Python_Part1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Python Part 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Getting Started"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello world\n",
      "hi friends\n"
     ]
    }
   ],
   "source": [
    "# Print Function\n",
    "print(\"hello world\")\n",
    "print(\"hi friends\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Python as a Calculator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning Variables\n",
    "savenum = 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savenum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "savetext = \"Melissa\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Melissa'"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savetext"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "mylastname = \"ko\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "othernum = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "200"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savenum * othernum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "savenum + othernum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import Libraries\n",
    "import numpy\n",
    "import os\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on function loadtxt in module numpy:\n",
      "\n",
      "loadtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, converters=None, skiprows=0, usecols=None, unpack=False, ndmin=0, encoding='bytes', max_rows=None)\n",
      "    Load data from a text file.\n",
      "    \n",
      "    Each row in the text file must have the same number of values.\n",
      "    \n",
      "    Parameters\n",
      "    ----------\n",
      "    fname : file, str, or pathlib.Path\n",
      "        File, filename, or generator to read.  If the filename extension is\n",
      "        ``.gz`` or ``.bz2``, the file is first decompressed. Note that\n",
      "        generators should return byte strings for Python 3k.\n",
      "    dtype : data-type, optional\n",
      "        Data-type of the resulting array; default: float.  If this is a\n",
      "        structured data-type, the resulting array will be 1-dimensional, and\n",
      "        each row will be interpreted as an element of the array.  In this\n",
      "        case, the number of columns used must match the number of fields in\n",
      "        the data-type.\n",
      "    comments : str or sequence of str, optional\n",
      "        The characters or list of characters used to indicate the start of a\n",
      "        comment. None implies no comments. For backwards compatibility, byte\n",
      "        strings will be decoded as 'latin1'. The default is '#'.\n",
      "    delimiter : str, optional\n",
      "        The string used to separate values. For backwards compatibility, byte\n",
      "        strings will be decoded as 'latin1'. The default is whitespace.\n",
      "    converters : dict, optional\n",
      "        A dictionary mapping column number to a function that will parse the\n",
      "        column string into the desired value.  E.g., if column 0 is a date\n",
      "        string: ``converters = {0: datestr2num}``.  Converters can also be\n",
      "        used to provide a default value for missing data (but see also\n",
      "        `genfromtxt`): ``converters = {3: lambda s: float(s.strip() or 0)}``.\n",
      "        Default: None.\n",
      "    skiprows : int, optional\n",
      "        Skip the first `skiprows` lines; default: 0.\n",
      "    usecols : int or sequence, optional\n",
      "        Which columns to read, with 0 being the first. For example,\n",
      "        ``usecols = (1,4,5)`` will extract the 2nd, 5th and 6th columns.\n",
      "        The default, None, results in all columns being read.\n",
      "    \n",
      "        .. versionchanged:: 1.11.0\n",
      "            When a single column has to be read it is possible to use\n",
      "            an integer instead of a tuple. E.g ``usecols = 3`` reads the\n",
      "            fourth column the same way as ``usecols = (3,)`` would.\n",
      "    unpack : bool, optional\n",
      "        If True, the returned array is transposed, so that arguments may be\n",
      "        unpacked using ``x, y, z = loadtxt(...)``.  When used with a structured\n",
      "        data-type, arrays are returned for each field.  Default is False.\n",
      "    ndmin : int, optional\n",
      "        The returned array will have at least `ndmin` dimensions.\n",
      "        Otherwise mono-dimensional axes will be squeezed.\n",
      "        Legal values: 0 (default), 1 or 2.\n",
      "    \n",
      "        .. versionadded:: 1.6.0\n",
      "    encoding : str, optional\n",
      "        Encoding used to decode the inputfile. Does not apply to input streams.\n",
      "        The special value 'bytes' enables backward compatibility workarounds\n",
      "        that ensures you receive byte arrays as results if possible and passes\n",
      "        'latin1' encoded strings to converters. Override this value to receive\n",
      "        unicode arrays and pass strings as input to converters.  If set to None\n",
      "        the system default is used. The default value is 'bytes'.\n",
      "    \n",
      "        .. versionadded:: 1.14.0\n",
      "    max_rows : int, optional\n",
      "        Read `max_rows` lines of content after `skiprows` lines. The default\n",
      "        is to read all the lines.\n",
      "    \n",
      "        .. versionadded:: 1.16.0\n",
      "    \n",
      "    Returns\n",
      "    -------\n",
      "    out : ndarray\n",
      "        Data read from the text file.\n",
      "    \n",
      "    See Also\n",
      "    --------\n",
      "    load, fromstring, fromregex\n",
      "    genfromtxt : Load data with missing values handled as specified.\n",
      "    scipy.io.loadmat : reads MATLAB data files\n",
      "    \n",
      "    Notes\n",
      "    -----\n",
      "    This function aims to be a fast reader for simply formatted files.  The\n",
      "    `genfromtxt` function provides more sophisticated handling of, e.g.,\n",
      "    lines with missing values.\n",
      "    \n",
      "    .. versionadded:: 1.10.0\n",
      "    \n",
      "    The strings produced by the Python float.hex method can be used as\n",
      "    input for floats.\n",
      "    \n",
      "    Examples\n",
      "    --------\n",
      "    >>> from io import StringIO   # StringIO behaves like a file object\n",
      "    >>> c = StringIO(u\"0 1\\n2 3\")\n",
      "    >>> np.loadtxt(c)\n",
      "    array([[ 0.,  1.],\n",
      "           [ 2.,  3.]])\n",
      "    \n",
      "    >>> d = StringIO(u\"M 21 72\\nF 35 58\")\n",
      "    >>> np.loadtxt(d, dtype={'names': ('gender', 'age', 'weight'),\n",
      "    ...                      'formats': ('S1', 'i4', 'f4')})\n",
      "    array([('M', 21, 72.0), ('F', 35, 58.0)],\n",
      "          dtype=[('gender', '|S1'), ('age', '<i4'), ('weight', '<f4')])\n",
      "    \n",
      "    >>> c = StringIO(u\"1,0,2\\n3,0,4\")\n",
      "    >>> x, y = np.loadtxt(c, delimiter=',', usecols=(0, 2), unpack=True)\n",
      "    >>> x\n",
      "    array([ 1.,  3.])\n",
      "    >>> y\n",
      "    array([ 2.,  4.])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Getting Help\n",
    "help(numpy.loadtxt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function print in module builtins:\n",
      "\n",
      "print(...)\n",
      "    print(value, ..., sep=' ', end='\\n', file=sys.stdout, flush=False)\n",
      "    \n",
      "    Prints the values to a stream, or to sys.stdout by default.\n",
      "    Optional keyword arguments:\n",
      "    file:  a file-like object (stream); defaults to the current sys.stdout.\n",
      "    sep:   string inserted between values, default a space.\n",
      "    end:   string appended after the last value, default a newline.\n",
      "    flush: whether to forcibly flush the stream.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(print)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class type in module builtins:\n",
      "\n",
      "class type(object)\n",
      " |  type(object_or_name, bases, dict)\n",
      " |  type(object) -> the object's type\n",
      " |  type(name, bases, dict) -> a new type\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, /, *args, **kwargs)\n",
      " |      Call self as a function.\n",
      " |  \n",
      " |  __delattr__(self, name, /)\n",
      " |      Implement delattr(self, name).\n",
      " |  \n",
      " |  __dir__(self, /)\n",
      " |      Specialized __dir__ implementation for types.\n",
      " |  \n",
      " |  __getattribute__(self, name, /)\n",
      " |      Return getattr(self, name).\n",
      " |  \n",
      " |  __init__(self, /, *args, **kwargs)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  __instancecheck__(self, instance, /)\n",
      " |      Check if an object is an instance.\n",
      " |  \n",
      " |  __repr__(self, /)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setattr__(self, name, value, /)\n",
      " |      Implement setattr(self, name, value).\n",
      " |  \n",
      " |  __sizeof__(self, /)\n",
      " |      Return memory consumption of the type object.\n",
      " |  \n",
      " |  __subclasscheck__(self, subclass, /)\n",
      " |      Check if a class is a subclass.\n",
      " |  \n",
      " |  __subclasses__(self, /)\n",
      " |      Return a list of immediate subclasses.\n",
      " |  \n",
      " |  mro(self, /)\n",
      " |      Return a type's method resolution order.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Class methods defined here:\n",
      " |  \n",
      " |  __prepare__(...)\n",
      " |      __prepare__() -> dict\n",
      " |      used to create the namespace for the class statement\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Static methods defined here:\n",
      " |  \n",
      " |  __new__(*args, **kwargs)\n",
      " |      Create and return a new object.  See help(type) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __abstractmethods__\n",
      " |  \n",
      " |  __dict__\n",
      " |  \n",
      " |  __text_signature__\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __base__ = <class 'object'>\n",
      " |      The most base type\n",
      " |  \n",
      " |  __bases__ = (<class 'object'>,)\n",
      " |  \n",
      " |  __basicsize__ = 864\n",
      " |  \n",
      " |  __dictoffset__ = 264\n",
      " |  \n",
      " |  __flags__ = 2148291584\n",
      " |  \n",
      " |  __itemsize__ = 40\n",
      " |  \n",
      " |  __mro__ = (<class 'type'>, <class 'object'>)\n",
      " |  \n",
      " |  __weakrefoffset__ = 368\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "type?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Working with Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/melissako/Desktop/Python-Lessons'"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get Working Directory\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Untitled.ipynb',\n",
       " 'Python_Part2-Copy1.ipynb',\n",
       " 'Python_Part1_full.ipynb',\n",
       " 'README.md',\n",
       " 'Python_Part1.ipynb',\n",
       " 'Python_Part2_full.ipynb',\n",
       " '.gitignore',\n",
       " '.ipynb_checkpoints',\n",
       " 'Python_Part2.ipynb',\n",
       " '.git',\n",
       " 'data']"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check Files\n",
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/Users/melissako/Desktop/Python-Lessons'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load a File\n",
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Untitled.ipynb',\n",
       " 'Python_Part2-Copy1.ipynb',\n",
       " 'Python_Part1_full.ipynb',\n",
       " 'README.md',\n",
       " 'Python_Part1.ipynb',\n",
       " 'Python_Part2_full.ipynb',\n",
       " '.gitignore',\n",
       " '.ipynb_checkpoints',\n",
       " 'Python_Part2.ipynb',\n",
       " '.git',\n",
       " 'data']"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['inflammation-05.csv',\n",
       " 'inflammation-11.csv',\n",
       " 'inflammation-10.csv',\n",
       " 'inflammation-04.csv',\n",
       " 'inflammation-12.csv',\n",
       " 'inflammation-06.csv',\n",
       " 'inflammation-07.csv',\n",
       " 'inflammation-03.csv',\n",
       " 'small-02.csv',\n",
       " 'small-03.csv',\n",
       " 'inflammation-02.csv',\n",
       " 'small-01.csv',\n",
       " 'inflammation-01.csv',\n",
       " 'inflammation-09.csv',\n",
       " 'inflammation-08.csv']"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.listdir(\"data\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on built-in function listdir in module posix:\n",
      "\n",
      "listdir(path=None)\n",
      "    Return a list containing the names of the files in the directory.\n",
      "    \n",
      "    path can be specified as either str, bytes, or a path-like object.  If path is bytes,\n",
      "      the filenames returned will also be bytes; in all other circumstances\n",
      "      the filenames returned will be str.\n",
      "    If path is None, uses the path='.'.\n",
      "    On some platforms, path may also be specified as an open file descriptor;\\\n",
      "      the file descriptor must refer to a directory.\n",
      "      If this functionality is unavailable, using it raises NotImplementedError.\n",
      "    \n",
      "    The list is in arbitrary order.  It does not include the special\n",
      "    entries '.' and '..' even if they are present in the directory.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(os.listdir)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = numpy.loadtxt(fname = \"data/inflammation-01.csv\", delimiter=',')\n",
    "# Check Data Type\n",
    "# Check Data Dimensions\n",
    "data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.14875"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy.ndarray?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 1., ..., 3., 0., 0.],\n",
       "       [0., 1., 2., ..., 1., 0., 1.],\n",
       "       [0., 1., 1., ..., 2., 1., 1.],\n",
       "       ...,\n",
       "       [0., 1., 1., ..., 1., 1., 1.],\n",
       "       [0., 0., 0., ..., 0., 2., 0.],\n",
       "       [0., 0., 1., ..., 1., 1., 0.]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Index into Array\n",
    "data[0, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 1., 2.])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:4, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.],\n",
       "       [2.],\n",
       "       [1.],\n",
       "       [2.]])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[0:4, 2:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1.,  3.,  1.,  2.,  4.,  7.,  8.,  3.,  3.,  3., 10.,\n",
       "         5.,  7.,  4.,  7.,  7., 12., 18.,  6., 13., 11., 11.,  7.,  7.,\n",
       "         4.,  6.,  8.,  8.,  4.,  4.,  5.,  7.,  3.,  4.,  2.,  3.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  1.,  2.,  1.,  3.,  2.,  2.,  6., 10., 11.,  5.,\n",
       "         9.,  4.,  4.,  7., 16.,  8.,  6., 18.,  4., 12.,  5., 12.,  7.,\n",
       "        11.,  5., 11.,  3.,  3.,  5.,  4.,  4.,  5.,  5.,  1.,  1.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  3.,  2.,  6.,  2.,  5.,  9.,  5.,  7.,  4.,\n",
       "         5.,  4., 15.,  5., 11.,  9., 10., 19., 14., 12., 17.,  7., 12.,\n",
       "        11.,  7.,  4.,  2., 10.,  5.,  4.,  2.,  2.,  3.,  2.,  2.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  0.,  4.,  2.,  2.,  1.,  6.,  7., 10.,  7.,  9.,\n",
       "        13.,  8.,  8., 15., 10., 10.,  7., 17.,  4.,  4.,  7.,  6., 15.,\n",
       "         6.,  4.,  9., 11.,  3.,  5.,  6.,  3.,  3.,  4.,  2.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  3.,  1.,  3.,  5.,  2.,  4.,  4.,  7.,  6.,\n",
       "         5.,  3., 10.,  8., 10.,  6., 17.,  9., 14.,  9.,  7., 13.,  9.,\n",
       "        12.,  6.,  7.,  7.,  9.,  6.,  3.,  2.,  2.,  4.,  2.,  0.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  2.,  2.,  4.,  2.,  1.,  6.,  4.,  7.,  6.,  6.,\n",
       "         9.,  9., 15.,  4., 16., 18., 12., 12.,  5., 18.,  9.,  5.,  3.,\n",
       "        10.,  3., 12.,  7.,  8.,  4.,  7.,  3.,  5.,  4.,  4.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  2.,  4.,  2.,  2.,  5.,  5.,  8.,  6.,  5., 11.,\n",
       "         9.,  4., 13.,  5., 12., 10.,  6.,  9., 17., 15.,  8.,  9.,  3.,\n",
       "        13.,  7.,  8.,  2.,  8.,  8.,  4.,  2.,  3.,  5.,  4.,  1.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  2.,  3.,  1.,  2.,  3.,  5.,  3.,  7.,  8.,  8.,\n",
       "         5., 10.,  9., 15., 11., 18., 19., 20.,  8.,  5., 13., 15., 10.,\n",
       "         6., 10.,  6.,  7.,  4.,  9.,  3.,  5.,  2.,  5.,  3.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  3.,  1.,  5.,  6.,  5.,  5.,  8.,  2.,  4., 11.,\n",
       "        12., 10., 11.,  9., 10., 17., 11.,  6., 16., 12.,  6.,  8., 14.,\n",
       "         6., 13., 10., 11.,  4.,  6.,  4.,  7.,  6.,  3.,  2.,  1.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  2.,  1.,  3.,  5.,  3.,  5.,  8.,  6.,  8., 12.,\n",
       "         5., 13.,  6., 13.,  8., 16.,  8., 18., 15., 16., 14., 12.,  7.,\n",
       "         3.,  8.,  9., 11.,  2.,  5.,  4.,  5.,  1.,  4.,  1.,  2.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  0.,  0.,  4.,  3.,  3.,  5.,  5.,  4.,  5.,  8.,  7.,\n",
       "        10., 13.,  3.,  7., 13., 15., 18.,  8., 15., 15., 16., 11., 14.,\n",
       "        12.,  4., 10., 10.,  4.,  3.,  4.,  5.,  5.,  3.,  3.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  0.,  0.,  3.,  4.,  2.,  7.,  8.,  5.,  2.,  8., 11.,\n",
       "         5.,  5.,  8., 14., 11.,  6., 11.,  9., 16., 18.,  6., 12.,  5.,\n",
       "         4.,  3.,  5.,  7.,  8.,  3.,  5.,  4.,  5.,  5.,  4.,  0.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  1.,  4.,  3.,  6.,  4.,  6.,  7.,  9.,  9.,  3.,\n",
       "        11.,  6., 12.,  4., 17., 13., 15., 13., 12.,  8.,  7.,  4.,  7.,\n",
       "        12.,  9.,  5.,  6.,  5.,  4.,  7.,  3.,  5.,  4.,  2.,  3.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  0.,  1.,  3.,  1.,  6.,  6.,  5.,  5.,  6.,  3.,\n",
       "         6., 13.,  3., 10., 13.,  9., 16., 15.,  9., 11.,  4.,  6.,  4.,\n",
       "        11., 11., 12.,  3.,  5.,  8.,  7.,  4.,  6.,  4.,  1.,  3.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  1.,  1.,  1.,  4.,  1.,  5.,  2.,  3.,  3., 10.,\n",
       "         7., 13.,  5.,  7., 17.,  6.,  9., 12., 13., 10.,  4., 12.,  4.,\n",
       "         6.,  7.,  6., 10.,  8.,  2.,  5.,  1.,  3.,  4.,  2.,  0.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  0.,  1.,  2.,  4.,  3.,  6.,  4.,  7.,  5.,  5.,\n",
       "         7.,  5., 10.,  7.,  8., 18., 17.,  9.,  8., 12., 11., 11., 11.,\n",
       "        14.,  6., 11.,  2., 10.,  9.,  5.,  6.,  5.,  3.,  4.,  2.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  0.,  0.,  2.,  3.,  6.,  5.,  7.,  4.,  3.,  2., 10.,\n",
       "         7.,  9., 11., 12.,  5., 12.,  9., 13., 19., 14., 17.,  5., 13.,\n",
       "         8., 11.,  5., 10.,  9.,  8.,  7.,  5.,  3.,  1.,  4.,  0.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  1.,  2.,  1.,  4.,  3.,  6.,  7.,  4.,  2., 12.,\n",
       "         6., 12.,  4., 14.,  7.,  8., 14., 13., 19.,  6.,  9., 12.,  6.,\n",
       "         4., 13.,  6.,  7.,  2.,  3.,  6.,  5.,  4.,  2.,  3.,  0.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  2.,  1.,  2.,  5.,  4.,  2.,  7.,  8.,  4.,  7., 11.,\n",
       "         9.,  8., 11., 15., 17., 11., 12.,  7., 12.,  7.,  6.,  7.,  4.,\n",
       "        13.,  5.,  7.,  6.,  6.,  9.,  2.,  1.,  1.,  2.,  2.,  0.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  0.,  1.,  4.,  3.,  2.,  2.,  7.,  3.,  3., 12.,\n",
       "        13., 11., 13.,  6.,  5.,  9., 16.,  9., 19., 16., 11.,  8.,  9.,\n",
       "        14., 12., 11.,  9.,  6.,  6.,  6.,  1.,  1.,  2.,  4.,  3.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  1.,  4.,  4.,  1.,  8.,  2.,  2.,  3., 12.,\n",
       "        12., 10., 15., 13.,  6.,  5.,  5., 18., 19.,  9.,  6., 11., 12.,\n",
       "         7.,  6.,  3.,  6.,  3.,  2.,  4.,  3.,  1.,  5.,  4.,  2.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  2.,  3.,  2.,  3.,  2.,  6.,  3.,  8.,  7.,  4.,  6.,\n",
       "         6.,  9.,  5., 12., 12.,  8.,  5., 12., 10., 16.,  7., 14., 12.,\n",
       "         5.,  4.,  6.,  9.,  8.,  5.,  6.,  6.,  1.,  4.,  3.,  0.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  0.,  3.,  4.,  5.,  1.,  7.,  7.,  8.,  2.,  5., 12.,\n",
       "         4., 10., 14.,  5.,  5., 17., 13., 16., 15., 13.,  6., 12.,  9.,\n",
       "        10.,  3.,  3.,  7.,  4.,  4.,  8.,  2.,  6.,  5.,  1.,  0.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  1.,  1.,  3.,  3.,  2.,  6.,  3.,  9.,  7.,  8.,\n",
       "         8.,  4., 13.,  7., 14., 11., 15., 14., 13.,  5., 13.,  7., 14.,\n",
       "         9., 10.,  5., 11.,  5.,  3.,  5.,  1.,  1.,  4.,  4.,  1.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  1.,  2.,  3.,  5.,  3.,  6.,  3.,  7., 10.,  3.,\n",
       "         8., 12.,  4., 12.,  9., 15.,  5., 17., 16.,  5., 10., 10., 15.,\n",
       "         7.,  5.,  3., 11.,  5.,  5.,  6.,  1.,  1.,  1.,  1.,  0.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  1.,  3.,  3.,  2.,  7.,  4.,  4.,  3.,  8., 12.,\n",
       "         9., 12.,  9.,  5., 16.,  8., 17.,  7., 11., 14.,  7., 13., 11.,\n",
       "         7., 12., 12.,  7.,  8.,  5.,  7.,  2.,  2.,  4.,  1.,  1.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  1.,  2.,  4.,  2.,  2.,  3.,  5.,  7., 10.,  5.,  5.,\n",
       "        12.,  3., 13.,  4., 13.,  7., 15.,  9., 12., 18., 14., 16., 12.,\n",
       "         3., 11.,  3.,  2.,  7.,  4.,  8.,  2.,  2.,  1.,  3.,  0.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  1.,  1.,  5.,  1.,  5.,  2.,  2.,  4., 10.,  4.,\n",
       "         8., 14.,  6., 15.,  6., 12., 15., 15., 13.,  7., 17.,  4.,  5.,\n",
       "        11.,  4.,  8.,  7.,  9.,  4.,  5.,  3.,  2.,  5.,  4.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  2.,  3.,  4.,  6.,  3.,  7.,  6.,  4.,  5.,  8.,\n",
       "         4.,  7.,  7.,  6., 11., 12., 19., 20., 18.,  9.,  5.,  4.,  7.,\n",
       "        14.,  8.,  4.,  3.,  7.,  7.,  8.,  3.,  5.,  4.,  1.,  3.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  0.,  1.,  4.,  4.,  6.,  3.,  8.,  6.,  4., 10., 12.,\n",
       "         3.,  3.,  6.,  8.,  7., 17., 16., 14., 15., 17.,  4., 14., 13.,\n",
       "         4.,  4., 12., 11.,  6.,  9.,  5.,  5.,  2.,  5.,  2.,  1.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  0.,  3.,  2.,  4.,  6.,  8.,  6.,  2.,  3., 11.,\n",
       "         3., 14., 14., 12.,  8.,  8., 16., 13.,  7.,  6.,  9., 15.,  7.,\n",
       "         6.,  4., 10.,  8., 10.,  4.,  2.,  6.,  5.,  5.,  2.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  3.,  3.,  4.,  5.,  3.,  6.,  7., 10.,  5., 10.,\n",
       "        13., 14.,  3.,  8., 10.,  9.,  9., 19., 15., 15.,  6.,  8.,  8.,\n",
       "        11.,  5.,  5.,  7.,  3.,  6.,  6.,  4.,  5.,  2.,  2.,  3.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  2.,  2.,  3.,  6.,  6.,  6.,  7.,  6.,  3., 11.,\n",
       "        12., 13., 15., 15., 10., 14., 11., 11.,  8.,  6., 12., 10.,  5.,\n",
       "        12.,  7.,  7., 11.,  5.,  8.,  5.,  2.,  5.,  5.,  2.,  0.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  1.,  3.,  5.,  6.,  7.,  5.,  8.,  9.,  3., 12.,\n",
       "        10., 12.,  4., 12.,  9., 13., 10., 10.,  6., 10., 11.,  4., 15.,\n",
       "        13.,  7.,  3.,  4.,  2.,  9.,  7.,  2.,  4.,  2.,  1.,  2.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  2.,  4.,  1.,  5.,  5.,  2.,  3.,  4.,  8.,  8.,\n",
       "        12.,  5., 15.,  9., 17.,  7., 19., 14., 18., 12., 17., 14.,  4.,\n",
       "        13., 13.,  8., 11.,  5.,  6.,  6.,  2.,  3.,  5.,  2.,  1.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  3.,  1.,  3.,  6.,  4.,  3.,  4.,  8.,  3.,  4.,\n",
       "         8.,  3., 11.,  5.,  7., 10.,  5., 15.,  9., 16., 17., 16.,  3.,\n",
       "         8.,  9.,  8.,  3.,  3.,  9.,  5.,  1.,  6.,  5.,  4.,  2.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  2.,  2.,  5.,  5.,  1.,  4.,  6.,  3.,  6.,  5.,\n",
       "         9.,  6.,  7.,  4.,  7., 16.,  7., 16., 13.,  9., 16., 12.,  6.,\n",
       "         7.,  9., 10.,  3.,  6.,  4.,  5.,  4.,  6.,  3.,  4.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  2.,  3.,  1.,  5.,  1.,  2.,  2.,  5.,  7.,  6.,\n",
       "         6.,  5., 10.,  6.,  7., 17., 13., 15., 16., 17., 14.,  4.,  4.,\n",
       "        10., 10., 10., 11.,  9.,  9.,  5.,  4.,  4.,  2.,  1.,  0.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  0.,  3.,  2.,  4.,  1.,  1.,  5.,  9., 10.,  7., 12.,\n",
       "        10.,  9., 15., 12., 13., 13.,  6., 19.,  9., 10.,  6., 13.,  5.,\n",
       "        13.,  6.,  7.,  2.,  5.,  5.,  2.,  1.,  1.,  1.,  1.,  3.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  1.,  1.,  5.,  5.,  3.,  7.,  2.,  2.,  3.,\n",
       "        12.,  4.,  6.,  8., 15., 16., 16., 15.,  4., 14.,  5., 13., 10.,\n",
       "         7., 10.,  6.,  3.,  2.,  3.,  6.,  3.,  3.,  5.,  4.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  2.,  2.,  1.,  3.,  4.,  5.,  5.,  6.,  5.,  5.,\n",
       "        12., 13.,  5.,  7.,  5., 11., 15., 18.,  7.,  9., 10., 14., 12.,\n",
       "        11.,  9., 10.,  3.,  2.,  9.,  6.,  2.,  2.,  5.,  3.,  0.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  3.,  3.,  1.,  2.,  1.,  8.,  9.,  2.,  8., 10.,\n",
       "         3.,  8.,  6., 10., 13., 11., 17., 19.,  6.,  4., 11.,  6., 12.,\n",
       "         7.,  5.,  5.,  4.,  4.,  8.,  2.,  6.,  6.,  4.,  2.,  2.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  3.,  4.,  5.,  2.,  1.,  3.,  7.,  9.,  6., 10.,\n",
       "         5.,  8., 15., 11., 12., 15.,  6., 12., 16.,  6.,  4., 14.,  3.,\n",
       "        12.,  9.,  6., 11.,  5.,  8.,  5.,  5.,  6.,  1.,  2.,  1.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  1.,  3.,  1.,  4.,  3.,  6.,  7.,  8.,  5.,  7., 11.,\n",
       "         3.,  6., 11.,  6., 10.,  6., 19., 18., 14.,  6., 10.,  7.,  9.,\n",
       "         8.,  5.,  8.,  3., 10.,  2.,  5.,  1.,  5.,  4.,  2.,  1.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  3.,  4.,  4.,  6.,  3.,  4.,  9.,  9.,  7.,\n",
       "         6.,  8., 15., 12., 15.,  6., 11.,  6., 18.,  5., 14., 15., 12.,\n",
       "         9.,  8.,  3.,  6., 10.,  6.,  8.,  7.,  2.,  5.,  4.,  3.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  2.,  2.,  4.,  3.,  1.,  4.,  8.,  9.,  5., 10., 10.,\n",
       "         3.,  4.,  6.,  7., 11., 16.,  6., 14.,  9., 11., 10., 10.,  7.,\n",
       "        10.,  8.,  8.,  4.,  5.,  8.,  4.,  4.,  5.,  2.,  4.,  1.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  2.,  3.,  4.,  5.,  4.,  6.,  2.,  9.,  7.,  4.,  9.,\n",
       "        10.,  8., 11., 16., 12., 15., 17., 19., 10., 18., 13., 15., 11.,\n",
       "         8.,  4.,  7., 11.,  6.,  7.,  6.,  5.,  1.,  3.,  1.,  0.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  3.,  1.,  4.,  6.,  2.,  8.,  2., 10.,  3., 11.,\n",
       "         9., 13., 15.,  5., 15.,  6., 10., 10.,  5., 14., 15., 12.,  7.,\n",
       "         4.,  5., 11.,  4.,  6.,  9.,  5.,  6.,  1.,  1.,  2.,  1.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  3.,  2.,  5.,  1.,  2.,  7.,  6.,  6.,  3., 12.,\n",
       "         9.,  4., 14.,  4.,  6., 12.,  9., 12.,  7., 11.,  7., 16.,  8.,\n",
       "        13.,  6.,  7.,  6., 10.,  7.,  6.,  3.,  1.,  5.,  4.,  3.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  1.,  2.,  3.,  4.,  5.,  7.,  5.,  4., 10.,  5., 12.,\n",
       "        12.,  5.,  4.,  7.,  9., 18., 16., 16., 10., 15., 15., 10.,  4.,\n",
       "         3.,  7.,  5.,  9.,  4.,  6.,  2.,  4.,  1.,  4.,  2.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  2.,  1.,  1.,  3.,  5.,  3.,  6.,  3., 10., 10., 11.,\n",
       "        10., 13., 10., 13.,  6.,  6., 14.,  5.,  4.,  5.,  5.,  9.,  4.,\n",
       "        12.,  7.,  7.,  4.,  7.,  9.,  3.,  3.,  6.,  3.,  4.,  1.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  2.,  3.,  5.,  2.,  4.,  5.,  6.,  8.,  3.,  5.,\n",
       "         4.,  3., 15., 15., 12., 16.,  7., 20., 15., 12.,  8.,  9.,  6.,\n",
       "        12.,  5.,  8.,  3.,  8.,  5.,  4.,  1.,  3.,  2.,  1.,  3.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  0.,  2.,  4.,  4.,  5.,  3.,  3.,  3., 10.,  4.,  4.,\n",
       "         4., 14., 11., 15., 13., 10., 14., 11., 17.,  9., 11., 11.,  7.,\n",
       "        10., 12., 10., 10., 10.,  8.,  7.,  5.,  2.,  2.,  4.,  1.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  1.,  1.,  4.,  4.,  7.,  2.,  9.,  4., 10., 12.,\n",
       "         7.,  6.,  6., 11., 12.,  9., 15., 15.,  6.,  6., 13.,  5., 12.,\n",
       "         9.,  6.,  4.,  7.,  7.,  6.,  5.,  4.,  1.,  4.,  2.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  2.,  1.,  1.,  4.,  5.,  4.,  4.,  5.,  9.,  7., 10.,\n",
       "         3., 13., 13.,  8.,  9., 17., 16., 16., 15., 12., 13.,  5., 12.,\n",
       "        10.,  9., 11.,  9.,  4.,  5.,  5.,  2.,  2.,  5.,  1.,  0.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  3.,  2.,  3.,  6.,  4.,  5.,  7.,  2.,  4., 11.,\n",
       "        11.,  3.,  8.,  8., 16.,  5., 13., 16.,  5.,  8.,  8.,  6.,  9.,\n",
       "        10., 10.,  9.,  3.,  3.,  5.,  3.,  5.,  4.,  5.,  3.,  3.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  2.,  2.,  5.,  1.,  7.,  4.,  2.,  5.,  5.,  4.,\n",
       "         6.,  6.,  4., 16., 11., 14., 16., 14., 14.,  8., 17.,  4., 14.,\n",
       "        13.,  7.,  6.,  3.,  7.,  7.,  5.,  6.,  3.,  4.,  2.,  2.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  1.,  4.,  1.,  6.,  4.,  6.,  3.,  6.,  5.,  6.,\n",
       "         4., 14., 13., 13.,  9., 12., 19.,  9., 10., 15., 10.,  9., 10.,\n",
       "        10.,  7.,  5.,  6.,  8.,  6.,  6.,  4.,  3.,  5.,  2.,  1.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  1.,  4.,  5.,  6.,  3.,  8.,  7.,  9., 10.,  8.,\n",
       "         6.,  5., 12., 15.,  5., 10.,  5.,  8., 13., 18., 17., 14.,  9.,\n",
       "        13.,  4., 10., 11., 10.,  8.,  8.,  6.,  5.,  5.,  2.,  0.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  1.,  0.,  3.,  2.,  5.,  4.,  8.,  2.,  9.,  3.,  3.,\n",
       "        10., 12.,  9., 14., 11., 13.,  8.,  6., 18., 11.,  9., 13., 11.,\n",
       "         8.,  5.,  5.,  2.,  8.,  5.,  3.,  5.,  4.,  1.,  3.,  1.,  1.,\n",
       "         0.]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy.set_printoptions(threshold = 10000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  1.  3.  1.  2.  4.  7.  8.  3.  3.  3. 10.  5.  7.  4.  7.  7.\n",
      "  12. 18.  6. 13. 11. 11.  7.  7.  4.  6.  8.  8.  4.  4.  5.  7.  3.  4.\n",
      "   2.  3.  0.  0.]\n",
      " [ 0.  1.  2.  1.  2.  1.  3.  2.  2.  6. 10. 11.  5.  9.  4.  4.  7. 16.\n",
      "   8.  6. 18.  4. 12.  5. 12.  7. 11.  5. 11.  3.  3.  5.  4.  4.  5.  5.\n",
      "   1.  1.  0.  1.]\n",
      " [ 0.  1.  1.  3.  3.  2.  6.  2.  5.  9.  5.  7.  4.  5.  4. 15.  5. 11.\n",
      "   9. 10. 19. 14. 12. 17.  7. 12. 11.  7.  4.  2. 10.  5.  4.  2.  2.  3.\n",
      "   2.  2.  1.  1.]\n",
      " [ 0.  0.  2.  0.  4.  2.  2.  1.  6.  7. 10.  7.  9. 13.  8.  8. 15. 10.\n",
      "  10.  7. 17.  4.  4.  7.  6. 15.  6.  4.  9. 11.  3.  5.  6.  3.  3.  4.\n",
      "   2.  3.  2.  1.]\n",
      " [ 0.  1.  1.  3.  3.  1.  3.  5.  2.  4.  4.  7.  6.  5.  3. 10.  8. 10.\n",
      "   6. 17.  9. 14.  9.  7. 13.  9. 12.  6.  7.  7.  9.  6.  3.  2.  2.  4.\n",
      "   2.  0.  1.  1.]\n",
      " [ 0.  0.  1.  2.  2.  4.  2.  1.  6.  4.  7.  6.  6.  9.  9. 15.  4. 16.\n",
      "  18. 12. 12.  5. 18.  9.  5.  3. 10.  3. 12.  7.  8.  4.  7.  3.  5.  4.\n",
      "   4.  3.  2.  1.]\n",
      " [ 0.  0.  2.  2.  4.  2.  2.  5.  5.  8.  6.  5. 11.  9.  4. 13.  5. 12.\n",
      "  10.  6.  9. 17. 15.  8.  9.  3. 13.  7.  8.  2.  8.  8.  4.  2.  3.  5.\n",
      "   4.  1.  1.  1.]\n",
      " [ 0.  0.  1.  2.  3.  1.  2.  3.  5.  3.  7.  8.  8.  5. 10.  9. 15. 11.\n",
      "  18. 19. 20.  8.  5. 13. 15. 10.  6. 10.  6.  7.  4.  9.  3.  5.  2.  5.\n",
      "   3.  2.  2.  1.]\n",
      " [ 0.  0.  0.  3.  1.  5.  6.  5.  5.  8.  2.  4. 11. 12. 10. 11.  9. 10.\n",
      "  17. 11.  6. 16. 12.  6.  8. 14.  6. 13. 10. 11.  4.  6.  4.  7.  6.  3.\n",
      "   2.  1.  0.  0.]\n",
      " [ 0.  1.  1.  2.  1.  3.  5.  3.  5.  8.  6.  8. 12.  5. 13.  6. 13.  8.\n",
      "  16.  8. 18. 15. 16. 14. 12.  7.  3.  8.  9. 11.  2.  5.  4.  5.  1.  4.\n",
      "   1.  2.  0.  0.]\n",
      " [ 0.  1.  0.  0.  4.  3.  3.  5.  5.  4.  5.  8.  7. 10. 13.  3.  7. 13.\n",
      "  15. 18.  8. 15. 15. 16. 11. 14. 12.  4. 10. 10.  4.  3.  4.  5.  5.  3.\n",
      "   3.  2.  2.  1.]\n",
      " [ 0.  1.  0.  0.  3.  4.  2.  7.  8.  5.  2.  8. 11.  5.  5.  8. 14. 11.\n",
      "   6. 11.  9. 16. 18.  6. 12.  5.  4.  3.  5.  7.  8.  3.  5.  4.  5.  5.\n",
      "   4.  0.  1.  1.]\n",
      " [ 0.  0.  2.  1.  4.  3.  6.  4.  6.  7.  9.  9.  3. 11.  6. 12.  4. 17.\n",
      "  13. 15. 13. 12.  8.  7.  4.  7. 12.  9.  5.  6.  5.  4.  7.  3.  5.  4.\n",
      "   2.  3.  0.  1.]\n",
      " [ 0.  0.  0.  0.  1.  3.  1.  6.  6.  5.  5.  6.  3.  6. 13.  3. 10. 13.\n",
      "   9. 16. 15.  9. 11.  4.  6.  4. 11. 11. 12.  3.  5.  8.  7.  4.  6.  4.\n",
      "   1.  3.  0.  0.]\n",
      " [ 0.  1.  2.  1.  1.  1.  4.  1.  5.  2.  3.  3. 10.  7. 13.  5.  7. 17.\n",
      "   6.  9. 12. 13. 10.  4. 12.  4.  6.  7.  6. 10.  8.  2.  5.  1.  3.  4.\n",
      "   2.  0.  2.  0.]\n",
      " [ 0.  1.  1.  0.  1.  2.  4.  3.  6.  4.  7.  5.  5.  7.  5. 10.  7.  8.\n",
      "  18. 17.  9.  8. 12. 11. 11. 11. 14.  6. 11.  2. 10.  9.  5.  6.  5.  3.\n",
      "   4.  2.  2.  0.]\n",
      " [ 0.  0.  0.  0.  2.  3.  6.  5.  7.  4.  3.  2. 10.  7.  9. 11. 12.  5.\n",
      "  12.  9. 13. 19. 14. 17.  5. 13.  8. 11.  5. 10.  9.  8.  7.  5.  3.  1.\n",
      "   4.  0.  2.  1.]\n",
      " [ 0.  0.  0.  1.  2.  1.  4.  3.  6.  7.  4.  2. 12.  6. 12.  4. 14.  7.\n",
      "   8. 14. 13. 19.  6.  9. 12.  6.  4. 13.  6.  7.  2.  3.  6.  5.  4.  2.\n",
      "   3.  0.  1.  0.]\n",
      " [ 0.  0.  2.  1.  2.  5.  4.  2.  7.  8.  4.  7. 11.  9.  8. 11. 15. 17.\n",
      "  11. 12.  7. 12.  7.  6.  7.  4. 13.  5.  7.  6.  6.  9.  2.  1.  1.  2.\n",
      "   2.  0.  1.  0.]\n",
      " [ 0.  1.  2.  0.  1.  4.  3.  2.  2.  7.  3.  3. 12. 13. 11. 13.  6.  5.\n",
      "   9. 16.  9. 19. 16. 11.  8.  9. 14. 12. 11.  9.  6.  6.  6.  1.  1.  2.\n",
      "   4.  3.  1.  1.]\n",
      " [ 0.  1.  1.  3.  1.  4.  4.  1.  8.  2.  2.  3. 12. 12. 10. 15. 13.  6.\n",
      "   5.  5. 18. 19.  9.  6. 11. 12.  7.  6.  3.  6.  3.  2.  4.  3.  1.  5.\n",
      "   4.  2.  2.  0.]\n",
      " [ 0.  0.  2.  3.  2.  3.  2.  6.  3.  8.  7.  4.  6.  6.  9.  5. 12. 12.\n",
      "   8.  5. 12. 10. 16.  7. 14. 12.  5.  4.  6.  9.  8.  5.  6.  6.  1.  4.\n",
      "   3.  0.  2.  0.]\n",
      " [ 0.  0.  0.  3.  4.  5.  1.  7.  7.  8.  2.  5. 12.  4. 10. 14.  5.  5.\n",
      "  17. 13. 16. 15. 13.  6. 12.  9. 10.  3.  3.  7.  4.  4.  8.  2.  6.  5.\n",
      "   1.  0.  1.  0.]\n",
      " [ 0.  1.  1.  1.  1.  3.  3.  2.  6.  3.  9.  7.  8.  8.  4. 13.  7. 14.\n",
      "  11. 15. 14. 13.  5. 13.  7. 14.  9. 10.  5. 11.  5.  3.  5.  1.  1.  4.\n",
      "   4.  1.  2.  0.]\n",
      " [ 0.  1.  1.  1.  2.  3.  5.  3.  6.  3.  7. 10.  3.  8. 12.  4. 12.  9.\n",
      "  15.  5. 17. 16.  5. 10. 10. 15.  7.  5.  3. 11.  5.  5.  6.  1.  1.  1.\n",
      "   1.  0.  2.  1.]\n",
      " [ 0.  0.  2.  1.  3.  3.  2.  7.  4.  4.  3.  8. 12.  9. 12.  9.  5. 16.\n",
      "   8. 17.  7. 11. 14.  7. 13. 11.  7. 12. 12.  7.  8.  5.  7.  2.  2.  4.\n",
      "   1.  1.  1.  0.]\n",
      " [ 0.  0.  1.  2.  4.  2.  2.  3.  5.  7. 10.  5.  5. 12.  3. 13.  4. 13.\n",
      "   7. 15.  9. 12. 18. 14. 16. 12.  3. 11.  3.  2.  7.  4.  8.  2.  2.  1.\n",
      "   3.  0.  1.  1.]\n",
      " [ 0.  0.  1.  1.  1.  5.  1.  5.  2.  2.  4. 10.  4.  8. 14.  6. 15.  6.\n",
      "  12. 15. 15. 13.  7. 17.  4.  5. 11.  4.  8.  7.  9.  4.  5.  3.  2.  5.\n",
      "   4.  3.  2.  1.]\n",
      " [ 0.  0.  2.  2.  3.  4.  6.  3.  7.  6.  4.  5.  8.  4.  7.  7.  6. 11.\n",
      "  12. 19. 20. 18.  9.  5.  4.  7. 14.  8.  4.  3.  7.  7.  8.  3.  5.  4.\n",
      "   1.  3.  1.  0.]\n",
      " [ 0.  0.  0.  1.  4.  4.  6.  3.  8.  6.  4. 10. 12.  3.  3.  6.  8.  7.\n",
      "  17. 16. 14. 15. 17.  4. 14. 13.  4.  4. 12. 11.  6.  9.  5.  5.  2.  5.\n",
      "   2.  1.  0.  1.]\n",
      " [ 0.  1.  1.  0.  3.  2.  4.  6.  8.  6.  2.  3. 11.  3. 14. 14. 12.  8.\n",
      "   8. 16. 13.  7.  6.  9. 15.  7.  6.  4. 10.  8. 10.  4.  2.  6.  5.  5.\n",
      "   2.  3.  2.  1.]\n",
      " [ 0.  0.  2.  3.  3.  4.  5.  3.  6.  7. 10.  5. 10. 13. 14.  3.  8. 10.\n",
      "   9.  9. 19. 15. 15.  6.  8.  8. 11.  5.  5.  7.  3.  6.  6.  4.  5.  2.\n",
      "   2.  3.  0.  0.]\n",
      " [ 0.  1.  2.  2.  2.  3.  6.  6.  6.  7.  6.  3. 11. 12. 13. 15. 15. 10.\n",
      "  14. 11. 11.  8.  6. 12. 10.  5. 12.  7.  7. 11.  5.  8.  5.  2.  5.  5.\n",
      "   2.  0.  2.  1.]\n",
      " [ 0.  0.  2.  1.  3.  5.  6.  7.  5.  8.  9.  3. 12. 10. 12.  4. 12.  9.\n",
      "  13. 10. 10.  6. 10. 11.  4. 15. 13.  7.  3.  4.  2.  9.  7.  2.  4.  2.\n",
      "   1.  2.  1.  1.]\n",
      " [ 0.  0.  1.  2.  4.  1.  5.  5.  2.  3.  4.  8.  8. 12.  5. 15.  9. 17.\n",
      "   7. 19. 14. 18. 12. 17. 14.  4. 13. 13.  8. 11.  5.  6.  6.  2.  3.  5.\n",
      "   2.  1.  1.  1.]\n",
      " [ 0.  0.  0.  3.  1.  3.  6.  4.  3.  4.  8.  3.  4.  8.  3. 11.  5.  7.\n",
      "  10.  5. 15.  9. 16. 17. 16.  3.  8.  9.  8.  3.  3.  9.  5.  1.  6.  5.\n",
      "   4.  2.  2.  0.]\n",
      " [ 0.  1.  2.  2.  2.  5.  5.  1.  4.  6.  3.  6.  5.  9.  6.  7.  4.  7.\n",
      "  16.  7. 16. 13.  9. 16. 12.  6.  7.  9. 10.  3.  6.  4.  5.  4.  6.  3.\n",
      "   4.  3.  2.  1.]\n",
      " [ 0.  1.  1.  2.  3.  1.  5.  1.  2.  2.  5.  7.  6.  6.  5. 10.  6.  7.\n",
      "  17. 13. 15. 16. 17. 14.  4.  4. 10. 10. 10. 11.  9.  9.  5.  4.  4.  2.\n",
      "   1.  0.  1.  0.]\n",
      " [ 0.  1.  0.  3.  2.  4.  1.  1.  5.  9. 10.  7. 12. 10.  9. 15. 12. 13.\n",
      "  13.  6. 19.  9. 10.  6. 13.  5. 13.  6.  7.  2.  5.  5.  2.  1.  1.  1.\n",
      "   1.  3.  0.  1.]\n",
      " [ 0.  1.  1.  3.  1.  1.  5.  5.  3.  7.  2.  2.  3. 12.  4.  6.  8. 15.\n",
      "  16. 16. 15.  4. 14.  5. 13. 10.  7. 10.  6.  3.  2.  3.  6.  3.  3.  5.\n",
      "   4.  3.  2.  1.]\n",
      " [ 0.  0.  0.  2.  2.  1.  3.  4.  5.  5.  6.  5.  5. 12. 13.  5.  7.  5.\n",
      "  11. 15. 18.  7.  9. 10. 14. 12. 11.  9. 10.  3.  2.  9.  6.  2.  2.  5.\n",
      "   3.  0.  0.  1.]\n",
      " [ 0.  0.  1.  3.  3.  1.  2.  1.  8.  9.  2.  8. 10.  3.  8.  6. 10. 13.\n",
      "  11. 17. 19.  6.  4. 11.  6. 12.  7.  5.  5.  4.  4.  8.  2.  6.  6.  4.\n",
      "   2.  2.  0.  0.]\n",
      " [ 0.  1.  1.  3.  4.  5.  2.  1.  3.  7.  9.  6. 10.  5.  8. 15. 11. 12.\n",
      "  15.  6. 12. 16.  6.  4. 14.  3. 12.  9.  6. 11.  5.  8.  5.  5.  6.  1.\n",
      "   2.  1.  2.  0.]\n",
      " [ 0.  0.  1.  3.  1.  4.  3.  6.  7.  8.  5.  7. 11.  3.  6. 11.  6. 10.\n",
      "   6. 19. 18. 14.  6. 10.  7.  9.  8.  5.  8.  3. 10.  2.  5.  1.  5.  4.\n",
      "   2.  1.  0.  1.]\n",
      " [ 0.  1.  1.  3.  3.  4.  4.  6.  3.  4.  9.  9.  7.  6.  8. 15. 12. 15.\n",
      "   6. 11.  6. 18.  5. 14. 15. 12.  9.  8.  3.  6. 10.  6.  8.  7.  2.  5.\n",
      "   4.  3.  1.  1.]\n",
      " [ 0.  1.  2.  2.  4.  3.  1.  4.  8.  9.  5. 10. 10.  3.  4.  6.  7. 11.\n",
      "  16.  6. 14.  9. 11. 10. 10.  7. 10.  8.  8.  4.  5.  8.  4.  4.  5.  2.\n",
      "   4.  1.  1.  0.]\n",
      " [ 0.  0.  2.  3.  4.  5.  4.  6.  2.  9.  7.  4.  9. 10.  8. 11. 16. 12.\n",
      "  15. 17. 19. 10. 18. 13. 15. 11.  8.  4.  7. 11.  6.  7.  6.  5.  1.  3.\n",
      "   1.  0.  0.  0.]\n",
      " [ 0.  1.  1.  3.  1.  4.  6.  2.  8.  2. 10.  3. 11.  9. 13. 15.  5. 15.\n",
      "   6. 10. 10.  5. 14. 15. 12.  7.  4.  5. 11.  4.  6.  9.  5.  6.  1.  1.\n",
      "   2.  1.  2.  1.]\n",
      " [ 0.  0.  1.  3.  2.  5.  1.  2.  7.  6.  6.  3. 12.  9.  4. 14.  4.  6.\n",
      "  12.  9. 12.  7. 11.  7. 16.  8. 13.  6.  7.  6. 10.  7.  6.  3.  1.  5.\n",
      "   4.  3.  0.  0.]\n",
      " [ 0.  0.  1.  2.  3.  4.  5.  7.  5.  4. 10.  5. 12. 12.  5.  4.  7.  9.\n",
      "  18. 16. 16. 10. 15. 15. 10.  4.  3.  7.  5.  9.  4.  6.  2.  4.  1.  4.\n",
      "   2.  2.  2.  1.]\n",
      " [ 0.  1.  2.  1.  1.  3.  5.  3.  6.  3. 10. 10. 11. 10. 13. 10. 13.  6.\n",
      "   6. 14.  5.  4.  5.  5.  9.  4. 12.  7.  7.  4.  7.  9.  3.  3.  6.  3.\n",
      "   4.  1.  2.  0.]\n",
      " [ 0.  1.  2.  2.  3.  5.  2.  4.  5.  6.  8.  3.  5.  4.  3. 15. 15. 12.\n",
      "  16.  7. 20. 15. 12.  8.  9.  6. 12.  5.  8.  3.  8.  5.  4.  1.  3.  2.\n",
      "   1.  3.  1.  0.]\n",
      " [ 0.  0.  0.  2.  4.  4.  5.  3.  3.  3. 10.  4.  4.  4. 14. 11. 15. 13.\n",
      "  10. 14. 11. 17.  9. 11. 11.  7. 10. 12. 10. 10. 10.  8.  7.  5.  2.  2.\n",
      "   4.  1.  2.  1.]\n",
      " [ 0.  0.  2.  1.  1.  4.  4.  7.  2.  9.  4. 10. 12.  7.  6.  6. 11. 12.\n",
      "   9. 15. 15.  6.  6. 13.  5. 12.  9.  6.  4.  7.  7.  6.  5.  4.  1.  4.\n",
      "   2.  2.  2.  1.]\n",
      " [ 0.  1.  2.  1.  1.  4.  5.  4.  4.  5.  9.  7. 10.  3. 13. 13.  8.  9.\n",
      "  17. 16. 16. 15. 12. 13.  5. 12. 10.  9. 11.  9.  4.  5.  5.  2.  2.  5.\n",
      "   1.  0.  0.  1.]\n",
      " [ 0.  0.  1.  3.  2.  3.  6.  4.  5.  7.  2.  4. 11. 11.  3.  8.  8. 16.\n",
      "   5. 13. 16.  5.  8.  8.  6.  9. 10. 10.  9.  3.  3.  5.  3.  5.  4.  5.\n",
      "   3.  3.  0.  1.]\n",
      " [ 0.  1.  1.  2.  2.  5.  1.  7.  4.  2.  5.  5.  4.  6.  6.  4. 16. 11.\n",
      "  14. 16. 14. 14.  8. 17.  4. 14. 13.  7.  6.  3.  7.  7.  5.  6.  3.  4.\n",
      "   2.  2.  1.  1.]\n",
      " [ 0.  1.  1.  1.  4.  1.  6.  4.  6.  3.  6.  5.  6.  4. 14. 13. 13.  9.\n",
      "  12. 19.  9. 10. 15. 10.  9. 10. 10.  7.  5.  6.  8.  6.  6.  4.  3.  5.\n",
      "   2.  1.  1.  1.]\n",
      " [ 0.  0.  0.  1.  4.  5.  6.  3.  8.  7.  9. 10.  8.  6.  5. 12. 15.  5.\n",
      "  10.  5.  8. 13. 18. 17. 14.  9. 13.  4. 10. 11. 10.  8.  8.  6.  5.  5.\n",
      "   2.  0.  2.  0.]\n",
      " [ 0.  0.  1.  0.  3.  2.  5.  4.  8.  2.  9.  3.  3. 10. 12.  9. 14. 11.\n",
      "  13.  8.  6. 18. 11.  9. 13. 11.  8.  5.  5.  2.  8.  5.  3.  5.  4.  1.\n",
      "   3.  1.  1.  0.]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(data)\n",
    "data[1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arithmetic on Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0.,   0.,  10.,  30.,  10.,  20.,  40.,  70.,  80.,  30.,  30.,\n",
       "         30., 100.,  50.,  70.,  40.,  70.,  70., 120., 180.,  60., 130.,\n",
       "        110., 110.,  70.,  70.,  40.,  60.,  80.,  80.,  40.,  40.,  50.,\n",
       "         70.,  30.,  40.,  20.,  30.,   0.,   0.],\n",
       "       [  0.,  10.,  20.,  10.,  20.,  10.,  30.,  20.,  20.,  60., 100.,\n",
       "        110.,  50.,  90.,  40.,  40.,  70., 160.,  80.,  60., 180.,  40.,\n",
       "        120.,  50., 120.,  70., 110.,  50., 110.,  30.,  30.,  50.,  40.,\n",
       "         40.,  50.,  50.,  10.,  10.,   0.,  10.],\n",
       "       [  0.,  10.,  10.,  30.,  30.,  20.,  60.,  20.,  50.,  90.,  50.,\n",
       "         70.,  40.,  50.,  40., 150.,  50., 110.,  90., 100., 190., 140.,\n",
       "        120., 170.,  70., 120., 110.,  70.,  40.,  20., 100.,  50.,  40.,\n",
       "         20.,  20.,  30.,  20.,  20.,  10.,  10.],\n",
       "       [  0.,   0.,  20.,   0.,  40.,  20.,  20.,  10.,  60.,  70., 100.,\n",
       "         70.,  90., 130.,  80.,  80., 150., 100., 100.,  70., 170.,  40.,\n",
       "         40.,  70.,  60., 150.,  60.,  40.,  90., 110.,  30.,  50.,  60.,\n",
       "         30.,  30.,  40.,  20.,  30.,  20.,  10.],\n",
       "       [  0.,  10.,  10.,  30.,  30.,  10.,  30.,  50.,  20.,  40.,  40.,\n",
       "         70.,  60.,  50.,  30., 100.,  80., 100.,  60., 170.,  90., 140.,\n",
       "         90.,  70., 130.,  90., 120.,  60.,  70.,  70.,  90.,  60.,  30.,\n",
       "         20.,  20.,  40.,  20.,   0.,  10.,  10.],\n",
       "       [  0.,   0.,  10.,  20.,  20.,  40.,  20.,  10.,  60.,  40.,  70.,\n",
       "         60.,  60.,  90.,  90., 150.,  40., 160., 180., 120., 120.,  50.,\n",
       "        180.,  90.,  50.,  30., 100.,  30., 120.,  70.,  80.,  40.,  70.,\n",
       "         30.,  50.,  40.,  40.,  30.,  20.,  10.],\n",
       "       [  0.,   0.,  20.,  20.,  40.,  20.,  20.,  50.,  50.,  80.,  60.,\n",
       "         50., 110.,  90.,  40., 130.,  50., 120., 100.,  60.,  90., 170.,\n",
       "        150.,  80.,  90.,  30., 130.,  70.,  80.,  20.,  80.,  80.,  40.,\n",
       "         20.,  30.,  50.,  40.,  10.,  10.,  10.],\n",
       "       [  0.,   0.,  10.,  20.,  30.,  10.,  20.,  30.,  50.,  30.,  70.,\n",
       "         80.,  80.,  50., 100.,  90., 150., 110., 180., 190., 200.,  80.,\n",
       "         50., 130., 150., 100.,  60., 100.,  60.,  70.,  40.,  90.,  30.,\n",
       "         50.,  20.,  50.,  30.,  20.,  20.,  10.],\n",
       "       [  0.,   0.,   0.,  30.,  10.,  50.,  60.,  50.,  50.,  80.,  20.,\n",
       "         40., 110., 120., 100., 110.,  90., 100., 170., 110.,  60., 160.,\n",
       "        120.,  60.,  80., 140.,  60., 130., 100., 110.,  40.,  60.,  40.,\n",
       "         70.,  60.,  30.,  20.,  10.,   0.,   0.],\n",
       "       [  0.,  10.,  10.,  20.,  10.,  30.,  50.,  30.,  50.,  80.,  60.,\n",
       "         80., 120.,  50., 130.,  60., 130.,  80., 160.,  80., 180., 150.,\n",
       "        160., 140., 120.,  70.,  30.,  80.,  90., 110.,  20.,  50.,  40.,\n",
       "         50.,  10.,  40.,  10.,  20.,   0.,   0.],\n",
       "       [  0.,  10.,   0.,   0.,  40.,  30.,  30.,  50.,  50.,  40.,  50.,\n",
       "         80.,  70., 100., 130.,  30.,  70., 130., 150., 180.,  80., 150.,\n",
       "        150., 160., 110., 140., 120.,  40., 100., 100.,  40.,  30.,  40.,\n",
       "         50.,  50.,  30.,  30.,  20.,  20.,  10.],\n",
       "       [  0.,  10.,   0.,   0.,  30.,  40.,  20.,  70.,  80.,  50.,  20.,\n",
       "         80., 110.,  50.,  50.,  80., 140., 110.,  60., 110.,  90., 160.,\n",
       "        180.,  60., 120.,  50.,  40.,  30.,  50.,  70.,  80.,  30.,  50.,\n",
       "         40.,  50.,  50.,  40.,   0.,  10.,  10.],\n",
       "       [  0.,   0.,  20.,  10.,  40.,  30.,  60.,  40.,  60.,  70.,  90.,\n",
       "         90.,  30., 110.,  60., 120.,  40., 170., 130., 150., 130., 120.,\n",
       "         80.,  70.,  40.,  70., 120.,  90.,  50.,  60.,  50.,  40.,  70.,\n",
       "         30.,  50.,  40.,  20.,  30.,   0.,  10.],\n",
       "       [  0.,   0.,   0.,   0.,  10.,  30.,  10.,  60.,  60.,  50.,  50.,\n",
       "         60.,  30.,  60., 130.,  30., 100., 130.,  90., 160., 150.,  90.,\n",
       "        110.,  40.,  60.,  40., 110., 110., 120.,  30.,  50.,  80.,  70.,\n",
       "         40.,  60.,  40.,  10.,  30.,   0.,   0.],\n",
       "       [  0.,  10.,  20.,  10.,  10.,  10.,  40.,  10.,  50.,  20.,  30.,\n",
       "         30., 100.,  70., 130.,  50.,  70., 170.,  60.,  90., 120., 130.,\n",
       "        100.,  40., 120.,  40.,  60.,  70.,  60., 100.,  80.,  20.,  50.,\n",
       "         10.,  30.,  40.,  20.,   0.,  20.,   0.],\n",
       "       [  0.,  10.,  10.,   0.,  10.,  20.,  40.,  30.,  60.,  40.,  70.,\n",
       "         50.,  50.,  70.,  50., 100.,  70.,  80., 180., 170.,  90.,  80.,\n",
       "        120., 110., 110., 110., 140.,  60., 110.,  20., 100.,  90.,  50.,\n",
       "         60.,  50.,  30.,  40.,  20.,  20.,   0.],\n",
       "       [  0.,   0.,   0.,   0.,  20.,  30.,  60.,  50.,  70.,  40.,  30.,\n",
       "         20., 100.,  70.,  90., 110., 120.,  50., 120.,  90., 130., 190.,\n",
       "        140., 170.,  50., 130.,  80., 110.,  50., 100.,  90.,  80.,  70.,\n",
       "         50.,  30.,  10.,  40.,   0.,  20.,  10.],\n",
       "       [  0.,   0.,   0.,  10.,  20.,  10.,  40.,  30.,  60.,  70.,  40.,\n",
       "         20., 120.,  60., 120.,  40., 140.,  70.,  80., 140., 130., 190.,\n",
       "         60.,  90., 120.,  60.,  40., 130.,  60.,  70.,  20.,  30.,  60.,\n",
       "         50.,  40.,  20.,  30.,   0.,  10.,   0.],\n",
       "       [  0.,   0.,  20.,  10.,  20.,  50.,  40.,  20.,  70.,  80.,  40.,\n",
       "         70., 110.,  90.,  80., 110., 150., 170., 110., 120.,  70., 120.,\n",
       "         70.,  60.,  70.,  40., 130.,  50.,  70.,  60.,  60.,  90.,  20.,\n",
       "         10.,  10.,  20.,  20.,   0.,  10.,   0.],\n",
       "       [  0.,  10.,  20.,   0.,  10.,  40.,  30.,  20.,  20.,  70.,  30.,\n",
       "         30., 120., 130., 110., 130.,  60.,  50.,  90., 160.,  90., 190.,\n",
       "        160., 110.,  80.,  90., 140., 120., 110.,  90.,  60.,  60.,  60.,\n",
       "         10.,  10.,  20.,  40.,  30.,  10.,  10.],\n",
       "       [  0.,  10.,  10.,  30.,  10.,  40.,  40.,  10.,  80.,  20.,  20.,\n",
       "         30., 120., 120., 100., 150., 130.,  60.,  50.,  50., 180., 190.,\n",
       "         90.,  60., 110., 120.,  70.,  60.,  30.,  60.,  30.,  20.,  40.,\n",
       "         30.,  10.,  50.,  40.,  20.,  20.,   0.],\n",
       "       [  0.,   0.,  20.,  30.,  20.,  30.,  20.,  60.,  30.,  80.,  70.,\n",
       "         40.,  60.,  60.,  90.,  50., 120., 120.,  80.,  50., 120., 100.,\n",
       "        160.,  70., 140., 120.,  50.,  40.,  60.,  90.,  80.,  50.,  60.,\n",
       "         60.,  10.,  40.,  30.,   0.,  20.,   0.],\n",
       "       [  0.,   0.,   0.,  30.,  40.,  50.,  10.,  70.,  70.,  80.,  20.,\n",
       "         50., 120.,  40., 100., 140.,  50.,  50., 170., 130., 160., 150.,\n",
       "        130.,  60., 120.,  90., 100.,  30.,  30.,  70.,  40.,  40.,  80.,\n",
       "         20.,  60.,  50.,  10.,   0.,  10.,   0.],\n",
       "       [  0.,  10.,  10.,  10.,  10.,  30.,  30.,  20.,  60.,  30.,  90.,\n",
       "         70.,  80.,  80.,  40., 130.,  70., 140., 110., 150., 140., 130.,\n",
       "         50., 130.,  70., 140.,  90., 100.,  50., 110.,  50.,  30.,  50.,\n",
       "         10.,  10.,  40.,  40.,  10.,  20.,   0.],\n",
       "       [  0.,  10.,  10.,  10.,  20.,  30.,  50.,  30.,  60.,  30.,  70.,\n",
       "        100.,  30.,  80., 120.,  40., 120.,  90., 150.,  50., 170., 160.,\n",
       "         50., 100., 100., 150.,  70.,  50.,  30., 110.,  50.,  50.,  60.,\n",
       "         10.,  10.,  10.,  10.,   0.,  20.,  10.],\n",
       "       [  0.,   0.,  20.,  10.,  30.,  30.,  20.,  70.,  40.,  40.,  30.,\n",
       "         80., 120.,  90., 120.,  90.,  50., 160.,  80., 170.,  70., 110.,\n",
       "        140.,  70., 130., 110.,  70., 120., 120.,  70.,  80.,  50.,  70.,\n",
       "         20.,  20.,  40.,  10.,  10.,  10.,   0.],\n",
       "       [  0.,   0.,  10.,  20.,  40.,  20.,  20.,  30.,  50.,  70., 100.,\n",
       "         50.,  50., 120.,  30., 130.,  40., 130.,  70., 150.,  90., 120.,\n",
       "        180., 140., 160., 120.,  30., 110.,  30.,  20.,  70.,  40.,  80.,\n",
       "         20.,  20.,  10.,  30.,   0.,  10.,  10.],\n",
       "       [  0.,   0.,  10.,  10.,  10.,  50.,  10.,  50.,  20.,  20.,  40.,\n",
       "        100.,  40.,  80., 140.,  60., 150.,  60., 120., 150., 150., 130.,\n",
       "         70., 170.,  40.,  50., 110.,  40.,  80.,  70.,  90.,  40.,  50.,\n",
       "         30.,  20.,  50.,  40.,  30.,  20.,  10.],\n",
       "       [  0.,   0.,  20.,  20.,  30.,  40.,  60.,  30.,  70.,  60.,  40.,\n",
       "         50.,  80.,  40.,  70.,  70.,  60., 110., 120., 190., 200., 180.,\n",
       "         90.,  50.,  40.,  70., 140.,  80.,  40.,  30.,  70.,  70.,  80.,\n",
       "         30.,  50.,  40.,  10.,  30.,  10.,   0.],\n",
       "       [  0.,   0.,   0.,  10.,  40.,  40.,  60.,  30.,  80.,  60.,  40.,\n",
       "        100., 120.,  30.,  30.,  60.,  80.,  70., 170., 160., 140., 150.,\n",
       "        170.,  40., 140., 130.,  40.,  40., 120., 110.,  60.,  90.,  50.,\n",
       "         50.,  20.,  50.,  20.,  10.,   0.,  10.],\n",
       "       [  0.,  10.,  10.,   0.,  30.,  20.,  40.,  60.,  80.,  60.,  20.,\n",
       "         30., 110.,  30., 140., 140., 120.,  80.,  80., 160., 130.,  70.,\n",
       "         60.,  90., 150.,  70.,  60.,  40., 100.,  80., 100.,  40.,  20.,\n",
       "         60.,  50.,  50.,  20.,  30.,  20.,  10.],\n",
       "       [  0.,   0.,  20.,  30.,  30.,  40.,  50.,  30.,  60.,  70., 100.,\n",
       "         50., 100., 130., 140.,  30.,  80., 100.,  90.,  90., 190., 150.,\n",
       "        150.,  60.,  80.,  80., 110.,  50.,  50.,  70.,  30.,  60.,  60.,\n",
       "         40.,  50.,  20.,  20.,  30.,   0.,   0.],\n",
       "       [  0.,  10.,  20.,  20.,  20.,  30.,  60.,  60.,  60.,  70.,  60.,\n",
       "         30., 110., 120., 130., 150., 150., 100., 140., 110., 110.,  80.,\n",
       "         60., 120., 100.,  50., 120.,  70.,  70., 110.,  50.,  80.,  50.,\n",
       "         20.,  50.,  50.,  20.,   0.,  20.,  10.],\n",
       "       [  0.,   0.,  20.,  10.,  30.,  50.,  60.,  70.,  50.,  80.,  90.,\n",
       "         30., 120., 100., 120.,  40., 120.,  90., 130., 100., 100.,  60.,\n",
       "        100., 110.,  40., 150., 130.,  70.,  30.,  40.,  20.,  90.,  70.,\n",
       "         20.,  40.,  20.,  10.,  20.,  10.,  10.],\n",
       "       [  0.,   0.,  10.,  20.,  40.,  10.,  50.,  50.,  20.,  30.,  40.,\n",
       "         80.,  80., 120.,  50., 150.,  90., 170.,  70., 190., 140., 180.,\n",
       "        120., 170., 140.,  40., 130., 130.,  80., 110.,  50.,  60.,  60.,\n",
       "         20.,  30.,  50.,  20.,  10.,  10.,  10.],\n",
       "       [  0.,   0.,   0.,  30.,  10.,  30.,  60.,  40.,  30.,  40.,  80.,\n",
       "         30.,  40.,  80.,  30., 110.,  50.,  70., 100.,  50., 150.,  90.,\n",
       "        160., 170., 160.,  30.,  80.,  90.,  80.,  30.,  30.,  90.,  50.,\n",
       "         10.,  60.,  50.,  40.,  20.,  20.,   0.],\n",
       "       [  0.,  10.,  20.,  20.,  20.,  50.,  50.,  10.,  40.,  60.,  30.,\n",
       "         60.,  50.,  90.,  60.,  70.,  40.,  70., 160.,  70., 160., 130.,\n",
       "         90., 160., 120.,  60.,  70.,  90., 100.,  30.,  60.,  40.,  50.,\n",
       "         40.,  60.,  30.,  40.,  30.,  20.,  10.],\n",
       "       [  0.,  10.,  10.,  20.,  30.,  10.,  50.,  10.,  20.,  20.,  50.,\n",
       "         70.,  60.,  60.,  50., 100.,  60.,  70., 170., 130., 150., 160.,\n",
       "        170., 140.,  40.,  40., 100., 100., 100., 110.,  90.,  90.,  50.,\n",
       "         40.,  40.,  20.,  10.,   0.,  10.,   0.],\n",
       "       [  0.,  10.,   0.,  30.,  20.,  40.,  10.,  10.,  50.,  90., 100.,\n",
       "         70., 120., 100.,  90., 150., 120., 130., 130.,  60., 190.,  90.,\n",
       "        100.,  60., 130.,  50., 130.,  60.,  70.,  20.,  50.,  50.,  20.,\n",
       "         10.,  10.,  10.,  10.,  30.,   0.,  10.],\n",
       "       [  0.,  10.,  10.,  30.,  10.,  10.,  50.,  50.,  30.,  70.,  20.,\n",
       "         20.,  30., 120.,  40.,  60.,  80., 150., 160., 160., 150.,  40.,\n",
       "        140.,  50., 130., 100.,  70., 100.,  60.,  30.,  20.,  30.,  60.,\n",
       "         30.,  30.,  50.,  40.,  30.,  20.,  10.],\n",
       "       [  0.,   0.,   0.,  20.,  20.,  10.,  30.,  40.,  50.,  50.,  60.,\n",
       "         50.,  50., 120., 130.,  50.,  70.,  50., 110., 150., 180.,  70.,\n",
       "         90., 100., 140., 120., 110.,  90., 100.,  30.,  20.,  90.,  60.,\n",
       "         20.,  20.,  50.,  30.,   0.,   0.,  10.],\n",
       "       [  0.,   0.,  10.,  30.,  30.,  10.,  20.,  10.,  80.,  90.,  20.,\n",
       "         80., 100.,  30.,  80.,  60., 100., 130., 110., 170., 190.,  60.,\n",
       "         40., 110.,  60., 120.,  70.,  50.,  50.,  40.,  40.,  80.,  20.,\n",
       "         60.,  60.,  40.,  20.,  20.,   0.,   0.],\n",
       "       [  0.,  10.,  10.,  30.,  40.,  50.,  20.,  10.,  30.,  70.,  90.,\n",
       "         60., 100.,  50.,  80., 150., 110., 120., 150.,  60., 120., 160.,\n",
       "         60.,  40., 140.,  30., 120.,  90.,  60., 110.,  50.,  80.,  50.,\n",
       "         50.,  60.,  10.,  20.,  10.,  20.,   0.],\n",
       "       [  0.,   0.,  10.,  30.,  10.,  40.,  30.,  60.,  70.,  80.,  50.,\n",
       "         70., 110.,  30.,  60., 110.,  60., 100.,  60., 190., 180., 140.,\n",
       "         60., 100.,  70.,  90.,  80.,  50.,  80.,  30., 100.,  20.,  50.,\n",
       "         10.,  50.,  40.,  20.,  10.,   0.,  10.],\n",
       "       [  0.,  10.,  10.,  30.,  30.,  40.,  40.,  60.,  30.,  40.,  90.,\n",
       "         90.,  70.,  60.,  80., 150., 120., 150.,  60., 110.,  60., 180.,\n",
       "         50., 140., 150., 120.,  90.,  80.,  30.,  60., 100.,  60.,  80.,\n",
       "         70.,  20.,  50.,  40.,  30.,  10.,  10.],\n",
       "       [  0.,  10.,  20.,  20.,  40.,  30.,  10.,  40.,  80.,  90.,  50.,\n",
       "        100., 100.,  30.,  40.,  60.,  70., 110., 160.,  60., 140.,  90.,\n",
       "        110., 100., 100.,  70., 100.,  80.,  80.,  40.,  50.,  80.,  40.,\n",
       "         40.,  50.,  20.,  40.,  10.,  10.,   0.],\n",
       "       [  0.,   0.,  20.,  30.,  40.,  50.,  40.,  60.,  20.,  90.,  70.,\n",
       "         40.,  90., 100.,  80., 110., 160., 120., 150., 170., 190., 100.,\n",
       "        180., 130., 150., 110.,  80.,  40.,  70., 110.,  60.,  70.,  60.,\n",
       "         50.,  10.,  30.,  10.,   0.,   0.,   0.],\n",
       "       [  0.,  10.,  10.,  30.,  10.,  40.,  60.,  20.,  80.,  20., 100.,\n",
       "         30., 110.,  90., 130., 150.,  50., 150.,  60., 100., 100.,  50.,\n",
       "        140., 150., 120.,  70.,  40.,  50., 110.,  40.,  60.,  90.,  50.,\n",
       "         60.,  10.,  10.,  20.,  10.,  20.,  10.],\n",
       "       [  0.,   0.,  10.,  30.,  20.,  50.,  10.,  20.,  70.,  60.,  60.,\n",
       "         30., 120.,  90.,  40., 140.,  40.,  60., 120.,  90., 120.,  70.,\n",
       "        110.,  70., 160.,  80., 130.,  60.,  70.,  60., 100.,  70.,  60.,\n",
       "         30.,  10.,  50.,  40.,  30.,   0.,   0.],\n",
       "       [  0.,   0.,  10.,  20.,  30.,  40.,  50.,  70.,  50.,  40., 100.,\n",
       "         50., 120., 120.,  50.,  40.,  70.,  90., 180., 160., 160., 100.,\n",
       "        150., 150., 100.,  40.,  30.,  70.,  50.,  90.,  40.,  60.,  20.,\n",
       "         40.,  10.,  40.,  20.,  20.,  20.,  10.],\n",
       "       [  0.,  10.,  20.,  10.,  10.,  30.,  50.,  30.,  60.,  30., 100.,\n",
       "        100., 110., 100., 130., 100., 130.,  60.,  60., 140.,  50.,  40.,\n",
       "         50.,  50.,  90.,  40., 120.,  70.,  70.,  40.,  70.,  90.,  30.,\n",
       "         30.,  60.,  30.,  40.,  10.,  20.,   0.],\n",
       "       [  0.,  10.,  20.,  20.,  30.,  50.,  20.,  40.,  50.,  60.,  80.,\n",
       "         30.,  50.,  40.,  30., 150., 150., 120., 160.,  70., 200., 150.,\n",
       "        120.,  80.,  90.,  60., 120.,  50.,  80.,  30.,  80.,  50.,  40.,\n",
       "         10.,  30.,  20.,  10.,  30.,  10.,   0.],\n",
       "       [  0.,   0.,   0.,  20.,  40.,  40.,  50.,  30.,  30.,  30., 100.,\n",
       "         40.,  40.,  40., 140., 110., 150., 130., 100., 140., 110., 170.,\n",
       "         90., 110., 110.,  70., 100., 120., 100., 100., 100.,  80.,  70.,\n",
       "         50.,  20.,  20.,  40.,  10.,  20.,  10.],\n",
       "       [  0.,   0.,  20.,  10.,  10.,  40.,  40.,  70.,  20.,  90.,  40.,\n",
       "        100., 120.,  70.,  60.,  60., 110., 120.,  90., 150., 150.,  60.,\n",
       "         60., 130.,  50., 120.,  90.,  60.,  40.,  70.,  70.,  60.,  50.,\n",
       "         40.,  10.,  40.,  20.,  20.,  20.,  10.],\n",
       "       [  0.,  10.,  20.,  10.,  10.,  40.,  50.,  40.,  40.,  50.,  90.,\n",
       "         70., 100.,  30., 130., 130.,  80.,  90., 170., 160., 160., 150.,\n",
       "        120., 130.,  50., 120., 100.,  90., 110.,  90.,  40.,  50.,  50.,\n",
       "         20.,  20.,  50.,  10.,   0.,   0.,  10.],\n",
       "       [  0.,   0.,  10.,  30.,  20.,  30.,  60.,  40.,  50.,  70.,  20.,\n",
       "         40., 110., 110.,  30.,  80.,  80., 160.,  50., 130., 160.,  50.,\n",
       "         80.,  80.,  60.,  90., 100., 100.,  90.,  30.,  30.,  50.,  30.,\n",
       "         50.,  40.,  50.,  30.,  30.,   0.,  10.],\n",
       "       [  0.,  10.,  10.,  20.,  20.,  50.,  10.,  70.,  40.,  20.,  50.,\n",
       "         50.,  40.,  60.,  60.,  40., 160., 110., 140., 160., 140., 140.,\n",
       "         80., 170.,  40., 140., 130.,  70.,  60.,  30.,  70.,  70.,  50.,\n",
       "         60.,  30.,  40.,  20.,  20.,  10.,  10.],\n",
       "       [  0.,  10.,  10.,  10.,  40.,  10.,  60.,  40.,  60.,  30.,  60.,\n",
       "         50.,  60.,  40., 140., 130., 130.,  90., 120., 190.,  90., 100.,\n",
       "        150., 100.,  90., 100., 100.,  70.,  50.,  60.,  80.,  60.,  60.,\n",
       "         40.,  30.,  50.,  20.,  10.,  10.,  10.],\n",
       "       [  0.,   0.,   0.,  10.,  40.,  50.,  60.,  30.,  80.,  70.,  90.,\n",
       "        100.,  80.,  60.,  50., 120., 150.,  50., 100.,  50.,  80., 130.,\n",
       "        180., 170., 140.,  90., 130.,  40., 100., 110., 100.,  80.,  80.,\n",
       "         60.,  50.,  50.,  20.,   0.,  20.,   0.],\n",
       "       [  0.,   0.,  10.,   0.,  30.,  20.,  50.,  40.,  80.,  20.,  90.,\n",
       "         30.,  30., 100., 120.,  90., 140., 110., 130.,  80.,  60., 180.,\n",
       "        110.,  90., 130., 110.,  80.,  50.,  50.,  20.,  80.,  50.,  30.,\n",
       "         50.,  40.,  10.,  30.,  10.,  10.,   0.]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  2.,  4.,  2.,  3.,  5.,  8.,  9.,  4.,  4.,  4., 11.,\n",
       "         6.,  8.,  5.,  8.,  8., 13., 19.,  7., 14., 12., 12.,  8.,  8.,\n",
       "         5.,  7.,  9.,  9.,  5.,  5.,  6.,  8.,  4.,  5.,  3.,  4.,  1.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  3.,  2.,  3.,  2.,  4.,  3.,  3.,  7., 11., 12.,  6.,\n",
       "        10.,  5.,  5.,  8., 17.,  9.,  7., 19.,  5., 13.,  6., 13.,  8.,\n",
       "        12.,  6., 12.,  4.,  4.,  6.,  5.,  5.,  6.,  6.,  2.,  2.,  1.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  4.,  4.,  3.,  7.,  3.,  6., 10.,  6.,  8.,  5.,\n",
       "         6.,  5., 16.,  6., 12., 10., 11., 20., 15., 13., 18.,  8., 13.,\n",
       "        12.,  8.,  5.,  3., 11.,  6.,  5.,  3.,  3.,  4.,  3.,  3.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  3.,  1.,  5.,  3.,  3.,  2.,  7.,  8., 11.,  8., 10.,\n",
       "        14.,  9.,  9., 16., 11., 11.,  8., 18.,  5.,  5.,  8.,  7., 16.,\n",
       "         7.,  5., 10., 12.,  4.,  6.,  7.,  4.,  4.,  5.,  3.,  4.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  4.,  4.,  2.,  4.,  6.,  3.,  5.,  5.,  8.,  7.,\n",
       "         6.,  4., 11.,  9., 11.,  7., 18., 10., 15., 10.,  8., 14., 10.,\n",
       "        13.,  7.,  8.,  8., 10.,  7.,  4.,  3.,  3.,  5.,  3.,  1.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  2.,  3.,  3.,  5.,  3.,  2.,  7.,  5.,  8.,  7.,  7.,\n",
       "        10., 10., 16.,  5., 17., 19., 13., 13.,  6., 19., 10.,  6.,  4.,\n",
       "        11.,  4., 13.,  8.,  9.,  5.,  8.,  4.,  6.,  5.,  5.,  4.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  3.,  3.,  5.,  3.,  3.,  6.,  6.,  9.,  7.,  6., 12.,\n",
       "        10.,  5., 14.,  6., 13., 11.,  7., 10., 18., 16.,  9., 10.,  4.,\n",
       "        14.,  8.,  9.,  3.,  9.,  9.,  5.,  3.,  4.,  6.,  5.,  2.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  2.,  3.,  4.,  2.,  3.,  4.,  6.,  4.,  8.,  9.,  9.,\n",
       "         6., 11., 10., 16., 12., 19., 20., 21.,  9.,  6., 14., 16., 11.,\n",
       "         7., 11.,  7.,  8.,  5., 10.,  4.,  6.,  3.,  6.,  4.,  3.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  1.,  4.,  2.,  6.,  7.,  6.,  6.,  9.,  3.,  5., 12.,\n",
       "        13., 11., 12., 10., 11., 18., 12.,  7., 17., 13.,  7.,  9., 15.,\n",
       "         7., 14., 11., 12.,  5.,  7.,  5.,  8.,  7.,  4.,  3.,  2.,  1.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  2.,  3.,  2.,  4.,  6.,  4.,  6.,  9.,  7.,  9., 13.,\n",
       "         6., 14.,  7., 14.,  9., 17.,  9., 19., 16., 17., 15., 13.,  8.,\n",
       "         4.,  9., 10., 12.,  3.,  6.,  5.,  6.,  2.,  5.,  2.,  3.,  1.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  1.,  1.,  5.,  4.,  4.,  6.,  6.,  5.,  6.,  9.,  8.,\n",
       "        11., 14.,  4.,  8., 14., 16., 19.,  9., 16., 16., 17., 12., 15.,\n",
       "        13.,  5., 11., 11.,  5.,  4.,  5.,  6.,  6.,  4.,  4.,  3.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  1.,  1.,  4.,  5.,  3.,  8.,  9.,  6.,  3.,  9., 12.,\n",
       "         6.,  6.,  9., 15., 12.,  7., 12., 10., 17., 19.,  7., 13.,  6.,\n",
       "         5.,  4.,  6.,  8.,  9.,  4.,  6.,  5.,  6.,  6.,  5.,  1.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  3.,  2.,  5.,  4.,  7.,  5.,  7.,  8., 10., 10.,  4.,\n",
       "        12.,  7., 13.,  5., 18., 14., 16., 14., 13.,  9.,  8.,  5.,  8.,\n",
       "        13., 10.,  6.,  7.,  6.,  5.,  8.,  4.,  6.,  5.,  3.,  4.,  1.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  1.,  1.,  2.,  4.,  2.,  7.,  7.,  6.,  6.,  7.,  4.,\n",
       "         7., 14.,  4., 11., 14., 10., 17., 16., 10., 12.,  5.,  7.,  5.,\n",
       "        12., 12., 13.,  4.,  6.,  9.,  8.,  5.,  7.,  5.,  2.,  4.,  1.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  3.,  2.,  2.,  2.,  5.,  2.,  6.,  3.,  4.,  4., 11.,\n",
       "         8., 14.,  6.,  8., 18.,  7., 10., 13., 14., 11.,  5., 13.,  5.,\n",
       "         7.,  8.,  7., 11.,  9.,  3.,  6.,  2.,  4.,  5.,  3.,  1.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  2.,  1.,  2.,  3.,  5.,  4.,  7.,  5.,  8.,  6.,  6.,\n",
       "         8.,  6., 11.,  8.,  9., 19., 18., 10.,  9., 13., 12., 12., 12.,\n",
       "        15.,  7., 12.,  3., 11., 10.,  6.,  7.,  6.,  4.,  5.,  3.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  1.,  1.,  3.,  4.,  7.,  6.,  8.,  5.,  4.,  3., 11.,\n",
       "         8., 10., 12., 13.,  6., 13., 10., 14., 20., 15., 18.,  6., 14.,\n",
       "         9., 12.,  6., 11., 10.,  9.,  8.,  6.,  4.,  2.,  5.,  1.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  1.,  2.,  3.,  2.,  5.,  4.,  7.,  8.,  5.,  3., 13.,\n",
       "         7., 13.,  5., 15.,  8.,  9., 15., 14., 20.,  7., 10., 13.,  7.,\n",
       "         5., 14.,  7.,  8.,  3.,  4.,  7.,  6.,  5.,  3.,  4.,  1.,  2.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  3.,  2.,  3.,  6.,  5.,  3.,  8.,  9.,  5.,  8., 12.,\n",
       "        10.,  9., 12., 16., 18., 12., 13.,  8., 13.,  8.,  7.,  8.,  5.,\n",
       "        14.,  6.,  8.,  7.,  7., 10.,  3.,  2.,  2.,  3.,  3.,  1.,  2.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  3.,  1.,  2.,  5.,  4.,  3.,  3.,  8.,  4.,  4., 13.,\n",
       "        14., 12., 14.,  7.,  6., 10., 17., 10., 20., 17., 12.,  9., 10.,\n",
       "        15., 13., 12., 10.,  7.,  7.,  7.,  2.,  2.,  3.,  5.,  4.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  4.,  2.,  5.,  5.,  2.,  9.,  3.,  3.,  4., 13.,\n",
       "        13., 11., 16., 14.,  7.,  6.,  6., 19., 20., 10.,  7., 12., 13.,\n",
       "         8.,  7.,  4.,  7.,  4.,  3.,  5.,  4.,  2.,  6.,  5.,  3.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  3.,  4.,  3.,  4.,  3.,  7.,  4.,  9.,  8.,  5.,  7.,\n",
       "         7., 10.,  6., 13., 13.,  9.,  6., 13., 11., 17.,  8., 15., 13.,\n",
       "         6.,  5.,  7., 10.,  9.,  6.,  7.,  7.,  2.,  5.,  4.,  1.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  1.,  4.,  5.,  6.,  2.,  8.,  8.,  9.,  3.,  6., 13.,\n",
       "         5., 11., 15.,  6.,  6., 18., 14., 17., 16., 14.,  7., 13., 10.,\n",
       "        11.,  4.,  4.,  8.,  5.,  5.,  9.,  3.,  7.,  6.,  2.,  1.,  2.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  2.,  2.,  2.,  4.,  4.,  3.,  7.,  4., 10.,  8.,  9.,\n",
       "         9.,  5., 14.,  8., 15., 12., 16., 15., 14.,  6., 14.,  8., 15.,\n",
       "        10., 11.,  6., 12.,  6.,  4.,  6.,  2.,  2.,  5.,  5.,  2.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  2.,  2.,  3.,  4.,  6.,  4.,  7.,  4.,  8., 11.,  4.,\n",
       "         9., 13.,  5., 13., 10., 16.,  6., 18., 17.,  6., 11., 11., 16.,\n",
       "         8.,  6.,  4., 12.,  6.,  6.,  7.,  2.,  2.,  2.,  2.,  1.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  3.,  2.,  4.,  4.,  3.,  8.,  5.,  5.,  4.,  9., 13.,\n",
       "        10., 13., 10.,  6., 17.,  9., 18.,  8., 12., 15.,  8., 14., 12.,\n",
       "         8., 13., 13.,  8.,  9.,  6.,  8.,  3.,  3.,  5.,  2.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  2.,  3.,  5.,  3.,  3.,  4.,  6.,  8., 11.,  6.,  6.,\n",
       "        13.,  4., 14.,  5., 14.,  8., 16., 10., 13., 19., 15., 17., 13.,\n",
       "         4., 12.,  4.,  3.,  8.,  5.,  9.,  3.,  3.,  2.,  4.,  1.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  2.,  2.,  2.,  6.,  2.,  6.,  3.,  3.,  5., 11.,  5.,\n",
       "         9., 15.,  7., 16.,  7., 13., 16., 16., 14.,  8., 18.,  5.,  6.,\n",
       "        12.,  5.,  9.,  8., 10.,  5.,  6.,  4.,  3.,  6.,  5.,  4.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  3.,  3.,  4.,  5.,  7.,  4.,  8.,  7.,  5.,  6.,  9.,\n",
       "         5.,  8.,  8.,  7., 12., 13., 20., 21., 19., 10.,  6.,  5.,  8.,\n",
       "        15.,  9.,  5.,  4.,  8.,  8.,  9.,  4.,  6.,  5.,  2.,  4.,  2.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  1.,  2.,  5.,  5.,  7.,  4.,  9.,  7.,  5., 11., 13.,\n",
       "         4.,  4.,  7.,  9.,  8., 18., 17., 15., 16., 18.,  5., 15., 14.,\n",
       "         5.,  5., 13., 12.,  7., 10.,  6.,  6.,  3.,  6.,  3.,  2.,  1.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  1.,  4.,  3.,  5.,  7.,  9.,  7.,  3.,  4., 12.,\n",
       "         4., 15., 15., 13.,  9.,  9., 17., 14.,  8.,  7., 10., 16.,  8.,\n",
       "         7.,  5., 11.,  9., 11.,  5.,  3.,  7.,  6.,  6.,  3.,  4.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  3.,  4.,  4.,  5.,  6.,  4.,  7.,  8., 11.,  6., 11.,\n",
       "        14., 15.,  4.,  9., 11., 10., 10., 20., 16., 16.,  7.,  9.,  9.,\n",
       "        12.,  6.,  6.,  8.,  4.,  7.,  7.,  5.,  6.,  3.,  3.,  4.,  1.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  3.,  3.,  3.,  4.,  7.,  7.,  7.,  8.,  7.,  4., 12.,\n",
       "        13., 14., 16., 16., 11., 15., 12., 12.,  9.,  7., 13., 11.,  6.,\n",
       "        13.,  8.,  8., 12.,  6.,  9.,  6.,  3.,  6.,  6.,  3.,  1.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  3.,  2.,  4.,  6.,  7.,  8.,  6.,  9., 10.,  4., 13.,\n",
       "        11., 13.,  5., 13., 10., 14., 11., 11.,  7., 11., 12.,  5., 16.,\n",
       "        14.,  8.,  4.,  5.,  3., 10.,  8.,  3.,  5.,  3.,  2.,  3.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  2.,  3.,  5.,  2.,  6.,  6.,  3.,  4.,  5.,  9.,  9.,\n",
       "        13.,  6., 16., 10., 18.,  8., 20., 15., 19., 13., 18., 15.,  5.,\n",
       "        14., 14.,  9., 12.,  6.,  7.,  7.,  3.,  4.,  6.,  3.,  2.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  1.,  4.,  2.,  4.,  7.,  5.,  4.,  5.,  9.,  4.,  5.,\n",
       "         9.,  4., 12.,  6.,  8., 11.,  6., 16., 10., 17., 18., 17.,  4.,\n",
       "         9., 10.,  9.,  4.,  4., 10.,  6.,  2.,  7.,  6.,  5.,  3.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  3.,  3.,  3.,  6.,  6.,  2.,  5.,  7.,  4.,  7.,  6.,\n",
       "        10.,  7.,  8.,  5.,  8., 17.,  8., 17., 14., 10., 17., 13.,  7.,\n",
       "         8., 10., 11.,  4.,  7.,  5.,  6.,  5.,  7.,  4.,  5.,  4.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  3.,  4.,  2.,  6.,  2.,  3.,  3.,  6.,  8.,  7.,\n",
       "         7.,  6., 11.,  7.,  8., 18., 14., 16., 17., 18., 15.,  5.,  5.,\n",
       "        11., 11., 11., 12., 10., 10.,  6.,  5.,  5.,  3.,  2.,  1.,  2.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  1.,  4.,  3.,  5.,  2.,  2.,  6., 10., 11.,  8., 13.,\n",
       "        11., 10., 16., 13., 14., 14.,  7., 20., 10., 11.,  7., 14.,  6.,\n",
       "        14.,  7.,  8.,  3.,  6.,  6.,  3.,  2.,  2.,  2.,  2.,  4.,  1.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  4.,  2.,  2.,  6.,  6.,  4.,  8.,  3.,  3.,  4.,\n",
       "        13.,  5.,  7.,  9., 16., 17., 17., 16.,  5., 15.,  6., 14., 11.,\n",
       "         8., 11.,  7.,  4.,  3.,  4.,  7.,  4.,  4.,  6.,  5.,  4.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  1.,  3.,  3.,  2.,  4.,  5.,  6.,  6.,  7.,  6.,  6.,\n",
       "        13., 14.,  6.,  8.,  6., 12., 16., 19.,  8., 10., 11., 15., 13.,\n",
       "        12., 10., 11.,  4.,  3., 10.,  7.,  3.,  3.,  6.,  4.,  1.,  1.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  2.,  4.,  4.,  2.,  3.,  2.,  9., 10.,  3.,  9., 11.,\n",
       "         4.,  9.,  7., 11., 14., 12., 18., 20.,  7.,  5., 12.,  7., 13.,\n",
       "         8.,  6.,  6.,  5.,  5.,  9.,  3.,  7.,  7.,  5.,  3.,  3.,  1.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  2.,  4.,  5.,  6.,  3.,  2.,  4.,  8., 10.,  7., 11.,\n",
       "         6.,  9., 16., 12., 13., 16.,  7., 13., 17.,  7.,  5., 15.,  4.,\n",
       "        13., 10.,  7., 12.,  6.,  9.,  6.,  6.,  7.,  2.,  3.,  2.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  2.,  4.,  2.,  5.,  4.,  7.,  8.,  9.,  6.,  8., 12.,\n",
       "         4.,  7., 12.,  7., 11.,  7., 20., 19., 15.,  7., 11.,  8., 10.,\n",
       "         9.,  6.,  9.,  4., 11.,  3.,  6.,  2.,  6.,  5.,  3.,  2.,  1.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  4.,  4.,  5.,  5.,  7.,  4.,  5., 10., 10.,  8.,\n",
       "         7.,  9., 16., 13., 16.,  7., 12.,  7., 19.,  6., 15., 16., 13.,\n",
       "        10.,  9.,  4.,  7., 11.,  7.,  9.,  8.,  3.,  6.,  5.,  4.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  3.,  3.,  5.,  4.,  2.,  5.,  9., 10.,  6., 11., 11.,\n",
       "         4.,  5.,  7.,  8., 12., 17.,  7., 15., 10., 12., 11., 11.,  8.,\n",
       "        11.,  9.,  9.,  5.,  6.,  9.,  5.,  5.,  6.,  3.,  5.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  3.,  4.,  5.,  6.,  5.,  7.,  3., 10.,  8.,  5., 10.,\n",
       "        11.,  9., 12., 17., 13., 16., 18., 20., 11., 19., 14., 16., 12.,\n",
       "         9.,  5.,  8., 12.,  7.,  8.,  7.,  6.,  2.,  4.,  2.,  1.,  1.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  2.,  4.,  2.,  5.,  7.,  3.,  9.,  3., 11.,  4., 12.,\n",
       "        10., 14., 16.,  6., 16.,  7., 11., 11.,  6., 15., 16., 13.,  8.,\n",
       "         5.,  6., 12.,  5.,  7., 10.,  6.,  7.,  2.,  2.,  3.,  2.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  2.,  4.,  3.,  6.,  2.,  3.,  8.,  7.,  7.,  4., 13.,\n",
       "        10.,  5., 15.,  5.,  7., 13., 10., 13.,  8., 12.,  8., 17.,  9.,\n",
       "        14.,  7.,  8.,  7., 11.,  8.,  7.,  4.,  2.,  6.,  5.,  4.,  1.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  2.,  3.,  4.,  5.,  6.,  8.,  6.,  5., 11.,  6., 13.,\n",
       "        13.,  6.,  5.,  8., 10., 19., 17., 17., 11., 16., 16., 11.,  5.,\n",
       "         4.,  8.,  6., 10.,  5.,  7.,  3.,  5.,  2.,  5.,  3.,  3.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  3.,  2.,  2.,  4.,  6.,  4.,  7.,  4., 11., 11., 12.,\n",
       "        11., 14., 11., 14.,  7.,  7., 15.,  6.,  5.,  6.,  6., 10.,  5.,\n",
       "        13.,  8.,  8.,  5.,  8., 10.,  4.,  4.,  7.,  4.,  5.,  2.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  2.,  3.,  3.,  4.,  6.,  3.,  5.,  6.,  7.,  9.,  4.,  6.,\n",
       "         5.,  4., 16., 16., 13., 17.,  8., 21., 16., 13.,  9., 10.,  7.,\n",
       "        13.,  6.,  9.,  4.,  9.,  6.,  5.,  2.,  4.,  3.,  2.,  4.,  2.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  1.,  3.,  5.,  5.,  6.,  4.,  4.,  4., 11.,  5.,  5.,\n",
       "         5., 15., 12., 16., 14., 11., 15., 12., 18., 10., 12., 12.,  8.,\n",
       "        11., 13., 11., 11., 11.,  9.,  8.,  6.,  3.,  3.,  5.,  2.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  3.,  2.,  2.,  5.,  5.,  8.,  3., 10.,  5., 11., 13.,\n",
       "         8.,  7.,  7., 12., 13., 10., 16., 16.,  7.,  7., 14.,  6., 13.,\n",
       "        10.,  7.,  5.,  8.,  8.,  7.,  6.,  5.,  2.,  5.,  3.,  3.,  3.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  3.,  2.,  2.,  5.,  6.,  5.,  5.,  6., 10.,  8., 11.,\n",
       "         4., 14., 14.,  9., 10., 18., 17., 17., 16., 13., 14.,  6., 13.,\n",
       "        11., 10., 12., 10.,  5.,  6.,  6.,  3.,  3.,  6.,  2.,  1.,  1.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  2.,  4.,  3.,  4.,  7.,  5.,  6.,  8.,  3.,  5., 12.,\n",
       "        12.,  4.,  9.,  9., 17.,  6., 14., 17.,  6.,  9.,  9.,  7., 10.,\n",
       "        11., 11., 10.,  4.,  4.,  6.,  4.,  6.,  5.,  6.,  4.,  4.,  1.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  3.,  3.,  6.,  2.,  8.,  5.,  3.,  6.,  6.,  5.,\n",
       "         7.,  7.,  5., 17., 12., 15., 17., 15., 15.,  9., 18.,  5., 15.,\n",
       "        14.,  8.,  7.,  4.,  8.,  8.,  6.,  7.,  4.,  5.,  3.,  3.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  2.,  2.,  2.,  5.,  2.,  7.,  5.,  7.,  4.,  7.,  6.,  7.,\n",
       "         5., 15., 14., 14., 10., 13., 20., 10., 11., 16., 11., 10., 11.,\n",
       "        11.,  8.,  6.,  7.,  9.,  7.,  7.,  5.,  4.,  6.,  3.,  2.,  2.,\n",
       "         2.],\n",
       "       [ 1.,  1.,  1.,  2.,  5.,  6.,  7.,  4.,  9.,  8., 10., 11.,  9.,\n",
       "         7.,  6., 13., 16.,  6., 11.,  6.,  9., 14., 19., 18., 15., 10.,\n",
       "        14.,  5., 11., 12., 11.,  9.,  9.,  7.,  6.,  6.,  3.,  1.,  3.,\n",
       "         1.],\n",
       "       [ 1.,  1.,  2.,  1.,  4.,  3.,  6.,  5.,  9.,  3., 10.,  4.,  4.,\n",
       "        11., 13., 10., 15., 12., 14.,  9.,  7., 19., 12., 10., 14., 12.,\n",
       "         9.,  6.,  6.,  3.,  9.,  6.,  4.,  6.,  5.,  2.,  4.,  2.,  2.,\n",
       "         1.]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.14875"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function on Arrays\n",
    "# numpy.mean(data)\n",
    "\n",
    "data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "averages = numpy.mean(data, axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60,)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "averages.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "otheraverages = numpy.mean(data, axis = 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "numpy.mean?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "otheraverages.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.14875"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mean on Different Axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Notes on Functions\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Mon Jul  8 14:04:09 2019'"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "time.ctime()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning Multiple Variables\n",
    "var1, var2, var3 = 212, 123, \"hi\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "212\n",
      "123\n",
      "hi\n"
     ]
    }
   ],
   "source": [
    "print(var1)\n",
    "print(var2)\n",
    "print(var3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Assigning Measurements of One Patient\n",
    "# Find max: numpy.max"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.0"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.max(data[1, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  1.,  2.,  1.,  3.,  2.,  2.,  6., 10., 11.,  5.,\n",
       "        9.,  4.,  4.,  7., 16.,  8.,  6., 18.,  4., 12.,  5., 12.,  7.,\n",
       "       11.,  5., 11.,  3.,  3.,  5.,  4.,  4.,  5.,  5.,  1.,  1.,  0.,\n",
       "        1.])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  1.,  2.,  1.,  2.,  1.,  3.,  2.,  2.,  6., 10., 11.,  5.,\n",
       "        9.,  4.,  4.,  7., 16.,  8.,  6., 18.,  4., 12.,  5., 12.,  7.,\n",
       "       11.,  5., 11.,  3.,  3.,  5.,  4.,  4.,  5.,  5.,  1.,  1.,  0.,\n",
       "        1.])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.0"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1, ].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18.0"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "patient1 = data[1, ]\n",
    "numpy.max(patient1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 40)"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Producing Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "# MatPlotLib\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(data)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plt.plot()\n",
    "plt.plot?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAD8CAYAAABw1c+bAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzsvXm8XNV15/vdZ6657qx7NQ+IUUwWYJuYBBxjPLcnYqcTxy9JO90vyeuk05nHzos7eemX9OvO4H5O7DiOJ/CAwQSMMTZhsAAJWQKhebiS7lz31lxnPnv3H6csgSPbAoME0vl+PvW5t84+59SuU1W/vc7aa68llFJkZGRkZJw/aGe7AxkZGRkZZ5ZM+DMyMjLOMzLhz8jIyDjPyIQ/IyMj4zwjE/6MjIyM84xM+DMyMjLOMzLhz8jIyDjPyIQ/IyMj4zwjE/6MjIyM8wzjbHfgVAwPD6s1a9ac7W5kZGRkvGJ48sknF5VSI6ez78tS+NesWcO2bdvOdjcyMjIyXjEIIY6e7r6ZqycjIyPjPCMT/oyMjIzzjEz4MzIyMs4zMuHPyMjIOM/IhD8jIyPjPOMHCr8QYqUQ4ptCiD1CiGeEEP+xv31QCHG/EOJA/+/A9zj+Z/r7HBBC/MyL/QYyMjIyMp4fp2Pxx8CvKaUuBl4N/KIQ4hLgt4AHlFIXAA/0nz8HIcQg8IfAdcC1wB9+rwEiIyMjI+PM8AOFXyk1q5Ta3v+/A+wBlgPvAP6xv9s/Av/mFIe/EbhfKVVXSjWA+4FbXoyOZ2ScbQ7d//vMPv3Fs92NjIznzfPy8Qsh1gBXAY8DY0qpWUgHB2D0FIcsB44/6/lUf9upzv0hIcQ2IcS2Wq32fLqVkXHG2bnzUyzf9teIb/6ns92VjIznzWkLvxCiCHwR+BWlVPt0DzvFtlNWd1dKfVQptVkptXlk5LRWHWdknBVmZnYwN/9hjEhR9Dt0unvPdpcyMp4XpyX8QgiTVPQ/rZT6Un/zvBBivN8+Diyc4tApYOWznq8AZl54dzMyzi6e12THjl9ASDCkIu8lzEx95mx3KyPjeXE6UT0C+BiwRyn1l89qugv4TpTOzwB3nuLw+4CbhRAD/Undm/vbMjJecUgpefDBn8Wyl1gx/J8B0BS0Jr9IkvhnuXcZGafP6Vj81wM/DdwkhNjRf7wZ+DPgDUKIA8Ab+s8RQmwWQvw9gFKqDvzfwNb+44/72zIyXnE88ugfYdk7MfR3c8m6G09st9stFhbuPYs9y8h4fvzA7JxKqUc4ta8e4PWn2H8b8PPPev5x4OMvtIMZGS8Hdu++gzD8DGFwCbfc8qcw99SJtmpUZmbmNsbH33kWe5iRcfpkK3czMn4Atdp+jh3/PcJwgBtu+Ac0TQO/daJ9QI7RbG2l1zt0FnuZkXH6ZMKfkfF9CEOXJ7b+LJpI2HTZ31IsDqcNQRrY5uKQ60YIYTAzc9tZ7GlGxumTCX9GxvfhG9/8EI4zy/DIr7Ny5TUnG/oW/xTj6I1Jhodez+zcHUgZnKWeZmScPpnwZ2R8D778Z79H6xsRSt3C1Vf93HMb/dTiN7UcWthhRfVmoqhOrfb1tDmMuOWv7+f+bx8+093OyPiBZMKfkXEKpqamODbT43i8gs3hW/5Vu6ylC9LLyeUADMQDOM5ypmc+B8BvfG47e6dC/nRLJvwZLz8y4c/I+C663S633347gWUS2DZzv/v7BAcPPmef6MBepMoTJWsBEIv7mRh/L43Gt/jy1se5a/ciALMy+4llvPzIvpUZGc8iSRK+8IUv0Ot18R2LyLKQTo6pX/plkk4HABUnJM0QSQGpVpLoDiweYHz8PYDGo09/glVlHWVrdKSgFkZn901lZHwXmfBnZDyLr3/960xOTnLzzTeQGOkyl/Dnf5NwaoqZ3/ptlJQE33oYQYxSDkpW8YwyLO7Dl0Psa17G9ROP8ZoVMcrSEZHkkXrnLL+rjIznkgl/RkafXbt2sWXLFq699lpGRp0T22u5CcZ+4zfoPvAASx/9O3pbjqLRRuohSpUJlI5aPMD/9bkdfOPoqylZbYrOTpSlIcKEh+Zb3+dVMzLOPJnwZ2QA8/Pz3HnnnaxcuZKbb76Z2tzRE231WouBn/4pym97G4sf+RhefRXCWOI77nslQbSneXL/Md792luxrTFWFLaAqWH5MQ+3umfpXWVknJpM+DPOezzP47bbbsO2bW699VYMw2Bxev5Ee7PeRgjB+B//F5zLbgJhoESHRE/bdZm6hP79pZL3X7eW8Yn3sMLZy5BTR0WSqSThqJfF92e8fMiEP+O8RkrJl770JZrNJrfeeiulUgmA1tJJ90y33SNJJMK2sVdtJmkcQg98Yk0CIPQyAL9waQLAYOmtAPzo4MMkUoBUPNzIrP6Mlw+Z8Gec1zz00EMcOHCAW265hVWrVp3Y3m37OJ4HQCJC2jWP8PEtJGqM4kU+ui6Jp9OaQqEaQwkNq3EAgN68yYH4In6k8igCybAreaiRTfBmvHzIhD/jvOXv7/gyf7pvkksvu5RrrrnmOW2un1Do9hBKoLSYxpxL75GDCFwGfuYdCA2S6dTiT8Qg0inD4n4AlmZqPBLdyKdn/4xXDTS4dDHikUYHqU4Wn2s88wRPfPq91A9uPXNvOCOjTyb8Gectd88usnPjlRxptUjrDZ3EjzXyrkuQaEgtojM5g7u0nPyyOTQjFfCmsFEyJFKDJAiopcLfqNWZab2Kx1YPk1TGWb3oU48Sdne9E+ef3/IZOuPb2b3vP5Mkmf8/48xyOhW4Pi6EWBBC7HrWttueVZRlUgix43scOymEeLq/37YXs+MZGT8MzblZOqYFwD9Lm6ce+OqJNiklIQZ5z8VXBsKKKe7ZCtgUbtx0IkHbjD2MCjokchAiF+qHIYmoNxrke+m5XdtmtJUK+wk/fxITijlQgiA3xb79f4BSpyxFnZHxknA6Fv8ngFuevUEp9RNKqSuVUleS1uL90qkO7HNjf9/NL7ybGRkvLk9/82u4dh6Ag+su4e7PfJLZA/sAaLVmkCK1+HuYCDMh1y5iWtNYV1yN6gv/EWcc5XdQYQUrDkBG0JhkwetQ6hv3HcckjmPWC52Hv+PnP/A1gnwX0x1j8MibmZ39AtMznz3j1yDj/OUHCr9S6iHglOUS+/V4bwWyb23GK4Ykjtn1zftxcwUq7QaJprP/itdy11/+V3rNBktLRwBS4RcWChepllO4NA3b9LtpqOee/EpU0EFElZMnX9zPnAzJhan/v5MzaWiK60KNLc0eoZTw5CcI8g3s3jhDB97F0OCPsn//H9NqbT+zFyLjvOWH9fG/DphXSh34Hu0K+JoQ4kkhxId+yNfKyHhROLz9CXqtJq6TZ8XCcTZ4LfZefQNet8vd/+P/YXFxCgDLj/ExiWUM+PCa1wEwvzAJwK7CWlTQQpelE+eWC/tY1CRGGtlJO6fRNgXXNRI8Kdk+exR58H7CwiJWbxwNg43LPoxjj/P0079EENTO5KXIOE/5YYX//Xx/a/96pdTVwJuAXxRC3PC9dhRCfEgIsU0Isa1Wy778GS8dTz9wH7mhEVynQDEKuO7YXiYjydi/+xWmdu9i9/bHATBCia4gRuGqAzRdE4DJY8cAGKg0EaKNECWmtBxKt4mnniZRBUIznSwOLI2epnHVVIAGPLT/STxLQ2kxVm8cADmp2LTpb4niFrt2/TJSZkndMl5aXrDwCyEM4F3A96w3p5Sa6f9dAO4Arv0++35UKbVZKbV5ZGTkhXYrI+P70l5c4MjO7Yxecz2JYVCRMWt3b6Okazw6vIqr3vQ2Os0lhJSQGNxA6pffE1g05lwAFmup53Mwv4jSOwjNYJ+9HoVCzu/Dior41smflmtq5Bd9Li86PNJN6Kx+DQBWL71TCA61KJUu5uKL/pRmaysHD/7ZmbwkGechP4zF/+PAXqXU1KkahRAFIUTpO/8DNwO7TrVvRsaZYtc37wdArErz6A/pGuHCPO8crXB3rckVP/FBEiOP44dEhs0m0lW5x/UxGnMuSim6HZ8QHaVrxHYaqTMlL0ZLQszOYZzAwbVOhodKC1BwvV9ne349jeELALC66UASzfUAWLbs7axc+X9wfOoTzM3deUauR8b5yemEc34W2AJcKISYEkJ8pwbd+/guN48QYkIIcU//6RjwiBBiJ/AE8M9Kqa+SkXGWkDJh1ze/zupNV7IQpO6U8UIOpSTvcMCXii8ttvBFnpwXUCxXycvlABQGdRpzPY7VXcw4INQMEiXw8mmoZuSl++nSY8z36DkCPUlDNJP+IPDqI9uJNYNFPPSgggqngIi47p8I59yw/jepVq9lz97fodPZfSYvT8Z5xOlE9bxfKTWulDKVUiuUUh/rb/+gUup/fde+M0qpN/f/P6yUuqL/uFQp9eGX5i1kZJyanTsf5e9+52McOrIHgMmd2+ks1bj89W9kttNj7VyEv2sAzyqyvLXE5aUcHz9WQ0UmOddjYMUmvpOc2a4IGnMujx5coiRcpKkYCR3ahXQWt9QxT7zu6mAOzxZY4SJCKSIrbbvi0D5slRDGR7F7E9xduYxINCFRJI10ANE0k8su+ysS32Trgx+gsTB75i5YxnlDtnI345xl37avE9bXsu2xfwH6k7rlCus3X0fND1k/FzHULuAt/ynq8/P81PgQR4KQKHLIex7W8Kuwk6cBMAuKXjPg4X0LDIo2ypQMRnkaemqpj7dC5swiAMPaNK4dE4saVugROCZhzkOPlnFtwUAZU+nEbthlwUhdRdHMySRuzekW6snrUIUGX/v730fK5ExetozzgEz4M85JksRFBWnd2257iW6jzqEnn+DSH309umHSkAlFT6JQTDTzfOVpjXeODVAMPFAaFaOK0PNE0bdQgGan+37r8BKjeoPYEORjm4YCpSSj3YTt+dUkSmfAmMY3QcgeVtShZ1t040li80J+dMhAGC5Wd5y6oVgSqeAHR9sAeN0Od/3Fh7EWLwIlEOZBtnz+M2frMmaco2TCn3FOMr9wD4lfACDxPJ558OsoKdl00xsBaCmNspcQm21q1UWGDph8bctxxuZT14o9eCEinKEeHCNUOhgRC7qi5ccMiBaxITCjHB0chOxiiRxH5MW4skrVnCI0BJp0MeIGnbxOI5onikcZ3f8AAEZvnD0jQ8wTAxBMtpEy4Z7/+d/o1uvkxcXYnVUUV0Y89qXbOLj1sbNwFTPOVTLhzzgnmZm5jbA3lj4JBU998z5WXHIZgxPLieOYrmFS8hISPaRT3kK9pJi8/Qhr6+mq3KfXbMBwH6AVOSRSEKuQY2a6GreER2wIVJwjkDY6TYRdYvnuAo14BVXjOKFhImQPkSzSzms0hOCIqlE/ngq4H41zbHCEBmluh3je5Vu3f5rJndu55jXvxtZy5JcuwRquMbZhDff+zV9QnzllAF1GxvMmE/6Mc45udz+t1nb8zgQAZmIx1Zni8r613+l08OwcBR+kFuLEOTT/bpSADYtpbP29y4tY4TdT4U8SPM9jJg9jhoGpJLGhkYQ5othENxoIu0xuKmE+3Iitd5C6gZA9lFwgMgTHChM8ZO5moBqiEptuMshsZZiO8JFIWt0aj99xO5tuuplish5bCAr1SxBawmW3XI9umNz5/36Y0HPP2nXNOHfIhD/jnGNm5jaEMIndKgBaYtMZEVxw3fUAtNttQj2HHUOsh5jSIvIP8cRag1wUgNJ4etBh/8gwrdBBJAmdbpdJFbNe9lfz6gIZ5YkiHV00EfkytjdLLV5Hy0gHD032kGoagIOmh46GqCbgjdOwdDTLILElXeHSjRuMTqzlxg98iN50iK1BrrkRlRi0Ojt566/8Jo2Zab76kf8vy+SZ8UOTCX/GOUWSBMzOfZmRkTdAnCZVk2Ee47IBDCtNldxqtdD6gZqtQhpG2RrIs22hTVLqoic2r93ncduGN+MmJiKOaHV6hEqxrpda3LEhkFEOFQl00UJYJSrt4zTj5TTMVPiNxEfIJQA6OY0b9SsRxgy57jizOQ1DCIKczhI9SuYgN93wc8j5AC9UGEKgJTaquRY/3sqqy67gdf/2gxx4/FtsveuLZ/SaZpx7ZMKfcU5Rq91HHDcZG/4JBOnCqSQooQZPFkFZarWxZDoIBNUIicQtlJEKyvjY0uKmp3yezP8YoWFhxCFR4KOhWJuk54k0DSVN8BWaaKDpOZwwwHUdpvsW//Ioh5ak6R08VrJ/vMygWKTQGWdJj0gQWLMLNDWXolnFahoEB5t4Sp7oq9a4AL1wlHZjns1vfScbX/0jPPLZT3L0qVOWwMjIOC0y4c84p5ie+RyOsxKTK/tbFElYpBsdP+Eime50KXmpuBoVgWe3sIwca4XASxTlxEePmrzliZg9F1yPEfsIFJeN5Cj1J2NDkS7K0vwEnTQ/v7BLFILDHLCHABho22hJEyElvjHAQyP9iKHeOMqtcem3H6Vy7BhN0UUgCI938Q818Tkp/HpjPUIojuz7BkII3vgf/iODy1dw9//8c9q1hZf8emacm2TCn/GKJemFTP3eI7QfOg6A6x6h2Xyc5RO30l4KkEkDv/UxIk+QUz1me6nwznS6FL10EPiF/X/HZze/jy+9+gO8flUVl5AZI2Lzt/+Kq3XBL3auoWT/CAA/Nukzn6zjI3O38+jDf0/QvZOgugJBE0iF3/RnmewL/4x5DIEiF7n0cgaNYvr6Vm+CYu8oP/LEfUytWoNUcwAoN8KfbCGkfuI9mp3lyMhhqfZoeqyT4+2/9rsMXHCch+5+70t9iTPOUTLhz3jF4h9qQazwdqQLtWZmbkcInfHx99BacFHxLMg2MmoxhMnupTT3zVzPpeRLBDHrrWO8buZBrj5+iJun2yihGGw2KEYt1luC5aZGvZpa9+0NFvHAHFcW7sKdeAYVHWKgVWN/cCEAYXklTm+Go1Yq/KGWWuRO2KGd01gTT4HUsNwxlNFGKNh69Q04cVrOIibBjcHRTiZ4yyU5lH8ZkThZpKU6NMbgCok5OoeKT94dZGScLpnwZ7xiiftZLeOGj5QhM7NfZGjoRmx7lNaih1JpO8pnOCmdEP4pCUVPktOaCAGvmXuG9xyqoSfpYqoVi3XsNddiopEAVWED8O4fX8HV4mFeU/oUq8202qhvBGzpvJ16LImqyxmqTzFtDgKwLJ7Hih3MuEk7r3FZPI3pjSKUgeOlVv5CZZRl6ihdfHwi2lJiP6vwew6LcvHVGPl5arOHAHB31NCdACMXE8y1XtqLnHFOkgl/xiuWuJb625UXU1v4BlG0xPKJ9wHQWuqhkjSXvlI+g7LC7vpu2n7AtokNVLyYkp5G3DhumY3JBEta6rKpui2MVTdwFI8HibjJTSeC9+0/gG2kRYJatbReb8cOyGkNtvYS/PIo1U6LppFHlz7rYhcnziP6wj9mzmD3i69U64dRhkbXLlHO9WhpLghYkgpbA0nqirKUweiqNAz16IFvANDbOodw0mikzrHDL+EVzjhXyYQ/4xVLXD8ZqTO/5z5sexlDQ2mRt07TR6l+cXPpY0Y2e5d282t7juLm8gz1fApaA4AhT7JSDjFpplb4IDm00iq+TMxXCBlRqfB/ttbE0JeQCrzF1A9vhR4rRm4nUooD1mqk0HBNCzvpsi6KsJIcKlki0QWu3sPqjZMAhCG6o6GEwC8WCXMJjjLxEjA1CX3hN9Cxx5aTBBUazS2EM13CqTbCSd97Z+bQS3yVM85FMuHPeMWSNMMT/8eHIybG34sQqSB7nRAl0wRoSvnIsEjDupqvNF2GWzXyPuT1VPg3itRPXqOLpkVUBq9AJQH3oHgKjwUEAg0FHK8MspTkSHyFpjs4gcd0xeLygktX5jiw/p0EpkU+7rE2ijESGynTNBANrYrZG0cDgljDtFJx9w2bukpFPiTE1iUCgUKhIWi5DUR0JcrcQefxGaTjgUiP7daPnYlLnXGOkQl/xisSFUuke7I2ba6xkfHxk1EukSdRMvXxK+WxX9vAQuWnuIgWZbeLlliYNEmUYMKIOJY7SgQYRoQ18iqS+jZ6gGYq7iYip0yu9XqElsl+L80BNFLaiJnEHDHHWeksMp7vMb3iJpxkGYXYZ0MYIZRBQhp1tMQQtjuOUAm92CRnpemWa+YAXTedR5BGj5zQEAhcESMQ9I40GKi+Bt1uMb1nO8alJ3+2nj+LSrIJ3oznx+lU4Pq4EGJBCLHrWdv+SAgxLYTY0X+8+Xsce4sQYp8Q4qAQ4rdezI5nnN/ES17qDenPg+bdDeRyy0+2+xJUPwGa8Pn4xBvJy0WubH8NyAEQJT5BksMRXeaXPQSAHecQhoNXTwuuR9LgXuXjKBM7FFzUOcCR3jDScZgwUn/9nDGIoMlFhYRQHcZOxsgFgnVRhFAgZDovUGcYy50AXLzEpGClA9dUbpwNpJlEHTPCFunPsmOnk829Ay5rNr4egKXi0xiXnPzZRmadaD7L35Px/Dgdi/8TwC2n2P7flVJX9h/3fHejSO+5/wZ4E3AJ8H4hxCU/TGczMr5DtND379sKqfvofhElU/dHHCaoJITUm85SMcTTTN4df5Sp3j5yceoOCpIEGASxhL3qEUAxEA+SdKZp+6m/P0l0CJfwMdC7Hmu94yx1bPauuohmIR1AGqKMQZOcyrFt5RZ8S2B3xxFJgaIKEbKDrmJq8QR6mCdRqYupmEuPP5zbwHJ0QpFQ0sAi7V9UTEc1fVYxvGwdUXeUYGQPasg/cR0Su/WcIi4ZGafD6ZRefAiov4BzXwsc7JdgDIHPAe94AefJOI9ptWrMTz/N0uLe5zy6k/uAkFCvE+eaIAVxPRVEtx2edPMAWhLwnr2HuMquMxXNsLaXRu+EFBBihETVsMzUuh6SVaLJR2jpRYRKqPodLmvtYzGBWIU02q8jjjXa1VE+tSIV6J5wMKljxA6TY4KeneAEOvc3f5XRxEWgGFB1FuP0jiRMZgCokSaRO+isY8kSHMwlaFZq+Xd10AbSfELljkE030PULkEf3IcfpHMGYccgcTqE0/9a+GWSoGTmAso4NcYPcewvCSE+AGwDfk2pvhlzkuXQd26mTAHX/RCvl3GeEcURvb+8ngkxf8r24/bvcuTaf0QevIGLuz9JONXGHM7Raz1L+C2HcrfLuqMhA5fHdDTFmk4dGEJYK5HU0DhAHK8DBAVlER17jMX1a/jlnXfwpsk0f/6TV1/NsQ0X0eu+CbiT3/mHjxIaOo9cuJIAA1vU8dDo5mKkblMID3EsvJr1rYM8MjzPMDXqjAAg1ZMsjK6koY9TdTvsGFjFG28EKLKxnfCZLS7/ZZNDrBL+OxGV2KT7xBzFxiX4ax+kNvsUSI2CdyFRoUZ0CuHf9tq3IAaGuearn3rxP5iMVzwvdHL3I8B64EpgFviLU+wjTrHte+aTFUJ8SAixTQixrVarvcBuZZxLfP7hzzMs6hyWa3nsqvex7VU/eeIxZ32IxcEOmD4z5TSWPTicli90WwGoVAwT28GIfSI/j6XaSK3MeLeLIKZor6ErTWzNxQtWpy+a1CDqMW0Ms6F5nJnyKDsuWMOu6mpCLaFQ3cCIZeNf9TraxTQZm4gTPOM7fva0D3a3RV6vMdJdT1GDIbFIQ68A0IgE3aExDCW5oraPStjl/9w1ybv3ePyHg2l8fqU9R91JVwwbaLhPzrNy9LUoJagvPIMelciFQyi7SzTbQyUnf1pJIrHdRfJHd5A0my/dB5TxiuUFCb9Sal4plSilJPB3pG6d72YKWPms5yuAme9zzo8qpTYrpTaPjIy8kG5lnGPs2vokFhHH5RoO5j/A5rd9hM1v+wiveuvfkvj/Bn9wEoBmLhW36Hgqut1GcMLil3YOgUJGOirpYeplKl6MpXVYqVYwR4SpScTsBQCY4VMAHLbHWNar0xxay1I+z7ydinxAwkXjF3Ng42oWB9PvqRO4TPdFOiEV7tF6E+EcI/Q2skIYDLFI08iRAJ2wi3JS//4FzWMUZZcrj3yLVx9yuaqW9nt9fZGuedJ2Un7C6KsvIuqsRjcXMMIyZlAF00PGEXHt5ARvc6qJGbloKqF111de3A8l45zgBQm/EGL8WU/fCew6xW5bgQuEEGuFEBbwPuCuF/J6GecfR2tHqbrp19MVJrt3bD3RlrRCVCgJBvYBoMx0oVZU81FK0VnyUbKHEhrS7IdJJhIlBctUFTM00IWLhcmkSI8VC6mIl/2dALR0h1LkUc4vI28NMt5J3U1T4RFG9WuZkl38XBEhFTnfYyrv0NM8Yj31no7Xa8zJmFCVuDAZYJAlEk2jbgvaURu9v94g7wcYKmEy7xL6CQf1dFL5qqhKT0+teIkES6dXMOnOXoiwekRBET2oIoQisdrP8fMvPXMU0b+5bn7+81nhlox/xemEc34W2AJcKISYEkL8HPDnQoinhRBPATcCv9rfd0IIcQ+AUioGfgm4D9gD3K6UeuYleh8Z5xhf/JcvkiNdoOUJk3xLZ89satHHNZcwt0CST1MujOPT1VziOCKp+3TqHkp2QHdQfSFGBSRRgcuiBC+pYghFQsIes0EkVxCGFoaQVIM0942RpBFB4/kxZHUlA0Hqfpz09mGoAcbiMUIrh5UkOL7LVK7IvLmE0tLJ2XKnydFOarGvFjaVfkGWaRu6sYaT9NcgRBqakizZw8yX9qIhkEg6cRvbTAXbI8I3BF/96C6MxiVpagcSlJ/ehcT51nP8/M39adWvZnkdwYED+Dt3vsifTsYrndOJ6nm/UmpcKWUqpVYopT6mlPpppdQmpdTlSqm3K6Vm+/vOKKXe/Kxj71FKbVRKrVdKffilfCMZ5w6JTFjYv4BF6r5whUmhvZzPPT4JQLTgUh9KC5EEQYFBTXLcnqOtdwlnunSbIUr1kLoFempZK+WTBEU2hh16ySA5keeIPcOsmdBL3khXeNhOl0KUhomWovS1S4VRHHsIz07PE2tHkUaTy+PVhKaFFSeUeh2OO0PMWgvIvvBXuw2qnTmKxjGG7AgRp4FxU7ZgkQqFMAChiBILTUpCUSJ06kyoISQwLxusKTiEQtHBY0fNp1XzWMk6UCB1n04vdS95wx7hs0I6u1PpIDW9/AaE49D4/Odfqo8q4xVKtnI342XHfdvvwwkdHFJZWebpAAAgAElEQVSr2Bc2RlRg166n8KOEuObRGN2J7ldxW8tQhqBhzGCgE0138dohSJfEsp5l8XskwTBr3Qa+qlDRh6gLlyXDwk1uoqu1se02RpRa+pUwFVKtMEzZqCBlGu5ZdkJ6A99imaoizAJmIil3WxzNjTNtzZyw+EtRnfWtoyT5A+TzLcIwnYeYsTUWBtKwzooZoBBM1Jaw4g6WN0JFK6ABNa3NRmHTNQU9EuYjxeYbxhjs6SCg6LSY76V9bVlNopnuiXUM3lLqvvKdQfTN19O+516Sbhbrn3GSTPgzXnZ864lvEWkR+Tj9evo4SC1ig2xwz9OzRAtdVPUQhfrFEBZRhsaIPEY5KbL/2F68bhrOGVvmCeFX0id0lzHeTS36nNBQYY510TVIKvTwyes9kih1zwz6XXwzj1YqU9VK2H466Zp3JPWhxxAIVjtrMOOEnO9yzBlnyp5DJ43cMXSXNfUZ5qvTmIUGcc/ETiSzjsItDwCwQaauq41TU0gK2L0RNMWJPD3F/XN0DEFsCJabgspSHay0H5bTYiZMI6gDuYgKJfGih1KKoJXerURGgfDiV6M8j/bd//ySf24Zrxwy4c94WTFTn0EsCLQJi1KcirCPTceuMR46fO6J43R7+9ANn3z9EpIwj6HDRUlazOSxzlaiIABCpGGg+q4elE/UrjLUS+8idC0i543xuuA1SOYIhE4eDxlqRIbBmFsnLgxhrykzIAqYSQJJjGFquGYqrOvtNViJwowCjjkTTJtLOAxgxIqlQonxdptGNbX0tcY6hoKIBVsROwUkgsuDVMQFoHmbkJYLfeGvyDz67AJdQ6DpJqsKAcZ0RCufunGEADF0HD0skchFlFJE013cVogW9oXfLNCzh7EvvJBm5u7JeBaZ8GecVY5OH+abT27lqakmT001+Yf7PouOjjNwAYX+ylMfg6XicYzmBPsmF+k6achlfukSlmQVBdRzabWrGW0Bo18KUdM0EBpK01DKw2mFBElqkdfseQzb4OJwDb5I8/RYiUsSaXimzbLeEmZhiPmKTzmxEIAhIzzhEKgBpJKUtSLjg1cyog+ztqOTk0UG5AT5SFIfugKjsoLlcSruzsImKkFALScQhoGbK/ANbQJQ1IoVhjoWkS3R+ottR8Qo+nyOjikwMNmn17GlgVyfDlxJbJFfNo3mVzDtFi0hCKe71Od6mLELZh6l2fSaAdX3vhf/mWfwd+8+cd0Pze0nioKX8qPNeBmTCX/G2SPo8r8++Sfc/8+f59a/fpC3//XDLB6aZ9Hscv+eOkUVEwvBirW7aOfmEXGOm+02naGn6QV5unKY/7r+g/wLr2d7qY0iYjQeoFZOBwYMAyuQKN1Aqh5Fr4krUzdL3Wqg8jUkkoZKBdFK2kShScfIM+Y1eHBsjp9a+B3CvnibxLjkSOQAgQzwE5e1697DG5d/gH963ONPjv8iE/FahhJ489BbKNz4B2zU8ygpKCxcQT7sUM/p2FISOHmOCpui6FEII3QJQV5H9Nc9jqhl5F0HT1NYGDS0BgrFwKY0kueIV8BecQzlOWhOk7pjEE53ac65mJFL7vpf5bUVG68TUXnbWxG2fWKSd35plj3PvJOPPvTnZ/DDzng5kQl/xtlBKbjrl5EyjyXz/PyKGX7tR0MqcZ6x9au5crCOLXrEpiCX64CWTmRebwu8gf202qMcKmnEmsF2NrOvaGCKGTZ4yzmWT5eVKMOi2PFQuoFSPQapUVdjKECqmEpicNA5zmJ/nVQxahCGNq5mY8qEY0MdYmeGQ+EeAGwtokcOLSniJh4PzHyKw7v/gYfmvsDvbuzwhyv+lmnHxQgUf6VqqNjHKXoEXhFNWuT8Ni3HIh8GBEY692CZPsXARaHwc/01BygGjRxKJESaxJE67X51sF4vrS98JDQYtkJ6no1yWiyGkmimS2Omixm10fLDDJgGI3EZvVKhfMsbaX/lbqTr8vieR8gJn5qaRmYx/uclmfBnnB22/A088yVQNp7u0V1cYHHXU8RazC+/7afRow6O3iE2BKYZUAhLLBWOM2DUEHqE317O4UKq2Hu4lGnbQhfTbAiW0RapH1waJuV2J/XzK49RY4ppbTUYMBBVGA8G2FHYS0OkVvTqqEUcGkRa+rOYG0ongg+Gu0HTKWoeLjmcuEAnadCNG3Tru5n1DvGUdZQnSrtwDQMnlLRFnthtIMot6l6JSAvIdxOUJggsm6i/TkDZAk1JpBbQ7WfrlKaiEAmkFhIRU0gEvoioiy6dpVncKMdMJNAFLGCTWC38jk/sx7gzPYyog2YVCOKQcTNH94lZqu99L7Lbpf3V+zi8kC58K2uL/Eu9c+Y+84yXDZnwZ5x5jjwE9/8B0QVvxkgcpgvThOMBoicwqgaVfAURSWy9RWwIdCMgH5U4Xt6PVj6CUiAaGziURk7iigKuvY6WXqOYVCn5NokAdJ1qs4nSDFABY9px6ixD6hEjlNHR2ZHfR1sUyOGxKvH7UT3pgLJQFaCg21sgzpcZEC08chSiPJ0wXZBF32Iud48CEOgW5VixGZMobJIUG9SDAo3iYcqtMgBdO4eKQhIhcHNpWcfAaWPoqfAnjkYuBqlFhCrCkRDoNlNag9it0Q6LLMTpwLFg6KBJSk6HRqxQiy6GlvZpvjbJXJTQvPMQ+shGrHXraN5+O/V+ds8xNcenZpde0o864+VJJvwZZ5bWFHz+gzC0gaOv+k1MZRJoAQ/aDwKgtTT2HZnCjC0crUtkaFiWh5M4TFf2w/Be5gKLvDfOZEGj5KX+9655GYfNBgKdS9y1eHaCFIqBdgOlGwjlY4oYL6kSax5FlSMUEW4VulqRCh1MFDKSGCpBClgsQ35xA2YoiYolBmgRYWJHObwoXZClRDpI5N05QOAZOvko4Ro0PHsO9IQl36ZbOMZQO51Y7to5rDjBM22a+fRuQzpNRpK+xV/R0AFbi7H71bWi3DBHjDoqqdMOSyzIdGI2MtK7knK+zW4THD/B6qeDUH6T7a4kyhnUP72Hyjvfh7djB+Vm6i4aYYGv1RrUwpOVzDLODzLhzzhzRD7c9tMQh/C+T/PMXGp5lpVHR3Sp5+vkc3nuvOMLmIGDpXVTV48RoinolqYIq4fYHUjKcZmjRZ1l7Tor5VFq5iaeMlJLd100iuvEzORnWHHx+tTHL5PUOI8cIs2joGx25w7zI8OvJ9BylOkgJehxjJXENIoGUTLMstllACRFk7Lo1/CNcnhxGoOf9IXf9htIrUrXEOTjmEEMglLqcnKDApHTpOymIt7Il7CVwrNsak4VIXWk2WGFn7Zry9I8/EVdsMZLz99zKizRAK1JL8oTCUkjFgwYaSy/U2qzRwNyGraWCr/w60QKPjOokbgxSXgJyjTZ+O00V6IuEkqqwedmX0i5jYxXMpnwZ5w57v11mNkO7/r/YfgCJuenABjr561RaxW33norXreNluhYuMSGQAjQ7B7XiiHQJPsCHVvkWHQMqr0OFyV7mDEu4ECS1sKtUCK0BEdKRyisWgVa6k5pyiJOoGERkcNiR34vI84oMXkqdIgTDU0pcnHAfBUSfzkTTUnXiUnKLfL9FBJRbOMn31kk1U+25rlE1mqUEJSitCCMGkknZJU7gKWboDqYcUQrV0RH4Zk2s9YYepwn0XxG/XTgMlYUARjQddb3a/GGdgklJInVohOlA8N8LBi2UuHP203GGgn/4zIby0iPEXHqxvnq5BLmW9YQHXcR132A1U8uosJ0QLkh3+Azs0tZIrfzjEz4M84MT34Ctn8Sbvh1uOgtAMy3UkuzlwzhxA5iQLBq1SpYtgq0mC28ishIBcq0O2yyNKTUOBJqtHJ5AOKe5ILgILGw8K3LUbRw9EFsrcJCboE9RR1Ihf8AmxDAMj3NcbOjuI+vHbsTISzKdPGjNNKmGLvMDSQod4Ll7iJzgzELhXnyKhX0KDYJE49Y6LRLFfREUnFDYjNNxTDcc+kID1lpoAcVxrwKw0mV6cp+ioFPz8xjJlFq8ZtDGHGehITBNCcd+nCRUCou1CvY/TKMyiqilESaPZpx+n4WIo1yPnX1SKtFRYDrB2hWOnCQSzN92jHcFQUUXjtOafg68tVXYewcAuAmq8YRL+TRZpbS4XwiE/6Ml56pbXDPr8OGH4cf++0Tm1tu6i7RolGG4jI7FncglaShudhBiQd5DUfDNQCYlsdYaZElt4wZVFgopFbtUsdhjXcMTcW0S6twpU/BHGJcpr7zT6o9qL7Ff0SsB2CZliMiZrZUp+NPAlChg5+klnQhCpivCgaaJXIyoFkeYGd+nhypyIaJTiQDXC1Hs1TFTCQFD6SR3nFUY5hPFkiKTezeBMuDMqNJhdnyIQqBS2QaOHGEZ9rU7Qp6kkcpsOOIBImm2XSlIodBt/+aQjPpmRpCQCdOB6j5SGAaCj+0ie0mVUPw+oMeoi/8qjILwEUFk89uPUa0SeeoWsS5+gMMPjmBUrAuOk7F0Pn0TDbJez6RCX/GS0pv7gjRp98PpXF4199B3/8M4IepqDm91Vxb30R1ZiWPPvwUdi1ipLWKQdnlkfkfZ3b6IjQtwizNM9nNc0HjQnYVIjSpMN0AFWqsV4eYHLOpRzolc4gJrcOK9hD7Ct+m4qSvMy1TYZ7QHJbyx/mx8gBjagKAvOwRKPNE3xaqsLKZmuDR0OV09Yjpfl3eMNFIVIyr51kqj2AmCblAIzHSnP6WkSOu7SWqNjBbg4xHVUbiKp3cLMXAw7NsdCXxTAvXtNFlKuShcolJSEJBKwiptxY4nktdOYbSaFhp/9xEILUyR1U6/xCHBoHdJGfAWB2EXcQTCVOrh0BJrtQiDtd6PPrIY3xycD9S87AvuZnet69hYb/g37YNDmxfYOfWOQ59e4GDT87zra89SNKPHMo49/hhau5mZHxfIqn46h1/xNv8Ogvv+WdG84Mn2uI4Juz79pc3NiEaV/BG4Kn9dVZzeVrHaukm5NC3OXjoGoZHJgEwZy9ngzvCfWt8Sn6XN5gHaEZlLmMndwxs5Cg6KzWLCcPjSn+CuytLiMIBQGdRFrlAg4Is0F31KNOFV3P/hlv44Ny9WJGPelax0HqhzPjiEi2zjO5cDdzOkzmBiCShFEgSPCPPQmWEDUclTqgjtLR4+kBiMFMss9zyMesFhlQVOxZoSIqBR8+2SYQg1FLXkW2l8wVd4VMUOeJWyJFOk65VxTH2cDETFBM4WEkt+Xys0x34AE85FxLzCygJnl0nrwt6wkQ6A3R1yVfW/jw3HOkw5kaU8hWe2fYk7nUrOD7+1wQTRxiYuYanH309Q8zyTuCRR0+mdACIk3u44U1ve5G/FRkvBzKLP+Ml408OzXBhdxdPVDbxT43Sc9rmFxdJAKUE07pizeineOS1/8RTN9zL7IZ7GTX28pPV32K1t4yxaJTx8X3I2OLL5W8zl1siyY0w0k1wlUkc2WwSO0EIdgykkTErDMElwRi6UsznUjeGl2iM9OcMvu6aPJLcRCIMmvkiehygfSdRDrDoLKccu1AaounZrAt0nsjbWHqAq9I7AcuocLTsYCYJRqJRkKn7aTTUiKvpIgNjwaQoKgzGFcpxmaKf3n24lk3UjxIynDZKQEu49LSEp7fM0nGGUJqO12qlmTojRbuYnrMSWET2BhK9ymPJNQilkHaDsf7CsyQ/ii8kS/lh7LCF3415++Vj6AuH0AZ1eiPToCUYZo81P/7n/MTvXctD7xrlq+8Y4dbfvYb1b0ijfmYOzb24X4iMlw2nU4Hr40KIBSHErmdt+29CiL1CiKeEEHcIIarf49jJfqWuHUKIbS9mxzNe3twx3+ALhw9wWe8gB+KVfLbtPSc9wL/70l3YSkdIk7am2C91Lt24nm3Jg2A2KRtLDJtHqeSb0FpNsdig0RmiaXVoWm1m8ga5nmJJ5iEyWc9BzDjmmdHUn1/SB1kdKjYFIXtK/VTGgc+gKfAJeXxggq6RThA38jZ+omPpEZEOvgkNsYpc7GIUqyx1I67xJN+2bUwtPOF3d4wS8+UQXUr0RDuRRnokMnCqadI4o2ZgaQWKMk8pKuNE/bw/hoVIUotf5rtg6DRFj7pUzO9t4nhprD2diJCEUqxQ+fS9EVeRRjo5+4B4A7qeIOwOloCcloBTxdMFDSuPFbbwQ503LYsxZQRmDyHSAU7oCc7gAQbGDd5x2ThbnYTJskZszyJFRGfx5ECYcW5xOhb/J4Bbvmvb/cBlSqnLgf3Ab3/3Qc/iRqXUlUqpzS+sixmvNHZ3Pf7T3mP8TC8t+VfY22FKGDzSSC3cDz9wL7uPLcdRCl2aeEKxXa7nqsGrCKVPS8XYfWs4Z3vodhfDiDjs2TjSwi+axJog6sKSKqDHOgYxqzrzHFpWIkx8BOMso8ZVvZXszwk0TeEEHqOGRlO4TJYvZll9HqEkrXyOjjLI6RGeBfNVcP3lWImPXa7SciWbPY9A04j1CK8f3VPWiyRiBikShNKxYwlK4SSQX7mIik30po4QAoGgFJVJSAU90SuMdNOBJ8oF2JpJU/Todh3I66w/dAcAZqgTi5hBPyawcsRSp+2k8xJ6eIz92iaa1gBCkyRml9gSaFaewDBoWBqm3sEnhzF3kG6hRFnUEXo/XYTeL3TjT/GO0SoFXeOfZhbpuW0SwyXsFAn6JSczzi1Op/TiQ0D9u7Z9rV9TF+AxYMVL0LeMVyDNKOZndx2hbOj8+7mHSELBhVuPUAkDPjW7xP37d/H3D/jk87PYykBPbFxN8ZRcywXOBYBgQeuhGelXLmdJ8mNpkrQdYchwMILsh03GvYQlmSeOUxfLxu4c9UqJg2YXj7UMi6Nc3t2MFILYFCxLTBwh2FeImcmv5a2PPEAl6NDKF2kpg7yeunAWqgK9N4SGwimViRLY5PvoStHTInyRCuZIeQDdOE6sSxACI1EgBB0T9PwsSWcA4vRn0hQh+dghFGk93IY1zJpaOtkcOAll6dARPi4x/ohFuZOmgDBik0hIBsKEjlmmGxapldIMo073YQAetX4sPY/VIGdomJqGUhotU6BZPqGRZ/LpnTTXXMogS4h+wjtp+BBpeN5xCobOu8YG+MpCkyUvJjZclF+l0djy0nxRMs4qL4aP/2eBe/83e28eZdl1lXn+zrnzm8d4MWZkRmYqM5VKzbaEZOMBzwJsaKBw4W5omtGuArqru2mabiigV+NiFbC6qrsGg8zQgA0YGyGwZUuWJdlyakhlppSjcoiMyJjjxZunO5/+4z7LpsoGI1vY7opvrVgR78V9N967ce53zvn2t/f+Cr9TwKeEEM8JIX7i7zqJEOInhBAnhBAn6vXdVca3ImKleN/566y5AffftI/0znMM6zaFW27lredP8dDqKu/74+eR0ucHZwRWrCNig5GADhmub/nYYY1to4lh1okFpDVBeuIiUWBxQXao9vbjG4mbJRgEhGai8QPc7jwFwOcqEMRz5MUlDnsCJ44ZmDCjJSUTPjWTQYtD3v75z1IY9Omk8rSFjhQKx4dmziI1TnCy7MTiqceCY55PU/MIxsQ5M1lFM1bxzEQSGbcPYM2R6MYqQb9AcgvAdTORhzxtCycI2DIr7NlJoYeKnqWT65dRQnGtukHUG2J7LWTkI2ODUEYU/Ii2XqLp5djO5HC8HlW1RjE4x2O8kQhJ02yxhxBdCISflJPwcwYIyfalJcyjx5hgEyEUKAiNHka9xGh0HYAfmiozihXPmnuI9BEictjaeOaVHDK7+AbhayJ+IcQvAiHwx1/hkHuVUrcDbwfeJ4T49q90LqXUB5RSdyql7qxWq1/L29rFNwj/emmTTze7/NrBGe6M62hxC0/NYh85zJs/+VdoJ8/je1l+9XvnWd0WmLGOVInUA/D5qy2cIE/DqYPZJNAFNhap2gX67Rlioai29uMbE1TcGMtT7CWm6ydBz8Plz+C4A05W02hkEeiUtYe5zQ3omyFTZoU6AU9OF7jr2knylQyFwYCmU2JHGkSexIigk8mRGmfmmnpyi/gY3DVyaUiXUCZ0ntlrIjSPoZ1MBMG4uNt62sc0GohhjUATKBXTlIm23zaaZL0h7ZTBJCukvBC2DpHxkjDZjrFDemcbAZh+F6FsQhmTDhShMNiMptnO5aj22sxr26T6n6YlyjzPbXSsBntkYtTzRslOw80lQXXL0xlOzTGhkoCtDG0is4vZKTMYJLuLW7ION2UcnsvdQKgln39r+epuVu//D/GyiV8I8cPAdwI/pL7CyFBKrY+/bwMfA179cv/eLr658amdDr+1tMUPTpb44ekyavFxAOLaqzAX9vN7Bw+gOjNkDjR4962vZnkQoKEhxxp/jgHPrnSZNBRKxFxzeoS6ROkjdKtPd/sGACqDOXpWmflBTBWfyWaR7aBAHEtiN8Pc2jUuVnIoklW/I69w5zBFqCsqVo1P1EL6psF3Pv4onb0lCsM+kdRZytToDhLS3EnnSI+J3xormj4Gd7kunuaBEAhp0Kwmev3QSpb6/thV08onNYhSoz0MHAtUjMuIGGgaPfTQpZcSWGLA/o0hE4szTMjEo68HQ3LdxIVk+h2UcIhkjB2NJxUxz9DKMdXtMuUHBP1T5FWbz/AmRvYWxvg8vqcQscLLJIsoW6XYMNPkRZI0pxAgYzSvzKC3CIAQgnfXMmynymznk2vR2bYYjZa/rmNlF994vCziF0K8Dfh54LuVUsOvcExaiKTQuRAiDbwFOPvljt3FtzYWhx7vO7/MzVmHX79hFiEE6vxDBCOJfuRefg+Pk+k3UylfZ2ffLXx2u0F7vMoXsYEUPlXR4XIrZk4fosU6FxyfUBcE6WSF2l+9HTMyyHgldpwss4OYqtUgG+k0VJogsBhsHWTv6hX6ts1SWuLGtwFweDjBpD6PIQ0endUojTxefeIFmhWd4jCpR7+Ym+G6m0hGK5kc6ShZoet+EmQO0Tga+MRy3LPXSHFFLoMSdFMJ8XuGhhZFjDKJjl9w99J2dEDgqwEDU6KEwvL7dFPQjSZ50wsh3UzMlKFIKwsn8EmPOgBY0ZBIyxAQYsfJrbpqzgNQ7bUp9idQxNyunuY0d9DKtF/6n0SxoNYIcFNJIDhrl9l0G2iMq31Gyfk0mWE0Wnnpdfflh+hRyIWFGRQxo/Yemq3Pfx1GyS6+mfD3JnAJIT4EvB6oCCFWgV8mcfFYwMMiqU74lFLqp4QQ08DvKqXeAdSAj41/rwN/opR66BX5FLv4huHM1Uv8zR++H//Yj1Iul3jf5y4Bitu9Eo2pH+MZs8bZ1RhHW+b3KxbfY+jcf+oqkZaspFupFJERkfU83Fjij6pM+g4vOBcJYoGbXSYelhGdfUx4ZQa2ZGRoTAw8dlIrZBt7acRpgsAGY8j86lUAni4LDlw/itiwyZbL7NUOsmLD2UqZ7zm/hKYUzSwUhgmxb2Sn2RmZzABXnDKvinpIXeE1jgN7iRAIXVAVyeSgmQ6XX/wr0lGKbmpIpQUj0yQVBJBaQylBoTfDsrWMkBquGjIcF0+zvTZDaz8tUUXGMQ/erfGeE5KcSjFQAywvydbF8Qn0LAE9bCS5uMOWOQVA2utgxBUow7y6QCzfyvHsQd4chwipIwfbvOXZgMHCAXKqjxQZIj+ZRJWCtfUi8wsDpGbhh5sopZIJe7jKwmYTb/AUgXg1w805Tn/iE2xNpV/6nzd6Pc60BvwPP/UTjO/vXXyL4e8lfqXUu7/M0/d/hWPXgXeMf14Ebvma3t0uvunx9L/9Bd750Hk+tedtfKacYbyg5OP7f/yLB5UVP/dHf0BZO8j33vNmHn50hZQcomJ44FXT9LSIzJMJKW32FphxXZ7JX6YeaHjZFWTjIAAzg1nquYRoKgOP68ZFMvptdGKLMLDQrS6FnqA0aPH5ks13nTFpf75E/aYR8/sX+LMpH6livv/KkwC46RhnOKLqN2ikpxkOdDppcFWNbHwdM+NTvuk5nKfehRAQ6oJ9KkcADG3F26dW2a6naWYt9gGuZTM56OI4GzCsYKgUoSYIiOkzRKrEe2+Na/kPUwHNyadYLn4ngj4p5YCUWF4L19BZtNfIijvwwx0kgoVojcvaLDKs03cElW4ByuBGihvlCxxP3Us4amOkK7hC58Ytn5MHYkrhkDAymSLZiUSBxvc3zvLMQgE0A4VLEDQxzTKdzhrT65/iXO0Ul4suR+qv58WHW7zI77z073zmlnt5/NvejnHiBX7mVbu3+LcidjN3d/GyEccxh08mpZV/fvE3ePbOwzx752GeF5/j4ufu402LP01qsQNScPHG1+MvLvKe6TJhKyQjhmzlSuzkTbyMw3aqwqRos9afZ6ZzCICThk1sDnHHrp1Jd5KNfBJILbg9+sYGr3vk5zGHHl5go1mJ6rjQXOH5ooExTnJS9Q4TosIn5jLc3jjFLeIJogJ0ehPkcnXmgxUGqSlkT9LPAEGeVNxHT4fYhRDL6CKJCXXBkTBxFA1mFY4GHhGNbIlIGigpKbl9StoG5jA5Tg9DVk03aQrjJ64iGSXE/5rpf8VU6nFiqaGQmMICKYE+3ZRGz0r0+NhN8gb2htsMZRrdv4Zr60ShhRZrdPuCN/IIO/oEzxZBKcXAzDFIzXBhuYOIR4SxyQF1CYCgb2GN6/AImUykX5B7Op0tNtNXAHhu4RpCK3D0v77Kf/Nvfpn3ffDDfN+h23CiJI7wG72IZzuDr9+A2sU/GnaJfxcvG6c+e5zydkJOxXaX/M5Z5rI2tbXH+WhK43ltgBgkW4Ar9gL960scTduM+oK8NuDC1DzauMNUvVTlkL7Cllem0J8mG8W8aCZWykYcE4mIol/hekFih4pM0GUj1QMlmBo06PkZ9LFl8sDKIgNT59JUDb1qM2FMcrxq0nYsJjefo5i5TP+IxnBYpFDcYJo1RqkJsl2BSoMeFnECFysjgJiC1sUkJNQl86NkMmnaySTTiaGVmiA0kqzamrvDJBsod4bA3iEVR1/YN40AACAASURBVCyOj50a5RFKIKIkKHzVmOKWwSZCKQQgxlVEfTNgJwftVKL1B14SV6gFXZSQaMEGgZUUu8sGWdz2kDt5mozq8sBCHlCocReue7d7fDqr4+tZDoQJoUcdEyNILJ1K85ED6yVL56XmEo1Mj8LAZGgs00xtcC24g6F7CvehTzH8s4/QLVXI9jsURwN+7Ow1trzdDl7fatgl/l28bFz/8IdxLYPNagVjS3D1+P8JUcCz65/n/yrnuCeaJRqMXTEphyf2T/Dwc9cIYg1TV1ytznDHpQaZbo+obLNgrxChsWW1uNN1WTPHAU0V0Ld3sL0C9azJnkEEYkTPCmhlYGLUouNn0Q0PJRXf9fFHAHimbOJNzZErHOBjszqZQY+gvYiUEaNyUjCuUNigZGwQ6SkkeVKpmChI4YQ+kZ0kdJVlB5uAUBNEriImoq8EkYJ2JPDtLKGRTFKT3go6Ib1wlmHxPIZus2okE9KMm8KJLVSc5Klcc6Y46jXJBwnxo4+DxLZgoxijqokSOxjX6Vfj2vwy7uNZyXXNBlmCbheDkNfyOI/PFtgZn8f0u0zIHCccm2fzBaZJ5DStJxGA9A1Cq4u9OYHrJju3J9qXkbHg+7ZfhVQaFyaO8/H+j7F48vNs/sqvkLr7brZrkxQ6DX740b+gG8b8+Lkl/Hi3vMO3EnaJfxcvC61Gk72fe4KnX3Uza3sq6JuCVvYFls78Mf9jOc+kivkR++dQ/WQ1GDgaD+25iz9/6hoA25V9hJrOHZe2Obi4SFyyqDlbCBQNq8PdI/elCNRlenjpTYSXp5+xWOjHqHEpzauTgtKoy7ZfQAjQ0zrVTpuJboNnyxrd4j529hzjeFXnphefoyd6tAYTNK0auu6R19pMGon2vVqbZsLxkFGcBH91HxDURJc0LqEucIchKg4gsniipxMjKGjgmUm/3IkwOdemmmVYvIqupWjLAbYyKSsdK3bwZA/D91hPVZkKRuwdJM6iwOijxdDPOKyXI/YeSdzP/rgGUFd8odCdRtfoIoBikEWPWvRUhjfwMKEmeXAquXCzq4+ipMnrPMEnciaGTCagtJvIPGagiMwu+k6N0WgFL/I47W8yt5XiUO0IR7TDXKo+i9NR/HrzLahKhZnf+k1atkOh1yG7do3fPjzHM50Bv3Jl/RUYZbt4pbBL/Lt4WTj+p3+BHfg8eO930K2m0RqgTMVHH3s/rhD8iwcj6t4EIlIQKfyU5AXvCCe2hmSFy3L1EJVel2o75OZLF8DUWMlMUYsjhtqIu12XwriH7llZR0tv40YFIkdnYaCQoYVQcGXGIpQaW35SxsBKazxz112kRtd4vqAxzBzkb47MEwvBsQvPoUWKx70DLJtz5AubVLcCpsdBz+uTM1Qz3ktWznUjJpYZarTJMMLXJEPXQyfAjE0+2xsTrDbEtRPXS0Ekq+plOYMquGgiQxQOsHFISYEVpnF1F9sbse0kstEdnWS17ZlNTB96uSyNHIS1aUQcEofJSn9NVkEpIr1GjzoZDPJBltCMaAVFZljj5saQB/fYKKCiXcUe1fk216AgPfSxFbUYJBOAE/iEZhfdrTIaXefhpYdxiTh0PU1lbg+vzb8WXx8x2XiYFxZu5P5/+YtoxSIdK0W508aNAt5VK/KTc1XuX9vhzzd3e/d+q2CX+HfxD0Ycx+gPfIyNmQon99zMoGgilEDfENyQjvjl+oj97gEuboxTPGLFyNRRQDuU5HM+7UyFo6sbeLbD3edPA3Axe5D5QEcJj31BSE1CP5S4MiKbbtLIJEHFvYOYKNaYbOtcm5L07BzzK0nSVFzWWZ2bw3DP42uCndpNPDBjcMOmT6HXwgokn02X6aochcImM60+ebeLHrpcn5wmnXUpREnc4pqM8bDYQwMpFH1sRp6LbbikY5PmOKlq3uozdJJa+bbZpaPyLNtZUtrt6DKN5Q+wYoO0FNhhCldzcbw+O1ZC/Df2kkqeQraxhjHdXI5GTjAwJjCDHvHYw79oTCKUS2jMMArbZJRNJsjimTHtXhI4/u41l5WswWpJkZ0dMr3xJD4a/9TYQAhwQ4OJaICndEzfJzQ7QIXRaIW/uPRnZEOTqYZNaWaW2yq3kXNLXM6f4r71j/NHUYk/Xtsh1A1K3RahlPSbDf73hWnuKWT4n15c4Uzvy6b17OKbDLvEv4t/MM48c5K55Wtcu6WCkhrDXLIi1esCZ2/MrZcl1o1HudIbvfSaoS6YMbcBgTeXR4tCDq83WZsos7C9hugHXE3tY8E3yTNEADOaRnMsHU+mhuyM/87eQUwUaezdkixNRDhWl+95IbFoilyyCjdHl9BixQduyLLpSI4t+iihkxulaIwtoYXCJmk3pN9OU2qvszQ5je7E1MZNyrcNxWYAcyp53FVpBl4fOzXCiSwQAgnMOR2GTiLDpFIdmtEE644k2KrQTaWxogDLC0lJsMM0vuaTC9rUjQotU+OGfhLE1USb9DDAdRzaaYOGKmGqIbEy6GuwZhbQoxaRMYE1CrCjFLafxdMUw83E+fTGdpe0H3F6QceeKlDbeg5UzGQxyb7d7NWo0eV5MfuS1INWYaW/yYntUxxslREIyjNzmEvLHGjexGpxlfv2PMVR7Tr/6+Vkd1TtJfLU8pnT6FLwH4/OUzR0fvTsEs3gC/Ubd/HNil3i38U/GIsf+hCuYVI/nLhQhumYGNC3AA02ChDuv5n6F7JzBfgaGJGJ0gSbtSr76is4vmS56hCbaex6n3p6iqqSFMclmYuaoB0KnNhk1gxpZDVErJgbxoSRZGEjJhIh33/m43TG9XpkEmMllD0Odnucz2vk/JAb1nwkGoVRGkOz0TWPjNHGCBXXRzrTO+tcn5pGCCjFSQbsyIq4PPSpxQkxd6MCvtbCzgzRooRoc0oybW0xdDKIOCab79Hz86w5gtG6z2omucVynTZSCGpeIknl4jo9LctaLsOU6xKhsFWTTD+xbkZWniXPQpcBSrO5mE+upR6somSe7MhiFKeQSIRyYDWZITVnk3uX+5ybz9IybyfKpCi0L2HnV1EKbBc0FM/KWUw/Ruk+hspzfCCRQrCwXgQpMdtd/N/9XfZ2DyBjybPdLO+Lfg1nzBilcd7W+qWkcmrVNLj/pr1seQHvPbdMtFvf55sau8S/i78bUQDH/x30EydKr9tjz2OPcvmuexhUs5j9AfEZwcm9FZZWJlh/bILzZpUTK1eYbj7Oa5pPcte5Z1BSUI/zOBOCQDc4unEdGes08pKBnae41SDSdDbLBnMiyVy19JB2JNjnTtD2S2zmJeVhiKFARZIDqy4/+0BMbhjzfx/5rwAwDA8AJWLm20nA8VijjR6DGUY4nknFraDSDXQvcbes6xZ719eoF6sMpEMx7GDaDnu9edaWFjieSYKhnbCENrmNYbpocSJvpbDIaANcJ4XljzCtiOHApmVJus2ALSvR1Sc2E7vkZJC4iVIqkXeupadIRTp9Q5GJh+TayUr6YDvP9PH30Y3rxHqaM/mE2K3uQ6Tbf0Jfb/B0OvHe62GGuJnshvz0Ovdc8wh0yacqt2JVBfMrn8YuXid0cxzUk/fxLHswg/E5MXmmr3Ob6+C0BabhsP1bj5OWBqaA+dZNPNFaJB03eae+zkR7yFLlboRS7Fz/Yh2f23Npfv2GWaZO/yH/4QM/9fUbg7v4umOX+Hfxd+PhX4JP/gKc/QgAT/7ZR0l5Ljt33kbLKXLwyVNMbm9Sz+bZiHPsLBbYWS6wsnSKue5ZjnXP8trjf43ueQyURjSbpthvM9ltIWKDW9KKkZNjemsDlOJazWBOtnGlBC0gP5jiVStVznXnqec0pnuJjBCHJgc24eYlxbVbX41vp4gCiWEkK+ZKlCdf/yx7ej7lVkKyjudhCBsrtmhn1qkPJzgZHWDbmeDwcjJJXE3Nkg0HOJbFG6+9g2NX381FJ/mbzbCMM9VBk+PWi7GJNu4d7zkWqTDRt6NWclv1UlXaYkggNSoriYd+Iky0+JJ/GYDjwd3EqsBI88j4MbnODiKOKIYWJ/J1usY2kZHmXE6S8z10/wp2/zE8Y4vnJx9EobCCLGoQoWINP71Buasz1+jzmYkJKtNbFPrnMbObDLeOYHkJ2S+KAq6f7FowRxh+ntdc6yH8gCl9Hq1wmOn3/BKx4XJk+x66UZ9zro0MlnnHyRYH1qsIc55OfetvDZe7R09z3/Sfc/jgI6ysPvj1GoW7+Dpjl/h38ZXxwp/DU/8u+bm1lHz/2EdZn55joFqcvnYj+XYiiwyHU7zp/DI3/8glbv4nlzj0NviTG36KR/d8BwCZzSZxRqdXynFo9XziI48N/mnNIbQzVIMmhW6PazWdGdmhYSXSzdu27+NNzwU836/RzWjMJAtivNjGHEvJC5W3MBuvE440dMMDpZj2q0SN8/zgh/8lgZ+ws+27KDMhu6v2Blfcw/yL/G+y02ly6HqiXV9JzWNHHlYQkRlOkPFL/EY9SbhqBCXS0z2iYXLbvDV1lIFIdhieY5Ia1yuU2wm59vIVvHjAwHQQoyZKKSphLrkewVXm1SKfmn4jkSoQyx6OB47bIjUY0jEL/ECvTypMAsBLWZMD/SEIncbsBwjEmwj0ISOzSzbIguYjRin8zAY+gmMbDc7mTdKVNu68iZCK3uqdeM0avtIQ0qPlJ6WgI7PLXr/MgbfNIQKPYnV/8jnMIpm8zdzgIEUqPOPm2XFdpneSaxhm78Ttdl8aLr3eGucv/CIy1rG687z44s8zGFx92cNvF68cdol/F18em2fhr/457LkHqoehtcT5U88zf/USo3e+k6v1LqvLExScHkpKBrkcWqS4vrQHrW5hTl3AjSJcJyHw7FaLaDaFjCNmdpLSATI2qEyV6GRs8nqbyZ0+ayUd0/ToWOMGKMMCcu0ip2UFJQVz/YRUQ6URaol3Ps5JanGd0B0TPzAX1Eh3NFIlF81P42sxVhgSptL4ImRReay5BWZKBlqjzvTOFlJFnHMWEFGIbPcYRokeP1LJKn0rLGAXfUbDJIB8e/4YrSjCVWZC/AyIfYG9kcQo6pkUetAnEBJPFwxjn2KcASXwnZA38Air6XnOZGukwk0IDGyvheH2SAdZvq/XR8o2vgbXUxY39HyECpBqyMg6DEDPbpINskSmi9HN4KU28JTiSLtBKAWPld9J73DiOHJbc2xvvYZVVeUWcY0dL5GdQrPLvFvGH7YQQEYlk1PU8sgX8mhWyK2D13Ku38NYyqIpSWB0sNUcUazhuy5RFPHoZ34Ix+kx88J7mTn1M4Sh4IUzP00Y9l+hQbqLl4td4t/Ff45hE/70h8ApwPf/PpQPQGuZF//oQ/i6TvU73sGnh3diFGIqRhN0g1ohIdzPPPUGNmQNJWP8MMZLJ8SfbnWJp9Ms7Gwgxw3HiXUmJstspyR5p8t8fYSSgnOFebzUOHq42Wc5XaLvJBPBnn6MT4QCYqkjiyVs9Tz5qEvkaph6IvXMjCYodU3IjbB9i5EFkWYSpTKYaCgEl4KAQn6HiU6MGYbUwi0upBaIIoU2/GImqhcnxLkxXq03hskOYk/mCKCxHaVwLYOM7BG1dMz+iFQUcjWlSPkjCDxc22YYueSUgxUZjOyQe/kspvL46EyVir9MYzODFge0zQ6ZMM0BPyZv7bBV0ImF4Mh4ca1HHQLnIEIJemaHbJAlzvoY7QKR3SbUhkzRx4gVjxW/HbXgI7tg2etsde9lJa5xN4uMxlJPZHYpRhWCsVSVVolDKer55HI5Qm3I/pU7kQimrhbwjQH93FUkGpp5hLWLZ3n4kR8hk1lhbfm1pJtHMbwymxffxHB4jfMX/ufdZi7fZNgl/l38bcQRfPTHobMGP/CHkK1BcS+Dxjqzn3mEq6++l//+wSvY0oNjGTKDHhJJaex8meh0+J3172Nl9VZA4KUT772Xd1CG5MjGEuG4hKePJJVKsZHxyGfb7NtWaFHMifxhIicmVoKV7RanqwdRmWSVPT+I8VXyeoHCOniAXHQSKwxwPTNZ8QtBamhgRJL1TEjG1fH1mE6lBlKjokJModikg7DWmBiXsa9561xJ7SFWAiUzL10SXzl46En555HGapxoTL2uJGXsox5EjEybrNEjauoEvmDS73M+ndhZ9WEX17IYRUOyyiHjCXoI8s2Yu9VTfHLaAqvD9nYS1G6kOggkTQpMOStslJKJ5khyiZFhE5XOUfKnaBl9zNgkzEisTiILadlNrpUzHG35nCjMoU91MFYEZfcUblRk0buTsgqZUTu4MfTtHabjKuv4KCAlkybw8Sgkn8/jqg56K8MbrPso90q49hqh0WdoDtDMY5x+/n50/Tg7nT3oi68nIsYXAWb7IK77ndTrn2R5+T9+nQfqLr4W7BL/Lv42Hns/XHkE3v6vYG7cMK0wz+frN5AZDnhy5tVsdn3eM/un9FJFrL6LpuDg9lVcy+LbR6dZ7O7lL5feAoBuecQCmgvTaEHIdKtOPG4POIpN1ptbbOSaZAodCl6GaqvD0/ljKFvR8XIcVxGnqwexnZiCalKI4oSe4ggtcrH2L1DSzxPGOiPPecnV444nohM5RcYVRFpIt1IDpaj0l1iwYnxrjZG4zmRbogQUOk3WUzViIYntCkKF2OY2oTIZCpNe6NBfS7OVHhN0o4tpHmLDcxkIh5zRI2oZgGDC7yDCZGeT7jQITZNR2COFRSnW6UYCuXqIN3nPMNAFD03MUBFJvkDDSQIZOxQ5ZF9lo6hR8AJmh4l9VoRboElyozkaRvI5Y9vCaCe1+u30dU6n09y60+Ny1qKZddC2BdUrl0jJJh33NhrxHlqZLbqRwDXbTPkVrkYCkdIw9GSyVn5ELpcjGLeNvGXz9QTSZ7l0BgBfX8HMSZzaE7huhj/q/zSTKs2KtcmquUUpznPqZIlK+e1cXfxNGo3PvgIDdhcvB18V8QshPiiE2BZCnP2S50pCiIeFEJfH34tf4bU/PD7m8rhd4y6+WXHx4/DEb8Ct74E7f/SLzxf3ElwWbBQm+At/gnuLDSZmm2hRiO6GKA0m1rfp5LNU+gPetvcRXhjNADBl7bBRmqIzUcWIw6RxhzRRCkZS8ut/8xk2q1toxQAjNploNriU3suOXaTvFjien+BseQHdgWnWMLWIMIb0cA0tDtE0D1vr4UcaAz+Frodo+DREj0jGLGdTZNwYhM8om0W6A7I76+zTdKRV52r/BJNNAyUE6e0OodTp5IqEmQlSbp20dR2FxJUG3SBDdzXFerYDxGx3O0TmIbb9DJEwSNMnaCakWXI7FIY9YgSl1jYxEjdMCH2vqNCJBDvdb+PGUZ19/YgPz9xCftzfNxzX1Hkx2suM0WCrINjXGmEqHSPWkcEGAEa0h4HmMtJGxHoKoz8DsYaTXWYpW+TYuMnMeXEMBGSWexy2H0UPpum7+3kwa+NGEBk9JrwKl10NoySQ2pgWFGTNNKGeSEDeFZPF8jmuFi4lE6+2yMw9/x4pFFb2Z+ixl0mV4Zq5xobeYiIsEIYRnvdPSKcPcPbczzEarb6iQ3gXXx2+2hX/7wNv+0+e+1+ATyulDgKfHj/+WxBClEg6dt1F0m/3l7/SBLGLbzB2rsDHfhKmb4P7fjPJuhrj0o5k39Iqn5i+k++9Y5aF9il6mQLZlzJOQwJXZ6lWwWm5vL16gv3ZxC+eMge8cOguRBwTaRrEMULqCKUTayEPXWvTLXWJRUKYhWaiu5wyjyEHGc6VDuDqJqOMw7Raw5QxYSwodJJib1onSSCKIkXPT+SZdNSlJxWjvI8mJjAikGJIpEv0QQ9ze8hklARsd9wtau1Ef7YaCVE2ChVCq0aqv0U63ECg8DWNSOnsbBRxrRgdjc5oRM86QDNI+tqmGeC2klV5rt+mMOwztBxyloZUMd1xv9s9sswghnWtgN2d412rAafz85yrVAklLCwVycR9Lot5RtKknjeZbyYWUpsy+jCxp8Z60oaxYTUQIoMhc2iDCazcBpu5MjNmSCoKOMvNRAWF5ocsBI8jEITuDTyRcsh7EZhdMmGBlZGJUYxR4Rf1+HRkEmsuQkAUKq7VOmw6m0Rqm7lbnscpLbP29H/LA0slblnpko/TXNd32NZ6VKIck9VJTp48x83H/j0QcebMe4ki9+swYHfxteCrIn6l1BPAf1qB6Z3AH4x//gPgXV/mpW8FHlZKNZVSLeBh/vMJZBffaHi9JJirGfAD/y+MSwx/Aac+8mlCIVm88Wb+j3ceJe9eoanKLxF/RhuhPMH52Vlsz2O0Psl7b/sAoDh1/hCXDx5lfuUqgW4QxqAJiYxNSrkIYW2Qt2JGw4SIM50e+aDHC/oxMuMRJ0zwbJvpqI4hBEGskeon9kvTv0grmEILhnSjJPiaDdv4ukVc8HDCieQcsg0CtEEX2jFWtwJhGhErSj0fESsMP0m2ahSrCD1ParhJtr2JJYYEY8N+082DAAuDwPdp6ymUvwBAKu4zalukdJ90q0lx2CPUHTL5HHoUsaMnxD9DDoXAbZ7H7u3lvvUQPQ55+NC9tNOS2kjijwJaKseF9H5iKZhtJnGFlCpDx0JEXXbyBVCCurONjDNoRhrRm8Qo7BBpGmcrWY4EK5zjZvy948zf7TZrVgvfnydGMjcKEGYPgaAUlGkVPYgVWj6ZwFIDncR7C9KUxNYECNAPnGNyepHNizcyXL+D5WGFO3fG1y8OqIsBEsn83BE2Nzdpt02O3vjb9PrnuPji/7Yb7P0G4+9tvfh3oKaU2gBQSm0IISa+zDEzwMqXPF4dP7eLbxIopXjd/b+I0Xk7N5Tvhr/eBrZf+r0IOvyg/QDPHTrAd7qP8Zf/+ip622fx4j6yekL8HXEPgVxnO5sMAW9Vp3q0y97KCtf0A3iOw80XnmVp/iCuXkBXQ7TI5Mj+CuZgmbLUGQ1LZACU4J7OSZ4p38x7V/8CNKgYg2TgeA0MdAKl0KKAULNwMsucdt+MVF8k/nTcAalhOQavvlIZf5A+QoE26hNKDXlFZ89CjdtyLbRYIBC40sIZDWgXKqS26/SLA1adCsf0Z4jGd0qntp+7thwCf4AMJa954QQ4cwBYQ49AaVTMAfX6DjItGaZsMuVJZHCRjhkQEFIJkk3v5MQjGMOfIR0o9g1e5MSh19DK/RU3za9yoruAZUf8Yfg6AKbagAGvP5sjbhj8WW2HZkZnolGmbbXQ24eReoa4P42cPM2tKxe41mpxML/Kc+IHOL55H7XXjZB6yCB/kcHQ53uePcr2DeewagOUiHjL+f00g3U+4v0B60GOvfYesicvoQUpVAQje4Mj9T65yTx3z2+w2E/xUGubVxmb3H5Z4+PzD/Ck1LgktxEyYjuzRKMV0a81ufjYRaanpzkoDsHmx3ji5Gl++j2PvJJDexd/B17p4O6X68T8Zad6IcRPCCFOCCFO1Ov1V/ht7eILePHZX6PpfJb17BWuDh2u1vtf8tXjZv23Cd7kkXnjJqK1TX1lGRUJmkaJyriqpK7dxcbEQfpGkhAkNwRRLHnt5BnuyDvoYcjB5QvIKGRoVLGUjowNJmpFiplVpjSdYFgiEhGaCLhrcIqGqNLpGRwRG9TsRIKZ6jfRlE6gINJt/HQB3YhZdfcB0I/GThwjkUUW2iZTnYRklXCxMEEpfEOSWfR4jenzhlpIPC5z39NyFDpN2sUK5fI5Lu+12SnlsOUAzMRJNDQy5PwsSkCkSW69fplKMAuAPXAJpUbJHCIC0FRMbnudTGWSjimJdYOR1qPgJZNR/SYPGRtE+hBj9AQjJ8OJW17NHtPlxtY10DTOTdxC2nNx3MR6WXZt7KBPrdFiZBeZDSfpG32MMI3U0oS9KYRUvGHjGbRRm1uM4wBcMg/TK2RomwWycZ8grbCMSfr9VCLjGD2m/Am0UcQgGlHaWKS5dYpmr4XjTqOIGVkbWEHAbXoeCfxpPUMzG3Cp8hQTgypBDNfsNTx9xEgbsGpushNu4Gd8VoerXG1f5eMtnWueZGJiieXzz79Sw3oXfw++lhX/lhBiarzan+JLl4lfxCrw+i95PAs89uVOppT6APABgDvvvHN3H/iPgGbzST658ThChshMi4+/+w50+4s2xuvXP8jlK+eRLcgfbvOuuM9vO9/FPvkfiHM25Y1rIE2EMBg4JaQPkdTJtV1a3Rqvy7zIJddgYughlSI96tNKVyh2EuIvVrL49R7TArqDIkO9S9rqcIeblGk+UbmFu/RVni7dgOOOKLcTbThQMZFVABI5oh4kNkbXT2yIvhOCFyHdLJ5TIHZ9hIgxhAWaxC1JplabRNY4mDqp0HqCrsxTbNW5tnCYbO4yQfRa7lGnsPHBTP5Wz93g6uxj/Jz7zzjLddAlqTjZ6dj9IV3dpuNME+UrTJeu0rvQwn7Vu1nMOghNx7PapN0kJtAJBSLWCc0OzdFxZrbfxRO3fxvfe+lx9l9bYxhc5M9vfz3TvR1ClXy207dW+eTS6/i22ZBzqSzFMIUvQ5BJPaHuIEsBMKohn50/yFvE/RRUC7+W4d3PfpQXztQ4MzdB/eB+tMwUtrwL+Gtcs8PWxDwf2/cAN2s/z3V3lXc+/GH2bvdZVTVE2iffvAW39nlyuqDtFnjNylu59dQprjrrqILih5Z+lH7teUYrNfTcJsfMBdYMj9t/5F4++MEP8t23fzen1toofpKCrvjIQz/LT059lEyx9AqN8F18JXwtK/6/Ar7g0vlh4IEvc8wngbcIIYrjoO5bxs/t4huM0WiNs+d+lkudRKZAttg48acv/b7VeoorV9/PcLFI6Xd0pB6xZTfoXV/EKQU0jDKFXgshEnllZJcod9oMUhMU+0MG7Soiu8wLmQ7FcV/dTL9LpxBjKB0RG4R+h5E2omJESLeIq3fZY7SZFBuU1A6np28GoF4oM7e1jtkd96SNPUapGlYuwI9s6uOSnHGoEytB6MSYwwE7eoVIzzCyE/tlToRIQ+DlJDOtaKZfTQAAIABJREFUBqVxECGYTNYZbVWg1K4zslJEuQ5SCWbYRCNCjXcLXdMmM9STfasQDB0Hf1wfKD0aEjoZzuZvR+t3KBlJ4NlbX6OTtlGaQZTewhpVETH0+zYgaMkB6WHMfU9+hoszh2g7OWRbcfjceVrpLJnhgKGW7HqyUQYnNjk6Nw1AOC5HERgeQzwukVhCp2dt9oolBHAjZzhl3YCfjjk3U0HhcT2/RqS71EdJeYZts81kUEUBJzOXuLT/JpYP3cOV+gnC4UWCdJ2zGRddRBTtHvVx7+G2lcLTY4SI2fYki/oG5ShDeXSAFXOLWpBiulCjUqlw8uRJVi99jjkzph9BdmGLB3/7/UThbs/ef2x8tXbODwHHgUNCiFUhxH8HvB94sxDiMvDm8WOEEHcKIX4XQCnVBH4NeHb89avj53bxDUQUeZw5+17iOGCnOy5nTI/T1xOftetucObsP8e25/H+sggrGoN+kbUpC2NjGSvj0khVyHYHCJkQv2cVmWy1GTlTFHp92q0pEIqRaVEd1/It9rqEpcTtI2OD6+svAJDTfUyvSECPG7QuviU5qs5wdv4ICmilskztbGGMO15FcY9BqkY+36IeLuBrid3QEjAIUkgrIN3t0MyVUMLGsxLivy3YQjNjPEcjNhXpTGKb9KaS99eOc0w0kg5am0YFx+lRE8lGdjCYwolduoZDfqR9seywlHRTSdBWD0JGs/tJRUOctUX8UaJ0bp58DoBYl8T562ixxR5/kp7vIGKDVdFn/wDeevwJtDjkkcq9iFDx4p4DxFJjcrBDPX8Ol4Bs4JDVPO6s7QHAH42zbDWPR82z+EoQR5KK2GIfVwlii5t4gbZh8wf+69BjxcLOBgTJZKXFSS0d1+iyJ7YhNvCjC2SGAz5+z5uxjD2Ew0fYGLQ5YSR+fttpUx9ViJTGlYl5ImuagtQIgeGwQEq3uKxn2NCb1IIc239ynttvu53V1VWmrWtoSmPD19mb9VnePMVjf3j/1zSed/EPx1fr6nm3UmpKKWUopWaVUvcrpRpKqe9QSh0cf2+Ojz2hlPqxL3ntB5VSB8Zfv/dKfZBdfHVQSvHipV+i1zvLwdRPsSW/qKo9o7vEsceZs+8jjn3K1q9TaPbppjJsbhyglzWYrGwStiz6VgZz4IHMEqkOnlVgutUmzkxi9Jqo5l7cyMbTsxTGLf+muz3csVVQxgZLrReT7FvDx/ELxPGAPbKLa0mOjc4wSKXZyBYZ2Cmq7QZmP1nxq7DJyKlSLG5TDw8QiSEjyyETj+gHaQwnojJoEBoGGjGR0UZGEYboYdgBnjLoTZsvfe5wUuFLDS/SqY4rea7JGUrFTfRMQnb99jRONGIkU5Q8QfwllsR+KiClBqx6t6E0jW/ffAyI8fsGoNge9sipENMcEWcTN9IBd55+7CD9LJtal3v8gFKvw5s2jvPR2bcQaBp/c/sbAbg7/RiR5vOocYaMZ+EYPe4oJiv+oSiSDnVkaLEp29Ta80gtxo+vspdrtClyY5wkXF2cOoxAcsN6RLVroZRPf9xtzNB71JRJOFjAGJ7jzReex/QlpN6BNFLYm09xNOohZUjG6tMaVgiUwU6uRE5OU9EEaQmF1kFs0+ZhI6Cu9TDRWdvos287jxKSanGdoFXFac+hCSi+XXL6k3/Nucc//fUc5rv4e7CbuftfGNbWP8TGxkfYu/efkf3cIl35xW5Jl40UF87+At3u89x442+wdSlDbjiklc2yXd+HigTlIy0621X0wEeEAaGhEYttIj3HtOcyU5oCFGfaGqvdAyghEY0dFDDZ7cEokSaEMlhxlyloioGXQVM6ZjCkLPu4luTmnWQ3cH4y8aoXhj2sQRLg1L1NlNTxnTz1YAHBkH4qS06N6PkZNDtmbpiUC9ZFE6RPptfjoZyG4fhEnsby/iTAKlclUS2iZeVIRQOygw526LKkzVMtbjIYN1LptqqkoiG+SlN2BQTjwjlKMbQVDkM6/jT2xjJ2vw+Ghte2MCxJqElSt3lkMg0CJ5lY5r0pBpGBEWZo612OjBI57D2bD9K0C3zu1lfx9MIR0l6fhcnTZDr7WddaMOohjB0qpoEW+3SyJQ429mAEDjeGs2TGcpim1ZljmaG0GD1jUmzvMFiY5Jl5G03BrUtZ2maHjlZDxArP7GBhkO8cQ483uNlsctuiBzLFE5W3kYqGHKk/iW0lSWi+WyGIDTRdoMz9pC2PojMk51VpmJJNE3ZEMml2FnK0nm5wPczjFDdgM0t2aT+rvqCSWWL2xqM88jv/D1vXdit5/mNhl/j/C0Knc4pLl36Vcvl1LOz7GbSdU3hiiIoSjXxK32Zz5wHm53+aiepbufbCVVJeQKdYJAwtGluTZGeGdDtFSuNyzCPHw6IHWp4b9r8NPTsJQNwdsTQ6CIDWXgZNJzvooUWJvCJRkFtiQhO0+gkJz4YWJj1cS6O63abY77A4mUga6TBABoksleomzT/aapbtYD8y7OHaaVIyoO9n0O2Q3LCHjCKUmdTZn9je5gW7g57yiXzJYM6EGFLPA0VFM5cmHY5bPvbWWTLmSOc2GKQSecn1dZxoRKhSZH0D5Y2bxQQxI0vHiVzs0gZGt8koMBCmhtc2UWGEaYXo+2My2Sa+NdbgvSr+eLM1iC1SbUEsBW/oPcv/x96bB0uW3fWdn3Pufm/umW9/tVd1bV3V1btaau2sAxJYshnAeGIGG7MNhJnAwdjhYcI4ZkzMmCFgcJidsbGYEULCCAmJljRC6kXdrd63qura6+3v5Z559+XMHzfVYAYRjLoVwbTqG/Hi5ct7310y7/me3/n+tqXJLn/8jm9l0qxyeHQdaRTUxhWOpAtMigFtsUumMqrCJ3VcDvlnEVrO/dkxktmEpMsckxS5k9J/vs65vcu82DnFCwcDEh1Wd0N23B6ZbqOnkLpl1PUHBvchlGCrvcUd12LW7IIX7Q7B/O2YwS6VqNwvj+cIMKkQI/I2dR3G9WsoCi7r0G46DIpyVbQT+nyupTGQNrrto9+U6JdskgIaWsb8B1exazU+/gv/M+Hkz8s838LXD7eI/xsEcdLlxRd/HNta4vSpX0QVYGuXUGKMSpfYb2p8m3sBPfY4cvin8Ecx/Z0LaEoRL5SpF5s3DiF1haxlHNksXTV+ZYCrAoR0MOoHeKjlohCs+ntcMA4CMGfepNBNiCeY6Yz4tZQHD6xxu60znZZRHYdoIcSU2JIw1lkZ9SikRBQFhibQ8jKypbpXNjXppfsZ5Uto8YRct9A0SZg62FbCyK9QmUxRMoVcsri9S6710N0UlQq0hQytC8bGrGnKioWXlxbq6mSL6/Yq0khIzXJ7XGQ4eUheVDAzjyQqJz4NSaSb2FlCvL+0hsPUQDch9Q2yXNI+1yO4XKNS6ZH4NYIiYS6polGuftamh6FrktUMMin4u5c/zbO3nUBVDfbH65CDkY+4IzpIRTmcSlyub15nmZgz3S0oMpqGRlEIYpL/7HufftFCzMHfSR/D112iOZe1jktzuM3Selk+IUtMTLv0bRyLbe6+8V1c2bbwYsWTWso/adcYL9eZtvfhiXK/NGoxUBa2yLAFNJXH1co1MpliTAr2tVzSRGeoTdD2unxCyzhTKT8z80aMd33MUlEhKGBj7w94/3/3z/AHPT75y/8rRZG/AU/8Lfx1uEX83wAoipSXXvoJ0mzEmbP/DsOoE33xIWI9RMghTc3lB9sBw8KEaYoQGusXBuhx6Yit3HYKFEx3q6gMGqu77NspLd60ss38rF59/8aX+PxcxMBtss/f44JbxvXvq1xGGQYqmWCn8eyiXBSKt9cnRH5J/IvWIpkWoaQgDWxWZnp7JxwSVDxk4VHImFq/h5UN6YYHKJTEinxAQ+k6SWJiWxHVgY+WlsSqpRqtwYCWGaFbJalUGyP0PYm+XTpg42UdLy+dxMv+Dpv2ApGy0Gc+iZQQp0igsNF0m3E0RChIvYhAeHhJzlCUkS5BYWJ9xYUgFU4rZXhjnkqlT7Y3z7TIqOcVGlnpmF2p7iG7GnkrJ1AW37/zSUShQAoO6VvIKRjZCJTGvfFBlMj544/+MXefv4CbJpibV2inNRIFoSjI1SxMNZHkocbae2/jrf6jCFWQ2Cfptlfwgl3+wUMxnvIJkwqGWU5ao47OPVvv5vYbb2MkCm7us/hgu0pku0yXDmA0JXkqiNOCnjIRArxZ3sRV8yZaYWCnimYmKKI6a+Y2c6ng4t6Usyt7qEKS9Qq86TrJ3kk2E4O5Yh19weS9//DHuPHCszz2+x96ox79W/gquEX83wB49h/8ANrPx5w88a+pVsoGHtmTn+SatYjQR+xfupfL8k5+czDP8y/9DP/Lz/wen/rQ07QHZSLd7iWFETcQQmCMCxrNHoeyUo9dlC4LlTMA7Ow+gy8iNqrzrE73mOg6ThGyWL9BoRmY0RQnTVBKEKcxH+o6KAVZ0KIQKTWnRTarrhlGNsvDHqIoWBpuUWgahVal0HxkofDEDoN0BVSEVuToRQGaThqbaLLAMlICu4yXl5mGEwYsZzGanYNQVCtDtLGDvgfkoOYTKvkUhGQuKC3Tkb+EmZTae5zHuLNInsh1SbMQEwPXHeHj4QQ5Y7+BpnJCpePN2oPVV31ufmGBrG1iWSHFTpugyPFUlUZeEv97b/tj9BGoTkIS6hy0tjm4VXb9uq26jZyAkQ5JlKRduAy0dSaDEd7ekMeOnKHIBQtRk1gpMiMlypvkqSDsWhz85g3O10/TSYbUwnVS+3ZG80fL+6stURdjJmkdaaQMKMCOGTt71JImV70x/u1N1iYxiWXjFDFmyyWeWBxf+xS+mqUBzXT/3WIXTUHkZCxc3aGXLrJmbbOcOphCUPNukI46RKaDnvcJdo5T11J0Ab/76R/j8Ss3SE7fx888L/nR//EXvw4j4Ra+glvE/ybH+d/7eezLXexLO8xVv/219/XxC3zJOMMBXfIF5x18bvK9bIUTug2FoU/RMgMnKvVolS3j+svoFcHC8zl5IVne/xJCNziZHMHVSgLw7SZZHnO9Ms/qaAeESScJsKwQ2zQwYx87zZCFgaHDywkMbmrIaZ2aEyOlRqGXFvkkr2GlMe98/lEeeOlhrDyksGoURWmV28YeE7FAc+li+XcSoTSdPJo1HW9YUJxFi5dwZg1G9qU5upVjVlOkVrCTHGJyaA76ErMT0czGIFwaURkzP+ydwejOo4AkT6gU5bFjtzTnLTyq5pQAD2uSMx5XMWRBojQaZkhlMebYyXeRBQbWXKl3q+06QQGucmmNbwdAWH30aUrehCiXCAlnr7+MeX3EsrGO5oMVjknQ0YXJ28KID/Ap3qm+xNX5RYbVOs2ZxZ9aKaOxy/aTcxQvWVSWQrr5HHaWIaPzZNYx+nNNMt0hNxqIXBKlLrkJWxTsH8XMa6/y4uIXiWqfAE3wiihIAKkUljdlGizihX1EWlr6muGzbXTJIg1HFGwtpDgjl2/XnmPD2KOqLM6lEmFehq06gesSWTbJzTnmDNhMDJZrV7Fti6vaPBuqyfnkVlLX1xO3iP9NjN2nH+Khj38BGU1QYZ/1Tz8FQBGG2PolnkgXuNtpkwuDrlphebSP8MTDPHD8Y4hcw43GTByHq6svYaQtLK+gdkFxfXwHjSPXkY5if3IQW5TWbWQ3qYwCrlQXsLMUE4/FqLSSm66OANykQBYGc25p7Xb2xsi4yYKpKJQiN8r9R6KFTGI+GATMX79GVcQUdhWVlCRu2EMy6bJw53EA3Lj0HRCV0s2FM+/HiyyePbKfbNYMpilSCqlhz7qF7frH+aWDP4i2o+M1JlSSACGquEmOpjKei7+J8/kZMl2QZzFuMdP7nfJ4Hx1VgU0SYWFOM7KpjqYVpGjU8dlafR+VhTsB8BqlVSy2XMJcIhHcnRyiEBlqGiGVIm8qJkZ5jn3hZY6ev0LOCBELPH9MBljofED7PKtqg3eLx7mv/wK9ZpWK8kgUBG5Aflmx91Kbim5DCnetXaQnJUX2HEpoRM1DmHfcQWtwiWjwdtLEAQlb+pR2rvN27ubCymfo268gi4ILtiLPC0BhugOSoMxUNr6SCyBzLtk38bMVDJFxpVnFyOH97nm6spxE7/FCNGuMviYJXJeRXUXuhiTTeZQ5z5yRcfxtGq/M8hIiTOLr11/vELiFr4JbxP8mRbh7k4//0v+GIxXE5eBLvngepRThpz9KMkq4oSnma6X1OnB1Tu6+lY2oiV9bBxXgRRGDWp3pkXUEkoWoQbars7b1HnQ7pX5kSKNYwmqZqMIntpp0Btusz4q1FcJjadY8pOGVhG6nEi23aMcmRqGzmmZoaYtFYTLMIlJdQKHoOovIpGB+ZZVIGZhSIkwPQp+0KlFmeU87vdIh29LL32ZaTkJxp81Dd7p0RueJDUFQbeDYKVFhYzVKSzWdLqCEg7aj0agNy2bpmkMsXQ6Gm+xVdUzDJ9UERZbjUhJ+Ys1yEVTGYL70Yxh5QsX3wRCkUqOWB4yjnJ1rl5GaRqXaYxQ3EXFOUJTHOVY4pMaUbKOsJ+RXPSatUkM/LNY5kg7ItIgCsJIRqQIdnUxJvif5WRKl8c7uU4xaNSxhkRSKYbOPdrNAyILWt+3H2IWf/MTv8qRjY8SvohUFu41lqnfcjxMPccdgJOVk6Zt7NISNq1dZtCpsd0LavQ2e8qaAwDBidD2BSdnwxUt9lNJQwBV7jZ46xna1z0aj7IX8iP5O9kQ5UR+cL/MXnKthafE7Js3xdfztk8yrPlEhOH/9t9jMSn9RjM5j//Fjr3cY3MJXwS3ifxOiyBL+5Od+nEkiOXJPk6/UxdN7A5IbY7Jn/5T1R1rsW1xnos0ailuS5fE5epEHtRC3cR03SRl1OrRWPDL2SPqn2Y3qWFdPEI9NmocHSFVBW20hmJCadeZGG6xV5kl0ncissOSbiMyiWiutbDvXEIWBh+Rtk3O0I5NqWqetPHqiS2JJ9AgmlQZaprFYL2P3C6GjaTYEIem8IJ0lUI12Sy18/6z8Qn2mm3ebQ55e1qjfvExOxqg9h15NGSUedjMmjSzytI0QNsY2aLKg5o1RuoGPy9HgJptVmyojMlkOE3emaUfSmN1LiOOVyWmaymhNx6SWQapJKlHINFHsXL2MkJKK12M3WUYUIUFREq2XuRTWkGS9DGcdGk2SVUgSnTPyGm91C9AUuS0gK4lfCMmHet/BJbXKs+oYbx89RVproEudLIvoe32mYxtvMSQqLlPNU9rZgMcdG5EJDo2mXGpVsQ+eA8AMPoMMymfA8q6iIcm0MQc7JqlWsDK6wpWmR3U6wrTLVYsKVpBC0EnHiMLCFzGX7JukyRKPzen0aqUk9pD9bQwKRSwS6t41lBLULvWgUSd1TOb7lwh2T2KIkJ62j/niBrY5YFGOmSqLT13bQaW3yjl8PXCL+N+E+NIv/ATX9xTv+aa7KNSfh/el43WmT2wxePg5Ul/nvuZVduJ9r22fuhbtrQUAbON57DQnXl6hLebQsufw0yUuLr+DxjSmd6FOdW5K6u0R6xq6mpDrddp+j7HlcWW5jL9fjBRZ0EJr9lCArgykMki0kPcNHmSru8C8riMQTLQesaUhQkkobGThsaC6CBSh0tGlCX5AtlAQ5gVaHhN1+8SmRceaIjOb6vBBAF5dSdA2A8az4ma9uRZFs2AU17EaCeHYRUgHhIleNrTCbfgUhmCqbI4GN9nwqnh5QCJKIsusHEvl+KrMJ6gxKZ3FgJAZneGI0HFJdQ07jfApGGyuI6wU2/LZTlagiIgUpLO+w7k1ouiWeRRp5pAvQKJJTog1bp8vLefUlJjHdkhnzuWXk1NITfGEOsHp9DpNzZv9v4/PkCC1qK74xFmfukopjts8btukwRFOj65wuWbQy2uEFYdW7xWcmUpm2mUUV089xz3N0vKeTzcJHY+5wQ7abMfUn8d2anSyMQYmvoi5Yq/RmgpebnXIzJzQFFR8HZVXWTd3sJxrJOM5jChl+S23kdoWNX+T6e4xlBK0k8OYEt576GMIVcplNxtLTD7/+dc3GG7hr8Qt4n+T4fJ/+hUef2aD2w97nP3Bn8PbnTkVBfijVwmf3SbsGux9q8VRZ8j2+Oxr/3uzPeD4xjJTfx7GNxCAfvAQ7ClOmZ/HUAHrK29FFRP6F+uoAkYrX2ScFugEKK1GKyhr9F84WBb/6oQBw9BFtbdBSKSQyMLg1fqLnApvYzM6xpwuyEnIjD6RJUkDk0AZ2HYLvXeZthUQ5xITA6KAfCnHVw5O1KWYDAltj17aoj48DZlOVmh4jNE2AmK9lGIG+2zQIB9a2I2EaGwjpYtdKIJ+SbB2I6GwChIVcnC6TiY1xrpLMguPHDshroJJXhLtYmUHn/K1JhSdyZih1yDRJBQZfeGjlMJqlYS5lywznbU1HM6079wcIyYpuTRw9kpiDxoahshpWeVqxlKSO/ffJJ0lfE2tFqKieNVcQKI4HZefeZKPsfxSWqmslNKXzAt25ws2DZ1ieph7Nr4AwKOTgHDxACt7Uy6q2aplVtOoNw34SkWLkbgAQGY71LTyelK/A1qLajahomwmIkQPAm4f32TSqHKQBL+u0/YLZFJlzdpGVW+SdVskus6+wyeJPQ+pMoapIB/vZ31D42Zkcf/ieaYzW8W3Xbof+ej/xxFwC38T3CL+NxH6Lz/Kpz78SRaqOe/9F7+Bnw6p9MuvuGhIivE2oFG7405efJsiLwR7yQEqUTnwr8716Ph1ttfegz6caeeHD6FfW+dc4xrLyeOk5gK+PiULDIbdeUbLj7Az8jH0FCFtanGB1AXXVsqVxELeYxJWKLwe0iytb1EYPFp/hYIc23mQji6JxVU8bYvYlEyDCqEycBoL0LvGgj1BzCJqVBqQr+T4hoUddBHhhNw2WZ++Hy1zOSB+h0laoZkP0JMM9NJy9ZdLC9sd5uh2TjSyUVjU05g+TcZRBasRk5mQizErkzLjd8tqERcziccZ4xQak6SOZkjmKl38sn0MVpbQSFJ2KosgBGGm4c9657rzJZN1gzm27bKo3XhWziA1JpjTkMhq0rpUkvagVa4A3Pw6VpRz79YuOyavObbHbgfN1hhoNr6yud0vw26TbERzCIWUuLMootSQvDAr2ewOl7j/D7+MmxU8bhXQPoaVFVwvytWZ0nIyFGG+gAp6aELjpn0TLY9RhoVnjghDCxJBnFRxsjEd5ZCIjKWBzfFwSFE1EKFBXNXpBAohOuxZmxT2ALYcMlOQbjew5ssM7yjzGe+d4DPDAzy6fYZlK+W2zpcB0FA89/IN0o2Nr3lM3MJfjVvE/yZBMtzl4//mX6FJxfv/+c+jezXOb30GbQgYDsacwooCoulN2PdWVpshNwZLjGyTA9OQSujTrypSmRBt3o7eK83LQyePc3j7FVpmRGfwGF6s6HslaU02T5BbY/r5o7iVUvc2ihpYkq25UjLqyBHBrK2itEu9XhYGG4bLpvMsK9XTVDWBxqt41hpKCnbDDmEuSWs6YrrBnBFizEovqzQgW1SknsQNthHZlEpqk+dHCLyrXDerTLIKdca0ZIiul5ONdEoLeyEufQHR0ETJglYSMbCr9CcN7GZCbugofcjSuMxQveEsEOc6CkVhd3GVZBy2kFZBuzrAV18h/hSjUKzXyqYs08xC6Qmm6+F0QsKwSjgy2TNLH8RoRvwhKZ4fEltN6tdG6BPFXtMhKSROeIEz5yfIQvHfLnUQSXkPlmbSLAJ0JI8XJznZL8Nuk3TIUs9mXLURElCKqaPxvK1RTTUiXUePc+7aHvJUW6c/W33MT0JUqlEYijUSbKPN1ssDbGuJTObs65/HzlKEl1KMJDJ8AiHr6EXEfFZOyPPTNlF9CQzJ2lrIJhmVWLG3fArhlNdX2zJo1absPnWd2qFyssniETvrp3l273ZGO2eJC7jvyGOzp1rwzNwxhh+9ZfW/0bhF/G8CqKLgT3/uh+kHGt/5X38/tcN3ALDefQw5zpCVFlUzQlMwDb7IaKFPReZsrZ1l5EpWRM7KZExu1LnUeRrZ72D4JpkmOXTkMLer64RKQk9g5hDpIxAKY3AWPW5hdD5HbRbdgmwjnBH9RpvadELsj1/rp1vY5eMmC53U6LDXfBhTlJqCoa5hWaXlejVaJcw1YiNBpl1qhnxtP5X7FHUQdkEl2EAVU9ygTSI3id3LfPq29zDNK3i6T4cp1kyfr9JH5bCoSrkiGpgUWko7jujbNUajKlYjRuk60hkgo5S5eMA1e4Uo1YnNAk/fxi0E46SC5qQ0ayOGWZkkZiYxiW0x8cp7DRMDSxTkCzW8dsB00mIwNglm1xPMcgsmucLxIyK7hunH6EONpKIYpxaN6Xnqk4zfOXIH1ywT0lLS8RDsC3YZRgs8WtyO65eTWx5uUwsMBg0FSmDHBTumxlOOxVzQILOnJA/CXRcvsO5JXjbGjBybE9euoaUS04i4LHtU9CZbTy8S6vuRStCclnKP4UWMghp++hSocgWlZaXTvhm12emUq7y0H3MjLFc5wlvGnXVQa2w5GG7Off7PotfLyC893OHL40PkSuf+9gWeH85xptWjbuwRYPDS0dsZfvRjqOzPiwnewuvH6+nAdQt/S/BT//5/4JVv+S5+7tKX2f/N/9Vr7/vTV2CSUmATl8oFveEL2KtrjNI6+cZ9TM9J9u08TGweYL26woX5xzi1+wCJd4Yo/SIvPfsUZypDrhSHGFll1q9GH80uWEj3od14G96xT8D/7YDxw4ROixrrJMY55gc9tp55EpYk6i5JOuvhnkgdpS1QP5vib8ZoSieJY3SnjBq5kh8kVAaT9cs8tNthPWlRH6/B/H08tVxj7z8eQ6Y3eX6+JB9lGEyd5wlci6eOn+E93T/Ccfs08fEoSdEQMdnEpNGYMs40srFAb6XUs4h1u4Y78tGPFhh2RuGMCTODY8F1znuHCRKDwMqZk5t4SjAtTHQYmBGzAAAgAElEQVQ3plafMsnbGGmBnsVMqlWUKCe3KNGpFoK9xQlnKjHT3RY7SYXloAcNCKMJ2LD3XMyrB+fJtCHnWwcoPitQmuJivJ+mGXHfWy7xufoh6HWReZlRXC8K/p7/YTrGhP+++CF+IO9jKEWW7IIO23M+ZpJjpoLrPYOBrtEO9iP0CdvH9/PA+S/wy7yVYOUsl1bXOHP5AmFiYJoha1qXppwnGWd8/8d3yYpVcm8NtXSEujXCH9occPocr/42D0XHUbNYfjdvsTPXQcszvPGEvu0BJp1xTs2ZYPiLVOlwSWvzKyd+lE7/Cv/YtKn4G7xgwMHKOkcXLvBrl9/Nfa2P8q/e/vMUuYUA1h702frifQjTJEsTYl/Qqv1T7nvH9349h9WbGrcs/v+f48Uv/hEX5o/ygnWO87cNUDNLDMBIN2E6RQqDcFBazJGWErUu8vLoTsxiFSUEJ9JXOZDcIDQb7FTW6FVCxq17oZPx5Cc+gi4yRhEMmicYeODGA6QDTWzk+lFAYbx9rcwRsFu003V8t8LccIAK9tAmE9JJg8Qqry3UdQqtzf4jf59n/IInxEuEVguc0nrspR1CTLDhStjGqXpobhly2Ex61A9MkG2T/vwCW8t3c+2wQhlwcfEgAFrgUjF85uWUqp5iKI2a22Pk18gWFeHIQeQFhUwwspC+XSMdlFJV3QoxDR0/N3jn8Cmer53gqrZKYOccYERFxigERVXiNULCvImdFGSawaTZQJt1G0tig0YG3bmyENp42iZB58SgnIG74yu80P8iXW2P+bGPl1eYH/vY81A/OMFzYrajKk8nS4xm/g0hSuJfTXzeZbzA/myPy8UykSorfY6cgNgo2KwP6V8/QSY9rvvlfY38O5B6yMXaUR6c/1PaUc7l1UM8efosdppgjk1cY0KgDalJHcu+l8byCv1WzqAJluUjJAyTNudqOxxrlPLNeFYN1KTJXtuj09vhgfAVJklOJmFumFKrddHG+5CVRbb0Fk8tnuHz++5lWqkw1UwGmuKMd4NqtU99OOXhDYfnQ8UL/QPc7B4muDGPe7VKq/Ug/t4cmumzfulP8EfxGzyavnHwNRO/EOK4EOK5v/AzFkL8k7+0z7uEEKO/sM/Pvv5LvoWvYOvGRXY/+4usW6WunBt7XL/+bwEI0zHLrxSQ+AghEUpQGGCeDikUvLh7golbksJyvMvh6AaFplPoDZ5b3SWo7MNfXOKAdh0ANQkZNI5yZdHA9SdIS1DFwmk/jRAQrw4QjEisBp1wi9A0mJsGeMEseqVvkRqAUkR6QWo2uPJCTD/V0Sr/F7XGCGXlyFyRZzaBMDGzCQrBgfsfxKiWPoNzxiX2vWMb97TO59/1Pp55zxmyLCDXDV5cPM49F17EShu4RkgiJK4tMDGwKhHdZJFsURFPHESRU4gYfUb8xaBc/LrWAFt4BJnJf7n9KTSV8ZmV9xBYOQeLnIZZavNGp5QepmkTJ8nJnTa+4+DMEsiyRGPRjHGcUlrqThdYTvu0Ex9ZpIh4yvn0BTLL48x6l3a2yD2TLdIzi+x7xzYHTu1ieimDSw38WcawrvlkSnEg8TG0gp3rLZaTEako6/REhsB3cibmhN/ufZCxm3FNExxKUjbTQxhaxKveQQxdcdf2kOcWW3zu3rdTCIkY5OhGTEHKlIha5QGWjtzJ7rJgb06gzXwku9ZR9rt9bC3HkBq7xS6xjClMh82Wy0J3k7vUOoWCSU1ncZRguQOScQdZXWR932Fiw2TsVHjljuO83DmMoRQrebnaO1d7BfNP9/GxnSYfDns8e+VOnhj9CN4v9og2tlh/9EcJdo9TWX2aT//GU+TZnxs6t/A3x9dM/Eqpi0qpc0qpc8DdQAD84V+x68Nf2U8p9XNf6/lu4T9HFEzx/8P38ZH8bXS1Umf+8uBerl77Jbrdz/P4l36J9odLK1mpnEIolFUgz8XcHOqoSZWxO3PuhXvclpQ17gttkbXGeWSesOM8yKJTRlREg4MUmsXVBbPsvGXqOJgE+0sLVpMJlcVnyYwG9XBIpEuaYUwlLCNb8h5kukTkGYkMCa0qFx7dpOuu8zZxmeXwGXJTYSYFjsgIhA1+STZy/hDVuBzgll3q3IYRE0mH79/6JKdefIkbc6tM7Crf/tzTVNI6UigyTaHyBEsqDDdnN1sib0PWnfkL8NGzkIFVRZvk5KnEsvs4qk6QGywmPb6p9ziPHXgbvq04aLVoz4rIOc2YPJZM8gp2kmLOHUAJQSMsJ4Y8lcy5I1aNAj/ymCQV7o1exTA1ZDpBZD6pU6Ezs+b1DOxmyp7WJixc9HZK8/CIYM9BG6YIZRACmYKFNCOaaIyuFxzKu0jhkeYRZqahrBQEpPImRZ5yxRHcFSZEWLiEXLPKzl1nt27QdwzcBCbzh1B7MRgKIXI2tT7SDJnsFawGNby0iuaVlv2hXhdzlrtQNyTjvEssM7p1h8DUWR7uoqbldxRUNZrlx4E/tFCVOTZWjrz2DD956hwvdI6yLCKiaZM0NViuj7BSjdtePovQQi4t/hlP2XOQ5+x+MiOPa5gYaEZEmH+ORz9y6fUOpW9IvFFSz3uBK0qpG2/Q8W7hr4EqCl789X/EXLbNhfrB195/tn8/vWw/L738U2i//CGEKGPMCz2iNycJTymoKexLFh3foO+V1mlvR+PgzAkgtX3MDa4zv/cs/eR+qvqYsHCZRKdAFWw3UmSmwDCRSJLKJkIsohQ0jjxBbtQwZ3HvXhzhJhmyKMi6BbkwEHlGJnxCy4K+x2bnEfbd8w/RJz6ZKTBjhasiEmGQhWWYaWDYVBKFylNSE4pQw9BjvCTgfVc+T6FyLiwfohpMaY995pIyZFIZKUWe4MyqR05VSfjuWnncggA9C+nZNaw8JJy4OG6Paj7HeBbC+QNbf8zEqbG1eJqqnGNBm5V1rgVMt1xC3cBOE+aWSsdmM/BRQpAVGk2rx0FDMpp2CIXBg/nLOA0Xla0DBalTpZ6X34GeK+xWytS22CpWUc2CxvHSiX7oyhSpHHaFTarAQ6P7YpXKKONAvI2umaRZSbZW+yvZWJu8sl0h1gQnZyUZXGL8ooysOtt7HoD9oyl65yjszkI+rQkbcoAUOUHfoxlrVLIGhtMlUxpnBiXRprlOzUhJ0zGREGw2y+img7FPGgbUHYNAy6gFkOQ2k90JN40RA68MAhCq4NX5g3h6yorRRRQmu7srtOdCcidmeQjR1gcYOntsmB+le8Jm0H03tlQ0MaCQLN/5BC9+YYMLX9r62gbSNzDeKOL/XuD//CrbHhBCPC+E+JQQ4vQbdL5vaDz5B7/AvcNP8YvVn8ZrlyRm5CmZZ/Fvnv1B0mCK+q6M6FRJXkZ9iFh0iO4tkAPIbi4x71fp1QLcLKSX72c12kGoAikWWeztsrz1KIWy6Y9PM0kshs5xYnOAGc+KjUmHqFBEwSJLy3+/rMu+fBXNK5CzEEctmiCAWgThQFIoA5FlKOUTWhqpTFh2H4G3/iSjUZXUlGiJwMtmZmIc41UcemGEnQlU6hOlGkVsYBgRd3bPI7sJvVaHG+0FHnzhaT576gT9mT9AGimFSrHNkhRjMSsYd62MqFEqQGYhY8vFLGKiSQW7NqYWLTGUpSX+nv6T1KdDtpbfRp62WfU8Ok4Pzw6YrHtEho6Tx6wulVEq1Tgg00wSTaOtd2maKZNJh8BxWPZinHaFLN8ABLHnoE/GZJqNnkXYzZTEllzlCFlVYbcSKqs+x26EmJnNdemSKoWlWUw3bPQCVnpbmFInyodYVgTLIXZqklvbvDyykEqxosoENVdkzCcDNqw5luWL7PcLQrfC1RWQ43JyWKjcZEP2sRAk0w5VFeJmFrY9ZI82B/UtIt9gHC/RMCfocUZY6OzUPTSlOG4bKFVwri0o0iFSwc3ufeSDLS5qm0RO6eF/YPc5Nhsd7suusE90MZI6o9157HrKsf2b1LIx2fhO2oPbUa0n+IMHjjGo3Ma+pI8ezoMoUPoF9p0d82e/d5G9m5Ovy1h7s+J1E78QwgTeD3zkr9j8DHBAKXUH8L8D/+mvOc4/FkI8JYR4am9v7/Ve1psWF778We58+V/znHUvj+t3EzWrdNQuzWBMZuvc9exFOr+hkS0rpvfvolBUFieYBwqSkwr3S5LNZJ521GTqZSxEMUv3fDumypgL+qDNsdQbUh9dQVN9rg3ewzQ0GdcOsd4YUfVLEtW0CtOiIFj7bsaU2b9CKhqHv0xmlkW8RFTGzDcinfHEQSoLkWeQ+xRS8PLSK9wvUqgucGO6n9QQyFTipTOJJwnx5pcYTid4SkDuEw90gsLFMGLu2j3P1qTGSyfuQgnJe596lHFjjl89VEpfmp6QqgTLKp2ijhGilMDYnn2YKiQUBc6s7k84rWFWEgzfYaDNmrhQcMfFp+g3T3I9XeXIgVVONctS0JMNj8iU2EXEartKo9HALjJiyybVJXW9PG88aRDUXZY6ErvTICu20UST2MywxlMiq4GRTrAbKYkFF+VtIEQ5mR4PcGLFvh2HC1anrNdjOqh8JtNtbGNISZAPOeIOmLYiWr7H1B5wU+ScjBNGogkUVBQspXtccg/gORvc3Uu4sNDhI0cuI/yS+JeddSKRILUclI6m6WgKPDukn9m0ayN2gjapWqVpdNGUIIoNutUGnShgeX/ZI/lksoYx69a11z2Nmo7Y0PpotqKe+pzcvElo2thtiSMzRFEh2SgjsG4/u4DHCKFyKnvvxA2W+ML8ZbrOGks3P8tI90CAEDpH3vEcTsXgU7/6ItH0Vl2fvyneCIv/24FnlFI7f3mDUmqslJrOXv8JYAghOn/VQZRSv66Uukcpdc/c3NwbcFlvPnS3b9L65A+xJztM3//rXNiesuMtcEBdww0jQsvmR1/4Q86Pj5ErSA4P8b9Jp3pmP9fOlRaR+5iGnbTRlIbv6CxEDnPGKUapw0qwQ2q0WRgkjD2HaT5lLz7B5eA9KKmxVg+pTcq0fpd5YmeX7pXb+PL1BlkhmewtUj/0ML5btmqU4QAFNDNJqCy03EbkCi8sr+WVpUvcVz1EXuRMdBuEwEgLKskYlMJIQkRrkenUx1IGQgUEI5M9o4Oph7hBxHZc5cVj51ge7LF/Z4tvawtCyiQpaSbkosCwR6SZQdseMMznXmuuolTERErcWcvFeFx20CrEiMBQZKokw1PnnwHg9xfvZuH0cW5vv8o0rhKMXRJDYqqE5YZNp9NBlxDaLqmmoc0WLoHfJPQc1LKDajfJ1QBdLpPIBHs0JbaaCD0kMgx0LeKCPPXnX/oBjaldcGRd43J1kbzIkLo126hYSXKkEMR5wFF3wFyYsBhWGBtj9ryEB6KI6/kiQp9iFhZ2FHDdXGZBH3JwFBKaOkMMImNWJlsvndGJXq6KcqccrjUjoTIYoRmKG+YxCnmUxqxCahLp7FUaNIdd9mb5Fnp3jUpRUoLvrxB7SwgFiS3YH4+53i0TuNY6ZQavSjXUTkoSSuLlFg3HxysmjJXNXTsPYGYunzn+G0R7j2JUSke/aR6j2/843/KPbsMfxzz0Wy9RFOprGVrfcHgjiP/7+CoyjxBiUQghZq/vm52v9wac8xsOaRKz+1vfR1VNiT7wu3ziwpSKmdM151lJtqjGMb7tslep8h/e+l40Cdb5CuPvChk1PKw5RXFNoA0Ebl5KMROrQitI0K8qBkmDA/EGsdFkfghb7SbD5nUkKVfkdyOKlCtLkvZkB6SiWiyh5l8kizVefXpAL1rm6toJzEqPyVITM/PRk4DQdmgXUKAwCgNRSKp+Sfya69PqnGA32MWoliuJahxT97tUch9N5cRuhXQ6QZM2kikvekdY91Yw9Jhp4fGl1XOMqw1Obl3Htm1uO76ff741yxS2Sv1cuhNGUY0lb4ebch+xU/o+VBEz0bXXWi6mkxkJaX2UXeDnBimC6nTMSu88H10+hn6oxvH2q1zvHSO2Sr1aI2O+WhK/EOA7FRJdIieCQVQnTR1SIfmUk9CVJTFp+j7yIsAd+8R2E+EEjKSHluf0aaPNjNex3uTSvoD5QY6mEvIsQWrGa8+FM1deQ5QHrHojDk8TDhWlRVxIuD+MuJyuIPQxQtZIIsEgtvGKCJMUoRSOup2RWzp9rSzCQGeil87cRGujaQmOnrO/V076Fzvn0OQJ6ma5UhKZRmRaHC1yXrp0mbTSIFQB8/ouE1cRRW32Fg+ykFps202W85Qn83n2+xtsN1oUhSKKNhFZn6Bn0s9vcNgc0UgmjNGZU5JvvfjfMLEm/PL7FI1X9kAJirRJlo0Q3mO883uPs3Z+wBN/dPUNG3NvZrwu4hdCuMA3Ax/7C+/9iBDiR2Z//l3gJSHE88AvA9+rlLo1JX8NePLXf5Rj6Us8f/e/pHX0LB9/fpN7D5ca/oq/zdEbL1BIyePfeZz5udJqa/1eDX1g8MryS9R0SG+CUlApPAI9IDSqWKN1jLFikq9wNFkjsio0pzpbrSq7c2scth+nECa1+ArdZovWtIfhZnh5laJzgb4s8DYiMI7zyN4dZHGF0YKLlg4QgO+10MMxiSkQCGShYc38BMuqRtQ8wPXuVYxaKYu4ccpZrjIXl3JfYJi4e3sIw0Uon4+/479gLGpoZoKPwxfueBA7CTnU3cJx52i25nmr5hHlNq7moxTo3pR+2GTB22NNrhLNzaSoIqVvOa8RfyeSZUJqZQfMgiAziZVGZOS8Zf0RdmybT/cvUzGnXOwdJ3FKK9mQBZoUdDqldRzNLH45ge1xeS6lcv4w2WbLHwEayjyIHYxxw5jIalKpDsgcRRA7IAQyKMm9q5pcXi0/m1PTCxRFjC71WaFtgZidcyw0rnkdTkwSTrvlikAv4Fyc8GpWJm9pok4aKsJhueLxRI/j44JR604GrZOIBNJIxzFgRw5Q5pggq2Ja5dJlvx8znLoMTY9E7aNmxIDCkOUsdY9ULC8vE60cpjAs6maPsBpSiYbEjsNSuseavYgeKwohONu9yGa9g9IkoX4ZgUTvLZDGQ9qLUxrpGF85SKFwo3netvPdPHdE8tG9T2EEbdIowLH3s7H5YU49uMypty/zzJ/e4PLT/y/x4Rb+El4X8SulAqVUWyk1+gvv/apS6ldnr39FKXVaKXWHUuotSqnHvvrRbuGr4aM//bOsXPkAP6y9ix8a/Dve8ms/TpDkXE6fA+BwepkTZ14AwKpUOdS5SZxYiJHPwh+ukBWCKJYM8hQJZPoCN9t7IARVrU+hFJZ2iEOzyJ7UmGO3qXNlruC4/BwAmrxKpreoT0fobo6jbDwrJ1x1WM01LP8BXlAHGF2/n6FXxUpL4varC4jpHswsXVEYUEwRRUJFNPn82qeZPvyTMIuL1yM4W19nJS714fdsGLwjGCAMF5UHPHn6LsxRhtQKBq7HhcOnOb5+CSPPcJyjfPdvXuTfv7RJErtUjCkxGoYzYZpWMGTGJivoB08ikIgio+tW8TIfBdxVeYVsYiBqXaQp8VOTsNAJrZx3bz1NRyk+tFHe10u9kyTV0tq2SkWIr0iUmWGSahI5FoxHZQtBhWI3D7i6exODJkp3aA7KCTC2Giw2dpCViCAsSTtOylXJZt7Ad3J6lQanJufJ8wApJNR1EIpsJsWsaVUerp/meBizv+1jZSZHQh0TxbV0BamPMWULMY2p9kriXwpf4L5+xpXlA8S1VeQE4kJDr4SkIudlL+XXgjM8oZfSSltkfNh+N/+2d5bvNAKyQsOWGV7uI5QifuQCwXMrvO+FP+F7fv8j3P2bz3HX04/jsIkdhtQfeZJEmtx+JeML1HhH/wFi3WKv2uIt9bfyPYf+KXfv/EuOf/a3sWofpJaNSZVNqiTrzoh0+FYeWJ/nY/cmrF91UGqH5eXvYTh8giC4xju+5zasuZSHfudptrbPvzGD702KW5m7f8sRnT9PbeiiW3W+bfhefuLOn6CVvI9OPcVsHaSlurTVHvO1MqTtJe84p6sXSa/VIQvZKhwu/9F+Xn3oANcWBNItCJwVtuqz9H/dpJcpXHmEA1F5jN32Art1xVqlirF+lVOv/B88u7xDobdwfR/NAUsI6mnKT//43Wg1g+7DB7DRGV25mx5tVsQ2qa5z5eBRcgFnt0qrWhY2SvnIrEuht9C9G/SvNikaCi0r2I3m2N8YshJvk0qLWnSM1m4GhkPP0okti4WtkjCvrSxRaDorW3uYSYKo7+cnv/kMp493KFKPiuljSwdNy4nzUnvuyn0s3nkvegEiz+i6Daq5D5rBfucyjAVaZYDUdR7ePcind48S2DmH0pC/V6/zSFBjPTnGZtygP1fGpFeMcuX1FYtfE4AQOOuStYvlPhKNxbRKPJhgq3IyWIhKeynXLOabQ2JbYsTlBLieHKa3LdkpylXFenWFSh4QFaXcIr5FYHyHz1SUoasv2vM8UrkPA6gUG7xz9y38YF8jtDSC3EPoEzzaeJOYb7K/g648wj2jP+GubkymafQ0gRZBokum+7aRSuMtep9vYcDJ6mUAalHCui141+ojjNIaXcelasTUkxENf0KtkVM5cpna2girmdD/VpvNQzqJNWRxY4/r95YTyIq+QLZ7nvm1RxFKMaq1OS9v8sLkOWJSUmuANbmLo50yZ2JS2GRml2fIuXN7BTdS/FkeIow9Fhf/DkJobGx+GM2QJCtfQGU2zzz2P5FltyJ9vhpuEf/fYuTDIc/9s39BJyuJZV4u8pb6B9nqm/zY2+9g1+lwkKvodkxbdZEqp1gJ6NhdmhfLEL7nXI24+/+w995Rmp31nefnufnNsdJbuapTdVZL6lZCSIBEhhHBgGEZe2zjMNhmzOCZXWZnz9jLemdtw2Cza/CM8TA2hsVgTJAISiiHbqlzqO6q6q6uHN4c7nvjs3/ckghiztgLxwZZv3PeU1V9n7dv+N3n+/ye7y/FCJct1pQ04bYioaLTMbbivZcqbPgSRexl1N4qRVzsZSPdxTMGKF9OoKszXBjQAA2j4yJMAWaTQrVBPmPx9g9cQ+DCm9smrXYCWyTYYW7QzOhUcybPTE0S87cKtGEhpCTb3mDV6ENJ+tSvxghTYDohl+xhFAFDcpOmnmNTaaI26gihcLJ/hLjdYXgpciouFAZQPJ/SZgXTcXBzfbz/Fdu54UAvrqeQ0lvkRBQpEkauJtLJSfpvvQ49kIggoBzLkgpaKKqO5a1g2gpaqoOmmFTcBOu1HJ4ekFd13r2thxCFh5S78AV4egTgWSsKUUwkEpiWhbFFZqpthf5WBD6eonF9ZQIAISIfS6K+1cpR1dHNkK6uMOBGLrAlq4+6JqiGBlLCCesgPhrtINpx6LFBYttcXKJzT1s9PG4exgNS9jov03o44DeoazFAoKgtEjLN67iGpNLDujLOgH6F/koLPQiZSVsogYIfF2hlweWeQa4qa7xPZBmK18ERqJ7khuue5D2DT6KLgMtKP8VYm4xdp9Bs0VQcBla+jd/WiE/4dN/cYGEwDgJccqzsjRax4S7U7BlGvvFn7KrMspbT8RXoJlQMqZE+uB2zPcjBHVFUWDOMk1ZblJHM1yU3n5Mcy3bo4COlR7HwClZWvsS55U2K+aikc7cmOHfuQ99XwuQl+a68BPw/oSKDgKV//SHuHRwjtzW5h0WOP/v6gxiawu2DKptWkTF5GTXtYXpZskGddtxCKoL81agXqu+GeGr08rfdPpzeyInnbcW0G+shm74kJEOq28b0uqwUeiinmsS9AfSmwuY1Wa5kmsS7bUQowVTR4pskNi6BlBRKSbo9DqVAoZaIXqlBfRNttEtgeDRHJ5gbi8BOk1GY5HBjlSWrH6RKbNMhjIPlhGx2E9TtIqrfZVPP8Kh+AbnVM/jJwZ0cuHIRx46ciguZAfLlGkkclCDAiwmklHzj5AN4vk7SaLOiRQucqXWpBxl2pvtITQ5iBD4i8KmZcRJBB2Wri1VSKaCokHyOvwFCTRJLZCnKaXbL0zylH0QCXRkBWSGZen5sT08RM4ySoTquQp9XoavprHlJcksGXT3gan+0GCSa0TjbiOidVlhkwljEkA6rsTxZLaARKEg/S5M0QWhhBxEYauVhYp0AFIUg9GkpGl3b4ISVINts0h/kyVFnNYx2IabSZX9TZ9LazlXlXlb3X8CTJj3MsL8WcKrUT4BBmJL0XWhyaSBPIEIqSpNJ3SFuB9QTOkJVmDj9a4xamzzT3k5et0nYLbLVBhVVY2o9quTZGC0gpUDpdtGdLPXkCE0zjyJD+m2JOnEt1WyWA8+e42J2HKRC2fS50A2xXjYGasBkJUr7qageceEDkmVziBvOq3gqHGtrtFozlErvwPMqPHryk+Tz84QiZHnzDjY272V+/lP/f6bfi15eAv6fUNn4xCdoPvYYy6UhRLwHGXiYikVVarx2dy+PfuqTSKFQcpdBgbgygdGUrARRKCWBZDMZQ/ElpycjSmG8oVLXRlF9G0Qc0+lieAUqfh1BBzfso1RfZ7mnl3KyBmo/bQtitx6mayRJbaXiK7qJo9ooQRta6wAsuk2eNj1qW9ZvxttkcHQNhKRtWSyNbnW56kSv3M7aMhtGllZ5imK9Q2BFZYTxYrTl27EDjZjhsSabBN1okapoKq/f/Dy1lkOZAhUzx671GTQlxA51bAseffRRLs7PkGhrJPUWa1tNSLJWneVggP3JGIqiEBMSEfg0VY1Y0MFK90Gih7waLVAp83ssRSGJFQaoVB/ndu6jjElYMGluhVWOFL4bodzb00vci0oSdwMNDA3bMOk4Gs5mE2cwzrmhqAxGphXgqTFCPbo/W43Tn13AwmbDzBHTQzqhILAjnQ6manhhRH+4joGsqeiqihu6mAJE2+eJZD+ptk/B8YnTZcGLnMuWcDjUirPYnmbFegKZ7HJV28aQ+ijXV0JmS4Osh32EKSjNrLFLP0HDiPF4bAkrVsV0e6j06szMX4/R6SedneZkMEVGjxbh4UMKstAAACAASURBVO5FBBJjNcBOhKwM5qhWB9AIUdwBOvE+amY/ha6HJyUnMnEev+VmDk2fwVc0jEwfS0qF836HzdU2iZ0aufVrKKo2qbSNEJJB0WIuWSJla0ysCp5oa2xsnqRQuBXDHCAZ3I1QAjxLsLo2RF/fG5md+0PK5Ud+lKn4opSXgP8nUJr330/5Tz7J9C/9CttmL6IkitSbZwHoi/Xz2ul7edaPwGLQiXj5XPYmUh2f1bCElGAPr7BQSOPogpPbYviqxvZqh9VwO2Z3CV/Pkew0UJQehNVBT64gGWeosspSbz+hIqnFFe49YGJNXkOo5Z8HflWNsWBHlju1eXzPY8OBEwmXRjqylI0rIaPFCprmkNVr2GoWKUHfirPes75AKBQqm4cpNJuEusR0AnYlXsZ6N6JErrcuQdcHJaJrJmdP07ok0KXkzFbS2E3nj+MZBg0lxrdxeOCBB5gYGSbbyWJpLg0haTtxBhLrLDPI/lS0AJnJFCIIsKXACBx6h4dh/Fb6K1E4YFL/bjKQGgriQ2NUK49ze7JNSlEIhuK0DYEWSEZ7+p8fWywW2epaSDfUcCwTWze53T5BzLdxc5NsJqLnmG4HOFYWY6v+T1hcJ5aooxJQUbPopoIXeoTdQRRCxotXkeFW9U8/yVojianECAOPQVRE2+fR9CQCyPMUAJfdKF4+QUArDHhq4x7CRPQ8V4YlcfUCR8qRX+GMvwdU6NnYZEyZwWq2wW/ixTYQ7eup1I8wufQqrlhlvOQMs7L0fEjnjelvYpgd2msmZyYM2imNlcUpfKHiKFkMEWfBGqDfltSEZDq5QTOZYsRz0D2XpaADSLqxVRbOVVD39KKg8SapUkdjcPA8I2yyYpZwBgd41XGfFU/hmStPIIRKg1fTG1sHdQA9F8O0Q0bGf4dkYgdnzn4A2174+07DF7W8BPw/YeLMXWb5t/8N1t693PPK13F45jJCNWjHywTNFW5ad7i/3WZ2ag8ZWSWz5cDq3/56CnaAbcSwl+J4LclaJsGlIQfPGsNOJjFaPmVvDF8u0InnSLVqKGqG1EiGSslAso2BzQ3W8j1IQAnLfPNwiXsqjxNoBbLNLZpBZFjuqpzwroXqFVYunqemGCQ0hbUeEdFBl25HUyS9vXOMyWVcEgip0RiILPCeUxsoQUDQmiRmOSAElhvSo0ywXr8XgFdZpxhqLCO2WjZOXnwK4UN7tMAZuZ9E0GL3zByuYdDWUtzT8Ojp6aFg18nIqHaOr0jqTpKE3mFNG2FHItqRxAaGEWFAwu8ggMFtwzD+chLtFfyORszoPK8T3VdhcIh64wT9+cO8qZgh7I1RTalYXkApn31+bLFYhK2sX09VCVQV2zC5ox1FYH2rvBeF6BkoMqRrFkhZbXw1RUAOAUgpqJOlraURYRvZHaKUXEGsqeh+tCA1ZIrLzT5MNYEMXSalgtLyOJHaiasI0kQBdPN+PyDJIvnzxjEcfJx4RGs1+2rUg5CpRpeE43HB2BFdl+Gze/Uie2YuoFpdUAKMrkF29f1M2aOciJ2hqi2yLFLE9a1ciVbAwdyDhI7C5v5x/LhNtdZPJ9kloE3cNbhqDTDaUYhJaHeqtGMZipPv5uDaLCf6xkk3q3RjqyxOV5hZ16n5Ia/042w0BhifeJbrC7MEWLT23sbN5ySmhIfq0UL9N+f3AZCK5ekZTJILBedXXPbt+xMg5NTpXyMI7B9lar6o5CXg/wmSoNVm8Td+HWEYxD76MU5Nzzzf2q490EOwcZ4Ddcm9N9zOld4+xplFFx5ISOa2kQ4ifnVjcZzKZj9SCAanVgi9QfqMBhtuCil16uZVaqkc6UaVuewxVnqLfL6vF18O0be6Tte0kEoK1V9ns2eQb81/i1DN09dYQ6ghBlkawuZryu1UF64yf/okFUUjpmmsp5MkuxK3OkC93k//wAzxbhoQKIFOOWujKgFBR/K+v/0coWcRZKNdgOZIzlefphlWEVLSY7R4WfMYQo+A3/Zb7J9d4+7u7ZwR+9nuzGAnEkhFAUtnJUxx3a13svj0CRJhZIWHCFp+ZOVryQkMJXrlM7sjoCh4UZRMYbgPJm4DQHYSmLE2jh4BdMwxqVs2Unrk8jfx80M9oAjW+g1iXkAx8VwmbQT8Uo2c8a6qogQhHd0krDiIWIJQT9FsbzW0CVy6VpaCViFMlIg7UdVyH5UGGarkIWjidweZzLoUJ99K3I1i6jtSY6GTxlRTSNFiHAXhhDiixFJKR5dRAtYV+tCULglHkHSWqWkZLgSjICWddo6T4TXEmeOass2FbAT8YUoyMFOFok/YGy2Aav1hdDQMDE7HL1CnQz49x4pSRFEks7MT5Bajc1oHDlBpZZEoKPoJYp01ZGiwZhYZtiVJQlKtFrvcHFlzgL2NOWaHxxiZuUqouqxVFzn3xCo1NhhV4uSdHHY7y56997LdrHG1dJCYCzfUkhx3bU5dXmZ+cwEhwO4uMj6eQkFwZrpMPD7Knt0fpdU6z4Xpf8dLaUSRvAT8PyEipWTlwx/GnbvM4Mc+yt9gcODCGZR4FBt+2hwk2LiAgUqfSLORKDDOHLrWRSgGD9z3CcrxKKJh2RthUbfIhB63D3c45FbYxSItT0dKn8XsKvVUmkK9xumev+GLyTX+i5qiaSfoX42Sv0K1F9VbI88wgQwQaoF8YxM94YObIBSSQAn586dCrpw6waYQqIZGJZEgbYfE2/N0Lt1BIlGjZpVQADU0qSlttLSP06Pyjvvuxrj6LEFuK5vVU5lrPklDSRBzfE7JCQrNKpgR8I/rIyym9zNjjdEQWXYFF2hkonDGhN5GEfC5h6cpqP2obuRwlQjcYCsqKrXj+eed2zYGQN6NdjHJbA5yo5AbQ+toGJkOnrHlH3BNqnIJRTHIZq5jbyaOVnOjXYrvoyjfdQRns1lCRUEKBU9V0L0AW9dpbHQojm/j5dpFmo1rEH4XNXRxzBw5bRWRG6I4cBjPzqCKgAYZauRRugEySDBVOsTIu34Wo9tGSkmgQDvUMNUYgVJmhOgaAreP2VRENnmqRkUkiQkX0w4YDupUrRxfXjtCp5WkfP5NPKOPoIs5jtQUVjN9bNADfQHWtEL6tZfZljsDwFF9nKqInOyHWxHNNphfYFaWSOkOQVUQHs/g9UkGzWOsrm6jqFTod6YpVue/6/S3Q9ZFAwXJATvHcftxChcjGvNqsRez28XW5qmv2xT7TuNJn9eLGPfOXINQfP7twU9zwVEJgdsvpXCl4K++82n2FC4C4Pt1Cv3RNV+Ziyi1YvF2xsd/k9XVv2Vx6S///pPzRSgvAf9PiDz8ex+i/q1vYrz/FyhPlfjM4ipHzp3CT0fAn/3ml/E2p5EyZGfNQQrl+VBO203w8YufZkCPqmLPG0O0DYV8mOCKo/BvrK/Rr2+l4MsK06UI0Prb60w4Glb7j+mtXWW1usZAOXLWqmKQlLuAoUbOwVDNk2g3MZIe7lYPxX7h0FA6XO74OEQ9dbu6QbodoIYBT9sBoWciR5YxQwNd6uiBQiaegkKRi7uu57oTj+H3Rrsav6yR8QLavkracbindph6OYZrpQllyO6eV3L3tpejFaLxu8PTNFIR8O8Jitw+kufb8w6lnr2oW/Hvcb2LJw2cwKBIL/PlNvPlNs0tJj7vRcCfyEWhmYy/nHijjWqGKGkPVwvJBTqV7jky6UOoanTvPZXIgWsF318YTFEUAsNAqhqeppKp1eldWcZzAybHJxlub/BbfoNUMwIqx8yR1xbQiiOkizE66zuJS5tQqCwyDFv9dLX0BrHRMUzHwZMBhjARKBiKRhhbp5SP7gMnw7lYtMNxDBMbjZQMMRxBx6nSHirhhDp//Oyv01zfRVcYIASHK9HCcZb96AMe5owgbjYoNctICWflPpLSIrSrvLp+I3fWbqJYcJiVJQp6m4TfJnm1S3lcw2/X6XbT7OUcu6pdhtamqSYineVtl3llA1UqpPQ05zYeZvjsIim/xbGb9jN6ZZ5urEFgVtGyq1zhDDcIj7VOnHPnbsU2Nnmd+Vmq8TiTMyEDesjp4F7uKJ6jIMYxtBzlzf8avU/La1CZg8oc4+nXUTIPM/PU/87Z4z+snuQ/LXmp5+5PgPzl//NO+g4/w6PBdj4R/zTuNx6n0fO/sO/icao3vAdN2ty+7xdY3vhN6MyT9CKH3YCzjDBDvnz0dnZ10uxM/C3f8j1Wk0X2BgFe0uQvyn38zPI0tWRER9T1VZZ7orLFU7k5Jg6tMHb0n+GKx6nXqvRvRnHiQi1hOBexkwPIlsDT8lidDnrOp+0lEcKhXKsRT+i0Mznyboe1hEIosqQ7Np6e5VjPn/P6lUOkRo7Su9nLoGvys503gEn0SQO7oDv+fyM6R6l8OcUhMc+3902Q7Lrc9e2HCVEJtifBd+nqFs9mihSKNklvgwF1idPpyPrcEwxjDKncP28yo2UYTUdAk9RbaIpktd3Lx/7iNB/beua9zjrvAApuBRDEM1s8/cRtZM99FRsVq+DSauvENJ9W5yKT/R98XmcTvsqaF5IKXlgRUrUsQk3HUxWuf/ZZsgtzTJcKJP6vj/IGP/i+sbZVIKstI9JvIqm7ONVRYqMRnTMvxwg7q4BkTRxFMd6MiiAIAww1Q0KJIpDCZJnC4DSichBsg+lkCldt0I4ptBoGg6JNzI0haSDzvUxaFS7OlRhI1NiLgRsOMdEOSXdszsT2ofTcA56g+cQw/RPLtB0NqyO5GPfZNXecy1Pb+Zer7+D3Rx9iRg3Yr59Elz6aH3AsvQ0xN4aqumxPnqaxpuKvXKSejHZ1brDIorZJj8zye/nvcCAQFDttrt84zXRpGz3bHXI0qORO85dLAxArA2Wm/By1Wo5jR+8CoP2mAQ7ffR83Jnz+xqsweX6FKdfjOyOvoTX6DIXeZ9jZaMAfvSd6n4EdwK/o72PtRIO7MyeZnDjw95qnLyZ5yeL/R5bN6W/RM34cRQlJjdn8m8H3MjX+r5hauky64xPT+1ClgSIEWm4Udf0iG4k4CdkiQCcMFXo7SWIioFHdTrbboJJMUqq2WMl4fGCjTqrj0kq8BgCHZTqJqApl/ukW57+zh243RWhnmc8JYq5DsVtFan0EYZVFq5//eeW3QYLWdVHiIX5ooAYWqd48XSONELBTXUePJ0EIMu0Wvp4l11YpXXkd+Cbb9txPPfT5pvsQl5S/4qnNr/G/7TF4KF4jyPgYroZxxOPM5E6kEGTo8tdTt3BipJf6YAncJvfi4SgK5VyBPZ05dN3BNaMFzZIG6atPkg46fN33iA32g4SXDT7OQGKNZb/Ef3z7fj76Mwf46M8c4INvPAhAr1clnsmgals20Pit9LkRr12I92Fokthg9Hcud9PzeutLGgyeKfMfrt/2Ap3G02mkqtLNGjx55AinJsfIJWKMf+QjLL71LZy//jrO7no3p/a8j3pmgphSh8wgwZnjuOUBUkQO+zl/O9IuoBltnik/jpQSKx7DD31MfZh+9QgASkbDzZ8hEWsjWh4b9PPEjkHOThjYgYWFS1xEhetErAf6TXaGVc4GGrHQxJPbEXgMtzucYT9hXuKZKrHHdJSkg9YxCYTCo4lVXLdKsPYIhtSxGoe4mhogY3SRQtDVVGr5XbRaBRynnyvOOL11iWV6dFJdLF/SEIu0RJftQR+Z9WeRSPqCFruvXGQp1o/dO0TYdDjy5JMcaqzw+vDb7HZ3MeWOERLSMAx0vYsbCzD0AtfrAl0KPpOf4mjqw4zP/Ty6XSR/w19xTpnkyq0fhbs+BXd9io8ffCtX7QN0vX6++Ynfoet0XqC7fyryEvD/I0q3ucqZZ94PCjTqPWSLmyTutXjWTvC6C1HN96Q+gLHVFMRIjCGWznAhrTLBDLPaJEvrkxhbyVjL3gi5Rpl6OsdwpcGAOcs/q1+l7v0slN+LUNLIYJ1ARI7PwdkyzY2ISsrlllgZGWVlaJBRZ4lAK+Li4AqftjVM3G4hJCiWQA90RGBQ9TWyooGlxplQy9jxCDxz7TqhluZI81py3WGWTt6GZTXJ7/0a9cYZHP1xrjTPcdSq8LHdSTyrjBH04pdgKRtF42RNl0pe41JPHwOBi3RafCXsMpBS6WhxpppLaLoLbMXbS7jw1MPcmRAcJWCtPwozjBlNirEq641eXrO7n7ccGuIth4Z485HtW98LSGRz31VKokiuZzuBq5Aw21gVE6vUQlWTpFJ7nx9WSJi4Tcn1k+Mv0Gu+WARVo6lYzI+OoDttxq89SObNbyZ3112cHx5mo+c6NnsO4GoObTWA9CCdJx9HrMZJEdFyG2qasFsibtps2BvM1edIFgp40kNTdTCj+H5rYIimcZr+5Dqi7VMRPTi5DjU1jUQBvY5QI+rL0vNs6AXepJ1nU02yEUaOd1upEUOjKTJcio1RHs0xtLSEYykEjRzNZBE6S9Qtl7GzD9FRYbxeoFPQn4/lP7trFzoJrHaJU+0dPNrdRdBRUTM+7aRg0A5x/BBJyETQR7rsUk95pAyPyVOXAaj12fyX172dpmmw/d7H2Fu7yFkvxc3hJKYi6OgKRrbMmm0gD/8LigtjHNAMLskMxY3rqQYOPSd+DUW3GTvyOe5XboID7+S+bJE/25hD+pGuN+IFPv6Hv/73nbIvGnkJ+P+RJAgCnv3Cawj6Q2ae3c/a6jim2eF8chpXws4Tz9JIpbDUGFJKbL+LiI9gN64yl1SYYJbL6iQXF6+lJQ0qYQyvkyZT3owidmyXNzHPtD5FLdzHRhAilDyGWydU+kh1WiRthyCRwojB1O6HSLYbPHH4MP3dTVxjq+Swv8FTvQHprRh+YWjkgziB3kYi+Rm+hqOPoIuQVBCNydubSDXFLd0bqKg1vmEWuDx3Ldn+C0xes0JaRHHru1YuspTL4FibqJ0JbC+GtwXk9WyKwdYi59O7MAKTtgy4pMBIIaI3xlvrCAGa5qJImO1eJHC6vD0f+SSO1b8MAjQRUStL9R4++IUTz9drN+MJxFaET/I5fn9LlInb8Bs6ZnqrPEJvnVzuBhTlu8xoMWlStz3cH9Lsu2dgEKlqhCGodgslDBk5cB0AExMTeLqOEj7XVrLJ15NxyAzReeJJYoEg7URhh7LbQvpp8vHomp9ceZLc6BgeHroQBLHoupPFbUjRZSSxiOj41NU+VM2n5kRWvmOuI4wYsVSeXKCxaWR5W2kWK3Q5FUZjTuVUGkbkGzgau4bEDg/UENdS6JSL7HZKmIHH41OjyErIbNznUE0w2XcZW1gEpsXc1BRSQqI5znocTqkH8DsqlaEUlYTFoB3S9TVqsQpeCTJ1FSXpoMRCBi6u0utU6Bgao94q/+sv/Gu8mMriozku2fN4MmRHOEDG6bIQTwGCi8kyvVfeww2axodWfo6ucBnU/z2VToL82f+JRM8M9cu/y1x9jg8/+mHy1WsjBQmHk2KQzNkF/uILv/8/nqwvQnkJ+P+R5PQX3o493mTh4jYq1RGqlcgSKfRucHClxuTVabqlQYQQtN0OQl4ikS4xNzBAoAjGmaXjJNA7Olf8HGoYkAp90vUKvm5QzaRZTwlq7ocoqn/LXBAitTxJ20WqBbLtOgqSIJ5CVwJCF248/igCaHTTdIw0EhXV3+Bo3no+eQtDp2tWCFUXEMTpstqxaIYx8u3IUi2468QUlW1iG9/OPkk37bK8vIvqwjUkdy0Sy0bAP7k5gyY8MDpo3V6U4EZC6SKSOR7p34OC5Gx8FyEJFs0EupQ0ChbjzXWUIAJDXXcwpMpy/TiVXC+5TcmNaQsr/AaKFyNnRiA6seMg951f5xMPRgXHhBBYia2aOT8A/EzchtJUMTNtjJSLiNvkv4fmASgkI+dwteO+QLeloRGkqiLCAK1dRyAZmop2C/l8nmwuhyKj+HepdPhiKonbAnd+nnTeINne6v5Vj6iIoZTJYHKQp1aeQgVc6aALMLcWLuViAwmMJq4iJLT8qCxHzYkc311zHSEEYa6fnCvxFB0j1uSAXuV0GKNOyAPFGPNJnaK/wQnjAPlMg2CLxdLW4ry6kadtxliMachAsByUGWuH7KBKrZLGHtwGMoz6EeghR/b1cdrbjtPRcYsGq/E4A52QdT3OpeQsD7hfR5WCXaZN24qRdTpMthaYiw3xltbD1FNpvnXLFEFX5T0nvsRjoc0BZ5Kkp1Buj5LJrHJGu4omh7n2zIfod4t8qv8L9ChXKKtr9K7eSuPSqzgw+hR/8O13YYQqQ9XbyAQOevo05/1dTPSvsfzl7/DE8W//jyfsi0xeAv5/BLny8O9TLpxELMRYOjtGaFjYWoqOnSCbX+dlz34N4fv4+ZsBsFdPkOxX0JO9TI9EWa1jzLF7aRUJaL5DX2oeRUSNTADWSj18oLSNvJ8knrpMG0EnUUQLBahZik6NcqEAqopcWKC1GCe31mTvybOotkQKhVAtkOqusW7GyTej4mEte5BZbYV+J6KLqqLAqquwGfYipESEITnZZsRQUITCt7KPobsLgGD++NvQ2hq1KYGRcim1F8kTRaOYjiSu3oqKTcPMETTbrKUGaOgZpEgwG8uwt13jbE5ld9mhs5XUpesOWiho2mvsuvY1xF3Jke1XyVtl3K1rDKXgHTfexF3XDPKx+y7y4IUocsnaighKZH8A+EduxGpLjJRPZiwKYcxv6eI5KW45yzeazgv025NJY5txBKA2awzEGpiz9zx/fGJiAiGjBSNDlxnD4OhTXwcgt3MIrR3RMqLhASE7smluGLiBo6tH6V6cxpdddAHGVhRp5ytfw5cKPbHovrpe1P+3vmXxu+YmhCFNI0He3Sp/YXc42BPgC8E3FJdn8umoMbs9x1llL4b0SU5FTuZcOURFoORD0p0mF0fHWXCjgn65zQlOpa5BGgb6chRVJntCbt5WpOEKuh0dL2Nhayr5doirZVhMLHFh7RSBkNxpLLNq5vF0jVCRVIwsSjMqyX2y53qK15qMby5jLzxJXFqMh70kNkMKvVdw0TgtL9Dv9/JQ6mnuzz7NjK7jxqPnoJ97Gyu1Xt6Y2+Rfrt7E2VBh0J6nmFylK5MUDBPPlNz/Rx9ndfOfVmbvS8D/DyzV+ce5XP4kakPwCO9BsdtITWfPgUNckHvIZFaRBEgh6MlFESuJxpeI7Z1CqDrT4ztI+i16vTaxFcFSkMHTdLb1noQwJK9EluT8wBhr5i6asRMgQPqwnot2FYGeZtTbYL2vF6RE2ajQnY0hfIGhJXmZfRKAAeca0lt18fvrq8iExXpthJ4wxWvDXQwEOarxCdZQsUUvTStO3LFBSTNqKmx2Vlk1yvhetBNwfIttFzqAYPzOReJqnQJR3kDKu4iljVJQYmyEgmS7ie5G1SsVxaKqquzlAp4iuK5soqQisNb1LtJ3EELhdcPXAOBl76PlJZhrRbTPRjfPnkyW/+OufezqT/Obnz/OfLmNlXzO4v8ejh/ATJLVt3Zgu6uoMkU8Pvl9Q4pbFn+5/UKLP2dotOJbDeddh3zGh2c+E3XBAcbHx2EL+IeDJjEJf7P8TdRCgeKhnai1iGYTTVCMTSbTRW4YuAHZaGKfPo1LFw0wFQh9F3t+Go2QoWSkK3/L0q9v/YwJF7XTwhHieeDf8ALSaocdMuCrgcPC1v1c11ykK2JcTfWhjUR0VGKphhtKdhizhAieuP4I57MGFR2c5TTruX5yS3OoTrRQFIcNbt5WjJLEXAMnGYXAplsBRpBBCkm8Cs1MQEFtMmuVuP/wzVyODwGw7hfIdbuU+8aJjw2zMV7glhNfoO7X2emXiClLrCsmmhKwaQSsqBsc6OxElYLPpbIUehXaOBQ0jWPnXo7tWxSnHiHQ25S6C0wNR7B3TN3O61+2HaMLf/qRD+D5L9Tli1V+HM3WrwghTgshTgghjv2Q40II8UdCiBkhxCkhxKEf9Zw/reLZdU4//POEMRgd+hCdBQ8liIB6YniKp6wb0HUPq99nenSCnG4hZUgmvoZeGiOUIZdGJhhz55CdAn6gcinoYXZyjKDuo9otikpEbcz1jTDsjhBceJBaQ0cEkvlCHFcz8HWLbcFVVvv7STYbiDAkvLKV9DNqcUc7Av68v5tcI6J4sq0andI2FCVgKB5ghyGv8PZSU7azAaiqxmYyQ8rtkosdIK4IlhtLCClRwudKGCj0dFskThhYBYee/evktzpxZrxnCaXHZGoPibCFbcU4NDNLDFCEQHFadAcUtDDk9qpF75YTWNcdPLdJb+82wqUuYcllv3qMJ9u3MNOJShOsuwViqkLMUPnUe65FCMEv/8UzGFvg/IMcP0DPQPSaWlmXbPIgWx1En5fClsW/+UMs/oym0kikv6v3wSFYOw3LUe/e8fFxIALVnLLBa9UsDyUX4cZryQ5mUWpR1JVo6SjWMpP5Hg4PHObWMxLhB4R+ByEEcUUQhl26e7SoMbvRRdUDZDeGL6HazSGUDjFfoHYaoIUkncgRu6lnEK01Xq/qXBWCwA2x/IAjNQchQx4rHqIbVwhdBWVxnk27Raodo5lLgaVxcWKMJ5NrrNqrjF6+wnBjDmWrKunosMFgNsaepGQmP0Q9FjVgF3aA7sYwXYVC00CNRe/cOXOMu29+BdlOg/HOAjOZKXo2Flgp9uOFfcQOdTmfG0G79CADMkdRxmhu7GO7P8i8skFjz8dIYXBTczf3JOOke3tZVioUNbAqe9FOvBtpVfml/Z9hKFZh79AkirHGI8F+ds4+RN+bXkZi2eGP//i3fvjEfRHKj8viv11KeVBKed0POfZaYPvW533An/yYzvlTJ8987g68QZ++9st4oFsk2agR6pE1dEpJckqJ4oqz2RWuTkTRIkGnitk/iT3doSscrvT3M6HMUrfjmFJnM4yxOlZibbqHlFOmQZZCrcJSsYdcOELs2QXmF3cCsJxss5mNwG7YW6RcyNNXiSzu2GY0acXECr3ddfTQoxKz2L9eItntYOkpVmVN8QAAIABJREFUQtUgaa0zZZ1m1plDRcGs3oYHCD2gaSVIOjbo4ISSNaeNgooZmkgp0A0PMwxpX7HYOJ4nUbJ5k4y6dsbcJuv2eUYTe+hrVNgYGaG/XSW9lZE6VF/imcl9HGrMkQwEIyLKZdC2LP7x7YdwLjfo7HgaFZ9H46+m6UXAXwt6n9fBSCHOx995kOm1Jme3ipO9gOoBeve9hedKufcOvOoFx79r8b8Q+BUhaG719Q0UFWf7IdDjkdUPJJNJND36zzPaKneJQRwdnrg2RqpoYdgWOAG4Boq1zEguT87M8brTOisjCfCjxT2hCBRcyrfGo3UkVIlbHZR2yFqYoWrnEFoDy9HQtvwv7lbC2oaRx3Q2uEONYyJRfMlAu0m+288YczySuZa6YVK384gwwJt/BL8ywnB+Fcv36Gs2WOmeoyCTHD6/QCHRQQlDCEOEjBaX2/OwOlTgijUFgBMEmF2F/nL0zo9rJqEUPNm3n0sj47xz+h6mKpc4nt9L38plVlNxFvQ8o9o6Hzn8XlpLTyPDgMPd7bxl7UZ2hj2EQtJeyLKa/VNeW305LVXyuH2VZbVCTGhMuAPsWZrkofNvY1/xAkO7FhjPjKMmZjgqd6GoC7z7ZW/F29uDfPIKX/z6J1+gzxej/ENQPW8G/puM5EkgK4QY+Ac470+UPPLpu2iPlUldGWPbwf/E5WevorYbhKYFAv705CWUroZTS1JILlDLZ3Ckj2ev8o3Bd/Ct6WXO5cBXVUbNOSqdOLuCEq8KltizeAavorJDjXjKQrPOWjbPuldidtsgT3RuwUdSMdZYK0aApDgSqaiU1laJa2kGjGHU4g4yRR2Ffga7VTpxC0OqvPPoA2DGKcgL1EKDxbLFjA6fFYuc8GMMUMaJ+QSKQtK2mQ8WWHA6eFqaQJgYgYEIdZJqVBen0bFoH0/SWokxKJbwQgM1hCvtsxiqxVhiB85onqzXJL1Fj/R3NjjfO8m11WO4AjK1NPg6uu5gSg0zXUD6Aavm3aixnfzseJrmVlPyUJS+Txe37ezlg3fs4FI9At/phuCJ2fL3fZ4Jd+I2Iqs+3/vKF+gzaWoYmsJm64fTA92tMhP1XC/pTBH23AVnvgRbvQWstMmaGjKjmlRXShQWx/mc0+DYQo2m10F9rmNZvEoqF8M+cYK+NYev73Wf9w8kFFD0gNZIE2NWEK8NkLM2EW2fea9EtZtB6A0SnRDhOhCqVKggpGRTz5KRbVKewo64ThhTybQ2iTkF9nCaE4kpnkxcw4Lcz8lrbmfGXOWiGKFfucBccYAdawtAwKu8/agDu0kko2sKbYdGJVpcro27dIsWV6wRCk5IV1VIt6G0HsfVQm6OX2JD5lncPoLpOrxi7hHi7RodLcawB6PrHg+YQ7juEEktzRd33UGnPMseOU4+SFGd+n/RYg3OdbZxT6bDmHyMPqfA1zcvsKRE1zCiqvjxfr6wdANnLu9gfOoK8w/+J6Z6jjOWu8wzuREe/9J7GNqjwbYOV5++nz/+z3/wd5/YP6Xy48jclcC3hRAS+JSU8k9/4Pgg8L2ek8Wtf1v53kFCiPcR7QgYGRn5MVzWT448+O0/RA6fJ1bvY+DSv2P+4lF0s06m5dLs7WfVyjDf28vYUgWxZpDaXwFdsORvECQM/tWRiHZ446noMY4zy2ZnipuCEn73DPsf/ktaQuU/Z97AnrBJqtthpjTC0ukGZ3f9Ju9tmTgGiHCTzVxEQTiOhSIDeq+uMrbng5jD0YIwfgYaQH8nRkMvcIcjud84zYBtksjPc79zC5/xJkEHJLxReYAnjE/we8av8Jh4F0NtgzWlhu4t4msZLC+FGZiooU5aiZxusaqHaRgsPd5H9i02lh9Z3iudVRpehaH8YdzkIqblM+J2wEyz2JNBCoXB6hLlrE5pMURJJdG1LiWtxIXF8yRzCQK5wmcXyxy9+BuMqBHFMNKz/wU6+bXbtnH1kRLu+fP8wl9PE4qZF4z56LYMMmxhWS+0U4QQDGVjPHxxg9+6YweWrn7fcd8ykUKwVBqjEE/Ctn8OJz4bgf+1/5xjPQW+WHOAtxCxXa+EC/CuC0+CDvp0A0SAFmsSTxus/fUXCWMmD0/5vOZYRBMpQhAoLhnLRayl0dcd+gYuc3VjnNnWdmpuCpFYIVUNkYZFqOisyDLZADbMAm/gNAIwh1KgKmj1DpqbYT8n+LpyFx9JRkXjoln5nPwOlECEIR85fTdWxaY5chCV7wBQ70KtGi3wfYFNKtHhaqyXPtsjp6qoQUhfe4JO7yzD1jxP6jtwBlPc+cwjNA3JjcZpvhS+mdn0OO99sAns4yvs42cAkrdwUhPcBFyqHedY+gT9gzUWZ27iMenw1dGTjNo/x7Fdt1Nq303DadGjxXnGd3F1lZnHx5hMzLNv2yL7AMYu0t26qwEeYOCVAPNU1pc5dfIV7D/w4mWlfxzAf7OUclkI0QvcK4S4IKV8+HuOix/ynReUyNtaMP4U4LrrrnvRlNC7snQKxf+vQJKRU+/AH63wVb2MnA/odst0rSHWUsMgBFeGCjy6/AZeof03sokVLvo9jLoN3vqQ5Gs3HeJyVpLwXXrVNdZqe+hKi3EtxUNNnea+YY4eejm7H7wbQ/rU40nuHAh4xXyTjoTp4UVUv0ItNQkypNrNkgmrGKlBTC3B+sJ3oLxI9xcXGVqymezexZcz1/EnQ5/lrbUbOdw9wiwL7Aqu8AH9OH8bXoPhFvgl8ysA3Nn9Dh/nXRyys2wgqFnrxLzdDNhjxPwYamCS0VaRikYqzFPr2GzWTL4Z3sn+9SvANB1NZ655koP528lVN9FiIb90+VHY9TbOlXpI+B2UmgN708jjZYSXZEDRmDBuIjQU1iY+BUqcX7n5T/hVxSQMQ2aurvKLN7z5BXpRFMHv/ttf4uj513BbKveC4wDexsfpt/z/rm4//PopfuEzx/jwl8/wB2/f/31+gJim8tm3/Cpr+V7+Q3IThg9Dzy549jM8nHodX6pLbs10+dXO77N+tgd3z8387uAxbhm8hcnHxvnD6/tJNj+JYUq6tUs07rmH9OtfR2B+A0f9bnnhhh81dUmO34H4/FcYf/ccR3kFM8v7aPkJdK2B2RUINQfCwUFSarZYLh5i8sJXWAc28hFttVrWWfFddnOG3179KHpfjfTV20gv6thPP0Q5t5vMHWfpur38+/FfJi6nkWsK8bGbaHcih7TtSWq1CPhPrneYFKssxpLsrrZJyg42Bl5ijBuTpzDdkM+nXg+awhuOPY1wB1ltX8fkrRc5MznErd7DPJDexifnPs5vKe+n01J478oz3Ou9ivzSJp3H9nPwladZunQ9b10fYY86x6/sPQRC4YF9r+LVT1/mhnaCx7ot0DUGOmtc/OoY8V6b4eo6f77v7RQClf/T+DiPJQ8zu5Cgb3eZfGGR40/9DtsnP0ssmfg7zfOfNvmRqR4p5fLWz3Xgy8DhHxiyCAx/z99DwPKPet6fBuk6HS4+9UECo0vp5PsR9m60KwGNlTKeW0cKAYrOYi6P6rQobdb5zKE3cjmcoJTaZEmpEHCBuL3E7oU2q0mTMW8NGSpYczbV4DI5bZRi3qF3JE1oKOCHWKEPQjBUc2h3Etz66hgzAxlUWaWZKJCw25QpYJo2Wk/Ev/qXH6ZmO2xYl9gcvMiYM01Hj3EyucjnUl9hzZ5novY2XsM81xsruGNLTAXrbBcLdA79MufjUZjpdldjxFPpJBxCJU5f1UQPTUSokxebhMVJUpUWoScIfYVtwVludp/CETo+AbPt83gCrl0voMUC8t2I8jg90s8N9ZNcDgfo3xdx9sKLY5g+qlBQGi062WMMDbyVw6VbuL7/eo6UjvDuHwL6z4lpmtxycBs3ThZ+6OfWGw6z4+BN/93vv3Kqj9985Xa+9Owif/nU1e87lglcVnpKhKpGPtUDQsC1P8fCwlV+46+OsbMvxSdf2eFG9Tx75i/w6psO8Ma9O3m2+Xn2FwV6UkfVlzAUnVPP/CKhb1N857vY37Ofiqg/f562WMUOBVNv/m30NQu9HPkrqkGUtSu0JlY3RFhDKFu02VB5nZXYEFdk1NoQpR6Vj7ZNHlSnCX2dntQSuznLkbLGnfZ9TM0vctvRr5Bzu9zMUTL4PBw7QLBxAQsNVzuCKkLUwKXVdQjDkKdqgnHFZ81SGOmo1DfvA0AfyrLvXLT7e6h0E2ajy97VNmcz/wKcs+y99CzteIp0cJZ8coFEagkrtsgl06LjnMWszlDJ7eINd89gVHwSsTr1YISTvJJGKseOma8jZMh/3FfC1QQt3aTXX8UMXUobLdorCZhNkr7Q4r7KIcSGgfp0l2ZrgI2NMVQ1gHaTT3/+Q3+3if5TKD8S8AshEkKI1HO/A3cCZ35g2FeB925F99wA/H/svXeYJGd17/+p1NU5d8/05Lg570q7qyytMiIISSAwIF+MwYBtbF9zAV8bB2x+4ADGYJFMvAgwSSAkhCSEwkq7Wm3W7k7amdnJqXPurnj/qLH4yRK+NgiB7f0+Tz1PPV2x3/PWqVMnfE/Rtu1F/hvg/u+9GSk8SdvIPjzFfmzc5AOPIRlNArkMpurFEgSmY0EkDbYf+yu8TYuP8j6kaAkBGLfCuFRYP7dAPhijzZ6gUfPTcm6BUv4MquSl60qb4JKHa47lEfUawbqTVtewvXS6BDZct4tFTwxBzFN3R/Gvblf8BlJyPXUtgz83T9UbxczaFMMu2uurXaHkBFEjyMGVe9DlMv3NN5Ab9dHe2sIW5SlMROY6XsEnUrcD0NKo0X3qaSxJQPPk6cxauCwF0VaIWFlGqyDl8jRlxzXSVxuny5gjb3lwaw3G+/p4LCmzqxFD8oiIq2yYZxMxLssfZUnpon1tFNErI+kBTMXxmZdSB7AFnba2176UIuZd+wa5cm2Cv/j+GY5O5579PbJaoKVYOv6Q86Kqr7+Vtxl/gGU0+fQbd+KtzWMjoNclvHv2cMvgLVT1KsMto7h1G9Eq4zIT1JQFym/zo27cyJ7UHmbFpWevI3rz5EmgRuLMXv8yrGILtmRTbzjxCUEu425KJHpiZL0BZFsmUUyTMyxO2nsB8FkZeiWbffJZDGwULUTSPefcfy2JIA9TjCSQLQ3/IR+1IFwSi/BE6y7s1c5wtrCNoNIgbJao2TLVapWjlhdN7cYSBLyFFcyVEeoK2HoSKRxgyNtHLhqnc2KGs51XUXQZYJVQXCaBapG72l/OxyY+xjklxaA9jyYqrLhUIvkRar4UmhIk+Ck3/rZ5DGTu6boOd6NGrzZJ9/QXWQoG+cBGN32SQldlEdnS2TKbxitqTHZ5uG5kBFOA75cu43Q1iBGIUF/ajG0LoAoMdj3IP3zqL37xk+iXgJ/X4m8BnhAE4STwNHCfbds/FAThtwRB+K3VfX4ATALjwGeBd/yc1/xPgbvvex/B+GH8i0F8c7dRDThNUlSrhstWoAG4U6QDIUxFJqnXuWC4yru//CkKQohPxt5Eh+gj5wuhYOB31zBlmW5xGKOgshKRyTWd7kOhylrCkU28fKbJxqUlosUCAGm/zlaPRHOuRsbjQZAK6GoYX7OBaBl0kkWKDmBmRhCwKYd8hFfAEgViq7n3stCJvyqiWXXG+r+MQBBTvY71vjAb1CcZpY9TQ5OseFIolk2H+SBbvEcQTZ2GZ5FATUJCRLQUYnaWkQkni6iiOoHXQK2MbNjkVhXV8d5N3NfpwmvLiLEdCC4vtm1TleHSwlHU1FpESUTtD6M2w5iuMk00Ch2PEQhsIRBY/1KKGVEU+PvXbqct7OHtXznGStnxGodFx7qOakWEQCu2bfO/fzjPsNXFx9RP0x0QoDiHabhQ125AjkTYntxOX6iP/a5DuDQTwariM+L4fyBS2ZhnYfHr7E7tZsGXf/b6kq+MVk0xNTXFIbdKZ30ZwaNhVZxHW5BLuDWRq6ufZj4Rx2+78NayFC0bt+DHwGbE10nH8hAxqU7V6EdtRpBX60HqtQBDoe0cC6SoeFMEn1mmEqhzcSTASijC1EAPFkt4hBZCSoOQUWLJCjI2n8GlVJl2bwAgP/8oAX+TSlCDchghYfP55CvBtLlkPMtcaBPu/I/QRZUz0i725J9gJLWWCaGVdn2FbcZZALLuMKbtvPie2PJq3IsWOx8cwSeXGE0NcuHEaVrFteQ8T3HB8AEeSilUuhS6GrOE9QIC0JkKoNseColpOqrzLKer+ONeEASCskClFCPYUiQ92sXanm9x991fe0nm0kuJn0vx27Y9adv21tVlo23bf7X6+6ds2/7U6rpt2/Y7bdvut217s23bz8v1/6+GA8e/QUi5B0ptbBgNI+BiKbQMQp1IM8mCCkqzRDXgZS7sWIM7NIsdM02UxgH2zBzilLCNQ2u6sBUFuZTHGnAaeq9xDWNmLWouhZiiYiglvJmNbL7kEgZUk4F0gVA1gGSajMdrCNjMjaXRqYOkYSoBvKaOqpXpL7sRJAVx+jgAFb9Ib9VAsMCrOL5jRUwhlU1ExaLiXqGpnEJOrGfHIQ2PWOYImzl75jQJXSLZsFk0F4ivqzLo19HVArbsWOyCpeARS8RWohR9Ag2Xo/j1moxi2GTqji81H0tyOC6h+STM8FUIio+6aBJuFOkqz9Pe6biU1P4wLi2IJTfQImfR/PO0v8TW/r8g5FX41Bt2Um4YvPOuY+imRURx/P1RvQKSwpcPTvOd4/P83i43V5oHYOh72PlZ9JKNb88ewAkYv3rw1Yw0RnE1neblgSoEH/YRDV7E2Nhf0O0yyUcs9NX2jYa3TPmEyDe/+U3CkQh7RycR1fKzUTSXUEO2ZVqVFf5X7nPIODxB3moZl+zjTFCkJnsR8jr93jpDZgxJcxIApGaItKawnHgjQ4E2ctH1ePMriEtNtq84/QTu23U9i+gEJZWQbOLSGyxYQfaPLjMgLDDldgrfWmtV9rWNYAbLBMoKbk+V76aupmO8Soc5QLAxREUqEPYOkm+4eTnDAPxR+ztp1bJcznHApqgEsbpiyHoFFB8PX7AF/xEY9bdiiRKvGn2KvogTlA00D7JjLstX14UwowG8tSb1gMrgRZdgWxL3XuDmxvQD1EQPpVA3shZEiqxQSg/gD+U5e6YDsBH5NBPjoy/FVHrJcL5y90XGQmaCxtxHwZLpXurBNLdiCzpmX4Cme46quZbeyVUedVeYmUgSLJsrRIFArcqZjhCTzbu50nqYe1rXci6SxJZlpkURxdRICQuUMiprF7K0ahdRDo3iKawhGfMSlfxMtlyNYiQJNGosxWPMmEscG55DqmewBa+Ti681oVoh2OzEsk3M7Gr3Im+dpEvHX1IxI2lCehlBjkPNRPEZ6A2ZcnIQfWY/cnEjaf0Gxu0eys06HhPCTYunJ2TKRS/7GveBDbWgk9YoWgqqUCYxIzDW76bu/Ixek5ENi2LdjS2I5PxhdOChXg+WezNiuIuCS+KilePMmkk2tTtBRPdA+FkFVei9HxEPLS03vbTC/v9hfSrIh2/dwuGpPH913zARlxMwjRgNDk/l+MC9Q1y9Psnv3HwVRPvh2Jew0xPoVRHfRXufPc8r+l+BIipYmkPhEFwpE7r2ejZt/ThuNcXwmXfR17cWfbUArOStUKp202w0uP3224m1X4uiZJ89n9ds4AvHkW6+kz3lk882R28vpGlIbg5EnDTMHWKB11c/zYy0iLg6rko9QUnX0IsdjIc6qHnimKKM7wkR9yPfIZHLcqSrjbN6EEkQiMqd2BZkdRePDS2wlllm3K3IpsXl/i7kdh05sIy/CffEr8W2vNxyqokklJGrB7FFgd1KP7qlsKbWxkB6lMl4H3e23UJIqPFG6SHKUgAr6SVSOIuPOJ/pehXVLRYHO7fRWsygKFV2xVO0ajGynjSJM3k6axb3XX0bmhyGtb10XXYLYLNzLIqCzhPtV1GXVDz1VlyBLNlCL6Jo05LI8MyZl+ELzvHkoT+l2WjwXwXnFf+LCMMweObR38b2pqkpt9Nx7gnq9nbqvnna+rexpBaxrX4EfRZLECi4BNLBKB11C/8Jx1faCO7C8Czxm9nH6Ten+PH6C8gk2hiyRVobaURspLpEb6ZIvZKl0nIUyVLJ3jvMGd0gHd1EtDBHsFEFAf58e5Rxy0JqZLDkVTbHZh0KRSxzI3kjC0YTU1Ro2XYEt9bEn49hhpt0NxYw5Shy00bxa2hNCW8ohis6iksYom7+JhEjQlRMUHQJyE2bwYUMtfAdxH0majOE4XF8+aIlI5QaCJUK22+4g4LXiSHoVRnZsMlrHoRIAluU+P3uFj4Rs7GwkaP9FFSJy0tHGKWTze0O/4wUc2NbDptkLX6aZOgGZNn/Uov8OXjF1jbeckkvXzwwxVjZ+YLx1HTecdcxOqNePvLabYiSCDveBDMHEarzGE0F786dz54j4o6wr2sfBs58iOXrhF9zG4oSZvPmO9GNIhcnZ9FtR/EX5CCWN0CXKpJMJvFfdgM+wQmhCaKOV7dI9nTBxlfxyPrf4FLtYVyij/Z8moIIR30FJMvkba//TaSBq+gIPElFc8ZRrsXxxRZYHMoTamvBpRVJJ7bjOSzRePwptoxPMBOKU6p6MbEJKg7VtahrnCrKbBeLzHtVWuoGedVHIaag+JyU5K+FXsdr9xfwmNAn/R9WRI1ItU7EkLgClUqunyutH1H1BfmC/yYsE94vf5lOb5lcYwJf6SxIIfyNEI/fsJVsNMkFwycZDQwQuuf7bKuuY8Wd4SAuPnS8jiErfOSNf4B78wV4kl14FJtAXWG0I0DSo4Ml4hNFtpaPoyghTFMi0FmnuOJhbOpq2lOH+fSX3/vSTKSXAOcV/4uI7939ZpToGMXcJVwb2YXiNjCtPkquHEdX8oyU21AEkYCwQsnlpRr2YUkivzPWpPXpwwjuMB1WJ4KpEM2u513iB1Esmwc27WElGKdTdx6af3D9OqYiYVUWqCeewcaiPFZnsi6QWDmG4U7jrjfQXTKn2hLctSGIpGUwJUfxqw2nKYZuD7LodrIr6u4YsdQUEhreWhsNj0hnY5GmK4qvLmN5DepNF75QgOhAjaj8IZrYXGdsYLO0hhVVQKhbBIuLeC++GvG1XybW/EkTchGLSt5xV8U2XM+aQYdXR6spKLpNyXBTTqSIKhJ/0NPKDX0Jnkg42cZlReDy2hGm5G66os6ngiAIGG7Ps+fv6P+1X7B0/3147w3r2NMX5atnXQgFjeFJP9WmwafesJOg23Fvse31IMoIWBDuRPR6n3OOW9bc8ux6Gy48O1ZdF4H1rF/3QVRzFsPWsG2LdCFKZ8hP9sRhaqUirq4WIuZqhpGi4W7aJLsd99jSxe9hJuQmQpRUMcuCOcaKP0CHqhAKBOCWf2Kr+xgrq4q/2ozSuqcXy7K5qFmihM5C6mLEBrjH8gwulNBlNwthiXN+HcHlMJDGjDwGEj22i3mPSGdD4Am8FEIybslxIV5+QqA9Bw96dYKNGQzqdGbLFJvLvBIXQjnF3tAxPPUKuqiQWQyyYkZ4d+g7aMUaUmMKgPWmycPSPlxag1c9/ACGoDAkG6wrdaBLOoZ7Hk/J5H0nCsy3dvGeWBcjBx6nrovY2CwmZukRc7gaSfzJUZLD86zrilAutBFoKeKaPcPrXvVB0unNbOj7AXd+9kO/kHnzUuO84n+R8LF/+i1C0QMsF9o50n01X334T3lCuQCA+/2LPHPvMyj51aYnYitYYZYicUTLYvPJAwRWxjDi64hlR+lNb8Sf20SMLC8vPUjR7cOQZPqMURo1PyktyXL3pVRdUdLjl1OmhmEIWFKZ9aNfoap6UKtNDFkhOTlDzudC0bJYosMB02gItLi7AJFCwnELlIMRjKNOoVLUt56mLdDaXKHsiuLWZBZdJidFjf3yQR4UTvIdy+Tb7kfwCS4S7g5MUcC0xjiwvsnDrVnu0ZZoGDbCKk+PhzqNvJ+6N8HImMWFG68EoFlWQYemIXHOH+AS8ognv84Hak9yLOS8lHRLo7uxhBkdeE5jc3G1haRUaSMY+tVooydLIp94/Q5Cqojr6TS5mos/7W7S8tTDFL77XWf50QE0v3O/cu/zi8subL0Q1XZktXbDc3mCWltfSUfHHRg0aaJjsIk1W7bR9Id44DvfYrhwjohVwhYA1cKtiUQ7nGzqTaEA79j0h7RZYRTLpGEWWAnGiLrdfGMpxzdKMNf5WhZsx02VbnqJD/TQ1u8jknczpygUQgNoIYc9tC2/mnHVKrHUMo9L6sQluuk3ZgCboJ1g3ivQXZ9ljX8/iazGnvwQhgixssW0tMSIorNQdgMCqUKFWXOc9Uh0WxHOmRcTz62wnGjjAfkC7ly8Ca/Q4OWJEVb8FrJWoMdIcyK1gx2Zp6luFggWigwLBTrzzj2qvjHyZp2X5VRufPJhDvdv4oMP/phYMoaAQNLoQBZsvLU2PJExzk7uQn7gHqrpQfyBIgFPlS//xYcJD2+iUYux6Pkxn/v8f35ah/M9d18EHB0+REfyMNWGl7+r5NCGPwxJ+O3Fm2gV69wrP8iv5z6Imn8Gy7eFiNrCnrnjfDd8O2tmzuE59DkAzqa6aVk+wvViD2qsE81Q6Q8f4aozrTyycQcbpHGMUhc31NyMdtzmXPw0LLpFBlUBWXoS2WzSMjtKMNUCrGX7uSFOhVppGhkENiPYNnpVJOnpxrKblOt1Kt5WMq1xupcEiJ/Gs3YL0yUXLSxhiAoVX4AT0grnAss8mfkYhFVn4R4KuQJ7m869TAce5B9vEuHoXwJweeR2UvUEmneFEEXqM3WsDXs5e2SFa37decmYZYmizwMIzIRbefOZz8Di91GB1xTWMxf+a+yG0xDF27bhOePesm0H2XEVqXzJ80jUfpmI+1U+fus6XnfXELsLI2z5m3/iX+cvexNNuq4E196XP+94URDZIl7G0zzIrpueH7cYHHgfB/gspuQhl2nw8P4noK3ndK3RAAAgAElEQVSXk1OznJyaxYy6IWBhhEJI6SCxdkfxr/W5KakRujE5JMkc7xjEkESOl2scH179SoheR00bZ4slMZez8IZCbEgd5EcTHeS9YXy2SaHzAuLNh/H1rhAtaUy1q6jqLMJEL+2eNhr6AuuYpSAPUlJE1mgP8Cb9PhgFqPBIsAm2zunyIq31BjOEUI0CpmgzIZ6kB4ujYokR/14KVhhblPju4JWsOzPG+/S38DHvnUTai+TNOcY64piywjXq/RidOYSROvW2VsbqdYRGK27fWRbEPWzCx+8/McSp/gEe330df5FS+NaH3k+qmUK3fSiGn5IZ44Kt/4NGI8Pw4gys+zGuPoHF04tc+8BhPlt7BQ9uuJ9W7Ye8LHMrrfH4iztxXkKcV/w/J/LlHM9894d0XlhgenorVx+Fm7e20L7yz+jaWvL+RTauvANLcuGqj1Cyk8TVJPfs3Eg6EEYwBD5607s41wzgaVvgj7+9Qpt3PUJMZLLqpc03y4eXPsDSQiu5K3M8WbiE8sYfcscTAubok5y+9jZ6grsRKzobbeeL4is33E7r/JRzf/Fu7ng4xxc2riCSwqs16PbXSWqDVPRJdheyPLr7XZSkh7jQ40yHxrqtDD0KF8tO2lwhFOV1W47w5KmLeHcig1geZzl9FUcn5ln0jPPXNy4BYd70+DpuGhwg9htvBuDe93wJwxzAU/Phk+aRlAbJG67AOGCSW1hl7KxJ5JqOqyMbTnDZpX8O6gcACP/560kZr6Nz2ofZK9De/1zF39q3DnfgYfyRxC9SxD8T9m7s5ehvu/HrmxD+6DUvuI8pWbjbul9w22de/37yhd8hHn/+fxNFhW1/fDu5TIbfDV8CwNihA+y/6/O87J3vpvzIFEPdn6fY+j6Gkv+DYMrhKnKJIuv8bjTVy+uM3URrfo4DX9rcwzqf4zZ79MQP+LB5Jw8fvZ7YqWG8gRD92TvZL/0J8Wg/nmqaldBV5N5zkpDrcfqXbuTEgJuuaIOaZNPu6ySbG+NjRz/C9BWfACCQvpIfrj+OmxK7hnNsyNzFw/ipereyqTyEJYjsOFfBkIPckE3zgT1VTmZc+BopooEC3nSdUwObuGb8cf7GeiN/aX8BKeJlzdGjfPamN9KensXjaqJv8nBPy5W8Zv8jLLe04iutoxrfz6JxFl1KobVs5aaD+/nkzW/ge/sPYMbDuMQASqmFgmhx2fQNCJKC19+OL1/A0F34O5qEnslz/J3v54fNg0iCTVoq8sAP38sdb/inn3ea/NJw3tXzc8A0TR789NfIJE4hCFAotBJp+ugYvZd4NYVLS3JvvJO1CzaCZRLreopsfYWQq5V8WwwEgZVIkMfkFNmQQD4dJlApY0U6MbE4ZdaIyAUioTJmsg1JsZiqtTLQchK/qwVPI4uw6EYpa+A3aHetB1FhOdjOwGo6YcMnIRhZLFcV5Aj+Ro0t9bOE5QhLjVniYhb60yj1AhGXhmV7OVabY6wpElilTG62hUh6TdqaPrrmn6Rz6xsI9myhahuE9AEKfqfhyfrpFQZ3X0tnoJPOQCeh1WYsTSOFWyjRcWmOtmsvINrm4+h9d6+OosBywwmEelvb6E71Q7QXor3oapSAXCHkK5DVgmzsfL6FFU6kkOVfTfsl1pZC7e7G1dn5gov8U5Q+gCzLJF5A6f8L/MEoXX1riEajRKNRdl55NW5ZZuroARJiGJcxjj//ZTLxdXx0ofDscVsDXhZliFTdpN0iimVw5dIjdHtUuj0qF6Z6kMw0xfwwLo8Xeekocn6YtetNVFrx1papIlOfeAuVxU30LuvoIsx6NzMeqRJT+zEti2AoyKzXyQ5K5IrI8QIT1R78NOnIL7HGbVMRfXQ1ZvBYNpFaAxEJqaryju1uiprJtCvM5uYpWsrLlIIR8qkIJhLD4lq2e+aZSthkonH2Tp1lPLuOBbmD4dQaBs+MIOsGl1TjKLbESmCWjGkTivRx1VNPAHBQVMlH+8EyCdaT7PELhGwvjUOfxDY1BglRynUSj68gYPGD0yOI4SNgC1iuDHL0ae78wgdepJny0uO84v858NW7PsWW3AC+wCKGIVMttuIJd9IRKDHvciyxB1Ih4hUVb32O0pUa2cYKsuhmItKHbBpEK1WayKwxNNYtOtWSRiBOSagy3HAUWjqksjzoBPgy9ShrXLPUg06g1lPJUzBtxlLnUJUwQscO4hVIZAO4NA3dq7ASDWPLZZouP/5amWDdoWmeqszhk9LEd60jJgRRxSy25OepxafImW7KcybYFkarH8sSuNyadppr9L+CHx95ArduovhupIGKYNu0FBfwbN/+7PgEkqtZNoKIlWmiBg3k/X9MOD5FJXsYZbXH62wtjKXIXJR8Lj2y6V39jzGNBSNBb+y/Jm/KiwHF7Wb9pVdy9qknCSlO5pOn+hgb5k7wDzMr3Jd2lP9mv4cVx4XPcFCiv7GC63vvhBUnb743tRZsKEl1fOGwQyWtBtlw015AArNGTfYSbnSxNHEZ/dUlRCwOz7nIRydxywkQIoiRThY8jvGRaFtGEEzO5Z2cflMtY5cyuK0GHqvGxNoLGelZi6xX0esSe+QJou1eLFFk27nTDOZnURs1Hu1y0l4Xw9tJicv84OKLUHSNtfMhhpfX8ShXE6BEaeMmLnjyIG4bdq/sZim4RFq3cbtD0L2VVKXIWKoLWVJQyhrrXG7Wii5GZp9kuTZFpnKWNneC6so63J4KpdYE/dUzCHKZNQtrAPjR3CCDHV/hi9+466US8YuK84r/Z8T9j9/NJUMb+OfQfXT4y2TKrQSEGE2vgGXDnPsiMi6BLdMnaHjaEM1l8tNB8prjPhmLdNJaXiFWrBITqrxm+wDb0uNoLhWXEmQFm3q+j7wB0wEf5aiT/ugRmwQaOjVfDVsQ8daWydgm9weOYul15O6L2Tg9SaPahb9ep+z2sWBOEtWDVFUvsUaTkNhGDY18M03dKlL2t5IUksjCEjU1wFOLT7G9ZSfLk2HC1QJVXwBDU9kmPAN9V/D9L36VZr3OBQ0bAQlDciGbEB9IIrrdz45Rx/reZ9etdJOKax+cuRvf0F8jKp14/e0ALNUD1Hw+LosEnjvIAae4TVJscp7u5wR2z+P52LLvOgxdQ7V+8gV0y+IZtge8/O7wDGerDbYEvORdAjYwGpRIxDvA5YOv/xo0irhVL15dpuoxCQY9MPRd2HwbsZ4EyV7HgrcFCWogNsKU3Wn6tHEOzmu4Yo41nfD0Ynp7mfcIBJo67g05QGa+2EPTVtCjImUd1lbHsBC519zKob37kC0Nq24jZM8SbpXBsumbXGDj6UkSuWVOdWxGlRqclDaRIcCJdTvYMjaK0OzkTLOfZ9jGVo6h7FxL1/IC9bqHZCOJT4iQNhwK7tmey9nXGmcp5BgVrVo769wiE4Fxgqe/w1Sqi2XfYSRZxb+yDQC5F4J6g55FL/9f5K3YppuRMuhakKT3Yxw4duylE/KLhPOK/2fA2MwwbQ8qrCg5TgWO4PNUOVvvpqe3h5qoMKRtJFrvYzRUZt8zB0EQUXoCpCcjTNsGk2qTvDfI3rkTePMN1khprrpmKzvSZ8mFQ3hsH9O2xCsqFqMNiVpcoFoZo6p76A1P4a/q1PwTiN44Ln2ZOavJaSODvnAENTLImmU/2HV89ToltxfLXSWidWFKEhs7+0iJMWaEMgBlrY45ukzS3Y4kpJkQwowXxtmixzFybjoaS2RdESIZA59Q5Eylk7mh01zz1t+m48K9uJt5dElE1Sxiu5+bWTNw0U/68ihaleau32Ky2c4lsXEuunArWsOxTG0ECsEoF0eem4cvRn/Co99MvLRUDP8Zkezpo6VvEDtfefa3/rZ+PrepB7co8ubT52hXFfKqwKJboOgS2BKPwWu+BIVp+M7bwLIImV6qboM1njkwGrDzDgA2XdoGkhOPqdbqSLbMMWGWC58+y0hbP6p3FM2u0eLpxlT7mPcYtKWXqfhG8fm2EBd0hu1ORJdOVfKypjqOInhouFQepA9TlBEMC2txmIKsIxaanKhcyNrxKcreIJYkk0xUOVqO88nwreiKi+ufehoBmVjIhw1cyY/IRJ0ah678NA2lSle1h2VlEk1vositXNPTgSVKTMXbuMK3hTJNRlz3EtKqzCUGqHRModXSDNS7aDa89LTM01BM1k+10HPDTjylViqBFZ458TIUtcjCxJ9SqlaeJ49fZZxX/P9BVJtVpr/4ND7TzdOtI/QHnCEs5HrY1HSoD77RehtRDbzFR2iUBxHNJrt//VqMvMK0O8WjKUfpXpM9xC0P3c16SceTXSLcLHM63EkQGV1o8OuNYZaqIi7Fok04w1K1hQ2xMXwFEz2+gBRI4a8tU2q4yDfKGBM/RhAlulUftqXhrRmU3V78sobPcvzJxukSPlSGbeceSppA53SGNq8PQbA4YTq1/p5nziFIFn3Msqi00LVSoWF7eeixCbbf8HI2XHolvosuQm0UsETw15v4L977nLEKtKSwLafaUTarPHLoUe6f7cMM97Kr+mFU0fXsvlq8g8QqjcO/QE10PLvuGbzgRZPhf2Vs2Xcdet7hWRIt6BvcTJvbxWc2djNZb/KesTnUgMpwyAmuXxkNQvdFcN0HYex+2P+3RMUgVbdBr3UaUtsg5bzQB3a20PA7cZac5cjqphOPsvPIcSxJYlQaZElepMXdjS30suARacnOUdXGSCYuJyAZDNGD164CAopt4Nm6C21rlGJTIBdxXEEzkyssBUL0TM1QXBFJZVbQgl5SmTnS3e0sVU3ubr+a1twClx3fj21bdNsyHdV51jHC/kyBmUCS1qVZBs0WsmqWaniWGavMNltm5uAogm0heDsREBlNPArTztfkyeB6Il3XM6WPEHeplLM9JKNzjHdUiBdsJvUSA9koqAUWzw1xdvb1RGJDfPPb/7mKu84r/v8gvvupz7G+1sfTO8/xZPkAa9wmTc3DNvc2UpNfwUcNq+n4ro3kKTRXF57aDA898H+QbAuxxeZoVMGtNdlrHeeK6aeIVQtM/eARAPb33AhAnzBOh7ACy07g1CM3SNfidAQWsHIidsckQqCFQGkZ0bTYPB/BKs3TqBcZ9JmIcoxgBWxRRFVVZNFJn2xbbRU4TR5Tkck1vewoV/BIDrPkmGoQsLyUhoqkNoZpsZZZccUJ1TRO5eKk1m3m8jf8BgC+3ReiC1UQBMLVGu4Nz826ARBMRwktpbzMnHmGy978uyhv/CYiBq9o+xbCKqlMtG/d8471tvc8u96x6V+zfZ/HC2HdxZchr7o13E2JtjXOl9LFkQDv72/jB5kiIxGZkYCIZNvsCq7GTS58K2x5LTzyQTpEgbrHwK8vPWvtAyiqRHDPRtRGHkOUsc0se0cKjPXOowowxFYWUzN45AC2EGLBo5JcnVex2MVEo1FmpHY0XcJr1si4YizuuQopqvLeG9cxnHBeME+sdGELIpdPHWFLepxa3Es3E0RrOQrhGGK0wUK8g1fNPkgwVsIkS2/OJH7OiWNMlFKcTvUxMD3Nhmofh+OHMQWDg/4xJEwmRk6TLBWYD0c5WjORO48SnakzF27hmBJlYOutfNbTjWVZuDIbUBQNb1JEwuafv/INbrIcF+Vki4ieiTK/eDk9Hffzj1/8m5dExi8Gziv+/wDu+sanuXxxO4+2HeeK7dcyGp+hLVBivtrB7oSB172E5hPx1NI03cuoQpGGpwXBnGPy2DksBLr75hkKRekoZAjhYWWzl0t++G2Wv/ENFr1RgqrzILZIR9FtCU92KwuaIyZdVxGwqdW8SP4MdiiCaJm4mhkum3KCqov1BVTbTVQWSDYdq67k8VFdPe8GSaIqNqkqTSr+IDnNi0vIIgtO7GEkWGPDTAxLE9l27asJaQ73+7w7xXCth5f//nuRVrNopHCYxTanOjfWrCFIz+1CBSBZjuKfk3S27LuezVddC/FBhJs/TUKaxC87L6I9255fgBXuGgCg0VDobf9v163zZ4LL46V72xYECzyajC/8kyYzb+1I8KpkmMckg/vbFFJNcEurKkAQ4Ka/h9ZNbKiMoMlQkNyw6dbnnP+iGwbxaI6C7Vw4QeOKXXxnW5lBt8aItBeh+yAAabeALokkY3WEOriLYeLxOAUxylLdh9dqMORby0Gvm51BH2+5uBe933lJLdhJPI06GCKbspMMt3XTL4wz0dKPS2swvWUdsqHz9uLdqB0GM3GFtqxJbcpPzoywkE+x0B/Ao2mk64v4DT8nosdoig0eUU7jqy2wOVdjOCQzI5oooRkGpuaYTXXSFOA3PnuGg64w6co8vXnHXTkY9FNyh7AmTnL94CZs3c9SrMbymePsu/pDlAt9DKa+wNfu+c4vWsQvCn418+B+BfH5v3o7sbqHWTFBy/wif//FP6C734PqyhIYD/GEvp9CcDM52gmQZdE/Tv3YdlCgJCwg6zYZV5SYWabs8nJpbooa/Zh9w2hDURLZee7v2c2V5bMQ3s2geIgRvRuvK8K5Soi2aJ6CMsuDORWlXcUoWvS1T3PZETgX/P6zSv47vfv5ncoggfASOWkYeDV5t5uy6ka0LHqLCnOuAnGhStYbI1taQhIyNJnGRGRCbrJ+wUfDr/Bn4z1srTifwH/b9Ub8A7vIFppQ+Akf/CMXOFkO7a4XbpomWSVMQA55OHXVKzk9tXqsewe7N74d/7kDNC2Zy3o6nnesLxbDNARKzSDJ84Hdfzc2XrMP9dHP4tfV5xS2CYLA363r5EShyhQ6F9X+1YEuL7z2K7R+3qmq/nj7WlpGn09JHF3tNeAT0mz62ztpuf9mrOoxzol7wLdM3cgx53Yqj43cIvMnW/jW199Fxe8n1LCZqkcxEcm62pio6fzPnhiCILDnovVwH+QDITZNjrJv425cj3yPh/zbWWOfpaH62D5xhOP9u+idmsRl6cx3xTkYjdKXtXDh5qtnbkO3FM6s6cL8oUCheJbOWooj8RPsnU0x63N6QG3PiPyoV2C2q0L8XBvd2jx0RZBsGBcEbpYWyPpPskF7LbPVEG2hCmokyHF3J58fPcWFzQRNT5lIw+SJr3wRTdhHuXkfodpf87DLYt/1tz5v3H6VcF7x/ztgzY2jrUwxviSx0m2ilV7J4Q2PsA/HpZPPXMjXdvUz5XkT73zqMerArHsGqzaA7K1w1rNMqgpTnnbUpTAMwuZcjZy0nn7hEE+515GysjQ8kJIEDJq4yXIyPwgtYKY3UQg+wdN2mnJNgoQBJRfevsNcBmCcZHHVPX4wcpx1wgGuLu5hMfIxsF/JfNBAk2V8TQ0vCtPWCqpYI+cOUzddrFiTCO4lGq4QwYqLaKHOI3sv50irTO7cZqJ6gXtarnEucG7puYPT2gK2zbUbXzgn3eWroDWX+ceX3U5xNvucbWLsNj7iO0RE0QkoLzwVs5UYGf9Wkj+j7P47IjW4lq57InSazx81nyTxhU093HZwlD2hwPMPjvTQs+e9qGMf55tSHk584nm7/G7g1XgaCQZu/TUUf4CbB2/mE8P3Q+sezrCZlsYphuUtgB/rpMZKKQpYkCvhAgwkZtyd+LGwgUtXg/rRvlZqoohkWWxs5IhNTGEJIkciG7jQGAIX7KwOMVJfT2XG4l5zLzerT5BdKaLjp8uQ+PHSJoKuEpW+Dsa6+4gsjrNmYCdHOMGyO8se26Jie+kJzqMYKWY7NTYcCGIKC3Tt7mDvSQnBtsk3z1HY+xC1p27ByvYSbj/NPdHX8aNL97D+7EluevgU4HxFzz39L51mQ6wA3dYXaFx2E26v+3lj96uC84r//wFba/LUJ79OdVlEkCzKc2dJ7P1nMsEqfV6ZaiPAbv83+HPfpwG4a+dF3P70U6zoS3jdPbhqZ0jYIotqgohW5PGeiwnWK7SbDUwG6LAM7t23j0PNfn5/5JMEpUsQhSUyVpilggotcMHcPnyeW3h/+x8QWJCp1sPQJrD2wAepqe9h67JMhzoI0nHe5v81pFM7UAMu/uTkB/m91gYyfehuhZTuWOUrUo7bb7iCv7zPaY0wLi9wjVliwhLZMh8GAS4spNldEvlcaYDkk1VezWO86zW/SWT9T1wufz+9xN9OLfOBwXZu6nxh1XzHnX+Gblq87adRKlz+MNK/Ycwn/37ivNL/D0IQBL79h4/81O3rQz5OXbf9p9JcbLj4bRza8xsO388LwH6DTeGrYzSHcjTGC9w8cDOfOvkZ3ILOGXsziZ77uDN+Gdg2yVdkuFW+lezv/w2eP/7ffGVsjMcHLqR8pECyWcQL7FiNMxzSsrQFw/RmV7jjI3/FySteyWy0G131sLgcxddeRY8F+bZ+mHP149zoehSPYHBD+zPIK+10i13csTzE7ts/z3K+hcmWFq4/fJpi/SZcpspK+DivLz+AYbsYOnclXcGtTCaCJMeqnGvrQD81xYeEvdjYnNh1igcXfPi1ZcL5rRhdJ0ivcyjBR/q28JaJNB9e9yVS5zZy6ZhGfJ2L13ke4msndyJnKrztS/v50tuv+XnE+AvFz+zjFwShUxCERwRBGBYE4YwgCO96gX2uEAShKAjCidXl/T/f7b70KH30I4xbp8AWUHqiSC6LueNT+OsisVAGsxDkrM+xdv987OPkFTcPbNxKvpigobgoWE+jCS6e8W8mYFaZTK6hPZ/B8Nq49G5sG1rVQ1zp06l7ZMJiCK8wz5lqL4JtE7DcnHMt49/YQJEsInUNV6yCv9ZHBZACrbRlLAJlA9MXRSz1U0VCavXS65YJ1TQKPpWS4iJVt8mJFTwxP52brkRu9jh/Um+iiDOMyBbds16i/X5mzSCfOziLR5F4T7+Fgk0oEUIWBWRR4NF8mb+bWubWlghv6fi3KRMUSXz2uOctkoggng81vdT4f3EbSZKMLL7wokgKsdesRU54yH1tmKQR4+L2vbgaw5y2txEL5emX6tAw+YdT70C95HbcySTyo4+BKKKaFfJqANE0iI2UkQU4m8nxx4UyhUCQHeUGeraEJz3JwXg/V6xNcGypnz5rklOBQTqTS/S6J7k3dAUWArdN/JBoYYy4JTLtaiWj7yUVn0NXTCTLwqwVSdYTzIfOYtgyhdBHWWhuZHClyqLcSqhQ5VxLjAFtD1ZIxFYsepb3cky2Oeg5Q2tmO5qtMBbtIlEysCWBY91d2FqUTKyMx9a43DhEqG7QGiyxWPajqlP86Se//9II82fAz/PEGcD/tG17PbAHeKcgCM9P64D9tm1vW13+UzWwrH/1ExRyeygsF3EndMpWjEZPCqkmc8tyElnW6coV+GroRtz1BlszWd49XGc0GuNQzyYa1o8x0bm/5Tr2uqqU4q1oqpv2QppC0EIyAmSFFi6WzvDGC/0YgoiqRhGFFYZyMSxPgLCosCBnyWecVNF4rYodruIrdNGISNSDEcKFOmIxR90fpToRpm9bgpxfJSgIRGsmZbebistNZ01kXsixfft2fnzXONFKEkEAsWwiojFVDiLp0L/mRn6srwVsvvzmCxEbVVQU5JATyD1Xa/LOoWk2+j389drOXymCtPN4aSCqMrE3bsA2bLJfGeK23luxqsfIiHHK0TCW201CzGKUBF759eMEbr6Z+oED+C2beDqLIcmYPoX0dInP7J/g1w4P0ZQkfJYfKZ9j/p5HELA5Fh/gD69bS8lcR5c9xZBvAD13jj4muav91Zz2DbLRniS14vQv0F0R/NYGFNGmNz5DU1GQMjO0NmIUFIP9wg6k3ZdxrrGbvfmTADwzuJ503EVU6SV0QYrAjnaminFKXgNX7CylRpCDjWuoS17eVq8hFDUe6Gwnkk/SDCwhyTqy38QC6LCpmS7eNPlNvtrfzve+v/+XJ6R/Az+z4rdte9G27WOr62VgGGh/sW7slw396YfIPTPIMeVu9KqMFPJwx+/9EWNug6c35IhHbGwbFrQ2zqkdJAt5vG0X8eo5i+sLRzjevZaR3gTDwQ3klAjvet9bObnJ4UVvK6TJROsAzMhb2WMs0S4s4JM9CIKLDAb1ehPd50dMjKLqKsWs03NUrVpYkoCn2EN6V5GlmIikNRFzc9T8IeyqQLzDzxNHVzAFGKwpNFwqliiRasCilEcqJJg8nuZV2woIXolmw5kGjXQM1eXhD4dimAjslOZIKTVqtRpuXIiqTNU0efPpc4jA5zb14JXOW+v/XaEkvERfsxZ9rsLW4x0kLYdr/2xoPemAmw2u02zZJDM3UeDPui4Gy2Jwbo5gzskUa7b62bMmzgfOLTHj9vPesAuPP4lQylF8/ABNScG7bRtrW4PcvKMfuV6lIalMLtcwZYkjyfWMC9sIhuu0FM6iYRLEReXYMpmKQGRrkenWdpJzY1xUdyLZD8jdfHtmEQsXrcI44XqJIxs20xnux7JNyvZxfBe08lD4KdyGwtbBUaabFvuFa4jby+zdESUwVyXj99Fpb0GQNNLtAl1GiXTITXKH00tBLc0Tr5X4oOVh+tz8L0dA/wZelKdWEIQeYDtw6AU27xUE4aQgCPcLgrDx3zjHWwVBOCIIwpF0Ov1i3NbPDCs9T/buDAIaS5WzSKqJ3LlAe2sLk/5JFlI24bYi1XKI+YzESjTOLh+4yl4sQeftB/+RZCHDgxdezcMdl7O7nsEd9XGqbZBYMYer2aAaWMIWTJriehKWyeTw91kbdCzncc2J1Bq+ILLphGE0oYihuajjBIzUajc/Fh9jOraaTWPqVP0R/GEXxx+aIdTux7s1wdb6T4qiEg0TKRXkxA8WGNiVZO8rt6B53eSaXvKaG7XgYyK1nhUB9sgyO7xZjh49Sq3ZwCOp2LbNH47MMlJt8MmN3XR7VM7jvzc8G2MEruqkcSTNu3kFfiPPCXEneVUlwTJfffVGom1+7j+ywkOvvIPuM2dwlZyiPiEeYtNaBaPLj3emzK2Da5DiCeRGGXPoMKdjvbzmIqeo67UXdFLLOgV/01qAb7ZehyGI9BUfgkMAACAASURBVGy6FlG08Sea6OYyXYbI3JKFdVRBbjEoRzx0Ls1xVWWclG5xxldl4ahTvFjtnWDL+DAn121gn+llvjbO9PGHmFJyPBE4xsXlXUh2gzF/mWFPF1fwMI8d+hp7ljMIhkW5owfbFphPaKiGzeHaIBfd+L9w+5vMVYP8yelvMhOQeOeBaTRN++UI6Kfg51b8giD4gW8Dv2fbdulfbT4GdNu2vRX4OPDdn3Ye27Y/Y9v2Ltu2dyUSvzyaXds0yX/8mxh2ilOxJ6ktibhbRPzJXh781t1kAiVubtTxhQtUMiEOeJ2K0tvXdKEUY9R9Ezw13cbLDj6CZFk0dyS50dPK6PcepeHrJVXIotbKRDNZap5l3KsVtbXKCAOrVcDnKhoCMrbqQ1tyPqI83hJWVaWhSkjVJFqbjx/N/d/2zjtOquru/+9zp9ets7O9wQLLUoRlqUIQRQFRUIliIpoYo8lPjU+eJ0WjMbElxkRNTHySR42KHWPFhlFRYwHpve6yy7K7s71Mr/f8/phRAdEoKrth7/v1uq85954zM5/7nZnvnHvu+Z7vG3SnZX2k3WvJRk3G7jD3stE4p+RTEvp4mqUx7MNX7yAjz8asJZWInEoiFgt9MTObuvORwKvqKNw2I7clLAw1FrBt2za8ET9mg4l7mzp4tr2Xa8rzmJnpPAafhsZ/As5TSjANy2DCxlJygw1sIhlTkhsLYbcW8vx3JqK36rnTPJYDBgeW7gAA7hwnfw1LioJ+TPsC/PDR9Sg5ychgh7+d7e5hzBudnExQlGkl0zgcSyLEdvtQHsmdzzi7mfGzTkdFj80dweGvxSkVuhIFuN60QhQMFcmefnZrO6VhJy3OWtIDkEBisrVTvWUr7WnZxB3P0JBYxc5tTdzx2h+JKQnO6pmJOHACWyolQkpOTLxNTnwTk51RFE+ILXkViEgRXkcnIaPCph1uwqEwjtwgB4LpTIi8ySl7OlmXb+Xyv3/6zfb+4Es5fiGEgaTTf1RK+YnIBSmlV0rpT5VfBgxCiAGdvcB3122EotWk5a2ivnMnSIE+J8HE6st5ZM9D6CRMVxMoigoHBPsLhuAM9DA0cgBToJDGjhZU9LiCw5m9cy3SqueW8blsqRNInY5CbydKJISyNU6LsY/0SC5Nip4qukgzJteuifja6HIU41QtqAmBXgljtXrRBXVIawKLr4S3cjcQU2OUVJwIumTP2xB3EvRFmX1xFWkuC8ZiB66DpmbEo10YY5nMvWw0BpMOhEDazUgEm3ryqLeWEDc5WH7VdDIXDKWiO5tYLIZPDdKclc0NdS3My07jymJtno3GxwhFkLV4OPp0M4taCpKLuAHFMjkLpshp4c/fHo+MS346+0r8qgFDIs4HRhPWeIynp43mD+eMZWNjL2/HP75CTZs4BYvx46DAU0fPYFikjmdyTqHWVsKSQlcy9qB4EpbcKIXt21KCCmlOPwHzBoX0qfXETHr8bSaMoaHEdBEEkpAlgG9fLtW7tgLwbkYlk7NW4Ism2Op9l3S/nZJIPvmdc1ifl8Pwlhi2PgvpmW1YzR50TQHCOjM2w2TqzSobjdlY4hEeuv9RbOkGYqqOWMTL5SUNjG0L8/KwHG6798Vj9In8e77MrB4B/B3YKaW841Pa5KbaIYSYmHq/riO1HQiEn7kPb9sULKa1iO/8P6JdEczZMVzl+8kwV7Ezdx8nBwLUZ+ahqgq+BjeN+UMoPrCPDU8sB6C5pwm7UoPDJ8jt6WLc7t2055q5fpQLVElBbyc+s5X4gRh7iGOIOdlHESVqjLiaR4gQMhYgs2QkMZx0mVTc7a0YDBFs4QQJewyTt4QXYq8xJnsMCxbMAbsbgIRMY9IZZZSMSl4FCCHIHZODLSYxJiThYJxTv1tFuvvjHK/uguRzVRS2O0Zyz4XVuJ1mbJPyKBlXQYZqw28082BZCWUWE3+qLNZu5mp8AsVqIOuCSqa3f5wHeVTu6I/K88pdXHhaBeGQjhfKp4FJEDSauHtoLsXpaZw+Jo/LZpSzIjkKg9dg5YzFJx3yHrOrCigIe+g1OLEkIizISf6xKENPwpoRI8+3l4BQ0RvSiRrN2N9VMJhi2HPCdHc4sAcLyA4UoaBDzdiHYbceZ8BPbkhlufsMynR+isrq8drCuEIVxM2wJb0Cn8HC2H0R6CjB4uihuHsZhV4PNm+YaM5o4kJwf98EAoqFurWrGXnSlYCkIZBF2oanuLMqG3dI5YGifFauXPu1fxafhy8zj38asATYKoTYlDr2C6AYQEr5N2AR8EMhRBwIAYullEcO8exnXvj1z6HOyIS8l+gMN9J+08PEfAWMdLZStF6yfNf3uER3HiMjZnoKlxH0ujEOOZuQ0crMmJOSyCyihggNmR24A+eAfxcdqpVpbQdozMyjy52GvTuAQU3gySjA1biH7lgbMBqdfgJE6wnKErxqcpW/E0+exrMvbcCstOOwJH8NzmCUSI5CXa+J+mADVfrTuf7957nA7sLU14gnt5O2uhdZ9eePz8ug6sgvnURQD+tc+dTVtkBty0f1Ad0USliH3giG+dN4UUZ4cVcyqbscZaZZTmSzTRBVFO4fVYZD/8llGTQ0AIz5dobOL6eoexvtujRGDj/5kPobZ1Swdm8rO/d6CTuMXGkTnDqk9KP6n542nNrt9fAW7HUP44KijENfX69QIJOTIqaH67DpJiUrymYCN+NO68MnfbgTTnpt46mLuom9DdGiKCGLkezucqZ7k2tCbc5fwdSXW6ktsmEP7mZ9RhmL83JRhcSYgBst23DE/8hzhdeRHQkw1BMjksjHOhzCthr+2B7kxboW7hlXzm3vX0Z5TxRThpmEGuGD37+EXhHs7MjhtIY9vLrxOi7Vm4g5QzQMa+Rd0x2cOG3q1/QpfD6O2vFLKd8FPrPrJ6X8C/DJ0L8BRt2uXfQ0jsRnKyLRbGZWyXvs7inDqMSptjSiCBeTApeil0a69D6s9k7C+2bQmJMcf5zW4cZkDLBb3U521whUm5HtZjOZsp24MHJBcy1LlUpq2pPJw73GUkKKCVt7LdI2i0y9i20Y0MUy8Ic6MTucTJwyir+9sJtSkSBtRjOJuJ4Sfxe1RUaeszSCMLHD/A3qI3BiSS5laiW91j7o/uT5jWpvJ6wzsCEqYGf7YbV6KrMUDhSU0W7T81pX3yG1Mj8T/H5utJkZZhu4kYgaA4O06gJmL1tOo8WE2TL5E/XPXjiVSbcuJ0sEuWbyoQFOep3Crd+fyTsvVZJ5+oIjvv68ETWsbtjFXN1DxONno9ebIX8ccdWA3R0hLbCdqH0yNoObzqxMRBcETCqJLIEpYkbEYV/GVgIBDzm9UVbU6AnEVxNXKtnkGIY7uI/ver3YW0z4s/ewKlvH9/Z5sYkgB3y52CMWQsU7cbTM5lyPkaWjVVZXnMCMLT2AQGewk00maxseweM04m8xMk63m5AwEl/YQ2J8gj3P/5gTpx1pHsyxY9BH7kZCId6/+xV8xrFkdm2hOWsKD9eHiYj1WAoEq2fkYFxzF0WhOD8pXIU7t49zBXi3tvPiN4JkeOPs9vayVR9mqTGPK6MhgpYE72a4+F/lMV5nFmLvXhY3bMc8cjQ9BiMtXrDbKhjTvpVolQdTws0WQznjExkEItspqhqDEAKdM4uhRWsw2jow++bhjD2EV81jp2UbiyrO4Nqa2Tz44INsH5LB1Fv/xq9yjnbs/RQAfvPVmVVjEPOb8374qXVmg47NvzzrU+td6VbOfuvTFzqbMmoud+mvo6VlLxs2nMfEic+DTk80Yww29yZKd9Uy7+4r2XDSTLLznAyv3kxtkZXW+0qoLyjGPn0UF1jP4OWWt4BdnGG6msU7cjnNDTHLKNKpYMmBJxHmAL9NewCAM5scrHW0ogTKaGrPp7RwH8+lv0NNbD1zu+bzfOEM/qW7gd/+n8K4aRWY03/AsLEzaWlYReIUIwWWOAvcRVw/vBMzMDyvi6v/ewm33vHw0Rn4K2DQT8J+5Lrb8cbGUtrwEkNNKzF5a0kYHciEguIU+HfezPCAgdvyHuK6sy9ktNhLPK5nyDtNtKZlUtrVSiymoyY7zs9qvEhjHoZQC7ZEH+PEbgwyRsKewRU3XE+fr4des522qBGLoxCJIKrfjxIqZ8zCB1FQCEW91LW/BsApozeRm19LT9Mk4vXJhbFWh7KIyAiLKhaxYsUKmpqaWLhwITlH7fQ1NP6zqBxxMzbbcHz+bezecwMApknnYHQkyInU09W4H2cogqMkOW10Y/pUGoe7Gdawj05vjL5ZCpY9cbrSMihOK8CaKdHLKFFTFdVlWfgn3YxRRHi5WMeUgMQWCxCMgy5uI7foDISQ5BTsJ6Y4KW7dT0JvImydxNOTh+BZOAe77jncYgaljpHsVtN5OaOH0e69mBWo73YRGi05d9VafnzDNf1mw0Ht+O+76TYigYlk+rZT3rqK9J/dwL3TM0lEtqAoLuyOU5jSmsWjWW+wx6kwIa+EYnMj4bZ0Xj1hGnGdHnegl6B5LVU100l4PAStBXSpAfRCsj1aRjmNSGuc7Mx0RCSIXzWSQOHcGQXkmrx0RtvQRzPYv3Y9AP5YL08Vd/Lq4xeSY/s/+rpLqKsbieLbT8QgeCceYkTGCML7w6xbt45p06ZRVfWp4REaGsclE6qfRKez0dT0EO3tK9ANnQVAga2FpjXrMEiVzPQmOow15FVeQnREgrSAn55Agt2t6yjd00pL6TB0Rh3Xjmkljp64eThd4R7cUxbziPky2sxZnFq/lBdMa5H6EAJBmprHgYiegtydfCDGE2mSFLW2ELbNoi43ypiabxK3PAOJHUzImkNXYAh/ykhnllmlI6JH130qQoG+GZKzXlrO3Uv7p9c/aB3/q888jfAMwSK6qdr8ALrcLCrGjOPslpdQZS8u2ySmt45nva2Hx1zP8M1h5/BO7b+wmb3o9+p5e8IMUCXFHR5Ewoe1JpfImgAIhR0WPQfMuTwbmcgQsZ+4EmP3tn0YE3G8AQOFapjKXTcyOqMNT18PAOb65AT8sCFMrjFCIOs9FBzs3PZNQiJOtmxhk81Eo+Lj5JyTefnllykrK2PWrFn9aUYNjX5Br7dTPf4JQGHb9qsI2R1EIgayMn10v7cKe14Ei8GPesKFTKs8FaU0eVNY39NLx4a3SPP7MWSPItETYdTeYk71NiCFga2BBJHaXl4rW0hGxMvFPY9Qpm5ncmpRgh1b9xKPjMFgiVCWtgthy2ZI417i5iF0unSYDGb2VCzAZb2VCEFm2M5hYsSO2xrH317Kdy+9kT09hXSfaMIZTJD9xJ/YW7//mNtvUDp+T9MB2lf2ElctlLeswBAPk5ibvNvvDLZhNpmpzhpGSIWWfbuQahqX1ZzOxt3J+DPdbkFtXjnmvgj2Xh9hp501uzejj2ShJCKERBeFaifLjLMxyuTl5htvJpdu7Y6audn2ILmRfdinX0RnV3J2a4G/mISMkzmkhCuLc7AqsKzei6pPJoV2Kz08Z7NjFEZ6V/Vit9tZtGgRuiMkP9HQGAw4HCMZPuxXSBln7fqz6YoVYHdHcG7ZSNqQIEE1Hdfsc1EUBVPuLJpzcyjyNENDMoAsJ7+Mpwv1XFwf5VdtZShSpTGRRf3uNt516Rnma6dVuljIPynRtWNQEoTa9HwjlkAfU8l3bwYkpVEfQo0TzqxmxYvPUzD1EjpEiNvSnsKit3OhsQShSioCyT+P1sZs0m0BXjm3jDH1AZ696bJjbrtB6fhf+/0TeONl6GxrcDfsQMkspvKq3/Pqq88RblUYV/AtDDoDa7q3opomcM6qyRj1etID24hFTHjilUTSDZT4e0jXFaPqdLz5xDIipiJEyMMI705m+NfjEBGejk/FgZ/2rgYAJrONmfJfNOSeTvmiX5BvTydsbkWRegKxPgomt5PQNRPwlPCuzkin/Q8gQSf9vGGwUJEYSswf47zzzsNms/WvITU0+pnCwgvIyTmdWKyLQKkOvVmlxNaFIy9Mj3sBOmMyuHHG2IvwDM+gYn89mbVBmt1uHizP4YmRXpRCK+amIIX+PsKmSpbGukgogtJ9dTzsPIWYNJKlv5UMRWAOF5Le8h45HRGU7BjesA9zPEZpRwth6zQe3fY6+bnDWZg1jTV561nvfYV4/g6c7cUMa0vOer9o/g34olZMFXFeH5/LGe/v539+/v1jardB5/jv/elN+CLV2M3rmOpZh4z4SJQle8373lzKGOcsiqWL1VVv8cgJKzGEduMSp/DIT67D5Wgm0pLGP8cuBCFYGLIx2TkBq8+PLaYjYsmhV/Vi0EW4/JfX8Zd52TyXmEqxbAJAlXCT4T48piEUXXw/ANXnLMGvS9aHjR2EeI3Cwgs5Z8lKzu40sDIjjMH+Hm9YTISEILvVxfz588nPz+8fA2poDDCqRv4Ri7mYtrLkXObcCX0IBRynftyTzs8swFeegTkaZfieBjpKS3k7R+HesUPIWTIKYdQxvjtK3FjG83kWcnvbsKg9/OD7N9A9+mosoptcw2rUsIu0ngTmAxL0kDm8lXDAR1VrI1JnZ6M7jbP+egUR5x4y+0YSqViJagjiaDqfdNXF5tXPU14xim2eIqpc+zHPOp09BSa+/cq7/PQ3vz5mNhtUjn/p7+8k4avBaahl/rUXo2uII0xOin53FwCZwREMT6thZXY3WTkP0Wzx8Ozot9HHAoREJkZTmEBzJrtLdZBQmdsAlmo3vcKIzp+MIgzSyUlX/pyCvBymT5/J94dF6VKT0YzZdBMVZpwXP43OmJwTX3TybPrwAKAvaCYtbQIVQ5N3+3/xgzcZ54/zTLaH+9Kd5CYUTht1GuPGjTvGltPQGLgoisKECc8Ss9kIh3UY7Qm6gnk4Kw6d9FA6ZQ6JVNR52OXm5tIEo7KGok8zkfXtEczsMIBQ8JosjPA0cN7i88hyZFG86Crq7KeTZ3oXiUJPrISuTTl4QjqG5m3mCd0MSlobcYYChFzjaXB+gM5fxpPe98gs8xLpMyJaLXTH/hvdq08BkB0ehV5J0BT/F9vP/j4Rg+C0F59m6eNPHRObiYEYSDthwgS5bt26r/Q17/jdHTj6jMSUOKpIAGCMRlB1emJ6PR1WCxsKS5i+rxbFsJceRaGbCLZwNhkBhfwhtZRUfMC+py7jrlOmk+aPctGrtR+9vjSmIRUjS0/UYck6dHVq9863GRbqoZI9fGP++eROOOOQ+lf+cDmjOxfTWr4MMaEbRTF+VOft7ubm+r206vXM9efwm8teRa8f9OEXGhqfoKdnLdHfnonbGmSzsYjG4rzDWkiUhwUljc2sWDIX/WGLy6oIbh97HnpV8v2mPzExK/zxM6XK0G1drGj6NTVpj9PQ3sKBCZ2cUNnJHWuvJK/NStMoM5vKxuDqbcYUUzDoojgyugkFbEQjNgyYcUYizN5ThzGhUjT+H+iMIRpWL0EVcfRxFYGgZtE0qqu/eGSvEGK9lHLC52k7KHr8mzdtxuwTeAxdxJQYcaEiZZyYThDRCQJ6wZNjxrKuIJ8nq0ag6iEQN2BImHEkDOhdAQrL1uPryscXL6Ijw8IQTxAU40ebiIcIRfagZucTEPKQrXbYNBxKD/v1ZZ9w+gBjz76UrrS1eNMOoKoR4nHfR5vVaeBSu52Tw1G+Nf5qzelraHwKGRk11KbPpLPXSktxGoohetgWwzOpkNUzx4Ee4olDNzUhGd+yhYmdr1OT3YkU/o82lCC1I/Rk6lvZ5D2XkGkIGdvSCakws+gNdhnyqNrxAa7eFsBEzGAgYRUEsOFT0wjrjfj1sDE3l+dHVhATcbo9lZitvRgzGlGBqE4loUjqD3z9y9IPih7/A7+4i/3GbkrD6RSfche6Lgvu3zoQFid5Ly3jpH++jSe7kDGe3WwqrOKk6HK2ep4mHp3JWMdUFubchYkwlSMf4/JlH7Bj7Bhuft/Ht84YwUbfXs7363D3tLPy9Jk4HEdIYK2hoXFcsOq19Wx5pg+jojLn52N4ftNplFj9/M9bv+ERUzs9xd24b32cHeOGYPruTlw9CerX5NHZ6cRbPAlvXpyHR3yTb+99iu9NWUhdy0XUtRegvpRcl2jY+Wcxf8Hio9Km9fgP4u9X385+YzdFsQy+c+t/YQxOx7Y3DxnuQy02sPjZp2h2lzK99j1WLPk2o7rW86bxTKLWcUw1jqba/QyZdGBwXY8zZKMzOxsRTXBqXIc3M8ZlbQEM8RhLR5drTl9D4zhnyuxqsodFCCYU3vjDBvKt38KoqEzOW8vqoJ6TL/olOxZOYqjcg10nCfQOp8TaSzykp8y5lZu+cx0ned7j8aFn8cI7D7HVU0SVux7F6Mc+tuqonf4X5bh2/LdfewseU5DMhB3X5GQmn6mn34/9TQOYbPzlxBo2lNdQ0bST+y/4HgBLwv+LPtyAN/MHZGU3Us063gp/kzPGLGDDmh10ZWaT0x3DVu1iyevv0JWWye8SvYzUomc1NAYF5/x4LunOAH1RI/6VI/BEdXyj8D02SSdqbw/n3HQvntMESjfU7y2hXJdc+NAQbsFssfDLcWMp9x/ggaGLYFcZil7FOMrIpddcf8zO4bh1/H+9+x6ETo8OhbAS4cwz5gOw9/4bkS2NbJxUxQvD5pDd3cbdo4Zhs9sJhfvYShrOrjvRS8kb1hreip3EK5GTSCQSvH2gkYTFyPiuOLc0r2FryXAu2reVRWfO7+ez1dDQOJace/Mc0gwxerttxBunU+hopTWtg/CWLax7exnm4hjqJgs1//yAdn85ZkcEX3fybvLIyhO5LLCFqDTwxPgz6Op0YR/hIRwKHTP9x6Xjb21uRvEE8YkwGVEDP7vh2o/qxLMb8JmN3Dz3AhRV5cqev1Balrz7/6/Vt/N+0IxL9fMjcSceCni8bwmt6Vm8vmot26zJD66cCI9VVDJh3w5uuXhJv5yjhoZG/2EwGph+VRVmRRLfejbRhJ5xBR+wYd1ONrXdgyphp5yJoqp0rxfkZHrxtVvp6Urmuki0m5nz+rO0u/L5h/4C8u0d/PlvVxwz/V829eIcIcRuIUStEOLqI9SbhBDLUvUfpJKyf+2s+MvTePS9lETT+cFvf/bR8WBvJ3FPN7/44Y/pcWZx1t5XGFq2nU1vzEdNJNje8D5hUzOXuOIMF7sZ39mM35WGvjPIww2tNGUWoAvF+fsQG+6uNh5ecKq2ZIKGxiClZGgh5bMtxGMWoo2TmJS7gfe8AXKyW+jsddOtd7H29GnktXSQ7o2gxnS8++SP2bDmbXpWbWTogXoW7l7BOxkn8k91LglX8zHT/mVSL+qAu4G5wEjgfCHEyMOafQ/okVIOBe4Efne07/d5ueea37Pf2E1+LIM5Vyw6pK7+fy7mr6fNYlvFGCbuW82dP7oJg3ccsYw+3lt+Pm+HLZyXGSLXGCCW/nOWnzOPrPY+4tlWVlpz6cpKQx+IA5IHSnPIyB7Q6YM1NDS+ZmaeNZXMkiCddTMw6aNYq17BqVfpaKkkxxbnklv/yua51WSviwKSgKeJd+54CFUGMA0Zxu0X/IApHet5VPkONreB9e+/dkx0f5ke/0SgVkq5T0oZBZ4ADk+bswBYmio/BZwsvsaErb+//jd0GiM4pRVDRTq5BYcGUq0wl/DU7LMobanlsfMvBODEM5/E0OMgbF9HUWYzE2wJ9ijnsqj6W+h0Ol6dfQJ6fxTVaUQadER18Ct/C+Nrqr+u09DQ0PgP4txr5mOO5BLuLWBkVj2RiAXZU8LFV10HwDm33kNjYQlWZ5iWPS7isg3FWsLlt9yCzZ7OT0uyyIu0c7fuxzyz5b5jovmo5/ELIRYBc6SUl6T2lwCTpJRXHNRmW6pNU2q/LtWm87Ne+2jm8S97Yhl3ZJmJCj1CSuCT59Wlc5GZ6OTK+LUYiRx2PhKXIU6tP4c5r/8I3UH/iWvsJq6cVQx6hdm7dvLwD8//Qto0NDSOb4LBMC//7UYyTlhG78559O74ZJaxUOg1IoGtmNPcjJ63HeWgbrcnms1PMq+jNNHAY1XVFORXfGENX2Qe/5cJAz1Sz/1wb/t52iQbCnEpcClAcXHxFxYTjcdwR4PEAdTYEdsUqQ1MjT1Pr04HWD8ha39AT/WqeRDrJHFQTXUPnLtB8q47jQcuWYSGhobGwVitZjLKa+ita8dXfxJ64ycHU2yiEqOph5GT9mMK2g+pKyXMhZ1PUGctpmm/86gc/xfhy/T4pwC/llKeltq/BkBK+duD2ryaarNKCKEHWgGX/Ddv+nWs1aOhoaFxPHOsInfXAhVCiDIhhBFYDCw/rM1y4KJUeRGw8t85fQ0NDQ2Nr5ejHuqRUsaFEFcArwI64H4p5XYhxI3AOinlcuDvwMNCiFqgm+Sfg4aGhoZGP/KllnqUUr4MvHzYsesPKoeBb36Z99DQ0NDQ+Go5LiN3NTQ0NDQ+Hc3xa2hoaAwyNMevoaGhMcjQHL+GhobGIENz/BoaGhqDjAGZelEI0QHsP8qnZwOfuSREP6JpOzo0bUeHpu3o+E/VViKldH2eFxmQjv/LIIRY93mj1441mrajQ9N2dGjajo7BoE0b6tHQ0NAYZGiOX0NDQ2OQcTw6/nv6W8BnoGk7OjRtR4em7eg47rUdd2P8GhoaGhqfzfHY49fQ0NDQ+AyOG8f/7xK/9ydCiAYhxFYhxCYhRL8nGhBC3C+EaE9lSPvwWKYQ4jUhxN7UY8YA0vZrIURzyn6bhBDz+kFXkRDiTSHETiHEdiHEVanj/W63z9A2EOxmFkKsEUJsTmm7IXW8TAjxQcpuy1JLuw8UbQ8KIeoPstsJx1rbQRp1QoiNQogXU/tfjd2klP/xG8lloeuAcsAIbAZG9reug/Q1ANn9reMgPTOA8cC2g47dBlydKl8N/G4Aafs18JN+tlkeMD5VdgB7gJEDwW6foW0g2E0A9lTZAHwATAaeBBanjv8N+OEA0vYgsKg/7XaQxv8Ga0ApCwAAAv5JREFUHgNeTO1/JXY7Xnr8nyfxu0YKKeW/SOZHOJgFwNJUeSmw8JiKSvEp2vodKaVHSrkhVfYBO4ECBoDdPkNbvyOT+FO7htQmgVnAU6nj/WW3T9M2IBBCFAKnA/el9gVfkd2OF8dfABw4aL+JAfLFTyGBfwoh1qdyCw9E3FJKDyQdCZDTz3oO5wohxJbUUFC/DEN9iBCiFBhHsoc4oOx2mDYYAHZLDVdsAtqB10henfdKKeOpJv32ez1cm5TyQ7vdkrLbnUIIU39oA/4I/AxQU/tZfEV2O14c/+dO6t5PTJNSjgfmApcLIWb0t6D/MP4KDAFOADzA7f0lRAhhB54G/ktK6e0vHUfiCNoGhN2klAkp5QlAIcmr88ojNTu2qlJvepg2IcQo4BpgBFADZAI/P9a6hBDzgXYp5fqDDx+h6VHZ7Xhx/E1A0UH7hUBLP2n5BFLKltRjO/AsyS//QKNNCJEHkHps72c9HyGlbEv9QFXgXvrJfkIIA0nH+qiU8pnU4QFhtyNpGyh2+xApZS/wFslx9HQhxIcZAPv993qQtjmpoTMppYwAD9A/dpsGnCmEaCA5dD2L5BXAV2K348Xxf57E7/2CEMImhHB8WAZOBbZ99rP6heXARanyRcDz/ajlED50rCnOoh/slxpf/TuwU0p5x0FV/W63T9M2QOzmEkKkp8oW4BSS9yDeBBalmvWX3Y6kbddBf+SC5Bj6MbeblPIaKWWhlLKUpD9bKaX8Nl+V3fr7rvVXePd7HsnZDHXAtf2t5yBd5SRnGW0Gtg8EbcDjJC/9YySvlr5HcvzwDWBv6jFzAGl7GNgKbCHpaPP6QdeJJC+rtwCbUtu8gWC3z9A2EOw2BtiY0rANuD51vBxYA9QC/wBMA0jbypTdtgGPkJr5018bMJOPZ/V8JXbTInc1NDQ0BhnHy1CPhoaGhsbnRHP8GhoaGoMMzfFraGhoDDI0x6+hoaExyNAcv4aGhsYgQ3P8GhoaGoMMzfFraGhoDDI0x6+hoaExyPj/XEsBImJTJ+0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for d in data:\n",
    "    plt.plot(d)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.,  0.,  1.,  0.,  3.,  2.,  5.,  4.,  8.,  2.,  9.,  3.,  3.,\n",
       "       10., 12.,  9., 14., 11., 13.,  8.,  6., 18., 11.,  9., 13., 11.,\n",
       "        8.,  5.,  5.,  2.,  8.,  5.,  3.,  5.,  4.,  1.,  3.,  1.,  1.,\n",
       "        0.])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(numpy.mean(data, 0))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# What is the max for each patient?\n",
    "# What is the min for each patient?\n",
    "# Create a plot for each of these questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Figure Dimensions\n",
    "fig = plt.figure()\n",
    "\n",
    "# Using Subplots\n",
    "# fig.add_subplot?\n",
    "fig.add_subplot?\n",
    "axes1 = fig.add_subplot(1, 2, 1)\n",
    "axes2 = fig.add_subplot(1, 2, 2)\n",
    "# axes3 = fig.add_subplot(1, 3, 3)\n",
    "\n",
    "axes1.set_ylabel('average')\n",
    "axes1.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "axes2.set_ylabel('max')\n",
    "axes2.plot(numpy.max(data, axis=0))\n",
    "axes2.plot(numpy.min(data, axis=0))\n",
    "\n",
    "#axes3.set_ylabel('min')\n",
    "# axes3.plot(numpy.min(data, axis=0))\n",
    "# axes3.plot(numpy.mean(data, axis=0))\n",
    "\n",
    "fig.tight_layout()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "# Labeling Axes\n",
    "# axes1.set_ylabel()\n",
    "\n",
    "# fig.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### String Manipulations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Slicing a String\n",
    "word = \"dinner\"\n",
    "\n",
    "# What about negative?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'dinner'"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'din'"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'e'"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[-2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'r'"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[-1, -1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  1.,  3.,  1.,  2.,  4.,  7.,  8.,  3.,  3.,  3., 10.,\n",
       "         5.,  7.,  4.,  7.,  7., 12., 18.,  6., 13., 11., 11.,  7.,  7.,\n",
       "         4.,  6.,  8.,  8.,  4.,  4.,  5.,  7.,  3.,  4.,  2.,  3.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  1.,  2.,  1.,  3.,  2.,  2.,  6., 10., 11.,  5.,\n",
       "         9.,  4.,  4.,  7., 16.,  8.,  6., 18.,  4., 12.,  5., 12.,  7.,\n",
       "        11.,  5., 11.,  3.,  3.,  5.,  4.,  4.,  5.,  5.,  1.,  1.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  3.,  2.,  6.,  2.,  5.,  9.,  5.,  7.,  4.,\n",
       "         5.,  4., 15.,  5., 11.,  9., 10., 19., 14., 12., 17.,  7., 12.,\n",
       "        11.,  7.,  4.,  2., 10.,  5.,  4.,  2.,  2.,  3.,  2.,  2.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  0.,  4.,  2.,  2.,  1.,  6.,  7., 10.,  7.,  9.,\n",
       "        13.,  8.,  8., 15., 10., 10.,  7., 17.,  4.,  4.,  7.,  6., 15.,\n",
       "         6.,  4.,  9., 11.,  3.,  5.,  6.,  3.,  3.,  4.,  2.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  3.,  1.,  3.,  5.,  2.,  4.,  4.,  7.,  6.,\n",
       "         5.,  3., 10.,  8., 10.,  6., 17.,  9., 14.,  9.,  7., 13.,  9.,\n",
       "        12.,  6.,  7.,  7.,  9.,  6.,  3.,  2.,  2.,  4.,  2.,  0.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  2.,  2.,  4.,  2.,  1.,  6.,  4.,  7.,  6.,  6.,\n",
       "         9.,  9., 15.,  4., 16., 18., 12., 12.,  5., 18.,  9.,  5.,  3.,\n",
       "        10.,  3., 12.,  7.,  8.,  4.,  7.,  3.,  5.,  4.,  4.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  2.,  4.,  2.,  2.,  5.,  5.,  8.,  6.,  5., 11.,\n",
       "         9.,  4., 13.,  5., 12., 10.,  6.,  9., 17., 15.,  8.,  9.,  3.,\n",
       "        13.,  7.,  8.,  2.,  8.,  8.,  4.,  2.,  3.,  5.,  4.,  1.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  2.,  3.,  1.,  2.,  3.,  5.,  3.,  7.,  8.,  8.,\n",
       "         5., 10.,  9., 15., 11., 18., 19., 20.,  8.,  5., 13., 15., 10.,\n",
       "         6., 10.,  6.,  7.,  4.,  9.,  3.,  5.,  2.,  5.,  3.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  3.,  1.,  5.,  6.,  5.,  5.,  8.,  2.,  4., 11.,\n",
       "        12., 10., 11.,  9., 10., 17., 11.,  6., 16., 12.,  6.,  8., 14.,\n",
       "         6., 13., 10., 11.,  4.,  6.,  4.,  7.,  6.,  3.,  2.,  1.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  2.,  1.,  3.,  5.,  3.,  5.,  8.,  6.,  8., 12.,\n",
       "         5., 13.,  6., 13.,  8., 16.,  8., 18., 15., 16., 14., 12.,  7.,\n",
       "         3.,  8.,  9., 11.,  2.,  5.,  4.,  5.,  1.,  4.,  1.,  2.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  0.,  0.,  4.,  3.,  3.,  5.,  5.,  4.,  5.,  8.,  7.,\n",
       "        10., 13.,  3.,  7., 13., 15., 18.,  8., 15., 15., 16., 11., 14.,\n",
       "        12.,  4., 10., 10.,  4.,  3.,  4.,  5.,  5.,  3.,  3.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  0.,  0.,  3.,  4.,  2.,  7.,  8.,  5.,  2.,  8., 11.,\n",
       "         5.,  5.,  8., 14., 11.,  6., 11.,  9., 16., 18.,  6., 12.,  5.,\n",
       "         4.,  3.,  5.,  7.,  8.,  3.,  5.,  4.,  5.,  5.,  4.,  0.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  1.,  4.,  3.,  6.,  4.,  6.,  7.,  9.,  9.,  3.,\n",
       "        11.,  6., 12.,  4., 17., 13., 15., 13., 12.,  8.,  7.,  4.,  7.,\n",
       "        12.,  9.,  5.,  6.,  5.,  4.,  7.,  3.,  5.,  4.,  2.,  3.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  0.,  1.,  3.,  1.,  6.,  6.,  5.,  5.,  6.,  3.,\n",
       "         6., 13.,  3., 10., 13.,  9., 16., 15.,  9., 11.,  4.,  6.,  4.,\n",
       "        11., 11., 12.,  3.,  5.,  8.,  7.,  4.,  6.,  4.,  1.,  3.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  1.,  1.,  1.,  4.,  1.,  5.,  2.,  3.,  3., 10.,\n",
       "         7., 13.,  5.,  7., 17.,  6.,  9., 12., 13., 10.,  4., 12.,  4.,\n",
       "         6.,  7.,  6., 10.,  8.,  2.,  5.,  1.,  3.,  4.,  2.,  0.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  0.,  1.,  2.,  4.,  3.,  6.,  4.,  7.,  5.,  5.,\n",
       "         7.,  5., 10.,  7.,  8., 18., 17.,  9.,  8., 12., 11., 11., 11.,\n",
       "        14.,  6., 11.,  2., 10.,  9.,  5.,  6.,  5.,  3.,  4.,  2.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  0.,  0.,  2.,  3.,  6.,  5.,  7.,  4.,  3.,  2., 10.,\n",
       "         7.,  9., 11., 12.,  5., 12.,  9., 13., 19., 14., 17.,  5., 13.,\n",
       "         8., 11.,  5., 10.,  9.,  8.,  7.,  5.,  3.,  1.,  4.,  0.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  1.,  2.,  1.,  4.,  3.,  6.,  7.,  4.,  2., 12.,\n",
       "         6., 12.,  4., 14.,  7.,  8., 14., 13., 19.,  6.,  9., 12.,  6.,\n",
       "         4., 13.,  6.,  7.,  2.,  3.,  6.,  5.,  4.,  2.,  3.,  0.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  2.,  1.,  2.,  5.,  4.,  2.,  7.,  8.,  4.,  7., 11.,\n",
       "         9.,  8., 11., 15., 17., 11., 12.,  7., 12.,  7.,  6.,  7.,  4.,\n",
       "        13.,  5.,  7.,  6.,  6.,  9.,  2.,  1.,  1.,  2.,  2.,  0.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  0.,  1.,  4.,  3.,  2.,  2.,  7.,  3.,  3., 12.,\n",
       "        13., 11., 13.,  6.,  5.,  9., 16.,  9., 19., 16., 11.,  8.,  9.,\n",
       "        14., 12., 11.,  9.,  6.,  6.,  6.,  1.,  1.,  2.,  4.,  3.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  1.,  4.,  4.,  1.,  8.,  2.,  2.,  3., 12.,\n",
       "        12., 10., 15., 13.,  6.,  5.,  5., 18., 19.,  9.,  6., 11., 12.,\n",
       "         7.,  6.,  3.,  6.,  3.,  2.,  4.,  3.,  1.,  5.,  4.,  2.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  2.,  3.,  2.,  3.,  2.,  6.,  3.,  8.,  7.,  4.,  6.,\n",
       "         6.,  9.,  5., 12., 12.,  8.,  5., 12., 10., 16.,  7., 14., 12.,\n",
       "         5.,  4.,  6.,  9.,  8.,  5.,  6.,  6.,  1.,  4.,  3.,  0.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  0.,  3.,  4.,  5.,  1.,  7.,  7.,  8.,  2.,  5., 12.,\n",
       "         4., 10., 14.,  5.,  5., 17., 13., 16., 15., 13.,  6., 12.,  9.,\n",
       "        10.,  3.,  3.,  7.,  4.,  4.,  8.,  2.,  6.,  5.,  1.,  0.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  1.,  1.,  3.,  3.,  2.,  6.,  3.,  9.,  7.,  8.,\n",
       "         8.,  4., 13.,  7., 14., 11., 15., 14., 13.,  5., 13.,  7., 14.,\n",
       "         9., 10.,  5., 11.,  5.,  3.,  5.,  1.,  1.,  4.,  4.,  1.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  1.,  2.,  3.,  5.,  3.,  6.,  3.,  7., 10.,  3.,\n",
       "         8., 12.,  4., 12.,  9., 15.,  5., 17., 16.,  5., 10., 10., 15.,\n",
       "         7.,  5.,  3., 11.,  5.,  5.,  6.,  1.,  1.,  1.,  1.,  0.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  1.,  3.,  3.,  2.,  7.,  4.,  4.,  3.,  8., 12.,\n",
       "         9., 12.,  9.,  5., 16.,  8., 17.,  7., 11., 14.,  7., 13., 11.,\n",
       "         7., 12., 12.,  7.,  8.,  5.,  7.,  2.,  2.,  4.,  1.,  1.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  1.,  2.,  4.,  2.,  2.,  3.,  5.,  7., 10.,  5.,  5.,\n",
       "        12.,  3., 13.,  4., 13.,  7., 15.,  9., 12., 18., 14., 16., 12.,\n",
       "         3., 11.,  3.,  2.,  7.,  4.,  8.,  2.,  2.,  1.,  3.,  0.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  1.,  1.,  5.,  1.,  5.,  2.,  2.,  4., 10.,  4.,\n",
       "         8., 14.,  6., 15.,  6., 12., 15., 15., 13.,  7., 17.,  4.,  5.,\n",
       "        11.,  4.,  8.,  7.,  9.,  4.,  5.,  3.,  2.,  5.,  4.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  2.,  3.,  4.,  6.,  3.,  7.,  6.,  4.,  5.,  8.,\n",
       "         4.,  7.,  7.,  6., 11., 12., 19., 20., 18.,  9.,  5.,  4.,  7.,\n",
       "        14.,  8.,  4.,  3.,  7.,  7.,  8.,  3.,  5.,  4.,  1.,  3.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  0.,  1.,  4.,  4.,  6.,  3.,  8.,  6.,  4., 10., 12.,\n",
       "         3.,  3.,  6.,  8.,  7., 17., 16., 14., 15., 17.,  4., 14., 13.,\n",
       "         4.,  4., 12., 11.,  6.,  9.,  5.,  5.,  2.,  5.,  2.,  1.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  0.,  3.,  2.,  4.,  6.,  8.,  6.,  2.,  3., 11.,\n",
       "         3., 14., 14., 12.,  8.,  8., 16., 13.,  7.,  6.,  9., 15.,  7.,\n",
       "         6.,  4., 10.,  8., 10.,  4.,  2.,  6.,  5.,  5.,  2.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  3.,  3.,  4.,  5.,  3.,  6.,  7., 10.,  5., 10.,\n",
       "        13., 14.,  3.,  8., 10.,  9.,  9., 19., 15., 15.,  6.,  8.,  8.,\n",
       "        11.,  5.,  5.,  7.,  3.,  6.,  6.,  4.,  5.,  2.,  2.,  3.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  2.,  2.,  3.,  6.,  6.,  6.,  7.,  6.,  3., 11.,\n",
       "        12., 13., 15., 15., 10., 14., 11., 11.,  8.,  6., 12., 10.,  5.,\n",
       "        12.,  7.,  7., 11.,  5.,  8.,  5.,  2.,  5.,  5.,  2.,  0.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  1.,  3.,  5.,  6.,  7.,  5.,  8.,  9.,  3., 12.,\n",
       "        10., 12.,  4., 12.,  9., 13., 10., 10.,  6., 10., 11.,  4., 15.,\n",
       "        13.,  7.,  3.,  4.,  2.,  9.,  7.,  2.,  4.,  2.,  1.,  2.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  2.,  4.,  1.,  5.,  5.,  2.,  3.,  4.,  8.,  8.,\n",
       "        12.,  5., 15.,  9., 17.,  7., 19., 14., 18., 12., 17., 14.,  4.,\n",
       "        13., 13.,  8., 11.,  5.,  6.,  6.,  2.,  3.,  5.,  2.,  1.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  3.,  1.,  3.,  6.,  4.,  3.,  4.,  8.,  3.,  4.,\n",
       "         8.,  3., 11.,  5.,  7., 10.,  5., 15.,  9., 16., 17., 16.,  3.,\n",
       "         8.,  9.,  8.,  3.,  3.,  9.,  5.,  1.,  6.,  5.,  4.,  2.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  2.,  2.,  5.,  5.,  1.,  4.,  6.,  3.,  6.,  5.,\n",
       "         9.,  6.,  7.,  4.,  7., 16.,  7., 16., 13.,  9., 16., 12.,  6.,\n",
       "         7.,  9., 10.,  3.,  6.,  4.,  5.,  4.,  6.,  3.,  4.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  2.,  3.,  1.,  5.,  1.,  2.,  2.,  5.,  7.,  6.,\n",
       "         6.,  5., 10.,  6.,  7., 17., 13., 15., 16., 17., 14.,  4.,  4.,\n",
       "        10., 10., 10., 11.,  9.,  9.,  5.,  4.,  4.,  2.,  1.,  0.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  0.,  3.,  2.,  4.,  1.,  1.,  5.,  9., 10.,  7., 12.,\n",
       "        10.,  9., 15., 12., 13., 13.,  6., 19.,  9., 10.,  6., 13.,  5.,\n",
       "        13.,  6.,  7.,  2.,  5.,  5.,  2.,  1.,  1.,  1.,  1.,  3.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  1.,  1.,  5.,  5.,  3.,  7.,  2.,  2.,  3.,\n",
       "        12.,  4.,  6.,  8., 15., 16., 16., 15.,  4., 14.,  5., 13., 10.,\n",
       "         7., 10.,  6.,  3.,  2.,  3.,  6.,  3.,  3.,  5.,  4.,  3.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  2.,  2.,  1.,  3.,  4.,  5.,  5.,  6.,  5.,  5.,\n",
       "        12., 13.,  5.,  7.,  5., 11., 15., 18.,  7.,  9., 10., 14., 12.,\n",
       "        11.,  9., 10.,  3.,  2.,  9.,  6.,  2.,  2.,  5.,  3.,  0.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  3.,  3.,  1.,  2.,  1.,  8.,  9.,  2.,  8., 10.,\n",
       "         3.,  8.,  6., 10., 13., 11., 17., 19.,  6.,  4., 11.,  6., 12.,\n",
       "         7.,  5.,  5.,  4.,  4.,  8.,  2.,  6.,  6.,  4.,  2.,  2.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  3.,  4.,  5.,  2.,  1.,  3.,  7.,  9.,  6., 10.,\n",
       "         5.,  8., 15., 11., 12., 15.,  6., 12., 16.,  6.,  4., 14.,  3.,\n",
       "        12.,  9.,  6., 11.,  5.,  8.,  5.,  5.,  6.,  1.,  2.,  1.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  1.,  3.,  1.,  4.,  3.,  6.,  7.,  8.,  5.,  7., 11.,\n",
       "         3.,  6., 11.,  6., 10.,  6., 19., 18., 14.,  6., 10.,  7.,  9.,\n",
       "         8.,  5.,  8.,  3., 10.,  2.,  5.,  1.,  5.,  4.,  2.,  1.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  3.,  3.,  4.,  4.,  6.,  3.,  4.,  9.,  9.,  7.,\n",
       "         6.,  8., 15., 12., 15.,  6., 11.,  6., 18.,  5., 14., 15., 12.,\n",
       "         9.,  8.,  3.,  6., 10.,  6.,  8.,  7.,  2.,  5.,  4.,  3.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  2.,  2.,  4.,  3.,  1.,  4.,  8.,  9.,  5., 10., 10.,\n",
       "         3.,  4.,  6.,  7., 11., 16.,  6., 14.,  9., 11., 10., 10.,  7.,\n",
       "        10.,  8.,  8.,  4.,  5.,  8.,  4.,  4.,  5.,  2.,  4.,  1.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  2.,  3.,  4.,  5.,  4.,  6.,  2.,  9.,  7.,  4.,  9.,\n",
       "        10.,  8., 11., 16., 12., 15., 17., 19., 10., 18., 13., 15., 11.,\n",
       "         8.,  4.,  7., 11.,  6.,  7.,  6.,  5.,  1.,  3.,  1.,  0.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  1.,  3.,  1.,  4.,  6.,  2.,  8.,  2., 10.,  3., 11.,\n",
       "         9., 13., 15.,  5., 15.,  6., 10., 10.,  5., 14., 15., 12.,  7.,\n",
       "         4.,  5., 11.,  4.,  6.,  9.,  5.,  6.,  1.,  1.,  2.,  1.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  3.,  2.,  5.,  1.,  2.,  7.,  6.,  6.,  3., 12.,\n",
       "         9.,  4., 14.,  4.,  6., 12.,  9., 12.,  7., 11.,  7., 16.,  8.,\n",
       "        13.,  6.,  7.,  6., 10.,  7.,  6.,  3.,  1.,  5.,  4.,  3.,  0.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  1.,  2.,  3.,  4.,  5.,  7.,  5.,  4., 10.,  5., 12.,\n",
       "        12.,  5.,  4.,  7.,  9., 18., 16., 16., 10., 15., 15., 10.,  4.,\n",
       "         3.,  7.,  5.,  9.,  4.,  6.,  2.,  4.,  1.,  4.,  2.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  2.,  1.,  1.,  3.,  5.,  3.,  6.,  3., 10., 10., 11.,\n",
       "        10., 13., 10., 13.,  6.,  6., 14.,  5.,  4.,  5.,  5.,  9.,  4.,\n",
       "        12.,  7.,  7.,  4.,  7.,  9.,  3.,  3.,  6.,  3.,  4.,  1.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  1.,  2.,  2.,  3.,  5.,  2.,  4.,  5.,  6.,  8.,  3.,  5.,\n",
       "         4.,  3., 15., 15., 12., 16.,  7., 20., 15., 12.,  8.,  9.,  6.,\n",
       "        12.,  5.,  8.,  3.,  8.,  5.,  4.,  1.,  3.,  2.,  1.,  3.,  1.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  0.,  2.,  4.,  4.,  5.,  3.,  3.,  3., 10.,  4.,  4.,\n",
       "         4., 14., 11., 15., 13., 10., 14., 11., 17.,  9., 11., 11.,  7.,\n",
       "        10., 12., 10., 10., 10.,  8.,  7.,  5.,  2.,  2.,  4.,  1.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  2.,  1.,  1.,  4.,  4.,  7.,  2.,  9.,  4., 10., 12.,\n",
       "         7.,  6.,  6., 11., 12.,  9., 15., 15.,  6.,  6., 13.,  5., 12.,\n",
       "         9.,  6.,  4.,  7.,  7.,  6.,  5.,  4.,  1.,  4.,  2.,  2.,  2.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  2.,  1.,  1.,  4.,  5.,  4.,  4.,  5.,  9.,  7., 10.,\n",
       "         3., 13., 13.,  8.,  9., 17., 16., 16., 15., 12., 13.,  5., 12.,\n",
       "        10.,  9., 11.,  9.,  4.,  5.,  5.,  2.,  2.,  5.,  1.,  0.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  1.,  3.,  2.,  3.,  6.,  4.,  5.,  7.,  2.,  4., 11.,\n",
       "        11.,  3.,  8.,  8., 16.,  5., 13., 16.,  5.,  8.,  8.,  6.,  9.,\n",
       "        10., 10.,  9.,  3.,  3.,  5.,  3.,  5.,  4.,  5.,  3.,  3.,  0.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  2.,  2.,  5.,  1.,  7.,  4.,  2.,  5.,  5.,  4.,\n",
       "         6.,  6.,  4., 16., 11., 14., 16., 14., 14.,  8., 17.,  4., 14.,\n",
       "        13.,  7.,  6.,  3.,  7.,  7.,  5.,  6.,  3.,  4.,  2.,  2.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  1.,  1.,  1.,  4.,  1.,  6.,  4.,  6.,  3.,  6.,  5.,  6.,\n",
       "         4., 14., 13., 13.,  9., 12., 19.,  9., 10., 15., 10.,  9., 10.,\n",
       "        10.,  7.,  5.,  6.,  8.,  6.,  6.,  4.,  3.,  5.,  2.,  1.,  1.,\n",
       "         1.],\n",
       "       [ 0.,  0.,  0.,  1.,  4.,  5.,  6.,  3.,  8.,  7.,  9., 10.,  8.,\n",
       "         6.,  5., 12., 15.,  5., 10.,  5.,  8., 13., 18., 17., 14.,  9.,\n",
       "        13.,  4., 10., 11., 10.,  8.,  8.,  6.,  5.,  5.,  2.,  0.,  2.,\n",
       "         0.],\n",
       "       [ 0.,  0.,  1.,  0.,  3.,  2.,  5.,  4.,  8.,  2.,  9.,  3.,  3.,\n",
       "        10., 12.,  9., 14., 11., 13.,  8.,  6., 18., 11.,  9., 13., 11.,\n",
       "         8.,  5.,  5.,  2.,  8.,  5.,  3.,  5.,  4.,  1.,  3.,  1.,  1.,\n",
       "         0.]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### For Loops"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "word = 'lead'\n",
    "\n",
    "# Iterators\n",
    "# Variable for Iterating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'lead'"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "word"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "l\n",
      "e\n",
      "a\n",
      "d\n"
     ]
    }
   ],
   "source": [
    "for char in word:\n",
    "    print(char)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'d'"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [],
   "source": [
    "del char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'char' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-144-dc9262092942>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mchar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'char' is not defined"
     ]
    }
   ],
   "source": [
    "char"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Count how many letters in your name.\n",
    "# What is your variable after the loop?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(\"hi everyone\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name = \"Melissa\"\n",
    "count = 0\n",
    "for char in name:\n",
    "    count += 1\n",
    "    \n",
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = 10\n",
    "num = num + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = 10\n",
    "num += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Is there a function to get the length?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Building and Manipulating Arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0 1 0 1]\n",
      " [1 0 1 0]]\n",
      "[[0 1]\n",
      " [1 0]\n",
      " [0 1]\n",
      " [1 0]]\n"
     ]
    }
   ],
   "source": [
    "ownarray = numpy.array([[0, 1], [1, 0]])\n",
    "# ownarray\n",
    "\n",
    "# print(numpy.matrix.view(ownarray))\n",
    "print(numpy.hstack((ownarray, ownarray)))\n",
    "\n",
    "print(numpy.vstack((ownarray, ownarray)))\n",
    "# numpy.vstack(ownarray, ownarray)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "125"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Exponents\n",
    "5 ** 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "496"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums = [1, 3214, 123, 123, 345, 34]\n",
    "# Make a list of odd numbers.\n",
    "# Take each element to the third power and add them together.\n",
    "\n",
    "oddnums = [1, 3, 5, 7]\n",
    "summ = []\n",
    "for o in oddnums:\n",
    "    summ.append(o ** 3)\n",
    "    \n",
    "numpy.array(summ).sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 27, 125, 343]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "summ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "496\n"
     ]
    }
   ],
   "source": [
    "sumvar = 0\n",
    "for o in oddnums:\n",
    "    sumvar = sumvar + o ** 3\n",
    "\n",
    "print(sumvar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "496\n"
     ]
    }
   ],
   "source": [
    "sumvar = 0\n",
    "for o in oddnums:\n",
    "    sumvar += o ** 3\n",
    "\n",
    "print(sumvar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sum var is 496\n"
     ]
    }
   ],
   "source": [
    "print(\"sum var is\", sumvar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "othernums = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3214, 123, 123, 345, 34]"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3214"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [],
   "source": [
    "nums[1] = 832"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 832, 123, 123, 345, 34]"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "tups = (1, 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tups[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'tuple' object does not support item assignment",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-173-15357396ceeb>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mtups\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m92\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: 'tuple' object does not support item assignment"
     ]
    }
   ],
   "source": [
    "tups[0] = 92"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "mydict = {\"hi\": 1, \"hello\": 2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'hi': 1, 'hello': 2}"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydict[\"hi\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mutable Objects\n",
    "# strings\n",
    "# tuples\n",
    "# dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [],
   "source": [
    "oddnums\n",
    "oddnums2 = oddnums\n",
    "# Making a Copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7]"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddnums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "oddnums[0] = 9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 3, 5, 7]"
      ]
     },
     "execution_count": 191,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddnums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 3, 5, 7]"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddnums2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {},
   "outputs": [],
   "source": [
    "oddnums2 = list(oddnums)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [],
   "source": [
    "oddnums[0] = 13"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13, 3, 5, 7]"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddnums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[9, 3, 5, 7]"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddnums2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nested Lists"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Mixed Data Types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding to a List\n",
    "oddnums.append(21)\n",
    "oddnums\n",
    "\n",
    "# Deleting from a List\n",
    "del oddnums[1]\n",
    "\n",
    "\n",
    "# Sort or Reverse\n",
    "# Index, with Skipping"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13, 5, 7, 21, 21, 21]"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddnums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "metadata": {},
   "outputs": [],
   "source": [
    "oddnums.reverse()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[21, 21, 21, 7, 5, 13]"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddnums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {},
   "outputs": [],
   "source": [
    "oddnums.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5, 7, 13, 21, 21, 21]"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oddnums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  5,  5,  7,  9, 10, 11, 15, 20, 26, 28, 34, 40, 43, 44, 46,\n",
       "        49, 58, 59]),\n",
       " array([19, 20, 18, 22, 18, 20, 19, 22, 18, 20, 22, 21, 21, 20, 20, 21, 22,\n",
       "        18, 22, 21]))"
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.where(data == 18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# range() Function"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading Many Files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Asterisk in File Names\n",
    "# glob.glob()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Looping over Files\n",
    "\n",
    "# Write a script to loop through each file, load in the array,\n",
    "# and produce a plot (your choice of mean, max, min, etc.)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conditional Logic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Boolean Logic\n",
    "True\n",
    "False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hi\n"
     ]
    }
   ],
   "source": [
    "if True:\n",
    "    print(\"hi\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Comparison Operators\n",
    "mynum = 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynum < 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 215,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynum > 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 216,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynum == 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynum != 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynum >= 30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynum > 10 and mynum < 40"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynum > 10 and mynum < 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mynum > 10 or mynum < 20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 % 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "metadata": {},
   "outputs": [],
   "source": [
    "range?\n",
    "\n",
    "# go through all num from 0 to 100\n",
    "# check if num is divisible by both 2 AND 5\n",
    "# print a special message if it meets both criteria\n",
    "\n",
    "# if\n",
    "# elif\n",
    "# else"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# and"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# or"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Writing Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# F to C is temp - 32 x 5/9\n",
    "# Using def\n",
    "# Functions take Arguments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# C to K is temp + 273.15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Nested Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Make a function for yesterday's analysis to produce plots\n",
    "# (of mean or max or min) from a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write a function to detect some condition in the data of a file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Adding Annnotations to Functions\n",
    "# using '''???'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting Default Arguments in Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Syntax Errors\n",
    "# Variable Name Errors\n",
    "# Index Errors\n",
    "# File Errors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Using Assert"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "assert 3 < 2, 'not true'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write a function that uses assert statements to make sure the\n",
    "# input and output are reasonable.\n",
    "\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}