Brainstorm 2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# numpy has an array type, with special methods to help us in array/matrix manipulation\n",
    "l1 = [1,2,3]\n",
    "l2 = [11, 22, 33]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "array = numpy.array([numpy.array(l1), numpy.array(l2)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "array.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.]\n",
      " [ 0.  0.  0.  0.  0.]] \n",
      "\n",
      "[[ 1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.]\n",
      " [ 1.  1.  1.  1.  1.]] \n",
      "\n",
      "[[ 1.  0.  0.  0.  0.]\n",
      " [ 0.  1.  0.  0.  0.]\n",
      " [ 0.  0.  1.  0.  0.]\n",
      " [ 0.  0.  0.  1.  0.]\n",
      " [ 0.  0.  0.  0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "#create float arrays\n",
    "print numpy.zeros((5,5)), \"\\n\"\n",
    "print numpy.ones((5,5)), \"\\n\"\n",
    "print numpy.eye(5) #identity matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  4  6]\n",
      " [22 44 66]]\n",
      "[[   1    4    9]\n",
      " [ 121  484 1089]]\n"
     ]
    }
   ],
   "source": [
    "print array*2 # we can multiply all members of array\n",
    "print array * array # we can multiply an array by another array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.        ,  0.5       ,  0.33333333],\n",
       "       [ 0.09090909,  0.04545455,  0.03030303]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#other operations are allowed too\n",
    "from __future__ import division\n",
    "1 / array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "one_dimensional_array = numpy.arange(0,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[100 100 100]\n",
      "[100 100 100   3   4   5   6   7   8   9  10]\n"
     ]
    }
   ],
   "source": [
    "# in numpy array, slices elements points to array element\n",
    "# it's avoid memory copy, we have just one array and \"view\" of it\n",
    "slice_of_array = one_dimensional_array[0:3]\n",
    "slice_of_array[:] = 100\n",
    "print slice_of_array\n",
    "print one_dimensional_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34],\n",
       "       [35, 36, 37, 38, 39],\n",
       "       [40, 41, 42, 43, 44],\n",
       "       [45, 46, 47, 48, 49]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# transposing matrix\n",
    "matrix = numpy.arange(50).reshape((10, 5)) # matrix of 10L and 5C with values from 0 to 49\n",
    "matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45],\n",
       "       [ 1,  6, 11, 16, 21, 26, 31, 36, 41, 46],\n",
       "       [ 2,  7, 12, 17, 22, 27, 32, 37, 42, 47],\n",
       "       [ 3,  8, 13, 18, 23, 28, 33, 38, 43, 48],\n",
       "       [ 4,  9, 14, 19, 24, 29, 34, 39, 44, 49]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7125, 7350, 7575, 7800, 8025],\n",
       "       [7350, 7585, 7820, 8055, 8290],\n",
       "       [7575, 7820, 8065, 8310, 8555],\n",
       "       [7800, 8055, 8310, 8565, 8820],\n",
       "       [8025, 8290, 8555, 8820, 9085]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.dot(matrix.T, matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1],\n",
       "        [ 2,  3],\n",
       "        [ 4,  5],\n",
       "        [ 6,  7],\n",
       "        [ 8,  9]],\n",
       "\n",
       "       [[10, 11],\n",
       "        [12, 13],\n",
       "        [14, 15],\n",
       "        [16, 17],\n",
       "        [18, 19]],\n",
       "\n",
       "       [[20, 21],\n",
       "        [22, 23],\n",
       "        [24, 25],\n",
       "        [26, 27],\n",
       "        [28, 29]],\n",
       "\n",
       "       [[30, 31],\n",
       "        [32, 33],\n",
       "        [34, 35],\n",
       "        [36, 37],\n",
       "        [38, 39]],\n",
       "\n",
       "       [[40, 41],\n",
       "        [42, 43],\n",
       "        [44, 45],\n",
       "        [46, 47],\n",
       "        [48, 49]]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numpy.arange(50).reshape((5,5,2)) # 3d matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45],\n",
       "       [ 1,  6, 11, 16, 21, 26, 31, 36, 41, 46],\n",
       "       [ 2,  7, 12, 17, 22, 27, 32, 37, 42, 47],\n",
       "       [ 3,  8, 13, 18, 23, 28, 33, 38, 43, 48],\n",
       "       [ 4,  9, 14, 19, 24, 29, 34, 39, 44, 49]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.swapaxes(0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import matplotlib and visualizing plots inline\n",
    "import matplotlib\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "points = numpy.arange(-5, 5, 0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dx, dy = numpy.meshgrid(points,points) # coordinate matrices from coordinate vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "z = (numpy.sin(dx) + numpy.sin(dy))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x1133bd590>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I1pYB7fqT/UyOUxPZyKYE5RwzSljshNnklevrU9RhfR3r6IGrFtCY3q8WI2YrMRLba2/K\nWwyMK/7O1c5aEQKmBOxxmBKWe3auPb55+pFtWEhNHlBVgRHolvNB8nXKeSR/4NuQOKofe380jnML\ndLOGFOtx1A+Cb3McyNQsrlQhyQPERS56TpC5KAqGPWhczptY3q0OOUS7nuIUchkqse0g4zleb1WH\ncbxWjSRNnFR5jixvYIDZ24aaKkxIDwQjGSVFYqrW1smcGjwUjMSUQSRGw0h1a6PTjKTYYiTe3Bmy\nPBUjNzXV6LfWzpoI7eX4kRkD0WyDzAjwNjdont5CHgO3rJtKSBxyPzEJbjlCA3iZNw/yKcD7uZ9u\nyIPg6RX47um5CX0620qFBEp4HTK0g3pYFMJjfeCMEGM4bLkt0qEt66UFsl7HQgo1yUnomzCCMXLS\ntz1YritlJBCGEB4jhK+DXRgL7QIZzmHE0iVLGBl5ipGYOrEjtXlOw0cPI7JNHxuVCkSNjTiMOKiJ\nriZHqpaF7YCjGNnGauFw2+ysiRCQDhQGEOYaL0dwA/CQeK8An/P0keSWQh7L/Vh+sFJAIBzy4KA2\noDMTxkwYc5qEPNx694wyjTC+RIIK7B6IS3NnWRjTQCqqdNuOKuSiClkflEjiUd3abyWW35lTbkjQ\nHImst+cBKTOGxBgpKQFmjEQYkDAiqxJMGJgxkuT8EhNAmOQKWzKUn3waRjLgGJHxvhocOeEeD46R\nPQ8VRorirTGyV2xkJhwUCxEjpq7Ln9wHttuXbV5937fGyBbGAPYt4d4iO2silPBBHwAjxMZacAOY\nkODelF4A9z0MrgadDGdCngjoGPIccgH9gZMQH5oQOVOlqrwS6IQYhhmlR5FcAO5RX8f7t+Zttpn0\nWeCaDDNJ7zMZoETgivHi+bCHZeXcSUiDgTGLikt6PQ45ISXuhscjEhKzK7zMCXsM2mh9px1byHoD\neKIKwfr+r/+2mgwzJGReixG7nNbjjDnKOTUYIwYjRCHDQpS91ImTIA/uJJiLWvZxxYhjJWAGOWAF\nAmpXgy1GggI8DSM3N8bxiv8521kTIaAgjpKmMQ9xQr4nkqCFO3OefgRhzzsPd8eoCCvv34TFAejt\nX5sT7Hr62IWSAjuGO9HLzxLgEYcec4NVgZCDorKHIpOTDPs5aWWY2R9UGth/08ii2GJI3IbHhzzg\nikadTk6CibKrQidFDADb64tGfglXlD1EbslQqsWYxUhdELk+RnoRg6dNTAHyMIsRU4OHJkpwB5Nn\nIoYeRvK2GNnKbksj+p6dNRGyemwD+1yjV28W0wH4PVN1nHAPFtrshAAx+Pg9lvE9D9WfeXqZTjgo\n0A+5hH4jJ1eAY04Yc02IXU8/AXr5M1XoXr4Jf4AA4PaSBL6ACScSz0/E1uIEnAjIDIKGyMkupBJi\nVs4hgHNCpgwiQs4JIwCi0RXNoL/zkBN2JOo45UF6kBlYwmJivQ9CglEVtrnCPQ8AQYejh7wjGHfI\nTkxzx0BFiEsYKe0Hp9FCxIg5v3s8IKPgpY0Y9jw4we9V9RlGLGUS1WCMGMYsx43Oshq6oyRxUkDV\n/+CWGNnCLorwPhpDwhdpOzbtIQSYglvmTQHehsR73oWQZtnTt2rwYGGQgttDouDtsxLgxNPHUKft\nZDMfBzjZhYnD9qIpl1nEq6NgEIilBOVkyPKuahUiZ/jLJXWuUNZjoCqeWI6Q9MFPqiRj0eSQVQkm\nK4qQFjlqVTgiI3EdIgOlh5mJMkQhxDUYAeZJMIbEeyXBEgrvnCB9XAnQSLBVg4eAjUOFk5YA0ceI\ndcRqJz3rQBUjudzrUzCyhTFKU6nbaGdPhJbIXqcIS9K7BXjsjdoVoQL6Hg+411GD+xynp57ecoRj\nLlXknqfPTOAMVYT2owohUvT+gQQrBWAXxIhtDujGEzo0XrC0GoNqZVgoUnOH5Pth2x8AhqhCIIEI\nqgoZUBV3yAlIQOaMQxYF6KpQ+y40ghk4I3Hp49HM5tVxfGwraoTGWmUWFTjQOoxYO7aIkb2mPSxa\nMIxYgaSQHjlGPM+pGDnkNMGIRQyHnDxiGIOztNxgztpzuZFixIgrwhgSEyhvi5Gt7BIa3ydjEPZ6\no5Z8TXxjxAjQmmn0AB5JMHp4A3lLiEJ6CnoF9yFLcSSOW6XYwZ0JnFMJiasu12slWAE8JsGjGgwg\nn4AdNbC9V2MFvAulbAs5kFxDhqZKACdEJnlIZXPyMFlyfYSU5Q4NJISYcsKOMvYxTEYGEoQIs4Tj\n1s+fFU7cqgrPAeABo4bFiUqDeSPE62KkjRZaRxnTJiVFMpSwWCMJw4hFDAfFyshLGEk1Ribd8k8d\n5QQjuSbAkzGykbFep9tqZ06EkuMbSHJ/bceedYPnmgDbCrGHMpoXvOckaKGN5g/zrqoAGgH2qoCx\nUmzglrBRq8V5LtxZQYIB4CVMlt9aPH2NZJ+vFVZWMqw4Rce9eqwPD2fy7eKrd5xVQaqyjYWTkQiU\nCURJFKDmSyU0FnWEDFwZIWrYvM8SIg9c+ntMQcnJfd51yDDhHgFDaEAt95o3wUhbQPNCmuNEHWa2\noX17RAhwHyKGGBrHiCHrPG+StOAkKxLM9xEjG5icziU0vi/GgIYucvekA9UC9PJupwwnFb8G0DaM\nIY+EO52wuAmJTQ3eyzsPdwzg+3HwkDjnVD7UFENiI70Y0hioI8CbimA9rUnz6PV7Fvt7Jy6KkIo6\nZAJImYYR5jHZHL8LHKTDXIicSLJ41qOyhMaiDh/NArOB2GPYxGUcCGExJFx2AgtkOFKWJjVNqJyC\nKpzDiOGkhxFziK2jNFzczVddEowYEVwMVWgcMTLmhMOY+iGx3vtChlDnuBIjRorXxchGdimW3Cdj\nDV0i4NuLbXmJ+MZI5eGPkmA/HLaQOAcSjB4+hjvMdUgsHr4Nd1AXSnpePk8J0EkTdZjs073rZhgn\n/a8hRE7KLzmQYSoqkTLJt02sSU2GbsCuXjKAlOAqZyQlxCzq8JATEpXCiYfIyaYH7/w26VAIUO7d\nHUguMCE5GQ6QNn0xVB7AqvTWY8Te+liLkRg5zKVNYoHEIoU2JC6tCDohsZJZzAseI8EKIwEnJ2Nk\nA2ONBm6rnTkRShOK0VoUcwl9qn7ivHlFeXHf2n7t0av61cWR+LlOmd55SPxozAvyIJ9i7Hj6WCXO\nYyrNZdqQJwM00jwJNsMqGR5zPwvevnyPOAxTIUQwypeKQuXaGlGz5QsHBkZZVrJwGZkIKQPjKNNt\niIxxUDLUEFdNFKFN73EXV+Uc9Bkqb2ccvEHzHYwYkXQoanEA4woH7AGvMLcYiThZi5G6iLar8DEh\nwWzKUBThvXHwItq9PFQYyUaKY6rSJo6R8USMVITYYCTiZA4j9t+GIm6uC7ZTjYheDuC5AN7DzB/f\nWf6ZAH4QwC/orH/IzN94k2OeNxEy4R4GDCGRkTsgL11jxdfigqcPDaNbEqyS36GZQ1sB9GFDgiUk\nLn8cwp1+cWQFwGMeqKcEF8MevX4xR5hrhchWIlECImsyo8UUBoOoLp5w2D4nPXeahshIWRSh02ep\nIktYLPlCZEiOMMGLJ4CExvcaaN4h4B5L8xjprOEAaHHFQuYljBhOehhZIsE+AQoeJhjhEhLHKnGL\nEf9ucQ8jTROqVRiZyxEew8imOUKpqm9k3wXg2wC8cmGdf87Mz93qgOdNhBay6keVAPiHdKIZmAF0\nwZ2bphA2P3r6u/kKh5x0XvIPdBvQ76n3H3PCXj/iPmYlzqpSnJq8oHl7BfhYSJBGqkDs8zNNwE0t\nyMtFqi0AvCTGyx8nU4zkBRFXhyNp2Cw5QleGdiCSHKHVYFnfEZZ8IeMwlqqh3ac7AA7EwFigVn3/\nOOYKgzK8QukYa2RCJqlQt6pQvi9MixiJ3WTNYUQcJlUYieRn0UIPI/fy4GS4zxI1GEYOY5pUiTOT\nfLxdP+beK6DRSNIxD8uQJjhZiZEeGfYwckNjbFcsYeYfJqJnbrKzlXbWRJhBuMtX1Zfl7Luy8aJH\noMd3h40ApWHszqvDrYfvkaAXRxTolvPZh5zPYRy8ODKOSoIR4CN5aFkaTEOAbdMGZgV3BLsP0QH5\nEQBb8UMmwh/HYQyJISTEKPRnYTJTOFwpnghvSfFkHBMwZOScsEfJF96zzQZ4Exu/b0n3Y7F8kmp7\nphT+SF/RS7jCiMxCiAOXbyLbN5IjASbka2EkV2S4TIL7UBwxR2l5QS+gGV5ajMSQuOcoj2CkziHX\nuFgkwTmMbGCbfpbguH0aEf0kgF8C8NXHPgt8zM6aCNlCFcQPa0/bKhngI7it8899k+yORHg3X3k4\nbAC3vM+9POCeKsJRQ2IBd/DwVbjTK45AgDkGL5/XAbxbGTymCgMOq7aDUQ1yWM/XN/UHV2ZOhjFM\nJvjrGazbe/tCIpgyRE44jPqltpxwIAZlRiIJh6WpjTSt6SrDjnkHr5DearKrQhmvMTLFyRxGStvS\n2kFmToIPjUqqRtNtm9JQHClDWsbImJwAJ46yxchY1inh8hQbN8LIDe2EN0ueTERvCdMPM/PDJx7u\nrQA+mpl/k4g+H8APAHj2ifuo7KyJMINwN1/pN2/1+8KLiXB7vSlV4C5dJ1Fp/hDyPZEEH827CuD7\ncagS3/uxTnyPY0K2BPgYE9+Q3E5Lgjrsgr5JgvcIEViX/yGUHKEBnrjMA8Orx7JPqbxGNVEC41hA\ngUXGGg4Bhb1EGXKSD9vvxwE5RXICcgq5PJUyURmOkCY3IyfpritJpVfUIOm3hjOu6IABoj7928IL\nGDGc9DCSOVaNhwlGvHgWlOC9bEWRnWNkPw7d4sgEI2Oqq8LjjBJci5E2l3wqRjaylR80ey8zf/JN\njsPMvx7GX0dEf4eInszM773uPs+aCJkhrdV5cOXQzRGyhWo1uFsi9LZiAeBWGKnD4cEBHosjHgoz\nHan+AbEdWCRBrCHB6O1bIryGt7cQyPODIt5Ccw1ZlqDFEVs+NmRIMWeIojRJutjCmPSYGQdK/gqe\ntSlM2o4w0eDFkVYZIgN3ceUduZpKzCydu4r+k3s7cNb3k+naGIl55Eh+IyfHhxdGJkqwX0A7jNrU\nKgtOJhjJ8CrxrBKMfyGXPMFIRxk+NooQaxXhjY2IPhLArzAzE9GnQND6vpvs87yJEIS7fAeAeH2z\ngXLVZil+UKnqMJPThPzia1JLXj4qQssLRoBbM5mcE/Ionr7kexTg45QEo9evwpwYKveGN8n/KLG1\nitDaFCI+QGxyEUXo2TxVhqXthZ2GNqsZIAUXFkI8AODmY0qVImTtsj8oQ+m5ekQmCbdzkmudwBgT\nYWD2HKEMs38sfSuMRJzEKKGHkVgcOYSG9dFRdjHCTbQQMIE1GFlwlo9FjtAKm1sYEb0KwGdCwuhH\nAHw9gCsAYObvAPBHAPyXRHQA8NsAns8885L5SjtrIsxa2U1UXsUakL0pjK8X2ohFgHv3SCB/E8AB\nzsW7W3XYcoKmBJcAvkiCRwCeRuqrwF6OMIY87un5KMhFrVEZt9xgr2jihGfHCEWUaochwRhOoSJD\nJIAZI8Vl4Z7GZi4WEtsQhEz6jRMlwhESIg+UnRAHyg0ZcoURQEjxOhgZkTwfaBh51KrCRzBib45Y\nODxLgrmJFsxhWj4wH8dIWyyJGCHv738BH3ZLN5KE0j/lZlXjFxxZ/m2Q5jWb2VkToSjCnQMdQAV2\noAa4TVsXWubpeyowc1oFcK8Qx1fnlAQ5QwDuzR9iqFMKI6YCJwDX6C95Mrz29nXTCJ7kCoEm/EGt\nAgkcFCCVkHdSLNFttUeasHeAuByO6pxhpQxDpcWUoTFs9YW4YZyEyVWNo2lGgwyvHI+UvJPXxFKt\nTtC3SvQNFbOIkzUYaVWgh8KhKmxYOeYoWxIsTagCRmK0ENqMevXYVOEcLlZipNc8psXINiZv1txW\nO28iZFWEqgJ6VtqGkU9H7x6/mRFVYN2TTCmI2DACfJr07gDck96Nl2/DYSXEnnefthergb3qFbuG\n5PwdYn3FzgkxKsKwfw+VYUOqlSHHjVGU4WirBWUYGK13qlERZg1rMxPGNOqwtB/MJIUSGw6UkbwJ\njYbGJ2KkJUDrUKOHEX/HvIORVglOogXDCXcwEhxlVSgZpxhpleFWGNnCGNspwsfCzpoIvUdjsOcf\nIti7APeshPaHAAAgAElEQVQcEAWAJ/fsRpI9Euy9NVKUYN1gGpkqNegAzwHg0XuHJjVdgE/Azt33\nSYFmvDHjOp8AigokUQ1OiIH0VKDJfq2XU9tNeJdXuHGFMiTpM3oMZNh+O9jCZGbSY+6Qk+UNk4fK\nV0naD+5SqRwnYlzRCHtneSuMWK85a0gwvjXiJKgNpnOMFHok2EQLxxzltKVBQ4BNntDu6WqMbGCX\njlnvkzFL8xmg7sCztZgIr0E+Bbd1nxWrft65pjZ/6Hr5TMjj4ADnmO/pKUGeAtzag6U5VZinBLgm\nES7kVhBtvGRgp6xKz8iQID1NZwCJXAkmU4TAJG/oIajfnFoZxh5rpFdr7btwQRla05pKGULu4U6/\nijcG8suQHGEiKZAcrCJtxZKNMGI9kBtG7o2D9ysYMdKNFvytkTpi6CpBU31VUW0lRiwvmDFRg9fB\nyE2N9b7dVjtrIpRiiXbhtBrkFvaQg9s+ImQAv2fNZkLzGPPwbTvBbmEkE3CYCXXacHikaZgzNh5+\nFKC2uaA2ER6LJC3YqYzIbIpgl3OyJjRV1bj6WFPZnz1QORwjQbvqH2zWTJjMgL+Kx6jC5CrKtiYt\nOtzpw2Shck6SDzyAsUsjDiydvSbK0ut1IMXrYsSihIgR6136njpOe7Wy6pB3JhzmDORDKimTsckJ\nNqQnw2WMeA555DrK6DrKIxgJXOUY2cAYwH67d40fuJ31mTOAe3mHBMYBdTLcLH5Iu3xPlyZkGD28\ng7khQWsIW3WiEAA+G+rkRgkeI8Ewr/LwbR7IyZDXv0cKoGosS7oBAaCi/qBDfxA4EFrYfQKk+JFt\nqbUnDAfrhckEaTisG7Y5Q717AAZwaHRtr9v5UNWhFE1YvoNMhRDByUmxh5HYyHcLjERHae0ETQlW\n4bClTDrNqGo8lGjBo4iuw1yJEb1xJ2FkEyvd4N1GO28iZMKj4849PoAK6E6C3lh2CnTrTca/McLk\nYU7bseqSEvRweI0SPDS5nwjwiggbFZgLuD0H1IY+cmEWX5b3kJhK3k4aU7P3MMPJvpEBYBB1mCHn\nw4MuG4QfUxgHijIsNqMMATBLcxoMpWmNNTeT/vkyMvtm5ZU74hAiZxwoY5cyEkmvNntiXGk+UZTh\nNhgxMowYmbxfvtSMKrw/vFoJdsjvKEZyUX8TjOi9WcKI4YK2aj6D2uncNjtvItTQ2EIhmdm8uO/h\nVQh9ArgtDGo9/KEtiswAvCqMzClBK4y0gD4G8E5l0L17B9yrGsyGcIddCUI6TiCE5jNcCiZ6WZNf\n3qk6tHySNflplSGf0rQmS/ddgPRliJzK2yApA+OAIWVvSpOJsEtyT3ZplLwgGHkkJ0Ajv8El72kY\nMUU4hxHvRWaGBKtoYU4JcsRDiBa4xYZN8ww+igKsQ+OVGLFbQ0srnG6PS0VIRM+A9Bf2FMjVfJiZ\nv5WIPhzAawA8E8A7AHwRM3+AxPV8K4DPB/BbAF7MzG9dOkZm4F4epI871F2w+zreNqwkay3vEz+z\nGcmwEGCpDFtVeLYh7FITGQbo0Hj8lviqIfeBHwhQAN6CPUgn6PyOGQ+Q5QdJSFG+USxqUFQhh+/o\niuoreUIueUNVcCnsGwwk1s5bbdmxAkrT6FpUJoGHjKT3bswJu2GEfQkwJ3J1mIirPGEslJyCEccJ\nDydjJJIgazh8tDDSRgsL2ChFEp4pmCxjBChK8RhGgq+6sTHT41YRHgD818z8ViJ6IoAfI6L/A8CL\nAfwTZv4WIvoaAF8D4M8DeA6kh4hnA/hUAH9Xh7PGCkwAVejTWg4ev/4WMZXmEurhGcB+HOSD5P4N\niSMAP9ZEpqcEuZkXSLAFeBpDiNP19mV5BXaUaTcLfTXk8U93KinKq3as3zTW7ZmlB+t2f528YbVc\nnyRTnpJzKspQVg/jrTK0HCKkC6+DKUHYEKVinEr7QWlLmFwZmho8hpGYT24JMGLESHivWIiOMr5f\n3lWCbROZNm/cYmImHxgxYsWSGifbYWQLY2CzV+weC7s2ETLzLwP4ZR3/DSJ6G4CnAXge5D1BAHgF\ngDdAiPB5AF6p7wT+KyJ6EhE9VfczcwzC3fGq8vytFRIsQ+byEXZ2Qqw9vH+YPRsRSjdJDvRTlGAv\n36PevUx3VGBUhxHgTYhcVQObXOHE/LU6exoK6IlUhUm5WKvG9kW1oA5ZCiRSNeY6o94ekkPTGj6i\nDFlVp5FhMrUo30IZhqy8zMis5JVypQiNEBNYe8TmogZPxIgpQCM7BmYxUt4d7mBkrrH0Ut54Rgmm\niJNOnpBaXGyBkU3s8s0SaG+yvw/AmwA8JZDbuyGhMyAk+a6w2SM6ryJCInopgJcCwENPeaJ8+tEa\na3RunDfBCO2Y3NMzObhlHoWqcOg8wRtKN11pHQP4ZLxDgkZ2kxxhAH0FdvXsY+Pl5cdq2zG9Hl0c\nc2kWkWL3+lxIMdmfEKI8PKYQhfiSjsqQy+tTMfe0K9MJJUzWO4kq18j1pswJNDCYk34iVJQhs4Ts\nSQspzELgLSEWErTQ/D5jJLYRzKErrV5OsIeRLuk1jrLBSJrkCBsCDBixeY6LEzCyhTGKo7mNdmMi\nJKIPBfAPAHwVM/96rEJpNzknuR3tpPFhAHjix34kV6HxzJ7iGwoW8tiNKV+ZI/8GcZcEDeCRBCPA\ne01k2nB4kudpvPkRgBu4HdQGeAV23YQmXAwbpTJw1QfJD2IgQDtXJSU7yiFPZ1+oMxWoCs72nyBk\nSCwiGYC8KmL9Eyrhri2gxNMWCpS3UJiTnEtiV4cpZTBLuJyV/EgxYaS4JUZcMbYkmKnuSmvu3eG2\niUwn/D2JBEP7weJUYyh8M4xsZY/bN0uI6ApCgt/LzP9QZ/+KhbxE9FQA79H5vwjgGWHzp+u8WWNI\nPg9YlyM0YBvYDdz2IZ2cRXGMnge0r82h7l5fQT7pRWZFqNMFuIO7UYdKgJ77CaGyJ8FtGYchAG87\n3ALZcoNJ5nuxJAMglqYxUawxAYOGv2zhrRGjFhpc9bGqRJIcX+eGxaY1BN3/jDL0cTYu1SKK/4k6\nJCb5gHsmDKmoQQqkeF2MxM9sRhXITBg1Iqi+QxOVYC9aiK/NtThZihYWMOI5wkNwjoaRMVaKV2Ak\nvl2StiPBx+2bJVoFfhmAtzHz3wiLXgvgRQC+RYc/GOZ/JRG9GlIk+bWl/CAAL5YQybdre0BvPT2A\n+huygRSrL83lQIKVl0fx8jkowfDu8KRNIHeU4AoSrOdxaTjbEKCFPIjeX35096ppo8DS8WZ4vQ6M\n0jdhqhgRRa2V8ba7f8jlkSxfD/ejrOxVat3JGmUIADlJCxp5hiWXSERAytIEyBSi5TyphMRrMcIN\nTlZhpOcoWyWYGyXYthtslOExEiyqsMGIh8aNkxzZcTGPEZOLBSNb2VYfb3os7CaK8A8C+OMA/i8i\n+gmd9xcgBPh9RPQSAO8E8EW67HWQpjNvhzSf+dJjB2AG9mPyNMZi/ocRAJ40rCrg9mGmKsxBDIfn\nAN6+O9xpCDunBFNTLHGwVyFRBHwMiQs5+gWpPP6MRy8XDB7LkhAkJfaHG0lDZjZihIfKViQxFRhz\nhDaPnOk6p2AFFL9RM8rQfxoByHIvBvntkiNMsAiOCBIahzxhCYvXY8RxwrQeI7GJDGMaLeQmQugU\nz+oIoo+R1JJkPgEjoUp8EkY2MGZgnx+HRMjMb0R067V9Tmd9BvAVpx1DGjqn4PV76wDwnI/N8+8L\n+zhmVOBKgLeJ77UAH6P6K4RImSXUCeAuzSIY8JAnKsIyT39o/8JlU3PWiJpqUkwohMiF/KxQgoE9\nxLXQ2HKEMW/ou5y5fxMyjK/jsXb7H9olMhIosYTJCR4qg9gJ0dRgNiK0Y52AEZ8+BSPeggCYfG3u\nmKOcFEi4mj+HkXTgOlKw7QwTASOCXbkTp2JkC5PQ+HFIhA/KIsGBqQrefHYIfUoTEE38a0LbPDx4\nJQk2oU4dJqOEyW2oMwmFppXhWYC7t9flCu4C9hIi0xzAEcKdpESRScgwlWIJ27Vk/Qvq0AnLFSFZ\nlwlChrr/NGoBxcLmJnwGFTK0s6VIhlAy9Dsq9wzhLyetGjMreQshEjFSKuS3FUawRIKMkDJBwUgk\nwagMA0YmJFj9nUCCPs4VAVYYUc+0hBFAe6PZME/4uHyz5EGYJKxT5eWjA2t7MqlBDgG1/WV9GNqq\nX/TyVV6nLYY0HShUCW+gqvwF0HcB3gG1e/geAY6qElsvPyPFyHt3FiVo5CevthHYCI/hr9hNlV2d\nPxRCmypDn27PIbCRqI4ZZQgphniYHENlLZpQUoU4CimCGCkVcoshcitwTsKIOslFjFR5vzKOFjOV\nQgz54zYnaBjx1EknJL4uRuakuty44iQ3MMbjvPnM/basD280IvaHAIj3vgE3MFWB3AA8evlAgl44\nqUiy9eStIiyg7ylBA/MSCYoKKB7fFKIBm1oy7F40OAECQiTgpGGyEA+SXgv7G0rbQe35TMhxBxhx\nEYkabAsoRBYnwxWhrcNQooDE0X7WTQGFiOQLevZgJtOLKCSZdH9MyBoyk58PVSR4I4zodNdRBiUY\nMZIUO1LQmmKlaiztWOmowxUkGCOHbTCyhW0XGhPRywE8F8B7mPnjO8sJJ76ue8zOnwhHLXEievsp\nwA3YMq+EWK4CG2AbIVZePpBgITVyjw8OYF1oAmHTlac/cA3wA1dAR2bx8B0CrIAOlAT4HNC9CY2y\nBBM4ZyHETOCUVD0E4DIcDekgBMggpAMLGRK5+msLKFLbNfWopzDG84kj5Zw9MOYQJpPuR9lPlKEc\nxAomTKoQlchMJfYwUl2iQH64LkaCEkTAiI2DY/EjKsImL+hNp0wVGoaCo7wORuxHn4KRjWzDb5Z8\nF+TjTK+cWX7y67rH7KyJUO4ned5nzr8ZsG2bCbg7KrDkBI8DPL7zOQmJY36nCm0C2Ntw2AB+yBpC\n5aICRwbl3PH0YTyA2wspdi1iNVC9vlSLKZDgKAoxZ1eHPCTQAcJqg75RMgi404E1yqIqFHbV56qs\nkKG9RVzdJFVusCY0YyBDU4ZJm9gkkqY41neixPelKRCX49r9n8OIz1vCSKsCQ6TQYgQNRqKjnGCk\naiXQcZTVshkSVPJzPKzBiP/QFRjZwKRqvM27xsz8w/q22pyd/LruMTtrIgQ0bAGw5Gw8wQ4UBRGK\nI12A67xeqBOn2/CmTXL35zegngO453oagFu+J+cJAVbNJPzHFyMOIIeCvnzByRs5s7bPk+wfIJou\n1eINRjT6sKtas1CYbDqH+6PLnBztz8JGhHneibWSIcUCipJhVhnoy8vPkf2w/9Y5jJSoYQEjzXCJ\nBEsRpMXMzTBSF0bqsJhGwwM2x8gWxusbVD+ZiN4Sph/Wt8lOsVWv655i502ErO23yDFcFU5iDsiB\nbQqQyzwPc3oAj+Ewd8DayfVEr143kSjA9pBn0kRGlWAD8GrIDIw5hD1lXH+4DFe0ESNlpEKIDAxJ\nwspkNd3keSUnQyUtC5N1h8COvfjhW49UhEXh3ECOENLT0BGtduNAhhYm6/3yTwuEzVj3ZyRt71Fz\ng49NMdI6xvY1Oi5YqDBShcNNMSQXjHRTJlEJXgcjc/hoMLKVrQyN38vMn7zZQTey8yZCiCIsyfk6\nAS4rxHBHlwVQI4C+B3BXABUJhuWYkuOyd6+Jsuvl9by6ADcPb6GPjduPzA3YuxctgFwJsCJEXdbQ\nmZKIjGNU3iGS8zISHcv98O8gUymk9IonnG1f8HtCmcp7yfq+MpPtTwsnQenHRuHQogqbIuRwTr1n\nscVI3OdajMw5yjg/LJ/DiByDJxixMJc4YOUYCbYYiQ7yFIxsYAw8yKrxya/rHrPzJkINVcr11YfB\nl1NZz4YdAqwAzjXApwRXwp0q8V0pxJr02l5kaq/fANsS3nMkqJ7dPXwb+gA16Htm4E6pyLNIiJnB\nQ7IrqopwBGMIbc+S1ii0I9VDhiXsxEGQtyP0SrKqQanJsJ+H5Qurpk8obQydDEdtYE2sr9Pp7U4Q\nYkS4t7oe9Pg3wgjCvWdMSLBtExhzyeCAizZX3HGMFZ4CRryYdliJkVyKJq06PBkjG9kDbFB98uu6\nx+y8iRAoSgAIsiKYA5xmwe4A75GgPRC98CejPDDu/ac5n6IouUyH0MfVwKiKLlb95gBuQI6KUOdx\n9Pw9U9VHVmZtCTEl0Fje+XAypKAME6tKKzlBZnZiAFh7upZlJW9YlJ3n8diUXpkuClArxcqzZMvs\nfWi79lZRTnpvLXdo+zsFI53pWRJUNdtzlNGBlv0U5TdRhQxVcMFJBgw6sRlGlhxlixFmYNRS/akY\n2cCsD9AtjIheBenT9MlE9AiArwdwJcfh78A1Xtc9ZreACFG8VzfsCcP4AGR4nmlWCVZg7eR8Ks+N\nKbAd0FPPj2oZlzZg1vxhjgTNs0cPrwBnB70S45GwhylBvlvcECLkmsZMXT9MRkUw3iSG5D9vuO2K\ncCFEtu0CUVbzdchKeoB15xraQ2oxxavPFQHeX4x0HWWlEm+AEXuLpMHIUUcZMZIz2B3miRjZyLYK\njZn5BUeWM058XfeYnTcRRmUHoAp5fJ0m3tIhhfyPjc+TYAPmFuDcB3S9HjvAJyFxExpbdXiRBHOu\nFaCBu2ojlvvXzcCdGGCqCXEYZP9DkmOGy1aRYYY0lrE8XkZ5Q0RfMrYQGcTaPAdViGzkGEnKm7x4\nvKz3xwgudciQ4EUR8lA4EGK898cwYuuejBGqsAD0sdMlvS5+TNV18oJLjrIlwXG8PkYSATz2l59o\njAeaI9zczpsIAfX6Nj5zoSMBVvmfDsDjss4D4evadCQ9Vw72QHAZ5np737c3a0AJibX5QwxrPCyK\nuUFm8Jj74F6sCJoy0EZ4iSHfDwZoHAsxKRlKMxXZhgCpJo/SxZWE0Fo86YXIWsCyUNhf5Uvl+s2G\nyDrOgJxTLvnCigy1KMPenAblTRQO+9sCIwEX5T6HecEBtpgxPEwwkIE2JK72Hef5u+YZVkSpMBJI\ncRYjRn5LGMEIb5m+kV2I8L6ZdInFHv50VrFFbY4wkF5LZO7Vc/0QuMdvpqucX8+72zqqBhE8fDfc\nYVEBlRJsAM5eJNH5gBNgyf/MeHvN1Uiuj5QQR1eHNKSSTxo0Xzjoqxt67kx67lYtZh1mlBA5oVSR\nQ1jc5giNHD001mvIQyDGXJYhy/NpVV7KDRnqjSbbJ+M0jBhOehhpKsT1GyZoMFD+akJsMNNEEG1L\nglI1zn79i8PsYCSGwlEF2j1dg5GU9P4ukeV64/XtCM/SzpsIDVwh/OFw46gNi/1vBuCsz0n09h3v\nvuTpKyXJqD09c7OcJ+vGIklsBBtzPZVnZ54nwLn8jy7nlOS3pSxkmABAvwNi22c5ORqleyubNrLm\nrKxELO0AqVxbVvLy3mz0J8RpK3xUCtvIkpWTbD8hRGY9LJT8iDVMznIObL2msG6rGAlvM6/GSJw3\ni5FIoC1GAunNYsTwYFFExEgu80ukUPAy+dNweEKCLUaWCiEBI1vZhq/YPXA7byIECuAszdTzOhVA\nFwDeI8fgqdd4+sqzd/dZ2oPNefpuuBOaQlShTgR41VZMgdyA3TuoSKL6WBNypO3tJEc4godBwmSz\nIVSSk7QdZGSAhlJFZpIH1YhMQ9VCenWu0AhQyIv9PsAI01RhmGcVYVeE0PUglWn/ZnNsR2j3nmbw\nEXGEGiPlHjd5QV8WMNL+xZA5zgvOs40YYMTYqEHHgznLgIleAc0xYsMbYGQLY4Z/evc22vkTYWw+\n07MAbmBKTC0xThTiEU/fzxlx8zBwRaYlLJp6+vpF+VxIMGchKFOAcwDPebFphLdgGEcBfJI8oKtD\na4Li4aaxVlQcOi+T9gGox/JGzqb+hNxorEnPcoUxdJ3kCo0MdZeuCitSLKcm6xVlCN+VkqvtaCE0\nbjFSqcNWzeUyfxI1TBzkTMTgqg6eA2wjBl8eMFLIsdOKwO5/L1pYixGwOLu5sPmadgmN76PZA1DQ\n3FgENzDv+cOydt2Jp5+AFQ2Abdu4rABv0jYrePro/d1zx/VzmD8H8AjutipoXjlpL9A512TIGiaT\nDcM5kRA3x9BY83um0CzU48rBGBHoMUNjaPZ17FpOO1+dEiD5PjmSIcLQ8GEQqGLtdRixc57DSEuY\ncxhZcp6tw3TlrIRnIXPEAkWMGD4maRMdHsNIr2pMyR3hVnbJET4AK2Q4vzyqup6Sq6dpFsi9vx4x\nloch5oaCMmhCnqN5n+jpNfczC/AAbm48PlUVYxTVp2Qo7QlNnmm+sKcK21yhD1GUoqq4qmGykVdv\n2Nyf+GfL4nInzGjsOrAiQ/jcGXzY/mcwQs38Np3i2y/go4cRTLaP6jFcay7k6BFDrpfDHNMJGGnx\n0cPIVjZ5/fUW2XkTYQDR4iXuADy+WhU9/WSbpf2gfhBobh/tQ9WGPLDtdcJADgTPzWjVYJl/BOBe\nWSjzKeXK8ztZGHGHELlVhX5cezDj8hSuK5dLQFYUQT2/Wm+GSEwVRiKtVGGIsGOE0MYJN8GID5t7\nZr+nt5/J/qt7Hcd5ghFfx+ZZPjk6I6AMzZwUQ0rlphjZyC7Fkvtp4SGZXOcGoK3i64G2VYKVN8c8\ngCcgj57d5gXgVSGPeXmgVohr1CAwD/AY9sRxSuDMNdCjKhy0oR5C/3G2HPDmKnYNTGkVkrKwlcI8\nXTc6IAuFw/3huJ+F+RNVGFjPw2GUbbfESDx+vL/9P56sU/YZWw2U+09GeCgYmaROdPtZjACIRZAb\nYWQDk8NfiPD+WvMgdJfPmN2aLuibed2Qutl+ljDjeIOto12n97y+j9c7mwC83ZZK2xMHek4lRB4G\n2BfkRAFqeBxDY9tvZpT++6HvAgdys3MdWmWleUI/6fpaVMTavR6oCK3KFfo6palNtc0CRlql5/PD\ncaJSnITUvfNsxydkWP8Ot+gcu/vm6XiFi3ifTsCId9NTMLKNEcZbXDU++zNfC8ZuCNvbtgX5KefQ\nHBPCIzrOTQjFqEPdMN+sDYXN4wNNM4jmBJbeI+XmIZhbvvQALD2gfu5l1JURZsJIG++QQs+xLFpn\nvZMJqw2Je9vOOc0OtubO3X9bxvyxljDSqsM4BCYE2FV/7b2snN22VePyEaz5v3O126EIzRqlgN5D\nNNvzyMLDtvAQzpLg0rwjRDLpQXjBOIC2zvms3D4zqO1B3cLjuJ9IkKmZ781o6hsQ1V202RyhTndJ\nprfM3mZpw16b1QuH12CknT5GpCuszR/31ScvYLC+D7ToqELzGDMLl2MxbSVGtjDG7Q6Nz14RTmwD\n0F5336u6LLrf2FvjxScqYE4dmvrsqI3e9AOy9eowknKc3wxPPPYp0cKNbKPjTEhx3UbbHNz3V8Tm\n0t+52u1ShI+xsX+C8jE0SpuHNBcrVt6PfqzP5PbZpWr8II3C8ESw8jGAUzO8jp0DFkJX0PJJzxnh\nT/rhj7nvV/irWPfhHBesG2G1GY/4el27TQcji/c+hthbqTRaAYWFFaTT24VNqbhk76fRF65wlht+\nqwQA+FIseYDW3jvLeVcPAc8/FGv3G4xp5iFr911/XXzhYCgfUlph5N3sp/obtNcFsr1215sPTEnR\nvoZHzTknndex9npNrh8V5eWv3YVlZZz793zufqyZBqYYOcVmtuP2vOIy78C280F1w0JzveePHxxb\nvI/W4/QWGLmm3ebQ+OyJkOOD0bNIhp0HbrKtPly+/poHB/MPd/WN9GodcpAzUbnSFZmEh8DHk3en\nPwtkexBacqoeqJo4qSXApQ97HyPyuDiROgvqk14cP+Jwji2brTNsjRGdXibp8jf3myfbd9aprMVI\nq9aPfYw9Kv9rYOSmdpurxmdPhABmATgLshgy2XDJYzeA5oXjdDu/CZ6+Ir3J7wigXuutjbxaVViB\nvgV780A0YC+9j+g+jSRbZZKoKMGEQni+o/nfObmWsOlmWTsM96JLJD6f6/txCkaAZYxMSswzv7NZ\nZ+43+3EI5V4kVPeM43VHvWyCldQSZCr39DoY2cBE8W1DhET0eUT0s0T0diL6ms7yFxPRrxLRT+jf\nl930/M8/Rzh5kMKimNch+NsJth6hLPN1dH9Vl/KsQ6qHiMURKvuUdfRzlPHeJnL979sTVZ+ftG6q\nZJ9UgMwM6VKfgZy0y6nkvch4ZZASKGlTmhkwVw9CoprojFhjfrAJi3kmPC73g/zBrcmLqmvckubq\nlMWEtFDeHuntbw1GbJlNcsBIe6wGI+3vrP/sQ/dlvYKjgpFYaLPrJGSu9wesB2TFERV8tBjhUR2Y\nYsTaE3oP3idiZCPbovkMEQ0Avh3A50I+3P5mInotM/9Ms+prmPkrb3xAtfNWhA0QJ00EaQrQ6NX9\ngUTZzyT31Hno2mMshVMF1FERGIDjwxpIhcIH13vhMVAILHr8AF5KNAlrqnkdJSiHp1pRlAXV+U0K\nJW1o5tdaf2cK16u5Vr37NPdXrleTwqiOyZNj2LFnMdKcz1zkUPbLs9t3zzn+hWPLkKbrhvMXpYji\nYMzJRDzMYMTtuhjZ0DbKEX4KgLcz888z8z0ArwbwvE1PtGNnrwirB2NuHUUW+bSNcwENakVnwCRg\nAmqi6TxYV/FEFcmSkoD3nGwqMQHICkxXgygHaP86qlB6ibHviiRQziUnqRU6GgCvEEZgG5ECrgZp\nGDwctnzkJCye5CzLw1mHyTTJD5pCiuQ3pxh7f3PEaY5sEhL3yPNEjLSq0BUdAnTCebRRQ4ws4u8k\nc7Y2r4cRu27HMGJRxFLkcFOM3NAYhLyuavxkInpLmH6YmR8O008D8K4w/Qjk28Wt/WdE9BkA/g2A\nP8vM7+qss9rOnggnlcOIZDf13qBZoMd3VckeqESlK6lIbEZkLDswUBcw6zjXoQ+IgSHukx3I8o5u\nAucsHplJu6CnOhRKBP3aeR/ogPYtGH9//epIRYAyoyZByw0OCS3xcYcALT8ov9vWmT787HlE+Dqt\nEoMDrfoAACAASURBVDqaf50hu978Uo29AUYSgzMVjCSdnwFOpXPZoymUQHxsrVfsOnCNEUraJ+NQ\ngNlNoThG7JsF4V4xqVqXnsZbMgSwiBHHyYZd9a8sCr+XmT/5hof6IQCvYuZHiejLAbwCwGffZIc3\nvgpENBDRjxPRP9LpjyGiN2mi8zVEdEfnP6TTb9flz1x3gJm/1JvPzcM5nTenOvzBCsN23fJXE0FP\nEVXjbfW4pwjbEDiEsBPVpuvN/SGlav0pCcZjJPmaHZF8wCkuGyIZ2lBJL/w+v24z13ju+s9uE65/\nrQbb9bmQ4EYY8TTKkfM/5fe0GKn3GbFUrvFRjMQc7zDUGNH7v4QRwcRCy4RTbbtiyS8CeEaYfrrO\nK4difh8zP6qT3wngD9z09LdwB38GwNvC9F8F8DeZ+VkAPgDgJTr/JQA+oPP/pq63bBFQieWPwl/i\nGvAJQArz7aFK7GqlqzxsGep17KGM6iY+9K6AEsBDmD/UpMcNwDmST0tMBmoD+jDIvCHV4LXx9m8Y\n6oemJcGk+4vbtA8REXiw8yFXgexVZP2NqTzg7L8V1bVGgq9XrmeZj/Y6puae+JDLdFgPqdz/UzFS\nO74yPcFIajARz9lxQQ02qFom1ywoar2WbNd3CNc9JcFIq9KtWZXiwe+1kaFhxObPYaTF0FbGK/6O\n25sBPFsF1R0Azwfw2rgCET01TH4Bav65lt3oKhDR0wH8JxBWBklM9tkAvl9XeQWAL9Tx5+k0dPnn\n0IoEhQF71hOnBvAVcaLy+pWHboC81uO3irGEiXXIaARaHgIDdSoef2iIKISqE6CHCrATYiS9QH5G\ngNRs62F6RXzTh42NDDtqsFYv5RrUBFHIsSXEnsIuRDWT/4uKLdzLss46jPTUYX1P9fhA+a2BpOO8\nqDp7ijiSYXtdpsqxwYhd6yW1buq/qv6vxIgu8whiI9tCETLzAcBXAng9hOC+j5l/moi+kYi+QFf7\n00T000T0kwD+NIAX3/Tcb5oj/FsA/hyAJ+r0RwD4oP4YQBKdT9NxT4Iy84GIfk3Xf2/cIRG9FMBL\nAWD48CcVsCwZQaS5dktCTNpEhf1VJcmXsH/9jAABcWx7I6uE/GCYx9qERefb93X9W7usxMuyL2Jo\nl2/aWSmjzhUiy3kqyMk+mDSobxpH+VnD4P0GlkRVhvcnOExzP7Fy2DbUpkoRyDgbIdswQVVrUCzx\nz9RgIlE59tBH8qjGozpCRR5FMTZq0dcNSpBkOhKgY2MFRkpPPgUjrF/nqzACzfMmlFwhk58HjfBx\nT0anKUaIbZ/w/fFQMMJ268K8ST7ZnJZ1nNv0U0gQApKOM0bZ2VqMbFg1ZgA5b0OqzPw6AK9r5v3F\nMP61AL52k4OpXZsIiei5AN7DzD9GRJ+51QlpBelhAHjoo59eenlaeknYmC0QoiSboV3VUyhyNECX\ngxavnAD7HGWlHpISmz2wRo76ZTdSMqBsoR3JQ8z2DWH4020AZyt+oDxP0kWWPT2siXBoBTDDEuQC\ncNu4XxH0/E8sjEQS7OSg2FUlGlWIEN6WazUlQAqKqFw/IzlXgQjjel98Xdh2K0gwPntrMOLXewEj\nyhGOES64QSA3u3HeBZf+zlhQA+uX95TgYkXZfrtgjZxMHSMDgdFgxBym31st9Bgu+JoYuakZvm+p\n3UQR/kEAX0BEnw/gCQB+B4BvBfAkItqpKoyJTkuCPkJEOwAfBuB9i0cw8Nv4nAVPL8iCe2YBb+g3\nj+U/8ZYGVptvxBjmIyxP4vEJwcObQmQ9jnl3aAXZt4UDRXpvTiDkKdCBQob2bYmchRTc8wP+Rr6X\nH1GDOrQjjO0RYbknzwUWNRhVoChF0lCZnPAkfEM9r6cGjfhs2WQ+PL/HiStlaPM4LndCbAhwrSqc\nwQhlI6DQczbkI1Xey7Zzh6rHgBHrPbtgqDhHu02GpQlG7JTCLexhBEzuHB0j1W/i62NkQzvnd4mP\n2bWJMMpTVYRfzcwvJKK/D+CPQBpCvgjAD+omr9XpH9Hl/5TXdKS2ightKECrwA4Ja3QsdBLKxYPp\ng4BIlkZeYf8OesBx5eCOhDiUcFtAH84PkPAHmCVD+cB6LgqHqAK7H9fMgB8az1JUDR0VyFEhRhJM\nyUmwDoWb4VAT3XR8Ot0Nia2QtUSCTrKR9AIh0vUxwgNLBBAwUtZXogybR+dIrSM1XgpONA+Qu9vB\niGCRjmIEA0vnuOEUhIztIHxzjGxhj0ciXLA/D+DVRPSXAfw4gJfp/JcB+G4iejuA90OqQcetBXtr\nFvKwruMhKGuDZvX8uhJn8vDWwGy5HiPNSg1C13OVqIAz9ZPhSrHkiORYZKBP2jPxYOQoYa708JxA\nPFZkiJxL3nBQ2RnADssdATI9BDBH8rPpquCRNGwv5FiR4FDC3x4JtkqvrZpWub4wPqm4Nn+FHI+Q\nYEuArTq8LkayhsrmMEOYXMJXlHyhYUSXmTr0/LH1Rm3LnDQDVpKpT1KHTxoOAxj0G9QD4PmaRhla\njtA9dA8jRpZLGNnEVjePOUvbhAiZ+Q0A3qDjPw95TaZd5y6AP3rSjgmgIcix/tHdi3IEuLnNLDvy\nHI/mnSirlyYAI3m+0PWZg1oOnaFYVSUh68ET3m2ILMeEE5XxLXYAHdTTAyBiMAYQ1coQlk8a9dsl\nEeydpLnuLFw7CkWTRgUGdTghwUTgnZKlq0IEEmxC4gHIXjxBpRTzgEKcPj/+RdLrk6CTIRByhDUZ\n+s++DkZ0cUl7KBkKw6B6WZlqjNjm7kvj/rhgBJBrlMTb1iGyqkUhqjqnnHfmDAmgQdh05BojQPn0\nw00wsoVdFOF9NA2NaRbk4R47GaGA3YCWQ2HDixxUhzeW89FlMfwpilCP1YRFbYgMoJ8LApx05TEJ\nyjCGyQpo9/wR7HFnpgL8egXys2HVPCZ5jpCJpiQ4dEiwrRI3inBKcHXOcCkvWCrEK0kwkCFFUlyD\nEbtUESNGkgEjXhSxy235w1Bgkw/eU4kmYohseAFJxwe2Ti+N4sefxwi5kiWph0SMAJI3vQlGtjAG\neKOq8WNh502ExKC04PEtsYwIroYQXRXCPTVr7of1ySAEoANwlxzzRU0LBB4MxDpdqYDi3T1fCCDv\ngHQo+ypkaMpQyTAl0Kg/JBEwZgc7gPJhHw5EUV03VT+hzVndZjDB2ql5YYRQKcE8pEKCTpKq/ioF\n2KpBJUv7S5Ina/OC1pRk0mwmsTzs3pDZCLMhwMSF+AjXx4jeRthrkRqBIqMUxRAwokRnOUTYPJtU\nc5U4lGUSVWhoHN1emy8E5Mk86CmZMtwlVYQRI0riPYwYER7DyGZ2IcL7ZmlJEYakMCkeSxVPUW7A\nJkg+yMJWYof3LNDjIccwH4Yx8hmSFJd5E6AHUs07Xc8IeoTsWB9szgCNXLx8BLuC20NuhKFfk44i\nDIQ4adjdFEZmSXBXh8B5CCQYSK8KgXUcFNfhMk4AD+zrIipBU39hvCJAIz8KLayOYMSVY8RIpkKI\nSnoSWioiqGDEw+Qlh6m3u6RTCkYA+Z0ZFiYXjGQANCofkzrMnZwTHQCmBBy02VXEyKgfh1+LkTY0\n3tLmBfnZ21kTobQDzj4+a+YFrbkDARbuODn6ujrMUjF0oHMgQye9xuOHfRVPL8eJzShKrqhTPIle\nHyhUas28kCqAM7JUDGMOCKi/S9zL/1TtCMnD4Ml4Q4J1OFyUYMwL5jCdK+Krm8pMQ2fuznfF1yPB\nBFBijw5aAiR733gFRsjuScSIOZ+IEUunqFMjiyJgmOo4TPuT76aL6nSczGBET+QYRvw4uwRkVhpV\nXCDLvm6KkS3sQoT3z5Iqgbn8j1WqWEMVeZUH4m1NKZqnJmgeQ1E6lpzQJEw2i8lyRj8xjiYxDtL2\nsVy8P0hDYy7LDwATISGjdO/FoNE8fwZoKN8UZtamHhya9vQVoXeKmlDUYEuAqhar6nBHCTr5NbnC\nuhhSSDBXiq+vErtKcOA6FE6B/AIOjPzKtP30G2Ak9EJU1CGKw+xFD63D9PsfotHgFFuMJMVhGa+V\nYZLN5BZmg5pGMyNKo29/g6mDkRYfPYxsYXo9b6udNxESYxiOKUIOEYAA3P4SMzinOnIhqcYiw724\nqMYa6F5llB1LYlzXz4PXcKqGsPH1Kt1MvX8Jj40MWcdpZGQkf4hlTZMU2g1X4/HZFYvMd4u5oNBG\nrOoAIhXys+lZEmyU4KTJSxsip5YcIwFyNyd4jATlT7CQOgToxHhdjBjBDJA0it0Dj7chDjNGD+g4\nTLBHJPHVOlQ4qTFSiiVxvChDc5giGPWNFK0aG0ZIKzPzGNFzNGJvMbKh9Tj3tthZEyEBhQgX1nOn\nyxSALuOZJHRgpFJI1h0yoESi8I75oJj7QWhWIxPe20y0ov5QeXrx7qZEi9eX9Qg42GtTKHlDDduk\njzrWcCuGPbp5qtFXKUHbP5E2Vq4JsGoO0zSVqZRg20xGVV5c1s0R+rrcJUUkFgK0ooiOU0uEkBSJ\nFL3ZyW9ChEcwYmQTMZJJMZNDFVwrtByucyFJUWQ9h+nNaqxJFcGaB3YxIvnAghEbrzCiPpsYwAET\njDBpjnAJI01RbYKRrexSNb4/RgTsPEc47268ZwtiZAUwMyFn6V3DCTFT+a4CM6Dk6IqRaqAbcZkZ\n0AnmpadYiiFPnK6AHhPvdh6s4Y55fuZCiMle+VLAyyb6M+ZCY51OGgITpgRIdT7Q51ekZ7lAqsiu\nLZZUSjAWUKISjOHwwKV5jOUDh1oFEvGEACsiBJAqRXgsNJZhxEjBRyHEbBEDUEJlxUPEyzGHSeNp\nDtPGiXQfxNLGVTYCdtD33TfGyEa28IievZ01EQKMITaTgAAfQPWhGMv9ZNYQl9WjDoyckwIhaRVP\nmqmwt5EgzdwIqHgogU/0+kV1FuDTiLr9mEZ3Vf6QZ/JBIGBnQOfgTZV8rXw4cr3fRAXwchb9K2fk\nB9QEqPO9i6glEuyEw5USjJXhUDjxpjFGfFSv531GriDBlHhKgigESMSa81/CCLsanMMIkWKF5F54\nBKGMwnapR7kHEtEWh2kvwLkybFmmwUhFhk2Y7CF0JFqyDhtQ5vEMRshC9BMwclOz33dL7ayJkAjY\nDaMDGyj3LV7zHDx+NpAruEYUvEzUoQMhl3DBk9LxiEUZsrZxSzMIkje2SNVi7fVjAUUeYplHuYRB\nICrq0J8FVY5Gll5l7FvJb5UQqOpUNRKgj0dyixXhTjjcNpsZCgmyKcNKCZbpKhxOFg4zKBBh0gqx\nkGB2Ahwa8htCtNDDSMTJWoxIS5RUY8Qc5jGMhCMzF8xMMALzdf0wOSrDOYwgl2r3BCOeEF1mObZ9\nbWJ09HjnbOdNhBDwpwbo0TITBg2JJUks88acFNSjg52ItM80VYcZiN+bqY9Qe/2oDCU0Dl652YG1\nKSxNJMqwOGkKC2vPXwgPnipkJsmdmecHSvgWrc0FESYECA2JXRl2iiJV/m9OCTb5QLgybJRgID4f\nDwRo+cCUahVoREgQ0iOSCCE5GZZyxXUwwooTolFUYcpKmLnGSGjK0sVIUG+uDIdwA23DplH+RBmi\ndpgFFwEj3rpAv0aXuYMR7fJrDiOKk02rxvYbb6mdNxES8NBO+nidAzkgoG49/kCM0T0/eegzqoeV\nTwYnD4OsLW309Ee9PmkIZLN20AbSwmXmzdM4VYaWB+IkyzmRhITm+bVpBNk7sIzS3s0eriGGP3ZO\nVE4XKEUSU5uRACfhbxgPqnBSHW7eHIlKMMfCSKsEd6J2MIRQeMgeCqchVyowEU8IcFBiNOKLanCJ\nCOPQMGG4YCaMwikVRogsX0jI0NYHHYxM8sqtJrV7MqKyiTIkXSfM40RII+TG6/1zjHAhxDmMtMW0\nHkY2sznSvQV23kQIxo5yN/8DFGBLYlnAk1m+DJf0b9RpICFRBmVRh+b5iRJGZCTNCVWEiDLRqyYT\nVO3YBq3Xb/NBFfCkXRllJUdv/KrufVSCzaoqYlqIdT5Eg0xMX5ty8tMHuFKHrQq0eSFUrkhwQQku\nkaCpv7ahdKkMT0lwGIoKHBLrsCbAIeVCgOjnkQ0j1vGK5AWnGGkJ0TBSurJPsDJIfFsz3GYhwzav\nF9fggJGmwjbJK9u5Ky4sbCZi7WKwwUh4Q6bFSBcfLUa2MN5yZw/ezpsICbgKOcJUwU8so3h7I8Yx\nJwf8oOGPKUSiNFGHQMkJwd7sCEdbpQxtuvGK0euLCtRwNwNpVEArYclQeyAhUXvM5N7fm2owgIFm\nq3QlR4ia/KIapFoFgmKRpFV7C01kPEc4owQHDYejEhyyqkFNeygJDoM4vZ2qwKuhEKDNS1ScYwyH\nT8GI44STT6ecnBB7GBlHwQhmHKZjZOxjpFKGqBc50Y1wh+ktEuhEjHi4fgJGNrKtqsZE9HmQTp4H\nAN/JzN/SLH8IwCshX697H4AvZuZ33OSY502EYNxJh6P5HxtmbcWaU8ZBgc1MPp1y0n2lxvOzAl33\nZQlya14z2tnU+Rp5KwSqRtmVaeeH6FBVINk2JXz2dnHJ+sJTgLOQC6maIGsIDgQZUVsMfZz8Ahmy\nkqFP9yrETmrTJjJtYSQPLO0OTVW2JGgVYiPBoeQDh6GEwruURRGaCgykuEsZCUUJJmIkMJL2wrwU\nHrehcWZxlIfMjpGD5hDnMAK/V4w8JlHaozpOv8dTjMg8zRlTwMgYVgvTmUQFxveOK4wkOaf43eVr\nY2RrAbcBERLRAODbAXwu5JtHbyai1zLzz4TVXgL9IiYRPR/yRcwvvslxz58Ih7Hy8pOmERQ9fi4e\nPrGCPZXukgAkJnWuxfMfxgEY7KWmDIxJE+RKhn74Okwm1P3HifKhKSAq72sPSJjn3p5hnUQQUQmB\nLexwwLOexwySLRRsCDGGyl7gMIW4RIKhGBKbyFThcCBBD4dNCUYS9CYydT5wjgSHlF0B7owElQCt\njanNazHiOKFWFXIHI4yRlQRzqjDilzWEyROMwG6rkqGzo72eZ1FsUfZuFjLHG+jvBjcYqd4fppA+\nKRgp3XAtYMQOs3XB5Ob2KQDerv2agoheDfkCZiTC5wH4b3X8+wF8GxHRqh7vZ+ysiTCRKMJhRdgz\nqiLMngcSUtwR4cADMmcMlDCykF/0/AC8Ldk4JlFsoybIozJEP0x2ZYjg9WNifBKGxHDHiC94fn1j\nX96yU++vyXAAJRG+Iuxp84RGghMCNCIzddi2DzylMNIqwV0IhYMSHAYlP2LsNAweUsaVqsA7aXQC\n3FFGoqIGdxRyhJRnMZIDEazByEFxUWOEsR8lVAaMEFGKKLE9qinDHU/C5JIebMLkHk6sfeAcRhIr\nLqYY8U4hVmJkK1sZGj+ZiN4Sph/WD7aZ+dcu1R4B8KnNPlZ9EfMUO2siJAAPJWEUC4F6ljlhR6oI\nQRXgD5yQmHHQNi2JQwg1DtI+AsAeAHKSpD4ymBPAPFWGxoQdZWjnHBtdz/84UnCXvKF7fvvkKAeF\nyNaQWrdnrpy/WQE4dQjRSC9Wj9u/KQnGN0YmSnCpMBJemZsjwZ2qwN0wTlTgoMsTGLs0VgRo5Cfq\nsBDiqRg5KE4iRpCBlBj3gKphtL3kMSqJiVrMSob6213wzytDvVNTZdhxmBLMcEOSASOqBtdipDrk\nlmqQgZWv2L2XmT95uwNvY+dNhMR4KB0mAE+azzHz3GBFggL4lAcBunr4rOGPef5DTtjrsQ6jDklU\n4RiVIdVev6cMI/nFAkrx5tCEeJkHImSrBprn18PVCXGU9oMMrYTPp2WcWGEqkDqEiIrk2rdF5goj\n/sbIUmFkl0MTGW0TGIoipv5MCV6lQnyDhr07ytilsSLDSH6RDAUXBSdrMXIFwj5gZMeEAw04aOQw\n6nYWKgPwDzpZoa2QYcDIQbbqKUO7J/ZecbxnqRlK653ymdiSOlE8qBqsMOJqcD1GNrENcoQoX7s0\ni1/CbNdZ/0XMI3bWRJjAeCjtq5xPr/mMjY+wKmBywHtXWHnAjrIoxJyQaMAhh4dIiXE/DkKG4Szq\nphMZPFog3C+gWGQzV0CxJHjM/Zg6tHbW7EBHSYg7wDnkpDrmBNiQXyDBWg3KetaTdFUhjiHz5N3h\nmXDYq8NChIPmBgcjwpRFASbGlSrBq2F0FXgnjZ4DNDK8apTgjkprggH5RhgxXOyYcMiDO82IEaIB\nyUNjGZbsRyBDguOlLbIxhdcrAe99KLY48FYG6jRnMZJQ2gzOYWSJmCJGNrKNqsZvBvBsIvoYCOE9\nH8Afa9a53hcxF+ysiZAIeCgtNKg2/DBhpIQdSrFkRMIhDxgGdkLcc7IoV0Nk+fl3ANwDgJyQk4XB\nOabfMWprQJmpobOeRCTD9t3k+DGoNh8+GYaCiXl/e2485LGkuFl7WYIKtOmKCL1qrPNCnrBWhzMk\nmGoS7BZGLBz2t0VUBQ5CWLth9KYxVyEUNjI0FbijjKs0VmrQCNDIz8ZvgpGs1zyBSmg8cOlvMGKk\nOQ7DyCeQoc4szkowQgDsEw+Td5M7DrPCh47ySowwQtOZYxjZyjYgQs35fSWA10Oaz7ycmX+aiL4R\nwFuY+bW47hcxF+ysiTAhOxEOC/mfsQp7koP8ikbsefA3TVIecEUZj+ZdaIw94KCIsWEE+qzXZw5A\n6jSbCJ5WmsxQ8fC2GolXT0CJmqjkB+0tAqsqtuExMPXCbRI85h5bAuzlCD0vaMucCFcowaYwEpXg\nMGSvDF/p+C5lVYQZd4YROxonKrAlwKuGCAfKruqvg5EBGSMSdjTiwANSHrzNKQDsOOMedjhoAWUf\nlGEiBmn/gKOGyUSyPw+TORRQWoxgipF471wNQlWfv38ecsi2qwYjQFSHHXzE42xl2yhCMPPrALyu\nmfcXw/jpX8Q8YmdNhATgikYHeC8ZnlkIb9RQx8gvsYbEIR/oqhDwoRRNdvK6Wi5hcrSoDJ0Mh+NN\na7wBtP9PZYeqTM2pVz+NEN4UYPf6LQlOVGbc3k6pDYvlRdZCih31hzA+R4Ki/OIbI5jkBFslaCQ4\nNCQo5Dd6U6mHhkM1FFKsCbDCBXgWI5YbjBhJYGSSAsmQsirDgpHESQsnBxkOB8FIghTFkobc+m4y\nyxeeXBkyNwUUv2eKkc67yYYd/9wEUClSb6gfugdri2oRF6dgZAsjxlah8WNiZ02EiRhPSHsH99C5\npVbBy5ww6l1NWYBuwN9jcO+feHB1aEPZRnJCdtzJccpZoUqOW9MaLlgXm3p97yrE2G9EKJqEPw5D\nDgoRcEKEjnetUYOlP8JWFQYybKcHwL8xElWhTnffHQ6NpZMSYE8JXg2i/K40BL6TDj4ctMnUlavC\nsSK+hDBO2TExixGC48Iwkt0ZDidjBOg7TCukmDKcYETPxd0kxZus7QwpKEODTT4NIwCayvEKjGxl\nl45Z74+RFksAeB6otStAQhEaHehDYgf8ngekxNhzaD6jzSPKJ+XUMpAT+TAzAUOhwCof1Dat8XeM\na2XY5oMmyjBGS0pUVkGO+Z/4rqoIRUbYabxoMjuGWVERRhJUJceBCEueMJBgQ5LeiwzBiyPWWHrS\ng0xQghYO90hwRzmQXyHBlghlWggwDtdgJINlnEhbD2Ts807aGho2LEdoTGLt7N0O7jABbaDd5JWl\nk2glQx5QkaHdP9BEGZbgImCEUPq9xH3CyEZ2UYT3yQiMJ5AS4ZH8TyFDSYpnTthjQKKMzAkDZwyc\nsde2Yp4j5BSaX9iwAL0Nk80E3zT1+lihDMGeN6pCmOD5vTu7NvfjIXIg0/qiyexOeDwhwDl1mJqu\n9UN43O9KK4f+BEs7QasOe2EkKME7acQuDK8o46HhgJ2S3y5lJ8JIgFc0BhLMVbFkDiMjJ1xhxGiN\n5wNGzGlaFDFwxh5DcZwaKjseiJHy9InvvwIqpd3qlc1yu6fKsMoRKka88XbYbmuMbGUXIrw/JmGQ\nhEtz3t6KIpbczvr2SCTBkQiJM1JWlcKqELH3MAiAAz4Cfan7L6L/r71vjbWuu8p6xlz7nK8WDW0p\nQilESmyMRKNgAyUYg1IuNsaqAUSNQoU08X6J0SI/8PajGqPiJaVfilqMUrBSaQBFWjD+UCtFUaqA\n1FLhq2ApN4nyfefsOYc/xmWOOddca1/OPu+7T989kn322muvs9baaz3rGc8YY15aZcgcSXOeD0Kn\n0gDWieYhufVSiYk0JGqrxXDAA2MPPEuGRzXYEyB6FSgk6KExdeFwHF4/DqUVlOCIBHsleJ0yrqct\nNk5+tn7r5HfVKMJKgFe0dfKTULn2KlnEiDV36TCSKSFxmRGiYSQxe6gMRFLU2deDLTlMPeBcGWLu\nML223PyMkE4x0kNYPgIjQHCOpzJePtZDsLMmQtIcITDODwIQTw/CFVkOiPXhKJUQLbZJwMTSWGui\ngmdx5aDLFhJTUcWYZ2FyScWH+Pf3CdLvNBGoSYwHr98sUZMEt4bXDswiL540Aa3dqCLIAcwLJ37R\nbL/181ARRmJcIsE+HO6G1/ehtKYaDnu3udBv2PKCFg5vUmgms4MEWyIsIVdY3EGu5ZAjRia96I4R\nWCVZ9mEq0PCBoqNga3g8WWFNZ18vnJth1yJGmBmYDCcQZQidNiLeSxv2nzuMMGTfrg6pve8WGtu6\nu2DkVHYhwvuxBMY1ZSe2kUnYA23WktXTE2550xDiBMkn3ULAbqC/xeTd7nplCLRhcrUJzAWZyAkR\ngCtCxwMLkQzJUH+O0qn3VjDCsnlsjRDRvSQHND+znYqwU4YeCveFk74w0g2vT54XFNVcR5FR9TcZ\n+bXV4eu0xXXKrgSfSlvPBz7VkGFdjgRony0/WCvHy+GxY8SVYYuRQgk3vHGMTCGnfGvJX9vthECG\nALLmlUeWE3jSOaqZ9BxTU+BFmTtMzxE2VWOTf/W+Ng73AIw4Tk5MhCvZq7O3sybCmCOsQ2K2dkUy\nrGq2RrVImJCc9Mz795aoenpRgoKKqAyjIpThvHS+i1QEl0WADvX00mQHDkIzAaT1PZ17fclzoQ/s\nPQAAIABJREFUsxIg1ZCpBK8eckEUHoC9FaEtp/59QIKdEkSzXF/toKqqCImbEWSm0GXOCyNBCW46\n9deTYCVCSZFc0dYJ0BydV44XMGIObg0j8v1CeJv0u0H0IJgo3rSmx0jS5jbTJM10iOW6iQokB0t0\nmKTxbu8wre+yj2YTyG4RIyMivC9F+IDtzIkQDnyz2E6suHKT0CezDsAKGXE6hseAqAXJ/dQwKBV2\nr9srw3qcMptvApCBGhKTh0DMclw7Q7Yqnvl6WlCGlj6S/4K2o6jA7gjR00Rr3j5cxL5RdSU89tC4\nFkxaJQgtlIC4VYM6oVIdWZpdCW6CEtwMGktfDcJhV4Npi+fR1sPha7JtWgK0SMHua9+MpsVIVpQs\nY2RCQSYpqhlG/KUYucU0qCKL2cCu14P7sc1TGMwDqGMVaVc8U3CJq8OMGEG9f+5p98DIWs7uXkjw\nEhrfjxGFqvHoKlMFeNKiiTWhyaj5QSHAgluuP9cT67XoO1OGcQgn5A1KImx0XTybbZ50IvrQFQ+A\nA90KJ4kd3iBdIkjjM91MwB7UoYVCIeRxb0+Yg2+gCJuwOAEgXmhGwzMl6CQ4KI7EQRTqeIJVCdZu\nc9JY2toHPjVtnQRjOPy8dOukZ+/XmiM04oupklg9vitGBAqVRJvCS8QIqsOUO6zNrBJwzRk3ADYg\nzw1K64TsO5omYS3viucYQQ2TaRAmF8gE7ntiREDQvS9h5BTG68R77nbeRAhu2ouNLCvtTArOTOrp\nNYww7z//v1RDoQSZ3S6EQEWndESyJHh20BeSLntIRaYBSF37MWaUBHCRWcU8kV2kuQSlECZrpZgY\n4NDbhC3uZWqS4b5stgDyBuyBDK0niBPhgASrUjQlqOeVGKOBVUnbCsbiSBxOy0eXJtaweC0cbkmw\n5ga3XjWOBNi2IzweI/L/rdS7Cs3oC0kTHCSZCuKKsmMkp6wplIARDZEnbWNYCoNT6TBiobENkBAc\nJuuQWgmzMHkfjOhqeE5xBSMnswsR3o8RgOfR+ix2V1CvS0UBT5iYBexaCUze31MelgkFN6EnAVAT\n6UjAs1pnLAq+rGMiGsDd8oTNlGWvLFVVZnmlAmCqOcP4qywPZMrQutERSMjQQmXz/IwW7N0+G4UY\nL15DhjwnP/29MxKcKmGaOoyTr0sj6TrR0iaQ4JWrwuJtBDeNCiyB/HITDo9I0FWhqcGOACd1lsdi\nBICGycVTKIKTrpF2AhLXvPKzuIKOYukmEcTWh/+P6RSeSIonASPEDOgMdI16K6TRgzpHVoyYAjR1\n6v9zd4ycxC5EeD9GYFyF/qRLVogU4CyTNVEd8QUE9/xy0zfIkFxOwRY32OAKuakoirfXqRxNEVLC\nJhUZ55CkD/OUCrJ6fUuMG9ALME+MJz0PVYSk28s7ACNIC4Ps3eUmHOwOYB6AGzE05lnzmTZUHihB\nMhLkYXGkTr4uDytRLY4QcVWB1o9YR5CxHiPWnjAqweQEuU6C18gNAUqbQl7FSIFO7zrCCOChcVRP\nN6iK8ApZh+1i77NsGEnqcK+IpHhCMqxXSTKHtgwMwU3xpJJhAUqqGLFzsBvJel8YdbzKFDAScoGL\nGFkiwH7dHY2AJ7dqTEQvAPBmAL8Gcsn/IIAfBvBNAD4ZwAcAfAkz/yzJ0BpfC+DVAP4fgC9n5v+4\nun/AiXAp7AEk9LmCDsVO5vEF/Lc8ueePQM/gpkroRGi9D4wIUfOFFiID8LxQzAXFLlZSSQ5Dd1l4\nA9ZmEeT5nGZUGSNDjiQ4ALtZj2Z1/Ys5wlGusA+Huwqxq0Gad5+bbCAFYq0M5yYktuLIU9N2XCFu\nXnMS7EPi2rNEiVBD7n0wYqNNR4yANGTuHOasAm15wrBsGCmUxGEGjMQ5UuSuzjHCnFAQh3czPMl5\nWBtscmdHzpa7MGIEuRdGTmGMJzpH+LUA/iUzfxERXQN4PoA/D+BdzPwGIno9gNcD+HMAfiuAl+vr\nMwG8EfO5CBpLYFwpQqaVm2bNVtzrKyHa2EU+8CUQgF4HaQAGzWcSzfKFOeSANtrQtm9sbSGy9zTR\nXBCz0qASKIqSUABw7W+qyLemEzQAu/+eDn1rOUK5qBoac5MfbEjQKsQrecFRo2mbjH3URMYGS1h6\nXS+QYNOOUFVlJMCplhoWMZIZDWG2GAGyeqHeYV6HHKGowNqbJFNyjGRkb05TMZJdHdY8c4sRH0u0\nzxfazSpcHWbMJe+BkSZS2IGRk9mTSIRE9NEAfhOALwcAZr4BcENErwHwObrZWwD8awgRvgbAN+hI\nsv+eiF5ARC9h5p9YO85EbWuF2IrFYarpmGyxhWLqyvusSW0OBuQO6Jll+C7ZJ3nxJFPy8OcqST/m\nLXgWItvLigbE5JPsgExJhdCHoblBPX9VGEwK6ARQYdgw7N6nFPAEuHvfAZhnajAuW76wI0HPB9r5\nkJCgvOu/E4YhsS3HwkgMia1I4gOpUvGQUvJ9YTCFUCG2fO4VZVyjNCrwmkpDgGsYsbRuWcBIDZHn\nDhOADvW21f0lUZNUvP+yKcMmRNb2hTFENozYnMxChpb/45ovtnuhza8sHAZYCnA9RhgeKh+FkVPZ\nk0iEAF4G4KcA/AMi+nUAvg/AnwDwcYHcfhLAx+nyaHaqlwJoiJCIXgfgdQDw0pdKjwAAmAYjZRjg\ns3o8FXPSR1TVIRAAzlNDhhkJ16SKT6vIlg+yMe48X8gyXt1GK4RbKtiktkIoHn/yirKHPyFXWMNp\nRY0nxhXoiJVk/RxUoEdIHvbEi9cuc1xvJBiVYFSLSbeOeUHNByKExPYg92rQ5x+mQU5wNRw25VdV\noOUETREaCV6hNCrQCNBwsAsj0wAjN9YWVS96gRRRrqFtEGnr6RKgNrqOqZQ+RL7lhEQJm5SxZWm+\nNYocijrO2NKgCZHTHhgp+jkdh5FT2kMOjdPuTRZtA+DTAbyRmT8NwP+FhMFuqv4OujzM/DQzv4KZ\nX/ExL0qYiHBFZC04cAXyl62biOTln+0B0RDKE+uh2hiUR2y8awqkJvHZCfDKJg+iOuF4VISWMyMy\nxYQ69SJEWUEVmVfsdNkLFH11ty92dArPL0Ja2CYh9CPmNlfYNJPRz/q9nzdBlQxU8bIXRRJVZWgq\n0KbcNAU4xWuj170WO7Q9oJObrK/V4VoQ6UnwiqQaPOn9PxYj15rDtGMJ8VrxJjTi9vO0UW+K46je\n/6K9Xyo2Ns33beRQr6vcZwr3ldI6RqoDQ3NfD8XIqfOEO19nandRhM8AeIaZ362f3wYhwv9tIS8R\nvQTAh/T7fWanaowU3BMIaeDtE2Sy7gnakV6BnrmGPtfaZAKQzzcETEzeLW8iwjVqI1sZpbhgYva8\nlihCqThuSPoYL3p8ki5mvccnst4lqE1XbIUpRU2CI4bFpgwVtd4Mwv/0F6195+ZBQpMjHD5c3nZQ\nw2IN2zwkdkUopB8dQJxzeFJiMDU4oQ6q2rYX1IIIVRVo1WH5zvoUG1FVFTgFfAA4GCOJgFu9kFkV\nLDghU2kwckVbHcBVl/eMHCQklsjB2pvGyEHC4xA5hMb4bNhIXFmLapjctEMFQnvVwzFyEmPce9WY\niF6EQSF2sF0G8AP68ceY+bfv2vfRipCZfxLAjxPRr9JVnwuZjd5mmIK+f6suvwPAHyCxVwL4+V35\nQQC4oqTgHb/67837X2GuDC3H5FVIT7yPPf408Pj9fLpHe3z31iGZQ/EVvH5YFz1/9PD9i2lAgt06\nf6E9pqm/+XtQgLrcXIOokNGqwTrfiKlyVYgI9yDkBRtFiKoERySYiFYx0n/vCjFihOYYcQx0qtAi\nB1eETWQwx0hC6yhirrCJHAj+eUkV3gtGTmX3rwhfDynEvhzAu9BFoMF+kZl/vb52kiBw96rxHwPw\nj7Vi/H4Ar4Vc+m8moq8A8D8BfIlu+x2QpjPvgzSfee2unROCt1/lbFF2GVzdoJXRVBlOxCgc3BZP\n0lyCtjJEE3Pj8SdWUKviq+GdPPi3DvSCRKIMKJADayGlODGiyRWaOoP2aLHRRZjqJD6c6qg0NXev\nQFcERxy3bcW4U4UYPDzzUCuqQUviU3jJdahkaKkAI4ENhfBYlWAcPaYZVDWQyTW1w+9PgUSsMHJF\nYxLchRGpRY0xUqMHeS/MWljTMS6prRjHyMHan2bSuVCU7Lc0weZa2epI2COM2HUrfn3RRA4todEY\nI1QxsIQRUPW1M4yc0E68u5EtFWLvbHciQmb+fgCjWes/d7AtA/gjhx7DwD1KhNed2wNQnPh6oBdm\nH2XEEuJX2AK8cY9vY9ZNrA8kMwpJJ/wrytgiOfldpYyS22rgzNs7CdbqIHEde7CC3YgbNfwpa0C3\nT+gAH5AYvT3B2w3OSFC/H6pBjNTKXN0YYW2SzSzHToaLleIuN7ikBkVZiXJbIsG7YOSaCDcdRhIJ\nWV5RVieqA/z6uQlGhPyKD+YxQVsSBGW5odq6oMdI0vtQsbKCEbQYqffOEyqLGJGlHRg5he1HhC8m\noveEz08z89N7HmGpENvb8/QYWwBvYOZ/vmvHZ96zhBzcq4qQiuR81Pcb0AvYq4YW/lgDVxmCyb6z\nBzHhFjqaiZKhNZW41ZxOCk1yIgn0xOAAj6dpOLYQxl01VTJCADtjDPSIdl/hF01WNySH9vsFhejN\nOICZEqTwAPtudNmvi1/P2mbPSNCueVV6kRC199BADdZeI+E+diS4CyOuBlcwYr3WLHKIA25ErExg\n3V+pYxUCHjWA4YTf4GSAkbnDCWSo72xf2i2moAoNN7gjRk5h+4e+H2bmkXgCABDROwF8/OCrr24O\nx8xEtHTEX8HMHySiTwHw3UT0A8z8P9ZO6qyJ0KyODjK/czJ2R5qRoY/poQ/A1Hn8zIRJlaHkjooA\nnIuHPpOGSZYQj+Fx7M5loY8cjmdnSRH0Gvp4mzGTfPGWOgnq57AZMHsmZjbuVdKpwXisoAZtG9ID\nNPnBEBbL7+6vQ1CCCPlW1O2qIqzkaYp8HzW4iwRHGHEa3YGRXhXa/c8koxdNRFpcqb93LYVi5GeX\nKF4v0s+GtaI3k3oHaTeaOpz0GOlAchBGTmDux+9ozPyqxWMQLRVi+318UN/fT0T/GsCnAVglwrs0\nn3kktg7wuj4+FACa3FFsYGvh2NIQTo2y6VROXI4P/WgbI40YKssXLQlV8mlD1TZ07RAWPHr06s3n\nJSXY7WNkaxFmDO/6z1EJAmiujYXGvaXu+o8sde/z70+LETufZmSbrmhiirYfGSkO39U4hvDbqblG\nmK/3dyxjBJ1DQ5//q+v2xsgdzQKKtdcdbakQW8+B6IVE9JQuvxjAZ0OKuKt21kRo98iAPFGaveL3\nsk3NGcl39X3px/YjHMfJoiwHJNuVBuh+jEAMcXn2e8zBY51s2n+y91ANjOsxqP5130UEzgi2C4s9\n9G3UYBsK7/qdKZBeDI0B1HAY85GlR2GxdZtz5depwZ4ET4GRfmDepUmhmm20emzbjwaAiKmFUS4Z\naDGyaJ3TjIR2DEZOZrzH6272BgCfR0Q/AuBV+hlE9AoierNu86sBvIeI/jOA74HkCHcS4dmHxhHg\nI5soIbN1k5K2WIBWm0l7FFjog5rviSHLwedElTB7FRjDIAc4UwU4hbDFyM1zhGjCZRuhZjV07n/D\nKUKeA123h4DAkAAAzJSTWcwPHmv7YkS2bTFSjLwCRmS7Fi8TbFRrLZYQ45bhqjBMbF33QTKm2ihE\nzrOtMcPIYi65D499B9xUiu8VIyM7QWi8unvmn8a4EPseAF+py/8WwK89dN9nrQjvyyLI+4dzKUTr\nJwU65MHdibe7xAwrXn6mAE4AfGs/GD+PlkefjzXvQnfHHzAKnUeNsOt36+c/miiq/59eXR5lMVQ+\n6v8XlnHCgskeYfGJ4HAv9kQS4cj6YZcmGofBd7HlItddd4z7CXXuYKciwYdo+/z2U12fPk+4aI8C\nI/cfGt+bnX1o/KisdD4hcxjK/45m959P2l5hcIC13S9UDw+1sudv2He7j0Tb57cfdH1WtqV997MP\nRu5o993F7j7tI0YRloG7Kf04bJBhtqyNmEzgQ7PvR5bDXMeFU5hBbz8LQ8+FdTHDPbdFkPMeyxiE\nIvt65B0PV9xNfKALtxNetd+tX6+177O/HycpLD+4hpHcYcXGncwBK0vOsjTYaH9zPpVD8JEU9t1+\nj2WcNly9hMb3aAZeA3NvcX2BtBMrKM1D04McaAG7RH6yXWpIsD12ffB9noqwf+7WmTECIToZ2it8\nbv6B9iM2XvkOa+Q6Xh/Pv/99dg19Jrcd5qQS1LYMZpBm27iz6n5H7CEk25VVjPTrljCyr8lADDQk\nuLKAo3h9mNtrxQEnMvhC6zRn+NmBkZkdgZGjbJ+w+EKEx5ldtwj0/hW/H1kJ7/1jEj159PiZae7p\nYaMRJ/3fJeUzJwX2h2DHDx19XvrO8ucjbxuWyR4Qnv/vfD2Fc6X2gQy/ZYn0qlOo164wNY4kD0iv\n/2z7j99nHYW8Hqs0Dm4JI/33vWXwDCO5O/4+KZL+N8Zr4ecwcJRL1tzOXWQ2u9/tNqsYedRkeKZ2\n9jlCmQE2OZBj5c/WWXOI6OlNOWSWpgqmLLKGOxHkfdgbLYbS9bjxgaf6jtbTF0ajBGSBGgJqlGED\n6EBSpgYDeEdhhq3jvolFNAZkvgtuvwv7liGgtOWFk6E9uHrNlRCNGJ28mLDR/RYmH9Q0KzlKT42k\nPXuSDFoAm10u6fwhxYfTn0inYdDTs0ETrF1h1u13YUSWywwj8P22ljtSzzYXsv+OGhYbRvq8sivm\nDiOtsm6jB44N/npSG2HE79+RGDlR5E4479B3l509EUagA3PP3pOg/x9aBSHr6iRMRoZGfNnBrj2P\nmTpFkvyB70NiPxcDc7fMPdDNOlKch8mDCxIBvhT+dl8L5hX5A2KUdotcpwSI5AzuCBB1eHm0YV99\n4Isr6A1kuej1vsLYbGY4wOYhDiTF2gOD2Zu7SFgruDCMAGgI0SxipDmmYsTUZuYxRqBHMocZo4KI\noXoNKkZi+qC/7MuRA1V13mMk7sDeB1HDPhhpHOYJjMrDZcKzJkKGkpuMST7cxsDtYId1moerwRjy\nRALMMG9uHl4AHJPgpgYM4HVdVUJzMiAs5X6EGEPo2Xt6XlGDBvCGqDoLDXd7YSiTApkkQP3GT4xc\nEZKfK/n5FiaQ/T7UdfW6sF+PeH2yDmBaOCGTDWCbVLEn7SVi3+tAF0w+TSuCU5vs5F3NVDKciBpn\nuAsj9p1FDGsYMZyMMJLXMIJ4jdqX48MxERU4qjqPGNH3U2DkRGJQjxOO+QDtrInQzMlw5XsfYisk\n040ER55ett0d8kgyv+YHK7BjHqgNEfvcTxPyLClA3xgNiIcAD/vvw5FmuCUekKGRJNsE82GfOhI2\n2QNoHNmQePtbLTzz6+IkaI7EiC9cc0pKgDLa94R5eGzzDieWGD1pKJ+jbA1kKD9+efQZwUOLkRIw\nIsvjiCE3ZFdVYQyLFzHC80JJxUsbLdg95yVMjCKGu2DkxMx1CY3vyRjALRckImTkYc+CEQEWADch\n3Lk1Lx7AfYOpenkjPVTPbttZ7mcU8mxLwpanSgAleciTi25bqFFVXgk0QizB0xfIOIS6fgnga2GP\n9cQCk49j10RAFg8VgHSa0ZEqlM8WogUCRA2PCwtRZU7YFhl3sXCZhccZCYlZhzIrToYTEm51PMik\n29mIQFEVgrWHBtcQOZJhAd8ZI3mAkVHEcMMTbnkj82V3RDnCiBHitiTPD5pa5vDOESvhXfARIoYS\n1GCPERPDB2PkRHYhwvsxRk2OAxXwQNtGMII7hsORBG+RcMsJM08Pwi1vUDgpuJN/zrru1siOE7Zl\nmgE9FwW6f56Hxo2nLwryLuyJ4Q5FcI8IcA/QCQ8K2CnpyMcFAMmsaLJ/Ak8sD5zunEnOESShbtLJ\nzIkINEnOMBeZhyXFcM8cBCdc6XXflkkGWEhFrmfZYEqM5H1/C24xyfh+mNrfpVx3RQU3nHBNBbd6\nqpllUFUfSmuAkYiTfTEyUoM3PA0jBk+bgAJOZP1tmZrrweY4S2qwUZQAS4l5QbQE2GOkoCHBGUZW\nSNBNyfCUPHhRhPdl6rEd7FhoExi+iwC3nE8Md6Knv8XkZFjBTkqOAmxbJ58t7DHiayuBDIgq3OXp\nO3DLb60vakgSs/AHCADuiUPXzfik6Fh3diGZRNrpOKVI3TkxfK5cEYj2WxIyAKI8C/W2JWGjOcDb\nMunozNyGjIhquwxzhVnzfjFEBqySy/4Q37AOvBsJ8UCMGAkaRm5QHeIN+4QCw4ghVoujM9iGzzFi\niIU0ixjaPHL7XiOFeUi8SoI9RiJOYq1uT4e6t12I8H6MUcGeMR+KPTeqsCPAAPBbTrhRL36DSUlt\no4pvIwpQH9aoBkeePnMF+pYTsirBbUlOgjs9vb/QhsQhNG4Arssz8uuBF8DeZ4GsWCKTgkNnyJOQ\nmJjAReNOlgvIpLlCbbvChVQVirIsJSETRBWWhC0xiBlbnpC0aLLlBBRgQxnbkkTxJSAVmQUvUcEN\nCwSbcfxsuUlw6gxz0DEPbSBV1DmL74qR6ChvlQTNQVo4fBtC49uyaSKGLU+4DRGDRQkxYsglNRGD\nYYQNOzEcNowAx2FkREw9Rk4lCWNo/gDt7InQEtkJY08PRG+/DvAbTLOQ2La9CXkfB3yZ3LPfcsJW\nQ+Pe049yg9HTF/P0JXp61HAn5gnXAB69/YgMoyKkbhWruksdGRpFWr6w6BNSdNe2nOCKkAigVJpc\noVWHCxdsi0xrsGFCglyrpMowMXs1+JYlbM6QnGGdLkFC5JgvtMnXZcpNOa/CNsL08RiJ4fAtT+4o\nLSQuphC5VopH+WPbpmg6wCKGpdzgLH+MqtDYgO+KMITEHUZi9HAURk6k4ige7wHamRNhHRp9fQ47\nsUiAOYQ6UQlmlgfNSNAVIQcCtLBYH4KtKsLbMikZtp7e1GAusq6U1Hh6WFjcqMGOBIuQXg/w5WYR\n9ffTANQ+qjFU3SUlw2JfVvngZAjNHRbUf44cXWSuDCP3XhUmYmxLAhKwUUIQBShj8k0oQILMB6Nh\nuQ17NvXd47rfYzPLgeU4Vvjw2QlPiBHDhinCWCBZem15wrbU9MlWsRJzgxEj5lRcDZYVjAQSdIxY\njrC0BHgsRk5ii12nzt/OnAiBG7YZypYvsnWJiuC2HE4ktEh+c3DPwx0B8FQJUElwa99paJw5EGAT\n7lhuEF1IHEjQQL0E8DIgQ6BKPoQqYDTiqgqVzCxqlKhSiyj6ZDRkSJD8oW8DMImSQyEQSWIxE4H0\ncwpkiAKZ0lKXJ2IkTkg8AQW4xYSkhDslJbHRQxQe3KxKMNvsguCGEE+JEVOCFSObMUZKi5FbjRgM\nG4dhhA7DiDnMngCPxMgp7KII78kYwK02qSjc9jYwiyPJzCp+muA2JTgiwZsAdK8cdwWSrVUBuRZK\ncgA4D0LiUjQMKqiePoQ8BwHcyVB+6yjkaUBIuq3GKwxTgkEdlqoSLdRyxVAwyxdaiMZIKCSBKBG0\ncMJIJCUNm750y0mIceJaPSYhr1vNI8aZ4BLH+WMKbqC5Qj2nawA3BGlS4+pQ7r9NnTnCSOzRsS9G\nWhKsLQl6EqxONNWQOKRPck+CDA+JS0k1baL3vpKh3sZjMAK5Vwdh5BQWlegDtDMnQtJQo15hG9Ay\ndk9qhkoK4C4hz3PTKcJKghXQz5Yr3PKE5xTsHu6EkPimTLOQ+DanJiTOeRTuwJPgawDvq4FkIVEX\n8qx536gEAdLcoHh/70JnZGjsp9taF2S2RFw9pO+bi7QnJG1S04fINzBynGon3rRFKtpjOKkCtMQe\n4M1prvQf7P0aWfOBCVfYSlGLCjKTd8kzhdhjJOJkX4z0JHjDGzynuFgLiZ8rm0YFVrVYQ+JtnhQj\nSyExagicd2BkhpVB1XgHMTkeTmT3XSwhoi8G8Bcg85J8hg7RP9ruCwF8LaQj0puZ+Q279n3mRAjx\nyHHyn6YKWO9iD+7aK2Q/EpQqIdUKYAx3Qkgcq8Xm6ZeqxMOcj4F9F8CDCmwUgF2Y+N6Zg5sCKVoR\nhGqhBElD5qLhMLFXionmxRPZn4TIsW3hLETm2sha8oXFFaGkCdnbFkZF6PcYgyeKtgBvkFF8Phr7\n/Tblpvwvexh8KEZGJDgroEUSDBiJDeybCnEIiRkYhsS9o/RCyV0xskaEtBAu38EeQdX4vQB+F4A3\nLZ4D0QTg7wH4PADPAPheInrHrgmczpsImXCDCdDqIgCfaDta7QTfgtsrwxryGtifLVfISI2nj2rQ\nPPxzZfKcz03Z4CZr84iScBs9fSiOlKygj3nBXEmQdF0D8BzygV4woZnnH5LhkgUybF5JQnROQnpe\nYVBFaH3rbOIonljOH0aqOifwpL8vhMhAN4eJnuQNNsCkijChyRfqhl48MbtCHQ/Ge6JQwRVtvfJv\nU2xmnTxpQhniwzBSu8fNCTBiRJym4CODZorQ84KhJcFNmZwEb8uE2yxV5G2ePG2Sc3ISLCWhOC5a\nR0mKGcMCZcsXwt8dI12YfBRGTmGMey+WMPMPAuKkV+wzALyPmd+v274VwGuwY0rP8yZCEJ4tV97r\nYDSxUhzs08YTjOCuZFjVYF8BNBJcAvhN2TR5QSPBbdYKckkC8pxqXjAncB6QoAE81xwQGch1m3Ho\nU3/zTqCrWuLg9fuwmFgrxOgKKVIugZMhrOcJdF3duCgp5lwZ7FbHO5N84YRrAFtiIA+gpo25M5Iv\nF5LlwvIuo9Js/V4bARoh3kLziwsYmY0ghM5ZHoiR6Ci3nPBc3ggJlgk3eWrygrdKglvDR0kzRznD\nSA6O8hFh5FS2Z7HkxUQUQ9qnmfnp050FXgrgx8PnZwB85q5/OmsiLJDeH+bJb9FOstSPbNyDOzaO\nvfEmMuNmENtiVcOuUqyKcJ+mMiWEOk1IzAHQuwDe5Aa7hHif+1kDub1rjaYhRNh7iC8OvdVPAAAg\nAElEQVRNMRatIJNRIkuYbAckgMPku1Y82ZUvFNUX5xIOk8FrJRlJlGBiHXW6wEkQkIkxZYANy/dp\nOEwU1F4bnx2Ckdqlchkj0VE2GFGVuoyRNC6g9UowRAt3xsiO0Lh5P4XtR4QfZuZXLH1JRO8E8PGD\nr76amWcTup/KzpoIRRFe6+TZBvB2/tg6qnQNe2ICPAeg14bSmxnAJfyREPjWlGDeeD7wRsOdrRZH\ntlkrgx7uiALkQuCcxMvnHUqweVXQ23fAAOS7Qh/qllUJcgyPLSTW/JKHygjr+zDZmtXkcGiy6Dbp\nDH3jlny3NIURebaaR0yiEu12hjBZCLCGxtZFb2JRgpOGuTZX8i2H+ZEH+cV9MNI2oZljxJTgCCOx\ngNZjJC8V0AwjTmrzcLiSX8BIJMUOGweFx8EpnsI0xXxnY+ZX3XEXHwTwSeHzJ+q6VTtzIoQnq7Ej\nNDbPDvsfts7xtQfJLhLsK8RbFmAb0Pu2YG3OZ1T9G5Fe6+WbdZ776cAd84Q4QhFaWGyfE6xtct1U\nm6WENGAbJpOoQSRnv6aSXLL807B4oo2sqUglORUGsAHSFsgb5EReSc6lhsReUbZ7rIWOa2yRQa4Q\nIw6mARn3GLFiiuUC98HIiARjO8G19oI5p9qFLisJxqJZDvc/OMohbnZhBNibCC0yOIkx40wGZv1e\nAC8nopdBCPBLAfzeXf901kRYmPAsX3lFcdRgtjaUTcHjJ28XaOAuHL19V/kbAPymbMS7h7zgNk+e\nFzQSLLn2EXWAW54nz5Vg4+VzLJoEFbgE9gO8vb/3ijCh9jKwfbCQYoJWisP+IxnWdXX/+gzKQfJY\nEdq9bG0LJ8Mg4q4o41lciSIs8CJJIVGAV8jSXpAYk+YGAeyNEfmcDsaI5QRHGDFHOcKIkODAUXou\nsOYGZxgJn2cqsGDmJB+nIlw91omMiH4ngL8D4GMBfDsRfT8zfwERfQKkmcyrmXlLRH8UwHdC4o2/\nz8z/dde+z5oIGdIH2CvGIQQCaqgDwD17M2Amt7mfAlolwVHPkds8NRXi2iC2kmDJmvRWDx/bCvYe\nfRHgMRc0ej82/9MrQuM0vXTelIYtl6gb2TZ5XkDxCnM4HmfySjLRoJKcSpMjBIDCskUJ97RQAnCL\nQjLIQ0nkFeSk99QJUQlMGlQfj5EMwm3Z+P5GGBmRYFSERoI9RqxCzLEwkgNG8gIJlgMwshQ17MDI\nSRWhHfMejZnfDuDtg/X/C8Crw+fvAPAdh+z7rImwaNOF1LQjDCAPYVAP7NkIMkVyQFYUsZFCrPJX\nG0xvmiYQEeDRyxevAlJtImMAzysAz0DKLUnGJhEjkHtF0EGuCzsVIc0VoRFcUA6cwv/YKAbGjHHY\nrgVlyP6PxSvJ9o8MgDK7ImyUoQ4ZU1INfY0UExUdnEGazpgylCJLVYiCCa7LqG0TD8WIOcw1jFje\n2KKF6Ci3OXkzmR4jHHFSBiSYpRJMOkQOWbOqu2BkL2d5IiZkAOcRGh9lZ02EzIRnedM0oo6kOJuO\nMwA7vhu4hQhDZTi2ASvTEOAW6pwK4EnXzzz8iBg9DOJhuLPkgWvPEg4EGEhR+YwYtYudpf78YdK4\nt2kbVsmQuerEekpzMvSeKjb1amqLGSURin3HhJyy32cZqzC7OkwkJJiYnRhtW8NIxMehGLFulWsY\nsTC4D4dje9IhRkKbQc8JRowMcoROhEdiZL/eRyckr4fLg3cjQiL6UwC+EnIJfgDAawG8BMBbAXwM\ngO8D8PuZ+YaIngLwDQB+A4CfBvC7mfkDa/tnDVluEcIdnmbbWfjTg9uA7QBnaeR6Uzb+bonwNYDP\nSFDD4abBdB/qmFfvSLBJgnMF/9jjB3B34fESGXrex4FuobGQIieqDalDTslqExyjVyNECYhlf5EM\nKZAh2eYxHydkmJuHLhRQzKxpjf0LgCui0LawEuEVZRlsAeT9lZO2J5yoDPFhGKmTxs8JMGLE+pU/\nV6TJVXSUuQuH+0b1kQSlPWnAyVJOsCG6AQkqPlKHk1Nh5FR236HxfdrRREhELwXwxwF8KjP/IhF9\nM6RC82oAf5OZ30pEXwfgKwC8Ud9/lpl/JRF9KYC/CuB3rx2jgPCcDtw5HJ0kbOdk6DkgCiBPMw9f\nQLjJbW8Aa/5gPQIOUoIL4bB4/ECOEcy5BXolQp55/BnYzbrL4g2juzxhVYNcB1EwTrMQORyrDbN0\noIN6FERl6E8VA4wETIyRMgSqMrR7JeuEiK45o6TsIbK9mzq0cDkRe17QVOIaRiIBLmHEnGTESMwZ\nW2Np71/eFUb2VoIL0YJjJkYLjUrslOApMXIiO5Oq8VF219B4A+CXENEtgOcD+AkAvwW1XP0WSCfp\nN0K6ufwFXf82AH+XiIh5meGYtWdJKCuOOtQDNREeQW4EWDTZbUDviyJGgrH5w0wJhsawfde5BuAx\nyR0Brh7fPXsPdlcEvJwQjx5+R/7HE+HhZT1NnBA9Hxj2V6Vf00wmQchwpAyloGLxth1XlgsSiKoy\njKdtYXJJJKPLwEhrKypQQ2VTh1MslpCEzGA0xZJjMGJFNB+Ad4ARI8FR17mGBHuMLJFg6UhwyVHu\ngZEZIR6KkVPYruOduR1NhMz8QSL66wB+DMAvAvhXkFD455jZugM8A+nyAoSuL1ri/nlI+PzhuF8i\neh2A1wHAL/345+O5spl1wpdx6FqAA2iAbVNK9gRYOjLMlhcMPQKGSlDbgJVtWleCfb6nA3jKFeAt\nGfI4ER7zQKFIsisM8UoxACaS6jAFctR9cxJCZO2DXIsjAGlrIGKJOI0Mw92CkyECGZoylMaIyEiq\nBFOTcrRG1nYvN6ijXW+oNOrwlhOuqKgaLDLeIWTZiK8nw2MwYkPsR4xY//KdGPFwOIG3eyjBreGE\n5qS3CyNNfrDDyEJIvIaRuxoh4PMB2l1C4xdCVN7LAPwcgH8K4AvvekLa7/BpAPjYT/0YvikaGq+4\nmxr6KMiZHOg9uG3Z8oHMhFvz9NrUYd4GbBDq5IXCSPTkvh7V+/ehTgFSDmFOJMOlHBAwBHv07k56\ngOQHC4SjErUFYA+rGDbQg5Mfz/OGiSUcrqm4sDMGeKPLWyVDbsNkDjuzYIBVGTJLcxl7lwmVMkoi\nJErYgrFJGRMlJUEhQms7eAxGfJzJjgBtAqrC5CrQhlwzjIzCYRQSErS88bbLCUaM5C5yWMAIDfDR\nOsrjMXJSK7s3OVe7S2j8KgA/ysw/BQBE9C0APhvAC4hoo6owdm+xri/PENEGwEdDiiaLxkx4Lm/c\n4/cVQaBWBQ3MABpw2/BI5vljmNN3jo8A3zVKyKwwEpXgLhJ0kHcevimicCBDdACXhd7jG64rAdIs\nPBZ2o9psxv6XoaEveWYvPidMqmplx2jjoIUwOWOxgAIAmAqQU9PqojABE2Q4M2ihhAs2SYskRQZf\n3aQsPY24kuIII7FqHB1kJMNDMNJ3rVwsjFgzqr4wMiO5wedm+TCMOC72xMgp2fCJVISQkPiVRPR8\nSGj8uQDeA+B7AHwRpHL8ZQCso/Q79PO/0++/ey0/CFg7wk0A99T0HPAeAx0Z+tSKXCdZMnDHuUbM\ny29tYNUQ6syUYGgruFMJbhcA3wC/Arx9Be/vAOeD8j81JOZax1AlSASwTt5kwziVSbZJ0O8mVX4T\nXDEmDY/deIUMozJkiAq0AgpPAFcFyFNBCiHypOrQQlsJkWUMyI0WThKxf7ZtT4UR5jrepGHE84FK\njLXb3EK0UA5Ugj0+BiTYY6SNGlqM7OxVondL8rknIq8nOEf4biJ6G4D/COkv9Z8gIe23A3grEf0V\nXff1+i9fD+AfEdH7APwMpMK84xjATZmQNBSCHiiaqcDYYLeZZc6S452HN1DvAvisMDJSgkZcs/Zg\nOwA+S4bXJPgQ3H0OaBCKNCqPJD9oStBmsfOmNVowSaD6f/7/NfcnG7cf/QB6MrL1gjL0Iom2M9S5\nT2QXwsDbbBNyygk4mSVRh9KImrBJ0mwmVpClYJJ0c94bI6YAzTnGtEmPERtubZEE+8LISAn2OUCL\nFkZqsPC4qMZ9LvlwjDhOaH5Lj7ez6Wt8lN2paszMXwPga7rV74cMjthv+yyALz5o/yDc5KoI08B7\nleDxDfA2QjCH3I/NJharwkU/+3iCo25zaz1GIvFtaUh6Bub6ziE32Hr4hgBLALYpAKACXZd7owxH\nN9s8xF4oIdhUnlxCgUSPVyb77DdNlF+jBPt3IT8ftGa1gAI4GaoyZCZMU/GxEUth8ETgJGUZ61cs\neUIZlGFKRZwjsTvIQzBinw0X+2DExhO0UWS87/A+TWT6aKGLFCJGKDOWiPFUGAEqQfIpE4VPaGh8\n72Zhik3huHRTe09v4HZPzxQm0CFscwW9TaSzCvCeBGdKcMmrY0iCM4A3yiCGPVybU/jkt/Kb+3yh\nmwKbCdLUmWS/ToqTfplUEbLOq5tETcm1q8covTK0w5mAYzmmheFRGTL5EobKEDKXcY7tDVPxvGFh\nQknFw+REjCkVzxMSWcGE98JIxElPgBEjNtHSNst4Rz6oaqFmIvahEhy1JXVs9J/nGBnnCrnmmbnD\nSIeXozByCrMQ/YHaeRMhIMUSBf1wGwM35mS4LXHi9dbDc0x4dx3jeVff4V4JruR73MsPqoFWLZYe\nA9ySo74bsC0BTvKD/PfPew2oLksV7NKmj2ENx4wQ2frbsW6ox7D8oFeNmf36zp4bj2ClmhyVYYKo\nO9uO4zuSVo2TEjIwTaykrOegipGIZ4QYSdBU4V0w4g6zixSaKRgCATYYWepa2SvBlWiBeieZW3zM\n8sd7YmR0SXqMnMwuivB+jLVaZwBfC3vMq9s6IzsDNwdFaMTnlWGmdlDVgv0Bvghse7UAT7Mc4QIB\nljmwR+0Je7MkOLIqMk2I06zZTCBE1iYxpg6N+EIYnCDrrHmNWwoHXimgDPsm674owWvVzEkbe7Py\nspBeT4gEVDUI7I0R+3wwRqwyPMLIUt/hEV4aBxkUXx5gZJQ6YXbcuAo7EiNc5t/dyR4uD545EULy\nOAZua73RN5Z1oCOEPab6DODq4ZmxHwnyQt/hUajThThDAPeh8Oz7HuAWDnEb9kCXd4E4jCgzG2ih\nI0RMutKPoWqO0RCik+GsaBJuDllYbF8FMqSWDKNKBLR5DbOSclCHJJ9TKmCeXA36e8DFXTHCh5Cg\nYSSSYOwxMgxzQxgcCW8fEgwYiQTYY8RJcAdGKPFJc4RUHm5sfN5EqKELgMWwx7YDIsBN5czBLUQY\ncz2QngBeGcZy5a/x4v3nTgmOAK7qsFGFkQDtcwR34Up85vnlR4cLoO8R00VZTy4eMCkhFn2fKiFa\nm0JThwVGjPO84aIybG6IEGlxdC0oQxMtTID2QimJNFRu1WEhCZeNAIUE4WR4SoyUosq41MKZT8Ae\nMTKKFhjSY2ShicxStODh774YsbxhCeR3F4zc1Ri7nfMZ23kTIYDbnBzww20c4PVzBLeFezMVaMBe\nAniT3F5oLN2rvejF8x4Az62HF6AH8Pfgnnn8wTXxvA/D+vt6Q2olRH8YPH1n1CSEVbvS6f9DJnCy\n7YfKcKYSCZSXR63R2FYfHoZ3yQPr+joMPzOJGiSpJpMSIhPrALB0FEaa4loJ7yOMeGEEYxJcUoJW\n4AhRw2K0ECvGUf31KtC2s+HxD8WI4oRpftuONQI/sQ2q792YpTGrqYClbYA29InArkTYqcAA8jqU\nFsZKMIJ6V7e53JGggrgnRCqMtO1U4JYbcIu3jx4/gB3hPVqp0PaeJcIS8Lg4KkSuShDeXIa9+GGh\nsZHfLEw2ZdinB7G7b3ITGrNQsIXGnjdkkazMyc9XmiGqMoSGwdiHCFuMzAgwqMAZRrwFwQAj/Sgy\nfbRQBoWRQbTQNKfKQNq2WIgq8E4YIZLtTt3H7kKE92fmtXeFPQZ25h7kAHjg4RlN+y+o8kOf6N4B\n8GF1uPmeu7C49fRgJUQOYFdwu0J0T6/gb0cuaC+GN40Q0qEEUTBGilOYtMQUIkO6jmg7Qic3piY0\nHoXJQkxUm9J05mTIRoGDMLmWmvV+1VdJRuAtIVp4bKQoP32dCNcwwlo9nkUKqgDbKTc7EmwU4Dxl\n0uaPx9HCLHIIJCiYMVzgxBg5od0zERLRF0NGsPrVAD6Dmd+zsN0HAPwCBJXbtelDzc6aCBnShksA\nPkjSIzq+lgyLtv+LqnAR4BwA7i/SSpyBOQCeI/Ar0HsF0Hr5+GB0Xt48fAP2UsFtk6Yb0FeT4Sxt\nBAHYyAuiCkUBEBdp0sIIvUiAKOkY8jB7eN2ExkFJ2FvudiOHhXVYlnmRhbRYz8tCcZ8qNEVpGTlV\nzqEkQMZFLEqEst+Kjf0x4pfvEIyMHKWFwx7yDjASHCUaDOwmweowO4wwhPwiRjRk9h98CEZOYbx0\nrJPaewH8LgBv2mPb38zMH969mdhZEyGYnAiX7lcEuX/uwA0ehMKMdYDrsgO89/IhrOkbS1sesPX4\npgRCDtBC4W317gZ8I8CG/MzT7/L2BU5gTOSqEETgiUBMEi5zfdEUyGDSXXTKcBYmqwpMYOk2FywM\nNi2nxW2YHPOSDAZxKKDYPbNwXRtKS05LJvckkvUtGR6PEVZM9JOuy1yl0HB4jJFxMQ3dZ25IcN6L\nZECC2+KFs14F7o2RxdC4YuRUdt9VY2b+QQCnD+lx7kQIJTBTHive3oANwEMcf5gsFB4BfKAEoR4e\n/efo5UtUAi3Im+YPzQMQq4DcvLuHN7DnBXBrHqj98Z3ZhUoSEjeqEEkHaGXEjslCJe0uemVohANV\nZLHoIv1MqVGEFL5nKICtUFJk//AmNUJqogztH1m3qftp9ic/xPdzvxihBiN0AEb6V1sRrsszJdhh\nBVpAo8xAKXrsUjFiRHQgRk5jvHysR28M4F+RAPZNOrTfqp01Ecr9VMWCZd/l66MC5ODhOYQ5qgbc\nyzOawkgMX3y2ucaLB49fgvLzbbglxBHAty0JUi4Ccg+PO3AbsAeePnZ05wjqdkBC6UVQJB9HOsgg\nl1LV4ZRquKzTDffKMBZQXBXq5bR8pz1gJkIbI/tjv0E/c6cMk5KqqcMk95MSez2nzsWiJHgsRkIe\nuQmFUfGBJg9IgQADZkbV4t4BhuJZjBxiYWSIkVzg6ZIOI63DPAIjpxJxXI+3w15MRDG393QkKiJ6\nJ4CPH/zfVzPztw7Wj+w36sDRvxzAdxHRDzHzv1n7h7MmQgDSbMEVxeB79/Y19GnAbV4+vFcP3oY6\nI2UYAT7L8eQwQkgO4XEgxF0A9zCnlAVPr+ssMR5/dAc8MlB7Do90tJkq0UwJcikSDluoyQVlI8s2\n9zpgIa4qwy4fZ2TYhMFZSc3IEUDT4No+A95fuQmTVTCChYjl4SYgSaFGSD3OzleV6N4YsctnTvIA\njLT54EHxbLa8w1H2GNmW+t1W8XEARgQHy6HxCCMns/1I9cNrxQtmftVdT4OZP6jvHyKit0MGgXnA\nRKiV3hoSzT2Ok58RH1BDnOjpeQHgMQQahTXdur4dGPrtY6jcNXPow+ERwGvup7h3pxxCniYRvuCB\nUwU5KXk0hAhoAcNqukma0+TixGig9hCXIFV2L5awzIhXLGzWbaj7H3vF0V4tNxXWe5isI+PUcRgq\nccn27JiIovcQjDg3HICRGP7G8HiEkzWM1NB5ASOMnRhxEgwYGeJkT4ycys6hHSERfRSAxMy/oMuf\nD+Av7fq/8yZCiNf2gUZDLiiOOR5zQDXZHjw8UHM5uq6CVr06R6XXgTy8RmPE9Y2kY8gzJMEFJViB\nHlRgBHtUhGugK4CXVgHJFepYXDL0FgNJCU/bDYITMGm7wE0CbQHYFJ8cnX29Hynrei1aGOl58aR7\nxhqVOAxk9dyUrKzfrOcNvdkPnHCtfeTJMBJC5D4cHjpKWz9ThjzfLmIjKMKmiUzeEyO9o+zD4+ZH\n74GRU9j9N5/5nQD+DoCPBfDtRPT9zPwFRPQJAN7MzK8G8HEA3q4FlQ2Af8LM/3LXvs+bCBWk1etz\nA27ZJnp7zIEOVICPSDAA3NUAYzHxDY5A7r18fXhrbqjz7mtKMAC8AXdcBmQ7YBl4zlQ6EGGxiyAK\nkadU9zHpZFQoAE1CKrmAKfnvYqpNiUCqckzBUVVnlZzgShEQjtXD+7Irw8I1dRiVYYE3wfHmNbVK\nooqGYfHwIkaa5T0w0pGgh8oNFkKxpMHOAiYatReJcYCRrr3gI8HIKczO4R6Nmd8O4O2D9f8LMo0w\nmPn9AH7dofs+byIEqhIAgFhBBKqgGJGhfm6JbYUETTH2xNeHO2Fg1SYfGMIdz/k0yoAlf7aLBIsm\nxC3nYwAPOSCO6nBkGvaQFS9SqvGq9uVFqlMduTI09tkkYFuATVKeYc8bMg0qyc173amLt5grRL8s\nRRIQtJmPfqfsKIVtzSJaxRlCoDXPhd0Ysd84wEgTDo8w0mCgD5Ghiq4jvW6blvgCATpG6staEOyN\nEVs+FiOnsjMIjY+1B0CEEMAbuPtrzeE9ev+oAvW7EQlWr76Q8/EHxfbZe33uFAM78UbQwz2+gRfr\nAG/CHVl2cNv4SUttxJR4mFIlPiPEEO9KTlBtSkLUE+RcKZBNFgKyARtMzXHS6+AkFkLkIgTaqkm4\nqLNr5ven1NtHRRWgbsNF1J83r5GtqjpUVWirVzGCgJOIkRVHWe8nVaXXqUQ023Uv5hZLXENiMNdj\naH7YulkuKsEVR7kXRuwaUALPLtYd7EKE92RGbgb44TZ9qAwFGznJjQC+CFonuNFrkPcJ3zUhMaun\n53lobM0f+sLIjARznoO79/T9oHI6d4cUGTKkDWEgxHjNqG1yIstaQCnQNofsxRMrknheruh/EQtJ\nAE2ITKbUAknF9oVs/25nQZCmMqWuM47zkNkqxFEd2s6MFI/ASCS53lHGcPhwjPB8eSkkVsdKmTV6\nKO4wFx3lLoyMBh0k7dOYRhfrSGOsK9Azt/MmQiAAm3ZvY+AOSmMGcH8IIqAHOZ/Svsdwp6n8cQQ7\nN8s1n2gkqR6/FPf+Q4CHMIdzacFtwFbQxYkAhahcDraAV0IU7qhEaFe2IcOUICPHFFAWckRGbVBd\nuIbIpIOm6mmRFjZISc2VnnFdUIamEtnOxfKFPRm68gxhNPWEeCRGIiZGjjIowD533OKs4sEx0yjG\npZA4YCSqwEF1eIQRtuLasRg5mYXjPkA7cyKU4Y5WpxzUr6jPEwaAVwWABtxNDtHIMbz68K0Jh5hn\nDwll2zYkvY38PMHNHv4sevk1gBeuwO66NDFQk9+Zhbh0SCvpV6xD3wM1jIlNcrwKKUNcoWj3PK3k\nyruSlFWfM2kRhF15a4cPn1ReRkWxhKEeWjk13pf4HfmyNNHxTjDcxr6kTMoeIh+HEcdDd6/bHiYd\nRjqFGMPmmDZp9u/bKn4sTRKLH06cgQSN/Lp0yVEYUZxQ+9XdjHHvxZL7tPMmQgORywZdTVxBbduF\n7cEDgFshJO43ADoSJnpgDzx93Xfw6lw9veeCGkKtRNOAOr401HFQmyI0cO+qBmb19kTglCohYhIy\nzHmuDNGqQssL9iEysnwn10JHN1aiIyU6VgLg1OYGnb8K2u55dlzSe6RnwYAXT6y9YqMMPezW/7XK\njPH7XTASsDBThCOMsKm6FjfNd75NzRfOWhIw5mmTGT6K3MPoKA/FCAwHdLqq8a7jnbmdNxECFXgB\n0zTK+fi2cyXoDWV1m/oAUAPckbefKcoB0Pf19KPuUN4LoNTvmnC4B/jA05v3bzqjp9pERkLdDCfD\nXhkW+SFUVAmqYmUnMlWwTLVwQlUVMldypFRTET68ll2PEJKyXjtGvYamAO0a2k+V7STHabdaOG9U\nGTkMI/W+zTFCBc059xFDjBx6LAwjhogRdZaGEcdDJD1b50qx/26BBI/AyEnsQoT3aE3zmYE5wDXf\nNQD4jMBG4LR9dZ6+fSg6Dx9APyZLrus8RK5g9nAnhsRGfqYOe4DbNn596jKDa7MIA7iCfUaGrrBU\novWqg0hDYyNE1lxjvaasIS/lSno1NK4q0a5rkyvsyNCUY0uKemoF2uNEz0d/7y4y3IWRRh32973U\n9bOoodT3JmIYEh83WGhxxfV7tm05kGOIGmJeMGJkRIIRI10BY4iRk5jdtIdpZ0+EpgaqdOisBzfQ\nAtwAG9YLsPfz9COSqw9S/C4kvAPhuRocePo+JK7hzg4SXKsIZsCbRSQSsEcy5CI5QygpUjinoSpk\nzxUy1weWGwdjRKCDrYaeIezbVFUY15naX1OFxtOE8G74UAx46L0LIxEHRnb2ucdId55ranAJI6O8\nsmHE89XuGPU94IAGzagQo4i7YOSEUbE4hkuO8F6NWLG8UOXqSdAAP1xvDxrGQB69ZsSIsA6tqmyO\naRY9ffxsHjR6UvXwsnoB4AHc3Hl8CU1VQkXVZ2EQEZAKgAmweYNHqrBUJjNCrOGyKjNVcUDrVHjw\nXk94fN0Z7fWOqrC9lgMdSHfHSJ82mZ1rOL9meUCMccbB+f+EdoO9YwRajJjFtElQgXL/dpPgIkbK\nKZmwO+cHZudNhAFEq4X+FbLrPf1o38D4AaAGtP0DzC3oZw8C13XFjlEB7SPJOGjDAxHIcIkEeeTx\nKfl6SmMyZNakPrGHyL0q9OMaeTffG2FSDWsZvk/J5YVnYkRso1cXMjeqMETYHiH0ZIgjMTIgN4rf\noROZ8Zz7/c/wExVfe+xZIc3yyT1GosME5hhZcZR7YeRkxg+6anxil3APxt37yvc19AlhW/e/TUgT\nP4fvZ/vv1o0Up4U87XECiPucTaO+Qt6nV4O+vAPg9jluMwqLvKJYxuuBStKoCjj+bl/fkIqdA4b/\nu1Oph+2aYy0RVTynNYyMjqVENzru7Ld2OJm/eBFL9d6Ha2FhsR+HZwQ4HFxjCSN+KG7wtzdGTmUM\nyBSs669ztfMnQmD2IMy88hqg7X20/cJDMnu40X2HASnE5e5+xwqyHGtAitE6NX8lce8AAAmkSURB\nVBhtBvD4AHH7nZNh16yiSaRzSKz3+2rOo/6W5mGPD5Nfy77Z0NK1QrM8Ih9/507xBcXWHrt7he9H\n6cMYMaymTuIx4jH73zj4PPq9DR5G1jiRDi9xuccIl8MxcirTnOXq60zt7Imwfzhm1j0szfKQKLvE\n+dq+MfDycXtGbZTaE0Sf+4nrR59jyAO04DbCGgG8tw7oi9/3oFx6KJfWl0pqroywQDbA2LFgTpR7\nRwDB9sVIPQ+ar991nBHR7rC9csh9frCPInpnuQMj9XzvgJFjrSfc0etM7bxzhL0xWlnQKQdZt9x+\nbPEhPfQcdq3bccNpRJBLhzuUoPrNCoOmbmUpwDSN91O4DkIYzCvI65k43TZcEp5/XlVYzf2N5eLw\n3dLy0mfM7/2SOjzW5sp3tP+VA/TXe4d6WsJFkxJ5lMQziF4ekp29IgQW1Nt93OMd+95ryKL7xt4+\nXnymOpfUoSmLTnX0+3nE+N7HYXlBY/Q/h2KkI7CTOMwDj3un3RxDePdBkhdFeL920r7hdzAmm1vj\nMZqPZX+x+zAfWPZ8n9kzNQZb984HaA+CCBuj8G5CZk/g7tyOuve1bda+P/F8sePj0LKHjX2IE8GH\n5pptpyNYj+avaIb6P+zUlgYRP/b/5XwG24Sb2fzPoRiJofepVBrtkURY2UD6V6/8K1WXTDYqj3+5\nh7M89dzAjLMuhuyyBxEau/X3bvjA8PJDse9+gzHt8SA7+e1nPpHSHuZ9QykJqZ3CRh3tl+a5teH2\nB/Nb8D6/YXTP+ntCC98Rr///0v7XPgNzjJzAuD+vkcUpBbr1B80qN3Js1m2un67zUZo1zVl7nant\nJEIi+vtE9CEiem9Y9yIi+i4i+hF9f6GuJyL620T0PiL6L0T06eF/vky3/xEi+rJ9T5D7h2V2gmG7\n/gEZ/a8+XIv7XTjOErnGbkrtNqq0elXVdHqn+hD4sg2smlbIKdV9Lam4EXGu7bPZ/x4PkP2eROos\njDDjfrrl8NkLt937yHYVee+EkdFy/NyT9OC19Jv9vNPKseJ96DHSq/XegfYOLRLksRg50hhSqNn1\nOlfbRxH+QwBf2K17PYB3MfPLAbxLPwPAbwXwcn29DsAbASFOAF8D4DMhc4x+jZHnXrYGxvi9bxNC\nJnsfeezuAeHu8+g4cZu6TM1yf1W59/Zrs4cl6h6OJKpQQ1wHbgP6HuzdA5GoKkvbn64H2f7TXJkk\nqkowwQnPiW/R8dD8WoZr0VzvhevO1P1vfw+WHNoOjNRjcN0fxt+NCHaRuEe/o7suTKjXNNl1ovqu\n1655ny0nxwj5vTkOIyeLMAB4s56PVEWoM8T/TLf6NQDeostvAfA7wvpvYLF/D+AFRPQSAF8A4LuY\n+WeY+WcBfBfm5Dq2DtD2gIwegGb4uQWibB6wAXjjexP6zbYlX++WqDvPSiIO9j50iUB2Lx7IaXY9\nOqAPXpSoyQ02RNfsq3vodL+8NucttevjdW7IceX67bQByYzWNxjYEyOz/1/AES/sMx535BDafXYO\nsjn2IEzu1brhI2Kkt9F9BfbCiH9/IuOcd77O1Y4tlnwcM/+ELv8kZC5RAHgpgB8P2z2j65bWz4yI\nXgdRkwDw3Af+5J9572i7M7UXA/jw4z4J7Ie38zjX/e0hne/5n2uLkV911939An72O9/Jb3vxHpue\n5XW5c9WYmZnodI0NmPlpAE8DABG9h5lfcap937c9pPN9SOcKPKzzfUjnCsj53nUfzLxfhHemdqwu\n/t8a8kLfP6TrPwjgk8J2n6jrltZf7GIXu9hjt2OJ8B0ArPL7ZQC+Naz/A1o9fiWAn9cQ+jsBfD4R\nvVCLJJ+v6y52sYtd7LHbztCYiL4RwOcAeDERPQOp/r4BwDcT0VcA+J8AvkQ3/w4ArwbwPgD/D8Br\nAYCZf4aI/jKA79Xt/hIz9wWYkT29/085C3tI5/uQzhV4WOf7kM4VeHjne3Kjo/opXuxiF7vYR5A9\nrJ4lF7vYxS52D3Yhwotd7GJPvJ0tERLRFxLRD2t3vdfv/o97P59PIqLvIaL/RkT/lYj+hK4/uLvh\nIzzniYj+ExF9m35+GRG9W8/pm4joWtc/pZ/fp99/8mM41xcQ0duI6IeI6AeJ6LPO/Nr+KcXBe4no\nG4noeedyfR93t9gHaawDbp7TC8AE4H8A+BQA1wD+M4BPfczn9BIAn67LvwzAfwfwqQD+GoDX6/rX\nA/iruvxqAP8C0s/glQDe/RjO+U8D+CcAvk0/fzOAL9XlrwPwh3T5DwP4Ol3+UgDf9BjO9S0AvlKX\nrwG84FyvLaQzwI8C+CXhun75uVxfAL8JwKcDeG9Yd9C1BPAiAO/X9xfq8gsfNS4e2T193CewcCM/\nC8B3hs9fBeCrHvd5def4rQA+D8APA3iJrnsJgB/W5TcB+D1he9/uEZ3fJ0L6gf8WAN+mQP8wgE1/\njSFNmT5Llze6HT3Cc/1oJRbq1p/rtbWeUi/S6/VtkG6kZ3N9AXxyR4QHXUsAvwfAm8L6ZruPtNe5\nhsZ7d8l7HKahzacBeDcO7274qOxvAfizqONLfwyAn2Pm7eB8/Fz1+5/X7R+VvQzATwH4BxrKv5mI\nPgpnem2Z+YMA/jqAHwPwE5Dr9X043+sL3GO32I8EO1ciPFsjol8K4J8B+JPM/H/idyyu87G3RyKi\n3wbgQ8z8fY/7XPa0DSSUeyMzfxqA/4s6ohGA87m2AKD5tddACPwTAHwU9h1E5AzsnK7ludi5EuFZ\ndskjoisICf5jZv4WXX1od8NHYZ8N4LcT0QcAvBUSHn8tZDQga0Qfz8fPVb//aAA//YjOFRC18Qwz\nv1s/vw1CjOd4bQHgVQB+lJl/iplvAXwL5Jqf6/UFLt1iV+1cifB7Abxcq3DXkATzOx7nCRERAfh6\nAD/IzH8jfHVod8N7N2b+Kmb+RGb+ZMi1+25m/n0AvgfAFy2cq/2GL9LtH5liYOafBPDjRGSjoHwu\ngP+GM7y2aj8G4JVE9HzFhZ3vWV7fwTlcusX29riTlEsvSDXrv0Oqx199BufzGyHhxH8B8P36ejUk\n1/MuAD8C4J0AXqTbE4C/p+f/AwBe8ZjO+3NQq8afAuA/QLpA/lMAT+n65+nn9+n3n/IYzvPXA3iP\nXt9/DqlUnu21BfAXAfwQgPcC+EcAnjqX6wvgGyG5y1uI2v6KY64lgD+o5/w+AK99HPh9VK9LF7uL\nXexiT7yda2h8sYtd7GKPzC5EeLGLXeyJtwsRXuxiF3vi7UKEF7vYxZ54uxDhxS52sSfeLkR4sYtd\n7Im3CxFe7GIXe+Lt/wMiI0d3KDX3DwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ffd36d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Plot out the 2d array\n",
    "matplotlib.pyplot.imshow(z)\n",
    "\n",
    "#Plot with a colorbar\n",
    "matplotlib.pyplot.colorbar()\n",
    "\n",
    "#Give the plot a title\n",
    "matplotlib.pyplot.title(\"Plot for sin(x)+sin(y)\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Can use np.where  on 2d for manipulation\n",
    "\n",
    "from numpy.random import randn\n",
    "\n",
    "arr = randn(5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.7235839 ,  0.        ,  0.        ,  0.57087962,  0.28737915],\n",
       "       [ 0.1015063 ,  0.        ,  0.55667718,  0.86806614,  0.38436627],\n",
       "       [ 1.30827364,  0.        ,  0.        ,  0.        ,  0.38961121],\n",
       "       [ 0.        ,  0.        ,  0.22533415,  0.15019898,  0.        ],\n",
       "       [ 0.12217088,  0.97646331,  0.        ,  0.        ,  0.        ]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Where array is less than zero, make that value zero, otherwise leave it as the array value\n",
    "# Useful to normalize a distribution with valid values.\n",
    "numpy.where(arr < 0,0,arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "matrix = numpy.arange(1,10).reshape((3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "45"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.0\n",
      "2.58198889747\n",
      "6.66666666667\n"
     ]
    }
   ],
   "source": [
    "print matrix.mean() \n",
    "print matrix.std() # most common variation in distribution\n",
    "print matrix.var() # variability of distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "print matrix.any() # true if any value in distribution is true\n",
    "print matrix.all() # true if all values are true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "from pandas import Series, DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1\n",
       "1    2\n",
       "2    3\n",
       "3    4\n",
       "dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Series([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ww2_cas = Series([87000000, 4200000, 300000, 2100000, 4000000], index=[\"USSR\", \"Germany\", \"China\", \"Japan\", \"USA\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USSR       87000000\n",
       "Germany     4200000\n",
       "China        300000\n",
       "Japan       2100000\n",
       "USA         4000000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ww2_cas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4000000"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ww2_cas['USA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "USSR       87000000\n",
       "Germany     4200000\n",
       "dtype: int64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ww2_cas[ww2_cas>4000000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "NanSerie = Series(ww2_cas.to_dict(), index=[\"USSR\", \"Germany\", \"China\", \"Japan\", \"USA\", \"Brazil\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "USSR       87000000.0\n",
      "Germany     4200000.0\n",
      "China        300000.0\n",
      "Japan       2100000.0\n",
      "USA         4000000.0\n",
      "Brazil            NaN\n",
      "dtype: float64\n",
      "USSR       False\n",
      "Germany    False\n",
      "China      False\n",
      "Japan      False\n",
      "USA        False\n",
      "Brazil      True\n",
      "dtype: bool\n",
      "USSR        True\n",
      "Germany     True\n",
      "China       True\n",
      "Japan       True\n",
      "USA         True\n",
      "Brazil     False\n",
      "dtype: bool\n"
     ]
    }
   ],
   "source": [
    "print NanSerie\n",
    "print pandas.isnull(NanSerie)\n",
    "print pandas.notnull(NanSerie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import webbrowser\n",
    "website='http://en.wikipedia.org/wiki/NFL_win-loss_records'\n",
    "webbrowser.open(website)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nfl_frame = pandas.read_clipboard()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>Won</th>\n",
       "      <th>Lost</th>\n",
       "      <th>Tied</th>\n",
       "      <th>Pct.</th>\n",
       "      <th>First NFL Season</th>\n",
       "      <th>Total Games</th>\n",
       "      <th>Division</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Dallas Cowboys</td>\n",
       "      <td>493</td>\n",
       "      <td>367</td>\n",
       "      <td>6</td>\n",
       "      <td>0.573</td>\n",
       "      <td>1960</td>\n",
       "      <td>866</td>\n",
       "      <td>NFC East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Green Bay Packers</td>\n",
       "      <td>730</td>\n",
       "      <td>553</td>\n",
       "      <td>37</td>\n",
       "      <td>0.567</td>\n",
       "      <td>1921</td>\n",
       "      <td>1,320</td>\n",
       "      <td>NFC North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Chicago Bears</td>\n",
       "      <td>744</td>\n",
       "      <td>568</td>\n",
       "      <td>42</td>\n",
       "      <td>0.565</td>\n",
       "      <td>1920</td>\n",
       "      <td>1,354</td>\n",
       "      <td>NFC North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Miami Dolphins</td>\n",
       "      <td>439</td>\n",
       "      <td>341</td>\n",
       "      <td>4</td>\n",
       "      <td>0.563</td>\n",
       "      <td>1966</td>\n",
       "      <td>784</td>\n",
       "      <td>AFC East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>New England Patriots</td>\n",
       "      <td>476</td>\n",
       "      <td>383</td>\n",
       "      <td>9</td>\n",
       "      <td>0.554</td>\n",
       "      <td>1960</td>\n",
       "      <td>868</td>\n",
       "      <td>AFC East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>New York Giants</td>\n",
       "      <td>684</td>\n",
       "      <td>572</td>\n",
       "      <td>33</td>\n",
       "      <td>0.543</td>\n",
       "      <td>1925</td>\n",
       "      <td>1,289</td>\n",
       "      <td>NFC East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>Denver Broncos</td>\n",
       "      <td>465</td>\n",
       "      <td>393</td>\n",
       "      <td>10</td>\n",
       "      <td>0.541</td>\n",
       "      <td>1960</td>\n",
       "      <td>868</td>\n",
       "      <td>AFC West</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Minnesota Vikings</td>\n",
       "      <td>457</td>\n",
       "      <td>387</td>\n",
       "      <td>10</td>\n",
       "      <td>0.541</td>\n",
       "      <td>1961</td>\n",
       "      <td>854</td>\n",
       "      <td>NFC North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Baltimore Ravens</td>\n",
       "      <td>181</td>\n",
       "      <td>154</td>\n",
       "      <td>1</td>\n",
       "      <td>0.540</td>\n",
       "      <td>1996</td>\n",
       "      <td>336</td>\n",
       "      <td>AFC North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>San Francisco 49ers</td>\n",
       "      <td>522</td>\n",
       "      <td>450</td>\n",
       "      <td>14</td>\n",
       "      <td>0.537</td>\n",
       "      <td>1950</td>\n",
       "      <td>986</td>\n",
       "      <td>NFC West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                  Team  Won  Lost  Tied   Pct.  First NFL Season  \\\n",
       "0     1        Dallas Cowboys  493   367     6  0.573              1960   \n",
       "1     2     Green Bay Packers  730   553    37  0.567              1921   \n",
       "2     3         Chicago Bears  744   568    42  0.565              1920   \n",
       "3     4        Miami Dolphins  439   341     4  0.563              1966   \n",
       "4     5  New England Patriots  476   383     9  0.554              1960   \n",
       "5     6       New York Giants  684   572    33  0.543              1925   \n",
       "6     7        Denver Broncos  465   393    10  0.541              1960   \n",
       "7     8     Minnesota Vikings  457   387    10  0.541              1961   \n",
       "8     9      Baltimore Ravens  181   154     1  0.540              1996   \n",
       "9    10   San Francisco 49ers  522   450    14  0.537              1950   \n",
       "\n",
       "  Total Games   Division  \n",
       "0         866   NFC East  \n",
       "1       1,320  NFC North  \n",
       "2       1,354  NFC North  \n",
       "3         784   AFC East  \n",
       "4         868   AFC East  \n",
       "5       1,289   NFC East  \n",
       "6         868   AFC West  \n",
       "7         854  NFC North  \n",
       "8         336  AFC North  \n",
       "9         986   NFC West  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfl_frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Team</th>\n",
       "      <th>Won</th>\n",
       "      <th>Lost</th>\n",
       "      <th>Tied</th>\n",
       "      <th>Pct.</th>\n",
       "      <th>First NFL Season</th>\n",
       "      <th>Total Games</th>\n",
       "      <th>Division</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Green Bay Packers</td>\n",
       "      <td>730</td>\n",
       "      <td>553</td>\n",
       "      <td>37</td>\n",
       "      <td>0.567</td>\n",
       "      <td>1921</td>\n",
       "      <td>1,320</td>\n",
       "      <td>NFC North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Chicago Bears</td>\n",
       "      <td>744</td>\n",
       "      <td>568</td>\n",
       "      <td>42</td>\n",
       "      <td>0.565</td>\n",
       "      <td>1920</td>\n",
       "      <td>1,354</td>\n",
       "      <td>NFC North</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>New York Giants</td>\n",
       "      <td>684</td>\n",
       "      <td>572</td>\n",
       "      <td>33</td>\n",
       "      <td>0.543</td>\n",
       "      <td>1925</td>\n",
       "      <td>1,289</td>\n",
       "      <td>NFC East</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>San Francisco 49ers</td>\n",
       "      <td>522</td>\n",
       "      <td>450</td>\n",
       "      <td>14</td>\n",
       "      <td>0.537</td>\n",
       "      <td>1950</td>\n",
       "      <td>986</td>\n",
       "      <td>NFC West</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank                 Team  Won  Lost  Tied   Pct.  First NFL Season  \\\n",
       "1     2    Green Bay Packers  730   553    37  0.567              1921   \n",
       "2     3        Chicago Bears  744   568    42  0.565              1920   \n",
       "5     6      New York Giants  684   572    33  0.543              1925   \n",
       "9    10  San Francisco 49ers  522   450    14  0.537              1950   \n",
       "\n",
       "  Total Games   Division  \n",
       "1       1,320  NFC North  \n",
       "2       1,354  NFC North  \n",
       "5       1,289   NFC East  \n",
       "9         986   NFC West  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfl_frame[nfl_frame[\"Won\"]>500]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.5524\n",
      "0.0135417051282\n",
      "0.573\n",
      "0.537\n"
     ]
    }
   ],
   "source": [
    "print nfl_frame[\"Pct.\"].mean()\n",
    "print nfl_frame[\"Pct.\"].std()\n",
    "print nfl_frame[\"Pct.\"].max()\n",
    "print nfl_frame[\"Pct.\"].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Summary Stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   2.,  nan],\n",
       "       [ nan,   3.,   4.]])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = numpy.array([[1, 2, numpy.nan], [numpy.nan, 3,4]])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dframe1 = DataFrame(arr, index=['A', 'B'], columns=['One', 'Two', 'Three'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "One      1.0\n",
       "Two      5.0\n",
       "Three    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe1.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "One      1.0\n",
       "Two      2.0\n",
       "Three    4.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe1.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "One      A\n",
       "Two      A\n",
       "Three    B\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe1.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>One</th>\n",
       "      <th>Two</th>\n",
       "      <th>Three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   One  Two  Three\n",
       "A  1.0  2.0    NaN\n",
       "B  NaN  5.0    4.0"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe1.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>One</th>\n",
       "      <th>Two</th>\n",
       "      <th>Three</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.707107</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.250000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.500000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.0</td>\n",
       "      <td>2.750000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.0</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       One       Two  Three\n",
       "count  1.0  2.000000    1.0\n",
       "mean   1.0  2.500000    4.0\n",
       "std    NaN  0.707107    NaN\n",
       "min    1.0  2.000000    4.0\n",
       "25%    1.0  2.250000    4.0\n",
       "50%    1.0  2.500000    4.0\n",
       "75%    1.0  2.750000    4.0\n",
       "max    1.0  3.000000    4.0"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe1.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rank</th>\n",
       "      <th>Won</th>\n",
       "      <th>Lost</th>\n",
       "      <th>Tied</th>\n",
       "      <th>Pct.</th>\n",
       "      <th>First NFL Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.50000</td>\n",
       "      <td>519.100000</td>\n",
       "      <td>416.800000</td>\n",
       "      <td>16.600000</td>\n",
       "      <td>0.552400</td>\n",
       "      <td>1951.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.02765</td>\n",
       "      <td>167.434531</td>\n",
       "      <td>127.578995</td>\n",
       "      <td>14.893697</td>\n",
       "      <td>0.013542</td>\n",
       "      <td>23.858146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.00000</td>\n",
       "      <td>181.000000</td>\n",
       "      <td>154.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.537000</td>\n",
       "      <td>1920.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.25000</td>\n",
       "      <td>459.000000</td>\n",
       "      <td>371.000000</td>\n",
       "      <td>6.750000</td>\n",
       "      <td>0.541000</td>\n",
       "      <td>1931.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.50000</td>\n",
       "      <td>484.500000</td>\n",
       "      <td>390.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.548500</td>\n",
       "      <td>1960.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.75000</td>\n",
       "      <td>643.500000</td>\n",
       "      <td>527.250000</td>\n",
       "      <td>28.250000</td>\n",
       "      <td>0.564500</td>\n",
       "      <td>1960.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>10.00000</td>\n",
       "      <td>744.000000</td>\n",
       "      <td>572.000000</td>\n",
       "      <td>42.000000</td>\n",
       "      <td>0.573000</td>\n",
       "      <td>1996.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Rank         Won        Lost       Tied       Pct.  \\\n",
       "count  10.00000   10.000000   10.000000  10.000000  10.000000   \n",
       "mean    5.50000  519.100000  416.800000  16.600000   0.552400   \n",
       "std     3.02765  167.434531  127.578995  14.893697   0.013542   \n",
       "min     1.00000  181.000000  154.000000   1.000000   0.537000   \n",
       "25%     3.25000  459.000000  371.000000   6.750000   0.541000   \n",
       "50%     5.50000  484.500000  390.000000  10.000000   0.548500   \n",
       "75%     7.75000  643.500000  527.250000  28.250000   0.564500   \n",
       "max    10.00000  744.000000  572.000000  42.000000   0.573000   \n",
       "\n",
       "       First NFL Season  \n",
       "count         10.000000  \n",
       "mean        1951.900000  \n",
       "std           23.858146  \n",
       "min         1920.000000  \n",
       "25%         1931.250000  \n",
       "50%         1960.000000  \n",
       "75%         1960.750000  \n",
       "max         1996.000000  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nfl_frame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas_datareader.data as pdweb\n",
    "import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prices_frame = pdweb.get_data_google(['CVX', 'XOM', 'BP'], start=datetime.datetime(2010,1,1), end=datetime.datetime(2013,1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prices_frame = prices_frame.to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "prices = prices_frame['Close']\n",
    "volume = prices_frame['Volume']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "rets = prices.pct_change()\n",
    "corr = rets.corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11740a810>"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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S2TBSX3TPX9E5+ExJ5F3HMdCgegXOblcvpOXk2EldEvySQOv6VWOdhLh23WnN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\nfkIIIVKOBD8hhBAp5/8B/gJVtOwJN7EAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1174d4290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "% matplotlib inline\n",
    "prices.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import seaborn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date        minor\n",
       "2010-01-04  BP            NaN\n",
       "            CVX      0.336602\n",
       "            XOM     -0.125348\n",
       "2010-01-05  BP      -0.138539\n",
       "            CVX      0.336579\n",
       "            XOM     -0.128109\n",
       "2010-01-06  BP      -0.137424\n",
       "            CVX      0.329826\n",
       "            XOM     -0.120683\n",
       "2010-01-07  BP      -0.145101\n",
       "            CVX      0.325259\n",
       "            XOM     -0.120131\n",
       "2010-01-08  BP      -0.140401\n",
       "            CVX      0.324500\n",
       "            XOM     -0.125204\n",
       "2010-01-11  BP      -0.109753\n",
       "            CVX      0.306835\n",
       "            XOM     -0.130811\n",
       "2010-01-12  BP      -0.125178\n",
       "            CVX      0.307480\n",
       "            XOM     -0.130083\n",
       "2010-01-13  BP      -0.116512\n",
       "            CVX      0.291262\n",
       "            XOM     -0.126942\n",
       "2010-01-14  BP      -0.113966\n",
       "            CVX      0.288676\n",
       "            XOM     -0.124073\n",
       "2010-01-15  BP      -0.115385\n",
       "            CVX      0.285367\n",
       "            XOM     -0.127729\n",
       "                       ...   \n",
       "2012-12-17  BP      -0.526453\n",
       "            CVX      1.605610\n",
       "            XOM     -0.182278\n",
       "2012-12-18  BP      -0.527175\n",
       "            CVX      1.620181\n",
       "            XOM     -0.186558\n",
       "2012-12-19  BP      -0.529812\n",
       "            CVX      1.610069\n",
       "            XOM     -0.195342\n",
       "2012-12-20  BP      -0.522501\n",
       "            CVX      1.613782\n",
       "            XOM     -0.194691\n",
       "2012-12-21  BP      -0.526156\n",
       "            CVX      1.604701\n",
       "            XOM     -0.204904\n",
       "2012-12-24  BP      -0.521724\n",
       "            CVX      1.603787\n",
       "            XOM     -0.199853\n",
       "2012-12-26  BP      -0.520479\n",
       "            CVX      1.602207\n",
       "            XOM     -0.197216\n",
       "2012-12-27  BP      -0.521994\n",
       "            CVX      1.607400\n",
       "            XOM     -0.199595\n",
       "2012-12-28  BP      -0.525443\n",
       "            CVX      1.582484\n",
       "            XOM     -0.200564\n",
       "2012-12-31  BP      -0.510693\n",
       "            CVX      1.597022\n",
       "            XOM     -0.199649\n",
       "Name: Close, dtype: float64"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "dframe = pandas.read_csv('reading.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>q</td>\n",
       "      <td>r</td>\n",
       "      <td>s</td>\n",
       "      <td>t</td>\n",
       "      <td>apple</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>pear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>s</td>\n",
       "      <td>d</td>\n",
       "      <td>f</td>\n",
       "      <td>rabbit</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>dog</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1  2  3       4\n",
       "0  q  r  s  t   apple\n",
       "1  1  2  3  4    pear\n",
       "2  a  s  d  f  rabbit\n",
       "3  5  2  5  7     dog"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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 },
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