{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Indexação de Numpys e Arrays\n",
    "\n",
    "Nesta palestra, vamos discutir como selecionar elementos ou grupos de elementos de uma matriz."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criando um simples array\n",
    "arr = np.arange(0,10) #* 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Mostrar\n",
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexação e seleção\n",
    "A maneira mais simples de escolher um ou alguns elementos de uma matriz é muito semelhante às listas de python:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtendo um valor através de um índice\n",
    "arr[8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtendo valores em um intervalo\n",
    "arr[1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[0:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transmissão\n",
    "\n",
    "Numpy arrays diferem de uma lista Python normal por causa de sua capacidade de transmissão:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([100, 100,   2,   3,   4,   5,   6,   7,   8,   9])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Configurando um valor com intervalo de índice (Transmissão)\n",
    "arr[0:2]=100\n",
    "\n",
    "#Show\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Redefinir matriz, veremos por que eu tive que redefinir em um momento\n",
    "arr = np.arange(0,11)\n",
    "\n",
    "# Mostrando\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Notas importantes sobre fatias\n",
    "slice_de_arr = arr[0:6]\n",
    "\n",
    "# Mostra a fatia\n",
    "slice_de_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99, 99, 99, 99, 99, 99])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Modifica a fatia\n",
    "slice_de_arr[:]=99\n",
    "\n",
    "#Show Slice again\n",
    "slice_de_arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Agora note que as mudanças também ocorrem em nossa matriz original!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99, 99, 99, 99, 99, 99,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Os dados não são copiados, é uma visão da matriz original! Isso evita problemas de memória!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([99, 99, 99, 99, 99, 99,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Para obter uma cópia, precisa ser explícito\n",
    "arr_copy = arr.copy()\n",
    "\n",
    "arr_copy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Indexando uma matriz 2D\n",
    "\n",
    "O formato geral é ** arr_2d [row] [col] ** ou ** arr_2d [row, col] **. Eu recomendo geralmente usando a notação de vírgula para maior clareza.\n",
    "\n",
    "https://pt.wikipedia.org/wiki/Matriz_(matem%C3%A1tica)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 5, 10, 15],\n",
       "       [20, 25, 30],\n",
       "       [35, 40, 45]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d = np.array(([5,10,15],[20,25,30],[35,40,45]))\n",
    "\n",
    "# Mostrando\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, 25, 30])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fila de indexação\n",
    "arr_2d[1]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# O formato é arr_2d [row] [col] ou arr_2d [row, col]\n",
    "\n",
    "# Obtendo o valor do elemento individual\n",
    "arr_2d[1][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Obtendo o valor do elemento individual\n",
    "arr_2d[1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 15],\n",
       "       [25, 30]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fatiando um array 2D\n",
    "\n",
    "#[[ 5, 10, 15],\n",
    "# [20, 25, 30],\n",
    "# [35, 40, 45]]\n",
    "\n",
    "# Fatia (2,2) do canto superior direito\n",
    "# arr_2d[Linha, Coluna]\n",
    "arr_2d[:2,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([35, 40, 45])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fatia inferior\n",
    "arr_2d[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([35, 40, 45])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fatia inferior\n",
    "arr_2d[2,:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Indexação \"Fancy\"\n",
    "\n",
    "A indxação \"Fancy\" permite que você selecione linhas inteiras ou colunas fora de ordem, para mostrar isso, vamos construir rapidamente uma matriz numpy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d = np.zeros((10,10))\n",
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Tamaho do array\n",
    "arr_length = arr2d.shape[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.],\n",
       "       [5., 5., 5., 5., 5., 5., 5., 5., 5., 5.],\n",
       "       [6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],\n",
       "       [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.],\n",
       "       [8., 8., 8., 8., 8., 8., 8., 8., 8., 8.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(arr_length):\n",
    "    arr2d[i] = i\n",
    "    \n",
    "arr2d"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Indexação \"Fancy\" permite o seguinte:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2., 2., 2., 2., 2., 2., 2., 2., 2.],\n",
       "       [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.],\n",
       "       [6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],\n",
       "       [8., 8., 8., 8., 8., 8., 8., 8., 8., 8.]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[[2,4,6,8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],\n",
       "       [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.],\n",
       "       [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.],\n",
       "       [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Permite em qualquer ordem\n",
    "arr2d[[6,4,2,7]]"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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+++32aC455oWFhaGhoQMHDuzevXtkZAR5UXLAwOM1GsC7du0aHx+vBGBjeMeOHZOTkxUC3N3dPT09XQnA3Lp79+6tEGA8x9zD69evx1l43XXXcVevcDEU8lg5MT7+9NDQa2fPmjHiCXHQPTl5ZZ5PpbOzs9/Yvj0O77ru7reMjzcniZ7auTNeoeUkSsBaxsoXAxVlACW0bds2nm733HPPV7/61fL3AiXU29vLi+mePXsYTioBuK+vj1GkQoCRbvv370drIt0qwTDSbd++fVUBDKUwDOAKieOtW7fiJkRlzs7OdnZ22l3xhvLfHJkgfPPu3XdNTfEbqQ1vyvcGB50SsrN4Tb43MBCxzPbwtYWFxApfPHToqd5ePFWJZ7PKvH1kJJGNrOpXPWKgeAb+/M//vKur6/z585/85CcroYR4EPf09MzPz4+NjVVCCQEY6YYSqgpguAUwSgjAVVFCAEZYoDWrAph72ABXwuvG+xJxhCghPMeEN23ZssU9qVaLZ+iKjo7IVJFRgE/IErhkkAhMWj29dy85z+/fj/OmOQ+NIzc9cfWWLTf19V23fTvYsDw9NWUzeiiz5ycnb21WjVm16U3n2q/0pnVWDOTBAE7vv/iLv3jjG9/4J3/yJx//+MfzaCLbOhk5eJ+mTuZBGE6yrTyP2sCJT4iap6amGLDzaCLbOk26UefExIQhz7b+zGtzgJFudm9k3kS2FQIYYpEXVQHM+xLE4jnmEfFnf/ZnUTZYWm8/R/fs+ae5uctHaf8HWgaapbWUdO7CmTMvTE1Ruf2enJggJ8lwMQ+bh9es4ZdE3Ib+2ln+dX2f2bUrpUi8kgxzvrt7dytNW9knd+wIh9REkfDKZSkGimHgzjvv5LnG9D+egGJabLGV4eFhABNgMRf2yG2xudaLM9oZ4JmZmdZrK6AGA8x7f4UAb7z0UxXASMxqAUb9rL30gxfWvwO5sfdckgdrXK6Ni1/v7v7+2FiKvMA+0DLQzAEISThxY5W7fxO1DhU6+0QDeupAutb/+/CwZTakKlzxVhIpaNFqnLVftGBiK03AbqJIYtPKFANtYQD1g7ubxxn/VkUJEQsCYAIs8NK3hbRGGzXARB9XBfCePXtMulUFsBPHVQHsxHFV1Lx5i3lfQhJF7n8nhqIxQ8SsEEPztc7O/3dggHki7Gr9BFoGmtVqJZLPNFYkxw6ZYCIOOvFUSuYrc3N21iaqLH3dZZe1v94qpZJMTr08Pw9+VrRZbW8eHIxU+8r8PH20XybUImf9Qyy5dqwX498mOPGrUloMlJkBC5dm+h/PEAvH/On/csImXLpaARYW323BvJVY1gRg5kFGR0cRcEziVGIdFoBZhwXgSjDMXxaABwcHUfMAdtHH5fyLM1RstMEEGUqIjcdS5tCXxNAH5uZuHx4m4oTyhK38YN8+ljs90dPzzMhIJJ430DLQrCESUS1de/a8Z2aG5VH2y6HV8PzERENV+cYIIPpIT9P1n18kqzSq5b+tXfu1ri6L44b/7omJay7HtzfRyivHj3PtUHL8S51cweI71QRsFREDDTHgwqV5uj311FMNlW2LsUWEsHCMt+pKhEtb9LGFS1clmNeij3G0VAWwE8cALr9088VxVcKlb775ZrYcw3P8yiuv1HlfiriMOFyYmWE65sCWLTaHYv8SzkJ+xDjQMtAsUnngITN6Dme8CB2xsyTiZ5kIc2ctXufRjRuZhEqpMF5JizkOgzXKzF1ihT6qxL5QysGOJLiUteY9zbL4CcHEPipTDAQywHoxm/6PO70DayjYjKDjagVYELligEFeMFfNNecAozWbq6HgUgC2sblCgIly40WiKoC/9rWv4Q0CMBttpFxcDPZckgcJq8lYasTv1pGRH05OMiPDyiZzFOGViaxCCrQMNAt5XcN/Mzc8fJq9Ci9tGRlSJNDGZsr8+bLAgi2aRWbl8OX8YGzsrunpyC5HV3d2Oh/Y9TX2E2L7AE6xcyNljShbnrboKxofv+3yurkWAau4GGgvAx/84AcfffRRc3rXedVrL9DLrfPSz7QCAwkv05WYVjDAcAvgSqx0AzB788A3EzeVAOzWEiKJKgEYYqu1lhDP8X333cctwYvTgw8+ePlvMe3/BDHkzBmAA8VBoGWgmQMQSTDdwx48kT2BIjYNHSJEXGAQ4g8lQU6tsKSGag43pl0zRr7QO0Qevwg+l29nmTiL5MSb+JnLIUecQg+ZvekhRK3EUJwx5VSOAdstjQ8UPPHEE5VQQgRYEHNTrb2Pibmp0FbCyKBqAR65vFlztcQxtwSisxLSrbmNNhLEEMG8OCdwCOFRcM9KvA58p8IdWiLQMtAsUnn8EInglBBuErdDT8rOy/FKEnNMAEU2Irpi48ZE45wykS9sdGR9gfy60icEBvLOxFDBCi8Em2zEQEMMuN2lCZeuRJAQARa8TBNzQ2wso0hDnW2LsUUfV3SzZhRG+WNuuKwmjiu0WbNpzQqpefsuYROe4yUxhGRhDCYM2Z+BIp6XlU1vHhjwZ20CLQPNwv/s2QjRjFFCTiukfJCrbs3IKbNhBZYzdmu13ASWO5V3As/Z63g8GdpKo26fa1dzK7WprBhoFwM4vX/t137tn//5nwmXTtgtrV2wardLuDTDnoVLk6htWJYzFhsLYKKPbcqpLMhq4HCACfhACdWwKlG2E8cVAsytWyFxzPvShz70IdtduonvEi6JIRY0+TeOuYIizhIzCLQMNPMbbSLdis+D8BprER8Yoso6S0CSZdYSQyz+MgNfkzWKnOao35eY1IAYtXpsTZ9fJ/bOAZbYLtIzvgYND59VUqsvfhNKi4FyMvCpT33qD/7gD8CGDEpZGVse8LZwDDzarDmniwLDeN2QFwTzVkJrVk4cs5YQhiuk5lv3HC+JIbtr+SoFriA+ExEZp+P3dKBloFm8/ngOVZnXCr1ChA0IkQjMncUtA3Oo4aZdu8zhxMfI6DUuIucYi2/2E1htiNliGPiBA3wR1skUmnauL6fSQqoyG5vZpAuuiD/RGZ/idGZKiIEyM8D0v+0R8sgjj7z//e8vM1TDZtHHFQqwsOhj5piqEi7too+rEi5duehjp+ar8iUTFy7diud4SQzhCmL4j6wXS3z0BFoGmiU2kZjZtXfvoUtxS+iVv960yWxajOwhTAdpRSgSziELr7Fq8b7EHS2JqFrJRA+5CG5XDz0ClTsMT0S64ApyIRI9fM5ACTFQTgYqFy5duWDeygFmRowtCiu3NK9a4pjFjxVaS2jh0jzBHnrooVY8x0ubLrIWKUQJ0WSgZaBZ+FP4TX19bAvpqx98RaxCD68hbonioQZ/Wor6E+ehrCyzUa6SVlxHhDb7jS7VuXv3e2Zn67rlnL1LXN3VFa+QnLeMjfmrzJy9EmKgzAzg9HYfl37uuefKv3DMIkIqtPcxgJlgMsC4LqoSfYwSqtDexzCMsCD6uCpOLMSxbQOBc6gSC8fYaAO30Lp16/gwSCtKaPFhmLIZUWlPsQMhv/FNIOOA2Z/Q9hVkT0UrFbexHGozg1r7E5qZ+5wZFdaqKjyftqzRdGzNVeg+PRsv7jqrTRfj5Cin7QwQG2S7pfF0azuYEAA8iHn153FK9HGIfdtt+AaWAbbt5tqOpy6AKgJGtHFLsJawbu/KYADDQK0QYEjL5LuEdNn+CiophsJvHSeG3KbM4WUTLW2jampLkRqJBUuVGdn2WjtQl+rqrHIwzIMkfly6tLS4vY8JsCgtSB+YA1yVrYQBbNKtKoCdOK4KYKc1q6Lmie+29yU22vDv7SbSTgwtxQyRpZ+6DNhaM6JwCogoqgtGBmJghTHgwqWJ6m3V6V0INRYuXaEAi+pGH1dls2YXfVy5tYRVWZqX10YbTSgpFREDYkAMZM4AL3kIGIJXeO3LvPI8KsS7DmCcFrxY51F/5nXy3l8twAzPBhhfS+Zs5FGhAUYcI93yqD/zOgFcra/mZf5dQm6wVTFNlvmtowrFgBjInAHUD1/Y4KlEEEDmledRYRUDLCwihECWqkg3A0z0cVUAV04cVw4wn1zlKcEEGR9hzerv2omhtdTIgX7EgBgQA21h4OGHH/7Yxz728ssvM+yxVqgtGBpq9KWXXmIUeeaZZ/r7+23Abqh48cZVBPyZz3zm8OHDbCiMJ6B4xhptEYY/97nP8b28CgH+/Oc/z95dAP7EJz5x7bXXNtrl4u1/67d+i92lM/8uIRGK/DkvhipKDBV/UdWiGBADjgHmFL7//e+7QyXEgBgQA4kM5PFdQieGFECdyLkyxYAYKIiBN7/5zbyYPvjggwW113IzNrnADj0t11RQBQA+/IPTa7b/u4Laa72Zv//G//js9/6Pf77Qek3F1PDYmjXfWLPmM8U0lkUrBpgQ7ywqK6KOffv2sVoz1y80SwwVcSHVhhgQA7UYuOKKK2688cbeyx8KrGVWnnx2gwRMxQD/44/XbN5aHg7rIPnhsavf8Ma31TEq0envXMJSOcAVuocL0G1LO1CX6M4SFDEgBsSAGBADYkAMFMWAxFBRTKsdMSAGxIAYEANioJQMSAyV8rIIlBgQA2JADIgBMVAUA4oZKopptSMGxIAYKA0De+55a2mwrJk6+pwP5uT/t26tf3wpzb7/V3R0xLJLkbHx8cfXHDzIF75LgSYJxIuzs6+eOePOXHny5JoXXnCHSsCAxJBuAzEgBsTAqmNgpO+u8vR59P7vLgNz7vy/WXa8eHBTf/8NZY2y/8mhIcTQ1pGRGOqyZHyrt/fc4cMOzasupcRlBjRNdpkJ/S8GxIAYEANiQAysSgYkhlblZVenxYAYEANiQAyIgcsMSAxdZkL/iwExIAbEgBgQA6uSAYmhVXnZ1WkxIAbEgBgQA2LgMgOrJYD6xPj49wYH3zI2duvAwOW+L/v/v61dWr5w/Y4d75yeXnb60sHZ2Vli8l+Zm+Poik2bburru6azM26Wa87L8/OvzM/TxHU9PVfG1lacmp5+audOB+CuqanEkMNXFxa+1tlJDYnddMWVEANiQAyIATGwGhhYFWKIsf/poaGrt2yppYTclba1kVd3dbkcl/DVkmU+vXcvqz1vHxmJixJXKvPEob6+Fw8dotpEoXN1Z6d14fnJyVeOH6/VOoDfPDAwNzqKRqzLSa1KlC8GxIAYEANiYGUwsCrE0Nzw8Gtnz6Ja6l6zlLWRV2zceH1vL2rj+kuul9NTUz8YH//Bvn2vLSz0TE7WrTkTg2NDQ6aEatWGp8q6sOjBqi2GKN61dy/4YUZiqBaZyhcDYkAMiIFVwsCqiBli1OdyMqvVykX9wPw8ogepwcQTvyTuujSV9vz+/UyftVJzYFkmyOhIVvt64RyCEAQTzqFAADITA2JADIgBMbAiGUjwDBF3gl/htTNnCIvBF7Kxp6dWz7HklEWlIAien5jgkK2xIkUCzSib3jRN4IZJjJWxSBrcNvEgHgZ73ELMZ7U4mRUvTjev6+7GVQOqWhRlmP+9S9FOeHSY3sqkWq4Uni2umpxDmfCpSsSAGBADYqCiDCwTQ8zCxAdaoonfMj4eFxl02GJ1/9czZwhkOX3ggFFADbcPD9/GjpyXf0LMQpr+wdgYgzeukfhk1lO9vTg5uicm4jiZzwLIddu3X4aT5f/FyCAQI+lgmADwuCZruj+mYqkWKRnnrelqVVAMiAExIAbEQLUYWCaGcAjh6sAbZGExjPS4DZgGQuu8t/ZMEB4LCqJRiDtmpRVrnfAqxVlINwtp+s2Dg4uejEtzVX79eIxQQsRHvylpIuz0JfcVnfKLZJJ+ZmSEdoklwlmVSYW1KrEAcFRp5i4c6kQMQdE1NRbZ1YKkfDEgBsSAGBADK4aBZWIo7gFCXjzR08NM0A8nJxOlBkQwlL5ndtZ3LTB4xwlKNwtp2k1LRcDgMaK5xJAgfB7MkXHWhxfHFp6Dh8aW1tMdaEGBdU9OZuitSUSCjgwMAE8snpKJjEMMWY9SzHRKDIgBMSAGxMAKZmBZAHWiYjCfyoszM7VYYJVWpGCiOEg3i9RgbcWbxjnEKZv5cnjwFZG2Uy7TErYlD/6PSH7Th7jKmAfkFyWETwgNFwmQarrmWgWJo8I5h+Mtv4Zwy9VqXfliQAyIATEgBlY8A8s8Q/QWp87iFjVzc26AND2RQkSiSyZuX9cspGkqYccgJsvc7j4WH33Trl2JcioOo8Uc2rU4IfhhXTrhUIRm/0xuC7LgBLcQqou46RaRJxZnPjQeJZZoqUwxUCEGFhYWxsfHz1yar+/v7+/JeSI7E2ZmZ2eBTVUdHR0lBwzU6elpo7erq6u3t7ez8O1nG+IctGA2wJs2bQJwyRn2e+duDEguJ8/Q6wP201DtH5Y5vUwMISyQGjavFB4Kk+gHivc53SywaSpBD1nkkAXQmJfIgpzijWae49wzRB+zbyH7OAOGZVkWjJx5c+gtwpKyjZvOHKQqFAOlYgAZNDQ0dPbS/DjARkdHd+zYMTk5icgoFU4DA9qJiYkDlxegkAnalNGlvV2IcOvA7Nq1i1MlZBglsT1p9Ux3dzeAyy+JuBN2Xv6owJ49e0YCdstzF6WwhEMYb/HixYvxzHLmLE2TEV5jSoi1YCwQu3thgW818FvXo9N6xxpqGuVBixYntOhM2r8f3ZZ5ZHFIp1Bm6CEsbU+BkCKN2pjbhvq/1dvrfq0SLhY5LW5xFJlwbBSe7MVA2Rhg8BgcHHRKyOAhNQbKukQgooTKxmcEz9zcXIRbM9i/f385fQDmbIv0gsNDhw4BuNbZuH27cpD17Wp6tbW75Blidsz24/FXxUNHAavHG2oaHwxhy4TsIKFejxaq/Zhj5yG6QIxwrte17kxii60n4oeBrK5O3qvhWuy+iouBcAbc4MGrP6/ROAb2XppiZrQmXVpPwMaNG5kBYYQO72m7LLds2YLctJkmxMTw8DC+N8AAHvdbX9KS3nZBtXYBDCr8Q+a4mpqaMsCoOgCXViUDnpu5EreEz3N7r3UrrS+JIVsPf0XMk8yyqVYaCCnbaNPESvNdMJxDhi0xdNraJZAIvxEiD+WUR1CRKUWTXJGe0qKJpMRdIiPGtQ7/bZKP0b6Slvhtslr11Mq3yLDEb7HVKqJ8MVBaBubn593gwTiHvGDMnpmZQQmBGR9MCcUQWo35GqD6EyKlZRgZ5M/UIC84BLnRDtVlE0PcANwVPp/mENq3bx+ZOLr8U6VKA9tEG2LueOq3lUoCm3vYvzdKgiocxtI0GftNU8zFTVsVx4aGiFkJr645y0abtskpvk2Bd4SNkdJVji1Ja1HSsaQrPiFFnBMBQ3Q5cUdHtBrh1fxGKG2OopxKGbY8NmHKCbCqFQMpDDAq21ncQjyaLe1CRvAMpZRt1ynGZge1XRjC202EWsJQofQeVQKwuay4k8umL9O5re7ZJc/Q4mfMh4eZkSESxX2LlEP74kSuPWy06cVgnd27TYikuIUMM30hrmhxa4Das2l1e0dsDeE7TM+hG8x5ZvsMUZCFbG2JWKqLua7B4udNzp6tqybr1iMDMVASBtyLvj/aOW+QH6RcEsArAAYzZU5lOt1Z2n7hbkEx44ozhDi6ygkVF4vdrkDFo1lOkBFUcIuA418UM3dCmecfI8jtcEkMoTD48qh9WMOCVJhgYh3T4jL7nGeym2gaiWNiqG58N0olsho/kYj0THw/JgqtUTOGH2Tc1hrh/TZHhn7KaaFZOuCQsxaEXldNhlQlGzFQKgYQQAzSDCT4XUoFbOWBIWbIQqpL7sPgTvDVMHNPCI5ER1fbrxF6AlaBwfIx7uSqiCHm8mzyEZ5JjI2N8QfoXkXazmpdAEtiCFPWjf8v8/OLDoNL2124UbzWeJ8Y0RJvMsSs0aYNYeDnVxc9T6OjRFs37cJh921+Wbzm5rzwD7ll9vEuk2MTc3nsDxTCZyIkP9N2dUKrNc2JX5vSYqAMDDgXBWAIPuWJTGAywUNlwLYiMTDaWVwLvSNdoT4SqlXa6SduXfSlybUKURqBShgZDPMn6btpIzalOlyKGXKwGOORQU4JufwCEuFNM6MHHltmXxcYigQvjhVJN7bl60RKJZrhwTJm+DddCdkMVPFSg3atC060JXaETNhgjiwPrVarReWLgSIZsEdwVR7ERTKTVVuoHzfNhBug5D4A4LGBEz/WfZCTU8Kl9Wh3i/evkLjcvXs37iv2tGRXIf7FoWUk4yuqUC8SxFBWfyo51YNLw8K6w2egEDHsHE0kOCHP6aiYH+S3rpJIr4Sz+bmF0pvGYWZdQOikWMIh4edM/MktlMKSTlWOAX88ZhKERdS+r6hy3SkzYIjFgWEIUULlDxDhfiBaiB9Ga2b0QI7rwmajSsWzMWmizQA7xUaCHCbRSgUYMCgenEDu9QOqnR7ib7BsaGvhWTZNVsuoPPm2qhw8eHr4Qmo4MEb99IE/k7knh4e9miLbNblTuSZwWYV0BHXIppq5IlHlYqCNDJgGigQMMWXWRkgrqWnohVsLFaqEEvLJZ8xmtLZNk3HDkPbPtj1trBJ2w08EDNO+/JR2H2ofLfTa/GmF3kYqJobsk6vs3EPYb/qKev/CKC0GxMBqYMCtZvLfnt27qe83Wg1s5NTHSish48TFTVdi/56crmOu1TpvlqM61+YyqbxiYojPg2TSbVUiBsTAymPAuYIY5JhQsEMS1lOJodaveOWUEFef6x4JHXMx9QQpt85JtjW4CSZXLZybl4i5M/qS8iEwV6TIhK2lj7SIv9BySv5H5781VUwMRRjXoRgQA2LAMcCYxxdDLf6U2AviGHjYuT2pXbSvsy9Dwgk49zJtgsOwMYlTquHELaRnzpGYWX58Dvv7+8sWPARglITJCIPKLWF3CIeOfL8X7U3Hp+2IzTIxxJ0QP9tetLSO7kFc+kvzOHQuN26JtiOsBcD9oZ0/fx4biaFaRClfDIiB6jHAaIEzgMALHsdu1Tfd4IW7nB77eGgI4F2mU0gluRIOjw/SYSuVbnOoSMCno9Tlo+dKqC0cvAolIn9rDjmrzEooNw2erYXENcidvG7dOjKrt5rMEa2EGBADYiDCAIoHMeRPfzDmVSLmNNIRHWbCAJNK/s3g6mScxjEQmT5zZ5UIZ6CrqyvOMDl4jEq7rh5nG35iFhVyD7ieyjPkqFBCDIiBlcAA/gmmQnjMmRuDwzKPeWzNUiHSXQBWVTAz7PHDneAPe6V1VySyivuqzB4sJkb58RnmhYSfxL60PROc3A8sykMNR7SaxFDbr44AiAExkD0DpZ2yyb6rqrEeA6jhagmgeh0q3flKMIwS4jaw/aWQRBESJYYihOhQDIgBMSAGxIAYWFEM4Bo0QVxrZyzFDK2o663OiAExIAbEgBgQAz4DzIiZEmKel0k9/5RLSww5KpQQA2JADIgBMSAGVhQDzIgRLk0YE6GEKbPnEkMr6qqrM2JADIgBMSAGxAAMECSEH4gtNgiXxieUvpBCMUO6Z8SAGBADYkAMiIEVxYALlw7cWUOeoRV1+dUZMSAGxIAYEAOrnAHCpW2LDcKlAzcmkGdold8z6r4YEANiQAyIgZXDAN8DsShppsZSgoQiHZZnKEKIDsWAGBADYkAMiIFKMoAfiA+iES5tzqHwPsgzFM6VLMWAGBADYkAMiIGSMoBDiN2l+Voza+nTw6XjHZAYinOiHDEgBsSAGBADYqAyDDQaLh3vmKbJ4pwoRwyIATEgBsSAGKgGA7a7NN/ZCA+XjndMnqE4J8oRA2JADIgBMSAGKsAAUdJ9fX0AnZmZCQ+XjndMnqE4J8oRA2JADIgBMSAGys4AsUE7d+4kXLqhhWOJvZJnKJEWZYoBMSAGxEDbGHjj1Vdv2PY/+83/48MPn56a8nPKk/7R448D5ljsQ+jlQfjK/Hx5wGSFxMKld+zYwVr6RsOl4xgkhuKcKEcMiAExIAbaycC/vPLKucOHfQSRQ/9U29NnLyGYGx1tO5JVAoBwaabGDhw4wHc2cA5l0mtNk2VCoyoRA2JADIgBMSAGcmfAwqVRQoRLZ6WEAC3PUO5XTg2IATEgBsSAGBADrTNgSoh6pqament7W6/Q1SDPkKNCCTEgBsSAGBADYqCkDOAH2r59u4VLZ6uE6LA8QyW96oIlBsSAGBADYkAMOAYGBwe7u7tZONZ6uLSr0yUkhhwVSogBMdAGBs6cOXP48OG1a9e2oe0Wmqwc4DVfHPS7u3b5oX+qDGnWPn24DDgawVA5wJW7hzMMl45fWImhOCfKEQNioDgGrr322re//e2j1VmJs2fPHtipFuDDPzi9Zvu/8y/q1O/c6x+2N73zP31lGYC//0bXuvN//H99bllmiQ/4HtbExARRLCXGuAxaRQFnGC69jI5LBxJDcU6UIwbEQHEMXHHFFZs2bco8AiC/DoCWyisG+B9/vGbzVp+TcuH/z8t3wfnhsQ3XvFwuhD53sbTJIAGOEZNZRgFCUwHUmV0tVSQGxIAYEANiQAxUkYEV7hn67NTQF/522UZYRx+4mHKdjvxw9tz5BQxu7ui8paMzxbJdpzJB+OsP9X5r/oDrwjs7d3zxvml3qIQYEANiQAyIgVXFwAoXQw1dyyfnp+9/aKcVuf9n93x650hDxQswLj/CAkhQE2JADIgBMSAGsmVgtYihvp7d92zrT+cON1K6QdvPZoXw070j5gBz4q/tXRMAMSAGxIAYEAPtYmC1iKEN6zre1dmbwjI64+jzh1IM2n4qQ4Tb3tTT9u4IgBgQA2JADIiBkjCwFEDNWPvswvKQ/hoYAy0DzWo0UjP7xR8vMFtE5fb76JFJcmpah52g4xZadHPHlrASRVuVH2HRjKg9MSAGxIAYEAMZMbDkGUIN8HvHTd392wfvvqPvuvUdtZoItAw0q9VKYj4CiGrjp1oM8fndhweok77jPUqs37WIKJmYGbPDd3ftTPc2uVKtJ8qPsPU+qgYxIAbEgBgQA21hYMkzZM0zVfTAw4N3/2Enoy8Ll1IwBVoGmqU05J868nwyJBTMxOy4bxme/vw3Rmxp1e99uH4Nz13yIZnO++ZcQVtslR9hONuyFANiQAyIATFQNgaWxNBjvz33qd5hmyc6d/7s5Oy+vvHtu8Z6GIkj81CBloFmDTFy3boOnECTAzOskLdfDq2GR49MNFSVGePp+fw3hklTTzkjacqPsAnaVUQMiAExIAbEQHkYWBJDbKvzifcM/fVvzyM1UAamivDrPDi9910jm3xHUaBloFlDXHyuf5IV775q+cR79loN/sY54XU+ODWE8qOzJVxIb70oP8JwtmUpBsSAGBADYqCEDCzFDDlwSA1+EQfEJj85P/XYkUlzFLEgy1ch2AdaBpo5ACkJfFQ4cgigRqWlmAWeooOPHd2PccgEmdXJZozOF0XMUGBDTZuVH2HTXVNBMSAGxIAYEAMlYSBBDDlkuHaOrKsZRu3MSARaBpr5NftpYpjuf6gXZeZntpJ+4FLcNPsvUwkCi39t9x1LkBPfh5ouFOlDKj/CVvhXWTEgBsSAGBADZWAgQQzZgqnHjk4+t3DcQUzctDDQMtDMtVUrgU/IKaFLIT7bbclb0zsHWm3Mr8Wn2AiZ4pdWipQ+8Y6XH2Ecs3LEgBgQA2JADFSLgSUxhGRhRowwZH8GimCa/p7Bvp4Bf6V9oGWgWThfNqWFva9RzKMTXoksxYAYEANiQAyIATHgM7Akhu7+wy7/hLmCEvfRCbQMNPMbbSKdvv4/vUIX/ePMWLpvXiLmzrbd1BOPCkJ7OUeUr8lcDS5xxwNrLZ1u5uwTE7kiTGxRmWJADIgBMSAGVhsDS2LIeh6y6WJDluEV1qWeqsxrhSIhkhpnFQlbGF+3bKJBfAqMTR1NDKGE4mcTK8k1M46hbAhz7b4qFwNiQAyIATFQAANLYghXEDNikfViiQgCLQPNEptIzGQV/acmFz+2iiRitb/ZbFi3MdFYmWJADIgBMSAGxIAYCGFgaZ8hlpeHKCEqDbQMNAtBaTb3bOtjW0hf/eAr+sJ9i6vAyvZDvJSDxOdNXFoJMSAGxIAYEANioGwMLHmGyoYsEQ/bQvJrQdNu3yO2ok409jNZM2+lEqOgnCXTUvGZKXeWBMVDmrO2sMc9xmp8v4YW05kgJNDKbSLQIh4VFwNtZ2B2dnZhYfFrzR0dHT09PYl4MBgfHz9z5gxn+/v7a5klls08MwSwNRpumTlIv8IQGNhMT08bw11dXb29vZ2dWT76fDx10yGAQYuZAd60aROA23hXhAD2e+3sIbktPDsAtf7ooNcH7Keh2j8sSbpiYshYSxc0iczaUnlOhUiZxBoayjzywxmzdxtkN1Q8b+PPTr8eGpV3Q6pfDOTHAOJmYmLiwIEDrokdO3YkPoKxHBoaOnv29S3KRkdHsZycnOQ57soWkGgIcGDXcoUdCDhCr4O0a9cuThVJciBgBvLt27c7nC7R3d1NDUVKokDADqEluMl37nx9y989e/aMjIxEDPI7DAfsEMbBXLxY338RL5V3ztI0Wd4trar6zTOUuVtoVXGozoqBdAYicqGWMSPH4OCgU0JmhoQaGBioVSSn/EDAtB5umRNUqzYQxtzcXIReK75///6CfQCBgM2PGKfu0KFDAK51Nm7fek4g4EhDKPtITmGHzQEuDF4rDVXSMxTe4bqTSuFVNWTJJ94asi/Y+IulDLQqmAQ1tzIY2LhxI9MEDGO1uuNGDt77eYfGK7B37+IHDRmqSRfpBjCEdQG7joRbuiJ5JEJgbNmyBcVpM02IieHhYdxvgOG64IHr6+vLA1itOgMBgwr/kDmupqamDDCqDsAFC+UQwK6z3M8pd7szyzURDpgbo+Cr33THV7gYapoXFRQDYqDkDKBpcNqjhPxZgwjm+fl5N3IwyGHMgD0zM4MSwpLX3CLFUAhgwx9uGelvtoeBMJBB/kwN8oJDLooxD9uFDYeBgLkHuDF8rswhtG/fPjJxdPmnck0HAnYYgG2iDZFx/PjSJyKcQd6JRgHzF+ffG3nDa6V+iaFW2FNZMSAG2sYAA1jdthmSzQa3EM9lS+MPMDGEZ6huDRkahAC25sItM4QXryoQhiPWr6HIUCHXbiBgZ+8nKgHYXFbczPTUVJHfhQLSrTBcALxWmlDMUCvsqawYEAOlZsC95ftDnfMG+cHXpe5G1cAxU+aEZmKocqk6hLsFFyM/hgpHV6ngOTC4WOyOdVDdqdIm4BYBh4Ti35LDlhgq7V0kYGJADGTGAAKIEZrhxA3SmVWtimIMEDNkIdX4MAqbI4uhqJ/BIL127Vo2ArAQe+aemDlNdHTVrytnC1QFrNIIy8ecms+5zQyqZy6PyUc0HP9CMshL+wcoMZTB9VYVYkAMlJMB/8lL5CkRDyvYz1+SS4ADwM3glNwZEGGM26O00o27F32JXKtKCE6EWzskjAyGi1yvlwgjMVNiKJEWZYoBMbDSGLCZMn++bKX1sAT9Qf24aaaxsbGS+zCAx45T/Bhz5roo4VBN7L9FuVVIXO7evRs3G3tasqsQ/+LQMpLxFZWzFxJDJXh+CIIYEAP5MOAPxrxSs4La9xXl0+bqrRVu3UYGKKGCF6g3wTu3BCH2/DBaM6NHDbgubDaqidryK2JMmmgzwE6xkSCHSbT8Wm+uZhQPTiD3BgLVTg/xZ9hcnbmWkhjKlV5VLgbEQCkYMA3EHJnvGWK7lFKAWxEgYBh6LVSoEkrIZ527wk0/4YbxT5UhbawSecO2zvZjuwCAjQQ5EF4GnOkYwGkG5XwhkRhKv3w6KwbEQIUZcEuZ/Fdn92Lq+40q3MkSQK+0EjL+XNx0W/bvKcE1zB2C82Y5qnNvspEGtM9QI2zJVgyIgUox4MKlGeGYTbBDEtYJiaFMLmbllBA3AJfe9xHCg3MIEaScCS0ZVuImmFydcG7L7Jk7oy/O6eIM2pvg3SOueJz7qjx/d06fQZfEUHvvGbUuBsRAkww4oeOeaDYqW3XMetiAx+dCLfiUwAuCGHhMuz2pXahvkwgaLBYC2KoMt2wQQmPmgTDcQnqmHYmZ5cdvpr+/v7DgoXDAKAmTEQaVu8JuEg5dJX4vckq7tlLuYZp2U3gOBrFZJoa4yeNnnVnmiUDA6B7Epb80j0PncuOWyBxYExW6x4XNQkoMNcGhiogBMdB+Bmww8HHwUHOZbnRhqMATwCmexW7JN6V4246/vPq1ZZ522FzNiYA5G27pqsojEQjDUe13x+Ep0g0QCNiwYRy3R88VqS3iAHwOHbGOzLYnwgFH/twcclaZOUXlMotPIM6cRrfYQcUMFX8V1KIYEAPFMYDiQQz5cx88+1BCRY55xfVWLQUwwKSSfz+4EozTeAsi02furBLhDLCPZZxhcvAYlWFdPX/7eKd4MvihhPIMhV9fWYoBMVAiBti/JBANzgmeeoxz9qpt02eBZTM0CwccbpkhvHhVgTBcDFa8hoJzAgEzx8QPNwO3hEPYFndFIGAH0iUYzvlxh4UlAgHjdOHHZxjlwU9hOFMaAhhL8Jg9R5b5wne1iKGJ2fEHHh584MNj/T0DiTTd8cBal//Ozh1fvO/1EEuX6RLhlhR5dmH+uYXFHSDuuKnnuvUdrpJ4ItwyXrahnCM/nD13fiFe5OaOzls6lt2s4ZZPzk/f/9Dryyap+Qv3Tb2rszfehHLEQBsZKHK+po3dVNOBDDAQtkUABcJbAWZlYxhxxhUnZDDRMbwqxNCLP154cGro5o4ttZSQu+3u/9nFXTJv2dTlcmolAi0/OdF39PlDVFJXH4Rb1oIUmP/Z6aFvzR+IG9OjT+9c9qoRbomQMkIeOzr53MLxeOXKEQNiQAyIATHQLgbwAhLQTSQTU3U4h+IwVoUY+vw3hs+dP/up5SN9nAtyImog0cYyQyw/OzVkSiilHjsVblm3qkCDvp7dG9Yt81S9u2vJteNXEmKJS8kIOfL8rMSQz57SYkAMiAEx0F4GXLj0zMxMLQ/xqhBDk7PjXIm77+gr8now7UW7+Eu+8Lej6e2GW6bX09DZe7b1B85khVs2BEDGYkAMiAExIAbyZoDYIDbR4HMrSKKUuKUEMUT8B8EiL/74zHXrNzFebntTTy2sWHLKxlSKPHpkcXsJxs5IkUAzyqY3bSEsicE3FnATj3qhTqKFcAvh3kgP2cEy25/ffXjREfeJ9+ytK4bCLbNFqNrEgBgQA2JADKxgBmqFS8e7vEwMMVkTH7mJJv69D49HQmutIouZfXLozCcn+1wYCjV8qnf4E+8Zco2FmIU0PTE7Njm7Lx7aQkP3P9TL7MyDfRNxnE/OLX4Tbtubtjs8BSRQYBBCvHZdBRZuWQBsNSEGxIAYEANiYAUwQLg0QUJsjMSOCSHr+ZeJIQI+7ripG0+PhY8Qd/zYkYnHju4ntnf/4NISxAhNDzw8cPSHs2gU4o6fPTPH4im8ShEbDtPNQpru7xlEDBGiG4nXwWOEEiI++p5tCRNhvl8qjiqPHIvXRkTWjdcOt8wcJ268fd8YplrkI0oxBWq4ZeYgVaEYEANiQAyIgUYZIFwanxALx2qFS8crXCaG4h4g5MWusR6igB89MpkoNagRtTE5OOu7ZBjj4y2lm4U0zewbWi0OBo8RzSWGBDF9xhwZZ314cWzZ5iD7aPTTvctWZiU2EW6ZWLyVTGSlFf/WmgOkJ2bGvnDfdKIfK9yyFTwqKwbEgBgQA2KgdQbY+Mq+BMInmcN3T1i2A3WiYrgcEjRTCyKrtCIFE8fUdLNIDdZWvOn+7YOcenJ+cebL/Tx2ZJK0nXKZlrA9fnDSRPLzO0Tz4UvDTxYJnIq3GG4ZL9tKzrabepjHZKk/85v8C9QN6zYiMZnrjFQbbhkpqEMxIAbEgBgQA8UzwIwYO4wTKI0kCldC4FzmGeJ4cWrs6CSzXcxbWTdMT6R0KdElE7evaxbSNJWwYxC+ik/1jpjksvjou+/YlSin4jByzaELREOjLYibTm8o3DK9nibO+pOMyE2bFSWuiyAnJhx9DRdu2QQMFREDYkAMiAExkCEDFi7NJ3hZOObvLh3SxDLPEMLi7j/sZKdmgqAJAwopj02iHyheNt0ssGkquftSYBCKzZqw+Oh31dgjJw4j1xw2NCJ6CR9YemfBEG6ZK2CrHD3E/CNpWw+Y0mK4ZUolOiUGxIAYEANiIEMGLFya72wQLo1PqFElBJIlzxDhNThdFjcn7B3u6xlww3niOq8M+0BVDTXN0n2LcSHm95IzaT+emJT432yhptdma/GQFHFVAY0b1ncQSGSul3DL9BazOovKYaYs8TMdkSbCLSMFdSgGxIAYEANiIHMG+PIgQUINhUvHMSyJISJvbD8ef1U8BUIGyHi9DeU01DSDMQvHGLmRUBYthHSr1Rw7D3HKLfuvZZZtfmJzAKaVCJnhltkijNdmwCJ7UsfNXBdCLBOLK1MMiAExIAbEQFYMsHDMYoMmJiYsbrq5mpfEkK2Hjw9yxPk2V3V4qUabZo39g9N7WQBl2BJDp611AonwGyHyUE4FBBUdfSDhM9r2YdfIt8nCLR2NdCHwm6+uSHjCmGSbzbpFwi1rVZVrR2o1qnwxIAbEgBhYYQzY7tJbtmwhSKjWdzYCu7wUM2QDoYubtvJM7hTwqalGmzZXEB+7wN1CsEu6ysGTRF8KkHSBjDdthvgjzJnfo5dj25uoCh7QIpGCBH1zlVGNvo8t3DJSW93DTDpStxUZiAExIAbEwApmYGhoyL6zgXOoRSUES0ueIQZConqZuPn1h3rf3bn4zc5vzk9xaFv75Epoo00Tz8TnNWz/mxS3kGEmtpq17kd+OLOm9mxarr0rVeXfnJsiXIlrSuiSeQHdd+Yjcd/hlqXqoMCIATEgBsTAymaAcGmUkIVLh+wuHcLGkhhCYbDtnn1Yw2JZcBXwQQmW2Vu8S0h1zdk00fS7OneaGKq7Yp/Y6shq/OZAtr2UzZERL2W+rubwsEs4l5UL6l9T6mTTy0i14ZaNIsmkI402KnsxIAbEgBhYAQyghAgSIlx6eHgYSZRVj5bEEDXiLfjr356376Fy6EZHf78Zv+HEwBffwNIhZo02bQG/gZ9fxfOEOwQXSFsWnYV0P4Qom+mru4NRnH8/Bwb4dVE7nEr8ui354ZZ+/SHpTDoS0pBsxIAYEANiYCUx4MKlw7+zEdj9ZWLIytja78Dy2ZqFN82MHk2zzD4EAAKCACOK1BVDTBFSITsv19J/rrlwS1eklcQlhXoWF07dLoS0QpRVeqCVqyTQEnifnV5U6HW3p8q2Iw6nEmJADIgBMbCyGWBGzFxB7CTUepBQhKsEMRSxKNshewvZjoXhE0ZMwxEQw2aSbO2YLiYS17onMhBumVi80cySe1Nw1AUSUvKONHpdZC8GxIAYEAPFMGDh0s3tqVgX4dqLFxNWg9ct1i4DW6ZO6wS+EOEU7klqF2C1KwbEQDoD3d3dhw8fTrfRWTEgBsQAu0uPjIw0sbt0CnVr167ds2cP1VbMM2SfXGUai0VkgRM9KSzolBgQA21nYP369bfeeutHP/rRtiMJBPClL30Jy2oBPvGjl9ZseYffwT0ffKt/2N706Fe/uwzA839/45Wv/urHf2lZZokPHn/88YMHDzKmlhjjMmgVBZzVwrFlXFw+qJhn6DJs/S8GxMAKYcB2j8X1XZX+VBHwgWMn19zzSZ/hi1+ouXG/b1ZMeu3948sa+vaX337Ny4eefGJZZokPCGQZHR2t0DSLALu7yXmGljZddOeUEANiQAyIATEgBsTA6mFAYmj1XGv1VAyIATEgBsSAGEhgQGIogRRliQExIAbEgBgQA6uHgYoFUK+eC6OeioFVy8C5+RePjR0pT/e3Dm7b0HldefC0juT6cxefGvpm6/VkVQN4Tm9Ym1VtJamnVAxv3nnLzb23lISZcsKQGCrndREqMbB6GTg3f+67o4fK038GkpUmhl66WCqGr7/7J1aeGCoVw/w1SQylP1I0TZbOj86KATEgBsSAGBADK5wBiaEVfoHVPTEgBsSAGBADYiCdAYmhdH50VgyIATEgBsSAGFjhDChmqOEL7D4JQkl2xP7ifcs2i7v0IdKFeKW1Pg7vLN2X5O+4qYePqbn8zBPhCMMt+eLY/Q/tdFC/cN/Uuzp73aFLBJphzxfo7v7Dzjve1BOh11WlhBgQA2JADIiBrBiQGGqSyft/dnHn9Vs2dUXK8/H2xE+WYv/pnSMRY//wkxN9R59fDBqtpSR841bS4QjDLZF6RshjRyefWzheC16gGcWRg309A1/429G639at1ZbyVxgDm3fcXEyPTh54rpiGytZKYQyfmj316tkLZet+AXjEcAEkN92ExFCT1KUrm76e3RvWLfPuvLtryXESb/KzU0OmhOKncsoJRxhiyXfijJAjz8+miKFAM+vyJ96zd3J2/PPfGO7vKdF3A3K6HKq2LgP3Tv9CXZtMDMbX/lEm9VSuksIY/krvX65OxSmGy/xHITGUy9W5Z1t/4jxRYmNMkDHq41nBEZJokEdmOMJwy2xx4hy6e1vf5Ow+OYeyJVa1iQExIAbEQIQBBVBHCGnD4e8+vOj5wBHShrbL3SQ6DICPHpkoN0yhEwNiQAyIgWozIDHU5uuH24MYo0/tHMk1aLrNnWy2efOuwQ/Os2brUDkxIAbEgBgQA3UY0DRZHYKaO40zY983hilLlMy2N22vFfXCmqkHp4ZYklbLoLnWQ0oFIqSqcMuQdhu1gRzEEMvQblHkUKPcyV4MiAExIAbCGJAYCuOpQSsiXazEt9YcWIx6mRn7wn3Tcd/PAw8PnDt/9tO9aavMGmw51DwQIdWFW4a23Yjdtpt6Fj1DZ+YaKSRbMSAGxIAYEAMNMCAx1ABZIaYM3u/u3LntTT1sF3T0+dlvzk0RHM1KsU9O9kW2zMHb8djR/cRNYxxSc1Y24QjDLbPCVqseFqnVOqV8MSAGxIAYEAMtMiAx1CKB0eL+kntCXvhlUT0bEuLeYA9Dp3uYICNuesO6jcXHTQcipGPhllEWsjuGvSIX2WUHXDWJATEgBsRAZRiQGMr9UqGH7ripG+cQwTdODLF9DvvxPPDhsfjcWe6AYg0kIoxZLWaEWyYWV6YYyJaB2dnZ6enpM2fOUG1XV1dvb29nZ2diEwsLC+Pj42bZ39/f01OoO9ZBCgdsRbAHOemOjo62YA4HHG7p2MgjEQ6DOwdjuyU2bdrEzVNyhn26QG43Bjd8rXvet88wHcgw9NZqFKprnWpjvsRQEeSjIRBD584vfabDvB3II34jCNiAccP6DgKJnHKKGORxGEdYq5Vwy1o1NJTPPGND9jJeJQygbIaGhs6ePRvp765duziFdPDzI8ajo6M7duyYnJyMmPlFMk9HMLj6awGemJg4cOCAMwNwyujizDJMhAMOt8wQXryqcBgM59u3b4/X0N3dTSWFSaJwwBGo3Ak7d76+i++ePXtGRgqKOm0IsEMYAc/hxYsX45ltz5EYKuISmAyK7ElNw8ydxZu3rah95RS3yTynFsJ4Q+GW8bJN5xC91HRZFVyRDMzNzcWVED3dv3///Pw8o53rNSPH4OCgO7QEOmNgYAA9FMnP7zAcMBgiSig/VCk1hwMOt0xprvVT4TDMpxJv8dChQzgtuH+KUcnhgCNQeQ2I5BRz2DTgYuC12Ir2GWqRwKDixEpjd936Tc766AMX4792lm+TcQoHjDO2BHvtUA+/xBtFTrV+GEdYq85wy1o1NJRvodPxb8A1VImMVyQDW7ZsGR4enpmZ4UWTyQ5eka2bDGm+ynEjB+/9U1NTFDEzZJOvmQqgKBCwQ7Jx40Ywu8PiE+GAwy1z7UU4DCy5YRCd3BL8uJsHhe3fPLmipfJwwA4J9zN3uDssONEoYOMZev2fgjEHNifPUCBRQWYIhfjX6QmUJjyIWGm+PBpUSw2jxfX5l77X0cqXXMMRhlvWwJtN9tEfLr7ix6VhNrWrlsoygLPHnx3gVZ5DnEA2TqCQ+vr66Bxv+W7kYJAjuoJXf86ihDjLWFjYnEggYLsge/fuZUoCtP6ESMHXKhxwuGWuXQiHYe4fHww5uIv27VvcEgX/h38qv3Q4YIeB+5lJXg4RGceP1/wktrPPNtEEYO5h/+80WzzZ1iYxlCWfBLigVwiXJtzHJsXcV9xLssd0OMJwyywZXF4X6+/Yhwk+2bty+RkdrXYGEoNG47MbiAljCheLK0K8iImhIj1DrnX/ysUB21nGZt+sLelwwOGWuXakRRi1rkV+mJsAzNwueLiZuUNMFeUHL15zE4DjlZQ2R2Ioy0vDbA4eIIJ+LO7Hqr65Y8vvfXi8dd/Gc5c+SUFtrVQVjjDcMksGl9c1MTtGRv/2aMDHcisdiYFFBnizd+LGhce6t3x/qHPeID9CuXgSEwEXDyO8xXDA4ZbhrTdhGQgDdwuiGVecNYH/o4m2MimSDhgXi92xQMWpmUmLLVaSDpjK4RYBx78IKf4qTcy12GhOxSWGsiSWr2rwS3CPCReqjs+apbRHqFDKWQvWaXFfonCE4ZYpmFs5RWjUY0cmEX8gaaUelV0lDBAMZCHVvDfbHJnfcQQQD25GkTI4XQxYOmAffEnS4YDDLXPtWl0Y3Ay+IGbuCcGR6P/IFaerPAUweoKzWBJ8w81cEjGUAtg6xVyeTT7CM4mxsTH+Bt3biOt4GRISQ01ehV9/aNGPzSonf2dCq4s5ncyndWzCKCtlEI4w0BJ4n51eXOBgIT61OA00s+JsxcQcGdOLtWpTvhhwDPCEdbMGpF2+8xWRQ+Qpj2OikouMkHVIIolagCNm5TkMBxxumWvvmoBBqFZcRucK0q88HTB3L1rf5Jpfqo3pdMCJwAjgg2H+Kn1PbaJl8ZlaTdYk56yKX9xUuqjPRGTiFmqyqwHFWG9vhCBfUswDzagBtxCfMSFaSG6hFD51yhjgoeymNnj1rPXeac/fMjyFAwGX5/qGAw63zLV3gTC4VdjAiR8Dw11EDh7EXLElVp4OGPluUW6YJRYvPjMdMHh2796N+4plnpHFnviKytMLnzd5hnw2gtLpk1lBVTRu9In3DPHbeLmCShDGFEJLoBmg2Zj7W3vb8EgqiC81kx0DvGW6xfMooUhQAmObmwdhBoSN4Mjx3UXZAQmtKR1waC0F2oUDDrfMFX44DLfQCQHElBl+C36Y+nH5ueJ0ldcFbHe1iTZbE+AUGwlymNorcnavLmC65ise3kCMUnPfspeB+5t1JLQ9Ic9Q2y+BAIgBMdAkAzyUGcMsVCiuhPxKseQQY98zxJSZb1NAOhxwAWBCmggHHG4Z0m7TNs3BcKM17RY8ixoC2O5wZD1q3n4sEAe0JMjh5m9geVGBAABAAElEQVSasUYLhgBOrBOclk8NiQbtzZQYai//al0MiIEmGQh5KLtlZcSfumZ4MbU0XiKXWUAiBHABMMKbCAccbhneehOWrcBwnpUi9+9pBXAT/LRepBXAzpvlqG4dT4Y1aJosQzJVlRgQAwUxEPhQxhVkgBjhmE2wQ5toIL9IMRQIuCD6ApoJBxxuGdBs8ybhMLgBuPS+j5BWnUOIIOXmQTRSMhwwK8giFVPW5n+ZO6MvzukSMcv2MBywraWPtO7cV0X+3UUwRA6dPiNfYihCjg7FgBioAANuTS9TXcRp8uOD5rv0FmbBgMeXUC34lBxWsvCYJi7EjF3YtV82p3QgYGvdaTj3sLZxyM4SflHAcBIOONwyJ26t2nAYWKIkTEZYWe4Ku0k4dOTnipbKwwHHY5iIuTExxJ0QP5sT8nDA6B7Epb80j0PncuPPMyeEDVXL35Q9JWwWUmKoIfZkLAbEQCkYcCqBB5mNCj4sXyswVOAJwIxnsVt+jzFv20W668MBgy3eI7+briq/y5mnXSt+064Vn+FwS1c8j0SjMCA5zjPaujBt0SjgPEhrqM6GAEf+3FxDrDIrTG66RuMJnglOq1nsoGKG4iwpRwyIgZXDAIqHB58/98GzDyVU2Ji3cqhcKT1hUsm/H1y3GKfxFkSmz9xZJcIZ6OrqijNMDh4jf5VZeIXZWoKBe8CeDK5meYYcFUqIATFQGQbQN+FYcWMwD8I4Z6+2HBY/4DUEmK1ZwnuXk2U44HDLnKBateEwmGPih5uBW8JBKt5dEQ7YgXQJpHzxaj4cMNNP/PgMozz4cfjbmAAYS/CYJGXmzn8OSAy18aKoaTEgBopjwJ/ZKa5VtVRWBhgIixdAZSUjF1xlYxhxxtQYc6O4AOMOKk2T5XITqFIxIAbEgBgQA2KgJAzgG0b7ooRqTdXJM1SSKyUYYkAMiAExIAbEQPYMMB9qXkD2GKvlDpRnKHveVaMYEANiQAyIATFQBgaYEWPzVebsiHmqpYTAKc9QGS6WMIgBMSAGxIAYEAMZM0CkPBtqdHd3o4T8cOl4M/IMxTlRjhgQA2JADIgBMVBhBixcGiUUuGOCxFCFL7agiwExIAbEgBgQAxEGLFyaXcXZNTu+cCxibIeaJkukRZliQAyIATEgBsRA9Rhw4dJ8pcdtM123GxJDdSmSgRgQA4Uy8IafWHvDnf9DpMnnpp+N5OiwaQb+Ze2aNjJ8YeFC08grVFAMt+Vi4QciTojwIPZUbGhrMYmhtlwvNSoGxEBNBv71ny+eOviPkdOP7PyrSI4Om2bgjRfXiOGm2QssKIYDicrQLDxcOt6oYobinChHDIgBMSAGxIAYqAwDhEvznQ0Ll667cCyxV/IMJdKiTDEgBsSAGBADYqACDKCE2EDo0KFDrXyAWWKoAldaEMWAGBADYkAMiIE4Ay5cmu9s4ByKGwTmaJoskCiZiQExIAbEgBgQAyVigHBp21SaqbFWlBBdkhgq0XUVFDEgBsSAGBADYiCEgZGRkcHBwc7OTnYVamjhWGLlmiZLpEWZYkAMtI2Bm3tvGbj4m21rfhU0/PTmN5SK4cH7x1ce66VieAXSOzCwb98+dpdGEqV/ZyOw7xJDgUTJTAyIATEgBsSAGGgzA5mES8f7oGmyOCfKEQNiQAyIATEgBkrHAOHSzIixcIxwaXxCGeKTZyhDMlWVGBADYkAMiAExkAsDbCptUdIzMzOtBwlFIMozFCFEh2JADIgBMSAGxEDpGOjv7ydc2pxDmYOTZyhzSlWhGBADDTBw5syZw4cPr127toEyJTCtHOA1Xxz0aVu7/NA/VYb04TVrKsewAOd95+zatYu19JmES8ehSgzFOVGOGBADxTGwfv36W2+99aMf/WhxTbbW0pe+9CUqqBbgEz96ac2Wd/j93vPBt/qH7U2PfvW7ywA8//c3Xvnqr378l5Zllvjg8ccfP3jwINsflxjjMmgVBcw02bJuZHqwwsXQ0ORTo48u+zO7+IXmd6jMlPnFyma/f2r62Mkz/7T4DeeuGzf03rG584YN8VZ6/9NXDhw76fJ3bN08/Tv3ukMlxEClGUAM3XbbbdnGQuZKCF566q8W4BOvnVzzjo/4tIyMlOhJOHp6+dL6b3958zUvV4hhvg+KGBJg/wbLNm0MZ1tnpLYVLoYivS3P4fjfHBva/9TZV16NQNq1fcv4fe/ruOaqSL4OxYAYEANiQAyIgZwYWC1iaPf7bu9/R2dOJDZR7dwL5+JKiHr2zxyfP/XS7H9Y/g63686FVxa9Rzs/+0gTbamIGBADYkAMiAExkMLAahFDHVdf1XvHzSlEFH9qy/XXDr5/a+/WzT0/dcPCyxeGH/mOzegdOnF68tvzfZ50w6B4eGpRDIgBMSAGxMAqYWBpaT3hNfOnzoV0O9Ay0CykRd8G3TB99Dkqt190Azm+QSXSyKD5kY8NffBtJnSYFxvpu6v71usN/Mz3T1WiFwIpBsSAGBADYmAFMLDkGcItwS/j8eDPbe3b3pkSthJoGWjWEIkIIKqNF9lzz1sRE/H8wBxU4Njjx8x457bNBfiQEgOlcV8FApaZGBADYkAMiAExkBUDS2LIamSOZvChJ4jtRQ+hilImaAItA80C+zN74nSiJQqJ1VgDP7c18WzdTMSQr7EKEENxSPi3Zk+87hDarnmxOEHKEQNiQAyIATGQDwNL02Rzw784vOtOAlloiNjefV9/evtn/mvPf/zyyFe/E5mHCrQMNGuoXx3XXIkTaOb//N9YIW+/HFoNE9+eb6iqshkTM2Qh1Tjn/IChsuEUHjEgBsSAGBADK4yBJc8QEzeEsPDL5jcIi8mZ+eOnX8Kvw+/e/QdZjeUcRYGWgWYNETr5Gzsj9ns/9DZz6vg78URsyn/ISnvnmhq/773lByyEYkAMiAExIAZWDANLYsh1iakxfgnBITZ56uhzqCJzFBHREpk1C7QMNHMAUhL4qPCgsFEhEi3FrNFT6DbnYSJmqNHiLdqjhJiatErG7ntvhOQWK1dxMSAGxIAYEANiIJ2BBDHkCnTecG1gSG+gZaCZAxBJ4LLq/exXErfniVg2eogYaiX+utHmfHs6RYSW5aCEmg578utUWgyIATEgBsSAGAhnIEEM2dIqmyZzFSVuWhhoGWjm2qqVGP7q61E1GODIIcqYECLS1d2K0Jd3UkK1rrvyxYAYEANiQAzkysCSGEKyIICIFvJnoGxjwIH3bfVX2gdaBpqFd4/dmc0YJeQcOew5FF5DqSylhEp1OQRGDIgBMSAGVi0DS2Koa+9/8VkwV1DiIvNAy0Azv9Em0rUW2zdUFYrKuZd8pZVYydr7X/+mYF3LxOKWKSWUQo5OiQExIAbEgBgokoElMWSthmy62JBleIV1u01V5rUigJpIapxViBjiqesWLKGBm/LbePWVeOMi+wLwGTUFD5XwqgmSGBADYkAMrEgGlsSQv3g+vauBloFm6W35Z/d+8G39fzxNDpJo07//UzuFmPBtqpJeePn179UTDx7fFKDn8nc5qtId4RQDYkAMiAExUF0GljZdHP+V9wUu6g60DDQL546tCNkW0lc/+IqmP31veA2tWxII5Srh+2IurYQYEANiQAyIATFQUQaWPEOV6IBtC2lB027fI7airgt+4ZXFz7tilhgFRWZIJRSfPnrS2sLvxYL8uu3WMpj+nQY0HAFG4K9VlfLFgBgQA2JADIiBVhiomBiyriYKmnQW+LoIv9gEip5atbnvybPzdS2bzPOH9h+MT6Vl3ooqFANiQAyIATGwOhmopBhq46Uyz1CLbqE24lfTYmAlMTA9PT07O3vmzBk6tWnTpt7e3p6ensQOLiwsjI+Pm2V/f38ts8SyGWaGA7ZG6R3ISXd0dLQFczhgoGJsDHd1dXEtOjs7M6QusKpwwOGWgU03Z9YcDHdjQHLBPAcCxqwWIdwbtU61M/+ifsSAGBAD7WNgx6WfRtufmZlJfG52d3dzKlLb2NjYxo0bfXvaZNiOmAUeFgOYViKAA+HFzRaretPta359zP+Nm0VywhmO02vId+3aFUiyD2wx/Za73/7O90Tw1D0MBxxuWbdRM9izZw9dDjR2Zk3DmJqacvcGTbsKwxMFAHYI44lwnM6yOcCueEoCeMbhUgB1HLFyxIAYEAPlZMD8JXFshw4d4r3TP8sb6uDg4NmzZ33jAwcODAzUjzX0i7SY9iH5VcUBc3ZiYgKEvlnx6XDAc3NzEXoN7f79+4v0AYQDDrfMlfamYQwNDeUKrFblTQOuVWGp8jVNVqrLITBiQAyEMrBly5a+vr7t27czhUQZXpdHR0dJMDBPTk46reNGDpxGIyMjzC/s3bsXM4Zq0kXOPQUCdv3Hm8UMCGrJ5RScCAeMJYoT6QOfDJnDw8N2LQDPteAyFYO8IcAhN0/esMMBOyTcz5W4JQywddCBL3NCYqjMV0fYxIAYSGaAcXd+ft4/Zw6hffv2kYmvwk5h40YORmW0BWZMT6CEMMABU5gYCgRssJFrRDiBFrfWzp07LbPgf8MBI4NQmQ4e2pRDkBvzsF2MGAoHHG7pOpVHogkY3M+mMhEZx4+//n2qPLAl1tkEYO5h/95IrLYkmZomK8mFEAwxIAZaZcBcRH4tLooTtxDPZTuFM8kSeIZ84+LTccCGgVHHoS0eVUqLiYAToSZaptSc06lwGOGWOUG1atNhmL+Tm7kYfRnS03TAITWUxEaeoZJcCMEQA2KgeQZ4Y0b34E2xKvBVWMK5iPxHtvMGtTEupxbg5inIuWRDgJkpc0LTSc+cAUarDwccbhltI9PjujBwsdgdy32OUzPTxpuprC5gKsUGAce/KGbuBDd53Ux7OZeRGMqZYFUvBsRAngzgRPE1DdMHjBlxX4XFsjCKYJ8nnPp1BwKuX1FRFk0AJmbIQqrb4sMIBxxumSvZITDQE7AKDJY+cTO3VwyFADbGmMuzmWv+SEmw8JC/Qfc2kiurjVauabJGGZO9GBAD5WWAaBt/BsH5J0BM5ClneY6XCn0EcKmwJYKpC5jRzuJaKE46sZIiM+sCdmDCLV2RPBKJMLh70Zem9fNotJU6EwEnVkgYGX+etValJRYpLFNiqDCq1ZAYEAPZM8Bbpm38Y1UzQWZOoHhLNlPmz5fFbQrICQdcAJiQJhoCjPpxc5S4ASgb0kS2NuGAwy2zRRiprS4MYv8t5L8M4hLwdQFjs3v3btxXttEU/9pGQeTjKypJLyJXoeF9olI2L9IpMSAGxECjDDS3h2G8FR64TMrYA852UcPGPYIth+X3mPl71sXrqZuTK+BI6w4qjUZOhR9StolNF+P1JzLsm7FwzG1uiRLyT6WnM9l0Md5EXcCuSLilK+In7Dbzc5pL14JhrHIduR/sB6lhtzoJcoiNa6jFvAEngnF/jE3czFkBjgODRionX56hqDrUsRgQA1VkAJePW8TLm3SkCzZfxhyZ7xlyI3fEuJjDdMDFYGiolXTAMAy9FiqEEipDqGw6YL/v4ZZ+qczTtWAYq4TdsM+C/VggDgBIkAPhmYMJqbAW4MSy4LR8f/I60bItmRJDbaFdjYoBMZA9Ay5u2m3B4pYyEX/q2uNN2tJ4+11mWxJxwG2BEd5oLcAlVELWqVqA410Ot4yXzTCnJDDCexQO2IUKuSLhrRRgqdVkBZCsJsSAGMiYARbSI2V4MfXrdQ4h4kwt34VLI48oYodu86EixVAgYL877U2HAy6JEgoHHG6Z6yUIh+EmmBweOLdFlEw5cRs7p4szyCMRDtjW0kcwOPdVkX93EQyRQ6fPyJcYipCjQzEgBirAAMuMGQxsJDC4PH8tyJRDp4FQS3wu1PKZuGElC2ZuT2oX6ltAhwMBGxKH3z2sTXDYWWYDCxhOwgFjafM4TDsSM8uPz2d/f38xU2YNAQ65efxe5JEOB+zmfx0MFpeZGOJOiJ91ZtkmwgGje3gz8dd1cuj8tdwS2QJrrjYeBYbw/Pnz1CAx1ByNKiUGxED7GWA8sCHBh8KQ7A8PpHmjZbTmWeyWfGPP23bx7voQwGCLdwr8LtMpJL/XOaVDADs8PkiHpwDd5toiEQLY7MMt/fozT5cERni/AgFH/txc/UR8O63vMotP+G8X69atA0A2Ymj8b44NPvTE2H3vHfi5rYm9Wnv/0m4TO7Zunv6de+Nm86fOTR89OffCOU5t+jdX9W3v7LxhQ9wsp5yFly/MnjhF5TRaq912IQxvN9wyWxpnv39q9sTputeuXfAinQUGv2T23HpDxzVX+WfbhRACF1654COxdPxuDEc4ffS5nZ99xNU59ekP9d5xszt0Ce78zr3/GSoS/yqdWdkSzAvwYufeNR08HrXseuJPn6F4EEO8Ajpj1BK+Cl8wueL5JcIB54ehoZpXMOCSdK0kMMLvinDAXV1d8a+nkcPfZjFuwvRO2QYQ4MFf5cIK17KiLL1Y3bP2MO24+qr5kY/VMjYxtOeet2LQdeOGiGbi+d73/0wfOnE6Uhz7kb67IpnZHtL08CPfYSB3rSc22i6E4e2GW2ZLILX5StdVvvt9t4/sustJjTbCc5Bcouc/ftkut68P2ouw9z995cCxkw6hS/h3Y6MIsR97/BhVTc7MHz/9kt9ZV78lhiafGn30uykvMxH7bA8jcTwNVY5Pgjc8VyT9jRNL82Hgq/DVkisemCgMcCCeumYAXry77vmkb3nxCwP+Ya10QwzXqqRufvQZ8u0vv/2alw89+UTdgnGDcMDhlvFWIjlMWuF0bG4wzRBGBFXKYTGA/a7xTsJPCqT0U60AjtRsVbENB+9IPAfWrl2Lk5hXoww8Q4iJs6+8ysgXaTJ+WEvZ8NRmcNpy/bV4g7b/FC/rV04dOcnTmV9el8d/5X3xqrLKoel9X3+a2jZefSX/0pHEmtuFMLzdcMvEDraSCXW9d2zuvH7Dzm2bqYdrN/71Y7DKtZv8jdfXUrYRXqRrDPxO+PqnyoAQBclLhY/KKLWcRhHiVbK/OLQ+YsivNpLe+6G3ccn4Q468pUTMSnjIsyxdAPmYC56v8Zt26YYAu1JtTKxgwCXpWklghN9j4YDDLcNbb8UScYYSYjMC/MfxjR8zEEM8RsGHjmkaJQNA5K0Ufz6qqP+PpxlTeVLXmrdqukVXkJppenEsv2FDrbdzjNuFMLzdcEvX96wS88Mfcx4g6uTa9b+jc/tn/uv+mePM/vT81A1tJDDSR/QEtyvuFnR25FQbCXRI4C1xJssM8kPI5ePvl7815rsrp4cce0qIATEgBmoxgBLixYnFE4SBI4niZgn7DBFqMPLV7/ACzb8MZvEyfg5PT7wpi2+0y2MvfJu6acbL+CO47x2d5q0hkKhuDU0boIFouq7YahfC8HbDLZvmqlbB+KUHTPet12Pv4mDaCM+HPfCni4535LWfaemSIIwDczm5IkSH0dDEt5c243HtKiEGxIAYqDQDTJEzSUeUIcvcEpUQvVvmGbLQgUifiXce/5X31pILU0efwx4vTqRUJoc0mjijkUnlmVTSLoTh7YZbZkKIq4RIMpdOSRQJD+FO5ASOwLh6KwnCFBgppzLh0DxS8IPzjApTmtMpMSAGxECFGGBGzASQ7ZNUC/kyMURsAS/0vVs3W6TCwsuvTszMMdlBdPPsf/hIYhXmtmGaKfFsK5mMpqaEei75GFqpKqey7UIY3m64ZbYU4VMkSAXHHmuUUmouEh5tDe1/CmUf90GWBCEwcMwMP7I4f9d5w7W8YIRAzZBDyEEM8Rc98HMSQyk3hU6JATFQGQaQQcS2u3DpFNzLxFDcA8RclS29mfz2POlIRbxEWsRxHq+ShHPSHA9oZgci7ZbksF0Iw9sNt2ydUvwutrR++thJC4ef/I3edB9MkfAGHvr6pTD/OxvqaZEIAWax/CQOHFtMj/3NselP31sYh7x1IIbsIjbEkozFgBgQAyVkgGX8Fi7NYjGiudMRLhNDiZoGRxFj28z3TyWKIWpHr6S30cRZ4pYIccW1gD5rongBRdqFMLzdcMtM6MKrwVBqVdmFS1exRcKjLRycxE2nQ4rwUCRChAjuWP7Fl8Z+V7Yij787nLIp2//kgRD3cIQHHYoBMSAGqsWAC5e2ZfMh4JeJIQrgdWdXEt4O3TMR909IRRnaELXNGECFLNdP1GcZttVcVe1CGN5uuGVzDMRLjey608KluXNwqLDdH2H1tbZFKBIetzRx0+izxLjpeEcsp0iEtOjvOkH4Dr9oIzhEX4IkUcNljpAW44vsavGjfDEgBsRAORkgXNo2WSVcOnyPx2ViiJkO4ips5qtuwEdOLDB09X72K2CILLbPqbkmqm0XwvB2wy2b6H6tIm7AZiAfeN9WNjVmoidxrXjB8FBmBDA1FDddMMJESqGRAD6cQ7jcHLfOsgwIHRglxIAYEAMlYYBNpU0AzczMNLS12JIYwgNkSmh4150MZi5SIXGJWU7ddo94ZjRCokdzgpFSbbsQhrcbbpnSzRZPcfNwC+FmYCBnUPdrKx6eeTtAwq+PhPTQ/oMdV38Xn5avNopHGEHlDm2G2m1P4PJzQsjcnGtCCTEgBsRA5RggNogvfhAujSRqdMPrJTHE7Bj+GKY2hj64bBeW+LPYEWRzWC5SxOU3l7BHPK/CYPBnDZqrLY9S7UIY3m64ZR78xOucP7Vs4+M2wku8S7nZwOzf4W1EGGfPgLHXon8qb4SlXbzpk6C0GBADYiDCgIVL79q1i7X0dcOlI2U5XBJDZ/5pcWOYyJOXnJQ9DxFDzKYhoVrfm8R/xNcKNPHR06IFM8W/tembZZhuF8LwdsMtjZZcObSBnCXi7hI0Co+CmSBM/AaTfQsp8rmudiF0FEUS9qfHR4tdfhMIXdm6CQsT5NOBdS1lIAbEgBgoDwNNhEvHwS/tQG3PXBc3babMkaV/1ch2GEoRTPEmE3OYoTOfUIgSoga+QEl4Kb/2qfnEOrPNbBfC8HbDLY2ZTDhkQRORvBGqCT6zVeL+bpyNwqPOTBBGsKUctgshHJqy97EN/OnXba8mJhxdfhMIXdm6CftTymPPsLpNy0AMiAEx0BwDhEvbdzYIl2aarLlKKLXkGeKZS6gpswl8oss2XSSGgEOL4qzVwM47bmbRMgvvWwnxYTCwsZNZFVqPtEUQbiuVR2qLH7oWbTBgutApQrequV0Iw9sNt4wz0EoONwlBOXxkl0HU3Iq2zxB17tq+xV24dsEL71obEV7i8BH+0JiiMg7tO/OAZ0Gli97LFSGKFhcvGMq5fjP8OspSDIiB1cMAm0qzcIz+NhouHadoSQzxzGWHN9a0I4AswIIpMNbgsMzeQivihclhtONtFSnjP7UTLUMyrd2IZWIQw/zpxQX/l8bgZfG5kbIhh5FGeR1PcYZFjK3+vBHSSni74ZaZcIjvx+SyyVkjhDsHbZ0Y+BUOj6oyQWiQwv8tHiEzUzDGX5n/h8a9zSZbkfBz60VDCAM7zgaPWA7+3JIXKrCgzMSAGBADbWGA2KDBwUHCpUk0tHAsEe2SGOI0a2rmRz7GO6IFfLgHceKo5qqzdUO8yzo3gDsVmKChxMCOlOI2MdfQzjG1agtpul0Iw9sNt3Q8ZMIhW3HySyyLm6/Et+EvzrLmmoBHwUwQuv76ifhFbyNC/nD4dQFS4MQ9E/fQNIfQ73WtNJePv1/kV9N/wrVqVr4YEANiIA8GLFx6x44dLBxrIlw6DmmZGLLT8ZEsXszPQZGMf/0YU2x1n6Q2IYUfJV1d+ZUnps2lX+Zn92pDiFuRoTrxYjWdudo4TBRATbNHQQhk74DFxIloUFekWv54mSPDuRvJ16EYEANioGwMEC7N1NiBAwd2796NTygreEsB1E3XyEDIY5SpJcJm0yvBvc+vi8hJN045S1QKZzNxC6W00sopIWyFPSsrDlvkEP+u/cUhdFKqwi3EywxznXVfZlIq0SkxIAbEQAEMWLg0Sohw6QyVEMgTPENN9IfHaPqTND4r0UQrrgg7IUU2Q3KnSpIQwtYvhDhskcPAaTVeZhb+719tsS0VFwNiQAzkzYApIVqZmppiBVm2zWXgGcoWkGoTA2JADIgBMSAGxECEge3bt7OvNCvIMldCNJSNZyiCWIdiQAyIgUAGzpw5c/jw4bVr1wbal8SscoDXfHHQp27t8kP/VBnSh9esqRzDApz3ncPCMZRQJuHScagSQ3FOlCMGxEBxDKxfv/7WW2/96Ec/WlyTrbX0pS99iQqqBfjEj15as+Udfr/3fPCt/mF706Nf/e4yAM///Y1XvvqrH/+lZZklPnj88ccPHjz4kz9bYojLob38/TXnT66pHGCmyZb3I8sjiaEs2VRdYkAMNMoAYui2225rZevYRlts0d6eyNUCfOK1k2ve8RG/4yMjA/5he9Ojp5evCfr2lzdf83KFGB4aGkIM3ZhxEEuO1+SF6UUxVDnAOTKyZo1ihnKlV5WLATEgBsSAGBADZWdAYqjsV0j4xIAYEANiQAyIgVwZkBjKlV5VLgbEgBgQA2JADJSdAcUMlf0KCZ8YWG0MnJt/8djYkfL0euvgtg2d15UHT+tIrj938amhb7ZeT1Y1gOf0hootJ6zb959/cveVP7GurlkxBi/+9Klz25Z2op+57pkfrTnxzs4dxbTeRCtHfzh77vzZJgo2XURiqGnqVFAMiIFcGDg3f+67o4dyqbqpSjfvvGWliaGXLpaK4evv/omVJ4a2/PXPNHW75VLoQz/9i3fd925X9dCRoe89MvrF+6ZdTtkSv/5Q77fmDxSJStNkRbKttsSAGBADYkAMiIHSMSAxVLpLIkBiQAyIATEgBsRAkQxIDBXJttoSA2JADIgBMSAGSseAxFDpLokAiQExIAbEgBgQA0UyoADqItlWW2JADDTJwOYdNzdZssFiJw8812CJFWJeGMOnZk+9evbCCmGtkW6I4UbYKtpWYqhoxtWeGBADTTBw7/QvNFGqiSLja/+oiVIroEhhDH+l9y9Xp+IUw2X+M9E0WZmvjrCJATEgBsSAGBADuTMgMZQ7xWpADIgBMSAGxIAYKDMDEkNlvjrCJgbEgBgQA2JADOTOgMRQ7hSrATEgBsSAGBADYqDMDEgMlfnqCJsYEANiQAyIATGQOwMSQ7lTrAbEgBgQA2JADIiBMjMgMVTmqyNsYkAMiAExIAbEQO4MaJ+h3ClWA2JADBTAwOzs7MLCAg11XvqJtMip8fHxM2fOkN/f39/T0xMx0OHKYyD9lpiensbAbolNmzb19vbqrmj0HkhhGHpr1QbVtU61MV9iqI3kq2kxIAayYYAn786dO62uPXv2jIyM+PUig4aGhs6ePWuZo6OjO3bsmJyc7Ojo8M0KTqcMJA6JswFq24dqByZRbnKWq2DaoquriwEPM9eR4hMptwRQt2/fHofU3d3NrdJGntMZ9gGHW/qlsk2nMExD7u8x3ujFixfjmW3PkRhq+yUQADEgBlplAK1Tqwoe2YODg5GzBw4cGBgYQA9F8gs7TB9IGJInJiYA6fCg3ijiDotPpACOaE2HbdeuXZxql+JMuSXMg+hwusShQ4fQcPPz823BnMKwQ2iJcMtIwWwPUxjOtqFiapMYKoZntSIGxEBeDPBQZhirVbt7ZPPej8eIV+q9e/divH//ftLtcgM4VImwI0oo0abgzBTAc3Nzzuvmo4JhhAUk+5nFpNNvCTBs2bKlr68P/5DpnqmpKfyF5NMRJDJCuRicfispDPtmpMMtIwUzPKzLsLVlPGfYbn5VSQzlx61qFgNiIHcGGG5tGOOxe/z48Uh7nHU6iUGOiRte/WdmZhinsURztEUMBQ4kGzduBLDDH+lakYd1AUM+7je4hU/8LsPDw3ZRAA/tyI4i0abfEiAx948PiRxg79u3j0y0nX+qmHRdhh2McEtXJPNEXYZdi9zAkTlrd6psCa0mK9sVER4xIAYaYMBe4vH6JI64bmoJAxfC4uJF2uK08AeSWv3Ed8WQzPBchoGkLmBkEDYM0qYs8bUAG8Ktd0jPWt3MKT/9lqjVaFumxgxMXYYd5nBLVySPRHMM54EkwzolhjIkU1WJATFQKAMMuhZYQ2xKYsPuLd8f6pw3yA/KSSyeR2bIQIKjwkm3PDA0VGddwIlQfcIbaq5F47q3RLx+FAb3j7uF4hFm8SLZ5tRl2DUXbumKZJ5oiGG4BTP3M/86hjOHlEmFK3ya7Kmhb353dFkwwcDF34wQd2r2hZPTz144c4H8DV0bNvfesqHzuohNTodtbDrbHgV25Cu9f3nywHOu6c07br53+hfcoRJioCEGeM4yHUMRlo+hb5jzSilu0zc8jnkup5jlfcofSNIB540ksP7mAOPTcl4354cLbLEVs4ZuCRriZvAFMZN99DdR27WCKr1sOMPhlukttnK2UYaZubbJR3gmMTY2xt+gextpBUnmZVe4GErn69j43z019OSrZxdlkP+zZVfX+8Y/cFXHOj8z23Qbm1ZHsmVAtbWLAeZlCHe1AawWBjckY4A9j2MCcdq1iKzRgaRWpwrLbxowItVCqmtNX+bUhZBbIqVpZicTJ1tTirR4KpzhcMsWIaUXb5FhYshgmL/KdjkOU3q3WqbJbt+97UNTP8+vz8W5uXNxJYTB8f1zX+n9K98y83Qbm862Lw115M6Rd8evQrZ4VNsqYQBBY0HQ4b53e/628Snc4kBS/JVtDjBXxKKnARx+dVrvXRO3BC4K9izgx1pngsw8iK2DCawhnOFwy8CmmzBriOHdu3fj+2TfKXYV4l/ct9YivqIi74rwbq4Wz9BVHVfd3HtLnJdrt2zYOriNqbEbem68sHD+O8OHbFrt9KFT85PPdPbdFi+SVU4bm86qC1ZPeEcgOdumVduqZcCCJ2wYsyhppmaMDRLkMNnBD2ObmwdhloGN4Mjx3UWFEdjQQFIYqpSGmgPMOOdibpgTge2UJrI9FXhL+I1yS9gh9wxTZvgt+MGt5fJ948zT4QyHW2YO0q+wIYZ9xcMbiFFqKpmNDNB2fs1lSC+JIcJrkAUh4TKBloFmjbKAZDk1e+rk1LNW8IbtN27uvbm5KS36e9fIux0AKuGQ+CGUEJmnZl7ITwy1sWnX30wSK6YjmbChSgpjwGZhEDpO67immQvjJ7IPtQmgSMAQU2auVN6JhgaSvMGE1N8EYEh2gxxKyGoIaSsTm0ZvCb9RG61t02SURzFiKJzhcEu/U5mnW2EYMNBrYqgtbyN12VgSQ3hE+L2++waGN0RAirwItAw0qwvRN4gHRNvZt+7p9mWNXyQlnaj88CGlFDk3/+KxsSNmsHnnLYneJs7WNWui6RRUTZyqi9DVmW7Z9o44nEqIgTgDLnqXkAt3lhdTSxfpt2hxIHHgC0s0CpgRDq1ppYpXQq3Tgh/RKonvVtV65Yk1hDMcbpnYUEkyne/WUV0SYAZjSQzZMU6RJwYfJ6wYPYQqSpnXCLQMNAsk5fTsC4mWCC8Wgm0d+OnEs+GZ5nYye3xO8YLn5s/RlsuvLYaCzFw9JOo27Ru3ng7sCA2FWxqqgjvSOhWqoYoMuBAEB57B2LxEzJ2hcuwt37mCGOGYO7NDt/lQkWLI4VyRiTIoocBbAv65Abj0kegxHEJ2aQjJX5HXqPVOhTPMu0dc8SCRDUN5/u6cPgPYkhj6xblfJkoGt8dLxxfDip/ed4RfHEWd/bdtHdjmO4oCLQPNGrpCV3ZchRMISE6lOV/R/MQzrYshYoYspHqx43kGDMV73cam42BayVkxHWmFBJXNm4H4RAYTNCaGeNS6swx4fCHLQq2Za2AlC49p4kIMnotuyRst9YcPJAWACWkiHHAZlBA9chfd9S7xluAsUUHcKiaazZi7wm4SDp2AdvXklAhnONwyJ6hWbTjD6B7Epb80j0Pncuvv788VZ2DlFiiG8fnz5/l3SQwx3/G2oR5+2TMGYYEwQhXh1+H34N4nWY3lHEWBloFmgbjNbOfkByP2b9vbba4afwObiE3gIcvdndfnveMfCCyViVkbm84Ev6tkxXTE9UiJqjPAExxPABMNPIvdKic6xQATf3nNr7PhAwkY3Hjs3lxNcBg8qirg3TocsFtITwwWC4j48Wlk5Cs4eMhvPSWNHuInYkAX4h2P2GR1GG+olnoLt8wKW+v1RP7cXIWsMnO3t8ssPuH/Qa1bt7iNzpIYcmhwuvBLCA566LmpZ/nXHEUE0zh/jBkHWgaaOQApCaZgcDy4GOcUy0ZPMYozP2il3jv2/khPXW0bOjfgmrJDYoZcfiQRaGalApuONNHiYTjCcMu2dKRFHlR8xTOA4kEM8ZLqXkwZ8Bie4wNMeaiID9KIOZfpFFJJADs8PkiHrQDd5toKTDCFiivI3Q+uFOM0Ww1Fps/cWSXCGejq6mK2McIwOdBbBmWMm8pg8GRwYYUJYsh1+NrODenRxI1ahlfoavYTuKzY/idxZyDfrIk0NRMmZQVRQinTbbi7QiK1A81oMbzpJvqVUiQcYaBluzqS0kedWlUMIG5q6RvGYwY/3gVt2OZQA95quDdq3RI4YPjhZuCWcDyUwV3hwFQlUYthpAY/PsO8k/BThn6BGU3GdqAoIf85kCCGbPWQTZM56EyTEanjDi0RaBloFqk8fvid4VmnhHDPEOBMCBFmj+xsaYNEX2OlK6E4pBZz2th0i8gjxVdMRyL90uFKYqBsLopaAwmcs09dCZmvBZhBpYRo60JiICybAKrFcLwv4ZbxsoXllJBhJBr7bhBHyDZIvhKCkyUxhGRBABEtRJCQI8v204sEUAdaBpq5tuom2BjabPyF9M9Nv77hUN3iiQZtHMXb2HQiFU1nrpiONM2ACooBMSAGxECZGcBNhfZl8QTBgkjJONQlMfRfuv7MP22uoMSl44GWgWZ+o02kay22D6mqiVEc7eUcUb4mizRX16yJpsfX/pG1ktJuBEbKYV2Ermy6ZRMdcTUrIQbEgBgQA2IgbwaYD7WowZQdsJbEkKEJ2XSxIcvwCuvSQVXmtVr8yPzCeVb7M04TT123YC0DN+925carFhfQTTzjW17aU6DVjYv8Cv10G5v2YbSeXjEdaZ0K1SAGxIAYEANlY8DWT4BqZmYmZa58SQz5i+fTOxNoGWiW3pZ/9m17e6b7HyUHSfSnmz5vp9Axvk1D6VcXXv9ePaFI8ZX51+f5Ia02Nt0QRXWNV0xH6vZUBmJADIgBMVAtBuxjeYRLs4IsPYJ76av17xv/QK315JHOB1oGmkUqTzlkF8Q7h9/lqx98RfdOL/sQfUrxSp8iAMvhZ8Mnl1ZCDIgBMSAGxIAYiDNAuDQbqxIujXMoXQlRdskzFK+ohDm2LSSzY2Bz+x4NXPzNulAvLFywUn4U1L3Tv1C3YMSA4iHN1TVrtGlmBg0J/jbWukdQNXFYF6GrM92yoY4QYMSFcDUrIQbEgBgQA2IgcwYIlyZIiK252DsK51BI/RUTQ9YlhueQvvk29nURckKkjF+wJOlTM68v8WPH7ZJAagLGwaFvxqcjm6hHRcSAGBADYkAMJDJAuDQ+IRaOpYRLxwtWUgzFu7Hic8wzlJVbaMXTpQ5WmoG1b1z7k2+/vtJdKDn4f117UQznfY3W3XHVNVddm3crqj/CgAuXnpqaamgfqRUuhtgtOmTD6AibJTz82Px9JUTVKKSG5tQarVz2K4aBi/9y8UeHT0e647aWiOTrsAkG3nBxrRhugreGipw/euH8mmVRAbqHGyKwCWMXLk0iZeFYYs0rXAwl9lmZYkAMiAExIAbEwEpigE+s8CXmHTt2sHAssrt0SDeXVpOFWMtGDIgBMSAGxIAYEAPlYcDCpVFChEszTdaEEqIvEkPluaBCIgbEgBgQA2JADDTAAN9gJjZo//79w8PDgQvHEmvXNFkiLcoUA2JADIgBMSAGSs0AC8csSnpiYoK19K1glWeoFfZUVgyIATEgBsSAGGgDA/iBtm/fzqSYW0HWCgh5hlphT2XFgBjInoH0fT6zb2/11fj05jeUase1wfuDtsWr1oV6y9zt7+rsrRbmCqG1cGm+s9F0kFCks/IMRQjRoRgQA2JADIgBMVBSBgiXZk9FC5dmmqy5cOl43+QZinOiHDEgBsSAGBADYqB0DKCECBJid2nCpXEOZYhPYihDMlWVGBADYkAMiAExkAsDLly6oe9sBEKRGAokSmZiQAyIATEgBsRA2xiwhWMECTW6u3QIYomhEJZkIwbEQF4MnDt37plnnrHHXF5tZFovLnrqqxjgly+sefRzPg29vRP+YZvTx04uA/DiC8+88V8qxDA3MPhPPLSsE/9+as916zctyyrNgQEuM8NHnz/04o+X+HrtzGK6s7Mzq3DppaovpySGLjOh/8WAGGgHA1dcccWGDRvyeNXLqTc2kFQL8MJrL635yVt9Qnp63uoftjd94IXlI9GrF9Zf+WqFGH7ppZdOnDix/k3LWLx92//0po0/tSyrNAcGuMwMv/B3z7z64oIj7F9fXfPai2vyU0I0tPwWdC0rIQbEgBgohIH169ffdtttIyMjhbSWQSMELlBLtQCfeO3kmnd8xO/8yMiAf9je9Ojp5Uvrv/3lzde8XCGGieQ9ePDgjb3LWPzf79td2qX1BrjMDJ96aPZb8yccoS9Mrzl/ck1WC8dctX5itYihY+N/98Tg4+8de//WgZ/2++/S/veEN++4OeX76nUtz82/eHL62XNz56j8qk1XdfbdtqHzOtdQJHFh4fz85DNm3Nl/2w09N0YM8jgMRxhumS3OJtqlyLn5Rc5v6Lnhqo51ETzPTT/7yM6/cpkfmvp5NrNxh0qIATEgBsTAamZgVYghBMdTQ09eu2VDLSXk7oC37ukmvaFrg8uplUi0ZDye7nv09KFTfqmDe5/E+K6Rd/uZlkaiAezVsxfs8Lujh27fve194x+IW2aVE44w3DIrbFZP0+065hOFzobODXbJkJ4vHV/UTPoRA2JADIgBMWAMrAox9J3hQwiOu0beVfeqJ0qWxFKJlngmUEKoLrxBN2y/8cqOq05OPYvE4ffCwoWIymHUx1lF5QzSm3fe8tL8iwijp/cduarjqsTKE2E0mhmOMNyyUQzp9s21+9TQNyMaNNIKzjlj9fTsCxJDEXJ0KAbEgBhY5QysCjF0bPwIlxmBkvfFRsdEZuKYi0EVTfc/isp5295uf74MiQYeXEGXpc8t13Zex1QOymnr4DbfMkPY4QjDLTOER1VNtIus5BKjKaEuWzCqTQyIATEgBlYDAwliiOgK3p4vnLlAvMtmxvLaUSxYwpHFXpyafWF+YnF5YTzwJdCMsulN0wT+lcSIEIZDPApMhcQ1BFNRuIXQHPE4kswvMFzF6UKEXbnxKjAQSLRh4PXgIWbukEcAQPc4GDB5ffcNeDiYynnbUI/LzzARjjDcMkN4VNVEu08MfI2CaE2JoWyvhWoTA2JADKwSBpaJIeYaYsPJk0QTv3f8A3GRAUEWkforZz5BuMbJA88ZZdRw5/C7/LE8xCyk6WNjRxAQifE3X+n9K+Y+eifuieN8bmpRsd2w/YY2XlFUWmQS59TsYlwRIikinlCfWJ6aeaFgtHGEtQCEW9aqobn8Wu0idrn3cMgVIHabQ65SYkAMiAExUHIGlokhHEJ4JhiPCWEB96sLF+Ym/vvx/XNonY/MfrRWT74+8DWGdjQKccesisJJg1cpbpxuFtI0ThTEEF6Ty/NKrzeCxwglZJE68XZPTi+qNDoVP1VMDk4gU0LXez42+kvreLkiGCx2+6VLq6Iip/I7TESY2Fy4ZWLxpjNrtUs+gVbo9bqh8U03rYJiQAyIATGw4hlYJobiHiCmeL7c8yWbuKkVc4Pa+Mhsv++SYYiKE5duFtI0ThQ3i+SDwWNEc36Oax1lZmu1fHjubDEJiw1iwPadQL5eXAx5GTtCaBFdIGwIVBE3Ut44ExEmNhpumVi86cxa7aKwub53Jq3Ua7otFRQDYkAMiIHVxsAb/A4nKgbzqaRM3LBKK1IwccIi3SxSg6GKN20RNjbz5ZDjKyLtB9+4UwQSkUaIuJyCE0RBMW/IdBhqr1bTNkVIkDWqqJZNfvkhCK31cMts0dZql3zclrgkfZWZbdOqTQyIATEgBlYDA8s8Q3QYpw7agtkum8chx/RECheJLpm4fV2zkKapxNafI61Mcll89JZdXYlyKg6jyBzm75hhpMW4EPRhGHIEEwup/PwC0oEIQRJumS3sWu1ytxA3DWnETWfbomoTA2JADIiB1cbAMjHk7wF4KbY3GtGSyE6iHyhumW4W2DSVoIcscsjCRMxLdPOlIKd4o23MYbQmrJtJnMhi+zgkQqAI0kpcJRc3zjAnHGG4ZYbwqCqlXSbOCBRT3HS2hKs2MSAGxMDqZGBJDDFHY7shsxZs68DSQvSkdV4Zc9VQ0yzdRwwRZIMYYrBkogTdVrb4WTeKM4mTiC2yybX7NIQ55AqY16uL0F3jcEtXJJNEeru27JHdHGxDB7/FgyyK7LiKQCJNn/m0KC0GxIAYEAO1GFgSQ8yO4cZgPx5/VTzF2NqnVuGs8htqGt3AwjFCjJFQr0cLDSxt1ROBxHpsctyy/8jZnA5tFAeht6FitCkLlI4Ds8BqOxstk91xCEJrLdwyO3SLNQW2GyeQshZ+XsB9m22XVZsYEANiQAy0i4ElMWTDcDxsha0C8wbXaNPESvPBL5xDhi0xdNowE46D3wiRh3IqJqjIH8Uj39/waUTSGTD0nB9NZfIucVckumDxWy1OqAUiBG24pXWtSIQDF3/T59PS9g3dxG+TxY1TcrLqSEoTOiUGxIAYEAPlYWBpNRn7TQPLxU0bRObICviQU6NNM4sHPL7AgA+AxfbpKmdz7+JSsgIk3WXGnjSfUIoSMkvTQMfG/s4O+ZfAKdhGJPnyaOns2BG2r+TXNmx0+Y0mmAwNRBhuaRiQpwUjbLTvgfZZdSSwOZmJATEgBsRAexlY8gyhMAhKZd7hK71/aZsu8pFRDm1rn1xRNto0YdTMQBE5BKoUt5BhJraauKJTM6e2DuTaicXKWextqPjqKjRG2iPayY8fYokZfiAj3PZjtDgYt1AuUjyTw3CE4ZaZAHOVtKtdB0AJMSAGxIAYWG0MLIkhFMa90z9vH9awUAxcFKzWWVxmf2jx2xH5/TTRNBLHZEeiE8WHiv6IrMb3z+aUNgIjlfs7UHPKek20L8aOcJSQL5j8Gpi74ZB4KRdt7Z9tNB2C0OoMt2wXwkb7Xtc+247UbU4GYkAMiAEx0F4GlsQQOFh987H5+9jZxYJP3aAb+fyFQ5wYt+HOukSIWaNNE85C/YGfX8XzhNMFN0wtneGgtpiAsZDOulbo9b3TvxAn3Bn4CfuuSIvb6oQjDLd0IAtG6Np1iYbId6XiiUw6Eq9WOWJADIgBMVBOBpaJIYPYxgXJ4U3b9xmYeAqhFQFBgBFF6oohm9vChVNL/7nmwi1dkVqJkF4jmAgDxy1Utwu1Wsk7v+QIgYcTDhLqRlyVvCN5X8cK1T89PV0LbW9vb+TUwsLC+Pj4mTNnyO/v7+/p6YkYFHDYEGDDMzs7C3LSHR0dxWNuCDBQsTeGu7q6uASdnZ0FsOo30RBgjMFsgDdt2gTgkjPs99TdGJBcJM/hDIdb+v1qYzpBDLURTUjT+IRsw73wCSMmpJh+emLwcSKU08VE4pRQIqpwy8TijWZaAHiLbqFGG23IvuQIcXYGXrKSd6Shi7KyjXfu3FmrgxcvXvRPIYOGhobOnj1rmaOjozv+//bO4LeL44rjuFHjqggXWogqRYmEm0MphwJ2iVopVds4UtQc0oOR6KFVowpz6dn42CP0PzCcqh4qYQ7NIVUlQKkaNVEi4zQHRA/YiAakCtJEgCo1QhX9uA8e4939ze/tb3dm1+ZZCGZn37z57ncXz3ffvJl9+eWlpSUURmiWulwL8NmzZy9evKiQABwZXdSs3YIRcIFexTA7O8upnCQbAaMkpqenFacWDh06BOCcksgIWBFKgSdBG87Pz586dapgkO5Q+y13UfhPZ7csu+qkZpOJIVk7DVPkM80svWqnDA0Ul0H2GRa7pR3eUEs2fyrs/zS0SWaDniO0z/r1/EIy39Yt0B0jx/HjxwsXgs6Ym5tDDxXqe3JYUEI9QTUIxurqqgrN0ObcuXNra2soj7CyD2UJtpWRrKysEB8Cc04BV4YxtAZlP9TGDeoysMnEkGzNzDQWi8jiK+rrEuH2zoAzsBkZ2Lt375EjRyLIdeTgvZ93aMbmhYUF7BmqKecMAwjIoYD1Wnbu3MkMCCO01nRSsADGBsUpM01IjZMnTxJ+Ay3gUZzxG9T6RRkBg4r4kOie8+fPC2BUHYARyq2jiji0ANbmPM+b4pEQwLUuTa+xk8ImE0OkG3dCk3fqDDgD/WQAuRCZJuAtX0cOBjmMGbCXl5dRQlwOAZj8YigOWEhGrjFfg2U4IdIV/0MBI4PCW4C84BDkwjxsZxZDQwFL+Cfkkxo03JkzZ6gk0BWeylAeClgx8DyLaENkXLt2TeszF+yA7ZaZL6Hc3eNNF8vnvMYZcAacgU3NAEOy4CcsxO9lKWu+SA9ncAQhY7Oi7T//lVB7PtNUZnVTAJaQFQ9zZn1Zpmvr1WyyyNDWuwF+Rc6AM9CEAd6VGSH4myEZlVOY4NC3/HCo02hQmKHcBEOttnHAtVzlMR4BMFEWFZoqPfOgpZe6gLFHNBOKE4TlDLPUyI2AibfJEwtUgpqpUUX8GwHjwW4Z6S7PKY8M5eHZe3EGnIEkDDBZwOwGgwR/M4whdHQYDvujnhGa4aTybGiZumwEnBqG3f8IgMkZkpTqTmIYdsBE4MbGxtgIgCcHwMw9ITIqA112ukawtABGVcAqzlk+pmp+hL5aaWIBLB3ZLVsB1sSJi6Em7HlbZ8AZ6BcD5Kkwg4DuEVih9CHzlFwcxr9eIS4A7hW2SjBDARO3kLwWmmu4pdJVnsqhgBUGj0cfpp8qAfP0ilwL07MUebeFSsCVkOyWlc2TVroYSkqvO3cGnIFUDBw7doz3eDbNY4MT/uaNWXriZbRyDJaZsnC+LBWyAX7rAh7gJl/1CIBhXqeZFhcXM8cw6gIGHhs48SOcSmRRlXQGoo2Ayf2XlP/KBzsDTu3CCBh7u6U677bgYqhb/r13Z8AZGJEBBgbe41Xi8MaseoiV0uI0HIwxoD6MFY3Y8ajNLIBH9Z2kXV3AcKsbGaCECvlbSSBudFoXMI8E2UL8IKaZ0cMZoQuZjdroONWREbAwKaJNAKtio0ANk2ipIG70awRMI7vlxh46O3Ix1Bn13rEz4Ay0y4BueltWPFLDHFkYGWIjn3YB1PUWAVzXVR77CGAYhl5JFepECVUyEAEc2vNU6PQTYZjwVOZyJWBhlcQ4zsqP7AIANgrUQHhmnNodvUu5/J9ObaRgtyw0zHPoYigPz96LM+AMJGdAX5c1B1aXMoWvzpVxo+TgqjooA66y6lHdIMD9VEIQNwhwmVN9ZphmLZ/NVmMHnA1SvCM7YLtlvMdEZ31pfW1i9ZMgtGRH7Pg+kHfX7txdu4vlnqk9fCKt3BmfWltbunp3dd2G785aPtpadtKkJo6Qs3yrS+CNf3V88sgL+Tf+jiPUa4+b3bjw8R9feVONf3z+dT7QoYdaMJphz437/eTvuK3xB0A9e6FdBhA3OnqpZ30/1tkxTZdmhGM2QQ4pSBM1Uw/pCkbA6QDU9WwH3BMlZAfMA8CtD2OEkKMBIdaU1eVqNHs7YJ3/1Y7gXJbZM3fGtWjQRQ1SFOyA7ZYpcI7m08XQaLxt+/b8+gTzxDcm4u0vHPnTJyu3sakcfflw7Psn3uNz9OLko9+sfPPY/u+f/mHcZ7tnByFEW+gp7fGDhfe48BdPfU9rMhQURiWHCiBuNjE5IbcM6Xnv2rr0rPwxmtEWabtvbj+3/cH3ogAACVBJREFUbOjXfys78sqGDKB7GL3CtT8c6js936UX/wx4fC5Ukk9JvMCeX9PkhchZTfVtCMbS3AhYXKmG05dpERxylgmdDDLODlgX0jPtSEo7PyEh3Is8yUO1AKMkREYIVJ4KeUg4VPLDq0hRtgPWKTyFQW6WiCGehPJZNWu3YAdst2wXYS1v/OfiF8L27duPHj1KQxdDtdh7bGwRBO+feFeU0ONmQQm18c7xP1PBIP3sK8/dW7uDMPr7mcvju8YtzgNPoxcjCAloAX7H3gmiQXumn3l61/jN8x8z9vOHL8BnU2wRhOFlDzUjoCWsfnLpVlQMmcyk6wMLh66cvvy3kyvxbwCHOL3cIgNIH13CHbplGUs4njFUEAkg66Jgz9t2ObYU+mm9XACg/guAqZdxTg0ogF8rVSGFBinKRsCKJwSpeDLoNu3LCFjs4VMpVQ/ouWzagk5rAVaQHRbsgO2WnVwObxdodN6L0G0HDx4Eg+cMpboRaB1GSolGVPbBIEo9oSAGaaZsGFBnll6lBrVB28om7VbGEaLJXlr8wU/Xfg489BAIKcycXUeIYusDQmUjfiFq1nqB4BDMIK0IDrXu3B3GGWCjvPJ0BjX8amMZS9gWxYMYCo0Z8FBCOcc88NgBh+A7LG9hwEwqhc+DkowqZYwsTJ/p2dYLW5jhnl8avxB4XyIcSPqghi09MtT6E/7Q4Ttzb1MieIC4KfdBxgmSgvp9x/frWQTH7kN7iMcwlXPgxJTWJyrEEZK9VE5gYux/euc483okEk3MfSURMHUbR1jXTO1bLJDmxX1cO3vVg0Mtsmpxxa8wfohJMHqJPaKHn8q2BCf4xYelxDA4zDbgKZ5agNk5SRt2VbADZmjpCmTYrx0wc0z8hA8PfsJoYug2XdkOuIwBKZ9ZzYPBDthuWb601DW8LDE/zk4KFMKwpYuhJMwTKrh58QaRlcqkabq8fWk9kQhhURAcz848hxi6vXwrCazA6VCEge2GIlk1kbm/DabNDowIjWbNsAxsLVnY3GuiU/lTywfCemJOoGnsY1j4i68rhmoB7gpk2O8WBtyTS+sJjPCmx8t2wHbLeI8tnkWlsRkB6WKkGAIv9OzTZCEb7ZSJ+pD9w0KzSLSAzBU6Yy1SoUvJyL73/wVohVMtHloQVnZHQ1FCu6eeqTRoq9KI0GjWFqpKP9xo6gmVVZ71SmfAGXAGnIHOGSAQyIsTSojJUGKZBSUEPBdD7d+jv8y9zUTS4eiSq88/fbiCjO4JKpD/y9QY5R2T63NPqUMvFoSVvEieE8N/IaBVadyk0ojQaNYEydC2ogtl94Ghxm7gDDgDzoAzkJkBZslRQuTLl3MKFYlPkykV7RTYqObauVXypu1yASVEE7o/uvqzdkBEvYyAUPzRkPwnpvZeSrz434jQaBYlo7WTEuprzZ07cgacAWfAGWiDAZIFZTKddOnIrLpHhtog+5EPZm3I+UUukDf9qG74v5JrQisWcA23bmYxGkL6vH3pFhv5UHjx1HeTJscYERrNmrFlas22CCY7N3IGnAFnwBnIywBZ0mxDz6SYrCCLdO6RoQg5tU8xi8RC60jedKVHlqwzoA7aorqyyciVoyFEebw18yZzf1xaJBFqZFRhQyNCo1no2cvOgDPgDDgDTw4DrBlkKzIWjlUmCRV4cDFUIKTRoayiZ6E1fwqOPjjx7ke7xkkkkumzwtbVsiiJJjLbIjm5BQ+tHNoRaneqhJj7S62E6NSI0GimV5GuwF6U6Zy7Z2fAGXAGnIG6DJAuzcIxdhUnXbqw8dggVy6GBjEzej0LrcuNJSeavZvllCRKly0lsVrOlp20VVPuF88FhNKXKCFOyeaQbQEY6seI0Gg2tLvmBqmX1zVH6B6cAWfAGXgSGJCFY+wuzYdiCA4ZL9nFkJEok9ncg1+V7eTDroXvahEKIkmIiScWkbGTobaSNWV7potL7jFg0Vn8m6/qJFKwI8RJqIQs39/IibDWhUQIaX5KgnmFUF9zt+7BGXAGnAFnoC4Dmi7NN/LCbxcO9eMJ1EMpSmUgGujK4uMvObB/IClHiKRQHmn3VxYv8911/siGjVqfrsBuSRITsighYORHmO7a7Z7ldrBbpr2JWzoDzoAz4Ay0zgAzYrJejCShWkoIJB4Zav12WB2yLIs4EBM9b838QSZZJA+G+kH7Vltdt2HHwnX5YAhfkAVhwSWfociQP1TotIeHLLIjvMdHVJKusOvhhTskZ8AZcAZ6xUCtdOkychdDZU4y1aB4XrvwOonV6CHJfSEmhBIaJDKYhAIZn5HXbOs8QCvzcipTZLpCmIeHyl4IhlEffmCu0swrnQFnwBlwBhIxQJIQSkh2l+aTbeXdpS39uhiysNTIpjK7RTyysuy1Cz8huiCJ1XGVc/PCDVrV2sHIiLsSIWAq6yM+MyMsI6kLuOyhVg05VcT2kKeD9Gstb27sDDgDzoAzUJcBTZeen59v8vFaF0N1mX9oLzNHBEjYJWhEF4+aWfaqlumYPo+7PUcIPIJwUB7PuDKaya1jryPmyAjmPbqT/q8z4Aw4A85APgY0XZrvbLCWvknHLoZGZK9y8mhEX4Zm8h3QFGEhQ+cmk54jJPZmuWVGMxghLHTl9GWyhTwsZHo+3KhTBnZ86YsvPP+1TiFs/c73ff1geJFfGHsqPPRy6wzw5XkRQKRLT01NNfTvYqg2gZnnYgTfgRNT/KmNNWODniM0zvoZzeCVlK9ffHYsI8HelTMwOgP3/nP/w3/8K2w/9svT4aGXmzNw5Z8fhk7e+O2PwsNelW/9dR3Ot3491itUtcAwI7awsGDcXdri2ZfWW1hyG2fAGXAGnAFnwBnoBQMEhFBCs7Ozlu9sGBF7ZMhIlJs5A86AM+AMOAPOQJcMtJUuXb4GjwyVOfEaZ8AZcAacAWfAGegXA6RLkxvEdzZIl26ycKzyqjwyVEmLVzoDzoAz4Aw4A85AjxiQ3aWXl5ebp0uXr2rswYMH5VqvcQacAWcgDwOHDx++evUqiZB5umveCy+mONlcgD/79+fbdj/f/Nozebhza8dT/z38nelM3TXuhgf4+vXrX97b2FEuB/c/3Xb/zrZNB5j/dKwgm5ycTMGTi6EUrLpPZ8AZsDJA6Nv+ZWmrU7dzBpyBrcUAGmjk3aUtTLgYsrDkNs6AM+AMOAPOgDOwZRn4Hx/MYoGwEPupAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Mais Ajuda de Indexação\n",
    "A indexação de uma matriz 2d pode ser um pouco confusa no início, especialmente quando você começa a adicionar em tamanho de etapa. Experimente pesquisar no Google \"indexação NumPy\" para imagens úteis, como esta:\n",
    "\n",
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Seleção\n",
    "\n",
    "Vamos examinar brevemente como usar colchetes para seleção com base em operadores de comparação."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,11)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr > 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "bool_arr = arr>4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False,  True,  True,  True,  True,  True,\n",
       "        True])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bool_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[bool_arr]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[arr>2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 2\n",
    "arr[arr>x]"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.16"
  }
 },
 "nbformat": 4,
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}