Inicio python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Esto es un titulo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## esto es un subtitulo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "b=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=\"Esta es una reasignacion\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function str.count>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<function str.capitalize()>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.capitalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cion'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[20:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=[1,2,3,4,5,6,7,8,9,10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ingrese el primer valor a sumar2\n",
      "Ingrese el segundo valor4\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "val_1 = input(\"Ingrese el primer valor a sumar\")\n",
    "val_2 = input(\"Ingrese el segundo valor\")\n",
    "resultado = int(val_1)+int(val_2)\n",
    "print(resultado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "val_1= input(\"Ingrese el primer valor a sumar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ingrese un numero: 4\n",
      "el numero digitado es par\n"
     ]
    }
   ],
   "source": [
    "val_1= input(\"Ingrese un numero: \")\n",
    "result = int(val_1)%2\n",
    "if (result == 0):\n",
    "    print(\"el numero digitado es par\")\n",
    "else:\n",
    "    print(\"El numero digitado es impar\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "8%2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7%2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int(val_1)%2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n"
     ]
    }
   ],
   "source": [
    "print(val_1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "def suma(x,y):\n",
    "    return x+y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "resultado = suma(45, 78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "Missing parentheses in call to 'print'. Did you mean print(resultado)? (<ipython-input-32-246d79d80f57>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-32-246d79d80f57>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    print resultado\u001b[0m\n\u001b[0m                  ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m Missing parentheses in call to 'print'. Did you mean print(resultado)?\n"
     ]
    }
   ],
   "source": [
    "print resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "resultado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "123\n"
     ]
    }
   ],
   "source": [
    "print(resultado)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "n = np.array([[2,4,6,8],[1,3,5,7,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[list([2, 4, 6, 8]) list([1, 3, 5, 7, 9])]\n",
      "[list([2, 4, 6, 8]) list([1, 3, 5, 7, 9])]\n"
     ]
    }
   ],
   "source": [
    "for i in n:\n",
    "    print(n)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([list([2, 4, 6, 8]), list([1, 3, 5, 7, 9])], dtype=object)"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'some numbers')"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "plt.plot([67,100,68,4])\n",
    "plt.ylabel('some numbers')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x124e7d550>]"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1xKNSysHHZreVIxKu7I+XdW1E6ALMubb7wv7fF+rbHU2x2V3kPoC4JPtByOF1DCX6wb4m07VwMpudFpRKgRjiDXD5RtvqyZO8RSRjdaVCMttrbQwmM4yn/AhMa72Sryiulyv7o1ke/cZSSXujKTa7zSVd0nNWdFgbnWa2x+KKEjYKhi7mGK0OAFjvtOQIK9fVztdnDTxb13B5XZvdZhaQ8JGk1ssIdyO7pnvStY0mK7anRgzXiFwpVsVEV2YsNo2N8uUGeIpOq4Fua2FQ4nMfCyeTjEqKSBiXdVkDJRmY1s9oo9wx9+IQQ388xUYnTcCw7GTik8/WICmlsNFpoS8cMLc3mqLbMggXMPSU2ABbY16M8iMMZlHXRkeOPsrrWu+2oDUwEJSk98czaL1wMvaaitdWoJJi0cdByaF1DLslXtq8Jjea9sYHjNGUl6suKn8AE01LDbB1Toskr7ySqFiWaP/fG09Fz1FY6Moiw44xeBKDsls6xmhjPp5hPY/ys8SglJZK6GR2R1NsFvbYeqeJfoQx3yrssY1uM8rJAEXE0JRTLKUoP4b+KaPSjQiKsexk7DWV6LLoI3dYmS6pMz0oOcSOYWGAmw2FjU4zKjIvGvPNbktc/16kpQBgqyuH5rvDKdpNhW7LXGaTmE1D/2xkEVhfEIGVkcy6jZoEa7tS0pXnGATXcjKbYzyd5ze+1SWnpRZRfrvZQK/dkCOG4SQvlwTijGYxYl3okke/rUZhj0Xs13LyeaPTxDi7JlzJ82sFKgmA6Jy5HBYgi/JH0zkmM71CS0kd80FJqgf1vE4p9ZBS6hGl1Fsdf3+nUuqL2b9vKKUuFf42K/ztvhTroUjZAK93WxhMYhDD4kZbj4jAyrrWOk3xTHp7jEqZJ0WtR9APZfonxpjvFUocgQJiENxo5Yg1p2wExsne6Oud5byAlBbsF6gkwFI28si8aMxjEcNmr6wrYl1ZObTRFb/HVgywaI+V9quNzCMQw0qOIUZXN35dBymt8Fv8opRqAngXgNfCPMP5fqXUfVrrr9r3aK3/x8L7fwnAqwoqBlrru2LXwZHZXGM4mecGAIi7OfqjBWUQrWs8XVpXDJdcjFjtusbTOaazOVpNXkywP5qioYC1ttG3nv3fH82ALd66BtmNvpYdZwyctk7TnrMYxGC/3+rothpoNpTIMGmtl6gkwAQMUie/X3YyMZF5GTHE5BhKuopJXussOLoaynEtR1McXe+wdRV12P9FjiHXZanPBLpyh/XCRQyvAfCI1vpRrfUYwAcA3O15/5sA/EGC7xWLnYlUNMBr7aYYmvcns9zIGb1yY94fz3JDCRjjKY0MB5MZ1tvLDsuuV7oue7PHQOB+bsxLcFpw/q0ue/7X8htNgBiyz1g0pJTCelvm5AeTGeYaS8Z8vd0SX8tiTgwwKEscmZfRR1e+98u6copRsDaLinL0EbHHVvICXXnwkSfFEyIZ62TazQY6rcYLMsdwE4AnCr8/mb22Ikqp2wDcAeAThZd7SqkzSqnPKKXekGA9QcmNSbsQmUdQSf1x2TE086iYK4MVJxODPlYdFiCr/hlMpugVztdaR35zDEqOOQYx5MY812X+lxyjNY4bxYBBSOUtOoKXdUmuZZ6wXDLm8rzAftmYx1T/DFfXBUgN8HQFFdnXJbqARXQfQ1eWKa4FkpHrKiL5jYiE/UFJNJUEwIUXq0pV3gjgQ1rr4lm4VWt9Vin1YgCfUEp9WWv9zZUvUepeAPcCwK233hq14EEeZS5H05INOJtrjKfzlchcyiUPxrOcpgHMzTHNvqPT4vnxwXi25PzW85tDYoBnJYpLHs0NxjMohTxhGYM+ytey3Wyg3VQiVLSfO5l4WtCe46IuafXPcDLHXC8iaCAOMeyPprkBt2scSSnG8RTHCjTPUpKXSTEOJ8v7NYayGUxm6GRUIBBXYmoZBhtk9doNNFSkrvZywPZCRAxPAril8PvNAM5WvPeNKNFIWuuz2f+PAvgUlvMPxfe9W2t9Wmt9+tSpU1EL7k+Wo0zAXCiJ1y5Hv+bnlphLLhtgu4Fk1MgM5TyKfZ0rg/K6oigb4/yKCUtA7rAALDnTtXZslF+k8mS0YLmJzKxLpss+9KnXKhkTYZS/YoCtYxY407KumGs5nCwHPxsRlUSjyTwPPIC4EtNRVhXVbRt9to9EYszza1k6/9caYkjhGO4HcKdS6g6lVAfG+K9UFymlXgrgGIBPF147ppTqZj+fBPDXAXy1/NnUUualAXMD9wVUkjWMvZIBlnZZDsazJV0x0fTQkfuQ6ipTXBsRugzF5ch9CNcFLF9LKf2zyDGUaEHRvlh1MtJKouHEGKYVYzKeifbYcDpHr100wPLyy+FkWVdMknc0nS0fY4STWdEV4WRslF90zNISX9e1fEEiBq31FMBbAHwUwNcA/KHW+itKqd9QSr2+8NY3AfiAXt7JLwdwRin1JQCfBPCOYjXTQcnQFWUKjcnAoWu9YxJwI2b99XQ2x3i2TEutRRrgJcTQjYvyXbSUDH1MsdZZbL2YBFx/bCpZitHheqclo5LyHEM6KqlcFSZ18ACWDPB6J6MYmYPcJrM5ZnOdd9bbdQGyvMBwMkumazSpcFhCZ1rU1Wo20G01xLqUAtrNBWsuTdjba9ktIaNrrSopRY4BWuuPAPhI6bW3l37/x47P/QWAV6ZYA0dciGFdWJW0qLBxG81iZBASJy0VRSVNkxnz/niWP6AdSEElLW89aQKuXC0FWCoppippmZbaETxz20UlSSnGnMooRqz2/I+WDTNV15IBjorMqxCDwGhOZ0v5ikW/gIx+K58X6eyl0XSGXqu5tMdMGbmcliojhvN7fbaug5RD2fncdxngbsuUGDJHPLicjP2Ze6MNXA4rJjE7KVE2bTn6GJZ1JaSlrD5pH8OqLmmU70J/Ml3lain7s4RidCIGYfXPQtfqHpNGwEVd9mfJTKIyYrDUjeSRu2XEABgnb6mcFLokx+i6ljGFBAclh9IxLBqsVnnu4ZR3gVxVBjYC424cVxntwgDzDMBkZlrv01FJ0yWD2czGIKSguAB5Am4wWdUlLQvdH5vx6cXKHGny2RqgtRJdKaEYF/RDfI5ngT5WEQN3X2itMZouJ3mtkxYZ81JeoJHtMZljmC3lBABjjKXGvIw+ep0mBkInAyxfy/WuvL/loORQOgZXJcuilFNmzMsljsW/8XXFl4W6q6XiqKRyZL4h3NAuxyBFDOXch9ElrUpa7lQG5LSUKzKXXksX/bMuHMvgXJdFDFInU6oIK34Pd23dUkn2WkcWmY+m87yKyEqv3czzi1xdq4ihIdQ1Q7up8jJawOyLmGdFHIQcaseQovmrn6MPRyknl0qaeHRxHYPjGA1Pyl8XsFrhBESU+I6nS2gNkBvzchmt0SWtMKugpSb86h9XZC5Ffy5jLjXALvTRFVI2o8nqMeZU0pgfTRsDvLrHJPvChRhi6B/XurjsgtE1X11XR0ZjH6QcSscwnCw3WAHyaoryrB5ADvPtzbTu4vKZxtxFSzUayhhz5rpyWqpdRgxS/n22oqsnvNGKT0mzIq0wK5c4Wl1S+qfVUCVaKg4xuAww25iXavKL6+LrWnVYzYZCpymnbJyOQYo+ylF+DPpwIRnhHltZV3bM3D12kHIoHUO5wQpIS/+sCZ1MVcIS4DcfuXSZ3/kTPl0Iy/wuKwt1JYylkaGr8ks638iVZJTuC5cxkfak+BGDLF9RjFqlTsZVk29+5+cFXPkKq1uSMB5NVyPzXgT66DoCGZnDmq/mK7I9J6HfDkoOrWNYpTJkMH/guGml83pcurot037PpWwWbfwuyia+WgrIDDATyWit0XckjLvthsgAuJLP61lkyKd/VpOMC8fAp39Wo18hleSI8q0x4e6xnP5Zqv6xunjn3yKGFNH0eDaH1qtOZq3TFFcllY25qUqSJMXdFJc0we7KfQCySq6DkkPpGAal0daAPAIbjGcrDVZrwgvtQh9KqSxqitcF2GiOeYyORHaui0n/TGYas7l2nn/JjeZKZK9lDYbc4yyPUbC6AIEBrqAfRLoceYF8vzLPv2u8RqvZQKfJv5ZViEFC/wwd+QqjW0ZLjVyJbGGUX6VrMtOYMBsMR64cQ0TC/qDkUDoGVyWLFM65Gqx6EU4GWDXAkkSXK8dg18bXlSXFS01pEpjvGmC40JWKljLXkh+Zu2kpo4uP2FwG0/yNG5mvViXZaJjrZBbGfPnW77YbfF15jmE1AuYa4Bx9JEo+OxPZ4ryAWxcgy8usnq8MsdWO4bkVV4OVlGcdTKYrumx0wdflpmziDHBJV4t/c1Q5LBGScQwwNLoabPpHa43yg42MbmGNvwMx5LSgIMlbnoYrDT5ssUSnuYpKuQlLnwEecdGHoyYfkNE/VlfPmWPg6Zplo0JcDmsoSPCaCqc09I+zJ0IYMBykHE7H4KAfuhGIoRyVNxoKnRYfmvfH07yqo7w2qZNxcvnMm6Mq+SxxWFUU11q7ibk2VBNVRtNsHLWjKgkQ3LQVVUmAJDJf1ZUbADb9YxxWEZW2mwoNFYEYHIZOcozms64O4/gEu9UlRh8OymY8NbOiePpWeyJy9MfOy7gdFrCgDK8FOZSOwcVLx9A/ZV1AFoEJjOZaqVoKMFG+OMfgoH+4GzBHMg76ja3LQ3EBPKPp6jpf0iWIWl1VMSJdrgqblmyPuZyMUiq5AWavq8IAy6gkd45BUmI6qqDL7OBGyTlb7T2QI4YqirGmkp5jKc8QAuQJIBctBchK9lx8+UIX12HZceDx0ZyXSpLmPqocMyNqrdZlDUC8ARZH+Q5dUlTq6gi2a0tlgCVJ3koDHIU+4nMMQ0d/RfF3znFqrZMa86E3+KippOdUBuNVzrDdNE97khi6csQKyG7awaRal8RhuWgpiZOpNMAtU5kxZVRmuJqiir9z1hZCDGzO3FlHL3MyLl2GDuJTBq7kp1mbIMp3NN4tdKUzwPx1VfVENA1lyKB/qiuc+LTgdK4x12l0ARVNlDViuDbEdXEAw7vyDUCVLhnMr3QMglLCXomXluuqppIAsHIWC447/uaojH4FlE3eYFWO8oXjIqron26Ln+NxURmADJW6IlajS54wXqV/BOjD0xNh/h4ffEhYgUokIx66uerk6wa3a0RcHa6A/OZw32gSJ7Oa5AKE/K/DMJl1CY7R8TwAq8t+F12XrYqJr9ipumklunwUi9EVjxjsWkX5Cte+kFT/VAQyMTmGJPSPpycC4AUMVSW5KXXl+0JAma3ssYiJtAclSRyDUup1SqmHlFKPKKXe6vj7zymldpRSX8z+/ULhb/copR7O/t2TYj0+MZGh++EmIghccaN1EzoZSVVSpWHKUBGnLNT11ClAGoFdBcQgclh+9MGlpSods6BcuBIxtGSNZO6AgR/l22tZpittJZFkj6Wo8XcNCgRkFWa+CifuuqpQacxzJw5Kop/gppRqAngXgNcCeBLA/Uqp+xyP6Pyg1votpc8eB/DrAE4D0AA+l332Yuy6qsRyhi7E0BV08lYjhiYuDyY8XdPZ0hO/irpkUabbYdm/U58uN5rO0Wk20Ggs01JdQTRdhRi6IifjrsnP+0g49EMF+mhkeZp0iEFCJc2x1VvdF2udJvthUCYoSoc+Oq3VfdHryPYYUI1Keca8KsqXI4aqctUUgYwdw/1CyzG8BsAjWutHtdZjAB8AcDfxsz8O4GNa6wuZM/gYgNclWFOlVEUTgPHcksRgVWTO1VXJ/7b4SMbVxg/I6Z9UukKIIUWUnzu/BLqMPh5iq6pkASKcvGO/dlv8h8W4ZggtdPGDonIRBxDL5adApf4oXxTIOB7UAzAdVgVdppQS5TcPUlI4hpsAPFH4/cnstbL8lFLqAaXUh5RStzA/m0xcY4etiGqmqzhbYQTmprgEScYKxCC7Ody6JNU/4RwDvyqpKsfASVhW6bKvcY6xqpIFkFKMq2MUALPH+IGMTxc/j+XbF6zIvCpfIegXqHQyAi4/lK+Q5Neqzv8LDTEox2tlcvFPAdyutf4+AH8G4P2Mz5o3KnWvUuqMUurMzs6OeLGuscNWuAljExlWR/mSGnOXw+q1m5jOmWWhlYhBluStQkUAbypnVSmhxABURfmdpilWUzRPAAAgAElEQVQLTYE+AP6+8DqZVkNmgJ0IV9Z74KSS2k2MZ7yuYFcXr9UFyKJpV76CrauClhLRPxXXst1soNVQrLErrsd6WukKqhgPUlI4hicB3FL4/WYAZ4tv0Fqf11qPsl//NYBXUz9b0PFurfVprfXpU6dOiRfrQwzcEtPxbPXRhrkuQVWSa1a71QUwOXNP7bvRxbvRqhwWwI+aOq4yWkECruqmNdCcdy2rShwB/k3r3WOCcuFKAyxApVXFEtKAwR1gyRCDM1+R0JhLRpX7A4amsIw2zbU8SEnhGO4HcKdS6g6lVAfAGwHcV3yDUurGwq+vB/C17OePAvgxpdQxpdQxAD+WvXZgUlV7DfD539CmkVFJqbj8EGLgoI/VUcGArJa7kpeOQQxOA9xgUkkhxCCgMpL1HlTnK2RD3DxRPvP8u859V0Dl+cq+2boq9kU3rzBLRTE2WBRjVX7N6rqWcgzRVUla66lS6i0wBr0J4D1a668opX4DwBmt9X0A/nul1OsBTAFcAPBz2WcvKKX+CYxzAYDf0FpfiF2TT/L5Ls6KHRllUFX9M5qastBydFwloSifE+mMK5PispK9KoQF8JN57uRnRI4hATSvmjpq9XNzMkZXVcCQsicizR6TVoVVldECYFFmVbm63JgncMz2HLKMeRBJMvZrRX4NkI8XPyiJdgwAoLX+CICPlF57e+HntwF4W8Vn3wPgPSnWQZEQYmAlUgNRJkAv2ZvPdbUxFyV5AwaAeZyp8hWjiuZC80AiXjQ9ms6hlCn3c62N52TcSUbzWpP1bAdf5Rt3VtV0Nsd0rr30D6cstMqYS6t/XLO98jJmZjTtLCFv8RHDcDJHw7EvJKXHvnu82+Kh0qqqJPvaHrP0+CDl0HU++7hkbvfnol66Omqi3miLfEV1kpcbTXsdFjOaThZlVlRembXxjKY1cq5omU8LesqYmU4mjBg456vaYUkTs/58ETOP5URrdo9x96u/74a+LqPLtS+MMZdUOK2urcMsSQ8j3GuHSjp0juEguGRflE/lbEde/lFW418VsRpdvAgsGf9bocuuLYWRA7KyUMkMJydnzksYew0As8HN9VhPK9JR5VVBEcAvC/VRn3yEW40YuCWmLl2AOf8s9OFxzNzks5dKegEmn59X4o0MW6YslPocV1/yeTH7nabLt2lk0Zy7Xl3e4OaImLKyUI4xr6qKsWvjGU13xArwGwy9BrjFq/EPVb6NGdNC/YaJV2Vjy6tdyf8FkmT2t1RQLFxdVRSXjEqq3mNd7rX0lpgyqU9f8NfiP1r1IOXwOYYAlwzQjaaPluJSSb4NyOXyZ3ONyUz7o0w2+qjIC3CTvB7E0OVC84qkOMB3Mj4DzO18HnkQQ49JjfjyFfa1MTGQ8ZVXLyp2eJF5Ol3ua2kn0nLRR+UeY469GU5n+ciKVV3pEAM3v3nQcvgcQ4BLBuiReSiRbXRxnUwCWsq7Afk9Eb4IbK3DpFk8iIF7o6V0MlUNVgC/vyVURgvwAwYfYqBGwD4aVVZi6kalIl0V+QrAXsvnBjFUIZl8XQlGzkt0HbQcQsfg55IBvjF3J82YVJIHMXArRrwOS1SuWm2AuTNegsY8kZORJJ9dD7Axunh5gdB4DYCeF/CWVzPr8nO6zFsuHJ/H6jHXZb/XHzDwEvauYwT4e8yXx+q1eSNJfAEbN/dx0HLoHIN3iB4zaeZzMlwDUPWgEqBY/sfLV7hutLxkL+HNwa1Kqo7m0jkZSbmqz8nMGCNJQuM17PeRdAXKJc33MferUxfPmM/mGuOZO8nbbir2SJKqAgezNh5iqBoHA2RNaQmqpfJ1pWqibPH22EHLoXMMvtp3bsWOr8EtzzEQI/NQFzVALzH1GRMgi06IxzjN5udUwmluia/XAPA5W58x5w738xkT8328fZEi9xRqsAI4VJJPF6+M2YeWF3kBZiLbey15+SIvlZSgWsroYqKPzGE5y2gF9NtByqF0DNW170z+lxIZUimDhMakaoiYFU6TlTWG1YiBX+JbTRlwbzQfYmB2pQYQg3lPAiqPXcbsL30FOKg0XY7Bl/sw38GkWSryFUYXP/nvQkVWFzeP4u+7YeZRPA7LvudakEPnGKqqHwB+WWiok7H4nqAujwFuNxUairEuTyLbfgfdyFVHhkBWypmqYoSbZKwYr2F1Daf0p4h5EYMgym82FNqOfEVXGnz4uoITIAabdCdXS3mQjHmd2y+QjrJJij58PRGCZjnf3gd4lVwHKYfOMYSSnwDDABAa3FIkss24CEaU70lkA9aY8xxW1c3B4fKrHm1ohUslhRCD1vRSzqrSS4BfSBBal30PbV0EKolMcVUHMq1sjDQ5XxGiKxmVXLa8unqPMZPPAcTALUrwRfmTmSaPKvciBkEfyUHKoXMMIV4aYMDp6bwyMuTy0mFoTo+afNUP5js4TsaPGDicrY/KsK9z8wK+RDbAQ1k+I2d00RFD5R7j6kqafK4OZABbZZMGMUj2awpdgJ8VYHc++wLJ7DvGjNyTj3oDairpORNvNMfm8sPGhBsZppgj5OuwNN9Bj/JDxpyDGHzUm309JWIAeBU7PlTE0hWolrLfR1uXBzEwdfkQA8AzwL4RIvY7uLpSoA/A3xPBfXyvLyfWEzAMPrqMo+ug5fA5Bm+DFf9Gq9rMBkmoJIlBgNfJ6+uwtLr4JbkJHFYgYu22Ghhno8opQkJ/ZKflTzJydPmqYrgU46IpylNiyg4+4mmWUO6JpSuEGBhRvu9521ZXigdeGV18hqFGDNeo+Iy5BDH4xh1zOmZJNEuiclVOlE8xJskiVoZjXoz9CJSYMs5/CH2QnTwpyUhHDM2KxjsbfKRy8pzOc98IEaMrIfpgdCtPZtXP2waMAeb0C6RMGIccFkfXQcuhcwxVA+EAWflf1aax+jgzcao6b40uOmIIGgBJXiBBviKMGOhRU5iX5vek+CpsWLoCVTH2+2jrcg+9s8JJ2C8S2fE5npS5p6AuxnwjSr7CvC9+X/D3WLjw5QWVfFZKvU4p9ZBS6hGl1Fsdf/8HSqmvKqUeUEp9XCl1W+FvM6XUF7N/95U/m1pC5WcAD5pXbWajj4cYfE6mx4DmwSiflRcI3bSLJ9WF1xXmuAFa1ERBRUAaJ88dCpcSMfgMk9XHT/6nSBiHq9W4pdpeh8UcLePLfRS/k7K2cL6IkZM8LH0MSqkmgHcB+AkArwDwJqXUK0pv+wKA01rr7wPwIQD/S+FvA631Xdm/18euJyQ+xGA7NjnGpGoDAtybI2AAGDA/ZABkOYZ4Q0fJMQA0xxyuo+dG5mkRQ5UuWxaaKmDgFBKEaUFGVRIBMbCbOwPogxZ8hHMfxff5hFJeDaTJSb4Q+xheA+ARrfWjWusxgA8AuLv4Bq31J7XW/ezXzwC4OcH3ioRyo9ETlu4hYgtdHDgdRgz8cQUpIrBwtRRANObBHAM9Mg8iBmZiloQYGInZ4B5LhRhYeYEZOq0GGo4R0kaXAH0kifJDxRINzDUwJfQLBNfFyGMFgyJm74Ev+OMimYOWFI7hJgBPFH5/MnutSt4M4D8Ufu8ppc4opT6jlHpD1YeUUvdm7zuzs7MjXqyvkQngRtPV9dJGF68s1LeuLrPBrdOsNgCmwildUhygUTZUxEA5Z8E6enZZqK+SiNmT4tFl9DGvZTCQiS+jzXWR+27CiC0VwuX0kfgaAoFCwMAKPtKUmPoeLMWdVXXQ0kqgw2V9nK5dKfVfATgN4G8UXr5Va31WKfViAJ9QSn1Za/3NFYVavxvAuwHg9OnTtHpGh/h6DwDe/JPhZI7jG2kMQCjK7DEbhvy6Fh2brgeQLOuyEdhVQAwcWoqIGCjnf5INCkzVkxK8liyjSckxpNLFT2SnKH0NGfOik98K6AoGHwxacBF8pMlX+B4s9UIcovckgFsKv98M4Gz5TUqpHwXwawBer7Ue2de11mez/x8F8CkAr0qwpkoJRXO8ZJ4fMbAogwBi4FX/VI/JBnilcSEDwEny0quS4iNDCWVQdc64PSnDQO6J1y8Q0MVAuCG6kkslKeV+sBGw2PuUvACZFmQEDMFhdYTzT2kUNesK68rHflTo4s6qOmhJ4RjuB3CnUuoOpVQHwBsBLFUXKaVeBeBfwTiFZwuvH1NKdbOfTwL46wC+mmBNTpnPNcah6h8GYghWEiVEDNwkoy/30WNQNmEqiQPz0/UxUCmuFE7G6KMnZn3NcoClBTno7+ohBlZ5b8WUYmCBMCmzqigNbvY7g+tKWK4aahTllKuGBltyZ1UdtEQ7Bq31FMBbAHwUwNcA/KHW+itKqd9QStkqo38GYBPAH5XKUl8O4IxS6ksAPgngHVrrA3MMi2fehvICjBrzUJTPqnAK5z5IEVgAMXAqdiwtVWUAOCMegrN6GJQNpSMbSOP8jD56tRoFMXCifL8uTiURYV2pUCnHABPmhAE8ujI8KoWiy18txSlXDa3L/u1a6WNIkWOA1vojAD5Seu3thZ9/tOJzfwHglSnWQJHQhQbMjcZ5tnKqCz2czHBqq1v5d1uZMZlpdFqBvECoWopjzEPVUgwnQ0cM8RTXAppfXcQwzfMVPifP2BfBMmYm9RnYF6xqqQAqsu/b7rWD6wLCyWdSwpiIGGhBEZGW4jiZUIHJCwUxPJ8kxCUDPMRgqKQ0eYEQxZXDVuLN4e2vYNA/vqYcsy56NEfuY2CVErrX1mgodIj0W4jjBkDuvg2NijDfw6h8S1hJREIfjGbFEPoA6FF+o+KpigAvkAmjD74xr7IXHdbe9+9Xs7ZrBzEcLscQKD+zf6MYTJuv8CcG6QO7wk6GsQkTRvmh5CdnjtBo6i+jFd20QaPJSIonQAyhTnGAOavqAPIC1brMuaTmBUK5D/M+2rXstT35Ck4gE+y74dM/VeeMU5RA36+1Y7jqEio/A+hwOs9XBJwMdVqob1a7WRc3L5Dopg3oWqAPmgEIReUAsWIkUPsO0AsJKPxvj0jZpEYMqUdipMwLUAIGmmMOoyLyuoKd+ukSxgB9wB8NMfAeSHSQcqgcAwUxUI0JhX7gJWZDXdQ8XcmcDKHE0Xwn0QBQaKkEMN/qYyGGUPXPVUYM+UiGYIlp2rwAjf6hBR/UgCFUeAFwkWSKPBYh+KMGDBTEwHyI0EHK4XIMFMRArMwIlcUBEi4/VZI33U0bHu6XDjFwarlplUS0aC7ES1tdNMqApotiTMazObQOR5l0VEpFDFQnH86JUVFWCBUB9IRxu6kqGzfzPUbcr0AY/VH3PhCiK+scw3MilAiA2pVKTVgCYUMXan6x6zK64qE5T1e4Kgag37S+m8wOMaQ1H/nLaIHnCDEQdaWkuMz3pssLpInyOZVvRIqRmq/wHKMtSuA1PsZTeaG5S+ZvNZX0nEioKsb+bZyVHJJ0Jaj+oejiVf+EeiJ4FSOp+N9Q45fVR6fe/NuXrOsAEEMI/Q0JPSnUpLh5L+04aZQNAc0Qq+ho6DtEMTJ0BWahGX20ykMa/UMLJEMUl/2emkp6DiRUR1/8W+gB36GKhaKu0MWm5Svo1T/BJC+z9NW3mVlRfoB+AOyNRjtG33UE6OXCzwVi0FlPildXvsfi6Z/5XGM8S5jkDU595Ub5KVFRKGAgsgLZ2I8UlUSU4IMzq+qg5VA5BgpioD7gmxPlh3VRHBYzLxDo1QA46MO/TagGOATzAY4BJjgZ4k37XOQYgLBjpiEG2rWkVsVQdAG07m6qrlBRwiIvEF9eDdArzKh0JSf4SFVhdtByyBwD4eYg37S0fAUQNua0GmcazLedtxQkkyKRbdbGoH+SGfOwk7mWEQMQNppDCmIg0j+kRCqnwiw0j4v1bA0/YshRaQIkA9CvJWnvk6kkWk6yTj4/B0IxwNRomnKjUZtpaE6GhhhsHbfPALcaCg1Fb0oLGXOyAQ7kK6yuFFUxAM9h+Tpv7bpIeQEKZUB0zLRRHbR9Qdlj3H4BCmJIkcgGGEUhpNwTfY8F0Qe1ipE6dqWmkq6+kEZiEPl32o1GQwzURhqjK7AuQpJLKUUu5QwlLM13EatsAhVOVheVS06FGGxkGKIMSHkBAmIg554IJblUY04tvaTomhDmQfHmG9EKCch0ZWiPEfsFUiKG0XSedUr7ryV1hM5By+FyDMSGFYBuzCnoIxwZ0teVwpiYtYU5c6111vmcKMonIAb66Im0iIGCioBwwEDJMVBzT9QoEyBQSSyKi5oTqz7GdjNDpYkQQ5c4w4wSfPSIY29C3d0Az2FRq+goPSkHLYfKMQynM2/zC1Ac/Uw1APGlhGlLX8PrMmsL85mTmYbWFCdDHFYXqHACbDRHjTLD0VzKpDhASfKmQwy00ldiwECiuDKHRUYfoWo1aoUZxQA3aTOcKMEHETGEengAcz5DFYwAES0znmFx0HKoHENoHDVAvzlGjHxFmP4Jl742GgqdZtgAU/IoZm3N8DESKinMd9EqdpL2HlCQDDECo5bRAjT0F8pX0CPz7PxTKBtyTiy+X4CKSikG2Iz9CAcM1FHlZLoyQQ+P0ZUuv8bJ8Ry0JHEMSqnXKaUeUko9opR6q+PvXaXUB7O/f1YpdXvhb2/LXn9IKfXjKdZTJcPAOGpAcHOkSDISEINZWziapqzL6qJTXITyvwSNd+a7EiYZ201SXoCFGAjONJSv4NNSFIox3pjz8xVpUOlc+5GM0UUvF05FfabMiYXGflhdAK3E96Al2jEopZoA3gXgJwC8AsCblFKvKL3tzQAuaq2/C8A7AfxW9tlXwDwK9HsAvA7Av8z0HYhQEMPipiXC/ARVHpQGN8BSIzQkEzR0hKQZhRbJ10Uso02VZCShP8vlE4wmOcdACBgovDTAoaWujjHn6qLcS9TcR6oof0QsV6UOyqQc43SuMQ3QP7QcAz1hf9CSAjG8BsAjWutHtdZjAB8AcHfpPXcDeH/284cA/IgyIdXdAD6gtR5prb8F4JFM34FIaFAdQE8Mjgj5CtMcw0nmUeB06EajIYYewQBz0Ae1wSpM2VAjMMK1JE4LHU3myRADpZIlKWIg5rEo48AX/QIpUWmaoIharjoklTHTEQPVXlCKQoKjOhgd3gctKRzDTQCeKPz+ZPaa8z3ZM6IvAzhB/GwyGTIQA+lCB3TZG42azEvBv1PKVQE7eZSW+0gRGVLKe+3fqR2ulBwDQKPyqIiB4uRTIQbqqGbSuoj7guLkKY/Itd9FbbxLMd/IPjyLlK+gzpZKGDD4mviMLnrn+UFLCsfgCpnLpG7VeyifNQqUulcpdUYpdWZnZ4e5RCPXb3fx4lMb3vf0qDcaIV8B0Awd1WhS6vIppYTmu8I3BzX30SOMHqbQItR1UROWXaqTT5gYZCEGwrX0PfGOtS4yYqNQjERUSjDAKfeFreYh5SsmtCGGlHEw9r1+XYQcA6Pz/KCllUDHkwBuKfx+M4CzFe95UinVAnAEwAXiZwEAWut3A3g3AJw+fVpU6Pubf/uVwfdQHyNIQR8ArZSTc3OE6Qc6YqBWS9GqT4i6CDet5WxbFc1A07lJWKZFDOmMOWVWj32vd12TMJKhzhFiIYbgtaQjhv546n0PhS6zfyeP/SAc41ybfeSrHqPc4+S+p8kM3a1ucF3AC4dKuh/AnUqpO5RSHZhk8n2l99wH4J7s558G8Alt3PV9AN6YVS3dAeBOAH+ZYE1isY05NCopfPoojWTDSTgytLrIXH6C3gNquWqv1cRkpr2jyslUUnaj+Wq56fmKaxMxcDriqXQlNcpPgiQJPRFUXfSSaEqpNhXJ0PuLyFWMBKdFqcijrOtqSDRi0FpPlVJvAfBRAE0A79Faf0Up9RsAzmit7wPwuwD+rVLqERik8Mbss19RSv0hgK8CmAL4+1rr5xRH2cYcyuiJUJQJUBOz4YoFwGzoK8NJQJe9OVLwv3QnY94/w0bXvaWoqMhG+aPJHOsd93vyiJVc/kehBdMhhs2Kc2DFPkSegrIodCUlMTuczKDUAmFUCY36pOUFaOuiV+SF6RpqRd5iv1Zdq/zhWYSCEIBSYcbJPb0wqCRorT8C4COl195e+HkI4GcqPvubAH4zxTpSCSWaHiZEDJRySYAaNVEb3DgUFz0Bt1GBlinlvQAtyueM/QjpAlIjhjlObFAChnBehlJHb9dG4bhNlZwflVKqwihTX+26qOPrw6h00axYdQxUWopyLekFIVTqmdbEB4RL5a+GHKrOZ6qQqmwIdckAbS4LpV6avC7mrCRfAo5KP1CiacrYD6B40/p0USuc6M1f6RADrSih1w7niygOCwBpXDOFygBoxpzc4MZxMgRaUGs/xcjp+gf8kTlntEzx/dVro439CK3rakntGBxCqr8m1CUDxJuDTCXRovwOJTJshaeF2g3aId5oXsdAGPth1kVBDDwD4DtnM8KTzYrfRUMfNMQQNOZkxECjkkLn3uoKzf4hj8Qg0FLUYgnK+U9pzCnTaIvf5dNFrqJ7gSWfX3BCrYAIQWmARhmYSaFU+oHASxMpLsDfZJXyRuM0RQF+zpZe4RRGDGMi+mg1G2g1VDLE0CUgBkqUCVApGxpdSaJRJzO0GqqyaixfVzvcw0NOihOaFanGnEL/cB2WTxdn7AdQO4ZrViijJ8ZExEB5jCDHmJBm9RApAyBE/9CifBaVRCh9BWgOi94vEG9MzPfRqBESLUhADJQo066LUsZMRQzUfAVF1zgwxJBTYlp8f9W67Pd6dRHKhelVXGFjTh77QRy6eTWkdgwOIUdgxJuDMpKBmq8YT+eY+8pCybx0OAIbZZUsvlpvoHjTUhLGRCqJYMzp9E+8MQGoDYYcJ58GMfTaYfpnSHh+hdFFQwwUiovSr0EtMU1J/5CMObEjmzJBmdwPlP2dMsb7oKV2DA6hlNmxkowEY0KKDImRDu2mDddf28qrcCVLWBdlTLlZV/rk89VCDLbEMRVioFSy2HVRck+pEMOQ8FQ/qwsIBB/EElPSfmWWMfupJHoexawr3skopdAhoL+rIbVjcAh1JDW9kihRsxyhmYYyxREgRvlEw0R5HjU9xxCmf6gVTjYvcLUQA+URrVYoOQZK7TtAN+ZUxEAbLU7fY/79SmvuZOWxEpQxkxvvSIEMTReQleW+QGYlveCEihioNwdtjAI9yg8ZYGp/hXm/32hSDRNFFxBusCIhBmI0Z99DcTKU4+wEEAM1ygTSIgZatRq9Wio0R2hILJagGGB6IMNIGCdofFz0aqRApYz9SnxWxEFL7RgcYrx29cWhdkUC5uYIzWvnR/n+6IQamRhdoXUxuOSAASZVspAQAy/KD0WsHF20Xg0aLZiu8ZHWd0Oly+wcoep1pdyv9CIO+36fruJ7K9dFaCSjolJL/yRDDMSHXh201I7BIaEyO2qFDUDf0DSYT+sK5iSfgzdtqtJXalKclDCmn38qYqB2nlMQAyn3FCg9tiOkKXmBtAljWk8K1ckAgf1Kdlj0YokwKiU0uBG7qM3a/MZ80cNDLG+vEcO1Kb1AYw6PyqDcaLTEIKUCgh3lJ0hkU3oPKBNMzbroiIFq6EjGnIo+SCW51DLmNMdImyNE76Iufr9LqI13tAIHIvogUIzkYglKhRMr+AugUgaSrBHDNSxhxGATqQxj7t2EdMqg+P1VayOhDyJny0IMoSgzFWLIbpxQR7Z9D8mYX2XE0KXSUqR8hUEMoX4BGsVIu5bpChyYVXQB6pOiizKqPCViYO2LGjFcu9JrNbM8gvticxKWIdi6eBZyGsRAeeoUQC99pRjMdrOBZkMFm9IojrTRUOg0w/RP6LGqVszzqNMhhhDyA4iIoTAUzrsuImIAAnOEngPEQClj5gwKDOmiOizKqPJF4x0VMVCo5xoxPK8lxOVzEpahqIlaYldclzfKn4Yf7gIQI0NifwVA4FmJlEGuK+RkiOsKFRIkRQzE5Kf5Pv9QOG5/hfmMW5cNPji9ByEkSeuJIFCMCdEH1ckABIpxOkOTUCxh10bpiagRw/NcQq3prORnIDHL4pJJyTxmwjgI82lbJDRenMpxA5ZmSeRkrlHEELqWnMqrUGTOc1jpiyX8+yJhvoLosABC8EGkPhe6KOijRgzPawlF06zkZ6Cagjreofh9VeuyUxxTQfMhAzFYnrtKqFNH7dpSUAZ2XdcqYgCqryUHMfQCkTmP4krH5VMRA+UYW43wkxWpDgvI8ohJ0QehifKwIAal1HGl1MeUUg9n/x9zvOcupdSnlVJfUUo9oJT6Lwt/e59S6ltKqS9m/+6KWU8qCUVNHGMeGgrH2jSBRDb1WcgAkctnGODQQ+RHRIoLCEdNQ2K+grQuTlNa29/8xc0xFL+/LNTpnsDBIIaq/aq1ZtM/wX1BOEbKkxU5iKEX6P2gHiMQLok+jIjhrQA+rrW+E8DHs9/L0gfws1rr7wHwOgD/XCl1tPD3X9Fa35X9+2LkepJIiGddGBNOBOanpahzbHy6OMYEsFFTqPOZHuWn6IkwugIRGLFaCggjhiHx+RVmXf7mryHLyfuNObVZy64LqKYFqTOEirqqjJPNiaTqiaAiBqMvXBXGQQz+nBhn74edDGXsh9X1QpiuejeA92c/vx/AG8pv0Fp/Q2v9cPbzWQDPAjgV+b0HKqEaf06UH9JFnS6Zel2A3dDxHdnmO0Pll4yEcQDm8/IVgdJjDi0VMHQSxJDCmIf4dy4q8ulijXcgTkSlU4z+MSKcPRaibIYTWt8NQOlJ4dxH4VlVV0NiHcP1WuunACD7/zrfm5VSrwHQAfDNwsu/mVFM71RKVTwx+OpKEDFwovyExqTTbEApQu6DYYCrommTr2By+alujhBiYOU+wk1p5MiQ6JhZZcwhipFjgKucDGdURyhfwdDVysqYgxVmnLxAAEmy0EdwHAx9v4ZQEZn6zII1X0/K1ZDgkSul/kwp9aDj392cL1JK3Qjg3wL4ea21vSJvA/AyAD8A4DiAX/V8/l6l1Bml1JmdnR3OV7MlNPuHFeUnLJSY6eoAACAASURBVFcN1V8vhojFVxJZyoAeNYW7xTkRWBAxcCiDVIghSDHSRjIAaemfUPWPqMAhgD6oFTu+aaGcsR9Glx8xsNFHsOs/1R7jOSz7/c+ltEJv0Fr/aNXflFLPKKVu1Fo/lRn+Zyvetw3g/wHwj7TWnynofir7caSUei+AX/as490A3g0Ap0+fPlB3Go7m+DdamEumG+Bw7oNRZpfAYdn3JaN/gjmGOU5s0A2AHWLoqkmnjmQACEUJxJEMAIGyYVWyhKJ8QUl0AocF2HLhNHs/9HAjljEP5cQmMxzb6JB0maAo3d4330/PcRyExFJJ9wG4J/v5HgAfLr9BKdUB8CcA/o3W+o9Kf7sx+1/B5CcejFxPEqE3pcU3uHGiOcA/fI0D8wE/YqCOMC7qCj4nghE1BfsYEkVgnBswXJTAiFgDz7Bg5SuCUT6nKoZWqp0m+ODtfUr1T6oGN54xNxRXdRc7Z13hMvKrIbGO4R0AXquUehjAa7PfoZQ6rZT6new9fwfAfw7g5xxlqb+vlPoygC8DOAngn0auJ4mEk4z0myP0EHmOkwH8sDVllM9FHz5jvshXpKnykERglf0CCXMMrAqbwDMsJFVJQfSXoF+Aixh8Bpgz9mOhy1dhxjPmwWY5RkWe1sBkVlWtxtEVbjy9GhKkknyitT4P4Eccr58B8AvZz78H4PcqPv/DMd9/UBIciTGdkcvPrL5qY05vZAL8iIFbrtprN3FlOKlYFxPme6o88nxFAidj18apcLKfcYnEmKd0Mj4kSc1XhAIZjjG3/QIpkuL2fSma+KyuC/sVPTyMsR+LdQUa7xj3EWDuZddgR17l2wsDMbwgJfSAEc6Ftvqqbw4+YkiRyAb8BphNcXmqPNjGJNRJyqwYAfxGkzxGgdD4yE1kV/e30PMVobLQxdiPdPuCY4BTjP2w7/Pld8y66IUXwco3hsMCPE7+eYgYasfgkHCOgV7jDNAQA5kzJ0RzrCg/EZds8xUunjWnpaiUQcs/RppTlRRGDAz+N9hgSM9XLPIC8esKz13i5Yt8iIFL//icPBsxeBoy2c2drfB0W64x9/WR1IjhBSChh8hz2uUB/4bOny1AoAwWuhJG+SFjwrjRqnhWdpLRM0Zakq8AfIiB1ywH+A0wZ4ibb10cYxLKY6VEDCkTxnz04QuweIjBN91Wa22i/GTVajVieMFIzzPhkwMzARuZV2/oToueryBF+YzO5/CgQGZXsMOZSnhpwH1zSHX5DAqn8sqsy+NkiOfLzqpKYUyAEGVDHyFtdCVEDARjzqtwis+jFL/TtbbJTENrHsIqrmF1bbyxH2ZdNWK4JqXnqZnmRKwhXRxe2uoaV5ZecmvMPXkB9tyl6puDM1uqqMt1ziT5iuLnVtdGbz4Kc/l8Y+6jRnj7wleUQB8hbXRRonwOKk1jzHueEersHh67LxzHyXmsp9EVKonmNd75dF0tqR1DhYSifOqNAfg7NvlOJmFeoNXEeDbH3DEUjo8+qksm+ZVXYV30aM5P2XDHK/t0cQavAZZ/91VLpUEMfIflQQxsirF67w8FiGFckRfgjP0ofqfrONk9PMFqNX5O7LkepFc7hgrxzWXhRnM+XZwZQoBtpvFH+dR8ha8sl/PM22Vdvig/AWJg91dUH6N5fKtm5wV8XcGcyDyEGDjGPIgYWA7L52S4kXk6xOCLzDmjrYvf6aQrs9fWEqyLO/ajRgzXuCSN8gO6WOjDM5OIU+II+KNpLpz2zYTiog+SLna+ojoypJ7/0LOtOd3d9nt95ZccY97xIYaUObEsKCL38Pj2PpdK8jxESNITAfgRA1uXZ79yO59rxHCNii/S4dTRW12+cQXcfEUlYmAaEwpi4HP5rihfVpXkRh98Xtp8rtqYcBGbb7giNTK0a6t08kz04UUMY3q1FBCO8rnoo9qRciuJfMacmxT3oQ9bxRWPlgdsJ1MjhmtafB3GY64BDlT/cKM5Q4G4DR03kW0/t7ouZhe1J8ofMqkkf46BT2UYXdUUF5uycRgAzmNV87UlzQuEKpzS5MQ4FTZ2XZOZxsyRx5KUqwJ+J0+lf3z5oiEz+PAhBu66QjmxqyW1Y6gQP8/KN8DeIW5MxABURPkCJ1OlaziZo6GAdpNIS3mrkmSIwVn6yq1w8kaGPANg9bkMwGRmHqvKpQWrDMCAUS1ldfka79Y6iRAD0/nZ97oq6QYTU0bbJufEfLRgOsQwGEujfB8tRVtXqIz5akntGCokaZTvg/lM/tcX6XAarIwuP2LotprkfEXIYQFpKpyss3DNpHGJ7+FGXCrDvNddMsmNMoFQ81fiqiRmTsxXrsqipbw5Hl4ZLQUxsI25N1/BzQt4aCmWvfCPBL8aUjuGCvF3BTOjfM+IB8ncJaC6XyCdk2Hq8pTsiWG+J19B1eV7uBG3xBGobrLiljgC/nk9aauS+HkBX4Mbj0ryGHMB9WZ0xdM/vkYy7twlL2IQ7TH/SPCrIbVjqBBfVzC3+sQ34oHzmEogkExl6vLBaS7F5ZsWKu5KdeiyyTwONVJFs3BLHIEM/fnKe9nlqu6RDOyeCC9i4Dv5qrzAYMKbExaq/pHQUr7IPEmDG3O/tpsKqmJU+XDM32O9GjFcu1LV/TmdzTGda5EBriq/lERglYiBWclSpYtNcXmipsFkhhaLS/ahDwE0rzDAi+F+3Kqkg0UM3HEk5nv9IzG4iMF+bkXXZEZGa0C4X4B7HavWZfN+3FJtXxUd9ZwppSqp5wWSYe6xGjFcm1I1FZL7bAGgEOVXGBSWk/FE5vxy1UxXRQQsczLuaE5GGVQnBrnGyVv6yu0895Y4xjssCZLxP6eDif68VTZpjbnIYVWcs1ToY8B0DHZtvtLXFAj3akqUY1BKHVdKfUwp9XD2/7GK980KT2+7r/D6HUqpz2af/2D2GNBrQqryAtyO4OJ7KyuJOAY4UAHBy1dUOyxJ6aVdQ1m4FTY+hJUyyStFH04HL1hXlcPiVtgA4So6boIdcOd42HkBX7cyt8Kp5VmXcI95zz/zvvTSUkzH/HxHDG8F8HGt9Z0APp797pKB1vqu7N/rC6//FoB3Zp+/CODNketJJlXD17hVMUC10cxHSDO7ZYHqGn9u4x3gRgzc0eK+MjtuNOcbe245Wy6aSVFKuNAVP0IEsNVqq8EHt8IGMIbH1d8yyahPDsIK1eVzEZb93KouJvrwIoY56xh9jzAdZtQndRqtXZvbYQl7ZZ7PiAHA3QDen/38fgBvoH5QGTLwhwF8SPL5g5YqOsNe6HUmNCx+1opN8PFuWj9i4A5LA6oRA4fiAqrL7LjrAuzN4W6W67XpXDJAoGwSViVJotZyUYKUygBc+1Xm/ICqyFxIJbkoG25HdqBclXOMSqlKA8ylPs3a3P0t3M7nXNfzHDFcr7V+CgCy/6+reF9PKXVGKfUZpZQ1/icAXNJaT7PfnwRwU9UXKaXuzXSc2dnZiVx2WKryAovmF0lTWkmX6KatplkGbJ7Vw+Uzm6IAW2bnvtE4zs/ocpdMSpxM5bqYXdRA9UgSKZUErF5LEWKoiMyldBmQhstPWa4aKjHlJP7N2qrLmDnny6wtLSp1OawvPH4Rv/xHX8LTl4estUkkePRKqT9TSj3o+Hc343tu1VqfBvB3AfxzpdRLALhCPvdz9gBord+ttT6ttT596tQpxlfLpOqmFUVzFU9lymvyRejDvQk5uvzNX7zqE7O2iqhpLLvRnM5vLFtXOsRQVfrKm69jdLnHdUTpqkAM3Gqp4metSMpofcac23djpwZXXUtOTgCw1zLe+RldVZVvfOqzymF9+/w+PvS5J68KzdQKvUFr/aNVf1NKPaOUulFr/ZRS6kYAz1boOJv9/6hS6lMAXgXgjwEcVUq1MtRwM4CzgmM4EKlKTnGbtYAwYhDxv9MyLTXHZMajpWzzlzNhLDXmFQnLzW5wqy1JJWJgGiajq7ostN1UaBInhQIexBAR5VflsXoJAgbu0EGgeo9JZkv5HlXJDT68uafJDMfWebUrVQ8RkqLS/ni68rqE+qxCDIMxv8JJKrFU0n0A7sl+vgfAh8tvUEodU0p1s59PAvjrAL6qTcbtkwB+2vf550oqEYOwXNKpS+RkqigDvi6rz00l8SPzKp5VxNl6EANfVzVi4ETlwGKI4XRWEZknGBeR19EnKDEVVdhUOCzuDKGldSUywKaUsyrK5yOG6vJqblCUpvDCrMutS8JWSCXWMbwDwGuVUg8DeG32O5RSp5VSv5O95+UAziilvgTjCN6htf5q9rdfBfAPlFKPwOQcfjdyPcmkakMvOm/jq5LyG40z4KyilDM3AMxooqr+fTCZ8XVVIQbxzVFVRiugDKoqrwS0FLBaycWdB2V0VSAG0Xwdd8JYmmAvfjbXFZFHcXP5fANsHlSVKGHsQwyCgKESeTN1VSfF+Y5ZKjx8XxKt9XkAP+J4/QyAX8h+/gsAr6z4/KMAXhOzhoOSlDmGyghMEOVXwWkpYnBF07PsqVMpufw1SQSWMMdQ1eEqcTL2s0V6bDSZQSl+IhuoRn8i+qciYGBNV61IGEscVqfCyUxmc8zm9KfnLdZWhUoF+6IyYJhhvSOhPtM5v1H2CNMiBTWczNBQ9Cc0xkjd+VwhVTctd4gbUGjMSUj/pKCl7NqSOZkKLp9bLeXTxa1kyXVVNMtxDDlQjRiGzKfn2XUB1QGDLPiocDIJpu5KdJmx2ipJgGXfX5XjkSCGgTMyFxjzCkpWtq4mtHaUMWdBEWePSaV2DBUSumm58+2BVZhvk0n8m2MVTue5DwbFleuqcjJsKsmHGNLokiCGbquB8WyO+bzcSCZLZAOuSiIJX15lgPmReWXwIZjVYynEFEjGvr9aF7/Awe71JX3MRlHABD5OXRPeoEDAViWlo1HNZ1fvy6uReAZqx1ApVTdtbswTJAalUb4rAh4IojmrqzL3IViXq8RRwuWvtZuV0RzXAFRRedyxH0ZXVY5Hxku71iWJzG3DZX8cfy3tfizrknTx2rWVDfBigKFA12S5+meeUZ/c87/Wqdpj/OBjrePOC3AHBVpddh1FGUz4TadSqR1DhVR1fw4mM3QYD0MHCmWhVTCfGeW72u9zx8BtSnNUU1g6QsblV5U4cm+O1ophAmQ3bXXpsYT/raJsJA6rOsnL3WNrFY5hKEiKt5sNtJvKoUsW5a93WugnQh9rnebKuiRltGZdq7oAWV5gvW1GlZdHkkho1Conz+1TipHaMVRIdTKPb5isPpcuQIoYSptGUEYLuGF+Xi+dADFIynsBG2U66sKFJY7ms8vnvz+eYa3NTDJWIAZZea97XeZZGLIoczUyFxpgx76I0VW+lnlSXKSrIihiU0mt/L5ZXhsf/VU6ZomubE+W+yIkEwSkUjuGCqlEDAKO2+pbKX0VUjauyFxOSzVWnscrzTF0HZVEkhJHIIvmJrOlAXNaa3F/BeAoPR5PWTOvjC43YhiMZ2xdVY2PEue3nr2/TI1IaCkgi/JLhkmaMHZF+XL0sUr/SNFH1R6TnP8qxzyc8qP89QpdUtsjkdoxVIgtCXNx+RI4V1VJ1G7SH2CT63JF5uKEcbUutsNqNVeSvFLEsNYxlRlFBziZacy1LGEJVCGGNDmG/njKLnH0zUrinvtWs4FOs+HMC9jKII64aBZJUrxal9TJrFKMUuS91jHNisXqH+keW9A/q1G+XJcjx3AVehiA2jFUSqOh0KnIC0g6D11VCxL+EXD3HsSgj3L5n6TDtfjdRWMuNSauBKjUYVUjBr6Tr0IMfYGuPPhw5IskkeGag34bTMwMIW6Jo6v6R2yAnXSlDMm4qKTYPTZIsMcW9M/qOeMmjHP04UBGNWK4BqTnyAsMBM1agHv0hPRCu3oPrO4k+YoIWqr4eUB+o7kiMCnH7XJYWmv0JxH0j2NfcHXlwYcLyQhQqSsy749nWGfOqfLpAvio1KXL7ov1rkTXdIn+sVVKa0zEtu4wwEMh8nbpAmTBh0WerlxKnXy+BsT1eE8pYnA94FuyaRa6VqN8CWXgRAwRtBSwbIDFCUvHzSHOoziqf8ZZ522qHENfkGMw+la7svvjGTaYBhPIuPyVPcbPo1Tp6o+naGYPZOLpWqV/7O/cta11mpiXKMYYXcXPA8D+yDiZDaGTKeoaT81DkjaETsZVelwjhmtAqpq/ZFVJbi4/tS42ZeB48pc0L+AqvxQjBgeVJK2jdzUMLRoCuXmBiqokQYWT0beae5JUSwEZZVMyJvtCY7LeWdVlnR93j7mqkqwBXmcepzWaxT22PxI6BgeVJEVFi+Tz4jgt2uXusYXDKucrZEGpRGrH4BFnknfMHy4HVFUS8Ttvq3Xx5/4A7id/ybn81UouaZTvipoWN5osmVeE+dIo04UYtNZZ8llaxrwamUt0OSmb8QwbIiqphX6pkaw/kqEiV/XPQGqAnbmnab5m3rpW8wLSfeHTxUYMDocFyHOSEqkdg0dczV/SWuJuxVgAaenrii7BOGqry66luC7uQDija5V/72fRHJcaWSTgFsZpkN9oPANgDaONUo1emQFoN82zgotR/mg6x1zL5uS7ck9yKmm1kUzqZNYc3comJyNAMo4Ks35Wkcd5drrVBZTpnzgqaTlgkDoZXyAjrDArUp9T87yVTcG+kEjtGDxSVRYqbXBzlSVKjElx+mKKddm15LqEw7pcTmZ/LOVsVyOwfesYmBGw1bXkGIR0melib66cL/M9iarVhFTSuoOykZTkAua8rKIPOZIxny8YzRG/vNfocuSexFG+i/6JdDJOxCChGBslXTKHJZXaMXjEhRgkTzYD3JGhVJedL1Omf6QRq1NXhJNxJgYF1SfFz5ufp0t/i9NlDQD/RtvoNnMnBSCP7CRGs7wvtNbYT0gl9cVUkmkkKwYf+xFUEoClCFiarF9QSQtjvp80ypcZc1dOTOpkzGdapWOUIW+p1I7BI2XEMJ/rrCxREAE4ykLNzSFLWAIlykYYGS7GMixHYKloqf3RFO2mEtdyu5KMXEPXbjbQaTVyAwLIYb79/mX0IUsyAqvVagYJ8h2p+f5VhCstfbX0TxHlSqmkXtsRmQvKe+26gGX6ZzA249M5j2gFKvIV0fTP6h5LUS7cHz2PEINS6rhS6mNKqYez/4853vNDSqkvFv4NlVJvyP72PqXUtwp/uytmPamlzOVbw8J9fjHgHny3N5qyE1NAgf6ZLhtg6bqAZQOwP5bpcj2rQOr8UiIGwCQAXVSSTNdyNJdHhsKeFGdSPBH90x9PRbpcIzbkVJIjMSumktyUTZSTcdCVUn3DRIihnON5viGGtwL4uNb6TgAfz35fEq31J7XWd2mt7wLwwwD6AP7fwlt+xf5da/3FyPUklfJTmaS0CGAMwGSmMcvGRWitsT+aimC+CzHsj6aiyKTnqLLZH8mSn65nFewLnZ9dlzvJKKF/WnkivKhX5Bi6TeyN4nlpu66iw9qPiAzL9I+dLSWLWFcHue2P5I13Rtfy+U+la18wjsToyvIVDscsLfFddn4pdT2PEAOAuwG8P/v5/QDeEHj/TwP4D1rrfuT3XhUpVxJZYxATTVsDPJ5lzS9RkflyRCGpWHBVEu0JHVa3CjEIdDUaaqX+vT+eotfmUwaAifL3S1QGEEMlrSY/Jbo2ey3sFXVFrGut01qif4aTjJaSOGZHND2YzESJVHf1z0wUMOT0T4lKkhxjM+s8LxvgtXaTNfI8X1upKdA6Vcm9VK4wyxHD88QxXK+1fgoAsv+vC7z/jQD+oPTabyqlHlBKvVMp1a36oFLqXqXUGaXUmZ2dnbhVE2Wz21wyAHnppeDilJuscr48Ipouc/my6gd3yV6MrmEpmpMcI7A6lXNfuC7AoDwXlyxFH0Unsyh95eva7LawN5rkv/cjKIPyGJH9COrNnUxNWJUkjPJdjWT7QscArDbf9ScytAysPpAohpYqV5gt8hXXCJWklPozpdSDjn93c75IKXUjgFcC+Gjh5bcBeBmAHwBwHMCvVn1ea/1urfVprfXpU6dOcb5aLJvdNgaTGaZZV7BFDJKLU66lz1vvBdHEQpfZePO5Fkfmm44af0MlCdaV3ehLNMtIlmMAVgem9Ucz8Y2x0VmmbGIog3K+IopK6rQwnMzzPWYpA1HncykyX+RR4nM8s7l5Ep8IyTiTvNIKJ1uuWuxilzkZo2+VspHOI1pvL+eeBuMZGoJ+IOe6rjJiCH6L1vpHq/6mlHpGKXWj1vqpzPA/61H1dwD8idY6D48s2gAwUkq9F8AvE9d9VcRGDvvjGY6sNXJjIKGStnrLRnM/AmaWdVnIKaGS7LEUjfneaCrS1WwobHSW+ff98RQ3HumxdQGrN0cUYug0sbM7yn+XVrIALipJXuG0mV1Lu8fiShyXI/MYxFBuMLTOJopKShDl5/TPZNkx37DdZuuya1uluGR7rNdp4nJ/nP9ucx/cfqB8XeNlRgC4hhBDQO4DcE/28z0APux575tQopEyZwJlztwbADwYuZ6kUmXMZZRBe1lXBGKwxnx3OInWZT+zN1yGrRL0AWSc+XD5po2K5hLUvgPmnO2Plx2W5HwVddkkbwxlYB1w2cnHUEl2PdK5P+b7l1FpjmSEqKi4LiBDDMLzv95prhQSSKP8MpKMmWC63i7TlfL9WoUYJBVmEol1DO8A8Fql1MMAXpv9DqXUaaXU79g3KaVuB3ALgP+v9PnfV0p9GcCXAZwE8E8j15NUcmM+tMbcRuYCY95bNsB7ETmGzbLDEk6EBIBOq4Fuq5HrGk1nWeu93GguIYbRVMzZuip2pMa8nGPYG05zx8/WlSV5baS5O5ygmSXLuVKmGBcGOCL4yPbYIMKYlJFkLJJpqMW6JrM5xrO52Mht9Vp5UGTWJkeSRtfyHpMa85X9GuUYWhhMZnkV4/54im6rgRZzsq1UoggrrfV5AD/ieP0MgF8o/P5tADc53vfDMd9/0LIwwMuRuQTO5VF+yQDEIYZlhyU1mlu9Vr6umKQ4AGz22rkuIA4xbPVaOHduf0nXqa3K+gSvlCPD3aGsVwNYjvLXO63cyUgog7IBttc0hq60RtP+v9Xj0yxlXTEVeUopbPXauJIA4QLAVre9ZMz3hvKAYavXwrfPLYok90cznNyU7bHifQTIezWsLsCc9yNrbfSFeT+p1J3PHlkYgGXOVhKdVN1o0gqndlPlOnJdwsh8s9sqoCJ5tyYAbHVb2MuO0Y53kDqZ7d6yAYjLMbQwmi6SvLsjOWKwN6ilM+KczDKStPsjzjEYXVey/yXHudFpQamiLrmTsWvIdQ3M/9tr8bpmc4398Ux8Lbd67SX0sTuciI9xu2fQsn207e5wiu012bq2szXkdLGwIkwqtWPwyCqVJK+jLxuAmLJEpdSSMe9HOCzA1tIv51FSUEmm2UpGiwDmpr0yWNy00gYrYLmQALDGXGYA1kvVV8bJyHSVqaTdrINdssfsGq7kiCEzwIK1NRpmj+0Ol5FMCgO8cDIR+8IGWNHrWqaSdiMoxq1eG1oDe+OFM41xpHY9QBzFJZHaMXikTCXtjWZig5nzrCtRfrwxj9blQAxR6yoZE3HUtNbC/tjwrFpr7A4n4igzj/LH9pxNsC00AOUS393hBFuxiKFAJcWu60oZfQj1bRcMsHXQEicDGEN3JZGT2S6ij2HsutrYG5sofzbX2B1No5AMgCVnGuNkyrqkxyiR2jF4ZHHTZlRSRPLTRvnFCKDZUKIaZ7O2Nso5BnmUv8gLxCTFgWWeNd6YLBDbcGLm0Ut15RU7BQMsNZgL9LFwzFIDsOoY5FFmM4/yF4hho9MUoQ9gOZqOdvJLuuKdzG4ZFQnXtdVt5VG+vQZSx7xVon+uDCdRxwgs7qErEUGRRGrH4BFrHG0EfHkwwZGIi7PVa+ebz+qSJCyBjMvPkMzlbPNI17bVW+i6Eqsro5K01otoLjICuzKcFHTJblp7PJcHZm0xVUnlgCHOyZTRh3xdwKoBljoZwG2ApcFHkUqKRgxr7XyPxSTYi2vYHU4LuuISxrtDg0D2RnL0l+vK78tplO3hSu0YPNJqNrDWbi4Z4JiLU6RsLg0mOBqjq0AlXR5M0Gk2RM92ADLHUHB+gNwxbPZMBNYfz/Iko1TXdoEzvxyJPhaOYWw6jedanGNY6FoYOqkx6bTMuOaik4lxDEtc/iCFroWTWe80xeWSWw76J8aYz7XJF8Uk2Itr2B1OonIywCIA2h1OsDeeQuuYY1ymkgz6qKuSrhkpGuArgzg4l1RXd9mYb0egj81ClJ8bYKljKDTyLfhfOf0AmJsjFskcXe8AAC71J3kUJjUmR9Yzx5B1ue5FJLIBsy+KJabxUb5NZE8iHcMylRSraxHlpzTmKRGDXVdKXfHoQ2sdbS+4cvVc0PNUtnqtPPK9lAAxXMqMyeXBBMcyYyXSVbhpLw/GOLoeZ5gmM43RdI7Lgwl67Yb4oeOb+YaeLHIMUsSwtoiabKAq1XW0EOXH3rTdVhPrnSYu9icYTmYYz+ZRRvPYehuX+vHoA8h6P/bGua6YPbZEJY1iHVYbs2ym1+5wgrV2E+0I9AGkNsATzOfLr0l1XRksHJZ0v/baTXSaDVwZTkwJrJYjGYnUiCEgJzY6OL8/yqPpGPrnyFoblwZpaKkja21cHkzydcXqAkw0fbkfp8uen4v9Bf2T4kbLa98jeGnAHKM1wrHHealwjDG6jm90cGF/nEfTsQa4WK6agkoyEWu8w7JrSrEuo6uIGOKrf2KR5IL6jHdY9rO7w2lOl9U5hmtIjm90cHF/kpdNRhuALJq71J9ERfknNjqYzs0NG+sYTmyYqPL8/giXBmMcXZNHmSc2M117I1wZmr4P7mM9rRQNQGwiu9lQ2Oq1cHkwwYV9cw2Ob8iP8+h6B5cH41zXiUhdF/tj9McGfcTsiyL9c6kfhyS3ei1Ms6mqsXusmC+6FBl8LAKGaY5wpXvMFjNcGZigFmhi5QAADGhJREFUyLwmW1s3yxddGRZ0RTj57Sz4WyDvOsdwzcjxjQ7O74+TRIYnNjrYHU0xnMxwZRh3cxwvGPPYm/b4hhkBcCE7zrhj7GbrGuNSfxxpTEz37aXBIsqPudGOrpsb7WISx9DGxf7CyRyL0HU8cwzn9+KdzPGNDi71xxhNZ7jYn+TXQyLFgOH83kg8KsKuCzB77Pz+KA8g4tZlzlnMuizVdn7fOPmGgph+U0oZe7FnjhFA9HGe3xst9n6NGK4dOb5hblprTKIMcLZJHr/Qh9bx9ANgbrTYCKysK2YDHtswn72wN8a5yJu21Wzg+HoHO7sjnNsb4chaGx1h3wcAHF0zRvN8AsdwbN3oSoEYjmWo9FwCY3Jqq4u5Bh7dMTOmTkbostduZ3eEc/vjqGO0M652dkc4vz/GiYh9sbKuCF3tZgPH1ts4t2d0Hd/oiPs+AODkVsfoyp183HGe2xvj3N4o//1qSe0YAnJ8o4vZXOObO3sAgOu24yOwrz+9CwDigXBGl/ns2ctD7A6nkbos/WOM+aktuQHotprY6rZwft9s6NjNfGqrmzuGGCMHmCj/Qn+Ci/0xuq2GaBqqlSMZ+kiCGDbaGM/meOKCGeYWY0xOZef7oWyPxRhNu6e+fX4f4+k8ymHZfXBub2Si/IjztdFtYb3TNAZ4dxSly67t3O4Y53ZHUeceMOffHuNGpyke4QIUnUztGK45sUbza0+ZG+26LdlDZ4AFZfO1p64AiHMMFn18PdN1XYSuI2ttNBsKz+6OcH5/FHWMgIl4z++bGy2FY3h2d4Rzu3HoAwBu2O7h2StDXMiiX2l5L2Bu0gv7Yzy7OwSAqKIES108/IwJPmIRAwB87WmzL2JQkdX19WzvxxjNo2tttBoKT10e4vJgEuWwABtNj6JpKatrZ88gmZMRQVG+rt0sKIq4J62uS/0Jnr48RLOhovYYV2rHEJDjuWOIN+Z2A3/1rNF1/bbcAFuH9ZVM13URuhoNhWPrHXz96SvQOg4VAbbKxkDz2Bvt1GYBMUTeaDce6eGZK0OcvTSIuo4AcNPRHuYaeODJyzi52Ymak2+Py+6xWPoBKBrzGI7bOhmj63iEAW40FE5sdnK0HOOwAEOR7eyayDzayWzZKD8eMZzc6uL8vtmvMeceKFzLp3dxYqODRgTFxZUox6CU+hml1FeUUnOl1GnP+16nlHpIKfWIUuqthdfvUEp9Vin1sFLqg0qpuDN5AHLTsTUAwOcfu4jtXktc3w8ANx1d6ALiovxeu4mTm91c1/WRxvymY2v43LczXZGI4YYjPTzw5GWMp3PcEOGwAIsYhjh7eRCt64Yja5hr4AuPX8LNx9ajdL0ou5Z/+a0L+XWVyi3ZHvv0o+dxfKMTRT9Yh/eFxy8urVMinVYDR9fbua7Y4zy11S2sK35fPPidy5jONV4kfHRsrmuzmwUMQ/FjaIu6JjONr5y9ghtidRWuZawursQihgcB/CSAP696g1KqCeBdAH4CwCsAvEkp9Yrsz78F4J1a6zsBXATw5sj1JJebj62hocw45FtPxBmTXruJG7Z72M0eviGdO2PlthPr+cC62Jv29qKuY3G67ji5kZdM3n5yI0rXzcfWMJmZkslYXfamH0xm0cdoDe5oOo/WZZ1UfzzDLcfj9thGt4WTmx1cGU5xcrMb/XCX204sruWtkWu77XhRV9y1vP3ERl7ff+uJOF13nFzHcGKeKndbtC7z+cuDCW6P1PXiTNeV4TRaF1eiHIPW+mta64cCb3sNgEe01o9qrccAPgDg7uw5zz8M4EPZ+94P89zna0q6rWZuBF58cjNa322Zc/mu6zajOO6iruu2ulFNUUbXYuPdEWmA7yicp9gN/bIbtwu64gzTd123WNeLI4+x6Ihj94UNGADgjshjBIDvvn4LQPz5AoDvzs7ZDdu9KLRcXBdgHH6M3FnQFb8v0um68/p0e794T8YGRVy5GjmGmwA8Ufj9yey1EwAuaa2npdevOfneFx0BAHz/rUejdZ2+/RgA4K5bEui67TgA4BUv2g68Myw/kK3rRUfiDUDxPN0WGWW+7IbFTft9N8eds6Ix+v7bjkXp6rWbuDMzmq+O1AUA/8ktR5KsCwC+9yajK8Ue+76bja4Ue8zquvnYWvQes7qAeCTzikLw8cqCXom86Ehxj8Wd/06rgVuOG30pbA9HgjhTKfVnAG5w/OnXtNYfJnyHKyzWnter1nEvgHsB4NZbbyV8bTr5lde9FC86uoafevXN0bru+Wu3Y380w3/zgy+O1nX3XS/CI8/u4Y2vuSVa1197yUn84t98CX7oZddF63rxqU28/W+9Ai86uhadMNvqtfG/vfGu6K5zwDQg/c7PnsZDz+wuRa9S+Z9/8pX42FefwQ/eeTJa1y//2Etxw3YPP/n98Xvszf/ZHZjPNf7bv/GSaF1/+/tvxjd39vF3/0r8PfeDd57Ef/c3X4Ifemn8Hvvu67fw9r/1Ctx0bC0aeR9Zb+O3fuqVaDYa0ci70VD4V//1q/HEhf4SEpHK//ozd+E/PnIOP3jnqWhdHFFaV9piuhKlPgXgl7XWZxx/+08B/GOt9Y9nv78t+9M7AOwAuEFrPS2/zyf/f3v3FyJlFcZx/PtjTUojxILIP6XCUklQyhJaEWJdaEkWGBVFIkU3RfaPsm6iiy6C6B+FEGoZhCUmtVQkYULdJO62UJpFYn/cMjVKi4Isero4Z2hn21VrZnbc8/4+sMy8Z9/dfQ7P7DzzPu+Zebu6uqKn519/yszMjkBSb0QMu1CoZiRaSduAzrwCaSxwPdAdqSJtAZbk/ZYCx3IEYmZmLdToctVrJPUDc4G3JG3K45MkvQ2QzyHcAWwCdgLrI2JH/hUPAPdI2kU657C6kXjMzKxxTWkljTS3kszM/rvjqZVkZmajiAuDmZnVcWEwM7M6LgxmZlbHhcHMzOqMylVJkg4AX//PHz8N+KGJ4YwGnnM1eM7la3S+Z0XEUd9GPSoLQyMk9RzLcq2SeM7V4DmXb6Tm61aSmZnVcWEwM7M6VSwMz7c7gDbwnKvBcy7fiMy3cucYzMzsyKp4xGBmZkdQqcIgaYGkzyXtkrSi3fE0m6SpkrZI2ilph6TleXyipHclfZFvG79M2HFGUoekPklv5u3pkrbmOb+aP/K9GJImSNog6bOc77ml51nS3flxvV3SOkknlpZnSWsk7Ze0fcDYkHlV8kx+PvtY0uxmxVGZwiCpA3gOWAjMBG6QNLO9UTXdn8C9EXEuMAe4Pc9xBbA5IjqBzXm7NMtJH+te8xjwZJ7zT8AtbYmqdZ4G3omIc4DzSXMvNs+SJgN3Al0RcR7QQbq2S2l5fhFYMGhsuLwuBDrz123AymYFUZnCAFwI7IqI3RFxGHgFWNzmmJoqIvZGxEf5/i+kJ4vJpHmuzbutBa5uT4StIWkKcCWwKm8LmA9syLsUNWdJpwCXkq9fEhGHI+IgheeZdCnikySNAcYBeykszxHxPvDjoOHh8roYeCmSD4EJks5oRhxVKgyTgT0DtvvzWJEkTQNmAVuB0yNiL6TiATR+0d3jy1PA/cBfeftU4GC+SBSUl+sZpMvivpDbZ6skjafgPEfEt8DjwDekgnAI6KXsPNcMl9eWPadVqTAMdcXwIpdkSToZeA24KyJ+bnc8rSRpEbA/InoHDg+xa0m5HgPMBlZGxCzgVwpqGw0l99UXA9OBScB4UitlsJLyfDQte5xXqTD0A1MHbE8BvmtTLC0j6QRSUXg5Ijbm4X21Q8x8u79d8bXAxcBVkr4itQfnk44gJuSWA5SX636gPyK25u0NpEJRcp4vB76MiAMR8QewEbiIsvNcM1xeW/acVqXCsA3ozKsYxpJOXHW3Oaamyr311cDOiHhiwLe6gaX5/lLgjZGOrVUi4sGImBIR00g5fS8ibgS2AEvybqXN+Xtgj6Sz89BlwKcUnGdSC2mOpHH5cV6bc7F5HmC4vHYDN+fVSXOAQ7WWU6Mq9QY3SVeQXk12AGsi4tE2h9RUki4BPgA+4Z9++0Ok8wzrgTNJ/2DXRsTgE1yjnqR5wH0RsUjSDNIRxESgD7gpIn5vZ3zNJOkC0sn2scBuYBnphV6xeZb0CHAdafVdH3ArqadeTJ4lrQPmkT5FdR/wMPA6Q+Q1F8hnSauYfgOWRURPU+KoUmEwM7Ojq1IryczMjoELg5mZ1XFhMDOzOi4MZmZWx4XBzMzquDCYmVkdFwYzM6vjwmBmZnX+BpIqIZxuTfYBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "x=np.linspace(0,100,5000)\n",
    "y=np.sin(x)\n",
    "plt.plot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}