appendix.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Programmierkurs Python\n",
    "\n",
    "## Appendix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_even(i):\n",
    "    if i%2==0:\n",
    "        return True\n",
    "    if i%2==1:\n",
    "        return False\n",
    "foo = is_even(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "def is_even(i):\n",
    "    if i%2==0:\n",
    "        print(True)\n",
    "    if i%2==1:\n",
    "        print(False)\n",
    "foo = is_even(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def is_even(i):\n",
    "    return i%2==0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "is_even(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import imageio\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = imageio.imread('rsh_logo.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1221c5150>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(630, 1200, 3)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "im.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x122508b50>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im[400:600, 0:200, :])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "im[400:600, 0:200, :] = [255, 255, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x122589ed0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PQaOrS7DhYIUq338Dy3Z+AA+PHvZe6nzYSKB47ipCLVL8hVU06tGlOZ5dXfMXgkBW+rW0veNQwPi9dxFan7q3Xx9J7CNDKMWg9PTudVYy9Y2ksFDGkUJHQjs2p+vvE/3jla5fvPWQ8JshIs2B14HLVHVNfJj6SsqknkEROVdEZovI7FWrViUTdYvAswZsiF62bl8+BbDK0p3vJ4xHz8iDiMKSdn8npCBsQFXqXbBHbTlW7jYJVnr0srcRXa7ViuLJWrqX3IUQqfO8NVgYXDB46yK0PLe//5IFJij291UgpXXr18W/8GTKZXiaS7fVNwFhVEBXFWOMIeP7GxJKSoog/e9DQGFpl1vJO/c/tHrqaAjUM1E8AKvknf4Kxd+vKPccLTIX0kvrfqDY4VN2rS3ZMp402tLxq3Pp+vt1sZBa+YHaAkjo7RCRFHzBPlVV3wgOr4iqW4LflcHxJUBGXPTuwbFyqOpkVe2vqv07dOiwcfAWS9RaJDN0IVZLAvO7Ov58M4L3Rx4lpjD2gBlpiSdQGjOjqV9CWOyaCMW/5tPk8J0D+3JL7uXvAdDbTgp8xic4wJgEYv01RH+X4wlnxKl+BDb8dwERY2h2dL+EJwIlhHrY/A1AC3quiPfeaVna506w6wnv3hmvhnu9fNBkUsfshBhDdpNraDXpGNo8ObZSQREClmTcyropP7B8r4cp/S4LBRbIGfT2nkzUWNKRJBFA1GPtQ7PIMjcC0E0nkja4q/9+NxL74kSsZQR4BvhVVe+PC3oL+Guw/VfgzbjjpwVWM4OA1XHqm60cv5e6YuTTGGlJ5w/GBVPV66YHaf2ZMyyWa/C0hN7efagavMz82JT4FEn3vQzU62Ic/oDfkla3AtD+rb8AsOq01wh5Hj3tXf5pUk9OaEWYHzqNnewrgAlmpPrkjnkWY9fS9rWTfB14HaFGyG5/O0oppr2vV7d4/tfDhlK6zL8RFV/3bVEieLEyafDP5q4nMmsJ696bQ5ZcSLfiO2l2zB6IhIiac5ZF8H/ST9wL1Tw8Csg96XUAeuuziEhDKN22LQL9Qhj404wj//L/4GkB3QP//LEvq4a2L64nEtG5DwVOBX4UkXnBseuAO4FXROQsYDFwfBA2DTgEmA9sAM6o0xJvRlQFUaH44z8xpJB2QJ86TByMKMUz/gQTpvufVwRqB0VTwvjCIezbuf+aS8rObevvc139xkrFghbgLVtPVtfxdHjvIpodvGNU7V8/WauyZvzHbB+ZUqaPEEEUck99FVUh1DMDwaKJKrATyTciGKuk9O+NJx6GMEKInEFPESJCynZt/PEOwFhYErocSxgQQghWAG2CYGi6Zx+6fP+3jXIosx1V8V1VCCHa3Hs4re4d63cPJPAjtJWrA7ZYgiULF8t4UqQLbe8fQ/OLB/k9dVO2cH1joUbhrqpfUPUrNKqS8xW4sJbl2iIRCVH8ZSYiITzNq9O0VSzewrUsPfgFuv95BaGerYPl85Rw1xZYFUQsqCFn10fwKAIsTffbibbPHYPp3ZrKHk7FQrDuZ+nCXFJ6twsGQeMfZhuMHQX7AgvlAlJpjUcLIr/nxvS/xGxW6v5FUCw5+z9Ni1sOLPfERTcLp84FwnRb6A9g14UM9JcpNCxOuxxIo/uscUGI/1VQOGshra4bHtwL8cdDOtyB0hLBYilETQtajx/G6ts/JcO7pUyIB2lEr2tW+nhsYRFIOO7rx8RdSVNNnTa6R+UrkfBAbSVRgw2/UanJFfHWQtlYqA3usJIVHo/aFFJ6N6Prn9dtdMEaX4PqXP4mg8Kyfe8nREt6rr0/JhjqAlHDkj73Ec5oSkqvNnFZGlDoobdR+Nbv5IydhEc7VJoQUsuGzxezvs/dhPDorvdUTNcaFoYvwmib2OBnD72jnEm+VX9SEAorhj5O0dfLCNOBCBF66sTYYGOsR4/WS+fSzl9Ly3vGkDYwo6KlgioqobrPN5CKIduE1OZNygZuraHw3f9RqmtpefvooAgKRuiaew0E9wUBrFJw1TRSduoQ6x36Dahh/bNzWXHWc7Q8cACmsASVphgtTtoSI4Kp8mVVIVggPfnqi/orDax75CuaXzIYrGkMsj2ukTIs3/N+7I/5RGQtPezDhLQRVDABto1a1hElK9cSllZ4bECap9Zp2gvMBVjJo3vmtcQP0ErsnyX98F3IsJPI0FtJP3MvXxWAQSSMUlIhTVVlUfg80nbogwn5D3tIlIVyefAZ6hOZn8siuYhsczUbZv4GUkwkVIxHvp+OBJ5QBPLPn0Z9aYMzd7yOtIEZweBtnBWOVbwfczEKauvWOkcQVuz+CMaEaT3tFCCYEGVg+diH6aMPlTVu4osMxfi3KNoQGCXvgffo/MulqPqrNpFXzEK5gHX3fUEf+zDtp5+KR6qfdNe2SY8XhIBFUvkH8dJhD2xyx7P0q+Vkhm7Eror4jXcjkQiKgZxiFsuVRH7MpfVTR9PbPgz4Xlapw/GaLRXXc08Av1NkWd7lViANCewYROtmDv6f5gLCpJFh769U5eEfCwe+xn3VQPunj0SfPpJlcgvWCBFN2cjm3LIodAmiLejy6+VgLAvlPEQ6k6phMmW8L7R1A+n770pPOwkVP36mjCdsheZ/PzymClh35TTW3fcVHsqGad/RKfP6oF/kEf8YabCMM/iNiwQO0hVYLJeRQjoiQlc7wZ9xK/7qUn+ac9hOnyxf39gFEFYdOwUroFriT+yqK8dsAiW/LAHCNBnaB7/EFiJK08G7UaYr969srFmLZq+Q+5fXCamNyfvFciEdXz2X3voYisVTSwjFoxBDGhnZ1ybcc7fWUjj1Z3JPewUV33wyFJiELu11N5HstRAo2WLJBe12ds+b6Jp5c/l5CIov2NSwKHQFYTVk6D2xdmrlIZMpmv4HPb17YpZg0WfyTzmfMK2wFNBLnwiS81i1/1MUf5kF6mGs0G39zUh6StnTaJVVp73G2qkzaHfjMbS6ZZTfkAisOuBp1s+YBxjCLVvQvWDCJuuXol8wa+6cQf51H6Gyga5LbiS1S5tYmv7Qtj+RrJFooKqkEVet7vBN7gyiFsWj6fDdgoDapRvzN0Ireup9CU8GUom+coaSoHftf7iXxc+/5RPCphWpHTsgYhE19LZP0fnTs2n36el0y76eHvYu+uhDdP74/JhgV1V/IWYRUnu1j72cNq8IK77tr2QXUfreH6DiD2rGsbjz9TFzUQl6wWuuf59MuYGQpGMFIprn66MFjHr8GRrHdqVP4T+OlfSoBArnZ/vCnVLq1P0jAAajvkbCx7Ig5WI6fXleIlEpfPlrepQ8yNJm15FlLqanPkr6sb4XR78DYEAsQnPfJ7/YhHuORgx5p79CCKHJgD19E1WFrNBVkL2OsPrNzWL5O6q+hc/Kc14l01yNZnsUzViAxllW2cB+dJG5kJA2o6u9J/bULGp6LSXTMwmRTmbq1USfssIXvydbrqAJbTAmjZB0JP+KdxBgkbmCws//AM8gGkZNmOzmtxFtDNdcN53M8A2sf3EeIWlD7q2vx56ZnBNepnjGQlJpR5gWyBqlcOo8EnWVHyV6+up7P2OhuZC866fT8Z3T6GkfIKVrm3LvqapiVCh84/dG76rH9dwTIejpGJphKSVtVJ9aDWKh4CmY/BJWHf9SbD3OhIsjgTdxBUNT31QwugpT0IVbd/OnqIRo9+FfQQwWD0OI8NAehCTes4nfg4ruS8zyQyl88BuajRvgLwyyQ5sg+SIimk7eIVOJYLGsAEkhRBsES6hli9hIZ+55byCpTWkz6RBa3nYwi0JXYDCBPxpfkmabC+itT8SN7FXW37AoxYimoyJ1Ormk+M3fQcN4AqIWEWG+nMH2+nw1k9N8qxYRfzFyq2ks2ekeumyYGPMnE6tH8GWyOPQ3wrSiVDb4vUdrEVFKv11Gyj7dQb3AZLI8C804wtIWK76Dumy5Fo+19NTHWBK+ES/mUCxMdptb8NauQ0jBBGmtOGgSvUvL1DZGhQXmQkKmLVh/kF4xLN3tXiiK+HNqpRQiYRaZi0lp2o7u62+l28l7s1AuIGzboBJh/YPfsuHFefS2D/rlDJ1PmDagYQhFWBy6HGtL6frxFfSYOJpMcyVoCinSlqXmRkoppEn/DLr8fBlLd38Io0JEIuSc+gapU2bS6b/nEF2oJjbYawNLIoleX0veuDcpePIjDE1pfvRe9NZHgiUQK39GRIQsuYlSE6GPN6HScxoLTrgnQvAJ6QUCsvjjhXD9pnfcrXgs6XorLLf01NvY1O9D3/tFIWEJ0/KIvmXpKKgpBSDUvbWvFrYhvxoi/heIVLHchETrG2HdHwtoFzRg4llCCl31XkpmLGT5AZMR1tD+/lNpcdlgssO3oDZM+5ePJksuImXkHnT+8NygoJbFoUsx2pJOn/wVIcTy45+l6NU59Azc6dZU/RRSgwmqoTodyE49YicirCFEc7LMzagUsJ19IbBd9yq9yYpBxPKnOZ006UaG3kHZTF2Dh42pb5SgrZPWoEoK7Vg5/CkKP/sFoYVv/moL2M4+XDEjoJc+QcF5b2PaptJq4sExFZe1lk6Rm1hqbkIpJcU0pVuB79Qs01yJaFqgRmpG5PflhHfpDEDhO7+RGmqPWovBY6G5jLBausy6mpQBGWTJxXiE6f7z9YR3bec/S/iXoY/6jXJ03/O8oI5KH+/xja6R/7UIftPc1d7DmpP/Rep+PWk+bqAvgK0HIUOGvZWl292Pt3ARxqQS+WAxmXITiOLpmqAeKXgSCYR9CiEtwdCMzt9dRtsnjvL949tQmSqwGsRYwlocNNCNzwQyihPuCaLY4EEQNnz0yybKY/87sOiFn2B5Ed2LJlJBX5okYUJYDdHmySOJ9sJV1f/8lFQiM7MIj94BDBT/5zdWHjWJHjqpSusKhZhOOyTN/ZcY2PDUd5Qa3/wzdVRvMvQOCISstYEliXgsH/MIve1jeNi43lMIkWaEJULu8OcoZjVQSi/7qH9ODZX3ixrGBCqOulx5SgR66YNseOMXmh25S/kJLBV60sFNX1vIolbX0Md7lkxzVYUxgsqcfKVaJSKK1fWktG1N+6UTyeoy0W8IxFZp1ylAmycPr3BMMBgLPeztgQ65LH7XlbeR1eFa0nbpQo9fri4Xt+lhO1PirSSFNGxKMzKy7iClQzpqfJVchj5SoQBlKZtY/gChUCjYr1h2ibPrj96xti+eWBYuQKjsmnWef2Vg6w9rn/qWovf+R8k3i0mlGepZUnbqTJP9etL0siGY9unoRj6NDCFiCdSAWqH1hKjL7MYp2MEJ98SQqH2x/yCEaBb0oJLtQRoWmotAld5xL9Emm7ERQaUpnq7BdGwaHA3KpAajysoxU2gyIoMNH39Pl9fOp0fgc74ylZI/gU8JawQraRi1ZMmVtLv3KLzMfHp6j8WdHVgPe75ets1zx9L8tL2CICW0kafGTm+dTu5hzxHaJ4NeM2+N2d4nVk/jD0yygY5vJ6AHT4KoeqvZ0TtT802w/CGns33hFHp6D7LYXEzXH66rNoYEA5hddEJMzSPBgGjba4bS7NrhmJab4F0zuLx+D1nKtQ3hdk1j6pKYCWs0XPB72dFJOxotk1dJY9ZwRNtUBZqfM4AW5wwoN6Er/h3xS77pqrkMvbn8NWmkNN5mq47xe8NrsKqoWP4MjYsJdq3CeZbFi33WqioLZRxoU3rroxDXWGzaQxZh/Qs/o6qkNGkF1hD1E+7r0Iv8gTssGz75ma4fX0zKMbtFVyGtNEt/iFbo9OffUS3EoxQljRVXvUQ3OwE18SaaQS8uZMAIzU6NWwbOVGw50g7dka46kU4zz0MwSevNW4wbSk+9l6aH7pBUvIQIrGE2RtWLCT+voJg/5Gy21ylIExtYrnik7tEx4bQluiuAEVrccQimZTrlnoWki17JtZS4PyoRYkYo9+wJm1WwxxMrNmVjS7FrFndO7Wj8gh1AEl7FvR7p37+/zp49e3MXo1oU8GZns2TgE6TYEB5KeNeWdPnpyiofFOuBCUFWyo2YSIQui67F9GxR+7LEnJddTFjb0lVvjYVFi6Jx+xF8Cxi1vqqkJsEaAYy1GGN8v9axHpNsFrcbUVPUhuyLxOe5bK/76Pja6YS2bxcLX5ROwBEAACAASURBVHfbp6TfsB8mgfECh6O+EJE5qtq/sjD3WCaALygt4f7dwUaIEAGxRH4tYOlO9/i980DPHU/+ea+xyFyB562nm06sE8EOBItBW0Laigga844Y7yVR1Fex+KaN6ncWTWI95pB6GOOPBajRWJzN5fLEz7aBH9Vgqb0F5lzaPn5CmWAPzA3zbnwdQcp9zTgcWxJO554AZcLF0kMnknvaK2z45xxUm6B/5LE4fD2iRaARfyKLaY7VfDo+dwbtJh8byKU67HlaBWOCYbS1ZYNe8cI3at5YIaBmoiZ58UJ1a/dtnSjRcRQRIVPOprdOLjeuoiiihpYTj908jY7DkSBOuCdBdHy+3fPH0X7K8f6ne1EppX+sQYxHuE8HaOrrM32TrPrp1VljWWLGo5JKj9jAmaMuECDv8jfZ8OgsMuzTcQN5wbqnIiwy59HLPl5tOg7H5sYJ9yQoG+iJ69k2SSNlj4qLjfg93fhBqrrr4RlCqKZgTU6dpbmt40/rtxTNzMRmraV78YSNXAQEDbYI4fT2dWpr73DUB+7p3ArJbnMrIkpP77EqLXUcySF4LDQXIasjtHv1lOjBjU4SFsuZdFs7AXH9IscWjntCtyYUkAje6kJETaAycLewNkQ75yv/73F65tyFaVf9oHeTMcPcWhqOrQInGbYmBEpnLsUQounxe/iDe9uCwW49IliWdJhAt1U1L369rPvddM38ewOUyuGoPU4tsxWh6pE96C4sHu1ePr5GCxbfNNOZ6m1M1EeiVVicdh2mR5tKz1PVOIdmULwkG8+9MY6tBNdz35qwIdJoQ6tPz6yyxx4d9PO369aDYmPBX5k2wspe99D5jTNJO3THcuHRa+hfO4vFY5G5ij76sFvjtI6If04d9UONwl1EmgCfAWnB+a+p6k0i0ht4GWgHzAFOVdUSEUkDpgD9gFzgBFVdVE/l3yaI6oUXhS/E0JRm+/Yp16NE4bkXn+WBB++kRYsW/mpA6vmLaZeUsO++I7j/zofLpbUtUjZrN8ICuZQmu+/kC3aF4046nFV5q2jatGmsUbTWEikppMTC64/6a546gZQMvvno/Q/dzTPPP0H7dh2IRGwsTFXp1KkL//7Xu9TpPBCHT3RmZVV/+LKgebCdAswEBgGvACcGx58Azg+2LwCeCLZPBP5VUx79+vVTR9VYVS36fLEulhu04KL31Kqqp1atjai1qhdderau27DeP9faivFtRKdMfVpVveBv28S/Np6WzMnWhTJOVVVPOPUoPfaEw3TuvG/V2kiFOBFV/f3MKXr5NRdpu67p/s1wJIS1VkceNEhLS4tj+5U9n7PnztQPPnyvoYvXKABmaxVyNSnfMiKSDnwBnA+8C3RW1YiIDAZuVtWDReT9YPtrEQkDy4EOWk1GW4Nvmc2Hv5jCYjMeEaVHZEKgFrBYT2NuVzfu+Wjsszf+uKWkJEJqat2u/7q1oKqsf/gb8i77Dz30LmbO/Jp99hkEVN0jVyzLt3+ALvOviB3Ly8uhbdv2DVLmrZtEeuPBguI2wqpVuXTq1KkBytV4qLVvGREJicg8YCXwAfAnUKCqUSPrbKBbsN0NyAIIwlfjq24cm4IadOk6QggtTtybqPdswcQJdqiw7mpMWJly52y7gt1DEPL/9i5dfroKgIEDB1fpSE2Df0tbTqDL/L+V04I5wZ4oiYgX/xxjwk6w1zEJCXf1v+X3AroD+wA71zZjETlXRGaLyOxVq1bVNrlGiarvkXFJt7vxWE+bqccg28Cq7XWNxR9/WGAupN3rp5KyS7tYSFVE139tcf9B+Ovnlg9P5ovX4dgcJDWCoaoFwMfAYKB1oHYBX+gvCbaXABkAQXgr/IHVjdOarKr9VbV/hw4Vp+9v8yhYERbKODzx6Ok9UG7BEEfiGISCM95CBNKP2ilutaXqrqUhq8UNtDx7oL+7sct0N7Dq2MKpUVKISAcRaR1sNwUOBH7FF/LHBqf9FXgz2H4r2CcI/6g6fbujCsRfWDlEO1K6NUecm4Fasfb52fS2D0ES1zHcsW39FcjhqGcSsXPvAjwvvrcsA7yiqu+IyC/AyyJyO/Ad8Exw/jPACyIyH8jDt5jZJikb1EwyHr5aYKEZh9CELlm319tcVN9ksupVeKLtcpUDjvVgrxxNc+Pf5BPy14NdHPo7aCGqoQrpbFy/qEOwvKNeptOflyVc1sTDygYZk61X5YPkiceFhvniqI9novb5+tespue9MVHjE6KqP6jq3qq6p6ruruov+6OqC1R1H1XdXlWPU9Xi4HhRsL99EL6gviuxJWJtJG7bJqejDRaKCNGBXvZhTLDYRlkatdO7R02lwCb1oPvxojv+T6IvcXz94/Ov6rrce98dFQS7alk6Rx0zuuZrKpbI4gKEJvTSR/x0aroNQZpr3vwUE5vLWk0WVdTfWhsniIn9+nMQKhYikeejrHmvbEnAjdOwxD8niazAVTfY2H1LhurOTyStRBuUsmep8SsTnAK3jpk3bx5jxo5g1MFDWL58KRdfdh4Dhu6RxIsVQURZKJeQduD25dfgFKHf4N1i+1988Rm77b0d2UsWUZO6ITNzEeeefzoHHjqCz774mO9/nMtufbenqKikxgc9OzuTYcP7MmTkAJYsyWTp0mw++XwGQ0bsTaINzaxZ3/Ds808zcsxgvp39NW+//SYD992bgoK8CueKCM9NfYZTzzqBDz58j//+dzpjxo7ggw+n8cEH73P+BWfTuk27mt0vYFjW6x7a3nQA4K8sNXDo/wGWqS/9k+EHDapk/VFhsZxLDzuJ6Ietqsdeg3fkznv85QzXrl3PgKF7MOLgoVXmLSLMnjuTkWOGsv/Bg5g7d165ma9z5nzLqEOGMHj4XsyePSuh52PWt98w9ugxHHTYfsydO9tf4zXgjz9+Y+iIflx2xQVkZmYSvy7rDz/MY9D+/8chRx5AxftlefWNqYwZO4Ivvvq00vuRFGoYOXpIUkJ07rw5HHLkSA48dN9Kygdvvf0Gg4ftzQ8/zq0yjXnz5tJvn904+PD9ueueiRXy/eGn7xk0bE+mTX+PaAPU6KnKAL4h/xrbJKbd+26vZ55zSmzCRr/Bu+nAoXskFNdaq6sOeFoXyw3BEX/SUcHqHP2/ATvFzvG8sslIo48YqRPvulmrn6DkqbVWRx8xPFYua63uf+DAhMr1+r9f0VPOPLbcsTPHnVLppJTqGDpiQCzOTbderSNGD1L1bLmyW2s1Y7sO5eKNPmJ4uf177p9QY15Lm47XxVyjwawv3bP/zuXCzzj7pApxrLWad8q/Y/s//TxPx4wdpZVd270H7lJt/tZaPf6UI3Tw/v3KHYv+Dh0xQE887ega6xEf11qrg/bbq9LwK6++rMq4U1+aogcfPkqtVpzottve2+mIMUMSLkd1fP7lJ3rEsaP1mvGXJxXv32+9qvuOGqh7Ddyp0vD7H7qz2vjROo0as6+OPmJkpefccPPVSZVpa4BqJjG5nns9EO2dRXsHu+y4C02aNU0sLkLxh4vpUXxL0IkxqMJxfzmSebN+ifX+jCm7dW+98T6ffvUZNX2Ila2F6qsLRISmTRNb17U0UoK15deKferRF5CNbQSroCxeWa/pphtuJy01nY1X3RYRMucv988Pkve0fI/uyr9dV21eALbQw6QaFA8MdOnSpdx5/3jqxQpxM80ltJlyJNEe5JXXXsYjDzxWaT7ffvVdlWWI1sO3oSyvHon+Jqu+iD1TVWjSqr4Xlg2Fa3ny0X8wYMju5UdvFObN+h+lxSUJl6M6Tj3zBN585T1++GFejRqwcqjw2Yff0LxZC4btX3FOjvUS62kXFhbTvGlzDjhkmJ9s3PW1dtsyI3bCvY6JCt/4Fy2nYBVPPf5ctfFsIMfmhy6n6+rxkCKxu/PCi/9gdcEaosu8bUzYCKvz8zjt7OrGrk25X1XhoDH7cfXlVydUr5AYjJYJp1326hX4z0rsEYpv8FQ9sDBwWH/CVa4vG9RVYnsJ5RPNK1uuBITuRRPK0vYsY8aO4KhjD60QR9Uj9/CpdJx2cbAvjL/xKvLz89mu946V1jNk0moujFZdbmstZlNUv1UOBVQlvAxZWVn0zOhBhw6dWLFyRUzoHXDoMMJhQ2lpzeMLNaGq3HfHQyD+M3/GWSeSaNsVLc/nM2bSonUL+u6zc/mGr4ZORNl7EeHVl98kLyeHQ48aVe59MVW1io0U5xWyjok+TIsWL2D3vfrQum1bvvhoNjXqpsUju8lNhLUEaZkSF2CZPv0dtBohAYZWrdqQn5+fUBlP+MvhYMKsWb+GnXbeFUhgQMoIOfl5nHX+qSxfvpwO7TsmFm8jWrZsy6579qZNu458+fFMwuEEBGRSWFCDJYRSAlK2HN70dz9i8LB+tGqr7DNsT6a//SFt23QM4oVY885XtH37JMD/gvr+hzlgvVpZf6gKrVq1Yt9RAyqEtWzZvMF0v6tXrwagoCCPMYeNYO63vwDQu/d2QTm94HfTLbyylmRy9FEnAHDisafy9rR/B5PuEu9DqgqPPDCZSy4/n5y8VXRo17HmSOXw85o76zf6DdyFwcP25usvqv/Caqw44V4PqCo9Mvrw32lfcM64vwYP2JzKz40K/dWllBTnsp0+SqTcIm6+4DY1vB82UsKgIfsnVL5/TX07tr3PsL0IS5ivPq/et4+NeLRt25ZnHn8BgB136ZH0iwuwZk0ev/6YiaWUA0bvz0fTv0oqfk2oCiV/rEBNCj1K7qhwLaMver8hu3Ly6ccx/c1Pff8x291Pb33U7y0Gsq1P7x3JycurVtjVJAxFlNWrV/PVJxUHA/cdNYBWrSr3Jb8pVNcBWJS1CICvP/2OMWNHAXDY0QfwzhsfAhDxSmuVtygcffyh7LPPkNgxY8LErz2bUDoibN9nB6b950PGjB3F1Vdcx/D9RlX7BVQZ1lrmzPyVkaOHsffAXfhu5s/oNja726ll6gljDCLK008+R5v27ViwaGGl5/l+YizLWt/Advo4FiW8kQpi/HU306ZN1UJARFi7vpDx19S8mlB5LGMOGkPT5mk16n431g//79fMGr4mKqdssCfEhg1FDB81iOpMIpNOH2H5Tnf6KqSQIVyF5nfOV7+wbu16/vPmawCULvTvT7wQeuj+R2ndunU5q5SN65KI0KqqbpvaSy7vU6iMVatWVBln9ep8ouaRq1Ys5euZX5Ca2qSsLLWVewKtW7fnsQcnx/4GDRjE2eedknRS0euVntaEu++/I5j4kZzaKDom9dH0L+jUqQsffzKDbU3cbVu1rWeiD2X8y6wqHDDiIC667JzK7ZsxLJJraDvtHCBSqV6we9cM8vJz6Td4DzZW76gqp551Al07dSb522n4etZMCgoKaj5VKur7RaTSwa/KiR9UVFSFbz6bS3qL5rFj1cdLrG4GMNIMixdcbxO77l9+9Wm5dNPTm7Pn3gNYJJfRw5tUIS2RECtX5jBg2J7lyh9Nb+rLzybQKFU2TlIxrWRo0aIVlan55v1Utfph7dq1RM0jv/36J26ZcCNvvPxO2QmyqZOc/Ib5nHGn8v7bM8qF3H7L3SxZvmwje//yxAa/4waeo2V47V/vcMKxJ3HIUSNJ/Nmu2FGY/taH3PPgXXz5zadVxGmcOOFeh4gICnTu3Jnf5/9B9MX+2yVXUbS+iEFD96rQj8ySi+jyx/U0Pbg3lRkvaTBxaOZnP3DMkUdVEDSHHTmS5ya/yOuvvFujdcK/g17qm++8DliuvPoS8nKWM+frn6t9qS3KAw/ey9KlS8kryKckUsqadfncc/8dvP/eF4lcGhT46NP/ktY0Pa5uHk9MeoZDjzqQb779qoI1Q3R/2cosRJT81XlsPDlnY1bf9TkKGK1ov3/DbdfEti++7HxWrlxJysB/0umZ0ytY7PjlU+Z+8xPT/j2DvkN394WD9Xvb//jnZO6+785qZ6dChEWLFmDCoTL1G9GJTB7hcJjMzEVVfhlURd//24tRY/blwUfuZu53s3jr3TfoO3gX5s38vdJyKJYWLVpQsMYfkxEJkZsb76zP0q5dO2bN+aJcORPDgAhZS5cwf0H5/BVLcXExE+68hapETVTwT37iMcbfeG1ZmYOvmtP+cjot0lvUMJ7ql3nW7JmEQimVdBQM7/7nv6SkpFSM2pipykayIf8ai527tVYLCgrUWquRSETz8/PL2RQXFBT4x9Q3vbYr1+t8c5Yms4CGtVaffPpRvfiy8/T7H+cmHG/NmgItKMiL+ytI2Ea9oKBAi4uLY3bWfloFunr16oTzLygo0LVr16q1tsJ1yc1dpfn5uRXiRM/Nz89Xz/O0oMDPt3L8a7hsp/s1kxs0u/MdldbvicmP6vgbLtd1a9arZ1UXcnHCC3BkZ2fqDTdfrXffd7suWZJV4/n5+fl+yTwvth0fZq3VoqIizcvLSXq+QCRSolNeeEavuOoCffW1l6o9Nzc3Vz3P08LCwtixiRNvj+UZLVt+fn5S9zRKtC4b35u8vLzgeVlTLu+Nic9zw4YNlV6LW24fX20ZrLW6fv16tdZqbm7FZ0lV9fc/fqs2ja0R6mqxjvpiW1isQ8vpVy0F189g9YT36aH3bhPL3pWvf31lAlnmUlRa0OSQnej49qmVrykYPPILzQX01scSXntQt7DlU8uu6eZboq66+xoNa5B7nzCNazm/Wi/W4ag95QckLasnfEJPvRdLze5OqqIhGua6yqNBXm6BEnx1jC4pAKmi/AJLW95Mt3evAvUSNsRIpgoNcW8qX5AluXLUtpzVWwpJjefUBYnVIbmxm8aAM4VsQESEDf+dT87Bz9NdbwOqnGyYcHr1zZbT40qMJrTEGkPRvMV+h3zj8lsoeedXOn59EeHd/RWV6qOGW8p1q6kcW0o5a0Niddh2hHqUba/Gm5nlox+mh715G5sr1wBo8Ne3K7Fx2UrGBu3KdWSPnURo97ab/snkcGwFOOHeoFh62/sg5hrfUWf4EwboPPtiRBRDOvb7Fb5fh4D1r3zP8oOepo991J8Us/V3Wh2OKnFqmQbEA0Kuz16vGIFQ13S8JWvJ7vs4SC5hbUExhTQ/ZHe6/nAJAPWz9InDseXguo8NSMitgVrvCIZuWdeheJhQKUIbrBhSdutMh3fOxF1/x7aC67k7GhX+IKqlp72DtVN/ItS2CeljdvIDXWfdsQ3hhLujUeHLbwMCLU75v3KOwByObYmEv1FFJCQi34nIO8F+bxGZKSLzReRfIpIaHE8L9ucH4b3qp+gOR800BlM/h2NTSEYBeSnwa9z+XcADqro9kA+cFRw/C8gPjj8QnOdwOByOBiQh4S4i3YFDgaeDfQFGAq8FpzwPHBlsjw32CcJHies+ORwOR4OSaM/9QeDvlE0LaQcUqGok2M8GugXb3YAsgCB8dXC+w+FwOBqIGoW7iBwGrFTVypcS2kRE5FwRmS0is1etmkNsFkqj/PPRYIHo+CXNEqfuV5GJdzWbqA8SVa/CuVHXvLXxUxLvdyfZeBpdSLuOZ5zG12fjqtXGZ8vGYYm4/K0svZqvd+WLoGwJzgId9U8i1jJDgSNE5BCgCdASeAhoLSLhoHfeHVgSnL8EyACyRSQMtAJyN05UVScDkwH6969h9dutnKhXvMOPPJC+fftjDHz55dd8MP1jKjPlUCx/PftEZs2aRdGGDYw94jgeuv/ROivPy69MZdJj9/Hr/35jr//rx1mnn8PJJ5xSaVl8LCedcjQ77rgjS5YsITMzmw+mly18cN4FZ9ClSxeKiopo164DV185PukyRSIl7D1gR4qKShg8aD+mPPtyQoOh69ev57wL/soHH31AOCXEkYcdw+233UHb1h2Syl8Vxh49mv79+6Oq/PDzPF5/6W2i1+T4kw9l1132BCAnJ49HH36y2vREhMuvuoTWrVsSiUTIXrKIf0x+kaiP/7nfzeI/b72BiDB79lzefev9GtMbddC+7LHHHrRq1YrffvuFf734byp9flQR8fjLaSew4447oqr8+MvPGGNIT09nv6HDOev085K6Po6tkKp8AVf2BwwH3gm2XwVODLafAC4Iti8Engi2TwReqSndfv02u0v5ev4rY8DQPfS4v4zVqsjNz9Frr78itm+tVauejhw9RL+e+YVaG6kybs142nfArjrz269U1Qv8Znt67fVX6WlnHlttTGutHnz4/lWERXT0ESPj0kwca1V/+uV7HbLfXrG4+wzbM4GYnl7x94v1oDH7abw//NLSYt131ICkyhDPhDtv0oMPH1FJOa2edsbxeulVFyaV3jffflFpeqqqixbP10efeCDhtKy1uip3uQ7ef++ErrNV1aEj++r6Davjjnl6+VUXJZynY8uGavy518bO/WrgZRG5HfgOeCY4/gzwgojMB/ICAe8IMMbQo1uPSsOe/seTPPnMY3z75fexY37vVZjx3peMGTuKV198nebNWm9S3occeRBvvP4uPTN6BWkDCLffMpHtd8uoNq6IUFpairVUWKxbJOpSwQSLZic+fi5iOeu807jnjgdjPfWZn39fQyy49vqrmPHJDOZ+8xPx+phwOJXPPpzFpvrt3n//Efz884+VlFNo3rw52/XaLqn09ti9b5Xrf7Zo3qrcOqY1ISK0adURaxMz8RTAi1jSUluUHVTDOWePSzhPx9ZLUk+/qn6iqocF2wtUdR9V3V5Vj1PV4uB4UbC/fRC+oD4KvjWi1eiVVZXX33yZD6d/WqVO9Oixx3LAmOGbpDMdftAQNmzYQI/uFYW4MWEW/LqsxjRUFYzWkH/y67gWFZUw4a5bAd/PV3XpaxD+3Q9z+G5mdHnAyvLcNDcD1qteVx6xyY0JiEbdTlQSJiESdiYfi6NYidR8IgAWa8HaktiRKS88w0477JZUno6tEzdDtQGYM3dmbDslJa3Sc0QEa6FVi6p75Wecdg5vvPlaleHVoepx6slnxPWyN425c2eDehV6jpvS4ET5bubP7HfAPowZO4rzzriQI488uspzReDxJ+tu/GFjli9bRokXKXfPohQWFhLaqqx6DSLKrNkzWbduHXPmzubgg0ZvUatJOeoPJ9wbgH59B8a2S0qKqjjLYoyhOnVCOBzG8yoK1kQwxpDRrXvS8Tamf98BVYRsujWPiPD5jG/5x7NP8uTzk3ji2YeY/mbVK9WXlBb73jWrXR5v09QyIpCSklLunkVp2rQpydfTVhPDYhvAlmDggCGEQiEOPvAQvvj6ExrbUnOOynF3eIvBvxVffv1Flb3g7OxM8gtygOR7ygUFBbww9bl6M42ri3lqZ55xHpMnTcFICv96bWqV551w7El4eJUK9uhgUm0e7bq4HmWYKtMriZSCTVTFUjui92dg/8G4137bwN3lBqY6IbhseTZXX/+3Ks854uhRfPvFTzWmUx7f1vm7mb+SuzqniniWv5x2XILp1S8Z3bqzww478dXXn1R5TufOXVmzZg0HHDKsQli0ftM/eKe+ipgUoVDVC7Pcee8ETjzulAYtT1VqQUfjwwn3BsYimJTUoDdX/oN93sxfCYdSmDXnK6B8D/LxJx6mTZv2m5Cjb1eNemgpDB3Rr0LaIiG++3F2jSmpxPeM445XM9mnZix79N+e+MlR337zNbfddF+1sb76ZG4wfmApdx3VQ0Q5cOShyRbEj47EGgh/8LbM0sWKwZhwEJZYRVNTU1m/fi3PPDu5QpwZH02nZctkLZ8MIUIVrvkOu/UsXw/V2G84XF776ofU/aQ4x5aF1O0n6KbRv7/o7Jply1aMf40HDP0/mjZNo7S0FAN88ekcpJL29dYJ1/PPl5/n5OP/Qk5ODtM/mM78X7JrWYYyPevI0UNYtWI5p//1PF57/UXUCjP++znNmrWoMu7eg3alWXpzcnNz6dWtN+9N+wiAadPe4Zobr6RVqxbk5eRw8UV/Y9w5lyRVriH79aVg7WrOO+sCHn3sQSY/MYXh+42qMaaqcvd9E3nmH49xxunjKC4u5rkXnmbIwGG8+ELyA8/7DNmdiLWkpadRvG4Dc2f9HgvrP2hXUps0wfM8SooKmTPzt2CMpOb6gWG/AwaxZm0+zdJbkpeXQ9dOXZnx3y+TKt9+I/uTk5dHh7YdWJW3Kia016zNZ/Kj/+SgAw6Onbty1RJGHDCMVm3aYW2EdevWISKoFyGclsp33/zmPGY2AkRkjqr2rzTMCfeGYNOusQYzW+uD+kzb4XA0DNUJd6eW2YIRkUrVIHWZ9uamrsqwJdSlOurrPjocVeFMIbdw6rN3vSX03OuqDFtCXapjSy+fo/Hheu4Oh8PRCHHC3eFwOBohTrg7HA5HI8QJd4fD4WiEuAFVx2bjlok388qrL9C0aVM8G0GDeTWhUAhjDLvsuAtTnn1l8xbS4dhKcXbuDcLmv8ZbCtGZuQeNGc4H0z8PjpZ3ZBX/TF548Zk8NukZ3Eemw1ERN4lps7P5r/HWjUVVnDmhw7ERbhKTY4th0zoTxgl2hyNJnHDfitgSvrJqixPSDkfD4IT7Fs5Dk+5DVfl29lcsXZ5Vzkuhw+FwVEVCwl1EFonIjyIyT0RmB8faisgHIvJH8NsmOC4i8rCIzBeRH0Skb31WYKsgyQ53zF0rMPVfzyEi/PXMkznxpGNqsUxe5S5eE/4a2Go/Gmw1ddx0t7flfcVUnUdlrp0djoYgmZ77CFXdK055fw0wQ1V3AGYE+wBjgB2Cv3OBx+uqsFstSWgiNG59UgFmff4jAL/8sIjPP/k2OGdTBEb5W22tTcr3usZJ9035eqi6zNXXo3KhmUic6DnV6es37cM16lHTEvVBX3UeIhVXhapfJ2KuIXH41EYtMxZ4Pth+Hjgy7vgU9fkGaC0iXWqRz1bPJ5/NYI9+O/DxJx/UeO6XX3/B0BEDGHHwYC782zkUrMmPhakqfznjOA44ZD/++dJzSZdDVTn5L8fx5rT/8OeC3zn4iH1r1IG//+G7jL/lahZn/sk+w/YCPxoaeAAADbJJREFUIiT72FhrueO+Wxg1Zl+GHzSMqa/8k5deeYFDjxjFeRecU23cV15/kX6Dd+OAQ4bx9LNPBEerz19EuPSyizjosOEcPHY4P/70HQCFhcWccOpRDBm+T7RkSdUD/Gu4/a7dycxeSEgMb/znVfYesBMLF/5Z6fkffPg+zzz7NCNHD2HKS8/x1HOT6Tt4F2bN/jrpvKP89r9fOeO8kxh5yFBGHDyYO++5lTvuvoWjTzyM/UcO3uR0HY2MaC+iuj9gITAXmAOcGxwriAuX6D7wDjAsLmwG0L+69Pv1S6gYW/Gfz5HHjNZjjj9UVVWttVodo48YWWVYSUlRtXGr4tgTjtBrb7oitm9tpNrzFy78U/fov32srNZTzcxcVGPZK8Na1b6Ddo2L66mq6pjDRtUYt7CwUPsO2jXpPFVVDz58RJB/+TKPGTtCc/JWJJXWkiVZeu31V1Q4bq3VvoN30TPHnVYhn+j+6COGlwvzy+Ullf/GjDh4cIVj7Tun1ypNx9YF8P/t3Xl0VNUdwPHvbxIDgpawhASQipRUG8EFEUFUFiFsFqzbgdpKLZZWqdWKVdAeqQd3qZSe46HigmI9YqsgKSIcQep6UIMLYABBiEtMSEQ2s5Ewv/7x7oQBMslMEjKZ19/nnDmZd999792bO/N7b+7cNzdXIwSeaC/BLlDVvnhdLlNF5KIjThBKjL2yIjJFRHJFJLekJJYtE4+6j+BLXnyV0opydu/eVe8Vc11dH6H9xaqwuJB169bVLNfXf78k5yU6tE9zXQsgAeje/eQGjXgR8a6oD/2OvPfSW/6fVfVvrAch0LBRNjVdXDVl9qblK/++nLGXjohpX9dcN4H7Zj1U6zFy3/mUwsJC9pfuOax9RIRg0Pu0E/5/q6wsj7UqR6ntdVBSWNro/Rp/iCq4q2qB+1sMLAH6AztD3S3ub7HLXgB0D9v8JJd25D7nq2o/Ve2XltbwCiSC8Df1rl0lDBt1Qb0BOtJE1t66hn2petPUWwgEkhkyvH/9mYGbb7yV1q1TOHdgH8rKvqfuLyfr511RHKrb6jVRBHZACUCwqfqoA0CANaveIbVdx6i3uv73k6mujtwlJSLs2/cdI8cOO6rtDpt3VeGJBf9g3759EfcVrfDjqCojxg5u1P6Mv9T76hKRtiJyYug5kA1sBHKASS7bJGCpe54DXONGzQwA9qpqYZOXPEElJ6fQ6vjWNVfDzenKyyfw6tKVVBw4wKhxQxg4+Jw684sIry5dAxrg8onjOee8rEaXu+CbfDZvziNvy3rS4npWj+7GqNC57MNPcqmoqKgzb+WBCg4Gq4nUl3/nXdMYMKQPmZmZ5L670e2/cf/PoqJvKCwsIG/TenfCMMYTzaVDOvC2iHwCvA+8oqorgAeAESKyFRjulgGWA9uBbcDjwA1NXuoEVl19AK2KfAV46M0eqOWNH3BXv40Z6x5g7ZsfM+TC4aSmprL5s0115hYRPli7gUcemktG125cduW4RhwbTurWg9NOyyLr1DPI27Qhyq0aOYwzgmi2D8X/Nm1OoG3btgQj9D6qKkmBZJKTWqFa+6eu+2Y9wto3PuHB2fdTcaCyESU/JCOjK126dOP0rDM5s3efJtmn8Yd6g7uqblfVM93jdFW916XvUtWLVTVTVYer6ncuXVV1qqr+SFX7qKqvfzUmFqrQqVMn5sx+NIrctQe06mAVKSmtG12W22+9k16n9OL6G6fUuj488KkqPzm1N4sWLubbPTsbfeyQCVf+IsqckbtCGkJVuea6q6isjD7APnz/Ixx33HEEIoxrFRE6dOjAO2veq/O4EGDQgEGMvzy7ZrumMn/ewibbl0l8dodqswny4Ox7KCoq5vyBF0XMFXqzl5eXs3f/d0etv+43v2zY4RVGjhl6WNLY0T+lc8dOEcvxq8k/h5o+8iAnnngiZaXlDb6hSd34cw1fVigrq7u7A6k+KimIcv6Fdd8f5x0pNJ4/WHNMEaF4VzHTp82Iuuz9+p5HQUEB2aNra7sg+fnbyco6A5GkCAE7uSb9zul3U1ZWxss5S2pOorF9Cjk0hr9Bmuz7C9OSWXBvBsFgkBFjh7J//14+XLs+qm1yFq/k6kkTWPTCPyktLSU/fzvjrhjF0CHZDS5HZXU5s+fc5wK28sBf72HRc0tqzauqXDruMgaF9cuff1FfPnh3fUw3ZYUcPFhFWloav5s6xXVkewFq0LBzadOm9k8ioZPB1h35dO6cwe7du2uCYNE3BWRnj4pwNG9EzNMLHgfgT9NvJvRSf3D2LPr0z2TFkv+SPWJsTHXYsG4LFRUV9Du/92Hnt+xLhrDiteU8fO+cOrZ2Jyg3ruyt199n/lOP8vSzTwDBGK/gA0CQlJRk9pfui+nEMPpnw3l77ZsxHMskKvvJ32ZQVFRARkZXYrni8kaVwG1/voWPPvqA3r3PYuYdM0ltV/uVdjRUlYXPPskTC+YxZPBwZv3l/jrLonqQ199Yxd33zKRH9x4sXLDIrQnWud2Rqqqq2LQljyQJkJ6ezs6dJShVFBcXU1VdzcgRYyJum/PKYjJ7/ZiO7TtTVPQNgUCA6upqdu/9jqGDh0es5/oN60gKpCABBQ2w89tCTjj+B/TvP8AFWIXA0XeP1id05T9j5jTeemMNY0Zfyh2330UQJcDR+8vbtB40QEZ6Oju+2E771I707NkLVWXV6hVkdOlO14x0OnaM/svlysoqtmzNo2tGN3buLOTLr/IZNfKSKIbXKvPmz+WG394cU51Ny2W/5x538f8fG2P8x37P3Rhj/s9YcDfGGB+y4G6MMT5kwd0YY3zIgrsxxviQBXdjjPEhC+7GGONDFtyNMcaHLLgbY4wPWXA3xhgfsuBujDE+ZMHdGGN8yIK7Mcb4kAV3Y4zxIQvuxhjjQ1EFdxFJFZEXRWSziGwSkYEi0kFEXhORre5ve5dXROTvIrJNRNaLSN1zoRljjGly0V65zwVWqOppwJnAJmA6sFpVM4HVbhlgNJDpHlOAeU1aYmOMMfWqN7iLSDvgIuBJAFU9oKp7gPHAMy7bM8Cl7vl4YKF61gKpItKlyUtujDEmomiu3E8BSoAFIvKRiDwhIm2BdFUtdHmKgHT3vBvwVdj2X7s0Y4wxzSSa4J4M9AXmqerZQCmHumAAUG8i1pgmChWRKSKSKyK5JSU/pGZaeF8+jDGmeUUT3L8GvlbV99zyi3jBfmeou8X9LXbrC4DuYduf5NIOo6rzVbWfqvZLS4t+5ndjjDH1qze4q2oR8JWInOqSLgbygBxgkkubBCx1z3OAa9yomQHA3rDuG2OMMc0gOcp8NwLPiUgKsB24Fu/E8C8RmQx8AVzl8i4HxgDbgDKX1xhjTDOKKrir6sdAv1pWXVxLXgWmNrJcxhhjGsHuUDXGGB+y4G6MMT5kwd0YY3xIvC7yOBdCZD+wJd7lOIY6Ad/GuxDHkNUvcfm5buD/+p2sqrWOJY92tMyxtkVVa/vC1hdEJNfql7j8XD8/1w38X7+6WLeMMcb4kAV3Y4zxoZYS3OfHuwDHmNUvsfm5fn6uG/i/fhG1iC9UjTHGNK2WcuVujDGmCcU9uIvIKBHZ4qblm17/Fi2LiHQXkTUikicin4rITS7dV9MQikiS+z3/ZW75FBF5z9XjBfe7Q4hIK7e8za3vEc9yR8Pv00iKyB/da3OjiDwvIq0Tuf1E5CkRKRaRjWFpMbeXiExy+beKyKTajpXI4hrcRSQJeBRvar4sYKKIZMWzTA1QDUxT1SxgADDV1cFv0xDehDe9YsiDwBxV7QXsBia79MnAbpc+x+Vr6Xw7jaSIdAP+APRT1d5AEjCBxG6/p4FRR6TF1F4i0gGYCZwH9Admhk4IvqGqcXsAA4GVYcszgBnxLFMT1GkpMALvpqwuLq0L3lh+gMeAiWH5a/K11Afeb/KvBoYBywDBuzEk+ch2BFYCA93zZJdP4l2HOurWDthxZBn90n4cmhmtg2uPZcDIRG8/oAewsaHtBUwEHgtLPyyfHx7x7pbx1ZR87iPs2cB7+Gsawr8BtwFBt9wR2KOq1W45vA419XPr97r8LZWvp5FU1QJgNvAlUIjXHuvwT/uFxNpeCdWODRHv4O4bInIC8BJws6ruC1+n3qVBQg5LEpFLgGJVXRfvshwjx2QayZbCdTWMxzuJdQXacnSXhq8kcns1pXgH96im5GvpROQ4vMD+nKoudsmNmoawBRkEjBORfGARXtfMXCBVREI/XxFeh5r6ufXtgF3NWeAYHZNpJFuQ4cAOVS1R1SpgMV6b+qX9QmJtr0Rrx5jFO7h/AGS6b+5T8L7oyYlzmWIiIgI8CWxS1UfCVvliGkJVnaGqJ6lqD7z2eV1VrwbWAFe4bEfWL1TvK1z+FnsVpf6fRvJLYICItHGv1VD9fNF+YWJtr5VAtoi0d59usl2af8S70x9vSr7PgM+BO+NdngaU/wK8j4DrgY/dYwxeP+VqYCuwCujg8gveCKHPgQ14oxjiXo8o6zoEWOae9wTex5tO8d9AK5fe2i1vc+t7xrvcUdTrLCDXteHLQHs/tR9wN7AZ2Ag8C7RK5PYDnsf7/qAK75PX5Ia0F/BrV89twLXxrldTP+wOVWOM8aF4d8sYY4w5Biy4G2OMD1lwN8YYH7LgbowxPmTB3RhjfMiCuzHG+JAFd2OM8SEL7sYY40P/A9zuV3zlYaZ/AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "im[500:700, 200:500, :] = [240, 240, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(10):\n",
    "    im[\n",
    "        500 + i * 100 : i*100 + 700,\n",
    "        800 + i * 10 : i*10 + 1000,\n",
    "        :\n",
    "    ] = [200, 1/10*255*i, 255]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x1227f9350>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "imageio.imsave('rsh_new.png', im=im)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = imageio.imread('rsh_new.png')\n",
    "\n",
    "for i in range(100):\n",
    "    imageio.imsave(f'rsh/rsh_logo_{i}.jpg', im)\n",
    "    # imageio.imsage(f'rsh\\\\rsh_logo.jpg')\n",
    "    im = imageio.imread(f'rsh/rsh_logo_{i}.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JPEG-PIL - JPEG (ISO 10918)\n",
      "\n",
      "    A JPEG format based on Pillow.\n",
      "    \n",
      "    This format supports grayscale, RGB and RGBA images.\n",
      "    \n",
      "    Parameters for reading\n",
      "    ----------------------\n",
      "    exifrotate : bool\n",
      "        Automatically rotate the image according to exif flag. Default True.\n",
      "    pilmode : str\n",
      "        From the Pillow documentation:\n",
      "        \n",
      "        * 'L' (8-bit pixels, grayscale)\n",
      "        * 'P' (8-bit pixels, mapped to any other mode using a color palette)\n",
      "        * 'RGB' (3x8-bit pixels, true color)\n",
      "        * 'RGBA' (4x8-bit pixels, true color with transparency mask)\n",
      "        * 'CMYK' (4x8-bit pixels, color separation)\n",
      "        * 'YCbCr' (3x8-bit pixels, color video format)\n",
      "        * 'I' (32-bit signed integer pixels)\n",
      "        * 'F' (32-bit floating point pixels)\n",
      "        \n",
      "        PIL also provides limited support for a few special modes, including\n",
      "        'LA' ('L' with alpha), 'RGBX' (true color with padding) and 'RGBa'\n",
      "        (true color with premultiplied alpha).\n",
      "        \n",
      "        When translating a color image to grayscale (mode 'L', 'I' or 'F'),\n",
      "        the library uses the ITU-R 601-2 luma transform::\n",
      "        \n",
      "            L = R * 299/1000 + G * 587/1000 + B * 114/1000\n",
      "    as_gray : bool\n",
      "        If True, the image is converted using mode 'F'. When `mode` is\n",
      "        not None and `as_gray` is True, the image is first converted\n",
      "        according to `mode`, and the result is then \"flattened\" using\n",
      "        mode 'F'.\n",
      "    \n",
      "    Parameters for saving\n",
      "    ---------------------\n",
      "    quality : scalar\n",
      "        The compression factor of the saved image (1..100), higher\n",
      "        numbers result in higher quality but larger file size. Default 75.\n",
      "    progressive : bool\n",
      "        Save as a progressive JPEG file (e.g. for images on the web).\n",
      "        Default False.\n",
      "    optimize : bool\n",
      "        On saving, compute optimal Huffman coding tables (can reduce a few\n",
      "        percent of file size). Default False.\n",
      "    dpi : tuple of int\n",
      "        The pixel density, ``(x,y)``.\n",
      "    icc_profile : object\n",
      "        If present and true, the image is stored with the provided ICC profile.\n",
      "        If this parameter is not provided, the image will be saved with no\n",
      "        profile attached.\n",
      "    exif : dict\n",
      "        If present, the image will be stored with the provided raw EXIF data.\n",
      "    subsampling : str\n",
      "        Sets the subsampling for the encoder. See Pillow docs for details.\n",
      "    qtables : object\n",
      "        Set the qtables for the encoder. See Pillow docs for details.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "imageio.help('rsh_new.jpg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "im = imageio.imread('rsh_new.png')\n",
    "\n",
    "for i in range(100):\n",
    "    file_name = f'rsh/rsh_logo_{i}.jpg'\n",
    "    imageio.imsave(file_name, im, quality=100-i)\n",
    "    im = imageio.imread(file_name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Scope\n",
    "\n",
    "Der *Scope* einer Funktion gibt an ob wir Zugriff auf eine Variable haben oder nicht.\n",
    "\n",
    "In Python hat man in der Regel nur Zugriff auf die Variablen aus der kleineren Einrückung und die über der Zeile stehen."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "foo = 42\n",
    "\n",
    "if bar is True:\n",
    "    print(foo)\n",
    "\n",
    "bar = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello\n"
     ]
    }
   ],
   "source": [
    "foo = 'Hello'\n",
    "\n",
    "def my_func():\n",
    "    print(foo)\n",
    "\n",
    "my_func()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'foo' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-27-31504fd18de0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mbar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfoo\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'foo' is not defined"
     ]
    }
   ],
   "source": [
    "del foo\n",
    "\n",
    "def my_func():\n",
    "    foo = 42\n",
    "    bar = 2\n",
    "    return bar\n",
    "\n",
    "print(foo)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## `import`\n",
    "\n",
    "Ein großer Vorteil von Python ist, dass die Programmiersprache eine große Standardbibliothek hat.\n",
    "Die Standardbibliothek ist bei jeder Python Installation verfügbar und die einzelnen *Module* müssen importiert werden."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Ein Beispiel kennen wir dabei schon."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "99\n"
     ]
    }
   ],
   "source": [
    "import random\n",
    "\n",
    "print(random.randint(0, 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Das `random` Module der Standardbibliothek ist z.B. dokumentiert unter [https://docs.python.org/3/library/random.html](https://docs.python.org/3/library/random.html).\n",
    "\n",
    "Alternativ kann man sich mit in Jupyter den *docstring* anzeigen lassen indem man ein ? dem Ausdruck voranstellt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mSignature:\u001b[0m \u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mb\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
       "\u001b[0;31mDocstring:\u001b[0m\n",
       "Return random integer in range [a, b], including both end points.\n",
       "        \n",
       "\u001b[0;31mFile:\u001b[0m      /usr/local/Cellar/python/3.7.6_1/Frameworks/Python.framework/Versions/3.7/lib/python3.7/random.py\n",
       "\u001b[0;31mType:\u001b[0m      method\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "?random.randint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Ein paar sehr hilfreiche Module aus der [Standardbibliothek](https://docs.python.org/3/library/index.html)\n",
    "\n",
    "Modul | Funktion\n",
    "--- | ---\n",
    "[`re`](https://docs.python.org/3/library/re.html) | regex (pattern matching auf strings)\n",
    "[`datetime`](https://docs.python.org/3/library/datetime.html) | rechnen mit datum\n",
    "[`random`](https://docs.python.org/3/library/random.html) | Pseudozufallszahlen\n",
    "[`os`](https://docs.python.org/3/library/os.html) | Dateisystemzugriff\n",
    "[`shutil`](https://docs.python.org/3/library/shutil.html) | Dateioperationen\n",
    "[`sqlite3`](https://docs.python.org/3/library/sqlite3.html) | SQlite3 Datenbank\n",
    "[`time`](https://docs.python.org/3/library/time.html) | Zeitoperationen\n",
    "[`logging`](https://docs.python.org/3/library/logging.html) | Logging\n",
    "[`email`](https://docs.python.org/3/library/email.html) | E-Mails erstellen\n",
    "[`json`](https://docs.python.org/3/library/json.html) | JSON En-/Decoder\n",
    "[`xml.etree.ElementTree`](https://docs.python.org/3/library/xml.etree.elementtree.html) | XML Operationen\n",
    "[`unittest`](https://docs.python.org/3/library/unittest.html) | Unittesting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dateizugriffe\n",
    "\n",
    "Eine Datei kann man öffnen, lesen, schreiben und schließen.\n",
    "Dabei unterscheidet man zwischen Textdaten (z.B. Quellcode, markdown) und Binärdaten (z.B. Bilder).\n",
    "\n",
    "Dateizugriffe sind immer sehr kritische Operationen da wir nicht wissen ob\n",
    "\n",
    "* die Datei existiert\n",
    "* wir Zugriff auf die Datei haben\n",
    "* wir die Datei schreiben können\n",
    "\n",
    "Daher immer aufmerksam sein da man schnell auch Dateien kaputt machen oder überschreiben kann!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# Einführung in die Programmierung mit Python\n",
      "\n",
      "Eine Einführung in die Grundlagen der Programmierung \n"
     ]
    }
   ],
   "source": [
    "data = None\n",
    "with open('README.md', 'r') as f:\n",
    "    data = f.read()\n",
    "\n",
    "print(data[0:100])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "mode | Funktion\n",
    "--- | ---\n",
    "`r` | Liest Text Datei\n",
    "`rb` | Liest Binär Datei\n",
    "`w` | Schreibt Text Datei\n",
    "`wb` | Schreibt Binär Datei"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "my_text = \"\"\"\n",
    "It can happen to you\n",
    "It can happen to me\n",
    "\"\"\"\n",
    "\n",
    "with open('my_text.txt', 'w') as f:\n",
    "    f.write(my_text)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Installiert euch *Visual Studio Code*.\n",
    "\n",
    "*Visual Studio Code* ist ein Code Editor für verschiedene Programmiersprachen."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Script schreiben und ausführen\n",
    "\n",
    "Jupyter Notebook ist gut zum partiellen ausführen von Code-Blöcken - jedoch eignet es sich nicht gut als Script welches man eine bestimmte Anzahl von malen ausführen möchte.\n",
    "\n",
    "Dafür legt man in einem Editor (z.B. *Visual Studio Code*) eine neue Datei mit der Endung `.py` an und schreibt dort den Code.\n",
    "\n",
    "Wenn man die Datei abgespeichert hat öffnet man eine Konsole und wechselt in das Verzeichnis der Datei und führt das Script mithilfe von\n",
    "\n",
    "```\n",
    "python3 my_script.py\n",
    "```\n",
    "\n",
    "aus."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## PEP8\n",
    "\n",
    "Wenn mehrere Leute an einem Projekt arbeiten ist es sinnvoll sich an sogennante *Style Guidelines* zu halten, die die Formatierung wie Namen von Variablen, Funktionen oder Art der Einrückungen vereinheitlichen.\n",
    "\n",
    "Bei Python nennt sich dieser Standard *pep8* und ist hier sepzifiziert:\n",
    "https://www.python.org/dev/peps/pep-0008/\n",
    "\n",
    "Art | Konvention | Beispiel\n",
    "--- | --- | ---\n",
    "Variablen | alles klein, wobei Leerzeichen durch `_` ersetzt werden | `my_variable`\n",
    "konstante Variablen | alles groß geschrieben ' \n",
    "Funktionen | alles klein, wobei Leerzeichen durch `_` ersetzt werden | `my_function`\n",
    "Klassen | CamelCase | `MyClass`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## git\n",
    "\n",
    "*git* ist ein *Versions Kontroll System (VCS - Version Control System)* womit mehrere Leute an einem Textprojekt arbeiten können.\n",
    "Jede Veränderung ist ein inkrement und "
   ]
  }
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