Data_engineering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9529f22a",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\DANIEL\\AppData\\Local\\Temp/ipykernel_17472/1153825410.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgeopandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mgpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpyplot\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mplt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mfolium\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mgeojson\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\DANIEL\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\pyplot.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     34\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mcycler\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcycler\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     35\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 36\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolorbar\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     37\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimage\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     38\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_api\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\DANIEL\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\colorbar.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     36\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     37\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmpl\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 38\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mmatplotlib\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m_api\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcollections\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcm\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolors\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcontour\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mticker\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     39\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0martist\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmartist\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     40\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpatches\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmpatches\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\DANIEL\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\contour.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolors\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmcolors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     16\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcollections\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmcoll\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfont_manager\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfont_manager\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     18\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtext\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtext\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     19\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mmatplotlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcbook\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mcbook\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\DANIEL\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m   1445\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1446\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1447\u001b[1;33m \u001b[0mfontManager\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_load_fontmanager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1448\u001b[0m \u001b[0mfindfont\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mfontManager\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfindfont\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\DANIEL\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36m_load_fontmanager\u001b[1;34m(try_read_cache)\u001b[0m\n\u001b[0;32m   1432\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mtry_read_cache\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1433\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1434\u001b[1;33m             \u001b[0mfm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mjson_load\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfm_path\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1435\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mexc\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1436\u001b[0m             \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\DANIEL\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36mjson_load\u001b[1;34m(filename)\u001b[0m\n\u001b[0;32m   1004\u001b[0m     \"\"\"\n\u001b[0;32m   1005\u001b[0m     \u001b[1;32mwith\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfilename\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'r'\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mfh\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1006\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mjson\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfh\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_json_decode\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1007\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1008\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\daniel\\appdata\\local\\programs\\thonny\\lib\\json\\__init__.py\u001b[0m in \u001b[0;36mload\u001b[1;34m(fp, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[0;32m    294\u001b[0m         \u001b[0mcls\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcls\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mobject_hook\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mobject_hook\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    295\u001b[0m         \u001b[0mparse_float\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparse_float\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mparse_int\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mparse_int\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 296\u001b[1;33m         parse_constant=parse_constant, object_pairs_hook=object_pairs_hook, **kw)\n\u001b[0m\u001b[0;32m    297\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    298\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\daniel\\appdata\\local\\programs\\thonny\\lib\\json\\__init__.py\u001b[0m in \u001b[0;36mloads\u001b[1;34m(s, encoding, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[0;32m    359\u001b[0m     \u001b[1;32mif\u001b[0m \u001b[0mparse_constant\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    360\u001b[0m         \u001b[0mkw\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'parse_constant'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mparse_constant\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 361\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcls\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m**\u001b[0m\u001b[0mkw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mc:\\users\\daniel\\appdata\\local\\programs\\thonny\\lib\\json\\decoder.py\u001b[0m in \u001b[0;36mdecode\u001b[1;34m(self, s, _w)\u001b[0m\n\u001b[0;32m    335\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    336\u001b[0m         \"\"\"\n\u001b[1;32m--> 337\u001b[1;33m         \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mraw_decode\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0m_w\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    338\u001b[0m         \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_w\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    339\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\daniel\\appdata\\local\\programs\\thonny\\lib\\json\\decoder.py\u001b[0m in \u001b[0;36mraw_decode\u001b[1;34m(self, s, idx)\u001b[0m\n\u001b[0;32m    351\u001b[0m         \"\"\"\n\u001b[0;32m    352\u001b[0m         \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 353\u001b[1;33m             \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mscan_once\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0midx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    354\u001b[0m         \u001b[1;32mexcept\u001b[0m \u001b[0mStopIteration\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    355\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mJSONDecodeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Expecting value\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\Users\\DANIEL\\AppData\\Roaming\\Python\\Python37\\site-packages\\matplotlib\\font_manager.py\u001b[0m in \u001b[0;36m_json_decode\u001b[1;34m(o)\u001b[0m\n\u001b[0;32m    964\u001b[0m         \u001b[0mr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFontEntry\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__new__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mFontEntry\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    965\u001b[0m         \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__dict__\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mo\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 966\u001b[1;33m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misabs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    967\u001b[0m             \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfname\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmpl\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_data_path\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    968\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mc:\\users\\daniel\\appdata\\local\\programs\\thonny\\lib\\ntpath.py\u001b[0m in \u001b[0;36misabs\u001b[1;34m(s)\u001b[0m\n\u001b[0;32m     68\u001b[0m     \u001b[1;34m\"\"\"Test whether a path is absolute\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfspath\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 70\u001b[1;33m     \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msplitdrive\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     71\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32min\u001b[0m \u001b[0m_get_bothseps\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "import geopandas as gpd\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import folium\n",
    "import geojson\n",
    "import numpy as np\n",
    "from folium import plugins"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c837e77",
   "metadata": {},
   "outputs": [],
   "source": [
    "from folium.elements import JSCSSMixin\n",
    "from folium.map import Layer\n",
    "from jinja2 import Template\n",
    "\n",
    "class FeatureGroupSubGroup(JSCSSMixin, Layer):\n",
    "    _template = Template(u\"\"\"\n",
    "        {% macro script(this, kwargs) %}\n",
    "            var {{ this.get_name() }} = L.featureGroup.subGroup(\n",
    "                {{ this._group.get_name() }}\n",
    "            );\n",
    "            {{ this.get_name() }}.addTo({{ this._parent.get_name() }});\n",
    "        {% endmacro %}\n",
    "        \"\"\")\n",
    "\n",
    "    default_js = [\n",
    "        ('featuregroupsubgroupjs',\n",
    "         'https://unpkg.com/leaflet.featuregroup.subgroup@1.0.2/dist/leaflet.featuregroup.subgroup.js'),\n",
    "    ]\n",
    "\n",
    "    def __init__(self, group, name=None, overlay=True, control=True, show=True):\n",
    "        super(FeatureGroupSubGroup, self).__init__(name=name, overlay=overlay,\n",
    "                                                   control=control, show=show)\n",
    "\n",
    "        self._group = group\n",
    "        self._name = 'FeatureGroupSubGroup'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9c469e93",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "ray_17_04 = pd.read_excel('Rayos_2017-04.xlsx')\n",
    "ray_17_04 = ray_17_04[[\"LATITUD\",\"LONGITUD\",\"TIPO\", \"CORRIENTE\", \"ERROR\"]]\n",
    "ray_17_04 = ray_17_04.rename(columns={\"LONGITUD\":\"COOR_X\", \"LATITUD\":\"COOR_Y\"})\n",
    "\n",
    "ray_18_jul = pd.read_excel('Rayos_julio_2018_CODENSA.xlsx')\n",
    "ray_18_jul = ray_18_jul[[\"Latitud\",\"Longitud\",\"Tipo\", \"Corriente_[kA]\", \"Error_[km]\"]]\n",
    "ray_18_jul = ray_18_jul.rename(columns={\"Longitud\":\"COOR_X\", \"Latitud\":\"COOR_Y\", \"Corriente_[kA]\":\"CORRIENTE\", \"Error_[km]\":\"ERROR\"})\n",
    "\n",
    "ray_18_jun = pd.read_excel('Rayos_junio_2018_CODENSA.xlsx')\n",
    "ray_18_jun = ray_18_jun[[\"Latitud\",\"Longitud\",\"Tipo\", \"Corriente_[kA]\", \"Error_[km]\"]]\n",
    "ray_18_jun = ray_18_jun.rename(columns={\"Longitud\":\"COOR_X\", \"Latitud\":\"COOR_Y\", \"Corriente_[kA]\":\"CORRIENTE\", \"Error_[km]\":\"ERROR\"})\n",
    "\n",
    "ray_18_may = pd.read_excel('Rayos_mayo_2018_CODENSA.xlsx')\n",
    "ray_18_may = ray_18_may[[\"Latitud\",\"Longitud\",\"Tipo\", \"Corriente_[kA]\", \"Error_[km]\"]]\n",
    "ray_18_may = ray_18_may.rename(columns={\"Longitud\":\"COOR_X\", \"Latitud\":\"COOR_Y\", \"Corriente_[kA]\":\"CORRIENTE\", \"Error_[km]\":\"ERROR\"})\n",
    "\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59c53134",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Procesar las Hojas de datos para extraer la información de ubicación necesaria\n",
    "\n",
    "#----CAMBIAR----#\n",
    "ray_18_ago = pd.read_excel('Rayos_agosto_2018_CODENSA.xlsx')\n",
    "ray_18_ago = ray_18_ago[[\"LATITUD\",\"LONGITUD\",\"TIPO\", \"CORRIENTE\", \"ERROR\"]]\n",
    "ray_18_ago = ray_18_ago.rename(columns={\"LONGITUD\":\"COOR_X\", \"LATITUD\":\"COOR_Y\"})\n",
    "#----!!CAMBIAR!!----#\n",
    "gdf = gpd.GeoDataFrame(ray_18_ago, geometry = gpd.points_from_xy(ray_18_ago.COOR_X, ray_18_ago.COOR_Y))\n",
    "#----!!CAMBIAR!!----#\n",
    "with open('ray_18_ago.geojson', 'w', encoding='utf-8') as f:\n",
    "    geojson.dump(gdf, f, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a20a5793",
   "metadata": {},
   "outputs": [],
   "source": [
    "bog = gpd.read_file('upz_bogota.geojson')\n",
    "cun = gpd.read_file('cundi_municipios.geojson')\n",
    "\n",
    "map = folium.Map(location=[45, -74], zoom_start=8, tiles=\"Stamen Terrain\")\n",
    "\n",
    "df = gpd.read_file('base_maestra.geojson')\n",
    "#----!!CAMBIAR!!----#\n",
    "df2 = gpd.read_file('ray_18_ago.geojson')\n",
    "\n",
    "#codigo para permitir que sea leido por geojson\n",
    "df = df.set_crs(\"EPSG:4326\")\n",
    "df = df.set_crs(epsg=4326)\n",
    "df2 = df2.set_crs(\"EPSG:4326\")\n",
    "df2 = df2.set_crs(epsg=4326)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b238891c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#----CAMBIAR----#\n",
    "df2 = gpd.read_file('ray_18_ago.geojson')\n",
    "\n",
    "ray_indi_mala = df2[abs(df2['CORRIENTE']) > 50]\n",
    "ray_indi_buena = df2[abs(df2['CORRIENTE']) < 50]\n",
    "\n",
    "#Agregar columna \"color\" a los datos de rayos\n",
    "df2['color'] = \"black\"\n",
    "\n",
    "pd.options.mode.chained_assignment = None\n",
    "for i in range(0,len(df2)):\n",
    "    if abs(df2.iloc[i]['CORRIENTE']) > 50:\n",
    "         df2.at[i,'color'] = \"red\"\n",
    "    elif abs(df2.iloc[i]['CORRIENTE']) <= 50:\n",
    "         df2.at[i,'color'] = \"green\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "90e6b308",
   "metadata": {},
   "outputs": [],
   "source": [
    "#----CAMBIAR----#\n",
    "with open('ray_18_ago.geojson', 'w', encoding='utf-8') as f:\n",
    "    geojson.dump(df2, f, ensure_ascii=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f791f6f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "grupo_rc = folium.FeatureGroup(name=\"RC\")\n",
    "grupo_sz = folium.FeatureGroup(name=\"SZ\")\n",
    "\"\"\"\n",
    "grupo_bog = folium.FeatureGroup(name=\"Bogotá dividido por UPZ\")\n",
    "grupo_cun = folium.FeatureGroup(name=\"Municipios de Cundinamarca\")\n",
    "grupo_ray = folium.FeatureGroup(name=\"Rayos\")\n",
    "\"\"\"\n",
    "grupo = folium.plugins.FeatureGroupSubGroup( grupo_rc, \"Disponibilidad buena RC\")\n",
    "grupo2 = folium.plugins.FeatureGroupSubGroup( grupo_rc, \"Disponibilidad mala RC\")\n",
    "grupo3 = folium.plugins.FeatureGroupSubGroup( grupo_sz, \"Disponibilidad buena SZ\")\n",
    "grupo4 = folium.plugins.FeatureGroupSubGroup( grupo_sz, \"Disponibilidad mala SZ\")\n",
    "\"\"\"\n",
    "grupo5 = folium.plugins.FeatureGroupSubGroup( grupo_ray, \"Mucho Riesgo\")\n",
    "grupo6 = folium.plugins.FeatureGroupSubGroup( grupo_ray, \"Poco Riesgo\")\n",
    "\n",
    "\n",
    "\n",
    "map = folium.Map(location=[4.5, -74], tiles=\"Cartodb Positron\", zoom_start=6)\n",
    "\n",
    "def style_function(feature):\n",
    "    return {\n",
    "        \"fillOpacity\": 0.1,\n",
    "        \"weight\": 1,\n",
    "    }\n",
    "\n",
    "folium.GeoJson(bog, style_function=style_function,\n",
    "               popup = folium.GeoJsonPopup(fields=[\"uplnombre\"])\n",
    "              ).add_to(grupo_bog)\n",
    "\n",
    "folium.GeoJson(cun, style_function=style_function,\n",
    "               popup = folium.GeoJsonPopup(fields=[\"munnombre\"])\n",
    "              ).add_to(grupo_cun)\n",
    "\"\"\"\n",
    "folium.GeoJson(\n",
    "    rc_indi_buena,\n",
    "    marker = folium.Circle(radius = 10, fill_color=\"green\", fill_opacity=0.4, color=\"green\", weight=3),\n",
    "    popup = folium.GeoJsonPopup(fields=[\"COOR_X\", \"COOR_Y\"]),\n",
    "    ).add_to(grupo)\n",
    "\n",
    "folium.GeoJson(\n",
    "    rc_indi_mala,\n",
    "    marker = folium.Circle(radius = 10, fill_color=\"red\", fill_opacity=0.4, color=\"red\", weight=3),\n",
    "    popup = folium.GeoJsonPopup(fields=[\"COOR_X\", \"COOR_Y\"]),\n",
    "    ).add_to(grupo2)\n",
    "\n",
    "folium.GeoJson(\n",
    "    sz_indi_buena,\n",
    "    marker = folium.Circle(radius = 10, fill_color=\"green\", fill_opacity=0.4, color=\"green\", weight=3),\n",
    "    popup = folium.GeoJsonPopup(fields=[\"COOR_X\", \"COOR_Y\"]),\n",
    "    ).add_to(grupo3)\n",
    "\n",
    "folium.GeoJson(\n",
    "    sz_indi_mala,\n",
    "    marker = folium.Circle(radius = 10, fill_color=\"red\", fill_opacity=0.4, color=\"red\", weight=3),\n",
    "    popup = folium.GeoJsonPopup(fields=[\"COOR_X\", \"COOR_Y\"]),\n",
    "    ).add_to(grupo4)\n",
    "\"\"\"\n",
    "#................................Parte de los Rayos....................................................\n",
    "folium.GeoJson(\n",
    "    ray_indi_buena,\n",
    "    marker = folium.Circle(radius = 10, fill_color=\"green\", fill_opacity=0.4, color=\"green\", weight=3),\n",
    "    popup = folium.GeoJsonPopup(fields=[\"COOR_X\", \"COOR_Y\"]),\n",
    "    ).add_to(grupo6)\n",
    "\n",
    "folium.GeoJson(\n",
    "    ray_indi_mala,\n",
    "    marker = folium.Circle(radius = 10, fill_color=\"red\", fill_opacity=0.4, color=\"red\", weight=3),\n",
    "    popup = folium.GeoJsonPopup(fields=[\"COOR_X\", \"COOR_Y\"]),\n",
    "    ).add_to(grupo5)\n",
    "\n",
    "\n",
    "grupo_ray.add_to(map)\n",
    "map.add_child(grupo5)\n",
    "map.add_child(grupo6)\n",
    "\"\"\"\n",
    "grupo_sz.add_to(map)\n",
    "map.add_child(grupo3)\n",
    "map.add_child(grupo4)\n",
    "grupo_rc.add_to(map)\n",
    "map.add_child(grupo)\n",
    "map.add_child(grupo2)\n",
    "\"\"\"\n",
    "grupo_bog.add_to(map)\n",
    "grupo_cun.add_to(map)\n",
    "\n",
    "\n",
    "\n",
    "folium.LayerControl(collapsed=False).add_to(map)\n",
    "#----CAMBIAR----#\n",
    "map.save('ray_18_ago.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4e34917",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se carga el dataframe generado previamente\n",
    "#----CAMBIAR----#\n",
    "df3 = gpd.read_file('ray_18_ago.geojson')\n",
    "\n",
    "# Se carga el geojson de los mapas de bogota por upz y cundinamarca por municipio\n",
    "with open('bocu.geojson') as f:\n",
    "    js = geojson.load(f)\n",
    "\n",
    "# A cada rayo se le adiciona una columna llamada 'ubicacion' donde se identifica la upz o municipio al\n",
    "# cual corresponde según sus coordenadas \n",
    "\n",
    "from shapely.geometry import shape, Point\n",
    "\n",
    "df3['ubicacion'] = \"otra\"\n",
    "\n",
    "for feature in js['features']:\n",
    "    polygon = shape(feature['geometry'])\n",
    "    for i in range(0, len(df3)):\n",
    "        if polygon.contains(df3.loc[i, 'geometry']):\n",
    "            df3.at[i,'ubicacion'] = feature[\"properties\"][\"nombre\"]     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8e7110b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se genera la base 'ubi' donde se consignan la cantidad de equipos con \n",
    "#nivel ceraunico alto, bajo y Nan(sin informacion)\n",
    "\n",
    "bocu = gpd.read_file('bocu.geojson')\n",
    "ubi = pd.DataFrame({'upz_o_municipio': ['x'],'cera_bueno': [0], 'cera_malo': [0]})\n",
    "\n",
    "for upz_municipio, i in zip(bocu['nombre'], range(0, len(bocu)) ):\n",
    "    cera = (df3[df3['ubicacion'].str.contains(upz_municipio)])['color'].value_counts()\n",
    "    ubi.at[i, 'upz_o_municipio'] = upz_municipio\n",
    "    \n",
    "    try:\n",
    "        ubi.at[i, 'cera_bueno'] = cera['green']\n",
    "    except:\n",
    "        ubi.at[i, 'cera_bueno'] = 0\n",
    "    try:    \n",
    "        ubi.at[i, 'cera_malo'] = cera['red']\n",
    "    except:     \n",
    "        ubi.at[i, 'cera_malo'] = 0\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32276730",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Se generan las columnas 'Hi_indi' y 'Hi_falla' donde se consigna el HI del municipio o upz según los datos\n",
    "# de fallas e indisponibilidades de RC y SZ que albergan\n",
    "\n",
    "for i in range(0, len(ubi)): \n",
    "        ubi.at[i, 'nivel_cer'] = (ubi.loc[i, 'cera_bueno']) / (ubi.loc[i, 'cera_malo'] + ubi.loc[i, 'cera_bueno'])\n",
    "    \n",
    "# Nota: al correr esta línea, se lanzan dos RuntimeWarning que no tiene mayor importancia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a3cc2419",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ahora a la base con los RC y SZ 'df' se le adiciona el HI según la informacion histórica de indisponibilidades y fallas\n",
    "# de acuerdo con la upz o municipio al que pertenece, se llama 'Hi_histo_ubi'\n",
    "# Ojo: Recordar que ese HI está asociada a la upz o municipio, no al equipo\n",
    "\n",
    "for i in range(0, len(df3)):\n",
    "    for j in range(0, len(ubi)):\n",
    "        if  df3.loc[i, 'ubicacion'] == ubi.loc[j, 'upz_o_municipio']:\n",
    "            df3.at[i, 'nivel_cer_ubi'] = (ubi.loc[j, 'nivel_cer'])*100\n",
    "\n",
    "ubi['numero_Rayos']=ubi['cera_bueno']+ubi['cera_malo']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "533106d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ubi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "12482b12",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "# Rangos: Buena 100 - 71, Aceptable 70 - 36, Mala 35 - 0\n",
    "# Rayos: Poco Riesgo 0-50, Alto Riesgo 50-100\n",
    "# Nota... El rango de indisponibilidades está con signo negativo por limitantes de np.interp, sin embargo,\n",
    "#          también se cambia el signo del numero a interpolar para compensar\n",
    "\n",
    "rangos = [100, 71, 35, 0]\n",
    "rangos_rayos = [0,25,50,75,100]\n",
    "\n",
    "# Se calcula el HI según las fallas e indisponibilidades\n",
    "#for i in range(0 ,len(df3)):\n",
    "#    df3.at[i ,'nivel_cer_ray'] = np.interp(df.loc[i ,'Numero_Fallas'] , rangos_fallas, rangos)  \n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eab0d57b",
   "metadata": {},
   "outputs": [],
   "source": [
    "#---CAMBIAR---#\n",
    "df3.to_excel('nivel_ceraunico_18_ago.xlsx')\n",
    "#ubi.to_excel('ubi_18_ago.xlsx')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c642597f",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Para generar el mapa de HI segun los historicos de fallas e indisponibilidades\n",
    "#Mapa 1: De acuerdo a la intensidad de corriente de los rayos\n",
    "import folium\n",
    "\n",
    "bocu['nivel_cer_ubi'] = (ubi['nivel_cer'])*100\n",
    "\n",
    "m = folium.Map(location=[4.7, -74], tiles=\"Cartodb Positron\", zoom_start=8)\n",
    "\n",
    "folium.Choropleth(\n",
    "    bocu, data=bocu,\n",
    "    columns=[\"nombre\", \"nivel_cer_ubi\"],\n",
    "    key_on=\"feature.properties.nombre\",\n",
    "    fill_color=\"RdYlGn\",\n",
    "    legend_name=\"Niver ceráunico (NC) por zona\",\n",
    "    bin=20\n",
    "     ).add_to(m)\n",
    "\n",
    "folium.GeoJson(\n",
    "    bocu, \n",
    "    popup = folium.GeoJsonPopup(fields=[\"nombre\",\"nivel_cer_ubi\"], aliases=[\"Nombre\", \"NC por ubicación\"] ),\n",
    "    style_function=lambda feature: {\"weight\": 0,\"fillOpacity\": 0},\n",
    "    tooltip = folium.GeoJsonTooltip(fields=[\"nombre\"])\n",
    "                  ).add_to(m)\n",
    "#---CAMBIAR---#\n",
    "m.save('NC_18_ago.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ad34562",
   "metadata": {},
   "outputs": [],
   "source": [
    "#MAPA 2: De acuerdo al numero de rayos en cada zona\n",
    "bocu['num_rayos'] = ubi['numero_Rayos']\n",
    "\n",
    "m2 = folium.Map(location=[4.7, -74], tiles=\"Cartodb Positron\", zoom_start=8)\n",
    "\n",
    "folium.Choropleth(\n",
    "    bocu, data=bocu,\n",
    "    columns=[\"nombre\", \"num_rayos\"],\n",
    "    key_on=\"feature.properties.nombre\",\n",
    "    fill_color=\"YlOrRd\",\n",
    "    legend_name=\"Número de rayos por zona\",\n",
    "     ).add_to(m2)\n",
    "\n",
    "folium.GeoJson(\n",
    "    bocu, \n",
    "    popup = folium.GeoJsonPopup(fields=[\"nombre\",\"num_rayos\"], aliases=[\"Nombre\", \"No. rayos por ubicación\"] ),\n",
    "    style_function=lambda feature: {\"weight\": 0,\"fillOpacity\": 0},\n",
    "    tooltip = folium.GeoJsonTooltip(fields=[\"nombre\"])\n",
    "                  ).add_to(m2)\n",
    "#---CAMBIAR---#\n",
    "m2.save('rayNum_18_ago.html')"
   ]
  }
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