{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T18:17:22.583915Z",
     "start_time": "2018-03-16T18:17:20.252530Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "# from shapely.geometry import Point\n",
    "# from geopandas import GeoDataFrame\n",
    "import os\n",
    "from os.path import join\n",
    "import pandas as pd\n",
    "import fiona\n",
    "# from cartopy import crs as ccrs\n",
    "# from urllib.request import urlopen\n",
    "# from zipfile import ZipFile\n",
    "# from io import BytesIO\n",
    "# from pathlib import Path\n",
    "\n",
    "sns.set(style='white')\n",
    "cwd = os.getcwd()\n",
    "data_path = join(cwd, '..', '..', 'Data storage')\n",
    "figure_path = join(cwd, '..', '..', 'Figures')\n",
    "file_date = '2018-03-06'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T01:30:57.926475Z",
     "start_time": "2018-03-16T01:30:57.915320Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPython 3.6.4\n",
      "IPython 6.2.1\n",
      "\n",
      "pandas 0.22.0\n",
      "geopandas 0.3.0\n",
      "shapely 1.6.4.post1\n"
     ]
    }
   ],
   "source": [
    "%load_ext watermark\n",
    "%watermark -v -p pandas,geopandas,shapely"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read NERC regions shapefile\n",
    "[Link to the shapefile](https://github.com/gschivley/Index-variability/raw/master/Data%20storage/NERC_Regions_EIA) for local download"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T18:30:35.188878Z",
     "start_time": "2017-11-02T18:30:34.659422Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4326'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# EIA NERC region shapefile, which has an \"Indeterminate\" region\n",
    "\n",
    "# path = os.path.join(data_path, 'NERC_Regions_EIA', 'NercRegions_201610.shp')\n",
    "# regions = gpd.read_file(path)\n",
    "# regions.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T13:50:57.087464Z",
     "start_time": "2018-03-16T13:50:56.795701Z"
    }
   },
   "outputs": [],
   "source": [
    "path = os.path.join(data_path, 'nercregions', 'NERCregions.shp')\n",
    "regions_nerc = gpd.read_file(path)\n",
    "regions_nerc['nerc'] = regions_nerc['NERCregion']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T13:51:08.346080Z",
     "start_time": "2018-03-16T13:51:08.307554Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>NERCregion</th>\n",
       "      <th>SHAPE_Leng</th>\n",
       "      <th>SHAPE_Area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>nerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>22.488030</td>\n",
       "      <td>11.459289</td>\n",
       "      <td>POLYGON ((-84.86300370899994 30.71266453000004...</td>\n",
       "      <td>FRCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>76.997961</td>\n",
       "      <td>130.425939</td>\n",
       "      <td>POLYGON ((-94.8320392469999 49.33080593000005,...</td>\n",
       "      <td>MRO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NPCC</td>\n",
       "      <td>48.574281</td>\n",
       "      <td>32.754394</td>\n",
       "      <td>(POLYGON ((-72.5509712409999 40.96617993100006...</td>\n",
       "      <td>NPCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>99.332877</td>\n",
       "      <td>71.157869</td>\n",
       "      <td>(POLYGON ((-87.92640864299995 44.5391396020000...</td>\n",
       "      <td>RFC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>128.958444</td>\n",
       "      <td>142.139208</td>\n",
       "      <td>POLYGON ((-80.20680177299994 36.54903791300006...</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>SPP</td>\n",
       "      <td>61.523164</td>\n",
       "      <td>56.496471</td>\n",
       "      <td>POLYGON ((-97.56098691099993 36.38533196100008...</td>\n",
       "      <td>SPP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>TRE</td>\n",
       "      <td>44.145641</td>\n",
       "      <td>50.605118</td>\n",
       "      <td>POLYGON ((-104.4498774409999 32.00686615000006...</td>\n",
       "      <td>TRE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>WECC</td>\n",
       "      <td>102.110211</td>\n",
       "      <td>323.227276</td>\n",
       "      <td>(POLYGON ((-122.6656125679999 48.3967777520000...</td>\n",
       "      <td>WECC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID NERCregion  SHAPE_Leng  SHAPE_Area  \\\n",
       "0         1       FRCC   22.488030   11.459289   \n",
       "1         2        MRO   76.997961  130.425939   \n",
       "2         3       NPCC   48.574281   32.754394   \n",
       "3         4        RFC   99.332877   71.157869   \n",
       "4         5       SERC  128.958444  142.139208   \n",
       "5         6        SPP   61.523164   56.496471   \n",
       "6         7        TRE   44.145641   50.605118   \n",
       "7         8       WECC  102.110211  323.227276   \n",
       "\n",
       "                                            geometry  nerc  \n",
       "0  POLYGON ((-84.86300370899994 30.71266453000004...  FRCC  \n",
       "1  POLYGON ((-94.8320392469999 49.33080593000005,...   MRO  \n",
       "2  (POLYGON ((-72.5509712409999 40.96617993100006...  NPCC  \n",
       "3  (POLYGON ((-87.92640864299995 44.5391396020000...   RFC  \n",
       "4  POLYGON ((-80.20680177299994 36.54903791300006...  SERC  \n",
       "5  POLYGON ((-97.56098691099993 36.38533196100008...   SPP  \n",
       "6  POLYGON ((-104.4498774409999 32.00686615000006...   TRE  \n",
       "7  (POLYGON ((-122.6656125679999 48.3967777520000...  WECC  "
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions_nerc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T13:51:14.699102Z",
     "start_time": "2018-03-16T13:51:14.564849Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a75666ac8>"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regions_nerc.boundary.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now using NERC region shapefiles [created by DHS](https://hifld-dhs-gii.opendata.arcgis.com/datasets/12582592aeb842f5a7aab556a57a10d1_0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T03:34:48.247805Z",
     "start_time": "2018-03-16T03:34:45.621456Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objectid</th>\n",
       "      <th>id</th>\n",
       "      <th>subname</th>\n",
       "      <th>name</th>\n",
       "      <th>address</th>\n",
       "      <th>city</th>\n",
       "      <th>state</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>country</th>\n",
       "      <th>website</th>\n",
       "      <th>method</th>\n",
       "      <th>date</th>\n",
       "      <th>source</th>\n",
       "      <th>shape__are</th>\n",
       "      <th>shape__len</th>\n",
       "      <th>geometry</th>\n",
       "      <th>nerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>WECC SOUTHWEST</td>\n",
       "      <td>WESTERN ELECTRICITY COORDINATING COUNCIL (WECC)</td>\n",
       "      <td>155 NORTH 400 WEST SUITE 200</td>\n",
       "      <td>SALT LAKE CITY</td>\n",
       "      <td>UT</td>\n",
       "      <td>84103</td>\n",
       "      <td>USA</td>\n",
       "      <td>https://www.wecc.biz/Pages/home.aspx</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>2017-04-21T00:00:00.000Z</td>\n",
       "      <td>EIA 861, eGRID 2010, http://www.nerc.com/</td>\n",
       "      <td>84.129616</td>\n",
       "      <td>60.209543</td>\n",
       "      <td>(POLYGON ((-106.940414613533 38.5297752935145,...</td>\n",
       "      <td>WECC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>WECC CALIFORNIA</td>\n",
       "      <td>WESTERN ELECTRICITY COORDINATING COUNCIL (WECC)</td>\n",
       "      <td>156 NORTH 400 WEST SUITE 200</td>\n",
       "      <td>SALT LAKE CITY</td>\n",
       "      <td>UT</td>\n",
       "      <td>84103</td>\n",
       "      <td>USA</td>\n",
       "      <td>https://www.wecc.biz/Pages/home.aspx</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>2017-04-21T00:00:00.000Z</td>\n",
       "      <td>EIA 861, eGRID 2010, http://www.nerc.com/</td>\n",
       "      <td>43.141024</td>\n",
       "      <td>59.568593</td>\n",
       "      <td>(POLYGON ((-124.166090785201 41.0947342364264,...</td>\n",
       "      <td>WECC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>ERCOT ALL</td>\n",
       "      <td>TEXAS RELIABILITY ENTITY (TRE)</td>\n",
       "      <td>800 AIRPORT ROAD</td>\n",
       "      <td>TAYLOR</td>\n",
       "      <td>TX</td>\n",
       "      <td>76574</td>\n",
       "      <td>USA</td>\n",
       "      <td>http://www.ercot.com/</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>2017-04-21T00:00:00.000Z</td>\n",
       "      <td>EIA 861, eGRID 2010, http://www.nerc.com/</td>\n",
       "      <td>61.102506</td>\n",
       "      <td>131.535864</td>\n",
       "      <td>(POLYGON ((-123.715621602827 44.0113707388719,...</td>\n",
       "      <td>TRE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>FRCC ALL</td>\n",
       "      <td>FLORIDA RELIABILITY COORDINATING COUNCIL (FRCC)</td>\n",
       "      <td>3000 BAYPORT DRIVE,  SUITE 600</td>\n",
       "      <td>TAMPA</td>\n",
       "      <td>FL</td>\n",
       "      <td>33607</td>\n",
       "      <td>USA</td>\n",
       "      <td>https://www.frcc.com/default.aspx</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>2017-04-21T00:00:00.000Z</td>\n",
       "      <td>EIA 861, eGRID 2010, http://www.nerc.com/</td>\n",
       "      <td>12.385353</td>\n",
       "      <td>170.579871</td>\n",
       "      <td>(POLYGON ((-85.7661778899989 30.3156300105005,...</td>\n",
       "      <td>FRCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NPCC NEW ENGLAND</td>\n",
       "      <td>NORTHEAST POWER COORDINATING COUNCIL (NPCC)</td>\n",
       "      <td>1040 AVENUE OF THE AMERICAS, 10TH FLOOR</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>NY</td>\n",
       "      <td>10018</td>\n",
       "      <td>USA</td>\n",
       "      <td>https://www.npcc.org/default.aspx</td>\n",
       "      <td>OTHER</td>\n",
       "      <td>2017-04-21T00:00:00.000Z</td>\n",
       "      <td>EIA 861, eGRID 2010, http://www.nerc.com/</td>\n",
       "      <td>19.384108</td>\n",
       "      <td>151.655607</td>\n",
       "      <td>(POLYGON ((-67.1335131587897 45.1268843657456,...</td>\n",
       "      <td>NPCC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   objectid id           subname  \\\n",
       "0         1  0    WECC SOUTHWEST   \n",
       "1         2  1   WECC CALIFORNIA   \n",
       "2         3  2         ERCOT ALL   \n",
       "3         4  3          FRCC ALL   \n",
       "4         5  4  NPCC NEW ENGLAND   \n",
       "\n",
       "                                              name  \\\n",
       "0  WESTERN ELECTRICITY COORDINATING COUNCIL (WECC)   \n",
       "1  WESTERN ELECTRICITY COORDINATING COUNCIL (WECC)   \n",
       "2                   TEXAS RELIABILITY ENTITY (TRE)   \n",
       "3  FLORIDA RELIABILITY COORDINATING COUNCIL (FRCC)   \n",
       "4      NORTHEAST POWER COORDINATING COUNCIL (NPCC)   \n",
       "\n",
       "                                   address            city state zipcode  \\\n",
       "0             155 NORTH 400 WEST SUITE 200  SALT LAKE CITY    UT   84103   \n",
       "1             156 NORTH 400 WEST SUITE 200  SALT LAKE CITY    UT   84103   \n",
       "2                         800 AIRPORT ROAD          TAYLOR    TX   76574   \n",
       "3           3000 BAYPORT DRIVE,  SUITE 600           TAMPA    FL   33607   \n",
       "4  1040 AVENUE OF THE AMERICAS, 10TH FLOOR        NEW YORK   NY    10018   \n",
       "\n",
       "  country                               website method  \\\n",
       "0     USA  https://www.wecc.biz/Pages/home.aspx  OTHER   \n",
       "1     USA  https://www.wecc.biz/Pages/home.aspx  OTHER   \n",
       "2     USA                 http://www.ercot.com/  OTHER   \n",
       "3     USA     https://www.frcc.com/default.aspx  OTHER   \n",
       "4     USA     https://www.npcc.org/default.aspx  OTHER   \n",
       "\n",
       "                       date                                     source  \\\n",
       "0  2017-04-21T00:00:00.000Z  EIA 861, eGRID 2010, http://www.nerc.com/   \n",
       "1  2017-04-21T00:00:00.000Z  EIA 861, eGRID 2010, http://www.nerc.com/   \n",
       "2  2017-04-21T00:00:00.000Z  EIA 861, eGRID 2010, http://www.nerc.com/   \n",
       "3  2017-04-21T00:00:00.000Z  EIA 861, eGRID 2010, http://www.nerc.com/   \n",
       "4  2017-04-21T00:00:00.000Z  EIA 861, eGRID 2010, http://www.nerc.com/   \n",
       "\n",
       "   shape__are  shape__len                                           geometry  \\\n",
       "0   84.129616   60.209543  (POLYGON ((-106.940414613533 38.5297752935145,...   \n",
       "1   43.141024   59.568593  (POLYGON ((-124.166090785201 41.0947342364264,...   \n",
       "2   61.102506  131.535864  (POLYGON ((-123.715621602827 44.0113707388719,...   \n",
       "3   12.385353  170.579871  (POLYGON ((-85.7661778899989 30.3156300105005,...   \n",
       "4   19.384108  151.655607  (POLYGON ((-67.1335131587897 45.1268843657456,...   \n",
       "\n",
       "   nerc  \n",
       "0  WECC  \n",
       "1  WECC  \n",
       "2   TRE  \n",
       "3  FRCC  \n",
       "4  NPCC  "
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.join(data_path, 'NERC_Regions', 'NERC_Regions.shp')\n",
    "regions = gpd.read_file(path)\n",
    "regions.columns = regions.columns.str.lower()\n",
    "regions['nerc'] = regions['name'].str.split().str[-1].str.strip('()')\n",
    "# regions = regions.loc[:, ['nerc', 'geometry']]\n",
    "regions.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T03:35:41.847789Z",
     "start_time": "2018-03-16T03:35:41.601677Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a7d1fe908>"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regions.loc[regions['nerc']=='WECC'].plot(cmap='tab20', alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "regions.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T03:33:32.370215Z",
     "start_time": "2018-03-16T03:33:12.543612Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a7d997c50>"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BVKEi82jONU3T6OoK0dwscfsnv0dFeUos/vtXv8JkMiFJ+rRrOlAUhebGejoaDmFIBijNsnJRlRdBmJrTg6Zp3L/+H7R39pKZ4cbtdvE/9/2JnNzcNFt+cnQBmUaau5rJ8p16jllncqjhGG738XMqCiJ5OXnk5eTNolXzl6HhKE2NFhYuvJ5bblk+xlPsRO7CkUiEh/70J8LBEUpKy7n0iivIzJwfKXhmi8HBQV554SXkUDtrFhWekWi8haZpfP83j3C0oZkDDW2svfASqqsqkJPJNFk9MXQBmSa6urpSWXiF6c/J/24hEUmghGM4nXocRDpobh7BYFjJjTdeN6FRxuDAAK88v4HFhXkc6RJYsWoVHs/8Tr8zXcRiMfwHDzPY2YvHaOPckkU8v/kARblLT7/xBPjez//Ia2/uwmy28qGP3cwXvvwdbDbbjKcA0gVkmrj/T/djL59YTWOd09O5pxmpo4MMo4BZL3Z1xnR2BkkmF3P55e+dUPv+/n5+f++9GFAZysziw7f+E3a7fn2/nf7+fhrqjhIPhDAqApX5xdQsXA6Apqokk+nJOPHKm/t49c1d9I3EyMnz8JWvfQ9JkmYlf5wuINPESHyE8pX64vmZIidkDr92iGpDhBs+eu5sm3NWEArHaG318NGP3jSh9oFAgN/+5jdcefGF7N63H2dm1pTEIxAI8Mc//hFBELjlllvweo+n6dA0bV4n0Kw7dIjeuhYWllTgzR3vVi6IIi67h6PNnVSXTaxM7cn4w2MbaOgaYXntIh574tlZrR6qC8g0sHvfbuKu+Ly+IeYKnf4Oah1RLrtk2WybclagaRqvvdbPzR+8fcz1ebIHeEdHBz+8+05uXncDDrudnv5+9h46zM4d21lUU8P117+XrKwTr/Opqsrrr7/O8xs24M3MJBqNUlpaSmZmJoqiMDIygizLfO5zn6O2tpbvfOc703bc00l7axv9R9pYs3TlKdvdeM0N/Ol/HzhjARFFie9885t8+p+/eEb7SQe6gKQZTdP465N/ZcF7Fsy2KfOeSCBCcNch1t6+dky6Ep2ps3PXANdd+2lycnKIxWI89/wzHDxwkIKCHG6/fWzGoP7+fn549z/jqzpev+VwnR+j0citN3+Avv5+vvfd73LrJz7B6tWrxwQZxmIx7r3nHlwOG9WVlZRVVSEIAuFwGJvNxjPPPIPX6+XgwYN84Qtf4Le//S3hcHjeTYupqsqBN3dxee15p20rSRIOi5uRYBi3c+rHWVxawdXvmdjU43Sj35VpZseOHXSEOiiRSmbblHlPLBzDbjPS1T2Cs0pPA3OmHDw0RHXVOioqqolGo/zP//yEQ4f2MDgY5fLL//VYu9/e89/U1+/EbIrwvusLEAQJf/0e1qy+jPNWreTqy1K1tt0uF/l5efzmV7/kmuuuo6qyClmWufvuu6kqL+eG668jOyuL5vYOIpEIdrsds9mM0Wjk8stTWYVXrFiBLMskk0neeOMNsrOzOXr0KO95z3uwWCYXkT0bHNizl2XF1ROebVA1DaPxzNyiw4EAZRVzI/uCLiBpZsNLGyioObMhqk6KjPwMWLOCPfuOsKDqeGRtUpYRBXFMnMPJSCQTxGIxjEYjVsu713vLfyREYcHVLF++moMHd7N+/X1cfkU+7e1mSkoWc955awH4618fJB5/gw/eePwFqLMrjMOeWq94Szzewm6z8dnbb+eV119npL+fYDBIRVkp5646h+zRqS23w0FfKDW6MBgMdHZ2Eo1G0TSNRCJBNBrlpptuYuPGjbz66qv4fD7q6ur413/9V+YyqqrS5m9i0aoLJryN1+Wlq3eIypKpRYp39g1yxfUfmNK204EuIGlE0zSGkkNU1VbNtilnDZqmMhwX+evf95HtMWGUVF7b3okiGrGJKtWVGbzvurHrI6qmkkwkaetupW+kj6RVRgjDRcsvnqWjmF1++tNnCQScfPnLl/L3v/8CpzPAxz++gHg8icWShaJE+Nujf2bvvhdZtdrDshWFhCJhJIMBo2TAYhawGE5el8LldLDumpNXvHO5nLT19RMKhXj++eeJRqOUl5ezatUq7r33XlpbW4nFYqxYsYJVq1ZRW1tLzTSmIE8Xf/rDH/Hv2MvVkxCQi869kEee+sOUBCQSjdPe1Y9gKp70ttOFLiBp5Mc/+TGGPIO+eJ5GMouzySxOxdIMDoboa+6h9uNXYTAZkBMy8a27xrTXNI3dB3cTkcKIBglTjgkDRpjZ+Ko5hcViZvnyTAThVZYutfFWURVFUVm61MzCRRlsfP4hXM4hHI7VuD3ZxONxJFFiaKAPg0mku6OTkqLSKfWvqSqtra3s3LWLO++8E6vVSk9PDz/84Q/p6+vj8ssv55prruGqq65K41FPL21tbdx37738+Ov/NqntrBYLXmc+Xb2D5OdMLrfYrx95hta2Nmw51Vx7/dwoD6ELSBoZig1RtKyIoaEhYrE4Ho9bL/6TRhwZDhwZjmO/iwaRupYgu/c1U1zsIBqL0jfUR9wex2K1EgnL9LXGEeUES6b48Dsb+Pznrzjh5zabmVWritDQWLSwiGzPEnYePEQoECArOwej0YjN6cJkNOH3D0yqz1QRLY1nXngRt8dDb28vX/3a13jwwQdxu91ccMEFmM1mVq5cyfDwMOvXr6eyspKKOTK3fzq+8dWvs7hiAauXnzPpbc1GM9/4r0f48dc/NikR+fSHruaXDz7LbZ+7Y9J9The6gKSJSCSCrcSGoirs3X+Q4rIquutbKC3MJSNjamUpdU6NKIoUnFdBd9BP5/4Y3lIPolPEaDSiKCq7t0dQpDxCTR287xt6MsCTISAgaxpDgRFWLVlMe083kqaSjEZQNJV4JEI0mkBRlNNGrD/8t8cIRyL09PZhs1opLS/ji1/5CpFIhA0bNnDw4EG+/e1v43Q6Wbp0KeXl5cTjcUKhEK+88gpbtmyhrKyMBQsW4PV6ZzXG4WR0dnSwoLiMS845f0Ln5J1ceeHlLK5exOZdG7n52okLiMfpQEDlpU0vc1NW1pzIAqAn908TW3dvxVntxGw2Y7fb6e5sxWaW8Hjcp99YZ8poiQSeEgc5C3Iwmk1IxtQ7UWdbkninzJpiE9/9+pWzbOXcx7doMXFNICknkZMyoiBgMhqxmExYTEZQRRRVPe1+mlpa6O3rJ5FMEopE+Na3UwvhBoOB66+/noqKCnJycujr6wNSVfjMZjOZmZmUlZVRUFBAd3c3//u//8t3v/tdduzYMa3HPRUCgQCDoQCbtr/O0PDQpLc3m82UlZQRik7eG+sLt17Pmxv/zn/e9d1Jbzsd6COQNJBMJtnfuR9zdSpVwepzVsyyRe8eZFlheChJVk7q3I8MJ/HXqRicXgQpRigSJ5mU9fQnp0EURYpKSujv66W7vx+D8a1MvSrhcIRYzIXJeOoywSPBIAUFhXz4ox/lkYceIi8//5grriAI7Nixg69//etASlCam5txu91j6oyIokhWVhZZWVmUl5dzzz33cNddd1FUNHeyWufk5uLyeLAZzWRlTj1ZqtPmJRSJ4ZhEAalkUsViNrHuhpun3G860UcgaeC17a8R98Zn24x3JUW1C+nuVkkmFPbtTdLY4aZ4zQWUrl7OkluvYdeBPrTTvzjrkKqIV1ZWQWVZOUW5uRTl5lKcl8/CykrMptOfxLojR/n8F75ARUUFRcXFfO6O43P1kiQdm3Lp7u5m/fr1XHrppacsUjUyMgLAkSNHzvDI0ktGRgZ3/uAuBLORnv6+Ke+npmoxb+5vmNQ2Dzz+AgZ7FitWr55yv+lEF5AzZGBwgB3tO3BmnVl6Zp2pIYgChtyF+DvzcVQspmT1ckzW1GhDNIhUVmRhsRwffaiqSjgcJhqLkkgmkBUZTdNmy/w5iXYCJ0JJSpI8Tarw7t5eKioq+PGPf0SG2z1mjj4ej1NTU8OGDRt49tlnycvLO623YlZWFrW1tXMyXbwkSSyoWYiiyFPeR0VJGfUtw5PbSBBoaW7ge1//Ahs2PAvAjm3b+PHdd9HSODkxSgf6FNYZkEgk+M/f/SclV+pR57OFIAhklxee8LvISBjecYMPjwzTNtSM2WxGU7VjP6N7S/0RhDHFqUY/Hbd/k8FCaVHpWVfISjIcn6rq64/S3enBZa1k0yuvkJmZydIlSzC+YzprYGgIl8fD008/hc1o4uOf+MSY7x9++GFWrVrFvn37sNlsOBwOJsKyZcs4fPgwy5bNrVxozz/9LCVWL/lnUIdGkiQUxYQsyxgME3sUf+2TN/Hi1j2sXlrNLx96irLSMnyLFzMSDvFfP/h3bvv0F1h93unTqqQLXUDOgIcffZjs1dmIE4iI1pl5eg62c23t2BtclpO4vE7M1jNPrR0YDBIIBHC7zi5HCVGSUDUVURBRNYWqknMoyi9AURRaOzrpHxgkP29s1bvnXtzI+266iVf+8Twmsxmzeez5zc3N5eDBgzgcDjo6OigsPLHon4i5NkLcsWMHOZKdpdWLznhfRXmFBCMxvK6JCaogCFy1NrXGumRBMc+sf4jPf+27rFixgi3/eJL1j/55RgVEf/JNkUgkQmusFZtXz9E0V1ESSUaCY9emZEVGENNz2UuSRCAUSMu+5hJmi4XkaI1th91EW2cLwVAISZIoKSyg/oifto6OY+1DkQj1jQ24XC40WeaSyy4ft8/KykpEUaS6uhpVVcnPn3gkdjKZnBMiEggEuOvOO9n41HNUFpWlZZ9Ws41AKDqlba8+fym9bQ386kff4Sff+Qo2p4ec/JlNo6SPQKbIfX+7D+8yrx51PkdJRBLEewbxy2aW15Yd+1xWlEmPGDUNYuHo8ZTngoAgQE9rL6uXzdzb3kygaRr9fb3kuF0A2KxGHBl+nnjhMAU5i6guLeHyteez90g9jI4i7nvgT9zx+X/m8Uf/iqYqLKmtHbPPZDLJjh07yMrKwmg0cs0110zYHlmWGR4eRlXVGavNHggEaDxaz0BvP7v37GbdDe+n+fBRLJrIgoxCrrzgEmxpChC2W+2EI10Tbv/j+9ZTWpDNusvO47//sJ5Dzb0sNxpJWrO541++TTgUSotdE2VeC4jP5xOBe4BlQBz4lN/vr5+Jvtta2yhfpheMSjeqoiKIwhkLc8sbfi5ansG5q31jPldUGaM4uQeRIsskBlVys3PROL5uUl12dk1dATQ3NZJht2ExpaagBEGgIM/BfsMQi2oWMzA4SP3uvWRlp9LLyLJMSVkZyUiYD151Bc0DQ+MC3AwGA5IkTSkrQ09PDxdccAGSJCHLMq2trZSUlEx4zWCiaJqG/9BhOuubcZmsFGTkUlNZy7LcCkL9QS5dvCqVrr729PuaDFazhXAkNuH2igp/e+51Gts6CUVVcgvyeeNwG1ddu5RIJMKf/vgHFvoWcP37bkyvoSdhXgsIcANg8fv9a3w+3/nAT4H3z0THVeVVKCgz0dW7hr7WXvzb6vDkuxEEkUVrFtO8rxGLw0pBdeptd7hnCMlgQDJKmCwmDCepE1K8upq61/dx7ju8HVVVRRQnd9kriorX6yXDO7ncRfMRg8GARTxBvIcQx+l04HQ6KCs97jSy/9Bh3ty+HYsEOWvXUlAyPmWMIAjj1kROhqZpqXUld0qcW1paWLx4MYODgzz55JOMjIwgSRKf+cxn0halPjw8zI5XXqcys4CLasamJnE7nbid0+dhabPaaGyPTLi9KKg0tnXhsltQDQ7cGV6uvCAPlCSbNm5ETkRZec7MufjOdwG5EHgOwO/3b/P5fKtmotNkMklbsI0C9LTt6SA0GKSjoZ3+5j6UpELH4U7yF+TR29yLnFTobenm4CsHWHCuj/7Ofrz5TsyqGbvgIJmQURQFRVXQNFBH/51UFKLt/by65ShWi4TZLGI2iXT0DLAwd5KZUDUN8SzztDoZgmjk0OFBJNGBJJgRBCuiYMEkiqPiO3b6Ly8vl/zcHMoKiwhHY5Tl5Jxwv6dzAT7WvyAcE4+nnnoKi8WCy+Xi7rvv5uqrr6asrIyBgQGeeeYZbrwxPW/ZQ4NDFNi9FE32ukgDZcUlvLLz1COQt5/3r95+Ewcauvnmf/yYqqoq9u/ZhdvjpbxydjKAz3cBcQEjb/td8fl8Br/fP3Xn7AnlBOQ7AAAgAElEQVTQ2NyIqVCPbE4X8Uicke4hlISGzW0hozATQRCIhEJEA1HKV5WQiMXprO/g/BvXsn/TXmouWEht9annE+KxOJFQlEgwTDyWIBCJ0tQzyMJJ2qeqGt0DXQwGBqgsrcZoOHVE9nwiHo8TDofweL2IgkgyKWBgKReuPG/MNGKdo4FINIrjHRUD83Ny+NJnP8P+AwfYvn8/WdW+d3ZBIBCYsIC8nby8PL7xjW/w+9//no6OjmOuw5mZmbS1taWtjrrD6eBHf/wf3n/p1Vx18XgHgNORlJPUtzZSXVI56ak1QRSxml0nFOe3+PUjz1BWkEN3/xAlVYu580c/oaKyGoDlMzjaOBHz3QsrALx9fClOt3gAHDh4gHhEjzxPFwc2HSA8GOeCD12I1WXFbDMRHgrjyLKy9Fof7lwXsVCc3MrUvLvJbMbE6QXcbDHjzfJQWF5IxaJyas6poax68m+ZVruFrLIMsGjIyWm/vGaU1uZGBrs68R8+RCAYQFNjhMIj4x7MTrudUDh80v0sXbKEBQt87N21a9x3W7ZsIXt0zWQyBINBGhoaaGtr4+abx6buMJlM1NenZ7kzOzubb3zvO2w7vHdK2+88uJunNz/Bpu2vTWn74twSjjafeCH9t3/dwOBQgN89+gLPbfOzdO3VPPH441PqZzqY7wKyBXgPwOgayP6Z6LSstAzOrufIrHLJxy+l9sql+N+oI7Mkg4rzisnzZRPsCyIZUgveVruN7voeGvYcoe1AKx7HFDORalNbtwoOhYiMRDEY5/ugfSxGk4ksrxe31cJwdxdWUcFs0mjraKehpRFFUXj1ze00tHdgPM3b9eGj9dx08/gcTYsXL2Z4eJIR18DatWt57LHH8Hg8eL1jM1rn5OSwd+/UHvgnoqysjNWrV0/YXXhwZJCNb27k8ZfWMxBp5LaPXMVIpB+A4cAIz7z6PD//y695c98u1BMkodQ0jcSoe/KiqoXsO9I17vuXtu6huXOIxq4Q5553CXf/6MeYzWY+9/kvnPkBp4n5fjc8AVzl8/leBwTg9pnodMWyFfxu/e8QTAIFC/R1kHTQ39FLxXnFtO3rQo0PoiVE2g9248lzk1HsxZnpJBFLUrG6hEpfCbn5uaff6YnQJq784UCY4FCYeDhOhjOTpQuWnVXTVwAmk4VEMoH9beV+s/N7GYz9g61bE3z6Y5/GaLayeHGqQmAikUAUxRNO1ZjMphN+XlxcTDw++RG7xWLh3HPPPeF3giAQjUZJJBJpWUzfs3M3y4omXtv8UOMhqmtsiKIdu81KLBEnpgb4/XN/wOkxsmR1OWvzruaRh5/lkef/wo+/9P1jdu48uIsDTXsYDozwiXWfwuP2MBA4PsX39Ku7eGbjNmwZRXzi//sSt3zoY7y5cw+7d+zgN3+4D5tt7sSezWsB8fv9KvDZme731a2vUn5xOc4cJ4qicLSxmVBMxuu0kpedic1m0+NDJoGqqPTU9+LItCKKIoW+VMlOVVUID0XIKstE1WSS8QSqrOIxe6aePkRTmejAOz6QpLrIh3MavXBmG4fLSXtDPW6nA4cttb5RkGcjFIqzdGEVoigSjYQ5dOAAAmA0GogrKotPUHI2EjlxQFxra+u0FFZzu91s27aNiy8+s1LFbW1tdPgbqT3/kglvU5JXTlPLG2TmOhiKBzDbbFxz49pxYnbrP61j29Y9vPzGa1y59lLq2xpo6D7A+9+7imAowqMvPIErq4iGQJIdBxvZ9OY+ZKOHe/60HkVR+Mj7bkKVFVZW1fC1H/zbpAIwZ4J5LSCzRX52PvGuOM4cJ7v27ScourA6suhOyLQ3dGNCxmk2kJPhJsPrSbvP+nxFSSqoiorBnDofb4msKImcd8Ma4uEY3uzj56piRRVNexvobezHd1EVzbvaUBIKBTlTH/VpmsJEBERVVOwO51ktHgBOp5PyhYtob2rE8bYXW4fDTCDciqwoXL5mbLDk63v2nXBfkciJ10jy8vKmtIh+OpxOJ42NjWckIMlkkt2vbeOqCdQ17x3o540D2+kLdWO0aaxYVUVRScFpXxZXnrOYP/3+af7xs5e4aG0VV1yWSkUSiyd5cds2LrzuBuwuN79+4FGsnnwW1Vbzva99izyjDS0QR0HjE1/5HCvPmXz1w+lGf7JNAa/HCwIoikIwmsSam3I7FCUDJntqbj6oaQz2RhE6BrAbRdx2C3nZGe/q0Ul7XSsGq0gimkQUBTQN5LhC6eJyzDYzZtv4WIHyZZU07W0EYYiB1iHKq4uPuXlOFlVVEYSJ5XZXVQ2DdPbfHgICVosV6QQvOTWLBbbv3cyalcffzIdHAmMW0wOhEAlZRkMg9yRvx2azmZKSEoaGhnC5XGm132w2T9kbS5ZlXnrmOS6sWXnCl7xEIsGeuv3UtdWhGuK4siwsWVPOJXkLJtWPyWTkU5+9MfW8CAToHhlCTcq8+vJhbrl6Hc/t3kFPVzu9rX3kxSx01P2db77/Fn719/U09Xfyxa9+mauvvXbSxzcTnP13yDTQ3tWN/40uPD2DKCdbcxMEjBYbWGzEga5Ekvb6bkyCjMNsINvrItPrGZfV9GzGaDEhGBQqVh8PRNv3/CEURcHIyc+DJ8dD6+FmNA2cZteUp6/kpIzBMLFFUk1TEd8FOt/X20tgeBDlbSOEoeE4ba0WLKYsZHns+QqEglSOxhxoGsRUKK/2IcsyQ6GTe2ldeOGFPPLIIzgcjpO6q06WQ4cO0dPTM6VtY7EYDz54LyWWfJz2VCJDVVVpaGtmb/1eAvFhzHaBigV5vGftOZhMZ36fSpKEx+vFM+oQcGN+Pk1NbcR2DNPd18cq37mowyFWLFvOT57+Kwfaj/LyllfnVDGtd6ILyCTRNI3th1s458ovs3fjH5ClMGqWgmg4dXoMSTIijXoOhdAY6o9CVxNWCZwWIzkZHtxu14zl+5kN8isLOPDq/tEU7JkYTAbKzymhr6OL8GAUp8dFVkk2JqtpzBvlcO8woiSiJBTyM6c+BywnZU7z33QMRVawGSdeKW6+omoqVoOE3ekkEk1y4ICEy57LebUXnDDfk8vhpL6zm+ysTAQh5bliMBiIx+OUlJy8rIEoilxxxRVs3LiRgoIzdzzp7OzkF7/4BSaTibvuumtS2w4NDXHvvT/j5psv4MlHthKOR+gLdiNZVPKLM1h1RQkZGWnOWXICHA4blZWl1B9tJDA0wrlXrOTxpx7ntbrduLI97D64b8Jp72cLXUAmyZPPbKBHdeAUBJZf8Umaj+ygO3oAk3Myi4QCRrMNzDZkYFBT6ekMILb2YjUKOMxGsjPceNzus0pQBFFgycVLGeoapO7VeqrPL8eZ5cCZ5UDTNEa6Awx09xDqD5FfWYQcl2na0YTLYWOkM4jL4cTr9p6+o5OQEpATj0DkpJKa4hIFREEgHIzgdEy9r/mCNyODloE+7FYbRqNIbkYpK5ccX/PQNI29hw4TT8rIioImQE5uKkV+OBpFGE1MabPZaGpqOmVf+fn54xaZpzr91Nrayr/8y79w6623Tmq7gYEBNm16io9+9GIcDhvL1hRjtRm4eMFFszK1/MADTxIJhrFnZfL9P/10tNyAi61vbJtxW6aCLiCTpGdgGEvW8SGlze4m0R/F5LQixxIkg3GCe/vxnJuLyTUxdztBEDFZHYCDJG8JSgiau1lQkEV+3onTQ8xHBFEgozATq8tG064mMou9ZJenIs89+W48+W56G/oQDRJ9u7tY9/5ryS/OIxQIYXfYz+gmTyZlJMP4NZCR/gCR/hgF+QUoqoqqKlgVO07H2b2ADmAymhBNFkLRCA6rjZHgIO1dHRTlp3KPNbS0YLQ7qSgaX79DlmXyi1KjDkEQaG5uPmVE9VvbHDx4MFW0SxBIJBKTLhalqir19fV861vfwv6OyPiToSgKdXV1PPbYH7jqquVs2bKP4eEYsmxgeLgfn69iUjaki7aOYXLzC2g4coSf/fJnvG/dulmxY6roAjIJXnn1Vbbt3EvtuuMpt3IKqxkcaKGvoQHjoI2KqgsIVPcwLLVMuZ+UoNhJqDJ2W/rdH+cCVqcV37k11G09jDvPeawMLUAimsSbbcZkNFJYkprucLnPfPE1GU+ecA1ETWqcs3xG0qjNSRwOJ3I4CIDV0cX++i4K825HEAQMkgHRdOJEiEaDkXA4jMmUiv9YuHAhbW1tlJaOT6j4doqLi4/VQt+/f/Kxv62trVxzzTVUV1dPqH04HGbjxo243W5qalbT3q5RW3s1breb7u4u1q9/hHA4gt0+s/EVyaSMLMucU+ujINs178QD5n8k+owxMDDII0/+A0/1+AfNwtorKTDXklNQRXZ+JUP9bTD6phzvixDrCCFHJx9IZdCSOJ1zew70TMkqzGKkJ1WUKRlPcvjlI/Q2DiAZJeLRRFr7khMxDCdYBDnbStJOllgsitWSEonqKhcZGQYi0VSCP5fDQWdXN63t7RxtGFtz22I2M9zbw5HDh1BVlYKCAl544YVT9uV2u5Hl48Gcoiiyf/9+ent7J2zv008/zdNPPz2htg0NDbzwwgssWbKE8vJyzj9/LWvWXIDb7SYWi/HHP97Dpz51zYyLB6Riav7jzk9x222XU1CQNeP9pwNdQCaALMs8/MzLLLj6FkoWnni4XVmzhopFa9j7xt9RSqIYR11STUE7K4pvJje0BEOzjUR9DHmCD0ajmJ5kcXMZR4aTQy8fITISJdATpLelD3e2B0EQsKSh7OzbSSbjGIzjL/l3u4BkZmUzHAge+91mUwmOFiZyu5wUZ2WQ73YiRyPvePgL5GR4EYF9u3bS196K//ChU/YVDofHuMzW1NSwfPly+vv7J2yv1WqlvPz0tXi6u7s5ePAgK1ee2E13YGCAtWuX4PXOXl0Xi8VMdraXlStrxpzb+YIuIBNgaGiIQdWKOIFCREvOuZYiViE1WUg0RLFLWVgdLkorV1Jbu45FJdeQGJ5ICUsNq/HsWUA/GWabmZoLFrN9/W46DvWQUeAlEky5g2pqesuYyok4BsN4sdA0LZUOntkvmzobOBwOEppGNBZD0zTsNgOhSCp3lSRJlBYVkun1kulxEwqF0TSNeDJJMBxmYGQESRJxOp0oonTMxfdkNDU1jXFdf2stZKIvSuFwmLKyMv793/8dTdNO6sYbDofZtGkTtbW1J913fn4+wWD6AxynwqZNWyY8qppL6GsgEyAciYJhYvl2zDYHpZUrKWUlqqrwzmeSwWzGFSsg0jCIkAVG94nfspPxOC7n3Ml5M10IokBmcRaOTDuynKTinFL6mwdoOdBELDa1WtEnI5lIYHCPF2Wj3UBd66GUR1DKqmOjEkEQTjJCSX0mJ2QWVdUgTbLK4Vyj2reI/r4+ugf6URMqjUcP0t0XYHg0UFA0GEjKKr5Fi8CSxGyx4HJ7sVitYzyr2jtPXp41Ho+zcOFCNE0jGAyiKApWq/VYsalTeWTJskxLSwuPPPIINpuNo0ePUlZWxk9+8hMuvfRSrr/+egCOHDnCkSNH6Ozs5Nxzzz2lF6Moimialc2bd3P++UtnLWOEpmnYbDaGhoZmpf8zQReQCVBSXESmtoOEoiBOwq32RCMWu9NL7bL3IicSbD/8EJxEQIREkIK8yinbPJ8QBAHfeTV0NXQc88TSNA1zVXqnsORkHINh/KDb7rZhn+IsxlD3yIQzuM5lJEkiNy8PQTLzi5+9xpVXnUfZoiVkZEwue0JxcTGNjY1UVIz3amptbaWwsBCXy3UsIeOBAwcwm83YbDYikQiBQIBgMEhRUREtLS0MDQ1hNBqx2WyUlZVx1113EQwGqaurY/PmzVitVh577DHy8/Pp7e1FkiSWLVs2Yc+u66//AA0NR3nkkVcpL3ezZs2yGRcSQRBob29jx47D3H77jOSDTRu6gEwAURS5bs0yHny9AU9+WVr2OTLYheqQiQ2FMLms44TJJPKuilK3e+w4M1w0be8AQUNTVZRema4DvWTnZ2J32ikqK8RxBk4FqiIjpjm8XJQEksnkWZP2JCfby7f+9UqefuoQV1993aS3LysrY/PmzeTn549LoNjU1ITP5xvj5hsOhxEEAZfLhd/vJycnB4PBQGdnJwsWLKC0tBSL5cQBnZs2bcLlcnHbbbfR0dFBVVXVlALvKiurqayspqmpgUceeZXSUhdr1ixLS/T529E0jZ/97EGsViuf+9zYtPcjIwEyzyBIdrY4O676GaCysgLX5vTVHzBarNjasynIWcJwVwcDiQYsFccvftMJ3pTPdnLL88Z9logk6B0aZqS+hZ7OPi655sKpdzDFWiCnQjgL8504HTZWrDTz4IM/5PLLb6WwcOKpNARBoKamhkcffZTbbrvt2Oc7duzAbDaPixFZtWrVsRel1asnV13v0ksvnVT701FeXkl5eSXNzU385S+vUlzsZO3aZZjN6ak++te/voAkGfjsZz845vNf/vJhGhs72bRpfVr6mUl0AZkggiBQ4LXSOclprJPh8uSwYlWqpnOWXM7goeNRvKoi47KfnfEfk8VkM5FpywQVHNLEgsZOyjQIiIZ2woJB852FvgJycgK89OLzVFQupWeoh2U1SykrKTvttpmZmaiqys6dO/H5fOzcuRNBEE44rTUXR9llZeWUlZXT3t7G3/62idxcMxdeuAKr9cxS2wwPh7jjjpvHTQc2NTURiSTJzZ1ijZtZRBeQSWAQBDRNBdK7YBoY6kFzHH8IJWNhsvMmXwL0bEZF5fAeP6IosuK8yUUuv0Xq/y69SAYJVTn7BATAajGzbbCOfXlOzJkW+ra9yqfeISDDw8M8tXsfoWiMWy9ee2wK6cILL2Tz5s34/X6WLFkyrqLgfKCoqJiiolvp6urk8cdfJiND4uKLV0wqZkTTNLZt24eiqESjCVpauqmqKj72/YED9TQ3d/FP/3TbKfYyd9EFZBK0tncgVp3e/3yy9Pc3Yso9PuIwqXEcjjN82z7LyC7OJrs4m33PHaTe30RwKMCH/s8HJnWeBNLzoNe0Y3GiZzU79jRhqyojb0FqCivo70GWZTq7unizsZmEwUQgmUSuXEgyGqF/cPCYgFgsFq688srZND9t5OcXcOONt9Df38eTT76I06lx8cXLcE8gO8JDD20gEgFFCbNu3UW8/PJ+du1q5PzzF2C324jFoiiKyH/8x/dn4EjSjy4gE6SxsZHmgTAV1el/coQTA2/L5qvhtJrSlvL6bKF9XzvKUAKLxUIoGMJitWC2TM5LS1PP3OdfVTX6GvtxO0drwGgGLBlnZ9beZYtLeObP+wiWFtDfL+Fy5vB/t+1Ettmx+GqRjEZEQNi1jVKrFduiydXJmG9kZWVzww0fZXh4mOeeexGbLcm6dSdfk7v//sdJJiVE0YimqVRVFZKR4eTw4RA9PSbMZgP33PNn+vtHZvAo0osuIBPkwMGDRLX0z9dqqkpMHcFE6iEkJxJ43Gd//MdkifSF+fA/3YTxTFws0zGFpWl4XF7KS2Yn+V460TSNWCyJ0SiNS/Eiyyp/3z5A8WWXY8rMZGAgSvGFawEQkgm0tw3B8hx2bnpH1cKzGY/Hw7p1H+Tll1+kvb2boqLxzh8tLV10dQ0BVjIzjXzsYymPtowMF6LYz0UXXYfJZOKnP/0BX/zil2f4CNKHLiATxLdgAbaj6X9TCA33oziSMCogaiKC0zY/8+JMJyLCmYkHEI1JHD6URNMUNFWhsFjE45m8h81IYARZkee0666qavT2BgnHZRBEKks8JBIyT27rQTZaiGkScURCMRXXUBcfu6YCo1EkEkkSiSToGY4zYM/GnpM9ur8k4XAYBIGhoSEkSSQ/L+V2OhxP0tLeTjwps6C8DICRkRF6e3snnPBwPnLuuWvYuvXJEwrIs89uwe3OxmCwACH6+obwelNTXuecU8xTT/2Fdes+wp///NS8Lps8d++AOUY0lsDlzUj7fvv6GjFlHp8CMVidNLR0cI7Hk/a+5itDXYNERyJnvJ+173nvsX+HAmHa9z/JZE+zKImYPKkCSgbb3L19hobC/PGwgLWkHEtbPZWjtZ56zZmYfT4gdfN7gERkAX/c6QerDdXsQLHYMDjMGDPNBINxZFnAbLYRCAYwGo1EIpExD71IURnrgwnEjjb+/sZ2ZLMFxeEk2N7G3ZWVZ+10rN1uZ9/eRiKhINevuwSn8/h6nM9XwsGDzWzffgBNi3D99WuOfWcyGbnggjJ+/vM78flW8cEPfmg2zE8Lc/cOmGMcPNqI1T21QJ9wYJjhvjYkyUhffwN5hYvIzk9NgQTjvUhvC1gSJYl4PL0pPOYbLW80oUZVEnISyWxAQaG3sw9VUxGFqT+MWvwNSAYJ0WgiGo4jClOLID95epO5g8VixO4yYsn0oHSn6oYbjRJScnzZWZPNCsuWAxCPyyRjClFVINhnpLx0ISazmfIyM339ffT3D1BWVorJeHzkZvZmYAbIyaN/oP+Yu27z1s309vaSlzf+DX2+097WhqIouIwOAp1Rdu06SEVFER6PG6vVwiWXnMP+/Q2oqsrQUIhHH32eO+74MLbR8gxWq4VYLMLTTz+uC8i7gesuv4i77/0ztlVXTWq77jY/dS0vIGaJCLKApdTJ0ZZN9PT5yXCXEpGHMKkm4pEAgiqTiIXJtqUncGk+0LanjehQFIPNgNFhxOaxQUzjw7fdhKIohEJhwoEQ8fMTZyQeqqrSeXQzpaUGVFVFkiG7cGqL32aLiaauBiRJQlBFfBULp2zXdBAIxnllXz+qKRVXILsz+cPWPhKIKNl5p7zph4YEiotWYjAaMJvMYwIls7OyCY1m6YVUfqpwOEwsHsfjdmM2m8fExBgzc2jv6DzrBETTNAb6egn191GU6WVBaTFH/a0ceGMzcTRqan1ce+1yDAZpNKakEJvNyQMPPM0ddxyPQLfZzLhcFpqamiaUXXguogvIBMnIyGBFZT6NQ304vBOL0VAVhY6ufbhqx7aXFhiJayO0ht7EUCQRCw1T5BAoLCjCaDTOyeCqaSOo8oEPv5fAcJDAcIDAUJCy5akiUpIk4Xa7JuQueTqSCRmH00hG1pl7TFnsFiz21H4C3aHTtJ55wqEYDRnVOPJT1525qACVAgyc+oZPJhVMJs9J08UIooDRYKS3t5fs7Gy6uropLSkh22zm0OFDlJWVAQKKrKT+73Kyad1Zz9lUquuI309nextOm42ujnZW+6oQRZGaqgpqqOBoazubXn6Za69dzvXXX8jwsI0NG15A0yQEQWXHjoOcc04NgiDw6U9/gO9//37i8cnXCpor6AIyCa678lLuvn89Du8lp22ryEne2PwQSk4COx40VUVTtWPuuoIgYLSbURSZZGiQ0prlx7KSvpswGCSsVitWq5Xc/Okr3auqKn09cSRJwyCCKGkYDBqSBJIkIkkCkiQgjv5bUTRs9tML+WwkUtQ0DUVRSSRk4nGFeFwmllAIxVWCMYXegShizuTPZSQik5tTcso2xSXFxGIxGhoayc/Pw+1JuTOXl5UTCocRRZHXt25lxfLl2F0e+oLBU+5vPtHV2clwTzcVeTm0d3ZiQhuzvqOqKi/v2Mmyc1Mp7Tds2Epubj6iKBGPByktLWT79jrcbifV1SUEAiHq61vm7egDdAGZFBkZGbz/ouX8o66ZjMKyU7bt62pCKFexOp0Edw1g0VxEYyOIRQKGmA1ZiSKIIxRUu1m+fPG7UjwADDOUBt1qs3DRDR9PlRFNyiiygpyUkZNJlGSCpCyjKEmUeBJFlqnf+wZXXnv6h/B0RLe/k0c3thC3upA1ARkBWRNRJQnNaEIzWFGNZjAZkUxGDE4TUpER26kiHTWQFZVEXEZRNZwOM8MjMqqagXUCJZQtFguLF9eM+czpdBIMBnG7XeTn5aUSKQqQmOdp7uEtd+cYLfVHKczKQFUUmhoauLB28Zh2wUiEYCxIKBRl585DmM0Gent7ychwsHHjLgYHo2RlZfKPf2ylurqEv//9ZQKB6LyecUibgPh8PgFoB46OfrTV7/d/2+fzrQP+DZCB3/v9/t/5fD4r8BCQAwSB2/x+f9+Ztk3XsZyKBeUlPPz0xtMKiNlqx9aeTaClmxW1H8CVkUsiHmWwp5XcJQtQ5CQHtvyBqtJiTGlK1jbfUGUVg3Hm3mGMJiPGCWZYjQzVT6jddFeMjEYT9Bg8WBfVpvoDTnUEqZGJgiKryIqGogjIsoggGFI/ohFRMGIyWrFaHanI8m4/AiZqapZM2U6D0UBJ6fjRS/ws8MAaGRmhbt9eMp0OBCApy7hslnHeZfF4Es//Y++9w+M4z3vte2Z2ZvsusIteCQLEEiTBKjaR6r1YVrEUW7EdxymOE8dffHJSTxInsZ3k5JwkX/Kdzy1WEjuOi2zJsmRJlqwukWKvIMFlAdF72cX2qecPgBRA9EpQwn1duEgM3p15Z4F9n3mf8nuysqg70UBray/5+UEURUbTYnzqU7/Kd7/7PY4ePcVdd+2mru48+/YdZWCg/5rOUpvPT28lcCQcDl/uDB8KhWTgn4CtQALYEwqFngMeB06Gw+G/DIVCHwX+LBQK/fe5jg2Hw53zeD/jkpeXh9PKTNr8BiA7p5jsnOJR4xS7k4Ky4RRKWaFi3Yc59caTbLprzYTneT+TTqTp7uihp7Nn6Wl/TeKaGuiMIiFhYWHpC2tAHA6ZQqOD3rSKzaGgaQZ9/dZwDYqILGfw+2XqT7QjqRKGbhIoX0leQRGK7BgymsNPuBMtVJIk0dnVsiDzzwgSpmle04vkYCRCrs+Lffjho7O7mxzf2LhcMNvPypxCVm0p5cDpMPV1zfi8Xrr7unE4gnz7299h48aNvPrq27S2ttLU1M7OnXNQl14CzKcB2QIUh0Kh14EU8AXADpwPh8MDAKFQ6B3gBmA38PfDr3sR+HOgZh7G/mge72dcOjo6SWomA50tZOUXk4pFcfsnrg+ZzMj4cxL773EAACAASURBVAroa1/HhboGKtetmHBca3snkVgCw7Iur2uCAE5FJpjlxef1XpPbYJffRc76fJ77wQv8yu9+fNLucYvPxK4pGZnqFQufeZXJaPz7qx3EDBtKIonNoWAaFj5PKcUlQ0/7B/a/wuBAlHgsw+68SgzTJKK4CARyaKg7BfEY6AaW30dlbe241wnm5BLMWRgDLvqyGBgYIBgMLsj5F4NEbBDviJ1rMDub+tYWCnJzsI34m5VEkVu2bQFgz5FjFGdnUVqQR293F5HOXr71zW/y0EMfor29A11XcTg8fOMbi+I4WTBmZUBCodCvMWQgRvI7wN+Gw+EfhUKh3Qy5nb4AjCzfjgF+wDfi+HjHZjt2wUmn05RkOdFEke7wESpcKj16JZ7g7FIVs0o28uIPXyS7wEcgZ3xD1BMZJK0Exij4JQyDjvYYgt6LXbRwKBIeh51glg+Px33VWnTOhGBpkL5wz5LqqzHU2nb0DkTTdCxzaDdpLkLcYwgBI5hHVnU1pqaTSWuYFqjae0WVPrwE4w7KhxdoSRRJdHbR0D+ApGbIVeygSHQZ8y9lPx3knFxaWtuuWQOiaRqDkQje4Htqwl6PB9nj4/iZs2yqCY27u3r0rtv5yWtv0jkQZWVREcloBIfPxz/90//HH/zB75PJdNLT07OYt7IgzGpfGQ6HnwiHw+tGfgEHgZ8O//wdoJihhX1knb4XiACDI46Pd2y2YxeUZDJJbm4Of/KHv086/A7t4WPsO3kWZbb9UAG7XSFn7Ud486ljE/aVqCwtREuMvT1BlFCcHmRvENOdQ1LOplNVON4ywN6T5+jsvjb+QGWbMqcaj/nGsiy4osgw0jqIknIgJRSCvsVxt8myRJXVx4rzB1jdeoTwt5/DMkopHNEVU1YU3HY78ogn4TJFoQDwixK9ySS9qkpG0xdlzlfiy82jubt7TueIRCIMDg7S19c3T7OaPv19ffjH6QOyrmY1GUHCmOAzK4oij9x+CzvXr8Pp8yO7XLS3tSHLMsFgFp2dnUSj137B8Hx+ar8I/B5AKBTaADQDp4FVoVAoEAqFFOBG4F1gD3Dv8OvuAd4G6udh7ILidDrp7u7GZrPxp7//eUwtQ+OFs8jK7IPgR06ewZ1XBp7dHHjt2LhjfF4v2XawzKmfIkXJhuLyIvvyONcRpbOnd9ZzWwwsy0JaYv5xwzCRrpiS2+WmqKCYksIScoOLY0BEUeD2rYXsWpdFbYWL+zbbaXzrtVEtXu1+P112hWYBMvpoteEewyB43XUUbN1KaMuWRZnzlXgDQTqjs9eQO3L4CM/8+Dmef/ZFnv3Jzzh96vSEY3VdR9fnx1DW153k7Kk6Whou4BxH9Vm22VhZsYKjFxrpi0z87JofyOKG2hoyiQR9XV38w//+32QyKs3NPcjyUnLZzo759HH8HfDdUCh0H0OZUZ8Kh8NaKBT6b8BLDBmrfwuHw22hUOhrwLeHYxcq8Ph8jJ3HexkXQRCorKwEoKevH5cvmw0+F9HeTrLyimdxRouUquOVZQJV27m47zQVNV3kF4/tTFZdUcqB+kZs3ukLLdo82ZxrHwCgIHfpCDSapknL6RbsToXGgxeprqm82lMaRSqR4s0fH+NCsQ9ECwsLWZA5l5sEhtxYgiggCGNb2lrmUJzq0r8wtJkxLQEswAIBAdPg8veY731ZhoVgCVi6hWWCaAjIgohDlAkILnYONnL0W19H8mdRfc/9lAzLhkT6B0ieOHG5V4ndJmMTBBwOB5JNAourEszuaDiPPMkCOxV2ux2v28/DDzyKaZp8/+lv43A66OnpYd26dSSTSdLpNLm5uXzlK3+DJIp88S+/OKfsuHQ6TSYeI8fnxZU1cRFrYX4+AwMDOJSJU/D7IoO8cfgogmVRsaKcU3UnCeYGyGR0ysvLZz3HpcK8GZDhgPZ94xx/DnjuimNJ4NH5HruYvPrGW7iLqnBEmzD02fqXBUryArR1t+LNLyV/88d46+n/n4c/mzP0oR9BNBYHZl60ZvNkc7a9HwHIXyJGpO10G9mCh0B2Njs+vZm83IUrIJwNhmFyT94Wbisbv7+FaZpD671pcqUHQ2TIfSFKE2c9zZXeeJxvHdrHSU1j6+OfAMDt8RAvLUVzOBhob6PMAqcAFw8fRrRMLMMkpqoES0vQMhmKK6tm3E9lNmjJFPY5hLdsso2LLUOVAaIocvP1t3OxoYFgIJcf/fAp1IxKWXEFJ+uf5OOPfppnnvsR//mf/8knP/nJWV9zMBrFOU2vgpZO43aN34pW1TS6+/vZUFVJXWMTF5uaeeiXPkphYSHf+96TbNmybEA+sOzadh0X3jlPPJNHy5s/44ZHfxNpFkHrNaurSR2vQ7UsJMWBvfg+3nj2NW57eEi90zRNzjY00Z20kL2zc53IngDh1l466rsIFvkJFgRweYZ6jmgZDdm+uBlcWkJj/Q215BUusdTdYQxdR5rkCVYUxSHf71VyvXkcDnpKyqh94KHLx2RFpqRyaDeSikQgkcAnK/gABAlEiXybDau3j7SmkciLLYoB8eXn0dQfQdf1aSd1DAwM8Pprb5BOp8jy5PDZX/093trzOolkgl3bb2D3ziEliJrQUPq7rutomoplmaQzGTRt9o3DLMuivbmJHPfUBZWmaSJMkO79ved/DggodjuK3Y4gigQ8Lg7ueYc//9KX+f73v8+NN94463kuFZYNyCw5da6BYFEZ9qo1rNx8I8IcFpP8nGzO9Qzg9AXwF6+m69BJLp5pIr8sj7rzjaiKH9k9tw+7hQuHsI5Mh8TZ+gZMoxfBptLZ0sA9n71zTueeKcm+OL7suetbLRS6ZiAuvkLJtGka6Cdjd9Jed4pkJoM7Px/BMgkWFuHN8iPYpHHrlERBAEFAEkX0OSyyMyE7v4D6QC719fXUTpBGfCWJRAKX4uPm7Xdd7qV+w/U3Y1nWuLu6Z59/mkMn9vHGnleJRCJ8/gu/M+v5DvT3Ixr6lHVemqbR2NpKjm/8lsqP33f35f8bhsFz7+wjv7QcVVX5/r8/gWBZbNiwAb9/UZJHF4ylFb28hqitWUW04yLAnIwHQGdXD4wIkOdseph9LzRy4HQDuisHSZ77k6KZMMkvWkXp6s3U7PgIa3d9mjXbf4v8ksWPP5iahbLIu56ZYBgGtiuj6EuIUG4ev6WIOM7VU+FwkBOJEIwOMjCcFmpTlAmzgwB6AMc0JEvmi4rdN3Hy7LmpBw6TTCQxDO2y8YCh+ONELsEHP/QRPC4ft+6+k9/9zBf4n3/3P+eUsaVZ0DYQxZykmPRicwvHjxwhNzB1jyBJknjghp0M9vagxmP4MAjl53DvXYv74LYQLO9AZsnamhqK9p8gnohhd3nmFLS7bvNGjtSdITP81CNJEtnrHqe3/icUXz8/C5mQknB6xj71C7bFeRIdicfrXlJpu1di6AbKEu/3sTYY5NjFRlraW3DnFSLZbPR2dNDe0YFfAJ882oevGQayJJHRdXwlJfgWsWFZ18F9PHLT9dMaa1kW+/Ye4GMP/8q0zy+KIn/6+3952U3m8/j58pe+gsNp52/+5m9m9NkMBIMEdl5P+PQpTENHlMa+NpVK09rUyMO33zLtc4uiyK6NtRQNd3hs7eyiu6ODD91/P8/97GfTnt9SY+l+iq8BPv3o/QQj9agdZ+d8roqSAlK9rZfVXZ3+PGzCOgYudszoPJZpkq5LoF/QSLfEyQzEMTUDRXCN2SklYxFkz+IvlI4lLhxp6Aa2JW5ABEHg8YoV3N3dSqqnC0EQqFIUVoki5XbHqNRo3TRpMAyaTYNWyyIrd/FiT+lEHL29hbLS0inHWpbFf/z7d1i/dsusimD/259+jh/8+Lv841f/Hssysdlss36wKy4rJ2OTiSdH12oYpklLexvrq1ZO+9w/feNtEqnUZeMBDPVQycqiv7Pjqig6zxfLBmQOuN1ufvNTHydrLmkmw2RnZZHndWCMyOXPXrWLwTMWamL67Vy1RIaKkp1s2/Rxrqt8nGr77WT1lJPjGeuqinS14JmH/hgzZalXyBu6gbSEKuMnQhQEKgIBJGPIXWUTxXGzhwzTpLC8nNU7dlC76/ppKe7OF53hM9y3a+ekY5784ZOoqkpTUxMrS6rZsHbjrK71u5/5AvsO7cHhsKPrJv19/bNenEVBYHBwEOkKV2bdqdNYyQQ+z/ixj/F44KbduJ2j3/Nzza1s21BLT28vN27bNqs5LgWWDcgcyGQyPPPc8/Rl5mexycryYaRHtxzNrf0InQfapy0bbsZ1/NlDDZkUh5NgQTlVq3dRXjm2kCw20EwgP3vM8YVEV/UlHf+AoRiItMR3IJdQJBuCrk46RhKEUQ8mi0XrmXrOfu/b3LRzYgPy+utvcOjgYX76zE95d+++We9OTdPk5ddewOf3kYgnUNUMq2tWz3oH0tLcjEcScF4xH7uiUFFciDyDh6BLc9h79ASJZJpYIkksmcQfzOXOu+5iVc1qzobDs5rn1WbZgMyB1tZWnnrlXZwF8xOIzvL5UFOjO9zZHC5c2TfSd3p6dZJCyobT7Z16IKCme/Fkjd99br65uK+B8++c48w7Z/BlTW9+l9izd++ibvMN3cC2wDLt84UsSQgjMqoM00QzDNKaRlrTMC2LtK6jq4tvQARBwGu3jwl+W5bFnj17OXToEPWn6/mr//F33Lb7PjbUXMe26ybfrUzE+YtnGRjs4+Of+GXcHjfBYJDPfOYzs577pVRgVddp6+klrQ4Zackmoc9SV6yjf4D27m5e3rsfT3YAX14ef/znf4HT7eG3fvM3OXt27q7wxWZp+xKWOC6Xi8KqtXPOwrqEx+NhTVk+57r7cPjeE5/zlayh+/hZUkVRnNmTp/0puKc9H1FWF60y2WEq3Hzvbvr7BsjNn5kPftWqqgXvuzESXVWXnLzKRNhEEXHEgtZiGmQXlyDJMslEHDOdxlNWRv4iBs0v0dNwgW2bNjI4OMgTT/wbhfkFFBUXE4vH8NmzaWhswufKwqE4cCgOsv2z3w0//9IzfPa3P0tOTg5/87dfQZmDvBBAXn4+zU2NOD1+8r1ZZAaGJIEEQcSYpQG5bs1qTjQ04fT5cWdlE+lo5399+UsImRQtzc089MD9HD1RN+e5LybLBmQO+P1+ZHN++xkXFxVwsbN/zPGctffRdfA/KL3NjShN/GuzSzPYUUiTuz7mE1m24c/245/CAI7HYleqa0l1uN/G0kcQBGwj3JtOh5PCcRo7XQ3aThwj3tVOOpom0h/htz/1BS42XWDPO0/yx7/3xXm9lsvpZnBwkJycHBRFwbIsvvH1b/DpX/v0rBbkvPx88vLzSaVS1NXV0VJ/GocsYgMc2SWzmmN5YT4tff0U5eTQ3NxEqKSI+rYObtm2mWBBEYePn+D/+fzn+drXvz6r818Nro1PyRLF5XLxyM2beXLfafJXzk9TKFXV0AxzzC9GtMn4V9xL1+HnKdy2ctzXGqqG2z51XjqAmk5icyyOW8g0TdpbuvjZkz+norqctRtrFuW6s+XcgVOsF68d+XHZMrn0TKymUrReuIChG8QGB1m9ceOidn0cSemaWsoDeXzq8d+4vNMNrarh9z77R/N+ra2bdvKDH/yAm266ib6+PupOniIyQw0uXddpaWmhtbWVvu5u0okYeipNod/DxoqSeem5s3vd0N/+yoJcTl9owFRVXn73AP78Ap559ln+8ovza1gXmmUDMkc2ra/l+bcPT1m5Ol1EUcA0xlcUdQWKSXWvItraib9krBtIi6XJypqeqONAZzvunMVJpxVFkfUPb8A0TZqPti55A1K1aTWO+sGrPY1pIxomKV1HNQz8loW7pxdRELCrKg0nTyJLIoJiZ0XNwjfBGokrk+Hxhz8+xk3qsM9/5t9AtJ/IQJQ3Xn2LG3bczCce/TV+/LPvXW5SlkqlUNUhl63dbqexsRGbzUZbaws9XV2oqTRaOkmWXaEoJ0BZYRBRXJh053gyybOvvckv3XsXoRXlvHT4OF/80peBITf2tcSyAZkjgiBQU1bAuWQMh3vu8hw2m4xgTBzwzK6+ia6j/4krmEa+sk9BQsRTND3BxPhAM3m1Mwtmz5V0LI3bs3gppLPFVI0x6ZtLmTZdxV5cTDQ2SDI6SEgQEAQBn92OT9dBh7ZYnAsn61hRUzNGqHMhGOjsIKWr7D26nztvuG3Br3fbTXdSVFhCTfWQJ8A0TSxzWHKksZGO9k4M3UTVMpypD9PUcJZbrttAQSDAluK8BY8FGoZJV28fPQMDXGjvxO3z85NX38DQDYJl5QiCwJNPPomaSS/oPOaba+dTsoS5dfd21J6meTtfMMuHmh6/9kMURYI1D9F5oHVMaq+kK8jTfLpLDLbiCyyuHlUikphxBtbVwNB1bOK106vBbbdTWF7G6nXryMnNHVezuVhRsEcipNMLv0C1HD+O9dLL/Nl9jy2K8bjEJeMBcLGpAU3TOHjgEIFhuZEVZRVkkhoffegTuOwOaqsqyQsGFiWRJJ3JsK/uNI3dvUh2BzaXm5Qos+2W2/gff/4XfPOb32DPO2/zR3/ypws+l/lkeQcyDwQCAdS+FvTiEDZ57hkUOdk++ntS4HCN+3PF5cPhvZ6+M4fJqXmvwtcujD9+PAyXg/31F5FFsMsSdpuE1+3A73HjdDpn7O+91GvCNE0Mwxj1ek3TOHawHtIauj3Nxm0bZnTuRccwh4QHrxFEQ0fXdEzTRDN0DNNEHKe/vAJkUincMyiCmy66phHp6SZYUISaiLGjqgaHsvhFqgCJRJzTZ0/y4Iceoq7+BD9rbOSWG27j0KHD3HbTXTzzzPcIlcyuBfVscbucPHzbTXT29vH2oaMIlodbbr2Vxx57jD/54z8Gy+L//ed/WdRsw/lg2YDME7//m5/k/zz1Gt4V6+d8LruiIAiTPyn6y9bTdew8qcJBnFk+LMvCLk3/6d6QLWzeHCwgDaSx6BtUMfv6EEwNGxZ2m4hiE7GZFvaMDVe2A1/Ah9PjRBAEogNRBqNx2ht7aW9ow+X3YivwYqoGFQUBKlevIBaLUX8yTOJMLg6XkzaxZW5vziJg6iaSdO2kUmal07Ts34doWTgQsI2TdZTUdfpMk/xZ9JSZDud+8jQhSaFdS2N4vRzsH2D3LGs65sozzz9FICebxsZGivLKSabipNIpTp88QklhKQ4sXHb7oi/WR07VU5ibi2kNGfgjhw7yja99jfaOdo4dP3HNGQ9YNiDzRiAQoCrHycX+LlyB8RvMTBdJEqdVOJe77kN0Hfx3Sm91YaR1fJ7pP1UVZK0h2tRGItOLsEJAUmzYZDuMUP5Vh79SjTGqfDcRu9hH78lOdL2TRLKfVMpAkv1Ub3iE7FAvWYFiBmNdpNU40XAvLx86gGwP4PXUUrU5RF5xFZHzP5z5G7LImJqOTbw6T8+zIU9WyEzSFQ8gZRqs3L59QXqAxPr7yU2k+NjjjwPQP9B/OXh9NdhQu4m39rzO3bffx8oVVfz7d7/BO2/9gg1lBdQd/AWSquOepNPgQrGiuJi3T9Th9mchSjbS0QFiA/383d/9T+xLXB9uIpYNyDzy0Q/fw+f/x5epvPWjczqPKIowQSbWqHE2GV/ZfXQdfp6sslz8eUXTvkbJilpKqOVc/dtEpcl3BZKukFu2ElFcNeGYHFYA4M8pHHH0wVFjMqkkDqeMhYWwhKVCMqkMAykR1TSRJQnvcA9yzTDQDQNBEFBstkndXJZlYQ5/Xfq/dcVx7zSfghujUS5kNARJxBRELEEgrqpsdSiU+P3YBTANY1y31SUk0yKdSk1uQCyYza/Fk5VNiywRj8fweLwEsqeXSr5QlBSVosaiNDWcw+vx0d50nq1VpdSuGcr+6+jo4GroFwayfNyzcysnz56n7ux5BiIRDAQ+/OCDU794ibJsQOaRaDRKLKNP+WGeCt0wsRkp9MEeBFcWkm3ieIQ7WEy6ZxWD587jqpx5tXFGj085V8Ofoaf9Avkl47d4nS6SZKO5TeY7X30TWbZw2AVMI43b68Trd+HLduHxufD6PHh8HuxO+1WRfe/sGeRMZRU2SaSn4QKPFQ8ZxV90dGIrKiGVSrIqGmVNztiMt1+0tqFlB4cWYkFEEIe+hpo5iUO91EWR/r4+7jBNsl1Tx636VZWcLdtH6UTF43Gip45SArhFCUPXJ/09OiSJlnNnkdeuxeV+LwYSHYjQ19qCLEmoQOWaNUMp6QiXjcm5U6dxiALdfX0Ur6ykoKhwzPn7LpzD41kaCRKqqlKZn4cY6eRf/vGLPHDrbgrz3ytGLSwcO/+FxrIsmto7OFx3mr7+AfYfP0E8k2H3Ddd2V8JlAzKPZGdn8ysfvp13wnV4K2YfKA4GAtwQCNDc0kJDJD2pAYGh1N6Bo4PouoYyQ8OlWWOzvSzTJBNNYve7hxY8Q0BCxjLNOcm22BSFVdsfGHPc1HVisSjdLVEyiQhmphtDvYBgZbApAjbJwCYJyLKIJIJNEXE4FVweO55sJ6ZpkluQi02S8GZ5keaYQWUaJsWlZUiSRKan+/Jxu9NJSfVq4vE41qmj49+j3UHZhk1TXkN2ONF7pifVLwsCmqED7xkQWZaJazrxTAZDy6BrKvIkbhCHLFOkacSjg6MMiGmZ5DqdeF0u2gYGaDl9GtMwyFgWiiwjCiIuoNjvp8jj5cC5s2MMSOPBAxQWza46e6EYiMfxKRJ3bNs0ynhcLX740iu0tbRwLHyOxz/xSf7tx0/T19d3zdV9XMmyAZlnNtbWsqcxNi/nSqk6inPqjBlRFMlafzenjx9mw9brZxSMU80UMjKmbqD2JRHjMm4xB79YRlf6NM5CH3K2g/r6l9mZ96vjBmjnimiz4ckO4smeuvrbsiwMXSOZTtI/MEiyOUJ/23l82Rni/V243E5E0UISLSRJQBJBVdMMxlLI8pDxEwQTLAtBsJBsIg6HjMOloCgSiiLT291PQlMRNIGBRIK6+CCCINCX0cjOZEhqKj39A8j6aE0kQRTpicXJGhbeuxTHsq78vwAx0+SNtlbcLjeaZaHB8L8C2CQEScKSbCBJpEyLSruDhN2OKYAwMECu3UFrMI93ZZlmp4d4ZADnFDsAmyjS1tSI1+/H6R7a+VgjOhcWZ0+uRSVJIhjmZVeXaRg8/xd/yoaSFXxoEdN1J0NVVVo7m2jpbmfLmgp8vqXROtk0TJq7unji29/h5ptvBiB3EfuyLBTLBmSeaWhqRGV+5MpTqoFon97TtCTbGVSCtDaEKa2cXsWxZZpYSZN0QxynnsW6itvwVOYgihJ9Xc10nD6FmWsg2WUUrwtpHqQc5oogCNhkBZus4PJmESgso6R68sy3SHcbedlO/LmjkwxM00RXVXQ1jZpOoms6SU2l4vaV2NfVIgoC5aUldAxGwTTJszuQ8vJwazqJYJCLwnvnAcCyCDocCAVFQ0ZcGBLfE4YL+xhuyyoIAl7LIrFjJ7IiY5MVZNmGLMtINhlxkl4kmYxK58EDiKJIZc1aAAyni8auninfO5skDe1CBgcvGxDJJo9K2LBME03NoCaTkEohpZLY1DQuLJxA5rXXaMzNYUVtLYIg4s/O5tMPfoxg4OpKv7S2NXPs1GHsTjuKqHPvjTvx+5eG8WhsambPu/v4ky99+bLxeL+wbEDmmf5oHJd/fj5Mac0Y6bWYEsWXS2PHWQK5Edy+qeMhgiiy4/pPjhuzCeaXsc33cU6f/jkp+wCiZsOyTATh2imwu4Suqdjkse+HKIooDgeKw4FrxPulRbvJzs5GECAre/z3Ma9w7nUEXt/MYwaSJGJeEel2KHaszMSinrqmEU+lSKaSDMbjiNEBEmccWMk4pFKUyjaCHjdOwCkKZNnt5LmcBLNceIsCOEcE+x9aUcLnnn+JFbW1dJ8NIwxErrrxADh4/F2yXBJyqp+gz4t3Gn//i8XXvvt9iioreeyxx672VOadZQMyzxytO4Nt5dzz33VdR7OEGe9lbAVV1J04ztadu6cdyJ9onN3pYtOWh4lHe5EkGfEaqs4eiWnOLKlhaMewgBOaA5JkI25XIJFElESCih2Xw8Fg4wWkaARJAkmQ6E0lkRU7siSBZRDU0mwqKqLA56Ukz0dBMEiWxzPjKuxvvv4WebtuAsCSJKoqJ87MW0zqjh3i8TtvwW6fudrzQhI+f4GkYfD2Cy9e7aksCMsGZJ75jccf4X9/56fY8ypw+XNmHTOIxeOY0sxzwwVBRMteQbjuKDUbrpvVta/E45+evtZSxTIMxGnqP+mZFKK+dPWIBAHWbN6Cqmbo7ehE6+zE7/Xy27/0sVEJDi/U17N+TQ2lOTlYuoaz+QJbVo6v4jxdTl1s5IRsp9jnp/ndvXSdOslnd98511uaMydPHUcyDeSrpDo8EZZl8b1nf8bW63df7aksGHN6x0Oh0EPAo+Fw+PHh73cA/wzowMvhcPivQqGQCHwV2ABkgF8Ph8PnF2rsXO5nPggEAvzRrz7MQCTKsTMXONHYh5K3EsXpnlFwu6dvAJt9+tIkI7E5PfT0RchpbyG3qHTqF7xP0TUVSbJhGPqUmWyXMLUMW9bOLV15oZEkEafTid3hwBiOXwiCgKrr9BoGhiCwtnYdhVWrOHfqFEFJxEhnyGgaFzo6WFM2u34hpgC3u+2s6WigNDeXJ6L9nAvXU1lRNZ+3NyMi0QHeeuFHFDjtvLt3LwUFRYiSQE4wyMDAIJlMmlXV89MxdKYIgkA8lebhRx65KtdfDGZtQEKh0D8DdwHHRhz+OvAI0AA8HwqFNgMrAEc4HN45bAj+AfjwAo696vj9fvx+PyvKy7ixv5833z1AU0sfMW85nuzppRT2xdJIc8ggUYIlhC+cxpcdxO6cnSG6ljF0jYMvfZ8VtZuxBGPaBgQshKtQezIbXB4PXR43cVkmHotjWSbV27aOqmouX72aF/7lH7itdh3PnmsgY0FpMol3GvUnV1K7BPL0GQAAIABJREFUYgW1K1YA8PapetyBfHbvunme7mZ2CIjkBoPYbTIp00RVAjzzwjN86LrdJJIxAoWLX5sSHRzk77/+r2gWdPb1XxZzfD8ylx3IXuAZ4DMAoVDIB9jD4fCF4e9fAm4DCoGfA4TD4X2hUOi6hRo7h3uZN3701NNYCDz2yEPA0I7kofvuxjRN/vW/fkR7l0ZW/uQ9O3RdQ7ME5powKxasou7YYTbv2H1N6uzMBckm4wvmU7Fhy8xeaFkTZkElk0kMwxwRI3nvX6fTuehxE4/Pi2fD5PVGit2OHAjird1MvttNNBLhYlcr61eUz+naxy9cIMtTzMmDB7F7PGzetm1O55sOuqYTTySIRWKk0yq6aqJldPw5tTh9dh64aTfnzp9FefVlEqpOQk+wfdXGBZ/XSJrbO/lf33yCuz78IF/5ylcwDOOqyrosNFMakFAo9GvAF644/KvhcPiHoVDo5hHHfMDILjwxYOXw8eiI48ZCjQ2FQrZwODy1BsgCsn3rdRw9Ucfv/8XfsHnNKo6cPM3D993J6lA1j91/J7/31//Ixns/Mek50ukMljj3lFlRkolbdtR0Evs06kmuNcKHXsUbyME0LCwTCleuGaWGPNjbNeNGX8IkO5BzDS14/dlDcoQj6joSsSjVK8twXtmf5SqjaTqtTY3cesfdl4sHfX4/7efPMFfJzyynh4rSUgK+LBr7+uY+2Ul46pkfUV9fz703P4TXlYVdcRLti9DcdhG308NgPIbNLvPs88/Q1xPhdz/1JfoGujhU9wbff/4Vbt+xCY/bg3MBdMBGEkukcJeUs+fwERzD8jc229KKy8w3U95dOBx+AnhiGucaBEbuF71ABHBdcVxcqLFX23gAlJWVUVxcTFIzKQhmE02bPPvSq5w9f4Ebd+3kNz/2IG+eOo0trxJ5AgG8RCqFIM/TH7tNIRWPve8MiGVZeAJBVm7eCkCsv4emU/txevxgWVgWFFSumtXOa6LX2O32cdNDr+zLslTIZDIERZGg972PiSAIJA1jkldNTUNbO4OCE9Pt42IsTnloYTsdOh1OKopC+FxBnI6hhmRnG06zc9PNOB0uOrrbePGtp1hdtZZ7dz8KgMu5ktKilQxE+3j13adJJQe474aN+LwL59IaMCweffCh9/WO40rmzTyGw+HBUCikhkKhSoZiFXcBfwWUAB8CnhyOVZxcqLHzdS9zRZIkPvbQ/QDccoXUTWUl1K5exQuvvUM8bnCxL0nOqtHSF519EWT75FXB00UJFHE2fJptuYvb/2ChMTRtVIabN5DL2hlWQ6vpJKZhXC72QxAw1MzEhXzXmBdQkkTMcUQDnXP0tb1y7DjuvErKqxcnhbd27Xq+f+IHCMINqJqKIitcv/kmzjeFWVe9icK8Yj79kc+P+9psf5CP3P0bxOKDvPTmt3jwtoWRmP/XH/yInIpKPvoBMh4w/x0Jfwv4L+AAcDQcDu8HfgKkQ6HQXuCfeM8dtlBjlzw+n4+PPngvv/5LH0LvHp041tLWTlSd+Cl4pgiiRMoQMPSJ2+Reixi6Ou3uixPhMuJsKsumtshDTYGL1XkO1lYUThxEvwoKrjMhmUheVpm1LAjX1zPepIU5LHKmaeJ0ODFNA8uyyExSwDgZHW2dNJxrZKB/YMzP0uk0scH35IBKiktJqXGefeVJevo6icWjJFJxEsn4tK/n9fhYXbWb1/YdxhzPqs6RvOJSvvTlr8z7eZc6c9qBhMPhN4A3Rny/D9hxxRiTIQNw5WsXZOy1Rm5uPj//9j+z9a6HCRaUklE1BNs8+9KdfjqaL1CycmFdDYuJrmvY5iitoijKjOQulqr9uHDmDFomw8DAANtvvBFBEGm+eJGAzUbOcCafpmnEYzESg1HQZv8wUd/UxPdfep31q9Zz+ugpikvKefijH5nxeZoamykKrKTlQhf+LD+iONQDp6uzm3D9WQwNqtespKS0mFQqRVV5Dd093YQbTuNxecnyBagNbZ7RNdeFtpHKpHnt3UNsW1+Dzzs/Qoa/2PMu/dHBy3GPDxLv7wjPEsOyLBoaLlJZ+V5B10fuv5O3XnmRtgM/J3D/r1G5ooye4/VYdue87ULsOaWca63Hl92LL/vaLgq8hKFpSM655am9HxLT+nt7sWkalbm5tMkypmnS3toKsUFKc3Opf/cdgoqMUxIp9LjJ9XrJWr9uVtcaiMV4/VAdj9/wYQqySuka6OTN8H4eNB+esqJ9MBpjMDJIJqNhGiY27EiihGJzkMlkcDqdYEHj+VbK8lZhmkPZbrqu03yxlfWrttDousjW9btmNfdLbF1/I9191ew/+Sq5/g421szdDZcfDBDVlmYcbKFZNiALzJFjJxiMxbj5hqE//KysIamFrq4unnz6GX7nM7/Bsz95io7OLr72wxfwVW+nuqyAuqYe7PNQAW6ZBpneFtyiijwP/dqXCjbFTndjmEhHJ5IssWL9zJ5GgXEbQjU2t5LRhoLMl346ZMiteel3P9/omk7+cGBYRKC7p4dkdzfVRUPNxQKyjbvXrZmXa33nhV8wOAh7m18D2cLtUMgN+Olsa6OodPKC1fNnG4j2xRGFoZ1GX6SXd/a/wR033E9nexeiIKGqGrF4DIctgtPp4sjRE9SFj3LL1vspLVhBe3fbvNxHXrCAe27+ZX6x50leefcwOzfU4J5FXQzA4WPH+foPfsQ3/uM78zK3a41lA7LAVK1cwf7DQ70jBEEgGBwSnsvLy6Ols5f/+MHTfPrxj1BYkM/nHv8QX/nXH5LOWonkmJ+sqWRLPeury8kp2vy+qgVxef2sWDNUe9B6/tgUo0ejq2lMNY0wjsfBMAwCOYvfcGi29Pf2kj3shnPbFWJ9fVTkv9dS2ZpE2XemOBQHJWVVXOho5bH772ddVYi+aAR1GoWXx04fYsPKnWT5h4rqqivWkc6k8Hmz0JIqoqygmCZ19cdYU7WRnv5O3G4vj93xGxyr34/fk01/tBfd0LFJ87Ns3bHrMSKD/Tzxw6/wuV9+ZFIV5CvJqBqpdIqvfv9J1qyrZdv27fMyp2uNZQOywPh8Pu645aYxxwVB4M//++dpaGy+fCwvN4cHdq3nb585THD13P8gLdPAa8bJLV4x53MtZWZiGLVMGiHexXVrV6MoY2MoV6PV6WzRNB2HaSAP1xr4nE58zqE0V8uyOHX4IMXMn2slVFaKkMnjw9s/RGtXO+uqQjjtDphGP2+nw0FxwWgJlZKCFUQHB8geVq9+8c2fsGPjzVSUvieN0tLRSHP7ReyKk5rK9XNuFnYlWb4AJUU1/MMT3+bzn/zYtHuTP/Xyqxw6fAhLtPHUs8/N65yuJa4NzYb3IfsPHsTr9bKhdu2o47fddAMlVi96JjXna5i6hjd47TetmYyhBlPqlONM0yAT6cKW7OG62jW4XM5xi7wEAXq72xdiqvOOJEmkBIFEeqz4YyaTpsjSubV2dvGOKzl4+iyvHzqFItuxKw5OXzgHgGKTifb3M9A/wGA0ijpBVpYyjqjoyrJq6s/XsffwG+w98gY+b/Yo4wFQnF/G9o03sXHNNgpyixdkF/3Yfb/ObTd+gufeOMCTz7+EMUWdTCqdwRIE0gi8e/DgvM/nWmLZgFwlfvr8S8Tj46ch/tLdN3Lx1f+a8zXSPS3kFxTN+TxLGUPXsLsmd/dpkU68RpRNVaXs2LwBxyQVyZUV5cjSDBcpy0KbQ2bTbBFFAVmxj2oIdQmbTUaapyro4+ca+OmbB7lj431Ioogs2UilNI6caSCVyZAjilg93ZidXZw9fmLc1N7xiutkm8wNW2+numItHpefm7ffNc49iqwsXTVvbquJ2LZhNx/98B+SsbLZd/T4pGMVRUZRFP74z/6c7Cm6OL7fWTYgV4ld1+/gX772rXF/ds/dd+Jzzq0SPdPTRK7DICvv2vHnzwbLNIl0ddB8+gRdF8cXY3bYFWpDVfh8ngXJvHK5vTQ0tRKLxbEsi0Qi8V6XwgVGsUw8w26rkUiSRJtq8OPTYf7twGFONTXN+hqJVJosdxDFNrSLeDd8kAdufISAu5B4KoPP7SLg9ZLl9VAeyCY5fP+qOvXOECA/p4hNa7Zx5kIdnT3zEyifLb/84Gd56/CpUbsQy7IuG+mMqrI/fIH//sW/4qMf/djVmuaSYTkGcpW4787bufeO8SunVVUlK39uC78U66L21rtG9Yh4PyLbHVRvvgVD12k5d5T8caTFp3oHIpFBIoODSKKIKAqk0mlm0pZIUewUla6kub0NXVOJx2JIosD2bdctaKrwYCSKPsEiLQgCa7YPVV0nk0leevYp1pbPXEAxGo/z8v6j7Fp9x+VjXqeHPcfeZtfmm/jP538ImNRWb+Cu67cTTSTp7BvAKQqIWOSuqMDpdCBMo4w/kJWDy3F1laMVm0JB/krefPcAN1+/A1EU+Lcnn+bk2bMIgsDNt93GH/3ZX7yvFXZnwvt7dVniTOTP7e/v5+yJw3M6t+J0v++NxyUEUcSmKNgm0A+zLHNcN88l+iMRXN4cZFc2ot1HbsHMe6gIgkBeQQlFpSupXrOBQF4hJ+pO0XCxEVXVSKXSaKpKfDBGdGBs9fVsSMTjlGRN7UJxuVyoNpl4cuZxNdlmI8sdwOd8r+ByV812vLKD82fruGvD7fzKTR8nW/Ty3Z/9jEjEwIoY5LicBFwuki0tnDl1ikQyMel1vv/cExw6uReP++r3Mf/Ew58jqvp5+uXXAYglkyRSaSKxOIeOHls2HiP4YKww1xh2u50Sr0R/05lZnyOZGKSjuWEeZ7X0mehpP6ZBV0/vhK+zrCEjJEnSUOxgHvzt2YFcCksqkRx+wheaON/Yyk/+6794/amn+eqX/prU8GKuTdPNcwnTtIhGolgWRAajdPT2TOt1focT2zS7Mo7kP57/Bb3R6Jjjt224mZ2rt1KWU4JiU6guWgUaSJaNtKFi+Pw4S0owfT6qQqvxeCYXMdxau4s1VXPVCJ4fHIqDR+/9NbDlcOD4SX7p3rvBsti5azc/f/kXV3t6S4plF9YSJDs7mztuv536jh7Uxn7SuoVqihg2J6biQrS7UZxebC73hH3KHVU7aWw8TmHZ3NqYXlNMYEAku2vKzJqFQBBFXC43ruEgf7IixE3bdrNq1X7+z19/kfXbd3Lh5AlqNm/B6fMiACf27aN4ZQVqKs3Wm26iuKyctuYm9r72Gqaq0t/VjU0Ucfm8FK3fQk9HG81dXWwOhXCPEwu5RGlBPo5ZtFd2yA62r9o4ZfaTIAgU+PPpi/ZhdzoI5uVit9vx+YedgcLk+dFVK5aezM6tOx/gmRf/hVQqRUlZOd/41reQ5yif835j2YAsUf7g858d9b2qqvT09NDd3U1bZw/Nnb109J4nkdFIqJA2DNKaRdqAjGEhOdysLJpe98P3CxMtcjbZTriplYK8PCRpvE339Io/zh3ejyLaLl9rpFtMVVXCp49TUllNzdYdE3SBHJrfxs3b0TWNSHSAz33uT9iz5zUajhynofE8q6vXku/JISkmeOdnzyN73DSdOcPd9zxMReUqXA4XisNBd1cHLak46zZupf7EEV45fJgP756k9/Ys3Zmd/T2cudjFR4Zl0icjraWJxaPYMcYkEQgIM+7NcrXJCxaysfYu3nj3BbZs2zHtGpEPEssG5BpBURSKi4spLi5m0yTj+vr6+Pt/+zHZoW3vux4gs0WUJESHh9ffPcTGmipyglf6sKe3qDklmZ1br5/w5zfu2E1rSxOtjQ2U10xef3Hd9vcW+1tuu5dbbruX48cOsKp67eUdC8CxI/t57MFPjKlZkWUFIzrk/qpZv5mmKbxTfbEYLT09lObOrC7oV+67g68++YtJF//Xjr8JNolsp4/Gjots3L5tSNtqBJIkoevaNSWnY1kWff0RKiqr+chjMxeM/CCwbEDeZ6TTaVwez4SurWuV/c/+B/68AkzTRNdUckpWUVQ5ugjTmmQnYbN01tWswu+bHwXWca9hk8kvKKItNjZmAJPPD2DDxrFtYTduHl+RQBAEMqnk5e9VbXQvNU3Xsdlsl02jv3wlidTMg+gep5OA3z3pzsHlcHH8Yh0Om0LcyvBYzWpamtrweF1kB4aC/KIoYpgm15ID6NU9L7L38Bs88tEH2Lx55lprHwSWDcj7jOLiYv7wUw/z8zff5XR7Ckf+SsRrvMmNqevklpWz+bZ7gaHA85FXX+BsfxvRnm623vtxLMvC1CduSKkIJjnB8TOW5tOrIokSPS2NaMPV4RYW4rC7K9rdNefzv/zzZ2ntaMMULDbfcufl45H4IO+eOQOCCJYJskJ5dhZFwxlDkdYWytfPXFTRMAzSmckN39ZVW2iLdFKUW4LbG6C/NYmi2OlNDlw2ILKsoOkqjjn2cFlMtq3fRbjhJNt2XHe1p7JkWTYg70OcTicP3X0rN/b189zr+2jNKLhyS68p//NI1Ex6VPdBWVHYfs+DAOx74Se0njuBZZrY3RPXELhcEweY51P/SrHbeeCehy6/1/P5np84fhjD4+b2X/7UmPOuXruOspJibDYJywJd1+k9dw7DNJFEEYci097fT5bHM6rF7VSkNQ3NTJBS0ziV8Rd/SRTJ9+exbf31pDIZbMMFh5ZpkUql6OvrpbunkxJ/aPY3fxU4c7GOtJYkJ/f90QJhIVg2IO9jgsEAn/rIvTQ0NvHiO0eJ2HNxZV172lhdTWfxTFDvsHLTdXiC+UMd8kwTXVWRbLYxNTDjSbe/x/wqKE7VG2O2WAKENl43yng0nD2Fmknh9/svp+kKAthsEqrDzoHzF9hRvYrimnVcSKcwGs5zz4baaV+zKCeH/OwcDHPi3V17fyctPa0EGk5jIaAksjl0cg/JdJzKFavwuLKpyK3F7722ZD+y3EEaGs9RXFx8taeyZFk2IB8AVq4o57fLyzh0/CRvHj+FnlWG3TX9p9CrTTLaR/WmW8b9mcVwkFyS6L94mpxAAF3XMSwTy7SwsFBVlbzy0YtAd08v8XgSQRCIJ5L4pqgNi/T1oKuza986H+i6xsWWRjauGv0Un5MTpLKiDF0fnaYsCALlK1eSSCRJpFJ4vF48Xi+tDePLvUxGMMvDsYYj7F5z45ifDabinGg9w+rKtTQ2nyMrkMdAXzub1mxHVTOsr7l23T/PvvJjBMF2zWWPLSbLBuQDgiAIbN24no1ra3hj70EON7Yh5lQgK0s/NVFX0yiOsS4oy7JGJQu4XC4qVo6VMolG+inIHS1OMhhP4A8MycW4s6ZOd+49f56d1+2YctxCsWfvmyg+/xiBxEuB+YmKBO1OB8lM5rJeVsPAAOdbWxEliZWF05PLKcnL4kj9xcvusJG09rYRSw4idpmsyC+jY6CL7kgv99127Wct2WSBDz1w74LtKN8PLBuQDxiyLHPHTddz/XUJXnprP/XtSex5FUi2pZsfY1nmuMqypmEgjKjrmKgfkKpmxgbKR3itpvN0qdjtOMYxYouF4nSxcuPYTCA1o2IYxrhqtwDFJSUc3bOXvKwsADbvupFwNEJ7wwUsoHIaRmRn7Rrae/vZH97L9TWja02qiiopyy3FM6xhtaZ0Nc2zkExZavQOdCPLNmLJwas9lSXNsmn9gOJ2u3n4nlv5nQ/vplBtIdF5AdNc/Grt6SCOW/w3VOktGAaJvi7ifV0oEwR5bbJMJDrzhSCdTJJOJlEz6SX73iAqHDg4pJtmWTA4GLscRI/F4jgcQw2f6i5eBMDj9VJUUsrG63dzrGtieZeRNLR1cvJ8MzWlY2tbFMl22XhcZqm+VzMgo2ZoaW6hq2N6UjEfVJZ3IB9wsrKy+OUP301Xdw/Pv7mfdtWOK7dsSfl8hQlapoqiSDB/KLZhWeAUxteVys7OIdrfMerYVGFzQ9c5t/ctysoqME2Dkryr21fF1DTazocxNB1D1zEMnaqNW8jNL0SSROrPNgACmYxKOpUgK5iDnkmzbk0126/fyf49e2jp7qY0b8hdZ7PZaG5vo62kkEQ6gyiJVE2wG+keiKDrkOWepkbxVZCNmS8ikQjNF8K4PD5ygwXEBsev6VlmiGUDsgwA+Xm5fPrR+2lsbuXne47QK/hwBQqXhCGRptWrevJA5xg58SksiGkYFBeXsm7thmlce+HZumUHmUwamyxjs9n4yTNPUrVxC6Zp4ssKYBvHBdnfM9RZURRFcnJz8V/h5ipft4FjyCh+D33nz1JVWIhlWZxtbcXtdGJZFqW5uWiawYaKTdP6WzBMk+RVaK41XzSE61gXNIinB7l9dQXnole3P8lSZ9mALDOKFWUlfKa0mDPnLvDKgToGlVxc2fOb+jvdrJaui2Ey8SiZZIqGI4cQRAGb3Y5stw//68AmK9gUBVGyITimb+wymQyZVBLZ7hg3SDoUV1g6Hw/FbkcZocVUvWo17ceOoKkq/ZE+Nt1535jXGIZ5OT6yorKKusOHCEkSLseQq69iVfXlsYn+Pp4+dQZTEDjf2orXYSfPrtDdG+XFvcd5cMcj05qnKAq4PQtX7T8ek8WApkMqnUJVVQxNIye/mOjgeU61HMbhiOJTBDo7OiiYZsLBB42l8wlZZskgCAI11VWsXlXJkROneOv4KVLuIpy++cnjTzTXsbrIT0Y1SekGGc0go5tkDAtLVDAkGVOUiXS1Ulm9nvVbbgC4LGOiqxk0NYOWSJHSo+j60PcNiQg2SUQQBARRQBDA7bCT5ffR2nyOhqNuQECURNobLrJx804Mj4fiquoxc2w6dYLayqVb+LZx43vpsQcO7bvcNU9TM0iSDZss48vO5c0332FleQmSKCIJwoStYcur31PDrdw0dO4L9af45osvcWf1zdPfiVpgahPXjMwXnS2NqPF+0FIkU2mKa67D6xvfxaYZOtHoAAORPuLxbmKJXkTSiFYaiRQuWSUVsxP0VGHLD9EV7efOLX42VlUQT6kcO/4uBYUPL/g9XYssG5BlJkQQBLZsWMfGdTXsO3yMEw3nSGomGQN0mwNkF3anG9nhnJGry+dUePTe28ccN02TVCpFKpUiHk/QvybAcz9/hYbuZlauvx5RFFHsDpRpymGYpkmOlKS6ohy27Rp1nNsgNhjhTNf4LgrFJpM/x66QcyGTTqPpGk6na8qn68L8QtqPH0UURWSbzLv73+b+X/0t7HY7+cXldDWcZ9uaGkpLZ9YoK4NIR2c3pTvKpjW+NxZjUBAILHDltqpp9LWEqSl0IToFLK+d0+dP4QrmMRjvJZ7owdSTSEIK0UojCynyfAJVWVBS5CE/yz2c9uwe/oKn3x4g3+ugrukUNrGZDZVD75XHqdB+7ADmnQ8up/OOw5wMSCgUegh4NBwOPz78/cPA/wJahod8EXgb+CqwAcgAvx4Oh8+HQqEdwD8DOvByOBz+q1AoJM517FzuZ5nxkSSJXdu2sGtY609VVQYHB+kbiNLdN0BvpI1ERiep6qRUg7RuoiFj2hwIihPF6UJWHJeNjF0ef0EURRG3243b7SYnJ4cVwOYNtXzlH79KJpWcQCJ9Ygxdw+MZa2wuLQSmYSJMIDp5NSM/F0+fQGu+gE+ROW8JbLj13svvnaqqQ7Itjvfuq7S0nNLS99rVvvLiM7z+n0/g9HqJZ9LYLIPta2eug3Wh4QIby6dftS4JArmFhXhnIJUyG2ySRG7lBlrb6ynLlhEEMGInyfbGuK7MQ2G2G5dDAZzDX1PvnB2Kzv9t784Doqz2Bo5/n5lhG1ZZVAQVFzyCC4p7LlloptXVsrLUFm92229m3dbbot0ye9vslktZ17JdW6xM07RwTzTNLY+KoKCA7MMOs7x/DBq5wDAwgHA+/8icOc/D7zyDz2+e85znHKvNQqhfGXo3/V++EO3cFM+utEeZ8/IrLmvTxcrpBCKEmAeMAXZXKY4FHpVSflml3nWAp5RySGUieBUYDywEJgJHgZVCiFggoh7qKi7m7u5OcHAwwcHBiMhz37fZbBQXF5Ofn09OvonMXBP5phyKy82UVlgI9K/d8xS3TxrP/y1eTs/hV9fqvoTVXIGxmint3T09KU5PJyUvFxt/nRPL1ohPnZcUFtC7dQgBPr74ZWWSckTSIbI7aceOsuOnleSVl9M3uhdaaTEHEw8TPTSOgvwcAtuEEhTSltvvuJ91HyzgjsGDyTXls/SHFexMaEO/AefO9lttHMUl6GsxosrdYKC0tBRcnEB0Oh1BIa1JPiEB+9+bj9HEFX0du1I6n45tDGQX7CNWeNE28K9XntMnDOGpj7epJ9LPoy5XIFuAb4C7qpT1A/oKIWYA24HHgGHAagAp5TYhRH8hhB/gIaVMBBBC/AjEAaF1qVuHtij1SNO0M1cS7drVffhrWFgY9948jkVLPyc8Jo6AkLaOxWGtwLOaRYC8vX0YOfzyOsdXnyxmMzablczCQgJ8fOkQHELS4f3sOnaEzh5uTBk8hIzcHNq3bgPAJe3b8+XWjQzuJsg8tJf1q76m3CcAD4MBd4OBNoFBzLj5Vl7++otaJ5CCjCxu6nPtOeU2m41ycwUlZcWUlBdTbi3Bai3n96TDjBhR88JTp/dhtdY8UOH0Sbvqv0VFRRTk5+KhmQE9haUlFJYVV7ufmvSIuPBVSmR4CN1a2Xhr3us8MGNmnX5Pc1NjAhFC3AE8dFbxNCnl50KIkWeVr8WeVJKwXzXcDfgBVQdTWyrLqj7ZVQB0rmtdIYRBSun6O3hKg+vWLZJZj/2T519biOfAcXg6sFiWZrPg7nHxLGB06sRx0vfsoF/bNvi3+jPxDu3UCbB33QBnkgeAm8HATcNHAhDRJpQBIooftm+lwPJnd59Bb0AEBbN1YzxDhl/qUCxpJ09gKjGxOymBEP8A9AYbBr2GwQ3cDBreXu6Et/EmwNcXf++2GD09+XLWWiYEBTm0/x83fkN+cTaTrpwO2O9L2WxW/vvBXFq1asXNV9/B8ZNJ/H7oVzy83bBZrWSezKVf+x7BZoUPAAAYjElEQVQcPLSb4zmpDOoWRXirKPKKCuje/q830BPTcvnf6t8wGP3x9TQQ096HUbHnTnPjqCdvHc1d85ZjeeDBOo34am5qTCBSyveA9xzc3/tSyjwAIcQK7N1O+UDVa1od9oRQtcwXyAOMdamrkkfz5uPjw4w7p/Dawg/p0O8KfPyrnwHRTQe6CzyE2BQl7tzGuG6R58w3ZXDghJWTfYqC/BzQdIyIOr3Qlo3Td3OuHDSMV79b7nACCQoOYVTfGG4Y2B9Pd/cau25yCwqwWh0/sbbv1I7ewX+O/Hr303kEtAqgi+jIqawMXvvfc+gNOjyMBvqE9wUbRHaNJP6HlZzMTMPLzUigjw8Wq5W0vBRGDfjr38Ls91dS5NOFN16cR2FBAQtffZHfTvzB/aO7YvSs/bQ9Rk8Pnpg8lHf++yr3zHi01ts3V/X2v0sIoQF7hBDhlUVxwE5gMzCuss5gYK+U0gSUCyG6VG43BvvN9jrVra+2KE1XaGgojz1wB/s2fEt52fnnXLKYzVgKMmkX5OCT001E9IhR7Dp+rNbbFRWa8HbX0a9PLzp3aIcpJ42s9GRysv6chkOn0+Hh4Og1m81G8t7d/K1vDF4eHg71+7vp9YAF63kWV0lNT8FssX+3W/zJW3z70zJKSkuIjOrMDxuX8fO21WTlpXPwyB6SUw8z+srLWfbNJyz7+lM++ngpjzw6k0cem8mtd97Mgs/fJ7KXIDHjOEt/Xs0r33xN+6COfLHh+Jnft/lQNkEde3PLbbcRHh5O96go3li8lPb9xzBn2Q6Wr//NoeNwtuj2geQm/sb2X7c5tX1zVG/DeKWUNiHEdOArIUQJcAB4F3s31GghxBbsX4emVW5yN/AxoMc+supXIURCPdRVmrng4GCmT7qaF157mX6jrye8cmlbq9WKpSiX1r7udOnR9Zx1xJu6sqIi8ipXMnRESXEhaSeSaRMSQliHcDRNIzAwkMDAQCoqKkj47XcCg+1Tl2TmZhPc1rF7R8cOHWRgSCABtXgg0MdoZEz/KD79ehFTrrubg0f3YrHYKDRn8fuBBIIDW3MqM4O8olxKc7LZJXPIL83AM0BPSXE2YyeM4q67/lFjsnJ3d+fNBfMpLi7mhmsn4uPhQaBPKw6lufHMB+sJCGrNKWsgry7+5Jx93Tx5Cnt69GTBszMYP8KC2wVmMK7OvWO6cdODt/PusrW1HhbdHGm2+lyOrQkTQkQASevWrSM8PLym6spFIC0tjRdefg2bTyi9h8Th72ajW0QYXo04a66zbDYbe1cu57LISIdH+uTn5dC6lRFfX9/zbvPHHwfR3L0JCm7DklUriB41hnY1LI6UmX6SVjmZDOjS2al2PLZoCcOGTGa7XI/Rz408Ux6xsX3x9PQkIyMDP19/YmJ6E9ouFHd3d4zG2g3NPlvcyMswVrgTEe5BUkYmfQYP59/PzbZPInkBjz/0IFlHduFlM/HfhyfV+nfOXvw9n2w+zIbt+2jduualAC52qampxMXFAXSSUiZXfe/i+oqmKFWEhoby1uv/x6J3/4dfeSYxvRpvvY76UF7rqVNsuLm5nUkeZw8zbd06hJOn8gA4YTIxqobkUVhYiDX1GP179qi2XnVuunwYr3y5iJFXjGD2rGdZ89NPBPj7U1ZWRkTlYID6FB0dTUZKGmE9etC+r5GH//VojTe5X3p9Hvv372fq+DhueGQeA/v34Z/XD8XDwSvWZ6ZfzbZ9rzFsYA9k0qkWPbT34rnDqCgXcNed0ygxZTd2GHWiaRph0X1IOCxZnvAreQUFNW6j0+kpKSmhoqKCxKPJ7Ny9j9y8vDPvFxcXo9PpsFgslFacf6bi0yoqKsjYu4tR0VFOnxCPpKURf/QofWK78+Yrc7ECyUePkpWdzdzZs53aZ03+O/9tvvjuKx5/5nkeffwJh0dI9ejRg4m33sv2DAsbjpRy6zMLa/V7X/jnVEb26c2bLz9PS+nFOR+VQJRmwcfoTuap9MYOo07adohA1z2GPmMn8tXOHRSVVL8wk4+vP4eTjnP46HGMfsF07NyNrOzcM+/rdDpysjPJzUpDZ77wA4E2m42kXTu4Mko4NOLrQkrNZvJN+bT39aVPhP1qo2uXLqxbuZLcwkKn9+sq/37mGZJTTvHNmnXsSTdzy9OOJ5GYiBAiRRcyD2wh/qfVLoyyaVNdWEqzcM1VV7J8xWpCWjt2o7gp0jSNiK72oa39x17Lki+WcN81E6qt37nrX6cosdh0ZGZmEhgYSFhYGKGhoeh0OsJ+C+HkiVTKCguxWcxUlJWh0+nQWS1Yy8sZ1r7dmWVvndWzfXvit2xh4eJ3MXr7MLh/fyK7dyesQwfuiGka0+KfTdM09Ho9RRVW8oscfxhRp9PoHGDjq9157Px9HyNHj3VhlE2XSiBKs+Dl5UVW5qnGDqPeFBea8PLz50jaCbJLyykuLKBf5y74eVf/AGVAYGsyszPw9PTE19f3zLxfUy8bicFWjnuANz5eXuh0Ogw63Xm7fE5mZvHB2rVMvvwyfI1GAv38HI77ly1bSc03YTRb2LVxI0cTEwkJCWHk6NG1OwAN7JW5b/DG8zNqtc21AzuxetN+jhySLoqq6VMJRGk2rJbm8xxp16heBLdph9lspmNgIMlHj/D9L6uZPLL6qVf0ej2+vgGknkgjqvufz9l2bNOGopISsk0mMkwm0DTa+fvT6qwRXPOWL6fYw5NSo5E1Sckc3b+f2bffxrGUFLw8PQk7a10Mm83G3E8/o52/Pxu2b8fbxwd3vZ7svDxWr16Nr9HIsMsuq9+D4wI3TpnC+m+W1mobnU5j+tWx/JikYbVaW+RsvSqBKM1GbEwPDh3cS7fujs8gC/aT4B97dlBSWkp0737oNB2GytFNjXlSCAj8c1qQ0LD27CouIiM3B7PNRljguVOGfLf5F37eupnXHnmKogI3bDYbaTk5nCoo4JTZQkhEBEE9e9GuMmmkJiWx7/hx3EpLCPHywsfTi8sHDGBp/Ab8DAZOHT9OzIgRvPzlV+RnZDAgKoobzkogxaWluGsa7yxfRmpePu0DAhg7aBC/7d/PIw88gKnMPinliRNNf2W/1sG1X+9mUPcw4g/u5ol/zWTuq2+4IKqmTSUQpdkYNmwor7wxn67dejh04rfZbCRs+YnSwjx6REcR1q4Te/buwGQyUVZupqyslHxTIVdffytubo07p5a3tw8Dx00kuaIcg5sbew7sJjY0FA0ICWjFjFfn0Gf4MGyajj0H91NeXkKaHsJ69qRrhw70NhrPOSatWrWC2FiKiopIT0+n0GwmwMODOROuZdPGjWzbEM+NN93ECwcOcCg7E+2wgeGxsbQNCqSkrIw7Z81GpqQQ3bEjZWYLFVYrPfrEoA8KJigrk4wCEzpNw2qzceLECcJqGEbcmNavWYOnwbnRVDOv6cUNz37Ms08+wawX59RzZE2bepBQaVZOnDjBj+s3ERndj+Aabqgf2PcbUR1DiI6OumCdtLQ0Fi7+kEm33dukxvtbLBZSEiVpqceJ0ll556vPsHj7kHRQUm6p4OapU3hw5kynYz59XtA0DYvFwi033siptDQCPTx4aeZD/GvOS+xMSaHCaiXEx4frr7mGCbfdRs9e9qu/VatWMX369DP7a+pXIEuXLOHg+k+ZOKwbsZG1n0H6ZEYWD7y9lmXxu5tdV1Z1DxI2r5YqLV5YWBi3T7mB/bs2kW/KrbauKeskUVHdq60TGhrKFXEj2JOwgdLS6ofVNiS9Xk9Et2j6D7+c1JISHph4M3/s3UNJeQmbE7Yz4+GH65TwNE07s71er+eTL79k7ebN3D9rFuNmPMS2Y8eIqUwWEydexz+feeZM8gAYO3YsHprGrh072Pf773VrbAMoKikh8VgKC9an8PQn29khU2u1fbs2wXQK1HHntFtcFGHTpLqwlGZHp9Nx25RJbNi8jSP78gnrJGgb+td5i9JOptAhvK1DJ9mhlwyhqKiIFT+sQ/QaiNHb8TmiXM3NzZ3ticm8vHIZfgGtmD33JZfNAaZpGiNGjOCXzZvx9fXFu4YRYUdTa3cSbkw9e/Vi16oAdKXZ/FHggd58iP6idj0Vz901nue/PkBOTg6BgdXPFN1cqCsQpVny8vJizKjLuGHCWDKPS3ZsWU955Q3dkpJidmxeR3R34fD+vL29ufbq0exN+IWyWkx42BD27NtFYGgoK39ay9ChQ2veoI7atm1bY/K42AwbNozDJgMb9iRSmnmEhybWflocHy9PJvYN4rZJ17kgwqZJJRClWfPw8ODG6ycwbtRQVn+zlM3xa9i47jvunDaF0LNGFNXEy8uLv111BUcO7nJRtLV36OBeYnpGs3rNmmonEFRqNn/BIvSevvh5ufPKpxuc2sfAqI48ceMAThxLqufomiaVQJQWISgoiEcffpCRg3sz6dqr7COQnBAYGAgVpZSXVz+3lCvk5/95T8dsrkDu/40ATxtzX3qhSd3gv1h1j4rC2+hNuc2NWX8f4/R+hnRpxZZVn9VjZE2XugeitBiaptG1q/PLmp7Wr29Pft2dQEx/13YXZWedIvNUOls3rjuzdniXblEc+D2BosJC5r44i4CAAJfG0NJsSdjJsEH9WPTtVu6ZcIlT+9A0jfg1P3DJNbc26aHL9UFdgShKLXXq1AmDVnHmnoqr5Ofm8O3ypXh7GvD10mMzlxH/0/fE9OzOwrffUMnDBTRN4+dN29hzCsrNFtJzC1i0Ymut9zOwkx8rvvrKBRE2LeoKRFGccOnQQfywbhMDh8a5ZP/vL3wdTdMYfvlYVnzxIe4GHbGxsTz/zgLc3Gq/prfiOHd3dx55/nXueeg2vD0MJCYf567xQ2q1j349upAeGuGaAJsQlUAUxQkhISF4up27iFN9MRj0JB46yKqVKyjIz+PndWvo5IIFmZTz69K1K9FDr2LxojcI8NBz4Hg2X/yyl+duHenQ9h3bBmFu5e/aIJsA1YWlKE7q27sHyYkH63Wf+fm5fLD4TY4eTaLQlMeAAf3Yvm2zSh6N4OHHn8SmGSiusPLVtkSuHXbhGQvO5u6mp7gw34XRNQ0qgSiKkyK7diHIW8fhP3bX2z4/eX8+xxMP8eumdQwdOoT3Fi1oEetuN1WvvfkOJRaN7zbsZu6Hji8c5abXUVHWdGYucBWVQBTFSZqmMWhgP9oGepFyLLFO+7LZbNhsNnLy8klI+JXeMTE89eQTanhuIxt39dXEb93Fht8O0qXPMMrMji0ZYLXZ+OTjj10cXeNTCURR6mhg/37kpCc7vTa22VzBx++/xWfvv8GObRspKSpkxdfNfwTPxSI0NBQPDw+MbSKY88lGh7ZJTsti0/btLo6s8akEoij1YEBsb3ZsXU9FRe0fMDQY3Jjy9/uxanqKiwt54J8PqSuPJuje+x7g6w37mPTUO8x86xvyCi/cRfXR+oN0i+rTgNE1DpVAFKUedOncicsu6Uf8qi/Iysyo9faappGbm09Epy489uhMF0So1JW/vz/jJ93OzrQKlm1L4rIZi7j+iQXnresV1I7HH32ygSNseCqBKEo9ad8+nGm3TSU1+bDD22yO/4ml77/NgnlzKC8r5d2F810YoVJXs198kSNJqaScOMXIMdfRKqgdVuu5XZfJiYk8/+J/GiHChuXUcyBCCH/gI8APcAdmSim3CiEGA/MAM7BGSjlLCKED5gMxQBkwXUp5xFV1nTwOilIvysvLKSkpcrh+QUE+NnMZOquZXTu3Exn5ugujU+rT6/MX8PlHS3huyQc8OfVy3A16dDoNm82GTq/D6N78H/h09gpkJrBOSnkpcDvwdmX5QmAyMAwYJISIBSYAnlLKIcDjwKsurqsojSYwMBBfL8dPHKOvHM/oq28gKTUDi8Xc7Faza+4mTb2dFVv2MeS+t3j5059ZsyuZv7+2mvR8C7PnvtLY4bmcs3+trwOLKn82AKVCCD/AQ0qZKKW0AT8CcdhP+qsBpJTbgP6uqutkWxSlXun1eszmCsfqGgyEtG5LSvJhdFrLWF66uZk2dQqdWvuxL8PC4++tISm3hIKSckT36le7bA5q7MISQtwBPHRW8TQpZYIQoi32rqwZ2LuzTFXqFACdK8urPpJpcVVdIYRBSunYQG1FcZFB/fsQv20rfQYMd2g0VUVFBYWmPNq2DmmA6JT6dtfjc/jb1Ps4cuAA33+znCMpqSxesrSxw2oQNSYQKeV7wHtnlwshegGfAY9IKeMrrxR8q1TxBfIA41nlOuwJod7rquShNAVhYWFEdckmKzOdkNY1L1rl6enFgEuGc+zwAdLS0mq90JXSuLy8vOgcGYmH0chHn33Ks7P+Q7vw2i2He7FyqgtLCBENLAMmSylXAUgpTUC5EKKLEEIDxgAbgc3AuMrtBgN7XVXXmbYoiiv07BFFdkaKQ3U1TePmqdPR6fQ8/cyzLo5MqS/fL1vG/XffzWcffMA9d07nvn9M561F7zCoAZYVbiqcnY13DuAJzBNCAORLKccDdwMfA3rso6V+FUIkAKOFEFsADZhWuQ9X1VWURufm5obRTaPAlI+vX82zsv60+lvSMzLoGhnZANEpdRU3dDAy+Sg2LJSa8okQgjfnt7yp9jVnp1+42AghIoCkdevWEd5CLi+VxpWZmcna9fHkm4oJ7yToKnpcsG5RYQF3T5uEl4eeb75aTlBQUANGqjiqoqKCX9atY/odt2LGzAtP/Yfrpk7Fz8+vsUNzmdTUVOLi4gA6SSmTq76nxgwqiouEhIQwedL13D39Foqyj7N86XzWrlxOgencab69fXyZfOvf8XD3YOPGTY0QrVJVakoKxUV/fZ5n7jNPs2XDBo4e2M/d99xHSkoWt997b7NOHjVRC0opiotpmsZNk24A7N/mlnz4MXp3L9zc3bFarXj7BjBkeBwjR49jS/xaNmzayIQJ4xs56pbNoGl4GY1/KXts9vMAXBrnmlUoL0YqgShKAwoPD+epJ/5FcXExOp2OiooKtm3bxoov/ofRx59u3boxOm5kY4fZ4rVV3dwOUQlEURqYpml4e3sD9iGgV1xxBTExMaz49nseeeDf6PX6Ro5QURyjEoiiNAFt2rThH3fe0dhhKEqtqJvoiqIoilNUAlEURVGcohKIoiiK4hSVQBRFURSnqASiKIqiOEUlEEVRFMUpKoEoiqIoTmlJz4HoAdLT0xs7DkVRlItGlXPmOU+4tqQEEgowZcqUxo5DURTlYhQKJFYtaEkJJAEYDqRhXypXURRFqZkee/JIOPuNFrMeiKIoilK/1E10RVEUxSkqgSiKoihOUQlEURRFcYpKIIqiKIpTWtIoLIQQ/sBHgB/gDsyUUm4VQgwG5gFmYI2UcpYQQgfMB2KAMmC6lPKIq+o22EE4ixDiWuAGKeXkytct9lg4ozm0oSohxCBgrpRypBCiK7AEsAH7gPuklFYhxLPAVdg/yxlSyu2uqttgDa9CCOEGvA9EAB7Af4ADjsbcnI5FTVraFchMYJ2U8lLgduDtyvKFwGRgGDBICBELTAA8pZRDgMeBV11ct8EJIeYBc/jr30GLPBZ10BzaAIAQ4lFgMeBZWfQa8G8p5XBAA8ZXfm6XAoOAm/jz/5Cr6jaGqUB2ZXxjgbdouceiWi0tgbwOLKr82QCUCiH8AA8pZaKU0gb8CMRhP9GtBpBSbgP6u6puA7T7QrYA95x+0cKPhbOaQxtOSwSuq/K6HxBf+fMqYBT29q6RUtqklMcBgxAixIV1G8My4Okqr8203GNRrWbbhSWEuAN46KziaVLKBCFEW+xdWTOwd2eZqtQpADpXludXKbe4qq4QwiClNNeqgbVQzbH4XAgxskpZsz8WLtAc2gCAlPJLIURElSKtMuGD/TPzx97e7Cp1Tpe7qm5mHZtVa1LKQgAhhC+wHPg38EpLPBY1abYJREr5HvDe2eVCiF7AZ8AjUsr4ym/HvlWq+AJ5gPGsch32k2C913X1yeZCx+I8XNK+2tS9CE+8Z7ftYmzDhVir/Hz6M7vQZ+mquo1CCNEe+BqYL6X8RAjxcpW3W9SxqE6L6sISQkRjvzydLKVcBSClNAHlQoguQggNGANsBDYD4yq3GwzsdVXdhml9zdSxcEpzaMOF7KpyhTqWPz+zMUIInRCiA/aEmeXCug1OCNEGWAM8JqV8v7K4RR6LmjTbK5ALmIP9BuE8IQRAvpRyPHA38DH2OV/WSCl/FUIkAKOFEFuw39yaVrkPV9VtKtSxqJ2vufjbcCEPA+8KIdyBP4DlUkqLEGIjsBX7F9D7XFy3MTwJtAKeFkKcvhfyIPBmCzwW1VJzYSmKoihOaVFdWIqiKEr9UQlEURRFcYpKIIqiKIpTVAJRFEVRnKISiKIoiuIUlUAURVEUp6gEoiiKojhFJRBFURTFKf8PyEf3O6DKqF0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regions.to_crs(epsg=2163).plot(cmap='tab20', alpha=0.5,\n",
    "                               linewidth=0.5, edgecolor='0.1')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is slow! \n",
    "\n",
    "regions_nerc = regions.dissolve(by='nerc', as_index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x68c397ba58>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regions_nerc.to_crs(epsg=2163).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "path = join(data_path, 'NERC_Regions', 'Aggregated NERC Regions.geojson')\n",
    "regions_nerc.to_file(path, driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fiona.open()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T01:31:38.713042Z",
     "start_time": "2018-03-16T01:31:10.957920Z"
    }
   },
   "outputs": [],
   "source": [
    "path = join(data_path, 'NERC_Regions', 'Aggregated NERC Regions.geojson')\n",
    "regions_nerc = gpd.read_file(path, driver='GeoJSON')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T01:31:06.504376Z",
     "start_time": "2018-03-16T01:31:06.438831Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'init': 'epsg:4269'}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = os.path.join(data_path, 'cb_2016_us_state_20m', 'cb_2016_us_state_20m.shp')\n",
    "states = gpd.read_file(path)\n",
    "states.crs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T01:32:08.768359Z",
     "start_time": "2018-03-16T01:32:08.755736Z"
    }
   },
   "outputs": [],
   "source": [
    "drop_states = ['Alaska', 'Hawaii', 'Puerto Rico']\n",
    "states = states.loc[~states['NAME'].isin(drop_states)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T01:32:10.797976Z",
     "start_time": "2018-03-16T01:32:10.090678Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x118104c88>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states.to_crs(epsg=2163).plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T01:32:14.647749Z",
     "start_time": "2018-03-16T01:32:14.610175Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>nerc</th>\n",
       "      <th>2001</th>\n",
       "      <th>2017</th>\n",
       "      <th>Reduction</th>\n",
       "      <th>Percent Reduction</th>\n",
       "      <th>Annual Reduction</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>TRE</td>\n",
       "      <td>609.480997</td>\n",
       "      <td>445.396025</td>\n",
       "      <td>164.084972</td>\n",
       "      <td>0.269221</td>\n",
       "      <td>10.255311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>WECC</td>\n",
       "      <td>520.628493</td>\n",
       "      <td>346.627554</td>\n",
       "      <td>174.000939</td>\n",
       "      <td>0.334213</td>\n",
       "      <td>10.875059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>USA</td>\n",
       "      <td>629.016312</td>\n",
       "      <td>438.047363</td>\n",
       "      <td>190.968949</td>\n",
       "      <td>0.303599</td>\n",
       "      <td>11.935559</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SERC</td>\n",
       "      <td>633.368212</td>\n",
       "      <td>426.814179</td>\n",
       "      <td>206.554033</td>\n",
       "      <td>0.326120</td>\n",
       "      <td>12.909627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RFC</td>\n",
       "      <td>676.181487</td>\n",
       "      <td>461.065520</td>\n",
       "      <td>215.115966</td>\n",
       "      <td>0.318133</td>\n",
       "      <td>13.444748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FRCC</td>\n",
       "      <td>648.223415</td>\n",
       "      <td>410.133060</td>\n",
       "      <td>238.090355</td>\n",
       "      <td>0.367297</td>\n",
       "      <td>14.880647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NPCC</td>\n",
       "      <td>414.395302</td>\n",
       "      <td>172.744904</td>\n",
       "      <td>241.650399</td>\n",
       "      <td>0.583140</td>\n",
       "      <td>15.103150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>MRO</td>\n",
       "      <td>855.109267</td>\n",
       "      <td>541.516736</td>\n",
       "      <td>313.592531</td>\n",
       "      <td>0.366728</td>\n",
       "      <td>19.599533</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SPP</td>\n",
       "      <td>853.801162</td>\n",
       "      <td>494.664676</td>\n",
       "      <td>359.136486</td>\n",
       "      <td>0.420632</td>\n",
       "      <td>22.446030</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   nerc        2001        2017   Reduction  Percent Reduction  \\\n",
       "0   TRE  609.480997  445.396025  164.084972           0.269221   \n",
       "1  WECC  520.628493  346.627554  174.000939           0.334213   \n",
       "2   USA  629.016312  438.047363  190.968949           0.303599   \n",
       "3  SERC  633.368212  426.814179  206.554033           0.326120   \n",
       "4   RFC  676.181487  461.065520  215.115966           0.318133   \n",
       "5  FRCC  648.223415  410.133060  238.090355           0.367297   \n",
       "6  NPCC  414.395302  172.744904  241.650399           0.583140   \n",
       "7   MRO  855.109267  541.516736  313.592531           0.366728   \n",
       "8   SPP  853.801162  494.664676  359.136486           0.420632   \n",
       "\n",
       "   Annual Reduction  \n",
       "0         10.255311  \n",
       "1         10.875059  \n",
       "2         11.935559  \n",
       "3         12.909627  \n",
       "4         13.444748  \n",
       "5         14.880647  \n",
       "6         15.103150  \n",
       "7         19.599533  \n",
       "8         22.446030  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "path = join(data_path, 'final NERC data',\n",
    "            'Summary table {}.csv'.format(file_date))\n",
    "index = pd.read_csv(path)\n",
    "index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T13:51:21.672922Z",
     "start_time": "2018-03-16T13:51:21.597805Z"
    }
   },
   "outputs": [],
   "source": [
    "for nerc in regions_nerc['nerc'].unique():\n",
    "    try:\n",
    "        val_2017 = index.loc[index['nerc']==nerc, '2017'].values[0]\n",
    "        val_2001 = index.loc[index['nerc']==nerc, '2001'].values[0]\n",
    "        reduce = index.loc[index['nerc']==nerc, 'Percent Reduction'].values[0]\n",
    "#         print(val)\n",
    "        regions_nerc.loc[regions_nerc['nerc']==nerc, 2017] = val_2017\n",
    "        regions_nerc.loc[regions_nerc['nerc']==nerc, 2001] = val_2001\n",
    "        \n",
    "        regions_nerc.loc[regions_nerc['nerc']==nerc, 'reduction'] = '{:.0%}'.format(reduce)\n",
    "        regions_nerc.loc[regions_nerc['nerc']==nerc, 'reduction value'] = reduce\n",
    "    except:\n",
    "        pass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T13:51:22.390072Z",
     "start_time": "2018-03-16T13:51:22.353987Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>NERCregion</th>\n",
       "      <th>SHAPE_Leng</th>\n",
       "      <th>SHAPE_Area</th>\n",
       "      <th>geometry</th>\n",
       "      <th>nerc</th>\n",
       "      <th>2017</th>\n",
       "      <th>2001</th>\n",
       "      <th>reduction</th>\n",
       "      <th>reduction value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>22.488030</td>\n",
       "      <td>11.459289</td>\n",
       "      <td>POLYGON ((-84.86300370899994 30.71266453000004...</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>410.133060</td>\n",
       "      <td>648.223415</td>\n",
       "      <td>37%</td>\n",
       "      <td>0.367297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>76.997961</td>\n",
       "      <td>130.425939</td>\n",
       "      <td>POLYGON ((-94.8320392469999 49.33080593000005,...</td>\n",
       "      <td>MRO</td>\n",
       "      <td>541.516736</td>\n",
       "      <td>855.109267</td>\n",
       "      <td>37%</td>\n",
       "      <td>0.366728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>NPCC</td>\n",
       "      <td>48.574281</td>\n",
       "      <td>32.754394</td>\n",
       "      <td>(POLYGON ((-72.5509712409999 40.96617993100006...</td>\n",
       "      <td>NPCC</td>\n",
       "      <td>172.744904</td>\n",
       "      <td>414.395302</td>\n",
       "      <td>58%</td>\n",
       "      <td>0.583140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>99.332877</td>\n",
       "      <td>71.157869</td>\n",
       "      <td>(POLYGON ((-87.92640864299995 44.5391396020000...</td>\n",
       "      <td>RFC</td>\n",
       "      <td>461.065520</td>\n",
       "      <td>676.181487</td>\n",
       "      <td>32%</td>\n",
       "      <td>0.318133</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>128.958444</td>\n",
       "      <td>142.139208</td>\n",
       "      <td>POLYGON ((-80.20680177299994 36.54903791300006...</td>\n",
       "      <td>SERC</td>\n",
       "      <td>426.814179</td>\n",
       "      <td>633.368212</td>\n",
       "      <td>33%</td>\n",
       "      <td>0.326120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>SPP</td>\n",
       "      <td>61.523164</td>\n",
       "      <td>56.496471</td>\n",
       "      <td>POLYGON ((-97.56098691099993 36.38533196100008...</td>\n",
       "      <td>SPP</td>\n",
       "      <td>494.664676</td>\n",
       "      <td>853.801162</td>\n",
       "      <td>42%</td>\n",
       "      <td>0.420632</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>TRE</td>\n",
       "      <td>44.145641</td>\n",
       "      <td>50.605118</td>\n",
       "      <td>POLYGON ((-104.4498774409999 32.00686615000006...</td>\n",
       "      <td>TRE</td>\n",
       "      <td>445.396025</td>\n",
       "      <td>609.480997</td>\n",
       "      <td>27%</td>\n",
       "      <td>0.269221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>WECC</td>\n",
       "      <td>102.110211</td>\n",
       "      <td>323.227276</td>\n",
       "      <td>(POLYGON ((-122.6656125679999 48.3967777520000...</td>\n",
       "      <td>WECC</td>\n",
       "      <td>346.627554</td>\n",
       "      <td>520.628493</td>\n",
       "      <td>33%</td>\n",
       "      <td>0.334213</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   OBJECTID NERCregion  SHAPE_Leng  SHAPE_Area  \\\n",
       "0         1       FRCC   22.488030   11.459289   \n",
       "1         2        MRO   76.997961  130.425939   \n",
       "2         3       NPCC   48.574281   32.754394   \n",
       "3         4        RFC   99.332877   71.157869   \n",
       "4         5       SERC  128.958444  142.139208   \n",
       "5         6        SPP   61.523164   56.496471   \n",
       "6         7        TRE   44.145641   50.605118   \n",
       "7         8       WECC  102.110211  323.227276   \n",
       "\n",
       "                                            geometry  nerc        2017  \\\n",
       "0  POLYGON ((-84.86300370899994 30.71266453000004...  FRCC  410.133060   \n",
       "1  POLYGON ((-94.8320392469999 49.33080593000005,...   MRO  541.516736   \n",
       "2  (POLYGON ((-72.5509712409999 40.96617993100006...  NPCC  172.744904   \n",
       "3  (POLYGON ((-87.92640864299995 44.5391396020000...   RFC  461.065520   \n",
       "4  POLYGON ((-80.20680177299994 36.54903791300006...  SERC  426.814179   \n",
       "5  POLYGON ((-97.56098691099993 36.38533196100008...   SPP  494.664676   \n",
       "6  POLYGON ((-104.4498774409999 32.00686615000006...   TRE  445.396025   \n",
       "7  (POLYGON ((-122.6656125679999 48.3967777520000...  WECC  346.627554   \n",
       "\n",
       "         2001 reduction  reduction value  \n",
       "0  648.223415       37%         0.367297  \n",
       "1  855.109267       37%         0.366728  \n",
       "2  414.395302       58%         0.583140  \n",
       "3  676.181487       32%         0.318133  \n",
       "4  633.368212       33%         0.326120  \n",
       "5  853.801162       42%         0.420632  \n",
       "6  609.480997       27%         0.269221  \n",
       "7  520.628493       33%         0.334213  "
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions_nerc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T13:51:28.909332Z",
     "start_time": "2018-03-16T13:51:28.881645Z"
    }
   },
   "outputs": [],
   "source": [
    "regions_albers = regions_nerc.to_crs(epsg=2163)\n",
    "states_albers = states.to_crs(epsg=2163)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T13:51:31.128947Z",
     "start_time": "2018-03-16T13:51:30.872423Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a76c7f908>"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regions_albers.plot(column=2001, cmap='cividis_r', edgecolor='0.1', linewidth=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T14:15:58.522009Z",
     "start_time": "2018-03-16T14:15:58.294639Z"
    }
   },
   "outputs": [],
   "source": [
    "def plot_nerc_annual(regions_proj, states_proj, data_col, text_col,\n",
    "                     cmap='cividis_r', vmin=None, vmax=None, title=None,\n",
    "                     cbar_title=None, **kwargs):\n",
    "    \n",
    "    states_ec = kwargs.get('states_ec', '0.6')\n",
    "    regions_ec = kwargs.get('regions_ec', '0.2')\n",
    "    regions_lw = kwargs.get('regions_lw', 0.75)\n",
    "    font_size = kwargs.get('font_size', 9)\n",
    "    bbox_alpha = kwargs.get('bbox_alpha', 0.7)\n",
    "    FRCC_x = kwargs.get('FRCC_x', 4.75)\n",
    "    SERC_x = kwargs.get('SERC_x', 2)\n",
    "    SERC_y = kwargs.get('SERC_y', -2)\n",
    "    SPP_y = kwargs.get('SPP_y', 1.75)\n",
    "    RFC_y = kwargs.get('RFC_y', -0.5)\n",
    "    \n",
    "    \n",
    "    fig, ax = plt.subplots(figsize=(8,3.5))\n",
    "    # set aspect to equal. This is done automatically\n",
    "    # when using *geopandas* plot on it's own, but not when\n",
    "    # working with pyplot directly.\n",
    "    ax.set_aspect('equal')\n",
    "\n",
    "\n",
    "    regions_proj.plot(ax=ax, column=data_col, cmap=cmap, legend=True,\n",
    "                      vmin=vmin, vmax=vmax)\n",
    "    states_proj.plot(ax=ax, color='none', edgecolor=states_ec)\n",
    "    regions_proj.plot(ax=ax, color='none', edgecolor=regions_ec,\n",
    "                     linewidth=regions_lw)\n",
    "#     plt.text(x=1.1, y=1.01, s=cbar_title, transform=ax.transAxes,\n",
    "#              ha='center', va='bottom', fontdict={'size':font_size})\n",
    "    plt.title(title)\n",
    "\n",
    "    for point, nerc in zip(regions_proj.centroid, regions_proj['nerc'].values):\n",
    "        text = regions_proj.loc[regions_proj['nerc']==nerc, text_col].values[0]\n",
    "#         text = '{}'.format(nerc, reduce)\n",
    "        x = point.x\n",
    "        y = point.y\n",
    "        if nerc == 'FRCC':\n",
    "            x = x + conv_lon(4.75)#-79\n",
    "            y = y - conv_lat(1)#28\n",
    "            rot = -67\n",
    "            plt.text(x, y, text, ha='center', va='center',\n",
    "                     fontdict={'size':font_size})\n",
    "\n",
    "        elif nerc == 'NPCC':\n",
    "            x = x - conv_lon(1.5)\n",
    "            y = y + conv_lat(2.1)\n",
    "            plt.text(x, y, text, ha='center', \n",
    "                     fontdict={'size':font_size})\n",
    "\n",
    "        elif nerc == 'SERC':\n",
    "            x = x + conv_lon(SERC_x)\n",
    "            y = y + conv_lat(SERC_y)\n",
    "            plt.text(x, y, text, ha='center', va='center',\n",
    "                     bbox=dict(facecolor='white',\n",
    "                                alpha=bbox_alpha,\n",
    "                                boxstyle=\"square\"),\n",
    "                     fontdict={'size':font_size})\n",
    "        elif nerc == 'RFC':\n",
    "#             x = x + conv_lon(RFC_x)\n",
    "            y = y + conv_lat(RFC_y)\n",
    "            plt.text(x, y, text, ha='center', va='center',\n",
    "                     bbox=dict(facecolor='white',\n",
    "                                alpha=bbox_alpha,\n",
    "                                boxstyle=\"square\"),\n",
    "                     fontdict={'size':font_size})\n",
    "        \n",
    "        elif nerc == 'SPP':\n",
    "    #         x = x + 2\n",
    "            y = y + conv_lat(SPP_y)\n",
    "            plt.text(x, y, text, ha='center', va='center',\n",
    "                     bbox=dict(facecolor='white',\n",
    "                                alpha=bbox_alpha,\n",
    "                                boxstyle=\"square\"),\n",
    "                     fontdict={'size':font_size})\n",
    "\n",
    "        else:\n",
    "            plt.text(x, y, text, ha='center', va='center',\n",
    "                     bbox=dict(facecolor='white',\n",
    "                                alpha=bbox_alpha,\n",
    "                                boxstyle=\"square\"),\n",
    "                     fontdict={'size':font_size})\n",
    "\n",
    "    sns.despine(left=True, bottom=True)\n",
    "    ax.set_yticklabels(labels=[])\n",
    "    ax.set_xticklabels(labels=[])\n",
    "    \n",
    "    cax = fig.get_axes()[-1]\n",
    "    cax.set_title(cbar_title, fontdict={'size':font_size})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T02:20:59.328190Z",
     "start_time": "2018-03-16T02:20:59.321135Z"
    }
   },
   "outputs": [],
   "source": [
    "# https://gist.github.com/springmeyer/871897\n",
    "\n",
    "def conv_lon(x):\n",
    "    newx = x * 20037508.34 / 180\n",
    "    return newx\n",
    "\n",
    "def conv_lat(y):\n",
    "    newy = np.log(np.tan((90 + y) * np.pi / 360)) / (np.pi / 180)\n",
    "    newy *= 20037508.34 / 180\n",
    "    return newy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maps of 2001 and 2017 annual values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T14:16:03.765622Z",
     "start_time": "2018-03-16T14:16:02.738744Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = '2001 U.S. Average\\n{} g CO$_2$/kWh'.format(usa_2001)\n",
    "kwargs = dict(\n",
    "    regions_lw = 1,\n",
    "    regions_ec = '0.1',\n",
    "    SERC_y = -1.5,\n",
    "    SPP_y = 2.25\n",
    ")\n",
    "\n",
    "vmin = regions_albers.loc[:, [2001, 2017]].min().min()\n",
    "vmax = regions_albers.loc[:, [2001, 2017]].max().max()\n",
    "plot_nerc_annual(regions_proj=regions_albers, states_proj=states_albers, data_col=2001,\n",
    "                 text_col='nerc', vmin=vmin, vmax=vmax, title=title,\n",
    "                 cbar_title='g CO$_2$/kWh', **kwargs)\n",
    "\n",
    "path = join(figure_path, 'NERC map_cividis_2001.png')\n",
    "plt.savefig(path, bbox_inches='tight', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-16T14:16:33.485866Z",
     "start_time": "2018-03-16T14:16:32.098285Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = '2017 U.S. Average\\n{} g CO$_2$/kWh (↓ 30%)'.format(usa_2017)\n",
    "kwargs = dict(\n",
    "    regions_lw = 1,\n",
    "    regions_ec = '0.1',\n",
    "    SERC_y = -1.5,\n",
    "    SPP_y = 2.25\n",
    ")\n",
    "\n",
    "vmin = regions_albers.loc[:, [2001, 2017]].min().min()\n",
    "vmax = regions_albers.loc[:, [2001, 2017]].max().max()\n",
    "regions_albers['arrow reduction'] = '↓ ' + regions_albers['reduction']\n",
    "plot_nerc_annual(regions_proj=regions_albers, states_proj=states_albers, data_col=2017,\n",
    "                 text_col='arrow reduction', vmin=vmin, vmax=vmax, title=title,\n",
    "                 cbar_title='g CO$_2$/kWh', **kwargs)\n",
    "path = join(figure_path, 'NERC map_cividis_2017_change.png')\n",
    "plt.savefig(path, bbox_inches='tight', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 443,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = '2001 U.S. Average\\n{} g CO$_2$/kWh'.format(usa_2001)\n",
    "\n",
    "vmin = regions_albers.loc[:, [2001, 2017]].min().min()\n",
    "vmax = regions_albers.loc[:, [2001, 2017]].max().max()\n",
    "plot_nerc_annual(regions_proj=regions_albers, states_proj=states_albers, data_col=2001,\n",
    "                 text_col='NERC', vmin=vmin, vmax=vmax, cmap='Reds',\n",
    "                 title=title, cbar_title='g CO$_2$/kWh')\n",
    "path = join(figure_path, 'NERC map_reds_2001.png')\n",
    "plt.savefig(path, bbox_inches='tight', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 444,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = '2017 U.S. Average\\n{} g CO$_2$/kWh (↓ 30%)'.format(usa_2017)\n",
    "\n",
    "vmin = regions_albers.loc[:, [2001, 2017]].min().min()\n",
    "vmax = regions_albers.loc[:, [2001, 2017]].max().max()\n",
    "regions_albers['arrow reduction'] = '↓ ' + regions_albers['reduction']\n",
    "plot_nerc_annual(regions_proj=regions_albers, states_proj=states_albers, data_col=2017,\n",
    "                 text_col='arrow reduction', vmin=vmin, vmax=vmax, cmap='Reds',\n",
    "                 title=title, cbar_title='g CO$_2$/kWh')\n",
    "path = join(figure_path, 'NERC map_reds_2017_change.png')\n",
    "plt.savefig(path, bbox_inches='tight', dpi=350)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Maps of difference from national average"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = '2001 U.S. Average\\n{} g CO$_2$/kWh'.format(usa_2001)\n",
    "vmax = max(abs(regions_albers['2017 diff'].min()), regions_albers['2017 diff'].max())\n",
    "vmin = -vmax\n",
    "\n",
    "plot_nerc_annual(regions_proj=regions_albers, states_proj=states_albers, data_col='2001 diff',\n",
    "                 text_col='NERC', cmap='PRGn_r', vmin=vmin, vmax=vmax,\n",
    "                 title=title, cbar_title='g CO$_2$/kWh')\n",
    "path = join(figure_path, 'NERC map_diverging_2001_2017_bounds.png')\n",
    "plt.savefig(path, bbox_inches='tight', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 446,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = '2017 U.S. Average\\n{} g CO$_2$/kWh (↓ 30%)'.format(usa_2017)\n",
    "vmax = max(abs(regions_albers['2017 diff'].min()), regions_albers['2017 diff'].max())\n",
    "vmin = -vmax\n",
    "\n",
    "regions_albers['arrow reduction'] = '↓ ' + regions_albers['reduction']\n",
    "plot_nerc_annual(regions_proj=regions_albers, states_proj=states_albers, data_col='2017 diff',\n",
    "                 text_col='arrow reduction', cmap='PRGn_r', vmin=vmin, vmax=vmax,\n",
    "                 title=title, cbar_title='g CO$_2$/kWh')\n",
    "path = join(figure_path, 'NERC map_diverging_2017.png')\n",
    "plt.savefig(path, bbox_inches='tight', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 447,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x252 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "title = '2017 U.S. Average\\n{} g CO$_2$/kWh (↓ 30%)'.format(usa_2017)\n",
    "vmax = max(abs(regions_albers['2017 diff'].min()), regions_albers['2017 diff'].max())\n",
    "vmin = -vmax\n",
    "\n",
    "regions_albers['arrow reduction'] = '↓ ' + regions_albers['reduction']\n",
    "plot_nerc_annual(regions_proj=regions_albers, states_proj=states_albers, data_col='2017 diff',\n",
    "                 text_col='arrow reduction', cmap='BrBG_r', vmin=vmin, vmax=vmax,\n",
    "                 title=title, cbar_title='g CO$_2$/kWh')\n",
    "path = join(figure_path, 'NERC map_diverging_BrBG_2017.png')\n",
    "plt.savefig(path, bbox_inches='tight', dpi=350)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read file with facility location, generation, and fuel data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T19:48:37.887827Z",
     "start_time": "2017-11-02T19:48:29.404221Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = os.path.join(data_path, 'Facility gen fuels and CO2 2017-08-31.zip')\n",
    "facility_df = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Drop plants without lat/lon data\n",
    "From 2001-mid 2017, 9 facilities (out of 8,435) don't have valid lat/lon data. All of them are from 2010 or earlier. Drop these rows or the spatial join fails. The amount of generation from these plants is tiny - well under 0.05% in all years."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:48:19.503509Z",
     "start_time": "2017-10-31T13:48:19.472547Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8435 total plants\n",
      "9 plants without lat/lon\n"
     ]
    }
   ],
   "source": [
    "print(len(facility_df['plant id'].unique()), 'total plants')\n",
    "print(len(facility_df.loc[facility_df['lat'].isnull(), 'plant id'].unique()),\n",
    "      'plants without lat/lon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:48:42.171526Z",
     "start_time": "2017-10-31T13:48:41.905246Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "In 2006, 0.000% of generation is from plants without lat/lon\n",
      "In 2005, 0.001% of generation is from plants without lat/lon\n",
      "In 2004, 0.001% of generation is from plants without lat/lon\n",
      "In 2002, 0.024% of generation is from plants without lat/lon\n",
      "In 2001, 0.031% of generation is from plants without lat/lon\n",
      "In 2010, 0.000% of generation is from plants without lat/lon\n",
      "In 2009, 0.000% of generation is from plants without lat/lon\n",
      "In 2007, 0.000% of generation is from plants without lat/lon\n",
      "In 2003, 0.011% of generation is from plants without lat/lon\n"
     ]
    }
   ],
   "source": [
    "years = facility_df.loc[facility_df['lat'].isnull(), 'year'].unique()\n",
    "for year in years:\n",
    "    total_gen = facility_df.loc[facility_df['year'] == year, 'generation (MWh)'].sum()\n",
    "    \n",
    "    # Plant ids with no 'lat' in year\n",
    "    no_loc_plants = facility_df.loc[(facility_df['lat'].isnull()) & \n",
    "                                    (facility_df['year'] == year), 'plant id'].unique()\n",
    "    \n",
    "    no_loc_gen = facility_df.loc[(facility_df['year'] == year) &\n",
    "                                 (facility_df['plant id'].isin(no_loc_plants)),\n",
    "                                  'generation (MWh)'].sum()\n",
    "    \n",
    "    percent_dropped = no_loc_gen / total_gen * 100\n",
    "    \n",
    "    print('In {}, {:.3f}% of generation is from plants without lat/lon'.format(year, percent_dropped))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:48:51.245565Z",
     "start_time": "2017-10-31T13:48:50.792098Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facility_df.dropna(inplace=True, subset=['lat', 'lon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:48:53.081482Z",
     "start_time": "2017-10-31T13:48:53.074928Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['f', 'fuel', 'month', 'plant id', 'total fuel (mmbtu)', 'year',\n",
       "       'generation (MWh)', 'elec fuel (mmbtu)', 'geography', 'last_updated',\n",
       "       'lat', 'lon', 'prime mover', 'datetime', 'quarter',\n",
       "       'all fuel fossil CO2 (kg)', 'elec fuel fossil CO2 (kg)',\n",
       "       'all fuel total CO2 (kg)', 'elec fuel total CO2 (kg)'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because I have monthly data for every facility from 2001-2017, there are lots of duplicate rows. No need to do a spatial join on every row. Just keep one instance of each facility in each year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:48:57.937818Z",
     "start_time": "2017-10-31T13:48:57.703166Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = ['lat', 'lon', 'plant id', 'year']\n",
    "small_facility = facility_df.loc[:, cols].drop_duplicates()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Use `Point` from Shapely to create the `geometry` list of facility locations. `crs` is the coordinate reference system that translates lat/lon into a specific map projection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:49:01.630618Z",
     "start_time": "2017-10-31T13:49:00.225123Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geometry = [Point(xy) for xy in zip(small_facility.lon, small_facility.lat)]\n",
    "# small_facility = small_facility.drop(['lon', 'lat'], axis=1)\n",
    "crs = {'init': 'epsg:4326'}\n",
    "geo_df = GeoDataFrame(small_facility, crs=crs, geometry=geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:49:06.221470Z",
     "start_time": "2017-10-31T13:49:06.202644Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>plant id</th>\n",
       "      <th>year</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2017</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2016</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2015</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2014</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2008</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat      lon  plant id  year                  geometry\n",
       "0   44.4936 -88.0303     10360  2017  POINT (-88.0303 44.4936)\n",
       "6   44.4936 -88.0303     10360  2016  POINT (-88.0303 44.4936)\n",
       "18  44.4936 -88.0303     10360  2015  POINT (-88.0303 44.4936)\n",
       "30  44.4936 -88.0303     10360  2014  POINT (-88.0303 44.4936)\n",
       "38  44.4936 -88.0303     10360  2008  POINT (-88.0303 44.4936)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:49:07.695554Z",
     "start_time": "2017-10-31T13:49:07.689889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88918"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(geo_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spatial join of NERC dataframe with polygons and facility dataframe with points\n",
    "Joining the 9 regions (NERC) with 100,810 records takes much longer with facilities as the left dataframe in the join (2 min vs 12 seconds). Not quite sure why this is. The faster method leaves me with polygons rather than points tho. There might be a better way to rectify this, but I just create a new geodataframe with the geometry set as the lat/lon points.\n",
    "\n",
    "**EDIT**\n",
    "Although the GeoPandas documentation says that the `op` parameter doesn't matter for point-in-polygon operations, using 'within' made the operation much faster (12 seconds). The first method below is probably preferable since it keeps the point geometry."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Method 1** (slow unless you use `op='within'`)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:49:33.051292Z",
     "start_time": "2017-10-31T13:49:29.161632Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facility_nerc = gpd.sjoin(geo_df, regions, how='inner', op='within')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:49:35.979853Z",
     "start_time": "2017-10-31T13:49:35.962856Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>plant id</th>\n",
       "      <th>year</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>NERC</th>\n",
       "      <th>NERC_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2017</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2016</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2015</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2014</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>10360</td>\n",
       "      <td>2008</td>\n",
       "      <td>POINT (-88.0303 44.4936)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        lat      lon  plant id  year                  geometry  index_right  \\\n",
       "0   44.4936 -88.0303     10360  2017  POINT (-88.0303 44.4936)            2   \n",
       "6   44.4936 -88.0303     10360  2016  POINT (-88.0303 44.4936)            2   \n",
       "18  44.4936 -88.0303     10360  2015  POINT (-88.0303 44.4936)            2   \n",
       "30  44.4936 -88.0303     10360  2014  POINT (-88.0303 44.4936)            2   \n",
       "38  44.4936 -88.0303     10360  2008  POINT (-88.0303 44.4936)            2   \n",
       "\n",
       "   NERC                              NERC_Label  \n",
       "0   MRO  Midwest Reliability Organization (MRO)  \n",
       "6   MRO  Midwest Reliability Organization (MRO)  \n",
       "18  MRO  Midwest Reliability Organization (MRO)  \n",
       "30  MRO  Midwest Reliability Organization (MRO)  \n",
       "38  MRO  Midwest Reliability Organization (MRO)  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_nerc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Method 2** (faster when using the default operation)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-31T15:16:05.049419",
     "start_time": "2017-07-31T15:15:52.623120Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facility_nerc = gpd.sjoin(df, geo_df, how=\"inner\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-31T15:16:05.120060",
     "start_time": "2017-07-31T15:16:05.051451Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NERC</th>\n",
       "      <th>NERC_Label</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>plant id</th>\n",
       "      <th>year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>1200931</td>\n",
       "      <td>40.389167</td>\n",
       "      <td>-91.394167</td>\n",
       "      <td>57953</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>1200919</td>\n",
       "      <td>40.389167</td>\n",
       "      <td>-91.394167</td>\n",
       "      <td>57953</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>1200907</td>\n",
       "      <td>40.389167</td>\n",
       "      <td>-91.394167</td>\n",
       "      <td>57953</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>1200904</td>\n",
       "      <td>40.389167</td>\n",
       "      <td>-91.394167</td>\n",
       "      <td>57953</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>1201030</td>\n",
       "      <td>40.389167</td>\n",
       "      <td>-91.394167</td>\n",
       "      <td>57953</td>\n",
       "      <td>2011</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NERC                               NERC_Label  \\\n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "\n",
       "                                            geometry  index_right        lat  \\\n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...      1200931  40.389167   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...      1200919  40.389167   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...      1200907  40.389167   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...      1200904  40.389167   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...      1201030  40.389167   \n",
       "\n",
       "         lon  plant id  year  \n",
       "0 -91.394167     57953  2014  \n",
       "0 -91.394167     57953  2015  \n",
       "0 -91.394167     57953  2016  \n",
       "0 -91.394167     57953  2017  \n",
       "0 -91.394167     57953  2011  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_nerc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make the new geometry of facility locations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-31T15:16:09.677274",
     "start_time": "2017-07-31T15:16:08.520811Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geometry = [Point(xy) for xy in zip(facility_nerc.lon, facility_nerc.lat)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Create new dataframe with the data I want to keep and the new geometry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-31T15:17:45.636359",
     "start_time": "2017-07-31T15:17:45.604018Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "crs = {'init': 'epsg:4326'}\n",
    "keep_cols = ['NERC_Label', 'plant id', 'year']\n",
    "facility_nerc = GeoDataFrame(facility_nerc[keep_cols], crs=crs, geometry=geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-31T15:17:53.146153",
     "start_time": "2017-07-31T15:17:53.132367Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NERC_Label</th>\n",
       "      <th>plant id</th>\n",
       "      <th>year</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
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       "      <td>2014</td>\n",
       "      <td>POINT (-91.39416700000001 40.389167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>57953</td>\n",
       "      <td>2015</td>\n",
       "      <td>POINT (-91.39416700000001 40.389167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>57953</td>\n",
       "      <td>2016</td>\n",
       "      <td>POINT (-91.39416700000001 40.389167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>57953</td>\n",
       "      <td>2017</td>\n",
       "      <td>POINT (-91.39416700000001 40.389167)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>57953</td>\n",
       "      <td>2011</td>\n",
       "      <td>POINT (-91.39416700000001 40.389167)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                NERC_Label  plant id  year  \\\n",
       "0  Indeterminate - various NERC membership     57953  2014   \n",
       "0  Indeterminate - various NERC membership     57953  2015   \n",
       "0  Indeterminate - various NERC membership     57953  2016   \n",
       "0  Indeterminate - various NERC membership     57953  2017   \n",
       "0  Indeterminate - various NERC membership     57953  2011   \n",
       "\n",
       "                               geometry  \n",
       "0  POINT (-91.39416700000001 40.389167)  \n",
       "0  POINT (-91.39416700000001 40.389167)  \n",
       "0  POINT (-91.39416700000001 40.389167)  \n",
       "0  POINT (-91.39416700000001 40.389167)  \n",
       "0  POINT (-91.39416700000001 40.389167)  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_nerc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Changing the crs to see what happens\n",
    "Just making sure that it does something"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-28T19:20:04.302495",
     "start_time": "2017-07-28T19:20:03.204775Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_test = df.to_crs({'init': 'epsg:3395'})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-28T19:20:26.814114",
     "start_time": "2017-07-28T19:20:17.102754Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1301c5f28>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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69X9z/Phxpk2fzquQEJRpeB7lNvKcyeXhw4e4urpKyblyOb8vOc6o8duRy2Vk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1301b3fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-28T19:20:35.322416",
     "start_time": "2017-07-28T19:20:26.815800Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1334af7b8>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TJuvR6fQEvTKOSgwG8/npm5NqNRpz9fDRRK9bWFgkGG4+Nj0//pg7Geiemq9E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\n0Wg0mlzzfy9jWRsUwaGKAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1301b38d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_test.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NEW - read file with only plant id, state, and lat/lon\n",
    "[Link to data file](https://github.com/gschivley/Index-variability/raw/master/Data%20storage/Facility%20labels/Facility%20locations.csv)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you need to load the shapefile, [download it here](https://github.com/gschivley/Index-variability/raw/master/Data%20storage/NERC_Regions_EIA)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# If loading the shapefile\n",
    "shape_path = ''\n",
    "regions = gpd.read_file(shape_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T18:30:45.607401Z",
     "start_time": "2017-11-02T18:30:45.585899Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = join(data_path, 'Facility labels', 'Facility locations.csv')\n",
    "location = pd.read_csv(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T18:49:32.271876Z",
     "start_time": "2017-10-31T18:49:32.265485Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8217"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(location)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `nerc` column is labels from a spatial join using this same shapefile and lat/lon data in QGIS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T18:51:46.859717Z",
     "start_time": "2017-10-31T18:51:46.847384Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>nerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>AL</td>\n",
       "      <td>33.458665</td>\n",
       "      <td>-87.356823</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>AL</td>\n",
       "      <td>31.006900</td>\n",
       "      <td>-88.010300</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>AL</td>\n",
       "      <td>32.583889</td>\n",
       "      <td>-86.283056</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>AL</td>\n",
       "      <td>34.012800</td>\n",
       "      <td>-85.970800</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>AL</td>\n",
       "      <td>33.644344</td>\n",
       "      <td>-87.196486</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   plant id state        lat        lon  nerc\n",
       "0         2    AL  33.458665 -87.356823  SERC\n",
       "1         3    AL  31.006900 -88.010300  SERC\n",
       "2         4    AL  32.583889 -86.283056  SERC\n",
       "3         7    AL  34.012800 -85.970800  SERC\n",
       "4         8    AL  33.644344 -87.196486  SERC"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T18:51:48.617485Z",
     "start_time": "2017-10-31T18:51:48.606127Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>nerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [plant id, state, lat, lon, nerc]\n",
       "Index: []"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "location.loc[location['lat'].isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T18:33:01.802719Z",
     "start_time": "2017-11-02T18:33:01.676764Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "geometry = [Point(xy) for xy in zip(location.lon, location.lat)]\n",
    "# small_facility = small_facility.drop(['lon', 'lat'], axis=1)\n",
    "crs = {'init': 'epsg:4326'}\n",
    "geo_df = GeoDataFrame(location, crs=crs, geometry=geometry)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T18:33:02.932814Z",
     "start_time": "2017-11-02T18:33:02.917012Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>nerc</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>AL</td>\n",
       "      <td>33.458665</td>\n",
       "      <td>-87.356823</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-87.35682299999999 33.458665)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>AL</td>\n",
       "      <td>31.006900</td>\n",
       "      <td>-88.010300</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-88.0103 31.0069)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>AL</td>\n",
       "      <td>32.583889</td>\n",
       "      <td>-86.283056</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-86.283056 32.583889)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>AL</td>\n",
       "      <td>34.012800</td>\n",
       "      <td>-85.970800</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-85.9708 34.0128)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>AL</td>\n",
       "      <td>33.644344</td>\n",
       "      <td>-87.196486</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-87.19648599999999 33.644344)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   plant id state        lat        lon  nerc  \\\n",
       "0         2    AL  33.458665 -87.356823  SERC   \n",
       "1         3    AL  31.006900 -88.010300  SERC   \n",
       "2         4    AL  32.583889 -86.283056  SERC   \n",
       "3         7    AL  34.012800 -85.970800  SERC   \n",
       "4         8    AL  33.644344 -87.196486  SERC   \n",
       "\n",
       "                               geometry  \n",
       "0  POINT (-87.35682299999999 33.458665)  \n",
       "1              POINT (-88.0103 31.0069)  \n",
       "2          POINT (-86.283056 32.583889)  \n",
       "3              POINT (-85.9708 34.0128)  \n",
       "4  POINT (-87.19648599999999 33.644344)  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "geo_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Every plant id is unique**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:50:43.257871Z",
     "start_time": "2017-10-31T13:50:43.253116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8099"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(geo_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:50:44.183062Z",
     "start_time": "2017-10-31T13:50:44.174410Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8099"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(geo_df['plant id'].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 1 - use defaults"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:50:50.604260Z",
     "start_time": "2017-10-31T13:50:50.091253Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df1 = gpd.sjoin(regions, geo_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:50:53.758238Z",
     "start_time": "2017-10-31T13:50:53.701578Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NERC</th>\n",
       "      <th>NERC_Label</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>plant id</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>nerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>4381</td>\n",
       "      <td>54930</td>\n",
       "      <td>IA</td>\n",
       "      <td>40.584150</td>\n",
       "      <td>-91.424530</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>639</td>\n",
       "      <td>1127</td>\n",
       "      <td>IA</td>\n",
       "      <td>40.703100</td>\n",
       "      <td>-92.425300</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>629</td>\n",
       "      <td>1104</td>\n",
       "      <td>IA</td>\n",
       "      <td>40.741200</td>\n",
       "      <td>-91.116667</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>630</td>\n",
       "      <td>1105</td>\n",
       "      <td>IA</td>\n",
       "      <td>40.747800</td>\n",
       "      <td>-92.873056</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>2562</td>\n",
       "      <td>7706</td>\n",
       "      <td>IA</td>\n",
       "      <td>40.816736</td>\n",
       "      <td>-91.146667</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NERC                               NERC_Label  \\\n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "\n",
       "                                            geometry  index_right  plant id  \\\n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...         4381     54930   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...          639      1127   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...          629      1104   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...          630      1105   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...         2562      7706   \n",
       "\n",
       "  state        lat        lon nerc  \n",
       "0    IA  40.584150 -91.424530    -  \n",
       "0    IA  40.703100 -92.425300    -  \n",
       "0    IA  40.741200 -91.116667    -  \n",
       "0    IA  40.747800 -92.873056    -  \n",
       "0    IA  40.816736 -91.146667    -  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can already see that there are more rows in this dataframe than there were power plants."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:51:09.971321Z",
     "start_time": "2017-10-31T13:51:09.966549Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8135"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 2 - use 'within'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T18:33:15.404330Z",
     "start_time": "2017-11-02T18:33:14.837525Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df2 = gpd.sjoin(geo_df, regions, op='within')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:51:19.971612Z",
     "start_time": "2017-10-31T13:51:19.947421Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>nerc</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>NERC</th>\n",
       "      <th>NERC_Label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>AL</td>\n",
       "      <td>33.458665</td>\n",
       "      <td>-87.356823</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-87.35682299999999 33.458665)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>AL</td>\n",
       "      <td>31.006900</td>\n",
       "      <td>-88.010300</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-88.0103 31.0069)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>AL</td>\n",
       "      <td>32.583889</td>\n",
       "      <td>-86.283056</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-86.283056 32.583889)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7</td>\n",
       "      <td>AL</td>\n",
       "      <td>34.012800</td>\n",
       "      <td>-85.970800</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-85.9708 34.0128)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>AL</td>\n",
       "      <td>33.644344</td>\n",
       "      <td>-87.196486</td>\n",
       "      <td>SERC</td>\n",
       "      <td>POINT (-87.19648599999999 33.644344)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   plant id state        lat        lon  nerc  \\\n",
       "0         2    AL  33.458665 -87.356823  SERC   \n",
       "1         3    AL  31.006900 -88.010300  SERC   \n",
       "2         4    AL  32.583889 -86.283056  SERC   \n",
       "3         7    AL  34.012800 -85.970800  SERC   \n",
       "4         8    AL  33.644344 -87.196486  SERC   \n",
       "\n",
       "                               geometry  index_right  NERC  \\\n",
       "0  POINT (-87.35682299999999 33.458665)            5  SERC   \n",
       "1              POINT (-88.0103 31.0069)            5  SERC   \n",
       "2          POINT (-86.283056 32.583889)            5  SERC   \n",
       "3              POINT (-85.9708 34.0128)            5  SERC   \n",
       "4  POINT (-87.19648599999999 33.644344)            5  SERC   \n",
       "\n",
       "                            NERC_Label  \n",
       "0  SERC Reliability Corporation (SERC)  \n",
       "1  SERC Reliability Corporation (SERC)  \n",
       "2  SERC Reliability Corporation (SERC)  \n",
       "3  SERC Reliability Corporation (SERC)  \n",
       "4  SERC Reliability Corporation (SERC)  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Same size dataframe as from Method 1. Yet again, there appear to be extra results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-09T15:21:53.966457Z",
     "start_time": "2017-10-09T15:21:53.960588Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8135"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Method 3 - use 'contains'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-09T16:20:00.433347Z",
     "start_time": "2017-10-09T16:19:59.207784Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df3 = gpd.sjoin(regions, geo_df, op='contains')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-09T16:20:07.101031Z",
     "start_time": "2017-10-09T16:20:07.036510Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NERC</th>\n",
       "      <th>NERC_Label</th>\n",
       "      <th>geometry</th>\n",
       "      <th>index_right</th>\n",
       "      <th>plant id</th>\n",
       "      <th>state</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>4387</td>\n",
       "      <td>55340</td>\n",
       "      <td>AR</td>\n",
       "      <td>35.861900</td>\n",
       "      <td>-90.025300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>6136</td>\n",
       "      <td>172</td>\n",
       "      <td>AR</td>\n",
       "      <td>35.689300</td>\n",
       "      <td>-89.994000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>152</td>\n",
       "      <td>8055</td>\n",
       "      <td>AR</td>\n",
       "      <td>35.311000</td>\n",
       "      <td>-93.235100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>6217</td>\n",
       "      <td>187</td>\n",
       "      <td>AR</td>\n",
       "      <td>34.211913</td>\n",
       "      <td>-93.110963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "      <td>2928</td>\n",
       "      <td>59947</td>\n",
       "      <td>AR</td>\n",
       "      <td>33.631538</td>\n",
       "      <td>-92.703611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  NERC                               NERC_Label  \\\n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "0    -  Indeterminate - various NERC membership   \n",
       "\n",
       "                                            geometry  index_right  plant id  \\\n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...         4387     55340   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...         6136       172   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...          152      8055   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...         6217       187   \n",
       "0  (POLYGON ((-91.71222639199999 32.9756623780000...         2928     59947   \n",
       "\n",
       "  state        lat        lon  \n",
       "0    AR  35.861900 -90.025300  \n",
       "0    AR  35.689300 -89.994000  \n",
       "0    AR  35.311000 -93.235100  \n",
       "0    AR  34.211913 -93.110963  \n",
       "0    AR  33.631538 -92.703611  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still too many results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-09T16:20:13.982363Z",
     "start_time": "2017-10-09T16:20:13.976363Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8135"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What facilities are missing?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:51:32.811339Z",
     "start_time": "2017-10-31T13:51:32.383580Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NERC</th>\n",
       "      <th>NERC_Label</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>index_right</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>Indeterminate - various NERC membership</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "      <td>(POLYGON ((-81.95506602699999 24.5196900770000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "      <td>POLYGON ((-95.07148604699995 49.36451082200006...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NPCC</td>\n",
       "      <td>Northeast Power Coordinating Council (NPCC)</td>\n",
       "      <td>(POLYGON ((-73.82134384999995 40.6045004290000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "      <td>(POLYGON ((-90.89359863199996 29.0467829150000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "      <td>(POLYGON ((-91.71222639199999 32.9756623780000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>SPP</td>\n",
       "      <td>Southwest Power Pool (SPP)</td>\n",
       "      <td>(POLYGON ((-93.73310749999996 30.4137588960000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>TRE</td>\n",
       "      <td>Texas Reliability Entity (TRE)</td>\n",
       "      <td>POLYGON ((-100.0008629659999 35.02950123100004...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>WECC</td>\n",
       "      <td>Western Electricity Coordinating Council (WECC)</td>\n",
       "      <td>(POLYGON ((-117.215183729 32.77737140300007, -...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             NERC                                       NERC_Label  \\\n",
       "index_right                                                          \n",
       "0               -          Indeterminate - various NERC membership   \n",
       "1            FRCC  Florida Reliability Coordinating Council (FRCC)   \n",
       "2             MRO           Midwest Reliability Organization (MRO)   \n",
       "3            NPCC      Northeast Power Coordinating Council (NPCC)   \n",
       "4             RFC               ReliabilityFirst Corporation (RFC)   \n",
       "5            SERC              SERC Reliability Corporation (SERC)   \n",
       "6             SPP                       Southwest Power Pool (SPP)   \n",
       "7             TRE                   Texas Reliability Entity (TRE)   \n",
       "8            WECC  Western Electricity Coordinating Council (WECC)   \n",
       "\n",
       "                                                      geometry  \n",
       "index_right                                                     \n",
       "0            (POLYGON ((-91.71222639199999 32.9756623780000...  \n",
       "1            (POLYGON ((-81.95506602699999 24.5196900770000...  \n",
       "2            POLYGON ((-95.07148604699995 49.36451082200006...  \n",
       "3            (POLYGON ((-73.82134384999995 40.6045004290000...  \n",
       "4            (POLYGON ((-90.89359863199996 29.0467829150000...  \n",
       "5            (POLYGON ((-91.71222639199999 32.9756623780000...  \n",
       "6            (POLYGON ((-93.73310749999996 30.4137588960000...  \n",
       "7            POLYGON ((-100.0008629659999 35.02950123100004...  \n",
       "8            (POLYGON ((-117.215183729 32.77737140300007, -...  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T18:55:43.052867Z",
     "start_time": "2017-10-31T18:55:43.047124Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frcc = regions.loc[1, 'geometry']\n",
    "serc = regions.loc[5, 'geometry']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "I figured out that plant id 641 is one that shows up in both FRCC and SERC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T18:57:09.262882Z",
     "start_time": "2017-10-31T18:57:09.258488Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plant_641 = geo_df.loc[geo_df['plant id'] == 641, 'geometry'].values[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T18:57:10.483414Z",
     "start_time": "2017-10-31T18:57:10.418570Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frcc.contains(plant_641)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T18:57:11.155804Z",
     "start_time": "2017-10-31T18:57:11.101613Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "serc.contains(plant_641)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So 641 definitely is in both NERC regions. And apparently the regions overlap? But they aren't supposed to. And I didn't have any plants show up in multiple regions when doing the spatial join in QGIS."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:52:00.314608Z",
     "start_time": "2017-10-31T13:51:59.174113Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frcc.intersects(serc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 36 plants with duplicate NERC regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-31T13:52:11.159223Z",
     "start_time": "2017-10-31T13:52:11.151692Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df2.loc[df2['plant id'].duplicated()].sort_values('plant id'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T18:53:12.392999Z",
     "start_time": "2017-11-02T18:53:12.389322Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "pd.set_option('display.max_rows', 200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T18:53:13.994147Z",
     "start_time": "2017-11-02T18:53:13.934496Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>state</th>\n",
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       "      <th>lon</th>\n",
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       "      <td>POINT (-87.2244 30.5661)</td>\n",
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       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
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       "      <th>406</th>\n",
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       "      <td>POINT (-87.2244 30.5661)</td>\n",
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       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
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       "      <td>FRCC</td>\n",
       "      <td>POINT (-84.8869 30.6692)</td>\n",
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       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>407</th>\n",
       "      <td>642</td>\n",
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       "      <td>30.669200</td>\n",
       "      <td>-84.886900</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-84.8869 30.6692)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>643</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.268600</td>\n",
       "      <td>-85.700300</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-85.7003 30.2686)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>408</th>\n",
       "      <td>643</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.268600</td>\n",
       "      <td>-85.700300</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-85.7003 30.2686)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>690</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.708571</td>\n",
       "      <td>-84.863869</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-84.86386899999999 30.708571)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>432</th>\n",
       "      <td>690</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.708571</td>\n",
       "      <td>-84.863869</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-84.86386899999999 30.708571)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>1773</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.726100</td>\n",
       "      <td>-88.662500</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.66249999999999 46.7261)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>963</th>\n",
       "      <td>1773</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.726100</td>\n",
       "      <td>-88.662500</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.66249999999999 46.7261)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>1774</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.696400</td>\n",
       "      <td>-89.208900</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-89.2089 46.6964)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>964</th>\n",
       "      <td>1774</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.696400</td>\n",
       "      <td>-89.208900</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-89.2089 46.6964)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>1775</td>\n",
       "      <td>WI</td>\n",
       "      <td>45.947200</td>\n",
       "      <td>-88.218900</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.2189 45.9472)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>965</th>\n",
       "      <td>1775</td>\n",
       "      <td>WI</td>\n",
       "      <td>45.947200</td>\n",
       "      <td>-88.218900</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.2189 45.9472)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>1777</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.131100</td>\n",
       "      <td>-88.225000</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.22499999999999 46.1311)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>967</th>\n",
       "      <td>1777</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.131100</td>\n",
       "      <td>-88.225000</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.22499999999999 46.1311)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1780</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.955300</td>\n",
       "      <td>-88.195800</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.19580000000001 45.9553)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>1780</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.955300</td>\n",
       "      <td>-88.195800</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.19580000000001 45.9553)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>1781</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.990781</td>\n",
       "      <td>-88.210514</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.210514 45.990781)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>970</th>\n",
       "      <td>1781</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.990781</td>\n",
       "      <td>-88.210514</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.210514 45.990781)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>1784</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.871900</td>\n",
       "      <td>-88.069400</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.0694 45.8719)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>1784</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.871900</td>\n",
       "      <td>-88.069400</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.0694 45.8719)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>1785</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.159200</td>\n",
       "      <td>-88.235300</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.2353 46.1592)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>972</th>\n",
       "      <td>1785</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.159200</td>\n",
       "      <td>-88.235300</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.2353 46.1592)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>1821</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.106278</td>\n",
       "      <td>-88.334531</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.33453100000001 46.106278)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>977</th>\n",
       "      <td>1821</td>\n",
       "      <td>MI</td>\n",
       "      <td>46.106278</td>\n",
       "      <td>-88.334531</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.33453100000001 46.106278)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>1848</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.788900</td>\n",
       "      <td>-87.905800</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-87.9058 45.7889)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>992</th>\n",
       "      <td>1848</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.788900</td>\n",
       "      <td>-87.905800</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-87.9058 45.7889)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>4043</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.253930</td>\n",
       "      <td>-88.409560</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.40956 44.25393)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1978</th>\n",
       "      <td>4043</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.253930</td>\n",
       "      <td>-88.409560</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.40956 44.25393)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>4122</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.316400</td>\n",
       "      <td>-88.314700</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.3147 44.3164)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>4122</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.316400</td>\n",
       "      <td>-88.314700</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.3147 44.3164)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4127</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.200020</td>\n",
       "      <td>-88.458171</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.45817099999999 44.20002)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020</th>\n",
       "      <td>4127</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.200020</td>\n",
       "      <td>-88.458171</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.45817099999999 44.20002)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>6192</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.573611</td>\n",
       "      <td>-86.215833</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-86.21583299999999 30.573611)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2191</th>\n",
       "      <td>6192</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.573611</td>\n",
       "      <td>-86.215833</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-86.21583299999999 30.573611)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>7715</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.592080</td>\n",
       "      <td>-87.135391</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.135391 30.59208)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2565</th>\n",
       "      <td>7715</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.592080</td>\n",
       "      <td>-87.135391</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.135391 30.59208)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>7822</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.274582</td>\n",
       "      <td>-88.319074</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.319074 44.274582)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2614</th>\n",
       "      <td>7822</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.274582</td>\n",
       "      <td>-88.319074</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.319074 44.274582)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>10250</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.266057</td>\n",
       "      <td>-85.520670</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-85.52067 30.266057)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2947</th>\n",
       "      <td>10250</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.266057</td>\n",
       "      <td>-85.520670</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-85.52067 30.266057)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>10416</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.595800</td>\n",
       "      <td>-87.252500</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.2525 30.5958)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3027</th>\n",
       "      <td>10416</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.595800</td>\n",
       "      <td>-87.252500</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.2525 30.5958)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3434</th>\n",
       "      <td>50250</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.596600</td>\n",
       "      <td>-87.326400</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.32640000000001 30.5966)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3434</th>\n",
       "      <td>50250</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.596600</td>\n",
       "      <td>-87.326400</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.32640000000001 30.5966)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3435</th>\n",
       "      <td>50251</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.795600</td>\n",
       "      <td>-87.955600</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-87.9556 45.7956)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3435</th>\n",
       "      <td>50251</td>\n",
       "      <td>MI</td>\n",
       "      <td>45.795600</td>\n",
       "      <td>-87.955600</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-87.9556 45.7956)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>50310</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.473600</td>\n",
       "      <td>-87.236100</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.23609999999999 30.4736)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3471</th>\n",
       "      <td>50310</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.473600</td>\n",
       "      <td>-87.236100</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.23609999999999 30.4736)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3681</th>\n",
       "      <td>50725</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.848333</td>\n",
       "      <td>-87.113333</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.113333 30.848333)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3681</th>\n",
       "      <td>50725</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.848333</td>\n",
       "      <td>-87.113333</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.113333 30.848333)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>50807</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.141984</td>\n",
       "      <td>-85.621103</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-85.62110300000001 30.141984)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3723</th>\n",
       "      <td>50807</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.141984</td>\n",
       "      <td>-85.621103</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-85.62110300000001 30.141984)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4327</th>\n",
       "      <td>54842</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.286900</td>\n",
       "      <td>-88.333900</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.3339 44.2869)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4327</th>\n",
       "      <td>54842</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.286900</td>\n",
       "      <td>-88.333900</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.3339 44.2869)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4352</th>\n",
       "      <td>54885</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.276900</td>\n",
       "      <td>-88.334400</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.3344 44.2769)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4352</th>\n",
       "      <td>54885</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.276900</td>\n",
       "      <td>-88.334400</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.3344 44.2769)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4527</th>\n",
       "      <td>55135</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.193600</td>\n",
       "      <td>-88.506400</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.5064 44.1936)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4527</th>\n",
       "      <td>55135</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.193600</td>\n",
       "      <td>-88.506400</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.5064 44.1936)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4610</th>\n",
       "      <td>55242</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.566400</td>\n",
       "      <td>-87.115000</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.11499999999999 30.5664)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4610</th>\n",
       "      <td>55242</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.566400</td>\n",
       "      <td>-87.115000</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.11499999999999 30.5664)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>56037</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.189036</td>\n",
       "      <td>-88.468492</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.468492 44.189036)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5022</th>\n",
       "      <td>56037</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.189036</td>\n",
       "      <td>-88.468492</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.468492 44.189036)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5397</th>\n",
       "      <td>56522</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.928333</td>\n",
       "      <td>-85.426111</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-85.42611099999999 30.928333)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5397</th>\n",
       "      <td>56522</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.928333</td>\n",
       "      <td>-85.426111</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-85.42611099999999 30.928333)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6162</th>\n",
       "      <td>57502</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.570000</td>\n",
       "      <td>-87.390000</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.39 30.57)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6162</th>\n",
       "      <td>57502</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.570000</td>\n",
       "      <td>-87.390000</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-87.39 30.57)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7160</th>\n",
       "      <td>58663</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.115000</td>\n",
       "      <td>-88.217222</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.21722199999999 44.115)</td>\n",
       "      <td>2</td>\n",
       "      <td>MRO</td>\n",
       "      <td>Midwest Reliability Organization (MRO)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7160</th>\n",
       "      <td>58663</td>\n",
       "      <td>WI</td>\n",
       "      <td>44.115000</td>\n",
       "      <td>-88.217222</td>\n",
       "      <td>MRO</td>\n",
       "      <td>POINT (-88.21722199999999 44.115)</td>\n",
       "      <td>4</td>\n",
       "      <td>RFC</td>\n",
       "      <td>ReliabilityFirst Corporation (RFC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7859</th>\n",
       "      <td>59689</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.515448</td>\n",
       "      <td>-86.518407</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-86.518407 30.515448)</td>\n",
       "      <td>5</td>\n",
       "      <td>SERC</td>\n",
       "      <td>SERC Reliability Corporation (SERC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7859</th>\n",
       "      <td>59689</td>\n",
       "      <td>FL</td>\n",
       "      <td>30.515448</td>\n",
       "      <td>-86.518407</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>POINT (-86.518407 30.515448)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      plant id state        lat        lon  nerc  \\\n",
       "406        641    FL  30.566100 -87.224400  FRCC   \n",
       "406        641    FL  30.566100 -87.224400  FRCC   \n",
       "407        642    FL  30.669200 -84.886900  FRCC   \n",
       "407        642    FL  30.669200 -84.886900  FRCC   \n",
       "408        643    FL  30.268600 -85.700300  FRCC   \n",
       "408        643    FL  30.268600 -85.700300  FRCC   \n",
       "432        690    FL  30.708571 -84.863869  FRCC   \n",
       "432        690    FL  30.708571 -84.863869  FRCC   \n",
       "963       1773    MI  46.726100 -88.662500   MRO   \n",
       "963       1773    MI  46.726100 -88.662500   MRO   \n",
       "964       1774    MI  46.696400 -89.208900   MRO   \n",
       "964       1774    MI  46.696400 -89.208900   MRO   \n",
       "965       1775    WI  45.947200 -88.218900   MRO   \n",
       "965       1775    WI  45.947200 -88.218900   MRO   \n",
       "967       1777    MI  46.131100 -88.225000   MRO   \n",
       "967       1777    MI  46.131100 -88.225000   MRO   \n",
       "969       1780    MI  45.955300 -88.195800   MRO   \n",
       "969       1780    MI  45.955300 -88.195800   MRO   \n",
       "970       1781    MI  45.990781 -88.210514   MRO   \n",
       "970       1781    MI  45.990781 -88.210514   MRO   \n",
       "971       1784    MI  45.871900 -88.069400   MRO   \n",
       "971       1784    MI  45.871900 -88.069400   MRO   \n",
       "972       1785    MI  46.159200 -88.235300   MRO   \n",
       "972       1785    MI  46.159200 -88.235300   MRO   \n",
       "977       1821    MI  46.106278 -88.334531   MRO   \n",
       "977       1821    MI  46.106278 -88.334531   MRO   \n",
       "992       1848    MI  45.788900 -87.905800   MRO   \n",
       "992       1848    MI  45.788900 -87.905800   MRO   \n",
       "1978      4043    WI  44.253930 -88.409560   MRO   \n",
       "1978      4043    WI  44.253930 -88.409560   MRO   \n",
       "2017      4122    WI  44.316400 -88.314700   MRO   \n",
       "2017      4122    WI  44.316400 -88.314700   MRO   \n",
       "2020      4127    WI  44.200020 -88.458171   MRO   \n",
       "2020      4127    WI  44.200020 -88.458171   MRO   \n",
       "2191      6192    FL  30.573611 -86.215833  FRCC   \n",
       "2191      6192    FL  30.573611 -86.215833  FRCC   \n",
       "2565      7715    FL  30.592080 -87.135391  FRCC   \n",
       "2565      7715    FL  30.592080 -87.135391  FRCC   \n",
       "2614      7822    WI  44.274582 -88.319074   MRO   \n",
       "2614      7822    WI  44.274582 -88.319074   MRO   \n",
       "2947     10250    FL  30.266057 -85.520670  FRCC   \n",
       "2947     10250    FL  30.266057 -85.520670  FRCC   \n",
       "3027     10416    FL  30.595800 -87.252500  FRCC   \n",
       "3027     10416    FL  30.595800 -87.252500  FRCC   \n",
       "3434     50250    FL  30.596600 -87.326400  FRCC   \n",
       "3434     50250    FL  30.596600 -87.326400  FRCC   \n",
       "3435     50251    MI  45.795600 -87.955600   MRO   \n",
       "3435     50251    MI  45.795600 -87.955600   MRO   \n",
       "3471     50310    FL  30.473600 -87.236100  FRCC   \n",
       "3471     50310    FL  30.473600 -87.236100  FRCC   \n",
       "3681     50725    FL  30.848333 -87.113333  FRCC   \n",
       "3681     50725    FL  30.848333 -87.113333  FRCC   \n",
       "3723     50807    FL  30.141984 -85.621103  FRCC   \n",
       "3723     50807    FL  30.141984 -85.621103  FRCC   \n",
       "4327     54842    WI  44.286900 -88.333900   MRO   \n",
       "4327     54842    WI  44.286900 -88.333900   MRO   \n",
       "4352     54885    WI  44.276900 -88.334400   MRO   \n",
       "4352     54885    WI  44.276900 -88.334400   MRO   \n",
       "4527     55135    WI  44.193600 -88.506400   MRO   \n",
       "4527     55135    WI  44.193600 -88.506400   MRO   \n",
       "4610     55242    FL  30.566400 -87.115000  FRCC   \n",
       "4610     55242    FL  30.566400 -87.115000  FRCC   \n",
       "5022     56037    WI  44.189036 -88.468492   MRO   \n",
       "5022     56037    WI  44.189036 -88.468492   MRO   \n",
       "5397     56522    FL  30.928333 -85.426111  FRCC   \n",
       "5397     56522    FL  30.928333 -85.426111  FRCC   \n",
       "6162     57502    FL  30.570000 -87.390000  FRCC   \n",
       "6162     57502    FL  30.570000 -87.390000  FRCC   \n",
       "7160     58663    WI  44.115000 -88.217222   MRO   \n",
       "7160     58663    WI  44.115000 -88.217222   MRO   \n",
       "7859     59689    FL  30.515448 -86.518407  FRCC   \n",
       "7859     59689    FL  30.515448 -86.518407  FRCC   \n",
       "\n",
       "                                  geometry  index_right  NERC  \\\n",
       "406               POINT (-87.2244 30.5661)            5  SERC   \n",
       "406               POINT (-87.2244 30.5661)            1  FRCC   \n",
       "407               POINT (-84.8869 30.6692)            5  SERC   \n",
       "407               POINT (-84.8869 30.6692)            1  FRCC   \n",
       "408               POINT (-85.7003 30.2686)            5  SERC   \n",
       "408               POINT (-85.7003 30.2686)            1  FRCC   \n",
       "432   POINT (-84.86386899999999 30.708571)            5  SERC   \n",
       "432   POINT (-84.86386899999999 30.708571)            1  FRCC   \n",
       "963     POINT (-88.66249999999999 46.7261)            4   RFC   \n",
       "963     POINT (-88.66249999999999 46.7261)            2   MRO   \n",
       "964               POINT (-89.2089 46.6964)            2   MRO   \n",
       "964               POINT (-89.2089 46.6964)            4   RFC   \n",
       "965               POINT (-88.2189 45.9472)            4   RFC   \n",
       "965               POINT (-88.2189 45.9472)            2   MRO   \n",
       "967     POINT (-88.22499999999999 46.1311)            4   RFC   \n",
       "967     POINT (-88.22499999999999 46.1311)            2   MRO   \n",
       "969     POINT (-88.19580000000001 45.9553)            4   RFC   \n",
       "969     POINT (-88.19580000000001 45.9553)            2   MRO   \n",
       "970           POINT (-88.210514 45.990781)            4   RFC   \n",
       "970           POINT (-88.210514 45.990781)            2   MRO   \n",
       "971               POINT (-88.0694 45.8719)            4   RFC   \n",
       "971               POINT (-88.0694 45.8719)            2   MRO   \n",
       "972               POINT (-88.2353 46.1592)            2   MRO   \n",
       "972               POINT (-88.2353 46.1592)            4   RFC   \n",
       "977   POINT (-88.33453100000001 46.106278)            4   RFC   \n",
       "977   POINT (-88.33453100000001 46.106278)            2   MRO   \n",
       "992               POINT (-87.9058 45.7889)            2   MRO   \n",
       "992               POINT (-87.9058 45.7889)            4   RFC   \n",
       "1978            POINT (-88.40956 44.25393)            2   MRO   \n",
       "1978            POINT (-88.40956 44.25393)            4   RFC   \n",
       "2017              POINT (-88.3147 44.3164)            2   MRO   \n",
       "2017              POINT (-88.3147 44.3164)            4   RFC   \n",
       "2020   POINT (-88.45817099999999 44.20002)            4   RFC   \n",
       "2020   POINT (-88.45817099999999 44.20002)            2   MRO   \n",
       "2191  POINT (-86.21583299999999 30.573611)            1  FRCC   \n",
       "2191  POINT (-86.21583299999999 30.573611)            5  SERC   \n",
       "2565           POINT (-87.135391 30.59208)            1  FRCC   \n",
       "2565           POINT (-87.135391 30.59208)            5  SERC   \n",
       "2614          POINT (-88.319074 44.274582)            2   MRO   \n",
       "2614          POINT (-88.319074 44.274582)            4   RFC   \n",
       "2947           POINT (-85.52067 30.266057)            1  FRCC   \n",
       "2947           POINT (-85.52067 30.266057)            5  SERC   \n",
       "3027              POINT (-87.2525 30.5958)            5  SERC   \n",
       "3027              POINT (-87.2525 30.5958)            1  FRCC   \n",
       "3434    POINT (-87.32640000000001 30.5966)            5  SERC   \n",
       "3434    POINT (-87.32640000000001 30.5966)            1  FRCC   \n",
       "3435              POINT (-87.9556 45.7956)            4   RFC   \n",
       "3435              POINT (-87.9556 45.7956)            2   MRO   \n",
       "3471    POINT (-87.23609999999999 30.4736)            5  SERC   \n",
       "3471    POINT (-87.23609999999999 30.4736)            1  FRCC   \n",
       "3681          POINT (-87.113333 30.848333)            1  FRCC   \n",
       "3681          POINT (-87.113333 30.848333)            5  SERC   \n",
       "3723  POINT (-85.62110300000001 30.141984)            1  FRCC   \n",
       "3723  POINT (-85.62110300000001 30.141984)            5  SERC   \n",
       "4327              POINT (-88.3339 44.2869)            4   RFC   \n",
       "4327              POINT (-88.3339 44.2869)            2   MRO   \n",
       "4352              POINT (-88.3344 44.2769)            2   MRO   \n",
       "4352              POINT (-88.3344 44.2769)            4   RFC   \n",
       "4527              POINT (-88.5064 44.1936)            4   RFC   \n",
       "4527              POINT (-88.5064 44.1936)            2   MRO   \n",
       "4610    POINT (-87.11499999999999 30.5664)            1  FRCC   \n",
       "4610    POINT (-87.11499999999999 30.5664)            5  SERC   \n",
       "5022          POINT (-88.468492 44.189036)            4   RFC   \n",
       "5022          POINT (-88.468492 44.189036)            2   MRO   \n",
       "5397  POINT (-85.42611099999999 30.928333)            1  FRCC   \n",
       "5397  POINT (-85.42611099999999 30.928333)            5  SERC   \n",
       "6162                  POINT (-87.39 30.57)            1  FRCC   \n",
       "6162                  POINT (-87.39 30.57)            5  SERC   \n",
       "7160     POINT (-88.21722199999999 44.115)            2   MRO   \n",
       "7160     POINT (-88.21722199999999 44.115)            4   RFC   \n",
       "7859          POINT (-86.518407 30.515448)            5  SERC   \n",
       "7859          POINT (-86.518407 30.515448)            1  FRCC   \n",
       "\n",
       "                                           NERC_Label  \n",
       "406               SERC Reliability Corporation (SERC)  \n",
       "406   Florida Reliability Coordinating Council (FRCC)  \n",
       "407               SERC Reliability Corporation (SERC)  \n",
       "407   Florida Reliability Coordinating Council (FRCC)  \n",
       "408               SERC Reliability Corporation (SERC)  \n",
       "408   Florida Reliability Coordinating Council (FRCC)  \n",
       "432               SERC Reliability Corporation (SERC)  \n",
       "432   Florida Reliability Coordinating Council (FRCC)  \n",
       "963                ReliabilityFirst Corporation (RFC)  \n",
       "963            Midwest Reliability Organization (MRO)  \n",
       "964            Midwest Reliability Organization (MRO)  \n",
       "964                ReliabilityFirst Corporation (RFC)  \n",
       "965                ReliabilityFirst Corporation (RFC)  \n",
       "965            Midwest Reliability Organization (MRO)  \n",
       "967                ReliabilityFirst Corporation (RFC)  \n",
       "967            Midwest Reliability Organization (MRO)  \n",
       "969                ReliabilityFirst Corporation (RFC)  \n",
       "969            Midwest Reliability Organization (MRO)  \n",
       "970                ReliabilityFirst Corporation (RFC)  \n",
       "970            Midwest Reliability Organization (MRO)  \n",
       "971                ReliabilityFirst Corporation (RFC)  \n",
       "971            Midwest Reliability Organization (MRO)  \n",
       "972            Midwest Reliability Organization (MRO)  \n",
       "972                ReliabilityFirst Corporation (RFC)  \n",
       "977                ReliabilityFirst Corporation (RFC)  \n",
       "977            Midwest Reliability Organization (MRO)  \n",
       "992            Midwest Reliability Organization (MRO)  \n",
       "992                ReliabilityFirst Corporation (RFC)  \n",
       "1978           Midwest Reliability Organization (MRO)  \n",
       "1978               ReliabilityFirst Corporation (RFC)  \n",
       "2017           Midwest Reliability Organization (MRO)  \n",
       "2017               ReliabilityFirst Corporation (RFC)  \n",
       "2020               ReliabilityFirst Corporation (RFC)  \n",
       "2020           Midwest Reliability Organization (MRO)  \n",
       "2191  Florida Reliability Coordinating Council (FRCC)  \n",
       "2191              SERC Reliability Corporation (SERC)  \n",
       "2565  Florida Reliability Coordinating Council (FRCC)  \n",
       "2565              SERC Reliability Corporation (SERC)  \n",
       "2614           Midwest Reliability Organization (MRO)  \n",
       "2614               ReliabilityFirst Corporation (RFC)  \n",
       "2947  Florida Reliability Coordinating Council (FRCC)  \n",
       "2947              SERC Reliability Corporation (SERC)  \n",
       "3027              SERC Reliability Corporation (SERC)  \n",
       "3027  Florida Reliability Coordinating Council (FRCC)  \n",
       "3434              SERC Reliability Corporation (SERC)  \n",
       "3434  Florida Reliability Coordinating Council (FRCC)  \n",
       "3435               ReliabilityFirst Corporation (RFC)  \n",
       "3435           Midwest Reliability Organization (MRO)  \n",
       "3471              SERC Reliability Corporation (SERC)  \n",
       "3471  Florida Reliability Coordinating Council (FRCC)  \n",
       "3681  Florida Reliability Coordinating Council (FRCC)  \n",
       "3681              SERC Reliability Corporation (SERC)  \n",
       "3723  Florida Reliability Coordinating Council (FRCC)  \n",
       "3723              SERC Reliability Corporation (SERC)  \n",
       "4327               ReliabilityFirst Corporation (RFC)  \n",
       "4327           Midwest Reliability Organization (MRO)  \n",
       "4352           Midwest Reliability Organization (MRO)  \n",
       "4352               ReliabilityFirst Corporation (RFC)  \n",
       "4527               ReliabilityFirst Corporation (RFC)  \n",
       "4527           Midwest Reliability Organization (MRO)  \n",
       "4610  Florida Reliability Coordinating Council (FRCC)  \n",
       "4610              SERC Reliability Corporation (SERC)  \n",
       "5022               ReliabilityFirst Corporation (RFC)  \n",
       "5022           Midwest Reliability Organization (MRO)  \n",
       "5397  Florida Reliability Coordinating Council (FRCC)  \n",
       "5397              SERC Reliability Corporation (SERC)  \n",
       "6162  Florida Reliability Coordinating Council (FRCC)  \n",
       "6162              SERC Reliability Corporation (SERC)  \n",
       "7160           Midwest Reliability Organization (MRO)  \n",
       "7160               ReliabilityFirst Corporation (RFC)  \n",
       "7859              SERC Reliability Corporation (SERC)  \n",
       "7859  Florida Reliability Coordinating Council (FRCC)  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.loc[df2['plant id'].duplicated(keep=False)].sort_values('plant id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T19:49:43.075229Z",
     "start_time": "2017-11-02T19:49:43.051240Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>f</th>\n",
       "      <th>fuel</th>\n",
       "      <th>month</th>\n",
       "      <th>plant id</th>\n",
       "      <th>total fuel (mmbtu)</th>\n",
       "      <th>year</th>\n",
       "      <th>generation (MWh)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "      <th>geography</th>\n",
       "      <th>last_updated</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>prime mover</th>\n",
       "      <th>datetime</th>\n",
       "      <th>quarter</th>\n",
       "      <th>all fuel fossil CO2 (kg)</th>\n",
       "      <th>elec fuel fossil CO2 (kg)</th>\n",
       "      <th>all fuel total CO2 (kg)</th>\n",
       "      <th>elec fuel total CO2 (kg)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>SUB</td>\n",
       "      <td>6</td>\n",
       "      <td>10360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-WI</td>\n",
       "      <td>2017-08-24T11:46:12-04:00</td>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2017-06-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>SUB</td>\n",
       "      <td>5</td>\n",
       "      <td>10360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-WI</td>\n",
       "      <td>2017-08-24T11:46:12-04:00</td>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2017-05-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>SUB</td>\n",
       "      <td>4</td>\n",
       "      <td>10360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-WI</td>\n",
       "      <td>2017-08-24T11:46:12-04:00</td>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>SUB</td>\n",
       "      <td>3</td>\n",
       "      <td>10360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-WI</td>\n",
       "      <td>2017-08-24T11:46:12-04:00</td>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>SUB</td>\n",
       "      <td>2</td>\n",
       "      <td>10360</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-WI</td>\n",
       "      <td>2017-08-24T11:46:12-04:00</td>\n",
       "      <td>44.4936</td>\n",
       "      <td>-88.0303</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   f fuel  month  plant id  total fuel (mmbtu)  year  generation (MWh)  \\\n",
       "0  M  SUB      6     10360                 0.0  2017               0.0   \n",
       "1  M  SUB      5     10360                 0.0  2017               0.0   \n",
       "2  M  SUB      4     10360                 0.0  2017               0.0   \n",
       "3  M  SUB      3     10360                 0.0  2017               0.0   \n",
       "4  M  SUB      2     10360                 0.0  2017               0.0   \n",
       "\n",
       "   elec fuel (mmbtu) geography               last_updated      lat      lon  \\\n",
       "0                0.0    USA-WI  2017-08-24T11:46:12-04:00  44.4936 -88.0303   \n",
       "1                0.0    USA-WI  2017-08-24T11:46:12-04:00  44.4936 -88.0303   \n",
       "2                0.0    USA-WI  2017-08-24T11:46:12-04:00  44.4936 -88.0303   \n",
       "3                0.0    USA-WI  2017-08-24T11:46:12-04:00  44.4936 -88.0303   \n",
       "4                0.0    USA-WI  2017-08-24T11:46:12-04:00  44.4936 -88.0303   \n",
       "\n",
       "  prime mover    datetime  quarter  all fuel fossil CO2 (kg)  \\\n",
       "0         ALL  2017-06-01        2                       0.0   \n",
       "1         ALL  2017-05-01        2                       0.0   \n",
       "2         ALL  2017-04-01        2                       0.0   \n",
       "3         ALL  2017-03-01        1                       0.0   \n",
       "4         ALL  2017-02-01        1                       0.0   \n",
       "\n",
       "   elec fuel fossil CO2 (kg)  all fuel total CO2 (kg)  \\\n",
       "0                        0.0                      0.0   \n",
       "1                        0.0                      0.0   \n",
       "2                        0.0                      0.0   \n",
       "3                        0.0                      0.0   \n",
       "4                        0.0                      0.0   \n",
       "\n",
       "   elec fuel total CO2 (kg)  \n",
       "0                       0.0  \n",
       "1                       0.0  \n",
       "2                       0.0  \n",
       "3                       0.0  \n",
       "4                       0.0  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:02:20.963770Z",
     "start_time": "2017-11-02T20:02:20.673806Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "plants = facility_df.loc[:, ['plant id', 'year', 'lat', 'lon']]\n",
    "plants.drop_duplicates(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T19:51:27.369014Z",
     "start_time": "2017-11-02T19:51:27.062773Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x1165f8ef0>]], dtype=object)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VygKQpEpZAJJUqendDiCpf8xdcXNH7mf5/BHO6dB91ewFXQBT8c3ayjfmxotPb/v9SlK7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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11606f1d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plants.groupby(['plant id']).max().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:46:16.715175Z",
     "start_time": "2017-11-02T20:46:10.267698Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = join(data_path, 'EIA downloads', 'EIA-860 2015', '2___Plant_Y2015.xlsx')\n",
    "nercs2015 = pd.read_excel(path, skiprows=0, parse_cols='C,L')\n",
    "nercs2015.columns = ['plant id', 'nerc']\n",
    "\n",
    "path = join(data_path, 'EIA downloads', 'eia8602012', 'PlantY2012.xlsx')\n",
    "nercs2012 = pd.read_excel(path, skiprows=0, parse_cols='B,J')\n",
    "nercs2012.columns = ['plant id', 'nerc']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:46:18.083637Z",
     "start_time": "2017-11-02T20:46:18.071464Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>nerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10867</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>50903</td>\n",
       "      <td>RFC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10671</td>\n",
       "      <td>SPP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2527</td>\n",
       "      <td>NPCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3305</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   plant id  nerc\n",
       "0     10867  SERC\n",
       "1     50903   RFC\n",
       "2     10671   SPP\n",
       "3      2527  NPCC\n",
       "4      3305  SERC"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nercs2012.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:46:23.806266Z",
     "start_time": "2017-11-02T20:46:23.793925Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nercs = pd.concat([nercs2012, nercs2015]).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:46:26.431119Z",
     "start_time": "2017-11-02T20:46:26.421839Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(9178, 7289, 8928)"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(nercs), len(nercs2012), len(nercs2015)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:46:31.530712Z",
     "start_time": "2017-11-02T20:46:31.499222Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.merge(plants, nercs, on=['plant id'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:46:34.731754Z",
     "start_time": "2017-11-02T20:46:34.720874Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>year</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1544548</th>\n",
       "      <td>1275</td>\n",
       "      <td>2006</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544560</th>\n",
       "      <td>1275</td>\n",
       "      <td>2005</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544572</th>\n",
       "      <td>1275</td>\n",
       "      <td>2004</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544584</th>\n",
       "      <td>1275</td>\n",
       "      <td>2003</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1544596</th>\n",
       "      <td>1275</td>\n",
       "      <td>2002</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         plant id  year        lat        lon\n",
       "1544548      1275  2006  38.947222 -99.528611\n",
       "1544560      1275  2005  38.947222 -99.528611\n",
       "1544572      1275  2004  38.947222 -99.528611\n",
       "1544584      1275  2003  38.947222 -99.528611\n",
       "1544596      1275  2002  38.947222 -99.528611"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plants.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:46:39.591707Z",
     "start_time": "2017-11-02T20:46:39.578003Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>nerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8923</th>\n",
       "      <td>60510</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8924</th>\n",
       "      <td>60511</td>\n",
       "      <td>WECC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8925</th>\n",
       "      <td>60514</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8926</th>\n",
       "      <td>60550</td>\n",
       "      <td>SERC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8927</th>\n",
       "      <td>60571</td>\n",
       "      <td>WECC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      plant id  nerc\n",
       "8923     60510  SERC\n",
       "8924     60511  WECC\n",
       "8925     60514  SERC\n",
       "8926     60550  SERC\n",
       "8927     60571  WECC"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nercs.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:46:54.035731Z",
     "start_time": "2017-11-02T20:46:54.028688Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['SERC', 'RFC', 'SPP', 'NPCC', 'WECC', 'MRO', 'TRE', 'HICC', 'ASCC',\n",
       "       'FRCC', nan], dtype=object)"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nercs.nerc.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:18:54.973582Z",
     "start_time": "2017-11-02T20:18:54.961702Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>plant id</th>\n",
       "      <th>year</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "      <th>nerc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>89242</th>\n",
       "      <td>1275</td>\n",
       "      <td>2006</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "      <td>SPP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89243</th>\n",
       "      <td>1275</td>\n",
       "      <td>2005</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "      <td>SPP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89244</th>\n",
       "      <td>1275</td>\n",
       "      <td>2004</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "      <td>SPP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89245</th>\n",
       "      <td>1275</td>\n",
       "      <td>2003</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "      <td>SPP</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>89246</th>\n",
       "      <td>1275</td>\n",
       "      <td>2002</td>\n",
       "      <td>38.947222</td>\n",
       "      <td>-99.528611</td>\n",
       "      <td>SPP</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       plant id  year        lat        lon nerc\n",
       "89242      1275  2006  38.947222 -99.528611  SPP\n",
       "89243      1275  2005  38.947222 -99.528611  SPP\n",
       "89244      1275  2004  38.947222 -99.528611  SPP\n",
       "89245      1275  2003  38.947222 -99.528611  SPP\n",
       "89246      1275  2002  38.947222 -99.528611  SPP"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:17:46.319652Z",
     "start_time": "2017-11-02T20:17:46.295244Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "88"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[df.isnull().any(axis=1), 'plant id'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T21:02:19.571139Z",
     "start_time": "2017-11-02T21:02:19.557630Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import neighbors\n",
    "from sklearn.model_selection import train_test_split, GridSearchCV\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.colors import ListedColormap\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:47:17.663675Z",
     "start_time": "2017-11-02T20:47:17.660112Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "nercs = ['SERC', 'RFC', 'SPP', 'NPCC', 'WECC', 'MRO', 'TRE', 'FRCC']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:47:35.495443Z",
     "start_time": "2017-11-02T20:47:35.454473Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_slim = df.loc[df.nerc.isin(nercs), ['plant id', 'lat', 'lon', 'nerc']].dropna(subset=['lon']).drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:47:38.181971Z",
     "start_time": "2017-11-02T20:47:38.175845Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unknown = df_slim.loc[df_slim.nerc.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:19:37.322247Z",
     "start_time": "2017-11-02T20:19:37.315816Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "n_neighbors = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T21:04:28.083243Z",
     "start_time": "2017-11-02T21:04:28.070127Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = df_slim.loc[df_slim.notnull().all(axis=1), ['lat', 'lon']]\n",
    "y = df_slim.loc[df_slim.notnull().all(axis=1), 'nerc']\n",
    "\n",
    "X_scale = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T21:06:11.018817Z",
     "start_time": "2017-11-02T21:06:11.010866Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    X, y, test_size=0.33, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:47:46.181994Z",
     "start_time": "2017-11-02T20:47:46.175564Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['MRO', 'WECC', 'NPCC', 'SERC', 'SPP', 'RFC', 'FRCC', 'TRE'], dtype=object)"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T21:06:12.573829Z",
     "start_time": "2017-11-02T21:06:12.570487Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "knn = neighbors.KNeighborsClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T21:06:13.311863Z",
     "start_time": "2017-11-02T21:06:13.046Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "params = {'weights': ['uniform', 'distance'],\n",
    "          'n_neighbors': [10, 15, 20, 30],\n",
    "          'leaf_size': [3, 5, 10, 30],\n",
    "          'p': [1, 2]}\n",
    "\n",
    "clf = GridSearchCV(knn, params, n_jobs=-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T21:04:53.477943Z",
     "start_time": "2017-11-02T21:04:48.784220Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=None, error_score='raise',\n",
       "       estimator=KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=5, p=2,\n",
       "           weights='uniform'),\n",
       "       fit_params={}, iid=True, n_jobs=-1,\n",
       "       param_grid={'weights': ['uniform', 'distance'], 'n_neighbors': [10, 15, 20, 30], 'leaf_size': [3, 5, 10, 30], 'p': [1, 2]},\n",
       "       pre_dispatch='2*n_jobs', refit=True, return_train_score=True,\n",
       "       scoring=None, verbose=0)"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T21:04:54.963439Z",
     "start_time": "2017-11-02T21:04:54.957868Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(KNeighborsClassifier(algorithm='auto', leaf_size=3, metric='minkowski',\n",
       "            metric_params=None, n_jobs=1, n_neighbors=15, p=1,\n",
       "            weights='distance'),\n",
       " {'leaf_size': 3, 'n_neighbors': 15, 'p': 1, 'weights': 'distance'},\n",
       " 0.96590695046148045)"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.best_estimator_, clf.best_params_, clf.best_score_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "No difference in score when X is preprocessed with `StandardScaler`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T21:05:09.696372Z",
     "start_time": "2017-11-02T21:05:09.638412Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98498422712933753"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_scale, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:36:04.390405Z",
     "start_time": "2017-11-02T20:36:04.371039Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='uniform')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = neighbors.KNeighborsClassifier(n_neighbors, weights='uniform')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:36:50.047487Z",
     "start_time": "2017-11-02T20:36:50.027872Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95693087224031848"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:37:02.106043Z",
     "start_time": "2017-11-02T20:37:02.084523Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "           metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "           weights='distance')"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = neighbors.KNeighborsClassifier(n_neighbors, weights='distance')\n",
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:37:05.078004Z",
     "start_time": "2017-11-02T20:37:05.044278Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95946435034382915"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-11-02T20:31:59.134545Z",
     "start_time": "2017-11-02T20:30:31.590699Z"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'str' object has no attribute 'view'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m~/anaconda/envs/py36/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36mmin\u001b[0;34m(obj, axis, out, fill_value, keepdims)\u001b[0m\n\u001b[1;32m   6441\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 6442\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mobj\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfill_value\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mfill_value\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mout\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mout\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   6443\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mAttributeError\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: _amin() got an unexpected keyword argument 'fill_value'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-95-cf8de1f4ef1e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     21\u001b[0m     \u001b[0mZ\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     22\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 23\u001b[0;31m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpcolormesh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0myy\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mZ\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcmap\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcmap_light\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     24\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     25\u001b[0m     \u001b[0;31m# Plot also the training points\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/py36/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3242\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3243\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3244\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpcolormesh\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3245\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3246\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/py36/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1896\u001b[0m                     warnings.warn(msg % (label_namer, func.__name__),\n\u001b[1;32m   1897\u001b[0m                                   RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1898\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1899\u001b[0m         \u001b[0mpre_doc\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minner\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1900\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mpre_doc\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/py36/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mpcolormesh\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   5601\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_norm\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5602\u001b[0m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clim\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvmin\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvmax\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5603\u001b[0;31m         \u001b[0mcollection\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_None\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5604\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5605\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgrid\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/py36/lib/python3.6/site-packages/matplotlib/cm.py\u001b[0m in \u001b[0;36mautoscale_None\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    343\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    344\u001b[0m             \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'You must first set_array for mappable'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 345\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnorm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoscale_None\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_A\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    346\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mchanged\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    347\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/py36/lib/python3.6/site-packages/matplotlib/colors.py\u001b[0m in \u001b[0;36mautoscale_None\u001b[0;34m(self, A)\u001b[0m\n\u001b[1;32m    887\u001b[0m         \u001b[0;34m' autoscale only None-valued vmin or vmax'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    888\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmin\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 889\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmin\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    890\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmax\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    891\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvmax\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mA\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/py36/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36mmin\u001b[0;34m(obj, axis, out, fill_value, keepdims)\u001b[0m\n\u001b[1;32m   6445\u001b[0m         \u001b[0;31m# fill_value argument\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6446\u001b[0m         return asanyarray(obj).min(axis=axis, fill_value=fill_value,\n\u001b[0;32m-> 6447\u001b[0;31m                                    out=out, **kwargs)\n\u001b[0m\u001b[1;32m   6448\u001b[0m \u001b[0mmin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mMaskedArray\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmin\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__doc__\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   6449\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda/envs/py36/lib/python3.6/site-packages/numpy/ma/core.py\u001b[0m in \u001b[0;36mmin\u001b[0;34m(self, axis, out, fill_value, keepdims)\u001b[0m\n\u001b[1;32m   5592\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mout\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5593\u001b[0m             result = self.filled(fill_value).min(\n\u001b[0;32m-> 5594\u001b[0;31m                 axis=axis, out=out, **kwargs).view(type(self))\n\u001b[0m\u001b[1;32m   5595\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5596\u001b[0m                 \u001b[0;31m# Set the mask\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'view'"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ef1cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = .02  # step size in the mesh\n",
    "\n",
    "# Create color maps\n",
    "cmap_light = ListedColormap(['#FFAAAA', '#AAFFAA', '#AAAAFF'])\n",
    "cmap_bold = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])\n",
    "\n",
    "for weights in ['uniform', 'distance']:\n",
    "    # we create an instance of Neighbours Classifier and fit the data.\n",
    "    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n",
    "    clf.fit(X, y)\n",
    "\n",
    "    # Plot the decision boundary. For that, we will assign a color to each\n",
    "    # point in the mesh [x_min, x_max]x[y_min, y_max].\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, h),\n",
    "                         np.arange(y_min, y_max, h))\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "\n",
    "    # Put the result into a color plot\n",
    "    Z = Z.reshape(xx.shape)\n",
    "    plt.figure()\n",
    "    plt.pcolormesh(xx, yy, Z, cmap=cmap_light)\n",
    "\n",
    "    # Plot also the training points\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y, cmap=cmap_bold,\n",
    "                edgecolor='k', s=20)\n",
    "    plt.xlim(xx.min(), xx.max())\n",
    "    plt.ylim(yy.min(), yy.max())\n",
    "    plt.title(\"3-Class classification (k = %i, weights = '%s')\"\n",
    "              % (n_neighbors, weights))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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