{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:46:03.602776Z",
     "start_time": "2017-08-08T16:46:03.376758Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "import glob\n",
    "import numpy as np\n",
    "import geopandas as gpd\n",
    "from shapely.geometry import Point\n",
    "from geopandas import GeoDataFrame\n",
    "sns.set(style='whitegrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import facility data and NERC labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:39:43.440142Z",
     "start_time": "2017-08-08T19:39:34.762652Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = os.path.join('Data storage', 'Facility gen fuels and CO2 2017-05-25.zip')\n",
    "facility_df = pd.read_csv(path, parse_dates=['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:52:17.840902Z",
     "start_time": "2017-08-08T16:52:17.804900Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "<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>NG</td>\n",
       "      <td>3</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
       "      <td>2017-05-24T14:26:30-04:00</td>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>1</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>NG</td>\n",
       "      <td>2</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
       "      <td>2017-05-24T14:26:30-04:00</td>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</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",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>NG</td>\n",
       "      <td>1</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
       "      <td>2017-05-24T14:26:30-04:00</td>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2017-01-01</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>3</th>\n",
       "      <td>M</td>\n",
       "      <td>NG</td>\n",
       "      <td>12</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
       "      <td>2017-05-24T14:26:30-04:00</td>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>4</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>NG</td>\n",
       "      <td>11</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
       "      <td>2017-05-24T14:26:30-04:00</td>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>4</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   NG      3     10275                 0.0  2017               0.0   \n",
       "1  M   NG      2     10275                 0.0  2017               0.0   \n",
       "2  M   NG      1     10275                 0.0  2017               0.0   \n",
       "3  M   NG     12     10275                 0.0  2016               0.0   \n",
       "4  M   NG     11     10275                 0.0  2016               0.0   \n",
       "\n",
       "   elec fuel (mmbtu) geography               last_updated      lat      lon  \\\n",
       "0                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "1                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "2                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "3                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "4                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "\n",
       "  prime mover   datetime  quarter  all fuel fossil CO2 (kg)  \\\n",
       "0         ALL 2017-03-01        1                       0.0   \n",
       "1         ALL 2017-02-01        1                       0.0   \n",
       "2         ALL 2017-01-01        1                       0.0   \n",
       "3         ALL 2016-12-01        4                       0.0   \n",
       "4         ALL 2016-11-01        4                       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": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:39:43.916170Z",
     "start_time": "2017-08-08T19:39:43.443143Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facility_df.dropna(inplace=True, subset=['lat', 'lon'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:47:29.919203Z",
     "start_time": "2017-08-08T18:47:29.555183Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = ['lat', 'lon', 'plant id', 'year']\n",
    "small_facility = facility_df.loc[:, cols].drop_duplicates()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:47:32.042329Z",
     "start_time": "2017-08-08T18:47:29.922204Z"
    },
    "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": "markdown",
   "metadata": {},
   "source": [
    "## Read NERC shapefile and merge with `geo_df`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:46:05.950909Z",
     "start_time": "2017-08-08T16:46:05.280871Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = os.path.join('Data storage', 'NERC_Regions_EIA', 'NercRegions_201610.shp')\n",
    "regions = gpd.read_file(path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:48:45.036910Z",
     "start_time": "2017-08-08T16:48:26.845859Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facility_nerc = gpd.sjoin(geo_df, regions, how='inner', op='within')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:49:03.540936Z",
     "start_time": "2017-08-08T16:49:03.518930Z"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <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",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>10275</td>\n",
       "      <td>2017</td>\n",
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       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
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       "      <td>2016</td>\n",
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       "      <td>10275</td>\n",
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       "      <td>POINT (-81.6006 27.9114)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
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       "      <th>27</th>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>10275</td>\n",
       "      <td>2014</td>\n",
       "      <td>POINT (-81.6006 27.9114)</td>\n",
       "      <td>1</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>Florida Reliability Coordinating Council (FRCC)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>10275</td>\n",
       "      <td>2013</td>\n",
       "      <td>POINT (-81.6006 27.9114)</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": [
       "        lat      lon  plant id  year                  geometry  index_right  \\\n",
       "0   27.9114 -81.6006     10275  2017  POINT (-81.6006 27.9114)            1   \n",
       "3   27.9114 -81.6006     10275  2016  POINT (-81.6006 27.9114)            1   \n",
       "15  27.9114 -81.6006     10275  2015  POINT (-81.6006 27.9114)            1   \n",
       "27  27.9114 -81.6006     10275  2014  POINT (-81.6006 27.9114)            1   \n",
       "39  27.9114 -81.6006     10275  2013  POINT (-81.6006 27.9114)            1   \n",
       "\n",
       "    NERC                                       NERC_Label  \n",
       "0   FRCC  Florida Reliability Coordinating Council (FRCC)  \n",
       "3   FRCC  Florida Reliability Coordinating Council (FRCC)  \n",
       "15  FRCC  Florida Reliability Coordinating Council (FRCC)  \n",
       "27  FRCC  Florida Reliability Coordinating Council (FRCC)  \n",
       "39  FRCC  Florida Reliability Coordinating Council (FRCC)  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_nerc.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merge NERC labels into the facility df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:39:53.186693Z",
     "start_time": "2017-08-08T19:39:51.180580Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cols = ['plant id', 'year', 'NERC']\n",
    "facility_df = facility_df.merge(facility_nerc.loc[:, cols],\n",
    "                                on=['plant id', 'year'], how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:39:53.834730Z",
     "start_time": "2017-08-08T19:39:53.797728Z"
    }
   },
   "outputs": [
    {
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       "    <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",
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       "      <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",
       "      <th>NERC</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
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       "      <td>0.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>NG</td>\n",
       "      <td>2</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
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       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FRCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>M</td>\n",
       "      <td>NG</td>\n",
       "      <td>1</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
       "      <td>2017-05-24T14:26:30-04:00</td>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2017-01-01</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",
       "      <td>FRCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>NG</td>\n",
       "      <td>12</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
       "      <td>2017-05-24T14:26:30-04:00</td>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FRCC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>M</td>\n",
       "      <td>NG</td>\n",
       "      <td>11</td>\n",
       "      <td>10275</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>USA-FL</td>\n",
       "      <td>2017-05-24T14:26:30-04:00</td>\n",
       "      <td>27.9114</td>\n",
       "      <td>-81.6006</td>\n",
       "      <td>ALL</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>FRCC</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   NG      3     10275                 0.0  2017               0.0   \n",
       "1  M   NG      2     10275                 0.0  2017               0.0   \n",
       "2  M   NG      1     10275                 0.0  2017               0.0   \n",
       "3  M   NG     12     10275                 0.0  2016               0.0   \n",
       "4  M   NG     11     10275                 0.0  2016               0.0   \n",
       "\n",
       "   elec fuel (mmbtu) geography               last_updated      lat      lon  \\\n",
       "0                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "1                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "2                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "3                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "4                0.0    USA-FL  2017-05-24T14:26:30-04:00  27.9114 -81.6006   \n",
       "\n",
       "  prime mover   datetime  quarter  all fuel fossil CO2 (kg)  \\\n",
       "0         ALL 2017-03-01        1                       0.0   \n",
       "1         ALL 2017-02-01        1                       0.0   \n",
       "2         ALL 2017-01-01        1                       0.0   \n",
       "3         ALL 2016-12-01        4                       0.0   \n",
       "4         ALL 2016-11-01        4                       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)  NERC  \n",
       "0                       0.0  FRCC  \n",
       "1                       0.0  FRCC  \n",
       "2                       0.0  FRCC  \n",
       "3                       0.0  FRCC  \n",
       "4                       0.0  FRCC  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filter out data older than 2014 to reduce size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:40:02.461222Z",
     "start_time": "2017-08-08T19:40:00.995134Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facility_df['state'] = facility_df['geography'].str[-2:]\n",
    "keep_cols = ['fuel', 'year', 'month', 'datetime', 'state', 'plant id', 'NERC',\n",
    "             'generation (MWh)', 'total fuel (mmbtu)', 'elec fuel (mmbtu)']\n",
    "facility_df = facility_df.loc[facility_df['year'] >= 2014, keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:40:03.232260Z",
     "start_time": "2017-08-08T19:40:03.075251Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "facility_fuel_cats = {'COW': ['SUB', 'BIT', 'LIG', 'WC', 'SC', 'RC', 'SGC'],\n",
    "                      'NG': ['NG'],\n",
    "                      'PEL': ['DFO', 'RFO', 'KER', 'JF',\n",
    "                              'PG', 'WO', 'SGP'],\n",
    "                      'PC': ['PC'],\n",
    "                      'HYC': ['WAT'],\n",
    "                      'HPS': [],\n",
    "                      'GEO': ['GEO'],\n",
    "                      'NUC': ['NUC'],\n",
    "                      'OOG': ['BFG', 'OG', 'LFG'],\n",
    "                      'OTH': ['OTH', 'MSN', 'MSW', 'PUR', 'TDF', 'WH'],\n",
    "                      'SUN': ['SUN'],\n",
    "                      'DPV': [],\n",
    "                      'WAS': ['OBL', 'OBS', 'OBG', 'MSB', 'SLW'],\n",
    "                      'WND': ['WND'],\n",
    "                      'WWW': ['WDL', 'WDS', 'AB', 'BLQ']\n",
    "                      }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:40:04.378330Z",
     "start_time": "2017-08-08T19:40:03.756290Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "for category in facility_fuel_cats.keys():\n",
    "    fuels = facility_fuel_cats[category]\n",
    "    facility_df.loc[facility_df['fuel'].isin(fuels),\n",
    "                    'fuel category'] = category"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:40:04.514333Z",
     "start_time": "2017-08-08T19:40:04.487331Z"
    }
   },
   "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>fuel</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>datetime</th>\n",
       "      <th>state</th>\n",
       "      <th>plant id</th>\n",
       "      <th>NERC</th>\n",
       "      <th>generation (MWh)</th>\n",
       "      <th>total fuel (mmbtu)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "      <th>fuel category</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NG</td>\n",
       "      <td>2017</td>\n",
       "      <td>3</td>\n",
       "      <td>2017-03-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NG</td>\n",
       "      <td>2017</td>\n",
       "      <td>2</td>\n",
       "      <td>2017-02-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NG</td>\n",
       "      <td>2017</td>\n",
       "      <td>1</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NG</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "      <td>2016-12-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NG</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "      <td>2016-11-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NG</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fuel  year  month   datetime state  plant id  NERC  generation (MWh)  \\\n",
       "0   NG  2017      3 2017-03-01    FL     10275  FRCC               0.0   \n",
       "1   NG  2017      2 2017-02-01    FL     10275  FRCC               0.0   \n",
       "2   NG  2017      1 2017-01-01    FL     10275  FRCC               0.0   \n",
       "3   NG  2016     12 2016-12-01    FL     10275  FRCC               0.0   \n",
       "4   NG  2016     11 2016-11-01    FL     10275  FRCC               0.0   \n",
       "\n",
       "   total fuel (mmbtu)  elec fuel (mmbtu) fuel category  \n",
       "0                 0.0                0.0            NG  \n",
       "1                 0.0                0.0            NG  \n",
       "2                 0.0                0.0            NG  \n",
       "3                 0.0                0.0            NG  \n",
       "4                 0.0                0.0            NG  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:40:12.389777Z",
     "start_time": "2017-08-08T19:40:12.379777Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "fuel                          object\n",
       "year                           int64\n",
       "month                          int64\n",
       "datetime              datetime64[ns]\n",
       "state                         object\n",
       "plant id                       int64\n",
       "NERC                          object\n",
       "generation (MWh)             float64\n",
       "total fuel (mmbtu)           float64\n",
       "elec fuel (mmbtu)            float64\n",
       "fuel category                 object\n",
       "dtype: object"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:48:02.434039Z",
     "start_time": "2017-08-08T18:48:02.403037Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['HI', 'FL', 'VA', 'MI', 'ME', 'MN', 'CA', 'AK', 'NY', 'MD', 'WI',\n",
       "       'NH', 'PA', 'OR', 'MA', 'IL', 'DC', 'RI', 'TX', 'CT', 'WA'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "facility_df.loc[facility_df['NERC'].isnull(), 'state'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Import state-level generation data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:54:51.321569Z",
     "start_time": "2017-08-08T16:54:51.311569Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "folder = os.path.join('Data storage', 'Derived data', 'state gen data')\n",
    "states = [\"AL\", \"AK\", \"AZ\", \"AR\", \"CA\", \"CO\", \"CT\", \"DE\", \n",
    "          \"FL\", \"GA\", \"HI\", \"ID\", \"IL\", \"IN\", \"IA\", \"KS\", \n",
    "          \"KY\", \"LA\", \"ME\", \"MD\", \"MA\", \"MI\", \"MN\", \"MS\", \n",
    "          \"MO\", \"MT\", \"NE\", \"NV\", \"NH\", \"NJ\", \"NM\", \"NY\", \n",
    "          \"NC\", \"ND\", \"OH\", \"OK\", \"OR\", \"PA\", \"RI\", \"SC\", \n",
    "          \"SD\", \"TN\", \"TX\", \"UT\", \"VT\", \"VA\", \"WA\", \"WV\", \"WI\", \"WY\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:55:04.590342Z",
     "start_time": "2017-08-08T16:55:03.431253Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "state_list = []\n",
    "for state in states:\n",
    "    path = os.path.join(folder, '{} fuels gen.csv'.format(state))\n",
    "    df = pd.read_csv(path, parse_dates=['datetime'])\n",
    "    state_list.append(df)\n",
    "state_df = pd.concat(state_list)\n",
    "state_df.reset_index(inplace=True, drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:55:05.447367Z",
     "start_time": "2017-08-08T16:55:05.439366Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "end                            int64\n",
       "f                             object\n",
       "geography                     object\n",
       "last_updated                  object\n",
       "sector                         int64\n",
       "series_id                     object\n",
       "start                          int64\n",
       "type                          object\n",
       "units                         object\n",
       "year                           int64\n",
       "month                          int64\n",
       "generation (MWh)             float64\n",
       "datetime              datetime64[ns]\n",
       "quarter                        int64\n",
       "total fuel (mmbtu)           float64\n",
       "elec fuel (mmbtu)            float64\n",
       "all fuel CO2 (kg)            float64\n",
       "elec fuel CO2 (kg)           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:55:25.906527Z",
     "start_time": "2017-08-08T16:55:25.784515Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "state_df['state'] = state_df['geography'].str[-2:]\n",
    "keep_cols = ['state', 'type', 'year', 'datetime', 'generation (MWh)',\n",
    "             'elec fuel (mmbtu)']\n",
    "\n",
    "fuel_cats = facility_fuel_cats.keys()\n",
    "state_df = state_df.loc[(state_df['year'] >= 2014) &\n",
    "                        (state_df['type'].isin(fuel_cats)), keep_cols]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:55:29.381719Z",
     "start_time": "2017-08-08T16:55:29.373718Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['COW', 'HYC', 'NUC', 'NG', 'PEL', 'DPV', 'OTH', 'OOG', 'WWW', 'SUN',\n",
       "       'WAS', 'WND', 'HPS', 'PC', 'GEO'], dtype=object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "state_df['type'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "## Total generation and fuel consumption for each fuel category"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "### Annual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:56:13.777231Z",
     "start_time": "2017-08-08T16:56:13.601215Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "annual_facility = facility_df.groupby(['year', 'state', 'fuel category']).sum()\n",
    "# annual_facility.reset_index(inplace=True)\n",
    "annual_facility.drop('plant id', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:56:15.795339Z",
     "start_time": "2017-08-08T16:56:15.780338Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
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       "</style>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>generation (MWh)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>fuel category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">2014</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">AK</th>\n",
       "      <th>COW</th>\n",
       "      <td>558292.181</td>\n",
       "      <td>7216953.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>1538738.000</td>\n",
       "      <td>14633403.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>3288022.319</td>\n",
       "      <td>32828304.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOG</th>\n",
       "      <td>56165.769</td>\n",
       "      <td>546450.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>445621.447</td>\n",
       "      <td>6927101.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          generation (MWh)  elec fuel (mmbtu)\n",
       "year state fuel category                                     \n",
       "2014 AK    COW                  558292.181          7216953.0\n",
       "           HYC                 1538738.000         14633403.0\n",
       "           NG                  3288022.319         32828304.0\n",
       "           OOG                   56165.769           546450.0\n",
       "           PEL                  445621.447          6927101.0"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_facility.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:56:29.680123Z",
     "start_time": "2017-08-08T16:56:29.666122Z"
    },
    "collapsed": true,
    "hidden": true
   },
   "outputs": [],
   "source": [
    "annual_state = state_df.groupby(['year', 'state', 'type']).sum()\n",
    "# annual_state.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:56:34.046370Z",
     "start_time": "2017-08-08T16:56:34.022368Z"
    },
    "hidden": true
   },
   "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></th>\n",
       "      <th></th>\n",
       "      <th>generation (MWh)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th>state</th>\n",
       "      <th>type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"25\" valign=\"top\">2014</th>\n",
       "      <th rowspan=\"9\" valign=\"top\">AK</th>\n",
       "      <th>COW</th>\n",
       "      <td>558292.17</td>\n",
       "      <td>7216950.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>1538738.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>3288022.33</td>\n",
       "      <td>32828310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>-2312.99</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>445621.46</td>\n",
       "      <td>6927090.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>62511.68</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WND</th>\n",
       "      <td>151957.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>0.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"10\" valign=\"top\">AL</th>\n",
       "      <th>COW</th>\n",
       "      <td>47301626.28</td>\n",
       "      <td>488993810.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPV</th>\n",
       "      <td>3101.38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>9466872.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>48270074.40</td>\n",
       "      <td>362215370.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NUC</th>\n",
       "      <td>41243689.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOG</th>\n",
       "      <td>180403.48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>140.51</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>98100.01</td>\n",
       "      <td>1199180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>46936.84</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>2732084.23</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">AR</th>\n",
       "      <th>COW</th>\n",
       "      <td>33220754.79</td>\n",
       "      <td>334098580.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DPV</th>\n",
       "      <td>4853.48</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HPS</th>\n",
       "      <td>67070.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>2639776.01</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>9613708.03</td>\n",
       "      <td>70429870.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NUC</th>\n",
       "      <td>14478259.00</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 generation (MWh)  elec fuel (mmbtu)\n",
       "year state type                                     \n",
       "2014 AK    COW          558292.17          7216950.0\n",
       "           HYC         1538738.00                NaN\n",
       "           NG          3288022.33         32828310.0\n",
       "           OOG                NaN                NaN\n",
       "           OTH           -2312.99                NaN\n",
       "           PEL          445621.46          6927090.0\n",
       "           WAS           62511.68                NaN\n",
       "           WND          151957.00                NaN\n",
       "           WWW               0.00                NaN\n",
       "     AL    COW        47301626.28        488993810.0\n",
       "           DPV            3101.38                NaN\n",
       "           HYC         9466872.01                NaN\n",
       "           NG         48270074.40        362215370.0\n",
       "           NUC        41243689.00                NaN\n",
       "           OOG          180403.48                NaN\n",
       "           OTH             140.51                NaN\n",
       "           PEL           98100.01          1199180.0\n",
       "           WAS           46936.84                NaN\n",
       "           WWW         2732084.23                NaN\n",
       "     AR    COW        33220754.79        334098580.0\n",
       "           DPV            4853.48                NaN\n",
       "           HPS           67070.00                NaN\n",
       "           HYC         2639776.01                NaN\n",
       "           NG          9613708.03         70429870.0\n",
       "           NUC        14478259.00                NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_state.head(n=25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "It's interesting that the facility data has fuel consumption for solar generation and the state data doesn't. Looking at a 923 data file, it's clear that the fuel consumption is just based on a conversion efficiency of 36.6% across all facilities."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:57:05.735159Z",
     "start_time": "2017-08-08T16:57:05.717158Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generation (MWh)     19030396.62\n",
       "elec fuel (mmbtu)            NaN\n",
       "Name: (2016, CA, SUN), dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_state.loc[2016, 'CA', 'SUN']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:57:06.679212Z",
     "start_time": "2017-08-08T16:57:06.669212Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "generation (MWh)      14354970.0\n",
       "elec fuel (mmbtu)    133773953.0\n",
       "Name: (2016, CA, SUN), dtype: float64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_facility.loc[2016, 'CA', 'SUN']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hidden": true
   },
   "source": [
    "How much generation from large sources (Hydro, wind, coal, natural gas, and nuclear) is missed by monthly 923 data? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T16:59:00.892661Z",
     "start_time": "2017-08-08T16:59:00.853659Z"
    },
    "hidden": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "HYC has an error of 24.60%\n",
      "WND has an error of 3.35%\n",
      "COW has an error of 1.15%\n",
      "NG has an error of 5.21%\n",
      "NUC has an error of 0.00%\n",
      "SUN has an error of 37.42%\n"
     ]
    }
   ],
   "source": [
    "for fuel in ['HYC', 'WND', 'COW', 'NG', 'NUC', 'SUN']:\n",
    "    state_total = annual_state.loc[2016, :, fuel]['generation (MWh)'].sum()\n",
    "    facility_total = annual_facility.loc[2016, :, fuel]['generation (MWh)'].sum()\n",
    "    \n",
    "    error = (state_total - facility_total) / state_total\n",
    "    print('{} has an error of {:.2f}%'.format(fuel, error * 100))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2015 generation and fuel consumption from annual vs monthly reporting plants\n",
    "The goal here is to figure out how much of generation and fuel consumption from facilities that only report annually is in each NERC region (by state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:06:18.811693Z",
     "start_time": "2017-08-08T18:05:36.522281Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "path = os.path.join('Data storage', 'EIA923_Schedules_2_3_4_5_M_12_2015_Final.xlsx')\n",
    "frequency = pd.read_excel(path, sheetname='Page 6 Plant Frame', header=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T17:56:22.758012Z",
     "start_time": "2017-08-08T17:56:22.738010Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th {\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>YEAR</th>\n",
       "      <th>Plant Id</th>\n",
       "      <th>Plant State</th>\n",
       "      <th>Sector Number</th>\n",
       "      <th>NAICS Code</th>\n",
       "      <th>Plant Name</th>\n",
       "      <th>Combined Heat And\n",
       "Power Status</th>\n",
       "      <th>Reporting\n",
       "Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015</td>\n",
       "      <td>2</td>\n",
       "      <td>AL</td>\n",
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       "      <td>Bankhead Dam</td>\n",
       "      <td>N</td>\n",
       "      <td>A</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2015</td>\n",
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       "      <td>N</td>\n",
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       "      <th>2</th>\n",
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       "      <td>AL</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Walter Bouldin Dam</td>\n",
       "      <td>N</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2015</td>\n",
       "      <td>7</td>\n",
       "      <td>AL</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Gadsden</td>\n",
       "      <td>Y</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>AL</td>\n",
       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Gorgas</td>\n",
       "      <td>N</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   YEAR  Plant Id Plant State  Sector Number  NAICS Code          Plant Name  \\\n",
       "0  2015         2          AL              1          22        Bankhead Dam   \n",
       "1  2015         3          AL              1          22               Barry   \n",
       "2  2015         4          AL              1          22  Walter Bouldin Dam   \n",
       "3  2015         7          AL              1          22             Gadsden   \n",
       "4  2015         8          AL              1          22              Gorgas   \n",
       "\n",
       "  Combined Heat And\\nPower Status Reporting\\nFrequency  \n",
       "0                               N                    A  \n",
       "1                               N                    M  \n",
       "2                               N                    M  \n",
       "3                               Y                    A  \n",
       "4                               N                    M  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:06:23.326924Z",
     "start_time": "2017-08-08T18:06:23.320923Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "frequency.rename(columns={'Plant Id': 'plant id',\n",
    "                          'Plant State': 'state',\n",
    "                          'YEAR': 'year',\n",
    "                          'Reporting\\nFrequency': 'Reporting Frequency'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:48:29.635576Z",
     "start_time": "2017-08-08T18:48:29.618575Z"
    }
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
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       "      <td>Bankhead Dam</td>\n",
       "      <td>N</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
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       "      <td>8</td>\n",
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       "      <td>1</td>\n",
       "      <td>22</td>\n",
       "      <td>Gorgas</td>\n",
       "      <td>N</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year  plant id state  Sector Number  NAICS Code          Plant Name  \\\n",
       "0  2015         2    AL              1          22        Bankhead Dam   \n",
       "1  2015         3    AL              1          22               Barry   \n",
       "2  2015         4    AL              1          22  Walter Bouldin Dam   \n",
       "3  2015         7    AL              1          22             Gadsden   \n",
       "4  2015         8    AL              1          22              Gorgas   \n",
       "\n",
       "  Combined Heat And\\nPower Status Reporting Frequency  \n",
       "0                               N                   A  \n",
       "1                               N                   M  \n",
       "2                               N                   M  \n",
       "3                               Y                   A  \n",
       "4                               N                   M  "
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:06:26.345094Z",
     "start_time": "2017-08-08T18:06:26.338094Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year                                int64\n",
       "plant id                            int64\n",
       "state                              object\n",
       "Sector Number                       int64\n",
       "NAICS Code                          int64\n",
       "Plant Name                         object\n",
       "Combined Heat And\\nPower Status    object\n",
       "Reporting Frequency                object\n",
       "dtype: object"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frequency.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make a dataframe with generation, fuel consumption, and reporting frequency of facilities in 2015 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:40:27.236621Z",
     "start_time": "2017-08-08T19:40:27.052605Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "freq_cols = ['year', 'plant id', 'Reporting Frequency']\n",
    "df = pd.merge(facility_df, frequency.loc[:, freq_cols], on=['year', 'plant id'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:40:28.030661Z",
     "start_time": "2017-08-08T19:40:28.005659Z"
    }
   },
   "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>fuel</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>datetime</th>\n",
       "      <th>state</th>\n",
       "      <th>plant id</th>\n",
       "      <th>NERC</th>\n",
       "      <th>generation (MWh)</th>\n",
       "      <th>total fuel (mmbtu)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "      <th>fuel category</th>\n",
       "      <th>Reporting Frequency</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NG</td>\n",
       "      <td>2015</td>\n",
       "      <td>12</td>\n",
       "      <td>2015-12-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>4344.509</td>\n",
       "      <td>55210.0</td>\n",
       "      <td>22133.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NG</td>\n",
       "      <td>2015</td>\n",
       "      <td>11</td>\n",
       "      <td>2015-11-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>4304.052</td>\n",
       "      <td>54695.0</td>\n",
       "      <td>21927.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NG</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "      <td>2015-10-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>4810.546</td>\n",
       "      <td>61133.0</td>\n",
       "      <td>24507.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NG</td>\n",
       "      <td>2015</td>\n",
       "      <td>9</td>\n",
       "      <td>2015-09-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>5058.453</td>\n",
       "      <td>64282.0</td>\n",
       "      <td>25770.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NG</td>\n",
       "      <td>2015</td>\n",
       "      <td>8</td>\n",
       "      <td>2015-08-01</td>\n",
       "      <td>FL</td>\n",
       "      <td>10275</td>\n",
       "      <td>FRCC</td>\n",
       "      <td>5404.571</td>\n",
       "      <td>68680.0</td>\n",
       "      <td>27533.0</td>\n",
       "      <td>NG</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  fuel  year  month   datetime state  plant id  NERC  generation (MWh)  \\\n",
       "0   NG  2015     12 2015-12-01    FL     10275  FRCC          4344.509   \n",
       "1   NG  2015     11 2015-11-01    FL     10275  FRCC          4304.052   \n",
       "2   NG  2015     10 2015-10-01    FL     10275  FRCC          4810.546   \n",
       "3   NG  2015      9 2015-09-01    FL     10275  FRCC          5058.453   \n",
       "4   NG  2015      8 2015-08-01    FL     10275  FRCC          5404.571   \n",
       "\n",
       "   total fuel (mmbtu)  elec fuel (mmbtu) fuel category Reporting Frequency  \n",
       "0             55210.0            22133.0            NG                   A  \n",
       "1             54695.0            21927.0            NG                   A  \n",
       "2             61133.0            24507.0            NG                   A  \n",
       "3             64282.0            25770.0            NG                   A  \n",
       "4             68680.0            27533.0            NG                   A  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:33:18.341118Z",
     "start_time": "2017-08-08T18:33:06.228439Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x16fb2e198>"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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AqHU+r5b83XffeU5JlqTGjRvr+++/l9Vq5arJAAAAAIA6wefK7YABAzRu3DgN\nGjRIFRUV2rZtmxITE5Wbm6s2bdoEokYAAAAAAKrkM9w++uijysvL086dOxUcHKz7779fsbGx2rNn\nj5599tlA1AgApnRk7m1VjkfM3hugSgAAAOo/n+FWkuLj4xUfH19pW7du3QwpCAAAAACAq+XzO7cA\nAAAAANR1hFsAAAAAgOkRbgEAAAAApke4BQAAAACYHuEWAAAAAGB6hFsAAAAAgOkZGm4/++wzjRkz\n5rLt27dvV0pKilJTU7VhwwYjSwAAAAAANAB+3ee2Ol566SVt2bJFISEhlbaXlZUpMzNTGzduVEhI\niO655x7Fx8erTZs2RpUCAAAAAKjnDFu5jYiI0PLlyy/bfujQIUVERKhZs2ay2Wyy2+0qLCw0qgwA\nAAAAQANg2MrtwIEDdfTo0cu2O51OhYeHex43bdpUTqfTrzkdDofP5/ha//VnDgB1k91ur/a+tdE/\nGlI/akjvFeZUm/2D479q9A/UBn+Pu2vpHQg8w8KtN2FhYXK5XJ7HLperUtitij8H15Gt1z4HgPqn\nNvpHQ+pHDem9ouG51v7B8V81+gdqA8dd/RTwqyVHRkaquLhYp0+fVmlpqQoLC9W9e/dAlwEAAAAA\nqEcCtnL75ptv6uzZs0pNTdW0adOUnp4ut9utlJQUtWvXLlBlAAAAAADqIUPDbYcOHTy3+klOTvZs\nT0hIUEJCgpEvDQAAAABoQAJ+WjIAAAAAADWNcAsAAAAAMD3CLQAAAADA9Ai3AAAAAADTI9wCAAAA\nAEyPcAsAAAAAMD3CLQAAAADA9Ai3AAAAAADTI9wCAAAAAEyPcAsAAAAAMD3CLQAAAADA9Ai3AAAA\nAADTI9wCAAAAAEyPcAsAAAAAMD3CLQAAAADA9Ai3AAAAAADTI9wCAAAAAEyPcAsAAAAAMD1rbRcA\nAABQXUfm3lbleMTsvQGqBABQ21i5BQAAAACYnmErtxUVFZozZ44OHjwom82mefPm6cYbb/SMz5s3\nT7t371bTpk0lSStXrlR4eLhR5QAAAAAA6jHDwu3777+v0tJSvf7669qzZ48WLlyoVatWecaLior0\n8ssvq2XLlkaVAAAAAABoIAw7LdnhcOiOO+6QJHXr1k1ffPGFZ6yiokLFxcWaPXu2Ro4cqY0bNxpV\nBgAAAACgATBs5dbpdCosLMzzODg4WBcuXJDVatXZs2c1evRo/eEPf1B5ebnGjh2rqKgo3XLLLVXO\n6XA4fL6vVW4EAAAgAElEQVRuGx/j/swBoG6y2+3V3rc2+kdD6kcN6b2ibvH32KvN/sHxXzX6B2pD\nIHoHAs+wcBsWFiaXy+V5XFFRIav14suFhIRo7NixCgkJkST17t1bBw4c8Blu/Tm4jmytepwDFGiY\naqN/NKR+1JDeK+qWQBx719o/OP6rRv9AbeC4q58MOy359ttv10cffSRJ2rNnj26++WbP2OHDh5WW\nlqby8nKVlZVp9+7d+vWvf21UKQAAAACAes6wldv+/ftr586dGjlypNxutxYsWKA1a9YoIiJCiYmJ\nSk5O1ogRI9SoUSPddddd6ty5s1GlAAAAAADqOcPCbVBQkObOnVtpW2RkpOfn8ePHa/z48Ua9PAAA\nAACgATEs3AIAAPM7Mve2KscjZu8NUCUAAFTNsO/cAgAAAAAQKIRbAAAAAIDpEW4BAAAAAKZHuAUA\nAAAAmB4XlIIHFw0BAAAAYFas3AIAAAAATI+VWwC1jrMGAAAAcK0ItwAaDPvj2VWO/3d4gAoBAABA\njSPcAkA1EZYBAADqDr5zCwAAAAAwPVZuAQAAANQ4rqmBQGPlFgAAAABgeoRbAAAAAIDpcVoyAAAI\nGE5TBAAYhZVbAAAAAIDpEW4BAAAAAKbHackAANQjnPZrDO5rDQB1Hyu3AAAAAADTY+UWAAAAhmHV\nG0CgGBZuKyoqNGfOHB08eFA2m03z5s3TjTfe6BnfsGGDcnJyZLVa9cADDyg+Pt6oUgAA14g/TuuO\nuv7foq7XB/PzdYw5Fo8NUCUA6hrDwu3777+v0tJSvf7669qzZ48WLlyoVatWSZJKSkq0du1a/e1v\nf9OPP/6otLQ0RUdHy2az+ZyXfzQBwDffvXJxleO1/b1MvjeK+oxw1nDR2wBjGRZuHQ6H7rjjDklS\nt27d9MUXX3jGPv/8c3Xv3l02m002m00RERE6cOCAunbtalQ5AVPTf1Bey3z1rUHW9X8Q6np9QF1S\n0x9UEhaqjw+N656a/vekrs9X02qyvob0XusCejmulcXtdruNmPjJJ5/UgAEDFBsbK0mKi4vT+++/\nL6vVqs2bN+t///d/9fjjj0uSnnjiCQ0bNkx9+/b1Op/D4TCiTAAmYrfbq7Uf/QMA/QNAdVS3d6B2\nGLZyGxYWJpfL5XlcUVEhq9V6xTGXy6XwcN8fDXNwAagu+geA6qJ/AIA5GHYroNtvv10fffSRJGnP\nnj26+eabPWNdu3aVw+HQjz/+qDNnzujQoUOVxgEAAAAAuBqGrdz2799fO3fu1MiRI+V2u7VgwQKt\nWbNGERERSkxM1JgxY5SWlia3260pU6aocePGRpUCAAAAAKjnDPvObU1zOBycFgSgWugfAKqL/gEA\n5mHYackAAAAAAAQK4RYAAAAAYHqEWwAAAACA6RFuAQAAAACmR7gFAAAAAJge4RYAAAAAYHqG3efW\nCA6Ho7ZLAFDLqntLDvoHAPoHgOrgdmDmYZr73AIAAAAA4A2nJQMAAAAATI9wCwAAAAAwPcItAAAA\nAMD0CLcAAAAAANMj3AIAAAAATI9wCwAAAAAwPcItAAAAAMD0CLcAAAAAANMj3AIAAAAATI9wCwAA\nAAAwPcItAAAAAMD0CLcAAAAAANOz1nYBqJuOHj2qxMREvfLKK4qOjvZsT0hIUHZ2tiQpKSlJkZGR\nlfYbMWKERo0apYSEBDVp0kSNGjWSJJ05c0ZRUVFauHChQkND5Xa79eqrryo3N1eSFBQUpPvvv19D\nhgypVr2lpaXKzMzUrl27ZLFYdN111ykjI0Ndu3bV0aNH61StQENhtj6yfPlyrVixQjk5Oerevbtn\n+/z585Wdna2DBw+qoKBAEydOVEREhCSpoqJCLpdL48eP1z333CNJOnXqlBYuXKg9e/YoJCREbdu2\n1aOPPqpbb721WnUBDYUZe8Zbb72lzZs3q0mTJpKkgoICrVixQmvXrtXy5cuVk5Oj1q1bS5LOnz+v\npKQkTZkyRZLkdDr17LPPateuXQoODtZ1112nadOm6de//rVf4wAuR7iFV40aNdKsWbO0ZcsWhYWF\nXTbetm1bbd682ev+q1evVocOHSRdDJ9paWnKzc1VWlqali5dqn379mndunUKDw/XN998o9GjR6tF\nixbq27fvVdf66quvqqKiQm+++aYsFoscDof+9Kc/KS8vr87VCjQkZuojknT99dfr3Xff9YRbt9ut\nXbt2VXpOVFSU1q5d63m8f/9+/f73v1dycrKsVqvGjh2rlJQULV68WBaLRTt37tR9992nv/zlL+rY\nsWO16gIaCrP1jGPHjmnJkiWaMWPGFcdHjhypSZMmSZLOnj2rwYMHq0ePHoqOjtb48ePVq1cv5ebm\nymq16pNPPtH48eO1detWNWvWrMrxFi1aVKteoL4j3MKrtm3bqm/fvsrKytLTTz99TXOdOXNGZ86c\nUfPmzeVyufTaa69py5YtCg8Pl3TxD8olS5YoJCSk0n4nTpzQxIkTL5tv/fr1lf7RO3XqlMrKylRW\nViabzSa73a4FCxaooqIiYLUCuJyZ+ogkJSYm6oMPPtC0adMkSYWFherWrZv279/vta5jx44pJCRE\nNptNb731llq1aqX09HTPeHR0tH73u9/p5Zdf1oIFC6r9/oGGwGw9IzU1VW+//bYGDBigHj16VFlP\naGiounbtqi+//FJWq1UnTpzQ5MmTFRR08VuCvXv3VmZmpioqKlRQUFDlOIArI9yiStOmTVNycrJ2\n7txZ6RQhSTp58qTuuuuuStsWLVqkX/3qV5KkP/7xjwoODta3336r66+/XqNHj9agQYP0xRdfyGq1\n6sYbb6y0b9euXS97/V/84hdVfkJ7ydixYzVhwgT16dNH//mf/6k+ffpo+PDhaty4ccBqBXBlZukj\nktSiRQvdcMMN+vzzz9W1a1e9/fbbGjx4sP761796nvPFF1/orrvu0rlz5/T999+rV69eeuWVV2Sz\n2bR3717ddtttl83bs2dPLVmyxK8agIbOTD2jefPmmjNnjp588kmf+xw7dky7d+/WuHHjtGfPHt1y\nyy2e4HpJbGysJGnfvn1VjgO4MtOF288++0zPPPNMpVPCfi4zM1MOh0NBQUHKyMiQ3W4PYIX1S1hY\nmJ5++mnPKUI/5e+pQe+++64WLlyopKQkWSwWBQUFyWaz+fX6/n562qFDB7311lvau3ev/vGPfyg3\nN7fS92oCUSuAKzNLH7lk0KBBevfdd/XrX/9an376qWbNmlVp/NJpyaWlpXr88ccVFhbm+QPZYrGo\nvLz8sjnLyspksVj8qhdo6MzWM/r166d33nlHS5YsUWJiYqWxnJwcvf/++6qoqFBwcLAmTpwou92u\nzz//3PMB/JUEBQVVOQ7gykwVbl966SVt2bKlytNBDxw4oE8//VRvvPGGiouLNXXqVG3atCmAVdY/\nMTExnlOEqmPgwIHauXOnZsyYoZdeekmRkZE6f/68jh8/rvbt23uet3XrVp06dUrjxo3zbPP309Ml\nS5Zo1KhR6tq1q7p27aqJEydq5MiR2rlz5xVXUYyoFYB3Zugjl/Tr10/33HOPYmJi1KNHj8tWTi6x\n2WyaN2+eBg4c6Fnh7dq1a6VV3ks+/fRTRUVFXcU7Bho2M/UMSZo5c6aSk5PVvHnzStt/+p3bn4qK\nitJf/vIXud3uSh98LVmyRH379vU53rt376uqD2goTHUroIiICC1fvtzz+ODBgxozZozGjBmjSZMm\n6cyZM2rbtq2aNGmi0tJSOZ1OWa2myu911rRp05Sfn6+TJ09Wa/+HH35YDodDH374oZo0aaJRo0Zp\nzpw5cjqdki5eIXHJkiWXXQHRX//85z/1/PPPq7S0VJJUUlKif/3rX7r55pvrXK1AQ1XX+8glLVq0\n0C9/+Us999xzGjx4cJXPDQ8P16RJk7Ro0SKdP39egwcP1rlz5/Tiiy/K7XZLkvLz87Vp06ZK38MF\n4JtZeoZ0sW/MmTNHK1eu9Ov5PXr0UKtWrbRixQrP2R47duzQpk2b1KlTJ5/jAK7MVMlv4MCBOnr0\nqOfxrFmztGDBAnXq1ElvvPGGXn75ZaWnpysoKEiDBg3SmTNnrvliBLjo0ilCP/3j7Erfe+nZs6dm\nzpx52f6tWrXS+PHjtWjRIsXExGjKlCl6/vnnNWLECFmtVgUHB+vRRx9VTExMteqbNWuWsrKylJSU\npJCQEDVq1EiPPfaYIiMjdfTo0TpVK9BQ1fU+8lNJSUl6/vnnK90SyJu7775ba9eu1Zo1a/TAAw/o\ntdde06JFizynQ7Zv315r1qzhAzHgKpmpZ0gXz/oYOHCgX2HcYrFo5cqVyszM1NChQ2W1WtWiRQut\nXr3ac+sgX+MALmdxX/po2SSOHj2qqVOnasOGDbLb7Z77BpaVlemmm25Sly5d9PnnnysrK0sul0tp\naWn685//rHbt2tVy5QAAAAAAo5hq5fbnbrrpJmVlZal9+/ZyOBwqKSnR+fPnFRoaquDgYDVt2lQ2\nm00ul6u2SwUAAAAAGMjU4XbOnDnKyMjwfBdh/vz5ioiI0O7duzVy5EiVl5crOTlZHTt2rOVKAQAA\nAABGMt1pyQAAAAAA/JyprpYMAAAAAMCVmCbcOhyO2i4BgEnRPwBUF/0DAMzDNOEWAAAAAABvCLcA\nAAAAANMj3AIAAAAATI9wCwAAAAAwPcItAAAAAMD0CLcAAAAAANMj3AIAAAAATI9wCwAAAAAwPUPD\n7bfffqvY2FgdOnSo0vbt27crJSVFqamp2rBhg5ElAAAAAAAaAKtRE5eVlWn27Nlq0qTJZdszMzO1\nceNGhYSE6J577lF8fLzatGljVCkAAAAAgHrOsJXbrKwsjRw5Um3btq20/dChQ4qIiFCzZs1ks9lk\nt9tVWFhoVBkAAAAAgAbAkJXbTZs2qWXLlrrjjju0evXqSmNOp1Ph4eGex02bNpXT6fRrXofDUaN1\norI2W++tcrxkyKsBqQPwxm63V3tf+gfQsNE/AFTHtfQOBJ4h4fZvf/ubLBaLPv74Y+3fv18ZGRla\ntWqV2rRpo7CwMLlcLs9zXS5XpbBbFQ4uYx3ZWvU4v3+YGccvgOqifwCAORgSbtevX+/5ecyYMZoz\nZ47nO7WRkZEqLi7W6dOnFRoaqsLCQqWnpxtRBgAAAACggTDsglI/9+abb+rs2bNKTU3VtGnTlJ6e\nLrfbrZSUFLVr1y5QZQAAAAAA6iHDw+3atWslXVyxvSQhIUEJCQlGvzQAAAAAoIEw9D63AAAAAAAE\nAuEWAAAAAGB6hFsAAAAAgOkRbgEAAAAApke4BQAAAACYHuEWAAAAAGB6hFsAAAAAgOkRbgEAAAAA\npke4BQAAAACYHuEWAAAAAGB6hFsAAAAAgOkRbgEAAAAApke4BQAAAACYHuEWAAAAAGB6hFsAAAAA\ngOkRbgEAAAAApke4BQAAAACYHuEWAAAAAGB6hFsAAAAAgOkRbgEAAAAApmc1auLy8nLNnDlTX3/9\ntYKDg5WZmamIiAjP+Jo1a7Rx40a1bNlSkvTUU0+pY8eORpUDAAAAAKjHDAu3eXl5kqScnBwVFBQo\nMzNTq1at8owXFRUpKytLUVFRRpUAAAAAAGggDAu3/fr1U1xcnCTp+PHjat26daXxoqIirV69WiUl\nJYqLi9OECROMKgUAAAAAUM8ZFm4lyWq1KiMjQ++9956WLVtWaWzIkCFKS0tTWFiYHnroIeXl5Sk+\nPr7K+RwOh5HlNnhtfIzz+0dts9vt1d6X4xdo2OgfAKrjWnoHAs/idrvdRr9ISUmJRowYoa1btyo0\nNFRut1tOp1Ph4eGSpPXr1+v06dN68MEHvc7hcDg4uAx2ZO5tVY5HzN4boEqAmkX/AFBd9A8AMA/D\nrpacm5urF198UZIUEhIii8Wi4OBgSZLT6dTQoUPlcrnkdrtVUFDAd28BAAAAANVm2GnJAwYM0PTp\n0zVq1ChduHBBM2bM0LZt23T27FmlpqZqypQpGjt2rGw2m/r06aPY2FijSgEAAAAA1HOGhdvQ0FA9\n99xzXseHDRumYcOGGfXyAAAAAIAGxLDTkgEAAAAACBTCLQAAAADA9Ai3AAAAAADTI9wCAAAAAEyP\ncAsAAAAAMD3CLQAAAADA9Ai3AAAAAADTI9wCAAAAAEyPcAsAAAAAMD3CLQAAAADA9Ai3AAAAAADT\ns9Z2ATXtyNzbqhyPmL03QJUAAAAAAAKFlVsAAAAAgOkRbgEAAAAApke4BQAAAACYHuEWAAAAAGB6\nhFsAAAAAgOkRbgEAAAAApke4BQAAAACYHuEWAAAAAGB6hoXb8vJyTZ8+XSNHjtSoUaN05MiRSuPb\nt29XSkqKUlNTtWHDBqPKAAAAAAA0AIaF27y8PElSTk6OJk+erMzMTM9YWVmZMjMz9corr2jt2rV6\n/fXXVVJSYlQpAAAAAIB6zrBw269fPz399NOSpOPHj6t169aesUOHDikiIkLNmjWTzWaT3W5XYWGh\nUaUAAAAAAOo5q6GTW63KyMjQe++9p2XLlnm2O51OhYeHex43bdpUTqfT53wOh8Pnc9rUwBwNFb87\n1HV2u73a+3L8Ag0b/QNAdVxL70DgGRpuJSkrK0uPPfaYRowYoa1btyo0NFRhYWFyuVye57hcrkph\n1xt/Dq4jW699joaK3x3qM45fANVF/wAAczDstOTc3Fy9+OKLkqSQkBBZLBYFBwdLkiIjI1VcXKzT\np0+rtLRUhYWF6t69u1GlAAAAAADqOcNWbgcMGKDp06dr1KhRunDhgmbMmKFt27bp7NmzSk1N1bRp\n05Seni63262UlBS1a9fOqFIAAAAAAPWcYeE2NDRUzz33nNfxhIQEJSQkGPXyAAAAAIAGxLDTkgEA\nAAAACBTCLQAAAADA9Ai3AAAAAADTM/xWQADgy5G5t1U5HjF7b4AqAQAAgFmxcgsAAAAAMD3CLQAA\nAADA9Ai3AAAAAADTI9wCAAAAAEyvygtKlZWV6a233tL27dt1+PBhBQUF6cYbb1RCQoKGDBmiRo0a\nBapOAAAAAAC88hpuP/zwQ61atUp2u13Dhw9X+/btZbVadezYMX3yySdau3at/vSnPykxMTGQ9QIA\nAAAAcBmv4fbw4cNat27dZauznTp1UmxsrEpLS7Vu3TrDCwQAAAAAwBev4fbee++tckebzab77ruv\npusBAAAAAOCqVfmdW+ni6ckrVqzQ6dOn5Xa75Xa7ZbFY9MEHHwSiPgAAAAAAfPIZbufPn68nn3xS\nnTp1ksViCURNAAAAAABcFZ/hNjw8XHFxcQEoBQAAAACA6vEabnft2iXp4gWk5s2bp8TERFmt/356\nz549ja8OAAAAAAA/eA23y5Yt8/x84sQJHTx40PPYYrEoOzvb2MoAAAAAAPCT13A7efJkde/evdJq\nLQAAAAAAdZHX5Prss8/q66+/Vvfu3dW3b19FR0crMjIykLUBAAAAAOAXr+E2JydHP/74o/bs2aNd\nu3Zp3rx5+uabb9S9e3fFxMRo8ODBgawTAAAAAACvqjznuHHjxurVq5d69eqlAwcOyOFwKCcnRx99\n9JHPcFtWVqYZM2bo2LFjKi0t1QMPPKDExETP+Jo1a7Rx40a1bNlSkvTUU0+pY8eONfCWAAAAAAAN\njddwe/LkSeXn52vHjh3avXu3IiMjFR0drUWLFqlLly4+J96yZYuaN2+uxYsX67vvvtPw4cMrhdui\noiJlZWUpKiqqZt4JAAAAAKDB8hpu77zzTsXExOjee+/VwoUL1bhx46uaOCkpSQMHDvQ8Dg4OrjRe\nVFSk1atXq6SkRHFxcZowYcJVlg4AAAAAwEVew+3MmTOVn5+vuXPnqnv37oqOjlZ0dLRatWrl18RN\nmzaVJDmdTk2ePFmPPPJIpfEhQ4YoLS1NYWFheuihh5SXl6f4+Pgq53Q4HD5ft42PcX/maKj43aG2\n+Hvs2e32ar8Gxy/QsNE/AFTHtfQOBJ7XcDt69GiNHj1aZWVl2r17t/Lz8/Xaa6/J7Xarb9++euyx\nx3xOfuLECT344INKS0tTcnKyZ7vb7da4ceMUHh4uSYqNjdW+fft8hlt/Dq4jW6se5wD1jt8daksg\njj2OXwDVRf8AAHMI8vWERo0aqUOHDurcubN+85vfqKysTLt27fI58alTp3Tffffp8ccf1+9///tK\nY06nU0OHDpXL5ZLb7VZBQQHfvQUAAAAAVJvXldvs7Gw5HA59+umnatasmfr06aPo6GhNnTpVYWFh\nPid+4YUX9MMPP2jlypVauXKlJOnuu+/WuXPnlJqaqilTpmjs2LGy2Wzq06ePYmNja+5dAQAAAAAa\nFK/h9ssvv9SAAQM0e/Zsv79n+1MzZ87UzJkzvY4PGzZMw4YNu+p5AQAAAAD4Oa/h9re//a0k6auv\nvtJXX3112XjPnj2NqwoAAAAAgKvgNdyOGTNGrVq1UmRkpKSLF4G6xGKxKDs72/jqAAAAAADwg9dw\nu2LFCr3zzjsqLi5WfHy8Bg8erJtuuimQtQEAAAAA4Bev4bZfv37q16+ffvzxR+Xl5Wnp0qU6efKk\nEhISNHjwYHXo0CGQdQIAAAAA4JXPWwE1btxYSUlJWrZsmebPn6/t27erf//+gagNAAAAAAC/eF25\nveTYsWP6+9//rm3btqmsrExJSUlavHhxIGoDAAAAAMAvXsPt6tWrtW3bNlVUVCgpKUnPPPOMbrjh\nhkDWBgAAAACAX7yG2yVLlqhdu3aKiIjQjh07lJ+fX2mcqyUDAAAAAOoKr+GW8AoAAAAAMAuv4fa6\n667TLbfcUuXO+/fvV5cuXWq8KAAAAAAArobXqyVv3rxZTzzxhPLz83X+/HnP9nPnzumjjz7Sww8/\nrM2bNwekSAAAAAAAquJ15TYjI0MHDhzQmjVr9Oijj0qSGjVqpPLyct1555164IEHfK7sAv+fvXsP\ni7LO/z/+GoHR4eApT99WMSFdLTUN2zI1BfIspbIJolBGrZppHkrNjK9rKGpmm7naqrvuil6pkRHm\n2sG0y7RdVsa0InX76opplriKOVBymt8f/pyNNZhx5B694fm4rq7L+/7M/bnfTPd8mBef+wAAAAAA\nvlDlo4Dat2+vhQsXSpLOnj0ri8WiRo0a+aQwAAAAAAA85fY5t5c1btzYyDoAAAAAAPBapdfcAgAA\nAABgFoRbAAAAAIDpuT0t+fvvv9eWLVtUUFAgp9PpWv/kk08aWhgAAAAAAJ5yG26feuophYSEqG3b\ntrJYLL6oCQAAAACAq+I23J45c0Zr1qzxRS0AAAAAAHjFbbjt0KGDDh06xDNtAQAAAKAS2dnZmjZt\nmtq0aSPp0uWdDzzwgJKTk6t9Py1atFBgYKDWrFmj6dOne9VPdHS0br75Ztdy79699dhjj1VXmdeF\n23D71VdfadiwYbrppptUt25dOZ1OWSwWffjhh1VuV1JSolmzZunkyZMqLi7W+PHjFR0d7WrfsWOH\nfv/738vf31+xsbEaMWLEtf80AAAAAHCdREVFae7cuZKk4uJixcTEKCEhQTabrdr28dZbbyk+Pl6t\nW7f2OthKUkBAgNLT06utrhuB23C7bNkyrzrOyspSw4YN9eKLL+rcuXMaNmyYK9yWlJQoLS1NGRkZ\nstlsGjlypCIjI9W0aVOv9gUAAAAAN5ILFy7I6XQqICBAX3/9tVJSUlRaWqpmzZopLS1N77zzjj74\n4AM5HA4VFRVp0aJFCgsL09y5c3Xw4EE5nU499dRTuvfeezV8+HA1adJEAQEB2r9/vw4fPqzFixdr\n/vz5+uMf/6ghQ4bol7/8pY4cOaJ+/frpiSee0LZt27Ry5Uo1atRIRUVFWrx4sVq2bFllzZs3b9ab\nb76p0tJSLVmyRGvWrNHBgwclSbNmzdLtt9+uVatWadu2bWrVqpWOHTumt99+W4mJiVqyZImaNm2q\nmTNnKj4+XqGhoZo1a5YKCwsVFBSkBQsW6PDhw/rTn/4kSfr666/17LPPqlevXlq7dq0yMzNVXl6u\nJ554QgcOHNDtt9+uQYMGaffu3dqzZ49mzJjh9j13G25vvvlmvf766/r73/+u0tJS3XPPPRo9erTb\njgcMGKD+/fu7lv38/Fz/PnLkiEJDQ9WgQQNJUkREhHJycjRw4EC3/QIAAADAjWjHjh06evSoTp8+\nrcaNG2vu3Lny9/fXokWLNHnyZN1xxx1avXq13nzzTdWtW9c1e5qTk6MVK1Zo4MCBKi0t1YYNG3T2\n7FmNGjVK27ZtU0FBgV555RW1atXKFR7r1q3r2u+JEye0fv162Ww29e3bV0888YSWL1+uDRs2KCAg\nQDExMVfUWlJSosTERNfykiVLJEktW7bUwoULtWPHDpWUlGj9+vX67rvvNHXqVC1fvlxbt25VRkaG\nzpw587P9XrZy5Uo98MADGjRokLZt26bVq1erV69eOn/+vF5//XV9+umnWr16te644w5t3rxZGRkZ\ncjgcSk9P17Bhw7RkyRINGjRI77zzjh555BGP3n+34XbRokXKy8tTbGysnE6nNm/erK+//lrPPfdc\nldsFBQVJkhwOhyZNmqTJkye72hwOh0JCQiq81uFwuC3Wbre7fY27uV9P+qiteO9wvXh67EVERHi9\nD45foHZj/ADgjasdOy6flnz06FE98cQTatWqlaRLk3uLFy+WJF28eFHdu3dX69at9atf/UqS1KlT\nJ82bN09Hjx5V165dJUmNGzdWcHCwvv/+ewUEBLj6+jnNmzd3TRzabDadPXtWDRs2dGWy22677Ypt\nKjst+fI1w0eOHNE//vEPVwAuKChQXl6eOnToIH9/f7Vo0eJna7r8+NgjR47o008/1euvv67S0lK1\nbt1aktSuXTtZLBY1a9ZMFy9e1LFjx1x9NmzYUBMnTpQknT17VqdPn9bXX3/t8f2f3IbbPXv2KDMz\nU3Xq1JEk9enTp8qE/lOnTp3ShAkTlJCQUGGb4OBgFRYWupYLCwsrhN3KeHJwHd967X3UVrx3uF58\ncdJsWDYAACAASURBVOxx/ALwFuMHgKsVFhamCRMmaObMmUpPT9ctt9yip59+WmFhYdqzZ48k6bvv\nvtOXX34pSfrss8/UunVrtWnTRrt27dLw4cN19uxZFRQUKDg4uMIjWS0Wi8rLyyvs778f2dqwYUOd\nPXtWRUVFCggIcJ1a7InLua9Nmza6//77NW3aNNeMaqtWrfTPf/5TpaWlcjgcOnXqlCTJarXqu+++\nU+PGjfXVV19Jkm655Rbdd9996tWrl3Jzc5WXl/ez+/vFL36h//u//1NZWZkuXryoqVOn6rXXXtOg\nQYM0b9483X///R7X7jbclpWVqbS0VFar1bX801OMK3PmzBk9+uijSklJUffu3Su0hYeHKy8vTwUF\nBQoMDFROTk6130UMAAAAAK6XmJgYvfXWW9q6daueeeYZzZ07Vz/++KOsVqtefPFFfffddzp69KiS\nkpJUVlamBQsWqGXLlvr44481cuRIXbx4UbNmzXKFzcs6duyouXPn6oUXXqh033Xq1NHkyZM1evRo\nNWrUSP7+/vL3dxv9KoiOjtbu3buVmJioCxcuaOzYsWrUqJESEhI0cuRINWvWzJURExISNH36dP3i\nF79Qs2bNJEnjxo3Tc889pz/84Q8qLS1Vamqq/v3vf1+xn5tuuknDhw9XQkKCnE6nxo0bJ0kaMmSI\nXnrpJc2ePdvjmi3Oy/PGlXjttdf00UcfafDgwZKkrVu3qnfv3ho/fnyVHaempmrbtm0KCwtzrXvo\noYf0ww8/KC4uznW3ZKfTqdjYWI0aNarK/ux2u2czt3M7VdkemvK52z5qK947XC9GH3uejh8A8N8Y\nPwAYZfPmzTpz5ox+85vfGNL/6tWr9eijj6q8vFzDhg3Tm2++6Qqj1WXAgAF69913q7XPy/Lz8/W/\n//u/Wr58ucfbuI3v48aN02233aa//e1vriTdp08ftx3Pnj27ypQdFRWlqKgojwsFAAAAAHimvLxc\nw4cPlyTFx8dXe7A10t/+9jelpaVpwYIFV7VdpeE2NzdXt99+u/bu3SubzVYhiO7du1d33XWX99UC\nAAAAQC12OXga5Te/+Y1hs8KXGTVr2717d2VlZV31dpWG29dff12pqalaunTpFW0Wi0Vr16696p0B\nAAAAAGCESsNtamqqJOn5559Xu3btKrTt37/f2KoAAAAAALgKlYZbu92u8vJyzZ49W/PmzXM9r6i0\ntFRz5szRe++957MiAQAAAACoSqXh9pNPPtE//vEPnT59Wq+88sp/NvD3V1xcnE+KAwAAAADAE5WG\n24kTJ0qSMjMzNXToUJ8VBAAAAAD4eRHPVO+9j+wvJnn82pUrV2rt2rX68MMPVbdu3Wqtozq4fRRQ\nly5dlJqaqqKiIjmdTpWXl+vEiRNav369L+oDAAAAANwAtmzZokGDBmnr1q2G3+3ZG3XcvWDq1Kmq\nX7++Dh48qA4dOuibb75R27ZtfVEbAAAAAOAGkJ2drdDQUMXHx9+wE51uw21JSYkmTZqkXr166bbb\nbtOqVau0d+9eX9QGAAAAALgBvPHGG3rooYcUFhYmq9WqAwcOXO+SruA23NpsNhUXF+uWW25Rbm6u\n6tWr54u6AAAAAAA3gPPnz2vXrl1au3atkpOT5XA4tG7duutd1hXcXnP7wAMPaNy4cVq8eLHi4uL0\n8ccfq3nz5r6oDQAAAABwnWVlZSk2NlYzZsyQJP3www+Kjo7W2bNn1bhx4+tc3X+4nbnt1q2bli5d\nqsaNGys9PV1xcXFatmyZL2oDAAAAAFxnb7zxhh588EHXss1mU79+/bRp06brWNWV3M7cTpkyRdu2\nbZMktWjRQi1atDC8KAAAAADAla7m0T3VJSsr64p1c+bM8Xkd7rgNt7feequWLVumO+64o8L1tnfd\ndZehhQEAAAAA4Cm34bagoEDZ2dnKzs52rbNYLFq7tnofHgwAAAAAgLfchtv09HRf1AEAAAAAgNfc\n3lDq5MmTGjNmjPr166f8/HwlJSXpxIkTvqgNAAAAAACPuA23KSkpSk5OVmBgoJo0aaIhQ4a4bgEN\nAAAAAMCNwG24PXfunHr27Cnp0rW2I0aMkMPhMLwwAAAAAAA85faa23r16unbb7+VxWKRJOXk5Mhq\ntRpeGAAAAACgouNzO1Vrf6Epn7t9TXZ2tpKSkvTyyy9r0KBBrvUxMTG6/fbbtWDBgmqtyVtuZ26f\nffZZjR07VseOHdODDz6op59+Ws8995xHnR84cECJiYlXrF+zZo0GDx6sxMREJSYm6ujRo1dfOQAA\nAADAJ8LCwvTOO++4lg8fPqwffvjhOlZ0Jbczt506dVJGRoaOHTumsrIyhYWFeTRzu2rVKmVlZclm\ns13Rlpubq4ULF6pjx47eVQ0AAAAA8Jn27dvr2LFj+v7771W/fn1lZWUpJiZGp06dut6lubgNtydP\nntS6det0/vx5OZ1O1/q0tLQqtwsNDdWrr76q6dOnX9GWm5urlStXKj8/X3369NHYsWO9KB0AAAAA\n4Ct9+/bVBx98oOHDh+uzzz7T448/bq5wO3nyZHXr1k3dunVzXXfrif79+1f6yKDBgwcrISFBwcHB\nevLJJ7Vz505FRka67dNut7t9TdNq6KO24r3D9eLpsRcREeH1Pjh+gdqN8QOAN65l7KiJYmJiNGfO\nHLVq1UrdunW73uVcwW24LS0trdZH/zidTj388MMKCQmRJPXu3VtffvmlR+HWk4Pr+NZr76O24r3D\n9eKLY4/jF4C3GD8A4JJWrVqpqKhI6enpmjp1qr7++uvrXVIFbm8oFRERoR07dqi4uLhaduhwODRk\nyBAVFhbK6XQqOzuba28BAAAAwAQGDRqkU6dOqU2bNte7lCu4nbl99913tW7dOkmXnnPrdDplsVh0\n8ODBq9rRli1bVFRUpLi4OE2ZMkVJSUmyWq3q3r27evfu7V31AAAAAFCLePLonup299136+6775Yk\n1xNvJOm+++7Tfffd5/N6KuM23O7evdvrzlu2bKlNmzZJunR+9mVDhw7V0KFDve4XAAAAAICfcnta\ncnFxsV577TXNmDFDDodDy5Ytq7ZTlAEAAAAAqA5uw+3cuXNVVFSk3Nxc+fn5KS8vT7NmzfJFbQAA\nAAAAeMRtuM3NzdXUqVPl7+8vm82mRYsW6dChQ76oDQAAAAAAj7gNtxaLRcXFxa5n3J47d+6qnncL\nAAAAAIDR3N5QKikpSWPGjFF+fr7mzZun7du3a8KECb6oDQAAAAAAj7gNt0OHDlXHjh2VnZ2tsrIy\nrVixQu3bt/dFbQAAAAAAeMRtuM3MzJQkBQUFSZIOHTqkY8eOKSwsTO3atTO2OgAAAAAAPOA23H74\n4Yf68ssv1bdvXzmdTn300Udq1qyZioqKFBMTo0ceecQHZQIAAAAAUDm34TY/P19vvfWW6tevL0ma\nOHGixo0bp40bN2r48OGEWwAAAADAdef2bsnnzp1znZIsSXXr1tX58+fl7+/PXZMBAAAAADcEtzO3\n/fr108MPP6yBAweqvLxc77//vqKjo5WZmammTZv6okYAAAAAAKrkNtxOmzZNO3fu1J49e+Tn56fH\nHntMvXv31v79+/XSSy/5okYAAAAAAKrkNtxKUmRkpCIjIyus69KliyEFAQAAAABwtdxecwsAAAAA\nwI2OcAsAAAAAMD3CLQAAAADA9Ai3AAAAAADTI9wCAAAAAEyPcAsAAAAAMD3CLQAAAADA9AwNtwcO\nHFBiYuIV63fs2KHY2FjFxcVp06ZNRpYAAAAAAKgF/I3qeNWqVcrKypLNZquwvqSkRGlpacrIyJDN\nZtPIkSMVGRmppk2bGlUKAAAAAKCGMyzchoaG6tVXX9X06dMrrD9y5IhCQ0PVoEEDSVJERIRycnI0\ncOBAo0oBABjs+NxOVbaHpnzuo0oAAEBtZVi47d+/v06cOHHFeofDoZCQENdyUFCQHA6HR33a7Xa3\nr3E3/+tJH7UV7x2uF0+PvYiICK/3wfFrLMYP3OgYPwB441rGDvieYeG2MsHBwSosLHQtFxYWVgi7\nVfHk4Dq+9dr7qK1473C9+OLY4/g1FuMHajKOXwAwB5/fLTk8PFx5eXkqKChQcXGxcnJy1LVrV1+X\nAQAAAACoQXw2c7tlyxYVFRUpLi5OM2fOVHJyspxOp2JjY9W8eXNflQEAAAAAqIEMDbctW7Z0Peon\nJibGtT4qKkpRUVFG7hoAAAAAUIv4/JpbAACAmqaqO4Zzt3AA8A2fX3MLAAAAAEB1I9wCAAAAAEyP\ncAsAAAAAMD3CLQAAAADA9Ai3AAAAAADTI9wCAAAAAEyPcAsAAAAAMD2ecwsAAACgVqnq2dQSz6c2\nK2ZuAQAAAACmR7gFAAAAAJge4RYAAAAAYHqEWwAAAACA6RFuAQAAAACmR7gFAAAAAJge4RYAAAAA\nYHqEWwAAAACA6RFuAQAAAACmR7gFAAAAAJge4RYAAAAAYHr+RnVcXl6uOXPm6PDhw7JarUpNTVXr\n1q1d7ampqdq3b5+CgoIkScuXL1dISIhR5QAAAAAAajDDwu327dtVXFysjRs3av/+/VqwYIFWrFjh\nas/NzdXq1avVuHFjo0oAAAAAANQShp2WbLfb1atXL0lSly5d9MUXX7jaysvLlZeXp5SUFMXHxysj\nI8OoMgAAAAAAtYBhM7cOh0PBwcGuZT8/P5WWlsrf319FRUUaPXq0xowZo7KyMiUlJaljx45q3759\nlX3a7Xa3+23qpt2TPmor3jtcL54eexEREV7vg+PXWIwfuNEZPX5U9Rng+AduPL747gHfMyzcBgcH\nq7Cw0LVcXl4uf/9Lu7PZbEpKSpLNZpMk3XPPPTp06JDbcOvJwXV8a9XtHKCV473D9eKLY4/j11iM\nH6jJrvX7B8c/cOPh91bNZNhpyXfeead27dolSdq/f7/atWvnajt27JgSEhJUVlamkpIS7du3T7ff\nfrtRpQAAAAAAajjDZm779u2rPXv2KD4+Xk6nU/Pnz9eaNWsUGhqq6OhoxcTEaMSIEQoICNCDDz6o\ntm3bGlUKAAAAAKCGMyzc1qlTR3Pnzq2wLjw83PXvxx9/XI8//rhRuwcAAAAA1CKGnZYMAAAAAICv\nGDZzCwAAzO/43E5VtoemfO6jSgAAqBoztwAAAAAA0yPcAgAAAABMj3ALAAAAADA9wi0AAAAAwPQI\ntwAAAAAA0yPcAgAAAABMj3ALAAAAADA9nnMLAABwA+HZwgDgHWZuAQAAAACmx8ytG/z1FACAGxe/\npwEAlzFzCwAAAAAwPcItAAAAAMD0CLcAAAAAANPjmlsA+P+4dg8AAMC8CLcmV9WXcb6IAwAAAKgt\nan24jXhmbZXtb4X4qBAAAAAAgNdMF24JowAAwNeq8/sH32UAwBimC7e1Db8AUR24lvT64H0HAPcY\nK2su/t/C1wwLt+Xl5ZozZ44OHz4sq9Wq1NRUtW7d2tW+adMmbdiwQf7+/ho/frwiIyONKgUADMEf\nnwDURgQWADcqw8Lt9u3bVVxcrI0bN2r//v1asGCBVqxYIUnKz89Xenq63nzzTV28eFEJCQnq0aOH\nrFarUeXAAO6/2L9YZXtN++XHL3vUZAR5ALh2fFcAjGVYuLXb7erVq5ckqUuXLvriiy9cbZ999pm6\ndu0qq9Uqq9Wq0NBQHTp0SJ07dzaqHJ+5kQOf2b+cVvcvBH7BVM7dsWJ/MclHlVQvs38GrsaNPBZJ\nNfcYM6Pa9Lmoqar7836jHxPVPX5U1d/1HitvdNV9rPC7AdfK4nQ6nUZ0/Nxzz6lfv37q3bu3JKlP\nnz7avn27/P399fbbb+uf//ynnnnmGUnS9OnTNXToUN17772V9me3240oE4CJREREeLUd4wcAxg8A\n3vB27MD1YdjMbXBwsAoLC13L5eXl8vf3/9m2wsJChYS4/9MOBxcAbzF+APAW4wcAmEMdozq+8847\ntWvXLknS/v371a5dO1db586dZbfbdfHiRV24cEFHjhyp0A4AAAAAwNUwbOa2b9++2rNnj+Lj4+V0\nOjV//nytWbNGoaGhio6OVmJiohISEuR0OjVlyhTVrVvXqFIAAAAAADWcYdfcVje73c5pQQC8wvgB\nwFuMHwBgHoadlgwAAAAAgK8QbgEAAAAApke4BQAAAACYHuEWAAAAAGB6hFsAAAAAgOkRbgEAAAAA\npmfYc26NYLfbr3cJAK4zbx/JwfgBgPEDgDd4HJh5mOY5twAAAAAAVIbTkgEAAAAApke4BQAAAACY\nHuEWAAAAAGB6hFsAAAAAgOkRbgEAAAAApke4BQAAAACYHuEWAAAAAGB6hFsAAAAAgOkRbgEAAAAA\npke4BQAAAACYHuEWAAAAAGB6hFsAAAAAgOn5X+8CYA4nTpxQdHS0/vSnP6lHjx6u9VFRUVq7dq0k\nacCAAQoPD6+w3YgRIzRq1ChFRUWpXr16CggIkCRduHBBHTt21IIFCxQYGCin06k///nPyszMlCTV\nqVNHjz32mAYPHnzVtZ47d06PPPKIJOnMmTOSpCZNmkiS/vznP2vSpEn69ttvFRgYKElyOBxq1aqV\nFi9erCZNmigxMbFC++Xt//jHP151LQDMNX5IUnFxsdLS0rR3715ZLBbVr19fM2bMUOfOnXXixAlX\nrRaLRSUlJWrWrJnS0tLUokWLCuOH0+mU0+nU+PHjNWjQIK9qAeCe2cYYAMYh3MJjAQEBev7555WV\nlaXg4OAr2ps1a6a333670u1Xrlypli1bSrr05TEhIUGZmZlKSEjQyy+/rC+//FLr1q1TSEiIvv32\nW40ePVqNGjXSvffee1V1NmrUyFXHq6++KkmaOHFihdekpqbq7rvvliSVl5dr0qRJWrNmjZ555pkr\n2gFcO7OMH9KlP4KVl5dry5YtslgsstvteuKJJ7Rz586frXXBggVatGiRlixZIqni+HH48GH9+te/\nVq9evRQSEnLVtQDwjJnGGADGIdzCY82aNdO9996rhQsX6oUXXrimvi5cuKALFy6oYcOGKiws1F/+\n8hdlZWW5vvy1aNFCS5Yskc1mq7DdqVOnNG7cuCv6W79+/c/+MvNEUVGRzp07p86dO3u1PQD3zDR+\nnDlzRiUlJSopKZHValVERITmz5+v8vLyn63n7rvvdgXb//bLX/5SgYGBysvLU8eOHb39kQG4YaYx\nBoBxCLe4KjNnzlRMTIz27NlT4dQfSTp9+rQefPDBCusWLVqkX/7yl5Kk3/zmN/Lz89O///1vtWjR\nQqNHj9bAgQP1xRdfyN/fX61bt66w7c+Fzf/5n/+p8i+vnpo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dzgFaOd471GQc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\nhBBC5/0froUFUImnKz4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x17f445748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x='fuel category', y='generation (MWh)', hue='Reporting Frequency',\n",
    "                   col='NERC', col_wrap=3, data=df, estimator=np.sum, ci=0, kind='bar',\n",
    "                   palette='tab10')\n",
    "\n",
    "g.set_xticklabels(rotation=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of NERC regions in a state"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T18:29:45.685110Z",
     "start_time": "2017-08-08T18:29:45.660109Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df['state'] == 'TX', 'NERC'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fraction of generation/consumption from Annual reporting facilities in each NERC region of a state\n",
    "This is development of a method that will be used to approximate the fraction of EIA-estimated generation and consumption within each state that gets apportioned to each NERC regions (when there is more than one). The idea is to take data from the most recent \"final\" EIA-923 and use the annual reporting facilities to approximate the divisions for more recent data. I still need to figure out if it's better to do the calculation by month within a year or just for the year as a whole.\n",
    "\n",
    "Determining if it's better to do month-by-month vs a single value for the whole year will depend on if the share of generation/consumption from Annual reporting facilities in each NERC changes much over the course of the year. There is the potential for error either way, and maybe even differences by state. Annual is certainly simpler.\n",
    "\n",
    "While looking at data for Texas, I've discovered that generation from Annual reporting facilities can be negative. Need to figure out how (if?) to deal with this...\n",
    "\n",
    "#### Conclusion\n",
    "While there can be variation of % generation in each NERC within a state over the course of 2015, most fuel categories across most states are quite stable. And when fuels do a have a wide spread over the year, they also tend to not be a large fraction of total generation within the NERC region. Given these observations, I'm going to stick with a split calculated as the average over an entire year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:36:45.097072Z",
     "start_time": "2017-08-08T19:36:45.042069Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def annual(df, state):\n",
    "    \"\"\"Return the percent of gen & consumption by fuel type in each NERC region\n",
    "    for a state\"\"\"\n",
    "    a = df.loc[(df.state == state) & \n",
    "                     (df['Reporting Frequency'] == 'A')].copy()\n",
    "    a.drop(['plant id', 'year'], axis=1, inplace=True)\n",
    "    a = a.groupby(['NERC', 'fuel category']).sum()\n",
    "    \n",
    "    fuels = set(a.index.get_level_values('fuel category'))\n",
    "    \n",
    "    temp_list = []\n",
    "    for fuel in fuels:\n",
    "        temp = (a.xs(fuel, level='fuel category')\n",
    "                / a.xs(fuel, level='fuel category').sum())\n",
    "        temp['fuel category'] = fuel\n",
    "        temp_list.append(temp)\n",
    "    \n",
    "    result = pd.concat(temp_list)\n",
    "    result.reset_index(inplace=True)\n",
    "    result['state'] = state\n",
    "    \n",
    "    rename_cols = {'generation (MWh)': '% generation',\n",
    "                   'total fuel (mmbtu)': '% total fuel',\n",
    "                   'elec fuel (mmbtu)': '% elec fuel'}\n",
    "    \n",
    "    result.rename(columns=rename_cols, inplace=True)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:56:34.974260Z",
     "start_time": "2017-08-08T19:56:34.901256Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def annual_month(df, state):\n",
    "    \"\"\"Return the percent of gen & consumption by fuel type and month in each \n",
    "    NERC region for a state\"\"\"\n",
    "    a = df.loc[(df.state == state) & \n",
    "                     (df['Reporting Frequency'] == 'A')].copy()\n",
    "    a.drop(['plant id', 'year'], axis=1, inplace=True)\n",
    "    a = a.groupby(['NERC', 'fuel category', 'month']).sum()\n",
    "    \n",
    "    fuels = set(a.index.get_level_values('fuel category'))\n",
    "    \n",
    "    temp_list = []\n",
    "    for fuel in fuels:\n",
    "        for month in range(1, 13):\n",
    "            temp = (a.xs(fuel, level='fuel category')\n",
    "                     .xs(month, level='month')\n",
    "                    / a.xs(fuel, level='fuel category')\n",
    "                       .xs(month, level='month')\n",
    "                       .sum())\n",
    "            temp['fuel category'] = fuel\n",
    "            temp['month'] = month\n",
    "            temp_list.append(temp)\n",
    "    \n",
    "    result = pd.concat(temp_list)\n",
    "    result.reset_index(inplace=True)\n",
    "    result['state'] = state\n",
    "    \n",
    "    rename_cols = {'generation (MWh)': '% generation',\n",
    "                   'total fuel (mmbtu)': '% total fuel',\n",
    "                   'elec fuel (mmbtu)': '% elec fuel'}\n",
    "    \n",
    "    result.rename(columns=rename_cols, inplace=True)\n",
    "    \n",
    "    return result"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is the percent of generation, total fuel consumption, and electric fuel consumption from facilities that report annually to EIA-923"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:36:51.258425Z",
     "start_time": "2017-08-08T19:36:49.361313Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_list = []\n",
    "for state in states:\n",
    "    num_nerc = df.loc[df.state == state, 'NERC'].nunique()\n",
    "    if num_nerc > 1:\n",
    "        df_list.append(annual(df, state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:56:46.356903Z",
     "start_time": "2017-08-08T19:56:36.828365Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_list = []\n",
    "for state in states:\n",
    "    num_nerc = df.loc[df.state == state, 'NERC'].nunique()\n",
    "    if num_nerc > 1:\n",
    "        df_list.append(annual_month(df, state))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:57:01.368750Z",
     "start_time": "2017-08-08T19:57:01.343749Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fuel_by_nerc_month = pd.concat(df_list).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:36:53.553550Z",
     "start_time": "2017-08-08T19:36:53.537549Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fuel_by_nerc = pd.concat(df_list).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:36:54.848622Z",
     "start_time": "2017-08-08T19:36:54.828627Z"
    }
   },
   "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>month</th>\n",
       "      <th>% generation</th>\n",
       "      <th>% total fuel</th>\n",
       "      <th>% elec fuel</th>\n",
       "      <th>fuel category</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>0.333333</td>\n",
       "      <td>0.000066</td>\n",
       "      <td>0.034382</td>\n",
       "      <td>0.000065</td>\n",
       "      <td>WWW</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>SERC</td>\n",
       "      <td>0.666667</td>\n",
       "      <td>0.999934</td>\n",
       "      <td>0.965618</td>\n",
       "      <td>0.999935</td>\n",
       "      <td>WWW</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>SERC</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PC</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-</td>\n",
       "      <td>0.276596</td>\n",
       "      <td>0.002770</td>\n",
       "      <td>0.001223</td>\n",
       "      <td>0.002772</td>\n",
       "      <td>NG</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>SERC</td>\n",
       "      <td>0.446809</td>\n",
       "      <td>0.219980</td>\n",
       "      <td>0.636033</td>\n",
       "      <td>0.175285</td>\n",
       "      <td>NG</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   NERC     month  % generation  % total fuel  % elec fuel fuel category state\n",
       "0     -  0.333333      0.000066      0.034382     0.000065           WWW    AR\n",
       "1  SERC  0.666667      0.999934      0.965618     0.999935           WWW    AR\n",
       "2  SERC  1.000000           NaN           NaN          NaN            PC    AR\n",
       "3     -  0.276596      0.002770      0.001223     0.002772            NG    AR\n",
       "4  SERC  0.446809      0.219980      0.636033     0.175285            NG    AR"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_by_nerc.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-08T19:57:16.667614Z",
     "start_time": "2017-08-08T19:57:16.646613Z"
    }
   },
   "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>% generation</th>\n",
       "      <th>% total fuel</th>\n",
       "      <th>% elec fuel</th>\n",
       "      <th>fuel category</th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2704</th>\n",
       "      <td>RFC</td>\n",
       "      <td>0.249526</td>\n",
       "      <td>0.382119</td>\n",
       "      <td>0.513470</td>\n",
       "      <td>NG</td>\n",
       "      <td>10</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2705</th>\n",
       "      <td>MRO</td>\n",
       "      <td>0.946647</td>\n",
       "      <td>0.666793</td>\n",
       "      <td>0.665857</td>\n",
       "      <td>NG</td>\n",
       "      <td>11</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2706</th>\n",
       "      <td>RFC</td>\n",
       "      <td>0.053353</td>\n",
       "      <td>0.333207</td>\n",
       "      <td>0.334143</td>\n",
       "      <td>NG</td>\n",
       "      <td>11</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2707</th>\n",
       "      <td>MRO</td>\n",
       "      <td>0.923901</td>\n",
       "      <td>0.658481</td>\n",
       "      <td>0.718664</td>\n",
       "      <td>NG</td>\n",
       "      <td>12</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2708</th>\n",
       "      <td>RFC</td>\n",
       "      <td>0.076099</td>\n",
       "      <td>0.341519</td>\n",
       "      <td>0.281336</td>\n",
       "      <td>NG</td>\n",
       "      <td>12</td>\n",
       "      <td>WI</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NERC  % generation  % total fuel  % elec fuel fuel category  month state\n",
       "2704  RFC      0.249526      0.382119     0.513470            NG     10    WI\n",
       "2705  MRO      0.946647      0.666793     0.665857            NG     11    WI\n",
       "2706  RFC      0.053353      0.333207     0.334143            NG     11    WI\n",
       "2707  MRO      0.923901      0.658481     0.718664            NG     12    WI\n",
       "2708  RFC      0.076099      0.341519     0.281336            NG     12    WI"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_by_nerc_month.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "split_states = []\n",
    "for state in states:\n",
    "    if df.loc[df.state == state, 'NERC'].nunique() > 1:\n",
    "        split_states.append(state)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['AR',\n",
       " 'FL',\n",
       " 'IL',\n",
       " 'IA',\n",
       " 'KS',\n",
       " 'KY',\n",
       " 'LA',\n",
       " 'MI',\n",
       " 'MO',\n",
       " 'NE',\n",
       " 'NM',\n",
       " 'NC',\n",
       " 'OK',\n",
       " 'SD',\n",
       " 'TX',\n",
       " 'VA',\n",
       " 'WI']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "split_states"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols = ['state', 'NERC', 'fuel category']\n",
    "a = fuel_by_nerc_month.groupby(cols).std()\n",
    "a.drop('month', axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "\n",
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       "        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></th>\n",
       "      <th>% generation</th>\n",
       "      <th>% total fuel</th>\n",
       "      <th>% elec fuel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NERC</th>\n",
       "      <th>fuel category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">-</th>\n",
       "      <th>HYC</th>\n",
       "      <td>4.928033e-09</td>\n",
       "      <td>4.608873e-07</td>\n",
       "      <td>4.608873e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>3.047858e-04</td>\n",
       "      <td>4.821993e-04</td>\n",
       "      <td>2.272056e-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>2.242653e-05</td>\n",
       "      <td>6.797735e-03</td>\n",
       "      <td>1.781255e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">SERC</th>\n",
       "      <th>HYC</th>\n",
       "      <td>6.275202e-09</td>\n",
       "      <td>8.077144e-07</td>\n",
       "      <td>8.077144e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>8.582103e-02</td>\n",
       "      <td>1.433441e-01</td>\n",
       "      <td>6.745018e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOG</th>\n",
       "      <td>3.920610e-08</td>\n",
       "      <td>4.330886e-06</td>\n",
       "      <td>4.330886e-06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PC</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000e+00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>2.242653e-05</td>\n",
       "      <td>6.797735e-03</td>\n",
       "      <td>1.781255e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">SPP</th>\n",
       "      <th>HYC</th>\n",
       "      <td>5.370029e-09</td>\n",
       "      <td>6.464797e-07</td>\n",
       "      <td>6.464797e-07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>8.551625e-02</td>\n",
       "      <td>1.428619e-01</td>\n",
       "      <td>6.722309e-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOG</th>\n",
       "      <td>3.920610e-08</td>\n",
       "      <td>4.330886e-06</td>\n",
       "      <td>4.330886e-06</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    % generation  % total fuel   % elec fuel\n",
       "NERC fuel category                                          \n",
       "-    HYC            4.928033e-09  4.608873e-07  4.608873e-07\n",
       "     NG             3.047858e-04  4.821993e-04  2.272056e-04\n",
       "     PEL            0.000000e+00  0.000000e+00  0.000000e+00\n",
       "     SUN                     NaN           NaN           NaN\n",
       "     WWW            2.242653e-05  6.797735e-03  1.781255e-05\n",
       "SERC HYC            6.275202e-09  8.077144e-07  8.077144e-07\n",
       "     NG             8.582103e-02  1.433441e-01  6.745018e-02\n",
       "     OOG            3.920610e-08  4.330886e-06  4.330886e-06\n",
       "     PC                      NaN           NaN           NaN\n",
       "     PEL            0.000000e+00  0.000000e+00  0.000000e+00\n",
       "     WAS            0.000000e+00  0.000000e+00  0.000000e+00\n",
       "     WWW            2.242653e-05  6.797735e-03  1.781255e-05\n",
       "SPP  HYC            5.370029e-09  6.464797e-07  6.464797e-07\n",
       "     NG             8.551625e-02  1.428619e-01  6.722309e-02\n",
       "     OOG            3.920610e-08  4.330886e-06  4.330886e-06"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.xs('AR', level='state')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>% generation</th>\n",
       "      <th>% total fuel</th>\n",
       "      <th>% elec fuel</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>NERC</th>\n",
       "      <th>fuel category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">AR</th>\n",
       "      <th>SERC</th>\n",
       "      <th>NG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.143344</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SPP</th>\n",
       "      <th>NG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.142862</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">FL</th>\n",
       "      <th>FRCC</th>\n",
       "      <th>COW</th>\n",
       "      <td>0.102601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.134697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SERC</th>\n",
       "      <th>COW</th>\n",
       "      <td>0.102601</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.134697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">IA</th>\n",
       "      <th>-</th>\n",
       "      <th>NG</th>\n",
       "      <td>0.303138</td>\n",
       "      <td>0.264098</td>\n",
       "      <td>0.300394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MRO</th>\n",
       "      <th>COW</th>\n",
       "      <td>0.132677</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.140241</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.211916</td>\n",
       "      <td>0.213591</td>\n",
       "      <td>0.214703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">SERC</th>\n",
       "      <th>COW</th>\n",
       "      <td>0.153661</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.152410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.121395</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.113601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">MI</th>\n",
       "      <th>MRO</th>\n",
       "      <th>PEL</th>\n",
       "      <td>0.579756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFC</th>\n",
       "      <th>PEL</th>\n",
       "      <td>0.579756</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">TX</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">SPP</th>\n",
       "      <th>WND</th>\n",
       "      <td>0.214635</td>\n",
       "      <td>0.214635</td>\n",
       "      <td>0.214635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>5.272077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.124454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">TRE</th>\n",
       "      <th>WND</th>\n",
       "      <td>0.214635</td>\n",
       "      <td>0.214635</td>\n",
       "      <td>0.214635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>5.272077</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.124454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">VA</th>\n",
       "      <th rowspan=\"3\" valign=\"top\">RFC</th>\n",
       "      <th>COW</th>\n",
       "      <td>0.169581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.185219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.115835</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">SERC</th>\n",
       "      <th>COW</th>\n",
       "      <td>0.169581</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.185219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.116589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.115835</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"4\" valign=\"top\">WI</th>\n",
       "      <th rowspan=\"2\" valign=\"top\">MRO</th>\n",
       "      <th>NG</th>\n",
       "      <td>0.159994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.143071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>0.187468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">RFC</th>\n",
       "      <th>NG</th>\n",
       "      <td>0.159994</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.143071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>0.187468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          % generation  % total fuel  % elec fuel\n",
       "state NERC fuel category                                         \n",
       "AR    SERC NG                      NaN      0.143344          NaN\n",
       "      SPP  NG                      NaN      0.142862          NaN\n",
       "FL    FRCC COW                0.102601           NaN     0.134697\n",
       "      SERC COW                0.102601           NaN     0.134697\n",
       "IA    -    NG                 0.303138      0.264098     0.300394\n",
       "      MRO  COW                0.132677           NaN     0.140241\n",
       "           NG                 0.211916      0.213591     0.214703\n",
       "      SERC COW                0.153661           NaN     0.152410\n",
       "           NG                 0.121395           NaN     0.113601\n",
       "MI    MRO  PEL                0.579756           NaN          NaN\n",
       "      RFC  PEL                0.579756           NaN          NaN\n",
       "TX    SPP  WND                0.214635      0.214635     0.214635\n",
       "           WWW                5.272077           NaN     0.124454\n",
       "      TRE  WND                0.214635      0.214635     0.214635\n",
       "           WWW                5.272077           NaN     0.124454\n",
       "VA    RFC  COW                0.169581           NaN     0.185219\n",
       "           NG                      NaN           NaN     0.116589\n",
       "           PEL                     NaN      0.115835          NaN\n",
       "      SERC COW                0.169581           NaN     0.185219\n",
       "           NG                      NaN           NaN     0.116589\n",
       "           PEL                     NaN      0.115835          NaN\n",
       "WI    MRO  NG                 0.159994           NaN     0.143071\n",
       "           PEL                0.187468           NaN          NaN\n",
       "      RFC  NG                 0.159994           NaN     0.143071\n",
       "           PEL                0.187468           NaN          NaN"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[a > .1].dropna(how='all')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-09T15:00:10.701750Z",
     "start_time": "2017-08-09T15:00:08.473624Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
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71VRl7qqdb5FefuYbzbzuFEX3bPrb9wHB3dVv5DWqripWVUW+HA5/+QdHmX6MG+P0\nCVTs4IvUK/GcHx9j23GPsRmGYejbrzPqfk4a2Utxg0fJ8Ew5+nfsqZF/YKT276tQ5VdHV+rIzDii\nooIKhYUHeDfpgP5KK3Zo47adcleXy8cvVMnDExUT0LK/X0kqK63Sfxfu0uFDcfLz7aGAgCrV1DhU\nWhYou71IgcHL6rbyqNU1MkjTLkhS/0FRDa5cUllVo+Wp+1VR7aOPt/bT5zvi1C24Qh7DptzSQNV4\njj53n32bqfMn8A0jAAAAAN45tsne18+pkFA/5eUeXUVtd3quykqrFBTs19jNAQAAAACAJJvRzNfY\nFy1a1ODxCy+8sMngu+++W9OmTdOkSZMkSZMnT9bSpUvldDr14YcfasGCBXr//fcVGBioWbNm6amn\nnlJ8fHyjeampjS9p2BoMw9C+XeXatqFY7hpzKzcEhjg0+Zwo2extu1+ju8bQ8g9zVFXpkSTF9gvU\n0FO6NHOr+r778ojyclySpC4RPjp1WmSb7Tvp8Rha+VmuSgprJEmBwQ5NPCdKDkfD9+/xGNq8tkj7\nM8rrjjmcNo0cH67uvbzbxqM15ZXU6GCeSzabFBPpq7Ag8ysYSNLhrEqt/Tq/7ucJ07spNPz4RgXD\nMLTsg8OqLHdLkgYMD1G/JPMraGQXuPT68iMq+/Hv5VhdQ5y6akqk13OXpLVf5+lwVsNbJfyc08em\n/kNCFJcYJHsjz7MkZeVV6aUluaYy776sl3wayBo9erSp2x/L6voC4ORAfQFgFeoL0DY8HkNfvp8j\n14/vjQaPDlVM30At+yBH1a6jnwMkDA7WwBEnz2oG3tYXagsAM3jtAsAq1BcAVmlJfUHTmr2yeOGF\nFyo9PV1r1qxRTU2NkpOTNWhQ88urBwcHq6zspz3VPR6PnM6jd9elSxcNHTpU3bp1kySNGTNG27Zt\na7LJQLLuD6Agr1wfvZWmzF1F9Y4nDOimfgOjtGblHhXklR93u/IStyqKQjVhaqIl82rMdyt2q6ry\nkCTJ7rDpwpkpCgsP9CojMjxPr/59tSSpMK9aYUExza5m0FpWL9+lksJDdT9fNGus+iZ2a/I2Y8YY\n+varDC1dvE0yjjZapK7I17QLknTKafFt1iAhSdl5ZXrhnY3akP7TxXC7TUoZ2lM3XTRMXULMfevl\n1e9X1/1738RuOn3quEbHFh/eppU/bo+Rl23o0tmjTP3Ormq3np//ZYMNBpKUX1KjTzZU6fE5p3j1\nGObllmpx1nJTY0cmx+r06QMV3MjjUuP2KC09Vys3Zmn1JnPbpths0pjRo+R0nNiqFMcyW19SU1Mt\nqUVW5VqZTa712eRan23lnGudrPWF57vz5lqZTW7bZNfiDTI6iupqt/bsPKKKcpdCwwLUJyFC9jZu\nxjcjfWuOXD++l7bZbZp+3jgFhfipsjhdXy/ZIUnan1Ghiy4ff9y2br8k3tSWzlafO1uuldnkWptr\nZXZnyz1We783sjKbXOuzybU+u7PlHqu96wvPd+fNtTKbXOuz26K+oHHNNhm8//77ev755zV16lR5\nPB7dcsstuvnmm/XrX/+6yduNGjVKy5cv19lnn620tDQlJv50IX7IkCFKT09Xfn6+QkNDtXHjRl16\n6aUn/tt4yfAYWrs6U18u3qZql7vuuJ+/U9POT9KIU2Jks9l0ymnxOrCvQOWlLgUG++rb5Rnavjlb\nkvTVknT1TeymXrHhbTLnaleNVi3bVffzyFNivW4wkKQ+fSMU1y+ybhuCFZ+nq9/Ahpewb02F+eX6\n6scPbyRp2OjezTYYSEe3xDj19H7qGhmk9/6zXjXVHhmGtOT9Lco7XKazZiTJ3ooXnBtzpLBCf35+\npfJ/3KqilseQVm06qH05xXrs1okKDmh664SD+wu1NyOv7udTT2966f9ho3vXNRkcySnVoQNF6hnT\n/OoVqzYd1JHCiv/H3n2Hx1WdiR//3pmRNOq99y7LkmzLvXdsY4rBNBMgEFjCkmSBBbIbNtn8EkI2\nhWQTkoVAQg3EdIMB495t2bIsW5Jl9S6rd2lG0rT7+2PkGY010ozcMOR8nofHuveee+bMHYln7rnv\ned8J25TVd3O2poupCYEO+zvv/O+NI6ERPtx4x7Qx+w1GE4WVHRw+fY6comYGBvVOvzbA1ITAyxpg\nIAiCIAiCIAjC5MmyORj8yN5KBrXW7/S+/u6sWj+FqTMiv8LRjVV0stHyc1JaCJ4jgdBzF8dz7EAV\nw0MGdMNGjh+qYdma1K9qmIIgCIIgCIIgCIJwzXMYZPD666/zwQcf4O9vfoj+yCOPcN999zkMMli9\nejVHjhzhrrvuQpZlfvnLX/L6668TExPDypUrefLJJ3nooYcAWLt2rU0QwtXQ1aFh63unqa/ustmf\nNCWEG27LwsfPWvNeUkhExwVYtgPv8OJcQw/9vUPIJpmP387nu08uxdVt8innJysvpw5NvzlFvVKp\nYNHK5Ivua+l1KZaHxefqe6gqaycpLeSyjNMeWZbZ9nERBr15Vb27hwurb0qfVB9pmeHc/72FvPta\nLgN95uuQd7SW7i4NG++ZidrBw/1L9c720jEBBqM1tA7wyf5K7lk3cbaPo/uqLD+HRfgQnxw0Yfug\nUG8iov1oaugBoPBko1NBBvmlbQ7bAGzeUcrGFckkRPri60T9UYPBfmaECylHBQIYjSaKqjo4XNDE\n0cJm+rU6u+coFBIm08QlS25anODU6wuCIAiCIAiCcOXs+aLE5t7mvN7uQT56Ox+93sT0OdFfwcjG\nGhrUWxYLAEybFWX5We3uwuyFcZbA7txDNcxfmoCb+sreXwqCIAiCIAiCIAjC15XDp+Imk8kSYAAQ\nEBDg1Gp3hULBz3/+c5t9iYnW1drr169n/fr1kxnrZSGbZHIP17BnW4nlYTeYJxXWbJhK1swoh+/P\nw9OVDZtm8PeXc0A2l1vY/skZbrpzutPjMBhN7D/ZwM7j9TR1DKB2VTE7PZQbFycQEeRl9xzdsIGj\no7MYzI3B19/dbltnxCYGEpcUSG2leUX9gR1lJKYGX7FsBiWFzVSWWB96r7ohHU8nHmhfKCLaj4ce\nW8zmV3NpbeoDoKq0ndf/fIRND87BL2DymR2cMThs4OCpRoftdh6v41tr08a9jt2dWkoKmyzb85cn\nOnXNs2ZGWYIMzpw6x+ob020e4tszrDdOePy8gsoOCkYCTgJ81CRE+hIf4UNCpC8JEb6EBXrapDsN\njXCuRmlIuDcFFe0jgQVN9GnsBxaolBLTU0JYPD2CmWmhvPhRAUcLm+223bg8ifmZEU69viAIgiAI\ngiAIV0ZHa7/dAIPRdnx6hvRp4VclIN+RksJmjCPB0m5qFSnptuUC5y1J4PihGvQ6I0ODenIP17J4\n1cUH9QuCIAiCIAiCIAjCN5nDO/3U1FSee+45S+aCDz/8kLS0tCs+sIulGdQzrDfi6+k65gFsZ/sA\nW989TUOtbd33lPRQ1t+Whbev2unXiU8OYv7SRHL2mydVTuc2kJQWQvo0xw8/B4cN/PzVY5wZlS6/\nFx2fH65h57E6/vPbs5mdHjbmvLyjdWgGzA9pzVkMkpwe73iWXJdCbWUOcGWzGQwN6tm+5YxlOyYh\n4JJWtPj4ufPA9xfy0dv5VJxtBaC9pZ9X/3iIO78zh6jYy1++or1bi86JFfzd/cNohgzjlkw4frAa\neWShvq+/u1O/MwBTZ0Swc2sxJpOMdkBHVVn7mImxsSbOCGBPV98QXX1D5JW0WvapXZXER1gDD+LC\nfVC6qzAOGibs69PCJtpy6+weUyokpqcEs2haJPMywvAaVfP0h/fOZv/JBrYdraGysReFJJGRGMiN\nixOYY+dvQxAEQRAEQRCEq+tUboPDNsNDBs4WNF8T2QwK8qzjnTo9ApWL0ua4h5cbsxbEWe7xjx2o\nYu7i+GsiQEIQBEEQBEEQBEEQrjUO75Z/8Ytf8MILL/DMM88gyzJz587lpz/96dUY26QcP9PMlv2V\nVNZ0oQBc3VUsnxPLbSuS8fZw5fjBavZ9WWqT5l3t7sK6WzLIyI6ccCW5dkhPXkkrPf3D+PuomT0l\nFLWbiuXrUqmpaKflnHk1/ecfFBIV629TasGeV7YU2QQYjKYzmPjVW3m89B8rCPG3rsjXDRs4ut+a\nxSB7XozD13FGXGIQsYmB1I2M58DO8iuSzWDvthIGRso8KJQSN9yWNeY19AYj2iEDHmoXXFQTr9AH\ncHVTcecDs9n12VmOH6wGQDOg480Xj7LhrumXrf6nwWgit7iFTw9OvErHZmzjjF+r0XEqt96yPXdJ\ngsNsBOd5ermRNCWE8mLzw//CvIZxgwz6NDpe+qiAnKIWu8dHUyklUmL8qWvuQzNkP2hgSGekpLaL\nklpreREvIA0JCfu/K83ItA3pbfYpFBLTk4NZNC2CeZnheI8KLBhNqZBYOTuGlbNjkEciMq5Uhg1B\nEARBEARBECavs23AuXbtzrW7knq6tDalErNmRtltN39pAicO12AwmBjU6sk7WseC5Yl22wqCIAiC\nIAiCIAjCPzOHQQZqtZof/vCHV2MsF+393WV8+WUpYUhkYX5gaxo0UnigmlOnGsn08aC1sdfmnNSM\nMNZvzMTLZ/zsBbIs8+HeCj7YU8HgsPXhq6daxR2rUrllWSK3fCubv/7vQQx6E0ODej7ZfIp7vzsf\nSTFOqvz+IfbnT7ziQ6c38uXRWr69Pt2y78SRWrTnsxioFCxaefnSNi69LoW3XhrJZlDXTXV5O4mp\nly+bQUNtF3k51tXsi1YkExTqbdmuaerlwz0VHC1qwmCUcVUpWDwjkttXphAZbL90xHkKhcSam6cS\nGOzJl1vOIJtkjAYTH72dT2eHhvAoXwpONNDVocHVTUXq1DCmz4nGfZyH26O1dw+y43gtu47X0dU3\nPKn3/Ju/5/HwhkxCLijdkHe0Fr3OXMJA7e5C9tyYSfWbNTPKEmRQVtzK0KAe9QUZE06cbeFP75+m\nu9+5MT925wyWzYxGlmXaugepaeql5lwv1U29VDf10daltXueCewGGAwj04LM+cIYCgmykoJZNN2c\nscB3kiUyRHCBIAiCIAiCIFx7XFyVjhtNot2VVHjSWvbOL8CD6PgAu+28fNRkz48l91ANADkHqpi9\nKA4Xl6/+PQiCIAiCIAiCIAjCtWTcIINbbrmFLVu2kJZmW1telmUkSaKkpOSqDNCRioZu9n9ZRjy2\nq8EVSAQCcp+O1j7ramp3DxfW3ZrJ1OkRDh9evr29lPd3l4/Zrxky8PrnxQzrjWy6LpXrbprKto+K\nAKit7OTwvkqSp0fQ3jNIZ88g7T2DdPQM0tk7RE1TLwaj4xT2+aVtliCD4SEDR/dZsxjMmh87qdIO\njsQlXZDNYEc5CSmXJ5uB0Wjiiw8LLVn7A4I8bco8nC5v49lXj9uUIdAZTOw50UBOUTPPfncBKTGO\nSx/MWhCHf6AHH751kuGR1fj7t5eNaVdf3cWRfZXc/dBcIqL9xo7XJHOqrI0vj9aSV9KCafLVBgA4\nXtzC6Yp27lqdyoaliaiUCgx6IycO11jazFwQO+nUmynpoajdXRga1GM0mDhb0ET2vFjAnHHjb5+e\nYdeoTAkAqTH+zEoPYcexejp6Bi37o0K8+Pb6dOZlhAPmh/mhAR6EBnhY9gEMaHXUNPdZAg9qzvVR\n29xHxKhrM4hMHTImQDPqtYP91PzvE8smHVggCIIgCIIgCMK1LTk9lOLTTY7bTXFU4u3KkmWZwjxr\nkEHWzKgJ73UXLE/k5NE6jEYTmv5h8o/VMXdxwtUYqiAIgiAIgiAIgiB8bYz7hHPLli0AlJaWjjmm\n0+mu3Igm6dPt5gwG4xm90jowypfUudH0qxTkl7XholLgqlLiolKYf3ZRWv7t6RvmAzsBBqNt3lmK\nwWBEM6gHb1foN1+XPdtK+PO2s9hf/+2cYb3R8vOJIzUMas2BEiqVggUrksY77aKNzmbQ6CCbgckk\nY5JlVE6k+T92oJq25n7L9vrbsiy1LweHDfzm73k2AQajaYcM/OqtE7zyo1VOvVZiaggP/GAR7756\nnJ6uwXHbaQd0/ONvx/nefyy3ZDTo7h9id24924/V2V25r1BIzMsIY+28OE6Xt/HxfvulE0L83Wnr\nNr/2sM7Im1+cZW9eA49uzGK4TYPmfDYKpYI5i+IdvqcLqVyUTJ0ewcmRzBAFeY1kz4ulqLKDP7yb\nb3ltMJdB2HRdGhuXJ6FUKrh9RQqldd30aXQE+qpJjvZzKpDEy8OVzMQgMhODLPu276sg93Pr/xvO\nIdNv59zkGH8RYCAIgiAIgiA4xWg00dbcj0FvJDDYE4/L+D1SpzeXZvN0d640mzOaGno4dbye7k4t\nbmoVqRlhpGeFW+53vuncL8ioZk98chDhUb5XYTTjO1ffQ1eHNRQ6c+bEZfV8fN2ZPifacs91dF8V\nM+fHolL9c3yugiAIgiAIgiAIguAMh8uo77zzTt577z3LtslkYuPGjXz22WdXdGDO6qjuxsNxM+ow\ncaKxm+2N3ZfttWUZ3t9TAZgvZAYSLkgokEgAzo6s7L4YUSHmMgHDQ3pyRj3QnrkgDu8JSjxcrDHZ\nDHaOzWZQVNnBJweqOFnaitEkEx7oyZp5saxfGI/azor87k4tB3ZaswlkzYoiPtn6oPpAfiP9Wv2Y\n80Zr7x4kt7iFBVkRTr2PkDBvNj04h5d+e2DCdtoBHaeO1+Md7ceXObXkjJRquFCQr5o18+NYPSeG\nQF93AGakhjA3I5xtR2opr+9GkmBqQiA3LEogLtyHXbl1vPH5WQYGze+tobWfH714hDlu1hINWTOj\nLvpzzJwZZZnwaqjp4qV389l2wrYER1y4D09syiYh0jqhp1QqmJoQeFGveaG+uh7Lz4PIjPdXtWr2\n5MpBCIIgCIIgCP98TCaZnP1V5B6qob9vCDAH+aZlhrPqhin4BThzx2dfVWMPH+yt4FhRM0aTjKuL\nkiXTI7l9VTIRQROXZht3vEYTn39YyOlc2+/gJYXNHNhRxt3/MpdAB2Xfvu6aGnr46O2TE7ZRKhVs\nuHvGVRrR+EZnMYiK9Xfqs1m4IolTx+sxmWT6e4c4ndvArAVxV3CUgiAIgiAIgiAIgvD1Mm6QwX33\n3Udubi4AaWlp1hNUKlasWHHlR+Yk13FWwV9eMSvoAAAgAElEQVTIcIXHYQCqkUkdyZzgjkSCSoEh\n2JMgP3eC/dwJ9FMT5OvO5p1ltI5T5/68li4N/Vodp4/WWrMYuChYeAWyGJy35LoU/n4+m0FtN9Xl\nHSSmBgPw+eFqXt5SZNO+uVPDG1+c5XDBOX7xyEI8R61kkWWZbR8XYtCbPx9XtQrfxAA+3FtBS6eG\n5g4N5fXOBXw8/85JYvaUE+JvTuVv+TfAgxB/dzzUtitoWs71OdXvtu1lFBjG/mZIEsxMC2Xd/Dhm\npoWgtJNFIT0+kPR4+w/s18yLY15GOK9/XsyekYf/foA8bH2tuUsmn8XgvOg4f/wDPejuNP8OnR4V\nYKCQ4Nblydy9JhWXK7TSpr21n9IzLZbtZuzXlMhODWFm2lebGlUQBEEQBEG4tsmyzNb3Tts8CAZz\n4MHZgibqqzt54AcL8Q/0nHTfeSWt/PKNXPSjS7Ppjew+UU9OURPPPrKA5GjHpdkutGdb6ZgAg/O6\nO7W888oxHnlq2aRLo31ddLQN8I+/Hkc3bM6+p1IpmDY7mubGHgb6h+nrMQeKGI0mGmu7mZIVPlF3\nV5TBYKT49DnLdtasKKfO8wvwIGtWlOVzPrK3khlzY+zeGwqCIAiCIAiCIAjCP6NxZz3eeustAH7x\ni1/w4x//+KoNaLJcXBTIw0aH7cIDPQn2ckWvN6EzGNEZTBgMRnR6EzqDCb3BiGz/WemEQgM8mJ4S\nTJCfO0G+7rQUt1I98gDW3yBz59oppGaE2ZwTGeLFf710FJ1+/HHXNffzwz8cIE5jfTA9a0EcXt5X\nLvV8XGIgMQkB1Fd3AXBgZxkJKUFUnevllU+Kxj2vsrGXP39wmpsWJ9I8EkDQWNHBcK01iKBsSMeR\n905f1Lj0BhNVjb1UNfbaPe7t4TIScGAOPpB6xi+VMJrBYHv9/bzcWD03hjXz4gi9hNVSAL5ebjx+\nVzar58Ty4kcFeLQMWI51I/ObD07z6MZpNpkGnGUwyuCrhpEgg0AkmpAJD/LkibuymRIfcEljd+Tw\nngrOxxWovVwZNhhgyPp7qpBgaXYUj26chkLhuByDIAiCIAiC8M+rvLh1TIDBaAP9w2zfcoZND82d\nVL+aQT3Pv51nE2Bgc3zIwK/fyuPlH61COYnvrINaHScO10zYpqdrkKL8RmbOj5vMkL8W+noHeeeV\nY2g15jJwkkJi430zSZ1qved997VcyotbATi6v4q0zDCnyrRdCZUlbZagfaVSwdTpzmXIA1i0MpmC\nEw3IMvR2D1KY18iMuSJTmyAIgiAIgiAIgiCAE+USnn76aXbt2oVGY65haDQaaWxs5LHHHrvig3NG\nfFIQ1SMTGOORgWe+txCfkXT3dtvIMkaTjE5vRG8wUVDezm/fmTj9I8ATm7JtUtAbZkTytz8coq3F\nXKH+s/cLiIjxs0mNnxYbwP88upC/flJEaZ31QbynWkWgnzv1I+caO7UMY14p4eKqZOHyK5fFAECS\nJJauSbXJZlBT0cHn+Q0OAzAOFzRxuKAJACWQOVI6AqAPmY4rOO5+rZ5+rTUIwQdIxfEKk+GRf7OS\nglg735x94HLVZz1vakIgT9+axVsv5lj2tSAzUNfNE/+7nxsWJ/CtNWljsjGMp665j99vzufcuV6y\nRt6jGonrMsP5l00z7JatuJy6OjScybeuBFp9/RQey47k+Jlmmjs1eKpdmJMeRsglBmkIgiAIgiAI\n/xzycmodtqkobaOnSzupsgn7TzagGZo4n11rl5a8sy3MzXB+pX3F2VYMTmTTO1vQ/I0LMhjU6njn\n5WP0dluDum+8fZpNgAHAgmWJliCDc3XdNNR0EXOZyrZNVuFJawBLcnoI7h6uE7S2FRDkScaMSIpG\n7n8O76lg2qwoFCKbgSAIgiAIgiAIgiA4DjJ48skn6e3tpb6+nlmzZnH8+HGys7OvxticsnptGi87\nCDJITg+dMMAAzA/YVUoJ1ciEweIZkXx6qIryc22oQhpQBjYjueiQ9W4YO8IxtEeTHhNK+gWrxlUu\nSm65J5u//eEQRoMJrUbHp5tP861/mYs0aoVMQpQP6290QVFaSqumHTelG7Ojs7g+ZTa7D3fw8Z4K\nwrC2D0sKxNPJLAZnWkvZXnGAiq4aJCRSgxJZl7yMtGDHQQpjshnsKKOo17xiXlIPoAqtR+HbjqQw\nYRryxNgWhbErHEaNNWpUgIEJmTpklAqJkAAPwgM9CQv0IDzIEz9vNX9+/zTD8iCq4FHXWOeGsTMC\nQ3sUPmpPnvpWNp29w7R1a2nt0tLWraWtS0tHzyAmO8EPfcAwMm5MvFpG66rgxceXEh3q7dR1PU+W\nZYpaS9leeYCqrloUKEgLTmRd8nJSghLGtD9+cNRKJ3cVA4PmVT8mGbYerObw6Sb+ZUMGC7MiqOpo\n5M3jX1DTX4URI76qANanLWNt+ny2Hqjm7e2lGIzmSc1+ZLxH3mOCl9uEAQZ9Q/3srDrI0fqT9A8P\nEODux5K4uaxIWIi7i3rc8y50eE+FJeDE19+dKTNCOVyfw77OHFo1HbgPu9HTMJ21bssI8rz4jAqy\nLFPQcpYdlQeo7q5HISmYEpzM9cnLSQqMu+h+BUEQBEEQhGuLU6XOZGht6ptUkMHZmi6n200myGBw\nUO9UuyEn231d6IYNbP5bLu2t1gxtq26YwvQ50WPaRscHEBnrz7mRgPqj+6u+kiADrUZH+VnrXMG0\nWWPH6siiVckUnToHsrkURvHpJjJnOldyQRAEQRAEQRAEQRC+yRwGGZSVlbFz506ee+45Nm7cyOOP\nP87jjz9+NcbmlNAIH9JX+HN2b7fd47L3MOtvS590v5Ik8dAd8fxs71ZMKq11v4sORUw/6ohGHlr1\n73bTPoaG+7Dqhins+KQYgOrydnIP1zB3ifkB9JBhmF8fepHitnLLOVqjhp1V+9lfe4R/X/AwhpZw\nGkeCJ4zIbD3bgsfuMu5YmTJuqklZlnm74GM+K9ttsz+n4SQ5DSe5I+MGbpu63uH7XnpdKn//i3nl\nfUNtNy4eShT+LbgmFiAprE/1la7DKH26MPY0o6uYgYtSRayvO/6d1pUtadlRPLI2lWA/d7v1KwdM\nnbxZ8jqS67Bln+SiQ+FZhiqkge/OfIQZqaF2x2owmujsHaKtyxp80Nqlpby+m/r2XpJkFdIEgQah\nkoLwwMmtuJdlmTdOfcCXFfts9h+pz+NIfR53Z21gw5Q1lv2d7QOUFbdYtm+/YxprlQpe3lJI28gK\noK6+IX79Vh5Rqb10+hwDSeZ8IoYu0zn+fvYd/pG7h4GSLJCt1zAo1p/huh4Aik83sWbDVFQq5Zgx\n1/ec49kDL9A7ZJ3A7R3up+Z0AzurDvLfyx4n0MNxLdqeLq1NKtvZS2N59uAfqei0BlH0AltLd7Gz\n8iD/sfhRpoakOOz3QrIs87eTm9lVdchm/+G6XA7X5XLf9I3ckLpq0v0KgiAIgiAI1x5nSxUolJNL\nt29yshae0eQ4K8Fovn4TB6+f5+PrfCDvtc5oNPHhWydpHJWFb/6yRBaMk2lPkiQWLEvkgzfzAHNJ\njI7WfoImGdx9qc4WNGEymn8P3D1cSEoLmXQfwaHepGeFc7agGYBDeyrImBFps4BAEARBEARBEARB\nEP4ZOczzFxgYiCRJxMfHU1ZWRnR0NHr9tbMqo3WgnS3a96hJO0afXysy5kkinesgrZFllKQc4IPK\nzybdr8lk4pVTr9sEGIxmVGn5y6nXMMn2J6XmLIonMS3Ysr378xJam8wPed/If98mwGA0nVHPHw++\nTluVtcBAG2AA3v6ylD++d2rcuqKH6nLHBBiM9v6Zz8ltPD3u8fPikszZDM7zGdLhmmAbYDCa0q8d\nl+gy/vTvS0l1scatBAZ7ctsd0wgL9LQbYGAwGdnR+pE5wMBO15Jay7amD5HHmSBUKRWEBniQmRTE\nqjkx3L0mjSc2ZfPsdxegjS+mPvkkOtdBm3PkUS+kGjay5Z1TmOylQxjH3uojYwIMRvtH4SfkNxVZ\ntnP2V1nem3+gB6kZ4cyZGsb/Pb2C21YkWyZVJfd+OrxHAgzsMHq1oIqsAMDPy41n7p/Dvz0013Jd\nhwb1VJxtG3Oe3qjnV4detAkwGK25v43fH/3ruNd4tCN7Ky3XyttXzTEO2AQYjDZkGOa3h/9C31C/\nw34vtLPy4JgAg9HeOv0RhS0lk+5XEARBEARBuPbEJQU5bKNSKYiKdRwUO1pilJ9T7XKLWymvtx+w\nbk/SlBA8vByn3J82e/Kr5q9Fsklm67unqSy13mtMmx3NqhumTHheakYYAUGelu2cA9VXbIzjKRgV\nIJ0xIxLlRZbGW7zKGjjd0TpASVHzJY9NEARBEARBEARBEL7uHGYySE5O5tlnn2XTpk089dRTtLW1\nOfVA8mrZUXkQvcmA3qcLjU8XyBKSLCFLJksG/73VR7khZSWers6vWj/dXMy5/pYJ2zT0NnGsIZ/M\n0DS7x1fekkzTH3sY1OrNqz/ezmP9g6nsrz02Yb/e5yLRDRkBULko0HuboM+80n/PqSpaenp4fFM2\nnmrrxyfLMp+c3e7wfX1SsoMpTpRNiJ0WZCmZ4GVS4jUQgMa3c9z2qtBGzuTX0NZifai8/KZkBk2D\nMGz/nLxzhbQOtJs3xlkIUt1dz4lzBU6N+TwtvSgDm+iXoN+vHa/eQFyHPTEpDPT7thNRNxXfbnNK\n1JLCZj79IJ+VN4+fIeI8WZb5tHSnw9ffUrKD5MB4NP06m4mtGQuj0Og1lu1bV8UwJ8uf1z8/S6Vx\n/CCO81Qh9WT6zeS7N8/Ax9MVAzoSpgRSccZ8DfNza4lKtV0dlFOfT4d24lSxFZ01nGouJnmCMgQD\nvcOczq23bKfODeSNpo8n7FerH2Rb+T7Wp66YsN1oJtnEVieu8edlu8kKm3hiUxAEQRAEQbj2zVkc\nT+HJxgnbTJsdjbuH4wf75/UODJNT1ORU2+ZODU+9cJDVc2K57/op+HpNXKJOpVKyav0Utr5XMG4b\nX393UqaGOT3ea5Usy+zcWkxR/jnLvpT0UG68PcvhvZNCITFvaQLbPjIHYBfmNbJ8bSpePlcnw0Nn\n+4ClXANA1qyLL3EQGuFD6tRQykYyDR7aVcGUzHCRzUAQBEEQBEEQBEH4p+YwyOCnP/0pp0+fJikp\niR/84Afk5OTwu9/97mqMzSmnm4ttd0gy8gWrwQ0mA/+27adX5PX/kPPqhMe9I0OIrZgFQGerht+/\n+hGm2PFTcioMKgJb4yzbzUHl9EWXMzopZzXwb9s/uKjxVnbV8uAnTztuKEO81zw8B8wZDUKakqnx\n6Rw3GMBl2JWcvTUoMKfr7w5q5P8VbYMi++0n4/kjL0/+pPPjlGQG/DoAa2aIxsQClOUuePWZV00V\n5Taxp/EAbVEVlz5YoKyjigc/eZqQxmRCDMkAGFTD/OncC8if2PnsA5z4QwQklZFS1Yc8setDyz7v\n4RBiMf9+VZS28fAHz2B0mXymkV8d+r8Jj4fVTSHIGA+Y38ubXa86kQcFPi75ko9Lvpz0eBwpaC3B\naDKiVIwtDyEIgiAIgiB8ffT3Dk143NVV6XDV/GiNbf38/G/Hae7UTNhOIcH5hGayDDuP13GksIl7\n16axdkH8hGUcYhICkSTzefYM9A/T3akhMNjL6XFfiw7vqeT4IWvmsuj4ADbeNxOFnSx19kybHc3+\nHWVoB3QYjSZyD9ew4vrJBwprh/R09w/j4abC38kghdGBK4HBnkREO5fZYjyLV6dYggxam/soP9tK\nasbXP5BEEARBEARBEARBEC6Ww2ebt99+O1u2bAFg5cqVrFy58ooPajJ0Rt1XPYQJ9fu30RlSR2Bb\nLABBrfEM+HYw4Ndut31QSzxKowsARoWejjD76eivOAnaIiuIL5sLgGd/AJ79gWh87GQzkCGiLgOF\nyfzA16DS0RJ97aazlxUm6pPziS+Zi7vWFzAHURhUOrrC6i7La0hGJQGtsZbtrpB6ZOXk6r06Y8C3\nHYNqGJXBDYWswLcrgq7Qy/MezlPpXAloi7Fsd4RXIysu/3uZDFmWRZCBIAiCIAjCOIYNOvZUH2Zv\n9VFaBtpQq9yYFZHF+tSVRPtGXHS/sixzvPEUOysPUtlViyRJpAUlsS55OdPD0yfdX3/fEFvfG1XO\nTWXCZJSRZAXSSNSwTmekvqaL5CmhDvsrqurgl6/nMjBoDbqdmupBt2sJnYoaJBcd6NwIV6by8OIb\nGR5U8sqWIpo6zAEJmkE9f9lSxM7j9Xz31kzS4wPtvs6hXeWWAAOTi4Hu4HpUKgV+LTHIOgVGg4kv\nPizk3kfmO1zxfyGTbOJYwyl2VR2kqqsOhaQgLTiJ65OXX3Imr/KOaraV76WgtQSD0UC0bwSrEhez\nNG7umO/V+cfq2PdlqWU7JNybTQ/OwcVl7Pfvdk0n28r3kdNwkn6dhkB3P5bGzWNN0lJmL4znwI4y\nAPKO1rFoZTKubs6EWENtSxd/3vspdbqz4KoFowpvQwx3TFvL2uyp454nm2SKRgUZZM2KsvkcTLKJ\nnIaT7Kw8SHV3A0pJQXpICtcnLycjNNVunxHRfiSlhVjKRhzcVU7K1NAxn29ZRxVflO+lqLUUg9FA\njF8kqxMXszh2ziXdu7QNdLCtfC85jfkM6LQEefhbrvFkMjYKgiAIgiAIgiAIwuXi8O4+KCiIvLw8\nsrKycHV1PkXl1RLlE06bZvw0/teClugSPPsCUA+ZU9lH1mRRmXEIo4ttgITS4EJgS5xluzOs7qJW\npV8uGp9ONF5d1mwG55Kp8R6bzcC3Kxzv3mDLdkt0yVc6bmeYlAZqU0+QcHY+bsPmWqER9VMxuujo\nDbz0Gpv+7VGojOa/F5NkpPMyP/g/T1bI9AY2W7Jf+HVEXvYgg8CWBBSyNYCkK6TewRlXXohnIC5K\nl696GIIgCIIgCNecAZ2GX+x/gepu63c2nVHP3pqjHKzL5fH5DzInavqk+zXJJl4+8Q77ao7a7D/V\nfIZTzWfYMGUNd2dtcLo/2STz6ebTDGrN9w0myUhV2hGG3QcAiCubY8k8tm97KUlpIRM+sN+b18Cf\n3j+FwWhNL7B2hS95us/Q6LTWJFzqQVo4zYuF9fz38sf589PL+eRAFe/tLmdYZy5ZV93Uy3/8+TAr\nZkVz//p0m9XzXR0aCkeVD2iOLKE7pAGAPlUP0dXma1tb2UnhyUamzYp2+pqYTCb+L/dNDtXl2uzP\nbyoiv6mI26Zezx0ZNzrd32g7Kw/w6sn3kLFen8quWiq7aslpOMnTix7BdeT7dUlhM198WGhp5xfg\nwbcenofafez37/KOan558M9o9YOWfS0D7bx35jP21Rzl6Tnf58heBQa9iaFBPaeO1zN3SYLD8RbX\nNfGz/X8AdT/S+bR+Sh0DrpW8WvoidV238t1V9hdA1Nd00dNlHU9mtrVUgslk4oVjr3G04aTNOXnn\nCsg7V8BdmTdxa/o6u/0uXp1sCTJobuylsrTNJvhlW/le3jhlm3GworOGis4ajjWe4qkFD6NSOhdg\nMVpJewW/OvgigwZr1o/m/jbeLdrK/poc/nv54wR5BEy6X0EQBEEQBEEQBEG4FA7vcIuKirjnnnsA\nkCQJWZaRJImSkmtjpfqqxEXkN5+ZsE2cXxQ/WfbYpPrtHe7n6e3PYZSN47ZRKVT8bu2P8XL1dNhf\n2/x+Nr90EpNRxkXvRmRNJvXJJ20e2Ac2x6M0mSdulK4SP/3O/ag9xk7k7D7RwBufF2MalZ9z0+pU\nen0Kxkz6Xej65BVsnGqeNBkY1PPB7nL2nKhn9Lp0lULBDYviuWlJIq2ZvXz8mrneqDmbQQAany5L\nW4VBRVi9ddVSVLwfjz/0mFMrdroHe/nhjucwMU6eUcBV6crv1/wEd1fna3fKssyP9zxPy0DbhO2e\nWPYAEasiee8v+Wj6zQEfsTXZ3Lw4k7gU+6uW3jz1IQfrjk/Y743Jq+ms8KYP8yTQ9DnRPHnzcxOe\n8/LeHeT27Z6wjctQEC/d+cMx17alsY/NL5onyTw0fvx24c8ICDavZmnXdvOfO385Yb9qlRu/W/MT\n1C5j688OanS8+ttj6DH/HSxZnsrTy9dgkk08s+vXtGu7xpwz2o+WfI+kgLgJ21zo1ZPvjpn0u9Cq\nxMWTXhUmCIIgCILwz+CveZttAgxGM5gM/PHYa/xx3f8jyHNyDyV3VR6a8F7jk5IdJPjHMC8626n+\njh+uobrcmt2tJbqUYY8By3ZbZIUlyKClsY+KkjZS0sdmM5Blmc07y9i8s8yyT6WU+NfbMvio+RU0\nOq3d1+/QdvG7I6/wm+ue4faVKSyfGc1rnxVz6LQ1gGBvXgM5Rc3cvSaNGxbFo1IqOLS7AnmkzoLO\ndZCeIOuK+d7AJvw6IvHuMwdg7/y0mOS0EDy8xn7Ptmdbxb4xAQajfVi8jQT/WGZFZjnV33mVnbVj\nAgxGK2g5y3tFW7l3+kZqKjv4+O18S6YGTy9X7vnuPLztlCkY0g/x28N/sQkwGK1N08lfCt5g8ezr\nyTtqDoQ+drCa2QvjJiy5IMsyv9r/N1D32z0uKU3sbtvCsnNTSY0cW7JgdKmE2MRA/AKsK/23lu2a\n8F7j3aKtJPjHMD18bKaE6LgA4pODqKkwl+I7tKvCEvxS2l41JsBgtPymIj4o/oJNWTeP28YerW6Q\n5w+/bBNgMFrLQDsv5LzGz1c+Nal+BUEQBEEQBEEQBOFSOQwyOHbs2NUYx0WbGZHFvKhsjjXm2z3u\npnTl4VnfwtttcvUwvd28uHf6rRNOFNw/43bCvR2n7QTwjvdi5fVT2PXZWQB8ekIJaIuhK9Q8AajU\nu1hWowMsWJJEsL+/3b5uWTSF2KBAfvXWCQaHDQBs/rKG5XOTCPUsp1XTYfe8KJ9wbs9Yj7vKnV25\n9by17Sx9Gh1gzVAxMy2EhzdkEjFSPzQo3YfcuHoaa7uB89kMjluCI8Ia0nDRmyfNlEoFN90xAx+1\nc9fa282Lu6fdwtsFH4/b5jvZdxLiHeRUf6M9MvsenjvwAnqTwe7xZXHzmR05DUmSuPe783nj/44y\nNKjHZJL5/B/F3PvIfKJix17/e6bdQkl7xbgP12P9osgwZfNZd5F5hwRLVqTh7TZxIMojy27g1Htn\n0Lu32D0uG1Tcl3UHPmrvMce8EjwJCvGio808KVtV1EXsuhDAfI3vyryJd4u2jvvaD83cRLCX/aCK\nE3tL0Y+s6FK7u7BoaSpqN3PgyyNz7uWXB/+M0WQ/EGdVwiJmhGeM+7rjuW/6bZR1VNM52G33eIJ/\nDGuTl026X0EQBEEQhG+6Dk3XuPdF5+mNej4v283Gqdc73a9JNvFZ2S6H7baW7GRqSIrDdu3NA+z5\n/Kxlu9+3bUw2Lq13NwM+HZZAgz3biglL8LAJNNUbjLzy6RmOFDRZ7mw91S48cfcMuqUqumt6JxxH\nXU8jJ84VMCU4CTd3+Nc70lg8K5g3vzjLuXbzd+tBo45Xt+WzI6+cOxYnUZjXYH0fEZXIilEP7iVo\njivGs2gxClnJoFbPl1sLuW6j4zIHJpOJL8omDjoG+LR0J6lBjjMBXHjOeAEG5+2qOsRMj1lsfaMY\no9Ecgu7qpmTD/Vm4eMv0Dw+MOedA7TF6h+0HApxX1VXH9ZlGpByQZejtHuTkyRrSpo1/H51TXsmw\neuLscpLSyOs5n/NfN95ls9+gN1I8KlAkZVqwZexGk5EvyvdM2C+Yr1diQKzdY7OWRlmCDBrrujl7\ntoGYpAA+LdnpsN8dlQe4LnEJrirnM7LtqT5Cv04zYZvSjioqO2tJCoxzul9BEARBEARBEARBuFQO\ngwx0Oh2vvfYaNTU1/OQnP+GNN97g4YcfvmZKJ0iSxL/N/w5hZ4LZUXmAQb01wj81MIEHsu8gYZwJ\nAkeuT1mBp4sH75/5zOahcohnIHdl3syi2NmT6m/ekgQqS9sskxLhDem4DnqhMrrgpvVCaTJ/HG5q\nFfOWTjxxlJ0Wwm9+sJif/e0YHT3mlSP7jreRkbqUoPgyiruKYWQiSUJBdtg0/nXe3ZxrGeYvH5+g\noqHHpr/QAA8e3pDJ7HTbupKSJLH0ulTeecUcbOLZH4hPVxhDnn2odGoC2mMsbRetTCIoZHLBHDel\nrcbL1ZMPij+nU2t9qBzmFcymrJuZHz1zUv2dlx6SzH8vf5w38j+gqts6Yenp6sH1ycvZmH695X2G\nhPtw14NzePvlHAx6E3qdkc1/O879319IcKjtQ30/d19+vvIpXst/j7ymQuSRJT5KhZKF0bP49ozb\nePdF68TulMxwAoIcZ7rwcnfjf29+kh9vfZVulwokpfXBvaQJ4t7MjayZkWn3XEmSyJwZZamZWnSy\nkeVrUpEU5vd3a/o6fNy8+LB4G12D1s893DuEb2XdMm663KFBPScO11i25yyKt0mRmhmaxk+WPsab\npz6gpsc62ert6sn61JVsmLLG4fu2J8DDj5+vfJLX8t8jv+mMZUJUpVCxKGY298+4HbXKudVggiAI\ngiAI/0zOtJVZvp9OZFvFPrZV7Lvsr1/ZXceDnzw9YRvJpCCxeCFqo/l7tkE1zLn4wjEl2QBaI8st\nQQbtzRqeeON5+v0vyFamBPdRyRNMwO9O73B6zM8feXnszmhwv6DKQSewdU8m/rL5wIVZDM7TqbW0\nRVYS1pgKQPHJFj7XfGKTDe5SlHVUObzGF8M0oOTdV0+gMpi/Z5skI2Xxx/hR7meX3Pefil4h2n8G\nvl3hAHz8WQ5V1UfsfuaTUW08yYOf2GYl8OkMI2bY/Athkoz8se4FTI32A8/HU9xWPv41liHea56l\npOBbH+ylZsrEme7O0+oH+dfPn5nUWJxV2FoiggwEQRAEQRAEQRCEq8phkMHPf/5zAgICKC4uRqlU\nUldXxzPPPMPzzz9/NcbnFJVCyd1ZG7h1ylpKO6rRGXVEeIcS5Rt+yX0vjZ/H4tg5lHdW0zvcj6+b\nDylB8Sik8dM7jkdSSNy8aTovP3+AQcjCGuEAACAASURBVK0eyaQgqC1uTLvps6Nx93AcxBEX7sPv\nHlvCs68eo7LRvErnTJkGyqLAJQiFZy8gYdL4ctTgRnfRKc7WdjF63tFVpeC2lSncujwJNxel3ddJ\nSAkiKtafxjpzEEBM1cgs3uhSD8GeLFyZ5NR1uNCKhAUsjZtLWUc1/boB/NW+JAXGXdQ1Hi01KJH/\nue4/qetppGWgHXeVmrSgRFxVY69tTHwAt903i/deP4FskhnU6nnn5WM88INF+Pq727QN9PDn6UWP\n0KHtoqa7AQmJ5MA4fNU+1FR20NxoXTE1f1mi0+MN8ffilW8/xpnaFvaWFDBs0JMaGsO67Km4qOx/\nNudlzYy0BBn0dg9SV9NJXKI1A8SqxMUsj19AWUcV/ToN/mpfkgPjJyw5cPxQDcND5gk5Vzclc5fE\nj2mTHpLMr677EXU9jbRqOszXODjJUs/1YgV7BvIfix+lXdNJbU8jCklBckCc3UwOgiAIgiAIgplh\nnCxe15KwhjTUg9bvdOfiizC46uy2HfTuod+n3VJ+IORcMv1+bZf8cPpiuA554NcRadluj6iyzWIw\nSmdYNX6dEZb3GVGbQWXGYWSFyW77r5pK50Zc6RxLgIGMTEPi6csWGAHQEVZtCTJw1/ri2ReIxrfz\nsvV/nl9HlOXnPv9WTMrL/DchQXtkJZ5lcwBzEL5Hvz9ab/tZ2K4WwzjZ5QRBEARBEARBEAThSnEY\nZFBcXMyWLVs4ePAg7u7u/OY3v+HGG2+8GmObNLWLmunh6Ze9X4VCQVrwxT1Av5CPrzspU8MoONEw\nbpuy4laWr0vD1c3hx0OAj5r/eXQRz79zkuPFo1Lt69WYeqx1M01AcY3tJNG8jDAeujmT0FE1Ku2R\nJAkfPzXUXXBg1JzasnWpqBw8CJ+IUqEkPST5os+fSKxfFLF+UQ7bpaSHcvOd0/hk82kA+nqHeOeV\nY9z/vQV266gGeQQQ5GFbyzZnX5Xl55iEALslFxzJiAsjI25sbdGJ+Pp7EJcUSG2leaKuMK/RJsgA\nzl9jx+lrAYaH9Bw/WG3Znr0wftzAF0mSiPOPJs4/2u7xSxHsGUiwp/1SDoIgCIIgCIItZ77zfpW8\neoJtSsR1htSNzUxwgbaoCrzPmoMM3LW+ePeE0u/feiWHaVdwUyIS5iBo/ThZDM6TFTJNcWdIKJkP\ngNuQF0HNCbRHVl6VsToimRRIJgUmpQGFUUVc2RxcddZ7wqa4IvoDLu81HvTqRePdiWe/+bt9UEvC\nZQ8yUOpd8e613gP1BJ2boPXFG/DpQOvZg4fGD4Dgc0nUpZ24Iq/lrFi/SMeNBEEQBEEQBEEQBOEy\ncvgUW5IkdDqdZcVzd3f3hKufhYnp9UbKRwcD2NHTpaXwZCOzFsQ51afaTcWP7p/Dd57dQVffsMP2\nEUGePHxLJjPTxq+DOVptZQdnCyauiXkmv4mp077+ExtZs6LRanTs3GquEdvRNsA/Xs3lvkfmOwz6\naGvuo7LUOkk6mSwGl0PWzChLkMHZgmbW3ZqJyzjZKRw5caSWoUE9AC6uSoflOwRBEARBEISvXlJA\nHHF+UdT2NOI66Il/RxSuQ56YFAb6/dvo828FSeY/Fz9KcuDYLFUT+Ufhp+ypPjxhm1umrOWG1JV2\nj2kHdPz9j7loMX/HDAj24PuP3ktBezEvnfj7uH0OevUgB2uQ2s0lyIIrZ9Cm0FmyGdy8JIHbV6Sg\nUNjeo3YP9fLD7c9hYvzyER4u7jy/5ie4qSbOwtXTNcgbeccsPbUGNTjMSjA7agURXv4UnWgCIKIl\nlR/ecS8BweMHeL95+iMO1h6bsN/bp65nbfKyCdtc6M+7tnNKswfv7lCCWuLx7DcHSutcBkGSbQIM\nFl6XwJxly53qt0PbzX/s/OWEbbxcPXl+zY9xUaqoTuvg07eKAPDuDeZXc39KcLi53J5Ob+SzwzVs\nPViF3mjELf0YCvXghH0PlczGXxXMwxsyyUoOIv9IAwdOmQM5PLxc+d97n0KhtM2O91r+exypz5uw\n37syb2J14uIJ21RPGfVe+oK5I3Ij75/7aMJzssMz+d7c+yZsc6G2gQ5+tPvXE7bxd/dlZkTWpPoV\nBEEQBEEQBEEQhEvlMMjgvvvu44EHHqC9vZ3nnnuO3bt3873vfe9qjO0bqbq8nUGt3mG7ovxzTgcZ\nAPRrdE4FGAA8/2+L8fZ0vqb9iSO1DtuUF7fQ2z04prTA19G8pYloBnQc2WueoGqq7+H9N/LY9OAc\nlKrxSzjk7LdmMQgK8SJlinNBHJfLlKxwtn1chEFvQjdsoOxMCxkzJh/4oRs2cOyANYvBzPmxeNrJ\n5CAIgiAIgiBcWyRJ4l9m3s2Lb3yBf3OszTH/ziiG1APErlaQHZE56b6/lbWBouYy2gbb7R6P9Ynh\n1vR1uNkpTSbLMp9vKUarMd8HKZQSt907iwBvX5Z6zeNUSzHHGvLt9uvt5sXNN89i699KAPCQFfgb\nXelTSHzvtmmsnhs77nnfnnE7r5963+5xCYlHZt9DkKfjzGP7DlYij8QUuKhVtPUG4hJejaS0H2hg\naInlaLGeldmuqNxUGIYNGI0y+7dWct+/zh83aP++abeSX1/KgKnH7vFwdRQ3T1njsDSZLMv0Duho\n69bS2qXlTK4HQa7TCeuOsGnnqre9d0vLjmTFdelOLyrwdvPi3mkb+XuB/YfrCknBo3PuJcDDvOI/\nK9OTo6E1tLcOAFCY08yGTTM4VdbGXz4upKlDg3mKQoW+JhPX1DykcYI59E3xyP2BdGHiV68XsG5+\nHMo663XLzI7C18NnzHnfnn4bFZ01tGnsZ1GYGpLCjamrcHFwjbOyPDkeWUfLuT4AdKXezEjJ4FTz\nGbvt/d19eXDmnXi7eU3Y74W83bzYlHkzm4s+tXtcKSl4ZPY9qBQXn1VQEARBEARBEARBEC6GwyCD\nDRs2kJGRwfHjxzEajbz00kukpaVdjbF9I2kH7NcbHdvOuYABS/thx4EL5xlM46/msedcveP6krIM\nTQ0934ggA4AV16ehHdBxKrceMAeHfLL5FLd+KxtJMXbSra93kKJT1nSc85cl2m13JbmpXUjLCOfM\nyDgK8xovKsjg5LE6tBrz76lSpbjqGRkEQRAEQRCEi3cuXzcmwOA89ZAXmoNqdPMNTpVmG62/Hzrz\nZ2AIOoMysBlJYb6nkI1KjB2RtFdkoV1kws177Ll5R+uoKLFm/Fp5/RTCIn0B84Pox+Z9h2ifcLZX\n7KdfpwHMQQDZERncnXErH+1oogcZv5H0BVGSgjsemsv01JAJx7wuZTk+ai8+OPMFTf3W9P/x/tHc\nnbWBaWGOS+11d2oozLOWRghNCcJYqMNUMheXmDKUPtaSdKZhNYaWOIytsejRseVQNQFA4kiZhbqq\nTp782U58o3wJD/IkLNCD8EBPwoM8CQ3woKCkj/a8GeZ+Ay64xu2R1DalUjO9n5QYf5sggrYuLa3d\n5n/N+wbR6Y2WcXkDYRrbAIMLDSETmRE66ayFN6atwlftzYfFX9AyYA1ASQqI4+6sm8kItc4dSAqJ\n+csS2fpeAQBn8s9RrtVx6Kxtpj9fL1e+c8NKfILn88rx9+mRrVn1XEyerIpdjptvAu81VWAaubfd\nl1NLJtaA8Gmz7JcO8XP35dmVT/PW6Q851pCPcSR6xF2lZkXCQjZl3uQwwADMAT2LV6XwwZvmrAiV\nJW1857q7iPU7xK7Kg2j05iwMCknBrMgsvj39tosuA3dL+lr83X356OyXtI66ximBCdydteGKlR0U\nBEEQBEEQBEEQhIk4nFkyGAw0Njbi6WlOT1laWkppaSkbNmy44oP7JvLycW5FuJePelL9BnircVEp\n0BsmTtvpoVbh7TF2ddFEnJ1o+iZV0ZAkifW3ZaLV6ig7Y570Kj7dhIenK2tvyRhzTY4frMFkNE9w\neXm7kTnzqykdkTUryhJkUFXezkDf0KR+l/R6Izn7rBkZsufG4D3J30VBEARBEAThq6EbNnBod8WE\nbQZ6hziRU8fCSQaSvrO9lIF+BfRnoW9IQ+HeD0iYND5gUtGJgY/2VvLQzRk257W39LNra7FlOz45\niHlLbEtxKRVKZvguonYwmPy6CoyygTDvUNIjUnjp3SqKKjvwBEuQgVoG1yGDU+NeGDObBdGzqO1p\npH94gAB3PyJ9wpy+xzm8u9LyINvbV03GrCi2FDYha33Rlc5BctMiuWmRjSpkjQ9gm/msCwhCxndk\n7J79w5wuaeXChP2ShLnkg9ENfXUW+vqx1xjgJy/nYDTJNkEEjoTi+L26Ai9szqeoqpO18+OICx+b\nBWA8S+Lmsih2NrXdjQzoNAR4+BHlE263bUZ2JHu3lTLQP4zJJFNz1hr8IUmwZl4c375+Cl4j96yz\nYv+bc70t1He14evhSVpwPAqF+RrPmRrO7zfn09DaT9Co96jydME/2HPc8fq7+/LY/Ae5f8btNPQ2\noZCUJPhHo3aZ3H1PWkYYIWHetLX0A3B0bzV337+BW6eso6ipiiG9nilhMQR5Oc6W4ciy+PksiZtL\nbXcDAzotQR7+RPiEXXK/giAIgiAIgiAIgnCxHAYZPPnkkzQ1NZGYmGgzESOCDC5OQnIwnl6uaBxk\nNMiaaX/lxXjUbioWT49kb17DhO1WzIxGpRw/5b890XEB9Hafm7CNpJCIjL30yZNriUKpYOM92bzz\n1+PUVZnTaZ44UountxtLVqdY2g0P6ck/VmfZnrM4HpXqq0lXmZAchJe3GwP9w8gmmTOnzjFvqfMT\nyKeO1zPQb86ioVBKLFiedKWGKgiCIAiCIFxmZwqakI2Os5bt3VtBr6s5QFmnN1r/NZrQ603oDEb0\nehN6o3n/kM5IcfWo9PIGV0z9Y1dlb8+pITMpiBB/d4L83FG7KPj4nXwMI4HQ7h4u3Lxp+piMX7tz\n6/jT+6cxP8s3p0Ko7tTxSq019bwG0KtVuIwEFxzYWc6UzHCnsodJkkS8f7TDdhfq7tRSMOr+atHK\nZLKnhBHs7057t3mlujzsgTzsMebcpTMiMcnQ3KmhvW0A72EjCiRckIgCarH9nGQZjKM/u3Gu8eCw\nc8EVAK4qBSEBHvi0a8HBr4UCCZXexBdHavjiSA1T4gJYtyCOhVkRuLo4vrdRSAoSAmIctqtu6qNV\nkjkfAhACNAOxEb48elsWqbEBY86J9A0j0nfsA/WkaD/+8MRS3tp2ltqDNZb9NZph/v2PB3liUzZJ\nUX7jjsVX7YOv2vlgigtJColFq5L5+G1zqY/SohY+3VnG7sImapvNZRR8PJu4bm4st69MxkPtOEPC\nRMzX2H6WEkEQBEEQBEEQBEG42hwGGZSVlfHll19OOm2iYJ9SpWDZ2jS++LBw3DYhYd5kZE9+Jfzd\na9LIK2mlT2M/gCHAR81tKyefSnH2ojjL6vjxpGeFfyNXvKtclNz5wGzeevEoLU3miaL928tw93DF\nL8CdpoZeGmq7GB6Z7HRxVTJz/lc38aNQKsjIjuTYgWrAXDLB2SADo8HE0b2Vlu3ps6O/MeUvBEEQ\nBEEQ/hmU1XQ5bgRoNDpe+mj8+5GLNaw38YvXjlu24xQKgkclWvNJCuTo2VaC/Nwt/7V1aUYFGIxv\nQVY4m5Yk8tafjwLmDAlnC5qYehHlwZx1eE+FTRaDGXOjUSokHrk1i+deOz7umBdPj+TJb820uYfe\ns72UI7vMWSaCkYhLDaZdb6SlU0Nn79BFje98EEFIgAeh/qP/dSckwAM/LzckSeLX//Wl5X7FWSW1\nXZTUdvHXT86wak4Ma+fFEhHsdVHjBBjQ6nhrWwnbj9WikGEaEsqR/26YGsoD356NcpLB8ACuLkpW\nTAnj7YO1AMjIdAL6ln6e+uNB7lydyu0rkycdaO+s9GkRHNhRRme7uczH3h1lNgEkfRodH+6t4GRp\nK798dBFe7pcWaCAIgiAIgiAIgiAI1wqHQQaJiYm0t7cTEjJxvUvBeTPnx6LXGdjzRSlGo215g6hY\nf26/fxYuTqwWuVBogAe/+t4ifr85n8qGHptjU+ICeGJTNoG+k39oHB0XwPJ1aez7stTu8aBQL9bd\nkmH32DeB2t2Fux+ex+t/Okx3pxaALz8ustt2SmY47pMsR3G5Zc2KsgQZtDT10drcR6gT6U4L8hro\nG5nglBQSC1eILAaCIAiCIAhfKyrnHqQ6znVw6XzAJsCgHZkThU1Q2GTTTqmQHAYYKBUS379tOt6e\nrqRMDaW82Jxi/8CucqZMizCXGbjMerq0FJwYlcVgRZIlW9mc9DB+8uA8XtlSRHOnxtLGzVXJuvlx\nfHt9+pgg/WWrU6goarGk1ld3DfKLp5aiUikZ1hupbuzhP//vsMNr4eGm4mcPzyc00BpE4EhouA/1\nDgJQFAqJ29emszu/gbqRMQL0a3Vs2V/Jlv2VTE8OZt2COOZMDRvz0L6tW0tOUTP9Gh2Bfu4smhaB\nt4crsiyz72Qjr39WTM+AOWOaEWgHzucm6G/su6RfysKTjZafXXzV6HvNWSaMJpl/7Cgl92wL/74p\nm+hQ74t/kXEoRrIZfLr5NAABQBNwYdhITVMfb3xezPdvn37ZxyAIgiAIgvD/2bvz+Kjqe//j75lJ\nZrKHLJCNbCQkQAgkhH2XTVxRwX1t0aqty8+13qqtl1qKXm/b67W19lEvVq2KIgjusqgILkAg7EvY\nIRC27PtkZn5/BEICWc6ETEL09Xw8+ngw53znM985k36dOed9vl8AADpDqyGDqqoqTZ06VSkpKbJa\nz1w8ff311z3asR+74eOSlJ7VU5vW5angeJm8rV5KSYtQXGLoec0aERsRqD89OFa5B4u0fX+BTDKp\nX2KoklqYJtKIMZN6q0dkoL77ercO7Kk7QeUfYFXmsDiNvChZPj/yOzICAm265e7hevV/VqqimZki\nJCl321GVFFUqqFvnzQAQGR2siKggHT01RefGtYc0+Yp+LT7H4XBq5bIzsxgMGBSjkLDm1zEFAADA\nhad33x7a+u0+eavl3xP+MmlQoE2KDJDV5iWrl0Xe3mZ5e1lk9TLL28ssq7dF3l6ntnmb9f7yXB07\ntURAcwJ8veXr46XioiolNrhoXCWXDjRzFdnR2lX1U212HixUVp8IjZuSWh8yOHG0TFtzDrdpFrjW\nNJrFIMhHmcMaLwUwuG+EBqX20JY9J5V/sly+Pl7KSOnR7J3qFotZl80YoLkvrZIknTxerlXLdmnc\nxamyeVvUNzFMI9KjteqsEMbZLh2VqD4J5y4p0JwDewt09NSMbC3pNzBaV03srWkTkrVtX4E+/W6f\nVm04LHvtmaRITu5x5eQeV2iQTZOHxeviYQnqFmjVKws3ackP+xsFJP75wSZdPDxe+46UaNPuk41e\nKyrcXzdPSdWX72yQ0+lSaXGVNq/P08Ah7i9pUVNdq20bj9Q/vvyyfpri563/fXe9CkrqQg27Dhbp\nwT99pdsu7asrxyS1eyilf2aMPpi/USa7UyaZFCVpbxN/719mH9Idl6cxmwEAAAAA4Eeh1ZDB3Xff\n3RH9+EnyD7Bp+Nhe7V7XZDIpJS5EKXEh7Vo3tX+kUvtHqqa6VrV2h3z9rIbWQP2xCAnzV4+oQO3b\ndbLZNpUVdn375W5N7eSZHdKzeuroR1slSZvX5WniZX1bPJm2eV2eigrqZmkwmaTRk9xfVgMAAACd\nK7NPhF7381Z4RetT41tKaxRgKtdlMwYoNe3c9e7PFhLoo9mvrW52v9kkPfXzYeqXGKp5c9fUBwFM\nJil9TC8lmaQTRZV1/yuuUkFJVf1FfCNqT13sjuoZrNT+kdqxOV+StGLJTvXLaN/ZDIoKKpSz+sws\nBqMmJsuriZnmzGaT0pPDlZ4cbqhubGKoskbEK/u7/ZKklct2KS0jWuGn7rC/5ZI+ysk9rvJKe5PP\njwj101UGl0KTpE3Zh7R43oZzZs87W2i4v6ZcWRdKNplM6pcYpn6JYbrzyv5avvagPv1un46cODNj\nQ0FJteYt2an3lu5Ut0AfFZScu9xDTa1TH67c22ibt5dZ107orekTesvqbdGxHce1KbtuWb7vvtqt\nAYN7uh243745X/YaR119q0V9+kfKavPSS49N0CsLNunr9XWzHNhrnXp18RZ9vzlfD16foV2HivXJ\nt3u1+1CxLKc+xyvG9FJ6krHPstbh1KFjZdqTV6y9h4t1wF6reNXN7hCmutkMqs8+JqdmrBjQu7tb\n7xEAAAAAgAtRqyGDoUOHKjs7Wzt37tT06dO1YcMGDRkypCP6hguU1eYlq63VP50fncqKGu3f0/o6\ntxvWHtSUaWkembbVqPRBMVr28Va5XFJpSZX25p5QUmrTJ7OcTpdWLsutf5yWEaOw81hvFQAAAJ3D\nYjbp+msH6v/+tUY9T61331CpXPK3WmSuqbvoXFZSrXn/t0bpWTGaelX/Fpf9GpEepZlX9tf/fbhZ\nrrOyAV4Wk+6/LkNpvcK07vv99QEDSbrokj4aPfHcAKvD4VRhabX+MPcH7TpU3Op7i4s8s/zXuItT\n6kMGJ46Vacv6PKVn9Wy1hlFnz2Iw6KxZDM7HhEv7aPvmfJWXVsvhcOrj9zfptntHyGQyqWePQP3x\nl6P0l7fXa8/hxsdkQHK4/t8NgxQcYGv1NVwul77+YqdWfLGzfpvZbFJaRrTyDhSp4FRgwGqzaEBW\nrMZdnCL/JuoGB9h09fhkTRubpI27juvT7/bp+8359cfG6VKTAYOmZKZ01z3TByg6/MzvjBHjk+pD\nBsfyS7Vr+zH17hthqN5pG9eeCYP0HRBV/zs10M+qR2/J0oj0KP11/gaVVtTNRrdlz0nd+9zyc2bQ\n+G7TEX236Yhuu7Svrp2Y0mhfeaVd+46U1AcK9hwu1v4jpaptEN4wSYqUSzaZZJJJaapbFqJCdUuF\nnF7M8IW3sjUhK1ajM2KUFBN8XrMYAgAAAADQmVq9Uvyvf/1LS5cu1bFjxzR16lT99re/1YwZMzRz\n5syO6B9wwSgtqZbLwN1W1VW1qq6yt3iS1tMCg33UK6W7du84LknamH2w2ZDB1pzDOnn8zJ1JzGIA\nAADQdY0aEC3nrYP1z4UbZSmrkY9MckgqlktDsnrqnmnpWrtyr75ZeuZC+qbsPO3ZeUKXTU9Xn/So\nZmtfNS5JWX166LPv9mnngUKZTCal9QrT1BEJigj104ljZfp80Zb69vFJYRp5UXKTtSwWs8K7+WrG\nhBTNeX1Ni+8pI6W7osLPLOUVGR2sPumR2r7pzGwGaRnRMlvMBo9S886ZxWBC07MYtJWvn1UXT0vT\ngjfXSZL27z6pDWsOKWNo3VIBidHB+svD47Rjf6FyDxbJbDapf68wxUcFtVS2Xq3docXzNmjz+rz6\nbT6+3ppxW5Z6pXSXy+lSUWGFamud6hbiK29r6+Fxs9mkjJQeykjpoZPFlVqy+oA+/26fThQbCxhM\nGhKrB67PPOeCemR0sHqldNeenXW/Wb77ardbIYOS4krtyT1R/3hAE0GTUQOj1S8xVC+9t0Grt9b9\nvbS0RMfrn2yTvdYpk8lUFyjIK9bRUzO+tcSluhCP7VSwxyKTLJKskrrJpJNyaY9cKiyp1vtf7tL7\nX+5SVJi/RmdEa/TAGCVGBxE4AAAAAAB0Ka2eUVi4cKHeffddXXfddQoJCdH8+fN17bXXEjLAT46v\nwbUzzRbTBTHTw4CsnvUhg+2b8lVTXXtOv1xOl75pMItB3wFR6hEZ2KH9BAAAQPsakxGj4f2jtHZb\nvo6cKJePzUuD+0aoR4ifJGncxalKTY/U4ndylJ9XIkkqL63Wu6+tVf/MGE29Kk1+zdwxHxsRqLuu\nSj9nu6PWqYX/Xlc/db2Pr7euujGz1dm9RqRHadSAaK3aeLjJ/YF+Vt199bmvN25Kan3I4OTxcm1e\nn6cBg2NbfC0jGs5iEBBk06Dh7TeLwWlpGdHasOZg/Xf1JR9uUUq/HvXH3GQyqU9CqPokhLpVt7ys\nWvPmrtGhfYX120LC/HTjzKH1SzKYzCaFhPk3V6JVYcG+umFyqq6d0FuP/e83yj1Y1OpzggNszV5A\nHzE+qT5ksG/XSR0+WKTo2G6G+rJ5XV7d1X3VhawTmlm2IiTIR0/9fKiW/LBfL723Qa3Fxt/+Yoeh\n1zeZpOjwAPWKCZapyq6K7cebbRsmkyrl0pEG246cLNd7y3L13rJcxXT31+iBMRqdEaP4yMBzjpfL\n5dKWPSe1auNhlVXY1T3EVxdlxSo2gt9uAAAAAIDO0eqVULPZLKv1zB3ZNptNFkv73ckBdBWBwT7q\nmRDS6KRdU/r0j5SlHe6iOl+p/SNltVlUU+2QvcahbZuOaOBZJ163bz6i4/ml9Y/HMIsBAADAj4K3\nl1kj0qOb3R8ZHayZD47RquW7tGLJTjkddZdeN6/P097c47p0err6Dmj++Wf76vMdOtJg2YPLZgxQ\ncIhvq88zm0169JYsxS4J1Mer9qi0wi6p7gJuVp8I3Tmtv2KaWMorIjpIfQdEadvGusu2K5bkqn9m\nzHnNZlBUUKGcNWdmMRg9oXe7zmJwmslk0qXT0/Xy81+pttapygq7lny4VdNuzGxzzeP5pXr71dUq\nanDXfWxiqK6/Y3CzgZHzYbGYFRsRaChkENDCDG+9UsIVGR2k/MN1YZfvvtqt6bdmtVrT5XJpw9pD\n9Y/TB/VsMdBiMpmUEh/aasCgOTarRQlRQeoVHazEmGAlRgcpITJIPqdC3AvezNZmtRyoSfK1acr4\nRK3anK9dZx23vOPlmrd0p+Yt3amePQJOBQ6iFR8ZpNKKGv3xtTXatPtEo+e8tyxXU0ck6J6r0y+I\n358AAAAAgJ+WVkMGQ4cO1XPPPafKykotXbpU8+bN0/Dhwzuib8AFZ8yk3nr7n6ub3W+2mJqdEraj\nWW1e6jug7i4pSdq49lCjkIHL5dI3S87MYpDSL0KRMcEd3k8AAAB0DovFrLGTU9Snf6QWz8vR4YN1\nIYHyshq9969s9Rt4WJdcnS7/ZU6odwAAIABJREFUwJYvUu/bdUKrvtxV/3jg4J5KyzAeUPCymHXz\n1D6aMbG3dh0sUrXdodgegereSkhh3JQUbdt0RHJJBSfKtWldngYOaftsBquW76oPW3hqFoPTQsL8\nNXZKipZ/sl2StGHtIQ0YEqvEZu7Gb8nuHcc1//W1qq6qrd+WnhWjK64bKC8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CEe4ubSn4THUefs7c0eCYzrY+mOU3SVKu1qjD2tRfoRG19Y5RCUo8GHA7bGTSjvGvmbuR\npm74dpWzfW9GJztvSbnHC8/iWFHDqxeGuw7GMDdpt5S8WpmDzVk7GxwTYOeHmb4TJeVqjFp8k7IF\nOtS/MqONoMJDAXdAKZN2UcMvGTuRoclpcMwcv1vga+shKfdowWmcKG54Fc5RbkMQ4jpAUm5qZSZ+\ny2p47dGe9gGY1elmk/v42oWIrIX1hYisxZL6Qg2z2mXAQ4cOxb59+zB16lScOnUKvXv3rtk3cOBA\nvP/++9BoNNBqtUhKSqq1vz78Bajf0KEiXjm+A6pGlrDvG+SLqbOl3Zbi3Y8PA3kNXz2vgYg5Dw9H\nYBc3s3Mz04vw01eN3+/vwSdHwc3DwexcvVaPz945AK3G0OC4m2f0R9DQLmbnAsAv30Uj9XJBg2P6\nD/LDlNvrb5gx5diBJETuS2pwjKOTDSbPHACd1gjdtdsWaLV6aDUG6LTXbmdQfXsDbdW2/LxyGHSN\nL22ngwgdAAMA47X/3fhvhUIGL3d7+Ho7ws/DAacPJVftbMDNt/VH0BBpx3j1m7sh1zYcrHC3R2ho\nqKTcxphbX2JiYqxSi6yVa81s5lo/m7nWz7bmnKt11PrCn3f7zbVmNnNbJrsa60vbyLVmNnOtn83c\nuurLt+nihLcOfwq9se7nDjJBhifDH0S4v/S59Snvi1f3vIP8StOrM07tNR53DZ0lOXeEYThKDlXg\nbHaC6ef16IFF4x6GjULaCXsA0J8Bfr1gutHAxdYZiyc8Dj8naSfsAcAlwANvH/kcBmPdzzPkggyL\nRj6E4V0GS87tVdYbr+55B4XqYpP7Z/S9GXMG3S45d7h+GIoPfowLuYkm9/f36oWFYx+CSq6UnK09\nZcS2hN0m97nZuWDxhMfh7egpOdepixvejfzS5G0A5DI5nh71MIZ2kvaZDgD0KO2JJXveQbGm1OT+\nO/rfgtnBMyTnDtMPw+v7P8DF/GST+4N9+uLxMQ9CKfEYi6II9UkDdiTuN7nfw94Nz0z4Nzwd3KVO\nuUGt/drFmtnMtX42c62f3d5yr9fa9YU/7/aba81s5lo/uyXqC9XPak0GkyZNwpEjRzBv3jyIoog3\n33wTa9euRUBAACZOnIj58+fj7rvvhiiKePrpp2FjY2OtqfwjCIKA8PGBOPFXPGT1rGagUMowdkof\nODo1fqxFUURmfjnikgtwvqgCPSHCpoFVEjIgokxnNCu7Ws++3ujV3weJcdn1jhk0zB/+3aS+obFB\nxKTe2P3HhXpHePk4ImRkNyiVcknJE6b2xfefHoXRaHoBEKVKjrGTzTvG1xs5LhBnY66itKT+q0Im\n3dofAwZ3lpR7+VIe1n16tMExIoAxM4OQWlCBhJQCXL5aDJ3exEl+vQGpOaVATtUbZj8AXSCrN7cC\nImKzS1ESlwW1Vg+1xlD1X60Bas21/9barkdFpR5arR59INS7KoceIhKKymE0ipDJuHIHERERERFR\nSxjs1x+vTXgGP5/7A6ey4mq2D/DujdkDpmGAd+MXj5ji5eCBN25ajE3ntuFw6gloDToAQGcnX0zv\nMxETeoyyKFcpV+L5MQvx24W/sSvpEIrVJQAAJxtH3NRjNO7of4tFDQYAMC94Bjo5+WBr/E6klWRW\nPZ9MgXD/EMwNvhVeDtKuVq8W0ikYS8c/jV/Ob8fprP99phHs0xezB0xFP69eFuX6OHrhjUmLsfHs\nNkSmRkN3rVGki7Mfbu1zE8Z1D7coV6VQ4b8RT2DLhR3YnXQIJZqqW6G62DjhpsAxuL3fZIsaDADg\n3kF3oIuzH7Ym7MLVkqpVGJRyJUb5h2JO8HR42lt28nt4l8F4ddxT2By3vVYDyiDf/rhzwDT09uxh\nUW4nJx+8Mel5bDq7DZFpMTXNOAEunTGj7yREdBthUa6NQoWXxy3Clri/sCfpMEq15QCqmlluDhyD\nmf0mS24wAKo+R3xwyBwEuHTGHwm7kVFa9dmcSq7E6IBhmBN8K9ztXC2aMxERERH9M1ityUAmk2HZ\nsmW1tgUGBtb8e86cOZgzZ461nv4facr4nthxKBluZVoobjhBq7RRYO4DofDxczb5WL3BqLv6jwAA\nIABJREFUiOSrxbhwpQBxl/MRd7kARaWamv0JAHoBsLsh1wgR6RCRB0Ah8YSvIAiYde9QbPkxFhfP\n1200CA7pjGmzpXePA0D4uEBoNQYc2n0RN94QxK+LC+Y+OExygwEABPTwwJ33h+K3n05Cc8OqEQ6O\nKsy+PxRePk6Sc+0dbTD/0TBs+OYECvLKa+2Ty2WYNKM/gkOkrQgAAN0CPeAf6IG0pPx6xwwe5o+b\nx/zvTbROb8TljGJcTC1EQkohElILkXnDnAAgE4AcInyBOg0BZRBxCSLOH0sBjqVInncSRHQHIL8h\nV3stt1wnQqs3wFZltRJGREREREREN+jl0R0vjX0SxeoSFKtL4WTjCDc7lybnutu74tHh83H/kDuR\nW54PlUIFHwdPk7fIk0IlV2JO0HTc0f8WZJflQoQIXwcvKORNey8pCALGdg9DRLcRyC3Ph1qvgaeD\nO+yVdk3KBYA+noH479hFKFKXoERdCmcbR7g2wzH2tHfHwhH348Ghc5BXXgAbhQrezXGMFSrMC56B\n2f2nIqs8FwCa7RiP7zES47qHI6c8Dxq9Fl4OHrBT2jYpFwD6e/dCf++nUFRZjBJNGZxtneBqa/rz\nMim8HTzwRNgDeGjoXORVFMBWYQMvB48mH2NbhQ3uHjgTcwZMR1ZZLgRBgI+jFxQy6Z9rXU8QBNwU\nOBoTe4xCdnketM14jImIiIio4+MZug5EEAQo3exwukwDD4hwuHaCtgwiCjRauJzNwGO9qt5AVqh1\nSEgpRNzlqqaChNRCaLT1315AA+AcRLhChAsEyABUXmsu0ANQyGXoK3nFAUBlo8C8h4YjI60I505e\nRUWZFo7OthgY0hne9TREmEMQBIyb0gdDRgTgdHQaCvLKoVIp0CfIFz16eUJowhXwfYJ88dQrk3A2\nNh1XU4sgEwQEBLqj/6BOFjUuVPP0ccLji8fhYlw2khJyodcZ4OXrhEGh/nCQuDJCNUEQcPdDw/DL\nuhgkxefW2T9omD+m33D7DKVCht4Bbugd4Ibpo6u2FZdpkJhWhPiUAlxMKcT55Hxo9UakQ0QOAM9r\nK10YABRChOnFAc1XCKAEIjwgwv7a73EpRBSgauUFB1sFbJpwrImIiIiIiMhyLrbOcGmGk7I3slPa\nIsBV2gp+5lDI5Ojs7NvsuYIgWLRkvzlcbZ2b5cT3jeyVdtY5xnIFujj7NXtu9Ql1a3C1c2mWBo4b\n2avsEKCy0jF2sc4x9rXSMSYiIiKijotNBh3IX5GXkZhWBADIBZAL8Yb9V5CZV47SCi0uXy1GPSv+\n1+Ln4YAuPo44ce2WBkUAilD3gWOHdoaLo+W3vOjk74pO/s2/DJuLmx0iJlm2ZGNDbGwVCB3ZDaEj\nmzdXJpehb7Af+gY335tGG1sl7nkkDFdTqxo5Kiu0cHKxxcCQLmavuuDiaIPQfj4I7ecDADh4Mh2r\nf4gBAGgBZACAid8LexsFuvo5w1Ylh62Nouq/KgVsVHLYVX993XZblQLrdlzApbQiGADk1JM7PtS/\nyVcCEBEREREREREREREREZF0bDLoIERRxNZDyY2OO3Wx7tXs1WQC0KOzC/p390D/7h7o190d7s5V\nS6RtP5yMz349a/JxPf1d8chtlt3WgFpO5wBXdA5onkaO4QN84WCrQPkNt4y40aK5QzBqUCdJ2fZ2\nCrz4yWHoDaa7YJzsVbh9bE9JmURERERERERERERERETUPNhk0EHkFamRkVcu6TE2Kjn6dnW71lTg\njt4BbrC3VZocO210D3T1c8ZvB5IQE58NvUFEJ08HTA7rhqmjusFWxV+lfxJblQJ3Te6Lr34/V++Y\nPl3dEBYkfTnKvl3d8fJDI/DeT7EoLtPW2ufn4YAXHxgGb3d7yblERERERERERERERERE1HQ8M9xB\nGIxGs8feO6Uvhvb1RvdOLlDIZWY/LijQE0GBnhBFEQajKOmx1PHMGNMDOr0RP+6Ih95Q+/dvSG8v\nPDc/FHILf0dC+vrgm5dvxpEzGbiUXgSZICA40BMh/Xwgl/E2CURERERERERERERERESthU0GHYSn\nqx2c7FUordA2Om7OTb2bdD97QRCgkPNE7z+dIAiYPaEXJg0PwIHYdGTml8PBVomwID/09G/6bRlU\nSjnGh/hjfIh/M8yWiIiIiIiIiIiIiIiIiJoDmww6CIVchptHBGDzvksNjrslvFuTGgyIbuTiaIMZ\nEYGtPQ0iIiIiIiIiIiIiIiIiagFc774DmXNTb/To7FLv/n7d3DEjokcLzoiIiIiIiIiIiIiIiIiI\niDoSNhl0IPa2Srz52ChMG9Uddjbymu0OtgrMHBuIZf8Kh62Ki1cQEREREREREREREREREZFleMa5\ng3GwU+LROwbi/mn9kZpVAkEQEODrxOYCIiIiIiIiIiIiIiIiIiJqMp557qDsbBTo09W9tadBRERE\nREREREREREREREQdCG+XQERERERERERERERERERERGZhkwERERERERERERERERERERGZhU0GRERE\nREREREREREREREREZBY2GRAREREREREREREREREREZFZ2GRAREREREREREREREREREREZmGTARER\nEREREREREREREREREZmFTQZERERERERERERERERERERkFjYZEBERERERERERERERERERkVnYZEBE\nRERERERERERERERERERmEURRFFt7EuaIiYlp7SkQUTsREhIiaTzrCxGZi/WFiKyF9YWIrEVKfWFt\nISJz8bULEVkL6wsRWYvU+kINazdNBkRERERERERERERERERERNS6eLsEIiIiIiIiIiIiIiIiIiIi\nMgubDIiIiIiIiIiIiIiIiIiIiMgsbDIgIiIiIiIiIiIiIiIiIiIis7DJgIiIiIiIiIiIiIiIiIiI\niMzCJgMiIiIiIiIiIiIiIiIiIiIyC5sMiIiIiIiIiIiIiIiIiIiIyCxsMiAiIiIiIiIiIiIiIiIi\nIiKzsMmAiIiIiIiIiIiIiIiIiIiIzMImAyIiIiIiIiIiIiIiIiIiIjILmwyIiIiIiIiIiIiIiIiI\niIjILGwyICIiIiIiIiIiIiIiIiIiIrMoWnsC1Hw+/PBDjBw5EqGhofWO2bdvH65cuYIHH3ywWZ+7\nT58+SEhIqPn6tddeQ2JiIj7//HMYDAa89tpruHjxIgDA29sbr7zyCrp161YrIz09HS+++CLWrVtX\na/uWLVuwatUqbN++HZ6enjVj77vvPuzduxdbtmzBypUr4efnV+txy5Ytg4eHB6ZMmYLAwEAAgNFo\nRHl5OWbOnIlFixYBAMrKyvDOO+/gxIkTkMvlcHZ2xgsvvIABAwZYdCwyMjKwbNkyXL16FaIoIjAw\nEK+++io8PDza3FyJLMV60zb+Pyyl3qjVagwfPhxLliyBQqFAnz590LdvXwiCAIPBAAcHB7z22mvo\n06ePRXMhsgRrSduoJfPnz0dWVhbs7e1rts2ZMwdjx45t8lz42oXaCtabtllvysrK4O/vj7fffhue\nnp711qN77rmH9YTaHNaVtlNX4uLicPToUahUqprtt912G5ydnbFu3Tp89NFH2LBhQ833o9VqoVAo\nsHTpUoSEhAAAzpw5g7fffhvZ2dlQKBQYOHAgnnvuObi7u1s0LyIpWE9av55s3boVO3bswJo1awAA\nFy9exK233orVq1djxowZAIB33nkHKpUKnTt3rnfegwYN4nskahdYd1q/7hw6dAhvv/02ACA1NRWe\nnp6wt7dHly5d8Mknn9R8dgsAoiiitLQUY8aMwZIlSyCXy2vtrzZu3Dg8/fTTkudCZDGROox7771X\nPHbsWINjPvzwQ/HDDz9s9ufu3bt3zb9ff/118b777hMrKipEURTFV199Vfzss89q9m/btk2cOXNm\nnYy0tDTx3nvvrbN98+bN4oABA8THH3+81tjx48fX7H/++edNzuv6cdWysrLEQYMGiZcuXRINBoM4\nb9488b333hN1Op0oiqJ49OhRMTw8XCwoKDD3269lwYIF4rZt22q+/uyzz8SFCxe2ybkSWYr1pq62\nXm/0er04e/Zs8aeffhJFsfZxFEVR/P7778U5c+ZYNA8iS7GW1NUataS+n0NT58LXLtSWsN7U1Rbq\njcFgEBcuXCi+9dZbJvdfP471hNoa1pW6WquuREREiHv27KnZlpSUJIaFhdV8f6Z+DmvXrhVnz54t\niqIoJiYmiqNGjRKPHDkiimJVzfn888/FadOmiWq12qJ5EUnBelJXS9eT7OxsMSwsrObrr7/+Wnzo\noYfE5557rmbbvHnzxOjo6AbnzfdI1F6w7tTVmudnTP08bvzstrS0VBw7dqy4f/9+k/uJWgNXMmiH\nsrKy8Oyzz6KiogIymQwvv/wyrly5gnPnzuHll1/Gxx9/jOLiYrz33ntQq9UoKSnBiy++iG7dumHD\nhg0AgE6dOmHKlClYtmwZEhMTYTAY8Mgjj2D69Om1nuvHH3/Epk2bam0bMWIEXnrpJZNzW7lyJZKT\nk/H555/D1tYWAJCXlwcPDw8YjUbIZDJMnTq11pUp5pg8eTISEhKwbds23HrrrZIee6Pc3FyIoggH\nBwdERUUhMzMTixYtgkxWdfeQsLAwrFixAkajsdbjdu3ahY8//rjWtu7du+P999+vtS0vLw+VlZU1\nX99zzz04e/Zsi86VqLmw3nSceiOXyxEaGorExEST+0eMGIF3331X8vdIZA7WkrZdS6w1F752odbA\netO+6k1FRQUKCwsxcODABsexnlBrYl1p+3Xl5ptvxt9//40JEyYAAP78809MnjwZSUlJJudkNBqR\nlZUFFxcXAMBXX32FuXPnYuTIkQAAmUyGf/3rX9i5cyf++usvzJw5s0nHgKga60nbrSfe3t5wc3PD\n5cuX0b17dxw+fBhPPfUUFi1aBFEUodVqceXKFQwaNAgpKSn1zpHvkaitYd1pu3VHqsLCQlRWVsLV\n1dXiDKLmxiaDduiXX37BuHHj8PDDD+PgwYOIiYnBggULsHnzZjzxxBPo06cPFi1ahOXLlyMwMBBH\njx7Fm2++iW3btmHevHkAgFmzZuHtt9/GgAEDsGrVKpSVlWHevHkYNGgQ/P39a57rnnvuwT333GPW\nvFavXo21a9fiu+++q/mjAACPPfYYFi5ciPXr1yMsLAyjRo2qWWbKXEqlEitWrMCjjz6K8PDwOvv3\n7t2L2267reZrlUqFn3/+GQCQk5OD2267DRqNBoWFhQgODsbHH38MX19fbN++HX379q35o1Bt7Nix\ndZ5j0qRJmDRpUqNzfeaZZ/Dcc8/ho48+Qnh4OCIiIjBlypQWnStRc2G9ad/15nqFhYU4fPgw/vWv\nf9XZJ4oitm/fjiFDhjT6nESWYC1p27UEAF5++eWaN+4ODg5Yv359k+cSFxfH1y7U4lhv2ke9sbOz\nQ0FBAVxcXDB16lQ88MADtfbfWI9YT6g1sa60/boSERGBV199FTqdDkqlEvv378eTTz5Zq8lgw4YN\n2L17N0pKSmA0GjFu3Di8+eabAICzZ8/illtuqZM7bNgwnDt3jk0G1GxYT9p2PQkLC0NsbCz8/PyQ\nnp6OgQMHokuXLoiPj0dpaSmGDBkChULR4Lwbe83C1zTU0lh32nbdacxtt90GvV6P/Px8BAYG4uWX\nX8agQYNq7b/es88+izFjxjT5eYnMxSaDdig8PBxPPvkkLly4gLFjx+Lee++tM2b16tXYt28fduzY\ngdOnT6O8vLzOmMjISKjVamzevBlA1VUkiYmJtf4wSOk+u3TpElatWoWXXnoJv//+O5ycnAAAQUFB\n2LNnD2JjYxEZGYlvvvkGGzZswMaNG2temJkjODgYs2bNwpIlS/Diiy/W2jdhwgSsXLnS5OO8vb3x\n+++/w2g0YuXKlUhKSsKoUaMAVHWn29jYmPX85nafRURE4ODBg4iKisLRo0exevVqbN++veaeXi0x\nV6LmwnrTvutN9YtmURQhiiImTZpUq8u4+oWoVqtFYGAgli1bZtb8iKRiLWnbtQQAli9fjhEjRjTr\nXPjahVoD6037qTexsbFYtGgRJk2aVOs+6qbqEesJtSbWlbZfV1QqFUJCQhAZGQk/Pz/4+/vXOmEB\nAPPmzcOTTz6J3Nxc3H///Rg8eDC8vb0BAIIgQK/X18nV6XRmzZXIXKwnbbuehIeHY//+/fDy8qq5\nT/3IkSMRFRWFioqKmuduaN58j0RtDetO2647jfn9998BAN9++y22bNmCiRMnmtxP1FrYZNAOhYSE\nYPv27di/fz/+/PNP/Prrr1i7dm2tMXfffTdGjBiBESNGIDw8HM8++2ydHKPRiNWrV2PAgAEAqpai\nqV4qrpqU7rOPPvoIKpUKhw4dwpIlS/Duu+9CFEUsXboUL730EoYPH47hw4dj4cKFmDx5MuLi4hpd\nFvNGTzzxBO644w788ccfkh4HVP0RWLx4MWbOnImvv/4ajzzyCIKCgrB+/XqIoghBEGrGvvvuuxg5\nciTCwsJqtpnTfVZUVIQ1a9bgpZdeQkREBCIiIvD4449j9OjRKCgoaLG5EjUX1pv2XW8aetEM8IUo\ntRzWkrZbS6w5F752odbAetN+6s3QoUMxf/58/N///R9+/fXXBj80ZD2h1sS60j7qypQpU/D333/D\nx8cHU6dOrXecl5cXli9fjgULFiA0NBT+/v4YOHAgTp06VeeD+5MnT2L+/PkSvmuihrGetO16Mnz4\ncHz44YdwdHTE6NGjAQCjR4/Gt99+i+LiYrzyyiuNZvA9ErU1rDttu+6Y64EHHsChQ4fw1ltvYenS\npc2WS9RUssaHUFvz1ltvYevWrbj99tvx6quvIi4uDkDV/bYNBgOKiopw5coV/Oc//0FERAT27NkD\ng8FQM6a6OzssLAw//fQTgKplYGbMmIHMzEyL51V99cmSJUsQGxuLzZs3QxAEJCUl4euvv665L016\nejr0ej0CAgIseo4VK1bgs88+s2iOCoUCixcvxpo1a5Cbm4vQ0FB4eHjg448/rjlGhw4dwpYtW9Cz\nZ0/J+U5OTti7dy9+++23mm2XLl2Ch4dHnT+6rT1XInOw3vwz6g2RtbGWtN1aYs258LULtQbWm/ZV\nbx588EGUl5dj48aNDY5jPaHWxLrSPupKREQEoqKicPDgQURERDQ4dujQoRg3bhxWr14NAPj3v/+N\nzZs348iRIwCqbie3Zs0aqNVqk7dRILIU60nbricuLi6wtbXFoUOHapZYDwoKQnJyMnJyctCtW7dG\nM/geidoa1p22XXekeOGFF/DLL78gPj7e6s9FZC6uZNAOVV/tsWXLFsjlcqxatQoAMGbMGCxZsgSr\nVq3C7NmzMW3aNCgUCoSFhUGtVqOiogLDhg3D888/D09PTzzxxBNYunQppk+fDoPBgOeee86iYn0j\nZ2dnrFixAgsXLsTQoUPx7rvvYsWKFZg4cSLs7Ozg5OSEd955B66urhblBwcH4/7778e2bdtqtt14\nHx2g6gOr6qWtrhcREYEhQ4bggw8+wPLly7FmzRqsWLEC06dPh0KhgJubG7744gt4enpKnptcLscX\nX3yBlStX4oMPPoCtrS28vb3x2WefQS6Xt6m5EpmD9aZ91xuitoK1pO3WEqmkzoWvXailsd60r3qj\nUqnw1FNP4c0332zwXquCILCeUKthXWkfdUWlUmHo0KEAYNZSxs888wymTp2K6OhohIaG4uuvv8bb\nb7+N5cuXw2AwICQkBOvWreOy5tSsWE/afj0ZPnw4jh07Bjc3NwBVVzMHBATUuZijvnnPnDmT75Go\nTWHdaft1x1y9evXCzJkzsWrVqprVKG78Prp27YoPP/zQ6nMhqiaIoii29iSIgKqutBdffBHr1q1r\n7akQUQfHekNEzYG1hIhaCusNETU31hUiai6sJ0TU0lh3iNoG3i6BiIiIiIiIiIiIiIiIiIiIzMKV\nDIiIiIiIiIiIiIiIiIiIiMgsXMmAiIiIiIiIiIiIiIiIiIiIzMImAyIiIiIiIiIiIiIiIiIiIjIL\nmwyIiIiIiIiIiIiIiIiIiIjILO2mySAmJsbssefPn7fKHKyVa81s5lo/m7nWz7bmnAFp9YWISIqO\n/PqFf0/ab641s5nbMtkA60tbyrVmNnOtn83c2qS+N2pv32d7y7VmNnOtm2vN7PaWW60tvHaxZjZz\nrZ/NXOtnt7fcam2hvvDn3X5zrZnNXOtnW7u+UMPaTZOBFGq1ul3lWjObudbPZq71s605ZyKitqK9\n1VD+PWm/udbMZm7LZEvV3o5fe8u1ZjZzrZ/N3KZpb99ne8u1ZjZzrZtrzez2lmsJ/lzab641s5lr\n/ez2lmuJ9vg9trc5t7dca2Yz1/rZbam+/BN1yCYDIiIiIiIiIiIiIiIiIiIian5sMiAiIiIiIiIi\nIiIiIiIiIiKzsMmAiIiIiIiIiIiIiIiIiIiIzMImAyIiIiIiIiIiIiIiIiIiIjILmwyIiIiIiIiI\niIiIiIiIiIjILGwyICIiIiIiIiIiIiIiIiIiIrOwyYCIiIiIiIiIiIiIiIiIiIjMwiYDIiIiIiIi\nIiIiIiIiIiIiMouitSdARNTWJWecxsXEHVCqCyBAhEbljG7dJ6Bft5FNys1OS0DCoR+gkpVCUAB6\nNSB36YnQyY9AobKxOLeivAgnd3wBQZMBuQow6gC9zAPBNy2Aq0cni3MNBgNO7vkO6uwzUNoBogHQ\n6GzRfcSdCOgVYnEuAMTH/I2c87tgY6sHBECrlsGt51gEhd/WpNzMlDgkHv4RKkU5BPm1Y+zWG6GT\nFjTpGJeVFODUzi8g02b97xjLvTBo0gI4u/lanGswGBCz+xtoc87XHGOtzg49Rs5Flx6DLc4FgAsn\n/kLuhd2wsTMAALSVcrj3GYcBI25tUm7GlXNIPLIeNoqKmmOscO+LkJsfgkKhsji3tDgXp3d+Cbku\nBzIVYNQCBqU3Bt38CJxcvCzO1eu1iN21Ftr8C1DaXvs91tuj18i70Kl7sMW5RERERERERERERET/\nFFzJgIioAQdiv0f+2XXw0uTBVTDCRRDhrStGxcVfsfPI+zAajRblnj28GaknP4eDpxpKdyUUzkrY\neiuhtEnB8Z+fR1lJgUW52WkJOPPHUtg65cLGsypX5aGEvVsJLu5fjctxkRbl6jSViFz/LARcgJ1P\nVa7STQlHbwNyLq7H8b+/tCgXAA5tXI6yvF1w8BWgcFVC4aKEvY8cmtLDOPjjfy3OPXVwI66e+QoO\nXloo3a47xsrLiNr0PCpKCi3KzbhyDud3LIOdc37tY+xahPg9q5CScMKiXI26AkfXPwu5cLHWMXbw\n1iPrwjpE7/rWolwAOLThNVQU7oWDrwwKl2vH2FcGdfFBHFz/isW5J/f9gIxza+Hopat1jBWKJBzb\nsBiVFSUW5aYnn0Lczjdg51IIVfUx9lTCzqUQcbveQNqlkxblVlaU4NhPiyGXX4Kd93W/x146ZJz/\nFrF7f7Aol4iIiIiIiIiIiIjon8SqTQanT5/G/Pnz62zfu3cvZs2ahblz52LTpk3WnAIRkcXOJO2H\nY95ZyATB5H6P8quIPCu9huVlXYa64AhktnKT+228lDj52xuScw0GA5IOfQqlm9LkfrmTAjlxm6BR\nV0jOjvzlddj7mF78RlDKIDPEW9TAELP7e9i7FUOo5xg7eGlxdOsHknOz0xKgL46CYGP6GNt6KxFt\n4TFOOfoVFK71HGNnBTJP/wi9ViM5+9gvy2DXwDGG9ixSEqMl5x7/+0vYu5fVu9/BU41jf6yRnJtx\n5RwM5SchqEy/lLDzVuLE5uWSc/V6LdKPfwuFi+ljrHBW4mrMd9DrtZKzj29eDjsf07mCSgZjxUlc\nTT4tOZeIiIiIiIiIiIiI6J/EardL+PLLL7F161bY2dnV2q7T6bBixQr88ssvsLOzw1133YXx48fD\ny8vypY+JiKwh4/JeNFaZtFkxKOg+DjKZ+T1b5/Z/Ayd30ye/q9l5CUg4vRcePt3Nzk05ewA2nqZP\noFZTOCtxYvsa9B01y+zcirJCODirAdQ/Z0EhQ+qJLXBy9zM7FwDU2TGw9Wp4SX2hMhnZVy9BLm/4\nmF0vfv9aOPo0PN7eC7h05gBcvQPMzk0+tQsqj4aPsdJViRN/fYpeI8y/1UNFST4cXHVoqPdPUMpw\n5ehGODh5mJ0LAPr8c5B7NnyMxbIE5GVdlpR78eD3cPJt+Pfe3t2A5LhIOEv4vUiM/hMq90aOsZsS\nJ3Z8iV6hU83OLS3IhoO7AQ0eY5UMl45uROceg8zOJSIiIiIiIiIiIiL6p7Fak0FAQAA++ugjLF68\nuNb2pKQkBAQEwMXFBQAQEhKC6Oho3HLLLdaaChGRZDq9Fh6GCqCeK+yruQjA5WPvSMp2cm98jCAX\nUJb9F8qyJUWbxc4pGylnpF25Xt+KANdz9BMk5zbWYAAASjcV0s9/LinX0afxMYJcQHHWHyjOMj9X\nbmYvicruqvRjXM+KANdz9IXkXJtGGgwAQOWukpzr5Nv4GEEpQ2H6ryhMNz9X1fh0r427Iv0YKxs/\nxjYK6St9EBERERERERERERH9kwiiKIrWCk9PT8czzzxT65YI0dHR+OGHH/D+++8DAD744AN06tQJ\nd955Z4NZMTEx1pomEXUgISEhkh9jqr5oDZVQFf3eHFMionZEX6KDonvdWz0BzVdfiIhuxPpCRNYi\ntb6wthCROfjahYishfWFiKzFkvpCDbPaSgb1cXR0RHl5ec3X5eXlcHJyMuux5v4CxMTEWOWXxVq5\n1sxmrvWzmWv9bGvOudqN+UajEYd3/Q6HhhcyIKIORq8GRjRzvemor1/496T95lozm7ktk12N9aVt\n5Fozm7nWz2ZuXVLy29v32d5yrZnNXOvmWjO7veVer7Vfu1gzm7nWz2au9bPbW+71Wru+8OfdfnOt\nmc1c62e3RH2h+rV4k0FgYCBSUlJQVFQEe3t7REdHY8GCBS09DSKiBslkMmicAuA2eCbpAAAgAElE\nQVRQlmpyvyiKEAQBOTJHhIc9Jik7PvJ3KHGxwTGGUj0Chj8KhdLMteNRdc/5/Ms/N7gkvCiKsHUZ\nC99uQWbnAkDivveh8mh4LuWFLug/9h6zMzVaA2L/+AideprOrT7G+SlqdB+7EG7ONubPN2orVHYN\nr9GvL9Gha9jjUCiUZucW5aWjKPU3CIoGjrFRhIPXJHh17m12rsFgwOXDH0Hp1vAxrij1QL9Rc83O\nBYDzf38CRz+h5njWmuu1bWWZBgyY/KSk3ITILbB1bPheE/piHbqNfAJyeeO326hWkH0FJRnbIcjr\n7/ARDSKcOk2Fh093s3MNBgOuRH4MhUvDP2+FWx+zM4mIiIiIiIiIiIiI/olarMlg27ZtqKiowNy5\nc/HCCy9gwYIFEEURs2bNgo+PGTfPJiJqYSMG3Y0Th1fBVah7VxlBEFAhAoOHzIero7ek3GETH0Lk\n+mdh72O6BItGEYJTEHz9pZ3s9PTtjqvn98LOpbDeMeW5KoROvlVSLgBkdZ8Idf6BehsYNLk6jJj1\nDGxs7c3OLCnXYlPqEDzhdcrkiV9BEGAo1+OXy8F49bbO8PQ2b9UbAHCb/gSOrn8WdvUdY4MIhetQ\n+HYxvxEAqDrGh87vh71bSb1jKvJtETplsqRcAMjpHAFNSWS9DQzqHB3C5zwNhcr8ZgsA6D/p37h8\ndA3kTnWPhSAIMJTp0XvcI/D0Nf+EPQC4zngSxzYshp236ZP2okGEymsEfDr3lJTr6dsdBy8cgIN7\neb1jKoocEHrLBEm5AJDpHQZ9RXS9DQzqHB1GzGPjIxERERERERERERFRQ+q/FLMZdOnSBZs2bQIA\n3HrrrZg7t+rqywkTJmDz5s3YsmUL7rnH/KteiYhakrODBwaHP41cuQNEsXajQZ6gQrchD8PPo4fk\nXLlcjuF3voGybDlEnbHWPn2JDpAPwLCbLTvROfrOl6Au94GxXF9ru1FtQEWBI0bf9bpFuUFhM2Dr\nNhq6Ql2t7aJBRHmWEQNvfVVSgwEAONopAVtffHEyCCVXNXWOcXmWBt/F9EKB0Q+ernaSsuVyOUJn\nvY7ybFndY1ysg6AaiJCb7pOUWW3M3FegLvWEseKGY1xpQEWRM0bNe82i3ODRs6B0Coeu6IZjrDei\nPFvEkBlLJDcYAIBXp17oMvQhqHN0dfapc3TwG3gf/Lr2l5yrUKgQcscylGdXzfF6+iId5HYhGDLu\nLsm5ABAxbykqi91hrDTU2m6sNKCy2A0Rd1l2jAePnQeFXQj0Jo8xMPSOZVAozF89hIiIiIiIiIiI\niIjon6jFb5dARNSeeDj7YcrEpcjMT8blqzEQRSM6+w5AiI+02w3cyMbWHmPnr0RR7lXER/0Og7Yc\nDp4BGDTh9iaf5Bx1+7PQqCtw9tAmaEpzobRzQdDEObB3cG1SbtDI22EwzMCFE9tRnHERMqUNAodM\nhrfE1QCqyWQCbh7RFZt2a/DuuYkISLqKAS7pkAGIL/VFUnlXAEAnLzsoFeYvt1/N1t4REfNXoSA3\nDRejtsKgrYSjVzcMnni7pOX7TRk16/l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s5SXNKM5v1C8vWRMxrs/zJdVt+MPrp/D3D84OSjCwtZLj0U2xePn/LkVUuNuo5goOc0VM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\nXX26ttDOlGyVlFod+0OC/PTwhL7qH92yLlIGAAA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emNvb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\nMrjUosOpokacK2uB2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eH3ffLG9WZs4no/ti+uL2if14oM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\nXvJxxR07aZCiY9svILBYzJp1yYjjTfu0ccMFw92PdxVUaMWmAp+e32q1K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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1778bdc88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fuels = ['NG', 'HYC', 'COW', 'GEO', 'WND', 'SUN']\n",
    "\n",
    "sns.factorplot(x='month', y='% generation', hue='fuel category', col='NERC',\n",
    "               row='state',\n",
    "               data=fuel_by_nerc_month.loc[(fuel_by_nerc_month['fuel category'].isin(fuels)) &\n",
    "                                           (fuel_by_nerc_month['NERC'] != '-')],\n",
    "               n_boot=1)\n",
    "path = os.path.join('Figures', 'SI', 'Annual facility seasonal gen variation.pdf')\n",
    "# plt.savefig(path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-09T14:57:45.549559Z",
     "start_time": "2017-08-09T14:57:45.511552Z"
    }
   },
   "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>% generation</th>\n",
       "      <th>% total fuel</th>\n",
       "      <th>% elec fuel</th>\n",
       "      <th>fuel category</th>\n",
       "      <th>month</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2265</th>\n",
       "      <td>SPP</td>\n",
       "      <td>4.484362</td>\n",
       "      <td>0.538867</td>\n",
       "      <td>0.562231</td>\n",
       "      <td>WWW</td>\n",
       "      <td>1</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2266</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-3.484362</td>\n",
       "      <td>0.461133</td>\n",
       "      <td>0.437769</td>\n",
       "      <td>WWW</td>\n",
       "      <td>1</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2267</th>\n",
       "      <td>SPP</td>\n",
       "      <td>4.132260</td>\n",
       "      <td>0.473945</td>\n",
       "      <td>0.564103</td>\n",
       "      <td>WWW</td>\n",
       "      <td>2</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2268</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-3.132260</td>\n",
       "      <td>0.526055</td>\n",
       "      <td>0.435897</td>\n",
       "      <td>WWW</td>\n",
       "      <td>2</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2269</th>\n",
       "      <td>SPP</td>\n",
       "      <td>5.280268</td>\n",
       "      <td>0.519683</td>\n",
       "      <td>0.550018</td>\n",
       "      <td>WWW</td>\n",
       "      <td>3</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2270</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-4.280268</td>\n",
       "      <td>0.480317</td>\n",
       "      <td>0.449982</td>\n",
       "      <td>WWW</td>\n",
       "      <td>3</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2271</th>\n",
       "      <td>SPP</td>\n",
       "      <td>2.961378</td>\n",
       "      <td>0.697669</td>\n",
       "      <td>0.725501</td>\n",
       "      <td>WWW</td>\n",
       "      <td>4</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2272</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-1.961378</td>\n",
       "      <td>0.302331</td>\n",
       "      <td>0.274499</td>\n",
       "      <td>WWW</td>\n",
       "      <td>4</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2273</th>\n",
       "      <td>SPP</td>\n",
       "      <td>-5.065479</td>\n",
       "      <td>0.586754</td>\n",
       "      <td>0.959101</td>\n",
       "      <td>WWW</td>\n",
       "      <td>5</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2274</th>\n",
       "      <td>TRE</td>\n",
       "      <td>6.065479</td>\n",
       "      <td>0.413246</td>\n",
       "      <td>0.040899</td>\n",
       "      <td>WWW</td>\n",
       "      <td>5</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2275</th>\n",
       "      <td>SPP</td>\n",
       "      <td>3.538606</td>\n",
       "      <td>0.417402</td>\n",
       "      <td>0.656162</td>\n",
       "      <td>WWW</td>\n",
       "      <td>6</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2276</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-2.538606</td>\n",
       "      <td>0.582598</td>\n",
       "      <td>0.343838</td>\n",
       "      <td>WWW</td>\n",
       "      <td>6</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2277</th>\n",
       "      <td>SPP</td>\n",
       "      <td>5.823124</td>\n",
       "      <td>0.481409</td>\n",
       "      <td>0.577494</td>\n",
       "      <td>WWW</td>\n",
       "      <td>7</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2278</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-4.823124</td>\n",
       "      <td>0.518591</td>\n",
       "      <td>0.422506</td>\n",
       "      <td>WWW</td>\n",
       "      <td>7</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2279</th>\n",
       "      <td>SPP</td>\n",
       "      <td>2.823845</td>\n",
       "      <td>0.542277</td>\n",
       "      <td>0.564253</td>\n",
       "      <td>WWW</td>\n",
       "      <td>8</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2280</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-1.823845</td>\n",
       "      <td>0.457723</td>\n",
       "      <td>0.435747</td>\n",
       "      <td>WWW</td>\n",
       "      <td>8</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2281</th>\n",
       "      <td>SPP</td>\n",
       "      <td>4.395278</td>\n",
       "      <td>0.587122</td>\n",
       "      <td>0.573002</td>\n",
       "      <td>WWW</td>\n",
       "      <td>9</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2282</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-3.395278</td>\n",
       "      <td>0.412878</td>\n",
       "      <td>0.426998</td>\n",
       "      <td>WWW</td>\n",
       "      <td>9</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2283</th>\n",
       "      <td>SPP</td>\n",
       "      <td>-12.152599</td>\n",
       "      <td>0.618150</td>\n",
       "      <td>0.499494</td>\n",
       "      <td>WWW</td>\n",
       "      <td>10</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2284</th>\n",
       "      <td>TRE</td>\n",
       "      <td>13.152599</td>\n",
       "      <td>0.381850</td>\n",
       "      <td>0.500506</td>\n",
       "      <td>WWW</td>\n",
       "      <td>10</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2285</th>\n",
       "      <td>SPP</td>\n",
       "      <td>4.745620</td>\n",
       "      <td>0.507283</td>\n",
       "      <td>0.534694</td>\n",
       "      <td>WWW</td>\n",
       "      <td>11</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2286</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-3.745620</td>\n",
       "      <td>0.492717</td>\n",
       "      <td>0.465306</td>\n",
       "      <td>WWW</td>\n",
       "      <td>11</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2287</th>\n",
       "      <td>SPP</td>\n",
       "      <td>3.483764</td>\n",
       "      <td>0.473996</td>\n",
       "      <td>0.558542</td>\n",
       "      <td>WWW</td>\n",
       "      <td>12</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2288</th>\n",
       "      <td>TRE</td>\n",
       "      <td>-2.483764</td>\n",
       "      <td>0.526004</td>\n",
       "      <td>0.441458</td>\n",
       "      <td>WWW</td>\n",
       "      <td>12</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     NERC  % generation  % total fuel  % elec fuel fuel category  month state\n",
       "2265  SPP      4.484362      0.538867     0.562231           WWW      1    TX\n",
       "2266  TRE     -3.484362      0.461133     0.437769           WWW      1    TX\n",
       "2267  SPP      4.132260      0.473945     0.564103           WWW      2    TX\n",
       "2268  TRE     -3.132260      0.526055     0.435897           WWW      2    TX\n",
       "2269  SPP      5.280268      0.519683     0.550018           WWW      3    TX\n",
       "2270  TRE     -4.280268      0.480317     0.449982           WWW      3    TX\n",
       "2271  SPP      2.961378      0.697669     0.725501           WWW      4    TX\n",
       "2272  TRE     -1.961378      0.302331     0.274499           WWW      4    TX\n",
       "2273  SPP     -5.065479      0.586754     0.959101           WWW      5    TX\n",
       "2274  TRE      6.065479      0.413246     0.040899           WWW      5    TX\n",
       "2275  SPP      3.538606      0.417402     0.656162           WWW      6    TX\n",
       "2276  TRE     -2.538606      0.582598     0.343838           WWW      6    TX\n",
       "2277  SPP      5.823124      0.481409     0.577494           WWW      7    TX\n",
       "2278  TRE     -4.823124      0.518591     0.422506           WWW      7    TX\n",
       "2279  SPP      2.823845      0.542277     0.564253           WWW      8    TX\n",
       "2280  TRE     -1.823845      0.457723     0.435747           WWW      8    TX\n",
       "2281  SPP      4.395278      0.587122     0.573002           WWW      9    TX\n",
       "2282  TRE     -3.395278      0.412878     0.426998           WWW      9    TX\n",
       "2283  SPP    -12.152599      0.618150     0.499494           WWW     10    TX\n",
       "2284  TRE     13.152599      0.381850     0.500506           WWW     10    TX\n",
       "2285  SPP      4.745620      0.507283     0.534694           WWW     11    TX\n",
       "2286  TRE     -3.745620      0.492717     0.465306           WWW     11    TX\n",
       "2287  SPP      3.483764      0.473996     0.558542           WWW     12    TX\n",
       "2288  TRE     -2.483764      0.526004     0.441458           WWW     12    TX"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "fuel_by_nerc_month.loc[(fuel_by_nerc_month.state=='TX') &\n",
    "                       (fuel_by_nerc_month['fuel category'] == 'WWW')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-09T19:44:25.935800Z",
     "start_time": "2017-08-09T19:44:25.854795Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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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></th>\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>plant id</th>\n",
       "      <th>generation (MWh)</th>\n",
       "      <th>total fuel (mmbtu)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NERC</th>\n",
       "      <th>month</th>\n",
       "      <th>fuel category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">SPP</th>\n",
       "      <th>1</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>24477.085</td>\n",
       "      <td>1031862.0</td>\n",
       "      <td>270598.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>23591.727</td>\n",
       "      <td>1002454.0</td>\n",
       "      <td>260114.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>23338.641</td>\n",
       "      <td>1055755.0</td>\n",
       "      <td>258287.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>23981.906</td>\n",
       "      <td>1079494.0</td>\n",
       "      <td>265683.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>22927.767</td>\n",
       "      <td>1020067.0</td>\n",
       "      <td>253919.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>19081.413</td>\n",
       "      <td>824972.0</td>\n",
       "      <td>210964.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>22039.664</td>\n",
       "      <td>994750.0</td>\n",
       "      <td>243501.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>22143.524</td>\n",
       "      <td>1014658.0</td>\n",
       "      <td>243921.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>22219.108</td>\n",
       "      <td>1071510.0</td>\n",
       "      <td>245327.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>15374.204</td>\n",
       "      <td>712726.0</td>\n",
       "      <td>169390.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>22856.243</td>\n",
       "      <td>1012581.0</td>\n",
       "      <td>253042.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>158335</td>\n",
       "      <td>21888.202</td>\n",
       "      <td>964300.0</td>\n",
       "      <td>241652.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"12\" valign=\"top\">TRE</th>\n",
       "      <th>1</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-19018.764</td>\n",
       "      <td>883010.0</td>\n",
       "      <td>210695.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-17882.569</td>\n",
       "      <td>1112673.0</td>\n",
       "      <td>200997.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-18918.668</td>\n",
       "      <td>975783.0</td>\n",
       "      <td>211310.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-15883.681</td>\n",
       "      <td>467792.0</td>\n",
       "      <td>100523.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-27454.045</td>\n",
       "      <td>718424.0</td>\n",
       "      <td>10828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-13689.060</td>\n",
       "      <td>1151472.0</td>\n",
       "      <td>110548.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-18254.812</td>\n",
       "      <td>1071581.0</td>\n",
       "      <td>178150.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-14301.901</td>\n",
       "      <td>856449.0</td>\n",
       "      <td>188369.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-17163.885</td>\n",
       "      <td>753512.0</td>\n",
       "      <td>182816.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-16639.300</td>\n",
       "      <td>440272.0</td>\n",
       "      <td>169733.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-18039.961</td>\n",
       "      <td>983507.0</td>\n",
       "      <td>220204.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <th>WWW</th>\n",
       "      <td>6045</td>\n",
       "      <td>75188</td>\n",
       "      <td>-15605.285</td>\n",
       "      <td>1070104.0</td>\n",
       "      <td>190996.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          year  plant id  generation (MWh)  \\\n",
       "NERC month fuel category                                     \n",
       "SPP  1     WWW            6045    158335         24477.085   \n",
       "     2     WWW            6045    158335         23591.727   \n",
       "     3     WWW            6045    158335         23338.641   \n",
       "     4     WWW            6045    158335         23981.906   \n",
       "     5     WWW            6045    158335         22927.767   \n",
       "     6     WWW            6045    158335         19081.413   \n",
       "     7     WWW            6045    158335         22039.664   \n",
       "     8     WWW            6045    158335         22143.524   \n",
       "     9     WWW            6045    158335         22219.108   \n",
       "     10    WWW            6045    158335         15374.204   \n",
       "     11    WWW            6045    158335         22856.243   \n",
       "     12    WWW            6045    158335         21888.202   \n",
       "TRE  1     WWW            6045     75188        -19018.764   \n",
       "     2     WWW            6045     75188        -17882.569   \n",
       "     3     WWW            6045     75188        -18918.668   \n",
       "     4     WWW            6045     75188        -15883.681   \n",
       "     5     WWW            6045     75188        -27454.045   \n",
       "     6     WWW            6045     75188        -13689.060   \n",
       "     7     WWW            6045     75188        -18254.812   \n",
       "     8     WWW            6045     75188        -14301.901   \n",
       "     9     WWW            6045     75188        -17163.885   \n",
       "     10    WWW            6045     75188        -16639.300   \n",
       "     11    WWW            6045     75188        -18039.961   \n",
       "     12    WWW            6045     75188        -15605.285   \n",
       "\n",
       "                          total fuel (mmbtu)  elec fuel (mmbtu)  \n",
       "NERC month fuel category                                         \n",
       "SPP  1     WWW                     1031862.0           270598.0  \n",
       "     2     WWW                     1002454.0           260114.0  \n",
       "     3     WWW                     1055755.0           258287.0  \n",
       "     4     WWW                     1079494.0           265683.0  \n",
       "     5     WWW                     1020067.0           253919.0  \n",
       "     6     WWW                      824972.0           210964.0  \n",
       "     7     WWW                      994750.0           243501.0  \n",
       "     8     WWW                     1014658.0           243921.0  \n",
       "     9     WWW                     1071510.0           245327.0  \n",
       "     10    WWW                      712726.0           169390.0  \n",
       "     11    WWW                     1012581.0           253042.0  \n",
       "     12    WWW                      964300.0           241652.0  \n",
       "TRE  1     WWW                      883010.0           210695.0  \n",
       "     2     WWW                     1112673.0           200997.0  \n",
       "     3     WWW                      975783.0           211310.0  \n",
       "     4     WWW                      467792.0           100523.0  \n",
       "     5     WWW                      718424.0            10828.0  \n",
       "     6     WWW                     1151472.0           110548.0  \n",
       "     7     WWW                     1071581.0           178150.0  \n",
       "     8     WWW                      856449.0           188369.0  \n",
       "     9     WWW                      753512.0           182816.0  \n",
       "     10    WWW                      440272.0           169733.0  \n",
       "     11    WWW                      983507.0           220204.0  \n",
       "     12    WWW                     1070104.0           190996.0  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df.state == 'TX') &\n",
    "       (df['fuel category'] == 'WWW') &\n",
    "       (df['Reporting Frequency'] == 'A')].groupby(['NERC', 'month', 'fuel category']).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-09T19:44:01.909443Z",
     "start_time": "2017-08-09T19:44:01.839439Z"
    }
   },
   "outputs": [
    {
     "data": {
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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></th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>plant id</th>\n",
       "      <th>generation (MWh)</th>\n",
       "      <th>total fuel (mmbtu)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NERC</th>\n",
       "      <th>fuel category</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SPP</th>\n",
       "      <th>WWW</th>\n",
       "      <td>72540</td>\n",
       "      <td>234</td>\n",
       "      <td>1900020</td>\n",
       "      <td>263919.484</td>\n",
       "      <td>11785129.0</td>\n",
       "      <td>2916398.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TRE</th>\n",
       "      <th>WWW</th>\n",
       "      <td>72540</td>\n",
       "      <td>234</td>\n",
       "      <td>902256</td>\n",
       "      <td>-212851.931</td>\n",
       "      <td>10484579.0</td>\n",
       "      <td>1975169.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                     year  month  plant id  generation (MWh)  \\\n",
       "NERC fuel category                                             \n",
       "SPP  WWW            72540    234   1900020        263919.484   \n",
       "TRE  WWW            72540    234    902256       -212851.931   \n",
       "\n",
       "                    total fuel (mmbtu)  elec fuel (mmbtu)  \n",
       "NERC fuel category                                         \n",
       "SPP  WWW                    11785129.0          2916398.0  \n",
       "TRE  WWW                    10484579.0          1975169.0  "
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[(df.state == 'TX') &\n",
    "       (df['fuel category'] == 'WWW') &\n",
    "       (df['Reporting Frequency'] == 'A')].groupby(['NERC', 'fuel category']).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### States that include more than one NERC region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-21T15:40:07.367990",
     "start_time": "2017-07-21T15:40:07.341486"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "NERC_states = ['WY', 'SD', 'NE', 'OK', 'TX', 'NM', 'LA', 'AR',\n",
    "               'MO', 'MN', 'IL', 'KY', 'VA', 'FL']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-21T15:50:04.437271",
     "start_time": "2017-07-21T15:50:04.318767"
    },
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "error_list = []\n",
    "for state in NERC_states:\n",
    "    error = (annual_state.loc[2016, state]\n",
    "             - annual_facility.loc[2016, state]) / annual_state.loc[2016, state]\n",
    "    error['state'] = state\n",
    "    \n",
    "    for col in ['generation (MWh)']:#, 'elec fuel (mmbtu)']:\n",
    "        if error.loc[error[col] > 0.05, col].any():\n",
    "            error_list.append(error.loc[error[col] > 0.05])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The dataframe below shows all states with more than one NERC region where facility generation is at least 5% below EIA's state-level estimate in 2016. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-07-21T15:50:11.212518",
     "start_time": "2017-07-21T15:50:11.159172"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>generation (MWh)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "      <th>state</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>COW</th>\n",
       "      <td>0.057014</td>\n",
       "      <td>0.058988</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>0.095835</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.413092</td>\n",
       "      <td>0.508736</td>\n",
       "      <td>WY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.212550</td>\n",
       "      <td>0.233675</td>\n",
       "      <td>SD</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.141895</td>\n",
       "      <td>0.122942</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>0.327894</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>0.359396</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>0.098354</td>\n",
       "      <td>0.089782</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OK</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>0.688630</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.053121</td>\n",
       "      <td>0.062912</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOG</th>\n",
       "      <td>0.132470</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>0.533980</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>0.247843</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>0.443587</td>\n",
       "      <td>NaN</td>\n",
       "      <td>TX</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GEO</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.090467</td>\n",
       "      <td>0.085517</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>0.638877</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.055065</td>\n",
       "      <td>0.050793</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOG</th>\n",
       "      <td>0.100225</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>0.580154</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>0.930467</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>0.516165</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>0.202653</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>0.086687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>AR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.089319</td>\n",
       "      <td>0.123133</td>\n",
       "      <td>MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>0.860146</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MO</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>0.942665</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.064268</td>\n",
       "      <td>0.084587</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>0.555342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>0.794821</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>0.711827</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WND</th>\n",
       "      <td>0.112260</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>0.596931</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NG</th>\n",
       "      <td>0.060489</td>\n",
       "      <td>0.059321</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OOG</th>\n",
       "      <td>0.961357</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>0.987482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>0.052267</td>\n",
       "      <td>0.059331</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>IL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>0.128468</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>1.139389</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>0.164274</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PEL</th>\n",
       "      <td>0.055163</td>\n",
       "      <td>0.055660</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>0.642315</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>0.095183</td>\n",
       "      <td>NaN</td>\n",
       "      <td>VA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HYC</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OTH</th>\n",
       "      <td>0.318424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SUN</th>\n",
       "      <td>0.317072</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WAS</th>\n",
       "      <td>0.585390</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WWW</th>\n",
       "      <td>0.466676</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     generation (MWh)  elec fuel (mmbtu) state\n",
       "COW          0.057014           0.058988    WY\n",
       "HYC          0.095835                NaN    WY\n",
       "NG           0.413092           0.508736    WY\n",
       "NG           0.212550           0.233675    SD\n",
       "HYC          1.000000                NaN    NE\n",
       "NG           0.141895           0.122942    NE\n",
       "WAS          1.000000                NaN    NE\n",
       "HYC          0.327894                NaN    OK\n",
       "OTH          0.359396                NaN    OK\n",
       "PEL          0.098354           0.089782    OK\n",
       "WAS          1.000000                NaN    OK\n",
       "WWW          1.000000                NaN    OK\n",
       "HYC          0.688630                NaN    TX\n",
       "NG           0.053121           0.062912    TX\n",
       "OOG          0.132470                NaN    TX\n",
       "OTH          0.533980                NaN    TX\n",
       "SUN          0.247843                NaN    TX\n",
       "WAS          1.000000                NaN    TX\n",
       "WWW          0.443587                NaN    TX\n",
       "GEO          1.000000                NaN    NM\n",
       "NG           0.090467           0.085517    NM\n",
       "SUN          0.638877                NaN    NM\n",
       "WAS          1.000000                NaN    NM\n",
       "NG           0.055065           0.050793    LA\n",
       "OOG          0.100225                NaN    LA\n",
       "OTH          0.580154                NaN    LA\n",
       "WAS          0.930467                NaN    LA\n",
       "WWW          0.516165                NaN    LA\n",
       "HYC          0.202653                NaN    AR\n",
       "WAS          1.000000                NaN    AR\n",
       "WWW          0.086687                NaN    AR\n",
       "NG           0.089319           0.123133    MO\n",
       "SUN          0.860146                NaN    MO\n",
       "WAS          1.000000                NaN    MO\n",
       "HYC          0.942665                NaN    MN\n",
       "NG           0.064268           0.084587    MN\n",
       "OTH          0.555342                NaN    MN\n",
       "SUN          0.794821                NaN    MN\n",
       "WAS          0.711827                NaN    MN\n",
       "WND          0.112260                NaN    MN\n",
       "WWW          0.596931                NaN    MN\n",
       "NG           0.060489           0.059321    IL\n",
       "OOG          0.961357                NaN    IL\n",
       "OTH          0.987482                NaN    IL\n",
       "PEL          0.052267           0.059331    IL\n",
       "SUN          1.000000                NaN    IL\n",
       "WAS          1.000000                NaN    IL\n",
       "HYC          0.128468                NaN    KY\n",
       "WAS          1.000000                NaN    KY\n",
       "HYC          1.139389                NaN    VA\n",
       "OTH          0.164274                NaN    VA\n",
       "PEL          0.055163           0.055660    VA\n",
       "WAS          0.642315                NaN    VA\n",
       "WWW          0.095183                NaN    VA\n",
       "HYC          1.000000                NaN    FL\n",
       "OTH          0.318424                NaN    FL\n",
       "SUN          0.317072                NaN    FL\n",
       "WAS          0.585390                NaN    FL\n",
       "WWW          0.466676                NaN    FL"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(error_list)"
   ]
  }
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