{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:43:07.409241Z",
     "start_time": "2018-03-07T20:43:05.714457Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import os\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "import numpy as np\n",
    "cwd = os.getcwd()\n",
    "base_path = join(cwd, '..')\n",
    "data_path = join(base_path, 'Data storage', 'final state data')\n",
    "fig_export_path = join(base_path, 'Figures')\n",
    "idx = pd.IndexSlice"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "heading_collapsed": true
   },
   "source": [
    "### Date string for filenames\n",
    "This will be inserted into all filenames (reading and writing)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:44:30.908116Z",
     "start_time": "2018-03-07T20:44:30.904830Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "file_date = '2018-03-06'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:44:37.588763Z",
     "start_time": "2018-03-07T20:44:37.558109Z"
    },
    "hidden": true
   },
   "outputs": [],
   "source": [
    "us_state_abbrev = {\n",
    "    'United States': 'US',\n",
    "    'Alabama': 'AL',\n",
    "    'Alaska': 'AK',\n",
    "    'Arizona': 'AZ',\n",
    "    'Arkansas': 'AR',\n",
    "    'California': 'CA',\n",
    "    'Colorado': 'CO',\n",
    "    'Connecticut': 'CT',\n",
    "    'Delaware': 'DE',\n",
    "    'Florida': 'FL',\n",
    "    'Georgia': 'GA',\n",
    "    'Hawaii': 'HI',\n",
    "    'Idaho': 'ID',\n",
    "    'Illinois': 'IL',\n",
    "    'Indiana': 'IN',\n",
    "    'Iowa': 'IA',\n",
    "    'Kansas': 'KS',\n",
    "    'Kentucky': 'KY',\n",
    "    'Louisiana': 'LA',\n",
    "    'Maine': 'ME',\n",
    "    'Maryland': 'MD',\n",
    "    'Massachusetts': 'MA',\n",
    "    'Michigan': 'MI',\n",
    "    'Minnesota': 'MN',\n",
    "    'Mississippi': 'MS',\n",
    "    'Missouri': 'MO',\n",
    "    'Montana': 'MT',\n",
    "    'Nebraska': 'NE',\n",
    "    'Nevada': 'NV',\n",
    "    'New Hampshire': 'NH',\n",
    "    'New Jersey': 'NJ',\n",
    "    'New Mexico': 'NM',\n",
    "    'New York': 'NY',\n",
    "    'North Carolina': 'NC',\n",
    "    'North Dakota': 'ND',\n",
    "    'Ohio': 'OH',\n",
    "    'Oklahoma': 'OK',\n",
    "    'Oregon': 'OR',\n",
    "    'Pennsylvania': 'PA',\n",
    "    'Rhode Island': 'RI',\n",
    "    'South Carolina': 'SC',\n",
    "    'South Dakota': 'SD',\n",
    "    'Tennessee': 'TN',\n",
    "    'Texas': 'TX',\n",
    "    'Utah': 'UT',\n",
    "    'Vermont': 'VT',\n",
    "    'Virginia': 'VA',\n",
    "    'Washington': 'WA',\n",
    "    'West Virginia': 'WV',\n",
    "    'Wisconsin': 'WI',\n",
    "    'Wyoming': 'WY',\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import & combine generation and CO₂ intensity data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CO₂ intensity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:46:56.217144Z",
     "start_time": "2018-03-07T20:46:56.189423Z"
    }
   },
   "outputs": [],
   "source": [
    "index_path = join(data_path, 'Monthly index states {}.csv'.format(file_date))\n",
    "monthly_index = pd.read_csv(index_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:47:08.005515Z",
     "start_time": "2018-03-07T20:47:07.975778Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>final co2 (kg)</th>\n",
       "      <th>generation (mwh)</th>\n",
       "      <th>index (g/kwh)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AK</th>\n",
       "      <th>2001</th>\n",
       "      <td>4.192039e+09</td>\n",
       "      <td>6743766.00</td>\n",
       "      <td>621.616856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3.829402e+09</td>\n",
       "      <td>6767322.00</td>\n",
       "      <td>565.866731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>3.474714e+09</td>\n",
       "      <td>6338738.00</td>\n",
       "      <td>548.171264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>3.492109e+09</td>\n",
       "      <td>6526716.92</td>\n",
       "      <td>535.048356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>3.531411e+09</td>\n",
       "      <td>6576658.54</td>\n",
       "      <td>536.961208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            final co2 (kg)  generation (mwh)  index (g/kwh)\n",
       "state year                                                 \n",
       "AK    2001    4.192039e+09        6743766.00     621.616856\n",
       "      2002    3.829402e+09        6767322.00     565.866731\n",
       "      2003    3.474714e+09        6338738.00     548.171264\n",
       "      2004    3.492109e+09        6526716.92     535.048356\n",
       "      2005    3.531411e+09        6576658.54     536.961208"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_index = monthly_index.groupby(['state', 'year']).sum()\n",
    "annual_index.drop(['month', 'quarter'], axis=1, inplace=True)\n",
    "annual_index['index (g/kwh)'] = (annual_index['final co2 (kg)']\n",
    "                                 / annual_index['generation (mwh)'])\n",
    "annual_index.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:47:37.915233Z",
     "start_time": "2018-03-07T20:47:37.749452Z"
    }
   },
   "outputs": [],
   "source": [
    "gen_path = join(data_path, 'Monthly generation states {}.csv'.format(file_date))\n",
    "monthly_gen = pd.read_csv(gen_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:47:45.151408Z",
     "start_time": "2018-03-07T20:47:45.111153Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>generation (mwh)</th>\n",
       "      <th>total fuel (mmbtu)</th>\n",
       "      <th>elec fuel (mmbtu)</th>\n",
       "      <th>all fuel co2 (kg)</th>\n",
       "      <th>elec fuel co2 (kg)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fuel category</th>\n",
       "      <th>state</th>\n",
       "      <th>year</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=\"5\" valign=\"top\">Coal</th>\n",
       "      <th rowspan=\"5\" valign=\"top\">AK</th>\n",
       "      <th>2001</th>\n",
       "      <td>564593.00</td>\n",
       "      <td>14858000.0</td>\n",
       "      <td>11124000.0</td>\n",
       "      <td>1.415224e+09</td>\n",
       "      <td>1.059561e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>575286.00</td>\n",
       "      <td>13086000.0</td>\n",
       "      <td>8005000.0</td>\n",
       "      <td>1.246442e+09</td>\n",
       "      <td>7.624762e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>549665.00</td>\n",
       "      <td>12792000.0</td>\n",
       "      <td>8153000.0</td>\n",
       "      <td>1.218438e+09</td>\n",
       "      <td>7.765732e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>648979.71</td>\n",
       "      <td>13566410.0</td>\n",
       "      <td>7815580.0</td>\n",
       "      <td>1.292201e+09</td>\n",
       "      <td>7.444340e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>624317.77</td>\n",
       "      <td>13285200.0</td>\n",
       "      <td>7445360.0</td>\n",
       "      <td>1.265415e+09</td>\n",
       "      <td>7.091705e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          generation (mwh)  total fuel (mmbtu)  \\\n",
       "fuel category state year                                         \n",
       "Coal          AK    2001         564593.00          14858000.0   \n",
       "                    2002         575286.00          13086000.0   \n",
       "                    2003         549665.00          12792000.0   \n",
       "                    2004         648979.71          13566410.0   \n",
       "                    2005         624317.77          13285200.0   \n",
       "\n",
       "                          elec fuel (mmbtu)  all fuel co2 (kg)  \\\n",
       "fuel category state year                                         \n",
       "Coal          AK    2001         11124000.0       1.415224e+09   \n",
       "                    2002          8005000.0       1.246442e+09   \n",
       "                    2003          8153000.0       1.218438e+09   \n",
       "                    2004          7815580.0       1.292201e+09   \n",
       "                    2005          7445360.0       1.265415e+09   \n",
       "\n",
       "                          elec fuel co2 (kg)  \n",
       "fuel category state year                      \n",
       "Coal          AK    2001        1.059561e+09  \n",
       "                    2002        7.624762e+08  \n",
       "                    2003        7.765732e+08  \n",
       "                    2004        7.444340e+08  \n",
       "                    2005        7.091705e+08  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_gen = monthly_gen.groupby(['fuel category', 'state', 'year']).sum()\n",
    "annual_gen.drop(['month', 'quarter'], axis=1, inplace=True)\n",
    "annual_gen.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State RPS information\n",
    "Use data from [LBNL](https://emp.lbl.gov/publications/us-renewables-portfolio-standards-0) to determine what year each state's RPS required that renewable energy be procured."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:48:51.501968Z",
     "start_time": "2018-03-07T20:48:51.371737Z"
    }
   },
   "outputs": [],
   "source": [
    "path = os.path.join(base_path, 'Data storage',\n",
    "                    'rps_compliance_data_july_2017.xlsx')\n",
    "rps = pd.read_excel(path, header=35, usecols='A:V', na_values=['-'])\n",
    "\n",
    "rps.index = rps.index.droplevel([1, 2])\n",
    "\n",
    "rps.index.names = ['state', 'Type']\n",
    "\n",
    "rps_tidy = pd.melt(rps.xs('Total RPS', level='Type').reset_index(), \n",
    "                   id_vars='state', var_name='year', value_vars=rps.columns, \n",
    "                   value_name='Generation').dropna().sort_values(['state', 'year'])\n",
    "\n",
    "rps_start = {}\n",
    "for state in rps_tidy['state'].unique():\n",
    "    first_year = rps_tidy.loc[rps_tidy['state'] == state, 'year'].min()\n",
    "    rps_start[state] = first_year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:48:53.799787Z",
     "start_time": "2018-03-07T20:48:53.794373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AZ': 2001,\n",
       " 'CA': 2004,\n",
       " 'CO': 2007,\n",
       " 'CT': 2004,\n",
       " 'DC': 2007,\n",
       " 'DE': 2007,\n",
       " 'HI': 2005,\n",
       " 'IA': 1999,\n",
       " 'IL': 2008,\n",
       " 'KS': 2011,\n",
       " 'MA': 2003,\n",
       " 'MD': 2006,\n",
       " 'ME': 2000,\n",
       " 'MI': 2012,\n",
       " 'MN': 2002,\n",
       " 'MO': 2011,\n",
       " 'MT': 2008,\n",
       " 'NC': 2010,\n",
       " 'NH': 2008,\n",
       " 'NJ': 2001,\n",
       " 'NM': 2006,\n",
       " 'NV': 2003,\n",
       " 'NY': 2006,\n",
       " 'OH': 2009,\n",
       " 'OR': 2011,\n",
       " 'PA': 2006,\n",
       " 'RI': 2007,\n",
       " 'TX': 2002,\n",
       " 'WA': 2012,\n",
       " 'WI': 2000}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rps_start"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dumbell plot of annual state index"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dumbell plot code\n",
    "https://github.com/iturki/Data-Analysis-and-Visualization-Projects/blob/master/dumbbell-chart-python/dumbbbell_plot.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:48:59.150602Z",
     "start_time": "2018-03-07T20:48:59.145847Z"
    }
   },
   "outputs": [],
   "source": [
    "sns.set()\n",
    "sns.set_style('white')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This version of the plot is probably better/more flexible"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-08T18:50:06.404382Z",
     "start_time": "2018-03-08T18:50:06.224353Z"
    }
   },
   "outputs": [],
   "source": [
    "def dumbell_plot(data, years, axis_labels, legend_loc=[], offset_divider=35,\n",
    "                 rps_start={}, fig_kwargs={}, figsize=(5,9), legend=True, rps_legend=False,\n",
    "                 text_h_align='right', palette='deep', style=None):\n",
    "    '''\n",
    "    This is an example to create a dumbbell chart in Python.\n",
    "    If you would like to provide your data and customize the graph, modify the variables in the section below.\n",
    "    Please be aware that you need matplotlib installed in order for this to work.\n",
    "    '''\n",
    "    prop_cycle = plt.rcParams['axes.prop_cycle']\n",
    "    if style:\n",
    "        plt.style.use(style)\n",
    "        colors = sns.color_palette()\n",
    "    else:\n",
    "        colors = sns.color_palette(palette)\n",
    "\n",
    "    # Styles to be used when plotting the different elements of the graph.\n",
    "    axis_label_style = dict(horizontalalignment=text_h_align,\n",
    "                            verticalalignment='center', fontsize=10)\n",
    "    \n",
    "    data = data.loc[:, years]\n",
    "    min_data = data.min(axis=1)\n",
    "    max_data = data.max(axis=1)\n",
    "\n",
    "    # Create the figure\n",
    "    fig, ax = plt.subplots(figsize=figsize, **fig_kwargs)\n",
    "\n",
    "    index = range(len(axis_labels))\n",
    "    \n",
    "    # Auto-set the state abbr text offset\n",
    "    label_offset = data.max().max() / offset_divider\n",
    "    \n",
    "    # Loop N times\n",
    "    for i, (data, year) in enumerate(zip(data.T.values, years)):\n",
    "        color = colors[i]\n",
    "        for value, label, j in zip(data, axis_labels, index):\n",
    "            facecolor = None\n",
    "            if label in rps_start and rps_start[label] <= year:\n",
    "                facecolor = 'w'\n",
    "            \n",
    "            ax.scatter(value, j, facecolors=facecolor, zorder=3, color=color,\n",
    "                      linewidth=2, s=50)\n",
    "            \n",
    "            plt.hlines(y=j, xmin=min_data[j], xmax=max_data[j], zorder=2)\n",
    "            \n",
    "            if i == 0:\n",
    "                ax.text(min_data[j] - label_offset, j, label,\n",
    "                        **axis_label_style)\n",
    "\n",
    "    plt.yticks(index, ['' for x in axis_labels])\n",
    "\n",
    "    if legend:\n",
    "        for i, year in enumerate(years):\n",
    "            ax.scatter(x=legend_loc[i], y=51, color=colors[i], zorder=3,\n",
    "                       s=50, linewidth=2)\n",
    "            plt.text(x=legend_loc[i], y=52, s=str(year), ha='center')\n",
    "        plt.hlines(y=51, xmin=legend_loc[0], zorder=2, xmax=legend_loc[-1])\n",
    "        \n",
    "        # Add filled and hollow circles to show RPS status in legend\n",
    "        if rps_legend:\n",
    "            ax.scatter(x=legend_loc[i], y=49, color=colors[i], s=50,\n",
    "                       linewidth=2)\n",
    "            plt.text(x=legend_loc[i] * 1.4, y=48.9, s='No RPS', ha='left', va='center')\n",
    "\n",
    "            ax.scatter(x=legend_loc[i], y=47.5, color=colors[i], s=50,\n",
    "                       linewidth=2, facecolor='w')\n",
    "            plt.text(x=legend_loc[i] * 1.4, y=47.4, s='RPS', ha='left', va='center')\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T20:49:05.435069Z",
     "start_time": "2018-03-07T20:49:05.423198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>final co2 (kg)</th>\n",
       "      <th>generation (mwh)</th>\n",
       "      <th>index (g/kwh)</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">AK</th>\n",
       "      <th>2001</th>\n",
       "      <td>4.192039e+09</td>\n",
       "      <td>6743766.00</td>\n",
       "      <td>621.616856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>3.829402e+09</td>\n",
       "      <td>6767322.00</td>\n",
       "      <td>565.866731</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>3.474714e+09</td>\n",
       "      <td>6338738.00</td>\n",
       "      <td>548.171264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>3.492109e+09</td>\n",
       "      <td>6526716.92</td>\n",
       "      <td>535.048356</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>3.531411e+09</td>\n",
       "      <td>6576658.54</td>\n",
       "      <td>536.961208</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            final co2 (kg)  generation (mwh)  index (g/kwh)\n",
       "state year                                                 \n",
       "AK    2001    4.192039e+09        6743766.00     621.616856\n",
       "      2002    3.829402e+09        6767322.00     565.866731\n",
       "      2003    3.474714e+09        6338738.00     548.171264\n",
       "      2004    3.492109e+09        6526716.92     535.048356\n",
       "      2005    3.531411e+09        6576658.54     536.961208"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_index.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### State CO₂ intensity changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-09T16:18:06.301243Z",
     "start_time": "2018-03-09T16:18:06.090034Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "      <th>2016</th>\n",
       "      <th>2017</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>VT</th>\n",
       "      <td>8.743736</td>\n",
       "      <td>2.627368</td>\n",
       "      <td>3.897625</td>\n",
       "      <td>3.771958</td>\n",
       "      <td>2.429857</td>\n",
       "      <td>1.476818</td>\n",
       "      <td>1.767715</td>\n",
       "      <td>1.110861</td>\n",
       "      <td>0.996470</td>\n",
       "      <td>1.299876</td>\n",
       "      <td>3.699465</td>\n",
       "      <td>1.945666</td>\n",
       "      <td>2.181743</td>\n",
       "      <td>1.995218</td>\n",
       "      <td>2.324067</td>\n",
       "      <td>3.152369</td>\n",
       "      <td>3.473664</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ID</th>\n",
       "      <td>41.224086</td>\n",
       "      <td>43.663795</td>\n",
       "      <td>61.658292</td>\n",
       "      <td>62.467341</td>\n",
       "      <td>59.917659</td>\n",
       "      <td>41.155225</td>\n",
       "      <td>62.334927</td>\n",
       "      <td>62.409781</td>\n",
       "      <td>55.274896</td>\n",
       "      <td>59.869191</td>\n",
       "      <td>30.078001</td>\n",
       "      <td>52.081016</td>\n",
       "      <td>91.250630</td>\n",
       "      <td>68.401312</td>\n",
       "      <td>99.726957</td>\n",
       "      <td>85.319666</td>\n",
       "      <td>71.727515</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>WA</th>\n",
       "      <td>173.633562</td>\n",
       "      <td>113.274490</td>\n",
       "      <td>150.414143</td>\n",
       "      <td>142.676842</td>\n",
       "      <td>145.794267</td>\n",
       "      <td>92.678299</td>\n",
       "      <td>115.408311</td>\n",
       "      <td>120.514381</td>\n",
       "      <td>127.461321</td>\n",
       "      <td>134.104207</td>\n",
       "      <td>65.859107</td>\n",
       "      <td>53.998259</td>\n",
       "      <td>105.457490</td>\n",
       "      <td>100.622720</td>\n",
       "      <td>101.152979</td>\n",
       "      <td>82.623000</td>\n",
       "      <td>87.188693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>NH</th>\n",
       "      <td>307.386766</td>\n",
       "      <td>302.778174</td>\n",
       "      <td>368.752308</td>\n",
       "      <td>351.076469</td>\n",
       "      <td>347.602944</td>\n",
       "      <td>321.294057</td>\n",
       "      <td>295.528926</td>\n",
       "      <td>287.338168</td>\n",
       "      <td>269.835337</td>\n",
       "      <td>247.668650</td>\n",
       "      <td>258.123841</td>\n",
       "      <td>226.484810</td>\n",
       "      <td>174.072251</td>\n",
       "      <td>171.495084</td>\n",
       "      <td>178.883640</td>\n",
       "      <td>125.623186</td>\n",
       "      <td>108.926910</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OR</th>\n",
       "      <td>208.215535</td>\n",
       "      <td>147.203056</td>\n",
       "      <td>187.189972</td>\n",
       "      <td>177.197624</td>\n",
       "      <td>189.193400</td>\n",
       "      <td>139.267282</td>\n",
       "      <td>195.526393</td>\n",
       "      <td>190.392149</td>\n",
       "      <td>173.988571</td>\n",
       "      <td>192.524596</td>\n",
       "      <td>112.124000</td>\n",
       "      <td>122.914629</td>\n",
       "      <td>163.462912</td>\n",
       "      <td>143.742115</td>\n",
       "      <td>161.691698</td>\n",
       "      <td>138.396176</td>\n",
       "      <td>112.772948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year         2001        2002        2003        2004        2005        2006  \\\n",
       "state                                                                           \n",
       "VT       8.743736    2.627368    3.897625    3.771958    2.429857    1.476818   \n",
       "ID      41.224086   43.663795   61.658292   62.467341   59.917659   41.155225   \n",
       "WA     173.633562  113.274490  150.414143  142.676842  145.794267   92.678299   \n",
       "NH     307.386766  302.778174  368.752308  351.076469  347.602944  321.294057   \n",
       "OR     208.215535  147.203056  187.189972  177.197624  189.193400  139.267282   \n",
       "\n",
       "year         2007        2008        2009        2010        2011        2012  \\\n",
       "state                                                                           \n",
       "VT       1.767715    1.110861    0.996470    1.299876    3.699465    1.945666   \n",
       "ID      62.334927   62.409781   55.274896   59.869191   30.078001   52.081016   \n",
       "WA     115.408311  120.514381  127.461321  134.104207   65.859107   53.998259   \n",
       "NH     295.528926  287.338168  269.835337  247.668650  258.123841  226.484810   \n",
       "OR     195.526393  190.392149  173.988571  192.524596  112.124000  122.914629   \n",
       "\n",
       "year         2013        2014        2015        2016        2017  \n",
       "state                                                              \n",
       "VT       2.181743    1.995218    2.324067    3.152369    3.473664  \n",
       "ID      91.250630   68.401312   99.726957   85.319666   71.727515  \n",
       "WA     105.457490  100.622720  101.152979   82.623000   87.188693  \n",
       "NH     174.072251  171.495084  178.883640  125.623186  108.926910  \n",
       "OR     163.462912  143.742115  161.691698  138.396176  112.772948  "
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barbell_index = annual_index.pivot_table(values='index (g/kwh)',\n",
    "                                         index='state', columns='year')\n",
    "barbell_index.sort_values(by=[2017], inplace=True)\n",
    "barbell_index.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len([x for x in rps_start.values() if x <=2008])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'AZ': 2001,\n",
       " 'CA': 2004,\n",
       " 'CO': 2007,\n",
       " 'CT': 2004,\n",
       " 'DC': 2007,\n",
       " 'DE': 2007,\n",
       " 'HI': 2005,\n",
       " 'IA': 1999,\n",
       " 'IL': 2008,\n",
       " 'KS': 2011,\n",
       " 'MA': 2003,\n",
       " 'MD': 2006,\n",
       " 'ME': 2000,\n",
       " 'MI': 2012,\n",
       " 'MN': 2002,\n",
       " 'MO': 2011,\n",
       " 'MT': 2008,\n",
       " 'NC': 2010,\n",
       " 'NH': 2008,\n",
       " 'NJ': 2001,\n",
       " 'NM': 2006,\n",
       " 'NV': 2003,\n",
       " 'NY': 2006,\n",
       " 'OH': 2009,\n",
       " 'OR': 2011,\n",
       " 'PA': 2006,\n",
       " 'RI': 2007,\n",
       " 'TX': 2002,\n",
       " 'WA': 2012,\n",
       " 'WI': 2000}"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rps_start"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:12:42.628341Z",
     "start_time": "2018-03-07T21:12:40.504975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAATQAAAIeCAYAAAA1XvNSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states_index = list(barbell_index.index)\n",
    "\n",
    "dumbell_plot(barbell_index, [2001, 2008, 2017], states_index, \n",
    "             legend_loc=[500, 300, 100], rps_legend=True,\n",
    "             rps_start=rps_start, palette='colorblind')\n",
    "\n",
    "plt.vlines(439, -1, 50, colors=['0.5'], zorder=1, #linestyles='dashed',\n",
    "           linewidth=1)\n",
    "\n",
    "plt.ylim(-1, 53)\n",
    "# plt.text(x=200, y=40, s='2017\\nNational Average\\n(439 g CO$_2$ $\\mathregular{kWh^{-1}}$)',\n",
    "#          ha='center', va='center', size=11)\n",
    "plt.text(x=680, y=3, s='2017\\nNational Average\\n(439 g CO$_2$ $\\mathregular{kWh^{-1}}$)',\n",
    "         ha='center', va='center', size=11)\n",
    "sns.despine(left=True)\n",
    "plt.xlabel('g CO$_2$ $\\mathregular{kWh^{-1}}$')\n",
    "\n",
    "# label as part of a larger figure\n",
    "plt.text(-0.02, 0.89, s='a)', ha='right', size=11, transform=ax.transAxes)\n",
    "\n",
    "path = join(cwd, '..', 'Figures',\n",
    "            'State CO2 intensity {}.pdf'.format(file_date))\n",
    "plt.savefig(path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "barbell_index.loc['USA', 2001] = 630\n",
    "barbell_index.loc['USA', 2008] = 580\n",
    "barbell_index.loc['USA', 2017] = 439\n",
    "barbell_index.sort_values(by=[2017], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:12:42.628341Z",
     "start_time": "2018-03-07T21:12:40.504975Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASkAAAIeCAYAAADnDvkKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMi4wLCBo\ndHRwOi8vbWF0cGxvdGxpYi5vcmcvFvnyVgAAIABJREFUeJzsnXlYVGX//18z7IKBCy4p7iamVuYG\nipqSpiLi8uBSomn208e0nnJDs9IyRbTsWz1uBZS45BLu2pNpaIoiabnmLoqKiAso+zLn9wfOkXEW\nBhhghrlf18V1yZlz7nNA+HDf57zm/VFIkiQhEAgEZoqyoi9AIBAIDCGKlEAgMGtEkRIIBGaNKFIC\ngcCsEUVKIBCYNaJICQQCs0YUKYFAYNaYZZHaunUrAwYMICAggOHDh3Pq1CkAVqxYQZ8+fejVqxff\nfPMNTyteBw8eJCAgQP58y5YtBAQEyB89e/akVatW3L17t8Kv+f79+4wbN45+/frRv39/jh8/Lo+1\nZ88e/P39CQgIYNSoUVy/ft2srxdAkiRmzJhBWFiYya9VYOVIZsbly5elLl26SElJSZIkSVJ0dLTU\nvXt3KTo6WgoICJDS09OlrKws6Y033pB27twpSZIkZWZmSl9++aXUvn17yc/PT+e4OTk50tChQ6V1\n69aZxTW/++670rJlyyRJkqSzZ89KPj4+UkZGhpSZmSm9+OKLUnx8vCRJkhQRESG9/fbbZnu9kiRJ\nly5dkoKCgqQXX3xR+v777016rQKB2c2k7O3tmTdvHrVq1QKgdevW3L17l19++YX+/ftTpUoVHBwc\nGDx4MNu2bQMKZlCZmZmEhIToHfe7776jevXqDB8+vMKvOS8vj+joaIYOHQpAy5YtadSoEX/88Qf5\n+flIksSjR48ASE9Px8HBwWyvF2DNmjUEBgbSp08fk16nQABgW9EX8DT169enfv36QMESYsGCBfTs\n2ZM7d+7g4+Mj71enTh2SkpIAePXVV3n11VeJjY3VOeb9+/eJiIggKirKLK75wYMHqFQqqlevLr9W\nu3Ztbt++jbOzM3PnzmX48OG4ubmhUqlYt26d2V4vwMcffwzAoUOHTHqdAgGY6T0pgIyMDN577z2u\nX7/OvHnzkCQJhUIhvy5JEkqlcZe/YcMGfH198fDwKKvLBYy/ZpVKpbFd/ZqNjQ3nz5/nv//9L7t2\n7eLgwYNMmDCByZMna91/M5frFQjKGrMsUrdu3WL48OHY2NiwatUqnnnmGerWrcudO3fkfe7cuUOd\nOnWMGm/Xrl0MHjy4rC4XKN4116hRA0mSSElJ0Xitdu3aHDx4kJdffpkGDRoA8MYbb3Dx4kUePHhg\nltcrEJQ1Zlek0tLSCAoKonfv3ixZsgRHR0cAfH192bZtGxkZGeTk5BAVFcWrr75a5Hipqalcv36d\ntm3bms0129ra8sorr7BhwwYAzp07x+XLl+nUqRPPP/88cXFx8hPI3377jfr162sstczpegWCssbs\n7kmtWbOGW7dusWfPHvbs2SNv/+GHH+jduzeBgYHk5ubi6+vLwIEDixzv2rVruLu7Y2dnZ1bX/Mkn\nnzB79mz69++PQqEgNDSUqlWr4u3tzVtvvUVQUBB2dna4urqydOlSs71egaCsUUhlcbNDIBAITITZ\nLfcEAoGgMKJICQQCs8bii5QkSUh5OWXyiL6skCSJnPw8i7lmSZLIyVNZzPUKKhdmd+PcWPLTU7i7\nYwEpf0SQ/ygZm6ruuHUdQ83+M7Fxdqvoy9NJSnYmC07uJeJSHMlZ6bg7OjOmWQdmvuCLm4NTRV+e\nFimZuSzYe5GIuASS03Jwd7FnTAcPZvo2x82p7B5ECASFscgb5/npKcTP70b2jVNarznUb0OjWQfM\nrlClZGfSbfdSTj1I1HqtTbW6HOg70awKVUpmLt3+e4hTiY+0XmtTtyoH3ukiCpWgXLDI5d7dHQt0\nFiiA7BunuLtT/3v4KooFJ/fqLFAApx4kEnJqXzlfkWEW7L2os0ABnEp8RMjeS+V8RQJrxeJmUpIk\ncWFybfIfJevdJyVHyeuH6wMKvfuUJxJwY2p/VM6OevdRpmdRf/EOs7hiCbjhNx+Vo34Pyt3FnqQ5\nvbXeLiMQmBrLm0nl5xosUABu9ipszel3R6kwWKCAgteVZnLRChuDBQogOS2H3HyL+vsmsFAs78a5\njR02Vd0NFiqbqu5cvBJvNn/lJUmi9k9zSM5K17uPu6Mz8ZevmMU1S5JE7Tm/kpyWo3cfdxd77Gwq\n/loFlR+Lm0kpFArcuo4xuI9bt7Fm8cuuRqFQMKZZB4P7jG3e0WyuWaFQMKaD4cSIsR0amM31Cio3\nFlekAGr2n4lD/TY6X3Oo34aafsHlfEVFM/MFX9pUq6vztTbV6hLcpmc5X5FhZvo2p01d3Uu+NnWr\nEuzbrJyvSGCt2MyZM2dORV9EcVHaO+LqNQKAnNsXkHIysKnqTvVek3n2rTCz0w8AHG3tGNG4IInh\nwsNkMvJycXd0ZnJLH8K6DDUr/QDA0c6GEW3rgQQX7qaRkZOPu4s9k7s0JmzYS0I/KGMkSYL8XFAo\nTTJjlSSJXFU+SoWiWONJkkRuvoRSQYXNnC3u6d7TyP+ZNnYWs/xQ/8DYKW0s4prVP6h2NsX7ARcU\nH1NLyiUViM1J5LX4IiUQVCSjRo1i6tSpvPDCC+Tk5ODt7c3EiRN56623AGjbti22trb89ttvuLq6\nArBq1SqOHz/OV199pTGWqSXlkgrE5ibyWuQ9KYHAXPDx8eHPP/8E4NixY/j4+BAdHQ1AdnY21atX\n580332TevHkAXL9+nXXr1vHpp59qjWVqSbmkArG5ibyiSAkEpaBz585ykdq/fz+BgYE8evSIR48e\n8ddff9GxY0cmTJhAfHw8Bw4cYM6cOcyZM4dnnnlGYxxJkkj5I8LguS5sWoji8T0lYz5CY3YZHG/h\noZ26j9t5zOBx4XHXy/XN5qJIWRnqv/IC0/D8889z5coVJEkiLi6Ojh074u3tTUxMDEePHqVr167Y\n2NiwcOFCZs6cyfPPP687dtkISbmGE9gZ+xtro4Rnqhje55kqBfsVRmkLVQwvKctb5BVFysrYv39/\nRV9CpUKpVOLp6cmBAwdwd3fH3t6ebt26cfz4cY4dO0bnzp0BaNKkCU2aNGHQoEG6B3osKRvCpqo7\n2Y8jc4r6UOXm4e7obHA8d0dnVLl5msflFdwkN3hcOYu8okgJBKWkS5curFixgq5duwLQrl07zp49\nC4Cbm3E3uk0tKZdUIDZHkVcUKYGglHTu3Jljx47RvXt3oKBDdNWqVenQwXCReBpTS8olFYjNTeQV\nCoKVMXfuXD755JOKvgyBHvLTU7i7M4SUA+FPPKluY6npF1xiTyrk1D7CLx6VPamxzTsS3KZnkZ5U\nyN5LhMddlz2psR0aEOzbTHhSgrJFFCnzQh1/natQYm9jKy+j5O3YYm9beuu8pAKxOYi8YrknEBjB\nqFGjOHnyJAA5OTm0a9eOsLAw+fXXX39da3l37949evXqhUql0hovPz2F62uncPzf1Tg3zpGjE1z5\naK4Psw/+xLXUhwTv/Ic686JxDN5F7Tm/MmPHWVIyc0t8/QqFQqMIFus4ExTJ0iCKlEBgBEVJm7dv\n36Z69eokJCTIx2zdupWAgACUSs1fs/z0FC5/3pW0/32JU2YqADVyM3n9agzt1k6m/dr5hO4/L0fl\nJKflEPr7Zbr991CpCpWlIoqUQGAERUmbnTp1YsiQIWzdulU+ZuvWrQQGBmqNdXfHAvJuntZ5nhbp\ndwm6uQ/cr2u9Zq2xzaJICQRGYIy0OXjwYHbtKrC8T548ybPPPkvt2rU1xjHGLB90+zS4JVIQ5KxJ\nedve5oAoUgKBERgjbdasWZOmTZvy119/sWnTJoYNG6Y9kDFmeW4mdjbZoNAuRtYY2yyKlEBgJMZI\nm4GBgWzZsoUTJ07QrVs37UGMMMvv2TmRm+8AkvbNamuMbRZFSiAwEmOkTR8fHw4fPkyvXr20bpiD\ncWb55jqtIaUuurodWWNss+U1YhAIKoh69epx/vx5jW1Lly7V+FypVPLrr78aHKdm/5mkntil8+b5\neeeaRNbrCVcaaL1mrbHNYiYlEJQDajlTkiRsnN1o+uEfuPSZQqZTQRDePTsn1jbuzPE3vuXP12cx\no7un/EZfdxd7ZvRoxv6Jnalir9B741ySJHIevwG5MiGMcytDGOfGExsby08//cSSJUsA+OWXX/ji\niy+4fv06e/bsoUGDgtnOvn37WLlyJWvWrMHGxkZjjKLigPUZ5/DE9k7PyyLk1D69EcDmFPVbFojl\nnkBgBDt37iQsLIx169Zx8OBBZs2aRWRkJA8fPiQ0NJTvvvtOZ4F6Og44/1Ey93aFknZytxwHrLBz\nwEHHORUKBRn5WXT/ZZlGwmZyVjqhp6PZffM823u8jf93xzWSNNXy5+5zd8o96rcsEMs9gaAItmzZ\nQkREBBEREdSsWZOBAwdSrVo11q9fz8KFC5kwYQIeHtrxJqaIAy4qAnjY7s1mFfVbFogiJRAY4M8/\n/2TDhg2kpqaSn58vb587dy5hYWFkZWUxcOBAreNMFQdcVARw7MN/0CV9qqkM8qcoUgKBAdzd3YmI\niGD06NFMmzZNfrNw9erVadeunc4CBZgmDtiYCGC7PJ3Sp5rKIH+KIiUQGKBhw4Y4ODgwcuRI7Ozs\nWLZsmXEHmiAO2JgIYEW+nU7pU01lkD9FkRIIjGT+/PmsX7+eI0eOFLmvKeKAjYkA7ujiiS7pU01l\nkD+FgmBlCAWh/DBFs8+iGnzqeronv14BjTzLAjGTEghKQGE5Ux82zm40mnWAGn4z5KWfTVV3avjN\nMLobsZuDEwf6TmRGmx7y0s/d0ZnprXtwoO9EGro+w4F3ujCjRzMt+VNdoCxd8hQzKStDzKSecOPG\nDT744AOaNGlCWloa3377rfxaly5dOHTokNYxRcmZ+pAkCfJzwcauRMuvlOxM5p/4jYhLcdzNztAS\nOtXnKBz1W1kkTzGTEggoSNvcsmWLwX3Uy7d7u0LlJ3dqOTN+fjfy01P0HqtQKFDY2pe4QHXbvZRF\nZ/ZzNzsDeCJ0dtu9lJTsTPkc6qjflMxcuv33EKG/X7b4hE9RpAQCYMqUKXzzzTfcvn1b7z6mkDNL\nQlFCZ8ipfdrH7L1YaSRPUaQEAqBWrVq89957fPjhhzpfN5WcWZKPooTOhYd2ah+z85jBYyxJ8hRF\nSiB4zIABA3B2dmbt2rXaL5pCziwJxgidz1Qp2E+N0haqGL4pb0mSpyhSAkEh5syZQ3h4OOnp6Zov\nmEDOLMmHMUKnu6Mzqty8J8fk5chP+vQeY0GSpyhSAkEhqlevTnBwMJmZmRrbTSFnlgRjhM6xzTtq\nnFehUDCmg/YbnjWOsSDJUygIVoZQEEqOKeTMklCU0Hmg70Stlunqp3uVQfIUMymBwABqaVOlUpHv\n6ELDmftLJWc+PbYxkqU+oXNGmx46CxSAm5NdkZKnpSBmUlZGZZ5JxcbGMmrUKJYsWUK/fv3k7f7+\n/rRq1YrPPvuMFStWEBMTg42NDba2tvznP//hxRdf1BpLLW0++CMC1aNkHthXIap2KzY/14PAll0J\nbtMTN1vbEsmZpZEsJUkiV5WPndLG6PM+LXlaGiKZU1CpaNKkCTt27JCL1Pnz5+X7S19//TX5+fms\nXr0apVLJzZs3GT9+PMuWLdMIrdNa1imUVMvJ4K2EOHzuX2V0Vjq7b54vmMXYGr5B/TS6lmHFSdJU\nKBTY2xTv17ZA8rS84qRGLPcElQpPT08SExN5+PAhANu2bcPf31/+9wcffCC3mqpXrx6vv/46mzdv\n1hhDLW3a121Bo49iaBmRR6OPYrCv24IW6XcZd/2oXomyKCqTZFleiCIlqHT06tWLPXv2IEkSJ0+e\npG3btty7dw9XV1dsbTVnIR4eHty6dUv+vLC0+ey4CKo080ahUFClmTfPvhUOPG6DLkk6JcoixcxK\nJFmWF6JICSod/v7+7Nq1i7i4ONq3bw/AM888Q2pqKnl5eRr7Xrt2jbp16z7ZoJY2FUqcmnpp7OvU\nzBsUyoI26JJKW6IsikomWZYXokgJKh0eHh5kZGQQGRnJgAEDALCzs6Nv374sWbJEjgBOSEhg7dq1\nDB48+MnBamlTUpF5WTPcLvPSYZBUBW3QFUotibJIMbOSSZblhShSgkpJv379SExMpHHjxvK2qVOn\nYmtry9ChQxkxYgSzZ89m3rx5GjfNC0ubt74fQ8bFGCRJIuNiDLfCxgKP26ArFFoSZVFUNsmyvBAK\ngpVRmRUEU6Hr6R5SwezrvHNNRr80jEa1Gut1lAxRmSTL8kLMpARWiaFkzcKJmsrHS78H9lX43qMD\nU7zGMvHlvkYXKEmSyMnPk89jCsnS0pM2i4uYSVkZlj6Terr1OcDixYtxc3Nj//79PHz4kDt37tCs\nWTMAfvjhB43OwsVN1lQnakpKW/IkldESZUp2JgtO7tXbGl09dnEky8qStFlchMwpqBRUr16dyMhI\nnUVMjbFtzwujUCjA1h4FYG/kwkPXe+0Kt0ZXz8KKI1mWVgK1ZMRyT2A1lFeyZkmSNIsc04olUFGk\nBJWCopZL5ZmsWZIkTSGB6kcUKYFF4ejoSE5Ojsa2jIwMHBwcDB9YXsmaJUnSLAorl0BFkRJYFE2b\nNuWff/7hzp07AGRnZxMXF0erVq0MH1hOyZolSdIUEqhhRJESWBQuLi4EBwczfvx4RowYwRtvvEFQ\nUBANGzY0eFx5JWuWJEnTqDGtWAIVCoKVYekKQmkor2TNkiRpFjmmFUugYiYlqLQ8LWyaou25MZQk\nSbPIMY2QQJ8WR8uLspZLrW4m9e2337J7924AunfvzvTp04mJiWHBggVkZ2fTt29f3n//fY1jpk+f\njpeXF4MHD+bevXuMHTtWfu3Ro0c8ePCAv/76q1y/jpJizjOp2NhY3nnnHbZv3y4nEyxevJgmTZrw\n8ccf07ZtWwCysrLw8fFh8uTJcjZUYYwRNkvb9txYSpKkadSYhdupGyGOlgXlJZda1UwqJiaGgwcP\nsnnzZrZs2cKZM2fYsWMHs2bNYunSpezatYvTp0+zf/9+AJKSkpgwYQL/+9//5DFq1KjB1q1b2bp1\nK5s3b6ZevXp8+umnFfUlVTrs7OyYOXOm1l9lV1dXIiMjiYyMZMOGDdy7d481a9ZoHW9sK/TStD0v\nDuokTVOeR6Od+uOlZejpaJKzCtpw6WrBbmrKs427VRUpd3d3goODsbe3x87OjqZNmxIfH0/Dhg3x\n8PDA1tYWf39/fvnlFwC2b9+Or68vffv21Tnezz//jJOTk5z8KCg9Xl5euLq66ixAahQKBWPGjGHX\nLm0fqaJaoVcUZSGOGnXecpRLrapINW/enJdeegmA+Ph4du/ejUKhwN39yaPpWrVqkZSUBMC4ceMI\nDAzUOVZ+fj7Lly9nypQpZX/hVsacOXP44YcfiI+P17tPzZo1efDggca2imyFXlEfZSGOmptcalVF\nSs3FixcZO3Ys06dPx8PDQ2MqLkmSUVPzP/74g0aNGtGiRYuyvFSrpFq1asyaNYvg4GA5oO5pbt68\nSZ06dTQ3VlQr9IqiLMRRYyhnubSy/HcZzbFjx3jzzTeZMmUKgwYNok6dOiQnP/nBTk5OplatWkWO\n89tvv2m0TRKYlp49e9K4cWOtJgkAKpWK8PBw/Pz8NF+ooFboFfVRFuKoOcqlVlWkEhMTeeedd1i8\neLH8A/7iiy9y9epVrl27Rn5+Pjt27KBbt25FjvX333/L+dmCsuHDDz/E0dERgNTUVIKCghg1ahQj\nR46kQYMG/Otf/9LYv7yETXOhLMRRo89bjnKpVUW1hIWFkZ2dTUjIk5unw4cPJyQkhMmTJ5OdnU33\n7t3p06dPkWMlJCRoLzcEpaJTp0506tRJ/tzFxYXff/8dQDOH3AA1+88k7eRuvcJmTb9g01ysmTDz\nBV923zyvVxwNbtOzbM7r25zd5+7olUuDfZuZ7FxW50lZO+bsSZmK/PQU7u4MIeVA+BNPqttYavoF\nm0TYLAv3qTSkZGcScmof4RePyp7U2OYdC7osl7EnFbL3EuFx12VPamyHBgT7NjOpJ2VVMymB+WJI\n5Jw5cybDhw9n7ty58v7z5s1j37597Nun5xH74/snBf+UwAR/iytKmiwSlS3S7cZI52wgPRvJ2QGp\nagNoVba/3m5OdoT0b8kCP88ybeNuVfekBOaNPpHTzc2NuLg4uWdefn4+p0+f1jlGYZlTlXYXFEpU\naXe1ZM7i8rQ0qVQoykWaLPK6CkmVd9NyQVJyNy23TKRKfRSWS8sCUaQEZoM+kdPW1paOHTty6NAh\nAA4ePIi3t7fOMQy1SC+NzKmWJlu4uhPjN4m80aHE+E2ihat7mUqTRV6XFSR2VkiRunHjBi1atJB/\n6NT07NmTGzduGDw2KChI/ndAQIDJr6tnT/03GkNCQvDy8tIKXROYDn0iZ//+/WXDfMeOHTot/8Iy\np74W6SWVOdXSZITPMLxrNUKhUOBdqxHhXYYBZSdNmpNUWVFU2EzKzs6Ojz76iLS0tGIdd/ToUfnf\nW7duNfVl6SUvL4/du3fTtm1bjffyCUyLPpGzXbt2nD17lgcPHpCSkkK9evW0DzamRXpJZM7H0qRS\nocDLXTO3yrtWQ5QKRdlIk0VhJYmdFVakatWqRefOnVm4cKHWa3l5ecyePZthw4bh6+vLxIkTycrK\nYt68eQDyW1XUtndmZiZTpkyhf//++Pv7s2XLFgCioqJ4//33GTt2LL169WLOnDkGxzdEdHQ0DRo0\nYODAgfz000/y9kmTJmkUrcGDB3P27FmuXbvGmDFjGDRoECNGjODs2bMABAcHM2HCBPr27cu+ffvY\nvXs3Q4cOZcCAAfTp04fjx48DcOHCBQYPHkxAQACfffYZvXr1AuDu3btMnDiRwYMHM2TIEGJiYor9\nvTd3dImcCoWC7t27M2fOHF599VXdBxrRIr0kMqdamlRJEkeSr2mMe/jONVSSVCbSpLlJlRVFhd6T\nCg4O5uDBg1rLvr/++gs7OzvWr1/Pnj17ePToEfv372f27NkAbNy4UWP/b775hmrVqrFjxw5+/PFH\nvvnmG86dOyeP9fXXX7Nt2zZ+//13zp8/r3d8Q0RFRdGnTx+6d+/OP//8w6VLBWv9gIAAdu7cCRS8\nHzA7O5vnn3+eGTNmMG3aNDZv3sxnn32mEf/i5ubG7t27eeWVV/jpp59Yvnw527ZtY9y4caxcuVL+\n3rz33nts3boVDw8P8vPzAfj8888ZMmQIUVFRLFu2jI8//rjYs1FLoLDIqcbf35+9e/fq9dgKy5z6\nWqSXROYsLE2OObiemKR4JEkiJimesYfWA2UjTRp1XVaQ2FmhCoKLiwufffYZH330Edu2bZO3d+jQ\nATc3N9asWcOVK1eIj48nIyND7zhHjhxh/vz5QEH/NV9fX44ePYqLiwtt27bFxcUFAA8PD1JTU+nY\nsWOxxr937x6HDh1i3rx5ODo60qNHD3766Sdmz55N9+7d+fTTT0lLS2PHjh0MGDCA9PR0Tp8+zcyZ\nM+UxMjIy5DfEvvDCCwAolUr++9//sm/fPq5evcrRo0dRKpWkpKRw8+ZNunfvDsCQIUNYtWoVUBA3\nc+XKFb7++mugYFaYkJBAy5Yti/39NyeMETlbtGih8VRPl35QWOaMn9dFo0V6aWTOwtJkl13folQo\nUD2+11OW0mSR11WOUmVFUeGelI+Pj9ayb+/evXz99deMGjWKwYMH8+DBA4M3/55+TZIkeeZRuIuI\nQqFAkqRij79t2zYkSZLfhpGVlUVubi5Tp06Vi9a+ffv45ZdfWLFiBSqVCnt7e417Zrdv38bNreD+\ngXqGkJ6ezr/+9S8GDBhAhw4daNGiBWvWrMHGxkbv9ahUKn788Ud5rDt37lCjRg29114ZkST9gXXq\n9E19Mqeyiis5+XnFFjHVaZsVIU0avK7HiZ3lIVVWFGahIKiXfeoOIIcPH6Zv374MGTKEZ555htjY\nWLno2NjYyL6MGi8vLzZt2gTA/fv32bt3Lx07dtR7PkPj6yIqKoqQkBBZHjx48CCurq7y06aAgAAi\nIiJwc3OjXr16VK1alUaNGslF6tChQ7zxxhta48bHx6NQKJgwYQKdOnViz5495OfnU7VqVTw8POQl\n6Pbt2zW+1rVr1wJw6dIl/P39ycysGEenuIwaNYqTJ08CkJOTQ7t27QgLC5NfHzlyJO+88w4HDhzQ\neXx+egpJ62dwYXJt/nnLgQuTa5O0foaW+2Tj7EbtoSE8900SLcOyee6bJBwCPmHW2YPU/mkODquC\nqf3THGbE7Sie3yRLk95wpivSOW+k241BVbF/69VSZdKc3mQv9CNpTm9C+resFAUKzKRIqZd9ubkF\n4llgYCA7d+7E39+f9957j5dffllWE3x9fQkICCA7O1s+/p133iElJQV/f39GjhzJhAkTDLY4MjT+\n05w6dYoHDx7IN66hYJk2evRo+QZ6u3btePToEQMGDJD3WbRoEZs2bcLf358vvviCJUuWaP3l9vT0\npGXLlvTt2xc/Pz+qVavGrVu3AAgNDWXp0qUMGjSIkydPyrOv2bNnc+LECfz9/Xn//fcJDQ2Vl7Pm\njo+PD3/++SdQkEbh4+NDdHQ0UNCaKjExkapVq+o8VitxU6HUmbhZGIWiIH0zNSer1CLm09KkkvKX\nJouirKXKCkMSmCXffPONlJSUJEmSJP3vf/+TJk2aZJJx58yZY5JxSsKpU6ekf//735IkSdKCBQuk\nP/74QwoICJAePnwoHT58WAoODpZmzJgh7d+/X+vY2z9Nl86MQro4o4WUfjFGUqlUUvrFGOnijBbS\nmVFIt9fP0Hve6Ue3S4RPkVr8HCLFJF2VVCqVFJN0VWrxc4hE+BRpRtyOIq99+vYzEh9sk1os2CvF\nXL1XMMbVe1KLBXslPtgmzdh+tuTfGIFBzGImJdDm2WefZezYsQwcOJDVq1czffr0ir6kUvP8889z\n5coVJEkiLi6Ojh074u3tTUxMDEePHqVr1646j5NKKWmaQsRUS5MRw1/Cu1H1gjEaVSd8WEHSa2WQ\nJs2VCr9xLtDN4MGDjY4nsRTshxIcAAAgAElEQVSUSiWenp4cOHAAd3d37O3t6datG9HR0Zw7d45R\no0bpvh9llKSpwk4JuU8HeRohYqrUIma+7hRQtTSpVIBXw2qaYzSqhlLxRJq0t61kSy0zQMykBOVK\nly5dWLFihTxrUpvkgPzEUotSSJqmEDHV0qRKgiPXNHPVD8c/QCVVDmnSXBFFSlCudO7cmWPHjskO\nmL29PVWrVqVDB/0Jk6WRNE0hYhaWJsf89DcxV+8XjHH1PmPX/10wRiWQJs0VEXpnZVhq6J1Wi/Sn\nJE1DHYifbnv+tIhpTFfhp9ucKxWgevybU9nbnFc0YiZloZw9e5Y333yzoi+j3LBxdqPhzP3U6DtV\nXvoZ2yL96bbn6iVecdqeP93mXL3Em/5KU36b0AlXR1skqWzbjVsrYiZlgSQkJLB9+3aOHTumIUMa\nQ3nOpBISEggNDSUlJYXc3Fw8PT2ZOnUqERER1KxZkxEjRsj7Dh06lC+//JL69etrjfN023SlS03c\nfEZT0/9DbF2qae1vCMkE0b+SJJGcmcHiM7/zw+OUzioKR6SUOmTerId7lSpl0m7cWhFP9yyA77//\nnoMHD8qfh4eHM3HiRMaPH1+BV2WYrKwsJk6cyLx583jxxRcB2Lx5M1OmTKF169ZGj6NrmadKu8v9\nX74g/fSvRc6inkahKGh7XhpSc7J49dflGs0PMqQscI0Hh2SSr7xE6O+X2X3ujlgGmgCx3LMAxo0b\nxw8//CB/KJXm/98WHR1Nhw4d5AIFMGjQIB48eEBCQoLR45RV0mZpMNTaHMd0cL8OVJ5kzIpGzKQE\nZUJCQgINGjTQ2l6/fn0SExM5ceKE/N5HQI6+KYwuiROQJc74eV24sGkhdYZpZ5KVKV/923Dn4Gq3\nIakxoCA87joL/DzFk79SUC5/kvPz85k0aZLGG2EXLlxIcPCT2Iz/+7//o1+/fvj5+RERESFvX7ly\nJa+99hr+/v4sW7asVNeRlpbG3Llz6d+/PwEBAQQFBXHmzBn59fT0dObOnUuvXr0YMGAAr7/+OocP\nHzZ6/NjYWI14Y4AxY8bw22+/yZ8vXLiQtm3bakQQ+/j4EBUVpXVsUaxYsUL+9+3bt5kxY0axji9L\nateurfP9kPHx8dStW5c333yTyMhI+aNZMx2RImWVtFkajGltbpsLioJbvZUhGbOiKZf/3nXr1uHj\n44OTU8FTlMOHD2ukLh49epQjR46wbds2fv75ZyIjI7ly5QoxMTFs376dn3/+mS1btnDixAl+/fXX\nEl2DSqXi7bffxtXVlS1btrB161beeecd3n77bTmqZcKECdjZ2bFz5062bdvG7NmzmTZtGrGxsSX+\n2r28vDh27EkOdUxMDC+99JK87dq1a1SpUkV3HG4xqFOnDjVq1CgyvK+88PX1JSYmRk49gIKwwurV\nq+PhYTioTaaMkjZL82FMa3Py7EAqmDkJybP0lHmRkiSJyMhIua15SkoKS5YsYcKECfI+HTt2ZNWq\nVdja2nLv3j3y8/OpUqUKZ8+excfHBxcXF2xsbOjatavGrKQwX3zxBb1792bYsGFMmjSJqKgojddj\nY2NJTEzk3Xffxda2YJXr5eXFggULUKlUHD16lFu3bjFz5kzs7QsiWZ9//nn+/e9/s3Tp0mJ/3T/+\n+CNBQUG8/PLL/PXXXwAkJSVhb2/Pa6+9Jt8I//PPP+nSpQtQEDPz9ttv89prrzFhwoRiN3wYOHAg\n3333XbGvtSxwdnZm+fLlLF26lOHDhxMYGMiJEyf48ssvjR6jrJI2S4Mxrc15UAcouCYheZaeMr8n\nde7cOapWrSpHcHz88ce8//77JCZq3ni0s7Pj66+/Jjw8nD59+lC7dm1atWrF/PnzGT9+PE5OTuzb\nt0+ng7Jv3z6OHTvGjh07yMzMZNCgQVpdX86ePYunp6fWTWe1+bx582Zat26t9QPVoUMHvvjii2J9\nzVFRUfz666+sXLkSR0dHrl+/TnZ2NgcPHqRLly506dKFSZMmMW3aNP788098fX0BuHXrFsuXL6de\nvXoMHTqUmJgYXnnlFaPP+9xzz3Hp0iVSUlL0v8WkHGnQoAHLly/X2j558mStbRs2bNA5RlklbZYG\nQ63NyXKG5IJ7cZUlGbOiKfOZVHx8PHXq1AEKpvt169bV2zPt3Xff5fDhwyQmJrJhwwa8vb0ZPHgw\nQUFBjBs3jnbt2mFnp/04NyYmhr59+2Jvb4+rq6vOoH6lUqmR0vk0CoVCZ/Bdbm5usf4SXrhwgY8+\n+ohRo0bh7OyMjY0NL774IqdOneLgwYP4+Pjg4eFBVlYWqamp/PXXX3h5Fdxv8fT0xMPDA6VSSdOm\nTeW44eJQp06dYj09M3fUSZs1/GYUW+I0JZIkkZNf8P6+p+VQgCoKR5xSG8GVl3CvUoUZPZqVSj8Q\nYugTynwmpVAo5OXVrl27SE5OJiAggNTUVDIyMpg/fz7Dhg0jJyeHli1b4uTkRO/evTl//jxpaWn0\n7t2bMWMKpvzff/+9zvsZSqVSo/2RLlq3bs3atWuRJEmj6Hz55Zd07tyZF198kcjISHJzczUK4d9/\n/03r1q35888/iYqKIicnh2eeeYaPP/5Y53mcnZ1ZsGABn3/+OV27dqVKlSp4eXlx/PhxTp48yaJF\niwDw9vZm7969VKtWTQ6tU3+f1N+3kvyA2tjYmI2icPHiRRYtWkRmZiYZGRl0796dyZMn8+DBAxYu\nXMitW7fIz8+nbt26BAcH4+7urn+wMmibbgyGWquHtPdjQbt+shwKlLrdeEpmLgv2XiQiLkGOArZ2\nMbTMf5obNmzIzZs3AYiIiGDHjh1s3bqVd999l549ezJr1ixu3LjB7NmzycnJIScnh71799KuXTtu\n3LjBxIkTycvL49GjR2zatIm+fftqnaNz5878+uuv5OTkkJaWRnR0tNYPSfv27alRowbffvutPGP6\n448/iIqKolmzZrRv355mzZoxf/58OSH09OnTLFu2jIkTJ9K+fXvmz5/P4sWLSUxMJD09XefXW69e\nPXr27EnHjh3lZgne3t5s3bqV5557Ti5EXbp0ISIiQr4fZSqSkpJ0WtvlzcOHD/nggw+YNWsWkZGR\nbNiwgQsXLrBu3TomTZpEr169iIyMZO3atQwZMoTx48frnMmWVdt0YzCmtbpaDlXnTpUmGbNw+mdy\nWsH9yOS0HLNK/6wIyrxIeXp68uDBAx490t0KGgruC73yyisMHDiQIUOG0LZtW/z8/PD09KR3794M\nGDCAf/3rX4wePZp27dppHf/KK6/Qvn17Bg0axP/7f/+PWrVqaS3tFAoFS5cu5fr163J/vu+++46V\nK1dSs2ZNAL799lvs7e3p378//fr14/PPP2fRokUaXUyio6Np2rQpzs6Gn/BMnz6d7du3c+bMGZ57\n7jlSUlLw8fGRX/fy8uLKlSt07tzZqO+jMVy4cIHGjRvj6upqsjFLyt69e+nUqRONGjUCCmZ4Cxcu\npHXr1lStWlVjSd65c2caNGhAXFyc1jgVKXOWd2t1a2iZXhLK5b17q1atQqlUMnLkyDIZ/6+//iI+\nPp5BgwaRm5vLsGHDmD9/Pp6eniY9T1RUFDdv3tR549ccmD9/Pp07dzZ4s7283ru3YsUKHB0dGT16\ntMb2Xbt2cfz4cbmHopovvviCxo0bawT9SZLEhcm1yX+UTKOPYmSZEyDjYgzx87pwLxO6abZhNB2P\npc0Yv0l412okb45JiqfLrm/hYQb8p3TungYTVhnsSOzuYk/SnN5W97SwXG5ejBgxgkOHDpVZV5PG\njRvLPe8GDx4sz8JMye+//85XX31FcnIyH3/8Mffv3zfp+KUlMTGRu3fvFutpYFny7LPPcvv2bY1t\nCQkJ1KxZU17+F+batWvUrVtXc2NFypzl3VrdSlqml4RyeVuMnZ1dqW1xQ7i5uRU7DaC49OjRgx49\nepTpOUpD3bp1i+UglTU9evRgxYoVjBgxggYNGpCbm0tISAidO3fm7t277Nu3T9ZEDhw4wLVr17Tb\nkD2WOfMfJZN5+YjGTEpT5kwy+exCkiRq/zSH5Kx0jiRf05hJFU70TMrNM8m5JUmi9pxf5XtRurBW\nMdQ8HgMJKh0uLi6EhIQwe/ZsgoKCGDZsGJ6enrz++ussX76cnTt3MmzYMIYNG8bPP//MypUrsbGx\n0RijImXO8m6tbi0t00uCyJOyMiwtmbM0iZylxRSJnsU631Ppn4Wx5vRPMZMSmDWlSeRUU1jELA6u\n9o781nsC01uXPNGzODyd/gkFS7zSiqGWjohqEZgtuhI5q/eZYnQipyER01CB0SVUvtl+KFP9m+Du\n7FimSy51y/QFfp6lFkMrC2ImJSgXYmNjef/99zW2LV68mKioKJ1Cq6626upEzmsLuhcpcRojYuo8\nTo9QuSj6Cq8ujyU1K6+E34HiUWlbppcAUaQEZklpJc6SiphCqDQ/RJESmB2lbatemtbq6nbq+hDt\n1MsfcU9KUG4cOXJEI300ISGBd999V3vH0rRVh5K3Vi+GUCnaqZcfYiYlKDe8vLw0IoP79++ve8dS\nJnKWtLW6up26IaxVqKxIRJESmB2llThLKmIKodI8Ecs9gVlS2kTOwumZXXZ9qyViBrfpqfs43+bs\nPndHr1ApkjbLH2GcWxnmapxLkgT5uWBjJ89U8tNTuLszhJQD4eQ/Ssamqjtu3cZS0y9Yr8RZuENx\nak4WIaf2EX7xaIEn5eDMmOYdmflCzyI9qZC9lwiPuy57UmM7NCDYt5nVCpUViShSVoa5FamnhU2b\nqu64dR1Dzf4z5UKkq4A9jT5xc+Jz3fjvgQQi/rzO3bRc3F0cjE66lCRJCJVmgChSVoapilRsbCzv\nvPMO27dvlyNWFi9eTJMmTfj4449p27atxv6LFy+mdu3aGtu03pdXiOK8L+/p99gVxjGvKlkXXgCV\n5p0Na34vnKUh7kkJSoydnR0zZ84kIiJCY6bh6upKZGRkkcerhU1dqIXN2kOLTt401PY8y/ZRQdvz\npCYa29ViZkj/lkWOL6hYxNM9QYnx8vLC1dWVNWvWFPvYwsKmPooSNp8WN/VS7TagvWAQYqZlIGZS\nglIxZ84cAgMDNfLbU1NTNaTNWrVqafcuVAubBlCnbuoUNtUUp+25pHlfSYiZloEoUoJSUa1aNWbN\nmkVwcDAvv/wyYORyr1Dqpt5djEjdLJygqZdCbc8LI8RMy0As9wSlpmfPnjRu3JjNmzcbfUxhYVMf\nxqRuFrfteWGEmGkZiJmUwCR8+OGHHDlS8BaWp5d7AB988IHWE7/CwubTFKeFuqG25455Vcl63Pa8\nMELMtByEgmBllIcnZYzXpKYkwqYuUrIzNcVNR2fGNOvIxOe6suyPmyYRM4U3VTGImZTAZBgjZj6N\njbMbtYeGUCtwgdGFTRduDk4Et+mJSqWShc6IS0cBiZm9fEuVdClan1csYiZlZeibScXGxjJq1CiW\nLFlCv3795O3+/v6kpKTInYj/+usvedk2Y8YMWrduDVRswwQou6YJojlCxSNunAtkmjRpwo4dO+TP\nz58/T2ZmJl26dJHjVdRP7iIjI+UCBRXbDh3KriW6SOqseESREsh4enqSmJjIw4cPAdi2bRv+/v5F\nHmeKJM3SfpQ0ibPIcUVSZ4UjipRAg169erFnzx4kSeLkyZNaT+R0UpHt0KHsWqKL1udmgShSAg38\n/f3ZtWsXcXFxtG/f3riDSpmkWdqPkiZxFjmuSOo0C0SREmjg4eFBRkYGkZGRDBgwwKhjKrIduvr8\nZdESXSR1mgdCQRBo0a9fP7Zu3Urjxo1JSEgw6pjSJmmWlpImcRY5rkjqrHCEgmBlmErmVAubktKW\nPEmFndIGVUaq0WJmWYiRuoTOsc07EtzGcBJnkeOKpM4KRRQpK6O0RUotbD74IwLVo2Qe2FchqnYr\nNj/Xg8CWXZn5gi+u9o56xczyECMLRwibcikmjPOKQSz3rJSVK1eyatUq9u7di4ODA8HBwZw5cwY3\nN7cCpSAlhTFjxjBkyBD5GF1JmtVyMngrIQ6f+1cZnZXO7pvn9YqTusTI5LQcQn+/zO5zd0wmRioU\nCuxtTP+jXdD6XBSn8kbcOLdStm/fTr9+/di5c6e8bdq0aURGRrJ69WpWr17NkiVLNBwgQ0maLdLv\nMu76UdHCXGByRJGyQmJjY2nQoAHDhw/Xm6p59+5d7O3t5WWNMUmag26fBkkSLcwFJkUs96yQjRs3\nEhgYSJMmTbC3t+fEiRMALFq0iOXLl3Pr1i2aNm3K//3f/z05yJgkzdxM7CQVuaKFucCEiJmUlZGT\nk8OBAwdYtWoVb731FmlpaaxevRooWO6tXbuWuXPncufOHRo0KJTDpBY2DXDPzolchVK0MBeYFFGk\nrIyrV68yZMgQwsPDCQsLY8OGDRw6dIj79+/L+3Tv3h1fX18++ugjeZsxSZqb67QGhUK0MBeYFFGk\nrIzLly8TEBAgf+7k5ETv3r2JiYnR2G/ixIlcuXKF6OhoeVvN/jNxqN9G57jnnWvyfYOORbYwb1O3\nqs7XhBgp0IfwpKyMknpSankzPyude7sW8uBAuOxJ/Vy7FVue68HQ57sVKU4WR4w05DsJZ8l6EDfO\nBQbRl7bZLOQ8No7OSEpbOkgqvjBSnHRzsiOkf0uDSZn6WqbPfMEXVLYiJdPKEDMpK6Bw6mZcXJw8\nk/L396dVq1bcvHmTOXPm0LRpU43jKiJt01DL9Odd68DVFzl7K0vrNZGSWXkR96SsBH2pm4aoiLRN\nQy3Tz6be5mz+WZ2vCRm08iKKlJWgTt3MyckBik7drKi0zZK2TAchg1ZWRJGyInr16kVCQoJxqZsV\nkbZZnJbpOhApmZUTUaSsCH9/f65du2Zc6mYFpG2qEzYNoqdlOggZtLIiipQV4eHhQX5+vlGpmxWR\ntlmalukgZNDKilAQrIwGDRqQmJhoVOpmRaRtGmqZ/rxrHbj/PGfR/XRPyKCVE6EgWBnGypxPy5ul\nbYOu7xy6ZE1DCZuobEVKppUhZlICDfTJm00fy5slbYNeGEOyppuDE6hskW43RjpnA+nZSM4OSFUb\nQCtbo2RQQeVCzKQqESEhIZw5c4bk5GSysrLw8PDgmWee4dy5c2zcuJHq1asze/Zszpw5w4IFC/D0\n9NQ4vjzkzaLaoW/v8Tb+3x0Xbc0FMqJIVUKioqK4cuUKU6dOBWDNmjVER0ezcuVKevfuzfjx4/nX\nv/6ldVzS+hnc2xWKfd0WPDsuAqemXmRePsKt78eQk3ieGn4zqD20dALnjLgdhJ6OpoWrOxE+w/By\nb8iR5GuMObie86nJdHJqQ2xcdf3H92hGSP+WpboGgWUhnu5ZAW+88Qb29vaMHz8eW1tbnQWqvOTN\notqhxz78B32yJghh0xoRRcpKeOONN9i/f7/W+/NkykPeNKYdul2eXlkThLBpjYgiZQU8fPiQzz//\nnLlz5xIbG0t6err2TuUgbxrTDl2Rr1/WBCFsWiOiSFkBM2fO5I033mD48OF4eHgwd+5crX3KQ940\nph16RxdP9MmaIIRNa0QoCJWc8PBwlEolr7/+OgBt2rTh3LlzbNmyhYEDB2rsWx7yZlHt0Nf3GIT/\nDf1P94SwaX2Ip3tWhi6ZUy1uYmNXrFbpJaWodujGpneWtlOxSPe0DESRsjIKFyl94mbN/jNRVnHV\n2yrdVBRVZPQVkSJl0CIoj1bvAtMh7klZGLGxsbRo0YJduzRzl/z9/QkODqZnz56sWrVK3n758mWC\ngoK0xlGLm/d2hcpP9fIfJXNvVyjx87uhykhFYWtfpjMMdTt0fecoaGuu1CpQ3XYvJfR0NMlZ6SgV\nCpKz0gk9HU233UtJyTYc5Kdu9R76+2WS0wqytdSt3rv99xApmbmm+wIFJkEUKQukqJTNH374gStX\nrhgcoyJSN02BOrmzhas7MX6TyBsdSozfJFq4uhts8S4fL1q9WxyiSFkg6pTNhw8fAtopm8HBwQQH\nB5Ofn6/z+IpK3TRlcqc+GVRfi3fR6t1yEUXKQunVqxd79uzRmbLZvXt3nnvuOb777jvdB1dE6qYp\nMEYGVbd410UxWr0LzAdz+zEUGIm/vz+7du3Sm7IZHBzM5s2bOX/+vPbBFZC6acrkTkMyqK4W76LV\nu2UjipSF4uHhQUZGht6UTRcXFz799FM+//xzrdcqInXTFBgjg+pq8a5xvGj1bnEImdOC6devH1u3\nbtWbstmpUyf8/Pz4559/tF6riNRNU1CUDKqvxbt8vG9zdp+7I2RRC0J4UlaGlidVxuJmWVCUDFrk\n8cVo9S6oeESRsjJ0GecqlQpyMsC+Ckql5dwBKG/jvLTnKwnCihfLvUrDjRs3+OCDD9iwYQMAAQEB\nvPzyywbzzA0Z5+Y8k1KjlkFLdbxt0b/4pTXcS4Kw4p8gilQl5NixYzz33HMcOXKEtLQ0XFxctPbR\nFRWsNs7TTu42SVRwZUBX3LHacN998zwH+k40eaFSW/Hq+2ZKxRMrfve5O1YXoWw5c3uB0WzcuJHX\nXnuNXr16sWXLFp37WKpxXt6U1nAv0TkfW/Et3J2JmdyFvEX9iZnchRbuzlZpxYsiVclIS0vj2LFj\nvPLKKwwZMoR169Zp7WPJxrmlGe4lOudjKz5i+Et4N6pecM5G1Qkf9hJgfVa8WO5VMrZt24ZKpWL8\n+PEAJCcnc/jwYby9vZ/sZJRxrsJOCbmq8rx6M8MIw12lNtzzTfSNemzFKxXg1bCa5jkbVZOXfrn5\nklH30yoDYiZVydi0aRPLly8nLCyMsLAwZs+ezZo1azR3slDj3NIM9xKd87EVr5LgyLUHmueMf4BK\nsj4rXhSpSsTZs2eRJInmzZvL21577TWOHTtGYuKTtuWWapyXN6U13Et8zsdW/Jif/ibm6v2Cc169\nz9j1fxec08qseOFJWRlqT6o8GoFWBopqZlpeT/dUj39LrbFBqphJWSk2zm40mnWAGv2my0s/m6ru\n1PCbUaoCJUkSOfl5Jb6xK0kSOY+XmeaAm4MTB/pOZEabHvLSz93RmRltepRJgQJwc7LjwDtdmNGj\nmbz0c3exZ0aPZlZXoEDMpCyalStXsmrVKvbu3YuDgwPBwcH069ePbt26kZeXx5QpU6hWrRqffPKJ\nvDwoPJMqLHIqXWri1nUM7v6zSlSgrCHSVxjnFYOYSVkw27dvp1+/fuzcuVNje25uLv/5z3/w8PBg\nzpw5Wj/cWtHBgCrtLvd3LyJ+fjfy01OKdR1PR/oClTLSV224l2exKLDilVZboEAUKYslNjaWBg0a\nMHz4cI2ndzk5OUyePBlPT0+mTp2q81i1yKmLkoicauFRFyLSV1BaRJGyUDZu3EhgYCBNmjTB3t6e\nEydOAPD555+TkZFBUlKS7gMLiZz6KK7IqRYe9SEifQWlQRQpCyQ1NZUDBw6watUq3nrrLdLS0li9\nejUAI0eOJDw8nAsXLrB161atYxXky0s8fRQrOvix8GgQEekrKAWiSFkg27ZtY8iQIYSHhxMWFsaG\nDRs4dOgQ9+/fp3nz5tja2rJ48WIWLVrE5cuXNY6VsCl4mmeA4oicauHRECLSV1AaRJGyQDZu3EhA\nQID8uZOTE7179yYmJkbe5uHhwbRp03jvvfc02l1RSOTUR3FEzsLCoz5EpK+gNAgFwcqYO3cus6e+\npylyFqIkIufTwmNhjBEen5YXNY63QnlRoImYSVkhyiquNJzx2xORE0olcj4tPELBEm96a+OEx6fl\nRTAsL5qb8CkoW8RMysK4ceMGAwYMoFWrVvK2Tp06ceDAATmVUx/56SlsndmfVqoLchKnq8+b1Ow7\nFZtn3Eu9pErJzmT+id+IuBTH3eyMEiVYGpIXLUH4FJgeUaQsjKdjgvVtexqt9+oVwhTv1Svtkq/I\n8cWS0GoRyz0rwdQC59OUVugscnwhfFotokhZIJcuXSIoKEj+0CtuPkYqA4HT1EKnsWmV+hDCZ+VF\nJHNaIM2aNSMyMlL+/MaNG4YPUCdxGkAtcJYoibM4QmdJEiyLIXxaS1qlNSFmUtaAOonT0C6lSOIs\nrdBpbFqlwfGF8FlpEUWqknDx4kUGDx4sfxw9elR+rXASpz5Kk8RZWqHTqPGF8Gm1iKd7VoJ4uiew\nVESRsiLy0h6wbVZ/WqkuPulY3G0sNf2CTRIVnJKdScipfYRfPCoH341t3pHgNj1xc3AqVmicLl8q\nJTOXkL2XCI+7LntSYzs0INi3mShQlRhRpMyE2NhYfvrpJ5YsWSJvW7x4MU2aNEGhULB582ZsbGyQ\nJIlx48bh4+Mj7zdhwgQAli9frnNsnSmcPqOp6f8hti7VdB5TGp4uRsVJ7TRG2BRpldaFeLpn5jx6\n9IjVq1ezc+dO7O3tSUpKIjAwkOjoaJRKJYmJiWRkZJCbm0tCQgIeHpr3bnQt81Rpd7n/yxekn/61\nTBouqBMsQfcyUF+bcl1LOl3txQvSKkVxshbEjXMzp0qVKuTn57Nu3TquX79O7dq1+e2331AqC/7r\nNm3ahK+vLwMHDmTt2rVax5e1xFkUxZE8hbAp0IUoUmaOra0tERERXLt2jXHjxtGjRw82bdoEgEql\nYseOHQQEBODn58euXbvIysqSjy0PidOUkqcQNgW6EMs9M8HR0ZGcnByNbRkZGSgUCrKysvj4448B\nuHr1KuPGjaNdu3bcvn2b9PR0pkyZAhQUre3btxMYGFgwQFlLnEVRHMlTUgphU6ATMZMyE5o2bco/\n//zDnTt3AMjOziYuLo4mTZowdepUUlNTAahXrx7VqlXDzs6OTZs2MW/ePLml+ldffaW55CtjidOU\nkqcQNgX6EDMpM8HFxYXg4GDGjx+Po6Mjubm5BAUF8cILLzBq1ChGjx6No6Mj+fn5BAYG4urqyokT\nJzSeBrZr147s7GyOHz/Oyy+/LEuc93aF6j1vWbZTV0ueoaej9e5TWPIc08GD0N8v699XCJtWiVAQ\nKjllLXEWRXEkTyFsCs02cUkAACAASURBVHRhM2fOnDkVfREC06L2lJQKBUp7J1y9RoAkkXP7AlJO\nBjZV3an+6iRqvvk9di5uZTo7cbS1Y0TjtgBceJhMRl4u7o7OTG7pQ1iXoRqelKOdDSPa1gMJLtxN\nIyMnH3cXeyZ3aUzYsJdEgbJSxEzKTImNjeU///kPzZo1AyA9PZ369euzePFievTowaFDh7SO0SVN\n/j+Plvy/63FkxkSS/yiZTEUVLjUfxsxMP65m2pdrumVpjXOBdSLuSZkxXl5eGvecpkyZwr59usPj\nnl5WKRUKsh7do93ayaSl3y3YSaHEScqgzYUIvrDZx2jXBSSnuWjJkmVFYcnTqH3FUzwB4umexZCT\nk8OdO3dwdXXV+bpammzh6k6M3yTyRoey3y6PFul3sa/bgkYfxdAyIo9GH8VgX7cFLfKvMS7zZ/l4\nIUsKzBVRpMyYI0eOEBQURL9+/Rg8eDC9evXC29tbaz9Jkoi4FAdAhM8wvGs1AqDKsSgAnh0XQZVm\n3igUCqo08+bZt8IBGJT1GxRa7QtZUmCOiCJlxnh5eREZGcmaNWuws7Ojfv36OvfLVeWTnJWOUqHA\ny71hwUa1yKlQ4tTUS2N/p2beoFBSQ0rFjjx5u2hnLjBHRJGyAKpVq8aiRYuYPXu2LHsWxk5pUyBF\nShJHkq8VbFSLnJKKzMtHNPbPvHQYJBX3FK7kFrotKWRJgTkiipSF0KxZM4KCgpg3b57Wa4WTMccc\nXE9MUjwA6e0GA3Dr+zFkXIxBkiQyLsZwK2wsAJsdX4VCT86ELCkwR4SCUEnQ9XTPOSeTH/9eT4tC\nT/eQCt6kd96mIaNdF/BI6QIIWVJgvogiVYnQlYw5vsHzvH09jsxDqwo8KaUzl5uPIDijj+xJGZtu\nqctzEj6ToKwRnpSFsHLlSlatWsXevXtxcHDgiy++4O+//5ZfP336NNOmTSPk9ddZ0K6fXExUGanc\nvXGcjMd/ixSSiu5Nq3PBrycqR1ejiosuSXREo3aQ7MG6Y3dEy3NBmSJmUhaCv78/3t7eeHp6Mnjw\nYI3Xtm/fzg8//MDatWtxcHCQt2u9b6/Qcs/Y9+3pWkaq1D8yWc5w5SWUki2qx5vEslFgasSNcwsg\nNjaWBg0aMHz4cNasWaPx2pkzZ1iyZAnffvutRoGCJ6mcumROY1M5dUmiMX6TaOHqDo7pjOyjIm9R\nf2Imd6GFu7OQQgUmRxQpC2Djxo0EBgbSpEkT7O3tOXHiBAD379/n/fffJzQ0lLp162ocUziVU5/M\naUwqpzpZUy2JKhQKvGs1IrzLMAD+d+ckAN6NqhM+7CVASKEC0yLuSZk5qampHDhwgPv37xMZGUla\nWhqrV6+mdevWvP/++7z55pu0b99e+0BjZE4nleFUzsfJmhqS6GO8azVEqVCQnJVOriofextbvBtV\nQ6kQCZoC0yJmUmbOtm3bGDJkCOHh4YSFhbFhwwYOHTrE7Nmz8fDw4PXXX9d9oBEyZ1GpnOpkTQ1J\n9DGH71xDJUm4Ozpjp7Qp2Bb/AJUkpFCBaRFFyszZuHEjAQEB8udOTk50796dqKgoLl26RFBQkPyx\nevVqeb/CrdX1yZxFpXLqkkQlSSImKZ6xh9YD8Jr7CwDEXL3P2PUFTxuFFCowJeLpXiVGPN0TVAZE\nkarkPN29OEtZhXp9JxertbouSfT1xu3hjgdrjyVZRctzfYF9QmYte0SRMmOebr3+yy+/8O2337Ji\nxQpCQkLIyMhAkiSeffZZZs+ejaOjo8bxutqrn1U+R0DIzhLlmlujca6vRfzE57qx9I8bBtvBC0yD\nKFJmTOEitXPnTsLCwli5ciXh4eF4eHgwYsQIAD7//HPq1avHm2++KR9riqWetWOoiYRjXlWyLrwA\nKs0H5GK5a3rEjXMLYMuWLURERBAREUHNmjWpV68e//vf/4iJiSErK4sZM2YQFBSkcYwpRE5rx1CL\n+CzbR+B+XWu7kFlNjyhSZs6ff/7Jhg0bSE1NJT8/H4ARI0bQv39/wsLC6Nq1K5MmTdLImTKVyGnt\nH0W1iKfabUB7ISJkVtMiipSZ4+7uTkREBKNHj2batGmoVCpiY2MZOHAgYWFhHDp0iDZt2jB//vwn\nBxklcha0VxfowZgW8ba5oNAuRiLh1LSIH1Mzp2HDhjg4ODBy5Ejs7OxYtmwZP/74I1FRBfnl9vb2\nNG/eHHv7Qi3KTSByWvuHMS3iybMDSfthgZBZTYsoUhbE/PnzWb9+Pf369SM6OpqBAwcyfPhwNm/e\nzPTp0+X9TCFyWjuFRVa9PKgDaH8PhcxqWsTTvUqKeLpXesTTPfNAtFmvpCjtHTXbq2enY1PVnX+c\n2uI1d0+lLFCF28ubYiajr0X8JE8fvusciJONAxfupot28GWMmEmZIQkJCYSGhpKSkkJubi6enp5M\nnTpVVhDUftSCBQtISEjgq6++0rwnhW6R063rGMLPKpn1aeXSD/QJlzNf8MXNwckk55AkieT0LBb/\nfoUf/rwhC5xvtq/P1Fea4e5iL5Z4ZYQoUmZGVlYWgYGBzJs3jxdffBGAzZs388svv9C6dWtq1qzJ\n8OHDmTdvHqmpqYSEhGBrq7nk0FrqFSLFvhadvjpfaWZShpZkbarV5UDfiSYpVCmZuXT77yFOJT7S\nPo9Y4pUp4sa5mREdHU2HDh3kAgUwaNAgHjx4QEJCApIk8cknn5CZmUloaKhWgYInIqcu3HLuVCqR\n05BweepBIiGn9pnmPHsv6ixQIATOskYUKTMjISGBBg0aaG2vX78+iYmJrFixguvXr5OUlKRzeVFY\n5NRHZRI5ixIuFx7aaZrz7Dxm8DxC4Cw7RJEyM2rXrs2NGze0tsfHx1O3bl18fX354YcfcHZ2Ztmy\nZdoDqEVOA1QakdMY4fKZKgX7lQalLVQxvDwWAmfZURl+VCsVvr6+xMTEcPLkSXnbxo0bqV69Oh4e\nHjRv3hyAzz77jE2bNhEbG6s5gFrkNEBlETmNES7dHZ1R5eaV7jx5BTfJDZ5HCJxlhihSZoazszPL\nly9n6dKlDB8+nMDAQE6cOMGXX36psZ+rqysLFy5k2rRp3L17V95eWOTUR2UROY0RLsc271jqr1Wh\nUDCmg4fh8wiBs8wQT/cqIeLpXgHi6V7lQMiclRBZ5IQCkTMnA5uq7lTvNZnNmW3p/lr/Cr5Cw0hS\nQZCe8vHExJCgqU+4nNzSh7AuQ03mSTna2TCibT2Q4MLdNCFwliOipZUZkpCQwKJFi7h9+zaOjo44\nOjoybdo0+X7UhAkTAFi+fLnhgR7fUyn4pwSShK73mpkLKZm5LNh7sSDtMiMDp3o3UbjdJkPKMiho\nujk4EdLeT6O9fFksvdyc7Ajp35IFfp6VOo3U3BDLPTMjMzOTwMBAPvvsM9q2LZghnDx5kkWLFhEZ\nGUliYiIzZswgNzeX0NBQPDy075VY4nJPYzmlzIMmf4NjutZ+plzCCSwDcePczPj999/x8vKSCxTA\nCy+8wKpVqwDYtGkTvr6+DBw4kLVr1+ocwxJlTg1Z0v26zgIFphU0BZaBKFJmxo0bNzRkzn//+98E\nBQXRp08fbt26xY4dOwgICMDPz49du3aRlZWlcbylypxPZEnpceKlfsIvHhXipBUh7kmZGXXq1OH0\n6dPy52phc+jQoURHR5Oens6UKVMAUKlUbN++ncDAwCcDFEPm1NtevbwpLEsqpILESwMUbu0uqPyI\nmZSZ4evry+HDh/n777/lbdeuXeP27dvs3r2befPmERYWRlhYGF999ZX2ks8CZU4NWVJSFCReGqBw\na3dB5UcUKTND/XaXH3/8kZEjRzJ8+HA+/PBDPvjgA65du4aPj4+8b7t27cjOzub48ePyNkuUOf9/\ne2ceF1W5PvDvDAwgYICKprkiJoWm1y0Rw8zcpa5yzbLwplaWtlhpgtbNuuaCdm372SZqmWXikiuV\nmcpVFJW6uZVbYS6kqKCyD8z5/YEzMszCDMzAAM/38/HzuXPO+57zDnEf3vc93/M8xrKk6kbGS8s4\nQtAUag7ydK8WIk/3hNqEzKRqIW4+/rSenkTDIa8Yln5u9QNpOHQaO5qONQQoRVEovLHsczSKolBY\nXGTztf3raUiaFM60vsEEenvD752pd7U13qqSqsyBXj5M69hXAlQdRHYeayHmsnL69X6cRkNj0P76\nrrE06eAS4ZXJkmlOlgScKmgKro8s91yIlJQUxowZw8KFCxkyZIjheGRkJKGhoYwePZp33nmnZLNZ\np6NPnz6MGzfO6BrWlnqezTuyTB3Jer/+TnkHrareoxPqFrLcczGCgoLYtGmT4fOxY8fIy8sD4M03\n32TGjBksXbqUxYsXs3nzZo4ePWrU35rIWXD2EP5XfnJahsmqypIp1C0kSLkYISEhpKenc+3aNQA2\nbNhAZGQkAM2aNWPFihUcPnwYtVrNV199xZ133mnoa4vI2SN79413+Mwzb/MBp2XJFAlTqAgSpFyQ\n/v37s3XrVhRF4eDBg4ZXZGbPnk3Dhg2ZOXMmvXr1Yt68eRQWFt7saIvIyXU0FFlu4O1fIlfaiw1Z\nMvUSpiDYgwQpFyQyMpItW7awf/9+unXrBkBBQQFHjhxh0qRJrF69mm+//Zbz58/z9ddf3+xog8h5\nRXULWivPSwJ9PdAVFTotS6ZImIK9SJByQVq0aEFubi7Lly/ngQceAECtVjN16lSOHz8OQEBAALfd\ndptRvT1bRM4Un15g5SlZRTNMVlWWTKHuIUHKRRkyZAjp6em0adMGAI1GwzvvvMO//vUvRo4cyUMP\nPYSiKERFRRn1azQsFs/mHc1e07N5R7IadKFj0/pmz3dsWp+YfsEVHnPsXf3oGNDU/LUDmhLT8b4K\nX1uou4iCUAsp60m51Q/EP2IcjYbGMGvBu7zwynTmbjvJkv1/Gjypcd1bEtMv2CGe1NxDP7LkxD6D\nJzWuXQ9iOt5XrfqBoigu5Vvps49K4rzykSBVy7BUXj0wcjpuPv688cYbvP7664Bz/4/iKkGhKkqw\n2zUeJ4q0tRUJUi5ESkoKkydPJjg4GEVRKCoqYsyYMdx111088MADhIaGGrVftmwZbm43N6JNRE6V\nGpSSfCyezTvSenoSsxa8awhStZ2ycqlapUJ349e9OuRSKeZQMeS1GBejZ8+eLFy4EICcnByio6N5\n6623CA4OZvny5Vb76kVOj6btafbEUuq17Uneqb2cXzyWgrOHbmTkrDvGt14ube8XyNLeo+gZ2Iq9\nGacZu+trg1w6t9vQqhuPDaXa5w67o8rGU1OQjXMXxsfHh1GjRhEfH19u29IiZ7MnluIdHIZKpcI7\nOIxm45cAJRk5Z86cWe1ZOKss2+cNuXRp71GENW6NSqUirHFrloSPAhxXgt3+7KPmkVLt5pGZlIvT\nsGFDMjMzOXnyJNHR0YbjoaGhxMTE3GyoFzlVauq17Wl0jXrBYaBS07CezrUycjqTG3KpWqWiZ2Ar\no1NhjVuVLP30JdiLq+AHYkepdg932UgvjQQpF+f8+fN07dqV69evW1/u3RA5i69nkHdqL97BYYZT\neSf3gKLDrX4gM16byOt1oNSioig0WTmTjPwc9macJqxxa8O5PRdPo1MUAr18uKAtqpKNfUVRaDLz\nezKyCy22kVLt5pHlnguTnZ1NQkICgwYNKrdtaZHz/OKx5J5IRlEUck8kcz6+JFOCf8Q4qyJnbaK0\nXDp219ckX0hDURSSL6QxbneJpV+Vcqlx9lHzSKl288hMysXYu3cv0dHRqNVqiouLee655/Dw8DBZ\n7kHJu3yl6+41GhZL9sFECs4eIm1WuMnTvUZDY+DXd6v0+1QnsXf1I/HcMQ5lphO+5QOTp3tVLZfG\n9mtH4m8XLT7dq4xIW5sRBaGWUZyTxaXNc8lKWmIicpb1pFwBfXZQVDo83NwdPpNwNbk0K0/rNJG2\ntiIzqdqI2fLqrkVWnpbXtx7m0993k+dzDty1eKu8eKJ9D97o0t9hAaSqSrDbPB4p1W43EqRchLlz\n53LkyBEyMjLIz8+nRYsWBAQEsG3bNr7++ms6dOgAwFdffcWlS5d47rnnTK5hLiunLvsSl7fEkX0w\nkdbTk6rs+1gjK09L+KIdHPXeBX43iy3kKvm891sSP6QfZ/fQSQ6d6ahUKpeq06dSqeQpno24zn+1\nOo5eJ1i7di2///47U6ZM4ezZs6SkpBAbG8uaNWuMMh6Yo7ysnK4ic87ZdoKjxUctllI/evWvKhct\nBddFnu65OK1ateKee+4xWOiWsLW8uivInHGbD0gpdcFmJEjVACZPnszu3bs5cOCA5UZ2lFevVtTu\n4ONncyl1QajuX1nBBjw8PJgzZw6vvvqqoSiDCTaWV5/x2uvVXlK9kY+nlFIXbEaCVA0hNDSUYcOG\n8emnn5o9X1rmtIQryJwqlYpx3VtKKXXBZiRI1SCefvppmjVrZvF8eVk5Gw2NMXuuqont14473e6E\nfPM50e/0u1WyeAoGROasRSiKQvH1DC4nvm02K2dFZU5HJrDTJ9rLKSzijR+O8snvu4w8qSdD7mbm\n3+6XIqKCAVEQagHm0gX79X6cRoOn4HZLYIUDiyOzWlrKSHnuqafw1rg5zTgXaj6y3HMBUlJSePHF\nF02OFxQUEB4ezuLFiy321Qucl7fEGZ7uFV/P4ErifE7H3Y8u92qFxqTPahl3eAcZ+SU+U0Z+DnGH\ndxCRuIisAgsb+OaudSMjZdz2U4YsABnZhcRtP0WfRcnkFenwdNdIgBLMIkHKhfnuu+8YMmQI69at\nQ6czn/PINoHTfhxZMt2WjJSCYAkJUi5MQkICUVFRhISEsHPnTpPztgqcpUVKW2XO8kqm25PVUjJS\nCpVBgpSLkpaWRl5eHiEhIURFRbFixQrTRs4SOG0omY4+q2V52JGRUhDMIUHKRUlISCAvL4/x48cT\nHx9Pamoqp0+fNm5ko8BZUKQzyJQzZ850WMl0nbbIJnkz0Nf6O4eSkVKwhgQpF6SoqIgtW7awYsUK\n4uPjiY+P56mnnuLLL780amerwGnvhnTprJaWsFW2lIyUQmWRIOUi7N69mxEjRjBixAgGDhxIaGgo\n/v43l0kjRoxg/fr1Jq/FOEvgdGTJ9Nh+7ZxW2l2o/YjMWQuwVlbdzcd4P8gembOyWS1LS6BX84ts\nzkgpJciF0ojMWcMxV1bdr/fjZgOUvVQ0q6VFCbR/P6sZKaUEuWAOWe65EJ988gm9e/emoKAAKEmE\nl5RkOZumOZFTl32JK4nzSZsdQXFOlkPGpc9qaWuAsiaBXi3Mx8NdbTZAWRI+I/5vN1l51lO7CLUX\nCVIuxMaNGxkyZAibN2+2qb2zRM7KUFEJVIRPwRISpFyElJQUWrZsycMPP2zeiSpDRUROe2TOCmfd\nrKAEKsKnYAkJUi5CQkICI0eOJCgoCA8PD3755RfrHVwxE2dFJVARPgUrSJByAa5evUpSUhKff/45\n48ePJzs7my+++MJ6pwqInLbKnBXOullBCVSET8EaEqRcgA0bNhAVFcWSJUuIj49n1apV7N69mytX\nrljs4yyRszJUVAIV4VOwhgQpFyAhIYEHH3zQ8LlevXoMGDCA5ORk3nrrLYPk+fLLLxv1c8VMnBWV\nQEX4FCwhMmcNQlEUKNaC283cS+WVVS9LVZRZNyeB/rNtN6bf1Y8AL2+LsqaUIBfMIUGqBmDWKL9n\nLI2GxRoCkbkAZo6qCFJ6MvNzeeuXH/js1AEuFeTSyNOHIHUQp4415PI1xaKsKca5UBoxzp1ISkoK\nkyZNYuPGjTRtWrIEWrBgAUFBQfTr14958+Zx+vRpiouLadq0KW+++Sb16xsveUxKp6vUJXnMS5VO\nd/PxL/k/s7v1zeeqJKsgjz7ffmjkTF0qyOESh6CxD2R3JiMb4rafIvG3iyRNCjcEKilBLpRG9qSc\njEajITY21sTxeemll+jbty8rVqxg5cqVdOrUiX/9618m/fXCpkfT9rR+LZk7lhbR+rVkPJq2rzZh\n0xasSZ145UDgn4aPImsK1pDlnhNJSUlh5cqV6HQ6unfvzmOPPcaCBQvw8vJi69atrF+/3tC2uLiY\n3Nxco5mUoigcf64JxdczaP1aMt7BYYZzuSeSSZsVzuU8iEio0q9lG+88Y92ZKtLAb2FAyYwp0NeD\nCzMHyPJOMEFmUlXAzJkzWbZsGWlpaUDJ7Kp58+ZGbdzc3EyWegZhU6WmXtueRqfqBYeBSu0apdPL\nYovU6a4F1c2/jyJrCpZwtV/vWklAQADTp08nJiYGnU6HTqfjr7/+Mmqj1WrZuHGjcUe9sKnoyDu1\n1+hU3sk9oOjMCpvW/jlT5rRH6qRIA8rNWZPImoIlJEhVEffddx9t2rRh3bp1NGnShICAAH744QfD\n+c8//9zoMxgLm+cXjyX3RDKKopB7Ipnz8eOAqhc2bcEWqbOkzPrNcYusKVhCnu5VITNmzGDv3pIZ\nUVxcHG+++SZLlixBq9XSsmVLZs2aZdKn0bBYsg8mUnD2EGmzwkGlBqWkvJUrlU4vS+xd/Ug8d8z8\n5nm+D2S0NHwUWVOwhmyc1wDsFTatURlPyly5dWtOU1mps5GnD23VbTl5rIHBkxJZUygPmUnVFG7s\n95T8TwWq8G+LuUybj7TuChkt+Cr1osUsmpYye4qsKdiDzKSqgbNnz/LSSy8RFBTEkCFDiIiIsNjW\nnMxZermnlzltxd6ZlD7Tpn7Zplap0Ol/ZfJ94PfOoLv5t65j0/pGYqYgVBbZOHdxqlvm1EuZ7f0C\nSR76LEX/jCN56LO09ws0kTJBxEzB8UiQcmFKZ99s9sRSvIPDUKlUeAeH0Wz8EsB89k1r/+zNzKnP\ntLm09yjCGrdGpVIR1rg1S8JHlQwy4C/AeDIuWTQFRyJBypWpbpnzhpSpVqnoGdjK6FRY41aoVSoT\nKRNEzBQciwQpV6aaZU69lKlTFPZmGJd433PxdMneVBkpE0TMFByLBCkXprplztJS5thdX5N8IQ1F\nUUi+kMa43V+XNCojZYKImYJjEQWhmnnrrbd45513AGjTpg1vv/220fnqljlLS5nhWz4wfbpXSsoE\nETMFxyMKQg2gumVOc5k2R7fpBhdb8GXqBcmiKTgVCVI1BEVRUIpKKvuq3D0qvJxytHGu0+nIKShG\n467g6W49K6ggVARZ7jmZlJQUJk+eTHBwyRKooKCAyMhIoqOjAXjwwQfp0qWLxcBhS+rgqkJfbh1K\n8pG/vvUwn/6+mzyfc+CuxVvlxRPte/BGl/74e9ar0rEJtRcJUlVAz549WbhwIQCFhYUMGjSIBx98\nkBMnTnD77bezd+9esrOz8fX1Nepna+rgqiYrT0v4oh0c9d4FfjlAiYmeq+Tz3m9J/JB+nN1DJ0mg\nEhyCPN2rYrKzs1Gr1bi5uZGQkMDAgQPp378/33zzjUnb6rbNLTFn2wmOFh8FrxyzJvrRq38x99CP\n1TI2ofYhe1JOpvRyT6VSodFoGDNmDF27dmX48OEkJiZy7tw5Jk6cyObNmw39XDp18NOfQZdfwV1L\n8tBnCWvc2nAq+UIa4Vs+INDLhwsPz5Q9KqHSyHKvCii93NPz5ZdfotPpmDBhAgAZGRns2bOHsLAb\nwcgm21yHRg1aXZV8jRLU7uDjB+5aqyZ6Rn4OWl2xYQ9LECqKLPeqidWrV/PRRx8RHx9PfHw8r776\nKitWrLjZwAm2uSPSB+uKCmnk4wlFGqsmeqCXDxq1W1X8KIVajgSpauDo0aMoikK7du0MxwYOHEhq\nairp6SUpUarbNreESqViXPeWN0xzyyb6uHY9ZKknOATZk3JhHJ1LChxTwdjo6Z7Xzad7ehP9Tr9b\n5eme4DBkJuXCuPn40yp2Jw0HTzEs/dzqB9Jw6LRq0w8A/Otp2D3xXl64bTj1rrY2LP28VV68cEef\ncgOUoigUFhfZlM5FURQKbyxphbqJ7Go6kJSUFMaMGcPChQsZMmSI4XhkZCShoaHs27ePpk2bolar\nKSgoIDQ0lJiYGDw9PU2uVVbiVPs2osGgl2kUOQN334Cq/Fpm8a+n4Z0HOrNQ6URhkQ5UOjzc3K0u\n8cylIR4b3J3Yu/qZBLWsPC1ztp1g6f4zFtMTC3UDmUk5mKCgIDZt2mT4fOzYMfLy8gyflyxZwvLl\ny1m1ahWNGzc2eeoHN5d5l7fEGZ7w6bIvceXbtzk9pw/FOVlV8l1sQaVS4alxK/eVGH0a4rjDO8jI\nzzE8AYw7vIOIxEVkFdz8GWXlaYn4v93EbT9FRnbJq0AZ2YXEbT9FxP/tJitP6/TvJbgOEqQcTEhI\nCOnp6Vy7dg2ADRs2EBkZabbt2LFj+f77702Ou6rEWRmspSE+lJluJH/O2XaCQ+nXzV5H0hPXPSRI\nOYH+/fuzdetWFEXh4MGD/O1vfzPbzsvLi4KCAqNjzkgZXJn0wY76V14a4nm7N99suznV6s9X0hPX\nLSRIOYHIyEi2bNnC/v376datm8V22dnZ+PiUKUde3SmDnYEtaYhv8S5pp3YHb+sPBCQ9cd2iJv2q\n1xhatGhBbm4uy5cv54EHHrDY7tNPP2Xw4MHGB50kcTpK5qyQAGpDGuJALx902iJ0RSWb5NaQ9MR1\nCwlSTmLIkCGkp6fTpk0bo+Pjxo0jOjqaRx99lJycHCZNmmR03lUlzspgSxpivfypUqkY272F1etJ\neuK6hcicLogzJE49jpA5K4K1IqMdA5qSNHiiQUPQP90zt3kuxUfrHjKTckFcSeK0R7y0hr9nPZIG\nT2Rax76GpV+glw/TOvY1BCi9uOnn5U7SpHCm9Q02LP0CfT2Y1jdYAlQdRGZSDqI8kXPu3LlcuHCB\nAQMGMHfuXNO9qBuYkzj9e//TYRKnrTMpe8RLeymbhtiauOnn5Y62WEHjppIlXh1FZlIOpDyRc+3a\ntYwZM4Yvv/zSbH9XkTjtES8rgj4NsT5AWRM3r+YX4eGulgBVh5Eg5UCsiZyKorB+/XrGjh2LVqvl\n+PHjJv1dReK0tQfxNQAAIABJREFUR7ys9L1E3BTKQYKUg7Ekcu7Zs4fbb7+dBg0aEBUVZZw7CudL\nnPbInPaIl5WWPEXcFMpBgpSDsSRyrlq1irNnzzJ+/Hg2btxIYmIi16+XmkG4isRpj3hZWUTcFGxA\ngpSDMSdyZmZm8ssvv5CQkEB8fDyff/45AwYMYN26dTc7OlnitFXmtEe8rLTkKeKmYAMSpJxAWZFz\n//79DBgwADe3m+l0H3roIb788kvDUsZVJE57xEuH3EvETaEcREFwIZwpceqxRUGwR7ysLCJuCuUh\nMykXwlUkTkvi5Ssd+vLDwKfw8/By3L3qaSolbkrmztqPzKQqSUpKCpMmTWLjxo00bdoUgAULFrBp\n0yaGDx/OCy+8YGi7detWvvvuOxYsWGByHWdLnHrsfS1GURQy8rJZcHgHy04dcLjYWfZetoqbkrmz\n7iAzKQeg0WiIjY01+mv+j3/8g02bNhkdW7NmDaNGjTLp7yoSpzmuFuZz//efMP/ITqeInaVRqVQ2\niZuSubNuIUHKAfTs2RM/Pz8j96lZs2a0atWKAwcOACXFP8+dO0f37t1N+ruKxGmOqhQ7bR6TCKB1\nCglSDmLmzJksW7aMtLQ0w7GHHnqI9evXA/DNN98QFRVl0q+qJM6KZuasSrFTBFDBHBKkHERAQADT\np08nJiYGna7kiVzfvn05cOAA+fn5bN68mQcffNC0o6tInOaoSrHTVkQArXNIkHIg9913H23atDFI\nmhqNhvvvv58PP/yQtm3bEhBgZgO8iiTOimTmrEqxUwRQwRISpBzMjBkz8PK6+Yh+5MiRxMfHm90w\nB9eROC2NrarETrvGJAJonUIUBBegKiROPfYqCFUpdto8JhFA6xQyk3IB1N5+tJr2Aw2HvFLtmThL\noygK3u4adg56xmpGTVuv5SjpsrICqFCzkDLrFeDRRx/l2WefJSwszHBs1qxZtG/fnpEjR/Lggw/S\npUuXcmcsZQVOt/qB+PV+nEaDp+B2S2C1LVksZeU8NnwaPhoPQ0ZNm67lJOnSv56GucPuYM7QEMnc\nWcuRmVQFKK0WABQWFrJ9+3aGDh1Kamoqt99+O3v37iU7O9viNUwETqD4egZXEudzOu5+dLlXnf49\nzFE2KydgkDf7fPshuUVauwKUs6VLWwVQoeYiQaoCDBo0iJSUFENq4G3bthEeHo63tzcJCQkMHDiQ\n/v37880331i8hl7gNEd1Cpx6edMc9sqbIl0KjkCCVAXw9PSkX79+bN26FSjJXT5q1Ciys7NJTU3l\n3nvvJSoqiq+++sps/9ICpyUcKXDaI3Pq5U1L2CNvinQpOAIJUhVk5MiRrF+/ngsXLnDt2jVCQ0PZ\nsGEDOp2OCRMm8Oabb5KRkcGePXtMO+sFTitUi8B5Q960iq3ypkiXgoOQIFVB2rdvT05ODp9//rnh\ndZfVq1fz0UcfER8fT3x8PK+++qpJLnPgpsBpBUcKnLbKnHp50xq2ypsiXQqOQoJUJYiKiiIhIYGh\nQ4dy9OhRFEWhXbt2hvMDBw4kNTWV9HTjPZ7SAqclqkPgLC1vWsJWeVOkS8FRiMxZTZgInKVwtMBZ\nmvJkzrLyZmnslTdFuhQcgcykHIyiKChFheVuCLv5+NN6ehINh04zLP0cIXDqpUmdTleh8uhls3IC\nFZI3QaRLwTHITOoGn3zyCcnJyajVJc7Niy++SIcOHfj666/ZsGEDarUarVbLiy++yN13323S35yY\n6X/PWBoNiy034CiKAsVacNNUePmjlybjU3/ncr1TqBr8heKmpZGnD+Pa3cyiac9rMYpiXA69MtiT\ndVMQSiPGOXDy5El+/PFHvvrqK1QqFb/++ivTpk1jwoQJ7N69m2XLlqHRaDhz5gyPPfYY69ato0GD\nBob+5pZuxdczuLwljuyDieXOjFQqFbhb32S2hmFZdSETgv4HXjno//JcKigRMRPPHSNp8ES7rqtS\nlZRDdwQl0qUEJ8F+ZLkHNGjQgPPnz7N69WouXLjAHXfcwerVq1m5ciVPP/00Gk3JsqRFixZ88803\nRgEKql/MNEiTgX+CV47ZNtWVRVMQKosEKUqC1IcffshPP/3EqFGjGDRoENu3b+fixYu0aGH8hKps\nTqjqFDONpUkFAv6yOo4lJ/ahIKt7oWYhyz3g9OnT+Pr6MmfOHAAOHTrEU089xR133EF6ejr169c3\ntN21axft27cnMPCG52SHmKnVOWHwemlSpQN36+/CZeTnUIyvEwYhCM5DZlLAsWPHmDlzJgUFBQC0\nadOG+vXrExkZyaJFiygqKgLgjz/+YMaMGajVpX5s1ShmGkmTigqKrD8tC/Tywc1qC0FwPWQmBQwY\nMIBTp04xcuRIvL29URSFV155hfvvv5+rV68yevRoNBoNxcXFzJ8/n4YNGxr66sXMy1viLF7fmWKm\nXpqM234KMm+FwDMW245r1wPV8QNOGYcgOAtREBxAdYmZesw93SuLXsR8d26cXZk5BaG6keVeBSkt\nbTpLzLQVgzTZJ4SGF7tDRgtUxSVLv0aeFRMxHYVOpyO7oNBQQUcQ7KXOzaTMSZtffPEFR44cwd/f\nn6KiIgICAoiNjTV5sgflS5uOEDMrg16adFdDkaIzETHtzXFeUU5fvcZDievYn/0bipsWVbGG7r4h\nrBo8nFZ+tzj9/kLtoU7tSVmSNu+8806mTp1KREQEAAcOHGDy5MmsWbPGqL/N0mYlxMzKUlqa9Kim\nifLpq9cIWbWQfPfr6HfqFTct+/IOEbIqjd8eelEClWAzdWq5Z0naLEu3bt3QaDScPm1ca666pc2a\nwkOJ60oClBny3a8zKtFyxlJBKEudC1LmpE1zNGzYkMzMTMNnV5A2qyIzpyP+7bv2q9Wf077sX2WP\nSrCZOrXcsyRtdurUyaTt+fPnufXWW28eqG5ps6ag8QBNkdUmipuWXG0Rvp7VtywWag51aiZlSdp0\nczNWHHfv3o2Xl5dxkKpmabMqMnM64l9xfp7hyaIlVMUavDV16u+jUAnq1G+KJWnzhx9+YP78+Xz6\n6aeo1Wp8fHx45513jPpWt7RZU1Cr1XT3DWFfnvm9O4AevncYW/uCYIU6pyBUhuqWNh1BVSgIRk/3\nyuBVVF+e7gl2IX/O7MCkHDrWpU19lkz9UqgimTJrIq38buG3h17k7np3GZZ+qmINd9e7SwKUYDd1\nZrmXkpLCpEmT2LhxI02bNgVgwYIFBAUFMWLECADef/99GjVqxCOPPGLU195y6EalxXNzqXfbOVT+\nf5Gr5BtKluszZdZWWvndwt6Hx6DT6cjVFuGtcZclnlAh6tRvjUajITY21q7ZjEk5dJXaajl0o9Li\nubkQ9D/y/NLIVfJRq1SGkuURiYvIKshz9Fd0OdRqNb6eHhKghApTp35zevbsiZ+fn/laeBbQC5we\nTdvT+rVk7lhaROvXkvFo2t6swKnPktk+0IdHBxWDVw7t/QJJHvosRf+MI3nos7T3C5RMmYJgI3Uq\nSAHMnDmTZcuWkZaWVm7b0gJnsyeW4h0chkqlwjs4jGbjlwCmAqe+tPiSUZ34/mLJBvvS3qMIa9wa\nlUpFWOPWLAkfVdLmxL46sUclCJWhzgWpgIAApk+fTkxMDDqdDq1WS25uruG80f6SXuBUqanXtqfR\ndeoFh4FKbVwO/UaWTLUKura4hYz8HNQqFT0DWxn1DWvcyrD00+qKnfVVBaFWUOeCFMB9991HmzZt\nWLduHYWFhSxevBiAixcvGiW0Mwicio68U3uNrpF3cg8oOiOBU58lU6dA6plrJSXJFYW9GcbvAO65\neBqdohDo5YNGLbkyBcEadTJIAcyYMQMvLy8KCwvZt28f0dHR5OXl0adPH0Ob0uXQzy8eS+6JZBRF\nIfdEMufjxwHGAmfp0uLjvv6FAYEdARi762uSL6ShKArJF9IYt/vrkjY2liwXhLqMyJzlYCJwqtSg\nlLycZ07gNCotri4yypSpVqnQ3fhx21uy3FFUVT4pQXAUdXYmZStuPv60it1Jw8FTDEs/awKnUWlx\nb2/4vTP1rrbGW+VlWOJNCe3DzkHP2BWgSouhglCXqNUzqRMnTjB//nzy8vLIzc2lT58+eHt7s3Pn\nTq5du8bFixcJDg4GYNmyZSYvGpeVONW+jfDv/U8aRc7A3TfA3C2NKF1aPKsgj7d++YHPTh3gUkGu\nzVKnkRiaXbLnNbZ7C2L7tcO/nvUXec0hMymhplFrg9S1a9d49NFHef/992ndujXFxcW88MILhIeH\n88gjj5CSksLKlStZuHCh2f72LvOskVWQR0TiIg5lpgO2L/uMlo5l6Ni0PkmTwu0OVBKkhJpGrV3u\nbdu2jbvvvpvWrVsD4Obmxrx584iKirKpv70SpzXmHNzGocx0u6VOQ/l0MxxKv87cbSdtHoMg1FRq\nbZAyVyLdx8cHD4/yE61VROK0WgY9eQtgWeqct3uzlfLpllmy/0/ZoxJqPbU2SDVr1oy//vrL6NiZ\nM2fYv39/+Z3tlTit4aaGW7ytSp3c4l3SrjT68ulWyMguRFssQUqo3dTaINW3b1/++9//8ueffwKg\n1WqZO3cux48fL7+znRKn1TLo2iKbpE6dtsh8+XQrBPp6oHETz0qo3dTaIOXr68vcuXN59dVXiY6O\nZtSoUYSEhDB69Ohy+9orcZZ3rbHB3QH7pM7SYqglxnVvKTKoUOuptU/3Kos83RME16DWzqQqir58\nutrbz7h0ejkSpzX8PeuRNHgi0zr2NSz9bJE6jcTQG0u/QF8PpvUNthqgRPwUahO1YiZlKetmkyZN\n2Llzp+EFYoDnnnuOXr162ZR9U18+Xe3t57DS6Zn5uRWSOkuLoZbGYIv4KTMpoaZRa2ZS5rJuent7\nU1xcTEJCAgCbN29Gq9WaDVDmsm9e3hJH2uwIdLlXUbl7VDpAZRXk0efbD3n7aBKXCnLtytRZUj5d\nbTVAGTKCZhcCJU//4rafIuL/dpOVp63U2AWhuqg1Qcpc1k2VSsWcOXP48MMPOXnyJB999BGzZ882\n6etIcdMaFZU6bbq2iJ9CLaXWBCkwn3Xz1ltv5fnnn2fUqFFMmTKFBg0aGPVxtLjpDKnTpmuL+CnU\nUmpVkCqbdVPP3//+d7y8vIxyRRlwpLhpjYpKnbYg4qdQi6lVQQqMs27ahAPFTWdInTZdW8RPoRZT\n64IU3My6aQuOFDfLu09FpE6bry3ip1BLqRUKQmVxpLhpjYpKnTZd20bxUxQEoaZRK2dStqCXNhVF\nwc3H3yHiZnml1M1KnZ4+vNKhb6VTCVdU/BQEV6fOzaSsSZtuPv4lAcZOcTOrII85B7ex9OR+MvJz\nyhU0s/K0zP7hOEsP/MmlbC2Bvp6VyrZZFmvip8ykhJpGjQpS5tIBP/fcc6hUKrZs2cL06dP57rvv\naNKkidn+zljW2buEc8b7ePYgQUqoadSY5d61a9d46aWXmD59OsuXL2fVqlUcP36clStXApCQkMBj\njz3GqlWrLF7DGdKmvYKmSJeCYB81JkhZSwd85swZrl69yoQJE1i/fj1arekrIM6SNu0VNEW6FAT7\nqDFBylo64NWrVxMVFUX9+vXp3LkzW7duNb2AM6RNewVNkS4FwW5qTJCylA543759bNy4kW+//Zbx\n48eTlpbGF198YXoBJ0ib9gqaIl0Kgv3UmCBlKR3wr7/+SocOHVi+fDnx8fGsXr2ay5cv89tvvxn1\nd4a0aa+gKdKlINhPjXq6d/jwYeLi4lAUhZycHPr27cuRI0cYOXIk/fr1M7T79NNP+fPPP/n3v/9t\n1F+e7snTPaHmUaOClD1Y8p2Kc7K4tHkuWUlLbnpSEeNoNDTGbIBSFAWtrhiN2s1yLqeCPOYe+pEl\nJ/YZPKlx7XoQ0/E+i57U3G0nWbL/z5LkdD4e/LN7C6b3CybAu/ySW5VBgpRQ06h1Qao8WVNPedKm\nvYKm/prlBbTSZOYW8tYPJ/jswBku5WgrXULdFiRICTUNlwhSKSkpjBkzhoULFzJkyBDD8cjISEJD\nQ9m3bx9NmzZFrb65hTZt2jQ6dOhgdB1HLeec+Y6d4R5lln1qFehu/Jdw5rJPgpRQ03Cv7gHoCQoK\nYtOmTYYgdezYMfLybqbTXbJkCZ6enlavUVrWbPbEUuq17Uneqb2cXzzWIGs2eah8YbO0oLm09yh6\nBrZib8Zpxu762iBozu02tFLfVy91tg/0YenDnenZKoC9pzMZu/J/Bqlz7rA7KnUPQagNuMzTvZCQ\nENLT07l27RoAGzZsIDIy0ub+jpQ1nZlBs6zUufThzoS1blByj9YNWDKqMyBSpyDocZmZFED//v3Z\nunUrI0aM4ODBgzz55JOkp5csucaNG2dY7qnVaj777DPjzjbJmjo0atDqsIwNgqZOL2gWW7uQFW5I\nnWoV9GwVYHyP1gGoVTelTg930RGEuo3LzKSgZA9qy5Yt7N+/n27duhmdW7JkCcuXL2f58uWmAQoc\nJms6M4Nm2UyaOgX2ns40vkdaJjpFpE5B0ONSQapFixbk5uayfPlyHnjgAbv6OkrWdGYGTaN73JA6\nx678H8l/XCm5xx9XGPf1/0ruIVKnIAAuttwDGDJkCOvXr6dNmzacOXPGcLz0cg9gzJgx9O/f36hv\no2GxZB9MpODsIdJmhZs83Ws0NMamMcTe1Y/Ec8c4lJlO+JYPTJ7uxXS8r7Jfk9h+7Uj87SKH0q8T\n/sFuk6d7Mf2CK30PQagNuISC4CgURSkp6pn4trEnZUXWNHcNra6YHG0h8w5vt1nQrAgmUqevB+O6\ntySmX7B4UoJwA5ebSVUEcwKnX+/HaTR4Cm63BNq0bCorbzby9CFIHUTx8W5wXYfi44lSvyWEOu5H\n5l9Pw9xhdzBnaEi5JdQFoa5SrUHqzJkzxMXFkZWVhVarJSQkhClTprB06VI2bdpE48aNAcjKymLI\nkCE888wzJtcwJ3AWX8/gSuJ8cg59a5PAWVbeBLhUkMMlDkFjH8juzKVsLXHbT5H420WHi5YlJdQl\nOAmCOapt4zw/P5+JEyfyxBNPsHz5clauXEmnTp14+eWXAXj88ccNT/PWrFnDmjVruHz5ssl1HJFt\nUy9vmsUrBwL/NHyU7JmCULVUW5DasWMH3bt3p1OnToZjw4cPJzMz02jDHCAzM5OioiIT49xRAqde\n3rRIwF/Aza07ES0FoeqotuXemTNnaNmypcnx5s2bk56ezi+//MLmzZtJT0+nSZMmzJo1C19fX+PG\njhA4b8ibVnHXgkoBpWRJJqKlIFQd1TaTatKkCWfPnjU5npaWRtOmTXn88cf54osvePfdd7l06ZIh\nt7kRDhA49fKmVYo0hgAFIloKQlVSbUGqX79+JCcnc/DgQcOxhIQEGjRoYJTLvEOHDjz55JO89NJL\n6HTG0yFHCJyl5U2LZN4K3LyGiJaCUHVUW5Dy8fHho48+YtGiRTz88MOMHDmSX375hf/85z8mbUeO\nHImvry9fffWVyblGw2LxbN6RwvRjpM0K59ex7qTNCqcw/ZjNAmfsXf3oGNDU/Ml8H8i4uSwV0VIQ\nqpZaIXOaTXRnh8AJptk1G3n60FbdlpPHGnD5mlJp0bJsQjxrVYadicicQk2jxsucZQOU2rdRichp\nR4AC8Pesx9xuQ5nTdYhDg4klSfTUsYaG4OfsbJyCUJOptuVeSkoK3bp1M6RiAViwYAFr164lPDzc\nqG1SUhIxMabLNr3IeXlLnOEpny77ElcS55M2O4LinCy7x6VSqfBwczeq8OLhrq5wgIpIXETc4R1k\n5OegVqm4VJDDvrxDXG68H9RFZGQXErf9FBH/t5usPNOipoJQ16nWLAgajYbY2NgKO0fOKJvuSKyV\nYBdJVBBso1qDVM+ePfHz82PFihV293VW2XRH/isvw6dIooJQPtW+JzVz5kxGjhxJ7969DceuXr1K\ndHS04XNWVhahoaHGHR2VidNZ2JLhUyRRQSiXak96FxAQwPTp04mJiTF4UH5+fob39pYvX87UqVNN\nOzqhbLoj/9mS4VMkUUEon2oPUgD33Xcfbdq0Yd26dTb3cUbZdEdiS4ZPkUQFoXyqfbmnZ8aMGezd\nu7f8hqVwVCZOZ2Etw6dIooJgGzVS5ixdfViXe9W0bPo9Y7llyFS8fBuWOzNxtlTpbEnUXkTmFGoa\nNSpIWSuhrvb2Iyv3GnOPJLH01IFyS6Nn5WmZs+0ES/efMaTudaZUKca5IFQMp+5JjRkzxvACcWFh\nIV27diU+Pt5w/rHHHuO3336joKCA8PBwFi9ebPFa5sTN4usZXN4SR9rsCDKz/qLP1sXEHdlJRn4O\nABn5OcQd3kFE4iKyCm5WQ9aXOI/bfoqM7MKStk6WKh0piQpCXcKpQap3794cOHAAgNTUVHr37s2O\nHTsAKCgoID09nZCQEL777juGDBnCunXrTDId6ClP3Nz6+XMWs2vqS6Pr0Zc4N9tWpEpBcCmcGqR6\n9eplCFI7d+5k5MiRXL9+nevXr/Pzzz/To0cPoCRFS1RUFCEhIezcudPkOraIm80ObgYrK9fSpdH1\nJc4tIVKlILgOTg1Sd955J7///juKorB//3569OhBWFgYycnJ7Nu3j3vuuYe0tDTy8vIICQkhKirK\nvH1ui7hZlI9GsWJt6kuj3yhxbg29VCkIQvXj1CClVqsJCQkhKSmJwMBAPDw8iIiI4KeffiI1NZVe\nvXqRkJBAXl4e48ePJz4+ntTUVE6fNpYfbRE3Mz280aosfx19aXR9iXNriFQpCK6D02XO8PBwPv74\nY+655x4AunbtytGjRwHw9fVly5YtrFixgvj4eOLj43nqqaf48ssvja5hi7h5tsNgsLIJrS+NXrrE\nucW2IlUKgsvg9CDVq1cvUlNT6dOnDwAeHh7Ur1+f7t278+OPPxIaGoq//83l14gRI1i/fj15eXlG\n1ykvA2f/Me9bzK5ZtjR6bL92dGxa33xbkSoFwaVwKU/KnDuk94vcb+SKslZCvaw4aa00enWUOHcF\nxJMSahouEaTMiZWPdG0MgWfYeGI3w49vZ8SFIwQU5qKuH4h/OSXUy4qT1qguqbK6kCAl1DQc/u7e\n2bNneemllwgKCuLIkSP4+/tTVFREQEAAsbGxRpVg4KZYWdpbysjN5b1z31D/r8t89r+vaZ9zyXBO\nZ0MJdb04aQtS4lwQXBunvmA8depUIiIiADhw4ACTJ09mzZo1Rm3MipWBf4JXDk+e2mcUoEqjz7zZ\n5KHqzb4pCIJzqbJULd26dUOj0RjpBYqisHT/mTItlZKMlYrC3/86bPWaWUlLRLoUhFpOleaTatiw\nIZmZmYbP2mLF8O6cAZUC7lo0io6G2jysUXw9oyQbgiAItZYqDVLnz5/n1ltvNXzWuKlMxUpFBUUa\ntCo1lzX1sIZb/UBwq71P4gRBqMIgtXv3bry8vIyClHmxUlWSsVKl4ptbO1i9ZnVm3hQEoWpw6sb5\n/Pnz+fTTT1Gr1fj4+PDOO++YtInt147E3y4ab55ntIT6V/i0ZQ96X/nD7Oa5K2TeFATB+Tg8SDVv\n3pxVq1aZHFcUhcLiIooL8ylSaQy5lPzraUiaFG4sVnp7M/q24dD4DC97+fD349uJKuVJBdhZQl0Q\nhJqL03OcZxXksWD/etj6PgPP/UxDbR6X3Xz5zncg3DuFKYO74l9Pw9xhdzBnaEgZsbIzC+8eZjDO\nVboicNPIEk8Q6hBW96QeffRR9uzZY3TsjTfeICIigujoaLp168Y//vEPoqOjSUhIMOmfVZDHoPVv\n0/XL5xidlmx4WtewOJvRV9fQddPDDHrvO0MmTHPZKvViplqtRuXuIQFKEOoYVmdSDz30EOvXrycs\nLAwoSQGclJTEt99+i7e3N9HR0cycOZO2bdua7T/n4DbCD26wKGS2Lz5Nrz+WMXdbW+YOu6OSX0UQ\nhNqI1ZnUoEGDSElJMWQk2LZtG+Hh4Xh7e5d7YUVRWHpiX7lC5vD8H1iy77RImYIgmMVqkPL09KRf\nv35s3boVgLVr1zJq1CibLqzVFZOVd71cIbOhcpWs7FzJhCkIglnK9aRGjhzJ+vXruXDhAteuXSM0\nNNSmC2vUbvjXq1+ukHlZ5Ye/r7dkwhQEwSzlBqn27duTk5PD559/TlRUlM0XVqlUjG3Xo1whc53X\n/Yzr0Uo2xAVBMItNxnlUVBQJCQkMHTrUrovH3tWP3Xc9wDGfRmbPH3NrRXKbxyUTpiAIFnF60rus\ngjzePrAB3db3GHT2pif1re9A1PdO5eXBXWp1JkxXQ5LeCTUNp8uc/p71+Hf4KJReD1FYXIS7rpgi\nlYbeUr1XEAQbcHqQ0qNSqfB01wAa3KrqpoIg1HiqNFWLIAiCvUiQEgTBpbF5uVdUVMRff/3lzLEI\nVUB2djZnz56t7mEIggm33nor7u6mIcnmp3tnz56lX79+Dh+YIAgClLx217x5c5PjNgcpmUkJguBM\nKj2TEgRBqA5k41wQBJdGgpQgCC6NBClBEFwaCVKCILg0Tn8tRqfTMXPmTI4dO4aHhwezZs2iVatW\nzr6tTWi1WqZPn865c+coLCzkmWeeITg4mJiYGFQqFe3ateP1119HrVbzwQcfsGPHDtzd3Zk+fTp3\n3XVXtY798uXLjBgxgiVLluDu7u7SY/7444/58ccf0Wq1PPLII/To0cOlx6vVaomJieHcuXOo1Wr+\n/e9/u+zP+JdffmHBggUsX76c06dP2zxGS21dEsXJfPfdd8q0adMURVGUn3/+WXn66aedfUubWb16\ntTJr1ixFURTlypUrSp8+fZQJEyYoe/fuVRRFUV577TXl+++/Vw4fPqxER0crOp1OOXfunDJixIjq\nHLZSWFioTJw4URkwYIBy8uRJlx7z3r17lQkTJijFxcVKdna28t5777n0eBVFUbZu3ao8//zziqIo\nyq5du5Rnn33WJcf8ySefKMOGDVNGjhypKIpi1xjNtXVVnB46U1NTueeeewDo3Lkzhw9bz3lelQwa\nNIgXXnin/QBDAAAHiElEQVTB8NnNzY0jR47Qo0cPACIiIkhOTiY1NZXevXujUqlo1qwZxcXFXLly\npbqGzbx583j44Ydp3LgxgEuPedeuXdx+++1MmjSJp59+mnvvvdelxwvQpk0biouL0el0ZGdn4+7u\n7pJjbtmyJe+//77hsz1jNNfWVXF6kMrOzsbX19fw2c3NjaKiImff1iZ8fHzw9fUlOzub559/nsmT\nJ6MoiiGFjI+PD9evXzf5Dvrj1cHatWtp0KCBIfADLj3mzMxMDh8+zLvvvssbb7zBlClTXHq8AN7e\n3pw7d47Bgwfz2muvER0d7ZJjHjhwoJH8aM8YzbV1VZy+J+Xr60tOTo7hs06nM2uVVhfp6elMmjSJ\n0aNHExkZyfz58w3ncnJyuOWWW0y+Q05ODvXr16+O4bJmzRpUKhV79uzh119/Zdq0aUZ/vV1tzP7+\n/gQFBeHh4UFQUBCenp5Gby642ngBli1bRu/evXn55ZdJT0/nn//8J1qt1mhsrjZmwGhPqbwxmmvr\nqjh9JtWlSxeSkpIA+N///sftt9/u7FvazKVLlxg3bhxTp07lH//4BwB33nknKSkpACQlJdGtWze6\ndOnCrl270Ol0nD9/Hp1OR4MGDaplzCtWrOCLL75g+fLl3HHHHcybN4+IiAiXHXPXrl3573//i6Io\nXLhwgby8PMLCwlx2vAC33HKLIdj4+flRVFTk8r8XYN/vrrm2rorTX4vRP907fvw4iqIwe/Zsi8VE\nq5pZs2aRmJhIUFCQ4diMGTOYNWsWWq2WoKAgZs2ahZubG++//z5JSUnodDpiY2Nd4j+qvjirWq3m\ntddec9kxx8XFkZKSgqIovPjiizRv3tylx5uTk8P06dPJyMhAq9UyZswYOnTo4JJjPnv2LC+99BKr\nVq3ijz/+sHmMltq6IvLuniAILo2LihGCIAglSJASBMGlkSAlCIJLI0FKEASXRoKUIAgujQQpQRBc\nGglSgiC4NBKkhFrF0aNHefzxx6t7GIIDkSAl1BrOnDnDjh07XNacFiqG67zpKwh2snjxYnbt2mX4\nvGTJEiZOnMiECROqcVSCo5EgJdRYnnjiCZ544onqHobgZGS5JwiCSyNBSiA7O5s33niDYcOG8eCD\nDxIdHc2RI0cM53NycnjjjTfo378/DzzwAKNHj2bPnj02Xz8lJYXo6GijY2PHjuWHH34wfJ43bx5/\n+9vfKCwsNBzr3bs3a9euNelbHh9//LFd7QXXRoJUHUen0/Hkk0/i5+fHN998w/r165k0aRJPPvkk\nmZmZKIrC008/jUajYfPmzWzYsIFXX32VqVOnGvIRVYSePXuSmppq+JycnEznzp0Nx06fPo23tze3\n3XZbpb+jULORIFXLefvttxkwYACjRo3i2WefZe3atUbnU1JSSE9P5/nnnzdkTO3Zsydz5sxBp9Ox\nb98+zp8/T2xsLB4eHkBJcrVnnnmGRYsW2T2ezz77jOjoaLp06cLPP/8MwIULF/Dw8GDgwIGGjfAD\nBw4QHh4OwJUrV3jyyScZOHAgTz/9tNFsS6j9SJCqxfz444+kpqayadMmPvnkE44ePWrS5ujRo4SE\nhJiUM+rTpw8NGzbk0KFDdOjQwZAPW0/37t05dOiQXeNZu3Yt33//PR999BFdunThzz//pKCggF27\ndhEeHk54eLjZIHX+/Hn+9a9/kZiYyKVLl1y6aIDgeCRI1WKSk5MZPHgwHh4e+Pn5cf/995u0UavV\neHp6WryGSqWiuLjY5LhWqzUJXNY4fvw4r732GmPGjMHHxwc3Nzc6derEoUOH2LVrF71796ZFixbk\n5+dz9epVfv75Z3r27AlASEgILVq0QK1W07ZtWzIzM22+r1DzkSBVi1Gr1eh0OqttOnTowNGjRymb\noPU///kPe/fupVOnThw+fNioEAGU5Kvv0KEDBw4cYPr06UyZMoU333zT4n18fHx4//33iYuLIzc3\nFyhZVv70008cPHiQzp07AxAWFsa2bdsICAgwVDkpXbhDpVKZjFWo3UiQqsX06tWL77//nsLCQrKz\ns9mxY4fJ7Kdbt240bNiQDz74wDBj+u9//8vatWsJDg6mW7duBAcHM3v2bEOgOnz4MB9++CETJ06k\nW7duzJ49mwULFpCenm5UmaQ0t912G/fddx89evTgvffeA0oC0vr167n99tsNgSg8PJylS5calnqC\nIDJnLebee+/l559/Zvjw4fj5+dG4cWOTpZ1KpWLRokXMmTOHYcOG4e7uTkBAAJ988gmNGjUC4IMP\nPmDhwoUMGzYMNzc3/Pz8mD9/PnfffbfhOjt27KBt27b4+PhYHdMrr7zCsGHDiIyMJDQ0lKysLEaP\nHm0437NnTyZPnkyvXr0c+JMQajJSiKEW8/PPP5OWlsbw4cPRarWMGjWK2bNnExIS4tD7rF27lnPn\nzvHcc8859LqCABKkajVZWVm8/PLLZGRkoCgKf//73xk/frxD77F9+3Zef/117r33XgAmT55crbXn\nhNqHBClBEFwa2TgXBMGlkSAlCIJLI0FKEASXRoKUIAgujQQpQRBcGglSgiC4NBKkBEFwaSRICYLg\n0vw/EfWQBUb9S+kAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "states_index = list(barbell_index.index)\n",
    "\n",
    "dumbell_plot(barbell_index, [2001, 2008, 2017], states_index, \n",
    "             legend_loc=[500, 300, 100],\n",
    "             rps_start=rps_start, palette='colorblind')\n",
    "\n",
    "plt.vlines(439, -1, 50, colors=['0.5'], zorder=1, #linestyles='dashed',\n",
    "           linewidth=1)\n",
    "\n",
    "plt.ylim(-1, 53)\n",
    "plt.text(x=200, y=40, s='2017\\nNational Average\\n(439 g CO$_2$ $\\mathregular{kWh^{-1}}$)',\n",
    "         ha='center', size=11)\n",
    "sns.despine(left=True)\n",
    "plt.xlabel('g CO$_2$ $\\mathregular{kWh^{-1}}$')\n",
    "path = join(cwd, '..', 'Figures',\n",
    "            'State CO2 intensity {}.pdf'.format('David Hawkins'))\n",
    "plt.savefig(path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Largest and smallest changes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:15:23.668653Z",
     "start_time": "2018-03-07T21:15:23.664033Z"
    }
   },
   "outputs": [],
   "source": [
    "barbell_index['change'] = barbell_index[2017] - barbell_index[2001]\n",
    "barbell_index['% change'] = barbell_index['change'] / barbell_index[2001]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:15:26.545372Z",
     "start_time": "2018-03-07T21:15:26.536847Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The largest absolute reduction is -466.4 g/kWh in DE, from 880.0 to 413.7\n"
     ]
    }
   ],
   "source": [
    "max_change_state = barbell_index.sort_values('change').index[0]\n",
    "max_change_value = barbell_index.sort_values('change')['change'].values[0]\n",
    "start = barbell_index.loc[max_change_state, 2001]\n",
    "end = barbell_index.loc[max_change_state, 2017]\n",
    "print('The largest absolute reduction is {:0.1f} g/kWh in {}, from {:.1f} to {:.1f}'\n",
    "      .format(max_change_value, max_change_state, start, end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:13:09.632311Z",
     "start_time": "2018-03-07T21:13:09.620952Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The smallest absolute reduction is 30.5 g/kWh in ID, from 41.2 to 71.7\n"
     ]
    }
   ],
   "source": [
    "min_change_state = barbell_index.sort_values('change').index[-1]\n",
    "min_change_value = barbell_index.sort_values('change')['change'].values[-1]\n",
    "start = barbell_index.loc[min_change_state, 2001]\n",
    "end = barbell_index.loc[min_change_state, 2017]\n",
    "print('The smallest absolute reduction is {:0.1f} g/kWh in {}, from {:.1f} to {:.1f}'\n",
    "      .format(min_change_value, min_change_state, start, end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:13:09.645642Z",
     "start_time": "2018-03-07T21:13:09.634867Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The largest relative reduction is -64.6% g/kWh in NH, from 307.4 to 108.9\n"
     ]
    }
   ],
   "source": [
    "max_relchange_state = barbell_index.sort_values('% change').index[0]\n",
    "max_relchange_value = barbell_index.sort_values('% change')['% change'].values[0]\n",
    "start = barbell_index.loc[max_relchange_state, 2001]\n",
    "end = barbell_index.loc[max_relchange_state, 2017]\n",
    "print('The largest relative reduction is {:0.1%} g/kWh in {}, from {:.1f} to {:.1f}'\n",
    "      .format(max_relchange_value, max_relchange_state, start, end))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:13:09.658426Z",
     "start_time": "2018-03-07T21:13:09.648255Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The smallest relative reduction is 74.0% g/kWh in ID, from 41.2 to 71.7\n"
     ]
    }
   ],
   "source": [
    "min_relchange_state = barbell_index.sort_values('% change').index[-1]\n",
    "min_relchange_value = barbell_index.sort_values('% change')['% change'].values[-1]\n",
    "start = barbell_index.loc[min_relchange_state, 2001]\n",
    "end = barbell_index.loc[min_relchange_state, 2017]\n",
    "print('The smallest relative reduction is {:0.1%} g/kWh in {}, from {:.1f} to {:.1f}'\n",
    "      .format(min_relchange_value, min_relchange_state, start, end))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Population"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:28:27.056486Z",
     "start_time": "2018-03-07T21:28:27.030343Z"
    }
   },
   "outputs": [],
   "source": [
    "path = os.path.join(base_path, 'Data storage', 'Derived data',\n",
    "                    'State population.csv')\n",
    "pop = pd.read_csv(path)\n",
    "pop.columns = pop.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:28:29.529513Z",
     "start_time": "2018-03-07T21:28:29.516938Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>United States</td>\n",
       "      <td>2000</td>\n",
       "      <td>282162411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>2000</td>\n",
       "      <td>4452173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alaska</td>\n",
       "      <td>2000</td>\n",
       "      <td>627963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Arizona</td>\n",
       "      <td>2000</td>\n",
       "      <td>5160586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Arkansas</td>\n",
       "      <td>2000</td>\n",
       "      <td>2678588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           state  year  population\n",
       "0  United States  2000   282162411\n",
       "1        Alabama  2000     4452173\n",
       "2         Alaska  2000      627963\n",
       "3        Arizona  2000     5160586\n",
       "4       Arkansas  2000     2678588"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:28:35.171808Z",
     "start_time": "2018-03-07T21:28:35.159348Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>930</th>\n",
       "      <td>Virginia</td>\n",
       "      <td>2017</td>\n",
       "      <td>8470020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>Washington</td>\n",
       "      <td>2017</td>\n",
       "      <td>7405743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>West Virginia</td>\n",
       "      <td>2017</td>\n",
       "      <td>1815857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>2017</td>\n",
       "      <td>5795483</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>2017</td>\n",
       "      <td>579315</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             state  year  population\n",
       "930       Virginia  2017     8470020\n",
       "931     Washington  2017     7405743\n",
       "932  West Virginia  2017     1815857\n",
       "933      Wisconsin  2017     5795483\n",
       "934        Wyoming  2017      579315"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:28:43.234668Z",
     "start_time": "2018-03-07T21:28:43.228735Z"
    }
   },
   "outputs": [],
   "source": [
    "pop['state'] = pop['state'].map(us_state_abbrev)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:28:44.166772Z",
     "start_time": "2018-03-07T21:28:44.154625Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>US</td>\n",
       "      <td>2000</td>\n",
       "      <td>282162411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AL</td>\n",
       "      <td>2000</td>\n",
       "      <td>4452173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK</td>\n",
       "      <td>2000</td>\n",
       "      <td>627963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AZ</td>\n",
       "      <td>2000</td>\n",
       "      <td>5160586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AR</td>\n",
       "      <td>2000</td>\n",
       "      <td>2678588</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  year  population\n",
       "0    US  2000   282162411\n",
       "1    AL  2000     4452173\n",
       "2    AK  2000      627963\n",
       "3    AZ  2000     5160586\n",
       "4    AR  2000     2678588"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:28:47.834640Z",
     "start_time": "2018-03-07T21:28:47.820654Z"
    }
   },
   "outputs": [],
   "source": [
    "annual_index_pop = annual_index.reset_index().merge(pop, on=['state', 'year'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:28:49.428169Z",
     "start_time": "2018-03-07T21:28:49.413203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>final co2 (kg)</th>\n",
       "      <th>generation (mwh)</th>\n",
       "      <th>index (g/kwh)</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AK</td>\n",
       "      <td>2001</td>\n",
       "      <td>4.192039e+09</td>\n",
       "      <td>6743766.00</td>\n",
       "      <td>621.616856</td>\n",
       "      <td>633714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AK</td>\n",
       "      <td>2002</td>\n",
       "      <td>3.829402e+09</td>\n",
       "      <td>6767322.00</td>\n",
       "      <td>565.866731</td>\n",
       "      <td>642337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AK</td>\n",
       "      <td>2003</td>\n",
       "      <td>3.474714e+09</td>\n",
       "      <td>6338738.00</td>\n",
       "      <td>548.171264</td>\n",
       "      <td>648414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AK</td>\n",
       "      <td>2004</td>\n",
       "      <td>3.492109e+09</td>\n",
       "      <td>6526716.92</td>\n",
       "      <td>535.048356</td>\n",
       "      <td>659286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AK</td>\n",
       "      <td>2005</td>\n",
       "      <td>3.531411e+09</td>\n",
       "      <td>6576658.54</td>\n",
       "      <td>536.961208</td>\n",
       "      <td>666946</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  state  year  final co2 (kg)  generation (mwh)  index (g/kwh)  population\n",
       "0    AK  2001    4.192039e+09        6743766.00     621.616856      633714\n",
       "1    AK  2002    3.829402e+09        6767322.00     565.866731      642337\n",
       "2    AK  2003    3.474714e+09        6338738.00     548.171264      648414\n",
       "3    AK  2004    3.492109e+09        6526716.92     535.048356      659286\n",
       "4    AK  2005    3.531411e+09        6576658.54     536.961208      666946"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_index_pop.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:29:01.234027Z",
     "start_time": "2018-03-07T21:29:01.223106Z"
    }
   },
   "outputs": [],
   "source": [
    "annual_index_pop['tonne CO2/pop'] = (annual_index_pop['final co2 (kg)']\n",
    "                                         / 1000\n",
    "                                         / annual_index_pop['population'])\n",
    "annual_index_pop['MWh/pop'] = (annual_index_pop.loc[:, 'generation (mwh)']\n",
    "                                         / annual_index_pop['population'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:29:06.704260Z",
     "start_time": "2018-03-07T21:29:06.642116Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>final co2 (kg)</th>\n",
       "      <th>generation (mwh)</th>\n",
       "      <th>index (g/kwh)</th>\n",
       "      <th>population</th>\n",
       "      <th>tonne CO2/pop</th>\n",
       "      <th>MWh/pop</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>850.000000</td>\n",
       "      <td>8.500000e+02</td>\n",
       "      <td>8.500000e+02</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>8.500000e+02</td>\n",
       "      <td>850.000000</td>\n",
       "      <td>850.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>4.425946e+10</td>\n",
       "      <td>8.056302e+07</td>\n",
       "      <td>547.438394</td>\n",
       "      <td>6.106634e+06</td>\n",
       "      <td>10.669395</td>\n",
       "      <td>16.792258</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.901864</td>\n",
       "      <td>4.218201e+10</td>\n",
       "      <td>7.348272e+07</td>\n",
       "      <td>246.740510</td>\n",
       "      <td>6.752970e+06</td>\n",
       "      <td>13.539141</td>\n",
       "      <td>13.050768</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2001.000000</td>\n",
       "      <td>4.732677e+06</td>\n",
       "      <td>1.986881e+06</td>\n",
       "      <td>0.996470</td>\n",
       "      <td>4.946570e+05</td>\n",
       "      <td>0.007559</td>\n",
       "      <td>3.181076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10%</th>\n",
       "      <td>2002.000000</td>\n",
       "      <td>3.530592e+09</td>\n",
       "      <td>1.043385e+07</td>\n",
       "      <td>209.386261</td>\n",
       "      <td>8.451272e+05</td>\n",
       "      <td>1.904900</td>\n",
       "      <td>7.319306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2005.000000</td>\n",
       "      <td>1.461421e+10</td>\n",
       "      <td>3.364504e+07</td>\n",
       "      <td>402.071114</td>\n",
       "      <td>1.802464e+06</td>\n",
       "      <td>3.828428</td>\n",
       "      <td>9.933583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>2009.000000</td>\n",
       "      <td>3.529086e+10</td>\n",
       "      <td>5.773346e+07</td>\n",
       "      <td>547.382227</td>\n",
       "      <td>4.363600e+06</td>\n",
       "      <td>7.615839</td>\n",
       "      <td>14.472842</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2017.000000</td>\n",
       "      <td>2.486620e+11</td>\n",
       "      <td>4.544390e+08</td>\n",
       "      <td>1111.411905</td>\n",
       "      <td>3.953665e+07</td>\n",
       "      <td>96.738933</td>\n",
       "      <td>90.521185</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              year  final co2 (kg)  generation (mwh)  index (g/kwh)  \\\n",
       "count   850.000000    8.500000e+02      8.500000e+02     850.000000   \n",
       "mean   2009.000000    4.425946e+10      8.056302e+07     547.438394   \n",
       "std       4.901864    4.218201e+10      7.348272e+07     246.740510   \n",
       "min    2001.000000    4.732677e+06      1.986881e+06       0.996470   \n",
       "10%    2002.000000    3.530592e+09      1.043385e+07     209.386261   \n",
       "25%    2005.000000    1.461421e+10      3.364504e+07     402.071114   \n",
       "50%    2009.000000    3.529086e+10      5.773346e+07     547.382227   \n",
       "max    2017.000000    2.486620e+11      4.544390e+08    1111.411905   \n",
       "\n",
       "         population  tonne CO2/pop     MWh/pop  \n",
       "count  8.500000e+02     850.000000  850.000000  \n",
       "mean   6.106634e+06      10.669395   16.792258  \n",
       "std    6.752970e+06      13.539141   13.050768  \n",
       "min    4.946570e+05       0.007559    3.181076  \n",
       "10%    8.451272e+05       1.904900    7.319306  \n",
       "25%    1.802464e+06       3.828428    9.933583  \n",
       "50%    4.363600e+06       7.615839   14.472842  \n",
       "max    3.953665e+07      96.738933   90.521185  "
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_index_pop.describe(percentiles=[.1, .25])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:30:26.782127Z",
     "start_time": "2018-03-07T21:30:24.515715Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAToAAAIgCAYAAAAV56U8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCp0Qotn7/+KJOdQbhQvsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barbell_pop = annual_index_pop.pivot_table(values='tonne CO2/pop',\n",
    "                                              index='state', columns='year')\n",
    "barbell_pop.sort_values(by=2017, inplace=True)\n",
    "barbell_pop.drop(['WY', 'ND', 'WV'], inplace=True)\n",
    "\n",
    "# states_pop = ['{}  '.format(x) for x in barbell_pop.index]\n",
    "states_pop = list(barbell_pop.index)\n",
    "# rps_states = list(rps_tidy['state'].unique())\n",
    "\n",
    "dumbell_plot(barbell_pop, [2001, 2008, 2017], states_pop, offset_divider=30,\n",
    "             rps_start=rps_start, legend=False, palette='colorblind')\n",
    "\n",
    "plt.ylim(-1, 53)\n",
    "plt.xlim(None, 25)\n",
    "\n",
    "sns.despine(left=True)\n",
    "plt.xlabel('Tonne $\\mathregular{CO_2 \\ Capita^{-1}}$')\n",
    "\n",
    "# label as part of a larger figure\n",
    "plt.text(-0.02, 0.89, s='b)', ha='right', size=11, transform=ax.transAxes)\n",
    "\n",
    "path = join(fig_export_path,\n",
    "            'State CO2 per capita {}.pdf'.format(file_date))\n",
    "plt.savefig(path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-07T21:31:21.269808Z",
     "start_time": "2018-03-07T21:31:20.805731Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAK4AAAIgCAYAAAAcKVJvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<Figure size 180x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barbell_pop = annual_index_pop.pivot_table(values='tonne CO2/pop',\n",
    "                                           index='state', columns='year')\n",
    "barbell_pop.sort_values(by=2017, inplace=True)\n",
    "barbell_pop = barbell_pop.loc[['WV', 'ND', 'WY']]\n",
    "\n",
    "states_pop = ['WY', 'ND', 'WV']\n",
    "# rps_states = list(rps_tidy['state'].unique())\n",
    "\n",
    "dumbell_plot(barbell_pop, [2001, 2008, 2017], states_pop, offset_divider=30,\n",
    "             rps_start=rps_start, legend=False, palette='colorblind',\n",
    "             figsize=(2.5, 9))\n",
    "\n",
    "plt.ylim(-1, 53)\n",
    "# plt.xlim(None, 25)\n",
    "\n",
    "sns.despine(left=True)\n",
    "plt.xlabel('Tonne $\\mathregular{CO_2 \\ Capita^{-1}}$')\n",
    "path = join(fig_export_path,\n",
    "            'State CO2 per capita insert {}.pdf'.format(file_date))\n",
    "plt.savefig(path, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## State electricity imports/exports\n",
    "Data is from [EIA State Energy Data System](https://www.eia.gov/state/seds/seds-data-complete.php?sid=US#Consumption). Most recent update was March 9, 2018. Code are described in [EIA documentation](https://www.eia.gov/state/seds/sep_use/notes/use_a.pdf). Negative values indicate flows out of the state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-12T20:03:08.982903Z",
     "start_time": "2018-03-12T20:03:07.872318Z"
    }
   },
   "outputs": [],
   "source": [
    "path = os.path.join(base_path, 'Data storage', 'State energy flows',\n",
    "                    'use_all_phy_update.csv')\n",
    "flows = pd.read_csv(path)\n",
    "\n",
    "# ELISP is electricity flows\n",
    "flows = flows.loc[flows['MSN'] == 'ELISP']\n",
    "\n",
    "# Drop all years before 2001\n",
    "flows.drop([str(x) for x in range(1960, 2001)], axis=1, inplace=True)\n",
    "flows.drop(['Data_Status', 'MSN'], axis=1, inplace=True)\n",
    "\n",
    "# Electricity flows are given in million kWh (see documentation)\n",
    "flows.loc[:, '2001':] *= 1000\n",
    "\n",
    "flows.columns = flows.columns.str.lower()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-12T20:03:13.854714Z",
     "start_time": "2018-03-12T20:03:13.844461Z"
    }
   },
   "outputs": [],
   "source": [
    "flows = flows.melt(id_vars='state', var_name='year',\n",
    "                   value_name='MWh flow')\n",
    "flows['year'] = flows['year'].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-12T20:05:29.104048Z",
     "start_time": "2018-03-12T20:05:29.092476Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>MWh flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>827</th>\n",
       "      <td>VT</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>828</th>\n",
       "      <td>WA</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>829</th>\n",
       "      <td>WI</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>WV</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>831</th>\n",
       "      <td>WY</td>\n",
       "      <td>2016</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  MWh flow\n",
       "827    VT  2016       NaN\n",
       "828    WA  2016       NaN\n",
       "829    WI  2016       NaN\n",
       "830    WV  2016       NaN\n",
       "831    WY  2016       NaN"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flows.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-12T20:03:34.878743Z",
     "start_time": "2018-03-12T20:03:34.840172Z"
    }
   },
   "outputs": [],
   "source": [
    "annual_index_pop_flows = pd.merge(annual_index_pop, flows, on=['state', 'year'])\n",
    "annual_index_pop_flows['MWh flow/pop'] = (annual_index_pop_flows['MWh flow']\n",
    "                                          / annual_index_pop_flows['population'])\n",
    "annual_index_pop_flows['share flow'] = (annual_index_pop_flows['MWh/pop']\n",
    "                                          / annual_index_pop_flows['MWh flow/pop'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-12T20:04:22.516029Z",
     "start_time": "2018-03-12T20:04:22.492522Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>state</th>\n",
       "      <th>year</th>\n",
       "      <th>final co2 (kg)</th>\n",
       "      <th>generation (mwh)</th>\n",
       "      <th>index (g/kwh)</th>\n",
       "      <th>population</th>\n",
       "      <th>tonne CO2/pop</th>\n",
       "      <th>MWh/pop</th>\n",
       "      <th>MWh flow</th>\n",
       "      <th>MWh flow/pop</th>\n",
       "      <th>share flow</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>795</th>\n",
       "      <td>WY</td>\n",
       "      <td>2012</td>\n",
       "      <td>4.610672e+10</td>\n",
       "      <td>49588606.28</td>\n",
       "      <td>929.784645</td>\n",
       "      <td>576765</td>\n",
       "      <td>79.940226</td>\n",
       "      <td>85.977142</td>\n",
       "      <td>-30130000.0</td>\n",
       "      <td>-52.239647</td>\n",
       "      <td>-1.645822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>796</th>\n",
       "      <td>WY</td>\n",
       "      <td>2013</td>\n",
       "      <td>4.886832e+10</td>\n",
       "      <td>52483065.48</td>\n",
       "      <td>931.125515</td>\n",
       "      <td>582684</td>\n",
       "      <td>83.867622</td>\n",
       "      <td>90.071232</td>\n",
       "      <td>-32933000.0</td>\n",
       "      <td>-56.519486</td>\n",
       "      <td>-1.593631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>797</th>\n",
       "      <td>WY</td>\n",
       "      <td>2014</td>\n",
       "      <td>4.506333e+10</td>\n",
       "      <td>49698557.94</td>\n",
       "      <td>906.733105</td>\n",
       "      <td>583642</td>\n",
       "      <td>77.210564</td>\n",
       "      <td>85.152470</td>\n",
       "      <td>-30055000.0</td>\n",
       "      <td>-51.495609</td>\n",
       "      <td>-1.653587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>798</th>\n",
       "      <td>WY</td>\n",
       "      <td>2015</td>\n",
       "      <td>4.567480e+10</td>\n",
       "      <td>48968928.92</td>\n",
       "      <td>932.730320</td>\n",
       "      <td>586555</td>\n",
       "      <td>77.869603</td>\n",
       "      <td>83.485656</td>\n",
       "      <td>-29420000.0</td>\n",
       "      <td>-50.157274</td>\n",
       "      <td>-1.664478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>799</th>\n",
       "      <td>WY</td>\n",
       "      <td>2016</td>\n",
       "      <td>4.273486e+10</td>\n",
       "      <td>46660041.39</td>\n",
       "      <td>915.877066</td>\n",
       "      <td>585501</td>\n",
       "      <td>72.988538</td>\n",
       "      <td>79.692505</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    state  year  final co2 (kg)  generation (mwh)  index (g/kwh)  population  \\\n",
       "795    WY  2012    4.610672e+10       49588606.28     929.784645      576765   \n",
       "796    WY  2013    4.886832e+10       52483065.48     931.125515      582684   \n",
       "797    WY  2014    4.506333e+10       49698557.94     906.733105      583642   \n",
       "798    WY  2015    4.567480e+10       48968928.92     932.730320      586555   \n",
       "799    WY  2016    4.273486e+10       46660041.39     915.877066      585501   \n",
       "\n",
       "     tonne CO2/pop    MWh/pop    MWh flow  MWh flow/pop  share flow  \n",
       "795      79.940226  85.977142 -30130000.0    -52.239647   -1.645822  \n",
       "796      83.867622  90.071232 -32933000.0    -56.519486   -1.593631  \n",
       "797      77.210564  85.152470 -30055000.0    -51.495609   -1.653587  \n",
       "798      77.869603  83.485656 -29420000.0    -50.157274   -1.664478  \n",
       "799      72.988538  79.692505         NaN           NaN         NaN  "
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "annual_index_pop_flows.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2018-03-12T20:05:08.067638Z",
     "start_time": "2018-03-12T20:05:08.036350Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>year</th>\n",
       "      <th>2001</th>\n",
       "      <th>2002</th>\n",
       "      <th>2003</th>\n",
       "      <th>2004</th>\n",
       "      <th>2005</th>\n",
       "      <th>2006</th>\n",
       "      <th>2007</th>\n",
       "      <th>2008</th>\n",
       "      <th>2009</th>\n",
       "      <th>2010</th>\n",
       "      <th>2011</th>\n",
       "      <th>2012</th>\n",
       "      <th>2013</th>\n",
       "      <th>2014</th>\n",
       "      <th>2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>state</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>AK</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AL</th>\n",
       "      <td>-7.835467</td>\n",
       "      <td>-8.475724</td>\n",
       "      <td>-9.253044</td>\n",
       "      <td>-8.264674</td>\n",
       "      <td>-8.455503</td>\n",
       "      <td>-8.083637</td>\n",
       "      <td>-8.581933</td>\n",
       "      <td>-9.436638</td>\n",
       "      <td>-10.417118</td>\n",
       "      <td>-10.419200</td>\n",
       "      <td>-11.692700</td>\n",
       "      <td>-11.484522</td>\n",
       "      <td>-10.776732</td>\n",
       "      <td>-9.903135</td>\n",
       "      <td>-10.978857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AR</th>\n",
       "      <td>-0.247439</td>\n",
       "      <td>0.073173</td>\n",
       "      <td>-0.761886</td>\n",
       "      <td>-0.933197</td>\n",
       "      <td>1.385065</td>\n",
       "      <td>-0.031895</td>\n",
       "      <td>-0.638197</td>\n",
       "      <td>-1.173747</td>\n",
       "      <td>-3.186918</td>\n",
       "      <td>-2.553735</td>\n",
       "      <td>-2.774628</td>\n",
       "      <td>-4.354582</td>\n",
       "      <td>-2.836078</td>\n",
       "      <td>-3.210409</td>\n",
       "      <td>-1.663951</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AZ</th>\n",
       "      <td>-4.479208</td>\n",
       "      <td>-5.011809</td>\n",
       "      <td>-4.669383</td>\n",
       "      <td>-5.711906</td>\n",
       "      <td>-4.520920</td>\n",
       "      <td>-4.207233</td>\n",
       "      <td>-4.789482</td>\n",
       "      <td>-5.827053</td>\n",
       "      <td>-5.143813</td>\n",
       "      <td>-5.216974</td>\n",
       "      <td>-4.345491</td>\n",
       "      <td>-4.611250</td>\n",
       "      <td>-4.863526</td>\n",
       "      <td>-4.590332</td>\n",
       "      <td>-4.481365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CA</th>\n",
       "      <td>2.190000</td>\n",
       "      <td>2.310202</td>\n",
       "      <td>2.196115</td>\n",
       "      <td>2.535800</td>\n",
       "      <td>2.199931</td>\n",
       "      <td>2.135548</td>\n",
       "      <td>2.150713</td>\n",
       "      <td>2.447797</td>\n",
       "      <td>2.196166</td>\n",
       "      <td>2.134108</td>\n",
       "      <td>2.210720</td>\n",
       "      <td>2.126722</td>\n",
       "      <td>2.066795</td>\n",
       "      <td>2.060944</td>\n",
       "      <td>2.035342</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "year       2001      2002      2003      2004      2005      2006      2007  \\\n",
       "state                                                                         \n",
       "AK     0.000000  0.000000  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "AL    -7.835467 -8.475724 -9.253044 -8.264674 -8.455503 -8.083637 -8.581933   \n",
       "AR    -0.247439  0.073173 -0.761886 -0.933197  1.385065 -0.031895 -0.638197   \n",
       "AZ    -4.479208 -5.011809 -4.669383 -5.711906 -4.520920 -4.207233 -4.789482   \n",
       "CA     2.190000  2.310202  2.196115  2.535800  2.199931  2.135548  2.150713   \n",
       "\n",
       "year       2008       2009       2010       2011       2012       2013  \\\n",
       "state                                                                    \n",
       "AK     0.000000   0.000000   0.000000   0.000000   0.000000   0.000000   \n",
       "AL    -9.436638 -10.417118 -10.419200 -11.692700 -11.484522 -10.776732   \n",
       "AR    -1.173747  -3.186918  -2.553735  -2.774628  -4.354582  -2.836078   \n",
       "AZ    -5.827053  -5.143813  -5.216974  -4.345491  -4.611250  -4.863526   \n",
       "CA     2.447797   2.196166   2.134108   2.210720   2.126722   2.066795   \n",
       "\n",
       "year       2014       2015  \n",
       "state                       \n",
       "AK     0.000000   0.000000  \n",
       "AL    -9.903135 -10.978857  \n",
       "AR    -3.210409  -1.663951  \n",
       "AZ    -4.590332  -4.481365  \n",
       "CA     2.060944   2.035342  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "barbell_flows = annual_index_pop_flows.pivot_table(values='MWh flow/pop',\n",
    "                                              index='state', columns='year')\n",
    "\n",
    "barbell_flows.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-10-27T19:43:20.420119Z",
     "start_time": "2017-10-27T19:43:19.026575Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAASsAAAIVCAYAAACJPNn0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSLASQjgFCVZCCKcgwUoI4RT+H9n7nxOAoTEMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 360x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "barbell_flows = annual_index_pop_flows.pivot_table(values='MWh flow/pop',\n",
    "                                              index='state', columns='year')\n",
    "barbell_flows.sort_values(by=2015, ascending=False, inplace=True)\n",
    "# barbell_flows.drop('VT', inplace=True)\n",
    "\n",
    "states_flows = list(barbell_flows.index)\n",
    "\n",
    "dumbell_plot(barbell_flows, [2001, 2008, 2015], states_flows, offset_divider=8,\n",
    "             rps_start=rps_start, legend=True, palette='colorblind',\n",
    "             legend_loc=[-20, -40, -60])\n",
    "\n",
    "plt.ylim(-1, 53)\n",
    "sns.despine(left=True)\n",
    "plt.xlabel('MWh flow/Capita')\n",
    "plt.vlines(0, -1, 53, colors=['0.5'], linewidth=1)\n",
    "# plt.xticks()\n",
    "# ax = plt.gca()\n",
    "# ax.set_xscale(\"log\", nonposx='clip')\n",
    "path = join(fig_export_path, 'SI', 'State MWh flow per capita.pdf')\n",
    "# plt.savefig(path, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "_draft": {
   "nbviewer_url": "https://gist.github.com/f7834c5c2616085cd9236adac70d94b7"
  },
  "gist": {
   "data": {
    "description": "State index & gen data.ipynb",
    "public": false
   },
   "id": "f7834c5c2616085cd9236adac70d94b7"
  },
  "kernelspec": {
   "display_name": "py36",
   "language": "python",
   "name": "py36"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}