Cell Analysis-Copy1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cell Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import libraries and load data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "df = pd.read_csv('tmp_alb_tx_sums_202004021132.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "    }\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>atoll_schema</th>\n",
       "      <th>site_version</th>\n",
       "      <th>site_name</th>\n",
       "      <th>fuze_site_id</th>\n",
       "      <th>site_record_id</th>\n",
       "      <th>latitude_degrees</th>\n",
       "      <th>longitude_degrees</th>\n",
       "      <th>street_address</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>...</th>\n",
       "      <th>antenna_longitude</th>\n",
       "      <th>number_of_tx_antenna_ports</th>\n",
       "      <th>number_of_rx_antenna_ports</th>\n",
       "      <th>tx_record_id</th>\n",
       "      <th>xmtr_location</th>\n",
       "      <th>duplex_type</th>\n",
       "      <th>coverage_type</th>\n",
       "      <th>threshold</th>\n",
       "      <th>fcc_market_number</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM1_PINON</td>\n",
       "      <td>355471.0</td>\n",
       "      <td>101508</td>\n",
       "      <td>36.457800</td>\n",
       "      <td>-105.598446</td>\n",
       "      <td>Highway 522 &amp; Blueberry Hill Rd.</td>\n",
       "      <td>Taos</td>\n",
       "      <td>Taos</td>\n",
       "      <td>...</td>\n",
       "      <td>-105.598446</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>104953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.263681e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>LSC_HILLRISE_ENB2</td>\n",
       "      <td>676486.0</td>\n",
       "      <td>101524</td>\n",
       "      <td>32.305697</td>\n",
       "      <td>-106.738075</td>\n",
       "      <td>2912 Idaho Ave</td>\n",
       "      <td>Las Cruces</td>\n",
       "      <td>Dona Ana</td>\n",
       "      <td>...</td>\n",
       "      <td>-106.738075</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>104954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.184412e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>000x</td>\n",
       "      <td>NM6_HOBB-TIME</td>\n",
       "      <td>1452362.0</td>\n",
       "      <td>102666</td>\n",
       "      <td>32.710036</td>\n",
       "      <td>-103.137736</td>\n",
       "      <td>915 N Shipp St</td>\n",
       "      <td>Hobbs</td>\n",
       "      <td>Lea</td>\n",
       "      <td>...</td>\n",
       "      <td>-103.137736</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>104955</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.476214e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>000X</td>\n",
       "      <td>ELP_HUNTER</td>\n",
       "      <td>676424.0</td>\n",
       "      <td>592872</td>\n",
       "      <td>31.761794</td>\n",
       "      <td>-106.363022</td>\n",
       "      <td>7564 Gateway East</td>\n",
       "      <td>EL Paso</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>...</td>\n",
       "      <td>-106.363022</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>104956</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.134106e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>000x</td>\n",
       "      <td>NM6_HOBB-TIME</td>\n",
       "      <td>1452362.0</td>\n",
       "      <td>102666</td>\n",
       "      <td>32.710036</td>\n",
       "      <td>-103.137736</td>\n",
       "      <td>915 N Shipp St</td>\n",
       "      <td>Hobbs</td>\n",
       "      <td>Lea</td>\n",
       "      <td>...</td>\n",
       "      <td>-103.137736</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>104957</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.166033e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  atoll_schema site_version          site_name  fuze_site_id  site_record_id  \\\n",
       "0  albuquerque         0000          NM1_PINON      355471.0          101508   \n",
       "1  albuquerque         0000  LSC_HILLRISE_ENB2      676486.0          101524   \n",
       "2  albuquerque         000x      NM6_HOBB-TIME     1452362.0          102666   \n",
       "3  albuquerque         000X         ELP_HUNTER      676424.0          592872   \n",
       "4  albuquerque         000x      NM6_HOBB-TIME     1452362.0          102666   \n",
       "\n",
       "   latitude_degrees  longitude_degrees                    street_address  \\\n",
       "0         36.457800        -105.598446  Highway 522 & Blueberry Hill Rd.   \n",
       "1         32.305697        -106.738075                    2912 Idaho Ave   \n",
       "2         32.710036        -103.137736                    915 N Shipp St   \n",
       "3         31.761794        -106.363022                 7564 Gateway East   \n",
       "4         32.710036        -103.137736                    915 N Shipp St   \n",
       "\n",
       "         city    county  ... antenna_longitude  number_of_tx_antenna_ports  \\\n",
       "0        Taos      Taos  ...       -105.598446                           2   \n",
       "1  Las Cruces  Dona Ana  ...       -106.738075                           2   \n",
       "2       Hobbs       Lea  ...       -103.137736                           4   \n",
       "3     EL Paso   El Paso  ...       -106.363022                           4   \n",
       "4       Hobbs       Lea  ...       -103.137736                           4   \n",
       "\n",
       "   number_of_rx_antenna_ports  tx_record_id xmtr_location duplex_type  \\\n",
       "0                           4        104953           NaN         NaN   \n",
       "1                           2        104954           NaN         NaN   \n",
       "2                           4        104955           NaN         NaN   \n",
       "3                           4        104956           NaN         NaN   \n",
       "4                           4        104957           NaN         NaN   \n",
       "\n",
       "   coverage_type  threshold  fcc_market_number           sum  \n",
       "0            NaN        NaN                NaN  8.263681e+08  \n",
       "1            NaN        NaN                NaN  2.184412e+08  \n",
       "2            NaN        NaN                NaN  8.476214e+07  \n",
       "3            NaN        NaN                NaN  1.134106e+08  \n",
       "4            NaN        NaN                NaN  5.166033e+07  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Select site_version== 0000 only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>atoll_schema</th>\n",
       "      <th>site_version</th>\n",
       "      <th>site_name</th>\n",
       "      <th>fuze_site_id</th>\n",
       "      <th>site_record_id</th>\n",
       "      <th>latitude_degrees</th>\n",
       "      <th>longitude_degrees</th>\n",
       "      <th>street_address</th>\n",
       "      <th>city</th>\n",
       "      <th>county</th>\n",
       "      <th>...</th>\n",
       "      <th>antenna_longitude</th>\n",
       "      <th>number_of_tx_antenna_ports</th>\n",
       "      <th>number_of_rx_antenna_ports</th>\n",
       "      <th>tx_record_id</th>\n",
       "      <th>xmtr_location</th>\n",
       "      <th>duplex_type</th>\n",
       "      <th>coverage_type</th>\n",
       "      <th>threshold</th>\n",
       "      <th>fcc_market_number</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM1_PINON</td>\n",
       "      <td>355471.0</td>\n",
       "      <td>101508</td>\n",
       "      <td>36.457800</td>\n",
       "      <td>-105.598446</td>\n",
       "      <td>Highway 522 &amp; Blueberry Hill Rd.</td>\n",
       "      <td>Taos</td>\n",
       "      <td>Taos</td>\n",
       "      <td>...</td>\n",
       "      <td>-105.598446</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>104953</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.263681e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>LSC_HILLRISE_ENB2</td>\n",
       "      <td>676486.0</td>\n",
       "      <td>101524</td>\n",
       "      <td>32.305697</td>\n",
       "      <td>-106.738075</td>\n",
       "      <td>2912 Idaho Ave</td>\n",
       "      <td>Las Cruces</td>\n",
       "      <td>Dona Ana</td>\n",
       "      <td>...</td>\n",
       "      <td>-106.738075</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>104954</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.184412e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM3_CERRO</td>\n",
       "      <td>355421.0</td>\n",
       "      <td>101443</td>\n",
       "      <td>34.797617</td>\n",
       "      <td>-106.820133</td>\n",
       "      <td>6mi S of SE Los Lunas, AT&amp;T Road</td>\n",
       "      <td>Los Lunas</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>...</td>\n",
       "      <td>-106.820133</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>104959</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.614895e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM6_ONYX</td>\n",
       "      <td>985315.0</td>\n",
       "      <td>100862</td>\n",
       "      <td>33.353914</td>\n",
       "      <td>-104.524059</td>\n",
       "      <td>3600 S Main Street</td>\n",
       "      <td>Roswell</td>\n",
       "      <td>Chaves</td>\n",
       "      <td>...</td>\n",
       "      <td>-104.524059</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>104960</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.872214e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM3_CERRO</td>\n",
       "      <td>355421.0</td>\n",
       "      <td>101443</td>\n",
       "      <td>34.797617</td>\n",
       "      <td>-106.820133</td>\n",
       "      <td>6mi S of SE Los Lunas, AT&amp;T Road</td>\n",
       "      <td>Los Lunas</td>\n",
       "      <td>Valencia</td>\n",
       "      <td>...</td>\n",
       "      <td>-106.820133</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>104961</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.407915e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5912</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM3_ZINC</td>\n",
       "      <td>676518.0</td>\n",
       "      <td>101438</td>\n",
       "      <td>34.057964</td>\n",
       "      <td>-106.890786</td>\n",
       "      <td>103 Manzanares Ave.</td>\n",
       "      <td>Socorro</td>\n",
       "      <td>Socorro</td>\n",
       "      <td>...</td>\n",
       "      <td>-106.890786</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4407973</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.454942e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5913</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM3_ZINC</td>\n",
       "      <td>676518.0</td>\n",
       "      <td>101438</td>\n",
       "      <td>34.057964</td>\n",
       "      <td>-106.890786</td>\n",
       "      <td>103 Manzanares Ave.</td>\n",
       "      <td>Socorro</td>\n",
       "      <td>Socorro</td>\n",
       "      <td>...</td>\n",
       "      <td>-106.890786</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4407974</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.250458e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5914</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM6_GRAPEVINE</td>\n",
       "      <td>676533.0</td>\n",
       "      <td>722741</td>\n",
       "      <td>33.327369</td>\n",
       "      <td>-105.585175</td>\n",
       "      <td>600 Spring Road</td>\n",
       "      <td>Ruidoso</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>...</td>\n",
       "      <td>-105.585175</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4509520</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.962542e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5915</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM6_GRAPEVINE</td>\n",
       "      <td>676533.0</td>\n",
       "      <td>722741</td>\n",
       "      <td>33.327369</td>\n",
       "      <td>-105.585175</td>\n",
       "      <td>600 Spring Road</td>\n",
       "      <td>Ruidoso</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>...</td>\n",
       "      <td>-105.585175</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4509521</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.680068e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5916</td>\n",
       "      <td>albuquerque</td>\n",
       "      <td>0000</td>\n",
       "      <td>NM6_GRAPEVINE</td>\n",
       "      <td>676533.0</td>\n",
       "      <td>722741</td>\n",
       "      <td>33.327369</td>\n",
       "      <td>-105.585175</td>\n",
       "      <td>600 Spring Road</td>\n",
       "      <td>Ruidoso</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>...</td>\n",
       "      <td>-105.585175</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>4509522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.351062e+07</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5137 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     atoll_schema site_version          site_name  fuze_site_id  \\\n",
       "0     albuquerque         0000          NM1_PINON      355471.0   \n",
       "1     albuquerque         0000  LSC_HILLRISE_ENB2      676486.0   \n",
       "6     albuquerque         0000          NM3_CERRO      355421.0   \n",
       "7     albuquerque         0000           NM6_ONYX      985315.0   \n",
       "8     albuquerque         0000          NM3_CERRO      355421.0   \n",
       "...           ...          ...                ...           ...   \n",
       "5912  albuquerque         0000           NM3_ZINC      676518.0   \n",
       "5913  albuquerque         0000           NM3_ZINC      676518.0   \n",
       "5914  albuquerque         0000      NM6_GRAPEVINE      676533.0   \n",
       "5915  albuquerque         0000      NM6_GRAPEVINE      676533.0   \n",
       "5916  albuquerque         0000      NM6_GRAPEVINE      676533.0   \n",
       "\n",
       "      site_record_id  latitude_degrees  longitude_degrees  \\\n",
       "0             101508         36.457800        -105.598446   \n",
       "1             101524         32.305697        -106.738075   \n",
       "6             101443         34.797617        -106.820133   \n",
       "7             100862         33.353914        -104.524059   \n",
       "8             101443         34.797617        -106.820133   \n",
       "...              ...               ...                ...   \n",
       "5912          101438         34.057964        -106.890786   \n",
       "5913          101438         34.057964        -106.890786   \n",
       "5914          722741         33.327369        -105.585175   \n",
       "5915          722741         33.327369        -105.585175   \n",
       "5916          722741         33.327369        -105.585175   \n",
       "\n",
       "                        street_address        city    county  ...  \\\n",
       "0     Highway 522 & Blueberry Hill Rd.        Taos      Taos  ...   \n",
       "1                       2912 Idaho Ave  Las Cruces  Dona Ana  ...   \n",
       "6     6mi S of SE Los Lunas, AT&T Road   Los Lunas  Valencia  ...   \n",
       "7                   3600 S Main Street     Roswell    Chaves  ...   \n",
       "8     6mi S of SE Los Lunas, AT&T Road   Los Lunas  Valencia  ...   \n",
       "...                                ...         ...       ...  ...   \n",
       "5912               103 Manzanares Ave.     Socorro   Socorro  ...   \n",
       "5913               103 Manzanares Ave.     Socorro   Socorro  ...   \n",
       "5914                   600 Spring Road     Ruidoso   Lincoln  ...   \n",
       "5915                   600 Spring Road     Ruidoso   Lincoln  ...   \n",
       "5916                   600 Spring Road     Ruidoso   Lincoln  ...   \n",
       "\n",
       "     antenna_longitude  number_of_tx_antenna_ports  \\\n",
       "0          -105.598446                           2   \n",
       "1          -106.738075                           2   \n",
       "6          -106.820133                           2   \n",
       "7          -104.524059                           2   \n",
       "8          -106.820133                           2   \n",
       "...                ...                         ...   \n",
       "5912       -106.890786                           2   \n",
       "5913       -106.890786                           2   \n",
       "5914       -105.585175                           2   \n",
       "5915       -105.585175                           2   \n",
       "5916       -105.585175                           2   \n",
       "\n",
       "      number_of_rx_antenna_ports  tx_record_id xmtr_location duplex_type  \\\n",
       "0                              4        104953           NaN         NaN   \n",
       "1                              2        104954           NaN         NaN   \n",
       "6                              2        104959           NaN         NaN   \n",
       "7                              2        104960           NaN         NaN   \n",
       "8                              2        104961           NaN         NaN   \n",
       "...                          ...           ...           ...         ...   \n",
       "5912                           2       4407973           NaN         NaN   \n",
       "5913                           2       4407974           NaN         NaN   \n",
       "5914                           4       4509520           NaN         NaN   \n",
       "5915                           4       4509521           NaN         NaN   \n",
       "5916                           4       4509522           NaN         NaN   \n",
       "\n",
       "      coverage_type  threshold  fcc_market_number           sum  \n",
       "0               NaN        NaN                NaN  8.263681e+08  \n",
       "1               NaN        NaN                NaN  2.184412e+08  \n",
       "6               NaN        NaN                NaN  1.614895e+08  \n",
       "7               NaN        NaN                NaN  2.872214e+08  \n",
       "8               NaN        NaN                NaN  1.407915e+08  \n",
       "...             ...        ...                ...           ...  \n",
       "5912            NaN        NaN                NaN  2.454942e+08  \n",
       "5913            NaN        NaN                NaN  3.250458e+08  \n",
       "5914            NaN        NaN                NaN  2.962542e+07  \n",
       "5915            NaN        NaN                NaN  1.680068e+07  \n",
       "5916            NaN        NaN                NaN  2.351062e+07  \n",
       "\n",
       "[5137 rows x 74 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.site_version.astype('category')\n",
    "df= df[df['site_version']== '0000']\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking at a correlation heatmap of features in relation to sum area with correlations being greater than or equal to .1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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kSVKbRnTRDtxWJkmSJEmSJGnQrEySJEmSJElqVxeV63TRo0qSJEmSJKldViZJkiRJkiS1qYu2TLIySZIkSZIkSYNnZZKGlP8+5IOdDkGSJEmSJM2HySQNKXudeGSnQ5AkSZIkLWaXf/rUToewxLnMTZIkSZIkSeqHlUmSJEmSJEltSheVJlmZJEmSJEmSpEGzMkmSJEmSJKlNXVSYZGWSJEmSJEmSBs9kkiRJkiRJkgbNZW6SJEmSJEltcpmbJEmSJEmS1A8rkyRJkiRJktplZZIkSZIkSZL0YlYmSZIkSZIktSldtGmSlUmSJEmSJEkaNJNJkiRJkiRJGjSXuUmSJEmSJLWpi1a5mUySJElaknbdZGs+vP/bGZER/PaGy/i/q86b5/r2G23Oh/Y/lE3X2ZATzhnHxbdd16FIJUmSBsdlbl0syfeTbLWY5vpUr+PRSW5eHPNKkrQsG5HwsTe+k2N/+g3edcrx7Lf1Lowetd48fR5+cipf+s0PufDmazoUpSRJWhwyojOfTjCZ1MWq6n1VNWkxTfepBXeRJKm7vHr9V3Lf1Ed5YPpjPN8zmwsnTWCvLcbM0+ehJx7nrkfup6eqQ1FKkiQtHJNJw1CSXyX5c5JbkoxN8pYkNzSf25NMbvpdnGSn5nhmki834y5Msktz/e4kb2n6HJ7k273u89sk+yQ5CVipmf+M5vJySU5tYrggyUpL+z1IktRpa6+2Oo/MmDr3/NEnp7H2aqt3MCJJkrSkJOnIpxNMJg1PR1TVjsBOwIeAK6pqTFWNAW4EvtrPmFWAi5txM4AvAn8HHAh8fn43q6pPArOaexzWNG8OnFxVWwPTgYP6G9sku65Ncu24ceMW+kElSRrKwov/B88CJEmStKxzA+7h6UNJDmyON6KV2Hk8yXG0kj4n9zPmb8CcHUEnAs9W1XNJJgKjFyGGyVV1Q3P854HmqKpxwJwsUv3oxAmLcCtJkoamR2ZMY53V1px7vvbL1uCxmdM7GJEkSVL7rEwaZpLsA+wH7F5V2wPXAysmeQNwCHDUAEOfq5r7b6U9wLMAVdXDC0nH55n3d2bF+YTybK/j2Zi4lCR1odsemMJGa67DeiNHsfyI5dhvq5254o4bOx2WJElaApLOfDrBv+APPyOBaVX1dJItgd2AV9DaIPuNVTWrjbmnAO9PMgLYANil17XnkqxQVc+1Mb8kScPK7Orha+f/hK+94yOMGBF+d+MVTH7sAd6791u47cF7uOIvN7LleqP50sHvZ7UVV2bPzbfjvXsfwL+M+2ynQ5ckSRqQyaTh5zzgqCQ3AbcD42ktMVsLOKfZnOuBqnrTIsx9BTCZ1jK4m4Hrel0bB9yU5Drg04scvSRJw8z4u25m/F2fmaft/7v03LnHtz04hbd967ilHJUkSVrcOlUl1Akmk4aZqnoW+Id+Ln2un7779DpetdfxCX36rdr8WcBh9KOqPgF8olfTNr2u9bfhtyRJkiRJWgaZTJIkSZIkSWpXF1UmuQG3JEmSJEmSBs1kkiRJkiRJkgbNZW6SJEmSJEltShftwG1lkiRJkiRJkgbNyiRJkiRJkqQ2dVFhkpVJkiRJkiRJGjwrkyRJkiRJktqULirX6aJHlSRJkiRJUrtMJkmSJEmSJGnQXOYmSZIkSZLUJjfgliRJkiRJkvphZZIkSZIkSVKb0kWlSVYmSZIkSZIkadCsTNKQcvmnT+10CJIkSZIkLbQuKkwymaSh5dDvfrrTIUiSJEmSFrOzjj6x0yFoMXKZmyRJkiRJkgbNyiRJkiRJkqQ2ddMyNyuTJEmSJEmShqkkb0xye5I7k3yyn+sbJ/lTkuuT3JTkTQua08okSZIkSZKkdg3Bcp0kywEnA38H3AdMSHJuVU3q1e0zwM+q6rtJtgJ+D4ye37xD8FElSZIkSZK0GOwC3FlVd1fV34CfAgf06VPAy5rjkcADC5rUyiRJkiRJkqQ2pUObJiUZC4zt1TSuqsY1xxsAf+117T5g1z5TnABckOQYYBVgvwXd02SSJEmSJEnSMqpJHI0b4HJ/Ga7qc/4O4LSq+p8kuwM/TrJNVfUMdE+XuUmSJEmSJA1P9wEb9TrfkBcvY3sv8DOAqroKWBEYNb9JTSZJkiRJkiS1KenMZwEmAJsneWWSlwBvB87t0+de4A2tZ8iraSWTHp3fpCaTJEmSJEmShqGqeh74IHA+cCutb227Jcnnk7yl6fbvwJFJbgTOBA6vqr5L4ebhnkmSJEmSJElt6tD+2wtUVb8Hft+n7fhex5OAPRdmTiuTJEmSJEmSNGhWJkmSJEmSJLVpqFYmLQlWJkmSJEmSJGnQrEySJElagrbfaHMO3+vNjMgILrr1Wn59/aXzXH/1eqN5z55vZuO11uUbfziLq+++pUORSpIkDY7JJEmSpCUkCUe89p848Tc/5PGnnuS/Djqaa6fcyv3TXvi23cdmTuc7F/2cfxrz2g5GKkmS2pUuWufmMrdBSvLWJFt1Oo6hLMlRSd7d6TgkSRoqNltnQx5+YiqPzJjG7J7ZXHnnTew8+tXz9Hl0xnTunfowPfP/Bl5JkqQhw8qkwXsr8FtgUqcDGaqq6nudjkGSpKFkzVVexuNPPTH3/PGnnmSzdTbqYESSJGmJ6Z7CpO6uTEryqyR/TnJLkrFN28wkJya5Mcn4JOsm2QN4C/CVJDck2bT5nNeMvyzJls3405J8M8mVSe5OcnDTvk+Si5P8PMltSc5IUwOX5PgkE5LcnGTcnPYBYr44ydeTXJrk1iQ7J/llkr8k+eICnu0VTb9RSUY0ce8/n3v9ZxPrH5KcmeTYpv3IJt4bk/wiycpN+wm9+lyc5MtJrklyR5J+a/eTjE1ybZJrx40btzA/PkmShrz0+3+VViBJkqRlW1cnk4AjqmpHYCfgQ0nWAlYBxlfV9sClwJFVdSVwLvDxqhpTVXcB44BjmvHHAt/pNe96wF7APwIn9WrfAfgIsBWwCbBn0/7tqtq5qrYBVmrGzc/fqmpv4HvAr4EPANsAhzfP0O+zVdU9wJebcf8OTKqqC/q7QZKdgIOamN/WzDPHL5t4twduBd47QJzLV9UuzTN/tr8OVTWuqnaqqp3Gjh27gMeWJGnZ8vhTT7DWKiPnnq+1ysuY9tSTHYxIkiQtKUlnPp3Q7cmkDyW5ERgPbARsDvyN1nI2gD8Do/sOSrIqsAdwdpIbgFNoJZDm+FVV9VTVJGDdXu3XVNV9VdUD3NBr7n2TXJ1kIvB6YOsFxH1u8+dE4JaqerCqngXubp5joGejqr4PrAYcRSsJNpC9gF9X1ayqmgH8pte1bZqqponAYfOJ95fNn/2+R0mShru7Hrmfl6++FmuvtgbLjViOPTbbjmun3NbpsCRJktrStXsmJdkH2A/YvaqeTnIxsCLwXNXcHTBn0/87GgFMr6oxA0z/bO9bDdA+G1g+yYq0qpp2qqq/JjmhiWN+5szT02fOnmbOgZ6NZknahk3/VYEZA9xjfvnN04C3VtWNSQ4H9llAnAO9R0mShrWe6uEHl/2GT/3j4YxIuPi267hv2iMcsvMbuPvR+/nzlNvYdO0N+Pc3HsYqL12JHUdvySE7v4Fjz/pmp0OXJEkaUDf/BX8kMK1JtmwJ7LaA/jNoVfRQVU8mmZzkkKo6u9njaLuqunER4piTOHqsqXg6GPj5IszT2/ye7cvAGcA9wKkMvKTucuCUJP9F6/fkzU1/aL2HB5OsQKsy6f4245Ukadi64d47uOHeO+ZpO3vCH+ce3/Xo/bz/x/+9tMOSJEmLWaeWnHVCNy9zO49WFc9NwBdoLQebn58CH09yfZJNaSVR3tssJbsFOGBRgqiq6bSSNBOBXwETFmWePvp9tiSvA3YGvlxVZwB/S/KvA8Q1gdZyuhtpLVe7FpjzdTT/CVwN/AGwVl+SJEmSpC6SF1Z0SfNKsmpVzWyWxl0KjK2q65bgLevQ7356CU4vSZIkSeqEs44+Eea/ncoy749nPtaRBMsb3jFqqb/Xbl7mpgUbl2QrWkvxTl/CiSRJkiRJkrQMMJk0RCU5GdizT/M3quqHi/k+awF/7OfSG6rqnYvzXpIkSZIkDVfdtGeSyaQhqqo+sJTu8zgw0LfSSZIkSZIkzaObN+CWJEmSJEnSQrIySZIkSZIkqU3dtMzNyiRJkiRJkiQNmpVJkiRJkiRJbUoXlSZZmSRJkiRJkqRBszJJkiRJkiSpXd1TmGRlkiRJkiRJkgbPZJIkSZIkSZIGzWVukiRJkiRJbeqi/betTJIkSZIkSdLgWZmkIeWso0/sdAiSJEmSJC20bqpMMpmkIeXqKZM6HYIkSZIkaTHbdfRWnQ5Bi5HL3CRJkiRJkjRoViZJkiRJkiS1KSO6Z52blUmSJEmSJEkaNCuTJEmSJEmS2tRNG3BbmSRJkiRJkqRBszJJkiRJkiSpTVYmSZIkSZIkSf0wmSRJkiRJkqRBc5mbJEmSJElSm1zmJkmSJEmSJPXDyiRJkiRJkqQ2ZUT3lCZZmSRJkiRJkqRBszJJkiRJkiSpXd1TmGRlkiRJkiRJkgbPZJIkSdISdNOE6zjuvR/g2MOP5jdn/WLAftdcdiXv/vsDufuOO5didJIkSQvPZNICJHlrkq06HcfikuTwJOsvwrgTkhzbHH8+yX6LPzpJkoaXntmz+dHJ4zj2i//JSad+k/F/upz77/nri/rNenoWf/jV79h0yy06EKUkSVocks58OsFk0oK9FRg2ySTgcGChkklJ5tlbq6qOr6oLF2dQkiQNR3fd/hfWWX891lnv5Sy/wgrsts9eXHfVNS/q94vTf8KbDnkrK7xkhQ5EKUmStHC6MpmU5FdJ/pzkliRjm7aZSU5McmOS8UnWTbIH8BbgK0luSLJp8zmvGX9Zki2b8acl+WaSK5PcneTgpn2fJBcn+XmS25KckbRyh0mOTzIhyc1Jxs1pHyDmzZJc2MR3XZJNm/aPN3PclORzTdvoJLcmObV5xguSrNTEtBNwRvM8KyXZMcklzfOcn2S9Zo6Lk3wpySXAh/vEclqv55uS5HNNTBN7vY9Vkvygie36JAcsxh+hJEnLhGmPT2WttUfNPV9z1FpMe+zxefpMufNupj76GDvstvPSDk+SJC1GViYNf0dU1Y60EisfSrIWsAowvqq2By4FjqyqK4FzgY9X1ZiqugsYBxzTjD8W+E6vedcD9gL+ETipV/sOwEdoVThtAuzZtH+7qnauqm2AlZpxAzkDOLmJbw/gwST7A5sDuwBjgB2T7N3037zpvzUwHTioqn4OXAscVlVjgOeBbwEHN8/zA+DEXvdcvapeV1X/M//XyWNV9Rrgu807Afg0cFFV7QzsSysht0rfgUnGJrk2ybXjxo1bwG0kSVrGVL24rdf/9fX09PCTU37AO8b+61IMSpIkqT3LL7jLsPShJAc2xxvRSrz8Dfht0/Zn4O/6DkqyKq1Eztm9iohe2qvLr6qqB5iUZN1e7ddU1X3NHDcAo4HLgX2THAesDKwJ3AL8pp/7rgZsUFXnAFTVM037/sD+wPVN11WbZ7kXmFxVN/R6ntH9vIdXAdsAf2ieZzngwV7Xz+pnTH9+2es+b2uO9wfeMmefJWBFYGPg1t4Dq2ocrQQdQF09ZdIgbylJ0tC3xqi1ePzRx+aeT33scdZYa82558/MmsV9U+7lv477DABPTJ3O/372S3zkc59iky02W+rxSpKkRTefxUbDTtclk5LsA+wH7F5VTye5mFai47mquf98OJv+380IYHpT1dOfZ3vfaoD22cDySVakVdW0U1X9NckJTRz9hj2f9v+qqlPmaUxG93PPlQYYf0tV7T7A/E8N0N7XnHv1fm+hVQ11+yDnkCRp2NnkVZvz8P0P8uhDD7PGWmsy/uLLOfqTH517feVVVuE7Z/9o7vmXPv4Z3n7k4SaSJEnSkNaNy9xGAtOaRNKWwG4L6D8DWA2gqp4EJic5BCAt2y9iHHMSR481FU8HD9Sxue99Sd7a3PelSVYGzgeOaMaTZIMk6wz2eYDbgbWT7N6MXyHJ1ov4PH2dDxzTa3+oHRbTvJIkLTOWW2453v2BI/nvT32OTx55DLvuvQcbjt6YX5z+k3434pYkSVoWdF1lEnAecFSSm2glU8YvoH0PY14AACAASURBVP9PgVOTfIhWwucw4LtJPgOs0Fy/cWGDqKrpSU4FJgJTgAkLGPIvwClJPg88BxxSVRckeTVwVZOzmQm8i1aF0EBOA76XZBawe/NM30wyktbvw//SWm7Xri80c93UJJSmMP89oSRJGpa232VHtt9lx3naDnrPO/vt+6mvfHFphCRJkpaALlrlRqq/jSGlznDPJEmSJEkahnYdvRUMvIXLsDDh/OkdSbDs/PerL/X32o2VSZIkSZIkSYtVN1UmmUwaYpKcDOzZp/kbVfXDTsQjSZIkSZLUm8mkIaaqPtDpGCRJkiRJ0kLqosqkbvw2N0mSJEmSJC0ik0mSJEmSJEkaNJe5SZIkSZIktSldtAO3lUmSJEmSJEkaNCuTJEmSJEmS2pQuKtfpokeVJEmSJElSu6xMkiRJkiRJalMXbZlkZZIkSZIkSZIGz2SSJEmSJEmSBs1lbpIkSZIkSW3qpmVuJpM0pOw6eqtOhyBJkiRJkubDZJIkSZIkSVKb0kWlSSaTNKTs/9VjOh2CJEmSJGkxu+DYb3U6BC1GJpMkSZIkSZLa1EWFSX6bmyRJkiRJkgbPZJIkSZIkSZIGzWVukiRJkiRJbXKZmyRJkiRJktQPK5MkSZIkSZLaZWWSJEmSJEmS9GJWJkmSJEmSJLUpI7qnNMnKJEmSJEmSJA2aySRJkiRJkiQNmsvcJEmSJEmS2pTuWeVmZZIkSZIkSZIGz8okSZIkSZKkNnVTZZLJJEmSpCVop9Gv5ujXH8SIjOC8iVdx1jV/mOf6thtuylH7HsQma6/Pl357GpfdcUOHIpUkSRock0mSJElLyIiED+53CJ88+2QemzGdb73r41x110TuffyhuX0eeXIaX/1//8fBO7+hg5FKkqR2pYtKk9wzaYhLMrP5c3SSd3Y6HoAkhyf59kL03yfJHksyJkmShqJXvfwVPDDtMR564nGe75nNJbf9mT023XaePg8/OZXJjz1AVXUoSkmSpIVjMmnZMRroeDIpyaJUs+0DmEySJHWdUautzqMzps09f3TmdNZabfUORiRJktQ+k0nLjpOA1ya5IclHkyyX5CtJJiS5Kcm/DTSwqQy6JMnPktyR5KQkhyW5JsnEJJs2/f4pydVJrk9yYZJ1m/YTkoxLcgHwoz5zvznJVUlGJVk7yS+amCYk2TPJaOAo4KNN7K9dUi9IkqRlgRVIkiQNT0lnPp3gnknLjk8Cx1bVPwIkGQs8UVU7J3kpcEWSC6pq8gDjtwdeDUwF7ga+X1W7JPkwcAzwEeByYLeqqiTvA44D/r0ZvyOwV1XNSnJ4E8OBwMeAN1XVtCQ/Ab5eVZcn2Rg4v6peneR7wMyq+mrfoJrnGAtwyimntPmKJEkaWh6bMZ21V1tj7vnaq67O1JlPdDAiSZKk9plMWnbtD2yX5ODmfCSwOTBQMmlCVT0IkOQu4IKmfSKwb3O8IXBWkvWAl/SZ69yqmtXrfF9gJ2D/qnqyadsP2KrXpmMvS7La/B6iqsYB4+ac/vyrx8yvuyRJy5TbH7qXDdZYm5ePXIvHZkzndVvuyEm/O63TYUmSpCUgXbT2y2TSsivAMVV1/iD7P9vruKfXeQ8v/B58C/haVZ2bZB/ghF5jnuoz393AJsAWwLVN2whg9z5Jp67a0V6SpN56qodv//FsvnTQ+xkxIpw/cTz3PP4Q797zTdzx0L2Mv+tmtnj5xnz2gPex2oors9um2/Ave7yJsad9qdOhS5IkDchk0rJjBtC7yud84OgkF1XVc0m2AO6vqr5Jn4UxEri/OX7PAvreAxwLnJPkkKq6hVa10weBrwAkGVNVNzSxv6yNuCRJWmZNmDyJCZMnzdP2oyt+P/f4jofu5bBTjl/aYUmSpMWti+oouqgIa5l3E/B8khuTfBT4PjAJuC7JzcAptJ8cPAE4O8llwGML6lxVtwOHNWM2BT4E7NRsCD6J1sbbAL8BDnQDbkmSJEmSln3xG0U0hNT+7pkkSZIkScPOBcd+C4Z57c7tf57ZkQTLq3Zcdam/V5e5SZIkSZIktamb9gs2mTSMJNkW+HGf5meratdOxCNJkiRJkoYfk0nDSFVNBMZ0Og5JkiRJkrpNFxUmuQG3JEmSJEmSBs/KJEmSJEmSpDZZmSRJkiRJkiT1w2SSJEmSJEmSBs1lbpIkSZIkSW1ymZskSZIkSZLUDyuTJEmSJEmS2pQuKk2yMkmSJEmSJEmDZmWSJEmSJElSu7qnMMnKJEmSJEmSJA1eqqrTMUhz+MsoSZIkScPXsK7duXvi0x35O+0m26681N+ry9wkSZIkSZLaNKKL1n6ZTNKQcsQPvtDpECRJkiRJi9kPjvjPToegxchkkiRJkiRJUruG9SK+eXVREZYkSZIkSZLaZWWSJEmSJElSm5LuKU2yMkmSJEmSJEmDZjJJkiRJkiRJg+YyN0mSJEmSpDZ10So3K5MkSZIkSZI0eFYmSZIkSZIktcnKJEmSJEmSJC3zkrwxye1J7kzyyQH6/HOSSUluSfKTBc1pZZIkSZIkSVKbhmJlUpLlgJOBvwPuAyYkObeqJvXqsznwH8CeVTUtyToLmtfKJEmSJEmSpOFpF+DOqrq7qv4G/BQ4oE+fI4GTq2oaQFU9sqBJTSZJkiRJkiQto5KMTXJtr8/YXpc3AP7a6/y+pq23LYAtklyRZHySNy7oni5zkyRJkiRJalM6tM6tqsYB4wa43F9Q1ed8eWBzYB9gQ+CyJNtU1fSB7mkySZIkaQnaZoNNeeduf08SLrvjen5/05XzXN9/613Ze4sdmF09zHjmaX542W94/KknOhStJEkaZu4DNup1viHwQD99xlfVc8DkJLfTSi5NGGhSl7lJkiQtIUl41+5v5OsX/ITP/PK77LrJNqy/+qh5+tz7+EN8/tzv89lfjePaKbdyyM5v6FC0kiSpLenQZ/4mAJsneWWSlwBvB87t0+dXwL4ASUbRWvZ29/wmNZmkparZSV6SpK6wyaj1eeTJaTw6Yzqze3q4+u5bGLPxq+bpc9tD9/C32c8DcPcj97PGKi/rRKiSJGkYqqrngQ8C5wO3Aj+rqluSfD7JW5pu5wOPJ5kE/An4eFU9Pr95TSYNMUlGJ7ktyfeT3JzkjCT7NRth/SXJLs3nyiTXN3++qhn7sSQ/aI63bcavPMB9Tkjy4yQXNfMe2bQnyVeasROTHNq0f2fOL1qSc3rd571JvtgcvyvJNUluSHLKnMRRkpnNL+rVwO5L+BVKkjRkrL7Ky5j61JNzz6c99SRrrLzagP1fu8UYJt5359IITZIkLWYZ0ZnPglTV76tqi6ratKpObNqOr6pzm+Oqqo9V1VZVtW1V/XRBc5pMGpo2A74BbAdsCbwT2As4FvgUcBuwd1XtABwPfKkZ97/AZkkOBH4I/FtVPT2f+2wHvJlWguf4JOsDbwPGANsD+wFfSbIecCnw2mbcBsBWzfFetDbnejVwKLBnVY0BZgOHNX1WAW6uql2r6vJFeyWSJC17+t/xsu+ely27bboto0etx3kTr1qyQUmSJLXJDbiHpslVNREgyS3AH6uqkkwERgMjgdOTbE5rF/YVAKqqJ8nhwE3AKVV1xQLu8+uqmgXMSvInYBdayaEzq2o28HCSS4CdgcuAjyTZCpgErNEkmXYHPgS8B9gRmNDsYL8S8Ehzn9nAL/oLoPnKwrEAp5xyir+RkqRhZdpTT7Jmr2Vra6zyMqY/PfNF/bZa/5X84/Z78eXfn87zPbOXZoiSJEkLzb+6D03P9jru6XXeQ+tn9gXgT1V1YJLRwMW9+m8OzATWH8R9+v7TaDHA9l1VdX+SNYA30qpSWhP4Z2BmVc1IK4N0elX9Rz/Dn2mSU/3N2/srDGv8D74wiLAlSVo2TH7sAdYduSajVl2daU8/ya6bbM0pF58zT5+N13w5797jTXztgjOZ8cz8CoolSdJQlgVvhj1suMxt2TQSuL85PnxOY5KRtJbH7Q2sleTgBcxzQJIVk6wF7ENrl/dLgUOTLJdk7Waua5r+VwEfafpcRmvZ3WXNtT8CBydZp4llzSSvaOchJUla1vVU8X9XncfH/v6dnPi2o5kweRIPTH+Ut+7wOsZstAUA/7zLG3jpCi/h/fsexAkHHMkx+x3a4aglSZLmz8qkZdN/01rm9jHgol7tXwe+U1V3JHkv8Kckl1bVI/3O0koS/Q7YGPhCVT2Q5BxaS9dupFWpdFxVPdT0vwzYv6ruTHIPreqkywCqalKSzwAXJBkBPAd8ALhnMT63JEnLnIn33fmiTbV/df0lc4+/et4ZSzskSZK0BKSLSpNS1f8mkBrekpxAa4naVzsdSy91hMvcJEmSJGnY+cER/wkDbKsyXDw05dmOJFhePvqlS/29WpkkSZIkSZLUpi4qTDKZNNwl+Vfgw32ar6iqD3QiHkmSJEmStGwzmTTMVdUPgR92Og5JkiRJkjQ8mEySJEmSJElqUzctcxvR6QAkSZIkSZK07LAySZIkSZIkqU0Z0T2lSVYmSZIkSZIkadCsTJIkSZIkSWqTeyZJkiRJkiRJ/TCZJEmSJEmSpEFzmZskSZIkSVK7XOYmSZIkSZIkvZiVSZIkSZIkSW1yA25JkiRJkiSpH6mqTscgzeEvoyRJkiQNX8O6dmfqQ8915O+0a758haX+Xq1MkiRJkiRJ0qC5Z5KGlCvvntjpECRJkiRJi9kem2zb6RC0GJlMkiRJkiRJapMbcEuSJEmSJEn9sDJJkiRJkiSpTVYmSZIkSZIkSf2wMkmSJEmSJKldViZJkiRJkiRJL2YySZIkSZIkSYPmMjdJkiRJkqQ2LbfiMx268wpL/Y5WJkmSJEmSJGnQTCZJkiRJkiRp0EwmSZIkSZIkadBMJkmSJEmSJGnQTCZJkiRJkiRp0EwmSZIkLUETr72e/3jfh/jEER/kdz87Z8B+Ey67in/9h4OZfMedSzE6SZKkhWcySZIkaQnpmT2bH5/8fT76hU9z4ilf5+qLL+f+e/76on6znp7Fhef+nk1etXkHopQkSVo4SyWZlGT1JO9fjPONTnLzQo45LcnBnbp/r7GHJ/n2Avrsk2SPXudHJXn3otxvcUpyQpJjm+PPJ9mv0zFJkjSU3X3Hnayz/stZZ711WX6FFdjldXty/fgJL+p3zo9+yj8cfAArvGSFDkQpSZK0cJZWZdLqwGJLJnWBfYC5yaSq+l5V/WhJ3CjJ8osyrqqOr6oLF3c8kiQNJ9Mem8qaa4+ae77mqLWY9vjUefrcc+fdTH3sMcbsutPSDk+SJGmRtJVMSvKuJNckuSHJKUlekeQvSUYlGZHksiT7AycBmzb9vtKM/XiSCUluSvK5pm10kluTnJrkliQXJFmpubZjkhuTXAV8oFcMyyX5Sq+5/q1pT5JvJ5mU5HfAOgt4lp2TXNnc45okqzXxXJbkuuazRz/jlkvy1SQTm/sf07RPSTKqOd4pycX9jP2nJFcnuT7JhUnWTTIaOAr4aPO+XtunImhMkvHNvc5JskbTfnGSLzex35HktfN51sOTnJ3kN8AFSVZN8sfmGScmOaBX308nuT3JhcCrerXPrfQa6FmTvK55hhuaZ1xtfj8DSZKGn3pRS8jc456eHs4cdxpvP/I9SzMoSZKktixyMinJq4FDgT2ragwwG3gd8GXge8C/A5Oq6gLgk8BdVTWmqj7eJJg2B3YBxgA7Jtm7mXpz4OSq2hqYDhzUtP8Q+FBV7d4nlPcCT1TVzsDOwJFJXgkcSCv5sS1wJL0qffp5lpcAZwEfrqrtgf2AWcAjwN9V1WuaZ/1mP8PHAq8Edqiq7YAzFvDqersc2K2qdgB+ChxXVVNovb+vN+/rsj5jfgR8ornXROCzva4tX1W7AB/p096f3YH3VNXrgWeAA5vn3Bf4nyYZtyPwdmAH4G203u/COBb4QPP78Vpa73QeScYmuTbJtePGjVvI6SVJGtrWGLUWUx99bO751MceZ/W11ph7/sysWdx/z1856bjPcux7juau2/7CNz/3ZTfhliRJQ9oiLXFqvAHYEZiQBGAl4JGqOiHJIbSqa8YMMHb/5nN9c74qrSTSvcDkqrqhaf8zMDrJSGD1qrqkaf8x8A+95tqu135II5u59gbOrKrZwANJLprPs7wKeLCqJgBU1ZMASVYBvp1kTrJsi37G7gd8r6qeb8ZO7afPQDYEzkqyHvASYPL8OvfzHk4Hzu7V5ZfNn38GRi/g3n/oFWuALzUJvR5gA2BdWgmgc6rq6eb+5w7moXq5AvhakjOAX1bVfX07VNU4YE4Wqa68e+JC3kKSpKHrlVtsxiMPPMijDz3MGmutyTWXXMG/feIjc6+vvMoqfOusH849P+m44zn0fe/mlVts1olwJUmSBqWdZFKA06vqP+ZpTFamlSSBVpJoxgBj/6uqTukzdjTwbK+m2bSSVKG/OvEX5jqmqs7vM9eb5jOmvzn66/tR4GFge1pVXM8sxNjneaHya8UB7vst4GtVdW6SfYATBhnvQOa8u9ks+Gf7VK/jw4C1gR2r6rkkU3gh5sG8w36ftapOapYYvgkYn2S/qrptEPNJkjQsLLfcchx29Pv4n898kZ7ZPbx2/9ezwSs24pwf/ZTRW2zKDrstbNGvJElS57WzZ9IfgYOTrAOQZM0kr6C1zO0M4Hjg1KbvDKD3fjnnA0ckWbUZu8GcefpTVdOBJ5Ls1TQd1meuo5Os0My1RVNRdCnw9mZPo/VoLd8ayG3A+kl2buZYLa2NqUfSqljqAf4FWK6fsRcARzX9SbJm0z6FVuUWvLBUr6+RwP3Nce/NEvq+LwCq6glgWq/9kP4FuKRvv0UwklZV2XNJ9gVe0bRfChyYZKVmv6N/GmD8FPp51iSbVtXEqvoycC2w5WKIVZKkZcr2u7yGk77/Lf77/2/vzuPsqsp8/3++AZoxhBlR0GAEARmCBGwBERUH1MYJxVlwiK04NF7bxuHaaP8UFH8irWgTaQUVRYELIiiDCAgoEBoSEgERJFwRNZAAIYCQcJ77x9kJlUoNJzlJnST1efuqV/ZZa+21n72rfJE89ay1v3sS//SW9n8mX/fONw+YSDr6y5+3KkmSJK3yljuZVFU3A5+hvYHzTcAltJdW7Q18qapOBx5PckRVzQGuTjIzyfHNPko/BH6bZAZwFgMkT/o5Ajgp7Q24++69cwpwM3BDkpnAybSrcs4B/kB7X6FvMUTSpaoep70n0teTTG/uZT3gm8C7klxDe4nbwwOcfgrt5Xk3Nee+tWn/HHBikitpVwoN5BjgzGbMfX3af0Y7iTNtgI203wUc3zzzicDnB7uvZXA6MCnJ9bQTdbcCVNUNtPeSmgacDfTfv2mRwe71X5rv+XTa37NfrIBYJUmSJElSD6Wq05Vg0krnnkmSJEmStAba95m7AX1eaboGeuihh3qSYBk7duyIP9dulrlJkiRJkiRplOlmA+7VUpJzgO37Nf9b/w281wRJXk57D6u+7qyq1/UiHkmSJEmStPobdcmk0ZRIaRJka1ySTJIkSZIk9Y7L3CRJkiRJktQxk0mSJEmSJEnqmMkkSZIkSZIkdWzU7ZkkSZIkSZK0oi1YJ70OYcRYmSRJkiRJkqSOWZkkSZIkSZLUparqdQgjxsokSZIkSZIkdcxkkiRJkiRJkjrmMjdJkiRJkqQuucxNkiRJkiRJGkBGU+ZMqzx/GCVJkiRpzZVeB7AyzX74wZ78m3arDceN+HN1mZtWKYee9MlehyBJkiRJWsHOOvLYXoegFchkkiRJkiRJUpeKVq9DGDHumSRJkiRJkqSOmUySJEmSJElSx1zmJkmSJEmS1KXWKHrBmZVJkiRJkiRJ6piVSZIkSZIkSV0qK5MkSZIkSZKkpVmZJEmSJEmS1CUrkyRJkiRJkqQBmEySJEmSJElSx1zmJkmSJEmS1KWWy9wkSZIkSZKkpVmZJEmSJEmS1CU34JYkSZIkSZIGYGWSJEnSSjTx6TtyxP6vZsyYMVx681TOveGKJfp33mY8R7zg1Txj86dwwsVncM0dM3sUqSRJ6oZ7JkmSJKlrYxLee8AhfOH873LUD09g/x32YNtNt1pizH3zH+CkS8/iqtum9yhKSZKkZWMyqYeSzEqyRZJNknywT/tTk5zVHE9M8srlmPuYJB9fkfEur1UpFkmSRtKzttqOvz44h9nz7mdh6wmu/sN09t5+5yXG3PvQA9w156+j6reZkiRp9WYyadWwCbA4mVRV91TVoc3HicAyJ5MkSVLvbbbRxtw3/8HFn+fMn8dmG47rYUSSJGllaVWrJ1+9YDJphCQ5N8n/JPldksn9uo8DJiSZluT4JOOTzEzyD8DngcOavsP6V/k048Y3x59O8vskvwSe3WfMhCQXNte/MslOQ8T5T0muTXJjkl8m2bppPybJaUkubiqqXp/ky0lmNHOv04w7LsnNSW5K8pUV9PgkSVotZYC2wgokSZK0enMD7pHz7qqam2R9YGqSs/v0HQ3sWlUTARYlh6rq8SSfBSZV1YeavmMGmjzJXsCbgT1pf19vAP6n6Z4C/HNV/SHJ84BvAi8eJM6rgH+sqkryXuATwP9q+iYALwJ2AX4LvKGqPpHkHOBVSX4NvA7YqTl/k+EeSpNYmwxw8sknDzdckqTVypz589hioycrkTbfaGPuf3heDyOSJEkrS42iJesmk0bOR5K8rjneDthhBc//AuCcqnoEIMl5zZ8bAfsCZyaLfz+67hDzbAv8OMk2wD8Ad/bp+0VVLUgyA1gLuLBpnwGMB84H/g6ckuSC5vOQqmoK7WQXQF180ieHO0WSpNXG7bPvZptxW7DV2E2Z+/A89tthD752yRm9DkuSJKkrJpNGQJIDgYOA51fVI0kuB9ZbzukWsuTyxL7zDJQGHQM8sKjqqQNfB75aVec1cR/Tp+8xgKpqJVlQT6ZdW8DaVbUwyT7AS2hXSX2IwSugJEla47WqxSlXnsdnDnk3YxJ+dcv13D13NoftcxB3zP4z18+6hQlbbcsnDn47G667PpO235nD9jmIo370tV6HLkmSltFoepmGyaSRMQ64v0kk7QT8Y7/+h4Cxg5zbv28W8GqAJM8Ftm/afw2cmuQ42t/XfwJOrqp5Se5M8saqOjPt8qTdq2qw9w+PA/7cHL+r4ztkcRXUBlX18yTXALcvy/mSJK2Jbrzr99x41++XaPvxdb9cfHzH7Lt5/2nHjXRYkiRJy80NuEfGhcDaSW4C/gO4pm9nVc0Brm420z6+37mXAbss2oAbOBvYLMk04APAbc0cNwA/BqY1Y67sM8fbgPckmQ78DnjNELEeQ3tJ3JXAfct4n2OB85v7vAI4ahnPlyRJkiRJq7iMpg2itMqrQ90zSZIkSZLWOGcdeSwM/KLTNcZt997dkwTLjltuO+LP1cokSZIkSZIkdcw9k0apJJ8G3tiv+cyq+kIv4pEkSZIkaXU2mlZ+mUwapZqkkYkjSZIkSZK0TEwmSZIkSZIkdalVrV6HMGLcM0mSJEmSJEkdM5kkSZIkSZKkjrnMTZIkSZIkqUujaQNuK5MkSZIkSZLUMSuTJEmSJEmSutSyMkmSJEmSJElampVJkiRJkiRJXSqsTJIkSZIkSZKWYjJJkiRJkiRJHctoenWdVnn+MEqSJEnSmiu9DmBluumeO3ryb9rdnzphxJ+reyZplbL/F97X6xAkSZIkSSvYVZ/+dq9D0ApkMkmSJEmSJKlLrVar1yGMGPdMkiRJkiRJUsesTJIkSZIkSepSaxTtSW1lkiRJkiRJkjpmMkmSJEmSJEkdc5mbJEmSJElSlwqXuUmSJEmSJElLsTJJkiRJkiSpS27ALUmSJEmSJA3AyiRJkiRJkqQulZVJkiRJkiRJ0tJMJkmSJEmSJKljLnOTJEmSJEnqksvcJEmSJEmSpAFYmSRJkrQSPe+Zz+GjL3szYzKG86ddyQ9+e+ES/XtstwMfedlhTNhqW445ZwqX33pDjyKVJEndaFmZJA0uySZJPtjrOCRJWtWNSfjYK97Kx884kbef/FkOes4+jN9imyXG/G3eXL74s+/yy5nX9ShKSZK0JkvyiiS/T3J7kqOHGHdokkoyabg5TSZpeWwCmEySJGkYOz91e+6eey/3PHAfC1tP8Mubp7L/jhOXGPPXB+dwx+w/j6rfZkqStCaqavXkayhJ1gJOAg4GdgHekmSXAcaNBT4CXNvJvZpM0vI4DpiQZFqSE5JcmuSGJDOSvGbRoCTvTHJTkulJvt/DeCVJ6oktx27C7IfmLv5877z72XLsJj2MSJIkjTL7ALdX1R+r6nHgDOA1A4z7D+DLwN87mdQ9k7Q8jgZ2raqJSdYGNqiqeUm2AK5Jch7tjOengf2q6r4km/UyYEmSeiFkqTYLkCRJ0gh6GvCnPp/vBp7Xd0CSPYHtqur8JB/vZFKTSepWgC8mOQBo0f5B3Rp4MXBWVd0HUFVzBzw5mQxMBjj55JNHJGBJkkbK7IfuZ6uxT/4+ZcuNN+W++Q/0MCJJkrSy9GrJet9/VzemVNWURd0DnLI40CRjgBOAw5flmiaT1K23AVsCe1XVgiSzgPVo/8AO+/+k5gd80Q95fe8LU1dWnJIkjbhb75nFdpttxTbjtuDeh+7noF325nPnntLrsCRJ0hqk37+r+7sb2K7P522Be/p8HgvsClyeBOApwHlJDqmq6we7pskkLY+HaP/AAYwDZjeJpBcBz2jaLwXOSXJCVc1Jstlg1UmSJK2pnqgWX73oh3z1Lf/CmDHhgulXc+d99/CeAw7h1r/cxdV/mM5O24zni4d+kLHrbcB+O+zOew54De+Y8u+9Dl2SJC2jWjXXsk8FdkiyPfBn4M3AWxd1VtWDwBaLPie5HPj4UIkkMJmk5dAkh65OMpP2D+ZOSa4HpgG3NmN+l+QLwBVJngBuZBnL5iRJWhNcc8dMrrnjM0u0/fevz1t8fOtfZvH6r39ihKOS376kGwAAIABJREFUJEmjQVUtTPIh4CJgLeA7zb/XPw9cX1XnDT3DwEwmablU1Vs7GHMacNoIhCNJkiRJUk+1qtXrEAZUVT8Hft6v7bODjD2wkznHdB+WJEmSJEmSRguTSZIkSZIkSeqYy9wkSZIkSZK61Fo1N+BeKaxMkiRJkiRJUsesTJIkSZIkSepSWZkkSZIkSZIkLc3KJEmSJEmSpC5ZmSRJkiRJkiQNwGSSJEmSJEmSOuYyN0mSJEmSpC61XOYmSZIkSZIkLc3KJEmSJEmSpC65AbckSZIkSZI0ACuTJEmSJEmSujSa9kwymaRVylWf/navQ5AkSZIkSUMwmaRVyqEnfbLXIUiSJEmSVrCzjjy21yFoBTKZJEmSJEmS1KVi9CxzcwNuSZIkSZIkdczKJEmSJEmSpC7VKNqA28okSZIkSZIkdczKJEmSJEmSpC61qtXrEEaMlUmSJEmSJEnqmMkkSZIkSZIkdcxlbpIkSZIkSV1qtdyAW5IkSZIkSVqKlUmSJEmSJEldKqxMkiRJkiRJkpZiZZIkSZIkSVKXWtXqdQgjxsokSZIkSZIkdcxkkiRJkiRJkjrmMjdJkqSVaOLTd+SI/V/NmDFjuPTmqZx7wxVL9O+8zXiOeMGrecbmT+GEi8/gmjtm9ihSSZLUjSo34JYkSVKXxiS894BD+ML53+WoH57A/jvswbabbrXEmPvmP8BJl57FVbdN71GUkiRJy8Zk0hCSnJJkl+b4Uyth/suTTBpo/iS/af4cn+StyzH34Um+sWIi7c6qFIskSSPpWVttx18fnMPsefezsPUEV/9hOntvv/MSY+596AHumvNXWqPot5mSJK2JqqonX71gMmkIVfXeqrq5+bjCk0n9LDF/Ve3bHI4HljmZJEmSem+zjTbmvvkPLv48Z/48NttwXA8jkiRJ6p7JpEaSDZNckGR6kplJDltUOZTkOGD9JNOSnN6Mf3uS65q2k5OsNcTc30pyfZLfJfncAP0DzT+/6T4OeEHTd1T/Kp8k5yc5sDk+IsltSa4A9uszZsskZyeZ2nwt7hsgln2S/CbJjc2fz27aD09ybpKfJbkzyYeSfKwZd02SzZpxH0lyc5KbkpzRwXOf3Dyb66dMmTLccEmSVisZoK2wAkmSpDVRq6onX71gMulJrwDuqao9qmpX4MJFHVV1NPBoVU2sqrcl2Rk4DNivqiYCTwBvG2LuT1fVJGB34IVJdu/b2X/+fuceDVzZ9J0w2AWSbAN8jnYS6aXALn26TwROqKq9gTcApwwR663AAVW1J/BZ4It9+nalXSW1D/AF4JFm3G+Bd/aJd8+q2h345yGuA0BVTamqSVU1afLkycMNlyRptTJn/jy22OjJSqTNN9qY+x+e18OIJEmSuufb3J40A/hKki8B51fVlclAv08E4CXAXsDUZsz6wOwh5n5Tksm0n/c2tBM9N62owBvPAy6vqnsBkvwY2LHpOwjYpc/9bJxkbFU9NMA844DTkuwAFLBOn77LmnMeSvIg8LOmfQbtRBm07+v0JOcC566YW5MkafV0++y72WbcFmw1dlPmPjyP/XbYg69dMmzhriRJ0irNZFKjqm5LshfwSuDYJBcPMTzAaVX1yeHmTbI98HFg76q6P8mpwHpdhLqQJSvK+s41WH3bGOD5VfVoB/P/B+2k0euSjAcu79P3WJ/jVp/PLZ78WXoVcABwCPC/kzyng2tKkrRGalWLU648j88c8m7GJPzqluu5e+5sDtvnIO6Y/Weun3ULE7balk8c/HY2XHd9Jm2/M4ftcxBH/ehrvQ5dkiQto15tht0LJpMaSZ4KzK2qHzT7FR3eb8iCJOtU1QLgUuCnSU6oqtnNfkFjq+quAabeGHgYeDDJ1sDBLJmgGWj+vh4Cxvb5PAv4YJIxwNNoLzkDuBY4McnmwDzgjcCidwxfDHwIOL6514lVNW2QRzEO+HNz3P8ZDKmJabuquizJVbSXxG20LHNIkrSmufGu33PjXb9fou3H1/1y8fEds+/m/acdN9JhSZIkLTeTSU/aDTg+SQtYAHwA+Eqf/inATUluaPZN+gxwcZNAWQAcCSyVTKqq6UluBH4H/BG4epDrLzF/n/abgIVJpgOnAl8D7qS9tGwmcENznb8kOYb2/kV/adoXbQr+EeCkJDfR/p7/msH3M/oy7WVuHwN+NciYwawF/CDJONrVWydU1QNDLBeUJEmSJGmN0KvNsHsho6kMS6u8OvSkYVcOSpIkSZJWM2cdeSwM/KLTNcYZU3/ZkwTLm/c+aMSfq5VJkiRJkiRJXRpNxTomk1agJNcC6/ZrfkdVzehFPENJcgTw0X7NV1fVkb2IR5IkSZIkrR5MJq1AVfW8XsfQqar6LvDdXschSZIkSZJWLyaTJEmSJEmSujSalrmN6XUAkiRJkiRJWn1YmSRJkiRJktSlJ6rV6xBGjJVJkiRJkiRJ6piVSZIkSZIkSV1yzyRJkiRJkiRpACaTJEmSJEmS1DGXuUmSJEmSJHXJZW6SJEmSJEnSAKxMkiRJkiRJ6lJrFFUmmUzSKuWsI4/tdQiSJEmSJGkIJpO0Sjn0pE/2OgRJkiRJ0go2GgoHqlq9DmHEuGeSJEmSJEmSOmYySZIkSZIkSR1zmZskSZIkSVKXRtMG3FYmSZIkSZIkqWNWJkmSJEmSJHWpsDJJkiRJkiRJWoqVSZIkSZIkSV0q90ySJEmSJEmSlmYySZIkSZIkSR1zmZskSZIkSVKXWi5zkyRJkiRJkpZmZZIkSZIkSVKX3IBbkiRJkiRJGoCVSZIkSZIkSV0aTXsmmUySJElaiSY+fUeO2P/VjBkzhktvnsq5N1yxRP/O24zniBe8mmds/hROuPgMrrljZo8ilSRJ6ozL3CRJklaSMQnvPeAQvnD+dznqhyew/w57sO2mWy0x5r75D3DSpWdx1W3TexSlJEnSslnhyaQkpyTZpTn+1Iqef1WU5NQkh3ZzXpJ/SbJBn76fJ9mk+frgcsx9YJLzl/W8lWFVikWSpJH0rK22468PzmH2vPtZ2HqCq/8wnb2333mJMfc+9AB3zfnrqCqNlyRpTdSqVk++emGFJ5Oq6r1VdXPzccSTSUlW16V7/wIsTiZV1Sur6gFgE2CZk0mSJKn3NttoY+6b/+Diz3Pmz2OzDcf1MCJJkqTudZVMSrJhkguSTE8yM8lhSS5PMinJccD6SaYlOb0Z//Yk1zVtJydZa5B512qqdmYmmZHkqKb98iSTmuMtksxqjg9PcmaSnwEXJ/lxklf2me/UJG9o5j0+ydQkNyV5f9P//SSv6TP+9CSHDDE+Sb6R5OYkFwBL1qsvfT+fbeaYmWRKkvTr/wjwVOCyJJc1bbOSbAEcB0xontnx/at8mjgOb45fkeTWJFcBr+/3ffpOE8ONfe91gFjHJ7kyyQ3N175N+4FJrkjykyS3JTkuydua7+eMJBOacW9s7nN6kl8P9Vya8ZOTXJ/k+ilTpgw3XJKk1UoGaCusQJIkaU1UVT356oVuK5NeAdxTVXtU1a7AhYs6qupo4NGqmlhVb0uyM3AYsF9VTQSeAN42yLwTgadV1a5VtRvw3Q5ieT7wrqp6MXBGcy2S/APwEuDnwHuAB6tqb2Bv4H1JtgdOAY5oxo8D9h1m/OuAZwO7Ae9rxg/lG1W1d/OM1gde3bezqv4TuAd4UVW9qN+5RwN3NM/xXwe7QJL1gG8D/wS8AHhKn+5PA79q7uNFwPFJNhxkqtnAS6vqubSf4X/26dsD+Cjt+34HsGNV7UP7+X24GfNZ4OVVtQdwyGDxLlJVU6pqUlVNmjx58nDDJUlarcyZP48tNnqyEmnzjTbm/ofn9TAiSZKk7nWbTJoBHJTkS0leUFUPDjH2JcBewNQk05rPzxxk7B+BZyb5epJXAJ38reuSqprbHP8CeHGSdYGDgV9X1aPAy4B3Nte/Ftgc2KGqrgCelWQr4C3A2VW1cLDxwAHAj6rqiaq6B/jVMLG9KMm1SWYALwae08H9LKudgDur6g/VTk3+oE/fy4Cjm/u4HFgPePog86wDfLuJ9Uxglz59U6vqL1X1GHAHcHHTPgMY3xxfDZya5H3AgJVnkiSNFrfPvpttxm3BVmM3Ze0xa7HfDnswddYtvQ5LkiStBKOpMqmr/YWq6rYkewGvBI5NcvEQwwOcVlWf7GDe+5PsAbwcOBJ4E/BuYCFPJsDW63faw33O/3uSy5vzDwN+1CeGD1fVRQNc9vu0K6Xe3Fxr0PHNErqOvmNNxdA3gUlV9ackxwwQ+7Lo+wzoN9dgMQV4Q1X9voP5jwL+RrsKaQzw9z59j/U5bvX53KL5Waqqf07yPOBVwLQkEzu4piRJa6RWtTjlyvP4zCHvZkzCr265nrvnzuawfQ7ijtl/5vpZtzBhq235xMFvZ8N112fS9jtz2D4HcdSPvtbr0CVJkgbVVTIpyVOBuVX1gyTzgcP7DVmQZJ2qWgBcCvw0yQlVNTvJZsDYqrprgHm3AB6vqrOT3AGc2nTNol3ddB0w3NvTzgDeC0zqE9dFwAeS/KqqFiTZEfhzVT3cXOM64K9V9buhxgO/Bt6f5Hu090t6EfDDQeJYlOy5L8lGTdxnDTDuIWAscN8g7YvcBezSVF2tR7vC6yrgVmD7JBOq6g7aFVaLXAR8OMmHq6qS7FlVNw4S7zjg7qpqJXkXy1hd1Fz/WuDaJP8EbLcs50uStKa58a7fc+NdS/4+58fX/XLx8R2z7+b9px030mFJkiQtt27ffLYb7f13WsAC4APAV/r0TwFuSnJDs2/SZ2hvkD2mGX8k7eRIf08DvtuMA1hUzfQV4CdJ3sHwS8suBr4HnFdVjzdtp9BejnVDswn2vcBrAarqb0luAc7tM8dg48+hvVxtBnAbcMVgQVTVA0m+3YydBUwdZOgU4BdJ/tJ336SqmpPk6iQzgV9U1b8m+QlwE/AH4MZm3N+TTAYuSHIf7QTTrs00/wF8jfb3Ik0cS+zb1Mc3gbOTvBG4jD4VXx06PskOtKuhLgWmAy9cxjkkSZIkSVqttHq05KwX0qv1dauaJBvQTvg8d5i9n7Ty1KEnDbsKUpIkSZK0mjnryGNh4BedrjFOuOSMniRYjnrpm0f8uXZbmbRGSHIQ8B3gqyaSJEmSJEnSshpNxTo9TyYluRZYt1/zO6pqxkjFUFW/ZPC3m3UsyTnA9v2a/22QDb97KsnLgS/1a76zql7Xi3gkSZIkSdLqoefJpKp6Xq9jWFFWp0RMk+Ba5ZJckiRJkiStjkbTnkljhh8iSZIkSZIktZlMkiRJkiRJUsd6vsxNkiRJkiRpdVfV6nUII8bKJEmSJEmSJHXMyiRJkiRJkqQulRtwS5IkSZIkSUszmSRJkiRJkqSOucxNkiRJkiSpSy2XuUmSJEmSJElLszJJkiRJkiSpS6NpA26TSVqlnHXksb0OQZIkSZIkDcFkklYp50y7otchSJIkSZJWsNdNfGGvQ1jpWtXqdQgjxj2TJEmSJEmS1DGTSZIkSZIkSeqYy9wkSZIkSZK61BpFG3BbmSRJkiRJkqSOWZkkSZIkSZLUpbIySZIkSZIkSVqalUmSJEmSJEldcs8kSZIkSZIkaQAmkyRJkiRJktQxl7lJkiRJkiR1qXCZmyRJkiRJkrQUK5MkSZIkSZK6VG7ALUmSJEmSJC3NyiRJkqSV6PfTZvKzU39MtVrs/eL9OfC1By/Rf80lV/Dbiy5jzJgx/MN66/L6ye9g622f2qNoJUnS8mqNosokk0mSJEkrSavV4qff+SHv+fRRjNt8U77xyS+y86Q9lkgWTdxvH/7xpS8E4Obrp3HB987k3Z/6aK9CliRJGtZyLXNLckqSXZrjT63YkEZeksOTLPOvAJO8dtFz6GUcy3iNA5Ocv4znfD7JQcOMOSbJxwdo3yTJB5c1TkmS1gR/uv1ONt96KzbfekvWXntt9th3b26eOn2JMettsP7i48cfexwy0lFKkiQtm+WqTKqq9/b5+CngiysmnJGXZC3gcGAmcM8ynv5a4Hzg5gHmXbuqFi7jfMscx3JeZ5lU1We7OH0T4IPAN1dQOJIkrTbmzX2AcZtvtvjzuM034U+337nUuN9edBlXXnAJTyx8gvf974+NZIiSJGkFcQPuPpJsmOSCJNOTzExyWJLLk0xKchywfpJpSU5vxr89yXVN28lNsmageddKcmoz54wkRzXtlyeZ1BxvkWRWc3x4kp8muTDJ75P8e9M+PsmtSU5LclOSs5Js0PS9JMmNzfzfSbJu0z4ryWeTXAW8BZgEnN7EvP7S0UKS45Lc3FzjK0n2BQ4Bjm/Om9DE/sUkVwAfbe7v0D5zzO9z/IkmrunN3If2j6OJc4tm/KQklzfHxySZkuRi4HvNszw+ydQmvvcP823dqHlOtyY5PUmaefdKckWS/0lyUZJtmvbF95Hklc15VyX5z35VTrs0z+CPST7StB0HTGju6fhh4pIkaY0y8F8qly49ev7LX8Qn/vOLHPzW1/Or//PzlR+YJElSFzqpTHoFcE9VvQogyTjgAwBVdXSSD1XVxKZvZ+AwYL+qWpDkm8DbgO8NMO9E4GlVtWtz7iYdxLIPsCvwCDA1yQXAfcCzgfdU1dVJvgN8MMk3gFOBl1TVbUm+18T9tWauv1fV/s213wt8vKquH+iiSTYDXgfsVFWVZJOqeiDJecD5VXVWMw5gk6p6YfP51EHmO5h2VdPzquqRJJtV1dwkH+obRzPfYPYC9q+qR5NMBh6sqr2bhNnVSS6uqqV/9dm2J/Ac2hVQVwP7JbkW+Drwmqq6N8lhwBeAd/eJez3gZOCAqrozyY/6zbsT8CJgLPD7JN8CjgZ2XfQzMsCzmAxMBjj55JPZcp9nD3XPkiStVsZtvikPzpm7+PODcx5g400H/yvP7vvuzTmnnA4cMQLRSZKkFalVrV6HMGI62TNpBnBQki8leUFVPTjE2JfQTnJMTTKt+fzMQcb+EXhmkq8neQUwr4NYLqmqOVX1KPB/gP2b9j9V1dXN8Q+a9mcDd1bVbU37acABfeb6cQfXW2Qe8HfglCSvp53MGkwn8x4EfLeqHgGoqrnDjB/Iec1zAHgZ8M7mmV8LbA7sMMS511XV3VXVAqYB42k/r12BS5p5PgNs2++8nYA/9klS9U8mXVBVj1XVfcBsYOvhbqKqplTVpKqaNHny5OGGS5K0Wtl2wnjm/HU2c2ffx8KFC5n+m6nsMmmPJcbc95e/LT6+9cYZbLHNsP/5lCRJ6qlhK5Oaqp69gFcCxzZLqwYT4LSq+mQH896fZA/g5cCRwJtoV8Es5Mkk13r9Txvk80Dtw21f+fBwMfaJdWGSfWgnx94MfAh4cQfzLr6XZinZPzTtGSDmgQz1LPpeJ8CHq+qiDuYEeKzP8RO0fw4C/K6qnj/EecM904HmlSRp1FprrbU45N1v4Ttf/BqtVotJB+7H1ts9lYt/8lO2feYz2GXSRH5z0WXcPuMW1lprLdbfcAPe9EGrkiRJWh2Npj2Thv3HftpvF5tbVT9o9vw5vN+QBUnWqaoFwKXAT5OcUFWzm+VhY6vqrgHm3QJ4vKrOTnIH7SVpALNoVzddBxza77SXNnM+SnuZ2KIlWE9P8vyq+i3tPZCuAm4Fxid5VlXdDrwDuGKQ23yI9tKswZ7BRsAGVfXzJNcAt3dyXp97+QnwGmCdpv1i4LNJfth3mdsA8y06/xfAG4a4zkXAB5L8qlleuCPw56rqOGEG/B7YctFzTLIOsGNV/a7PmFtpV5ONr6pZtJc0Dme4ZyRJ0hptpz13Y6c9d1ui7WVves3i40MOf/NIhyRJkkaRZjXYicBawClVdVy//o8B76Vd0HIv8O6B8jh9dbLMbTfgumbp06eB/69f/xTgpiSnV9XNtJdHXZzkJuASYJtB5n0acHkz76nAomqmr9BOjPwG2KLfOVcB36e9NOvsPnsc3QK8q7nmZsC3qurvtDccODPJDKAF/NcgsZwK/NcQG3CPBc5v5r8COKppPwP417Q3+Z4wwHnfBl6Y5DrgeTTVRFV1IXAecH1z/x8fJI7PAScmuZJ2pc9gTqH9Rrkbksykva/RMlUFVdXjtJN3X0oynfYz3rffmEdpv5ntwrQ3L/8bMNSyR6pqDu09nGa6AbckSZIkSSMn7ZeinQQcDOwCvCXJLv2G3QhMqqrdgbOALw877+pShpXkcNo396F+7eNpb4K9aw/CGnWSbFRV85tleycBf6iqE1bQ9HXOtMGKxyRJkiRJq6vXTWy/p6rXcaxMnzjzGz1JsHz5jR8a9LkmeT5wTFW9vPn8SYCqOnaQ8XsC36iq/Ya6ZieVSVJf72uqqX4HjKNdBSVJkiRJklY9TwP+1Ofz3U3bYN5De6udIY3IBsnNa+fX7df8jqqa0ekcVXUqT+6r1Ld9Fu23kK0QSc4Btu/X/G/LsLn1KiHJbrSXBPb1WFU9r5t5myqkFVWJJEmSJEnSGqFVrZ5cN8lkoO/r0adU1ZRF3QOcMmAFVZK3A5OAFw53zRFJJnWbwBhJVfW6XsewIjSJuom9jkOSJEmSJK08TeJoyiDddwPb9fm8LXBP/0FJDqK9T/YLq+qx/v39+ep2SZIkSZKkLq2ie1JPBXZIsj3wZ+DNwFv7Dmj2SToZeEVVze5kUvdMkiRJkiRJWgNV1ULgQ8BFwC3AT6rqd0k+n+SQZtjxwEbAmc3b5c8bbl4rkyRJkiRJktZQVfVz4Of92j7b5/igZZ3TZJIkSZIkSVKXVtFlbiuFy9wkSZIkSZLUMSuTJEmSJEmSutSyMkmSJEmSJElampVJkiRJkiRJXXLPJEmSJEmSJGkAJpMkSZIkSZLUMZe5aZXyuokv7HUIkiRJkiQtMzfgliRJkiRJkgZgZZJWKR/94Vd7HYIkSZIkaQU78a0f63UIK50bcEuSJEmSJEkDsDJJkiRJkiSpS1YmSZIkSZIkSQMwmSRJkiRJkqSOucxNkiRJkiSpSy1c5iZJkiRJkiQtxcokSZIkSZKkLrVarV6HMGKsTJIkSZIkSVLHrEySJEmSJEnqUpV7JkmSJEmSJElLMZkkSZIkSZKkjrnMTZIkSZIkqUstl7lJkiRJkiRJS7MySZIkaSXaaZvxvH6vAxmTMVxzxwx+efPUJfoP3Om5PH/CbrRaLeY/9ig/vOYi7n/koR5FK0mSlldVq9chjBiTSZIkSStJEt446cV881dn88CjD/G/Xv42Ztx9B3+bN3fxmLvn3stX/nA6C55YyH7P2p1D9jyA066+oIdRS5IkDc1lbssgyWuT7LIKxHFqkkNXwry/af4cn+Sty3H+4Um+saLjkiRpdfWMzZ/CvfMfYM7DD/JEq8UNd93KbttOWGLM7bP/xIInFgIwa85f2GSDjXoRqiRJ6lL16H+9YDJp2bwW6HkyaWWpqn2bw/HAMieTJEnSksatvxEPPPzkkrUHHpnPuA3GDjr+Hyfsxi33zBqByCRJkpbfqE8mJTk3yf8k+V2SyU3b/CRfSDI9yTVJtk6yL3AIcHySaUkmNF8XNudfmWSn5vxTk/xnkt8k+eOiKqIkBya5PMlZSW5NcnqSNH2fTTI1ycwkUxa1dxD/S5LcmGRGku8kWbdpn5Xkc0luaPoWxbZlkkua9pOT3JVki0X33Ux7HPCC5j6P6l9xlOT8JAc2x0ckuS3JFcB+fcZsmeTs5p6mJlncJ0nSaDHgf8wHedPLpPE78/TNtubSW65fqTFJkiR1a9Qnk4B3V9VewCTgI0k2BzYErqmqPYBfA++rqt8A5wH/WlUTq+oOYArw4eb8jwPf7DPvNsD+wKtpJ2cW2RP4F9oVTs/kyQTMN6pq76raFVi/OW9ISdYDTgUOq6rdaO+B9YE+Q+6rqucC32riA/h34FdN+znA0weY+mjgyuY+Txji+tsAn2vu4aUsWbV1InBCVe0NvAE4ZZA5Jie5Psn1U6ZMGe6WJUlarTzw6Hw22fDJSqRNNtiIBx+dv9S4Hbd+Oi99zj58+4pzeaL1xEiGKEmSVpBWVU++esFkUjuBNB24BtgO2AF4HDi/6f8f2su+lpBkI2Bf4Mwk04CTaSeQFjm3qlpVdTOwdZ/266rq7mpv8z6tz9wvSnJtkhnAi4HndBD7s4E7q+q25vNpwAF9+v/PAPewP3AGQFVdCNzfwXUG8zzg8qq6t6oeB37cp+8g4BvNszkP2DjJUnX9VTWlqiZV1aTJkyd3EYokSaue/zvnr2w5dhM223Bj1hozhuc+Yydm/vmPS4x52qZbctg+B3HKr3/K/Mce7VGkkiRJnRvVb3NrlmodBDy/qh5JcjmwHrCganF67wkGfk5jgAeqauIg0z/W91KDtD8BrN1UGH0TmFRVf0pyTBPHsLcwTP+ia/W9h46Wz/WzkCUTj31jGywNOob2c/VvxZKkUatVxdnXX8YHXvQGxiRc88eZ/PXBORy82778ae5fmfnnP/KaPQ9g3bXX4fD920XJ9z/8EKf8+qc9jlySJC2r6lGVUC+M6mQSMA64v0kk7QT84zDjHwLGAlTVvCR3JnljVZ3Z7HG0e1VNX444FiVn7msqng4FzurgvFuB8UmeVVW3A+8ArhjmnKuANwFfSvIyYNMBxiy+z8Ys4INJxgBPA/Zp2q8FTmyWBs4D3ggsuv+LgQ8BxwMkmVhV0zq4J0mS1ig333MnN99z5xJtv5jxm8XH3/zV2SMdkiRJUldG+zK3C2lXBt0E/AftpW5DOQP412bD6wnA24D3NMvkfge8ZnmCqKoHgG8DM4Bzgakdnvd34AjaS+1mAC3gv4Y57XPAy5LcABwM/IV28qivm4CFzQbkRwFXA3c28X0FuKG5/l+AY4DfAr9c1N74CDApyU1Jbgb+uZN7kiRJkiRpdTSa9kzKaCrDEjRve3uiqhYmeT7wrSGW6o20+ugPv9rrGCRJkiRJK9iJb/0YLN+2K6uNd3z7mJ4kWL69RdqmAAAGX0lEQVT/vmNG/LmO9mVuo9HTgZ80S9YeB97X43gkSZIkSdJqxGTSKizJScB+/ZpPrKrvLu+cVfUHYM+uApMkSZIkSUsYTSu/TCatwqrqyF7HIEmSJEmS1JfJJEmSJEmSpC6Npsqk0f42N0mSJEmSJC0DK5MkSZIkSZK69ES1eh3CiLEySZIkSZIkSR0zmSRJkiRJkqSOucxNkiRJkiSpS27ALUmSJEmSJA3AyiRJkiRJkqQuWZkkSZIkSZIkDcDKJEmSJEmSpC61RlFlkskkrVJOfOvHeh2CJEmSJEkagsvcJEmSJEmS1DErk7RKOe7w23sdgiRJkiRpBTv61Gf1OoSVzg24JUmSJEmSpAFYmSRJkiRJktSlVrV6HcKIsTJJkiRJkiRJHbMySZIkSZIkqUvumSRJkiRJkiQNwGSSJEmSJEmSOuYyN0mSJEmSpC65zE2SJEmSJEkagJVJkiRJkiRJXWphZZIkSZIkSZK0FCuTJEmSJEmSuuSeSZIkSZIkSdIATCZJkiRJkiSpYy5zkyRJWole+e6tmDBxAx6Z9wT//Zk/DTjmoLdtwYTdN2DB48UFp8zmb3c9NsJRSpKkbrVarV6HMGKsTJIkSVqJZlw1j5/8/38ZtP+Zu2/Apluvw8n/9n+58NTZvPydW45gdJIkScvOyiRJkqSV6E+3/Z1xWwz+V64d9tyQmVc/BMA9dzzGuhuMYcNxa/Hwg0+MVIiSJGkFaLkBtwRJNkxyQZLpSWYmOSzJrCRbNP2TklzeHB+T5LQkFzdjXp/ky0lmJLkwyTo9vRlJklZRYzddm4fmLlz8+aH7FzJ2U3/fJ0mSVl0mkzSUVwD3VNUeVbUrcOEw4ycArwJeA/wAuKyqdgMebdolSVJ/WbppFP1iU5KkNUZV9eSrF0wmaSgzgIOSfCnJC6rqwWHG/6KqFjTnrcWTyacZwPiBTkgyOcn1Sa6fMmXKiopbkqTVxkNzFzJ2sycrkcZuujbzH1g4xBmSJEm9ZTJJg6qq24C9aCeDjk3yWWAhT/7crNfvlMea81rAgnoyRdpikP25qmpKVU2qqkmTJ09e0bcgSdIq7/ZpD7PrfmMBeOqEdXns0Zb7JUmSpFWaC/I1qCRPBeZW1Q+SzAcOB2bRTjD9AnhD76KTJGn1cMg/b83Td1qf9Tdaiw9+dTxXnTuHMWu117ZNu2wed0x/hGfuvgHv//IzWPBYi5//9+weRyxJkpZHMXrWqZtM0lB2A45P0gIWAB8A1gf+O8mngGt7GZwkSauD8/7rb8OOueT79wH3rfxgJEmSVgCTSRpUVV0EXDRA144DjD2m3+eNBuuTJEmSJGlN0xpFb9BwzyRJkiRJkiR1zMokSZIkSZKkLpWVSZIkSZIkSdLSTCZJkiRJkiSpYy5zkyRJkiRJ6pIbcEuSJEmSJEkDsDJJkiRJkiSpS1WtXocwYqxMkiRJkiRJUsesTJIkSZIkSepSuWeSJEmSJEmStDSTSZIkSZIkSeqYy9wkSZIkSZK61HKZmyRJkiRJkrQ0K5MkSZIkSZK6NJo24DaZpFXK0ac+q9chSJIkSZKkIWQ0Zc4kSZJWFUkmV9WUXschSZK0rNwzSZIkqTcm9zoASZKk5WEySZIkSZIkSR0zmSRJkiRJkqSOmUySJEnqDfdLkiRJqyU34JYkSZIkSVLHrEySJEmSJElSx0wmSZIkSZIkqWMmkyRJkiRJktQxk0mSJEkrUJINk1yQZHqSmUkOSzIryRZN/6QklzfHxyQ5LcnFzZjXJ/lykhlJLkyyTk9vRpIkaQAmkyRJklasVwD3VNUeVbUrcOEw4ycArwJeA/wAuKyqdgMebdolSZJWKSaTJEmSVqwZwEFJvpTkBVX14DDjf1FVC5rz1uLJ5NMMYPzKC1OSJGn5rN3rACRJktYkVXVbkr2AVwLHJrkYWMiTv8Rbr98pjzXntZIsqKpq2lv4dzVJkrQKsjJJkiRpBUryVOCRqvoB8BXgucAsYK9myBt6FJokSdIK4W+7JEmSVqzdgOOTtIAFwAeA9YH/TvIp4NpeBidJktStPFlJLUmSJEmSJA3NZW6SJEmSJEnqmMkkSZIkSZIkdcxkkiRJkiRJkjpmMkmSJEmSJEkdM5kkSZIkSZKkjplMkiRJkiRJUsdMJkmSJEmSJKljJpMkSZIkSZLUsf8HnqRXcQze7dMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x792 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Looking at a correlation heatmap of features in relation to sum area\n",
    "fig, ax = plt.subplots(figsize = (20, 11))\n",
    "cmap = sns.diverging_palette(150, 275, as_cmap=True)\n",
    "sns.heatmap(df.corr()[['sum']][round(df.corr()['sum'].sort_values(ascending=False), 1) >= .1], annot=True, linewidths=.4, fmt=\".1f\", cmap=cmap, ax=ax);\n",
    "#plt.savefig(\"heatmap.png\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "atoll_schema            0\n",
       "site_version            0\n",
       "site_name               0\n",
       "fuze_site_id           64\n",
       "site_record_id          0\n",
       "                     ... \n",
       "duplex_type          4647\n",
       "coverage_type        4647\n",
       "threshold            4647\n",
       "fcc_market_number    5137\n",
       "sum                     0\n",
       "Length: 74, dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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>max_power</th>\n",
       "      <th>radio_model</th>\n",
       "      <th>main_propagation_model</th>\n",
       "      <th>antenna_max_gain</th>\n",
       "      <th>antenna_h_bw</th>\n",
       "      <th>site_support_structure_height</th>\n",
       "      <th>antenna_centerline</th>\n",
       "      <th>extended_calculation_radius</th>\n",
       "      <th>sum</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>49.542425</td>\n",
       "      <td>RRH4x45-4R-AWS</td>\n",
       "      <td>WPM_RU_AZNM_15M_AWS_052215_PHO</td>\n",
       "      <td>15.562</td>\n",
       "      <td>81.75</td>\n",
       "      <td>41.000001</td>\n",
       "      <td>37.0</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>8.263681e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>UHBC B13 TRDU 2x40</td>\n",
       "      <td>WPM_SU_LCR_ELP_TALL_700_083016_ALB</td>\n",
       "      <td>13.300</td>\n",
       "      <td>71.00</td>\n",
       "      <td>45.011525</td>\n",
       "      <td>38.0</td>\n",
       "      <td>29.999801</td>\n",
       "      <td>2.184412e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>WPM_RU_AZNM_15M_700_052215_PHO</td>\n",
       "      <td>13.540</td>\n",
       "      <td>79.75</td>\n",
       "      <td>231.059161</td>\n",
       "      <td>120.0</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>1.614895e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>47.781513</td>\n",
       "      <td>2x40AWS</td>\n",
       "      <td>WPM_SU_NM_15M_AWS_052215_ALB</td>\n",
       "      <td>16.000</td>\n",
       "      <td>81.00</td>\n",
       "      <td>197.050453</td>\n",
       "      <td>100.0</td>\n",
       "      <td>29.999801</td>\n",
       "      <td>2.872214e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>WPM_RU_AZNM_15M_700_052215_PHO</td>\n",
       "      <td>13.540</td>\n",
       "      <td>79.75</td>\n",
       "      <td>231.059161</td>\n",
       "      <td>120.0</td>\n",
       "      <td>99.999751</td>\n",
       "      <td>1.407915e+08</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   max_power         radio_model              main_propagation_model  \\\n",
       "0  49.542425      RRH4x45-4R-AWS      WPM_RU_AZNM_15M_AWS_052215_PHO   \n",
       "1  47.781513  UHBC B13 TRDU 2x40  WPM_SU_LCR_ELP_TALL_700_083016_ALB   \n",
       "6  46.000000             Unknown      WPM_RU_AZNM_15M_700_052215_PHO   \n",
       "7  47.781513             2x40AWS        WPM_SU_NM_15M_AWS_052215_ALB   \n",
       "8  46.000000             Unknown      WPM_RU_AZNM_15M_700_052215_PHO   \n",
       "\n",
       "   antenna_max_gain  antenna_h_bw  site_support_structure_height  \\\n",
       "0            15.562         81.75                      41.000001   \n",
       "1            13.300         71.00                      45.011525   \n",
       "6            13.540         79.75                     231.059161   \n",
       "7            16.000         81.00                     197.050453   \n",
       "8            13.540         79.75                     231.059161   \n",
       "\n",
       "   antenna_centerline  extended_calculation_radius           sum  \n",
       "0                37.0                    99.999751  8.263681e+08  \n",
       "1                38.0                    29.999801  2.184412e+08  \n",
       "6               120.0                    99.999751  1.614895e+08  \n",
       "7               100.0                    29.999801  2.872214e+08  \n",
       "8               120.0                    99.999751  1.407915e+08  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[:,['max_power','radio_model','main_propagation_model','antenna_max_gain','antenna_h_bw','site_support_structure_height','antenna_centerline','extended_calculation_radius','sum']]\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Created a function to fill numeric and non-numeric nulls with either their mean or mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def fill_na_with_mean_or_mode(col):\n",
    "    if col.dtype == int or col.dtype == float:\n",
    "        return col.fillna(round(col.median()), inplace=True)\n",
    "    else:\n",
    "        return col.fillna(col.mode()[0], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#passed my function through a list comprehension so i could fill all null columns with one line of code\n",
    "[fill_na_with_mean_or_mode(df[col]) for col in ['max_power','antenna_h_bw']];\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 5137 entries, 0 to 5916\n",
      "Data columns (total 9 columns):\n",
      "max_power                        5137 non-null float64\n",
      "radio_model                      5137 non-null object\n",
      "main_propagation_model           5137 non-null object\n",
      "antenna_max_gain                 5137 non-null float64\n",
      "antenna_h_bw                     5137 non-null float64\n",
      "site_support_structure_height    5137 non-null float64\n",
      "antenna_centerline               5137 non-null float64\n",
      "extended_calculation_radius      5137 non-null float64\n",
      "sum                              5137 non-null float64\n",
      "dtypes: float64(7), object(2)\n",
      "memory usage: 401.3+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    5.137000e+03\n",
       "mean     5.685331e+08\n",
       "std      1.227489e+09\n",
       "min      9.001435e+02\n",
       "25%      3.005578e+07\n",
       "50%      1.627407e+08\n",
       "75%      5.791687e+08\n",
       "max      2.926708e+10\n",
       "Name: sum, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['sum'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploratory Data Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x1440 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "fig, ax = plt.subplots(figsize = (20, 20))\n",
    "sns.distplot(df['radio_model'], kde=False,color='blue' ,bins=20)\n",
    "\n",
    "plt.xlabel('Radio Model', fontsize=19, labelpad=11)\n",
    "plt.xticks(rotation=90, fontsize=14)\n",
    "plt.ylabel('Count', fontsize=19, labelpad=11)\n",
    "plt.yticks(fontsize=14);\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 792x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "fig, ax = plt.subplots(figsize = (11, 8))\n",
    "sns.distplot(np.log(df['sum']), kde=False, color='#A15EDB', bins=10)\n",
    "\n",
    "plt.xlabel('log(Total Area)', fontsize=19, labelpad=11)\n",
    "plt.xticks(fontsize=14)\n",
    "plt.ylabel('Count', fontsize=19, labelpad=11)\n",
    "plt.yticks(fontsize=14);\n",
    "\n",
    "\n",
    "#looking at the distribution of my target after taking the log"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a1edecb10>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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MIAAlm+hkr9TXOW54a3DHNWO+/3RKQ8cBYcv9olI42MhEFABKMZ3q6kynbKVaNZmbfACmDldGAEo2PNkbqZTJXumvMzrv4tNzWoSed/HpksYOANHedfpJ+35oBk46Hc2d41g8FrpPUnAcAEozXFdn5Dm7WuvqTKdspVrlOE7oeddxxp7jAZhaBIAAlGyik72SX2etdu18WouWL9SVG1Zo0fKF2rXzacmOfXeIid30EwQ2/E5gOpo7gc2zG7Vm/ZJR++QS6v8AQIkcx9G8+XO1fuNq/f23r9L6jaurNvN2ojewMJUmdpMPwNTiqAegZCMne+PpAlbq62LxmI498SPZzJBSW4NPpzR0ZHieG77NvGhqAGX2yUPGvS8DAA5wHGdaLKGiC1gVMMre5Btu/rBr59M6Z8XJlR4ZMKNwdQRgXCY62SvldY3N9Tq4vVWXXbNMxjGygVUs5qqxuX7M1zGxm36aWxu1el2Hbrlha3abrV7XoeZWMnAAAKXxfV+J7p6czl7DzQVGmugNLEydltZGnbjok9oy4ry/al2HWjjvA5EiAASgagRBoN6e/rygwOyDgzEnaUzsph/HcdTQWD8q2OdFts0oHA4A05vv+3rlpT15AYV58+cWDAJNh2ylWuV5nubNn6u1160sGrADUD78xgGoGonunmzwR8rUhLnlhq1ae91Ktc2ZPeZrmdhNL8lEr27ccFveErCoOrLQEQYAprdEd082+CNljuNbSpwzoDI8z2PbABVGAAhA1Uj76dDCwGk/XfS1QRAomeglA2ia8FO+3vnuP9Nxp35cjuMoCAI99uBPIivcTeFwAJi8Sp57xztnYJ5QeWwDoPIIAAGoGl6sQGHg2NiFgYMgUOerb6jz9TdVNyumgf6U2g85SO2HHczEokrF6+I67pSP64093dltdtwpH1e8Lh7J57sFilC7ERWhBoDpLrOU9nVt3nTPiKW0SzRv/iGRnHvHcxzPzBO61Pl614h5QpvaD2tjnhARll4D1YHfNgBVwxhHyy45I6dF6LJLzpAxYx+qkvv7tO/N/br31od047W36d5bH9K+N/crub8vimFjAnw/rf37kjnbbP++pPwSsr2mguOE72tMQgGgNM922mYAACAASURBVPv39WSDP9LwUtp7tH9fTySf3zy7UavWdeQcx1et61Dz7Pyiwj37+7TvzcSoeUJCPcwTIlNo6XUy0VvhkQEzCxlAAKpGajCl7fc8mtMidPs9j+qCz51d5HWD2dbxUmZS8d1vP6ArN6yQRHeJapT2/fBt9ncrIvn8Qvvayis/G8nnA8B0lxpMhS7BSg2mIvn83mSfdmx7Iuc4vmPbE+q46FTNPqg5b6w/2vFUznN/tOMpnXPBSZGMFZml1y2tTTnb4Ifbn2TpNRAxAkAAqobrOUp0J7Xl+q3ZxzLp3GNnZQRpGzoJDdK2LOPE5AWBDZ0IBkE028z13AL7GkvAAKAUxjGhS7CMYyL5/NRgSi8886JeeObFnMcXnb8w5NlGnzr5o7rjm9/PLj86/7IzJRPNWCHF4jGdvuT47M2f4czbWDxW6aEBMwq57gCqhuM4Wn75WTnp3MsvP6voshzXc7KvGVZK4AiVMzwR3Hb7Tt147W3advtOnb7k+MgmgtYqdF+zxAwBoCSOW+Cc7UZz7nWc8HN/6JzB2GzwR8rcJLrjm9+XxEE/KkEQhGb+BkFQ4ZEBMwsZQACqRpC2qm+cpcuuWSbjGNnAKh2ki2byuJ6nVes6su1gh+sAuB6HuGoVBOnQieDa6y6M5vPTQYF9jYkoAJTCcVw1NjVo8cpTsoWVG5sa5DjRZFLG6+K69AtL9UbngWYCB7e3hjYTSPtBeMcwjvmRqfSSQQAZXB0BiEQprT8dV+pJ9OalBzc21Y/53p7rKhbzciahsZgnz2U5T7UqOBn3o5mMO65RX0+/br/p/uy+tvzys9TY1BDJ5wPAdBek0/rWP9yZtwTsqq+sjObzrVVfb7/uvfWh7HF8xRVnq7Elv/af6zrhHcMo/B8Zx3F01AeP1DEL3p9d+v30rp/TfAGIGAEgAONSSiAn7DWltP5Mp8PTgzPFnAsbGBjQD7Y+qmMWvF91isn3ff1g66M6Z8VJamweO3iEyig4GY9o2V6QDrLBHymzr91+0/2RZSABwHSX9tMFAvnRdHP0Uynd9vXv5RzHb/v690KP47F4TBet/az2J3qzN4qaWxqoPxMhNxbTwrMW6J+/dm92LnjRVYvlxtgGQJQIAAEoWRAE6ny1S52vd2UnUO2HtKn9sLYxg0DJRK/+/clf6NJrlspxHAVBoKeeeF7HnfJxtbQ2HXj/QsWcixUGtkYLFh6ju27enp1UnHfx6ZKluGO1clxHV/ztchnHkbWBjHFkgyCyO4HpdBBahJrlAABQmkpndKT90o/j9Y11auxrUN2suuyyX89zVd9YF8lYken+ORz8kTLzu3/+2r1ae100GWMAMsoaADLGnCDp65JcSbdaa//fUd9fLul6SX8aeuib1tpbyzkmABPXs79P+95M5KRbL7vkDDU01at5duF261bSX330PfrWxjtz7vqMDut4MTc0K8Qr2pnJatfOp3Mmgbt2Pk171ypmHFe+3683Ot/Ird1QPyuSz4/FvfBuJDHuiwBAKeJ18dCMjrAaPOUwnuN4X++genr68sZa1zBLzS0c96MQpMMzxoJ0NBljADLKFqI3xriSviVpoaS/lNRhjPnLkKf+L2vt+4b+EPwBqlhqMBW6RKtYAb90KvyuTzrl5zzPGBPaUcQUadNqHKPjT/14Tkep40/9uIxLBlC1soGvwYHB0Mej6AhCNxIAmJx0Oh1+bo/ogt4GNvQ4bkOyhlMDA6FjTQ0MRDJWSI5jCnRtY64WlSAIlOhOqquzW4nuJHOeGaqcIe8PSfq9tfa/JckYc6+k0yT9Zxk/E0AZBUF44d5iJ5BSl9ukBn19/85Hcp73/Tsf0QWfO3vM97dW+s43tuVM7L7zjW2kFVcxa6WB/sG8bLKenj4lE3159aGmWqWLUAPAdJcaTIWe26Pq6hTYAsvGbX4AKF1giXm6SJdRTB3H9UK7tjkuGVhRKLUeJ2pfOX/j3iLp5RFf75b04ZDnnWWM+YSk/5J0pbX25dFPMMaskrRKkubPn1+GoQIoRSweC12iVayIYjweC03Tjo96nes6SnQnteX6rTnv77pjn5gqXYhyppjKY3FQoOD35V86Xzf9/R1av3F1Tn2oqeZ6lS1CDQATUU1zYi8WvgTLi2gpbSzmhc9JQj7fdR0dd+rH9JFPfiCnFmGx+QWmklVPsjfnxs/yy89SfWM0S79numSiNxv8kTLzrs2b7i77fAvVp5xH6LB8vtFh9ockbbXWDhhj1ki6Q9KxeS+ydoukLZJ09NFHE6oHKqShqV6r1nVoyw1bsyfvVes61FCkTXu6wHKbvAwdY3Th2nPUk+jL3h1qbKmXiiwBcxyjsy84Ue/5q3fKWitjjH713G9JK55iU3ksLnjnNsg87o9aHjjmexXpTBf+fVfLLjkj78LFcYrVm6puE+nSB2D6qKY5caElWFdFlH3b1NKgNeuXaPOme0ZkNCxRU0tD3nNjdXU6+mNH5dQiXLWuQ7G66iwCXYvH8nQ6Hdp9s1inV0wNP+WHZuyNZ76F2lDOANBuSW8d8fXhkl4Z+QRr7RsjvvxnSf+zjOMBMEn79yW1Y9sTOSePHdue0DkrTlLbnNaCrys1Q8dxJAXKuTt04efPUbE5T3xWnd5+5Nv0Txu+kzOxi8+qzokdMndjw7rHdHftyxT+LvEOcrGU5kLfr5sV1/Z7Hs3Zl7ff82jR5YbVjPRuAFEazxKsconFYlq88pTsTaNYgZbiqYGB7M2r4XFuuWGr1m64UNLYN7GiVqvH8oKdXlmGF4lYgWz8Yln8qD3lDAA9I+kdxpg/U6bL12JJS0Y+wRhzmLX21aEvT5X0mzKOB8Akpf1ALzzzol545sWcx89atnDM17lugeU2o1Kvg0B6ZPuPcy7KH9n+Y52z4uQx3z81MBgemLrgJEkH7gSOvqPW0DRLvcn+mrrDNl04jqvTOo7XG0P7hOd5Oq3jeD216/mCd3DDFEtpLvT9K/9uRV7Kc0trU8WzxiZz15f0bgBRGs8SrHJIJnr1wN2P6JgF71edYvJ9Xw/c/YiWrDot75iXTheo+xbSMr7SkolePXzf4zlzmofvezz0/zWduJ4beuPHLdrpFVMhCAL9aMdTOfvVj3Y8pY6LTq300BCxsh2hrbW+MeYySY8o0wb+Nmvtr40x10l61lr7oKTPGWNOleRL6pK0vFzjATB5pQZyRqubVRe6dKxudIaOlRYsPEZ33bw9+7zzLj49f/FoHhP+uhErUYMgUOerXep8vSt7p3BOe6uSPX2yQaCB/pTaD2lT+2FtBIEiYfO6gA0ODOoTx39oXBNyP+WHTuqHU5oLfd+Lx0LbF3sVvBM22bu+xX4WADCVGppmFVgWHk1NlyAIQs/9YY0pXNcUmL9U31LxdIH/V3qad2xyPTf0vEsAKBqBtTrxrAVK7OuRlLnxduJZCyLN2EN1KOtVjrV2h7X2f1hr326t/erQY18eCv7IWvsFa+27rLXvtdZ+0lr74tjvCKCS4nVxXfqFpbrkC0t15YYVuuQLS3XpF5YqXhcf83W+72czdK7csEKLli/Ujm1PyPdHX5gG2QmPlLl4vevm7ZKKTXpsgdcdOKn17O/TvjcTuvfWh3Tjtbfp3lsfUmJfUpKyX+97M6Ge/X3j+IlgoqxstgvY8M9/oH9QxnF001fvUDLRW9L7eEN3oEcauYSs0PfTvh/evjhvn4xOoQyeqfpZAMBU6k3269mfvqBLr1mqa79+hS69Zqme/ekL6k32R/L5QRB+7g9C2sC7sZguumpx9hiZDT4UWDJWSTYdPheyVZitNB7+YCr0vOtH1DVupjOS+vsGcuZd/X0DoUV7Udu4zQ2gZLMa4vJ9P+fk4fu+ZjWMHQBK+2m98MyL2nL9Vt147W3acv1WvfDMi3k1gIYLAI80XBh4LKW8LjWYCi1W2dzSmPN1VO1rZ7rADy8MLo2vCHSmCOi5OZP61VcvURAESnQn1dA0K+/7a9afW7gNfAUn2KkCGTypSfws1qw/t+TldAAwHkEQ6IMff6/e2NOtRPd+vbGnWx/8+HtDM3DKwQbhx3Fr8z/fBoG8mKvLrlmma79+hS67Zpm8mCtbhVk16QL/r6h+ruUy0TkepkbaT4fOu9JpOubONNwWBFCy3mS/brk+t4jiLddvLVpjxPXcAi23c9N+nQJLzJwiS8wcJzy1e2Q9l6DghMqO+np6T7Cq3XCNm7EmguPJWnEcR/Pmz9X6jauVSvkK0oHu/+5OvfDMi9kAyKGHz9H6jatz6up0v5EI3ycruPzPmPD92BTpgjds5M+CulYAys0Yo2DUxWOQTpd8zJr05xc494d9vjFGD937eF79mWqsf1JouX2xuVC1c70CZQS86f3/mi4Kzrsowj3j8BsHoGQTrTHiep5WrevIyUxYta5Drpd7kW+M0Yorzs553oorzpYpVpjXGJ138ek5rzvv4tNz2sfH4rHQ5THdXftyvqYbQvkM17jZdM0t2YngSG3trQqsHXfWiuM4amltUizm6evXfSdbpHx4CVVvsl8trU1qa28dKvTslLTPRM1xjM6/7MycMZ1/2ZnjKkw9/LPI+b8CQBkEQaB4PKY5cw9SS2uz5sw9SPF4TEFIBk55FDiOhyxqcV1XZy1dqEPntaultVmHzmvXWUsXynWrr/5MPB4PXa4Wj4+dbV3tSp0LojwKzbsIwM08/MYBKFmpmTyjDfb3F+jSdaLUfKD9qjGOmluadNk1y2QcI9dxZBwjP5VW1959amltlBc2UbBWyUSPrtywQkE6kOM6+sN/vay5hx6cfUrz7EatWb9Emzfdky0+uHpdh/6/bU9k/x9r1i9R8+zGSf6UUMjIGjfxujqtvrojm1GWWbrVofqGWWo/5KBxBS6Gs4oG+ge1aPlC/XD7k/rj73ZLGjtAuWvn0zn75K6dTxftOFdOruuqqakhu//bwMoGQVVeoACA4zgaTPl5RX0bTEQXlNaGH8cvOCnvqekgUF9ff17B6nh99QVVBgcGtfuPrw7Naawc1+jFF/7P0JL16bukNz2iHmTOXHBF/vbC1IvX1Wn1ug7dMuJ3YPW6DsXr6oq/GDWFABCAkjmOo2WXnJFdQ9zW3qpll5xR9GI9nbYlto+3evONzJrkltYmnb7k+JzPWrWuQ/Pmz80LAnnxuOYeNkc3XntbznO9EXfLMstjDslZHtPQNEtLVp2mcy44ieUyERidQeZ5nhavPCXblc3zPDmOM+7gz+jOWeddfLoe3PqY/vi73QWXk7meU6AbSeW2v5W0P9GT9/vVOHv6tv0FULsKFdNfu2FFJJ9fN6tOJy76ZPEOo5LSKT/7vOGxbrlhq9Zed2EkYx2PeF1chx9xWM6c5qKrFhdtuFHt0ukgfC54/ui5IMoh7fsKgiBn3hUEQUWbX6AyuNIBULLUYErb73k0p5vX9nseLVo42XUdHfXBI7Xq6g5duWGFVl3doaM+eGRe+/iRBeo+ffpf5xWr23LDViW6e/Le3x8cDO8yNjiY91xUzsguVYP9A3pq1/M6eG6rWlqbdfDcVj2163kN9g+M6z3DOmfddfN2ffr0vx6zCLI/6Gvn/bty9pmd9++SP1i5iZBfoFA5HVIATIXh4vhdnd1KdCcnXfMunQ6vKZKOqKbIwMBA6Ll/YCD/PJJOV1/h/0IGBwdDA2uD03xO4w7VbBopU3uPPlRR8FO+dn7/X7MdeH1/6OsSG02gdpABBKBkXsxTojupLddvzT5WSsFe13PDsy1iuUtbRhaoa2yqD5+s+WHdCowWLDwm2zY1rA5AWKbIqnUd2rHtiZyCwfPmzyULqEyGu1Rt3nS3HNfVX330PfrWxjtz9gnHdRUEQcnboFBdqrfMP1TrN64umNUVBKVmpUWn4AVKEGSXuVHcGcBEZM6Br+csg16zfonmzT9kwseSQsWKR9/cKZe0XyCjJOQ4XrBZRAXrvhVSsEulX33BqvFwXE8XXbU4by7ouFyORsEYJ3SubKJasomqwW8cgJLVN87SpV9Yqjc6u7Ppowe3t6q+cdaYr0un09lsi+F13zvv35W37ntkjaGeZN846g3Z7AlNOpAFsnbDgdTusEyRLTds1aLlC/XCMy9mCwYX62iGicvtUpUquHTglZdeL/miZDiraPR+UjcrXrQz3VEfPDKvI0yxelblVLBDiuvkBS8JVgIYj/37erLBH2m4SP49uvqrqzT7oOYJvacbC7+gd0vs4jhZXoHjuBdyHHdcRxet/az2J3qz85fmloaq7KxV6cBauQSF5oIhNZtQDsXnypgZCAABKFlvsk89yV7de+tD2cne8svPUkOyvsgEsniGjpS5Q7d6XYf2dSfV2FSvy790vh6465Fshs6qdR1qac0v0lxKi/dCmSKNTfU5X5MKW17DXao6X+squHTg4ft+pI6LTi3poiSTVbQk7652sS5irueF1o6obDeSTBewO775/eyYzr/sTEkmL3hJsBLAeKQGU6HH3GJLuMeSTqXlxdycwvXpIF0gU3fqefFYaHaxF9LN03GdoRo6vdnH4nXxqgwAxevioYG1aV8DaGgJ4kiJ7qTSk1yKiNKMlWWMmYUAEICS+Slft990f86F6O033a8r/65IwUdb2l2HIG01OJjKCTBddNVinXPByZJRwS5gjhvenWzkxK5QpkhPsi/n62LL2TA1xrrDuWDhMSXVZQiCQPv39ahuVlyXXbNMgwODSuzrUSwWy3ve6OVT6VQqvCBoBe+Eua6jxx/+t5y7o48//G86Z8VJoZM2gpUASuUUOOZOLgAS6KF7H8/LwDnnghMnP+ASDA4MhGaSXrlhhUZ3y7JBprtW7usHVd9Yr2rT0DRLab8lpwuY52YaV0xnsZinz644SfsTmSCc52W+jjHvikTBeReZxDMOWxxAyawNL/ho7dgFH0u+62CsvvONbXmTORmrtjmzw1vAKxPcWbWuI1tcMNsFbMSkYrj+zOjnPL3r59mvCxUMxtRzYzFddNXinO2RWToQ0103b8/M1scwXNPp+i9u0bWX/5O+ufG76u3t145tT+imr96h5NAEMwgC7evar8GBlNLptAYHUtrXtV/pglljlbsT1jy7USefc6y23b5TN157m7bdvlMnn3OsYrFYaOHMsGDlVBd5BVAbhrt4jjzmltLFs8i76jNnfiJ7bvY8T5858xNSRDVFRtYNHNbV2S0b5M9JrA000D+oe299SDdee5vuvfUhDfQPyqr6jpHpdKDenj69+nKnurv26dWXO9Xb01eVBavHK2wbIBqxunjoXDk2zTPLMH6EXAGUzHEK3EEsMoEcq7bJSIUCRUGRSU+Q9uUYk9Pa0jFGQfpAhoTjODr08Dlae91Kpf20XM9V8+xG2sBXyODAgH78yM906TVL5TiOgiDQYw/+RAvPWqB3vvvPcpbvhSnU/WvR8oXacv3WbHZMX8+A9id68pZ6NbU0lOFu+OTFYrGc/TgWi6m+sU6Xf3GZOl9/M/t4+yEH5QUrRxc6P+qDR+qsZQvluI5iFd6/KWINVFYq5We7eA5n62y/51GtuOLsCb+n4xql+nOzdjNBpckXVi7lmOGOY04SpIPQLotFM5groGd/r/bvS+b9XOsbZ6m1raXSw5uwkZ1epereBrVocGBAe17dm5NZ9of/elmNzfUanTGH2kYACKgx5b3QMjrv4tOL1vIZzXVcLbvkjOyJf3gy4zruqOcV6NJRZDIZBFabr78n73Vrr7twxHMC7e/uGQomWQXpQL37+4oNHWXiOo56kn16/ZW92YuRnmSfjDE67pSPKz5r7FT3sWo6jcyOGegfCF3qddVXVmr55WdllzQO17MyFewIk0z06oG7H9ExC96vOsXk+74euPsRLVl9ugYHci+yVq/ryOuWlkz06uH7Htei5Qs1u7VZDU31uv+7Oyve5S6sAx9FrIFoua4b2sXTcSde+D6dTutHO57KCSr9aMdTOnuSS8BKP2YUmJOEHMcLZQsFRTKYK6FWAyUFt0GRGz6YGl4srrf9+eFKDfgyjlHat3rbnx8uL0YG0ExDAAioIeVo8zrqE/Tqy3v0+WsvkLVWxhj96rnfau6hbWO+yvfD7zxeeOU5Oc9zXE+r1i3WlhvuHZGtUbxFaMGWqSMyhwplgtAGvjIc19WJixbkbet/feRnevf7/0fRznIjazod8Y7D9enT/1rNLY1qbGrQ5V88P5sdUyirzAaZrnaji5cWWXlWVkEQ6FMnfzSvCHQ67euWUUGsW27YqrXXrVTbnNk5rw8rtp7oTuqPv9tdscLRYdlaFLGubWR8VR/Xc8I7dnkT3y5maAlYTyJTS294CZiZZIWJko8ZxmrXzqdz5ha7dj6d12FUKj0TuRrUaqDE9Rwdd+rH9JFPfiCb+fvUE89Pah/EeFj19fXnzYPj9QSAZhoCQEAN2b+vRw/f96OcydB4OioV48Xr9PYj36Z/2vCdnJOHF68b83WO64Tfecy7ILCKeV7ORbkNMhk7Y75/ocyhEXcBC2WC0Aa+MoJ0Ohv8kYa3x73Zzm/F7iAPd/96+L4f5QU91qxfkn1eoaKHjmvU19OflwHU2FS5NGgb2GzwR8r8TO745vd15YYV4QHOUZ12giD3YsiLeWpoatDyyxcpnU7rsQd/UlLh6Km+eC+UrUUR69pExld18gd9/fdvX8q7gXPUB4+c8Hs6rlE6lc7rDOq4k8uk9FO+3vnuP9Nxp348Z4nw6GOG4zg69sSP5GUXh+5ntnCXxWrjeuGNLdyQ9vbTSSwe19EfO0rf2nhnzhwyFicAEYW074c3v7iONvAzDWfiGkYx0OpUzu2STgehBRmnqnCgPzgYevLwB8cu4ud64UWaR7fctsp0Adu7500luvdr7543NTiYki0WAHILFLcccWevUCYIbeAro9D2cBxT8h3kWCym05d8Oq/D3OZN92j/vh5JUtPsptB9L0gHoR3titWbmkqjjwXpdLpADSwbWgR69J1r4xgtWHiMtt2+Uw/c9UOlBlK66Su36+8+90/61sY79YnPfLhoG+Hhi/dN19yiL13yNW265ha98tKeSR2nhrO1Ro+/XB33qv3cV+3jm6xC2RvDhdlRGa7n6cj3vF2v/WmvursSeu1Pe3Xke94ut0iG7VjGexz1fV9de/ep87Uude3dJ98PP9/G6+L6xGc+rG9tvFMbrvh6weNXavBAdvGVG1Zo0fKF2n7Po0oNhr2v1a+e+60uvWaprv36Fbr0mqX61XO/lapwCVhjc2PoeauxubHCI5ucVIE5ZKrIHBJTo2C2vF9b5yAURwZQjSr/UiBMRLnvjDpOgbtxU1CQURqjm1eRi2Z/cFA7tj2Rk5m0Y9sTQ61iR0xobKZQ5ejxS1KiO1kwE8FxHTXPbsopnts8uyknAFQo/Zs28OUXllFSaHu8/sobisc9OW1mzEyUZKJXN331Dp1/2Zmh+2RqMCVJ6u/tDy16WN94eEW7gIUdC9ZuuLDgXd9V6zry0ra9eG67exvYbDBs0fKFebU5dt6/a2hpROEsp3Is1xruwDf6uDeejnulZiUFQaA3O7uVSqVlHKPBgZRSA4M6qIRi9VGYztkxpW4DMr6qlS1YWHi0Urf1eLop+r6vva92aW9nd/Y8PdjXqjmHteV1+BwcHAxt757JVDhw3HC98LpGYZkybszTX330PTnZJ5nOk9V3zu9N9uj/vPh/87K1mmc3qq5udvE3qFJpP1BLa1POeemH258kABGRgssgWYI341TfUQ9TYv++nmzwRzpwV/zqr66akqVAmJiRRVoPLNF6XEtWnTYly44K3Y1bu2Fq0jsLLacptoY+nbZ64ZkX9cIzL+Y8ftayhTlf28DqsYd+mvPzeeyhn+qcC07Spi/dUvBCKe0H+sHWR3OK5/5g66M6Z8XJI8bu5tU/WLWuQ8/+9IXs/4M28FMv7IJ39dVL1Dy7USuuOFu3ff17OfVqHtz6mBLdSV25YcWYF8qpoYvMnmRfwW5eie6k0kGgppZG3XjtbTmfM57uMeUQFmix1oYWS3ccEx5AHbF/S5K1B+pGtB08O7QekOzYweByXLw7jqN58+dq/cbVJS0rG33x2dA0S6/t3ltS0KQn2a9ksi+vzkm8fpaaq+B3e7rWQxr5e9zS2qQTF31Scw87WLMaZskGgdJ+ekRwtzaXr0x3pRYWHk+QcjzH0Z5EnxKhAah6zW7LnZeWmqnQPLtRl35hqd4YEVQ6uL1VzbPzM2X8wVRoUOnKDdVXWNkYR3/+zvk5y+0vumqxjJneF+qxuKfTlxyfd46LxbkcjYLjOHnzrhVXnF31Nx8w9fiNq1GpwdSYd8VRGYWKtE5V1sFEM3RK5Xr5QZRMEcmxJ/alB45MaBFcyWQvlNZet1KyNuciMu2niwaY0n5az/3br3Lajj/1xPNacMIxWnDCMRQqLZOwC95brr9H6/5+leobZulzf7tc+95MqCfZpwe3PqY//m63JClI2zEvlI3J7EM/3P5kXheYVes6JGuUSqXkxWL6z1/8Lm+7zz1hzoQ62k2VsECL76dDi6Vf8LmzQ/fv0XWSYiMKY8fqYrpr1MXOXTdvLxoMHllce1ihzLhCWQJhj5cq7OLzii9fUHLQJDUwEJ45sGGFqqHNrZ/yQ++AV3t2zPDvcUtrk07tOE533bxdLa1NeRdza9afq5aDmgsEMjm2VlKphYXHF6QsvTOo7/sld7Zy3fC6fu6o2kL9vQPq6+3PCSqtuOJs9fcOKDY795gVWBv6u1eosHIlC5kH6XSB49j0rtUSBEFo17iwot0oB6P6hlk52fL1DbNUjXWwUF4EgGqUU+G72wg3ukjrgY4VJxd/cQkKFkOeZEHGYYMDKf34kZ/lXEw/9uBPdMKZC8Z8XXxWXegSlvisUcWjTXgR3OECdV2d3RocGNT+fUkN9KfUfshBaj/s4ML/75ylb0Z/+b535KR/H5ioVl8Ngzx4YQAAIABJREFUgFpRKKMkSKf1g62P6rQlx+dsc+lAcHDsTJQDFx4Pbn1Mi1eeovZD2+S6jrbdcaD1+RV/e0Fo2r9VEPq7ePYFJyrRnSz7pN+LeTrqg0fqmAXvz36+V2g5Q4GLodF3g5taMh3QOl/vkuOE//yK1dMqdblWoSyBQw+fk5etc/kXz5dk1fn6m9lJZ/shbWo/rC3vZxt28Zno3l9yVlI6HX6Rm05P7Hd8qi8CY/FYgTvgseIvrqDh3+NFyxfmLDMcfUG/edPd+vy1FxTo+vjZCv8vZrZSM7NSBY7ZqZDft7GOo6MFgdVHj/2APvTX780ua/r3J38ZGoDx4vECy15H1QBKpbLZDMPjvO3r3wstahuLeTpz6WfyCv/HCgS3K1lGodw38yrGmgllpmJqBGk/PFu+SNMN1B4CQDUqFo+F3oGr9klmrTNDBW7zWqZOUY0eLx4PzdDxYlPTYcH1HP32P/6gp574efaxtvZWnXTOsWO+zk+lFI/ldveStfJTKUkHijCP1c591dUdenrXz7X39Tf17X+480D6eFNmycHl1yyTlbLvb6RRE9vwieqi80/Um290j3lRiokrlFEiGZ2y+FPyPE+f+9vl2vPqG9qx7QklupPZ+jZjZqJYm5fZ43muvnf7Dh2z4P361MkfVU+yT4ENQu+kXvWVlVp45t9o/1BhWs/ztPDMv5HjONp0zS1TPukfDiQEQaAgsHI9Vycu+mTOBc5VX1mpi9Z+VvsTvQdqWbU0SMYJvcseXtvLZj/vki8s1Y5tT2SzqtraWxWLjX0OcBxHhx4+R2uvW6m0n5bruWppbSwpUDOcoTf68f37exT46bylHw1N9XlLNcIChvsTPSVnJU10mWqYctTrKXQHvOOiUyf0flEZ/j1ubKrP/mxH/ntYV2e3HCe866MxXORVUryuLnS5VLwu90aMMYUCzvnbz3Xd0OOo6+ZnBdfV1+kDx7xbr/1pb/bzP3DMu1VXn99F1E/5I2q3BXJcR3/4r5fV1JJ7vChYUyYkUBIEBZbIhwSLKl1GodQMqGoxOOgruS+pdDqQ6zpqmt2keOiyLpvXtKFQZmolM7BqlxN6DUJPqJmHAFCNamyu1+yDWnLS/GYf1KLG5vriL0bZGBml+lN5F0JmitIv076vnffvCin8OjUZRsPdvEbflRvZzSvspG2t1cDAYN6FbV1D7sTPcU1eVsTTu34uxzHadvtOXXTVYv34kZ9JGpE+vmGFZtXXq6+nP29im9Na1Dihd576evt147W3jXlRiolraJql1es6dEvOPrNYbsyR0+/o9VcyFwOStPTiM5RIJIcKhJ80ZiZKrC6ezewZrklyyLw5OuO8z+iBux7JZgB97svLQy9SXc9TQ2OD6mbVZYOGruvK9bwpn/QPBxIevu/x7D547prTdPfmH+R8VjLRK+M4OceHVes6ZIyywcu2g2crVhfTQP+ggsAqCIJsACzR3ZO9m55MZLqgdaw8RVtvfUiJ7mRJNa6CIAitt3Po4XPUm+zP/l4HBYq/pv38TmbNLY164K5H8tP+L8hP+w8LGD696+daffUS3XL9PaH7Qs5ntTaFHqOaJ1Bfpxz1eqy1occhW4WdiEbKZIYtUXfX/uz2KVR/SyZ8WdBUNSPAxPipwbysOT/ly08NSjpQCNqLeVr7dysV2EDWBjLGkWOcAg0SzFBnrgMd3jJf529rGwRKdO8vqQi14zh6y/xD9erLndlz+lvmH5oXAChYUyZkrOPpgFTpMgpuLPxmnjtFN/Om0uCgr9d278k75h56+Ny8IFCpRcOrpVh+rQWhHDf8GsRp49g80xAAqlGO46j9sDbVN86qmQNXLQjS4UUYp2pddym1cCYjSAehnSne9+F3Zb5f4KTd1NKggf7BvJPOaJ7n6bSO4/XG0M9n+Gtv6KL8n792rxYtX5jNQMosJbJKDQ6O0d1kaHJpw+88XXrN0uzXU7ktkNHXM6AgCHKC0TawskEQus1kpBeeeVFnLVs4ZuHg4YKeLa1NOnvFiepJ9Km7a58G+lM6reN4nb38RA0OpgoWKbXWyvf9/LvhNneCPdakv9TJ4XAgYdHyhdlAzpy5B+VNhAcHBvWdb2zL2Ue33LBV6/9hjU4+51MHAkgjLgpGLrvq7x8IzbS56KrFslahmTyFxjpyDA/f97hOOvvYnADM6quX6LhTP6Y/f+f8nGBt2DIT1w0Pvioko2A40DBy6cXJ5xyrQw9vL6mIdCzm6bC3ztXaDRdm70Y3tzaFXhAWU46C2EFQ4A74dSsn/J4H3ru8FyuxWEzNLY3ZgO4Ptz+Zl2m8Zv0Sua4Tmm25ZNVpUzYWjJ+1Nq9ezrJLzlBTyA2PgYGBvGPj/8/emcfJUZT///30XHtns8kGgoDwRQUVEJD7kHAoJApyBExUMAQICgghBH+KSggoXxXkEMEvCOFQBAEBUUEFOVXkklu5lEDCkQQ2e+/O0VO/P6pqpqenZ3cm2U02oT6vV7+mp7q66unq6qfqeeqp54mnor+h6LG3fLExl63eB1C1Ecvy+Xz0nCrCqserYFUTtUXe87wKi1FrZg7teUJzcxNzF8wuUcKtSSVq1eNbV29kaHcb1TIIr0rrsrHgLH+sKKFGEn4FGWQsOkJ3GF04BdB6DM/zxnRUkfcjKq2aj5QTaM+rbEFTDW3DDfYiUiEyhS6/0qB92tmzqxp0lBp60texopNxrUVLDO2/wBsyuom1kKi08hQU7kfyXThoZDNZ7r7twZI953ff9iBHzJoW+c7mLpjNtjttRSymw8Dncr425xe/8C5BO0zuWNHJUScdGrmipQR+cdntzJk/k3lnH0cmkymx9FF5v0JfK7VKK/clpVHL5NAqEoKRub7+na+UTYQbmxsi+2hmMM1Gm07iyNmf44G7Hyl3ZD51Nx57+GmmHLgL2azPSWcexb13/pVH7n+K6y+/nVO+O4ufnHttVZPXKKXHrlO25/G/PlNW716f3plLzinygjnzZ9I8rrHMcsvzpILSI1rZmkgkShSGiUSipvHMOsNeXVTjELtWpUswUptFx4rO1bYAGm1hpbe7n0u/fx3TZ03lHw88xfRZUxnX2kxLaxNHn3QYIhTeVWNzPZ87cr+SiGGHHXUgSqmSb9hhzSLvRytLyqKA+bmqeCOA71dQ6kQIlPl89U6Y/ZzP80+9XMZz9p40PpSv+rDi8UQi2q9QxLbYZCrJ1MOnlEcTTK0ZCxw/l6Onu6fManpcvCUy/0grf2vxgeT71W/Di8Xjke4pglbkMDrK91oxFpRQI418BR95lRyhO6y/cAogB4c1iHiikl+TkfHNlEgly/yKzJk/k8Qwk5Z8Ps+KtztYsaxjSCetw0WmqOjwt8roI8OtTrS1t9LQ3FA4txOHzGCmYvl9PQM0j2us6BukrqGu5P9IvQsHA4l2+lipT+RyOaZN34dUQz0r3u4oWYVOD7TSPrmNeDxOPB7jiGOmMbF9PBedvShSqJm74Biee/Iltt5hS3563vUl34QXq68oDG2701aFLWRHn3goXoQ/i1omh1aREIzM1bmyu2QivO1OW5Gs4PfIi3l4nkcsHot0aO15Hp/cfRsuDIS6P/70GQDaWk6K9J3x/TmISEVBIUrpseFG7YyfMK5Q7/4H78HeB+xCPp8vUTZdecGNfOO8E8p8COX98m1h1nrPOtxO1deRHhjE9/MopXjqH88XLP3a2lv55g++RiaTLfFLFI+P7hRmOIfYlYSkDTduL9kuF2zjRAWl0qpYKAUx2sKKdQzc2FRfsDKdc8ZMrr745rJn+cZ5J7DRppP4f//7VTo7urji/BtL2m9dXkFfl5GvoHzMh5SPfq46RRHUJlAmknHmnD6TfF4r9Se0tzLn9JmRAYhi8XgkrwsrCmoNK97U1FhmVRMF34+OGrrf5/aIzD/SUEqVfSOe50Uqiqudv9WCnq4+fn/zfSVKnd/ffB8zjz+4bDt0be9AkapLlij4U3VJwoE4vFi0w/KosXi0MBaUULUo9qrJG4uPnI88h3UbTgHk4LBGoSJXP2qNQlWJ0eey2WhT3Aor7RZ9PQN0rewe1knrcJEpKq2Yx+Ix9j94D3bbZweSyYQxRU8TMxMrO0jl/QorhL7ixG8dxYSJ44h5wsJLT0MEHnv4GSZsML7yoBb3ChY+IhLtGD0R57SFs4sOdx1GFhW23s0757jId9bX0881P7mVeQuPJR6PseEH2guTdWX6fWtbC8m6FFtts0XFPpnJ5LjsvOu1Dx2TZq9decGNnHrWMdEWYdkcnz1iX6YdPoXurj4SdQnyERGkapkcWkVCOqCo/O0N93DE7GnMOO4gWsY1Gg4gzJk/gysvKK46z5k/g1hcKyX9bAUfX8dMi1TMnnTmUbz0/GsF+apjRSfpwQw/OffagtLp8KOn4sU8EoaPRCk9kqlEQYG22z7b88ndtyko3cLKpigfQlapFrZMFIEfnXkFUw/fm0023yikuJ7B/gftybK33+WtN5Yx0DeI7/uIJ/iDPitX+Ixvb4lUAoUdkiZSSbLpTM0r48M5xI5yFPv7m+8r2y4XVHpUG2WtVoy2sGIdA/f1DhTe5eSNJ1Ws0wqrVvljr63rK+jrMqp1kF7tgg1ogdKO7UFFSZRAmUgm6V7ZXba1rGV8uVVL3vcr8LpSv2G1bAEDRU9Pb5lVT+uECKsaEXbco1QBNWf+jMhtq6MBESEejzFx0viC5arK5yMdcVc7f6sFfi5fGANBb8efdviUis61q30H2UyOXy/6A585ZK+CRfCvF/2BY045oiRfIpmIdFi+JgPZVGMBOprI5/OsXNFFNptDPCGTzpJNZxnfPq5sDKtWCVhpHlwpEE0ul6O7s2+NLrw4rBm4t+iwSljbjtFGu/7RKj+bzUWGx5196hHD3xygrRKjr8XJYQldmWxVvomGmkB2vNsFCk44Y2bZim88EWe3KTvQ1zsAjRQG9a6VPWTSWeoaUnieRyKZ4AuzP1sSUeQLsz9LIpmgriFFNudz2Q9+WTJ5E9HWEbO+fnhZeFfPi5lIY5DL+pFtf8wpRxScQB9zyvTCNj3nN2tkUGnrnedJ9Dsz4d/z+TwDA+kya7aUsdjKZjL09faTz6vIPtnZ0VVQ9oS3I3Ss6KwYErmggDrnODre6+JPtz1U4kS9MCHy85x10Sncc+fDJZYqUZNDq0joXtlbqHPxK0u5ZdFdTJu+D40tDVy0YBHzFh6HyqsIf0lWiBf2+9zuXPfT2wpt8pWTDwM8ps+ayp/veLgQ8atjRSfxeIwT5s+k472uQLt0F5xJKyjZwmVN/MO+l4KKq/0P3rMgFNl6gsqmfF7x+5v/UvKdAZGWiXlfMX3WVDbdfHLBesmWeeUFusxbr72bM74/h/7egTJhoK83zbjW0vbOZHK8t6yjLO+9v/srLz3/Wk1R3YLKrC233pxPH7wXK9/NmYlwU6Sj2F2nbM8fbgmvnP+FL875PC2tTXieN6Rvq1VFPBEvKGaCjsIRGZFtV56nHTv/6+lXCltjps+aWlHhD2NjBd0hACV85eTDyvlHKAR3teHiQVs17zZlB95bXvzedpuyQ6QlbTaTJpPOlKRl0hmymTRBJ9QaFcKFh8yFapnz+Nkc/33pjTIfhp/YaavyvLlcQRFvy7zygpuGXUwbKYgIiPDu8pUlfCxKAVTt/M2iGqHeiwmNjfU0NDUUFmBEqcjt0ENFbw0jFpPICIHh6GbZTIZUKlWyAOSJRzZT6rB8NNHU0sA3f3AimfRgYTEhmaqjoal8K+RoyAz9vYP09vaXKSxT9amyBYO+ngEGBwZL0gYHBgsW8BZ+LnoeHCWD5HI53nqj3Ln3RptOckqg9QDuDa7HGC0lxtpyjGafJ5vNkffz/Ob6uwvbNEay/tF8vliF8Li1lFuJ0ff3DhKLCaefexw93X0FYTDs5DCqX1T2TVS64hdLxCtEpoiz5L9vkapLUFdfZ0K3qsLqu5/LgVKMb2vBz/lMnDSeTDqjt/UMpunq6uEXl93O/O8dX8FZtGKwP821IQe5dvtZLpvl5RdeK5nYPfbwM7S2jStMWmNxj5323JaNP7ghSinGTxjHsacegXjCwkvnEot5dK7sAaX4zok/dtsVRgixWCzS+iObzXHbL0ojQ932iz/xlZMPL3wTduKx2Yc35jOH7EU2kyWXydLXN0jez7PktbfZdsetOPWsWfi5PPfc+TAvPf+aVgwCc86YaSzISieitvyolbC2Sa3svu8O+L7PrdfezZz5M2lsbqTj3S4T3rqnZEJkrV/6egc4/OipZDNZujt7S/htPp/nvWWddHX2cPK3j+bdZSsLIe9b21pAQUtrE0rlefKR58tW0/c+YBdNuCiy2RzzFs4uTEjfeetdEEU8HueI2dO4ZdFdhe/ez+VRwGMPPk1bu3bcnPNz3HrZ3ZFRyGzEs/CqcdDCLuZ5kbwiFotx6lnHgKJMcJu3cHakZeJpZ8/m1mvv5tQKkdo8U5dS0Q7D6xtTZW3d39sfmfegGfuxzatvVtzGEAW7rWrLrTfnUwfsUraNsKW1qUxQntA+PlJJF/Qttqr++YYS3Bqa6pg2fR/uuvV+Pn3wnlxZ4ii8stKr2nmC52nHzp//4qcLCsA/3/FwWbSvo088tHB/eAV9sw9vzLTp+5iIdb1Owb6mIbB08TtlCpAJk9pKsnmex6nfnYV4Xon1ZWT/8Sv5UisX0pUicnyPdn+lIh2Jhy2AagmXHksk2WKrD5b4MJwzf2ZkZC0/l2f3fXdg570+UTKnGG4xbaQwnD/EElorWWZH+DOsVqj3YjGyKoefyxkLpByxWIxExBasWDzaYXakFVgqFemHKZEKR4SNk00XrT5t/fHUmlH+AGQyeTKDGew0OJ+HzGCGeDJBXV3ANUINljo11Z/ORCosm1sagVIFkO/7kd+W7/sl+bwaZJDuzj7uuvX+km/wrlvv58jZn6Nt4rhVfi6HsQGnAFpPoa1E3mPFspUBK5HxtE+esNoTrrXhGC1KKfPlrx1Cd2cvi19ZOqL1j+bzebFYpAKlln3NUYz++HlfYOV7XSXbDr78tUN44O5/sO+03QoDcSXlVkNTXaQZdzwRK/joiCfiqLxi/MTxRgBVxGJCPJkiZ7ZZpVJJ8vk8FwV8kRw790jaJo0n5/tcFtg2Mnvukfz13sfZd9puhYhIFZ05L5zNpMkTSiY5n/vCvuy81yfw/TzJuiRb77Bl2cQukSquQqbq6vjotlvwzpvvFr6JiZPGk0gmyOcVfi5Hc3MTybqiw2m3XWH10djcyIxjD8bPa8VtW3srm22xCXnlR05EEsm48dETKyh/bJSvfD7PW0uWM3FSK82tLWyx1QdLtiLNmT+TQ750INlMGt/Ps0lLA8fNnUEsGeOsi04pcQKdy2Z5NRDRzot5+BmfbDrH9rt8nFRditMWzua1l5fQ1NLAhWddzcnfPrpEkbHl1puTSiX57BH70dfbF2lN43lewUQ/qGw6/vQZjBvfQktrI92dfXxh9meJxePstf/OhUmbIOy1/87E4nG6O3tJJFM0NTeW+PqZM38miaSePHsIn//SpwvOr//z0uuMG9/CgYdN4bNH7EsileKH37ycjhWdtLa1VFy1DY8dG2+2YUFZ5lWwAozHPS5csIiTzjyKt5csL5m0xhKJaEsQ48i7UplWaTKUT5ILzr6qpK2Hcgh/67V385WTD6u4jcEqQjRPyJNJawufKKsnu7XWtsvUw/dmq223IO8r6hvq2H3fHfj9r++jY0Un1/30Nk6vEOUrWG+qLkV6MB0ZvSxKcDvhjJm0Tmgla6wqrrzgRo45ZXpZJDmr2AsrvcLzBPH0llkvHtOWZ0qRSiXJZDKghM8esQ/9vQOFshe/spQ7b7yX6bOm6u1g73aiBAYH0iilqG+sKwh7La1NBevOle91jYifEofakEgmIhUg4W014nmR1pfJ+ijlg899dz1SIiTed9cjHHHMtLK8lbYKRUYgEuGgGfsR82KIJzS3NHHQjP0itmB5ZUpIHWGwvE/5lbbIR1jKpOpTbL/Lx0vmC9vv8nFS9eXWH6MBP1ehXWeVt2vFrUoRVhrdnX088bdnywMJtOxWItQrlScW022vVB6JxfBEKwTDiMVjTJs+JWLrcvm8VilFy7jmwiKhFxNiXrzMt5FSeRLJBF5eyPt5vETM5ItWwGWzOXo6eyN5ZxjpdI6+7mLexpYmUhER7jLpNJlMmncD1m0TJ7UST8epqyvm7+8dpK6xHhkY0GUmYqTq6+nvHYzc2lut0j2WSLDVNqVz1q222YJYhHWd7/vkfL9scSisAAKJ/l6iHHFFLOZ8+WuH1OqxwmGMYlQVQCJyIHAJEAOuUkr9IHQ9BVwPfBJ4D/iCUmrxaNI0ljCa25h6ewboWtlTvnrQ1EDLKu4Jtqhk1p0dRbPuKKXML392B9NnTeVKs91opMzKqzFbX9V9sfm8H22CvPNHq6YvylFyT3d/4V3btF/+7A7mLphNNpPFLgRFteMr/3qNHXbfhikH7EZe5c3An2DKAbuRy+S4OCDUzlt4XJkFxJz5M5kwqY3Jm7SDoswh79UX38zcBbMBYe6CYxARMpksj9z/z8JWAn1d+x7YcuvN2f/gPQuTk/++9IYemoTClpsJk8aXKHwW/GRuRd9HdmKRzWTxxGPSRhMQ9Ha2WDyOn8uz8NRLCkJ5sr64GjiS/er9ikw6w0B/+fad+sY6TvrmlxHPK/Fx4HkeT/ztWaZM3ZVtd9qKQ798APUN9Ywbl8H383gxD1FCenCw4mQ+k9YrcX4uRzwWI5VIkUzFC5OzZF09vu+z0x6fIJf1C3ljsZietOYVed/nogWLOGH+TGLxRIlFCsBu+2zP/gftybvLV9K+YVuEr5NfMe+cY2ltaykx0bfWTLlsjnxgcpYe1FHKIkPT1yf50ZlXMG/hsUMKMJ7nMWnDCcxdcAyLX13KRz72P/i+jp7m53zyfp7Tzj62MLkOCg0nnDGTTTafjJ/zSSTjTJw0np7uXkB/O9ZsXCR6AplJ55g+ayoNjfWRQuYRx0zjlmvuKjyvFlBifPeir5MZyESuCj/1jxeAoSNnbbn15sTjcXq6B/Cz2SH9l1hlzLxzji0ou2Ixj6ZxTaxcsZIVy1bSMq6RRDLBeys6ad+wjW132op4PB6tLMvlmTBpPPO/N4fuzp4Sxfec+TMBCkog8fQ22bAvIetEevd9d2DrHbYsa4PWCeNQSpEZzJBMJthy68155P6n6FjRyRXna19Wl5xzDSedeRQdKzppHtcUSWsmEO3QIjhPaGltYvpXpnLLtXcVBO9UfZJc1tfRu3yfpnGNKL90y+XiV5Zy67V3c9RJhyIi/OKyopLztLNnF1aQN9lsMu8tX1kYp+y33bGik2QqWbBEdf4mRg/ZTGYIH4FFQbUWRYmIx2FHHYDnxVAqz/gJrRx21CQkQgFT0WF0hI+1WFxv+QlvgYrFw+XqyHJlVpERZkXD+TAspdWnsbmRVF2iUG48kSzh2UEMDubo7ykqFRqam0qUBLVCPC/SklAiZAQRov26RMj0nudFLjKUR3z18P1suQWOlD+Tn/W569YHQpYi5f6aQM+vH7j7kcJiYy6X55H7H2XK1N3K6MxmsoXXqPKQV3kSUVsLsznefSe07Xcww8QN28qUQOl0jlwuh9Zi6EP/p0wJpIylYrkVVn1pW8U8Bnr6yeWKc4m83099c7TyZ2AgQy6bNf1OMTCQob4+WfYOVL76aHzJVB2TNpxQMveZtOEEkmUWU9VZ1tm8/3r6lTJl4aQDJ0TkdVjXMGojq4jEgMuATwNLgcdF5E6l1L8C2Y4FViqlPiQiM4AfAl8YSTpGmimPFGphAquCbCYzxKrM6imAKq025P38qIV4raSUaWyqL9Rfi2O2oRQ4Q+1/z+fz9PcO0vFu5yrti/W8WGQYdc+r3gJIRQg4qbroFfauld0kU0kaAj4ZggqWVF2K7s4eVF7R29tX9kwtrc0lk0BQFSeQFy1YxFe/8cWKdPzm+j8ybfo+TNxgPD1dfUw5YDcQxde/8xU8D869bB6JVJLDjp5G2qykJBIJtttla/73G5eV0DWurZnzz7yyUJcnVDTXtqtQ4nkk61IFSxSFnlT4OZ/zrjiDvO/TubIHP5vjtIWzi2bMEatYDtXDz5VOYo44ZhobbdIOCC0TxpHuD6yaNdSTTWf52HYfBoTPz/w0Mc8jl8kVzLBVXvtH8HPRkaV8P1+yVef402eQqEuUWc2Ma4sOpwswMJgm1VivhWwj+Mw5YyaJZJH3TT18Ch0rOrnpqt9x7NwjI2nxvBid73Xj+3nmLjiGl57/LxMmjS9Rnnz1G1+ivjHF80+9zKSNJlSc8Nlnq/TMN131O44+8VCaW5vIZX0+/LHN6ezoLrM2bGlt4s3X32VCeysnffPLXPaDX3LErKmMn9ha1kav/Gsxf7/vn8xbOLtgrTXnjJmFMOClE8hp/PelN9j0fzaqyCP+8vu/lzzX1RffTHdnL6eeNYvf3nRvman5rlO2L5inR/Fkz/PY/6A9Aejq6CrUE51XOPfyeXo7oFJ0ruzhgm//vGBJ09BoInspRV+vVqhvufXmTD18SkXfZ/l8nkvOua7iFre5Z8/mQx/9IBMntZHNZPBiHi+/8F/u/s2DnDB/JuMmjCs4kd517+0i/CDdyDzjoN76MAo63O5Y0Ul3Z09BOam3vpTSuts+2/Ppg/cCZbbqJeKA4GezKKUKQuMxp0xn0SW3cNy8L5DL+vzsxzfwjf/9Krl0riCE+dk88XicE874Yom1qd5qkOfBPz5a8g5zWb8QMezsn8wtUYJOmborl37vuoJvJbstzLZjwUG5JySSCbddbARQrb+cWhQl8USc3u7yuUNrQ31Z3mqdUIMWvqsRflP1dYxvG8dbS1aUKIpSEdZKtWwXSySTpPvTJdt/cpkcqYZy4XtwMEfswRnZAAAgAElEQVR/d5+JpqbI5xX93X1AY5m8Ub1MogrKH6BEeR1GNhPtWzLsWBlAPKG/f6DcEXZ96TY4pfKR+ZL1UU6YJVIJGGVVkvd97r3zb9x7599K0j/1mZ1D9UfPSVtT5VuP+nr6aWlrKVHWpRrq6evRASOCyOVy5RYsSqeHFUC5XK4qizU/59PfPxhhMVfeVwYGMpHKIoDG0Pa+WqLxKZWnrzfifdWV0hBLxAsLr8F8sSjfhbEKUUfXYCQ2h9HDaI6mOwOvKqX+q5TKADcBnw/l+TxwnTm/FdhPojycrSIGB3Msf2s5Fy64mgVfv4gLF1zN8reWMzi49lf0BwYyvLesgwsXLGLB1y/mwgWLeG9ZBwMDmeFvrgKCx5Spu3LrtXdz0YJF3Hrt3UyZuisyAq+8qaWBE874Im3trQCFFeDfXH83PSZiwEjDKp2CaGvXEUmsEFVtFBVrSn/hWVfpfnHWVbz1xvLCKkA8kWDO/Jklzzdn/kziiQRvvbG8ZLIDxYl6d+fwz573c5HRevJ+9X3SrtwHkR7MRrZPT3cfV5z/q8KqVTKV5FMH7MJl5/2ChadeQi6b48oLbiTv5yKfKUxXxYmhmVjWN9ZF0pHzfQ6euT83XfU7zpn7E66/7DZ6e/u4+Zo/sPDUS7hwwSL6evuJxWO8tzz0XSzv4NTvziqhy8+WCv+p+rqCRdCCr1/MxQuvYesdtjTm2tpviucJ3V16lX7B1y/mogWL6O7qIdWQIj2Y4cIFi4jH4yTr6grfjBb+3GC3OghuyfncF/Zli60+yIULFpFIJSJ5YCKV4Jc/uwOM35dkfYre3j4uXrjIvNtF9Pb24XkS2dfEk7Lvy8/lyvo2KPr7B/jpedez8NRL+Ol519PfP0As4el7splC/lwux63X3k1PVy8nfesoXY9I4bmi+v3+B+9Bd2dPYfy5eOE1bLL5Rry9ZHkJLf/3oxuIxXTI40oTPiugWQEm/MyxmATy+vz0vOvxc5V4jVYW9XT1Ut9Uz4zjDiqJwBVso1323o6OFZ088/iLBZ745zseZt9pu5WNLTdfc1fhGSrxiJPPPJqFl57GjOMO4o5f3cPiV5YaJUYvzz7+IleefyMXLVjEleffyLOPv8jkjScxfdZUrNl6eMwBoaerl3giXqD/38+8Gsm/874qfPv2W5979jEFS5oli9/ipqt+Ryzmce/v/kbHik622XErfv7jmxBPR04Jlnn0iYcSj8eGVMwlUynq6utMH9A8Z5PNN2Lq4XtzxQU3kgs4kfYrWEf4vvYFcuBhUwrvcP+D9yzQ0dOtx53Old3an0peFWjdbZ/tC76L7Byo670uMgNpLlxwdYnvN2s51NzSWNJvwt9ed1cPrRPG8Y3zTuB7l5/OGd+fw+RNNmCDjdrL5hyxRKykv9oyP3PIXvzyZ3eU+FYaCAhQVkF0yTnX8N2TLuRHZ17BW28sL/Gj5FA7KvFML6QAGYrPhJHLRFsL5SIszryYDtYQ/I6s4/8whuOFFtlMpqAoumjBogJv086CS5Gqr4/kDan6cmWV7/uR4075lhq9VSgqbyadLslXi0xSi3ProGNlyz+7O3sj35efzUaOC342G8oXPX74ERbR8WScwYFMyfMPDmSIR4SBt9EEg7DjaRC5CtZquYj3mkglK8wlyn07AZFzzChUa7FWKfpuLlv+Dfg5n76+0nlHX98Afq68X9USjS+XyUS+r3B7ZQYzPPSnRznpzKNYcMmpnHTmUTz0p0fJDJa360jIKw5jF6OpAPoAsCTwf6lJi8yjlMoBXcCI2Zb19/RGfpT9Pb0jVcUqI90fvX0h3T84zJ3VIjr08khs3vQ8j8bmBqbPmsppC2czfdZU7rzxXp59/MVCyO2Rhg2dGxy4Tzjji2z2oY35xnkn1OSot7tzaAVOemCwYLZun++uW+8nPTDA//3ohhIB00IPzNGmwUEMNcmvFs3jGvnqN0oVcO0bjC9L+/LXDuHPdzysBdispi0TGiSUyg9jWVBKV8UJpIkMIRUEtbyfL+uPV15wI7tO2T7w/ybS/QOR7yZo9m3pDdIx1CRUKejp7CWbTlecUHjmnV55wY1kBgcK13/+45vKopY41IbgJGbnvT5ReAeV3nW6f6Awybn+8tsrTgRj8VikUN7TVcrfo/pxx4rOISe4wXu04BMr9NFEMs7JZx5d8lyxWKys3+99wC6RdG/zyS3LaMn7vp5YDTPhiyWSkQKMdWJq83as6CQ3hGPQoLIol8tV/P7tRPeWa+6ipbWZuQtmc8wpRzBxwwkcf/oMFl56Wgn///mPbxqSR5xz2k8AxeX/+4tCtDKAnu6+yHveXrrc+IjKF8zWLU9+4O5/AFpItHwM4Ib/+y1LXnuL0xbOZuGlp3Ha2bMZ19bMJedeU/YuJk4aX/jf2FRfUAZZvmTTghEEbf13/OoeMhk9Ea4k1OQy0Txnq223KONjQwnd119+e4HHdqwoWvucYKyxAH57wz0k6hIMDKRpHtfEjOMOYtr0fcr6+JU/vgnf9wt9pVi/LjPYFyp9e9l0uuAAe9z4Zu00XJXPOTo7ugvfqAiFumy77n/wngX6gtsrrYIorCjtNdEhHVYNlXhmeJEjnozmM/FkhLPkmrZVKWKJGDOOO4jTFs5mxnEHEUvEIreAVSv8VvL5FTUfy2aytE7QTukXXnoa8xbO1j60IuattSggqs1bi0wy3FwriOHGhZL2qnIOWstctZa28mJeZB8MKwFrqX+oucTq5LXBD4LQuwFCtNagrKtWUaPrj1Wov3xRstr2isWEl55/jXNPu5SFp17CuaddykvPvxatLBwBecVh7GI090JFWfKEe001eRCROcAcgE033bRqAmoZmNY0Rpu2ij4TRsp7l1Lceu3dJXVYk/zRwEiGzq24dcRMGHw/XzBbD+Lwo6eaCUi+4hax4VCLCXQl6LbYoKwtAOYtnM3K97rp6e7jzhvvLUYBM5OG8EBlBZdKWyyCJtP2GaP2mttnz+fzJfuLm1oa+e2v7mG/z+0e2eZ2C5/9X82AY+kK+gwZ6ntK1SV1OGSoWLb1VxCuy75vB41V4cXBLZVBvjTUu7Z9cqg+kfdVQdC1pv8trU3c8at7SvJGrVxrQXfo+u23Gpyc2j7V3dXLxA3GF54r3O/7egcKDo7D5YedXQaFbjvpj+IPbe2t+Lks/wk4rrY+xD6+w0cKee23bh1uh8uyEdGsMHXl+TfyvcvnDbk1QitjlnHpudex8NK5fOdrF9DW3sq8c44tceLdsaJzCB7hGWuDcl7zjwee4oQzZhb8KNl77vjVPZqORDIyjHwskaRjRWeBjwWVQG3t2mdZV1c3TU0NQ/IVa01q03WUFQoWpgoV6bC8s6MLgOeefCnSh9FQfde+K+tPyQrd4TLiyWRBcWfrTRgh+o3/vsW06fuwdPE7LH5lKX+67SEOP3oqyboEqbpkRb4opo/8+Y6HmDN/BldeoJWPOiJT0cdPLQKAny+v67br/8jRJx7KjOMOwvO09ce1l/6m0K5BpU9wXLUKonC9zh+bxqrOifO+oq4+VcIz6+pTZQoYEaGpqZG5C2aXhOCOMtCvZVsV5PnTbQ+x65TtSZEgl8vxp9se4sgIh9HByIMl5YaE71qsJPK+z31/+FuJ/5kH/vgoUw7ctSxvTX2/aqVKLVvrEpF8NB7lALjCuBDlW7LaOWgtc9Va2sqLxUnVJUv6YKouiRcrFUdHq/6aaPViBf5Y5MnlLhsqjdtRyrqalCoKZs89kkUX31yof/bcIyPX8attr6HGmVUt02HdxGgqgJYCmwT+bwy8VSHPUhGJA+OAMls8pdSVwJUAO+64Y9UajLHceUebtrCDT1t+lAO1VS0/anAKR5MYSaxq6NwwhvLxA0O/m7b2Vu69869lkby0v5zhfSsl66JDYIb36Q6HSm3hxWL4Ob+gnAsraMLP/tjDzzBn/kyefeLFSLr++/ISps+aSnNLI01GKGporC8ZvBsai36YHnv4mRJBbdudtmLa9H3o7uyNbFMrdBXbeOjJZFu7Dbvt8fw/XzITVDXkO2tqaSgIDpXydHX2lPwPXh/NPr2uYVV4cSwe44T5M7nightLBPXK70xMFLBonyY2H+jQ5xMnjS84ka5vbCgIxMF+7HlF3z0F5UGFchWq8E3qfp4s+EBpa29lxTsdtLQ2EYvHC99M58pu9p22Wwk/PO3s2UPyEZvvq9/4UiHt3RUrC0Kyva63SMSYPmsqsXg80sHyn257sHD+6ENPA/D3+54s+6aPP30G9/z24SItRpiKJRKR0QljCT2OfOXkw7j9l3+mrb218A51lJfSKYR+L1KmmGse18RT//gXR594KC8++58yuqZN34e29jbmnXOs9s+lFF2dvRz65c+QHsziZysIODt9tMB3ovgXqIKAOZSC+8tfO4Q7b7y3kN7U0khbu97udvSJh/LXex4vK3/W1w/ntl/8ibb2VrbY6oMFC6mgwIyoyHq9mBT4slUcKqUihW7f92lrb6XH8FD9beh+375hG6m6VCAqY2n0G0trWR83wvEj92vroZPPPJpEMkHzuCYy6WzhWSvz44iINbHycbW7s5e6hno2/R/t4LmpuZEZxx1Ey7hGjj99BipfbJ/guGoVROF6a/Hztz5jVefEoLjrNw+UKGDu+k2Us17Ry7OqNClqzbYWgTJZVx+pyE3WlW/B8jyP2acewaJLbikKv6ceUbboV62iCCDVUMeOe2xb4tNkzvyZpBqi/AVVP0cfDaVKPBmnpbU5tMDRHLmtKhaPju4Wi5fPXVINdZHvK9wGsUS8wpgQUX8Nz9XcUs9gXz89AWu+uvoUzS2lfaBaOmutvyY/VErxxN+eK3OCvM+03UvLHGZhdFXrR7SSPriw9Oc7HuLI2Z8ry1pteyVTqchxJpkql0ESqehvu9LWOod1CxJejRyxgrVC52VgP+BN4HHgi0qpFwJ5TgK2UUp91TiBPkwpdeRQ5e64447qiSeeqIoGu9823HknbTRprTuCHm3aKoX7rmWr1HDlr3i7gxXLOgJh5teNkK5R4XSDTpyz2RxvLym/vuHGk1j+1rv8349uKDiujMVjxOJaGVNNtJLBwRzpgTR+LlMIox6LJ0nVp0bkvQ8O5li5oqMsitD49jbq6uKRzz7v7ONAFLFEAj9bpCvVUE9mME0u6+N5QizukUil6O7oLiu/pa2FnpU9eDGPuoY6cpliOcm6enJZvU8/uMI/Z/5M7rr1fp59/MWCQDlhgwm8t6yjPMrYBm30dvUWQnj/8fYHeOn51wrfDDDk95TN5shkcpFljxs/jivO/yXdnb2Fus6Y9X3zzRTDS1cDEXlSKbXjar/IdQDV8uLBwRw9K7vxfZ/6xjq6Vuooct86/6SK7/r1V5fQPqmNgYE0nifaUiXi3Q72DRin3iacbCyOiEculykJMZuoSxhn08U+GU/EI/vMuLZmUB7xZIzXXnqDCe2tPPXoC/z9vn9ywvyZNI1r5P67HqGuPsVOe3wC39dRszLpbMl3MWlyGwP96TJF8QYfmER6YLDEes/387y9ZDl33Xo/Bxz2Kfq6B0rCzt5zp3YA/K3zT6K/u78QmUU80WG601liMeGZx1/klmvuKmkj63DU84QH//Qo9975N9raW40T4lZ+9K2fMfXwvQsRw6wyLRaLkWpI8cZ/3uSuW+8vfB/jxjcz0DdIPBbDi8W48OyrSieHiTgiYqKuaaVELJHAz2WJJxLkMtkhw53bsaWrs4fWtmbyvqJpXDMdK6L7SsfylVxx/q+ME/jtCgrh5tYm8nlY9qb27xaPx8vub2tvY6Cvn99cf3eBD51wxkzq6+t45613C6HRx7e1EE8m8LN6u1ws7qHyilzOR+UViUSclrZmerpKAwvkctF8qaGhDt/3mbDBeN5ZuqIQBcz2p+A7ePjex9hpz0/Q2NSAIk88FqexRYcYHs4aNnIsO30G9Q31hS1xlqbJm0xCRG+R9jyPvO8TSyQKzrXDecPRdYYbV+27tZFPE8kEiLAyEFBh/4P3YO8DdkHEo6+3r2S8qGX+4vhwNKqddw4O5sp5qxenrrE+2qlxd18hgqgVKBtaoh0gpwfTJfOMWCJJqq58/pPL5Vjxdvlcpn1yW8lcq5a5dCaTI5vNlYwFqYZ6Eok4yWQ5rdWWW0u7VlumnbOEaU0m45HfXueKbnIB3hGPxWhtbymblw4O5vBzORNow5RbX08sHi+j1Y7bQX7UPL5llZ/fopoIyIODOXy//Pljsfhq1V9r3hVvryhxeH/CGV+kfXJ7WVsNNe9e1fqz2RzvLF1ewgdPOEPLIuE+UGt7VeOIvBY+UAnvJ168rmHUFEAAIjINuBgdBn6RUur7InIO8IRS6k4RqQN+AWyPtvyZoZT671Bl1jLYwdiNAgajT9tohplfE+WPJoYL457N5ujp7C0TUEbimUf7vQ9XfqVnH+rZgvc0NDeQ7h805RcHGVtnsi5ZFJTM9VxWhxDNDA4WhMJUQ11gsPKIJxMolSeeSJSkh/PFEnEyg5myZxvuuYsTqgDt9fVkMxmymVzhnszg8IJVJbyfBrtaBQ/7blL1KXIZHf2wtC/pd53382TTmZLrqfo60gODZe82/M7tale4zHBapfu1EjRLqqGO/p5+YjGPRCpJ2iiiQIzDVCHv+yWKjETSRFfK5cxWCSGWiAW+BR1uPCxoWFieY5febUTFQn8PKBXCz9zf0x/67jwaW5pKoppEfR/JpFf45sNt3DSuCRFK+GA8mSA9kC7wRKsw0LzEI5lMkklnVns8sLwon8+TzyuUUtQ11EW+w+F4cjqdo6+7l2RdneE/pt3q6+jv7S/pj/a5YjFvyDJrGQfC7Z5IJVH5fOGeYFlhxVgsEQelCiGRV6Vdw2NZMApYlAJuuPuHyjvcuFrLPasz1jo+XBm1CH/VzlNGK2+1/amWMjOZHL1dvVXx5NF4rlrKHK1vb233gWoxFvrg2m6rWvrA2n4HUXg/8eJ1DaOqABoN1DrYOTg4OKwpvJ8GO8eLHRwcxiIcH3ZwcHBY+3g/8eJ1DeuGuYaDg4ODg4ODg4ODg4ODg4ODwyrDKYAcHBwcHBwcHBwcHBwcHBwc1nM4BZCDg4ODg4ODg4ODg4ODg4PDeg6nAHJwcHBwcHBwcHBwcHBwcHBYz+EUQA4ODg4ODg4ODg4ODg4ODg7rOZwCyMHBwcHBwcHBwcHBwcHBwWE9h1MAOTg4ODg4ODg4ODg4ODg4OKzncAogBwcHBwcHBwcHBwcHBwcHh/UcTgHk4ODg4ODg4ODg4ODg4ODgsJ5DlFJrm4aaICIrgNcjLk0E3l3D5FSLsUwbjG36xjJtMLbpG8u0wdimb1Vp+6BSqn2kiRmLGIIXD4Vgu0adr+vXxxIt76dnGUu0uGcZvbKqhePDw6Padq2l/dd23rVdfy1513b9teR9v9dfS961XX8teUer/iDeN7x4nYNSar04gCfWNg3rIm1jnb6xTNtYp28s0zbW6RvLtK3LR7Bdo87X9etjiZb307OMJVrcs4xeWe4YuaPadq2l/dd23rVdv6N1/azf0er48Pp4uC1gDg4ODg4ODg4ODg4ODg4ODus5nALIwcHBwcHBwcHBwcHBwcHBYT3H+qQAunJtEzAExjJtMLbpG8u0wdimbyzTBmObvrFM27qMK4c5X9evjyVa3k/PMpZocc8yemU5jByqbdda2n9t513b9deSd23XX0ve93v9teRd2/XXkne06ndYB7DOOYF2cHBwcHBwcHBwcHBwcHBwcKgN65MFkIODg4ODg4ODg4ODg4ODg4NDBJwCyMHBwcHBwcHBwcHBwcHBwWE9xzqpABKRRSKyXESeD6SdLSJvisjT5pi2lmjbRETuF5F/i8gLInKqSW8TkXtE5BXzO34M0TZW2q5ORB4TkWcMfQtN+uYi8qhpu1+LSHIM0XatiLwWaLvt1jRtARpjIvKUiPze/F/r7TYMfWOp7RaLyHOGjidM2lj4ZjcL8jmTdraIzDfneRHJiMjzIvI7EWk1/39qrinzv0NElojIX0Uka86fE5FBEXlJRLpExDf5/xO4t9pjWY353eEOd7hDSe28Jnj4IpKuUJYvIgMi8rj57TVpWZPvHhHZSEQONGWkTZ5e0Tzx3yLyhIi8Ze5fLCLfNHz38yJyR4Anf0tEXg38P0hE7lxT44SDg4ODg0NNWNtx6FflAD4F7AA8H0g7G5g/BmibDOxgzpuBl4GPAT8CvmnSvwn8cAzRNlbaToAmc54AHgV2BW4GZpj0/wO+NoZouxaYvrbbztA1D/gV8Hvzf6232zD0jaW2WwxMDKWNhW92syCfM2mF7xXIAm+Y8+uAbwMZ4KdAGvCBI4H7gH8Cb5l73gQGgXfMvTcBCsibOheb/1ngXXO+DOgz5wp4InD+krlXATng2cA1P3RfDngw8N8eeVOfb4584Np7ofv9iPv7A+cDpiwVKsf+D6fZI1shfVWOSnVEHekRrHdVn6daemt5rpFsr2woz1D57bVMIC03yu8xqk+uq8dIPUul97UE6Aq9m1XpV28Gyl4OdETU10WRt6w0z5ZF8yTLl7IU+eXLJq0XeMqc96P5pW0bW0+Q7nzgepiGfIi2FRHP0h/ImzP/+yLy5SnldZ9G8+j/htrTnlse4AfSo95vre/cD9U1OESeasqupf7R4kHucMdYPIYbx8NznahvKQs8TWWeUqnsqGs5iuNxntL5WiaQPhCiJYfmEwNoHha8ZstYCnSieWSa8ud5LYKma4FFaP4epDmLnvO+E0h7ETgVPY/uCZS9GHjStE+XqXswQPNVwCaGbjsHfdU87xJgofmvgH8Bu4Tm7S8bGqYH0q4N/d/M1JU3v+8Cfwf2ZWg54E7gqMC1nwNnDCdfrJMWQEqph9CD6ZiDUuptpdQ/zXkP8G/gA8Dn0QIa5veQMUTbmIDS6DV/E+ZQ6M5/q0lfW21XibYxARHZGPgsmkkhIsIYaDeLMH3rCNb6N1sjHiH6e84DjwFN6AERc74CPXgBbEVxIPSA9sD9dpxoA+IU+30mkOdDaCWpzb9J4JoAdaHymkM0KpMvb66Hx6aW0P1RY1dd6DwWUYelR4hGvEL6qsCvIW+Y1lVFmCfVUm6lNqkl30jwxEplxEN1D0WHvZYIpK1qG1f7TGtyPrWq7TzcfcFvZHXrylD5fTUA9YHyY0S/z3Dd6dB/+04FaCX6HbyOVkjH0BPjrMnXT1G53Ufx2283aQ3A3ejv+D/oCTnm/vvMuYcWBqBU+LLn/zG/PcDFgbwvUMo/VwIpk3fQ/O9Cj+GDaOX724F6vkSRv3zZ/E4OXP8nRWEMigol2z5Bq1JL67PmvzJ5gwgKVaCVY31oocm+kwzF8cXWaa8tN/f+Ez3vjIJ9/7lQ+jND5CVAqwr8BvvNYODcKteCsILiYsr720Dov23PfARNwfLC55Xyh+vLR6SF/+cof4YwnZXuDV/Lh/4PVUa4TpsW7ivBca+a8lcXwXKD9eUi8uQozW9pte2XprQfWSVxpTp7Q+XZ/p+n2PfzlC5EKErnXjm0cgA0n3gvgjar4LHl2XvsfTG0EgJ0f3+SYlt0md/n0PwuY8rOUFTQBHmYxWMU52UKLXPbfAPodnmCouL8qsD154Fuigt3r6GVOksD7eEBfzN53zJ0LkHPTU9DLxhbvtGP5n8d6Lljv7m2OPA81wLfMPn/bdK3NjR1o5VAPcABwEloPn8NmpcOmHqXGTp+BXzCpN+JGRuUUkuAX5o8PWjetAA4HfgMsL95zseVUo/ahhSRjwMbGZpSDI3X0QqkJvP7LHDiMPecApwjegfA7sAu6PFmSKyTCqAhcLKIPCt6i9ga364RhohsBmyPthbZQCn1NmhFDDBp7VFWRhuMkbYTvU3oafTHfQ96AtWplLKMZSlrSWkVpi3wgX/ftN1FIjLcxz1auBjN/CwDn8AYaTeDMH0WY6HtQA8afxaRJ0VkjkkbU9/sENjQ9MsfoAe3IDzgBnTbfwjdL0APoh8ANhGRPHqws+PBVygKznG0YAV6kpGkOPneKVCPXf2FojBG4L8X+r9NiE5bZjIizdIRlR5VRvh/+HdNoRaFw0gpgCq1wZrC6tS3tt7TcBiL86RVbaPh7rPXVURarRhqy3EbRR5TjSLPIjxGtAXOE8C4iHu3QVs6CzAbzUs8k7fB5HnVXO9B866g8kqhV2atoBMDPkKpoGuF6aASO4cWIAaBRuC3FIW+j1MqaKZMeWmKQmQbWhFk0URRwHqDojA5zdQbVI59yPx2GnrTlC5aTQ7k9QzNbRQVWGFhv9fkC7ZJyjybFZAbQ3X0B56lz9w7YPJVgkK/n55A2kcDZaZDeTF0BBU7mcC1sBI++AwW9r09Gbhm5ymLQ3mDfTo4lwn3BYvOiLxDQRh+4eBVynlSXVRGSpUOhMoO95nhvvN44H77PK+iFbnBZw6OZbb/Bq1QVhe2bvu+rYLCKissukN02TzdoTT7/u39QYVPlvK2DSqRBikunEGx/3sUv/VMIP09U0dQKWn5hD23PEzM/3pzv1Ds2z56DveuySPALeY3RtEKB4oK12fRltfj0O89CfyE0v7hBc6D32kvWunRZepuNGV0mzaKmXJ9tBJlMvr7/4e5fyO0NWWDefZutNX4xhQVvBk0X+tAK8g/jVauKFN/3DxLI/qdvBVosz2NYUivaYMHzLUWdB+cCNxu0vpMOc1o6/mbKPKPN027/Ecp9aKhcQWl/eok8xwpYDvgx0qpm03bHGnKPZdSXIiWZ5cAO1IFjOz2d/RiavcweRcDV6J3LVwOnKyUGvZbG4sTm1XFz4At0C/kbeDHa5MYEWkCfgPMVUoN+fLWNCJoGzNtp5TylVLboRnDzujBvyzbmqXKVBqiTUS2Br6Ftp7YCT2B+n9rmi4R+RywXCn1ZDA5IutaabcK9MEYaLsA9lBK7QBMBSh8T70AACAASURBVE4SkU+tRVqCGG5Vzk62NkNPYD8SuJ4InP8C/c3biccgevVoiVLKQw+8diIxDb1iY2H7UpiPxUPnURPrShhJS5tq61zTGGuKjHURY8bK8n2GWvpuJYF1qHdXi3XcUAjOYYOCTNBa51HgdxR5ot3OFQfGo5/V+u+xQsXr5vcIc/0VisqiLPA/FIV1KzT1Ba7bsmydPcDxaOHCKogsP82jhbwetBDUYO6xq/WgBRhrNRkDLgvU+6a5x1piWAtLQSv8rQVmjKLQPJFS2LJypl2sosPy0zpKeWu9ybMJRaHU9pl0II99PxuZ309Sav2ZD/xK4H9TIE9w0SEWym/T8pRa+3mBa3WUWrtEKdvjwGER928VkReKixpRFnPB8+EWU8PfmVWADZXnI5TLbmGlgoVVeIXbzyJohVctjcG0DStct7AKWts/RmJMtM8eXhQKl98a+G+vJSj2vzzF9qgL5F0aoLmV8jYL1jOeypapzYH/Vilkv7tN0P3aKlK3RSsvbHnBeuy9Ni1m7hlPafsGrUTSgXK2Nr8zgVmmDMvLTjX5gt+Opd8qzXvRfasNzaOstaaHtlBsNOdHmrKepvj9Xmry290Tnea5J6HnrJujLWfq0fxrdzSv2MLQ/Ywpu8kcuwMPmbK2ATYwdW5g0uwC6BxTz6Po72U58Li5tgna+OHNwDNbJfiOaEXOaSLyLzS/+qJ53kPMQuvjJn+dqeP/mR0Xc4Efmme0il9E5APAXmiLpteAPRkeW4jIM6bM3dDWVVtI0V/q08BXQ/dcABwIvGCUYcNivVEAKaWWGQE9jzbz3Xlt0SIiCbSwdYNS6jaTvExEJpvrk9EdckzQNpbazkIp1YnW4u4KtIqIZWYbU5ykrRUEaDvQbKtTSqk02pxwbbTdHsDBIrIY7ctlX7TFzVhptzL6ROSXY6TtAFBKvWV+l6NXCnZmbHyz71E+iWxDCwegBZAe4IPowegkk/4WxZWpk9HfUSN6QmNX4oIrPJ0UJ4uboQdmCyvQdFO6MhyccEYpdEZrfBlqsrrejGkOQHUCg1MSjTxqEdRWxXptpJSjwbqDSgFrKZQAPgwcbK41oYWfd9CCilUKfBbdjz5s0l5Dr8pvZurYzNwbLN9uRbU0WIHOWgxMNOVZYeF4isqSVorWmNbaqB694pwz5b8TqMvW4aH9+Vnh0Cp7UmhrFWudELQ+GKBoiRTeQgdF4Xwj82v9C0HRCiFBUWlnrSisH6UNKf0GgxYTtr5lgWt2PAuuUNuxJLilLwg7li2LuJ6lKChD8d0Ht1MFhegwv7BlPRtxPWxBE4a9N2zhGrwXSrdOD4UoCzxCaeExrpK1E5RuMwynWR8u4Xqj6g4vrNj8Leh3N9z3bOcWSyi1aquEqO1mFir0G3UtCtYnjX2WnkB6sN7NIu4Ntm1w2+cAReVvnuJ2qzCsstD6AbNbo6wS2frMyVH67UKpYvTJAC0JtBW3tTb6dSB9X8r7bhat+LA+FhVajrH+0Qj82sVAn6KyaZypz1oIDgL3oq1pLE94A90nrAL5kxQthtoCz70UrRTKoueerWgZpQ9tyXQu2oroO6acf6CVVQ+ZZxCT1ofmGc1mV8s2psyLDI1LTPssRm+LAi1rzEXzptOMMiUF/N60xYYmXw/al+Zyk/cOpdR2xgjgHpP3DrRy5yi08uVtyi1frwNeUUq9Zq7/zzC7bD5ofvNoq/6vAf+LtkraLkDD/4Xu29a0y1YiUtU8eL2ZLFtBzeBQSvc4r0k6BLga+LdS6sLApTvRWyswv78dK7SNobZrF5FWc16P3k/5b+B+YLrJtrbaLoq2FwMKAkH7iFnjbaeU+pZSamOl1GbADOA+pdSXGAPtNgR9Xx4LbWfqbxSRZnuOXpF4njHwzRq/U2+LyH6Gvjb0QPPXQLZO9MrFKeitXnH03mo7yG8CfA/Yj+JEoxe9B9oOVv9GD6QKPdBaCyBFcTV8Y0rHjNsD53aSEpx0BFd3w4gSRMJ4d/gsZZP1dRXrEu1rktah6opafV9VrIpFymi2Q7Vlrw4NtQpQlVDJ6i4olIfzBd/ZICPzHQeVQUG/JMeileC23mVoQSroYNQqLSx/a0eb/ls8a/LZbRiWzoHAfdZ/RBz9rH9HW1muRAsrVwWe7QWKQqFVWNSjLU5SaL4c9PkziBaaZqOVWRtQbE+rFGmlqASzPoqUocFuz7L83grhwa1TOfRYsjxAk1BU7ttfRdG3kVVEBfl5UEC1lgB1FBVRBK4HLRuCCG+pssfSiPxPUhy7guX/h2IftPTZ9g+OTVboPSvwTBZhCxoi8oQRFLqDfbkaPiWh36hrUBzHg+n1lCNqYcb2gWD7D0dj0KIqnN5LZYTL25TSbZqVEKY7Sik3XHsGrZitAjDoGzCG7mdLA/msdQ2h8oPPbRUjoL8rq7y120qDsNuj7H3/pqi4tX3T0mEthqw1TjJwr22PXUO0WEV0Am2tGHxeW2fQItEqT6wVz57mftsnLG+zFjH21yqW7X1xc96G5j+2nsloKx1rdXgKun2TaAWztZrZFK0gz6OVSlk0vztHKXUUWk5oRSs1QCu0foZWtkykqHjvRX/bObTPzk1NW5xgrlsF2W5o66dmtILl76bci4wyJYtWHl2B5s0XoQOqHEjIl47oaMUfRyu8jgD+AEwxz7ozui98LHDLTsDWIpJDK4rGU75FLIjX0cqe7ZVSZ6PlkCEXx43C53JT/itopdGwWCcVQCJyI9rh6ZYislREjgV+JDqs8bPAPmgnUmsDe6Bfwr5SGlb9B8CnReQV9N7GH4wh2sZK200G7jd0PI72s/N79NageSbM6gS0Emus0HaDiDyHdrA2ES1ojxWMhXYbCmOl7TYA/mpMLh8D/qCU+iNj45sFOBr4jlmpuA9YqJT6T+D6peiVkmvQA1wOvRLzNnog/i56FSIBXE9ROfMK2krsJfRAZxU4V1PcSgZ6uwMUJy4WMwPnwUmNhPIGTeUtqvH3lKiQXsncfqSsCtYG1iXa1yStQ9U1knSsTSuW1Sl7dWgYTsisFkPNI8PCdFR6Hav/HYf5S0Pg/EK0UtuWvRF6oeujFAUfu43DWvXsgOZRVnjaE63cWYGe7Peb/E8H6pkAPByoZztTRj16pX3QnAt6/tqDFl6sINiH5tU9FAXrqRRX+H20guNJ9NYIq/ToQituPmKeI4ee62HotKvVQd8pYcfIUBTOrDNqzK/divYiRcF7E0P7eHP/u4FyrEBrFVc5tKCYpugvxkYRsriaolD9HKV95V2K/mfCwQOguE3LKuEsNjO/GYrv2Qrvts2DfW23wLlVioUVR/Z9QWWlcfB61Leh0BHbguUGYa1JhkJYuTqchRKURkYK3xf8DX9/UWUHnRIHF3sU0VvRgghb94TbcTjFtKUvqo2CytkkpUoyaxln77dblyYHyn8gVNcrlCq47DWrnLVtaX3uvBLKZ5XBNm1LtBVQ0M8PFCNe3UvRsXOGUkX2O+i2u8P8j1GMetVDMbpXF0WFTAatCAX9zR5L0UopQ3FrrKXRKiY8iu84QdHP1otoa5rFaEsZHx2VGzRPfQ1t5fMm+lu9B91+efQcf7JJ+6W55y5TdhyteLE42dA3ydz7HPp9WmVMF8V5adzQ+mu0+4IsWpGTM222EvizOc8CH1ZKWetKRORAU/bBFPvPRqaMgwn4IzOL1T9Dz7Xz5jlORyuqzlNKLUW/3x+Kht3qu6lSKo6efy8k2rVJJexJcRG2Ek5AWxk9gN5q9g0RaR/6Fljr4Zfd4Q53uMMd7nCHO9bnAz1ZftoczwCfNem7oye4z5jfY4cp5wFgx8D/04GrzfliYGLg2qXAd815b6icWcBPhyofHVr3FHO+CdpXzqRQ/mspDWU72+R7FTgmkB5DT4a/h7YMuQU9Oc+hHdXbCDjPmGMlWjDaDL016t/AX9BC1l/Q211vpbjK3g18L9AOK9G+5zBlvGrK8dCCxEqK1kevG5q60UJM1tRto/V0oH1SPE/RqXIOvVIdtFCxEZqs4sQqQv6NFijs9qrvU1Sa2HvPMu/kdYrbSdKBcq21SpSlpQql2/PHgWMMHYtN/XmKwmI3xfDxAxFlR9UTPnyGDlMdFXY6fPxuiHzBENBD0WeP/grpuSrudYc7gsdwfXttHOHvPCrke57ob+V1tPIqnB7FV2qhI3yEo56Fj1zot5prVtGcjrgnT/Qzp9HKqIFAWhbNj29A75IIbpvtQltAvoxWJmcC1/vQTqlXUOwTOfS49QJFayerYH8QvVjwj9BYeaR5B5PN//fMfUvRURJfMvTaecLjmB0SoXH3bGA+Wkm2GNgwcG0ecM1wcxIxmR0cHBwcHBwcHBwcHBwcHEYIJjz3jcBhqjwgynpfv8PYg1MAOTg4ODg4ODg4ODg4ODg4OKznGK1QvA4ODg4ODg4ODqsAEbmd0mh8AP9PKfWntUHPWIWIfJtSB6gAtyilvj/UtQplHYAO5WuxAdpvzWKKEb06KfqaOUop9dwwNG2AdmjaT9E3kS3jNaXUoeaefwCfMNetk9Wgk2IbqdH6H7G4lWKwB4sURR84SYp+2KxPm070dodBdB9rDtX1FrCzUuo9EXmUos82+yw58yxB/25Z4EGl1GdEZIKhMxwi226Rg9IIatafkd2KsQIdme1stF+PYCj4AXR0H4stKHVQbSOXdVOMOBZ24pxGbwnciNIomzaf9WPTaWixPlKa0Y5mrX8fG91rOfp9NgAfotyPThrtl28Tiv5U7NZF67cpyufdMrRfwk3RfpSsU9sw8uitLh1oX06bBmhIUeqo29YfdG6dRW9FWQacA1yGfs/B8hej+5R1OuxR/pwWaohroJ/3XbRz9LOAX5hnmxC4z27tWYH+djYL0Rysa5k5GikGq7DtOWD+p80zgG4jm7ca2KhYWVOuoujrym4JTVN0Mh10rB6j+A0Gny2H/gZz6LYO0lfGo0RkCcUw8vZ7GDTlBv122bqXo9sz7Gzdbm2LobcvvWau9VEaGRYC/MnQcBnav9gHKDp4X4Hud/9Bf4ug+2o7pbzyPYqRDy3+ppQ6KcB3LY/FtIftl0G/Upcopa4JlRPFuzHP9gTlY0CQ1hJawuVWi1rGGhHZBt3ng0grpXYJ5x1NOAsgBwcHBwcHBwcHBwcHBwcHh/Uc62QUMAcHBwcHBwcHBwcHBwcHBweH6uEUQA4OaxAisirhhh0cHBwcHBwcHBwcHBwcVgtOAeQwpiAim4nIiyJylYg8LyI3iMj+IvI3EXlFRHY2x99F5Cnzu6W5d56ILDLn25j7GyrUc7aI/EJE7jPlHm/SRUTON/c+JyJfMOmXi8jB5vz2QD3Hisj3zPmXReQxEXlaRK6wyh4R6RWRc8xe+t1GuQkdHBwc1ihEZIqI/N6cHywi31zL9MwSkZ+ubh4HBweHWiEiO4rIT9Y2HaMFEZkbnFuLyF0i0jrUPWsLZq4/f3XzODisb3AKIIexiA8BlwDbAlsBXwT2BOYDZwIvAp9SSm2PdiB3nrnvYuBDInIocA1wglKqf4h6tkU7GtwNOEtENgIOA7ZDO2PcHzhfRCYDDwF7mfs+AHzMnO8JPCwiHwW+AOyhlNoO7WTtSyZPI/C8UmoXpdRfV61JHBwcHNYsjEK8pnmCUupOpdQPRosmBwcHh7EMpdQTSqlTRqv8MWBJPpeiQ3OUUtOUUp1rkR4HB4ca4RRADmMRrymlnlNK5YEXgL8o7a38OXQkgHHALSLyPHAR8HEAk38W2rv6g0qpvw1Tz2+VUgNKqXeB+4Gd0QqdG5VSvlJqGfAgsBPwMLCXiHwM+BewzCiGdgP+DuwHfBJ4XESeNv//x9TjA79ZzTZxcHBwGHUYK8x/i8jlwD+Bq0XkCRF5QUQWBvIdaKw1/4pWnNv0gmWNiHxQRP4iIs+a302HqPdaEfmZiNwvIv8Vkb1FZJGh5dpAvpnGOvN5EflhIP0YEXlZRB5ERyqx6e0i8hsRedwce+Dg4OAwBFbTGj1oEXm24WMPGL5WUTEUqPM6wzNvtZY2IrJYRM4y/PYIEdlORP5h8t0uIuNNvgdE5GJ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\n",
      "text/plain": [
       "<Figure size 1296x792 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plotting some of the features from the correlation heatmap to see the linear relationship beyond numbers\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "fig, ax = plt.subplots(nrows = 2, ncols = 3, figsize = (18,11), sharey=True)\n",
    "fig.subplots_adjust(wspace=.2, hspace=.35)\n",
    "#sns.set(font_scale=.5)\n",
    "\n",
    "sns.scatterplot(x=df.max_power, y=df['sum'], ax=ax[0, 0], color='#69547C', )\n",
    "sns.scatterplot(x=df.radio_model, y=df['sum'], ax=ax[0, 1], color='#69547C')\n",
    "sns.scatterplot(x=df.main_propagation_model, y=df['sum'], ax=ax[0, 2], color='#69547C')\n",
    "sns.scatterplot(x=df.antenna_h_bw, y=df['sum'], ax=ax[1, 0], color='#69547C')\n",
    "sns.scatterplot(x=df.antenna_centerline, y=df['sum'], ax=ax[1, 1], color='#69547C')\n",
    "sns.scatterplot(x=df.extended_calculation_radius, y=df['sum'], ax=ax[1, 2], color='#69547C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "corrmat = df.corr() \n",
    "f, ax = plt.subplots(figsize =(18, 8)) \n",
    "sns.heatmap(corrmat,annot=True, ax = ax, cmap =\"YlGnBu\", linewidths = 0.1)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['radio_model'] = df['radio_model'].astype('category')\n",
    "df['main_propagation_model'] = df['main_propagation_model'].astype('category')\n",
    "df_new=df.loc[:,['site_support_structure_height','antenna_centerline','extended_calculation_radius']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### One Hot Encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# label encoding the data \n",
    "#from sklearn.preprocessing import LabelEncoder \n",
    "#le = LabelEncoder() \n",
    "#df['radio_model']= le.fit_transform(df['radio_model']) \n",
    "#df['main_propagation_model']= le.fit_transform(df['main_propagation_model']) \n",
    "from sklearn.preprocessing import OneHotEncoder \n",
    "le = OneHotEncoder() \n",
    "df_new= le.fit_transform(df_new).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 386 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   0    1    2    3    4    5    6    7    8    9    ...  376  377  378  379  \\\n",
       "0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "1  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "2  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "3  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "4  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  0.0  ...  0.0  0.0  0.0  0.0   \n",
       "\n",
       "   380  381  382  383  384  385  \n",
       "0  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "1  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "2  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "3  0.0  0.0  1.0  0.0  0.0  0.0  \n",
       "4  0.0  0.0  0.0  0.0  0.0  1.0  \n",
       "\n",
       "[5 rows x 386 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new=pd.DataFrame(df_new)\n",
    "df_new.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Set the training and test data sets\n",
    "#df_features = df.drop(\"sum\", axis=1) # drop labels \n",
    "df_features=df_new\n",
    "#Select the label\n",
    "df_target = df[\"sum\"]\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "scaler = StandardScaler()\n",
    "df_features_std = scaler.fit_transform(df_features)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       8.263681e+08\n",
       "1       2.184412e+08\n",
       "6       1.614895e+08\n",
       "7       2.872214e+08\n",
       "8       1.407915e+08\n",
       "            ...     \n",
       "5912    2.454942e+08\n",
       "5913    3.250458e+08\n",
       "5914    2.962542e+07\n",
       "5915    1.680068e+07\n",
       "5916    2.351062e+07\n",
       "Name: sum, Length: 5137, dtype: float64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_target"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "train_x, test_x, train_y, test_y = train_test_split(df_features_std, df_target, test_size=0.2, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try RandomForestRegressor() and DecisionTreeRegressor()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,\n",
       "                      max_features='auto', max_leaf_nodes=None,\n",
       "                      min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                      min_samples_leaf=1, min_samples_split=2,\n",
       "                      min_weight_fraction_leaf=0.0, n_estimators=10,\n",
       "                      n_jobs=None, oob_score=False, random_state=42, verbose=0,\n",
       "                      warm_start=False)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import r2_score,mean_squared_error\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "model= RandomForestRegressor(random_state=42)\n",
    "#model= DecisionTreeRegressor(random_state=42)\n",
    "model.fit(train_x,train_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred=model.predict(test_x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2.12046326e+09, 2.73654578e+09, 5.67987190e+07, 9.10548183e+07,\n",
       "       1.18310689e+08, 6.67802244e+07, 3.54310447e+08, 1.84017305e+08,\n",
       "       1.58309764e+08, 4.67980768e+07])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred[0:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1097    3.046767e+09\n",
       "1253    3.694258e+09\n",
       "4568    6.965031e+06\n",
       "1053    2.522053e+08\n",
       "4331    4.460817e+08\n",
       "5772    1.616554e+07\n",
       "3388    1.880570e+08\n",
       "5210    1.466685e+08\n",
       "754     1.353617e+09\n",
       "5375    3.135829e+07\n",
       "Name: sum, dtype: float64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_y[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Root mean square value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "897525445.367455"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mse=mean_squared_error(test_y,y_pred)\n",
    "rmse= np.sqrt(mse)\n",
    "rmse"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### R- squared value\n",
    "#### R-squared is a statistical measure of how close the data are to the fitted regression line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5012235418976279"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2_score(test_y, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "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>Actual</th>\n",
       "      <th>Predicted</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1097</td>\n",
       "      <td>3.046767e+09</td>\n",
       "      <td>2.120463e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1253</td>\n",
       "      <td>3.694258e+09</td>\n",
       "      <td>2.736546e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4568</td>\n",
       "      <td>6.965031e+06</td>\n",
       "      <td>5.679872e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1053</td>\n",
       "      <td>2.522053e+08</td>\n",
       "      <td>9.105482e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4331</td>\n",
       "      <td>4.460817e+08</td>\n",
       "      <td>1.183107e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3752</td>\n",
       "      <td>1.007604e+08</td>\n",
       "      <td>1.741457e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4407</td>\n",
       "      <td>1.121789e+09</td>\n",
       "      <td>8.790536e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5625</td>\n",
       "      <td>3.201431e+08</td>\n",
       "      <td>3.952044e+08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3542</td>\n",
       "      <td>1.091077e+06</td>\n",
       "      <td>1.041358e+07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>530</td>\n",
       "      <td>1.449409e+09</td>\n",
       "      <td>1.859151e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1028 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Actual     Predicted\n",
       "1097  3.046767e+09  2.120463e+09\n",
       "1253  3.694258e+09  2.736546e+09\n",
       "4568  6.965031e+06  5.679872e+07\n",
       "1053  2.522053e+08  9.105482e+07\n",
       "4331  4.460817e+08  1.183107e+08\n",
       "...            ...           ...\n",
       "3752  1.007604e+08  1.741457e+08\n",
       "4407  1.121789e+09  8.790536e+08\n",
       "5625  3.201431e+08  3.952044e+08\n",
       "3542  1.091077e+06  1.041358e+07\n",
       "530   1.449409e+09  1.859151e+09\n",
       "\n",
       "[1028 rows x 2 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "actualvspredicted= pd.DataFrame({'Actual': test_y,'Predicted': y_pred})\n",
    "actualvspredicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "actualvspredicted.to_csv('Test_predictions.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 55% indicates that the model explains 55 percent variability of the response data around its mean."
   ]
  }
 ],
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