final-project-notebook-uj-no-gas-data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 1: Gathering Data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pathlib import Path\n",
    "import time\n",
    "\n",
    "import os\n",
    "import requests\n",
    "import pandas as pd\n",
    "\n",
    "def fetch_and_cache(data_url, file, data_dir=\"data\", force=False):\n",
    "    \"\"\"\n",
    "    Download and cache a url and return the file object.\n",
    "\n",
    "    data_url: the web address to download\n",
    "    file: the file in which to save the results.\n",
    "    data_dir: (default=\"data\") the location to save the data\n",
    "    force: if true the file is always re-downloaded\n",
    "\n",
    "    return: The pathlib.Path object representing the file.\n",
    "    \"\"\"\n",
    "\n",
    "    data_dir = Path(data_dir)\n",
    "    data_dir.mkdir(exist_ok = True)\n",
    "    file_path = data_dir / Path(file)\n",
    "    # If the file already exists and we want to force a download then\n",
    "    # delete the file first so that the creation date is correct.\n",
    "    if force and file_path.exists():\n",
    "        file_path.unlink()\n",
    "    if force or not file_path.exists():\n",
    "        print('Downloading...', end=' ')\n",
    "        resp = requests.get(data_url)\n",
    "        with file_path.open('wb') as f:\n",
    "            f.write(resp.content)\n",
    "        print('Done!')\n",
    "        last_modified_time = time.ctime(file_path.stat().st_mtime)\n",
    "    else:\n",
    "        last_modified_time = time.ctime(file_path.stat().st_mtime)\n",
    "        print(\"Using cached version that was downloaded (UTC):\", last_modified_time)\n",
    "    return file_path\n",
    "\n",
    "def fetch_and_cache_gdrive(gdrive_id, file, data_dir=\"data\", force=False):\n",
    "    \"\"\"\n",
    "    Download and cache a url and return the file object.\n",
    "\n",
    "    data_url: the web address to download\n",
    "    file: the file in which to save the results.\n",
    "    data_dir: (default=\"data\") the location to save the data\n",
    "    force: if true the file is always re-downloaded\n",
    "\n",
    "    return: The pathlib.Path object representing the file.\n",
    "    \"\"\"\n",
    "\n",
    "    data_dir = Path(data_dir)\n",
    "    data_dir.mkdir(exist_ok = True)\n",
    "    file_path = data_dir / Path(file)\n",
    "    # If the file already exists and we want to force a download then\n",
    "    # delete the file first so that the creation date is correct.\n",
    "    if force and file_path.exists():\n",
    "        file_path.unlink()\n",
    "    if force or not file_path.exists():\n",
    "        print('Downloading...', end=' ')\n",
    "        download_file_from_google_drive(gdrive_id, file_path)\n",
    "        print('Done!')\n",
    "        last_modified_time = time.ctime(file_path.stat().st_mtime)\n",
    "    else:\n",
    "        last_modified_time = time.ctime(file_path.stat().st_mtime)\n",
    "        print(\"Using cached version that was downloaded (UTC):\", last_modified_time)\n",
    "    return file_path\n",
    "\n",
    "\n",
    "\n",
    "# https://stackoverflow.com/questions/38511444/python-download-files-from-google-drive-using-url\n",
    "\n",
    "def download_file_from_google_drive(id, destination):\n",
    "    URL = \"https://docs.google.com/uc?export=download\"\n",
    "\n",
    "    session = requests.Session()\n",
    "\n",
    "    response = session.get(URL, params = { 'id' : id }, stream = True)\n",
    "\n",
    "    needs_confirm = needs_confirmation(response)\n",
    "\n",
    "    if needs_confirm:\n",
    "        params = { 'id' : id, 'confirm' : 't' ,}\n",
    "        response = session.get(URL, params = params, stream = True)\n",
    "\n",
    "    save_response_content(response, destination)\n",
    "\n",
    "def needs_confirmation(response):\n",
    "\n",
    "    return response.headers[\"Content-Type\"] == 'text/html; charset=utf-8'\n",
    "\n",
    "def save_response_content(response, destination):\n",
    "    CHUNK_SIZE = 32768\n",
    "\n",
    "    with open(destination, \"wb\") as f:\n",
    "        for chunk in response.iter_content(CHUNK_SIZE):\n",
    "            if chunk: # filter out keep-alive new chunks\n",
    "                f.write(chunk)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [],
   "source": [
    "# download required datasets\n",
    "\n",
    "file_dict = {\n",
    "    \"aqs_sites.csv\": \"1fMfkw-NJ03VrQxYpDjM_4T6VDaWhvegi\",\n",
    "    \"greenhouse_gas_emmitter_facilities.csv\": \"1yjTRv1OrsdWk-xNW4ZbFYB7_8Tt_x_fV\",\n",
    "    \"greenhouse_gas_emmiter_gas_types.csv\":\"1akEokx_wqsgYqGNnNJsgebA6DDGHJLR2\",\n",
    "    \"2020_daily_global_weather.csv\":\"15pjzsCiIE0uL69a4tZYgXoog8TKjIJNJ\",\n",
    "    \"daily_wind_2020.csv\": \"18MqsjkN0EYPuLb0iR0U9sZYmNVCdd94h\",\n",
    "    \"daily_temperature_2020.csv\": \"1Q62JlGtc65L2eU9FfQZcP9UyMCdcem1T\",\n",
    "    \"traffic_volumes.csv\": \"1sZyjZSWz1xEoB26u_OrhKokZYhTIJRMD\",\n",
    "    \"county_aqi_2020_daily.csv\": \"1uNH90XRceOfb16ctuUsYXVIEDeR2yaeC\"\n",
    "    }\n",
    "\n",
    "for k, v in file_dict.items():\n",
    "    path = f\"./datasets/{k}\"\n",
    "    if not os.path.exists(path):\n",
    "        download_file_from_google_drive(v,path)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jamshedu/miniconda3/envs/msse-python/lib/python3.9/site-packages/IPython/core/interactiveshell.py:3169: DtypeWarning: Columns (1) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"
     ]
    }
   ],
   "source": [
    "aqs_site_df = pd.read_csv(f\"./datasets/aqs_sites.csv\")\n",
    "gh_gas_facil_df = pd.read_csv(f\"./datasets/greenhouse_gas_emmitter_facilities.csv\")\n",
    "gh_gas_type_df = pd.read_csv(f\"./datasets/greenhouse_gas_emmiter_gas_types.csv\")\n",
    "weather_2020_df = pd.read_csv(f\"./datasets/2020_daily_global_weather.csv\")\n",
    "wind_2020_df = pd.read_csv(f\"./datasets/daily_wind_2020.csv\")\n",
    "temp_2020_df = pd.read_csv(f\"./datasets/daily_temperature_2020.csv\")\n",
    "traffic_volumes_df = pd.read_csv(f\"./datasets/traffic_volumes.csv\", low_memory=False)\n",
    "aqi_2020 = pd.read_csv(f\"./datasets/county_aqi_2020_daily.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### aqs site data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['State Code', 'County Code', 'Site Number', 'Latitude', 'Longitude',\n",
      "       'Datum', 'Elevation', 'Land Use', 'Location Setting',\n",
      "       'Site Established Date', 'Site Closed Date', 'Met Site State Code',\n",
      "       'Met Site County Code', 'Met Site Site Number', 'Met Site Type',\n",
      "       'Met Site Distance', 'Met Site Direction', 'GMT Offset',\n",
      "       'Owning Agency', 'Local Site Name', 'Address', 'Zip Code', 'State Name',\n",
      "       'County Name', 'City Name', 'CBSA Name', 'Tribe Name',\n",
      "       'Extraction Date'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Site Number</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Datum</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Land Use</th>\n",
       "      <th>Location Setting</th>\n",
       "      <th>Site Established Date</th>\n",
       "      <th>...</th>\n",
       "      <th>Owning Agency</th>\n",
       "      <th>Local Site Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>CBSA Name</th>\n",
       "      <th>Tribe Name</th>\n",
       "      <th>Extraction Date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12931</th>\n",
       "      <td>37</td>\n",
       "      <td>81</td>\n",
       "      <td>11</td>\n",
       "      <td>36.113333</td>\n",
       "      <td>-79.703889</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>229.0</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>1979-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>North Carolina Dept Of Environmental Quality</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KEELY PARK, KEELY RD,</td>\n",
       "      <td>27301.0</td>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Guilford</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>Greensboro-High Point, NC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4311</th>\n",
       "      <td>16</td>\n",
       "      <td>79</td>\n",
       "      <td>8</td>\n",
       "      <td>47.545977</td>\n",
       "      <td>-116.136588</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>700.0</td>\n",
       "      <td>INDUSTRIAL</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>1974-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Idaho Department Of Health And Welfare-Environ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KELLOGG JR HI SCH 800 BUNKER AVE</td>\n",
       "      <td>83837.0</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Shoshone</td>\n",
       "      <td>Kellogg (Kellogg-Wardner)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20075</th>\n",
       "      <td>55</td>\n",
       "      <td>139</td>\n",
       "      <td>9</td>\n",
       "      <td>44.016928</td>\n",
       "      <td>-88.547614</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>0.0</td>\n",
       "      <td>INDUSTRIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>1992-01-23</td>\n",
       "      <td>...</td>\n",
       "      <td>Wisconsin Dept Of Natural Resources, Air Monit...</td>\n",
       "      <td>TRAILER IN PARKING LOT AT 455 MARION RD</td>\n",
       "      <td>455 MARION ROAD, TRAILER IN PARKING LOT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Winnebago</td>\n",
       "      <td>Oshkosh</td>\n",
       "      <td>Oshkosh-Neenah, WI</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3254</th>\n",
       "      <td>12</td>\n",
       "      <td>57</td>\n",
       "      <td>80</td>\n",
       "      <td>27.875862</td>\n",
       "      <td>-82.062356</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>32.0</td>\n",
       "      <td>INDUSTRIAL</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>1973-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Florida Department of Environmental Protection...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RT 2 BOX 279 NICHOLS RD</td>\n",
       "      <td>33547.0</td>\n",
       "      <td>Florida</td>\n",
       "      <td>Hillsborough</td>\n",
       "      <td>Not in a City</td>\n",
       "      <td>Tampa-St. Petersburg-Clearwater, FL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6050</th>\n",
       "      <td>19</td>\n",
       "      <td>153</td>\n",
       "      <td>61</td>\n",
       "      <td>41.622767</td>\n",
       "      <td>-93.657997</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>277.0</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>1980-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Polk County Physical Planning</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2916 30TH ST. NW, EASTER SEAL</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>Polk</td>\n",
       "      <td>Des Moines</td>\n",
       "      <td>Des Moines-West Des Moines, IA</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11430</th>\n",
       "      <td>34</td>\n",
       "      <td>1</td>\n",
       "      <td>9000</td>\n",
       "      <td>39.465017</td>\n",
       "      <td>-74.448791</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>5.0</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>1991-09-18</td>\n",
       "      <td>...</td>\n",
       "      <td>National Park Service</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Brigantine NWR</td>\n",
       "      <td>8205.0</td>\n",
       "      <td>New Jersey</td>\n",
       "      <td>Atlantic</td>\n",
       "      <td>Not in a City</td>\n",
       "      <td>Atlantic City-Hammonton, NJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16891</th>\n",
       "      <td>47</td>\n",
       "      <td>145</td>\n",
       "      <td>11</td>\n",
       "      <td>35.904797</td>\n",
       "      <td>-84.565214</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>282.0</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>1976-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Tennessee Division Of Air Pollution Control</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.6 MI W OF KINGSTON STEAM PLANT</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Tennessee</td>\n",
       "      <td>Roane</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>Knoxville, TN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15009</th>\n",
       "      <td>41</td>\n",
       "      <td>43</td>\n",
       "      <td>1003</td>\n",
       "      <td>44.398180</td>\n",
       "      <td>-122.721196</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>155.0</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>1985-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Oregon Department Of Environmental Quality</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1301 LONG STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>Linn</td>\n",
       "      <td>Sweet Home</td>\n",
       "      <td>Albany, OR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13331</th>\n",
       "      <td>38</td>\n",
       "      <td>55</td>\n",
       "      <td>101</td>\n",
       "      <td>47.404163</td>\n",
       "      <td>-101.140707</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>640.0</td>\n",
       "      <td>INDUSTRIAL</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>1980-04-01</td>\n",
       "      <td>...</td>\n",
       "      <td>North American Coal</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FALKIRK #1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>North Dakota</td>\n",
       "      <td>McLean</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17217</th>\n",
       "      <td>48</td>\n",
       "      <td>61</td>\n",
       "      <td>6</td>\n",
       "      <td>25.892518</td>\n",
       "      <td>-97.493830</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>6.0</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>1993-07-01</td>\n",
       "      <td>...</td>\n",
       "      <td>University of Texas Rio Grande Valley</td>\n",
       "      <td>Brownsville</td>\n",
       "      <td>344 Porter Drive</td>\n",
       "      <td>78520.0</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Cameron</td>\n",
       "      <td>Brownsville</td>\n",
       "      <td>Brownsville-Harlingen, TX</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-05-18</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      State Code  County Code  Site Number   Latitude   Longitude  Datum  \\\n",
       "12931         37           81           11  36.113333  -79.703889  WGS84   \n",
       "4311          16           79            8  47.545977 -116.136588  WGS84   \n",
       "20075         55          139            9  44.016928  -88.547614  WGS84   \n",
       "3254          12           57           80  27.875862  -82.062356  WGS84   \n",
       "6050          19          153           61  41.622767  -93.657997  WGS84   \n",
       "11430         34            1         9000  39.465017  -74.448791  WGS84   \n",
       "16891         47          145           11  35.904797  -84.565214  WGS84   \n",
       "15009         41           43         1003  44.398180 -122.721196  WGS84   \n",
       "13331         38           55          101  47.404163 -101.140707  WGS84   \n",
       "17217         48           61            6  25.892518  -97.493830  WGS84   \n",
       "\n",
       "       Elevation     Land Use       Location Setting Site Established Date  \\\n",
       "12931      229.0  RESIDENTIAL                  RURAL            1979-01-01   \n",
       "4311       700.0   INDUSTRIAL               SUBURBAN            1974-01-01   \n",
       "20075        0.0   INDUSTRIAL  URBAN AND CENTER CITY            1992-01-23   \n",
       "3254        32.0   INDUSTRIAL                  RURAL            1973-01-01   \n",
       "6050       277.0  RESIDENTIAL               SUBURBAN            1980-01-01   \n",
       "11430        5.0       FOREST                  RURAL            1991-09-18   \n",
       "16891      282.0  RESIDENTIAL                  RURAL            1976-01-01   \n",
       "15009      155.0   COMMERCIAL  URBAN AND CENTER CITY            1985-01-01   \n",
       "13331      640.0   INDUSTRIAL                  RURAL            1980-04-01   \n",
       "17217        6.0   COMMERCIAL  URBAN AND CENTER CITY            1993-07-01   \n",
       "\n",
       "       ...                                      Owning Agency  \\\n",
       "12931  ...       North Carolina Dept Of Environmental Quality   \n",
       "4311   ...  Idaho Department Of Health And Welfare-Environ...   \n",
       "20075  ...  Wisconsin Dept Of Natural Resources, Air Monit...   \n",
       "3254   ...  Florida Department of Environmental Protection...   \n",
       "6050   ...                      Polk County Physical Planning   \n",
       "11430  ...                              National Park Service   \n",
       "16891  ...        Tennessee Division Of Air Pollution Control   \n",
       "15009  ...         Oregon Department Of Environmental Quality   \n",
       "13331  ...                                North American Coal   \n",
       "17217  ...              University of Texas Rio Grande Valley   \n",
       "\n",
       "                               Local Site Name  \\\n",
       "12931                                      NaN   \n",
       "4311                                       NaN   \n",
       "20075  TRAILER IN PARKING LOT AT 455 MARION RD   \n",
       "3254                                       NaN   \n",
       "6050                                       NaN   \n",
       "11430                                      NaN   \n",
       "16891                                      NaN   \n",
       "15009                                      NaN   \n",
       "13331                                      NaN   \n",
       "17217                              Brownsville   \n",
       "\n",
       "                                       Address  Zip Code      State Name  \\\n",
       "12931                    KEELY PARK, KEELY RD,   27301.0  North Carolina   \n",
       "4311          KELLOGG JR HI SCH 800 BUNKER AVE   83837.0           Idaho   \n",
       "20075  455 MARION ROAD, TRAILER IN PARKING LOT       NaN       Wisconsin   \n",
       "3254                   RT 2 BOX 279 NICHOLS RD   33547.0         Florida   \n",
       "6050             2916 30TH ST. NW, EASTER SEAL       NaN            Iowa   \n",
       "11430                           Brigantine NWR    8205.0      New Jersey   \n",
       "16891         2.6 MI W OF KINGSTON STEAM PLANT       NaN       Tennessee   \n",
       "15009                         1301 LONG STREET       NaN          Oregon   \n",
       "13331                               FALKIRK #1       NaN    North Dakota   \n",
       "17217                         344 Porter Drive   78520.0           Texas   \n",
       "\n",
       "        County Name                  City Name  \\\n",
       "12931      Guilford                 Greensboro   \n",
       "4311       Shoshone  Kellogg (Kellogg-Wardner)   \n",
       "20075     Winnebago                    Oshkosh   \n",
       "3254   Hillsborough              Not in a City   \n",
       "6050           Polk                 Des Moines   \n",
       "11430      Atlantic              Not in a City   \n",
       "16891         Roane              Not in a city   \n",
       "15009          Linn                 Sweet Home   \n",
       "13331        McLean              Not in a city   \n",
       "17217       Cameron                Brownsville   \n",
       "\n",
       "                                 CBSA Name Tribe Name Extraction Date  \n",
       "12931            Greensboro-High Point, NC        NaN      2021-05-18  \n",
       "4311                                   NaN        NaN      2021-05-18  \n",
       "20075                   Oshkosh-Neenah, WI        NaN      2021-05-18  \n",
       "3254   Tampa-St. Petersburg-Clearwater, FL        NaN      2021-05-18  \n",
       "6050        Des Moines-West Des Moines, IA        NaN      2021-05-18  \n",
       "11430          Atlantic City-Hammonton, NJ        NaN      2021-05-18  \n",
       "16891                        Knoxville, TN        NaN      2021-05-18  \n",
       "15009                           Albany, OR        NaN      2021-05-18  \n",
       "13331                                  NaN        NaN      2021-05-18  \n",
       "17217            Brownsville-Harlingen, TX        NaN      2021-05-18  \n",
       "\n",
       "[10 rows x 28 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(aqs_site_df.columns)\n",
    "display(aqs_site_df.sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gh gas facility data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['V_GHG_EMITTER_FACILITIES.ADDRESS1',\n",
      "       'V_GHG_EMITTER_FACILITIES.ADDRESS2',\n",
      "       'V_GHG_EMITTER_FACILITIES.CEMS_USED', 'V_GHG_EMITTER_FACILITIES.CITY',\n",
      "       'V_GHG_EMITTER_FACILITIES.COUNTY',\n",
      "       'V_GHG_EMITTER_FACILITIES.COUNTY_FIPS',\n",
      "       'V_GHG_EMITTER_FACILITIES.FACILITY_ID',\n",
      "       'V_GHG_EMITTER_FACILITIES.LATITUDE',\n",
      "       'V_GHG_EMITTER_FACILITIES.LONGITUDE',\n",
      "       'V_GHG_EMITTER_FACILITIES.PRIMARY_NAICS_CODE',\n",
      "       'V_GHG_EMITTER_FACILITIES.STATE', 'V_GHG_EMITTER_FACILITIES.STATE_NAME',\n",
      "       'V_GHG_EMITTER_FACILITIES.YEAR', 'V_GHG_EMITTER_FACILITIES.ZIP',\n",
      "       'V_GHG_EMITTER_FACILITIES.FACILITY_NAME',\n",
      "       'V_GHG_EMITTER_FACILITIES.SECONDARY_NAICS_CODE',\n",
      "       'V_GHG_EMITTER_FACILITIES.ADDITIONAL_NAICS_CODES',\n",
      "       'V_GHG_EMITTER_FACILITIES.COGENERATION_UNIT_EMISS_IND',\n",
      "       'V_GHG_EMITTER_FACILITIES.EPA_VERIFIED',\n",
      "       'V_GHG_EMITTER_FACILITIES.PARENT_COMPANY',\n",
      "       'V_GHG_EMITTER_FACILITIES.PLANT_CODE_INDICATOR'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "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>V_GHG_EMITTER_FACILITIES.ADDRESS1</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.ADDRESS2</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.CEMS_USED</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.CITY</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.COUNTY</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.COUNTY_FIPS</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.FACILITY_ID</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.LATITUDE</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.LONGITUDE</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.PRIMARY_NAICS_CODE</th>\n",
       "      <th>...</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.STATE_NAME</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.YEAR</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.ZIP</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.FACILITY_NAME</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.SECONDARY_NAICS_CODE</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.ADDITIONAL_NAICS_CODES</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.COGENERATION_UNIT_EMISS_IND</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.EPA_VERIFIED</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.PARENT_COMPANY</th>\n",
       "      <th>V_GHG_EMITTER_FACILITIES.PLANT_CODE_INDICATOR</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>43305</th>\n",
       "      <td>1231 POPE STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>MEMPHIS</td>\n",
       "      <td>SHELBY COUNTY</td>\n",
       "      <td>47157.0</td>\n",
       "      <td>1005312.0</td>\n",
       "      <td>35.166770</td>\n",
       "      <td>-89.958930</td>\n",
       "      <td>325199.0</td>\n",
       "      <td>...</td>\n",
       "      <td>TENNESSEE</td>\n",
       "      <td>2011.0</td>\n",
       "      <td>38108.0</td>\n",
       "      <td>PMC BIOGENIX INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>325611 325613 311999</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PMC GROUP (100%)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32249</th>\n",
       "      <td>140 SAMSONDALE AVE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>WEST HAVERSTRAW</td>\n",
       "      <td>Rockland</td>\n",
       "      <td>36087.0</td>\n",
       "      <td>1000780.0</td>\n",
       "      <td>41.204400</td>\n",
       "      <td>-73.968900</td>\n",
       "      <td>221112.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>2010.0</td>\n",
       "      <td>10993.0</td>\n",
       "      <td>Bowline Generating Station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GENON ENERGY  INC  (100%)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33398</th>\n",
       "      <td>2217 Oakland Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>VAN BUREN COUNTY</td>\n",
       "      <td>5141.0</td>\n",
       "      <td>1007545.0</td>\n",
       "      <td>35.631540</td>\n",
       "      <td>-92.392770</td>\n",
       "      <td>211111.0</td>\n",
       "      <td>...</td>\n",
       "      <td>ARKANSAS</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>72031.0</td>\n",
       "      <td>Hilltop Compressor Station</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BHP BILLITON (100%)</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35383</th>\n",
       "      <td>1374 BRYAN GRIFFIN ROAD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BUTLER</td>\n",
       "      <td>PENDLETON COUNTY</td>\n",
       "      <td>21191.0</td>\n",
       "      <td>1005271.0</td>\n",
       "      <td>38.735550</td>\n",
       "      <td>-84.408180</td>\n",
       "      <td>562212.0</td>\n",
       "      <td>...</td>\n",
       "      <td>KENTUCKY</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>41006.0</td>\n",
       "      <td>RUMPKE OF KENTUCKY INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>RUMPKE CONSOLIDATED COS INC (100%)</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71480</th>\n",
       "      <td>1010 Muldoon Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Staten Island</td>\n",
       "      <td>RICHMOND COUNTY</td>\n",
       "      <td>36085.0</td>\n",
       "      <td>1002843.0</td>\n",
       "      <td>40.565216</td>\n",
       "      <td>-74.193377</td>\n",
       "      <td>562212.0</td>\n",
       "      <td>...</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>10312.0</td>\n",
       "      <td>Fresh Kills Landfill</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>CITY OF NEW YORK (100%)</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50787</th>\n",
       "      <td>45 Route 446</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SMETHPORT</td>\n",
       "      <td>MCKEAN COUNTY</td>\n",
       "      <td>42083.0</td>\n",
       "      <td>1000193.0</td>\n",
       "      <td>41.859383</td>\n",
       "      <td>-78.445170</td>\n",
       "      <td>324199.0</td>\n",
       "      <td>...</td>\n",
       "      <td>PENNSYLVANIA</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>16749.0</td>\n",
       "      <td>INTERNATIONAL WAXES INC</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>THE INTERNATIONAL GROUP INC (100%)</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54789</th>\n",
       "      <td>2210 S. Azusa Ave.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>West Covina</td>\n",
       "      <td>LOS ANGELES COUNTY</td>\n",
       "      <td>6037.0</td>\n",
       "      <td>1011449.0</td>\n",
       "      <td>34.036441</td>\n",
       "      <td>-117.902294</td>\n",
       "      <td>562212.0</td>\n",
       "      <td>...</td>\n",
       "      <td>CALIFORNIA</td>\n",
       "      <td>2017.0</td>\n",
       "      <td>91792.0</td>\n",
       "      <td>BKK Corporation</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BKK CORP (100%)</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14799</th>\n",
       "      <td>1111 Bagby St. Sky Lobby 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Houston</td>\n",
       "      <td>HARRIS COUNTY</td>\n",
       "      <td>48201.0</td>\n",
       "      <td>1008396.0</td>\n",
       "      <td>29.758940</td>\n",
       "      <td>-95.370960</td>\n",
       "      <td>211111.0</td>\n",
       "      <td>...</td>\n",
       "      <td>TEXAS</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>77002.0</td>\n",
       "      <td>EOG Resources  Inc. 515 Powder River basin</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>EOG RESOURCES INC (100%)</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14355</th>\n",
       "      <td>103 FUNSTON ROAD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>KANSAS CITY</td>\n",
       "      <td>WYANDOTTE COUNTY</td>\n",
       "      <td>20209.0</td>\n",
       "      <td>1005745.0</td>\n",
       "      <td>39.142019</td>\n",
       "      <td>-94.615315</td>\n",
       "      <td>327993.0</td>\n",
       "      <td>...</td>\n",
       "      <td>KANSAS</td>\n",
       "      <td>2015.0</td>\n",
       "      <td>66115.0</td>\n",
       "      <td>CERTAINTEED CORP.</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SAINT-GOBAIN CORP (100%)</td>\n",
       "      <td>N</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57857</th>\n",
       "      <td>19801 NE 36TH ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>HARRAH</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>40109.0</td>\n",
       "      <td>1000859.0</td>\n",
       "      <td>35.508900</td>\n",
       "      <td>-97.178900</td>\n",
       "      <td>221112.0</td>\n",
       "      <td>...</td>\n",
       "      <td>OKLAHOMA</td>\n",
       "      <td>2019.0</td>\n",
       "      <td>73045.0</td>\n",
       "      <td>Horseshoe Lake</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>N</td>\n",
       "      <td>NaN</td>\n",
       "      <td>OGE ENERGY CORP (100%)</td>\n",
       "      <td>Y</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      V_GHG_EMITTER_FACILITIES.ADDRESS1 V_GHG_EMITTER_FACILITIES.ADDRESS2  \\\n",
       "43305                  1231 POPE STREET                               NaN   \n",
       "32249                140 SAMSONDALE AVE                               NaN   \n",
       "33398                 2217 Oakland Road                               NaN   \n",
       "35383           1374 BRYAN GRIFFIN ROAD                               NaN   \n",
       "71480               1010 Muldoon Avenue                               NaN   \n",
       "50787                      45 Route 446                               NaN   \n",
       "54789                2210 S. Azusa Ave.                               NaN   \n",
       "14799        1111 Bagby St. Sky Lobby 2                               NaN   \n",
       "14355                  103 FUNSTON ROAD                               NaN   \n",
       "57857                  19801 NE 36TH ST                               NaN   \n",
       "\n",
       "       V_GHG_EMITTER_FACILITIES.CEMS_USED V_GHG_EMITTER_FACILITIES.CITY  \\\n",
       "43305                                 NaN                       MEMPHIS   \n",
       "32249                                 NaN               WEST HAVERSTRAW   \n",
       "33398                                 NaN                       Clinton   \n",
       "35383                                 NaN                        BUTLER   \n",
       "71480                                 NaN                 Staten Island   \n",
       "50787                                 NaN                     SMETHPORT   \n",
       "54789                                 NaN                   West Covina   \n",
       "14799                                 NaN                       Houston   \n",
       "14355                                 NaN                   KANSAS CITY   \n",
       "57857                                 NaN                        HARRAH   \n",
       "\n",
       "      V_GHG_EMITTER_FACILITIES.COUNTY  V_GHG_EMITTER_FACILITIES.COUNTY_FIPS  \\\n",
       "43305                   SHELBY COUNTY                               47157.0   \n",
       "32249                        Rockland                               36087.0   \n",
       "33398                VAN BUREN COUNTY                                5141.0   \n",
       "35383                PENDLETON COUNTY                               21191.0   \n",
       "71480                 RICHMOND COUNTY                               36085.0   \n",
       "50787                   MCKEAN COUNTY                               42083.0   \n",
       "54789              LOS ANGELES COUNTY                                6037.0   \n",
       "14799                   HARRIS COUNTY                               48201.0   \n",
       "14355                WYANDOTTE COUNTY                               20209.0   \n",
       "57857                        Oklahoma                               40109.0   \n",
       "\n",
       "       V_GHG_EMITTER_FACILITIES.FACILITY_ID  \\\n",
       "43305                             1005312.0   \n",
       "32249                             1000780.0   \n",
       "33398                             1007545.0   \n",
       "35383                             1005271.0   \n",
       "71480                             1002843.0   \n",
       "50787                             1000193.0   \n",
       "54789                             1011449.0   \n",
       "14799                             1008396.0   \n",
       "14355                             1005745.0   \n",
       "57857                             1000859.0   \n",
       "\n",
       "       V_GHG_EMITTER_FACILITIES.LATITUDE  V_GHG_EMITTER_FACILITIES.LONGITUDE  \\\n",
       "43305                          35.166770                          -89.958930   \n",
       "32249                          41.204400                          -73.968900   \n",
       "33398                          35.631540                          -92.392770   \n",
       "35383                          38.735550                          -84.408180   \n",
       "71480                          40.565216                          -74.193377   \n",
       "50787                          41.859383                          -78.445170   \n",
       "54789                          34.036441                         -117.902294   \n",
       "14799                          29.758940                          -95.370960   \n",
       "14355                          39.142019                          -94.615315   \n",
       "57857                          35.508900                          -97.178900   \n",
       "\n",
       "       V_GHG_EMITTER_FACILITIES.PRIMARY_NAICS_CODE  ...  \\\n",
       "43305                                     325199.0  ...   \n",
       "32249                                     221112.0  ...   \n",
       "33398                                     211111.0  ...   \n",
       "35383                                     562212.0  ...   \n",
       "71480                                     562212.0  ...   \n",
       "50787                                     324199.0  ...   \n",
       "54789                                     562212.0  ...   \n",
       "14799                                     211111.0  ...   \n",
       "14355                                     327993.0  ...   \n",
       "57857                                     221112.0  ...   \n",
       "\n",
       "      V_GHG_EMITTER_FACILITIES.STATE_NAME V_GHG_EMITTER_FACILITIES.YEAR  \\\n",
       "43305                           TENNESSEE                        2011.0   \n",
       "32249                            NEW YORK                        2010.0   \n",
       "33398                            ARKANSAS                        2015.0   \n",
       "35383                            KENTUCKY                        2019.0   \n",
       "71480                            NEW YORK                        2019.0   \n",
       "50787                        PENNSYLVANIA                        2019.0   \n",
       "54789                          CALIFORNIA                        2017.0   \n",
       "14799                               TEXAS                        2015.0   \n",
       "14355                              KANSAS                        2015.0   \n",
       "57857                            OKLAHOMA                        2019.0   \n",
       "\n",
       "       V_GHG_EMITTER_FACILITIES.ZIP  \\\n",
       "43305                       38108.0   \n",
       "32249                       10993.0   \n",
       "33398                       72031.0   \n",
       "35383                       41006.0   \n",
       "71480                       10312.0   \n",
       "50787                       16749.0   \n",
       "54789                       91792.0   \n",
       "14799                       77002.0   \n",
       "14355                       66115.0   \n",
       "57857                       73045.0   \n",
       "\n",
       "           V_GHG_EMITTER_FACILITIES.FACILITY_NAME  \\\n",
       "43305                            PMC BIOGENIX INC   \n",
       "32249                  Bowline Generating Station   \n",
       "33398                  Hilltop Compressor Station   \n",
       "35383                      RUMPKE OF KENTUCKY INC   \n",
       "71480                        Fresh Kills Landfill   \n",
       "50787                     INTERNATIONAL WAXES INC   \n",
       "54789                             BKK Corporation   \n",
       "14799  EOG Resources  Inc. 515 Powder River basin   \n",
       "14355                           CERTAINTEED CORP.   \n",
       "57857                              Horseshoe Lake   \n",
       "\n",
       "      V_GHG_EMITTER_FACILITIES.SECONDARY_NAICS_CODE  \\\n",
       "43305                                           NaN   \n",
       "32249                                           NaN   \n",
       "33398                                           NaN   \n",
       "35383                                           NaN   \n",
       "71480                                           NaN   \n",
       "50787                                           NaN   \n",
       "54789                                           NaN   \n",
       "14799                                           NaN   \n",
       "14355                                           NaN   \n",
       "57857                                           NaN   \n",
       "\n",
       "       V_GHG_EMITTER_FACILITIES.ADDITIONAL_NAICS_CODES  \\\n",
       "43305                             325611 325613 311999   \n",
       "32249                                              NaN   \n",
       "33398                                              NaN   \n",
       "35383                                              NaN   \n",
       "71480                                              NaN   \n",
       "50787                                              NaN   \n",
       "54789                                              NaN   \n",
       "14799                                              NaN   \n",
       "14355                                              NaN   \n",
       "57857                                              NaN   \n",
       "\n",
       "      V_GHG_EMITTER_FACILITIES.COGENERATION_UNIT_EMISS_IND  \\\n",
       "43305                                                  N     \n",
       "32249                                                  N     \n",
       "33398                                                  N     \n",
       "35383                                                  N     \n",
       "71480                                                  N     \n",
       "50787                                                  N     \n",
       "54789                                                  N     \n",
       "14799                                                  N     \n",
       "14355                                                  N     \n",
       "57857                                                  N     \n",
       "\n",
       "      V_GHG_EMITTER_FACILITIES.EPA_VERIFIED  \\\n",
       "43305                                   NaN   \n",
       "32249                                   NaN   \n",
       "33398                                   NaN   \n",
       "35383                                   NaN   \n",
       "71480                                   NaN   \n",
       "50787                                   NaN   \n",
       "54789                                   NaN   \n",
       "14799                                   NaN   \n",
       "14355                                   NaN   \n",
       "57857                                   NaN   \n",
       "\n",
       "      V_GHG_EMITTER_FACILITIES.PARENT_COMPANY  \\\n",
       "43305                        PMC GROUP (100%)   \n",
       "32249               GENON ENERGY  INC  (100%)   \n",
       "33398                     BHP BILLITON (100%)   \n",
       "35383      RUMPKE CONSOLIDATED COS INC (100%)   \n",
       "71480                 CITY OF NEW YORK (100%)   \n",
       "50787      THE INTERNATIONAL GROUP INC (100%)   \n",
       "54789                         BKK CORP (100%)   \n",
       "14799                EOG RESOURCES INC (100%)   \n",
       "14355                SAINT-GOBAIN CORP (100%)   \n",
       "57857                  OGE ENERGY CORP (100%)   \n",
       "\n",
       "      V_GHG_EMITTER_FACILITIES.PLANT_CODE_INDICATOR  \n",
       "43305                                           NaN  \n",
       "32249                                           NaN  \n",
       "33398                                             N  \n",
       "35383                                             N  \n",
       "71480                                             N  \n",
       "50787                                             N  \n",
       "54789                                             N  \n",
       "14799                                             N  \n",
       "14355                                             N  \n",
       "57857                                             Y  \n",
       "\n",
       "[10 rows x 21 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(gh_gas_facil_df.columns)\n",
    "display(gh_gas_facil_df.sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### gh gas type data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['V_GHG_EMITTER_GAS.ADDRESS1', 'V_GHG_EMITTER_GAS.ADDRESS2',\n",
      "       'V_GHG_EMITTER_GAS.CITY', 'V_GHG_EMITTER_GAS.CO2E_EMISSION',\n",
      "       'V_GHG_EMITTER_GAS.COUNTY', 'V_GHG_EMITTER_GAS.FACILITY_ID',\n",
      "       'V_GHG_EMITTER_GAS.GAS_CODE', 'V_GHG_EMITTER_GAS.GAS_NAME',\n",
      "       'V_GHG_EMITTER_GAS.LATITUDE', 'V_GHG_EMITTER_GAS.LONGITUDE',\n",
      "       'V_GHG_EMITTER_GAS.STATE', 'V_GHG_EMITTER_GAS.STATE_NAME',\n",
      "       'V_GHG_EMITTER_GAS.YEAR', 'V_GHG_EMITTER_GAS.ZIP',\n",
      "       'V_GHG_EMITTER_GAS.FACILITY_NAME', 'V_GHG_EMITTER_GAS.COUNTY_FIPS'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\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>V_GHG_EMITTER_GAS.ADDRESS1</th>\n",
       "      <th>V_GHG_EMITTER_GAS.ADDRESS2</th>\n",
       "      <th>V_GHG_EMITTER_GAS.CITY</th>\n",
       "      <th>V_GHG_EMITTER_GAS.CO2E_EMISSION</th>\n",
       "      <th>V_GHG_EMITTER_GAS.COUNTY</th>\n",
       "      <th>V_GHG_EMITTER_GAS.FACILITY_ID</th>\n",
       "      <th>V_GHG_EMITTER_GAS.GAS_CODE</th>\n",
       "      <th>V_GHG_EMITTER_GAS.GAS_NAME</th>\n",
       "      <th>V_GHG_EMITTER_GAS.LATITUDE</th>\n",
       "      <th>V_GHG_EMITTER_GAS.LONGITUDE</th>\n",
       "      <th>V_GHG_EMITTER_GAS.STATE</th>\n",
       "      <th>V_GHG_EMITTER_GAS.STATE_NAME</th>\n",
       "      <th>V_GHG_EMITTER_GAS.YEAR</th>\n",
       "      <th>V_GHG_EMITTER_GAS.ZIP</th>\n",
       "      <th>V_GHG_EMITTER_GAS.FACILITY_NAME</th>\n",
       "      <th>V_GHG_EMITTER_GAS.COUNTY_FIPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66102</th>\n",
       "      <td>8901 WISCONSIN AVENUE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BETHESDA</td>\n",
       "      <td>16.092</td>\n",
       "      <td>MONTGOMERY COUNTY</td>\n",
       "      <td>1000756</td>\n",
       "      <td>N2O</td>\n",
       "      <td>Nitrous Oxide</td>\n",
       "      <td>39.000798</td>\n",
       "      <td>-77.094409</td>\n",
       "      <td>MD</td>\n",
       "      <td>MARYLAND</td>\n",
       "      <td>2012</td>\n",
       "      <td>20889</td>\n",
       "      <td>Naval Support Activity Bethesda</td>\n",
       "      <td>24031.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80078</th>\n",
       "      <td>4000 TULIP TREE DR</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PRINCETON</td>\n",
       "      <td>28.000</td>\n",
       "      <td>GIBSON COUNTY</td>\n",
       "      <td>1001828</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>38.292700</td>\n",
       "      <td>-87.564500</td>\n",
       "      <td>IN</td>\n",
       "      <td>INDIANA</td>\n",
       "      <td>2013</td>\n",
       "      <td>47670</td>\n",
       "      <td>TOYOTA MOTOR MFG IN INC</td>\n",
       "      <td>18051.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105761</th>\n",
       "      <td>38-54 Vernon Blvd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Long Island City</td>\n",
       "      <td>35.250</td>\n",
       "      <td>Queens</td>\n",
       "      <td>1000567</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>40.760000</td>\n",
       "      <td>-73.750000</td>\n",
       "      <td>NY</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>2012</td>\n",
       "      <td>11101</td>\n",
       "      <td>Ravenswood Steam Plant</td>\n",
       "      <td>36081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11653</th>\n",
       "      <td>6 MI SW OF</td>\n",
       "      <td>NaN</td>\n",
       "      <td>PORT FOURCHON</td>\n",
       "      <td>24896.300</td>\n",
       "      <td>LAFOURCHE</td>\n",
       "      <td>1003000</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>29.069510</td>\n",
       "      <td>-90.323700</td>\n",
       "      <td>LA</td>\n",
       "      <td>LOUISIANA</td>\n",
       "      <td>2011</td>\n",
       "      <td>70357</td>\n",
       "      <td>TIMBALIER BAY PRODUCTION COMPLEX</td>\n",
       "      <td>22057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86386</th>\n",
       "      <td>1905 KENTUCKY HIGHWAY 3249</td>\n",
       "      <td>NaN</td>\n",
       "      <td>STANFORD</td>\n",
       "      <td>5.200</td>\n",
       "      <td>LINCOLN</td>\n",
       "      <td>1003693</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>37.468889</td>\n",
       "      <td>-84.675556</td>\n",
       "      <td>KY</td>\n",
       "      <td>KENTUCKY</td>\n",
       "      <td>2011</td>\n",
       "      <td>40484</td>\n",
       "      <td>TRI-K LANDFILL INC</td>\n",
       "      <td>21137.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>189610</th>\n",
       "      <td>BUILDING 82 MOWRY ROAD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GAINESVILLE</td>\n",
       "      <td>90.592</td>\n",
       "      <td>Alachua</td>\n",
       "      <td>1000402</td>\n",
       "      <td>N2O</td>\n",
       "      <td>Nitrous Oxide</td>\n",
       "      <td>29.639700</td>\n",
       "      <td>-82.348600</td>\n",
       "      <td>FL</td>\n",
       "      <td>FLORIDA</td>\n",
       "      <td>2011</td>\n",
       "      <td>32611</td>\n",
       "      <td>University of Florida Cogeneration Plant</td>\n",
       "      <td>12001.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146608</th>\n",
       "      <td>440 NORTH 9TH STREET</td>\n",
       "      <td>NaN</td>\n",
       "      <td>LAWRENCE</td>\n",
       "      <td>50501.000</td>\n",
       "      <td>DOUGLAS COUNTY</td>\n",
       "      <td>1003370</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>38.979930</td>\n",
       "      <td>-95.214430</td>\n",
       "      <td>KS</td>\n",
       "      <td>KANSAS</td>\n",
       "      <td>2015</td>\n",
       "      <td>66044</td>\n",
       "      <td>ICL</td>\n",
       "      <td>20045.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>211861</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Offshore</td>\n",
       "      <td>11532.750</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1011643</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>28.971577</td>\n",
       "      <td>-89.514681</td>\n",
       "      <td>LA</td>\n",
       "      <td>LOUISIANA</td>\n",
       "      <td>2018</td>\n",
       "      <td>0</td>\n",
       "      <td>WD 79 A &amp; F (Complex ID # 20491)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65910</th>\n",
       "      <td>1230 Independence Parkway S</td>\n",
       "      <td>NaN</td>\n",
       "      <td>La Porte</td>\n",
       "      <td>4582.200</td>\n",
       "      <td>HARRIS COUNTY</td>\n",
       "      <td>1004227</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>29.718368</td>\n",
       "      <td>-95.085830</td>\n",
       "      <td>TX</td>\n",
       "      <td>TEXAS</td>\n",
       "      <td>2013</td>\n",
       "      <td>77571</td>\n",
       "      <td>INEOS POLYPROPYLENE</td>\n",
       "      <td>48201.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197885</th>\n",
       "      <td>6100 N. Western Avenue</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Oklahoma City</td>\n",
       "      <td>10.430</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>1008529</td>\n",
       "      <td>N2O</td>\n",
       "      <td>Nitrous Oxide</td>\n",
       "      <td>35.533760</td>\n",
       "      <td>-97.529760</td>\n",
       "      <td>OK</td>\n",
       "      <td>OKLAHOMA</td>\n",
       "      <td>2011</td>\n",
       "      <td>73118</td>\n",
       "      <td>Chesapeake Operating LLC  350 - South Oklahoma...</td>\n",
       "      <td>40109.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         V_GHG_EMITTER_GAS.ADDRESS1  V_GHG_EMITTER_GAS.ADDRESS2  \\\n",
       "66102         8901 WISCONSIN AVENUE                         NaN   \n",
       "80078            4000 TULIP TREE DR                         NaN   \n",
       "105761            38-54 Vernon Blvd                         NaN   \n",
       "11653                    6 MI SW OF                         NaN   \n",
       "86386    1905 KENTUCKY HIGHWAY 3249                         NaN   \n",
       "189610       BUILDING 82 MOWRY ROAD                         NaN   \n",
       "146608         440 NORTH 9TH STREET                         NaN   \n",
       "211861                          NaN                         NaN   \n",
       "65910   1230 Independence Parkway S                         NaN   \n",
       "197885       6100 N. Western Avenue                         NaN   \n",
       "\n",
       "       V_GHG_EMITTER_GAS.CITY  V_GHG_EMITTER_GAS.CO2E_EMISSION  \\\n",
       "66102                BETHESDA                           16.092   \n",
       "80078               PRINCETON                           28.000   \n",
       "105761       Long Island City                           35.250   \n",
       "11653           PORT FOURCHON                        24896.300   \n",
       "86386                STANFORD                            5.200   \n",
       "189610            GAINESVILLE                           90.592   \n",
       "146608               LAWRENCE                        50501.000   \n",
       "211861               Offshore                        11532.750   \n",
       "65910                La Porte                         4582.200   \n",
       "197885          Oklahoma City                           10.430   \n",
       "\n",
       "       V_GHG_EMITTER_GAS.COUNTY  V_GHG_EMITTER_GAS.FACILITY_ID  \\\n",
       "66102         MONTGOMERY COUNTY                        1000756   \n",
       "80078             GIBSON COUNTY                        1001828   \n",
       "105761                   Queens                        1000567   \n",
       "11653                 LAFOURCHE                        1003000   \n",
       "86386                   LINCOLN                        1003693   \n",
       "189610                  Alachua                        1000402   \n",
       "146608           DOUGLAS COUNTY                        1003370   \n",
       "211861                      NaN                        1011643   \n",
       "65910             HARRIS COUNTY                        1004227   \n",
       "197885                 Oklahoma                        1008529   \n",
       "\n",
       "       V_GHG_EMITTER_GAS.GAS_CODE V_GHG_EMITTER_GAS.GAS_NAME  \\\n",
       "66102                         N2O              Nitrous Oxide   \n",
       "80078                         CH4                    Methane   \n",
       "105761                        CH4                    Methane   \n",
       "11653                         CO2             Carbon Dioxide   \n",
       "86386                         CO2             Carbon Dioxide   \n",
       "189610                        N2O              Nitrous Oxide   \n",
       "146608                        CO2             Carbon Dioxide   \n",
       "211861                        CH4                    Methane   \n",
       "65910                         CO2             Carbon Dioxide   \n",
       "197885                        N2O              Nitrous Oxide   \n",
       "\n",
       "        V_GHG_EMITTER_GAS.LATITUDE  V_GHG_EMITTER_GAS.LONGITUDE  \\\n",
       "66102                    39.000798                   -77.094409   \n",
       "80078                    38.292700                   -87.564500   \n",
       "105761                   40.760000                   -73.750000   \n",
       "11653                    29.069510                   -90.323700   \n",
       "86386                    37.468889                   -84.675556   \n",
       "189610                   29.639700                   -82.348600   \n",
       "146608                   38.979930                   -95.214430   \n",
       "211861                   28.971577                   -89.514681   \n",
       "65910                    29.718368                   -95.085830   \n",
       "197885                   35.533760                   -97.529760   \n",
       "\n",
       "       V_GHG_EMITTER_GAS.STATE V_GHG_EMITTER_GAS.STATE_NAME  \\\n",
       "66102                       MD                     MARYLAND   \n",
       "80078                       IN                      INDIANA   \n",
       "105761                      NY                     NEW YORK   \n",
       "11653                       LA                    LOUISIANA   \n",
       "86386                       KY                     KENTUCKY   \n",
       "189610                      FL                      FLORIDA   \n",
       "146608                      KS                       KANSAS   \n",
       "211861                      LA                    LOUISIANA   \n",
       "65910                       TX                        TEXAS   \n",
       "197885                      OK                     OKLAHOMA   \n",
       "\n",
       "        V_GHG_EMITTER_GAS.YEAR  V_GHG_EMITTER_GAS.ZIP  \\\n",
       "66102                     2012                  20889   \n",
       "80078                     2013                  47670   \n",
       "105761                    2012                  11101   \n",
       "11653                     2011                  70357   \n",
       "86386                     2011                  40484   \n",
       "189610                    2011                  32611   \n",
       "146608                    2015                  66044   \n",
       "211861                    2018                      0   \n",
       "65910                     2013                  77571   \n",
       "197885                    2011                  73118   \n",
       "\n",
       "                          V_GHG_EMITTER_GAS.FACILITY_NAME  \\\n",
       "66102                     Naval Support Activity Bethesda   \n",
       "80078                             TOYOTA MOTOR MFG IN INC   \n",
       "105761                             Ravenswood Steam Plant   \n",
       "11653                    TIMBALIER BAY PRODUCTION COMPLEX   \n",
       "86386                                  TRI-K LANDFILL INC   \n",
       "189610           University of Florida Cogeneration Plant   \n",
       "146608                                                ICL   \n",
       "211861                   WD 79 A & F (Complex ID # 20491)   \n",
       "65910                                 INEOS POLYPROPYLENE   \n",
       "197885  Chesapeake Operating LLC  350 - South Oklahoma...   \n",
       "\n",
       "        V_GHG_EMITTER_GAS.COUNTY_FIPS  \n",
       "66102                         24031.0  \n",
       "80078                         18051.0  \n",
       "105761                        36081.0  \n",
       "11653                         22057.0  \n",
       "86386                         21137.0  \n",
       "189610                        12001.0  \n",
       "146608                        20045.0  \n",
       "211861                            NaN  \n",
       "65910                         48201.0  \n",
       "197885                        40109.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(gh_gas_type_df.columns)\n",
    "display(gh_gas_type_df.sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### weather 2020 data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Unnamed: 0', 'Station', 'Date', 'TAVG', 'Latitude', 'Longitude',\n",
      "       'Elevation', 'PRCP'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>Unnamed: 0</th>\n",
       "      <th>Station</th>\n",
       "      <th>Date</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>PRCP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>424025</th>\n",
       "      <td>424025</td>\n",
       "      <td>CI000085406</td>\n",
       "      <td>2020-04-26</td>\n",
       "      <td>207.0</td>\n",
       "      <td>-18.3500</td>\n",
       "      <td>-70.3330</td>\n",
       "      <td>58.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>635668</th>\n",
       "      <td>635668</td>\n",
       "      <td>CA00401HP5R</td>\n",
       "      <td>2020-06-24</td>\n",
       "      <td>230.0</td>\n",
       "      <td>49.7000</td>\n",
       "      <td>-103.8000</td>\n",
       "      <td>589.0</td>\n",
       "      <td>117.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240432</th>\n",
       "      <td>240432</td>\n",
       "      <td>CHM00054208</td>\n",
       "      <td>2020-03-06</td>\n",
       "      <td>-33.0</td>\n",
       "      <td>42.1830</td>\n",
       "      <td>116.4670</td>\n",
       "      <td>1247.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>255105</th>\n",
       "      <td>255105</td>\n",
       "      <td>CA007098603</td>\n",
       "      <td>2020-03-10</td>\n",
       "      <td>-102.0</td>\n",
       "      <td>48.0500</td>\n",
       "      <td>-77.7833</td>\n",
       "      <td>336.0</td>\n",
       "      <td>70.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>869673</th>\n",
       "      <td>869673</td>\n",
       "      <td>CA008402644</td>\n",
       "      <td>2020-08-28</td>\n",
       "      <td>123.0</td>\n",
       "      <td>49.6833</td>\n",
       "      <td>-56.0833</td>\n",
       "      <td>48.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>454683</th>\n",
       "      <td>454683</td>\n",
       "      <td>USS0008L02S</td>\n",
       "      <td>2020-05-04</td>\n",
       "      <td>75.0</td>\n",
       "      <td>38.4200</td>\n",
       "      <td>-108.3800</td>\n",
       "      <td>2865.1</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>848367</th>\n",
       "      <td>848367</td>\n",
       "      <td>IN009120400</td>\n",
       "      <td>2020-08-22</td>\n",
       "      <td>259.0</td>\n",
       "      <td>14.2830</td>\n",
       "      <td>74.4500</td>\n",
       "      <td>9.0</td>\n",
       "      <td>386.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>685236</th>\n",
       "      <td>685236</td>\n",
       "      <td>USS0019D03S</td>\n",
       "      <td>2020-07-07</td>\n",
       "      <td>90.0</td>\n",
       "      <td>45.1100</td>\n",
       "      <td>-119.5000</td>\n",
       "      <td>1569.7</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>910938</th>\n",
       "      <td>910938</td>\n",
       "      <td>USS0010J17S</td>\n",
       "      <td>2020-09-08</td>\n",
       "      <td>-25.0</td>\n",
       "      <td>40.8900</td>\n",
       "      <td>-110.8300</td>\n",
       "      <td>2675.2</td>\n",
       "      <td>76.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>882051</th>\n",
       "      <td>882051</td>\n",
       "      <td>SZE00116090</td>\n",
       "      <td>2020-08-31</td>\n",
       "      <td>147.0</td>\n",
       "      <td>46.8167</td>\n",
       "      <td>6.9500</td>\n",
       "      <td>490.0</td>\n",
       "      <td>107.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Unnamed: 0      Station        Date   TAVG  Latitude  Longitude  \\\n",
       "424025      424025  CI000085406  2020-04-26  207.0  -18.3500   -70.3330   \n",
       "635668      635668  CA00401HP5R  2020-06-24  230.0   49.7000  -103.8000   \n",
       "240432      240432  CHM00054208  2020-03-06  -33.0   42.1830   116.4670   \n",
       "255105      255105  CA007098603  2020-03-10 -102.0   48.0500   -77.7833   \n",
       "869673      869673  CA008402644  2020-08-28  123.0   49.6833   -56.0833   \n",
       "454683      454683  USS0008L02S  2020-05-04   75.0   38.4200  -108.3800   \n",
       "848367      848367  IN009120400  2020-08-22  259.0   14.2830    74.4500   \n",
       "685236      685236  USS0019D03S  2020-07-07   90.0   45.1100  -119.5000   \n",
       "910938      910938  USS0010J17S  2020-09-08  -25.0   40.8900  -110.8300   \n",
       "882051      882051  SZE00116090  2020-08-31  147.0   46.8167     6.9500   \n",
       "\n",
       "        Elevation   PRCP  \n",
       "424025       58.0    0.0  \n",
       "635668      589.0  117.0  \n",
       "240432     1247.0    0.0  \n",
       "255105      336.0   70.0  \n",
       "869673       48.0    0.0  \n",
       "454683     2865.1    0.0  \n",
       "848367        9.0  386.0  \n",
       "685236     1569.7    0.0  \n",
       "910938     2675.2   76.0  \n",
       "882051      490.0  107.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(weather_2020_df.columns)\n",
    "display(weather_2020_df.sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### wind 2020 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['State Code', 'County Code', 'Site Num', 'Parameter Code', 'POC',\n",
      "       'Latitude', 'Longitude', 'Datum', 'Parameter Name', 'Sample Duration',\n",
      "       'Pollutant Standard', 'Date Local', 'Units of Measure', 'Event Type',\n",
      "       'Observation Count', 'Observation Percent', 'Arithmetic Mean',\n",
      "       '1st Max Value', '1st Max Hour', 'AQI', 'Method Code', 'Method Name',\n",
      "       'Local Site Name', 'Address', 'State Name', 'County Name', 'City Name',\n",
      "       'CBSA Name', 'Date of Last Change'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Site Num</th>\n",
       "      <th>Parameter Code</th>\n",
       "      <th>POC</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Datum</th>\n",
       "      <th>Parameter Name</th>\n",
       "      <th>Sample Duration</th>\n",
       "      <th>...</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Method Code</th>\n",
       "      <th>Method Name</th>\n",
       "      <th>Local Site Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>CBSA Name</th>\n",
       "      <th>Date of Last Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196576</th>\n",
       "      <td>18</td>\n",
       "      <td>89</td>\n",
       "      <td>34</td>\n",
       "      <td>61104</td>\n",
       "      <td>1</td>\n",
       "      <td>41.653501</td>\n",
       "      <td>-87.435561</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>Wind Direction - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>68</td>\n",
       "      <td>Instrumental - RM Young Ultrasonic Anemometer ...</td>\n",
       "      <td>East Chicago-Marina</td>\n",
       "      <td>East Chicago- Marina/ 3301 Aldis St.</td>\n",
       "      <td>Indiana</td>\n",
       "      <td>Lake</td>\n",
       "      <td>East Chicago</td>\n",
       "      <td>Chicago-Naperville-Elgin, IL-IN-WI</td>\n",
       "      <td>2021-02-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240029</th>\n",
       "      <td>24</td>\n",
       "      <td>31</td>\n",
       "      <td>3001</td>\n",
       "      <td>61104</td>\n",
       "      <td>1</td>\n",
       "      <td>39.114313</td>\n",
       "      <td>-77.106876</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Wind Direction - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24</td>\n",
       "      <td>INSTRUMENTAL - VECTOR SUMMATION LEVEL 4</td>\n",
       "      <td>Rockville</td>\n",
       "      <td>Lathrop E. Smith Environmental Education Cente...</td>\n",
       "      <td>Maryland</td>\n",
       "      <td>Montgomery</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>Washington-Arlington-Alexandria, DC-VA-MD-WV</td>\n",
       "      <td>2021-01-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>445170</th>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>7001</td>\n",
       "      <td>61104</td>\n",
       "      <td>1</td>\n",
       "      <td>41.945874</td>\n",
       "      <td>-112.233973</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Wind Direction - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>INSTRUMENTAL - VECTOR SUMMATION</td>\n",
       "      <td>WASHAKIE</td>\n",
       "      <td>8600 WEST 24000 NORTH PORTAGE, UTAH</td>\n",
       "      <td>Utah</td>\n",
       "      <td>Box Elder</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>Ogden-Clearfield, UT</td>\n",
       "      <td>2021-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>223193</th>\n",
       "      <td>21</td>\n",
       "      <td>111</td>\n",
       "      <td>1041</td>\n",
       "      <td>61103</td>\n",
       "      <td>1</td>\n",
       "      <td>38.231580</td>\n",
       "      <td>-85.826780</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Wind Speed - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>129</td>\n",
       "      <td>Ultrasonic Wind Sensor MD1425A - Vector Averag...</td>\n",
       "      <td>Algonquin Parkway</td>\n",
       "      <td>4201 ALGONQUIN PKWY</td>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Louisville</td>\n",
       "      <td>Louisville/Jefferson County, KY-IN</td>\n",
       "      <td>2021-04-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>259517</th>\n",
       "      <td>26</td>\n",
       "      <td>125</td>\n",
       "      <td>12</td>\n",
       "      <td>61104</td>\n",
       "      <td>1</td>\n",
       "      <td>42.679172</td>\n",
       "      <td>-83.130141</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Wind Direction - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>INSTRUMENTAL - VECTOR SUMMATION</td>\n",
       "      <td>Rochester Hills</td>\n",
       "      <td>ROCHESTER FIRE STATION, 277 E 2ND STREET</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Oakland</td>\n",
       "      <td>Rochester</td>\n",
       "      <td>Detroit-Warren-Dearborn, MI</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>266478</th>\n",
       "      <td>26</td>\n",
       "      <td>163</td>\n",
       "      <td>26</td>\n",
       "      <td>61103</td>\n",
       "      <td>1</td>\n",
       "      <td>42.359175</td>\n",
       "      <td>-83.255146</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Wind Speed - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>INSTRUMENTAL - VECTOR SUMMATION</td>\n",
       "      <td>NORTH OF JOY RD./WEST OF SPINOZA/NEAR DETROIT ...</td>\n",
       "      <td>22000 JOY ROAD (DET-JOY RD)</td>\n",
       "      <td>Michigan</td>\n",
       "      <td>Wayne</td>\n",
       "      <td>Detroit</td>\n",
       "      <td>Detroit-Warren-Dearborn, MI</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144958</th>\n",
       "      <td>8</td>\n",
       "      <td>67</td>\n",
       "      <td>1004</td>\n",
       "      <td>61103</td>\n",
       "      <td>1</td>\n",
       "      <td>37.303890</td>\n",
       "      <td>-107.484167</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Wind Speed - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>INSTRUMENTAL - VECTOR SUMMATION</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Weminuche Wilderness Area - Shamrock Station</td>\n",
       "      <td>Colorado</td>\n",
       "      <td>La Plata</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>Durango, CO</td>\n",
       "      <td>2021-03-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129534</th>\n",
       "      <td>6</td>\n",
       "      <td>111</td>\n",
       "      <td>7</td>\n",
       "      <td>61104</td>\n",
       "      <td>1</td>\n",
       "      <td>34.210169</td>\n",
       "      <td>-118.870509</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>Wind Direction - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>INSTRUMENTAL - VECTOR SUMMATION</td>\n",
       "      <td>Thousand Oaks</td>\n",
       "      <td>2323 Moorpark Road, Thousand Oaks, CA  91360</td>\n",
       "      <td>California</td>\n",
       "      <td>Ventura</td>\n",
       "      <td>Thousand Oaks</td>\n",
       "      <td>Oxnard-Thousand Oaks-Ventura, CA</td>\n",
       "      <td>2021-02-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76243</th>\n",
       "      <td>6</td>\n",
       "      <td>43</td>\n",
       "      <td>3</td>\n",
       "      <td>61104</td>\n",
       "      <td>1</td>\n",
       "      <td>37.713250</td>\n",
       "      <td>-119.706200</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Wind Direction - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>INSTRUMENTAL - VECTOR SUMMATION</td>\n",
       "      <td>Yosemite NP - Turtleback Dome</td>\n",
       "      <td>TURTLEBACK DOME, YOSEMITE NATL' PK 95389</td>\n",
       "      <td>California</td>\n",
       "      <td>Mariposa</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-02-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42268</th>\n",
       "      <td>6</td>\n",
       "      <td>19</td>\n",
       "      <td>4001</td>\n",
       "      <td>61103</td>\n",
       "      <td>1</td>\n",
       "      <td>36.597442</td>\n",
       "      <td>-119.503659</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>Wind Speed - Resultant</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20</td>\n",
       "      <td>INSTRUMENTAL - VECTOR SUMMATION</td>\n",
       "      <td>Parlier</td>\n",
       "      <td>9240 S. RIVERBEND, PARLIER 93648</td>\n",
       "      <td>California</td>\n",
       "      <td>Fresno</td>\n",
       "      <td>Parlier</td>\n",
       "      <td>Fresno, CA</td>\n",
       "      <td>2021-04-28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Code  County Code  Site Num  Parameter Code  POC   Latitude  \\\n",
       "196576          18           89        34           61104    1  41.653501   \n",
       "240029          24           31      3001           61104    1  39.114313   \n",
       "445170          49            3      7001           61104    1  41.945874   \n",
       "223193          21          111      1041           61103    1  38.231580   \n",
       "259517          26          125        12           61104    1  42.679172   \n",
       "266478          26          163        26           61103    1  42.359175   \n",
       "144958           8           67      1004           61103    1  37.303890   \n",
       "129534           6          111         7           61104    1  34.210169   \n",
       "76243            6           43         3           61104    1  37.713250   \n",
       "42268            6           19      4001           61103    1  36.597442   \n",
       "\n",
       "         Longitude  Datum              Parameter Name Sample Duration  ...  \\\n",
       "196576  -87.435561  NAD83  Wind Direction - Resultant          1 HOUR  ...   \n",
       "240029  -77.106876  WGS84  Wind Direction - Resultant          1 HOUR  ...   \n",
       "445170 -112.233973  WGS84  Wind Direction - Resultant          1 HOUR  ...   \n",
       "223193  -85.826780  WGS84      Wind Speed - Resultant          1 HOUR  ...   \n",
       "259517  -83.130141  WGS84  Wind Direction - Resultant          1 HOUR  ...   \n",
       "266478  -83.255146  WGS84      Wind Speed - Resultant          1 HOUR  ...   \n",
       "144958 -107.484167  WGS84      Wind Speed - Resultant          1 HOUR  ...   \n",
       "129534 -118.870509  NAD83  Wind Direction - Resultant          1 HOUR  ...   \n",
       "76243  -119.706200  WGS84  Wind Direction - Resultant          1 HOUR  ...   \n",
       "42268  -119.503659  NAD83      Wind Speed - Resultant          1 HOUR  ...   \n",
       "\n",
       "        AQI Method Code                                        Method Name  \\\n",
       "196576  NaN          68  Instrumental - RM Young Ultrasonic Anemometer ...   \n",
       "240029  NaN          24            INSTRUMENTAL - VECTOR SUMMATION LEVEL 4   \n",
       "445170  NaN          20                    INSTRUMENTAL - VECTOR SUMMATION   \n",
       "223193  NaN         129  Ultrasonic Wind Sensor MD1425A - Vector Averag...   \n",
       "259517  NaN          20                    INSTRUMENTAL - VECTOR SUMMATION   \n",
       "266478  NaN          20                    INSTRUMENTAL - VECTOR SUMMATION   \n",
       "144958  NaN          20                    INSTRUMENTAL - VECTOR SUMMATION   \n",
       "129534  NaN          20                    INSTRUMENTAL - VECTOR SUMMATION   \n",
       "76243   NaN          20                    INSTRUMENTAL - VECTOR SUMMATION   \n",
       "42268   NaN          20                    INSTRUMENTAL - VECTOR SUMMATION   \n",
       "\n",
       "                                          Local Site Name  \\\n",
       "196576                                East Chicago-Marina   \n",
       "240029                                          Rockville   \n",
       "445170                                           WASHAKIE   \n",
       "223193                                  Algonquin Parkway   \n",
       "259517                                    Rochester Hills   \n",
       "266478  NORTH OF JOY RD./WEST OF SPINOZA/NEAR DETROIT ...   \n",
       "144958                                                NaN   \n",
       "129534                                      Thousand Oaks   \n",
       "76243                       Yosemite NP - Turtleback Dome   \n",
       "42268                                             Parlier   \n",
       "\n",
       "                                                  Address  State Name  \\\n",
       "196576               East Chicago- Marina/ 3301 Aldis St.     Indiana   \n",
       "240029  Lathrop E. Smith Environmental Education Cente...    Maryland   \n",
       "445170                8600 WEST 24000 NORTH PORTAGE, UTAH        Utah   \n",
       "223193                                4201 ALGONQUIN PKWY    Kentucky   \n",
       "259517           ROCHESTER FIRE STATION, 277 E 2ND STREET    Michigan   \n",
       "266478                        22000 JOY ROAD (DET-JOY RD)    Michigan   \n",
       "144958       Weminuche Wilderness Area - Shamrock Station    Colorado   \n",
       "129534       2323 Moorpark Road, Thousand Oaks, CA  91360  California   \n",
       "76243            TURTLEBACK DOME, YOSEMITE NATL' PK 95389  California   \n",
       "42268                    9240 S. RIVERBEND, PARLIER 93648  California   \n",
       "\n",
       "        County Name      City Name  \\\n",
       "196576         Lake   East Chicago   \n",
       "240029   Montgomery  Not in a city   \n",
       "445170    Box Elder  Not in a city   \n",
       "223193    Jefferson     Louisville   \n",
       "259517      Oakland      Rochester   \n",
       "266478        Wayne        Detroit   \n",
       "144958     La Plata  Not in a city   \n",
       "129534      Ventura  Thousand Oaks   \n",
       "76243      Mariposa  Not in a city   \n",
       "42268        Fresno        Parlier   \n",
       "\n",
       "                                           CBSA Name  Date of Last Change  \n",
       "196576            Chicago-Naperville-Elgin, IL-IN-WI           2021-02-25  \n",
       "240029  Washington-Arlington-Alexandria, DC-VA-MD-WV           2021-01-15  \n",
       "445170                          Ogden-Clearfield, UT           2021-02-09  \n",
       "223193            Louisville/Jefferson County, KY-IN           2021-04-21  \n",
       "259517                   Detroit-Warren-Dearborn, MI           2021-03-01  \n",
       "266478                   Detroit-Warren-Dearborn, MI           2021-03-01  \n",
       "144958                                   Durango, CO           2021-03-01  \n",
       "129534              Oxnard-Thousand Oaks-Ventura, CA           2021-02-12  \n",
       "76243                                            NaN           2021-02-16  \n",
       "42268                                     Fresno, CA           2021-04-28  \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(wind_2020_df.columns)\n",
    "display(wind_2020_df.sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### temp 2020 data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['State Code', 'County Code', 'Site Num', 'Parameter Code', 'POC',\n",
      "       'Latitude', 'Longitude', 'Datum', 'Parameter Name', 'Sample Duration',\n",
      "       'Pollutant Standard', 'Date Local', 'Units of Measure', 'Event Type',\n",
      "       'Observation Count', 'Observation Percent', 'Arithmetic Mean',\n",
      "       '1st Max Value', '1st Max Hour', 'AQI', 'Method Code', 'Method Name',\n",
      "       'Local Site Name', 'Address', 'State Name', 'County Name', 'City Name',\n",
      "       'CBSA Name', 'Date of Last Change'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Site Num</th>\n",
       "      <th>Parameter Code</th>\n",
       "      <th>POC</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Datum</th>\n",
       "      <th>Parameter Name</th>\n",
       "      <th>Sample Duration</th>\n",
       "      <th>...</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Method Code</th>\n",
       "      <th>Method Name</th>\n",
       "      <th>Local Site Name</th>\n",
       "      <th>Address</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>CBSA Name</th>\n",
       "      <th>Date of Last Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>217323</th>\n",
       "      <td>48</td>\n",
       "      <td>233</td>\n",
       "      <td>1073</td>\n",
       "      <td>62101</td>\n",
       "      <td>1</td>\n",
       "      <td>35.676200</td>\n",
       "      <td>-101.440100</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.</td>\n",
       "      <td>Borger FM 1559</td>\n",
       "      <td>19440 FM 1559</td>\n",
       "      <td>Texas</td>\n",
       "      <td>Hutchinson</td>\n",
       "      <td>Borger</td>\n",
       "      <td>Borger, TX</td>\n",
       "      <td>2021-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>258835</th>\n",
       "      <td>56</td>\n",
       "      <td>1</td>\n",
       "      <td>802</td>\n",
       "      <td>62101</td>\n",
       "      <td>1</td>\n",
       "      <td>41.269167</td>\n",
       "      <td>-105.614722</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>INSTRUMENTAL - ELEC. OR MACH. AVG. LEVEL 1</td>\n",
       "      <td>WEST MET SITE</td>\n",
       "      <td>WEST MET SITE, Mountain Cement, Laramie</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Albany</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>Laramie, WY</td>\n",
       "      <td>2021-03-08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>161716</th>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>9</td>\n",
       "      <td>62101</td>\n",
       "      <td>2</td>\n",
       "      <td>36.742227</td>\n",
       "      <td>-107.977567</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>INSTRUMENTAL - ELEC. OR MACH. AVG. LEVEL 2</td>\n",
       "      <td>1ZB Bloomfield</td>\n",
       "      <td>2200 N 1st Street, Bloomfield, NM</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>San Juan</td>\n",
       "      <td>Bloomfield</td>\n",
       "      <td>Farmington, NM</td>\n",
       "      <td>2021-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>163581</th>\n",
       "      <td>35</td>\n",
       "      <td>45</td>\n",
       "      <td>1005</td>\n",
       "      <td>62101</td>\n",
       "      <td>2</td>\n",
       "      <td>36.796667</td>\n",
       "      <td>-108.473138</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>42</td>\n",
       "      <td>INSTRUMENTAL - ELEC. OR MACH. AVG. LEVEL 2</td>\n",
       "      <td>1H Substation</td>\n",
       "      <td>Shiprock Electrical Substation, Waterflow, NM</td>\n",
       "      <td>New Mexico</td>\n",
       "      <td>San Juan</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>Farmington, NM</td>\n",
       "      <td>2021-02-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>205552</th>\n",
       "      <td>48</td>\n",
       "      <td>141</td>\n",
       "      <td>55</td>\n",
       "      <td>62101</td>\n",
       "      <td>1</td>\n",
       "      <td>31.746775</td>\n",
       "      <td>-106.402806</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.</td>\n",
       "      <td>Ascarate Park SE</td>\n",
       "      <td>650 R E Thomason Loop</td>\n",
       "      <td>Texas</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>El Paso</td>\n",
       "      <td>El Paso, TX</td>\n",
       "      <td>2021-03-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>276996</th>\n",
       "      <td>56</td>\n",
       "      <td>35</td>\n",
       "      <td>100</td>\n",
       "      <td>62101</td>\n",
       "      <td>1</td>\n",
       "      <td>42.790700</td>\n",
       "      <td>-110.055100</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41</td>\n",
       "      <td>INSTRUMENTAL - ELEC. OR MACH. AVG. LEVEL 1</td>\n",
       "      <td>Daniel South</td>\n",
       "      <td>DANIEL SOUTH ~ 4 MILES SO OF DANIEL WYO OFF OF...</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Sublette</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-03-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>118037</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>42</td>\n",
       "      <td>62101</td>\n",
       "      <td>1</td>\n",
       "      <td>42.329500</td>\n",
       "      <td>-71.082600</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.</td>\n",
       "      <td>DUDLEY SQUARE ROXBURY</td>\n",
       "      <td>HARRISON AVE</td>\n",
       "      <td>Massachusetts</td>\n",
       "      <td>Suffolk</td>\n",
       "      <td>Boston</td>\n",
       "      <td>Boston-Cambridge-Newton, MA-NH</td>\n",
       "      <td>2021-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87910</th>\n",
       "      <td>16</td>\n",
       "      <td>59</td>\n",
       "      <td>4</td>\n",
       "      <td>62101</td>\n",
       "      <td>1</td>\n",
       "      <td>45.181897</td>\n",
       "      <td>-113.890285</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>Instrumental - Vaisala 435C RH/AT Sensor</td>\n",
       "      <td>Salmon - Charles St.</td>\n",
       "      <td>618 North Saint Charles Street</td>\n",
       "      <td>Idaho</td>\n",
       "      <td>Lemhi</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2021-03-31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99623</th>\n",
       "      <td>19</td>\n",
       "      <td>163</td>\n",
       "      <td>15</td>\n",
       "      <td>62101</td>\n",
       "      <td>1</td>\n",
       "      <td>41.530011</td>\n",
       "      <td>-90.587611</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>40</td>\n",
       "      <td>INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.</td>\n",
       "      <td>DAVENPORT, JEFFERSON SCH.</td>\n",
       "      <td>10TH ST. &amp; VINE ST. DAVENPORT</td>\n",
       "      <td>Iowa</td>\n",
       "      <td>Scott</td>\n",
       "      <td>Davenport</td>\n",
       "      <td>Davenport-Moline-Rock Island, IA-IL</td>\n",
       "      <td>2021-01-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175859</th>\n",
       "      <td>40</td>\n",
       "      <td>47</td>\n",
       "      <td>555</td>\n",
       "      <td>62101</td>\n",
       "      <td>1</td>\n",
       "      <td>36.512363</td>\n",
       "      <td>-97.845959</td>\n",
       "      <td>NAD83</td>\n",
       "      <td>Outdoor Temperature</td>\n",
       "      <td>1 HOUR</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>60</td>\n",
       "      <td>Instrumental - Vaisala 435C RH/AT Sensor</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11826 N 30th St, Kremlin, OK 73753</td>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>Garfield</td>\n",
       "      <td>Enid</td>\n",
       "      <td>Enid, OK</td>\n",
       "      <td>2021-02-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Code  County Code  Site Num  Parameter Code  POC   Latitude  \\\n",
       "217323          48          233      1073           62101    1  35.676200   \n",
       "258835          56            1       802           62101    1  41.269167   \n",
       "161716          35           45         9           62101    2  36.742227   \n",
       "163581          35           45      1005           62101    2  36.796667   \n",
       "205552          48          141        55           62101    1  31.746775   \n",
       "276996          56           35       100           62101    1  42.790700   \n",
       "118037          25           25        42           62101    1  42.329500   \n",
       "87910           16           59         4           62101    1  45.181897   \n",
       "99623           19          163        15           62101    1  41.530011   \n",
       "175859          40           47       555           62101    1  36.512363   \n",
       "\n",
       "         Longitude  Datum       Parameter Name Sample Duration  ...  AQI  \\\n",
       "217323 -101.440100  NAD83  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "258835 -105.614722  WGS84  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "161716 -107.977567  WGS84  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "163581 -108.473138  WGS84  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "205552 -106.402806  WGS84  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "276996 -110.055100  WGS84  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "118037  -71.082600  WGS84  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "87910  -113.890285  WGS84  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "99623   -90.587611  WGS84  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "175859  -97.845959  NAD83  Outdoor Temperature          1 HOUR  ...  NaN   \n",
       "\n",
       "       Method Code                                 Method Name  \\\n",
       "217323          40   INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.   \n",
       "258835          41  INSTRUMENTAL - ELEC. OR MACH. AVG. LEVEL 1   \n",
       "161716          42  INSTRUMENTAL - ELEC. OR MACH. AVG. LEVEL 2   \n",
       "163581          42  INSTRUMENTAL - ELEC. OR MACH. AVG. LEVEL 2   \n",
       "205552          40   INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.   \n",
       "276996          41  INSTRUMENTAL - ELEC. OR MACH. AVG. LEVEL 1   \n",
       "118037          40   INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.   \n",
       "87910           60    Instrumental - Vaisala 435C RH/AT Sensor   \n",
       "99623           40   INSTRUMENTAL - ELECTRONIC OR MACHINE AVG.   \n",
       "175859          60    Instrumental - Vaisala 435C RH/AT Sensor   \n",
       "\n",
       "                  Local Site Name  \\\n",
       "217323             Borger FM 1559   \n",
       "258835              WEST MET SITE   \n",
       "161716             1ZB Bloomfield   \n",
       "163581              1H Substation   \n",
       "205552           Ascarate Park SE   \n",
       "276996               Daniel South   \n",
       "118037      DUDLEY SQUARE ROXBURY   \n",
       "87910        Salmon - Charles St.   \n",
       "99623   DAVENPORT, JEFFERSON SCH.   \n",
       "175859                        NaN   \n",
       "\n",
       "                                                  Address     State Name  \\\n",
       "217323                                      19440 FM 1559          Texas   \n",
       "258835            WEST MET SITE, Mountain Cement, Laramie        Wyoming   \n",
       "161716                  2200 N 1st Street, Bloomfield, NM     New Mexico   \n",
       "163581      Shiprock Electrical Substation, Waterflow, NM     New Mexico   \n",
       "205552                              650 R E Thomason Loop          Texas   \n",
       "276996  DANIEL SOUTH ~ 4 MILES SO OF DANIEL WYO OFF OF...        Wyoming   \n",
       "118037                                       HARRISON AVE  Massachusetts   \n",
       "87910                      618 North Saint Charles Street          Idaho   \n",
       "99623                       10TH ST. & VINE ST. DAVENPORT           Iowa   \n",
       "175859                 11826 N 30th St, Kremlin, OK 73753       Oklahoma   \n",
       "\n",
       "        County Name      City Name                            CBSA Name  \\\n",
       "217323   Hutchinson         Borger                           Borger, TX   \n",
       "258835       Albany  Not in a city                          Laramie, WY   \n",
       "161716     San Juan     Bloomfield                       Farmington, NM   \n",
       "163581     San Juan  Not in a city                       Farmington, NM   \n",
       "205552      El Paso        El Paso                          El Paso, TX   \n",
       "276996     Sublette  Not in a city                                  NaN   \n",
       "118037      Suffolk         Boston       Boston-Cambridge-Newton, MA-NH   \n",
       "87910         Lemhi  Not in a city                                  NaN   \n",
       "99623         Scott      Davenport  Davenport-Moline-Rock Island, IA-IL   \n",
       "175859     Garfield           Enid                             Enid, OK   \n",
       "\n",
       "        Date of Last Change  \n",
       "217323           2021-03-19  \n",
       "258835           2021-03-08  \n",
       "161716           2021-02-04  \n",
       "163581           2021-02-04  \n",
       "205552           2021-03-19  \n",
       "276996           2021-03-03  \n",
       "118037           2021-03-25  \n",
       "87910            2021-03-31  \n",
       "99623            2021-01-12  \n",
       "175859           2021-02-10  \n",
       "\n",
       "[10 rows x 29 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(temp_2020_df.columns)\n",
    "display(temp_2020_df.sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### traffic volumes data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['OBJECTID_1', 'OBJECTID', 'District', 'Route', 'Rte_SFX', 'County',\n",
      "       'PM_PFX', 'Postmile', 'PM_SFX', 'Descriptn', 'Back_pk_h', 'Back_pk_m',\n",
      "       'Back_AADT', 'Ahead_pk_h', 'Ahead_pk_m', 'Ahead_AADT', 'Lon_S_or_W',\n",
      "       'Lat_S_or_W', 'Lon_N_or_E', 'Lat_N_or_E'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>OBJECTID_1</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>District</th>\n",
       "      <th>Route</th>\n",
       "      <th>Rte_SFX</th>\n",
       "      <th>County</th>\n",
       "      <th>PM_PFX</th>\n",
       "      <th>Postmile</th>\n",
       "      <th>PM_SFX</th>\n",
       "      <th>Descriptn</th>\n",
       "      <th>Back_pk_h</th>\n",
       "      <th>Back_pk_m</th>\n",
       "      <th>Back_AADT</th>\n",
       "      <th>Ahead_pk_h</th>\n",
       "      <th>Ahead_pk_m</th>\n",
       "      <th>Ahead_AADT</th>\n",
       "      <th>Lon_S_or_W</th>\n",
       "      <th>Lat_S_or_W</th>\n",
       "      <th>Lon_N_or_E</th>\n",
       "      <th>Lat_N_or_E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4065</th>\n",
       "      <td>4066</td>\n",
       "      <td>4066</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td></td>\n",
       "      <td>LA</td>\n",
       "      <td></td>\n",
       "      <td>29.160</td>\n",
       "      <td></td>\n",
       "      <td>BURBANK, OLIVE AVENUE</td>\n",
       "      <td>15000</td>\n",
       "      <td>239000</td>\n",
       "      <td>224000</td>\n",
       "      <td>14500</td>\n",
       "      <td>231000</td>\n",
       "      <td>215000</td>\n",
       "      <td>-118.3110085</td>\n",
       "      <td>34.17873575</td>\n",
       "      <td>-118.3109035</td>\n",
       "      <td>34.17886594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>972</td>\n",
       "      <td>972</td>\n",
       "      <td>3</td>\n",
       "      <td>32</td>\n",
       "      <td></td>\n",
       "      <td>BUT</td>\n",
       "      <td></td>\n",
       "      <td>15.211</td>\n",
       "      <td></td>\n",
       "      <td>CHICO, HUMBOLT ROAD</td>\n",
       "      <td>720</td>\n",
       "      <td>7100</td>\n",
       "      <td>5700</td>\n",
       "      <td>430</td>\n",
       "      <td>4500</td>\n",
       "      <td>3800</td>\n",
       "      <td>-121.7366167</td>\n",
       "      <td>39.76246931</td>\n",
       "      <td>-121.7366167</td>\n",
       "      <td>39.76246931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>884</th>\n",
       "      <td>885</td>\n",
       "      <td>885</td>\n",
       "      <td>3</td>\n",
       "      <td>20</td>\n",
       "      <td></td>\n",
       "      <td>SUT</td>\n",
       "      <td></td>\n",
       "      <td>16.838</td>\n",
       "      <td></td>\n",
       "      <td>YUBA CITY, SUTTER STREET</td>\n",
       "      <td>5100</td>\n",
       "      <td>50000</td>\n",
       "      <td>47500</td>\n",
       "      <td>4450</td>\n",
       "      <td>50000</td>\n",
       "      <td>48200</td>\n",
       "      <td>-121.6122085</td>\n",
       "      <td>39.14260755</td>\n",
       "      <td>-121.6121875</td>\n",
       "      <td>39.14245663</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1762</th>\n",
       "      <td>1763</td>\n",
       "      <td>1763</td>\n",
       "      <td>4</td>\n",
       "      <td>29</td>\n",
       "      <td></td>\n",
       "      <td>SOL</td>\n",
       "      <td></td>\n",
       "      <td>0.000</td>\n",
       "      <td></td>\n",
       "      <td>VALLEJO, JCT. RTE. 80</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>1600</td>\n",
       "      <td>11000</td>\n",
       "      <td>9700</td>\n",
       "      <td>-122.2320721</td>\n",
       "      <td>38.07591487</td>\n",
       "      <td>-122.2320276</td>\n",
       "      <td>38.07648984</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7056</th>\n",
       "      <td>7057</td>\n",
       "      <td>7057</td>\n",
       "      <td>12</td>\n",
       "      <td>91</td>\n",
       "      <td></td>\n",
       "      <td>ORA</td>\n",
       "      <td>R</td>\n",
       "      <td>17.950</td>\n",
       "      <td>R</td>\n",
       "      <td>COAL CANYON ROAD RIGHT</td>\n",
       "      <td>9100</td>\n",
       "      <td>145000</td>\n",
       "      <td>139500</td>\n",
       "      <td>9100</td>\n",
       "      <td>145000</td>\n",
       "      <td>139500</td>\n",
       "      <td>Left Skipped - Input PM on Right Ind. Alignment</td>\n",
       "      <td>Left Skipped - Input PM on Right Ind. Alignment</td>\n",
       "      <td>-117.6872347</td>\n",
       "      <td>33.87186882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4898</th>\n",
       "      <td>4899</td>\n",
       "      <td>4899</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td></td>\n",
       "      <td>SBD</td>\n",
       "      <td></td>\n",
       "      <td>33.291</td>\n",
       "      <td></td>\n",
       "      <td>REDLANDS, REDLANDS BOULEVARD</td>\n",
       "      <td>10200</td>\n",
       "      <td>147000</td>\n",
       "      <td>141000</td>\n",
       "      <td>11100</td>\n",
       "      <td>160000</td>\n",
       "      <td>153000</td>\n",
       "      <td>-117.1538342</td>\n",
       "      <td>34.03969472</td>\n",
       "      <td>-117.1540122</td>\n",
       "      <td>34.03944549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2519</th>\n",
       "      <td>2520</td>\n",
       "      <td>2520</td>\n",
       "      <td>4</td>\n",
       "      <td>580</td>\n",
       "      <td></td>\n",
       "      <td>ALA</td>\n",
       "      <td>R</td>\n",
       "      <td>1.622</td>\n",
       "      <td>L</td>\n",
       "      <td>BEGIN LEFT ALIGN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>6800</td>\n",
       "      <td>83000</td>\n",
       "      <td>72700</td>\n",
       "      <td>-121.5879994</td>\n",
       "      <td>37.73991124</td>\n",
       "      <td>Right Skipped - Input PM on Left Ind. Alignment</td>\n",
       "      <td>Right Skipped - Input PM on Left Ind. Alignment</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6159</th>\n",
       "      <td>6160</td>\n",
       "      <td>6160</td>\n",
       "      <td>10</td>\n",
       "      <td>165</td>\n",
       "      <td></td>\n",
       "      <td>MER</td>\n",
       "      <td></td>\n",
       "      <td>33.616</td>\n",
       "      <td></td>\n",
       "      <td>FIRST ST, HILMAR</td>\n",
       "      <td>1600</td>\n",
       "      <td>21100</td>\n",
       "      <td>10800</td>\n",
       "      <td>1500</td>\n",
       "      <td>24700</td>\n",
       "      <td>11800</td>\n",
       "      <td>-120.8501266</td>\n",
       "      <td>37.40789324</td>\n",
       "      <td>-120.8500313</td>\n",
       "      <td>37.40788624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5579</th>\n",
       "      <td>5580</td>\n",
       "      <td>5580</td>\n",
       "      <td>9</td>\n",
       "      <td>203</td>\n",
       "      <td></td>\n",
       "      <td>MNO</td>\n",
       "      <td></td>\n",
       "      <td>6.866</td>\n",
       "      <td></td>\n",
       "      <td>MERIDIAN BLVD</td>\n",
       "      <td>1050</td>\n",
       "      <td>11000</td>\n",
       "      <td>8200</td>\n",
       "      <td>1050</td>\n",
       "      <td>10700</td>\n",
       "      <td>7400</td>\n",
       "      <td>-118.9460037</td>\n",
       "      <td>37.64376601</td>\n",
       "      <td>-118.946289</td>\n",
       "      <td>37.643352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>65</td>\n",
       "      <td>65</td>\n",
       "      <td>1</td>\n",
       "      <td>29</td>\n",
       "      <td></td>\n",
       "      <td>LAK</td>\n",
       "      <td>R</td>\n",
       "      <td>36.289</td>\n",
       "      <td></td>\n",
       "      <td>RENFRO DRIVE</td>\n",
       "      <td>1200</td>\n",
       "      <td>12000</td>\n",
       "      <td>11500</td>\n",
       "      <td>1300</td>\n",
       "      <td>14100</td>\n",
       "      <td>13500</td>\n",
       "      <td>-122.857047</td>\n",
       "      <td>38.98399113</td>\n",
       "      <td>-122.8568738</td>\n",
       "      <td>38.98408767</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      OBJECTID_1  OBJECTID  District  Route Rte_SFX County PM_PFX  Postmile  \\\n",
       "4065        4066      4066         7      5             LA           29.160   \n",
       "971          972       972         3     32            BUT           15.211   \n",
       "884          885       885         3     20            SUT           16.838   \n",
       "1762        1763      1763         4     29            SOL            0.000   \n",
       "7056        7057      7057        12     91            ORA      R    17.950   \n",
       "4898        4899      4899         8     10            SBD           33.291   \n",
       "2519        2520      2520         4    580            ALA      R     1.622   \n",
       "6159        6160      6160        10    165            MER           33.616   \n",
       "5579        5580      5580         9    203            MNO            6.866   \n",
       "64            65        65         1     29            LAK      R    36.289   \n",
       "\n",
       "     PM_SFX                     Descriptn Back_pk_h Back_pk_m Back_AADT  \\\n",
       "4065                BURBANK, OLIVE AVENUE     15000    239000    224000   \n",
       "971                   CHICO, HUMBOLT ROAD       720      7100      5700   \n",
       "884              YUBA CITY, SUTTER STREET      5100     50000     47500   \n",
       "1762                VALLEJO, JCT. RTE. 80                                 \n",
       "7056      R        COAL CANYON ROAD RIGHT      9100    145000    139500   \n",
       "4898         REDLANDS, REDLANDS BOULEVARD     10200    147000    141000   \n",
       "2519      L              BEGIN LEFT ALIGN                                 \n",
       "6159                     FIRST ST, HILMAR      1600     21100     10800   \n",
       "5579                        MERIDIAN BLVD      1050     11000      8200   \n",
       "64                           RENFRO DRIVE      1200     12000     11500   \n",
       "\n",
       "     Ahead_pk_h Ahead_pk_m Ahead_AADT  \\\n",
       "4065      14500     231000     215000   \n",
       "971         430       4500       3800   \n",
       "884        4450      50000      48200   \n",
       "1762       1600      11000       9700   \n",
       "7056       9100     145000     139500   \n",
       "4898      11100     160000     153000   \n",
       "2519       6800      83000      72700   \n",
       "6159       1500      24700      11800   \n",
       "5579       1050      10700       7400   \n",
       "64         1300      14100      13500   \n",
       "\n",
       "                                           Lon_S_or_W  \\\n",
       "4065                                     -118.3110085   \n",
       "971                                      -121.7366167   \n",
       "884                                      -121.6122085   \n",
       "1762                                     -122.2320721   \n",
       "7056  Left Skipped - Input PM on Right Ind. Alignment   \n",
       "4898                                     -117.1538342   \n",
       "2519                                     -121.5879994   \n",
       "6159                                     -120.8501266   \n",
       "5579                                     -118.9460037   \n",
       "64                                        -122.857047   \n",
       "\n",
       "                                           Lat_S_or_W  \\\n",
       "4065                                      34.17873575   \n",
       "971                                       39.76246931   \n",
       "884                                       39.14260755   \n",
       "1762                                      38.07591487   \n",
       "7056  Left Skipped - Input PM on Right Ind. Alignment   \n",
       "4898                                      34.03969472   \n",
       "2519                                      37.73991124   \n",
       "6159                                      37.40789324   \n",
       "5579                                      37.64376601   \n",
       "64                                        38.98399113   \n",
       "\n",
       "                                           Lon_N_or_E  \\\n",
       "4065                                     -118.3109035   \n",
       "971                                      -121.7366167   \n",
       "884                                      -121.6121875   \n",
       "1762                                     -122.2320276   \n",
       "7056                                     -117.6872347   \n",
       "4898                                     -117.1540122   \n",
       "2519  Right Skipped - Input PM on Left Ind. Alignment   \n",
       "6159                                     -120.8500313   \n",
       "5579                                      -118.946289   \n",
       "64                                       -122.8568738   \n",
       "\n",
       "                                           Lat_N_or_E  \n",
       "4065                                      34.17886594  \n",
       "971                                       39.76246931  \n",
       "884                                       39.14245663  \n",
       "1762                                      38.07648984  \n",
       "7056                                      33.87186882  \n",
       "4898                                      34.03944549  \n",
       "2519  Right Skipped - Input PM on Left Ind. Alignment  \n",
       "6159                                      37.40788624  \n",
       "5579                                        37.643352  \n",
       "64                                        38.98408767  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(traffic_volumes_df.columns)\n",
    "display(traffic_volumes_df.sample(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### aqi data 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['State Name', 'county Name', 'State Code', 'County Code', 'Date', 'AQI',\n",
      "       'Category', 'Defining Parameter', 'Defining Site',\n",
      "       'Number of Sites Reporting'],\n",
      "      dtype='object')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>county Name</th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Date</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Category</th>\n",
       "      <th>Defining Parameter</th>\n",
       "      <th>Defining Site</th>\n",
       "      <th>Number of Sites Reporting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29921</th>\n",
       "      <td>California</td>\n",
       "      <td>Solano</td>\n",
       "      <td>6</td>\n",
       "      <td>95</td>\n",
       "      <td>2020-11-01</td>\n",
       "      <td>79</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>06-095-0004</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>172010</th>\n",
       "      <td>Nevada</td>\n",
       "      <td>Nye</td>\n",
       "      <td>32</td>\n",
       "      <td>23</td>\n",
       "      <td>2020-10-17</td>\n",
       "      <td>30</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM10</td>\n",
       "      <td>32-023-0015</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149661</th>\n",
       "      <td>Minnesota</td>\n",
       "      <td>Scott</td>\n",
       "      <td>27</td>\n",
       "      <td>139</td>\n",
       "      <td>2020-12-25</td>\n",
       "      <td>27</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>27-139-0505</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297677</th>\n",
       "      <td>Vermont</td>\n",
       "      <td>Rutland</td>\n",
       "      <td>50</td>\n",
       "      <td>21</td>\n",
       "      <td>2020-09-14</td>\n",
       "      <td>26</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>50-021-0002</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>336642</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston</td>\n",
       "      <td>56</td>\n",
       "      <td>45</td>\n",
       "      <td>2020-11-03</td>\n",
       "      <td>36</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>56-045-0003</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98021</th>\n",
       "      <td>Iowa</td>\n",
       "      <td>Bremer</td>\n",
       "      <td>19</td>\n",
       "      <td>17</td>\n",
       "      <td>2020-04-17</td>\n",
       "      <td>42</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>19-017-0011</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96501</th>\n",
       "      <td>Indiana</td>\n",
       "      <td>Vigo</td>\n",
       "      <td>18</td>\n",
       "      <td>167</td>\n",
       "      <td>2020-11-20</td>\n",
       "      <td>50</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>18-167-0018</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>291908</th>\n",
       "      <td>Utah</td>\n",
       "      <td>Box Elder</td>\n",
       "      <td>49</td>\n",
       "      <td>3</td>\n",
       "      <td>2020-12-25</td>\n",
       "      <td>31</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>49-003-7001</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>108171</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Daviess</td>\n",
       "      <td>21</td>\n",
       "      <td>59</td>\n",
       "      <td>2020-10-01</td>\n",
       "      <td>31</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>21-059-0005</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165948</th>\n",
       "      <td>Montana</td>\n",
       "      <td>Sanders</td>\n",
       "      <td>30</td>\n",
       "      <td>89</td>\n",
       "      <td>2020-03-31</td>\n",
       "      <td>16</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>30-089-0007</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Name county Name  State Code  County Code        Date  AQI  \\\n",
       "29921   California      Solano           6           95  2020-11-01   79   \n",
       "172010      Nevada         Nye          32           23  2020-10-17   30   \n",
       "149661   Minnesota       Scott          27          139  2020-12-25   27   \n",
       "297677     Vermont     Rutland          50           21  2020-09-14   26   \n",
       "336642     Wyoming      Weston          56           45  2020-11-03   36   \n",
       "98021         Iowa      Bremer          19           17  2020-04-17   42   \n",
       "96501      Indiana        Vigo          18          167  2020-11-20   50   \n",
       "291908        Utah   Box Elder          49            3  2020-12-25   31   \n",
       "108171    Kentucky     Daviess          21           59  2020-10-01   31   \n",
       "165948     Montana     Sanders          30           89  2020-03-31   16   \n",
       "\n",
       "        Category Defining Parameter Defining Site  Number of Sites Reporting  \n",
       "29921   Moderate              PM2.5   06-095-0004                          3  \n",
       "172010      Good               PM10   32-023-0015                          4  \n",
       "149661      Good              Ozone   27-139-0505                          1  \n",
       "297677      Good              Ozone   50-021-0002                          1  \n",
       "336642      Good              Ozone   56-045-0003                          2  \n",
       "98021       Good              Ozone   19-017-0011                          1  \n",
       "96501       Good              PM2.5   18-167-0018                          2  \n",
       "291908      Good              Ozone   49-003-7001                          1  \n",
       "108171      Good              Ozone   21-059-0005                          1  \n",
       "165948      Good              PM2.5   30-089-0007                          2  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(aqi_2020.columns)\n",
    "display(aqi_2020.sample(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "## dict to use to convert fro county abbreviation to county\n",
    "\n",
    "county_abbreviations = {\n",
    "\"ALA\" : \"Alameda\",\n",
    "\"ALP\" : \"Alpine\",\n",
    "\"AMA\" : \"Amador\",\n",
    "\"BUT\" : \"Butte\",\n",
    "\"CC\" : \"Contra Costa\",\n",
    "\"CLV\" : \"Calaveras\",\n",
    "\"COL\" : \"Colusa\",\n",
    "\"DN\" : \"Del Norte\",\n",
    "\"ED\" : \"El Dorado\",\n",
    "\"FRE\" : \"Fresno\",\n",
    "\"GLE\" : \"Glenn\",\n",
    "\"HUM\" : \"Humboldt\",\n",
    "\"IMP\" : \"Imperial\",\n",
    "\"INY\" : \"Inyo\",\n",
    "\"KER\" : \"Kern\",\n",
    "\"KIN\" : \"Kings\",\n",
    "\"LA\" : \"Los Angeles\",\n",
    "\"LAK\" : \"Lake\",\n",
    "\"LAS\" : \"Lassen\",\n",
    "\"MAD\" : \"Madera\",\n",
    "\"MEN\" : \"Mendocino\",\n",
    "\"MER\" : \"Merced\",\n",
    "\"MNO\" : \"Mono\",\n",
    "\"MOD\" : \"Modoc\",\n",
    "\"MRN\" : \"Marin\",\n",
    "\"MPA\" : \"Mariposa\",\n",
    "\"MON\" : \"Monterey\",\n",
    "\"NAP\" : \"Napa\",\n",
    "\"NEV\" : \"Nevada\t\",\n",
    "\"ORA\" : \"Orange\",\n",
    "\"PLA\" : \"Placer\",\n",
    "\"PLU\" : \"Plumas\",\n",
    "\"RIV\" : \"Riverside\",\n",
    "\"SAC\" : \"Sacramento\",\n",
    "\"SB\" : \"Santa Barbara\",\n",
    "\"SBD\" : \"San Bernardino\",\n",
    "\"SBT\" : \"San Benito\",\n",
    "\"SCL\" : \"Santa Clara\",\n",
    "\"SCR\" : \"Santa Cruz\",\n",
    "\"SD\" : \"San Diego\",\n",
    "\"SF\" : \"San Francisco\",\n",
    "\"SHA\" : \"Shasta\",\n",
    "\"SIE\" : \"Sierra\",\n",
    "\"SIS\" : \"Siskiyou\",\n",
    "\"SJ\" : \"San Joaquin\",\n",
    "\"SLO\" : \"San Luis Obispo\",\n",
    "\"SM\" : \"San Mateo\",\n",
    "\"SOL\" : \"Solano\",\n",
    "\"SON\" : \"Sonoma\",\n",
    "\"STA\" : \"Stanislaus\",\n",
    "\"SUT\" : \"Sutter\",\n",
    "\"TEH\" : \"Tehama\",\n",
    "\"TRI\" : \"Trinity\",\n",
    "\"TUL\" : \"Tulare\",\n",
    "\"TUO\" : \"Tuolumne\",\n",
    "\"VEN\" : \"Ventura\",\n",
    "\"YOL\" : \"Yolo\",\n",
    "\"YUB\" : \"Yuba\",\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data wrangling\n",
    "\n",
    "### decisions log\n",
    "- we are going to only use data for the state of california because we are interested in using vehicle emissions data which we have for the state of california in addition to the factory emissions data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "daily_global_weather_2020 = pd.read_csv(\"datasets/2020_daily_global_weather.csv\").drop(columns=\"Unnamed: 0\")\n",
    "\n",
    "us_greenhouse_gas_emission_direct_emitter_gas_type = pd.read_csv(\"datasets/greenhouse_gas_emmiter_gas_types.csv\", low_memory=False)\n",
    "\n",
    "aqi_sites = pd.read_csv(\"datasets/aqs_sites.csv\")\n",
    "\n",
    "daily_county_aqi = pd.read_csv(\"datasets/county_aqi_2020_daily.csv\") \n",
    "\n",
    "daily_temp = pd.read_csv(\"datasets/daily_temperature_2020.csv\")\n",
    "\n",
    "daily_wind = pd.read_csv(\"datasets/daily_wind_2020.csv\")\n",
    "\n",
    "traffic_volumes = pd.read_csv(\"datasets/traffic_volumes.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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>Station</th>\n",
       "      <th>Date</th>\n",
       "      <th>TAVG</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>PRCP</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AE000041196</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>211.0</td>\n",
       "      <td>25.3330</td>\n",
       "      <td>55.5170</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>AEM00041194</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>217.0</td>\n",
       "      <td>25.2550</td>\n",
       "      <td>55.3640</td>\n",
       "      <td>10.4</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AFM00040938</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>54.0</td>\n",
       "      <td>34.2100</td>\n",
       "      <td>62.2280</td>\n",
       "      <td>977.2</td>\n",
       "      <td>23.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>AG000060611</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>71.0</td>\n",
       "      <td>28.0500</td>\n",
       "      <td>9.6331</td>\n",
       "      <td>561.0</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>AGE00147708</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>99.0</td>\n",
       "      <td>36.7200</td>\n",
       "      <td>4.0500</td>\n",
       "      <td>222.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>1064278</th>\n",
       "      <td>USW00094847</td>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>96.0</td>\n",
       "      <td>42.2314</td>\n",
       "      <td>-83.3308</td>\n",
       "      <td>192.3</td>\n",
       "      <td>157.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064279</th>\n",
       "      <td>USW00094849</td>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>67.0</td>\n",
       "      <td>45.0717</td>\n",
       "      <td>-83.5644</td>\n",
       "      <td>208.5</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064280</th>\n",
       "      <td>USW00094860</td>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>77.0</td>\n",
       "      <td>42.8939</td>\n",
       "      <td>-85.5447</td>\n",
       "      <td>237.1</td>\n",
       "      <td>165.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064281</th>\n",
       "      <td>USW00094908</td>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>73.0</td>\n",
       "      <td>42.3978</td>\n",
       "      <td>-90.7036</td>\n",
       "      <td>321.9</td>\n",
       "      <td>361.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1064282</th>\n",
       "      <td>USW00094910</td>\n",
       "      <td>2020-10-22</td>\n",
       "      <td>70.0</td>\n",
       "      <td>42.5544</td>\n",
       "      <td>-92.4011</td>\n",
       "      <td>264.6</td>\n",
       "      <td>747.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1064283 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Station        Date   TAVG  Latitude  Longitude  Elevation   PRCP\n",
       "0        AE000041196  2020-01-01  211.0   25.3330    55.5170       34.0    0.0\n",
       "1        AEM00041194  2020-01-01  217.0   25.2550    55.3640       10.4    0.0\n",
       "2        AFM00040938  2020-01-01   54.0   34.2100    62.2280      977.2   23.0\n",
       "3        AG000060611  2020-01-01   71.0   28.0500     9.6331      561.0   10.0\n",
       "4        AGE00147708  2020-01-01   99.0   36.7200     4.0500      222.0    0.0\n",
       "...              ...         ...    ...       ...        ...        ...    ...\n",
       "1064278  USW00094847  2020-10-22   96.0   42.2314   -83.3308      192.3  157.0\n",
       "1064279  USW00094849  2020-10-22   67.0   45.0717   -83.5644      208.5   99.0\n",
       "1064280  USW00094860  2020-10-22   77.0   42.8939   -85.5447      237.1  165.0\n",
       "1064281  USW00094908  2020-10-22   73.0   42.3978   -90.7036      321.9  361.0\n",
       "1064282  USW00094910  2020-10-22   70.0   42.5544   -92.4011      264.6  747.0\n",
       "\n",
       "[1064283 rows x 7 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#PRCP is average percipitation in tenths of a mm, TAVG is tempearture average at each station for each day\n",
    "daily_global_weather_2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V_GHG_EMITTER_GAS.ADDRESS1', 'V_GHG_EMITTER_GAS.ADDRESS2',\n",
       "       'V_GHG_EMITTER_GAS.CITY', 'V_GHG_EMITTER_GAS.CO2E_EMISSION',\n",
       "       'V_GHG_EMITTER_GAS.COUNTY', 'V_GHG_EMITTER_GAS.FACILITY_ID',\n",
       "       'V_GHG_EMITTER_GAS.GAS_CODE', 'V_GHG_EMITTER_GAS.GAS_NAME',\n",
       "       'V_GHG_EMITTER_GAS.LATITUDE', 'V_GHG_EMITTER_GAS.LONGITUDE',\n",
       "       'V_GHG_EMITTER_GAS.STATE', 'V_GHG_EMITTER_GAS.STATE_NAME',\n",
       "       'V_GHG_EMITTER_GAS.YEAR', 'V_GHG_EMITTER_GAS.ZIP',\n",
       "       'V_GHG_EMITTER_GAS.FACILITY_NAME', 'V_GHG_EMITTER_GAS.COUNTY_FIPS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_greenhouse_gas_emission_direct_emitter_gas_type.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State Name', 'county Name', 'State Code', 'County Code', 'Date', 'AQI',\n",
       "       'Category', 'Defining Parameter', 'Defining Site',\n",
       "       'Number of Sites Reporting'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_county_aqi.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State Code', 'County Code', 'Site Number', 'Latitude', 'Longitude',\n",
       "       'Datum', 'Elevation', 'Land Use', 'Location Setting',\n",
       "       'Site Established Date', 'Site Closed Date', 'Met Site State Code',\n",
       "       'Met Site County Code', 'Met Site Site Number', 'Met Site Type',\n",
       "       'Met Site Distance', 'Met Site Direction', 'GMT Offset',\n",
       "       'Owning Agency', 'Local Site Name', 'Address', 'Zip Code', 'State Name',\n",
       "       'County Name', 'City Name', 'CBSA Name', 'Tribe Name',\n",
       "       'Extraction Date'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aqi_sites.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The defining site column in daily_county_aqi is defined by \"State Code\"-\"County Code\"-\"Site Number\" for a given State and County in aqi_sites. Thus, the City name can be determined by the defining site using the aqi_sites table, if increased granularity is required."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State Code', 'County Code', 'Site Num', 'Parameter Code', 'POC',\n",
       "       'Latitude', 'Longitude', 'Datum', 'Parameter Name', 'Sample Duration',\n",
       "       'Pollutant Standard', 'Date Local', 'Units of Measure', 'Event Type',\n",
       "       'Observation Count', 'Observation Percent', 'Arithmetic Mean',\n",
       "       '1st Max Value', '1st Max Hour', 'AQI', 'Method Code', 'Method Name',\n",
       "       'Local Site Name', 'Address', 'State Name', 'County Name', 'City Name',\n",
       "       'CBSA Name', 'Date of Last Change'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_wind.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Aggregating Daily temp, wind speed and wind direction by state and county"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning/Prep on Daily Temperature Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Prep on daily temperature, wind speed and wind direction\n",
    "\n",
    "# Extracting relevant columns\n",
    "truncated_daily_temp = daily_temp[[\"State Code\", \"County Code\", \"Site Num\", \"State Name\", \"County Name\", \"City Name\", \"Latitude\", \"Longitude\", \"Date Local\", \"Arithmetic Mean\"]]\n",
    "\n",
    "# Array of US States and territories\n",
    "US_States_territories = us_greenhouse_gas_emission_direct_emitter_gas_type[\"V_GHG_EMITTER_GAS.STATE_NAME\"].unique()\n",
    "\n",
    "# Only US States and territories should be in our data\n",
    "truncated_daily_temp = truncated_daily_temp[truncated_daily_temp[\"State Name\"].str.upper().isin(US_States_territories)]\n",
    "\n",
    "# Impute missing 0s, with mean of the arithmetic mean, there are no null values\n",
    "truncated_daily_temp[\"Arithmetic Mean\"] = truncated_daily_temp[\"Arithmetic Mean\"].replace(0, truncated_daily_temp[\"Arithmetic Mean\"].mean())\n",
    "\n",
    "# Rename Arithmetic Mean column to include temperature and units (Temp - F)\n",
    "truncated_daily_temp = truncated_daily_temp.rename(columns={\"Arithmetic Mean\": \"Mean Temperature (F)\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
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       "      <td>43.84539</td>\n",
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       "      <td>43.84539</td>\n",
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       "      <td>56</td>\n",
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       "      <td>Weston</td>\n",
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       "      <td>43.84539</td>\n",
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       "        State Code  County Code  Site Num State Name County Name  \\\n",
       "0                1           53      1000    Alabama    Escambia   \n",
       "1                1           53      1000    Alabama    Escambia   \n",
       "2                1           53      1000    Alabama    Escambia   \n",
       "3                1           53      1000    Alabama    Escambia   \n",
       "4                1           53      1000    Alabama    Escambia   \n",
       "...            ...          ...       ...        ...         ...   \n",
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       "290329          56           45       800    Wyoming      Weston   \n",
       "290330          56           45       800    Wyoming      Weston   \n",
       "\n",
       "            City Name  Latitude  Longitude  Date Local  Mean Temperature (F)  \n",
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       "1       Not in a city  31.09210  -87.54350  2020-01-02             67.342857  \n",
       "2       Not in a city  31.09210  -87.54350  2020-01-03             26.580000  \n",
       "3       Not in a city  31.09210  -87.54350  2020-01-04             44.830000  \n",
       "4       Not in a city  31.09210  -87.54350  2020-01-05             51.550000  \n",
       "...               ...       ...        ...         ...                   ...  \n",
       "290326      Newcastle  43.84539 -104.20512  2020-12-27             31.000000  \n",
       "290327      Newcastle  43.84539 -104.20512  2020-12-28             26.000000  \n",
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       "290330      Newcastle  43.84539 -104.20512  2020-12-31             24.833333  \n",
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     "execution_count": 20,
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   ],
   "source": [
    "truncated_daily_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Prep on Daily Wind Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extract relevant Columns\n",
    "truncated_daily_wind = daily_wind[[\"State Code\", \"County Code\", \"Site Num\", \"State Name\", \"County Name\", \"City Name\", \"Latitude\", \"Longitude\", \"Parameter Name\", \"Date Local\", \"Arithmetic Mean\"]]\n",
    "\n",
    "# Only US States and territories should be in our data\n",
    "truncated_daily_wind = truncated_daily_wind[truncated_daily_wind[\"State Name\"].str.upper().isin(US_States_territories)]\n",
    "\n",
    "# Seperate wind direction and wind speed before imputing missing values\n",
    "daily_wind_speed = truncated_daily_wind[truncated_daily_wind[\"Parameter Name\"] == \"Wind Speed - Resultant\"].reset_index(drop=True)\n",
    "\n",
    "daily_wind_direction = truncated_daily_wind[truncated_daily_wind[\"Parameter Name\"] == \"Wind Direction - Resultant\"].reset_index(drop=True)\n",
    "\n",
    "# Impute missing values or 0 values with mean of the column\n",
    "daily_wind_speed[\"Arithmetic Mean\"] = daily_wind_speed[\"Arithmetic Mean\"].replace(0, daily_wind_speed[\"Arithmetic Mean\"].mean())\n",
    "\n",
    "daily_wind_direction[\"Arithmetic Mean\"] = daily_wind_direction[\"Arithmetic Mean\"].replace(0, daily_wind_direction[\"Arithmetic Mean\"].mean())\n",
    "\n",
    "# Rename Arithmetic Mean columns to include the measurement and units\n",
    "daily_wind_speed = daily_wind_speed.rename(columns={\"Arithmetic Mean\": \"Mean Wind Speed (knots)\"}).drop(columns=[\"Parameter Name\"])\n",
    "daily_wind_direction = daily_wind_direction.rename(columns={\"Arithmetic Mean\": \"Mean Wind Direction (compass degrees)\"}).drop(columns=[\"Parameter Name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
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       "      <td>2020-12-28</td>\n",
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       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
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       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-29</td>\n",
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       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
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       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-30</td>\n",
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       "      <td>1.687500</td>\n",
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       "        State Code  County Code  Site Num State Name County Name  \\\n",
       "0                1           53      1000    Alabama    Escambia   \n",
       "1                1           53      1000    Alabama    Escambia   \n",
       "2                1           53      1000    Alabama    Escambia   \n",
       "3                1           53      1000    Alabama    Escambia   \n",
       "4                1           53      1000    Alabama    Escambia   \n",
       "...            ...          ...       ...        ...         ...   \n",
       "239426          56           39      1013    Wyoming       Teton   \n",
       "239427          56           39      1013    Wyoming       Teton   \n",
       "239428          56           39      1013    Wyoming       Teton   \n",
       "239429          56           39      1013    Wyoming       Teton   \n",
       "239430          56           39      1013    Wyoming       Teton   \n",
       "\n",
       "            City Name   Latitude   Longitude  Date Local  \\\n",
       "0       Not in a city  31.092100  -87.543500  2020-01-01   \n",
       "1       Not in a city  31.092100  -87.543500  2020-01-02   \n",
       "2       Not in a city  31.092100  -87.543500  2020-01-03   \n",
       "3       Not in a city  31.092100  -87.543500  2020-01-04   \n",
       "4       Not in a city  31.092100  -87.543500  2020-01-05   \n",
       "...               ...        ...         ...         ...   \n",
       "239426  Not in a city  44.373056 -110.830833  2020-12-27   \n",
       "239427  Not in a city  44.373056 -110.830833  2020-12-28   \n",
       "239428  Not in a city  44.373056 -110.830833  2020-12-29   \n",
       "239429  Not in a city  44.373056 -110.830833  2020-12-30   \n",
       "239430  Not in a city  44.373056 -110.830833  2020-12-31   \n",
       "\n",
       "        Mean Wind Speed (knots)  \n",
       "0                      4.349492  \n",
       "1                      4.349492  \n",
       "2                      4.349492  \n",
       "3                      4.349492  \n",
       "4                      4.349492  \n",
       "...                         ...  \n",
       "239426                 1.795833  \n",
       "239427                 1.212500  \n",
       "239428                 1.441667  \n",
       "239429                 1.466667  \n",
       "239430                 1.687500  \n",
       "\n",
       "[239431 rows x 10 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
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   "source": [
    "daily_wind_speed"
   ]
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       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>179.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>108.857143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>271.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>230.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>251.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>242333</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>190.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242334</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>171.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242335</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>181.166667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242336</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>173.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>242337</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>157.958333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>242338 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Code  County Code  Site Num State Name County Name  \\\n",
       "0                1           53      1000    Alabama    Escambia   \n",
       "1                1           53      1000    Alabama    Escambia   \n",
       "2                1           53      1000    Alabama    Escambia   \n",
       "3                1           53      1000    Alabama    Escambia   \n",
       "4                1           53      1000    Alabama    Escambia   \n",
       "...            ...          ...       ...        ...         ...   \n",
       "242333          56           39      1013    Wyoming       Teton   \n",
       "242334          56           39      1013    Wyoming       Teton   \n",
       "242335          56           39      1013    Wyoming       Teton   \n",
       "242336          56           39      1013    Wyoming       Teton   \n",
       "242337          56           39      1013    Wyoming       Teton   \n",
       "\n",
       "            City Name   Latitude   Longitude  Date Local  \\\n",
       "0       Not in a city  31.092100  -87.543500  2020-01-01   \n",
       "1       Not in a city  31.092100  -87.543500  2020-01-02   \n",
       "2       Not in a city  31.092100  -87.543500  2020-01-03   \n",
       "3       Not in a city  31.092100  -87.543500  2020-01-04   \n",
       "4       Not in a city  31.092100  -87.543500  2020-01-05   \n",
       "...               ...        ...         ...         ...   \n",
       "242333  Not in a city  44.373056 -110.830833  2020-12-27   \n",
       "242334  Not in a city  44.373056 -110.830833  2020-12-28   \n",
       "242335  Not in a city  44.373056 -110.830833  2020-12-29   \n",
       "242336  Not in a city  44.373056 -110.830833  2020-12-30   \n",
       "242337  Not in a city  44.373056 -110.830833  2020-12-31   \n",
       "\n",
       "        Mean Wind Direction (compass degrees)  \n",
       "0                                  179.900000  \n",
       "1                                  108.857143  \n",
       "2                                  271.000000  \n",
       "3                                  230.100000  \n",
       "4                                  251.100000  \n",
       "...                                       ...  \n",
       "242333                             190.375000  \n",
       "242334                             171.375000  \n",
       "242335                             181.166667  \n",
       "242336                             173.666667  \n",
       "242337                             157.958333  \n",
       "\n",
       "[242338 rows x 10 columns]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "daily_wind_direction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging the wind, and temperature data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "wind_data_merge = pd.merge(daily_wind_direction, daily_wind_speed, how='inner', on=[\"State Code\", \"County Code\", \"Site Num\", \"State Name\", \"County Name\", \"City Name\", \"Latitude\", \"Longitude\", \"Date Local\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "weather_data = truncated_daily_temp.merge(wind_data_merge, how='inner', on=[\"State Code\", \"County Code\", \"Site Num\", \"State Name\", \"County Name\", \"City Name\", \"Latitude\", \"Longitude\", \"Date Local\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Final dataset which includes weather data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Site Num</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date Local</th>\n",
       "      <th>Mean Temperature (F)</th>\n",
       "      <th>Mean Wind Direction (compass degrees)</th>\n",
       "      <th>Mean Wind Speed (knots)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>53.540000</td>\n",
       "      <td>179.900000</td>\n",
       "      <td>4.349492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>67.342857</td>\n",
       "      <td>108.857143</td>\n",
       "      <td>4.349492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>26.580000</td>\n",
       "      <td>271.000000</td>\n",
       "      <td>4.349492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>44.830000</td>\n",
       "      <td>230.100000</td>\n",
       "      <td>4.349492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>51.550000</td>\n",
       "      <td>251.100000</td>\n",
       "      <td>4.349492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>228161</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>190.375000</td>\n",
       "      <td>1.795833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228162</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>-4.833333</td>\n",
       "      <td>171.375000</td>\n",
       "      <td>1.212500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228163</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>-1.625000</td>\n",
       "      <td>181.166667</td>\n",
       "      <td>1.441667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228164</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>8.291667</td>\n",
       "      <td>173.666667</td>\n",
       "      <td>1.466667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228165</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>18.875000</td>\n",
       "      <td>157.958333</td>\n",
       "      <td>1.687500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>228166 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Code  County Code  Site Num State Name County Name  \\\n",
       "0                1           53      1000    Alabama    Escambia   \n",
       "1                1           53      1000    Alabama    Escambia   \n",
       "2                1           53      1000    Alabama    Escambia   \n",
       "3                1           53      1000    Alabama    Escambia   \n",
       "4                1           53      1000    Alabama    Escambia   \n",
       "...            ...          ...       ...        ...         ...   \n",
       "228161          56           39      1013    Wyoming       Teton   \n",
       "228162          56           39      1013    Wyoming       Teton   \n",
       "228163          56           39      1013    Wyoming       Teton   \n",
       "228164          56           39      1013    Wyoming       Teton   \n",
       "228165          56           39      1013    Wyoming       Teton   \n",
       "\n",
       "            City Name   Latitude   Longitude  Date Local  \\\n",
       "0       Not in a city  31.092100  -87.543500  2020-01-01   \n",
       "1       Not in a city  31.092100  -87.543500  2020-01-02   \n",
       "2       Not in a city  31.092100  -87.543500  2020-01-03   \n",
       "3       Not in a city  31.092100  -87.543500  2020-01-04   \n",
       "4       Not in a city  31.092100  -87.543500  2020-01-05   \n",
       "...               ...        ...         ...         ...   \n",
       "228161  Not in a city  44.373056 -110.830833  2020-12-27   \n",
       "228162  Not in a city  44.373056 -110.830833  2020-12-28   \n",
       "228163  Not in a city  44.373056 -110.830833  2020-12-29   \n",
       "228164  Not in a city  44.373056 -110.830833  2020-12-30   \n",
       "228165  Not in a city  44.373056 -110.830833  2020-12-31   \n",
       "\n",
       "        Mean Temperature (F)  Mean Wind Direction (compass degrees)  \\\n",
       "0                  53.540000                             179.900000   \n",
       "1                  67.342857                             108.857143   \n",
       "2                  26.580000                             271.000000   \n",
       "3                  44.830000                             230.100000   \n",
       "4                  51.550000                             251.100000   \n",
       "...                      ...                                    ...   \n",
       "228161             15.250000                             190.375000   \n",
       "228162             -4.833333                             171.375000   \n",
       "228163             -1.625000                             181.166667   \n",
       "228164              8.291667                             173.666667   \n",
       "228165             18.875000                             157.958333   \n",
       "\n",
       "        Mean Wind Speed (knots)  \n",
       "0                      4.349492  \n",
       "1                      4.349492  \n",
       "2                      4.349492  \n",
       "3                      4.349492  \n",
       "4                      4.349492  \n",
       "...                         ...  \n",
       "228161                 1.795833  \n",
       "228162                 1.212500  \n",
       "228163                 1.441667  \n",
       "228164                 1.466667  \n",
       "228165                 1.687500  \n",
       "\n",
       "[228166 rows x 12 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "weather_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "368"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(weather_data[\"City Name\"].unique())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Incorporating land use, elevation and location setting for the AQI sites into weather data set as features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "truncated_aqi_sites = aqi_sites[[\"State Code\", \"County Code\", \"Site Number\", \"State Name\", \"County Name\", \"City Name\", \"Elevation\", \"Land Use\", \"Location Setting\"]].rename(columns={\"Site Number\": \"Site Num\"})\n",
    "\n",
    "# Only showing US and territories aqi sites\n",
    "truncated_aqi_sites = truncated_aqi_sites[truncated_aqi_sites[\"State Name\"].str.upper().isin(US_States_territories)]\n",
    "\n",
    "truncated_aqi_sites[\"State Code\"] = truncated_aqi_sites[\"State Code\"].astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Site Num</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Land Use</th>\n",
       "      <th>Location Setting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Brewton</td>\n",
       "      <td>37.00</td>\n",
       "      <td>RESIDENTIAL</td>\n",
       "      <td>SUBURBAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>2</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Brewton</td>\n",
       "      <td>50.00</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a City</td>\n",
       "      <td>89.61</td>\n",
       "      <td>AGRICULTURAL</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    State Code  County Code  Site Num State Name County Name      City Name  \\\n",
       "80           1           53         1    Alabama    Escambia        Brewton   \n",
       "81           1           53         2    Alabama    Escambia        Brewton   \n",
       "82           1           53      1000    Alabama    Escambia  Not in a City   \n",
       "\n",
       "    Elevation      Land Use       Location Setting  \n",
       "80      37.00   RESIDENTIAL               SUBURBAN  \n",
       "81      50.00    COMMERCIAL  URBAN AND CENTER CITY  \n",
       "82      89.61  AGRICULTURAL                  RURAL  "
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "truncated_aqi_sites[(truncated_aqi_sites[\"State Name\"] == \"Alabama\") & (truncated_aqi_sites[\"County Name\"] == \"Escambia\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_set = weather_data.merge(truncated_aqi_sites, on=[\"State Code\", \"County Code\", \"Site Num\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "feature_set = feature_set.drop(columns=[\"State Name_y\", \"County Name_y\", \"City Name_y\"]).rename(columns={\"State Name_x\": \"State Name\", \"County Name_x\": \"County Name\", \"City Name_x\": \"City Name\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Site Num</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date Local</th>\n",
       "      <th>Mean Temperature (F)</th>\n",
       "      <th>Mean Wind Direction (compass degrees)</th>\n",
       "      <th>Mean Wind Speed (knots)</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Land Use</th>\n",
       "      <th>Location Setting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-01</td>\n",
       "      <td>53.540000</td>\n",
       "      <td>179.900000</td>\n",
       "      <td>4.349492</td>\n",
       "      <td>89.61</td>\n",
       "      <td>AGRICULTURAL</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-02</td>\n",
       "      <td>67.342857</td>\n",
       "      <td>108.857143</td>\n",
       "      <td>4.349492</td>\n",
       "      <td>89.61</td>\n",
       "      <td>AGRICULTURAL</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-03</td>\n",
       "      <td>26.580000</td>\n",
       "      <td>271.000000</td>\n",
       "      <td>4.349492</td>\n",
       "      <td>89.61</td>\n",
       "      <td>AGRICULTURAL</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-04</td>\n",
       "      <td>44.830000</td>\n",
       "      <td>230.100000</td>\n",
       "      <td>4.349492</td>\n",
       "      <td>89.61</td>\n",
       "      <td>AGRICULTURAL</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>53</td>\n",
       "      <td>1000</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Escambia</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>31.092100</td>\n",
       "      <td>-87.543500</td>\n",
       "      <td>2020-01-05</td>\n",
       "      <td>51.550000</td>\n",
       "      <td>251.100000</td>\n",
       "      <td>4.349492</td>\n",
       "      <td>89.61</td>\n",
       "      <td>AGRICULTURAL</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>228161</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-27</td>\n",
       "      <td>15.250000</td>\n",
       "      <td>190.375000</td>\n",
       "      <td>1.795833</td>\n",
       "      <td>2230.00</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228162</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-28</td>\n",
       "      <td>-4.833333</td>\n",
       "      <td>171.375000</td>\n",
       "      <td>1.212500</td>\n",
       "      <td>2230.00</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228163</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-29</td>\n",
       "      <td>-1.625000</td>\n",
       "      <td>181.166667</td>\n",
       "      <td>1.441667</td>\n",
       "      <td>2230.00</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228164</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-30</td>\n",
       "      <td>8.291667</td>\n",
       "      <td>173.666667</td>\n",
       "      <td>1.466667</td>\n",
       "      <td>2230.00</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228165</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-12-31</td>\n",
       "      <td>18.875000</td>\n",
       "      <td>157.958333</td>\n",
       "      <td>1.687500</td>\n",
       "      <td>2230.00</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>228166 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Code  County Code  Site Num State Name County Name  \\\n",
       "0                1           53      1000    Alabama    Escambia   \n",
       "1                1           53      1000    Alabama    Escambia   \n",
       "2                1           53      1000    Alabama    Escambia   \n",
       "3                1           53      1000    Alabama    Escambia   \n",
       "4                1           53      1000    Alabama    Escambia   \n",
       "...            ...          ...       ...        ...         ...   \n",
       "228161          56           39      1013    Wyoming       Teton   \n",
       "228162          56           39      1013    Wyoming       Teton   \n",
       "228163          56           39      1013    Wyoming       Teton   \n",
       "228164          56           39      1013    Wyoming       Teton   \n",
       "228165          56           39      1013    Wyoming       Teton   \n",
       "\n",
       "            City Name   Latitude   Longitude  Date Local  \\\n",
       "0       Not in a city  31.092100  -87.543500  2020-01-01   \n",
       "1       Not in a city  31.092100  -87.543500  2020-01-02   \n",
       "2       Not in a city  31.092100  -87.543500  2020-01-03   \n",
       "3       Not in a city  31.092100  -87.543500  2020-01-04   \n",
       "4       Not in a city  31.092100  -87.543500  2020-01-05   \n",
       "...               ...        ...         ...         ...   \n",
       "228161  Not in a city  44.373056 -110.830833  2020-12-27   \n",
       "228162  Not in a city  44.373056 -110.830833  2020-12-28   \n",
       "228163  Not in a city  44.373056 -110.830833  2020-12-29   \n",
       "228164  Not in a city  44.373056 -110.830833  2020-12-30   \n",
       "228165  Not in a city  44.373056 -110.830833  2020-12-31   \n",
       "\n",
       "        Mean Temperature (F)  Mean Wind Direction (compass degrees)  \\\n",
       "0                  53.540000                             179.900000   \n",
       "1                  67.342857                             108.857143   \n",
       "2                  26.580000                             271.000000   \n",
       "3                  44.830000                             230.100000   \n",
       "4                  51.550000                             251.100000   \n",
       "...                      ...                                    ...   \n",
       "228161             15.250000                             190.375000   \n",
       "228162             -4.833333                             171.375000   \n",
       "228163             -1.625000                             181.166667   \n",
       "228164              8.291667                             173.666667   \n",
       "228165             18.875000                             157.958333   \n",
       "\n",
       "        Mean Wind Speed (knots)  Elevation      Land Use Location Setting  \n",
       "0                      4.349492      89.61  AGRICULTURAL            RURAL  \n",
       "1                      4.349492      89.61  AGRICULTURAL            RURAL  \n",
       "2                      4.349492      89.61  AGRICULTURAL            RURAL  \n",
       "3                      4.349492      89.61  AGRICULTURAL            RURAL  \n",
       "4                      4.349492      89.61  AGRICULTURAL            RURAL  \n",
       "...                         ...        ...           ...              ...  \n",
       "228161                 1.795833    2230.00        FOREST            RURAL  \n",
       "228162                 1.212500    2230.00        FOREST            RURAL  \n",
       "228163                 1.441667    2230.00        FOREST            RURAL  \n",
       "228164                 1.466667    2230.00        FOREST            RURAL  \n",
       "228165                 1.687500    2230.00        FOREST            RURAL  \n",
       "\n",
       "[228166 rows x 15 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "feature_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Adding the observed values for AQI to the features dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Extracting the Site Num from Defining Site\n",
    "daily_county_aqi[\"Site Num\"] = daily_county_aqi[\"Defining Site\"].str.extract(pat=f'\\d+-\\d+-(\\d+)').astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Renaming columns to allow for merge\n",
    "daily_county_aqi = daily_county_aqi.rename(columns={\"county Name\": \"County Name\", \"Date\": \"Date Local\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Merging with the feature set\n",
    "final_data_set = feature_set.merge(daily_county_aqi, on=[\"State Code\", \"County Code\", \"Site Num\", \"Date Local\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Dropping redundant columns and renaming \n",
    "final_data_set = final_data_set.drop(columns=[\"State Name_y\", \"County Name_y\"]).rename(columns={\"State Name_x\": \"State Name\", \"County Name_x\":\"County Name\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Code</th>\n",
       "      <th>County Code</th>\n",
       "      <th>Site Num</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>Latitude</th>\n",
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       "      <th>Mean Temperature (F)</th>\n",
       "      <th>Mean Wind Direction (compass degrees)</th>\n",
       "      <th>Mean Wind Speed (knots)</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>Land Use</th>\n",
       "      <th>Location Setting</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Category</th>\n",
       "      <th>Defining Parameter</th>\n",
       "      <th>Defining Site</th>\n",
       "      <th>Number of Sites Reporting</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815000</td>\n",
       "      <td>2020-01-09</td>\n",
       "      <td>52.125000</td>\n",
       "      <td>137.125000</td>\n",
       "      <td>4.904167</td>\n",
       "      <td>177.0</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>56</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815000</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>65.750000</td>\n",
       "      <td>163.708333</td>\n",
       "      <td>7.583333</td>\n",
       "      <td>177.0</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>44</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815000</td>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>63.937500</td>\n",
       "      <td>208.916667</td>\n",
       "      <td>3.166667</td>\n",
       "      <td>177.0</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>30</td>\n",
       "      <td>Good</td>\n",
       "      <td>NO2</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815000</td>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>58.012500</td>\n",
       "      <td>75.458333</td>\n",
       "      <td>5.945833</td>\n",
       "      <td>177.0</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>31</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815000</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>54.945833</td>\n",
       "      <td>105.375000</td>\n",
       "      <td>5.491667</td>\n",
       "      <td>177.0</td>\n",
       "      <td>COMMERCIAL</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>34</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>94814</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-10-05</td>\n",
       "      <td>45.708333</td>\n",
       "      <td>162.666667</td>\n",
       "      <td>2.287500</td>\n",
       "      <td>2230.0</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>54</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>56-039-1013</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94815</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-10-07</td>\n",
       "      <td>44.083333</td>\n",
       "      <td>174.791667</td>\n",
       "      <td>2.087500</td>\n",
       "      <td>2230.0</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>85</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>56-039-1013</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94816</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-10-08</td>\n",
       "      <td>46.375000</td>\n",
       "      <td>169.375000</td>\n",
       "      <td>3.016667</td>\n",
       "      <td>2230.0</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>69</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>56-039-1013</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94817</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-10-09</td>\n",
       "      <td>45.041667</td>\n",
       "      <td>168.666667</td>\n",
       "      <td>2.612500</td>\n",
       "      <td>2230.0</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>55</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>56-039-1013</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94818</th>\n",
       "      <td>56</td>\n",
       "      <td>39</td>\n",
       "      <td>1013</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Teton</td>\n",
       "      <td>Not in a city</td>\n",
       "      <td>44.373056</td>\n",
       "      <td>-110.830833</td>\n",
       "      <td>2020-10-10</td>\n",
       "      <td>43.875000</td>\n",
       "      <td>191.791667</td>\n",
       "      <td>4.179167</td>\n",
       "      <td>2230.0</td>\n",
       "      <td>FOREST</td>\n",
       "      <td>RURAL</td>\n",
       "      <td>70</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>56-039-1013</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>94819 rows × 20 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Code  County Code  Site Num State Name County Name  \\\n",
       "0               1           73        23    Alabama   Jefferson   \n",
       "1               1           73        23    Alabama   Jefferson   \n",
       "2               1           73        23    Alabama   Jefferson   \n",
       "3               1           73        23    Alabama   Jefferson   \n",
       "4               1           73        23    Alabama   Jefferson   \n",
       "...           ...          ...       ...        ...         ...   \n",
       "94814          56           39      1013    Wyoming       Teton   \n",
       "94815          56           39      1013    Wyoming       Teton   \n",
       "94816          56           39      1013    Wyoming       Teton   \n",
       "94817          56           39      1013    Wyoming       Teton   \n",
       "94818          56           39      1013    Wyoming       Teton   \n",
       "\n",
       "           City Name   Latitude   Longitude  Date Local  Mean Temperature (F)  \\\n",
       "0         Birmingham  33.553056  -86.815000  2020-01-09             52.125000   \n",
       "1         Birmingham  33.553056  -86.815000  2020-01-10             65.750000   \n",
       "2         Birmingham  33.553056  -86.815000  2020-01-14             63.937500   \n",
       "3         Birmingham  33.553056  -86.815000  2020-01-16             58.012500   \n",
       "4         Birmingham  33.553056  -86.815000  2020-01-17             54.945833   \n",
       "...              ...        ...         ...         ...                   ...   \n",
       "94814  Not in a city  44.373056 -110.830833  2020-10-05             45.708333   \n",
       "94815  Not in a city  44.373056 -110.830833  2020-10-07             44.083333   \n",
       "94816  Not in a city  44.373056 -110.830833  2020-10-08             46.375000   \n",
       "94817  Not in a city  44.373056 -110.830833  2020-10-09             45.041667   \n",
       "94818  Not in a city  44.373056 -110.830833  2020-10-10             43.875000   \n",
       "\n",
       "       Mean Wind Direction (compass degrees)  Mean Wind Speed (knots)  \\\n",
       "0                                 137.125000                 4.904167   \n",
       "1                                 163.708333                 7.583333   \n",
       "2                                 208.916667                 3.166667   \n",
       "3                                  75.458333                 5.945833   \n",
       "4                                 105.375000                 5.491667   \n",
       "...                                      ...                      ...   \n",
       "94814                             162.666667                 2.287500   \n",
       "94815                             174.791667                 2.087500   \n",
       "94816                             169.375000                 3.016667   \n",
       "94817                             168.666667                 2.612500   \n",
       "94818                             191.791667                 4.179167   \n",
       "\n",
       "       Elevation    Land Use       Location Setting  AQI  Category  \\\n",
       "0          177.0  COMMERCIAL  URBAN AND CENTER CITY   56  Moderate   \n",
       "1          177.0  COMMERCIAL  URBAN AND CENTER CITY   44      Good   \n",
       "2          177.0  COMMERCIAL  URBAN AND CENTER CITY   30      Good   \n",
       "3          177.0  COMMERCIAL  URBAN AND CENTER CITY   31      Good   \n",
       "4          177.0  COMMERCIAL  URBAN AND CENTER CITY   34      Good   \n",
       "...          ...         ...                    ...  ...       ...   \n",
       "94814     2230.0      FOREST                  RURAL   54  Moderate   \n",
       "94815     2230.0      FOREST                  RURAL   85  Moderate   \n",
       "94816     2230.0      FOREST                  RURAL   69  Moderate   \n",
       "94817     2230.0      FOREST                  RURAL   55  Moderate   \n",
       "94818     2230.0      FOREST                  RURAL   70  Moderate   \n",
       "\n",
       "      Defining Parameter Defining Site  Number of Sites Reporting  \n",
       "0                  PM2.5   01-073-0023                          9  \n",
       "1                  PM2.5   01-073-0023                          9  \n",
       "2                    NO2   01-073-0023                          9  \n",
       "3                  Ozone   01-073-0023                          9  \n",
       "4                  Ozone   01-073-0023                          8  \n",
       "...                  ...           ...                        ...  \n",
       "94814              PM2.5   56-039-1013                          3  \n",
       "94815              PM2.5   56-039-1013                          3  \n",
       "94816              PM2.5   56-039-1013                          3  \n",
       "94817              PM2.5   56-039-1013                          4  \n",
       "94818              PM2.5   56-039-1013                          3  \n",
       "\n",
       "[94819 rows x 20 columns]"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_data_set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## US Emissions data set\n",
    "\n",
    "## Incorporating US GH emissions by state/county as features "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['V_GHG_EMITTER_GAS.ADDRESS1', 'V_GHG_EMITTER_GAS.ADDRESS2',\n",
       "       'V_GHG_EMITTER_GAS.CITY', 'V_GHG_EMITTER_GAS.CO2E_EMISSION',\n",
       "       'V_GHG_EMITTER_GAS.COUNTY', 'V_GHG_EMITTER_GAS.FACILITY_ID',\n",
       "       'V_GHG_EMITTER_GAS.GAS_CODE', 'V_GHG_EMITTER_GAS.GAS_NAME',\n",
       "       'V_GHG_EMITTER_GAS.LATITUDE', 'V_GHG_EMITTER_GAS.LONGITUDE',\n",
       "       'V_GHG_EMITTER_GAS.STATE', 'V_GHG_EMITTER_GAS.STATE_NAME',\n",
       "       'V_GHG_EMITTER_GAS.YEAR', 'V_GHG_EMITTER_GAS.ZIP',\n",
       "       'V_GHG_EMITTER_GAS.FACILITY_NAME', 'V_GHG_EMITTER_GAS.COUNTY_FIPS'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_greenhouse_gas_emission_direct_emitter_gas_type.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>V_GHG_EMITTER_GAS.ADDRESS1</th>\n",
       "      <th>V_GHG_EMITTER_GAS.ADDRESS2</th>\n",
       "      <th>V_GHG_EMITTER_GAS.CITY</th>\n",
       "      <th>V_GHG_EMITTER_GAS.CO2E_EMISSION</th>\n",
       "      <th>V_GHG_EMITTER_GAS.COUNTY</th>\n",
       "      <th>V_GHG_EMITTER_GAS.FACILITY_ID</th>\n",
       "      <th>V_GHG_EMITTER_GAS.GAS_CODE</th>\n",
       "      <th>V_GHG_EMITTER_GAS.GAS_NAME</th>\n",
       "      <th>V_GHG_EMITTER_GAS.LATITUDE</th>\n",
       "      <th>V_GHG_EMITTER_GAS.LONGITUDE</th>\n",
       "      <th>V_GHG_EMITTER_GAS.STATE</th>\n",
       "      <th>V_GHG_EMITTER_GAS.STATE_NAME</th>\n",
       "      <th>V_GHG_EMITTER_GAS.YEAR</th>\n",
       "      <th>V_GHG_EMITTER_GAS.ZIP</th>\n",
       "      <th>V_GHG_EMITTER_GAS.FACILITY_NAME</th>\n",
       "      <th>V_GHG_EMITTER_GAS.COUNTY_FIPS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1919 S. BROADWAY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GREEN BAY</td>\n",
       "      <td>58024.000</td>\n",
       "      <td>BROWN COUNTY</td>\n",
       "      <td>1000589</td>\n",
       "      <td>BIOCO2</td>\n",
       "      <td>Biogenic CO2</td>\n",
       "      <td>44.492500</td>\n",
       "      <td>-88.032300</td>\n",
       "      <td>WI</td>\n",
       "      <td>WISCONSIN</td>\n",
       "      <td>2017</td>\n",
       "      <td>54304</td>\n",
       "      <td>GEORGIA-PACIFIC CONSUMER OPERATIONS LLC</td>\n",
       "      <td>55009.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>850 12TH AVE</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>134.500</td>\n",
       "      <td>New York</td>\n",
       "      <td>1000766</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>40.771100</td>\n",
       "      <td>-73.991100</td>\n",
       "      <td>NY</td>\n",
       "      <td>NEW YORK</td>\n",
       "      <td>2018</td>\n",
       "      <td>10019</td>\n",
       "      <td>59th Street</td>\n",
       "      <td>36061.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3379 HWY 482</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Noble</td>\n",
       "      <td>6.854</td>\n",
       "      <td>SABINE</td>\n",
       "      <td>1009343</td>\n",
       "      <td>N2O</td>\n",
       "      <td>Nitrous Oxide</td>\n",
       "      <td>31.621528</td>\n",
       "      <td>-93.724774</td>\n",
       "      <td>LA</td>\n",
       "      <td>LOUISIANA</td>\n",
       "      <td>2012</td>\n",
       "      <td>71462</td>\n",
       "      <td>Baker Road Treater</td>\n",
       "      <td>22085.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1012 BEAUCHAMP ST</td>\n",
       "      <td>NaN</td>\n",
       "      <td>GREENVILLE</td>\n",
       "      <td>53562.000</td>\n",
       "      <td>WASHINGTON COUNTY</td>\n",
       "      <td>1003557</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>33.392476</td>\n",
       "      <td>-91.017584</td>\n",
       "      <td>MS</td>\n",
       "      <td>MISSISSIPPI</td>\n",
       "      <td>2017</td>\n",
       "      <td>38701</td>\n",
       "      <td>TEXAS GAS TRANSMISSION - GREENVILLE STATION</td>\n",
       "      <td>28151.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>487 CORN CREEK ROAD</td>\n",
       "      <td>NaN</td>\n",
       "      <td>BEDFORD</td>\n",
       "      <td>7635064.700</td>\n",
       "      <td>Trimble</td>\n",
       "      <td>1006542</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>38.584700</td>\n",
       "      <td>-85.411700</td>\n",
       "      <td>KY</td>\n",
       "      <td>KENTUCKY</td>\n",
       "      <td>2018</td>\n",
       "      <td>40006</td>\n",
       "      <td>Trimble County</td>\n",
       "      <td>21223.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>219923</th>\n",
       "      <td>14353 McFarland Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>1929.800</td>\n",
       "      <td>SKAGIT COUNTY</td>\n",
       "      <td>1010898</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>48.448041</td>\n",
       "      <td>-122.433615</td>\n",
       "      <td>WA</td>\n",
       "      <td>WASHINGTON</td>\n",
       "      <td>2016</td>\n",
       "      <td>98273</td>\n",
       "      <td>Sierra Pacific Industries - Burlington</td>\n",
       "      <td>53057.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219924</th>\n",
       "      <td>7628 Thorndike Road</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>2776.500</td>\n",
       "      <td>GUILFORD COUNTY</td>\n",
       "      <td>1010033</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>36.075650</td>\n",
       "      <td>-79.965030</td>\n",
       "      <td>NC</td>\n",
       "      <td>NORTH CAROLINA</td>\n",
       "      <td>2018</td>\n",
       "      <td>27409</td>\n",
       "      <td>QORVO</td>\n",
       "      <td>37081.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219925</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Calumet</td>\n",
       "      <td>28352.800</td>\n",
       "      <td>CANADIAN COUNTY</td>\n",
       "      <td>1011778</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>35.516370</td>\n",
       "      <td>-98.182280</td>\n",
       "      <td>OK</td>\n",
       "      <td>OKLAHOMA</td>\n",
       "      <td>2014</td>\n",
       "      <td>73014</td>\n",
       "      <td>OFS CS Cedar Lake</td>\n",
       "      <td>40017.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219926</th>\n",
       "      <td>1186 Calkins Rd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Troy</td>\n",
       "      <td>23.840</td>\n",
       "      <td>BRADFORD</td>\n",
       "      <td>1010571</td>\n",
       "      <td>N2O</td>\n",
       "      <td>Nitrous Oxide</td>\n",
       "      <td>41.730488</td>\n",
       "      <td>-76.707873</td>\n",
       "      <td>PA</td>\n",
       "      <td>PENNSYLVANIA</td>\n",
       "      <td>2012</td>\n",
       "      <td>16947</td>\n",
       "      <td>Granville Compressor Station</td>\n",
       "      <td>42015.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219927</th>\n",
       "      <td>1186 Calkins Rd</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Troy</td>\n",
       "      <td>23.000</td>\n",
       "      <td>BRADFORD COUNTY</td>\n",
       "      <td>1010571</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>41.730488</td>\n",
       "      <td>-76.707873</td>\n",
       "      <td>PA</td>\n",
       "      <td>PENNSYLVANIA</td>\n",
       "      <td>2013</td>\n",
       "      <td>16947</td>\n",
       "      <td>Granville Compressor Station</td>\n",
       "      <td>42015.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219928 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       V_GHG_EMITTER_GAS.ADDRESS1  V_GHG_EMITTER_GAS.ADDRESS2  \\\n",
       "0                1919 S. BROADWAY                         NaN   \n",
       "1                    850 12TH AVE                         NaN   \n",
       "2                    3379 HWY 482                         NaN   \n",
       "3               1012 BEAUCHAMP ST                         NaN   \n",
       "4             487 CORN CREEK ROAD                         NaN   \n",
       "...                           ...                         ...   \n",
       "219923       14353 McFarland Road                         NaN   \n",
       "219924        7628 Thorndike Road                         NaN   \n",
       "219925                        NaN                         NaN   \n",
       "219926            1186 Calkins Rd                         NaN   \n",
       "219927            1186 Calkins Rd                         NaN   \n",
       "\n",
       "       V_GHG_EMITTER_GAS.CITY  V_GHG_EMITTER_GAS.CO2E_EMISSION  \\\n",
       "0                   GREEN BAY                        58024.000   \n",
       "1                    NEW YORK                          134.500   \n",
       "2                       Noble                            6.854   \n",
       "3                  GREENVILLE                        53562.000   \n",
       "4                     BEDFORD                      7635064.700   \n",
       "...                       ...                              ...   \n",
       "219923             Burlington                         1929.800   \n",
       "219924             Greensboro                         2776.500   \n",
       "219925                Calumet                        28352.800   \n",
       "219926                   Troy                           23.840   \n",
       "219927                   Troy                           23.000   \n",
       "\n",
       "       V_GHG_EMITTER_GAS.COUNTY  V_GHG_EMITTER_GAS.FACILITY_ID  \\\n",
       "0                  BROWN COUNTY                        1000589   \n",
       "1                      New York                        1000766   \n",
       "2                        SABINE                        1009343   \n",
       "3             WASHINGTON COUNTY                        1003557   \n",
       "4                       Trimble                        1006542   \n",
       "...                         ...                            ...   \n",
       "219923            SKAGIT COUNTY                        1010898   \n",
       "219924          GUILFORD COUNTY                        1010033   \n",
       "219925          CANADIAN COUNTY                        1011778   \n",
       "219926                 BRADFORD                        1010571   \n",
       "219927          BRADFORD COUNTY                        1010571   \n",
       "\n",
       "       V_GHG_EMITTER_GAS.GAS_CODE V_GHG_EMITTER_GAS.GAS_NAME  \\\n",
       "0                          BIOCO2               Biogenic CO2   \n",
       "1                             CH4                    Methane   \n",
       "2                             N2O              Nitrous Oxide   \n",
       "3                             CH4                    Methane   \n",
       "4                             CO2             Carbon Dioxide   \n",
       "...                           ...                        ...   \n",
       "219923                        CO2             Carbon Dioxide   \n",
       "219924                        CO2             Carbon Dioxide   \n",
       "219925                        CO2             Carbon Dioxide   \n",
       "219926                        N2O              Nitrous Oxide   \n",
       "219927                        CH4                    Methane   \n",
       "\n",
       "        V_GHG_EMITTER_GAS.LATITUDE  V_GHG_EMITTER_GAS.LONGITUDE  \\\n",
       "0                        44.492500                   -88.032300   \n",
       "1                        40.771100                   -73.991100   \n",
       "2                        31.621528                   -93.724774   \n",
       "3                        33.392476                   -91.017584   \n",
       "4                        38.584700                   -85.411700   \n",
       "...                            ...                          ...   \n",
       "219923                   48.448041                  -122.433615   \n",
       "219924                   36.075650                   -79.965030   \n",
       "219925                   35.516370                   -98.182280   \n",
       "219926                   41.730488                   -76.707873   \n",
       "219927                   41.730488                   -76.707873   \n",
       "\n",
       "       V_GHG_EMITTER_GAS.STATE V_GHG_EMITTER_GAS.STATE_NAME  \\\n",
       "0                           WI                    WISCONSIN   \n",
       "1                           NY                     NEW YORK   \n",
       "2                           LA                    LOUISIANA   \n",
       "3                           MS                  MISSISSIPPI   \n",
       "4                           KY                     KENTUCKY   \n",
       "...                        ...                          ...   \n",
       "219923                      WA                   WASHINGTON   \n",
       "219924                      NC               NORTH CAROLINA   \n",
       "219925                      OK                     OKLAHOMA   \n",
       "219926                      PA                 PENNSYLVANIA   \n",
       "219927                      PA                 PENNSYLVANIA   \n",
       "\n",
       "        V_GHG_EMITTER_GAS.YEAR  V_GHG_EMITTER_GAS.ZIP  \\\n",
       "0                         2017                  54304   \n",
       "1                         2018                  10019   \n",
       "2                         2012                  71462   \n",
       "3                         2017                  38701   \n",
       "4                         2018                  40006   \n",
       "...                        ...                    ...   \n",
       "219923                    2016                  98273   \n",
       "219924                    2018                  27409   \n",
       "219925                    2014                  73014   \n",
       "219926                    2012                  16947   \n",
       "219927                    2013                  16947   \n",
       "\n",
       "                    V_GHG_EMITTER_GAS.FACILITY_NAME  \\\n",
       "0           GEORGIA-PACIFIC CONSUMER OPERATIONS LLC   \n",
       "1                                       59th Street   \n",
       "2                                Baker Road Treater   \n",
       "3       TEXAS GAS TRANSMISSION - GREENVILLE STATION   \n",
       "4                                    Trimble County   \n",
       "...                                             ...   \n",
       "219923       Sierra Pacific Industries - Burlington   \n",
       "219924                                        QORVO   \n",
       "219925                            OFS CS Cedar Lake   \n",
       "219926                 Granville Compressor Station   \n",
       "219927                 Granville Compressor Station   \n",
       "\n",
       "        V_GHG_EMITTER_GAS.COUNTY_FIPS  \n",
       "0                             55009.0  \n",
       "1                             36061.0  \n",
       "2                             22085.0  \n",
       "3                             28151.0  \n",
       "4                             21223.0  \n",
       "...                               ...  \n",
       "219923                        53057.0  \n",
       "219924                        37081.0  \n",
       "219925                        40017.0  \n",
       "219926                        42015.0  \n",
       "219927                        42015.0  \n",
       "\n",
       "[219928 rows x 16 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_greenhouse_gas_emission_direct_emitter_gas_type"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "US_GH_gas_emissions = us_greenhouse_gas_emission_direct_emitter_gas_type[[\"V_GHG_EMITTER_GAS.STATE_NAME\", \"V_GHG_EMITTER_GAS.COUNTY\", \"V_GHG_EMITTER_GAS.CITY\", \"V_GHG_EMITTER_GAS.CO2E_EMISSION\", \"V_GHG_EMITTER_GAS.GAS_CODE\", \"V_GHG_EMITTER_GAS.GAS_NAME\", \"V_GHG_EMITTER_GAS.YEAR\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.options.mode.chained_assignment = None\n",
    "\n",
    "# Renaming columns and making sure the names match with the final_data set we will eventually merge with.\n",
    "US_GH_gas_emissions.columns = [\"State Name\", \"County Name\", \"City Name\", \"CO2 equiv emission\", \"Gas Code\", \"Gas Name\", \"Year\"]\n",
    "US_GH_gas_emissions[\"State Name\"] = US_GH_gas_emissions[\"State Name\"].str.title()\n",
    "US_GH_gas_emissions[\"County Name\"] = US_GH_gas_emissions[\"County Name\"].str.title()\n",
    "US_GH_gas_emissions[\"City Name\"] = US_GH_gas_emissions[\"City Name\"].str.title()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>CO2 equiv emission</th>\n",
       "      <th>Gas Code</th>\n",
       "      <th>Gas Name</th>\n",
       "      <th>Year</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Wisconsin</td>\n",
       "      <td>Brown County</td>\n",
       "      <td>Green Bay</td>\n",
       "      <td>58024.000</td>\n",
       "      <td>BIOCO2</td>\n",
       "      <td>Biogenic CO2</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>New York</td>\n",
       "      <td>134.500</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Louisiana</td>\n",
       "      <td>Sabine</td>\n",
       "      <td>Noble</td>\n",
       "      <td>6.854</td>\n",
       "      <td>N2O</td>\n",
       "      <td>Nitrous Oxide</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Mississippi</td>\n",
       "      <td>Washington County</td>\n",
       "      <td>Greenville</td>\n",
       "      <td>53562.000</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kentucky</td>\n",
       "      <td>Trimble</td>\n",
       "      <td>Bedford</td>\n",
       "      <td>7635064.700</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>219923</th>\n",
       "      <td>Washington</td>\n",
       "      <td>Skagit County</td>\n",
       "      <td>Burlington</td>\n",
       "      <td>1929.800</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>2016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219924</th>\n",
       "      <td>North Carolina</td>\n",
       "      <td>Guilford County</td>\n",
       "      <td>Greensboro</td>\n",
       "      <td>2776.500</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>2018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219925</th>\n",
       "      <td>Oklahoma</td>\n",
       "      <td>Canadian County</td>\n",
       "      <td>Calumet</td>\n",
       "      <td>28352.800</td>\n",
       "      <td>CO2</td>\n",
       "      <td>Carbon Dioxide</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219926</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Bradford</td>\n",
       "      <td>Troy</td>\n",
       "      <td>23.840</td>\n",
       "      <td>N2O</td>\n",
       "      <td>Nitrous Oxide</td>\n",
       "      <td>2012</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>219927</th>\n",
       "      <td>Pennsylvania</td>\n",
       "      <td>Bradford County</td>\n",
       "      <td>Troy</td>\n",
       "      <td>23.000</td>\n",
       "      <td>CH4</td>\n",
       "      <td>Methane</td>\n",
       "      <td>2013</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>219928 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            State Name        County Name   City Name  CO2 equiv emission  \\\n",
       "0            Wisconsin       Brown County   Green Bay           58024.000   \n",
       "1             New York           New York    New York             134.500   \n",
       "2            Louisiana             Sabine       Noble               6.854   \n",
       "3          Mississippi  Washington County  Greenville           53562.000   \n",
       "4             Kentucky            Trimble     Bedford         7635064.700   \n",
       "...                ...                ...         ...                 ...   \n",
       "219923      Washington      Skagit County  Burlington            1929.800   \n",
       "219924  North Carolina    Guilford County  Greensboro            2776.500   \n",
       "219925        Oklahoma    Canadian County     Calumet           28352.800   \n",
       "219926    Pennsylvania           Bradford        Troy              23.840   \n",
       "219927    Pennsylvania    Bradford County        Troy              23.000   \n",
       "\n",
       "       Gas Code        Gas Name  Year  \n",
       "0        BIOCO2    Biogenic CO2  2017  \n",
       "1           CH4         Methane  2018  \n",
       "2           N2O   Nitrous Oxide  2012  \n",
       "3           CH4         Methane  2017  \n",
       "4           CO2  Carbon Dioxide  2018  \n",
       "...         ...             ...   ...  \n",
       "219923      CO2  Carbon Dioxide  2016  \n",
       "219924      CO2  Carbon Dioxide  2018  \n",
       "219925      CO2  Carbon Dioxide  2014  \n",
       "219926      N2O   Nitrous Oxide  2012  \n",
       "219927      CH4         Methane  2013  \n",
       "\n",
       "[219928 rows x 7 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "US_GH_gas_emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grouped_US_GH_gas_emissions = US_GH_gas_emissions.groupby([\"State Name\", \"County Name\", \"City Name\", \"Gas Code\"])[\"CO2 equiv emission\"].agg(\"mean\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>Gas Code</th>\n",
       "      <th>CO2 equiv emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>CH4</td>\n",
       "      <td>9.502500e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>CO2</td>\n",
       "      <td>2.049189e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>N2O</td>\n",
       "      <td>1.132698e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Billingsley</td>\n",
       "      <td>CH4</td>\n",
       "      <td>6.356875e+02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Billingsley</td>\n",
       "      <td>CO2</td>\n",
       "      <td>1.375766e+06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23017</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Osage</td>\n",
       "      <td>CO2</td>\n",
       "      <td>7.340513e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23018</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Osage</td>\n",
       "      <td>N2O</td>\n",
       "      <td>1.081740e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23019</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>CH4</td>\n",
       "      <td>1.263103e+03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23020</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>CO2</td>\n",
       "      <td>9.864694e+04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23021</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>N2O</td>\n",
       "      <td>2.266904e+02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23022 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      State Name    County Name     City Name Gas Code  CO2 equiv emission\n",
       "0        Alabama        Autauga  Autaugaville      CH4        9.502500e+02\n",
       "1        Alabama        Autauga  Autaugaville      CO2        2.049189e+06\n",
       "2        Alabama        Autauga  Autaugaville      N2O        1.132698e+03\n",
       "3        Alabama        Autauga   Billingsley      CH4        6.356875e+02\n",
       "4        Alabama        Autauga   Billingsley      CO2        1.375766e+06\n",
       "...          ...            ...           ...      ...                 ...\n",
       "23017    Wyoming         Weston         Osage      CO2        7.340513e+04\n",
       "23018    Wyoming         Weston         Osage      N2O        1.081740e+03\n",
       "23019    Wyoming  Weston County     Newcastle      CH4        1.263103e+03\n",
       "23020    Wyoming  Weston County     Newcastle      CO2        9.864694e+04\n",
       "23021    Wyoming  Weston County     Newcastle      N2O        2.266904e+02\n",
       "\n",
       "[23022 rows x 5 columns]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# CO2 equiv emissions are the average annual CO2 equiv emissions for the gas.\n",
    "Grouped_US_GH_gas_emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Convert to daily GH gas emissions\n",
    "Grouped_US_GH_gas_emissions[\"Average Daily CO2 equiv emission\"] = Grouped_US_GH_gas_emissions[\"CO2 equiv emission\"] / 366"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "Grouped_US_GH_gas_emissions = Grouped_US_GH_gas_emissions.drop(columns=\"CO2 equiv emission\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "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>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>Gas Code</th>\n",
       "      <th>Average Daily CO2 equiv emission</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>CH4</td>\n",
       "      <td>2.596311</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>CO2</td>\n",
       "      <td>5598.875820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>N2O</td>\n",
       "      <td>3.094803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Billingsley</td>\n",
       "      <td>CH4</td>\n",
       "      <td>1.736851</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Billingsley</td>\n",
       "      <td>CO2</td>\n",
       "      <td>3758.924495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23017</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Osage</td>\n",
       "      <td>CO2</td>\n",
       "      <td>200.560474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23018</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Osage</td>\n",
       "      <td>N2O</td>\n",
       "      <td>2.955574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23019</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>CH4</td>\n",
       "      <td>3.451101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23020</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>CO2</td>\n",
       "      <td>269.527162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23021</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>N2O</td>\n",
       "      <td>0.619373</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23022 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      State Name    County Name     City Name Gas Code  \\\n",
       "0        Alabama        Autauga  Autaugaville      CH4   \n",
       "1        Alabama        Autauga  Autaugaville      CO2   \n",
       "2        Alabama        Autauga  Autaugaville      N2O   \n",
       "3        Alabama        Autauga   Billingsley      CH4   \n",
       "4        Alabama        Autauga   Billingsley      CO2   \n",
       "...          ...            ...           ...      ...   \n",
       "23017    Wyoming         Weston         Osage      CO2   \n",
       "23018    Wyoming         Weston         Osage      N2O   \n",
       "23019    Wyoming  Weston County     Newcastle      CH4   \n",
       "23020    Wyoming  Weston County     Newcastle      CO2   \n",
       "23021    Wyoming  Weston County     Newcastle      N2O   \n",
       "\n",
       "       Average Daily CO2 equiv emission  \n",
       "0                              2.596311  \n",
       "1                           5598.875820  \n",
       "2                              3.094803  \n",
       "3                              1.736851  \n",
       "4                           3758.924495  \n",
       "...                                 ...  \n",
       "23017                        200.560474  \n",
       "23018                          2.955574  \n",
       "23019                          3.451101  \n",
       "23020                        269.527162  \n",
       "23021                          0.619373  \n",
       "\n",
       "[23022 rows x 5 columns]"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Grouped_US_GH_gas_emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "Pivot_Grouped_US_GH_gas_emissions = Grouped_US_GH_gas_emissions.pivot_table('Average Daily CO2 equiv emission', [\"State Name\", \"County Name\", \"City Name\"], \"Gas Code\").reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "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>Gas Code</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>BIOCO2</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HFC</th>\n",
       "      <th>HFE</th>\n",
       "      <th>N2O</th>\n",
       "      <th>NF3</th>\n",
       "      <th>Other</th>\n",
       "      <th>Other_Full</th>\n",
       "      <th>PFC</th>\n",
       "      <th>SF6</th>\n",
       "      <th>Very_Short</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.596311</td>\n",
       "      <td>5598.875820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.094803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Billingsley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.736851</td>\n",
       "      <td>3758.924495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.072728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Prattville</td>\n",
       "      <td>4362.405137</td>\n",
       "      <td>72.520516</td>\n",
       "      <td>860.282705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.186514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>Summerdale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>499.213388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>Eufaula</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.055024</td>\n",
       "      <td>105.047787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>7672</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Uinta County</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.354679</td>\n",
       "      <td>36.397404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7673</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Worland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.038934</td>\n",
       "      <td>66.135656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie County</td>\n",
       "      <td>Worland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.047558</td>\n",
       "      <td>79.295663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.067885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Osage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.155055</td>\n",
       "      <td>200.560474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.955574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7676</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.451101</td>\n",
       "      <td>269.527162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.619373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7677 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Gas Code State Name      County Name      City Name       BIOCO2         CH4  \\\n",
       "0           Alabama          Autauga   Autaugaville          NaN    2.596311   \n",
       "1           Alabama          Autauga    Billingsley          NaN    1.736851   \n",
       "2           Alabama          Autauga     Prattville  4362.405137   72.520516   \n",
       "3           Alabama   Baldwin County     Summerdale          NaN  499.213388   \n",
       "4           Alabama   Barbour County        Eufaula          NaN    0.055024   \n",
       "...             ...              ...            ...          ...         ...   \n",
       "7672        Wyoming     Uinta County  Mountain View          NaN    5.354679   \n",
       "7673        Wyoming         Washakie        Worland          NaN    0.038934   \n",
       "7674        Wyoming  Washakie County        Worland          NaN    0.047558   \n",
       "7675        Wyoming           Weston          Osage          NaN    0.155055   \n",
       "7676        Wyoming    Weston County      Newcastle          NaN    3.451101   \n",
       "\n",
       "Gas Code          CO2  HFC  HFE        N2O  NF3  Other  Other_Full  PFC  SF6  \\\n",
       "0         5598.875820  NaN  NaN   3.094803  NaN    NaN         NaN  NaN  NaN   \n",
       "1         3758.924495  NaN  NaN   2.072728  NaN    NaN         NaN  NaN  NaN   \n",
       "2          860.282705  NaN  NaN  31.186514  NaN    NaN         NaN  NaN  NaN   \n",
       "3                 NaN  NaN  NaN        NaN  NaN    NaN         NaN  NaN  NaN   \n",
       "4          105.047787  NaN  NaN   0.065589  NaN    NaN         NaN  NaN  NaN   \n",
       "...               ...  ...  ...        ...  ...    ...         ...  ...  ...   \n",
       "7672        36.397404  NaN  NaN   0.019948  NaN    NaN         NaN  NaN  NaN   \n",
       "7673        66.135656  NaN  NaN   0.054552  NaN    NaN         NaN  NaN  NaN   \n",
       "7674        79.295663  NaN  NaN   0.067885  NaN    NaN         NaN  NaN  NaN   \n",
       "7675       200.560474  NaN  NaN   2.955574  NaN    NaN         NaN  NaN  NaN   \n",
       "7676       269.527162  NaN  NaN   0.619373  NaN    NaN         NaN  NaN  NaN   \n",
       "\n",
       "Gas Code  Very_Short  \n",
       "0                NaN  \n",
       "1                NaN  \n",
       "2                NaN  \n",
       "3                NaN  \n",
       "4                NaN  \n",
       "...              ...  \n",
       "7672             NaN  \n",
       "7673             NaN  \n",
       "7674             NaN  \n",
       "7675             NaN  \n",
       "7676             NaN  \n",
       "\n",
       "[7677 rows x 15 columns]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pivot_Grouped_US_GH_gas_emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Resetting index\n",
    "Pivot_Grouped_US_GH_gas_emissions = Pivot_Grouped_US_GH_gas_emissions.rename_axis(None, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>BIOCO2</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CO2</th>\n",
       "      <th>HFC</th>\n",
       "      <th>HFE</th>\n",
       "      <th>N2O</th>\n",
       "      <th>NF3</th>\n",
       "      <th>Other</th>\n",
       "      <th>Other_Full</th>\n",
       "      <th>PFC</th>\n",
       "      <th>SF6</th>\n",
       "      <th>Very_Short</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.596311</td>\n",
       "      <td>5598.875820</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.094803</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Billingsley</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.736851</td>\n",
       "      <td>3758.924495</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.072728</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Prattville</td>\n",
       "      <td>4362.405137</td>\n",
       "      <td>72.520516</td>\n",
       "      <td>860.282705</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>31.186514</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>Summerdale</td>\n",
       "      <td>NaN</td>\n",
       "      <td>499.213388</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>Eufaula</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.055024</td>\n",
       "      <td>105.047787</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.065589</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>7672</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Uinta County</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.354679</td>\n",
       "      <td>36.397404</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.019948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7673</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Worland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.038934</td>\n",
       "      <td>66.135656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.054552</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie County</td>\n",
       "      <td>Worland</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.047558</td>\n",
       "      <td>79.295663</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.067885</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Osage</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.155055</td>\n",
       "      <td>200.560474</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.955574</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7676</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.451101</td>\n",
       "      <td>269.527162</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.619373</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7677 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State Name      County Name      City Name       BIOCO2         CH4  \\\n",
       "0       Alabama          Autauga   Autaugaville          NaN    2.596311   \n",
       "1       Alabama          Autauga    Billingsley          NaN    1.736851   \n",
       "2       Alabama          Autauga     Prattville  4362.405137   72.520516   \n",
       "3       Alabama   Baldwin County     Summerdale          NaN  499.213388   \n",
       "4       Alabama   Barbour County        Eufaula          NaN    0.055024   \n",
       "...         ...              ...            ...          ...         ...   \n",
       "7672    Wyoming     Uinta County  Mountain View          NaN    5.354679   \n",
       "7673    Wyoming         Washakie        Worland          NaN    0.038934   \n",
       "7674    Wyoming  Washakie County        Worland          NaN    0.047558   \n",
       "7675    Wyoming           Weston          Osage          NaN    0.155055   \n",
       "7676    Wyoming    Weston County      Newcastle          NaN    3.451101   \n",
       "\n",
       "              CO2  HFC  HFE        N2O  NF3  Other  Other_Full  PFC  SF6  \\\n",
       "0     5598.875820  NaN  NaN   3.094803  NaN    NaN         NaN  NaN  NaN   \n",
       "1     3758.924495  NaN  NaN   2.072728  NaN    NaN         NaN  NaN  NaN   \n",
       "2      860.282705  NaN  NaN  31.186514  NaN    NaN         NaN  NaN  NaN   \n",
       "3             NaN  NaN  NaN        NaN  NaN    NaN         NaN  NaN  NaN   \n",
       "4      105.047787  NaN  NaN   0.065589  NaN    NaN         NaN  NaN  NaN   \n",
       "...           ...  ...  ...        ...  ...    ...         ...  ...  ...   \n",
       "7672    36.397404  NaN  NaN   0.019948  NaN    NaN         NaN  NaN  NaN   \n",
       "7673    66.135656  NaN  NaN   0.054552  NaN    NaN         NaN  NaN  NaN   \n",
       "7674    79.295663  NaN  NaN   0.067885  NaN    NaN         NaN  NaN  NaN   \n",
       "7675   200.560474  NaN  NaN   2.955574  NaN    NaN         NaN  NaN  NaN   \n",
       "7676   269.527162  NaN  NaN   0.619373  NaN    NaN         NaN  NaN  NaN   \n",
       "\n",
       "      Very_Short  \n",
       "0            NaN  \n",
       "1            NaN  \n",
       "2            NaN  \n",
       "3            NaN  \n",
       "4            NaN  \n",
       "...          ...  \n",
       "7672         NaN  \n",
       "7673         NaN  \n",
       "7674         NaN  \n",
       "7675         NaN  \n",
       "7676         NaN  \n",
       "\n",
       "[7677 rows x 15 columns]"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Pivot_Grouped_US_GH_gas_emissions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State Name     7677\n",
       "County Name    7677\n",
       "City Name      7677\n",
       "BIOCO2         1360\n",
       "CH4            7599\n",
       "CO2            6908\n",
       "HFC              91\n",
       "HFE              36\n",
       "N2O            6507\n",
       "NF3              63\n",
       "Other            17\n",
       "Other_Full       69\n",
       "PFC              96\n",
       "SF6             226\n",
       "Very_Short       50\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get number of non null entires (to see which gasses to add to the data set)\n",
    "Pivot_Grouped_US_GH_gas_emissions.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Based on the counts of non null entries, we have a reasonable amount of data for BIOCO2, CH4, CO2, and N2O. So we will try to use these as features. We can revist this if need be."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Truncating data to the gasses we want and filling the NaN with 0s\n",
    "Truncated_US_GH_gas_emissions = Pivot_Grouped_US_GH_gas_emissions[[\"State Name\", \"County Name\", \"City Name\", \"BIOCO2\", \"CH4\", \"CO2\", \"N2O\"]].fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>BIOCO2</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CO2</th>\n",
       "      <th>N2O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Autaugaville</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>2.596311</td>\n",
       "      <td>5598.875820</td>\n",
       "      <td>3.094803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Billingsley</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.736851</td>\n",
       "      <td>3758.924495</td>\n",
       "      <td>2.072728</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Autauga</td>\n",
       "      <td>Prattville</td>\n",
       "      <td>4362.405137</td>\n",
       "      <td>72.520516</td>\n",
       "      <td>860.282705</td>\n",
       "      <td>31.186514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Baldwin County</td>\n",
       "      <td>Summerdale</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>499.213388</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Alabama</td>\n",
       "      <td>Barbour County</td>\n",
       "      <td>Eufaula</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.055024</td>\n",
       "      <td>105.047787</td>\n",
       "      <td>0.065589</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "    <tr>\n",
       "      <th>7672</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Uinta County</td>\n",
       "      <td>Mountain View</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.354679</td>\n",
       "      <td>36.397404</td>\n",
       "      <td>0.019948</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7673</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie</td>\n",
       "      <td>Worland</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.038934</td>\n",
       "      <td>66.135656</td>\n",
       "      <td>0.054552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7674</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Washakie County</td>\n",
       "      <td>Worland</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.047558</td>\n",
       "      <td>79.295663</td>\n",
       "      <td>0.067885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7675</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston</td>\n",
       "      <td>Osage</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.155055</td>\n",
       "      <td>200.560474</td>\n",
       "      <td>2.955574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7676</th>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Weston County</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.451101</td>\n",
       "      <td>269.527162</td>\n",
       "      <td>0.619373</td>\n",
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       "<p>7677 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     State Name      County Name      City Name       BIOCO2         CH4  \\\n",
       "0       Alabama          Autauga   Autaugaville     0.000000    2.596311   \n",
       "1       Alabama          Autauga    Billingsley     0.000000    1.736851   \n",
       "2       Alabama          Autauga     Prattville  4362.405137   72.520516   \n",
       "3       Alabama   Baldwin County     Summerdale     0.000000  499.213388   \n",
       "4       Alabama   Barbour County        Eufaula     0.000000    0.055024   \n",
       "...         ...              ...            ...          ...         ...   \n",
       "7672    Wyoming     Uinta County  Mountain View     0.000000    5.354679   \n",
       "7673    Wyoming         Washakie        Worland     0.000000    0.038934   \n",
       "7674    Wyoming  Washakie County        Worland     0.000000    0.047558   \n",
       "7675    Wyoming           Weston          Osage     0.000000    0.155055   \n",
       "7676    Wyoming    Weston County      Newcastle     0.000000    3.451101   \n",
       "\n",
       "              CO2        N2O  \n",
       "0     5598.875820   3.094803  \n",
       "1     3758.924495   2.072728  \n",
       "2      860.282705  31.186514  \n",
       "3        0.000000   0.000000  \n",
       "4      105.047787   0.065589  \n",
       "...           ...        ...  \n",
       "7672    36.397404   0.019948  \n",
       "7673    66.135656   0.054552  \n",
       "7674    79.295663   0.067885  \n",
       "7675   200.560474   2.955574  \n",
       "7676   269.527162   0.619373  \n",
       "\n",
       "[7677 rows x 7 columns]"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Truncated_US_GH_gas_emissions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Merging Gas data with our Final Features Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Merging Gas data with our Final Features Dataset\n",
    "complete_data_set = final_data_set.merge(Truncated_US_GH_gas_emissions, on=[\"State Name\", \"County Name\", \"City Name\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>County Name</th>\n",
       "      <th>City Name</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date Local</th>\n",
       "      <th>Mean Temperature (F)</th>\n",
       "      <th>...</th>\n",
       "      <th>Location Setting</th>\n",
       "      <th>AQI</th>\n",
       "      <th>Category</th>\n",
       "      <th>Defining Parameter</th>\n",
       "      <th>Defining Site</th>\n",
       "      <th>Number of Sites Reporting</th>\n",
       "      <th>BIOCO2</th>\n",
       "      <th>CH4</th>\n",
       "      <th>CO2</th>\n",
       "      <th>N2O</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.8150</td>\n",
       "      <td>2020-01-09</td>\n",
       "      <td>52.125000</td>\n",
       "      <td>...</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>56</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194.591845</td>\n",
       "      <td>145.217054</td>\n",
       "      <td>0.555493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.8150</td>\n",
       "      <td>2020-01-10</td>\n",
       "      <td>65.750000</td>\n",
       "      <td>...</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>44</td>\n",
       "      <td>Good</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194.591845</td>\n",
       "      <td>145.217054</td>\n",
       "      <td>0.555493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.8150</td>\n",
       "      <td>2020-01-14</td>\n",
       "      <td>63.937500</td>\n",
       "      <td>...</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>30</td>\n",
       "      <td>Good</td>\n",
       "      <td>NO2</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194.591845</td>\n",
       "      <td>145.217054</td>\n",
       "      <td>0.555493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.8150</td>\n",
       "      <td>2020-01-16</td>\n",
       "      <td>58.012500</td>\n",
       "      <td>...</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>31</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194.591845</td>\n",
       "      <td>145.217054</td>\n",
       "      <td>0.555493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>Birmingham</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.8150</td>\n",
       "      <td>2020-01-17</td>\n",
       "      <td>54.945833</td>\n",
       "      <td>...</td>\n",
       "      <td>URBAN AND CENTER CITY</td>\n",
       "      <td>34</td>\n",
       "      <td>Good</td>\n",
       "      <td>Ozone</td>\n",
       "      <td>01-073-0023</td>\n",
       "      <td>8</td>\n",
       "      <td>0.0</td>\n",
       "      <td>194.591845</td>\n",
       "      <td>145.217054</td>\n",
       "      <td>0.555493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\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",
       "      <th>30140</th>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>Riverton</td>\n",
       "      <td>43.024210</td>\n",
       "      <td>-108.3637</td>\n",
       "      <td>2020-09-18</td>\n",
       "      <td>59.291667</td>\n",
       "      <td>...</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>106</td>\n",
       "      <td>Unhealthy for Sensitive Groups</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>56-013-0004</td>\n",
       "      <td>5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.998634</td>\n",
       "      <td>167.363388</td>\n",
       "      <td>0.091191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30141</th>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>Riverton</td>\n",
       "      <td>43.024210</td>\n",
       "      <td>-108.3637</td>\n",
       "      <td>2020-09-19</td>\n",
       "      <td>61.708333</td>\n",
       "      <td>...</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>60</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>56-013-0004</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.998634</td>\n",
       "      <td>167.363388</td>\n",
       "      <td>0.091191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30142</th>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>Riverton</td>\n",
       "      <td>43.024210</td>\n",
       "      <td>-108.3637</td>\n",
       "      <td>2020-10-05</td>\n",
       "      <td>56.416667</td>\n",
       "      <td>...</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>54</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM2.5</td>\n",
       "      <td>56-013-0004</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.998634</td>\n",
       "      <td>167.363388</td>\n",
       "      <td>0.091191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30143</th>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>Riverton</td>\n",
       "      <td>43.024210</td>\n",
       "      <td>-108.3637</td>\n",
       "      <td>2020-10-11</td>\n",
       "      <td>50.333333</td>\n",
       "      <td>...</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>52</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM10</td>\n",
       "      <td>56-013-0004</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.998634</td>\n",
       "      <td>167.363388</td>\n",
       "      <td>0.091191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30144</th>\n",
       "      <td>56</td>\n",
       "      <td>13</td>\n",
       "      <td>4</td>\n",
       "      <td>Wyoming</td>\n",
       "      <td>Fremont</td>\n",
       "      <td>Riverton</td>\n",
       "      <td>43.024210</td>\n",
       "      <td>-108.3637</td>\n",
       "      <td>2020-11-07</td>\n",
       "      <td>54.500000</td>\n",
       "      <td>...</td>\n",
       "      <td>SUBURBAN</td>\n",
       "      <td>52</td>\n",
       "      <td>Moderate</td>\n",
       "      <td>PM10</td>\n",
       "      <td>56-013-0004</td>\n",
       "      <td>4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.998634</td>\n",
       "      <td>167.363388</td>\n",
       "      <td>0.091191</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>30145 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       State Code  County Code  Site Num State Name County Name   City Name  \\\n",
       "0               1           73        23    Alabama   Jefferson  Birmingham   \n",
       "1               1           73        23    Alabama   Jefferson  Birmingham   \n",
       "2               1           73        23    Alabama   Jefferson  Birmingham   \n",
       "3               1           73        23    Alabama   Jefferson  Birmingham   \n",
       "4               1           73        23    Alabama   Jefferson  Birmingham   \n",
       "...           ...          ...       ...        ...         ...         ...   \n",
       "30140          56           13         4    Wyoming     Fremont    Riverton   \n",
       "30141          56           13         4    Wyoming     Fremont    Riverton   \n",
       "30142          56           13         4    Wyoming     Fremont    Riverton   \n",
       "30143          56           13         4    Wyoming     Fremont    Riverton   \n",
       "30144          56           13         4    Wyoming     Fremont    Riverton   \n",
       "\n",
       "        Latitude  Longitude  Date Local  Mean Temperature (F)  ...  \\\n",
       "0      33.553056   -86.8150  2020-01-09             52.125000  ...   \n",
       "1      33.553056   -86.8150  2020-01-10             65.750000  ...   \n",
       "2      33.553056   -86.8150  2020-01-14             63.937500  ...   \n",
       "3      33.553056   -86.8150  2020-01-16             58.012500  ...   \n",
       "4      33.553056   -86.8150  2020-01-17             54.945833  ...   \n",
       "...          ...        ...         ...                   ...  ...   \n",
       "30140  43.024210  -108.3637  2020-09-18             59.291667  ...   \n",
       "30141  43.024210  -108.3637  2020-09-19             61.708333  ...   \n",
       "30142  43.024210  -108.3637  2020-10-05             56.416667  ...   \n",
       "30143  43.024210  -108.3637  2020-10-11             50.333333  ...   \n",
       "30144  43.024210  -108.3637  2020-11-07             54.500000  ...   \n",
       "\n",
       "            Location Setting  AQI                        Category  \\\n",
       "0      URBAN AND CENTER CITY   56                        Moderate   \n",
       "1      URBAN AND CENTER CITY   44                            Good   \n",
       "2      URBAN AND CENTER CITY   30                            Good   \n",
       "3      URBAN AND CENTER CITY   31                            Good   \n",
       "4      URBAN AND CENTER CITY   34                            Good   \n",
       "...                      ...  ...                             ...   \n",
       "30140               SUBURBAN  106  Unhealthy for Sensitive Groups   \n",
       "30141               SUBURBAN   60                        Moderate   \n",
       "30142               SUBURBAN   54                        Moderate   \n",
       "30143               SUBURBAN   52                        Moderate   \n",
       "30144               SUBURBAN   52                        Moderate   \n",
       "\n",
       "      Defining Parameter Defining Site  Number of Sites Reporting BIOCO2  \\\n",
       "0                  PM2.5   01-073-0023                          9    0.0   \n",
       "1                  PM2.5   01-073-0023                          9    0.0   \n",
       "2                    NO2   01-073-0023                          9    0.0   \n",
       "3                  Ozone   01-073-0023                          9    0.0   \n",
       "4                  Ozone   01-073-0023                          8    0.0   \n",
       "...                  ...           ...                        ...    ...   \n",
       "30140              PM2.5   56-013-0004                          5    0.0   \n",
       "30141              PM2.5   56-013-0004                          4    0.0   \n",
       "30142              PM2.5   56-013-0004                          4    0.0   \n",
       "30143               PM10   56-013-0004                          4    0.0   \n",
       "30144               PM10   56-013-0004                          4    0.0   \n",
       "\n",
       "              CH4         CO2       N2O  \n",
       "0      194.591845  145.217054  0.555493  \n",
       "1      194.591845  145.217054  0.555493  \n",
       "2      194.591845  145.217054  0.555493  \n",
       "3      194.591845  145.217054  0.555493  \n",
       "4      194.591845  145.217054  0.555493  \n",
       "...           ...         ...       ...  \n",
       "30140    9.998634  167.363388  0.091191  \n",
       "30141    9.998634  167.363388  0.091191  \n",
       "30142    9.998634  167.363388  0.091191  \n",
       "30143    9.998634  167.363388  0.091191  \n",
       "30144    9.998634  167.363388  0.091191  \n",
       "\n",
       "[30145 rows x 24 columns]"
      ]
     },
     "execution_count": 226,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "complete_data_set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30145, 30145, 30145, 30145, 30145, 30145, 30145, 30145, 30145,\n",
       "       30145, 30145, 30145, 28016, 30145, 30145, 28558, 30145, 30145,\n",
       "       30145, 30145,  7076, 29787, 28599, 28421])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Non-zero count for each column\n",
    "np.count_nonzero(complete_data_set, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For this final aggregated dataset the BIOCO2, CH4, CO2, and N2O average daily emissions non-zero counts are 7076, 29787, 28599, and 28421, respectively. These values seem reasonable but if the lower values for BIOCO2 cause issues then we can remove that feature. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### From this EDA our final dataset is complete_data_set, and has the following features: State Name, County Name, City Name, Mean Temperature (F), Mean Wind Direction (compass degrees), Mean Wind Speed (knots), Elevation, Land Use, Location Setting, BIOCO2, CH4, CO2, and N2O. The dataset also contains the AQI which is what we will be trying to predict, along with some other columns which can be considered meta data.\n",
    "\n",
    "#### If time permits precipitation from the daily_global_weather_2020 should be added."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## EDA and Feature Selection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Encoding Categorical features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Define any additional helper functions you need here\n",
    "def scale_feature(data, col):\n",
    "    data[col] = (data[col] - data[col].min())/(data[col].max()-data[col].min())\n",
    "    return data\n",
    "\n",
    "def process_pipelines(data, pipelines):\n",
    "    for function, arguments, keyword_arguments in pipelines:\n",
    "        if keyword_arguments and (not arguments):\n",
    "            data = data.pipe(function, **keyword_arguments)\n",
    "        elif (not keyword_arguments) and (arguments):\n",
    "            data = data.pipe(function, *arguments)\n",
    "        else:\n",
    "            data = data.pipe(function)\n",
    "    return data\n",
    "     \n",
    "def get_cols(data,numeric,encoded):\n",
    "    selected = numeric\n",
    "    [selected.extend([f\"{e}_{v}\" for v in data[e].unique()]) for e in encoded]\n",
    "    return selected\n",
    "\n",
    "def root(data,col):\n",
    "    data[col] = data[col]**(1/2)\n",
    "    return data\n",
    "\n",
    "def cube(data,col):\n",
    "    data[col] = data[col]**(3)\n",
    "    return data\n",
    "\n",
    "def encode_categorical(data,**kwargs):\n",
    "    data = pd.get_dummies(data,**kwargs)\n",
    "    return data\n",
    "\n",
    "\n",
    "def outliar_bounds(data,col):\n",
    "    \n",
    "    quant3 = np.quantile(data[col], 0.75)\n",
    "    quant1 = np.quantile(data[col], 0.25)\n",
    "    \n",
    "    iqr = quant3 - quant1\n",
    " \n",
    "    bound_l= quant1 - 1.5 * iqr\n",
    "    bound_u = quant3 + 1.5 * iqr\n",
    "    \n",
    "    return {'variable': col,'lower': bound_l, 'upper': bound_u}\n",
    "\n",
    "def select_columns(data, *columns):\n",
    "    \"\"\"Select only columns passed as arguments.\"\"\"\n",
    "    return data.loc[:, columns]\n",
    "\n",
    "def remove_outliers(data, variable, lower=-np.inf, upper=np.inf):\n",
    "    \"\"\"\n",
    "    Input:\n",
    "      data (data frame): the table to be filtered\n",
    "      variable (string): the column with numerical outliers\n",
    "      lower (numeric): observations with values lower than this will be removed\n",
    "      upper (numeric): observations with values higher than this will be removed\n",
    "    \n",
    "    Output:\n",
    "      a data frame with outliers removed\n",
    "      \n",
    "    Note: This function should not change mutate the contents of data.\n",
    "    \"\"\"  \n",
    "    return data[data[variable].ge(lower) & data[variable].le(upper)]\n",
    "        \n",
    "    \n",
    "# Please include all of your feature engineering process inside this function.\n",
    "# Do not modify the parameters of this function.\n",
    "def process_data_fm(data, is_test_set=False):\n",
    "    #encode_cols = ['County Name','City Name', 'Land Use', 'Location Setting']\n",
    "    #select_cols = ['Mean Temperature (F)', 'Mean Wind Direction (compass degrees)',\n",
    "    #   'Mean Wind Speed (knots)', 'Elevation','BIOCO2', 'CH4', 'CO2', 'N2O']\n",
    "    \n",
    "    encode_cols = ['County Name','City Name', 'Land Use', 'Location Setting']\n",
    "    select_cols = ['Mean Temperature (F)', 'Mean Wind Direction (compass degrees)', 'Mean Wind Speed (knots)', 'Elevation']\n",
    "\n",
    "    m3_pipelines = []\n",
    "        \n",
    "    m3_pipelines.extend([\n",
    "    (encode_categorical,None, {'columns': encode_cols}),\n",
    "    (select_columns, get_cols(data,select_cols,encode_cols), None)\n",
    "    ])\n",
    "    if not is_test_set:        \n",
    "        select_cols.append('AQI')\n",
    "        m3_pipelines.extend([\n",
    "            (remove_outliers, None, outliar_bounds(data,'AQI')),\n",
    "        ])\n",
    "\n",
    "\n",
    "    data = process_pipelines(data, m3_pipelines)\n",
    "    \n",
    "    \n",
    "    # Return predictors and response variables separately\n",
    "    if is_test_set:\n",
    "        return data\n",
    "    else:\n",
    "        X = data.drop(['AQI'], axis = 1)\n",
    "        y = data.loc[:, 'AQI']\n",
    "        return X, y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(94290, 20)\n",
      "(94290, 20)\n"
     ]
    },
    {
     "data": {
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       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Mean Temperature (F)</th>\n",
       "      <th>Mean Wind Direction (compass degrees)</th>\n",
       "      <th>Mean Wind Speed (knots)</th>\n",
       "      <th>Elevation</th>\n",
       "      <th>County Name_Jefferson</th>\n",
       "      <th>County Name_Denali</th>\n",
       "      <th>County Name_Fairbanks North Star</th>\n",
       "      <th>County Name_Cochise</th>\n",
       "      <th>County Name_Coconino</th>\n",
       "      <th>County Name_Maricopa</th>\n",
       "      <th>...</th>\n",
       "      <th>Land Use_FOREST</th>\n",
       "      <th>Land Use_DESERT</th>\n",
       "      <th>Land Use_AGRICULTURAL</th>\n",
       "      <th>Land Use_INDUSTRIAL</th>\n",
       "      <th>Land Use_MOBILE</th>\n",
       "      <th>Land Use_MILITARY RESERVATION</th>\n",
       "      <th>Land Use_BLIGHTED AREAS</th>\n",
       "      <th>Location Setting_URBAN AND CENTER CITY</th>\n",
       "      <th>Location Setting_RURAL</th>\n",
       "      <th>Location Setting_SUBURBAN</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>85058 rows × 656 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Mean Temperature (F)  Mean Wind Direction (compass degrees)  \\\n",
       "0                 -0.219065                              -0.782889   \n",
       "1                  0.516111                              -0.367534   \n",
       "2                  0.418313                               0.338830   \n",
       "3                  0.098612                              -1.746408   \n",
       "4                 -0.066859                              -1.278971   \n",
       "...                     ...                                    ...   \n",
       "85053             -0.452882                              -0.020536   \n",
       "85054             -0.565295                              -0.383809   \n",
       "85055             -0.529323                              -0.278994   \n",
       "85056             -0.601267                              -0.290062   \n",
       "85057             -0.664218                               0.071258   \n",
       "\n",
       "       Mean Wind Speed (knots)  Elevation  County Name_Jefferson  \\\n",
       "0                     0.082434  -0.497328                      1   \n",
       "1                     0.958050  -0.497328                      1   \n",
       "2                    -0.485422  -0.497328                      1   \n",
       "3                     0.422876  -0.497328                      1   \n",
       "4                     0.274444  -0.497328                      1   \n",
       "...                        ...        ...                    ...   \n",
       "85053                -0.618876   3.142369                      0   \n",
       "85054                -0.772755   3.142369                      0   \n",
       "85055                -0.534446   3.142369                      0   \n",
       "85056                -0.666537   3.142369                      0   \n",
       "85057                -0.154513   3.142369                      0   \n",
       "\n",
       "       County Name_Denali   County Name_Fairbanks North Star   \\\n",
       "0                        0                                  0   \n",
       "1                        0                                  0   \n",
       "2                        0                                  0   \n",
       "3                        0                                  0   \n",
       "4                        0                                  0   \n",
       "...                    ...                                ...   \n",
       "85053                    0                                  0   \n",
       "85054                    0                                  0   \n",
       "85055                    0                                  0   \n",
       "85056                    0                                  0   \n",
       "85057                    0                                  0   \n",
       "\n",
       "       County Name_Cochise  County Name_Coconino  County Name_Maricopa  ...  \\\n",
       "0                        0                     0                     0  ...   \n",
       "1                        0                     0                     0  ...   \n",
       "2                        0                     0                     0  ...   \n",
       "3                        0                     0                     0  ...   \n",
       "4                        0                     0                     0  ...   \n",
       "...                    ...                   ...                   ...  ...   \n",
       "85053                    0                     0                     0  ...   \n",
       "85054                    0                     0                     0  ...   \n",
       "85055                    0                     0                     0  ...   \n",
       "85056                    0                     0                     0  ...   \n",
       "85057                    0                     0                     0  ...   \n",
       "\n",
       "       Land Use_FOREST  Land Use_DESERT  Land Use_AGRICULTURAL  \\\n",
       "0                    0                0                      0   \n",
       "1                    0                0                      0   \n",
       "2                    0                0                      0   \n",
       "3                    0                0                      0   \n",
       "4                    0                0                      0   \n",
       "...                ...              ...                    ...   \n",
       "85053                1                0                      0   \n",
       "85054                1                0                      0   \n",
       "85055                1                0                      0   \n",
       "85056                1                0                      0   \n",
       "85057                1                0                      0   \n",
       "\n",
       "       Land Use_INDUSTRIAL  Land Use_MOBILE  Land Use_MILITARY RESERVATION  \\\n",
       "0                        0                0                              0   \n",
       "1                        0                0                              0   \n",
       "2                        0                0                              0   \n",
       "3                        0                0                              0   \n",
       "4                        0                0                              0   \n",
       "...                    ...              ...                            ...   \n",
       "85053                    0                0                              0   \n",
       "85054                    0                0                              0   \n",
       "85055                    0                0                              0   \n",
       "85056                    0                0                              0   \n",
       "85057                    0                0                              0   \n",
       "\n",
       "       Land Use_BLIGHTED AREAS  Location Setting_URBAN AND CENTER CITY  \\\n",
       "0                            0                                       1   \n",
       "1                            0                                       1   \n",
       "2                            0                                       1   \n",
       "3                            0                                       1   \n",
       "4                            0                                       1   \n",
       "...                        ...                                     ...   \n",
       "85053                        0                                       0   \n",
       "85054                        0                                       0   \n",
       "85055                        0                                       0   \n",
       "85056                        0                                       0   \n",
       "85057                        0                                       0   \n",
       "\n",
       "       Location Setting_RURAL  Location Setting_SUBURBAN  \n",
       "0                           0                          0  \n",
       "1                           0                          0  \n",
       "2                           0                          0  \n",
       "3                           0                          0  \n",
       "4                           0                          0  \n",
       "...                       ...                        ...  \n",
       "85053                       1                          0  \n",
       "85054                       1                          0  \n",
       "85055                       1                          0  \n",
       "85056                       1                          0  \n",
       "85057                       1                          0  \n",
       "\n",
       "[85058 rows x 656 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "scaler = StandardScaler()\n",
    "\n",
    "#encode_cols = ['County Name','City Name', 'Land Use', 'Location Setting']\n",
    "#select_cols = ['Mean Temperature (F)', 'Mean Wind Direction (compass degrees)',\n",
    "#   'Mean Wind Speed (knots)', 'Elevation','BIOCO2', 'CH4', 'CO2', 'N2O']\n",
    "\n",
    "print(final_data_set.shape)\n",
    "#display(final_data_set[final_data_set.isna().any(axis=1)])\n",
    "\n",
    "#remove NaN values\n",
    "final_data_set = final_data_set.dropna()\n",
    "\n",
    "print(final_data_set.shape)\n",
    "#print(complete_data_set.shape)\n",
    "# Processing data\n",
    "X, y = process_data_fm(final_data_set)\n",
    "\n",
    "#display(X.iloc[:, 0:4])\n",
    "\n",
    "# Resetting columns to avoid NaN values when joining below\n",
    "X = X.reset_index().drop(columns='index')\n",
    "y = y.reset_index().drop(columns='index').squeeze()\n",
    "\n",
    "# Select numeric values and encoded values seperately\n",
    "X_encoded = X.iloc[:, 4:]\n",
    "X_numeric = X.iloc[:, 0:4]\n",
    "\n",
    "scaler.fit(X_numeric)\n",
    "\n",
    "X_numeric_scaled = pd.DataFrame(scaler.transform(X_numeric), columns=X_numeric.columns)\n",
    "\n",
    "X_scaled_encoded = X_numeric_scaled.join(X_encoded)\n",
    "\n",
    "display(X_scaled_encoded)\n",
    "\n",
    "# ONLY training and validation for now\n",
    "X_train, X_val, y_train, y_val = train_test_split(X, y, test_size=0.2,random_state=69)\n",
    "X_train_scaled, X_val_scaled, y_train_scaled, y_val_scaled = train_test_split(X_scaled_encoded, y, test_size=0.2,random_state=69)\n",
    "\n",
    "#X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.2,random_state=69)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(68046, 656)\n",
      "(17012, 656)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_val.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Feature Selection Exploration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature Selection using variance thresholds \n",
    "first we want to make sure we arent including any features that dont have predictive power anything without variance should be thrown out. If we have time we could look into refining a threshold on the variance but might be more effective to just use other feature selection tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 52.375   , 163.291667,   5.6125  , ...,   1.      ,   0.      ,\n",
       "          0.      ],\n",
       "       [ 80.      , 109.716667,   3.158333, ...,   0.      ,   0.      ,\n",
       "          1.      ],\n",
       "       [ 60.791667, 223.625   ,   3.7625  , ...,   1.      ,   0.      ,\n",
       "          0.      ],\n",
       "       ...,\n",
       "       [ 27.325   , 186.866667,   2.7625  , ...,   1.      ,   0.      ,\n",
       "          0.      ],\n",
       "       [ 84.958333, 111.4375  ,   4.2125  , ...,   1.      ,   0.      ,\n",
       "          0.      ],\n",
       "       [ 53.5     , 149.25    ,   1.395833, ...,   0.      ,   0.      ,\n",
       "          1.      ]])"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.feature_selection import VarianceThreshold\n",
    "\n",
    "\n",
    "selector = VarianceThreshold(0.001)\n",
    "X_selected = selector.fit_transform(X_train)\n",
    "\n",
    "\n",
    "display(X_selected)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(X_train.shape)\n",
    "print(X_selected.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Regression models\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### define a model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.model_selection import RepeatedKFold\n",
    "import sklearn.linear_model as lm\n",
    "\n",
    "\n",
    "# Attempting both lasso and Ridge regularization models\n",
    "lasso_model = Lasso()\n",
    "ridge_model = Ridge()\n",
    "linear_model = lm.LinearRegression()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Fit model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE using Linear regression: 120.85269561777532\n",
      "Training MSE using LASSO regression: 172.09675265538297\n",
      "Training MSE using Ridge regression: 120.89103762214157\n",
      "\n",
      "Validation MSE using Linear regression: 140564487472.8448\n",
      "Validation MSE using LASSO regression: 173.03889926954184\n",
      "Validation MSE using Ridge regression: 123.52308075475845\n"
     ]
    }
   ],
   "source": [
    "#Fitting the models\n",
    "linear_model.fit(X_train, y_train)\n",
    "lasso_model.fit(X_train_scaled, y_train_scaled)\n",
    "ridge_model.fit(X_train_scaled, y_train_scaled)\n",
    "\n",
    "y_pred_train_linear = linear_model.predict(X_train)\n",
    "y_pred_train_lasso = lasso_model.predict(X_train_scaled)\n",
    "y_pred_train_ridge = ridge_model.predict(X_train_scaled)\n",
    "\n",
    "y_pred_val_linear = linear_model.predict(X_val)\n",
    "y_pred_val_lasso = lasso_model.predict(X_val_scaled)\n",
    "y_pred_val_ridge = ridge_model.predict(X_val_scaled)\n",
    "\n",
    "# Model Predictions\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "print(f'Training MSE using Linear regression: {mean_squared_error(y_train, y_pred_train_linear)}')\n",
    "print(f'Training MSE using LASSO regression: {mean_squared_error(y_train_scaled, y_pred_train_lasso)}')\n",
    "print(f'Training MSE using Ridge regression: {mean_squared_error(y_train_scaled, y_pred_train_ridge)}')\n",
    "print(\"\")\n",
    "print(f'Validation MSE using Linear regression: {mean_squared_error(y_val, y_pred_val_linear)}')\n",
    "print(f'Validation MSE using LASSO regression: {mean_squared_error(y_val_scaled, y_pred_val_lasso)}')\n",
    "print(f'Validation MSE using Ridge regression: {mean_squared_error(y_val_scaled, y_pred_val_ridge)}')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 285,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[38.94354823 38.585987   37.22908431 ... 38.38601    37.31170737\n",
      " 37.66131433]\n",
      "73709    71\n",
      "77487     9\n",
      "84270    35\n",
      "39740    45\n",
      "73155    74\n",
      "         ..\n",
      "44624    74\n",
      "24154    31\n",
      "48612    63\n",
      "41587    24\n",
      "44030    39\n",
      "Name: AQI, Length: 17012, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "print(y_pred_val_lasso)\n",
    "print(y_val_scaled)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualize results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 286,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 287,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ridge Regression')"
      ]
     },
     "execution_count": 287,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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WP8sSiUTSSMyewWAeh/X/1z9nrcrYdzZUiEN3rG+3fDZr8ffU5Sgep41To1Fu6mulv72ZO9a3M5XM8NT+AVKZHF6njTcvTnLn+g4A9p4aZ2WLB7/HhtNmY3tfayEGmcW6gVCcr7x8mtOXo+ze1EVv0EsoNlOIzbFUlhWtbnasaed2Qzk397Ww71yYZqeNeDrHI7f2Mp3M8NU9Zwi47WztaaHZacebv96pZIbnjo5wc18Lbw1OFcU9/fjo4Pkwx4YizAI39bZy+9pgUax/dFc/LR4HLx4f5UIowQe3djM4mSwcbxU7zfpZ3zdAUb9cC3FUKIqy0HWoid7eXmVoaGihqyFpIJFEmvf/t71EU1n8bjs//4PdS/YDVQ79dfpcNhQgPpOb8zXry83kZrE3CQIeBz//g90A3PflPUwmMiXHtXod6uQpX4cffP4OPv61fURTWZpdNgQQm8kV/d+qrgOhOPd+aQ/a08RhEwTcjpqvK5JIc++XXiaSzBbecxiuR3+v6OvscdqYSpZe52JCAPYmQXZWoQnI6d7f88V7iyaZ5T4XA6E4u7+0p7rz2QTZnIK9SeBzqxPmaCpLZlYp7PPM43fymW8eMr2H9AHPWB+w7o+F/iwLIS4pitK7ICdf4sg4K5FcXbTn63QyQ3ZWwWFrKhon6OOw1fjBbCzgc9tRgIhuDNDmdfDyH95r+gXi7i+9zJQu/uoJuG1EUzlqmTUIMI11QFUxTF+O3SbI5Gqfs/jdNmKGemtx77FvHDQdH5Wrh89tJ5oqbSMtvhtjpx59HzXn+1HrG61fgCUxJq4UY+USWcmCc3RoimgqSzo3SzSV5dilqYWu0rygv87pVLZh16wvVwEys0qhTG2bGVFDHZ4/OlL0etri/2Z1fe7oSNHDO5NT6rquo0NTTBvqa3Y95nVe3JNLoNA/ClcCrvb+88dGivYt97l47mjxvmXPl1OK7otp3eRS2+fJAwOW91C5+pTvj2v3syyRSCSNRHuWavHBbJygxWGr8YPVWMA4BrB6NpcbL6jH1Ta5BCxjXbUxTH9cPZNLoGRyqZX31P6BstdrVY+YxTFWsVOPMWbqz28VV5dqHJUTTMmCs623Bb/bjtPWhN9t58ZVLQtdpXlBf50Bt71h16wvV6D+4qeVqW0zw2+ow0PbVha9Dlj836yuH9q2EqF77bCJuq5rW28LAUN9za7HvM6Oms61EGj9IwCb4f2HblxZtG+5z8WHthXvW/Z8NlF0XwTcdhxNomifx3b2W95D5epTvj+u3c+yRCKRNBLtWarFB7NxghaHrcYPVmMB4xjA6tlcbrygHmcrivPVYBXrqo1h+uMctlrPruIzqbcAHt3VX/Z6rerhszjGKnbqMcZM/fmt4upSjaNyiaxkURBJpDl2aYobVy3MevO5rnevRpumvaddJ1DxmqvVbGrlrg56uRhOFMrXtBjHR6ZBgS09gaL/vzs8jRCwa117YYmNvn77z4VQFFVboJVr1Hto+oG+Ng+vnpng7g0dRJKZuvsykkjz4vFRLk4mePD67pKyjPeK/vVgOMFTBwZ4dKeqwfyLl8+wucvH0GQSn9tOwONgQ6eqwdx3LkQincPeBBu7/Txyax8nR6McHZxCQWFjtw+vww4CEuks716aIhRPc8+mTm7ua+OZI8NMp9K47DbavE7eHZmmp8XNL92wgh8fGebcRIzfumsdOUXVcDY77WzJt6PWT60eR6HNzHSr5T4XA6E43zkwwEw2x7beVjZ3+3n1zATbe1vYfy5Mf7uXHWuCRefT33fauR+68YoWxXgPWd1n1fbHQi7rkUtk60fGWYlk/jHTr5vFcX18e3L/AB+7uYdIMlsUu/VlmpWxX9Nguu0lxxjr9OKJUU6NRhHAmxcj3LGunetWBtiV1zL+7WvnuDSZorfVzfHLUe5Y347gigbTaW8imsyye3Mnuzd3cjGcwCbgh0eGeWxnPzf2thbi/GtnxhkIJdi9qZOgz8k7l6YZnU7htAlGpmbY0OXjrg0d7FgbLIqZ23tbOHA+jNdhI5HO8St5DebX95zFaVcnZhu7/HQGXOxap9b7+WMjbO9t4cjQVFHc238uVBgTHTof5uilCCiwra+VHWuChffcdhuf3JnXYJ4YZSCU4ANbuhmKJBmfnuHiZILruv0F3anVeLJknHU2BIa+XCxxtByVYqycYEqWPXPVgFajTatnDf1cNJuVrqmaa66mjPu/vIewQT+wmB6GVtcwlz43aigq6VPrraNkbsgJZv3IOCuRzC+1Pvcb5XMw1/rVM7aAylp9/T5z8ahYiHhq7JtWr4NEA/w1FjtSgymRVGCu692r0abVs4Z+LprNSuevpn7VlKHXSy5GrYDVNcylf4zHVtKn1ltHiUQikVyb1Prcb5TPwVzrV8/YohqtfqM8KhYinhr7RsZzFTnBlCx75rrevRptWj1r6Oei2ax0/mrqV00Zer3kYtQKWF3DXPrHeGwlfWq9dZRIJBLJtUmtz/1G+RzMtX71jC2q0eo3yqNiIeKpsW9kPFeRS2QlEua+3r0abdpc6wWVNZuV6lTL9mrLMNMPLCasrmEu/TOXfqmljpL6kUtk60fGWYlk/qn1uT8QivP8sZE5+xzMtX71jC2q0eo3Kq4uRDzV+uahG1fS4nEsi3guNZgSSYOxMgSqlHC+WsOeas+vT6RcT3LeSvtWup5GlTsXzMoaCMV5+vAQ/UEvD27ptuwjKO6DgVCcp98YYk27lwe2dJdstzpW+3+t/VHNfWSsQyO4FhI4V4OcYNaPjLOShaQRz6hr4TlXTSx98fgo712Oct0KPw/k4512XJvXwU+Pj5rGQisGQnGeOjAAiuqy2t/eXDammtWz2jFSte8bY6tV3H/u6Agf2nbFvGc+46iRuYyPluq9WinG1ubPK5Esc6oVvhtF7PrXjRCw65Mx12MsVKtQ30yUXymJcL3mQtViVtZUMlMktm/x2Nn7xftK+sjYB9/6jR18+CuvF8pu8ThoEle2W/WfZvITTWVr6o9q7qNGGAhV02ZLKaBJJJJrm0Y8o66F51w1sfTeL+8hkriS/7nV6+DHX7iTj39tH9OpTFHeSH0stGIgFGf3l/YUXv/ta+f5u8/dziefOHjFwMZjZ4+unGrHCrWaBlmNdczK18f9L//0JM88fief+eaheYujRqq93+bLEHKxIjWYEkkNVCt8N4rY9a8bIWDXJ2Oux1ioVqG+mSi/EeXORQBvVpaZ2N6sj4x98OT+gaKyo6lM1f03XUd/VHsfzdVAqJo2k0gkksVCow3ylupzrtpYqkcfq/STS21bNUZCehTg63vPFcXU6QqxzGqsUKtpkNVYx6x8fdxXgCcPDMxrHDVS7f02X4aQixU5wZRIaqBa4btRxK5/3QgBuz4Zcz3GQrUK9c1E+Y0ody4CeLOyzMT2Zn1k7IPHdvUXle13O6ruv0Ad/VHtfTRXA6Fq2kwikUgWC402yFuqz7lqY6kefaxy2ETJtmqMhPQI4Hd2ryuKqYEKscxqrFCraZDVWMesfH3cF8BjO/vnNY4aqfZ+my9DyMWK1GBKJDVSrfC93GuYu4Bdn0i5HmOhWoX61ZbdCHOhajErayAU5/uHh1jd7uWB60s1mFZ9MBCK873DQ/TnjzNutzpW+3+t/VHNfWSsQyNYLoZCUoNZPzLOShaSRhvkLdXnXDWx9MUTo5y8HGXzCn8h3mnHtXocvHB81DQWWjEQivN3v1A1mJ/ceUWD+f03h1jd5i3oPMvVs1ZzoErvG2OrVdzXTHY0DeZ8xlEjcxkfLdV7VZr8SBY1mrGKz2Ujls7xyK299Lc3L3S1gIURY1cykpmLaL6a9yKJNPvOhgC4Y327paBev9/WnkCJuU0112Q8n1aOJug3/qs343nu6Ag397Ww71yY/qCX61b4eeX0BPds7ODk5ShHBiN4HDbuu66TfefCdPqcJNI5jg5G6At6uTiZ4I517eze3MW7w9OMR1OMR9M8cltvITi9cHyUl98b5fRYnM/vXofLaSc+k6XZZWdrT4A9J8fYfzbMTX0teJ02xmNpHrm1lxaPo6gNp5IZnjs6wj0bOxicTBa1rVlbGdv/2FCEv3n1PLf1t/Lw9lWFNrQyFXrh+CgD4QQf3NJdON/WngAHz4d5ezCC22ErmDdUY5ZQrj8Xs0mBnGDWj4yzEsnCozfr+fGRYVKZHDf1tZY17Dk2FOHb+wf49K5+buxtBa480+/Z2MFkIkN/u5eD58MlcaKvzcMTr57j0IUwHoeNG1e1snmln3g6xx3rgrz03jiXIklGIkluXxvE6bCxrSfAc8cuc2t/Kxu7fPzgreGicxtjGlDyWotne0+OsefkOB1+F1t7AjQ77cTTWc6MxXDZ1bg1nczwN6+e59b+Vu7Nx+/ETLawRFagLpNNpLOcGY0xk5vFZWuip83DcCSJy27jI9t7ePPiJIcHIvzW3WsLdTW24cdv7iE7S8UxmNn11GqOuBSRE0zJosUoKAf14bDni/cu+CRzIcTYViY0ViL1WkTzZnU3vveDz9/Bx776OuG8aUCb18GP8oYBxuPu+/IeJvP7CSgyt7Ey9TEa6/zg83fw0a++XlSO3SbI5hTsTYLsrIJNQE65Uv63fmMHD3/ldebzqfXs43fy6N/+gkgya7mPFsTM3ve5bURTOQAC+f8b9w16Hbz0h/eWtJW+XYNeB1/59Zv59ScOFvbxu+3YmwQxC1Oh3V96mSmTehvrK6DICMHMLMHss7iUTArkBLN+ZJyVSBYW7Vk7lUiTNQQQK8OeY0ORIrO6Zx+/k4DHUWR8ZwNy81v1wrn7gl7u//KewpiixWNHCFEwJmrNv47ltZYLxbOP31mYZBrb0GET+F12U2NGY8xu9ToQmJsDLqbY2Cgqxdh51WAKIVqFEEd0f6eEEFkhRFAI0SWE+IkQ4rQQ4h0hxF3zWRfJ4sMoKAd1EPz8sdL3rzYLIcYuZ0JjJlKvRTRfzXvPHx1hWmcaYCWoN5oLGM1tqr0mrWx9OZmcKujXhP1Zpbj8J/cPzOvkElSDAH07mGFVBwWIpa6Eb7PJJZQaJUCpacN0KsvX954r2ieWb8NypkLV1FcBnto/UNK3RrME42dxuZkULAVknJVIrj2056pxcgnWhj3fNpjVPXVgoMT47mpMLrVzHx2aKhlTGGOcFs8WkqcOXGk3YxtmcoqlMaMx5kYtxjjLNTbO6wRTUZSIoijbtT/gr4F/VhQlDPxX4ICiKBuB3wD+Tggh06YsI4yCclB/NXnoxtL3rzYLIcYuZ0JjJlKvRTRfzXsPbVtJQGcaYCWoN5oLGM1tqr0mrWx9OQ6bKujXhP12UVz+Y7v6iwwH5oPHdvYXtYMZVnXQfsHU8LttpvsajRKg1LQh4LbzO7vXFe3jy7dhOVOhauorUHOcVTJLMH4Wl5tJwVJAxlmJ5NpDe67aTQKI1XP20wazukd39pcY39m4Ojy6s59tvS0lYwpjjNPi2ULy6M4r7WZsQ4dNWBozGmOu32KMs1xj41VdIiuEeBf4PxRF+ZEQIgasVRRlPL/tIPBHiqLsKVdGo5bu7Dszztf2nuPzu9dxx4bOeV0nrV8T3xf0liR3L6d3ulbR6+j2nwvT7LKRmMnxK3kN23z1RS39PFcxdj33lJkwfXXQy7vD0whgl067p639N75fS90jiTT7z4ZQUHV67w5Pk0irOsNd665oMPV12nc2RHwmSzKTZTCcpLfNQ5ffzS4T/YHxvFPJDM8fHeEhXTLkF/OawQ9s6SaSzBQE/cZ/9WY8zx8bYXtvCwfOh1nd5mXzCj8vHB+l3edEIDg9FsXjtHHvpk4OnAvT4XOSyOQ4OjRFwGXjzcEp/sXWFTywpZufvTta0AB/QqehnIil+MXZMO+NRnn/dV1sWuEHoNlpp7fNw9NvDDIcSbF1VYBwLI3HZeOT7+unxeNg/7kQKGq/TCUzfO/wEB0+J80uO81OO1ssdKuRRLroWIBnjlxi/7kwu9YHefgmVYO5/1yIeCpLs9vOlpVXygJ48cQoF0MJHtzSzVAkCQps6Qlw6HyYo0MR3E61nkYjBCuzhHL36GI2KViuS2QXU5yVSJY6V9N3wco8xybgqf0XEUIhkshgbxLs2tBBf3szfW0eXjk9QU/AxfePDHPPhnZePjnO7k2d/Nrtq4ti5t0bOogkM7R6HDxzZBjRBB/e1sPQZBIE9LZ6+NvXzrH/bIhUZpbVQQ/rO/24HIJURmEyMYPTZmN4KonX0cT6Lh/dAQ9nxmPsXBtkRcDNd35xkd/Oj6u1a9ifHzN48/4Fx4enQcCude2FMcHdGzt4a3CSvScn6PA52boqgNdpZzCcYO+pcda0e7l5dRvJtBrHd64LsntzF8dHponP5H9FVCCRzjE4mcDrsPHO8LT6Bb3HTk+rh+HJFC5HEw/f1MNbFyd582KE37zLXIP51IEBHriuizPjcR7atpIWj8PSiMgY741jnMUWGxvFotFgCiF2AT8EeoEWYFBRFK9u+3eB5xRFebJcOY0IfPvOjBfpmv76U7fwv/3wnXlZJ21czx1w20hllBI92WLRHl4NBkJxS53XfCZIvprJlxt1rlqTE9dbvjGZsVl5kUS6SE+hJ+h18EMTvaaVHrPR+r1q28N477V47CTSs2V1qWYaVCtdqVW7VZOIutprKqdrvdY0HnNhOU4wF1OclUiWOvM9ZqimfH2MrnUpqZXWv9w59THSDL/OYwC0X/gcZeNaveMYq7qYXZfxPJncLPYmQcDjqKvf6un7qznGXAwsqAbTwGeBJxVF0RYsG+8Z09/IhRC/L4QY0v5isdicK/I1g67pL146M2/rpI3ruaOpnKmebLFoD68G5XRe86nnuppasUadq9bkxPWWb6bpM9vXSps4XYXeYD71e9WWY7z3jNrWapJGl9OVWrVbPbqMavq+mvNLlhWLJs5KJEudq+m7UCl+1KNTLKf1tzqnUbNpRO8xAKpGsVJcq3ccY1UXs+sylqd5OdTbb/X0vfQjKOaqTDCFEM3ArwLfAFAUJZR/v1O3Wz9w0Xisoij/XVGUXu3P5/PNuT6fN+iafvf+DfO2Ttq4ntvvtpnqyRaL9vBqUE7nNZ96rqupFWvUuWpNTlxv+WaaPrN9rbSJgSr0BvOp36u2HOO9Z9S2VpM0upyu1Krd6tFlVNP31ZxfsjxYbHFWIlnqXE3fhUrxox6dYjmtv9U5jZpNI3qPAci7rFaIa/WOY6zqYnZdxvI0L4d6+62evpd+BMVclSWyQojPAJ9TFOUu3XvfAi4oivLHQogdwPeBdbpvXk1ppAbzL/eeK6wVn8910tp67kd3qhpMY3L3cnqna5Vy1z2ffXE118M36ly1Jieut3yj1tFq3/1nQyBgy8oAx0emC9qDcgmWy9V5vtvJiP7eM9NVVJM0utYkztUmoq72mq52EumlyHJbIrsY46xEstSZ7zFDNeVr+7R6HDx9eJDhyRS3rW2jP9hMb5uHV89MsNLv4odHhvnU+1arTrFKqTdDteccCMX5/uEhOvxOOn1uets8vHB8lNXtXh64vlvVGeo0ndXEtXrHMXr9qOYpYHVd+vKqGc9Uop6+v5Y1l0YWhQZTCPEq8A1FUb6pe68beApYC6SBzyuKsrdSWY0IfAOhON/ZP8BUMk3A4ywkHZcUcy0niJ0LjW4Xrbz+dq+p8Yu2T7lkvlb1g+KkxuUeyvpjyiUKvpr3hVm9+ttV06NK11TPuazayuyarRJIx2dUk6Rq6lbunOWOqdT+8rO7LCeYiyrOSiQSayqZ+5jFhYFQnCdePceFiQQrW93sWBPkwS3dNcVpfdlbLczmytWh3rpXaguzY6zGRuWurVzc08wl79nYweBkEqBgkrQQRpvVjP3m87xzPd+imGA2krkGPivR8N5lYrBTLctNrFwtjW6XakTplZL5WonpfS4bs4pCJKn+WNHmdfByBWF8s0tNqxGzSBR8NRMHW9Urk9dXlLumes6lNzDSl2tlTlSSQBoKbQ3WRgT6c+r7tdL+xjap1lRouX52l9sEs5HICaZEMn+Ue0ZbxaKpZMZ07NrqsfPjx++qKk4bY46aGqzU2K9cbKpU93pimtkxgKkBYblrm4t50dU22qzFYHE+ztuI8cFiMvlZFFiJhpeLwU61SLGyOY1ul2pE6ZWS+VqJ6adT2ZIkx9WY0OiNb4zC/auZONiqXvrPb6PqYJYQupwRgdn+RgMkKyMC4/VVu79VXerZRyKRSCQLQ7lntFUsKmd4U22cNsYcK2O/crGpUt3rjWnGY6wMCCtdW73mRVfbaLMWg8X5OO/VON+ym2BaiYaXi8FOtUixsjmNbpdqROmVkvlaiekDbntJkuNqTGj0xjdG4f7VTBxsVS/957dRdTBLCF3OiMBsf6MBkpURgfH6qt3fqi717CORSCSShaHcM9oqFpUzvKk2ThtjjpWxX7nYVKnu9cY04zFWBoSVrq1e86KrbbRZi8HifJz3apxv2S2RBfWn8r/7xQBTCVWD+amdUoNpxnISK9eCllhXUa7oBepdSx9JpHnh+CgXwwl2rQtyZHCK7X0t7DsXpj/oLegrBkJxvnd4iP6glwe2dAOw/2wIhVJNJlwxfdH205IaW+k1+9uvCOLNEgXvPxviQijOiZEov7ajl+GpFBdCCR65tdfUpOno0BRtXgc/fnsYFPjI9h4mE5kSDWM1WkKjmc3qoJfjw9PE01m8TntZTaqxX/T9ox2jvdfmdfCz46P0540MtH7VdJU+l73IzGjf2RATsRTj0TSfuLWXFo+D/edCxFOqBrOcEYG+fi8eH2UgnOATJm1ZqU2qNRUy274Q2o+riVwiWz9yiaxkOVGvJm0uWrZyz2gt3nf6nHT43YVxxkAozt++ep4zY1EcNsGu9R14nDbGY2m29QR4/thlbulv5SPbVwGl5m9a3EqkszQ77apB0OmJQqw31m//uRAosKUnUPA92Jr/v8iPKYCSGHt8ZJp4KovXZS/oPMvpKIHCuXYZNJj7z4aImcRfbSzw7vA0A7qxSU6B1cHiOK/pO/vyhkjbe1t4+b1xEHDf5k6ODE2xvbeFtwanClrMavWfmq7TqOGsRovaSEOiWmjU2F5qMCWSBmNcw65pAmpdS2/UWgDYAH2WqRaPnWcM+gpNA2ilm6x2Tb2VttDsvXv+5CWmDfmvoFS3UNAVpDJkcsXPFruAFq/T8jy1BvZK12/UONibRKF/fC4bChBLZUu2WeksNS2MVvdGaCcWQi+5UNqPq42cYNaPjLOS5UK9z+D5enZXej7rt1vlxmz1Othj0D5ajVsq1d84TtHrNvVl6D0kjO/bBOQU8+MqnbtararG//zc7fzu3x8p+FDkFIWpvDdC0Ovgh1+4k49+9fUizee3P3s7D3/ldZT89T3z+J185puHKuo/v/UbO4qO08ZC9WhRlyJSgymRNBjjGnZNE1DrWnqj1gKKJ5eApeagnG6y2jX1VtpC8/dKJ5dQqlso6ApypYEvq1D2PLVQzfUbNQ76/pnO61jNtpXTWZbThlytZM5zZaG0HxKJRLLYqPcZPF/P7krPZ/12K8zqYzVuqVR/Yxy00kPqPSSM72cV6+MqnbtararGX+49V+RDYdR3aufWv/fk/oGCPlMBnjwwUJX+03icNhaqR4t6LSInmBJJjRjXsGuagFrX0hu1FqD+gqnHSnNQTjdZ7Zp6K22h+XvGmqkYdQsFXYGtVO1gF5Q9Ty1Uc/1GjYO+fwJ5HavZtnI6y3LakKuVzHmuLJT2QyKRSBYb9T6D5+vZXen5rN9uhVl9rMYtlepvjINWeki9h4TxfbuwPq7SuavVqmr89u51RT4URn2ndm79e4/t6i/oMwXw2M7+qvSfxuO0sVA9WtRrEblEViKpA+Ma9nrX0msaAwRsWRngYjhBq8dRlNTYKoGxmT6x1jX1lcrVv/fMkUu8eTHCb961loDHwfPHRnjoRnPthpYU+tm3h0HAh7f1FCVktjpPLVRz/cZ+0fePdozZNqMGxKhhXehkznNlobQfVxO5RLZ+ZJyVLCfqfQbP17O70vNZv/3QhTAXQwl2rguy/1y4yEOgUn2rrb/ZOKXS2Kdc7K313Fb7afUaCMU5cTnK5+5ay429rSVjA6O+U68v3aXTt+rHNJWuS3ttPK6obia60msJqcFcgsgk6UuHWhL+VmtqU+1xjUh2XGs9qz3nVp0pgGbCUy65czVtYxTTHxuK8KcvngIFPnf3Wk6PxTg8EOHXdvQSSWYZj6YYj6W5Y12QtwanuLmvhZdOjhdMh967HOXtwQgAm7p9BTOFqWSGp/YPkMrm2N7byuYVfr5zYICTl6d539p2wok0l6dSrOlo5l/csKLEGGDf2RAXQ3EOnp8klclweixGf3sz/+HDW7mxt9X0es2ufyAUV+uRyXFTX2tRQm2rvtW3UYvHoRo6zGQLy3gS6SynR2O4HTYe3dVfEhCNZg3vDk+TmFHNGrSE1Pds7CgybKo36fV8IyeY9bMc4qxEUi3z9SwrZ0I3lczwdN7YT//s1xvjGGNqf7uXg+fDnLocZdMKP7evDRbFYatJ576zISaiKcZ08dLKtObIxUlePjnGLatbmU5mCXjsHDgfptPn4oHru1nd3lyI+c8cucS+cyFu6m2lw+cikc7yzvAU0WSWL9y3gb6gt3D9B8+Hef3MBGfGYmzt8fP5+zbS395ciIPTqTQzGXVp6u7Nndyyuo0fHxkmlcmxsdtHp9/N1p4A/3RshOeODtPe7ORT7+vn9Hi8ELPavA6eOTKMQqnxoD52H88bBkWSWQZCcd4enOKmvhY6fS68rmJjQTPjwFpMHpfyWF9OMJcYMkn60sGqr6o1zzGboFV7XCOSHddaz1rOKaAwqWn12FGgILQXUDADqLZt9EmSBfBXn7qFf/2dN+vuOytaPA6mkpnKOxrQjAEe+8bBQhuY8ezjd9IX9FY0WzBLqq0ZPn3sq6+XGA9p36Tq28jntllqZ7U6600JtDpppgzZMqFBM2yqN+n11UBOMOvnWo+zEkm1zLeZj5kRjtdlI6KLI60eOz/Wmf0ZzWsEYLcJU+8DDbNxgDFu6zHGBzNDHStavQ5ys7Nl4w9AwG0jlVFI52ZNt//Pz93OJ584WDZ/ZbXYmyBrOI3DJgi4VVMivfFPJVo8dmxCEJvJFcyNfG57TWaLiyFGzhVp8rPEkEnSlw5WfVWteU415dVyDmM5GlYC81rrWcs59QHBKLTXmwFU2zb6JMkK8OcvnTHtk7kSTdU+uQS1Tk/tHyi6TjOeOjBQldmCWVJobV8z4yEobaNYheBuZkqgN2Uoh2bYVG/Sa4lEIlkKzLeZj5URjp5pw7PWNKaWmVxqZVgZ/5hhjA9mhjpWRFPZivFH3S9nObkE1bCnUT+BGSeXoLaZPo5Vi95YUD+eqcVscTnESDnBXGTIJOlLB6u+qtY8p5ryajmHsRwNK4F5rfWs5Zx6+wGj0F5vBlBt2+iTJAvg9+7fYNonc8XvdtR1nAAe3dVfdJ1mPLqzvyqzBbOk0Nq+ZsZDUNpGPgtjJn2djaYEelOGcmiGTfUmvZZIJJKlwHyb+VgZ4egJGJ61pjHVxFjPWIaV8Y8ZxvhgZqhjhd9trxh/1P1sOG3W05Df3r2uJA7Wi93kNA5bsSlRteiNBfXjmVrMFpdDjJRLZBchC2H6ISmlWs2kVTJjLRFxLaY2mmhdoVhXcXxkGkUp1i+CeSLlo0NTqtbg7WFS6Rzb8vo9KNUHGBMWx2fUb/H0SY31DITiPH90pCgxs15HcnxkGhTobfPws+Oj9Ae9PLClm6lkhu+9MUR/h5cda4JFRjuaTrDZbS9qL2O7vHh8lIFwgk/c2lvQYP6PF0+BEHz2zjUcHZri5++NsWtdO6vbvUxEZzg6FGFjlx8F2LkuyJ6T46SyOXpaPFyaTJLO5XDZbQS9Tt4bi7FrXZBbVrfx7NErbbe52893fjHA8UtTBJtdjMdSNDttXN/Twi9tXcGRoSnu3qBqPLQ2uDgR59CFSZLpDCcux+jwufjjh7dwx4ZO03vB7N44NhTha3vOEnDb2LG2vcjwycx4qNA/ecOBFo+DF0+McvJylL42D16XnYnoDPvPTWBvauLfPbCpoAk13guaKcPxEfWeaHbZ6W1Vk1TfvaGjyLDJ6r5e6OeYXCJbP8shzkquPeZTK1nOaKYavwOzMrVxgmacozeUGQwneOK189y6upWHt68CKBpXwBXzmi09V44/dCHMqdEovW0evA47CGh2lsZz7fyJmSyJTJYzozHcThu3rW7jxRNjPLqrnxt7Wwu6/pv7Wth/Lsx0Ms1rZ0KsanHjtNvo8Dk4cH4Sv8tGW7OTO9Z30O5zIYAzo1F+fnKMO9a3owAzmRzRVJbxWJpf39HH4YsRQvEZHt3Zz5nxGD85djmviwzw+fs20pI3PDw2FGEmm2MmO8tkXI2zv3TDikI839CpajC39AT452MjPH90hGCzg49uX8W+82EevK6LQwOTTCfTINRlsQ/f1MPQZBKFKz4R+8+FuDgR5/jlKL96Wy9TySwD4ThHB9V7qsPvAgW8LjtbewIcH54mNpPFlx+7gLXZoJVPwVIe60sNpkRSBwuVfNksyXKzy1ZY269PZmws23is/pPd6rEjhCBuog8w01fotX3lrg1K9ZPG98rp9OrViprto7+GVo/6bWQkr1HRrgco2q/N6+BJXZJlUPUVe794X8m17/7Sy0WaFzP9Yi261XI0Qp9hlljbSr95LSInmPUj46xkqbEQmrZqY1itdTV7dpvF0LmUb/RMsOd/hVOgME741m/sKIqNDpN9rDwDrLDSi7Z6HSR0ZULlsUW5dtC3gX4Zrha7WzyOmu8XfZk+l62kHay+HF/qWksrpAZTIqmDhUq+bJVk2Wy9v5WeImOYXMIVHWS1CYvLlV9Js1ltQue5aEXN9pk2lGWmVTTuZ0yWXOnaNaz0i7XoVsvRCH2GWT9Y6TclEolkKbMQmrZqY5jVcVZ1rTaGzrV8DW1cYRwnGGOj2T5WngFWWOlF6xlblGsH/b7G8z9/bKSu+0V/TLkxldUxyy3mygmmRGLCQiVftkqybLbe30pPoR2rR9NsVJuwuFz5lTSb1SZ0notW1GyfgKEsM62icT9jsuRK165hpV+sRbdajkboM8z6wUq/KZFIJEuZhdC0VRvDrI6zqmu1MXSu5Wto4wrjOMEYG832sfIMsMJKL1rP2KJcO+j3NZ7/oRtX1nW/6I8pN6ayOma5xVy5RFYisWChki+bJfgFyiZeNjv2eF5TqWkbtTLKJSyOV6GFNNN9VnqvnE6vmmTEtehXNW0iYKpVNNMwDoTifP/wEKsrJKl+8cQoF0MJfiWvA62mfguZxNusH6z0m9cacols/cg4K1mKLISmrdoYZnZcubpWG0PnUr5Rw6kfa2jHabp+vfbeuA9QtN9QJFnwYtB0+0OTSdDpTVs9Dp49OgwKfHJnPy0eR11ji2raQNOmGmN3PfeL/hizdqinL5YqUoMpkdSAmRi7WrOfaswFrAwB6klWrzf0eeX0REliZKs69rcXG/o05xMHN/rBZ0wi3Yj2mw+M567HtKHW+tey/9Vqm6We9NmInGDWj4yzEok582kkVM0445kjl3hjIMJv3b22yKStUXXQYp/eULCasZDV2KnSeAco2UdvQOTVjU+qGVNUasdaxmr19PO1FkfLUSnGVu/LK5Fc41RrYlOPCY22n9FgRjOeMTu+GvOY6VSmoGf48k9PFoxnrK5tOpkhM1v6pVK15gTVYmZWNNf2mw8aYYRTa/1r2f9qtc21bEQgkUgkjWC+npPVGtnd86WXmc4bzT3z9jDPPn5nwyaZZmZ/Dp2hIFiPhazGTmYGSPpyml02FEUpmPEFvQ5++IU7+ehXXy8cB2oc/tEX7iwYHVmZHdZqntRoYx4ZR4uRGkyJJE+9ovJqRdxmBjPlzlGNeYxeLK83nrGqo9nkEqo3J6gWK7OiubTffNAII5xa61+vUcF8ts1yNiKQSCSSapiv52S144yYwYzvqQMDDTm/Vr7R7E9vKFirmZ2VAZJxX6M5n2ZopMdodGRldlireVKjjXlkHC1GTjAlkjz1isqrFXGbGcyUO0c15jF6sbzeeMaqjo4mcyl+teYE1WJlVjSX9psPGmGEU2v96zUqmM+2Wc5GBBKJRFIN8/WcrHac4TOY8T26s78h59fKN5r96Q0FazWzszJAMu5rNOfTDI30GI2OrMwOazVParQxj4yjxUgNpkSio15RebUibiuTlXqS1WvbWj0OXj0zwUM3VtZgFgyARqaJp65oMGsxJ6gWM7OiubbffNAII5xGGzDMpex6udaMCKQGs35knJVIzJmv52S144xn3r7E4YEIn7trfjSYWuzTzHiqHQtZjZ3MDJCMRjnGfbTj4jNZdXyy7sr7lcYUtZonlWuLhTLnWypIk58GMxCK89zRkYKhylwNYI4NRfj2/gE+vau/4Q+L5UQjhdXzbdjSKAMZY1lTyQzPHR3hno0dTCYylvXR7mH9fkDVJkPaecejKcZjabb1BHju2GVu7W/lI9tXlRXdG8+jbUulszz5i4t8fvc67tjQWXSOwXCSzSv83L42WDAnElDIu6WZFIFqFnAxFOf1sxMIYHtfG5OJNAqwqcuHx6l+MzoUTvCLC2F+9dZehqdnCm3R5nXw03dHsQl482KEVCaLramJFQE3CgqRRJq+YDO/uqOv0HbHh6f4L/90glR2lk1dPoLNLla1eQjF0jQ7bZwcjbFrfZCPbF9VdTtXeg3qs+Ore87Q0ewq1Ke/3cvB82FOXY7SG/TgddpLTJzK3YNmRlDNLnuR4YPVNdTDfBoiyAlm/Sx0nJVIlgKNen7pDft+enyU/qCXB7d0Fz2zXzg+ysnLUdp9TsKxNJtW+HlwSzdTyQxPHx6iy+fE47STSGcZDCdp9zkJxdL0BT00O+3E8+83CTh6aYrfu38Dd2zoLIwHbu5r4eX3xklmcmzva+UBrew3huj0O+n0uwvjjKcPD9HstHF+Is5QOIHbYeffP7iRkUiS//qT9wi4bGxe2cKWngAA7w5PcW48TiyVZVWbF1C4bU0QAQX3eq/Lxh3rO2j3uWh22Wn12Hly/wAuRxOdfjf3b+7kh29d4tRYjN0bO/E4bRy6EMbvdgAwk83x8e2rODo8TX/Qy+1rgwyEEgVDIKMxkNm4RIt7J0em2XtqjP72Zu7e2MnmFf6ajBQXm8HPfNVLTjAbyEAozr1f2oOCuhzxmcfv5DPfPFS3AcyxoQgf/srrhX0bKdheTjRSWD3fhi1movd6yjeWFfDYiSazhUmXXUCL11lSH/09DFeWmShAfCZX0WQIKDEC0BNw23jlj+43Fd37XLai8/zg83fw8a/tYzKeRp8K+a8/dQv/6w+OWZ7DjFavA3RmAfVgb4LsbOX9CvsL8DibiM5Ud5C2ZDhWoZ21drF6/fM/2M1gOFH07ACwATmLc+sNpazuQX09Mnmdi4YA7E0Cn9uOgJJrqIf5NkSQE8z6kRNMiaQ8jXp+WRnwtXrs7PnifQDc+6WXTWOb39VEbKb4WV0Lf/rINn7/6aOmx/vd9hItpN9tI5bK1X2+q42jSZCdVbAJyCkUzAb1MbXZZUOg6jyzec8IKwRUNFJcbAY/81mvSjFWajBr4LmjI4WbTwGe2j8wJwOYb+8vFmg3UrC9nGiksPpqGLZoVGOsU8kASCOWyhY9GLOKuUGN/h4GVSg/ncpWbTJkZgSgJ5rKWYrujefRRPvG6dlfvHSm7DnMz5ut+RgjtUwuId/GVU4uQe2j6SraWW9mYPb62KWpkmcHWE8uodRQSmPaoh7GIKs3VTC7hnqQhggSiWSp0qjnl5UB33QVMTc6h8klwP/4+RnL442GQup7S2dyCRRMBrMKRWaDxpg6rTMkLEc1RoqLLZ4tZL3kBLMGPrRtJZpFigAe3dU/JwOYT+8qFmg3UrC9nGiksPpqGLZoVGOsU8kASEP7ZUnDLswNavT3MORtyN32qk2GzIwA9PjdNkvRvfE8mmjf+BD63fs3lD2H+XntNR9jxF7j09Au1G+Qq8WXr2OldtabGZi9vnFVS8mzA9RfMK2oZLxgrIfRCkpvqmB2DfUgDREkEslSpVHPLysDvkAVMdfvKn1W18K/ff8Gy+ONhkLqe7Y5ne9qo5kM2gVFZoPGmBrQGRKWoxojxcUWzxayXnKJbI0MhOI8f2ykYKhSjaD32FCEJ/cP8FheZ6nXXYL6y+WjO6UG0wy95rXF4yib5Lda4XaltegDoTjPHx3hoSrX2/e3Vzax0R+jF7TDlbX/ZomDB0Jxnn5jiDUdXh64vliTse9siIloisHJJAI4eD7E5pUBPvW+fiLJTOFBYix/Kpnh+WMj3L2hg6FIEkWBvjYPPzs+SodP1Vpomjuza4sk0rx4YpS3ByN4HDauX+Hnu28OcXt/kM0rAyW6Pn17tngcBZG+vvyJ6RTfeP0Cv3v/Bj5ww8qC2cBAOM7bg1PsWh9k96YuDl0Ic/JylL68vjCRzjIeTfOJW3tp8Th48cQoB8+HGJpM4mhqYvvqVibjGRAKG7p86jEzWQ6cC3NmPMZDN6xg//kw21a14HLY2LUuyJ5T44xNzzA6nWImM0sik2V9hw+HXTAZT9Pf0cwjt/YxFEkST2W5GE7w4olRsjmFjV0+gj4nq1o9hBNpvHYbJ8di7FrXzsPbewBMjRD2nQ0hKDY5MBoPGY87NhTh63vO0uF38sitfUSSGVYHvRy6EObUaJTe1rwG020vMZQyM17QthWMoIaniaezNDvtbOm5Yvhgdg31Mp+GCHKJbP0sdJyVSJYC2rN0bDrFUDhJb9BT0CrW8jzTYuT2vhYOnA+zus3LA1u6ATV+25vgyQMXaXHbWdvRTDiRxmNX9f2rgx4GJ5Ns7vIRz+Rob3YSiqdpb3YSjqcLWvzETJahySTTyTSvnw3zb+5Zy+p2H21e1SRwe28Le06NMzqdIp7KcvemTlYG3Hxz3wU2d/uYTGT53N1rCXgcfP/wEF6njfOhOEOhBG6nnd+8aw17T47zoyOX6PI72drTSnfAzVuDkyTSOXKzs8xkZ+lp8YJQuK0/SDKd45/fHSEcS+O0N/HwTT1s6PLT7LbT4rbznQMXcdoFAbeDnlYP712e5uxYjK6Ah+lkmthMjnUdzTgcTaQzs3x0ew9vXJxEUeC+zZ0cGZxie18L+8+G6fA76Qq42bJSHdu0eR387Pgoq/N6zYvhRCHunRyZZu/pcVa3e1UNZrff0kjROEatxqjpaus05yvOSg3mAmPUbf7Vp27hX3/nzcJ2qbu0xqgXbPU6SMxB+1VtMuP5TMRrVsZ0MkN2VinoA7SyjPeOtvbfSrOh8T8/d3vBKEevqTMmJjae30xzZ0xkbLx2TVcZy+sXjNdgpeMspzmsV5toVY6xzfUaRCMtHjs2oeokNW2Gmd7QmJRa0zjOxz0pqQ85wayfpRZnJZKFIJJIs/tLLzNl0EfWEg/K+SwY47w2DphOZko0+OVitkaJB4NNnbyZjTnMsNIgmvlLfPuzt/PwV14vlNXisbP3i/cVxVCztttrKN843sjMzhJNXRGE6Ms1jmnsNkE2pxSNIQWqD4TeZ6De+G01RrPiWov3UoO5wBh1m3/+0pmi7VJ3aY1RLzjXdeTVJjOez0S8ZmVoa/+NZRnvHW3tv5VmQ+Mv954rqaNZYmLj+fVYJTI2lqvpKq2uoVwC5nIaQ7NjK2kTrcoxq7sVRo2hld7QqIuZz3tSIpFIJIsPq3hSy7O8ks+CPs5r4wAzDX65mK1R4sGQK97fuN3sHGYaRDNt/5P7B0rGb2b+DEaM5RvHG7FUsdtAuTFNJqeU1MHMZ6De2Gs1RrNiucV7OcGcZ4y6zd+7f0PRdqm7tMaoF5zrOvJqkxnPZyJeszK0tf/Gsoz3jrb230qzofHbu9eV1NEsMbHx/HqsEhkby9V0lVbXUC4BczmNodmxlbSJVuWY1d0Ko8bQSm9o1MXM5z0pkUgkksWHVTyp5VleyWdBH+e1cYCZBr9czNYo8WCwFe9v3G52DjMNopm2/7Fd/SXjNzN/BiPG8o3jDZ+72G2g3JjGYRMldTDzGag39lqN0axYbvFeLpG9Chh1m8eGIlJ3WSX6tqtmfXslqk1mPJ+JeM3KsEocbLx3zI47PjLNe8PTHBwI87v3qbmtqi2/Gs1dpWTFQFXnKJew2aotK+1XbTnGumsaxN689rTT56Iz4GLXuvbC9VTSG+qTUus1jrWynBIzX03kEtn6WYpxViJZCDRPAk333hlw1xwPKsW/Vo+jRAN4bCjC3752nutW+ukPNleM2RramOLuDR0FrwbjmOP7h4dY3e4taA+397ZwZGjKVIOor6tR268vS+8hYWy7o4MR3A4bn9zZb5kCRB+Ttfbe1O0vKdc45tHGSHrfC/2YZ67x22qMZsW1FO+lBnOJs1gTty4Fqmm7ubZvueMLBj3tqmDf+BAvZ16k3649tMwSA2vHmJ1Ln7zZLEmwVXna//UJit8dniYxk8XrshcMgLR9950NAXBH/uG9L584udllL5gdVLoeY4DQJz3Wyjbbp9p+04x09GVFEmn+/uBF9p4aLySdLlf+QCheSDAdT+d45NbegibWWLbZ+a3a92p8tvXGYsvlSy05wayf5RZnJRKY23igXHyuZrsRLd70B708qBs/aLG+0+/E61Td4xUoxNupZKZobGEW94zxXR/PtW17To5xeCDCr+3oLeTg1Mf48WiKwXCSvryxUavHzj8cUscgs1CIj1btpJ23zevgp++O4nPZiM3keOS23or1thoX6M+hHW8cr1Tbv/r+0rdpNZNIszKuxfG7nGAuYa41QfDVpJGGPvWcYyAUZ/eX9hT2bfU62JMXkVcyL5pKZkqE4y0eR4mxTjx/zLd+Y0eR4L/V6+DHX7iTj39tH1OJNNn8icyMgqKpbJGZjdG0xyiS13A0CXxuO4qiFIJPq9cButdQKvY3u55KxgZaOS/l26/WfjMzIPjhF+7kQ3/xWpEGxMwcyaxPNATwzON38ulvHCxr9lOLKdJ8cGwoUnR/LBdjMTnBrJ/lFGclEpjbeMAqnpoZ25Uzj9MwjhE0I5upZKZoXGEk4LYRzeeqFKhpRTRDHC3uaUZ4mdwsNgE5BRy2pkK9ovnYZDYzaPXYQQgiFiZ5esxMb4zGgzZBYXyip8XjYCp5Jab+KF9voyGhVbzVx3t1qWxTybipXP/q6+l12QrXW42Rj1kZ1+r4XZr8LGGWmyC4kTTS0Keeczx3tFjsXU5IbyzDTDhuZqyjHfOkQfCvN7zRP7zNjIKMZjZG0x6jSF5DMxMwGt0YE0Ibxf5m11PJ2EArp5xxUDnMDAiePzpSkkjazBzJrE/07fnU/oGKZj+1mCLNB0ZDCGksJpFIJMXMZTxgFU+tjO2szOM0zMYIWhwqhza5BPJfZF4xxNHint7kJqtQFIemU+amf/oyypnk6TEzvTEaD5pNLtXryOj+f6XexrqVi7f6epiNm8r1r7G/yl1TtWUsx/G7nGAuYpabILiRNNLQp55zfGhbsdi7nJDeWIaZcNzMWEc75jGD4F9veGPXncjMKMhoZmM07TGK5DU0MwGj0Y0xIbRR7G92PZWMDbRyyhkHlcPMgOChbStLEkmbmSOZ9Ym+PR/d1V/R7KcWU6T5wGgIIY3FJBKJpJi5jAes4qmVsZ2VeZyG2RhBi0Pl8LttRbFWb4ijxT29yY1dUBSHAhamf/oyypnk6TEzvTEaD9otTuR3O3T/v1JvY93KxVt9PczGTeX619hf5a6p2jKW4/hdLpFd5FxLguCrjZkBjJkuYv+5EIpivZ6/0jmsjh8Ixfnb184xNp3mC/etL1qWqInf+3RJfvV9bCYcNzPW0Y45NhThiVfPs2Wln772Zrb2BDh0PsxAKMGu9UFTgb6+vKlkhuePjvCQLlmwJo6Pp7IgIDGTYzCcoDfoodllh/y3n8l0lvFYmk/cqmonnnn7EvvPhLhpdSseh40zYzFS2RxtHmeRiF9LLv2QiTb02KUpkjNZvrnvAu9bE2TTygB9bR5+enyULp8Tj9OOz2VnV15zomkjz43HOD8RJ5XJ8f7ruklmcvz03VHavHb6O3y47QKHzUZvm4ehySSD4QTvXZ5mddCDQhO//+BGtvS0sO9siIlYimOXpogms3z85lUcHJjkzGiUd4enWBFws67TR8CjJoAeiSRx2W3cd10n//TOZS5HkmzpaSkkxN6xpo0XTozx2K5+Ah4Hzx8d4e6NqsnC6uAVTYmmFzHTxVj9394E/3BoiOtX+ulvby7RrLR5HfzjoUHCiTS/s3s9fUFvQUMzHkuXaGUaoT1ZDMglsvWz3OKsRAJzG2/pxwJb84Y7q4PFz3F9/N5/NoSC9bjDaJADV7SPZ8dizGRnceaXtr4xEOGB67v4tdtXMxhO8OSBAT62vYfTYzFeOTmOy9HErAJfuG8DI5Ekf/7SGR6+aSVvXYzgcthQFIV0dpaP3byK185MMDSZotVj59UzE7jsTaxocdHb1ozPZccm4KX3xkCAz2XnfWuCXI6m6fY7eXdkmq09ASLJLL96Wy/ZWQrjrUgizY+PXOKVU+PqZNHWRCarcHk6yUx2lsl4mlRmlk6/E5/LTneLB7/LzoZuH81OO4l0lvFomhtXBXju2GXWtnsJJ9IoCgSbnRy8ECKZnuUL965nZDrFK6fG6Wn18C9uWMH+c2FWB72sanXzw7eG+djNPQUpj1XMfeH4KBfDCT6wpZs3L07y5sUIn7trLTf2tlb0VNDea/M6ePX0RMkY51pBajAlEionM54vHaZVEt5Grs8vSS7cJCrqFGptG5/LRk5RCkmRNWMB7f+alvBbv7GjKLmykb0mGlCz6zfqBvXn02jzOnjSkMy5EQTcdpKZHJlc45+NAlVPk0jP4nfb+cHn7+CjX329SC+i18XodSNmWtloMlOyzEivtTFqcI2aUe19M22ux9nEdDJbdRLpxYacYNaPjLMSSW0YY9oPPn9HQe9ojHH1+Ajc/+U9Rc9tM/7n527nd//+SGFp5kJh18UwrS0+9BevFvSgiw27IeZajXWMOlYzT4WF8FlYKKQGUyKhcjLj+dJhGrWH1RxX77UVdJNV6BSquQYz7aCGvny9hsOYXNmIlQbUWD+jbtCszGrOVw/Rmey8TC5BvQ69/kbTlui363UxRt2IUStrpmHRa22MGlyjZlR736pfrO5fiUQikVzB+OzU6x2NMa4eHwHjc9uMv9x7bsEnl1Cq7VQ9Dxbn5BLMY66GPsQadaxmngoL4bOwWJn3CaYQwiWE+IoQ4rQQ4l0hxHfy73cJIX6Sf/8dIcRd810XyfKlkuZvvnSYVkl4G7k+vyS5cBU6hWquwUw7qKEvX6/hMCZXNmKlATXWz6gbNCuzmvPVg99lx2FrdKkqAor0N5q2RL9dr4sx6kaMWlkzDYtea2PU4Bo1o9r7Vv1SSxJpycIh46xEsrAYn516vaMxxtXjI2B8bpvx27vXFcpdSIzaTtXzwFbxuIXCLOZq6EOsUcdq5qmwED4Li5V5XyIrhPhTwAb8W0VRFCHESkVRRoQQ3wAuKoryx0KIHcD3gPWKopT9mkYu3ZHUS6VkxvXqXMsdXy4JbyP1tabJhWtIIlxN2wCFRMpbegKF8resLE7wrE/kfHI0ytHBKVyOJj5lSKJc6fqPDUV46sAAH93eQ06BVo+DF06M0uFz0um/ksxa06p4nTbOT8Q4NxEnlc5x//XdpNKqBrPT7+T9W7rxOm0MTSZp9zoJxdMEfU6GJ5OEYmnGYjP8+wdUDaaW3Prpw4NMRNN8dHsPZybiBFw2/u7gILf3t6nfbArY0OnD67LT7LLT2+rh6cODDEeSbO1pYTKexu20ce+mzoIOVtO46rXB+nbV+k9rU8BUe6v93ybgu28MFRJu79JpMM2SdEcSafafDTEeSzERTfMrJhrMIm1uDUmkFxPLaYmsjLMSycJj5vtgFeNqjf/ac1uLuVr8bXHb+dGRYR7d2V/QB2pjgUMXwpwajeJx2BgIJfjNu9YSTWX4y73n+Nj2Hk6MRkllcrR5nQyEEvzL23o5Mx7j8ECEezd28OMjw4xGU2xe4ee6FQHC8TRNwGtnQ3icNq5f6eeXtqoax06fi86AqzAe0McwrS2eefsSB86G2djtI5nJ4XXYODkao7/Nw4nRaeIzWZpEEx6njV3r2+nwua40gALNbjXGvnpmgu29Lew5OV7wdzh0IUQyo/A7u9cxPZPl1OUom7r97FgbLLSVduzdGzoYmkwWjV+MMbdorDMyDQolsVUfh81itrENrjUWVIMphGgGLgG9iqLEDNtiwFpFUcbzrw8Cf6Qoyp5yZcrAVx5jEttrNcGrRqMS2dZajtX+WoLf+EyWZpedvjYPr5yeKLi/PXd0hJv7WnhrcIp7NnYwOJks7KtPCKyZq9yzsYPJRKZswmZ9QuKpZIan9g+QyuTobfMQS+dMEx6Xu16rhMZgnqQ4kkjzwvFRBsIJy+TKZvUul4haMy769v4BPr2rv2BMA5i2k2ZCY3X/WyWXNvucVFOPiWiKsWiaR27rLTqv1fkaeX8uZeOdq8lymWDKOCuRzA9aPIf6TQD1cWEgFOfpw0P0B708uKW7JI7qn/FPvzFEp1/9MlWLv8YY2OZ18OO3h5nJ5NjW28rta4O8OzxdGFOUi9tW1zoQinNiJMpv3V29oY2+XnrDunLjB+18E9EUF8NJ+oIemp12FCjU3Sy+G/vDLPbuOxsiMZMtWt6qlVnohzeGWNPu5YF8P1QaE1mNP5549Rzj0VIjx0aNTRczCz3B3Ab8EHgaeABIAn8MHAEGFUXx6vb9LvCcoihPlitTBj5rjGYv17K4GBpnlFOP4N7KFKecEN/MqMa43d4k8LpsBXMVUFOFBNyOkoTNRjF6wGMnmsyWnKMWgyH9Nr2Oo9XrQFCapDiSSLP7Sy8XCeLNzGBqSUTtd9v5i3+1nV9/4mDheL8uYbRmKuR1NjGlM6F55vE7+cw3D5Xc/5rZQsEEySbI5pQiM51q6xFw20u0MM/mz2tm7pDJzWJvEgQ8jobcn0vdeOdqsowmmDLOSiQNxhjPK5nlmR2vjyVGA7xWj509X7yvxFDP67IRMYwhWjyqXCGii7NNQCVVo1XcNqvrfV/eUzCb09CbBunjthbX/G67pSFOuXoYze3MaPE4mEpeMb8zGtS1eR38SGe4U025Qa+Db3/29iIDwVavgx8byjGOifT3QWH8YdJPzz5+Z2FS3igTx8XMQpv8OIB1wHFFUW4DHgf+AbBTeh+aip6EEL8vhBjS/mKxmNluEkrNXq51cXGjjHLqEdxbmeKUE+JX+ipHM+jRm6sAZHKKacJmoxg9liqdXGrlVmswpN+mJ2qRpNgsqbGZGUytiai/vvdc0fF6gwC9mF5vQvPU/gHT+9+YpDmTU0yTTFdTj+hMaf8+eWDA0txB36eNuj+l8Y7EgIyzEkmDMcbzWp/hxue20ZBuuozZoBEtZmooVJ5casdVM7YxxnENvWmQPm5rca2cIU65ehjN7cyPuTJ5U1DjrLE/jEZKlcqdzveDsW6VDJmMbW/VT08dGCgc0ygTx6XMfE8wB4BZ4O8AFEV5GzgPXA8ghOjU7dsPXDQWoCjKf1cUpVf78/l881zlpYvR7OVaFxc3yiinHsG9lSlOOSF+JdsYzaBHb64C6i+YZgmbjWJ0n+E4fbnVGgzpt+nxWyQpNktqbGYGU2si6t/Zva7oeL1BgF5MrzeheXRXv+n9b0zS7LAJ0yTT1dTD7yrt38d29luaO+j7tFH3pzTekRiQcVYiaTDGeF7rM9z43DYa0gXKmA0a0WKmhkAVXFfCKm5b1dWI3jRIH7e1uFbOEKdcPYzmdubHOIrKfWxnf0l/GI2UKpUbyPeDsW6VDJmMbW/VT4/u7C8c0ygTx6XM1TD5+RnwZ4qi/JMQoh94A9gG/L/ABZ35wPeBddJ8YG4YzV6Wuri40jr2RhnlmJVjpavTJ03WEinrNX3Hh6cZj6UYn07THXDyD4eG+L37N7B5ZYDnj42woaOZF0+M8dGbe5hKZtV9o2l2rQtyZHCKh/J6Tc0oJ5LMFB5Qmv7h+Mg08VSWeDrL0KSqX+jwuelr8/CdAwOcHI2yc00Qp9PGr9xyRSeoaSXvyJ/rboMOVNNKaNemF7dr7xvbaN/ZEBOxFEOTSdx2G6dHo+xc3869m7tKdJBF5jFHRwrnB3XJ0A/fGuaxXapZwb4z4/zFS2e4fU2Q3qAXgSry14vyD54PMziZ4MHru5lMZAqJje/eeKXdppIZnj48xJqgVxX8j0wzNp1iKJwk6HMSjqXZtMLPdSv8vHJ6gpv7Wgr90N/eXDAbenSnqsHUDHLGo2k+cWsvLR5HISnzJ2690tbGz2E57aSZZlPrb2OS7mqMd+aqHboWWC5LZEHGWYlkPtAb62imcmZjEqvnrT4OaJrJF06M0tHspCOvrTTuqz3jv3d4qGCcs2udGn9fPD7K0aEIigIbu31MxNK8eHyU7KzCzatb+NTONZwcjfL2YAQBrGz1cCmSxOuw8eGbekr8HDQ0bajPZeP8eIzoTI6P3tTDwQuTpLI5tve1smNNkIPnw1wMJ/jAlm4iyYxqJHQ+zMELIS5HUqxsdbO1pxWvy4am40iks1wMJWkS8ObFCO+/vot/dftqQDXSGY/mY3Gzk0uRJChw0+pWHri+u9AO/fnY/e7wNBOG2FvwRIileeTWXqaTGZ549TzXr/SrJkHaul0BikLBG+Nnx0fpb/fywPXdReMTra/0MbjN61D3z9dDi+mD4QR/9uIpsjmF+6/v4iPbVxWO6W8vHYNfa7rMBdVg5iuwDvgG0I76q/5/VBTlh0KIbuApYC2QBj6vKMreSuXJwLd8WMh17NUkTQZMNa/avlPJNFndStNnH7+TvqDXtNxKulmjhlGBovX/ZroAvUbPqJWEK8mF9U+AoNfBS1XqTPR18rlspHM5YjNXLlirUzmdZya/3EaPprd47BsHi7QUeg2MlabTqHmcSma490t7inSaeh2HFXrdayU9rl67YtV+A6F4UT3MNLH6e8BMp1qL9mcu2qFrhWU2wZRxViKZZ8zGJEDZGFDNWKLaeGuM4UYCbhtNTU0l2kC44vFg9APQxyZQf5k0+kBAsSZS0z9+7Kuvl8RSgaoZTaRni3wSiurpsfNKXn+qXZtZGwJFYwW9b4IvPw6K6fJW6zWgxvFPNXG62nGFfgxSbdy+FnWZC63BRFGUc4qi3Ksoyo2KomxXFOWH+fdHFUX5gKIoGxVF2VpN0JMsLxZyHXs1SZOtNK+FJPeGXMdPHRiwLLeSbtZ4nHH9v5kuQK/RM9NYZJVSzcR0De1s1IPqJ5fGOlnpPK00o5qeUo+Z9tNKG6Lt+9zRkSLNolHHYYVe91pNG2hYtZ+xHmaaWOM9YNSpVstctUOSpYeMsxLJ/GM2JqkUA6oZS9Ry7nJEUznLfaz8APSxCcx9INSyM7r/q9dhFksVKMSuaZNyQPWLMGsjjelUqSeC0TdB039qk0vt3Pr/W41/rOJ0teMK/b7Vxu3lqMtc2GysEkkZFnIdezVJk600r4Uk94ZP16M7+y3LraSbNR5nXP9vpgvQa/TMNBaaBlFPoIZ2NupBfa7iC7a6Hv1xVppRTU+px0z7aaUN0fb90LaVRZpFo47DCr3utZo20LBqP2M9zDSxxnvAqFOtlrlqhyQSiURSitmYpFIMqGYsUcu5y+F32yz3sfID0McmMPeBUMt26P6vXodZLBVQiF0Bk3JA9YswayONgLvUE8Hom6DpPx22K2fQn6vc+McqTlc7rtDvW23cXo66zHlfItto5NKd5UWjNJaNOLeVTtNM86q9bxMUJUEuV24l3awxue/+syHi6SzNTjtbegJFWj8zjV4kkebFE6NcDCV4UKehOD48XShnV416PWOdnnn7EgfOhdm5LsjuTV2W16O/ZmMSZK3ekUSa/eeu5BTVNDBW57Zqw4FQvKg9NF1NPJ0taDOaXfaiJMyafrPqBNj5pMzl2s9YD6v2MCZrrufeN9MOLTeW0xLZRiPjrERijtU4oFwMqGYsUe259TH85GiUo4NTIBS2rWrlgS3dwJWxQWJG9WjYtMLPjjVBy3isxSZ97JtKZvi7AwMg4JPv66fF4zDVo2qeBGdGY7gdNj65U91Xryf9/ptDCAVOj8XYuT7IwzetMm0jszY0xkazGNnqcRTGDoDl+KeaOF3tuMKsbpXi9kKOZ+eDBddgNhoZ+CSNwsxYpZLeziyhr94gx+zB/dzREe7Z2FEQ2ENpsmL9cceGIvzNq+fZkhepj8dmODoYYVtfK50+V1EiYoAXjo9y8nKUzSv8PJgPMGYmBC8cH+XU5WiRoY3ecEZ/PRP5c27s9jMLPKIzr9l3VhXmj8fS3LEuyL6zYda0q+L3g+fDDIQTfHBLt+X1lrvub+8f4OM395CdpWR//b8Hz4c5eTlKX9BDOJbmp8cvk8sp3NLfyvuv6+YHRy4RcNu5oacFj9POYDjBz98bpQmYyc4S8Njp9HmIpzN0t7jpCXg4H0rwoRtXcHR4mibgrcFJYskM8XQOp10ATdx/XRftPieD4STNThvnQwl+bUcvkWSWeD6FyVA4wetnJ9i6MsBkMlvYDsUJp/vbvUWJqAfDCb69f4BP7+on4HEU7pv3LkdL2lRrN+1eWdvuJZRI47bbuP+6Tt4anCocq7VTp99dkvxaM0Dq9DnxOu00u+xFiaT1dT10PsyFUIJHbrtyLxgTcFdzf18t5ASzfmSclUiqM2WpZhxRzjBQM3/TxghtXodlbAboa/MUtgM8/cYQNgEnR2OsDnp473KUbC7Hui4/v3zDCvadDeNz2RiKJGkCWr1O3r00RdDn5IZVLXiddhLpLO9cmmJ0OoXD1sTo9AzrO5tp8Th46b0xZmcVZhWFTr+LZpeDFQE3CgqxVJbb1gY5dXmaF46PsmVlC5tW+LEJOHBhkl/e2s0LJ0ZJZ2dZ3+WjvdlFe7OTk6MxuvxOjg5F2LQiwKM7+3nvcpTXz4xzZixGX9CL321nJqswk8nx2K5+To/F2H8uxOYuP/FMDhvw+tmJgjZy1/oOOvPjpVdOjTMRm6HV42BtRzNrO31cmkzidth4dFd/wRwokR/rHBmKsLnLTw5M42ytY0Wr+2QuxnuL0SBITjAlEhPMBNrlhNdmQn2jwN0oHC8Rz9sEfpddFaansqbnPTYUKUoCXI4Wj1rWtE70H/DYsQlRlFgZsDQH0ITwLR5HkTGM2X6VDHL0AntQl7P4PWqC5Wj+eu02QTanlAjmjddtbxLkZpWCqF9/XGZ2aT2z9LTk+0czDtCuxO+2EU1VzmymNx8aDCeqvlc09H3kd9uIpXKmJktGwwKjGdOzj9/JZ755qMQMQTNeiFsYI1xN5ASzfmSclSx3qjFlMRrWmT3vypn8GE1w7E2C7OwVw5pqYrOkNgRq2rNy8dah60egYh9XYq7Ge4vVIGjBTX4kksWIlUC7UiJivVDfKHA3CsdLxPO5K4mJrc77bUMS4HJEU1lihjrEUqWJlcuZA2hCeKMxjNl+T+0vb5BjnKhklSsJorXrzeQUU8G88bqz+f01Ub/+uKWM0ThAI1bF5BKKzYdquVc0jOe0MlkyMznQ8+SBAVMzhGnd/WdlKiGRSCSLnWpMWYwmNGbPu3ImP0YTnKwuvlUbmyW1oX7BXz7e6vuxmj6uxFyN95aqQZCcYEqWJVYC7UqJiPVCfaPA3SgcLxHP264kJrY676cNSYDL4Xfb8Rnq4HOXJlYuZw6gCeGNxjBm+z26q7xBjlHMbxdXEkRr1+uwCVPBvPG67fn9NVG//riljNE4QMPnriZtdrH5UC33iobxnFYmS2YmB3oe29lvaoYQ0N1/VqYSEolEstipxpTFaEJj9rwrZ/JjNMGx6+JbtbFZUhvaL5jl0PdjNX1cibka7y1VgyC5RFaybKnWXMe4v16orzfaMROOm4nnobxw/NhQhCdeu5IoeCI2U1h73+FzgQLNbntR8uVTY1E2dfkLIn8zE4IXT4xyajTKpm4/m7v9RYY62j77NQ1mXD3nxi4figK/otNgaqL+iWianeuC7D8Xpr/dy441QQ6dD3NxMsGD13dbXm+5637qwAAf3d5DTqFkf/2/hy6EOXU5Sm+bh3A8zU/fHSU7O8stq1UN5o+ODON329i6qgWvw87gZIKXTowhBMxkcqoG0+8hPpNhRYubFS0eBkIJfvmGFbwzMo1Q4MjgJNOpDIkZowbTxeBkgmaHjfPhBL96Wy9TyWzBNGhoMsFrZye4Ia/B1LZr5gj69jg+Ml0wHRgMJ3jqwACP7lQ1mNp9c3I0WmTMpG837V5Z2+4lHE/jdtq4d1MnR4am1GMvRzk1FqW3VdVgbukJFEyVdq1TNZjfPzxEh9+J16FqMPWGUfq6HroQZiCU4BO6e8FohlDN/X21kEtk60fGWYmkOlOWasYR5Ux+NFM+bYygN6wxxmajGR7A9w7rNJhtHt4bjZLJ5djQ5eeXtq5g/7kwPqeNS1NJANq8Tt4dnqK92cnWnla8LhuJmRzvDEcYnZrBYROMRvMaTLeDl94bJzc7iyKgs9lFs9vOioCbWRTiM1lu61c1mD87McrWlS1sXumnCfjF+Ul+aWs3L+o0mB0+F8FmJ6dGY3T6nBy9NMWmFX4+9b5+To5Gee30OGeNGszsLI/uXM2ZsRgHzofZ1Okjkc3RpMDr50LYmyDgdrBrQ4c6XorO8MrpcSaiM7R6nazt8LK208dwXoOpmRBpBoLa+Gpjlw8FisYulcwca72X5mK8txgNgqQGUyKpAzNRN5Q3L6lGxG1ljGIU/usNX/qCXo4OTRXE/5oZgJlpDuRNf0ajXNd9ZcJpZk6UmMniNZi6VCNq17bp6zM4mSxcN1BkSPDT46P0B708uKWbwXCCr718Bp/bzvpOH7F0rmAgVK6tjO1sJXbXbzs+PMWfv3SG960JsnlloMhgR2+k0OJxmJoi6Y144jNZEuksp0djCGBbX2vBUElv9GQ0yDHeD2Z1jyTSvHh8tMhAx+o6NdMovQFEtTTSJGAxGg4YkRPM+pFxViIpT6OegcZxw1Qyw1MHBpjJ5NjY5aPD7y6Jq1ZxptVj5x8ODXFrfyu3rG7jx0eGmcnk2NDtw+tUf0HTGxIOhOI8tX+AqWSaFo+zyABnIBTnlVPjTCbSrO3w0d7spLfNw6XJJAqwvU91rdXHpqf2q66zj+7st4zpmkng6bEYHruNT+3qZzqZKRrvaIaEQZ+TeDrHtp4AP3jrEp0+F5+7Zx2gmht1+q+Y01VrnFOp32oxdqqm75dCrKwXOcGUSGrEzADIaHhiFHpXI+LWC7X15RmF/y6HKBKg+902UunZIv2hvQlysxRMcLQ65hSlyPSnxeOgSVAw/fnB5+/go199nUmdYYBm6qI3BbIStWvbplOZogTHV86nLvmJmBgK+Vw2YjOl2gfNzMBoKW4mai8ndtdvc9ohNjNbdJ5Wjx0hBNFkhqxy5dwBj51kerbk+rX+t3pC+t02bE1NRAxtac8vockpSsFYKeh18MMv3FnoY/257v3ynqIy9uaNHYzXOZXMFEyjzNqsHI00CVishgNG5ASzfmSclUisadQzMJJIc9+X9xTicYvHwVSy1MynNW/op8UTLWb78jHfzMCvHEGvg29/9nYe/srrRfFNAH6PvWgMUY5Wr4M9f3hvUWzS2GsS083MiozmgD5XU0nsrgajyaIZlfqtVmOnSn2/VGJlvUiTH4mkRqwMgKbLCL2rEXEbhdrTKXPhv1GAHkvlSsxtsrMUmeBo5RhNf6KpjKnBgB6j2VA5UXuhbUwml9p1WxkSmE0utfM/f2ykbFvp29lK7K7fZhagNAOarK7qSr7OZtefKTO5BLVfzNoyM3vFzEl/bn0flzNg0owdjPvqTaPM2qwcjTQJWKqGAxKJRNIIGvUMND7/oylzp1hjPNFitvH9aplOZXly/0BJfNMc7qvFLDZpmMV0s7GB8bh6JpdQarJoRqV+q9XYqVLfL/dYKSeYEokBKwOgQBmhdzUibqNQO+A2F/4bBeg+t63E3MbeRJEJjlaO0fTH73aYGgzoMZoNlRO1F9rGZm62o12XGT6XubBeMzMo11b6drYSu+u3+VyljzbNgMauq7rI19ns+rX+t8Lntpm2paPpipmT/tz6Pi5nwKQZOxj31ZtGmbVZORppErBUDQckEomkETTqGWh8/vvdDtP9jPFEi9nG96sl4Lbz2K7+kvimGuBUX55ZbNIwi+lmYwPjcWaxuxqMJotmVOq3Wo2dKvX9co+VcomsRGKCJqi2CfjhW8M8ltcG6M1ZxqZTnBmNoQi4qbeV61b4eerAAKcuT/NLN6zk125fXbL84sdHLvHmQIRf3dFLJKnq+rxOVbu35+QYbw5EeOjGFbxyZoLLU0nu3tjJLavb+NnxUZqdNsKJNKtaPXT53fS2eXjm7WFSmRw39ap6iOPDU/x/Pz1Js7OJ3Ru76GtvZqvOtKVgTpQXuAMoCvhcV0yKBkJxnjowQDqbo6fFQyieprdNNYnRNJVdfieKAqGYavTz8slxAB6+qacoYfLqYDOgMBad4ZO3r+aFE6McHphk8wo/K1s8tHgcfDKv1zDqCzVRvEKxtnMgFOe9kSi/uqOX7CwlekpNCH/wXIg/ffE0uzd10Nrs5OD5MG57E7OK4K4NQZ47dpnb1wTxOm0cGZxi1/og927u4t3hacajKSZiaT6wpZuTl6McvRRhIprmzFiM7OwsKwJubljVwsXJBGuCXgZCSXatb2f35s4ig5z950IFEx+tbZ8/OsJDhiTaL54YZSCU4AO6JM9AiTHEi8dHGQhfMdqp9l7W9KT1GBRY6UYXm+GAEblEtn5knJVIzNE/Tw9dCHNh4op2vlq9nd7H4KfHR+nyOen0u9m1vp3jw1P86Qun6PS78bttBDxOPpU3pnnm7UvsPxui0+fi9FiM37hjDSPTKZ49MsxEbIabV7dyeixGe7OTB7d08703L+GwCTqaXcTTWVa2eNixJsgDW7oLWs+3LoR5e2gKn9vGxi4/77++mw6fi2OXIvzTscs0O21s6PKTyeVoa3bhsjeRyubIZGd58PpufnpilIDbzroOH+cnYkRTWT61s59LkRQD4QQfzMe0/nYve06O8dKJUeJp1ThvdVsz/R3NvHc5yvBkkn//4Ea29LTw9ZfP8I+HLpLOzXLz6jZaPA7Ojsdx2AT/+y9fj9/tKJjbhWJphIBtvcWaUH1bv3h8lCODEQCCzU4uhBP81l1rubG3FSj2NtCPIQDT/rSK41aeEYs9VtaL1GBKJHUyEIqbat4iiXSJbs6MFo+DvV+8tzA5MB7jsAmyOaWgozT7JNqAWYq3ab8oZmZnC1pNAfzVp27hX3/nzaLjHWWSApvpA6aSGXZ/aU9V7WMX4Pc4UBSloLk06inKoaZtcVjqC/U6xGaXDQVK2twsMbXWR9qxbmdT1ZoSY/1bPXYQomJfG89vxlz0H/VqOeaqAVnKGhI5wawfGWclklL0z0NPPq5oMeuZx+/kM988VPFZqZUxlUgXeQHs+eK9ACVaRrDWTNZDq9fBUxXKMvolBNz2wvJWv9tmmUNZAHabKJHQ2IBqMj23eOx86Ve2lYxjzK4hkffD0J/JqMMsN1Yza3OrMUQlHwhgycbJuSA1mBJJnVhp3sx0c2ZEU5kS7aCeTE4p0lGakaN0m6bx02s1FeDPXzpTcny5pMBWOr9qySqlmstagl8mp5hqOLS2NtbPrM3NElMbr60WTYmx/rVoXCrpIuei/6hXyzFXDchy15BIJBKJhvF5qI9ZTx4YqOpZqZVh9AJ4/tiIqZYRrDWT9RCtoiyjX0J05koMtJpcQt6DwMSfoZrJpVY3s3GM2X5pw+QSSnWY5cZqZm1uNYao5AMh46Q5coIpkVhgpXkz082Z4Xc7SrSDehw2UaSjNMNG6TZN46fXagrg9+7fUHJ8uaTAVjq/arGLUs1lOc1iSd1swlTDoU8wra+fWZubJaY2XlstmhJj/WvRuFTSRc5F/1GvlmOuGpDlriGRSCQSDePzUB+zHtvZX9WzUivD6AXw0I0rTbWMYK2ZrAd/FWUZ/RL8risx0Oe2WR6rutuWbjV3XzCvm9k4xmw/zSNCj1GHWW6sZtbmVmOISj4QMk6aI5fISiRlGAjFef7YSFHSY7iimzs6FAFFzYu4Y02QvSfHOHAuzM517Ty8vadkCaReaxdJZgqJe/WJ7HetC3JkaKqQeHl10Mvx4WkQsGVloEjj9+KJUS6GEvxKXpN3bCjCE6+d59b+VnZv6qqouTPTBwyE4nzv8BCdPhde55XQ0Oy2F5I8a3Uzag239AQ4PjzNQDjOofNhVrV5uGt9By++N8YD13Xx2tkJxqNpHt25mpxCyXmNba2vH8D+syHGYynGo+lCG5olpjYeOxhO8PU9Z3Ham+j0u7hvcyf7z4Xp9LlAKAyGk2xe4WfHmiDHh6eJp7M0O1VdauG80RRnxmLMZHO47Hbamh0MhBL88g0rODMRLzl/te1d7fZ6tRxz1YAsVQ2JXCJbPzLOSiTm6J+HU8lMUcyq9lmp7WcWu7Q4ePeGDoYiyRIN//ffHEIAA6EE//K2XoanUrx2epyBUIJ7NnbS7ncyEVW9EQ6cD9Phc4ICQ5Ekm7r9PHB99xU/gGMjZDI5vrX/Al0+Fyta3bz/uhU8vL2HwXCCpw4M8OhO1X9i/9kQCNi1rr1w3Rs6mvnRkWH8bhs71rSzY22waDxzMZTgQd1Y59CFsDpmQtVM7lgT5NCFMKdGo0V123dmnP/4zHEiyTS/eddaYukcXoeNeDrHJ27tLegkNT+MeErNQ73LJBdmYayW12D2tHpIZHL8yi29JW1ebgxRSVe5VOPkXJAaTEldHBuK8NWXz9Lpd/K5u9fVnNTdjEYlnD02FCkk5dVE2mbl6/frC3obmuzWKArXJ0Dec3KMwwMRfutuVUSu7XvPxg4mE5mCKc09Gzt473KUk6NRruv2lwjUtcTFqayacLkzn3BZ0wG8kDd82dYT4Gcnxvj4zT1kZ1WH2R+8NczHb+5RjYRmssTT2cIk6va1wYKRzXgszSMWhjF6M4N3h6eJz6gP8a09Ad4dngauJIZ+7ugIN/e18NbgFPds7GBwMlnYrtVXa3+gKLG0frt2Ln3ZTx8eotlpIxRPs7rNg9dpx+uy0+qx88Sr55lOZQi4HXzu7rUFLag+6bLx3FoS5035ttAnrDYzs9HXVTv+5OUom1f4eTDfZ5FEmr8/eJGfnxjlzvUd9Aa9wJWk1mZl10IjkzU3MkH4Yk4gLSeY9SPjrGSx0+jnj748LaZ9aFvlLw21Y41xQh9ztNimxRqtvj97Z4T/9PxxcrMKj9zax8dv7WUglCiKucl0lrFYmg9u6WZwMmkZt83q8OLxUX5xPkR0JstjO/vJzkJ/u5eD58OcuhylN6jGUwFcDCd46b1RfG47uzd1kUrneP3sBFtWBhiLzXBTbyvNThtjsXRhzPGB67s4PR7n5r4WXjo5DgrcvqaNH7w1TMBj4/Y17aQyWf7m1Qt88n19ZGYpGh/0tXkKYyG9uZ12Hdp2bZylb0d9exqP+em7o6xp9/LAlu7CtrhumW8oNsPxkSi/dffawtjQOPYo96X8coif1SAnmJKaOTYU4cNfeb3wutak7mY0yizEWLdnH7+TG3tbS8r/i3+1nV9/4mBhP7/bxkxGaYgAW2/+A9bJkQH++lO38G++82ZhX83QxwwtabH27aKZ2L/N6+BHX7iTj3zltcJkSk81JjvGfcz6V2vP6WSGrCEfpP74QIWkzFp9P/61faZmPcbt6dyVHFjl2rUSbV4HL//hvcAV8b3PZSsyRtJw2ppodqnLfmIzuSLhvj4JdqvHziwUXW+Lx84zj9/FQ3/+imX+rhaPHZsQRWUvlNFOIxOEL3ZTAznBrB8ZZyWLmUY/f8qZ91Qa+0QS6eI44XUggPhMrhBXoqks2VkFR34J5c//YDcHz4VMzWwcTaIk77UZRkOa+7+8h3C+Di0eOyBK4qe9Sc2hvRhxNAl8brupmR+o7RrPt6M9b5Bo147RGQ3qafE4EJhv0++TTBcbBmnjh2qMEa/V+FkN0uRHUjPf3j9Q9LrWpO5mNEoEbazbUwcGTMv/+t5zRfvFUrmGCbCNQnyr5MigGu/o97WaXKrlXKmbldg/msry/NER04TFUJ3JjllyZbOkyNFUloxhcmk8vpKBjlZfK7Me4/biY+ubXGrlGsX3RmMkDa1e0xbCfQ31+OLr1epfLjm0Wdm10EgDgUYnCJemBhKJ5GrT6OdPOfOeSmMfY5zQYpw+rmhxVF/f/2FhZlPN5NJYt6NDU0VjArUOpfFzsU4u4YohoZUpj348ohkkasdYjYeiqYzlNv0+RsOgWowR62G5xE85wZSU8Old/UWva03qbkajRNDGuj26s9+0/N/Zva5oP5/b1jABtlGIb5UcGVTjHf2+Jvp3XTlX6mYl9ve77Ty0baVpwmKozmTHLLmyWVJkv9uOo0mY7q9RyUBHq6+VWY9xe/Gx1u1aCTPxvdEYSUOrV8BCuK+hHl98vVr9yyWHNiu7FhppINDoBOHS1EAikVxtGv38KWfeU2nsY4wTWozTxxUtjurr+28tzGwcTdVE8VJDmkBJHUrjp30Rj/g1Q0IrUx79eEQzSNSOsRoP+d0Oy236fYyGQbUYI9bDcomfcomsxJRjQxG+vucsHX4nv3lX4zSYjRBBHxuKFMTnRg2mJvweCCWwN8GPjgwXROrViu+t1sXrkyP/+O1h0tkcK1s8eW2gF6/LporpwwleOzvB+6/v5pdvXMnB82FOj0Vp8zpJpHPsWhfkwLkw7T4nyUyONy6EaW92ctua9oI+ElQtwbNHhxmbnmFoMsFUMs3KFg+/vXs9kWSWi+E4J0ai3LuxgxdOjOGwN9Hlc7FjTRv/9M5luvwu3rs8TSY3S3fAAyjMZGa5c0M7PzoyTKffRafPTavXwanRGDf1tdDhcxGKzXBkMMJNfa2saW8uGPdo5jo39gR47thlblvTysM3reL48BR/8dIZNnX5iKSyPHTDCo4NT9Ppc+J12kmks5wajeFx2nj4ph5ePzPB80eHcdlt9LZ58XnsrGr1EI6n6WtT2/P1MxPcsb4Dj9PGGxfCuOw2xqMzhOIzzM5CUxM0O22MR1OEYhlam+381t3refPiFC6HGnymk1nu3dzJhi4ff/vaebK5Wda0N/OLC2GS6Sy5Wbipt4V3hqdpAsamkzidDlw2QSKdxeu0c+/mLt67PM3o9Ay/cecahiaT7D8XYmgyQZffjd9t5471HaQyOc6MRTk6NIW9SeBy2Fgd9PL+Ld10NLsYj81wdHCKbX0teJ22goZmOpnh2/sHivSz/3BoiFv7W7l3c1dBhyMABCRmVD3OHeuC/PM7l7k0mWL35g5uWd3Gj98eBgXuv66Ttwan6Am4+MfDQ2xb1YLTYeOOdUH2nQvjc9q4NJnE5bBx/3WdvPTeONOpNE67rUjrC+V1o7V8no36oKuxHEguka0fGWcli51GjCeM+nytvOPDU/zl3nP89u513LGhs6py9p9TdX5ep+pToDfj08Yl+veODk1xMRTjv79wmllF4aPbe1jX6VNj7KoAzx+7zPUr/XhdNiZiaXauDfLdNwZ562KEtR3NuJ02ru/28/eHBun0OVnX6SOTUxiLzvDR7Ss5eCFCs7OJfWdDBJudBNwOzk3E6A646Wvz5J/7WVx2QbvPyURshtkcdPidtPucjE6lCcXTtHibcNrsuB02XPYmxmNpXDbIzEIqnWUWWN3mpalJMBRJMDMzi93ehMvRBArMZNVVQ6vavPS0uLmpr40Xjl/mvcsxVgZcbF/dylg0xWQik1/22kSLx06TEITiM1wMJbE1wYYuH5tW+FGAyXgaAYxMzfDYrtW8PTTFeyPTrO/yI1B46b1xAm47m1YECMVnCEVn2LwiQNDnwGW30ey08cbAJLeubiOSzDCdzPKxm3s4NjxNf/CKdtPM0+POfBz92M09DEdSXAgleOS23sJS5Wp1lVZGQWY+EItVqyk1mJJlxVzWtleT6H46lTHN8wTmyX+1xMPZ3JWlpppGI5bXE1h9ArXkynrNqYYNmAUctia8zqYijUGLx8GsUqo1rAe/28arf3Q/QMn1C+DvPnc7n3ziYMk12PNa06X1dFlYyulzoTp9baOYq25Uj1GjZEyGPV/ICWb9yDgrudaxivd6/4Na/CeqHXvo98uY5HLUo52/xePgrv/v52WlGJLqsYqldgEtXic/+PwdBV8IM08PM559/E4+881Ddesqze4fYFFrNaUGU7KsmMva9moS3VtNLsE8+a9eL6Dfz0rfqEdLrmxGLl+2pi0srkfGVGtYD7FUrkiPqL9+Bfj63nOm15CVk8uaKTe5hKvbnnPVjeox07Jeq5oTiUSyNLCK93r/g1r8J6ode+j3q/RM185/dGhKTi4biFW7ZxVz3wijp4cZT+0fmJOu0uz+WepaTTnBlFxTzGVtezWJ7s2SCGuYJf/V6wX0+1npG/VoyZXNsOXL1rSFxfVwmGoN68HnthXpEfXXL4Df2b3O9Brsojo9qOQK5fS5cHXbc666UT1mWtZrVXMikUiWBlbxXu9/UIv/RLVjD/1+lZ7p2vm39baU1flLasOq3e3C3DfC6OlhxqO7+uekqzS7f5a6VlMukZVcc8xFm1FNonstOfLdGzo4ORrlYijBrxiT/w5Pg4AtKwOFxMPHR6YLSZOBon3jM1ma3Xa2rAwU7act2fnb185xZiyG12nns3euIadQpOmYSmb43uEh+tu9PHC9qh948cRoUVLjzd1+Xj0zwYaOZv7pncvcsrqV3Zu72HtynAPnQmzrbaHD72IirxfcuT7IwzetKkkkbEwOPRCK8/3DQ3T4nHQG3MXXnNduajk4d6wJsvfUGK+cGqfZZcdjt+F0NLGh04fXaafZZafFY+cf3xji+hV+OvyuwteNodgMr58NkcrkUJRZvE477T4X+8+FubmvhU/t7OfA+TBep43z4zFC8TT3bOrk5r42vvvGICNTSdZ3+DgzHscmFMZjM2zu9vPu8DTJTI7LU0l8bic2AbGZDF6HnQ/esIKxaJrT41E+vbOfi5NJTo5M897lKP3tHjI5uH1tEEUBj8PGoYEwo1Mp7LYmtve18Kmdqm5zPJZiKJykt80DAiaiaX4lr8F86sAAH93eQ05RJ5nffWOIW/pb2b2pS7030lmanXa29AQ4dD7MxckEO9cG+cm7lxmeTKnXuLqVZ4+qGsx7N3dyZGiKlX4X331ziG09LbicNnauzSfebnaqdcjrevacHGcqmcblsLGh009nwMWudVfu0UYkjtY0Svr7er6RS2TrR8ZZyXLAKt4PhOI8f2ykEOPmWp7Vfsaxgvb/3lZPUYzVjvmHQxd55dQ4v3zDCo5emmZVi5t/eGOQzmYn1/UE6Al4OB9OcO/GDvafD7NrbZD958M8cF0XP39vlGOXpuj0uXhwSzfffP08w5NJnHm/gOFIijavkwe2dDGZSHNieJpLUyl6Wtz0Bb3E0zkcTYLBySSKoqbtOjUewyGaWNPpZevKAHtOTTAeTdLb5sXrtBGKpklmZ7l7YwfJzGzB5+G5I5d49UyINe1eHr9vA88du0wkPkMkmcFma2Jjlw+7TXD6cpTzoTgoChu7/Nx3XTeTyTQzmRyxmRxj0RS/vmM1bw5GGImkuG1tG5F4mp+8c5m2ZicrWzyMx1Kk0jnuv66bvnZvIZZqY5Mzo3FcjiYevqmHSDJT6DtjX2reHw9c18WZiXhh/DcQSvCJW69oMOcSM610mY2Kw41GajCvYTTR8ad39ReSxS5GIfBix8yAxCisNu4zlczw9BtDNLtshGJpOnxOJmJpNq/wF4x6BkJxjg5G2Lm+nVtWtxUlDNYS/za77EXJhLWHlFmiYWOg0+qoJU4eCCe4Y12Qfzp2mYn4DF+4dwN9QS8vHB/l7aEIHruNh7f38N7lKCcvR9m8ws91K/wFc5iP5LcNhBN8cEt3IfGxlY5En/S4zevgp++O0uVXjX0UoNllp9Vj5wdvDfNxE0G8WXnG9k7MZAtlbe0JFExvtNfGRMtaEuo71gV5a3CKm/ta2Hc2jC/fT71BDwCnx2K47TY+sr2nkPT5Dt2EXkuyDeqSqXs2dvDe5ShvD0ZwO64cF5/JMhGb4e28KVJ/e7Npn9Uq/l8sn2VjPy+GOlWLnGDWj4yzkuVAtc/aes1X9LF8a0+gEKe1+Ko9V82er5FEmh8fucThgQj3bergp8fHcDnUFUsbunwk0zkOXQgT9Dm5saelEHdPj0Z55fQEn3pfH0cvTXFuPFEw/bm1v5WNXT6eePU8yUyOLr+LFo+D9Xlzt609AfacHGP/2TB3rA9y8+o2fnxkmKlUGo/dhtthY9/ZCdq8Tj5+8ypePjVRKFOL85ciKQbCCR65tbck/hmvVxu7vD0YIZ3LEXA7+cj2Hk5ejnIhlOCDW7t58+Ik+8+GuKmvlQ6fi2aXvcgoThsHf+D6Lk6Px7lnY0dR29Ybs/Tja6OZpN4YSj+WMzOwMxvbLNWYakROMK9Rjg1FisxfAh47qfTsohQCL2bMDEh++IU7iwTeP/j8HXzsq68Xkhj73bY5Gei0eBwlCZBBXbbxzON38tg3Dhbqo9+mNxuo1ijA5xTE0vV9xjXBu/F+Mp7bVkfy5r0W16K190e/+rppGyiG1w5bE80um2Vy5lpo8zp48rO38/BXXi8YPMzl6ag3aKhWqL+YEjAXjK2SmZIk4Uvh+SInmPUj46zkWqceU55azFeMYwsjmlGfZgJobxIEPI5C+Xf/yUsNMeqrhUYayZnFv0xuFnuTIDurYBOq5rEeNKO4wXCiogliPTHLOL5+9vE7ubG3tehe8LlszCpKkcGi0cDObGzz8a/tW7Ix1Yg0+blG+bbB/CW2hIXAC4mZAYlR4P380ZEiI525GuiYJUAG9cH+5IEB00TDRrOBao0C6p1cwhXBu/F+Mp67nuTNVteiF9gbMV6JPnG1VXLmWojmTZX0Bg9zQW/QUK1QfzGJ+gvGTiZJwiUSiWQpU48pTy3mK8axhRHNqE8zAczMKkXlN8qorxYa+XOTWfzTrlOh/sklXDGKM46DNfQmiPXELGO5Tx1QX+uvZTpvhGdWLw2rsc1yialygrlE+bTB/MW3hIXAC4mZAYlR4P3QtpVFRjpzNdAxS4AM6jd+j+3sN000bDQbqNYowOes3xpGE7wb7yfjuetJ3mx1LXqBvRHjlegTV1slZ64Ff95USW/wMBf0Bg3VCvUXk6i/YOxkkiRcIpFIljL1mPLUYr5iHFsY0Yz6NBNAR5MoKr9RRn210EgjObP4p12nQB1f1ItmFGccB2voTRDriVnGch/dqb7WX0sgb4RnVi8Nq7HNcompconsEkYTHT+6U9VgLlYh8GLHzIDEKKyOJNLsPxsCAbvWtRdMdZqdNsKJNMFmJ+F4mk1dfnasDXJ8eJqBcJyjQ1PsXBfk5r62gmi/xeNgv7Zu320vEfTr69PbVir219dbMwo4dCHMxVCCneuC/OSdy0zE0/zO7vX0Bb2q2c9gBLfTxoe3qfqGU2NRNnX52bzCXzCH+fBNPQXToge3dBcJ3s3aTJ88utXj4GfHR+nMazABmp2qYc+Pjgzz0e09DE+ligTxZuUVtXc+cbVWlibM15ve6BNX7z8bYjyWYiKaZue6IEeGptje28L+c2GaXTbC8bRqsqMIzoxFC+0xNJks9KumwdQMHkD9FvbuDR2cvBzl6KUIbruND9+kHqdpMI8OTbGtr4X+YLNpn9Ui1F9Mon5jPy+GOlWLXCJbPzLOSpYDtZry1Gq+oo/lW3oChTitxVftuWr2fI0k0jzz9iXeHIiwe2MHPzsxhsue12B2+0ikc7xxPky7z8nWVS2FuHv6cpRXzkzwydv7OHZpinMTCT5y00reGIhwy+pWNnT5+MbrF0iks3T5XQQ8DjZ0qhrMLT0B9p4cZ/+5ELvWq+OWZ98eLpjAeex5DWazi49t72Hv6Qlu6W9lQ6fvSpyPpLg4meBXbuktiX/G610d9HLofJijQxFmsjlaPM7COGQglOADW7p56+IkB86H2baqpaDB3GXQYBrNd/RtW2/M0o+vjRpMrd9BHXdoYxIzAzuzsc1SjalGpAZTYsliMhNZLOgF2O8OTwPljXb0x2libwF4daY0mlHNYDjBwQthPnvHGs5OxNl7apzP3rEGt9NeEIy/cHy0YMDz4JbuomBjNCLSv5+YyeLVicz1RjWaqdB7I9O89N4obV4nv3Pvek6PxTg8EOFDN64oiOONJj9tXgfffWOQS5Mpdm/u4N7NXRw6H+a90Sir2zx48s6v+jYCePrwEP1BLw9uUR1t9XXU2mU8qrrLrg566PC7S67r6NCUah50fJROn5NOv7tgGnTnuiA/eGuY2EyG39m9HrfTjr2JwrbXz4X5+M09ZGfVbxGnkplCe2h9uO/MOP/9hdOs7/TyqZ1rCsYAh86HOTIYIZ0PePdd18lLJ8eZTmZw2pvY3tvKA7rr0q5D32eRRJoXjo9y6nKUTXnjJytBfzUGEvWaTCxn5ASzfmSclVyLzMXUZ67nMZrDmJWvxe2b+1p4a3CKezaqX2weGYwgBGzIm/HckXei10yAfm1HL5ciKY4ORgjF0pwLxejyufjUzn5ePTPBpUiSHWuCdPhchGIzHLwQxm1vYlYRfPzmHl58b5STl2O8L++IPpPNsf9cmDvWBckpkM7lEAgctibam528OTiJQHBzXyvxdK5gctjhczIRT9PX5qHT76avzcM/HhpkeOrK+TWaXXbsAr6x7wK3rwly3coAdgF//eo51nX6+MJ9GwA1v2Qqm2NTl89ynKC1o3Hco2/vF46PcnI0ynXdfnpa3QVTouys6r/wyumJgkFQuTit7Ws1HqzWxKea+2GxIyeYElMWk5nIYkHfJulcqbDQaLSjP66cmL8S9iZ12WxOUZjWCcZbPXb2fPE+AO7/8p6CyVCb18HLf3gvQMl5zYxqfHWaEtlQtQx6qjEB0O/jd9uwCVEkhLdCf12auUxmdm7PJ4dN0OyyFwyAtD68NJng1584WLSvZgxQzRlbPA4ESsl1tXrs/Pjxu3j4K68xZdjmNBH0V2MgoRkD1GoysdyRE8z6kXFWcq0xF1OfWp6tlZ7pvrwpXXwmV1T+QCjOvV/aU1X8afU6yOZyxGbqMEBY4mhGOlAcA7/1GzuKxj0Bj51kepZml61kbKWnCTXua5iZG2p9OpVIF7SjZuNBowGi3rjJyijR6n5YCkiTH4kpi8lMZLGgbxMzjEY7xuPqJTurGszELATjR4emisTkRqMBPWZGNfWaBZgdVU3w0+8TS+VKhPBWGK9rrpNLUA0U9G2k9eHX9p4r2VczBqiurhnT69KbRBkx+6xVYyBhNJ2q1mRCIpFIJCpzMfWZ63mM5jBm5T93dKSG+JNdlpNLKB4X6dvROO7RbzOOrfQYW9HM3FA7l96YyGw8aGZoVMko0ep+uBaQE8xlymIyE1ks6NvEDKPRjvG4elF/wbTjsxCMb+ttKRKTG40G9JgZ1dRrFmB2VDW6fP0+PretRAhvhfG6HE1ztxxw2ERRG2l9+Pnd60r21YwBqqurw/S69CZRRsw+a9UYSBhNp6o1mZBIJBKJylxMfeZ6HqM5jFn5H9q2sob4Y8fnWp7D94BFDDSOe/TbjGMrPcZWNDM31M6lNyYyGw+aGRpVMkq0uh+uBeQS2WXMfJiJ/PDwIF/62Sm2rPTzwRtWFukIrxa1aNqM6+SPDUV4cv8AD17fxbFL0/R3eNnc7S8y6NGO07SIfW0emp12Epkshy9Mcmo0SpffzUe397Dn1ARr272EEmlGIknOh+K8/7pumgTsPTXBv757LR0BN6uDquZzIppiKJIsmAUdPB/mjQthhiNJVra6uWFVCx0+VYcwlczw9OEhuvLGOol0lsFQkt6gB4Djw1NMJXN8/OYejl2aJpPN8cqZcRQFbl8T5O2hSUamZrhjfTuzCH71tl6ODk2x5/Q4n921hsvRFC8cHyWVyeFoaqIv6KXV4+D1sxM4bU00ux1sXeFnZHqGezepJgTtzU5uXd3Kdw8Psa23hUgizfBUCr/LwUw2xw09LVwIxzl9OYbTDl6Xg742D1t7Wor0nP94aJDBcBwFwcV8QuZEOksoNoPN1kTQ4+DkWBS33cambj/ZWYVEOsdMdpZOv5NoKsuONUEiyQznxqJMJjLYm5q4bqWfW/rbSKZzvHhilHPjcdLZLGs7fIxH08wqCm3NDqby+6dnZxGKwvRMBo/DQZvXQW+bh9HoDH6Xgw6/i6lEGrtNcMf6DibjaV44McoKv5uJeJrc7CzdARd3b+qi0+/C67QX+u65oyNs7GzmH94YZDqZweO0ccf6Dvrbm9mqMzAqGA8dHeHujR0MTiYBNRA+9YuLPPq+1YxMpzg8EOH2Na385PgY92xoz5suuWlvdnJTX2uRPlT7LGj1MNOemOl4gYKGFjBNLN3Iz+1ckEtk60fGWUmjWQya8VpNfVYHr4wPAEsfBONYAigxdtl3NoRANfvZc3KMA2fD7Fwf5N7NXew5Ocbek+PkZmc5NxGnvdnJtr5WHrm1j5OjUd4ejJDJzjKLgstuY1Wrh6NDU7w1MIkQcM+mDi6Gk4zHZmhCMBadocVjZ8vKFhLpDIcvTpKbhXWdzdzYE+DlkxPE01lc9iY6/G4SMxkmYjPMZBQcdoHHIZhMzuKxg80GDpudTG6W+MwsNsBhF+RmFTL5n/+aHeB2OHDaBMlcjlaPk3h6lk1dXo5emsbrsDGLgl00kchkmcnM0h1ws6nbx4ELYVrcdlKZWdq8Ti6GEzjtgjavi4DbTjSVJT6TpalJsK6zmd+9fyMHzoV5/tgwa4LNuB02RqMpPnn7avacHufIRbUvAFa0uFjX4WMilsbeBAfOhVjT4aM/6OVCOMGv3tbLVDLLkYuT/PTdy3T6nXx4+yoevmmVaaz83htDqmFgIs3GLr/p+NbM0MhMi2k0C1osxn61IDWYkqvGDw8P8u+fPlr0nqYjvFofmlo1bfp18vo1/KB++6RfP29MPD+XT45qT95UVmN375deLtH4acd5XbYiXeHffe52PvnEQcs62QX4PQ4UKBxnJOCxW+oUGpmAebli1ndW6BM2V9IGV0uLx84zj99VuNe8ziamklnT+30qmSnogdRvg21Mmyy1NiaWrpf50ITLCWb9yDgraSRL0fNBX+dmlw1FuaK5N9MBWmnujOXMKjCVrM6v4dnH7+TG3lYiiXSRD4Nk7gS9Dn74hTv58FdeKxn3PPv4nfQFvUU6yZyiFHwV9OO3cvfyUrzva0FqMCVXjT/7+ZmS94yJZ+ebWjVt+nXy+jX8ULp+3ph4fi7ok+xaaezMNH764/TvfX3vubJ10nQF5bSi5XQKcnI5d8z6zoppk/tuLpNLoORem05lLe93vR5I1bOY63gb9fmWmlKJ5NplKX6+jXXWx2MzHaCV5s5YTjRV/STxqQMDhTKq9TKQVIfml2A27nnqwICpTlJDH8vL3ctL8b5vJPM+wRRCXBBCvCeEOJL/+9X8+11CiJ8IIU4LId4RQtw133WRzC//7v0bSt4zJp6db2rVtOnXyevX8EPp+nlj4vm5oE+ya6WxM9P46Y/Tv/c7u9eVrZOmKyinFS2nU2hkAublilnfWREwue+stMHVYrzXAm675f2u1wNpv2BWqudckJrSuSHjrGQxsxQ/38Y66+OxmQ7QSnNnLMfvdlRdh0d39hfKqNbLQFIdml+C2bjn0Z39pjpJDX0sL3cvL8X7vpHM+xJZIcQF4EOKorxjeP8bwEVFUf5YCLED+B6wXlGUsl/TyKU7i5sfHh7kyy+cYsuKAB+8YQUPLJAGs1JSZKtktwOhOM8fGykk6zWuidcfd+hCmFOXo/QGPXT6tCTFY7xyaoKeVg+/dEM3R4amuHtDB0ORJPEZ9dZudtrZYtDYWdX5xeOjHBoIE4qluWdjB7s3dxWOm0pmeP7YCA/dqOZjGgjF+f7hITr8Tjp9blo89kLy45xCUWLg8ViKdy9NEYqnuW1NkP72Znatay8qczqZ4YnXznPr6jZ2b+7k+PA0A+E4bw9OsbnbR06BXeuChWs8eTnKxckEO9eq723vbeEn71xmeCrJDStbyKHu/8/vXGYwlKC7xcXWVS1qgueVAY6PTIMCvW0enj06zFQiw0w2RyyVZWtPCwOTCS5MJHjfmjamkhnGY2luXBUgFE8TS2WZTmW4saeFeCZHX5sXr8vGRHSGvafGmUpkWNPRTLvPycYuP4lMlqODUwTcNg4NTLLC7+JyNM1MJseGLh+zijohG51OEY7NkEjn6Gn1srG7mVavk31nJgh4HNy2Jkgyk2NTt58da4L88PAQzx4bpq/ViyKUgo5kW28rO9YGS/puQ0czP3p7GBRIZ2e5bW0b/cHmkoTN+vtOayd9/54Zi/HmxQi39bfys+Nj3LWhnRdOjNHpc9HhU/U8D1zfXXKvafUwu9+1z4KmPd5/NgSCor4ySyzdyM/tXFhOS2RlnJUsdubD82G+Merk9p8LlTz3rMYS5cp58cSVfNc71gTVccNp1dsgkkzjddj5tw9s5Mbe1qIy9p8NEU9nSczkePPiJGfHY6zvbOa6FQGGIykUFHpbPYQSaYJeJ4l0jht6AvzNq2e5EEpy25o2Ht3Zz4+PXOL48DQrAm7Ss7O0eV2MTyd5e2gKj9PGyoCL86EEPpcDj9NGi8dJNJlmMJzAYResavMSS+UIxVLkFGhvdrGy1YVAkMzkCHodXI7OcH13gFNjMW7o8XN6LI5AYTKRJp6epb/dw/vWtnN6LE4ul+NCOMGqFg+TyQw2oTCTnaXFq45lxmMp4qksN/a28Kmda3jxxCjPHR1mc7ef1W1e3h6e4l/e0stbQxEGJuI0u+ykc7Pc1h/E67QzHpth17og+8+F6cyPj7Q+jCTSPPP2JX5+fBSHzVbU7lb9bxy/VXsPLZX7vloWXINZJvDFgLWKooznXx8E/khRlD3lypOBT2LkaiVPtmLfmXG+tvccn9+9ji09LZYGQ5ox0IVQgkdu6y3kT7Kqlz6x70+Pj9If9PLglm4AU8MiYwJg4/v35A1i4jNZml32EoMWq8S/xvOZ1dl47L6zIRIzWcZjM5wYifJrO3oL+pW+Ng9PHRjg1OUo92zqpMPnZCyW5pFbezk5Ms1/f/E071vTRiSZ4eRolKDXqU7k0jk8ThunRqOsDno5MTJNMpPD67Sxva+NWeCDW7p56+Ike05OEHDbcTgEbruNNq+TfWfGGZ6aoafVzS2r2zgzHsPvthNL5jgzHmNjp48VbW5swIsnxlBQeN/aIOcnEvS0ulEUuBBK4LKDEE0kMzk2r/DT7Xez/+wEtqYmPM4mWj1OYuksfpedSCKNzdbEHes76PC5Cu0+lczw9BtDdPrV9hwMJ1kd9KAAZ8ZiuBw2Ht3Zz3Qyw7f3DxQlhH7i1XMcG57iweu62djtx6vry2NDkcL+kWSWxEyWRFpdmnNpMsksCt0BD51+Fx+5qYfJRKbEnOKZI5d4I5+8OztLxfvM6vNVzedtPo0/5ARTxlnJtUEjnxONKKtSzK7mmWc0GNRv02J+s9NGKJ4uGAnG01kGw0k2ryg2mBkIxXn68BCdPieXJpO8eGKMG1e18NCNK/jZiTHuXBfk6cNDXJpKsnNtkKlkli6/i0MXJpnJ5nDZm5hMZHDZm+jyu+hp84AiuBRJMJ3M4HU2YbfZiCYzDEWSOGzQ3uyht83NuYkEoViSVq+LLp+TZGaWwXAch93OjjVB/G4be05N0N/mJjqTIzur8Mn3read4SjXr/RzOZLkn9+9TLDZSZfPRU+rm4MXJmlvdjAQSpDOQaffSS43W0hd9ss3rMTtsLH31Bj9HeoEOxRLszroocPvZmtPgIFQglQ6yzf2XWD3pk5++caVDIQStHkd/PjIMIgrvxA//cYQa9q9hR9EIok0LxwfZSCc4JFbe0tyn0tUFssEcwp1Oe4vgP8NNfXMoKIoXt1+3wWeUxTlyXLlycAn0XO1kidbse/MOL/+xMHC60DeDc3MYCiT12lo7P3ivbR4HKb10hsK6fNBBtw27E1NxPJJeTWDIGMC4Gcev5PPfPNQ0ftGrIxk9Il/m102BBTOV8k0ySiGl5TS4nFUbfKgxyYgZ9GXbV4HX/31m4vuxWqwN0FuVjUs8LlsZGZni/SWNqC12Vno4+lUhky+EvpE08bPl5lxldm3+vNpgLAMJ5gyzkquORr5nGhEWVZlVFO20ShQbxQDalwtF7P1aAaKekM2yRXMjAntTWrecStavQ5+/IU7+chXXit8Ia6Pc5JiFoPJzz2KotwE3AKEgG/n3zf2vanMSwjx+0KIIe0vFovNY1UlS42rlTzZiq/tPVf0OjpjbTBkvOGfPzZiWS+9oVBR+akc0yaGRcYEwE/tHyh534iVkYwx8e90qnrTJKMYXlJKLSYPeqwml2qZWb5uuBerITt7xbBgOpUlZjDzyVFsDpTRVUKfaLrcPWH1eVvuBggNRsZZyTVJI58TjSirUsyu5pmnGQWaxdVqJpdwJX7rDdkkVzBrk3KTS7gS6/SGSvo4J6mNeZ9gKopyMf9vBvgz4G5FUUIAQohO3a79wEWT4/+7oii92p/P55vvKkuWEFcrebIVn9+9rui132VtMGQc2T1040rLeukNhYrKd9sImBgWGRMAP7qrv+R9I1ZGMsbEvwF39aZJRjG8pJRaTB702Mr0pd9t53cM92I12JuuGBYE3HZ8BjMfG8XmQA5dJfSJpsvdE1aft+VugNBIZJyVXKs08jnRiLIqxexqnnmaUaBZXC0Xs/Vo8VtvyCa5glmb2CvMeLTYpTdU0sc5SW3M6xJZIUQz4FAUJZJ//fvARxVFuUcI8S3ggs584PvAOmk+IKkVMwMfK32Emdh6LpqMY0MR/uQn7xGbyfLFD25mS0+LaYLm/WdDjEVTnB2L4Xba+OT7+mnxOApaDE0wDhTee3d4mkRaTTJ8KZJkU5efB7Z0MxhO8OT+AR7b1c+Nva0MhOJ87/AQXqeNC6E4F0Nx7Hnd35qOZnpbPbxwfJR2n5Nmp71o7Ug8nS1o/j5yUw+RZIbVQfXcE7EUg6EkHqeNgVCCh25cwdHhaTp9TsKxNK+fneCO9R30Bb0k01neHZ5iPJZmx5ognT4XE7EZ9p4aI5LMsKrFQzo3y5r2ZjZ1+/irV86Rzs7S0+Kiw+dhPDbDTb0t/OJ8mIFwgm6/m+yswnQqjaNJ4LTbSGRyBFx22rwOktlZslmFy9Ek3X53flluBo/DRiqTw2ZrwmO3MZ1Kk0jP4nU24XXYiM5kcdpspLNZklkIuJvIzc4ST0Ow2c66jmZSmRwnRtRfcPraPIxGUwSbXURTGWaVWVIZBZdDkMmqS5yaXXYS6SyzgLOpCVsTNLsc+Jw2xmNpbE2wudvPTG4Wr9POfZs7+dGRkYK+Juh1MJlIs77Lr+p0L4SZjKe5obeFO9a28903h9jRH6QJePnUOC6bYCCc5MZVAW5bGyQUTbMpr8l5+cQoX/rZSbasaOGe6zpIzuQ4eH4ShMJI3gQiFFfb9EPbetje30Zvq4efHR+lw6fe+29djHApkuTXd/RxLpSgw+fE61QdZ5vddlrcdv7x0BC39rfy8PZVRffs8ZFpFEVNRK7d9wqU6H1r/ezWw3JZIivjrORapxFGKXrtYzkznnLPn0plWJn1GbXpelMgbaygaQOjqQwT0RnOhxLcs7GdZCbH5akUbc1OFOD8WIyx6Az/yy9dx33Xd/PMkUu8cHyUmewss4qijhcmk9hsgt2bOonN5EBRCvHZ6wRFsdHR7CKUSBFNqT/reRyQzoLbLphVFDI5Nb1ZtTMEOyCE+pfO/1LYBHidgnha/cXWDigC2rx2uvxudqwN8ubAJOcm4tiaBEIoJFKz5BQIeJpIp2dJzYLDpn7Bms1Bkw0CLjWnt00I1nU2M5nIYm+CmaxC0Odkc7ePwwOTAMzOwqYVfm5e3UqX341NwNf2nKXFY+ff7F7P0aEpXjwxyl0bOti0IsAd69sZDCf42stnCXjtrGlvJhRLW+perbwxlgMLqsEUQqxDDWg21GHtOeDfKopyQQjRDTwFrAXSwOcVRdlbqUwZ+CTlqFVjMRdNxrGhCB/+yuuF1/qkyEY92ke/+jqT+STJWoJfo0YNSpM2Z2eVouTNer2FprX89DcOWiZgDnjsPPv4XUXn+sHn7+BjX33d9Jhn89pNo/azVnxOQSwtF+7UiplupNr9/W5bSb5K/XYzDaffbccmKOhNKtWl1esAXcLxFo8dmxDETPS61egw9TRak7mMJpgyzkokZahFH2m1Tz3Pp2rL1Gvba8HraCKRmVt+5OVEwG1jWhcjjTHO0STwumxMJ7OmcdhK9xpw27A1NRHXeVUsh0nmgmowFUU5pyjKzYqibFMU5UZFUT6iKMqF/LZRRVE+oCjKRkVRtlYT9CSSStSqsZiLJuPb+weKXuuTIpvp0TSmU+YaNbOkzcbkzXq9hQI8eWCgbALmmMm5jBqDomvIazfnMrkE5OSyTmptNf3+Rv2kcbvZ+CVmSCBeqS7GhONGja6ZPvhqfBaXMzLOSiTlqUUfabVPPc+nasusZ3IJyMlljRi/gDW2ujbWsuoNK91rNJWTscuE/z97/x0l13Xf+aKfkytXdXVudEAGARIgGEASDCJFUcGmkiVr5LGCkzxjS7Lneu7YM/e9++57a5bvzF3Xst+Mx2meJXusMOOxZNmURCuQoggxgCQIEgRIZKDRAZ0r5xPfH6eqUF1dnYAG2A2ez1q9uuuk2tW19/7tfc7+fn83wuTHw+OGsVqNxbVoMn7p4NC8141JkVvp0WpEfK01aq2SNjcnb27UWwjAZ+8bWjIBc6jFezVrDOZ9hqp2s1n7uVpCqqcKuRpW+19rPL5ZP9m8v5WGM9SUQHy5sjQnHG/W6LbSB9+Itujh4eGxGKvRRy52zNX0Tyu9prKUwH4JAoo3hF8N4aYY2fxfr421Fvs2FtO9hn2SF7tacN3TlKw13tIdj+VYrV7jWvQdJ8bTfO2lET5z39CCpMjN2rLmJM2L6TWa9RnNWo+RRIEnT0zy+N4r+S4PX0iAAFGfzF89P4xh27xndzcfvn1Ty/eqnTObL3N+Oo9PkfjUfUP16524nCHmV3jq5DQBTaJYsbhva5yXhpN0BFVKusUb4xn2DUTpCGkAzOUrHB/PcN+WOB/ev4lMyeBbR8cJqhITmRIA+/pj9EV9/M9Xx9kSD1AwLNqDKsmiTkCWeG0sjWXbdIY1VFlERKBsWkyky6RLOr0RHx/dv4nDw0m2dwb57huTfOj2Xl44P8flVImK5eo3bu2NENRkzkxlGUsV6QhpfGBPD0XTYi5X4dhYmlTRYHd3ENMWmMyWuL0/xrt2dZLI6TxzeoawT2ZHV4iTUzm2dQS4MFdkIObjjctZBmJ+8rrrmBv1K3SEVAoVi5AmUzFt7t7cRkCVOTeTQxBgU9TP5XQJnyqxuzvMPxyb4H17unh1JM1QPEDJsNhZTbp95FKS4+NpfLLEh27vYzxVAsFd//j1l0f59L2DWFBP+HxyIgsCHNxa1Y48e4HOsMqvPeia/jx5YpKHtneQLhlIAnzl+UuYlsV79rj1A1ytZKFi1iNuUJPZ0+teu6Cb4LjbDjZoK2vvCcxLRt1cz25UW2zmnbJE9nrgxVmPm42V9C3LHXM1/dNKrxnzKzx1apqAInFxLk++YvGbD28j4lf4+6PjdIRVAorM+Zkch87O8TuP7eCere18543LHL6QZFdXiERRRxAgoEgcHU3xnt3dPLCto6rRtHjxfAIHB8eGHT1hfLLAU6dm0GSRj9zex4/PzNIZVLicKZMpGiiSK38wTBufKmDZUDEcZMm9oVnSHWzbxnFge1eIgm4RDyqMJEpIoqvj7I35MC2YzJToDms4gmtu9NH9m7h/e0f9M0+kS4wkihy/nEaTJO4cirGjK8yx8RSG5RDzqxR0g5CqMJMrUTZsHt/Xx77+KH/36jjdYY0Lc3nagyq9UT+vj6WRRYFfe3CLmzatGq9q45Kh9kA93o4kirxvTzfpksHeTVEyJaMeN89M5zg7lWNnd7ieJxPccdjfHx1nsD3AY7tdDeZaxa6NwtueB3Ot8QLf+uB6Jkh/u1kqEfJixz99cprT0zl2dYdXJPhOF3VevJAAXCOZxsS/Ub/Ci9UBf1CT60mDh9oDfP/EJN9/c4rNHQEe2t7JY9X3evFCgtlcmXPTeXdCE3MnNA6woytEZ9hXN145Pp5BFuFvj4wTUEReH8/wGw9tIVsxOTqS5oN7ezg3W+BdOzoYS7kTw9q5L15IMJcr8+ZEhmzZ5GP7N/Hkm5OcnMjx+Ye3oqkyo4kCRy4lCflkwppCf5ufuYLOLd1hDmyJ88pwkiOXEszldbpCKkdHM+zsCnJqKsfW9hC9bT76Yn4uVydWQUXi+QtzRHwK79rZSVm3eOKNy0xnSwRU1yygYlpIosCOrjCKLDGTLYPjEAto5HWDoXiQd+3o4NuvT+CTRRwcZrJljGouMlUUuZjI0x3R6A77OXR2BtuyQRSxLZuATybqU5jJl4n6VfpiPrrCPlJFA8eBnqiPfNng3EyeoXgA03aQRIE7B9uwgH19Eb53Yoq7h2I8vKur/n2+NZEF4Na+CN8/McnTp6Z5z+5ufnZvL69UDY/u3xrnxYvJuplAbeLWXEeODCe5lCjy/lu7531vteOfOHaZH745xWxOpzOs8tBO11z08IUEhmmxvTvM5x5yJ6XfOz7JB/f11nN/jSQK9W01c6qVtP23s5/wJphXjxdnPdYjjXFzMeOwtX6/5v5rqT6tVr65XJmZvM4n7uqv96FLlT1d1Hnq5HS9v399LFOPv7V4Gva5Ny9ncxU+dc8gFxJFukIqBd3ipQsJKqbNYHuAW/siXE6V+OFb04R9IiFNIaQqjKQKmLZDR9Us6PRkBlWSCPpkHNtBtx0CioQii0xlSuimjSqL9EUDZEo6qYJOe0ilI6RxZjqHLAn0RgJkyzoIAu1BhZJuky3r6IYFggg4lAwH24G2oIIESKJAR0jlUrJIWJOJBlS2dQQ5Oprm1t4ot26K8PpYitlchZlshaF4gJ09IZ47O4fpOPTF/Ay1BwlrMpva3HGCX5HY0xvmfx4d5/ZNUSwHBuJ+OsM+Yn6Zrx4eJeKXua0vgl91V+AUdZPZnM7929z/9x0DUV4fy/DBfa5jbHO8awso/PTcHO/a0UGqaNyUY9/V4k0wPdac650g/e2k8bPVjHZqBjuLTRQf+dKzpBsMcyI+CVkU62YnrYT97/7Ss3XTn2aifoVM6co+AeqGP82tNeKXEQVh3vsvRswvIwgC2aLBQrXe0kT97rKRVmYwHqtjqe9zOWJ+mSe++OACk6bFzIFqhlIf/C/PLdCfrKScz/7uIwDzDA1iAYXiCswM3u5+wptgXj1enPVYb6SLOo9+6dl6v9cWUPjJv3nkuvUprfovYNE+rVVcr/WhUb+yaNnTRZ2H/+An7lM2j7edxlgq4I598k35SZVlxoXvFN5Wkx+Pm5Ob2YyjldHOUp+xdnwjubI1z+xkMWH/YuTK8yeLjYY/zeTL5pLXaiRbPXa1k0u3TIubwXisjqW+z+WoGUQ1fxdLmRI8eXyypQHQctQSTC80NFhZ27+Z+wkPD48by/HxzAKDsevZp7Tqv5bq01rF9VofulTZlxsPeNxYnKa/s02TS1h+XOjh4k0wPVbNzWzG0cpoZ6nPWDu+kbBPmmd2spiwfzHCPmXe60bDn2ZCPnnJazUSqR670ApmeZrNXTyunqW+z+WoGUQ1fxdLmRI8vq+3pQHQctQSTC80NFhZ27+Z+wkPD48by77+6AKDsevZp7Tqv5bq01rF9VofulTZlxsPeNxYhKa/Iz4ZuSnALjcu9HDxlsh6XBVracaxFD96c5L//Mx5/tWj23nfbb3XdK1GbWVN+1bTQjRqzACePD7J/oEox8YyPN6gRVvsut85NsHhCwl2dYcwHXj/rd2Mp0v1pPOttBpPn5zmzHQOvyLxxlgKWZL4tQc3czld5ux0DlGAoyMp7hxsQ1Mk3renm+8dn+AfX79MV1jjjsE2PnBbDy9eTNIVVilWLH56bpZ0UaczpJEs6AzEA3SENLZ3hwgoMg5QMkxeupDgxOUsAVUkVzbpivjoi/pAELh/a5wnT0yxb1OU6VyFS3MF3rWzk4F4AAGYK1T44YlJxlIleqI+AqrE+dkCn7yrn4lMBVFwePFCgq6wRnfEx3CiQKFs0hv1s6MnTFtA4YdvTTGVLmHZbuLlLfEAl5JFYn6Zsumwqc3P1o4g6aLBRLrIaLKMJIAii4iCU0+DIuK6o2qygOmAiAOiAI6r9QgqEo4gIAkCJcMiXTIRceUhlu3eoVQEsKsJpVURVFWipFs0O8DX8kj6JJAlAVEUsR3X5MC0QBAFNNlNBm1aDpIIdw7EubU/yt1DbXzj5RHmCjoPbOsgUzK4OJtjKlOhaJjc2htDkwUuJoo8dksXfW1+Tk5kGUsW0C0HVRYIKjKz+QofuK2Xn9nby8mJLLP5MmOJEv1xPyXd4tRkjgObY/ztkTFKukVXxMcHbuvhkV1dHDo7ww/enGI6U0aVJW7pCXF0JI0oCnSGFXb3RrmjP8Z/OzxC1C/zGw9vqy+JHmjz8903JrAd+Mj+vroZQm2J12Ka5VbmUjdKk+ktkb16vDjrsRJutMa60dDu4NbVaTBXW9aaZlJoeK+RRIFvHh1nc9XYpVVcP3zR9UOYy+t8/M75GszGso8li/zN4RE+dkcfl9NlXjg/y+V0iY/c3sfx8SyP7e7iyEiKy6kS52dyAHSEVKZzOj1hlVcvpbAcCPlEdNOVXQgidIf9ZEsVkkUD0QGxGutswHFAlQXKhoPN6vMuNyPiXrfGUtdrtU+sbg/5RCqGTTyoEfbJnJspIOKODQKKAIJASbcxHZCrF/OpcPumODYOpuWQKRlMZkq0B1V006E9pBHySaiSyKVEkYE2H+mSyWA8QHtQo7/NT1632NsX4anTM7z3li7OzxV4fG8v2ZLBl58f5s7BGI/s6mI0WSTmV3ju/FzdMK/ZnPFm9SRZCk+D6bFh+dGbk/yLr79Wf/3/+/SdVz3JbNZW1mp9W0Dhq796Dx/+kxdwqN6x8ssUKxZm1QBmpVqzTFGft5RiKa1as56kxlIddMQnkStbS+yXWy5jlagGl0XOaySsSeQqiy+njPllLMdZtZ7vnUzYJ/FfP30Xv/jlV9bsmhGfzHd/a74WU8CdVJstcpiGfRLP/d6jgKshShX0eQODkCbyR5/YP6+9NdKoC67pimqOwyvVLN9oTaY3wbx6vDjrsRxvt8Z6Nay2rK2Oz5SMuha9sQ+8Gk6Mp/nQn7yw6H4JrkrK4rFy2gIK//iFB/jYn724QGfbqKWNBxSeWULru5HawVrjaTA9Niz/+Znz817/l6bXq6FZW1kjVzb56uGR+januq2mkVuN1qx5nf5yCZZbTQaXmgQuNbkEyFVa6zisZa47/xpLh7Vs2bwqPd87mXzZ4s8PXVzTa+YqC7WYDrScXNbK0Kghak7Pna/Y/PES7atRF1zTFcHqNMueJtPD4+ZhI7Xn1Za11fGNWvTGPvBq+JvDI0vu9yLs9SdX9SdYTGdbI+t5DVw13gTTY93yrx7dPu/1bzW9Xg3N2soaYZ/MZw8O1bcJ1W01jdxqtGbN6/SXS7DcStO4lC4v7JOW3q+11nFIy1x3/jWW1upFfPJV6fneyYR8Er/58NY1vWZYW6jFrLnTLlaGRg1Rc8cf0kR+e4n21agLrumKYHWaZU+T6eFx87CR2vNqy9rq+EYtemMfeDX80sGhJfd7Efb6E676Eyyms60R8bwGrhpviazHuuZHb07yX545z2+tkQbzxOUMg/HAvOT0NW3FkycmeXyvm/eodtxosrginWljsuTnzs/Nu85SCZYPX0i4ieyZn9weAaI+mb97dZxbesMMxYMc3OYmCf77o+N0hFQ6Iz76Y/557/f0qWnOTuWIh1xN5nuryYMbP3N/zNXTZUo60YDK3YNtPPnmFHcNxfjw7ZvqiYg7q7rON8Yz3D4QrZcB4DvHLvPjU9PIksQn7+7n/FyB/f1Rjo1n2N4RrF9ve2eIv3p+mKJh0hX20RXx8cjOTp44NsHLFxNkSgZdYR8/u7eHH5ycZmt7gNPTOfZuivJrD27lzHSOZ07NcGw0BQJYtoMsCuiWjSJJ3DEQ5fxsgTsHooykSkxnKwiOQ9gn4dcUusIaPlliLl/h5GSOkmFgWg6qLCECOd2kI6CRN0wimkLEL6NIIh+/cxPfeHmUs9N5VFlAEqAz5Gd3XwRVFpjNVaiYFoIgUtEtprIlfKrMx+/YxNOnpsmWDQKqzEfu2MQvHBgkFlA5MZ7my88Ps7s3jODAixfnSOYrZEomt22Kcd/WOJ0Rn1sHJrMUKiZz+QpvjLn//7Ju8fz5Od67u5tP3jNY1zTWdD17eiOMJotIAvzVC8NkSjoBReY9e7r58O2bFugiSxWT/3b4Eu/a2Vkv44vnZ/mTn5znXTs6XZ3nZBYc6nWv1kYal4Y1tqvl2suN0m6Dt0T2WvDirMdKuJHt+VpZbVlbHd84Trja5bE1Toyn+dpLI3x0fx+ZslnXbb53d0PMrsaBYsVkruDuG0+VeOniHE+8fhndsumN+tFkka6wj0ShQnfET1E3qh4CNmXdJlU0CPnclGYhVWIkWWQo7iddNkgXDJIFA0FwU3JYNhQNk6AmkSpYruZRhPaAQlY3kQR3pUxYU4kGZIbnilg2BBWRsF8hW6pQqC546Qi6qdFEQaA9qJEvG8zkyhg2dIc1ApqEA8SDKptiASJ+mXzJ4Jkzs3SHNdrDGgFF5nK6hCpBvmJRMmyKukl7UOVDt/fR3xZgLl/h8EU3n3NbQCOvm7QFVTRJAASiAaU+ztnTE2awI1gf+7X6nmta2lrsW+n4byO0g7XE02B63NQslrz4WkXXyyVYBlr+vZyu48ULrgHAWLJEe0jlcrpEvmxQNmzuHIzxxBuT/PMD/UzldIbiAe7ZEuetCTfIgDsJHWjz88QbE2RLBhGfwkf29zGWKgGuGcuXnxvm5GSWzz+8FcuB09M5Btr8ALx5OUO+bPLA9nb+8dgEWzqCvHd3N397ZJySYbKtM8Svv2trPblwzRDpzGSWQ2dnGGoPMdQe4FKiyAf39nDkUoqSabGjK0RRt/jp2RmKus2//cAu7t/eyYvnZ/mzQxfZ2xvmiTcm2NEZRpEFLs4VOLC5jclMGcNyCKoS52fzZMsG/VE/4+kyYZ/MUDxAtmKyqzvEa6NpsiUDATfAPb63l21dIb792hhvTeQI+ySKusVQPER/3M/lVLnq6KtyfiZLqqjjU2V294RJFHSmM2V6ohrjqQqKBHv7Y4R8CpdTRTIlk5hfoSOkUtQtHBwuzBRwHAfHcQhoMrmKxf3b4kxnK+TKBtu7QhwbTdMT8/Hbj+4gXTIZSRQ4PpZmIB7g9FQOw7Lojwe4Z3M7793TTaZk8M1X3cl8Sbd45VKSjpDG1o4gF+cKZEsmH7ujj+MTWTpDKgLUE3hH/QovXkjMm4ju6g5hQX3/U1UjqYG2K0mn//aIa1IxlS0xmighCLC9M8Q/OzDA6akcb4yl8SkSj97SOS/hd7FiEtBkbu2L8Mpwcl5S8MaE1CtNSn698SaYV48XZz02Gmvd1yxlXrbce9ZifaFiEtRk7q/eoGs0Evzmq24/3Bfz8bdHxrlrKMZH9m+qxwRJgDMzOW7vjzHUHqz3u0cuJZlMl4iHNDRZBBwSOZ3hZIHd3WGOjqQomzZbO4KMp4qUDJugKnFwWztnZ/IICBR1A8uGgm6QKZgoEhRNCKgCuu4gyQJxv4IkCpRNGwSBoCoSUGUm0iWyZQtZcJ8ChjSFqVwJ2wbTWagf7QmrFAwLy3LoifqQBPApEgXdwrIdJEEgqEmkSwYz2TKGBTu6AlxOlRBEkU0xP21BFdNyTewCmswHbu3BASqmRa5kMpYqsq0zxEM7OtnVE+aJYxPM5Stkywa39UVJFnV00ybiV7hnqI3vnZiq/78Bnjh2maMjaX7hQD+mzaLjuuXGhWsV467GkOrtiLPeBNPjpqU5sXFNjA2LJ0Ne6XWXSrAc1NylqvmKRah6B66wTOL5VkmY1zNRv0xJt9GtZrXeyvl//Mwu/sP3z6xhqW4uwlXTpqtBqJ6fXeR815nv6q9/NeWJ+mWKur2ipOQ3pEzeBPOq8eKsx0ZirY1WatfLloxFzf4We89WBn5hn0S+6qGwlJHfciZ7HmtLWBMRBHGel4EsQNiv1Md4S8WzVtuuNcathSHVjYqznsmPx03LYmLsaxVdryTBcrb6d7Zsrui9Nloy5dpnuhb+7Nm1Nba52bgWsyTXjGrx851rvP7VlCe7TJvxzA88PDyuB2vd19Sut5TZ32Lv2crAL99g0Le0UZ83ubyR5Cr2AnNE02HeGG+peHY9YtxaGFKtF7wJpseGZTEx9rWKrleSYDlS/Tvik1f0XhstmXLtM10Ln39kbY1tbjauxSyp9gRzqf030oyplpB6pUnJPTw8PNaKte5ratdbyuxvsfdsZeAXajDoW9qoz7P3uZGENXGBOWJt6e9K4tn1iHFrYUi1XvCWyHpsaBYTY1+r6Hox4XdtG9Dy7+WWMhy+mGA2W2Y8VSIeVJnIlMiWDHTLYX9/lO++McknD/Qzk9cZbAtwYEvcFfqXr2gw+9v8fPf4BJmiQcSv8OHb+xhPl8CB/jY/X3ne1WD+5ru2YgFnp3P0x/yAwJsTafIVi/u3xnnijUm2dAR47JZu/u7VcYpVDebnHto6z+jo5GSTBjMeYCRZ5Gdv6+HV0RRl3WJ7d4hixeKnZ2cpGja/9/6ddQ3mXxy6yK29YZ44PsnOzhCKLHJxLs+BoTYmszUNpsz5mTyZss5A1M94pkGDWbbY1R3k9dE0mUYN5r5etnaF+Iej47w5kSXskyjpFkPtIfrbAkykSwR9ElGfyvnZLKmCjk+RuaUnTLKgM5Wt0BvRGE+XUSSBvZuirgYzXSJTNIgFXA1mQbfAcbgwW8Cu3tEOau7y0/u3xZnOVciVTLZ3uWXsi/n44qM7yJRMRpIFjo9nGGjzc3oyh2HZDMT9HNjczmNVDea3jo7TGdIoGiavXkrRHlTZ0hlkeK5ArmTy0f19vDmRpSPs1q1aAu+oX3FNoqoazOPjGXZ2hbAF6vufPjnN2Zkc/bEAnRGtbhw1FHc1mGPJEgiwvSvEJ+4a4Mx0juNVDeYjuzo5Np7hoe0djKdLdT3Rnt4IRy4lGU0UuW9rnGPjmUVNrd5O8wNviezV48VZj43GWvc1KzEvW+w96wZ+FZOgT+bg1vkmaQDfOjrOUHuAvqiP//nqOHcNtvHh/X31mCABZ2fy7Ksa7O3pi3CkqsGcypRpC6lo1ZvAiXyF4URVgzmapmRabGsPMZYqUjIsQprMwa1xzs3koabBdKBQMUkXDRTR1WAGqxpMURFo9ylIskjZsIArGszLmTK5kokkuhOxsKYwmS3h2GC00GD2VjWYZlWDKUvglyTyuo3l2K4GU5VIlQxms2V0C3Z0BZlIFxEEkU1tVQ2maXNyMkuwpsEUoGLY5MoGY8ki27pCPLijk13dYb7zxgRzuQq5isGtvVFSRZ2KadfNfv7pzSnuHIzx4aoG8zvHJjg6muKTd/djOSw6rltuXLiWGsxrNaS6EayZBlMQhH8P/BGQAb4H3Av8S8dx/n4tCrpSvMDnsRpWItRf6TVq565U6L1So6DGstW2tfr7iWOXefrUNCIC+wdidTOXWsL7mtnRrX0Rvn10nCffnOKBbXHCPpX7t8V55vQs2bKOKknsH4hxYEuckUSRtoDCd45N4ACP3tLJPx6b4OJsnk/dM8hEtsIdA1G+f2KKy+kSB7bECagSr4+mOD2VozOs8bH9m/jhyRlKusGlRJFb+yJ0hDSevzDHQDRAwTAoVmyifpnpfIUHt3WgyRLZss6hM7PctinKrX1RXjg/y1S2Ql/Ux6VkEcOyOLi1g/fu7uY///gcE9kSUc01HuiPBxho8/PqSJoP7u3hJ2dnmctXME2bsmXjkwUiPg0EcBywbJtNMR+X02V8ikTUJ3NmJo9g2wR8CpIgMJXTUUXoj/spli0SRZ1N0QAdERUch4rpMJEpUtQtusIaj+zs4q2JDBPpMu0hlaCm0BP1ockiiiyyoyuEAIymStzSHeaxPd0APHVyum6Q8+LFJJ0hlYlUiUPn5vjXj+2gN+bnbw6P8L7dXTx9eoaLs3n+xUNbMR04Nprin05Moskit22KctdQGx1hn2v8dGwCAfh01QK/ZiKxqyfMT8/N1c164IohVqv2Acwzzmp8HfPLfPv1CX7p4BAD8UBLg63F6v6NZqNNMNdLjAUvznpsfK6mD1rMyG8lfVtjX/rWRBZobTzYFlB44tgECPCR268Y9MX8Ml99aQQRgbJhcdsmd2KULZsk8zq9MY3eiJ/XRlMYts1gPEhPxMeLF+aYzVV4ZGcnYb/Kto4A33hlDFWC87MFTMthS9zPG5ezSKLr3jqZrdAeVJnL6+CAIIAkQkfYR0AVOT9TRBbd2Gk7VyaMYU0kpMps7nAnr8mCjohNvsFaQgREESwbVMH927DBr4rs6AwxldVxHJuyadMWVKgYNp1hlY6Qj4tzBYoVk46wRjyocsdAG8dG0xy7nGawzUe2bLGnz31ymCjoDLb5KeoWL16Yw3EE7hx0x0WN5nM1t9+RRKFusjTUHmQkUeCbR8cJqRJ53Wo5lhpo8/PTc3PzrrMa1kMcvJ6s5QTzDcdxbhcE4b3AF4HfA/6H4zh3rk1RV4YX+DxWykqE+iu9Rk1A/e3P38/H/uzFZYXey21rNAcyLBu5mj+wtq3ZSEi3LPKVhZpIAfjOFx/gs3/1yqoNhARAlgQMa2OtYtioRP0KjmMvasyzVixlIlEjHlD4hy88UK/LtToY8sk4jkO65D4xjwUUaHjdSEgT63WyZrBVm7S+neY+NTbgBHNdxFjw4qzHxuZq+qDGcxYz71vK2KdxrFHrf9sCCj9pMB7Mlg0v3t5ABODZ330EgEf+4Nm6ydI3PncPn/ryK/Pi5FJjqdp1VjPJXC9x8HqyliY/tdHtw8A3Hcfx7CE91jUrEeqv9Bq1c588Prkiofdy2xrNgRzAsJ0FhkGNRkKtJpfgTiS+dnjkqgyEHPCC3Q0kVzZuiKvrSr7RbFNdrtXBWr2r0fy6kcY6mS3PN7lYr6YD6xwvxnp4rAFX0wctFp+bDXyW2m40TC6BBbHfi7c3Fgd48sQk3zs+Oc9k6S8OXVwQJ5caS9Wusxq8OLi6CWZBEIR/B/wC8JQgCCJwc03HPW4qViLUX+k1auc+vq93RULv5bY1mgMJgCIKCwyDGo2EQlrrpioAnzk4dFUGQgKgSEtZDnisJWGfsqQxz1qxkm800lSXa3WwVu9qNL9upLFORnzzTS7Wq+nAOseLsR4ea8DV9EGLxedmA5+lttfGGjWaY78Xb28sAvD43l4+uK93nsnSbzy8dUGcXGosVbvOavDi4OqWyG7HXbbzU8dxvl19/QnHcf7j9SxgM97SnfXJel1rXhM/x/wKz52b4/GGtfTLlbm2XxbhH16f4LMHh9jbH2MkUeDJ45M8tKODM1M5LiWKvP/WbsbTJRzH1UC+NZGlWBX47+mN1PVtmZLB118aoWxY9MX8btLikkm+YnD35jhBVWI2r/Pzd7mrDr7+0giz2QonJzOMJIo4toMjuGY/sYBKT8TH5vYAhy8mCPtk4kGV3T0RXh6eYyxVojviR5MFknmdomFRNixKFZv2sEJbUCVVMMiVdIomdAQlBESyZYOKBT4ZNEWiza8wmytTMd2lkKmigelQX24SkKFggia5d6xMByQHKg4EVAipCsmCgdHQ1SgC815rErRyaFdE8CmQqyz+HWuC+143EgX389tceewE4FdAFkXCmoIkuVoaHIG2oMqtfa45jm3b2A7EgyqSKHBrX4SjoymSBR3HEdjTE+bNySzxoIJuOuQqJh/b38ep6TztAYWXLiYQBYGOsAa4mpq5gk5PxEdRN3FwTRPOz+RIl0wCsoiiSHzq3kFeH0sT9sncs7mdA1vivDWRZS5Xrv7W2dTm4+hoioAisbs3wq9VDZ/++oVhfvTWNHf0R/jByRnuGGzjf3lsB+PpErPZMudm8miyxGcODhH1K7x4IYEgwMGtCzVIN6qP2IBLZNdFjAUvznqsLVfb9q+lz1jO+KSVt0JjvwWtzfuar9uovRxNFl1jvIksCNAf8/PDt6bpDKsEVBkBODGe5gdvTdEX9fH+29xJy6uXklyczXMxUUIEVBlEQXT9CBJFcFyNpGG7MdGyYbE1SyLzY9JGQcItd2MoV0QQbNBx77SZAgQUga6In1t6IszlSrwxngEHuiI+bumOULEtVEkkXTL41D2DXJgrEtQkTk9lGU0UObA5ju1AQJU4NpaipJtEAyq/+sAWJtJlTk/lGGz3E1BlihWT2bzO+/Z0kyoaDLUHODKc5FKiyCfudo30lhtDHr6YwHGu+Bmsx3Hy1bJmGsz1ghf41h/rfa15q/LB0glya+dkijpmtYnU1uj/8l8fWaC1AFevVqxqKmvbY34ZURDIV3WVluOQbaFnaybml7FhRcd6eFwNAu5gZKlFuzG/zP/98X38i6+/1nL/f2+hZYn6ZUq6vSLt0vVko00w1xNenPVYK6627V/PPmMl3grXWsaRRIGH/+DZNSmvx/Wn2bugNp5r9MNoHNuBGx+LTbGukZXoejcya6bBFARhsyAIfy4Iwo8EQXim9rM2xfTYyKz3teZXkyC3tt9s6E0a1+g3ay2AeXq2GtmmhL35FWols6s41sPjanBYenIJbj3842fOL7r/z1toWVajXfK4ghdjPW5GrrbtX88+YyXeCtdaxu8dX51mz+PtZak4lm0xtgPmje1a1ZmV6HpvZlajwfw7IA38CfCHDT8e73DW+1rzq0mQW9svNyzUb1yj36y1AObp2WpEmhL2hlaolYys4lgPj6tBwF2WtBQRn8xvP7p90f2/2ULLshrtksc8vBjrcdNxtW3/evYZK/FWuNYyfnDf6jR7Hm8vS8WxSIuxHTBvbNeqzqxE13szsxoN5nHHcfZd5/Isi7d0Z33ydiZUXwlXkyB3nn7z/ByP7+2t50mqJWA+cinJSKLIz9/VX080PxgPcHIyCw4c3DZfywHw9Mlpzs7k6G/zA3B+Jg8ObO8OEVBkgppcP+/pk9McGUlyfCzNeKpEe1BFNx38msTW9iA20BVWeeVSii0dQfpiPvqifhIFnfagSrKoIzrwwoU5FEmkULGYyBTpjQYIqAKZkslsrkzZdLi9L8xMwaRsmGSKBgFVpi2oEtIk0iWDYsWiJ6IxkiwCDqbtpjlRJZFCxaQ74mPvpiivXEoh4mDYMBT3ce/WDp45PcOF2QKW497VCvtEdNPGskB3IO6XKBoWIlCxQbRdPUY8pCAJIol8BcMBnwh+TSZbMnFwdaJBTWau4L6u6U9qd858MgR9Cqm8sUCzEtLAJyuYtoVlu3cwSxUbSQS9KmIRcbfLIu7NBgE6w346gjLn5gqEFBlEgUxRR5MFQj6VgCIzEPeTKZtkijpBTSHil+iL+Dg8nCKsSfhUCUUS2d4Z4sHtHTx9eoZNUR/fev0yt/dFGEkV0WSJ7ogP24H37e7i8HCSj+7vI1MyKegmc/kKx8cydIVVzszk+ZWDm5nKlfnpmVlCPplC2eLkVBZFEhiMB/j0vUO8OZGlI6zSGfKxpy/CaLJIzK/w3eMTlA0LvyLxynCSofYgD27v4LE93cQCKi+en+UvDl3kwe3tPHVqhq0dAT7/7h31fGLfeHkEx4FP3zdUbwfLaZeuNxttiex6ibHgxVmPteVq2/717DNaaSnXuowjiQLfOjpOZ0ijM6KxpzfC909M8uPTM9y/tZ1beiP0t/n57vEJjlyc483JHAFVpDPop2CYvHtHB0+dnsF2QBIgVzLojvrRLZtErkKlqskMqALZsoMmUc/9rIhQNiGkSRR1qx7TJJZfuXI9qfkvVMMpDm5Zgz4ZvyJRNix008ZxHGRJJOJTyJZNBuM+MiWLqF+mO+rnVx/YzA/fmuIfX79MW0Dho/v76Y8HKOom52fz+GSJR3Z1cvhikqAqMZEpgSPQ1+Yjmdfpj7vjr/FkiZ09YQ5sjrccz9XGbUuN+ZaqM431o3ad9TpOXi1rmQfzvwO/5zjO2xp1bvbAt17Ncm4Ea/XZWyWPX63RSCsDgFavZRG+8vwwumnz2J5uHtnVNS/JcqZk8JfPXWQqXeKhnZ18ZP8mYgGVE+Np/ubwCB+7o490dZI3liqxqzvMLT1hnjg2QcW02N4VojPsqyf83dEZ5HsnprhrKFZ/r0LFZDxZ5AdvTpGrGGyK+UiXLD7/8FY0VWYuV2Y0WUIS4PkLc/gVmS0dAe7d0k5fzMdXD4+gySIRv0JAkXjq1DTposEtvSEiPo0vvHsbAP/nk6cYnstjAx1BlWLFxrAtdnSFsB2BeEghXdR5dThB0YCQKhDUFCqmTXfUhyxApmyxpT1AybCYypSpWA4fv6OPQ2fnuJwpUDZs+mMBdNPmcrqMLIIiwdaOMJPZMoWKScQvY9g2uukgiyI7u4KcuJxFUyRifrk6udZIFCrkKjY+2Z0oRvwaPkXEsm0MC2JBBd2yKVcsCrpJe0jj9v4oR0ZSCA74VImYX2F4rkBIk0gWdCzbQRAFwqpM2bQpGSZ+VSLiU+mL+blnc5yCbtEeUkkUdDqCKhfm8kxnKsSDijvJL1uMpgpEfQrj6RL7B2J87qGt9YTbjcm5a3Xx6ZPTvDGWxgF2dIcAmM3r3L81zj+dmGKuUOELj2xnIB5YkChcFuFvj4xz11CMj+zfBMBTJ6cZSRZ5f9W8oNnoYjZX5vxMHk2R+Ex10rhcAvK1bMNrwQacYK6LGAs3f5z1uHbWU1tvZCXlWuqYpcYPS73nixcSFCsmAU2uG/3BFXOXWp/7ibv66zeqnzo5zfHxNBXTomLa5MsmBzbHcYDjY2n2DcQAeGM8TcwnVydLMhYO/TE/hmWTKxmcm8mzpzfCqaks+YqNBPg1EcOyERDQTWfepFIAVNG9mVujzSeRq1jzZEE0ndO4SwJEEWy79YQ14hcoVdwbzTXifhlRBFkS6QxpXEoUUGWRQtlElSW2dQa5nC5RsRxifoV82aQv5mNrV4iwT2FHV4iAKlPSTUaTJQbjfvzV10dH01yYybGnL8Jjt3Tzo1Mz/NLBIQD+5vAIv1Q1aWz+jmvfU+N3dmufa8zYWAeABfWitm29tYEbxVpOMH8A3AM8D5Rr2x3H+WfXWsjVcDMHvvVulnM9WavP3pzwWKkuR1iJsc9iZWk2AKi9bjQAakXUr5ApzU/YG/ZJ/NdP38UvfvmVVX82j3cGteTctQnfI196lnRT4ufFiPgkyoZTNxTIlYx5dbSWJqUxH6csQDSg8u3P38/P/ekLJFu8V83wYCmjgvXWf23ACea6iLFwc8dZj2tnvbX11ZRrqWMa9xmWjSwKRPzKkp8vXdR595eeJdXQbzZOxqJ+GYcrhn01s8DPfOVl0p6J3w3lu198gIF4YN53XPuemifQsgCWA4ok1uNevmzWx5U3o2nPalkzkx/gfwC/A/w98GTDj8ca8U42wlirz96c8Hg1xj6LlaXZAKD2eqnJJUCuvHCgni9b/Pmhi1f12TzeGbQyx1n5udY8Q4HmOpovW+TL8+83mw71ep1d5L1WYlTwTu6/1ggvxnpsCNZrW19JuZY6pnGfAxi2s+zna9VHN3a7zeZ+DvDVl0YW7Ws9rh9fe2lkwXdco3k4V0vF1hj3GseV70TTntWyYhcRx3H+5noWxOOKILh2Z+2dIAKusVafvXad5ieYteut5D2ay/L4vl6+8vzwgtfLPcEM+xY+wQz5JH7z4a08d27uqj6fx81PK3OclT7BDC/zBDO0yBPMWr3+8nMXWz7BDPvklk8wG9vQO7n/Wgu8GOuxUVivbX0l5VrqmMZ9tSeYy32+2jmLPcEM+xY+wfzsfUM89daU9wTzBvOZ+4YYiAfmfcdr9QRzvbSB9cRqlsj2An8JvLu66cfAv3Qc54Z6Md/sS3fWu1nO9WSxz76cpmIkUeB7xyf54L7eeUne+2N+njs3x+P7XHOe2rFPHp/k8eqxzbqytoBST4zcFfGxpzdS1zkKAgRUua6pOHE5gyTAXz0/jGE57B+IcSlZ4MJMnu1dYXb1hDk+luHNiTSdIY3t3SFu64viV2XGkkUOX0hw52CMVElnLFnCtGwCqkRQk3nhQpKIJlE2be4eauPn7tjE118e4eJsgYJusr0zxK8/tJUfn57hrYkMtm1zdrqAAIT9IumiTU9EpT2kMpszCPslNkX9HB5O4NgQ0FxBfbZcwXFENrX5CGkqE+kC0zk3ULYHJCxb4MHtHZydyXFhxr2+qoBpu8mgCxW3Uw4qIIoisiSQKi5UZGgSWJabHFoSIKBCruLuU3A1HA6urkOWoWIu1Hs0J2GuIQvUJ1ErNTAQAJ8EZetKPkhVFQhrCtmyjm5CQBMJqwpz+UrdIKEZCfApENAUeiM+JBEqFgQUkcvpMncNxjg3UyBXMV0NpiiSLFYwTIe2gMKF2TxRv8ZvvWcbZ6cLzOYqfObgIJfT5bpeB+DrL40wmyuTq2p0UgWdZ87MctdglIlMGdN22NoR4pMHBkiXDGJ+hR+enGZzPEBfzMffvTrOnYMxHt7VxZHhJK8MJ8hXTD66fxMnJrIMxgO8d083AN954zJPn5zGtB22d4b4tYe2EvUrHL6QwMHVp4wmiy37qPXUf23AJbLrIsbCzR9nPa6d9dTWG0kX9Xpf1axnbzxmsbI3mviNJosMxq9o7jIloz7WqI0palrKs9M5BuJ+irrFKxdTdIZVtnQGyVcs3r+nm9dHUxwdTfO5B7cQ8Sv8yU/O8dpImrJhsSnqYzxdwqdIdAZVzs3lkUWRTF7HcNzYJ+PGNhHQFDdWlUwL07QomlcM7jYCigC2Mz9WS9UJXY2gAgXDje99UR+m4xDWFGbzZcqGTcyvEPIpJIsVtrUHmSsY5CsmQU1kIB5gOluhK6Txc3ds4tC5OQbjAWzgE4sYMu7pi3BkOMlIssjBrXGOjWV4aEcH6ZLBYDzAK8NJzk675oxdYd8CA8fVeHrcLKylBvO7wGHgz6qbfgN4wHGcD11zKVeBF/jeWSynqRhJFHjkD57FwZ0oRKuJb1utj4crGszG5LmNd6cM+0p7iPoVwCHTcOdxMU2Gl1TZ43pR0z/W7qj7VZFsufU0WgC+8bl7+NSXX6m3iWd/95G6qcSjX3p23hPKxru2bQGFr/7qPXz4T16ob4v5ZZ79XXe+sx41V0uxASeY6yLGghdnPTYua6kPbbyWXxXr7uW1fjXqV+bpLyM+ueXSVwGI+GVKuk1Ak1a8IsVjbQhpIvmqo1FjTGxkMf+Ob3/+fj76py/Me0Ld6JOwHOtVr7wWrKUGc8BxnP/gOE66+vN/AQPXXkQPj8VZTlPxveOT9cGww5XEt63Wxzdfq/nYxskluBrKRm3FUpoML6myx/VioSZo8We0DvDnhy7OaxNPnnDr5vHxzILBT7NW6KuHR+Zty65Sv+xxTXgx1sPjGlnLvqr5Ws39arP+MldpveTVgXnX8bix5BvschtjYiOL+XfUPDcaWU29eifHztVMMEVBEHpqLwRB6GJhblIPjzVluWTLH9zXW6+EAlcS37ZKatt8reZjFXF+dQ77FMK+KzJlAVAW0WR4SZU9rhe1Onul/kmLHisAv/nw1nlt4vG9bt3c1x8l4pMXHN/4Pp89ODRvW2SRtuPpTa4LXoz18LhG1rKvar5Wc79a218jrLW2NRFg3nU8biwh7cpUpzEmNlL7LhVRcKVA1e/q8X29C76z1dSrd3LsXM0S2c8A/zfwXdybAD8L/G+O43z9+hVvId7SnXcey2k9RhIFnjwxyeN7e1smx208b7Gkt7W/Y36FH52cZqg9wGO7XU3a4YuJ+hr9xbRntXJ85fmLTKRK3L05TkCTeOtyhlzF4tP3DpIpmRRqdzhrkcqBom4yni7RHnBzJ1YMi8PDSXZ0Bjk3W+BnbuvhY3f28/0Tkzx5YgKfIrG7N8In7hrg9dEULw0niWoSh4eTdIZ99Md8vHghQVdYozviYzpbYU9fmPu2tPNXLwxT1C2ifoWSYTKdrRAPaDx+ey9+VeLHp6YZTRSQJYl7Nrcxni5za2+YN8ZTjCbLbO0I8N49PXz3jUke3N7Ot167TLqgs7M7jCBCT8TPTLbEeKZEPKCSKupUDJtd3REyZYNUoQKCwL6+CMcnstiODQgICNy3Nc5H92/iGy+PMJ4uoUoi+/ujHBvPopsWQU0mXTBIlSsookRIk3EcGxuB7rDK5XSZmF9hJFlAU0QsC/K6mwdTk0Uqpo3jgKaIhH0qOzqDvDGepTuqsbsngoNDvmLRGVQ4Opbh4NY4Ub9KrqTz318ZQ5EcNMXVpQJs6fDz/lv76AhrBDWZ/pibMLtsWPgViROXM3zizn5OTeco6xbbu0PgCIyliuzqCdMX9fFXz18CweFXH9jCRKbMSKLI+/Z0c2Y6x2iiyMeb9CK1+jeWLPKV54e5pTfMUHvQ1Rufn+Pxva42qLFNNC4FqumTCrpJUJXZ0xfh5EQWBDi41dUrjSQK/P1r4wy2BXhsT3fLtrMRlvhswCWy6yLGghdnPTY2a9lXNV4rUzIW9Kvpol4fIxzc1s5YssiXnx/mrsE27hiM1fvlxrFJpmTwlecvcn4mT0CReXxvD4fOzXFLb5h0QeeZ07Nsbvfz8qUkFcNGFBz8qoxfkbEduK0vTECTmc66+vt82WQ6VyGkyszlK8iSgK5b6PYVbaYEiAIYjptbWhJFbNsm5FcIqe61mvNlNtKo8RRxdZSGA7LoakINw33yJ0kigmCjyTKSIBDyKcT8MiXDZDxVxqeIbOsMM5EuIQgwm6+wuT3Ats4wz56ZRhBEtnUG2T8Q4/CFBEXd5sEd7czmdXAczs8WyJcNbuuLIksiFxN53rW9A1WRiAdVJtJuPum2gMpIosivPbiFgXiAp09N12Nq8/LY5u+6MdbW0oUdvpigUDEJanI9Vl5NHdoIsXOlrJkGs3qx24BHcIfHP3Yc5+Q1l3CVeIFvY9AoaoYbk4y2luwYFhf3L3f+Uol0AZ4+Oc2lRJFP3N1fNwlqPv6pk9O8MZauLmt0iPlUPry/r57QPlMy+KOnznBsNM32rhC9UT/9bX7mCjrtQZWLs3nGU0XKho3jOPz8Xf28cilNm1/m6GiKom4xl6+wJR4gXTbpDmuMJosAbO0MkSzqCI5ApqTTFdHY3BHCAWazZYq6QSJvkK2YRDSZ9qACgkBBNwmpMm1Bjfu2xvnm0XEG4wHCPoVLiQLT6TJBv8wDW9u5mKiaCWkyPz03S1CVsQFNErFsh1xFRxREHCDqUwlqImXD4d6tbZycyDI8myfsV/CrMgFFJFexKFQMDBNKuolPldBkkVzZYHdvGFmSmMlVqstMDGRBwnIcQpqCJAo8sL2dkWSRTNFkZ3cIvyLx/TcnqJg2IU1lsM3HxWQB3XAoGjaaCO1BlcmcTkAREASBvG6jimDYsCmmsa0zzLmZHLYFhm3TF/ORKBh88u5+Xh1Jkyrq3L+tnUxJZzJTJqjKjKeKTGfLPLC9g0zJxKdIlE2L2/qiJIs6U6ky52bz7B+M8sm7B3h9LFM3ixhJFPjaSyOUdYv2oMqZ6TwHt8W5c7CNJ96YAAc+c3CorqVsrHeN9e/kRIY/fuY8j+zs5BfuGSRTMvjLn15krlDhC49sJ+JX5plUNF+rLaDw03Nz9SfyzYYWje2kLaAsKNt6YqNNMGF9xFjw4qzHFW5Wg5JW/ehbE1mAupFf4+de6fii8bhbq8YxrcYMh87M8OzZWcKajCBAqqDjOAIXZnOYtkOxYnBLbxSfKiECF+cKtAdVon6FM1M5dMvBtG02xfxUTJuxZBHLBk0WcHCwLHfyJ4gCRWPl4/yVUDPkEXHfo2aCJ1Z/BBEs271LJuL+1mR3m1yNs5vbAxR1C02R2NrhxqJU0SBTNsgUDXZ2hTCBnrDGv3x4G+dm8vz41DRF3UKVBKJ+jYplsbcvSkG3uKXHNVb8zrEJSobFju4QnWFf/btKF3X+xyuj/PjUNO/Z3c0/v2dwgZnkUt/vzdoOroU1nWCuB7zAt/5pFDXfqGS0zcmO4wGFZ1Yowm4sc6PAu9EIKKhJ2A7z0o5E/W7qhkbbatO2FzVgkQUI+WTPmtyjTs2U5xe//MqKjv3OFx/gl//6SL2eypKAaTnIooBPFefpM4OqSGER+9vGa9Xs2qUGN95G859mo6D3/OEhsmUDo8HybzHjhLeTjTjBXC94cdYDbl6DkuZ4L0vCvP4sFlAQuDJuaTZ6WWx80TwOaU59URszNPs9eFw/2gIK//iFB3j8j386T4sZ0kSe/7fvqU8+lxo/3qzt4Fq5ZpMfQRC+Vv19RBCEV5p/1rKwHjcHjaLmG5WMtllsn13ley0m8M42lD1Xnu/81jrx7uIGLKaDl1zZYx4O8BeHLq742K8dHplX7wzLqZv/5Jvq3mKTy9q1vvrSyDwDocZ8mc0JqBuNgtwJ6fwB0mLGCR7L48VYj/XKzWpQ0hzvm/uzXNO4pdnoZbHxRfM4pHka2cpM0OP6Uvv+GieX4Br/1L7D5caPN2s7uN6sxOTnP1V//xvgd1v8rAhBEP7fgiA41SVACILQJQjCDwRBOCcIwpuCIDy4uqJ7rFcaRc2tzHau53vWiKzyvRYTeEcayh72KfPOaT7e/ayLG7DIAgtMVjze2QjAbzy8dcXHfubg0Lx6p0hC3fwn1FT3guri3Xst2XejgZAszN/f+HejUVDYJ6NI871nFjNO8FgR/6n624uxHuuKm9WgpDneN/dn4aZxS7PRy2Lji+ZxSLNDVyszQY/rS+37azT6AfcJZu07XG78eLO2g+vNakx+ftVxnL9abtsi594J/J/AHuBxx3HeFAThr4BRx3H+P4IgHAC+BWxzHGfJRzze0p1r50asJV/MTOda8lEtV+ZmsT0srv1cTG/ZnGD5leEko0nXeOX0VI7nz89xOVXiX793B5vaAnzr1XGCmsTldIls2SBdMAhqMqmijgAEVIlEwWCo3c9bE1nuHIwxlSkzkSkzkS4R0lxnuragSlm3yJVNyqaBX1UIqzKzRZ3eiMZs3uDAUBtnp3NcTpcwbPCJ7lMnvyqQrzjzljiqIggCiFUBhGGBXxExLBtFFjFMGwSBNr+C5TgkCwa2A6oMHUGNy5kKEhD2S2RKVl3cH5Bdob9huaYB+gq6j5o5QPNyoRoCbhLpVpnBBFwzAUcARYTa6uJaLrLa9QKKu1TUsFu/R6vrLnWcWv3fOo57XEhxE0N3hn2MpMqAmwhalmSKFRNNEbAdAduxifkUbEFAFgQcoD2k4lckMiWDyUyZ9qDKwzs7SZdNPnl3P+mSyZnJLM+cngYEsmUD23H4+B39vGdPt2scpFvs7Y9xz5Y4b01kEQRcY59zc/Vk0Hs3uRrM//KT8zy8s5NfODDIWLLIf3r6HAIO/+qxnQB8+blhdveGGWwPcmvVuKpW52N+pW5KAfCNl0co6RY7ukIAzOR03n9rN+PpEjPZMhdm8/hkiU/d52kwr5X1EmPBi7M3G9cS828Gg5JmT4gXLyQoVkwQ3D4+pLmGZ0cuJRlJFPn5u9xu48njkzzeoJP/1tFxhuIBDmyJ88pwkjfG0ggC7OuP8d6qIdpIosDXD48gCPCh2/t4fTTN4QsJdna7/ggA27tCTKRK/NObU1iWu2RWEFwfg+lsBaBu0BPxS0iCQLpkosqgG9SNeCTcuChIUNrAqTVl0f0csiRS1F0zvoE2H4blUKiY3LMlzli6TK6kk6uYKKKIJosgCuzsDCHLArf1RrmULHJ6Kks8oHJgc5yiYbGzO8x797iGjf/fp87UfQV+57275i2BffFCgtFkgVMTOT730Bb29sfmlXEl7WC5dnaz6TjXTIMpCMJrjuPcudy2FudpwLPALwI/AT5YDX55YIvjOLPV414Bfs9xnGeXup4X+K6NjbiW/GrKvNQ5jftqyesjfmXBMY1J6ZsnJAIQ8klLLon18FiOpSa6jcm8m+trqGqZn19C29zcBlabMHokUeCRP3i2ZfliAYXiddZVXysbcIK5LmIseHH2ZmIjxvy1pPHzBzUJx3HqPggC1OP/tz9/Px/7sxdbekc072vltRDzyzzxxQfn9bGxgILjOGQ834VVIwruTd2rpTG2tgUU/vQX75jndfDdLz7A3v5YvX5kivo8D4LVegos185uxna4FhrMuwVB+ALQIQjC5xt+/jdgJf+dfw983XGc4YZrtgNiLfBVuQQMtnj/fy0IwnjtJ5/Pr+AtPRZjI64lv5oyL3VO474ryesXHpNdQkvhwALNm4fHalkqfjYn826ur9ll2kRzG1htwujvHZ9ctHwbrQ9Zz7zdMbZ6vBdnb1I2YsxfS5o/f3Ncr/Wntf6xlXfEwn0LY3+2RR9b03J6rJ5rlao2np4rm/x5k9fB114aAa7Uj2YPgtV6CizXzt6J7XAlGsxNwN1AEDjQ8NMD/PJSJwqCcLB67J+12N1cfVouTHcc548cx+mv/YRCoRUU2WMxNuJa8qsp81LnNO67krx+4TGRJbQUtSeYHh7XwlJqnOZk3s31NbJMm2huA6tNGP3Bfb2Llm+j9SHrnLc1xoIXZ29mNmLMX0uaP39zXK/1p7X+sZV3xMJ9C2N/pEUfW9Nyeqyea5WqNp4e9sn8ZpPXwWfuGwKu1I9mD4LVegos187eie1wNUtkf8ZxnO+v6uKC8O+A3wb06qZ+YBr4HK4eZLO3RPbGsxE1FVdT5qXOWSyhbvMxhy8kQIA9vW5C+tl8mbmczsfvcnNa1ZLvFismb01kSOR17t4SpyOkEVRl+tv8PHVymopp8dpomj29YWZzOju6Q6QKOrmySbKoEwsoGKbDhdk8juNwx1Ab79nVxT++MYFu2GTKBr/z2A4A/uCHZ3HHjjYVEzpDKsmCjl+VOD2VwzBtNneE2NoZZCZXZjpTplAx2d4VpmJaPLKrC58icfxyhn2bopR0ix+8OUW6pBPSJNpDGmdn8miSyFA8wEiySKFiIIoiUb+CXxGZzev4FYmyYZItWfSEVabzOrYNARVsR6Q9qFI0LEKaTKJQwbAcAqpI2Od+1mSxgm5CV1hlb3+U8zN5RhMlgj4J23awHIj6JPyqQkiT+OSBAX5yZpbJdImJbImAIlPUDcqGw629EXTLZniuQFG3afSL80sgyxKKKCBJAiHNTfwsyxKmaZKvuNrXfMUioIoIgoAmifg1dzAymiyxuzdMd0RjOlPmtbEUhmETD/uI+WRSRYP2kEY8pLK7J8z52QIhTUJTJHAcNEVie1XDeH66gIPj5ugK+djTF+HkZJbZbJnxVImAKnHicpZNbT5+7cGt85J5N9dXWF7b3NwGVpsweiRR4O9fG6cj5B4zm9P5+WrdX+99yAZcIrsuYix4cfZmYyPG/LWk2ROi5tWwp6pBb+wfF/OOaN739Mlpjl9Og+NqMB+rajBbeUEcvphgNuf28f0xP51hH/1tfr77xgQzuTKpgkF3VKMv6ufE5Sw+WWQ0WWAqW+G2vjABVWE0WSCgSpydcVcX+GWZ+7a2MZoq0RPx8dNzsxTKFs3PVgOygCQLlMo2a/0s1ScLGKZT90Sgmh9TgHmeEI1okpuHOlnUiflVREGkbFoMxf3cPhDjzHSeX7l/M35NplQx+evDl9jVGWI6rzPU5ufcbMG9uSoLbmztDF35fx6foKRb3D4Q48DmOCcnsiBQj3MnxtN87aURPnPf0DyNZe27bfQguBpPgeXa2c3WDtc0D2bVSGA/4Kttcxyn1Z3Txc6/xBV9yH8DLjUYEPw9sNUz+Vk71lpQvFEEykuVc7Ek9Y0J5puTyr94wR2Q19b0C0BAkxck460dW6yY9f21xPf3bo6zqzdCzC/z1cOjaLKIprgOdtmyyfBcAUUUMWybLR1BOkMabQGVE5czhP0KumExmS2zpT3AG+MZtraHMGyLNydztAdUKoZrf94f8zOeKiEIAo7j0B1RuZwu0xnSGEkW0GQJvywzWyhzYKiNdNkkXTSYypTRFAFZkugOa5i2g2U7mJZNuljBsEBTJPyKSMmwsGynrl1J5CsMdYSYy1bIVgwsCwKaiF+VCSgiMzkdnyISC1QnwopIxbIxTZuQpuJXBWRRZCpbxnYcdNPVGUqigG7ZyAJ0RwM8sL2D10ZTXE6V+OX7N3MxUeDYaJqh9iDZUoWpTAWfIuFXJcZTRXIVm86gwu7eKFPZEqYNPllkKlukbDjEgyp3D8XpimgcOjPLbL6Cadt0hzRKpsMtPSFA4NhYiopp864dHUznKkyky7QHFTSltgBExK+I3DHYRlG3CKoSZ6bzDMb9jKaKRH0yr42muX9ru5ucWoBtXSGCqkxAk7m1LzKvDt4xEOUHb04xk9P57MFBJtJlTk/naA+qFHSLT9zVX89J+eKFBHO5MjN5vb4d3Ilhzcygduy1tt2RRIFvHh0nqErzyrFe2WgTTFgfMRa8OOuxfrje445mA6Dmv9sCCk8cm0AAPn1wiKhfqW9vHDM0ji1eGU5ydipHPOT22e/f082ZqRzHxtIkCzrDcwWCqsimeBBNFsmVDMZSRXoiPvK6a2LjV0R+em4OEYfaKlsBiAXcXNq390eJB1V+fHoWB3c54u39YSYzFaZy7v2moAqF6q0nqXqMwRXjPZ8EugWLJ7QCTQTbnm/CJ+MaDS03e5CqZY74ZbqjPjIFg5l8BcuBoCaxvTPA7f1tvDycAsHh3s1xzs7kSRV1trSH2NTm5yP7+zg9lePIpSSTmRLxoEbYJ7OjGkNn8xVevDCHCDy6u5tHdnXx1kQWoB5bm+vOYt/5eh7XrkfW0uTn3wKfxNVwHALeC/zYcZyfW0VhLnEl+HUDXwO24N59/bzjOIeWu4YX+FbGWguKN4pAebXmPqbtLEgw35hUvtHop5lGg5TmRL0AQVWgsBKbVY93NAIgiWAuFeWbjv/OFx/gs3/1yrz6Vqu7QN2cp3bsL//1kWtqu60Mf67GCOFGstEmmOslxoIXZz3WB9d73NF4/UZjn9rfuZIx7ymcAET9MgXdqufObO5jjapW3uPtx01BI86rO82mT8uZ5XkszjWb/DTwGeB+YNxxnI/j6j70pU+Zj+M4mx3HebP697TjOO9zHGeH4zi3rjTweayMtRYUbxSB8mrNfVolmG9MKp9dQqDfeP3mRL2AN7n0WBEOK59c1o7/6ksjC+pbre42mvM4wNcOj1xz221l+HM1RggeS+LFWA+PBq73uKPx+o3GPrW/m5d4OrgrjmqTy9q2Wn+se5PLdYUDC+pOK9On9T6u3aisZoJZdhynDIiCIAiO45wBNl+fYnlcK2stKN4oAuXVmvu0SjDfmFQ+soRAv/H6zYl6wX2C6eGxHK5V/uqO/+x9QwvqW63uNprzCMBnDg5dc9ttZfhzNUYIHkvixVgPjwau97ij8fqNxj61v+WmTk/ANfNRJGHetlp/XBtbeKwPBFhQd1qZPq33ce1GZTVLZH8KvAf4CjAFjAO/7jjO3utXvIV4S3dWzloLijeKQHk15j6tEsw3azAPX0hQ0Oc/LWplkNLKQKWW+P6ezXFu6YkQ9ct87aWqBrMavXItNJgdYY24X+XERJawT0Y3LSYzZTa3Bzh+uarBtCzemswRD9Y0mLAp5mM8Va7mkHLoDauMZSp0BlVGUlUNpiQzVyhz91AbmaoGczJTxlfTYEYaNZgO6apBj9qswZQEIj6FZKHCUHuIuVyFbLlRg6kQUASmczp+VSTm10gUKgRUkYppY1oOQU1xNZiCyHS2jA3ohoVUNeTRTQdZhO6onwe3VTWY6TK/dHCI4WYNZraCJksEVJmxVKGuwdzTF2UyW8KywKeITGZcDWZ7SOWuoTjdEY1nz8wym6tqMMMaFdNhV08IHIHXx1NUDJuHqxrMyUyZeECtajAdEEQCisj+gTZKhkVAcY0YBtr8jKVKRHwSr41lOLg1Xr+7vb0rRECVCWoye3oj8+rg/v4oP3hritmczmfuG2QiU67reYoVi483aDBr5hFzeZ2P3zlfg/nkicl6fV6Ltlsz/AkoEkX9SjnWKxtwiey6iLHgxVmP9cP1HncsZuxT+zvmV/jOGxMIAnzq3qG6wVnzmKFxbHHkUpKz025sLlYs3runmzPTOd4YS5PM6wwnCgRViU1tAVeDWTYYSxbpjfrIV0wUSUKTBZ6rajBraTRFIBqQyZRMbu+PEQ8qPN2gwdzfH2aiQYMZUiFfXQMhA4IAhrNKDaZU1WA2TBVWrcEMKHRHNDJFk5lcGcuBUE2DOdDGK8MpHBzu3VLVYBZ0tnSE6G/z86Hb+zgznePIcJKpbIm2gEbEJ7O9242hc7kKhy/MgQDv2d3Nwzu76uY+tdjaXHeWMnPyWDlrqcG8DRgGAsB/ANqA33cc59galHPFeIFv7VhMPH+tovq1FOXXjEwA7q86si1W5prBTq1G10x5glXDHaBu2FPUTWZzOp+4220b33x1nM6wWh/0D7T5+em5Od61o4PTUzlGkkXu3xrnxQtJOsMqRd3ixQtzCLid2kf2b2IsWeQvnxtmc3sAG/jEXe61v354hPF0kclMmX2bXLfUZLUDbQ+r7OwKUdQtjlxKEg+p7O2LcnIiyw/emmKwzc90TqdiWnSGVWRRYktHAFUWyZVNbuuLcilZ5MxUlpAms60zSHfEz9mZHDGfzLNnZtEth/ft6cIGzk7lGU0VqJg2e3rCyJJIpmgwmixgOqCIoEgyg21+EkWdqE/GdGAmW6E7ohHUJCqGzVSugiqK3LOljddGMyTyJfpibrCczVUAqJg2/W1+2oIqF2cKmLZNwTBRRQlNFrFxE1Fbto2ASLKgEw8ojKaKOI5DxQJFAMdxA+Bg3E8iX6Fi2QRVkXzZxnSgI6Qw1B5AQODUZBZVkegIKiQKOobl8PE7NnFkJIVlw4HNbZwYz3ApUcAnSwiiwP6BKANtAV64MIcgQCKnk6+Y7OoJcX62yKaoRrJg4Nck7hxq4723dPPt1ycwLYtUyeDT9wzy0nCCtyZybO8K8cD2Du7ZEufIcJLT0zl2dYd5755uMiWDrx0eQQA+vL+PsVQJmG9E0FhHg1UDoLcmsowkCpyazPELB/oxbebV/2ZDn1btcLF2sxg1Q5+heID3Vt0RW7XN9WyOsAEnmOsixoIXZz3WBysx7Fvt+KXV/tq2mnHPu3Z0MJYqUayYzOUrHBtPc3t/jKH24AJzv1pfGVIl8hWLT9y90GztXTvcm6PfPzFJvmJyW1+Eo6MZciUdTRFJ5HVCqoxh2xi2w67uCL/+0Ba+8fIIr1xK0hf1E/bJjCaKJEsmIq5ZnOm4kz1RBL1hlihUfxonjhLUHWZ9Muimew2B+ZPHxZAFiPgVTNsmoMropolhgSw5mBbkK/a899RE2BT3U9It5rI6Pk1iV3eIXNnCsC00WSKRryCLIrt7o5QMg+PjaQTBvfH++N5ehjpCHBlOEvEpVCyL2VyFT90zyES2Uv+OGmNlo3Fj8+/a973U97/SurTeY9+NYi0nmHHHcZJrVrKrxAt8a8Ni4vlrFdWvpSi/2Tgn5pcRBWGBILuVwU4zsYCC4zhkSmtt1O0SVEUK+sL7gLVJrsc7m7BPXjLhtiqJBKvGEukm457m+qNUnxz/+H99mEzJmGfo02hQ1cq8YiVtstnQJ+aXefZ3370gwK53068NOMFcFzEWvDjr8fazUsO+1YxfWu0HeM8fHiJbMjDs5aN1PKDwTNXcr5X5GcChJrM1j+uPKzW5YtxoOSBLAqZ1xfH+25+/n4/92Ystv/+V1qWNEPtuFGtp8nNOEIRvCoLws0LtFoPHhmUx8fy1iurXUpTfbJyTXUSQ3cpgp5lcVbR/vWg1uQRvcunhkl+m7tXqdCvjnmYMy6nX/2ZDn0aDqlbmFStpk82GPtkW52wU068NhhdjPTyqrNSwbzXjl1b7a9tWMrmE+f1hK/MzWGi25nH9aTZudHBjZW17rmzy5PHJRb//ldYlL/atnNVMMAeBJ4F/C4wJgvAfBUHYeX2K5XG9WUw8f62i+rUU5Tcb50QWEWS3MthpJlwV7V8vgmrrpuSNEj0AQsvUvVqdbmXc04wiCfX632zo02hQ1cq8YiVtstnQJ9LinI1i+rXB8GKsh0eVlRr2rWb80mp/bZsirixaN/aHrczPYKHZmsf1p9m40U1RItS3h30yj+/rXfT7X2ld8mLfylnxEtl5JwnCNuDfAb/qOI605qVaAm/pztqxmHh+OZOc5daeL3XdlZzbeEy6qPP0qWlGEkV+/q7+eQL75iTHT5+a5sxUjoG4Hxw4P5PHAXZ0hekIa9zaF+HQ2RkOX0gwFA8wma3wybv7OTeT55lT0+i2Q3dYozPs48BQG0++OcXunjAI8MZohq6IytGRFGG/ws4uVxtQNCwUSeSOwTYObonz1ZdGcBybgKYQ9SvM5SrkKyY+WWA4UWZ7V4BSxeJiooAkOMSDPqJ+hVRRp2jYbOsIkqtY5Mo6k+kKPgUMCwwbAoqAJIpIAhiWhYOIbdloqrsEUpNEypbN7u4QZ6bzONhky277DqkQ1FTSBR3Dpr6kstVzVwlX0wHu+9YQubJks7bZJwtUTPcuoQzIskC5wds9rIoYpk15mTQcNdOBpQjIUKw+4FOZn7+hWXPS6nqqCO1Blcnc/MwPMqBpImFVpmLaZEqma4AgQsV2/x8+VcCyIRZU+ejtfTxxbIKCbtId8fGbD2/jyRNTvDqSZH9/jEd3d1HULX58eprJdIW9/VHu2xqnpFscOjNLpmywoyvElvYgw4kinzzQT6ZsUiibFHST8VSJ9qCboPt9e7p5fTTN0yenUWWRX31wM5mSqzOuaU7aAksYVF1MUCi7x4c0mYNL6Jgbz3vq5DTnZnLs7Arz2BIazMMXEzgOC3RJ64GNtkS2kbczxoIXZz1WRrNHwlr3ASOJAk8en+ShHR2kisYC3dxqxy+L7a9ti/kVfnRymoAqkSzoDMT9FHXL7S83RRlqD3KwhQbzGy+PkC7qxAIqH9rXV9cGlnST8zN5MmWD0bkio8kCZdNmKO5nPF2mrFt17WSjHKLNLxP1K4wmS8vGxfWMhBuHa59LAUxc1/T2oMpcTkcQoTOoki4ZFBvGDiFV4Pb+NuYKOo7j0Bv1UTRsfvHAAMPJIn5FIpnX0U2Lly+l+Jlbu3nu/BxdYY1NsQAf3t/H66Npnj0zS2dY5e7Nce7ZEuetiSyCwDyTxkaDpka9Zq3+Pd7C38AzBlpDDWb1YjLwYeBXgHuAbzqO88VrLuUq8ALf28e1rD1fyblL6SMat7XSnUX9yjzNmdWgt6wl2w2oYn1wDgs7v8XwdJQeS/H7H9nD//7EyfrrkCaSr6x8WCAAYb9MtkkfrDbV2caE3tmSgWk7C5JIN5Iu6jz6pWdJVjWdbQGFf/zCAws0KFerrVzvWpSNOMFcDzEWvDjrsTzN3geN2sS1un5NG7lcX7dWXM1nauwHW+noPdaOQ9WxXmNcW4zF4rBS1WMuF/ta6TXXU3xbD6yZBlMQhD+mapsOfB0YeDsCn8fbx7WsPV/JuUvpIxq3tdKdtdKc0XBMbXvjRHElNtu18z08FuO/PHNh3uvVTC7BrV+tNJrNddYBvnZ4pK4XapVEupHj4xmyDdddTIPSfM5K27inRVlbvBjrsZFo5ZGwln1AozZyub5urd+zxko+U3M/eD29Ht7p1MZ62RX8jxeLwzU95nKxb7lY6bE8q9FgTgN3OY7zM47j/E/HcfRlz/C4qbiWtecrOXcpfUTjtla6s1aaMxqOqW1v1ETUcjQth6ej8FiK33p027zXIW013apbv1ppNJvrrAB85uBQXS/UKol0I/v6o0QarruYBqX5nJW2cU+LsuZ4MdZjw9DKI2Et+4BGbeRyfd1av2eNlXym5n7weno9vNOpjfUiK/gfLxaHa3rM5WLfcrHSY3lWtERWEAQJOPZ2JHxuxlu68/ZyLWvPV3LuUvqIxm3NieSbjwM4fDEBDuzpu5JsN1MyePLEJA9t7yBdMhiMBzg5kWU2X2Y8VaI/5qcz7GNPX4STk9n6+Scns8xmK5yfzeGTJT50ex/jqRIjyQKvDqfoi/n4xN0DnJnOcWYqR0dQ5cJcnulMhaGOAA9u6+Cf3pzizqEY2ztD/NdDF5jJVdjaGUKRRaYzZYKaxOO39fLUqRnAIV0ywHEIajJTmTKD7UEUSWAiXSZd1BEFmM3r9MX87OkN41ckXh5O8YFbu/nJ2VkqhkWqoKMqEj5ZojfqYyZXJuRTCKoipyZz5HUDEYH2oMammJ+pXJlbusOcns7THpC5OFckVzaQRIGusEbZcLAc1wa8ZJqENRVREDAtm/aQiixJJPJlMiUDnyJx11AbF2bynJkpENZE/IqEaTvolo1jORiWgyQLdAZ9pEoVTNshrEnoJkR9MpGAQq5iYtkOB4baeHMiR8k0eXRnFxPpEkdHU1iODY6AX5EwbBvdtPHJEh1hjUzRYDZbQVYEtncGaQ9qHL6YxKeCaUFYq+X1UvgX79rC2ek8L16YYyxVZFtHCN1yMC2bX7x3kJeHUwiCwz+7a4Afn57m4lyR33lsB/dv7+TEeJqvvTTCZ+4bYiAe4OlT0xwZTpDI69y9JU5HUAMBihWT87N5fLLEI7s6OTae4fG9vUT9Codr+S998rwE0bU62yqhd6sk0s3t6fCFBDRoTq5Go3QtbfrtYiMtkV1PMRa8OOuxMmo6bBwWaBPX6vor7evW8j1X+5kWjD0uJCjoJkFVpr/Nz3ffmCBT0qmYDvmymwczWdSZzJR4bSRNQXd1+bmKRUG3eGhbO8NzBYbniliOgyJBsuhmsRRwb47HgjKJgln1X3DzRdf8FbSqf8By1LwKFHG+30IzzTIhCVAkd1t7UKViO7QHVJKFCqmiiU8GTZHZFPMxldUJqRLZsklbQCGvm8SDKr94zyDPnpnDsCw2dwR5bTSFTxZRJZHTUzliAYUP3b6JeEhlNqfz83f1zxvr1eKaBHz95VHet6eLFy4kifhkDmyJ89jubsaSRb78/DB7esIMdgTnxdWVxL71HN/WA2uZB/P7wMccxymtVeGuBi/wrX9WkpwWVpf4fbH3qRkM1BLSw3yzgeb3bExif3/V8OSpk6450K6eMPdsic9Lev/EscscvpBk/0CUgCoxmizRHlIpVCzef2s3p6dyPH9ujpFEgfff1sPP7u3l2TMzvHQhwb6BGJ0hjYJu8vpImrcmM0iCwP6BKG+MZ7Fsm21dYdqDKju6Q0ykSjx1apqIT2Y0VSSsKewfjOGTRX58agZFFNBth7JhIIkS/TE/tuMwmSlj2TZtQR8hTWIur1Mo61RM8KkiPREN04aDW+PM5nVCPpl0weDYeBrLtvDJEhXTZntnCMN2aA+qTGTKCMCe3iiGZfHj0zNosohPFsnrFvGAWs2rqLC5I8BEpoRlO9i2w3SuQldYw7YdchWTrpBGvmKSKukossBd/W0cn8hgW+DXJD5x1wD3bo3zBz84zcVEiXuGYqRKJt1hjdfH0twxEGU8XUaR4MJsAU0SuW9bO2en8szlyyTyBjbQFnCTP4uiSDzg/t/39EYIaArDs3n8iogD5MoGiixxe3+UN8YzpAs62bJJSBMxbQFBcLhzoI0LcwXagwpTmQqqJJIq62yK+ulvCzKbL5OvmOzqjrC5PcChs9NMZCo8uqsTRZZwHNjRHaIz7KOim3z5hUu8b3cXR0bTfPbeQWpeBssZY9Tqb3Oy6JXubz7myHCSY2NpBGB7d4hEXufQ2Vl29YT4wrt3LDAyqCUf/+HJaYbiAd5bNfzZCImmN9IEE9ZPjAUvznpsLK6lP1qqD23VBzaOEWpGMN87Psm7dnQwlnKb7v3b2smUDL53fJI7BqK8PpZhR2eQvz0yRq5s4lMkNsV8vHIpydb2EKoicGI8w0yuDAL4ZYls2UKRwK9I5MpWfTLVFtIo6jr5ijNv0tfK2K6WDxKYtxIm7BPJNDnvrcRoD9wygCsxkgBVBtt2c1CG/AoDbX7GUyUMy6GgG5RN97h9A1H290f5u6Pj9IR9dEV9dEU0IprC9u4QAVXm3HSO7785iV+RefeuTgqGhQhs6wpR1C1OTeb4hQP9pEsms7kyYw2GeO/f081YqkSx4prlzeZ13r+ne4E5VI2RRIFvHh2fF9c8VsdaTjD/M/AA8HdAvrbdcZw/u9ZCrgYv8K1vVpKcNqhJCEB+hYnfF3ufxYTeNWE+MM/4x3Yc0g1GKjG/jA0tzVWCmoRlOyta6+/hcbW0BRR+soiJRGObMSy7niy6VZtqtb/xGjWjjOV6+0O/+0j9Kel7/vAQ2bKBYV05K+qX+c4XH9wQ5gcbcIK5LmIseHHWY+OwFuaDrYyEwB0/ZIo6ZlPHWTOK+W+/coAP/8kLC/rViM+dFHr+DW8/rUx9RhKFulEkuHHt0O++e13GsfXMmpn8ABHgBLAbOFD9ufvaiudxs7HS5LTZaxRPLyX0rgnzm41/mo/Pls1FzVVyZZNcxZtcelxflqr/jfW3MVl0qzbVan/jMcYKJpfgmijMO8+af5ZnfnBd8WKsh8cqWQvzwVZGQrV9zZNLuNLXfvXwSMt+1Ztcrh9axcVGo0hYOg57XD0rViM7jvMr17MgHjcHNaF07W5ic3LaVk8wr0Y8XRN6t3qC2SjMr71nqyeYEZ/3BNPj7WWp+t/YZmpPKBdrU632Nx6z0ieYj+/tnX9e0xPMmvnBV54fXtDGPa4NL8Z6eKyexcYcqzm3+Qlm4/hhsSeYYZ/MZw8O8a2j4wv61bD3BHPd0MrU54P7evnSD8/Uvx8vjl0fVrNENgL8e2Cz4zgfFQRhD3C74zj/43oWsBlv6c7acD00VI16hefOzWGYFt94ZYzffnQbnz64ZYEQfiXi6aX0nC9eSFDUXT1l1CfzP4+Mc9dQjId3dfHWRJZC9QlkSHOF9j98a5qusEZAkwiqV5LOP31ymrMzOeJBlfFUCRGBvf1R7tkS5x+OjvPkm5Ps7YtwMVFkc0eQnrDGqyMptnUGeWsii2VDvmxw1+Y2fva2Xv76hWGG5wqIgsjDuzq4nCozl69wOV1EFkVEwZ3oSgJ0BDXSZR1REMhXbFTJ1U0UTFcrEdQEVEmkbNgYhoPJfLG9X4KydWVbKx2FIgACaLKAZTtIIpR0V0PRSEB2hf6afGV/UBEJaiIz+dVNtEVAFMB03LtYdkO5/DLU5vQC0BFSkASB2Zw+r0yqCLrtliuoKcwVjPrnjFf1lgV98TQyIu4+BdABVQC9erBPBkGAUsP9Cal6jgUENAFFkqiYFqbhYON+lohPJKQpVEwL3bSRRBFJEDAdC9sWuKUnTECVaQsqqLKEJomIArxyKcUjOzt49uwc92xuQ1UkdnWH6Yv5+PNDF0gVdB7f18c/v2dwnsaxLeAm/u4MqXRGfPOSQ8NCE4xaouhGPRFQP+bIpSTHx9MAbO8KkcjpPHN6hnhQ5d+8fxcAf3N4hPft7uL4RJbN7QF2dYd56uQ0g+0BHtvdvWHMDzbgEtl1EWPBi7MeG4tW5iyN44ZGneVbE1mKFZNAgw9DYx8a8ys88cYEFcP1Gjg7nWNHd5jRZJFLiQLv2tFJR1jl3FQeTZF49JZOnj07y3SmzES6iGHD5x/exmS2zGujaR7Z4fb7fkXghfMJyqZNrqQTC2ikSzohzfVAKOs2xk0wI11MyykJrk9CqmiCAyGfiIODboEsOLQFfCiywFS2QlAV2dkVJlk08CsStmOTKppUTJvOkEpbQOPOoRhF3SIeUplIlZjLV5hIl3lkZyfxkMpcQee+LXGOjWV4aEfHAi3mSKLA3x8dnxfXmtkIXgNvJ2upwfzvwEngFxzHuU0QBD9w2HGc/WtS0hXiBb5r53okSG/WMoiiO5mp8fsf2cOnD25Zk3I2b2/UQbRKWh/2SeSrdxMF4NmqzqyRkUSBh//g2XnbGs/z8FgNza57Mb87o21MwF27695IWBP53m+/q65xNKrLX2H5pN8r0WQudY5flciU5q8IWKy9bAQ24ARzXcRY8OKsx8aleXzw7c/fz8f+7EWyJQPDnh/NmzXwrcYBy9Hc13vcOA797iMA8/SUAN/94gMMxAOL6mtX6gy81uPkm4211GDe4jjO7wMGQNXpzksRuAG5HgnSm7UMVlNH3pyM/lrK2by9UQfRKml9Hc1NcwAA0/ZJREFU4yTR4YrOrJHvHV+4zZtcelwtzfUm2yIBdyv9b65iz9M4Nl5nuaTfK9FkLnVOrrxwufli7cXjuuDFWA+Pa6R5fFDrT5snl7BQe9dqHLAc3hjh7ePJE5ML9JQAX3tpZEl97Uq4HuPkdxqrmWDOS/pcvbvqBb8NyPVIkN6cFFkS51eN5mT011LO5u2fPTg0z4K7OWl9yCfN21/TmTXywX0LtzWe5+GxGprrTaRFAu7megruE8zGBM+N11ku6XdjuxBYPKH0YueEfUrLz9GqvXhcF7wY6+FxjTSPD2r9qSIubEqttHmrxWugbx+P7+3lg/t6F3wHn7lvaMGYdLXj3esxTn6nsZolsv8RyAGfBj4P/GvcxND/x/Ur3kK8pTtrw/XQUDXrwU5NZPjK85f4raoGcy3L2bx9JFGoJ6NvTlp/cGv7gmT1rRhJFPjW0XE6QxqdEW3eeds7gjx9eobP3DdExK/w90fHEYBzs3n29UfpCGkENZn+mJ/vHp/gcqrEdLbMp+4Z5GKiSFCTSOZ1/KrEkeEEc/kKHSGNoXiAwxdTmLbNXK7MwW3tFHSLS3NFREEgHlTQFJGP39nPj07OMJEukS8bpMsGkuBwYKidiWyFnZ1BXhvNgOAQ1mQuzhWQRegM++kIqTgIZMs6OA6aLJEo6Ai4Okk3N6bE9s4ws7kKnWGN8XQJw7KI+TV2dodQZYkzU1nGU0W6Iz4cB1JFg1TRQJPcIFuyQBagM6wS1BT8ikRRd5/SabJEpmwQ8cnolo1uuj9+RSTiUynqrmtvMq8jiWA70BFSyVZMeiI+3ru7m787OoZuOrT5VXpiPtpDKhdnclycLSIKuMuyTQdRFoj4ZLojPkaTxWoeMYO+mJ/JbBm/IrOzO8hYssTlVBkb2BTVGGoPEg0ozOYrmJbDz9zWw1OnZggoIpPZMgIC2zqD1YmYQ8V0GE8VKekW27tD3DUYr+t79/RFODmRrSfbrul9m+vlWLLIl354mmRR5/G9ffxCgwaz1pZOTmZXnPT7ahKTN7alsWSRr700wmO3dHF+rrBke1nvbMAlsusixoIXZz02Nq00mY39aS0XdrOmHdxxwDdeGqFsWPTF/CSLOju7XL383706zp1DMe4YaOO7b0yAAJ+6dwiAb7w8wky2jG45fPreQTIl10hhT2+Ek5NZZnNlxpMlKobF8xcSDLT5cXBQRImpbImAIjKcLBH3K5i2Q9m0SRfLlHVAANFhgQcDXFmiq4iuh8JiiIBfgcLChSorWuYr4WorHdw47+ooJUKaTNm0KBkmjiMQ8SmUTZN0yUYD4hEVywZBcBiIB9jRGeKVkRQDMT+Zss5E2h2XbGrzMdAeJOpT2d7lxpzxdIn2gMrFuXxVmylRqFgMtQf4tYe21mPTSKLAV567yFxe5zcf2cbe/ti8erCaeNjIRvAaeDtZSw2mDPwu8FHc+vgd4P9yHOeG2mx6gW8+Xz88zB89dY47B9v4f31oz5oNBluJm2vGOrAwQfzViqGXMvFZ7HqLlaOVsP/F6oC+1oEGNZlb+yK8MpzkzFSOwbgfv+o+SWrsZItNiXqH2gO8MpzkxHiakmER86t8+uBQ/f/9ozcn+aOnz7G1I8AbY2kcBz5x9wD3bo3z1cOjdIZVPvfQVgC+eXScoCpxOVWiYloUKibDc0U0CRRZomTYTGWLWCYMdQR4eGcXE9kS48kSfkXmFw70c3wiS2dV3P7suTk+fkcfr42lMUybiXSZoCoS9auMpYoYtkNvxMdtm6IcvjDHpUSR3qgPVRbJlU1u64tydCSJbjncNRjjxESWsmmxJR5gMlOhbFrYjkPUp+BTReZyOiFNRpUFJtJlwGFHVZAf8cvkKxa6YbsTs1wZSQBZFMmWLfpiGtGAyt2DbZydyTGeKlHULW7rixDUFC7OFfApIooo4FNkdveGefNyhrm8TkAV8KsybQGNfMVAAC7MFvCrErmSgU+RiAUUChWTiUwZWQIcAcOy6QxrvP/WXmwHcmWdp05NY5g2lu3qI9tDGgXdtRiK+hVCmsz92zso6RYvXkgQ9yu8fClJR1DDERwkUWB7Z4iIXyZXspjJV/jo/l6OjWd5YGucFy4m2dkV5JtHxxmMBwj5FHJlg4d3dvKR/ZsAeOrkNCPJIp+4q5+oX1k02XcjI4kC33x1nM3tAR5rSA69VLLw5vMbk39/cJ87gVxs+1Jtfr2zASeY6yLGghdnPZZnPRug1PqzWj+2Glb7uUYSBb55dBwReH00RdmwkCSB7ogPTRYBAcdxEAR3hJErm4wlS8SDKsmCTntApWCYDLX5+d6bUwRVERDRLRPHgZLuIEtgWCCLsH8gyniqTL5iEPWppEtl8tW1D6oIitR6ElkjIEPFcs3vDNyJo940DWiecIY1kXzFRhaoGxFFNBHbcbBth86wzzUrRESSBKI+hbaAyvnZPGCjyTLxoIokCtw12MZ4qoRhO1yczdMT9fGe3d0MtQeJ+WX+4tkLJIs6j97STXtIrY/DxlKl+lhuNl/h1UtJemN+Pnn3wAIDn5XGw1as53q93lizCeZ6wQt8V/j64WH+9ydOztt2aA0MOVqJmwEe/dKz9bQgjeL4qxVDr9TEpzlxfKtyAAuE/R/90xdItUhjsloUScC0FqZ4qBmgnJnM8i++/tqKruUZAnhEfDLgkC1f8cyNBRSKFWtJc55mA4pYQOHZprq/3PnNZggC8I3P3cOnvvzKgu3f+eID/NJfvdKyzW8ENtoEcz3hxVmPpVjPBiiN/dxqTcpW+7la9akeNx6lIebByuJhK9ZzvV6PrJnJjyAI/14QhJjg8qQgCHOCIHx8bYrpcTX8cQvjnLUw5Gglbj4+npmXE3Ip051rFVEvdb3FyrGYsH8tMFpMLuGKAcp/fub8iq/lBSKPXMUkV56fJGYl5jzNBhSt6v5y5zfXPwf4i0MXW27/6ksji7Z5j7XHi7EeG4X1bIDS2M+t1qRstZ+rVZ/qceNpjHlXY3ZXYz3X643Iakx+PuI4Thp4DHcp+APA//N6FMpjZfx2C+OctTDkaCVu3tcfrT55cVnKdOdaRdRLXW+xciwm7F8LFEloKeSvGaD8q0e3r/haniGAR1iTCfuk+dtWYM7TbEDRqu4vd35z/ROA33h4a8vtn71vaNE273Fd8GKsx4ZgPRugNPZzqzUpW+3natWnetx4GmPe1Zjd1VjP9XojshoN5uuO49whCMLvA6cdx/m6IAivOY5z5/Ut4ny8pTvz+frhYf7o6XPcNdDG/34NGsxW+sVmcXO6qHP4QgIEFk343nx84zr4xt9vTWQBGGjz8503JnBwnb8atWjNouzG652cyNbLAcw7ZzDuXr9QMSnpJudm8lRMG00W2dEdwnHg5ESWubzO3r4IiaIrYNjRFQYczs/kKZtW1XlM4vbBGH1RH197aQQRgdFkntmCwa/ev5mZnM7lVInXRxKkSia7uoMMJ4rYjkM84ENTBKYyJaIBjbaAQr5sktdNHBscbKRqvtBMyaIzpGHYJtmShWG7d38CqkhXRGM6U6JguNsG437yFYNkwUSqrrkN+mVkCVJ5E1GEoCZQMd3kxiFNqZrouHf0amJ/HCiZ7uvac6p4QKKoWximq/fQbdBksG3qpkCl6sM3tbp/MSTAarG9NmWRqroSh4VPdjUJgppEUFWYzpQRRNfEoGy4RgM+BRwHKq6PApIIluOaD+j2wutJQF/MR0G3cBybVKlVydxrySJE/DKiILClPcCbEzlEbPINq61FATqCCoZlo8oipu3QHfYxkSmzoytIQJUp6ibnZgr0Rn3M5XR6oxr7B9v4wG09PHN6lrl8helsmYAi8WsPbcFyqJtQOA7c2hdZoB85MZ7my88Nc9fmGB++fdM8rbHQYCjhOK01kyfG03zt8Aj7+iP86OQMn753EJ8q0xZQeO78HPv7oxwbz9TNsl68kKCou4YYe3oXlmc9s9GWyK6XGAtenL2ZWImm7Gp0Z2tpgLLa91/u+EbTv9qYaDFfiWatXqZk8K2j43SEVDrDvnn98FiyyF8+N8ye3jCDVc3gnx+6wFiiSF43q7FVwnHclU8+VWSuUKEr5AfBwbIcLNvBsBxEAUzHQTcs0uXW8ehmZKjNhyxJ7hgmryOI8MDWdsYzFWayJQplCwvXeO89e7qZyersG4jSGdYoVtzxXK5sMJOrIIsi929rZ6gjuGAseHIiS75iIggQUGUG2vz88OQ0Q/EA92yJt4xljbG0lQHUYrxTdZtrafLzPPA94HO4d1ZngeOO49y2FgVdKV7gW3uux7rz5qTvklCdAIgCpt16ySlA1C9T0u2W2suV6DW//fn7+bk/faGuG1uN5jHml0EQSLfQbXraSY/rTWNy6Fb6EaClNrpVUvHF9Mvv+cNDZIo6ZkNllgWIBtQl29tS112vbMAJ5rqIseDF2ZuFlcT2t1t3ttr3v5ryLuYr8Z4/PES2ZGDaDookEtQkHJg3BnBvOgr4NYls6Yb7bXlUifllHHDdeZuIBxT+4QsP1GNUUHNTzOWrvgaSwLyYB/N1m9fDS+SdwJppMIFfBnqA33McZxrYCnzj2ornsR64HuvOm9fBmw719fBLTdSW0l6uRK/55PHJebqx1UwKs2VzUd2mN7n0uN40JodupR9ZTBvdSnu8VBtqDrSms1BfuZrreqwZv4wXYz3WkJXE9rdbd7ba97+a8i7Vd9bGJLV9zWOAWj+cXyNPB4+rY6nxWbZFjMo2jT+badZmrrWXiMcqJpiO45x3HOd/AV4UBKG7+vo/Xr+iedworse68+Z18LJAfT38UpqFpbSXK9FrPr6vd55ubDX6iIhPXlS36eksPK43jcmhW+lHFtNGt9IeL9WG5KbKLAsL9ZWrua7H2uDFWI+1ZiWx/e3Wna32/a+mvEv1nbUxSW1f8xig1g+H1sjTwePqWGp8FmkRoyJN489mmrWZa+0l4rG6JbL7gP8O9OG2uXHgnzuO8+b1K95CvKU714frkVC2Oclt4++ahnJPb4Qjl5KMJIr8fDUf4GLlWKyMrZIq17Sie3rnJ7zf0xeZl2i5plmrJbIH6uf2x/w8d36ubhLw5IlJHtrewQsX5jh0dpZ/dmc/p6ZzjCdLnJ/JoykCv3r/Fi4mikgCvDaaplA2KBoWm2IBMmWdTMlAkwQqFgRVAb8iIwgClxJFfungEBdm87w6kqJkWOzoDAECQU3izFSekWSBkCqzpTOAZTmcmsrhVyV6Ihq66bCjK8Sx8TQRv8K+TVGOX86wtz/GP7urn//604u8fHEOTZZ49JYucmUL3bI4P5PHsGw02dWN7OmNEA8p5MsW52aySKLI+/f04FcljlxKAQ7nZ/NkSybbOoNMZcrMFcpgX9FmyoAiCwzEA/hkkVTRIKDKXJzL49dEOgIacwWdrpCGKAkogsBIMk9ed3WWQU2kI+RjZ3cY3bQ5M52nM6QR8cmcmspStix6Qn43H6UgUNItbtsU5c3LGQKqTK6sky6b6IaFYYFfEdnaGaA7EmAiU0KT3bQzM7kKFdOmYpj4FAVJBFUWkUWBff0xLswW+MCt3fzk7By2ZZEoGmSKOm0BFd2yuXuojYlMmb6on5BPIl+2GE0W2NMX5SP7+3j2zCwIsLs7zD8cm+DT9w7i12RifoWnTk3TEVIp6hanJ3P82oNbVpQcejGtc6uk4ku1oZjf1Vw+tL2DdMlYUXvbaEmnN+AS2XURY8GLszcTK2m3b3fbXu37X015l+o7G/tacMcAjWOG2r6xZJEvPz/Mnp4wgx1Boj6Zr700imnaTGVLbOsKcedgnKJhcnwsQ9Qvc3Q0zcEtcRwEyqaJT5bpi/lIFnXiAZWXLiZ4+eIclgOaLNId9pMp6xSq+aRVGYpVrwG/DGXT9SEACCgilmFTobXngciVYxsJKmA5AoLtULLnH9/oiSBVz2+Vok2t+h7UzhvsCGBaDuOpEgKuh4IkChiWQ1G30GSRoCZzYEucvZuiDM/lOTddIFM2+OTd/fzkzCwXZ/MYloMqCWztCvHFd293l8Q2+G0cvpgAh/pYrjZ2a45RwLzvNuZXeOrkNIPtAQ5sji+IrYvVkZXwdreft4u11GC+AvyB4zjfrL7+eeDfOo5zYE1KukK8wHd13CgRcmOC45phz40QPi9mKLSvP8rJiQx/dugiBwZj/OjUDJ97YDOa6t4Ju7UvwivDSUaSRe7fGufFC0lCmsRcXueWnnA9mX26qPPUyen6cc+cniVXNlzdhirx0nCSvpiP9qDGzu4QRd3ileEkPlmibNrs6Aoyma3w6w9tYSAe4Iljlzk6kuaDe3t47vwclxIFeiI+tnaGmEiXyJZNRuZyTGd0/JrM3k0RhtqDvHIpQSKv0x5SuXdzOxOZEpdTJbIlnWTRIKjJ5CsmPkViW0cQGxiI+XljPE1HSCNd1MmWTQKKiF9TGIj76Yn4efNyBsu2mchUeOyWTn54ahrBcRMa27aN4ziUDNfcRpYENncE6A77ODeTBwcsx9WwqLJIRJNpD2t8cG8vPzw5TaZocHoqiyAIdIRUPnBbL+0hlZcuJjk/k+fzD28lVzF56tQ06YLOZLZMWTcRBIGoX6UzrNEWVLlvS5yvvzRK2TBpC6iUTZtHdnYyk6twfjbPB/f28tz5OQQcRpIltncGkSSReFAFRyBZ0KkYJqemskT9Kts6g0znKtX6Y7imC5aNLIiUTIuusEamZJApGwy2BUgUdXoiPgbaguimxd1b4gRUiddHU7w1kUUA9g/G+MK7dwCuhf0dA1FevJgkqEok8jodIZW5vM6unjD3bInz1kSWuVyZ0WSJXT1h3runG2DRdlMzIYDWJj5X02Yajb1uJqOCDTjBXBcxFrw467FxWU0/1tyfwuJ9b+342jjgE3f1M9QerG87M50joEicnc5xcFs7j+zq4q2JLLO5Mudm8ghAX8zPpbkCk5kSiigynCigmzYHNrdxaipHMl8hmTcI+iQqukVPzIdhQqas0xHUyFQMUsUry0RlAAHagyq5imv6V9JtVEVEEhxy+pXxfXtAomzYlAwHGwirIo5jo9ugN8xMJdwJ5RL+ffX3jQUVVEkkVzFRRJG2gEx3xMds3mBr3M9Pzs6yqyfMLX0RBKBiuMtSD2xpq8bONOdmctzWF+XT9w3x2miKoyNpfv2hLUT8Ct87Psm7dnRweio3b4y2uT3AgWr8rD0sGGjz89Nzc7xrRwepolE3bPraSyPgwGcODi1p+HQ19eedzlpOMF91HOfupm1HvAnm+udGiZCbExxH/TLFFoY9a81ihkKKJOJTxHmazNUSCyg88YUH+MifPE96jQT+QVWgoK+s3Xm8s4j4JGRRJF+xWpr0PPqlZ+sGVm0BhZ/8m0euql3dDCY+y7EBJ5jrIsaCF2c9NiarGeukizrv/tKzpKr9aazqHN6q760d//Af/KRuMiMA3/niA3zmKy+v2djA4wrLGSsutV+RBIKavMCs6dnffYSoX1m0jryTDXuuhrU0+TkuCMJDDRd+EHj5WgrncWO4USLk5gTH2RskfF7MUEi3bHKVa+v4WxkHXSve5NJjMXJla9F2c3w8M68eXku78kx81iVejPXwuAZWM9apHVsj22AM0+rc5uMd4GuHR9Z0bOBxheVGSUvtNyynpVnTkycml6wjnmHP2rLsBFMQhCPVpTt3AocEQTgjCMJZ4KfAwetdQI9r50aJkJsTHEdukPB5MUMhVRIJa9cmzG9lHHStBFXPMsijNWGftGi72dcfnVcPr6VdeSY+6wcvxnp4rA2rGevUjq0RaTCGaXVu8/EC7rLLtRwbeFxhuVHSUvsVSWhp1vT43t4l64hn2LO2LLtEVhCEh6t/OkBX9fdcbb/jOIeuW+la4C3duTpulAi5McHxUoY9a81ihkJ7N7kazL84dJG7BmM8fWqGX31gMz5VvmIyNJxkNFXkvi1xDl9MElIl5gquTu6x3Vc0mE+fnK4f95Mzs+RKBoosElQkXrqUpC/qpyOssr0rRKli8cqlFD5ZnKfB/NyDrgbzO8cu89pomp+9rYfnzycYSRboiWhs6QgxkSmRLZlcmssxk9Pxq1UNZjzIkUsJ5qpavnu2tDOZKTFW1WCmiiZBVSJXMQkoEls7Qzg49Ef9vHE5Q0dIJV00XA2mKuBXFQbaAvREfLw1mcU0LSazFR7d1cmPTs1UNZhlV4MJlHRngQbzbIMGU5UbNJghH4/v7eGpUzOkizqnp3IIQHtI5WcaNZizeX7zXVvJ6SZPn5wmVTCYypYo6SaiIBIJKHSFfMSDKvdsaeMbL49S0l0NZsV0eHhXB7O5CudnCzx+Ww/PX5hDcKoazK4gsiTSFtAASBV0SobJqakcMZ/Ctq4QM9mym/esZCLguBpMUaRkWHRFNDJFg0zZZLDNT7Jo0BPRGIgH0S2LuzfHCSgyr40mOTmRRRBg/0CMz1c1mE+emGR/f5SXhpMEFIlEwdXOJvM6O7vDHNgS5+Skq9EZT5XY2eVqfoElTXpqJlSrSQS9VJvZqCY+y7FRlsiutxgLXpz12Lisph9LF/W6aUzN5G+pc9NFnadPTTOaKPLxBg3m0yenOTuTwy9LnJvNc9+WOA/v6nL792yF8zM5APra/AzPFZhq1GAargbz9FSeuXyZZMEgpEmUDYveqA/dcjWYnUGNTNkg2azBFKE9UNVgilCquBpMWYRs5YqSsiMoUzIsSnpVg6m5GkzDgspVajDbgmpVg2kgVzWYPREfswWDLW1+fnJullu6w+zuiwACFdMmXza5e3MbAVXm9dFUXYP5qfuGeH00zdHRFJ970NVg1owVz0znGE0UuW+rO0Ybqhr1nJzMUii7Gsz+Nv8C47pMyeAbL42AAJ+6d74Gc7Vmkh4LWRMNpiAInwP+D6B2oTHg9x3H+cs1KeUq8ALf9afZMGelYvlmYXSjgP7WvkhdkA0Q1GRu7YvUrw9XxPWZkrGoUVDNROhdOzoYS5XqAu/7G1zEmk1QatvmcmVGUyUG2vx0hn11Uf+LFxLzhOJPHJsgW9YByJcszkznKBkm+wfauHdrHIDzM3kcB4KqxKFzs5R0i4Aqu+VKlnhrMsOOrhCKLHJprkhQE9nSESIeUHl5OEFH0EeiUGYyW0GTRbrCGrIkcnIig2nbyIJExTBRZJFYUGEupyOJENRU5nIVOsMKoiASUCWmcmUKFXcS6FcEBtoC5CsmM5kKFtAZUkAQKJR0CiYoAkT8cjUHGIR9IiFNIVvSKeoOqgB+n4xt25i2Q0dYo2LYBDQZvyJybjpPUJPIli00CUomaBKoigCOq3vd3hVmNq9T1k1KhgUCBFSJbFknU7QJqO5nKZYNQj6FdMlAESFffX/DcR1lowGFVNEgFlAIqDK2Y2OYoEiQKBq8b083AvDDk9MMxQPs64/ywvkkgmBjmDbpkollOUgiiKKATxZJlixEqk+5ZYGAKqFIAmXTIaBKJPIVfKqIX3YDsl+ReP+tPRRNi9MTWS5nSrQHVCzHwbYFNFnklt4IPRGNo6MpBtr8vDHuOvg+flsPPzo1w/t2d3F8IktnSKUz7JvXHhrr72rbaHN7W67dnhhP8zeHR/jYHX2YNje1kcFGmWDC+oqx4MXZm4W1NAW72VjMkBCo96NvTWQpVsy6r0RBNxlLlbilO8yunjA/PDmNBLw5kaE35ueW7hB/c3iUPb0RHt/bw/dOTBHzy7w2mqKomyQKBrf2hjk/W8BxHObyBkEFFFkiVbIW6AqjmkjJcI141iPN5Q1rAo7jyoACqsDu3ijFikVAc2Ps8FyRh3d24DgwmSkjijCWLPHY7m52dofdO2v5Cs+cmmYyU2FXT4itHSEObG7jR6dm+KWDQ+TKBn/8zHke3tnJz+7t5a2JLDB/vNcqDi5m3uOZ+lw91zzBFAThl4HfA/5X4HB18/3Al3Ad7/56bYq6MrzAd31pNsyRRYGIX1lWLN8sjAbmCehbCbIFXCOekCbhAIWKRUAVyZSudOgRv0ypahT0337lAB/+kxdarr2PBxT+4QsP8NE/faH+nrVtP/enL9SNURqJ+t0UIekW+zw8rifN7SEeUHhmhYY9i7W35drtifE0H/qTF+rXkQWIBtSb1shgo0ww11uMBS/O3gw0m9ispo+52VnMkDCoSQhQvfnqeSVsJNoCCv/4hQfqZnWNcXAxEzvP1OfaWAuTn38NfMBxnO87jpOu/vwT8LPA76ygAD8SBOG4IAjHBEF4ThCE/dXtXYIg/EAQhHOCILxZNTTweJtpNswxbGfFYvlGYXQrQXwzNSOebNmsn58tm/OMghqv+9XDI4sKu7MNRiXN2xYT4eeq7+vhcaNprsfZVRgKLNXelmq3f3N4ZN51TOfajII81oxrirHgxVmPhbQysfHaustShoRZb3K5IWk2q2uMg4uZ2HmmPteXlUwwJcdxRps3Oo5z6f/P3p9Hx3Hd977op7qq5xGNGcRAcBRJkRooyqQGi7IlO4lkO46nnNiW41jJjR3n5J68OGu9e99a9/xx1h2ec3JfTmwnN3HsWLKdOPIoS3FsSRZpSSRNihJFSuBMEEQTczcaPc/1/qiuZnV1daMbBEmArM9aXARq2LVr1+79q93Y3+8PZbn2YnxcluUdsizfCfx34Bvl7f8ncEiW5Y3AZ4HvCIJgqqVvMHrDHKtFaFosrxVGGwni9ahGPD6HVDnf55CqjIK05T6xZ6iusNunMSrRb6snwveWr2ticr3R92NfC4YCjT5vjT63n9kzVFWOJFydUZDJsnG1MRbMOGuiw8jExvysKzQyJPQ5JKwW04hvtaE3q9PGwXomdqapz7WlmSWyZ4HtsixndNudwAlZljc0fTFB+Azwp7Is3yMIQgIYlmV5trzvMPCXsizva1SGuXTn2qM3zFE/dI30XUbCaK2AfmufjxGtBtMhsbXXV1W+ev5COs8P3ggx0Obi3uFg5RhVg/n9oyE6PTZcdolUtsBsIsdHNYL7Z9+6zOsXlWS92/sDFWOU2USGc9OKdvKOwQC71iqJelM5pU7heJZfX4zgkESS2TxtbhvJTJF3JhbIlUoEXTZ+6/ZexiJJfv7ONO0eGw9s6OCNS1GmYylKJYH3buliJp7lzUtRev0O7JKFibLWAFkGLMjI9PkcXJhLUCiCRQBBgDaXnal4FuUoRWRvASQLdTUYIkqA1P4dVr/80mFRftfo/bFbqn/32Cwkc6XKeVK5nLymPAkoln8Wyz8b4bEJ2CQLkVSx8g1WCbCJIBeVMkHRWBZLSnmLfV9sFUAq6z21BF0iTkliKpbFKoBQPsZqUeqtbTf1lUF7LfXeRJTnYBGUdpFQ2lQAbBa4Y8BPvlhiLJImliogWQVkzbfc6zo93Ls2yBvj82TyJeYSOfwOifVdXgRBZmuvn0yhSH+bE5dN6bdnZxLkCiV8Tiuf2n3FgAAaayxVndDBC2ESmSsazoV0nuePT/LgxismB3rtyXgkxdOHxvjtO/soytzURgaraInsssXY8nlmnDUBak1sVltS+Ub1W6zuWo2ldmxVz31xZJqxSIqP7lSGCHXsPDUV51IkxZ51QX785mVOTsZY1+lh51CQVL7AW+ML3Nnvx2ETOTwa4a3xKH6HhGAR2NTl5fjlBW5f42drr5d/enWUQrFEIlvAIijxbkOXm9G5FPmCzM24fsomgJqJzesQsCAiCDICIAgWtq/x0ea2MRPPkEgrfzG+azDAaDhFr89BoShz/PI8qVyRDredOwbbGAy6ODebYPsaP5lckdcuzPHAug7WBF0IKO+Te9Zd8dRIZQskcwXmdO+G6jsmUBVLjUx9VvpnYyWwHBrM/wtYB/yhLMvR8rY24B+AUVmW/7KJSjwFPFz+9TeAKWBclmWX5ph/A56TZfmpRmWZge/6o65Tj6XzFEoy1vK3PddqvXqjdfHafVrtpnrcQjpfpa3Y96W9Vc5h9c792/90J7/39cOVOoiAz2VV9BjZAvli/c+JxyaQMHNb3vK4bRZSmkm6FqtGC2KkCW5zWXm5rI9aTGPpdUj88Av3VemNA+W+mtQlCb+VNSaraIJ51TG2fI4ZZ02aZqWPDc2+BxjVXa+xrPceoI6lv/O1A8TS+aqlsfovaj12CzZRJJ4pUCgpjupGrwUBl5Wv/d5dVe8TJleQLOBzWCvvX3arQDxT7+tqBf0X2kbxDqp9CNRHo9ceN9PvV/pnY6WwHBrM/w/KHx1CgiC8KQjCGygOdwXgf22mErIsPyHL8kC5rC+rm/V1NTpXEIQ/FwQhpP5LJBLNXNJkGVHXqedLckU3eS3XqzebCFer3VSP02srnj8x2dS5f7f/QlUdinBFj9FgcgmYk0sTgKq/AOvRakGMNMGL6UL02/R647jBZ6FeWSYrjquOsWDGWZPWWOljQ7PvAUZ1b/Y9QDuW6nWX+g9OIluq6DNljCeXgOH7hMkVCiWq3r8Si0wuoXa1lFG80/sQqOi1x830+5X+2VgtLDrBlGU5L8vy7wF3AP878H8Ad8qy/J9kWW7JflOW5W9x5RtWBEHo1OweAox0KH8ty3K/+s/j8bRySZNlQF2nbrUIFd3ktVyv3mwiXK12Uz1Or614bHtvU+d+/qF1VXUQ4YoeQ2ysx/DYTL2GifIXzHo9QasFMdIEL6YL0W/T6429Bp+FemWZrCyWM8aWyzPjrMmirPSxodn3AKO6N/seoB1L9bpL/VjusVsq+kwBqPdaYPQ+YXIF5S+YV+KVx7G4zFx/hFG80/sQqOi1x830+5X+2VgtNJUHc8mFC4IP8MiyPFH+/cPA3wIDwDeBi7Is/1dBEHYBPwDWybLccFm6uXTnxmCky7yWSwaaTYQLtevnx8JJnj8xyWPbjbUX9c49EYrW6NPUYwJOK6+cm+Ox7b28cTHCl39xhrsGAvzlb96G32nl2bcu88ZYlM89MAzAP706ym29XlxWibMzcRKZAqH5FMlcke1rfOwebuepQ2NIogW31cJELMt7N3fy0ulZen0OQKbNbQdkZuJZ4uk8s4ksVtGCXVKG2+l4hv6Akx0DAYbb3ZyaijEyGSOTK5HKFwAZuyTx6JYu0vkiF+aSJLMF1ra78TokcgWZ8WiKbK7I/Rs6mC9rDiYXMvzGtm4cksjP3plGtMBCKsdAm5P5dIEtPV5skoUzM0l8dpFL82m6vXZC0TQ+h8Rfvv82Xjw5zQsnZ1jf6cLrUJZqdnjtbO/zc2oqTjJXwGOTGOpwcXg0wkI6Tzxb4J7BNkbnkggIzCWzdHntOCSRDT0eioUSL5+ZI+CQyBRL9Pgc+JxWun0OAELzKSaiaeaTWbb3B+gLOLk8nyaSyiFZLAy2uYhmcszGsxRLMu+5rYuFdIHTUzG8Dit9AQe5osxkNM3mbg9vhRYolGRu7/Nz11AAUPKfLqTzzMazICu5PcPJHO+/vYff3TVY0Q+7bCIT82kcNpEP7Oir0UQePB8mmbuSE3bPuvamNM3abc0mCb9VE0evliWyV4sZZ02WykofG5p9D6inwWzmPUAdS9X3m5HJWMU3Yv+ZGQ6dj7B7fZAP3rEGoOo9KJ0t8O1fX+LDd/ZxIZxiqN3FI1u6K+8Tf/PiWSLJLOl8kXWdHta0Odm7qZN9p2cJzac5Mx0nmsphlUR8dpHzs0msIrjtIgvpInIJbFYLNlEgVyiSL0GppPxFz22FTEH5S6qA4hugfhvllGr9CiRAsCh/QZRRvAUsAkiiIu3Q2jzYy7moSyiTOxnF16Akw97NnQQ9NkZnk0QzeeyiQCpXwiJAh9fB3k2dzCdz/OLkNOs7PAQ9NrKFIrmCzKd2D9a8W21fo3gDVN6ZbCKh+TRBl41UrsijW7uJpvNVz6ZevDN6jvW0x4v1+5X+2VgJXLUG8yovPoAS0JwofXUW+AtZlo8JgtANPA0MAzngC7Is71+szFs98DUjPG4loeyNEjI3kxS+lbJeHJnmYjjFx+7pJ5bO1ySTB2OTIjURtTbhPVBJTr2tz1eVyHc8kuIfXxnlnqEAD23uqpR1+EKYv37xLO/b2s1HdvbzzkSMuXiGS5E0m3u83Nbj5amDFzk1GaPb5yBbLBF028kVikxG07xruJ2iDO0eG+FEjoGgYgZzbjrOc8cnkIEP7OgjUyjy/IlJ2pxWPnbPAM++NUGuUKLX72BiIYOAjN9p5VIkze19ft69uROXTcJtl5AE+Oq+81iQcdgkunx28gWZC3MJnFYRu2RhqN3Ntj4fcwmlXd67pYser52v7b9Ap8fKbCKHAGzp9XFbr48LcwlmFrIMtLt494YOvnngIudm4mQLRdZ1eLmj389boQXWtrsZbHfxzsQCNsnCbDzLfDJHNl9CsAhs7vZwbDyK0ybS7XXQ3+ZCFmRmYlnSuSKZQhGnZOHsTBzRYsFpFeny2hmbTzPY5iCcVEJrSZbp8TnY2OOlz+fkjfF50jllgY3LKrJnQwcAB8/PMRvPMRVL4bFLOKwS4USOwaCDD989wN7ys5Us8K9HQmzp9dLpseOySwScEl9/dZRiscTu9R10euzIKBPFgTYnzx5TnteH7uxjPpWnzWXl5+9Ms7bdxSNbu1vu58vxGdV/3tpcVn51ds7QAONm4RaaYJpx1uSm5lq/p+jLV98LQHkH0I6ZGzvdPHdiiuF2F6lckaDHRjipfAHb6XUw0Obk5yPTzMXS/PLUDGvaXGzu9nDwQqTyl9J8Sea9t3UTTeX4+cg0HpvE+HyK9Z1uErkic/EM2byMyy6SyRWxSkI5RktkiwVKJVjX7mY8miKRLZLPlygADqtAu9uO0yZyeT5NKl9CQjHIyxeVianHBgICBVmgVChhESFfUNbjO60CuYJMSQabBF0eB9FUVnk3cdtJ5oqsCTgIum2cnUngtkv85u3KX4f/4+0pFtL5it/AI1u76fAox9mtIp/ePYTfaeV4aKESV3t8di5F0uxZH6zE3KF25UvnE6EotvJ5AM8cDTEUdPFoOYYaPbNGfWSlm/as9PoZcUMnmNeCWznwXY04uRnjkOslZF5O06BoKsfev9pHNGW8kkwSwOu0VpIna69nZLjidyppUqL6r/9QlmXo82ZaLQI2CZKmDvOmo5FTbrNIFoGCRtsTcFnZ10Ky8+UwG9CWkS+WEMvfYkOtAcbNxK0ywbwW3Mpx1mRlca0NV4xMf/TvBZKg5A02uToCLivJOqaJAiCJQt196taAU+InX3yA3/nagRqjpnp9ZKWb9qz0+tVjOUx+TFYIVyNObsY45HoJmZfTNEifTFqPmkw+ZnA9I8MV9VgjEgbb8yXZnFzepFzt5BKomlwCLffz5fiM6s0PCpq1UHoDDBMTE5OVxLV+TzEy/dG/A5iTy+Uh3sA0UYaG+1RiGmMmvVFTvT6y0k17Vnr9loo5wVxFXI04uRnjkOslZF5O0yB9Mmk9ajJ5n8H1jAxX1GON8Bhst1oE3KbJz01JsxnuGyHpjCNa7efL8RnVmx9ImlFfb4BhYmJispK41u8pRqY/+ncAyQzxy4K3gWmiAA33qfg0xkx6o6Z6fWSlm/as9PotFXOJ7CrjasTJqrmIjKIn1Irbr4eQWZ8ovmKec3aOx5rQgjXSlr54cpqxcIr3be3mjUvz/PpChB39fjq8dlw2iW19Pi5FUgScVn4xMs1geS0/oBiuZAu4HRJbe30V/eT4fJp2t40LcwnCiRzb+vxMxdJML2QYbHfzm7f38G+vhzgeijLQ5uTtiRgf2NHLR3YO8OyxCS7OJjk1E6PH52BLr49fvDPFVDyHVQCvU6LDY2c2niGdLzHc4SaaypMvlnDaLFhFEQtwKZIiV1IE+m6niCzLxDIlRKDTayOeyWGVlGCYzRdoczkoyCXmkzkKJfA7LRRLYLVY2NHv51hogUKphCBDNl/CZZdI5QrYrRZEUWBzl5fZRJ5IIkMqX6TNZUO0CMSzOSwIpPMlZBlkGda2u5hNZMkXS9hEkb6Ag3OzCXJFcFgg6LGRLhQUAwCg0+0kXSzQ5rIxFk6izr2KsqIPAbCLIFrAabVikywkcnmS2RJumwVJshBLFRDL+ceCbivRdB6LAKWiIj5z2yUQSuTyJexWCz6HonONpnIUS0qd7VaRuUSWbKFIvlhCEATskoWFVAFJEri9z4fDJtHnd2CXRH51ZpYOr41Ht/TQ7rVzdirOv789idMm8fDmTtK5EjIyawJOzs8mOD+TIJrO86E7+jg0GmH7Gj8yMNDmosNrr3z2mvmMqLrJkckYsozhuVq9UL2y9SZdWtOqm3F5LJhLZK+GWz3Omlx/GmnqwNjAbCycrGjz7h0OLtnPwcj059ljExwdm+cTu/qZWMjw69EwkUSODZ0eLs2n8TtEDl+M0uuzk8wV2L2unWS2SDxbIJ7JYwFePTuLJFno8zsJzafo8TuxSyJTsQwdZb+FcEo5trRYJVcIFsBVzv1tFWC4041oEQjNp8gXSthtFpySRI/fTl/ATSKbZ22Hm4/tHOCNS/McvRTl3qEAPzk2gVW0MBXL8MEdfWzt8/G1fRdw2SzsGg4yn8rhsIp88l2KBvMHb4QYbLviY1DPqKmh6dPxyabeNW8Eq9FUyNRgmlS4keu8r1YD2or+VNV2SqJAoSgj1Ulyr01urz9fnxNLj1Se5BglmfM4xEUTB5usTvQ6XH0y7vrniUgWS1Vi6Eb6aY9drCSidttFBCBhcG40leM9f7Wvbp++lTEnmEvHjLMm1xMjHWQjTR0oE4a9X95XNf7arsLPoV7ZRmO814zxLRFwWSmWSi212U+/eD/b+wPLcv3VqnFc6ZgaTJMKN3Kd99VqQFvRn6pay3yx/H+dJPf1kjcvNrkERZNhdJQMTSUONlmd6HW4zX49l8gUW9JPaxNRq7rgegnHG/VpExMTk5WOkQ5ysfHyueOTNePvcr3baMs2GuPNGN8a8Uyh5TZ7+tDYsl3/ZtU4rnTMCeYtxI1c5321GtBW9Keq1tIqlv+vk+S+XvJmfcJlIyShNhEzXPkLpsnNiV6H26w0x+MQW9JPaxNRq7rgegnHG/VpExMTk5WOkQ5ysfHy8R29NePvcr3baMs2GuPNGN8aXofUcpup6UmWg5tV47jSMZfI3mLcyHXezSSPb/X8eseoWjP1f32SewRqktvrzx+ZjJHMFkjlCoQiaYJuG6l8kUe3XEn8+6M3Qvzs7Sl2rwvid9r4yM5+/E4rBy+EGZtLcuRihA6PneFON0dH5zkyFsEuWdg5FGRjt4cjF8PE0wXuHmzj7EyC+VSONQEX+VKRQlHm1FQct03C77QqLqCyzFwii9uuaDh9Tiu3dXs5NR3DarHw8XsGeH1snrcvL/DOxAJtLhuZfBFJtPDe27rIFIpMLWQpFIucm02ytpzLq9fvQBRF3relix8dm2AimqYky3hsEiVkdg61cTGcIpXLs5AqMhPPcO9QG5PxDMhK8udOr52L4QTpfIk/fc8Gktkizx2foCQrzm/rO9y47BJ3Dvj5/hshhtrctHmsXJ5Pc+LyAv0BJ8MdHiKpHIIAXV470VSe01Nx9m7qwGoVQVbyWR4dj3LfunYQ4FdnZvE7rCTzRX5v1wD7z87x+sUILpvIb23vRQb+48QU8Wye2/t8BNw2LAh4nVYe3tzJwQsRPDaRkakYl8Ip7ljjZzqR445+Py6byLnZBA5J5JPlPF4vjkxzZibOpm4vu9YGGZmIgQD9ASe/GJlGFGA0nGLvxg72n53j7sEAH7zzSpLuZvXT2uMbnbtYn75VMZfILh0zzppcb1rV1IGylFXV5u0aDlbF+qtlLJzk+ROTFRO07x8N0em10elxsKecE/vrr44y3O6iKMP7tnZzejrOmWklNmzu9vLM6+NMLGTY0OlmMpblyQeG8TmtfOfQGDPxDJfCaSYW0risFhAEJhZSFPLKBDZdKNHutOJ32wjNp4hnFe8Fr1MklS2S06SaAuVL77zudd4ClWW+/W0OMvkS+VKJbL5AupyFxSnB+i4PPqeNtyeiFIoyxaJMrgg9PjtOu0g+XyJbLGEVBUqyQI/Pzvh8ih6fg/VdHuxWEWQZuySyocsLyITm0/QHnZX2Anjx5DSXwike3dpNKJpmNpZlNpHlozuV/OV/8+JZQObPHtm0bMtjVVajxnGlY2owTRblRiR4bfaajY5rdh9geNxYOMlzxyd5fEcvfqeVF0amGYuk+NhO5fPy1ZfPcWE2wR89uA6HTWJHv5/xSIp/fGWUrb1enDaR2USO+9YF+fcTU1yKJOnxOdi2xo/bJuGyS2RzBb62/wJDQSc9fidBt40z0wk2d3tI5ooMBp3IwHgkzUDQSSSR42dvT5HKF9g1pEw6HVaJ9Z0utvb5SeeKvDgyxVQsy2DQxdRCmkS2iNMm0uW109fmIpcvMj6vlNfusdPusnF6OsFQ0MnZmSQdXhu71gbxOyT+6cBF7lzjJ5krkiuWyBaKZPMlntgzRDRdUMyP7FIlefRQ0MVtPV5+cmyC0bkko3MJ7uj3c3IqTr4g0+mzcceaAG+FoszEs9gkC/euDRKaT2OXRLKFEtvX+JiIpYmnC7x/azc/PHaZ+WSO997WjcMm8lYoSsAhcfBChKDLilW0MDIVx20TES0WNnZ6yJWK5ItKTtMurx2/00pvwMnEfBq7VWRtu5NvHRxjQ6eH3eXgNpvI8f6yCdSLI9PYJAuPb+/l5yPTdHjt/NbtPfzs7SnOTiewSQJ3DbSRzBexAG9cmuf+9R04bCIHzs1hEWD3+g4A3hpfYHO3h8lYmrl4nu1rfBSB+9YF+eWpWQTgU3uGGGp3V/W5lWg0cDNhTjCXjhlnTVYajUz+rmZ7NJXjxZFpTk3Fua3HWzGROXBulv/xy3Ps3dTJ7947yHgkxbcOjvGZPUMMBF0cOB8mlS2QzBU4N5PAbhX50B19nJ6Kc2oqTofHxlwiR7vHRjJX5P1buxmfTzNbNhDscNu4MJckli7w/q1dfO/oODOxLH9w/1qOjM1z7FIUl03k/EwCSRT4xD0D/Oztaabi2fJE00KpZOHBje2cnYlzZiZV1V42Czhtin4/VyjiddoolmSyhSLxrDI7dUkgWgQkSUIUZKyiSCJbQBBksoUSTquEyyaSyObJ5Eq0uazYrCLFksxQ0MVcMkdRlgm6rAx3eLh9jZ9OrwNJgKd+fYkvPLSO+zZ0AtTEPtWcLpm9IvFI5wrMlOP0fCrPULuryrhpsfdGI8O7G/F+e6tgTjBNGnIjxM/NXrPRcc3u05qlaI/Ti/jddguJbH0fNwvgNoX9JktEAL7z5L188uuHK31u35f2mpPMa4g5wVw6Zpw1WUnUi/dXu/2HX7iPD331NaJlkzRQDGn+v7+znT/69huVbW6rhWT+yvuB126pTNJMGvPdJ+9lTZur6n3r2S/ez2e+cbhiTmeEaqRoLS9rXcz4ycjw7sd/cv+iZlEmS8c0+TFpyI0QPzd7zUbHNbtPb5aiHqcX8TeaXIJiIW4K+02Wigz8/f4LVX3u+ROTN7BGJiYmJquDevH+arerZkJa4pkCf/PLc1XbtJNLwJxctsDf779Q87711KGxKnM6I1QjxWaNn4wM75oxizK5dpgTzFucGyF+bvaajY5rdp/eLEU9Ti/i99gbfxQsmMJ+k6UjAH/80LqqPqdqe0xMTExM6lMv3l/tdtVMSIvXIfFn79lQtc1trX4/8C7yvmByhT9+aF3N+9YTu4eqzOmMUI0UmzV+MjK8a8YsyuTaYS6RNTEU11/rNevNJr1tJMxebN/B82ES2QLpXIHZeI6P3qMY8KjJ6/ednuGXJ6cRLRY+sKOX59+eJJrK47aJrG13c/BCmESmyNZeH8Ndbj54Rx9vXJrn0IUId/b7kQU4MhrBJonMJjLMJ3NYBIGhoIt8sUTAbePCTJyTkwm6/Xa8DhsDAQfJXBEZ6PY5SGYKnJyK0xtwsKHDxctnZomX1feD7S6mYhkki4U2l42FdJ5CsUS2UCCZA6cNBFmgUJJx2a0EXRIDQTfjkSSxTBFkGZ/TyrpON4lskXAiy0Q0RdDtYLjDzempOOFkFrdDpFgCEYFiqYTdKuK2iaxpc7Gpy8Pbk3HanBKHRsO47Vb+064B9p2eIZ4tMB3P0u6yEUsXSObyDLW7sVoETk0lkESwSRb621x4HFZkWSaXLzKXzBFw2ijKMk6rhVSuyHQsw12DAXp8Dl48NQMliGXy2K0WiiWZUklGFC3kCyUcNhGLIJMrKMtgurx2nHaJXKHIyEScUqlEl89BPJvHY7PSH3SyqcvLG5fmES1QLAlMx9JYJZF717YxGk6ztt3JbT0+jo7Nc2h0jlIJNnV5QBDY3O3h+OUY2XwRu2RhLpHDJlrYNdzGOxMx0vkid6wJcGJygS6vnfvXdVAS4PZeHz96c4JOr53PPThc0WD+4GiIgXJi8HcmYsAVvYi+D7eiLar3+blV9SfmEtmlY8ZZkxuNkVbSKN5rjfm04ykYG6MZve+8eHKa01NxNvd4eWTLFQ3m3758jnuHgvQHXYxHUhy5GOGz962lIMNcIsN4JI0FOHB+DhmZbAEckkC2UEK0CMQzBQaDLsKJHOlCEY9NYrjDxeGxCCICMuC0ihRKMqFoBgG4f10bb15eIKn7K6kENP6b39JRc32q02arBbIlsApQkkG7dstqAYsANquAgAWXVSSZKyJYZO4dauc3bu9FFOAr+87R5rLy3i09uGwi70zEiCSy7FwbRACOlb0L5tM55BJs7PaAAHOJXJWRota4SX1vfHBjB/OpvGFs1BveqdtkjOOsydIxNZgmLXE9NJnX6xra9fgAfqeEKCiDfqGcK7MVlNQnV/QAH/zKqyykr9WQf/NhlLB6pSCgmB0UDHKgtlrvn37xfgaCroYaoFg6X9UH21xWXv6LvYtqjFv57NzqyaXNCebSMeOsyY2k1bHLSH+nHU+vph4P/9U+5jXvEQIglWOFaIGCuVq2CrdNQLSIiy6B1bPYM9PHTvVdrJm+cSvHwWuJqcE0aYnrocm8XtfQD3Bqwvr8EiaXYKwHMGmelTq5BKVuRpNLdV8rPH1obFENkL4PNqsxbuWzYyaXNjExWY20OnYZ6e+WY7xT66FFhsr4bU4ua0nmZOLZ1t+Nmo1natu30jfMOHhjMCeYJlVcD03m9bqGfo2/mrDeahEMkycvhpEewKR5ltLm1wv1W+l6+1rh07uHFtUA6ftgsxrjVj47ZnJpExOT1UirY5eR/m45xju1HloEqIzfkvkGXYPbJuC1t/5u1Gw8U9u+lb5hxsEbg7lE1qSG65GQ9npd42A5z5LbIbFn3RVdxmDQxchEjLFIkpOTcR67vYe3J2IIApydTrCxy0O6UCToshFO5tjc42XX2upEzqp248x0nP42JwCh+TRBl41IKkfQZeN4KMrp6TgPbOjALols6lESML9ybo4HN3Rw4NwcL5ya5pEt3axrd/OPr47S6bHT4bHR1+ZkIpoGwCmJvHp2jnxJpstrZSycptNrY6jdg9chsaHbQ6fHQX+bkxdOTiPI8M7EAu0eG7uG29nc7eXbh8Z4ZyLGhi4P3V47+07PEMsWCDglSiWwSyL5Uokur4NwMse2NT7eu7mL59+ewikJvHY+jNcu8YcPruNCOIXHJnI5mgYBXFaRty4v8Nk9azlwfo5n35pkS4+H/qALv9PGB+7oI5rOk84W+OaBi7xrOIjDKnJyKs7ejR3sOzvHzsE2NnS5+adXLxLP5JEs4LJbWUjn+cMHhpmKZ3hpZJp8ScZlFQknc7x7UycDQRfIcHY6znMnJskXSqztcLNnXTvzqRwOm8jeTZ38xztTjM2laHPbsEsCdqvIhi4PnV4HW3t9XIqkEAXF9W4+meO+De3YJZGPlJNA/92+89gkC3ZJwO+ycc9gGz8+NkGHx8YDGzp48dQMn949VEkQ3YxmaGQiVqUXaeYz0spn51ZOLm0ukV06Zpw1udG0OnYZ6e+Wqx4HL4QrOaHVWKHqAwPlHNrZfJHDF8P0+V14HBLZQpFEpsi2PuX4k1Mxur0OHt3axQ/euAzI+Bw2buvxcvjiPBdm40gWCx+/p5+XTs8wFU1jFUV2rPHx6vkwTptINl8kW1QMcGwWyBTBZQePzUYsk8dlkwi6rSykC+QLJXLFItmcTB4QAUFQfBF8DomdQ20cHZsnW5TZ2OkimskjYGFLr4/berxMLKQJx3NcmEtitaDoLAWBNreVTo+DZDbPUIebB9Z38N1fXyKczPLYHX387q5BAJ49NsHBC2Hu6PfjsomEomn625y4bBLheJaRqTifuKdfkRk1+cy0sVP7LtbMM7xV4+C1xNRgmlxTlsNERC1Dn1R3qdeJpnK8MDLNWCTFx3ZeMfZpdK62fKDm56F2xUBADTL3rW9nIZ3n6UNjZHJF1pQngzJwZ3+AXcNBDo9GGIuk2NHn47kTU2zp9eK2icwkcpV6/d3L5/jp8UnevbEDSbJgQRH9H7oYoc/nIJkt8NblBQRkPn7PIB+4o49/e32cs9Nx7JKFPes7SOWK/OztSeLpPL1+J4MdLpKZAqem4vgcVnKlEpLFQiKjiOLfta6ddK7IkYsRPA4Ju2ghnikysZDmoU2duGwiI5NxHt/ew/HLMfKFIodGI/QFnKwJOHnPbZ0cOB9BFGA0nOLhTR389PgUkVQWZJmP7uzn5VOzyMCTDw5zdibBSyPTyALcPdBGOJnDaRX54J19jM+nSWYLpHIFzs4kQIZCscS+07PcvsbHPWuDjM4leSsUZXohw+f3rieZLfLT4xMMt7sZ7vTQ5bXxjQMX6fc7+d8+uA2A//uFMyDAkw8Mczma4UQoSkmGOwYC3Nbj5ecj03R6bLhsyjetbrvEtj5fxSRiW5+P7x4a4yfHJtjc42Fjt4/33NbJm+MLvFtnMKBN7rytz1d57vWSRasJp+8a8NctT99fF9um7bNmcmkFc4K5dMw4a7IaGQsnefrgGAjKShLVWO2545M8rjMU1L53aMd99eeBNie/OjvHuzd2MD6vfNEbcEp849VRSrLAkw+upVCi6t0glSsQiqTpDzrp9DrY1uerjP0AB86HSWULyMBcIsuR0QgOSWSmbMAXzxZ4cEMHJydijEXSbOxy0+V3kEjneHsijixD0CUhSSLtbhtnZ+LkC2CVYLjDTWg+Tb4o47CKeOwSpZJMOJElX1JSrVmA9V1uhoIuouk8D27o4AdvXCaVy+OxW3HYROYSGbJ55Z3BLlrwuawkswUG2138L7+1FYCvvXwOm2QhXywxEU1z73A7qXwRhyRW3g86vUp8TecK/OrMLCOTcb70vk08vKV7Se97i70nmjHv+mNOME2uGcshntaWkS+WkCwCPqe1qqxWjU32fvllohrznYDLSipbrHuutny3XUQAEtkiHruo5Mg0MAXyO5W/rF0NLquFVN4UcawUWjHzkQTwu2z88Av38dtffa3KBEKLiBLYVUOCf/7sLj74lddqrmMt93ujZNJATf/XbtP22WYSUt8qmBPMpWPGWZPVxlg4yd4v76saW7/75L188uuHkVHG931f2stQu7uu2Vqrhm6SRaDYwNNBlV54HBKyLFe9l9yquG0C+aLQ0vse0NDgxzTyuTGYJj8m14zlEE9ry1DF880antQrz8jcp9G5+vJj5Z9jmYKhIYtS5tVNLgFzcrnCaOXFoiBXJ3KuR5Fqc6inDo4ZXkft90aJoY36f70+ayaXNjExuRV57vhkzdj69/svVLbJwPMnJoH6Zmut/rllMTd67TtNq66qNyvJnNzy+95iBj+mkc/KxJxgmiyZ5RBPa8tQxfPNGp7UK8/I3KfRufryfeWffQ7J0JBFKdPa8r3qcVnNj99KohUzH0moTuRcD5Fqc6gn9gwZXkft90aJoY36f70+ayaXNjExuRV5fEdvzdj6xw+tq2wTgMe29wL1zdZaNXSTFjEM1L7T6N9LblXcNqHl973FDH5MI5+ViblE1uSqWA7xdDPC7VaNTV4cmebSfIqP3K1oHRc7V1s+UPOzasiSzBVw2yT2lDWY3/m1osHsC5QNeQTY0R9g19ogR0YjXJpPcXuvj39/e4rber24bCJziVylXn+/7xzPvjXJQ5s6sJYt6ZxWkcOjEXr9ZQ1m6IoG8/E7+njm9RBnZ+LYRIE9GzpIVzSYBXr9Dgbb3SSzeU5NJfA5JPJFGdEiKBrMgQDvGg6Syhd5/WIEj13CLikazMmFDO/e2KFoMKcU46MTEzHy+SKHLs7T53fQH3Syd1MnBy9c0WDu3djBcyemiCRzgMxH7l7DvjOzyLLA5x5Yy7mZJC+dmkaWZUWDmcrhtIl8YEcfoWiaZKZAKlfk3GwcZMgXS7x8epbtfT52Dpc1mONRZmJZ/vihdSRzRZ4razDXdXnocNv45oEx1gQc/G8fUDSYf/PSWZBl/uCBYSYWMhwPRQHYsSbA5h4vL4xM0+Gx4bJLICsazK19PkYmYyDD1j4f//LrMZ49NsGmbi+berzs3dzJsdACD27oIJrOV5k9HbwQrpx35GKES+EUj25tkCz6xCR39vvrlqfvr4tt0/bZRknJbyXMJbJLx4yzJquRsXCS7xxSNJiffNcVDebzJyZ5bHutBlNvtra111f5uT/grJjxheaV2O53SHzjtdFKbCvKXHk3yBZI5QuE5hUzm06Pg61lgx91jFbNgpBhLpnl9dF5bJKF2USWy/MpEtkiD25o5+RkjIthRYPZ43cQL2swSzIE3Vaskki7y8qZmQT5ooxNhHUdbsajGXLFEk5J0WAWSzKRZJZc8YoGc0OXh6Ggk2gmzwPrO/jhm5dJZvN4KxrMLNl8CdEiYJcs+J1Wkrkig0En/++yBvPv9p/HKgoUCjITC2nuXRsknS9WDPUOXojQ6bHjsoukcgVeKWsw/+JRRYO5lPe9xd4TzZh3/TE1mCarluU0ENKbn6hCcSPBuGraMhvPMD6fZnO3l0e3dgPw4sg0x0JRkGFjt+JAet96xZ1WNRa6b12QX56eBRnWtjv5xmtjdPvsfPa+tRy/HEMU4PR0gj3rg+zd3MW/n5hk/5lZ7ur386NjE/T67Lzv9l5cNpHZRI73b+1mfD7NpXCSY+MLbO72UJThvvVBfvb2FGPhJFbRwkI6zx89uA6HTaLNZeUnxyZYSOewCAKxTIHxSIp2j5XpWJY2l413lw19QpE02UKRgxci3N7n5UI4RYfbRr5YotfvZLjDTSJb5GP3XDFManNZ+dXZOR7f0Ussnef/9+IZZFngAzt6+OGxy0wvZOj2OfjL37gNgK/uO0c4nuHcTJIP3NHHb9zew1MHx/A7JXp8Tk5PJ7hzwM9gu7tirrCx082P3rxMrlAinMxyW4+PU1NxXDYLU7Esj2zp5s7Btorh0jNHQ3R6bLhtEq6yERNQMeC5b30745EU3zo4xv3rgvx8ZBqvU+L2Pj8um8RcIstb41HuGAjgtomcnUmQKSjGBRu6PLhtEslcgfFIms09Xu4dDrLv9AwHz4fZs76dvZu7avoUsKQ+fK0NC/TGRPXMFlYz5gRz6ZhxdnVSL94t92d7JRiq1KvDYtubNZdZSOd5rmzAp5r83Le+vapdtXE2X5CJZQrcvsZHKldkc4+3YignonwZ+/j2Ho5PxIilchy+OM//9OAwRRleOTvLicsLZHJFHFaRcDKLxQL3resgFM1gE2F0LkWb206uUCCXL+FxSooDqyzT7nEwn8wq7vVuOz6HxGw8i1UUSOeLWC0iJUoUSkWcVis+h0QkmcfrtHJ7r5+gx0Z/m5NfnZnh6MV5XHaJ9V0etvX6OHAhTCpb5LZeHx0eK1ZRJOi28dZ4lGyxyGCbizaXjbcuL/Cf37OB+zZ0Aspk/5nXQ3jsYs37g9b0SNuuJqsLc4JpsipZbgMhvfmJKjAvlOQqoTnAw3+1r8a0xecQEQSLobGP3ykhA7EVIuBX7+taEHBZSZa1ECsFn1Mini7UaGH8TgkBKsYKPod0zXUw1nLbW0VLxSQq2cBgyohrbVgQTeWq+rgANaYJNwPmBHPpmHF29dEo3i3nZ3slGKrUq8Ni2+uZxOjLdNosxAxiStBl5Ud/cj+/87UDxNL5FRUHVwrfffJe1rS5eOjL+2r2+Z0S6VwJdzk2RssxqM1l5eW/2HvTxJ5bBdPkx2RVstwGQnrzE1Vgrheaq+foiWeKdY194pkCiRUk4L9Wk0ugIrRfSSQytS8CQI2xQjx77Z+R1oRANYlqtQ9fa8MCfR83Mk0wMTFZXTSKd8v52V4Jhir16rDY9nomMUbnGsWUmKZdV1ocXCn8/f4LPHd80nCftn21MciMPTcn5gTTZEWy3AZCevMTVWCuF5qr5+jxOsS6xj5eh4RnBQn4JUurVgXNowrtVxIeh2RotKA3VvDar/0z0poQqCZRrfbha21YoO/jRqYJJiYmq4tG8W45P9srwVClXh0W217PJMboXKOY4tO060qLgyuFP35oHY/v6DXcp21fbQwyY8/NiblE1uSacbU6jbFwkuePT/JYOUHyUsrTC7/1QvGA08ovRqZx2USSuSIf26n8tf/7r4cQLXDickyZQNolNnZ7mE/k2Hdmlr6Agw6PnR0DAR7ZckWfeWk+xe7hID85dpmD58LMJjNIFgslGXas8TPY7iIUSXNsfB6LReDuwTZS2SIjUzHanDam4xkkASTJQjpbQhDg9jVetve34bKJ7D8zSzJbIFeQ8TklFtJ5irKMzWIhV5J5cEMH6XyJbp+Nt8YX6PDYSOaUv7DOJfJYLDKZXIGiLNDltSEj4LFLJLJ5JqMZnFaRXEnGa5foDThY1+mhx+vgYiTFJ+7p53I0w5npOAJw+GKEoXY3PV47L52aJpYu8JG71/DquTDxbB67aGFdlxe7ZOFyNMXFuQThRAGfU8AuWXFaLXgdNmbiaSKJPGvanGzo8vDY9l4OjEbY2Onmu78eJ1coAsry3EJJ+YtbOJFlKOjiw3ev4a7BNn7y1gTxdB5ZlskVZOLZPNv6/KRzRVw2kYvhFNv6vPzzgTHyhSKP7+jl1fMRhoJOPA4rofkU86kcmWyRbKnEXf0Bzs4msIsWwsk8+WKRwaCLbp+DuWSO39jWw29u7+WHR0P85Nhlev0OPnv/MCcmYrhsIqlckfdt7eb0VJzTU/FK4m29hkerBVYTfKva0WYMC7RaSiMdS73PzFg4yfdfDzHU4WLX2mBds4XVjLlEduksR5xdCTq9Ww298deB82EEYE8DjdtyxNVmz1lOPXq9OmhjvFYTP9R+Jea/cnaOx3b04ndaq7To70zESOUKyDLMJbIcH1/gjgF/xQ/hfWU/hGS2gMcu0d/m5Jmj45ydjpPJl0hm81gsFmKpPB0eGzarBaskks3mOTWdoM1lI10oIQGzyTxDQQd3D7bx0qlZEpkCRZS/+sjlf+0uiflUAZsAGRncVsgXIWeQ4UzN4anN5WlBMfcRUVJmqdscVoFMXqaE6nQLFlHZBuAQweu04nNYmU1kKRaLyFhY2+5ibaeHC7NxxiNpXDaRgTYnF+aSgMAn7hng8w9vIOCycSIU5X//9xHGyp4OfpedLb1exiNpdpf9J1RzJNUzwahfLBbjmukrJtcGU4NpckO4Wp3G9dCTRFM53vNX+4jo9JYBl5VUtki+vJS2HlaDJMGgvLwb6Q9WIz/94v38/jePVJa2NIMa1K4GyQKFFZwmVAC+8+S9/N7XDzc8Rt9/2lxWflzW8KhaYFGAonzl2Gb1KHotZdBl5Zea81rVKd1smBPMpXO1cfZW6WMrlWbb/3o9p6VeZznOU/0WfE5r1XuExy5SlGXFKAdlvFb9C/TjtgCG4/SP/+R+PvzV12reIW5lAi4rP/mT+3n8b18hnikuelyj97rFYpwWc8y5/pgaTJMbwtXqNK6HnuR4aMHQ9EWr02yEUZJgoK7+YDXy9MGxliaXcPWTS1jZk0tQXjL+fv+FRY/RY6QFLsjVxzbbv/VaylgTCasbbTcxWS7MPnZjabb9r9dzWup1luM8rc+CduxVNfIqWl8GPfXG6eePT15z47jVhtouiQaTS+1xjZ7vYjHO6FhzzFk5mBNMk2vC1eo0roeeZEe/3zD5sVan2QijJMFAXf3BauTTe4Yq7dEsyzGoSCt8ZBJQtCaLHaPHSAssCdXHNtu/9VpKXxMJqxttNzFZLsw+dmNptv2v13Na6nWW4zytz4J27FU18ipaXwY99cbpx3b0Gr5D3Mqo7eJxiE0d1+j5LhbjjI41x5yVg7lE1uSacbWJb+vpJ5dTKxZN5Th4PsxsIsNcPMdHdiq5mioJmCdjzMYzhObTbOrysrnHW0m+HE3n69ZlLJzkvz03wtGxeT50Ry9vT8YZ7nDx6d1rOXBujh++eZl4psBdgwH6A04OX4zQ7raRyhdJZ/OEUwXCiQwCAjuHgnzgzj52rQ2y/8wML41Mk8op3w7aJAsdXjtOScQqWegPOJlL5uhw20iUdYChqKIZSWULhKJpnJLI2xMxHJKFbKHEtj4f8+kcsXSe+WSONo+dLo+dT+4eqmhfte2RzBZI5Qqcm06AABs6vaRyBU5OxfnEPf2V5NNHRiMcvxzFYVWSL//H21Ocm00QT+eIZ4q8//Ye2lw2fv7ONKH5JFt7/Wzu8fKBO/qIpvMEnFZ+enyCTK5IX8BJKl/k0S3K/czGsswmsny0rJl9/sSkkhC7vC8UTRF02UjliuxeF+RYaAGfXeSbBy7S7XPw+3vWcm4uyYMbOjg9HefIaJhwIkenx8axUIwP39XHO5Nxen12jo5FmIrlFBOoAT+pXJGP3N1fSeKtJvb+wI4+QvNpkrkCbpvE1j4laXcyp3wD67ZL7FnXXtWXVS2wNtm3ekzT/fdCGGRjndViOqWbTXepxVwiu3SWS4N5s/exlUyz7X+9ntNSr3O156ljrNF7BFAZP7f2+a6MxeU4B1TG8nrjtPoOkcwVSGWLvDMRZWohi8chkc2XuGdtG9FUjv94Zxq3zUJRBr/DyoW5JMMdLj54Rx/ff2OCbL5ANJXHZbPQ43cwGcsy1Obk2HiMLq+d2USWPeuCRNN5LoVTJPIFgk4r8yklHm/sclMoCeSLRSYXMgRddnr9Ni7NZ9jS7eHY5QUKpRLD7W7WdXmZT+Z4azxKoVTELonIJZmFdJHhThePbe9jPp0jmy9it4pYgAMXIvzmth4e2drNtw9dZGQixtY+P9vX+Hjq0CU2dLp5z5ZuHtnSXWmXfz1yiZdOzrCt18tMPMfGbg/pXJFNPd6q4xo938Vi3HL0FZOlYWowTZadpZoCXK34upUyjJIqq4mTHy+bBi1WvpERi/Z/7XEHzodJZgs1Qnu3XWKgzcmvzs7R57PzT6+Nki/J3NUf4NRUjKEON3cPtuGyScwlshw4P4cAbOnxMRZJMRR0MRnL8vj2Hg5fnCeeyWOVLARdNt6ZWCDotvGu4Xb6Ag7+9UiI4XYXRRnev62bNy7N8+LIDMVSiXVdHj5xzwDj82lOT8Y4dDHC5+5bS0FGmUBH0mzq8bIm4ODrr4ySyhfo8TqRBZlsvsQTe4a4HM1weirOQNAJwNnpBLliEZsk4raKHL00z33rO2j32Hj78gLTCxl6Ak629flw2yRcmrZ498YOTk3FOR6KYreKfOiOPt64NM+vTs/S1+bkN2/v4cCFCJ0eG51eBwGnxFMHx+jw2vnEPQOVexMEJei4bSInJhaYWcgyEHTxiV0DnJqKc2YqzqYeL/cOB3ln4srk+O3LynKaP3l4Az6nlW8fHENG+YstUEmwfWoqzlgkxX3rgnzvSIiRyRhfet8m3nd7b01/GQsnefqgMtn80B19zKfyDftqo8TfY+FkTV9tNbF4syx2/mo1TjAnmEvHjLM3B6v1s9ss2vsDxdRoNp5hNp7jY/coXxY3M7YNtStfiF4MpyrnvTAyzVgkVTH+e+74JHcN+CtxyW2TqmJ9OlfgUiTNQNCJy6a40Kox7ydvTTAXzxJL59nQ5eFSJEW3187xywuIgsBYJEm3z85g0M2lSJLJhQzDHR4+ee8g3z18iVNTMUqlElv7/LR7HQwHXfzLkXH8Tol8UcZjl4imcyBDu8dOKlukUJLxOkQuzadZ3+Emky+SK8qIFgGXTSQSzzKfyfPw5i6sosj52Theh8Tl+QxdXjuPbO1GBl6/GKHX7+TdGzt47sQUO4cC7N3cVYmnWq8Bt11iW5+v5n3p8GiE01NxNpdjsT7e1XumN2OfvZkwJ5gmy8pShNTLIb5upQyjpMoum4WFcuJkAdj3pb1Vk8x6pkJqGZIoUCjKFaG/mqj5h1+4j9/+6msVEbrJ6sfInEfP//2xHfy3fz9V6S///NldfPArr1WdZxUFfI5aEygw7qNqv15I59n75X1VfdXvtF4Tw57Fzl/NxgnmBHPpmHF29bOaP7vNoL0/t11ElmWi6Wo9ZMApkcqVFh3b9IZ+XruFePaKEUAzMcFEQYDK+5JkEQzzhdYzSLzZ++zNhmnyY7KsLEVIvRzi61bKMEqqHNMkTpZRllQ2Kl+bTFkG8kW5SuivP87k5qGZF4m/eelcVX95qvwXUC35orEJFDRO/P3c8cmavnqtDHsWO980TjAxWZ3c7J9d/f0Zme3Emhzb9GO3dnIJ5uSyFbTvS0aTS6hvkHiz99lbDXOCadISSxFSL4f4upUyjJIq+zSJkwXgse29hufoTYXUMqyiUCX01x9ncvPQTPrsP3vvhqr+8sSeoZrzrKKxCRQ0Tvz9+I7emr56rQx7FjvfNE4wMVmd3OyfXf39GZnt+Joc2/Rjt9de/WrcTEwwUdC+L1ktxi1XzyDxZu+ztxrmElmTllD1hsISzEiuVnzdShlGJiqHRyOMR1J8ZGd/xbzmeGiBNpe1ogtUjXsW0nmeORpibdDFruFgJUHzL0am6fDYcNslZFmT3FiAVK7AuEaDgQypfIGjF+c5Mx0HIJLMIiDQF3AyPp9mjd/BnYMBjo0vYBMFRueS9PgddPscTMcydPuc2KwWfvuOPl46Nc3xUAzJAtFUnkS2QH+bk7sG29jU7eH/2X+BdK6IwybxxJ5B9p2e5WI4RYfbSsBl587BANOxNMcuRbkYSdLudrCl10ssnSeZLbCxx4vfYeWlkzPkSyV6vA7sVgEEgXxBJlMoMhBwVQyBbJJAriBjlyxE00p9gi4buaJMp1sitJDjjjU+HtzUSTpX5Nj4An6nyNHxBe4bDhLN5Dk5GUMUBB7c2Mn4fIqTkzHuHGjjt27v4fm3p1jb7mI+mWM2keX8bAKLIPD+rT1kC0V+/vY0nT47dw0EOD+bIJ0vcm4mwfY1frb1+Xn78gIdXju7hoPsWhvkZ29P8uLINDuH2piOZbgczfB7uwZ4ZzJOOJEllinQ4bFyaDRCl9fOe2/rxmUXOTedJJ0vMBFNMzIZ44N39PEHD6zjnYlY1efgRCjKl39+ilimwGd2DzEyFUeW4T23dXLgfIROr6InVZNFj4WTPH98kgc31hpGnQhFefrgGJ/eM8T2/kBNn9ZrgOsZ+TSjZWnGZEFvtrUaNDLmEtmlY8bZm4OVZHpSb9wYCyd55miIoaCLR7d214wxQN3xZiyc5PtHQwwGXdw7HGRkMsaluSQjk3GefHAYn9NaGWNVTby2vIV0vrL/zUtRjo7NV8779qExsoUivX4no3MJIokc6zs9XIqk2dTjYWohw8RChna3DXvZMO/cTIzL0Qweu0R/m5PfvL2XDV0e/unVi0zH0syn8gRdViRRYDaeUzSMQolkpoRUnlQlcjlSWejw2ritx8vJyQVmE8pfZy1Aj89OtlAknKr9i61TApfdiiyXiKaKVSnDLChfkNutAtmiTNknkB6vjUyhhCzLeO1WoukcLpvIe2/r4mIkRTiZo91lZ6jdyRuXFvit7T18+O5+xQAxlmF8Pk2720Y4mWNzj5dda4OVd64joxFOTcXp8NiIJHP0B504rRIeu1TXsGcl9VmTxpgaTJNlY7WujzeqN8B7//t+FlI5CuWPgKp3Aww1cO/5q311EyoHnBKCIJDMFg21dFdD0GXlW39wLx/4ymtXWdKN4R8+dTd/9O03mj5eAHxOqWJU0Kj9FtPGqM8vls5XtZ/PIZLOleou4VHxOkQSmaJh4m2tbhLgoS+/XEna3Qg1QXe9BNONPmc3OoH6ahoDzAnm0jHjrMlyUm/cGAsnq2Kk3ynx7BcfqIyNHruIDFVxVTsWamOyflx120UElHyLqs5dW566P5Et1t1eL/9zK5pMv9PKQvrm8Wgw8gXIF0tIOl2l9tkKwLNfvJ/f/+aRVRE7TJrD1GCaLBurdX28Ub3VbQVNlFD1bvU0cI0SKqtJm+tp6a6GWFnjt1r5H78819LxMlTpExc7drH9z5+Y5Fu69otniotOLgHDyaVarlF/aobFEkw3+pzd6ATqq3UMMDExuXHUGzf0MVI/NhrFVW2Z2phsNK7GDLwY9Psbba9HK3E9nrl5Jpdg7Aug6i3rPVsZePrgmBk7bjGu6QRTEASHIAg/FgThjCAIxwRB+A9BENaW93WVfz8rCMLbgiA8cC3rYnL1rNb18Ub1VrdJGomAqnerp4FrlFBZTdpcT0t3NfjKGr/Vyn9+z4aWjhegSp+42LGL7X9sey+f0bWf1yHW1Ydo8TjEuom3jfpTMyyWYLrR5+xGJ1BfrWPAzYwZZ01WOvXGDX2M1I+NRnFVW6Y2JhuNqz4DLwb9/kbb69FKXPc6rC0cvfIx8gVQ9Zb1nq2AkgLMjB23Ftd0iawgCA7gPcDPZFmWBUH4IvBBWZbfJwjCN4BLsiz/V0EQdgHfB9bLstzwzwDm0p0by2pdH29Ub3VbwGnllXNzPLb9Ss7BsXCS509MVm1TEyojwNZeXyUZs9susWddO0DNNcbCSb5zaIyZeIZEtsiuoTYlT1W+QGg+zaZuL31+B997PUSvz85kLMsn7ulnIVMgmSmXrdHsff9oCBF4czyKDNy/vp3BDjf9ASffPnSRE6EYHV47n3rXIK9fmgcZPnBHH6H5tJIIOlfgjbEoI5NK/q13b+oknSsBMhu6PCDAuekkMjIbuz10ehz4nRLfeHUUBIGP7+zn1fNzTETTbOvzk84VcVpF3plcwGOXyBVlZuNZPnRHL8cvx/j0bkVDqK37mdkEO/r9uGyKvhFkNnR7QIa5RI6P7FTykKl6wyMXI5yZjhN0KzqOTd1eNnd7K88MlG9UN3S4efHUDL99Zx8T0QyX5lN85O7+yvM7EYry9KExPr17iIGgq+rZP7ihg9B8mmPj87x8epaHN3Vy51Abe9a1KzqdE5Pc2e/n4IUIQ+2uis5E359ePDnNpXCKR7d2c3o6zlg4xZ51QQ5eiNDpsdPps1cl6K73WVrqvqUc1yqrZQy4VZbImnHWZDVQb9wYCyf5wdEQg+0uHtnSXTM2Qm1c1ZapxmSjcVU9V/Vi0Je32M+qjvDMTJz+gAuXTcTtkNja66vEpf6AE5dd8Vw4Ox3n5yNTtLlsbF/j53MPrsPvtHLwfJjZRIZz0wkcVpF7htr40bEJHJIFmySQLcjMJ3ME3FbyhRKTCxnuHQ4iy5DLF/n5yDSJbAG3TeS+9e2cnUmykMozGUtjF0EURTo8Dh7d2oVVElnX7uIr+84xGc0giQIOyYLLJtHX5mBTl7ecU7uAIMDOwQAXw2nWtrvY3OtlIprGYRXZu6mTQxcidHhsuOwSqVxBic+amKr3ujB6ttr3qNUSO0yaY0VpMAVBuAf4V1mWNwiCkACGZVmeLe87DPylLMv7GpVhBr6bk2udLN7oWKME93rjHzXRvb78aCrHiyPTnJqOs7n7SvJgtUzJAv/0yii5Yon7N3QAMDIZ5/HtPZydTfLujR2Mz6eZjWc4N5NABtYEnISTOdxWkdFwise393B8IkY+X+S183M4rBY6PU6S2TxDHW4+fs8Ap6biHA8pk807+wM8srWb8UiKf3xllC29Xoba3QScEv96JMRwuW4XwwneNdxOUYZ2j41Tk3FOT8dwSBacNon1XR5+6/YeDpyPsLbdRV/AwVMHx7BbLfgdVtZ3eej0OtjW5+OdiRhAzc9GiZbHIinuWxfkwIUIQ0EXt/V4+fnINJ0epe1DkTRBj41wIkeHx8ZcMsdt3V58Dol/eGWUdZ0uPr17Laem4pyZirOpnLRZTfjstkvct16Z6Guf71C7q3JMKldgNpHj/Vu7mU/lqwyexufTVfVvc1n5yVsTICvfvvqdVg6cD3NqMsbhixH+4L61LGQKnJ6Oc1u3l0e2dgNKsm+gysznueOTlb6k7Yf6Y6+G1WK+c724VSaYesw4a7IaWWz8UsfRuwb8vDm+UPlfO64amQOp4z8Yj7PacVg79v/8nWnWtismf+p7glpmm8vKz0em6fLYcNqqV6647RLb+nzsPz3DCyPT2CULf/bIJnxOa6X+/35iircvR5lP5fnQnWu4vc/HP7xyAbdNJJzMsaHTy+ZeLyfGFzh0IcxCOo/DCjZJosPrIJ7OMp8q0OaSGAi68TmtTMcyDLS58DklSjLkCzJTcSWmDQbd9PgcvH05hs+p/LW2L+BkIpomUygiAJFEjrMzCTZ2eWjz2HBIIk6ryOHRCJt6vPzJwxvwO628MDLNz9+e5PjlBe4ZDPKpPYM1z6GZ52my+llpE8yngDDw34BxWZZdmn3/Bjwny/JTjcowA9/Nx7VOFl/vWL0wHYyNf/Ti9B9+4T4+9NXXiOoMfyQBijJYyv/fCLx2kXi2eF2upTU60JseqG0hiQL569gYAaeERRBIZIvkiyXEcj2MaiBZoGAgsxEAUbdPQFkuG8/Ub1u/0wrIFaMf1Zzpg195rcowSv3CQm9Q8fJf7L0qh+XVYr5zvbiFJ5hmnDVZVSw2funNgLQYmc5ojXrymjyX+nE2msrx8F/tY748Dtcb+7UGQfF0vsq7wYhWTIBWE94GMVAf38x4dPOzYkx+BEH4X4CNwP9a3mRkzGh03p8LghBS/yUSiWtZTZMbwLVOFl/vWL0wvZ7xj16crhoJ6CmUJzI3anIJXLfJJVR/gPW3rLbF9ZxcgmKIFNM830KdySUYTy5BOV6/T0Yx/GlEPJOv6heqOZPeMAqMDSquxvTANN8xATPOmqxOFhu/GhnmGZnOxHVxQKWeaZq2LKOxX2sEtNjkUj3nZqRRDNTHNzMemVyXCaYgCH8B/A7wm7Isp2RZDpe3d2oOGwIu6c+VZfmvZVnuV/95PJ7rUWWT68i1ThZf71i9ML2e8Y9enK4aCeiRhPI3oDcwK7PXLl63awl1foYrbWG9zo3h0xg0CJp6GCHVGf0Eg33qXzAb4XVYq/qFas6kN4wCY4OKqzE9MM13TMw4a7JaWWz8amSYZ2Q649XFAZV6pmnasozGfq0RkNRESLuBrwDXlEYxUB/fzHhkcs2XyAqC8OfAJ4FHZFme12z/Z+CixnzgB8A603zg1uRqxd+tnN9ImF7P+Mco2fyLJ6c5Mx1nU5eXXcNXkgtfiqQQBfjGaxfJFYrcv74DWYCTk3Eeu72Hc3PJiqmMqsFEgD6/k0gqh6uswXzs9h7enoiRLRQ5cG4Oh1Wk0+sgmcsz1O7mYzsHOF3WYCLAjv4Aj2xRNJj/9Ooot/V6GQq68Tslvvd6iOGgi4sRRYO5W9Vgum2cmopzeiqOwyrgtEls6PTwG7f3VAxt+vwOvn3oEjZJWU68oVPRYG7t8zEyEbtieqT5WdsWqlHPpXCK3euCHBqNMNjmYnOPlxdOTtPhLmswo2mCLhuRVK7K0Mdnl/jHV0dZ1+HmU7uHOD0dV9q9W0nqPDIZqzJEgmpjh8GgixGNBnMumePRLd1E0/lqk59oGmTY2qfUP+C08tPjigbzk7sVDebBC2FOTcQ4PBbhs3vWEssUODOj1OWRLYoG8+CFMMhUmTPpDaPUvqY3qLgaTAOFam6lJbJmnDVZ7Sw2fqnj6J39fo6FFir/6434jIx61NhkNM5GU7nKmK0d+38xMl1j6KaWGXBaldjlseGySlVrYt12ia19PvafmeHFkRlsksCfvVfRYKr1/4+3pzhxOcp8UtFgbuvz8Q+vXsBjFZlL5tjYdUWDefB8mFhZg2mVrHR67cTSWebTBdqcEoPtHnwOielYloGgE69DUlYPFWSmYxlAZqCswXxnIobXIeF1SvT5nUxEM2QLylAQSeY5OxNnU6eiwbSrGsyLETZ1e/lCWYP54sg0P39nkuOhGDuH2vjU7sGa59DM8zRZ/dxQDaYgCP3AOHABiJc3Z2VZfpcgCN3A08AwkAO+IMvy/sXKNAPfzUc9E5Trjd78R2sAc3oqzsVwivdvU8xhdvT7WUjneeZoiE6PDbdNwqUzmZEs8MM3J/jMnqGKwP/xHb34ndaKUcD3jowzuZDmnrVBOj12XGWDAK1hzr7TM+w/PYvXIeGwilglC/0BJ3PJHO1uGxfnkiykCzyxZ5DL0Qynp+K0e2xcnk8TSWa5MJck4LKyd1MXTptIKJKmP+gEYDae4/3buhmfT3MpnOTYeJQ7BwLMJ3O8cHKa4XYP7V4bPqeV92zu5MD5CJ1eG51eBwNtzkr7vHZujp8en8BpFVnX6cYhibx4chrRYuF/evcwp6YSnJuJYxEE1rQ5WdfpIZkr8v6t3VVmR5l8EYsAMgI20cLGbg/pXJGD5+fIFkt0+5x0eu18evcV052xcJK3xqPcMRCgw2OvmCyoZkv/eiRUMTza1ufjyGik5lmOR1J89eVzdHjtfOKeAUPDH73p04HzYVLZAi67VGmLjZ1unjsxVbleq+Y9rRr/LJeRglE5q92k4VaZYJpx1qQVWhljrucY0My16h1jtF37XqHGCrgypjdzrN40bv/pGV4fi/KHDw4zEHRVth8ejfDWeBSHVeTT5ZRYzxwNMRR0ce9wkH8/Mcn+M7M8vr2HwxejbOn1IqAY/63x2/ne6yE293jZ0OXBAvzy9AxyScZpl+j1OZiKZSiUZCSLQCSVY1uvD0m0cHw8SjJfoMtjZy6RJZWT8TlFckWZB9Z1EM8VGJtLKl+muqz0BZy4bCKnpuKkcwXsosRA0MFgh4d8ochMPMsn7x3kfDhVMd8bj6QZCDorMf/nI9MMBV08urXbMFao7aU1RKy372r7g8mNZUWZ/CwHZuC7udCK97Ui8euN3vzHAtRTG1gtAi67SCxdqNFaBJwSgiAQS+UNzxcAn1Mi2aSW42Y1C1gOfA6RWAPDAdEiUCjJNdu1W6yigNNqqVsOVJsBqaZPT3zjcMUYohGtmPfoDSeCLiu/bHDuchkpGJUDrHqThltlgnktMOPszUkr5mLX06ilmWvVO8Zo+0I6X/Ve4dXECmUZ7BVzP+2xoJi1LaTz5Z8lxLJpXK5YLc70OSUyuVKViZDKrRC3/U6J/V96GMDQXEk1RPydrx0w3NesGeNqjT+3AivG5MfExAiteF8rEr/e6M1/Gtm5qMZARgFENQKod74MTRsFqMebGNPI0VUxa6htPf2WfFFuWA5Umz5oTZ+aq2PzBgd6w4lYC4ZVV2OkYFSOadJgYnLz0Yq52PUcA5q5Vr1jjLbr3yu0Y7ze3E9vIBTP5DU/XzEL0pMwMBHSXuNmp16siGVqDRGN9l2P2GZyYzEnmCY3FK14XysSv97ozX8a2bmoxkBGQn7VCKDe+cq3qc0ZBajHmxjjXcRwQLLUtp5+i1UUGpYD1aYPWtOn5urYvMGB3nDC14Jh1dUYKRiVY5o0mJjcfLRiLnY9x4BmrlXvGKPt+vcK7RivN/fTGwh5HVbNz1fMgvR4DEyEtNe42akXK3yOWkNEo33XI7aZ3FjMJbImN5x6JihaWlmPPxZO8pVfnmV0LsmfP7qJrX3+ps7Vmv8cuRjh2HgUl03kAzv6ePNSlIMXwmzsUjSBm3q83Nbj5afHJ4imcjgkkR0DAXatDfLORAxBAL9D4sfHJvjtO/s4O5PgjbEoTz44DMDXXj6PIMhMRNNEU3l2rwtyKZImlimwczDA5Wia3oCTj98zwEsnp/np8Ql8dolQNE2Pz0GPz8FkLEOPz8FCpkC+WOQzu9ey7+wsJy4vcO9QG+dmE4zOpkjn8gRcdtYEnfQFnDglkXS+yHwqT6/fznCHh8vRNOFEjovhJHYRkrkSkwsZBtqcbOrx0eW1c89QGz96cwK71UKnx86utcrvHodI0GXj5dMzxDMF+oNO1ne42X9mjkyhxNqgk+l4FlEQsFlF5QVHLoFg4fMPracoK8/s9YsRbJKFUCRFKJqhx2fH57ThkCycnU1gFQQ6/Xa29Pj43IPriKXzfP2VUdpcEodHI5SAvRs7sUoie9YHOXghQjZf5OD5MHarQLdXMVECmQ1dXn7z9h6OjS/w2I5eYuk8f7fvPF6HyNY+P+lckSOj89itAjOxLMlckc09Hu4abMNlk0jnCozPX9Gm9AecvHByGmQYmVjA45CwSyJep5VP7x5qOgG11nBiT5MazOUwUjAqx8jYajVpYswlskvHjLMrl1Y+h3pfAVUHpzUXg/q6uOs5BjRzrXrjnd7cR/U4eOHkNANtLvwOiW8cuMjDmzr5je29VaY9x0MLZHIFnv71JT7/0DrWtLn4+isXOD+TwGEVefLBYYoyBJxW/u31ccLJHJ9/aD0+p5XvHw2VNY0xTk7EkYE+v53QfIZ0ocjWHh99AQe5QpFfnQ1z90CA0EKGoaCTXr+D/WdmmY6lWcgUaXdZkRHo9Fi5MJciU5CxWsBlFxCwkMkWKchK6q1+n52ZZI5CUaaE8oW4+jdal6Ssugm6bSRyeVJZGRloc0nYrSJ20cJ0PEMhLyNYYCDoJp0rMBHL4rUL3D0YVHKEixbWdbh5eyLGAxs62NzrQwS+eeAiD23u5Ddv763yilDbdCGd5/njkzym0bMKUGXA14oZo2kStHIxNZgmq55W1uOPhZM89OV9Vdt8DolMvtT0Wn69LvTZL97PZ75xuKJbUQk4Fbe2hXSh8rulrNfQatm0mpdGiYqNaPX4a0HAKfGTLz7Ah7/6WlUb6HUm2t99DrFmaZIRavv+/jePEEvnyRssa9UTdFn51h/cywe/8prhUiRRgFKD/JcqAZeVlO5Zvfe/72+6HmpdfvkXeyvnxussp9q/yhNQr8Z6mxPMpWPG2ZVJK59Dva+AVneonbAtpbyVoMlc7DyPXYlBiWyhKh/zT794P9v7A5VjF1K5imRFANx2C4ls9Rj+3Sfv5U//5VilPj/8wn389ldfa0qHfzMgAN958l4++fXDlfcij+bdxCgOqu2fbEJzabI6MTWYJqueVtbjP3e8VsMZz7a2ll+v33j64FiVbkVF1Vtqf9drDPSal0aJio1o9fhrQayspdC3gX4KVq1jKTZVdxl46pCiaWx2UhfLFHjq4FjdCWSxicmlUkdj3WGz9VDrotehGLHaE1Cv1nqbmNxMtPI51PsKaHWHV1veStBkLnaeGp+1k0uApw+NVR2r9UNQJqS1Y/jf779QVR9VW3irIKO0gfa9SBvfjeKg2v5mzLh1MSeYJiueVtbjP76jVsPptbe2ll+v3/j0nqEq3YqKqrfU/q7XGOg1L40SFRvR6vHXAl9ZS6FvA73OpFrHIjZVdwF4YreiabQaaCbr1eeJPUN1dS6i0JwGpp7usNl6qHXR61CMWO0JqFdrvU1MbiZa+RzqfQW0usOrLW8laDIXO0+Nz1axejz/9O6hqmO1fggC4LHXjuF//NC6qvqo2sJbBQGlDbTvRdr4bhQH1fY3Y8ati7lE1mRV0Mp6/LFwkq+9fJYLcyn+yyMb2drnb3ktv14XGk3lOHg+TDKnfGvptksV/YpWMwe1GgP1XFXzspDO851fj5HJFelrc5LKFdk9HOQ/3p5iYiHNtl4/6UKRTd1eHtnSrWgaTkzS67Xzb2+EuHdtkHa3nVA0RX+bE5dNwm0rJ3c+PcMbl6J8/J5+zs0meGlkBgS4sz+glNnlZddwkCMXI1wKp3h0azeh+bRyXzKkckXOzcbJ5ovYrSI71gR4pJzvqtIG2QJuh8TWXh9HLkYYC6f46E5llcQPjoYYbHfxyJZuAF48Oc2R0TDhZI571gbpcNuVa8zEcdhEPvmuoUr7qvrXn52Y5KVTM+wcCFAEOtw2wskc/UEnnR5HRZuoPqMHN3Tw5vg8R8eifOKefooyleTayVyBVLbI+HwKt1Xk7ckF+vxOPvfgOvxOa13dYcBp5dm3Join88QyeWYTOf7wAUWLo7aV2y5V6SS19zAyGWM2lmU2keWjO/tvigTUq63e5hLZpWPG2ZVLK59D7ZikauQW01ku17WvlqVeS6/HPHF5AVGAHx+b4NO7h9jeH6g5NuC08sq5OR7brugGlbgVIZYp8IW96ytLavUa0YMXlHiYyhU4N50kUyiQzBS5MJcg4LJy79p2ZAF2DyueAEqua7ESr3/29iTPvzWB12llY6eHkak4OwcCXIqmORGK0ua00ua2cVuPjxOXo0zHszitEg9uaOfZ45N0eWxMx7I47SL9ARepfJE/fGCYWLbAkdEwF+aSLKTz9PqcfPJdg7w9EcNlF4kkczitIhfDKT5xTz/f/fUlfvHOFPevb2djr4+gy0YyV2TPuiDHQguV9yDte5HfaTX0CzBq/9USM0xaw9Rgmtw0XGuDgWYTOKuJqpPZAm67VEnGrG5TtYj6fXAlsbWa2PndGzuYT+Ur5Z8IRfnHV0bZORTgQ3euqaqLNhk0KImcLcCZ6Th71rfzoTvXAPDCyDRjkRT3rQvy5vgCj++4Yp6krbtRPdVze7w2nv71OL+xtZvf2dlfJeZXfw44JX745gSf2VMbtF8YmebMVByXTWQ0nOJ3d/VTKCmOrEbnNHouB86HmYtnmEnk2NHn44fHLuO1S7xruJ3NPV5+PjJNl8dGh9dRlUQbqDG4UF8MjBKNaw0x3pmIkcoWcNkltvX5ODwaYSyS4mOayaKRgYa+j+iv0SyL9fXVZrhzIzAnmEvHjLM3B9qY0Ux+6Rs1rjQ7XuqPW0jnq+5Pu18bC/Rx9JnXQ6xtd/HIVuWL0OOhhYaxSR+HPlb+QvWZ10N0em10eh0MtDn5ybEJsoUiG7o8uG1SRa+oxhF9bFJ/PnA+zGw8w2wix/u3dnNqKs6Ri2Hm4jl2DQcZanezrc/HkdEIx0JRBCDosnF6OsGe9UF6fQ7+4ZVRujx2gh4bG7uvXD+VK1TKffXcHPvPzPL49h4OX4yytdfLYLnsw6MR5ZqJHNv7/BRluG99kAMXIgwFXTxa/qK50TNcjr5jxrbVgznBNLkpuJYGA60kcAZ4+K/2VYn7A04JBIGogeBfbwRkZFBjLZsv/O1/upPf+/rhyrleh8grf/meyoRUazxk9Kn12i0IFguxdLU2RAD2fWkvfqe1ynBIi79cT/25zaI1Tnjoyy9X7leLKCj6SP059YimcjVtvRgCYBUtVQYDWoMLvTlDsyY92vLVtlQNgQolGWt5GZBRH1Gv0Wx/Xayvr0bDnRuBOcFcOmacXf3oY8a+stFYPW7UuKIf5+uNl9FUrsYwL5Ep1jXjU2OB9l70JoB+pxWLAPF0vkqLqY1NS4lD9bCJFtx2EQFIZIt47CIlWSbaRNytF/evF36nxP4vPVz3C8/l6DtmbFtdmCY/JjcF19JgoJUEzuo2LXqzn0b7jAxqVPOFv9t/oercRKZYqYveeMiIeLZEwqAeMorJjN5wqOrcTMHw3GbRGycYofNaqJxTj0Zl1UOGGoMBrcGF3pyhWZMebflqW6qGQOo16/WRWIv9dbG+bhrumJiYLIY+ZqhGY/W4UeNKs+OlkWFeIzM+/bgMtSaA8Uy+xugHqmPTUuJQPdT6qGaAqjFgM9zoPwU16hPL1XfM2HZzYU4wTVYF19JgoJUEzuo2LXqzn0b7jAxqVPOFzz+0rupcj0Os1EVvPGSE127BY1APAcVkRm84VHWuQzI8t1n0xglG6LwWKufUo1FZ9RCgxmBAa3ChN2do1qRHW77alqohkHrNen3E12J/Xayvm4Y7JiYmi6GPGarRWD1u1LjS7HhpZJjXyIxPPy5DrQmg12GtMfqB6ti0lDhUD7U+qhmgagzYDM1bz10bGvWJ5eo7Zmy7uTCXyJosiVbXyWuPh/qJnRcro1XBuL6ejbSWRoYv+iTBqlbj6UNjeO0St/f76fAo2r8jFyOcnozT4bFxYS7BpUgaySJw92AbqXyRzd3eimHOWDjJD94IIcgwGklx71CA505MsanLw1gkiWSx8LkHhimUlEF3IZ3nKy+f5eJckj98YB2TsQyHLkQYbHNyfi6Bxy7R4XFw79o2fvjmBF0+G3f1B/jhsQk+/9A67tvQWbmfVNmoSDW/GQg6QYZLkTQdXhupXJEuj42nD11iS6+Pzz0wzOnpOKen4gTdNiLJHJt7vPT5HRXjhIGgq6IjOTedIJ4tMJ/M4rZL5IoyO4faWNvuxu+U+N7rIXYOBvigTmOqfV6qDlLVip6bjvPS6Rl2DgaYS+TxOES8DitrAk4mFtIA7OgPsLnby7PHJohl8pRkGYdVZGOXB5dNwmWXFJ1M2bzH57Tyqd1DVdqdlGp2lC9wbjqBDKwJOLkcTZPIFEjnSzyxZ5BCCdpcVl45O8eDGzuIpvO1xk4GBghX29e17aM17TB1K7WYS2SXjhlnVzfqeDYbzzCXyNUYjTU6zygOLvfYoi9zLJzk+6+HGOpwsbnby6/OzhnqRqOpHC+enGZsLsX7tnVzeirOWCRVuT/t/j3rFWOdQZ1+8EQoytdfHWXnYBsfvLOPhXSe778ewiLAxUiKJx8YZnt/gLFwkqcPjoEAH7qjj1A0zVg4ycnJOE8+MIzPaeX7R0N4bCKJbJHta3x870iIeCaPKEKby042X2R7v59ktojLppjqPLa9h8MX5xEEeHhzJy+fmmU2kSE0nyZbKHL3QBtnphNEkjkkUWA+maMv6GRbr483xuaRgXROkX7IssBjO3qQZcVQL+C0MjafptNjI+C2Mdjmosfv4OJcir2bOviXw+OMRVLcMxRgLJLC57DyG9t7eGhTF0fKGsyphQxtbjudXju7htp4/sQUO9cG+OAdiseD9rmdCEX5mxfPkC3I3D0YoASGfa2VPrTazORuZUwNpsmy0+o6eaPkx9cj+a6+nj/8wn38ztcOLCmptHquNikzGGv+6i2zbHNZebmsLYmmcuz98suLai8kAdwOyVDTCIrGsyTLxAxyTqqaDVWf8vvfPFJzP7F0virvo15jGM8UcJfvTasx1epkFtOoCFClgaz3DLRtrm1Dr0OqWqLkc0gIwhVdq1q+1yGRL5UqyZ/1dbAA+j1tLis//pP7K3VS9ZqF8tLXekgWKJYwTF5+rWhFK2wGZnOCeTWYcXb10qymsdmylntsaRSXnTbFQ6CeblQ910j7rh0L1f3qGK7GXqDm2kaa/IV0vqJfVfmpLoaqenv1eq3kT76eCIAkCjX5QLX71XbWPhutVtTo/UnvGaGifc8BU1d5M2NqME2WnVbXyd+o5Lv6eqr6u6UklVbP1Ws1jDR/9dDrO5vRXhRkGuo/lOvWTqjUuqn/P31wzPB+9EFRrzFUjzfSneq1qvUw0kAaPYN6Oki9NjSera6PWn4sU6hK/qyvg9EefZ3UshZ7VSiUqu/remhFWtEKm5iY3JpcrQbcqKzlHFsWi8uNdKONtO9G+1X0+nj9tVXUttLqV1X0MVRb3kqdXEI5TtWZXKr71XbWt0+swXPSe0aoNIrrZny6tTAnmCYt0+o6+RuVfFdfT1V/t5Sk0uq5eq2GkeavHnp9ZzPaC0mgof5Dua5ouE+vTzG6H6tFqDlHqzFUjzfSneq1qvUw0kAaPYN6Oki9NtRrr66PWr7PIVUlf9bXwWiPvk5qWYvpXSRL9X1dD61IK1phExOTW5Or1YAblbWcY8ticbmRbrSR9t1ov4peH6+/toraVlr9qoo+hmrL08fRlYSyyqp+/bTtrG8fX4PnpPeMUGkU1834dGthLpE1WRKtrpO/Ucl3jZIjLzWptDYp8wsnpxlsc7FrOFjRwqn3NRh0lfUMEcKJHLvWtjHY4WbPuvYaPd2LI9Ncmk8xHHTxzBsh3r2pk/vXd/DKuTke3HBF27eQzvPtQ2MIAnxgRx+h+TQIsGedog1Vy9k9fCUxMlBJiqwufzG6H7W+l+ZTfOTu6jyP2md28HyYZK6A2ybV6ApVzeFsTNGS9Le5qhJK6/WC9Z6Btk4jk7GKhnE8kqrSzQAVjaO2fFC0KMfHF0CQ2dDpodPrqBwTcFr5xcg0nR47nT575ZnoE5Kr109mFU2m2yHhd0j8+NgEv31nH0WZhsnLrxWNtJmmbqUac4ns0jHj7OrmajXg+rKWe2xpFJcX0vmquFXv3Hrjb1UMmYhV4qR2yab+2kZtNRZO8p1Digbzk+8aMoyhNdebjDEbyxKKpgi6FT+DR7d0E4qmK3rY3cNBxT+hXdGbvjAyjcsmVrwE+gJOLs+ncdpEtnR7+e6RcdYEHKxtd3NmOsHGbg9TC2mmYlkGgy4+fs9A5X1ABP7x1VE6PTY6PHZ2DATYtTZYiX0/PT5BOldkY5cXBJm5eI6P6DSTjd7V9Pd/IhTlb146Q64gc//6dsP3nGvVh0xuPKYG02TFcz0MSrTmKEbJl5dS1mLGQdok136n9Uoy5XiOj91TO6irCaIDTol/PRJiS68XATg2vsDmbg/JfBEROHZ5gT97zwa29vlrkjWDkoBa/T2ZLZDOFbgUSbO5x8uj5cTSal3G59MMtDlxa0xwfj4yTadHSR6tJoceanex7/QMR8ei/OGDw5UcYWPhJM8cDdHlsdHhdVQlyVbvf2Onm+dOTFUldX5nIgZQ+Xk2niEUSdMfdNJZp5y7BvwcuBCpqVuby1oxhVDbOZlVlj2FIileOjWNx27lt7b38NDmrpr7MDIWctulSh20z1dN7P3ujR3Mp/KG/anZ/qGnlaTozZa51M/WzWAaZE4wl44ZZ29OVtrnupVxTBvf6uVi1JdlZCb09VcucHEuydoONx+/Z4D5VL7qHO04DFSN99pYq42fasxaSOf59sExopkcAgLZQpFIIle51umpOKem4rR7bIQTOQaDTjq8jkrMFwWZ186H6fLa2dLn4/Y+P+lckZHJOI9v7+HsbJKNnW6+8dpFLkVS9AXsPLKlB6dNZDaeY8caH/96ZJx0vshA0Mm2PqW+s/Ec79/Wzfh8mlOTMQ6cD/PeLV381vbeSnxXY99cIsvJyTh/+OAwA0FXXVPEF0emuRhOVd5lVlrfMrn2mBNMkxXN9RCAa6+hmrgs1ZilWeMgfZJrj0Os0Uru1wjrl5LI2eeQyORLNSY8fqeEIAhVpjwqXoeIKAhNJXaGKwYBhaJcY3jgc1prjBBUgb+RSYK2zMVGnUZmC/q6afUlfqeVhXTz7fjdJ+/lT//lWKVvaK8TdFn5kcYASGtAAcq3xSWqk3lDrYlEM8ZSrSRFb/Yzs9TP1s1iymBOMJeOGWdvPlba57qVcew9f7WPSDme6U1k6pUF1WPxP392Fx/8yms1sUQSwO+y8dL/66GqeKOPU6ocoijLhqZ7XodYlZtztSAJSo5qo3r7nBKZXKnGSGnvX+2rer8wMkBajTHDpDVMkx+TFc31EIBrr3G1xizNGgfpk1wbmc/ohfWtEs8am/AYmfKoJDLFphM7wxWDgBrDg0NjhkYIahsY7dOWuRiNzBb0dau+fmuT9L/ff6Gqb+jrUM+AAhTTIL3RxFKNpVpJit7sZ2apny3TlMHE5OZjpX2uWxnHYrr4pj/WqCz9tqcOjhnGEtVITx9v9MeqRnKNYutqm1yCcv/16p1oYCqn5alDtQZIJibmBNPkhnI9BODaa1ytMUuzxkH6JNdG5jN6YX2reO3GJjxGpjwqHofYdGJnuGIQUGN4sHvI0AhBbQOjfdoyF6OR2YK+btXXtzZR+hX++KF1VX1DX4d6BhSg/AVTbzSxVGOpVpKiN/uZWepnyzRlMDG5+Vhpn+tWxjGfLr7pjzUqS7/tiT1DhrFENdLTxxv9saqRXKPYunKtfuojCfVjsqeBqZyWJ3bXGiCZmJhLZE1uOMshAF9s/b/WoOeVs3M8Vtbr1TunUXn6+o6Fkzx/fJLHdNq5sXCS509M8uCGDsbn08wlMpybToAA29cEeHRrd0XTd9eAn4PnIwx1uOjzO/i310Pc1uMlky/y6rk5dg62IQswMZ/mV2fn+OS9A4iSSJfXRofHwUCbk2deH+dyNM09a4N0eOykc0UOXgjzdihKm8tKp9fJ//zoRgaCrrIhT5bQfIqgx8bl+TQC0BtwMp/K0d/mxGWVkAGPXTHq2X9mhkMXImxf46fDY8dd1my+cHKaDo8Nl03CZZMqusq5eIbZRI517S7+7WiIdw0H2dzrY2uvjyMXI5yeihN025iIpoln8iQyBe4ZDtLhtiNDRQe5kM7zgzdCuKyKEYIswx0a84J0tsDTv77E5x9ax9Y+PwfPh5lNZBgPp8kXi7x0aganVeJ3dq7h/vUdPH3oIqOzKf7Loxu5b0NnjUlDMlMAAVw25frjkRRff2WUoXYXkVQOl01k76ZOjo0v8ODGK0ZMjUwktKYJ9bREan+pZ27RqA9e7XHLdd5Kwlwiu3TMOLs6aFWLPdSujHGybKxlrKd1bEUf3my91GsJKOY6UDs2VtW7rBF02aUqbwBVAw+1hjQHzodJ5QqVuHR4NMLrFyNMxTIMBV08sKGD509McfdQgA/duaZiXPP1V0e5ezDA3YNtvHBymna3DbdNmVTJgCBAOJ7lWGiBTV0eUrkiLpvIicsLOK0ikVSOZCbPTCKHw2qhL+DknqEg0VQOGVgTcDIRTZMtFPE5bewaauP5t6dIZ/K8diHMmoCLuwb99PicHLkYIZ0v8MTutVwIp8jli/zinWlmExk6PDbev62XdL5YWdO7/8wsQbeV3oCLRCZPu8tOh8/OB+/o481LUX70xjihaIZda9v4/N4NRNP5iiHSbCLD0bF5JuYz/JdHN7K1z1/XVO7Fk9OMhVN8dGd/XQOkpWBqOVcPpgbT5KZnKZo0T1m3mCwnEdae04pWZbFj9YmfVT2jZBFw2cUqTZ9WC6JNGq2W/X98+Hb+6Ntv1NRBEqjJz1kPvb7PSP/Z5rLyY432UKtp0R+raiUBwwTXgbIeVNvORuWo+J3KXwlVjahWB2mUXFuvmdn3pb34ndYqzW2lLi4ryHKlbCMtj9om6vkuu1ijZRXK9UzptCmL0YyWyGT5MCeYS8eMsyufaxH3tOOykQ5+MX14s/Uy8jL47a++VnXtH+likFbPni+WEMvaQW080MfwSty1CA1jByhj+rNffIAPf/W1yhjtd0qIgqDkwK4T1xrlvjZCvTf9/X7rD+6taEQF4DtP3ssnv364ck2fQ8RisRjGI1H3DqDXjwZcVp7WlK/id0rs/9LDlfeNGx2fVppO2KQxpgbT5KZnKZo0VUthdE4rWpXFjtUnflb1jKoOVDvYa7UgRmX/j1+eM6xDs5NLqNX3Gekp6mkHjY6NZWoTTsu6/c2Uo722Vm+j1UEaJdc20i7qNbf1yq73bPXtbtSGsSb7h77cZq5vYmJishjXKu6pGOngF9OHN1uvelp17bX1MUj7u8wV7WCje6nE3UViB1yJe/oxOrZIXGuVWJ371WpEZeDv9l/Q1a9YNx7p3wGMvBGMNKj6940bHZ9Wmk7Y5OowJ5gmq56laNJULYXROa1oVRY7Vp/4WdUzqjpQrfZBqwUxKvs/v2eDYR2kFoQfen2fkZ6innbQ6FifozbhtKDb30w52mtr9TZaHaRRcm0j7aJec1uv7HrPVt/uRm3oa7J/6Mtt5vomJiYmi3Gt4p6KkQ5+MX14s/Wqp1XXXlsfg7S/C1zRDja6l0rcXSR2wJW4px+jfYvEtVbx1blfrUZUAD7/0Dpd/cS68Uj/DmDkjWCkQdW/b9zo+LTSdMImV4e5RNbkpmApmjSg7jmt6AkWO1afGFqbIFpNKv3ghlotn1HZB87N8vf7L/DhO/uYjGcr5w0GXRy5GOHMVLySS3Jrr4+fnZjkpVPT7Bxsw2YV+cjd/TXLm9RE02r+RzVRcr2k0gcvhElmyseur01grU9wbdTO2muqqNcGahJfN0qubaRd1Osq1bIADp4P1yTfbvRMKxpQm0gqV+Qjd/fjd1qXrG1s5vomV4+5RHbpmHF2dXAt4p5+7IXW9OHN1stIq15v3Nfr2Y1iab0Y3ujYsXCSHxwNMdju4pEt3VfqoRmj1faqF9fUGDM2l+TkVJxP3NPPQqbA2FySt0IL3NGveBYggNsmVd2b/n717TwWTvKDN0IMtrl4pJzDuuIxUM5trXoRqO8Al8IpPrJTGfa+fzTEkObexsJJvvPrMTL5Ijv6A5XtVc//Bsenm0H/f6tgajBNbjqaNRBoJdm91kzgnYkYQMVIQDUQWKw8vUECKKYFqbIxwX06I4OBNie/OjvH4xrDoTaXtWabZIF/PRJiS68XARiZjPO7u/qZiGY4NRVnIFid6Pnpg2MIwMO3dfLm+AIbO9384uQMn9kzxEDQxTdfHeXHxy7T6bVz92AbU7Esv7urn0KJyr2+MDLN6ek4t3V7eWTrlcBbr+4vjkxX6uIqmyG47VcMf9T23H96htfHopUkzo2SZ9d7JvtOz3C0XMb2/oDhM1fbWZ3ApnKFSlJst02qmAhpzSKOjEY4ForikEQ+dGdfVQJubV3UPqFvk3oJwFvpt80cZ5ogNIc5wVw6ZpxdvbQyzqhjlzbW6cc1/b7lrp+RgZDR9YfaXRwejTAWSfH+rd3Mp/KV2JDKFkjmCswmcnxMYzrTjIkfwE+OXebFkRkKpRL9bU62r/EjA+ORNANBJwBnpxPkiyW8Divvua2TX56aJVcosr7LU4m/2vvZ2OnmuRNTbOn1ks4VOXB+jvvXd7C511cxHDo9HafDbeP8bIJIIseuYcWgTzUy0se6sXCSZ14P4bGLzCVyuG0iJyYW6PM72TkY4HtHQzy0qZMHNnTwb0fGmU1keWLPEIUSVW1VzyjJKNZp4+9iMa6V5361x5nceMwJpslNxVINBBolu9cbAmg/EepSVq0xglF5UG1cE3BZkXUJmQNOpRx9kmYBJaFxMlOoaCnUbYl0gdoMmsb4nBIxgwTQWpwS1DtEEsDntFIolYhp8nYGXFZ+8if3V5kfVF3XIdXNrak3G9DisVtIZBUNi2ooYWS0pDXq0Zf30y/ez/b+QNXx7rKRhd4MoR71Ek1bRQGfw1p53nqTISNjil/WMUVYiiFHo75qmiAsjjnBXDpmnF2dtDLOaA1d4MryUf24pkg7ao10lqN+//zZXVXGNqpJm75uVlGoyXlsLRvm6cdtAXj2i/fz+988YtgO+lhRLOe3vFr0Rj3LzT986m5Dk79mUA2R9Fh15kf6WCfp2nipxj/LFf9MVhamyY/JTcXVGAg0a8SjRZtcuVF5eoOEeKY2IXO9JM1y+XitUF/d1uzkEpSkyIvRaP5ZkNU6Vl/VyPygan+2fqGNAq06uYQrhhIq9Yx69OU9fWis5nijtm9EvUTT+aJc9bz1RhFGRg3NGG00a8jRqK+aJggmJiZ6Whln9ON5vXGtnpHOctRPb2yjmrTp66afXKrbjMZtGXj64FjTJn6N4lcr6I16lpu/rWPy1wxGk0uoNT/Sxzp9Gy+1DyxX/DNZXZgTTJNVxdUYCDRrxKNFm1y5UXl6gwSvozYhc70kzUL5eK1QX90mttA2njrGOVqcDQ6RBLWO1Vc1Mj+o2m+vX2gj/yGP/crw43PUN1rSlqEv79O7h2qON2r7RtRLNG0VharnrTeKMDJqaMZoo1lDjkZ91TRBMDEx0dPKOKMfz+uNa/WMdJajfnpjG9WkTV83q1g7QquGeXoE4NN7hpo28WsUv1pBb9Sz3PxpHZO/ZjBoPqDW/Egf6/RtvNQ+sFzxz2R1YS6RNVl1LNVAoFkjHlXIv7XXVzEFgNokzvWMa6oMZXTmOeo2ZOhvc/LKuTke295bMY0JOK0120QBvvd6iC09XhDg5KRiJDCxkOH0VJyBNhedPjt71ikazG8fGkMQYO+mTo6FFtjQ4ebFUzN8endZg/naKD85dplOr4O7BwNMx7J8/J5+ijKVe31xZJozM3E2dXsNzQ/6A9V1f/HkdKUuLptYMTTY2uermOxs7fOx/4yiKXnyAUWDaWQosdgz2X96lqOX5nnygVoNptbI4uD5MMlcAWRI5YqEoin6A05cdglkcDukyjNWDRKOj0dx2EQ+sKOvynSpnslQPVOMpfbbZo4zTRCaw1wiu3TMOLt6aWWcUcdzbazTj2tb+3x1jXSWo371TNr0dRsMKjr5S/MpHt3SXTG3G5mMkcwWSOUKzMVzfESjwWzGxA/g2WMTvHRymnypxECbk21r/CBDaD5Nf9AJssDZmTj5Qgmv08rDmzt5+fQs2UKRDZ2KBlNv1LOhw82/vz3FbT1e0vlqDebWXl/FlK/dbeP8XIJIMsc9axUNptsulWPdDG9civK5B65oML9/NITHJjKXzOG2irw9qWgw7xoI8MwbId69qZP713fwzNFxZuM5Pr17kKJMVVup5RuZHxmZI2nNja5Gg7kc8c9k5WBqME1uaowMAq6GZg2E9GY+RgZAevH8Uq6vNzvQivTv0zm4queNR1J86+AYn9kzVDUBg+r2Uk2EGtUbFLOc2XiG2XiOj92jOKjqzX7evbGjyhRHfz+qic7FcIqP3dNf9SKhNxRaSOcNn6nemMHoOdV7fs0aB4yFkzxzNMRQ0MWjGnOjVgyjmqGZezG5OswJ5tIx4+zqpZUxsNE4VC8eLOdYtdR4+8LINGORVMXQp9l71I/fRvfTyPBPb3xTLxYvZvanxri7Bvy8Ob7QMB43Uzc1tt63PlhV3k+OXa6YBGlN9eoZ/LT6bExubcwJpslNy1g4yd4v76syCLiaSWazBkJaAwK/U0IUBBIGBkD5YgnJIuBzWpsSqxuZE2nNFvSognugcp7DKlQZ9KgmOFDbXn6nRCpXqltvj0NClmWiOuGm1yHW6DThiimOeq+NzJP2l80c9n755aryvQ6JRKZQ80y1beMpm/iopkv66+mfX7PGAdr2UZ/ts198oMrQaTHDqGbQm0wIUOk/pqnB8mFOMJeOGWdXJ62MgUDdMdUoHujN7pbb8KfZeCtDxcyuXowwukf9fRmNverxsXSefOlKxBKgYngjWYSqfVAdixcz+/OV46f+zTvgspJaxEiwXjzVIwBum0Aid+Uor0OqaGv196N/RzHNdkyawTT5Mblpee74ZI1BwNXQrIGQ1oAgnikQq2MAJKOI6JsVq9czJ6qH3mBIOa964qea4EBtezVTbyNjn4TB5BKumOKo99rIPKmemYM6uVTrqD5T7T3qTZf012t2ux5t+wA17dKMYVQz6OsTu8ryTExMTKC1MbDRmGoUD+qNu8tdV/0x+nirNbOrFyOMY2Pt+K0fe7UxS4vW8Ea/T61Xs2Z/RpNL9dhmjASN4qkeGaoml0BNu2nvZzEjJDMumSwFc4Jpsmp5fEdvjUHA1dCsgZDWgMDrkPDVMQASuJLmpBmxej1zonroDYaU86oNelQTHKhtr2bqbWTs43EYWw+ppjjqvTYyT6pn5uBxSIbPVHuPetMl/fWa3a5H2z5ATbs0YxjVDPr6+K6yPBMTExNobQxsNKYaxYN64+5y11V/jD7eas3s6sUI49hYO37rx15tzNKiNbzR71Pr1azZn9chGhoBNWskaBRP9QiAx1Z9lL7dtPezmBGSGZdMloK5RNbkutJoXf9S9JQnQlGeOjTGE7uv6A2vRjvQrIHQwbIucSaR4/1bu5syhGl0Ta2uQiiL6UEx2zk9FWdzr5fN3V5+8c40nV57lanPc8cneffGjorpwb7TM7xyZo7egIMnH1xXpZkMOCW+93qI23q8dHjseOyKEY+qxxiZjDEbyzA+n2ZTt5c1AQffOxJiuN1FEXjf1m5OTcU5M60Y+qRyBd4KRdnU5aVU3q8mwFbLvBRJIQrKX1M7PXY+9+C6Kg2m1lBo19og+07PcOhChD3rgnzwzjVVy3bUZzMeSfHUwTE+fFcf0XShKnn0yESMRNl63l3WvACVZ/LOhGJ0oO4faHPy83em6fTaSOWKHA8tsGddkIc2d1XdgxpkD5wPk8op17w0n65oR1vpa3qTCdPUYPkxl8guHTPOrhxajWeqQU8iU6iMf0bGdGPhJM+8HmJth4tHtija9+ePT/JYOf6qcS6RLeBxSJV4oz1mKXWviXfUGqRpjzk8qpjhDLQ76fA4KprDMzNxNnZ5uXc4WBnTU7kCofk0mzVjsnrfAae14hegxmuoHXtPhKKV2LKQKZDMFpBlSOeKiulNu5NUtshb4wts7vFQLMFHy94Eap1VY7s969sZmVjgr184w0Cbi/s3dlxp67IR0PNvT3H3YICNXR5+9OYEj27p4vhEDBG4GE7x2PYezs4mK7pK9ZkIghK//A6Jpw+N4XNIBJw2jl9e4LP3rWUhU+DIaJhEtsjn965nIOjixZPTvDUeJVso4nfa2DXUxgsnZ3jCwK+h3rvQYv3R1G7eOpgaTJMVQ6N1/UvRUzajubhW2oHl0n/qdYrahMcf/uprFe1JwCkhCEKV/mUhna+qg5pceiGVq+TUVNKdiBVdpkB1zkdtuW67aKi5VM979ov388Q3DtfVhKrHqUmd1XsxSqZt1FZGyb+DLiu/1CV21msltfjL9xM1KAOoKb8eem2tvm/ldfk5Ay4r+5aQgNrk2mFOMJeOGWdXBkvRwkVTuSod4GJjqDZ2NKtfbKYui8Xneh4F2phYo4MsH6/WpZEeUXvfzcbreu2ijal6rKKA126sTz0RivKBr7xWOdbvtLL/S3srE1/tc1oMo+ekja310Ma/h778cpUeVFv2Ut+59JNPU7t562BqME1WDI3W9S9FT9mM5uJaaQeWS/+p11VoNRha7YmR/kVfBzW5tDYQylCly9QHIn25RppLffmNkIGCXJ2c2yiZdr220F8/ZvAM9VpJLUaal5imb9S7P6Ny6ulzcrrJpXq8qVMxMTFZTpYSz/Q6wMXGUO3Y3op+cbG6LBafF9P/Geogy8erdWmkR9Ted7PxupmYqidfrK9P/dbBsapj45l8jS62WWTgqUPVz0kbW+sRyxjrQ/VlL/Wdq5X9JrcW13yCKQjC/xAE4aIgCLIgCLdrtncJgvAfgiCcFQThbUEQHrjWdTG5sTRa178UPWUzmotrpR1YLv2nXleh1WBotSdG+hd9HdTk0pJGeqH+BVP7uxZ9uUaaS335jRAASahOzm2UTLteW+iv7zN4hnqtpBYjzYtP0zfq3Z9ROfX0OaouSX+8qVMxuRGYMfbmZSnxTK8DXGwM1Y7tregXF6vLYvF5Mf2foQ6yfLxal0Z6RO19Nxuvm4mpehT3dGN96mf2DFUd63VYa3SxzSIAT+yufk7a2FoPn8NYH6ove6nvXK3sN7m1uOZLZAVBeDdwAXgVeFyW5bfL278BXJJl+b8KgrAL+D6wXpblhl/pmEt3VjeNNI5GCZeXUt71StS7lPoaUU+zqU02rWoy9felr4NWb/LKuTke217WbWiSZo+U9Srusq5GWy7AwQvhSjLm/oCzUk5Fl6Mta1KjZ7RJlaTc+ntptq3Ue07mCrhtUo02R9/2D27oIBRNV+qr3k+9MrTlIysalv42J78YmabTY8dlEw3bRd+31KTfqnb0kS2taTBNrj23yhLZ5Y6xYMbZlcRS4pl2nF5sDNXHjkaxtNW6NCqzkUeB9hhVz6jGFiOvAzWmqRoQt702djQbgxaLqQ9u6OD0dJyxcIr3afwXwFhLfyIU5euvjrJzsI0P3tlXs6RUfU79bUqsvbPfz6HRCC6bSCpXZPdwkGOhhbrPSRsLtfUKRdM1zz+ayvHiyWnOTClxa3OPtyq+N8NifeB6vX+Z3HhWjAZTEISLVAe/BDAsy/Js+ffDwF/KsryvUTlm4Lu1aTVZ9FJZzHBIb1SQyirpNdxloxmj5MX6xNXaJMeqWU8zBkcnQlH+8ZVRtvR66fTYSeYKjJfNZvoCDv71SIhen523QgskswUevq2LwaCrJiG0tl5q4ur3b+1mfD4NKMmY35mIMRfPcHYmgSzDHQMB7h0O1k3OrLbbXQN+DlyIMBR08ejWbgDDpN1GCanV37WTaG3bnAhF+dbBMX7nrj4KJRhqV4yNfnpsgtlEjg/d2cdHdvYvmki6FfTJs1tJ2L2c/dI0UGieW2WCqbJcMRbMONssN/rzuNi41OwkUHu8Or5+pmz8oo9b6nFG117qPWivr4+t9e4NlHihfrmojSHql54CGMY9vYnQW+NRBAE2dHlw26Sac/TX/cmxy/zy5DSiaOHJB4YrcejIaIRT03Ha3TYiiRz9QWelvG19Pg6PRhiLpNjR5+MXJ2f4zJ4hBoIuw/YFJfY9fXCMbKFIX8DJRDStLMdN53locyd7N3dxZDTCxXCK92+rNtgzaiM1pqttCtS0favP9Gr7m8nqZUVOMAVBaAfGZVl2afb/G/CcLMtPNSrHDHy3LouZBixX0vrFDAHqmRCoaM0I9ImuVVMCySJUTH1cNgsL6UJThkF604BWURNCawNnPeF/PRSLc0tDoyYtvrJhTjJbrBg7eByKKUJUY2IEVAyG1HrqjYz+n0/dzR99+41K2aIAJblWWwrKciqtcdJS+4ORacaP/uT+phJ2xzO1Scyvph6mgULz3MoTzKuJsWDG2Wa40Z9HvSlam8vKjzXj0lKMeP72P93J7339cGX/d5+8lz/9l2OGJnS//dXXGhoJNXsPRkZC+thqNOZq4weUY4jO4E1FG/cWi9/6c6B6LM8ViySypapjG8WhxfA5RNK5Uk2sWkjneejL+1oqyyoKFIrK+4XXIVHSmfZpTf4CLisCV+KS3lhQ/65gRKufgRv9mTFZXlayyY/+s2i4lFwQhD8XBCGk/kskEtehaiYrkWZMfZYjaf1ihgD1TAjQnKM3L9Cb+WhNfWKZQtOGQXrTgFapZ6jQCloDn3pGTVoSGgMEbdvojSj0CbWNjIz+xy/PVZVdbBDU9cZJS+0PRqYZzSbsNkpifrX1MA0UTJqkqRgLZpxdCjf686g3LVsOI56/23+hav/f779Q14RuMSOhZu/ByEhIH1uNxlwjM7d6saxeLG5EvbFcP7mExnFoMeKZomGseu5468aB+eKV9wt9TIXqOqptWM9YcKlGTst5vMnq5oZMMGVZDgMIgtCp2TwEXDI49q9lWe5X/3k8nutVTZMVRjOmPsuRtH4xQ4B6JgRoztGbF+jNfLSmPj6H1LRhkN40oFXqGSq0gtbAp55RkxaPxgBB2zZ6Iwp9Qm0jI6P//J4NVWWLQv23Zr1x0lL7g5FpRrMJu42SmF9tPUwDBZPFaCXGlo8342yL3OjPo960bDmMeD7/0Lqq/X/80Lq6JnSLGQk1ew9GRkL62Go05hqZudWLZfVicSPqjeUee+1rc6M4tBheh2gYqx7f0bpxoFW88n6hj6lQXUe1DesZCy7VyGk5jzdZ3dxIDeY/Axc1BgQ/ANaZJj8mjVjMiACWJ2n9YoYAVWYvF5VE0P1BJ50eh6EZgaqxUA1wRiZjJDNXjHbUxMvq5FKvx9SW8YOjIX70RgiraGH7QIAtPV4iyRz9bS7CiSy/PDWNZBHK34jKbOz2sLXHRyiaRgC29wd4dGt3Rfe5sdPN914fRwD+4IFhJqIZxiJXjALG5pIcuRihw2Nj19p2+gIOfvTmBE9o9COq3uPFkWnOTMdpc9uYjKaRgTv6A+waDlbuGQFkWQl2qXyB8XCado+NuUSOwaALBJmZeI6P7VRWXnz74BiZQrFSzr+fmORXZ2b5+M5+RqbiZPJF2l029p2ZYXIhy2/f1cfvvWuoymxoMFire4VazedYOMkzR0N0emwIwExCqYfWKGmPQeJyo353LfqlaaDQPLfyEtny7//MEmIsmHG2WW705zGayvFiWT//0Z39NaY0vzo7x+M7FKO3erq3sXCS549P8lh5QvPVl88yOpvivzy6kfs2dDIWTvLtQ2Nk8soY/MjW7ismdIsYCTV7DwcvhJHlK7pArcFPMlvAZZOq9m1fo/gWfP9oCLdNJJkr8tHyOP3iyWkOXwgTTubYtTbIUIe7Ytqm1XYKAvQHnPzb6+Ocn1H+Yt8fdNLnczIaTvHkg8Ns7w9UYsLaoItdw0HemYhxKZzkV2dmiGcK3D3Yxkw8x44BPxbgWGiBTd0epmIZpqJpuv0O7l3bzq7hIEcuRrgUTnF7n48fvzWBTRTwOqzcMRBg19ogIxMxZK60g6r1zJdk/A4rqVwBt91KMltgqN3Fb9zew8unZ4mlc1glkYGAk/H5NPliEask0u6ycWIiRqfHxrY+HzIQiqTZ3KsY02nb08hY0EiDWs+Q6WqMn0xWJzdcgykIwleBDwE9wByQkGV5gyAI3cDTwDCQA74gy/L+xcozA5/JSmIpiYeBuucY6T/9TmuVftNI4/jTLz5QpYlR8TslUtlizXIgr0Osyo+pLUsShIqeUJ9cW5/YOeCyksoWq3SI+WIJUaAqd1jAKWEpl5s3yCOpRdWJaPUicEVfIuuOq1yjXBd9m+r1r1bRgscuUpTlivY06LLyrT+41zBpdbNJqE1WFrfKBHO5YyyYcXa1UC/+6PXwi42N8UyhygsAlNjx7Bcf4ENfebWi47sWY2GjGFpvn3a7quv3Oa388Av3GdZXH0ONYoAeAfjOk/fyya8frsQjn1MinSspGlCdvlHFahFw2UViurbc/6WHq+K8Vl8pAM9+8X5+/5tHKlrPenVTtZOJTEHxcxAF8sXF3+O18bIZfaWKqZs0qccN12DKsvwn5WU3kizLPbIsbyhvn5Zl+X2yLG+UZXlbs4HPxGQlsZTEw43OMdJ/6vWbehIGmhiVelqThMHkUi1LqyfU6170iZ2NdIgy1CSmjun2N0LW/a+SL1bfv35/vTbVt59WF6mtX72k1YvpYk1MbiRmjL11qRdL9Hr4xcZGvReAeo5el3ctxsJG8bDePu12ra6/Xn2biQF6ZBQNqjYe6eOdEWpd9G2pj/P6az19cKxGt2+Eqp2s+Dk0MblUr1GvPo0wdZMmS+VGmvyYmKx6lpJ4uNE5RvpPvX5Tj8dAE6NST2vicYiG9+PR6Qn1uhd9YmcjHaIANYmpfbr9jRB0/6uo+hL9cUZ1qZe4W6t71WuI6iWtbjYJtYmJicn1pF4s0evhFxsb9V4A6jl6Xd61GAsbxcN6+7Tbtbr+evVtJgboEVA0qNp4pI93Rqh10belPs7rr/XpPUM1un0jVO1kxc9BbE792ag+jTB1kyZL5bppMJcLc+mOyUpjKYmHG51jpP/Uaz6PjIaZS+R4aHMnH7xjTZUmZjaWZTaRrWhSqrSi00qC5Ue2dFd0n3f2+zl4IcJQu6uuLsMosfNj23sr5Wt1hlrdo5oke4+BtkZNnj0yGauqM1ClR1WTSEfT+ZpzVU3LRzT3uljiblUfC9RoiMbCSX5wNESHVzl/LpHjI3f3m8tjVyG3yhLZa4EZZ1cP9WJJvXG6kYZuIZ3nB0dDDJZjgTr+vzgyzaX51DUbCxvFw3r7jMb0RvVtFAOS2QLI4HZI9AecvHJurhJ/G8U7Nd6Goik2dXnZNRyslG3UllrGwkm+c2gMBPjku4aq9LM1dQOlfnapJpZeiqQIOK28cm6OO/v9HLoQwWUXiSRytLttzCVzbO7xVjSeen3l1T4fk1uXG67BXG7MwGeyGIsl8l2ORL/NJiQ2SlK9kM7zzNEQQ0EXj27trgmaRnUzSkatXn+gzVkxc1AD4jOvh+j02nDZpEoSav15atJlNYDpk1WrxxglbW4mgbU2afRYOGloXPSNV0f5xcg0f/7IRu5d1151bpvLys/fmabTa6PT66g24nk9xNp2F49s7a6qb71nob9v7T0t1hfqnauaJL17YwfzqXxVGYs9R6Mk4ibLiznBXDpmnF25LCV+GZ2jmtcMBV3cWzavSWYLpHIFZsvmZuqk58D5MLPxDGenEzhsIh+6o4/x+TSwfOOXep25eKZirqZe3yimNIrB9c7RX6uZ+p8IRfnHV0bZ0utlqN1diZupbAGXXar8DtUx1V3eZ3T9VtpEHy/UMttc1qq4r95TKntlea4+9muNAn8xMs2n3jXAQqbY0AhKLVe9JzNemaiYE0yTW4qlmO4sJTm0PsG1kWDeyIigkfi/GUMDo4TIKqopgTZRtoqaqFprBKQ3yQHjZNVWi4DHIRmaGtRLYK1NGq01BtKaLjzwf75IInelBh67hWxermtc0Oay8tQf3MsHvvJaZZvfaUXgSr2MnoX+eYGyRMpTTtadNDC/qHeusiTJUmOIIQngd9kamjjp22cphgsmzWNOMJeOGWdXJkuJX0bnLKTzVSZARqjGM09843CNeZyWoMvKL69y/Iqmcjz8V/uqrqM3vtGb+WhjoFEc0p+jHYebid+gTC618UatV7120+9T48VS3jW095ErXsm9KQCiBQql6naq95zaXFZ+/Cf38ztfO0AsnTf0ZNAaGOljlj52LsfzNrk5uOEmPyYm15OlmO4s5RrNJCSuZ0RQT2zfjKGBkZGBimpKYIQ2UbX2eKPj9OYCar2NrlkvgbVqpKA3BtKaLmgnlwCJbKmhcYFaVvW2/KLPQv+8oNrkoVFf0J+rNYjQ1rAgGyfmbtQ+jepsYmJiomcp8cvoHL0JkBFa45lGxJZh/FLrWO/6i5n5NGMApN3fTPwG+JYu3qj1qod+nzYOttpG2vvQl1koVf/e6DlpzfqMJpdqGfVilj52LsfzNrk1MCeYJjcVSzHdWco1mklIXM+IoJ7YvhlDAyMjAxXVlMAIbaJq7fFGx+nNBdR6G12zXgJrbdJorXmO1nTBY6uugcduaWhcoJZVvc266LPQPy+oNnlo1Bf052oNIrQ1lATjxNyN2qdRnU1MTEz0LCV+GZ2jNwEyQms80wjfMoxfah3rXX8xM59mDIC0+5uJ3wCf0cUbtV710O/TxsFW20h7H/oyJUv1742ek9asz8jwTy2jXszSx87leN4mtwbmElmTFUsrWhPtsdA4qf1ihgLNaiCNEhLrjx9qrza8kWVFM/nCyHSNkYL2eCNDgxdPTnPsUhQBaHPbODOT4L71Qe4aaOOVc3M8uKGD8fk0Y+Ekh86HyRWLdHuddPrsfGq3YiJw4Nwsf/3CGfrbXGzu8RJJ5hhoc+KySSCAy6boRlQzABH4xoGL7N3UyW9u71USX2cKIJQNB3T3PhZO8v2jIVw2kZQm+fU3Xh3lhZFp/ssjG9nc66toF18cmeaFk9P8z+9VNJhqgvBn35ogms7htIps6PTisosgK7rPgTYnvxiZZqjdxa61inZoLp5hNpGrJBs3eg5HLkY4PRlnIOik0+tgq4GWRq+X0epaAGYTWY6PL7B7fZC7B9t4YWSado+NLq+DPevbq3SZ0XS+oTHFUg0XTJrDXCK7dMw4u3IZCyd5/vgkj2n07Cr1dHjb+nw1cWUsnOQ7vx4jmsqRzZeYiKaJppQcjEG3nc89uJZousBYOMnx8QW6fTZeH5sn6Lbzxw+tYyFTqDJwu1pdaMWkLp4hFEmzsdvLo1sVM7rvHw3RUR5nt5bH6aH26jFUWwegEkteOTtX01ba+L2111ej7dTW60Qoyj+9OsptPV46vHZS2QJnpxMIgsCOAb9inqPGRSCVLzAeSbO5WzH9OTIa4dRUnHaPjWS2yPu3dXNqKs7p6Ti3lY8xijvHQwu0uaz84p1pOrw2BAHOTScpIXPnQIA+v4MfHZvgid1DbO8PVN4RTk8pMQ6Zqpio3vPFcJKXTk4xn8rz2fvWEssWrxjqGfQr9bxkroDbJlUM8UxMTA2myaqkFa3JciUCblYDuVj5RseDsS6v2frrE2eraDWNeq2EljaXla/+3l2G+kytRkNbh/FIqkbvuP9L9bUXRnqNgMtKoVgkkS3VHG+UtDuayvHQl1+uJJhW9SWf+cbhGs0MsKjWRt2vT1wdcEpYBIFEtki+WEIUoChX62W05XvsIvlSibgmf6jXIWK1WEiUNZxGWlPTffbGYU4wl44ZZ1cmjWKFkY5RxUg3px9rWyHglNi3iHfA1dyHus9tF6u0/wGXFYFa3XyrMbdePX74hftq4qC2XvX0iNpraWORNibUQ6/T1Jal115qy/HrNJPa89x2EQEqsUndr4+taoxarncok1sLU4NpsippRWuyXImAm9VALla+0fFLSSatpZ5mRqtprJf4GRR9xd/V0WdqNRraOuj1J/FMftHnoK9DPFMwnFxq664vQ68TferQmKFmphmtTb3E1bGyplQ9tyDX6mX05yc0k0uARKZYKaOe1tTk/9/e3UfJVdd5Hn9/09Wd7k4/pZOQpEnSSQggETIIARIQkR1QXFlXxYezMyI+jIMiM571rDvnuO7qmXXPOuvDnD2O7owKKIijA6iDDD6ACqIJIYIhQJDwkHTShISkO/2Ufu789o+6t/rW7XurbnVXV3VXPq9zctJddevW7/666verW1Xfz09EiiXJPBIlqm4u1/b59MWMszOpC427Ljz2J52jk7QrKt8gV7vi6hHj5qLgnBAn17wT3i4o3/H2xVwf3J8/RxXrNZRIkE4wZU4qpNakWAsBJ62BzLf/qO2ns5h0UFzNTLCmMW7hZ0jXV3wspj4zWKMRbEO4/qSxtjrv3yHchsbaFA0Lo4eZqEW7w7U4Brx/S3tkzUySWpu4haubvJpS/7Ypm1ovE759Q21VVlsbaqsy+4irNRURKZYk80iUqLq5XNvn0xQzzs6kLjTuuvDYn3SOTtKuqHyDXO2Kq0eMm4uCc0KcXPNOeLugfMfbFHN9cH/+HFWs11AiQfqKrMxZhSzuW6yFgPMt6px0/1HbT2cx6SC/ZmZ4bIK25joGxyamLCTt10oMjkxw8PggqxfXs6xpYabO76nOHr71231sXNHIkoaFLKqdrKOMaoO//YVrFvO289sS/R3C9RoA9z75Mk909PDh16+jqa46s3B11FdI/VqSA12DXBeqHwnXLMYtth3Vr5BeuDpYNxS8bdQ+wrd/8Nkj7D3Sz1nLG7nqnOWZffi3CS7Kra/Hlpe+Ijt9mmfnrnzzyPaXujJrG+O8Wvkc6wM/+OwRdnf20Dc0zvHBEVY01XHRutZ0beGhPo4ODHOsf5Qt61t5dF83axan1x+eyfyY5DjyjdtJ5ugk7Qpvk7d/Y+oR4+aijq4T3PNEJ/XVVZwYneBNG5fz3OF+9r6ankcuWtsaO+/42Q042NjWxM793XR0DWayDXIdb1Q/Rc2tM/kbyqlNNZgiBZjOItbB2yZdvLmj60QmDOb44Fjk4tGvDYQZxC3WHG6vv99rIwIg8rU1HJTkhwwEF3OO6yP/fl+3upk/HOydsvizv4D3we4h1rTWUVeTilwEetOq5kxQTnAf4fsrJIwpbrsH9qQDEc5ekQ6TALL6Prx49rH+YV7tH+Xdm9Pj6V2/72TtkuwXW7keP/kW/56OmTxeK51OMKdP82xpzWSMy7evpHOEPybuPdzPWSsauXhdK4/t66aje5BNbU3c99RhNq5spK6mis7uocw2SceymY5V/lxytH+Yg8eHWL04HdaWb64tZP/+nPfzZ45wWmMN9TUpHJNBSR1dgwyPjnP7jgPcdMV6Lt2wLOv2D+w5Qkf3IG/euDxrXi/k/oPzA0SHJ3V0neCO7R1gcP2WdprrqqfM3YX2c9K/TyGPValsOsEUSWgmhe7hgIVcixGHA3tSBs31Nfzwpkt5+9d+l9mHQWT4TFw4UNKQmai2/igQ8uMHBPSPjGfWowyGCSVZtDsqnCdK+L7raxbQOzSeOYbgQtv5whuSBjb0DI7yxi/+OhMcAdBUW8WCBQvoiWhrroW1IR088VAo6CHX3yoqkGg6FMyQm04wp0/zbOkUElCT7zmfbwyMmyNmEvhTEzE3JTnGQk9+CgkwKpTfvr6hsdi1Iv2vqAav/d5fXMylG5ZFzinVBYzxUfNDY2365DYcaBQV+NdSX83gyAQNC6sib5P0+PP9faYbpiSVSSE/IgnNpNA9XECfazHicGDPuMsO2vHFhc/EtTdpyExUW8PhBn3DkyeXwf0lXbQ7KpwnSvi++4bHs44huNB2vvCGpIEN0WFEE7GBF/negis0yCkqkGg6FMwgMv8VElCT7zmfbwyMmyNmEvhTiiC+QgOMCuXvP+7kEtL9Fb72H73gvKg5pZAxPmp+8IPpwn0WNd+Gw+wK7eekf5/phinJqUknmCKemRS6hwvocy1GHA7sSVl20I4vLnwmrr1JQ2ai2hoON2iqTVFdNdnKYJhQuI+iAoiiwnmihO+7qTaVdQzBhbbzhTckDWyIDiOqig28yBfSUGiQU1Qg0XQomEFk/iskoCbfcz7fGBg3R8wk8KcUQXyFBhgVyt9/9YL40d6YOhd81AvOi5pTChnjo+YHP5gu3GdR8204zK7Qfk7695lumJKcmvQVWZGAmRS6+wELfhhBvhrMf3vqFS7fsJSeobGsgAF/Hxu9xbHjAmyi2ps0ZCaqrVEBAS111TzywrGs/UX1kX+/569qZldnb2b77OChcTqPD7GqtY76mlRWSEJwn71DY1nHUEh4Q9LAhp7BUR7ccyQdtHBaI1d5NZjBxbeD4Qp+2MXR/vTC1QB3P95J+5J6rjpned52Ba/L9fcslIIZ4ukrstOneba0ZjLG5dtX0jnCD4DxQ8wuWtvKzv3dHOga5Ny2Ju5/+jCvWdlIfU0VnceHYgNqCjnGQvto+0tdHO0bofP4IKta61jWUJt3ri1k//6c94s9R1jm1WACLKpJZebjoZFxvrvjAB+NqMF8cM8RDhwf5OpzlmfN64Xcf3B+gOhAIz/wDwd/7tVg5gr3KeT+891uumFKUnlUgynzUpKi8blcWF6stuUKDsoX5hAODUgtgB/+4RA3bG3nvFUtRT2eXG2B4oQOBI/HD+CJC3gIH3vw/2cO9XFiZDwruCEcvBMOWwreppBQibn8GK10OsGcPs2zlSPJGBQMnTt4fAiAlroUtz/aQVNtivVLGxgYnciE1xQzqGwmx5FvzgsGG931eCftrfVcHUrADe4/HLCXLywuVxuKMffnmkMLaVOSvkxyO5EgnWDKvJOk4HwuB5wUq209g6P8uy89lAnJWVxfza+9MIN8YQ7B38cmTrLACwvy/eTmyxKfZM4kWMIPDBqYYehAcJ/BBaijAh6CgQ3jJx2pBZb1fziMKFVljE+4TPDOD2+6lHd87Xex4URJQyXm8mP0VKATzOnTPFsZkoxBUaExuaRyBM/NliThMuE5zx/Pg8FGkD5xfuhTV06ZM4JzLUx+9TcuLC6qXbm2ne7xRgX3hI89V5vyzddJA6REwhTyI/NOkqLxuVxYXqy2hYMDCgm0Cf7uyD65BLjj0Y6iHU++tvQVIXQguM+gqICHYGCDH5gQ/D/IAWMTk9f795UrnChpqMRcfoyKSOVLMgZFhcbkkit4brYkCZcJz3n+eB4MNoL4OSM85ucLi4tqV65tp3u8UcE9SQPlkszXSQOkRAqlE0yZc5IUjc/lwvJitS0cHFBIoE3wd3+5k6Drt7QX7XjytaWpCKEDwX0GRQU8BAMb/MCE4P9BBlRXWVbwzls3rcwZTpQ0VGIuP0ZFpPIlGYOiQmNyyRU8N1uShMuE5zx/PA8GG0H8nBEe8/OFxUW1K9e20z3eqOCepIFySebrpAFSIoXSV2RlTkpSNO4X/TsXX4s30zbMZOHhXGEvUduHa/78n/cc6uNo/zAHjg/xmuXpQJqW+hqe6uzh6w+9wJKGhXzk8vU011Wz/cUuHOn+ONg9yO3bO3jH69qYcDA0Ms5t2/bzhrOW8e/PW5mzhsavWWmoqcrU3sSFFvhtHxwZp96ra9y5r5uOrkHetTn97Yl/2/0Kb92UTiy86/FOljXUZC2SnasP17Sm62kW11fzyPPHuPzMpXT2DGUCigC2vdiVVVf5zKE+zGBVSx2PPH+M81c3s+tgL5efuZTnjvTz5MEeaqureNuftNEzNMaa1nr2HOrL9N2eQ7189VcvcPG6VpYsquFg9xCrl0SHSuSrFTUm26n6ltLRV2SnT/Ns8SStpYuqE/fX4A3Xfvu3SS2A7+/s5ML2Fi5Ys5jfPH+MazdNhqM9sOcIzx3uZ82SOpY2pMdbgAf2HKGje5B3X7iK5rpq/nXXyzxxoIf3bl7Fod5hHtvXxe7OHl7pGeHctkZOb62nNlXFGac1sGhhihMj4xwbSAee+fc1nbnSnzty1bcH+yccKNTRdSIztwQD4fygnODc8cCzR1izuD4T6Bb+m7QvSc8BfshbMGhn24tdmXE8X9BeOCwvnKGQq/4xuC2Qmc9f29Y05diTBMrlCoIK7tt/LQDTCwiSU5NqMKVizWbNwEwWHs43wUbVkoTrP3yt9dV850MX8x/+4XeZy1rqq7njQxdn1ZVAuq7yA7ftpH94nPqaBfQOjWcW1L735st4/62PZRaqNsjUqITbHFWTE1yUO+p4wvWOwXoXM+PEyERWm4LH96OPX5aoxiVYVxOsGwn3XfpTyQWZ2pWB4XHGT7rMZRPOZRYU9+spYbKmpbba6BueiPxbxNV75qsDKlZtjiSnE8zp0zxbHElr6eLGz6DwWNU7OMp4xIb+eH/9LTvoGZr82qf/id7YyZP0e+ObAQ21VZnfW+pSTDiX+d23gPSJbpWRdZ/5xu+4vvDHynCte3iMzTW/FqPWMF9GwExeY/QMjnLllx7KzLm5+ioqb+HHCft1OsLzdinraaVyqAZTKtZs1gzMZOHhQvcbVf/h6/NqSIKi6koA7tjekVW3EVxQ27+OwGVxC0FH1eQEF+WOOp64Osdg/UiwTcHrk9a4RLU5qu/8+iD/vv02BS8L3v/UmpapJ5fBbaOOP18dULFqc0Rk/khaS5erTtwXHquiTi5hcrwPj4tjJx19w+MMBMa39Btwk7+Hr/ed9LYN32e+8TuuL+Jq3cNjbClqDXNlBMzkNYZ/2yR9FZW3MJtzRvhxp3lJZoNOMGXems2agZksPFzofqPqP3xNXg1JUFRdCcD1W9uz6jaCC2r71xG4LG4h6KianOCi3FHHE1fnGKwfCbYpeH3SGpeoNkf1nV8f5N+336bgZcH7n1rTUjXlOIPbRh1/vjqgYtXmiMj8kbSWLleduC88VqViNvTH+/C4WL3AaKpN0RAY3/xPMIP30RAx/i3wtg3fZ77xO64v4mrdw2NsKWoNc2UEzOQ1hn/bJH0Vlbcwm3NG+HGneUlmg74iK/PabC7wO5OFhwvdr18T4dd/7HmlL1Nj6K/jdffjnbQvqeeqc5ZnLgsuthysQTnv9GZ6h8ayFtT2a1ZxZBaNjmtzR9cJ7nmik/qaKgZHJ7juglVTvh4bPp5g3Ytfy7J1fbqWJNime57oZOmidA3m1kBdSr56kuAC1OGvMW3363hqU1PqZ6IWr/b7YWuorslvw8HuQe54tIO3n99G7/D4lG1z/T3j2qvFqEtLX5GdPs2zxROuyYtbpD48fp4YnfxEa1FNKnKsqjL4we87uXDNYl63poVHXjiWNd4/uOcIB44PcvU5kzX0AA8+e4QDXYNc59VgBsdDgHt3vczPnjnM0OgEH7viDOoWprLbF5qfpjtXZsbu0fEpxxh3m6TX5bo+39+kkPvIJTjn5uur4GuAresL69fpyDeviuSTb46Nj0oUmYPCBfIt9TVcfuayouwrqvi+f3icB/YciQ0gADJt6Bkc5X7va6SrF9fxg50HOdQ7xEVrW1nasDATQBMsqPe3v/SMJWw9Ywm7O3tprqtmVUsd39neQXNdip6hcU6MjHPOyiZe29aUCdRxwPmrF2dCbZ451Mdr25o43DvMjn3dvPvCVdz0xg1Z7XzLuSvp6DrBHY92MDo2weHe4ayFp4MLUn/osnVZ/QLw2L5uOroH2dTWxH1PHebC9hb+4/mnZ47fOXj5+CC//OOr3OB98hoMU+joGuRDl63LXA7wVGcP39newQ1b22MDhHzOwcHuQe7ccYBrN62kua6abS92cax/mAPdQ6xurWNgOB0Y4esfHmfnvm6c9/PqxXX87vljdA2MsGpxHb1DY5lFxvuHx9n2YherF9fRvmQRpy+up3FojG8+so9nX+njJHDp+la2vdjNssb0SfLqxXXs7uxlaGSc+546zOb2Ft52/umZY7lzx4GsBcz99r/hzKUcHxwrKPQnyQLa4VCJpLcVkemLe34F5yh/jAzrHx7nmUN9meds39AY39newTtf18buzl7u3ttJc12KSzcso6PrBPftfiUT5rOxrZltL3bx4J4j7NjfTdPCKr68v4cL21tY2VTLY/u6MQdnr2yid2iMZw714Rysaa3nlkde4sFnX+WSda30Do2z7cVjXLC6hfufPsyS+moOdA+xv+sErw6Mcuj4IAe6B1neWAsGm9e2ZkLRWuprOO/05szx9w6NZeaR4PwS3M4PNYp74y7cfz2Do/xm79HM7fx+Ds//4VClcH+H55S4/QfvJ3hsEB3c44cVDY6Oc3RglHd7AUhvOXdlbJuCAUaP7evmyYM9mEHHsROZcD1/Tgq/dgj2Yb4AqbjXOH7fBd881jwhxaJPMGXeKGaoT74FkpMEEIT3FxfUE+SH6zTWprLCZlrqUiwwY2BkIjZkxr99rmds8PqoYJ6OrhNc8cWHsm7TXJfi4U9dSe/QWFa4T3Ndintvfn2mX8JrUPoaa6u4768u551f30bv0Cjjgc2a6lIMj57MClMIhl2Ej/UnN1/GeatagOg+TQVCJsy777i+aq5LfyU3GHRRKk11Ke788CVTgpjCUgbN9TWJHstJQi3CQRG/9h6vp+Ii2voEc/o0zxYmyfMrScDb4vpqvvZnr+PPvvVY5P38/bs38cm7dseGt5VDOLwmHObmzy/hcSgqtC1O0tvlC/ABpgTvhEPexiZOUmXptaOrqxZk7WeRN3f1hMJ4wq8VYHL+ba6rjgx6CobQjZ3M/zo8X3hdXIBU3GucpI/RSp8nZPoU8iMVo5ihPvkWSE4SQBDeX1xQT5Af5BAVNjMZNhB9wuTfPt/+gz+Hg3nu2z01qMfvy3C4T7hf4gwMT2S2Gz8Zvm5qmEJ22EX2sd7x6GSgUVSfBkMm0p9IxveVf5/lMBATxBQ27kj8WE4SahEOitAi2iKzL8nzK0nAW//wOP/v4Zdi7+f//vKFnOFt5RAOrwmHucWNQ7mC5sKS3i5fgE9U8E445M0PMwqG3wT30z88NYwnap7x59+4oKdgCF0SScLroo417jVO0seoyHTpBFPmjWKG+uRbIDlJAEF4f3FBPUF+kENU2Mxk2EB0yIx/+3z7D/4cDua5dtPUoB6/L8PhPuF+idNQW5XZLrUgfN3UMIXssIvsY71+y2SgUVSfBkMm/E8w4/j3WQ4NMUFMYSkj8WM5SahFOChCi2iLzL4kz68kAW+NtSk+dsX62Pv5xJ9uyBneVg7h8JpwmFvcOJQraC4s6e3yBfhEBe+EQ978MKNg+E1wP421U8N4ouYZf/6NC3oKhtAlkSS8LupY417jJH2MikyXviIr80oxi95zLZCcNIAgvD+/SH9VSx13PX6QQ8eH2Ly2laWNC1lUk8oK1wGmhCvEhcyc8OoKN7alA4CO9o1w8PggZy9v5KJ1rZnQhY1tTezc350JcIgK5vHDgYbHJti0qiUTGuRfd8/jnSxtrGFpQ22mvtPw9r2vmwPHBzl3ZRP3P32YC9pbeNufnJ7Vf1UGP951iOu3tLO6tT4yTCHqWK/f0p75emywrX640UVr08f5at9wZpFvgLt/38mihVUc6hnCOThzeQPLmmozAUPbX/JqY0bStTFb17fy06cP03VilI9dcQZNddXc83gndTVVdJ8Y5ewVjZy9vDETmPHy8UG++usX2NTWzMLqKrasb+Wh545m+u/sFeltNyxdxI92HaKptorNa5dw9cblWaFG9V5d6ImRcboGRrl642TwRiE1mEkW0A4GRSS9baXRV2SnT/Ns4ZI8v/IFvPnP2ac6ezLj/+6Xe3l471H+6soNmRrMqPC2P77Sx7YXuzhnRSPHToyyZV0rK5pquW37fi5pb+XstqZ0gNyhPo4ODHOwe4iBoVF++dxRli5K5xlsWN7AWac1cOeOg6xoWsjTh/p41wWn88wr/ew7NsDAyDjLm2pZ0VjLlg1LaG9dFBle0zs0xj2Pd7ImEEoX7oNCw2WS3M6vhbSIgDmYrFsMhxSF97/nlb70nFubitxPVBiPHzQ3ODbOsf7RrPk3LlTI/3lNaz0793Wzu7MHDNpa6hgcneDqc5bTeXwoE/4XF163prU+PU+Hxv18r3Gm+zgWgfxzrE4wReaZ2a6TmCuLMCepk/V/j6pBSbpgdtSC2L/KUbsIxC7gnWux7HxtlOLSCeb0aZ6df5LOC8Ht6moW0OfVS/o1nR+4bWdWLWLUepvBusq5It8Yn3TOnG91iPOtvVI5VIMpUmFmu05irizCnKRONtci5rn2FbVgty9YaxtXNzWdxbLztVFEZLqSzgvh7aJqOoO1iFHm4viVZIxPMvbOtzrE+dZeOXXoBFNknpntOom5sghzkjrZXIuY59pX1ILdvmCtbVzd1HQWy87XRhGR6Uo6L4S3i6rpDNYiRpmL41eSMT7J2Dvf6hDnW3vl1KGvyIrMQ7NdJzFXFmFOUidbjAWzoxbEznW76S6Wna+NUjz6iuz0aZ6dn5LOC+F6yXBNZ7i+b+d+rz7QwabVLVPqKueKfGN80rF3vtUhzrf2SmVQDaaIiJxydII5fZpnRUQkF9VgioiIiIiISEnoBFNERERERESKoqwnmGZ2ppltM7O9ZvaYmW0sZ3tEREQqieZZEREptXJ/gvlPwDecc2cB/we4pcztERERqSSaZ0VEpKTKdoJpZqcBFwDf9S66B1hnZmvL1SYREZFKoXlWRETKoZyfYK4GDjnnxgFcOs72ALAmuJGZfdLMOv1/AwMDZWiqiIjIvKN5VkRESq7cX5ENr5EyZVlf59xXnHOr/H8NDQ0lapqIiMi8p3lWRERKqpwnmAeBVWaWAjAzI/1u64EytklERKRSaJ4VEZGSK9sJpnPuVeAPwPu8i64D9jvn9perTSIiIpVC86yIiJRDqsz3fyPwbTP7NNAH3FDm9oiIiFQSzbMiIlJSZT3BdM49B2wtZxtEREQqleZZEREptXKH/IiIiIiIiEiF0AmmiIiIiIiIFIWll8WaP8xsBDha7naUQQOgxcnS1BeT1Bdp6odJ6ou0Zc65heVuxHx0Cs+zs03Pzdml/p1d6t/ZMx/7NuccO+9OME9VZtbpnFtV7nbMBeqLSeqLNPXDJPWFyNyk5+bsUv/OLvXv7KnEvtVXZEVERERERKQodIIpIiIiIiIiRaETzPnjK+VuwByivpikvkhTP0xSX4jMTXpuzi717+xS/86eiutb1WCKiIiIiIhIUegTTBERERERESkKnWCKiIiIiIhIUegEcw4ys1+Y2W4z22Vmj5jZ+d7lt5rZc97lv/Evr2RxfRG4/gYzc2Z2bZmaWBI5HhNmZp8zs71m9rSZPVTels6+HH2x2cy2m9kfzOxZM/uvZW5qyZjZZ73nwbne76eZ2c/M7HnvcfH6crdR5FRmZmea2TZvrH7MzDaWu03zlZnVmtmPvb7c5Y11a73rNPYVieaV2WFmC83sH7x+fMbMvutdXlH9myp3AyTSe5xzPQBm9nbgVuAC4MfAXzrnxr0Tqn8BzipTG0slri8ws1XAjcCj5WpcCcX1w18D5wHnOudGzWxl2VpYOnF98U3gs865e82sFfijmd3nnNtTtpaWgJldAGwBDgQu/gLwqHPuGjO7CLjbzM5wzo2XpZEi8k/AN5xz3zazdwG3AFvL3Kb57BvAT51zzsxu9n5/Exr7ikLzyqz6AnASOMt7/K4MXF4x/atPMOcg/8Wzp5n0AxHn3L2BB9qjQLuZVfTfMK4vPN8A/jMwUso2lUOOfvgU8DfOuVFvu1dK3LSSy/OYaPH+XwSMAt2laVV5mNlC4GvATUAwse093uU453YCR4B5/W6oyHxlZqeRfhPsu95F9wDr/E/dpDDOuWHn3P1uMqXyUWC997PGvhnSvDJ7zGwR8EHg0/7jN/C6raL6V59gzlFmdjtwpffrNRGbfAK43zl3MuK6ihLVF2b2MeAZ59wOMytb20op3A9m1gQsA95hZtd5l/+9c+4HZWlgCcU8Pz4I/KuZfZ50v/ylc+5wOdpXQn8LfNc5t89/HpjZEmCBc+5oYLv9wJrSN09EgNXAIf8NYu9TiwOkn5P7y9mwCvHXwE809hWN5pXZcwbQBXzGzK4ChoDPAbuosP6t6E+/5jPn3Pudc6uBzwBfDF5nZu8j/U7HjeVoW6mF+8LM1gEfAf5HeVtWWhGPiWqgBqhzzm0h/Zj4il8vUclinh+fAj7lnFsDvBb4X2Z2drnaONvMbCtwEfD1iKvD60+dGu/CiMxdek7OAjP7NHAm8N+8i9TPM6B5ZdZVk/60fY9zbjNwM/B90h/4VVT/6gRzjnPOfQe40nv3CDN7L/BZ4Grn3KtlbVyJ+X0BXAq0Ac+a2X7SdQK3mNlHyti8kgn0A8AA3teunHMHgN8Bm8vUtJILPD+WA+9wzv2Ld/lLwA7Sj5VKdQXwGmCf9zxYBfwcuBjAzJYFtm0nu5ZGRErnILDKzFKQDmcj/ammnpMzYGb/BXgn8Bbn3KBzrsu7XGPf9GlemV0dpMt67gRwzj0J7APOgcrqX51gzjFm1mRmbYHf30H64/RuM3sP8HngKu9koqLl6IvvOedWOOfWOufWkq6/+LBz7ptlauqsyvWYAP6Zya8NLyY9CewuRztLIUdfHAOGzewK7/KlpN94eLosDS0B59wXnHNtgedBJ/Bm59xPgbuAjwN4YQErgN+WrbEipzDvzeA/AO/zLroO2O+c21+2Rs1zZvZJ4D+RfrO9J3CVxr4Z0Lwyu5xzx4BfAm8GMLN2YB3wHBXWv6rBnHuagXvMrI70uxxHgWu9mo07gcOk68z87f/Uf9euAsX2RXmbVXK5HhOfBm4zs5u8bf+3c+6JcjW0BOL6YsJ7A+Yr3qcE1cCXvEL5U9HfAHeY2fOkw46un69JdCIV4kbg296Y3QfcUOb2zFtegvyXgZeAX3uvh0acc5egsW82qW+L46PArWb2d8AE6byIV8ysovrXTr3X6iIiIiIiIjIb9BVZERERERERKQqdYIqIiIiIiEhR6ARTREREREREikInmCIiIiIiIlIUOsEUERERERGRotAJpkgRmdl+M/ujme0ysz1m9vEZ7u+NZvZ77+fN3lI1+W7zOTOrCfz+t2b23pm0Q0REZC7QPCsy92mZEpEiMrP9pNdlfNrMVgNPAW9wzu32rl8A4Jw7mXB/byS9nuPmAtrggEbn3EBhrRcREZnbNM+KzH36BFNkljjnDgJ7ge+Z2R1m9kNgF7DSzN5sZr81s8fNbIeZvcG/nZl93sxeMLOHgWsDl2feZfV+f6uZ7TSzJ713ci8xs3/0rt7mXXaamX3bzG72btNgZrea2dPev88G9veQmf2dmT1iZi8G9oWZ/YX3TvEuM3vKzC6ZpW4TERFJRPOsyNyUKncDRCqVmZ0HvAb4CXAlcIFz7lUzWw98FrjGOddnZhuAh81sLXAN8DbgfGAI+FHMvs8CbiH9ru1eM6sG6p1zHzWzG4FL/XdWzSx40/8O1ACbgDrgt2a2xzl3l3f9GcAbvW32mNlW59x24MvAOc65Q959LZx5D4mIiEyf5lmRuUknmCLFd7eZDQODwIeAc4F+59yr3vXXABuA34QmpdWkJ8gfBCatW4HPRNzH1cD9zrm9AM65MaA3QduuAj7hfXXohJnd7l3mT3zfd85NAENmtov0RLgd+BVwu5n9BPipf78iIiJloHlWZA7TCaZI8b3LOfe0/4uZnQsE6zQM+Jlz7v3hG1poJpwFBoQLr4O/Dwd+nmByjHgncCHpd13vN7PPOOe+P1uNFBERyUHzrMgcphpMkdL7BXCNNyECYGYXez/+EniPmS0ysyrgAzH7+DnwFu8rPJhZtZk1e9f1A80xt3sA+IilLQLeBzyYq7FmlgLOcM793jn3JeBu4OJctxERESkjzbMiZaRPMEVKzDn3vJm9D/iWmdWRrsN4Avhz59x9ZrYVeBJ4GXgYWBWxjxfM7MPAP3u1GhPAjcBjpOs4fmVmQ8CbQjf9n8BXSafuAdzlnLs7T5OrgNvMbDEwDhwFPljocYuIiJSC5lmR8tIyJSIiIiIiIlIU+oqsiIiIiIiIFIVOMEVERERERKQodIIpIiIiIiIiRaETTBERERERESkKnWCKiIiIiIhIUegEU0RERERERIpCJ5giIiIiIiJSFDrBFBERERERkaLQCaaIiIiIiIgUxf8HsyfRHB5vCA0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1120x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Predictions vs Observations plots for validation\n",
    "plt.figure(figsize=(14, 6), dpi=80)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(y_pred_val_lasso, y_val_scaled, s=5)\n",
    "plt.xlabel(\"Predictions\")\n",
    "plt.ylabel(\"Observations\")\n",
    "plt.title(\"Lasso Regression\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(y_pred_val_ridge, y_val_scaled, s=5)\n",
    "plt.xlabel(\"Predictions\")\n",
    "plt.ylabel(\"Observations\")\n",
    "plt.title(\"Ridge Regression\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Ridge Regression')"
      ]
     },
     "execution_count": 288,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x480 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot residuals for validation set\n",
    "\n",
    "plt.figure(figsize=(14, 6), dpi=80)\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.scatter(y_pred_val_lasso, y_val_scaled-y_pred_val_lasso, s=5)\n",
    "plt.xlabel(\"Predictions\")\n",
    "plt.ylabel(\"Observations - Predictions\")\n",
    "plt.title(\"Lasso Regression\")\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.scatter(y_pred_val_ridge, y_val_scaled-y_pred_val_ridge, s=5)\n",
    "plt.xlabel(\"Predictions\")\n",
    "plt.ylabel(\"Observations - Predictions\")\n",
    "plt.title(\"Ridge Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 289,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R^2 for Linear on validation: -784013430.4371523\n",
      "R^2 for LASSO on validation: 0.03485707074747524\n",
      "R^2 for Ridge on validation: 0.3110368333756022\n"
     ]
    }
   ],
   "source": [
    "import sklearn\n",
    "\n",
    "print(f'R^2 for Linear on validation: {sklearn.metrics.r2_score(y_val, y_pred_val_linear)}')\n",
    "print(f'R^2 for LASSO on validation: {sklearn.metrics.r2_score(y_val_scaled, y_pred_val_lasso)}')\n",
    "print(f'R^2 for Ridge on validation: {sklearn.metrics.r2_score(y_val_scaled, y_pred_val_ridge)}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Hyper parameter tuning to improve predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### data is already scaled above"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=4, estimator=Ridge(),\n",
       "             param_grid={'alpha': array([1.00000000e-05, 1.70125428e-05, 2.89426612e-05, 4.92388263e-05,\n",
       "       8.37677640e-05, 1.42510267e-04, 2.42446202e-04, 4.12462638e-04,\n",
       "       7.01703829e-04, 1.19377664e-03, 2.03091762e-03, 3.45510729e-03,\n",
       "       5.87801607e-03, 1.00000000e-02, 1.70125428e-02, 2.89426612e-02,\n",
       "       4.92388263e-02, 8.37677640e-02, 1.42510267e-01, 2....6202e-01,\n",
       "       4.12462638e-01, 7.01703829e-01, 1.19377664e+00, 2.03091762e+00,\n",
       "       3.45510729e+00, 5.87801607e+00, 1.00000000e+01, 1.70125428e+01,\n",
       "       2.89426612e+01, 4.92388263e+01, 8.37677640e+01, 1.42510267e+02,\n",
       "       2.42446202e+02, 4.12462638e+02, 7.01703829e+02, 1.19377664e+03,\n",
       "       2.03091762e+03, 3.45510729e+03, 5.87801607e+03, 1.00000000e+04])},\n",
       "             scoring='neg_root_mean_squared_error')"
      ]
     },
     "execution_count": 290,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "params = {'alpha': 10**np.linspace(-5, 4, 40)}\n",
    "\n",
    "grid_search = GridSearchCV(Ridge(), params, cv = 4, scoring = \"neg_root_mean_squared_error\")\n",
    "grid_search.fit(X_train_scaled, y_train_scaled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 291,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-11.08572018, -11.0857201 , -11.08571997, -11.08571975,\n",
       "       -11.08571937, -11.08571872, -11.08571762, -11.08571574,\n",
       "       -11.08571255, -11.08570714, -11.08569795, -11.08568237,\n",
       "       -11.08565603, -11.0856117 , -11.0855376 , -11.08541523,\n",
       "       -11.0852171 , -11.08490635, -11.08444302, -11.0838049 ,\n",
       "       -11.08303105, -11.0822877 , -11.08195137, -11.08271859,\n",
       "       -11.08576107, -11.09295375, -11.10728273, -11.13346077,\n",
       "       -11.17829143, -11.25007238, -11.3570181 , -11.50522173,\n",
       "       -11.69545358, -11.91799539, -12.15025799, -12.36450381,\n",
       "       -12.54142512, -12.67671285, -12.77741407, -12.85523652])"
      ]
     },
     "execution_count": 291,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.cv_results_['mean_test_score']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 292,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-11.08195137069128"
      ]
     },
     "execution_count": 292,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_score_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 293,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ridge(alpha=1.1937766417144382)"
      ]
     },
     "execution_count": 293,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 294,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([45.06884502, 35.50825322, 33.02976446, ..., 43.91204329,\n",
       "       35.11200897, 36.64537621])"
      ]
     },
     "execution_count": 294,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_values_on_ridge_model_train = grid_search.best_estimator_.predict(X_train_scaled)\n",
    "predicted_values_on_ridge_model_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 295,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([38.61737399, 39.81486269, 43.68242277, ..., 43.09664252,\n",
       "       32.99782406, 34.43227263])"
      ]
     },
     "execution_count": 295,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted_values_on_ridge_model_val = grid_search.best_estimator_.predict(X_val_scaled)\n",
    "predicted_values_on_ridge_model_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training MSE using optimized Ridge regression: 120.90144806553806\n",
      "Validation MSE using Ridge regression: 123.52944066525569\n"
     ]
    }
   ],
   "source": [
    "print(f'Training MSE using optimized Ridge regression: {mean_squared_error(y_train_scaled, predicted_values_on_ridge_model_train)}')\n",
    "print(f'Validation MSE using Ridge regression: {mean_squared_error(y_val_scaled, predicted_values_on_ridge_model_val)}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 297,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Optimized Ridge Regression')"
      ]
     },
     "execution_count": 297,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gqp1PnBxp+ty3o9Frzu4aWKx2KBpDTUQUCoVCoZgHe3pbCPtclb/4Rwy/717XYrmN/rzda7W2r9eGiM9lua9xG4H2V2n930bqqFWvS9Tf1ty/iG9xV4WLch3m/j+yZ21VXbX6+pY9ay3L1cco3ECbjds/smdtVVuM24R9zjn7Gduo7+t21h9cc5/esmdtpTz9GxGdiMU5oZ8LxjGrPp+q22pE37dWP8zHWh+f9x3oq2rnI7vXNn3u29HoNWd3DSxWOxSNoZZmKRQKhUJRg3rrzU9enal8WNF/N6/Ft3v+yf5RrkymeOddvfS1Bytr6oWAA5trOw3zacOGtgBXJlOVf83bNTsuJ6/OEPW7eeb8OA9s7eDMaJwTgzNIJLf3Rtm3qW1OPdOpHEfKjkgqX2BoMs32NWF6Wnx85YUh7twQ5eCOLvpHYppHImE8meXUSJx3393LTNkROT+WwOty8tbbe5hO5y37PzCR5GsvDtHXHuChW7trujsDE0k+9ZNznB1N8Iu71vCLu9dWxurV4RipXIHxRJYTQzPs39TG1q4Qj//sCiGPk3s2t7NvY3VfjcdlJp3niZMjPLJb8zaOXNT6H/S62Lk2Qv9wDAzH3Di2T/aP0hH2gBSMxbO8aVc3Q1Ppqu2NDEwk59SFhAMWjojxnLBqN8BT/aNcmUrx8K2z9e5cG6mMjd52u34cuTBRtY/x2DxxcoS9vS28NDhTcVqMx2++robdNVer343ur7Bmvkuz1EREoVAoFAoblmq9+HQqxxs+foip8vr5toCbb5bdgJt5bXoz47WQsW1k32b9HKu6F6OMxWC1egtmp+XQRx+sEtdXY59XK8oRUSgUCoVikVmq9eJ6uToxC6/gZlyb3sx4LWRsG9m3WT+n2XqW0yVYrd6C2Wl54uSsq7Na+6yoRk1EFAqFQqGwYanWi+vl6kQsvIKbcW16M+O1kLFtZN9m/Zxm61lOl2C1egtmp+WR3bOuzmrts6IatTRLoVAoFIoaLNV68elUbs76/dWwNr2ZPiykv43s24grUK/uxShjMVgN54YVRqdFX5als1r7vBpRjohCoVDcBNzooKwbXf9rjYXItsawQD30za48K/HaGGq3FMdcr+OO9bOysfGD5EJDFOuNXTNju1TbmrcH60DJZsqtFcLYbDjmzcBitruZMb4Zx2oloyYiCoVCscK50fLlja7/tcZ8x7sSNmcItBPAdz5yH7/198fmlGeuxxwWF/G7SOdKi3rMjZKxjlE2NgfHOQUUJZXgvGaCGq22Xyopvtlj1kioZTPlWt3EwBxCeKPF+cVkMdu9GDcgUMwfJasrFArFCudGy5c3uv7XGvMd70rYnCHQTgJfPDpQN7DQKixuKY65UTI2tlGXjc3BcQU5GzzYbFCj1fZLJcUvJFDSLtSymXKtbmIw33DMm4HFbPdi3IBAsfyoiYhCoVAsEzdavrzR9b/WmO94V8LmDIF2Anjf/r66gYVWYXFLccyNkrGxjbpsbA6OcwmqgvOaCWq02n6ppPiFBErahVo2U67VTQzmG455M7CY7V6MGxAolh+1NEuhUCiWkRstX97o+l9rzHe8zWGBushbKxzRHORnDLVbimNuDKQ7PjQzRzZeaIhivbFbKim+2WPWSKBkM+Va3cSgkTJu1mt7Mdu9GDcgUMwP5YgoFAqFQrHCMIvMuoS8vtXP0+fGef22DqZS+XlLswMTSb764hB9bQEe3tld030wCtBQLVXrr+tp3/o2Zmla708mV+BvnrlE1O+iJAX7NrXSGfIigaDXxa6eCK8Oxyr7zqTzFbH9x6fHmEnn8Lud7O6Ncs+mtsq2+n56O3b1RCqCO2gp35cnUrzr7t45d1gyjredGF/rdbPAbNz2+UuTnBmNs77VT2fYV2lnKlsgYOivVbv1sp7qH+X0aJwd3eHKsbI7P4zjp5dllP6PXZrk8kSKe7e08dzFSfraApVxTGULlWVzxmP5ZP8oA5Mp3ryzu+FzbjqVa3i/+dwQoN5xWoy6FMuDmogoFAqFQrGCMIvMUkqm04U527kEtAQ8TUuzZmE86ndx6KNvsPxL+Rs/fojJsgAd9bsQQpAsS9Xf+NC9vP2Tz1YEaYBowA2G9rYG3HyrnPw+ncxRbGIcIn4X8XRhjlPSCAJwOQQhn4uShJn0bBsPG1K49X4aBXmXQxDxuy2lfvPrQJXA/I0P3VtJuc8VSw2109w/d7n8b3zoXt72yWeZNo6v38W3P3J/pY6g14mEqm3MY1AoyYr038xYRv0uilISz8wetUbOuelUjoMf+wkzhnPW7RREfO5FuXlALJ2nUJJzbmCwmDcqUCwfK1JWF0JEhRDHDT9nhRAFIUSbEKJLCPEvQohzQohXhBD3L2VbFAqFQqFYTqxEZisKknlJs2ZhPGZTxomhmaq6Y5lCVbv0RHcj5vYat2tmEgKQyMxvEgLaB+58SRLPFIhnqj+kG1O4Ya4gr+9nJXubXzcfK2PKfaPtNKOXbzW+MVMd8fIxqTUGRum/GWKZAolM9VFr5Jwzi/MA+aK03G8+NwTQ+1Tv5gsLqUux8lnSiYiUclpKuVf/Af4G+Gcp5STw58BRKeU24LeBLwkhXDWKUygUCoXipsFKZLbCJZiXNGsWxiM2ZezpbamqO+JzVbVLT3Q3Ym6vcTtnU62EkM81R2xvFIH2zULY5yLsc1e9ZkzhhrmCvL6flextft18rIwp942204xevtX4Rkx1hMvHpNYYGKX/Zoj4XIR81UetkXPOLM6D9o3IYt08QO9TvZsvLKQuxcpnWZdmCSFeBf5PKeW3hBAJYJOUcqz82vPAn0opD9UqQy3NUigUipuXm3lt93zabhaZnzo1ysBEigOb2zh0doxMrkhr0MOl8RS/98Am1rcF5rgcxscz6TyPl2/P++iBPgD+9pmLXE/k+PCDW9jdG7Vtx5ELEyDgwGatXLPc/rUXhuiMeOgM+ThQrtu4jzH5PZ0t8NmfXiLodjCezNHXFqAt5GVbd4iAxwUSUvkCg5NptneHWRf18a2Xhnno1i5euDLFTCoHCDwuB1u7QgS9LoJeF71RPz98dbTSjp09kYrgPpPO86WjWt/fu7/P1hExC/JmV8P4ulFW1iVxKWfHXt/22OVJzl6L09saoDPiZefaCP0js07IzrURjl2e5OXBaXxuJ2+9vYczo3HNZ7mrlxa/m6dOjXJ2NM72rjAPGRwR4/mhj7dePhJ6W/08c26cB7Z1MJ3OE/W7+c7LwwjgwR2dHL04yYb2APs2tlXapH9tEvS5Ksf7qVOjXJlI8fDObqbT+YbFffN+5jG1OtcbvSGA3XF47sIEwnDe2e2vZPOVw4p3RIQQB4BvAr1ACzAopQwYXv8K8D0p5RdrlaMmIgqFQnFzcjOv7V6MthvLCHgczFh4EyGvg0RWWw4UDbiRUlbW6Ed8TuKZYtU+//CBe3jP556vhBceMnkTS9k3c/geQIvfhVMIEtki+fISKB23yckweitGB2WxAvsWEk7Y7DE2j4XRi5nvcbFqk9FdWY2hqDfze8RrnRXpiJj4HeCLUkp9waH5/dfy20YhxB8LIYb0n0QisaSNVCgUCsXScDOv7V6MthvLiNl4E/okBJjjDZgnIQCfOXyxKrzQ7E002675BC8aMYb6mdtqdjLsHJTFCuxbaDhhM8fYPBZGL2a+x8WqTbXGaDlY6mv4Zn6PUMyPZZmICCGCwLuBvwOQUk6Un+80bNYHXDHvK6X8Cyllr/4TCoWWo8kKhUKhWGRu5rXdi9F2YxkRG28i5J39b9nsDYR9zjn7fPDg5qrwQrM30Wy75hO8aMQY6mduq9nJsHNQFiuwb6HhhM0cY/NYGL2Y+R4XqzbVGqPlYKmv4Zv5PUIxP5ZlaZYQ4reAD0gp7zc893ngspTyz4QQ+4CvA5sN35hYopZmKRQKxc3Lzby2ezHabixjJp3niZMjbO0I8tTp6zy6v4/1bYGqMDug6vFMOs+XfjaAlLOehDG8cD7LfxbSN92r0F0Jo3+yoS1A/3Cs4jxYOhk2DspiBfYtJJxwPkvvzMdqocfFqk03+hpa6vpvdP8U82NFOyJCiGeAv5NS/r3huW7gMWATkAM+JKU8XK8sNRFRKBQKxXLQrJxeL6TNavvnLkwwFs8wOJmmPeQhmSvWDI6r1abFbO98xXxzAGKjbViqMZ4vjdZj1X67Ps23rwsJu/zeiRHeskebCFkdn/kcs5uRZo7TzcZK6ceKnogsJmoiolAoFIqlZiGis1WYntX2ZtHbiNti/1ptmm97rULlgHmJ4cb+tAXc/PhPHlxUObxeEN5i0Wg9Vu0H67FbTnEeqsMuBfCdj9zH+/7u+arj880P38c7Pvls1Q0DflLnmN2MNHOcbjZWktx/M8jqCoVCoVDcFCxEdLYK07Pb3g6r/Wu1ab7ttQqVW4gYrmMXrmi1z2K0eTFptB6r9tv1aTnFeagOu5TAY0cG5hyfJ06MzLlhwGqUw5s5Tjcbq6EfaiKiUCgUCoWJhYjOVmF6dtvbYbV/rTbNt71WoXILEcN17MIVrfZZjDYvJo3WY9V+uz4tpzgP1WGXAi1zxnx8Htmzds4NA1ajHN7McbrZWA39UEuzFAqFQjFvlnN98nKvhW5Wmh2YSPLEiZFK8Jz+oaCW03Hk4gRjsSyDUymCbieXJlK8e18vM5kCUsKunggDE6mKq+BywDdfGuYdd/RQKDFnzXszgXJ6aJyVSK735ZE9jYvWZlnb2Pdav5vbXGs9f1/7bFCh3ma7shspr5b7YRe4Z7WdsR16G837WEnn5na3Btz8oH+UvrYA92xqsxT87fwTs+thvIlBi9/NcxcmSOW0sMOA11UJbTQHXdZyRqzaXOt6rDXOZofFjmbrtCvD6jxbDVL8SumHckQUCoVCsaws5/rklbQW2oqFrkM37h/0arfpjWcKFEoSl1NQKEqcDijMxozgdgoiPnsPpZm21nMfFhLeGPI6kUAyW6z0LZEtLti7qFVPrX1rlTdfFuKA6O1OZAvki7OfyaJ+F4c++oa6HlA9P8fq3LIa/+lUbk7I5E/mUU4jY2J2WOwCH5utU3HjUI6IQqFQKJaV5VyfvNLXQi90Hbp525jBUcgXtX+NkxDKz89nLOq1aynCG819Wwzvol49dvsuxbm0EAdEb7dxEgJzPZt6/kmj+9mNv1XI5HzKaWRMzA6LXeBjs3Uqbj7UREShUCgU82I51yev9LXQC12Hbt42YnAU3E7tX5fpf2y3s7aH0kxbm3m92Toi5WBGY98Ww7uoV4/dvktxLi3EAdHb7XZWR0CaPZt6/kmj+9mNv1XI5HzKaWRMzA6LXeBjs3Uqbj7U0iyFQqFQzJvlXJ+8UtZC27HQdehm38DoKOj/Rv1unjk/zgNbZz2U+YxFvXYtdnij3h/z743W3WjQoVXZy+UHLCQ8UW931O/myVOjbGgN8NDO7oZDHc1+TrNjVFWOKWRyPuU0MiaNBnE2W6fixqAcEYVCoVDcVKyUIK561JJla8nDT/aPMjCZ4l139dquf68nUTcSOmgUnB8uf3hdqOBrJ1I/fW6ct+zR5Odmwxv17V8djjEwkeTE4DR71kcBODUS5/ce2MT6tgD/+PwVfnRqlLs2tFKUsL7NT8DjIlgWrK0+HD/ZP8qZa3E2tPnpCPsqIrZxfDpDHoIeFxKqyrI7Ds9dmODKRJL+kThv2b2Gc2NJ7ljfwkuDM3MEa128fv22Dgan0iSzBQSQzBUYnEpzS3eYh3Z2A7My+PpWP0+fG6/sA7M3JzAe/yf7Rzk5NE06X8TndrKnN8rDprLutbg5wHMXtMR7DP2tdy4Ypff1rX5+8OooG9sD7NvUVmmXXvZ4PMP1RI533dU7K8NnCxURfj43C1iq94Sb5b3mZkZNRBQKhUJx07DS5XOdWjL0Nz50L7/6qecs5eGDH/sJM2ntQ6CVjNuIlK2XXyt0cCaVo2D4b7zF7+I7H7m/0q75CL524ny+JCv9afG7SGaLDQULmsMea33q8LsgbR+vYiliP/ixnzBt2qnF78IpRFW7rcr65ofvm3MMgZphk/oY6MfUKF7XIuJ34RCC6Trl6uP5jQ/dy9s+8dM5fQOI+JwIISrnWLRcdqJ8QwAJc+oxbmN3owC7fmtLA7WlgEUpK/Ua+xYzPKeL7tD4zQKW6j3hZnmvudlRsrpCoVAobhpWunyuU0uGfuLESEPysJWM24hErZdfK3SwYPr0a9WuZgVfO3He2J+YTbvqlVfvw3qtSQhYi9hGwVrHqt1WZVkdw3phk1B9TI3idS0S5fOnXrnG42/VN4B4pjhHUDceZ6t6zNvY3SjArl35kqxcA1Z9q25f8zcLWKr3hJvlvea1ipqIKBQKhWLZWenyuU4tGfqRPWsbkoetZNxGJGq9/Fqhg65qv9myXc0KvnbivLE/EZt21StPWG4xi98+4xGwFrGNgrWOVbutyrI6hvXCJqH6mBrF61qEyudPvXKNx9+qbwBhn3OOoG48zlb1mLexu1GAXbvcDlG5Bqz6Vt2+5m8WsFTvCTfLe81rFbU0S6FQKFYZN8t66PkIw7WciaVonzmgDqplWbs+DEwkefzoAAJ4z/6+OY6IMVDQKAVbhd7ZBevpr0X9bp7sH6Uj7KEjNOtHNCv4Gs+bmXS+EmgI8MSJEfaub+H40EwlIE+v+5lz45bBh8bAOr2MB7Z1MDSdZmA8yYmrWl3pXJHnL0/yh2/Yys6eFr587Ao/OnWdOzdEkUBb0MPVqTQ+t5P37u+r+Agw60Z85+WrHD4zRnvIwz2b2tm3sY1Xh2OksgWSuQJXp9L0tvoJeF2kcgXG4jnetLObwak04/EMg1NptneHqxybp06NcuzSJPFMgbfv7eH8eJK9vbNj0NcerPJ09JsIDE2lSea0bwhSuQJDk2l62/x0hHzs6onQPxwDAb1Rf9U+WIRLTqdyPNU/yomr02TyRXwuF3vWt/DQrZoj8tSpUQYmUpW+6I7G+lY/Pyx7MQGvi6DHVQmZ1M8nq+vHKL33lsvoaw+wb2Nb1TVw5OIEY/EM44kcv3an5ogcuag5KUGvqxKOaA6hNHomdtJ9s8J/I+91K/1GF6sB5YgoFAqFYlWvh9b7ZuVMLHYfFzKOyxEYuJjtNe9v9EqsQgLrBezB3MC6Fr+LVK7UVBn660ZvIRpwIw2Ogu55vOOTz1aC+HQ/JJEtki+WcDkEEb97jp9g5avUchuaCexbzGNjh9m9cQooSioBmMZ+L/X5V6tttXyqxaxjNb3X3YwoR0ShUCgUq3o9tN63Rt2ExahrPnUsR2DgYrbXan/dJbAKCWykTnNgXa0QvUbGSsfsP+iehzmIT69Pdxus/ASrP8M2E3rY6Jgvh/sggYKkKgDT2O/laE+tOux8qsWsYzW9172WUBMRhUKhWEWs5vXQet8adRMWo6751LEcgYGL2V6r/XWXwCoksJE6zYF1tUL0GhkrHbP/oHse5iA+vT7dbbDyE6y8jmZCDxsd8+VwHwTgEvpdt8Scfi9He2rVYedTLWYdq+m97rWEWpqlUCgUq4zVvB66ljOxVHXNp47lCAxc7DLnE0DYaGCd7pQ0W4b+ujGsD5gT3mcO4tPbXMut2dAWoH8kRjJTAEHFbWgm9LDRMV+qa9J8PZj/Xc7zr14dK/GcVyweyhFRKBQKxargZpLtF9pOYxjfjjWzsvRKxu6GAc2MR7Nj14yUbBTZa00gaoXsgX3wn7mOets2EoC5WP1frP2Wq7wbVYdi8ZnvRKTOjfIUCoVCoVg+bhYBdTHaaRXG1+J3cfijb1iRfYa5grQuRDcjIzc7ds1I4Uax3Rx+WKs8wFLWt5LrzfK8oDGZf77C9nzPtcW+lm6E5L5Sr3/F4qEcEYVCoVCsGG4WAXUx2mkVxreS+wxzBWldiG5GRm527JqVwnXM4Ye1yrOT9e3keh1dnm9ExJ+vsD3fc22xr6UbIbmv5GtBsTioiYhCoVAoVgw3i4C6GO20CuNbyX2GuYK0LkQ3IyM3O3bNSuE65vDDWuXZyfp2cr2OLs83IuLPV9ie77m22NfSjZDcV/K1oFgclCOiUCgUihXFzSKgLkY79cC6s6Nxtq8J89CtN4cjYiWCNzMe8wmua1QKN4vsjZZnJ+tbLe0yy/ONivjzPWeWe7/lKu9G1aFYfJSsrlAoFIpVzUqRWM1S83MXZhOlGxGk+9oDldTvgNfFrp5I1WNzGcaE8ha/u5Lk/fS58Upq+VdfGGJje4Ada8I8fW6c12/rYCqVr6Skf+/ECK/f1lGVvn3vlnb6h2f41OGLfOjgZu7d2lnVVrOQ/dyFCVLZAhLtDlO7eiJzZHUridsotevPGdt1x/oWXhqc4Y71LTx3cZK+tgDroj6+8dIw7z/Qx+7eKNOpHN8+rqWnd4Y9bOoIMZ7IsaHNT0fYV2mLPi6v39bB6Wtxjl2eYCKe4+5NbQjg+OA0B7a08+COLl4djgGwvtXPD14dZWN7gJ6ojy8f08YylS+yvtVPZ9hnKaWfHJrmC0cGeNOtXZwbS1b6oY+9ywFfPjbEzrVh2kPeypi9OhwjmS2QyhUYnExzy5pw5bht6wzyw1PXef+BPta3BSzPrUYEeLtrpdFtzefqlYkkx4emub03Sl97sOrY68fxLXu0tPn5slKu75XGzTIuaiKiUCgUilXLSpFYzQnkEpgui8vQmCCdK5Zq1qGne0cDHsuE8mSuSL6o/d8twDKYD7RciaDPRSxdsNwm6BEkc7Ov/MMH7uHerZ1Mp3K88eOHqpLKBVRJ9Xrderr9Nz50b91085DPVZHAAx4HMzbtMvMPH7iHP/jSS8yk8zW3cwLFBsqbD1G/CyFERUr/q9/Yy29+7nn7tpRTzs3UOl5mwj5XlY+ip8gbhXcrAR6sU+HrSfpWz1mlz+v9cDsd+D2OyvklgEMffXBek5GVcn2vNG6mcVHJ6gqFQqFYtawUidXcDuMHRWhMkK6HsX9WCeV5wyfcWh9qC1Iry24b4yQE4DOHL1baapVUbsaYbt9ourkxsb3RD+SfOXyReKb2JASWbhICzEmY/3R5rGzbYtO5Zv70m7A4t8zCu5UAb3etNCLpN5I+r/dD38d4fj5xcqSJHs6yUq7vlcZrYVzUREShUCgUK56VIrGa22EUl6ExQboexv5ZJZS7nbOZ4Fbp4DouoZVlt03QU/3KBw9urrTVKqncjDHdvtF0c2Nie622m9sV9rnrbudssLz5YE6Y/4PyWNm2xaZzjfYZIGRxbpmFdysB3u5aaUTSbyR9Xu+Hvo/x/Hxk99omejjLSrm+VxqvhXFRS7MUCoVCcVOgS8JSMserMPsKS8nARJInTozwSNnZOHJhgmSuQNDjmiNIG9d3D06m+OKRAd5xRw8zmUJl7f/OtREt4bv8uMXn4hsvDfOrd/QwnS4wHs8wnszxa3dqqx6eODHC3vUtHB+aqXzw+9qLQ/S1B9jRHeaZ8+M8sLWD6XSe3eu0NfxPnBzhga0dDE2nK/Uc2NzOT06N8r9+dJ4/+oWtvOHW7ir3xZxUfuSi5isAICGZKzA0lWZ7txbEOJPO87UXh+gMe+gI+Vjf6ueZc+NaWwdnKuOli8h6u/b2an3Z29vC0UuTbGjVXI1vHR/m0f2zrsTgRJJnL4wDcMf6VtKFIr1lh6M36ueHr44S9DqZTOZY1+pHAK8OzzCRzHH3xjYATgzOsH9LGwe3d9E/EgMJva1+vvvyMJl8Eb/byXMXx/G6HGzpDLOrp4XOiLcqrV2XqE8OTfPY0QEeuqWL8+PJqn4cH5xha2eQJ165xs41YdrDXgCS2QL9wzHG4zl290ZI5orsWBOuHLetHUGeOn290u8jFyYYS2QYi+d451299LUH55x/z12YQAjYuTZS5eIYxXvzcU0YvBNzv/TztpI+PxxjYDLJiUGtjL6OIDvXRio3KtCP4yO7G3NE7EInF+vmDzeDT9EMN4u8rxwRhUKhUKxq9PXSsXSefGnu/13ucrjeUq6jbmbNtnFboxNRay39yaFpfuUTz8553soPWGg/jf4JaOF8KYtgPqt+GUP9YNYJiWcKFEoSl1NQKMqKK6G7JM222ezkWAUNmtsjoBK02Eh9Vv3ROdyk82B3ftjVUa/8WqGIobKjlMwWa46NeX+jy2P0kZaDRkMn51v2zeJTrEaUI6JQKBSKVY2+XtpqEgKzHsJSrqNuZs22cdtYg2vpv3BkwPJ5Kz9gof00+idA0/0yovsf+ZLUnJCi9m9BVvsEzba5kaBBc3uMTkoj9Vn1R6dZ56Gen9Fs+bVCEc3eSq2xqeXyLKd30Gjo5ELKXs0+xWpETUQUCoVCcVOgr5d2O6xXrusewlKuo25mzbZx20iDa+nff6DP8nkrP2Ch/TT6J0DT/TKi+x9uhyjfUUn71yWqfYJm29xI0KC5PUYnpZH6rPqj06zzUM/PaLb8WqGIZm+l1tjUcnmW0ztoNHRyIWWvZp9iNaKWZikUCoXipqFq7brBqzCuWV/q5RjzDe5rdC297h68fa/mkhjD+RZ7vfjARLLSJqO/0Ui/dGdE902AqqBD87/zbXMjQYPGkMGdPc2fC5X+ZAqkckXGEtmKk7GQ9lq1cSyWbar8WqGIQENjY97f6P8s9/KlRkMn51v2zeBTrEaUI6JQKBSKG85yy6LNhLktxf526CGExmBBq7KM4q4u3OshcsbnzGGAxrZaib/1XrPqZyNBeeb99Da1BtyVUMCHdnZX7W8MNLRqXzJbqORrGAVqY/DhV18coq8twC1rwvygf5S+tgAP75xfCr1V+/W2mY+bMTjSOGmoN7Z29diNRTNttwqNtCpvsa9Fu/KMgZsLCTScb/2KlYGaiCgUCoXihrLcsmijAW2NyOTz2d8OswTuEtAS8Mwpy1KydoqqnBBdvDaGARrbagweNIrH9aTgWhK0XVCeWX7WBel4tjrbJBpw8+2yWB9L5ymU5BxZ3RyaaCTqd+EohyEGvM6qwEjzdoc++oZ5y+9G2Tvsc/H5397HWz/x7OxxcwgKpdngSP0GA+a2WwnfdpK9Hu44n5sqmI+pcZzM5+piX4t25ZkDN+cbaDjf+hUrByWrKxQKheKGstyyaKMBbUu1vx1mCVwPFjSXZSlZm5LwrMIAjW21E4/rScG1JGi7oDyr/cwBi3o79P11ed2qHKuQRL2txr7aMR/R2XwDAWNfvnhkoPq4GW6KYLzBQK1xt6rHLtxxvm3XMY9TreO70GvRrjxz4OZ8Aw3nW7/i5kdNRBQKhUKxKCy3LNpoQNtS7W+HWQLXgwXNZVlK1qYkPKswQGNb7cTjelJwLQnaLijPaj9zwKLeDn1/XV63KscqJFFvq7GvdsxHdDbfQMDYl/cd6Ks+boabIhhvMFBr3K3qsQt3nG/bdczjVOv4LvRatCvPHLg530DD+davuPlRS7MUCoVCsWgstyxqlNetwtxquQ527bUqcz7Ls/QQQT1Y0MqzaA24+eGro3RGPPjdLkJeFzt7Ihy7NMnAZIoDm9v4yZkxJPC223vmlDWdyvFU/ygDkyneeVdvJeAOYH2rn+8cHyZTKNIT9ZPMFXmXQZA+OTTNJw9doDPk4QMPaEnhX3txiIDHSTJX5N7NbRy5MElH2EPA46r4GzPpPI8fHSCdK7KtO0RX2Edvq5/Hj17m5FCMiN/N6za1kcwX2dDmx+92IQRIScUFGU9keXlwhjURD68Mx7mtJ8xoLMeBrW289fZ1DE6m+Owzl7i7L8rWrhD/9MIQd/VFuWN9K989MYyU8N79fbT43XW9gelUjif7RzlzLa61x1PdnlSuwJXJNO0hD1en0+SLJbL5IvFMgS2dIUZjWfZtjPK9k9e4Z2Mbt6yNaGP78jCgtQOY4wTNpPOV4MFYOl8JsyxK2NAW4NilSS5PpLh3SxsvDc5wx/oW/vmVa1ydynDPplY2tAcr/onuhqSyBcaTWU4Nx3n3vl6m05pfY7yZge6iPH9pkiuTKd60s7vSJrD2jOycGePrfe1auKGk2k9xOeCbx4d53/4+dvdG55RZ67jUc5LMDtVy3ZBC0TzKEVEoFArFa5Ja68fnu7Z8KdekG8vOF0tzQv90P0P3K4z/S5sD8MzOQ1FKZtLa8h39Q78RfR0/wMGPHap6Pux3EUvbL4UCLbhQQtV2Ub+LT73nTn7zc89b7uMS2vK0RhDAlz5wT1VZxoBCo7tiF+JnZDqV4+DHflIZk8XCOLYtfjcz6VmPxSUg4ndX/BO/x0HMEGb5nY/cx6N/97yt+2KkNeDmWx++j7d/8lnLwEWjfwSzflOuWKpqq9vpmOPEmPexGk+71/WyEpnCHAfIuE+t41LLUdLLWarwQ8XioxwRhUKhULwmqbV+fL5ry5dyTbqxbKvQP7NfYcS8Bt/KedCx+uyvr+P/3omROc8navgYOvFMYc52sUyBTx++aLtPo5MQvR2fMZVl9CrM7oqdI6FTK6xwIVQHQVZPEAqSOf6J0aP44tGBhttk7LMVRv/IeC6Y22rlxFg5UVYektXrellWDlAj1049R8lYjs5ihh8qVg5LPhERQniFEJ8QQpwTQrwqhHi8/HyXEOJfys+/IoS4f6nbolAoFIrVR6314/NdW76Ua9KNZVuF/pn9CiPmNfhWzoOOVeyjvo7/LXvWznk+VMPH0An7XHO2i/hc/MHBzbb7uKzzJy0RwAdNZRm9CrO7YudI6NQKK1wI1UGQ7qrXXII5/onRo3jf/r6G22TssxVG/8h4LpjbauXEWDlRVh6S1et6WVYOUCPXTj1HyViOzmKGHypWDku+NEsI8T8BJ/BHUkophFgrpRwRQvwdcEVK+WdCiH3A14AtUsqafyZQS7MUCoVCYaaWmzJfb2UpfRejh2IV+md8/djlSQYmUrYBeOaAO2OwX/9wjGSuoLkFiRy/ZihjYCLJl44OgID3vE7zLY5cmGAskWE8nuPhnd0MTadJZrVQxaBvNrjwqf5Rzl6Ps70rXMkNOTk0zf966hy5Yok710cpAm/a2c10Ol8VQImE8WSW0yNxDm7r4MilSR66pYvz48lK2OPARJKvvThEX3uAfRvbLMemVoifeXyeOjXK2Wtxetv8dIZ97FwboX8kBhJ6W/08eWqUgFtzYw5sbuPQmTEyhSJbO0N0hn04BXz+yGX29WmOyM6e2f0PlL0ZsxNkbJs5zFJv08CE5gEdH5phb28L//LKNYanM+zb2MqGjmAlcNAYAtjb6ueZ8+OW/pE58NMc8Gg1XvXGs16AolVgZSPXTq2gRmM5SxV+qFhcVqQjIoQIAleBXillwvRaAtgkpRwrP34e+FMp5aFaZaqJiEKhUNy8NBIEt1T11pJnByaSldC8RoLyaom1etq4XeBcraA/o8T+9LlxXr+tg8GpNGAfbqj/Xqu8hYbAmYXm5y5MMBbPMJbIVQnwjYxRLTHavK/eJ2PfrPpeq7x6Nyswno/G8MR7NrVV1Wl33tYK9KsX6thoG43hjnpdRlHf+LzdsTCfm822wwq7/tUKw3yqf5TjQ9P4XE4ePdBXN3dEBRneHKzUicge4JvAV4GHgDTwZ8BxYFBKGTBs+xXge1LKL9YqU01EFAqF4uakkSC4paq3ljxrDiCsF5RnF4RoDuiLBtwIrOVgq6A//bWZVM7Sq7AKN1yoNNzs+AXL9RlFa6sgu3phkVbitPkGA/oY6cGCZuHaKE/biev1bmRglKEjfhfxdKHK//CU6zTeBMAoTdcK9DPflMAuyLDRmy2YpfeI30U6V5rzvNWxMJ+bVuJ3szdosOtfrTDMBz9+qO65Y1eHCjJc2axUWd0NbAb6pZR3Ax8Bvgy4sL6ZxxyEEH8shBjSfxKJhNVmCoVCoVjhNBIEt1T11pJnzQGE9aRYuyBEc0BfvIYcXEvytZO7rcINFyoNz2f8zOK0VZBdvbBIK3HavK/eJ2PfzPvFbH63C2C0qkcnkSnM+WBidRMA4/lRK9DPfFMCuyDDZm62YKzL7nmrY2E+N63O8WbPF7v+1QvDNGLV3oW0SXHzsdQTkQGgBHwJQEr5MnAJuBVACNFp2LYPuGIuQEr5F1LKXv0nFAotcZMVCoVCsRQ0EgS3VPXWkmfNAYT1pFi7IERzQF+4hhxcS/K1k7utwg0XKg3PZ/zM4rRVkF29sEgrcdq8r94nY9/M+0VsfrcLYLSqR0f/psmI1U0AjOdHrUA/800J7IIMm7nZgrEuu+etjoX53LQ6x5s9X+z6Vy8M00i9EEQVZLj6WQ5Z/YfAX0opvy+E6ANeAPYA/w24bJDVvw5sVrK6QqFQrF6mUzmOXJgAQUXEtdqmmTA0sHcNjNsbJVvz9gMTSb7+8yE6gh46wr66/oqtWHthgmSuQNDj4kDZEakVmHjs8iSXx1O8eZcWOqeHxl2PZ7g6lcbvdvLK1RnWtfr5xdvWcHxwhkfKfoBRhP7ai0N0hDwEvS4CHhfrW/384NVRusIeOsO+OYF3zay3r4QmTqR4593ayouvvThE0OsklS1WBPiBiSSPHdHk90fLYYPGMdcdhZC39tjo26XzBQYn0qxv94OEsUSuEtp45OIEUlaH3AEcuTBBouxB7OqJ8OpwjCsTSU6NaAGAhRJznBWjDD2TzvP1F4fY0B5gR3eYH746qgnzm9qq5HTz0j5dVNeDA80hhDAr+1v5K33tmvAd9WtukNH10MX9DW0BblkT5pnz4zyye+45oMvwujti9mrM56bdtVcvDNR87Z28OkPU7+aZc+M8smdWxreS1Qcmkjx+dIBYOkeL38Ov3N5TNWbmcbEKKK13riqf5MawIh0RACHEZuDvgHagCPxfUspvCiG6gceATUAO+JCU8nC98tRERKFQKFYvjawJNzsL9ULtGi1/udejm90Ut1MQ9rqqnA/z/9Buk2dgtf7fHCAYDbg59CcPAs07I+byW/wunELMGW9zX2A2fLFRN8iqL2b0gD87B8HofFjhdgoivrmehlW/mwnTswvos3KB7M63z//2Pt76iWerXI8Wv7spr8rs11jV2yj1PJ9mXaRGQgzreT3NtldNRpaPleqIIKW8KKV8UEq5W0q5V0r5zfLzo1LKN0kpt0kpdzUyCVEoFArF6mY+YWj1Qu0aLX+516Ob3ZR8Uc5xPsyYPQOr9f9mx2QhzoiV12M13ua+wOza/0bdIKu+mGnWQTCTL1p7GlZtaSZMzy6gz8rdsdvni0cG5rgezXpVtRykZqnn+TR7XjUSYmhX73zbq1j5qGR1hUKhUKwY5hOGVi/UrtHyl3s9utlN0f5aX+18mDF7Blbr/82OyUKcESuvx2q8zX2B2bX/jbpBVn0x06yDYMbttPY0rNrSTJieXUCflbtjt8/7DvTNcT2a9apqOUjNUs/zafa8aiTE0K7e+bZXsfJZ8qVZi41amqVQKBSrm2bD0KDxNeT1yl/KEEMrjH6BOQhPd0jOjmrBgfs2tc0JjtPbrHs3O9dGKq7BD/s1v+GhW7urlgM12z+z16O3z2r9vzEg0XwL2XpukHm73uhscN/QVLpqX7t+GJ0PPXRwLJZlLJGthCs2c540E6ZnF9BnFfhnt49+PujBh82MnbnMWvU2ip0L1chzzYyR1XFcaSGkitqsWEdksVETEYVCobg5WYpwvYUI2M2E31mF1pm3rRVsZ94eqoP4zGF55qA9gG8fv8qLA9P8+r5eptMFxuIZzl9PIKWWtp3IFnnX3ZrMbQyv06XtpC5NG54bj2e4MplmQ5sfv8dVCbszB+c92T/KwGSKeze38ePTY8xkcgjA63KytUtLH9cFeYeAV67O4BAwEsuwtTPMLWvDXBpPMJ7IsW9jGx0hb6Ud3z85wuGzY/zOvRvxeVz0tQc4dmmS44PTTCSyXJlKsaunhUf393HmWpzTo3F2dIdZF/Xx5WND3NUX5cEdXRw6c52nTo0igHu3dNAe8iKYzQoIejWJ/9svD4OEN97SyU9OjyGBt+3tmRMc2dce4NXhmDbOowkQsC7qZzyRY305oX1XT4TnL01y5lqcHWvC3LOprTKu1xM53ryzuyJj62N6x/oWfnJ6jOlMDoHm/NzT18r3Tl7jrr4ob9u7rnJ+9LUHeP7SJCeGpoml8ySyBbZ0hrgWy/J7D2xifVugcqzNfTXecMEqkDKZLZDKFRgztFPvs3Ec6l1Xzd44YrFZqSGprzXUREShUCgUK5bFEkkbFV7n0xa7561C61r87rqisV3AnzmUzxgQqMvYb//ksxVROup3UZLU9Sd0WvxuZtLVoXFWwV12//sb9xdAyOsgni01VPdCcQooSfu2LQei3I7iIrXD7RAEfa6qIL9ahLwOPE4niWyRXLH2uIdrHBtdsAdqBlLquCz67Kkju9ud18sli6/UkNTXIitWVlcoFAqFYqnC9eyE1/m0xe55q9C6RkRjuzrNoXxGKdrYJ51YpkA829gkRCuj+kOm1YfpWh+wjftLWLZJCCzeh/+FINGE/8Vqh36DgUZJZEuVGwLUo9ax0QX7eoGUOlZ9bkZArxVSuVSs1JBUReOoiYhCoVAolpylCtezE17n0xa7561C6xoRje3qNIfyGaVoY590Ij4XYW9tCdtI2OeuemwlvdtkJs7ZX6D91X25cIrabVsOBNq3A4vVDv0GA40S8joqNwSoR61jowv29QIpdaz63IyAXiukcqlYqSGpisZRS7MUCoVCsSwslkjaqPA6n7bUCmKzkojricZ2dQJVv5tlZLMoDfCd48O8eGWKd9/dy0y6wFgiw/nRJBJJb9RPIlecDfwzhNft7InQPxwjmSuA1PyBisgdzzA0laa31U/A46qE3ZkD8p7qH+XKVIr9m9o4dGaMmXQOEHjdDrZ2ao5Ib6ufH/aP4gBeHYkhJFyLZdjSFdIckbEkE4ksd29qoyPorbTjn0+O8PS5MX7rwEb8XldF0j8xOM14IsvgZJqdPRHeu7+PM6Nxzl6Ls31NmJ4WH195YYg7+6Ic3N7F4TNj/OjUKCA5sLWDjpBXG/jyermgx0Vvq5/vntAckQd3dHLo7BhSwltv72FoOl2R3K9MptjQpoVLjiU0FwegJ+pnMpmjN6o5Ijt7Ihy7NMnZ67M3FNDHdTyR4+FbZwV5fUz39rZw6OwYM6kcCEGLz83dfa18/5Vr3NkX5a23r6ucH7NjMcNMJkcyU2RLV5DRWJbfvV9zRPRjXaF8jA+YHBHz+aY7IuPJ2XbqfTbe+KAZAd14Xi+nq9GMzL+Y9SoxfhbliCgUCoViVbJSpFBjCrZZ4h2YSPLVF4foDHkqErMu/VoJtFaCbT2B3ljv4GSKLxwZ4P0H+ljfFqjspwvRr9/WweBUukpYN6Zs15J7deleL0MXr99VTlA3tkkvTxfp79kY5V/6r/Ohg5sB+NThi7zvdRvweVy0BmZTwwG++sIQG9sDPLSzu+5xPTk0zWefucTdfVHeunfdvML59DE0ivswV+zW69PHd3dv1LKMVLZQcYIChjL0fd90axfnxpKVmxeYj6Pdv4t9ns/3+lnodbeU1+1KeU9QzKImIgqFQqFYdawUKbRWYvVMOj8nVdwog5sFWqvU7m/WSAs31+tzC2KZYqWuiM9JJi8JeBzMpAu2boPbIQj5XEgpmU4XKnUbE8OtEtKNfTKL+iGvk1yxSKKOR+JyCAolOWdsYDb53e64nhya5lc+8exsf/0unv7oG5r69ktvb75YsuybcRzM9X33I/exvi1QOQ75kvUItwbcfPI37+A3P/d81fMC+M5H7uO3/v5YpQ26DO9yCgpFWXm8kCT0en1fzpTypbxuV8p7gqIaJasrFAqFYtWxUqTQWonVVqnixsd2ido6sQZSpqvrLUJV+cWKLFzrT4u6NB3L2CeGW/XF2CezqB/LFOpOQoDKJEQvp7r9tY/rF44MVD1ONHkeGNtr1zfjOJjre+zoQNVxsCOeKfDpwxfnPC+Bx44MVLVBF8PzRVn1eLHP8xuVUr6U1+1KeU9QLA5qIqJQKBSKFctKkUJrJVZbpYobH9slautEGkiZrq7XWVVX2OesyMK1BGtdmo747BPDrfpi7JNZ1I/4XIQakNldjtlSzeXXO67vP9BX9TjU5HlgbK9d34zjYK7v0f19VcfBjrDPxR+Ul6QZEcCjB/qq2qCL4W6nqHq82Of5jUopX8rrdqW8JygWB7U0S6FQKBQrCqvAwiMXJkiUfQfzev6lWC9uLNPoXehCr1nife78GH/14/Pcs7GNW9ZGKuJ2Z8hDR9hn2eanTo1qQXjdmuSsB9d5XU4ePdBHi9/NiaEZXA74p2NDrIl4uTKZ5vb1LaRzRZ6/PMlv37uRWKbAwESKN+3q5vS1OCevTlOSsL0rDEIyFstxYEsbRy5OsqEtwC1rwlrqelu1n6F7K2PxDBeuJxiYSHJqJMbWrhAup5O2gIeOsJdHyx/UnzgxwiPlwMNPHzrP909e4y271/DqSJwPlj+QG8ekxe/iWy8N8+iBPiJ+N5975iITiSx/8OBWdvdGqzwbgLF4jnfdrXkp+vge3NHJr+/bYBv6aHZp9OdaA26+8/IwsXQeicTndrK1M6QJ+mVpXndHgl4XLgGP/+wKHzy4mXu3dlbG5+TVGaJ+N586dIEXLk/QGfTRGfFwYIsmxwfK+37+ucts6woxnSnwgfs3sbs3WrkBQTJTIOhzsXOtdhOBRLZAyOeiN+rnh6+O0tegN2M8T/U+uxzwjZeGeX95jPUAxeODMzxiE7RpPPb6WNZLPW/kejMmvNdyX+YbSKpE8ZWFckQUCoVCcdNjtf4bmONU6Ov5l2K9uLFMv8dBLD0rJZuDCoE5gYe6D2Dlk9h5ImYE0OJ3kcwVyRft/582+xZup6hsL9B8ilS2SKG8tEt/3twmczBcPaIBN6lycN1f/cbeKi/iux+5r/LB2y7szhwA+aUP3MN7Pve85dKp7xr8CrtzIhpwI8rl54slXGUfRoDtkipj4J/5WAi0b3Eifvec43b///NjEtninPLs0B0hwDaMM+h11nR3rDC7Lw7ArlUuAS0Bj+X10UwoYLPXW73tle+xelCOiEKhUChueqzWf1s5FfVCCBezDbWCCmFu4KHuA1j5JOY67JDlftaahOjbGTFuL6GqHcbnrVyURpPbKZerl2H2Ih47OlAp0y7szhwA+ZnDF239jS8eHah7TsQN5UuqfRg7r8Mc+GfEWIb5uDUzCdHbZhUsaPaCark7Vpjdl1qtKkh7F6eZUMBmr7d62yvfQ6EmIgqFQqFYMVit/7ZyKuqFEC5mG2oFFcLcwEPdB7DyScx12CHK/XQ7a8fqmV81bi+gqh3G561clEiN9pgxjo/Zi3h0f1+lTLuwO3MA5AcPbrb1N963v6/uORE2lC+o9mHsvA5z4J8RYxnm4xbyOmkGc5vtwjhruTtWmN2XWq1yCXsXp5lQwGavt3rbK99DoZZmKRQKhWJFYbX+2xzw10gI4WK1wRjuZ7fG3hxmaFwfbxcKZ+yTMWBwPJHj1+7Uggl1J+GH/aMEPU4mUzl6owECHmeVZ2AMoIv63TxzfrwSRqi3o15QnTEYrjfq529/eomTV2d46NYu1rX6OT+axOt28N79fZVy9TJODk3z2NEBHt0/m7thHkeoDrszj9nARJKv/3yIjqAHEIwlsryznF3SyDmhl28cc+Nzxy5NcnY0Tm+bn86Qb07gX8XfKDsjtY7b3//0Et89OczWjiAbO0P8yp4ehqbSlfHt130Tn6sqZK9WGCdge47XO0/1PjsFfOv4MI/u1xyRJ06O8MDWjkqool2ZzYQCNnu91dte+R6rA+WIKBQKxSpnJYd4WbXNSoBdivoAy7prhQMaRfhaY6qH023vCvLVF4fY1hnmDbd28XBZJrYrzxhSp4ffGUPvzGOjC/FvqSEUm/tgF4A3ncrxVP8op0fjdAQ9JHLFShihXXv1sEE9xBCohCD2tQcqIr0E1kX9JHNF3ryzu7Lt+lY/3zk+jATetreHM9finL4WZ32blkCu9/GrLw7R1xbg4Z3d2mNDqCHMDVocmEhqS72k9k2T3odvH7/KkQuTHNjSxoM7uirjMJPO84kfn+PSeJI/fng761oDc8a13tjbnct251uj4ZT1zlmrthnLT9a4WYO+j11w5fpWPz8o36DA7ty1q79ZVtr71Eprz2pFTUQUCoViFbOSpc5mBfPFrC/odSKARFmC1uu2GitzO42ysNWYmoPtjET9Lr79kfur9tfL0yV1l0PM8RNaA26+9eH7eMcnn63IwRGfk3imWFOIt+q7LmUbherpVI4HP36IaZN0bg7VM7Z3JpWjYPFRQDAbuLfQTwphn5NEuY8AYa+DuCF/pMXvBiQzBln7C79zT9X46314z+eOVgU66vJ9wOu07LdxXFv87ioxO+J3ESvXaTVG5vPJLN1/40P38vZPPls3nLJWGcZjZxV0aSxff954swbzTQasgiuNtPhdfMd07uptbFRat2OlvU+ttPasZpSsrlAoFKuYlSx1NiuYL3Z9MZu67cIB7WRhc/vMwXZGrEII9ce6HG4lSevbVcvBsx/Q7YR4q75bCdV2Erw5VM/YXqtJiL5PfhEmIUDVJASomoQAxDP5OefLF03jP9uH4pzn9T6ZMY+rWcxOGH63GiPz+WSW7vUxNLbb6ryqVYbdsYtZlK8/X+smA1bBldVjbd/GRqV1O1ba+9RKa49iLmoiolAoFDcBK1nqbFYwX+z6IjZ124UD2snC5vaZg+2MWIUQ6o91OdxKkta3q5aDnXWFeKu+WwnVdhK8OVTP2F6XjSVuDNxbKCFDH0H7RsRI2Oeec768zzT+s31wznle75MZ87iaxeyQ4XerMTKfT2bpXh9DY7utzqtaZdgdu4hF+frztW4yYBVcWT3W9m1sVFq3Y6W9T6209ijmopZmKRQKxU3CSpA6a7kXVjLxd45f5YUr0/xeOdRtMdthlqCNwWn6c1ZCtp0sbLWWf3Ayxed+egm/S3D04iQdES9vvb2Hg9u7Kv5E/0gMKbX1+ccuTVbCBfXww/6R2aC8A5s1qfq5CxNcmUhy7NIk61r93L+1g28eH6Yj5OEDD2h3ofrqi0M4gVeGZ2gLedjSESKRLXLvljaOXJjEIeDyRIp37+tlOl2oeCi7eiJ88+dDfP/kNQ5sacPrdrKtK0yLz8XfPHORzZ1B3r53Hc9dnKQz5EEIOHphgpNDM4T8LuLpIulcgV09EdZE/Qgk4/Ec/ddiFIsl1kUDbF8TZmNHkNMjMYan0zy8s5uvvXiV8USW3esiBL0uhqcz7N/cRjJb1L4JSOcZmEzR4ndxz8Z2RmIZBidTlW9D7tgQ5fS1OKWSZO+GKB9+wzZi6Tz/7funuDCWwOkQPHRrNyVgcCJJ/0iMzpCPiN/FmhY/uUKRK5MpIj4348kc77xzHUcvTuEQkt+5fxOFEvS1B/jbZy7yjZ8P4XY6+K17N3L6WpyOsJcPPLCZWDrPJ39yAZ/bARImk1lagx6EEMTTeV6/o5NtXSH+6dgQfe0BrsXSnLuWYCSWxiWcRPwu7t/WQTpfxOdy8oZbOnlpcIa37FlLLJ3ns89cYlN7gJFYholEltdv7+Rte9dVzonTIzGeOXudbWsiPLq/j6lUHpcD/urH5xiaSrN7XZTXbW4D4NxoAocAn9vJz69Mc9+WdnrbAggBUpYnYQKCHhctfhf/dGyIW3vCtAe9BL2uWa9HaHc6a/G7ee7CBKnc7LnaaACh0WMRQNCnBTQ+fW6ct+zRJta6h6MHdS5mwGEtGnnfbKRO5ZrURjkiCoVCoVhSml1vbQ76q+U+LHfbGi3H6JHoTkbY56qs8dfX+ycyharQQLv19Xr5dl4GzA0pbJZw2TnRificJDPFmjkTK5WQ19l0bgeAU4AxgkX3XWrlsvzPd+3h3331xDxaWZ9axzTsc+J0OOb4LVAdUNkITqrzRMzOSq44uyTO3KYWv4t0rlTz+mnEB9PKcjOTzs+pRw/qTNnUcyOcjkbqVK5JfZQjolAoFIolpdn11uagv1ruw3K3rdFyjGvpdSfDuMZf/90cGlgrPK6WlwELm4SA5mMYid+kkxBgXpMQqJ6EwKzvUov/9aPz86qrEWrVnMgUbcMtm5mEwNxQQ7OzUqtNjVw/jfhgWlmzkxJzmKbZ6apX/lLTSJ3KNVk61EREoVAoFA3R7Hprc9BfLfdhudvWaDnGtfS6k2Fc46//bg4NrBUeV8vLgLkhhc0SMjkUYZ+zZtjdSqbZ8EAdcw6k7rvU4o9+Yeu86mqEWjWHfE7bcMt6bTZjHi2zs1KrTY1cP434YFpZbst69KDOlRRw2EidyjVZOtTSLIVCoVA0TLOeijm0biW1rdFyrMIJAUtHRQ8NrBUKp5cX9bv57olhkPArt2vZG1emUvzandrqhq//fAgBvDo8Q3vQw+aOEIlckTft7GZoOs1YLMtYIlt5bPRQBidTlYDB9W0BTl6dIZ0t8PdHLnNweyf3bengyf5RNrQH2NEd5qsvDHL+eoJCqUQ6V2AmU+S2ngh7eqNMJnP4PU6OX5kiXyyxtSvMu+5ez9BUmoHJJKdH4vzybWv40elR+ofjbOkKccvaMKlskYd3dnNmNM6JwRkyhQICgdftoCfqZzKZoy3o4cTgNGdG4+zra2VkJku+VGJrV4jfvX8zLX433zk+zHdfHmIsnuPg9k68HicBt5NXrsbwuh1k8yVu64lweTLJ1ekMv3f/JvxeF04B//TCEHf1RTm4vYsrkyk2tAX4h58N8LUXBvF7NCH+/FiyEsR4cmiaz/30EpvaNI/jWixDNODG53LidTvY0xtlR3dYC5j0Ork0luTieJKZdA6PUyAQ3LulAylge3eYHd3hSrgkwNdeHCLodTI8nQYJe9ZHeehWLUPlyIUJBiaTnBiaYf/mtkqbo343jx+9zNnRBK/f1sn69gCpbJFz1+MIAa1+D5cnU7z77l6KkoqXZAxGNJ7D+ms7eyIcu6w5Te+8q3dOSGUz15k5EPJAOZ9Fv/aByu/16rkRLlyjHsmNdvRWMsoRUSgUCsWSs5jCZqNlLUadViGAwJwguFohgbVCBO3qczngGy8N8/4Dsx90P/vMJXauDdMe8gIQLAvmev2HzlznxYFpfr0soesTDHMb9bDE0yMxnr88yb9541Z29rTwZP8oJwanQcCe3ii3rAnz9Llx7ljfUhGn+9qDlbDGX72jh+HpDMcHpxHA7euj7NvUxqvDsaqxMYcg6n371Tt6KJSwDNH79vFhcoUiu3ujlRDDv33mIkPTafZtbKOvPUjU75pTjp7W/oUjA5WxM4cv1gp/rBdcafVYl63TuQJXJtPsWBPmnk1tVX0fmEhWhTIClTbpgYH6PvrxgdrhgNOpHE/2jzIwmaoETw5MJGvK3TdCnDYfj3rUGnOgqeupVrn1XlOS+fKgJiIKhUKhWFIWU9hstKzFqFMvQw8adDsdhLxOinI2QM8Y3mcVElgrRNCuvulkrmrNfi0ZWqAtJ6rljhjbaLdt0A3Juc7znHL++r138q8f/3nNbYyCsbu8JKUS2pjJV/kLLoe2HEeCpXQNc0MNrXAC0aCHv/qNvfzm556vPP8PH7iHP/zH48TS+Uo+i90NEOoFV1o9NgZMmsdB7/vnf3sfb/3Es5X2R3xOXA6Hdl6ZOuU2hVnaBXpOp3Ic/NhPqs7DL33gHt7zuecrN3mImCRysA7sXErM4Z7f/ch9NScjtY6B+QYP+vg20o9a7wd2Ir2SzJcHJasrFAqFYklZTGGz0bIWo069DF0oN0rmOsbwPruQQLsQQbv6zOJwLRlaUnsSYm6j3bb1JiF6Of/7x7XFbLNgPCe00SRRF0qa7GwnXcPcUEMrimjlfPrwxarnP3P4YuUYGttldQOEesGVVo/twv+Mff/ikQHTDQmKxGxuPGAOs7QL9DSL3rLcV+NNHmqFIy6XOG0O93zsqH3YJ9Q+BuYbPDTTj1p9ryXSK8l85aImIgqFQqFoiMUUNhstazHq1MvQhXKjZK5jDO+zCwm0CxG0q88sDteSoQXUFNjNbbTbNui2ft5czr95Y20x2ywYzwltNEnU2jci1WNqxhxqaIUTrZw/OLi56vkPHtxcOYbGdlndAKFecKXVY7vwP2Pf33egz3RDAicRmxsPmMMs7QI9zaK3KPfVeJOHWuGIyyVOm8M9H91vH/YJtY+B+QYPzfSjVt9rifRKMl+5qKVZCoVCoWiYxRQ2Gy1rMeq0E86PXJyoiLu6zKy/bhWEaPe6XX1OAd86PjxHht65puyIlAPnjPUfPnOdn1+Z5l/d3ctMukAyV5izjf5v/0iM08Mxnh+Y5A/foDkiT50a5cTQNAB71kXZsUYTpvf2tnB8aKZy44CTQ9M8dnSAt+/tYXgmw8uD04iyV7JvY1uV1Gzss7lvb9/bQ1EyO6YXJkBAb9TPd14eJlsosmddlId0R+SnFxmeynD3plb62oK0+F1zytEdEV241x0RXfLXBfBajkit4Eqrx0cuTJDMFUjlCgxNpdneFWbfpraqvg9MJPn6z4fY0BrgobIjordJl//3bWyrOj5GadyurU+dGuXKRIpfMzgiteTuGyFOm49HPWqNOdDU9VSr3HqvKcl8eVCOiEKhUCgWzI2Q0ZeC+SYlN9tmo1QMs+nRfe3BKsFdl5bXt2pp06/f1sFUKm8p6+ry8plrcXasCfPwzu7KBzldan7zzm4Gp9KMxTOcu55AAD1RP8lcsfJaKltAAqlcgbF4jjfv6mYqla/Iwnod69v8BD0uAl5XRRx//4E+In433zsxwrbOIN84fpWOoJd371vP4FQa0CT2758c4UenrnPflnZ2rI2wvtXPD14dpSvsoSPs497y3ZMeLy9revSAluD9ZP8oLw9OA7CtO0TAo30rIKCSDv/8pcmKwK2P7eu3dVT6FvC6KhK4fhzuWN9SSYzvDPssBX+rmxLoNxG4dW2YvvZglaDfPzzD//7xee7Z2MYtayNV4rn5GCez2jIr480FFpLWbTy/9EmX3T5mmV8/x/b0tjA4mbIU/82yPmhiPdD0tWuW0Y3lzTdc1Cy3z+e9RAnsy4OaiCgUCoViQdwIGX0pmG9SMjQnthqT42FW8BbAdz5yH7/198cqgrvV/7ROoARVsi5QJS8DRP0uvv2R+3nrJ35a9fx80JeVFUolYpnaYYELTXgHTbSOpwtV5YR9rpouSTPtaA24+eLv3FMlkZv3dTkEhZKspK3rj/VxN4vxOh6nA69bVKXUw6x4DlTdxMBcv1F0n09at/H80sX8Fr/bch/jDRmMforbKfB7nMQM540u/tvJ+lG/CyEEyWxxXhJ50OtESsl0uU47Ub/R8nS5vZn2WJWjBPalRcnqCoVCoVgQN0JGXwrmm5S8kOR4oEow/uLRgSrB3Yoi1TK0XUq1MR17ocwmw9dPLF+MP1MmMoU55SSa7EetdlhJ5OZ9dSm6IKsf6+NuFuN1csXSnJR6mBXPzTcxsKp7IWndxvNLF/Pt9jHekMFIvijnjLcu/tvJ+rpMvhCJ3Fxes9e/sbz5tMeuXUpgX3moiYhCoVAogBsjoy8F801KXkhyPFAlGL9vf1+V4G6FE+bIulYp1cZ07IUymwxfP7F8oQnvACGfa045oSb7UasdVhK5eV9dinaJ6sf6uJvFeB2P0zEnpR5mxXPzTQys6l5IWrfx/NLFfLt9jDdkMOJ2ijnjrYv/drK+LpMvRCI3l9fs9W8sbz7tsWuXEthXHmpplkKhUNjwWlxHvJQyer0gskbWlNcLpmu0H3p9gur06ecuTDCeyHA9luNdd/dWhckBVUF+b7q1i6dOX+fstRgHt3cB8PzlSf7wjVu5d2tndZp1OXG9N+rnyf5R2kMegh5XZdmN7jsMTqb4y6fOMpHIUCjB/Vs6cLmc3LuljecuTuIEzl5PcHtvCwGvk/OjCcYTWQYnU6xvC9AR9rK2RUstX98aYCKR5Qf91yiWJB0hD+OJHB990w7u2dzOU/2jHLs8wZWJFIWSdueroNfFtViGXT0t7N/UxldeHKKvzc/PB6eZTubwupx43ILusJ99G9s4PjjFiwNTuJwOOsMednRFeGFgii2dQXweJ3dvbGMqmePbx68ST+doD/u4racFIQTnrse12wBLSaEkiWfyZAsldve00NMW4Mp4glimwLv3rednlyZJ5wr43S7GkzkObG4jmSuSKxQJ+zxsbPfz989dxu92EvG76Qx78bmclKQklS1yeTLJtq4Q3WEfT58bI5sv0t3i4xdvW8uDO7r4+otDPHFimE0dIXpafSDh2OUp3vO69Rw6M8YLA1PsXBsmGvBydTrNwe2d+D1OXrg0SXvYy6aOIJfGE1ybyeBxOrkWS7OlM8QD2zsryeknhmaYiGf462cu8aad3fzaXb08f2mSY5cnGU9kObi9k7ftXcdMOl/xXb51fJhzozHetGstt/e2VM6782NJHil7SXrA4j2b2ugfjjEwkeTUSJxHdq/h/FiSB7Z18NKVKV68Ms0H7t9UJf7vXtfCTDrPZ5+5yJWJJK1BL50hL2/b28N0Om8rhNtdc0+dGuXyhOYwDU2nSWYLBDyueXkiVnK7WUJvNBBVCexLj3JEFAqFYhG5kY7DaqReENkbPn6IqfIa9Vrhb7WC6ho9RrXWjZuD+qIBN6lssbJOPW4RXmfGLmjPXHe+WKp4C26nA7/HyUy6gSAQQz0up5iT6aET8bls8zH+4QP3sK41wMGPHWq4vtXKYrgwtfhu2ReaTObq1hPyCJI5WXc7l9C+WZox+DcRnxOHw1EJlDT6MXauysBE0vYcONxAWKSxPLPTYvSkFvs9VL0/rzyUI6JQKBSLiFpHvLg0EkSmUy/8zS6YrtFjVGvduPlDvXmder1JCNgH7ZnrNnoLWlsan4To9dhNQgDiWXsX4zOHL/K9E9ZtfK2x1H+OfeyI5gs1Uk+igUkIaOdNzFRmPFOcG9JZJzSw1jnQSFiksTyz02L0pBb7PVS9P68elnwiIoS4LIQ4LYQ4Xv55d/n5LiHEvwghzgkhXhFC3L/UbVEoFIpGUeuIF5dGgsh06oW/2QXTNXqMaq0bNwf1mdep1wsdBPugPXPdRm9Ba0sDaYSmesztrWq7197F+ODBzZVbDr/WWQwXphaPHtB8oUbqCXlEQ9u5hHadmAMW54R01gkNrHUONBIWaSzP7LQYPanFfg9V78+rhyVfmiWEuAy8RUr5iun5vwOuSCn/TAixD/gasEVKWfN2GmpplkKhWC7UOuLFpV4QmR4uWC/8rVYw3ULaYhWYZwyTA6qC/B66pYsXrkyRyRXZ2hUGJOPJHL92Z69t0J6xHmMwoR7qNjiZ4tOHL+BxCroiPh7c3snxoZlKGOEDWzsYmk5XBQ1G/W6ePDXKhtYA+za1VYXoDU6m+PShC3hcDnKFEmOJLP/uoW3cu7UT0JbTfOlnA8ykcgB4XU56Wv2kskX2b27j6MVJOsIe0tkih8+OMZHMEfG72NwZYldPhFS2yNPnxpiI52gLe9jRFeKV4Rh397VSlLC+NUAqX+DHp65zZSJJyOfitnUt3LImwvBMmli6wPVYmnS+wFg8TyxT4KFbOgn53cTSBbKFEm+6tYvD58bpawtwLZYmniny3v0bGJ7JcGJoGp/byd0bWvnKi4PkCiXu3dJBR9hLKltkcCqFE3hxcJp7N7fj9zp5+sx1plN5tq8Jc/+2TvZtbOPY5clKpkrA42JwIsWxgUnedWcvLw1NMzyV5u6NbaRzRZ67OFEpS3cUOkJeLQhxMo3f7eTVkRl6Wvz87gObK5kyJ6/OMB7L8NmfXuLhW7v51bt66R+JMTCe5MTVGfZvauOtZUfkiZMj7O1t4dCZMTL5InvWR+lp8c0JfpxJ5+cELOqBkjvX1g7p1BmYSPKlowNkCkV6WrQsmnfeZX8O17rmjEGMxn4vxXuoen9eWaxYR6TGRCQBbJJSjpUfPw/8qZTyUK3y1EREoVCsVlaSHL8S2tJMGxba3vmGnpm30QPo9OA9mA0xfMuetRX5XQ/Uswqfs6vLWLYehnjozHWePjNGe9jLrp4InYYQQV1ifnhnN/3DM/w//3KGgMfJL962hgd3dPHqcIyxeIaxeI57t7Tx0uAM2zqDfOWFQRLZAn3tQaJ+N0cvT7KxLcBdfa1I4JWrM1yPZVnfFqgEHOqC9D0bo3zv5DXu2djG+rYAqVyBC9cTlID2gIeTV2M4hGQklqEj6GEymePWnghv37uOf37lGlenMkT9Lo5enGT/5jam0wU6wx529kQQwJWpNE7g8LkxcoUSLX43Ia+bh3Z2Vfr0swvjPHlqlIdu7WZt1M/LQ9Ns7wpzLZYmlinw4Qe3EvG7eezoAEh4294eplJ5XA743DOXEULylt1r+UH/KC1+Fzt7tEno4GRaE/6TOdqDHq5OpxmZznDmWoyOkJfbelv45dvW8M+vXOPyeJKQz81MKofT4eC+re1saA9WwgK/ffwqRy5Msr07REnCm3dpIZTJbKEqDNEYvAhU3Tjhqf5RTo/GWd/qrxx38w0cQAsWTGYLVTdF0Msyhm1aCeVWoZzzDShUrG5W+kRkBm0Z2M+A/4CW4TQopQwYtvsK8D0p5RdrlacmIgqFYjWykuTLldCWZtqw0PbON/TMvN/nf3ufbbAeaMtVWvwuUrkS+WIJB1qWiI7bIYj43ZZ1mcMTXQJbX8UcIhgsC9AKe3Spe6lp8buQZb+jFkbR3FUOodRD/YJeJyXJnBsbtAbcfOvD91Vu4BAs32BBl9eNbXAKQSJbJFcsVZ433yTCGJJYKM26K60BNz9pMqBQsfpZybL666WUtwN3AhPAF8rPW4WQzkEI8cdCiCH9J5FILGFTFQqF4sawkuTLldCWZtqw0PbON/TMvE2tYD3Q/tOLGUR1c1ReviRt6zKHJ9aS5s0hgmoSUp/lmISAdvODWjcR0DGK5rMhlLPnmtWNDaxu4GAVgqkHDhonITD3JhHGkERp2l/J4YrFYsknIlLKK+V/88BfAg9IKScAhBCdhk37gCsW+/+FlLJX/wmFQkvdZIVCoVh2VpJ8uRLa0kwbFtre+YaembepFawH2l/bIgZR3RyV5y7/5duqLnN4Yi1p3hwiGPQstY598+NyLM8YhX2umjcR0DGK5rMhlLPnmtWNDaxu4GAVgqkHDnqc1R8BzTeJMIYkCtP+Sg5XLBZLujRLCBEE3FLK6fLjPwbeLqV8vRDi88Blg6z+dWCzktUVCsVrlZUgXxrXhNcSXOvt36yrYedoNDoejWxbL1CxVugZMGffgYkkjx0ZQAh47/4+AP7ih2c4NRLnDw5uxudxgYAWn4t/emGIW9eEaQ95CXld9Lb6eebcOFs7g/zTC4PMpPO0+Dz824e3sbs3WmnXc+fH+NThi3zo4GbCPjefOnSBkNfJ6za309Pi4++evaQt0ZECiSTocfHGW7u4c0MrT/aPEvA6SWSLbGkP8NlnLjGRzBL2utnWHcLjdjA8nSGdK3Dflg6kgFgqxw/7r4Ms4XY5CXrdxNM5bl8fZUtniGfOjZPMFfA4BV63g2JJm/icvRaveCDJXIHWgIeBySRO4SDqd+NxOcgWS0zFs5QAp1PgQOIQTtxu8Ltc5EvaBGsymSVThJ6Il+6Il6lUnky+REfYi9sB1+NZppI5XC6Bx+mkIEsIHOxe18LaFh/nRmO8OhynPehBSonX7cDvcTGdzhNwO9i+pgWPSzCdzNHXHuRdd6/nzLU4z5wb5+xojNagh22dIX5+ZQqvx8m6qJ/xeJYTQ9OEvG4CXicRrwu/x4Xb6eDMtRiZQokNrQHWtfoZT2aJpQoIB8TSeXxuJ1s6g2TzkpDXRTyXJ+x1kyuW2LU2QgnY3B7gKy8Osae3hXSuSLpQpNXv4e6+Vp44eY07+6Ksjfh47GdX+NW9Pbx6LV4u21ERzN+0s7viDhlvfvC5Zy5xa0+YgMfJWDzHO+/SgjqfOjXKmZE47WEPyWyx8rw5MPTIhQmuxzNcnUqzfU24EtBo5Z406jdZhSHeaCdNsTBWpCMihNiMNsFwok3wLwJ/JKW8LIToBh4DNgE54ENSysP1ylQTEYVCoVgalsK1mG/A4FLcYWe+dVjtO5POVzkbYZ9rzjKY737kPta3BXjjxw8xaVinr6/R15e9mNGD5J47P8Zvfu75yvMhj5NEbnZBV60gvha/i8d/93UVZ2WpQ/tuVqIBN4/9zj013Z5ahMoTvfkQ8Tl5+k/fyOBkil/5xLPzLsPlcJDIFskXS7gMnpH5HNXDOa18JpeAsN+NABLlbXRXyhw2+s0P38fbP/ls5TkdO3fEym9qCXgq1+BKcNIUC2dFOiJSyotSyjuklHuklLullG+TUl4uvzYqpXyTlHKblHJXI5MQhUKhUCwdS+FaLEe9S12H1b5mZyNhsRb/saMDnBiamSMm62v0rSYhMBsk96nDF6ueN05CoPbEwuysqEmINY24PbWY7yREq7vIyaszfOHIwILKMHpHRs/IfI7W8pkKstodMbtSOjGDhzK3LY37TcZtV4KTprhxqGR1hUKhUABL41osR71LXYfVvmZnI2SxFv/R/X3s6W0hYnpNX6PvtvES9CC5Dx3cXPV8yFNtldSyGszOirJErGnE7alFyGs2fZqp28nudS28/0DfgsowekdGz8h8jtbymVyi2h0xu1I6EYOHMrctjftNxm1XgpOmuHEs+e17Fxu1NEuhUCiWjoV6KosZMLjYLKQOq30HJpJVYXIz6Tyfe+YiE8kcf3BwS8X10NfZJ7MFgj4XBzZrOQ56qOHhs9c5fGaMda1+fvf+zVVBcs+dH+Mzhy/ywYOb2dnTwlOnRhmYSPHOu7QVEE+cHOGBrR2cGY1zYmgagD29UR66tbuSa6IHzAH87U8vMjCeorvFy+bOEMPlcMQ966Ps29hWaYvX5SSZLdAadNMV8fGe1/URS+f59OELhL1OdvVEmUhkee7iBL1RHy9dmdY+FPs99Lb6yRVKPHXqOj63g9t7o4R8boan04zFsmSLJTZ1BIil86yJ+JhO55jJFOkIesrZKGNkCkXu3dzBe/Zv4NvHr9I/HGNLV5i+tgDHB6cA2LuhlalknuHpFOevJ9jZ08L+ze2k8gVeuDQJCCaSWe7Z2MZ0OsdEIseunhamUjkQc8dJP5Z6KGRSv7uVhPPX4/zLK6O0Blz0dYSI+FzsWa/tPziZ4nM/vcSm9gDJXLESiqgHHPa2+gHB+etxYpkCiWye12/r5K1711XOpZND0zx2dIC37+1heDrD2etxtneH2dEd5of9o/S1ByqBhg/d0sUrI7GqEENjQKb5HNWPvzGc03huPLC1g+l0viq00+xKmcNG9eeSmQIICHq187qWI2Kuy+yI3Gg/TrEwVqQjshSoiYhCoXitMJ3K8dyFCWB1hIg1KqQ2K66at59O5Xiyf5SByRTvqpMQbQx+a6bOk0PTfOHIAO8/0Mfu3mhFxn3LnrWV+vTjl8pqt9NN5QoMTqXZ0R3mljVhvn18mHgmR9jn4dHyX8X1MvTfX7+tg9PX4py9Fqct5CGZK3Lv5jaeuzhZCSsETRw+MxLjx6dHiQa8PLp/A88PaKnv7UEPZ0YTHNjSxrauEF88cgWvy0FnxMsbd3TyzZeucmokRsTn5spkijUtPu7d0sFkMocQ0Brw8NyFccbiWd6+dx0P7ezm28eHuTSe4OxonJDXRbZQAiTdER8Ht3cB8PylKTL5POfHUuxZF2FnTwtnR+P0tQXoH4kxkchyPZFle1eIiN/Lm3d28YP+URxCEM/maQt68TgFDiHIFIpMJXO0BT1kCyXOXouTLZb4hVu6mE7nOXMtgZBwPZEmmyty69oWIgE3WzpDnLyqLf25b0s7bpcTtwO+cHSA3T0Rbl/fytnrcW7vjSKA/pE4b9m9hidPjXL2eoKD2zq5ZW2EXT0Rjl2a5HQ5fX0ykeOp06P43U42dwa5racFv8fF4GSK5y5oNyE4P5bkF27t5vbeFr58bIhb14bpDHmRUAktPHZpkssTqUqgpH7sHz8yQDpfZO/6KPs2tfH8pcnK+WwUyoHKuf7mnVooon6+6XUYgwhn0vmq89R83tY6j/UyzKGJxgmLcbtmrynFzY2aiCgUCsUqYjqVq5Kcb/YQsUaF1GbFVfP23/jQvbz1Ez9lJq39NVsAh8ryt91+oXLwW9Ig6daq8+TQdJVc/DfvvZPff/znFSn80EcfpMXvrpJ862GUyZsRyyM+J0KISn8VNw92x9n8vPnciPhdpHMlQl4n+VKJeKYxT6XF766EIArgSx+4h/d87vnKeWt+bHUe67K6HppolNqN71fG0EQloL82WJGyukKhUCjmh1lyvtklzkaF1GbFVfP2ZpFWMit/2+1nDourV6dZLv6rH5+vksKfODkyR/Kth7T5vR7xTLGpehQrB7vjbH7efG4Yz9tEg5MQoCoEUQKfOXyx6rz9tOmx1XlslNXNUrv5/cosvisUVqiJiEKhUKxAzJLzzS5xNiqkNiuumrc3i7SCWfnbbj9zWFy9Os1y8R++cWuVFP7I7rVzJN96CJvf6xH2OZuqR7FysDvO5ufN54bxvA35GpfljSGIAvjgwc1V5+0fmB5bncdGWd0stZvfr8ziu0JhhVqapVAoVgWrMRBLl5wR1BRBF7O+pRpDff24qNEXo1OhS92NOiJG0XU6leOpU6NcmUjxa3UcEWNgoZWM/tUXh+hrC3DPpjYGJlKVdflffv4KPzo9yhtv6eY37tnA4GSKx44M8GjZGdH7Mp7IcP56AimhPejh5FCMdW0+fnHXGv75lWtcmUyypsXPxvYgJwZn6B+ZYVtnmJlMjtPX4rz3dRsYjmV4+co0a6M+MrkimzpCHB+aZnNHkDUtfta1as7CRCLDj05fJ+Jz09sWYGQmzcb2IKOxDENTKdZEfIS8LjKFEt1hL4NTaZK5AkGPi1imQIvPxZWpFF1hH6WSpFSCdD4PDsgVJPm8ZGNHkHWtPs5eTzCTypLOQ8ANLpcglZEUJEQDTpCCVL4AEgpFWNviJVMoUiiCz+0gmSuQK5TIF8HnFqxtCdDb6uNnlyZxOQT5YgkkeNwOHELgdTnIFkoIobUrkS1RQvtrajTgJJUrgoBiAYoSwn4H61qCJHMFYpkc2UKJtoCHiXgWKQTZgqQ14Ob2dRF+PhSjp8VLV9jL0LTm8JwYiuF1C7Z3R+ht9fOGHZ38zeGLPH95glvXRChJydBUmqKkfL24uTqV5nosS75Ywu9xMp3Oa6GKaHfXyhdKTKXytAXdRAM+7tvaDgJODcd5cHsHP+y/TsTvYmNHkLPX4py7HmdDWxC3S3B1KkWhBB86uIWChCsTSZ6/NEnE78bjFESDHh7c3smRi5M4oOK9dIS8pPKFSpAhUCWvP1X2S95Zvk6Mcrt+3QxMJPnai0N0hjx0hH0V/8NSaje8X1ltY3UNrrb37dcqyhFRKBSvWVQg1sJZyjFspOzpVG7OWvQf30AnxhzCBuBxOgh6nUgpmTY4GVG/CyFExTH5xofu5Vc/9RyxdJ5CSVouwYkGtPC4ZDmI7ub6n1ixWAjQJl42mTJWOICS6bmwz4nb4SCeLZAvzpbldgoKRVkVdNhMiKC+nX4uu8vfcCzG+4N6315dKEdEoVC8ZlGBWAtnKcewkbKt1qLfyONoDmEDKu03BxSaHRN9DX3eZhIC2hp6fR81CXntoocQNoN5EgKQKAcbGichAPminBN0CM07W/q5vJjvD+p9WwFqIqJQKFYBKhBr4SzlGDZSttVa9Bt5HM0hbECl/eaAQrNjoq+hdzuErQcQNuyjwgZfu+ghhM1g9cEtVA42dDury3I7xZygQ2je2dLP5cV8f1Dv2wpQS7MUCsUqQQViLZylHMNGyrYKTruRDEwk+fqLQ2xoD7BvY1slLA7QwtyyhUqQGzDHU9FD5vqHYyBg59oI/SOxSv/0fTa0BTh2aZJjlye4NpOhNehlOpnl7FiSDx3cTEnC0UuTbOsMkc4XCXicnBlNsKMrRKpQpLfVT2fYR4vPxVdeGKKvPcC1mQyxTIF37O3hR2eu0391hq6ID4Hg9Ts6uGN9K3/704ucvBrj/i3ttAQ8bGoP8M3jw9y/tZ0nT10n4HEyMp2iJAUBj5N8scSmjhAel2AsnmUymWU8kef23ii7eiL8+PQolydSdIW9CIG2JMgp6Ip42d4V5oWBKdwOgcvlBCRIKJUkuWKJN97STaZQ5AevXiPodpLMF8nkirQGPTiEYG2Lj1S+SL5QolgqMZbIM5XMIqVkV08El9MJAq1uBJlCiTfd2sWTp6+DlCRzBYSE/pEYhRLcuiZMJODhvi1tfPvlEdZFfdyyNsJkMoff7WRgIsW/uruXmYzmuezsifCPPxvgO8dHOLijg7DPjd8zu93wdIZjlycYT+TZvS7CZCpHNl8kni0yFs/ym/vWc/TSBP0jcda3+ukIe9nTG62cVxvaAhy7PMnZ0dkgwydPjRJwOxmeSZPNF2nxe/iV23uYTudxCnjs6BUiPhf7NrXx0K2zwYZRv5tnzo9XwgOtgg6h8evdeC5blbMQ1Pv26kE5IgqFQvEaZ6WKn3bhZwsJLWy03ucuzE4YrEIhrQLcjM/p4XGtATc/6B+dI64bxXZjGOHjRwZIF4rc3huthA6awxP72gM8f2mSM+WQPAFcT+S4d7MWbnfH+haeuzhJZ0ir49xoAoeALV0hBHD0wiSvluX2ta0+1kX9nBqJc34szq6eCHvWtfDZZy7h9zj544e2M5Mp8PLgNLliEa/LSWvAUwkYHJhKcXtvlHSuyD+/MkI8nacz7MXrduJ2OphOZTl/PcltPWG2r4nw9Llx1rX4mEzlyBclv3zbWrxuJz8+PYrP7WBdaxAhYSKZJezTBPltnSHOXU/iczm4MpVkeDpNV8RHyOtEIJASAl4nt66JcOpajMlklslEnoDHyfV4BoRgY1uAy5MpZEkS8LpIZHKUpCDo1T6Qb2gNcPTSJFG/i+vxLJ1hH+993QaeeGWE/uE47z+wgcuTaWLpPFJK8gVJLJtnd08Lk6kcuUKREiAQxNMFBqeS+N1Obewd8McPbyeeLfLt41fJF0u0BtysiwYI+930Rv2MJ3LcsiZMT9THZw5fYGQ6g9ftwOVw8Ke/uIOdPS18+/hVXhyY5vce2FQJw/zs0xcZnkmzu6eFZK7ILWvCPLSzuyqg8+WhafKFEmGfm0cP9FWfm6+OEvI6SeSKvHlnN1OpPHt6W5hJ5y1vuGC+DvVzUQ9G1Pe3crnqXYeLtc2NZKnat9L7vRDUREShUChew6xU8dOqXcCCQgsb6Zs5EBLmhkIahXQ9wA2oktSjATfJ8hp5HQFV0u5MOl+1jzmQTheJE9kiQa8TASSyRXJFq9X+itVM0OMgmZs97n/z3jv514//3HLbaMDNtz98X1VAZ9XrfheJTIGCxcc4l4Cgz0UsXag6F90GaR1mr0PzDRNcAloCnqprrdGbTizGNjeSpWrfSu/3QlGyukKhULyGWanip1W7Fhpa2EjfzAFrMDcU0iik6wFuZkk9bpqE6Nsa22Lex/y5UBeJjbK7moS8NjFOQkALw7TDKqDTSMxmEgJQkNr+5peN0rrxujJvp+9vvF6auenEQre5kSxV+1Z6v28UaiKiUCgUq4CVKn5atWuhoYWN9M0csAZzQyGNQroe4GaW1HVR14hZ2jXvY1aPdZHYKLt7nOq/39ciQU/1cf/DN2613dYqoNNIxOfCZeO5u4S2v/llo7RuvK7M2+n7G6+XZm46sdBtbiRL1b6V3u8bhVqapVAoFKuElSp+WrWr2bbOp296wFqyRkCiXYCbMfRNF4CfPDXKhtYA+za1zZF2jfsAfOlnA2RyRfasj1aJxMbwRF1SP3s9Tm/UDwLGEzn2b2rj+NAMe3tbOHpxko6QBwScH02AgK1dIUBzRPqHZ9japTkJPa1+To/EuXA9wc6eMLvXtfC5Zy7j9zj4dw9tJ5YpcGJommyhiNftpNXv4ez1BH2tfq5MpdnT20I6rzkisVSerogPr9uhOSLJHOeuJ9hddkQOnxtnXdTHVDJPrlTikV0GR8TjYF1UG8upZI6Qz0U2X2JrV3DWEZlMMjyTpitcdkSEg5KUBD0ubl0b5tSI5ohMJPIEvU6ux7NIYFNbgMtTKUpFSdDrIp7VHJGw183dG1s1R+TyJC0+zRHpivh4zz0b+P4rI/SPxHnf/g1cmUozk8oj0RyReLbArp4IU2XBXEMQzxQYnEzi9zgrfs6/e3g7iSpHxMO6Vj8Rn5t1UT/jyRw71oTpafHx14cvcC2WweN04HI6+eibt7Ozp4XvHB/mxStTfOD+WUfkb396keGpNLf1tJDMF9mxJsxDt846Ik+dGuXE4DS5Qomw38179/dVnZs/7B8l6HWSyhZ5eGc30+k8u9dpjsjXfz5ke94aRfRjlye5MpGq2t9qWVUjN51YjG1uJEvVvpXe74Ww5I6IEOI/AX8BzADfA14H/L6U8uvNVroQ1EREoVAo5sdyiZLmepZKTLerp689MEfKbbSek0PTfPaZS9zVF+Vte9fVLFff9ta1YYIeJ1cm0+xYE+aeTW28OhwjmS1ooYW5AoOTada3+QEYS+R4885uBqfSXJlIcnxomh1dYZL5IuvLd8Da1RPh1eEYY/EMr1ydYTKR4+5NbXSGvEioyPcz6TyPHRkgWyjSE/UzPK2J2FOpPEGPk1yxxK6eFvqvxvjZpXHypRK/eNta3rRrLUOTST59+CLroj4e2dPDVDLH90+OAOB0CPweJ71RPy8MTLG9K4zDAQ4h2NwV4pbuEH/99EWSuQIeh6Ncd4bb1kU4dz2JywETyRx+l4Mi4HM7aQ96cApwu5z0tvq5bV0L6VyR44Mz+N0OjlycYHt3mEKxxPHBSaQUFEuSkgSHgF09YSQwMpMlly8ihWBTe4DJVJ6N7QGS2QKxTBEJTKWybG4Pki2W2L+pnWSuSDyTJ5EtsKUzxPmxBD6Xg0xesrUryKmRGEII7lgfJZkrVo7VK8MzDE2mQUq6In46I17ednsPp6/FOXstzvY1YdZFfXzumcsUSkVuXRNhYDJNV9jDiaFpNraHuG9bR0UUbw24efrcOK/f1sFUKl85p1oDbr7ywiBXpzLs29RKX3uwcg4AlTTz5y5MVB5bndtWN18wn+96G/SbMdiVaXV96dvu6ok0dI0tJqtZ9F4NLMdE5GUp5e1CiIeBjwB/CvyjlPLOZitdCGoiolAoFM2zXKKkuR49ZXyxxfRa9eSLpTlJ0o3UMziZ4lc+8Wxlm4jPyXf/8IGqlHRdUv+r39jLb37u+cUZtHnS4nczk87X31CxInACRcNjlwOKJXA5xZwgQqi+6UHU70JCRVpvC7j5seHGC2B98wV9MlJJSM/kK3UJtBspxDLFOWVaXV/v+OSzVTd/cNe5xhaT1S56rwaWQ1bXDauDwFellGearUyhUCgUN4blEiXN9eiy7WKL6bXqsUqSbqSeLxwZqNomninOSUnXt//04YvzGZ5FJZ5Rk5CbiaLpcaFUTla3mIRA9U0PYplClbQeszi3rW6+oFNJSDfUJdHOcasyra4v880f6l1ji4kSvVcvzUxEkkKI/wP4deBJIYQDUNNRhUKhuAlYLlHSXI8u2y62mF6rHqsk6Ubqef+Bvqptwj7nnJR0ffs/OLh5PsOzqIR97hvdBEUTOE2PXQ79VtDWxrnx2YjPVSWtRyzObaubL+hUEtINdenfiFiVaXV9mW/+UO8aW0yU6L16aWZp1la0JVlPSym/UX78Linlf1vKBppRS7MUCsVSspzrkJd7zfNCRcl67TW6FEYpdjHFdGMbwD7N3Fy/cW27vu5+V4+WdC4lFd/if/7wLMcHp9i7oZXfvX9TZR1//0iM67EM4/EcB7a08c3jV3n27DhT6Rwb2wMkMiXCPieZQgmXw8GtPWFuXRPh9LU4xy5OMJnOs7UjSEFCvlBkNJFlW2eIVL6ofcByCF4diWsfTF0Ogm4X1+IpHMJB2Osmnc+TypXwuKAt4KW7xUfQ4+bMtRiZQomSlIQ8LooSJhJZHA7wu514XA6klEwlC5W/yLf4BF2RAIlMgZFYFgCPA3IWdxT2u8AYYeEEnA5wOyG5iF/ImLNXGsXB7HINvRwnYPzbvd8tcDkEQkgSGYkDcLkg4nMznsjjdWpllKRWns/jJOB2smNtmOHpDFcmkvjcTgIeF0Up8bgcOITA5RQE3E5i6TypfJGilDgQOIQ29pOpHD0tPrpb/NyyJsLgZIpt3WEAHt7ZzbPnx/nW8avkCyX8HifpXBGPy8Hrt3ficTl5085uzozGOXMtTm+r5qycH03g9zh5z35t0vy9EyMV36Q14Oafjg1yLZbmrr42+tqDlfP6qy8O0Rny0BX20eJ38djRK4R9Tnb1tBD0ugh6XBwoOyIDE8mq7Y3Pf+3FIfraA5VEeH1CYOVqmV2Uhb7XGd8X7OpU/siNQwUaKhQKxQJZznXIN9ua53rtXY7+LEawofEDb4vfhVMIEtkiAY+DGVP4mx5cGPQ6kcB0Si2FUiycFr+Lj/3aHtsgQwG4yt82lKRk2iLM0OwHuRzaUi8zYZ+TRFngB4j4XSBlxQsBLTjxUNkNMXomelsPf/QNgHUIqZ2rZXZRWvwuUrnSorw3LNRDUywNS+6ICCE2CiE+LYT4oRDix/pPsxUqFArFSmU51yHfbGue67V3OfqzGMGG5rBCPVwwZhH+ZnRC7ELlFIpmiWcK/O8aQYa64xQrn5/WZVRPiq0mIUDVJER7XKjyQvT26NeSVaBnrRBSO1fL7KLEFvG9YaEemmJl0Ywj8hVgGvgE8D8MPwqFQrEqWM51yDfbmud67V2O/ixGsKE5rFAPF4xYhL8ZnRC7UDmFolnCPhf/pkaQoe44Rcrnp3UZ1X6Qy+bTXMjnrDqvQz5XlReit0e/lqwCPWuFkNq5WmYXJbKI7w0L9dAUK4tmHJETUso9S9yeuqilWQqFYilZzsCpmy3cql57l6M/Cwk2RMDOtRH6h2Mg4MBmLZtBL28mnedLRwfIFIrs6Y3OWQevl9Eb9fP40cu8OKCFA97d18rQdIada8Ocux4nni7wyO09/NJtazl89jrffHGQ4Zkc925pI1uUCCk5emmSA5vacLqcvPvuXk4MzfDt48Ok8wWcAlwOB9diWfxuB7t7oySyeV65GsPtFPS1h+hr95PKFjh1LUHE5yJfLNEScONxOjh9LUbA7WZN1EtnyMvgZIrBqTSFUoFCUbBzTQifx00mX2IsniGWzSElxDLan9WdaM5EwAM9LQGuJ7IkMkUcaB9knQ4QQjCW0P4q7wKcLsia/nivfxR1ObSlRrmCxCHA64ZErvyaACHB6RJQkmQMf9n3OsDjdhDPlrRJodD8lJwEWYKWgBuvU3AtnkOWt28PeWkPebg2k2EilafF78DjdGuTSa+Ds6MJhEPQHfGxuT3I8wNTBFxOvG4H+aIklS/QGfLgdbn4hVu6SOeLPHX6Ols6Avi9LkamM8yk8oT9Lta3BsgVSwxMpNjQFgBkJS/G43aSyBS4rSdCKlekLeRhMplje/dsUOFz58f47z84iwOJ1+1gQ1uAfZvaLc+7ZG52cHWfYyad54mTIzywtYPpdL4SbNgZ8hLwOCshnsZQw4d2auGaT/WPcvZ6vKo9OgMTSb7+4hAb2gNVr9lde+bn9cdRv5tnzo9XBYMu1nuDXZ03y3vpamQ5ckT+AfhTKeUNnQWoiYhCoTCiJMXaLHR85rP/QgMNm60DmBM4aPWc8QOVWaa1C3/raw9UhROmcgWux3O8eVc3P78yxYsD0/z6vl4KJe0vtboY3BXy0FEOJnz+0iRnrsUrYYffPznCj06Ncu+WDvweJ6dG4rxhewfPXpzkvs1t/KD/OhG/i9t6Ivg9Ll68PMFTp8f41w9sojXo5dlz47wyMkM2VyRbLOJ1Ogn53NyzsZVLE0li6TwTyRzxTIHOsId8AXLFIj1RH1cmUmSLkrDXxWQyj98t6Ar7kALWRQNMJLNcuB4nkS3hc0J3i58tnSEKpRJup4OL40kS2QJIyXQqjwDWloMUhYBowEOxJEFIXMJBOl8k6vfQHvJwemSGEhDxeynJIk7hoC3oIZ4pkMoV6WvzcS2Wo1gqsS4aIOx38crQDDPpAn6PNq0plQApWdsWwCkcdEd8bO0M8NUXh2gNuMkUSuQKJdoCHmYyBbrCHtqCXloCbrL5Eutb/bw6EufeLe34PU6euzDOdDJHLFPkzg0tnLoWp1CSbGwNkiuV6I542dIZ4tRInAtjcda3BukMe/C5nTx7YZyA20VP1EdX2McbbunkpcEZ7ljfwnMXJtnYPvvB/7kLEySzBYJel21QYSpbIOB1kc0V+Nyzl/mNfb3MZIq8ZY929yujmG4+n5/qH+XyRIp33d0751yudU2ar496zzf62KpOu+cUq4PlmIj8C3AP8FMgoz8vpfxXzVa6ENRERKFQ6Nxswvdys9Dxma8cvtQiqbGOoFdbepLIFitBhqHyMivjc3rwGmjSbSydJ1+alWktw9/KIYaN/C/pBEJ+F7H0XNdE8dqlxe8GZCWIEExBhQE3Ula/boVxH5eAloCncj4/+PFDVTdSOGw4l8H+mjSHdNqFgpqv4XqPjddZvefU+/XqYTkCDf8R+HfA14EnDD8KhUJxQ7jZhO/lZqHjM185fKlFUnMdughrDDK0es4o3eqTEKgR/tbgJAS0sLq4hfCueG0Tz+Tn3OjALIM3ciME4z4FOVciN2I8l6GGUG4K6bQLBTVfw/Ue28nt6v1aYUXDExEp5ResfpaycQqFQlGLm034Xm4WOj7zlcOXWiQ116GLsMYgQ6vnjNKt21Et01qGv5VDDBvBiSb2Nrq94rVB2Oeec6MDswzeyI0QjPu4xFyJ3IjxXIYaQrkppNMuFNR8Ddd7bCe3q/drhRXNLM1aC3wWeEP5qR8Bvy+lHLHfa/FRS7MUCoURJSnWRhe1Jdp69GbHaL5yuJ1ICliuETeGDtZrp75tKlcg4NHW3F+Z1IThw2ev88Llad6yew3nx5I8sK2Doek0yYy2/l5fk3/y6gxOAd96aZhHD/SxuzdaKX9gIslnn7nI8FSGfZtayeaL/PT8OPdt7cDvdvLy4Azbu0MMTKa4Op3m/i3tfO/kCDt7WnjktjV86/hVktkiF8aSRPxOMvkSuXyJVKFAX1uQ0ZksQkiyhRIg6Yr4Obitg++/Msobd3Tw4zPjuBywuzdKX1uAf37lGgMTSfraA5qfAcQyOfIFSa6o/UXR5dCC+EpSkshqxrdAk84daH9RdwnIS3CjBf7p//u7BRQl9LUHuJ5Ik8zOfi7wOsDjEnSEfIwns6SyJapv/toYVoGFznK78yWtne5ye4uA3yXwe5xMpWa/ZTKGLrqA9pAHKSCTyxPPSnxOrQ7hcIAsUShqH9jThSIuob22qSNEPFukp8XLtViWeCZPMpMnU4CgV+AQDnwuB1u7wgxNpxmPpwl6PeSKJdK5An3tQe7Y0IrP5eBfXh0hmS3SEfIC8LrNbVybyZArltjQFmBzR4jz4wmGJlMkMgXG4lnagh7+7UPbiWULnBmJ4/c4ef7SBIlsgfu3dFCUkCsUee7SBPduatf6l9fOE5/LyZauEOlckf6ROL/3wCZ290aZTuX4zvFhXhyY4pHdazg3lpzjPdldk+bwz4GJJE+cGOGBbZoAb37+kXK55mtavx6RzLnOjO8di/l+rXyTlcVyOCLfBY4Anyo/9UHgPinlrzRb6UJQExGFQqFonJXk0di1xRw62Bpw85NywJpdGeb17T/69wcZnEzxK594trKt2ykIeV1VYYR62WC9Xn1gIsnBjx2y7UPI66h80Fe8Ngl6BEI4SGTnMyXTCPucc/I8jAjAKbRlWObnjU999yP3sb4twC/8j8PMpHKV7c3eUyPUuj7t3kPsfKpa1/BisJLe1xQay+GIrJdS/t9Syunyz58D65utUKFQKBTLx0pal10rFM0Y3FarnWZ/w1jWF44MVG2bL8o5a/DrrVf/3onaX/KrSYgimZMLmoSAFjRYC8ncSYj+vJHHjg5UzmXj9mbvqREaDS00Xpt2PpUKhFU0SjMTEYcQYo3+QAjRBWo5rEKhUKxkVtK67FqhaMbgtlrtNPsbxrLef6Cvalu3U8xZg19vvbp+q1Q7Qt5m/ttUrEaCHkHI66y/YQ1Cvtr7C7SldFbPG3l0f1/lXDZub/aeGqHR0ELjtWnnU6lAWEWjNLM061HgvwPfRZts/zLwH6SUjy9d8+ailmYpFApFc6wkj6ZWKJoeGHhgc31HxGp9O8DJoWkeOzrA2/f2UJTMCSM0lm3XloGJJF/62QCZXJE966P0tPj41vFhHt3fx/q2AEcuTpDMFEBA0OvCCXz2p5fY3BHgvfs3MjSV5vS1GD8+NYrb6cTrFnidDi5PplkX9ZErlnj41m6++/IwhZLkbXvXsae3hW8dH+ahW7p44coU6VyR29dH2dEd5rsnhplJ5YhniwxOJNnQFiTkczGRyHL2epyoz43DAbkCBL1Ozl1PEPC42N4V5KWhadr9HjJFScDjIJ4psK+vlXPXE8SyBdqDHoolmE7n+fW7e/n54BRDkyk8biev29jG2esJ0rkiv3TbGv75lVFi6SwjM1lKpRJOJ6RylEMDHUynCji0XEIcDkF70EuL381UOkehWCKWzuNyQKEIAa+T3tYALqeDWDpPrlCiBKxr8TISy9IZ8rKmxcfl8SRXJpOsaQnw4Qe38OSpUY5emqQj6OEPDm7h6KVJXroyDUKytTNMb5ufuze08q3jw4R9TloDHp49P44QAinhjbd2sb4twHg8y7HLU0R8LiaTOfpHZlgT8bGzJ8K+je3s29TGN38+xDePXyXideF2OsgXSzxyew+/vm8DAF8+doWnTo1y29oI1+M59qxvQUjovxbnA/dvIuJ387fPXGRgMoXH6WBoKk1X2MOfvPkW7Ty6MMHAZJIjFyZwOwS/c/8mZjKaZ7Gz7D1F/W6+e2IYJPzK7T1Mp/Oa21Q+H3W3ySpEsJllWY1cn3bvIcbr0RgWqgJhX1ssuSNSruQ24EG0yfaPpJT9zVa4UNRERKFY/SgJcWVSK8QM7AMErfa1K9tu/2bbaRTf9bbZBRha9UsPoEvnClyZTLNjTZhb1oT5waujhLxOxhM51rf5SeWKHLs8ScSn5UFcj2fJF4v4PS52rokwMJlm7/oW2kNezo/GOXRunA8+sImihNPX4mxo85PMFTl2aZL2sAe/y8mhM2N43Q7euKOLVK6IBNqDHn52aYLr8RyZfIFEtkDA7eTBHV2cvx5naCZDwO1kfWuA0XiGcPnD8/nrcTpCXq5MpigWS6SLmiQuBaxvDdAT9fLqcAyBYOfaCPFsnuHpDB4nXItrXk2Lz0HA7cbphLF4Do8bBA6S2SICCPoctAe0ScfZsQRelyZ8TyRyrG/1c3kijdcFHqeTfKnEhrYg12IZ3E5BKldECIFA0B3xkiuUSOWL+N1OfC4HIzMZELC1I8i1eJaA20XI5+DSRJpdayO4nQ6OD05RkrJ8DCCTL9LT6mdDW4CL40muTqXobQ2yY02IqWSO/pEY61p8HNzRTTJXLIctaoGFWzvD3NEXBeD8aIJMoYjP7SRfKPLjM2O0+t34vC4+fHALI7EMh8+O0RHysm9jGw+Xwwv1c2lwMsWnfnIej8tBxO9ma1cIgLFEjjfv7Ob0tXgl6PKWNWGePjdeFVg4k87zvRMj3LG+hZcGZ3jLHi2hvN5NHQYmkjx2dIBcvsju3ij3bGpr6ppq5sYR5v3U+/Zrl2WZiKwE1EREoVjdKAlxZVIrqDDkdSKBRKYwRyCvJ7say7YS0Odzl683fPwQU2U5PRpwl8MNC+SLcwMMrfr1jk8+WxHnFYpGCfucuB2axO51i5oyei3cTkHQ66oKKdRp8buZSdvf1MHuZgtuQ6hnvW8bjddPW8DNjxuQztX7tmLJZHUhxGPlf48JIZ43/8ynsQqFQmGHkhBXJrWCCmNlIdxKILfa13xMawno822nji6r65MQqBZ5rfplFOcVikZJZIqVIM16Mnot9JssWBHP5A2/z71G7G62YAz1rIX5+onNM8hUvW8rGqUR6+4vy//+CfBRi5+GEEL8RyGELC/vQgjRJYT4FyHEOSHEK0KI+5trukKhWI0oCXFlUiuoMFIWwq0Ecqt9zce0loA+33bq6LK622kdYGjVL6M4r1A0SsjnrARp1pPRa6HfZMGKsM9t+H3uNWJ3swVjqGctzNdPZJ5Bpup9W9EozcjqvyOl/Lt6z9nseyfwX4GdwCNSyleEEH8HXJFS/pkQYh/wNWCLlLLmn6LU0iyFYvlZ7rW/SkJcfqyOsdnb6GsP0D8SQ0rmBJYBHLk4QSJTIOR1cWBLtRT+3IUJhIXEaqxDDyXU/9XrfHU4RrIc5Bf0agGGdmve9brG4xnGEjneeVcvLX53ReR9sn+U9pCHzrCPXT0RXh2Okcpq4rkejvjqcIzxeIbBqTQO4MUr09y1Icq1eJYL1+OsifjIFSVrW3xEA26ePjtGsQQdIQ8XxxIkcwV8bi3dfSaT57a1LXRFvFydSvPKyAx7e6PcsibCj8+MkkhrQYvXExkcCFoDHk1IdkDE66Ej7KGn1U+hUOLFgSlG47nKbVJdgMMBhXIgIGiTLDcgnNrvmSJ0Bt1MpfKWt4MNeSCRm93X7hNBrdd0HMy2Yz6EvYJCSZI2rEjSyzSGGer4nOD1OJhJl+Zs73dooY15wz5uB7idIKSghKQz7COdL7KpI8BEMs94PE0mL+lp8eP1OFkfDZArag7LqeEZMoUSayI+Al4379u/gZeGpjWPJF+kJOFDD24hlinw8tA0AG0BD0cujjMyk2HPuii9rX6evTBOxOfige1dCAHHLk3SE/XzwNYOnjx9nXeUb7IQ9bt5/OgAF8YSvH1vDy9fjfG+8s0Snuof5cxonO3dYR7e2T3nGhiYSPKpn5zj1eEYu3paeO/+vqpwQiuM1/9MOs/jR7VbYb93f1/D0vtivW/P9/8b5ajcWJYj0PDnUso76z1nsZ8XOAT8JvAT4C3liUgC2CSlHCtv9zzwp1LKQ7XKUxMRhWJ5UWt/Vz9WxxiY423oLkgyW7QNNptPGJrdPuaQNB0BuCzWvNdzTczr381lOssJ4y6noFCUOMsf8mvRyAf0hbIcdSiscToExdLs6P/DB+5hZ09L5bzNFec/9TIeVwGV8/Xzv72Pt37i2apj7nYKIj73HIepEU8kGnBzqIbnYbwOg+Vr3BwAulzv+fP9/0b9P3XjWUpH5G4hxIeBDiHEhww//wFo5Cj/J+BxKeUlQ5ntgEOfhJS5DGywqP+PhRBD+k8ikWigSoVCsViotb+rH6tjbOVt6C5IrWCz+YSh2e1jDknTkVivea/nmpjXv5vLLMhy2UVt/3qTEH2/pUZNQm4cxkkIwGcOX6w6bxeCNP2un69fPDIw55jr3ojZYWrEE6n3vm2+Dq0CQJeL+f5/o/6funlpxBFZB9wNBIF9hp81wG/V2lEIcaC87acsXrb6I9fcjaT8Cyllr/4TCoUaaLJCoVgs1Nrf1Y/VMbbyNnQXpFaw2XzC0Oz2MYek6Qis17zXc03M69/NZbqE/pdpUf7Wpf7YLUeqr0oOvnE4HdWj/8GDm6vO24UgTL/r5+v7DvTNOea6N2J2mBrxROq9b5uvQ6sA0OVivv/fqP+nbl6aWZr1S1LKf26qcCH+D+DfAOUVqPQCo8AH0JyQjWpplkKx8lHOxurH6hhbBQcCdYPN5hOGZrePHpKWzGlBb0GvqxL2Vi9czWqb6VSOIxcnKqFx/SOxqgA5o6OiB8o9c36cvb0tHDo7RiZfZGtniIDXRdCjteXY5UmuTKTYv7mNf3n1GpfHk7QHvUgkiWyRfX2ttIe9pLJFzl+P4/M4eXB7J98+Pkz/8AzdER8z6RzJXInt3SHu7GslnS3y8tAM+ze3cXBHF/3DMV4enOI7x4cpSklP1Iff7eTM9QQOIJbMk5PaLWTXRf0USlJ7Plvkt+/tY2gqzc8uTnBpPInDAV1hH9mCZFtngJPDcbwuQTpfIpsvkiuBWwACSiVoD7pxOARjZT9F/x7A64C+jiDJbIGRmSxrwm78XheTqRxTqSI+F0R8btpDXgrFEslsiXg2T6FYolCS5Evgc4GUsLEjxN71UXKFIk/1j1KUkrDPw7auANfjeUqyRC5f5Op0Frcbgh4X0YCH3miAC2MJrs1ouSTbukLEMkU6wx4ujSeYThdwOwQOh8TvdtMZ9vGOO3r4Yf8oIa+L6XSe121sQwKxdI5DZ8fZ0hkk4vfw6P4NFCWkswUe/9kVPnhwM/du7Zxznh0+e50XB6Z59929zKQLjCUyDE2m2d4dpic6G4YJ8Lc/vcQta8P0tQfZuTZSCQDcubb6nNZDNaWEt5aDDPXXGgn/HJhI8rUXh+hrD/DQrXM9klrXP1gHgC4X8/3/Rv0/dWNZrkDDO4G9gE9/Tkpp9W2H3f6XmXVEPg9cNsjqXwc2K1ldoVC8FrnZREu79jbyvFVQmy7E2oULgnW4mt32qawmgetiuznM0Bha+P+y9+fxcVyHnS/6ra7qfUVjI0GQ4CpKlEhRq0UtlhfJTixviWMnE0d2nDgzGduTz0xeknnvzb33cz/35c3MvfbkvpvYyczYsWPJvjOJ7NiRLSe2NLZkLaRFUaJICRJ3ggCxEECj97273h/d1aguVHVXAw0QIM/389FH7O6qc05VnSr0Ac73/LLFMrPpIgeGQvzw5DTbe31k60F385laKF9/0MPWHi9/+8o4U/EcNw+FyRUrRAMu5tNFtkW9eF0KfrdCxKvwFz89y3gsx/4tIbZGffzkjWnms0U2hdxcSZcYCrtYyJR4194BErkSr11awOGQ+NCBIXYPBplLF/j56VkuzKaZzxRxyRJhr4uIz8UDe/rIliqoKpQrFX50cppssYJLhs0RH/ft7uPoxRjnrqRBhZIKwxEPw1EvPT4Xl+aznJ9LUyipvGNnD7lSlVPTCYoV2NkX4PZtYZ49PUe+XGZnb4DLC7laDkupCnLtL0eqJFEuqwS8MrliBUWSCPlceJwOXIqDXLHKDQMBXr+cIOhSmM8VyOQreJwOfE6FaMBFqVwlX6oQz5dQqf1VoFiqEvG7qFarLGTLuJ0Q8dSug0txoMgSpXIVCYmg14nHKeNSJJK52iIJuVKFnX1+ihUVxSHx0L5BNoc8/Md/epsryRx9ATdlFW4cDNIbqA0WJSDodfGRW4d49dICz52eJehxEvQoHByOsHdTkCePT5IrVdjS4+XyQg5Jgt0DAfqDnqbQTG1xBYCIV+GxI2OEPQp3bu9thBaa9UHqxz+bLvDWVIrfuGuYcpWWYaGdPjNaBZJuhGeOYH2zFrL6vwV+nZrH8RzwMLV09V/poJEXWRyIDAKPAzuo/cXkc6qqPteuDDEQEQgE1xobTbTsVEzXv+91OUjmyk1zc7WAwbDX2TJc0CjOmoURfvQrLy6R0WWgoqvryS/cx6e//rIILRR0hUZoZt1PaofdPigDoUYgZ4VSpdpYpEEfKGrnmdEqkHQjPHME659Vk9V1PArcC0yoqvoxau5HsfUuzaiqul1V1Tfq/55RVfV9qqruUVX1ZjuDEIFAILgW2WiiZadiuvF941c1LWCwXbhgO9ldC1k0oo+WU4HHD4+J0EJB12iEZtoYhECtDz52pH0frNTL1kIS9Ys06ANFOwkqNAsk3QjPHMG1SycDkbyqqnnAIUmSpKrqKWD76jRLIBAIrh82mmjZqZhufN8o4moBg+3CBdvJ7lrIohF9tJwEPHpoRIQWCrpGIzTTYezZ5kjAp+5p3wfletlaSKJ+kQZ9oGgnQYVmgaQb4ZkjuHbpZGrWz4H3An8NTAMTwO+pqrp/9Zq3FDE1SyAQXItsNNGyUzFd/34iV+Kpk1McHA5zfCLBI/ubHRH9/u3EXNPtz9fm3fvdSkMC1qRzrS6t3EyxTLZQZi5d5JahED96Y5qRXl/N//C7iGWKDPf46A+5GY54eeKVcSYTOW7eHCZXrhD1uYhliwz3ePE5FfwehbBH4cs/O8v4fI79w3VH5M0Z5jOFmiOSKrE57CKeLfOuvf0kciVevRTHIUl8+NbN7B4IMpepOyJX0sxli7gcEmGvm4jfyQN7+sgVKyBBsVThH9+YJlOoOSJDPT7u21V3RGbTqGot1G9rxMtw1EOP383YfIbzsxkKpSr37OwhX6ry1nSCUgV29ge4bWuY587Mky+V2NkbYCJed0SKi44IUi1rJeCRyRYrOB0SIa/eEVHZM+DnxOUkAbdMLFsgXajgURz4XQo9fjelcoV8uUoiV1x0RMpVIj4X1apKLFvCozki2SIuRUZxSBQrFRxIBLX6ZIlkvoLfJZMrVdjVH6BYqeJ0OHjvTYNsCrn533/8NjPJHAMBN6WqxI2DQaJBV82UB8I+Fx86MMRrlxb4+ZlZAm4nIa/CgeEIeweD/ODEJLlihS0RL5OJHKiLjsghXbjntqivsQBC2KvwrV9cIuiWuWtHL3sHg5Z9UDsBc+kCb0+l+MSdw1RUmhaIMC7A0Okzw+xe2UjPHMH6Zi0ckVuAC4AP+PdAD/Cnqqoe77TSlSAGIgKB4FphObLoSgXV1WifPh1dE3WNAq8m2SZyJZ54ZYL+4GK6uZagbpWWrmdsPsMPT0zxwQObCXudDdnX71ZMZXar/U5MJOjxOfnx6Ax+l8xkPIeqQtTv4vRMim3R2mpMm8Ne3rmnj//+yjjJXBFFdhBwKcylC+zdFOTcbJbd/T7emEwS9bv54/fvZf9wpFFfuVzhbw6PMRzx8IEDQ/hcMuOxHH6XzKuXFrg4l2YiXiDsUbhpKMhMqkAyV8IhSQwG3eTLVa6kCvzSzYOk82WefmsGv0uhiopbdpArVQGVkV4fvX43b0+lyBTLVCpVcDgIe2QcDge5UoWoz0mhrOJSHFyKZRnwe4gGndwyFOL4RIJcsUpVrVKpquwZCJDIlbi0kCGdq6BScyEy+RLZcu239X6PA1WFfKmKIoHP7URxQCJbQlEchDxOvC6ZkNeJLElIEqiqWguplCQeuWUTPxmdYUe/nz39Ab7+4kUiPic9PicDQS+zqTwnJxKEvAo9fhepfJlcsYwkSaTzRUJeN0GPk5uGgoxE/bxxOYnDoTKxkKt7FWX2D0V4x64oAG9OJhiP5VBR2Rb1c8tQiEyxwusTcbb1+Dg7m6Y/4GYw5OH0TIrBoJvjEwnCHoXZdBHFASoSjxzYzP27+xrieTJX4qvPX2Df5iDbev1s7fHy4zdnkCW4MJ/l9x7YQcjrbPQ//aDb2H+1e6bVYg/GbVrdp3bu4dV+Dgkp/vpgLQYiUVVVYx23rMuIgYhAILgWWI6g3uk+K5Hg7e5rlYIe9irIkkQqX26knPvcciOxWY/TIZkmoRsZm8/wri8+2/jtedAjk8wvGiBWKdDG/cJehUyxQqmyOlGB//W3budffOtVEUQoWIJ+YYZ3f+nZpoUVNOk9U6hYLvbgd8sNeb2ddG7nHl7t59BGW4hDsHzWQlY/I0nSE5IkfUCSJJGvJBAIBCtgOYJ6p/usRIK3u69VCrom2RpTzs2wSkI38sMTU406VCCVrzR9brWvcb9kvrxqgxCAP//pWTEIEZhiXJhBjya9t1vsIZm3J53buYdX+zm00RbiEKw9nQxEtgFPAf8WGJck6T9IknTD6jRLIBAIrm2WI6h3us9KJHi7+1qloGuSrTHl3AyrJHQjHzywuVGH9hcRPVb7GvcLeRSc8ur9Pu0P3rNbpKELTDEuzKBHk97bLfYQ8tiTzu3cw6v9HNpoC3EI1p6OAg0bO0nSLuD/CfyOqqpyu+27iZiaJRAIjOgdBTuewXphbD7DUyemeEQXcGZnPvdKBNVOsLuvWQq6z1ULExydSpLJl/F7FA7t7CWRK/GdYxP0B9z0h9wNmdwqCd3I2HyGp05O8cDuPsYXcmS1xPV6+cCSAMR4tsjTozOML2S5Z0eUn749S6FcE48vzKW5FMuSyZdJ5MvcsS3ChfksIY9CLFMk6ncxsZBjPlMkX6qldPcFPCQLJdyyg1imiFtx4HbK7B4M8oV37+ZyPM8PX79cE9AdVTJ5Fa9LJuqvSdhTiTxuxUGmVG06Nr8TQCJTqv1cdgLaxJ2IV6GiVkjlzX9mS9CVv8JIgOyAcrXtpraQAYejJsxrOABZgqjfSTxXxu+R8TtlJuIFfAr0Br1UVZW5dJ5iGYJumVK1gqQ7NwAuByiyxLZeH70+F29OJ3FQc1HyxTL5MmwKugn6nITcChfmM/icMgu5Erdvi3DPzprUPzqV5PatES7FsgQ9TjaF3Pz8zBwu2YFLkalUVbKlMvliGRWJu7ZH2d7n46VzMT5wyyZuHgrxjZcusncwwEK2zCP7N3FyMokMXIhl+ez9NUfku69OsLXHx8P7BoFaP83WF0t4czJJIlfiU4dGOHslw0vn5jm4Ncy2Xj/37qrdN9qzIux1cvjcPOnC4l9UJGgK8NRckpfOzSO1SUg3u8/bPYuMz65WCCn++mCtktUV4MPAZ4C7gSdUVf1Cp5WuBDEQEQgEeoyOQjvPYL1wrQaMtZrP3q1jahWc+J4vPdsUgPj9z9/XOK8+l4NEbmmOSbcIuGUkaemUMcH6xOeEbmRaGgdvmgei/4Ku77MBt4xKzQXxu2XK9WwQK4zuiDHoU6tTkWsrmSn1JX5VlvomdmjndQjvQ2DGqjsikiT9OfUle4FvAVvXehAiEAgERoyOwkaZi3ytBoy1ms/erWNqFZxoDEDUn9ekSZhiN0kXKqTFIGTD0I1BCNT+EqX/C5LmgejR99mkwQVJtwk2NLojxqBPrc5SRW2EHhrr6KYfJrwPQTfpxBGZAe5QVfWXVVX9W1VVO0pVFwgEgtXA6ChslLnI12rAWKv57N06plbBicYARP15DZmEKXaTgFsm4FnT2cqCFeBzdqccCVAcza8f2b+5aRt9nw0ZXJBAm2BDoztiDPrU6nTKUiP00FhHN/0w4X0IuomtqVmSJMnA8bUOLzRDTM0SCARG9I6CHc9gvXCtBozpjwNYlWNqFZxoDEA0hil+99gEfUEX/QEPwz1enn5rBkmFi7Esv37nMIl8uZHtIANffeECfpeDmWSBHr+Tu0Z6iWWLzKcLnJvNIDvgwwe38Bt3bQPgmbdm+NlbVzg+EcfrlLlhUxCXLLFQ90zens7gd8nEMgVCXieqKiFJKiN9frZGvDx3eg6HBCGvQixbIuBW6A+4UVWVeK5EOl9Gdkj0+J0UyypqVSVTqpDIFRkMeskUS5QqKkGPk2q1SrZUxaNIFCtVMoUKskNipNfHZLxArlimXAWnDD6XQtTvIuJ18fZMHNkhsz3q43Iij9shka9UqFTB61RQUSlXaqGDDgkGg25ypQrpQhlZgk0RL8VSFZciITsczCTzlKtV8mWI+hR6fC58LplCpUrY48QpS5ybzXLLlhB37YhyairF29MpBoIuHt43yFMnp9ke9fLCuXlmk3lGen3sGghSLFe5c0cUVDh8bg6Ag1t7OHMlzamZJHeP9BDPVxgMujg1k+bde/t54ew8v//gTvYNhXny9cu8OhbnE/XrrgURfv3Fi6CqfOLOrQ3f47XxBQAO7epDAo5PJDi0s5cH9/abhmbavS8Annz9MsfG4nz2/h0AfO2FC9y0uZaPog9MNAZ9Zgo1/8rMtVrJvdfuWXStPKsE3WMtckT+EfhVVVVznVbSTcRARCAQCMxZq+Aws3o6DU6DRalcL9jaCWjTy+iwNDRR3ybjtvrXiVyJJ45NEHDJXI7ncDtl3rO3n5fOxegPuphPF3n5Yoxfv2OY0akUKvCeG/v5pzemOX0lRb5YJZkvITsc7B7wUypXeXMqiVpVuX1bFI/Twc9OX2FPf4BSpcpUooAig+xwgKridSs8uKefhWyR0akkM8k8UZ+LSws5NgXdFCtVYpkCPpeTgEchnitx/65eLsxlOTudAgfUw85xKTVZOeBWiPrdpHJlxmKZ+m/Kwedy0uN3kc6XUWSJ+XSB+3b1UVYl8qUyo9MJZEnCrcgkcyXSxSoOwKVIDEW8KA4H6UKR6UQRnxt6/bX3LsczVCtQVGtTLPoCCuWqVBtoZQt4nLXk9dpfDGrlh7xOUvkSe/qDZEtlLsdzFCsVNof9eBQHuXKFTUE3lxN5HEjsGvDjlmsDt8Gwhx6fixOXE7xnbz/fe22SbLHMB27ZxHdfu0w8U8TndrA96mf/1h4+cedWFrIl5lN5/uJnZ/G7ZCJ+F7dt7SFbrLB3U5AbNwX5+Zk53rmntgACwNYeb+O9Vy8tcGwszrtv6OMnozMEPAq3bAnjcyn43UotwHB0hpFos4g+m8ozmy7y8TuGGyGaWuinNtC9eSjEm5NJsoUyPl0op1VIaCJXWhLM2cn9Pjaf4YljE422ikGEoFusxUDk/wLuA/4OSGvvq6r6l51WuhLEQEQgEAiWslYCqVk9QEfBaX63XP/tfm2ee21qi0TI62wb0KYPgdMk3rQuNFHfJr24HvHW5N1Evc6QRyaVr4i8j+sAB9ClRcDaEvLIOCSp0bdBF6JZqDSFfpqhX2DBGBKq77Namdli1fb9rg/2pL7/c3/8bjEYEXSFtQg0DAEngZuAu+r/3dlphQKBQCDoPmslkJrVs5zgNL1sqwm2dgPaNDSJ12yhAqO4rsm7i/uKQcj1wloNQqDWr8xEcn24Z+v9F++BpSGhi31WK7OT+10f7KnVJURzwdXG9kBEVdXPmPz3O6vZOIFAIBDYY60EUrN6lhOcppdtNcHWbkCbhibxmi1UYBTXNXl3cV9ZhA5eJ3TyG9eVEvTIpiK5Ptyz9f6L98DSkNDFPquV2cn9rg/21OoSorngatPJ1KwQ8L8B21VV/agkSfuAW1VV/W+r2UAjYmqWQCAQmNNKIG3nXeg/s/Iw9J+bBaDp5duXzs0zl8pzRTdH/snjNSH31+8aJp4r18PcKpy+ksKryHz44BAL2RIjvb5GEKL2E8pfnz8PcPj8PLPJAldSBd5/8yCnplO8fHGe+XSRB2/o58MHtwDw9OgMp2dS7B0M8tC+wVqY4isTjPT5uGt7lJcvxHjlYoxLsQz5UpVsoULQKzPc46NUVnl7OkWxUuG+Xb1cWsiRzJW4fVsPb02nODuTJFOo4HKC4lDo9btI5IqkCzUfwuNSSGZLlKvgd0ukCrUjcdW9DgCnBB6nRLGqUi6DnYV/o16l9tvyZfw5x1H/TzIEDLZD+/Lazb8gSUDQ7SBZqLkoTqlWvt+jkC2UCXicFMu1AMGKWqVaBZfiIOx14XUpXElmKRarFKq1v3gMBpzMpktU68cYcDvwOGUGQx76g15uHQ7xt0fHccoSsWyRzWEvfQE3AyE36XyZM1fSDEe8zGeLBD0K/QEPhXKFW4bCTCVzzCTyKLKDqUSesNfJO3ZEmc8U8Thl7hrp4e9emUBV4V8/vIdUvsSfPX2aiNeJU5F5300D/PDkFKgSv/vAdhK5MpliGb9LYZ8W+lkPJ1TVWl9XJHj8F5d49B3bqNTnYmmBoPpATwk4tKvXtqs1Np/hu8cm2Nbr46GblueIrJWLJthYrIUj8n8Do8BvqKp6iyRJXuCwqqoHO610JYiBiEAgEHRGK3/ELFjxo195cYmHYTcYzehxQO1LZ8DtIFVY/PbrdEj43fKSufSKQyJQD2OLG4Ieoj4nP/2jd5HIlZrmuhsJeRUc0CjbGGyoBS1q018E1weKQ6LcpeutT7LvNNX+OUPYIZjfNxo/+MJ97B+ONG1rFei52p6YCDMUWLEWjsiNqqr+KVACqK+eJf6yLRAIBOucVg6HVbCihjFMrd2ccqPHAbUvafpBCCyGrhm301wRs6TpZL1+41x3I2mDg2LmniTFIOS6o1uDEGgeeHRaqjHsEMzvG43Hj4yZbmsW6LnanpgIMxR0m04GIk0BhvW/iIiBiEAgEKxzWjkcVsGKGsYwtXZzyo0eByxOw9Gjha4Zt9NcEWMZUJsTv39LeMlcdyMBg4Ni5p5oc/YF1w9KF6+3ZPFvOxjDDsH8vtF49J4R023NAj1X2xMTYYaCbtPJ1Kz/AKSA3wI+B/whtZDD/2X1mrcUMTVLIBAIOqedP7IkLO38PKiYhqnZqevw+VqOwly6yMdurzkiz4zOcGkhy8M3DRLPlRoBg985NsFIb83b0IexaYFtSOB3KU1z4cfmMzx1cqrxpe7bR8bIlysc2BLhoXqWQ6tgQ+2YIl4nT4/O4HPLXJzNkMiXefSebZy9kubIhRgHhsP0+d1ki2Um4jmGe7wAHDk3z6mZFPfv7sPtlPE6Zd6cTNLrd7KzL8DRsQVOTsTZtzlIVYXj4wkcDok7t0U4eTlBMl9iKOKlUoV8qYJLlphK5nHJEm6ng3S+StjrJOh1Uq1UmErm8bud3LgpyGyqSDJXIpEv4pQdDATcjC/kiHgVUoUKarVKWYVKtbZscSJfZiDgRFFkdvUFyJTKXI7lmc/kKVTA6YBev4vBoIsTk7XV+Z0ybO3xE/YqzKbyZIpVXLLEXLqIIkO+DAGXRLmiIjugXAWvSyJbUnErErLkIFes4HEq7N3kZz5TZCFTIl+qMBj0oqKyo8/Pa+NxdvT5+OVbNvO91ya5aXOQC3MZNoc9BD1OUvkyb08nyRcrDIY9jPT5ccsS04kCb0wmcDokihWVXf1+ylWVA8NhLi/kcSsOYtkiDgl29gf4xJ1biedK5AplvvrCebZEfNy+rQdQeXMywXSiQMCjkM6X6fE7CXlc7B7w43Mr+F1KLfhydIa+gIv+kId9m0OMTiZBgn2bQxy9GGNsPsuv3TFMMlfir1+4wI2bg4z0+gl7FL71i0v0+V387gM7TcMO9feNFqr4/eOTPHrPSNO0rHb381oEDYowQ4EZa+GIKMAfAx+l9guAJ4H/qKqq+d8SVwkxEBEIBBpCmrTP2HymEYRm9UWom6zmtdGO5batYV46F8PvlpmsBxLePdLDD09O11Kpe/2NsLn+gAu/q+ae6EPo+gO1ts2mity7K8rP3p4lnisS9rm4e6SHvz9+maC7FmCXLVZ4ayrF7z1QS7/+n79/kgvzWd69tx+n7GBiIYfikBiKeHjp3DyVikpVVenxu1BVGAy5mVjIkyqUUBy1xPFfvmUTW3q8vHE5wYW5DIlskbDPyU2bw3zizq08PTrDNw9fIJGrsCnoIup3cTmRR3FA1O/mjm0RXrkUZzaZJ1OooGohE2pNfnc5wO2UKFdBUlWyZQi6Jf7tL93Ej05OcWysNlja1usnni2SLVZ4ezqJ1ylTrqrki2VKVfC6ZPwuhWS+SKmiolZhpNdHrlRhKlHglqEAV1JFCpUq26NeEvkKm0Nujo/HifrdIMENA0FimQKvTyRRgZDXwZ6BEDdvDvHKpQVcci3oMVWssCXs5eF9A3z/+BT9ATdIKhfmMgTdMkMRPx6nhFOW8blkXr4Yo9fnIluqoMgS+zaFOHMlTcjrxKlIeBSZ3QMB+oOexnUPuGTmMkVk4NilBe7Y1kMF2DsY5O4d0aZwTC0E0O+SubyQQ5LgwHCEu3dEm8IJ7zUMlH94Yop37uljIVviwHC4EaCp73Mfv3O4o2BCfdChMcCzW4jnqmA5rPpAZL0gBiICgQCENNkJ+iAzCXjWRJbtJqt5bYyhbALBaqBNPfqbz9zFh7/8oq3+ZrWYglOuJc0ncmXTciI2gwn191WpUm2EgHbz/hLPVcFyWXVZXZKk/02SpIhU4ylJkuYkSfpYpxUKBAJBNxDSpH30creKuSzbTVbz2rQT1QWCbqD13ccOj9nub1aLKZQqtYUZrMqxG0yov6/0Czt08/4Sz1XBWtOJrP4RVVXjwENAGbgP+Her0SiBQCBoh5Am7aOXuyXMZdlusprXpp2oLhB0A63vfurQiO3+ZrWYglOuLcxgVY7dYEL9faVf2KGb95d4rgrWmk4ckddUVb1NkqQ/Bd5WVfVbkiS9qqrq7avbxGbE1CyBYG3oxjzh1Z5rvB6lydU65pWWq5e7W8my7eowCz807qMFImYLZXy6IEJtO/2/tX1OTsT55uExPn1ohK1RX9M8+JFeX2MuvgTMpgu8cjFGuVJlYiHP3dsjXEkX6Qu42BTy8sZkErcikS6U6fG5KVWqnJ9LE/Qo7Oj1s7M/wMRCDocEPT4Xr40vIAG3bu1hJpXn/JUUpSp84JZN/OiNGZwOleEePzPJPMcvxVAlB5vDHhayeeLZKjv7fPhcMmOxDCDhlh3MZmp5EA7A44RiGapqLXwPagF+sgNcThlVVZcsbxx0ywTdTiI+mdHpDAAuQO0wjNAKt0IjRDHscVAs16RzSZKWtKVTfApky0vzNbwK5AxWqSJBf8DJQq5EqQwOCZBAliDocVJWVQYCbtKFCldSeUpV6PU5yRTLRLxOon4Xc+kCuVIFSar99UGWVMoV6At4SBfLOBwqEa+Lu7ZHee+Ng3zv+CQSak2eL1fIl2oZOWPzWW4f6eFXDm7hh29Msyno5u3pJMl8mVyhjEtxkMiWSBUr7Or38YcP72UynufUTIq9m4I8dFNtkYQTEwl6fE6ePzvHweEwx8cTPHJgM8lcia+9cIGbNgeRVBidSjXCPSXdogoaVkGjErBvKNS0sIP+3jILI7XjlOj9E63s1XpuCwfl2mMtZPUXgB8Cn6X215BZ4ISqqrd0WulKEAMRgWD16cY84etxrvFqHfN6CSozCz/UQgK1fYAl89gD9d8GpwsVAm4ZleaAxPFYlg99+cVGPWGvk1yxQqlSRZagorbPapCpfclXZIlSxebPNbqbFi64/oj4nGTrfdl4P+hfayGa+nsgnS9Trqo46399aBc0arzXjCGGAbdMRVVJ1Ed7+jDSdk7JWj2vr8efC9cLaxFo+NvAJuBPVFWdAXYC3+60QoFAsP7pxjzh63Gu8Wod83oJKrMKP9TvYzWPXZsHnzQJSPzm4ebAtlS+1Ni/bGMQArXf7KtgexACYhAiWDmt7gezEE39PVCqqqjQUdBoqxBDrVx92+w6JWv1vL4efy4IWmN7IKKq6llVVf818JIkSYP11/9h9ZomEAiuFt2YJ3w9zjVerWNeL0FlVuGH+n2s5rFr8+BDJgGJnz7UHNgW9Dgb+yuSvcA4mXogomzfIBGuiWCltLofzEI09feA0yEhgek9Z+deM26nlatvm12nZK2e19fjzwVBazqZmnUA+L+BIWrP7wngn6mq+sbqNW8pYmqWQLA2dMO/WI8Ox2qzWse8XoLKzMIPjfto722L+prmsRvDBPX7nJyI8/iRMR69p+aI6PffFvUxOpkkU1z8bW+2WObNySSpXJnfumcbFZWm7Y9ejHFiPI7HKXPnSA/fOz5J2KNw544oeweDPH92riHtf+fYBP0BN/0hdy2c7kKsEbw4Ec+RydeEh/H5LE++PslcusD2Xi+3DIU5Np7g3Tf0E/W7eOncPKVKFUmC0zNpenxOZIfEYNDNxEKO2XSBQrnKloiXwZAHn0thPJYhma8Qz+SRZQdel0yuWGEg6EFySLxjJMIPTk4jOyTu39XL+fkMV5J54rkyjipIcu18FCq13yxuCrm5Z2eUF8/NE88UqargdcuASqFUZSjspVxVSRVK+FxOwl4nN20K8Op4gh6vQl/Qw5mZNHOZLOBAdkiUK1VUFSI+F8lcib6gm1s2h3jx3DzlahVVrVIqg9et0BtwMRj0cH42Q75cJlusUK1C0Ougz+9lOpGlXFWJ+Fy4HA5KVZUbNgVI5ipMxnN4XDJBt0y2WEGRHdy0OciNm0JcmEvzxkSSy4kct2wOcW4+ww0DAUZ6/ficMkcuLhDxKkzGc+RKFVAl7toe4cyVNIoMIY+L9+4b5MEbBhidqvlG2WKZuVSRh/cN8uK5Of7HW1d4740D3Le7j6ffmkFS4fh4nGK1ymDQjdspk8qVGF/IsW9zkM+9ew9hr7Pl/WAWoqn/t/4esRM02i7EEDANI21VTyf3fze4Hn8uXA+shSPyMvBFVVWfqL/+NeDfqqp6V6eVrgQxEBEIBAL7rIYYqkmzQFOI20rq04uyb04mTcs21j+XynMlXeTj9TTrbx4e41dvG6JcZYkUDzWJN1MokyuWG/uFvc6msu7dGeWl8zH8rlpIogoMR7ycn00zly6yf0uY+WyRVK5MKl9iIOjihXPzyJLUGGAcPjfPLVtC7OoPcOzSArv7/bwxWQsJHIr4QFKJZ0oossSFuQypXJmyWqFYUZElib2DAaYSeRayJbZGfdw5EuUD+zfxd0cnePnCPB6XzI2DITLFEicvJ2rJ31EfB7aEOXZpgYlYlioqmVwJySFRLKuEfE4+cMtmErkyo1MJcsUKhXIFFQi4FPqCbrZEfBTLFc7PZqhSJeCSOXMlgyQBkoTbIZHKlxkIuamoNZcnkSsSyxSpVmtBkZWqSrJQAcDvlOgPergcz7EtWgs/lCUoVlQWskWifjd9fifpQoVyVeX+3b3Ec2VGoj7emk6hqrAl4ubw+RibIx7+3Qf2MRXP8WfPnOGde/o4uK2HiViGv3ruPFsiHj54YIjegLvRTzQHKFssc+ZKGo8i82j9r29a2OD4Qo5L8xlGp1J8cP8mTlxOsr3Xx0P7Bhtf/p8ZneHt6RQ3bgo2vW9cUEG/sILWfxO5UiNIVB9cqPVN4/76EEWz/fT3gz7Y87XxRNuw0k7vTSGVCzphLQYir6iqeqfhvaNiICIQCATrk9UQQ+PZIu/+0rMsZGurQmkhbkZxtpP6tP2SuRLl+rx5gB6fk5/Vy7aq3wxFgpDX2ZDi/XU5OG7YRwICHplUvtLhWRBsZOwsUhDxOfmHz9/HR77yYlO/0d7/1b98qdFfFVmiXFGXLKwQqvctVbdv1kRWVxxSk7RuDFHUC/HaPWUW7NkqrLTTe1NI5YJOWQtZ/YQkSQ/oKrwf+EWnFQoEAoFgbVgNMVQrUyNpIc52Up+2X0k3CAFMyzDWb0ZZZYkUb7ZP7cugGIRcb9j59ateELd6X+uvpYpqurCCfhCi7WsmqxuldWOIotk9ZRbs2SqstNN7U0jlgrWi7UBEkqSj9WlZtwPPSZJ0SpKk08DPgUOr3UCBQCAQLI/VEEO1MjVCFuJsJ/Vp+2nyroZZGcb6zVAklkjxZvtofxERXF/YWaRAL4hbva/1V6csmS6sEPTIS/qzmaxulNaNIYpm95RZsGersNJO700hlQvWirZTsyRJerD+TxUYqP9/TvtcVdXnVq11JoipWQKBQGCf1RBD49likxBrJc526ohoUu3oZBJMAt6M9c+m8syli3zs9poj8viRMT56cIiKiqkcfPjcPJliXVKu7xf2OpvKumdHlCPnY/hcMpOJHKiwJeLl/FzdERkKE8sWSebLpAtl+v1OXjgXQ5ZgS4/OERkKsXMgwKuX4uzqq3kDHpfMlogPaHZEkvkylWrNEXFIEjc2OSJ+7trRwy/dvIknXpngF+fn8bo1R6TMyctx/E6F4V4fB4bCHBuvOyKqSjpfQpJqjkhYc0Tyi45IvlRBAvwuhb5QzREplCucv1JzRIIumdN1R8QhOXA6atOJBkJuKkiEPQrxXImFbIFqBXxuhYqqkqz/lcnvkhgIeJiI5xipOyKOeuhgzRHx0Btwkq3/deD+3b0kChW29nh5eyoFwFC45ogMRbz8vz5wE1PxHP+//3GGB/b0cXBrD+OxDP/Z6Ihoc6/q39SzhQpnZ1N4FJlP3lNzRJ46OcUDu/uYWMgxFsvw1lSKR27ZxMnJJCO9Ph66SeeIvDXDqenF0EK9OK5fIEG/sILWfxO5UiNIVC+2a33TuL923+gDSI1CvIa2zcHhMMcnEi3DSpdzbwqpXNAJq+qISJL0WeB/AbQKxoE/VVX1q51WuFLEQEQgWF30giIsTcAWXL9YSepWUquxL2n73jwUWiLoAjw9OsNYLMv79w2ykC2Z7vfmZJLZVJ4zM2k8LplH7xkhmSvx1ecvsG9zsCEr+90KW3u8/PzMXEPm3dPv54cnp9kccjMWyzIYdPP6RII9gwF+5bYt/OiNaS7NZ+jxuyiWVUYnEyxkC4S9LgZDboZ7fBTLVU6OJ1jIFRkMeShXa6tR+d0Kk4kM+RL0BhRuGAixNeqtraRVrpKvVPDJMtOpIirgdTpwOKrkCrXvy5JUm9qjOMAlQ7UKufqsMW3qglxPVQ+6JTIFlSrgkqAn4CJbLJMpVNFnogfckC7U/u2Va/5CVYUdfT5S+TL5cpVSqUy5CooMKhLZklr/LT14XA6KxSqFKgTcDvqDHtyKxKnpTKMen0tCRiJVrOJTwO2U6yuHSfhdCkgSHsXBTLKAA5WD2yKcmIiTK6lEfDLpQpWwR8GpKKiqykDQyVSyyJaQi9cvp0CFgbCbW4d7ALgwn6ZUrjKfKfKBmwc5MrbAQMDN79y3g6MXF4jni3gUmQPDEbZEPDx2eKwm26vwK7dt4cxshqGQm+8en+RzD+5k31CYJ49f5tlTs4Q8TvqCbj5ycIjxhVxTP9fE8D39fv7+tctIkkShVOWuHT30Bdz43UojyVzrxx+/Y5iRXj9j8xmeODZBf8CFz6U0tjWT3q2etZ08l+1K5kJGF3STVRuISJL028CfAP8P4HD97XuBL1FbResbnVa6EsRARCBYPfSCoj4JWMiKgni2yHu+9CyxuririeSAqdRq7EuqqhKvJz5L0BB7nbKDgFumVK02SeNOWSLoVqjq9hMIuo3f5SBTrFp+3uNz8tjv3N0kj1sR8Tmp6PqxBHz7s3fzya+9vGTfqM/J9+rSeypfbpl8bkxP1xZhMHsu25XMhYwu6DarKav/IfBLqqr+o6qq8fp/PwI+APwbGw37iSRJJyRJOi5J0vOSJB2svz8gSdI/SZJ0RpKkN+ryu0AguIoYBcWkkBUFdU5MJEgaUpuNSepWic9aX9LQi72avGuUxksVlaRhP4Gg27QahACm8nirbfX9WAX+83PnTfdNGtLSWyWfm6WnWz2X7UrmQkYXrBfsDERkVVUvGd9UVfUitTDbdnxCVdUDqqoeBP4T8PX6+/8ROKKq6h7gM8C3JUlqbSAKBIJVxSgohoSsKKhzYDhMyJDabExSt0p81vqShl7s1eRdozTulCVChv0Egm7jd7X+GmQmj7faVt+PJeD3H9xpum/IkJbeKvncLD3d6rlsVzIXMrpgvWBnatYZYL+qqnnD+17gpKqqu21XJkmfBv6Vqqp3SpKUBnaoqjpb/+xl4E9UVX22VRliapZAsLpYJQGLAKzus5zz1Y3AwFZz0dvtaxTJNW9EkmDf5lCT86F//+ULtaRzSYLNES/ZQoX33TxIPFdq9LVn3prh9EyKLREvfreCz6Vw81CI0boX8sZkgvl6qOBUIsd8usgDN/SzOeThGy9d5B3bowz3+kCtOSKyBF9/6SIHtoRJ5IqMTiaZSubZ0esnX6oQ9DgZX8gxHPHymfu286M3pjh+Kc72Xj+JfJHzsxkS+Qp+p4TDIeFzKeSKZaqqSqGsUq1CFXBKUFSb8yncMvjcMolsTdDW5OmyveiujnA5oFxvix2UenNaLVxslrVhJ3/DrBx0+w0FnSRyJbLlmrdSqIBHqZ0Xl+Ig6HaykCviVRzE6pKMR4FNIR/b+3xMxnOkC7UwyT6/m8lEnu29Pt6xs5fXLi00an3vjQPkShWeeXuG4YgXpyxz81CIKrCz18f3jk/y+w/uZEuPj689f55L85lawGLQzYdvHeKls3M8/dYM771pkA/s38xzp65w+FyMrVEvp6ZTxDJ5UoUq7983yJ7BIEg0+uvRizFOz6TYMxDk4X2DjMeyfO35C9w0FKTPX/NJ9uk8KTNZXUN/72nbvXwhxqVYll+rOyhGzCRzs+dGJzK6eLYL2rGajsj/DuwEfk9V1Xj9vR7gvwIXVFX9ExuNewx4d/3lLwHTwLiqqj7dNn8H/FBV1cdalSUGIgLB+kTMOe6M5ZyvlQYGtpuL3mmdVk6R/t8Bt0xFVUmYeB7GwEJjsKE+4O1DX36xsZ8EBL0KSZ1zom2rD3x78IvPtj03GkGPTNqQ+yBYf0R8TjKFMqWK/SslAWGvQrZYtfSY9O+fnIg39bd2ZT/5hfv47W8cbZTz95+7t+F+mLl2YO5VGTG27+8/dy8f/cqLpmGirVjps1k82wV2WE1H5H8CSsCEJEmvSZL0KrVVs8rAv7NTiaqqn1JVdWu9rC9qbxs2M/3LpyRJfyhJ0oT2XzqdtlOlQCBYY8Sc485YzvlaaWBgu7nondbZyinS/q3NaTfDqjyzgDc9Wiq1/rVZ4FsniEHIxqA2mO7sSqlg6rtZ9e1vGvpbu7IfPzzWVI7e/TBz7ZbrcRgDFpNduIftIJ7tgtWk7UBEVdWSqqq/CdwK/HvgPwAHVVX9Z6qqljqpTFXVb7L4lxEkSerXfTwCmLkof6aq6rD2XyAQ6KRKgUCwRog5x52xnPO10sDAdnPRO62zlVOk/Vub026GVXlmAW96akGEzc6JWeBbJwQM4XOC9UnQo+CUO7tSEpj6blZ9+9OG/tau7EcPjTSVo3c/zFy75XocxoDFUBfuYTuIZ7tgNbGVI7LswiUpBARUVZ2sv/4V4C+ArcA3gIuqqv6vkiTdBXwX2KmqasslUsTULIFg/SICsDpjOeerG4GBZnPRl1unlVNk/Pfh8/NkCouPd79bMQ0stGrn2HyGb/9iDFT45D0jtSDCc/NQd1DMjmlsPsN3jk0w0utjKOzh6y9eoFRWObSrl76ge0lbtPA5Leju7akkL52fZ9+mIJcTeSRU5tJF3rmnHyT4x5OT5IpVbtwU5I3JJLdsCRFwK/zs1CwDQTdBt0KqUCbsczIQ8LCQK1IoVkgWyngVB3OZIulCmXt29HJuLs18ukjIrTAY8eB3Krw2HiORr9Lrc7It6gXJwUK2UJuaVK5QqqhE/W72D0colSu8MhYjkavQ45MJeVyUqyrlqorfLTOXKeB2OChVYVPIjeyQmE/XMk3ShSKKLHPrljDn57P88wd2kC5WeOr1STL5EuPxPKiwKeymUK6SLZbxOhU2hd2UqzCTyNecHrdMLFNkU8jNZKKAz+kg4HGRypdwKzLpQpmPHhzig7cO8fRbM/icMrFsEa8ic2Y2zYHhMD6XzNmZNIVyhUK5ykKmRE/AxUDA3bjuJy8niHidfPvIGGdn0/zzB3aSLJQ5PZ2i1+8iXazwvn2DnJpOcWkh2wiuNOvDVn375EScv37hArePRHjwhgFGJ5NkimX8rprfcfRijEvzWT5W9zSM5bRz7ezex2blWoWJtmKlz2bxbBe0Y1UDDZeLJElbqQ0wvNQ8ulngj1RVPS5J0iDwOLADKAKfs5PSLgYiAoHgWmUjCqGt2txKkNfvl8iV+OGJqcZfMZ54ZYLtvT7u2hFlbD5Lj8/Jj0dnGIn6eHjfIIlcqbHNQ/sGgcWAN6hJ8tlCmdl0gaMXYgQ9Cm6nTMjr5NF7Rri8kOXPnj7NloiXTSEPxy4tcN+uPnoDLi7FcmyNevG7FHz1UMQnXhnn9EwKv0vhHTujfPsXl3ArDn7pls30BlycuZJmKp7n7JUUvT4nE4k8u/v9FCtVErkyAZeMqkK2VMHnchDyutje6+fVSwuEPE72bwnz1nQSVYX7dvcB8MxbV5hcyDLS5+fde/v59i8uUShWyJQrHNreSyxXYmuPhzcmU7x7bz83DAY5PZPie69dJp4uEgk4cTtl7hrp4e3pNJliGcUhUSpXmEkW2DUQ4N88dANPnZzi2NgCEZ8Tp0MiXazUV5KSSOXLOBwSXsXB6ZkUQY+L997Uz8X5LJfjORK5IgGnAg6JW4d7uGEwwMsXY8SyRYYjPhK5IvlShft29ZEtVZhLF5iIZdk1EOS2bRFyxQpHL8bYHPFyx9YI3z0+yafesa0h9Ee8Co8fHgMJ5lJFsqUy771xEJ9L5vh4nEO7evnIwS2N69/jczYFWH7wwGLSuDEQ8JnRGS7OZ3n/zYOML+SYTeUZX8hx42BwSZ8y9lmtnnfu6VsSfKgP/dSCO1vdGysNJez0nhQsRZyv7rAuByKrgRiICASCa5GNKITaFdmNgjwsyro+l4NErqwtKtXkaUjU5g/rV3cKuh2kCovrQ4W9ThxSLeDNGJwouPYJuB24ZLnhFumRgGf/+F2Evc6mQMCyqjYWOjAj5FVQJMlUME9ki6Yrn/X4nHz/8/c1yeQSmC4M0e5eX8mzYCM+R64m4nx1j9WU1QUCgUCwymxEIdSuyG4U5I0Bbdr3OuP3O7MlZvWDEIBUvmQZnCi49kkXan3IOAiBWv956uTUkv6WbtNH0i0Ec6vll81kcquFIdrd6yt5FmzE58jVRJyvq48YiAgEAsE6YCMKoXZFdqMgbwxo09Rjo4IssTQ1N+hu/rEV9DgtgxMF1z4Bd60POR1LBXYJeGT/5iX9LdCmjwRaCOaKhSdvJpNbLQzR7l5fybNgIz5HribifF19xNQsgUAgWAWWMwe8EyF0bD7T8CrCXmdTWWs559mOyC5L8L3XJvnUoRH2D0canx0+N0+6UGYuXeDERIJDO6Pctq2Hn4zOMNLr467tUS7Fsswl8/yX5y/w4J4+9gwGmW1s38uDe/t5czLJXDrPlVSRe3dGOXwuRn/QzXy6wE/fngGgosL2Xh9DER8DQRdff/Eim8Nubh2O8MZUkvt29eF1yhy9GCNQHxylcmX6gy6eOz1HulAi4HJy4+YAR8diqKqE3yWTKVYolqr0+J0kC2UUB2QLKoqjHtDncVKsVCiUKlRUyJVqP3NdEkgO8LqcuGQJlyJxOV7A4agF+BVLUFLBq0goskqqUDunDmphiTnDn4o8Cpj9ot/vhFwJfC4Jl1wTxovV2vZbIj5imSKpXBnJAdVq7S9QErWQROrHsCXk4dxsFhXwOiUqVZVCFSrVWnuqgEuG24bDnLqSpliqsDniJ1css5ArsaPXSyJfplAqkymqDAY9eFwONoXcnJvNEM+VODAU4lI8zwdv2cTRsQV29vs5sCXMY0fG6PO7mEkVyBUr7NscIuBRSOfL3LwlTEWFe3dGOT6eYHe/n2feusJDNw1wdi7DI/sX7w3FsdgHt0Z9PPPWDKemU0T9LmKZIg7g2KU4D900wG/cvQ2Aw+fmuTif4eiFBbb0ePjEnVuJ50pEvE6ePzvXWNTAGPCpyeT7hswXUWh339j5vBVj8xmeOjHFIzpHRmCNEPG7g3BEBAKBYJ2wmnPAofZF411ffLbhVejD2vRhauthzrOxrc/+8bsaqwy950vPEss2rwJvDGlrFS4X8TnrA4ZS05QZRYKg12nLF4n4nDz7R+8CMG2PYH0S8sikdNkvMvWUe12wJSz1OvTeyLu/9GzD59DQf/7gF58lkSst+Ww9f7kXzoPgaiEcEYFAIFgnrOYccKiF9em9Cv18dmOY2tWe82xs61Mna0GDJyYSpj6HMaStVbhcqh6WaJy3X1ax7YvoHQDhl2wcUoYAygpLgy3NvA6jN2Kk+fOS6WfrGeE8CDYaYiAiEAgEXWY154BDLaxP71Xo57Mbw9Su9pxnY1sf2V9bovfAcNjU5zCGtLUKlwvWwxKN8/YVCdu+iN4BEH7JxiFoCKCUWRpsaeZ1GL0RI82fO00/W88I50Gw0RBTswQCgWAVWM054FCfB35yqjEP3ipMbT1My9C3VT+tRfNEMsUyqPVgQZOQtpMTcR4/MsZHDw6RyJUbwXKHdvXWPq8H3Gnz9uO5UlOQ4mwqz1y6yD07ohyfSHBwOMzh8zFGen08dNNgk6Nz+FwteDFbKjMRy+Fzyrw2EadUqbK1x8vOvgCXEzmSuTJjc2kuJ/JUqioP7ukjWagdx6VYloqqMhzx8sl3jPD82TnG57OoqIxOJUnnS/jcMtuiAfoCLuK5Endu6+G/v3KJfKlKxONCkSXKVZUen5NUvkIiV0SRHTx4Qx/Hx+NcnM/hckCpCg4JfG4HhVKVXr+bgEfmSqqIU5YIe5ykCmV++ZZNbAp7eWVsAZfsIJ4t1FeakiiXyyTzFQrFCqliGZfiYFPIQ9DrZPdAgN+9fyfJXIn/9ONTTCZyuBUHlSoMhNxMxnNkChXK1Sq/efc2Tk4mKJWrHNrVR75U4YWzc9wyFKq5FMB8psiB4TBXUkUODoeJZYv87NQsh3ZGCXtd3DIU4kdvTHP7tgi3betZdDHiOTL5MkjmAZTxXMk0rFO7F7T+oe+Dep9juMdr+vmTxyd57vQsWyJefveBHet6WpbGerv/BdcHwhERCASCa4ROZHO723YqsNuV7fXhbgvZkmV42z8cv8yxsTi/cdcw5SqN7U5OxPnm4TE+rRPZjXWM9Pp4czIJ1ELi3pxMMpvKM5sq8vE7F5Otzdpj9VoLu9Okf304nRZEN9Lr4+iFGG/PpNja46U/6OHeXb2Mx7J89fkLRLwKp6+k+Z17tzeF8H3thQugwocObOaJYxNcnM8Q8jpRHBKPHBjiA/s386OTU3znlXHmMwWCbifxfIk9fX6qSBwYDjM2n6Wqqjy0b5A9AwG+9sIFZpN5ZtNF1GqVdKGC2wn5korHqdDjc5HIFzk4HGFLpPalemefn4DHyXQyR75YoVKFfUNB3pxM4nE6iHhdnJ/LkMgVKZareJ0y+7dECHqcTKdyOJCI+JxcSRVI5coEvTIDQQ/xbJFipYpTlpFQOT+X5fZtEa6kity8OchMqshI1MvoVIp0oUQsU6BcUblxU5jeoAtUFSRwKzJDES+XF3KUKlWqqoqke38+XWRr1IvPpeB3K0sCAk9OxPnq8xe4YyTCRw5uMe2n+kUdzAYR+gBCLZRwbD6zJFRTC+VsF87ZaXBhO1qFgnajfIGgW4iBiEAgEFwDdCKb2t22U4HVrmyfzJcoVRZ/hjhliZBnaXjbO/+Pn5LMLy7zpEgQ9rn4i392kN/82suN93/whfuaVtV67396jmSuRLmqLskY0fODL9zHb3/j6JL2KI7aXxUWX0O5HkMiAd/+7N188msvLylb0u1r/CzoUUzdAsHqow8INPadkEfm53/ynqZ+arVQgoZxwYQen5PHfufupsURJECWaquuKbJEuaJahnO2CvVcjjjeKhRU+8unENMF6wUhqwsEAsE1QCeyqd1tOxVY7cr2+i/9AKWKVXhb81qzmkz+V8+db3r/8SNjhv1qQXXtfl322JEx0/aUq8bXi/9Wgf/83HnTsrUgOrPP2oXhCVYPfUCgse+k8pUl/dRqoQQN4wIFqXyZxwyLI6jU+qtKrX+3CufsNLiwHa1CQbtRvkCwHhADEYFAIFhHdCKb2t22U4HVrmzvlJstcadsFd7WHEuoyeT/8sGdTe8/es+IYb9aUJ1FhlyDT90zYtoexWF8vfhvCfj9B3ealq0F0Zl91i4MT7B66AMCjX0n6JGX9FOrhRI0jAsUBD0KnzIsjiBR668Stf7dKpyz0+DCdrQKBe1G+QLBekBMzRIIBIKrQLt55XZlU/22QKNM/b+XI7Dble3NJHGz43ny+GVevRTnE3cOU1FhW7Q25z1fLPONwxd5555+/tnd25oCGUd6a/JxxOushRxGfezdFOTJ45Mk8yXcTgc9XhenrqS4YSBIrlRpBNPdvi2CCvicMm9OJujxu9g/HCZTqHBsLIbTURuV3Le7lx+PXuFXDw7x2kSci3MZAm6FQknlli0hppJ5ZpI5/C6FQrnKloiXI+djzKcLFMplwj43f/TwDVycz/KDk5OEXQoXYhkqVZWhsJdipUq6UCaTL5Etw+4+HxG/m8uxLDOpAlVqX3QrNW0CnxvK5ZqErgC7BgNEfE7enk5TqVZJ5mvHWAHcDijW/8oTdDvIl6p4nDIqKplCFb/bARLkC1VKKjgdtTJLVcBR+/Key1dQJXA4QJYcOCTqroZExOfCJUtMJwsE3Qrlqoqj7nC4FAdhr4tkrsRUMsfB4TDTqSJRn8LleB4kiWqlitclM53IUVXB71EYDHqoVmtyvdel4HMqXI5n8ThlPIrMprAHl9NBKlvm3GyaKip7BgJs6fHx4VuHmvrYS2dn+YufneXAUJhMsYLHJfORW4dYyJYaXtFsKs/lhRxb6o6P5hhlCot/CZG00YoKPrfC1h7vklBNTYRvJcSb9X1juOByXK2TlxOm9Ro/N3NIBIK1QjgiAoFAsEFYjbnd+jIDbhkVyBQq63LuuJX/0eNz8v3P39cUyKgPaAy4ZSqqSqJNSOFa45HBMPtMsEwiXgUVqSlIUKPH5+Rn9bBLK08JFsMNzb7dSCbvR7wKSBJxnSvyM12o5nIx3uerFTYqXBHBekA4IgKBQLBBWI253foyk/Wgv/U6d9zK/zALZNS/1o5rvSEGId2jdo3N0+3N/AjjIAQWww3NMHvf2K9W455czbBR4YoINjJiICIQCARrzGrM7daXGaoH/a3XueNW/odZIKP+tXZc6w2DAiNYAbVr7DT9zMyPMHpBsBhuaIbZ+8Z+tRr35GqGjQpXRLCREVOzBAKB4CqwGqFjRl9kPYea6ee2j04mQYJDO3tNfRbjcWkhdPuGQjx36gpHLsQ4MBymz+9uBBEO93jxuWtfLrPFMrOpIu/bN8hEPMdsssAbk3HShQofvXWIs3MZHtjdx6mZFCcm4hRKVVyKgz0DQXzu2igjWygzEc8hqfCLizH8TpnpVIEPHtjMZ+7bwehkgv/zmTOEPQrZUoVSpYLPqfDefYPctrWHL/34FK9eWuDQjiiFqkoyV+RKskCyUObGwQCFUpWZVIFbt4Q5MZkkVyzT43Nx81AYp+IgXSjT53dydCzOppCbbLHMO3b0MpXMc2k+yzt2RFnIFjk/l2EhU0JVVcJeJ6VKlelUgWK5TJ/fQ65YAQkcksTugQBVFbb3+fj4HVt57dIC//TGFPOZIr0BN++8oZ98ocIvxmJ84vZhXpuIM7mQ484dUUZ6/ezbHOLohRiXFrI8fNMgEws5ZtN53rycZDqZZyTq4/7dfXz/9UlQVYrlKjcPhcmVKo3rky2WmVioXa/+gKcRUvnMWzMcvRBjPl3kzh09jPT6G/3D2IciXic/ODFZy26peyT6frVvc2jJvzOFejiiLhjz8Ln5pn7YzX6+2mGjIsRQcLURjohAIBAIgMWQttlULQDv43cMLwlzaxf0ZlWuUbTVlxP2OpcI8vowNn0oG7Dks5cvxDgxEcetyHzk4FBTQKJZsBvQFEaXyJV44tgEI1Efd9eD6MzEfePx6NugBSdu7fHy49EZ+gMufK7FAc2rlxa4vJDnowc388MTU1xJF/ide7cznSwiS/Dz07OMxbLs6vfxew/s4uWLC+TLFfYMBAB4/vQsx8biOGQYCnmI+F3s2xRidDoJKjy8b5CgW+E/PX2aYqXKQNCF4pDJFEv0Bdwc2BJhdDpJqVLFLTs4M5PG71G4d2cvM6kih3ZFkSX485+ew+d0MBTxcH42Q6ZYJl+sEvY5eeimAS4n8jgdDn7jrq2cmEwScMnMZYrcOBhkKOLha89fYCFTRAU+fWiEwxdinJ5O8c4b+gF47swsEU8tiHFHr48TEwmGIz58boVfvW2Ip96Y5uREnPt39xHyuZr6YDxb5OnRGU5PpxiOegGYTRe5d2eUn749S6FU4cDWCA/vGySRK9WWdVbh0UMjDeH76dEZxmLZRrlan59L5bmSLnJgKMTfvjKOqkr8m4f3NOXTmPXRTiTv5YaIrlb4qECwHhADEYFAIBAsCWmDpWFu7YLerMo1CrGJXKlRDkDE5yRbF+T1Ym6pUm2EwjllB363jASkC5VGUFupuvRnkRaQaCxLqS9jWlVV4nVxPeiRSecrjbbUllt1WIr7RmFeC6vbWD8RNw5aPwt7nTz4xZ/ZWnAg6JGbMmgk4Mkv3Mejf/2LxnXX3vvU119mIWvulkAt9HJr1LdieXy5IaJ26xLiuWCjImR1gUAgECwJaYOlYW7tgt6syjUKsfpyAEsxVx8Kp32eNAS1maEFJBrLKlVVkvUyNPSDEO24Won7RmG+JAYhq4rWz7Tzboe0YRUAFXj88FjTddfea1fm40fGuiKPLzdE1G5dQjwXXG+IgYhAIBBcQxhD2mBpmFu7oDerco1CrL4cwFLM1YfCaZ+HDEFtZmgBicaynA6JUL0MjYBHbmqLVpeVuG8U5rWwOsHqoPUz7bzbIWBYBUCiNj1Lf92199qV+eg9I12Rx5cbImq3LiGeC643xNQsgUAguMaIZ4scrjsic5kiH7vd3BF56uQUj+zvzBExCrH6csJep6mYaxYGByz57OjFGCfGE7idjiXhdWbBbtAsGCdyJb57bIJtuiC6VuK+Wfs0qXk44uXp0Rn6gi58Ts0RqfDqpRiX43k+cutmfnRyiplUkc/cO8JMqohMzZ0Ym190RF4Zqzkiu/uDIKk8f3qOY2MLyA6JzSE3PX4XN20K8dZ0zU15775Bgi6FP3vmDMVypeaIyDKZQom+gIf9w2Henk5SKldxyQ5Oz6QJeGuOyJVUkXt2RnFI8Bc/PYfX5WBL2Mv5+TTpQplCsUrY6+Khm/qZTBRwKhKfuGMrb0wl8blkYpkiNwwGGQp7+PoLF5jPFIFacv2RCzFOz6R4555+qLswYY+TRL7E9l4fJy4vOiK/cnCIH70xzcnLce7f1UfY5+JjBkfkmbdmOD2TYrjHCyrMpWttf/b0LPlShQPDER66qeaIfPvIGEjwyXcsOiLPjM5waSHb6NvxbJHD5+t9Pl3kls0h/u6VCVTgXz/U7IisVB63u89y6xLiuWAjIhwRgUBgCzsi5EaQJTdCG1eD5SQzL2d7vRTe6fk11mkmxtuR5fXlAE0C/r07o7w2nuCde/p49dICx8bi/N4DO9ga9TUE9puHQkvE9kyhzHy6wOhUit97YAf7hyONtrxzTx/jCznG5jO8Ph7n0K5ePnJwS9O+c+kCr0/EuWEgyHQyz0wix0DYw86+ACcm4rxxOUGv38X+LRF+ef8mfvTGNFPxHHduj+JzybwxmWAilsMlS+zdFGoMgM7NpokGXGwOeTkzk2Jr1Merl+JMJ3Mc2BJmz0CQC/NZ7t4e4b8dHadaVTkwHObcbIbbt/WQyBU5P5slVSgxHPESzxXJlarcv6uPTLFCsVJhOl7g7Zkku/r9gISKyraon4jXyS8uxtgS8uL3yHicMrsHAkjAlfpiBwBPHJvA75J5ezrJW5MpytUqvX432/t8vGNHL3ftiDYWHMiXKhQrVQqlKr962xZOTCbxu2QyxUrTtXt7OsWp6RS9ARfz6SJbo4sJ6M+dusJL5+c5tLN2HaycCu3a+Oup6D8/M2far8zuhW70d4FAIAYiAoHABnZEyI0gS26ENq4GnR73crfXBG5nfXpIJ+fXWOfffOYuPvzlF5vEeKCtLK8vx18XzuMtZGSNoEdp8gVcdTneav//+lu38y++9aqlHxJ0O5Bl2Vbd1zJmieTL2Wa5BD0yz//Je5b8RevdX3rWVFI39iuzewFovKctghDyOq+b54lA0E2ErC4QCNpiR4TcCLLkRmjjatDpcS93e03gXs75Ndb52OGxJWK8HVneWI59wbl5u3b7//lPz7b88pwqVNdlmvtaY2eAsZq/1kznK0v6YSvx3divzO4F/XvaIgjX0/NEIFgPiIGIQHAdYUeE3Aiy5EZo42rQ6XEvd3tN4F7O+TXW+alDI0vEeDuyvLEc+4Jz83bt9v+D9+xuKakH3Y51mea+1tgR+VdT9g945CX9sJX4buxXZveC/j1tEYTr6XkiEKwHxNSsZXC9zk0XXBtYiZDG+fjLlSXX6v5YbaFzLe/zTurq9LjNhFn9dTabM69PPFephQUa59SbhRLqg9u0Mt6cTDKXyjMRz7ElUpv/r4UPfvfYBFujPh7eN9jUFn25RjF9LJbhrakUj9yyiTcmk/QGXMQyRY5eiPGB/Zt44WwMj9OBS5GQqOWQ3LW9hx+enGZnn49MsUI6V+TZ03Ps2xzm5i0hjl6MkcqXuXdXL1dSBY5fWiBVKLF3METI6+RTh0aYTOR5+cI8Z2bSTMVzOGUHSFAqV3E7FaL+WobKpYUMAO/Y3stg2M3To1eQJdje52cw6Oa5M7MoDpnegJOhiJe5VJEqaiM80CU7iGWKbOv1cSVZIJ0r4XE7cMoOPE6Zu7ZHOT4eZzaVJ+B2UqqoVKpVvE6ZYkWlolbpD7iJZYoUShVGev0MBD2cmkkRzxbJlVV6fQpIEkNhN9uiAS7Hs8ykCkhAvlihosJQxAOoXEkV2NrjZ0vEw89OzSKhsqXHy1SigN8lE/AoRHxOdvYH2dHnZzKeI5YpMjafoS/gZjKeo1xVGQy5OTAcYTpZ4JFbNvHK2ALxXJFYusTZKylGev04FYk7R6L43DJHzs1z/FIcv1fhAzdvZu/mUFM/1PfvZ96a4fXxOOl8mbl0kaEeD9t7/cxnimztqfU5LaBye30hA7071Kq/d0K3fRPxXUOwkRCOyBpxvc5NF1zbdKtfXyv3x1oex9Wqyyror1W7gCZvwxhKqJ9f32r+ftTn5Hufv68R8KYvq1W7zNqfzpcpV1Vkh0TZIo9Ej2JzOyMBl0S6uLF+Xq5HXLIDn9vauQnVQwyNZ9pl4Su16mdW6IM3jQGXy70Pu+FXmZW30Z+lgusH4YisEdfr3HTBtU23+vW1cn+s5XFcrbqsgv5atcv4njGU0Cww0IykScBb0ka7zNqv+Sx2BxfLGYQAYhDSJbTraoXZIES/XyeeiHUd1gGXy70Pu+FXmZW30Z+lAkE7xECkQ67XuemCa5tu9etr5f5Yy+O4WnVZBf21apfxPWMooVlgoBkhk4C3kI12mbVf81kUi1BEI3a3MxJwibjDbqBdVyuChmBK436deCLWdVgHXC73PuyGX2VW3kZ/lgoE7RBTs5aBCBsSXIt0q19fK/fHWh7H1aoLWrtAZu0y218fMmicNnP4/DyosG8oxOhUElQ4VJ+Db9WWVu1qVb8swd+9MsGNm4L4XDIT8VwjoO/7xyf56MEhKirIEnzryCWCXoVNQQ9vTtWyP27eEiZbqHD43BwqEjdtDjKTLPC799fySZ4ZneHE5TjJXImFbIkenxOPIqOi4lZkhiJe3p5Ocjme5/fu38H5+Qw/fmOGTWE3W6M+tvV4eezIJXb3+7lnZx8+t0y2UOHslRTJfJmFTAGQmIjnuH93L/lShfGFHLlCiXxFZe9giPv39NUDBy9SqlTZHHZzdCzOjl4fvUE3btnBUMTLhfkM0/EcI31+7t/Vx98dG2dyIcd8psh9u3qpIvG79+8g5HXy3Vcn8DllLsylOT+XIZOvsKPPj4rKhbkMPX4XIz1enj09R7mq8lvv2MblRJ6PHhwikSuTKS7+RcLvUhju8fL82Tke2N3Hi+fm+B9vXeG9Nw7wy/s3N/qJ5ghJEpy5kubAcJiRqJ9DdX/or352llMzSd5/y2Z++ZbNpv3L2M8uzWUYnU7x63cOM5nIc3q6FpjYH/IwHKm1ySx4s1V/X869ZXU/LLe8jf4sFVwfCEdEIBB0BSFIbhysAtq0QL9Ow92WW6+WdH1xPsvH72xOcTcGzhnFd62t9+7qBWgqJ+x1tgwnNB5j2OtsyMJvTiYt99OH3/3D8Ukk4N039vPaeILbtoZ56XyM/kCtjWevpFFV6PW7eO3SAqWqyq7+AB/Yv4mXzsUIuGUmFnKkCiUWMqVGZsnn37W7Ea44Np/huVNXmM+UcCsSikPi04e2c24+S8Alky5W+Pgdw1xeyPL//dFbFEtV9g2FuXFzkItzGWZTRYZ7PIxOJbljWw/TyQJnZ1PsGQgwEvVzaibFDYNBZpJ5phM5tkb93LgpwNdfvEjYo3DLcIQHdvfx969dxuOUKZQrTCXyHNgSJp4r88H9m3j54gJz6QIX51Ik8xX+1bt3U1bh1HSKrVEvuWKF4+NxbhgMEs8WyZcrALgUmYPDEe7aURPAR3p9vHwh1thPCyd8czLZCKP8+B3NfcQYKKnvD1aLIRj7n/G1dp0lwKfrdyt9vonno0BgjhiICASCFSMEyY2DlUxuJu7aCXdbbmDh33/uXj7ylReb5OPn6nWZicRRn5Of/tG7AHjPl54lVv8s7FUAiURucduw19l4LQFO2UHALVNVVeK5pV6AJiEXK9WmY1fq08YqqkrCZL/VwhiueC0jUftLU9nkK4Ux6FDfH8fmM41wSz0Rr4IkSY3FCDT5++8/d29jkQOr1x/9yotL7oEen5Pv6xZIWImQLp6PAsFShKwuEAhWjBAkNw6tZHIjdsLdlluvJp3r0eoya09S19ak7rNa6GDzl0f9a00ATtbFdjO0dhmPvVRVG3L7WmIMV7yWUTEfhGifGV9rfUQfbqnHuBiBsb+1e23EbNvlCuni+SgQdA8xEBEIBA2EILlxaCWTG7ET7rbcejXpXI9Wl1l7Qrq2hnSf1UIHnU3b6l9rAnCoLrabobXLeOxOh9SQ29cSY7jitYwEKBY+v/FtfX/Uh1vqMS5GYOxv7V4bMdt2uUK6eD4KBN1DTM0SCARNCEFykU7mg+vnuS9kS5b7WHkdnc471+bBZ4tlfK7mOfCaIB72Knz/tUkePTTC/uHIkvY+dWKKR+r+SCftMtv3yeOTHBtb4LMP7Giqa2w+w3eOTdAfcOFzK/hcSsMZyBbKZItlZtNF3rdvkPGFHOPzGUanUnz2gZpI/a0jY+RLFXYPBPDX99/a4+UnozP0B11kCxVeH09ww2CAyUSOyws5ZFni9q09ZEsVhnu8DAQ9HNIclLdmODEex+2UuWukh799ZYJSpcpNm4JMJwu864Y+fvLWFSRU4tkiSBIRr5OFbJFMvsx8psRA0MXmsI90sUzU7yJTKHNyIkEFFZ9TYUuPhwf29NMXcJMrVvj56SscHZunWq1NUqoC23t8zGdK3LsrynymSKFSxaFKvDEVp1iq4nXJ9cDCKvFckV6fm0y5QtBVG7DFc0UGQ242h70UK1X6/C6ePztPvlRBrUKpWqFYqhLxuwh5FSJeJxfmskR8Cul8hZDPxZaQm1fHE/zqbUO8fSXN2GyGfLlMplDlgT199AZcXIrlkFDxuRTmM0UODIc5fSXNfKqA36UQ9Dp56KZBbtsW4cnXJ8kVa+d8MpFDVeHWrRH2Dgb5u1fGGZvPsL3Pz+/ev3OJI/LUySke2N3HqekUY7Esv3ZHbYbHUyemeGBPH/FcqSmU0xjSefjcPOm6+3NzfWGETL4MEvjdCod21q7/S+fmkVhcMKFTxPNRIDBHOCICgUDQRTqZD26c565IEPa5Og4JtDvvXF+OWZhgu/abeR7GufNW7erkGKwCFJc4HLJEuaI2wgYVh0TAo6BCk3uieQgVdXEfOz/BenxOfmbipVwrBNwO0oVq+w27gNH3ACu/RyFXrOJ3y1RUlWTdzTH6Sno6Cdw07qd3kTQPqd29JwYSAkH3EI6IQCAQdJFO5oMb57mXVZYdEmhn3rl+H7MwwXbtt/I87LSrk2OwClDUowKl+oBC8wG04zHO9dc8BP0+dtC308ov2cis1SAElg5CwMrvWewPelfG6Cvp6SRw02w/jaTJ9sLvEAjWJ6s6EJEkySNJ0vclSTotSdJxSZL+SZKk7fXPBuqvz0iS9IYkSfevZlsEAoGgEzqZD26c565ILDsk0M68c/0+ZmGC7dpv5XnYaVcnx2AVoKinthqW1DgO/fEY5/prHoJ+Hzvo22nll2xkAu61+52i2Tk393sW+4PelTH6Sno6Cdw0208jZLK98DsEgvXJqk7NkiTJA7wH+EdVVVVJkr4AfFhV1fdJkvR14JKqqv+rJEl3Ad8Bdqmq2vLXVWJqlkAgWCs6mQ+un+eun89up8zlzDu3E57WqlyzefZ227XcbWExgFALN9w3FOJSLNs4Dv3xABw+N0+mWMbvUky3HZ1MkimWyRYqnLmSIp0vky9XefSebbWleiU4tLM5u+TwuXmQYN/mpS7Bvs0hRieTzKbzTMRqgYh7NwV5/uwcB4fDHLkQw+eUiWWKjbC8sEfh8SOX8CgO+oJuPnzrEBPxXOP4njt1hZ+fmSPgVgCVhUwRlyKzkC3yzx/YyXQqz3OnZmtfpiWJK4kcpUoVlyKjOByMzWfZMxDA55HJFMrcORKlL+gGFfyemv8wHsvyV8+eA2AqkWcmmWNLj5f7dvYRyxYbf2HYM+BnKlngs/fvYCqe4y9+epbfuW87qUKZIxdiDPhdvDmd4t5dvWyN+sgWy0zEcrVjDXrYNxTi6MVYLSywnhOi+RfPvDXD2HzN79CHBkItH+bSQpaP3T5sOi2rVX+xc1/o3Sgr/0P4HQLB6rEhHBFJku4E/ruqqrslSUoDO1RVna1/9jLwJ6qqPtuqDDEQEQgE652VhgXqQ/4SuRI/PDHVCCU0fr5SId5Oee2OETD9d8TnYmw+wxOvTLC918dD+wZbhsrp39eOW5P/9aF2wJI2649DH2I4HsvylZ+dI+yVuWt7b6MNVsd+ciLOV352jv6gqxYCePwyYY/Cndt7ebi+70tnZ/m/fnqWe7ZH8bhkXh+Pc2hXL7dv6+HHozP0B1z0Bz1s7fHyt0fHOT4eZyqRpdfvxiFJRP1OppMF7t4eJVuqcvf2CP80eoWPHRzi2dNzHL0Yw+N0cOOmIFG/m1u3RrhxU5C/PTrOVDzHnTui9AfcqFCT/VNF3n9zTfbP1BcAODuTBgl2DwTwuZRGgOOP35zB75aZTxfZuynIjZuC/PzMXCNMcDaVZ3whx97B2mfa8UjAm5NJErkynzq0jXiuXAsoTBW5d1e0EQIpAadn0nicMh85ONS0cIPx+n7t+fOcuZJCAnYPBPnsAztt9/F2/V0gEKwtG2Ug8hgwD/wpMK6qqk/32d8BP1RV9bFWZYiBiEAgWM+sNCxQL90aA/2e/MJ9fOrrL3cs5YK1TK6XtzWpuxNZ3u+WkYB0obJEMP6bz9zFh778YmO/iM/JP1iEyunL9LocJHPlRfnfAZWqeTBh1Ofke5+/j1/5yotNErrTIeF1yw1JWt+GZ03E9R6fk6/85m385tdetjzusFfhix87wD//1qstz49gEWd9IQX9ggg+l8M0kFLr45/++su2+6SQ0AWC9cG6l9UlSfp/A3uAf1d/yzgCMp3uK0nSH0qSNKH9l06nV7OZAoFAsCK6ERaoYQz0e/zw2LKk3FYyuTFUsFNZPlUPGDQTjB87PNa0X6tQOWOZTfJ/1TqYMFkv0yihl6qqaaCglbieypf5q+fOtzzuVL7Mn//0bNvzI1hEW3hAf92tFgzQ+ngnfVJI6ALBxmZNBiKSJP0R8KvAL6uqmlVVdb7+fr9usxHgknFfVVX/TFXVYe2/QCCwFk0WCASCZdGNsEANY6Dfo4dGliXltpLJjaGCncrywXrAoJlg/KlDI037tQqVM5bZJP87rIMJQ/UyjRK6s74EsBErcT3oUfiXD+5sedxBj8IfvGd32/MjWERbeEB/3a0WDND6eCd9UkjoAsHGZtWnZkmS9IfAJ4GHVFVd0L3/N8BFnaz+XWCnkNUFAsFGZyVSrFG6TeRKPHVyikf2Lzoiy5FyW8nkmrytl7o7OUbAUjDWAg1Hen08dNOiI9JObNeOW5P/l0jshnNglNC1bcdjWf7y2XOEPDJ37ehttMHq2E9OxPmrZ8/RF3Rx/64+vv/6JEF3874vnZ3lL356lnfsiOJxypy4nOCeHVFu29bD06Mz9AVd9Ac8DPd4eeKVcV67FGcykaXP78bhcBD1KUynFh2Ru0Yi/GT0Cr9ycIjnztQcEbfi4KbNIXoDLg5sibB3U5Anjo0zGc9x5/YofQE3ANlChdl0gfftG2RCc0RKZc5eqc0e2N2/6IgM14Mg/S6ZWLbIDQOLIv4Du/uYiOeYTRaYiGcbnz391gx9fhdINUckmSvXFgLIl5lNFphNFzi0M8qRC7HGdmevpPEoMh+6dcgyiDCRK/HXL5zn7Eyq1s6BIL+rc0Q66ZNCQhcIrj7r0hGRJGkYGAfOA6n62wVVVd8hSdIg8DiwAygCn1NV9bl2ZYqBiECwPtAEUb1EvJIvAdeScNqNY+mkjOUkwH/QJFEdmkVzO+VaicWt+oexXK0MTX7++J3NKyuZtbHH52ySrPX1a9tr25gd6+hkgr987jyfe3An+4bCPD06w6npFFvrK0FpwrtWxp5+P48fGWMhW+Q9Nw6yNVpTHLUk72dPXeGZt2aQgJs2hThzJU3Q46Q/5OY9e/v50clpJuM5btkSZiaZYzZVYmvUw5uTSe7Y1kNFhW1RLyrwxuUEEws5SuUqkgSK7GAg5EZSJc7OJrkUy+FzKbxv3yCz6SJvXE4w3OPhM/fu4Mxshtu2hvnp27Ncjmc5dyVNuaoyGHTz4N4BAI5PxDk4HMHrkhmdTHJqKsFCrsyhnVGupIv0Bdzs6PNzOZ7Do8i858Z+Xjofw+8yl9w1IV1/bX785gzbe33ctSO6ZJGBTD0BvZ2IbmdRAzuLNazms+Vaem4JBCthXQ5EVgMxEBEIrj52kr2XW95GF067cSydlLHcBHhNDP7tbxxdIp1bpa2bLQFsJrsDlv1D/5lWz0e/8mJDwNd4rp6+bZa2nc6XKVWX/uzSxPVf/cuXSOZLlCq1bYzH6lYgpQsBDLgcpIvNoYBaint5Y/2IvCooEgS9TiQgVSg3zjvUs1/q0+RUIK67zq1EdKt+3a6/Gz+304+Xy7X03BIIVsq6l9UFAsG1g51k7+WWt9GF024cSydlLDcBXi+/G6XzVlK5sW4zsbhV/7BKdTeipW+bpW2bDUJgUVyvDYAWt1lyrIYkcuMgRNtHDELsUVZp9B/9eQearr/xOrfqr60WWGjVL63612o8W66l55ZAcLUQAxGBQNAxdpK9l1veRhdOu3EsnZSx3AR4vfxulM5bSeXGus3E4lb9wyrV3YiWvm2Wtu10mGeqh3TlOeXFbZYcqyGJPOBa+qNQS3EXtEeRaPQf/XkHmq6/8Tq36q+tFlho1S+t+tdqPFuupeeWQHC1EFOzBIINTLfnJ3cahGeV7L2cdtkRTjfKfOx2x2LXvegkVdpK/jbWMzaf4buvTrC1x8fD+wYBePL1y7xyMc5v3DVMRaVt2rpx/v2Txy9zbCzOZx/Ywf7hSMP5yBbKZIplJhZyRP0uMsUKHzekbjeE9lcmCLhl0sUKv3bHoiOilSVRSyk/eiHG2HyW/VtCfP/4ZVyKjEuR8CgKmyMeMsUKm4Iu/tvRCd530wDHLsX5/Qd3sqXHxxPHasGKeweDPHbkIhfnsvzmXVs5N58l4JaZXMihArdujXDX9iijU0muJPOMTiY4NZ1iKp5DkhwM93i5b3cfY7EMF+eyvPOGfgqlCj96YwrZ4cDrdLCQKeJUHPjdTt6xvYczV1IsZEsMBD2MxbKk8kWGw14uJ/NsCnm4eUsYSVX56alZerwucsUKiUIBlyyTLVSI+l3sHvDz1lSK2VQRl1Pil27eRCpf4fXxBbZGfdwwGOD4RJJfvW2IF87OcWEuQzxbBGAw6OGhfYMsZEtMJ/P0eF0UyhXG5rOUqhWSuQp3jkQ4P5cB4MEb+smWKkwu5Dl5eYFcqcqWiJeBoJuD23qIZYpIEgyFvQ1v5K4dUS7FsuQKZb7x0kXedUM/v7R/c+P5kMiV+M6xCfwumYzuOlv5RFb9T+sPh0ycJM1F0e+3mjK7EOUFghrCEREIrjO6PT+5kyC8tWzXape71qzVcdidY/8X/+xgU4jfD75wH/uHI7bLTORKpt5JMleiXFVNA6Oerfsf7c6HMThR7xhILA2jaoW2vXE//fthr0K2WG1yCxK5IuWlM7fWBRIQ8irkilWKlfaN1HwNs+uiGHwYCfj/fGQf/9M/jNpuT9Tn5Ju/czcf/vKLjXP6rInro/eGtPOs9Rdn/a8LVk7ScrwRgUCw+ghHRCC4zuj2/OROgvDWsl2rXe5as1bHYXeOvTHE7/EjY2bFWZZp5Z2UTL7satto/kerdpp9pncMOv0Vmmr4v9n7Zo7Meh2EQK3N2vmxu73VdTH6MCrw5z8911F7kvUQS/05NXN99N5Iw+mpt6udk7Qcb0QgEKxfxEBEINigdHt+cidBeGvZrtUud61Zq+OwO8feGOL36D0jZsVZlmnlnTgdEmaKhcSi/9GqnWaf6R2DTvUNyfB/s/fNHBllHf+UlKBxfuxub3VdjD6MBPzBe3Z11J5QPcRSf07NXB+9N9JweurtauckLccbEQgE6xcxNUsg2MB0e35yJ0F4a9mu1S53rVmr47ATHKiF+D1+ZIxH7xmxnJbVqsyx+cyS0EXNHxqdTNZC9opl5jJFPnZ7c0ZIq3YaPwOaQguPXoxxejrFcNSLz6WQLdTq2BH18b3jk/zKwSGmUoXGl2GtjVb/NrorWt2yBI8fuUTII7OzL0C6WOF9+wZ5bXyBw+diHNoV5batPTz91gw+l8xkPEehVAHArcjsHgjgc9cGUdlimTcnk8ynityyJUQsU8TjkvnkO0Y4NZXk/3zmDA/e0EfE5+LERIL+oIsTE0luGPTzW/ds57XxBX5+eo6hsIfffWBno83boj6eO32FY2Nxfv3OYSYTeU6MJyiUy7idMgeGa97LpVi2cV1m03nmUkUe3jfYCI08eiHGpYVs4zr95I0p/v2P3kaRJW7ZEuL+3f3NIYgLOTLFMn6X0nA2jP3BeC2NXlkr32y5fVogEKwtwhERCARdwRh41y02imi+UloFBHarHKsQQbP99QJwJ+3R6rAKn9O3L5Er8cSxCUaiNQG+kwGrdhw3D4V4czJpekxanzQLL9Q+f+KVCfxuuSHEawMi4zHoz4HV+Wi3aAPA06MzjMWyTXU1wvxGZwi4auL9++uCuL4+/TUZj2X56vMXuGMkwrv2DnD0QoyL81nef3NtP21bLSQw4Ja5HM/hVmQ+cnCo6XwATedzbD7LSK+vcV71IY0/Hp2hP+CiP+hha4+36bW2nTEQUN+GdKHSaKM+OBJYVl+w01eu5vPjatcvEKx3xEBEIBCsGGPgnV4qXgnXi0xqlKv1AYGdHLNZiF9GFzSoDwCM+pz81BAKZyYGBzyK7fbEs0Xe/aVnm0IG9fXoy/e5HCRy5YYXEPEqPPvH77Y10DHWYVaXvk/q0cLwErkSD37x2abPNGn+019/uRG2CBDx1UL3MoWK5fVpt2hDwC1TqlZJ5StNdWmCvlnGidNw/rVr4nMrJHKL7TOK9IoDKlVQZGlJPoeRsFdBkqSmwEAzAV120NZ70aR2o0xudmya/G52DGGvwnM2+kI7rvbz42rXLxBsBISsLhAIVoxRPNZLxSvhepFJjceZXOYxm4X4WQUAJtuEuunFYLvt0fbXk7SQg5P5ctOXZ7P22K3DrAx9n9Sjl+WNaNJ8Mr80QK/d9Wm3aEMyXyZdH4To62oVtGg8/4vXpHkQZty7XK0L5m0GIfpja9rfREC3I99byeRmlA3v6191616/2s+Pq12/QHAtIwYiAoGggVE81kvFK+F6kUmNxxla5jGbhfhZBQCG2oS66cVgu+3R9tcTspCDQ/Xf9Ldqj906zMrQ90k9elneiCbNhzxLA/TaXZ92izaEPAoBj7ykrlZBi8bzv3hNnEvarUdx1AVz2bxcs2Nr2t9EQLcj31vJ5GYohvf1r7p1r1/t58fVrl8guJYRU7MEAkHTXP2tPV6ePzu3RDTtRh2dBPSt1/nYrdrWCFuT4NDO2px97ZgTuVJH7o1e4H1zMtkU4DY2n+E79Xn4D1nMw49nixw+N0+6UCbgronEiVyJp05M8cCevoa30Gp61jNvzXBqOsXeTUEeumlwyfSvZ0ZnuBjLcu/OKEcuxNjWY92esfkMjx8eA6m2MpfmVRw+Pw9qLaxwdCpJJl/GZ3BSNPn5gd19nJpJMTaX5dfuXHQznhmd4fWJOOlc7a8O79zbx0cObmE8luWvnj1LwK1w985eHrppsBaq98oEI70+hiIevvfaJJ86NMLWqK/5HjgzxyP1azU2n+GJYxMN7+PenVF+9vYsyXwRtyKzayAAwFy6yM5eH393bIIDW8LkyhWSuTILmQJbe/38+p1bmYjneHsyyS8uxviD9+wG4M+ePs1wj4+RqI+XL8ZI5cu858YBbtocqp2XySQX5zOcGE9ww2CAhWyJZL7IfLpEyKNQUeHz766tcPXV5y+wbyhIn9+N36MwHPHyk9EZRqI+7toR5ejFGCfG4yTzZeYzRXb0+vilWzbx7OlZ4tkiHkVh14CfgZCHQzt7m2TyiNfJT3T+y6GdUY6PJzi4NczxiUTjFxffPlJbwve36tfZ2K+s7h/tPMvAqZk0h3ZFedfegYbr0k5kt1vPchAyvEDQGuGICASCZWGcq2/mHKx1e9brfGy74XvGz5br3qwkZNK4rzbX3xgoZ3d/43adHJOZ5/Gcyfbt6rQ6JrPwxLDX2eRfhL0KT37h/ia/RqO2DK5MUud9aOfnbz5zVyOgT48DaDXLySpwMehWSBUWp1AF3A7SBfOSIl6Ff/jC/fzKV15suC49Pidf+c3bmkIozep0yhIBd7MXZHUsYa8TUEnkFtulOTitBqqdhgvavUeMx9Sur9ptm0AgWD2EIyIQCJaFca6+3Tn+q92e9Tgfu5PwPf1ny3VvVhIyadxOm+tv9Ebs7m/crpNjMvM8zLZvV6fVMZmFc9qchwAANRlJREFU9Bn9CzO/RqMWDNjsfWjnRx/Qp6edamH1K750oWx4bV1Sst5mvetiFkJpVmepstQLsjqWVL605LzY9Yg6CRe0e48Yj6ldX7XbNoFAsP4QAxGB4DrHOFff7hz/1W7PepyP3Un4nv6z5bo3KwmZNG6nzfU3eiN29zdu18kxmXkeZtu3q9PqmMxC+oz+hZlfo6H9RUT/Wjs/+oA+Pe1+eFpZHQG3YnhtXVKo3ma962IWQmlWp1Ne6gVZHUvQ41xyXux6RJ2EC9q9R4zH1K6v2m2bQCBYf4ipWQKBoGmu/iGLXIq1bs96nY9tN3zPbP67Wcjbcuqze36M23USHGennk6OaWw+w7eP1ByRT75jqTtgt85WxzQ6mWwK10vkSnz32ATben0Nx0Xf14d1PlTY62zyVfTnRzvOg8M1F+KB3X3EcyUiXidPj87QF3Dhcyv4XUpTmVDzJRK5ImGfi0++Y4Sw18mTxyc5dmmBz96/g61RH4fPz5PJl8mWypydSYMEB7ZEGr6N5vtQd4+0EMqvvXCBHb0+Kir82h21GRGaSxPPlRpfwPXnS38sh8/HGKmfG6DWjnrmilZPK5YTLtjuHvnusQkkCU7PpDm0s5cH9/Z35IbY7UcCgaC7CEdEIBCsOutZIr9adPOcmIUQriQIsdX8/lbCsCbVJ3Mlvvr8BW7aHGSk198QyO3UYRaMaRbEaBUeqBfHtbC8sNdpGeSo7TObyjObKvL+mwc5NZ3i7ZkUeweDbIl4+O9HJ9i3Ocg23bEYwxKzhZosbxYIqIUGPjM60wgd/MHrk/zjG9P8i3fuwO1UGknvfpeCCo0gxfFYlm8eHuPTBjH+3vqA6VuHx7iSypMplHnghn72DARatld/fTaF3EwnC/zGXcPE656HPpRQWyjhnbpFCqAWfnhpPsPxiTiHdvbykYNbTK+JMXhSH4poFlzYyT2hD0vUrnMngrtAIFgfiIGIQCBYVYQAupRunhOzEMJOBF277elEqjf+dIj6nHzv8/c1pHerOsxE9rDXuWRRBLOyANOgQ6NQrpepW4UjWtHjc/LY79xtKm9r9SmyRLmy6J+EvQogNUnwdgh65CYHRf/aKNVbEfU5+WaL9pq13yk78LnlppBDbZpTVVUbgxaNkEfmB//qgaZr8vefu7dJloelQYn6EMtO7glt22S+1MhKMS58IJ47AsHGQMjqAoFgVREC6FK6eU6sQgiXG4RotW8nUr0RTZ5uV4eZyG62KIJZWVZBh0ah3EyQ7oRW8rZWX6nSLMHXQgM7G4QATQGIxtd2y0u2aa8RFRrnVU+pqpKsS+xGUvnKkmtilOVhaVCiVdilXeFdH9hoXPhAPHcEgmsbMRARCAS2EALoUrp5TqxCCJcbhGi1bydSvRFNnm5Xh5nIbrYogllZVkGHRqHcTJDuhFbytlafU26W4GuhgU6LPazRByAaX9stL9SmvUYkaJxXPU6HRKgusRsJeuQl18Qoy8PSoESrsEu7wrs+sNG48IF47ggE1zZiapZAcA2yGmFemrugiaNAx3WYOQIr2W65dOP8mJ0Tq4BDsD4OfVuAJpl8W3TRFQGWzNs3OwY7svdL5+aRqEnZehclni3y9OgM4wtZPnb7MMlcib9+4QI3bqo5IpoA/sSxCQaCLvoCHsvjOjkR5/HDY3z0tiHKVRrHoBfC9UF1Ea+zyQXRtgt7Fb7/2iSP1t0KTdoejiy6I5orsW8oiKTC8fp58bsU3pyMMxXPE/AoFMoq+7eEqFThfTcPMr6QYy6VZy5T5OGbBpmI5xqy9r7NtSDBdKGMVP+e7HPV3JGjF2OMzWd5375BvvvKOE+enOJ9Nw2wb0uYiYUcwz1efK7al/dsocylWA6fS2Ysll0iqPvcChGvwreOjJEulLkcz/HB/UPcPBTiGy9d5O4dUW7cFGqcrx5fTZDvDbiIpYu8eH6OmzeHSOTKfOLOYSYTeU5NpYgGXGSLFVOJfVvUx8sXYpyeSeGQ4NVLC9y+tQenU+bjdwwT9jqXLAigP+/Pn5nj4NYwz56aNQ0u7DS8VAtLtApSXU/iufBVBAJzhCMiEAiA7s+pXkmonrEcO8GJqx2w2I3zY9fFaHccdsPfAm4ZFcjUg+n04YSdHIO+TL9bbgq7s1OmMXROkSDsc7UMHjTzXYzHrQ/a0zsCVufHKvwOWvsXEhDyKuSKVYqVxfyOdtfGeK70x9HqGhvbaeVSlCpVZAeUdZEiEZ+TrMm18btlVIPfofkysNSvMbbJ2GYJkHXOR6twylZ98npwN4SvIhBYIxwRgUAAdH9O9UpC9czK0bAKTlztgMVunJ9OXAwNs+OwG/6WzJctwwlXEvSW7LBMY+hcWTUPvmvnuxjboXcf9I6A1fmxCr+D1v5FzTMpNw1CwN61SZq0o901NrbTyqVQaR6EoGun2bUxOhut/Bpjm4zbqDQ7H63CKVv1yevB3RC+ikDQfcRARCC4xuj2nOqVhOqZlaNhFZy42gGL3Tg/nbgYGmbHYTf8LeRRLMMJVxL0FuqwTGPonCKZB9+1812M7dC7D3pHwOr8WIXfQWv/ouaZ1MrTY+fahEza0e4aG9tp5VJIgGL4adzqehudjVZ+jbFNxm0kmp2PVuGUrfrk9eBuCF9FIOg+YmqWQHAN0u051SsJ1TOWYyc4cbUDFrtxfuyUYec47Ia/QXMw3XKPYaVlaoF4+tC8VsdlFZ5orMsqHNGqTfrtk7kSjx8Z49F7Rhr+hXbOE7kS3zoyhlQPUtT8h21RH6NTSdvXxniu9Nu0usZj8xm+++oE23p8jYBCq3Ok9yTMPA19W6zCB7X2ZPL1z0zapG+zFt7YytHo9Jxcy6wnX0UgWE8IR0QgEHRMt8TLVkF8reow7reSIL/Vpt25MgvwsyqnVagf0DgnxiC9VsF/+qA5q2A4O8dh99j1QXQ/Hp1BBi7MZ/ng/k2cmc00wvNGemti9Fgsy/v3DTYF6p2YSKA44O9fm+RXbxtiMp7n4nyWj9853BReuLXH2wjPu3tHtGUfMV6HsflMTa4PuOgLerh5KMTLF2K8PhFHUuHWrRHuMpRpDNnTjqXH5+Qfjk9SKFWa9jPrv1ALDMwWyk3BhmYDlSdemaA/6KK/3j59kOKl+QzHx+Pcu6uXDx/cQqI+6EKFjxwcapzPRK7EE69MEHDLpAuVxjm002esFkBo11fM+q1x3/VwT69G6Oh6fEYJBFcTMRARCAQd0S3xspWYDNZSeyPMLFeiXFVRHBLlqoqzPu1hPYmg7c6VWYCf1SDgPV96thEO1+Nz8n1dqJ9eANZ7DBGvgkOSmmRpYIl07AA0Q8KsHcu55q0WK9AH0ZmhOJa6D4oEQa8TCUjmS0s+1zCGAOpxWfQR43X49mfv5pNfe7lt9kZtalSt32pieCJbbHIntP5p3E+WoKIuhh8qDomAR1kilIO5zP7gF59d0h6lXqax3dogw7it36OQyC3NBYl4FbLFass+YzxufZBhq8UL7C58sNxwzm6xWqGj6+0ZJRBcbYSsLhAIOqJb4mUrMdmOjF2qqo399CFs60kEbXeuzAL8rMrRi8ZGEVkvAOvRAuhaBf+pLA5CrNqxkkUGzOpuNQiBpYMQWBTck/my5SAEloYA6rF7Hf7qufO2AgD1/Va7HsbQPuMgRNuvrDaHH2rlmAUGmsnsZpRNBiHAkkGItq1VmKOdPmM8bruLF9hd+MBssYK1ZLVCR9fbM0og2KiIgYhAcJ3SLfGylZhsR8Z2OqTGfvoQtvUkgrY7V2YBflbl6EVjo4isF4D1aAF0rYL/JEAfm2fWjpUsMmBWtz6IzgyjgA2LgnvIo5h+rmEMAdRj9zr8ywd32goA1Pdb7XoYQ/sUx9KSNNFbH36olWMWGGgms5uhlWkk4F56TrTzaYadPmM8bruLF9hd+GC54ZzdYrVCR9fbM0og2KiIqVkCwXVMt8TLVmKyHRlbH+BnFQ54tWl3rqxka7NytHA4TTI2E4CNMrX2vvHcdiodL3eRAavFCiJeJ0+/NYMEjM1n+cAtmzg7l2kKzzt6IcalhSwP3zTYENy145El+P7xST56cIjJRJ6x+Sy/Vg/V045tuMfL02/NsK3Hx107oi37iPE6aLJ4X6DmYOzbXAskPDEeBwkODEe4a3tzmcaQPe1YIl4nT74+SaFcadrPrP/ColAO4HeZi+Nj8xm+c2yC/oCb/pCbfZtDjbJGJ5OMxTK8Pp7g0K4oH7615oh8+8gYSPChA0ON85nIlfjOsQkCLpl0PcjQKLxb9RmrxQrshGO2W/hgPdzT3RTMhawuEJgjHBGBQNARnUqXdrZvlfbdTlhvJ7ivZ0l0pW1rJ/u/dG5xhSS7SfMruV6tPrd7rFYCfidydCuxejnn0vi5fjEAo2uiCe+a8N2u7Z2ee6tzZPVvs0UIWi18oC0C8OlDI+wfjtiub7n323q+PwUCweojBiICgcA2nUqXdpPEzbaxmx7eKrXd7L318mVnpQJrO9lfL7eDvaT5lVyvVp+DvetgJjKn8uWmxQjaydF//7l7+ehXXjQVq9vVqy2AYFz4wPi59tNPSyY3prZLQNirkClUWra9XZ+2e47ShUrTggWNc1dY9HG0RQjCXqflwgfxTLHJF/rBF+5ja9Rnmopulhpv9zp30t8EAsG1jZDVBQKBbTqVLjtJErdKn26XHt5KcF/PkuhK29ZO9jdKz3aS5ldyvVp9bvdYzURm42IE7eRo7bWGHenZuACCVV8s6QYh0JwObxTe7bS9XZ+2e47MEsuThkUBtEUIWi18YFTaHz8y1jIV3Upq7+bzQSAQCMwQAxGB4DqkU+mykyRxq/TpdunhrQT39SyJrrRt7WR/o/RsJ2l+Jder1ed2j9VMZDYuRtBOjtZea9iRno0LIFj1Re1zDf02RuHdTtvb9Wm758gssTxkWBRAW4Sg1cIHRqX90XtGWqaiW0nt3Xw+CAQCgRliapZAsI5ZzXnXepnUTphgO+lc8xgCJmnOmiCrqkvn4xvboc1ZN9Y1Np/hqRNTPNImMHC552Klc/qXm3L+0rl5ZlN55tJF3rdvkIl4ruk8aXJ7pljG71LYp3MltDaYBeolciWeOjHFA/UwPiuXQS8bG10Is+OyK+uenIjz2OExPnWolnbeEPAnk6gshjRq7785mSRTKCMBPrfSCB48PZNi76YgeweDPH9mbsn1N/NKrCTpkxNxvvr8BfZtDtIXcAO1DA59Mjk0C++a8B3xOvnJmzOM9NYS0jU5fFvUx8P1xHQtPLE/4EKS4EqqyMfvqM1UMAu71Lf1zckkEksXJkjkSnznlQn8bplMscL7dKGQ47EsX33hAndui/Dhg1uAxRDFuXSBt6dT/O79O5ocEX192WIZ7SuAJIHPpTSuy9OjM1yK1RYOsHO/dSJxd8upEj6KQLB+EI6IQHCNsRbzrtvNqbdbhnGu+s8MDsNyfIROPu/GOehkTr9+jv1y22M8bwARXy3oz6pcK69Ac0u0YMiARzH1MuwcWzcC6KwCHq28E/15kFgMB9R+OoW9CrIh0LGdf2Tk5EScD335xcZrpywR8tg7xni22BQEGPE5QRdY2ONz8tjv3M2Hv/zikvwPicVMkE5DJs3q1fqHzy0T152zJ79wH7/9jaNt72X9PV/SZaNoDk7Qo1BR1UZAoh0nqRO66VQJH0UgWD8IR0QguMZYi3nX7ebU2y3DOFfdjm/Qzc9XwnLm9Bvn2C+nPWb+R6pNuVZegT4QUh+oZ/e6tvJUloNVwKOVd6I/D/pwQP15MXoMVuVZ8c3DY02vSxX7x2gMAtTao3/92OEx0xBC1fDvTkImzerVb6sv9/HDY7buZf09b2xnqao2+raGHSepE7rpVAkfRSDY+Kz6QESSpD+XJOmiJEmqJEm36N4fkCTpnyRJOiNJ0huSJN2/2m0RCDYSazHvut2certlGOeq2/ENuvn5SljOnH7jHPvltMfM/wi2KdfKK9AHQuoD9exe11aeynKwCni08k7050EfDqg/L0aPwao8Kz59aKTptVO2f4zGIECtPfrXnzo0YhpCKBn+3UnIpFm9+m315T56aMTWvay/543tdDqkRt/WsOMkdUI3nSrhowgEG59Vn5olSdI7gfPAC8AHVVV9o/7+14FLqqr+r5Ik3QV8B9ilqmrZujQxNUtwfbEW4VndCB4zC+mzqsdOONpyPl8JdstuFeC23Hr1/odVcGG7NlgF6nVyXbsdQGcV8GgVjqj1H2Ogn9anrM5LJ/3i5EScx4+M8dGDQ1RUOg511IIAtetk7PNaeOK2Hh97NwUbwZJAy7DLdv6VsV69P6Iv1+411G+nhWbqww2Bpjqv1v22WvsLBILus+4dEUmSLtI8EEkDO1RVna2/fhn4E1VVn21VjhiICJbD9So3mgWeGT9fjfNiJyivVUiflRTeqv2ttlsLOgkHBPOAv3blGcuwc221OrRAvHe2kNfbHZteitfkcr9bYWuPt6nsdgsf2DnGkV4fL1+IcWIijgocHI7w0L7BxrmzCn0E8Otk97FYlvfXBW+rUEJjWODJiTjfPDzWFAbY6jq2C43UFiSYiOW4YVOQu3dEa7J4odyYupUtlplNFXn/zc1tNQtebBcEaazbeJ+1Owa7YZit+t9KWY1n0/X6c0AgWAs21EBEkqReYFxVVZ/u878Dfqiq6mOtyhEDEUGnXK9yYzuJfLXOix3xvFVIn5WQbSby2tluLehEtrcK+Osk6DHgllsKxUb53AFN2RKKBGGfy7aorV/QQJPirX5yOOuiud3wQbNjNIrUGmGvE4fULPPD0tBHMxQHVKqLoYhaKGEiW6SsrRwF/Jffup1//q1XG/v94Av3Na061UmfM+vrdlAkqKhLpX19aKF23qzCFa3us+/p9l9umKGdBSpWwmo8m67XnwMCwVqxEWV1s8VFliBJ0h9KkjSh/ZdOp9egaYJrietVbmwnka/WebEjnrcK6bMSstu132q7taBT2b6dSN4uTLCdUGyUz40Bd2XVfFGBVseml+Fb/fpKE83thg+aHaPZIAQglS+1ld2tKFebQxG18L+yrioV+POfnm3a7/Eji5J7p33ObtuWtFW1lvbbBUHq7yOz+8y4/XLCDO0sULESVuPZdL3+HBAI1jtXZSCiquo8gCRJ/bq3R4BLJtv+maqqw9p/gUBgrZopuEa4XuXGdhL5ap0XO+J5q5A+KyG7XfuttlsLOpXt24nk7cIE2wnFRvncGHCnSOaLCrQ6Nr0Mb/pbozqaaG43fNDsGI0itUbQ42wru1uhOJpDEbXwP0VXlQT8wXt2N+336D2Lknunfc5u25a0VbKW9tsFQervI7P7zLj9csIM7SxQsRJW49l0vf4cEAjWO1fTEfkb4KJOVv8usFPI6oLV4HqVG+PZIs+MzjBmEUxmPC9j8xnL4DW7c6u1ueOSQVzXz/0/eiHGqZkUW6Ne+gOeJiFW218LW5MwD50zth9WLo+vBLNgRiupWmurXjI37tNK6laBm4dCjE7VPA0tiM6svojXyZOvT5LIlYh4nXzo1iHiuZKlGG3mHoz0+poE8nShjCQteg37h0I89cY0N24Ksr3Xz76hEEcvxrg4n+Xjhn6nL/PNyWRTP2n0HWry9NG6I1IoVwh7XXzo1iFevbTAq2NxPvvADrZGfYt96mKMU9MptvZ46Q952Lc5xD+enOK507N85t7tVFRIGwI39edIE8s1R+TxI2M8dOMAZ2YzvHNPH+MLOaif9yapu349zM6/Fi4IMJcp8MqFGJsjXj5x51ZOzaRq7Y168bkUsoUKs+kCh3ZGOT6e4IE9fUws5Brn2u+u3QOJXIknXpkg6JZJFSq8/+ZB3p5OcSmW5X37Bhvt3Nrj5cdvzhBwy8xniuwdDPKQLnhRC2O8e0d0idhu53lpZ4EKsz5ll9V4Zl+vPwcEgrVg3ToikiR9BfgIsAmYA9Kqqu6WJGkQeBzYARSBz6mq+ly78sRARCCwTyfzojsJoWv1pcNsW6OzoJ/zbuWtFCvVRrndDlVbLZYzD30l59fKD9Bv/64v/qwRvGcWqNdJ2YCpI2G8pvpwP7O+ZBa6py/brO9o++h/YkW8Ctli1TRgMpErNfXnkFchV6zavi76+0GPlc9kbLMxiFDVOT0Rr4KjTUCjmb+hP6ZO0Ic3Aqvqd+gRXoZAcP2wbh0RVVU/X59WpaiquklV1d3192dUVX2fqqp7VFW92c4gRCAQdEYn86I7CaHrtD6js6DRyo3Q0+1QtdViOfPQV3J+rfwA/fbGsEBjoF4nZbcKVNQwhvuZ9SUzP6Zd3zFzU7T6zQImjf250+ui399YZ7tARasgQn0ZdgIajdtYtakd+vDG1fY79AgvQyAQtEMkqwsE1zCdzIvuJISu0/qMzoJGKzdCT7dD1VaL5cxDX8n5tfID9NsbwwKNgXqdlN0qUFHDGO5n1pfM/Jh2fcfMTdHqNwuYNPbnTq+Lfn9jne0CFa2CCPVl2AloNG5j1aZ26MMbV9vv0CO8DIFA0I41c0S6hZiaJRB0RifzojsJoeu0vqYQNV1QnZX3oQ9bW41QtdViOfPQV3J+7QRBPjM6w6WFLB+7fakn1GnZVq6L8Zq260tmoXt2+s7RizHG5mvOU9jrbOkI6dug39buddH2f2B3HxPxnGlfbNXmJQGIFsGE7Vwis2M6OBzm+ESiqW376t4QKgz3eHn+7BwP7O5b4gTZ9Tu6gfAyBILrg3XriHQbMRARCBZZi4CuTsLNzAIKjcFnmmy7vdfXkGfNyoKaGKyFuWmheZpIbwzZW08hZfpzlsiV+OGJKcsgwXbBhSsNcey0fKv9u3meT07E+erzF7hjJMJHDm5ZchzGPtPuc7O+dvNQqNFe4/5ridX1svq3fsBhtnCEVqbxOM2CD9u1RzuX/+3lS/yPt2Z46KZBfnn/ZstyVtpvzI5TIBBcG4iBiEBwnbEWIqidOsyC0zQBFprF2JBXIZlbnJ8e8Tl51iCr6yVfCfPAoSe/cB+//Y2jptLz1f6Coz9nXpeDZG4xPVsvDVvJzmAdKKff3kzQNrs2nZTf6lhKlWrbkEI7nJyI86Evv9h4HXQ7cMpyQ8r++8/dy0e/8mKT6C3pjtP4uSaPw9JgQ6dDIuBRrlrYpdX10ovodkR7/QIDZveIMWTSSkA3W5Dgg3/+c1KFZidLw46Y3+64tX5zNa+DQCBYfdatrC4QCFaHtRBBlxNuBliKsWmL7Yz1aZj9mkQFHjsyZik9X22M50x/DHpp2GzbdoFyZqGG7a5NJ+W32t9OSKEdvnl4rOl1qlBtkrI1Sb7xueE4jZ8nLfoauvZerbDLVterE9Fev8CA2T1iFPnt9oenTkxZDkLAnpjf7rj1/eZqho4KBIL1iRiICAQblLUQQZcTbgZYirEBi+2M9WmYibkS8Kl7Riyl56uN8Zzpj0EvDZtt2y5QzizUsN216aT8VvvbCSm0w6cPjTS9DrodTVK2Jsk3Pjccp/HzkEVfQ9feqxV22ep6dSLa6xcYMLtHjCK/3f7wyIHNBN3WXwPsiPntjlvfb65m6KhAIFifiKlZAsEGpl2IXqv99PO3jfPLoXn+ut1ws0yhjN+jMByphan1B11kixWOXVwgGnCyoy/AZDxHsVwh5HXxW/eMEPY6m+p6enSGExNxvE6ZD906xGuX4hwbW+CR/Zt4YyrJ1h4fD+8bbLTLSnpei7noVvXoBd1ErrQoPC/kloTfjc1neOrEFI8c2Nw4F1qAoF4aN7se+n9bHbuZgK6FBtpZBMBMLofluSkaJyfi/PULF7h9W4QP1x0R/TE9MzpTC/vr9dIX8DSFCGrHoInf+wwuyDNvzTA2l+V9Nw82JG2t/G3RWj/XXCa9g6Tv9z0+Jz9+c6bJY7Lbp4zbadf3gXoooiTBvs21erOFcmP6lTG08+REnMeOjPGpe0bYPxxZUsfh87X7TVUh4FZqovpkkkzRPOCyaV9dCON4LMsXf/w2C5kij9w6xC/fstlykQh9X2216IFWj1m/EeK6QHBtIhwRgeA6pVNXxBgQp8gS5cri1A6rsDW7jM1nePCLz7bdTpEg6HU2zZWv6ELfAMJeJ4nc4lz4cD3ArlW71ipEbbnn3U5AoFXIXaeBh2YBhys9N3a9oeXUY/SNJGjp/3QS8GjmMukdpLBXQZakxpQ/jYjPyT98/r6WwZHt2qN3mayCIDv1MZbr/1g5TK3CLq3qE4MJgUCgIRwRgeA6pVNXxBgQV6o0zy+3Cluzyw9PmAfmGSmrmM6V15PKL35pVMFWu9YqRG25591OQGA7j2S5berGubHrDS2nHrMAxk7qaRXwaOaQGL2KpGEQor3fLjiyXXuMLpNZEGSnPsZy/R8rh6lV2KXdNgkEAkGniIGIQLDB6dQVMQbEOeXm+eVWYWt2+eAB88A8I4qE6Vx5PUGPs/FvCWy1a61C1JZ73u0EBLbzSJbbpm6cG7ve0HLqMQtg7KSeVgGPZg6J0asI1e8LDO+3C45s1x6jy2QWBNmpj7Fc/8fKYWoVdmm3TQKBQNApYmqWQHAN0GlomHH+tjE8EFY2l3tsPsN3jk3QH3CDpDKxkGO4xwsqzKWLPLxv6fx97d+Hz8+Tydfm8B+q5450Gkq3ViFqyz3v7QICW4XcLTdQcrltXk4dK6lHH7a3b3Noif/Trp5W9TZcpmIZv2vRq9CC/aB2/iNeJz8ZnWGk18dDNw22LddOe8xciVahncs9x53up7+/7HofwvEQCARGhCMiEGwQ2kmvnYrWyxWzV9KOTkPurEIKjdK2PugQauL6qekUezcFebguDY/NZ/jW4TFU4NFDI035Ct0U1NdKeG9XN9A4d1qoo1U44tXEqs1mgXv6sEGjCJ7IlXj8yBiozde3FScn4nzz8BifPrQodl+NsM92r9uVZQz+NAYatloc4WouzrCa5VzN+1AgENhHDEQEgg1At2XilUjBy22HUXZvFybYKqRQC15L5EpNgnvY60RVqyTzlcZ7Ea/C47/7Dj785Reb5tY/98fvIux1dlWkvZpirr5ufz3sLq4TrDWcXQgX7Bat2mwWuAeL05T0grnX5WharKCdQA1LAxJ/8IX72Br1rXnYp1GUbyXOm5Wlv0eMizQ826KPr9fFGbpRjhDkBYKNg5DVBYINQLdl4pVIwctth1F2tyvwapgFrxkF91S+REo3CIGazPtY/S8hep46OdV1kfZqirnGuo0Cv0Y3wgW7Ras2mwXuAZaCuZ52AjUsDUh8/MjYVQn7NB6HXcFdX5aGcZGGVn18vS7O0I1yhCAvEFz7iIGIQLCGdFsmXokUvNx2GGV3uwKvhlnwmlFwD3qcBD1y03shj8KnDo0sCTl8ZP/mrou0V1PMNdZtFPg1uhEu2C1atdkscA+wFMz1tBOoYWlA4qP3jFyVsE/jcdgV3PVlaRgXaWjVx9fr4gzdKEcI8gLBtY+YmiUQrDHdlolXIgUvtx1mAm67KWT6ADq9IKx3RL5zbKIhCEMt2O70lRQ3DAQbwXJj8xm+/YsxVBV+655mR6SbIu3VFHONArsmbw9HvDx/do4Hdvc1ZP/1MlXFqs2acL4t6mtcd6OEbpSnv31kDCT45DvsOyKPHxnj0XuaHZHVvn7tRPlOFxnQ7hHjIg3t+vh6XZyhG+UIQV4g2BgIR0QgEHSNbgqiY/OZhnQb9jqbhFzty9ozozNcnM9y764oL52PMRL1ceOmID8/M9fYb7kS8EajWyKvXXFav/CA2SICnbbbbAGDbkvW3V7wYTVYyTEvp/0rOea1WiBDIBBcu4iBiEAg6ArdFETH5jO864vPNpKbg16FZF1G7vE5+f7n7+MjX3nRVMbWMCaqdyIBbzS6JfLaTRzXb2eV8t1pu0uVKopOpAfztO+rsdDCWmHVhtVKpV/JMa/VAhkCgeDaRsjqAoGgK3RTEP3hiamm5OZ0k5C7KPS2wpio3okEvNHolshrN3Fcv51Vynen7dbk9HZp31djoYW1YiXHvJz2r+SY12qBDIFAIDBDDEQEAkET3RREP3hgc1Nyc6BJyF0UelthTFTvRALeaHRL5LWbOK7fzirlu9N2a3J6u7Tvq7HQwlqxkmNeTvtXcsxrtUCGQCAQmCGmZgkENlnpPHez7czm1UMtDC5bKONzK0vC39qVr5U10uvj5QsxxmJZPn7HMCO9/iZfw0wC1kLVZlN55lJF3nfzoGVwnlZfj8/ZcDn0ZerLmk8X+dgdw4S9zkaytarWBhmz6QI/fXuGWLrEZ+4b4VIshwq8e28/xycSPLC7j/GFHBKLqe8vnZtHoia+W52btQhfM37W7vzaqQPsp9qPzWd46sQUj5jUZ0ek1vpLQyY3Sfm2e/xau80WMGhX91ostGAMDFypW9KuPP2CDsbwxtVIpV+J1G3WVqDldRcCuUAg0CMcEYFgFVnJnG+r/YElwYABt0xFVZeEutkJDdSXpcgSpYraVMa3P3s3n/zayw1fwxgUZwxV03CZ1K3Vl8gWKauLdWhlGsvSOwdW9WgEPTKFkto2JM7vlpGAdKHS1pFYjfA142d/85m7GmGLdoL47NSxGvt1sm8nx78W7V4urfrjctoVzxZ5z5eeJVYvTwvm7IbvcbUwhlJa3VsCgUBghnBEBIJVZKXz3M22MwsGTJoE2HUSGqiVpR+EaGX85+fON/kaxqA4Y6iaRisHoayrRl+msaykyTmzIp2v2A6JS1qc+7UIXzN+pg9btBPEt9K2roUX0Mnxr0W7l0ur/ricdp2YSJA0+E7d8j2uFsa2Wt1bAoFA0E3EQEQgsMFK57mbbWcWDBgyCbDrJDRQK8spS0vK+P0Hdzb5GsagOGOomkYrB0HRVaMv01hWyOScWRHwyLZD4kIW534twteMn+nDFu0E8a20rWvhBXRy/GvR7uXSqj8up10HhsOEDL5Tt3yPq4WxrVb3lkAgEHQTMTVLILDJSsPErOatG+fVAxw+P0+mUMbvVpaEv7UrXytrW9TH0YsxLs1n+ZjOETGGpBnLsFu3Vl/E6+T5s3NLyjQGtBnPmVZPtljmzckkqVyZf/muXWyN+myHxIG1T7EW4WvGz9qd3+XUsRr7dbJvJ8e/Fu1eLq3643LaFc8WG6GN+mDOlZR5tbF7bwkEAoER4YgIBB2yFjKzcTsruVX7TBsAaIJ6j8/Jj0dn6A+46A96mvZrFSBnbJt+m9HJBH/53Hk+9+BOgh4n3zw8xqcPjbA16mvI5eMLObb2eBt1AqZSvZkc/8KZOS7Hs3zy7m2cm88yEvVx945oQ5x//75BxhdyZAtlVCBbLPOLczHOzKb44/ft5X23bG5I37dtDfPTt2dJ5ou4ZZndgwF8LoVcscyVdJF7d0Z5bTzRUr63WmBAC1H8+J3DtgcNdkMCV0I3w/86Ee2t9m8neFuV12mgYrtFHFrJ5fo++OZksnEftWrzctpj7PcvX4hxaibFjYNB7toRNa3b6hzarWe5oY2rFZzYzf0FAsG1gxiICAQdsBYys3G7VvK2XnyF2tQe2QHlanM52n6wVHS3kon127gVSBUMhdYJuh2mn0V8zpq4mi83SfWq7j1FlihXVLrxNPnTj+zjf/6H0Y7KspLvrRYYeNeXnkUfovicDbHcWN5qBCuudFEEO2W1+0y/fzvB2+65sROoaFzEwSwc0Uou17YrV5v7oFWbOznP+vboJe5ipflekWBJ3d/7/H38yldeXCK168tsV4/Z9Wl3/exe35U8AzeSiC8QCFYfIasLBB2wFjKz2XYaRnlbL75C7QuNcRCi389MdLeSiZu2sRiEgPUAJVUX6M2ker0c361fafzFT891XFYr+d5qgQE9dsRyuyGBK6Gb4X8rFc3tCN5W5bVbZKDVcbUKR7SqU9vO2G+s2tzJebaSuI2Y1f3UiSlTqb2Tetod+3I+t7tNKzaSiC8QCNYvYiAiuC5ZC5nZbDsNo7ytF1+h9ttVxeTu1PYzE92tZOKmbdzWt7zVZ8G6QG8m1evleMl07875V+/Z1XFZreR7qwUG9NgRy+2GBK6Ebob/rVQ0tyN4W5XXbpGBVsfVKhzRqk5tO2O/sWpzJ+fZSuI2Ylb3Iwc2m0rtndTT7tiX87ndbVqxkUR8gUCwfhFTswTXLWshMxu3aylv14P+/C6FfUM1STzidfL06Ax9QRf9Ac+SoDmrADlj2/TbjE4m+PLPzvLAnn5uHQ7z/eOTPHpPzRE5fH6e2WSBiXiW4UjNEdFCBK2k+nS+TMBda/PRCzF+9vYVzs6m+djtW3hzKsUd2yI8uHeAoxdiXFrIcs+OKIfPxegPuvG5ZbKFMs+fmWN0KskfPXwD775pkKdHZzh5OU6+VCGTrzAWyzAS9RMNuNgzEAAJ5tJF7tkR5fhEoqV8b7XAwDNvzTA2n+XX7ujMEWkl0neDlS6KoG3bLjDQKmTQ6Cy0E7xblacPybNqi91FHFrJ5foFGkankmTyNU+jVZs7Oc9mEve2qI+jF2KcvpLihsEgd22PMjqZbNzDh3SOiJnUbrceq+vTrj/Y6S8r7b8bScQXCASri3BEBAKBLVbTj4HW8/vtzsFP5kpNGSUaigRhn0vMR29Bt32StW6LYCniPAoEgvWOcEQEAoEtVtOPaTe/3+7ceLNBCEBZtQ6PE9Totk+y1m0RLEWcR4FAcK1inSomEAiuSbS53dpvV1fqxxjL0d7T/iJiNgffah87fxER89Fbs5zr260+sVblXm+I8ygQCK5VxNQsgeA6ZDX9mHbz++3OjdeCEg8Ohzk+keCB3X3EcyUxH90Gy7m+qzXfX3gE3UGcR4FAsJ4RjohAIBAIBAKBQCBYc4QjIhAIBAKBQCAQCDYMYiAiEAgEAoFAIBAI1pyrOhCRJGmPJEkvSZJ0WpKklyVJ2nc12yMQCAQCgUAgEAjWhqv9F5H/AvxXVVVvAP4P4K+vcnsEAoFAIBAIBALBGnDVBiL///buNFauuozj+PdHi6hUAVEE0mJZBYJKkMUaWUwEasQNESQ2IqjBAJHE+EbEgOgL3NAAGiRSkEVRippAwJXIEnCFC1RE1ktBjGxRKJu2PL44Z+xwafHO3OlMb/v9JJPe8z9z5j79ZZb7nP85Z5JsBuwGXNgOXQpsnWTuqGqSJEmSNByjnBGZAzxYVcsAqrl81xJgq+47Jfl0kgc6t6VLl46gVEmSJEmDNOpDsyZeOzgvuEPVaVU1u3ObNWvWkEqTJEmStLqMshG5H5idZCZAktDMkiwZYU2SJEmShmBkjUhVPQTcBCxohz4AjFfV+KhqkiRJkjQcM0f8+48GzktyAvA4cMSI65EkSZI0BCNtRKrqr8C8UdYgSZIkafhGfbK6JEmSpHWQjYgkSZKkoUvz9R3TR5JngYdHXcc0NwvwC1kGwywHxywHxywHwxwHxywHxywHxywHZ/Oq6vmUj1GfrN6zqtpg1DVMd0keqKrZo65jbWCWg2OWg2OWg2GOg2OWg2OWg2OWg5PkgX6289AsSZIkSUNnIyJJkiRp6GxE1k2njbqAtYhZDo5ZDo5ZDoY5Do5ZDo5ZDo5ZDk5fWU67k9UlSZIkTX/OiEiSJEkaOhsRSZIkSUNnI7IOSXJUkluTLEty3IR16yU5I8ndSe5Kcsyo6pwOkmyf5PokdyT5fZKdR13TdJHk9CTjSSrJLl3jmyX5WZI7kyxO8rZR1rmmS/LSJD9tn4NjbXZz23Vm2aMkv0hyS5vltUl2bcfNsk9JTup+nZtl79r3ytvb5+VYksPacbPsUZINkpzZZvbnJBe242bZgyQbdz0fx9rPoGVJXtVPltPue0Q0JX8CDgU+u5J1C4CdgR2AjYAbk1xVVbcPsb7p5DvA2VV1XpJDgHOAeSOuabpYBHwFuG7C+KnAb6tqfpI9gEVJtq2qZUOvcPo4G7iyqqrduXA2cABm2Y9Dq+qfAEneBywEdsMs+5JkN+AtwJKuYbPszyFVtXjCmFn27lTgOWCH9j1zi65xs5yk9n1y185yks8A+1bVY0kW0mOWzoisQ6rq5qr6C80LcaLDgLOqanlVPQb8CPjQUAucJpJsRvMHyoXt0KXA1p290XpxVXVNVa3si48OBb7V3ucPwD8A90ytQlU9U1VX1IorjvwW2Kb92Sx71GlCWhux4n3SLHuUZAOazI4Buq+IY5aDY5Y9SLIhcCRwQuc9s6r+3q42y6k5kmZnLPSRpTMi6tgKuK9reRzYfTSlrPHmAA92Ovx2z8oSmgzHR1nYdJVkU2C9qnq4a3icJlNNzqeAy8yyf0nOB97eLs43y76dAlxYVfcmAXyNT9FFSdYDfkdzRMNzmGWvtgUeBU5M8g7gaeBkYAyz7FuSecCmwOX9vsadEVmLtMc1P7KK25xJPET3nqusrjrXEhOve21eU2emfUpyArA98Ll2yCz7UFUfqao5wInAVzvDE+5mli+i/cNkD+DbK1ltlr3bp6reRDML/yjwvXbcLHuzPs2M8W1VtTtwHHAxzQ55s+zfUcD5XYde9ZyljchapKr2rqpXr+J2///ZfAkwt2v5dTz/2F6tcD8wO8lMgDS7/OZgXn2rqkcBkryma9jn4CS0x+ceDLyzqp4yy6mrqu+xYmbELHuzL7AjcG+ScWA28HNgTzDLXlXVkvbf/wDfBPb2Nd6X+2hmki6C5lB14F5gJzDLfrSHux1Gcz5d35/jNiLquAQ4OsmMJK+ieXL9cMQ1rZGq6iHgJpoT/AE+AIxX1fjIilo7XAIcC9Ce5LY5LzyhXV2SfBo4HNh/wjkOZtmDJK9MsmXX8vtp9j4/hln2pKpOraotq2puVc0FHgAOrKorMcueJNkwycZdQ4fTfPaAWfakqh4Bfg0cCJDkdcDWwF8xy359ELhlwkWNes7Sb1ZfhyRZQHN1iE2AfwNPAu+uqpuSzABOB+a3d/9GVZ05mkrXfEleD5xHc2zk48ARVfXnkRY1TST5FvBemjeoR4ClVbVdktcCF9B8OPwbOKaqrh5dpWu2JLNpZufuAZ5oh5+tqr3MsjftoauXAi+j2Wv6MPCZqhozy6lpZ0UOqqrFZtmbJNvQPC9n0Bzicg9wfFWNm2Xv2jwX0nxuLwe+UFU/Mcv+JLkWWFhV53aN9ZyljYgkSZKkofPQLEmSJElDZyMiSZIkaehsRCRJkiQNnY2IJEmSpKGzEZEkSZI0dDYikiSSjCe5PclYktuSHDvFx9svyR/bn3dPctEktjk5yUu6lk9JcthU6pAkrbm8fK8kaeL3PcwBbgX2qapb2vXrAVTVc5N8vP2Ar1XV7j3UUMArqmppb9VLkqYjZ0QkSc9TVfcDdwDfT3JBkh8DY8AWSQ5Mcl2SPyX5XZJ9Otsl+VKSu5JcDRzUNf6/2ZF2+V1J/pDk5nYGZq8kZ7Wrr2/HNktyXpLj2m1mJVmYZHF7O6nr8X6T5MtJrk1yd9djkeTj7QzPWJJbk+y1mmKTJPVo5qgLkCStWZK8AdgRuAx4O7BbVT3UfjPxScD8qno8yXbA1UnmAvOB9wC7Ak8DP1nFY+8AnEMz23JHkvWBl1fVJ5McDby1MyOSpHvTzwMvAd5I8w3o1yW5raouaddvC+zX3ue2JPOq6gbg68BOVfVg+7s2mHpCkqRBsBGRJHUsSvIM8BRwFLAL8ERVPdSunw9sB1wzoUmYQ9Ow/LCriVgInLiS37E/cEVV3QFQVf8B/jWJ2t4BHN8eGvZkkvPbsU4jcnFVLQeeTjJG05jcAFwFnJ/kMuDKzu+VJI2ejYgkqeOQqlrcWUiyC9B9vkaAn1XVRyZumAmdyWoQYOJJjd3Lz3T9vJwVn28HA2+mmS25IsmJVXXx6ipSkjR5niMiSZqsXwDz2wYFgCR7tj/+Gjg0yYZJZgAfXcVj/Bx4Z3uIFknWT7JRu+4JYKNVbPdL4BNpbAgsAH71YsUmmQlsW1V/rKqvAYuAPV9sG0nS8DgjIkmalKq6M8kC4LtJXkZzPsaNwIer6vIk84Cbgb8BVwOzV/IYdyX5GPCD9pyN5cDRwO9pzue4KsnTwAETNv0icAbN1bwALqmqRf+n5BnAuUk2AZYBDwNH9vr/liStHl6+V5IkSdLQeWiWJEmSpKGzEZEkSZI0dDYikiRJkobORkSSJEnS0NmISJIkSRo6GxFJkiRJQ2cjIkmSJGnobEQkSZIkDZ2NiCRJkqSh+y/Jn6viy5tyqwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6), dpi=80)\n",
    "plt.scatter(predicted_values_on_ridge_model_val, y_val_scaled, s=5)\n",
    "plt.xlabel(\"Predictions\")\n",
    "plt.ylabel(\"Observations\")\n",
    "plt.title(\"Optimized Ridge Regression\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Optimized Ridge Regression Residuals')"
      ]
     },
     "execution_count": 298,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 960x480 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 6), dpi=80)\n",
    "plt.scatter(predicted_values_on_ridge_model_val, y_val_scaled-predicted_values_on_ridge_model_val, s=5)\n",
    "plt.xlabel(\"Predictions\")\n",
    "plt.ylabel(\"Observations - Predictions\")\n",
    "plt.title(\"Optimized Ridge Regression Residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "a1b85603758daa886bf0ecc973668da6bcb4d22219496b87928d46b0d6d05015"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}