aqi-code-pattern.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2019-02-04 23:04:50--  https://aqs.epa.gov/aqsweb/airdata/hourly_42602_2017.zip\n",
      "Resolving aqs.epa.gov (aqs.epa.gov)... 134.67.21.26\n",
      "Connecting to aqs.epa.gov (aqs.epa.gov)|134.67.21.26|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 28767326 (27M) [application/zip]\n",
      "Saving to: ‘hourly_42602_2017.zip’\n",
      "\n",
      "100%[======================================>] 28,767,326  1.25MB/s   in 23s    \n",
      "\n",
      "2019-02-04 23:05:14 (1.17 MB/s) - ‘hourly_42602_2017.zip’ saved [28767326/28767326]\n",
      "\n",
      "Archive:  hourly_42602_2017.zip\n",
      "  inflating: hourly_42602_2017.csv   \n"
     ]
    }
   ],
   "source": [
    "# Air Quality datasets \n",
    "# https://aqs.epa.gov/aqsweb/airdata/download_files.html#Raw\n",
    "!wget -nc https://aqs.epa.gov/aqsweb/airdata/hourly_42602_2017.zip\n",
    "!unzip hourly_42602_2017.zip"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 912272\r\n",
      "-rw-r--r-- 1 dsxuser dsxuser 905396106 Nov 27 18:13 hourly_42602_2017.csv\r\n",
      "-rw-r----- 1 dsxuser dsxuser  28767326 Nov 29 13:13 hourly_42602_2017.zip\r\n"
     ]
    }
   ],
   "source": [
    "!ls -l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/IPython/core/interactiveshell.py:2698: DtypeWarning: Columns (17) have mixed types. Specify dtype option on import or set low_memory=False.\n",
      "  interactivity=interactivity, compiler=compiler, result=result)\n"
     ]
    }
   ],
   "source": [
    "# Load dataset \n",
    "aq_data = pd.read_csv('./hourly_42602_2017.csv')\n",
    "#aq_data = pd.read_csv('/Users/kkbankol@us.ibm.com/Downloads/hourly_42602_2017.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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 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>Date Local</th>\n",
       "      <th>...</th>\n",
       "      <th>Units of Measure</th>\n",
       "      <th>MDL</th>\n",
       "      <th>Uncertainty</th>\n",
       "      <th>Qualifier</th>\n",
       "      <th>Method Type</th>\n",
       "      <th>Method Code</th>\n",
       "      <th>Method Name</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>Date of Last Change</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>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FEM</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API Model 200EUP or T200UP - Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>2017-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FEM</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API Model 200EUP or T200UP - Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>2017-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FEM</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API Model 200EUP or T200UP - Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>2017-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FEM</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API Model 200EUP or T200UP - Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>2017-04-19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>73</td>\n",
       "      <td>23</td>\n",
       "      <td>42602</td>\n",
       "      <td>1</td>\n",
       "      <td>33.553056</td>\n",
       "      <td>-86.815</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>0.1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FEM</td>\n",
       "      <td>200</td>\n",
       "      <td>Teledyne-API Model 200EUP or T200UP - Photolyt...</td>\n",
       "      <td>Alabama</td>\n",
       "      <td>Jefferson</td>\n",
       "      <td>2017-04-19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   State Code  County Code  Site Num  Parameter Code  POC   Latitude  \\\n",
       "0           1           73        23           42602    1  33.553056   \n",
       "1           1           73        23           42602    1  33.553056   \n",
       "2           1           73        23           42602    1  33.553056   \n",
       "3           1           73        23           42602    1  33.553056   \n",
       "4           1           73        23           42602    1  33.553056   \n",
       "\n",
       "   Longitude  Datum          Parameter Name  Date Local         ...           \\\n",
       "0    -86.815  WGS84  Nitrogen dioxide (NO2)  2017-01-01         ...            \n",
       "1    -86.815  WGS84  Nitrogen dioxide (NO2)  2017-01-01         ...            \n",
       "2    -86.815  WGS84  Nitrogen dioxide (NO2)  2017-01-01         ...            \n",
       "3    -86.815  WGS84  Nitrogen dioxide (NO2)  2017-01-01         ...            \n",
       "4    -86.815  WGS84  Nitrogen dioxide (NO2)  2017-01-01         ...            \n",
       "\n",
       "    Units of Measure  MDL Uncertainty  Qualifier Method Type  Method Code  \\\n",
       "0  Parts per billion  0.1         NaN        NaN         FEM          200   \n",
       "1  Parts per billion  0.1         NaN        NaN         FEM          200   \n",
       "2  Parts per billion  0.1         NaN        NaN         FEM          200   \n",
       "3  Parts per billion  0.1         NaN        NaN         FEM          200   \n",
       "4  Parts per billion  0.1         NaN        NaN         FEM          200   \n",
       "\n",
       "                                         Method Name State Name County Name  \\\n",
       "0  Teledyne-API Model 200EUP or T200UP - Photolyt...    Alabama   Jefferson   \n",
       "1  Teledyne-API Model 200EUP or T200UP - Photolyt...    Alabama   Jefferson   \n",
       "2  Teledyne-API Model 200EUP or T200UP - Photolyt...    Alabama   Jefferson   \n",
       "3  Teledyne-API Model 200EUP or T200UP - Photolyt...    Alabama   Jefferson   \n",
       "4  Teledyne-API Model 200EUP or T200UP - Photolyt...    Alabama   Jefferson   \n",
       "\n",
       "   Date of Last Change  \n",
       "0           2017-04-19  \n",
       "1           2017-04-19  \n",
       "2           2017-04-19  \n",
       "3           2017-04-19  \n",
       "4           2017-04-19  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View first 5 rows\n",
    "aq_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['State Code', 'County Code', 'Site Num', 'Parameter Code', 'POC',\n",
       "       'Latitude', 'Longitude', 'Datum', 'Parameter Name', 'Date Local',\n",
       "       'Time Local', 'Date GMT', 'Time GMT', 'Sample Measurement',\n",
       "       'Units of Measure', 'MDL', 'Uncertainty', 'Qualifier', 'Method Type',\n",
       "       'Method Code', 'Method Name', 'State Name', 'County Name',\n",
       "       'Date of Last Change'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View titles of all columns\n",
    "aq_data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.558824e+06\n",
       "mean     8.250414e+00\n",
       "std      9.166491e+00\n",
       "min     -5.000000e+00\n",
       "25%      2.000000e+00\n",
       "50%      5.000000e+00\n",
       "75%      1.130000e+01\n",
       "max      1.296000e+02\n",
       "Name: Sample Measurement, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "aq_data['Sample Measurement'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  23, 2059,   34,   19, 3002, 4011, 4019, 4020, 9997, 1011, 1028,\n",
       "          5,    7,    9,   11,   12,   13, 2005,    8,    2, 1002, 1004,\n",
       "       2007,  242, 2016, 4001, 5001, 1005, 1003,   14, 2012, 6001,   16,\n",
       "        113, 1103, 1201, 1302, 1602, 1701, 4006, 4008, 5005, 6012, 9033,\n",
       "          4,    1,    3,    6, 1016, 8001, 8005, 9001,   10,   15,   26,\n",
       "         27,  306, 1234, 2002, 9004, 1006, 1008, 1014, 1017, 1022, 3005,\n",
       "       4002, 8002, 1001, 1013, 1018, 1021, 1025, 2004, 2011, 4003, 3001,\n",
       "       7004,   28, 7001, 7003, 9003,   25,   41,   43,   50,   51,   35,\n",
       "         32,  108, 1065,   21,   18,   56,   63,   76, 3103,   22,   78,\n",
       "         87,   30,   17,   67,   75, 1024, 1100,   29,   40, 2006, 4005,\n",
       "       3003,   42,   44,   93,   94,   95, 1010,   20,  423,  480,  962,\n",
       "         85,   86,  760,  761,  762,  540,  561, 1501, 1502, 1233,  110,\n",
       "        133,  124,  125,   45,   60,   73,   37,   38,   48,  101,   65,\n",
       "         33,   97, 9021, 1127,   80, 1376,  100, 4000, 5200,   46,   59,\n",
       "       1069,   69, 1067, 1044,   55, 1034,   24,   47,  416, 1015, 1035,\n",
       "       1039, 1050, 1052, 1066, 1037, 1051, 1053, 3009, 3011, 1068, 7011,\n",
       "       3006, 3013, 2003, 7022,   31,  123,  456,  892,   99,  232, 2601,\n",
       "        700,  200,  300])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print list of all unique monitoring site numbers\n",
    "aq_data['Site Num'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(66,)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# get number of aq sites in a single state, \"California\"\n",
    "aq_data.loc[aq_data['State Name'] == \"California\"]['Site Num'].unique().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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 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>Date Local</th>\n",
       "      <th>...</th>\n",
       "      <th>Units of Measure</th>\n",
       "      <th>MDL</th>\n",
       "      <th>Uncertainty</th>\n",
       "      <th>Qualifier</th>\n",
       "      <th>Method Type</th>\n",
       "      <th>Method Code</th>\n",
       "      <th>Method Name</th>\n",
       "      <th>State Name</th>\n",
       "      <th>County Name</th>\n",
       "      <th>Date of Last Change</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>308750</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308751</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308752</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308753</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308754</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308755</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308756</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308757</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308758</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308759</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308760</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308761</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308762</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308763</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308764</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308765</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308766</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308767</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308768</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308769</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308770</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308771</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308772</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308773</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308774</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308775</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308776</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308777</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308778</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308779</th>\n",
       "      <td>6</td>\n",
       "      <td>37</td>\n",
       "      <td>2</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>34.13650</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-01-02</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Los Angeles</td>\n",
       "      <td>2017-06-27</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>494017</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494018</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494019</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494020</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494021</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-29</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494022</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494023</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494024</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494025</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494026</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494027</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494028</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494029</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494030</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494031</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494032</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494033</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494034</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494035</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494036</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494037</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494038</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494039</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494040</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494041</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494042</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494043</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494044</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494045</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-30</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>494046</th>\n",
       "      <td>6</td>\n",
       "      <td>59</td>\n",
       "      <td>5001</td>\n",
       "      <td>42602</td>\n",
       "      <td>2</td>\n",
       "      <td>33.92513</td>\n",
       "      <td>-117.95264</td>\n",
       "      <td>WGS84</td>\n",
       "      <td>Nitrogen dioxide (NO2)</td>\n",
       "      <td>2017-12-31</td>\n",
       "      <td>...</td>\n",
       "      <td>Parts per billion</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>FRM</td>\n",
       "      <td>74</td>\n",
       "      <td>INSTRUMENTAL - CHEMILUMINESCENCE</td>\n",
       "      <td>California</td>\n",
       "      <td>Orange</td>\n",
       "      <td>2018-02-20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>101524 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        State Code  County Code  Site Num  Parameter Code  POC  Latitude  \\\n",
       "308750           6           37         2           42602    2  34.13650   \n",
       "308751           6           37         2           42602    2  34.13650   \n",
       "308752           6           37         2           42602    2  34.13650   \n",
       "308753           6           37         2           42602    2  34.13650   \n",
       "308754           6           37         2           42602    2  34.13650   \n",
       "308755           6           37         2           42602    2  34.13650   \n",
       "308756           6           37         2           42602    2  34.13650   \n",
       "308757           6           37         2           42602    2  34.13650   \n",
       "308758           6           37         2           42602    2  34.13650   \n",
       "308759           6           37         2           42602    2  34.13650   \n",
       "308760           6           37         2           42602    2  34.13650   \n",
       "308761           6           37         2           42602    2  34.13650   \n",
       "308762           6           37         2           42602    2  34.13650   \n",
       "308763           6           37         2           42602    2  34.13650   \n",
       "308764           6           37         2           42602    2  34.13650   \n",
       "308765           6           37         2           42602    2  34.13650   \n",
       "308766           6           37         2           42602    2  34.13650   \n",
       "308767           6           37         2           42602    2  34.13650   \n",
       "308768           6           37         2           42602    2  34.13650   \n",
       "308769           6           37         2           42602    2  34.13650   \n",
       "308770           6           37         2           42602    2  34.13650   \n",
       "308771           6           37         2           42602    2  34.13650   \n",
       "308772           6           37         2           42602    2  34.13650   \n",
       "308773           6           37         2           42602    2  34.13650   \n",
       "308774           6           37         2           42602    2  34.13650   \n",
       "308775           6           37         2           42602    2  34.13650   \n",
       "308776           6           37         2           42602    2  34.13650   \n",
       "308777           6           37         2           42602    2  34.13650   \n",
       "308778           6           37         2           42602    2  34.13650   \n",
       "308779           6           37         2           42602    2  34.13650   \n",
       "...            ...          ...       ...             ...  ...       ...   \n",
       "494017           6           59      5001           42602    2  33.92513   \n",
       "494018           6           59      5001           42602    2  33.92513   \n",
       "494019           6           59      5001           42602    2  33.92513   \n",
       "494020           6           59      5001           42602    2  33.92513   \n",
       "494021           6           59      5001           42602    2  33.92513   \n",
       "494022           6           59      5001           42602    2  33.92513   \n",
       "494023           6           59      5001           42602    2  33.92513   \n",
       "494024           6           59      5001           42602    2  33.92513   \n",
       "494025           6           59      5001           42602    2  33.92513   \n",
       "494026           6           59      5001           42602    2  33.92513   \n",
       "494027           6           59      5001           42602    2  33.92513   \n",
       "494028           6           59      5001           42602    2  33.92513   \n",
       "494029           6           59      5001           42602    2  33.92513   \n",
       "494030           6           59      5001           42602    2  33.92513   \n",
       "494031           6           59      5001           42602    2  33.92513   \n",
       "494032           6           59      5001           42602    2  33.92513   \n",
       "494033           6           59      5001           42602    2  33.92513   \n",
       "494034           6           59      5001           42602    2  33.92513   \n",
       "494035           6           59      5001           42602    2  33.92513   \n",
       "494036           6           59      5001           42602    2  33.92513   \n",
       "494037           6           59      5001           42602    2  33.92513   \n",
       "494038           6           59      5001           42602    2  33.92513   \n",
       "494039           6           59      5001           42602    2  33.92513   \n",
       "494040           6           59      5001           42602    2  33.92513   \n",
       "494041           6           59      5001           42602    2  33.92513   \n",
       "494042           6           59      5001           42602    2  33.92513   \n",
       "494043           6           59      5001           42602    2  33.92513   \n",
       "494044           6           59      5001           42602    2  33.92513   \n",
       "494045           6           59      5001           42602    2  33.92513   \n",
       "494046           6           59      5001           42602    2  33.92513   \n",
       "\n",
       "        Longitude  Datum          Parameter Name  Date Local  \\\n",
       "308750 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308751 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308752 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308753 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308754 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308755 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308756 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308757 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308758 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308759 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308760 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308761 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308762 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308763 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308764 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308765 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308766 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308767 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308768 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308769 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308770 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308771 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308772 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308773 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-01   \n",
       "308774 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-02   \n",
       "308775 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-02   \n",
       "308776 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-02   \n",
       "308777 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-02   \n",
       "308778 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-02   \n",
       "308779 -117.92391  WGS84  Nitrogen dioxide (NO2)  2017-01-02   \n",
       "...           ...    ...                     ...         ...   \n",
       "494017 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-29   \n",
       "494018 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-29   \n",
       "494019 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-29   \n",
       "494020 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-29   \n",
       "494021 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-29   \n",
       "494022 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494023 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494024 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494025 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494026 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494027 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494028 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494029 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494030 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494031 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494032 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494033 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494034 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494035 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494036 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494037 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494038 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494039 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494040 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494041 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494042 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494043 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494044 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494045 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-30   \n",
       "494046 -117.95264  WGS84  Nitrogen dioxide (NO2)  2017-12-31   \n",
       "\n",
       "               ...            Units of Measure  MDL Uncertainty  Qualifier  \\\n",
       "308750         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308751         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308752         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308753         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308754         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308755         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308756         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308757         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308758         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308759         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308760         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308761         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308762         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308763         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308764         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308765         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308766         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308767         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308768         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308769         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308770         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308771         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308772         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308773         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308774         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308775         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308776         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308777         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308778         ...           Parts per billion  1.0         NaN        NaN   \n",
       "308779         ...           Parts per billion  1.0         NaN        NaN   \n",
       "...            ...                         ...  ...         ...        ...   \n",
       "494017         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494018         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494019         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494020         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494021         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494022         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494023         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494024         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494025         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494026         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494027         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494028         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494029         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494030         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494031         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494032         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494033         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494034         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494035         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494036         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494037         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494038         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494039         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494040         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494041         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494042         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494043         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494044         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494045         ...           Parts per billion  1.0         NaN        NaN   \n",
       "494046         ...           Parts per billion  1.0         NaN        NaN   \n",
       "\n",
       "       Method Type  Method Code                       Method Name  State Name  \\\n",
       "308750         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308751         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308752         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308753         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308754         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308755         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308756         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308757         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308758         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308759         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308760         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308761         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308762         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308763         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308764         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308765         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308766         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308767         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308768         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308769         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308770         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308771         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308772         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308773         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308774         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308775         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308776         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308777         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308778         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "308779         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "...            ...          ...                               ...         ...   \n",
       "494017         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494018         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494019         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494020         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494021         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494022         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494023         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494024         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494025         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494026         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494027         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494028         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494029         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494030         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494031         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494032         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494033         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494034         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494035         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494036         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494037         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494038         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494039         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494040         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494041         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494042         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494043         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494044         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494045         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "494046         FRM           74  INSTRUMENTAL - CHEMILUMINESCENCE  California   \n",
       "\n",
       "        County Name  Date of Last Change  \n",
       "308750  Los Angeles           2017-06-27  \n",
       "308751  Los Angeles           2017-06-27  \n",
       "308752  Los Angeles           2017-06-27  \n",
       "308753  Los Angeles           2017-06-27  \n",
       "308754  Los Angeles           2017-06-27  \n",
       "308755  Los Angeles           2017-06-27  \n",
       "308756  Los Angeles           2017-06-27  \n",
       "308757  Los Angeles           2017-06-27  \n",
       "308758  Los Angeles           2017-06-27  \n",
       "308759  Los Angeles           2017-06-27  \n",
       "308760  Los Angeles           2017-06-27  \n",
       "308761  Los Angeles           2017-06-27  \n",
       "308762  Los Angeles           2017-06-27  \n",
       "308763  Los Angeles           2017-06-27  \n",
       "308764  Los Angeles           2017-06-27  \n",
       "308765  Los Angeles           2017-06-27  \n",
       "308766  Los Angeles           2017-06-27  \n",
       "308767  Los Angeles           2017-06-27  \n",
       "308768  Los Angeles           2017-06-27  \n",
       "308769  Los Angeles           2017-06-27  \n",
       "308770  Los Angeles           2017-06-27  \n",
       "308771  Los Angeles           2017-06-27  \n",
       "308772  Los Angeles           2017-06-27  \n",
       "308773  Los Angeles           2017-06-27  \n",
       "308774  Los Angeles           2017-06-27  \n",
       "308775  Los Angeles           2017-06-27  \n",
       "308776  Los Angeles           2017-06-27  \n",
       "308777  Los Angeles           2017-06-27  \n",
       "308778  Los Angeles           2017-06-27  \n",
       "308779  Los Angeles           2017-06-27  \n",
       "...             ...                  ...  \n",
       "494017       Orange           2018-02-20  \n",
       "494018       Orange           2018-02-20  \n",
       "494019       Orange           2018-02-20  \n",
       "494020       Orange           2018-02-20  \n",
       "494021       Orange           2018-02-20  \n",
       "494022       Orange           2018-02-20  \n",
       "494023       Orange           2018-02-20  \n",
       "494024       Orange           2018-02-20  \n",
       "494025       Orange           2018-02-20  \n",
       "494026       Orange           2018-02-20  \n",
       "494027       Orange           2018-02-20  \n",
       "494028       Orange           2018-02-20  \n",
       "494029       Orange           2018-02-20  \n",
       "494030       Orange           2018-02-20  \n",
       "494031       Orange           2018-02-20  \n",
       "494032       Orange           2018-02-20  \n",
       "494033       Orange           2018-02-20  \n",
       "494034       Orange           2018-02-20  \n",
       "494035       Orange           2018-02-20  \n",
       "494036       Orange           2018-02-20  \n",
       "494037       Orange           2018-02-20  \n",
       "494038       Orange           2018-02-20  \n",
       "494039       Orange           2018-02-20  \n",
       "494040       Orange           2018-02-20  \n",
       "494041       Orange           2018-02-20  \n",
       "494042       Orange           2018-02-20  \n",
       "494043       Orange           2018-02-20  \n",
       "494044       Orange           2018-02-20  \n",
       "494045       Orange           2018-02-20  \n",
       "494046       Orange           2018-02-20  \n",
       "\n",
       "[101524 rows x 24 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# show data associated \n",
    "los_angeles_aq = aq_data.loc[ \\\n",
    "                     (aq_data['Longitude'] > -118.455937) & \\\n",
    "                     (aq_data['Longitude'] < -117.842111) & \\\n",
    "                     (aq_data['Latitude'] > 33.764836) & \\\n",
    "                     (aq_data['Latitude'] < 34.173729) ]\n",
    "los_angeles_aq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date Local</th>\n",
       "      <th>Time Local</th>\n",
       "      <th>Site Num</th>\n",
       "      <th>Sample Measurement</th>\n",
       "      <th>State Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>308750</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308751</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>01:00</td>\n",
       "      <td>2</td>\n",
       "      <td>12.9</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308752</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>02:00</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308753</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>03:00</td>\n",
       "      <td>2</td>\n",
       "      <td>13.6</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308754</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>04:00</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>California</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Latitude  Longitude  Date Local Time Local  Site Num  \\\n",
       "308750   34.1365 -117.92391  2017-01-01      00:00         2   \n",
       "308751   34.1365 -117.92391  2017-01-01      01:00         2   \n",
       "308752   34.1365 -117.92391  2017-01-01      02:00         2   \n",
       "308753   34.1365 -117.92391  2017-01-01      03:00         2   \n",
       "308754   34.1365 -117.92391  2017-01-01      04:00         2   \n",
       "\n",
       "        Sample Measurement  State Name  \n",
       "308750                14.4  California  \n",
       "308751                12.9  California  \n",
       "308752                15.2  California  \n",
       "308753                13.6  California  \n",
       "308754                15.2  California  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#trim down the aq grid\n",
    "los_angeles_aq_trimmed = los_angeles_aq[['Latitude','Longitude','Date Local','Time Local','Site Num','Sample Measurement','State Name']]\n",
    "los_angeles_aq_trimmed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  from ipykernel import kernelapp as app\n",
      "/opt/conda/envs/DSX-Python35/lib/python3.5/site-packages/ipykernel/__main__.py:4: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\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>Latitude</th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Date Local</th>\n",
       "      <th>Time Local</th>\n",
       "      <th>Site Num</th>\n",
       "      <th>Sample Measurement</th>\n",
       "      <th>State Name</th>\n",
       "      <th>Timestamp</th>\n",
       "      <th>Weekday</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>308750</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>00:00</td>\n",
       "      <td>2</td>\n",
       "      <td>14.4</td>\n",
       "      <td>California</td>\n",
       "      <td>1483228800000000000</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308751</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>01:00</td>\n",
       "      <td>2</td>\n",
       "      <td>12.9</td>\n",
       "      <td>California</td>\n",
       "      <td>1483232400000000000</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308752</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>02:00</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>California</td>\n",
       "      <td>1483236000000000000</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308753</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>03:00</td>\n",
       "      <td>2</td>\n",
       "      <td>13.6</td>\n",
       "      <td>California</td>\n",
       "      <td>1483239600000000000</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>308754</th>\n",
       "      <td>34.1365</td>\n",
       "      <td>-117.92391</td>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>04:00</td>\n",
       "      <td>2</td>\n",
       "      <td>15.2</td>\n",
       "      <td>California</td>\n",
       "      <td>1483243200000000000</td>\n",
       "      <td>Sunday</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Latitude  Longitude  Date Local Time Local  Site Num  \\\n",
       "308750   34.1365 -117.92391  2017-01-01      00:00         2   \n",
       "308751   34.1365 -117.92391  2017-01-01      01:00         2   \n",
       "308752   34.1365 -117.92391  2017-01-01      02:00         2   \n",
       "308753   34.1365 -117.92391  2017-01-01      03:00         2   \n",
       "308754   34.1365 -117.92391  2017-01-01      04:00         2   \n",
       "\n",
       "        Sample Measurement  State Name            Timestamp Weekday  \n",
       "308750                14.4  California  1483228800000000000  Sunday  \n",
       "308751                12.9  California  1483232400000000000  Sunday  \n",
       "308752                15.2  California  1483236000000000000  Sunday  \n",
       "308753                13.6  California  1483239600000000000  Sunday  \n",
       "308754                15.2  California  1483243200000000000  Sunday  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# generate a timestamp from the date and time features\n",
    "los_angeles_aq_trimmed['Timestamp'] = pd.to_datetime((los_angeles_aq_trimmed['Date Local']+ ' ' + los_angeles_aq_trimmed['Time Local']),format='%Y-%m-%d %H:%M').values.astype(np.int64)\n",
    "# add name of day of week\n",
    "los_angeles_aq_trimmed['Weekday'] = pd.to_datetime(los_angeles_aq_trimmed['Date Local']).dt.weekday_name\n",
    "\n",
    "los_angeles_aq_trimmed.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYgAAAEKCAYAAAAIO8L1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzsnXecHWW9/z/fOW2z6Z1AgIRACL0FROSignpFUbk/xatevahcuSpe27UXDIhC8IIUBaQmYEIJLdRASE9IQhIS0utmN7ubbdmSbafP9/fHnDlnyjNz5pw9ZXfzvF+vfe05U5+ZM/N8n+dbiZkhkUgkEokVpdwNkEgkEkn/RAoIiUQikQiRAkIikUgkQqSAkEgkEokQKSAkEolEIkQKCIlEIpEIkQJCIpFIJEKkgJBIJBKJkKIJCCJ6nIiaiWi7YdlfiGg3EW0lopeIaJRh3a+JaD8R7SGify1WuyQSiUTiDSpWJDURXQGgG8CTzHx2atmnACxl5gQRzQYAZv4lEZ0J4GkAlwA4HsA7AKYzc9LtHOPGjeMpU6YUpf0SiUQyWNm0adMRZh6fbTt/sRrAzCuJaIpl2duGr+sAfCn1+QsAnmHmKICDRLQfmrBY63aOKVOmYOPGjQVrs0QikRwLEFGNl+3KaYP4NoA3U59PAFBrWFeXWiaRSCSSMlEWAUFEvwWQADBPXyTYTKj7IqIbiWgjEW1saWkpVhMlEonkmKfkAoKIrgdwDYD/4IwBpA7AiYbNJgM4LNqfmR9m5pnMPHP8+KwqNIlEIpHkSdFsECKI6NMAfgngo8zca1j1CoD5RHQ3NCP1aQDey+cc8XgcdXV1iEQitnUVFRWYPHkyAoFAPoeWSCSSY4qiCQgiehrAxwCMI6I6AH8A8GsAIQCLiQgA1jHzd5l5BxE9B2AnNNXTTdk8mJyoq6vD8OHDMWXKFKTOAQBgZrS2tqKurg5Tp07t07VJJBLJsUAxvZi+Klj8mMv2fwLwp76eNxKJ2IQDABARxo4dC2m3kEgkEm8Mykhqq3DItlwikUgkdgalgJBIJMcmzIzNmzcjkUiUuymDAikgJBLJoGHHjh1YuHAhVq5cWe6mDAoGpYBwSh9SrLQiEomkf6B7L/b09JS5JYODQScgKioq0NraahMGuhdTRUVFmVomkUgkA4uSxkGUgsmTJ6Ourk7oraTHQUgkEokkO4NOQAQCARnnIJFIJAVg0KmYJBKJRFIYpICQSCQSiRApICQSiUQiRAoIiUQyaJCu7IVFCgiJRCKRCJECQiKRDBpkvrXCIgWERCKRSIRIASGRSAYN0gZRWKSAkEgkEokQKSAkEsmgQdogCosUEBKJRCIRIgWERCIZNEgbRGGRAkIikUgkQqSAkEgkEokQKSAkEsmgQRqpC4sUEBKJZNAgbRCFRQoIiUQikQiRAkIikUgkQoomIIjocSJqJqLthmVjiGgxEe1L/R+dWk5EdB8R7SeirUR0YbHaJZFIBi/SBlFYijmDmAPg05ZlvwKwhJlPA7Ak9R0ArgZwWurvRgAPFrFdEolkkCJtEIWlaAKCmVcCaLMs/gKAuanPcwFca1j+JGusAzCKiCYVq20SiUQiyU6pbRATmbkBAFL/J6SWnwCg1rBdXWqZDSK6kYg2EtHGlpaWojZWIpFIjmX6i5FapDgUzhWZ+WFmnsnMM8ePH1/kZkkkkoFEsrUVABBvbi5zSwYHpRYQTbrqKPVf/xXrAJxo2G4ygMMlbptEIhngRA9WAwDihw6VtyGDhFILiFcAXJ/6fD2AhYbl/5nyZroUwFFdFSWRSCSS8uAv1oGJ6GkAHwMwjojqAPwBwB0AniOiGwAcAnBdavM3AHwGwH4AvQC+Vax2SSQSicQbRRMQzPxVh1VXCbZlADcVqy0SieQYox95u27fvh2nnHIKKisry92UnOkvRmqJRCLpO/0sTq63txfPP/88/vnPf5a7KXkhBYREIhk89KOZA5CJ7D58eGD63EgBIZFIBh/9ZCYx0CO7pYCQSCSDj4HdL/cbpICQSCSDh34ycxgsSAEhkUgkEiFSQEgkEolEiBQQEolEIhEiBYREIpEUCenFJOmXHDlyBLNmzUJTU1O5myKRSAYoUkAMUnbu3AlAC/OXSAYjbW1tSCQSDmsH9si9vyAFhEQiGXDEYjHcd999ePnll4t6nsa7NqLznZqinqM/IwWERCIZcMTjcQDAgQMHHLYoTEBEoiWMzneO3doSUkBIJJIBR/bcRv1DxeRkpE52xaBGnNRjdhJtEcTquwvVLM9IATFIGejeExKJG/PmzdM+qGp5G5InDX9aj8Y7N3jevvHODWi+f3MRWyRGCgiJRDJgUTs7y92EvFF7vc8gyoUUEIMUPc2wRDKoKcNM+a6392DtgdaSn7ccSAEhkUgGHcUcHt2/dD+++si6Ip6h/yAFxCBF2iAkkvJT6PcwvLO0MxcpICQSyaCjUN1yMx1FBPGCHKsQwqL1yZ0FaIl3pICQSCQSB14JbcTrwU3lbkbZkAJCIpEMWMhhVF5I36Z2paeARxtY+MvdAElxkF5MkmOZeqX/jX2ZuU/vZRRxKCUe0/e/uygpCNJILZGUn0K+h09VrMQzodUFO54XyiIgiOgnRLSDiLYT0dNEVEFEU4loPRHtI6JniShYjrZJJBJJfyVKCZcMtoWn5AKCiE4A8EMAM5n5bAA+AF8BMBvAX5n5NADtAG4oddskEskAp4Cq1f46C3/rrbdKdq5yqZj8AIYQkR9AJYAGAFcCeD61fi6Aa8vUNolEUmY+8+Jn8M1F3yxrG0QC4oPajjK0xEwpi4CVXEAwcz2A/wNwCJpgOApgE4AOZtbnTnUATih12yQSSf+gtqsWm5rycC8t8qj/C39fk9P2cVUcQ3FIOYKj1FuIJhUVRy8mIvqp247MfHc+JySi0QC+AGAqgA4ACwBcLTqFw/43ArgRAE466aR8miCRSAYphRQPhVAxRRNR4fK3gx8AAM7Cv/b5HMXEbQYxPMtfvnwCwEFmbmHmOIAXAVwGYFRK5QQAkwEIE74z88PMPJOZZ44fP74PzSguzc3NWLlyZbmbIZEMamxxEP3MvZsMWaH6InB2+moL0ZyccZxBMPMtRTrnIQCXElElgDCAqwBsBLAMwJcAPAPgegALi3T+kvDoo48iFovhsssug98vw00kklwYHRmNjlB59f2lMFLv3r0bM2bMyLrdu4G9RW+LiKw2CCKaTkRLiGh76vu5RPS7fE/IzOuhGaPfB7At1YaHAfwSwE+JaD+AsQAey/cc/QHdFa2/BKx1r2tAtPpouZshkWSloaEBVzZcibPaz8q6bTQUMn03vm3hcLjsnkicRen1zDPP5HPQkuHFSP0IgF8DWsYqZt4KzS01b5j5D8w8g5nPZuZvMHOUmauY+RJmPpWZr2NmsfJugKD2s0pXHS/vR8tDW8vdDIkkKz09WmqLUdFRWbdVfT7Td2PfOXv2bKx9b20hm5YzxRggxptLZ9z2IiAqmfk9y7L+Xwqpn5C9dq5EIjGipNJkUAGqOsxdPjfvfUWzj3PpAL7uW+z5GB3thVGTKVARSGWVVcOFyS7rBS/K8SNENA0p4UxEX4LmnirxQOcALokokQw0Nh85YvreVyGz4KKPY0S4B7NS318J/R4A8M/kJz3tv25NYQoLXYfXcQb2Y3byGwCNLMgxveBlBnETgH8AmEFE9QB+DOC7RW3VIGLDBu+FySUSSf4wM47GCzO6TiaTiMfjaB02EgfHH9+HRhk+9sEecgb2AwB+6Xsq/7bkgZcZRA0zf4KIhgJQmLmr2I0aTDQ3N5e7CRLJgCJfvX0hDdKPPPIIGhsbgY/2LaFDNiN1fscsHV5mEAeJ6GEAlwLoLnJ7JEWkSmlCLBYrdzMkElcK2dHnq2JqbGwsWBuykTjaf/1xvAiI0wG8A03VdJCI/kZElxe3WYOHcrnZLVu2zPS9iTqwNLgdixYtKkt7JJKykMfrV9B31sOhulfUFe58BSargGDmMDM/x8z/D8AFAEYAWFH0lkkKSow0xzNpNJf0d8qtYlq9Or+aCw0NDZg1axaOGAzlnlRM/SNUSoinZH1E9FEiegBacFsFgC8XtVWDiHIH6kgkxzL5qJi2bs0vXkjfb+9eQ9QzAxfNXIipU50TD4Yd8jXpHNi0Pq/2FIKsRmoiOghgC4DnAPycmY/dAq0SiaQkrA5MQKVSUZZzFzK4jZlRWdmJysqdjoPFpYfW4z9wpuMxDm7eiGkFa1FuePFiOo+ZpV4iTyKRSFnPT0T49lvfxnfwubK2QyLJhf1dJ+e8T3+cratqpk319fWYMmWKbZukmrQvSyZBRFrQIJWvMrSXMx9XyFxMktKzoVHGYkiOVXKfDRRqBpFIJHC4vj79fc6cOcLtokm7Z+Ef//hHLFy4sKDtyYey5GKSlA59VPW+/2CZWyKRFBfRDOL4znIpZ4D33rNmKBJT3y2OlfrgA61mREkDHyzIXEzHCC2K1BJKjj3yGXsXcgZRCBoOlC8LsxcBIXMx9WMWLVqEmpqacjdDIumX5DP4LrZKp/3l/TltH4uULzO0zMU0wFm3bh2eeOIJx/XWh72/1KeQSApNfzNSO71rPesGzvg6qxcTM1cBMOViIqIvApDDVolEMugoVKCe6DjhHa0la08h8Ow/xcw9hkR9fy1SeyQFZE7kYjyzp3S54yUSSQaxgDiCNupbSrv64cP7tH8u5OtgK/UU/Rx9MPN2TSrFhhpCh1qewCOJpBQwM76D+fgN7jcuLVt7hCN/Bl4M9S0yeuS47X3aPxfyFRD9S9l3DJBIJLBmzRokk5mgmlx0ri/GzsXLsXOK0TSJJC/C4XDBA0lPQBOCfXSy9KbSya8LrO1pyms/I7+ocbY5FhpHGwQRbYP4LhCAiUVrkUTI2rVrsWTJEvh8Plx66aUAnAVEXV2dlOCSfs/s2bMBALNmzSpvQ/KABG+YF4eQg13ZM7caB4Hlxm0GcQ2Azwn+rgEwvfhNG1gsXboUs2bNgqoWxyUtGtUSesUNFbOcBMSjjz6K/qYF3L7jJ9i770/lboakzHR3d2Pjxo1FObbwfaD8h0pj0I4vNC8VrlMchmAXYDsC0Tbt1Hkal5csWWL6nujDNfQVxxkEM0svpRxYuXIlAEBV1XTR9XLS32YQTU2vAACmn/bbgh53+4o6DB1dgannjivocSXFYcGCBaipqcHUqVPL3ZSsfB9Pwb8riYUTrrStE80gQtFW/CsWo2vzIeDj1wiP6eW9bG01ezrVD7Nne2Xmkng3lb8nk+SNmw2ivwmIYrHi6b1444H80jNLSk93t+bB05eZdi5VERP+3rzP44ezqkfUNZOq2T4CsXYAmfdz4f6rUdM5Oe92iOjd0HdbhhekgCgwxapBXaggIBkoJxnovP/++8LlYhVT4VS+vb0ZYaPA+bhW4fdK1dW4dd0vUt9yf4+j7LMvO1ia9BtZBQQRXUNUxnyz/YhoNIpXX301bQ8Q8fDDD5ekLeFwGLfddptteeYlyQiCRw78QbBeIhmYlOsZfuGFFzxtF4vFMH/+fIQTYdu6fJq+Jl4+dZyXjv8rAPYR0Z1EdEYhTkpEo4joeSLaTUS7iOjDRDSGiBYT0b7U/9GFOFchWbNmDTZt2uQ5S2Mx6ejoEC7XX55WrkwvmxzL3emsvr4eq1atyq9xEkkRcZoFF1tw6OoxQGyDMLJ3715UH622CATzPpXcjY/xavSoZlOw9frC7KVsT3HwUpP669BqUR8A8AQRrSWiG4moL+F89wJYxMwzAJwHYBeAXwFYwsynAViS+t6v0KeO/XkUrrftqENQXFyN47Wq17Ie55FHHrF5U0iOLc6Zew4e2PJAUY5t7AQ7YwMj07Cxze4Cwlv/cCWvwsdoAw4n3AXAl5Vlno5XDDypjlIV5V4A8AyASQD+DcD7RPQ/uZ6QiEYAuALAY6ljx5i5A8AXAMxNbTYXwLW5Hnswoz+cegphJyGVTXjtbtmFX6/6NZp7i2MrkQwO9OfowQ8eLPq5/uut/8pp+3g8jqrkGNQkS6tkML5bRjdXVVUxa9YsbN6y2bw92CYq6sMtmf1Sa9VUN7x48WJbTEi4qxOXKjsL0Pr88GKD+DwRvQRgKYAAgEuY+WpoI/+f5XHOUwC0QJuNbCaiR1OJACcycwMApP5PcGjPjUS0kYg2trS0iDYZlOgqpRUrVgAwFBOxoD/ETqboSV1jAQAJtXAlPXp7e4sW/yEpD3rXdnzP8Xjx9RcLNmsWHWdX266cjtHW1oaV8WlYFj8167Hzwek4TU0Zz6FbA3Pw8l/+iO3LFtuffZdm9MR60htdQOa032vWrAFgnqm0N9RDSIm0GF5mEF8C8FdmPpeZ/8LMzQDAzL0Avp3HOf0ALgTwIDNfAKAHOaiTmPlhZp7JzDPHjx+fx+m9k0wm8eijj+LAgQP6uQEU1xPo6d1P41er7LfDahi3frcLy8wDtDtYlf4cTOk7qUCBdL29vbjzzjuxdKk4oEgyMNGf9Q83fxhbN2xFbW1tyc4dTbgPNk455ZQStcSZL/pW4cDG9XjroXvTy46oQwEAMWheR5qQNb5nDErd1/OxEwESu9FWVVUZvomVWaVScnsREA3MvNK4gIhmAwAz56OkrgNQx8x6xqrnoQmMJiKalDr+JAAl0YHccsstWLRokXBdb28v6urq8NJLL7keIxw2eyu8++67ebfnz+v/jNerXrctzyaUVq9eDUB7sZfFpmFNIvMSNc2Yl3d7sqG7/u3aldsoUNK/YTBmtM9IfzdG8BeCQ52HHNdFLAKiurraNHovuat2ltG63p4IB7TNDUKBAfzA9xKmkXkmMBoZN1WrCDAO/srtlu5FQHxSsOzqfE/IzI0Aaono9NSiqwDsBPAKgOtTy64HsDDfc+TQFjAz1q1bl3U7J/bv35/OKaOzbFnhjUpeHxRmRo06xrQsGLS725X7wZP0b8KRMM7qOKtox79p6U2et50zZw4efDC7LcT4niaY8Fr0jPSovi+4xTwYzyvaLoQofhZYgGeDfzS1bUvieE/nbuhxKC5UIpWuW7K+7wH4PoBpRGQMVR0OYE0fz/s/AOYRURBAFYBvQRNWzxHRDQAOAbiuj+fISiF0locO2UdC+Y62mpubMb1jOvaO2mtb57VDt7ZnqCUatFCqJSv92bNLkjuslu/3zFZCN9mZiaR2amYrD8URHob34if2uT1Xta4DcJHn7fUmJWPJ9OcKxNIrIwjAa660xt4mDBMsjx2qBXCm5zbli9sMYj605HwLYU7Wd1HK9TVvmHlLyo5wLjNfy8ztzNzKzFcx82mp/219OUcxyNcGYfSfduOBBx7AOe3noDJeaVuX7Zz6+q1bZdqJUhJJRHDXxruEQVEDGdUyGi7lAGDd2rWZdghGyrHDmfdpbWKK67G4AAOip3b8Jq/9GrY2pM9PYBhfYZXN7TJGaVsRXkM/MFIzM1dDq0ndZfgDEY1x2W/AUKqHPlcPn480fcS2LBcVk/sGUrVUSObtmoc5O+Zgzo455W5KQSn2uxFMBj1tp7t1O7E/mUnSaGxz2K8dvzdU/CJZ+nkzOQwEqcD1bdP/ze/hwoVijbrja18iee0WoTEfWmrvTanmmM3xmrvqMU8xdPkVSe8P9aHkKCyNn4afdmsvUiKLaqBREUdgS/IjrmrqxEK6DfcH7B78BTpuqjP9eMPHXc6dBQ+vXDgUAnqBiD/gvXH6+fMUjqKRPqfG4JrQ0CIjCIBq2JbA2LNnj/igDv1LPFmagZ7jDIKZr0n9n8rMp6T+63+DQjhkexD0zt9LMfJCQjmM8pfGTwMAbDyi/ZSv1np7IYplizjW8NKRdsW6cM7cc/B29dslaFFhKKdN6WByLCagHePRIWxHW6TfaZ9NWEfS2jLtUzTl2somAeF2LHEXHYmVJj2em5H6QrcdmVmcUnEQMxAMsdXd3h4c6cVUOmo6NaPr49sfx6emfKrMrcmPF/e9iF+c+ovsG2bByzsUYT/eq9C8nBa+b0/tXddVB8CewM54bD3eINldvHdWV6t4SbtPlqWebSNlfk3dVEx3uaxjAPYqGgOM/trhi0b32draBntK4GLQ3NyMeDyOE044oSTnk5QH6/P2VvVb+AVKIyCMnee2tTsAqx8PibctuecVkdBY7CW0TfXY8xNlSwtYXNwqyjkrCSVpyiVkrJ5R2xsz5i8jfW0dx1VQIDMreeABLXnbQKwjXEwGm8quWM+1Nag0KwJd++7QqAK1xpkI+22j94qKTiBiXEIwvmG6sEpCQZwVBAy1KIzdPMGqYnK510RIKqUZ/InwpI8gorOJ6MtE9J/6X7EbVgpyjVfQXdGqqqrQ06PlVNHTcLihcm5eTH4P6X33798vXJ7ttdZTB3vt0DjefwqoD1T660zVDattpVACUFQNblzYuVxsMBCxLVvd4SAgDE3uy7hbTSaxODbdtvziSyyeRg63hMBoSQXoDR3aJtzUOIOYSg7BcNBmEKqwHE/53VwBAET0BwD3p/4+DuBOAJ8vcrtKwttvuxsNrUZqPUHegQMHMHfuXNM6N57b6q3QiIgdO3Zg1qxZplQW+jn3sDkXVUdHB6IR52JGAPBs9IK82+JGW1t+hsN4vANLlk7DkdbleZ+boWLZsmWuhZz6AwNpllEsLyYAiLGCbs64uV7edHn6s9UlPEiCesyW8db69evR1tYGNq7wKJTj8Tgee+wxVFdXp5c1N7ahlb1EYIudWIzrxk+sSn1jEGnb1SRHm2YQtwbm2nc3HEakjupN5F9KNRe8Juu7CkAjM38LWhbXUFFbVSKciu6IsLqh6cnxFCX7LWxbnn/BD1ESPF1QrecppuX79+8Xv9YCryivRuqW1iOetsuX7u7dAICamvwr8fUOq8GKFSuwfPnyArVKmy12dXVl33DgTQ48UcxZz2uxM/F89Lz0d4Uz79ChQ4dMt9Q6E1BZRThhnlW8+eabeOKJJ0y/ReNI51mJkerqatTW1mLOnDnpZavjfbOvmRRPnAmUSy9K9uJ3gez50faNPwFNDcOEBu1YsrC5sZzwIiDCrInmRKqWQzMGSQxELi/B008/LVzu1NE2qcOwJj4FzICSzF9AiILs9MRlqodOPpklj0w2uro9dJJ9InUNOarhdBiM3mFaptFsQVVuWLPh3nnnnbjrLjc/jdwp5qi80FjfjUK2vZOHmL4bZ1aJRAKNI5zjcI+EjyAgqtEcjZoERFVj/vXM8r1SkxCwrcvwLb84OaiVJWdejJ8PHY9kGSs+eznzRiIaBeARaEFz7wMof83NAsDxvie8Es0gOoYMw6LYDOxLjs8p1L8mORpPRi5CgvN/IMIB++Ru99Hc5XkgEMaIkU14+bVX8m6LJ1IPfyE6oMOHD+e1386dO/H3v/8dO3cWpzBLWlU5gAREb4Gzt+ZCg3H0T3ZbiF+1vx/MjGg8Y9+gmAqC9ueGaIAn1vmLdtb+dXd3Q1ESmHbautRiY1yx9l8h998+yQQ/EhiVyvLaHcwEy7KgjaVSVnopOfp9Zu5g5oegZXa9PqVqGgT0/YW1CoiGEWPwzCWfMB05iW5Po9tl8VOhQkFVMv9MJlFB5Ohj+3PPe3j+BW/gvPPeRnePtzxS+ZIZPfZdWHtSCQnQZw8NDc7Gwmy4qewyRZwGjg0inuz7u5FPEam6ujrTd+sdc7rP+8cch/N2m3+/VaEfY3Pov3NuQ9jnLQ2IPvhbvnw5QqFexCjTqb8Tn57axrwHAHxCsYeQxeHDdXgNP8bjABjPXPKJ9DqhS2yJxhpejNRX6H8ATgIwKvV5wJNNw+RFT28VEEeH2HMvRtGQtb5za2tr+nO9OjLrefvCjWv/ipaqHtdtKipKYwRLpxIogM77opneM26WEn3mMJAEBFtUfrm2vbq6Grfeeqsw27Eb2exICilIwD67OTh2oun7NKrHZDqCUeT+nIuIOHgRHu4+zvR91egPA8gMADqRXa01FYeEhYIIjNNxMP094cu0wWvMRDHwMpf6ueHv9wBeBTCriG3qN7hGSHrM7PpWTCt70d7e7rhNTU0N7r///pzbZT21V5uKAgV7V5S2XOtt6/4Xrx74V9tyveMpxIDIH8zf1lMKBlL0ulUdNrYnN8Ot7v598ODBLFtmb4l9kfl3bqscDrIEyS0KiotUhsNhrFmzxvX9ZYdUN2sOX2L6vnX42ant2dJO56e5ksVxICIhoDSGQZ2xdM3qcpD1jWLmzxm/E9GJ0FxdjxncOt5sXkxNnH1UYZw9eGHv3r16w+wrizj17Evd6YOdJ+Ng58m4x7ZGv38Ds6a1V7tCRaICVKIEa4XAelXBHBJI9v3kmbPHVbNBWjSTaR4+GpQ0j8qdynm++eab2Lp1KyZOnIhTTz1VuI1TgjwnwWFsnW0fy3dRAB4AdIeGQC8Zoa8OfqC5jnOl4LhZbBqFIh/RVAfg7EI3pD8QiUTSAXAi6pMjMCdyMdrUjBeGUUAwgLXTUqMKa/YVD7ORzHHcH8R42B5slAvhyvq83BhzFWReoJRBMJFM4qfPbkFnJDfjaLQiU5m2PxuBP1v7WZy4u+/Fa6yEw+GsBXbyobfL+h4U5t4mkT2+gIdl7GgdSbPK1knVRR694CIRzUVWtwkKZ3UOlyp6L5NDhqYHTrWYkmqj+FhuE8jOCnMNGOrKvAei81ZRczqTcDHxYoO4n4juS/39DcAqAB8UvWUlwNqh3HPPPfjLX/6CqqNVwu13JDUdZLOaeWiNAiLu8yMa8GbgMrUjV4NgTPwyeO30u0ccQDhRGPfVaELFnMjF+CAxKb/9k5qw29nQjhc31+PRVbmpJBKBzHUwGOFwOPd0DimKpQLSn7OK3sKPwufNm4cnnniiTy6+IrraM/d1YfQsNPZOgZoapYcTYaxr0Dx2mBl3vHcH9reLI/utvBnL7lHHocyswVpYxwmFGXDwSjQl8XPI0GxlAuwq4YQg31nvlDPSAmJfq5ZAcBx1YmHwd8LjOkV4H+fLqHw3Tz4Vn1n3Jr7texOAWP0UoRiORo/alhcaT26u0NxbNwFYC+CXfa0o11/RRxc3LLheuP6wwHjsJVAuG/FG82hN9AgtuOjj2DFpCs6ccGrePm5f9S0Nb+LXAAAgAElEQVTBeGjBgS3dfauApkee9kS1TmNnYqLL1s6srl8NAFACqYe9D8ZqjiYxe/ZsW41wz/sLzu3VyFouA3S+rr3ZMA6e2rkS+6Ln4Z25DwEAblt3G77z9ndw8OhBNPQ0YN6uefjeku95Om7Ugwt3YGsmKt86eu7q7EJnxRDrLgAYwffFM9xcBQQDuDUwx7Z8H88Qbt9adwiPd3wUscZMW89TqsAM+PwZwb09fpxodwDA5yLL0p93HD8VjwXvws2BpwA4eTGpOafwyQcvvdsCAJtTf88zc1/rUfdrnp15JdqPvxcdHZlc9LZ6EIbPaXtAn8jeKbYOG4lV08+H4uKjXV9fbztUFNp0fTK14PbAY3go+FfteN32HDe5YIw8Bfril22PNM2XxJ7CB/U9/vjjruvLrdYqVsQzRe2dT+1GLd3LgQ7NAN0d685ZMCqG+3UgORZzIhejKjkGzJxOfTM8kRkwqQaBctufb8PDDz4MQRgEAoEIqDO76tU6S3SaNYrualxgst2bGIej3W2IcsCmQtpReQK292TSkq+PnYIOFgk3mBLy+dhsPxEXIso9x1s+OPY2RBQgonsA1AJ4AsBcAFVE9KvU+uIk9SklgqegfegINA5RcM8992D79u2ZTR3eQ1HyMSuBQBhdXV2ejbxJF7mtBfdlHpgf+F7C4wHNZ0BPwSFsA7SRzGjonWhhOpa+HkUviOIDcNy4N9CVaDKtr6qqyqo+ibIPLerQPguZgeRlpOM0iAGA2267DU899VRex43D/lwf79c6u1zS0Xd1daGxsTH9XTFstyquqZtWxk9Bb28v3n33XQDAt32ZSOO4oTRpIpaATxBFDQICvgiOg3gGUdeeETj6+2rNr2a7Ho+C793EVMdBQpMyAivrP5z+foPvDcdtVSVzPsVqkxQFypFSkiSQbjOIu6AlYp/CzBcx8wUAzgBwChE9CODForeuyBh/rLZ59twouoseEaGNM0akXDsiIhWHDx/GO++842l7kSpLJ9HcC9Xg0vezwAJc6dti264SEYxA5sWwtjmeh0eSSMD1dQStv4jDVT8+3T0eKzruTa+rr6/Hk08+6XjfotEowkMPY1FsBl6PnVm0Ou6JlIsyM+PAgQOm+xBvj+OLB7+IeFv5Io+dSCQSnrINW1lRuwKvrXsp63YMdhSq+vKNGzfioYceSi8XRxSTKbOy8VlNOqSpWRj8Haorvqa1goFPta7F+uD/CLd9am3GiK/fD71z3bLF/u4QcgtO05+7cbDbBIw2lN8H/okraLPwGCYBYXnP/En7e1fpH1GS2aubgPgMgO8wc3rezsydAL4H4CsAvlrkthUfw/3dff/f7Kv70OMYH6+KIVo08r59+/I+nhEvesH1oZuwteI7tuV6h5zPteWyz4EDB7Bu3brsG6ZulH5NbHB31dOrHzkiThio56Rq14V3nhOAbNdV8xXtUd+9ezeeeuoprF+/Pr0u1qSNSNtXOse5lGKkV8hz/GDpDzA24pzszpo65IsHv4hTGrylc2l1ULHoWGv+OF3VeYrmSBJAEm29IUw74mwrigs6WDf0sEbvEH7ifx4vh242H0dgYK8gscbBqHrzW1RHTvegFComtzgIlQVPHTMniaiFmT28/QOH7efYPXd1+4LKKtjQLYtGF8tj01CjjhYeOxR0j0p2e7Wto3ZClhlMatVwcjdCs+FBjUQiICKEQu5JeoU1KByaoqs2Lr30UrT1uKnhUiPNlnMR5gD8CcIDDzyAkSNH4pJLtMAkr50fhzLeZ52dnRgxYoSn/bKxhydjGjKpPEzuvgNPK5UVn+peoEakYjq+7fiCnNuq2jkSGInRcWdvnVPoMMKJ0Ui6eDsl2u1BoW7PFIExkczp61t7Y2isHQuIHNEI+JHfrlBhAJHEEFMv6/Tu+g12Bx/MKtVowm/rqRlALByGh+DtPuE2GN0pKgxERF8HsEuw/YDmsEsJzWjMbNDtYbsra7U6xlFvGQ7bf0VVZfR6MKodPWp+OXb7D+OUwHiHrQGnHtv6YCb9mbw1d9xxB+68M3vso5uKTFGcCwvdt+Exx3VEChKqgif3Xoc3Y2fgjMaT0dzcbJttLVq0CG+88YZpmfUlj1Om4/ZiG/LKpkump9qa3+wr0YcAQzeMgqqQM4hra651Xe+L+3D+kfORTBSnmBQZ1FBr2uyFe4yP8hPBO9NuDk707rOrkbLdr4sVs/PJmzurnTd2jJuwMw7iEgOKYTZgTLMBiAekKgHvPpR/inyvuAmImwDcRETLieguIvo/IloB4IcAvt/XExORj4g2E9Frqe9TiWg9Ee0jomeJKPeAghzxOkWrUM1N6WRtGFFbW+u4j/HhUFX7RO2VuStw9+x70duZe5GbABW+BGEymf1l9/vt16FfZ8DvfB0L9j3rclSC/nL3cgDBhD3ZIACsW7cO771nTiJsLKIEAA09GaHn1AEsWbIEr776qkt77CT92gywNxVfoebYGe9uKE7KdOP9sF7v9W+KXbX7hnaO8YfGY1rXNDQczD+5YaHwe4jAz/X3Er1dh7vt7sQ/8Gl2moTj4QlXWRLzjSHxs+BTM+/fV5reNK0TDTy74h1or84tz1U+OAoIZq5n5g8BuBVANYBDAG5l5kuYub4A5/4RzDOR2QD+ysynAWgHcEMBzuFKbpGImR+pRtWyrT72mMvIOMvRtlavQSLQg7bWzvTL7UMSN/ufxHhBkE6pqeo4GTe8fR/2JjK6aJFBMq57GLlc8PCY8zzYqK7IdQxsnV156QdWrVqFTZs2idvi4sWUTCaxLFW8aW9jp6d9dKKxjIqxNVy4aHS3c7/fbM8YWmhKVUo1EHBWl6Znxi4/Q9S5BxfyMWy3LaupO5oyimf4WWABAEBVKm3bA9rz/BXfck/nDPRmBljfbHjZtE6Ui+loiGwJFYuBl3TfS5n5fma+j5ndU5J6hIgmA/gsgEdT3wnAlQCeT20yF4D7PLcQ9ApGzUkVSFhvvMV3OseuzO93Vne89H4mvfG/KNvwbf8i/DlgFzxKfQ8q3qrHhBN3oCnhpJPN3i6v7nt/eu9/AQCbE+5J2ub9U/P+crsnw+P2DLc6rfuNut6+KfSHJ72UidTINdpaD6IEgJjh+fASBb99+eL052W1y1y2zB+vnfWOHTvQ2dmZdbskE2Iil1ILpQgQvPTDz2PCBLE3FoEBYtfnui3e6LjOippUMVqxPxtHdzpr1d2yrSrkrRP3G2YQqkWgCVVVyf1ld3MtJvcA+AUyGdrGAuhgZt06Uwegb3X/PJAMMx766LXYesI0AECCFISWNqBiSWbqHGY/OtQQGEAFovh33zKc5Mut/vLJU+w6UJ3ath4ku/QXVvvB/bALLn+N5rL6x3E/RzyoefXY9ZkM6+M0jbTJnrEDj2eJZjXGHRgffdEDaVTTMTPe27YXf1m027TN+J7Jjudq3tlieLktAYlZRufW9gQMWT5F+xpVgiJ7itekjJzIjPYSndlTXIwMZ4zlhexQc61fkUgksGDBAluQo4il8VMxP3ph0VyHrej3vovFjhIjR2XiY4xxAT4PKqaGsd67EgYwVOC643YbnNYRAR9SdjusNWO8DqP30+OBO4UziDoeYnf5KgIlFxBEdA2AZmY2zvNFb43w6onoRiLaSEQbrWUicyWaMgZtOllLyf3MJZ+AVeAviJ6HZ+NnAAB+6X8GswOP4IphO4THq0QEQTVmi+jsSWg2C7XbqNLKpLmOxZK2Tjsaddbps6riPN6OV/Fb03IiRpLMT/fzwVvM+4JwRB2KSpdRvdUYfOjQofQLvDZ+Mp6MZOouJCljItyyZQveeGE+3lu2wbT/yPAEx3OlGpVmWMw+Xfc6UgpnEXxGlWAu9cgBs8BJtNWbityLSCaT2LhxY58y4GbDmKTPyz3St7Gq5kTUq6MAAOHsCZ8LSkQR26CMGK+UwFmzrIaDuXmznRiye+u5zVA8V6BzwRo9rXOlbwtGwp5AlFAaFZ/rlaUMyd6iu7zzEQCfJ6JqAM9AUy3dA60Qkf40TgYgTDLDzA8z80xmnjl+vJs3T3b0UbV+m7sr7J2TLr0ZwHjSXqwhEHfeOyu+jXc2/Rd8zWavpwf2/yeOVlSCbaorACrj5uoA5kUznS4BjgWGfPW9OEk9iH+jxTiOzLYKIkYIMZxFB9PLRpO9Ilw2NZMxvw+B8fjjj6cNonuSE0wjmuWnXwgAaI+OQlubNrO62OLdUhkvjLupkUQiYfNU+nXDx7LuF3VRm3ipCqdTX+9uhlu3bh1ee+01vP++2RZgPYdV1eVV9ZVt9uBq98kyMzMOTMMGjz3r7MctUM6JydSCn/qfg3X8p9/fnmBF1jmWL555vkZRDw61Ow92AOACdk8kGPP5TWqiz0eWZ2lBhulUC1Uw4weAAHu3cTrNIADg3/3i9pRdQDBzEkAvERWsxBkz/5qZJzPzFGgBd0uZ+T8ALAPwpdRm1wNYWKhzOtHGKdWOh4f8aEVGv23VOYYNqo3pvTWgDnPH1dg5Hs+c/lHTMjUVhzCueycOx/SfIXPcaFz80PkP9cAHeyfyPd8rGDasDbPxD7we+q1lLeOp4B2G9sM1n7wxNYKO02ztsKF+MDMjGOzFyIl70ssSiW6MDYjVMAcPHgSRIXgvhwntU089haoqcdZdvS1WGtXheDp6IWqTI3Pu2KzHa+lyz2Wld/R6sJ+IvXv3Yvbs2emI/bq6OsyePduU4sXp2P/4xz8c2xcOh/Gp+k9lvQYn1iamZPbJsq3TMZ2WPxy4Gz/0v4xp5JBkMEudgzD7EW81D7TOpoOug54ZSWdhrjLw5HFXYMmU81MNB8LkPevuq8HfoTIono1+JLnV83GMamVPvxIBoyYXXQvv6Y2MANhGRI8Z0n7fV4S2/BLAT4loPzSbhLOLUIEgAL7qbrCee91FpyfKh6LzTszsq+1rNc8wHg7chWs2LIKIyliTcHnCpS1R2DvcXwaeQSAQwQWwR2v7kcSkVOAPA4g6qA1EiQdj5PeccllVVZx73ts4dWZmhPjB1htx2mWZhHfGzLUvvvhiuk25kq0GgkgAHFE1Ib82PsW0PJ+RWG/U/hvUNGZsU/r5D+93VmXp16DPRnTBnK0Km1tuqlmzZmXNZptNOO5LGmfmWexAOf56oVSOp0mWQDT9XiSyZEfu5QCsT8wZiru7Jwncwt9++20kEgl0ogL+A12orU0JBQYiAg97JwEUojhOmCK2MxzH3j3WfAYB4aWCXIIJJ10y0/Px88WLgHgdWqnRlcik/Rb7CeYIMy9n5mtSn6tSLrSnMvN1zJx7gEAeBPYche89TVUTWuHN26FVMU9puy2Bcz/3P4NPKJvwWOAvCCGGT/k24ZHg3WCBXeFw2K4C+pgvS7kNp1EWMbJ5vhGAVfFpwhd7/vz5AABFyUyN4+zDW7HTPXWi27ZtQ0WFORlgR8d6k4G87v6MykXvqBIl03Nr7ehFEM1Re6exYsUK5z0t168m4rblO7dnRoz6tbUdtv++2Y6d7V6LUsznYoMwkkwmTXmQbPtkPWp+/DN4O4Yggi8oWrr3hQs1hcEHE8xV3v4voOVx0oPnEvDZPOayeRWSrwL33ntvekACAN3d3di2bVum4zcMyPb5civspDrYvqxJ99wwVsDzUoM6BsWT5qOveCk5OrforSgT1p+PHArxWLdNHFXRExiCfclxONd3OJ1SW+cm/yvpz3t838wcQ2Cw1DuSEGKWaabbwyV+MPZ2nQzAPRu7YtB1rlu3DosW2Wc20059D9h6Wfq7VjZVFY48fY0ZdZeeisJKd3woJlMzergCanIYotEoQqEQiAhxxY8eFvuR9wVRW41LEh7qEriRCGszobbeOKJqJcaSsypJZYIKwE9cEL1xIbPOPvLII2hsbMRnP/tZHDlyBFdffbVpvbW1kUgEw9s0+4Z+LbsSEzBByT0YcJb/Sfy7fzlqoxknht49cVOv9CXfSvyjYyZerr0aF3MXtiUmmfIWpclyS9rb22114ZPJpFbgK5r5/s7Sd7AxNBOXx82DNCchAABxQSAsAHiceAOw2CA8CAgCobu5+HXl3dJ9P5f6v42Itlr/it6ykmD/IS5XtuEzinuaKQZhWWwatiROwLrEyTmf1ahCCJKWvGxPxTfxaPCuzDlcQjGc7Af7wsdnnZ7qD18NVWLx4sW29T09PQgKckf5fO9BNJ4M7BX71Bv7sHcPX4zVoR9jZegneF+pxe23346uri4kFQV/m/k1PIf/cG1zoTC57Oa4r1PHfqh3FF6LnYUqdYzja/1mbAb+GdXUAdvqs3sQZRMAovVupXJ1dI8q4wxEV2u9/vrrpiSEOsY4mKii4o477jCtZ2asT5yMV2Nnm2JFvDAplRplWJa8YY9t/zp2tZ2ORnU4VJBNQBDym+kwM3xqEmdQDYLhKB577DGsXbsWcVFqC7dOW1SgArlF9ZgHh9n39BNwYKnzjLdQuPUmP0r9vwbA5wR/Ax/B7/DP4O14IGg3sQSDmYeYwEikbl2Ys7vlWfHiZtgUdn7kyWE0syc2CdMUb77xXRC3OxaLCR/QUMV8DBnjnFrE0LrUf7YtG05hPAYFrUNH4LSNB1Cb8hrbUCMuLZJv7iMAWLavDUd7M6qTZXuaUZty3dSObdh2+Uq0q+5ZRt+qfku4vCup6a6PWvY3Zjxt4YxKsqVXnJnWSLbrFa1/8MEHsx5XT6fiVgXReuwGQ+r5miFR27aqYeR7xx13mOwjzJquHNBsI1bPPF01lG1Q09yrzTCWxjX1k2KJfSC4B8o53U1mxji04c3Qr/Fb/7y0956oHovbLxJNiNsfjXoXEUYBMZWyv8MJJHDiUHGFu0LilmqjIfW/xvgHzQX1F0VvWQkwvgvJLL7Mw0Y3mh4Sa3T0CGTXNet4URG49REj/OL9p1aKOx8yfdYOPNwlY6fIr5wBxA0zl/qkN9dVZvOI6CgHUTVOy/y5s1LLfqs05RbVvGOHOA4FANbFT8KL0XPws5f34vvzM6aybz2xAQ2qIWDN8BtsSEzGwtjZ6FTtQVpzIzOxIzERd6w3j5xJ8AngrEkCN7Vn8kDloirau3cvnn3WLaeVd9zOW91Z7biuLaR1/pWV7Rg2rBXMdpWZ7n7LzNiUmIx/Rmem1XmrVq0yt8PiZp4NfVB2pWKuqZBvoShmTtdMOV/JRGqLhM3lPmfPsmXDPylcnnAQHCKMRmpfFk8uAAirAVAB4i+y4ekMRHQ+Ed2Zil24DYC38MB+jvFnWDH9fNdtX9/7UXONh5B5Sr8s9L9Zz6fvrygKEkw4mBzjvHHEeZYx4iRx0ZGE4j2Jn585PaKsSo7B+vhJ6XUid9M38DnTDWtQR7gm+Nt84nRsmXwqADIJnFzCxkQziEOHDmHlypWO++xOTkwnU6w+4pZmPdOmFlUb4UcsJjlduG1InAQ7dkPpzm0f4M9//jPa2toynbC1XK3hpbYG0en1LUTMnz8fu3bt8pQmoy+4zV70nEgXzXwNF1z4BqC6G9j3JTUX6ITheTJ25k5i6hplrXA5pfY/mZpsy92wXtGeiSeiafgoqKqaXmdUIbHP/pT+WJDOW6f6gDgeKxfB5SXpoJGRlF2lWAjcbBDTiehmItoF4G/QSo8SM3+cme8vSeuKjPHnaxg51r7ew+8bCWgeTGMdsjQaafdrKo/Ozk6sT5yMFfFpaddLKy1tzgJix5HTszfMgOhBVQwvxMr4NOxKTkx/F133OlxuOaZYraHvu27qOVg3TauxYTxc0jKfse1vPzW6uzOzs+eee87zyNstzfYrdZnZwpGUCkgXFHqqcWMVwcq4WYVUU6W5BMeREUKdRzWXVmMa7i6LO+zIwHHpJJF6ic3m5mYAwIYNWgT65s2ZAUBXVxdmzZqVaeuRI8UNkHK5teeca7ZZMRi8y/vM2QnjiP0EtLiqSUXNy6ZisrJsxkV46cKPIZlMpu0Zxv1Hq7kJ4fhRJxuE99+pknJz2vxDIL9SsrniNoPYDeAqAJ9j5stTQqE4CeDLALMKU14Ny/M1YeIBHGVzwIzo59YFRC4sXrwY3RxEEHHHhGinTn9euBwAWiN2Yabh/SVx6mTVeFyoYiKopusnsLDS2xGMxx5YdaOG0RkAUBL+fZ0gQXWtfRPseZus5/EqIJo6o46j8tpe+33XVVDz5s3DjsREvBo7K73ukx+YbTb66J8dXgm9jUlLPEt9eCcufOpCk8eXUQAC5lG4NaW80SWVWZv96Zt7SdluPb4VxaVLqKy0dJzMwEvNWY8tqp8CiDvQIS4dJQOoV0cKjNT5q5j0R914hJ91FUaVV3TKHEn9RQCNAJYR0SNEdBX6mm6zH7F9x49wyaWZtLp+Sz2D009/19NjRx6zNVr5nLIGeyuuxxh04A9+uyex4jP6RQNKp6FmryI+p5Mn3mjYZzeKKtaVt738omNEc7ZR2saTT8dPfA/ijtX/A/8+bQZEZM+V092bhL+qC50d9g7NKHBFgqC7uzunYkBejLfp8xl+cataaUqL9r1TDaUNr4A5QaAY8VN0110Zj7XemPdxl6qq6U54b3I8VsanYU8qsE1PdeLYEg8dipPwnTzMHo1MXYz9ilkA6+cw5hIzClrjPb7Mt1Pbx3O3Qqlt7dfh6mXk4G+qqirq4iNT+ytgBsL+opehyZsjySKXjxPgZqR+iZn/HcAMAMsB/ATARCJ6kIjscfwDjObmN0y+zdZOt65rEhbGzjEtu0Kxe/d6sCeZaH9RU19cpWgG1Iloxbf8Yg8ZQBskbKzU6v0OQQQnwNn3WXV4uddX/MC2TEmKDcMbqtdgW+As23ItKZrjqQEAG6doSQ2V3iT8VZlRcdhQp5GBtCGCU+6B1J1Rwxztye4VZir5WUDcuqntI6aAGXgxdi6WxTPBXEOSmuop6je3W+9o40FzlO+YTvtN3HzIe/0Po90inPJEy9WTLhwOO3rSOWWb9ZFqqvQGALGOLqiWAdLq1Vrgm1vwXV+5yme3wYk8j7xgLVU69yOfyes4IvKd2TjR4tExpJB4qQfRw8zzUhHPkwFsAfCroresBBhHHWrE/GOua9D81i+iPfi6bzFGoBsjHYKhnIxqVsZPqMKB7ttxzjmL02fOFjW5LzkOO9vGYjw6sKvi21hT8SP4Hcp7nqW6p2gAjA+t+LzdiW68NeRf8WnlPVxIe037iSpzjcNRvBn8JS5TnL08tnLGjZUZGFKpd04qqC1qaklje0bXn2hyr+UN2F/wXDHWdnAzFPZUTkrfuXqDu6xuU9kdnZAOxnUahR9Mjsa9/0jaBsDkt1+nUw4nUXbYD5K55+RxSgYparsCFTVdk9MBYc2943DD2/dh9X67+m7//v1IqAnHeyBaqs8gegOhrHOJE6kJFynmdDIEdnWVdeumdY8hTr0Z1jxqXnASBGcr1Tkfyw2rEBw28eKCHl9ETmKXmduY+R/MfGWxGlQKOJUIjw0ziOhm84hHT//wQugW3BZ4AhtCN5nWH+45Ttsv7sPfgt5s9uPG1eDo5BUYNbrR4APu/kr0Qpvy3h14IL1s4tBm4bZnx8VFVYzo510ePE64vrnidDATHgregxdDs0zrRLaJl0O/xxlKLeYH/wylWTwriRtjLiiTNoEIUHoseYUMp+h4xT0LJwBsyVLQKBvTf5cp73ic4mycjIRGCFUhbChHm20U+35iMkIJ4IvVXzQt94UaUHXUnHhQrxG+dat51jps2DBHVVEubrPW46aPIbjGqoqv46nA7XirWnvt3286FwBwIDnOti0AXPDUBTgScXK5trddd/F88rKrbeusDBVkUlbArvfeSYWlqiooNShQmdAxZChC6wsXnTxT2ZN9oxywvn+//8i0gh5fRLkKBpWVyD7N22QlPp5ext3mB3cjPmT6HiKzAFFTxuVINJc8QmT7mHR4eK0CZChlolRbSexWp3qoVa2fLUx+7EmMt/n+J5QKNPomObTH3tbJlOkI/LuNagt2MKI56LipBQEkzFEFHoyunQ4FZgpNT7AHu5PmuhZr1qyB4jA8JSIwA1sTGUHslt6jNy6eMezebfYoDwQywjZXFYYoGaOV7ZvMM0G9A/0X33a8tP8aAJnANgI76vebusTOAaI0Gb/zz0vXZvmMYo/mzoYCFR1h52qCTvepbne7IRaDsPDsf8n53NnaVUgqLGrhjtxjdHPmmBQQzUe1Tu0lXOe4TQuci9zkq9So68m4ksZTL0RnhfjB1ksVNg/XgskuVDKjabuXkE72DsP40K5NTMHrsTPS3yNpPbr9OGTJDnVYtetDP5zcDkS1Tn158KfY9a4WcB/LVFUViplh6MXq0I9we+BR9ET9OKzrWjmJigp3l8O2AuZxsnrbjEAPxkA7f8wXsxmuFy9ejJFjM15G1o6oXh2B9xOZxG9hiA2gATWAcE9uwYL5YE3HYfR6SjKhNjkS65ab08z8j89cHznCPryw7/MAsqS4aDW7BW+MT0ZDcjhGCeqTnKEcwvwPaWbNT/s22NYbEbtssynWQrSXPhBSGdpMlxlbmyOmuH9anZ9tKwixvcVLtbtcIMuAK3Ew9/xXuXJMCohOQQZVALiYMqM1hZP4nu8V4Xb5srzl0vRnXc3ipGJSUuuTgrQIPRALFZ+HIuYnKeYptDH195yPfNYgJNw5wsMQsXjwPKv8ERXLtdw+U5QmjE7YH2AGEItp52g6rOny9QJMVyhbwUx4O346etmPxKQtuPiShRg12qF2AIAo+zGDDqG64mvpzJ862UbYW+vMqbituvz3Q/+N9yu+m2q3+HcKDhG/pEQkVHu0jxplWzY6NhrJhHcXVe8V9vxoUp2L6RjtEO8nTsCS+HQ0qmZPmYstJTO3JzKzS6f7e0H9J4Ck+dq3JyfhrfgMDHfIuxTr0TrubCpX8TndbRDRKOHF2LmoSo7BruREBDe3QWkMo2b8CDQltOv1kmLbiTsDj+TQ1vyJJC2BnCnwQ7EAACAASURBVPXZbXR95ZgUEGlvDNaMrBeQZvRaELo1vc21te/gl4FnhPvn+7PXBjI+/h9O7kgdS/xCtEdGoq5uHkJDo9gX+oZpnSKqTAfA57GT2Rr6LwyBrrIyn3/zSaeZwp1v8GnlR3sbfTYdaF6eIwz0hjWvJv14ojsQYz+OjtT08kMrO7B74knYddzJtu1UUFrX+yWfc4S1iM//TZz59oiqzUr8HlyYzffEvXPzIYn9p54qXCeyH+QSNW2UGQeSY3FUrcCz0QvwZmqGuG3bNjQ3mW1XxqSR3SlVnVXoWzs54/NqnVXqnBIbCaWjwpbl2I3Q+hYEN7flFPCmo4CRcKkUmIhrx2xVK9OzRIomsfKMyWmV1xW+bTmfV+fjirjm/IgsSQhzpSnaD72YBiMqaWqBf+nYiI0V38NLoT/YtnGLbCxkMEh7pdi3+Z6N38eevTfj/9W/Y8oVDzgLglEe80GNoF6cQvbaF8H1LQgFOxDYk7El/D7wTwBAy9ZhYBAmoRXVFV/DxbQb2xJiQ7cbKrHZXdJhNLw/OQ7NvtT1EGP5jAux4nR7Ur9hFHPU9TIor1iiRoH6zNHQaRAQDK1qXW9cnBqdAPQMdVCJCRp69913G84D1CZHorazVjiDYGQ8n1bFT8FCQ+wBM+OFF17A9h0W+4KgjcYjPxr4Cy73mfNeGWckRIBBe4jdifGoSWozpJ48uxbRz3U+7cc4pOJqBOuVLDMIfZ/q5Bi0syWpouXZGY7cR+Ve6jcUgi0dxa8gZ+WYFBBQtB/05pp/OG6SbXr4UeUDbAx9FxUO9am90l4pHhXo51cEo9ihLB6ZHEfugVJGRA+10hFDfd1IKG3ia2IAzwa1WdaC0K3YbUjP4cY0ygRZMWASEIFdYn98BiGm+vHT5X/E3LqrEXxPU42NG1eNf7niKWw95WQsPO/yrFreXR7bmA3r/apKjsG8yAU4Gsv8fjH4sCg2A39YLC5xmYSCpkl2BwAAULvcM3juTk7Ekvh03PmCViJ+XuQCbE5kZqQMwksvvWRob+bVjsXEOnJdQPRwADWqlhcsYoip+IQg3uAIm1VWtxoExrrEFCyLnwYAqPTl17VMJHsFvpdDN+O10G9S38T2MTd0z+AehNCgjsT5tB9jktpz12vJlrA5dGPObc43BiNXmAnPVGY8vfwuVScLxTEpILbEFNyGWzDUwXMEcJ8ljKRu/MY/D+OoE2dSjefz6kameDIzHV7Q8GuH87NjO77b8bJgqdhDxIkTSOyGmOglfEd9VbhOZcVmw/DCktDPTd8Vh+hC49JJSif2dp2Mo7GRqItMhNIeAxIqxk/Q1CLvnngBGkaNg+oY2qXhpoN3RjBCtyxaGZ+GOPzY2ZpxGNBnGXtbIjjQkUh3ltb1RhrVYYizgtZO91oKumrEH9OcJ+LWxIIgdPX0IibwlHrhzXdcj21MVb4pMRlDEcYoQfS9FbeOeWiePcsEgYAAgOOoHTf4XheuU7LkYgpZjMgvh27GK40/A9jsHnsCWmxqxRa/3WZkpVQzCLJcpb/MqTYGLR0dXdhFZ1uqRJlv9nHk7NHwx8Cc9Odcsiqmp7Mefle9s58Ae5TtSFV8zlzc6j7nywT3hRBLe+tcG12N3/nnCfcRxUHkCoNAFt9QUUezPnESFhw2p1GuWNIAq8js5grXjopBeOWAd2eDvU1dtnPoxxEen81bAUBMZdy8Kru6L8I+LIqdgeXxaQgF3F9Fo9uzk4opHI/iPUHm2V314khtfQZhvH8JKFgR+gm2VPy3bfuxMM/23PT+nUrhDai/8c8X/tZOthCdXwlsiVMTDQCbB1Wf8dldbL0Zr0sjIBhk8mQa7j15c94ckwIimPZ9zjDR0hH/0C8epevoD+q3fPaSnc77UPpTNnQXua/6l9nW+ZPiovW5CAjjizY/+Ke0t85QOBvWOhPOvua5UFN7fNZtuixT/zTCRILuEve3q3/rqV0A0BkWq2OcRokxQxxJq56Z16U5xvrl+jFb1aH4zc7/Q4IJa+JTbIZiI9ZDX0R78APfSykBpqJTtd83UfQ1oAmIpbFT8UHS+HsQxpHYOP5qyHwftyXF6rIjaiXaBO6sfcVHYjGtQM3LuA2VTW61ohm4U/oaM/mN5OMOlegcz8Lmc51eIe4HCskxKSD0jloxJEn7mO8Dp81dyVYu0XzeVAZQTzMI587e75BB1Jfng2pOXWB/Ia7zLcc4HMXrRy6zrbMymVpMFbF64w4dfZ4wgJ2t0xFa0QjEtXtkVSEY6WD3SnFWiICNhtgFnYoh2T2KrColEa9Fz8TbsekAgLqUQTeCABiMg8kx2Jccj40G20KzOhQxVrA91Rlbg+1eCN2CnwUWgKGl325mu9OD6hJweEgdLTTKizjeo43rtdhZWBEvTpSvUxxEXjBwgz8TSf9zf2mzuO7ozT5QsmJ0ZS9F531MCgglZaQ25p3/is8+UncjE2CTQ4pt/UH2oKpxFxDikUOhQ/t1/hJ4GP8I3o2kSySwzurQj0zFk14+9FnTenFsdSaa9Sw6iLWhH2AkunEuHUB1xddwPmWCBKu7TsCze/4NFEnCf0DTk4tUCDqdXIFg0nuGzlib3RZwT+BvOH5ClWBrjV/4n0F1xdfS33WFRwgxLA/+BJcrGRfKCAI4nCrj+W5iKkKIYSS6AcrkBdJVNwlW8EbsTJPgSUDB4RpzAkAAeDp6oWNHyQZj5qrYVMyJXIwuNZhTao5caeXCzDatCFVM5K5i0hBsYRmpidyavbzfpUtxzQgYBodSQBQJ0XuRa1i811xKRiLNAVQfPRHNveJUGUbcZgNBEo8Ire6wbjiPusTLx6IT+bwKNZhqWSJKBqed8zhqx22BJzCJ2vBhZSeuTHnRfMyX8TN/aP9X015Q/hqPbr3Rkdk3SqEm7c/Btb53EVCcp/Pf95ttHHqYyknUjClKE272P2nbpypVTfCl4B/wQcWNYDBWpkbdelSwnoZFj8sAtLu3dK/YkAtV/PvEVTVdmOqAquVPWhGfhrpwLmlinDmLqrMatb/vW2gS9PlyW+Bx2zIvBYNOp1p81bcE8wO3ZRZmeeWPv2KpJwHhFPxXaBgMX0xrdCsPh48Kl0XAicI8IQOM7p4ewOKc4KS2yUbAYTQv4uf+Z3Hz+p+jEhFcX/Ft122t/tlGtqjTTDV08yHX683XX8KaIlp0nBlKZkR8QSqlCIPSQts6c8lVpTA86lRgyU7YwSX0hf2f87C3OZGIm21EFwZnKnYvOGt+LqPH0s7kcbgyKT7uQVV8ncvbR6E1eRy+EdqYXnaEh+Hx2nw8vOy8HvoNDqiTcFXsLsdtfhEojPrmHEGG1GxeTICmhrw98Jhpme467cQp2C8eTRaI3I/MCKVsTK8kL9O99YvKMTmDCIftfv5nCV5UN/SX/3zFWfVgZXSqLKmX2YpbHpedqqhGcm6Ijn+b/zHBlhr5piJQPRjiHg/+n20ZI9PGJMzuGjVddhuBzpd9y/Bk4HbTss6I8/ZWDhzOP5unU6UzL6PQXCKynWqQWH36dVqTFZ7bkS/TlIaCJ6fzimKpdihCdO1K2H2QNB7NOQ+MvnPmrBz3yI3pBscAp0SRhaTkAoKITiSiZUS0i4h2ENGPUsvHENFiItqX+j+6WG3Y3+PTjJx9wK1urhPp4DcPj53bNtflmFJC3Bb7y/x1/5KCdyEHqsydc9ih/KSIjIDw/pjeGXjEljahuuMqz/vHWnJzz8ykLHEW/F46ZjWemdI2qCMwJ3Ixeg1Ba8crGRfTmIPR2T0iJP9ZoFc+rGSirj9Eu1Bd8TVTkGSx8PI+5XPtY5B76g/rYMaNXNtEDPgM78JgtUEkAPwvM58B4FIANxHRmdCKEC1h5tMALEERixLVBivxjbh399RCoT/IXpJ4KaTizYPiji0XW4MTTjMUp7YVY/R5Ee3BNx3chBmUbqMoU+codOE0qsP1PudqfPmwps1dQChQNaNyit/7nzKsy78L7qz/hm2ZMXusbtgGgK27d+V1jliRNcrGJ+TzvncBAB9Wdhb1nID2rGSb4YrK6H5BWe26jw8JHBzj3X6VK/m8UcZ9jjjkZCskJRcQzNzAzO+nPncB2AXgBABfADA3tdlcANcWqw3D1FbcFniiWId3RNf7e1UxPb/vC0Vriw8qKhC11cN2EhDFiBZ9IXQLZgXsBlxAe+l1+84Vij2R2muh32Jx6Be4JTDXtq4v7E+pHcZBnALkN/55+KDiRgxL5ewxxgxY753xjp1NVQghU63s677FJs8nNW4vvmMUCkacBgjWX+5i2o0zqTr9fXseubNEjEYnPq+867pNLuq1vhJCPOtZRLPQb/ndB4kn4hAOTczNG6vQGVzNsEm9GBvskdRENAXABQDWA5jIzA2AJkQAcUEGIrqRiDYS0caWlvz0xWfGnNNHFxNdMHh5ZfoyGvXalm/63rLVw3ZqW+FedG/XxQDGpGw2H/XZq59NdkgV0leCqaCyh4N2g6sfCfxXym9+eCqg0HhfrLMyvbMYTx14LfQ7U1XA7/rF6Uy8MIxEZTHt93VB6Fa8kc5hVLi8VA8G78V9wb9hEtzqJ+QuIK7zLc+rPTf438SJJK6yqCMa4GRr2WVYjZAvtxKkhyrEwYMirA4cWbc3CAQGeQzi6xtlExBENAzACwB+zMye8xoz88PMPJOZZ44fn91dVITqyy14qpDc7H8yHbXsRrENfiqUnAqanK7UZd/IA15HWEYvplJypl9zYDAWaNLZHMqkn9DbZux4nNo7NiXoPut7L70s17QlX1BW4++Be7R9BesJbCt4JCKEmMlWkA/HQxPOAUpgskPHbCzE45W/BB7Ou03WgY69PfaWnObBPuJ3cW8WsW34dLwz05sgPndobvYZ7Z5mnrExwwfpDIKIAtCEwzxmfjG1uImIJqXWTwLgPiToA6yUx7uXAHw7y7RWp9gziJeTH3ENWhNhjJDOF/26RsA9hxWD4C+DgIgNFc9K/UiY/N31wZtZQHj/zXIdPd4bfMAkYGzHA7A3OQEj0INpVO94f58PzsLTwT8VrBjWSY4CwjiDyH6thXi23BD9Nm4p/dPkMUhPVHjf6d+jv8/p2MarGDEYczGRFr75GIBdzHy3YdUrAK5Pfb4ewMJitSFJ/d+7t9DlCq1oOlnBtNul4xrtIcOnFZ8l3oLAuFzZhq0V33Hdz2ikLiUr26cIl9tdWFWEEMMMci45Wix99JuGMrGZ9mn36rngrVgS+rnp/lYaPK30OIJrfe4GWjdyiS7WZoLZ74Mx+r4Y5PtbFFuLs57tv6WRFyZ8wnFdvql1cqEcPeVHAHwDwJVEtCX19xkAdwD4JBHtA/DJ1PeikCxBHvW+Umz1ilNwkVsAnVOKcDe+4VtsO+/FHlKCaALC3pYhiOA08qLuyv83NhqTdaxp3X1QcZv/cZO7s/U3K10KhowAm6HU2tadTE22ZX2xKRk722yfP65swdWK88ynVOR/tXk8RznsMrNjk+v6oKoFbv7wdK0sgGK4klKk+y65roWZV8P59/LusN4HJiaKY+DMxlWCAixO+JE0+dgXGqdR3Q/8zhM3Y0nPo+wtzF8PDtShVFo5LxhnED/0vYgY/LhU2eUpsaKX9AtOfNW31LbsdkvdYQJwoSnJYfYR3ark2ab2mTFHYeeK2whZmKU0NTY8HkcQQwBH4N2dU68JcqWyGfs4U+VM1IZP+zaYMqaWC7fMBO7k3gnnYrf4UPsGwMW5rCWopWQ5px7oBNCjaiVr31XPwugSjHOPyVQbFWWK+MyF2YFHcIZyCEmmdBK3QuItwZkZ4wi502MytnzVLmw5308DzwMwVzxzww8V8TwnyKIiUGdYRuZTqNEWLGnthE6yjNyNKUWs+KA6BlmNhzH3kiZIplKDyRXX7b6KXDz12JJ3K34IAJgSme+4vxOzAk/i67FMwatTqAFhCmEDzyiyu2fu5Ct62aJjagiOw6SY+wDTq1pq99HsTja3nfLf2DTiTIyrn44RWI0e9TScHXkU3ajElwe7m2u5aMnBFa1cnJHqTIohHID8dLJG//tcjaw6Xg25TkZqryk/vuNQfQxgfF5Z45hDawS68WX/iqzH/6l/gW2Z9doeDN5r+j6eOvFJZSNEuN2X54K3pD+PTBmfl4X+FwtCt6aX5yognISRU/uceCJwZ/rzrMCTWBC6FQEkcJ2/79H+hSRvG4Tl+90nXy/cTudD7B4fYqQ7Eczark81+PDCxE+lf0EfFHRDm737S5CM6ZgUEOvjpS/+3d+YhDaMdSgM44TRJjCZjuAsOph1H6uax+uLepN/IXyCgDCvAXvnKFX4mf9ZvBf6vmn5J5T3cV/w7/ix/3nT8l/6n8bq0A8x1GON8RjsMxkvwk/31rHeBzeb01QlMxP5lC93ASNCFJ0+FkfxSPBuwdYZjIF3gDho7wf+l2zLykUDayqaV0K5eQtlyO2+3oS/5rR9tqc5npqO6PWnhxieu0AJvDGPSQERSzgXmBlMhBVnv/ifBp7Hd/xv5HQ8q1rp9ZD3Sm06QcTxIw8dyKXKLqHB3KtdgaDZU6w1jnX3z0mW4jff87+aU/CdqB2+/9/eeYdHVaUN/PdOTe8hDUJCaKEFSAARpCguWLABrgXXdWFVXOvqKpZP0dUFdW2suvbFgtjXVdeGgtgLIk2RIs1QQocQSJs53x93MiVzJ5lJgbCc3/PwMLnl3HPvzD3vect5X5N6AuGcB80PSmhIQPja9h0zwLIq4JhiWRlWhl//hXeh6BZWEMGh4bP4/s1sofFsxP5EEnkXzm/51M3GWNVzh+HLs/mvn7G0fpzrUSkgDm18yeHjksJpLdreShV+VtQ66guDf9j/Efa5Zi9buLn3G0uc5z+DP9nyddh9qsNMEwqMujIfSuoWHNb/BYY7sIQaVARlGn3lf62J1o9CtvuE4/4WS6cSrlN6SvVVLXK9hkh2R6Yl1zEnaaTxoZ5Dob5PoqnsqIrhh12Nm7oH7HKx8INyMg/4vtuT8pKpLk5FWcNPfNlUjkoBEauODg3iw7QhLdpedJjml4YYag1/FW9z1kFE+ZUh/ZvtKXIwIm/Os30cdOwM+1MRt19cL4IJ4Am7zzxTIObpXMZbP+V9xw1B2+s0AMM3EnqeWq6iTfcLiltsL5ieUycs+0j91PTK7xhlmtCuNXGntP4A17M6/HT8/tT99uo/aWkhx/Cza4spr42kHK9PMCXarbjTosIpTNlsjkoBURQTHCuuaZxQM9TWoqlFnABsfrbx82zz+NR5NeAzrdRpAINkBQkSWYrvUPinI6lpIECwu+XXIBOXBTexHGR11O+4yvqGd/sF1g8Djiu2rDY1JwmKC2zmGkLdvdYvTtRffELOgpteEdQ2aQlqc+N5wzW0Va/R1Domddpg24vG8iV11wKilbCo+mUw/zdJVg0lU4ucKDm0mlck1frqU38QtYoKcEDXLd5KlPDKlkZKpAOLBcXpnhTZE2wLGGz5kXT28Ff7rIDjLrb919R8Nstxd6N96VWvGpt/fWNBNWiCag0Uwnuugab7pna+ukWusc7etIhFi6pLrBn4PVZanc3uU3OpCyC0WZqf9r8xjkoBoVRS4wf9DzCWQPv/vGTzlzFcog6xBtEcARFnCzaHFfjN8J1itN1a6agj1X6suPmbpySmoJjjuIvnHNNDHlufhiobhnJgZ/hpMRbUYcl9tZ/WTZy529q0eg5eE5OJz+Ht3d2b1adwKavy+evq9Aa3X0HaQ6HbHJUC4mhxUvcmME32axm/aVZ7JzeQLK41CCcqKBS93ME1u99yBIY6WnAHhXW2VIoTe4QC4irb697POWJofl1DRAM1RTsBKKs3MYoV30p9K25szRDITUEJ7FdNFxCzLGe1YG8CcVjqtOV6Cz2VYn1VqxW7DKDGxNLx0ebnqayJPCdaUzk6BcQhTNZ3T8eLDtm16pONL53wuqgcdtlbrzpWaxDpIBt5+8EDoqUZQsmfSAfbiSbO81CLJCNd8yAoustGVroDo9D8tadoqW6RSoWRUoG5ozacaKFd0oqWAKd5CKmRm9a8bysLIisu1BiLdn7Eqr2edS+e57GnehsV+ww/WoxLr6RuFRyJKYe7C4ecIyGDbX1ae0Zrpi0018wyrtB4oVtTuEUa3fWQ/WHed04NqtVdv53wkiC2HAphb7L5IF8RTs2WMLy0FZZIIoV8JLMbCM4Y0FCOr9KcljWXrd73PWUH13uu6+O4n3/k2hWV/PbX1tckjrxRowWITWuZylrh0NBP+IXo0UHbtttaZ1akkDYXkdEYzYliCoeRlsVB28wyyEbCfesMk9WxzSzK0xAzI1hLAr7kevVxEBh0kNJKDvuG2JdqrtW+0W4U+1Xw4P6r3ffuNuY/qrBE81pC4/k/XzUxvXZQGz3XCERQ1O5rPRP15XlTvZ9Pzv7Ze/10ZwUnZBhFrKxKce7GGuw6F1Mr4ae+7rMEZyWtIHSkQpk1MvtjQ4Py9phgTWavLS6i9htjeMksAOxS400dfKTgaGUN4lHHzKBtKU2oeWHGX+yvtEg7ZowMI5ttOHQOsVbjUFKel4bbRBP4A4/ztOukoO3+mnBj4+Pj7SeEpTnHE7yYrk5xqL/u4eT4OVhboFzAyAyfj+w71cP7eV7iMQHH7agqpcZdTnb0c3RLNAS992lZ9UrqVkH8BES1BOfU8f+51qrAR/RFQmQRDGepl0Pus8UEvxg3droiovYbw1fZC1bEFrRo261NUzSIRfENF2Cp45eo9qbbX3H+NeJrHqmcb+L3qGNPC09UzFAIWCzsN/FDKCymGoIz3pc6pbEot1S205NlDR4DkHcgWFCWpRuTRLfV6MN9eRfw8bA0rDEHAgTaVkdqo+2b0T9lM9cWfgbAM+7Tvdvru51q3FX8tHsaTstKX5laTwReVLfWD9c/SgWE7/MBW7Dd0H/W/yN5AfvMkpw1uR/1tIvc4+ay196yjq4tDiOl8L+zhrMxOoucYaEHhaay2N6lxdsEsDXBYdy/fEVYxzWWsjkUs7JPb/ygCGgoX9bh5Pou13JRz0MjLCtMQl3tVJvW7S7t4HteXWr6NdjuYPmcZBpfC7TDEWjWPeBIYGeqcZ0ai43M4Qt4KvdM736FMGd9EU9tHkL/Y4Kz+kZCiiNwkabbGnp8cVprSbIfpH/yRyTaniB5XLdmXTscjkoBEePwrXJtzC4/ieu9n39I6sI6Z8v5LwrV8oC/q1tosFidH8PiXgkA7LXHkzl8Aa/nGLZYl7R8Bsh1juwWb7O1iXGHV4zpzfTjA/6e2uXPLdqPh3IvaNH2wuGmzuHlQHovbVhYx92Zf0mT+jEWY8W4WajrED4LyA2132lo+v4yI0k1rOVYcYXlyK5fx2NlTon3s9uTFr2+b2rzwQR2VcVx8advN9p+fWbX+vwiFxV8H6ApuW2h+2sVxaTOC+mSsJN421tYonQ211ZhfI5vVtGhKrgco9vieyw7UtN8nx2JbI4zn+F/WWLum2jo55mPyeKmFgiz3Ng+mp0meW7SlHmB+Uh5p97AcWZF4/UTjkQezJ3IFd0Ds5cep+Y3el56z1rmquKAbaEWKZZbw6vM1xQey5lgun1xfMvOPB/ucG6TzjvgcGBTNRwk+LdqwR0wcJYnGu9krc33blqVufPdH6e7cT9W/ZDaaofP7OwKISCaw79d9XOk+a5vFt57OENLjkoBEbveZ2ZZGhdsHvH/Qqp6BDqS0zA3TVRGWTioAn/o58v1QTZFfyzKTebwwMG1sXxH21VCg/vrM1k9CkAc+7kVIz33xF7NK/f9eVJzUyib80Vi31Zpt6nMyP8jNZZAH1UHQleFq+O7X37HiRJYa7hWQsfVNwez328dsW7zzLcboxrX+CLqVxMynG6IymJ/vBUrtSHzViVn+N6FlV3iWNQ7gYPRvufY0fpMg9dQAvG2xrP/1vhp1a+2G43yKxlq97yP4Xzv/nxSHNrSUL+WiH9tx8jrPLYuR6WAsG32vbzn9wkeLD+o9Dl/lNXKs3lGOKq7gYgIZZEgp9p6MtiS4UQBp/R9JOiczZmGav1RyjH8nOOpPWtZ1GDfX3cNB2CraiCayu+FHcHHnKdmMYWZpLKL2WocH6UO5v7c3zV4HX8ORBhLflv7S4O2hXO9cUUPRnSdOib3uJ0ruvlKX1aZBB60FPYmphupaWZxl1sKrmCTM7hE5WdJxUHb/tn+twCsiskzbWuHPdDm/nT2mUHHLGtA8FzVbWqDgikc/t2/B135GSvuIAGxxWk4focf9L0LLquwOzlwAtbOJKOuP6XZUcTHVDTalyc7ncba6BxuKbiCXpsvR/ktGExlFzeq25lCcMRbvM0wU5474A5GFgdmBHbFuji2MvgcgKWqU70tnjQaSkAUz9aeyMcun39F8sIz9bUGR6WAEJtvANlri/d+nl5zLvfuOY09OPgisS/LYztzq7qZvTnVvJIxmhdzTyTesZ+/1Fxs2u48d+AMOLqyhgMxNuYNS+P3CQ8xf0A75nbv6t1fEWvMhib2vptNBRZeUOPo10DJx7JUJyMtPwBQqnyDxZBKX2nLckdgiK4Ap/A28X7hm3lqLa56M9oHcyeGvO4Ba6CAsKlAdXtJXFdO6+uLzX+84Fw2RvkqsVeKg9q8MMxbjcxEF8b3MN0+NPEt5qcYJpznssZyc+crG79WE6mvQY4ofobCY99q9Lxol3mq9HBTe0S5qyl1Bla3P2bgbGbkTw469s5OFzNw4ByezBlv3li95+z/W5jS/RYyh33C2hhj1fXJ/R7l0sLAFCUvZ57EFd0jLxZVR0W0ld62JQhgpTYozHViiVFitdzWWMCGUGOimZ1RdQe31VyIy2bBnRToBB5eMitgMgFQ7ozh2IEvsiw1h/haIXZnr4D9vVhKFIYwiNrv++2f0W4RNmqYFD2Tm2OnUp8yAidxLiXkVc6mvn5WN7F0YWHYxrncVnsRexNc1AAAHPxJREFUk2r+QqLduGZ2V5PIycGXB29rBY5KAWGzO/k+vgcPdTjf66Ca7yricddYllR0wBZVy7i+D3Fx8VS68TO1VitXdr+JLdHp9Epdwew+gTlgTqoykqr9ueYyhlY9xE5lCB2rn7qYwi7c0W4s7Xbx3MBinu97TJCDXIBoa3Dq6U2xxg9teY84b0rpEssq/pT5O/IrX2BAgS9HUlmCz3EXF0K7vou/kItRLvTd1KFc3e0GZuQFDzTj+hjlE/0FxOuuoXSPDtRyajL2UpjwLf0HvUqnoe8DgTPbsf0eoTOBFcxCsdtPYNdnQtEDfGlihnJZLVxif5DM4QtY0SWhUfPIdJN7DYfnssbSj4XeGTrAz3EFzLRN4tze95ie82+Pk3tpfNegfa9kjGZ1QqC5Z1qny0zbSq/exTvpwwO2rY9uH2AeqcMlNjZGZ4cUuPeqK5iT6Vtj4L9q+c30473nxaj9LEroyZvtRgWcP139mZWxPi37eTWeZ/sM5foufyZvaGB6cjNq/RyxlzGTY60/Be73+AB32xo2pwqKYwbOCXpei1VnnnWNxrZJSHFsY59flNTK2HxezRxDlZ/WciFP8aC6lKvWPYRDlpFbGrrIkF35BHqWs5xbeBjBeHe7/BK40LB+AaYlqgAz413dOODCwklrfanes2PKuajTQvoeNzi4Iz2Dtb7W4KgUEIiFU/r/k+mdLiaftVy9dxKf7sjngj1vULRvGUoJD6pLuYVbAQIKqYhAcfoSRvd/nFtqLmJi9Y2sUB0Bw7ZYqtI5q/p2bqqZRChLbk7UBrIT1iDAHeoG/qH+6N0X7azknITbOb7q795tq3tZWNQnAUT4a+35gDFQI2L0zc/RYVc++2lSh9AhmXtjDHV9WXou8RnrmM34gLDGapsQZTV+8E6rT02/suBG1qRkssjqG/BiLRX8hvfYHNWOA57BZmqXaxhR8i9KBr1MStyvJLKP9Y7QqZdf61nMMDWPQYPmBGgj/hy0RnFun+DBc4tk0oMfma3GMYoPiFO+F3x63mTWRAfmIFqY0NP7eVVMR9NrLYvrzP3qMiarR8kaNp/MYZ9wfdfrEOC5rNMCjrWgmJ8yyLSdHxKMdRnO6D1B+6Z2u5L+8Z8GbHus/dnMTxkUlO764dxzeSpnnFc7qtOmZnIJ0wonBRx7nnqW89UsrlV/M+1TPOU85KcxzsydyGvtTmRq56u5XYzZtVXVEIN5nYxcNtBd/cii+EIeaX8OFhQ1SdU8l306lVYnPQf/h56D/xN03p+638yrGb9haQ/fJKADgTUqHm1/Dn+QJwH4KLfI9Po+FJuiMgKe/SnW2wAjMskdb7wXNX4mxyHK8Pl93Ntn5klmN+lsRxXW0M55I0n250jca76oNHW3sT0zKliI5G6qpG+Bz2QdVS9SLlQOrR1uY1K3QnWk7x8Ca1qnOA8iysRB3r4keFsrcHQKiBTfgrHLKv+BxeWiuG97rk15x/gSFaSz3esw7j0vm177VnARxg/3au5lXNy/KC1K4HN3bwAK1vkG0Q0qkxddJyAoHC472Vm/JRQFrCGFwOIxn/QbwU+jSrgt4xLWWzNxOYXdScaAPss9mpFV93G9n5krdr+L2ztNAWBpbhbJ1kEM7fYcyunTJjplBPoAYtI2c0q/h8lOX84I5gGwI9VBn2Pe4MTix/hqYDJTPBXSDib7hI6rYxyJ7OEVuy/8syLGUPMfVpMZoL7mr+p67pTr+EvM/5Hp3MB4XgJgZsEpIZ9DeuxGLuYR9tni2VQvlPiVjNG8nzqEK9W92MVnqsmrfJGONXMokcASl33wrTSenXUKUwpvDdi/Lro9Z/efwfCSWQwreZb/K7icUmc71kbneI85qd/jZFDGSD5GiQVEeFoZwnldTHuGl8wie9g8pqvgsNd1UUY7/0kfydM5ZzG5x+0sb9eeWwYZg/KK2Hxm5E3iSS6kH9/zrEfgFB3zunf2PivnTK+dP3P4An5nf5zOrOLpnHEcV/Isp/Z7lNlqHKnspEv6t9xa8Cfv9U/hLU7mbfrzPTcXBC68HDLgeeIpZ310e07p+wiDB8wmy/IrlxfegjO7lAJ+YZJ6jOlcy01Mo0Ct4mz1AkviAjWgwXzOyf0f468Fxu8un7VMVYZpaKcjiZ2OJO7P/R0rYvN5LGcCE/rcx+sZv+GubhdR7ZcIry7nEcAZRTO5o2AKORgFvd5LGBH0bP0RgRudW+i4u5QvByTzbb8kdngy1p5jXUjUMmN4U34ayxRm8ryagCVxH2+mH89lJdcENnpzGVzwJj273U2XzsFmNItLcU33zzgvz2Q1+7kvk9pxAsOHLaWoz1NE11ay3J3n3V1XkbFYVgacVumCidU38vvq6+mc2ZPTLJ8zyt/UXD8Sq1PDz6Ulaf1A2ggRkTHAQ4AVeEop1byQGzNyirl/yTWkp/3KqjXDqR40kSFjC3FPN/LxK3e9mX90KkN/+Jkd6hTaDTciJwpYQ0HaGj7BmL1klgXbmGOp5NgRi7BYnWzeEnpFtSkW4c1uI9nbzcVY3vRuFgXrVBa3Hn8vtsoa/rtuNBft/pQpfaaS2WEBM9Uf6THo7zijslE/vwuAw5FOx8Kbic8YxpKlhnklqbKCP8dPC7jklfydXY5UohyV1GKh1gZflSRxMNrK8Xn/JrVmD8+pCVhws71XCpcsn8Ip+XOJTTRmx8ns5mruDWjzGnwz/tEpPlv96OyH+GCzLx6/1ulGEG5Wt/IrgU68Kz2hprPVOIrxvTiVwzMZYfk4aEVtud0IHf1b3mQy7Rsp8aQpn543mUc6nMsoeY8LnLPYTjrPcDFPtp/Ak+0n0H3/L3zy/R9wIzwqf/C294CaQiJ7vBOG2WocH8ScxCUsIgMjTPoqZdz3j3H5nFA8C7u7huu4iz/xAIvTirmQt4lyVvHYsMFMkxmedv6DAJ936cQNXRcwWH3OneoxoqhEIVzb/2aWSV/OVrPpz/cUsIY56gI+ix3JM8oXWprJFq5tfzbp1bvZHy8M5APvvt7ZczkjbiZvLjE0jwHRxgx6onqGFxKNexyl3mMtXbB7cjMdz1zv+XdgaBSvdx9D0cJVvJU+gliWcwIf8i8C1z/0ZilPq/P5mR4ksZub8//OPfk+7eYhdQmxBJphoq15VKm9OKWGvMSl7FBRXr37RrmDx9qfzeLkAiZ4JmcAO5PspO6pAUccVw0ew5W/LGDxTznsit+Kw2rcw8nbP8YaZbS0L6uatI2wqHeCxxzkxmJxs6uwjLGeyQuA1RoD9igoGEk0kAuUlb3NvnIjbX7+hgPklh7EEsqG2W0MADZbLGlpI3HJi4ytvhMLinzZwt32JwAotARGRGVZtvOR26MR2GOY6TAiD2tH341t80LI6Am2KKj1aCSnNi2Yoym0KQEhIlbgEeBEoBT4TkTeUkr91PCZEdJtDPlbH+eAayMitTx8/gAAfkwpgbXgrvEpVkXL97LjN6P48r8fkpzfIVSLbLF1Dtp2zR8uwGoPL8696+r9rOoSuPDnfoIdUXUOrSy24IirZW30eVgUXMff2KYySWUXFovhqE5PO4HNm1+ib9HTWCw2YmONWWBcXC9GDH+D6uqtLPx+AtXVxiBnQQU5YSuibGxemMyAAV+yIOoErB4b7Akx8yHC+kM2q5t7BpyGpaaCM+JfAv8MB56Zcw9+JMf5Kxfl3kbimu186C4JmKXbqGVhfA9Kyn/iCedFxBIcpbI2LYWLC2/jv+nDuJcribOU02noexywRDOGdzif5wBIXlPEhM4vsZT+nKLeZFTsB2QNm0+6azsPcJm3vXYEO9hH817A3wP5mnnHpaLYx2w1LsC6OIivvZ87s5poVcG5PO/dNoEXOU7NJ8cvPTsoruYe3lFncjKGYE1kL5fyMJeqh4P685yaQE2+PSgBX7rs4KxEY+KzJqa9d7JxEv9lsPoCC25s1JCg9jGQr4LaraMo5mt+3/NO4pI2cSbLEeBWdVOQ9htFJX0xfFTj1Rx+oJj+LKQzq01DxLvG3cUttd/jYA1jeTNgMhRXBomdPqWAwLUn6zrGkLpnL2T3AxGk8wj62KZT+fIZvFh9D+/bBtK7ag2bv0zC0bsLs7cUkZG5loLkQE2zTrh7rxcXnKalR4/7+PqbEwHotMHc5NazxwMcOLghaHt25Rb22hNxAWtUe4ota3jM/gAj6iWJ7Cyb+QjPWguH4Zjf33EUcYP9ogFvKYP7CqF8M1gP3er7NiUgMIacNUqptQAi8hJwOtCyAgLo3uce5s6dTHWtLzKm51/eIer7b4hqt5mfV18HQOoVZexYaYTTWRw2Bg54i2+/89mgf9djDgWJ68lOPh/e9bU/plcmQ7v4Io3apZ/Etu3vkZx0O7v33Mb+/Rn06jWe9euN8NeswQ+yasctxMQUgJ9zubIyhqgo44eZYCvx+kO2LP8t506ZjOXYJFjzERmuD7HtnEtu+0k4PPlh0tKOZ8TwH7F6nMxRUdl06XwTaWknYLVaiY7OoaT4Fb78KtD5WUe2msC7Ty4nK3ofkwf8k/Mrn2f9liLy/NTr1MQR9Op1Lwu+GNDg8+7fbw5lW9+GLS9S5zP8tl8SRYsqeNb1G86zd+GbmhcASGQfveIW8A/3xXSKKSWzugyHw2fPXdw3hnfUOI7Dt4YkNrYrFRWGI3yIfMbX6W6e4Glv9MltlhuJ25JIUpaRZbXrh88gWOix1s2QAe+wJdkYsF9gAjQxB1o4NQwEeIpAc58Vdz3hYBBFldc81xhW3Fgxj5Tqui2dD4e2Q6QqwKachM8vMg4jueCw435g3fqZ2GwJbNv2LllZ48nMOI3PvxjMb1Mf8WoZAN0INJXU50xe40xeC9hWWHg3rtr9rFpt+Lsc0elcRgGrRj4UcJyt1s3xK1cyjysh48WAfd173w9LJnkHUwBrfA6xB13Eyg7+aHuXfdYopFZIW96FzF75bNrkYOwZF7By5U0U93+Jb7871XtuUZ+n2LL1Dbp3uzPoHpwec2eMsz3kFUL+MJh/l+dmToPfPk9m0FkGJ+z+jOK9P/BCe5+2N8b6XdBx19pexSKKS61vgW0rXLGIuHgTf116V0NANDNkOhJEHYKUseEiIuOBMUqpyZ6/LwAGKaVMY7pKSkrUwoWhw0Ib44svvqCwsJCUlMDFcC7XAT5Z0BuHI53jhn5NeXk59913HxdeeCH5+fmUbXsPuz2JDeueZtceY3Zzwsg1DPrbR5SVG2aI9TMC7e1KuVGqBhEHGzY8TkbGKURHd2D79g+xWmNJSRniOU7R/pMluICpSx6loiKZs846BpHv6d79Tp5asJq/vbeSRVOHkpTUMqnBt+/4mIMHNhAVncOePQvpah0CMWlsr7Dy3E1/oXu/3gz7w0U88NizuN1Wrr32XOz2RESs3heovHwFO3Z8THLyMcTGdsHlquDrb8bgclUwcsRPXq2msmwnzpQkxG7lu7deZ8KXUd7ntXTp52zduo7OnSzc/8JG3qnsQ1frNo61r2PgoH/jdB6gsOszvPjyqyQnbOfY4zpjtyeRknwsKSlDcLkqvcLwkwVFuFyGOcNhSyfv3XuYl7CCnVFLGDnoZHq0H8b2fy7Bmuwk64aBuFwHWbfuHzidGfyy9u8kJvSjsHAG33x7MlHObLp2vZV95csQLFRX76B004tkZZ2J3ZaE1RbHmjXTycmZSMfcSTidmaxafSfRUTns2v0V+/Ytpm/Rv3C5DrBhw+Ps2v0FAGlpo0hNHY6IFXd5Las2Gb6SvI5TiHP1wSHprFhzIwedvnj/jl/djrU6nqhL3CQkFrHi5xvZu3chTmcWQ4d8zr59y9iw8Qnc7mpyO0xiZ+mnpG48E3en7RyI+9k7MPuTnj6Grl3/D4vYcDjSgvYDVFVtY8/2H6it3sfWPf8myplFQcG1REVls23b+yxb/qeA44cPW4zNE5H2zbensn//CvoWPUNqqjEZqa0tp6pqGzExndj57E8cWLWV+EnJJOQVopQby7I3sJStgN7jWTnvVPbk5DDoeM/gqhR8ei/0vxDiPf6qmkq4y+e72rMumi3fGNF/3X76EZfLhd3uc1avWHEjm7e8woAB/yEhPjCstT47dy4gPr6n79nUHAR747UfSh85h6WL13JZquE4X9/jScgbAvOCBZGXaXtD7zu4GzZ+Dd2Cs9xGioh8r5Rq1NPd1gTEBGB0PQExUCl1hd8xFwMXA+Tm5hZv2BCs2rUEbncVInakkXTBW7f+B6czk+TkQew9UEPRHUaYX30BEQm/Vlaz8WAVads206FDB2JiWi8dQ2Os+uYL8vr0wxEdQ2lpKQ6Hg3bt2rVY+7+ffANdLaXc9MRs77bKykpmzJjBOlcy5w3vzVefG5E+06ZNo6qqiunTp9O3TxFnnNV4qJ/LVYnF4qR2+0Fe++hNVq5axTnnnEP37t2pXL0be7sYrImHvxB9uOx9bx0x/dthz2jZpI7Nxe2uYvuOj0lKLMbhaBeQMdnlOkBVVRkxMU3MPrplCWT2CW/F9jSjvoS7z0WsvOkDch58kIQxwXVXDglPjYLS7/hn7ViqsXHVaUNg4B9hz0YoXQi5x0B0CqyZCy9PNHwLJYemAuWRKiAGA9OUUqM9f98IoJQyrd7eXA2iNfh4RRluBSf2OHRFiY5kastWYolLxxIbqMVVV1djs9kQEd5//3169OhBx45GSOrmzZtJS0vD4YjMFvvCCy+wZs0azjvvPLp2DV6XoPkfYO8m+O5JGDXtcPcEdq2Dj26D6gOGELh1N1hCTDg3LfL6VA4F4QqItuaD+A7oIiL5wCbgHOC8w9ulyDihUAuGSLBlmCeO8x/8TzopUKXOzm5a9li323CwW0K9pJojn8SctiEcAFLy4eznwjs2p3XymzWXNiUglFK1InI58AGGq/AZpVTr1W7UHFXYbLaA/zUaTcO0uTdFKfUuAfFAGk3LMHbsWL755htyc3MPd1c0miOCNicgNJrWIj4+nlGjRjV+oEajAY7WVBsajUajaRQtIDQajUZjihYQGo1GozFFCwiNRqPRmKIFhEaj0WhM0QJCo9FoNKZoAaHRaDQaU7SA0Gg0Go0pbSpZX6SIyHagddK5hk8amFRCOXLQ/T+8HOn9hyP/Ho7G/ndUSqU3dtARLSDaAiKyMJysiG0V3f/Dy5Hefzjy70H3PzTaxKTRaDQaU7SA0Gg0Go0pWkA0nycOdweaie7/4eVI7z8c+feg+x8C7YPQaDQajSlag9BoNBqNKVpAeBCRZ0Rkm4gsb+S4ASLiEpHxftvuEZEfRWSFiMwUT8V2EflERFaKyGLPv3Zt+B7uFpHlnn+/9dueLyLfiMhqEXlZRCIrBH34+z9LRNb5fQd9D1f/RWSEiOz168utfvvGeH4ra0Rkqt/2NvP8m9j/Q/b8W+AeTM8VkRQRmev5DuaKSPIR1v9pIrLJ75yTw+6QUkr/M8xsw4D+wPIGjrEC8zAq3o33bDsW+MKzzwp8BYzw7PsEKDkC7uEUYC5GAalYYCGQ4Nn3CnCO5/NjwJQjrP+z6o473M8fGAG8E+KefgE6AQ5gCdCjrT3/Jvb/kD3/5txDQ+cC9wBTPZ+nAncfYf2fBlzXlP5oDcKDUupTYFcjh10BvA5s8z8ViMJ4MZyAHShrjT42RjPuoQewQClVq5SqwHjBx3g0oeOB1zzHPQuc0bK99tHS/W+dXoYmzP6bMRBYo5Raq5SqBl4CTm+jz98M0/63aOfCpBn30NC5p2M8e2i730Gzzg2FFhBhIiI5wJkYszgvSqmvgPnAFs+/D5RSK/wO+ZdHrfu/OtPT4SLUPWAMqCeJSIyIpAEjgQ5AKrBHKVXrOa4UyDlU/a1PE/pfx10islREHhAR5yHqbigGi8gSEXlPRHp6tuUAv/odU/ec29Tz9xBJ/+toS88fzO+hITKUUlsAPP+3qqk4DCLtP8Dlnu/gmUhMZFpAhM+DwA1KKZf/RhHpDBQC7TFeiuNFZJhn9/lKqd7AcZ5/FxzC/ppheg9KqQ8xTDZfAnMwzGS1gJlAO5xhb5H2H+BGoDswAEgBbjhkvQ1mEUaKgyLgH8Cbnu2hnnNbe/6R9h/a1vOH0PdwpNCU/v8TKAD6Ykxi7wv3YlpAhE8J8JKIrAfGA4+KyBkYM9qvlVL7lVL7gfeAYwCUUps8/5cDL2Ko4oeTUPeAUuoupVRfpdSJGC/8aoz8LkkiYvOc3x7YfOi77SXS/qOU2qIMqoB/cRi/A6XUPs9vBKXUu4Ddo/GUEqjx1D3nNvX8m9D/NvX8Pf0JdQ8NUSYiWQCe/7c1cnyr0ZT+K6XKlFIupZQbeJIIvgMtIMJEKZWvlMpTSuVh2IQvU0q9CWwEhouITUTswHBghefvNADP9lOBBqNzWptQ9yAiVhFJBRCRPkAf4ENleLjmYwzGABcC/zkMXQci77/n77oXWzBsx4ftOxCRzDozo4gMxHj/dgLfAV08EUsO4Bzgrbb2/CPtv+e4NvP8Pf0IdQ8N8RbGs4e2+x00dE6W359nEsF3YGv8kKMDEZmDESGQJiKlwG0YDmeUUvVt3v68huFIXIahVr+vlHpbRGKBDzzCwQp8hCG9W41m3IMd+Mzzu9sHTPSze9+AMWu/E/gBeLp1et9q/Z8tIukYWsVi4NLW6X1Y/R8PTBGRWuAgRnSSAmpF5HLgA4zfyjNKqR89zbal59+U/h+y59/MezA9Vyn1NDADeEVEJmFMCCccYf2/R4zwYgWsBy4Juz+etjUajUajCUCbmDQajUZjihYQGo1GozFFCwiNRqPRmKIFhEaj0WhM0QJCo9Fo2hgSZuJKz7HDRGSRiNSKXwJLzz7TRKLhogWE5qhBRFLFl9FyqwRmuPyyFa+bJyLntVb7mv9JZhF+PrGNwO8xFuN6EZFjgSEY64J6YaxmHx5JJ/Q6CM1Rg1JqJ0a6AURkGrBfKfX3Q3DpPOA86r3AGk0olFKfikie/zYRKQAeAdKBA8AflVI/K6XWe/a76zeDL5Go0IREolqD0GgAEdnv+X+EiCwQkVdEZJWIzBCR80XkWxFZ5nlJEZF0EXldRL7z/Bvi2T7cTyv5QUTiMRZaHefZdo1Ho/jMYxZY5JnpRXLtWSLymKeNVSJy6uF5appDzBPAFUqpYuA64NGGDg4jkWijaA1CowmmCCMB4y5gLfCUUmqgiFyFkW78auAh4AGl1OcikouxirgQ48X9k1LqCxGJAyoxaghcp5Q6FUBEYoATlVKVItIFI8FgSQTXBkMrGY6RhG2+iHRWSlW23iPRHE48v6VjgVf93AgNZsaVwESiAHNFZJgnLXhYaAGh0QTzXV16ZxH5BU9eJ4x0KiM9n0cBPfxe1gSPtvAFcL+IzAbeUEqVmvgF7cDDnvQHLqBrhNcGeMWTfG21iKzFyJi6uBn3rGnbWDBSv0dSkc+bSBRAROoSiYYtILSJSaMJpsrvs9vvbze+SZUFGOzJINtXKZWjlCpXSs0AJgPRwNci0t2k/WswbMFFGJqDfxnRcK4NwWm/dc6c/2GUUvuAdSIyAYzkhyJS1MhppolEI7muFhAaTdP4ELi87g+PNoCIFCilliml7sYofdodKAfi/c5NBLZ4NIALMBLcRcoEEbF4/BKdgJVNuw1NW8STeO8roJuIlHoSBZ4PTBKRJcCPeKr2iVGjvRQjieDjIlKXKPE1jFKwyzCKai1RSr0dST+0iUmjaRpXAo+IyFKM9+hTjEylV4vISAzT0U8Y9UHcGBlPl2CELz4KvO6ZDc4HKppw/ZXAAiADuFT7H/63UEqdG2JXUOirUuo7fH4G/+0uIsjcaobO5qrRHGGIyCyMwvWvNXasRtMctIlJo9FoNKZoDUKj0Wg0pmgNQqPRaDSmaAGh0Wg0GlO0gNBoNBqNKVpAaDQajcYULSA0Go1GY4oWEBqNRqMx5f8BMmoVrn//osoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba4504ec18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.ylabel('Air Quality Level')\n",
    "plt.xlabel('Timestamp')\n",
    "plt.legend( los_angeles_aq['Site Num'].unique().tolist(), loc='upper left')\n",
    "# plt.legend( los_angeles_aq['Site Num'].unique().tolist() )\n",
    "# colors = [plt.cm.spectral(i) for i in np.linspace(0, 1, los_angeles_aq['Site Num'].unique().shape[0])]\n",
    "# ax not initialised - not really needed\n",
    "#  ax.set_prop_cycle('color', colors)\n",
    "# make sure to use the trimmed version\n",
    "for site in los_angeles_aq['Site Num'].unique():\n",
    "    plt.plot( los_angeles_aq_trimmed.loc[los_angeles_aq_trimmed['Site Num'] == site]['Timestamp'], los_angeles_aq_trimmed.loc[los_angeles_aq_trimmed['Site Num'] == site]['Sample Measurement'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA3IAAANrCAYAAAADDqqyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDIuMS4wLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvpW3flQAAIABJREFUeJzs3Xd4FNX6B/DvSUKVJk3pkS4WVFBRsSBwRfBn7+V6LVever2W61VU7KDYe8OGFeyChg4ivUPoJUDoJbQQEiDt/P7Ync3s7PSd3Z0J38/z8JDszs6ebDlzynveI6SUICIiIiIiouBIS3UBiIiIiIiIyBl25IiIiIiIiAKGHTkiIiIiIqKAYUeOiIiIiIgoYNiRIyIiIiIiChh25IiIiIiIiAKGHTnyjBDiCSHEp6kuBxEd2VgXEVGqsP6hZGJHjjwjpXxRSnknAAghMoUQUgiR4eZcQoj2QogRQog8IcQeIcRYIUQHb0tMRJWRl3VR+BxDhBCrhBDlQoh/6NzfWgjxhxCiQAixSwjxShzFJ6IAS2b9I4Q4Mdw+2iWEiNkYWghRXwjxqxCiUAixQQhxo+q+HkKIJUKIfUKI3eHjmrktJ6UGO3LkV/UAjATQAcAxAOYAGJHSEhHRkSobwL0AFmjvEEJUBTAewCQAxwJoDuCbpJaOiCozw/oHQAmAHwDcYfDY9wEUI9SOugnAh0KIE8L3LQdwkZSyHoCmANYA+NDDclMSsCNHjgkhHhNCbAmPPq8SQvQM3/6sEEJpwEwJ/79PCHFACHFW+JjbhRArhBB7w6NIrfSeQ0o5R0r5mZRyj5SyBMCbADoIIRok+u8jomBIRl0EAFLK96WUEwEc0rn7HwC2SinfkFIWSikPSSkXe/dXEpEf+aH+kVKuklJ+BmCZTvmOAnAVgKeklAeklNMQGiC/JfzYHVLKraqHlAFo6/R1oNRiR44cCYc3/hvA6VLK2gAuApCrc+h54f/rSSlrSSlnCiEuB/AEgCsBNAIwFcAwm099HoDtUsrd8ZSfiCqHFNZFWt0A5AohRofDmyYLIU5yeS4iCgAf1T9m2gMok1KuVt2WDUCZkYMQoqUQYh+AgwAeAcCw8IBhR46cKgNQDUAnIUQVKWWulHKtzcfeDeAlKeUKKWUpgBcBnGI2EgUAQojmCIUHPBxPwYmoUkl6XWSgOYDrAbyDUHhSFoAR4ZBLIqqc/FL/mKkFIF9zWz6A2sovUsqN4dDKhgAGAFjpcRkowdiRI0eklDkAHgTwLICdQojhQoimNh/eCsDb4YW1+wDsASAAGC6uFUI0AjAOwAdSykSMWBFRACW7LjJxEMA0KeVoKWUxgNcANABwvItzEVEA+Kj+MXMAQB3NbXUAFGgPlFLuAfAlQoNQrhOzUPKxI0eOSSm/k1J2R6gykgBe1jtM57ZNAO6WUtZT/ashpZyh9zxCiKMR6sSNlFIO8qr8RFQ5JKsusrDY4DmIqBLzSf1jZjWADCFEO9VtnaGzni4sA0BjxHb+yMfYkSNHhBAdhBAXCiGqIbTw9iBCIQZaeQDKAbRW3fYRgMeVjElCiLpCiGsMnqcOgLEApksp+3v5NxBR8CWrLgrfX1UIUR2hUfMqQojqQgjl+vkNgG5CiF5CiHSERul3AVgR559IRD7ll/pHhFQHUDX8e/VwmSClLATwC4DnhRBHCSHOAXAZgK/Dx14Z/jvSwtFPbwBYGJ6do4BgR46cqgZgMEINle0Ijd48oT1ISlkEYBCA6eHwgW5Syl8RGrEaLoTYD2ApgIsNnucKAKcDuC2c6Un519L7P4mIAihZdREQigw4COBsAEPCP58XPv8qADcj1Djbi1BD6dJwmCURVU6+qH8Qmg08iIpZtoMAVqkeey+AGgB2IpRQ5R4ppXJsMwBjEAq1XIJQh/MKm38/+YSQkhEhREREREREQcIZOSIiIiIiooBhR46IiIiIiChg2JEjIiIiIiIKGHbkiIiIiIiIAsYXm/41bNhQZmZmproYROSh+fPn75JSNkp1OeLF+omo8qkM9RPrJqLKx2nd5IuOXGZmJubNm5fqYhCRh4QQG1JdBi+wfiKqfCpD/cS6iajycVo3MbSSiIiIiIgoYCw7ckKIz4UQO4UQS3Xue0QIIYUQDcO/CyHEO0KIHCHEYiHEaYkoNBERwPqJiPyJdRMRJYOdGbmhAPpobxRCtADQG8BG1c0XA2gX/ncXgA/jLyIRkaGhYP1ERP4zFKybiCjBLDtyUsopAPbo3PUmgEcBSNVtlwH4SobMAlBPCNHEk5ISEWmwfiIiP2LdRETJ4GqNnBDiUgBbpJTZmruaAdik+n1z+Da9c9wlhJgnhJiXl5fnphhERDFYPxGRH7FuIiKvOe7ICSFqAngSwNN6d+vcJnVug5RyiJSyq5Sya6NGgc4ATEQ+wfqJiPyIdRMRJYKb7QfaADgOQLYQAgCaA1gghDgDoVGkFqpjmwPYGm8hiYhsYv1ERH7EuomIPOd4Rk5KuURK2VhKmSmlzESoAjpNSrkdwEgAfw9nYOoGIF9Kuc3bIhMR6WP9RER+xLqJiBLBzvYDwwDMBNBBCLFZCHGHyeGjAKwDkAPgEwD3elJKIiIdrJ+IyI9YNxFRMliGVkopb7C4P1P1swRwX/zFIiKyxvqJiPyIdRMRJYOrrJVERERERESUOuzIERERERERBQw7ckRERERERAHDjhwREREREVHAsCNHREREREQUMOzIERERERERBQw7ckRERERERAHDjhwREREREVHAsCNHREREREQUMOzIERERERERBQw7ckRERERERAHDjhwREREREVHAsCNHREREREQUMOzIERERERERBQw7ckRERERERAFj2ZETQnwuhNgphFiquu1VIcRKIcRiIcSvQoh6qvseF0LkCCFWCSEuSlTBiYhYPxGRH7FuIqJksDMjNxRAH81t4wGcKKU8GcBqAI8DgBCiE4DrAZwQfswHQoh0z0pLRBRtKFg/EZH/DAXrJiJKMMuOnJRyCoA9mtvGSSlLw7/OAtA8/PNlAIZLKQ9LKdcDyAFwhoflJSKKYP1ERH7EuomIksGLNXK3Axgd/rkZgE2q+zaHb4shhLhLCDFPCDEvLy/Pg2IQEcVg/UREfsS6iYjiFldHTgjxJIBSAN8qN+kcJvUeK6UcIqXsKqXs2qhRo3iKQUQUg/UTEfkR6yYi8kqG2wcKIW4FcAmAnlJKpcLZDKCF6rDmALa6Lx4RkXOsn4jIj1g3EZGXXM3ICSH6AHgMwKVSyiLVXSMBXC+EqCaEOA5AOwBz4i8mEZE9rJ+IyI9YNxGR1yxn5IQQwwBcAKChEGIzgGcQyrRUDcB4IQQAzJJS/ktKuUwI8QOA5QiFDdwnpSxLVOGJ6MjG+omI/Ih1ExElg6iY2U+drl27ynnz5qW6GETkISHEfCll11SXI16sn4gqn8pQP7FuIqp8nNZNXmStJCIiIiIioiRiR46IiIiIiChg2JEjIiIiIiIKGHbkiIiIiIiIAoYdOSIiIiIiooBhR46IiIiIiChg2JEjIiIiIiIKGHbkiIiIiIiIAoYdOSIiIiIiooBhR46IiIiIiChg2JEjIiIiIiIKGHbkiIiIiIiIAoYdOSIiIiIiooBhR46IiIiIiChg2JEjIiIiIiIKGHbkiIiIiIiIAsayIyeE+FwIsVMIsVR1W30hxHghxJrw/0eHbxdCiHeEEDlCiMVCiNMSWXgiOrKxfiIiP2LdRETJYGdGbiiAPprb+gOYKKVsB2Bi+HcAuBhAu/C/uwB86E0xiYh0DQXrJyLyn6Fg3URECWbZkZNSTgGwR3PzZQC+DP/8JYDLVbd/JUNmAagnhGjiVWGJiNRYPxGRH7FuIqJkcLtG7hgp5TYACP/fOHx7MwCbVMdtDt8WQwhxlxBinhBiXl5enstiEBHFYP1ERH7EuomIPOV1shOhc5vUO1BKOURK2VVK2bVRo0YeF4OIKAbrJyLyI9ZNROSK247cDmXaP/z/zvDtmwG0UB3XHMBW98UjInKM9RMR+RHrJiLylNuO3EgAt4Z/vhXACNXtfw9nYOoGIF8JIyAiShLWT0TkR6ybiMhTGVYHCCGGAbgAQEMhxGYAzwAYDOAHIcQdADYCuCZ8+CgAfQHkACgCcFsCykxEBID1ExH5E+smIkoGy46clPIGg7t66hwrAdwXb6GIiOxg/UREfsS6iYiSwetkJ0RERERERJRg7MgREREREREFDDtyREREREREAcOOHBERERERUcCwI0dERERERBQw7MgREREREREFDDtyREREREREAcOOHBERERERUcCwI0dERERERBQw7MgREREREREFDDtyREREREREAcOOHBERERERUcCwI0dERERERBQw7MgREREREREFDDtyREREREREARNXR04I8ZAQYpkQYqkQYpgQoroQ4jghxGwhxBohxPdCiKpeFZaIyC7WT0TkR6ybiMgrrjtyQohmAP4DoKuU8kQA6QCuB/AygDellO0A7AVwhxcFJSKyi/UTEfkR6yYi8lK8oZUZAGoIITIA1ASwDcCFAH4K3/8lgMvjfA4iIjdYPxGRH7FuItLx7MhlyOyflepixFi2NR9SylQXQ5frjpyUcguA1wBsRKgSygcwH8A+KWVp+LDNAJrpPV4IcZcQYp4QYl5eXp7bYhARxWD9RER+xLqJyNjQGbmpLkKMiSt2oN870/Dj/M2pLoqueEIrjwZwGYDjADQFcBSAi3UO1e3CSimHSCm7Sim7NmrUyG0xiIhisH4iIj9i3UQULOvyCgEAq7YXpLgk+uIJrewFYL2UMk9KWQLgFwBnA6gXDhcAgOYAtsZZRiIip1g/EZEfsW4iChAhUl0Cc/F05DYC6CaEqCmEEAB6AlgO4E8AV4ePuRXAiPiKSETkGOsnIvIj1k1EAeTTJXJxrZGbjdDC3AUAloTPNQTAYwAeFkLkAGgA4DMPyklEZBvrJyLyI9ZNRN5q9+QoDPxjeaqLkTIZ1ocYk1I+A+AZzc3rAJwRz3mJiOLF+omI/Ih1E5F3SsokPp22HgMu6ZTqoqREvNsPEBERERERVVpSP/9QyrEjR0REREREpCF8nu2EHTkiIiIiIiIDlS7ZCRERERERUWXl7/k4duSIiIiIyCf2FhYjv6gk1cUgAuD/feTiylpJREREROSVU18YDwDIHdwvxSUh8j/OyBEREREREQUMO3JEREREROQL0oeZRfxYJoAdOSIiIiIi8gk/9ZmUJXI+KlIUduSIiIiIiMgX/NRp4j5yRERERERENvgxjNGHRQLAjhwRmZBS4sPJa7Fz/6FUF4WIiIiOAH7qM/l8Qo4dOSIytmpHAV4esxL//m5hqotCRERERwC/zn75ETtyRAF1+9C5eG3sqoQ+R2lZqDY9cLg0oc9DREREBADSV3Nyzu3cfwh3DJ2LgkOJ39ieHTmigJq0cife+zMnKc8V7CqViIiIgkJvRu61savw/dyNyS9MmJPO5dsT12Diyp34beGWBJYoJCPhz0BEgaXEhvtx4TEREREdGZSB6+tOb5nU591TWAzAWbhnpO2UgPJoxTUjJ4SoJ4T4SQixUgixQghxlhCivhBivBBiTfj/o70qLBEll4DPV/maYP1ERH7EuonIH+bl7sE1H81AcWm57v35RSV4a8IaAM46ZclsO8UbWvk2gDFSyo4AOgNYAaA/gIlSynYAJoZ/J6IU2LLvIDbtKTI9Zm7uHpSVV8oZN9ZPRORHrJtsKCkrxy8LNjMixGfGLtuOaz+e6cn7smDjXhwuLYu5XXvqqWvy4n4uPY/+vBhzc/dio0E7aW9Rsavzbss/CCA5SVtcd+SEEHUAnAfgMwCQUhZLKfcBuAzAl+HDvgRwebyFJCJ3zhk8Cee+8qfh/XNz9+Caj2bi3UlrkliqxGP9RER+xLrJvg8nr8XDP2RjZPbWVBeFVO7+ej7mrN+DouLYDpgTG3YX4soPZuDZkcti7tOuR3vkx+yYYw6VuHt+9ePSwjGQ5TZ6XOqOa8GhErw5fjVKy2Jn8nYWHMKEFTtjHpMo8czItQaQB+ALIcRCIcSnQoijABwjpdwGAOH/G+s9WAhxlxBinhBiXl5eYnraRGRue35of7g1Ow7o3u/3/VNMsH4iIj86Iuqm3xZuiawtcmtnQej6lH8w8Zn/yLkbPpkV1+P3FoXe12Vb98fcp+3/aEMVc3cVouNTY/DDvE2OnnP1jgJ0fGoMRiwKJSFJC5/2wOFSbNl30PSx6jINHr0Sb09cg6wl22KO232g4nPv9zVyGQBOA/ChlPJUAIVwEAogpRwipewqpezaqFGjOIpBRG5VLMg1r2427ilCUXGgtiBg/UREflTp66ZNe4rw4PeLcN+3C+I6j9Jw9no8cXrOLjz+yxKPz3rkWbw53/VjDxaX4ZtZGwDov7/aFol2UHn1jgIAwLhl2x0979ItoTK/Gt66SZmRu/GTWThn8CTTx6o7codKQjNxemvr1Ns1+Tq0EsBmAJullLPDv/+EUOW0QwjRBADC/++Mr4hElCjKKJdVZVNUXIa/fzYnCSXyDOsnIvKjSl83FYfDzXbsP+TNCT0ODbnp09kYNid1aewJeHHUCvw0f3PoF533VxuS6NUn4GA4rHLz3tDs28rtoQ6h0jGLKYfqZ3X4ZUVG79jH3DCkYqZy+bbY2Uavue7ISSm3A9gkhOgQvqkngOUARgK4NXzbrQBGxFVCIp+TUmLRpn2pLoYrZpWR1rwNexNbGA+xfiIiP2LdZCyzfxbu/XZ+5PcjNcVJUXEpxi/fkepiJNQeiyQisTNy+l05ZS2aXelxDAqoy6SEZOqtrStVJY+LdFYTKN595O4H8K0QoiqAdQBuQ6hz+IMQ4g4AGwFcE+dzEPnarwu34OEfsvHejafikpObpro4jlhVaQFeIwewfiJKieLScuw/VIKGtaqluih+xbrJwKglFaFyOQZrtyu7Ab8uxS8Lt2D0A+fi+CZ1Ul2cxFD1f3RDKy168UYdOwdPa+94VUHUZapIkuKqGJ6KqyMnpVwEoKvOXT3jOS9RkKzNC11scncVprgk7lmtkQsi1k9EqfHv7xZg3PIdyB3cz/CYQyVlGLN0Oy47panrRllQVfa6yat1QXNy93hzooDJ3R1qSxQejl2XnrOzAPuKStA1s36yi5Uwul9/zWfIKhGJXVafTSklSsslMtJETL2kbicp95X5YGuMePeRIyIfKC4td7UXnJPQSiIiO8bZCAt7adQKPPj9IsxYuzsJJaIgO7K6+RX0Oji93piCqz+amfzCGJiz3l1nO6pTpHP/wKzlkW0JCg7FZi11+5lQP6/e1gB7i0rQ7snR+PCvtaHnUb0JvyzYEvl54opQHffVjFyXJfEOO3JEHkllZ6j9gNEuUwGHk50Y3nukXkKJKJF27D8MANjP1PJkIVETtn7daNyfpdJ37cfxdyr1ZuR/nL8ZQ8OdJDeD1EbUb7ne269syfRzeG2b3mekvFxiZ0Go/srJsw7/HZ7gxDrsyBFVEm5GxjgjR0SpICLJAtyf489VO5G1OHYfJ/IJkw7YaS+Mx91fz0teWQLpyBhItV6r793rIA1+VijJS8yqpZnrKqIIbjijpeVzKrN7icKOHBEZOsKWrhBRkijJAuJZn3vbF3Nx33fx7VVGzkzP2YXM/lnI2VkQ13n2FBZj7DJ7mRkZGVK55R04bHp/mpdvvzQPrdTepD1iW/5B/POrigGIY2pXt3zKDbuLHBXRKXbkiAKmtKw8EjtuZtX2+C60RESJ4sWMHCXfH4u3AgBmu1wb5UbiQisTc954+bVcXlL/jYcN9nBTvDl+Tcxtbj8T63dVdKr06h6r5CX//GoeiorLIr/7IVEcO3IeKy+Xvo27pmgzwiOLeQXmo0F+M3XNrkjsuJmBWcstj6moC0OfWe3nl+OgROSW2bVQCZfi9TI4lm/dj2FzNgGwOUtm461dsDE4+5Mmi/KyMSIm5PPp6z07l3p2T68TVlYe6lQqh2nfgqVbojf4NtpIPJnYkfNY6ydGof/PS1JdDLLh02mhyiE7zs28kx32kZHu3fNVNKaA/IMlaP3EKHw8ZV3k/r1FTERARN4rDw+Hsx8XHCMWbbE+yKHpa3ZZHqN3xSsqLsXbE9agtCw1DWkpJTbtSWzI3JHCzayW205umqonp1f3KLN0a/MK8cDwhZYl+yjB69/sYEcuAb6ftwlz1u9B4eFSfD93o6cZd4iqptv72qbZqOmUIySAXeE49R/mborc70VGKiI6Mpl10rKWhJKU+CE0iWxy2ni2cfzr41e7KsrbE9bgzQmr8VM4u6Ab8XzyhkxZh3Nf+ZNLGDyQzMHwIaqB6mVb98fcr26vj1i0NRARA+zIJci1H8/ECc+MxWM/L0H/nxdjb2Gx589xuLQM387eEBnZJGcSWXXc+eVc9Hhtsu59S7fko8sL47HbYoGvkQybHTk7I1Z6x/DTREResFOXBKCdRGHqBneqw/6UdUrFKZqRU7JEJ2RWLvylqMyRlat3pL4DvHFPYcxt5ZoKyUn9VFRcmpKJG3bkkuDH+Ztd7vFl7v1JOXjy16UYmb016vZDJWW466t5mL+BsedmEvl1m7BiJ9bviq0kgNBU/O7CYkx3uRFuus0UTuoZuXyDvZr2hUMngzDqRESVj9t2z0FVwgFKjh/nVURrJLOTkepOox5lWYK24U/WRi/ZhrV5+u2jZNKbCdS+nT8vsB9O3OnpsWjzxCjd+/5aneeobE6wI5ckK7cXYOX22GnceOwpCs3y7dfsen/O4EkYt3wH7vhyrqfPV1nFc5G48ZNZeO/PHEePUSoKt09r93GTVu6M/PzIj9m6x/w3fDsvRUQEAJe9Px1vuAx3A0IRBwo7A0RuB5Ee/2Wxq8dRyDezNuD1cascPWa37cgiZ+9pSVk5MvtnOXpM5JniuHipP3t3fjkP7QeMtv1YZTyVHbmQH+dtwoujVmBt3gFk9s/CFJOOy7Sc6HWRkQy2Nkd1Vm0vwO1D49+DUK/tV1waPcPr1Rq4V8as9OQ8etiRS6I+b021ddzSLfl4b1JsulUtZTRBW48olS3rl8Sb4WJWTan47axh84pVZk5+VogICCV/emei9fXHyNZ9Bx0d77buWbPzgLsHEgBgwG9L8e4kZ4OQiTJhufl+cnozJ15fPies2BHTiDeTFpmR87YcgDprpQ+nInXk7irE/35ajCFT1mFebijkVNmmQo9RVFH25tjEcxNXxH42nvptqcuSRtOrex76YZHj85SUlVsulUlkxOUR25FbvaMAmf2zInHObnQdOB7vxnHBM3LJu9Pw2riKEdGsxduiRjkVyneBYXHuKK/bHV/GP7LjREVHLvr2L2fkooODEUEn1J+Rscu2Y6Em5bPeJ4hJeogoHnZqEDczGjPW7tJNVECJ8cyIpTEzZuZ9DGcdkHiuNF63fy57fzoW2chknRZuPSdyRs6P3ThtBBgALN0a2z41ox3E3pZ/CIB+8hG99pnb/q12iYleoqV9LjJ1X/HBdHQZOMH0mES204/Yjtzf3pwCABg0aoXrc+w6UOw645IdSqrf+75bgEvenRZzv0jgiNCRZo8HyWjsvg2R0EpNZfTMyGU4XFqekOQ16pCYu7+ejys+mBF1/5TVeVEXjcOlZYax3uR/izbtw2tjnYVNEXnNTtvFTW134yezo343C+Oi+H05c4PtY8cs3YZvZ9s/Hogd1NQyy2w6eul2R88Vfd5Y2Zv24dmRyywfe6S2v7q8MD7mNq+iiwbYnGlz+3QfaJbBlJR68+Zp95bTk8gO/xHbkVNkb9oXMzuRbA8MX4i3J8TO7A3KstfJNPp4JGOmruBQCXYWHEr48ySCOmzhNJ3KySufTl2HO1WjSlZhE2UJeN827w2FO02zsWcPABwqTv0ml+Te5e9Pd7x2k8gL6nqtQGf0XsusgfPt7A34daF1evm/fz4HG3anPnlCkM1cu9tRaKFRyvh/fbMAX0zPNX3sMs0MjlVHYJ1B4jAAmB1HVJURO1fgtARuaO/nIKuSstjC6b17Zn9DvNE+rjuOmod9MDl510hfh1YKIdKFEAuFEH+Efz9OCDFbCLFGCPG9EKJq/MVMrFRndxyxaCvenBA7s3fYolIVPgit7PHaZJwxaKLjx/08fzP6vDUlASVKvdnrotfNDcxagQmqOG+pWSN34yezMGRKxYLaRIY03vzZbOuDUBE2EnSVoX4iCiqjJEtqZpevJ39dioe+tz4HAJz/6mSbpfKHVNRNy7fux7b82DWMy7fuxw2fzMKgrOX2T2ajLW10Lbv50+jrkFXD/OO/1pner5BSYo0qrf1fq/NiOqe21nDaaFMps4jjlpmv73NDmYFM1hK5nJ0HsHG3+20UnJYz3r/LbUcuQzP1mxvH3+yU32fkHgCgnjp6GcCbUsp2APYCuMOD5/DU9vzoGSTtCGKpw31J1uwowMcJ2N3d6o232kQxGd27XQeiQxLbDxiNB4cvtHzcf3/MxspKupHmdUOMt5o4WFyGCStC2SSVd2/G2t14cVRFRiM/ZMFKZiKWBAtc/URUWWzLt47WOILXeCe9bur7zlSc9dKkmA7WvoOh6/gqB3t72XnftGuSCg+Xori0POb64mbgUO8K9c2sDej95hTMWb8Hq3cU4NbP5+DRn6IHAhbrJNSIPbn19U/5G5SN7RPBrI3ndh9aPb3e+AvnvfqnZ+ezYqdeMLPFYUIlxcY97h7nhUS2qOLqyAkhmgPoB+DT8O8CwIUAfgof8iWAy+N5jkRYvi16Wn+X6gtx0rPjIinZ7br0vel4afRKz9c2WdWTfkx/W1xajt8WGWcrqsym51iHLQ6dkRv52ejiZTUjl4w+VmXoxwW1fiICQkkFDpcGb680p1WHf65eyZPquumzadEzXEqHoVyGsmbb6aQNm7PJ8hh1m2jH/kM44Zmx6P3mX0jTzIy4yc6o95gl4aRw63cdwGdT1wNATHtE3Yk0+jPtlCYZ10iztYH3frsg8QWwTbVRvI1Xb7xFllIrRnv07txv3kFsVq9GXM/rV/HOyL0F4FEAyhRWAwD7pJSl4d83A2gW53N4pri0HFmLY0dPPpwcPZs2wmFH5GBJYi622spOVuLwAAAgAElEQVT0lwWb0WHAaJSEZwwrQ2O7MrETq6+e7TW6eJXHsTztt4X2N6888ZmxurdXohHyQNVPRGonPzsuJqlH0NhKdlJpqhtHUlo3KWumFUq/as76Pbjk3Wm2Ommb91qHpanHJD+ZEuo8bthdhHTNtU/7ux16D1E6aWXl+tkVQ4/zpuFkp8PiVsVes9HPoe6ouE3QtrPgEDbtSVxIoVnnM9G0+9NpNatXPUklSS7XHTkhxCUAdkop56tv1jlU910VQtwlhJgnhJiXl5ecjFPvTFyD+75bELVRspX8gyW2FmwD8Y8sFhWXRv1eWBzdQRyYtQKHS8uxPxyuICKLbRNUoCRIZachFf1g9UikUfhiPMlOZq2zv6/dgcOlhvcFvXEVxPrJipQyIRlNyX+UejHV67fj5adoEb/wQ92kfVu0nZvVNkIs7by16ugSddWlzVJptK+YmRKdJTDK9bVcShQV6w+wq5/KqG1np6/nosi2GWW2/mb2xsjPbl4zADhj0ESc+0rywiiTyep9084EVxbxzMidA+BSIUQugOEIhQW8BaCeECIjfExzALrTW1LKIVLKrlLKro0aNYqjGPYpcbl7bewTsa+oGKOWbEPn58bhpGfH2Tp/vJ2Srx2k+AUqUi570bbLP1iCdyeu8ayhqEydZ2/aF9lGQU8q26UlKXhy9cJroyrFzkXUSDyZotR3G+2jYzTK6UOBq5+sXP3RTLTmlhBHBH+FTblnp4atRBEAdvmubtK2b+28J9rO3+HSMozWrBcz6shrG9Q3fep85lndUVMSdaiXm/xlsCWFegB1nsFASTJCKy97fzoy+2fFRIQBJt8b1euZke7PTkkiZyrjfW43M79B4LojJ6V8XErZXEqZCeB6AJOklDcB+BPA1eHDbgUwIu5S2lRWLnHIJMwxUncYfEvUjez7hy10fDFN9uVISRbixVT2c78vw+vjVzuarVTTvu5KrPpl70/HA8MXGT4ulRfxVOw99LZqA3mjOuUDnYrdjHpk0uyz8M0sq4GC0GNzdxcZXlin29y+INX8WD/FK+izM2RfPHtj+YmdGbnXxiVuL1Y/8kPdpL1OuAk3vOnMlpGfsxZvQ4cBY/DupOh07kZvv9vZJDV1o92qDbTrwGHkFymRTOryuW9/xNthyQ4Plr48ZiWklHh6xFIs3RKdv0H7tqhL69dOSSpDK/VektMHTcDjvywGAPw433orkyBKRJLxxwA8LITIQSju+7MEPIeuB4YvRMenxqCkrBznv/onfpgXHectVKM1etRpaW2lqNVIVZ/E6HlLHcw4FYbD7EpdLtDq+NSYqN8F7O1Ztt1icWplZhhaafEeaC8gizfnGxwZzSrLlZ3Pb8Eh43DMgEhZ/ZQKZg2VMUu321rnQgSEZunnb7BeB6y+vtqpU8xCvBUrt1tvuFsJpKxu0varxqmSURhlCPw9e2uk3TB+eWjgYYXmfVIvE1A38AsPx59XQH35VK6lh0rC+QM0x3YdOAGnDRwfdSxQ8fnUfgbtdGy97EftLSrBVzM3RLYHstPB9KIz7BWf9ikBAHkFhyNrPivrYKgnHTkp5WQp5SXhn9dJKc+QUraVUl4jpfQuR6pKzs6CmP1B/ggnMsnetA8bdhfh0Z8WR92vNICNviPqC1CGi3y4flsPkKgkLHbNXGfdkUtVrLYfwnmMKj+n+8i5TcWrZaeSS8Rm5YmWivrJC19MXx9JEOClpVvysWXfQfzrm/no/vKfyOyf5dlnyM9ydxXinYlrfPHd94uSsnK8OGoF9hVZJ064/P3puOrDmZbHfa2a+ffqmtjnramenMdvklk3qZdNTFgeHXmjHVRUp4c/Z/Ak3fOt21WIx39ZAqBipki7T5f6Wqb+KOzyIHW++pleH7cK5eUSP+nMuJzTtkGkLGXlMrojp1NOIHQtXGWxPZJXSVMAYF3egdA5tc+huUX9Gnr5/F5yO1N5zzfzrQ8iXYHc9nd7/iH0emMKnvt9maPHRTbQNpj6LZexxzqR6PaBUQPErGEyeZW9UEkvyq4uhxCJz1j2e/bWSAXolJ3O0nUfz8R9ca5VydlpXD6jGbnSMmcvygJVB+zA4VJk9s/Cl6ptDhRr8/RT9ir6hy/KZhK5WTlFe+735Rg0aoXh/eXlMiYUx45L3p0W0zgzaqxVJrd+MQdvjF+NHft923dPulFLtmHIlHUYlGX8OXNqoyojnt8GN49kb6nC+rWRMG77BHkFoe+S8janapbot0Vbo7JGjzNIb//RX2uj/lbl8/nPL+fFHHvRW1NMnzOefpS2zXb1RzPD54yebIgNrax4XCpml7STJ4o5NjJ2W/EinNyvnVsAyD+YuGimQHbklM0r5+Y6+/BEOnIG15Z4R2oTERts9MVRM2tb/+OLuQ6f0f0X4Q/V1g5mozLa7Jxu3T9sIS58/S9Xj7UTdjp7/R7Hm31qP0O93qgon3bvlB0GYaVWZTuk2VtKvTed0kgdqtORc5uuWI0NM//48K+1uOTdaViw0fiCrn27rPbZseORH7OR2T8r7vMk20GDLHZek1LipVErsGG3+cCJHyjf52KdDIBqbq+NrC78Y6xJQ9loUNFKxeB4SDLXbWk7o+rv21SDZR2vjl2l2zKZ47AtCUS3lDL7ZzkK/7X6XihtyUS+nPk2kv5pGUV5jV3mjzW9/u3GeTMLbSSQHTml0jFq7xqNVFj11uMNG0vERevcV2JHyp/4dUlUo8QvF0v1B1UI4+QvA35bGvdzTVwR34aSeqmLvWDWCfvnV9GjfkZJYKxmva75yDq8Sa/hNdPB1gRGmP7e2t8/n5OUjs7yraGGg5P1vP/8Ov7wFb3wpSB5ekSo/pFS4reFW3TrggnLd2Cvy4GPtXkH8PGUdbjrK/+HClktN1C4zZYaT30xppIkfPELs+aPUUfufz9mm55zxtroa4pZenevQ5q1nTWjiBLt065RRcnEU6TdB6Lrh98W2t9/2OhplVdv9Q4l1NI4tBII1TVqCzbuRWb/LFtrn/v/stjymJjyGby9fpkI80s5ki2QHblI8kmDb+FLo1eaPs7o2hJv2Fgimrh6YUBjl+3Ad3Mq9hPxYiZQOYObL7dC+x1SUgJrGd2++8Bh23v23aETCuFEokIEvahH9DqD+4qKbY2gWSRmjRv7cdaSnQ3VrDGivctt56QyUF4LJezqj8Xb8OD3i2LSf+8tLMadX83DXV+7q2OU70gi15P+NH8zHhy+MO7zaGdUjDj5U9THGtUXU9dYf0cWmsw0k3NGnbXFm/eh7zv6axDtZvlT2mJmHXevvg0Fh0osE3epaTub0XvbWZdqW/5BHC6NnYkao5mFctKJsNupXb8relZf+6iemoikYeF95qZbbIwNhLaccsrJzG0qmgqxHd8jo8ESzI6czYuPlvIhnGAwm6N+z93sfJ/MsLOolPMePu0+F9Ptii81++DZXZ+n6DJwArq/nJzkJyUO1qE5qQy8eCv0slae8vx4dH7eej9Dq/DheD3/x/LEnJgcc1MPBnXEcuX2/YahyG7tDSf42FkQfd7D4XD2DQYDTlYi61vcF83SIz9m47dF9mcA7JixdpetUH4njAYZ+/9svR5X+8iFG/filwXBng1OJaP8bZe+Nz3ucyvvVWESwpe7v/wnugyc4PrxNaumR37+bvZG03qluLQcZ700CY/8aD3ALQBs3ltkugWW5Tk0lca/XCYASdRebnbOqkwyuMn8Hi9tgr2R2d7WkX4VyI5c5OPksLFq1YhRd8T0KqTCw6WRKWu9xn0yO/+lUXuHxc+Lr7169EgAuPD4Y3SPM/uCuxklAkLx8Xoba3rxPMmehdroYhBBEQmVSuFeLpQcFQvjTUbBpcTavANR9UUQ9XlrKrq9NDGuc2hfJqM6T3ket51er9e33P31vJjOpleUz9DSLfm48ZPZrhuORsol8MmUdcjsn+X4M6j9XF/xwQw8/IN5qB8Zc7sOzhaTy82klTuQ2T/Ls4a92zaCIlc1QDN7/R6c+aJxvaIMmE8wSJ6iVi5Dncz7h1XMlM/fsEd3Ztnt1dmoqv929gZ8MX29y7M6eH6D29Udx0Xh/fE27C7C/kMl+GFu9DZgbtbm2TUjJ3r21e1gXNAEsiPndkbOqhqz6ojd8MmsyIyR7rE2CmRWmSl7oNhRrJpR8uP0sQRwXMOjIr+rQy625ps3SqSU+Hpmrq39hRS3fDYHL49ZGcmiZeXS96bp3j5/w148MHyh4/ALxZa98V+stJ8DN2GgPvxIkMfsNMs27ClCz9f/witjV1ke68d6RC3+4hll/dU/2u2odsWMnDcN57HLdhjOCMQbIq6UUFnfPGmlsygKK1JKvDkhtOH3YYezfT7/OAaKlNL2fqOuzm/S+Bke3sNr0ab4nn/o9PWRTbTj8dFf9gd8nVDaper1+1d9OBNXfDAj5ljjz7Z5nWH0Oj/561I89/tyx23isnKJwQZLkXSf36DgRoPPj/+yBI/+vDjyvu0pLLYVWeSWtnTq4vb/2f2yIb8LZEdOGVlav6vQNL27llWyk9rVMyI/t2pQM+Z+dUWo93G20+B/d9Iaw/us1lTsVY9kONx01YqdmGonyqWMKti3szeYHB1tWs4uPDViGZ53sL2EUpFssrnBcZFBCMgdX87FiEVbo2LwnXTkCj3IyNlLM5P55vjVth+rZN9iI6jyU5I6mTXmd4bX2Crpoc1qwMr+mYn5+5QZTYPjXc/IKR05DydAinXW6ADAk79ahygCoT0J9ZKAeVHG/IMlUSGZ0dsPVNSfZs+1eHNsA72SfxyTys2SCW2WZTNGdcct3Vqp3vf43tFnf1+Oy96PPww0UbQ5GMxmDhMeMWPwXdOuYZyes8tRx9ZJqQ+VlCErnMm8qLgMW/cdxIpt9jN7uqFtq43I3hL5ebhmZrAyCWRHTv0ZVad3j5f6I9CmUS3D4/IKDuuOTNhLL+rN1V39fZSQyNl5QPdiaJfXse2hC3jF79ssZuGiynI4VJa9Bhcfs07nlTqjX04o6ZNL1DNySY5K692pcdTvZunljRwJGzwH1e/ZW3Hq8+NM12bY2SNIeY/tbCuhfJq1mdbU1Be9ymi35nWyqond1tSJaKTNWqefHv2XBfbes+d+X46rPoytG4XLZQpqnZ8bh1s/n6N7X7mUMTOUm/YUxUROPPqTN6PlzKqrz82ros2ybOS+bxcYdvrU69F2mdQ9fmdnwEN7zEpNp2VnwaHIoJthFIDJ83z011p8/Nc664IAhjOXH2o6bU5n9J0M9qknRqSUOHvwJNz06WxHz+dUuZToOnA8Pp0aep3WWeydW1kEsiPnltWXUd05UxbC67n245m6FeO9NjaP9mqUVt1YKJehDq2bRcuZ/bN0L/DxOqxZ8Ovs7w6P4IZ/G71kW1SctTbm3KuQMPUaP/V6jkRmn9NjtmExN+QOvvuHLcTeohI8brIBu9l3ctLK6EaT2R5gkXpCShQeLkWBSbjyQ987W3+UilBMt5kMzULajRtVFZVWebnE+3/m2Mqqq5xv5fYCV6/RfpuZe4H4O41eXY+MtjbZV1QSMyN37it/xnxmvUoUZmd/0CNRIhPvZC3ZZvi6B/3dmKX6XH89a4NpEjxtKLV6K4axy7bjjEET8cpY8zBGs4EIoxDId1UbvStfo29nb4w6Jr+oBAWHSvCnNnTa4IOx+8Bh5OwsiL3D5RuarK9leXlowGBg1gr0fH1ycp7UBwLZkdMu2v1kyjpbF0yrxb7fzVZ3Foxnt9bvKtS9+G+2sT7KrAROKlv1FyOeTJOAvdF/pz6ess51I0N5bZU04fd8uwCPquKbte1W7d//xjjr9UB6hkypGK1SZ7V00sioUSXd+iALb5iEUnJD7srDbaf8nm+iB4xKzTKwqu4yCid2KxVt5md/d5Y1dcW2/fhx3ibd2U+9y4HRdWTiyp14dewqvOAwa6ub93iawWbGeuJ9D+xsV2J3SxgjVrMQgH4HzOh4s6QprB/95bNp6zF2WXx7vqaSss1RUXEZnvptKW74ZJbhse/9mRP1u7p6uTu8f6eSNMXoY7q7sBg/zHMWAvi6qr1glBSp8/PjcMrz46PqPCmN25y935yCXm9MAQB8PTMXmf2zUFYuXbfpkvW9VNffa4+Q2TggoB057QV40KgVmLzKel8aq47S5w6y/uh9oI12vVcz60w6+airvxfqdLdO1gy69cb41RixyDqkR31ttlr4r15jYfU6amfIrv4oevbinUnRFaobpap4SifhOlXSnX2l7DSS1B8ZzshVflaDUtrPWNN6NYzPpfrf63DbVDSanc4sXPz2VPzvp8VIN9moWF3zGv1JSv1UcMh6Daz6dXHzCjl5Wb2qD8wSSz0WZ5IApYhmjUC9ECij441m/4DKv87TrVS9LJXtepVfVILSsnLdfeXskDa+C9owY+2m32bUm6T/6+v5KFKt2S8rlzFLXH7P3qZ7HnW4/stjQgPjg0evwCnPj7ddFnV20O0ebx1jpJJ93GwLZEduis5mohNXWo/6eLn4/LYv5rp6nGkZnOxXFpXspOJnL9YMahuS//0hGyc9Mzby+zsT1+CB4YscVdKm7Sggan8gdciZ3uirtnzejbyIyPvzzayK5CxOwnWcNiS+0uy95/X5KXisNuFVOiXntmsIAGhYq6rhserPy+UeJwoI0uzHdp01upGtOqLWG6vuVw+ghA9Ks6rItOdz8RIZNfJKysqR2T8Ln0yxt04GCA1CvW4SoWDnmrhlX3IaYVpGr53Zaxqkz2Qy8XXxhkRoaU2HAWNMj8vZeQBXfzQz5nblfXDydrjdbmHMsu3o9PRYTFW1l7VRYz9b7Ms4N3cPMtJDlcQnU40nOmpXyzC8D/BuDawVr5bBNKxVzZPzJEsgO3JP/rrU8WMKDpWYjuTNcJi1ccZa43MlgzT42QvajsvPCzbrrq2xynak953S6/x1fGo0+qs6b+o01S+PqYgLP1hchkMlZbY6kEpnb8rqPEehlsrI+zezKmLMnczIeXXBPHC4FJn9szB/w56o2UyztZtUOVhl9lI6cmkWWRcBdw0Hu8rKJQ4cLnUUCqg8zknjJN7QPgC46+vY/dGe0Mn4qP7+bt57MFKPKJ0nO3txqUef+74zFQBw6+dz8NKoFbbKajUraBZ6rTU3dw/eNY1QsP57vBr/9OozaJaEI9nrmYOCL4s3DhwuxQKTZTeKwaP1v+u5u0MbhitZhO2I9/s3y6Tda+W6j2fa+uz45eNld/spK/YSF/pHIDtyeqwusCc9Ow5Ltxg3kG60yKbj1WaWc3P3YqZBJ3B3YTFOenas7n1a6s7FiEXmu9ev3qGzaNWE3Zm2zRap/vVCxAZlxVZwZvvnqUeBjn96DM4YNMHW9LnyN/z98zl4Z1KO7S/mfp3QKSeNA6cV2mfT9Ee5hoUXK1/14cyoSl/v9SN/klLijfGrDUNjphkMHlW3WGep1HSRKk+GMgFm9s/C8q3RdVxFaKX3l9rScomHv1+Emz+b7ah+fGbkUnR+bpzt8CR1RyQRexqrv97aulQZwV6yJbT1jI0Juch6GKAi1P2v1Xn4eMo6fDp1neXfbfROKdc4OyH8Cr26/NmRyyLrge28nots7t01Mtv8OuTVJ9BsPzoZ7H3vE8bve0RWNhNWGO/H+MDwhbhtqP2IrpHZW7Fht/uoI6NlLXqDwtrPSbm099nh5ytWz46NcUGHRkl5rkrTkUtkVibAXiITO1Zs22+4YPbb2RttrcEAnF0UZ6tGZB4cvjCyvs0oO5p6Ru53k4uzVef5Y50QIKuLvZX9h0ptVRra9ou9zqn+Mcpj356wBpn9s5C7y7hSdTojZ5Q6vkX90D6GvTsdE5XhbVs+txUIit2FxXhn4hrc9ElokGjEoi34z7CFkfuNPpNW6yzT0gQG/rE8si5YQkbSfxstlDcbxDIjpcRn09brfk5leUVHxUnn4reFoTrg3Yk5UWs4jBxycG431J3cqTph+2p2ZuTMDMxaYZlCvMhgvZpXs/1DZ+TixVGhSAcne1RaUX+2veCmccgQQn3faLIYUupM0maOtPDF9Fxc8s40189nVGWt3B47wD9qyfaY2/QGt8naxSc1wSkt6iXluVx35IQQLYQQfwohVgghlgkhHgjfXl8IMV4IsSb8/9HeFddYoqvvRI04OEk1rebkgqXumP22aCseGL4IAHD/d/oX3jJVFrz7TS7O5gkE9HkxZW2nU+bmgj5sjn4jWAlpenNCqNFzwWuTUVYudUMub/Zsn5TQubWvsJ2wDvJH/aS8d0pH/IHhi2wNZFh9rdIE8KlqJres3PhiHW+9tWRLPl74Yzke+dF8awInT6OU6b0/c/DqWOuwZ/V3WftnHi4tw+O/LImrXvlhXsU6Eb31sM+MqAjlTxMCeQWH4+pcmiUWAYw7rq4Sp1jcr9eYSxSndbKbvzcIoZWpqJveUaWop9QqMcsybMBs2xgrTlppbutR/3/rKrd4ZuRKAfxXSnk8gG4A7hNCdALQH8BEKWU7ABPDvyfcRIOpbL1F7m54fYGYsXYXysslnh2xzNXjnYwMKx0fbfjTul36IV9KxkarRqCTMizctA+vWuyhYpedybXQRrTRB97y2Wz8OG+T/v4oJi58/a+YdUBtnhiF/+ks4HWy8bmZyCa6iZ5qrrxSXj8p+5CVS/1OvxGr75V2n8GoLImaz3y81ZYSxqasaVN3MiSkq1AIdZEO2BjtVec7Ug9kLNq0D9d9PAvD5mzEix6FHGvfJyGAL1UJidIEcPqgCej5enRSqZKyctsh6VYvmeGeXC7eTO3WMqkMgXL61G6Kui8Ya4hTXjfREcRBI+KZke7aowEYP6nUzFPNmJBSbgOwLfxzgRBiBYBmAC4DcEH4sC8BTAbwWFyltMEotbZnySc8nl6+8ZPZqJqeZrqZrxknk2ElZRKjlmyL2bC83OCpD4Ubb1b7Ti23SMqgNnXNrqjUuPGw856WlcuYtRTxlEEvU6pVxqd4KH+h1bYNpM8P9VNkCZsEJq+2Dqf5dOo6HFOnuuOZ7tIyGXmu0Uu3R3U8vKKc/wlVUqJ5ue72n1R/fVfb2C7FqBN8+9C5kZBPr9oR2s6YtqpRZtO27DuIJZvzcbCkDNd+HMpO17rhUZj0yAUx5zSagZNSYv2uQrRuVMv0ORVOU2sfLi2LSYwyfK6zPaqcWmCyYXtxabnpTObavAOomp4WCSt3s67z3Uk5ePv6Ux0/Lpn8UDfRkWO2w2Qn83LtJ2KpzFrWr4mNJhvAWxEA7uh+HN6akPjZcE/WyAkhMgGcCmA2gGPCFZVSYTU2eMxdQoh5Qoh5eXnWe8C5paROjVeTusZ7NbnlthMHOJsNe3nMyphOHGA8OlsS7gBZhQ95sfm1G3ZGlYuKy6I6ck7j0lNNPSPXqkHN1BYm4FJVP93yeSjMVkqJ4lLrz+zArBW4f9hCx+uwisvKIrN/OzVZu7xOcrJ0a37k519t7CWpRz0Qk20jkYbRDJV63Z5X2Vy1g0Tat2KuqpGzZEt+pBMHAOt2FerOyv37O03dGz7nlzNyceHrf+FHzbpGw7ff4q3Uhuk/r7N5urphst5kra/CaRa4Kz+YYXzfh9PR8SnjtO09X/8L577yp6Pn0wraPlJ+bjtR5TDbQYZMALrbJlhJRDKtVGtar3rc56hdvQpyB/fzoDTm4u7ICSFqAfgZwINSSttTNFLKIVLKrlLKro0aJS6zS2Wd0RAexNwZXfR+CzfQtI2SnJ0F6K/aHNZs9DWR7Mw4nPniRHR+blzkd6MMgX6lVIxCABt2ux8VOtKlsn5SEoxYXeK25x+K2mbDqcMl5SZr5Fyf1vp8UecO/XLx21Nx0ZtTTM9hlnVQT4mNAS+zBER2Ld2SH5Nt7jtNkgh1lILe9gV6W7IoSWkUP4XX5M0Nz2hqQ7SN3rMck42BR2ZvxcnPjsPSLRUd7TU6s53qyd4er002PJ/ir9WxHQW367o37XGWqGn/QedRMEFKduL3tpOZlvU5uEgVAvS1q5Ti6sgJIaogVBF9K6X8JXzzDiFEk/D9TQCkdCpEQkZt7uxWiVEcYorEu7fSoZIyw72clIZH9ub8qNt7vTElKjTHboZNPyh22HjUcrL5uRf+yN4GoPIORCSDX+onq4vcwz8swoeTKzoAGZrQyjFLt5k+vrRc4mmDtbZeX2DVDeVyKWM+nSu27ccqk+1ONumEquQXlZh2xNQzR0YbzyqzmKVl5Ri9ZBuklKZrYZX0+4pJK3dgtM7r/MHk6OOsws2ttmQBQtlMAf1R7G9mbcAgg/3mbvpUP9sxULEP6hJVR04vQtdJfXLQ4G+12u4mXpNXhb6SrjIcB6RB6Ze6ya0eSUqrTsEQkK9dpRVP1koB4DMAK6SUb6juGgng1vDPtwIY4b54sRwv1pbAgN+cbyCuVeoi01AiOUn3raf3m38ZnkN5jc02Xg2awjiyPgHG+70lyphl4TTA7Me5kqr6qVRn9siqztKGMGtn19ZZzDaZnd9prWWVLEWa3GfHsq35Mbf1fWcqLnhtMjL7Z+luhq7unBhlb1u3qxBP/bYUQ6auwz3fLkDWkm0xSWHUlPT7ituHzsP7f8bOpjnlZLxvzY6KGbP8gyUoPFyKr02iDcz221TWVao/f3ohuk6WXz41Yqlu1IV6zWIikqf844u52LHfXdIop3umpkKq6iYv3Xlu61QXgfzEX81jX0hmorp4ZuTOAXALgAuFEIvC//oCGAygtxBiDYDe4d8943RixKuJFL9dIH5Z4G59isIszCVo6wzsmGGwCbvfGY2Kk6WU1E96oXUS5pX63qLomXFtspM/srchs3+W4eO9nC3u/eYU3VkzpfzqdvuYZduxNq8w5natkrJyZPbPwoycXboh4epEVb8tdF+vfT1rAxaGs1ru2H84JeE+uw32hdSjDn3s/Nw4dH95kulsphkluuI51bo4vQy6TkLyN5h0yrgAACAASURBVOwujAktBULr7EYs2oJNe4oSdq3Qe147jq0b/7qWJEh63VTVYm9Kp9LTBP5+VitPz0nBVRnXyMV7/Ujm9SeerJXTYDxf0NPtec2s31VoK65fbfZ6bxrw8czqPdSrfWQPsiCojF/KoApakha/SEX9BOg3nouKywxnhL+ZtSEm6YR2JsUqO6xZY9rpoGDOzgN4fdwqpKel4fnLTsALf4Q6BsrAj90ZmA27C5EmBBrVroZPpoQ2wL7x09kYcksX08fl7ra31s3o9VQ2Ri8rL8evcXQK3Ypn5l/boXdC+dyVlksUHi7FUdUydJOZOBklzt4UO3sKREcn5Ay62FlBbXrb5b5nm/c6W4eXCqmom/7Tsy1eG+dtG4TBIqSojIP/di51N5zREsPm6A86JfMlcd2RSwWnnTgAkc2vU+nu81sHqiNXVq4fIkb+Muyf3XDDJ8brZhLhAq6NMGVUeT/8g/6G2noDRE738jFL8PC1i/XBv4XXQHVqWicS1rh9/yG8OGqF4d83PWcXnv29otznvzo58nODo6pGfraaERq7bAdydh5A28a1TI/7drb53yVlYrcHMeJV9kwnduw/hLaNakX2jHt6xDK8fm1nVEkXMZsPO8mIaierst/ab8c3qZ3qIviSF8nRos/n/TkpuJKdQyAeC5/qjTQh0Pn5cabHmV1XP7q5C3oe3xgrtu037MipvXPDqWhxtPeZ7xXezrdTpZCeBtz6xZxUF+OIdOOZLW0f26lpnQSWJNaVpzbD0NvOSOpzBk0qNlw2u4jqZR20S/u3DJmyznDG49nfl2N6jn70gzrc0M4arZxwyOH2/ENRWRjVrNoN8a6JdWvldnuhkTM9DPU+88WJ+F61hYGStEUvs+BUnf0w4+G3LJHPXXpiqotQqb12TWcAQH3V4AxRkNSrWQV1a1axPM6sbutz4rGokp5me2Ds0s5NcWrLo22X0alAdeQa1a6W6iK4ErSBq/87ualho6wyaX+M+ah/KjSq5d/POEdgraUiuW2iBkP1rmPxjrza+Qgp4ZznvfonLnl3mu4xVv2HrTohrn6SyJl0JbNmjw6x25DNdbmJu5EZPrtOBLWNEBRXd2mO3MH9UC0j3fF+l268ctXJCX8OCj6rCA41u+2YjLTgdI+CU1JUjAYFTSpTyDtZ5Nw8PPX70mj3e1oFyeodxvsypYo29bwXqlfx5muegKJVOqlYX5qoWZF4tzjRYydiO+9AKNuk0ZYheQWHYVWtTQ/YvpGJoE2akwi3DZ2b8Oeg+HnR6fruzjOjfjc7ZWMPOtQ3ndkS157eIu7zUOV3k4NIJrveuv4UnNayXtRtJzevG/V7Rrrxl6DjsckL8w5UR6529UAt6fOFZg7icqt4nNmKnEs3qRjc8mqUmhNy1lIRabY9QbNP70zK8fyc31msbQOs93w8fdAE/DTffP2bXtKZI0lZOVNWUQUv6m5tRlCzU36j6fTZ9b+LOkR+7ndyE1fnoCNPRgLark3r1cDjfY+Pum34Xd0w+ZELIr93OCa2s9bv5CZY9HRvnNisbsx9iRKolnt6QFuSqSr2zd1aOppF8WoE99quzT05T5C0qO/NQla/fcb/07NdqosQGEXFpfjRooORCG4SmqTKn6vsrdHaqbOPWM2q6ZGf/Tib7idtnhiFIeFsoW51b9vQo9JQZaAd6E0zaS9Uy3DXtLyvR9vIz2e3CX3+eh1/jKtzUWJ9c4e7zrqVj27uEokOsyveSCa7M3o1q2Ygs+FRkd/1wjT79+mIejWTu4Y0UB25ZMRkJ8qJzZKbmAIAHuvT0VGcr1dhfXed18bymLPbNPDkubROaVHP+qCwQVd4tzB+0OUneXIeR51pm0PuVeKI9e5zwrGRn49vkvzPcJAs2LAv1UWoNM54cWLMbWe1TkydQfrcbspN/hPvlb1TkzpooUmeY9Ye00u0o+fRPh0sjzm2Ltc9+s1FJxyDVg3svcdmOuu01/7W6Rgcp+osWWnd8CjUq2GdvMSMUVPqaFWH7Mvb7SV6S0VkW7A6cikorVfLDFocHf+H3qna1auYxvBqOTnWTNvGtTCj/4Wmx7hJmlCrmnVo7Qab+1AN+2c33HSmdxuabsv3Zv+iRITmxVOxdGpaB60bhSrVc9tx6wEzDGZLLK/qp6C47JSmKX1+9YblFGzxjoFn/ae75TnVg9V2E0pkNqhosFc1mMV7rE9HW+ei5GlarwZa1K+JJzWhh069cW1n1Na069LSBNbl2WvHAcCkRy5AnxOPtT7QxNVd9KPI2jauhZ/+dRZWDeyD89vba/+kYr4pWB25FLxC8TSClRmudCHwytWpyb7kpPxGiQjcZI5qWs98atwqQYM2C9H/LuqApc9dhKNU4VVa91zQBr072QvDOMtkRvDSzs4bUF5ldBwy1V44lHotgZW4Oxjsn9jyy4Lkb0B9JBm7bEeqi2AoEYm4ruvKRA/kDXXCNTczKXrXt1zNhvPxZlzOuj/UWfz13rMx/qHzIrfXrh7fbAt5z6jT7VRGmtBdvrFln/7AuFGeDLvtL3WEkdppLY9G7uB+uonhumbWR7UM43anoqZJ2zTR2JGz4DbWGwByXuyL3MH9kJYmUlYZOdnXasW2/bq3H6UZMZk/oFfU7+o1cdrRFQD4/B9dY27TZvLMfvpvuKP7cZHf37/xNHRtVbHvRo0qoS+J2Rf2sT4dUbOq+azdnCd6Yv1LfU2Pueu81qb363GSHdTMZTY7keq1BImmfIICHNmcFL8uZEfO73IH97N1nLousqPfSd4nZuD4CXlFXXf/cX93jFN1lLTshpDN0OyFqL02P9jL2fpqZW+6U1sejXY6SSSOBNoB/x4dYmeBLo5z9kmP2edBzx/Zob0qtQlwnMpIT3M0m3bjGc6zU75w2QmRnz+46bSY+wf0q5hVfKBne8fnVyj9k1Q0kwLVkUtFUkUvK5TXr+mMhrWSuwgye3P0hrpOYo8V6tmxz27tigaakbd+JzfFlP/1AABU1xmVuLDjMZH7AaBhrWp44fITce8FobV0LevXRN2aVfDUJZ0ix3Q4tjZ+uufsyO/KaMeB8Ea/T/TtiLEPxlY+xzeJfr+O1mz82LhOddPO4L97tI266NldR+dVWN05DhIM1DAZAbry1GZeFAdAxWAA+3HkJT9vZ6Etm15oWdTxHl6bGtWuhod76zco6tnYyJZI6+ZuoWUEmQ1qonb1Kmhv0q6pa3O9UZdW0RscaxvZ/zy3YkBUb83cC5efGLUm3KuBer3BZCe8zmew8oU+yH7mb+h7knmH5bSW9WIikU7SyXzYvV18SYj0QgSdvvYXdgztUXlJnJlFq2WkYWfBYcP7G9euhv9d1AGXq8LM/3Oh/iC20WBaDdXgvjpBz5T/9cA9F7TB7edUDNpdH8d2FyLmh+QJVEdO2eg0mdo28m7T6Ku6NMe8Ab1dPfbdG071pAxm07962aGe/b9O6HBsbfzvog549eqT0VPnGCklGtcJde7UYYnf/fPMyAhTS1U4xyd/74IOx9ZG3/AXz862EpdoZqqOb1IHHXT26bhWE4409qHzItnX/m1jFmvdruh1Idef3jJmhq5z89jK1e7ibitO6tOqGWm6oaTN6tWIqkziXXdXMSPn45Y3BY6dNa+J8PM9ZxmG2ADAiuf74J4LouuKE5rWRevwIJh6wElhlVDo4d7tse7F2EiAL247PfLzuzecig9vOg1zn+yF//Rsp/u9PdVBMicnUvVeUHJUr5KO3MH9MFk1oPrV7WfgjOPqR34/L9zAr1XNXoiYdlBBuR430Zml0QvnvKVbK/TsWLFpfbxZMI+pUw0f3HQafr/ffNDFSvN6NbFqYB9HjzHaN29Av+NRvUo66taoghOaxrYb1Jkf09MEjmsQPdCeliaw/PmLom6TMnY/M7UbLTIw5urkEchsUBM3d7Oe7VLS7SshiNo2wZWnNcP3d3WzPI+iYa1qpgnezmvfCPf1aIuOqkRr9/ZoG/W5URgNcpUarBlq2aAmHuvTMepzd/RRVfH7v7tjukWOBz0F4UmGVAhURy53d1HSn/OJfvEt5nRLO+rQ0CD+XC/rj5mTmxsfr/0izH2yF/4RHq24r0dbXGOwZuPUlkejepV0LHvuoqjFr2e3aRjTsdJj1j+4/ZzjkJ4mIg2Nl64MZYc0+rvVFcvcJ3uhce3qeKh3ezQ/ugbuPt86ZHLtzsKo7FzpaQJPqP6mG85oid/uOwdLnv1b5LY/7u+OLq3qw4zdtXtedJb++7f2nm5CrzQo2Y0jr6R7GG4+8PIT8VCv9hhx3zl44XLrGfQurerj7LYVa2TPb98Ib113SuT3GlXTI2FeQEUmXCWaQRm0Ubc/0tIEHvlbxSyaNvLh9u7H6TZUL1CNjvc9qQkuVo0q683yv3J154SkY//W5b5fRmpUSecaP587r30jDPtnN1x1WnOM+s+5+PzWrlj+/EVo29heFJKSqOSxPh1x/ekt0PzoGnisT0cM+2eoIa9eB280mKje/8tshn7OE70ME1IoZj/RC31PahJ3bEzVjDRba6LU7Yl2x8QO+DetWx13qmYl9TaI7t6uIT66uQuAUDh3Lc2g9pnHNdBdLjLy390x8b/nR36fP6AX5g3ohfkDeuHFK07CYlX7RCtPZwYsIz0tst0DoJ8n4Jd7z8bv93fHPRe0wQO9YiMG0gTw4hUn4czWDTDnyZ6Gz69QBhHUWy6tfCHUgVY24laW1CgkQoMSn/0jNACmzn5utG6vpFyice1qtnMfnNS8bmgw3KWSsuQHxQeqI3d8EndKV7hd+9T/YneZllo1qIk7uh8XMyKshLe1aXQUbjij4gJZX9X5utPWuo7oD5l6ywFtAg07G0l/8veukVCMo6plmI6qKfHJrRuGKj0lEUur+sbhnk//XyesVY1k33BGS+QO7oc64Ubgf8MhSOrU+K0a1MSVpzWLlL9Lq6Mx7bELbTUcdxcWo071Klj3Yt9IpQIAv/87NMrXu1NjCBHdCLWz8eN1XVvEhKJYeahXe3Rrbd5B1Hu1q2Wko3enihGrZkfXwHOXnqBzpD3KjKlX+wwSvX/jadh/sCSucyx77iJ8c8eZuLlbKzzQqx06t6iHW7pFZ6Lt3LwurjytWcyMwHWqEJo7uh+Hy8OhyOfphB0NuaVL1O9SSqx8oU8ktFupN5U6u2bVdIx+4NyovYmqhxsZr6uSojSrVyMycHNMHfORaUWj2tXw6a1dcWf34/DFbafbqqPtsDMgaDYLoNWqQU28nKIEX2RfeprA69d2RqemdZCRnhbpNNgJ4b33gjb47s4zcc8FbTD4qpMhhMA9F7SJ7LOlNMKf+b9OUWtOB195ErKfju1omIX31a1ZBfcbhNRp1VeljFevJ1MPzrxxbWf8+K+zdB8/wObg/eMXHx9JBKc3w3/zWdF1kTaaSWm79DnxWOQO7oc+JzZBlfQ03H7OcRjQ73jMeaJnJCnb29efEuncKO9NG1W0WINa1dCwVrXIspc61avorgcDQpta5w7uh7lPhnIdKB3Mi044Fv+5sC3+uL873ri2M364u+L1eeWqk3Fay6NRNSMNj/XpqDuDv+6lfqgefs8b146dlX384o44sVkd3NcjvD1VuCmqdIJb1K8ReXy/k0OdLqVOVAYC1J+RGf0vxOf/qIho6NEh1Ob55d6KJTlAKKpuzpO98I5HUW1aSoe0SjircrxbIbgRqHgKrxfADr+rGx7+fhG25lfsl3PJyU1w2zmZuOrDmQCAMpdxaXebJMyY8PD52H3gME7PrI/WT4yKuu8vVejD/Re2xbuTcgCEMudc17UFHujVDtUy0jBsziYAoZCfP1flYeS/z0GaEPh02noAoQyOetQzew2Oqoo5T/ZCm3AZGtWqhpxBF2Pqml22G+16YRRG+p7UJCrZQIdja+PN6zrjb50qKsGpj/ZwFAp4f8926NGxcdQGkurX0CllY++0NIHqaRWjQSc1r4uVL/SJVDRAaLNs9d5Wox84F6VlEv/33rSY81arkhYVHvLzPWfjqg9nxBzXThUjf/+FbdGrU2P0e2cavvjH6UhPE1i9owDVqpiPFh4sKcPVXZpj1cA+mLB8J7q3a4icnQU2/vpoygXj01u7Imvxtph9hCixbjqzJb6dvTEh5876T3f0e2da5Hme6Hs8DpaU4Y/srXj29+UJeU61QyVluoM+nVvUQ/Yme/vxHVUtw3K9yK/3noO0NIGychk1Q6AecVc6b7Of6Km7PqhxnVAdd0f34zBx5U6c0qIeqldJR7tjamPqoz0io7fpaQJjHzwP9Y+qiupV0nGj6v1TZh6u6tIcTevVwA2fzMLwcAjS6oEX6+7hWWqyRcuAcHjnrMd7QkqJgVkr0Kh2NdzXoy0y+2eZviZumWUa/uK203HbF3Mjv1vVUYpbz2qFk5vXw39/zI67fDe4SIRA+j6+uQuuGzILQEUDVSsjPQ1nm6zpzkhPi1zv1dsCXW/wPlkFo7RqcBSmPdYDE5bvMK2j6qo6oW9ffypGDxgNAFjwVO/Id6NujSo4PbM+1r3YF0OmrsPg0SsBhNbEK993tdUDL0b78HmAiuv0NV2bo1vrBmjZoCaeGrEs6jH3nB/bBvv8H11x+9B5AIBTWuoPnjz9f7Gh25ed0gwXn9gEI7O3Rq0Fu+u81oZrAvue1AQP9WqPNyesxpN9j8ed5x6Hw6XlkTZMw1pV8fjFHdEvvM4tPU3g4b9VDOafnlkx8HyVyWzo/AG9TLNY/vW/C1BcWo52x9TG3ee3QVFxKSau2InH+4YmO5SwXzWlPad08m45qxXW5h2IatdqM6Of174RVg3sg2oZofMp77VZhnIvfHX7Gdh/sAR//3wOVm4viCtBolsJe0YhRB8hxCohRI4Qon+inseMVfbBTk3rRHXiAOCNa0+JushXd/Gm/HLv2aYhcm0b18KZrRuYzl4BFWvWru3aHFUz0vDy1Sejab0aUZ2sB3u1w8/3nI2Tm9eLGq3QS9P/9CWdcP+FoeOBUIak9DSBG85oiY7H1kZamkBGehp6dGysOzKtNvXRHrj3gjY4oWl8C4OvOLV5VFbMFvVrRq2ns+PEZnVRr6a7JDJ/3N8d7914KtqHK4yBJqFZ1TWNk4d7t4+qJI5vUgcnNa+LFy4/MWYtYkZaGu4OV+xznuiJLq2OjkoAo2harwbWDLoYawZdjLQ0gROa1sXaF/tG3pM7z20dNetwaXgR8FWnVVS0vcOfm2oZ6eh3chPUrVHF1ULyiQ+HQjea1K0RFSISdMmom5wMcBi5RTOiq6WeMbarVYOaGH5XN5zQtC5+uPssXHFqMzx1SSccVS0DDWtVw7Wnt8D1p7fAmccZzwTfqinXaaoGifqCvPCpivXAT2vWlRUWl+pmZKtvMhOw6Oneuj9rqRv0Sh2bniZitmIZ99B5UTMDx9SpHvMdVzu7bUPkDu4X1dBrUb9mVD3e4djakVmyjsfWwRmZ9WMSl5zVpgFyB/eLDIxUzUjTvRYcOGS95iI9XGc/e+kJhplsv7+rG964Nv7tEcoNtqcBQmsEV77QBy9ecRI6N6+LgZfZSxL17KUnmDYSnXhAJ4150Pih3QQAZ7ZuELkWntUmvuQaQOi6dkydalEZBLWq2whnbH50TdxyVibeuLYz2jWuFVOvKN694VS8evXJqJqRhsmPXICfwrNv790YmpVpFw4fTUsTUUlZzlH9replIlUzQp1SpdM0MhyhI4SItFdGP3Au/nNhWzzZ93isf6mvbhvwwo7HIPuZv+GxPh2jnsuOqhlpuLpL86jzPtH3eNxv8rl/oFc7zOh/Ie489zgIIaLqNyEE7j6/DZob7HEshEDu4H7IHdzPdGC/Qa3/Z+++49uozz+Af57EIWGHEVbSYigpFGgZDbtQKNBCaZmlpQtoobQ/WkonhA1lBCh7hE0GexQIkD1JyMTZwxmO4+zYznJsx9vP74872SfpdLo73Ukn+/N+vSC2dJK+lk7Pfefz7W472+m2C4/CtacX4tD9do8bhNltlwKM+ttZOOHrqWco/eiYg/DBn05rS6CzR/cCPHblcWkT8Vjr7k/94niMvPlMx+OD0KNbVxywVw8M/f3JGPy7k+KmC2dLKCNyItIVwPMAzgewDsBXIvKpqmbc1fu38/pixMKNOKTnrthW24j566pwxhH7YdH6Haiqa8K1pxfijRmrceSBe+K2C4/Cy5NT78u1u2Xu8ai/nYmD99oVuxR0aUvc8b0j9o/7UCb88/v4weNfADCmJFp7TK8+7VAMnb4ah++/O050OEETPXjZseizz2645vVZ+NEx8UPvx32tJ178zYk4+8j4hZ17dC/A7rt0xX2XHIuCrl3apuxZM2L+wWzEfnnrOfjeIxMBGOs0AGOqobXCFVt35sXX9t0Nt3SAjTqP7b03ju29N04q3BdjlpTbLkj26renHoqjD94LV7wwDfvtvgu21Dbi2332xh7dC+Le9977xPcoPXKF8TkkVjidguhPvnMIfmJOQ3jcobLmdoT17etPwa9enYkrv9snKTtpRxBmbLKacss5OOKOkUm3L73/Atz5ySJcc1qh7chtzM3n9m2bPgwYF/Erv9snboTOqdGRinW0+uTD9o1LdgAYF9mHr/gOXvpiJWau2pr0+LF/Pwt9D9wT911ybFuP52H774EXfvNdNDYbNf3nfnUCpq/cgn123wVj/34WDtirByYsjd8DrkdBVzzx8+PxybwNcbdfcnxvTFxWmfS63/9mL/TcbRdX2wc8dNmxmLi0AjeeYz8rIcYpcx8AjPjrmajKYPpn1y6C91NM33LjaLOT7NYLjsIjo5a6ftyg352EHgVd8ctXjBGVU8xZA/94392o179++E0c23tvXGsZYQOM+PLK5FJ8NHc9fnDUAZiwtKLtvtO/YXRM/uqUr6dNtgAkbwPRvaALGpodWooJrjntUJx91AF4fkIJilZvA5B5KvRcy1Zscus3px6KfoX7BJLIq1vXLph5+3m2903619mYvXpb2o7tmK5dBJef2AeXn9gHqor/fJ789vzUsh6qcP/d26Z7/uQ7h+Cibx8c1xiyXhsLLWtb/3DW4dhvj13a1sgCwMR/n43ahmbbjNHfOnivuCUeqey9a7eUM6bCkG5P37D80WZE0ouTCp2XlaRzaYCZu904YK8etqO52RDW1MqTAZSoaikAiMi7AC4BEEBD7pv4m7nQsrmlFbNWbcUph+8X92W817IeqOTBC3HLhwswYtFG7NG9GzbXGAs9j/taz7apMM+MX4EjD9yz7ct9wJ49MOeu89vmuvbuuSt+eMyBONyck3zGEfvhsSuPw+CpZfjlyV/H/HXbccnxvXHsIXunHclK9OtTjN7tVBWUC45NTqla0LULFv8nuTc+1hPcc7dubT0TffbZDZef0BtnfjPzXrWO7MC9eiStr8nEdw/dB0V3npcySQ1gXEBm3XEuTn5wPADgFyeFNzXIWskZ/8/vY8P2Osws3YpWVSwvr8G4YqOiffoR++Pzm76Ho11ckPJUaLGp7OGLMGVFJdZurUNB1y54+qrj8dn8jbj/0mNQXd+M0spa9OjWtW3z6LKHL8L1Q77CuOKKpOeJGXnzmTjigD3aGve3XngUbnp7btuC9Sd/cRzembkWA39zIl7/chUGTlqJKbecgzMfnYjfnnooZq7agn133wX/OP9ILFjnbsoiAFx/5uF4a+YarNnanmDq4uMOietZnX/PD/HIqKX46w/64kDLBczauRA7/vyjDwLQ3pC4+PhD0KWLQMRY/3DHj7+Fg3v2wE++cwjWb6/Dko070LvnrvjZd/vgs/kb2mK+GyKCGbenX2yfztEZzjbI1Dd67dF2Lvzq5K9jN5fZBGNrRebf80NUVrfPOJn873Pw42emtG3hkkrvfXZN6jwEjIrqE784Hk/84nioKkYt2oQfHnOQYyfRdd87DK+Z0/1j7rWZOuZ2xsDfzuuLnrt2a0vCdc6RB6Cw//COssdlaLHJr6MOCv87YG1oeSUiuPOib+FED+vP7UfKDmjLSmt1+Ynxo8X7m2vRiKJEvGwY7fpJRX4G4AJVvd78/bcATlHVv9gd369fPy0qKgq8HInqGlswq2yr7T4aTlpb1XVvUa4VlW3FPrvvErcQlqKtvsnYVsPPCIsXizdUoe8Be9rOZ6+ub8KWmkbfF1Q7IjJbVZN3g88hr7EJCD8+Ld20A90LumLJhh048qA9k/YSsmMXk1paFTUNza73gUpnU1U9XplSip67dsPjY5dj9p3nZTRKGxvBG/W3M9sqiFV1Tdhc08B4lSU76puwo64Jj49Zjt49d8VzE0tw2Qm98eQvjsf67XW4Z9giPH3VCdi9ewH+9u7cuBFTt5upJ9rZ2IyttY0oqajBsb33tq0Iu13XZ1eGV6eU4tTD93OVdMoqavEpirGJiLLPa2wKa0TOrtUT12IUkRsA3GD+WiMiy0IqCwDsD2BziM/vRZTKAkSrPFEqCxCt8kSpLIC78gQ3xBmctLEJyGp8ypvPdf9HgnmBb7l/nrx5b3IgsLI8BeCpq9p/f+1a++PE+XPLynuTpgwxbssStfgUtdgEdNBzPiBRKk+UygJEqzxRKgsQQt0prIbcOgDWTWT6AIhbDKGqLwN4OaTXjyMiRVHpeYtSWYBolSdKZQGiVZ4olQWIXnk8SBubgOzFp6i9j1EqT5TKAkSrPFEqCxCt8kSpLB5FKjYB0Xovo1QWIFrliVJZgGiVJ0plAcIpT1jpVb4C0FdEDhORXQBcBeDTkF6LiMgtxiYiiiLGJiLyLJQROVVtFpG/ABgNoCuA11V1cZqHERGFirGJiKKIsYmI/AhtQ3BVHQFgRNoDsyMr0xBcilJZgGiVJ0plAaJVniiVBYheeVxjbHIUpfJEqSxAtMoTpbIA0SpPlMriScRiExCt9zJKZQGiVZ4olQWIVnmiVBYghPKEkrWSMMeKfAAAIABJREFUiIiIiIiIwpP9LciJiIiIiIgoI3nXkBOR10WkQkQWpTnuJBFpMfdmid32qIgsFpFiEXlGzJ0hRWSSiCwTkXnmf8m7oQZflkdEZJH53y8stx8mIjNFZIWIvGcuenYlpPIMFpFVlvfm+CDKIiJni0iV5Xnvttx3gfl5lIhIf8vtob03PsuTi/fG9rEisq+IjDXfm7Ei4nqH1JDKc6+IrLc85sduy5OvohSbAihPoPEpSrHJTXmyGZ+iFJsCKE+g8YmxKThRik9Rik0hlod1pw5cd4pUbFLVvPoPwFkATgSwyOGYrgAmwJhr/jPzttMBTDXv6wpgOoCzzfsmAeiXxbJcBGAsjDWKuwMoArCXed/7AK4yf34RwP/luDyDY8cF+d4AOBvA5ynKtxLA4QB2ATAfwNFhvzc+y5PV98bpsQAeBdDf/Lk/gEdyXJ57AfzL63uTz/9l8P0LPDZlWJ7A41NIZfH1/XNTHp/xIJT3xmdZsv7eOD0WPuNTSGW5F50sNrl5Ly3nFOtOwZTH13cwpHjAulMHjk15NyKnqpMBbE1z2E0A/gegwvpQAD1gnFDdAXQDUJ6jshwN4AtVbVbVWhgn+AVmL9cPAHxoHjcEwKW5Ko/b182gLHZOBlCiqqWq2gjgXQCXZOm9cV0eH88TRFmcHnsJjPcEyN57k9FjO5ooxaYMyxN4fIpSbPJQHjuBx6coxaYMyxN4fGJsCk6U4lOUYlMY5XH7uhmUxQ7rTv4em/exKe8acumISG8Al8HodWijqtMBTASw0fxvtKoWWw4ZZA5l3hWbNhBWWWB82S8Ukd1EZH8A58DYCHQ/ANtVtdk8bh2A3kGUxWd5Yh4UkQUi8qSIdA+qPABOE5H5IjJSRI4xb+sNYK3lmNh7EOp746M8Mdl8b5wcqKobAcD81/UUvJDKAwB/Md+b191OV+jIohSbnMqDHMSnCMYmIFrxKUqxKVV5nIQZnxibAhCl+BSl2OSzPDGsOyWXJ6Yz1J2yEps6XEMOwFMAblXVFuuNInIEgG8B6APjZPqBiJxl3v1rVf02gDPN/34bZllUdQyM4flpAN6BMVWhGYBdEAwyrajX8gDAbQCOAnASgH0B3BpQWeYAOFRVjwPwLIBPzNtTvQdhvzdeywNk/73JFT/leQHANwAcD+Pi/3h4xcsbUYpNKcuTo/gUpdgERCs+RSk2OZUnFxibghOl+BSl2OSnPADrTp297pS12NQRG3L9ALwrImUAfgZgoIhcCqM3ZYaq1qhqDYCRAE4FAFVdb/5bDeBtGEPCYZYFqvqgqh6vqufDOOFXANgMoKeIxPb36wNgQ0Bl8VMeqOpGNTQAGISA3htV3WF+DlBj75xuZo/WOsT3aMXeg1DfGx/lycV746RcRA4GAPPfijTHh1oeVS1X1RZVbQXwCoL7TuWzKMUmp/LkIj5FJjaZzx2Z+BSl2JSmPE5CiU+MTYGKUnyKUmzyUx7WnTp53SmbsanDNeRU9TBVLVTVQhhzgm9U1U8ArAHwfREpEJFuAL4PoNj8fX8AMG//CQDHzEWZlkVEuorIfuZrfgfAdwCMUVWFMYUhlhHpGgDDgiiLn/KYv8dOcIExdziQ90ZEDopNwxCRk2Gci1sAfAWgrxhZlnYBcBWAT8N+b7yWxzwu2++Nk09hvCdA9t4bp8ccbPn1MgT03uSzKMUmp/LkIj5FKTaZzxmZ+BSl2JSmPE5CiU+MTcGJUnyKUmzyUx7zd9adOnHdKauxST1mR8n1fzCGrzcCaILR4r8OwJ8A/Mnm2MFozy7UFcBLAIoBLAHwhHn77gBmA1gAYDGApwF0DbksPcwyLAEwA8DxluMOBzALQAmADwB0z8J741SeCQAWmifUmwD2CKIsAP5ivt/zzdc83fLYHwNYDiPj0R3ZeG98licX703SY83b9wMwHkZv4HgA+2bpvUlVnjfM92YBjEB5cK5jR9j/ZfD9Czw2ZViewONTSGXx9f0L4JwPND6FVJZcvTeBxqeQytLpYlOG30HWnfyVh3WnDlx3CqksvmKTmA8mIiIiIiKiPNHhplYSERERERF1dGzIERERERER5Rk25IiIiIiIiPIMG3JERERERER5hg05SklEbheRV3NdDgqHiLwuIhUikjbFrYicJSJzRKRZRH6WcN+jIrJYRIpF5JlYyl2iMDE+dWyMT5SvGJs6tqjFJjbkKCVVfUhVrwcAESkUEZX2TSU9E5GXRWSZiLSKyLUJ911l3ldlfkGGiMhelvv3FZGPRaRWRFaLyK8s910kIl+KyHYR2SQir4jInn7L2YkMBnCBy2PXALgWxqavbUTkdABnwNg/51gAJ8HYZ4goVNmMT+b9h4vI5yJSLSKbReRRy32MT8EbDMYnykNBxiYR+aaIDBORShHZKiKjReTIhGP+bsaWKrOR0d1yX6GITBSRnSKyVETOs9x3rYi0iEiN5b+zff7ZnclgRCg2sSFH2TQfwI0A5tjcNxXAGaq6N4w9TwoAPGC5/3kAjQAOBPBrAC+IyDHmfXubxx4C4FsA+gD4bxh/QEeiqpMBbLXeJiLfEJFRIjJbRKaIyFHmsWWqugBAa+LTwNhLZxcA3QF0A1AefumJApcyPomxoe1YGPsfHQQjxrxpOYTxKWCMT0QAgJ4w9hQ7EkZ8mQXLptUi8iMA/QGcC6AQRv3pPsvj3wEwF8Z+aXcA+FBEelnun66qe1j+mxTen9IxRC02sSFHEJFbRWS92dO8TETONW+/V0RilZXJ5r/bzV6b08xjfm8OC28ze4oOTfU6qvq8qo4HUG9z31pV3Wy5qQXAEeZr7A7gCgB3qWqNqn4JI7D91nzs26o6SlV3quo2AK/A6Okg714GcJOqfhfAvwAMdDpYVacDmAhjc8uNAEaranHopaROIwrxCUaP6gZVfUJVa1W13rw4Mz5lF+MTRUY2YpOqzlLV11R1q6o2AXgSwJEisp95yDUAXlPVxWZ8uR9GvIKIfBPAiQDuUdU6Vf0fjA2nrwj8zaCcxSbf01CoYzCH6P8C4CRV3SAihQC62hx6FoBVAHqqarP52EsB3A7gpwBWwOgVegfA6T7L8j0AwwHsBWAngMvMu74JoEVVl1sOn4/Uw9BnAVjspwydmYjsAeOz+0Dap2p3T/0IQESOQPsoAwCMFZGzzB4rooxEKD6dCqBMREbCmAKzCMZFeyEYn7KC8YmiJIex6SwAm1R1i/n7MbCM0MGIPQeaDb1jAJSqanXC/cdYfj9BRDbDGGF6A8CAWDnJnVzHJjbkqAXGCXe0iFSqapmHx/4Rxpe+GABE5CEAt4vIoaq62mtBzJ7svUWkN4A/AIiVZQ8AVQmHVwFIWmciIufD6KE6xevrE7oA2K6qx3t4zGUAZqhqDQCYFd1T0d4LSZSJqMSnPgDOAXAxgPEAbgYwzJw+w/iUHYxPFCVZj00i0gfGNO5/WG5OjD+xn/e0uS92f2/z58kw1methtG4ew9AM4ABHv4WynFs4tTKTk5VSwD8DcC9ACpE5F0ROcTlww8F8LQYi/i3w+jREbQHCb9lWg9gFIB3zZtqYIzSWe0FwNrLBBE5FcaC0p8l9I6TC6q6A8AqEbkSAMRwXJqHrQHwfREpEJFuMEYhOHWJAhGh+FQH4EtVHamqjQAeg7Hm5FtgfMoKxieKkmzHJnNd2xgAA1X1HctdifEn9nO1zX2x+6vNv6FUVVepaqs5u+A/AH4G8iTXsYkNOYqt4fgejOCiAB6xO8zmtrUA/qiqPS3/7aqq0wIoVgGAb5g/LwdQICJ9LfcfB8v0JBE5Aca6lN+b61woDRF5B8B0GPPt14nIdTASNVwnIvNhvL+XmMeeJCLrAFwJ4CURib33HwJYCWPe/XwA81X1syz/KdSBRSQ+LUjxGgDjUygYnyjqshWbRGQfGI24T1X1wYS7F8OINzHHASg3p14uBnC4xGfJjYtNNmXl9hxpRC02cWplJ2fO8+4NI2tkPYyeZ7sGfiWMrDuHw6i4AMCLAO4XkXmqulhE9gbwQ1X9IMVr7WI+twDoJiI9ADSqaquI/BrAFBgB7usAHoQxhQmqWisiHwH4j4hcD+B4GF+S083nPRbGCN5NvEi7p6q/THFXUlpdVf0K7XO5rbe3wJgmQhS4qMQnGBkq/ylG6u6JAP4KYDOAYlVtZHwKHuMTRVm2YpMY2zCNBjBVVfvbPP9QAINF5C0YSTPuhJEeH6q6XETmAbhHRO4EcCGMdPdXmM99IYA5qlpuThO/C4BtfKR2UYtNHJGj7gAehlEp2QTgABiLcOOo6k4Yjaup5nSAU1X1Yxg9UO+KyA4YCQAudHitMTCC3ekwMvzUwVi4CwBHA5gGYyrAVADLYKyTi7kRwK4AKmAsCv4/VY31bPwTQC8Ar0n7XihMJkCU/yIRn1R1GYDfwKiAbYPRULvYnGYJMD4RdTbZik2XwUiw9DuJ3+/t6+bzjwLwKIwOptXmf/dYHn8VgH4w4tbDMKZ2V5r3nQtggYjUAhgB4CMAD/l6NyhnRDXVbBEiIiIiIiKKIo7IERERERER5Rk25IiIiIiIiPIMG3JERERERER5hg05IiIiIiKiPBOJ7Qf2339/LSwszHUxiChAs2fP3qyqvXJdjkwxPhF1PB0hPjE2EXU8XmNTJBpyhYWFKCoqynUxiChAIrI612UIAuMTUcfTEeITYxNRx+M1NqWdWikir4tIhYgssrnvXyKiIrK/+buIyDMiUiIiC0TkRC+FISLygvGJiKKIsYmIssHNGrnBsNmtXES+BuB8AGssN18IoK/53w0AXsi8iEREKQ0G4xMRRc9gMDYRUcjSNuRUdTKArTZ3PQngFgDWHcUvATBUDTMA9BSRgwMpKRFRAsYnIooixiYiygZfWStF5GIA61V1fsJdvQGstfy+zrzN7jluEJEiESmqrKz0UwwioiSMT0QURYxNRBQ0zw05EdkNwB0A7ra72+Y2tbkNqvqyqvZT1X69euV14igiigjGJyKKIsYmIgqDn6yV3wBwGID5IgIAfQDMEZGTYfQifc1ybB8AGzItJBGRS4xPRBRFjE1EFDjPI3KqulBVD1DVQlUthBGATlTVTQA+BXC1mYHpVABVqrox2CITEdljfCKiKGJsIqIwuNl+4B0A0wEcKSLrROQ6h8NHACgFUALgFQA3BlJKIiIbjE9EFEWMTUSUDWmnVqrqL9PcX2j5WQH8OfNiERGlx/hERFHE2ERE2eArayURERERERHlDhtyREREREREeYYNOSIiIiIiojzDhhwREREREVGeYUOOiIiIiIgoz7AhR0RERERElGfYkCMiIiIiIsozbMgRERERERHlGTbkiIiIiIiI8gwbckRERERERHmGDTkiIiIiIqI8w4YcERERERFRnmFDjoiIiIiIKM+wIUdERERERJRn2JAjIiIiIiLKM2zIERERERER5Zm0DTkReV1EKkRkkeW2/4rIUhFZICIfi0hPy323iUiJiCwTkR+FVXAiIsYnIooixiYiygY3I3KDAVyQcNtYAMeq6ncALAdwGwCIyNEArgJwjPmYgSLSNbDSEhHFGwzGJyKKnsFgbCKikKVtyKnqZABbE24bo6rN5q8zAPQxf74EwLuq2qCqqwCUADg5wPISEbVhfCKiKGJsIqJsCGKN3O8BjDR/7g1greW+deZtSUTkBhEpEpGiysrKAIpBRJSE8YmIooixiYgyllFDTkTuANAM4K3YTTaHqd1jVfVlVe2nqv169eqVSTGIiJIwPhFRFDE2EVFQCvw+UESuAfATAOeqaizgrAPwNcthfQBs8F88IiLvGJ+IKIoYm4goSL5G5ETkAgC3ArhYVXda7voUwFUi0l1EDgPQF8CszItJROQO4xMRRRFjExEFLe2InIi8A+BsAPuLyDoA98DItNQdwFgRAYAZqvonVV0sIu8DWAJj2sCfVbUlrMITUefG+EREUcTYRETZIO0j+7nTr18/LSoqynUxiChAIjJbVfvluhyZYnwi6ng6QnxibCLqeLzGpiCyVhIREREREVEWsSFHRERERESUZ9iQIyIiIiIiyjNsyBEREREREeUZNuSIiIiIiIjyDBtyREREREREeYYNOSIiIiIiojzDhhwREREREVGeYUOOiIiIiIgoz7AhR0RERERElGfYkCMiIiIiIsozbMgRERERERHlGTbkiIiIiIiI8gwbckRERERERHmGDTkiIiIiIqI8k7YhJyKvi0iFiCyy3LaviIwVkRXmv/uYt4uIPCMiJSKyQERODLPwRNS5MT4RURQxNhFRNrgZkRsM4IKE2/oDGK+qfQGMN38HgAsB9DX/uwHAC8EUk4jI1mAwPhFR9AwGYxMRhSxtQ05VJwPYmnDzJQCGmD8PAXCp5fahapgBoKeIHBxUYYmIrBifiCiKGJuIKBv8rpE7UFU3AoD57wHm7b0BrLUct868jYgoWxifiCiKGJuIKFBBJzsRm9vU9kCRG0SkSESKKisrAy4GUf64YWgRjr1ndK6L0RkwPhFRFDE2EZEvfhty5bFhf/PfCvP2dQC+ZjmuD4ANdk+gqi+raj9V7derVy+fxSDKf2OWlKOmoTnXxehIGJ+IKIoYm4goUH4bcp8CuMb8+RoAwyy3X21mYDoVQFVsGgERUZYwPhFRFDE2EVGgCtIdICLvADgbwP4isg7APQAeBvC+iFwHYA2AK83DRwD4MYASADsB/C6EMhMRAWB8IqJoYmwiomxI25BT1V+muOtcm2MVwJ8zLRQRkRuMT0QURYxNRJQNQSc7ISIiIiIiopCxIUdERERERJRn2JAjIiIiIiLKM2zIERERERER5Rk25IiIiIiIiPIMG3JERERERER5hg05IiIiIiKiPMOGHBERERERUZ5hQ46IiIiIiCjPsCFHlGX3f74Ew+atz3UxiIiIiCiPFeS6AESdzWtfrgIAXHJ87xyXhIiIiIjyFUfkiIiIiIiI8gwbckRERERERHmGDTkiIiIiIqI8w4YcERERERFRnmFDjqgT+apsK7bUNOS6GERERESUoYwaciLydxFZLCKLROQdEekhIoeJyEwRWSEi74nILkEVlogyc+WL03Hxc1NzXYysYHwioihibCKioPhuyIlIbwB/BdBPVY8F0BXAVQAeAfCkqvYFsA3AdUEUlIiCsX57Xa6LEDrGJyKKIsYmIgpSplMrCwDsKiIFAHYDsBHADwB8aN4/BMClGb4GEZEfjE9EFEWMTUQUCN8NOVVdD+AxAGtgBKEqALMBbFfVZvOwdQBsdz0WkRtEpEhEiiorK/0Wg4goCeMTEUURYxMRBSmTqZX7ALgEwGEADgGwO4ALbQ5Vu8er6suq2k9V+/Xq1ctvMYiIkjA+EVEUMTYRUZAymVp5HoBVqlqpqk0APgJwOoCe5nQBAOgDYEOGZSQi8orxiYiiiLGJiAKTSUNuDYBTRWQ3EREA5wJYAmAigJ+Zx1wDYFhmRSQi8ozxiYiiiLGJiAKTyRq5mTAW5s4BsNB8rpcB3ArgHyJSAmA/AK8FUE4iItcYn4goihibiChIBekPSU1V7wFwT8LNpQBOzuR5iYgyxfhE1LEt3lCF7gVdcMQBe+a6KJ4wNhFRUDJqyBERERHlwkXPfAkAKHv4ohyXhIgoNzLdR46IiIiIiIiyjA05IiIiIiKiADQ0t6Cl1XYHkcCxIUdEREQ51dTSivqmllwXg4goY0feOQq/G/xVVl6LDTkiIiLKqcsGTsVRd43KdTGIiAIxeXllVl6HDTkiIiJKqbVVsa22MdTXWLR+R6jPT0TUEbEhR0SONlbVYVNVfa6LQUQ58vzEEpxw/9hIxYEolYWIKFfYkCOKiMsHTkVjc6vr4zdV1aM1C4tpTxswAacOGB/66xBRNI0tLgcAbNoRncbTvz+cn+siEBHlHBtyRBExZ812rNla6+rYjVV1OHXAeDw5bnnIpSKizk7Mf1XddRzVN7WgIuRGn5dOLyKijooNOaI8VLGjAQAwaZn7xbRuK2FWDc3MIkdE3vx+8Fc4+aFwR/Gzk9ibiAhYtdldJ3susCFHFCmS/hAA4u6wjD3weXF2XoiIOoxpK7eE/yJsyRFRFkxaVoFzHpuEYfPW57oottiQI4qQqromT8erh9qMjwE5LCuv9v4gIupYzJ6jKLWdvMQ+IiK/lm0y6kGLN3jPrFtd761O5wcbciHYVFWPASOLs5KIgjqWawfNcnWcmCN3TNlNRGHzOwEgzKnZfjqmiKhzU1Wc/OA4vDtrjfvHZPB6w+ZtyODR7rAhF4J/vD8PL31RitlrtuW6KJRnquubQ3tuu2A0o3QLCvsPx+aahtBel8iPaSs3Y0ZpFqboUVrz1m4H4L3xdNcni0IojYHtOCLyo6K6Af0/Wuj5cX46tPzkJvCKDbkQxLJpsceQou7VKaUAgDmr7TsdsrQUjyjJr16ZiatenpHrYlAcbxe1GaVbQypHdipIRNSx+AkbmYSabEQpNuRClK2EFNT5+Dm3nCo+kuIJl3ONHBH51BLi8gI244jIq/Lq7O6FefewxaG/RkYNORHpKSIfishSESkWkdNEZF8RGSsiK8x/9wmqsPmCFxgKW7Y6o7ftDH+hblgYn4gyU7a5FpOXt29x4jXutJoPeODzJbj302ArNPk8IMfYRJQbc9dsz3URApfpiNzTAEap6lEAjgNQDKA/gPGq2hfAePN3Isoxu3pPPleGXGB8IrIxZFoZijemT5R09mOTcPXr7hIw2YmNyL365SoMnlbm+3nSaW7Ju83BGZuIciCjOo/LmVBbaxszeBHvfDfkRGQvAGcBeA0AVLVRVbcDuATAEPOwIQAuzbSQRBSvsqZ9ekBsyuSf356DY+4e5erxsbVxsSQGHW0WMOMTUWr3fLoYFz49JfTXybSf6KfPftkWq5ye+6lxKzJ8pexhbCKKjtZWRVVdE9Zt24nP5ttnmPS61cmVL04LomiuZTIidziASgCDRGSuiLwqIrsDOFBVNwKA+e8Bdg8WkRtEpEhEiiorK+0OyVuxinVHqxxT5jZVBTM/+/eDi9p+jvUwDV+wEbWN7tJ9PzDc2Oh7S5Z7jrKI8YkoYG6qM9be6EwTkixcX9UWq5y8OXN1Rq+TZYxNRDmSmA7g8bHLcNx9Y3DOY5Nw0ztznR/rsla/srLWb/F8yaQhVwDgRAAvqOoJAGrhYSqAqr6sqv1UtV+vXr0yKEZ0MdkJJTrnsUk5e22nOpWbczXb0wUyxPiUQk1DM2oawtvmgjouN+0y61q4MKduW0NWbX6dz4xNRDmSGJOGL9gIAGhqyd91Jpk05NYBWKeqM83fP4QRnMpF5GAAMP+tyKyI+Sd/TwcKW11T8BvkBnG+ualwnXj/2ABeKWsYn1I49p7ROPae0bkuBkXQoKmr0PeOERk9R3Nr+3o1P7HJzyhemNkxQ8DYRBQRiRm783FbE98NOVXdBGCtiBxp3nQugCUAPgVwjXnbNQCGZVRCojz0xzeKcPnAqVl5LVXFqEUb0x/nUK26J+CMcrnG+GQvscLb1NKK1vyqBFOI7vtsiWPPtNdKTquPSlHsdKxP0+llrX/l0ynM2ESUWmV1A8YuKU97XGur4tnxK1BVl1lm7VWb46dB2oWsTNt2qdbeBaUgw8ffBOAtEdkFQCmA38FoHL4vItcBWAPgygxfgyjvjF6cPhAFRQH86c05GT3H+u11wRQmWhifEjw7IT4pRN87RuKCYw7Ci7/9bo5KRFGkqrZ7S3qtz/jbfFcBCK5+zTlbZmLpquubsGePbt5fMDcYm4hs/ObVmVhWXo2l91+AHt26pjxuwtIKPD52OVZtrsUTvzje9fOnW0biFLKsj1VVlG6uxTd67ZH2NQdOWomfHneIuwL6kFFDTlXnAehnc9e5mTxvPlNVyz4VXCTX2aWqEAVph8seqfeL1oVajqhhfEpWUlGTdNuoxZtyUBLKldLK5HMgSNaEAH5H5Bas245ZZVudXychrt7+8SI8+8sTPL9eLjA2Edkr2+IuUUiTueVIbaO39bHpQlKsIymdzxdsxE3vzMWga0/COUfZ5iVKeM7wZLqPHCVYs3Vn289MdkIDJ60M/TXWbnM3mnbXJ4vifs9l4hUKX/mOelz9+qyMp55Qx/LuV2vTHhNYvcPPiBwUFz/nfVp6+Y5gMgITUe6li0F+69fpthJwG7KWl1cDABasq0r/miFP/WZDLmD5NFefwhfLiBRFiXPDKTomLq3IOBPfwIklmLy8Eh/NaR+J3VGfV9n9KASZ9A57fWji4Us3tW9C3tqq2FLT4Ps1Eutx7Dcloky5nUUQmxHg5ng/MxO8YEMuYJLiZ+qcwhiVzcesSuRe2eZa/G7wV7jlwwUZPU/sQmM9XRqbg8+aSh1PYANyCbHqPcto4PMTS/DdB8ZhQ8L6XNcNOV5giTo9L9WhFeXV+MvbznvFOW7TZPm5i8SOT1+AsGtsnbIh19qqOOnBcTjj4Qmhvo4CKOw/HA8OXxLq61B0hVHZeO3LVXG/OwWSD2evQ0lFdfCFoNDE5vwPXxjMaO5/Pl+C5yeWYGrJZswodV53FKTv3Dsaz08sydrrUbwZpVswcVlyBntruJi9ehuaW1qTjkkl3bSkRLWNLSiyrHWzvvb4pUbZ/jC0yNVrjFi4EfPXbm/73e3mvBSsFeXVmLNmW66LQR2U+zqTceCYJeW4dpBzYqSYT31mj7SrY3VpG5Hz9/ggdbqGnKri8NtHoLK6IZRMfYlZbQDglSmruAFvJxVGZWPKis1xvzuFiH99MB/nPTHZ1fMWlW1FYf/hWGuu89xRz7VVuRDUOWONRW/NWI1fvzoz7v55lkpxGHbUN+O/o5eF+hqU2lUvz8DvBn2VdLs1XlzxwjQ8NW5F8jGpKh4+6iM/e3G67fPGzs/FG3bEHZ+qYnTjW3NwyfOp185xhC47zn9yMi4fOC3XxSBqM2lZpavj3ISIWIiBSbEzAAAgAElEQVRqamm13Z9SVfHRnHVobDY6wNx0bnFELmCvTlmV/qDAtJ82c1azByuKdtQ35V3vYroKS2H/4VjoYgFuoveLjGlP01YaDcVbPshsah/5M2ze+kCeJ12D8FKHSjFFw06PGdncSFyvsXST+xH7Fhc9y04jydZHpzo77SpPH89NzrjLhlvHs6mqPuO1wdQxpGsgrdu20/F+WwlBY+3W5OeIxce+d4zEFS8kd1hMLdmCf7w/H8+Zs01cTWhgspNgpUtpnKlUlSe7ixPl3h+GFOHygdPSbj7rVzYqG3Z1q58+92XGz7uxqkPuLRd5L00uDeR5vJx7s1dnb8oluTNq0SYcffdoX50yTpLjhU2vc4rHZnoZczPD6OQHxyXd9tIX7d+J8h312L6zMen8bnU/Q5Qi6tQB4x1HXYliHhhe7PkxiZdEu0y31hCVOGtFBL5m1zHZScCy24nHxlvUxVLHBvlFW7DOuo4jtZKKGgya6n2E2O02vUOnl7X9/JiLKW6Jb0EzOx/ympdYd8UL05lEJ2K+WG5MFwqiU8Zq8LQy29s/nB3+PpPWXvZU+2s2NCe3yKyn5ikPjUe/B8YldZqGXVmicL1idmDZ7XVJnY/Xr/OM0i1pj0kMOXYxyO46GLvp+Ykr22Ystd3HqZXZ1yXkIZL4NXKhvhQFwOvifTesyUiWlVdjTIoNlweMKMZ9n4WXCOfuYYvbfn7OR9IJu/OXlf38EReLclcM8mm1y41xg2Lt+Anrax7rG6qub0KZh+1PEhtpza2KLgm1FzfTPim6HhzhfYSFOp76ptjaM29mu1i+5Gb9eXV9M75558i426xlGTp9NVLemQJH5ALGefVkJ6wMaPVNrbjhjdm299X6XP+Sqic7U4nTf+1ehnWl/BHWeULZMW1l+h7mIFnPFz8dP25srWkEAPzylRnYUtvo+nE7G5OnvieNyHEGQaiq6ppw0TNTcl0M6iS8dho3twTz/V9RUdOWyMSNxFe1ywK8dmu4y1TYkEsjk8yW67ZxjVHUZdowKew/3HZBrBt+R4cTHxVU4ypWsWpqUTw7fkVSNjmAIzth22yzQbJfXjejZyO9Y1peXu1qvas1rjwzPjmTJeC9cvXT4w6J+32UOTth0frk2OLE7jr8ZUn8FCe248L1i5em214T7KgqBowoxvJybn3TkX2xvBKF/YdjS0DXLWsCE69fZzcj8slTK5OP+dcH8z29rjUmnvXoRBxxx0jb48LsaOp8DTkPIy/D5q3HGQ9PwLSEC4ZVbUMzCvsPxydzkzPN1VkSaIQxhY+C46dNtX2n0fBxM6RvJ6hpvkGdWbE1OW/NXIPHxy4P6FnJi4d8LOBOxbqQO6xG2ruz1qCw/3A0cKPxUFVUJy/Kd2LtUf7hk5Nx2oD0e6Z2CWEA96C9urs6rrI684ogE4qFy0t20801jXhpcil+9crM9AdT3np1irGuceH6YBIyVdf7z1ja4iLbkZsQ5zUWWY9fY5MFM2bJRm+dV17kdUNuWslmrPDY47Nog/sTbu4aI2lFsUMA22D2FD47wejB7IyzmZpbWkPL+hi2JnMY3E8q2/97c05Grx3Vc6XYIeBwjVz+CCtZzcdz16Gqzthj8LExRoO/amf8noM8T4K1yGNFyU+jxs1UXK/Tdd2eBrHrZyaY7CR6GAfIiwJLb5LXU8fVGjmfdS6nsnwyz90m42F+FfK6IferV2fi/Ccne9p3ZPUW9xX22IfuFIy6mifeyspaqGrcha57QV6/va5d9fIMHHXXqFwXw5dYfeeuTxY7H2hjbYrGn9svbGAjclm8VvKyHK4uHoZFNlXV47P57i4ibrj5bJeXV+Pv783HP983pp+0TQVNKLbTOcmN5r0Law2vlZtTz2vFPPHoghQvkpRAwAc25IjyW9z1z3PWyvRb6ORy3XiY8alDtDSOuWd0KM/r5uLZ1XLiLVhXFXcx3HvXbr5e951Za/D9/0709dhcKOqkm51bv5eF/Yc77sW11WZhf1BTmbLZ68m6UjjWb69DYf/hGF9c7voxP39pOm56Z66rhdlBTe2uM5NO2O2/Y5XqorV4QxW+c++YwDY9T2dFeTUK+w/H9CwnDslHYTQWE0+DIw/aM/DXiOHUyvzT0NyC2oZmVKSJJxRNfhtGFTvq8dvXZibN5IgbkXO4Zq3xMCDjZEiKrVi8emb8ChT2H+54zPilFYG8lp2MG3Ii0lVE5orI5+bvh4nITBFZISLvicgumRczvTAqs27O0aKy9kZMi2rcxfC6IUWuXmfisoq4vcdu+2hh3MjhGzNWu9ojg/zzE48Sz7kxS1JXwt+w6XH2OyKXy+pKvq31jEp8Sie26fO2nalHq56fWIIXJq1s+z2WACKMtU2puD1lU50lS8xkCbH1mGGLxc3hC4MbucwHvr6nCZ9taWXm+3klliPMtla+dTLlS2wK00+f/RLH3DM6KR40tbRixMKNjgkiWls17TGUHV4/gRe/KMWUFZvxwey1cbd3dXkxe3iUv7Xk1uuXCDDMxbTIJS4S/DzhIqdAqgRSQQhiRO5mANZ39REAT6pqXwDbAFwXwGvEWVlZg/kJO6676Y2zSwvqJPaZOw2J/tOS4cbpQuJ03+8GfYWLn5ua8v67PlmEq16ekfoJyJdMKyperh8KTdroNB/Tw+dbZQk5iE9+uDkV/jt6GR4ZtbTt91jMc9Mh4OZcDbIzLN1TZWOqIND+HfMY+julxE+kxmbJgt3HWtfYglGLNto+JvE8CHP2QB7uI5cXsQkI53PbUd+E5eXGNTGxAn/xc1Nx41tz8MTY5Zi1yn6my5szV+PGt+YkNQYoe/xG8VSXLOu1zOmU83v9sD7O7Sn9ftFaLCsPL1FJEDJqyIlIHwAXAXjV/F0A/ADAh+YhQwBcmslr2Dn38S9wyfPxDR83FZWP5iRP59mZYi+vp8etwKuWjZ1jmlpacenzU22n6jgFuwqfWbky2f6AnD01rr2HxE+balPidJA0DflrB82Kuy2wqZXBPE2Hk6v4NGFpedzmym6kOhUmLUs/HSNu429VrN9eh6Wb4i88QdfD0mUpSxVXs32uxiqIeZ90wWOs8PLnjis2zrHEDgHbDgKb573vs8X405tzMG/tdjyYJuvq0k3VKSvmmfKy/j3XchWb/PK6fMLN9XScZQZL4vGx+PXcxBL8/KXpto/fVGVcf4PIeEoZ8hheUw2SNFiWCTg+pc+6k596XkurYsTCTf5eMEsyHZF7CsAtAGLv/n4Atqtq7Cq+DkBvuweKyA0iUiQiRZWVmU+zSTwhBowsxr2fxiewsNuAOVUD8Mlx7UOl1qfesL0O89Zux63/W5D0GKcT77aPFnpO0a2qOOPh9GmjE01buRljFkf7xOtsFMmVK99TKxOeKN/rqCHKSXz6/eAi3D3MW/KcVKOz1w76ytNj35y5Bmc8PAEXPBW/ca+bhdaZnEZdRFC+ox7H3jMaNwwtiusksXuR/81Z5/q5/SZ0aWppxUazI4yJMNKbnjB9f6ZNg8tuymass3FHXROq69NnL01VMe9kIlN3csNuxtOPnpycUbbqf7zfPpsp8Vro5croZ2bLmzNWo7D/cE8bP1NwurR1sMXffv2Q9utdKMulfDzmjRmZJ2IKm++GnIj8BECFqs623mxzqO2noaovq2o/Ve3Xq1cvv8Vo06qKllbFrR8uwPLyarz0RSkGu1jIGGQFp2pnE+auSd1z5TVo+D2Pf/XKTNzwxuz0B3Zy1rc3kJjhFCVUk3qD3M4HT+S0V0nY8qU+HLX4ZLWxqg7LErY0CWqi4V2fLLK9vSGE7UGsm0wLgFMHjEdNQzPGLCl3jINe3fTOXF+Pu++zxXhmQgmA/Dlvo+T+z5ck3Wa3VVOsIm03FZPLl5JFOTal0q1rclVxWXl10nKBmNi6IrcdKImNMTeNs0xOrcfGLAMATxnPKTixTzfxM1xZWdv2s9Pn6/d6OWDk0vQH5aFMRuTOAHCxiJQBeBfGtICnAPQUkQLzmD4AsrLKvKXVWIP0XtFa/NGmEVNd32Tbq6QeO2RicamyuiEuQQkAXD+0CP/3lv+9xZJGWnw/k3sjFm5EYf/heL+I88wz5jS1EskNNy+p5huaW7BqsxHkYpkD2587e7WlPFqHkvP41NDcgtMGjE/KRHnagAn40VOTw3pZW7WNwTTkUq0xEJG43+evs596mc1z1ZpAwct5+9IXK7EygEQfQfJacbGbvtjc0pqy4u224Z04ZRcAtu80MvL+9Z25SRXwsPYyzHM5j01epWpXpWqoxZYRWJM3rdu2E0+PW2E70pJ4KWQG0nCtrKxp20PXq/s+W4yb3/XXuRYT2xz74Q7asMo23w05Vb1NVfuoaiGAqwBMUNVfA5gI4GfmYdcAGJZxKV1obW2vJMQqvDG1Dc349r1j8IDN/P1b/jc/bdpQu1hV19SCMYvdpwp3472v4htTfoaWhy/Y6On4G82G5y0fJk8VpeAIgK4JV8OuLmtndY0tOPLOUTjnsUmoqmtCQULv6K9emRlQKdMLct+yMEUhPm2qqsfGqnrc91nyyEaixghk4/AabuJHtKNX8bJO13Kb2a6usQUDRi7F+U98EVaxssKuIfrIqKU4L8XfNcrlVPxBU8uSbovNNGlu1SylsMlvuYxNqorFG7xtLg8Alw+cZnt7qq9V4jUKMEbWnxy3HCtsOhMSr41u2IWc2au3YmaeZvguKtuKP781J/QsnBur6nDu41/gAZsRdzcGTS1Lzvbo8eObsmJz28//SXF9dEx2koeJ4vbqUZD+IJ/C2EfuVgD/EJESGPO+XwvhNZK0qqb84J02oB3tojFm7UW29uwGnfZ7Q1V88gw/X+d3v1oTTGE6gaArnzvTjHokLsp2u0aubdNlGBXNbKabT2S3H16eyVp8io1euRmFutHDSH5VXROOuH2E73Jlwvq3WL8/1ikxTm7938LAy5SKn69J7O9rVeSsQtjSqkmVrCAqLnZr3mLcVh7tlgfYTbuLmb5yc8r7KEnosWnYvA246Jkv27KMWlVWN2BbbSOufHEaNrhMslZTbz81sZvNRaq5xTjHEjusAX9JKOxc8cJ0/CJPM3xfP7QIwxduxPa61PXVIMSu4U7xwI1h89Zj9RZ3cd/J61NX2XYu/P29ecZssa/W4tv3jm7LOr9kww7PGeijIMzmeSANOVWdpKo/MX8uVdWTVfUIVb1SVbOSUqjFoSGXKevzfj6/PQCG3Svg5+/xm0CDMpduUWx1wnz8dGvk1m7dabvxci57o2IX43ySq/g0fmm5+frG9LPC/sNts1B6vSgt21QdiSlr1vj0ScgbfPvppbZ+T/x8ZxakmB4atmkrN9tmTM6U03olt+dTYgwD4jOYJr7NZXmUSTIXsh2blpcba3MTp9iu2bITJz04Dj9+Zgq+KtuGlyeXunq+B0fYZym1G5Hr0c247bWAz22nr7aqRnK2gJ22dWNZKm+m9Yib350X2Pf7g6Lk5FdflhidQLf8bwGq65uxs6kFa7fuxI+fmYKRi+xnEOTLZx20MEbkcqKobCtGp5geEmSdZ2dT+4Xsw9nuM6+5knASJl547/98Sdq1G34TaHRG1nc3Nmc7CLGLZRyboJm0fUGCMx+diFMeGp/UoM9lW92azZWcxaZUqgLFG41zYqBlQ++Yx11sJmqVWEeqqHY+j9xyM3I4d037uuC4NXKBlCA1NxuuJsr0e5JJpssV5dX4wOe6Y9u13AFUUJyeIqg1Sbz6RFvsO5F4LqzbZlTIN1YlxxLb65kptj7SKlWni9PIbbozx65DM1282lzTgMNuG4E3HTpYo1TtjzWsolSmbHEbq7fvdB6tjED/Zmohlq3DNOT+9OYcPJ1i5/SXvkiuPHlhvchZ53IHvcfbMxNKHBtqr325CtcOmoUnxy5vy7Y0s3RL3KJVXkg9sHyx0gUIL5ZuSr7wvW7TC2mdJ+7EesES4Wecj7qZCyLLNidPRSn22ImQ2JO6rTaYc9dNW+Fdy7Qoa0Mn3YU4kzTlQHziErfiN5f1fhWd6GIPv1TOf3Iy/u1z3bFdSYMYCXeq5AydHv0U22501h55t9qneyfdEcf6Pj6daisR2HcAXPnSdFTZTA90ihHp4ofTdiUtNt+NkorqtuzOH9rsHxxF7SNy4b5OFL8ibtdIpjssytvMRH5qZTb5aTzZbd7thbU3+MC9evh+HrsPMjFb5LmPty9Gtzsn126tw9PjV+CpccuxcF0VfvHyDDxiyfyTy9GaodPLfPdCd3R2qbndSh6RY1Mun6zfXteWobTCsk5yhdnT7XWhf+Lx2TwdCiwj/m4vTBOXVeCou0ZltObMz9+Y6eSEGaXhbFztx/VDi9Ies3brTvzt3bkpt7kJspEzbkk5CvsPR0XCSEmuY1PUN+7NtdjH88TY5XH1InHoHnT6SBMrzsUbd2C2x83Dgfi4aKdbl+Sq6o4645pqN6Ph+iFFmGZOzXPeFSg6Ff8tWV5/HqVahJcM3k4St/Wx6sgbx+ddQ26Yj7UYQa0bq29qQc/dugXyXIBRwXPKFuk0daC+qRWPjjYacHZZoHLh7mGLXfdCV+yo9/VZhinTUQMAuOIF++xemUg8C6J08aF4qZLB2EWg2Mhtuspv4hq6xOnTAuOc+PHT8ZuAe+V0Wv3xjSKc/OC4uAuu9TyMVarsTDZH0zJJQuAngttVTp8dvwIfzw14SnzQXHy9VRVvzlgdtxXJ7R8vxCfzNmBGFpK0DDWnqy1OGE3O9dT+rTZT/aid9dP55SszUlZuraegl/qT09o6p/iyKM161LFLkpPSJW4+b9VFBI+NST8dOypT8azXjXRTRhubW5O2IIqCmobmtim6XgXV/zO+OPUsiussm43nQpj1trxryPlplAV1khx11yjc/O68QJ5r3trtbT3yqaT73K1T88YXl5up4aPUz5La1a/Pws3vzsMtH87PWRkSA+Z9ny12/dhUFxE/vZHp5GJ/QfLn1Sn2FRm7uBVbMzKu2DlzbuLnnZiYQgQoWr0t43WeThWI0YvLUVHdEDfKZe1F/9RhW4pUcWytx43txy4p97T3kfUt/8RMl/342OX4+3tGzNnZ2Iz/fLYkcpUiN2sVJy6rwJ2fLMJDlmQTsQ6BVIm/sjHtyE8a+SC15GE2u2yauCx+ivJJD44D4JykxqnBFAtFM0q3oLD/cNf7L17z+qy4399LM5NnVpnH0XHL32NXgY4tpcjmvpZOvrL8fem+ple+OA3funtUyCXy7rLnp+J7j0z09Vi39fpMpla6zcQaljA7DfKwIeft+Ns+Wmi7ZilXFq2vQmllDS59fiquHeTcQ+B0UlpT1FbVNeG6IUW46Z25tif6nDXbUlYwE4/Lltii6vdtshXlSiwhhRte9+vLhPUsGDq9DKUuU71T9qW6INndXFXX6Grad2IYeHhkYqY4CaQx4qaeb51aeVWGab7PfNT9RX/+uir8YWgRnp1QEnf7ovVVGGfTWw8kj3Qm7hd69N2j8frUVRg0LdgsetnYouP3g42pltYRqNi+lKpqe+0IsiKxzmyE2639zaUoZHONMmuGUavE8DTZsia11iG2xBpJIxYa10O3mV79rHlNem2H+6x/z3yHMkVlcssf35jt+linvyeXMpkZ5rZe7zQFGLBvtL8xvQzTIrANSkuIJ1t4O9SFxOuI3DuzorWv2k+e/dL1sU4fu3VjZmtPvN0UhNhmntefebjj610+cBrKHr7IdfkA+2xS+SrV2hKrOWu2YfrKLei1R/cslMhg/f4/PzGzxD0UrlTrHOwu1G73VEvsNV6TkPK5iwQz62DAyGK8OWMNVg34ccrpnrne3iRx6k4sntrFLbeVg6C31PjNqzMDfT6rVNNzNlXVt422vD1zLb576D6uH+vHarMhl5iwKd3IStjYkPMn07WNbk6toOux1vN5Z2MzdtulvTpb2+CuYyuxTLHnzOVaz6g0LrMpqBE5u7furmHGTKv999jFY6mCxamVFpOWZd6T40fiom4/vH6Oro9PcdzORv8JNty66Bnv63JU1TarVbYlvr+NLqblXD5wGv47elmWKwydMLLnqXlrt9vebpfW2y1VxE0p3FCVnGAiXU+lG2/OMDq9dqTY5BdI3aOfiaaWVsxevRUbttehuaUVNQ3NSaNnMV7+Trd1MWscSBVzVdVVRw8ALEszZd5LeRLLcNhtI/Dg8PYR2W21jdi+sxE3vzu37bZxxeXovc+uyY/PqFTxopodLqhtFDqbVN+V0soazHLYONrLux30NMZWy9exOiFmpdvaJyaxTIfdNgJXvDANLa3qa+/KIIQ93XOHWfeKUs60j1xkFlVN//2OaFgCwKmVcWKbBIZl9uqttpWIBpcXcUdeP8gMP/iLnnE/+ufX5hrnaUTTV25J+vI5TdVwq7VVcdcni5I2NvUi8UvvZf3N7R+7G00JQpSDE8Vz3CopAx85pN8O2uzVqStuO318d9N1kDw2ZhmueGE6Tn94Av7z+RLMyDDLcIzbXl43Faf/zVmPb9450tW6PmvPq5/KYKrveyyOvj61fTrjtJVbcPx/xmJmQmW73uZzamgKbv1YVGMSG3L+pPqmXPLcVMfHdS8wAl4u1ppZOxP8JtmxO13mrNmOb9w+ApenSVzW1NKalIgqCKm+W3WNLTjvCWtWc3/v+a8SZgyoaiCJ3jLhNhu901psILmDKVeNcTthdn7lXUMubB/Otu8ZCKL3wmuwc3t8quNW2exZZeW2h9mvaSWb8ctXZmDgxPh1LUGc0KWba/DGjNW4+jX/05i8fh6lLhdyB+38Jyfn5HXJu0Xrg9tYPkbVuSOpVTXQ3tWgrzdvz3Se3r7UsjZ16PTVjqn2vfydbqdHxW1sbnnIed86AIAx6vWkmeJ8booR1z+/PaftGOvb5+etTPWYFg/Tvm75X3L24KD2PXWzHnPvXYPL7uxFVBuYUZfqlKpOs23On885wvVrBD610vKz3ynfTo2hVLMrYo65ezTOeGSCr9d1ojBizhNjlsV1TCzdtCOu4/rtDJcNxdbyvvhFKY66a1RW1vZmQgRoSNPgTGy33fHJohBLFB151ZDzMmLiV6p4kIu1dm4bWk0u13jEpi2VVFRj1eZaPDZmWSbFSys2vSExk1UQAb2rua9M4jSzTNg1MJtaWjFkWhmaW1rxA8sef0TZMnrxJsfKu6oGmqv2iAP2CPDZnGnAjdDWVsUOM8ue26e1fuutU7Ri4eDnL01vawSlisnDF2zE0+OTN07202lVkGJ0ITaVLNejTs2t6a9LUZg6T+7NWuUv0dmu3boGXJLUErNnxk+Jdv5OTF+5BQvNJCHWDtlM6iKNLa0o39Hg+/v4h6FFeNiyB3B7mRR3f7oYz0wowRfL29PpJ14D5q5xbmimE5vu/8lcY/Ciojr6+Q7SdWIlds5b6+0duZMnr5KdfJZmWDUT67btxPcemYgD97JPYhFEkgmvJ9I9n7pPh+/Gvz6Y35aGGwDO+9aBgT6/awF8oewqO62tikdGL8XvzzgMB+7VA2OXlONfH8zHzNvPRQ8XFxzr57OzsRlH3z0ah+63G1Zv2ZmVTgSrdFMIqPMo3rgDX99vt5T3t2rmvbMAsGf3AlQ3NGf1gjd6cbmnRmi6Y58avwLPjF+BuXed75jsJO77bPmDL32+fSrZ+KVGJcqaja2hOf1olPX9szbkPl+wAd/p3dPxswRSb45rLVsudeD6UKf1yKjkBoUXsdO8i4S3Fqh8Rz327NE+0mvdtiXdS/7yFSPD7l49CvDAZd9uf1wAZZ2wtALnH+29LjV2STnGohz9Lzwq7nZVYIL5t1k76ROjQtBL3C54ago++8v38O0+ewf8zMFJm+ykkwanvBqRCzNj2uINxpSo8h3h7f7u9RxLXPeQKWsjDrBvDAXZkxr7UiX2oriZ0rilpiEpO59VV5uNib8q24qXvijFvz4w9okaMKIYVXVNWLfNfkpRcsYq6+sb0wxWm2XIZg/zmMWb8Nd35qY/kDqFVlXHJB8PDC/G5wFsh6EJ/2aD115gayix299qpJkGvbKmIeX3HgBufGtO28/Wime6pDRep6NbY8pf3p6LC572P0060yQqURPG9MuoJmHpqBLfbadGXOafTOoY6PZz31HfjI2WKcZBnC+ZPkfinnpAex6BJyybmidWf/1Uh9N1RPndiiUqeVOc1sSlyijdEeRVQy7MLDvvRmybAgChL0DtVpD88f/fm+73M0kn9pVK/NjS9djtbGzGdx8Yh7P+OzHlMdZGfWzqZmyKQ1tly+PmkdbpGYmxeej01c5PFqAnxi5PfxB1Gi2tzqn0JwewJ5NVNivDqt5SfVv3nfz2vWPafo5dwGMdPE0trXEblieybtMyf13qKUqJU7ZUjZGx6wY77wHaVi7z8UebG/hak8VMWFreti5lyYYduPLFaahpaMZ9Ac/ECFpQp8e9Fx8dzBNZsBmXZRr3T6icwsSwuRt8TXGMwvmSuKeedePqZeXVbR1WiZ15fjIVDxiRZuQ1Cm9IBmLZem8YWoTrh6Rea50LYV5W86ohF+aI3MQcbWvgxE+GOC/spqpOW7kF23c691z89rWZ+N2gWWkXA7dVgiTF7abEpCxvzkjfaLKO6qkaPfGJ2ZictLYqxhVXxN3mVKHM5ohcrvfqsuM3Kxhlrr65Bf0/Cj9Lao2Z2CCbgxqtqpiwtCL9gRaF/Ydj9OJNcbfVmZ1e3cy0oV4qdbFEMsttRrxmlG5NOnbe2u1t0y7TOf8JYwTOGstVFTUNzfj94CJc+PRktLQqfvzMFHxVtg3PTShBaZokVTnn8q3dP81em0FsmZEo1ebwlFoQm3Nng9PZ8uCIYgydXubqeayhwdppdecn/mLs1tpG1DY0B5bB8t8fxicqitWPghiRc5qlEJSG5hZ8vsB7w/r9NHtQurkuHby30ZAbs6Q8buptR8eGXBaFuSFgkC5iFRsAACAASURBVNJlN5uyYjMmLqtMu2ajfd688+d2zmOTbB8HAGc9OhH/fH9+0mP+78058b+/NSfpGCc3pji+amcTFq6rykk65ZgonuZnHLF/rovQaTlNMQ5DNuNU4obSbiVusB5b8xbrcPCyz2Nsj9ARC5Onp8bW1sS4WSNnZRdL35yxuq2SU76jIS4TXRjpzIO2oSp9ZbBwv91SJm2JSdzcPQi1Wdg7taN5dUqp78fGsqO6ChkZhpV0I/drXGwNYhTDfuZNbB9Nr277aCGOuWc0jrhjpK/HJ3LbAPJTT2hJl6gogLrHkXeOwl/enotHR3tbd3nLh8mZdr16YVJJ+oM6IN8NORH5mohMFJFiEVksIjebt+8rImNFZIX57z5BFTasPZqyJewRtqA0u8yCmU6styvxep7u2a0Bas3WnfifzR5a6UYD073iqIQe/ZirXpmBnz4X/v57TmLrNaMkgm1LR7mIT272GPMj2+t+spkU0etoXCqxtyjWePASw8o8NJQPTUhU0tqqcQ0xu5H75ybEZ7N8aMTSuJhY05BfGR7dVDTf/sOpaUfxw1yPHmW5iE1OgsiCumqz89Y822obsbk2s887djZ9uWKz7VrVQVPLXD3PQEviuih2ric20NpzDWT+3JU1zp9BkKPkL33hv4PAjpu/f76ZmbSzyaRp1Azgn6r6LQCnAviziBwNoD+A8araF8B48/dA5HtK4ydzsPbp5y9O9/wYa292U0sratPsJZNKrJc8MTjYVUzfnrkGc9YYKZAzHXlNtTbPjeKNRiMqgvGdvMl6fAo6OVHMtIA2yHYrHxNGrNpSi2WbqjOq7LiZdmTt0D7loXF49cvSuE16P52XvA/pY2Pi435dU0vKPbDenJm9tbh+uckA3EUkbUNOBHjzulOCKpbxnIE+W2iyHpsApLyOB9FxmzgFOdEJ949FaWVmU4ZFgAXrtuM3r820TdvvVo3lfQiz00pV8cz4FZ5nVCTGofb6TPzZ/c6stajYUY/C/sNxx8ft00KbWloxYGSxbX152abk6ePpkihNK9mME+8fG/e+JUr3Nhb2H457zbW/D3y+JM3RKV5D3TU0JwbUOZhPfDfkVHWjqs4xf64GUAygN4BLAAwxDxsC4NJMCxnzVZm/vU6iwumLEJZZZd4rl9bK0A1Di3DMPaN9vXYsacd7CXOf7eqJt3+8EJcPnAYgdS99RXU9yne4z3KXSYz+5wfJ0zk7q5t+cEQkp3s6yUV8yqRyESX50JBLbCNcPnAafvTUZN/XiLdmrnbVYdVomfpYvqMBDyUkD7hrmLtEJWppEFobcvVN0Z9aebGLGQsiqffDazsGwPf6Ok/ZTvcc+SgXsQmA7cwWID++74BRb4hlHkzcm9avu4ctwqCpqzzO8HFn/fY6PDF2OX43ODkrpRexUUO7a/C55t62b81cg5IKo0E2bN4GvPRFKR4bbewTXN/UgvE22xnY2WAzFfzR0cuwtbbRdg1xzHCHrMmL1hujZIOnlWH04k149Ut/mTEBoKBr+njwwWzntXYdUSCTFUWkEMAJAGYCOFBVNwJGwAJwQIrH3CAiRSJSVFnpbrFtvof0bO9FFpNu0Xki6/vsJwnMNjPYbttpP4Ka7sKRagTi5AfH45SHxifdnvhss3yOjFhToc9end+dBkH65w+PzHURMpKt+LQ5zbSVfJEP9bqg10vf8fEijFxkP93aasCI4kBezxoDp670t04wV9wsERDEV7rOtGmwuclW+rfz+noq27WnF3o6PteyFZsAoCLFVNYih2vdQXv1cP38YVMALWZDJKgELTNXbcV9ny1Jud7/uQkr8IOENfxuqGpb1ly7zpk6D8tsYpHCLuZVWzqfLnx6CoD2dbzN5vSBh0YU47ohRW0znqwSp6hOLzXqXh8UrW0bQYs1JLfUNKZM778tRWr/0Ys34SfPtnf8JK5t9kSBA/ZMX5fNdOQ3H2XckBORPQD8D8DfVNX14h5VfVlV+6lqv169erl6TL4nO8nV1NBs92qe8/gkAKn3Capt8L5WsDVhuqcrbQ9p//vfmrk65VqmdL1Vnc07fzgV7//xtFwXIyPZjE8dRT700HtJZhKkHfXBzKqwvsePjloWyHNGTdcu7dWLPXsUJN3v5nLu9VQ87ms9vT0gh7Idm7xWn1QVu+2SfhpttgydXobSNGvxgvbYmOUo3Vyb1ICxrou18+bMNW0zjGKKN+5om5V141veGzTpPr+mFsUXyytxx8eLALTXl532wk2VK+DfHy7A4GllANCWRfcPQ4swZHqZ7fEbUuy/+cKklba3+3Vs7/SblS+1mT7a0WXUkBORbjAC0Vuq+pF5c7mIHGzefzCAwCas1nvMGBY1c9YEP3zvhtd2nFNP6RvTy9I+frs5Enf1aYfa3n+Jw9ScVPPJY0EFAL5wOUrYNrfc/HNWb6nFHR8vSspE15k9+8sTUt532jf2w8mH7QsgP0fDsx2fOooctZE6lXxJfOVXt65d4pKT2TXI3Kx3OcmMP27ly6mb7di0aH0VPrFZv+nkuiFFrrbCuHaQ89TBwv7DPb1uKoOmliVNZc6WBevjk2jc8IbzHmXTLaPssRGtC5+egt+8OhMtrYqpJe7XPce+O246NZ4a174eN3FW0Z9sRsPSJXv5dP4GVFs6r1JlGY7lFkgU5JRVheZFJ2MuZJK1UgC8BqBYVZ+w3PUpgGvMn68BMMx/8eLl+4hcvrB7l894eALWbNnpeg2Ik1qHSoy1wWa1oqK9l2Vbwj536RZrx/6e7/93EoCOMwUuCD897hBXx3nZtDkKchGf8kXfA/ZwvL8jXCyjmI3O6rKBzlu35LtuBV3iRuTszik3HYynHr6f4/03nxs/9TLiHzuA3MSmzxZswNqt3vYQc5tRdlIE9+ANmqpiU1U9Hh21FFNLNqM+TUfMqs3tHdIKYItZ55i3dju++8BYtHg6UWNblaTPDzDXMliQODLVYJPp86lxK5Jus/rrO3Pjfg8iw2km0iXV6ayS5zu4dwaA3wJYKCLzzNtuB/AwgPdF5DoAawBcmVkR2+23+y5BPRU5sKuzr99eh0HTvC1S9fOlX7g+fQ/OawmLZf+bYr+SVJW5fEgoQBnLenwK2pEH7pk2o5gfX993N6xwmBqUai0NBWdzjf2ako5it25d46b024XiLgFM+U++VuVBSy4Hsal7QXSmSOYjBXDqAGN9/kAX0wWtI1SqwA8eb89suz1F7oBUYktRrn7de9KUZ8c7N9RWuRhxtcplQ04VOOHr+TN1Opt8N+RU9UuknnF1rt/ndXL1aYUZZbwhd2IX3cRhcbf7tMRYe51GLdqEC449KG1Peaqsc9aUvIk9TXYJWVTbt/1szINNdnPh3z9yn8Qk6iMciXIRn4L229MOxZ2fLAr8edN9kn96czbKHr4o8NfNpveL7DP0Ufj67LMrunSJnzhpd85l2ox74ufHYZ+Ezt2g9kANUy5iU0fM/plNscQfbo77/pHx6xbrmloyyo9w9euzsPKhH/t67OM+trxyaqx9WWI/tXKP7gWhZ2XXtv+l9s0D98Dy8uyuo3TrshN6h/bcebXFdrcCBiM/yqu99bC3qv4/e/cdJ0V5/wH8871G7xwizZMiTYp4IqIiigXF2KJGo9HEQuzRxJ9BxRaxJjYSe8MW7LGhIEUEEZADpPd+1KMd9frz+2Nn92Z3Z3Zndmd2Z+4+79frXne7Ozvz3Ozsd57+4Onxy01ncrKi/2OT8NWvW0KPb3o/0D/7cHliY0PM+mabqVLVBdKhz09P6Jh+c2Hf6G6SOVnOfMW9nz2qea46sYPrx2jXrJ7rx0gHs6nWvaJlQ//1LrF6PTbXClf6KeIN64GSvJ1f0q8dTu/aCr/cNwQDOgbG0tnrslZ7xFvTj2JbaHGh6TE/r8ef3p4T9pwTk9y9PSN1DRhGSxDEk4qK3i9/3Ry3279XC3EA8M9Le7u2b18V5DJ9Nk7HK+w2h6/Ytt9S94FYduwvxVaTmYxSQSmFjbrZKWOtc+JXdbPDv77n9Gwdtc1953YzfT9v7t7m1rhE/U135LAerhyDap6zehxhabvg5aVffsYoo2dlshMrWjWuG4pl6R7D41VskfO3yEXC3RRv8hojqahA+cc3S327LNRfhnRBVqZ7xS1fFeSc6FNP8bk1tfcb09eix4OJLS5uV+Qadk+bjKPzq//eeCIm3nVa2HN2xqH0ad/U1ppLrOiumbIzBcN6H5nuZNQ6qRwj9+HwAVHP/XlQR9v7ybGYEVEW2++dvJ0HJ0JjQc4YK+38bYLJMgFuWJPAOmyp+N4pFXt8opU15oBAN9CaxlcFOdYqpYZbX8pR45xZTNeKyHFxG0yWNfCrKoNhf0YZKLMFPD/+8wDUzeYAeK969Q/Hx93mvzeemNC+f39i9bIgSoH9Zmu4bq0bRT13y+mdbe8n1gQ5esHY1PWI6uMa9qxM4nYeWTB5/OJeuKBPG5zUKfYsl7UVC3L+ls7eTVakoiA36a+DcGQT8wXqm1ucDLEmVvb4qiDnlxa55Y8OTXcSkvKQxYG9Xvb1gi3xN/KxSqWiWsnstJrpuzW9c13/uNsbZQbJnuxM6/HrmCPin+/gbGZ2ndXjCJzapWXosdUWFPInoy49Teplm25vtP5n26b1wrqqxxIcx6JfBDyRrpVT/naa6Wvj/3Jq2OP2zetj9JXHcXZGE6wEJzelomzUuVUj9DuqmenrVlvaPrnpJKeSZJnbo8J8VZDzyxg5tnSk38dzNqU7Ca6qUiosA56TmYEGdaKvO7P4qv8q9c+Lv+ju3TZmuCRjHZrXt7ytlcHjizZbG4Cvd5E2IY7VbnKUPpf0c2aWM7tdif5x4bFRz2VnCrJtXjMvXtUv9LdRRi/eYt8dc83XO+xioaKDqunX9COqiRrWtRbnjm3bBH8965i42/19aDf8PgUTjjnBV99udg8gq9baXB/FDW6OPerSqmFY5miZSSuwWXkgQ1eSs1I/YjcTR9Eev7iXo/tLpJb9kYhMugLQtL7/ZlD0oz7t7a2BdNnx7ZM+ZnCJkciJkew659jWaN3YfAzKKZ2rW3iDmZ8jGld3gzKKMacdkxv9JLnC6Ra5zq3MC9lETmvbNP7synVttMbfMaRL3G2GD+qIxy6KrtTyIl/lzowKcn6cxplqh+YuZZBbNsxBu2b1w1ptMjMEAzq2iGpdu/i4tuhnsIhmvPv67WfYH0ND1YxaOevnODvIupHFGsignm0ax+xSZ6YkwSVDapIXruib9D6+vPVkW9sHp9RPxvWnHA0AKCk3X0vzgfN7YN0T5utUPXFJL9xzTjec3yd6eZOgYC+U1/5wPK45KS/0/N1nB2q+k5ksafo9pyf+ZgLgfCX4Py7s6ej+Yln12LkpO1ZtdWzbxra2b1bf/n0kGV/dFj921stxtidcZoYkNHP0eb0Cs4ffdFontNHG9LldEe6vgpzBST2v15Fo2bC6ptDOWJ54XVduPb2T9cRF6K/rNjJ35JkJ74f8y+2ewJF5o/o5Wfj4ppPCZqNs1iAHn98SHQQlToucUde7S45riwtiZOao2qMXHYuzI6ZrL60IFIisjJWzku89r1fiLb6Du7UCAHTKbYDDZdEFteLD5fjy18249YN5OP7RiQkfx8yb1+Y7vk83JTuJxnCbs0S2b17PkeUnrOyiqkrFPFbzBjnIzBA0SKAi4lKtVfHy/OrWRbvnonWMCQ7Imp0H7K0lG1cKh9WyH5b7Xvx9v/gb6bR1aP3Rz26OP16tYOSZaKHl8QfHaMXPMIlhz1zWx/D5QTH29dnNA0N/tzGIPwUjz8QTlxj3sAmmI69F/VDll9u9CX1VkNNPdhJsZbg8vz1O1NVcWr35ndy5BZ69vC9evsreBWzVx3+uvkBbNLQ2LSr5U8eWDaKeC9ZEu8lsHNXJum5OQT/+32As/cc56H6ktZo3oz0/+7u+GH3lcXaSWGt1ym0QlYnu0CIwRs4sRv1Ld8Mx6wqV16J6nJ3dfP5e3ZIcV5/YAQsePBsdcxviupOPjtr2+Ecn4i8f/opf1u/GBX3b4nf55t38EmmtGtL9CMPvjRvaN08+05Ftc4zRN7efEvbYTgv3FSe0x/R7zjB8zW4B2Mq48niL7AYzJrG6Zz54fg+c1eOIqMxR6yZ1sf7JYRjW+0j848KeGDmsO+47rzvWPzkstM25x1avf2l0LTIjn7xt+5yd9bCZxRkCE3HDKdXXwNt/PIFDaiIksnRIPEe1aID6Nlq0+nUwnnSki80ut0c2iR+b9Q01l+W3x1Etoseat2tWD2aNXnVM4pbZVTWkWyscH2NSFQCok5WBK/t3MOzhoi9QXp7fHg3rZOF8l5f48VVBTm/URb2w+JFzcGzbJmHdmIKnsEWMQNOheX2886fATH1uLbpLtcefTs6Leu783m1MA0Wy41WCgcIs/2XUJSsQqLMw9sYT8fktA8Ne0++nV9smAJh5SlZWZkZoVr7BXXNxyXFtQ5lqgfEN79Lj24VimVk3EX1llt3uapv3Vi8qKyJoonWP6dWuSVTXlRtO7YjPbxmI2fcOwROX9MJTl/Y23a9Za1XwWjJzutYq6KarB3Qwram1w+6Myce2bWJrchs9/T0pcgbkId2tLcodZOV/j7dJ8F+PvFf2aVf9+XZoUR+vX5Mfc6Kva07Kww2nRmdCX776+FBFwTFHRH8veI9OnlOLrwc1qZeN37jUO2PqyiIAge7pp3drZfvzv/6U6MoAvzDqft26cXiL0NEuVYBNuHOQ5W3N4ooIcMvgTpYnWKpSCn1tjh1uXDe68KSUeavXhCXbDZ83u6xaRYwFNrr+crICebgFD50d9Zo+HZ1bNcTiR85Bu2aJ3Qus8m1BTkGFLhajzy/WlOr1sjNDUzLvLyk33Y6LIJMVWZkZuPHU8JtHZoaYBpDIbotWWjQa6QLj+zcE1g8zm6WpkUGgC2paPyeqNk0feIJ/Z2f5NjR4xh1DuqBzq4Z44Yrj8Ozv+oZufhki+OSmk8JqGoPjgF6+uh9e/H0/tGoUuHmvHFU9PmTksO6hfXx280kxK6vsCvbhz8wQvP2nEzDi3G7o16GZYQEmcuC5WavP1xGtUpFSMSX6Tad1wqX92oUeX3Z8uxhbhxdOgqxkcE7uHF2Y1ccEK2Mkgr049LXBZpmTvu2b4oHze8Tdp9Hn1zgibsTL5Btl2k7u3AKvXeN891iFwALmE++qPueRR+9t8BlRbA0NZjRORlaGRF1HTinX1oBNtPzuxPgtp+deuKBPG5zUMXb37L7tm6KrwdCgWfcNCXtcEWeu//VPDsMJebFblIy0j6h4MmqVCsacTrnGhclBXXJxz9BuWPzIOZaOWTc707SLoh2X57c3LfDrY/rXt1Xfk8wur8i4arTbWDHTiYpDu3yXW4vXNSzYTcToZF5z0lH448A8vKJbbLekwnwQeKoM7srZu/wsM0OipnfOyJCo7iz9TabbvrBvW7wbZy03fZANrjFmpVuCFdmZGaF+4MHKEbsTaVC0Hm0aY9JfT4vqfpGZIWhaP8dwPG+LhnXCZjvNycrAy1f1w2c3D8QNp3bEWdq4u/bN6xuuD6bXq20TXHfy0ZZqcIOFqrwW9XF619gtZd/fZV6wufPMLmhWP9vSzdlql6kvbj0Z4+88Nf6GBto1q4/bzuiMGSPOwOS/nYanI1oWI2cvW1AYvaRD19aNUEdXsdEptwG+i1jH7L3rohdn/0q3lqVZQU6fWep/dHNMv+d0/FY3dtvofeufHIYvbj05rOVhWK8jcUm/triyf/yZLsdHFEyN8h0LHjw7dF/KbRQ9NGD4oE5hs1ImK1gpVScrAwM6tghbXkCfvjn3n4mPhqd+HSi/c3oN3laN67q2sHKwovGyGN25Y7mgT/LLdsy+z5l5Db689WTMuncI/nVZH3xwQ3SM+EVXSDNa4sOogqiySmFgnDG7Y/5knp+45Ljw86NvEfv2juq4ph92dHrXXCx6+Gxcd3IePhw+AFcPiF5rEgBGnNstZrr0crIy0LJhnagunfEmeYq8lpc/OhR3DOkc1qCj77p9nK7iupeuUFdpcPk+fWlvS5OSxfo6BeNVKstzvivIHd0ykKE1a7oNBpcsgwkFRg7rgYcv6BmWsTk/YsKAyBvhEAvdf9rpBn6O+dMJob+b1c9GzzbxxySNMpji1Mp0q06x0zfaSOTCra0MbvxelJOZgUl/NV901qrsTMGQ7uHXSWQrxZvX5uN1rQbbKGNk9KX/4e7Bob9bmpzTRy/sibf/eILha3bccnpgDM/Tl/bG/53TNeaYKIrNbExWsNvZzYMDkyjdqBvr0C7G4PFzex0ZyvD/39ldMeveIaEWuydjFJjO730kHvxND/zbwrjGYNqszGrZoE4W7tXdsIMFyv55zXHnmcdg/oNn48r+4evv/J9uHcL3rjfPZDxyQfRseH3bN0W31uZxNDhpVW6jOoaLSIsI2jath065DSEimHr3YLxydSCTctoxuXg0Yga+/95wYthgdyB8bdDjOjSLqlDMyJComUr3l1SE/jYrtEZm7to3rx9Vs9zSwhjrF6/qh2cv74vHL+4Vc5bHJvWy0Sbi3tKzTXQLV5P62Xjjmnz875aBOFbXRTb4bzi9dMDdZ3fFPUO74sK+0Zlw/fnIbVTH8dnpagMnehcFKz2CrT3JzsJ746lHG058E8zI6yu6PrWxiHOylZAN62Q5Mi6vaf1s9GnfFK2b1EVOVgYyMgRjbxwQev2RC3qilX6JDggyMySskshozGhllQp17TPTIEbXxot0BbmHftMDn+tiXY82jTH5b6dh7sgz0apxXXx+y0DkNqqDV/5wPBrVzYZIYHZso9avxy/uFbdyEQj0iAOA47UCVr2I7tgX69KXb9Aq+Jch4eON62ZnQkRw8+DOOLlzi6jJBUUCcxb8L2JISfCeqz/+5Qb5Hv2/uviRc/DtHafG/D9vO70zerdrgnN6tjbdxmm+q3Z/6re9cenx7XBUi+rCmL415O5zuuKOsfPR3qBPqtHF36xBDh7+TQ/kNqqLOet3Y+Sw7hj7S2Ax6Uv6tUXbpvWxYfdBDH1+eug9HZrXx/g7T0VlVWBJ5pzMDHR7YDwAYP3Og4CWZ5n/YHT/WSPNDbpIvX5NPs4bHThmq0Z1sGO/8axTnXIbYE1R9JppLRvmYNfBMksBfMkj5+DNn9YBAEaNW2YpzXr6hVtHDuuOIxrXxe1j58d9X3amoNyoWgTA5L+dhiHP/Gj63jO7t0LnVo3wyo9rbKf3t/3a4bN5hWhSPxudWzXEJf3a4vN5m23vZ+rdg3HPpwtxZvcjoroz1om41oJjW576bS+c2iUXrRvXRcf7vg29btRUr69waFQnCw+c3wN7D5WFbfMH3VTfep/dPBAHSisMXzNy9YCjQrVst57OpQcScWzbxli8eZ/pzJ71cjLDpnk/7ZjcsJpDKzIyJGwWvyv6d8CIzxcBAI7r0BTzN+7FyZ1bYMbqXaEJa4JrPsXq2tO+eX08dvGxOCvGGKybTusUWkts+KCOqJudid/0aYMm9bIx5k8n4Lj20Tfdji0b4LL89mE3w+CaY/oa/f/dMhDdj2yMov2leOirJYbHz8nKQFlFFdY+fl7ou/PziDNwZJO6eOayPpbH0uS1bIC8lg1C5/74o5rhm4VbMXvdbmRlCAZq6WtaPztsgpi+7Zvi10178bsTAjf72fcNwYmPTw5lPO8b1h0XvTgjlGG466xj8Of35pqm44S8ZqibnYmRw7qjdzvzcSKxZhz8740nhlUaiQjaN6+PX+4fgtU7DoSer5OVgdKKKsMMsdkYx6zMjLDabABY9PA5YRMhjbvjFMMxK3bVy8nELYNjx51UT3lekyRbjrv/vO64cVBH5Daqg6FaBnX4oI74YcUOrDNYs7VJvWwUHzYftgIA9w/rgR9XFuG1aWtDz028axA6t2qI07u1ClVWAUC+wXIuRn5/YgfTiVhO7dIS01ftjLsPs4ku/j60G54av9xSOgDjynh9hV3w78h0dT+yMRY8dDb2HS6P6u4IBMZwBb/zfxyYh+tPORpN62ej18Pfh20XjG9Tlm9Hfl5zrN95ECO/WIxTOrfEmsfPw5ItxYZxp5MuP9evQzPMud9a62Rk48ljFx+LjbsP4dUfA5/vO9f1x7Vv/YIr+3fAWzPWhbaPXMf0ouPa4u0Z67F8237DccZndDsC39x+Cs7/909hz7dtWg8f3DAgavsjm9TFbWdErxv34Pk9cNoxuTinZ2vkjRhnuGQQEN67r2GdLPQwaZx59MKeGD1lNfJaNsBXt8UeVuA01wpyIjIUwAsAMgG8oZR60on9NqqbjTO6hWc2fnt8W6zecQB3ntUFjetmY4U2rmTK307D6Mmr8MWvW4x2FfJHrdYj2KVp8SPnICtDQrWw3Vo3xvonh2HXgVIcP2oS7jqri2ltVGOTGu03r83HluISDOzUAh/P2YQ62ZkYPXkVHrmgZ9i+lj86FEX7S9G+ef3QF3z2fUNw3Zg5+GFFEVaOOhfHjPwulN6HftMDf/90IZ77XV98OrcQo8Ytw+9P7IAHz++ButmZyBsxzvT/fvmqfujZpglEJDQI/dO5hVi+bX/M89WtdaPQNpEX/w2ndoRSKqwg17Flg6gFuif99TQcKqvArLW7MHxQoIWiaH8pTnhsEoBAMBnSrRUmL99hmIY2TethxLndcErnlrj6zdkAApmK+jlZOP1fUwEE1vN48re90VsLcPVzMvHxn09CzzaN0aF5/dDn/fRve4cV5BY+fDYqKhWaN8jBla/Nwsy1uwAAb/0xPzC5zmOTAQQyhB/rMkav/eF4DH9vLvp1aIom9bKx4KGz0eeR78O6CvzuhPCWiuBsdpF50GCt1CXHtcXn8zfjHxf2tDX7abxZl2ozt2LT5zefjO37SmK2prsxccOb1+Zjw65DOOfY1vhg1gac0rklVehFXQAAIABJREFUZqzeFZpspG52pqUC41UnGneXCdJ3mxERXKtb5mKwSXfMKVqr8uu6zFrwHOhviI3qZsWcKAMAJt11GvYeLkNGhuDd6/rj20Vbo1qXgMB353/z7VXMjL1xAF6fvhYX9K0uhP8aURF3fu8j8eumvaFjHtG4bth57du+adTjSHec0RnfLNqKNk3qhab8NpoARO+Tm07CZa/MNHxtYKfoGWoBoFWjumEZ4Ql3DsLn8zeHLeQ8Y8QZKNJVEI69cQDW7jyAWCJr+o1a89zwwhV9TWfKq0ncik3XnnQURk9eBSDQOn7r6Z2j8gZtmtTFluISLHr47KhCQbD3wB90XepaN6mLH+4ejNU7DuDMZ3/EqIuORW6jOmjZMAftm9XH2F824blJK2Oma1CX6ut36t2DkadVXuqv3aDljw7Fpt2H8NGcTXhDq3iOdNWJ4ffXHkc2xtKt+wAA/7y0DwY8Ebh3B+/NRkZfYdyD4YK+bcIKcqd3zcUPKwITs0TGnKsHdMCfB0UvX6Xvjhzswv7Gtfm4+f15YfG1Sb3ssN4RK0YNRUlZFWau3YVzeh6BBZv2YvLyQEwKFvae/11f5BuMjQvml3u3axpWwIhVeWTFG9fk46nxy7FKqzCKjHfB+8meg2X4uKAQp3ZuifVPDsPMNbvw1ox1oc84JysDfzo5D2/PWI/3ru+POlmZ+O4vp+L9WRtwgUELPYBQLwGzwhcArH38PBQfLjct2NfNzgy1mi16+GzUsbGguN7ATi2wfV8J/nBSnmnFutvEbArzpHYqkglgJYCzABQCmAPgSqXUUqPt8/PzVUFBgePpCHpp6mq0aVIvrEnZaRt3HcL9XyyKO3OXmVlrd6FOVngN6KGyChTtLw1rfQQC6/68P3sDLs9vH/dYvR6egP0lFXj/+hOxdGsxrj+lIx79ZinG/Lweax8/L6q/8aGyCkxbuRON62bhzo9+xY79pZh41yAsLCzG3z5ZgJev6och3Y/AM9+vQGaG4P/O6QoRQd6IcTipYwuMHR6oEfm/Txbgk7mFmH3fENTLycS+w+X4dG4h1hQdjNnV66M5G7Fs6348fEFP7C8px/RVO/Hp3EJMWb4DZ/c4Auf2ao3vl2zHs5f3DXWx2bL3MBrkZIVm4ftCy7AEv+y/btqLFyatxMtXH296vgY+MRlbiksw5W+nhbUwGllbdACVVSpsHIeZn9fsRK+2TWJOQAIAP6/eid+/MRsn5DVDv6Oa4aZBnVyd4jkVRGSuUspTC4bZjU2A+/HJaUop7D1kfgNLh4/nbMI9ny3EHwfm4WGt+6RSCkffG2hZW/aPoaiXk4mqKoWO932LTrkN8MlNA1FRWRXW/ciKkvJKXPvWL7j19M4x1wqySymFfSUVCS2qnozSikp0HTkejepkYZHFSQQoPq/FJ7dj01nP/ohVOw6EKhuCBbkf7h6MisoqdGhRH0oFMrgfF2zCPZ8uDL03XkVQ8eFyNK6bFVZRtWNfCfo/Hig49evQFPM27g17T2Q6rPZOmLJ8O64bE/0/v3Nd/1CX3wtfnIEFm/Zi6t2D8czElVi8uRg/3D047FgLNu3Fv75fEdYaNqBjc3yojcHUF3T/eWlvXJbfPvTcyGHdce3APHw4ZxN27CvB5fntcerTP+CqEzvgtjM6xxy/3u/Ridh9sMx2bwy9sooqzFizM+545lSYvGw76uVkmlYqVVYpHCitjptKKXwweyMuOq5taIhURWUVpq/aaWsm4w27DiK3UZ2ku/jGc94L00OVAcl8ZnbYjU1uFeROAvCwUuoc7fG9AKCUesJoe79llPyk+HA5SssrbWeG7Copr0RWhljqI52ITbsPIbdRnYQKyVZs2HUQ4xdvw59PS3wR+GT8vGYnfv/6bPRt3xRfGExB7EdeyygB9mMTwPjkhLKKKjw3aSVuGdwpbqXGobIK1MnK5PpROm7Hv9rIa/HJ7dhUXlmFyioVuobem7ke+XnNDSeQC1aoAIGhH/MeOMveP6Pt4/4vFiFDBCOH9UD3B8dj5LDuGDVuGVo3rhuajbGkvBKl5VWhilgrthYfxklPTAnr7WMlk12wfjeWbdsf1rJ4oLQCxz40AQAwtGfr0GR4m/cexslPTsHJnVuEuuwFC3Lj7jgl4dboHftKsGN/adjYU/KuJ75dhlenrcWD5/fAdSla2sIrBblLAQxVSt2gPf4DgBOVUrfpthkOYLj2sCuAFY4npFpLAPE7R6eGl9ICeCs9XkoL4K30eCktgLX0HKWU8tSUrFZik/Z8quKTHz/XVPFSWgBvpcdLaQG8lR6rafFUfPJgbAL8+bmmipfS46W0AN5Kj5fSAriQd3KrTdKoKjWsxKiUeg3Aay4dPzwxIgVeqXnzUloAb6XHS2kBvJUeL6UF8F56bIgbm4DUxSevnUcvpcdLaQG8lR4vpQXwVnq8lBabPBWbAG+dSy+lBfBWeryUFsBb6fFSWgB30uPW8gOFAPTzeLYDEHvGESIi9zE2EZEXMTYRkW1uFeTmAOgiIkeLSA6AKwB85dKxiIisYmwiIi9ibCIi21zpWqmUqhCR2wBMQGAa3beUUsaLA6VGSrohWOSltADeSo+X0gJ4Kz1eSgvgvfRYwtgUl5fS46W0AN5Kj5fSAngrPV5Ki2UejE2At86ll9ICeCs9XkoL4K30eCktgAvpcWWyEyIiIiIiInKPW10riYiIiIiIyCUsyBEREREREfmM7wpyIvKWiOwQkcVxtjtBRCq1tVmCzz0tIktEZJmIjBYR0Z6fKiIrRORX7cfS8vJJpuUpEVms/fxO9/zRIjJbRFaJyEfaoGdLXErPGBFZpzs3fZ1Ii4gMFpFi3X4f1L02VPs8VovICN3zrp2bBNOTjnNj+F4RaS4iE7VzM1FEmllJi4vpeVhENuvec57V9PiVl2KTA+lxND55KTZZSU8q45OXYpMD6XE0PjE2OcdL8clLscnF9DDvVIPzTp6KTUopX/0AGASgH4DFMbbJBDAFwLcALtWeGwhghvZaJoCZAAZrr00FkJ/CtAwDMBGByWYaACgA0Fh77WMAV2h/vwLg5jSnZ0xwOyfPDYDBAL4xSd8aAB0B5ABYAKCH2+cmwfSk9NzEei+ApwGM0P4eAeCpNKfnYQB32z03fv5J4vvneGxKMj2OxyeX0pLQ989KehKMB66cmwTTkvJzE+u9SDA+uZSWh1HLYpOVc6m7pph3ciY9CX0HXYoHzDvV4NjkuxY5pdQ0ALvjbHY7gM8A7NC/FUBdBC6oOgCyAWxPU1p6APhRKVWhlDqIwAU+VKvlOgPAp9p27wC4KF3psXrcJNJipD+A1UqptUqpMgAfArgwRefGcnoS2I8TaYn13gsROCdA6s5NUu+tabwUm5JMj+PxyUuxyUZ6jDgen7wUm5JMj+PxibHJOV6KT16KTW6kx+pxk0iLEeadEnuv72OT7wpy8YhIWwAXI1DrEKKUmgngBwBbtZ8JSqlluk3e1poyHwh2G3ArLQh82c8Vkfoi0hLA6QgsBNoCwF6lVIW2XSGAtk6kJcH0BD0mIgtF5DkRqeNUegCcJCILROQ7EempPdcWwCbdNsFz4Oq5SSA9Qak8N7EcoZTaCgDab8td8FxKDwDcpp2bt6x2V6jJvBSbYqUHaYhPHoxNgLfik5dik1l6YnEzPjE2OcBL8clLsSnB9AQx7xSdnqDakHdKSWyqcQU5AM8D+LtSqlL/pIh0BtAdQDsELqYzRGSQ9vJVSqleAE7Vfv7gZlqUUt8j0Dz/M4CxCHRVqABgFASdXB/CbnoA4F4A3QCcAKA5gL87lJZ5AI5SSvUB8G8AX2jPm50Dt8+N3fQAqT836ZJIel4G0AlAXwRu/s+4lzzf8FJsMk1PmuKTl2IT4K345KXYFCs96cDY5BwvxScvxaZE0gMw71Tb804pi001sSCXD+BDEVkP4FIAL4nIRQjUpsxSSh1QSh0A8B2AAQCglNqs/d4P4L8INAm7mRYopR5TSvVVSp2FwAW/CsBOAE1FJLhQezsAWxxKSyLpgVJqqwooBfA2HDo3Sql92ucApdS3ALK1Gq1ChNdoBc+Bq+cmgfSk49zEsl1EjgQA7feOONu7mh6l1HalVKVSqgrA63DuO+VnXopNsdKTjvjkmdik7dsz8clLsSlOemJxJT4xNjnKS/HJS7EpkfQw71TL806pjE01riCnlDpaKZWnlMpDoE/wLUqpLwBsBHCaiGSJSDaA0wAs0x63BADt+fMBxJy5KNm0iEimiLTQjtkbQG8A3yulFAJdGIIzIl0L4Esn0pJIerTHwQtcEOg77Mi5EZHWwW4YItIfgWtxF4A5ALpIYJalHABXAPjK7XNjNz3adqk+N7F8hcA5AVJ3bmK950jdw4vh0LnxMy/FpljpSUd88lJs0vbpmfjkpdgUJz2xuBKfGJuc46X45KXYlEh6tMfMO9XivFNKY5OyOTtKun8QaL7eCqAcgRL/9QBuAnCTwbZjUD27UCaAVwEsA7AUwLPa8w0AzAWwEMASAC8AyHQ5LXW1NCwFMAtAX912HQH8AmA1gE8A1EnBuYmVnikAFmkX1PsAGjqRFgC3aed7gXbMgbr3ngdgJQIzHt2finOTYHrScW6i3qs93wLAZARqAycDaJ6ic2OWnve0c7MQgUB5ZLpjh9s/SXz/HI9NSabH8fjkUloS+v45cM07Gp9cSku6zo2j8cmltNS62JTkd5B5p8TSw7xTDc47uZSWhGKTaG8mIiIiIiIin6hxXSuJiIiIiIhqOhbkiIiIiIiIfIYFOSIiIiIiIp9hQY6IiIiIiMhnWJAjUyJyn4i8ke50kDtE5C0R2SEicae4FZFBIjJPRCpE5NKI154WkSUiskxERgen3CVyE+NTzcb4RH7F2FSzeS02sSBHppRSjyulbgAAEckTESXVi0omTESu1fZ1g+45EZGnRGSX9vO0/qIWkb4iMldEDmm/+0bss5+ITBORAyKyXUT+kmw6a4ExAIZa3HYjgD8isOhriIgMBHAyAuvnHAvgBATWGSJylV/ik4jUEZFXtLi0W0S+FpG2yaazFhgDxifyIadjk/b+g1r+5oC+kJhkbHpYRMp1+z0gIh0TTWctMgYeik0syFFKiUgzAPcisL6G3nAEFobsg8CFfT6AP2vvyUFgkcb3ATQD8A6AL7XnIYFFSccjsNZNCwCdoS3KSeaUUtMA7NY/JyKdRGS8FvCni0g3bdv1SqmFAKoid4PAWjo5AOoAyAaw3f3UEznPjfgE4C8ATtLe1wbAXgD/dvUfqQEYn4jC9FFKNdR+btA9n0xsAoCPdPttqJRam4p/xs+8FptYkCOIyN9FZLOI7BeRFSIyRHv+YRF5X9tsmvZ7r1Zrc5K2zXVas/AeEZkgIkfFOdwTAEYD2Bnx/LUAnlFKFSqlNgN4BoFaDAAYDCALwPNKqVKl1GgAAuAM7fW/ApiglPpAe32/UmpZIueC8BqA25VSxwO4G8BLsTZWSs0E8AMCi1tuReBz4Lknx9SA+HQ0At+L7UqpEgAfAuhp/0wQGJ/IQ1Icm8wkE5vIOWmLTSzI1XIi0hWBFehPUEo1AnAOgPUGmw7SfjfVam1mishFAO4DcAmAXADTEVix3uxY/QHkA3jF4OWeABboHi9AdWanJ4CFKnz1+oW61wcA2C0iP0ug3/LXItLBLB1kTEQaAhgI4BMR+RWBFs4j47ynM4DuANoBaAvgDBEZFOs9RFbVkPj0JoCTRaSNiNQHcBWA78zSQcYYn8hLUhmbNNNEZJuIfC4iebrnk4lNAPAbrcv3EhG5OU4ayEC6YxMLclSJQLNuDxHJ1pqB11h8758BPKGUWqaUqgDwOIC+RjVLIpKJQA3F7UqpyCZmAGgIoFj3uBhAQ62vd+RrwdcbaX+3Q6BW6i8AOgBYh/hBkaJlANirlOqr++ke5z0XA5illDqglDqAQAZ1gOsppdqiJsSnlQiMk9gMYB8CN+9/WPwfqBrjE3lJSmKT5jQAeQC6AdgC4BupHnOXTGz6GIF4lAvgRgAPisiVFv8HqpbW2MSCXC2nlFoN4E4ADwPYISIfikgbi28/CsALIrJXRPYi0GdYEKhdiHQLAjVDM032dQBAY93jxgAOaDVJka8FX9+v/X0YwP+UUnO0rkuPABgoIk0s/h8EQCm1D8A6EbkMCA2i7hPnbRsBnCYiWSKSjcANh12XyBE1JD69jMBYiBYAGgD4HGyRs43xibwkhbEJSqlpSqkypdReBCqsj0agAAYkEZuUUkuVUluUUpVKqZ8BvADgUpAt6Y5NLMgRlFL/VUqdgkBwUQCeMtrM4LlNAP6slGqq+6mnBYRIQwBcrHUN2IZAM/QzIvIf7fUlCAzWDeqD6gkHlgDordUwBfXWvb4wIn3BvznNdAwiMhbATABdRaRQRK5HoNvX9SKyAIHze6G27QkiUgjgMgCvikjw3H8KYA2ARQh06ViglPo6xf8K1WA1ID71ATBGKbVbKVWKwEQn/SUwSROZYHwir0tRbDI8NKrzN8nEplj7JROei01KKf7U4h8AXREY+FoHgdlz3kIg0wEEapre1/6uj0BXgmN0770YwGIAPbXHTQBcZnKcpgBa635+RmCSkiba6zchUBvRFoGZ3ZYAuEl7LQfABgRqouog0C99A4Ac7fUzAOwB0BeBmX+eAzA93eeWP/zhT3I/NSQ+vQ3gM+342QiMjdmc7nPLH/7wJ/GfFMamnlreJhOBrpLPA1gBIFt7PZnYdCECs1kKgP4IdP++Nt3nlj/2fpJec4d8rw6AJxFopi9HIAMzPHIjpdQhEXkMwAytGXioUup/2iDPD7W+3cUAJgL4xOD9e/WPRaQMwD6lVLD/9qsAOiJQOwEAb2jPQSlVpg0OfkNL6zIAFymlyrTXp4jIfQDGIRA0fwLw+wTPBxF5h+/jEwIzmI0GsAqBjNViBDJyRORfKYlNAI5AoHt2OwAHteOcr5Qq115PJjZdgUABtA6AQgBPKaXeSfB8UJqIUkatvkRERERERORVHCNHRERERETkMyzIERERERER+QwLckRERERERD7DghwREREREZHPeGLWypYtW6q8vLx0J4OIHDR37tydSqncdKcjWYxPRDVPTYhPjE1ENY/d2OSJglxeXh4KCgrSnQwicpCIbEh3GpzA+ERU89SE+MTYRFTz2I1NcbtWishbIrJDRBYbvHa3iCgRaak9FhEZLSKrRWShiPSzkxgiIjsYn4jIixibiCgVrIyRGwNgaOSTItIewFkANuqePhdAF+1nOAKLGBIRuWUMGJ+IyHvGgLGJiFwWtyCnlJoGYLfBS88BuAeAfkXxCwG8qwJmAWgqIkc6klIiogiMT0TkRYxNRJQKCc1aKSIXANislFoQ8VJbAJt0jwu154z2MVxECkSkoKioKJFkEBFFYXwiIi9ibCIip9kuyIlIfQD3A3jQ6GWD55TBc1BKvaaUyldK5efm+nriKCLyCMYnIvIixiYickMis1Z2AnA0gAUiAgDtAMwTkf4I1CK1123bDsCWZBNJRGQR4xMReRFjExE5znaLnFJqkVKqlVIqTymVh0AA6qeU2gbgKwDXaDMwDQBQrJTa6mySiYiMMT4RkRcxNhGRG6wsPzAWwEwAXUWkUESuj7H5twDWAlgN4HUAtziSSiIiA4xPRORFjE1ElApxu1Yqpa6M83qe7m8F4Nbkk0VEFB/jExF5EWMTEaVCQrNWEhERERERUfqwIEdEREREROQzLMgRERERERH5DAtyREREREREPsOCHBERERERkc+wIEdEREREROQzLMgRERERERH5DAtyREREREREPsOCHBERERERkc+wIEdEREREROQzLMgRERERERH5DAtyREREREREPsOCHBERERERkc+wIEdEREREROQzLMgRERERERH5DAtyREREREREPhO3ICcib4nIDhFZrHvunyKyXEQWisj/RKSp7rV7RWS1iKwQkXPcSjgREeMTEXkRYxMRpYKVFrkxAIZGPDcRwLFKqd4AVgK4FwBEpAeAKwD01N7zkohkOpZaIqJwY8D4RETeMwaMTUTksrgFOaXUNAC7I577XilVoT2cBaCd9veFAD5USpUqpdYBWA2gv4PpJSIKYXwiIi9ibCKiVHBijNx1AL7T/m4LYJPutULtuSgiMlxECkSkoKioyIFkEBFFYXwiIi9ibCKipCVVkBOR+wFUAPgg+JTBZsrovUqp15RS+Uqp/Nzc3GSSQUQUhfGJiLyIsYmInJKV6BtF5FoA5wMYopQKBpxCAO11m7UDsCXx5BER2cf4RERexNhERE5KqEVORIYC+DuAC5RSh3QvfQXgChGpIyJHA+gC4Jfkk0lEZA3jExF5EWMTETktbouciIwFMBhASxEpBPAQAjMt1QEwUUQAYJZS6ial1BIR+RjAUgS6DdyqlKp0K/FEVLsxPhG5a+mWfcjIALq1bpzupPgKYxMRpYJUt+ynT35+viooKEh3MojIQSIyVymVn+50JIvxiWqzvBHjAADrnxyW5pQ4qybEJ8YmoprHbmxyYtZKIiIiIiIiSiEW5IiIiIiIiHyGBTkiIiIiIiKfYUGOiIiIiIjIZ1iQIyIiIiIi8hkW5IiIiIiIiHyGBTkiIiIiIiKfYUGOiIiIiIjIZ1iQIyIiIiIi8hkW5IiIiIiIiHyGBTkiIiIiIiKfYUGOiIiIiIjIZ1iQIyIiIiIi8hkW5IiIiIiIiHyGBTkiIiIiIiKfiVuQE5G3RGSHiCzWPddcRCaKyCrtdzPteRGR0SKyWkQWikg/NxNPRLUb4xMReRFjExGlgpUWuTEAhkY8NwLAZKVUFwCTtccAcC6ALtrPcAAvO5NMIiJDY8D4RETeMwaMTUTksrgFOaXUNAC7I56+EMA72t/vALhI9/y7KmAWgKYicqRTiSUi0mN8IiIvYmwiolRIdIzcEUqprQCg/W6lPd8WwCbddoXac1FEZLiIFIhIQVFRUYLJICKKwvhERF7E2EREjnJ6shMxeE4ZbaiUek0pla+Uys/NzXU4GUREURifiMiLGJuIKCGJFuS2B5v9td87tOcLAbTXbdcOwJbEk0dEZBvjExF5EWMTETkq0YLcVwCu1f6+FsCXuuev0WZgGgCgONiNgIgoRRifiMiLGJuIyFFWlh8YC2AmgK4iUigi1wN4EsBZIrIKwFnaYwD4FsBaAKsBvA7gFldSTUQExieimmLPwTLkjRiHrxbUjIYoxiYiSoWseBsopa40eWmIwbYKwK3JJoqIyArGJ6KaYcPuQwCAN6avxQV92qQ5NcljbCKiVHB6shMiIiIiW+pkBbIjZRVVaU4JEZF/sCBHREREaZWjFeRKWZAjIrKMBTkiIiJKq5xMtsgREdnFghwRERF5QkUVC3JERFaxIEdERESeoAyXwSYiIiMsyBER1SJ9Hvked3+yIN3JIDLEchwRkXUsyBH51PZ9Jfhi/uZ0J4N8pvhwOT6dW5juZJAHVVYp3PbfeVhYuDdtaWCLHBGRdSzIEfnUVW/Mxp0f/YoDpRXpTgoR1QBb9h7GNwu34pYP5qUtDTsPlKbt2EREfsOCHJFPbSsuAQBUJViFXV5ZhVd+XBNzlrh9JeV44IvFOFxWmdAxiMg/Ji/bnrZjsyWOiMg+FuSIfE4SfN87P6/Hk98tx5s/rTPd5sUpq/HerA34YPaGBI9CRH7x8NdLAQCSaFAhIqKUYkGOqJY6WBpoZTtUZt41s7IqUE3O2nIiIiIib2FBjqiWi1X5Hiy/7TpYloqkEFEtpThfJRGRbSzIEfmUSmEz2Ss/rknZsYjI//aXlGPL3sPpTgYRUY3GghyRz4mLA1rYpZKo9pGER95Wu/A/MzDwySmWt2esISKyjwU5olqKXZmIyKqKSvPZbY2s3XnQpZQQEVEQC3JEPuVYMYxT1BFRHJv2eLOb5ANfLMZlr/yc7mQQEaVFUgU5EblLRJaIyGIRGSsidUXkaBGZLSKrROQjEclxKrFEFC3RYlhN78rE+ETkH4mGo/dmbcCc9XtQfLjc0fS4ibGJiJyScEFORNoCuANAvlLqWACZAK4A8BSA55RSXQDsAXC9EwklIrKK8YnIWV5vt3/4qyXpToIljE1E5KRku1ZmAagnIlkA6gPYCuAMAJ9qr78D4KIkj0FEBlLRoubzcXSMT0QJ2Lj7UMqPGTkLb0l5JT6es8ny7Lz7S8zXw/QgxiYickTCBTml1GYA/wKwEYEgVAxgLoC9SqlgRC0E0Nbo/SIyXEQKRKSgqKgo0WQQ1XrJDnHzek17Ivwcn8oqqlBSXpnSYxLFk+qhtP+csAL3fLYQk5ftSO2BXebn2ERE3pNM18pmAC4EcDSANgAaADjXYFPD6jSl1GtKqXylVH5ubm6iySCq9RJtmbPyNr+Oo/NzfBo2ejq6PTA+pcek2uVwWaXnKwt2HigFADw/eaXpNuMWbk1Vchzj59hERN6TTNfKMwGsU0oVKaXKAXwOYCCAplp3AQBoB2BLkmn0tTemr8WCTXvTnQyqgZzq9rj7YJml7Q6XeTvjF8G38WnVjgPpTgLVcN0fHI/8UZNsvWfx5n2WuzkmInLPwQbAxZv3oXCPcVfPZVv3xdiDZ/k2NhGR9yRTkNsIYICI1JfAisRDACwF8AOAS7VtrgXwZXJJ9LdR45bhwhdnpDsZRKbem7UBizcXx93u1v/OS0FqHMP4RBTDgVJ7Y8pu/e883D52vkupid36v+egf2aktICxiYgck8wYudkIDMydB2CRtq/XAPwdwF9FZDWAFgDedCCdRGTCiXro5dv2x91m5ppdDhwpNWpCfHKz9YPIisjul9+42JXxvZnrbb8nfNyeP0b71oTYRETekRV/E3NKqYcAPBTx9FoA/ZPZLxHFl3Q+X7cDK4UGv60b7vf4dLi8EvVzkgrRREl5bNyyhN63tsh+92ArlUmx+afiw++xiYjop+N/AAAgAElEQVS8I9nlB4ioBjhkYfxbht9Kcj5XWeV+xrS0ohIVlVWuH4f8aUOCyxCc8cyPSR9bGG+IiOJiQY4oxQ6UVjiaeXaiC95DJovp6vfNbFVquVGMKz4UPtao68jxuPiln104EtUEVQlUJpjFo2ve+gUv/rDa/H22j+TfWXWJiJzCghxRih370ISkJw4pKa9EaUVyhUFLyw/oH7Akl1JuZFLfnbk+6rlFFia6odopkUax16evNXx+2soi/HPCirDn8kaMw6hvlgYeJHC97zlUPeMuC3VE5DU79pWgaH+pq8dgQY4oDSYs2Z7U+zcm2OUpGSzHpRgzpuQxLRvWibvNd4u32drnGz+tAxB7OZXnJxmvJffB7I2hv9kTk6j2mbV2F35ZtzvdyTDV//HJOOExe0u92MWCHFEN8eWvm20tkGulBlu/DcespNaMNTsd3yfLhmRHZIw4q0erhPbzhkkrndmxig+HdwGevHxH6O+KyqrQYuFEVLtd8dosXP7qzHQnI61YkCPyuWD+5y8f/uq3td4oBrc/y1RMpkL+VhVRkkuk+6JSCqMszH5ZsGFP6O/bYlz7D361BPmjJuFQmb118IiIaiIW5Ih8KB3jQQ7aXECYkuPGZ6zf53MTjburEQU5UZBLxLqdB00nTfluUaDXweGomXbZY4CIah8W5IjSbNzCrZa6HplJNHP1nxgzyBmpYAuO77zy45pQxhcIH4f048qidCSJfMSJglui+zB7W/B5dvUmIkpyQXAiSl6wC90Np3a0/J5YEwPEs6iwGActdktK5jiUfk9+txwAsP7JYQDCM9WRrS1EkSIvES/FAxbjiIhYkCPyP5t5q9/85yd30kG+wnIcxePIGLkEj715z2Hj/fG6JSIKYUGOyIckRfXRzDTVXPxoKZ6oglyKjlu45zAKTQtygVRksGslERHHyBH5kb6Lk5vdnZjZ95eb35+LG98tsLTtsq37XE4NecGU5dvx0ZyN8Tc0EDksNtFZK50U2ltEOY7lOiKqjdgiR1QDTF6W3ALjlF5jf9mImWt2YfSVxyW1n1iLMW/YdRAvTF6V1P7JP9bvPIjt+0pw3ZhAwf53J3SwvY/IQpgXxsjtL+HsueS8sb9sRLP6ORh6bOt0J4XIFhbkXLB0yz6UVFSiX4dm6U4K1QJKAa/+mPisl9H7UyirrEKdrEzH9kmx3fv5IgBIuiAXy5ItbIGrTQb/a2rS+4iaqDaFY+TiKa+scmnPVBsFY3BwYigiJ5VXViE7051OkOxa6YLzRk/HJS/9nO5kUC3iZE35c5NWoevI8ThgsG5c8aFyx45D0dxcpJvjHcmuyDFyn8/fnKaURKuo5AVNRP7gZrxKqiAnIk1F5FMRWS4iy0TkJBFpLiITRWSV9rvWNkslszYY1T77S8pRlUBGXsHZTPonBZsAAPsOl0ftt6LKP7XgXo1P8zbuwYe/GI9ZuvOjXx07ztPjl4c99kK3OPKXVK0j9+ZP6+zv18fXs1djExG5w80xvMm2yL0AYLxSqhuAPgCWARgBYLJSqguAydrjWmnUuGXpTgL5SK+Hv8fILxcn9F4nszQ1qOXGk/Hpkpd+xgitG0+krxdscew4L01dE/Y4kc916ZZ9OGbkd9habDyDINVsiaw1mMh19ug3S22/x8XG61TwZGwiIv9JuCAnIo0BDALwJgAopcqUUnsBXAjgHW2zdwBclGwiiWqL/862NrucPrOklHJ8ZjggUIMUbJ2rfs4fU8MxPkVLJFP+3qwNKKuowpTlO1xIEXmdE4vGG7WcHS6rxEM2K6127C8JK/C5EfNSgbGJiJyUTItcRwBFAN4Wkfki8oaINABwhFJqKwBov1sZvVlEhotIgYgUFBUVJZEM+/aVlGPS0tTO8jd/456UHo9qF0db5HR7q/Bvtben4pNbhe1023OwDGuKDqQ7GTXW1BU7cNUbsxLqcm1FSXllzNdXbnfns31v1nq8M3ODrffc/7/FYV0wI79OCwv3OpG0VPBUbCIif0umIJcFoB+Al5VSxwE4CBtdAZRSryml8pVS+bm5uUkkw76/jJ2PG94twOa9zncXMpusYFtxiePHotorrEUO7nSHTNWi4y5JS3xSSuF/8wujZtR7f/ZGHH3vtyjaX2p5X06at3GPK9fI0BemYcgzPzq/Y0JJeSX++PYczFi9C9+7VPFYWlGFCpdnfzS67hJZzLusIjydkfvdvi89360E+DbvlA5zN+zGb/79U9xKB6LaKpmCXCGAQqXUbO3xpwgEp+0iciQAaL891yenYH2gdez1ac5PRmJWkPNJjzTyqZrX1pO0tMSnrxduxV0fLcDLEePTPp1bCADYtOdQ1Hsuf2Wmk0kwNHPNrgQnh4j9Hh9lnn3nmjd/Cf1dWuFOJvaL+ZvR+f7vsGHXQVf2b8aJabid6PaZJr7NO6XDA18swaLNxVi9gy3/lJxZa3fhhUnpWUvVzXCVcDRVSm0DsElEumpPDQGwFMBXAK7VnrsWwJdJpdAF+7Vp1f/nwlTK5pklluQIOPNZZ1ov9BUDKkaT3I59JdhXYm/JAP/mj6qlKz7tOVgGANh5wHoB55f1u20fp7JK4eo3ZmPBJmvdyUSAr351biIVcl8i14UVew+Vhf4et3ArAGDFtv2uHMtMRob9+2FkHPNrQc7PeSciP7vitVl4btLKdCfDcckuCH47gA9EJAfAWgB/QqBw+LGIXA9gI4DLkjyGa9y4EZjtki1yBMCxWsXI68zsSu7/+GQ0b5CDeQ+cZXnfO7Tuf0bXrM8u45THp1SNg1u38yB+Wr0TP63eaWkBW4HghxUcT0PAQ18tCf3tVmExnkTiyPyN4ZUW/izGhfg670RE9ri5XEpSBTml1K8A8g1eGpLMflNlf0n0gsdu8VkGmDzuoa+qZ3yLFyB2HyyL+bodfso8pTM+RX7fnf/+2/skWJFEQYfLortpujkbrVHdhhOH8/PkQX7POxGRdyTfUZ3CmLfIMSdF0SInxbBqzvrwWVBTlafhVRxbqrKWdi+beJ+bW7MikjN8XGYx5MRESrxkawdmnagm8OQYOTJm1jrCWERG3p9lbwpuQ8qZZvvVO/aHjZ8xumZ5U/UGu93Cnfzcdh4ojZpBkPzNya915LVmFJucaZFLfh9ERH7HgpzDErm5/Pbln3Hrf+c5nxjyvLVFzswWF++627grerbESGc+Ow3nvTDdkfSQMacyn06P7zXfW3SOO3/UJNz10a+OHp9qLqOWMycuX79OdkL28GMmr9m46xB+XrPT1nvcvIxZkHOY2YeVEeNMz92wJzR7GNUucxyYbMDKOnKD/vmDpX1tibPeIW+qsf2qzSIZ2ZXaqAXi3ZnrEz5OVUSDWEVlVdiNJXI2y3hd2ayONwpuN25R6uIV149yRvAeY/UrXFZRhRyLywQU7S/FgVLjMeflBq23TvQgYEGO3LbrQCmWbtmX7mSQxwz65w/4/euz426XqnVja11BbsmWYkf3d/krM5E3Ylwog2OWIfL54srkcW5kaaavslfjRMCXcab437i7ugX2wS+XxNgytshM7OjJq8JuLK9PD18jc8+h2BPemF0/wfXFZq8NVDiY5Z3dKmy9N3M9uj0wPuF1zg6aFC5qo9emBdY2nGhxcfFjRn5nucB1wmOTcOxDEwAAy7aGZ3w/d2GZHyC6MoNqpnR25z9v9HScN5q9VCgx+nVj3ZycyZcFuSnLt2P84sRqhIeN/snRtASnb56v1YCzjpD0Nu89jLwR4zBluXHmyYnvtlLWg8TeOBl6vb99siD6WJbfTUbu+ij6nCYisiC3uih8WYtvIlr4S+OMaTO7fGau3QUA+GpBoIBq9vmbtcYk6wGtsGu1C/L2fSVYWBiIxb+s242eD03A1BVcVxmI39puJJEJRUrKw6+1igQndIpn1LilruyXvOGJb5dZrnRwy/Z9qWlRoZqhrKLKcFZggF0ro1w3pgA3ve+tMWXbtZvkhp0mY5HYIOdJq7bvx4s/rE7ovaUVlVi1PfZCusEubp8UFBq+7ubaIkYOmgQZq/w85Xc6Of31j8xgx/tY4i1QbvU6DBYgI2vJ3e7mZrVW/sxnfsQF/5kBINBlHQBmrtnlVrJSxok4URmjVOZmq0eGwc7fnrE+6f3OXpeeNfAoNV6dthY3vlsQehwvxGzafQh/eHM2W+EprvGLtyFvxDhs2XvY0f1e8J+f0P3B8Y7u0wpfFuS86OYPAgXL3/zHuMWP5ThvuuTln/HPCSsS6hp23+eLcdZz02Ku0xa8+ZhllBxpkYPC8m3VBcpYhS1OM58aTmWMR3y2MOo5pRR27LPXuhKvy6fpsikWt3O7fG9UGDCyX5eJCw7vqi1jqe75dAG+XmD+OVcpheLD5SlMUfVxI63eccBgS6qNqqoUvl201fTeZDWWPj1hBaav2olJy9Lbikfe93HBJgBwfPyjPh8GhN8XufyAi2LVUhpRSmHuht22Wya4jpw3lWrdgBL5eGavC9T0x6oBDNakm42RTOS7vXpH7FbAWJdmRZIFudqRJfaOD+dsMnwuWHGUatXXczi3C0tWC3JG73GpZ5/nfFxQiNvHzjd9varKfgVArPvcf6asCk3uEwvrjiiWT+cW4pYP5uE9J5biIbJgf0mgQqumZMtrfUHusM2WmK8XbsVvX56Jz+dtDg0et6KGXC81jttdG0P5INMWOfvHf+b7lcbHCD6O8V67FReUGDcnN/rZha6CZl0vIy+Xn7QJcOx27UxEua4ElpHA6QwW5BIpZE5auj2pWUWBQPq91BVZwTzjYvZ8rHDxr+9X4qIXZ8Q9buT5L68tJWuypEiLPVNX7EDxodS3GFPts25nYMx1SgtybJFzRmlFdKHN7EY7cen2UKZFb712AXy3eBse/3a5swmktEkm411SXomtxcZ9reOU41z5bsfKuH40Z2NS+/ZQvtTzfvPvn3DDO4ExHk62yEcWatYWHUj6cznlKWvLU0xebjxxiBstcvqxbaUVVdhmc7KO4ClPpDB1w7sFSc0qWrS/FF3u/w5jfl6f8D7ckfoqxcjz3+X+71w7llsTq1C4xZuL8UlBdG+BZPywoggXvxy/YoAoWTsPBIbD2Mn3rd6x3/Y9KFV9mHxfkPtl3e5Qf9d47jDodmJ2mm98twBXv2m+TkSFwdzHscYfebkJd9jo6fh8nvFkHDXZjv0lKK8MfGbTVhYlXHs+/L25OOmJKYavBffpZtfayFTH+jecWoCcYhMBFm0udmW8RmQ3Q7u9Cpyi/764UcDP1q1hdsO7BRjwxGRb7w+ep3dmpr7LVqE27fQXLk29nyijykwgEBeen7TS8DW95g1ybB9zawKzZSaK9Uypcf6/f8L/fRo9fjcR+nDG+xMlKl7+zahhxk4PpTOfnWb7HqRfR87N3l++L8hd/upM3GMhoLwxfS0mLInOVCWaATG6ADbHmAHHy+vILdmyD3/92Jlp0f3k7k+qr5sb3i3AhCXbEtpPsJneaGr/0GQnJu9N5PqL955YAaMyyRx3qmfZrAnu+XRBaAbFSPM2Gj8fS2SdQLpiSyJpBwKFCf0UzZGPjSTSJTgjkf6YLiqrqMIT3y4Ljc+ww87XNtbETWbL74watwzPT1plIR3RCYnXVfK7xYnF1USwx4B1+0rK8eEvG5Pq/jtjdfJrjXo5b0T+Ee8yNmqYcXLyJ6OGnFTNru/7gpxV/zGbYt5mDAvWLtpdLNnLLXK11aGISUrsN5uH+2B2dLfF0OQQDo6Ri7ePWLtMeogcM0qW6Lvafmyy9AQAzN8Yf7KIbxeFrwkX2SKXysK1/sj6a8moa+Wq7fvxvUHlyJBnfgybojnycdCrNsYgW6GUwvdLtoUKHh/M3oBTnzZuSXeM7rP6uGATXp22Fi9YKDBFZgqMvtN7DpYZxo9uD5hMf+3gZbJ4c3Ho72cnVrfkGaUnlbOGsqLJuhGfLcSIzxdhQWFx/I1NfDY3+Z48Vutb+NlSLIlcHU5eUTsPVre+rSmKnpWXs1a6yG5wKD4cf6p5I14tx3lpMH6qRXVJdOMYuha5Tbuj1xh085in/2tq1GtcfiA1vl1krRXCyvcv3liUVH6F9Qt/62NaMA3vz9qAM56ZCgA467lpGP7e3Kh9FO4JFHIPllZg1fb9oceRpq4oinrOrGugkfD0KUxdWYTh783FvycHClL3/28xNu0+jIL1zq9HFuydsUA3q2Nw/JaVyT72GLTu623afQjHPToRr09fm0Qq7QteavpF1oPjxgHjiqJUxpxafDuzLdjtqzSJrtllDoxJNKrk1Lcqe6HFzk4+aWvxYYz6ZiknFkuxRPKyTlYy6StYr3o9uvXPzauhxhXk5m3cg23FJXjgi8VhN0ynvlQJj3VKfywyVJtjTSJdnGIxujSq15ETvDQ1uoUhK4HuX5GVD5GxqPuD43GgtCLU5VNvWO8jbR8v/NiUbkY3nw0GlQSJ2FdSjvP/Pd10iQv9enT66z2YppFfLLY8zqXnQxNw1nPTQo+/XrAFy7bGXtcn0TFnT3y3HDu1jGthRBf4iS6MY3wnyUlOnvjOeCKtTbsPYc763dikjcGbvMx48hkjTnx3g5eeWbdVo8wUY4Y3xfssjew6UBpWyHIiX2VUUNO3KjvZErd57+GYQ2DM2Mnv/+3jBXjjp3Wm3enJHYlcJXWyrBWBrBQS9VdxiY0KRyckXZATkUwRmS8i32iPjxaR2SKySkQ+EhH7o6Mtisyo/nPCclzy0s8Y8MRkvDdrQ1iN7v4S47W+4n0+/5kS3g0m0fKYF2qVjNSWxXKNHIoYl5PsJ2T0GetnrTQq6F16fHvbx7HykW3YZZyZbtO0nu3j+Vk645MVU0xmgTTy9PjlePzbZVF3LKWAQocKcj+uKMLizfvwnIXuf/pvjBNR5Pax83HuC9NjbhN57b83c73p2mn679tr09Ziy17jrtOVlc7HwDnrzTNxVo72qUmXtVOf/gGXvTIzofuJk70v9LXP+tZDBaBRnayIAyd+nLY245Wfbmfpjk3xZlQ2cvyoSbjitVmhx8muSwrEH3ayeLNzizaf/OQUnPyk/e7UdvJJwcItW+RSK5HvfudWDS1tN3tdda+NQ2XGZQl9TDRKi5u935xokfsLgGW6x08BeE4p1QXAHgDXO3AMQ5GZoBd/sD+mIt6p/df3K8O6hsRqkXOq5qiqSoXVehXuOYSyCnemVa7NBTk7668ZifxMjFvkYs9amZ3pTgHfrY/Vh5dL2uKTFXbWhHtp6hq8Nm2t8XXqwGVk90ajv6Qj32s04UZ5ZVVoNkenPPDlEny9YEv8DQE8ZzIroxOZUSsiY0BFZZVrcd0twc85U/e/lOsKwkoB5/cJb/VP5h6TY7HGPHR8f7X/pTU2Vd+b7L1Pvwh8ot1myyqcjwVuVpXb+TeTWb+SEvfjyiL8YKNiFLDewKIvlC80GVMaXpBL7WefVEFORNoBGAbgDe2xADgDwKfaJu8AuCiZY8Sy3qDrmF3Fh8uxY1/sSS4+003NH+sDivVlt7MI6mPfLkO3B8ajvLIKh8sqccpTP+CyV2dafr8di5IY6Ox3yX7ZrLw7VOuZ5F1mw66DeGnqasM0HzSoITK7idSmMZHpjk9uifxsq5Qy7XFgRzKXxsw1u8LGgBrFuwe+WGx5vToz+u+Rfs2wrwwKc2Y36chCptFSMqlw0UszcMzI5NZUS/W3OV48CxSkwl9MZqZcu3HTL40gXohN1ecq8ZtTop/tiM8W4pSnfjBs3Vi53bhbt1Vu3OJ+WWd9HC0ntkuPG98twJ/GzHFl3/pCmmnlhe5zN6q49/IYuecB3AMgeCdsAWCvUir47SwE0NbojSIyXEQKRKSgqCh6ULsV782KvT6Qle/T6f+aiv6Px14bYu8ha2OpgoP8jfztE+vT+3/4S2D2w5LyylCmQz9o3kmXvuJOAdEP3PpilVdW4WBwUgjdZCdGrNba3T52Pp4evwKbdkf371++NfrGZxZrkp+00ic5pYC0xie3RH6260260drfb+zPdtJS87FkD3y5BKc+XV1IM7qRGU1eYpe+cKZfbHuyjXFu3y7aFt7LQttnVZXCM9+vwPY4FXvxmE3IsvtgoAtisAXLTpexyE8meHrtZDAdYXCJGE16gzjPWdWkXjbO6nGE5e19VFGV9tjkRCVjot0Hp64MpNto2RH9RDp2ODmVfKRYawqb8c+l6G1h+akIwZjqNv06y2aVF+kswCdckBOR8wHsUErppyUz+lcM/2ul1GtKqXylVH5ubm6iyYjphncLAMD0IrBKn3mN9d2M9cXVLwyoF+9CZPO8e5w+tcGL/7JXZqLnQxOglApbfsBoLI7V++A+7SZVUVUVdQ3qF04Omr3Wepc9O/xyOfohPlkVWSiKzKw++s1SR44T71oMxtPqdJhva3Sit9koIAUrs2LtWJ9xs3sPNUr6gsK9+PeU1bjzw1+jXlu1fT9e1Jaw0f8dtV+lTMfgvKDNljkxRoHYTU58dY3uR+FdbKMzNKVJdB8dOawHhg/qaHl7P4Qnz8SmYNdKi5vrl1QJSjR/EpxfpVKpqPhm1pIe71A/aoVDqzNpulUICP47Pqv0dNWuA6XYksAkMwBw/TsF6PnQBMPXjh81MZlkWfaJbsyyWeWF0azkel5dfuBkABeIyHoAHyLQLeB5AE1FJDjauR0AawMYXPTEd8vibxSDmx/ArgPVBbwKgwAUL3M1c80uzLQxzsZpff/xPS55aUbKj1u0vzTuFyeenQeMC9d20qAXDODBMQSTl+3QLT8g+MhgGnm715ZRS4fROBKzioNadG/xTXyKJ7KbYuRH6FR80mc8xi3citU7otfCiXyHmWRrx0d8vsjwebPWH6PvRawaUn1hONiaF4y1hw3G9/325Z/xzwkrUFJeGfa3kZ0HwjOIizcXI2/EuNDjA6X2z82bP62z/Z5IkZM7JeJgWWVURqakvPr6dDrz2jm3oa1Cuk8qmtIemw6VVYTWj4tcl9KMUQVHRYITBQWPafR5JduyYdTKBwCPjauu8Ppm4Rb0e3Qi5m7Yg8I9h8K+n8ny6sR26XT8qEkYmMAkMwAwTSugBwvqesl+3xOJV2aVF8NG/5RcYpKQcEFOKXWvUqqdUioPwBUApiilrgLwA4BLtc2uBfBl0qm0lh7T15IdPxIcI7d6xwHsjbO+j136oHVIyxgE/xMFYGaclpUrX5+FK1+fFXMbN+09VI55FhY1dtoJj00K68qVCLcnOSipqAxlDs1uTh/OMWl5iBArpUazRxv9a0X7S5POaPkjn+S9+JSMyMxSZKxzqtW+pKwqbKHn0ZNjz1wZ67D6rihu0V/LkV8B/f9h/N5qW4oDLYXB76hRjau+VSlWC5PRObn/i8VhjxvWyY7aZl9JOb5fsg07D5Qads2MXJYhnVnFTvd9i1HjqitHF+nOdZVytjDVpH62vYy9DwKUF2LTRl0laOT5fWr88qjZugHj+2XiLXLVE4I4fS2b3ddfn15dGTJLy1ct3VKMpVucmxVTzyeVCr5x7Vu/OL4mZSKfkZUGX6OKTK/PWhnp7wD+KiKrEej3/aYLx4gS6xxlJrBWl97K7YGa6TOf/REzVjvd+hVjFkwF3GEytbaZiUu3I2/EOMcLnBRfZE2cQMK6Vl7Yt03UezbssteqKEDU5DxGGZ13Z66Pes6J6ZC/W7Q16X2kWVriUzIyMsK7bUTOdOnUve2ezxbg1WnVC0x/vTB2g0Cs4/7r++oZIp/9fgXmb3R5TaWI78CstbtiZhANWwK03/G+JzG71xs817BOZtjja086KmqbO8bOx/D35iJ/1CTc8E5B1OsA8H82xlmny7EPTcBYs26xCbN+//Z5d7a0xKbgfWvBpr04WFqBl6euCfv+BhkVkBK9pwTvWUZvT3itXo1SCsWHymP2CgiNi3XhcuFkJ+7peN+3hnkbP3g7ybVFY3GkIKeUmqqUOl/7e61Sqr9SqrNS6jKlVHL91yxavs18pqNEFl12Q7zpdg9oLYehTEYCQeZ1LSMW63wYSTb49HrYuA+zn7hRYRLc58HSSmwrTnwSBX3a9keM+fx/9s47Tmo6/eOfZ5cmTYqACsqCIigKiKhgRRBB8Q57/dlOj7N7p57ieZbzzt49PRU926lYUVQURKpIky69l6X3ssCy5fv7Y5LZTCbJJJlkJrP7eftamcmkPJNJnnyf79OsQjmsHroKKu3vaJyJzxWioJ/SxfjwMhdfCmqmz1w4I9Vu3c7GvzJqCS78zwTXcgQ16+oYWmmhXPUB5LwUTcmdcPNbVLPIaTX2RP158WbL7Yx5GsbB7tQVW5OO2+elcagqGHtBBdlzLBNEQTeJxDzC/V77xTYXaeWWIsuCa36jWvSrVymVlKuW7mitrFyh42M/ouM/frQ/vp7HFqKXJKenFCLMw0PmZvX4fq+ZMCczw/DIhYrdSTzvFftGsoXb/CVZBk3vF50fru9PXAGgohz2ss2pclSSydN+Ua8hD+kqzyDKn2cbt2GOOuYecOaBo0iFMv9m1tqEppKAvyI8loNTlz9erpTmJsk49q8M6Hf12tMsrEJMz/240PazX1dYV2n0mpdiFv2LaYWu9mD3lf/y6UzXeTZWRmQ63vJL3piYdG97nciLMqkmGW8/68j4629mrQlZmsqB+X6xuvefHrYgXphn8Qbrscjswh2WY7JRCzbg9GdG2eoUXZ89PWwhdpsnJm1+79Lycvy8eFPKVk5uWiLEDcmUa3pH4vl/fOBasXJLEe4YNCMSPTTd/ERWtSv8MGlZeBWGc86QGz7Xe8Uvrwm5dkns6VLkMtlcL0/9gEXC/7ode1EwYCiGz11vuW089tzjtZduOEO28NKfz4iVkl1keFht3LkvZcuHVIMviR3I9nMvhpwxp8GcnH7Du+56pyiVvkeOZJ512/c5XudmD61fSjzqySe+D8c7+58xS20/+2yqsadnxTVXLGoAACAASURBVHIr9TXKoTmsWcff+/ks1/2r9AGIXkl22Jx1+GpGzICwCucyD5qt7sF0vZCVedCY6slk9KBW4tMQKMYgJRHrc/z6mKX4o1ap1qkw2I8WVVgf+nouVm/da9vKQ79fv521NqGNSEw261/8ioGTcM1/p+B5i7BPI9/MTF0jxjjeCWvs8+4vK0LZby7zxPfz8cDg3/DtrLWZb53ikx/mJI61o6hics6Q27TLe3ia17j5f3yb7LrdtS+cHiUL1lc8hNzMKs/VQkc++zW5AiJQkQ+YjbYFKzYX4VtTY94taVaGTMW1/53ia7uf5lsP8pZs3IV/j1yMHs+PRb/XnKtxphp7lZQ7X3l+czf9PnaUiqYSIs4s21yUkUGB27LdOtkKY7MqxGC+J5RynvSz6mlnFya2ZvteywIn12kTKDd/OD2+zGq98UusQyWNeG2svNNkMFbm+zrVQDusYhWVjRWbi7BS6zlZaCgFn6qo0ZbdxbZVZIHUE5IrNheh1QNDE6rgOv2kdhWX9YmmFZud+2Z68Ubb3XZ3fzYTo332s9OxqrJY1Rk4blmk2jO4kaHU5BUxXjNhj2/dknOGXFizJzNXb48riF9XJMeyhtVs8vaPK4qZJIfmJS74YOKK+MyYSKyEsBl9G68DgyDo/dI43GEozjJ52Rac8K+fMGxOeAUyUlX1tGOjzYTAZW9OwvMjFiWFe7jB7ElI5QFb6DP8KUedp4QEwnM/LkryqJnviVQP6Llrk6ta5tvcWHZ94cyVJAF30R9m79ne/WWuKqEZMYefVmZPVCp1R33oju7PjcGZz47BkJlrEqI4hs/dgA077Qek93852/cxbx80A6+MXAylgK9nVIS9Ok1av2rTozEM7G6bwdPXuI50McPL0Zmw2zP88Ns63PWJtyKBTizdaD9xYE6XMfLIkDm2nwVNzhlybnue6AyZucbSMDNzwWu/oPtzYwBkL0zl9TFLMd6Q6G4eKDw8ZK5hZkzQ/4NpMKM7eYzfYfGGXSnLgQdxa5lno/Wy1E4Xe9Sw60Gjo5SyDYPaWrQ/KdzS6Vra6eDlLS9XeGPs0qR10lGCu4tLK3UIFiE6qYowNK1XK2lZnoWH3Ov9Yp69tcK8yzsGzfB8HHPIWlgRI35pUDu5xYJfUj3yjWOCmSnC4Qks+846nWOrvopG7v5sFuas2YEnf5ifdB3PWr0dgzUDLgoeGJ08hzw2c1Vor3BiwR1hDUVu+Wg6hrgIr3Urg3liYf2OCm+2U57f+xNXupIhCHLKkFu5pSje080td1k0sXSirFxh6SZn1326KKVw96czsdgiJ+OOQdMttrDGKmRHV1CDplSEXvZ6cRzu/sy5dHUYysfJezpsznpc+saErBkW67ZbK+tUD5t7PpuF1n/73vbzIwyfpQpldPrqYxdvwlM/LMCN7yXPCnqdzND521e/JclzVLO6eOLC43ztj5BsYryWzRMczwyzL5gCAE3q1UxaZlFMMil/x6wf1u1ILKTlJqTdvMZP8zdgS5G3djHbTJVL90WgcIARc2XVMDHa3049/kgMq2vU6ZFyQPV8+w81zv/3eLw5dpltWCSQ+LzLdiHxiqqVyZPYbib+Hfed1taVn4rWE/7HfkHVsfAjwaPfVjSWj4rRnlOG3LSV2zBtZbj9iN76eZnl8qDsjY279mHB+l0YPGMNellUsUy3sqB+XY2wSEA2Y7wZwrSnrPZ984fT8OuKbVmrpOg3fGPwDPdV0ZZtLnI8r07fvVj7bX5dsS3BK5eO4rCq3ioQdG/bxP9OSZwJLnKhwsBYfr0qYby3pq705vX/86fJE3xWEySfT02cOCwqThxA/JSkZ1PfoOG0OYmOtyNoUkUhGCcMB5zbLmxxch6r547z5ePhoaOtumZ78rPGeNxsF1dzOnpUBud2lJSV44UfF1qm1uQCQfz2n0+1rhFRVckpQ86vJ8ILejKwmaAOPXzOesdqh26rl9nJ40XO5w15FqXlCss2eW93YIW5X4ZTMvpHk4NxP5eUxUoTp4v5gTa70H+ozisjFzvO+Ixf4k7eVYam4WXlyvess1LJ3y/qD61cQq/wlmmyPbsdBYKIoqiWl/w4NLeAmbPGlFtnuoHczDKHcc/tK6m8nqjUoZUVr2tYuVVJApZVUwOaCHAyuo3e7KiorDGLNmKpadyTSp+u3FKEggFDMWFpdibuPpu6Gq+MWoKXUxSpiTppXXEBKVG7CbDSsvKUaTax7QMRI21ySutlYtD59Qzr2NqgZpCs8jCCoGDAUK2PUcX+UxXsMOcT9Hh+bCCymJv/Tlmx1dZAnbHKu6FkNGx0XhixCNf8dwrGGSpFfThpJQoGDI1X1VqycTeWbNyN1xy8ceYb8/evVlSuNHowrUIerXAyzI3hrzqtm9RJkqO4tOK4d306M6F5sBesSkgrRWMuKMK6t1MRlYdJJgnjO1v9fKkMpC+nJXrs3AyIy8pVQlGoIDj7hWB0d5j8s1/7kPZsLCUf0iFyHGMkk9lwAWITzEHgdP7fHLusoo1Kmr/Trn0lWG1oyfPFNG8pN0N/ixVg+2XJFjz5wwLTp87X02StH9hX062jc8L2NuoGRklpbir++NmJsPjXvjMFRz88LOV6qXKxM0WOGXLha2m7xN6MPR/SPJDRODv2keE445nR8feTfVZ4DAJzromOn5nAITOTFajuTbz2nYp2BG+Oi1WR3LI7ln9y9gtjcfYLY/HscPv8GaccuXYPVdzYIx16VBmxemg6YnF4Y55nqt52vg5AAsFvO4l0KVcKPds1zcqxs4XS/gsSV48X0zrmybCtLnLdZhduT2rTUhVoWKeGr+28eOSINa+PqZi8tCoI88oo+8nNX1e4D1tO9VN8NX0NikvL0h5PXfz6BJxuGNvc+7lzDQAz63bYFzRJqQfiOV6xf7fv2Y/Hvp0XL3wR9uWoj5ly1fkc1DB+0JRVae/D+AT5rXAH+n8wFRt37sMEi4JAVtz7+Szc+tE0fBKALOlQLatH90g2FXYmQg8AD6GVNsv3lyYaosZG0htNicjploEtLnV2PRv3btcYdN2OfVi7fS8ObXCA6+MmlRo3/Tb7S8tRo1pefNbei+Kw+pmXbdrt21s5yaPxrF9nRjGsctusyJPUOZbm73dk07qhlwOuKmSywIMRheznnGSaMDxybs5hqjWuemtyyn2MtuhhVxWwa++QLtWrVYxoq6J32g3pOA68tF7asbcEjesmFxLSue/L2ZixelvaaTKLNsQmSC9+fQIW+Wzj44Y8kXgrpx/nrsc57Q+O6wB9IumpHxbgk19X49jm9XFR5xahe4V1p2YmUo3CJJ2JOAHwgENvQ9cyGET43avjAXjvpzpt5TZ8/1swHm2/5JRNn80LN1MPiCIXcbmAvV/FKbxr3rqd+GqGtxAEJ277yD48aOPOfdhueADYyTVl+VacYurT9H9vT8atHyW3VtAxD7iGz92Q0Pz3T/+L5Sr5+c2sNkmnpPUKizDQVOv7zcvr3f5gz9s8c0kHhiPlOir+vypDGN/WzW2wc19uFhiIAlaG8mtXdU69XYpfpomD4UBiZKoYzlwXzdmnrdwW2DNn2spt2OWj56sTxnGmUcyXflqc8Ll+SvXwukyF2emTvWsdvIpRJurDjWJTOL1TpM0FnQ7FpAd64uUrOoUtliM5Zchl8wIIyiMXVDiQXZ6Uk7H7+pil+MunhhAEi1V/K0xulGvHT/PtK2Oe9MRITFle4Y1K5U3dvqciJGn8ks2OMxzGdYHkcCbzjLc3j1zy75PpkLnJy7b6MkLdyGnebZ2aOeWUJxYs21yEn+a7C/WtTJjvkXQHq248cncGnNtWmbm2W8uE91ant2+HQ1LuJ9XPckv3I1yvW1Ux3hn1a4Wn893cgQKJTG6RFcZLyOp60msi6WNCq969QfHO+OVJFUD1nP+oh2eXlpXji2mFONU0Ua+jVKx4lJcxpxfWWlRONfP3r5Obdk80RVE5FScUEYgIDjnQfURZGOSWIZdFLR2U3nnwqzk4/9/j097Pko3WuVfpniHdvRwEk5ZVxNanCqv5w3u/otuTI1G4LbUH662flye837jLpiecD8VqtUWmw3VEgP9NWuF5Ozcea6vvwrEPyTWe/D65+TCJFp0PbxjIflKptZrVcmoYkxWM45ca1VL3hfOLm3tSBFgWcq/eoDB6g/Xr0OyR09+XK2Dayq2BTapt2LkPj303Dze8OyVhubEJddjtuNLh3V9W4N7PZyUZovo4ftCU1Tj/3+MDHXMa6fn82JQF/+avS+1BdkO28xVzSgNmM0fOySqPEm6M3T37S1FaVm47gE+V+9b/g6l4ccQiT3Klqug3fdV2rNuxL6Fvk7F/mhN2oTf6L+ZlAsDqOWTVcypMZqzanmAEu8WNR85LvoPOG/93gudtCAmTjyavsvDIpbdPGobBEtS8a6rQSuOzhR45a4zX9rY93prPV2kcrifdI6ePL8rKFS5+faKn3RcVl9qOLfXlO/cmGiNGb6abEvnZwqpKNoB4tVGniK4g2FtSlnZLrVSNx/XLI9v5ir4NORE5TERGi8h8EZkrIndpyxuJyAgRWaz9G8y0HGKD/WyRKw95q5unab3EHIJjHh6OWz+abrsP41fdV1KG9yesSCjC8uO8DZ57mLhNdC8xJJo++b25LLA/JrqsQBQV9NLIXnFjyK228nim2KxxXX/V5rJJNvRTrnBYo+yGgQRF0HNrOTJXFxodWhwY6P4yNbjJ9iDKK9nQTUZjIcxJ6aBSUJxYtDG84iZm9lv0bNUNt4qJYiS8d4tSCu0fGY4Hv7Iu2mF3WRvPcY5d+gCAxTbRZEYWrN+J0QvT92ym6/l1W8As2zooHY9cKYB7lFJHA+gK4DYROQbAAAAjlVJtAIzU3geC51LuAdLrxXFZO3a6WA3wf5y3IamKpRWvjlqCR76Zi68tSv57wW2PLaMhV5xiNiQVur7zWpo4KA45sFZGj+fGWDb23tGppFUrM66f0uH2s47M2LE+vqlrxo4VJOb7yTxoTH8IWcUtuYAxqyOvWubPZ7ex3I+ZanmCs9o28bj3rJJx3ZQJAwuoqKjoRLopMmGHZbptdzF0dmzCtaIvmrdzrK/+ya/J/WQT1tP0kt4ruKQseo3VrbBrOeWGPi/9jBvete/V6/YScmszjF1kXUnYTTsZIHuth3R8G3JKqXVKqena610A5gNoDqAfgPe11d4HcEG6QurozZKJN+zcw3YFU4xKf/ve2IX8zDD73mtu0Gcshs1ZrzUut2a2IfHV7cNn/U7rpNag+0z5oUHt6hk7lhtj2W0rAyM54oxOIBv6KR3O75i66ENQHNaodsaOFSR2PT51vPS7sqKqe+SCxu0sdfV86/V0wz3VXmrk50VA07snG7qp3FtFdd8cXD/15GWUjQ8nzDlyQCy00bdHLtXxbM5UgpdQYuO7N8YuRanHsvlhU2zhzXRi+qptKBgwFNNXpc77c1sgxY3Rv37HPlz3zhTLz1KFIeuTEtn2jAaSIyciBQCOBzAZQDOl1DogprAABNap9qLjWwS1qyrFNo/9rawGNOu1PnAn/HOELxn0ZNCbP7RvKwAAk5dXDMZKNEFWb92DCUs2o2DAUMyzKG+80KaPTBQMkGp5mUtDdZNwa1VMJttKKGwypZ/SoZJ6RQPj3GMPxr3ntE1YZp7o2eZy9tSOKOiLbBL09zdf0XaemMG3nOq8n1QeEmOOXI7dR5nSTZnyyNV1URFzXkAFJtzQ64Wx6PDo8ED3Wd30oNWvuaBP8dode+P7Nab2GKOWBIL/jF6Cp35YgM+m2reWKi9XuHLgpEDCFcNizIKYbOMM3jG7tBi39RPceOSK9tsXRNlS2T1yOiJSF8CXAP6slHJ9h4pIfxGZKiJTN21y1yC1sg84o0JCDLbpwej2wjZjDAdwy/jFmwEApz8zGle9HWu0e94rPyetV9OmCldVG5e5mQHfvLtqJbpnUj+lgwjwj9+3D/04ucrr/3dCytDKFzwWYDITBQ9+prisS/iTome1c2eH2A3+K549lfPBn0ndlKkre9yiTZbh+9li8cbdgfd+NOsd/bG7zmNfN6u6C5t3F8dDKC/6zwQAwMZdxXj+xwrdVmpwryqo+PfbW1KG0Qs2ot9rv2BfSRnW7aiIvtlXWoaJy7bg1g/tayNEkSvfmmS53G0D7uWbixLqO1jR8/mxtp9ttSnYYiaXc+QgItURU0QfKaUGa4s3iMgh2ueHALCcAlBKDVRKdVFKdWnSxF18e+VU59FDGTziqcq3usXPIGvH3hLLZGMzdsnbRj2ZjbCDTM/w+1UmqbbKlUI/ZjKtn9KSFcB1pxSg/aH1Qz9WrlLDVGbefFm6SaJ3oqqFVh7ZtG6o+69VPXGCzU6PpNI/dmqtTdO6+LR/buZ7Zlo3Zcoj98KIRTjj2dEZOZYXvp6RXo6/EfN1rF+eX0yz94iZWbZpN7o+OTJp+d2fWefzvzp6Sfy13aS4ALjrkxmYtXo7Ov7jR3R7clS8AvlkrQr23pKyuPy/Fe7A/yatxGRT37TycpWyWmMohGAMFZeWJ7U/8MLqFKkoHQ+LFYjKWUNOYnES/wUwXyn1guGjbwBcp72+DsAQ/+KZjxnUnsLl3etPzLYIaWF0W38VkAL0WynrqL//kHKdUtsEgIpjPvdjerP1flBQGb1mOx3WwNd22ezPGBbZ0E/pUAl/gsAxh68EPTTN1QkLP2QqBPHqkw+Pvw7aUB5x95k4uXVjALkVFpsN3ZTJ8xPF38JLCyE7XTxnTcxpumZ7heetTCnD89P9F3/qhwWW0TG7XYQMJuSe2RxSX0c3+m54r6JwiD45/7tXx+Ohr+fg8oExr9eOvSX4dcVWPPbdPLR7aFjkcu78ssymFoQbflmy2fHzPu0PBpDboZWnArgGQA8Rman9nQfgKQC9RGQxgF7a+4DIjdGO25CSqHL/l7Mtl38+1bm6Uiq+9DBj5QU3bSnmrnWXHBskG3a6c8v7pdcxzRLeH9EknBn2CD6X3ZAF/ZQOuaHbokRVMryCJi8vM+fPzQSF7Wy2JPzj8oBeVs4aGddNvFOCYcPOfXji+/nx92XlFZO1Xm6nH+cl9lArL1eum1MXGaKkvpm1FuMWx8JrrW4jq3tcoaLippE/vj8Vl74xMV5tMlU6zNs/L0Nfi1QX34Skj5amEamxt6QMNZyKD8QL4Pg+RCCkzky1QSk1HvZqs6ff/TrBWevMsGGndaz3X7+wNvDc8uM8d3HNQXD8Yz96LvKSazRvkLof2KEH1sLaFLH7lfG2yoZ+SgfqttSYvUh+KvHlib1nKFPhZ9Eg+YIL8hq88PjmFkttQsKc7ThXEQN62G22Q5zckA3dxEkP9/zhvam2ny3asCvhXJaVq7heSkd/vPPLcvxr6PzUKyKxIbixdYGXK/+2j5Nz5aZ4rPqry7utaD/qH1AdI+atR+/2B6cd4RNktECdGvlYttm/IacU0LBO9ZST8tnWO5krqRcA2T5ZVQWlgJ/mbbCscJgOE5ZkrjG32YibuTp7zeTD4vYeib3HrG6Pyhg2WRkJ81dq26xeiHsPn0kPxMa25mIkfoqT/F/XlrafcaybHs9e0gEA0L1tk/hrI3bnN1XbFDf3xhMXHoc/ndkapx15kIu1qx5Va5IiPMy9WkvLyuPP3XRCh+evc9/k3C7kUURcacTikuTtrapAGvXrZW9MxDvjl1vu78+fzsSb45bi5g+nuy5CYmb7nv3xEMggi04d0bQulm70H1q5rWg/GtWpafu5bnS67ZMcFjllyHFImhmWbS7CTR9MxfUODRn9sCugwim+jh1w5aowsOunZLu+qbWBVYLyn85sbbu93oTa+Gw6/nB/eXYkGNK1u//au23SsrWGZO+Xr+iU3gGywMEHWvemciq3bYdTLkMujHX1JtnpYnWdpTvY189tgwOqo5oWjmScXbfbu3lwXCFjco+mkwoaWa7bpF5NPHDu0VnPVYkqxp/WMVSMOCIiCQbbX7+YHS/pn8794yX1o9Su2InA8ia7x1RA5e3xy5LW2bffvriJUgpTtNw5K8Yu2oQ1WlEQK0+fG859+Wd8F2+wHtw93PqgOli2eTf2OLQYcGJXcSka1bHvBazrJjsdlily6o4O+lyZq6CRRFakkSRKYni5ZD/p383Tvs0zV1bVma7tVmC7fdP6sZkmo+KsxoFQTnPbWUcmLcs3TBDYterIBYJ4wJt7QBlxMxA75pDsVhWtXSOY3y+Mu7z9obEKbj2PrsjdNT6zS2w8CV5UDgMM/GG8tJvUs/cwEGfe+jnRCBq7aFNF0+k05kHs0lms2G9zH42cv9FysvzL6YkTXnuKvVWkNHsat+wuxoL1ifl86c6BeW3d4JYjmtTFhp3FeH3MUt/7aFi7Rsp1sj1syilLJuhKW9d1sw+zISTTtGmWXrGSAw+wnzmyIhc8EFUFtz/F17c5N0+2giHpFThNVLi5H7x6zYMmqN8yjEui7cH1sOCfffC7jocmfXZZlxbYYzPrnyr82/jc57XsD2MT7oNoyPlm1AL7htrpRBx5ua7tWjKNXZTcU9D1I97h8MYJLqUUTvjXT+jzUmKRE2PeYHFpWXbaF1jQWisAN3XFNt/7aFTH3pDTTxtDKz0QtA7/23lHB7tDAGcfndsVK42UVrXGSiFgzmNzIt3Lu0XD1MVPUh14r4UCpsEXPvo5TnWu26TZ+6tzFQ+ddTTkXAx71nuYOQ8Dc+hgR58tR9wOHM295lJ57M29487WvHPXdiuwzV9LXewk9brEmVu7HxF/3aQuDblsMWfNjoRG3Tr9OlkVB7LGzrNthZXR5zYH7V9D5+OuT2YkPJPusyl4Z1yn7d+Hod1Dw1zLaCbICuP65PjEZf7rMzgacppCyvYEU04ZckETRiEILzckqfxc41BcwUy6yiAIu3vlFusCN34HjCRY0lVZTetb55ulw+VdDgst5PDmM49IvZIHZhXaDxI+mrwq5fZhtxRJhdmQ8zsR7Hazly5PzKl0GtRYccZRTbD8yfNwbPMDcVij2p5ksbrWsz1gylWOP7xh/HWTet5+Q+KfggFDcdP7FbUGzv/3eHR7clTSeluK3OsVu9BKK858Jrk5u9Vk4ZiFyd68jyevwpCZaxM8cp/btJAKcrLX3JohHVo2rp123qxTaKW+Zzc5cmE67XLKkEtXh9er5bvbgmusLpqg8hpIbnFL9yM8TRZ4vb7NyrNxnRq4w8IDaLwk+5+RXPwksdhJw6TPFRSG+AjpI96555yjHD+P4kD2hJYNUS2kkENjKGMQX329Qy7GbAcjLyqYf/87e/orftLfh4E87e9nezbkAP8Tpvpmxs31tgaH2xiFxBrjL0CPXGb5ab59OKbOuu0ecuRsQiutKLIIZ164PrlC5sSl9h6rHs+NSXmcICtNBknNavk4zG+kkkZDFzovz4UlFeYZyjFDzv0D4bBGyT9epwx4FawMuekP9Qr9uCR63N+nXaj7P8A0QVCugHvOSa5aOOqe7vHXfzvvaFyr5Ybqce3GK7a64frt2tq6QhwJjx7tmuHJi44LdJ+ptGa6lSzz8oLOXjbs26Dzg6hIWLN6OI+8mi4KZ3VscWDaxzGfg8Ma+jNo3PSgNNO4bs1QJhK8PNePalYPP919Jn78yxmBy1GZMZ5iFjuJHl56uNnlmrpl6srkfDGnQk+p+tDGtk9LpFA5okl66QiNHUMrY/9me4I1tww5D+sebBFC9Kczgg3TscIqh8CcN0CIFV7LQpuvqzo1ra+zZqZ7QS9GcKpFzopxoHjusYcA4Ox3lPDzwAj7GVM/xEgH4/V4goW32CuH2LQySBc3ocdHBdDPz+qnPNThO3kpzmI1ljMvczPz7MRPd5+ZtKxWCuPabOgd2bQun6lpEEZ4NXFmyUb3feKyQbqGWJT7FLZuUiet7Z1CK/XnU7bbnuSWIefiXOmDzmoWT5wyi4vthlML0hUrgWz/oNmgKn5nMz3aJRa5efpi716VdHI25z/WB7VruBtQn1jQCCue6os22sDSeNyjDblO13ZriXmP9UYLn7P+JJEg2p04XSJ+Lp8gctt6tGsamrVoVC1BVAa76XT7vopO3G7R1sHIwGtOSLmPf15wrK9jGzGf5q1F+zH4VvuwZy/jKzfFktKdeTYXTwGA2jWqWYZstjooti6fLulj/NnME3skfM55cVy2RXCkPF1LLrp2XLxypV+cwsl1OyPbGQ+5Zci5UOn6zIBVzkbX1o1w6QktEpYd1zz9cBcj+g/ru4JgDsIGo8nGbMvG6c0CpeLta7vEXx94QPWkMEsjNavlof2h9fHG/3W2/LzMoMS7HdE4/lpEEozDCQN6YNQ9yTPqxB1ujSanMD2ngbT9oN1+m84tGzhOILixnfIkxNDKNI23s9o2SXh/gE9PTqrqs9VMOnCERehfEF4k82RNq4PqoLTcfc6MHR/fdDLO75DcNsBMkHmEV598ePy1OY/lfzeeFE+FYNXK9DHeoU0ZWplxyhXw3i/Lsy2GLZt3p1fEafCMNQFJEjzphlbWtol0AioiFNgQ3APGczX+/rPir//auyIvSB/MWDV+rVktH89e2jFhmfkB7JU7TQ94fUCfiw8cN3keVhjd6r3bN3NY054gDN/WB4VrPNnRrH7NhD4qZv5zdcyA+sOprfDdHafZrufnkpnxUC/8MqCH4zp5eYKhd56OPlqopBmV0CfGfj+HNjgg7dmtqsJrV3XGuccenLDM+f6qOPH9OjXHvecche4mIwSIXSOzHjnHkyxOuqhDc+eQQDdeYpHEY3QIIBdMJ10PkFl+o1d0hofcZa9GWANTOI5eaMs4AQPYF8L6pH9Xy+XG8/HsJR3QpF7NhIkYLyzVGhnfdFornHLkQRl/Zj38u2Pir83foGWjCl0e3jRBFcJwChmWmh0e/XZetkWwZfJy9zl6uUa6oZVO2kd33Lh5B0Sx5wAAIABJREFUTrUNILTejtwy5Ayv9RPXt8MhuM0Q9qIbFU4eCiO10gx3uuvso/DKlcfH3+uewPt6B1foolGdGhlJULbKmXLD7w0NYA/1kUQPWOdOOFG7Rn7SIGjonaf7OraRvh2sjR0nJj3QMynG3HitnnvswXjtqs64/9y2OLb5gfjhLn9yGgv46IdrWKcG6tZMnKWf/lAvHNu8vusiO8ZBp1IKDWtXx9/7Bt9jsapQIz8PfTscgvaHJnrgLjZFAxgxGtD5eYLbe7TBq1cle1BFvDd+d6pylqpXpBuHmBg8ck9ceBw+uulk3Jui+qZb0p3pNG9tbFTbsE4N9Gl/MFLhJkfUfByzvu6l9VMzn2+7AUC7g60f+kbPf09tn27CJ+/pZf976JMzegEU43XbtH54z52a1Sr0tzkP5fDGFec8FydFo0Yjw/k1RiulW+iIkKhjVazEi9PCTcRKqsiR9/9wEj666WTXx/RKThlyxqelCDDz4V5JfW50Q85pAHBdt5Z4XvPMeTVequUJPjb8IPl5kmDI6A/a33VMHaaiYwwx0Xn4/IrZSrcJ63+yKC3vBn3/5xzjz5tmrLLnN5+qVvV8PHNJB9frn9/hkKQBjFvjXceySIAC6njcj4hg977SpGXG1307HBIfuBx9SP0E479iPftjXNS5OQZeUzGb76Q3GtWpge/uON1TqfCTWsUqVOblCWY8fI7vXCJSUYr5lu5H4rs7TotPOJzvMElg9dvXrVkNLRvXNq2XWhdcZdInO/aWJK1zX59YFEOjOtUdvckCwfj7z8LgW09JccxYJdRz2jdDvVrVcXsPf2Xxk47vYxBvrE5sPl9bi/YnvHfTkkYPqzy9jb+JLqCiWqY5DLK41LoCndmjZ9yPHsquf7OWjWvj6pMPx/WnFNge/zQXsh9/eEN8e/tp+Pb2WNRAl5YNM5ZP9eLlNCjCxFiMp3peHr69/TSMvre77xQAqzY2hEQR8zPgzp5tXI+Va1XPc34+unxAnXlUEzQOse1HThly5hCLBrVrxEMo9WIT+oRn/zNa27Yb+Ee/Y+Oz40493i7olGyMfXjTyWjpEMJnNCCH3nkaxtzb3XI9YxjgY/2Sk+CNM681q+UnJNO3bFwbt3Y/Av/s1z5hmwfO8+dFWfz4eZjxUC9cfuJhnrf905mtE8JTbzANJl69KtlgscMYSmY26i7unOjNyM/Ls+xd8vN9ZyUts+OiztYeEmPvlScudC5aog8EvZQPBoDfaYN6c7U73YA//vAGWPFU3/jyJy86DkcfUj9+Po8LMHwNqDDi7Zr2Evfoej8/T3Bs8wMx/aFemP9YnxThF9af2VU6nDCgB768xdq40sOU69W0N1L+dMYReOvaLujd/mDH0DyR2ORMZ5uKkXpJ/UtOaIEVT/XFQWk+rN674cSE93aFlLq1bmy53LyN8ZR3b9skaX9/N0yY6Zhb1+i/28+LN9se0+qnNeZj68+pElMz35Iyb2GRNfLz4g2B9dwNEcHjFx6HIwwhRK2b1MGXt3SLv3cqhGSU/bgWByIvTzD70XMwyCa8M12m/K0nfn3w7IRlxkmnh0y/SbZLe1c28vMEx7U4EK0OqoNd+5IneVLR65hm+JvPsQYh2cCoG68/pQBXd23paruSMoV6tawjYMKqgOyHnDLkjGExZiN54DUnYN5jvePWc7P6tfC1iybGThb1S1ckGyHlSjn24Cnctjf+uv2hB6LAxuh7yJAfkK95+YyGp3G80aLhATj+8IZY9K9zcfOZR+DbO07DfX3a4eqT3V2MbmhYp4avqonmECCzi9kqgf6b20/FO9dXeJf0AYexaEofU35Rm2aJuVnV8gR1aybfYI3rOnuhvrzlFHQ+vAFmP3oO7rYJN+p7XIXn5KqTD8fMh+1DFL+53TrnrWh/qeVyHRHB3H/0xuBbTsGLl3eMG/Z60vGMVdsBVCSm69688zscihVP9UXTesEqkRtPa4XZj57jq78UcaZW9XzP3mKdpy+29lIf2uAA24kqfcLrqq6HJ0wGGMnPE/Q6phlExLL09KJ/nQsAuC9FL0SncFE/mEWxG8Tb6VUg0Vi64sTD4n0TWzQ8AG1N+soqTPXLm0/BEMOz4zSfIefGfOwrTjxcky3xG/Zs1xQntLQ2kq0iBmpUy8Pzl3bEsc3rJ4QmAom69+RWjXFCy0a4WwtxdQrN37Znf9Ky+rWqW+aZ2+kHL0WQmtav5SjPjae1SpTlgPDaW1RFjC2SzPeXXUivkaD7XNphrgRNiF+M3jCBu0gMoKIQnFNouplJD/SMv17xVF/bZ3CQhGbIiUgfEVkoIktEZEAQ+3SK16+Wn4faNarFcxD0mVfjoNyOyX/riab1auKA6vn4txbyZtcbKVU+gt0D6ooTD4vH5b58RSe01DwfBVro1ClHHoTnLq0YtBkfyq9puTI1quVhwLntUF+bIcjLk/ggw5wn5XcS0yk8xwq919hfe7eNG2fzHuuNR353DOY91ttym6Oa1UOPds3i50pvamtMwjaf53Kl0MU04Pn85ooZ5+F/jlWJMz+YzAOPE1o2xOBbT0X9WtWRlydJ4Ydl5QrPXxYbgH3wh5MA2Ic5AdZ9A2PfKbVBVKdmNeTnCS48vgVG3dsdIoK/9DoKfTscglkPxwpaTHnw7IwoAhGJX1eVnTB0kxE7FeHkWbD7qIFDPpxdeG18X5ogdoUzdKzqPdWolocVT/VNGlTrXHnSYdq29t/JOAHiFPlgpMxk6NjlHjjVqDJW5et5dDO00oy+fBFXEyBN69dK8JQfbGFQfW/Kx7X7bd+6tgtG3XMmjtHyzsweuZeu6BQfsF5/SgE+7d8VC/7ZBwDwhkVLg5LSclx8Qgt8d0dynm11Q8sdXZxbux+JFU/1dQyzfm74QtvPgMRG5mu270347L/XdUG7g+sF0mvy0d8dk5QqASTm0lV271zYuglIvKfM5/PjP3bFbWdZ99td+K8+CV73d67vgnevP9Fy3XQ5saAh3jHs+2ELzzkhThhTDJ4yTD5Ur5bnudL6bWcdidGm6Lo7bNIHDj6wFpY+cV5cj2eCUKa6RCQfwGsAegEoBPCriHyjlEqrbE/Navn44A8n4ZnhC2wNphcv64SXRi6Oz7S+dnVnDB0w1HG/zerXwhRDqMcpRzSOVzfrf0ZrDBy3LP6Z3Uzw3H/0xk/zN8Rzjcw8dXEHPHnRcXGvl563cvmJFRfbkU0rZsOMbREaOjyEX7isI056YmR8QHVHjyPRrXVjXP/er5ZFDv7auy2e1R7csx45Jykf7O5zjsJ7E1bYHg+IVaX7+I9dUVRcGje+jAVnateohhtOrRgANqtfExt2VpS31Q3a5y7tiBd+XBgfZBgTUM0GUnm5wtVdD8fUldsAAH/pdVTC4ESfaTc+mK46+XD84/ft0ebBHwBYh5pNf6gXdu4rQYdHfwQAzF+/E7Wq56c0nn6+7yzMWL09Hop4epuDEkKvGtXxF2JWPT8vbriT4AlLNwExb+/Fr0+wrXJlHDN9fnM3HHhAdWzeVYzHvptnW7XVKYnazoOuL9XzhXXPnTk8WeeCTs2xfFMRbu/RBnPX7nDV765nu2YYNGW1bcglEJsA+WvvtthdXIqbTmuFjbuKUbtGPob+tg7PDLM2HvYbDJ1LTmiBS0wyf3fHaSgrV1iycTc+nLQqvvylyzthzfa9+H3HQzF20Sb8NH9j/DO9d9lJrWLhmI/1a5/wIG/e4AD0PLopPv11dUIF5Lt7HZXwnBl5z5no+fxYAMAxh9bHd3echnlrd6K4tAy1qufj9as745aPpifkR/cy5R3rE283nFqAO3u0Qb1a1XFtt5ZYtqkIf+l1VIKHsEOLBvi0f1d0PKwBer04Fqu37o33frTidx0PxX1fzgYA3HtO26TPR91zJnpo8htJVaBq8K2nxq+lK086HIOmxM77jae1Qs+jm8WLrqTL9adaTxoAwB9Pb4W3fl6OZiEWX8k2YeomO4yFTyY90BON6tTA3b3a4rXRS5PWNXuAe7RL/3e/pmtL/G/SStxwagGOP7wh7hw0AwDQvW2FN65nu6b4w2mt8Nh3sdMw77HeeGbYwpTjFFI5GXjNCej/v2kp1zvLcA21blIXT198HA5tcEDc6XHlSYej/aH18fev56TcV16exCcEgVjqz2VdklORLu9SMcGZn5e56rDilMjne6ci3QA8qpTqrb1/AACUUk9ard+lSxc1derUwOXQuf+L2WjfvD6u7VbgeVulFMrKFarl56G0rDyeD7Zm+16Ul6u0copKy8qRnycJA7ICzehc8VRfvD9hBZrVr5UUZmikpKwcbR78AU9ddByuOKnCKJyzZgd+mLMuSSEvf/I8tHrgewDAsifOsxwovj5mKZ4etsDyePl5ggkDenhKgl+9dQ9mrN6OjyevxJrte/Hzffbl8l8ZuRhfTCvEuPvOwt+++g0fT44NGl6+ohNKyhTu/XwWAMQNrWFz1mFrUUl89qWsXOGIv8W+38h7zsQRTerioa/n4H+TVuKJC49LKgShs3HnPpz0xEgM+mPXhF5qOsPmrEOz+rWQJ4KjmtVLCpX7dtZa3KE9hL6+7VTbsLeqhIhMU0p1Sb1m5vCqmwD3+km/Fy/q3BwvXJbsWdCvzSOb1vVUpXXN9r2YvnIblm0qwl1nJ84Cnvvyz5i/bmf8/Yqn+mLYnHW4+cPpeObiDrhMy3vdsHMfGtWpYRku50WOU58aFT+OUR96pWDAUNSukY89+xOLfYy+tzvOem5M/Bh2KKUwcekWXPX2ZBzW6IAEnaKUwpTlW3GyIY+ucNueQBrbb95drFXMjWa4n1IKJWXK1hjfs78U+0rK0ahODZSWleOLaYW4/MTDXIfVF27bgz9/MhNvXHNC2vmQXigtK8fm3fstvaN+iZp+ClM3AcD4xZuhoHB6m4pc9KWbdscnJ4z3W79Xx2OWqWeg3f14wWu/4KhmdfHZ1EK0bVYPeXmC+et24rs7TsOHk1Zi+eYi9DqmGf41dD4u7twCX04vBADMevgcHFi7evze1PVji4YHYOxfz0J+nqCsXCFPYpNWKzYXYfnmIpylebALLCboX7q8Ey44vjneHLsUT/6wAI/1a4+Hh8wFEJso/mVAD3T510+W3+PT/l3x6dTVGDx9DQ6qWwP1a1XHg32PRrcjGqPzP0dgX0nFJFOPdk0xasFGy/0AMWfAgvW7koorAbHidh9NXoWXr+iEfp2ao6xc4cqBkzzn2UeZV648HsUlZdhatB+zCrfjofOPQbcnR/naV9N6NXHX2W3w4Fdz0Kf9wXjuso449pHh8c+PalYXw+46A8c9Ojxe36BjiwPxxS2nuHreFRWX4skf5qNr68a4/ePYGK5fp0PRoUUDXHJCi4TJtbs/nYnBM9Zg7j96o45DDnq6eNVNYRlylwDoo5S6SXt/DYCTlVK3G9bpD6C/9rYtAOf4jvQ4CIB9pnpmiZIsQLTkiZIsQLTkiZIsgDt5WiqlkpuhZRE3uklbnin9lIu/ayaJkjxRkgWIljxRkgXIQf0UQd2kE7XfVodyuSeKMgGUyw5Puiksk9Jqei/BYlRKDQQwMKTjJwojMjUqM29RkgWIljxRkgWIljxRkgWInjweSKmbgMzpp6idR8pjT5RkAaIlT5RkAaInj0sipZt0onouKZd7oigTQLmCIqxiJ4UAjAGkLQCsDelYhBDiFuomQkgUoW4ihHgmLEPuVwBtRKSViNQAcAWAb0I6FiGEuIW6iRASRaibCCGeCSW0UilVKiK3AxgOIB/AO0qpuWEcyyUZC0NwQZRkAaIlT5RkAaIlT5RkAaInjyuom1JCeeyJkixAtOSJkixA9ORJSQR1k05UzyXlck8UZQIoVyCEUuyEEEIIIYQQQkh4hNYQnBBCCCGEEEJIONCQI4QQQgghhJAcI+cMORF5R0Q2iohjO3YROVFEyrTeLPqyZ0RkrojMF5FXROuCKiJjRGShiMzU/pra7zkwWZ4WkTna3+WG5a1EZLKILBaRT7WkZ1eEJM97IrLccG6SOx37kEVEuovIDsN+HzZ81kf7PZaIyADD8tDOjU95snFuLLcVkUYiMkI7NyNEpKEbWUKU51ERWWPY5jy38uQqUdJNAcgTqH6Kkm5yIw/1E/VTZSRqOioAmQIfR4Usl2+d5VauTOuukOXK9vkKXJ+FglIqp/4AnAGgM4A5DuvkAxgF4HsAl2jLTgHwi/ZZPoCJALprn40B0CWDsvQFMAKxYjN1AEwFUF/77DMAV2iv3wBwS5bleU9fL8hzA6A7gO9s5FsKoDWAGgBmATgm7HPjU56MnhunbQE8A2CA9noAgKezLM+jAO71em5y+S+N+y9w3ZSmPIHrp5Bk8XX/uZHHpz6gfqJ+ivRfGvdhKDoqTZlCGUeFLJeve9KLXD51Rejny6dcWTtfTtsiDX0Wxl/OeeSUUuMAbE2x2h0AvgSw0bgpgFqIXSg1AVQHsCFLshwDYKxSqlQpVYTYhdtHm+HqAeALbb33AVyQLXncHjcNWaw4CcASpdQypdR+AJ8A6Jehc+NaHh/7CUIWp237IXZOgMydm7S2rWxESTelKU/g+ilKusmDPFZQP/nblvopAkRNR6UpUyjjqLDk8nr8NOWyIhTdFZZcPvYTtFyh6LMwyDlDLhUi0hzAhYjNKsRRSk0EMBrAOu1vuFJqvmGVdzW36kN6yEBYsiB2Y58rIrVF5CAAZyHWCLQxgO1KqVJtvUIAzYOQxac8Oo+LyGwReVFEagYlD4BuIjJLRH4QkfbasuYAVhvW0c9BqOfGhzw6mTw3TjRTSq0DAO1fT+EtIcgDALdr5+adrIceRIAo6SYneZAF/RRB3QRQP3mVxQnqpxwgajrKSSZkaRyVhlw6YeosnajpLj9y6WTrfDkRtj7zRKUz5AC8BOB+pVSZcaGIHAngaAAtELtIeojIGdrHVyuljgNwuvZ3TZiyKKV+RMwVPwHAIMTCFEoBWCnAIPtDeJUHAB4A0A7AiQAaAbg/IFmmA2iplOoI4N8AvtaW252DsM+NV3mAzJ+bbOFHntcBHAGgE2IP/ufDEy9niJJuspUnS/opSroJoH7yI0u2oH4KjqjpKFuZsjiO8isXEK7O0oma7vIrF5Dd85UzVEZDrguAT0RkBYBLAPxHRC5AbOZkklJqt1JqN4AfAHQFAKXUGu3fXQA+RszVG6YsUEo9rpTqpJTqhdiFvBjAZgANRERv1N4CwNqAZPEjD5RS61SMYgDvIqBzo5Taqf0OUEp9D6C6NntViMTZK/0chHpufMiTjXPjxAYROQQAtH83plg/VHmUUhuUUmVKqXIAbyG4eyqXiZJucpInG/opMrpJ2zf1k3dZnKB+yg2ipqOcZMrWOMqvXKHqLJ2o6a405Mr2+XIiNH3mh0pnyCmlWimlCpRSBYjF/N6qlPoawCoAZ4pINRGpDuBMAPO19wcBgLb8fACOVYrSlUVE8kWksXbMDgA6APhRKaUQC1/Qqx9dB2BIELL4kUd7r1+sglgccCDnRkQO1kMwROQkxK7FLQB+BdBGYlWUagC4AsA3YZ8br/Jo62X63DjxDWLnBMjcuXHa5hDD2wsR0LnJZaKkm5zkyYZ+ipJu0vZJ/eRdFieon3KAqOkoJ5myNY7yK5f2PjSdpRM13eVXLm29bJ4vJ0LTZ75QWay04ucPMVf1OgAliFnyNwK4GcDNFuu+h4pKQvkA3gQwH8A8AC9oy+sAmAZgNoC5AF4GkB+yLLU0GeYBmASgk2G91gCmAFgC4HMANTNwbpzkGQXgN8RuoA8B1A1CFgC3a+d7lnbMUwzbngdgEWKVjB7MxLnxKU82zk3SttryxgBGIjbzNxJAowydGzt5/qedm9mIKb1Dsq07wv5L4/4LXDelKU/g+ikkWXzdfwFc89RP1E85+ZfGfRiKjkpTplDGUSHL5VtnBXQ/BK67QpYr2+crcH0Wxp9oQhFCCCGEEEIIyREqXWglIYQQQgghhFR2aMgRQgghhBBCSI5BQ44QQgghhBBCcgwaciQwRORvIvJ2tuUghBAj1E2EkExBfUMyCQ05EhhKqSeUUjcBgIgUiIgy9CbxhIicLiK7TX9KRC4OVuqqi4i8IyIbRSRlSV8ROUNEpotIqYhcYvrsGRGZKyLzReQVvZQvIVEhSN2k7aOHdj/sFJFlItI/OGkJQP1EcpcQ9I0SkSLDWOhtw2ciIk+LyBbt7xnjNS4inURkmojs0f7tZPjsLBEZLSI7JNYPj7ggarqJhhyJJEqpn5VSdfU/xPrT7AYwLMuiVSbeA9DH5bqrAFyPWMPXOCJyCoBTEeuVcyyAExHrMURIpUTrl/UVYuXYDwRwOYAXRKRjVgWrfLwH6idCdDoaxkQ3GZb3R6zHWkfErvPzAfwJALS+bEMQK93fEMD7AIZoywGgCMA7AP6ama9QaXgPEdJNNOSIZ0TkfhFZIyK7RGShiPTUlj8qIh9qq43T/t2uzSB109b5gzb7sE1EhotIS5eHvQ7AF0qpomC/TdVFKTUOwFbjMhE5QkSGaTN3P4tIO23dFUqp2QDKzbtBrG9ODQA1AVQHsCF86QlJJkO6qRGA+gD+p2L8ilh/rWNC/GpVDuonEnWyNBYycx2A55VShUqpNQCeR8xwAIDuAKoBeEkpVayUegWAAOgBAEqpKUqp/wFY5vPYVZKo6SYacsQTItIWsQaKJyql6gHoDWCFxapnaP820GaQJorIBQD+BuAiAE0A/IxYw8VUx6wN4BLEZpNIuAwEcIdS6gQA9wL4j9PKSqmJAEYj1jRzHYDhSqn5oUtJiIlM6Sal1AbtsxtEJF8bmLUEMD7I70MsoX4ikSALY6FxIrJeRAaLSIFheXvEmlnrzNKW6Z/NVokNo2cbPifBkTXdREOOeKUMsdmDY0SkujbbsNTltn8C8KRSar5SqhTAEwA6uZiJuhjAZgBjfUtNUiIidQGcAuBzEZmJWOjYISm2ORLA0QBaAGgOoIeInOG0DSEhkUndNAjAwwCKERuEPaiUWp2m/MQB6icSMTKpb84EUACgHYC1AL6Tipy7ugB2GNbdAaCulm9l/kz/vJ5LOYkLsq2baMgRTyillgD4M4BHAWwUkU9E5FCXm7cE8LKIbBeR7Yi5pgWxi9iJ6wB8YJpVIsGTB2C7UqqT4e/oFNtcCGCSUmq3Umo3gB8AdA1dUkJMZEo3aSEznwK4FrGwmPYA7hORvul/C+IA9ROJDJkcCymlximl9iultgO4C0ArxIwAIFY7oL5h9foAdmvjJfNn+ue7XMpJ3JFV3URDjnhGKfWxUuo0xJSRAvC01WoWy1YD+JNSqoHh7wCl1AS7Y4nIYYjFeX8QgOjEAaXUTgDLReRSIF4NK1UBh1UAzhSRaloRiDMRyxciJONkSDcdC2ChUmq4UqpcKbUQwFAA5wb1PUgy1E8kamRyLGSxT73C4VzECp3odNSW6Z910LxzOh0Mn5MAyLZuoiFHPCEibSVWersmgH0A9iIWYmBmE2LJna0Ny94A8ICItNf2daB+4TtwDYAJHkIWiEtEZBCAiQDaikihiNwI4GoAN4rILMSUfT9t3RNFpBDApQDeFBH9QfAFgKUAfkMsNn+WUurbDH8VQjKpm2YAaKMdS0TkCMQqxc2yWZ/4gPqJRJlM6RsRaS+xFgL5Wgjf8wDWoGLQ/wGAu0WkueYRvAexqooAMEaT6U4RqSkit2vLR2n7zhORWogV2hARqSUVFS2JDVHTTb77WpAqS00ATyHm1i8BMAGx8rcJKKX2iMjjAH7RZhv6KKW+0hTRJ1os+A4AIwB87nC8awE8G/B3IACUUlfafJRUVlerzNfCYnkZtFLHhGSZjOgmpdRSEfkDgFcQm4nfAeAjAP8N52tVTaifSMTJ1FioGYDXEbu+i7TjnK+UKtE+fxMxI/E37f3b2jIopfZLrLDK25qs8wFcoJTar617BmIFN3T2IlaLoLu3U1G1iJpuEqYdEUIIIYQQQkhuwdBKQgghhBBCCMkxaMgRQgghhGQIEfmLiMwVkTkiMkjLTWolIpNFZLGIfMpcJUKIG2jIEUIIIYRkABFpDuBOAF2UUscCyAdwBWIVD19USrUBsA3AjdmTkhCSK9CQI4QQQgjJHNUAHCCxps61AawD0AOxSnYA8D6AC7IkGyEkh6AhRwghhBCSAZRSawA8h1gfqXWIVSychlhD4VJttULYNIcWkf4iMlX7S6qSSAipWkSi/cBBBx2kCgoKsi0GISRApk2btlkp1STbcqQL9RMhlY9s6ScRaYhYj6lWALYjVnLeqpm8ZUlxpdRAAAOBmG7q0qXLmyGJSgjJAl51UyQMuYKCAkydOjXbYhBCAkREVmZbhiCgfiKk8pFF/XQ2gOVKqU2aHIMBnAKggYhU07xyLQCsTbUj6iZCKh9edVPK0EoReUdENorIHIvP7hURJSIHae9FRF4RkSUiMltEOnsRhhBCvED9RAjJMVYB6CoitUVEAPQEMA+xxsyXaOtcB2BIluQjhOQQbnLk3oNFt3IROQxAL8SUks65ANpof/0R60ZPCCFh8R6onwghOYJSajJiRU2mA/gNsXHYQAD3A7hbRJYAaAzgv1kTkhCSM6Q05JRS4wBstfjoRQD3ITGOux+AD1SMSYiFChwSiKSEEGKC+okQkmsopR5RSrVTSh2rlLpGKVWslFqmlDpJKXWkUupSpVRxtuUkhEQfX1UrReT3ANYopWaZPmoOYLXhvavKS5s2bfIjBiGEJEH9RAghhJCqgGdDTkRqA3gQwMNWH1sss628pJTqopTq0qRJzhe2I4REAOonQgghhLhlX0kZSsvKsy2Gb/x45I5ArGzuLBFZgVh1pekicjBiM9yHGdZ1VXmJEEICgvqJEEIIIa5o99Aw3PRB8NVfp6/ahrlrdwS+XzOeDTml1G9KqaZKqQKlVAFig6POSqn1AL4BcK1uBYOVAAAgAElEQVRWHa4rgB1KqXXBikwIIdZQPxFCCCHEC2MWBp9CcdF/JqDvK+OxcP2uwPdtxE37gUEAJgJoKyKFInKjw+rfA1gGYAmAtwDcGoiUhBBiAfUTIYQQQqLKf8YsCXX/KRuCK6WuTPF5geG1AnBb+mIRQkhqqJ8IIYQQElWUZSZ+cPiqWkkIIYQQQgghxJ6Q7TgacoQQQgghhBCSa9CQI4QQQgghhJCAUSHHVtKQI4QQQgghhJCAYWglIYQQQgghhJAEaMgRQgghhBBCSI5BQ44QQgghhBBCAoY5coQQQgghhBCSY3z/2/pQ909DjhBCCCGEEEJyDBpyhBBCCCGEEJJj0JAjhBBCCCGEkByDhhwhhBBCCCGE5Bg05AghhBBCCCEkx6AhRwghhBBCCCE5Bg05QgghhBBCCMkxUhpyIvKOiGwUkTmGZc+KyAIRmS0iX4lIA8NnD4jIEhFZKCK9wxKcEEKonwghhBBSVXHjkXsPQB/TshEAjlVKdQCwCMADACAixwC4AkB7bZv/iEh+YNISQkgi74H6iRBCCCFVkJSGnFJqHICtpmU/KqVKtbeTALTQXvcD8IlSqlgptRzAEgAnBSgvIYTEoX4ihBBCqh5//mQGrhw4KdtiZJ0gcuT+AOAH7XVzAKsNnxVqy5IQkf4iMlVEpm7atCkAMQghJAnqJ0IIIaSS8fXMtZi4bEu2xcg6aRlyIvIggFIAH+mLLFZTVtsqpQYqpboopbo0adIkHTEIISQJ6idCCCGEVGaq+d1QRK4DcD6AnkopfTBUCOAww2otAKz1Lx4hhHiH+okQQgghlR1fHjkR6QPgfgC/V0rtMXz0DYArRKSmiLQC0AbAlPTFJIQQd1A/EUIIIcSKwm17MDlFSGZRcSmGz12fIYnSw037gUEAJgJoKyKFInIjgFcB1AMwQkRmisgbAKCUmgvgMwDzAAwDcJtSqiw06QkhVRrqJ0IIIYS45fRnRuPyFEVSBgz+DX/63zQs2rArQ1L5J2VopVLqSovF/3VY/3EAj6cjFCGEuIH6iRBCCCFuUZaZ8Yms2hoL5ikqLk2xZvYJomolIYQQQgghhFQaXNh8WYeGHCGEEEIIIYTAusR1VKEhRwghhBBCCCE5Bg05QgghhBBCCHHJtqL92L5nf7bFoCFHCCGEEJIpRKSBiHwhIgtEZL6IdBORRiIyQkQWa/82zLachBB7jv/nCHR6bITlZ1t2F2dMDhpyhBBCCCGZ42UAw5RS7QB0BDAfwAAAI5VSbQCM1N4TQnKQez+flbFj0ZAjhBBCCMkAIlIfwBnQ2qQopfYrpbYD6AfgfW219wFckB0JCSHpsjuDbQtoyBFCCCGEZIbWADYBeFdEZojI2yJSB0AzpdQ6AND+bWq1sYj0F5GpIjJ106ZNmZOakEpOSVl50jI3PeeskAzWvaQhRwghhBCSGaoB6AzgdaXU8QCK4CGMUik1UCnVRSnVpUmTJmHJSEiV49NfV8dfSwo77OsZa0KWxj005AghhBBCMkMhgEKl1GTt/ReIGXYbROQQAND+3Zgl+QipkuwvrfDIpfLEvTxyccjSuIeGHCGEEEJIBlBKrQewWkTaaot6ApgH4BsA12nLrgMwJAviEVKlMIZTOtlub41bhkeGzKlY12/MZQhUy7YAhBBCCCFViDsAfCQiNQAsA3ADYhPrn4nIjQBWAbg0i/IRUiXYW1JmudwcWvn49/MBAP/od6y7HWcuRY6GHCGEEEJIplBKzQTQxeKjnpmWhZCqSklZuYtiJtYrRMcfx9BKQgghhBBCSBXi6R8WJFhkXsIlIxRZSUOOEEIIIYQQUnV4e/xy39uqFD45c2RlmDl1NOQIIYQQQgghVQo7gyzoFLcwPXgpDTkReUdENorIHMOyRiIyQkQWa/821JaLiLwiIktEZLaIdA5PdEJIVYf6iRBCCCF+8Gtged0uzEhMNx659wD0MS0bAGCkUqoNgJGoaGZ5LoA22l9/AK8HIyYhhFjyHqifCCGEEJIhPBty2QytVEqNA7DVtLgfgPe11+8DuMCw/AMVYxKABnqDS0IICRrqJ0IIIaRyoJTCk9/Px8otRdkWJS3M7Quy7ZGzoplSah0AaP821ZY3B7DasF6htiwJEekvIlNFZOqmTZt8ikEIIUlQPxFCCCE5xtJNRXhz3DL88YOpGTleKgMrKEdaeQ4VO7HKD7SUXik1UCnVRSnVpUmTJgGLQQghSVA/EUIIIZEl9kguK89MfX+/IY9rtu/1eBxfh3GFX0Nugx6SpP27UVteCOAww3otAKz1Lx4hhHiG+okQQgjJUSLUps0XEnjdS3v8GnLfALhOe30dgCGG5ddq1eG6AtihhzgRQkiGoH4ihBBCco7MGUBAosFo9JqJOckt3eOEaJlWS7WCiAwC0B3AQSJSCOARAE8B+ExEbgSwCsCl2urfAzgPwBIAewDcEILMhBACgPqJEEIIqXTkkEuuuLQMNavlO66TqoF4OqQ05JRSV9p81NNiXQXgtnSFIoQQN1A/EUIIISRbTFuxDacceVDCsqSqlRHMkSOEEEIIIYSQYMlQhKXRwHLrNdu4a1/Cezd1WaLYfoAQQgghhBBCgiVDoZWpjDerT7+blZha76a1QFYbghNCCCGEEEJImARcY8QTCcVOvGznYp0wuynQkCOEEEIIIYREAi92T3FpGTbvLg7/QDablCuFaSu3Yf2OipDLJIOUhhwhhBBCCCGEVHDrh9PR5V8/+drWj321fPPuhPfLNhXh4tcn4MxnRzsch6GVhBBCCCGEEBJn5IKNGT3eh5NWJbz/53fzAADFpeW227BqJSGEEEIIIaTSksUUOUv8GmDm7Vi1khBCCCGEEEICIkxPWeJxGFpJCCGEEEIIqeSEafjYHjPIfdEjRwghhBBCCKkqiFbuMVNmnF0RknTbIJj366bXnF9oyBFCCCEkNAZPL0TBgKHYsack26IQQnKATOXKpbKv3hi7FKVl9kVMzCzbtBtrt++1OJBHwTxQLbxdE0IIIaSq89/xywEAq7ftwYG1D8yyNISQqBJ2SOXEpVscjp28bNSCjRg8fY3r/fd4fiwA4ORWjRL37XoP3qFHjhBCCCGhkYV0F5LjTF+1DWc+Oxq7i0uzLQrJApJubKMNG3ftS72SiX2lZWkfl+0HCCGEEJKT0I4jXnlm2AKs3LIHswu3Z1sUkkFmro793mXl4WgNs4EYlm4y75cNwQkhhBCSk+jhUiFNspMcZH9pOR4YPBsbd3r3kJDKy92fzQIArN8RznWRZ9JBXkM5F67f5eu4kfXIichfRGSuiMwRkUEiUktEWonIZBFZLCKfikiNoIQlhBC3UD8REi0kcu1+SbYYOX8DBk1ZjYeHzLX8nOG4xMznU1dj065iAMD1707xtQ8/Osi4Re+Xxvk6biRz5ESkOYA7AXRRSh0LIB/AFQCeBvCiUqoNgG0AbgxCUEKIM9//tg4TlmzOthiRgPqJkOigD8rpkSNmjCFnYxdtwqRlicUoaPwTAPhqRiH++sVsnPj4TwCAMQs3+dqPWQcZJwz0a7GkrBy/rtjma/92lIcUKgqkH1pZDcABIlINQG0A6wD0APCF9vn7AC5I8xiEEBfc+tF0XPX25GyLESWonwiJAAs3xMKRaMgRHf1aMA6kr3tnCq4YOCm2PAsykegybWUwhpU5tNKKnlrlyXTIpKrzbcgppdYAeA7AKsQGSDsATAOwXSmllxkqBNA8XSEJIcQL1E+ERA96V4hXaPxXURw8Z+ngpiXcqq170j6Ok+cvaNIJrWwIoB+AVgAOBVAHwLkWq1qKLyL9RWSqiEzdtMmfi5QQQqygfiIkenBQTlxDl1yVxqwqgroc/vndPNvPwjS2olq18mwAy5VSm5RSJQAGAzgFQAMtlAkAWgBYa7WxUmqgUqqLUqpLkyZN0hCDEEKSoH4iJGLQjiMVxK4G2mskk6w3VUk1Gm+F2/YGdhxz9EEkPXKIhSx1FZHaEmvM0BPAPACjAVyirXMdgCHpiUgIIZ6hfiIkYtAjR3R4LRAvhGUI7S+raPY9aMqqwPZrvr5L3MR0+iSdHLnJiBUNmA7gN21fAwHcD+BuEVkCoDGA/wYgJyGEuIb6iZAowtG7jojki8gMEflOe19pWqPMW7sTVw6chH0lZalXTgGvGBIjHEvu7Bf8tRNIhdmQ0ws+hUFaVSuVUo8opdoppY5VSl2jlCpWSi1TSp2klDpSKXWpUqo4KGEJIRV8N3stCgYMxbai/Uklmwn1EyFRI2gvzKZdxTjx8Z+wKMRBUojcBWC+4X2laY3y0JA5mLhsC+as2ZFyXfaLI27I6nUSgOJ68vsFAQhiTbrtBwghWeK/45cDAJZtLsKaAGO7CSEkDIL2royYtwGbdhXjHU0X5goi0gJAXwBva+8FlbA1itPYe8eeEse1wiwOQaKPn6qPKiRrz89+d+0rTXgflmwADTlCKgV85BFCoo6ElBiVg/lWLwG4D4CeONMYLlujhFlRd/GGXSgYMBSTMxDhcd+XswEARcXO4ZdhXTMk2iQVC8niKGd2YWrPcqptwpSehhwhOU+4Cm7Rhl34/rd1oR6DEFL5CXpInoteGxE5H8BGpdQ042KLVS2/XJgVdX9ZshkAkvT9xl37sHZ7MFEfSin849u58fdlFp6Kd3/JLQ8rCZ9yVx65kI7t5uApiGrVSkJIFsnEPOW+kjKc8+I43PrR9AwcjRBSmQnauTJ4+hp9z8HuOFxOBfB7EVkB4BPEQipfgsvWKNngpMdH4pSnRrle3+nX2Lm3FO/+siJp+RqDoThy/kYP0pFco7i0DI99Ow879pbYruMntPKdACYAlmxMzrcdPGONxZreiGofOUJIJWfIzPQVGCEkWlz7zhR8MysydoJvpq3cBiC3QiuVUg8opVoopQoAXAFglFLqalSV1iim32rK8q3Yvmc/TjUYimXlCr+u2JZhwUim+Gr6Grzzy3I8N3yh622MhtDg6YWW6yxcn37Ro8venJT2PqwIwKlnCw05QogtYSofQkh2GLdoE+4cNCPjx83LJYsr82S9NYpbdV9SVu6qIqUVeRaXgLkwROH2PfHXvGQqH3o4bamXAYZh1bs/mxWwRBVsLdofyn4ZWkkIIYQQYkGujvWVUmOUUudrryPfGkVvavzs8IU4/9/jsThF2werwaub4iXl4fVOJhFAvy6cGnDv2Z9YBKc85/tUMLSSEOJAWKVtc3WARAiJHjNWbw9lv/TaZIZeL4xFwYChGDhuGYBYHz8nRi90l+vm9Pvxp63cfDZ1tW1I5Iad++Kvcz06iB45QogtYSqIHNedhJAIsX1POGFLJFjsvGYrtuxJeG9VcdLI62OWJu/bYr1MO1uUUnHvIsk8xp/7vi9mo/dL4yzX22bQF24ukSiPV9h+gBCSBPvrEEJyCeqs6LBrX0lKj1oqrLwkr41egqkr7QuVWA1ozWFzYTZPBoBPfl2NNg/+kFApk0QPYy+5sK+JsGFDcEJIVjAOu6at3Jo1OUhwdHrsR/SxmQElJExoxkWH7s+OwYmP/5TWPqz6az2bohKh1YDWvBtPRTB88K1WsXXF5qJQj0PSwzjv49cO+nXFVlwxcGLWPbD0yBFCss7Fr0/MtggkALbvKcGC9bswY9U2nPvyz9hrSionJCxYtTI6bHGozpdt70eu50ORYDBqi1Vb99iu58Q9n83CpGVbA2to7xfmyBFSiflw0sqU1b8ICZrHvpuH+et2Yt66ndkWhWSQbA7SaccRN6GVxve8ZiohPnTQbz7bXUSFMKtu0pAjJMv8/es5OPfln31vrxDtJF9CSHTI8VQTSz6cZF/GnLhHt5mMl8iQmWts11c+njxurr/cLzVPgsCrER/lyybM0E4acoSEwLSV23Dco8NdV2nzkxPAiUpCiFeyOdb5YOJK28/W7diL3wpze9Y919GL0RgHxHPX2nvswxo4J+6XT7qqwu7iUtOS4H77bBt5+0poyBGSU7w6ajF27SvFNIfqXbkAw1qqAhGexiSB4yW0csPOfYEWOSrcZp/ncspTo/C7V8e73tc2hxwv4g8rfR94KK7F7szHoEeuarJrX0la21t5iKvCGCYtQ05EGojIFyKyQETmi0g3EWkkIiNEZLH2b8OghCUkV7Ca2QwLPvOsoX5yJh5GxeunSuHl5+71wthAixw5jam8Xod//nRmWrIQe9yGTBp/s827/bcyGL1gU8J7q2qYpPJj1gGejTDLSQLf4uQM6XrkXgYwTCnVDkBHAPMBDAAwUinVBsBI7T0hVQqrXAOScaifXFAVZixJBV4GNjv3mUOd0iMvz9vFdtVbk1AwYChWW1Ss21KUXg80kozV5I5T77//b++8w6Oo1j/+PemUUBIgdELvvUiRDgJGxXot167XrqhYwPITFBTLtVeUYkH0KioldAi9dwi9hE5CKCEB0s/vj9nZnZ09U3dmd3ZzPs+TJ9nZKSdT3jlvF1dbcygbXcYtxtfLDmoeg6Ukjp+7x+uzVqNxTmijdHXlyw2/mlQ2COf3nGlFjhBSCUAfAJMAgFJaSCm9CGA4gB9dq/0I4GZ/B8nhhBqBFBrhLKDMwuUTh8NGydtCKcXbs3fjYJZ9FXSNth9Yc+gcAGDFgbMaa3KsoKBYyOMxqkbtcOU2fjBfvYccoM+QIM0n4u+3souaEYGJyr0VzrYBfzxyjQCcBTCFELKVEPIDIaQCgCRK6WkAcP2uwdqYEPIYIWQTIWTT2bNcSHPCEztLfe87k+s6hm2HCGW4fNIJv3/KFqUKOfcnLlzF5NVH8NDUjbYdW+qQO5iVh9f/3okSHWF0hGFqZy3j+Me4VMEzJpUJau8w8TtxFS2H63vz9uCzJQf8GqMRzuUV6Lq/OIFF6ZZSutdYHnm9lAVDgD+KXBSATgC+oZR2BHAZBsKUKKUTKaVdKKVdqlev7scwOBzn4c6Rs/EYuT4Vnmw4hsWhVQGEyycZlFK88c/OYA+DE2RWani3rFDsT128ivm7zqiu89S0zZi2/hgOZuX5f0AEv4l1KDPit61IHpXqs/z4+Sv4fuUR3ftR86DM3HYS3y0/jKlrMgyNTWmPl/KLkDwqFXN2nGJ+vzD9DDqPW4wPF/h6CfmtYg05V4uw38IeuD45cq7fu3T2kDvqh8IXyvijyJ0AcIJSut71+U8IE6dMQkgtAHD9zvJviBxO6BEuhSTWHT4X7CGYhcsnGQXFpcx+W2XBYsnxUGiin9GYWel4etoW3evf8vVqPPHLZgDKClZkhDD9UOqvJFUsmNUUZWYy7ngxz8xtcmVIOJlrDmWrbkfdv7VP/ldp2vlzRjiaLUzav11+iPn9Yz8L99/8XactPS7Hwx3frsF1n6wwvJ3ed464nt711arihjOmFTlK6RkAxwkhzV2LBgLYDWAWgAdcyx4AMNOvEXI4IY39swt/LNFDPlmBruMXK36/eE9o6jlcPmmz5djFYA+BEwS0xMWJC1d9lk1dk4HUnfonxJmXClzHoorHE8PwrCo1z0vWW4d4KrVOqZIHhYXR/EiryDjnO7nnxitr2J9pzpuudF/tlHnejIZPa9+v4Skj/K1a+SyAaYSQHQA6AHgXwAQAgwkhBwAMdn3mcMoU4ovCiNxoODoVafsCqzjty8zF2dwCTN/g66kJA7h80kGYvts4Cui53FaVf884d0WmYJnLdStmeO3k23FFzjqMnkpxfTUFae8Za4vo6G2RIJKbX4RHf9yErNx8xXXemrlL1bDJsZdVB9U9wFpo3REXr/jXp86p+KXIUUq3ufJI2lFKb6aUXqCUnqOUDqSUNnX9tq6bKIcTIoiTDCOvGkqBLwKYCC5l9F/hlzvF5RPw+M+bkDwqFadzrrqL43DKNpeuak9mipQqohjkkakbLQl5/CqNHT4nhetxwcD7pNtRgEZLQd918pLmPjYfvYA/N5/A4j2Z+Gqpcojnj2uP4myu/rYWKw+cNV2Ig1KKCfP24kj2ZVPbhzJK/7P87vEYBqy5r16ZscOS/TgNfz1yHA6HgdnQDdNzET6J4TBYkJ4JADiQmYfhX60O8mg4TkApV0k6X7aq0l9Bcamm50SPrMxU8aKIcEVOP1uPXXD/PWZWus/34jXTOqVrD8lyqG0IWfxowX6/95F1KV/iNSSW3Sv3TdqA3h+kmXpejp+/im+XH8LDNlaJdSp6C94Y7j6gcRkuXik0tsMQgStyFjB2djrGzvYVhhyOXLBkXsrH2NnpyM1nW8V3n9K2LnI4RiksVvaw8Plv2ULPpLOohL2OVAHQeywrJs1RjLr28kkeD63UJq+gGOmncvDOnN3uZaxJtd5T+ePaowA894VS+4HbvlljaJxS1h4+h7S96ikHVwtLNPdjawVphfe5GqKyzNsjKGO4jxzjKp+/LChvC9LPmNhfaMAVOQuYsjoDU1ZnBHsYHAfhzpGTCZZr3l2CKasz0HbMQuZ2BSoTbiWseA0kVIixYC8cp/Lyn9sVv7MqH4oTGihd7kV7Mt1/K00u9YSdXZa0RTkj8YQIeD6Iy5/7bavmPvWE7HFFTp3SUoo2by1AyuerNN8ZRs+kWBRL6TptPmrMACDnp7UZPsukx8rOE+7L/KIS3PndWly47O158crSJNYXOzETUhrqt+sDkzdYvk9/rwvrnIotlCatOhKUzpMjBzez/RhckfMTucAoLinFAQv7anBCE3eOXIhUUapXtVywh8CxmCuFngn1BZUk70d/2hSI4XACTGkpRQ4jHy6hQjRzfamXRkkp0mPRlhsNtIqdHD7rmy8jVxj1TPCcIUmdi7wioBqUAqsOZCPPYB9RuxweWvfd7tNCJMu41N1Yf+Q8Or6zyGcd8V1LQHAmRwjVnbntpDXj82MmHapOouX71ftRmkGuEPeasBQT5u3VfY7UZAAhwPojgU+JH9Cyhu3H4Iqcn8grHH24cB8Gf7ICh89a0+SUE5qIzbpDZXJRLLPArzqQjf9tPB6k0XCsYOKKw7rWC+Gm7xwVPlq4D+3HLkSOTIlPqhSnua2iIqfjuHN3ejcCV5KBarLxlq+98zn1RCpQa+qzhC3S8x2pMTPOys3HvZPWY/zcPYaOYSaiRA9aBk+x3ymrT6awvXdlzcOuYht/bbFIkTOxTajMDQLJakbVSqU+gWqczS3AQ1M2eMm+YIWwRirFG1sIV+T8RD4B/m65MHkyUvmIE36scFmr7PK4Ld2bqb2SAcTQFJF7J60P2wpPTuf2b9agzVsLNNdbujcTl1RyM5QaLXPKBnN2CH3fLsgS/HWFICqsYsZ7YCbkkdXLzo7jlFW0erqZrcRo12Q5bZ+396eklBquSyCmOdgxrfanR16oOeRKS6ltUWeHFapZ7jmtr3ZAnsuAPnHFIaTtO4vfN3kU++y84BQ6CUT/xDKnyK05mI3kUamqvUSsgKedcLQ4f7kQwz5baXi7Uxev4uGp3uFwShP69+buwec6WhqIDXwB4BijgSoncGw6esH9QlJCvAfaKeRacjiePF1v9HQWsPL9pcdTJvcamoErcup4GRU15pasJtpOYlPGeWwykHcnnUtL/7bqjjEzV3dKWoVRJq48jMGfrPBapqfYjD/IU5iUEIt6iaG4ZWUeXuYUuSmuCk1bj1209ThGm1VywhM1WT1352lTTVKvFvkKzXGp7BCY71YcxseLjJVv3n1afy4Fxz5YTZBF8hn3gNPIvJQfEuMMV44qTMb1vJuUc+SMj2OxpIiK1PMvnch+usT/EvP8jaufDUHIFbKCsbPT0er/5vtM0LWKjUhDK+3An30Ho5LiiQtXTHtdtzHmzoM+Xu7vkCxVbN1GLAcIhUBc3TKnyInhqrZXanPADcQJPmpW4lMXjYcPAb6CwSphVVAsTrpDLdgjPBk7e7f2Sg7mmneX4KlpW4I9jDKPfIKk59WnnCNnXDYo5WoWSgwVRmTY/sxcJI9KxS5Z8Q7ukVMnHM7OlNUZuFJY4mOMMFIMJxChbk7n2vfT0PuDNFPbsgxBJ03OZaRsPW6dc0WUU2rG0EARiNutzClyYoPcJ6dtwbk8+/LY7vlhvW375oQOajkDV2wOR5Cix9ol5qUUOkD4hTNfpR3Eg1O0Szcv26/cO8kpk5HC4lJVr9tSjf5PnMBTr2p5zXUUxYWZEDIFFUJardLI7fzhgn0AfBVSrsepE85tRiatOoJl+5RlzZpD2Zi17ZTwQRpaqXHTUEpxOoetpPjrQRK3zlaop5BfVOLugVYWUKuQatRrKa5eVuYyZU6Rk9J53GLDBQGy8woU8+uOnmMnanLKLmqy3uxcXEuosXLcRAOGGuL9+9x07b5OHPN8uGAflu3zr3SzQ/Q4XPv+UrR4c36whxG2/LHpOJJHpRrOIRPLqwO+npgO9Sprbl9KqWXFclgyUB6NYMTTt2g3W5Zxj5w6H7gU4HDl5T+Vi3NNW3/M3aJAitId89rfO9F+7EJMW38MPd5b6uP9BTwGBX/JLSh253ZJeWDyBnSStVEY8N9lePxn33YxxSXqBrVQwMrnN8JBoZWBIOQVuT2nL+HrZQdNb896gNToMm4xuo1fontfyaNSMWaWsepKnNDhwuVCVa+bWj6KVgloJbS2GvbZCp9lT/yyWbOiWEa2sxPcOc4ji1fntZUpqzMAAMcvGHs2pXLnh5WH8d+FnknnmzO130elFJi364zPcjMSq0Q2mzp8Ng89Jyz1WrYxw5OzlTwq1Wcfd3app3mcsjJpM0uo5sUxYVxrvfemHqPBr+uPIedqEda62hocYVRT/HqZpyy+mVtPer8WMyoQsXqeHT57mWmUfeznzSFjUJu1/RRzeQQhqBATyfxuiYEq3Qcyc/FVmnBtlOZf3Rom6N6f//CqlZrc+MUqfDDfvGXESiuA0p6mugqscMKLi1cK0fGdRfhg/l7FddR0pwib+otcdoVsrjrg3ZNF3mJAzjGTyc8c/VhlNXX6pMUwlhYAACAASURBVNWqxPVdJ3NwMMueUtfhTFSE59U+fcNxfLHUmLGTUooSxuTyoonqknID0oD/+hZG0GpWHRWpLSu5R67s4M+V9qpaqbEjdxNxh0RAKBHIEHY9j9mwz1ai3RjfFjonL15VjPghRDna6Ph5/Tl4907ypDU5QSSI/1LDahVsO0bIK3JiHzezEwcrr7MTbhpO4Mi5KkxqflybobiO2uTCfGilvvW2n/BOHpYPRZ4zkcFDg23ntw3shrUs1ORJWZm03vDFKgz62NfDzFHHXxvRN8sOYXyqr4Hq+d+3Gd6XFb3FSinwz9aTqsULysgjwYFv/15A/3tRaTVWzQR3E3ENr8rVwhIT93n43rB7Tl/CJUbOm1oEnJlCSiykIsLCVF9HE/KKnIhZIa7W4ybnahFG/7VDd65AWZlccQTEsLL8IuX7Y8bmE4rfWVWw4qyCpy1SNpuThxnIX4a8f5z9lCiIiFnbT+F/G4/r3k/5mCiLRmQPaqJw3eFztjUO5gj8teWkX9v/sfmEpgdfL2aae8uZsfkEnv99GyavPqK4Dn//lh2e+mWz6W2VXrs/rzvqs0y8pbQMI13HL8bI/xk3csiPE25kKDT4VsLq3npKMiHcTrffihwhJJIQspUQMsf1uSEhZD0h5AAh5HdCSIz/w9RGHoevF7UcpvZjF2L6huOYxnjAmfvS8Hhwwos7vl2ruc4WlX6FZnPk5LBCFUpLqc/LR35/LtztyYGJjiQ4fuGKI8r1WolT5JOI0ovquelb8coM5WT9UENJ8q0+mI27Jq7Dt8sPKazBsYJNR/XlQx06m2fzSKxBrD53ViUnk79uyw6XGRWf9Xp0jHh+St2hldrb/LONnfulhPRVIL91ndwsXMlwzGJBujDHePOfXczcVym2ZJoonEabslqChhUeuREApN2I3wfwCaW0KYALAB6x4BiamPbI6dguV6UsqhS59l/EyDHgcETM6nF6PHkrD2b7WOXlt/qOE568lPoJ5VFUQnE6h12RNYRxhHwyg5ps0tPU2QpKSik+XLAXF68YK4OtNBERqxWGigIR7oRapeVTKvLJyZNfKYSQeoSQNELIHkJIOiFkhGt5AiFkkcvItIgQUjXYYw0ldIdWMtbLKyjGdkYfM9E4YHeOnPzeXXUwW2HN4GOkQKB43ljeTt+VrfKUefayL5OdX21VGKdT8EuRI4TUBZAC4AfXZwJgAIA/Xav8COBmf46hFz0TG5Y1T8+Nw4rHZiFX5ELkvcIJEkZ7o4joKZKSX1SCvWe8hZj8ZSFV2uonCL2lZu8wZlV0Mk6QTzlXikx75s9cUpu0spe/PXs35u86bep4LEb8thVfpR1CyuerDG3HRZ//GFWepegN2w61CU3qDuV7m1Xlz6EUAxhJKW0JoDuApwkhrQCMArDEZWRa4vpsCcFUckNBwX78501IY7aE8R37wvQzmt4lo1AI7+xX/9yB85cLDfWPW2qgomOgMSJfCIglc2bpPpTa/Di9eI1R/PXIfQrgFQCiip4I4CKlVHRhnQBQx89j+PDvH9ahw9sLvQSE9OJlXcpH8qhUr5LGgBDHLEdPXD0rn6OzrL+HsC/NXTE5ceGKrc3JOYFF78RdSZZoVTa8oEPIy/s0Ab6T/8xLvoqcPxVgHUhQ5JNI1qV8tH97obs9yq/rjxkKS1Fva8Fm8uojeOKXLUaGCUCwSLNY7bIMn5TcT/vO5Greo0pi1azxoqwxY/MJdHh7EbP3lR5Yp5k5oQ6jyzH6r53BHoIuKKWnKaVbXH/nQogYqANgOATjEmCxkWmGnzmT/pCrIFuM0uyNearf624/wHg4timkQGw+egGA0HBc5ONF+3UeSZ2JKw67/6alQjGf3zcdxwfz92oqNBPm7XWnQTw81bevnBnemrkL4+bsNr39sXNX0OO9JV4N1OWnWrMBuwUmQOebDazHtCJHCLkBQBalVJp1ynqWmOeVEPIYIWQTIWTT2bPGmuOuPngOF68UKTYGve3bNQC8c5iUCjlkqli9RVghkucYk2m5a146EVOb+Fz7fhq6MJRMANh96hI+X3JAc4wc5/D9ysPaK0HZal6okaf2v03aRTFY5YjlMjRLcu8nVY5DTFTY1D4KqnwS2eAyJH2y+AAysi/jtb934rvl6veGmsdBZEH6Gfy9RbmIjojYS0cPh7L0hTrmXC3CkE9X4KU/tquup/RCNmudz8oNu5BfVVYe8LNhPONWTz/lqxSGkR4XkhBCkgF0BLAeQBKl9DQgKHsAaihsY1g27TtjziBgBVY55LRC+vQaiYzc8xdc7TbSNdpjmOEPSSE0CoqFrvlsKaWaCs23yw9h8R5rWw78uPYofpAorEaZtv4oTufkY6bBXEGRo+cuqxaO04ued4xee+J/ejf0czSBwZ+ZWy8ANxFCMgD8BiFk6VMAVQghYkm1ugCYV5VSOpFS2oVS2qV69eqmBiB1P0uvHavnxHmFMJWUz1fh763qk6JihVJzi2WKpDwOWFqAZY+GZVXp3rvxy1X4eNH+kMtlKMto9UQSURImrHvhpT+24+avVgPQ50VeeUA7xj6pUpxnLCBo4PLKhQlBl0+iV7SklKpWvpUaeebJwiLXuRrSSnn85834aKG6VXi/Qm6AEkpGhQuyvmFXXUUGtJoLX2UUI/CH1//e5bPs8Nk8JI9KxS4bJlnBpLSUGi6cIIc1EVQzWvpT5CivoBhfpR3klUgNQgipCGAGgOcppbo1LTOyKTLCPiPd8C81wq4DdFucZEShWIVVFaaVyCsodhtfKdWn/J67rC+6o7C4FKNm7PAy3OqBUspUipTG5i4MI1lmJAJjfOoe7ZV0oOd20xvy+XpKK/8GEyBMP92U0tGU0rqU0mQAdwFYSin9N4A0ALe7VnsAwEy/R6lnPBqXT+2yrT/sPSkpLinFa397wjSUJs7bZB44eUjdtHWenlF6bmhWToT4cuz74TLN7UWycvNxwOBEjuMfZ3LyUVAsTF61lHYRxTuCcbv9ufmE+34zXdhHtmMvQwgoGiTa17Ay0ARLPp3NLUDfD9OQkX0Zl64KIUX9m1dXlU7ScNY5Mo/cXRPXmRqHnkgDo+sXlZS6jQ9at+BWRuEAwHxoJStcefEewZB2wxfG8vecjt6cbDVYMoK1W/F66C3oxUrPfX/eXny4YJ+PEYKjDCEkGoISN41S+pdrcSYhpJbr+1oALHO5RNoYbLH9ROgbUrTkkt0R4WNne0IaKfS94/UWHVm8JxO/bTyOMbPTDY2p4ei5aDh6rs9yxZL+jMIwRk6bntx/PQQyJbO+AeO3nbeQHY/3qwBeJIQchJCTMsmGYwDwvmG0Lt6x88o9suQ35uajF/Dreo8Sppzv4f1Z/gLW8vTJ+XzJQUPrK9HngzQM/oQ30Q0k3d9bghHThT4yh856vKdP92+suI2S4NIySpiVU/L7WFpMg1KgQWJYeeSUsFU+pe44haPnrmDK6iP4Mk14npUmCduPX8S24xex5dgFK4cAALp7X4o8+pN2nsXEFYd1v4zKRUcyl5sNrWSdQn8LdRQWlxo+T4GANVGSL7p4pZBZZU9rv/IUg7O5BZi/6zQO6+z3xPJMiPmVBRaERZUFXEWXJgHYQyn9WPLVLAjGJcBiI5OdHjk1OtSrYkl/v2oV7ekS06JmPNYfPqeYIywi78lqNVJv2bFzV3SdM6VIMTla+/p08X48+qP+PDut3mxqclltJDlXi1S+1Y+VoZVaxMc5o5+rJaOglC4DsMz192EA3azYr6ExaHz/LKPXlojcUimfYCuFjMjvF7lrX6rY6blv/Lm5CopLcLmgBJ0YRVg4gWHRHt+cTenER141VSlcQ0sOmffIebhSWOxjhU9WUeQGtqiBzTYoHIEgkPKJdWkI2NdsuCtU1l+KS0oRJTO5F+l8yRshO68AV1whk5SqN3tVyvHSyv+UctlrcuX7rPj7Mm72xjzUrhyHNaMHei2fsvoIxs7ejX3jhiI2iq2Q2gnrXpEbd+74di0OZOUhY0KKgf1SrD3sHXIt5jqOHtZC1z5YxifxOmSWsTxGP+gF4D4AOwkhYhfp1wBMAPA/QsgjAI4BuMOqA1rVs9Qo8XFRlihy1zRMROpOazy+xyVG/eEd6uBOHVEPdhdpkk4xd57MQeXy0e7PlFLm8YtLfUMfWeu6PWUKs9BPF3vXYMgvKkGcgiEO0J4Pe3nkgnDblcUA75CubiC9MSmlmLH5BHpNWGp4P/KwHfm7SkkQXZUVMJHf4IfPGstrM3rPl5ZSTF19BPlFJejx3lJTSlxpKUWP95bgn63Bq2oVzkiv6ShZw2clI5+2IDInqqRCP/NSgc939VVCK+sllOcN7g0gfZna/TJbzDAg2FXyW6wMmJ1XgPxij/yTH+/8ZY91VTpxEnPdtKpeAkCWxPCRnVeAmdtO4tU/hWdox4mLGMfIqfhxTYZXyDulFAvTzyjeu6y+ZF8sFTypesMNrYblkZd7BA4witNsyjjvrtTMuvwlpcpGoK+X6SuME8OI0RPfw2FW8dY2KKWrKKWEUtqOUtrB9TOXUnqOUjqQUtrU9duyfgrBLBZbYpMsMstfJuY64uN34XKhpvdOD/KwSGlefWQE8Srkp/TarVu1nM/zzDYCudB5D2jlN/szDfhg/l7zG+tFx/isUsy1cifb1a1syXE0xxGQo9jEK5KJMQXw6owdliS8yi+y0o07yY8KPyx+WHVEs4hAcUkpflh5GIXFpZi36wzGzN6N/y7cZ6jviJT84hKczsn3ygnkmIP5SEvuJbnir1zsRLjhsnLzsZ5R7MIs0tv4jGwCS6m6R+785UJcyi9m9mLkBIas3HxmWwlWTpXZMBVKKaasPoLcfPb2SkVUfCcQngUFxb4TA6PFULYdv4gRv23D75uOo7SUepXulvLWrHR3USAA+GfbSTz282Z8uFC/kiE+lsGaf7LeN3pCu27/dq27UjNr8vzbxmM+y0T0Xg9miCsvfel4gnWJ9Bbu0KJSuWjtlSTUrhynvRL0l7vPzivEbxuOoeM7i3Digv9zzFnblYsZyZ91JaNcg8TyPqMPhMhS8siJNR7UqlYuSHduzzszaMm+irFRqOK6d/s0M1c0TQ8hrchJoVQ552j+rjPq28o+y7VsK6zbShdcPsl5atpmHDqbp5hfN33DMYxL3YPvlh9yKwasVgiAvgIGojXVivCHso7SGbxaWIKcK0U+94BSqIMoJ2/8YhUz7ONygbmKgF69Fl1hUMPa1BS+A1CnSjnFbcUXzy+yyqwcb9iPEVHN0dVLt/FL0FNnxMGrM8wZZlYdzMbY2bsxZpZvPyFKle9xnwmFZAHrve9P5Gd+cYnPeVaq6vvzWuF+/UanxwkIvmLCet9UiDGWBcGabKkZOfV6Teyu3sexh2Bdtv2ZuXjzH9+Ks0ZJqGBMkdPbSsfItGeUhX0K1XJz5UY4RZlLfWUFO79WWKb3FpDv4eKVQiSPSsXy/UK4vJIid8gVai/1Lob7tFLNs/dE38b45M4OSKwYi9WjBuCNlJa2jSNsFDlQ5VC12SrWDxZ6QyuNIJ20z95+Cg9P3QiAPSkf/PFyvPC7b5+mH1YexpszhcpDeQXF7kpUSmFD4jHUsKIBI0eAdfsRANd9uhzt317o+51iaKVwTeThjyJq1jx1PNda9MjVdFkuKYVPnhULXmLcOIQIBppQQOzjk3OVbRySev6l7TWLS0txMCvP3UBXKjJ3ncxB8qhUHJHk1JUwenPKUTKgXS0s8XnYWD3SPl9yAFsUGv3aCaUU//5hnWKfUy38fcRO51w1/JzqXZ8VKrTmoHarE05wsTvHS4ms3AJ3fzR/MDoFc/pbauY2/eGd4v8uN1YtSD+jakATGfGbkIZJCEFG9mV0GcfuWezZh/dORNn63fJDzO/VeHvObsNF/0KJKJVIiVHDWrhbPNWpUk7X/MosYaPIUVDFhF6jZU3lVsdNR/0v8iDd5bPTt7p7huz2mYAQxRe5NCeEwpOkmp3HnnRd0BFuKb7A/a0Ap0RufhHO5ZWtcDxpiCIhnr6Gqw/qC5NUk5N7/WjsKt3vmUv5qBATifhYY5Z+K0qjlzVyrhbZUnxERLTM/t9M/y3f0n1q8b4k32HXyRwM+ng5bvtmjbC9ZIrxtysnZdFuT2SEnmdB6V4rpb5GE9a6Hy/y7rW39dgFJI9KVS3SIiDs3ayRq6SUYvXBc3jsZ/2V4KQY6d3E6v/2yp873NZzn/2YGpEHliLHyjPkcKzE6H3rlAijJXsymekR6w7rT38U/xe5Yf7rZYd8/k+1/3v29lOYvuEYsjXmY2pn7uKVQsXnXWkGyXJKhAvBKiIkJ2wUuaISqvji19Lj5C9O+bUxWrTECPdOWi8fje5tj7pKSe/W2beMhXjK7Kqu23PCUnTWsAAFm4LiEr8a4ko5fDYPGZIS30ZLhAPqwvifreYbBV+UhGxkXSpAkiSPQO+k9dvl+kPUyiKi7JCGUmrlvfpLKaWYvuE4flrrf9irOz9Mx7oXJH0v5ZEFUlEsGsbkzcW1mKtQpY6t6FCs1vAM/bVFUCjlSo78GS10hbv/sk45p0wNcXRm55Ks19jSvYJXo/kb8zA+1RP2yjLiKR6XekJNzaL1P/3MQ68diUPmm6YxWmhLNJ4Gmvsnb/CKvnjkx01e6RGv/LndbewyCiuP1aiMmbxau66D2j5Hq4SXyvsqB4tcC4rR6MXuthR6CRtFrteEpShgNEhMHpWqmnwJeBehWHUgW7HRIqtht/XouzGkkxl/wt3c8dM2SfpgVX4zQvM35iPlc/+bChMCDPjvcq9lafvYlnE11ASpP4qUWAgBEDxySfFx7uvuEANm2CB63ANBcQnFkWzfKoZG2Xz0Ai4XKj+vcgVKWsHw/skbZOt6/hZFi9ECLNPWsxWpUuorry5dLcK/f5AbxbyZvUN4D/y+8bjX8rmSRta5+UW45JJZny/xLsutF6/8QBOymVVQZoyrYXBBcSm+X+mZjCkZfRpX961AWzehvF9GP+F46t9bkQ/FsR67Im4CxXcKxY38xerqviv2n8Xcnco1Gf636YQ7/Fwv4hD1iBItT6SeyBC5UVfc5Tod/fZChf/0bmjJfqIinfFchZwit0IhZMRIfyI5YiWd/Zm5uHfSeoyd7ZvoD8DyHm2XGJXh9OpT0ufVH2EkCgdn3I7BY59CNT45WZfyFRs4WxU+JxfGSve8P5zJyUfNynHu+43rcc5FqyJtKaV+51UBwG3frHHnU+hBK4xHRJQtcqVmYfoZVdmlVCG1hFIfeZWnowDQRZdHUK7MSCe5VvT2k54Xo+8lpZBIAEjb52scENsNSFl1MBvt61XxWX5X13qGxsIi52oRjp+/giuFxbbIJTPMfLqX++89fiqq4YpDHAeO46OF+7VXsgi1AidqiIoVu5CJ+meTB8Q6STioWOCMZUALVXo2ruao/fhLyCly5y7bl28lWoyPKORQWJ0eNEjmvQHsFbhFJaU+XkW3cAiP59N2Bn28HLd+bS40Qo6S0JUvl3s7/D8uRVZuvjsRV3UwnKCjVSmUUutzQljiQO5dVyok8n8zd3kpMGJopdzQ8djPmzFnh/Emv6Wl1MfgJS8ewFJwlJDuy4oweumlkEZLnMsrwNjZ6YoTuozsy3hg8ga8rWBIfGiKb/EqpfGKYaRSzHoYpUzfcAy9P0hDm7cW4P7JG7wmfGoYzcU1glRp5VV12YTJ/DukmbHZXNGPomJBhmQxDFvFsqJRRt8DxxnVlCmAuyThoJslhmtuEPAwcnAzPNG3UbCHASAEFTk7yx+Lz4DZHkxGYT2Y8ibnSuh5XOXrjPhtKzq87e1VdEpSsBM4naMdVy+GXSWPSrVtHHZfkvOXC1FUQpFUKdZ9v/G7wBqC8Z77aOE+Q/kJn97ZQXOdJXuzfIw+ehvpynP1POG7vnfZobPGQ0JZz8feM94e9SMGFLLSUup3f8TLBcXuCAupTC1xGU2SR6Wi87jFmLI6AwsVeimJqQFGwh+X7s1CVm4+c0ImR36O/EHUT7eqVAX99zX13X+Pur6FZcfmGCfUQytDjWnrj/oUFdlgwLgk5f0Fyk20247xroZt9D3+wu++ERhy+SotVhcu00UrnobaVco5xkMZcoqcnSfO6nhp731bu41XaKXO/Uljt9P2ZeHO79aiuESsWln2yM0vwjxJQYUHJ2u3a7ASpQIjpZSaKpKilzOu/oI1K8VhYMsaAIDBrZJsO15Zwq6inmp5sFm5BaoTajkNq/nmT7G4+3v1nDO9iFZcliJYXCJU22z2xjyvKphq3DlxreY6BRphTEM/XeH++7sVh9F1/GK/DHjdxi9GuzELkZWbj9ZvLXAvLy2lPkqlkvFs1F87DB9358kcdBu/RDUk0046MEI4AaGPV0KFGPfn+gnlmetxAoND5ptlhtf/3oVlsvx4lpdcDztO6JftlApG5nFzBK++lheQZdyRz0ukrx475MzDvazJVzNCuD0PIaXI/bXlBJ6bvtW2/dtZWf3GL1fpipGW3mBqwzFSGvtKYTH+2uL9QD8zbQvWHznvzr8xqyCHcnuBl//YgSenbXF/vqjQO8suflUs5kAtydVRIsvVn65GpTi0qVMZGRNS0K4ue0LGMYYdHm5KgXm7jIcgKtGIUQiDhVX5RmpRFFuOXcBPa4+isLgU3yw7pEuWnM7Jx8oD6hUqC4rUc+ZYExhpOJGctL1Z+F6l4MJlV0W5LbJCBiWl1CfMU+l0GFHG5QRrYqLUeDmSePuAlDxCD/ZM9uv4/ZtX92t7DidUiNHRh0zMQ/5hlVAQaeQf6qX/WcVLAu11q10lTnsli7HCIeQk52RIKXJ2hlVqYYW3jhVKKUf6H6pNCo0MZ+ys3Xjxf94PtHgj3/DFKtdn/fuT0umdRY5vL6DEiYve4UiBvr+kbQqk2C0g3B65yoEXoOEOq3KuFVjViD06kgT8Pl+2X7mC55pD3jlWr87Q55XSKgBjpt+hmuL60NSNGD9X6OO5MeM8dp3MYa4nt9WVUOpTUOHEBd8QbiWjjvNhn+fICOL1UlHKrXnzhlZ+HV1+nVvUquTX/sIVp4SAccyjp3DS9ys9xqae7y0xdZxAKyix0ZEBPqIveqNU9PL69S0t3Z8WIaXI2S2LJq1i99ioWSnOkolUSQnF87+pexSlzRatsu6fvqTdsNXMqV26N9PWRsdazNx2Elk6/jcl5KeXNcGllCJtb5apMuJmsbvy2hnXPVYjPtbW45RFPlywz/J9UlBd1lg9EBJ4RS6/SL9yeym/GJ8s2o9HpvoX5mxVnnPyqFSv55FSiju+Xes2gG07ftHL41YiEyrnGH3e6lYtB0BQRrNyhWfxtb+V+zPp4fW/g1P2X0n+EyJT3hRuOX/7MMnfy61rc0WOBVfjAs+i3cptCOzi62We9kRKjbu1sDPFiEWcglc/kPRtZq1n/1GL2hvoJfhn0AB2N99bvIedhH7mUr4lYZcllOIfjZ52UtSep6lrMiQrGt9ejhmL3aoD+iqWSaGU4rnpW7FeZ7UzJS4XFGPEb9s0e0epkX5KW2GaveM0Hpq6ET+tzTB9HKOk7bU33yUrNx/VKsYg2iLlgGMvO07kWCb7oiKIo/MDNhw5j8+WHMASP/vwHT3nf/VJkWGfrXT/vT/TuzjLzV+t9urPuCDde/Im3VYkLkqwQHd6ZxG6jV+CwyYKvjiFNxT6xkVGEK+mzHYZD+TGziheVo+Jk5/5cGWBpKjR9A3mPe6BDnUM9PGc4JELdUJqJhfM0EorvGNGR6/3kEZHxrK4aIUqsfhlvfFSz1eLSjBr+yk8MMW/kvrirXBMR7U2o/uUcsZVydKsdcsM9hbdoTiTI2s9wHE0S/dm4bGfN1uyr0jibEXOKnacYIc++stXaQdVv0/V0U7h0Z824W5JPt5QhrIXKhzMYiuhkYRgq6RsuV233D3XNPD6HMw5gpMJpbNSrWL4RYpMmKevkJMa9RLKWTASbUopRcsAhijHBskjJzWOsuZcWqJEbZ4W6FBm02eQEFKPEJJGCNlDCEknhIxwLU8ghCwihBxw/a5q2WCDWLHSirm1UWVQDLvRQuusyP83q/SEQkk+UM4VfaFM7nL3fo5B3N5MLowSLK+HdJx/bzXXB8Yo8vAsKymlwJlLBahpUJFLTgytqnPBkE9bFRrFOwkh5C2UpnXmYOWhWcEsSaNzf673WklEQqFNeZXBJCKCeBkYI2zylN3UvrbX56jI8L+3w5246JDyL+jCn4gKM0Z2f3h2+lbb0zukBEuRk8KacX15d6eAj8Ms/pzBYgAjKaUtAXQH8DQhpBWAUQCWUEqbAlji+mwJds4/Go6eq/q9FR65r9IOaa8kQSlsxV/sUBPav71QeyV4rqGZMRRLkn3F7a0qAgEAR89dwTtzdiMrNx+UUny3/JCXEH3hd/UKUFah91+K1jlpkVYpLKUUWZfyUcOgIlc+xr6GvjYRcPnkT5hvoAhGjly4csvXa4I9BMcSFUG83pkEwA3taiFR0pLADqx8H4QTdinSdmBVPrCT8EeRE6r0ng1YyKNd0QxKxEb5hlYGwitbXhLSyTq3WpXhnVRAyPQTQyk9TSnd4vo7F8AeAHUADAfwo2u1HwHc7O8gRYJ52qxQ5IxaOS7q9HIZHVlufhGz7KyTWXngLJq8Ps/d+Niu8MNJq46g2/gl2Hb8It6btxffuUqOB/Le03uvPdAjWdd60ryRguJSnLtcaNgjp1dpdAplTT4ZIVTGyQld5FZ2Qgi+vKcTRltYzY0VJaA3MqSsEUrP/A8PdAn2ECznrI6K5WpsO3bRtiiDYBPL8MD++UQPw/sZd3MbQ+tL51kspc3oFDOYnmRLjkwISQbQEcB6AEmU0tOAMJkCUENhm8cIIZsIIZvOntVX3GGXjuIUdmFFNbrdBhW5nQolruUoKTVKFoVu75orS2spBh+S5a7mmhuPCBXibmgFTwAAIABJREFUjBpe00/loNv4xbrDFOQhm4G08+qtkPnSkOa61pN6YDLdrQeMWbyiQthKGij5JPYSczKE8MIHHHXi4/z3vkdFRqCcxOJdMVbYp1WOofi4KCwZ2c9neSh5ngJKCD30japXDPYQHEcIXT7DRBLiU6QoEP+vNIVleIc6hrcXh/jerW1dn4N3kfyenRFCKgKYAeB5SqluTYVSOpFS2oVS2qV6dX2lP0VvTDD4aa3xwh6BQmkCqWRRUMrJeG76Vuy2UFk+mOXbdNcTWmlMNfJZ26Bm9XXaIWTlFmD1QfVGwiLyRzKQj2idKvqSmuN0Vnu6t7unIIC79YBBj5zdFWPtIpDyKRR468ZWjgoJ4TgQC6xWURHEq+9V85rxAOCl3PnDnGevZcqkrskJluyfw3ES4RwxTAgQY0GenNHXWu+mwnv9wPhhTLmh95QPaV3T1PGtxK+zRwiJhjBJmkYp/cu1OJMQUsv1fS0A/tWR5gSMWdtP4ZnpWyzb3y/rfEvuSpXLo+cumw7xVFIE84tKcJmxT3F9vQ9bMB/Knk0SLd1fnaoexfC0S5EL99BKgMsnFu3rVgn2EDhlgNjoSFzXqqbPciMGobu61lP8TqnqbqganOwm0GflhUHNLNnPljcHW7KfUGd/pq9RPFwgID6tkMx4t3Yc1xfB1rG+8A784u6OWPZSP/exP/5XewxulWS4p5wTJI4/VSsJgEkA9lBKP5Z8NQvAA66/HwAw0/zwZMe0akdlBFNGHAstP2rKEKVA3w+X4a6Ja5VXUtmnkrdx8CfL0fqtBczjAUYEhPd6gTSIPTx1k6X7kyaPi+0UjChyg1omISoitEIrgyGfOJxQ4e5u9RW/s0LWRUUQ9G5azWe5kd6VLWrG45+ne1kwGk6gCxy1q1vZr+1/uL8L5jx7LRJsLo4TKmT5mWNnB42qVdBeSQeEADlXi3yWGWXj0fO61ru9c10AQkRTsuR/uLVTXXx/fxd3GLjWEBIrxpgeq9X4MzvrBeA+AAMIIdtcP9cDmABgMCHkAIDBrs+cICAqL0aSNimAA5m52KUzP0/K6ZyreOmP7Vi0W2iEqaY0iTlou04aC+Ucl7oHP6w87J2oSikmrjiEnCtFXk1oWYSCR85qpPHn6w6fR0xUBKqUj9a9/cd3tg9FjxyXTwzCOEKHYwB1I5v/d8mwNjWZ0t+Ix2zerjPoUI/tQQ4n+RwIjJyvWzsZzxeSYzR1QmTOs9cCAAa1SkKbOv4pg+GEnb1lzWKVkm2ZkUHnKbpHxYgl7EZf9Fa/5kJ6fTBz40T8qVq5ilJKKKXtKKUdXD9zKaXnKKUDKaVNXb/1qck64MLbGNl5BXjxf9sMbUMpxeBPVuCGL1YZPt7dE9fhz80n8J+fNiH9VA52nzamDCaPSsWjPwreqKIS7zw+qRybtv6Y1zO75tA5vDt3r2oLBLkcPKPR4Dsv3zs8c6KremUoUi3eU9hk1cFsJFWKNZQnFUlIyIUsBUM+WUnlcvoVbQ7HKHY/zY9c25B5ECOTtvVHlB9NJ0yewpUHeyb7vQ+zk/OyprzpDUF1Yo5cvQRrestWiPXNm7Vzrq819zEcvUW8fgWFkIqX4qLbOH9tOWlofX/kRca5K+6/Uz5fhXWH9c2Rh3yyAvlFQsGWxXsykX4qB01fn4clezKZ6xN4K2YFxdrVAotLBcVQ3G7MrHTV9e+fvEF74CFAvYRyPvkkRvPjYqMicH3bWlYOi6NBXHQEqserVxad/3zvAI0mtImPDbkeiEGF9Q6IiYowVF6bF9RxFv5eDb0FuNq6FDErQznFqoDhyIhBTXWtt/noBZtHYhyrrnCDRN8QTan8eP+2tkh97lrN/Vil67oVOdcQZjzZU9d2wZR5IaXIcezn+Pkrmuv8ufkEthwzJ1jm7Trts2xfZi5avDnf/VmsTrpYQZHLLSjGpgxjjpTFe4SaFsv3C7/np58xtH2o8teTvXxKfuutWPnO8NZ4Z3hrREVGmCrPyzEPAcEkjX5KZcUrkVTJv+awvCS9L0bnHIXFpXi4V0ND27CiwUSDGgA80KOB7wo6kY//wZ7JupUNjjqsqtZ6qwq2rCVUJ2V5Wcyils9pBzwvL3CMGMhWZKWP98CWSWhdW9tTW6LDbbnylf6a67wwuBma1qiIXk2EHN/ODaqqru8Em1VIKXLNXCWMOcYwEq8ufRb+3HyCuc5Lf2zHrV+vMTWWd+fu1Vwn0vVkSB/MJXs9St3Z3AI8Oc1TXVNP+HhKO8GjdE3DREdat+yienysj3VUr0cuuVoF3Kez6TjHempVVp+YWqGfNEtyfs8mUQxMe/Qaw9v2apKIpjWc/z8GmoIidgsaQFmeGrV4sxSrohLPXsYON9bAV40xN7XG6lEDLNtfuCGe9TtchR5EujHKrtdiXDe5qFnxcn/8/Eg3r2XxcVEYc1NrfPyv9uhUX33yGwzevKGV1+d6CWz5aiQf/N1bQsNb2FCjMEnTGhWZrUFu7lCbuf4P93fBgJbMFqyGeGEwO7RUOmXRM79LqhTrUzBFyoqX++OXR67RFQ7avGY8Fr3YVzW9QalhOeu5CAQhpcjde415Cx7HOFNWH7F8n2d1VF8SLehzd3q8ZkfPaXsKpRSXlKLLuMVIHpWKl//YjliXRbGUUtz2jTklNBRghT/JLUZ6FTmt0D6Ofeix8pkJ5ZBXGtMbNhJMSl2aXKPqxqukTX6wK76/X92zyaJHI2tbgFi9P71kTEjButEDfZar5byWKsycjFScBOBVEU5EnvtsFgcYwUMK8ZpKr/t/72iPyQ91RfOkeKwbPRBLRvbFZ3d1QJ0q5XyqTg5uleT1uX5iefRuWh0ZE1KQ4gq7v7lDHZSPicKtnerqkk3JieXxUK9k9+df/2PcUGMEuQc4obz/nrdbOgqRKp/e2cHvfUmJi47AwfHDLNtfcqK3AnNje28FbdGLfTFvhG+ofieGN2rqQ10xqFUSbmhXG1tl7SE+uqM9AKBCjHmPbOcGVREb5dlerljLvcMbXx+E1a8OwIwnPcpVSrtaXtvVTyyPaxlVdM3ShWEAIfA8F4EmpBS5qNCrnOcITlxQr+SohFjt8Mc1GfhowT4rh6SK6JEz22MOAHLzi5GdJyiNf2w+4Q4XCeWiJXpghdvJPXI1dIaqtahZyZIxcYxDoO1JNxPSIZ9gxcc5v6hKj8aCElSRkev291PqimhsVCSqmgiVemZAE8PbqKH07uplcc9IvahZuZW+ipVNoLom6/O6SCfQbQ0Us9AKaeLoR2x4LEam9G9eHbd1rouKsVFY8EIf1Kwch8bVK7pD6G+STPTH39IGrwxtgY2vD2Lu+6t/d8KUB7vijRtaao7jqX6N3X8ve7k/3rqxtfuzngJPz/Rvgu/u66y6jjQUu2alOOx9Zyi2/d9gr/fgoJZJiuGiSsuvaeg7eS8XE4mMCSk+ipEZ7u5Wz+0Vyy8qRZQBw8kgmXdM/mw2kUUlRDEMOdGM/1usQK60L7lsFRXGFrUqYeJ9nd2KnREiCUF5iSJYRaZwr3jZOzyyXEwkoiIj0KSGJ2IvKT4Ob6R4e2D94e3hnvtUrlg6oaBoSClyge6FEi6ouZzVEAXJW7PS8WXaQb/GcMyAR81ohcTvlvsqZ6sOZnt9nrNDyM07kJVnaN/hgNnQSo49PN63kc+yTvW9y6wTQjRj2ayShvd2D2wOilE+uqM9lozsi/i4aB+resf6VREfp7+YidQDoPY6UcsJYoUgyVkysq/XZyUPhZKn7of7u3hNZswgn7xJMVseXkpiBW2DUMaEFK8QSiOV7p7s21jxO15MxRjNkuKRMSEFvZtWx8pX+uObe9WVoaFthGbuvZok4q6u9REZQVA9PhaNq1fwmtSK9G9Rw8uLAgAznuyBYW08TeHvuaY+nuinck11SLSXhjRHC40Um9nPegpjCEV6IlGlfAwiIgi+uLsjRg9rge/v7+wzXpGpD3XDH4zQObXwRH/C3De/MQhb3hyM925t5+P5vKGdd5ExpTxQadRBlfLRGDHQO2TxmQGeXLSRg5vhjRRfpbt6Rd/n+en+vgYttUgdUbFrU7sSrmtdE7d3rotlL/VjKt9t6rANxTe2r+XV+1ZOzcpx+PhfgoJICNvAR0HxgAXVV0WkBePkuXhia4hgiqSQUuRCSXYbqfBlN2ZPm5Ul5/t8mKY7rEZanOBgVp5mBckNjMInz07famyAYYz8MsqrWOolJjICiTwR3G8qMbxgcmWbEKCShoXan8nsM/2bYMqDXQH4b1GUe2qsJi46Eo2rC0rJa4wJyEiFPAsWUg/Aje2Urei1ZRMmqQLZuEYF1JBNZjrKFHFxvCJKV0rJ6j6oVRKzNHnGhBTduWDynCCR5wc1Vb3mSmOV97KShro+3b8xqsfH4tdHr8GqV7ULCugh31WNePp/uuseI0ebegnlEadhjKhbtTwyJqRg2qPdveYBS0b2w/0686Y7N0jwUhjfvaUtKsVF4/E+jbw8cyJq843p/+nubovQILECvrynIwDgs7t8QxprxHveb/J93ti+Nh7v2xiEEKFFBoPG1Su6PZhSyskMK2tHe55DqSx+TubNl8sGOYkVY90FVsbfIhg9Fr/YV20T/CrLF5Yef96I3l5hhONuboPK5aKx+MW+uKVjHTzRrzESK8b6eMflxqveTauhu8vQdH+PBsiYkIKMCSmKCjAgnLt/nu6F1yXesORqFdBHFm6YMSEFc571DeUcMbAp7u3eQLNAlViI5KeH2floUqOAP2GeLOQ1VcrHCIrkK0NbWHocIzhH29BBZAhpcu3qqD+8geTCFXMeuZ0njDcFV+P85UJd6z0nUcLGzk7Hiv1nLR1HOMBKqK1dOQ6Vyvlap+QTfrOK3K6xQ7DuNd98G44+6lQph1qV49x5FVLkuQjZeQWqk61Rw1owJ7Na1mqRl4Y0R/8W/ierA8BKhYl7/+bsXAHWJE4vsVGReKyPt0fzQUk1xc/v7qh7X9L3sThJ7JacgIwJKT5hXlKv0sf/6oBZz3iXw76ZUdW1SnnPPpReXWpvtP/08fXcAt5W+YwJKe6/B8iuZxXX/yA3Kl7ftpapvlRy5e9JyXUc0CIJG18fhJ5NqqFuVXWv23u3tkXPxtohpacuCikBrHPHK5GGDlMe6orJD3o8RqOvb+k16Z326DWoHh+rWnipR+NEjLnJY4S5oV1tZExIwfAOdZBQIcZdtOW2TkIxF9FrrtYTr7eBnKln+jfBixKDUaW4KMViVCMkBph7u9fH30/1QsaEFGx6Y5CXcYYVjhkfF42MCSlub7r4yIn/x6xnhH31bFINo4e1wJf3dMSusUMAAH880QOf3dXBPa4VL/fH430a4d/XCBEXTWpUxCd3dnDnurK8jlKZ/YOranLGhBS8rVKYSAyB7ttM2LZDvSo+SmG5mEj83w2t8O29nXxCI6U8N7CpLgNlUqU4t4eZRevagrfvn6d7YelL/TT3p0XPxonufT4j81LGREUgY0IK7u0evBoeIdVgJ5RCK+XWm1DkalEJfll31GuZ3CprBDOVLlceyNZeyYGktKuF1B2+rRasQi7A5o3ojaRKccjNL0LfD5epbmv23tRbgprDpl5COfz2GLva1a2d6njlbyo9ZnOevRZ1qpRD1QoxOHrustd3Q1on4fO7O6L5G/PZGyswqGUSpq0/ZmgbKVILuJQ7u9ZH2j5fI4zcW2WUHo0TFXNdb2pfG/N2nsa8XdrtRbo1TMA7w1uDEILPFh8AAFzXWghtkuaQLHyhj9d2zZLikZvvMY71bVYd93VvgLdcvSnFPBFpeFCrWpWwTHYuKsZGeSmTd3aph983HWeG3uph8oNdkTwq1f1ZDIGS55g0S4p3l4lnofc1K7XMG3k1392tvqGS8qHz1uew6N9c3WDUq0k1xfw7PWyRFdwAgD3vDNXcTvTWdapfBVuOXVRd96UhzQEI1R0PZOWhgkpvSnG/1zRMwLibPVUtq1WMxYhBTVFYUoKv0g5hoA5D2nWtkpC64zTu7V7fS5EFgMdlocdyL2L9xPIYfb1y3mJcdCT+e0d7r/nA5Ae7ouHouQCg6nmTcnOH2th89AK6MXIIpTys4AGVIvWgfnF3Ry/52Lh6BWYYpZxKcVG4tZNgWOtQzxqHSnxcNFKfc27f1tBS5AxY4RpVq4DD2ZdV13nk2oaYtMr6yoxvpLQMWQVEzhv/7HL/vfnoeb+KhZy8aK7oSijSsma8jyL3we3t8MqfO/zeNyt+vGUtwVrEe+A4j0/v7IDnf9+mug4rtJJFG0k4jDynpFblcrpfvlJYnrnujRKw7rByr8bODapqtvEY0jqJubxO1XI4OH4YXpmxA39tOWlssBAS2eXc3rku2rte2lJPmEjr2pXcIU4ZE1Jw6uJV1Koc57b+iq8Wsbqf+Iw1SCyPZkmC0jPn2WuR4VKepVbjV4Y293o3sfo0vji4GQa2THJXzD387vUAgG9XHHKvw6ryqIaWYUUaHhofF4XcfE/xqB4Sj9gbKS1xR5d6aD92oe5jt6gZ75X0r5bTosWG1wei2/glprcPJwghQwF8BiASwA+U0glBHlLYQgjB/Od7o06Vcmg7xvfe/+fpXvh1/VE81c/jgfnojvYY/tVqH+83IEQDdKgryJj944Yphoq+PKQFnh3QVDO8FQCGd6iDIa1r6lrXDLfJ2lEQIuRC6qkuLnJXt/ooKC7VHXLLYspDXbFF9j6ReyyXjOynuZ/944YhgpS9HNrQUuQMXJs6VctpKnJ2VJtpUqMiHu3dCCvCRJGTcts3a4M9hJDho4X7vT7f1qmuZgnyB3o0wI9rj6quA2i3BVg6si8G/He59iA5ttCkRkUcNFhURy7bRMWueVI89mXmMrex6101oEUN5BeVqK6j59BKL9MIQhAVGcEs/x0VQVCsEffXqrZvkry0OhrLQyi3pspz4ERFTDx0RATB1Ie6einObepUdn+W/mdKzWofvrYhJswT+mZGRUZ4VWF0H891wBY14905Z00l1deUnuWdY65TjVDxUWZlp1T67muQWAGVy0Vjw+sDUVBUijWHsvHqjJ2K+waEayi9vq0Z10QvSh5dEfE4tSvH4VROvunjOB1CSCSArwAMBnACwEZCyCxK6e7gjix8ESszvzO8Nd6cmY5qkoIfHepV8fHotK9XBT893I3pfZJW+tQyshhRzOxS4pRIfe5anDRQ6Tw6MgKP9jYXRSDSv3kNTc+tHspq1FBI/ddGim/cpKMc7L+61rU8Uf/D29sBgC63Oafs8N9/aZfh7auQUyQnQuOWbeRn6BrHHJMe6IK5z/X2UXJ6ukrM39VVLZzMeytxkn5vD+W4eysVuZ6NE5FQIQYZE1Iw+cGuGHtTa7dsNNK/7aXrtAuPsMT4p3d2wN53hvpMnOY8e63vyhqMGNgUfZtVxwYD+ZyPXtsQ3Rsl4F9d6rmX9Wtew2tiJ0U899IKlvLiJE+4Qp/UqkeKYaZP92+CIa1rYvYz1+K2Tp58u0bVKzJLhcfHRbvDu6TeMNHIM1zj/ScNURJLl9eIj0O9hPK4s2t9zH/eN4xIqguOlF3nQFjA1cLZwoRuAA5SSg9TSgsB/AZgeJDHVCa4r0cypj7UFbOe6aW5bp9m1QOuXAWSGvFx6OjAhu4cZUJKMkotkC8OboaPFwlej1s71fEJ0WGVio2NikBBsadyYoualdCwWgXsPcO2eJtBfKHd36MB3p+/F12TE/BUv8a4c+I6y47BcQ61KsfhtIaV+DpZSeHoSIKiEs+0KH3sEFy8WqRYWljO1/d4KoH1bVZdsYyvlJWv9EfvD9J07R/wnZRytBnYUrjO0gnvbZ3qokZ8nFdRChbyubr4Uc2La2XO8K+yyoBNk+Kxb9wwXLxSiHIxkVhz8Bx2nMjBJ4v3K+xB4JkBTdG7aXVcLlTuAcma9CdUiEFcdCQm3NYWgz5e4V6uVLxlyoNdsWxfFvO7iAiCHxWqmSlRo1KcYv6iGtLm2U/0a4T0Uzl4ZWhz9zKt6z6sbS3Mfa6328vYtq6vd69LclXVMNf1rw10V3hc9Wp/5FwpQqJMARVHKU5UpWGcrOvRomYlZExI8cq5u7lDHfc7V7zX7UQMHRaHVwaipeoAOC75fAKAvV2yOW76WeAR4nCCQUgpclJB3rdZddzSsQ6qVYzF4j2ZbkWude1KSD91iSn0myXFY+dJoRKj6DGb+lA3rDqYjbdnp+NSfrGXsvfnEz1w+7e+4YQJFWLcFRjXvzYQcdGR7vwCMW+AEILdb3sSbq9tUs2nt5nI9P90x93fc0UvFFkysi/Kx0R5TXjkVHWFkIlu/471qnq1TKgQG6Xb2tytYYLXZE9pwrrq1f64XOAJj6vMyBticVP72pi1/RRGDGqqvTKHibQgkN4gAvlkWvzYpEZFZExIQdreLGw5doG5jl7+01s72VyOWCijf4sa6N+ihqIi9/dTPd1hie01EszFcyJVNq66Qjmb1IjH7491dxu+lDw94niCRZTLLf7vazwe09ioSEyU9HNisXRkXxyV9dRkhYpK+fKeTugybrHi9ZY25Y2NikSNSsregpqVPWGMB8YP8+mJJKdeQjkcPy+EWdVP1N8DzgrkYVKRWqEIoQ/rCvtcIELIYwAeA4D69Z3dA5LD4dhPSElGafuByAiCegnlUS4m0p3E/ttj3d2lVVkyUew9AQCTXD2UalaOw+2d62LHmCH47K4OWPC8UKGsWVJFdElOQCOZZ+/bezt79Q9JqhTnVapa7Ckh55dHlQ1r1SqqF6gwE15khBUv97cttthIWJYTaJ6kr3y7iOgVSWnr3bizRc14d+GRjUcFpS2pUhy+vKcjvpU0x7y+bU2v7dRCfdeOHoAfH9LnaahbtTyaS7wZleKisfedoTjy3vWq233iCnHjmEfan0fJc7FkZF+vfltyhS9aVjyif4saGHldc69l8hL5WijlchklnmF06Fi/qlcOmBqi8vCoRLFsX9ej/F0j8UI6tcp8TFQE9r4zlNlYV41G1SsaVkDF3o2dLAh3ipIoQ9GREZohYnqafr9ooI+fERq4FEex/2jWpfDNj3NxAkA9yee6AE7JV6KUTqSUdqGUdqleXV84PofDCV9CSpGThhJJ8w7E5pXdGyW68wvqJ5T3aRb5eJ9GGNamJiY9wLaaDu9QB8nVKiBjQgoWviA0ZHx5iPfkaWibmqqV4aSVvOSI/aOWjuzrlb/RuHpF9GqS6NV0VkqbOpXdylx8XBTu7S5Y4V6/vqXP+ERYOQ5K1E8sj/3jhqn291BDOvGXN299uJeyF0A+IZz5tHZ8ukifZtWx+Q3vksUrXu6va1JRPiYSvZokYnAr30l2vQRPeONdXeu581yUEBWvUcO8m0GOuak12rk8Z4fPeoru3NCutlvBq1U5Dl/e3clru9ck5YLH3tQa790qlC9+qFcyalUu51dbi7joSM1clsgIEtbx/4Fgwm3t8NdTPbFv3FAMbVOTuU7j6hW9+m3JwyT15AOXj4nCP0/3cueV9WnG7ovUNbmqUG2tg3besBYLX+iDpS/1Q4kflaJEj5RUWZX3PxQNaE6uPhYXHRmQfmaEEMx59lpMeair6X2IXmKjvViLSwUlSsz9HjGwKe6XvKcyJqTguYH2eO/FvlRrD50DAJy7XOjTxDjM2AigKSGkISEkBsBdAGYFeUwcDsfh2BZaaUcZ3YgIgnkjeqN2lXKKk817uzfAPdc0QGQEwfAOdQTlzBX2VrVCDL65tzNzOyWkxxkheWGNubGVV1VMsRdJJRUr+Qe3t8Po61u4q3R9f38XUEoREUEw7dHuoJS6qxZueG0gfll3FE+6St+2qVPZXbI6IoJg7E1tEEEE6/aHC/Z5HWdQyyS0qFkJb97QCu/M8S549e4tbfHa356KZP/q4ik/WyHW87/WrBSHMxIL6KY3BuHX9cfcORIit3Wqi7joSPz2WHfUqVIO9RLKI+2lfvhk0X6Mv6UNYqMisf7IeSzbm4XcAiFv5o2Ulpix5ST+erInbvtmDXafvoQejRK9QrIe79sID/ZMRo/3lgIQFOr2davg3knrAQA/uUIKxSp3d3aph/qJ5fHcwKb4Mu0gCl3hsR/c1g77M3Pxw6oj7hLcLw5u5lVlqaSUovFrQu+UjvWrokJsFGZuO4UHeyWjRc1KeLJvYzz162bsO5OL7DxPU/Npj17jnmjWreqd39aqdiW8fVMb9Pkwzd2QU8rWNwcjNjrCZyL4QM9kJFaMwdDWNREVGQFKKSrERvnk2XHMY3eJ77joSN3ekyrlo3HxSpHP8u4aFU5FOtSrgn+e7oWC4hIfA5NY8fDp/k0sy/8QS/GPubE1bv1mDf55St34MvnBLnh46iaktK2F1J1CO474ON/XjlyR/f3xHkg/lWPJmMOBNn4qMEUuL2hstDHbbd0q5bHr5CV3lb4XbPK+SUmsEINzlwslrSE898YfT/TAlUL1iqqhCqW0mBDyDIAFEGTTZEppepCHxeFwHA7xp8Gz4k6FMrr7ISmjC+BupTK6Xbp0oZs2bbJ8HCJbjl1AtQqxpmL8l+8/iwcmb0DvptXw8yPK4ZHFJaW4eLVIscqZXt6auQvt6lbx6e+hxqoD2WiWVBHrjpxHSttaXtb81//eiSV7srDs5X5IP5WDTvWrYtHuTDz282a8OLiZlzU1v6gELd4UmgnvfWcoBny0DMWlFJMfFMpwU0rx5sxdqFW5HGIiIzBr+yn87/EefnmJ8otKMD51D166rjkql4/GpfwiREUQd4hqYXEpFu3OxPVtazKt85RSlFJ49Q4pLC7F879vRatalfDMAOH/y84rQOVy0Zi/6wyul50jAFh3+BzGp+7BP0/3AqUUe07n+hQeuHilEB8u2IcWtSphSOskzbLZZR1CyGZKqXrSUIAxKpsAe+XTu3P3YOKKw9j+f9eh/dueXkYHxw9DlMneXKLh6sh71zu8iyGJAAAKx0lEQVTCo3X+ciHi46LQ9PV5ALzH5bSxhiurDmTjp7UZ+O6+zobOc25+ETYcOW97cRPxOQCEVILYqAg0dRkNdp3MwQ1frMLtnet6tZnwFyfKJ6PYPXficDiBx6hsskuR6wFgDKV0iOvzaACglL7HWt/Jwmjz0fO47Zu1uKl9bXx+d8dgD8cysvMK/FY6ORw1nDhRMiqbAHvlU0kpxaWrRahaIcat1Lw8pDme7t9EY0tlXvx9G/7aelKzYmKgEf8/6bhYyzhlkxu/WIWdJ3Ow/OV+aJBof261E+WTUZw8d+JwOOZwiiJ3O4ChlNJHXZ/vA3ANpfQZyTruyksAmgPY57Mj66gGwCkdup00FsBZ43HSWABnjcdJYwH0jacBpdRR2fh6ZJNreaDkUyhe10DhpLEAzhqPk8YCOGs8esfiOPlkFELIWQBHbTxEKF7XQMHHo4yTxgI4azyWz53sypHTLKNLKZ0IYKJNx/ceDCGbnGJ5c9JYAGeNx0ljAZw1HieNBXDeeAygq8R3oOST086jk8bjpLEAzhqPk8YCOGs8ThqL3ditiDrpXDppLAAfjxpOGgvgrPHYMRa7qlbqKqPL4XA4AYbLJg6Hw+FwOGGBXYocL6PL4XCcCJdNHA6Hw+FwwgJbQisdWEY3ICGcOnHSWABnjcdJYwGcNR4njQVw3nh0wWWTJk4aj5PGAjhrPE4aC+Cs8ThpLKGOk86lk8YC8PGo4aSxAM4aj+VjsaXYCYfD4XA4HA6Hw+Fw7MOu0EoOh8PhcDgcDofD4dgEV+Q4HA6Hw+FwOBwOJ8QIOUWOEDKZEJJFCNmlsV5XQkiJq2+UuOwDQkg6IWQPIeRzQghxLV9GCNlHCNnm+qkRgLG8TwjZ5fq5U7K8ISFkPSHkACHkd1dBBl3YNJ6phJAjknPTwYqxEEL6EUJyJPv9P8l3Q13X4yAhZJRkuW3nxuR4gnFumNsSQhIIIYtc52YRIaSqnrHYOJ4xhJCTkm2u1zueUMVJssmC8Vgqn5wkm/SMJ5DyyUmyyYLxWCqfuGyyDifJJyfJJhvHw+dOXD4FRj5RSkPqB0AfAJ0A7FJZJxLAUgBzAdzuWtYTwGrXd5EA1gLo5/puGYAuARxLCoBFEIrNVACwCUAl13f/A3CX6+9vATwZ5PFMFdez8twA6AdgjsL4DgFoBCAGwHYArew+NybHE9Bzo7YtgA8AjHL9PQrA+0EezxgALxk9N6H848fzZ7ls8nM8lssnm8Zi6vnTMx6T8sCWc2NyLAE/N2rbwqR8smksY1DGZJOecym5p/jcyZrxmHoGbZIHYTF38mc8atsixOVTyHnkKKUrAJzXWO1ZADMAZEk3BRAH4YaKBRANIDNIY2kFYDmltJhSehnCDT7UZeUaAOBP13o/Arg5WOPRe1w/xsKiG4CDlNLDlNJCAL8BGB6gc6N7PCb2Y8VY1LYdDuGcAIE7N35tG244STb5OR7L5ZOTZJOB8bCwXD45STb5OR7L5ROXTdbhJPnkJNlkx3j0HtePsbAI+7mTn+MJW/kUcoqcFoSQOgBugWB1cEMpXQsgDcBp188CSukeySpTXG7MN8WwAbvGAuFhH0YIKU8IqQagP4QmxYkALlJKi13rnQBQx4qxmByPyHhCyA5CyCeEkFirxgOgByFkOyFkHiGktWtZHQDHJeuI58DWc2NiPCKBPDdqJFFKTwOA67fuEDybxgMAz7jOzWS9oQrhjJNkk9p4EAT55EDZBDhLPjlJNimNRw075ROXTRbgJPnkJNlkcjwifO7kOx4RLp+UMSSfwk6RA/ApgFcppSXShYSQJgBaAqgL4WYaQAjp4/r635TStgB6u37us3MslNKFENzzawBMhxCqUAyAJQSt7A9hdDwAMBpACwBdASQAeNWisWwB0IBS2h7AFwD+cS1XOgd2nxuj4wECf26ChZnxfAOgMYAOEF7+/7VveCGDk2ST4niCJJ+cJJsAZ8knJ8kmtfEEAy6brMNJ8slJssnMeAA+d+LyKUDyKRwVuS4AfiOEZAC4HcDXhJCbIVhT1lFK8yileQDmAegOAJTSk67fuQB+heAStnMsoJSOp5R2oJQOhnDDHwCQDaAKIURs1F4XwCmLxmJmPKCUnqYCBQCmwKJzQym95LoOoJTOBRDtsmidgLdFSzwHtp4bE+MJxrlRI5MQUgsAXL+zNNa3dTyU0kxKaQmltBTA97DumQplnCSb1MYTDPnkGNnk2rdj5JOTZJPGeNSwRT5x2WQpTpJPTpJNZsbD505cPgVMPoWdIkcpbUgpTaaUJkOICX6KUvoPgGMA+hJCoggh0QD6Atjj+lwNAFzLbwCgWrnI37EQQiIJIYmuY7YD0A7AQkophRDCIFZEegDATCvGYmY8rs/izU0gxA1bcm4IITXFMAxCSDcI9+I5ABsBNCVClaUYAHcBmGX3uTE6Htd6gT43asyCcE6AwJ0btW1qST7eAovOTSjjJNmkNp5gyCcnySbXPh0jn5wkmzTGo4Yt8onLJutwknxykmwyMx7XZz534vIpMPKJmqgaE8wfCO7r0wCKIGj8jwB4AsATjHWnwlNdKBLAdwD2ANgN4GPX8goANgPYASAdwGcAIm0eS5xrDLsBrAPQQbJeIwAbABwE8AeA2ACcG7XxLAWw03Uz/QKgohVjAfCM63xvdx2zp2Tb6wHsh1Dx6PVAnBuT4wnGufHZ1rU8EcASCNbAJQASAnRulMbzs+vc7IAgJGsFW3bY/ePH82e5bPJzPJbLJ5vGYur5s+Cet1Q+2TSWYJ0bS+WTTWMpc7LJz2eQz53MjYfPnbh8Coh8Iq4NORwOh8PhcDgcDocTIoRdaCWHw+FwOBwOh8PhhDtckeNwOBwOh8PhcDicEIMrchwOh8PhcDgcDocTYnBFjsPhcDgcDofD4XBCDK7IcThlFELIZEJIFiFEs7wtIaQPIWQLIaSYEHK77LsPCCHphJA9hJDPxXK7HA6HYxYunzgcjhNxmmziihyHU3aZCmCoznWPAXgQQtNXN4SQngB6Qeif0wZAVwh9hjgcDscfpoLLJw6H4zymwkGyiStyHE4ZhVK6AsB56TJCSGNCyHxCyGZCyEpCSAvXuhmU0h0ASuW7gdBLJwZALIBoAJn2j57D4YQzXD5xOBwn4jTZxBU5DocjZSKAZymlnQG8BOBrtZUppWsBpEFobHkawAJK6R7bR8nhcMoiXD5xOBwnEjTZFGVmIw6HE34QQioC6AngD0modqzGNk0AtARQ17VoESGkj8tixeFwOJbA5ROHw3EiwZZNXJHjcDgiEQAuUko7GNjmFgDrKKV5AEAImQegOwA+UeJwOFbC5ROHw3EiQZVNPLSSw+EAACillwAcIYTcAQBEoL3GZscA9CWERBFCoiEk6/LQJQ6HYylcPnE4HCcSbNlEKKVmtuNwOCEOIWQ6gH4AqkFIsn0LwFIA3wCoBSH59jdK6duEkK4A/gZQFUA+gDOU0taEkEgIseB9ICTvzqeUvhjo/4XD4YQXXD5xOBwn4jTZxBU5DofD4XA4HA6HwwkxeGglh8PhcDgcDofD4YQYXJHjcDgcDofD4XA4nBCDK3IcDofD4XA4HA6HE2JwRY7D4XA4HA6Hw+FwQgyuyHE4HA6Hw+FwOBxOiMEVOQ6Hw+FwOBwOh8MJMbgix+FwOBwOh8PhcDghxv8DAoafqKQgQXoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fba39d8a7b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print aq changes throughout the year, and create a seperate chart for each air quality monitoring site\n",
    "ncols = 3\n",
    "nrows = 4 #los_angeles_aq['Site Num'].unique().shape[0]\n",
    "plt.subplots(nrows, ncols, figsize=(15,15))\n",
    "idx = 1\n",
    "data_summary = los_angeles_aq['Sample Measurement'].describe()\n",
    "for site in los_angeles_aq['Site Num'].unique():\n",
    "    plt.ylim(0, data_summary['max'] * 1.25)\n",
    "    plt.subplot(nrows, ncols, idx)\n",
    "    # note use of trimmed frame\n",
    "    plt.plot( los_angeles_aq_trimmed.loc[los_angeles_aq_trimmed['Site Num'] == site]['Timestamp'], los_angeles_aq.loc[los_angeles_aq['Site Num'] == site]['Sample Measurement'])\n",
    "    plt.title(\"site \" + str(site))\n",
    "    idx = idx + 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f8455658b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Print aq changes throughout the day from 0:00 - 24:00. Break into seperate plots by weekday\n",
    "nrows = 4\n",
    "ncols = 2\n",
    "plt.subplots(nrows, ncols, figsize=(15,15))\n",
    "weekdays = [ 'Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday' ]\n",
    "idx = 1\n",
    "values_by_weekday = {}\n",
    "for day in weekdays:\n",
    "    plt.subplot(nrows, ncols, idx)\n",
    "    # for loop 0:00 - 24:00\n",
    "    # sum measurements where weekday == and time ==\n",
    "    times = []\n",
    "    for hour in range(0, 23):\n",
    "        if hour <= 9:\n",
    "          time = \"0\" + str(hour) + \":00\"\n",
    "        else:\n",
    "          time = str(hour) + \":00\"\n",
    "        # select subset matching given weekday and hour\n",
    "        # note (again) the use of trimmed frame\n",
    "        subset = los_angeles_aq.loc[ (los_angeles_aq_trimmed['Weekday'] == day) & (los_angeles_aq['Time Local'] == time) ]['Sample Measurement']\n",
    "        #         print(subset.mean())\n",
    "        times.append(subset.sum())\n",
    "    values_by_weekday[day] = times\n",
    "    plt.plot( times )\n",
    "    plt.title(day)\n",
    "    idx = idx + 1"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.5",
   "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": "3.5.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}