HouseSimple.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install geopy\n",
    "#!pip install python-google-places\n",
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "from datetime import datetime\n",
    "from geopy.distance import geodesic\n",
    "import geopy.distance\n",
    "from geopy.distance import vincenty as VIN\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "import seaborn as sns\n",
    "import pylab as plot\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import datasets\n",
    "from sklearn import preprocessing\n",
    "from pandas.plotting import scatter_matrix\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn import metrics\n",
    "import statsmodels.formula.api as sm\n",
    "\n",
    "\n",
    "\n",
    "data = pd.read_csv(r\"unique_mls_db_GIT.csv\")\n",
    "cpi=pd.read_csv(r\"CPI.csv\")\n",
    "\n",
    "\n",
    "#coordinates of major go train stations in mississauga and nearby cities: Lisgar, Meadowvale, Streesville, _\n",
    "#Erindale, Cocksville, Dixie, Kipling, Clarkson, Port Credit, and Long Branch\n",
    "\n",
    "go_station_msga=[[43.5905809,-79.788362],[43.5976,-79.75369999999998],\n",
    "                 [43.5762,-79.70910000000003],[43.5670973,-79.6689102],\n",
    "                 [43.5819,-79.62369999999998],[43.607605,-79.5776749],\n",
    "                 [43.6357039,-79.5372283],[43.51251816552179,-79.63435649871826],\n",
    "                 [43.5555,-79.5874],[43.59133291164543,-79.54609036445617]]\n",
    "\n",
    "#coordinates of major hospitals in mississauga\n",
    "\n",
    "hospitals_msga = [[43.5713, -79.6084],[43.5594, -79.7033],[43.4507, -79.7639],[43.4962539,-79.8716229]]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mlsno</th>\n",
       "      <th>stno</th>\n",
       "      <th>stname</th>\n",
       "      <th>city</th>\n",
       "      <th>area</th>\n",
       "      <th>soldprice</th>\n",
       "      <th>solddate</th>\n",
       "      <th>type</th>\n",
       "      <th>style</th>\n",
       "      <th>bdrm</th>\n",
       "      <th>wshrm</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>house_condo</th>\n",
       "      <th>aptno</th>\n",
       "      <th>address</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>E3853125</td>\n",
       "      <td>46</td>\n",
       "      <td>Hinsley</td>\n",
       "      <td>Ajax</td>\n",
       "      <td>Northeast Ajax</td>\n",
       "      <td>595000</td>\n",
       "      <td>6/30/2017</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>2-Storey</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>43.891599</td>\n",
       "      <td>-79.028612</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>46 Hinsley Cres, Ajax</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>E3833751</td>\n",
       "      <td>508</td>\n",
       "      <td>Norfolk</td>\n",
       "      <td>Pickering</td>\n",
       "      <td>Amberlea</td>\n",
       "      <td>677000</td>\n",
       "      <td>6/30/2017</td>\n",
       "      <td>Detached</td>\n",
       "      <td>2-Storey</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>43.827530</td>\n",
       "      <td>-79.123617</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>508 Norfolk Sq, Pickering</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>N3788507</td>\n",
       "      <td>36</td>\n",
       "      <td>Innisvale</td>\n",
       "      <td>Markham</td>\n",
       "      <td>Cornell</td>\n",
       "      <td>725000</td>\n",
       "      <td>6/30/2017</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>2-Storey</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>43.900148</td>\n",
       "      <td>-79.238976</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>36 Innisvale Dr, Markham</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>E3855735</td>\n",
       "      <td>14</td>\n",
       "      <td>Shudell</td>\n",
       "      <td>Toronto E01</td>\n",
       "      <td>Blake-Jones</td>\n",
       "      <td>755000</td>\n",
       "      <td>6/30/2017</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>2-Storey</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>43.675379</td>\n",
       "      <td>-79.337634</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14 Shudell Ave, Toronto</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>W3854388</td>\n",
       "      <td>2500</td>\n",
       "      <td>Hargood</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Streetsville</td>\n",
       "      <td>1050000</td>\n",
       "      <td>6/30/2017</td>\n",
       "      <td>Detached</td>\n",
       "      <td>2-Storey</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>43.576264</td>\n",
       "      <td>-79.729552</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500 Hargood Pl, Mississauga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      mlsno  stno     stname         city            area  soldprice  \\\n",
       "0  E3853125    46    Hinsley         Ajax  Northeast Ajax     595000   \n",
       "1  E3833751   508    Norfolk    Pickering        Amberlea     677000   \n",
       "2  N3788507    36  Innisvale      Markham         Cornell     725000   \n",
       "3  E3855735    14    Shudell  Toronto E01     Blake-Jones     755000   \n",
       "4  W3854388  2500    Hargood  Mississauga    Streetsville    1050000   \n",
       "\n",
       "    solddate           type     style  bdrm  wshrm   latitude  longitude  \\\n",
       "0  6/30/2017  Semi-Detached  2-Storey     3      3  43.891599 -79.028612   \n",
       "1  6/30/2017       Detached  2-Storey     3      3  43.827530 -79.123617   \n",
       "2  6/30/2017  Semi-Detached  2-Storey     4      3  43.900148 -79.238976   \n",
       "3  6/30/2017  Semi-Detached  2-Storey     2      2  43.675379 -79.337634   \n",
       "4  6/30/2017       Detached  2-Storey     4      3  43.576264 -79.729552   \n",
       "\n",
       "  house_condo aptno                       address  Unnamed: 16  Unnamed: 17  \\\n",
       "0       house   NaN         46 Hinsley Cres, Ajax          NaN          NaN   \n",
       "1       house   NaN     508 Norfolk Sq, Pickering          NaN          NaN   \n",
       "2       house   NaN      36 Innisvale Dr, Markham          NaN          NaN   \n",
       "3       house   NaN       14 Shudell Ave, Toronto          NaN          NaN   \n",
       "4       house   NaN  2500 Hargood Pl, Mississauga          NaN          NaN   \n",
       "\n",
       "   Unnamed: 18  \n",
       "0          NaN  \n",
       "1          NaN  \n",
       "2          NaN  \n",
       "3          NaN  \n",
       "4          NaN  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>soldprice</th>\n",
       "      <th>bdrm</th>\n",
       "      <th>wshrm</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.955240e+05</td>\n",
       "      <td>195524.000000</td>\n",
       "      <td>195524.000000</td>\n",
       "      <td>195524.000000</td>\n",
       "      <td>195524.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.656679e+05</td>\n",
       "      <td>2.830855</td>\n",
       "      <td>2.653142</td>\n",
       "      <td>43.727473</td>\n",
       "      <td>-79.468918</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.719666e+05</td>\n",
       "      <td>1.097000</td>\n",
       "      <td>1.238492</td>\n",
       "      <td>0.136501</td>\n",
       "      <td>0.233164</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>43.288120</td>\n",
       "      <td>-80.127424</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.390000e+05</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.642452</td>\n",
       "      <td>-79.650354</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.310000e+05</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>43.721190</td>\n",
       "      <td>-79.446030</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.880000e+05</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>43.822972</td>\n",
       "      <td>-79.345323</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2.588889e+07</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>44.155510</td>\n",
       "      <td>-78.687984</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          soldprice           bdrm          wshrm       latitude  \\\n",
       "count  1.955240e+05  195524.000000  195524.000000  195524.000000   \n",
       "mean   7.656679e+05       2.830855       2.653142      43.727473   \n",
       "std    5.719666e+05       1.097000       1.238492       0.136501   \n",
       "min    0.000000e+00       0.000000       0.000000      43.288120   \n",
       "25%    4.390000e+05       2.000000       2.000000      43.642452   \n",
       "50%    6.310000e+05       3.000000       2.000000      43.721190   \n",
       "75%    8.880000e+05       4.000000       3.000000      43.822972   \n",
       "max    2.588889e+07       9.000000      35.000000      44.155510   \n",
       "\n",
       "           longitude  Unnamed: 16  Unnamed: 17  Unnamed: 18  \n",
       "count  195524.000000          0.0          0.0          0.0  \n",
       "mean      -79.468918          NaN          NaN          NaN  \n",
       "std         0.233164          NaN          NaN          NaN  \n",
       "min       -80.127424          NaN          NaN          NaN  \n",
       "25%       -79.650354          NaN          NaN          NaN  \n",
       "50%       -79.446030          NaN          NaN          NaN  \n",
       "75%       -79.345323          NaN          NaN          NaN  \n",
       "max       -78.687984          NaN          NaN          NaN  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mlsno</th>\n",
       "      <th>stno</th>\n",
       "      <th>stname</th>\n",
       "      <th>city</th>\n",
       "      <th>area</th>\n",
       "      <th>soldprice</th>\n",
       "      <th>solddate</th>\n",
       "      <th>type</th>\n",
       "      <th>style</th>\n",
       "      <th>bdrm</th>\n",
       "      <th>wshrm</th>\n",
       "      <th>latitude</th>\n",
       "      <th>longitude</th>\n",
       "      <th>house_condo</th>\n",
       "      <th>aptno</th>\n",
       "      <th>address</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>W3854388</td>\n",
       "      <td>2500</td>\n",
       "      <td>Hargood</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Streetsville</td>\n",
       "      <td>1050000</td>\n",
       "      <td>6/30/2017</td>\n",
       "      <td>Detached</td>\n",
       "      <td>2-Storey</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>43.576264</td>\n",
       "      <td>-79.729552</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2500 Hargood Pl, Mississauga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>W3817888</td>\n",
       "      <td>551</td>\n",
       "      <td>Cavell</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Cooksville</td>\n",
       "      <td>668000</td>\n",
       "      <td>6/29/2017</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>Bungalow-Raised</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43.563612</td>\n",
       "      <td>-79.622475</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>551 Cavell Dr, Mississauga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>W3850685</td>\n",
       "      <td>3582</td>\n",
       "      <td>Birchmeadow</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Applewood</td>\n",
       "      <td>670000</td>\n",
       "      <td>6/29/2017</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>Bungalow-Raised</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>43.612874</td>\n",
       "      <td>-79.607933</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3582 Birchmeadow Cres, Mississauga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>W3848786</td>\n",
       "      <td>4186</td>\n",
       "      <td>Uxbridge</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Rathwood</td>\n",
       "      <td>804500</td>\n",
       "      <td>6/29/2017</td>\n",
       "      <td>Detached</td>\n",
       "      <td>2-Storey</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>43.616948</td>\n",
       "      <td>-79.613434</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4186 Uxbridge Lane, Mississauga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>W3835920</td>\n",
       "      <td>4597</td>\n",
       "      <td>Centretown</td>\n",
       "      <td>Mississauga</td>\n",
       "      <td>Hurontario</td>\n",
       "      <td>889500</td>\n",
       "      <td>6/29/2017</td>\n",
       "      <td>Semi-Detached</td>\n",
       "      <td>2-Storey</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>43.595464</td>\n",
       "      <td>-79.664838</td>\n",
       "      <td>house</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4597 Centretown Way, Mississauga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       mlsno  stno       stname         city          area  soldprice  \\\n",
       "4   W3854388  2500      Hargood  Mississauga  Streetsville    1050000   \n",
       "38  W3817888   551       Cavell  Mississauga    Cooksville     668000   \n",
       "39  W3850685  3582  Birchmeadow  Mississauga     Applewood     670000   \n",
       "40  W3848786  4186     Uxbridge  Mississauga      Rathwood     804500   \n",
       "42  W3835920  4597   Centretown  Mississauga    Hurontario     889500   \n",
       "\n",
       "     solddate           type            style  bdrm  wshrm   latitude  \\\n",
       "4   6/30/2017       Detached         2-Storey     4      3  43.576264   \n",
       "38  6/29/2017  Semi-Detached  Bungalow-Raised     3      2  43.563612   \n",
       "39  6/29/2017  Semi-Detached  Bungalow-Raised     3      2  43.612874   \n",
       "40  6/29/2017       Detached         2-Storey     3      3  43.616948   \n",
       "42  6/29/2017  Semi-Detached         2-Storey     4      4  43.595464   \n",
       "\n",
       "    longitude house_condo aptno                             address  \\\n",
       "4  -79.729552       house   NaN        2500 Hargood Pl, Mississauga   \n",
       "38 -79.622475       house   NaN          551 Cavell Dr, Mississauga   \n",
       "39 -79.607933       house   NaN  3582 Birchmeadow Cres, Mississauga   \n",
       "40 -79.613434       house   NaN     4186 Uxbridge Lane, Mississauga   \n",
       "42 -79.664838       house   NaN    4597 Centretown Way, Mississauga   \n",
       "\n",
       "    Unnamed: 16  Unnamed: 17  Unnamed: 18  \n",
       "4           NaN          NaN          NaN  \n",
       "38          NaN          NaN          NaN  \n",
       "39          NaN          NaN          NaN  \n",
       "40          NaN          NaN          NaN  \n",
       "42          NaN          NaN          NaN  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_msga = data.loc[data['city']=='Mississauga']\n",
    "data_msga.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifyHouseFees():\n",
    "    \n",
    "    global data_msga\n",
    "    house_fees = {'house':1, 'condo':2}\n",
    "    data_msga['house_fees'] = data_msga['house_condo'].map(house_fees)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def mapHouseArea():\n",
    "    \n",
    "    global data_msga\n",
    "    house_area = {'Streetsville':1, 'Cooksville':2,'Applewood':3,'Rathwood':4,'Hurontario':5,'Central Erin Mills':6,'Mineola':7,'Clarkson':8,\n",
    "                 'City Centre':9,'Meadowvale':10,'Malton':11,'Erin Mills':12,'Churchill Meadows':13,'Lisgar':14,'Port Credit':15,'East Credit':16,\n",
    "                 'Creditview':17,'Meadowvale Village':18,'Lorne Park':19,'Fairview':20,'Mississauga Valleys':21,\n",
    "                 'Erindale':22,'Sheridan':23,'Dixie':24,'Northeast':25,'Mavis-Erindale':26,'Gateway':27,\n",
    "                 'Meadowvale Business Park':28,'Sheridan Park':29,'Southdown':30,'Port Credi':31,'City Centr':32,\n",
    "                  'Lakeview':33,'Huttonville':34}    \n",
    "    \n",
    "    data_msga['house_area'] = data_msga['area'].map(house_area)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjustSoldPrice():\n",
    "    \n",
    "    global data_msga\n",
    "    #add soldyear, soldmonth columns\n",
    "    data_msga.loc[:,['solddate']] = pd.to_datetime(data_msga['solddate'])\n",
    "    data_msga.insert(len(data_msga.columns),column='soldyear',value=pd.DatetimeIndex(data_msga['solddate']).year)\n",
    "    data_msga.insert(len(data_msga.columns),column='soldmonth',value=pd.DatetimeIndex(data_msga['solddate']).month)\n",
    "\n",
    "    #merge with CPI file and convert nominal sold prices to real sold prices with base case = 2015-07-31 \n",
    "\n",
    "    cpi_targetrange=cpi[(pd.to_datetime(cpi['Date'])>=datetime(2015,7,1)) & (pd.to_datetime(cpi['Date'])<=datetime(2017,6,30))]\n",
    "    cpi_targetrange.insert(len(cpi_targetrange.columns),column='soldyear',\n",
    "                           value=pd.DatetimeIndex(cpi_targetrange['Date']).year)\n",
    "    cpi_targetrange.insert(len(cpi_targetrange.columns),column='soldmonth',\n",
    "                           value=pd.DatetimeIndex(cpi_targetrange['Date']).month)\n",
    "    data_msga=pd.merge(cpi_targetrange, data_msga, on=['soldyear','soldmonth'], how='outer')\n",
    "    cpibase=float(cpi_targetrange['PX_LAST'][pd.to_datetime(cpi_targetrange['Date'])==datetime(2015,7,31)])\n",
    "    data_msga.insert(len(data_msga.columns),column='cpibase',value=cpibase)\n",
    "    data_msga.insert(len(data_msga.columns),column='adjustedSoldPrice',value=\n",
    "                    round(data_msga['soldprice']*data_msga['cpibase']/data_msga['PX_LAST'],0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#take a house's coordinates and calculate minimum distance of a house to GO stations \n",
    "def closestGoHospital():\n",
    "    global data_msga\n",
    "    \n",
    "    data_msga_coordinate = data_msga[['latitude','longitude']].values\n",
    "    mindist_GO = []\n",
    "    mindist_Hospital = []\n",
    "    for i in range(len(data_msga_coordinate)):\n",
    "        tempGO = []\n",
    "        tempHospital = []\n",
    "        for j in range(len(go_station_msga)):\n",
    "            tempGO.append(geodesic(go_station_msga[j], data_msga_coordinate[i]).meters)\n",
    "        mindist_GO.append(round(min(tempGO), 2))\n",
    "\n",
    "        for j in range(len(hospitals_msga)):\n",
    "            tempHospital.append(geodesic(hospitals_msga[j], data_msga_coordinate[i]).meters)\n",
    "        mindist_Hospital.append(round(min(tempHospital),2))\n",
    "        \n",
    "    #add the minimum distance to the dataframe\n",
    "\n",
    "    data_msga.insert(len(data_msga.columns),column='mindist_GO',value=mindist_GO)\n",
    "    data_msga.insert(len(data_msga.columns),column='mindist_Hospital',value=mindist_Hospital)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def classifySeasons():\n",
    "    \n",
    "    global data_msga\n",
    "    data_msga['season'] = 0\n",
    "    #summer season is from June to August\n",
    "    data_msga.loc[(data_msga.soldmonth<=8 ) & (data_msga.soldmonth >=6),'season']= 1\n",
    "    \n",
    "    #shoulder season from Aprint to May and September to November\n",
    "    data_msga.loc[((data_msga.soldmonth<=11 ) & (data_msga.soldmonth >=9)) | ((data_msga.soldmonth >=4) & (data_msga.soldmonth <=5)),'season']= 2\n",
    "    \n",
    "    #winter season from December to March\n",
    "    data_msga.loc[(data_msga.soldmonth==12 ) | (data_msga.soldmonth >=1) & (data_msga.soldmonth <=3),'season']= 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def rescaledistance():\n",
    "    global data_msga\n",
    "    data_msga.insert(len(data_msga.columns),column='mindist_GO_min',value=data_msga['mindist_GO'].min()-0.000001)\n",
    "    data_msga.insert(len(data_msga.columns),column='mindist_GO_max',value=data_msga['mindist_GO'].max()+0.000001)\n",
    "    data_msga.insert(len(data_msga.columns),column='mindist_Hospital_min',value=data_msga['mindist_Hospital'].min()-0.000001)\n",
    "    data_msga.insert(len(data_msga.columns),column='mindist_Hospital_max',value=data_msga['mindist_Hospital'].max()+0.000001)\n",
    "\n",
    "    data_msga.insert(len(data_msga.columns),column='mindist_GO_score',\n",
    "                     value=(data_msga['mindist_GO']-data_msga['mindist_GO_min'])/\n",
    "                     (data_msga['mindist_GO_max']-data_msga['mindist_GO_min']))\n",
    "    data_msga.insert(len(data_msga.columns),column='mindist_Hospital_score',\n",
    "                     value=(data_msga['mindist_Hospital']-data_msga['mindist_Hospital_min'])/\n",
    "                     (data_msga['mindist_Hospital_max']-data_msga['mindist_Hospital_min']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def houseType():\n",
    "    \n",
    "    #Categorize house types: 1 = town house, 2 = detached, 3 = semi-detached, 4 = condo; \n",
    "    #Drop the remaining house types including vacant land, link house, mobile trailer etc. \n",
    "    global data_msga\n",
    "    house_types = {'Att/Row/Twnhouse':1, 'Condo Townhouse':1,'Det W/Com Elements':2,'Detached':2,'Semi-Det Condo':3,\n",
    "               'Semi-Detached':3,'Comm Element Condo':4,'Condo Ap':4,'Condo Apartment':4,'Condo Apt':4,\n",
    "               'Condo To':4,'Det Condo':4,'Leasehold Condo':4,}\n",
    "\n",
    "    data_msga['house_type'] = data_msga['type'].map(house_types)\n",
    "    data_msga=data_msga.dropna(subset=['house_type'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def logrealsoldprice():\n",
    "    global data_msga\n",
    "    data_msga.insert(len(data_msga.columns),column='log_real_prices',\n",
    "                         value=np.log(data_msga['adjustedSoldPrice']/1000000))\n",
    "    data_msga = data_msga[(data_msga['log_real_prices']>-2) & (data_msga['log_real_prices']<1.5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def excludeoutliner():\n",
    "    global data_msga\n",
    "    #exclude Malton, and replace group 11 (Malton) with the last grup 34\n",
    "    data_msga=data_msga[data_msga['area']!='Malton']\n",
    "    data_msga.house_area[data_msga.house_area==34] = 11\n",
    "\n",
    "    #exclude distance outliners\n",
    "    data_msga = data_msga[data_msga['mindist_GO_score']<0.15]\n",
    "    data_msga = data_msga[data_msga['mindist_Hospital_score']<0.18]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:5: 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",
      "  \"\"\"\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:11: 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",
      "  # This is added back by InteractiveShellApp.init_path()\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py:543: 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",
      "  self.obj[item] = s\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:4: RuntimeWarning: divide by zero encountered in log\n",
      "  after removing the cwd from sys.path.\n"
     ]
    }
   ],
   "source": [
    "classifyHouseFees()\n",
    "mapHouseArea()    \n",
    "adjustSoldPrice()\n",
    "closestGoHospital()\n",
    "classifySeasons()\n",
    "houseType()\n",
    "rescaledistance()\n",
    "logrealsoldprice()\n",
    "excludeoutliner()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 21227 entries, 0 to 22471\n",
      "Data columns (total 38 columns):\n",
      "Date                      21227 non-null object\n",
      "PX_LAST                   21227 non-null float64\n",
      "soldyear                  21227 non-null int64\n",
      "soldmonth                 21227 non-null int64\n",
      "mlsno                     21227 non-null object\n",
      "stno                      21227 non-null object\n",
      "stname                    21227 non-null object\n",
      "city                      21227 non-null object\n",
      "area                      21227 non-null object\n",
      "soldprice                 21227 non-null int64\n",
      "solddate                  21227 non-null datetime64[ns]\n",
      "type                      21227 non-null object\n",
      "style                     21227 non-null object\n",
      "bdrm                      21227 non-null int64\n",
      "wshrm                     21227 non-null int64\n",
      "latitude                  21227 non-null float64\n",
      "longitude                 21227 non-null float64\n",
      "house_condo               21227 non-null object\n",
      "aptno                     9969 non-null object\n",
      "address                   21227 non-null object\n",
      "Unnamed: 16               0 non-null float64\n",
      "Unnamed: 17               0 non-null float64\n",
      "Unnamed: 18               0 non-null float64\n",
      "house_fees                21227 non-null int64\n",
      "house_area                21227 non-null int64\n",
      "cpibase                   21227 non-null float64\n",
      "adjustedSoldPrice         21227 non-null float64\n",
      "mindist_GO                21227 non-null float64\n",
      "mindist_Hospital          21227 non-null float64\n",
      "season                    21227 non-null int64\n",
      "house_type                21227 non-null float64\n",
      "mindist_GO_min            21227 non-null float64\n",
      "mindist_GO_max            21227 non-null float64\n",
      "mindist_Hospital_min      21227 non-null float64\n",
      "mindist_Hospital_max      21227 non-null float64\n",
      "mindist_GO_score          21227 non-null float64\n",
      "mindist_Hospital_score    21227 non-null float64\n",
      "log_real_prices           21227 non-null float64\n",
      "dtypes: datetime64[ns](1), float64(18), int64(8), object(11)\n",
      "memory usage: 6.3+ MB\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data_msga.info()\n",
    "n,bins,patches=plt.hist(data_msga['mindist_Hospital_score'][data_msga['mindist_Hospital_score']<0.18],100,facecolor='brown')\n",
    "plt.xlabel('min_distance_Hospital')\n",
    "plt.ylabel('frequencies')\n",
    "plt.title('Distance to Hospital')\n",
    "plot.show()\n",
    "\n",
    "n,bins,patches=plt.hist(data_msga['mindist_GO_score'][data_msga['mindist_GO_score']<0.15],100,facecolor='green')\n",
    "plt.xlabel('min_distance_GO')\n",
    "plt.ylabel('frequencies')\n",
    "plt.title('Distance to Go')\n",
    "plot.show()\n",
    "\n",
    "n,bins,patches=plt.hist(data_msga['log_real_prices'],100,facecolor='orange')\n",
    "plt.xlabel('log_soldprice')\n",
    "plt.ylabel('frequencies')\n",
    "plt.title('Log_Soldprice')\n",
    "plot.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "#clean unwanted coloumns\n",
    "data_msga.drop(['latitude','longitude','Date','area','soldyear','house_condo',\n",
    "                'soldmonth','city','stno','stname','PX_LAST','style','cpibase',\n",
    "                'aptno','mlsno','type','address','solddate','soldprice','adjustedSoldPrice',\n",
    "                'mindist_GO','mindist_Hospital','mindist_GO_min','mindist_GO_max',\n",
    "               'mindist_Hospital_min','mindist_Hospital_max'], axis=1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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>bdrm</th>\n",
       "      <th>wshrm</th>\n",
       "      <th>Unnamed: 16</th>\n",
       "      <th>Unnamed: 17</th>\n",
       "      <th>Unnamed: 18</th>\n",
       "      <th>house_fees</th>\n",
       "      <th>house_area</th>\n",
       "      <th>season</th>\n",
       "      <th>house_type</th>\n",
       "      <th>mindist_GO_score</th>\n",
       "      <th>mindist_Hospital_score</th>\n",
       "      <th>log_real_prices</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.033823</td>\n",
       "      <td>0.046684</td>\n",
       "      <td>0.024730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.041841</td>\n",
       "      <td>0.021686</td>\n",
       "      <td>-0.427527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.051859</td>\n",
       "      <td>0.078459</td>\n",
       "      <td>-0.424538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.063518</td>\n",
       "      <td>0.086782</td>\n",
       "      <td>-0.241594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.065637</td>\n",
       "      <td>0.086465</td>\n",
       "      <td>-0.141156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.087531</td>\n",
       "      <td>0.099936</td>\n",
       "      <td>-0.123881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.013384</td>\n",
       "      <td>0.030630</td>\n",
       "      <td>0.034209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.090723</td>\n",
       "      <td>0.103307</td>\n",
       "      <td>0.029480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.037859</td>\n",
       "      <td>0.042870</td>\n",
       "      <td>0.052901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.034355</td>\n",
       "      <td>0.014216</td>\n",
       "      <td>0.354376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.032987</td>\n",
       "      <td>0.122189</td>\n",
       "      <td>-0.352565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.019138</td>\n",
       "      <td>0.076760</td>\n",
       "      <td>-1.472231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.105966</td>\n",
       "      <td>0.117018</td>\n",
       "      <td>-1.059696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.043893</td>\n",
       "      <td>0.063558</td>\n",
       "      <td>-0.979872</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.032413</td>\n",
       "      <td>0.095587</td>\n",
       "      <td>-0.845040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.015664</td>\n",
       "      <td>0.028184</td>\n",
       "      <td>-0.518193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.073390</td>\n",
       "      <td>0.131458</td>\n",
       "      <td>0.276044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.077141</td>\n",
       "      <td>0.031643</td>\n",
       "      <td>-0.494064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.095500</td>\n",
       "      <td>0.054579</td>\n",
       "      <td>-0.439575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.021585</td>\n",
       "      <td>0.118728</td>\n",
       "      <td>-0.421556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.072493</td>\n",
       "      <td>0.052446</td>\n",
       "      <td>-0.435795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.094632</td>\n",
       "      <td>0.064656</td>\n",
       "      <td>-0.318431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.017376</td>\n",
       "      <td>0.053207</td>\n",
       "      <td>0.115702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.031538</td>\n",
       "      <td>0.032572</td>\n",
       "      <td>0.417415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.014005</td>\n",
       "      <td>0.113922</td>\n",
       "      <td>-0.268682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.100394</td>\n",
       "      <td>0.068715</td>\n",
       "      <td>-0.091269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.039308</td>\n",
       "      <td>0.098838</td>\n",
       "      <td>0.049086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.061580</td>\n",
       "      <td>0.079549</td>\n",
       "      <td>-1.297024</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.061580</td>\n",
       "      <td>0.079549</td>\n",
       "      <td>-0.994279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.036336</td>\n",
       "      <td>0.057619</td>\n",
       "      <td>-0.942853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22441</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.004832</td>\n",
       "      <td>0.116522</td>\n",
       "      <td>-1.296454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22442</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.038628</td>\n",
       "      <td>0.058777</td>\n",
       "      <td>-1.227583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22443</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.062474</td>\n",
       "      <td>0.023397</td>\n",
       "      <td>-1.262308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22444</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.049193</td>\n",
       "      <td>0.055586</td>\n",
       "      <td>-0.746548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22445</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.012970</td>\n",
       "      <td>0.049594</td>\n",
       "      <td>0.473124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22446</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.050478</td>\n",
       "      <td>0.046021</td>\n",
       "      <td>-0.994252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22447</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.042583</td>\n",
       "      <td>0.016868</td>\n",
       "      <td>-0.242072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22449</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.023716</td>\n",
       "      <td>0.062077</td>\n",
       "      <td>-0.381993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22450</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.112053</td>\n",
       "      <td>0.061175</td>\n",
       "      <td>-0.954512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22451</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.045577</td>\n",
       "      <td>0.055042</td>\n",
       "      <td>-0.105361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22452</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.016125</td>\n",
       "      <td>0.133222</td>\n",
       "      <td>-0.669431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22453</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.008669</td>\n",
       "      <td>0.128319</td>\n",
       "      <td>-0.321584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22454</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.030666</td>\n",
       "      <td>0.036471</td>\n",
       "      <td>-0.879477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22455</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.027722</td>\n",
       "      <td>0.050916</td>\n",
       "      <td>-1.360627</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22456</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.035647</td>\n",
       "      <td>0.054288</td>\n",
       "      <td>-1.263015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22457</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.040908</td>\n",
       "      <td>0.036587</td>\n",
       "      <td>-0.673345</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22458</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.074110</td>\n",
       "      <td>0.037466</td>\n",
       "      <td>-0.290352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22459</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.072459</td>\n",
       "      <td>0.062185</td>\n",
       "      <td>-0.575364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22460</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.040445</td>\n",
       "      <td>0.063135</td>\n",
       "      <td>-0.400478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22461</th>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.040927</td>\n",
       "      <td>0.064347</td>\n",
       "      <td>-0.385662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22462</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.057953</td>\n",
       "      <td>0.050761</td>\n",
       "      <td>-0.703198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22463</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.042155</td>\n",
       "      <td>0.068421</td>\n",
       "      <td>-0.627359</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22464</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>16</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.065696</td>\n",
       "      <td>0.078018</td>\n",
       "      <td>-0.356818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22465</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>14</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.072862</td>\n",
       "      <td>0.067600</td>\n",
       "      <td>-0.287682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22466</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.098437</td>\n",
       "      <td>0.047241</td>\n",
       "      <td>-0.086648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22467</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.034531</td>\n",
       "      <td>0.039650</td>\n",
       "      <td>-0.148500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22468</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.074470</td>\n",
       "      <td>0.090477</td>\n",
       "      <td>-1.328025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22469</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>1</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.093347</td>\n",
       "      <td>0.074347</td>\n",
       "      <td>-0.705220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22470</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>33</td>\n",
       "      <td>1</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.031262</td>\n",
       "      <td>0.087687</td>\n",
       "      <td>-0.400478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22471</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "      <td>1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.033387</td>\n",
       "      <td>0.044519</td>\n",
       "      <td>-0.517515</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>21227 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       bdrm  wshrm  Unnamed: 16  Unnamed: 17  Unnamed: 18  house_fees  \\\n",
       "0         4      3          NaN          NaN          NaN           1   \n",
       "1         3      2          NaN          NaN          NaN           1   \n",
       "2         3      2          NaN          NaN          NaN           1   \n",
       "3         3      3          NaN          NaN          NaN           1   \n",
       "4         4      4          NaN          NaN          NaN           1   \n",
       "5         4      4          NaN          NaN          NaN           1   \n",
       "6         4      3          NaN          NaN          NaN           1   \n",
       "7         4      4          NaN          NaN          NaN           1   \n",
       "8         2      2          NaN          NaN          NaN           1   \n",
       "9         4      4          NaN          NaN          NaN           1   \n",
       "10        3      2          NaN          NaN          NaN           1   \n",
       "11        1      1          NaN          NaN          NaN           2   \n",
       "12        2      1          NaN          NaN          NaN           2   \n",
       "13        1      2          NaN          NaN          NaN           2   \n",
       "14        3      1          NaN          NaN          NaN           2   \n",
       "15        3      2          NaN          NaN          NaN           2   \n",
       "17        5      4          NaN          NaN          NaN           1   \n",
       "18        3      2          NaN          NaN          NaN           1   \n",
       "19        3      3          NaN          NaN          NaN           1   \n",
       "20        3      3          NaN          NaN          NaN           1   \n",
       "21        3      2          NaN          NaN          NaN           1   \n",
       "22        3      4          NaN          NaN          NaN           1   \n",
       "23        2      2          NaN          NaN          NaN           1   \n",
       "24        4      4          NaN          NaN          NaN           1   \n",
       "25        4      2          NaN          NaN          NaN           1   \n",
       "26        4      4          NaN          NaN          NaN           1   \n",
       "27        3      3          NaN          NaN          NaN           1   \n",
       "28        1      1          NaN          NaN          NaN           2   \n",
       "29        2      2          NaN          NaN          NaN           2   \n",
       "30        2      2          NaN          NaN          NaN           2   \n",
       "...     ...    ...          ...          ...          ...         ...   \n",
       "22441     1      1          NaN          NaN          NaN           2   \n",
       "22442     2      2          NaN          NaN          NaN           2   \n",
       "22443     2      2          NaN          NaN          NaN           2   \n",
       "22444     4      4          NaN          NaN          NaN           2   \n",
       "22445     3      3          NaN          NaN          NaN           2   \n",
       "22446     4      2          NaN          NaN          NaN           2   \n",
       "22447     3      3          NaN          NaN          NaN           1   \n",
       "22449     3      2          NaN          NaN          NaN           1   \n",
       "22450     3      3          NaN          NaN          NaN           2   \n",
       "22451     4      3          NaN          NaN          NaN           1   \n",
       "22452     3      4          NaN          NaN          NaN           1   \n",
       "22453     4      3          NaN          NaN          NaN           1   \n",
       "22454     3      3          NaN          NaN          NaN           2   \n",
       "22455     2      2          NaN          NaN          NaN           2   \n",
       "22456     2      2          NaN          NaN          NaN           2   \n",
       "22457     3      4          NaN          NaN          NaN           2   \n",
       "22458     4      5          NaN          NaN          NaN           1   \n",
       "22459     3      3          NaN          NaN          NaN           1   \n",
       "22460     4      4          NaN          NaN          NaN           1   \n",
       "22461     4      3          NaN          NaN          NaN           1   \n",
       "22462     3      3          NaN          NaN          NaN           1   \n",
       "22463     3      2          NaN          NaN          NaN           1   \n",
       "22464     3      3          NaN          NaN          NaN           1   \n",
       "22465     4      4          NaN          NaN          NaN           1   \n",
       "22466     4      4          NaN          NaN          NaN           1   \n",
       "22467     4      4          NaN          NaN          NaN           1   \n",
       "22468     1      1          NaN          NaN          NaN           2   \n",
       "22469     3      3          NaN          NaN          NaN           1   \n",
       "22470     3      2          NaN          NaN          NaN           1   \n",
       "22471     3      4          NaN          NaN          NaN           1   \n",
       "\n",
       "       house_area  season  house_type  mindist_GO_score  \\\n",
       "0               1       1         2.0          0.033823   \n",
       "1               2       1         3.0          0.041841   \n",
       "2               3       1         3.0          0.051859   \n",
       "3               4       1         2.0          0.063518   \n",
       "4               5       1         3.0          0.065637   \n",
       "5               5       1         2.0          0.087531   \n",
       "6               6       1         2.0          0.013384   \n",
       "7               5       1         2.0          0.090723   \n",
       "8               7       1         2.0          0.037859   \n",
       "9               6       1         2.0          0.034355   \n",
       "10              8       1         3.0          0.032987   \n",
       "11              3       1         4.0          0.019138   \n",
       "12              5       1         1.0          0.105966   \n",
       "13              9       1         4.0          0.043893   \n",
       "14             10       1         1.0          0.032413   \n",
       "15              7       1         1.0          0.015664   \n",
       "17              4       1         2.0          0.073390   \n",
       "18             12       1         3.0          0.077141   \n",
       "19             13       1         1.0          0.095500   \n",
       "20             14       1         3.0          0.021585   \n",
       "21             12       1         3.0          0.072493   \n",
       "22             13       1         1.0          0.094632   \n",
       "23             15       1         2.0          0.017376   \n",
       "24             16       1         2.0          0.031538   \n",
       "25              8       1         2.0          0.014005   \n",
       "26             12       1         2.0          0.100394   \n",
       "27              8       1         2.0          0.039308   \n",
       "28              5       1         4.0          0.061580   \n",
       "29              5       1         4.0          0.061580   \n",
       "30              9       1         4.0          0.036336   \n",
       "...           ...     ...         ...               ...   \n",
       "22441           8       1         4.0          0.004832   \n",
       "22442           9       1         4.0          0.038628   \n",
       "22443           6       1         4.0          0.062474   \n",
       "22444          22       1         1.0          0.049193   \n",
       "22445          15       1         1.0          0.012970   \n",
       "22446          22       1         1.0          0.050478   \n",
       "22447           2       1         2.0          0.042583   \n",
       "22449          33       1         2.0          0.023716   \n",
       "22450          12       1         1.0          0.112053   \n",
       "22451          33       1         2.0          0.045577   \n",
       "22452          14       1         3.0          0.016125   \n",
       "22453          14       1         2.0          0.008669   \n",
       "22454          16       1         1.0          0.030666   \n",
       "22455           9       1         4.0          0.027722   \n",
       "22456          21       1         4.0          0.035647   \n",
       "22457          12       1         1.0          0.040908   \n",
       "22458          13       1         2.0          0.074110   \n",
       "22459          13       1         1.0          0.072459   \n",
       "22460           1       1         2.0          0.040445   \n",
       "22461          16       1         2.0          0.040927   \n",
       "22462          13       1         1.0          0.057953   \n",
       "22463          10       1         3.0          0.042155   \n",
       "22464          16       1         2.0          0.065696   \n",
       "22465          14       1         2.0          0.072862   \n",
       "22466          12       1         2.0          0.098437   \n",
       "22467           6       1         2.0          0.034531   \n",
       "22468           5       1         4.0          0.074470   \n",
       "22469          13       1         1.0          0.093347   \n",
       "22470          33       1         2.0          0.031262   \n",
       "22471          20       1         3.0          0.033387   \n",
       "\n",
       "       mindist_Hospital_score  log_real_prices  \n",
       "0                    0.046684         0.024730  \n",
       "1                    0.021686        -0.427527  \n",
       "2                    0.078459        -0.424538  \n",
       "3                    0.086782        -0.241594  \n",
       "4                    0.086465        -0.141156  \n",
       "5                    0.099936        -0.123881  \n",
       "6                    0.030630         0.034209  \n",
       "7                    0.103307         0.029480  \n",
       "8                    0.042870         0.052901  \n",
       "9                    0.014216         0.354376  \n",
       "10                   0.122189        -0.352565  \n",
       "11                   0.076760        -1.472231  \n",
       "12                   0.117018        -1.059696  \n",
       "13                   0.063558        -0.979872  \n",
       "14                   0.095587        -0.845040  \n",
       "15                   0.028184        -0.518193  \n",
       "17                   0.131458         0.276044  \n",
       "18                   0.031643        -0.494064  \n",
       "19                   0.054579        -0.439575  \n",
       "20                   0.118728        -0.421556  \n",
       "21                   0.052446        -0.435795  \n",
       "22                   0.064656        -0.318431  \n",
       "23                   0.053207         0.115702  \n",
       "24                   0.032572         0.417415  \n",
       "25                   0.113922        -0.268682  \n",
       "26                   0.068715        -0.091269  \n",
       "27                   0.098838         0.049086  \n",
       "28                   0.079549        -1.297024  \n",
       "29                   0.079549        -0.994279  \n",
       "30                   0.057619        -0.942853  \n",
       "...                       ...              ...  \n",
       "22441                0.116522        -1.296454  \n",
       "22442                0.058777        -1.227583  \n",
       "22443                0.023397        -1.262308  \n",
       "22444                0.055586        -0.746548  \n",
       "22445                0.049594         0.473124  \n",
       "22446                0.046021        -0.994252  \n",
       "22447                0.016868        -0.242072  \n",
       "22449                0.062077        -0.381993  \n",
       "22450                0.061175        -0.954512  \n",
       "22451                0.055042        -0.105361  \n",
       "22452                0.133222        -0.669431  \n",
       "22453                0.128319        -0.321584  \n",
       "22454                0.036471        -0.879477  \n",
       "22455                0.050916        -1.360627  \n",
       "22456                0.054288        -1.263015  \n",
       "22457                0.036587        -0.673345  \n",
       "22458                0.037466        -0.290352  \n",
       "22459                0.062185        -0.575364  \n",
       "22460                0.063135        -0.400478  \n",
       "22461                0.064347        -0.385662  \n",
       "22462                0.050761        -0.703198  \n",
       "22463                0.068421        -0.627359  \n",
       "22464                0.078018        -0.356818  \n",
       "22465                0.067600        -0.287682  \n",
       "22466                0.047241        -0.086648  \n",
       "22467                0.039650        -0.148500  \n",
       "22468                0.090477        -1.328025  \n",
       "22469                0.074347        -0.705220  \n",
       "22470                0.087687        -0.400478  \n",
       "22471                0.044519        -0.517515  \n",
       "\n",
       "[21227 rows x 12 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_msga"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check if any value in the dataframe is Null\n",
    "data_msga.isnull().values.any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\seaborn\\axisgrid.py:2065: UserWarning: The `size` parameter has been renamed to `height`; pleaes update your code.\n",
      "  warnings.warn(msg, UserWarning)\n",
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x206c3a0a630>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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rq/fdtBD3rlgQl/bLWKBUl+pt9NG7vjZkQH/ujAI8etfXklQjopGFG1exzBftOU002ydKtES2TfYzlOk2rl48JHE8tTgbG1cvTlKNiChRqqvKULGw1HcnsigKqFhYiuqqsqDzr1w6GyuWzILJIEFWAJNBwools/i+ZCJKGYI6+MWEGejkyZNYvHgxdu7ciUmTJiW7OkRpjfFEFB+MJaL4YTwRxXY9dTIAACAASURBVAdjiSg+GEtE8cN4IooPxhJR/IyVeLqhujbktJfXL09gTSgVpMSdyUQ0vNYzfcmuAhEREREREREREREREY0xTCYTpbi365vwL+v+il67O9lVISIiIiIiIiIiIiIiojFESnYFiCg4t8eL//zzAbz5wQkAgNPtRbYlyZUiIiIiIiIiIiIiIiKiMYPJZEo79Q1tqKlrRFNrDzxeBZJOwJRiKyorZqJsTpFveluHHWajDgIE2F0yigosvnlSXeuZPjz+3G40nuwGAMydMQ5mE8M11fi3tVDta7h5wlk+mm0SZZJY4izWGCUiYOuOg6jddQwOlwyzUcLy8ulYuXR2sqvlk+r1o/STyH4h3G2xnVMme+jpd3HgaIfv97kzCvDoXV9LYo2I4ottnIjCtX5LPXbta4GiqBBFAeUXlaC6qizovBzLIKJE42OuKa3UN7RhU81+tLT3osfuhtMto8/hQUt7DzbV7MfWHQexqWY/Om0OiFBxsq0XTW09EAWg0+bAppr9qG9oS/bHGNbuz1rxwK/eQePJbggCUFkxE2v+11eQZdInu2rkZ6AtdtocyDFLQdvXcPOEs3w02yTKJLHEmX9/EE2MEpGWwNr+1mE43TIkEXC6ZWx/6zC27jiY7KoBSP36UfpJZL8Q7rbYzimTDU6yAcCBox146Ol3k1QjovhiGyeicK3fUo+6Pc1QFBUAoCgq6vY0Y/2W+iHzciyDiJKByWRKKzV1jZAkAXanDFEUIImiduexU4YkCajddQySJMBkkNDd54YoCBBFAd29bpgMEiRJQE1dY7I/RlBeRcXvX2/Aw//9P+h1eJBj0WP1LWW47brzYTbyruRUM9AWTQYJgiAEbV/DzRPO8tFskyiTxBJn/v1BNDFKREDtrmOAAO14SxAhiSIg9JengFSvH6WfRPYL4W6L7Zwy2eAk20jlROmGbZyIwrVrXwsAQBDO/viX++NYBhElAzNUlFbaOuzIMUvwyAp0/ZdCCALgkRUY9To4XDKKC8wAtDJREHzTAcCo1+FUhz1Z1Q+pq8eF9Vs+wr4j7QCAGaW5eODmhZhabE1yzSiUgbbob3D7Gm4eFRhx+Wi2SZRJYokz//4g0mUZU0Qah0u7E9KfTtDKU0Gq14/STyL7hXC3xXZORERElPkG7kgOp5xjGUSUDLwzmdJKUYEFLo8XeknEQF+qqoBeEuHyeGE2SnB5vAC0MlUFlP7pAODyeDGhwJKs6gfVcLwD9/+qzpdI/vqiKfjF97/KRHKKG2iL/ga3r+HmCWf5aLZJlEliiTP//iDSZRlTRBqzUYJ30NiFV0XKPDEl1etH6SeR/UK422I7JyIiIsp8oiiEXc6xDCJKBiaTKa1UVsyELKuwmCQoigpZUaBC+12WVSwvnw5ZVuF0y8jNMkBRVSiKitxsA5xuGbKsorJiZrI/BgBAVVXU7jqKHz/9N5zpdsJo0OGuG+fj7n+cjyyzIdnVoxEMtEWnW4aqqkHb13DzhLN8NNskyiSxxJl/fxBNjBIRsLx8OqBCO95SFciKAqj95Skg1etH6SeR/UK422I7p0w2d0ZBROVE6YZtnIjCVX5RCQDtpqmBH/9yfxzLoFTgHnRBQ6TTKf3wcmZKK2VzioDKeaipa4TXq8LjVSDpBJQU5qCyYibK5hTh3Mn5qKlrxKkOOyYVZUOAAIdLRr7V7Jsn2exOD57avg/v7dfee1EyPgs/uHkBzps6Lsk1o3D5t8VTHXZMKLAMaV8jzjPC8tFskyiTxBpn/v1BVDFKNMatXDobgPZuVodLhtkoYXn5dF95sqV6/Sj9JLJfCHdbbOeUyR6962t46Ol3A94fO3dGAR6962tJrBVR/LCNE1G4qqvKAGjvSFYUFaIooPyiEl+5P45lUCow6HW4obo25PSX1y9PYG0oEZhMprRTNqdoxIRbKneeJ7604bHNH6K5vQ8A8JULi3H3jfORm2NKcs0oUuG0teHmiaatpnr7Joq3WOIs1hglIi2RlcpJq1SvH6WfRPYL4W6L7ZwyGZNqlOnYxokoXNVVZaiuCm9ejmUQUaIxmUyUQHUfNWHjix/D5fFC0gm4+erZ+NaVMyBJumRXjYiIiIiIiIiIiIiIiCgAk8lECeCRvXim9hO8/v7nAIBxuSY8cNMCzDu3EIIgJLdyREREREREREREREREREEwmUw0yk512LH2ud040tQFALhwxjj8YOVCFOZbklwzIiIiIiIiIiIiIiIiotCYTKa4qm9oQ01dI9o67CgqsKCyYqbv/Q0D05pae+DxKpB0AqYUWwPmGTyfwy3DI3sBAAZJB4+swKuovnnnzihA5VWzsPnVz9Dc3guvokAv6SDpBHi9Kpxur29eSSegdEI2vnvdBQl7p0R9QxuefOEj9Ng9EAB888oZuOWaOTDo+VjrVBCsvQIIu2zzq5/hRKsN6tkmCVEEBEGAKAgoKczCtIlW7G44BYdLhtkoYdGcCThjcw2Jg7kzxuHA0TPDxo7/tCNNnajddcy33uXl0/kuPUoJA+31i1YbnC4ZHq8KxW+/LYra0xkECOjscQEASguzseq681E2pwj1DW34j5qPcarTERBbggAU5pnx9UumhIwV/+2Hmh7p52jrsMNs1EGAALtLjmmdRMHc8cgbaO9y+X4vzDPi2TXLklijQHev24kTrb2+36cWZ2Pj6sVJrFGgqjWvwmaXfb9bLRK2PHJdEmtEqWD9lnrs2tcCRVEhigLKLypBdVXZsMtE2tbDOe8J1Rdt3XEQtbuOoc/hCVinxSgiO8sU8fqI0tkN1bVDyl5evzwJNSEaHWzjRGNX5b/WwnN2aBp6HVDzy9Dx/50fvwyHW/H9bjaI+MNjNwSdl8eHRJRoYrIrQJmjvqENm2r2o9PmQI5ZQqfNgU01+1Hf0Oab1tLeix67G063jD6HBy3tPb55/NfR0t4LW58LLrcXigIoCuB0ewMSyQBw4GgHHvmvD9DU1gOvV4GiAC63F30OOSCRDACyV0VTaw82bNvj295o8Soqnn+jAQ//9wfosXuQbdbjX28rw+03XMBEcooI1l43bNuDp7bvHbHsqe178cTz9fj8y8BEMqC1Va9XhaIoOPFlD+r2NMPh8kASAYfLg7o9zWhs6giIg+MtXdj+1mG0tPeGjB3/7a/7/W5s23EITrcMSQScbhnb3zqMrTsOJufLJOp3dh/eg54+N1weJSCRDGgx0t7pxKlOBxRFhaqqaGrrwVPb92LrjoN4YstHaOtwDIktVQVOdTrwwpuHgsaK//aD9UPRfI5OmwMiVJxs60VTWw9EAVGvkyiYwYlkAGjvcuGOR95IUo0CDU6uAcCJ1l7cvW5nkmoUaHAiGQBsdhlVa15NUo0oFazfUo+6Pc2+/kdRVNTtacb6LfUhl4m0rYdz3hOqL9q64yC2v3V4SCIZAOwuBV02R0TrI0pnwZJsw5UTpRu2caKxa3AiGQA8Xq08mMGJZABwuBV858cvD5mXx4dElAxMJlPc1NQ1QpIEmAwSBEH7V5IE1NQ1+qbZnTJEUYAkitpdXk7ZN4//OuxOGSqAcN4mrAAQRQHqiHMCigo4XLJve6Ohu9eF//vM37H9rcNQVWBGaS7W3XsFrphfyvcjp5Bg7dXhkmF3yiOW2Z0y7C552PX7589UFRAE0Zccc7iUwDhwyIAA2J1yyNgJqJPbC0WFtrwgQhJFQABqdx0bxW+MaGQB+/AwdsqKqkInihBFbZnaXcdgDzK4PliwWPHffrB+KJrPYTJI6O5zQxQEiKKA7l531OskCmZwInmk8kQbnFwbqTzRBieSRyqnsWHXvhYA2hMtBn78y4OJtK2Hc94Tqi+q3XVs2JMct6xGtD4iIiIiSj2DE8kjlQ9OJA9XzuNDIkoGPuaa4qatw44cc2CTMup1ONVhhwogxyzBIyvQ9V/CIAiAR1Z88/ivwyMrWgIuzG2LAsJKXADaXcMD24u3gyc68Pjm3Tjd7QQAfP2SKfje8rmwmBhqqSZYe/X23yU5UpnsVUZsb6o69P8Bj8Pub9yCoCWeDf3xMGBw7IRa9wCdoF0oQZRMAfvwMOYfaMuioMWVx6v4LiQabvlgseK/fX/+0yP9HAPbEgXB12dFu04iIkqMwU/EGKk8GuGc9wSbBmjHa5IIhBhHjHh9RERERDS2xGvsg4goErwzmeKmqMAC16DLq1weLyYUWHzT9JLou2NTVQG9JPrm8V+HXhIRyU28ioqw59eJgm978aKqKl569yh+tPFvON3thNGgw903zsc9/3gRE8kpKlh71YkCJJ04YpmkG7l9+k8XhKFl/nEgCoC3Px4GDI6dUOse4FUBs5FtjZIrYB8exvwDbVlRtbgyG6URE8lA8Fjx374//+nh8l+PXtKeKqD4xWg06yQiosQQxeA9UKjyaIRz3hNsGqAdr3nDyGuHuz4iIiIiGlt4fEhEyZAyyeQXX3wRy5cv9/1cfPHFePjhhwPm2bhxI6666irfPFu2bElSbSmYyoqZkGUVTrcMVdX+lWUVlRUzfdMsJgmKokJWFKjQfh+Yx38dFlN4CQVAa8SKooaVuBAFbQBnYHvxYHd6sO75j/DMnz+BV1ExcXwWHrvrclx92TlxHbSi+ArWXs1GCRaTNGKZxSTBMkLiVhyUTFZVxZc4MxvFwDgwS4AKWExSyNgJqJNBp93JqShQVQWyogAqsLx8+ih+Y0QjC9iHh7H7EwUBXkV7r7LFJGF5+XRYzPoRlwsWK/7bD9YPRfM5nG4ZuVkGKKoKRVGRm22Iep1EwRTmGSMqT7SpxdkRlSea1RK8Lw5VTmND+UUlALQL9gZ+/MuDibSth3PeE6ovWl4+fdiTHIMkRLQ+IiIiIko9el1k5WZD8DRNsHIeHxJRMqRMMvkf//EfUVtbi9raWjzxxBMYN24c7r777oB5PvnkEzz55JO++aqqqpJUWwqmbE4R7qych3yrGb0OGflWM+6snIeyOUW+aSWF2cixGGAySMgy61FSmOObx38dJYXZsGYZYTToIIqAKAImgw66QcnZuTMKsOafvoLJRTnQ6USIImA06JBl1sNkCOydJZ2AycU5uO+mhb7txepEqw3VG3bh3X3NAIBLLyjGE/d+DedOzo/L+mn0BGuv9920EPeuWDBi2b0rFuDBW8pwzkTrkISZKAI6nQBRFDF1Yg4qFpbCbNRDVgCzUY+KhaWYObkgIA6mleRhxZJZKCnMDhk7/ttffesi3LT0PJgMEmQFMBkkrFgyCyuXzk7Ol0nU7+w+PAc5WQYY9eKQi2pEESjMN2FCvhmiKEAQBEwuysG9KxZg5dLZeLDqYhQVmIfEliAAE/LNuPnq84LGiv/2g/VD0XyOfKsZKgRMKsrG5KIcqCqiXidRMM+uWTYkcVyYZ8Sza5YlqUaBNq5ePCSZNrU4GxtXL05SjQJteeS6IYljq0XClkeuS1KNKBVUV5WhYmGpr/8RRQEVC0tRXVUWcplI23o45z2h+qKVS2djxZJZyApy8ZTFKCLfao5ofUTp7OX1yyMqJ0o3bONEY1fNL5cPSRzrdVp5MH947IYhiWOzQcQfHrthyLw8PiSiZBDUwS8DTQFVVVW45ZZbcM011wSUX3HFFbjwwgvR3NyMRYsW4Yc//CGMxsABOJvNBpvNFlDW2tqKqqoq7Ny5E5MmTRr1+tPYULfnJDa+uA8utxc6UUDVstmorJgJnS5lrtGIGeOJKD4YS0Txw3giig/GElF8MJaI4ofxRBQfjCWi+BnL8XRDdW3IaS+vXz7idMosKfcMuPfffx9Op3NIIrmvrw9z5szB6tWrMXXqVPzoRz/C008/jQceeCBgvs2bN2Pjxo2JrDKNMR7Zi/9+6VO8+t5xAECB1Ygf3LwQ88+dkOSaxR/jiSg+GEtE8cN4IooPxhJRfDCWiOKH8UQUH4wlovhhPBFpUu7O5HvvvRdLly7F9ddfP+x8n332GR566CH8+c9/Digfy1eK0Og71WnH48/txuEvugAAF84Yhwdvvhjj8sxJrtnoYDwRxQdjiSh+GE9E8cFYIooPxhJR/DCeiOKDsUQUP2M5nnhnMvlLqTuT3W43du/ejbVr1w6Z1tLSgvfffx833ngjAEBVVUjS0OpbrVZYrdZRryuNPXsOnsITWz5Cj90NAcA3r5yBW689H3opcx5rPRjjiSg+GEtE8cN4IooPxhJRfDCWiOKH8UQUH4wlovhhPBFpUiqZfOjQIZxzzjmwWCxDpplMJqxbtw6XXnopJk2ahC1btmDJkiVJqGXmqG9oQ01dI9o67CgqsKCyYibK5hTFvM5fb/sI3b0eAIAgAFcuKMWVCyejpq4RTa098HgVSDoBU4qtmDtjHN7b34KW9j7IXgWjcZ+8KADlC0pRXVUW1fJeRcW2HYew/S+HoKpAtlmPu78zH5fPK41zTSlZBsfCQLtsPtUL2RvYKKcWZ8OabcIXrTa43F443d6A6YIAGPQ6uAaVR0IQgMI8M86fVoDdDafgcMkQoLXFAXoJ0Ik6uGUFZqOE5eXTsXLp7Ki3OZLR2F9Q+qtvaMN/1HyMtg6Hr6ww34QLpo3De/u/hEdWAuYXBGBKUTbaOhxDYicSep2APKsRgiBCVRX09HngdHshigIMkghVVeGRFfiFDIwGHcwGCZOLc4a03/qGNmzYtgddvW5fWcXC6PsNf4wdGkmwK3lT6Qpe1o/S0d3rduJEa6/v96nF2di4evGQ+dZvqceufS1QlNAnIWajCINeguxVodeJvn7knT1NAcvqdAJUFUOOy7buOIjaXcfgcMm+aedOzkdNXSMONJ4esr0JBRZffwEANXWN+OzYmYDjwFCfhyheEnn8kon7cR7/kb9MbOOJEqwPHc1xD6JwRNIuI43/SOb3P44VRQHlF5XEZQyDiCiUlLqlsqmpCcXFxQFl3/ve93DgwAEUFBTg4Ycfxve//30sW7YMqqri9ttvT1JN0199Qxs21exHp82BHLOETpsDm2r2o76hLaZ1rt38oS+RDACqCtTtacbazR+ipb0XPXY3nG4ZfQ4Pjrd0Yeubh9DU2gNZHp1EMgAo/XVYv6U+4mW7e1342TN/x7a3tETy9JJcrLv3a0wkZ5DBsdDS3oOtbx7CF609QxLJAHCitRefHj2NXrsnaDJMVRFTInlgHac6Hajb0wyHywNVUQMGEAHAIwNOtxcCAKdbxva3DmPrjoMxbTeU0dhfUPqrb2jDE1s+CkgkA0B7pxN1e5qHJJIBrW2faO2NKZEMAB6vivZOJ7psdrR3On3rUxQVTrcXLk9gIhnQ4tLW50JLe29A+61vaMPjz+0OSCQD0fcb/hg7NJJQj4Qa7lFRicT6UToanEgGtL7n7nU7A8rWb6lH3Z7mYRPJAOBwKeju1c5heuxutLT34vHndg9Z1utVoSpqwHHZ1h0Hsf2tw3C6ZUiidsy2bcchPPF8fdBEMgCc6rCj0+bAU9v3YsO2PWg4fmbIcWCwz0MUL4k8fsnE/TiP/8hfJrbxRAnWh47muAdROCJpl5HGfyTlg49jFUWNyxgGEdFwUiqZfO211+JXv/pVQNkzzzyDuXPnAgCuvvpqvPLKK3jzzTfx2GOPwWAwJKOaGaGmrhGSJMBkkCAI2r+SJKCmrjGmdbo8Q5MHAODyKLA7ZYiiAEkUIUCA3SFDhZbsTcSLu3fta4lo/kMnOnD/r97B3sPtAICvXzIFj999BSZNyBmN6lGSDI4Fu1Nrl8Nd3KCoGDKoN1rUEeJDUVVIoggIQO2uY6NSh9HYX1D6q6lrhN3hGXnGUeSWI5tfBWB3ygHtt6auMWRyO9J+YzDGDhFR4g1OJIcqH9jHC0J465VEEaKoHSsO9BuDl1X75xs4LqvddQwQtDJBECGJIhQVsLuG78BMBgl2pwyHSw56cWOwz0MULzx+iQ2/P6L4CNaHjua4B1E4UqVd+h/HDvz4lxMRjYaUesw1JU5bhx055sA/v1Gvw6kOe0zrHI5HVqDrv3xBEDDkrrHRNtJdBwNUVcWr7x3Hf7/0CWSvCqNeh+9980Jc/ZVzRreClBSDYyHY3ZTJNNId+wPTdQLgGGFgMlqjsb+g9NfWYU/IhUDxpKpajPu33+H6rnD7jVAYO0REqSuafbwohHes6H9cJgW5fDucJzLJXiXsRDdRPPH4JTb8/ojiw+GSh/ShoznuQRSOVGmXoY5jYx3DICIaTkrdmUyJU1RggcsTeCeWy+PFhIKh76uOZJ3D0UuiL4GsqtpgTCKJYWzQ4ZLxxPMfYdOfDkD2qpg4PguP/8vlTCRnsMGxoA824pdEIw0iDkz39r+jbzSMxv6C0l9RgQXpNsYtCFqM+7ff4fqucPqN4TB2iIhSVzT7eEUN71hx4LjMbJQQ7MbicJLEkk6ELtEnTETg8Uus+P0RxUewPnQ0xz2IwpEq7TLUcWysYxhERMNJrawJJUxlxUzIsvZOL1XV/pVlFZUVM2Nap1EfvEkZ9SIsJgmKokJWFKhQYTFLEKAllRPR1ZVfVDLs9Ka2HlRv2IVd+5oBAJdeUIwn7yvHjMn5CagdJcvgWLCYtHY53CCfKCBhg3vCCPEhCgJkRQFUYHn59FGpw2jsLyj9VVbMhMWsT2odDBGerwkALCYpoP1WVsyEyaALOv9I/cZIGDtERIk3tTg7rPKBfXw4dwkDgKwoUBTtWHGg3xi8rNA/38Bx2fLy6YCqlamqAllRIAqAZYQBR6dbhsWkJaMlXfAjwVCfkyhWPH6JDb8/ovgI1oeO5rgHUThSpV36H8cO/PiXExGNBiaTx6iyOUW4s3Ie8q1m9Dpk5FvNuLNyHsrmFMW0zh+tugS52WeTC4IAVCwsxY9WXYKSwmzkWAwwGSRkmfWYVpKHlVefh8nFOZAkcdQe4yb216G6qizkPLv2nsQPfv0Omtp6oBMFrLpuDh767iJkW/he7kw3OBZKCnOw8urzMKU4J+jg3dTibFwwYzyyLfqgCShBAIwhElPhEgRgQr4ZFQtLYTbqIYjCkOS1XgJMBh1UaO/VW7FkFlYunR3TdkMZjf0Fpb+yOUV4sOpiFBWYA8oL802oWFga9M4tQdBiKFTyNlx6nYDCfBPyrRYU5pt86xNFASaDDka9OOTpF0aDDtYsI0oKswPab9mcIvzwtkXIyw7c34/Ub4SDsUMjeXn98ojKE431o3S0cfXiIYnWqcXZ2Lh6cUBZdVUZKhaWjngHh9koIjdbO4fJsRhQUpiNH962aMiyOp0AQRQCjstWLp2NFUtmwWSQICvaMdtNS8/Dg7eUYe7M8UG3V1RgQb7VjHtXLMB9Ny3EnGnjhhwHBvs8RPGSyOOXTNyP8/iP/GViG0+UYH3oaI57EIUjknYZafxHUj74OFYUhbiMYRARDUdQ1XCvxU5fJ0+exOLFi7Fz505MmjQp2dWhFOKRFTz78id45W/HAQD5OUY8WHUx5p1bmOSapS7GE1F8MJaI4ofxRBQfjCWi+GAsEcUP44koPhhLFMyHn7ais8eFpZdOgTBad3ploLESTzdU14ac9vL65SNOp8zCF03QmNXe6cDjz+3GoS86AQAXTB+H1bdcjHG55hGWJCIiIiIiIiIiIiJKPx5ZwTO1B/D6+58DAObOHIeS8XyNChGFxmQyjUl7Dp3CE89/hB67GwKAb1bMwG3Xng9Jxye/ExEREREREREREVHm6exxYu3m3fjseAcAYNH5RZg4LivJtSKiVMdkMo0piqJi+18OY+uOg1BVIMusx30rLsJlc0uSXTUiIiIiIiIiIiIiolFx+ItOPPq7D3Gm2wkAqKyYiduuncNHXBPRiJhMprCs31KPXftaoCgqRFFA+UVa8nWgLBXpRAEq4KuzURKhk0T02j0AgGklVvzwtkUoLeQjPMayrTsO4o9/bYTL7U12VSIiigIkUYBer4Mse+HyKAHTdSKgl3RwebwQBAGSCOj1EvQ6EZOLczB3xjgcOHoGbR12FBVYUFkxEwBQU9cYUFY2pwj1DW1By4nqG9rwu1c/xYkve5JdlZicM9GKVdedDwD49z9+jFOdjoDpekmExSRhSrE1IFa+aLVB9qpQFRWCKECvE5GXY4QKFQ6XF0UFlqCxxvghf8HeMZRK7xZi/Sgd+Z+7AEBetgGAAI9XgaQT0N3rDntdc2cUoPKqWUOOhX71Qj1sdnlU6j9wvvXpsdNo73INnS4AtU/Et51XrXk14PNYLRK2PHJdXLdBFEyi9uPBxjSqq8rivh2iwXisQpRZ7l63Eydae32/Ty3OxsbVi4POG2n8RzL/HY+8EXCcWJhnxLNrloVcNwC8Xf8FNr74MTyyAqNBh3u+Mx9XLpg87DJERAP4TF8a0fot9ajb0+wbjFEUFXV7mgPKUpFXUQPq7HB7fYnkstkT8MS9X2MieYzbuuMgtu04lHaJZEBr025ZQZ/DMySRDABeBXC6vVDVgXlV2J0e9NjdON7Sje1vHUZLey9yzBI6bQ48tX0vNmzbg06bw1e2qWY/tu44iE01+4eU1ze0JeFTUyqpb2jDhm170j6RDACff2nDE8/XY+3mD4ckkgHtXUI9fW60tPf4YqWlvQd9Dg/sTg/sLhkOlwe2Phe+aLXhZFsvRKhoae/tj7Uexg8FFWywYLjyRGP9KB0NPncBgK5eN7p6XXC65YgSyQBw4GgHHv3dhwHHQj/7rw9GLZEMnD3fCpZIBgBFBZY/GL92PjiRDAA2u4yqNa/GbRtEwSRqPx5qTGP9lvq4bodoMB6rEGWWwYlkADjR2ou71+0cMm+k8R9J+eBEMgC0d7lwxyNvBF2H16vgmT8fwK+27oVHVjAh34zH/+UKJpKJKCJMJtOIdu1rAQAIwtmfdCUAmFKUDQWAQc8b88e62l3HoKhauxgLVFW708XukAEBsDtlCIIAk0GC3SnD4ZJhMki+MkkSULvrGCRJGFJeU9eY7I9DSVZT1wiHa/QG0hPNuSMDogAAIABJREFU7pKDXpgxQFG1mBmIFbtThuC391BVQIX2IwoCuvvcsDuHxhrjh4hodPmfuwwmidGd/npkJeBYaLSFc74Vz2t6QyXGRzNhTpRIocY0BsqJiIjCMTiRPFL5aAl1wWGw8u5eF/7Pf/4dL717DAAwd+Z4PHn/lZgxKW9U60hEmSfqM2Gn04mdO3eiq6sroLyqqirmSlFqSeW7jyMh6QRkW/Qw6nVoPpXYTp5SUyYlwsIlCoBHVWEQBHjks4kz2asMGbg06nVwuGQUF5iHlJ/qsCeiupTC2jrs8GZI/wBoyeCReGQFqqoNQHoVFaIg+JbzX14Q0B9fAnS+/2sYP0REoytTzl2IKH5C7Re4vyAiokx2rLkbv/jt//iewPaNr03H7TdcAEnH+wuJKHJRJ5P/+Z//GTabDZMmTfKVCYLAZHIGEkUh7U+yTAYd9JIIW68H2WY9JhRYkl0lSgFmo4Q+hyfZ1UgoRdXumvSqgEE6e/CoHUgGxrnL44XZKMHl8QbchePyeBlDhKICC7p7nJC96d0/DBCEkRPKekmEogCA9q49WVZ9y/lfjKGq2ryAALfsDYg1xg8R0ejKhHMXIoqvUPsFURwrz6giIqKxZtfek9iwfR/cHi8MehF3fXs+Fi+akuxqEVEaizqZ3NbWhtdffz2edaEUVX5RCer2NId111YqsmYZ4HLL6LF7kG3RQ5ZVVFbMTHa1KAUsL5+ObTsOxfUxgalMELSr7y1mCQ6nDItJgqqqcHm8vv873TKMeh1cHi9kWcXy8ul4e3cTnAgsZwxRZcVMbNi2By5PZO+eTFUWowTZq4R81LUoABaTBEUBVFWFTicEvHdTEOC7HkNRVeRmGeHxqnD3eANijfFDRDS6hjt3kZXQrzMYjl4SA46RRls4513xzIFZLVLQR1pbLXwtEGWGUPuF8otKklMhIiJKS1OLs4M+0npqcXZC61GYZwz6SOvCPCO8iorfv/YZ/vjXxv4yM3783UU4d3J+QutIRJkn6mcazJo1C+3t7fGsC6Wo6qoyVCws9V21K4oCKhaWBpSlIp0oID/HgD6HBx6vCotRwrSSXNxZOQ9lc4qSXT1KASuXzsZNS8+D0aBLdlUiJooCDJKILLMeRv3QXblO1O7IF4SBeQVYTHrkWAyYVpKLFUtmoaQwG70OGflWM+5dsQD33bQQ+Vazr+zOynlYuXQ27qycN6ScMURlc4pw300LMXViTrKrErNzJlrx4C1l+NGqSzAh3zxkul4SkZNlQElhji9WSgpzkGXWw2LSw2KUYDbqYc0yYkqxFZOKsqFCQElhdn+s5TB+KKiX1y+PqDzRWD9KR4PPXQAgL9uAvGwjTAYJudmGiNY3d0YBHvruJQHHQv/2T18Z1UTrwPlWYZ4x+HQBqH0ifu18yyPXDfk8VouELY9cF7dtEAWTqP14qDGN6qqyuG6HaDAeqxBllo2rFw9JHE8tzsbG1YuHzBtp/EdS/uyaZUOOEwvzjNhQ/Q94+L8+8CWSL5g+Duvvv5KJZCKKi6jPgJctW4ZrrrkGs2bNgiSdXc1zzz0Xl4pRaqmuKkN1kCeYBytLhtNdDjz+3G4cPNEJQOss//XWMhRYTUmuGaW6lUtnY+XS2cmuRlKsDFIWLMlVNqeIyS8KKhPbxn//dGlY80XyuYPFGtGAVB9MZP0oHYU6d4nF4P1+piVaM+3zUPpI1H58NPYLROHgsQpRZgmWOA4l0viPZP5n1ywL+P3zL234wa/fQesZOwDguq9Ow/e+eSF0fD8yEcVJ1Mnk3/zmN7jzzjsxZQqftU/Jte/wKax7/iPY+rTHjVZWzMBt110AXQrfNU1EREREREREREREFIv39rfg11v3wOn2Qi+J+H7lPCy5dGqyq0VEGSbqZLLZbMb3vve9eNaFKCKKouLFtw9jyxsHoapAllmP+25agMsunJjsqhERERERERERERERjQpFUbHlzYP4w18OAwDG5Zrwo1WLMHtqQZJrRgS4PV4Y9KFfLTnSdEo9USeTv/rVr2LLli1YsmQJDIaz753Ky8uLS8WIhtNjd+PJF/agvqENADCtxIofrboEJeOzklwzIiIiIiIiIiIiIqLR0efwYP0LH2H3Z9rY+Oyp+fjxdy/hKx8pZRj0OtxQXRtyOl8DkX6iTib/9re/hdvtxiOPPOIrEwQBDQ0NUVfm1ltvRUdHh+8dzA8//DDmz5/vm/7+++/jscceg8vlwjXXXIMHHngg6m1lqvqGNtTUNaKtw46iAgsqK2ZG9F7H+oY2bH71MzSd6oGiqFBVQBQARR3FSsfIYpLw5ek+3PnYX4ZMKyow458r52fcOz0pNgNx8umx01CUZNcmPU2dmIPvXncBY2uMq29ow9N/3If2Tmeyq5JUogBAwIj7E50oYMWSWWP2Pe0UXLCTq1Q6qWL9KJ0MN1gRT3od4PEmZFMhxdLOg50z/uy/PojrNii9JXLfmqhtJfIzxTouQ5mFxyrR43dHqSiSdhlpGw53/qa2HlRv2AWHSwYAZJkklBZmDZtIZt9ERLGK+g3sL7zwAg4ePBjwE0siWVVVfP7556itrfX9+CeSnU4nHnroITz99NN47bXX8Mknn+Cdd96JenuZqL6hDZtq9qPT5kCOWUKnzYFNNft9d++Gs/xT2/fii1YbvF4tkQykbiJZAJCbbYDbo8DpDj6a09bhwBNbPgr7O6DMNxAnDUwkx+TElz3YsG0PY2sMq29ow7rf7x7ziWRA6yfD2Z94FRUvvHkIW3ccHP1KUVoIlfhKVEJsJKwfpZNE/t2TnUgGov+8wc4ZgyWSY9kGpbdE7lsTta1EfqZYx2Uos/BYJXr87igVRdIuI23D4Zb/zydf4p4n/upLJOdlG+DyKNhZfxLrt9QHXQf7JiKKh6jvTF69ejVef/31uFXk2LFjAIA77rgDXV1d+M53voNbbrnFN33//v2YOnUqJk+eDAC44YYb8MYbb+DKK68MWI/NZoPNZgsoa21tjVs9U1lNXSMkSYDJoP1ZTQYJTsioqWsM60qjmrpG2J0yVGiJ2hTNIQMAdDoBWUYJtj63L+kdit3hCfs7oECZGE8DcSIzkRwzhyv8/ctYl6mx5AhxIQ8Nr3bXMd6dHINMjCeiZGAsjU3BzhkpNowl8hfruMxYx3giig/GUuZRFBXb/3IYL7ypXZwuigJyLHr02N2+G8F27WtBddXQZdk3xYbxRKSJ+szxvPPOw8svv4yLL74YFovFVx7tO5NtNhsuu+wyrFmzBh6PB7fddhumTZuGyy+/HABw6tQpFBYW+uafMGEC2tqGXj2zefNmbNy4Mao6pLu2DjtyzIF/UqNeh1Md9rCXl70KVFVLJqcqk0EHUQBsdk9Y86tA2N8BBcrEeAoWJxQdr6IytsKUqbE00sU8FNzAFcQUnUyMJ6JkYCyNTTwWjj/GEvmLdVxmrGM8EcUHYymz2J0e/GrrHnzwiZbA1EsijHodbH3ugPmUEI8XZd8UG8YTkSbqs8idO3fijTfeCCiL5Z3JCxYswIIFC3y/33jjjXjnnXd8yWRFUSAIZ1OcqqoG/D5g1apV+Na3vhVQ1traiqqqIJflZJiiAgs6bY6Aq8tdHi8mFFiGWSpw+e4eFxTVm7K3JVuzDLA7PZC94VdQAML+DihQJsbTQJxQ7HSiwNgKU6bGUnsnE8rRMBs5iB+LTIwnomRgLI1Nwc4ZKTaMJfIX67jMWMd4IooPxlJmefCpd9HU1gMAWFw2Ge8faEGvw4PBqRFRDH57GPum2DCeiDRRn0EeOHAgnvVAfX09PB4PLrvsMgBasliSzlavuLgY7e3tvt/b29sxYcKEIeuxWq2wWq1xrVu6qKyYiU01++GEDKNeB5fHC1lWUVkxM+zln9q+F55eL1LtCcCiKCDbrEev36M7wmUx68P+DihQJsbTQJxIIvio6xiZjRJjK0yZGktHmzphd/FR15FaXj492VVIa5kYT0TJwFgam4KdM1JsGEvkL9ZxmbGO8UQUH4ylzNLU1gOdTsA/feNCXH/FdHgVBXV7modc3F9+UUnQ5dk3xYbxRKQRo13Q6XTipZdewu9+9zv89re/9f1Eq6enB7/85S/hcrnQ29uLP/3pT1iyZIlv+vz583H8+HGcOHECXq8Xr7zyCsrLy6PeXiYqm1OEOyvnId9qRq9DRr7VjDsr54X97oOyOUW4d8UCTCm2QqcTfFc3hbioKWEMehEmg/bojkgTyUUFZjxYdTHf/0A+A3EyZ/p4iFHvAWnqxBzcd9NCxtYYVjanCKtvXYTCfFOyq5J0ooCw9ic6UcDNV5/H9yWTz8vrl0dUnmisH6WTRP7d9bqEbSqkaD9vsHPGf/unr8R1G5TeErlvTdS2EvmZYh2XoczCY5Xo8bujVBRJu4y0DQ/XtnOzjfj5nV/F9VdoF6ZXV5WhYmGp705kURRQsbAU1VVlQZdn30RE8RD1nck/+MEP0NrailmzZgV93HSkrrrqKnz88cf45je/CUVRcPPNN2PBggVYvnw5/vM//xNFRUVYu3Yt7rnnHrhcLlx55ZVYtmxZzNvNNGVzimLqCGJdPlYfH2nHuufr0d2rvfOhsmIGbrvuAuiSndGmjJLsdk6UKcrmFOHZn16d7GoQpbVUHxBj/SidsD2EJ9ixML878pfI9pCobSXyM/F8k/xx/xo9fneUiiJpl5G24ZfXL4fTJePX2/fivY9bAAAzSnPx0zsuwfi8wEdSV1eVoTqCpyyzbyKiWEWdTD5y5AjefPNNiHG8te/+++/H/fffH1BWW1vr+/9ll12Gl156KW7bo9ShKCr+39tHsOWNBigqYDFJuP+mhbhs7sRkV42IiIiIiIiIiIiIaNS0nunDL377IT7/0gYAqFhYinu+swCGVHgsDhGNeVEnk8eNGwdZlmEwGOJZHxqDeu1uPLl1D3Z/1gYAmFZixY9XLcLE8dlJrhkRERERERERERER0ejZd/gUfvn7evTYPdCJAr57/flYXj4jLk+EJSKKh4iTyQPvRS4sLMStt96KxYsXQ6/X+6bffvvt8asdZbzGpi489txunOqwAwC+fslkfL9yPq+4IqKwKIqK1jN9ONLUhcaTXTh6shstp3tx4z+c63uXDBERERERERERUapRVRV/fucofvfKp1BUwJplwL/ecjHmz5qQ7KoREQWIOJl8+PBhAEB2djays7Nx/PjxuFeKYlPf0IaaukY0tfbA41Ug6QRMKbaismImjjR14o9vH4HLoyS7mgEEaJ3l2/Un8ZcPmyJfXgDG55lw17cv4vsfaFjrt9Sjbk9zsquR9ibkm/H9b89PaLwNJI4bT3ah8WQ3Gpu6cLS5C3anPGTew190JaxeY9XWHQfxwpuHkl2NtFOYZ8Sza5YluxqUQm6orh1Slkrvh2P9KJ0Eaw+Zyr+db91xELW7jsHhkmE2SlhePh0rl86OaH2MJfKXyPaQqG1l4mei9MD2ED1+dxStgbHxtg47igosqKyYOez4VSRtLZ7zOt0yNv7hY7yz9yQAQK8T4fUq+Ommv4+47kiP/yL9ToiIBov4hcePPfbYkJ+f/exnuOeee/DYY4+NRh0pAvUNbdhUsx8t7b3osbvhdMvoc3jQ0t6Ddb/fjRfePJRyiWSdKCDbrEd3nxuKoka1DlUF2judWPf73ahvaItzDSlTMJEcP6c6HXji+fpRizdVVdFyuhfv7m3Gsy9/ip/8+3tYueY13Ll2J9Y9/xH+VNeIA0dP+xLJJoMOs6bkYemlU3DXt+fhrhvnjUq9SMNEcvTau1y445E3kl0NShGhEl+pkhBj/SidjLW/+8Dn3brjILa/dRhOtwxJ1AYlt791GFt3HIx4XeGWU2ZLZHtI1LYy8TNRemB7iB6/O4rWwNh4p82BHLOETpsDm2r2hxy/iqStxXPeU512/Og3f/Mlks1GHQQR6Bt0w0Sw9UR6/Bfpd0JEFEzU70x+66238MEHH+CBBx7AN77xDfT09ODuu+/GqlWr4lk/ilBNXSMkSUB3nwxRFCAKAhRFhd0pw+n2Jrt6Q5gMOqiqih6HJy7rc7i9qKlr5JVVFNSufS3JrkJGsbvkuMSbqqpoPWNHY/+jqrXHVXcNOYAeYDToMKUoB9NKcjGjNBfnTsnDpAk5MOp1EEW+SyYRancdS3YV0lp7lyvZVSAiogxRu+sYIACSqF0nLgmArCio3XUs4ruTiYiIiNLdwNi4yaClPUwGCU7EZ/wqXg40nsba53bD1ueGIAC3XXs+fv/aZwj3HqtIj//S4TshotQXdTJ506ZN+MUvfoEdO3bgoosuwsMPP4zbbruNyeQka+uwI8cswSMr0PXfdy4IgEdWoEZ30++osWYZ0Gt3h91RhkNV4Xv/MtFg0d75TsFFE2+qqqKtw64ljZu6cKSpC0ebu9EX4oISo0GHqQOJ40m5OHdyHiYX5cCg10EQmDhOFocreKKfiIiIEsvh0u5I8acT2FcTERHR2DQwNu7PqNel1HjxTze9D0VRkW3WY/UtF2Ph7CJsfvWzsJeP9PgvHb4TIkp9USeTVVXFeeedh2eeeQbl5eXIzs6GmmrZyjGoqMCCTpsDekmE7FUgClrCRy+J8CrelEgoi6KALJMetj533NctCMCEAkvc10uZQRQFJpTjaKR4G5w41u447kZvqMSxXocpxVrieKYvcWyF0aAbrY9AUTIbpZAXABAREVHimI2S9ohDv2vsvKpWTkRERDTWDIyND9yFCwAujzelxosVRcXkohysueMSTByfHfHykR7/pcN3QkSpL+ozTFEU8dprr+Hdd9/FD3/4Q7zzzju8SywFVFbMxKaa/bCYJHT1uKAIKgQAFpMeAgC7K7mPujboRYiCgB57/BPJAGA26FBZMXNU1k3pr/yiEr4zOY4sRskXbwOJ46Mnu3GkqRNHT3bjaHMXeuwjJY6tmDkpDzMn5WHqRCsMeiaO08Hy8ul8Z3IMCvOMya4CERFliOXl07H9rcOQFQU6QRtIhKqVEzlcMhwuGfk5Ro7XEBHRmDAwNu6EDKNeB5fHC1lWU2q8+LK5E/GDlQthivLiv0iP/9LhOyGi1CeOPEtwV111FV588UVUV1ejsLAQ//7v/46f/OQn8awbRaFsThHurJyHksJs5FgMMBkkZJn1KCnMwepbF+Hmq8+DUR/1nz0m2WY9vF5lVN7dLAhAYb4Jq29dxHc9UEjVVWWoWFia7GpkhHFWI669fBo+O34Gaza9j6r/8zq+9+hfsPa53fjjXxux70i7L5Fs0IuYOTkPSy+diru+PQ9P3l+OF35+LZ68/0rc850FuOar03DulHwmktPIyqWzcfPV5yW7GmmpMM+IZ9csS3Y1KEW8vH55ROWJxvpROhlrf/eBz7ty6WysWDILJoMEWdHegbdiyayI3pfMWMpMfQ4P/tfPd2DVz97EbT97E//3mb/j+dcb8PcDX6K90xHyyXKJbA+J2lYmfiZKD2wP0eN3R9EaGBvPt5rR65CRbzXjzsp5IceLI2lr8Zj3lmWz8eNVi4YkkiNZd6THf5F+J0REwUR9Z3JLSwsaGxvx0ksvweVyYcOGDSgq4g4oFZTNKQrZGZTNKYpoYCEaiqLij389gudfb4CiAlkmCfetXIjLLpw4qtslCkd1VRmqq8qSXY20oaoq2jsd2qOqfY+r7sYZmwsv7jwyZH6DXsSUIiuml1oxc3Iezp2cj3MmWiHpknMRC42elUtnj3p/QqNPVVXeqZRkqT4gxvpROhmr7SEeffJY/e4ymUEvIsdiQI/dg64eFz46eAofHTzlm56bbcCM0jzMmJSLGf1PCpqQb4YgCAltD4naViZ+Jkq+cI6l2R6ix++OojXc2HgwkbS1SOb9lxvnY9Of9kP2qsgySaiuKsOi80PXK5J1R3r8F+l3QkQ0WNTJ5J///OcAgH379uHtt9/GypUrMWHCBGzbti1ulaP002t341db9+LDz1oBANNKrHjou5egeFxWkmtGRCMJlTgO9Vh6gyRiSrEVM0pzMWOy9o7jc4qtkCTeYUyUClweL850O3Cmy4nT3Q6c6XbiTJcDp7sdON3lxJluB2x9blx7+TT872/OTXZ1iYgoA3hkBWe6HTjdpf102JyYfU4Bzp82LtlVG3P0kg6/+dd/wIkvbWg82YUjTdox/hetPfDICrp73dhz6BT2HDqbYM6x6IckmIvHWXjhGVEQL+48jG07DqEw34LppbmYVmLFtBLt3wKriXFDNIZ5ZAWb/rQfb35wAgAwaUI2fnr7JSidkJPkmhERRS/qZLLb7UZ9fT3+9re/4b333gMAnHvuuXGrGKWfxpNdWLt5N9o67ACAr18yBd+vnMdH1xKlIFVV0d7lwNGTWsK4sX9wydY3fOJ4emkuZk7Kxawp2h3HOt5xTJQUdqdHG6jvTxCfsTlxuktLGA/8G+pCkMFaTveOcm2JiCgTeBUVXT3O/kSxE+1dDrR32X2J49NdDnT2uDD46cnWLAOe/9kyJlaSQNKJmDEpDzMm5eHqr2hlsldBU1sPjvYnmI80deFEqw1uj4Ieuwf7jrRj35F23zqyzHrMKM3tPw/QEs0l47Mhivx70tjW0e2EW1bQ3N6L5vZevLuv2TfNmmUISC5PK8nF5KIcPrGLaAzosDmxdvNuNHzeAQC45PwiVFddDItJn+SaERHFJupkcllZGXJzc3H77bfj17/+NaZNmxbPelEaUVUVO/7nC2z60354ZAV6ScQ/V87D0kunJrtqRAQtRk93Oc/ecXyyC0dPdqG7N3iiSS+JmFqc4xswYuKYKHFUVYWtz60lhG39ieLu/juLfXcYO+BweSNab45Fj3yrCfk5RhTkmFBgNWFcngnjc82YP2vCKH0aIiJKF/79z8BPe3/S+HS3A+2ddpzpdsKrBH/PbjA5Fj0KrCZcdfEkJpJTiKQT+xNcufj6Jdo5u9er4OSpXhxt7sKRL7rQ2NyF4y02uNxe9Dk82N94GvsbT/vWYTZKAcnlmZPyUFKYDR0TzDSG/NM35+IrcyfiWHM3jjV343hLN06e6oVX0fanHx85jY+PnI0bSSdgSpEV55RYA+5kzrEYkvgpiCieDn/RiV/89kN02JwAgJv632PMC7CIKBNEnUx+9NFH8e677+KFF17A+++/jyuuuAKXX345704eY5xuGf9Rsx87dzcBAIrHWfDjVYswvTQvyTUjGptUVcWZbieONHX133XchaMnu9HV6wo6v14SMaU4BzNKczFzcj7Om5yHKXzHMdGo8L+ja2iCWHvs9JluJzyyEvY6RUFAbo4B+dlG5FtNGJerJYrH55q1ZHGeGeOsZpiMOoiCAFEUOKBPRDQG+Z5oEeKO4vYuJ9ye8C9UMhl1GGc1ocBqxrhcrd8Zn29GYb4ZhXnaj9kosd9JEzqdiKkTrZg60Yp/KJsCQDtuaWnv9Xv9TReOt3TD4fLC4ZLx6bEz+PTYGd86TAYdppXkYsYk7fU3M0rzMGlCNi9IpYylEwXMP7cQ888t9JV5ZC+a2npxvKUbR5u7cby5G8e/tKHP4YHsVXGspRvHWrrxdn2Tb5nxuSZMK9Uu8Jjefydz8bgsJp+I0sxfPvwCv/l/H0P2KjAbJTxw80JcduHEZFeLiChuok4mX3/99bj++usBADt37sT69evx+OOPo6GhIW6Vo/Ct31KPd/Y2BzxSTBQBJfzx6JiZDDqc6XLgviffGdXt5GbrYTToYTbq4HDK6OzRkmSlhdlYdd35KJtTBACob2jD7179FC3tfUGn09i0dcdBbN1xaMjj98YKvSRCL4kYGNPzelV83mLD0ZPd2PE/X4S9HlEAyheUorqqbJRqSulg+YO1iOAGJYqQpBORbzVqdxP3D9aPyzViXK42SF+Qa8a4XDP0ksg7gdLYDdW1Q8peXr88CTUJjvWjdBKsPWQqo16EyxP8ZE8nCjCbJHi9KlweL5QoOmudKGDWlHyMzzNjfK52cVJhvhnj88yYkG+BNcvAJHEGC7VvVRQVX57p8z0iu7GpC8daumF3ynC6vWj4vMP3WE8AMOh1mFZi1e5gLs3FzMl5Qx71m6j9eCL7C/ZNY5de0mF6/2PhFy/Sym6oroUgaBdt6Pr3mwVWE1o7+qCqwOluJ053O7H7szbfekwGHc6Z2P+Y7P67mKcWW2E2Rj2Mm5YYSxStu9ftxInWs69zmlqcjY2rF4ecP5K2Fmze6y+fhlfeOw4AmDg+C2vuuBSTi3IibsORzB/pZ9y64yBqdx2DwyXDbJSwvHw6Vi6dHXJ+IqLBoj4K+eCDD7Br1y68++67cLvd+PrXv45HH300nnWjMK3fUo+6Pc1DyhOZSLZaDOixu5GInEJ3rwdGvYz2DhUqtIEOQQCa2nrw1Pa9uHfFAgDAhm170GP3QBQAFYHTmVAem7buOIgX3jyU7GokjH/iWFEAr6JA9qrwOOWY162o8O13mFAem5hIjo0gaINIxeOytEdO52o/2t1cFozPNyEv2whR5N08mSxU4uuG6tqUGChj/SidjKVEMoCQiWRAu6O01+4JOT03y4CicRboJRFtZ+xweby+xLAKFYpXhaICx5q78ct7vhb3ulNqG2nfWlqYjdLCbJQvmARAezJS6xk7jjZryeUjTV041tyNXocHbo8Xh0504tCJTt969JKIcyZqCebX//75sNtK1GeKJ/ZN5G+gPagqIMsKBs7EvzzThz88eh1OfGnT7lbuf0z2iS974PJ44XR7cfBEJw76xY4gACXjs3CO3x3M00pyMS7XlJEX9zCWKFqDk6wAcKK1F3ev2xk02RpJWws170Ai+eLZE7D6ljJkmfURt+FI5o/0M27dcRDb3zoMCIAkak8a3f7WYQBgQpmIwhZ1Mnnt2rVYunQp1q9fj1mzZsWzTvT/2bvz+Cbq/H/gr8nZ9G6hB+WUlksBQaqIu4KKglqxyg9XsAorq6Ku4roIVmFCeSFcAAAgAElEQVR1xQMPEA9WFv26noiKVllcDxQXccFVDhFc7kOE3tC7zT2f3x9p0ytp0zbNTJLX8/FQOpNJ8p7J553PZN4zn+mgzbsKFHtvjSQhMkKHqjrP917tLla7QMOusiwEDFotZCFQZ3Egb9NhAIDZ6nAP6dmwXMPjLCaHp3WbjyodQrdpWjh2yoDslOFwyh0aLrczNu8qwLycbn0LUikWkr3TSIBG47paWKt1De8pAe4RAYRwHaQf0CsWf711nKKxEhFRaNFIrr5Hq5HcJ93qtBoIAWi1EhJjjYiLjsBfbx2HRX/fggijFqcqLUqHTUFMkiT06hmFXj2j8NuzewNw7euUlJtx5GSF+yrmI/mVqKq1we6Qcai+6NwWm90Jg14biFUgUoTJqMPQAYkYOiDRPa/h6v9jBZU4VlCFIycr8EthFU5XWiAEkF9ai/zSWmz5qfE4YEykwV1YPqP+fsx9kmOg1/GkVApPLYus7c33l2mXDMJNVwwLyBD1HV3HdZuP1heSXd8LOglwyDLWbT7KYjIR+azTxeSPP/7Yn3FQF3Rm2DJ/MNQXrmrM3s96704Na90wXLFGAhxOGSVldRBwnZGvaXJ2ZtPHKTyZrV2/IlcNmhWOnQKOABWOPVHq+4dIKRpN4wF6jcZVJHZViQWEXH9Fl3Dlpt3hhLWNLvJkSff+mCUiotAiSYC26clKDTPr+yBZCAjhGo3Gapfdv5OMei0AAacMJMQY3f1P4ek6xJjCa8hUCgxJkpCSGImUxEhcMDINgKvAfKrCgsMnK3Ak31VM3nWgFLKX+w/97sF/oV9qjGuI7D7xSO/juqeskQVmCmEajeS++r/h5AwAqKq1uQvMR/MrcKygCidKquF0ClTX2bD78CnsPnzKvbxOK6FPcgwG9o5rUmiOQ2yUQYnVIgppEoDYaANmZZ2pdChema0OtDy/RCuFznFSIgoM/nIMARqNFPCCTrRJj1qLXdH7zroOmzRe6SUL11n3yYmRAIDKagtk2fvjFH5MRh1qFTr5obNaFo7tDtn9nxoE4oxLokBpWiTWaiRXHycBQhaQUf+vAJxOGXY//OZif6RuNWY7vvz+OGrNdkiSBE395eUayXWQXKr/t+W0JLmuDJQASBqp8bloWLZhuba/P//7c6HrpLiG12t4HuqnNWhy1bvUOK/pdMuYWk03nydBgkbTON0Wq93Z5L2abwMi6jgJqL+iWOO+oliuny/LArIQkGVAlmU4nAB82KWVpMYTb2Xh2q+02p3u/iclMRLlVeZuWiOi5iRJQlKC697b40b0AlB/L1nU30u2/neF3SlDlgWcssCxgiocK6jClz/8CsD126NvcrS7uJzRJx4D0+IQEWb3kaXwExtlwNmDknD2oCT3PLtDxsmS6iZXMVfil8JKVNfZ4XAK/FJYhV8Kq5q9To+4CPcVzGekue7tnNojyp1/RNQxWo2ECKMWlTWBHbGzo0xGHSw2B3RNUt0pEHb3YSeirlHVN8aKFSvw2WefAQAmTJiABQsWtHr8ww8/RGxsLADgd7/7HXJyOL7q+FFpHu+Z3B0kyTWETlWtsp2kUS/BZncdGdFIEpyy68z76Cg9pl6UAaDxnsmQXfdWbvk4hZ/s8QNVfc9kvba+cKwBHA4Bh9OpqsKxJ+NHpSkdAilEIwXPUNcSAI17yE9N40lGsusKLiFEfT8iYHMEZqWMeg37I5X76ofj+Mf6/yn2/o+/9oNi7+2LabmfeH2sdYG9RSEdDScj1Rev0bw437Ro31hQb3ythmJ4Wxa8+G2T12x4/Zav2fy1NBoPRfsWRfrICB2yx6cjtUeUvzYlhYmGE5Yabn8AtO6HnLJr/89vhOt9HbIMCCAySgeHQ7j7n6kXZWBV3m7/vR9RJwigfqSl5vMfvuV8HDlZgcMnK3D4ZCVOVZghywLHi6pxvKgaX28/AcDVP/RuKDD3jkdGH1eBLDJCH/iVIQogvU7jvuK4gRACZVWW+nswV+FIfiV+KahE4elaCAGcrrTgdKUF2/cVu59jNGgxIDUWZzRcxdwrDgPSYllkoqDVPzXa43DP/VOju/S6LY+FG/VaCCFQaw781b0dXcfs8QPx3pcH4ZBlaCVXIRnCNZ+IyFeq2TPYunUr/vOf/+Cjjz6CJEm45ZZb8OWXX+Kyyy5zL/Pzzz/j2WefxejRoxWMVH3m5WQCAL75Mb/ZlcIaDSD7sQal02qg10qKF5LjovWIMOgRYdTCbHGgvNoKAOiTHI1ZWWe674d8z/Rz8Pq//oeC0lpIHh6n8NNwH5A1Gw4oelU90KRwLAGO+uFw7U4Zdqd6C8dNaSRg/Oje7u8fCj/rlmYj+751iheUXUN+Nl7JhfqrsGRZhizqr+aSBZxO13+u67yUFRelx59mjGF/pHJjz+qF/b+Uo7zadY86WQhANA4jK0TDyWqi1eNA43KNz6l/HhqXFwKoqrGh4eYdDelkMuraeH7je6qV3HAWH4DGtQqsfb+Uddtr67Qa/OHq4d32+tQ565dlY8q8dYq8d9NbIGg1EgQa+x9ZuK6wbPgPfjreaNRrYLV77tMMOg16JUVBgoTyagscTgG9VoO0pGhMvSjD3f9kDksBpo5E3qbD2NNkeNQG65dl+ydYCirecqk72kN779V0X6myxoojJyvdQ2QfOVmBknIzZAGcKK7BieIabNpxEoBr/zCtZ5S7wPz4HRdg4cqtqlgnCi+Bbg+SJKFHnAk94kw498xU93yL1YHjRVU42jBMdn4VjhdVwWJzwmpz4sCv5Tjwa3mz1+rVMwoDm1zFPCAtFknxpoCNQsNcos5aMX8i7npmY7Nia//UaKyYP9Hj8r60tWMFlc1O9o026VFrtrt/6Xhqlx1twx1ZvqPr2HA8dN3mozBbHTAZdcgeP5D3SyaiDlFNMTkpKQm5ubkwGFz370hPT0dBQUGzZX7++WesWrUK+fn5OPfcc3H//ffDaDQ2W6aqqgpVVc2HcSkqKure4FVgXk6mX4s6R/MrseSNH1B02nV/4UvP7Yfb/9/IoLo/UeawFB6o76JQzKcZk4Z2eWepotrqutfXycYDGacqLR6X1Wok9EuNQXrvOAzul4CMvvEY0CsWel3w5BJ1XSjmEuAqKAOuwlZVra3+THczTlVacLrCjFOV5sZ5FZYO348nLtqAxNgIJMZFICnedVAkOcFUf4AkAj3jTBzWMAwFKp969YxC7qxz/f66/iTLTQvarmHYRZNid0NRV3ZPNy+ItyqQNxS6ZdGkWF7/XNl78bz1dMN7tyiEN8yHa7he0eJe46iPUYjm7yOLJsvJja8jICA7mxfvRYvXaBgauHEZ4b49jPt1W2yj1tuicdqg1+KKCwYE+JPuHqHYN/1z6dWoMdtRWm7GqQozSitc/zb8XVphRlmlGQ6n7yc5xEQa0DM+Aj3jTegZb0JyvAlJCZHoGe8arjcxNgI6rab9F1Ip/mbqulDMpUAWa3x9r7hoI84Zmoxzhia751XX2eqvXq50/z4rOl0HIYD80lrkl9Zi84+No7j16hHlHh47vU8cqutsiIn0/z1kWezqPOZT94gw6jCkfyKG9E90z5NlgaKyWhzLr8KxgkocLajEsfxK97GNwlO1KDxViy27G4/NRpv0zYbJPiMtFv1SY7rt+IYatl2wCsVc6ghvRVVv2mpr3+7Kx/Pv/QirzQmDXoO7fzcaF53Tp8uv29XlO7qO/jgeGq5CNZ9sdicMQVTrIeWp5gjsoEGD3H//8ssv+Oyzz7BmzRr3vNraWgwbNgzz589H//79kZubi5deegn33ntvs9d54403sGLFioDFHYq++uE4Vn64GzaHDINOgznXjsSk8/srHRYpgPnkOgP+8MkKHD7RfIg1T7QaCX1TYpDRJw6D+jYWjtkxUzDnklMWqKh2DYd2qqJ5cfhUpRllla5/OzIcu0YjISHGiMTYCHdRuEe8CUn1B+p7xLnm86QL8iSY88nfGu9bz/vcUccFYy6ZrQ5XYbi8eaHYVSyuw6lKC6w234eJNhl1rn6ovv9JijchJTHS/XePeFNQnUxLygjGXAoVMZEGjBqcjFGDGwvMNWY7juZX4PCJSvcw2Q1D/BaerkXh6Vr856fG4lhyYiQy+sTVD5HtKjLHRRs9vR0FAPMpcDQaCWk9o5HWMxq/Obvx9lXVdTb8UlBfYM53FZlPFNfA4ZRRY7Zjz5FT2HOkcTSLhuMgA9Jim13JzDxSFnOp65yywNuf7cMHXx8CACTFm7Bo9lgM7B3XzjMp1IRqPhn02jZHduIJPdSSJIS6Bsk7dOgQ5syZg7vvvhvXXnut1+X27t2LBx98EB9//HGz+d7OFMnJycHGjRvRp49vZw6FI6vdiVV5u/HlD78CAFJ7ROKBWeexkwxj4ZZPDUOnHTpZ7vr3REWbheM+ydHI6BOPQX3jMahfAgvH5JVac8nukFFW1VAkdhWIT1eZcbq+UHy6woyyaqv7Kj5f6LUaJMQam11R7DooH4mkBFehOD4mwjUkNVEnqDWfiIKN2nLJ7nC6TlTycEVxw981ZrvPr6fXaZAYG+EuDPeMd41ukZTg6o96xpkQZeI9Vanr1JZL1FqdxY6j+ZU4fLKxwFxQWuP1di1JCSak945Dep/GAnNCTERggw5TzCd1cjhlnCypcReYjxW47snc1m3wEmMj3IXlgfXDZKclRfN3YIAwl7qmxmzH0re3Y8f+EgDA8PQeyJ15Lk+SCFOhnE/tFZO7+3EKLqq5MhkAduzYgblz5+LBBx9EVlZWs8cKCgqwdetWTJs2DYBrGDqdrnX4sbGxiI2NDUi8oaTwVC2efGMbjhZUAgDOOzMF994wBtE8wBLWQjmfGgrHh09WuP8rLfdcONa4C8dxyOibgCEsHFMHKZFLFquj1TDTp1tMV9RYO/SaEQYtEmIjXAfn4yLQo/4q4oZCcVK8CbFRhoDdR4vCUyj3TUSBpIZcWrvxILbuKcSpcnOH+iSNJCEh1ogecSb0jHeduJSUENlYLGZ/RAGkhlyitkVG6DE8vSeGp/d0z7NYHThaUNn4m/BEBU6WVEMWcI1+UG7Gf39uHMKyR1xE/dXLriJzep849IgzKbE6IY35pE46rQYDesViQK9YXDymLwDXcdmyKguOtbiKuehULWQBlFVZUFZlcRfjANdVcAN6xdQPke26inlAr1hERvDYo78xlzrv16IqPPbaDyg8VQsAuOq3Z+CWq4dDG8S3NaGuYT4RuaimmFxYWIg//vGPWL58OcaNG9fq8YiICDzzzDMYO3Ys+vTpg9WrV+Oyyy5TINLA276vGHmbDuPIiXLUWX0ftq2zYiL1+GFvMWYs+rTb36s9kUYd0vvGY0R6D+w5chrFZXVISYxsNT31ogze64u8qqq14fDJCrz0wS4Ul3kuGDel02qg12mgkVzD2jicMn4tqsavRdX4evvJAESsPv17xeD3WWcxz1SkoLQGhadrXcXhCtd9ihuuJj5VaUFtB67eAlz3v0qIMSIhtn7Y6TgjesSbkJwQ6T4w3/IKrrbOMAxXEoCeCRE4XWlx36cVAGKj9EiMNaHO6kCkUQcBAbPVyT6MPOaRms7QZXzUXRxOGe9uOACbh9skxEUZXCcsxUa4riKONyE5MRLJ9f96G+EiXPulERk9fepLGn5XNv0NBQB5mw5jz+FTrZZnLoWvQH63Buq9vL3PmWf0cE9bbA78UljlLjAfOVGBX4ur4ZRF/QmZRfhhb2OBOSHG6C4sNwyT3TM+Alff98+ArBMFh1DdV5EkCT3iTOgRZ2rW/1hsDvxaVO2+gvloQSWOF1bDbHXAZnfi4K8VOPhrRbPX6tUjCgOa3Id5YFockhJMzCVq5sGXvsWeI2Xu6RHpiXjizgs9LtvRvPO0vF6nwZ3/72xcel6/Tr+2P+Lwtvyy1duxeVcBZFlAo5EwflQa5uVken1tIqKuUk0x+dVXX4XVasWTTz7pnjd9+nR8/fXXmDt3LkaMGIHFixfjjjvugN1uxznnnIObb75ZwYgDY/u+YqzK240ai63bC8kaSUKUSY/qOu/D1ARandWBg7+WYe/R04iPMSI+2oCC0pr6aQPio40orzJjVd5uYOpIHownVNfZmtzf2HWP45KyOq/L67QS9DotNBpAlgWcTgG7U4bD6vv9X8PB8cJqPP/uTtwz/RzmmQp88d/jWLF2l0/LSgBiow1IiIlw36O4R/1VxUlxjcN9mowd2yUI1wP27REASsstreZX1dpRVWtHQowBJ+qHz0+Kj2AfFua85dGUeetUcaCM8VF30mk1WDR7LA7+Wo6E2AgkJ5iQ2iMKPeIioNd1fPSXcO6X9hw+hdKyujb7kobflTqdhBiTDuVVZjz/7k5IkoTyas9XhTOXwlMgv1sD9V6+vk+EQYeh/RMxtH+ie57N7qwvMFe4h8k+XlQFh1OgvNqK7fuKsX1fsU8xMJ/CTzjuq0QYdBjcLwGD+yW458myQHFZnXt47KP5FThWUIXS+t9FDfcz/25PYbuvH8rbjrxrWUgGgD1HyvDgS9+2Kih3NO88La/RSDDoNT4Vkr29tj/i8Lb8stXbsWlnvntaloV7mgVlIuouqikmL1q0CIsWLWo1f8aMGe6/J0+ejMmTJwcyLMXlbToMnU5CndnRre9j0GkgSVBVIbmB1SZDq5VQZ3EgIcaIOosDkFA/HYEIgw4WOJC36TAPxIeZpoXjhrPHi30oHGs1EmQh4HTKsDsFzNbuza9QYbYyz9TCqNe4r8iKizYiseEexS3uU9wjLgJJCZEw6rUc6lMlKmvs0GolQACVtTb0TopmH0ZEYWv0kGSMHpKsdBghQaeT2uxLGn5XRhhchwAiDLr6W7xw/4CoLQa9tlVhzO6Qcbyoyn3/5cMnKnC8qBp2DyMtNPX6J/9z34c5tUck988pbGg0Enr1jEKvnlG4YGSae35NnQ3HCqua3Iu5CieK28+lpW/vwMDese7hsuNjeB/bUNeykNze/K4w6DSABNR287H4rti8qwAA0LQbEcI1f16OQkERUchTTTGZPCsuq0OMSQdZdN97RJv0qLXYIbrxPbpKK8G9M2l3yM2mAcCo17Z59SkFv5o6m/tK44YCsrfCsSQBaT2jMbB3HAb1icPg/on46yvfweGUWTjuAqcsmGcqcdGYvhg3Mg1CFtDrtR6H+iR1koWAXpIg0NiPsQ8jIqKuaq8vafhd2ZRTFhBq/hFIpFJ6nQYZ9UXhhssdHE4ZJ4qrMe/5zdBIrpOXHQ4ZTTPsw38fdv8dZdIjvXf9/Zd7xyGjbzx69YiChvv1FEaiIw0Ykd4TI5rcz9zhlJFfUoM/Ld8ErUYDQMDukJsdF/3mx5P45sfG6YQYI85Ii8PIjJ64enw69Dre25Z8c7Kkutl0ZIQOZqtD1cfIAdeVyB2ZT0TkDywmq1xKYiTKq8zQSPB7QVmSgJhIA6pq1Xc1cktOUX9mGFw/3GwOp3saAKx2J5ITI5UKj/ysps7mvtK44b+i0+0XjjP6xGFIvwSk94lHRIshey227r/feKjTaiTmmYoY9R0fApSU5zq4CEDAfZCDfRgREXVVe31Jw+/KhiuTAdSfjCbBKXM/mairdFoNzkiLa3VFpSQBOo0EjUaDM9JicaywClabE7VmO3YfPoXdTe5XbjLq3PdfvmxsP/RPjQ30ahApTqfVoH+vWDicAg5n8/7JlUsSxg7vhWMFlcgvrYEQQHm1FeUHSrDzQAkG9o7jqCfkk217i7B09Q73dFy0AZU16j9GDriu9vdUOOYJSUTUnVhMVrmpF2VgVd5uRJp0qKnz3xWVOq0Gep0mKArJRoMGDodAZIQOQrj+tVU73dNWuxMOh8DUizKUDpU6ocZsdw0P1mS46sLTtR6XlQD0SorCwDRX4XhQX9dwYy0Lx56MSE/sluFvwonJqGOeEXVRXLQeVbV2199RRlhsDvZhRETUZe31JQ2/Ky1wwKjXwmp3wmTUQZIkWO0sJhN1FyEAu1MATieemTseTlmgoLSm/rdvBQ6dcN071mx1wGx14Ocjp/HzkdPYvq8Yf8+dqHT4RKrikAUgCyy4yXVPWKvdieOFVThW4BoqGwDOGthDyRCpm3g7pjciPdHD0m2TZYG1Gw9i9Rf7IYSrABtp1AVNIRkAxo9Kw6ad+a2uoB4/Ks3zE4iI/IDFZJXLHJYCTB2JvE2HceREOeqsXf+hbzLqYLM7g2K430ijDul94zEivQf2HDmNkrI6pCVFY/L5/d3TyYmRmHpRBu81GQRqzXYcyW8oHLuGq26zcNwzCmekxSGjbxwG901ARt94REboO/XeT9x5IR586VsWlDupf68Y/D7rLOYZua1flo0p89YpHYbqSAB6JkTgdKUFcpMLU2Kj9EiMNcFsdaBvSgQEBCxWJxJiTezDwpi3PFq/LFuBaFpjfBRMwrlfGpHRs92+pOnvyobfUH+4egQA1/2U9zS5OrIBcyk8BfK7NVDvpaZ10mok9E2JQd+UGFw8pi8AV2Gj8HSt+wTr40VVOP+sVL/HRoHHfZXO82XbGT3c05xCk6djeiPSE/HEnRe2WrattmO2OvDcuzuxdXchAGBwv3gsvHksZj3yhcflO/LaXVm2o8vPy3GdULF5VwFkWUCjkTB+VJp7PhFRd2AxOQhkDkvp9EHmr374FSs//Ak2hwy9ToPbrhmBy8cN8G+AATKjnWlSl8bCcaXrjOuTFSg85b1wnNojCmekxSKjbxwy+iZgSL+ETheOvfG0k0lEnceDIERdp/Y8YnwUTNge2ubtdyVPaKKWAplLgXovNa+TRiOhd1I0eidFY8I5fbopKlIK+6bO47ajpjpyTM9T2yk8VYvHX/sex4tc90m+9Nx+uHPaSOh12g61te5atqPLz8vJxLycDr08EVGXsJgcoqx2J17+aA82fH8cAJCcYMIDs85FRl+erUfdx2Jz4PVP9uLHAyUoaKNwnNIjEgPT4pDeJw4ZfeMxuG8ioiP9WzgmIiIiIiIiIiKi8LZzfwmefns7as12aDUS/nD1cFz12zMgSbzHMBGRr1hMDkFFp2ux5I1tOJrvul9I5rBkzLthDKIjDQpHRqHu8IkK/GvLMfd0Q+F4QK84pPeOxaC+8RjSLxHRUWyLRERERERERERE1D2EEPho02G88a+9kAUQF21A7sxzMTy9p9KhEREFHRaTQ8z3Pxdi+ZqdqLU4IElAzuXDcN0lg6DR8Ewr6n5nntEDt14zHGarA+m94zCobzzioiOUDouIiIiIiIiIiIjChMXmwIvv78LmH/MBAOm947Dw5rFISjApHBkRUXBiMTkIbN9XjNf/9T+cLKmB0yl8eo5GAqJMerz92T68/dm+bo7QP2Kj9Lh3xphW9+zavq8YeZsOo7isDimJkZh6UYbH+3r5uhx1H41GwtUXpisdRpumzFundAjdKi5aDyELVNU5Wj120Tm9sWlnfpvPlyRgxqQhmDFpaHeFSCFm9qOfo7TCqnQYASVJwITRvTEvJ1PpUCgEeOqX1HR/OMZHwSTU9/Ma6LXAgzef79ffPswlaiqQ7SFQ7xWK60TBge2h87jtqKmOtIeWy5qMOjx194Uw6rV+f+22lp26YB3szsZpvRbIe9p7G16zYT/WbT4Ks9UBk1GH7PEDeXyOiFRDo3QA1Lbt+4rx/Ls7caKo2udCskGngUGvRXWdvZuj86+qWjuWvP49tu8rds/bvq8Yq/J2o7zKjBiTDuVVZqzK291smY4sR+EtHA4wVtbYPRaSAbRbSAYAIYB3vjiANRv2+zs0CkHhWEgGXHmyaWc+lq3ernQoFOS89Utq6a8YHwWTcPrc7U7gkf/7r99++zCXqKlAtodAvVcorhMFB7aHzuO2o6Y60h5azouNMsBidWBa7id+f+225rcsJAOufbipCzy/xpoN+/HelwdhsTmg07iurH7vy4M8PkdEqsFissrlbToMs9UB2bc6MqJMOjhlGRabs/2FVcjmEMjbdNg9nbfpMHQ6CREGHSTJ9a9OJzVbpiPLEZFv1m0+qnQIFATCsZDc1OZdBUqHQEREYYy/fYiIiIhchBBYt/mIe1ojATGRelTV2uDjYXW/allIbm/+us1HAQnQaTSQJA10Gg0g8fgcEakHh7lWueKyOjh9qCRLkutMq6paG4QSPaQflZTVuf8uLqtDjKl5MzXqtc2W6chyROQbs9Xz1c1E1Ej29UwvIiKibsbfPkRERBSurHYn/rZ2F/694yQAQKfVQK+VgmrUTrPVdUVyU1qJx+eISD14ZbLKpSRGQquR2lxGp5UQadShsib4C8kAkJwY6f47JTES1hanbFntzmbLdGQ5IvKNychzjYjao2mnfyYiIgoU/vYhIiKicFRabkbu3/7jLiSbjFoAAuYgG7XTZNSh5R0unYLH54hIPVhMVrmpF2XAZNTB2/Fqk1EHCUCtJTTOUjLoJEy9KMM9PfWiDDgcAhabA0K4/nU4RLNlOrIcEfkme/xApUOgIJAUb1Q6BEWNH5WmdAhERBTG+NuHiIiIwt2fn/sGh09UQJKAmVcOg8XmhKNlVVYBem3H5mePHwgIwCHLEEKGQ5YBweNzRKQeLCarXOawFNwz/Rz0TY2B1KKgHBdtgMXmgF0FHaQ/xEbp8cDvxyJzWIp7XuawFMyZOhIJsSbUmB1IiDVhztSRzZbpyHIU3tYvy1Y6hG4XF61HbKTnsxYvOqd3u8+XJOCGyUMwY9JQf4dGIegff7k8LAvKkuTKp3k5mUqHQkHOW7+klv6K8VEwCafPXa8FHr7lfL/99mEuUVOBbA+Beq9QXCcKDmwPncdtR015+9y1GgkVNVZEmfR46A/n47qJg/HPpR1rOx2Z35Fl857OblU41mtd8z2ZMWkorr9sMCIMOjhkIMKgw/WXDebxOSJSDY6TEARGZvTE0P6JOF5YDQBIijfhgVnnYlC/BIUjC4zMYSk+HRjxdWFr8uMAACAASURBVDkKb+H+w4PFL/K3f/zlcqVDIApqau+XGB8Fk3BrD/787RNu247aFsj2EKj3CsV1ouDA9tB53HbUVEN7sDucWPnhbnz5w69wygJ9U6Kx6OaxSEuKbrVsR1/b38t6Kxx7M2PSUBaPiUi1WExWuaLTtXjqzW04fLISAHDOkCTMvzET0ZEGhSMjIiIiIiIiIiIiIup+pyvNWPLGNhw4Xg4AGHtWKubljOF9hYmIAoDftCq2bW8Rnn1nJ2rMdkgSMGPSEFx/6RBovN1AmYiIiIiIiIiIiIgohOz/pQxPvP4DyqutkCRg+mVDMGPSEEgt7wtJRETdgsVkFXLKAqs/34e1Gw8BAGKjDJiXMwbnDElWODIiIiIiIiIiIiIiosDY8P1xrPxwNxxOGSajDvNuOAdjh/dSOiwiorCiqmLy+vXrsXLlSjgcDsyaNQs5OTnNHt+3bx8WLlyI2tpaZGZm4pFHHoFOp6pV6LKKaiueeXs7dh8+BQAY3C8BD8zKRM/4yIDGsX1fMfI2HUZxWR1SEiMx9aIMHDpRjnWbj8JsdUCvlRAfY4SA5H48c1gK1mzYj7x/H4bF5oQkAT3jI3Dn/xvFexlT0POUE97a9bLV27F5VwFkWUCjkdA3OQqnKq2os9ghSRIi9Fqk941v8zWIwt2aDfvdfY5WAiBJcMoCBp0GMVF6SJLGYy56y9WO5DARNXrwpW+x50iZe3pEeiKeuPNCBSNqrmWfO35UGublZCodFgWxQPYXauqb7npmI44X1bin+6dGY8X8iYrEQuEl5y//QlWdwz0dG6nD6kezFIyIyL/Yxok6z+6Q8X/r9uDTrb8AAHRaCRqNhMde+4G5REQUYBqlA2hQXFyM5cuX45133sHHH3+M9957D4cPH262zPz58/HQQw/hiy++gBAC77//vkLRdo+9x07jnmc3uQvJWb8ZgKfu+q0iheRVebtRXmVGjEmH8iozlq7egTVfHIDF5oAEAatdRnGZGXa7A+VVZqzK241lq7fj3Q0HYLE5AQBCAKXlFjzz1jZs31cc0HUg8idPObEqb7fHdr1s9XZs2pkPWRYAAFkWOF5Ug1qzHUK4puusDhwrqPT6GkThbs2G/Xjvy4Ow2BwQsoDdKWB3yBCygMXmRGm5BTabvVUuesvVNRv2+5zDRNSoZSEZAPYcKcODL32rUETNeepzN+3Mx7LV2xWOjIJVR/b5gum92tOykAwAx4tqcNczGwMeC4WXlkU2AKiqcyDnL/9SKCIi/2IbJ+q88moLFv19i7uQHGHQQquRUGu2A2AuEREFmmou6926dSvOP/98xMfHAwAmT56Mzz//HHfddRcAID8/HxaLBaNGjQIATJ06FS+88AJuuOGGZq9TVVWFqqqqZvOKiooCsAadJ4TAP789itfW/w9OWSDCqMVd087GhHP6KhJP3qbD0OkkRBhczSPCoEOduRYCgE6jgc3hdC9bWWNHjzgTLHC4rgoRgATU/89VUDbbnMjbdJhXgAWhYMyn7uApJyxweGzXm3cVAACkJjnQoCE3hADqzA7ERxuYG2GCudQx6zYfBSRXn2N1NvY5Aq48EgCqau3oGR/ZLBe95eq6zUeREGPwKYdJ/ZhPgdOykNze/EBr2ecCrj52864CzMvx8iRyYy611pF9vmB6r/a0LCS3N5+aYy51XssiW3vzKfSFWj6xjZNSgj2XjhVUYvH//RenKi0AgGiTHmarHU65+XLMJQqEYM8nIn9RTTG5pKQESUlJ7unk5GTs3r3b6+NJSUkoLm591vYbb7yBFStWdG+wflRnseOF93Zhy27XwbA+ydF44PfnoV9KjGIxFZfVIcbUvGmIpn/XT0gA5PoJo17rvioEUrOnQgigpKyue4KlbhVs+dRdPOWEUa/12K7dedAOWQivr0Ghh7nUMWarAzpvY6fUV5MbUq1pHnnLVbPVgdREU6v5zL/gxHyiBt76XF/74nDHXGqtI/t8wfRe1L2YS0T+w3wi8o9gz6U3P92HU5UWRBi0uGf6aDzz1nZwF5+UEuz5ROQvqikmy7IMqcllBUKIZtPtPd5g1qxZuPbaa5vNKyoqanX/ZTX4pbAKT77xA/JLawEAvz07DfdcPxoRRmU/lpTESJRXmd1nyQONV4IBrqs/hHBNa+o/A6vdCY1Gch28a7h0rMnyyYmBHaqb/COY8qk7ecoJq93psV2786AdGkny+hoUephLHWMy6mCxOaBr3c27OyNN/WNN88hbrpqMOljtTp9ymNSP+UQNvPW5Go2nLw9qibnUWkf2+YLpvah7MZeI/If5ROQfwZ5LV14wAFEmPaZdMggDesXiqTd5GxtSTrDnE5G/qKaYnJqaiu3bGzuG0tJSJCcnN3u8tLTUPX3q1KlmjzeIjY1FbGxs9wbrB//ecQIr1v4Em90JnVbCzVPOwpTfDvRYIA+0qRdlYFXebljggFGvhdXuRKRJjzqzHQ5ZhkYCnPXH7eKi9bDYHHA4BMaPSsPmH/NdZ4o1Oa5nMmgx9aIMRdaFuiZY8qm7ecoJh0N4bNfjR6Vh0878ZsNbNxDu/wGRJp3X16DQw1zqmOzxA/HelwfhkOXmJzM1+Ts2qrH/acgjb7maPX4gvt52wqccJvVjPgXOiPREj0Naj0hPVCCa1rz1ueNHpSkTUJBhLrXWkX2+YHqv9vRPjfY4pHX/1OiAxxKMmEudFxup8zhEaWykag5VUYCFWj6xjZNSgj2Xzj0zFeeemeqeZi6RkoI9n4j8xdsgkgF3wQUX4LvvvkNZWRnMZjM2bNiA8ePHux/v3bs3jEYjduzYAQBYt25ds8eDhc3uxN8++AnPvrMTNrsTPeMisOTO3+LqC9NVUUgGgMxhKZgzdSQSYk2oMTuQEGvCfTljMGPyEEQYdBCQYNRrkJJogkGvQ0KsCXOmjsS8nExMnzQEEQYtANcVyUkJEZh/07m8JyUFNU85MWfqSI/tel5OJi46p7f7qiiNRkL/1GhEmfSQJNd0pFGHM9LivL4GUbibMWkorr9sMCIMOkgaCXqtBL1OA0kjIcKgRVJCBIwGfatc9JarMyYN9TmHiajRE3de2KpwPCI9EU/ceaFCETXnqc+96JzemJeTqXBkFKw6ss8XTO/VnhXzJ7YqHPdPjcaK+RMDHguFl9WPZrUqBMRG6rD60SyFIiLyL7ZxIv9gLhERKU81p++kpKTg3nvvxcyZM2G32zFt2jSMHDkSt956K+bOnYsRI0Zg6dKlWLRoEWpqanDWWWdh5syZSofdIcVldXjyzW04fKICAHD2oCQsuGkMYqOMCkfWWuawlFYHMjKHpWDGpKFtPm/GpKHtLkMUjDzlhDfzcjIxjyOdEHVJZ/sTb7nakRwmokZqKRx7wz6X/C2Q/YWa+iYWjkkpLARQqGMbJ/IP5hIRkbJUU0wGgClTpmDKlCnN5r3yyivuv4cOHYoPPvgg0GH5xfZ9xXj2nR2orrNDkoDfTRyMGyYP5T3diIiIiIiIiIiIiIiIiEiVVFVMDkVOWWDNF/vx3lcHAQAxkXrcO+OcZvd9ICIiIiIiIiIiIiIiIiJSGxaTu1FljRVL396BXYdKAQAZfeKRO+tcpCRGKhwZEREREREREREREREREVHbWEzuJvuOleGpt7bhdKUFAHD5+f1x6zUjYNBrFY6MiIiIiIiIiIiIiIiIiKh9LCb7mRAC6789in+s/x+cskCEQYvbp47ExHP7KR0aEREREREREREREREREZHPWEz2ozqLHS++vwv/+akAAJDWMwoP/P48DOgVq3BkREREREREREREREREREQdw2KynxwvqsKS17chv7QGAHDByF645/rRiIzQKxwZEREREREREREREREREVHHsZjsB5t2nsSKtbtgtTmh00qYeeUwXDMhA5IkKR0aEREREREREREREREREVGnsJjcBXaHE6+s+xmfbf0FANAjNgL33TQGwwf2VDYwIiIiIiIiIiIiIiIiIqIuYjG5k0rK6vDkm9tw6EQFAGBEek/Mv3EMEmIjFI6MiIiIiIiIiIiIiIiIiKjrWEzuhO37ivHsOztQXWeHBOD/XTIIN14+FFqtRunQiIiIiIiIiIiIiIiIiIj8gsXkDnDKAms27Mf7Xx2EEEB0pB5/un4Uxg5PUzo0IiIiIiIiIiIiIiIiIiK/YjHZR5U1VixdvQO7DpYCANJ7x+H+mZno1TNa4ciIiIiIiIiIiIiIiIiIiPyPxWQf7D9ehqfe2IZTlRYAwKXn9cPt146E0aBVODIiIiIiIiIiIiIiIiIiou7BYnIbhBD45D/H8I/1P8PhFDDqtbjt2uGYNHaA0qEREREREREREREREREREXUrFpO9MFsdWPH+LmzelQ8A6NUzCvffNAbpfRIUjoyIiIiIiIiIiIiIiIiIqPuxmOzBieJqLHnjB5worgEAnD88FfdcPxrRkQaFIyMiIiIiIiIiIiIiIiIiCgwWk1v4ZudJrFi7CxabE1qNhBsvH4qpF2dAo9EoHRoRERERERERERERERERUcCwmFzP7nDiH//8Hz7ZcgwAkBhrxLycMRiZkaRwZERERERERERERERERETBz2Z3wqDXdvpxCjzVFJN37NiBJUuWwG63Iz4+Hk888QR69+7dbJn8/HxcddVV6NevHwCgZ8+eePXVV7v83iXldXj6ze048Gs5AGD4wB6478Yx6BFn6vJrExERERERERERERERERFg0GsxZd46r4+vX5YdwGjIF6opJs+fPx8vvfQShg4dig8++ACPPfYYVq5c2WyZn3/+GVOmTMHixYv99r4795dg6eodqK6zQQKQPSEdN115Jgw6DmtNREREREREREREREREROFLFRVTm82Ge+65B0OHDgUADBkyBIWFha2W27NnDw4ePIjs7GzMnDkTBw4c6PR7OmWBNV/sx1//7ztU19kQbdJjwcxM/OHq4SwkExEREREREREREREREVHYU8WVyQaDAdnZrsvWZVnGihUrcOmll7Zazmg04uqrr8b06dPx7bff4o9//CM+/fRTGAwG9zJVVVWoqqpq9ryioqJm05U1Vjz7zk7sPFACADgjLRYLbsxEn5QYf68aUVDzJZ+IqH3MJSL/YT4R+Qdzicg/mEtE/sN8IvIP5hKR/zCfiFwCXkz+7LPPsGTJkmbzBg4ciNdffx02mw25ublwOByYM2dOq+fefffd7r8nTJiAZcuW4ejRo+4rmgHgjTfewIoVK7y+/8Ffy7HkjW04VWEGAFyS2Re3Tx0Bk1Hf1VUjCjnt5RMR+Ya5ROQ/zCci/2AuEfkHc4nIf5hPRP7BXCLyH+YTkUvAi8lXXHEFrrjiilbza2trcccddyA+Ph4rV66EXt+6uPvWW2/hqquuQkJCAgBACAGdrvkqzJo1C9dee22zeUVFRcjJycG/t59A3tZSOJwCBr0Gt0wZjssvGABJkvy4hkSho618IiLfMZeI/If5ROQfzCUi/2AuEfkP84nIP5hLRP7DfCJyUcUw1wAwf/589O/fH4888gg0Gs/3LN62bRssFgtuvfVW/PDDD5BlGQMHDmy2TGxsLGJjYz0+f/UX+6GPTERqj0jMv3EMBvdL9Pt6EIWStvKJiHzHXCLyH+YTkX8wl4j8g7lE5D/MJyL/YC4R+Q/zichFFcXkvXv3YuPGjcjIyHCf5ZGcnIxXXnkFa9asQUlJCe655x4sXLgQubm5WLduHYxGI5YtW+a18OzNuWem4J7rRyMu2tgdq0JEREREREREREREREREFBJUUUw+88wzceDAAY+PzZgxw/13SkoKXnvttU6/zzUT0nHLdedBp+1YAZqIiIiIiIiIiIiIiIiIKNyEVVX1qt8OZCGZiIiIiIiIiIiIiIiIiMgHrKwSEREREREREREREREREVErLCYTEREREREREREREREREVErLCYTERERERERERERERERkeJsdmeXHif/0ykdABERERERERERERERERGRQa/FlHnrvD6+fll2AKMhgFcmExERERERERERERERERGRBywmExERERERERERERERERFRK2ExzLXT6Ro/vaioSOFIiLomNTUVOp2yact8olChdD4xlyhUKJ1LAPOJQofS+cRcolDBXCLyD6VzCWA+UehQOp+YSxQqlM4lIDTyqU+fPkqH0GUnT55UOoSg15F8koQQopvjUdz27duRk5OjdBhEXbZx40bFv+iZTxQqlM4n5hKFCqVzCWA+UehQOp+YSxQqmEtE/qF0LgHMJwodSucTc4lChdK5BDCfKHR0JJ/CophssVjw888/IykpCaWlpcjJycHq1auRmpqqdGg+KSoqCrqYAcbdHdRw5lXTfNJqtYrG0hlq/nwDhdvARel8aplLwfi5BFvMwRYvEBwxK51LQPt9k9q3I+PrmlCKT+l8Ctb9PLW3AV9xPfwnlHNJDdvX37hO6qV0LgG+5ZPat7ea41NzbEBoxad0PgXrfl5Tam8P/sB1bJ/SuQQEfz6FQzvzVbhvi47kU1gMcx0REYHMzEwAcCd3amqq4mewdFQwxgww7lDTNJ+CGT9fbgOleculYPxcgi3mYIsXCM6YA8nXvknt25HxdQ3j67pg388Lhm3sC65H8AtELoXi9uU6kScdySe1b281x6fm2ADG5w/Bvp/XVDBs767iOqpbqORTMH8G/sZt0T6N0gEQEREREREREREREREREZH6sJhMREREREREREREREREREStsJhMREREREREREREREREREStaP/617/+VekgAs1oNGLs2LEwGo1Kh+KzYIwZYNykTvx8uQ3UKhg/l2CLOdjiBYIzZjVS+3ZkfF3D+ChUtjHXg3wRituX60Rdpfbtreb41BwbwPiouXDY3lxHCgR+Bo24LXwjCSGE0kEQEREREREREREREREREZG6cJhrIiIiIiIiIiIiIiIiIiJqhcVkIiIiIiIiIiIiIiIiIiJqJayLyXv37sXw4cOVDsNnO3bswLRp05CdnY1Zs2YhPz9f6ZDatH79elx55ZWYNGkSVq9erXQ4PlmxYgWysrKQlZWFp59+WulwqIvaa4P79u3D1KlTMXnyZCxcuBAOh0OBKLuXr3m4YMEC5OXlBTAyAoCCggLk5OTg8ssvxx133IHa2lqvy27ZsgWzZs0KYHTNBVs+BWPbby/mr776CtnZ2bj66qtx5513orKyUoEog5Pa90lqampw1VVX4eTJk0qH0ora942ef/55XHnllcjKysJrr72mdDhePfXUU8jNzVU6jJCl9hz3xlN+bd26FVOmTMGkSZOwfPlyhSPsmKbtXG37BaEiWNt6W9Tez3QFv/u7R3t5sGLFClx88cXIzs5GdnZ2wHOlrf06NXw3thWf0tuuve8Dpbdfe/Epvf1CXSj2gS3ddNNNyMrKcrehn376SemQ/Kbld08w7/OGgnDIp7awPXaSCFN1dXVi+vTpYvDgwUqH4rOLL75Y7Nu3TwghxNq1a8Xtt9+ucETeFRUViYsvvliUl5eL2tpaMWXKFHHo0CGlw2rTli1bxPXXXy+sVquw2Wxi5syZYsOGDUqHRZ3kSxvMysoSP/74oxBCiAceeECsXr1aiVC7jS/boKioSMyZM0eMHDlSfPjhhwpFGr5uu+028cknnwghhFixYoV4+umnWy3jdDrFq6++Ks477zxx4403BjpEIUTw5VMwtv32Yq6urha/+c1vRFFRkRBCiOeee048+uijSoUbVNS+T7Jr1y5x1VVXibPOOkucOHFC6XCaUfu+0ffffy+mT58u7Ha7MJvN4uKLLxZHjhxROqxWtm7dKsaOHSvuv/9+pUMJSWrPcW885df69evFhAkTxK+//irsdruYPXu22LRpk9Kh+qRlO1fTfkGoCNa23ha19zNdwe/+7uFLHsyZM0fs3LlTkfja269T+ruxvfiU3Ha+fB8ouf18iU/J7RfqQrEPbEmWZfHb3/5W2O12pUPxu5bfPWazOWj3eUNBOORTW9geOy9sr0x+8sknFb3Cq6NsNhvuueceDB06FAAwZMgQFBYWKhyVd1u3bsX555+P+Ph4REZGYvLkyfj888+VDqtNSUlJyM3NhcFggF6vR3p6OgoKCpQOizqpvTaYn58Pi8WCUaNGAQCmTp2q+jbaUb7k4fr16zFx4kRcccUVCkUZvux2O7Zt24bJkycD8N4Gjxw5giNHjuDRRx8NdIhuwZZPwdj224vZbrfj4YcfRkpKCgD17weoidr3Sd5//308/PDDSE5OVjqUVtS+b3TeeefhzTffhE6nw+nTp+F0OhEZGal0WM1UVFRg+fLluP3225UOJWSpPce98ZRfv/zyC/r374++fftCp9NhypQpQbEuLdu52vYLQkWwtvW2qL2f6Sx+93cfX/Lg559/xqpVqzBlyhQsXrwYVqs1YPG1tV+nhu/G9vY7ldx27X0fKL39fPm+UnL7hbpQ7ANbOnr0KABg9uzZuPrqq/H2228rHJH/tPzu2b17d1Du84aKcMintrA9dl5YFpM3btwIi8WCyy+/XOlQfGYwGJCdnQ0AkGUZK1aswKWXXqpwVN6VlJQgKSnJPZ2cnIzi4mIFI2rfoEGD3Dulv/zyCz777DNMmDBB4aios9prgy0fT0pKUn0b7Shf8vCWW27BddddF+jQCEB5eTmio6Oh0+kAeG+DgwYNwuOPP464uLhAh+gWbPkUjG2/vZgTEhJw2WWXAQAsFgtefvllVe8HqIna90kef/xxZGZmKh2GR8Gwb6TX6/HCCy8gKysL48aNc59woRYPPfQQ7r33XsTGxiodSshSe4574ym/JEkKynVp2c7Vtl8QKoK1rbclGPqZzuB3f/dpLw9qa2sxbNgwzJ8/Hx999BGqqqrw0ksvBSy+tvbr1PDd2FZ8Sm+79r4PlN5+7cWn9PYLdaHYB7ZUVVWFcePG4W9/+xtef/11vPvuu9iyZYvSYflFy++ecPg81Szctz/bY+eFdDH5s88+w/jx45v99/vf/x4rV67EX/7yF6XD88pb3IDrCuX77rsPDocDc+bMUTbQNsiyDEmS3NNCiGbTanbo0CHMnj0bCxYswIABA5QOhzqpvTYYzG3UV+GwjsHC0/f6vHnzWn0eav18gi2f1BaPL3yNubq6GrfddhuGDh2Ka6+9NpAhBq1gbA9qo/Z9o7lz5+K7775DYWEh3n//faXDcVu7di169eqFcePGKR1KSAv2HG+aX3379g26dfHUzoP9M1GrUN6uau9nOoLf/d2rvTyIiorCK6+8gvT0dOh0OsyePRvffPONEqG2ovYcVsu28/Z9oJbt5y0+tWy/UKWWz787jR49Gk8//TRiYmKQmJiIadOmhWwbCofPU824/Zvj9vCdTukAutMVV1zRavjKtWvXYtWqVcjJyXHPy87OxurVqxEdHR3oED3yFDfgOsvtjjvuQHx8PFauXAm9Xq9AdL5JTU3F9u3b3dOlpaWqHL6xpR07dmDu3Ll48MEHkZWVpXQ41AXttcHU1FSUlpa6p0+dOhUUbbQjgjUPQ5Gn73W73Y6xY8fC6XRCq9Wq+vMJtnwKxrbvS8wlJSX4wx/+gPPPPx8PPvhgoEMMWsHYHtREzftGR44cgc1mw7Bhw2AymTBp0iQcOHBA6bDcPv30U5SWliI7OxuVlZWoq6vDE088wfz1s2DO8Zb59cMPPzTrT4NhXTy1c0mSVLVfECqCua23Rc39TGfwu797tZcHBQUF2Lp1K6ZNmwbAdUC4YSQopantN1NLath2bX0fqGH7tRWfGrZfKAvVPrCp7du3w263u09GCuU21DKfQ/HzVLNwyKeOYHv0XUhfmezJddddh6+++grr1q3DunXrAADr1q1TTSG5LfPnz0f//v3x3HPPwWAwKB1Omy644AJ89913KCsrg9lsxoYNGzB+/Hilw2pTYWEh/vjHP2Lp0qUh8SM23LXXBnv37g2j0YgdO3YAcH0PqL2NdlQw5mE40ev1yMzMxKeffgoA+Pjjj1X7+QRbPgVj228vZqfTidtvvx1XXHEFFi5cyLMkOyAY24NaqH3f6OTJk1i0aBFsNhtsNhs2btyIMWPGKB2W22uvvYZPPvkE69atw9y5c3HJJZewmNANgjXHPeXX2WefjWPHjuH48eNwOp345JNPVL8untr5kiVLVLVfECqCta23Re39TGfwu797tZcHEREReOaZZ3DixAkIIbB69Wr3rWKUprbfTC0pve3a+z5Qevu1F5/S2y/UhWIf2FJ1dTWefvppWK1W1NTU4KOPPgrZNhSM+7yhJBzyqSPYHn0Xmqe3hKC9e/di48aNyMjIcA9rmZycjFdeeUXhyDxLSUnBvffei5kzZ8Jut2PatGkYOXKk0mG16dVXX4XVasWTTz7pnjd9+nTMmDFDwaios7y1wVtvvRVz587FiBEjsHTpUixatAg1NTU466yzMHPmTKXD9itftgEp6+GHH0Zubi5WrlyJXr164dlnnwUArFmzBiUlJbjnnnsUjtAl2PIpGNt+ezEXFRVh7969cDqd+OKLLwAAw4cPx+OPP65w5OoXjPskaqH2faMJEyZg9+7duOaaa6DVajFp0qSQKUaQ74I1x73l15NPPom7774bVqsVEyZMwOWXX65glJ2npv2CUBGsbb0tau9nSH182c9fvHgx7rjjDtjtdpxzzjm4+eabFY1Zrb+ZPMWn5Lbz9n3w9ddfq2L7+RKf2tpeKAnFPrCliy++GD/99BOuueYayLKMG264AaNHj1Y6rG5hNBpDZp83GIVDPnUE26PvJCGEUDoIIiIiIiIiIiIiIiIiIiJSl7Ab5pqIiIiIiIiIiIiIiIiIiNrHYjIREREREREREREREREREbXCYjIREREREREREREREREREbXCYjIREREREREREREREREREbXCYjIREREREREREREREREREbXCYnIQ+f7773HVVVcpHYZHeXl5mDNnqk0afwAAHb9JREFUjt9eb82aNXj55Zf99npEROSbPXv2YO7cuR16TllZGYYMGQIA2LhxIx577LE2l9+0aROef/75dl/3xRdfxOLFi1vNv+SSS7Bnz54OxeiLW2+9FYcPHwYAzJ49G2VlZW0ur+Z+mYiIiChQ1LxPtHbtWqxevVrpMIiIKAj52r/NmTMHeXl5AYiIKLTl5ubi1VdfVToM8oLFZFKlGTNm4LbbblM6DApRaiqWAUB+fj5yc3MxefJkZGVlYfLkyVi+fDnsdrt7GYfDgZUrV+LKK69EVlYWsrKy8Mgjj6CioqJD60HUnhEjRuCFF17o9PMnTpyIRYsWtbnMnj17UFlZ2en36C6vvPIKMjIyAABbtmxROBoiIupOai5+EZH/7NixAxaLRekwiHyi5r7pP//5Dy6++GJMmzaNOUVERBSGdEoHQB1XXV2NRx55BPv374ckSbjwwgvx5z//GTqdDt988w2WLl0KjUaDYcOGYevWrXjnnXfQp08fr6930003IS4uDkePHsWMGTNwzTXX4PHHH8fBgwdht9sxbtw4LFiwADqdDh988AHee+892O12VFZW4tZbb8UNN9zgU9wnT57ETTfdhAsvvBA//fQThBB46KGHkJmZiRdffBG7du1CSUkJhgwZgv79+6O8vBwPPfQQjh07hoceeghlZWXQaDS44447cOWVV6K4uBiLFy9GYWEh7HY7srKycPvtt8PhcODRRx/Fzp07odfr0adPHyxZsgRRUVH++ggoyPmjWDZx4sQ2l/G1WFZcXIzrr78ec+fOxZIlSyBJEmpra5Gbm4unnnrKXZT785//DL1ej3feeQfx8fGw2+14/fXXcf311+PDDz9EdHR0p9eHwsf333+PZ599Fr169cKxY8dgMplw22234a233sKxY8cwadIkTJw4EY8++ig++eQT5ObmIjo6GgcOHEBRURGGDBmCp556ClFRUdiwYQOWL18Ok8mE4cOHu98jLy8PX3zxBVatWoUNGzZg5cqVkCQJWq0WCxYsgMFgwLvvvgun04mYmBjce++9XVqnr776CitWrIAsy4iKisIDDzyAkSNH4siRI1i4cCFsNhuEEJg2bRpycnLw4osv4vjx4ygqKkJpaSmGDh2Kxx9/HNHR0bjkkkvw/PPP45133gEAzJo1Cy+//DL279+PVatWwWazoaysDNdccw3+9Kc/+RzjCy+8gC+//BJ6vR4JCQlYsmQJkpOT8dNPP+Gxxx6D2WyGXq/HggULMG7cOGzfvh1PP/20e/6f/vQnjB8/Hnl5efjggw9gNpsRHR2Nt956C2vXrsWaNWsgyzLi4+Pxl7/8Benp6V3aphQ6amtr8cADD+D48ePQaDQ466yzsHjxYmzatAkrV66E3W5HREQE7r//fowePRqnTp3CQw89hNOnT6O0tBS9e/fGc889hx49euCdd97Bu+++C71eD6PRiMWLFyMjIwOHDh3C4sWLUVFRAUmSMHv2bFxzzTX4/vvvsXz5cvTt2xeHDh2Cw+HAI488gjFjxii9WYiIqBvU1dXh3nvvxdGjR2G1WvHYY49hyJAhXo9dDBkyBN999x0SExMBwD1tNBo99l0ajQZff/21x/7Lmy+//BJff/01tmzZgoiICLz55pt46KGH8Jvf/AYAsHDhQgwePBhVVVVe9w+9HXcgCjf/+te/cN111+HOO+9UOhSigKqrq8PcuXNx/PhxxMbGYvHixYiMjERubi5KSkqQlpaG06dPu5cfPnw4Jk6ciP3792Pp0qW44YYbcPPNN2Pr1q2oq6vDXXfdhc8//xwHDx5EcnIy/v73vyMyMlLBNSTyv+zsbOTm5mLcuHH45JNP8MADD2Dbtm2IiIjAwoULkZaWhn//+9+QZRmA6+r+yZMnAwB+/PFHTJ8+HadOncKgQYOwbNkyREZGMrfUQFDQ+O9//yuysrLEggULxKOPPipkWRZWq1XMnj1brFq1SpSVlYnzzjtP7Nu3TwghRF5enhg8eLA4ceJEm6974403igceeMA9nZubK958800hhBAOh0Pcd9994uWXXxY1NTXid7/7nSgrKxNCCPHjjz+KUaNGCSGE+PDDD8Vtt93W5vucOHFCDB48WPzzn/8UQvz/9u49rsZs/wP4Z2u3RRG5n3EZg4Q5JJO020lEOyrlVoYIuYSOhog0J7pxiCbFZAyRZlTjljJHiJJSbuNVueQykjnIJaPSUO39/f3Rq+dIe4f5DeJ8369Xr1f7tp61n9eznrX297vW8xClpqaSmZkZVVRU0IYNG8ja2poqKyuJiGjDhg20cuVKIiJycHCgmJgYIiK6c+cODRs2jEpLS8nFxYVSUlKIiOjZs2fk4uJCBw8epDNnzpBcLielUklERGvWrKFz58695l5mH7qsrCyaMGECLViwgOzt7cnJyYlSUlLI1dWVLCwsKCgoSGhLRETe3t4UEBBAkydPJisrK5o3bx6VlZUREVFycjLJ5XJydHSkr7/+mvT19Ymo9vGenJxMDg4O5OjoSOPGjaPTp0/ThQsXSCqVkomJCa1fv77e+vr7+wvH+ouKioqEdnj+/HkyNzcX2seL5syZQ1u2bKl3G/fv36dp06aRg4MDOTg4UGhoqPBaZGQkWVtb06hRo2ju3LlUUlJCREQRERFkY2NDtra25OHhQffv3yei6vPFvHnzyMbGhqKjo6mkpIS8vb3J0dGRbG1tKSgoSGU9WcOQlZVFvXr1oosXLxIR0YwZM8jJyYmeP39Ojx49oj59+tCBAwdqtY+a1ysqKsjBwYF2795NDx48oAEDBtC1a9eIqPo4UtU+hg0bRr/88gsREaWnp1N4eDgR1T7H12fDhg1kYmJC9vb2tf769OlDOTk5dP36dZJKpVRYWEhERJmZmWRmZkalpaW0bNky2rx5MxFVtwFPT09SKBS0YcMGGjx4MD148IAUCgUtXLiQVq9eTURElpaWlJOTQ0RE+vr69OjRI1IqlTR58mS6efMmERHdu3ePevXqRY8ePap1LlHnzp07ZGRkRM+fPycioq1bt9KRI0eooqKCzMzM6Pjx40RElJubS7a2tlRcXEympqZ04cIFIiK6evUqDRw4kAoLC2nPnj1kbGxMpaWlRESUnZ1NX375JZWXlwv7WC6Xv3K/sv8d+/bto+nTpxNR9Zhu+fLldPPmTeFYI6o+xszMzOjp06e0fft2od0olUpyc3OjrVu3UlVVFfXp04eKioqEcmNjY6myspKGDRtGycnJRFTdPszNzen8+fPC+ebSpUtEVH3sT5o06V3vAsbUysrKIktLS/L09CR7e3uytramM2fOUElJCS1atIhGjRpFtra29K9//UsY29T0DTVqHpeVlZGHhwfZ29uTg4MDLV++nBQKBRERpaSk0Lhx42j06NHk5ORE58+ff2Xdjh07Rk5OTuTo6EgWFhbC2C0rK4vs7OzIycmJbG1t6fnz52rLf/DgAbm7u9OECRPI0tKSJk+eTA8fPvyrdyNjRPTfMWbN+CUqKoqmTJmiNnZBpL49qeq7CgoK6u2/6uPt7U3ff/+9UK9//OMfRERUWlpKgwYNoidPntQ7PlQXd2DsbWiofdOWLVvI2NiYZDKZ0DY2bdpEDg4OZG9vT+7u7nTv3j0ionpjBGFhYWRra0uOjo40ffp0YWzJWEOVlZVFBgYGQlw5NjaWxo0bR3PnzhXGZwUFBWRoaEh79uwhouo2uG/fPqEMfX192rFjBxERbd68mfr370/37t0jhUJBjo6OQpycsY9JeHi40F8sWbKEzMzMKD09nZRKJZmZmdGUKVMoKSmJiIguX75MK1asIKLqcdu4ceOovLycqqqqyNHRUWhP3LbeP16Z/AE6ceIEdu3aBZFIBIlEAmdnZ+zYsQNdu3ZFt27dYGBgAABwdHR85aV4a3zxxRfC/6mpqcjNzcXu3bsBQLh8jba2NiIjI5GWloaCggJcuXIF5eXlb1R3XV1d2NnZAQAsLCygoaGB/Px8AIChoSHE4tqH5O+//44rV65g/PjxAIAOHTrg6NGjKC8vx5kzZ/DkyRPhUsLl5eW4cuUKZDIZNDQ0MH78eMhkMlhbW6Nv375vVE/2YcvNzYWfnx969+4NNzc3fPfdd4iOjkZZWRkGDx6Mv//977Xen5eXh+joaIhEIkyYMAGHDh2ChYUFfHx8EBsbi+7du2Pz5s0qt7VmzRqEhITA0NAQJ0+eRHZ2NubPnw9nZ2c8fvz4lasuz549iwULFtR5vm3btnBxcQFQPSNLVfsAAKlUiszMTLi5uandRnx8PDp27Iht27ahvLwcy5cvR2lpKU6fPo29e/ciPj4eurq6WLVqFWJiYtC2bVukp6dj9+7daNq0KcLDw2vds6J58+b4+eefAQDLli1Dnz59sHr1aigUCixduhRRUVGYOXNmvd+bvT8dO3ZE7969AQCdO3dGs2bNIJFIoKenB21t7Tor6s3NzSGRSAAA+vr6ePLkCc6dOwd9fX3hktBOTk5Yv359nW2NGjUK8+fPh4WFBczMzP7UcTFy5Ej885//rPXc0KFDAQBZWVkYNGgQOnXqBAAwNTWFnp4e8vLyMHz4cHh7eyMnJwempqbw9fVFo0bVd/eQy+Vo3bo1AGDcuHEIDg6Gt7e3yu2LRCJERkYiNTUVSUlJuHHjBogIf/zxx2vVv127djAwMICjoyMGDx6MwYMHw9TUFBcvXkSjRo0wZMgQANWzlxMTE5GWlobOnTujX79+AIAePXrAyMgIp0+fhkgkQs+ePYUrEaSmpuLWrVtwdnYWtldSUoLff/8dLVq0eK36sY/bgAEDEBoaChcXF0ilUkydOhUZGRm4f/8+XF1dhfeJRCIUFhZi6tSpOHv2LKKiolBQUIBr166hX79+0NDQgFwuh7OzM4YMGQKZTAYLCwvcvHkTz58/x4gRIwBUH+8jRoxAeno6TExM8Le//Q29evUCAPTu3Rv79u17H7uBMbXu3buH0NBQ9OvXD9u3b0d4eDjat2+PFi1aIDExEZWVlXB3d8e2bdvqvQXPkSNH8PTpUyQkJEChUMDPzw+3b98GESE0NBTR0dFo2bIlrl27hmnTpuHw4cNqZ8kTEbZt24bVq1fj008/RVFRESwtLTFlyhQAwLVr13D06FF88sknKCgoUFv+wYMHYWhoiFmzZoGIMGvWLCQkJGD69OlvZV8y1qlTJ2H8YmBggD179uDq1asqYxf1tSdVfVeXLl3www8/qO2/amIgrzJmzBhs3LgRxcXFOHToEIYMGYLmzZsDUD0+9PDwUBt3GDly5J/ZTYy9UkPsm9zc3HD9+nX06NEDM2bMwP79+3H16lX89NNPEIvFiIuLg6+vL7Zs2YLg4GCVMQJbW1vs2LEDp06dgkQiwbZt25CTkwMrK6u3tSsZ+0v07NkTRkZGAKpj7StWrIBSqRRiCF26dIGJiUmtz7wYZwcgrLjs3Lkz9PX10a5dOwDV8ZmGeAswxv6/hg8fjoULF2LJkiU4e/YsXF1dkZGRAW1tbXTu3Bk2Njbw9/fHsWPHIJVKsXDhQuGzVlZWaNKkCYDqmFhxcbHwGret94uTyR8gpVIJkUhU63FVVRU0NDRARLXeWxM4f5UXB4xKpRJhYWHCZTJLSkogEolw7949ODk5YcKECRgwYADkcjmOHz/+RnXX0NCo811qnlM1aK1Jnr34fX/99Ve0adMGRITY2Fjh5FJcXIzGjRtDW1sbCQkJOH/+PLKysuDp6YkZM2Zg0qRJb1RX9uH6kJJlRFTr+P7++++RmJgIAHj48CEOHjwIoPqeyapUVFTU+rwq5ubmmDVrFu7evQupVIpFixahWbNmOHXqFORyOXR1dQFUJ4YBYMGCBRgzZozQJqdMmYLIyEhUVFQAeL3JJ6zhqjnWa6iapPAiLS0t4X+RSCT0My/2N+rK+OqrrzB27FhkZGRg79692LZtm3Cs/BVe7g9r6lVVVQVLS0skJycjMzMTp06dwsaNG7F3714AtfsipVJZb19ZXl4OR0dHWFlZ4YsvvsDYsWNx9OjROv2tOo0aNUJMTAxyc3Nx6tQpBAcHw9zcHPb29nXqfvXqVSgUCrXfSVNTs05/PXr0aCxevFh4fP/+faFNM9apUyccOXIE2dnZyMrKwrRp0zB79myYmprim2++Ed539+5dtG3bFmvXrkVOTg7Gjh0LExMTVFVVCcd6SEgIrl69iszMTHz33XdISEjA3Llz1R6vgPrzB2MNRUNMfr1qElOHDh3wySefAMCfmhzC2Nuiqakp/F9zzlcXu3hZze8MQHXf5e/vD6VSqbb/el3NmzeHXC7HgQMHkJiYCD8/P+E1VeNDpVKpNu7A2NvSEPumlx0/fhy5ubkYO3YsgOo2U9NPqYsRqJtky1hD93K8QCQSQVNTs96YyMsx7hf7yBf/Z+xj1bNnT1RWViIlJQWffvopLC0t8dVXX0EsFsPa2hrOzs6wtLRERkYG0tPTERERgUOHDgGo3Z5ejiNw23q/Xi/TyBoUmUyGmJgYEBEqKioQHx8PqVQKIyMjYcUwACQnJwuJ4Dctf/v27UL57u7uiImJQV5eHvT09DB37lzIZDIhkaxQKF677OLiYpw4cQIAcOzYMWhqakJfX1/t+3V0dNCnTx/s378fQPWPxYkTJ+LZs2cwNDREVFQUgOqE98SJE5GSkoLjx4/D1dUV/fv3h4eHBxwcHJCXl/dG+4B92N51suzHH3/E559/jr17977xpIX+/fvj9OnTwmM3NzckJCQgISEBDx8+hFKphJGREXJyclSuhMzOzq73PmEA0LdvX6SkpMDJyQn/+c9/MH78eOTl5UFDQ6PW+aGkpAS//fbbK4M+qiaf1NT5p59+qrOKlH18jI2Ncf36daG/qUnSvqiqqgpDhw7FH3/8gYkTJ8LPzw/5+fmoqKiAhoaG2gkSb8LU1BQnT57E7du3AQCnTp3C3bt30a9fPyxatAg///wzRo0aBT8/P+jo6KCwsBAAkJKSgtLSUiiVSsTHx8PS0rJO2TV1vHXrFsrKyuDp6YmhQ4ciOzsbFRUVwn1dXuXKlSuwtbVFt27dMHv2bLi6uiI3NxefffYZRCIRMjIyAAAXL17E1KlT0a9fP/z666/IyckBUL0C7cyZMxg4cGCdsmUyGQ4ePIj79+8DAHbt2oWpU6e++Y5kH60ff/wRy5Ytg0wmw+LFiyGTyfDkyRNkZGTgxo0bAIC0tDTY29vj2bNnOHnyJKZOnQoHBwe0atUKmZmZUCgUKC4uhoWFBVq0aAFXV1d4enoKx7FYLMbhw4cBAEVFRUhOToZUKn2fX5ux1/ZXJ79mzZqFsrIyTJs2DceOHROSXzXjpISEBMTHx6NHjx5q61QzienixYvo3bs3lixZArFYLIxLXx6HqSt/7dq1CAsLQ8uWLeHk5AQzMzOe0MHeOXWxCwDQ09NDbm4uACApKUn4jKq+69KlSzA1NVXbf9Xn5XHnpEmTEB0dDSKqdQUzVeNDHR0dtXEHxt6Whtg3vUypVNaKXezZswe7du0SXlMVI6iZZLtq1Sq0aNECwcHBWLNmzZ/ZRYy9U/n5+bh8+TIAIC4uDgMGDMCwYcMQFxcHALhz5w6ys7PfZxUZa5CsrKywbt06mJmZoVu3bigrK0NiYiJGjBgBZ2dnXL58GWPGjEFAQABKSkrw4MGD911l9gq8MvkD5Ovri8DAQNjZ2aGyshLm5uaYM2cOJBIJ1q9fD29vbzRq1Aiff/45xGKxMIP2dS1fvhxBQUFC+VKpFG5ubqiqqsLu3bshl8shEokwcOBA6Onp4datW69dduPGjZGQkICQkBBoaWlh48aNdVYrv2zdunVYuXIldu7cCZFIhKCgILRp0wYhISEICAiAnZ0dKioqYGtrC3t7eygUCpw4cQK2trZo2rQpdHV1ERAQ8Eb7gDFjY2P4+vriypUrMDAwUJssGzFiBL799ltMnDgRMpkMI0eOfKNkmbu7O5ydndGzZ0/Y2dkJn6sJzDdq1AiGhoYYOHAgli5dCn9/f+jq6kKhUGDLli0oKCjAunXr6t1GSEgIiAiLFy/GsGHDkJ+fj2vXrkEqlWLNmjVwc3ODjo4OwsPDQUQwNzfHnj17MGrUKDRt2hQ7d+6EsbFxnSQ98N/JJ/7+/sLltmQyGdzd3V9zT7MPkZ6eHkJCQuDl5QVNTU0YGxvXeY9YLIaPjw+8vLwgFoshEokQHBwMiUSCQYMGwcvLCwEBAfj666//dD26d+8OPz8/zJ8/HwqFAlpaWoiMjESzZs0wd+5cLF++HHFxcdDQ0ICVlRWMjY2RnZ2N1q1bY+bMmXj8+DGMjY0xZ86cOmXL5XK4uLggLCwMQ4YMgY2NDSQSiXDFglu3bqlsEy8zMDCAjY0Nxo4di6ZNm0JLSwu+vr6QSCQIDw8XgiiampoIDw9Hq1atEBYWhoCAADx79gwikQirVq1C165d8csvv9QqWyaTYebMmZg+fTpEIhF0dHQQERHxxpPI2MfLwcEBp0+fxsiRI9GkSRN06NABLi4u6NatGxYuXAgiglgsxrfffgttbW3MmzcPa9asQVhYGDQ1NWFkZITCwkLo6enB3d0drq6u0NLSgoaGBgIDA6GpqYlNmzYhMDAQ4eHhUCgUmDdvHgYNGsQBFfbBqkl++fj4oLKyUmXyy8LCok7y69y5cwgJCYG5uTkePXqES5cuQS6XY8OGDbhx4wa6deuGtLQ0eHl5ITU1Fdra2iq3/+IkJolEgv3796udxGRqaqq2/JMnT8LDwwNWVlYoKipCZmYmRo8e/XZ2GmNqqItd1Lzm7++P5s2bQyqVok2bNgDU9126urrw9/dX2X/VZ/DgwVi9ejUAYPbs2TAwMICurm6t24QAUDs+VBd3YOxdet99k6r6xMXFwd7eHjo6OggLC8OlS5cQFRWlNkZgaWmJRYsWIT4+Hn379kXr1q2FhSOMNWSfffYZIiIicPv2bbRq1QqrV69GkyZNsGzZMtjY2KB9+/avvaqfsf8lw4cPx9atW4X+SiqVIj8/Hx06dICXlxeCg4PxzTffQCQSYf78+ejYseN7rjF7FRHx9OSPRllZGTZt2gQPDw80adIEFy9exOzZs5Gent4gAsu//fYb7Ozs6gTDGfsrZWdnIyAgQPgR5e/vj5YtW8LDwwMAYGJiAg8PD8TGxiIpKQlLly4V7vsDoNbjtLQ0rF27VkiW7dixA/n5+di7dy+Sk5OxefNmHD16FGFhYUKybNq0abCzs8P58+fh5eUFS0vLVybLioqKEBERgQsXLgAAnj59CkNDQ2GVIlB9BYCoqCgkJCQAqJ5xbGJiAk9PT+jp6dVb/oMHD7B06VIUFRVBIpGgZ8+eWLlyJSQSCbZu3Sr8gOvevTsCAgKE+yQfPnwYSqUSXbp0wYoVK9C+fXu4uLhg0qRJkMvlAKqvNhAUFIT8/Hxh8omPjw9fWoQ1WOHh4Xj8+DGvoGeMsf9xL48Zax7v3LkTgYGBwtjG3NwcS5YsgUQiwcGDB7F+/Xoh+ZWQkIADBw5AS0sLPj4+yM/PF5JfwcHB0NXVxb///W9ERkYKyS8fH5869/p6kVKphK+vL7Kzs4VJTNevX8fSpUshkUhq1RmA2vIPHz6MkJAQNG7cGJqamujatSuISOVtWxj7X1JYWAgXFxccOnRImHjP40PWUDTUvgmoHStRKpXCJUlFIhE6dOiAoKAgtGvXrt4YQUREBJKSkmpNsq25RRljjDHGGjZOJn9kQkNDcfToUYjFYojFYixbtgxVVVVYtWqVyvebmJjAx8fnL9t+cHCw2pUoU6ZMQWBgICeTGWOMAQCysrLeWf9U420EC1+81/nLZsyYwatXGGOMMcbekgMHDmDr1q0qX7Ozs4Obm5vwOCwsDPHx8Vi5ciWsrKyE5zmZzBhjjDHGWP04mcwYY2/Ru0qWffnll3j69KnK13744Qfo6Oj8JdthjDHGGGP/P2+S/GKMMcbeBe6bGGOMMVYfTiYzxhhjjDHGGGOMMcYYY4wxxhiro9H7rgBjjDHGGGOMMcYYY4wxxhhjjLGGh5PJjDHGGGOMMcYYY4wxxhhjjDHG6uBkMmOMMcYYY4wxxhhjjDHGGGOMsTo4mcwYY4wxxhhjjDHGGGOMMcYYY6wOTiYzxhhjjDHGGGOMMcYYY4wxxhir4/8AT9lPDFMgfSsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1944x1944 with 90 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "col = ['log_real_prices', 'mindist_GO_score', 'mindist_Hospital_score', 'season', 'house_area', 'house_type', 'house_fees', 'bdrm','wshrm']\n",
    "sns.set(style='ticks')\n",
    "sns.pairplot(data_msga[col], size=3, kind='reg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# def linearRegressionModel():\n",
    "#     X = data_msga[['mindist_GO_score','mindist_Hospital_score','season','house_area','house_type','house_fees','bdrm','wshrm']]\n",
    "\n",
    "#     y = data_msga['log_real_prices']\n",
    "\n",
    "\n",
    "#     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3)\n",
    "\n",
    "#     #Create an instance of a LinearRegression() model named lm.\n",
    "#     lm = LinearRegression(fit_intercept=False)\n",
    "\n",
    "#     #Train/fit lm on the training data.\n",
    "#     lm.fit(X_train,y_train)\n",
    "\n",
    "#     #print intercept\n",
    "#     print(\"The intercept of the model  \")\n",
    "#     print(lm.intercept_)\n",
    "\n",
    "    \n",
    "#     #Print out the coefficients of the model\n",
    "#     print(\"The coefficients of the model  \")\n",
    "#     print(lm.coef_)\n",
    "\n",
    "#     #print R^2 \n",
    "#     print(\"R Squared of the model is  \")\n",
    "#     print(lm.score(X_train,y_train))\n",
    "\n",
    "#     #evaluating performance by predicting off the test values\n",
    "#     predictions = lm.predict(X_test)\n",
    "\n",
    "#     #scatterplot of the real test values versus the predicted values\n",
    "#     plt.scatter(y_test,predictions)\n",
    "#     plt.xlabel('Y Test')\n",
    "#     plt.ylabel('Predicted Y')\n",
    "#     plt.show()\n",
    "\n",
    "\n",
    "#     #evaluate our model performance by calculating the residual sum of squares and the explained variance score (R^2)\n",
    "#     print('MAPE :',\" \", metrics.mean_absolute_error(y_test,predictions))\n",
    "\n",
    "#     # explore the residuals to make sure everything was okay with our data.\n",
    "#     #Plot a histogram of the residuals and make sure it looks normally distributed. Use either seaborn distplot, or just plt.hist().\n",
    "\n",
    "#     sns.distplot(y_test - predictions,bins=50)\n",
    "#     print(X.columns)\n",
    "#     house = [[0.033823,0.046684,2,32,1,1,4,3]]\n",
    "#     showMe = lm.predict(house)\n",
    "#     print(showMe)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The intercept of the model  \n",
      "0.0\n",
      "The coefficients of the model  \n",
      "[-1.09994383e+00 -1.47540793e+00  3.22867735e-02  1.38088720e-03\n",
      " -7.41431723e-02 -5.65329492e-01  4.65915718e-02  1.39299308e-01]\n",
      "R Squared of the model is  \n",
      "0.756062294092595\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAPE :   0.19942849165260618\n",
      "Index(['mindist_GO_score', 'mindist_Hospital_score', 'season', 'house_area',\n",
      "       'house_type', 'house_fees', 'bdrm', 'wshrm'],\n",
      "      dtype='object')\n",
      "[-0.03252786]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "linearRegressionModel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "def OLSModel():\n",
    "    y = data_msga['log_real_prices']\n",
    "    data_msga_final = data_msga[['mindist_GO_score','mindist_Hospital_score','season','house_type','house_fees','house_area','wshrm','bdrm']]\n",
    "    x=data_msga_final.values \n",
    "    result = sm.OLS(y,x).fit()\n",
    "    \n",
    "    #checking on residuals \n",
    "    errors=result.predict(x)-y\n",
    "    norm_erros = (errors - errors.mean()) / errors.std()\n",
    "    n,bins,patches=plt.hist(norm_erros,100,facecolor='g')\n",
    "    plt.xlabel('Normalized_error')\n",
    "    plt.ylabel('frequencies')\n",
    "    plt.title('Error Terms Distribution')\n",
    "    plot.show()\n",
    "    \n",
    "    print(data_msga_final.columns)\n",
    "    house = [[0.033823,0.046684,2,32,1,1,4,3]]\n",
    "    showMe = result.predict(house)\n",
    "    print(showMe)\n",
    "    \n",
    "    \n",
    "    return result.summary(xname=['mindist_GO_score','mindist_Hospital_score','season','house_type','house_fees','house_area','wshrm','bdrm'])\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Printing the OLS result Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['mindist_GO_score', 'mindist_Hospital_score', 'season', 'house_type',\n",
      "       'house_fees', 'house_area', 'wshrm', 'bdrm'],\n",
      "      dtype='object')\n",
      "[-2.27732369]\n",
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:        log_real_prices   R-squared:                       0.892\n",
      "Model:                            OLS   Adj. R-squared:                  0.892\n",
      "Method:                 Least Squares   F-statistic:                 2.183e+04\n",
      "Date:                Sat, 13 Apr 2019   Prob (F-statistic):               0.00\n",
      "Time:                        11:43:56   Log-Likelihood:                -1738.3\n",
      "No. Observations:               21227   AIC:                             3493.\n",
      "Df Residuals:                   21219   BIC:                             3556.\n",
      "Df Model:                           8                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==========================================================================================\n",
      "                             coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------------------\n",
      "mindist_GO_score          -1.0347      0.077    -13.473      0.000      -1.185      -0.884\n",
      "mindist_Hospital_score    -1.5250      0.059    -25.992      0.000      -1.640      -1.410\n",
      "season                     0.0297      0.002     12.479      0.000       0.025       0.034\n",
      "house_type                -0.0742      0.002    -45.528      0.000      -0.077      -0.071\n",
      "house_fees                -0.5632      0.003   -170.897      0.000      -0.570      -0.557\n",
      "house_area                 0.0013      0.000      5.138      0.000       0.001       0.002\n",
      "wshrm                      0.1424      0.002     61.100      0.000       0.138       0.147\n",
      "bdrm                       0.0449      0.003     17.732      0.000       0.040       0.050\n",
      "==============================================================================\n",
      "Omnibus:                     1951.546   Durbin-Watson:                   1.537\n",
      "Prob(Omnibus):                  0.000   Jarque-Bera (JB):             3927.473\n",
      "Skew:                           0.610   Prob(JB):                         0.00\n",
      "Kurtosis:                       4.719   Cond. No.                         642.\n",
      "==============================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(OLSModel())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}