Untappd_JK.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Trying it out: my own data \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Preparing the data\n",
    "\n",
    "### Gathering and loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from numpy.polynomial.polynomial import polyfit\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn import metrics \n",
    "%matplotlib inline\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "os.chdir('C:/Users/jasper.kuller/Udacity/Project_1_blogpost')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_abv</th>\n",
       "      <th>beer_ibu</th>\n",
       "      <th>venue_lat</th>\n",
       "      <th>venue_lng</th>\n",
       "      <th>rating_score</th>\n",
       "      <th>purchase_venue</th>\n",
       "      <th>checkin_id</th>\n",
       "      <th>bid</th>\n",
       "      <th>brewery_id</th>\n",
       "      <th>global_rating_score</th>\n",
       "      <th>global_weighted_rating_score</th>\n",
       "      <th>total_toasts</th>\n",
       "      <th>total_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>256.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.270000e+02</td>\n",
       "      <td>4.270000e+02</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>427.000000</td>\n",
       "      <td>427.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>6.435152</td>\n",
       "      <td>25.255269</td>\n",
       "      <td>49.165191</td>\n",
       "      <td>0.832045</td>\n",
       "      <td>3.228516</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.118869e+08</td>\n",
       "      <td>6.853097e+05</td>\n",
       "      <td>41826.391101</td>\n",
       "      <td>3.443513</td>\n",
       "      <td>3.468923</td>\n",
       "      <td>0.803279</td>\n",
       "      <td>0.100703</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.728012</td>\n",
       "      <td>25.101446</td>\n",
       "      <td>5.186930</td>\n",
       "      <td>19.873734</td>\n",
       "      <td>0.737180</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.231865e+08</td>\n",
       "      <td>9.376225e+05</td>\n",
       "      <td>80508.490850</td>\n",
       "      <td>0.337279</td>\n",
       "      <td>0.325827</td>\n",
       "      <td>1.020305</td>\n",
       "      <td>0.377382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>36.067200</td>\n",
       "      <td>-115.177000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.683053e+08</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>47.447100</td>\n",
       "      <td>3.257030</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.858136e+08</td>\n",
       "      <td>8.653000e+03</td>\n",
       "      <td>388.500000</td>\n",
       "      <td>3.270000</td>\n",
       "      <td>3.330000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>52.075700</td>\n",
       "      <td>4.487940</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.432986e+08</td>\n",
       "      <td>8.041800e+04</td>\n",
       "      <td>3359.000000</td>\n",
       "      <td>3.470000</td>\n",
       "      <td>3.500000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>7.500000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>52.164500</td>\n",
       "      <td>4.895905</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.964939e+08</td>\n",
       "      <td>1.334346e+06</td>\n",
       "      <td>32280.000000</td>\n",
       "      <td>3.640000</td>\n",
       "      <td>3.640000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>53.348100</td>\n",
       "      <td>28.227400</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.603580e+08</td>\n",
       "      <td>3.537592e+06</td>\n",
       "      <td>389645.000000</td>\n",
       "      <td>4.530000</td>\n",
       "      <td>4.530000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         beer_abv    beer_ibu   venue_lat   venue_lng  rating_score  \\\n",
       "count  427.000000  427.000000  256.000000  256.000000    384.000000   \n",
       "mean     6.435152   25.255269   49.165191    0.832045      3.228516   \n",
       "std      1.728012   25.101446    5.186930   19.873734      0.737180   \n",
       "min      0.000000    0.000000   36.067200 -115.177000      0.500000   \n",
       "25%      5.000000    0.000000   47.447100    3.257030      3.000000   \n",
       "50%      6.000000   22.000000   52.075700    4.487940      3.250000   \n",
       "75%      7.500000   38.000000   52.164500    4.895905      3.750000   \n",
       "max     11.900000  250.000000   53.348100   28.227400      5.000000   \n",
       "\n",
       "       purchase_venue    checkin_id           bid     brewery_id  \\\n",
       "count             0.0  4.270000e+02  4.270000e+02     427.000000   \n",
       "mean              NaN  5.118869e+08  6.853097e+05   41826.391101   \n",
       "std               NaN  2.231865e+08  9.376225e+05   80508.490850   \n",
       "min               NaN  1.683053e+08  2.000000e+00       5.000000   \n",
       "25%               NaN  2.858136e+08  8.653000e+03     388.500000   \n",
       "50%               NaN  5.432986e+08  8.041800e+04    3359.000000   \n",
       "75%               NaN  6.964939e+08  1.334346e+06   32280.000000   \n",
       "max               NaN  8.603580e+08  3.537592e+06  389645.000000   \n",
       "\n",
       "       global_rating_score  global_weighted_rating_score  total_toasts  \\\n",
       "count           427.000000                    427.000000    427.000000   \n",
       "mean              3.443513                      3.468923      0.803279   \n",
       "std               0.337279                      0.325827      1.020305   \n",
       "min               0.000000                      0.000000      0.000000   \n",
       "25%               3.270000                      3.330000      0.000000   \n",
       "50%               3.470000                      3.500000      0.000000   \n",
       "75%               3.640000                      3.640000      1.000000   \n",
       "max               4.530000                      4.530000      5.000000   \n",
       "\n",
       "       total_comments  \n",
       "count      427.000000  \n",
       "mean         0.100703  \n",
       "std          0.377382  \n",
       "min          0.000000  \n",
       "25%          0.000000  \n",
       "50%          0.000000  \n",
       "75%          0.000000  \n",
       "max          3.000000  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers_raw = pd.read_csv('Data/Untappd/20200205_export_jk.csv')\n",
    "beers_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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>beer_name</th>\n",
       "      <th>brewery_name</th>\n",
       "      <th>beer_type</th>\n",
       "      <th>beer_abv</th>\n",
       "      <th>beer_ibu</th>\n",
       "      <th>comment</th>\n",
       "      <th>venue_name</th>\n",
       "      <th>venue_city</th>\n",
       "      <th>venue_state</th>\n",
       "      <th>venue_country</th>\n",
       "      <th>venue_lat</th>\n",
       "      <th>venue_lng</th>\n",
       "      <th>rating_score</th>\n",
       "      <th>created_at</th>\n",
       "      <th>checkin_url</th>\n",
       "      <th>beer_url</th>\n",
       "      <th>brewery_url</th>\n",
       "      <th>brewery_country</th>\n",
       "      <th>brewery_city</th>\n",
       "      <th>brewery_state</th>\n",
       "      <th>flavor_profiles</th>\n",
       "      <th>purchase_venue</th>\n",
       "      <th>serving_type</th>\n",
       "      <th>checkin_id</th>\n",
       "      <th>bid</th>\n",
       "      <th>brewery_id</th>\n",
       "      <th>photo_url</th>\n",
       "      <th>global_rating_score</th>\n",
       "      <th>global_weighted_rating_score</th>\n",
       "      <th>tagged_friends</th>\n",
       "      <th>total_toasts</th>\n",
       "      <th>total_comments</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Super Bock</td>\n",
       "      <td>Unicer Bebidas</td>\n",
       "      <td>Lager - Euro Pale</td>\n",
       "      <td>5.2</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-01 00:08:59</td>\n",
       "      <td>https://untappd.com/c/168305269</td>\n",
       "      <td>https://untappd.com/beer/8048</td>\n",
       "      <td>https://untappd.com/brewery/1308</td>\n",
       "      <td>Portugal</td>\n",
       "      <td>Matosinhos Municipality</td>\n",
       "      <td>Norte</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168305269</td>\n",
       "      <td>8048</td>\n",
       "      <td>1308</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.97</td>\n",
       "      <td>2.98</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>1664</td>\n",
       "      <td>Kronenbourg Brewery</td>\n",
       "      <td>Lager - Euro Pale</td>\n",
       "      <td>5.5</td>\n",
       "      <td>20</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-01 00:09:29</td>\n",
       "      <td>https://untappd.com/c/168305415</td>\n",
       "      <td>https://untappd.com/beer/5939</td>\n",
       "      <td>https://untappd.com/brewery/203</td>\n",
       "      <td>France</td>\n",
       "      <td>Strasbourg</td>\n",
       "      <td>Grand-Est</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168305415</td>\n",
       "      <td>5939</td>\n",
       "      <td>203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.11</td>\n",
       "      <td>3.11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Stella Artois</td>\n",
       "      <td>Stella Artois</td>\n",
       "      <td>Lager - Euro Pale</td>\n",
       "      <td>5.0</td>\n",
       "      <td>24</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-04-01 00:09:48</td>\n",
       "      <td>https://untappd.com/c/168305509</td>\n",
       "      <td>https://untappd.com/beer/4010</td>\n",
       "      <td>https://untappd.com/brewery/265</td>\n",
       "      <td>Belgium</td>\n",
       "      <td>Leuven</td>\n",
       "      <td>Vlaanderen</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>168305509</td>\n",
       "      <td>4010</td>\n",
       "      <td>265</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_name         brewery_name          beer_type  beer_abv  beer_ibu  \\\n",
       "0     Super Bock       Unicer Bebidas  Lager - Euro Pale       5.2         0   \n",
       "1           1664  Kronenbourg Brewery  Lager - Euro Pale       5.5        20   \n",
       "2  Stella Artois        Stella Artois  Lager - Euro Pale       5.0        24   \n",
       "\n",
       "  comment venue_name venue_city venue_state venue_country  venue_lat  \\\n",
       "0     NaN        NaN        NaN         NaN           NaN        NaN   \n",
       "1     NaN        NaN        NaN         NaN           NaN        NaN   \n",
       "2     NaN        NaN        NaN         NaN           NaN        NaN   \n",
       "\n",
       "   venue_lng  rating_score           created_at  \\\n",
       "0        NaN           NaN  2015-04-01 00:08:59   \n",
       "1        NaN           NaN  2015-04-01 00:09:29   \n",
       "2        NaN           NaN  2015-04-01 00:09:48   \n",
       "\n",
       "                       checkin_url                       beer_url  \\\n",
       "0  https://untappd.com/c/168305269  https://untappd.com/beer/8048   \n",
       "1  https://untappd.com/c/168305415  https://untappd.com/beer/5939   \n",
       "2  https://untappd.com/c/168305509  https://untappd.com/beer/4010   \n",
       "\n",
       "                        brewery_url brewery_country             brewery_city  \\\n",
       "0  https://untappd.com/brewery/1308        Portugal  Matosinhos Municipality   \n",
       "1   https://untappd.com/brewery/203          France               Strasbourg   \n",
       "2   https://untappd.com/brewery/265         Belgium                   Leuven   \n",
       "\n",
       "  brewery_state flavor_profiles  purchase_venue serving_type  checkin_id  \\\n",
       "0         Norte             NaN             NaN          NaN   168305269   \n",
       "1     Grand-Est             NaN             NaN          NaN   168305415   \n",
       "2    Vlaanderen             NaN             NaN          NaN   168305509   \n",
       "\n",
       "    bid  brewery_id photo_url  global_rating_score  \\\n",
       "0  8048        1308       NaN                 2.97   \n",
       "1  5939         203       NaN                 3.11   \n",
       "2  4010         265       NaN                 3.25   \n",
       "\n",
       "   global_weighted_rating_score tagged_friends  total_toasts  total_comments  \n",
       "0                          2.98            NaN             0               0  \n",
       "1                          3.11            NaN             0               0  \n",
       "2                          3.25            NaN             0               0  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_columns', len(beers_raw.columns)) # To view all columns\n",
    "pd.set_option('display.max_rows', 100)\n",
    "beers_raw.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cleaning \n",
    "Dropping columns that are not useful for predicting ratings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "cols_to_drop = ['checkin_url','beer_url','brewery_url','checkin_id','brewery_id','photo_url']\n",
    "beers = beers_raw.drop(cols_to_drop, axis=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Handling missing values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_name                         0\n",
       "brewery_name                      0\n",
       "beer_type                         0\n",
       "beer_abv                          0\n",
       "beer_ibu                          0\n",
       "comment                         293\n",
       "venue_name                      171\n",
       "venue_city                      196\n",
       "venue_state                     196\n",
       "venue_country                   171\n",
       "venue_lat                       171\n",
       "venue_lng                       171\n",
       "rating_score                     43\n",
       "created_at                        0\n",
       "brewery_country                   0\n",
       "brewery_city                     14\n",
       "brewery_state                    20\n",
       "flavor_profiles                 397\n",
       "purchase_venue                  427\n",
       "serving_type                    198\n",
       "bid                               0\n",
       "global_rating_score               0\n",
       "global_weighted_rating_score      0\n",
       "tagged_friends                  388\n",
       "total_toasts                      0\n",
       "total_comments                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropping columns we won't need for our analysis\n",
    "beers.drop(['comment','tagged_friends','purchase_venue','flavor_profiles','venue_name','venue_city','venue_state','total_toasts','total_comments'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# coordinates are not required for running the model, so these are dropped\n",
    "beers.drop(['venue_lat','venue_lng'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# missing ratings are beers I added to get the list going -- these need to be dropped as they don't have predictive value  \n",
    "beers.dropna(subset=['rating_score'], inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_name                       0\n",
       "brewery_name                    0\n",
       "beer_type                       0\n",
       "beer_abv                        0\n",
       "beer_ibu                        0\n",
       "venue_country                   0\n",
       "rating_score                    0\n",
       "created_at                      0\n",
       "brewery_country                 0\n",
       "brewery_city                    0\n",
       "brewery_state                   0\n",
       "serving_type                    0\n",
       "bid                             0\n",
       "global_rating_score             0\n",
       "global_weighted_rating_score    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing missing categorical values with fillna\n",
    "beers.fillna(\"Missing\", inplace=True)\n",
    "\n",
    "beers.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transforming \n",
    "Binning & one hot encoding\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "beer_name                        object\n",
       "brewery_name                     object\n",
       "beer_type                        object\n",
       "beer_abv                        float64\n",
       "beer_ibu                          int64\n",
       "venue_country                    object\n",
       "rating_score                    float64\n",
       "created_at                       object\n",
       "brewery_country                  object\n",
       "brewery_city                     object\n",
       "brewery_state                    object\n",
       "serving_type                     object\n",
       "bid                               int64\n",
       "global_rating_score             float64\n",
       "global_weighted_rating_score    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "cat_beers = beers.select_dtypes(include=['object']).copy()\n",
    "num_beers = beers.select_dtypes(include=['float64','int64']).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>beer_name</th>\n",
       "      <th>brewery_name</th>\n",
       "      <th>beer_type</th>\n",
       "      <th>venue_country</th>\n",
       "      <th>created_at</th>\n",
       "      <th>brewery_country</th>\n",
       "      <th>brewery_city</th>\n",
       "      <th>brewery_state</th>\n",
       "      <th>serving_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>312</td>\n",
       "      <td>190</td>\n",
       "      <td>67</td>\n",
       "      <td>13</td>\n",
       "      <td>384</td>\n",
       "      <td>23</td>\n",
       "      <td>150</td>\n",
       "      <td>74</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>IPA</td>\n",
       "      <td>Brouwerij 't IJ</td>\n",
       "      <td>IPA - American</td>\n",
       "      <td>Nederland</td>\n",
       "      <td>2015-11-25 22:35:12</td>\n",
       "      <td>Netherlands</td>\n",
       "      <td>Amsterdam</td>\n",
       "      <td>Noord-Holland</td>\n",
       "      <td>Missing</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>9</td>\n",
       "      <td>15</td>\n",
       "      <td>36</td>\n",
       "      <td>161</td>\n",
       "      <td>1</td>\n",
       "      <td>162</td>\n",
       "      <td>41</td>\n",
       "      <td>73</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_name     brewery_name       beer_type venue_country  \\\n",
       "count        384              384             384           384   \n",
       "unique       312              190              67            13   \n",
       "top          IPA  Brouwerij 't IJ  IPA - American     Nederland   \n",
       "freq           9               15              36           161   \n",
       "\n",
       "                 created_at brewery_country brewery_city  brewery_state  \\\n",
       "count                   384             384          384            384   \n",
       "unique                  384              23          150             74   \n",
       "top     2015-11-25 22:35:12     Netherlands    Amsterdam  Noord-Holland   \n",
       "freq                      1             162           41             73   \n",
       "\n",
       "       serving_type  \n",
       "count           384  \n",
       "unique            5  \n",
       "top         Missing  \n",
       "freq            155  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cat_beers.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Of the categorical variables, a couple might be relevant for predicting ratings: \n",
    "- beer_type\n",
    "- venue_country (perhaps?)\n",
    "- brewery_country\n",
    "- serving_type (perhaps?)\n",
    "\n",
    "Problem is, there are too many unique values per variable. Solution: creating umbrella values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Barleywine - American',\n",
       " 'Barleywine - English',\n",
       " 'Barleywine - Other',\n",
       " 'Belgian Blonde',\n",
       " 'Belgian Dubbel',\n",
       " 'Belgian Quadrupel',\n",
       " 'Belgian Strong Dark Ale',\n",
       " 'Belgian Strong Golden Ale',\n",
       " 'Belgian Tripel',\n",
       " 'Bière de Garde',\n",
       " 'Blonde Ale',\n",
       " 'Bock - Doppelbock',\n",
       " 'Bock - Hell / Maibock / Lentebock',\n",
       " 'Bock - Single / Traditional',\n",
       " 'Brown Ale - Belgian',\n",
       " 'Brown Ale - Imperial / Double',\n",
       " 'Brown Ale - Other',\n",
       " 'Dunkelweizen',\n",
       " 'English Bitter',\n",
       " 'Farmhouse Ale - Saison',\n",
       " 'Fruit Beer',\n",
       " 'Gluten-Free',\n",
       " 'Golden Ale',\n",
       " 'Hefeweizen',\n",
       " 'IPA - American',\n",
       " 'IPA - Belgian',\n",
       " 'IPA - Black / Cascadian Dark Ale',\n",
       " 'IPA - Brut',\n",
       " 'IPA - English',\n",
       " 'IPA - Imperial / Double',\n",
       " 'IPA - Imperial / Double New England',\n",
       " 'IPA - International',\n",
       " 'IPA - New England',\n",
       " 'IPA - Rye',\n",
       " 'IPA - Session / India Session Ale',\n",
       " 'IPA - Triple',\n",
       " 'IPA - White',\n",
       " 'Lager - Amber',\n",
       " 'Lager - American',\n",
       " 'Lager - American Amber / Red',\n",
       " 'Lager - American Light',\n",
       " 'Lager - Dark',\n",
       " 'Lager - Euro Pale',\n",
       " 'Lager - Euro Strong',\n",
       " 'Lager - Helles',\n",
       " 'Lager - IPL (India Pale Lager)',\n",
       " 'Lager - Pale',\n",
       " 'Lager - Red',\n",
       " 'Lager - Vienna',\n",
       " 'Märzen',\n",
       " 'Pale Ale - American',\n",
       " 'Pale Ale - Belgian',\n",
       " 'Pale Ale - English',\n",
       " 'Pale Ale - International',\n",
       " 'Pilsner - German',\n",
       " 'Pilsner - Imperial / Double',\n",
       " 'Pilsner - Other',\n",
       " 'Red Ale - American Amber / Red',\n",
       " 'Red Ale - Other',\n",
       " 'Rye Beer',\n",
       " 'Scotch Ale / Wee Heavy',\n",
       " 'Sour - Berliner Weisse',\n",
       " 'Spiced / Herbed Beer',\n",
       " 'Stout - Imperial / Double',\n",
       " 'Wheat Beer - Other',\n",
       " 'Winter Ale',\n",
       " 'Witbier']"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.set_option('display.max_rows', 150)\n",
    "beertype = beers['beer_type'].unique()\n",
    "sorted(beertype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPA                                  110\n",
       "Lager                                 53\n",
       "Belgian Tripel                        30\n",
       "Pale Ale                              28\n",
       "Belgian Blonde                        27\n",
       "Belgian Strong Golden Ale             18\n",
       "Belgian Quadrupel                     12\n",
       "Pilsner                               11\n",
       "Hefeweizen                            10\n",
       "Witbier                               10\n",
       "Blonde Ale                             9\n",
       "Belgian Dubbel                         9\n",
       "Farmhouse Ale - Saison                 7\n",
       "Barleywine                             6\n",
       "Red Ale                                6\n",
       "Belgian Strong Dark Ale                5\n",
       "Brown Ale                              4\n",
       "Spiced / Herbed Beer                   3\n",
       "Bière de Garde                         3\n",
       "Bock - Hell / Maibock / Lentebock      3\n",
       "Dunkelweizen                           3\n",
       "Winter Ale                             2\n",
       "Stout - Imperial / Double              2\n",
       "Bock - Doppelbock                      2\n",
       "Gluten-Free                            2\n",
       "Märzen                                 1\n",
       "Rye Beer                               1\n",
       "English Bitter                         1\n",
       "Wheat Beer - Other                     1\n",
       "Bock - Single / Traditional            1\n",
       "Sour - Berliner Weisse                 1\n",
       "Fruit Beer                             1\n",
       "Golden Ale                             1\n",
       "Scotch Ale / Wee Heavy                 1\n",
       "Name: beer_type, dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# grouping beer styles in larger bins\n",
    "beers.loc[beers['beer_type'].str.contains('barley', case=False), 'beer_type'] = 'Barleywine'\n",
    "beers.loc[beers['beer_type'].str.contains('brown ale', case=False), 'beer_type'] = 'Brown Ale'\n",
    "beers.loc[beers['beer_type'].str.contains('ipa', case=False), 'beer_type'] = 'IPA'\n",
    "beers.loc[beers['beer_type'].str.contains('lager', case=False), 'beer_type'] = 'Lager'\n",
    "beers.loc[beers['beer_type'].str.contains('pale ale', case=False), 'beer_type'] = 'Pale Ale'\n",
    "beers.loc[beers['beer_type'].str.contains('pilsner', case=False), 'beer_type'] = 'Pilsner'\n",
    "beers.loc[beers['beer_type'].str.contains('red ale', case=False), 'beer_type'] = 'Red Ale'\n",
    "\n",
    "beertype = beers['beer_type'].value_counts()\n",
    "beertype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still too many types, so further grouping is required. We see a long tail of categories with a low amount of values, so let's group categories with <10 values in 'Other'. Beer purists will hate me for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "IPA                          110\n",
       "Other                         75\n",
       "Lager                         53\n",
       "Belgian Tripel                30\n",
       "Pale Ale                      28\n",
       "Belgian Blonde                27\n",
       "Belgian Strong Golden Ale     18\n",
       "Belgian Quadrupel             12\n",
       "Pilsner                       11\n",
       "Hefeweizen                    10\n",
       "Witbier                       10\n",
       "Name: beer_type, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers['beer_type'] = np.where(beers['beer_type'].isin(beertype.index[beertype >= 10]), beers['beer_type'], 'Other')\n",
    "beers['beer_type'].value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next: creating umbrella categories for brewery_country."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Austria',\n",
       " 'Belgium',\n",
       " 'Croatia',\n",
       " 'Denmark',\n",
       " 'England',\n",
       " 'Finland',\n",
       " 'France',\n",
       " 'Germany',\n",
       " 'Greece',\n",
       " 'Ireland',\n",
       " 'Italy',\n",
       " 'Japan',\n",
       " 'Mexico',\n",
       " 'Namibia',\n",
       " 'Netherlands',\n",
       " 'Scotland',\n",
       " 'Singapore',\n",
       " 'South Africa',\n",
       " 'South Korea',\n",
       " 'Spain',\n",
       " 'Thailand',\n",
       " 'Turkey',\n",
       " 'United States']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "country = beers['brewery_country'].unique()\n",
    "sorted(country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Africa',\n",
       " 'Asia',\n",
       " 'Eastern Europe',\n",
       " 'Mexico',\n",
       " 'Scandinavia',\n",
       " 'Southern Europe',\n",
       " 'UK',\n",
       " 'United States',\n",
       " 'Western Europe']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers['brewery_country'] = beers['brewery_country'].replace(['Croatia','Czech Republic'], 'Eastern Europe')\n",
    "beers['brewery_country'] = beers['brewery_country'].replace(['Denmark','Finland'], 'Scandinavia')\n",
    "beers['brewery_country'] = beers['brewery_country'].replace(['Austria','Belgium','France','Germany','Netherlands'], 'Western Europe')\n",
    "beers['brewery_country'] = beers['brewery_country'].replace(['England','Ireland','Scotland'], 'UK')\n",
    "beers['brewery_country'] = beers['brewery_country'].replace(['Greece','Italy','Spain','Turkey'], 'Southern Europe')\n",
    "beers['brewery_country'] = beers['brewery_country'].replace(['Japan','Singapore','South Korea','Thailand'], 'Asia')\n",
    "beers['brewery_country'] = beers['brewery_country'].replace(['Namibia','South Africa'], 'Africa')\n",
    "\n",
    "country = beers['brewery_country'].unique()\n",
    "sorted(country)"
   ]
  },
  {
   "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>beer_name</th>\n",
       "      <th>brewery_name</th>\n",
       "      <th>beer_type</th>\n",
       "      <th>beer_abv</th>\n",
       "      <th>beer_ibu</th>\n",
       "      <th>venue_country</th>\n",
       "      <th>rating_score</th>\n",
       "      <th>created_at</th>\n",
       "      <th>brewery_country</th>\n",
       "      <th>brewery_city</th>\n",
       "      <th>brewery_state</th>\n",
       "      <th>serving_type</th>\n",
       "      <th>bid</th>\n",
       "      <th>global_rating_score</th>\n",
       "      <th>global_weighted_rating_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Houblon Chouffe</td>\n",
       "      <td>Brasserie d'Achouffe</td>\n",
       "      <td>IPA</td>\n",
       "      <td>9.0</td>\n",
       "      <td>47</td>\n",
       "      <td>Missing</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015-04-01 00:10:44</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Achouffe</td>\n",
       "      <td>Wallonie</td>\n",
       "      <td>Missing</td>\n",
       "      <td>230</td>\n",
       "      <td>3.72</td>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>Mc Chouffe</td>\n",
       "      <td>Brasserie d'Achouffe</td>\n",
       "      <td>Other</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>Missing</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2015-04-01 00:11:10</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Achouffe</td>\n",
       "      <td>Wallonie</td>\n",
       "      <td>Missing</td>\n",
       "      <td>52336</td>\n",
       "      <td>3.67</td>\n",
       "      <td>3.67</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>Trappist Westvleteren 12</td>\n",
       "      <td>Brouwerij De Sint-Sixtusabdij van Westvleteren</td>\n",
       "      <td>Belgian Quadrupel</td>\n",
       "      <td>10.2</td>\n",
       "      <td>38</td>\n",
       "      <td>Missing</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2015-04-01 00:11:30</td>\n",
       "      <td>Western Europe</td>\n",
       "      <td>Westvleteren</td>\n",
       "      <td>Vlaanderen</td>\n",
       "      <td>Missing</td>\n",
       "      <td>4680</td>\n",
       "      <td>4.53</td>\n",
       "      <td>4.53</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  beer_name                                    brewery_name  \\\n",
       "4           Houblon Chouffe                            Brasserie d'Achouffe   \n",
       "5                Mc Chouffe                            Brasserie d'Achouffe   \n",
       "6  Trappist Westvleteren 12  Brouwerij De Sint-Sixtusabdij van Westvleteren   \n",
       "\n",
       "           beer_type  beer_abv  beer_ibu venue_country  rating_score  \\\n",
       "4                IPA       9.0        47       Missing           4.0   \n",
       "5              Other       8.0        20       Missing           3.0   \n",
       "6  Belgian Quadrupel      10.2        38       Missing           4.0   \n",
       "\n",
       "            created_at brewery_country  brewery_city brewery_state  \\\n",
       "4  2015-04-01 00:10:44  Western Europe      Achouffe      Wallonie   \n",
       "5  2015-04-01 00:11:10  Western Europe      Achouffe      Wallonie   \n",
       "6  2015-04-01 00:11:30  Western Europe  Westvleteren    Vlaanderen   \n",
       "\n",
       "  serving_type    bid  global_rating_score  global_weighted_rating_score  \n",
       "4      Missing    230                 3.72                          3.72  \n",
       "5      Missing  52336                 3.67                          3.67  \n",
       "6      Missing   4680                 4.53                          4.53  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Exploratory Data Analysis\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Let's explore the numerical variables first, with a histogram plot\n",
    "beers.hist(figsize=(10,10));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# next up: correlation plot\n",
    "cmap = sns.diverging_palette(220, 20, sep=20, as_cmap=True)\n",
    "htmp = sns.heatmap(beers.corr(), cmap=cmap, annot=True, fmt=\".2f\");\n",
    "bottom, top = htmp.get_ylim();\n",
    "htmp.set_ylim(bottom + 0.5, top - 0.5);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jasper.kuller\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  \n"
     ]
    },
    {
     "data": {
      "image/png": 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gz7sv2hpshSZ5pZRqnj5do5RS/cTz4OGH4UMfgnvvhUwGvvKVrgylxVBKKdVppRL83d/B1762+msz990Hb3xjx0PRJK+UUs1aWICHHoqT91e/Co8+2vw2Tp6E1742/nXXXbBnT8fDhAFJ8oN8Q6VfbHYTsZ5Wjstyr/MXr5QRMdx0YISrx7Lxy+eElbHTCXu1n3zZ44WZIkUvXOnBPl/0eOCpS1yecyt90PdycCTNfDlgcmqR716Yx0SGw+PZdf3UN4qrugf7VSOZdTdwgZWbqNOLJR47O8t8yV9ZJ5WwN7xZ+/fPTa27wfuyq0bW7XtgdWzHZiTtkEo6W9rPf33mIp/85gtML7rsHUrxntuv5nU3Hmh6/o160C/HMFf0eOLcLHNFf8u98Zv15LkrfOH0BaYWPSaGkrz5FQe55UgHkuX0NHz966tn4U880fw27rprNYnfeSfk6z8MsLLvpgpdyV87/sbroJcr94PNSvTraeW4PH1xnk88epaMYzFf9PANzC95XLM3x8RQilsOjZBwHK4UPa4aSrJ3KMNssczfPn2ZtJPg8J40fgjfvzTP+bkS+0eyRGFIKTRcKZQ5vi9PwrL49rk58pkECQtGMilKQch7X31Nw0S/HNdYNslw2mahHPLSXJGbj4xx/VXDK+0RSn7AvqE0I5kkz04v8vAz0wynkxweTVMMIs5OLzGUdrhu31DdVgh//9wU/+Wrk4xlkyvtE16aL3P3yyZ41TV7Vvb9pYUyguGq4Uxl7PhF6q88PEI2ldhwP//1mYv85688zUgmyWjaZq4cMl/y+KdvvL5hoq83/9mit7LP6h3r87NFnro0z3guTS5ls+SGLJQ9Xn/j/o4l+ifPXeG+bzzLWDa1sr9miy6/8Jpjmyf6ixdXz8K/9jV46qnmBnec1QT+2tfC7bdDOt30HDqVv/qin3y3VJcrAyvVijMFl0Nj2V6GNjCqS/QBsilrZflt14zXXaeV4/LAU5cYyyZZ8gKyqSTJhMX8UsCLs0u8/KphLsy73HgwzeXFMhcXXA6O5ThzcYHRTIp00mbJi9ibT/Hs9BKhgVTCwjhCzraYWXB5bqqALRbJhM3EUBo3CImMYSyb5IGnLjVM8stxLT/OOZ53uDhb4jvn5zhx9R4uL5YZySYpz8evyDswmuX0uTlSjs3eoSRuaBjPp3jyhVkWSh4nr4332Wgu/kf88OQUR/fm+fzj5xnLJpkYipPFRCLBzKLLqeenec3L963s+7liXFV77cQQlxfKjGRS+FHEudkSrzi8+shpvf38yW++wEgmyb7hONHuS64ub5Tk681/efn1B0bqHutnLi8iWAxn4vfTDmfirz1xbpZ7O5Tkv3D6AmPZFHuH4rj2VorTvvDkeW4J5muvhz/3XHMbz+Vqk/irXgWJxu/abdV25K8dn+TdICKbrP0fL2ELRS9ssIZqVqEcMLbmjD2dsJgt1m8dAK0dl+lFj0Ojaa4seWST8YfeloiiF5J0hEU3XtcScP34z/NFn/F8CktWS9RLXtxSIAgNico/GssyldYFFiPpxEo8JTdi/4jN+bnypnFVsy2z0qLACyLSlX+kbhjHsFAKGM04WJbgVypoIxMRrqm6zKdtLs7HY08tuhxeM07CMsyXavdZGK1W0nphPLYlwnwlno328/Siy6GR2gKg0bTN+fnGPVzqzX84vbrP6h3rJTcgn65NirmUzeXFDvSKMQaeeorrPvcnvOKZJzj8nVPkZ+r3KmpofLw2id98M9jb/5P/duSvHZ/kl8uVq/uN7JZy5e2yXKK/fAYPtSX69bRyXPYOJVkoh6QSFn4AyQSExiKbBC8wZCs/vkYGUpU/j2QTlNyQdNImYcfbziQdQhP3gokig20LUSTkkg62WCuJ2A8N6YTFQjlk71Dj+wvLcS2fwQKEkZCrJLHl9ggAqUoMwxmHchj33VnuTWOJhb1ml1W3QpgYSjFfDpmoOmP0I2EkU7uSba3uw6RtEUbgR4Z85Setjfbz3qEUc+Vw5QweYK4crpwNb3X+1fus3rHOpRyCNf+hLbnxPZNNRRE8+WTtmfiVK+u+7d0bbePgwdokfv31ILLRGj2xHflrxyf58XyK87NFgHXXtFRnHJ8Y4vGz8T+ytdfkG2nluLzhhv0r1+QLrkd8ghtxeCzHbNHllkMjFN0Ax7a4aiiJF0TceGA4vibvx9fkF0o+x/bmOD9XwvWjlWvyYhmunVi9Jj+1WF65Jr98fXmzuICVa9KWGG4+NLbSHuHclRIGw8RQmqIb8Iojozz8zDTTix6HR9PMFFxGskmG0g5zS17tNfkbDgPwjhOH+C9fnQRYucYcRIYfOj5B0Q1W9v1oNolg4rHTds01+eVy/Ub7+T23X81//srTADXX5H/uNdc3Nf/qfVbvWF+3b4inLs2zUPLXXZPH9+Gxx2qTeLHYcPy6jh3jysk7+OKelzF14g7MNVc3d02+T2xH/trxN15Bn67ZDvp0jT5ds9Wna9LlInu//AXM/feT+voWng+v58Yb4Z574rPwu++Oz8zr6NrTNduoE/lL2xoopTpnZgY+9Sn4+Mfhm99sbRsnTqxeSrn77vgauWpZx56uEZEMcLUx5umORKaU6j8XLsAnPhEn8e9+t/XtnDwJ738//PRPw969nYtPNWXLSV5E3gr8ByAJXCsitwL/2hjztm4Fp5TqgsnJOIHffz+cPdv6dl772jiJv/OdMDzcufhURzVzJv+vgNuBrwIYY54QkaMdj0gp1Z7Tp+MEfv/9ceVmq978Znjf++Ctb40baKkdqZkkHxhj5qUPH0NSndWpG9mtbKeVderdFK6+Mbt2O/XGKPvhhjeWW7khKoZ1N4w3u1m9PJcrBY/QGPaPZJjIp1bjNwYeeWQ1iZcbP9+/mctvehvz7/5pJn7ybaSz6br7a67oMXl5kRdmZrgwv0TGcTg4lulIi4JW3h3QCbvtQY0t33gVkd8HHgR+HXgn8E+AhDHmFzsVjN547b1OlVm3sp1W1qnXcqG67UG9x9LWjjG9WOKlRY892WTdtg312g1cni9z98snOHH1aruBlxZKGIT9w2m8MOT0i/OAWWnHsFkriLmix+PPTrH31MOMfeZTHP7C57a8v9cyto28733x5ZS774bKs/XN7K9cyuHMhXlKfsjpc3MkEjZiDAdGM4TGtNWioN6xXij7iDEMZZJda1EyqG1QOnXj9ZeBfwG4wJ8AfwX8m/bDU/2kU2XWrWynlXXqtVyobnuwdjvx32vHuLjgEkWmYduGuu0GFso8+twV7nrZaruB2aIPwLGJPGcvLjGWS4JhpR0DwLPnZ3jVdx+Nz8L/+3+vmcso8CNb2LdAfA38fe/jzI++ncWbbiVbVV1adANsm7otJ5rZX0+cm2VPNsUPLhcYyiTIpRO4fkjBC7lqKN1Wi4J6x7roBiAwPtS9Ev/d2AZlS0leRGzg/zTG/O/EiV4NqE6VWbeynVbWqddyobrtQb3trB3D9cN1FYbVbRvqtRtI2sJsubatQxhF2OUSI597gLv+6OMc/PtvNIx7M+6+/bz09ndz8W3v4uKhY9xxbJyiF3JsoraT4eWnX2JszXw2ajnRzP6aK/ocHMmw6AaMVapuE7ZQ8MK2WxTUO9aRWb6+VRtHJ0v8d2MblC0leWNMKCK3dTsY1XudKrNuZTutrFOv5UJ124N621k7RiphE0W12aW6bUN1u4FUYYEbv/4l3vWXn+Nlk6fhH6+uc3PDKOu47jr4wAfgve+Fa+LK0cfOzhCGUPACjAHHEkpeSM6Shvuh2ZYTzeyv0WyCsh8xlIrXydk2fmjIJOyttyhooN6xtkRgzS2/Tpf478Y2KM1crnlcRP4M+DSwtLzQGNP6hUPVdzpVZt3KdlpZp17Lheq2B42uyVePcWA4xUuLHkU3ID83Tf6zf8o1n/1vDD0dPyP+75qaeaxw4818+0fexg9e92Ze9orjW7omvzyXhG2xUPKJDHhByPUHhhvuh2ZbTjSzv249MsaZC/McHstw+twc5SBauSa/0qKgRfWOdTblIMY0PG6dsBvboDRz4/UP6yw2xpif61QweuO1Pwz00zXPP4//sfuRj38c57lnm57TssXbbsf+2Q/w/bt/jPlUbvuertni/Dcap5n9tfy9L8yU9OmaPqZtDdTu8b3vrT5e+NJLrW/nx34sfkb8J34CsoN7lqcGQ0eerhGRw8BHgLuIT1C+AfyKMebFjkSp1GaMgVOn4gT+8Y/D4mLr2/rJn4wfL7z3XkhufHat1E7WzDX5PyR+dHK5jfN7K8s6/3pxtTsZE5+JV7egbfVs/Gd+Jk7i99zTk5dBKNUvmknyE8aY6uvyHxORX+10QGqAhSF8+9u1SXxurvntZLNxAn//++N3a2oVtlINNZPkp0XkvcAnK39/DzCz0Qoikgb+FkhVxvqMMeY3WwlUNacnN5c8L76cUkng5mtfQ5osu/euPcYzN76KM9fdyqUT/5Abbn8FNxwYYTyfYq7o8cS5WeaK/krf+P0ifOn0eT7xyFlmCi7j+RTvveMa7rlh/7r5L69/dqbIlaUyezIpDoxluHo8y3A6Wffmn+uHzBc9Fr2QMIrYP5xmOJ2AyusGi55PNuEwnEms28ff+MFLfPrUOaYWXfJJh5sODbF/JLcS+2g2uS7GS/OlDfvNL6s+vq4XMF8OcINw3Y3XVlo4dGqduserQ+93XXZpvtT1MXa6Zp6uuRr4XeCHiK/JP0x8Tb5hGzuJG93kjDEFEUmweh3/kXrfrzdeO6NrpdulUtw3pfpMvMkb9+UbbsK/626Cu++mePsPUR6fWInr6YvzfOLRswylnMobnaBQ9nnbrYdIORZPXVpkPJeqedOQBfzBQ88xnEkwmrKYcyPmij4/c8dRXn/T/pX5vzi3xPcvLGBZ8P1LBSzbwvUCJkbSpGzhh6/fx1g2zWLJw4gwnE5QdH2+/eIckTGkHQvbsim4PmPZFEkbbNsik3CIjGFvPoUIK3P5xg9e4v958AeMZpMkMTwzU8QLQt7xqkMcHR/mSqHMyw8Oc3g0txLjc9MFvjk5zd6hdO2bo04crkn01cc3jnEex4Ije7IYIyuPUKYT9rrPwVShzNR8mbFcqm4Lh3qfnemCy+X5UlPrnJ8t8tSlecZz6XVvhupUEr40X+LBM5cYTie7NsZO0ZEbr8aYF4Cm2gqb+H+QQuWvicqv/nmcZ0C1XLq9sAAPPbSawB+p+3/xxm67DV77WmZuu4Pyq38IZ2K1j/gLV5bAwNXjcfm8AySDaCWuB566xFg2iW0JxrHJ2YJjwSPPTsdxGxjOxAU4w5l4br/7108znEmwbzj+R70vFVdv/sUT53nTLQdX5j/5UgEjwuUFl6F0kkza5qV5w3zR4+X7hjlzcYF7rs+y5IdgYG8+xVMXSwynk8yXPLwArhpJsFD2mCt5jOWS+H7EeN7BDyOWvIA9udTKXD596hyj2ST7htKcnV5iPJek7EU89IMr3HJ4nJkll8mXChzbO7QS45MvzmLbFqO5+Ax5NBf/h/zw5FRNkq8+vk9fKjGcSZCwLBbLIfuG48rcyalF9g9n1n0OLs2ViWjcwqHeZ+fifBETSVPrPHN5EcFad7zaaYWw1hPnZhlOJ7s6xiBo5uma+4nPwucqfx8D/uNmz8lXWiI8BlwH/J4x5tE1X/8g8EGAq6++urnoVV2NSrfLly7DV7+1msSfeKL5jd911+obfe68E/LrLyUAzE8V1petR+v/f68uKZ9e9Dg0mma+FJKo/MeUT9pcWvAYzQQrL6pelkvZzBV9Do/UvoQ661jMrCnrX3Lj9Qt+yGg6jsvGUAoiMil7pa9NdYxLXsBI2iFeFC8XhLIf1nyfYwklP6yZS3UrBDeKSNtCNmUxVYj72yQsoeAGNTEulHxG07VP+uTTNhfnay95VR/fghvHKALlIH5B+XJbAze7/nPgBhu3cKj32YnbPjhNrbPkBuTTtRWx7bZCWGuu6LNvzQvIOz3GIGjmmvwtywkewBgzKyInNlvJGBMCt4rIKPB5EbnZGPOdqq9/FPgoxJdrmohHrXXxInzta+z78oOkHvoGie8/1dz6tl37hvvbb2+5j3jdsnVL1v0cV11SvncoyUI5xBWl+lMAABseSURBVLGFKIrDKXghe/IJcmkHP6hdebm0fs6N2Ff1b70YRIxlapNlLuXgR4Z8wsb1IWNDiJB2LEpuyEilRL86xlzSwQ0MlsTJHcBgSCfsmu8LIrMy3+W5TAylmCuH7EskSFkWYRRR9iJGK2edfmTIrflPaziTwK0k6mWFcsh4vnYu1fs2n3JwQ0PCEpJ2PPZyW4N6xyDl2ERrDkJ1G4S66yRsTG1Ym66TSzkEYf3j1Smj2QRLbrhyBt+NMQZBMw0brMrZOwAisofmLvfMEb9w5N4mxlTVnn8ePvYx+NmfhWPH4qdKqn8dPAjveQ/5P7yvboI3uRz8+I/Dhz8cX4rxvPia+vKvIIAHH4R/+S/jJN/GiyLG8ym8IMQLIkylVD2XsMmmnJplXhCu3Ox8ww374zNQP6TsekwvuswVfe44tpfrJvIg8dluGEUslHwWyh4fuPNaFko+lxdKeK7L5YUSJT/iLbceqhnn+FV5xBj2DSdZLMfbDoKQkWySuZLLjQeG18V49Z64fN9gSDrx2JYIo5kkCYnPZotugOuH5JJOzVzeffIIc0WPy4tlRlIWM0sesyWXu162h4VS3FL3+FX5mhhvOTxGGEbMLXkEYcjcksdsyePO4xMN9+2RsQwLJZ/5kstQ2qboBiy5Pscnhuoeg/2jaRISd3yMoqjm+xsdtwMjWRyLpta5bt8Qhmjd8br1yBidcuuRMRbKXlfHGATN3Hh9H/AbwGcqi94N/FtjzB9tsM4E4Btj5irvh/0y8GFjzF/U+/5dfePVGHj66dqbmhcuNLeNPXtWzsLdO1/D9LUvxzXSs9LtVsrWn744zwNPXeLynEs6ZXH70fGNn64ZyejTNfp0za5/uqZjbQ1E5CbgdcS94h40xnxvk++/BbgfsIl/avhTY8y/bvT9A53kowiefLI2iV+50tw2Dh6svZxy/fX6jLhSqmNtDY4Dk8aY74nIPcAbRORC9XX6tYwxTwKbXrcfCL4P3/pWbRJfWtp8vWrXXruawO+5B44e7UakSqldpJkbr58FTorIdcB9wJ8Ttzn48W4E1nfKZXj00dokHjb5ooEbb1xN4j/8w/GZuVJKdVEzST4yxgQi8pPA7xhjPiIij3crsG1XKMDDD68m8Icean4bJ06sJvG774bx9a9fU0qp7dRMkvdF5D3A+4C3VpbtnGeVZmfh619fTeKPPdb8Nu64o/YZ8ZGRzse5zba7/UE3xnvy3BW+cPoCl+bK5NIOd183wfUHRuKb2bL+xnO/9BPfqK/7Qsmn6AdkkwmG086O7HneL/t5t2vm6ZqbgF8E/s4Y80kRuRb4KWPMv+9UMG3deJ2dhQceWE3i3/nO5utUE6m9qfnqVw98H/HtfnN9N8Z78twV7vvGswynkmBCioFhfsnjnhv3ce3eIQ6PZbAta92bobZrzo3MFT0eP3uFXCqx0irgStHjqqEkQ+kk0wUXS4TIRIzn01hVLRN2gu3+bO122/LSEBH5rDHmne1so60kv9lTJul0bRI/eXLX9xE/P1skMqyUowN4QbSSUHbCeB/64nco+3ERUmQMtmXxwvQSqYTw/ruOYwnsH8msjANs65wbWX6fa7aqIOr56QKWJRwazRAZSNgWfhAhEj+Lvt0xtmO7P1u7XUeertmCYx3cVvPuuw/+/M9Xk/grX6l9xDex3W+u78Z4U4seh0fTLLrxe0EBMo7FbNFdaTewdpztnHMjhXLA2JpX9FkStxBwg4hM5WzXsde3TNgJtvuzpRrrZJLvbUuCn//5+Jfasu1+c303xpsYSjJfDlfP5EUoBREj2eRKu4G142znnBvJpx3KfrTS7AvinzBSCZuUYxFEhoQtBKEhaVs9ibEd2/3ZUo3pHt/F6pWjV5fm74Tx3vyKg8wWXYpuSNn1mVos4wUhtx3dw5IbMJJJ1Iyz3XNu5PjEEEuuX9MqwLEtDgyn4p45fhi3TAgCcimnJzG2o1/2s+psktfSyx0mnbA5NJbFEih6Yddv7nVjvFuO7OEXXnOMXNpi0Q1JJyzedfIId123j4MjaSJDzTjbPedGRrNJTlyzB9uG2aKHbcMdx8a58eAomaTNSCaBbcNINkU2ae+4G5b9sp9VZ2+8/qgx5svtbGOg2xoopVSXdKqtwWnWX3efB04B/6bdBK+UUqrzmrnx+pdASNzKAOCnK78vAB9jtUBKKaVUn2gmyd9ljLmr6u+nReQhY8xdlRd8K6WU6jPNJPm8iLx6+fV9InI7sNzoOmi8mtqt6pXtj2bXF6C10ne+2nJP8alFF9uKn1w5PJZdt87yOE9dnOebz89QdiPG8kluu2aMvfn0hm0Ethpj9TI3CJktevhB1LG+7fW0u/+2opUWBcvrLJSDDfvuV9vqZ6Ydu63dQjNP1/wCcJ+IPCcizxN3ovyfRCQHfKgbwamda7lsPwxhLJskDOHxs1eYW/Pu1eXy98jERUqRgWcvLzI5VahZdn62SNlfX0hzab7Eg2cusVQOEQHPNzz2/AwvLZRr1lke55nLi3zx9EXKviFhw0vzZf701As8+eIcYQjzRZeiF9ZdtzqeyakCz15ebBi3H4acPjfLS3Mu2aS9bv71tnnm4gKPTk5vus8223/1Ymu0/7ai3hibbW95naIXMl90431b8imt2bfVtvqZaUcrc9nptpzkjTF/b4x5BXArcKsx5hZjzDeNMUvGmD/tXohqJ5qcWiSXSpBNOViWRTblkEslmJxarPm+mYJL0rFJOhYiQtKxWKo8I169LOmsvmy72hPnZhmuvPw67diM5pIMZ1J8/6XFmnWWx3nk2WlGswn2DqWIxCKddMg4Dt9+cY5syiHlOCy5Qd11q+MpugFLftgw7heuFBnOpBjKOCyWw3Xzr7fNi/NFgohN99lm+69ebI3231bUG2Oz7S2vs+QGpBwn3rcJmyUvaLjuVj8z7WhlLjtdM0/XpIB3AkcBRyq9YjZ605PaveqV7acTFrNrzsrqlb9H0frHehuVxM8VffYNpVjygpVqykzSYqbg1ayzPM6Vgs/+4WTNOKmExdySD9RvI1A3RmPWPWtWHXdciJVEgHLl5dzV86+3TdcPSTm1/yTr7bOadbYYWzstBVppUbC8jhdWtWiwNm7RsNXPTDt2Y7uFZi7X/A/g7cTX35eqfim1znLZfrWyH1+brrZc/l7NsgRrTcO5RiXxo9kES25IwrIIK0m25EUMZxI16yyPsyefoFD5B21Zgojg+hFD2bhrdr02AnVjFMGyamOsjjuXcnD9iCCKt7d2/vW2mUrYrC1bqbfPatbZYmzttBSoN8Zm21tpa2BbBJV1l9tMNFp3q5+ZdrQyl52umZkdNsb8lDHmt4wx/3H5V9ciUztavbL9Jdfn+MRQzffVK3/PJWyyKWdLJfG3HhljoVy5zh2EzC15LJRcXn7VUM06y+PccWwvc0Wf6UUXy0SUvYBSEPDKw6MN2wjUizGbcsgl7IZxX70ny0LJZbEUMJS2182/3jYPjGRxLDbdZ5vtv3qxtdNSoJUWBcvr5FIObhDE+9YPySUbt2jY6memHbux3UIz/eQ/CnzEGHO6W8Foxetg0adr9Okafbpme3Skn7yIfA+4DngOcIl71RhjzC2dClSTvFJKNa9T/eTf1KF4lFJKbZNNk7yIDBtjFoDOPceklFJqW2zlTP5PgLcAjxE/mFV9297Q6zdCKaWUamjTJG+MeUvl92u7H45Szev0jbR2bmTuhJt62xXjTtgXu8GWH6EUkQe3skyp7dTpMvV22izshJL57YpxJ+yL3WLTJC8iaRHZA+wVkTER2VP5dRQ42O0AldpIp8vU22mzsBNK5rcrxp2wL3aLrVyT/5+BXyVO6I+xek1+Afi9LsWl1JZ0uky9nTYLO6Fkfrti3An7YrfYyjX53wF+R0R+2RjzkW2ISaktWymfd1afB+hECX/19ixL1vWCqTdGp2Pphu2KcSfsi91iy8/JG2M+IiI3AzcB6arlH+9GYEptxXg+xfnZIhCfKfqhwQtCDo1lO7a9XMLGiOAF0YZjdDqWbtiuGHfCvtgtmrnx+pvARyq/fgT4LeBtXYpLqS1JJ2wOjWWxBIpeiCVwaCzb8lMc9bZ3bN8Qxyfym47R6Vi6Ybti3An7YrdopuL1XcArgceNMT8rIlcRvzhEqZ5aTijd3t5Wxuh0LN2wXTHuhH2xGzRzgaxsjImAQESGgctoIZRSSvW1LZ3JS/yGkCdFZBT4/4ifsikA3+xibEoppdq0pSRvjDEicqsxZg74ryLyJWDYGPNkd8NTSinVjmauyT8iIv+w8q7X57eygogcAT4O7Aci4KOVRzLVLlBd1g6AMSDScs/zRr3Gm+1BXhNXJSag6dL7dsv2t7r+Zt+30dc71VpAWxTsXM1ck/8R4O9EZFJEnhSR0yKy2Zl8APyaMeZG4A7gl0TkplaDVTtHdVm7JXBhrsiF+TKW0FSrgGVzRY/Hz14hDGEsmyQM4fGzV3h+ulB3+VyD94Kui2u+zIW54kpcWy29b7dsf6vrb/Z9G329U60FtEXBztZMkn8TcBx4HfBW4s6Ub91oBWPMRWPMtyp/XgTOAIdaC1XtJNVl7XNFn1wyQS7lMF/ym2oVsGxyapFcKkE25WBZVvyKu1SChyen6i6fnKrfGbs6rvmSTy7lkEsmmCv6TZXet1u2v9X1N/u+jb7eqdYC2qJgZ9tykjfGnK33a6vrV3rdnAAeXbP8gyJySkROTU1NbXVzqs+5lcIhAC+McGzBsWTl0k0UGaI1byVL2KtfX6tQDkgnaj+u6YTFTMGru7xQDjaNyw0iHEtwbMELo01jaLSdrcTf6vqbfd9GX283xmZjVf1pW2qMRSQPfBb41coLSFYYYz5qjDlpjDk5MTGxHeGobbBc1g6QtC2C0BBEq2XtliVYUps4Nip7z6cdyn5tUin7EeP5ZN3l+XT9203VcaUciyAyBKEhaVubxtBoO1uJv9X1N/u+jb7ebozNxqr6U9ePkogkiBP8HxtjPtft8VR/GM+n8IIQL4gYzSZY8nyW3ICRTAIviMglbLIpBy+IMMbgBRFeEK7ckF3r+MQQS65P0Q2IooiiG7Dk+tx5fKLu8uMTQ5vGNZJJsOQGLHk+o9nEpjE02s5W4m91/c2+b6Ovtxtjp+aqemvLL/JuaePx8/X3A1eMMb+62ffri7wHiz5d05n19ekatZmNXuTd7ST/GuDrwGniRygB/rkx5ov1vl+TvFJKNW+jJN/Mc/JNM8Z8g9p3wiqllNpGeudEKaUGmCZ5pZQaYF29XKPUTjeINxwHcU6qMT2TV6qBQSznH8Q5qY1pkleqgUEs5x/EOamNaZJXqoFBLOcfxDmpjWmSV6qBQSznH8Q5qY3pkVWqgUEs5x/EOamNaZJXqoHlF1FbAkUvxJL4Zd47+UmUQZyT2pg+QqnUBpaT4iAZxDmpxvRMXimlBpgmeaWUGmCa5JVSaoDpNXnV9+qV4cNqL3rXC5gvB7hBuNJPPp2IC3wWygFFzyebcBjOJLa1n/qgtw/Yap/7Th2DVmJQeiav+ly9MvzJqQLPXl4kMuAHIacvLHBhrkg2aROG8OjkNGcuLlD0QuaLLmEI8yWfkhe2VMLfSiuAQW8fsNn8lr/eqWPQSgwqpkle9bV6ZfhFN2DJD0k6FudmSwxnEoxkUiyWQ7IphyCCi/NFltyAlOOQTTmkEjZLXtBSCX8rrQAGvX3AZvNb/nqnjkErMaiYJnnV1+qV4UfGEEVx1WbBDUjZgm2BF8al+SLg+iFeGOFU1nUsWdlWsyX8rbQCGPT2AZvNb/nrnToGrcSgYprkVV+rV4ZviWBZ8T/ufMrBDQ1hBEk7/jgbA6mETdK2CCrrBpFZ2VazJfyttAIY9PYBm81v+eudOgatxKBiujdUX6tXhp9NOeQSNl4QcWQsw0LJZ77kMpS2KboBjgUHRrLkUg5uEFB0A1w/JJd0Wirhb6UVwKC3D9hsfstf79QxaCUGFdMkr/pavTL84xN5ju0bwhJIODavODjMwdEsRS/EtuHVx/dy44FhskmbkWwK24aRTIJM0m6phL+VVgCD3j5gs/ktf71Tx6CVGFRMjDGbf9c2OXnypDl16lSvw1BKqR1FRB4zxpys9zU9k1dKqQGmSV4ppQaYJnmllBpg2tZANU1LyZXaOfRMXjVFS8mV2lk0yaumaCm5UjuLJnnVFC0lV2pn0SSvmqKl5ErtLPovUzVFS8mV2lk0yaumaCm5UjuLPkKpmrac6JVS/U/P5JVSaoBpkldKqQGmSV4ppQZYV6/Ji8gfAG8BLhtjbu7mWGpnmCt6TE4tUigH5NMOxyeGGM0mex3Wplpp5aDtH1Q/6PaZ/MeAe7s8htoh5ooej5+9QhjCWDZJGMLjZ68wV/R6HdqGWmnloO0fVL/oapI3xvwtcKWbY6idY3JqkVwqQTblYFlW/Bq/VILJqcVeh7ahVlo5aPsH1S96fk1eRD4oIqdE5NTU1FSvw1FdVCgHpBO1H7l0wqJQDnoU0da00spB2z+oftHzJG+M+agx5qQx5uTExESvw1FdlE87lP3aJFf2I/Lp/i7XaKWVg7Z/UP1CP3Fq2xyfGGLJ9Sm6AVEUUXQDllyf4xNDvQ5tQ620ctD2D6pfaJJX22Y0m+TENXuwbZgtetg2nLhmT98/XdNKKwdt/6D6RbcfofwkcA+wV0ReBH7TGPP73RxT9bfRbJLbrhnvdRhNa6WVg7Z/UP2gq0neGPOebm5fKaXUxvRyjVJKDTBN8kopNcA0ySul1ADTJK+UUgNMk7xSSg0wTfJKKTXANMkrpdQA0ySvlFIDTJO8UkoNME3ySik1wDTJK6XUANMkr5RSA0yTvFJKDbD+fiWPGjhlP2Sm4OIGESnHYjyf0h7rSnWRnsmrbVP2Q87PFokMZJM2kYHzs0XKftjr0JQaWJrk1baZKbgkHZukYyEiJB2LpGMzU3B7HZpSA0uTvNo2bhCRsKVmWcIW3CBqsIZSql2a5NW2STkWfmhqlvmhIeXox1CpbtF/XWrbjOdTeEGIF0QYY/CCCC8IGc+neh2aUgNLk7zaNssvtrYEil6IJXBoLKtP1yjVRfoIpdpWy4leKbU99ExeKaUGmCZ5pZQaYJrklVJqgGmSV0qpAaZJXimlBpgYYzb/rm0iIlPA2TY2sReY7lA4vaZz6U86l/40SHOB5udzjTFmot4X+irJt0tEThljTvY6jk7QufQnnUt/GqS5QGfno5drlFJqgGmSV0qpATZoSf6jvQ6gg3Qu/Unn0p8GaS7QwfkM1DV5pZRStQbtTF4ppVQVTfJKKTXABiLJi8i9IvK0iDwjIr/e63haJSJHRORvROSMiHxXRH6l1zG1S0RsEXlcRP6i17G0S0RGReQzIvJU5Rj9UK9japWI/NPKZ+w7IvJJEUn3OqatEpE/EJHLIvKdqmV7ROQrIvKDyu9jvYxxqxrM5f+ufMaeFJHPi8hoO2Ps+CQvIjbwe8CbgJuA94jITb2NqmUB8GvGmBuBO4Bf2sFzWfYrwJleB9EhvwN8yRhzA/BKdui8ROQQ8E+Ak8aYmwEb+OneRtWUjwH3rln268CDxpiXAQ9W/r4TfIz1c/kKcLMx5hbg+8BvtDPAjk/ywO3AM8aYZ40xHvAp4O09jqklxpiLxphvVf68SJxEDvU2qtaJyGHgzcB9vY6lXSIyDPww8PsAxhjPGDPX26ja4gAZEXGALHChx/FsmTHmb4Eraxa/Hbi/8uf7gZ/Y1qBaVG8uxpgvG2OCyl8fAQ63M8YgJPlDwLmqv7/IDk6My0TkKHACeLS3kbTlt4F/BgzCm7qPAVPAH1YuP90nIrleB9UKY8x54D8ALwAXgXljzJd7G1XbrjLGXIT4ZAnY1+N4OuXngL9sZwODkOSlzrId/VyoiOSBzwK/aoxZ6HU8rRCRtwCXjTGP9TqWDnGAVwH/rzHmBLDEzrkkUKNyvfrtwLXAQSAnIu/tbVRqLRH5F8SXcP+4ne0MQpJ/EThS9ffD7KAfPdcSkQRxgv9jY8zneh1PG+4C3iYizxNfQnudiHyityG15UXgRWPM8k9WnyFO+jvRG4DnjDFTxhgf+BxwZ49jatdLInIAoPL75R7H0xYReT/wFuAfmTaLmQYhyf898DIRuVZEksQ3kP6sxzG1RESE+JrvGWPMf+p1PO0wxvyGMeawMeYo8TH5a2PMjj1bNMZcAs6JyPWVRa8HvtfDkNrxAnCHiGQrn7nXs0NvIlf5M+D9lT+/H/gfPYylLSJyL/B/AG8zxhTb3d6OT/KVGxT/K/BXxB/UPzXGfLe3UbXsLuBniM96n6j8+vFeB6VW/DLwxyLyJHAr8O96HE9LKj+NfAb4FnCaOA/smLYAIvJJ4O+A60XkRRH5eeDfA28UkR8Ab6z8ve81mMvvAkPAVyo54L+2NYa2NVBKqcG148/klVJKNaZJXimlBpgmeaWUGmCa5JVSaoBpkldKqQGmSV4ppQaYJnk1cETkaHXr1h7F8AER+d1exqAUaJJXaksq3RqV2nE0yatB5YjI/ZUXL3ymUsJ/m4h8TUQeE5G/qup1clxEvlRZ/nURuaGy/GMi8p9E5G+AD9cbRERuF5GHK50pH65qewBwpLLdp0XkNyvf/2ER+cdV6/8rEfm17u0GtdtpxasaOJU2zc8BrzHGPCQif0Dc8uIdwNuNMVMi8lPAjxljfk5EHgR+0RjzAxF5NfAhY8zrRORjwN7KOmGDsYaBojEmEJE3AP+LMeadIvIB4EPAzUCRuMfSB4AQ+G1jzGsr638PuNcY80JXdoba9fRHUDWozhljHqr8+RPAPydOuF+Je3JhAxcrbZ3vBD5dWQ6QqtrOpxsl+IoR4H4ReRlxi+tE1de+YoyZARCRzxH/p/PbIrJPRA4CE8CsJnjVTZrk1aBa+yPqIvBdY0zNe1krZ+JzxphbG2xnaZNx/i/gb4wx76j8BPHVDWJY/vtngHcB+4nbMCvVNXpNXg2qq6tetP0e4teoTSwvE5GEiPyDyktZnhORd1eWi4i8solxRoDzlT9/YM3X3lh5wXSG+HV0yz9ZfIq4/fK7iBO+Ul2jSV4NqjPA+yttgfcAHyFOqh8WkW8DT7D6oox/BPx8Zfl3ae4dwb8FfEhEHiK+BFTtG8AfVcb6rDHmFEClFfYQcH75lXVKdYveeFVKqQGmZ/JKKTXA9MarUlsgIj8L/MqaxQ8ZY36pF/EotVV6uUYppQaYXq5RSqkBpkleKaUGmCZ5pZQaYJrklVJqgP3/OwZEz1Sn3LIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Zooming in on rating_score & beer_abv: how does a scatter plot of those two look like?\n",
    "\n",
    "X = beers.beer_abv\n",
    "Y = beers.rating_score\n",
    "X = np.array( [ [value,1] for value in X ] )\n",
    "m, b = np.linalg.lstsq(X, Y)[0]\n",
    "plt.plot(beers.beer_abv,beers.rating_score,'o', alpha=.1);\n",
    "plt.ylabel('rating_score')\n",
    "plt.xlabel('beer_abv')\n",
    "\n",
    "# best fit line\n",
    "x= beers.beer_abv\n",
    "plt.plot(x, m*x + b,'r',label='Best Fit Line');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jasper.kuller\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  \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"
    }
   ],
   "source": [
    "# Zooming in on rating_score & beer_ibu: how does a scatter plot of those two look like?\n",
    "\n",
    "X = beers.beer_ibu\n",
    "Y = beers.rating_score\n",
    "X = np.array( [ [value,1] for value in X ] )\n",
    "m, b = np.linalg.lstsq(X, Y)[0]\n",
    "plt.plot(beers.beer_ibu,beers.rating_score,'o', alpha=.1);\n",
    "plt.ylabel('rating_score')\n",
    "plt.xlabel('beer_ibu')\n",
    "\n",
    "# best fit line\n",
    "x= beers.beer_ibu\n",
    "plt.plot(x, m*x + b,'r',label='Best Fit Line');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "beers = beers[beers.global_rating_score != 0.0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jasper.kuller\\AppData\\Local\\Continuum\\anaconda3\\lib\\site-packages\\ipykernel_launcher.py:6: FutureWarning: `rcond` parameter will change to the default of machine precision times ``max(M, N)`` where M and N are the input matrix dimensions.\n",
      "To use the future default and silence this warning we advise to pass `rcond=None`, to keep using the old, explicitly pass `rcond=-1`.\n",
      "  \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"
    }
   ],
   "source": [
    "# Zooming in on rating_score & global_rating_score: how does a scatter plot of those two look like?\n",
    "\n",
    "X = beers.global_rating_score\n",
    "Y = beers.rating_score\n",
    "X = np.array( [ [value,1] for value in X ] )\n",
    "m, b = np.linalg.lstsq(X, Y)[0]\n",
    "plt.plot(beers.global_rating_score,beers.rating_score,'o', alpha=.1);\n",
    "plt.ylabel('rating_score')\n",
    "plt.xlabel('global_rating_score')\n",
    "plt.title('User 2')\n",
    "\n",
    "# best fit line\n",
    "x= beers.global_rating_score\n",
    "plt.plot(x, m*x + b,'r',label='Best Fit Line');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "sns.violinplot(beers.beer_type, beers.rating_score, order=['Lager','Other','Pilsner','Hefeweizen','Belgian Blonde','Pale Ale','Witbier','Belgian Strong Golden Ale','IPA','Belgian Tripel','Belgian Quadrupel'])\n",
    "plt.title('Ratings per beer type (user 2)', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "sns.violinplot(beers.serving_type, beers.rating_score)\n",
    "plt.title('Ratings per serving type', fontsize=16)\n",
    "plt.show() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WPOWXsOZSJ+oxrO7Fa7HmvC4la4EvGGO2zbajUkplOi3rVUoppZRSSinlOF1KRimllFJKKaWU4zQ5VUoppZRSSinluIyac1pWVmbWrl3rdBhKKaWUUkoppVJg//79XcaY8qnuy6jkdO3atezbt8/pMJRSSimllFJKpYCItE53n5b1KqWUUkoppZRynCanSimllFJKKaUcp8mpUkoppZRSSinHaXKqlFJKKaWUUspxmpwqpZRSSimllHKcJqdKKaWUUkoppRynyalSSimllFJKKcdpcqqUUkoppZRSynGanCqllFJKKaWUcpwmp0oppZRSSimlHKfJqVJKKaWUUkopx6U8ORWRIhH5hYgcF5E6EdmY6nMqpZRSSimllFpc0jFy+r+BF40x1wI3A3VpOKdSSiml1Jx1dnbyd3/3dzQ0NDgdilJKZa2UJqciUgC8FXgYwBgzbozpS+U5lVJKKaUSdfjwYbZs2cITTzzhdChKKZW1Uj1yuh7oBB4RkRoR+YGI5Kb4nEoppZRSCYlEIgCEQiGHI1FKqeyV6uTUA7wR+I4x5lZgCPhy/A4i8kkR2Sci+zo7O1McjlJKKaXU5cLhMADGGIcjUUqp7JXq5PQMcMYYsyf29S+wktUJxpjvG2NuN8bcXl5enuJwlFJKKaUuZyenSimlnJPS5NQY0w6cFpFrYpt+HTiWynMqpZRSSiXKTk5FxOFIlFIqe3nScI6/ADaJiA9oBv4oDedUSimllJozLetVSinnpTw5NcYcBG5P9XmUUkoppeZLy3qVUsp56VjnVCmllFIqo2mXXqWUcp4mp0oppZTKenZyGo1GHY5EKaWylyanSimllMp6dnKqI6hKKeUcTU6VUkoplfXGx8cv+aiUUir9NDlVSimlVNbT5FQppZynyalSSimlsp6dlI6OjjociVJKZS9NTpVSSimV9cbGxmIfNTlVSimnaHKqlFJKqaw3kZyOjjkciVJKZS9NTpVSSimV9UZGRgAt61VKKSdpcqqUUkqprGcnpaNjOnKqlFJO0eRUKaWUUllveHgYsMp7jTEOR6OUUtlJk1OllFJKZb3RWFmvMUaXk1FKKYdocqqUUkqprDccS07h4iiqUkqp9NLkVCmllFJZzRjDyMgIfrf1tkiTU6WUcoYmp0oppZTKauPj40QiEU1OlVLKYZqcKqWUUiqr2cloTiw5HRoacjIcpZTKWpqcKqWUUiqr2clojsd9yddKKaXSS5NTpZRSSmU1OxkNuDU5VUopJ2lyqpRSSqmsNjg4CEDAY70tGhgYcDIcBXR1demSPkplIU1OlVJKKZXV7GQ0ECvrtZNV5Yzh4WF+7/d+jwcffNDpUJTKGCMjI5w+fZrTp0/T3d3tdDgp43E6AKWUUkopJ9nJqdflwuN20d/f73BE2c2+OLB161ZnA1Eqg3z2s5/h+PH6ia8fffRRqqqqHIwoNXTkVCmllFJZ7cKFCwD4XC58breW9TosFAo5HYJSGSUajdLc3MzadRHuusv6+zhx4oTDUaWGJqdKKaWUymoDAwO4XS7cLsEj6Mipw3SuqVKX6u7uZnw8xJo1UW64MQJAW1ubw1GlhianSimllMpqFy5cwBdb49QDXOjrczagLDc2NuZ0CEpllNOnTwNQWGjw+SA3Vya2LTWanCqllFIqq124cAGvSwDwuV30anLqqNHRUadDUCqjnDp1CoCSkigARUURWltbHIwodTQ5VUoppVRW6+vtnegQ6XO5dOTUYZqcKnWpkydP4vMJubnW18UlUVpaTmKMcTawFNDkVCmllFJZrbe3d6Ks1+t2MTQ8rE15HKTJqVKXam4+QWlpFLEKPCgtNQwNjdDR0eFsYCmgyalSSimlslpvXx8+l/WWyB/7aHfwVek3PDzsdAhKZYxoNEpjYyOlZZGJbWVlVnlvY2OjU2GljCanSimllMpaY2NjjIyMTIyc+tzW0ERvb6+TYWW1+OR0KZYtKpWItrY2hodHKC+/+LdQVmYQgYaGBgcjSw1NTpVSSimVtewk1D+RnLov2a7SLz451c69KtvV1dUBsGxZdGKb1wslJRfvW0o0OVVKKaVU1urp6QEuJqf2x+7ubsdiynbxyamW+Kpsd+zYMbxeobT00iqC5cvDHDt2dMlVF2hyqpRSSqmsZSehdlmvPedUk1PnaHKq1EVHjhxm2bIIrklZW0WFYWBgaGKZmaVCk1OllFJKZa2uri4AcmLJqdsleN1uTU4dFJ+QDg0NORiJUs4aHh6mqekEK1ZGL7vP3nb48OF0h5VSmpwqpZRSKmt1dXUhwkS3XoAcj4vOzk4Ho8pu8cnpyMiIg5Eo5ayjR48SjUZZOUVyWlRkCAaFgwcPOhBZ6mhyqpRSSjkgGo3y+c9/ng9+8Pf53ve+53Q4Waurq4scjwexFxAEfAKdS3D9wMViZGQEt1iNqbSsV2WzAwcO4HLDihWXJ6cisHJlmAMH9i2peaeanCqllFIOGBoaYt++fZw/386OHTucDidrdXR04HfJJdv8bveSXNx+sRgeHibfFwR05FRlt3379lKxPIrXO/X9q1ZH6e7upbW1Nb2BpVDKk1MRaRGRIyJyUET2pfp8Siml1GIwMDAw8fngwKCDkWS38+3tE02QbDluFz29vYTDYYeiym5jo6Pk+3Ktz3UpGZWl+vr6aGxsYtXqyLT7VK22RlT379+frrBSLl0jp/cYY24xxtyepvMppZRSGc1OTnP9hQwManLqBGMMnZ2d5HgmJaceN8YYbYrkkNHRMfJ05FRluf3792OMoarq8pJeW0GhoahIqK7ek8bIUkvLepVSSikH9Pf3A5CfU8LY2KiOEDngwoULjI2Pk+N2X7I9EOvc297e7kRYWW98fJxcbwCAUCjkcDRKOWP37t3k5AjLls08n3R1VYj9+/cvmf8h6UhODfCyiOwXkU9OvlNEPiki+0Rkn3bGU0oplS0uXLgAWMkpXExWVfqcP38egIDn0uQ0J/a1fb9Kr/HxcYKeHEDLelV2ikaj7Nmzi9VV4cvWN51szZoo4+MhDh06lJ7gUiwdyembjTFvBN4N/JmIvDX+TmPM940xtxtjbi8vL09DOEoppZTz+vr6ACiIJaf21yp9zp07B0BgUllvIDaSat+v0isUCuFz+xBE5/2qrFRfX09fXz9r1kw/39RWWRnF4xF27dqVhshSL+XJqTGmLfaxA3gaeFOqz6mUUkplut7eXkRcFARKJ75W6XUxOb105NTtEnK8Hk1OHRIKhfC43HjcbsbHx50OR6m027VrFyLWqOhsvF6oXBVm587tS2JJmZQmpyKSKyL59ufAO4GjqTyn08LhMKOjo/piqpRSakY9PT3k+ILkePMAtPmOA86dO4fP7cY7Rd1cjktoa2tzIKrsZowhHAlbyanLoyOnKivt2LGdigpDIDC3/detjXLu3PklsaRMqkdOlwPbReQQUA08Z4x5McXndExHRwfvfve7eec738k73/lODh486HRISimlMpTVJTaXgNdaMkOT0/Q7c+bMZSW9tqDbzZkzZ9IckbIbILnFjdvl1oZIKut0dXXR2NjEmrVzvzCzZq1V/rtz585UhZU2KU1OjTHNxpibY7c3GGO+nsrzOe306dOMjY0RKruKaDRKc3Oz0yEppZTKUF2dXQS8eXjcPnyeHLq6upwOKeucOX16ojPvZAGvm+7ubm3Ik2Z2Mup1e/C6tKxXZR87wVy3bvaSXlt+PpSVw86dO1IVVtroUjJJZM8XCq24EdDmFkqpzNTV1cXx48edDiPrdXR2EoiV9AZ8eXR0dDgcUXYJhUJ0dHYSnDTf1BaMrXWqpb3pZSenHrHKenXkVGWbnTt3UlAglJQkNn907ZowR4/WLvrO75qcJlFPTw8AxhtEfAEt0VJKZaQvfOELfPKTn1z0/8AWs+HhYQYHBwj6CwEIePN1Tc00a2trIxqNkuv1THl/bixp1dLe9LJHqq2RU4+OXKusMjY2xr59e1mzJoRIYo9duy4SW4JmT2qCSxNNTpOop6cHxAUeP8YbmEhWlVIqk5w8eRKAwcFBhyPJXvb6mbm+gtjHQtrPaXKaTqdOnQKYduQ01+u+ZD+VHnYy6nP78Lm8mpyqrFJTU8P4eIi1CZT02pYvNwSDsujnnWpymkTd3d2IPwgihD1BOjt1/pBSKnPpXC7n2KWiubGR01x/IYNDgwwMDDgZVlaxk047CZ3M43KR4/VocppmIyMjAPjcXnwu78TXSmWD3bt34/EIlZWJJ6cisLoqTHX1biKR2ddHzVSanCZRZ2cnkVjXReML0tHZ6XBESik1PR2RcM7Zs2cByPMXXfJR5zemT0tLCwGvZ8plZGxBt2ui0kClx9DQEAABj5+Ax8/Q4JDDESmVPrt376RyVRjP1LMNZrVmTYSBgaFF3VdCk9Mkaj/fQdQbBMD4crnQ16vrcymlMpYmp845e/YsPo8fn8daxC4vx0pOdX5j+rS0tEzbqdeW63HT2tKyJBa2XywuSU69fp1+oLLGmTNnaGtrZ01V4qOmtqqqKCIs6nmnmpwmiTGGzo4OjM8eOc2ztunoqVIqQ42OjjodQtY6deoUef4SJNbxIs9fDFhLkqnUi0ajtJw8Sd40Jb22fK+HkdHRiTnCKvUuXLgAQJ43SK43yMCANm5T2aG6uhqAqjXzT05zcqy5p9V7q5MVVtppcpokfX19hELjRP35AET91vIA+g9NKZWphoeHnQ4ha51qPUV+TvHE1x63l9ycQp3fmCbt7e2MjY+TP02nXluez0petbQ3fexl+HK9QfK8QYZHRrTKQ2WFffv2UVAoFBYurFJj1eoIx+uOL9qqA01Ok+TcuXMAmFhSan+0tyulVCaIn2qgyakzhoaG6OzqJD+n9JLt+f4STp5scSaoLHPixAkA8mZLTmP32/ur1Ovu7ibgzcHv8VEYey+lqx+opS4SiVBTc4BVlYkvITPZqlVRotEohw4dSk5waabJaZLYSWjUby0LYHzWC6o2t1DKKtPavn07O3bs0G6kDou/krpYr6oudi0tLQAUBsou2V6QU8qpU62LusviYtHU1ITArCOnXpeLoM9LU1NTegJTdHd3UxirQrM/dnXp6gdqaWtqamJoaJjKVfMv6bVVVERxu4WampokRJZ+mpwmid150R4xxeVGcvI1OVUK+O53v8tXvvIVHnjgAX74wx86HU5Wi784oMmpM5qbm4HLk9PCYDmhUEibIqVBY2MjuT4vbtfsQxR5bqGhoT4NUSmwLvaXxC70lwSspZba23UNYLW02aOc81lCZjKPByoqrJHYxUiT0yQ5c+YM4s8Ft3diW9iXz2l9k6GU9caiEKRAtNTdYfHJqY5iO6OxsRGfxz+xxqmtKFgOoKN0aVBff5w8z9zeAuX7vJw926Zl8Glyru0cZQGre7X9Uf9vqKXuyJEjFBQKeXnJOd6KlVGamk4sytctTU6T5PTp04RjV/ps0ZwCTp/W5FSp8x3nMXmGaDBKZ5d2sHZSf//Fzpd2V0yVXo2NjRQGyic69doKcspwiZvGxkaHIssOfX19dHZ2UeDzzr4zUODzYIzR30sa9Pf30z/QT3mwBACf20dRoEC7WKslzRjD4cOHWFGRvOUnV6yIYoyhrq4uacdMF01Ok+TUqdNEcy69Ch7NKWRocGCi85xS2cheUskEDSZgtIO1w+zXo2Dc5yp9IpEITU1NFAaWXXaf2+WmMFiqSVCKNTQ0AFbSOReFsf3q67W0N9VaW1sBWJFXPrFtRbCM1pZWp0JSKuXOnz9Pb28fFRULL+m12ceqra1N2jHTRZPTJOjr62NgoP+y5NTEylHsF1ulslF/fz9jo2NWNpQL/Rf6dX1NB/X29gKwHOjVDphpd+rUKcbGxijJrZjy/qJABcfrjmPMwpYSUNOzRxLmmpz63W4CXg/Hjx9PZViKi12RV+ZdvHizIrecky0ntVGYWrLs16TlSUxO/X4oLkZHTrOVnXzayagtqsmpUhNNwUyugdxLt6n06+npwStCKdDT3e10OFnHTnCKc5dPeX9JbgUDgwM6xy6Fjh07Rp7Pi9d16Vugut4B6nqnnoed73FTe/RoOsLLavX19eT5g2xp2cXjdS8AsKZwJWNjY7oGsNL+QgQAACAASURBVFqyjh8/jtstlJbOflHyV9s8/Grb3C6sLVsWoa5OR06zkr0sQHRScmp8eYjbO3G/UtloovNoHpg864XX7m6t0q+rq4sCEfKBvv7+S9Y9Val37NgxfB4/BZPWOLWV5FVM7KeSzxjDsdpaCrzuy+4bGA8zMD7130Oh38O59nYthU+xY7XHqMpbwZmBds4MWB161xSstO7Tvwm1RNU31FNaGsV9+cvSZTq7XHR2zS19K18Wpaenj+5FdiFak9MkaGlpQTzeibVNJ4gQDRRNLBugVDY6deoUCJAH5MdtU47o7OggLxqlAOuN+mL7p7XYHT1aS3Gw4rJmSLbCQDket3dRzhNaDNra2rjQ30/RHJsh2ez9NUFKnb6+Pk62nOSq4jWXbF+eW0qeP5eDBw86FJlSqWOMoamxgbKy5Jetl5dbZcKLrY+BJqdJcOLECSI5xTDFm41wThEnNDlVWay1tRVXngvcgBdcQZdWEzio/dw5igB7hrw2qEqf4eFhTp5spjRv5bT7uMRFcbCCo0e0hDQV7KS/yJ9Yclro8yKyOJuLLBZ28nl1ydpLtrvExdVFaziwf7/OxVZLTmdnJ/39g5SVJf+5bR9zsS1PpsnpAhljaDpxgkigeMr7o8FiLvT1aSmQylqNTY1ECi5eEYzkR2g6sbheKJeKcDhMV08PRYA9CUEXt0+fY8eOEY1GKcurnHG/srxKGpsaGRkZSVNk2ePIkSN43S7ypijrnYnbJRT4vBw+fDhFkant27eT6wuwrvDyv48byq6ks6tr0Y0AKTUbuwlYWVnymiHZ/H4oKJBFV8GpyekCdXd3MzgwQDS2Jtdk9nb7yadUNhkZGaHtbBum8OIVQVNoaG1p1bmODjh//jzRaJQSoBir2npiTrBKuSNHjgBCSe70I6dgJafRaFRLSFPg4MGDFHg905ZVz6TI5+HYsWOEQqEURJbdQqEQu3bu5Mayq3G7Lr9wcNOyaxARtm3b5kB0SqWOnTiWzKEZ0nyUlIRpalpcF3U0OV0ge6h8+uTUanqhyanKRk1NTRhjMEVxL7rF1gjeyZMnnQssS9kL2ZcCHoQil0uT0zQ6dOgQxbnl+Dz+GfcrzasEJJbMqmTp7++ntbWV4gRLem3Ffi+hUEjXO02BnTt3MjA4yO0Vb5jy/nxfLteWrOPll14mGk3+CJNSTjl58iT5+UJOTmqOX1JqOH369KK6qKbJ6QLNlpziDSD+4KKr91YqGSbWBYz78zAl5tL7VNrYy1qVxb4ui0Zp0YsEaREKhTh69Chluatm3dfn8VOcW64NYJLMLsmdb3Ja5PddchyVPM899xxFOQVcX3rFtPvcVXkr7efbOXDgQBojUyq1mpubKC5OXSVZaUmUSCS6qC5Ea3K6QCdOnEBy8mGGK+GhnBIadJ6EykK1tbW4gi4IxG3MBfGLNhZxQHNzM/kuF7lYJY3LsBJWLbFOvfr6esbHxynPnz05BSjLXcXRo0cX1dXuTHfo0CHcLqFwnsmp3+0iz+fViwZJdurUKfbs2cNdK2+ZsqTXduuya8nz5/LEE0+kMTqlUicSidDaeiplJb1wsVx4Mc071eR0gY7X1xPKmboZki0aLKW1pUXfZKisYozh0OFDhEsmJT4C0ZIoBw/rG7x0a2psZFlcSdxyIBQOL6orqotVTU0NAGX5q+e0f3nBasbHx6mrq0tlWFmlpqaGAp8X9zzmm9qKfG4OHTqkF3SS6Gc/+xkel5t7qt40435et5d7Vt3Brl27dFqIWhLa2toIhcKUpjA5LS42uFwsqr8ZTU4XwGr2cpZo7tSLqduiuSVEIpFF9cRQaqHOnTtHd1c3lF9+nyk3tJ1p0zU202hsbIzm5mbiW/HYn2uJderV1NRQFCwnxxuc0/7l+asBmUhq1cIMDg7S2NhIic+zoOOU+H2MjIzoVJ0kOXfuHC++8CJ3rbyFAn/erPvfXfUm/B4fP/7xj9MQnVKpZfejKSlJ3TxqjweKihZXx15NThfgxIkTGGOIBstm3M++X1ugq2xiv6k25ZdfEbS3aXlc+jQ0NBCJRokvKi0H/CLaFTbFQqEQR44coSxvbiW9AH5PgOLccg4c0OQ0GQ4fPowxhpIc34KOU5JjlQTrRYPkePjhhxHg3evfOqf983xBfqNqA6+++qo2plKLXnNzMyJQUpLa9XuLS8KcWERL+GlyugB2sjnbyKnJKUA8Xk1OVVbZt28froALCqa4sxjEJ+zbty/tcWUru4lLFfA8hucxuBBWG8NhvUiQUnV1dYyNjbGsoCqhx5Xlrebo0SOMjY2lKLLsceDAgQXNN7X53W7yfF5NTpOgoaGBzZs3c8/qOynOmeofxdTese4u8nxBHnroIYxJ7Zt6pVLpxIkTFBUJ3oW9LM2qrDRKe/t5hoeHU3uiJNHkdAEaGxsRbw7GlzvzjiJEAiU0NDSkJzClHBaJRNhTvYdweRimmt4lEC2Psqd6j765SJOaAwcod7nIQzgHnIttXws0t7TQ19fnXHBLnNVdVGKlunO3vKCKUCikzcOS4MCBAxQucL6prdjn4dDBgzrvdAGMMXzzm98kzxfk3et/LaHHBjw5/PYVd1NTU8Prr7+eogiVSr3GxnpKS1P/OlJWbjDGLJplLTU5XYD6hgbCgRKYwz+7SLCUxsYmXZ9LZYX6+noGBwahYvp9TIWhq7NL52KnwdjYGIcOHuTKKV5/7IUb9u7dm96gssj+/fspyV2G3xOYfec4ZXmrEdF5pwvV39/PiRMn5r2EzGQlfi8jo6N6wXkBtmzZwtGjR3n/lW8n6E3s7wLgLatuozJ/Od/+1rcZHR1NQYRKpdbAwADt7R2Ulac+Lygrs86xWF6zNDmdp3A4zMmTJ4nMUtJriwZLGRsb5ezZsymOTCnn7d69G8RKQKdjVlj37dy5M11hZa0DBw4wFgpx1RT3rQTyXC527NiR7rCywujoKLW1tZTnJ1bSC9Z6pyW5FVr+vkCHDh3CGENpkpLT4ti8VV1vc36GhoZ46NvfZk3hSu6qvHVex3C73PzBte/mfMd5Nm3alOQIlUo9O1Fctiz11WN5eRAMiianS11rayvhUIhocI7JaSyJ1XmnKhts+9U2KAWmX/4XAiDFwvYd29MVVtbatm0bfhHWTXGfC+GaaJRdO3fq3MYUOHr0KOFwmGXzSE4ByvOqOH78+KKZK5SJrPmmrgXPN7X53S7yfV5qNDmdl0ceeYSenl4+dN17cMn834ZeXbKWN624kcc2bdLlsNSiY3fJL0/DyKkIlJeHqatbHFNENDmdJ7uN/JyT00AxiEvbz6sl79y5czSfaCa6cvYX3MjKCHXH6nRJmRQaHx/n9dde4zpj8Ew5ARhuAEZGR60Rb5VUNTU1uMRFWf7cO/XGW1ZQRSQS4ejRo0mOLHsc2L+fIp8HVxLmm9qKfR4OHz6s65cnqKWlhSeffJI3V97K2sLKBR/v3mveiRs33/rWt5IQnVLpU1tbS1GREEi8qn1eli03tLaeZmhoKD0nXABNTuepqakJcXkwgcK5PcDlhmCRjpyqJc9uUGEqZy9VMZXWJP3t23X0NFV27tzJ4PAwN82wzzqs0t6XXnwxXWFljf379lOcW4HXPb8lTMryKnG53Ozfvz/JkWWHvr4+Tra0JG2+qa0kx8vY+LiuEZygb33rW/hcXt5/9a8n5XiF/nx+a/1b2LlzJ9XV1Uk5plKpZoyhtvYIy5enr6naihVRjDHU1dWl7ZzzpcnpPDU1NRENFkECJSnhnBIadeRULXGvbX0NKRaYfT11KADJF1599dWUx5Wtnv3lLyl0uSYaH03FjXBLNMrOXbvo6upKW2xL3fDwMMfrj8+7pBfA4/ZSkrtCmyLNk72Ekr0+abIU+62LDbpW89zt37+f6upq3rP+reTPtspBAt6+ZgPlwRK+89B3tOmkWhTa2tro7b3AihXpe75WVEQRufiamMnSkpyKiFtEakTk2XScLx0am05YnXoTEMktoae7mwsXLqQoKqWc1d7eTt2xOiKVkbk9QCCyKsLBgwfp6elJbXBZ6PTp01Tv3csbo1Fc05T02m4HotEov/zlL9MTXBY4cuQI0Wg04SVkJivPW01DQ8OiKMfKNDU1NdZ8U19yk1OfPe9ULxrMiTGG73//+xQHCrl79R1JPbbX5eG3r7ibE80neO2115J6bKVSwb6otbIyfcmpzwdlZXDoUOZfUEvXyOlngcwfR56jnp4e+i/0EQ0mlpyaWDLb3NycirBUnB07dvDBez/AB373/Xzgd9/Pn376T3RNujSw3xiY1XPvPmdWW6W9ul5d8j355JO4RZjLW8FShKuBp596ShsjJcnBgwet+aZ5Kxd0nGUFq4lGo4viinemOXzoEIVJnm9qK/J5JhpeqZnt37+furo63rPurXjdyb1QAHDHihtYmb+Mn/z4J7p2tsp4Bw8eJBgUiovT+1xdWRmmtrY24//Hpzw5FZFVwHuAH6T6XOliJ5fRQHFCj7OT2cWyCO5i9vrrr9Pf08n1/nMsN+c5WntM19NMg5c3vwwlzK2k11YIUihs3rI5VWFlpb6+Pp775S+5yRjyZxk1td0F9F24wEsvvZTa4LJETc1BSnIr8MxzvqmtNHclLnFx6NChJEWWHYaHh2k6cYJinyclxy/2exkdHdX/6XPw+OOPU5CTx4bKm1NyfJe4eMeajZxsOalLL6mMZoxh375qVq4Mk+g1s19t89DVKXR1Ck895eNX2xJ7bVu1Ksr4eCjjG+ylY+T0m8D/ApbMRICJ5DSYWHJqvAHEm6NJUho01B/nqsIQn7h+mI9eYy3BoM2oUuvUqVOcaDpBdHXif+qR1RGOHjlKe3t7CiLLTj//+c8ZD4V4SwKPWQ+sEuHRn/5UR4MWaHR0lPr645Tmzdylt+bUq9ScmnnOtcftpTi3goM1mV+OlUlqa2sxxlDkX9jFgekUxZosHTlyJCXHXyra2trYs2cPb628Ha8rNRcKAO5YcSMFOXk8/fTTKTuHUgvV2tpKd3cvq+bxXqmzy8X4uDA+LrSdddHZlVgaV1kZxeUi4xvspTQ5FZH3Ah3GmGl/CiLySRHZJyL7Ojs7UxlO0rS0tCC+AHiDiT1QhEhOkSanKTY8PExLSyvrC6w31xXBKAGvLIoOZYvZ5s2bQRIr6bWZKusxW7ZsSXZYWamvr49fPPEEbwDK5zhqCiAIdxtD+/nzvKidexekrq6OSCRC+SxLyPQNd9A33DHr8cryVlHf0JDx5ViZpLbWWtOvyJ+ahCjgcRPwejh27FhKjr9UbN5sVcW8ufKWlJ7H6/LwpuU3snvXbvr7+1N6LqXmy+4qXVWV/jE7nw+WLzdUV+9J+7kTkbpLWJY3A78jIr8F5AAFIvKoMeY+ewdjzPeB7wPcfvvti2KiwMmTJ4n457iEzCSRQBEnT7ZgjEFSMAdGWW8Ko8ZwVaGVnLoErswf58hhLYlLFWMML738EiwD5rNmVy5QCi+9/BIf/vCH9W9jgTZt2sTY2Bj3zOOxVwOVIvzoh4/wjne8A7/fn+zwsoI9mla6wPmmtrK8Surbqzl+/Dg335ya0silpra2lny/F48rddfhCzxuHTmdxStbXuHK4jWUBIpSfq47V97IltZdbNu2jfe+970pP99SMzAwwN/+7d/S0NAwp/3f8IY38NWvfpVgMMHBmixWXV1NcTEUFDiT8lRVhamubqK3t5fi4sQqQNMlpSOnxpgHjDGrjDFrgT8AXo1PTBcjYwwtLa1E5vkiGw0UMTQ0SG9vb5IjU7aamhpE4Kqii2WJ1xaHaT7ZQl9fn4ORLV1Hjx6l/Vw70QVcCYyuidLa0qrztxbo/PnzPPXkk9wCLEtg1NQmCO8who6uTp555pnkB5gljh49SmGwFL8nOSus202VMn2uUKYwxnDsWC0FHndKz1Po93L+/Hn93zKNc+fO0dLawq3Lrk3L+Vbnr6AkWMSuXbvScr6l5Pz58/z5n/85+/btIxAIEAwGZ7zl5OSwc+dOPvOZz+gSZHM0NjbGwYM1rK5ybtpM1RprvdO9e/c6FsNsdJ3TBPX19TE0NEg0Z37JqYk97tSpU8kMS8XZv28f6wsiBOPqAq4vDgFw4MABh6Ja2jZv3ox4BFM5/yuBZpVBXKLNeBbo4YcfxkQivH0Bx7gC4QqEH//oRwwMDCQttmxhLbB+jJJgckZNAfzeIPmBYi0hnaOOjg4GBgYpSFEzJJt9/LmONGUbu4TxhrIr03I+EeENJVewf98+nTefgL1793L//fdz+vRpbrrpJq677jquvfbaGW/XX389N954I83Nzdx///265u8cHDx4kPHxEGvWONeGZ9kyQzAo7NmTuaW9aUtOjTFbjTGLvsbCTipNYH5lvdHY406fPp20mNRFFy5coK6ujptKxi/Zvr4gQq4vs/8YF6tQKMSWV7YQWRGBhawQ4IdoRZSXt7xMJDLHdVLVJRoaGnjppZfYYAxF8xg1jfebGAYHB/npT3+apOiyx9mzZxkY6Kckd0VSj1scqKD2aG1Sj7lU1dfXA1CQ5PVNJ9PkdGYHDhygOFDI8tyytJ3zutL1DI+MTDwH1PRGR0f5z//8T77whS8QiUS47bbbKCmZ+zKJZWVlvPGNb2RsbIzPfvazfPe739V58TPYvXs3Ho9Qmcb1TScTgdWrw+zZsztj32vpyGmCzpw5A0A0Z37JqfHlIi73xHFUcu3atYuoMdxSFrpku9sFNxWPsWvnjoz9Y1ysqqurGRwYxKxZ+PyJaFWU3u5evQI7D8YYHvr2twmI8NYkHG8Fwi3AL574BW1tbUk4Yvaw3xSX5FYk9bgleSvo6e3REro5sKcH5HtTO3LqdbkIej06HWEKxhgOHTzEVUVVae0jcFXxGgD9PzKLXbt28dGPfpQnnniCyspKbrvtNnJzcxM+Tl5eHrfddhsVFRU89thjfOxjH8voklEn7dq1k8rKMJ5Ud/yZxZq1Efr7BzL2Ao4mpwk6c+YMiAvjT2QhxzjiwuQUaHKaItu2vU5JDqwruDwBvX3ZOH0X+nUh+yR7+eWXkRyB5Uk42EoQn/Dyyy8n4WDZZefOnRyoqeHt0SiBBY6a2n4DkGiE733ve0k5Xraor6/H5XJTEEjuaFFJcPnE8dXMWlpayPV5cbtSnxQF3S6aNTm9zJkzZ+jp7ZlIFtOlwJ9HRV65JqfTaG5u5otf/CJ/+Zd/ycDAALfeeivXXHMNbvf852d7PB6uu+46brnlFnp7e/nCF77Al7/8ZVpaWpIX+CJ39uxZ2trOUeVgSa9t9eooIhfL7jONJqcJamtrQ3LyQeb/owv78jmlZb1JNzAwwJ7du7mjfJSp3o/cXBbC7xZeeeWV9Ae3RA0PD7N9+3YilZHkvJq4IbIywtbXt2ppUALC4TAPffvblLtc3J7E4xYgvNkYXnvtNW3Ek4CGhgaKAuW4XcltxlMUXAaIlpDOQXNzM0F3ekbr8rweTp85o3McJ6mpqQHgmpK1aT/31cVrOHL4sP5O4pw5c4YHH3yQP/qjP6KmpoYrr7ySO+64I6kdW0tKSnjTm97EFVdcQXV1NX/4h3/IP/3TP3Hu3LmknWOxskeTnZxvagsErLmne/bsdjqUKWlymqAzZ88S8uUv6Bgmp4D2c+0YsyhWzlk0tm7dSigc4a6K8Snvz3HDG8vGeO3VVxgfn3oflZjt27cTCoUm1ilNBlNlGBkeYffuzHzRzETPPfccp8+c4R3RKO4kjZrafg3Id7l46Nvf1tesOTDG0NTURGGgPOnH9rh9FASKtYR0FtFolLa2NnJT3KnXlut1Ew6H6eiYfb3abHLgwAEKc/JZFixN+7mvLl6r805jTp06xT/8wz9w33338corr7B69Wo2bNhAVVUVrhQss+RyuVizZg0bNmxg1apVvPjii3zoQx/iG9/4BmfPnk36+RaLffv2UVAgFBZmxv/R1asj1NUdZ2hoyOlQLqPJaYLazrbNv6Q3JurPZ2xsVJeTSbLnn3uOlXmG9VOU9NresnKMgcEhduzYkcbIlq4tW7YguQLJfO9RDpIjbNmyJYkHXbpGRkZ45OGHWSNCKhZr8CPcE41ytLaWnTt3puAMS0tPTw/9/f0UBmdPTmtOvUrfcAd9wx28dvxn1Jx6ddbHFOSU0djYlIxQl6zu7m5CoRCBBJLTut4BBsbDDIyHqT7fS13v3LtU2+fRudkXhcNh9lZXc33JFQnPN3287gVOD7RzeqCdf6t+hMfrXkj4/NeVrkcku5sg1tXV8Vd/9Vd85CMfYfPmzVRWVrJhwwauvPJKvN7UNgoD8Pl8XHXVVWzYsIGKigpeeOEFPvShD/G1r30t66o/otEoNTX7qVwVIlOWcV+1Kko0GuXQoUNOh3IZTU4TMDg4yNDQIMY//cipr3UXvtaZ19eyk9v29vakxpfNmpubqT12jLtXjMz4h39DSZiyAPz3f+v6jQs1MDDA3r17iayKMNtgnRwU5OAcX5FdEKmMsHPXToaHhxce6BL31FNP0dPXxzuNQZI8amp7I1AqLv7P975HNOp8SVIms+dYFc5hvmnfcAehyBihyBidA6fpG5595K0gUEZ7+zkte5+BXUKYSHI6MB4mbAxhY+gdCzEwPvdy0GDsPFq6eFFtbS0Dg4PcUJ74EjJnBtoZCY8xEh6jobeVMwOJv1fK8wVZV7gq6y6oRaNRduzYwV/8xV/wqU99il27dlFVVcXGjRu56qqr8Pv9aY8pJyeHa665ho0bN7J69Wq2bdvG/fffz+c+9zn27NmTFRU5J0+eZGBgiMqVmfP/s2JFFJcLTU4Xu/PnzwPWyOd0XEPduIa6ZzyO/Xj9R5Y8Tz/9NF638JYVM5frugTuWTnC/v0HaG1tTVN0S9P27duJRCKYVbP/Y5E+QfrmnjiZ1YbQeEhLe2cxPDzMY5s2cTVQlaLEFMCNcI+J0tzSwtatW1N2nqXAfl0pyElNKWNBTgnGGG2qNwO7m3HOAhq8JMLvdl1yXmVNs/G6PdxQdpVjMdyy7FoaGhqyYkR7dHSUZ555hvvuu48HHniAhoYGrrzySjZu3MgVV1yBz+dzOkT8fv8lMdXW1vKlL32Jj370ozz77LNL+oKb3bNhxcrMScQ9Hli23HDkSOY1CdXkNAH2SOdCy3qNz3q8neyqhRkYGOClF19g47JR8n2z/+HfXTmGxwVPPvlkGqJburZu3WqV9Cavl8JFZVZpryZCM3vmmWcYGBzknjSc60agXFz89Cc/yYor3fN1+vRpvB4/Od7El2SYi/yAtQahvea2ulx3t3WB2E4aU80lgt/jmThvtotEImx97TXeUHolOZ70j9TZbqu4HoBXX529XH6x6u7u5gc/+AEf+MAH+Ld/+zf6+/u5/vrrufPOO6mqqsLj9JolU/B6vaxZs4aNGzdy3XXX0dPTwz//8z9z77338sgjj9DX1+d0iEl37NgxgkGhoCCz/ncuXx6hoaEh4xqHaXKaADuZtJPLefP4EI9fmyckyTPPPMPo2DjvqprbVbdCn+Gu5WO8+MLzXLhwIcXRLU3Dw8Ps3beXyMrZS3rnRayuvbt271rSV1MXIhQK8fjPfsZ6hFUpHDW1uRB+zUQ50dycse3nM8HZs2fJ8xelbF3HPL91NSgbRoPmq6+vDxHwpmEZGZvPLUvyTfV87N27l+6eHu5ccZOjcZQFirmiuIoXnn9hyV1Qa21t5Rvf+AYf/OAH+clPfoLf7+fWW2+dWG80FY2Oks3lcrFixQpuv/12brnlFjweD4888ggf+MAH+Nd//VdOL6FVLRob6ykrC2fMfFNbeXmU8fFQxl3szPxnbwbp6Oiw1in1BhZ8LOPP1ZHTJBgbG+OJnz/OjaVhqvKnb4Q02W+tGWV0bJynnnoqhdEtXdXV1YRDYUxl6v7hm5WGsdGxieUI1KVef/11enp7+TXS96brJqzOvU/+4hdpO+dic/bMWXJ9hSk7vtftI8cX1OR0BkNDQ3jd7pRdIJiKO3ZeBc8//zx5viA3Lbva6VC4a+UtnD5zeskshVVXV8dXvvIVPvKRj/Diiy+yfPlyNmzYwE033URxcXFan/PJIiKUlJRw8803c+edd1JWVsazzz7Lfffdx1//9V/T2NjodIgLEolEaG09RUlp5l0gKY3FlGnr0WpymoCOjg4kJ49kXPoIe4K0a3K6YC+88AK9fRd475qRhB63Ki/KrWUhfvHEz7Xpzjzs2LED8Se5S+9ky0A8knUNLebq/z79NKUuF1ek8ZwehNuiUfZUV2tyNAVjDOc7zhP0FaT0PEFvoTbUm8HQ0BCeNI8ceUQYGJh7h9+lqq+vj+2/2s6bKm7E43K+pPT2ijfg9/h4/vnnnQ5lQerr6/nSl77Epz71Kfbs2cPatWvZuHEj11xzDcFg0OnwkiY3N5frrruOjRs3UlVVxY4dO/j4xz/OAw88QFPT4uxS3tXVRSgUpqgo85JTO6ZMW+JHk9MEnO/oIOxJzouA8efR0dGZlGNlq1AoxKZHf8pVRRGuL068Xv5960YYGBzimWe0c28iotEou3bvIrI8ktpXEDdEl0XZsXPHkivJWqi2tjYOHznCrdEorjSU9Ma7DSsJ27x5c1rPuxgMDAwwPj5OYIFrYc8m4NX/HzMZGxvDneYBJJcIY2Oj6T1pBnr55ZcJR8L82qo3Oh0KADkeP7cvfwOvbHllUV6IPnfuHH/zN3/DJz7xCWpqali/fj0bN25k/fr1GdHkKFX8fj9XXHEFGzZsYN26dVRXV/Pxj3+cBx98cNFNibOrJPPzM+99jNcLwaAr4yo5NTlNwPnzHRhfcppcGF8uA/0XdD7dArzwwguc7+jk/WuH5zWYfWVhhBtLw/zXY5sYGUls5DWbNTY20n+hHypSfy5TYejs6NTOypO88sorANzswLmLENYibH75ZQfOntk6O62EMbjQvgSzeACNqwAAIABJREFUCPjyJ86lLheJRFK2rNJ0XAKR8NynlixVLzz/AmsLK6nMX+50KBPeXHkro2OjvP76606HMmfhcJhHH32U++67j23btrF27Vo2bNjA2rVrM7LJUap4vV7WrVvHhg0bWL16NVu2bOHDH/4wP/vZzzKuic90enp6AAgGMy85BQgEovT29jodxiU0OZ2jaDRKT3cX0SQmp4B295un8fFxfvLjH3FFYYSbSuf/AvV764bpu9Cvc08TsHfvXgDM8tS/0JoK6xz79u1L+bkWk19t20alCEVpfgNuux7DqdOnl1TDimSw34SkqlOvLccbZHh4iPHxmZfOylaRSATSOBcbrL5w1nmzV3NzMyeaTzjeCGmy9UWrKc8t4eVFckGtra2NT3/603z/+9+nqKiIO++8k/Xr12dVUjqZ1+vlyiuv5M477yQ/P5+HHnqIz3zmMxk34jcVe8TegSVm58TnizI4OOh0GJfQ5HSOLly4QDgcxviSU9ZrJ7mLrTwhUzz77LN0dHZx7/r5jZrariqKcHNpiMc2ParNLOZo//79SJFAThpOlguSL5qcxunr6+N4fT3XOFjqfG3s4549exyLIRPZ3b/9SZr+MR37+NptfGruNK1vGs9AVicPEFteTITbK97gdCiXEBHuWH4DNQdqMv5vpq6ujk984hM0Nzdzww03cOONN5KTk45/totDIBDgxhtv5Prrr+f48eN84hOfyPi5qKFQCAC3OzNHTt1uk3Gj0JqczpGdRCazrDf+uGruRkdH+cmPf8S1xRFuKFn4H9S9V1hzTx9//PEkRLe0hUIhDh85TKQsfSMEkbIIBw8dzPpRCduhQ4cAWD/Pxz+P4RxwDngYw/PzGGEqRih2ubST8iT2G1+fZ+Ed3Wfi9+Rccj51Ka/XSzTN7wOjxuBdwnMA52LP7t2sK1xFwQLXgk+Fm8qvJmqiGX2h89SpU3z+858nEolw2223sWzZMqdDoqGhgerqanbu3El1dTUNDQ1Oh4SIUFFRwW233cbo6Cif+9znFkWDvkxtnWEMGdflWZPTObLn9yx4jdMYewRW5w0l7qmnnqKnt2/Bo6a2dQUR7igf5+eP/0zXqZtFQ0MDofEQpjyNr7LlMDw0nHGtzp1SW1uLW4TKeT7+HDAWu7XEvp6PqmiU2iNH5vnopcmuvvC6U1u/ZR8/00qxMoXP50s4OQ1HowQCAe69914CgQDhaDShx0cM+LM4OR0cHOR4fT3Xl873stlFI+GxS34XI+GF9+ZYU7iSXF8gY5NTYwxf//rXCYVC3HzzzRnTgbevr49IJMJ73/teIpFIRr1Hys3N5ZZbbmFkZIRvfOMbGds4MRCwLlaGQgt/wzo+xiV/G+NJaFsTDrsybnRek9M5SvbIKW4f4vHryGmCBgcH2fToT7mpNMS18+jQO517rxhhZGSUTZs2Je2YS9EROxkpS985TWwdriOaCAHQUF9PBdayLk6qBHr6+ujq6nI0jkwyNDSE2+XB7UptWamdnOpUhKnl5+cTiiZWaRGKGt7znvfwmc98hve85z2EEsxuI8ZQUJi69W0z3cmTJzHGsLZwvpfNLhoJjV7yuxgJLbwLsktcVOWv4ETTiQUfKxVqa2upq6tj3bp1E8lMJgiHw5f8LjKt/DMYDLJ27Vpqamo4cSIzf7eFsdeF0SQ08x4b55Lfx1gS2g6MjromYswU2T1BIgEdHR3gcmG8yXvRMP5cHTlN0OOPP87A4BAfvD653XUr86K8ecUYTz/1FL//+79PeXl5Uo+/VNTV1eHKdRHJSWOJbS64clwcO3aM97///ek7b4Y6efIk64wBh5NTuxdna2srZWVpvFqRwcbGxvC4vSk/jzt2Du32PrX8/HxCkShRY3DNsbzG6xKee+45AJ577jm8rsT+vkLGOm+2am5uBmBl3sJLUQPenEt+F2Xe5LxxXpm3jO0tNRhjMq6Msb6+HiDjXks9Hs8lv4tMnFdd9v+z997RcV33ve9nn+mD3gtBggTABhKFFItYRJESyUii1WVJji25y5aLfH3z7nK8rlde4thy7CzlJpLLi99tL74veXFk3zg2feM4uXFcJVmiWERSlFjBCqIMZgbT55z9/pjCIrQB5pwZYPZnrVmg5pyz908YzDn7u3+tvp63336bEydO0NXVVWhz3kFmPRkMClpa5ubddTm54fMom6O/TEoIBmVRhJBfj/KczpCrV68iXOXkJY40TdJRxpUrxV9prFgYGxvju3/7/7GpMc6yyvyLo4c6ouh6gu985zt5H3uhcOz4MfQai3M/Beg1OsffPG7tvEVIKBTCNzZGXaEN4ZrzXLX5uUY8Hsemmb94swl7dj7FO8l4AeL6zENz7ZpGJBLhxRdfJBKJYNdyWx7FdaPovA9Wkgn3rHbNXaB77K4bPguPPT9h8tWuCmKxWFG2jnOlS7kWW20Fu91+4/eiCMVp5ndWrH1fW1tbEULg881dci1aZFBWFuIn//RdyspCLFqUW/rBzfj9AsOARYvmHvGQT4rvr6xIuXLlCsk8V2CUzjKuDF7M65gLmb/+678mFovxcKc5D5ZGj8HO1ig/+uEPefzxx2ltbTVlnvlKMBhk8Mogcq31eR2yWjLw5gCxWCz7EC9Frly5AkBtge0AKAdsQsyLUv5WkUgk0IT5lWK1dNiwEqcTk/FUxHQDt938zyNpGCR0vaQjbsbHx3HanaaHtM8Ft/1aOHyx5HRm6OtLda0eHBxk2bJlBbZmfnHlyhWEEPT2FlcLowxut5tFba0MDw3MeazbduQ3rHp4KOVw6+zszOu4c0V5TmfIpctXMPKwI3g90lnOeDCY7YGkmJyRkRH+5/e/x5amGIvK5rZTNBX3L40ipM5f/dVfmTbHfCUTtiWrcxOn4qCAMWAMtJ9pqf/OEVktkVKWfFGkTI56MfhnNATVQqi8+eswDMOScEGRDuku1gIghSYjEqM5eE7nQmaeUhanUkq0AqcaTIcmUkteI8diV1awePFibrvtNgYGBtSaMAfGx8e5cOECu3fvpqmpafoLCsSa7rUMDtqLrmLv5SsaDoe96MKhlTidAclkEt/oCDLP5dEzYjfjDVFMzt/8zd+QSCR4sCMPGeVTUOuW7FoU5Sc/+cd5UZrcSrLCMEdlJMYEIpF+DQnE2CwWMJWpH2fOnMn92gXEyMgIAMWS2VZuGIyogkhZlFgsDpqbmwGIJK0JkczMk5m3FPF4PMSS8aL+DsT0VKRBsXlNM3zmM5/B6/Vy5MiRbG9MxeTE43HeeOMNKisr+eQnP1loc6Zk/fr1hMOSkeHi2sC5eMFOT08vDof5tRJyISdxKoTwCCFWmmVMsTI4OIiUEplvz6kSpzPC5/Pxg7//n2xtitHsNX/H896lUTRpqNzTmxgYGEDYBRSikGA5oKVsKGVGR0eB1K+jGCgHJU4VRUdVVRXl5eWELRKn4URqnra2NkvmK0bKy8uRSMJJczeQ50IoHkYTWlFVw72exsZGvvSlLxGLxTh06JASqFMQj8c5dOgQyWSSZ599ltraYkh2mZyNGzcCcPZs8fgEAwEYHobNmzcX2pR3MOPfkhDiXuAg8I/p/+4XQvyDWYYVExcvpvJC8x3Wa7hT4ykP3dT83d/9HfF4gvuWWfPQq3FJbm+N8pN//EcVsngdFy5cSKmRQmz8aaCVadnvYqni8/lwa1rB28hkKIOi6ntXaOx2O4Y0fwPNkHp2PsXELG5rI2SROA0ldTweD9XV1ZbMV4xkQipHI8V7PxiJ+qmrqyvq7826dev40pe+RDgc5vXXX1cVuScgGo1mfzd/8id/wtq1awtt0rTU19fT3b2ak6eKx0N58mQqP3zHjh0FtuSd5CLh/xDYRCp7DCnlQWBp/k0qPjLiMd+eU+xuhN1R8gvuqQiFQnz/+99jY2OcVhNzTW9mX3sMw9D57ne/a9mcxc6FixcwLPBcT4bu1blw8ULB5i8G/H4/xRSQ5gXGw+Gi631XKBwOR1Y4mklGABfzIrvQLOvoIJS05n41nkiybOnSomtPYiUtLS0ADIV9BbZkcoYjPlpaWwptxrRs3bqVr33taySTSQ4cOKD6GV9HMBjkwIEDSCl57rnn2LBhQ6FNmjF79uxleAiGiyC0V0p464SDVatWFl2lXshNnCallH7TLCliLly4gNDsSOccGwrdjBAYrkolTqfgRz/6EeFwhH3t1oYKNXgMbm2K8cN/+AHj4+OWzl2MSCm5evUqsqxw+UTSK0u+Mqzf78dTRDldZaT+NtR3JIXL5ULXzQ/F043UHG632/S55isdHR3EkkliJhdFklIS0g06i6ygiNUsWbIEgMuh4uzdLqXkcmiIpUuXFtqUGbFhwwZeeOEFXC4XBw4cwOcrXtFvFSMjI7z++uuUlZXxjW98I1vheL6we/du7HYbR48WvqL14KBgeBj27XtXoU2ZkFzE6RtCiN8FbEKI5UKIF4Bfm2RXUXHhwgWkpzKvPU4z6K5Kzg2cz/u4CwFd1/nei3/HyuoknVW5eyO+c8LDd07MPrfk7iUxItEYP/7xj2c9xkIhHA4Ti8YKk2+awQsBf6Ck83B8o6OUFZE4zXhxVWhvCo/HQ0JPzLgoTEKP4fF4eOSRR9LXziyEL5kWwMWaO1cMZFojjCfM9erHdIN4Uqejo8PUeYodj8dDS1MzF8eLMxUmEB8nFI/MqzYtK1eu5C//8i9paWnh0KFDJb05e/nyZQ4fPsySJUv49re/XXStT2ZCVVUVu3bdwYk3HRS6C9iRw3Y8Hjd79uwprCGTkIs4/TSwBogBfw34gX9nhlHFxtlzAySdlaaMbbirGLxyuaQX3JPx0ksvcWXwKr+zeHZe03NBG+eCs9+hWlaps6Ja5/vfe7EoS89bSaYQD4V01KTbm5byDvKYz0ee4zfmRMYWJU5TeDwepDTQ5cwEUSIZY9++fTzzzDPs27ePRHKG4tSIZ+dTTMzy5csB8MfNfbYG4qnPesWKFabOMx/oXN7FhfHiFFADgctA8fVznI6Wlha+9a1vsWbNGo4ePZqq/VBiDAwMcPz4cdatW8c3vvGNed2y6ZFHHiEelwX1no6Pw8mTdu65Z1/RVq6ekTgVQtiAP5JS/kcp5cb06wtSyuIty5YnkskkV65cxvCY01nQ8FRhGIYqijQBP/jB31PjhlsaCifcd7dFuHT5CgcOHCiYDcVARnxIVwHDetNzBwKBgtlQSKSUjAUCRVOpF66J0+zmRYlTUZGqSzBTkemwu9i/fz/PP/88+/fvx2F3zei6eHr8zHyKd1JZWUlzczOBmLmeU388gRAiK4ZLmeXLl3N1fIToDP/+reR8INUVodj6Oc6EiooK/uzP/oxt27bx1ltvce7cuUKbZBlnzpzh5MmT7Nq1iz/90z+lrKyYtmdzZ/Xq1fT29nLooBPdmnpt7+D11+1IKXj00UcLY8AMmJE4lVLqwC0m21KUXLhwAUPXkW5zqvBlxs32kFQAMDQ0xCsvv8KO5gi2Albe3tCQoNwp+NGPflQ4I4qAbE6hs4BGpOcuVXEaDAZJJpNFJU4z0kiJ0xTl5alPJ67PbN/WYXMRiUR48cUXiUQiOGwzE6eJ9PiZ+RQT093dTdDkir3+eJKl7e0q/5dUGKpEZr2UxcS5wCUWtS6at98Zl8vFH//xH3PHHXdw6tQpy9qqlZeXY7PZsNlsVFdXW/r7O3v2LGfOnOGuu+7iD/7gD4quF+dsefLJJxkflxw/Zr33NByGo2842LNnT7aIWTGSy7L/dSHEPwghnhBCPJR5mWZZkZARjYa3xpTxDY8SpxPx05/+FENKbmstbGC+0wZbGiP84hc/L+miL9lqgYUsDpp+LpVq5cKRkRHgmiAsBryATQiGVa9TgGwrkXgyYuo8sfT4VVXmRPQsFLq7uwknkkRNEqhSSgIJnbU9PaaMP99YtWoVAGf9xVfk8VzwEqu7VxfajDlht9v5whe+wI4dOzh58iRXrlwxfc4VK1ZQUVFBRUUF69evtyx8/dKlS5w+fZo9e/bw+7//+9hshS8ilC82btxI95puXnvNidWF7l97zY5hCJ588klrJ86RXMRpLTAC3AHcm34VZ5mnPJIVp+7pFwHOc79BC4+ghUdwH/sRznO/mX4CmwPhriypMI2Z8NN/+ic6q3SaC9i6JMO2ljiJRJJf/OIXhTalYESjaU9QIcVp+tlUqn3fhoZSVTCLSZwKBJVKnGbJiNNoImzqPLFEGK+3bMF4Eswi0/9wzKS801BCJ6Hr86LPohXU1tbS0tTM6bHiyoscjYzhiwTo7u4utClzxm638wd/8Af09/fz5ptv4vcvvCYaPp+PEydOsGnTJj7/+c+jaQUMnzMBIQQf/chHCQYlbxyxTnQHg/DGEQe/8zu/w+LFiy2bdzbM+BOXUn5wgteHzDSuGDhz5gzCUwm26RcBWmgEoScQegJb8ApaaGRGcyTcVbx98uRcTV0wXLhwgVOnT7OlqThESGelTr0HfvaznxXalIIRz5SWK+QzIn0Pjxe6zF2BuHo1VQWz2HxlFYbB1RKuInk9NTWpCJtowlzvfjQRoqbanFSThcTy5ctxOByMxcwRp7606FXi9Bq9/X2c9A/MuGK1FbztS4XA9vb2FtiS/OB0OvnSl75EY2MjR48eXVDPxFgsxrFjx2hra+OP/uiPFmwv51tuuYUNG27htdecWLXf/vLLdoTQ+OAHP2jNhHNgxktNIUSbEOJ/CiGuCiEGhRDfE0K0mWlcMfD2yVMk3OaE9GYwPLVcOH9eVexN88tf/hIobCGk6xECbqmP8uqrvyUSMTdcr1jRM5n7hRSn6U5OpVo5+erVqwiKy3MKKbGsxGmK6upqNE0zXZxGkiEaGudvxUqrcDgcdHd3MxY3J3bOF41TXVVFW9uCXwrNmL6+PoKxEJeKqKXMidEzlHnL5l2l3qmorKzky1/+Mrquc/z48aLaDJgtUkqOHTsGwJe//OV5X/xoOj72sY8TiUhee818AT48LDjxpp2HH36E5uZm0+ebK7ksNf8b8A9AK7AI+GH6vQVLLBbj0sULGB6Txam3Bl3XLUtwL3Zefukl2iokDZ7iESHrGhIkEkkOHjxYaFMKQlE8+NLitChsKQBXr16lQtOwk/9+y3OhCrg6PFyyn8v1aJpGbU0tkXjQ1HmiyXHq6+tNnWOh0NfXRyCWIGnCppY/odPX348woQf6fGXz5s0AvDH8doEtSSGl5I2Rk2zavGlB5S1CKjLgE5/4BCMjIwui48P58+fx+Xx85jOfYenSpYU2x3RWrlzJnj17OHzIQdDcRwa//pWDsrIynnjiCXMnyhO5iNMGKeV/k1Im06//Dky5dSuEcAshXhFCHBJCHBVC/NGcrLWYs2fPYhgGhrfW1Hky4586dcrUeeYDsViMw4cP0VtTHCG9GVZWJ3HaBK+88kqhTSkI2ZyPQuqP9NyluhAcHByksggFYBWplluq12mKxqZGwiaKUykl4VhwXvf6s5K+vj4k5D20N5LUCSeS9Pf353Xc+U5DQwNdnV0cvHqi0KYAqeJM/miQW2+9tdCmmMJDDz3Ehg0bOHnyJOGwubnuZhIKhTh9+jTbtm1j3759hTbHMp566ik0zc5vfmNe/YBz5zQGBjQ+8IEPUllZado8+SQXcToshHifEMKWfr2PVIGkqYgBd0gp+4B+4C4hxLy5Q2TEotniVLqrQbMpcQocPXqURFJnde3cFhLfOeHhXNDGuaCNL71azndOzK1ZvUODFVVxDr5emv1Os4VXCunMTs+9UHNQpmOoSMVp5lGXyYktdZqbm4kkzROn0UQIw9DnRWhWMbB27VpsmsZonsWpL5bK8+vr68vruAuBO3ffyemx8wyFC99i6qXLh3E4HNx2222FNsUUhBD8/u//Pm63m+PHj8/LtBfDMDh+/Djl5eX8h//wH0pqA7qpqYlHH32Mt07YGBzM//+3YcCvf+WktbWZBx98MO/jm0Uu4vRDwKPAFeAy8Ej6vUmRKTL9NxzpV/Gtribh5MmTCJsd6TZ5p0HTkJ5qTqqiSBw5cgQBrKiaW+n/c0EbEV0jomu8OebgXHDu4TwrqpOcPnO2JFvKOJ3pJqOz+VgS4PF4eOSRR/B4PDDbNaJ+ky0lxsjISF7yTaPc+HnMrCPn5GRsyrS6KXWampoIxQKmhTmH44HsPIrp8Xg8rFi5El+exeloNEFZmZeOjo68jrsQ2L17N0IIfnOxsGkwCT3Bq4NH2bZt27ztbzoTGhsb+ff//t/j9/vnZeeH06dPEwgE+NznPkdtrbnOoGLkve99L9XVlfzql07y/dg4dszGyAg8/fQn51V191yq9Q5IKe+TUjZIKRullA9IKaf9FqS9rAeBq8BPpZQv33T8KSHEq0KIVzOtEoqFkydPontqQJhfBSbpqeXtt5U4PX78OC3lkjJH8e1hdFUlkVJy4kRxhCtZiceT9jzPpq5IAvbt28czzzyTCteZ7RoxeZMtJUQ8HicUiZCP5VWUGz+PuYrTjE2jo4X3khQDra2tGIZOJGHOJtZ4bCw7j2JmrFu3Dn88SdLI33NlLKHT379uwbW5yAdNTU1s2bKFn198jbheuMKGL18+wngsxAMPPFAwG6xiz5497Nmzh7Nnz+b9XlxeXm6auB8eHmZgYIB77713wXq3p6OsrIyPfOQpLl0SnDqVv/tJPA6vvOykt7eHHTt25G1cK8ilWu//I4Sovu6/a4QQ/3W666SUupSyH2gDNgkh1t50/NtSyg1Syg3FlEMjpeTtt0+ie+ssmc/w1jI25sPn81kyX7Hy9ltvsrS8OMuit1ekXHdvv10chR6sJFs1bzbi1AH79+/n+eefZ//+/an4idmQXuMs5B3wyQgEUt4ybx7GcnPj5+Ge43gZmzI2ljoZ0TgeNScHNyNOW1paTBl/IbJu3TqklPjz1O80mtQJxROsW7cuL+MtRB577DGCsRAvXTpUkPkNafDPA7+hq7OrZD6n3/u932Px4sUcO3Ysr50FVqxYwYoVK/I2XoZQKMTx48fp7OzkmWeeyfv484l77rmHpUvbeek3TvS5BQ5mOfCanXBY8slPfmrehUrnItF7pZTZp62U0gfM+BufvvZnwF05zFkwhoaGCIXGMTzWhBhk8lpLObQ3GAwyNDzK4vI8fTPzTJVTUuUSnDlzptCmWE5FRTp4czb7Bg6IRCK8+OKLqQfmLMWpiIsbbSkhMoUuXHkYy82Nn8dcxamTVCHl+VyMI58sWrQIgPGYORuN49Ex6urqcLny8ddQGvT09KBpGqPRyW9gFU47diGwC0GNy0GFc/Lc9kz+qso3nZz+/n66V3fz4zO/IJGj97StohmP3YXH7mJFTTttFbnnV//28htcDg7xvifeN+8W5rPF6/Xyla98BafTyeHDh4u6PWE8HufIkSN4vV6effbZkr+f2e12nn76E4yNwdGjc09DGx+Hgwcd3HHHHaxevToPFlpLLuJUE0Jke6oIIWqBKSuTCCEaMt5WIYQH2A28ORtDrcaqYkgZVMXeVBlxgNay4k3ob/XGOXfubKHNsJzq6lTQhIgW8CEfu9GWUiLTZL0YS0EJBA4hiFnVSbzIaWxsxGazMx41SZzGfCxevNiUsRcqXq+X5cuX44tNHvqxuqaCCqedCqedTU01rK6ZfBPMF4vj9Xrp6uoyw9wFgRCCpz72FL6In5+d/21O1z62+m4WVzSzuKKZ39v0QR5bfXdO1yeNJD88/TO6OrvYuXNnTtfOdxYvXsyzzz5LPB7n0KFDJJPm9PidC4lEgkOHDqHrOl/96ldVFEiaW2+9lf7+Pl79rZP4HAMIf/tbOxKNp556Kj/GWUwu4vQ54NdCiD8WQvwx8Gvga9Nc0wL8qxDiMPBbUjmnP5qdqdaS8Y4ZXnN7nGZxeBAub0l65TJcvHgRgCZPcXpOAZo8BpfSdpYSNTXp70Eh9Uc01dJmvpRCzyeZ4jrFvP9fKt6J6bDb7bS2thA0SZyGYmNKnM6CVN5pAj0PFUfG4jp9vb0Lrm9mvlm/fj0bN2zkx6d/wXg8ZNm8/zrwCkOhUT728Y+VZE5wf38/X/ziFwmFQhw8eLCoPKjxeJyDBw8SjUZ59tln6e7uLrRJRYMQgo997OOEw5LDh2Z/b/H7BceP2bnv3vvmbW2CXAoi/RXwMDBIqrjRQ1LK70xzzWEp5TopZa+Ucq2U8otzM9c6Tp8+jXCVg926UIOku4ZTp05bNl+xMTg4CEC9u3g9p/VugzF/oOS8RC6Xi7LyMshfGkvuRKG6prokFxuZ9jnFuG0jkeigFurX0d7ezng8/+I0lggTTYRZsmRJ3sde6PT29mJIiX+OVXtjusF4PEGvCumdEZ/69KeI6jF+cPJfLZkvEBtn/+mfc+utt7J582ZL5ixGtm3bdoNALYY1SzQazQrTr3zlK2zcuLHQJhUda9asYevWrRw86GS2H9krr9ix2x08+eST+TXOQnIpiNQJnJJSfh04Auy+vkDSQuP0mTMk3FWWzml4qjl37ty87FOVD0ZGRvA4BO5ijF1MU+NKfTal2DajsbERES6cd0yERcm2z8jk2c61sq4ZJABdypLMBZ6M9vZ2ghEfhszvvTwQTVXhVOI0d3p6egDm3FJmLN3ftLe3d842lQLLli3jwQcf5BcXXmMgcNn0+b7/1j8TNxJ86lOfMn2uYue2227jq1/9KvF4nAMHDhAKWee9vpnx8XEOHDiArus899xzbNq0qWC2FDsf+tCHiEZn5z0dGxO8dcLGgw8+RF2dNQVdzSAXF8T3AF0I0QX8Z2AZ8NemWFVgDMNgYGAA6bFWexueGmKxaMk2s/f7/VQ6i6+FzPVk7BsbM6cSZzHT3NSMFimc19IWsdHclHthjIVAVVUVQgiChTZkAjI2ZUO/FSxZsgRD6nmv2BtMi9P29va8jlsKVFVV0d7ePmdx6ovplTVdAAAgAElEQVQlcDgcrFq1Kk+WLXw+9KEPUVVZyd8c/3HeN2yu59TYeX5z6SCPPvqo2sBJs2nTJl544QWcTicHDhwoSEeIkZERDhw4gNfr5Zvf/Cb9/f2W2zCfWLFiBVu3buXQodxzT197zYbD4eDxxx83xziLyGWlaUgpk8BDwF9IKT9LKqd0wTE0NEQ8FsNwWyxO057agYEBS+ctFoLBIGX24vYalzlS9o2Pm9PDsJhpbW2FEFCI/QMJRsiYt/kTc8XhcFBfW0sxdhLN2KSKWlxj6dKlAASiw3kdNxAZxul00txcmps0c6Wvr49AQs/mcM8GfzzJ6tWr51VD+0JTUVHBx59+mtNj53nl8hFT5jCkwd+++b+oq63j/e9/vylzzFdWrVrFX/7lX9LS0sLBgwe5dOmSZXNfuHCBw4cPs2TJEr797W/T2dlp2dzzmSeeeIJoVHLs2My9p+Pj8NYJO/v2vWtee00hN3GaEEK8B3gSyBQ1WpB35wsXLgDXxKJVyPR8mflLjXA4jFsrxqy6a7jT94l89hCbLyxatAiZkIUpihQGjGttOkqRZR0dDIriy7cdTP/MCDLFNc9mIJLf8P9AZIQlS5aUZN51Pli7di0JXWc8MbvnjG5I/PFkNkRYMXPuuusuVq5Yyd+f/Bdiyfz3Mn/50mHO+S/x9CeexuvNR0fohUVrayvf+ta3uOWWW3jzzTd5++2357RJMx2GYXDixAneeusttmzZwje/+U0aGxtNm2+hsWbNGnp7ezh8yMlMM/0OH7ZjGILHHnvMXOMsIJcn3AeBLcCXpZRnhBDLgP9hjlmFJbOrJN3W5lBJhwdhc1i6q1VMxGJRHHlac0WSAo/HwyOPPILH4yGSzE+upENL3cyLobiA1bS1taX+UYjY0uBNNpQgq1av5qo0iBXEdT05F4DmxsaSbPEzGV6vl8bGxryL02BsVHke5sDatWsBGIvPLrQ3EE8gpWTNmjX5NKsk0DSNZz7zDL5IgJ+e/XVex47rCf7+5P9m9erV7N69O69jLyQqKir42te+xoMPPsj58+c5cuSIKa1mEokEhw8f5uLFizz++ON8+ctfVhsGs+Dd736UQEBy5sz0C+NkEo4ddXDbbbctiAizXKr1HpNSPiOl/Jv0f5+RUv5J5rgQ4ntmGFgIrly5AkIgnWXWTiwE0lWemr8EMXQdTeRn4R1OCvbt28czzzzDvn37COdJnNrSw+h6cXt4zSCTwyOC1hdFysxZyrl269atwwDOFtqQ69CRnNY01t1yS6FNKTo6OjryGtYbT0YJxQLKQz0HFi1aRHl5+awr9o7FUwt5JU5nR09PD9u3b+efB14iFA/nbdx/O/9bxqIBnn76aRVVMA12u53PfvazfPazn2V0dJTXX3+daDR/pfYikQgHDhzA7/fzuc99jk984hOqkvss2b59Ow0N9bxxZPoqoSff1ohGJQ899JAFlplPPr/FHXkcq6BcvXoV4SqDAoTQJR1lXBkcnP5ExZR47ZL9+/fz/PPPs3//frz24vI2zUeam5txOB0QKMDkASgrLytp71xPTw9ul4s3C23IdZwHIoahKi9OwLJlywhERjGM/GxkBSLD2XEVs0MIQXd3N4FZhvX6YwmaGhtV8a858OEPf5hIIso/n3spL+PF9Tg/OfsrbrnlFlVoJwcefPBBvva1r5FMJjlw4EBe6mgEg0EOHDgAwHPPPce+ffvmPGYpY7PZuPfe+zh/XiMQmNopcOyYg7a2Raxbt84i68wln+prwaz+h4aG0e2FCUGQTi9DQ0MFmbvQ2Ox2dJkfr5zHLolEIrz44otEIhE8eRKnyfQwmb6TpYSmaSxZsgQxzU3SlLkDGsuWLUOIwrWyKTROp5Nt27dzTGgk53C7bQFc6ddS5lbV7jDgcjjYsmXLHEZZmHR0dGBInWAsP9Uxx9LitKNjwewDF4Tu7m7G4wmSRu7foWDSoFt5TedEZ2cn27dv5+cXXiWuz61yMsBLlw4TjIVUEaRZsGnTJr7xjW/g9Xp5/fXXCQRmv/M8NjbGwYMHqays5Fvf+hbr16/Po6Wly913340QgjffnFyu+f2CS5cE99yzb8GskVT8wwSMjo5iODwFmVvaPQT8AVMT1YsVl8tNoriL9ZIwUl98l8tVYEsKQ1dnF7agxSE6EkRA0NXZZe28RcjevXsJS2NO3tN7ELSQEqUfRnAPs3uYxZG8ITS23XabyieagIyI9IfzE9obiAzj8XhKttdvvlixYgUSCCZyy7WL6wbhRIKVK1eaY1gJ8e53v5vxeHjOlXullPzs/Ct0dXbR19eXJ+tKi66uLr75zW9SV1fHwYMH8fv9OY/h8/k4dOgQTU1NfPOb31SpB3mkqamJvr5e3n7LwWSy4K23UlJuz549FlpmLvkUpwtDrgP+gB9pL4z4kA4Xup4saLPkQuH1eonoxZ2bkCmsVKqL8c7OToywYW3F3gjIuFQeI1I73U0NDbxcBLfbw0BEGjzwwAOFNqUoaW9vR9M0/JH8iFN/ZIiOZR0LZme8UKxYsQJIFTfKhYyYzVyvmD39/f20t7fz60sH5zTOucAlLgav8uBDD6rvxRxobW3l61//Oo2NjRw+fJhgcOZVDwOBAEeOHGHRokV8/etfV5tnJnDnnbvx+WBkZOK/8VMnHfT0rF1Qv/t8itPP5XGsghIKhcDmLMzk6XlLUZxWVlYSTha3Mz+USN0cKiqsreRcLHR1pb2XYxZOmp5LVSlN5aA89MgjnEVyoYCZFAaSXwmNzo4O5bGYBKfTyaJFbfgjc0/TkFISiI7Q0ak2aOZKQ0MDlRUVBOO5eU4z52fvgYpZI4Rg7969nPINMBKZ/cPklctHcNjt7Ny5M3/GlSiNjY38xV/8BdXV1Rw5cmRGRZIikQiHDx+mvr6eP//zP6e2ttYCS0uPHTt2IITg9Ol3ro/9fsHwMNx++07rDTORGSsBIcQRIcThm16/EEL8JyFEnZTyn8w01CqklMRjMaStMC1cM/Pms3rafKG6uhp/TEwaulAMBNLitFQL82QWZmLMul1qMSYQQihxmub++++n3Ovl3wpowzFgWBq874knlMdiCrq6OvNSsTeSGCeWiKjogTwghKCrq4vxZG5FkYKJJDU1NSV77883d955JwCvDx6f1fVSSl4fepONmzaV7GZxvmlqauJP//RPEULwxhtvYEzRYFPXdY4cOYLT6eS5556jvr7eQktLi5qaGrq7V3Pu7DtrnZw9m5Jx27dvt9osU8nFTfW/gP3Ae9OvHwI/B64A/z3vlhWIeDzdHFoUKLw0PW/WjhKirq6OmC7z1vbFDEajGkKIkt0hrK6upq6+zlLPqfAJWlpbSjaU+ma8Xi+PPv44b0JBvKcGkv8tNBa3tSmPxTR0dHQwHh0joc/tfp4JDVYbNPmhs6uL8YSeU22H8aSuvKZ5pLW1lSWLl3B0+OSsrr8SGmY0PMbWrVvzbFlp09HRwRe+8AUCgQCnTp2a9LyTJ08SCoX4wz/8QxYvXmyhhaXJ5s23cvWqIBK58f2BcxqLFrUuiN6m15OLON0mpfy8lPJI+vUfgZ1Syq+SKvq4IMg+rArlDUjPO9WO1UKlubkZgOFo8Yb2Dkc1amuqcTgK41kvBlatXIVtbOabN7JaIh3pV4NEVucmqGx+G6tXrc7VzAXNo48+SlVFBT9FIC0WqAeBIWnw0aeeUv3rpiHj6QzMMe/UHx66YTzF3Fi2bBm6YRDRZ/aclVISSiRVG588s2nzJt4eO0fCyC3EGuD4SEo4qTZW+ee2227jvvvu4/z58xMWSPL5fFy8eJF3v/vd6vdvEbfccgtSwv/4jof/8p+vvQYGbGzcuPA+g1z6YZQLITZLKV8GEEJsAsrTx3K/sxQpBQ9RK+KQVrPJ7PwMhjXaK/LTGzDfDEZsLFrSVmgzCsqKFSv41a9+lfrWz+AOIvtlNgzY2JnjpksMjJChipDchNfr5ckPfIAXXniBtwCr6ofGkfyLprFq+XJ27Nhh0azzl2zF3sgwdeWz39n2R4apra2jsrIyX6aVNO3t7QCEEkm89uk3WCK6gW5IVYU0z/T39/Piiy8yELhMZ3Vu3reTvgGaGpuym9qK/PL000/zy1/+kqNHj97w/JVS8tZbb9HY2MhHPvKRAlpYWqxZs4YPf/jD+Hw3tibTNI0HH3ywQFaZRy7i9CPAfxVClJOqzBsAPiKEKAO+YoZxhSDbvzJPjdNzRuo32lFCZEJDLoZswNz7n+UbKeFS2MGu9qWFNqWgZFsp+IAGkyfz3TSnIssDDzzA97/3PX5y+Qpd0sBmQQXfXwIBw+DZT38aTSveCIdioaWlBbfLPeeKvYHoMMu7VUhvvrgmTnUaZtA1LpSu1LtkyRIzzSo5enp6gJTQzEWcSik56T/Pxu2bzTKt5CkrK+OjH/0oX/3qVzl8+PA7jn/84x/H7XYXwLLSRNO0kurlO2MFJKX8LdAjhKgChJTy+qyz7+bdsgJhs9mwOxzEZxFmgh7H4/Gwb98+9u/fz/gs8oxEel6PpzB9VguJ1+ultbmJ8+PnC23KhPhignHV0iS7iyp8Atlgrqtf+MQNcyqu4XA4+NSnP83nP/95XgbMzrwaQ/JLIdi1cye9vb0mz7Yw0DSNpcuWMnpp9hV7DWkQiIywrGNnnqxSVFZWUl5eTniGRZEy56ncuvxSU1NDS1MzZ/0Xc7puLBbAHw2yZs0akyxTANxzzz2sWrWKROJGZ4HT6VQh7gpTmbE4FUK4gIdJ5ZfaM+GvUsovmmJZAfF4vERmIyyTcfbdt49nnnkGgO/+wz/mPnl63rKystyvXQAsX7mK478dBIqvlc6ZYOrrUupCqb6+ntq6WoZH89O/cSrEqKB1USvl5eXTn1yCbN26lY0bN/Kvr75Kr5SUm+g9/Qkg7Haefvpp0+ZYiHR0dHDu9OxrK4diY+hGsuQ3xfJNW1sbQ2dvLPhS4Zx4SRRO6HjcblWp1wRWda/m8Muv53TNWf+l1LWrVplhkiKNqpKvKBS5xGX9ALifVKZZ6LrXgqOqqgqRyL2Vi7Q72b9/P88//zz79+9H2nPvlSoSUYQQJVsavbu7m6th8MeLr2LvSb8Nm01j+fLlhTal4HSv7s6pKNJssY3Z6F7dbfo88xUhBM888wwJIfhnE+c5g+QN4H1PPKFyvHJk2bJlROIhoonwrK73h4ez4yjyR1tbG5GbUuBX11Swuuadz95IUqeltbXwNSkWIKtWrWIkPMZ4fObLyXOBS2iapqonKxQLlFzEaZuU8jEp5deklM9lXqZZVkAaG+rRZrGQMCpbGBcevvsP/8i48GBUtuQ8hkiEqaquKdl8rrVr1wJwwje3nNv2Ch2PzcBjM1hVnchLgaUTYw5WLF+u8ixIbSLIoAQzOx5FwAgbrF6tKvVORXt7Ow8/8ggHgIsmVFTTkfxYCJoaGnjPe96T9/EXOhlROVHF3mpvIw6bC4fNRUPFYqq9je84J5OvmsmTVOSHpqYmIonkjNrJRKVccK0aioVMJNJA4MqMrzkfuMzS9nZcLpdZZikUigKSiwL4tRCiR0p5xDRrioTGxkZsR9/K+bp4+5Y5z63FQjS1vnOBUiqsWrUKt8vFMV+UTU2zL4r0xMoI54Ipz94XNozP2a5oEk4F7Dx69/o5j7UQyIZT+YAmkyYZvWkuxaR84AMf4J9+8hN+HAjwESkReQzvfQ24IiVf/PSn1WJwFmQqvAaiIzRW3lhQZ92SOxgLXwVg16rHJ7w+EB2huam5JOsQmElzczNSSmK6gXuKir1SSiIJnaYms250pc01cXqZ7vqZhZAOjF9h64btZpqlUCgKSC7uue3Aa0KIE0KIw0KII0KId5bwWgC0trYiY+Mwm6JIc8SeCLK4rXRblTgcDtatX8+RUTc59Ec3nWM+B7qheqplyFTPFaPmhbmJUYGmaSWf4zsTysvLeepjH2NApsJv80Uk3Tqmr7eX22+/PY8jlw4NDQ14Pd5ZV+wNRkdY1qFCevNNY2NqEzg6Ta/TpJQkDSN7viK/VFRU0NTYxPng5Rmd748FCUTHVXqNQrGAyUWc3g0sB/YC9wLvSv9ccGQq8onoO5sPm4qRREaDtJWwOAXYsmULg2G4FC6e0ObXhx14PW5VpTRNRUUFrYtaTRenS5ctVd66GXL33XfT2dHBTzWNZJ7Ce38ORKTk0888o/LtZokQgval7QQjozlfa0iDQHRU9dc0gfr6egCi+tQpH7FkSrw2NJjdN6t0Wb5iOefHB2d07vl0+K8SpwrFwmXa1b8QItP1OzjJa8GRWQhoYd/UJ+YZLTIGUjX63rZtGwCvXs29oJQZGBJeG3ax+dYtOByOQptTNPSs7cHms2FCmiNI0MY01nSrVgEzxWaz8YlPfhKfYfBKHsbzI3lJCPbu3au813Nk6dKljMdzF6ehmB/D0FW+qQlkxGZsGs9pxrOaEbOK/LN8+XKujo8QS05fxOB8MCVOVTEkhWLhMhPX1F+nf74GvJr++dp1/73gaG9vx263o4VHLJ1XC6XmK/UdwYaGBtau6ealq8XhMTvmsxOIwa5duwptSlGxatUqjIgBERMGD4KMS7q7VaXeXNi4cSO3rF/PzzWN2Bx3DX4GoGl8+MMfzodpJU17ezvh2DjxZCyn64LRlKBdsmTJNGcqcqWyshIhBPFpxGncSB2vra21wqySpKurC4nk0vjVac+9ELxCc1OTai+mUCxgphWnUsp3pX8uk1J2pH9mXguy8ZrdbqejsxN7yPw+jtejhYZwezwsWrTI0nmLkd179nI+qGWLGhWSX1124vW42bJl7gWvFhLZKrq5O4SmJRMurMRp7nzkox8lZBi8PIcxfEgOILjv/vtV65g8kEkVyYjNmRKIpDYslec0/2iaRlVl5fTiNH28pqbGCrNKkkwP34szEKeXQkN0qN6bCsWCZsZJfUKIf5nJewuFnrVrsYWGwZj6wZVP7KEh1nR3l2wbmeu58847sdts/Nulwob2RpLwypCbXXfcqXIfb6Krqwu73Y4YMSEXcRTcHrfyGM2CNWvWsGnTJn6taSRm6T39JaDZbLz3ve/Nr3ElSubvOFdxGoz6qKqsKtm+12ZTXV2d9YxORtwwsNk05akzkZaWFlxO17SeU93QGQyNqJ6/CsUCZyY5p24hRC1QL4SoEULUpl9LgQXb+KunpwepJ9DCFnlPkzFEaEQV3ElTVVXFbTt28KtBD/G5tyidNb++4iSWlLzrXe8qnBFFitPpZPmK5Wi+/G+m2Hw2Vq9ejc1WeM/5fOR973sfIcPgwCyuHUdyQAjuuvsuVQQmT7S2tiKERjCaWx2D8dgobWmvqyL/VFVXkzCm3sBJGAblZeWqIJiJaJrG4sWLGZwmWm0o4kNXOdgKxYJnJqvKj5HKL13FjfmmPwC+YZ5phaW/vx8AW2Bm5c3nii2d5L9u3TpL5psPPPDAA4Tikt8Mzs572l6h014xe2UrJfzLRQ9dnR0qvHQS1nSvQfgE5DPAQAc5JlUxpDnQ19fHqhUreFloyBy9p78l1T7j8ccn7rupyB2Hw0FTYyPjsdzEaSjuZ/Hi0q7ebiaVlZXo0/QEThiSysrKKc9RzJ0l7UsYnKaidUa8qogahWJhM5Oc07+QUi4D/o+bck77pJRft8DGglBbW8vSZcuwBS5aMp/NfxGny6VE0HX09/eztH0JPznvmVXP0ydWRnhi5eyr9Rz32RkIajz08CNq13wS1qxZg0xKyGfXJR9gpMZWzA4hBA+/+90MSYPTOVynI3lV09i4YYNaAOaZxUsWE4qNzfj8pB4nHAuqGgQmUlFRQWKasN6kYaiwagtYtGgRI+ExdGPyDeWhdAcF9Z1QKBY2M47Hk1K+IIRYK4R4VAjxZOZlpnGF5tbNm7EFB0FPmDuRlDgCF1i3bh1OZ3G0TykGhBA8+tjjDAQ1jvrsls//vwbcVFVWsGfPHsvnni9kNlPymXeaGUtt1MyNnTt3Uu715hTaewoIGAb33X+/WWaVLIsWLSIUn/kuTiiWOrfU+16bidfrJTlNWK8uoVyJU9NpaWnBkAa+aGDSc4YjPrwej/JkKxQLnFwKIv2fwAvp1y7ga8B9JtlVFNx6661g6Nj85npPRdQPkQBbVTXYd7Bnzx5qqqvYf85j6bznxzVeH3bw0MOPqEJIU9Dc3Ex1TTXkseuSGBE0NTep6phzxOVysXvvXo4LMeO2Mq8DVRUVbN261VzjSpCWlhZiiQjxZHRG54+nxWlLS4uZZpU0Xq+XhK4jpwjN0WXqPIW5ZKqCj0Yn38AZjfppbGxSkUwKxQInl0omjwB3AleklB8E+oAFvWrv7e3F4y3DNjZg6jx2X2p81arknbhcLh597HGOjNg55beuOM4Pz7pxu1w89NBDls05HxFC0LO2B9tonj4bCbZRG329ffkZr8TZvXs3CSl5cwbnxpG8JQS379qFw+Ew3bZSo7U1VT8w4xGdjsx5mesU+cfjSW16TuU81a87T2Ee9fX1AIxN4TkdiwVpbGq0yiSFQlEgchGnUSmlASSFEJXAVWBB9jnNYLfb2bZ1C86xAZDmtZSxj52jo7NL9ROchAceeICK8jJ+cMaaBcKVsMZLgy4eePBBqqqqLJlzPrN27VrkuISZOYSmJgxGxFD5pnli7dq11NbUcHwG554C4lKyc+dOk60qTTL395mG9obiftwut7oHmYjb7QZAn8pzahgqesYCMpXBfbHgpOeMxYJZEatQKBYuMxKnIhVDcVgIUQ3836Sq9R4AXjHRtqJgx44dyEQULXDFlPFFPIQWHOSOXTtNGX8hUFZWxqOPPc6BYQdnAuZ7T//+jBuHw6Gqlc6QtWvXpv6Rh9BeMZwK1+rp6Zn7YAo0TWPL1q2cEgJ9mtDetwCP201fn/Jam0FWnMYm9wxdTzgWoLFJhTCayUzEadKQ2fMU5uH1enE5XQTj4xMeN6RBMBairq7OYssUCoXVzEicylRCRr+UckxK+X8Be4D3p8N7J0UIsVgI8a9CiONCiKNCiM/kwWZLufXWW3G6XNhHc6l5OXNso2cAuP32200Zf6Hw8MMPU17m5funzfWeXgpp/PqKi/sfeJDa2lpT51oorFy5ErvDnhWWc2IEPF6ParKeRzZv3kxUSi5Nc94ZTWP9LbeokF6TqKysxOVyEYnPTJxGEkGam5tMtqq0yRQgNCYRp1JKdMNQhQotQAhBbU0N/tjE4jSUiGBIQ9UiUChKgFzCel8SQmwEkFKelVIensE1SeD3pJSrgVuBTwoh5lUJTrfbzfZt23D6zpkS2usYPcOyjg7VVHoaysvLec/vvpfXhx2cNDH39PunPTidLt773veaNsdCw+l0snLlSrSRXG4nE2MbsbFmzRpsNuvyixc6GU/omSnOCSAZMQzVZ9lEhBA0NDQSjk8etng9kUSQpiYlTs0ksxEzWc6pvOk8hbnU1NUSjIcmPJZ5X20aKxQLn1xWk7uA3wghTgkhDgshjgghphSoUsrLUsoD6X8HgePAvGtQdeeddyITkbxX7RWxIFpwkD27d+d13IXKww8/TFVlBS+eMqdy4kDQxkuDTh5597vV7myO9PX2IXwitR01W+Igx6QqhpRnampqWNTSwoUpzjmf/pkN0VaYQlNjI5HExJ6h69GNJJF4KJuHpzCH6TynmfeV59QaampqCCbCEx4LpD2q6tmsUCx8chGndwOdwB3AvcC70j9nhBBiKbAOeDmHOYuCzZs3p6r2juQ3tNeeHu+OO+7I67gLFa/XyxNPvp83Ru0cG81/39O/O+WhzOvhPe95T97HXuj09vYiDQmjcxgknbOq8k3zz+o1a7isTX67vwTYNI3Ozk7rjCpB6hvqiSYn9gxdTzSROkcVfzEXuz31HJksJiojWVUkhzXU1tYSmCTnNKA8pwpFyTBjcSqlPDfRaybXCiHKge8B/05KGbjp2FNCiFeFEK8ODQ3lZr1FOJ1Odu28HefYWdDn4hq6adyRU3SvWaNaBeTA/fffT0N9Hd895WWKGhY58/aYjdeHHbznd99LhWq4njM9PT0IISbNO5XVElk99QcmhgWaTaO7e15F/s8LOjo6GDMMounldkv6lWEQWNzWpqqSmkx9fT2R2PiUfTWBrHdVFX8xl4zonOzzyLydEbEKc6mtrSUYDaEbOm0VzbRVXOtgkPGcKnGqUCx85p4kNg1CCAcpYfr/Sim/f/NxKeW3pZQbpJQbijmEae/evchkAtvYjPT4tIjwCIRH+Z29e/MyXqngcrn4wAc/xEl/Skzmi7875aWmqpJHHnkkb2OWEhUVFSxdthQxNIk47ZfI/qkX5NqQxsqVK1VlTBNYunQpAJntv3sQ3MO1z2pY01imvKamU1tbiyF14vrUfZcynlO1EDeXrDid5LhMH1GeU2uora1FIhlPhHls9d08tvru7DF/LIjDbqe8vLyAFioUCiswVZymW9D8F+C4lPLPzJzLbPr7+6mrr8cxfDIv49mHT6LZbOzatSsv45USd999N4taW3jxtHfK5ukz5eionWM+O0+8/wN4vebks5YC6/rXoY1qk8fITUUShE/Q39efd7sU0NbWBkwcda0j8RkGixbNu3IA846M2MyIz8mIpvPulDg1Fy0d6j6ZIzvzvjZFSLwif2TC2P0T9Dr1x8apq61TrZUUihLA7DvuNuAJ4A4hxMH06x6T5zQFTdPYu2dPqihSIjK3waSBa/Q0t27eTHV1dX4MLCHsdjsf+vBHGAhqvHJ1bt5TKeHF014a6uu4994Zp1ArJqCvrw+ZlOCbxcWjIA1Jf78Sp2aQ6bE5NsGxcVL7CZlzFOaRKeYSm6ToS4bMcVX8xVyy4nQS36m86TyFuWTC2Mei7xSnY7EgdSoHW6EoCUy940opfymlFFLKXillf/r1YzPnNJO9e/eCNLCPTNWUYXq0wBVkLMSePbw9glIAACAASURBVHvyZFnpcccdd7B0yWK+f6ZsTt7TwyN23h6z8eT7P6Dy7eZIpmXJZKG9UyGGBEIIVQzJJNxuN+VeLxN12My8V8xpFQuFzGZkLDn1BmcsGaGsrEzlOprMdKJTiVNryXhOxybynMaDNDSqe5RCUQqoO24OdHZ2snTZMpyjp+Y0jn3kJG6Ph+3bt+fJstLDZrPxwQ9/hEvjgpcHZ+c9lRK+f8ZLU2MD99wzLx36RUVtbS1ti9tmJU61IY3Ork6VT2QitbW1TBRMOn7dcYW5VFVVARBLTuM5TYapqqyywqSSZtoQ0XRcrxKn1lBbW4smNHzRG7fRpJT4ogFVvVqhKBHUHTdHfmfvXkRwEBGdyAcxA4wkTt9Zdu3cqTx1c+T2229nafsSfnB2dt7TN0btnPKnvKaqyXp+WL9uPdpIjnmnOohRwfp1602zSwFV1dVMJIky71VWVlppTkmS+R3Hk1MXRIono1RWqc/DbDLidNKc05vOU5iL3W6npqaGsdiN66toMkYsGVfRHQpFiaDEaY7s3r0bAPvI7LyntrHzyGQ8O45i9miaxhNPvp8L44LXhnIXlz8466Ghvo677rrLBOtKk3Xr1iETcuLkxskYBamrfFOzKa+oIDbBIjuW/qlaKJmPw+HA5XITnyasN6FHs15WhXko0Vl8NDQ0vCPn1JcWq0qcKhSlgRKnOdLU1MTatT04R0/P6nr78CmqqqtZv155ifLBrl27aG1u4kfnPDn1PX17zMabPjuPPf4e5TXNI7PJO83km2auVZhDWVnZhOI048PzeDzWGlSiVJSXE9djU56TMGJqs6CIUCLWOurq6/DHx294zx9TfX8VilJCidNZsGfPbgj7EOGJGjNMgR7H4b/AHbt2qb5pecJut/PYe36XU34bb/ln/jv98YCbivIy3vWud5loXelRX19Pa1trTuJUG9Lo6OxQi3GTcblcJHnn55IAHHa7uidZRFl5OYnpxKkeU/nXipKkrm4icRrMHlMoFAsfJU5nwc6dOxFCYB/JzXtq851HGknuvPNOkywrTe6++24qysv4yYB7RucPRTReG3Jy3/0PqL6mJnDLultSeacz8WSn803X9a8z3a5Sx+VykZjgQ0kCThU9YBnl04hTKSXxRIyysjILrSpNsjmnkxxXOafWU1tbSygWRjf07HvBeCh7TKFQLHyUOJ0FNTU19PX14xw7m9N19tHT1NTWsXbtWnMMK1Hcbjfvuvc+Xh1yMhKdfhHxLxdcIDQeeOABC6wrPfr6+pDxGead+kAmVb6pFdjtdpITxL7roELbLaS8vIyknpj0uCF1DKmrjTMLmKnoVOLUOqqrq5FIQtf1kw/GQ9htdrVho1CUCEqczpJdu3ZCeAwR8c3sAj2BI3CRnbfvUGXpTeD+++9HAv92aeoKyEkDfn7FzbZt22hqarLGuBIjm3c6PP2CLhP+29vba6pNipQ41Sd4X08fU1iDx+NBl5OL06SROqbEqflcE51zaJatyCuZQmDjiWu1xUPxCJWVFWqTQKEoEZRKmiW33XYbAPbRszM63+a/gNST7Nixw0SrSpfW1lY23LKBn1/2TNlW5sCQg0AM7r33XuuMKzGamppobGqcUd6pGBYsaV9CdXW1BZaVNjabDWMCz6mB6uNoJR6Ph6QRn/R4Uk8dc7tnlqagmD3ThfVmDihRZB2Z2gPhxLV2S+FkhIpyVZNAoSgV1IpkltTX17Ny1SocY+dndL7NN0BZWbmqSGoid99zD8MReNM3uRfoF5dd1NXWsHHjRgstKz3W9a/DNmKb2iEhQRvRVL6pRdhsNiQgb/pQDJTn1EpcLhe6npz0uG6kjilxaj7TiU6Vc2o9mUJgket6AUeSMcorlThVKEoFJU7nwG3btyPGryLiE7W2vw5p4AxcYOvWLWoRaCLbt2/H43bx6yvOCY+HEoLDow7u3L1HVSY1md7eXoyoAeNTnOQHmZAqpNciMn/zxk3vK8+ptbjd7mzo7kRkjilxaj7Zv/tponqVOLWOTDh7NHktuiCqx1WYu0JRQqgVyRzYsmULkArZnQotNIyMR9i6dasVZpUsbrebrdu289qwG/3mFTjw2pAD3UBVS7aAnp4eYOq808yxzLkKc8mI05vX4fK6YwrzcblcJPUEcpLGzBnPqdM58SabIn9MX603dURt3lhHpt9y7LqK1jE9rjZrFIoSQt1x50BXVxfVNTXYxqYWp7ax8wgh2LBhg0WWlS633347wbjkLf87PdSvDTlobKhj1apVBbCstFiyZAll5WUwMsVJw1BbV6sKU1lERoDeXBRJB2wqosMyMqLTkBPsoAGGEqeWkRGd05VDUp5T63C5UkUN49eFvseNRFa0KhSKhY8Sp3NACMHmTZtwBC/BJLvgAPbAJVasXJmtQqcwj02bNmG32Tg4fGNrjIQBb/hcbN12m1poWICmafSs7cE2OrlHzu6z07O2R30eFjFVWK9KN7COa+J04rxTXaa2D1R7H/PJRhNM8vzOvK2+H9aR8ZDG9WthvXE9kRWtCoVi4aPE6RzZsGEDMhFFC0/iItLjaONX2aQK8FiC1+ulp7eHI6M3eh3eHrMTS0o2b95cIMtKjzVr1iD9EiZKr4uCMW6wZs0ay+0qVTKi6GZJpPqcWktG6BjGRI19rr2vPKfmM1moewZ503kK88n83ceN6zynekKF9SoUJYQSp3Nk/fr1AGiByxMetwUHQcrseQrz2bBhIwNBjWD8mkfuqM+Opmn09/cX0LLSYvXq1al/jE5wMP2eEqfWkRGgN4vTJOBUXgnLyIrTycJ6055T5a0zn2ue04mPZzyqKufUOjRNw+lwktCv7WomkspzqlCUEuqOO0caGhpobmnBFrwy4XEtcAXNZlOLcAvJCNATY9cWdyfGHCzv6qSsrKxQZpUcmdxe4Xtn2K4YFQghWL58udVmlSwZz8PNjuyEEMorYSGZTYKMCL2ZjGhV4tR8shsFk/hOjZvOU1iDy+UinhanuqGjS0OJU4WihFDiNA/09/XhCF2dcPvVPj7IiuUr1OLPQlasWIHdZuNkuiiSbsCZgIOeXtVj1koqKytpam5CjE4gTn2CJe1L1PfCQjKLOyVOC0s293cSz6lU4tQyMr/jyTynRvqA+iysxe12E0uL01g691QVRFIoSgclTvPA2rVrkfEIIha48YBhYAsN09OztjCGlSgul4uOzg5OB1MLikthjZguVZXeArB61Wps/nfma9n8NlatVJ+HlWSiBmI3vR+77pjCfK4JosnCelPvqzxH87nmxZ66IJLKybYWj8eTFaWZn2oDTaEoHZQ4zQPd3d0AaONDN7yvRUaRRjJ7XGEdK1eu4m2/nWdfK+dbb6QW3itWrCiwVaVHV1cXxrhxo7suCkbEUCG9FlNeXg5A5Kb3o1IqcWoh03pOUeLUKrLidJLjGdGqilNZi9frJZpMbaNFk/HsewqFojRQsSp5YOnSpTgcThKhYfT6ruz7WmgYgJUrVxbKtJJl7969nB8YQDcMqoDdTU0sXry40GaVHF1d6e+DH6jn2r+vP6awhMrKSgCi172XQBKX144pzOdab83JwnpTgkiJU/PRNA2bzTap5zSTi6o8p9ZSVl6Gb/AqQFakKnGqUJQOSpzmAbvdTmdXJ0cv3NhORguN4PF4WbRoUYEsK136+vp4/oUXCm1GybNs2TIAhF8g62X23wAdHR0Fs6sUyQjQ0HXvRW46pjCfrDidTBClPaqqQqw1OB2OScWpnn5bhZRaS3l5OZf0AQDCydR2WkVFRSFNUigUFqKefnlieVcX9qjvhsoKtsgoXcu7EOKdBWEUilKgqakJl9sF16djB6CisoLq6uqC2VWKeDweXE7nDeJ0PP2zpqamECaVJFmP6GRVeFCeUytxuVzok4lTQ2bPUVhHRUUF4URKlIYTkex7CoWiNFDiNE90dHQgE1FEfBwMAwwdW8RHp/IOKUoYTdNYsmQJInhtg0YLaixbuqyAVpUuNdXVWUEKSpwWgsxmpZykfUnGo6o2Na3B5XZlRejNZDyqSpxaS1VVFePxMFJKxtPitKqqqsBWKRQKq1BhvXkiE77oPfi32ffkde8rFKVK+5J2Tr10CiOdY6eNpwSrwnrqGxoIXr2a/e9g+mddXV1hDCpBpgvrzYhWFdZrDR63h3DAP+ExXUpsNk3lnFpMZWUluqET0+OE4mFAeU4VilJCidM80dvby2c+8xlCoWtBcw6Hg927dxfQKoWi8LS1taUq9uqAAUbUoK2trdBmlSQNjY1cOX4c0p6iTLR1fX395Bcp8sq1gkhTe06VOLUGr9dLcJLKyUlD4na5lRfbYjIpH8F4iGAiTHlZueo1q1CUEOrbnifsdjsPP/xwoc1QKIqObEGwMJC86T2FpdTX1xOQEolEIAgCleXlqlWGhWTDeqfJOVWCyBo8Xm+28NHN6FLidnusNUiRFafj8TDj8ZAK6VUoSgy1NatQKEylpaUl9Y8Q2VKx2fcUltLY2Ehcymw7GT/Q2NRUSJNKjmseUeU5LQbKysrQJzmWNCRl5aoHsNVkxGgwHiIYD6uceIWixFBPP4VCYSpNafEjwgIRTnmDmpubC2lSydLY2AhkW80S0DSa1GdhKdMWRFKeU0spKysjOUlBpKSUlHmVOLWabFhvIsx4Ikx1jarsrlCUEkqcKhQKU6mrq0sttMNAGJwupypuUSBuFqd+rm0eKKxh2oJIUiKEUOLUIqYSp7qUlKt7leVcC+sNMZ4Iq7BehaLEUOJUoVCYit1up6a2BiJABOrq69TCu0BkhKgfiCGJGEZWsCqsYbqwXpAqpNdCysvLSej6hJsFSZk6rrAWj8eDw+FI55yGVU9shaLEUE9AhUJhOvX19YiIQEQFjfVKDBWK2tpa7DYbY1zznipxai3Te04NtXljIRnxmZxQnEolTguAEILKikqGI2Pohk5lZWWhTVIoFBaixKlCoTCd+rp6tJiGLW6jtra20OaULJqmUVdbS4Br4lSF9VrLtVYyE7cvkcpzaimZFIPETaG9UkriSV2lIBSIqqpKhsL/f3v3HiZXVad7/H3TSafvnXDxAohR8cagIxIYQcQgjMOMoxyP8CjjqKjPQfGMDs6o43NmjgflUUfREa8oaowC3sAb4gUcJIKgAgISUG4DURx4FESBQJLuqvqdP9aq7kqlqtMdUrU6Xd/P8/TTu3bt2nvtvWqvWr+91tr73jxNt16gl/ALCKDjli9fLk9YsSkITgt7xKMepfs0HZzyjNPumk3L6aJFfd1MUk+rt4xO1ra8WFALqRZBcFrI2Nj4VHBKHgC9heAUQMeNj4+rtqmmmAjGDxW2++6764FFi3R/fk1w2l19fSnwrEWbltOg5bSb6l1GJ5sedloPVgmMyhgeGdam6oQkxv0CvYZfQAAdt88++0i1NJbo8Y9/fOnk9LTddttN90foAUnjo6NasmRJ6ST1lHpw2q7ltKaaFvfRctotU8FpU8vpRH7NeMcyGgPS4WEe5wP0ksWdXLnt1ZL+VtLvI2K/Tm4LwPx15JFH6rDDDpMk9ff3F05Nb9t11101GaE/SNqVVtOumw5Oqy3fT916uW7cLe2C0/oYVMY7ljE0NDQ1TXAK9JZO/wKukXRUh7cBYCfQ399PYDoP1Mf83iVpl113LZuYHjTVrbfNo2RqUdPixR29bowG08Fp6269tJyW0RiQNgaqABa+jganEXGJpHs7uQ0AwOwtX75ckrRJ4uZUBdQDz9oMLad9BKdds2TJEg0ODEx1462rj0Gl5bSMwcHBqWmCU6C3FP8FtH2CpBMkae+99y6cGgBY2Bor21S8u68+xrdWS8HpsqEtnzNbq1VpOe2ysfFxTT7wpy3mMea0rIGBgalpetwAvaX4L2BEnCHpDElauXJl635OAIAdguC0rOmW0xT87L/387Z4vxZV9XOTqq5atmyZ7rnvj1vMm6jVNDgwQGBUSGNwartgSgB0G3ddAIAe0vhoDB6T0X31YKddt95qVAmIumz58uWqNN09ebJa0xgXb4pZunRp6SQAKITgFAB6SOP4LZ4f2H31br3VWqXl+7VaVUv6aTntpvHxcU02PXZ2ohZaRnBaDBdogN7V0eDU9pck/UTSk23/1vZrO7k9AMDMGrvIEZx236JFi7R48eKpMafNalHdoksjOm98fFwT1S3zoxLSsnzzMHQfLadA7+romNOIOK6T6wcAzN2+T32qbrzxRu25556lk9KT+pf0qxqtW06rUZlqXUV3LFu2TJVaTdVaqG9RungzGaFly5YVTlnvouUU6F3Fb4gEAOiuT5x+umo1nqdZSn9/v6q1yZbv1aJCy2mX1YPQiVpNg4vSc2gnKlWC04K4QAP0LsacAkCPqXctRRlLBwbajjmt1Cp0aeyy+l2rJ/PjY6q1UKVWIzgtiOAU6F0EpwAAdNHAwIAqbVpOq7VJgtMum2o5rabgtP6MUx61VA7deoHeRXAKAEAXDQ4OqFptHZxWqpMaHBzscop623S33vQ4mUmC0+Lo2QH0LoJTAAC6aHBwsGXLaUSoUp1kzGmXTXXrrbec5v906y2H4BToXQSnAAB00eDgoKqxdXBaqU1MvY/uGRkZke2p7rz1FtSxsbGSyeppfX19pZMAoBCCUwAAumhoaKhly2kld/UdGhrqdpJ62qJFizQ6MqJJuvXOG7ScAr2L4BQAgC4aGhpSpTqx1fzJPI/gtPvGxsamWk4nqzXZ1ujoaOFU9S6CU6B3EZwCANBFw8PDmqhs3mp+vVvv8PBwt5PU85YtXz495rQWGh4aomtpQRx7oHcRnAIA0EVDQ0Oq1iqq1qpbzJ+sbp56H901Njam+pNnJ2s1xpsWtmgR1VOgV3H2AwDQRSMjI5Kmg9G6ydyaSstp942Pj6uSGk41WQvGmxZGyynQuwhOAQDoonrwuVVwml/Xg1d0z+joqCaqqSW7EqExgtOibJdOAoBCCE4BAOiidi2nEwSnxYyOjqpSq6kWoUqImyEVRssp0LsITgEA6KK23Xqrm2Sbbr0F1IPRyVposlYjOC2MllOgdxGcAgDQRVOBUGXTFvMnK5s1ODjEzWAKmA5Oa5qsVGm9LqwenHKhBug9PEgKAIAuqgc+m6tbBqcT1U0aJSgqop4nmyo1hejWW5ptvfCFL9Rzn/vc0kkB0GUEpwAAdFG7ltOJymaNLicoKqEenG7MN0Wi5bQs23rrW99aOhkACqDvEAAAXTQ4OKi+vj5NNAen1U08X7OQevfRTRWCUwAoieAUAIAusq2RkVFNNHXrrdQ2E5wWMhWcVtPDToeGhkomBwB6FsEpAABdNjY6qolK06NkKhsZ61hIc8spN+IBgDIITgEA6LKx8XFNVDZOvY4IbZ7cRHBayODgoCRpcy21nBKcAkAZBKcAAHTZ+PiYJmvTLaeV2qRqUdX4+HjBVPWuvr4+Le3v1+ZKCk7rwSoAoLsITgEA6LLR0S3HnNZvjkTLaTmDg4OqREhizCkAlEJwCgBAl403deutT9NyWk5jayktpwBQBsEpAABdNjo6qsnKhKq1dAOeeisqLafl1APSvr4+LV7MY+ABoASCUwAAuqz+yJiJamoxpVtvefXgdGDp0sIpAYDeRXAKAECXTQWnOSjdTLfe4gbzONOBgYHCKQGA3kVwCgBAlzUHp7Sclrc0t5j203IKAMUQnAIA0GVbB6cb1d/fPxUgofvqx56WUwAoh+AUAIAuq7eQ1m+ENFHdpLHRsZJJ6nlTwSkXCACgGIJTAAC6bCo4rUzfEIkuvWX19/dLkpbScgoAxRCcAgDQZcPDw7IXaaKyWZI0Wd2ksXFaTkuqB6f1/wCA7iM4BQCgyxYtWqTh4eGpR8lMVjfTclrYkiVLJBGcAkBJBKcAABQwNjqqydxyOlGlW29p9aC0HqQCALqP4BQAgAJGR0enb4hUoeW0NIJSACiv48Gp7aNs32T7Vttv7/T2AADYGYyMjmiyukm1WlWV6gTBaWEEpwBQXkeDU9t9kj4u6a8l7SvpONv7dnKbAADsDMbGxlSpTWiymrr2joyMFE5Rb1u8eHHpJABAz+t0y+lBkm6NiNsiYkLSlyUd3eFtAgAw7w0PD2uyulkTBKfzAsEpAJTX6eB0T0l3NLz+bZ4HAEBPGxkZ0URl81TL6fDwcOEU9TaCUwAor9PBqVvMiy0WsE+wfZXtq+6+++4OJwcAgPlheHhYleqkNlfS42RoOS2rr6+vdBIAoOd1Ojj9raTHNLzeS9KdjQtExBkRsTIiVu6+++4dTg4AAPNDvaX0oYn7JUlDQ0Mlk9PzaDkFgPI6HZxeKemJth9nu1/SyySd1+FtAgAw79WD040TGyTRclpa/fjvuSejjwCglI5eJoyIiu1/kHSBpD5JqyPihk5uEwCAncHg4KCk6eCUltOyDjroIH384x/Xk570pNJJAYCe1fE+LBHxXUnf7fR2AADYmUy1nE4+IGk6WEUZfX19etrTnlY6GQDQ0zrdrRcAALRQD0Y3TT6ovr4+9ff3F04RAABlEZwCAFBAvRvvxokNGhwYlN3qBvcAAPQOglMAAAqot5xurjykgYGBwqkBAKA8glMAAApoHGPKeFMAAAhOAQAoorG1lOAUAACCUwAAili6dOnU9OAQwSkAAASnAAAUYHsqQGXMKQAABKcAABTT3780/+cxMgAAEJwCAFBIveW0sYsvAAC9iuAUAIBCCE4BAJhGcAoAQCFLc3deglMAAAhOAQAopn9pCk4ZcwoAAMEpAADF1IPSJUuWFE4JAADlEZwCAFBIX1+fJFpOAQCQCE4BAChu8eLFpZMAAEBxBKcAABRGt14AAAhOAQAojuAUAACCUwAAiqNbLwAABKcAABRHcAoAAMEpAADF1e/aCwBALyM4BQCgMFpOAQAgOAUAoDiCUwAACE4BAChmr732kiQNDQ0VTgkAAOVxqRYAgEJOPPFErVq1Svvvv3/ppAAAUBzBKQAAhYyMjOjAAw8snQwAAOYFuvUCAAAAAIojOAUAAAAAFEdwCgAAAAAojuAUAAAAAFAcwSkAAAAAoDiCUwAAAABAcQSnAAAAAIDiCE4BAAAAAMURnAIAAAAAiiM4BQAAAAAUR3AKAAAAACiO4BQAAAAAUJwjonQapti+W9KvS6djB9hN0j2lEwFJ5MV8Ql7ML+TH/EFezB/kxfxBXswf5MX8shDy47ERsXurN+ZVcLpQ2L4qIlaWTgfIi/mEvJhfyI/5g7yYP8iL+YO8mD/Ii/lloecH3XoBAAAAAMURnAIAAAAAiiM47YwzSicAU8iL+YO8mF/Ij/mDvJg/yIv5g7yYP8iL+WVB5wdjTgEAAAAAxdFyCgAAAAAobsEHp7b/1fYNtq+zfa3tv+jQdlbZPj9Pv8j223fw+vewfe6OXOeOZruaj3H9b87HIB/HQzqRvrz+tbZvakjjvD6mOxPbL7Ydtp+yjeW+a3tZt9JVwo4qd5rPB9trbB+z41LadrvH27676Xzet9Pb7TbbK2xf3zTvZNtv2cbnVtr+SJ7erjLL9nrbu7WY/xrb6/J353rbR+f5x9veYxbrndVyvW6mvG88z2zvYvsa268uk9LOsf0h2yc1vL7A9mcaXn/Q9j9tx3pPsj20o9LZtO4Vtjc2lU2v7MS2FpL823xmw+vFuYw/fzvXt8Prub2qVd3J9qm5DnFqi+UX/LFfXDoBnWT7YEl/K+mZEbE5VwT6O73diDhP0nk7eJ13Sup4pfRh2hgRz3iY61glaYOky2f7AduLI6Iyh228PCKummvCbPdFRHWun+shx0n6saSXSTq53UIR8TfdSlAJO7jcWaU5ng8zpMtKQzlqs/zIVyLiH7ZzWwv6XMnlR70MWaUdl0d7SfpXpe/OfbZHJNWfA3e8pOsl3bmN1cx2OWyD7XFJF0g6IyI+Vzo9HXC5pGMlnWZ7kdKzE8ca3j9E0kmtPrgNJ0k6S9JDs/3AHMuM/9reusZ21BcWigcl7Wd7MCI2SvpLSf+9vSvrRD23h7WqO71O0u4Rsblxwfz9XfDHfqG3nD5a0j31zI2IeyLiTtsH2r7c9i9sX2F7NF+Nu9T21fnvEGnqqvha2+favtH22bmSJ9tH5Xk/lvQ/6xvNV64/lqfX2P5I3t5tDVdjR2xflLe1ruHq+Ptsv6FhXSfb/ufGq7zt0jpf2X6H7StzK8AZDcfvTbZ/mVsIvmx7haTXS3pzvhr6HNu72/5a/vyVtp+dP3tyXteFkr6Qj/nXbX/f9i223z/HNG7RImV7Q/6/yvbFtr8oaV2e9095X66vX3XOeXKj7c/n/Tm3fuXY9gG2f2T7505Xph/9cI/pfJMr0c+W9FqlAla2H237kpyX19t+Tp4/1WJk+5v5uNxg+4RiO7BjtSx3JMn2EU6tMOtsr7a9NM9vPCYrc5mzQk3nQ17/Yc3lSf7cW/M5cp3td+Z5K2z/yvYnJF0t6TG2N9h+dy7/fmr7kbPdMTf0EMmvP2b7+IZ9eEcuD4+1/Yy8/utsf8P28rzcWtun5X243vZBef5wPiZX5mN09NwP/Y6V0/o+p9+Jmxu+w6tsnz/HMmtX2xfmffuUJLfY5CMkPaAU7CoiNkTE7TmfV0o6O29n0C3K1TbLtSx/3FT+dvZI7pRGJH1P0hcj4vTSiemQy5QCUEn6M6WLGg/YXp7LpqdKukZqW74M2/5OLkuut/1S22+StIeki21fnJd7vu2fONVXzsm/F63KjJbn22w5/27n6WNsr8nTa2z/R07P+5xaw7+Z9+Wntp+elzvZ9pm2f+hUj/hfDevbav93Qt+T9II8fZykL9XfaFf+OtV3Vufpp+V8HvKW9dxH5jL+F/nvkIbPblFXwpbcuu50nqRhST/L51Tz93c2x37nrltFxIL9U/pxuVbSzZI+Iem5Si0Yt0k6MC8zptSCPCRpIM97oqSr8vQqSfdJXYrgBwAADwNJREFU2kspmP+JpEMlDUi6Iy9rSV+VdH7+zPGSPpan10g6J392X0m35vmLJY3l6d0k3ZrXs7+kHzXswy8l7S1phaTr87yWaS39J6maj3f976V5/i4Ny5wp6YV5+k5JS/P0svz/ZElvaVj+i5IOzdN7S/pVw3I/lzTYcMxvkzSe8+bXkh7TIo1rJd3UkMZTG/LpmIblNjTk/4OSHpdfH6AUpA7n79cNOc9WSApJz87LrZb0FklLlK5O757nv1TS6tJ51YG8/3tJn83Tl0t6pqR/lvSveV6fpNE8vV7Sbo3fDUmDShWjXUvvyw44FluVO3l+vcx4Un79BUkntTgmKyWtbXM+rFHr8uT5Snfvc37vfEmH5e9lTdKzGtYRmj4H3y/p31rsw/GS7taW5/NgPh/Ob1juY5KOb9iHtzW8d13Dvr9L0ml5eq2kT+fpwzRdrr1H0t/n6WX5+A13OK9W1LffMG/qmOe0fjBP/42k/8zTU8ehRR61K7M+IukdefoFOR92a9p2n1JL3W8kfa6eTw1pWdnwul25OrWcZih/1KL87aW/mfJe6Ty7V9L7S6ezC8dhff6evk7pQssp+bv+bEmX5GXalS8vqZ/LebnxhnXWy7PdJF1SP5cl/UvDebBeW5YZLc+3Fvm2UVuWTc/J721oWO4YSWvy9Jqc5r78+qOS/l+efp6kaxvy/xdKZd1uSuX1Hu32v3TezTGfN0h6uqRzlX6LrtWW5VjL8jfv7yWSXqzUW6Rexzle0/Xcr2j6t6xPqR7Wsq5U+jjMtz+1qDu1+C43f39nPPZ5eqeuWy3obr0RscH2AZKeI+lwpUx8t6S7IuLKvMz9UrpqJOljtp+hFGQ9qWFVV0TEb/Ny1yoVjhsk3R4Rt+T5Z0lqd3Xim5G60v3S060UlvQe24cpVR73lPTIiLjG9iOcxgztLumPEfEbpyv0dUtmSGtJ7br1Hm77bUpB9S5KhdS3lSqvZ9v+pqRvtlnnkZL2tacaGcZsj+bp8yJ1T6m7KCLukyTbv5T0WKUfl2Zz7dZ7RUTcnqcPlfSNiHgwb+frSt+v8yTdERGX5eXOkvQmSd+XtJ+kH+R96JN01xy2vbM4TtJpefrL+fW3Ja22vUTpHLi2xefeZPvFefoxShdb/tDpxHZSq3LHaXzINUplxs150c9L+t+aPm6z1ao8eX7+uya/HlE6lr+R9OuI+GnD5yeUfuikdIHnL9tsZ6tuvQ3nYTtfycuNKwU8P8rzP68UVNd9SZIi4hLbY05jkJ8v6UWeHu85oBzcbWujD0O729U3zv96/v9zpbJ/W9qVWYcp97CJiO/Y/uNWG42o2j5K0oGSjpD0IdsHRMTJLbbTrlxt9GS1L39mU/4uZNvK+x9KOtr2ByLi911KUwn11tNDJP2HUl3kEKWL8vWu6u3Kl0slfcD2+5SCnEtbrP9ZShfSLsvfwX6li/x1X2lafjbn2/Z06z0nprsNH6oUWCsifujUq2E8v/etXK/YmFuqDsrLt9r/S+aYhqIi4rpclzxO0neb3m5Z/kbEr5x6x1wn6VMNdZxGz5P0yryNqqT7bLerK13T4vO9rFXd6eoWyzV+fxttdezz/J26brWgg1NpKrPWSlpre51SZbDVj9KbJf1O0p8rXSna1PBeY5/vqqaP22yfw9P4+XqN5eVKwecBETFpe71SYSClK1vHSHqU0pd1LmmdV2wPKLUerYyIO2yfrOn9fIFShe1Fkv6v7T9rsYpFkg5uCkLrleQHm5Ztl0+zUcnbqo/Naxwj2LidmWrnzd+HyMvfEBEHzyEtOxXbuyoVkPvZDqUKcEh6m1L+vkDSmbZPjYgvNHxulVJF/uCIeMj2Wk1/N3ZqLcqdVyldqW5n6vunbR+DVuWJJb03Ij7VuGCuiDSfJ5ORL6nqYZwnbdLavK122p0rL4mIm+aQnofrD5KWN83bRdLtDa/rx3u2x2qmMmubvxk5b66QdIXtHyi1oJ7ctK6ZytUtFlX78mer8jd6ayzetvL+y0rjwL5r+/CIeKCbieuiy5WC0acptbDcodTr5X6lHkBSm/JFSsNWlFo532v7woh4V/Mikn4QEce12X673/G5lk3SlufXTGVTq9/xaPrfOL/t/u+EzpP0AaVW010b5s9U/j5RqUFmLjda2+aVzF7Xru6ULzo2m+1v64KoWy3oMae2n2z7iQ2znqF0FX4P2wfmZUZtL1bqhnBXbpF4hdKXZCY3Snqc7Sfk1+0K3nbGJf0+B6aHK7Xy1X1Zqe/5MUqBaqvPziWtJdVPiHty3/r6mNtFSt1uL1YKYpYpXY18QNJow+cvlDTVepNbizthvVI3FEk6Wql1upVLJP2PPOZiWKmrS/1q8d5ON8ORpge43yRp9/p820vaBOE7s2MkfSEiHhsRKyLiMUoVvMOUvuOflvRZpa6+jcaVegY85HSXumd1NdUd0qbc+bVSmbHC9j55/isk1VsW12v6+/eShs82nw/tXCDpNZ4ey7Wn7Uds3x7M6NdKrYJLc0vDEa0Wyj0Y/ujpMWON+yql7qXKV9fvy8tfIOmN+eKQbO/fgfQ3p3ODpLtsH5G3uYuko5TO3dmabZl1idJFSdn+a20dGMnpruyN50n9u9O8nZblaovlWpY/M5S/PWM2eR8Rp0m6SNI3bHf8ZoqFXKZ0A7d7I6IaEfcqfR8O1nQLZ8vyJffweigizlIKeOrf3cbv4E8lPbte7uXfzk719vqd7afm7/eLZ1iu8VxcpXSPgPvze0fbHsiBwypJV6p75Ws3rJb0rohY1zS/Zfmby/kPK/2e7+rWd4u/SNKJefk+22Oaua6EpF3d6dA5rKPVsd/p61YLveV0RNJHc5exitK4zhOUrkR/1Pag0tiFI5WuQn/N9rGSLtY2rlJExCanQcbfsX2P0g/afnNI29mSvm37KqUWlRsb1n1D7gb23xHRqgvonNLaRYNO3Z7rvh8Rb7f9aaWxB+uVCnopBdRn5YLPkj4UEX+y/W1J5zoNxn+jUtfYj9u+Tun7eonSuJiH42zb9VaNeyLiSEmflvQt21conewtj2lEXO10k4Ur8qzP5K7YK5QufLzK6WYnt0g6PSImcmH+kbyvi5W6cNzwMPdhPjlO0r83zfua0jiJB21PKl11bb7d//clvT7n7U1KlZiFoGW5k8uMV0s6J18Qu1LSJ/Nn3inps7b/j6SfNayr+XxoKSIutP1UST/JdYsNSmNZHs4dc1+ag8e6N0TE5ba/qtTF6xbN3EXrVZI+6XRjsNskNT6K44+2L1ca8/+aPO8UpXPjulxBWq9Uae60VyqVMR/Mr98ZEf81h8/Ptsx6p6Qv2b5aKVD/TYt1LVHqJrmHUo+YuzVd3q1ROp4blQKHVuVqq+ValT83q0X5O4d9Xiha5r0buq9HxL/Y/pxS74/jYvZ3u95ZrFMaX/nFpnkjEXGPNGP5so+kU23XJE0qV5KVxmd+z/ZdEXG4U7fQLznfAE7Svyl9B7fXE5rqGqsj4iOS3q40ZOEOpVbgdhdcTpb0uXyOPqRUVtVdIek7SkMKTol0M7s72+z/TtfdOw9R+3CLt9qVvx+S9ImIuNn2a5VudNXcnfkfJZ2R369KOjEiftKqrrTDd2jn1q7u9HdzWMdWx14LoG7l6d5dALZXDk7Pj4i5XKAAeo5TF6O3zHHcNwB0lFP3+A0R8YHSaQF62YLu1gsAAAAA2DnQcgoAAAAAKI6WUwAAAABAcQSnAAAAAIDiCE4BAAAAAMURnAIAAAAAiiM4BQAsWLZX2L6+dDrmE9vLbL+hdDoAAGhGcAoA6Gm2+zq8/sWdXP92WCapZXDa6WMBAMBMCE4BAAvdYtuft32d7XNtD9leb/sdtn8s6VjbT7D9fds/t32p7afY7rN9m5Nltmu2D5OkvMw+todtr7Z9pe1rbB+d3z/e9jm2vy3pQttn1t/L759t+0WtEpu3+wHb63Ka35jnH5G3sS5vc2mev972bnl6pe21efrkvNzavB9vypv4d0lPsH2t7VNtr7J9se0vSlpn+xTb/9iQnnc3fBYAgI6Zb1dzAQDY0Z4s6bURcZnt1ZpuNdwUEYdKku2LJL0+Im6x/ReSPhERz7N9s6R9JT1O0s8lPcf2zyTtFRG32n6PpB9GxGtsL5N0he3/zOs/WNLTI+Je28+V9GZJ37I9LukQSa9qk94T8vb2j4iK7V1sD0haI+mIiLjZ9hcknSjptG3s+1MkHS5pVNJNtk+X9HZJ+0XEM/K+r5J0UJ53u+0Vkr4u6cO2F0l6WX4fAICOouUUALDQ3RERl+XpsyQdmqe/Ikm2R5SCxXNsXyvpU5IenZe5VNJh+e+9+bMHSroyv/98SW/Pn1sraUDS3vm9H0TEvZIUET+StI/tR0g6TtLXIqLSJr1HSvpk/f28jidLuj0ibs7LfD6naVu+ExGbI+IeSb+X9Mg2y10REbfn7a2X9Afb++f9uyYi/jCLbQEA8LDQcgoAWOiizesH8/9Fkv5Ub0lscqmk10vaQ9I7JL1V0ipJl+T3LeklEXFT44dy6+uD2tKZkl6u1BL5mhnS6xZp9gzLVzR9sXmg6b3NDdNVtf/db07rZyQdL+lRklbPsG0AAHYYWk4BAAvd3rYPztPHSfpx45sRcb+k220fK0l5jOmf57d/ptSqWouITZKulfQ6paBVki6Q9Ebbzp/df4Z0rJF0Ut7mDTMsd6Gk19dvpGR7F0k3Slphe5+8zCsk/ShPr5d0QJ5+yQzrrXtAqZvvTL4h6SilVuILZrFOAAAeNoJTAMBC9ytJr7J9naRdJJ3eYpmXS3qt7V9IukHS0ZIUEZsl3SHpp3m5S5UCu3X59SmSlki6Lj+y5pR2iYiI3+W0fG4b6f2MpN/kdf5C0t/lwPjVSl2P10mqSfpkXv6dSuNDL1VqHZ1R7qJ7me3rbZ/aZpkJSRdL+mpEbHOdAADsCI5o7jkEAAB2NNtDSkHtMyPivtLpmUm+EdLVko6NiFtKpwcA0BtoOQUAoMNsH6nUNfejO0Fguq+kWyVdRGAKAOgmWk4BACjA9l9Jel/T7Nsj4sUl0gMAQGkEpwAAAACA4ujWCwAAAAAojuAUAAAAAFAcwSkAAAAAoDiCUwAAAABAcQSnAAAAAIDi/j8TAXFFmJ4xKwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "sns.violinplot(beers.brewery_country, beers.rating_score, order=['Scandinavia','Eastern Europe','Asia','Southern Europe','United States','UK','Western Europe','Mexico','Africa'])\n",
    "plt.title('Ratings per brewery country (user 2)', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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scyoCzeVhcEpERY3BKREREVGeGR4eBoCZzCkAON0MTomoqOU8OBWRgIg8ISLHROSoiNyc62MSERERFbJk5tTlnVno9GBwaMikFhER5Z51BY7xUwAvK6U+KyJ2AO4VOCYRERFRwUqXORWXB0NDXWY1iYgo53IanIqIH8AHAXwBAJRSYQDhXB6TiIiIqNAlg1PHzDV9cXoxOjwCpRRExKymERHlTK7LetcCOAfg30Rkn4jcLyKe1BVE5M9EpFlEms+dO5fj5hARERHlv6GhIVhcbojFklymuTyIRMKYnJw0sWVERLmT6+DUCuB6AHcrpa4DMAHgG6krKKXuU0ptVEptrKqqynFziIiIiPLf8PDw+f1NAYjbm3yMiKgY5To47QTQqZTambj/BPRglYiIiIjmMTg4COU8r9gsGawODg6a0SQiopzLaXCqlDoL4IyIbEgs+giAI7k8JhEREVGhGxgcgrh95y0z7g9xxF4iKlIrMVrvXwD4VWKk3ncB/OkKHJOIiIioYA0PDUGqLjpvGct6iajY5Tw4VUrtB7Ax18chIiIiKgahUAjT01Owu2f1OXW4AdEwMDBgUsuIiHIr131OiYiIiCgLRp9SbXZZr6bB4vayrJeIihaDUyIiIqI8YgSns0frNZYxOCWiYsXglIiIiCiPJIPTWZlTAFBuL/pZ1ktERYrBKREREVEeuVBwqrm8GOBUMkRUpBicEhEREeWRwcFBiKZBnK45j4nbh9HhYcRiMRNaRkSUWwxOiYiIiPLI4OAgNLcPInNP08TtQzwex+joqAktIyLKLQanRERERHlkcHAQcM8dDAmYKfUdZGkvERUhBqdEREREeWRgYBDimtvfFJiZXoZznRJRMWJwSkRERJRHBgYHIfNmTvXlzJwSUTFicEpERESUJ6LRKMZGR9KO1AvMlPVyrlMiKkYMTomIiIjyxPDwMID008gAgFht0BxOZk6JqCgxOCUiIiLKExea49SguX0MTomoKDE4JSIiIsoTRtCpXSA4VS4vg1MiKkoMTomIiIjyRDJz6ko/IBKgZ1X7BxicElHxYXBKRERElCdmynovEJy6vBjmgEhEVIQYnBIRERHlieHhYWgOF8RinXcdcXsxPT2FUCi0gi0jIso9BqdEREREeWJwcBDaBbKmAKAlSn45nQwRFRsGp5SRO+/8Ce66806zm0FERFTUhoaGoJyeC64jDE6JqEgxOKWMvPTSy3jxpZfMbgYREVFRGxwavuBgSMBMf1RjTlQiomLB4JSIiIgoT4yMZBCcuhicElFxYnBKRERElAcikQgmJyYgrgXKep1uACzrJaLiw+CUiIiIKA+MjIwAwMLBqcUKzeFMrk9EVCwYnBIRERHlgWRwusCASACgOT0s6yWiosPglIiIiCgPGMHmQplTAFBODzOnBAA4cOAAPvOZz+BTn/o9vPjii2Y3h2hJGJzSguLxePJ3pZSJLSEiIipeo6OjADLLnIrTjeGR0Vw3iQrAiRMnMD4+jlg0hqNHj5rdHKIlYXBKCwqHw8nfI5GIiS0hIiIqXjNlve6FV3a6mTklAMD4+DgEgoCvOnmBg6hQMTilBU1PTyd/n5qaMrElRERExSuZOXU4F1xXnG6MjTEQIf1143R64LL7MDY2ZnZziJaEwSktKDU4Tc2iEhER0fIZGxuD5nBBNMuC64rDhWgkwovGhNHRUbgcXrgcXoyOMjilwsbglBaU+sWXGqgSERHR8hkbG4PmdGW0rlH6y0wZjYyMwGX3wuXwsdSbCh6DU1oQy3qJiIhyb2xsDMqeYXDq0INT9jGkkeEReJxlcDt9GB0dPW8gS6JCw+CUFpQakDI4JSIiyo2xsTEgg/6mwEy/1ImJiVw2iQrA0NAw3E4/PE4/lIrzggUVNAantKDUzCnLeolosV544QX88Rf+BHd87Q7EYjGzm0OUd0bHxiGZZk4T642Pj+eySZTnYrEYRkb1zKnHFQAADA0NmdwqosVjcJrG22+/jW9+4xsYGBgwuyl5gZlTIloOu3fvxtnuHrTsb8Hw8LDZzSHKO+MT4xmN1AvMZE4ZnJa24eFhKBWH1xWANxGcDg4Omtwq8x05cgS//OUv8cgjj3AwzwJjNbsB+WjTpk3o7OzEiRMnUFFRYXZzTMfglIiWw/jEzEn0xMQEP1+JZglNTAL2DINTO8t6CclEitcdTAanTK4ADz30EJqbmwEAV1xxBa6++mqTW0SZYuY0jWg0CoDTphhY1ktEy2FsbBzQBABPqIlmi0QiiETCyaBzQTYHAL6XSp0RiPrcQfjc5ectK2WhyRCcFv29NDk5aXJrKBs5z5yKSDuAMQAxAFGl1MZcH3O5MEuo42i9RLQcxsbHAL8dGJ7m9BdEsxgn0GJ3ZLS+WCwQq40n3iWuv78fAOBzl8NmtcPl9OLcuXMmt8p8oVAIZfYyTIWmEAqFzG4OZWGlyno/rJTqX6FjLRsGYrrU4JTZZCJarPGxcUi1E4rBKc1jYmICra2tAIC1a9ciEAiY3KKVkwxObTPB6dQ7mxEf6Ene1yrq4Hz/J2bu2x088S5x586dg6ZZ4HGWAQD87goGp0gEp44y9IZ6eT5fYNjnNB2lAIAf+Anbtm1L/s6yXiJajHA4jOmpKUiwDOgY4VQHlNYvf/lLvPDCCwCA97znPfj+979vcotWTvKcIyU4jQ/0INbTPu82YnMwc1ri+vr6UOapgKbpPfX87gr09faZ3CrzhSZDWO1arf/O8/mCshJ9ThWA34jIHhH5s9kPisifiUiziDTny5Ueo88pP/B1AwMDEAA2i8bglIgWZWRkBAAgZQ5AZu4Tpert7YUWDELq6tFz9qzZzVlRxgm02OyZb2Sz81ylxPX29sKb6GsKAH5PJXp7e01sUX6YDE0i4NArL/geKSwrkTl9v1KqW0SqAbwqIseUUm8bDyql7gNwHwBs3LhRrUB7FmR8QfDFrIvH49AEsFsZnBLR4iSnjnHboLntnIeP0uofGAB8fojXi4GO02Y3Z0XNBKeZ9TkFAGW1l1zJ4qlTp/D8888jHo/DZrPhc5/7HILBoNnNMs3Zs72oD1yWvB/wVmIyNImxsTH4fD4TW2aeSCSCcCQMj9UDh4XVBYUm58GpUqo78bNPRJ4GcBOAty+8lXlisRgmEi9izh2mi8fjEBE4LMLglIgWxQhGxW2DcloZnFJa/f39QMMqiNuDifFxhMNh2O1ZZBILWDLItNky38hqx2SJlSy+9NJLeGHzZjhcPkxPjmLt2rW47bbbzG6WKcLhMAYHB3BZQ1VyWZlX/723t7dkg1MjGHVZXXDZXBzRusDktKxXRDwi4jN+B/A7AA7l8phLlfoC5oAdumTm1CIld4WWiJZHclJ4tw3KbcUAJ4mnWcLhMMZGRyEeD8TjAYCSGtjF+H4Va+bBuNhsCIVK63u5v78f3kAdPvT5uwCU9rQpfX19UEoh4JsJTgPeagDA2RIri09lnMu7rW64rW4GpwUm131OawBsFZEWALsAbFZKvZzjYy5JakA6yj5RAGaCU6eFncqJaHGSJ5BuG+CxoX+g4AZwpxwzAlHx+iCJjE9fX+kM7JKsTLJmkzm1YTpcWhVN5871w+EOQLNY4XD7k1OplKKeHn0k56C3Jrks4Ks+77FSlBqcuiwuVkIWmJyW9Sql3gVwTS6PsdyMflFem50DdiTE43FoAJwWYd0+ES1Kf38/NJcNYtUAjx3DrWcRi8VgsVjMbhrlCSPTI14fxOs9b1kpMIJTySI4Faut5Lrb9PX1wV1zBQDA6a0o6cF/jADUCEgBwGl3w+X0oru726xmmc4IRt02PXM6PsbgtJCsxGi9BcUISBv8AQwPMzgF9H64mgAum2CCV5+IaBHOnTsHePVyRfHaoZQq6XI8mss40Ra/H/B6AZGSDE6zypxabAiXUHAaDocxNDQIV6KM1emtxNmzpRucdnV1wWa1w+s6fz7goLempINTowrSY/PAY/Wwm16BYXA6i9Evak1ZOUJTIfaxRCI41QRum4bxcb7BiSh7PWfPQnkTJ90+PUgtpZJNWlh3dzfEagU8HoimweL3l9QJth6cCqBlXk0gVhsi4UjuGpVnjIsVbn9V8mdv71nE43Ezm2Wanp4eBH01EJHzlge81ejqKp33zmzJ4NTqgcfmYVlvgWFwOstMcBo8734pi8VisAjgsWkYY2kEEWVJKYW+3t5kUCqJn6WUFaOFdXZ2QisrS55oK38ZTnd0mNyqlRMOhyFW65xA44IsVsRiUcRisdw1LI90dXUBANx+vY+lu6wG0Wi0pAbOStXV1Y1ASn9TQ9Bfi3PnziEcDpvQKvMZwanX5oXX5sX4xHjJXsAoRAxOZxkYGECZ04UKtyd5v5SFQiEopWDRBD6Hhqnp6ZLr30JESzM8PIzp6WmIPzF/o0//yeCUUrWfPg3lL0vel0AQXZ2dJRN4RSIRaNYshwJJrF8qQUgyOA3MBKeAfmGj1MRiMfT0dKPcPzc4LffVQKl4yX7Gjo6OwmaxwW6xw2vzQinF7GkBYXA6S39/P8pdbpQ73cn7pcwYIMoqAp9dLzXi/IQ0m1IKTz/9NP7t3/4Nu3fvNrs5lGeSo0YmglOxatC8jpIq2aQLm56eRu/Zs5BgMLlMgkFEIpGSGfAmHA4DluyCU9H09SOR0ijtPXPmDBwuH+xOfTRnT6A+ubzUnDt3DtFoFEFf7ZzHyv36slL9jB0dHYXf7gegZ0+NZVQYGJzO0tfbi3KnO5k5LfU+UUZwbtWAoEsPTks9m0xzdXZ24u6778YjjzyCf/mXn5ndHMozRlZDAs7kMlVmx5kSzHZQeqdPn4ZSClJekVwm5eUAgFOnTpnVrBUViUSSwWbGEqNdl0pw2n76NNyJgBQAHO4y2B1udJRQ+bfBCDyDaTKnRsBqZJpLzcjICDxW/TzeZ/cll1FhYHCaQimFvr4+VLm9cFpt8DqcJXPFdj5GPw6rRRB06l+CpR6w01zGRYxgUL94oZQyuUWUTzo7OwFNkn1OAQBlTpzpPMPXCgEA3n33XQCAVKQEp8Hy8x4rdpFIJBlsZkospZM5VUqh/VQ7fOWrkstEBJ7yVSVzASOVEXiWp8mcuhxeOB2eks2cDg8Nw2/TM6c+mx6cGpWAlP8YnKYYHR1FaGoKlYmsaaXLU7L1+gajHM+mCSrd+pdmqT8nNJeRTa+oAKanw5wPl85z5swZaGVOiGXmK0eCTkyOT/CEgQAAJ0+ehGazQ1L7nNpssASCaGtrM7FlKycajWZd1msEs9FoNActyi+9vb0IhSbhrWg8b7mvvBHvvnuq5Aa86e7uhs1qh88dnPOYiCDoqyndzOnwSDJjapT38rumcDA4TWFcYarx+BI/vegp4aG4AT04tWh60sNp1eB3Wmf6jxElGNn0yqrz7xMBwKn2dqig4/yFQb3E9/Tp0ya0iPJN6/HjQEXFnJFqVWWl/lgJiEajWU0jAwAooT6nxkUKf+Wa85b7KtcgFJosuXOTrq6uxDQy6U/lg96akp1OZmh4CGV2/UKXEaQyOC0cDE5TGMFpdTI49eFsX2/JjBSYTsfp07BrMycLtR4LOjp4Mknn6+3thculwa9foGR2nZLC4TDO9vQA5a7zlkviPoNTikajONnWBqmqnvOYVFVhcGCgJMY6iEQiUFp2p2VSQn1OT5w4ARENvlmZU39VU/LxUtLd1Y2Ad+57xhD016Kvr68kXhupQqEQpsPTyYypTbPBbXMzOC0gWdaPFLfOzk6ISDJzWuv1IxaL4ezZs2hoaDC5dStPKYXTHafhsMwEpw0+K3a1JwauyGYuNipq3d3d8HgUvN6Z+6UqFArhlVdemVNmZ7FY8NGPfhRe40kqER0dHYjH49BmBafw2KA5bSXZV4zOd+rUKUQiEVirquY8plXVIAbg2LFjeP/737/yjVtBi8uc6uuXwkX01tZW+CoaYbHaz1vuK18FzWJDa2srbr31VnMat8LicX2amOvWXTnvOkFfNZSKo6+vr6TOYY0ZJYzMKQAEHAHONFFAGJym6OzsRLXHB1viSmS9V39hd3R0lNQb29DT04NQaAp+z8yX5Sq/DW+0D6O3txe1tXM74VNpOnOmA2VlCg4H4HBoJdvPBQCefvY9Gu4AACAASURBVPppbNq0Ke1jAwMD+OIXv7iyDTLZzEA37vOWiwji5U60nSyN/oQ0vyNHjgAAtDTfKVJZCdE0HD16tOiD00g0CmSZOTXWL/Y+p/F4HEePHkPlRe+Z85hmscJftQZHjh41oWXmGBwcRDgSRsA3d6ReQ9CrP9bT01NS57CDg4MAgDLHTHDqt/lLovqiWLCsN8Xp9nbUJbKmAFDv01/YpTh/FjBTIuO0zrxMmsrs5z1Wqvr6+vCDH/wAW7duNbspppuYmMDg4BD8fkAE8PnjJTmsP6CXsD773LPw1QHX/4mcdytrBDa/+AJCoZDZzVxRbW1tEJsFCDjmPljpwqlT7SWR9aH5HT58GJrHA3jmVhWI1QqpqsKhQ4dMaNnKWkrmtNiD046ODoRCkwjUXJL28UDNJThx4oQ+V2wJMPrXBrxzqw0MAV/1eeuWimRwmpo5tQcwODBoVpMoSwxOEyKRCM50dqKxbGbUM4/djnK3p2TLzo4cOQKbRTuvrHeV3wabRXC0hK5QprN//3689dZbePDBB81uiumMQLQs8T1Q5gdOnXq3JKcIeeWVVzA0OIS6awUW+/m3+usFE+OTeO6558xu5oo6ceIEUOFK2w1AKt2IhMMlewGQ9O4jLQcPArV183YVkZpatB4/XvSBx2KCUymRsl7j4kSwbn3ax4O16xGNRErmwrkxroMRgKbjdZXBarGVXHBqZEiDjpnz+YAjgMHBwZI8LylEDE4TOjs7EYvF0OgPnLd8la8M7548aVKrzHXo4EFcHLQh9XzBZhFcFLDj4IED5jUsDxjZr2I/WcqEUbYZSLx1AgFgbGw8efWyVExOTuLBhx6Ar1bgT1NB5a0WlDUKHnn0EYyOjq58A00Qi8Vwou0EUOVO+7hU69N2lcoJJc3V09ODoYEBaLV1864jdfWIRaNFf1E0Go1BWNab1sGDB+H0BODypw/GjKD1QImcm/T29gIAyjyV864joqHMW1lyo+cPDAzAqlnhtc1UYgQcAUyHpzExMWFiyyhTDE4TjCHKm8rK8eCB3XjwwG4AwJqycnScOVNyQcjo6ChOnjyJ9eX2OY+tr7DjRFsbxsbGTGhZfhgfHwcAKFVa86qlc/LkSdjtkqzICwZnlpeShx56CMNDI1j1HsybAVp1ExCanMS//uu/rnDrzNHR0YHwdBgyT3CKgBNis+B4iUwVQnO1tLQAAKR+/j5xWm0dIJJct1jFYjFgnmlB5lUCZb1KKezbvx/Bukvn/Wy1u/zwlTegpYSCU587CKvFdsH1/O4K9PSU1uj5/f39CDgD571WjCxqf3+/Wc2iLDA4TWhra4PdYkWdz4/TI4M4PaJnfZrKyhGLxdDe3m5uA1fY3r17EVcKV1U75zx2VZUT8Xgc+/btM6Fl+cEIzEut/2A6x4+3IhhUyQx7sNxYXjoBx/Hjx/HUU0+hcoOeIZ2Pu1xQfSXw4osvlsQV/tbWVgCA1KQfoVg0AarcOHL0yEo2i/LI/v37obndkEBg3nXE4YBWWYl9+/evYMtW3uL6nOqncfF48V4oPXPmDIaHhlDecNkF1ws2XI5Dhw6VRDKhr68PPnf5guuVeSpx7ty5FWhR/ujv70fQHjxvGYPTwsLgNKH1WCuaAuXQZl21XBusAFBaJ9oAsGPHDngdFlwUnJs5XRu0w2O3YPv27Sa0LD8YZZmjo2Ml3YchHA7j5Ml3UVExs8xmA8rKBMeOHTOvYSsoFArhhz/8W9hcQON7Fp5eqeEGgatMw4/+/u+SGfhidezYMWgOa/rBkAw1Hrx78t2SOKGk8ymlsHfffqCufsGpyaS+AceOHi3qC4LR2CJG602csxRzn9O9e/cCACpWzT9tCgBUNlyB8PR0SXz3nOs7l1Fw6vNUYHh4qKTmOj3Xdw7ljvOfm3Knfr/UAvVCxeAU+tXKtrYTuDg4t3a/yu2Fz+EsiQ87w/T0NLZvewfX1zqhpTlhsGiC62od2LbtnZI9oTTmy4rFYiVd3nzixAnEYjFUzHrrVFQoHD16pOgDd6UU/umf/gldXd1oulXB6lg4OLXYBE23KvT39+Mf/uHHRZ3xOHT4MFSN+4KBh9R69L6p7Hdactrb2zEyPAStYdWC62oNqxCLxXDw4MEVaJk54rHs+5xKCfQ5bW5uhqesGm7//CPTAkB5w2UQzYLm5uYVapk5lNK/P/yeigXX9ScC2FLJGBrPjRGMGoKOIARSMs/DbKFQCIcPHz7vdvTo0by9aMHgFHrfuHAkgnXlc4NTEcElwUocOXzYhJaZY+fOnQhNTePGete869xU70YoNIUdO3asYMvyR3//zNW3Up4763DifVE5661TVaUPilTso7A+88wzeO2111B/g8Bfv3BgavBWC1bdJNi2bTsefvjhHLbQPOPj4zjT0TFvSa/BePxwCX3Gkm7Pnj0AAG1V44LrSm0dxGJJblOMYrH4ost6izVzGg6Hsb+lBRWNVy+4rtXuQrD2EuzatXsFWmaeyclJTE1PZZY5TaxTKgMUjoyMIBKNzMmcWjUrypxlJTc4lOG+++7Dl7/85fNuX/rSl/DEE0+Y3bS0GJxiZojy9eXpR4HbUFGNzq4uDA8Pr2SzTPPi5s2ocNtwWeX8pXiXVTpQ7rbipZdeWsGW5Y9z586hJtFFqlSvxAH6CIp+vwbXrOsYVdUzjxernTt34p577kFwDVB/Xfbb11wJVFwCPPDAA3jrrbeWv4EmO3IkkTmvWyA4ddugBVwMTkvQnj17YAkGId4Lv0aAxHyntXVoLurgdBFlvUU+lcyBAwcQnp5G5eqFg1MAqGi8Gu++e7KoLxobf5vXNX8/bYPXHThvm2JnBJ+VzrnJpgpHRcmW9e7buxcbylfja+/5o+St3leZLJnPNwxOoZ9AV3t9CLrSjyi5oUI/0y6FScC7urqwd98+3NLoSlvSa7Bogg+scmHv3r0lN4fWxMQEJidDaKzS3z7GkO6lRi+xO4CqqrllqT4f4HJrRTvoz7Fjx/D9//V9uCuAiz4sC/aXS0dE0HSLwFcr+NGP/q7oRiI9fPgwIAKp8Sy4rqp14+Chg0Vd4kznm56e1j8fMijpNciqRpzp6Cja7MfiRust7szpzp07YbHaUdFweUbrV6+5FgCwa9euXDbLVEa3Io+rbMF1PYkAtlQyp8Znw+yyXgAod5Sj92zpna/19fWhu6cHN9ReiquqLp65VV6Mo0eO5mX3vJIPTmOxGA60tOCyipp511kbrIDDai26k8d0nnzySVg1wQfXLHxC+aEmLzQBnnrqqRVoWf7o7u4GADRWCqwWSd4vNW1tbZicDKGmdu5jIkB1VRz79u0tun6np0+fxrf+8lvQHFGs+096H9LF0qyCSz4K2HwK3/7rbxfVwGsHDh6AVu2G2BYuU5Q6H8ZLoAycZhw8eBCRSASSQUmvQWvU1y3W0t5YLAbJsqxXinhAJKUUtm/fgfKGy2Cxzh2cMR1v+Sq4fRVFPWCjEZxmkjl1O7wQkZKp/DOSBWkzp049c1ps5yQLMT4vr6y86LzlV1auRTgSzssKt5IPTk+ePInxiQlcXpnmDDvBqlmwobwa+/I0/b1choaG8JtXXsH7Gt0IOBf+ggw6Lbh5lQsvvfRi8sOyFHR2dgIAKvyCoFeS90uNMZVQzTzXdWpqgeHhEZw+fXoFW5VbXV1d+NrX7kA4Pol1tynY3IsPTA1Wp2D9xxRgDeMb3/wGTp06tQwtNVc4HMaxY61QC5T0GqReXy8fvyQpN5qbmyEWC7T6+oy3kWA5NK8Xu3cXZ5/CeCy2iLLe4g1O29vb0dfXi+qm6zPeRkRQueY67N27D1NTUzlsnXlGRkYAAG6Hb8F1RTS4nf6SCU77+vrgsDrgsc1NsFQ6KxGOhEvmuTDs2rUL5S4/VvnO77p4WWUTbBZrXlYZlHxwatRbX1ldd8H1rqyuQ8eZM0Vdr/7oo48iGo3itoszO6EEgNsu8SESieCxxx7LYcvyS3t7OzQBgl6gwqdwur3wg4nF2Lt3D4JBmdPf1FBba6xXHBd1enp6cPvtX8X41CjWf0zB6V96YGqwewXrPq4QiU/ijjtuL/iA/ujRo4hFo5D6hU+eAAB+BzSvo2jLwGmunbt2QerqIVZbxtuICNCwCnv27i26YEwppZe1Z13Wq19ILsbRerdt2wYAqFqTXaf+6qbrEQ5PF+1c7CMjIxAIXI7MztXcDm9y+rtid/bsWVS6KtN2tal0VSbXKRXT09PY07wH11Wvn/OcOCw2XFFxEba9sy3vssklH5zu2bMHawLlCDjnH5kWAK6qrk+uX4z6+vrw/PPP432NLtR4Mz9ZqPPa8L5Vbjz33LMlMzDQqVOnUO7XYLUIqsoEZ3v7MDk5aXazVlQoFMKhQ4dQUzv/B5rXC/j9gj17Cn9Y/56eHnzlq1/B6MQw1t+m4CpfvsDU4PTrAepUdAK3F3iAagSZkmnmVASqzoP9LS159yWZS8ePH8cTTzyBJ554Ak8++WTJDFpy9uxZdHV2ZlXSa9AaVyM0OYkjR47koGXmSQbbFs5zatj6zjsI1FwMp2fh8tVU5fWXwuZw45133slRy8w1NjYGh8MNLcMScKfDVzLBae/ZXlQ50k85VOXSl5fSOCG7d+/G1PQUbqy7NO3jN9Zdht6+3rzrUlTSwenExAQOHzqEq6ounDUFgEZ/AAGXu2jLiTZt2gRRMXxqvT/rbT+13g8Vi2HTpk3L37A8o5RCa+sx1AT0E+jaoEAphba2NpNbtrJaWloQjcZQt8Bbp7ZOoaWlBdPT0yvTsBzo6urCV77yZYyOD2H9xxXcFcsfmBpcAcH6TyiEwmO4/favor29PWfHyqWWlhZoVR6Iw5rxNtLgw/DQELq6unLYsvzydz/6Ee677z7cd999uPfee/Hggw+a3aQVYcxDqa1enfW2WsMqQKTo5rJMZj45lQwAPYA42daG6os2Zr2tZrGicvW12L59R9E9LwAwOjoKl2PhcUEMLrsXIyPFH5wqpfTMaZr+psBMP9RSypy+/vrr8Ds8uLS8Ke3jN9RsgEWz4I033ljZhi2gpIPTffv2IRqL4drahUcLFBFcW12PvXv2FF35zIkTJ/Daa6/hty/yotKd+cmkocpjxW9f5MGrr75a9EHauXPnMDQ0jPpE5sz4eezYMTObteJ2794Nq1VQnX72paT6OiAcjhRsuWZXVxe+ensiY5rjwNSQDFCj47j99q8WXB/UcDiMI0ePQNVn3j0AQLIEeP/+/bloVt7p6elB55kz0N5zFax/+mlIUz127NxREiMWNzc3Q/P5IGXZZcQAQBwOaDW12FVkF4qNICr7AZEEomlFd15ilPTWXHTDoravWXsDxsZGC/a750LGx8fhtGf++eq0ezA+Pp7DFuWHsbExTIYmkxnS2ZxWJ/wOf8kEp6Ojo9ixYwfeW3cFLPP0ZffYXbi26hK88frreXUhp6SD0507d8Jtt2NdefoX8mzX1q7CxORkUc3Hp5TCz3/+c3jtGj6xLsP+YWl8cr0fHruGe+6+u6jL8ozphFZV6m8dt1NQ7tNw6FDpDOSilMKuXTtRU6NgWeA8qroGsFikICsOjMB0bHJkxQJTgysg2PBxhemYXuJbSAHq0aNHEY1EIQ1Zfp6U6f1OS2FUdAB48803AQDaxY0Quw3aRaswODBYdOWqs0WjUezdtw9Y1bioKZgAfUqZk21tRTUQ30zmdBGnZZql6ILTLVu2wlfRCE9g/sEqL6Sy8WpYrPZkkFtMxsbG4bCln/owHafdjYkSCE6NoHO+4BTQs6elMv3hG2+8gWg0ilsar7ngerc0Xouh4WHs3LlzhVq2sJINTuPxOHbu2IGrq+phzfDL4KrqOlg1C3bs2JHj1q2cN998E4cPH8ZnLvXBbVv8y8Ft0/D7G3w4cPAgtmzZsowtzC/79++H066hOmV6sdVVCgdaWvLqqlMunTlzBr29fchkkE2rFaiuUdi5s7DeM93d3aYFpgZnIoMajk0U1CBJLS0t+vymGfY3Ncz0O91f1Be4AP375+VXXoFWWwnx6eV5clEDxGbDyy+/bHLrcuvIkSOYCoWgLaK/qcHYtlgGWwOWUNYLPdtaTN8/Q0NDOHz4MKqbFpc1BQCrzYHKxquwZcvWoqtGmJiYgMN24XFSUjnsLkxNTxXVaySdTILTKmcVzvYUf+ZUKYXNL7yAiwJ1aCq7cP+ra6vXIeD0YfPmzSvUuoWVbHDa2tqK4ZERXF+X+QTgTqsNl1fVYPu2/BvZajGmpqbwi/vuw+qAHbeszrz/wnw+tMaDxjI77rvnnoLuYzgfpRR279qJpmoFTZsJVi6q0TAZmir6jIfBuLpW35DZ+g31QE/P2YKZcqevrw+333E7xiZGsP42BXcOBj/KlLNMsP7jClOxCdzxtdsLoj/mvv37oFW5s+pvapAGH0aGR9DR0ZGDluWP3bt3o6e7G3L52uQysVkhlzTi9TfeKKqM4Gx79+4FRKA1ZPgBkoZUVkJzuooqOE0GDovInIqluMp6t2/fDqXiqL04+/6mqWrWbsTg4ABaW1uXqWX5YXJiIqvMqbFusQ/caMw5f6HgtNpVjb5zfUUfqB84cADtp0/jI6sXfg9ZNA23Nl6H5ubmvDnHKNngdNu2bdBEcG1Ndl+QN9Q2orunpyhOnh577DH0Dwzgc1eUQVtkeVUqTQSfu6IMff39ePzxx5ehhfmlra0NA4NDuLju/LdNU43AoklRZdQvZOfOHQgGBZ4Mr2cYQWw+lYzMZ2hoCHd87Q4Mjw5g3W3mZExncwYE629TmJgawx1fuyOvp7OamprCsaPHsu5vajBKgYu5tFcphQcffBCazwNZe372ULt6PaLRaFF+fhqa9+yBVl0DsTsWvQ/RNKC+Hrubm4viQjEwkznNts8pgKIr6926dSs8ZdXwli8+uw4AVWuuhWiWohu1dzI0CYc988ypPZFlnZiYyFWT8kJPTw/8Dj9c1vmfmypXFWKxGPr6+lawZSvvqaeegtfhxs0NV2a0/m+tuQGaaHjmmWdy3LLMlGxwun3bNlxWWQNPll+Q19fpH5aF3o+hv78fjz32a9xU78L6isWfJMy2odKBjXUu/PrRR4puWoQtW7ZAE+CS+vMDFqddsLoa2PL220VzojSfiYkJHD58GHV1mf+dXi9QFpC8D05DoRD+8i+/hb6+s7jkdwBPpfmBqcEVFKz7mMLwyAC+8c2vY2xszOwmpXX06FHEYrHs+5sa/A5oPkdRD4q0ZcsWHD9+HHL9ZZBZ04ZIwAdZtxrPPPtsUQ7aMT4+jhPHj0OWkDU1aA2rMDw0hDNnzixDy8y3lD6nxVTWOzExgX379qOq6YZF90k22BweVDRchi1bthbNd3M8HkcoFMqurDexbigUylWz8kJPTw+qnBceQ6baVZ1ct1h1dnZix44d+MjqG2C3ZDY1ZNDpw3vrrsDLL7+cF9MOlWRw2tnZiY4zZ5KBZjbKXW6sDVZie4EHpw888ADi0Sg+e3nZwitn6bOXlyEajeKBBx5Y9n2bJR6P4/XXX8OaGoHbMfcL8/JGDWd7e3H06FETWrdy9uzZg1gsnnFJr6G+XuHgwYN5e+U2FovhB3/7A5w8eRJrPwL4avMnMDV4KgUXf1QfqOlvvvc3eZkpmelvurjgVO936kXLgQNFczKZanJyEnffcw+0igBkfVPadSw3XokY9IHqiu05OJD4v2r1mXenmY/WoO+jWEp7Z8p6F5c5jUQiy9sgk+zatQuxWBQ1a5dW0muovmgjenq6C6bP/kKmpqYAzGRDM2G3OQGUQFlvV3cy+JxPtVt/3CgBLkaPPfYYrJoFH226MavtPnHxzZiensZzzz2Xo5ZlriSDUyPrecMiglN9u1U41tpasJnBjo4O/OaVV/BbTZ5FTR2zkGqPFR9e48ErL79cNFe1W1pa0Nd3DleuSf+W2bBKYLMKXnnllRVu2crasWMHHA5BZfppxObVUK+ffOXrieSmTZuwa+curH6fILA6/wJTg79esOYW4EDLAdx9991mN2eOlgMten9T+yJOsBOkwYex0dGiOZlM9Ytf/AIDA/2QD1wP0dK/zsTrhtxwOXbs2JEc0bdY7N+/H2K1Qmpqlrwv8fuh+XxFUwK+tLJerWgyp++88w6c7jIEai5elv1VN12f3G8xMLKfRsCZCWPdYs6cRiIR9Pf3LxicBh1BWDVrUVamAPqYGf/x6qv44KprUebIrntNo78G11avw9NPPW36a2VFglMRsYjIPhF5YSWOt5Dt27ZhTaAcVe7F9Ysygtrt27cvZ7NWzCOPPAKrRfDxJUwds5BPrvfBqgkeffTRnB1jJW3evBlOu4YNDelPKB02waWrgDdefz1vs4NLFY/HsWvXTtTWqqwrzyqrALsjP0t7t2/fjl//+teouhSovjx/A1ND5TpBzVXA888/j9dff93s5iRNT0/j2LHWRfc3NUhi+4MHi2t6pu3bt2Pz5s3QrloPrbbigutqV62DVlOBu37606I6idrf0gKproEsNAdVpurq0XLgQFGMxrqU0XqLpc9pOBzGrt27UbnmOogsz+mp0xNAoObigj1fm83Ifi6mrLeYM6d9fX2Iq/iCwakmGqpcVUWbOX388cehlMInL37forb/vXW3YGx8DM8///wytyw7K5U5/RKAvKh3HB4expGjR3FD7eLLilb5Aqj2+rCjAD/senp68Mbrr+PDa9zwOxb+Enz40DA6RiLoGIngR+/04eFDwxkdx++w4ENr3HjttdcK/uSqr68PW7duxdVNgM06f/BywyUapqani3YqiOPHj2N0dAyL6S6maUBtrT4/aj6dSA4ODuLH//BjeCoFq2/O/8DU0HiTwFcruOund+XN+6u1tRWxaBRSv8SLXn59vtMDBw4sT8PyQE9PD370938PrTII7aaFB6gQTYP2WzdhOhrB33zvewiHwyvQytwaHx/H6fZ2SF0Gc1BlSKurx/jYWFFk2TnPqX5BaioUQnXTdcu636o11+H48eMFW+2Wyggws8mcOkpgQCRjlFmjbPdCqp3V6O4qvuC0v78fL25+ER9ouAaV7sCi9nFJcBWuqlqLxx973NTsac6DUxFZBeATAO7P9bEysXPnTiilFl3SC+j9om6oWYW9+/aZnvrO1nPPPQcR4HcuzuwE8sxIGKGoQiiq0DoQxpmRzE+SPnaxD1DK9CswS/Xkk08CUNi4bubt8uq+GF7dd34ZVV25hsYqDU8++UTR9P9JtWvXLogA851b7mnWb/NpqAeGh0fQ1taWmwYuws9+9s+YnJzARbcC2gUuPCxWx/Y4OrYvfzAumuCiW4FIdBp33nVnXvRNNDKdUjt/5jS2tQOxrRce6VxEoGrdOHDoYF78XUs1MTGBb//1X2MqGoH20fdmnDUUvxfy4Rtxsq0Nd96ZH//jpThy5Ije37S2dsF1o9u2Irpt64LrabV1yX0XumRwacm+q40qkuB0165dsFhtqGi4fFn3W910LQB9CqdCN5M5zXwqGXti3UI7X82GMcDRQplTQA9ge3p6Cv4zdbZHH30U8XgMn1r3gSXt5/fXfwgjoyOmnruvROb0LgBfA5D2DE1E/kxEmkWkeSWmSNixYwfK3R6sKStf0n6uq2tENBrFnj17lqlluTc1NYVXXn4Z19c6EXQuU1nVBQRdFlxf68TLL71YsPOeDg4OYvPmF3DFaqDMMxO89A0r9A3P/WC7+VJBf/8AXn311ZVs5orYuXMHKioFjnkGdx4a0m/zMYLafCntbW5uxtat76Duen003FyYHNBvueDwCRpuBPbt3YctW7bk5iBZOHjoILQKN8Q5/8m16p+E6l+4tEzqfBgaGERvb+9yNnHFRaNR/K8f/AAdHR2Q334vxJ9dybPW1ADtxivw2muv4aGHHspRK1fG4cOH9cGyqhfubxofGEA8kyyX3w/N5dL3XeCMC5qL7XNaDMHpzp27EKy/DBbb8s0gAADe8ka4vOXYtWvXsu7XDEb2M7uyXj3LOj4+npM25YPu7m44LA6U2Rce5LPaVY3QVAjDw5lVAhaC3t5evLh5Mz606jpUu4NL2te6YCOurroYv37016Zl23ManIrIJwH0KaXmjeCUUvcppTYqpTZWVV14COilCofD2NPcjOtrGpY8RPmGimq47fa8OdHOxPbt2zE+MYFb12Q4QeUyuLXJg7HxiYJ6nlI98sgjiEYieN9lmZ0wrK0V1FdoeOjBBwo2IE9ncHAQJ060ob5+8VcanU6gsjI/+p3G43Hc94v74PRrqL3K7NYsXvVlgLtccP8vf2HqyWksFsORI0ehapfns0Xq9CCukDNi8Xgcd955J/Y0N8Nyy/XQVi1uECDtussg69fgwQcfxEsvvbTMrVw5x44dg1ZRCbFlNrVBJkQEqK7BkSIYJX1JZb0WC8IFXq3T09OD7u4uVDZevez7FhFUrLoKe/fuK/iBo4wA02HPPHOqaRY47K6iLuvt7u5Gtbs6o3P7Yhyx91e/+hWgsOSsqeEPNnwYY+NjeOqpp5Zlf9nKdeb0/QA+JSLtAB4F8FsiYtrl35aWFkxNT+O6JfQ3NVg1DddU12Pnjh151YfuQt58800EXFZsqFzeq5IXcmmlA2VOK954440VO+Zy6ezsxAsvPI9r1moo92V2MUNEcOtVgv6BQdPe1LnQ3KzX6zYssbtYXb3CiRMnTL9iuWPHDrSfakfd9QqapXD6ms4mmqB+I3C2p9fUkV07OjowFQpBapbpwle5C5rdWrAZMaUU7r33Xrz66qvQNl4B7bK1i96XiMDywY2QxlrcddddePvtt5expSsjHo/jWGsrkIML0FJdje6uroLPCi2lrBeateAzp/v27QMAVDZekZP9VzReicnJCRw/fjwn+18pxuvcac/us9Zpdxf8e+RCeroWnuPUUGxznXZ1deE3v/kNPrJmIypcIYXrxQAAIABJREFUyzM95NpAPW6o2YAnn3jSlHlPcxqcKqW+qZRapZRqAvCHAF5XSn0+l8e8kF27dsFuseLyqoX7vGTiutpVGB4ZKYgPu1AohObm3dhY54S2xKxxNjQRbKxzYNeuncn5uQqBUgp33303rJrCBy7P7m2yplrDunrBIw8/XBQDMAD6e8ft1hBYWrUI6uv159YIds3yzDNPw+HTULE8sxWk1bE9nizrPfZCbvqeAkBgNeAKanjmmWdysv9MGPP7Xqi/aTZEE6gqV8HOG/zv//7vePrpp6FdeQm06y9b8v7EosHy0ZshNRX42x/+MC+qD7LR09OD0OQktKqF+4Nly9hnPvVlX4ylTCUjmoZIpLCD0/3798PpCcATWL4Bs1KV11+WPE4hGxsbgyZaslQ3U067F2NjYzlqlbni8Th6ensy6m8KAFWuKgikaDKnDz74IGyaBZ+8+P3Lut8/2PBhhEIhPP7448u630xkddYtIi4R2ZCrxuSSUgo7d+zAFVW1sC/mymQaV1fXQ0QKoh9DS0sLIpEorqnJ7gNtOVxT40IkEi2o0Te3bduG3bt34/2XC7yu7IP5j1xrQTQaxj333JOD1q2sWCyGPXuaUVsbx1Kva5SXAy6XZup75ty5c9i/vwUV69W8c00uh8kBIBbWb2M9uet7KiKo3KBw/PhxdHRceLChXGltbYXmtAFly1iVUe3Bu6feLbjy+IcffhgPP/ww5NKLoL3v2iV3ITGIzQrtYx8AysvwN9/7nukXeLJx4sQJAIBkO0FyBox9FktwutjRegt5ED6lFFpaDiBYd+myvV9mc7j98JU3FNR5SDqjo6NwOX1ZT7XjsnsxMjKSo1aZa3BwEJFIJKORegHAptlQ7ioviszp6dOn8cYbb+Cja25EwLk8F4cNjf5qvKf+cjz7zLMrXu2W8atbRH4XwH4ALyfuXysiz2W6vVLqTaXUJ7Nv4vLo7OzE2d5eXFuziHkw5uFzOHFJsBK7CyA43bNnD+xWDevLV66k17ChwgG7RSuYkfImJibwLz/7Z1QHtPNG6M1G0Cu4+VLBW2+9VRAXLy7kyJEjmJiYRP0yvHVEgNraOJqbd5vW98eY76588ZWWecf4W8yaaP7osWNQVa5lPbGUag/isTjefffdZdtnrj322GPYtGkTZN0aWG65YdlPtMVhg/bxWxD3e/Cd73ynYLJAp06d0gdDCi5tIMJ0xOmCxeMtqNdJOsngcjEXzy2FPVrv2bNnMTQ0iGBdbnMfgdoNOHzkSEH3Ox0aGoLH6c96O7fTj+Gh4hkAKFU2I/UaqpxVONuTH9OwLcVDDz0Eh8WGjy9yXtOFfGb9hzAdnl7x7Gk2Z97fBXATgGEAUErtB9C0/E3KDSMwurpmeUtGrqlpwPE86EO3kJb9+7AuaIfNhP51Novg4qANB1paVvzYi3H//fdjYHAQH7tBYFlCZu29l2qoLNNw1113FvRABLt27UrMU7o8+6uvB8bHJ3Ds2LHl2WGW9u3bB6dPgytQuH1NZ7N7BO4KzZRgJRwOo6PjNFCV+QAdmZBqfX9G1i3fPf3007j//vshFzfCcuvGnGXlxWmH9okPIuZz46++/e3kFD757NSpU7AEghlNoxPdthVqoB9qoB/h55/NaEqZeHkQbSdPLkdTTTOTOV3MaL2FHZwa5fuB2nU5PU6gdh2mQiHTKkyWw9DQENyO7INTj6sMQ3l+nrpYiwlOq13VBV/We/r0abz99tv4aNON8GUxQFY26ryVuLn+Sjz37HMrGudkE5xGlVIFWxPQ3NyMOl8Zqj1LnCB+lqur66GUSnbmz0ejo6NoP92BDRV209qwocKB9tOn877Pw969e7F582bcuE5DQ8XSumRbLYKPbxQMDAzg3nvvXaYWrrxdu3aishKwL9PLp65Oz6CalUk/1noM7urCGMQsG56qOFqPt6743G3t7e2Ix+KQymX+cvTaoblsOFkAQcfmzZtx9913Q5oaYPnwTZDFlGZmQVwOaJ/4IKIuB/7yr/4Kra2tOT3eUp1qb4cKZtZhPT4wAITDQDgM1dOd0ZQyWqAcXZ2dBZ0Rm5lKJvvXjmgWRKOFW9bb2toKq80Bb/nyVbalE6jRS0zMujC6HAYGBuFdxFQhXlcAU1Oh5DypxaSnp0cfkdlZkfE2Va4qDA0PFdRYKLM98sgjsFtsuG3tzTk9zu+tuwXhcHhFB/nM5lPwkIh8DoBFRNaJyD8D2Jajdv1v9t47SK7rvvP9nts5Tk9PzhGTBxmDSDCCBEiRIEjJsmV5bbm06+K6nv1W2i1LZdlrPUcGlW1Z5lvnfc+SvW8tlShZFiGLMiWZpAiABIgwyBN6cs90zvGe98ft25wZzWD6dt845KeqCxyi7zkHd244v/T9iUomk8HVK1cwWtck+thd1W7YTSZV1//w7Rh21Mif0suzo8YISqmqBU5isRheevEF1DgZHB8RZ3PZUsPgYD+Ds2fPak7EBAB8Ph+mpqbRLGLCgdEE1NYRnDv3tniDlkgqlYLf55esr6mSWKoJEvGE7FkcvPEotnFKCAF1m3F3Qt21hD/84Q/xp1/+Mpj2JugeOQSik6N9OECsZjAfOY6MQYfPff7zmJ6elmVeoWQyGSx7vSAul2RzkGoXstkslpeXJZtDaoqGdTmRU50OuZx2DfM7d+/CXtMGppx/uwCsVQ3QG82aTQFnWRZ+vx8Oi3Dj1GHlUup9Pp/Yy1KcxcVF1FhqoGdKT4mvs9QVj9UiS0tL+OEPf4iH2vdKFjXlabbX4kDTIL79rW/LlgUo5C36fwAYBpAG8A8AwgD+TykWJTbXr19HOpPBSL34xilDGAzXNuLSxYuyRyxK5caNG2AIQWeVeP3lhNLlMoIQqNo4/cpX/gyBQAAfGSMw6MUzXu4bZlDvYvClL72k+vTv9fDRTTGNUwBobqKYmJiUXc04EAgAAASq8GsC/t8k9zmdnp4G0evEFUPiqbHA4/Gotl3XxYsX8fzzz4NprAVz4rBshikPsVnAPHEfkjSPz33+86o0zpaWlkApBamS0Dh1cmNrOU3v/VYy5aX15jWa1kspxeTkJBzudsnnIoSBw92GCY0ap8FgEPl8Dk5b6RFCHv4YNT4jKmVxcRG1JmFia7xxurSkzbrTb37zmyAATnYdkmW+J3qOIJFMyNZru6Q3KSFEB+CLlNLfpJQeKHy+QCnVRDz80qVLYAjBYG15TdC3YqSuCT6/H7Ozs5KMXyk3btxAq9MAk17ejdNqzHoGrU4jbqrUOH399dfxb//2Oo4OMWh2i3ue9DqCJ8cYRCMR/Mmf/LFqnRgbcf78edhsDMTeV/LiSnKn9iaTSQCATgY/TT4DmM1mnDlzBmazGfmMtPPpCmnXcqdteWY8INVmSVQ2iduCTDoDr9cr+tiV4vF48Dtf/CKoywHmsSOcgV4G+TffQ/7N8muFidMO5tQxhKIRfOG3vlC8xtUCH5kgDuF1cqVCnM41c2mRonEqUIWVO0S7NaehUAiJeBx2tzAP6I03voobb3xV8Hy26mbMzqhzr7YVvCHlcghvyeSya9sYuxfeJW/JPU55+O+r8d2yFfF4HGdffRWHmobhtkj3XF1Nt6sZ/e52vPLNV2QpnyjpKUgpzQPYJ/FaJOO9S5fQXV0Lq0GamsvhQrqwGpUTWZbFndu30O1SLmrK0+Uy4NYt+WvitmJ5eRlf/vKformGwZFBaQz4ehfB/aMEb731E5w9e1aSOcQmk8ng3XffQVNT5S1k1uNyAVar/C1liteeDFm9uQxw6tQpPPfcczh58iRyEhunSuHxeECrpSkZINVc6yu1Of5isRi+8Fu/hQwBmJNHQUzlv1uoPwTqryyjgtS4QB45hOlpD1544QVVPWP5zR9xiqv3sAarFYRhNLnR5MnlciCMrjwnD8OAZbWZ1svf29YqYYp7Ef8MIn7hwkY2VxPC4RBisZjgY5Vmfn4eQHnGqcNaDb3OoOnsgo3IZDIIBAPFSGipOI1OmHQmTRrrP/jBD5BMpXCic0zWeR/tHMPyyrIsQQUhO/FLhJBvE0J+gRDyDP+RbGUikUwmcev2bQzXiiQ1ugH1NjvcVhsuq1CNdmFhAfFEEp0u5cSQeLpcBsTicVV5t1mWxUsvvYhsOoknxxgwEva9HOtj0FHP4P9++WVVnYPNGB8fRyqVFqWFzHoIAZqaWLz77juyevwtFgsASB7FBAC9EXj11Vfx8ssv4+zZs9BLfAvmC3ooVqu09SerSafT8Pv8IC6J+idXcePymzI1QCnFS196CcvLyyAnDoHY5Tvf94JpawRzaCfefPNNfPOb31R6OUVWVlY4kR+LdOeJMAwYu13TKYv5fL4kNeMNYRhQSjUpCMU7FKxOYcZFuVgctWvm1RJzc3NgGF0xCioEQhhUOxtV5+irFP6er7UIS+slhKDGUqPJZ8arr76KjqpG9FRLKyC2nr2N/agy22VJ7RVinLoB+AE8BODJwkexvqWlMj4+DpZlMSBRSi/AXeSDNQ24cvmyqjzWwPttGDpVEDntrOJ257dv31Z4Je/z7W9/G++9dxkP7WLgdkgbTiOE4IkDDCibxYsvvKDaOjqec+fOQacjorWQWU9zC5BMpnDt2jVpJtgAt5sThcjIUNOvM3ICTK+88gpSqVQx7VYq+H8T/2+Ug6KTRYp6UwCw6MGY9Kry9n//+9/HW2++BXJgBEyjsA2R1DCjO0A6mvHXf/M38Hg8Si8HAFcDzdjskqR9r4ZabbLXW4tJPp8vK6UXQFFESYvG6crKCgDAbBdeR1kO/DxaNEo8Hg/cjgboBAj/rKbG2YwZj3bb6GwEH/kUotTLU2Oq0Vyv0+npaUxMTOB4627Z59YzOhxtHsX58+cRiUQknavkJyGl9FMbfH5ZysWJwbVr18AQgh1uab1yAzX1CEciqvLwA8Ddu3ehZwiaHcobpy1OA3QMUU1riIWFBfzN3/w1upsY7O6WR721ykZwYg/BtfFxvPLKK7LMWS7nzv0E9fUU+vLeg1vS2AgwDJE1tddiscBdU41USF1OJDFIBiksFjOqS2zZIQbFlE3H1sZp/o0ZwJcAfAnkXrnJ/bwFhBDAoZ7Uq0gkgv/xF3/BCSDt7FN6OT8FIQS6+/eB1TH48pe/rApnqd/vBy1kLEiKxQKfxo3TcgW1SMGo1aJxGgwGYTRZoZM6taSAyVoFAJoTJwSA6alp1Lpayz6+ztWKJe+S6urSK6EYOTULdxTWmmux7NWWk+JHP/oRCCE42DysyPyHW0aQz+fx5ptvSjpPyU9CQkgrIeSbhJBlQoiXEPINQkj5d4lMXLt2De1VblgM0hpn/TX1xfnUxOTkJJodBuglTFctFd5InpqaUnopoJTiT/7kjwGaw6l9jORe/dWMdBD0NDH4u7/7W9Wm987Pz2N+flGSlF4egwGor6eyt5Tp7xtAfEU5cTCpSKwQ9PX1yXotF1PjHFtvLKkvAWRY7rMQ434uAWo3YFElxuk//dM/IRaNgjm2B0QFz9SNIBYzsH8YV69eVayX8GqCoRBglijtezUWC0IhzbZiL0ROy03r1W7kNBKJwGCRsB55HUYzN1c4rK1rJZFIYHFpEQ3VHWWP0VDdDkqpattOlcPy8jIIIag2CXfK1phrEIlFNGWsv/nmm+hzt6PKpEzLgQ5nI+ps1XjrLWk7iQrZof0dgG8DaAbQAuCfC/9PteTzedy+dQt9bulTr5ocVbAZTaprlTI9NYkWh0ShrzJosesxNal85PQHP/gB3nvvMh4YAZxWeTeZhBCc3Mel937lK1+Rde5S4aOZLSK3kFlPcwswOzsnq5G+e/dupMIsUhHlo0pikU1QxH0Uu3fvkXVev9/PFRBbJHT+2Qzw+5XvzZdIJPDKt74F0tsGUiNdWxQxYAa6wDjt+Mf/9Y9KL4VL/5LBOCVmM5KJuOrLJTaDM07LfBcVHCVaVOyNx+PQG+Sr22b0RhBGJ1u/RrG4e5fr99zg7ix7DP5YvtxrO7C8vAy32Q1dGT1y+VRgPrVc7fj9fkxPT2N3Xa9iayCEYFdtL9679B4yGenEO4QYp3WU0r+jlOYKn/8JQJ4K9jLxeDxIpdPoqZZ+mQwh6Ha5VdUqJZlMwh8IotGuHuO00a7His+PdDqt2BqSyST++q/+Ek1uBnt6lImgOa0Ex4YILly4ILtibSmcO/c2nFUM7BI7tPn+qXKeg8OHDwMAAsr7SEQjUEhGOHLkiKzz+v1+6GxGSaOIxGZAPBaX9EVYCj/+8Y+RTqXAjOxQdB2lQHQMMNiF8WvjipeaxONxEJNENcmrMZpAKdWc0cGTz+c54ahyKKT1atEwT6VSYASm9N5446uI+jyI+jw4960/ENRShhACvcGk6B6kHHitjqba7rLHcNpqYLM4cevWLbGWpTgrKytlRU0BwG12F8fQAleuXAEADNd2KbqOodpOpDNpSZ0cQp6EPkLIJwkhusLnk+AEklQLf+K6q+UptO921cIzM6Oahx5fp1VvU49xyq9FyXTWb3zjGwgEQzixh8iaArme/b0M3A4Gf/WXf6GqdKxkMokrV66guUn6jY7TCTidBOfOnZN8Lp7GxkaMjo7Af5uAstJFT601nCiSzgg4mrifpYBSCt9NoLunG11d8r60QqEQqEXi54uVi8oqnYZ37tw5MA4bSL18glOVwPS0AQDeflvetPnV5PN5pFMpwCh9PSEpzKGlFL3VsCxbfuS0cJwWjdNcLgdGYNQr4p9BLpNELpNEcOGm4JYyjE6PbDYr6BiluX79OlyOetjM5fe1JISguaYXN66rJ4hSKb5lH6qN5RmnfOTU51M+M6cUbt68CaPOgHandN1HSqHP3VZcj1QIMU5/GcDPAFgCsAjgo4X/p1omJiZg0hvQaC/9Zv77KxfgCQXgCQXwe//+Pfz9ldJrdjpcbrAsqxqVxEAgAACoNpdZxyIBLjN3yQWDQUXmj8Vi+Po//W/0tRC01JTnpf7+pTy8IQpviOJrr+fw/UvlGZY6HcHxEQLPzCx+/OMflzWGFLz33nvI5fKS1puupqmJ4vLly0ilUvJMCOD06aeRirAISlj+3H6YgbWGM0oHPsKg/bA0UfrwHJAIUJx5+owk49+LUDgEapL4+WLmjF+ljdPrN26ANtaI6tDKv/lesc9p7ts/RP5N8XplE4cNjMOmqDp60VGrl0GQr6ArkUiUVsusNjjj9IMXOaWUlm+Uf0CglGL82jhaaivP2mip24G5+TlNCkKth1IKn99XjIAKxWXiyjO0YpxOTEygzVkPXZkZFl8d/x6+8KO/xGd+8GV84Ud/ia+Of6+scapMdrgsDknFTYWo9c5QSp+ilNZRSusppU9TStVhhW3C5OQk2pwuMAIefJ5wAIlcFolcFjd8XnjCgZKP7ajivDdqUaPlHz4OU/mb4mSWhdlsxpkzZ2A2m5HMVvbycxY2skoZp9/5zneQSKZwbKj8DfVyiCKdBdJZYGaFYrkC5deBVoLaKgb/3//6R1UoawLAhQsXYDAQ1MmUtN/cAmSz2WLKihwcPXoUrW2tWLhIwEoYPZUaSikW3gHq6mvx0EMPyT5/NBYDpHZ+FZ4ZsVhM2nnuQSaTQTAQAHGVH7XYCOoPAZkskMmCLq5wP4s5fpUds3Nzoo4pBN44JVJJfq+m0CNU6fTvctnMSKOZ1Jp3MM38tBOPd5io5R0iBIZhQAUa1blMcs05yWUERsspC6bcFGoFWFhYQDAURFtDf8VjtTcMAFCfeGc5xONxpDPpTdN6v3bra/jara9teryBMcButGvGOJ2fnUOTrXwNnZs+D7zZEI6dfBDebAg3feWbcE3WGszNSvduEaLW+/8QQlyrfq4mhPytNMsShxmPB62OKtnmq7fZYdDpVNPkmI9EmcuUpweARI7i1KlTeO6553Dy5EkkcpW9/Ew67iWqROpzPp/Hd/752+ioZ9BQrQ5PLSEEB3YQTE5N4/r160ovB5RSnD9/DvUNFOX2gxdKfT2g1xNZlUV1Oh3+46f/I5IhFssSvqP5yKlU+G4BcR/Fp37pl2GQWJF8I+LxOGCU9kIhhfGVrCUszm1UviWXIIwGxBPKnbeiQI8MhgApPLC0lq7Jcy/jdPU7eCPjFBo2Tk0mE9i8MIdCNp1Yc06yaWHR8nwuC5McddAicfnyZQBAe/1AxWM11XTDoDcVx9QyfF9jPgK6npnoDGai9075rjZVF7MM1Uwul0MgFESdpXybJpFLrdvPl5+tVmd1YUXCXsFC3Jk7KaVFty6lNEgIkVcaUgCRSAShcBjNbfKpWjGEQZO9CjMz6mhyzNcxVmCbwqonePXVV0EpxdmzZ1FvrMyo41vaKFFjefXqVSyv+HD6kHrSnAFgqI3gB+8RvPbaaxgeVqZ3Fc/c3ByWl1dw4ICw4959B+CD4a99H6iuBvbtL+1YnQ6oq6d4553zAH5V2MQVcOjQIYyNjeHdixfg6qQwO8V3WEiVygsAmTjF3HmCkZFhPPzww5LNcy/SqTRgkFiJVc/dr0rW8hcNf62lTbIsDHKk1G5C8TkvR5RKw6mtwOaGJTGa17yDiW1zpWgtGqcWiwX5nLBNrsFkXXNODPaGko+lLItcNg2zHO2NROLSpUtwWF2oqapcPl+n06O1rg+XLl4SYWXKwhuVLmP56ukuowt+n6rlcwBwNg2lFM4KWshY9WufJQ2G8s+b02hFuLAmKbRbhLwxGEJIMXZOCHFDmHErK7zgToNNvv5Z3Hx2LC4syDrnZvBpK/kK3lcWA4NUKoVXXnkFqVQKFkNlm4x84eWpRErNm2++CYOeoLdZHVFTHqOBoKcJeOONf1d8Y/Xuu+8CAJqahB0XDALZLPdZXn7fUC2VpiZgfn6xKOIlB4QQ/Pqv/zpMBjOmXoem0nspSzH1Q4CBHp/97GcVE/bKZjKATuK59cplW/DYbDbOQI1rS2yHJFKorZFHEHAjisaSHJcno93o4fv89IkiRvOadzAxbmBUabhm0263I5cWFt3XGy1rzoneaCn52GyGi7I6HPLuDcuFZVlcunQJHY0joj3nu5pGMDM7o5l01s0oGqebRE5LocpYhWBAmTIzIfBCb2Z9+RF/i8G0bj9fwVh6E3L5nGSZKkIshC8BeIsQ8ruEkN8F8BaAFyRZlQi8r1Qr7wOozmqH1+tV3MgAAKuV6x2Wyim/Fp5kIS3YYin9ZSIWFy++i7ZawKhX34u8u4lBOBxRvDn2xYsX4XBI30JmPbwxfOmSvN7curo6fOYzn0VsmWLunHY2tfMXKSILFL/6n38VLS0yKVetg2VZsCzLtS2RksL4SvZwJISgrb0dNKCsKJMQKEtBgxG0tbUpvRR5KBilSiqwf4hwqqqqkE5GZXMqZJJRAIDTKW79uFTcvXsXkUgE3c2joo3ZVRiLd0ZrFV67pMpUfqprlbEKwVBQ9U4t/v2nL6OfqxTwfWWlyoIUIoj0/wJ4FoAXwDKAZyilfy/JqkSA9wi5LfI1dwaAGqsNmWxWUfEOHt4zGEurxziNZ7i1yO21jMfjmJ2dQ2utOjcubYV1Kdl/LJ/P4/Ll91DfIP/14nQCFguD994TT620VI4fP46nn34a3mvAyi11v6AAIDBJsXgJePTRR3Hy5EnF1lF0wEl9S5F18ynE0OAg4A2A5tXzPL0X1BcEzeYwNDSk2Bp0fOG6HFkJCmbliEeZ50nlG+t7UVNTAzafQzYlz54pHQ8W59UCvBZDV5N4xml9dTscVpcqe6wLIRgMQs/oYdGVH+xwGp3I5XOq2LPfi2ImpAoCXwDAUm4dOonESYQIIvUAmKCUfgXAVQCPrBZIUhvcRauDzSB9f7XVVJm4lBs1FFjX19cDAPxJ9fTQ9Cc47w+/Nrng64DrXeo0Tl02LqKrZOR0YmICyWQKDaWX74gGIUB9PYtLly4q4sH8lV/5FezZuwczbwDhefVu9GJeiqkfAYNDg/i1X/s1RaNEcv+elPZs79u3DzSbBV3SRiocnVkEIQS7d+9WbA16XqWXlf4dRAsefCWEwcSgontZw1HjuoIsfDImT90fP0+dXHL0FXLu3Hk01XbDVoEQznoIIehu3oV3372oqh7rQgmHw3CanBVd9w6joziWmuGzDdMCxcOkIpXLgCFEsuetEBfjNwDkCSG9AP4aQBeAf5BkVSIQDofhNJtlf1g7TdwFpIYeUrwBuJJQLh1uPSuJPAghshunKysrAIAqqzpf3oQQOG0MliVUP9sKvpWLzL+aIvUNQCgUxoICNds6nQ6//Vu/jbb2dkx8H4ivqM9ATQYo7n6PoKGuAV/8nS/CaJTX8baeYoRK6lNF182nEPv27YPRZAJ7Vx2Cd/eCUgpMzmF4eBjV1eU1qBcD/hqlORk2wIVNtl6OtjUSQAipOAKq9D1SDo2NjQCAZESed18ysqLIHqQcwuEwbt26iZ5m8R1MPS27kUjEMT4+LvrYchEOh2HX2ysaw2l0FsdSM8VMyIw6+jjHMkk4HJU5Bu6FkCcZSynNAXgGwJ9SSv8LAIGyKfIRj8dhUUCl0KqiRuAWiwUN9XWYj6pHWn8umkVjQ73sSnl8yoZF2f38PTEbWEXbZYyPj8PhYGCVNxO+CL9XUKr/ms1mwx/+wR+iproWd84SJAPqMVBTEYrbZwnsVif+6I+eh8ulfNIKwzAghIBKnbKpknRNs9mM+44dA5maB82qx+G3EXQ5ADYYwYkTJxRdR7Fdhxz1wjnuPaclFdbVVGKc0kKKnRYjp83NnAJtIuyVZb5EeAl1dfWaiLBfuHABlFLsaN26Mcb3L/w9vvq93yt+vn/h3lV3XU0j0DE6nDt3Tqzlyk4kHIHDUFmJmN3AGbeRSESMJUmGyWSCzWpDIBVVeikAgEAqArfbLdn4Qt72WULIzwH4DwC+U/h/qr27k8mkIsYpP6cajFMA6OruwWxEPWkbc9E8Oru6ZZ+XLyZ3l2LnAAAgAElEQVRXs2NZxyjXQJ5SivHxa6ipUa6ewekETCZG0X6vtbW1eP75F2C3OHH7VYJkSHkDNR2luP1dAiNjxfPPv4AmoVLKEkEIgd5gAKSuwSwIuqmhL+GpU6fApjOgk9I1HxcD9sYkjCYTjh8/rug6DAYDV5MkR+/RgsNACbE9MdDpdAAt814qGLVS1X9JidVqhbumBrGgPBkz8dAi2tu1IRL29ttvw251obGmc8vvegMezHhvFD/egOee3zcZrWhrGMS5t7VtnPLGZbloxTgFgMbGBqwklM/KBABfKozGpkbJxheyVf8UgMMAfp9SOkUI6QLwVWmWVTnZbBYGBSwRPaO8suRq+vr6sBTNIJFVvog6nmGxFM2gv79f9rn5l7aatUzyrHL1UisrKwiFwqipVWR6AFzdabWbxa1bN5VbBICWlha8+OJLsBjsuP1dgpSCBmo6SnH7Xwh0rBkvPP8COjs7FVvLRhiNhqLxKBkqMk5HR0fR2tYGOj6heA3sZtBUBpiYxcMPPQSbrfyeeGJhsdlAM9K3AeLnUMO/uRx0Oh1ouWInheOUzi4ol67OTsSD85LPw7J5xIILqnuObkQul8M7F95BT/NuECLN77W3dQ9mZmeKrRe1RjQWhVVfWaoXf3w0qo6I5L1obWvDUkL5nqx5loU3HpC0U4AQtd7rlNJfo5T+Y+HnKUrpH/F/Twj5hhQLLJd8LgdGgRQXHVGXcTowMAAKYCqofBH1ZIhbw8DAgOxz8/n6KeVPw6aks4xivdfu3r0LAJAwS6Mk3G7A4/EoFkHmaW9vx4svvgSzzoZb31UmgpqOUtz6FwKmYJj29vbKvoatsFisQKmOr0weZrMZZ86c4VIvMyVmdBQUvtUQESOE4OnTp8GuBECXKxe9o5nsmnNCM5VHGNmbU6C5PE6fPl3xWGJgt9mAdOn3M81k1p2TEo9Np2EwGBSvxS4XhmHKj5yy0ipnSk1XVxdigXmweWn3TYnQEth8Fl1dXZLOIwbXrl1DIplAbwkpveXCpwtrMbWXUop4Ig6boTJnlEVvAQHRhHHa0dGB5XgQqZyy+yNvIoBsPiepk0dMd4z8uZr3gDCMIurq/JRq8WAODg6CYRjcCijXwJ7ntj8NhmEwODgo+9y8bHwkqdJoB6WIJKhi8vaTk5MAAAW1U4rz5/NsUV1ZSTo7O/HSS1+CRW/H7X8hSAblu3bSEc4w1VPOMO3r65NtbiFYbdbSjcx0HqdOncJzzz3HtcBJl3YczXLfsypVDL2ORx55BGaLBey1u5UPlsmuPScVGqeUpcCNSYyOjqK7Wx2vZFdVFWg6VfoBmfS6c1Lau4um0rAr5NwTA4PBUL6qMattMaje3l7k81nEQ9Km9kZ808X51M758+eh0+nR1TQi2RzVjgbUVjXjbQ2m9qZSKeTz+YojpwxhYDFYVFOKdy/469YTVjbSPV2YX8r7SEwLSlW7foPBgKwM8vXryansJWGz2bCjtxc3fcqHDG/6M+jv26FIBIQXXQhGVXWZFkmkgXSWLa5TbjweDxwOBkpftrzOj5ItdVZTNFCNDtz+rjwGamqNYfqiag1TAKhyOks2MmHS4dVXX8XLL7+Ms2fPAqYSozxpLpqiVFbBeqxWK04+9hgwOQeaEGB0bYTRsPacGCtL66czi2AjMTz99NOVrUtEXC4XmFSy9AOMpnXnpMR07lQSVVXitduQG71eX2yHIxTKaruNzo4dOwAA4eUpSecJL0/BaDShvb1d0nnE4Ny582ivH4DRIK3AV3fLLly9egXJpIB7VAXwxmSlxikAWA1WRcUoS4UviZuQ2ImzFRPBeZiMJnR0dEg2hzrCexJgNBqRUcA4TRfSUtRQH8Wze88eTIUySCpYd5rIspgKZrB7z15F5ne5XKhyOrAcVqdxulxIG1WqFoYzTpUvyLXbudrT2dlZpZdSpKOjA1966UuwmgoGqoQpvukIV2NqgAUvvfil4qZNrTgdTpBUic9Zow6pVAqvvPIKUqkUYCzROC2MrxbjFACefPJJUJYFe6uyzTQxGtacE1KpcXp9AtVuNw4fPlzROGJSXV0NCNj4EqNx3TkpMU03mUStQpknYmAwGEDLTWstGLVaTettaWmB1WpDaHlS0nnCyxPYsaNX9efJ6/VidnYGPS3S9yjuad6NbDZbbCWnFXhj0qyv3Hi36CyaME6rq6vR2NCAO0Fl90d3QnPo7++T9D4S0zhVlYa53W5HPCt/tDBeqI+x2ytTEBOTAwcOIM9SXPcpl9p7fSUFllIcOHBAkfkJIejrH8BiQFWXaZGFQtsSJYwRSikWFxeghr2/TgfY7YzqBBra29vx0ovvR1BTEjg50jFOlVcPM1588SX09PSIPofYuFwuICmtEitNZLk+wE6npPMIoa2tDaM7dwI3p1UjjESjCbCzS3j81CnVZO4AgNvtRj6RKEb3pIIkE5K2NpAazjjNl3c95XPQGwyabCUDcGVQA4MDiHhFSJXfBDafRdQ3o0hZkVDeeecdAEBX86jkc7U19MGgNxXn1AqpFJe1YtFVnoln0pk0EzkeGR3F7dCsYu+dZC4NT3gJwyPSpZsD4hqnvyHiWBXjcDiKhqKcxAr1MWry8g8NDcFmteCyV7mb77I3BbvNpogYEs/w8DBWwiwSaXVsJlczs0LR0d6miFMjGo0inc7AphJ/itXKwutdUnoZP0V7eztefOFFmHQ23P4uQTom3nWUTVDc+S4Bkzfh+T96QROGKcB5ctlkFlRKGexkFg6nQ3XRjlMnT4KNxECXfEovBQDA3uFaRzz66KMKr2QtxTr6hHTvH8qyyMfjqK1VUG68QopCTmVET2k+p9mUXp7hoSFE/HPIZaS5TsIr08jnsxgaGpJkfDG5ePEinDY3aqukU0Pl0euMaKvvx8WLlySfS0x4Y9Kk2zhL8Wu3vgZP1ANP1IM/fOcP8bVbX9t0LDNjRlLC55OY7Ny5E5FUHAsxZd47dwKzYCmLnTt3SjpPycYpIeQqIeTKus+/E0L+mBBSQyn9VykXKpTa2loksxkkBfZXS2TXKgUmBEZfgykuD14pYZuN0Ov1GDt4CJe9GeRZ+Q2zPEvxnjeNg4cOKerR372bS5HxLFd2DtJZuuYaSWcrGy+Xp5j3Q7GUZ5+Pe8hZK3BAZrNYc04qaWtosb6/JrXR2dmJ5//oeehYE+68SpBNVX4/5TMUd84C+ZQev//7f6DqGtP1vG94SKiyGcuq0ug4evQojCYT6B3lxbsAABOzGB4eVk0fXJ76+noAAI3FpJskkQAoRV1dnXRzSEwlxinyWRg0qlLMMzw8DIAiuHRHkvFDi7dXzaNeKKW4cvkK2huGZIuEdzYOY3Z2Bn6/8m1KSiWd5gJBmxmnM9EZJPNJJPNJ3AzdxEx08+e0SWcqjqd2du3aBQC47p9WZP7r/mnodTrJ7yMhkdNXAfwLgJ8vfP4ZwI8BLAH4n6KvrEL4zYw/KSyPPJFdq56YELjL9icT0Ol0XJ2Nijh27Bii6Rxu+eW/AW/40ohn8jh69Kjsc6+mv78fdpsVE4uVRXlSWay5RlIVZjV6limyOeVSnkMhrqmzuYLSjUxm7TmpJGnBYgZCoXD5A0hMb28vfvd3fw/ZOIO7/wqwufINVDZPcfc1IBki+J3//juq3zitp2g0xqXLUiGJHOpq1Wd0WCwWHDp4EJhe4FRyFYQGI2ADYTzwwAOKrmMjGhoaAAA0Jl2rBn5s3hDWIrxOBc0Jf6HQXFZVOhflwHcWCBaMSLEJLt1Gc3OL6vZm65mdnUU4EkZHg3zpx+2N3FxXr16Vbc5K4dN6jbrKnTJGnVEzxmljYyPq6+px3SeteNhmXPdPYWBgkGsHJyFCjNOjlNLPU0qvFj6/CeABSunzADqlWV758N5jb1zYC9FqWKueaBWYKuONRdFQX6+aVjI8Y2NjsFjMeHtOfrnst+cSsFktGBsbk33u1eh0OowdPISJRQK2gs2k2YA114i5wmyqOwsUZpOx6BGTm1gholGqKOZGGI1rz0klTnyjEchms4r3Or0Xo6Oj+PznPo+Yl2Lqx7Ss+g9KKWbeoojMU3zmv3wG+/fvl2Cl0vJ+VEzC31Uso1qj4+jRo2CTKdBlZSMOrIdTbzxy5Iii69iI4jUSjUg2B41wY6staiyEonFZhnGKXBZmk7SbRamxWCzo7e1FaOmW6GNTyiK0dBujo9LWyYnB+Pg4AKC1oV+2ORvdnTDoTbh+/bpsc1YKvz8wMJWnsxsYg6r3G6shhGDP3j24EfCALbcvcpnEMklMh5awZ690vXd5hFhQdkLIQf4HQsgYAL5KbcM8FEKImRBynhBymRAyTgj5YgVrFURraysAYDEqLAJjNaxVCrQahO2yF+MRtKlQptxkMuG++47jnaU0Ujn5LuhkjsW7Sykcu++4KpqjHz16FIk0i5mV8o1Tk4GsuUZMhvJTb1iW4s4CsP/AmGKeb16S3VBBxrXBgDXnpJLyJ71h7brUyn333YdPfepTCEwAS2UIHa7c5D4f//jHVVcnWCpFozEqjdeZZvJgU9li9E1t7N+/H4QQ0FmFa6Rnl9DZ1aXKtFaLxQKny1U0IKWARiIghKjWiVEKfCSC5oRvkmk2C7NZ25FTgKunC3snkd/iHDhr2lHdPFD8OGvuveeKBeaRScUlr5MTg5s3b8JissPtaJRtTobRoammGzeu35BtzkoR2zjNVthjWk727t2LeCaJ6bC8750b/mlQUOzZI71xKmQ7+mkAf0sIsYNT5o0A+DQhxAbgDzc5Jg3gIUppjBBiAPAGIeRVSunbFa26BBwOB6pdLsxFQlJPVSTHsliMRnBYwt4/lXDq1Cn867/+K87PJ3G8wybLnOfmEkjnWDz++OOyzLcVBw4cgNlkwo3ZDDoblI9uz6xQxJIs7r//fsXWkC2krjMq0ZvhdW+04Mn82Z/9Wdy5ewdvvvEG7A2Ao7E0R0XCTzH7E2D//n34pV/6JWkXKSE2mw02uw2JiES/qwhn9DY2yrdRE4LD4UBfXx/uLKwotgaazYF6/dh/RrlnyFa0NjfjZiAo2fg0EkZNba0qHKDlUkyTK6dgP5eB1eISd0EKMDo6iq9//esIL0/B3bx55HDw2CcFjRtY5KKxIxIrjIrB7du30ejulF15ubGmE5fuvIZcLqcqte/N4PcteqbyteoZPbLlZCwoBK+fMu6bRLerWbZ5x31TsJjNsgiblrw7p5ReoJSOAtgNYDeldCel9DylNE4p/d+bHEMppbwKgqHwka04Z0dfH6Yj0r0Q1zMfDSHH5tHb2yvbnEIYGhpCR3s7XvfEZZGhppTidU8CnZ0diqr0rsZsNuPI0aO4NU+Qyyuv2js+w8JiMePgwYNbf1kiWJaLpKulCwG/DrW06LgXhBB89jOfRX1DA6Z/SJDPbL1mNkcx+TpBVZULv/Ebn1OdCq1QmpubgbBEkdPCuGpO19y9ezfY5QBoVkJRqHtAlwOgeVaxsoBSaGtrA5EwcopwGG2FbCmtYrFwinQ0K/xeIrlM8XgtMzw8DEIIgos3RR03uHATtbV1qnVy8eTzeXg8HjS45Q9wNFR3IJvNYn5+Xva5yyGX4563OlL5+1NHdMXxtEB1dTU62jtw3Tct67zXA9MY3blTFueFELVeEyHkEwB+FcCvEUJ+mxDy2yUcpyOEvAdgGcD3KaXn1v39fyKEvEMIeWdlRVzv844dOzAfCSElk0dkMsjVHanVOCWE4PTTT8MTyuC2X/qo1E1/GrPhDM6ceUZV/dcefvhhJNMsJhaVNX6yOYpbc8CxY/dJXlx+L/j6aLXYgvw61Fa3vRk2mw2f+43PIR1jMXdh65O4cIkiGWTx3/7rf0NVVZUMK5SWluYWMFGpIqec6IWajdPh4WGAZUFX5HOEroZ6OWVrNbfIaG1tRT4RB82I78SglALhENpVWE4jhEqMU2S3h3HqdDrR0dmJwIJ4daeUUoQWb2HXrp2q2odsxOLiInK5HGpd8jta+DlnZlSiPr4F+TzXN1msyGmuHJVsBdm9ZzfuhGaRk7h/NE8wFcVi1FeM2kqNkN3ftwCcBldfGl/1uSeU0jyldDeAVgBjhJCRdX//l5TS/ZTS/WLXywwODoKlFBNBedpS3PYvw+lwoqVF+t5U5XLixAk4HQ6cnZBOOZHnexMxuKqcePjhhyWfSwh79+6Fu9qFK9PyFpOv5/Y8RTpL8cgjjyi6Dr4/Xl6eZ9yW8OvQUt++4eFhPPXUaSxfB+K+zQ3UZIhi6Qp3H2pRAGkjWltbkY+kJOl1SkNpOKucivT/LZXBQU7pUilRJOoNoLW9TVW9tdfT1tYGAKAhCcpsEgmwmUxRZ0KrWK1W7j/KMOBpJgWbTZ5SHanZtXMnwt47YEUyFhLhJaQSYU2k9C4scMJmctab8rgdXF3/4uKi7HOXA2+cMqRyJzYDBpSWJ2yoFLt27UI6l8VESJ5I900/10dbrrptIb/VVkrpxymlL1BKv8R/Sj2YUhoC8EMAJ4UuslyGhrg+Ubf8y7LMdyuwguGRYVV750wmE54+cwaXvSnMhreOdrRVGWHRE1j0BP01RrRVlVbTMxPO4Io3hdNPn1FdHZBOp8MjJx7F5CJFLKncw+jKNIv6+jrF0/H4TZFaSi74dRQ3axrhF3/xF+FwOjB3bvPvzJ2nMJvM+PSnPy3fwiSmtbWVK9aQIrU3lEJba5v444pIVVUVGhobQZcDss9NKQVZCWJoQL62E+XQUdBhoEHxo8s0FFwzh1bhn3frI6dMTRMnpW40QdfUyf28Djab0dzzcjNGRkaQy6YR9YsTweNb04yOjooynpQsLXECN1V2+YXNTEYrrGZHcQ1qp2icCjJjNoY3cPNq8dCXAH893yoYjaXQUdUIi94Ei96EAXcHOqpKd4LcDHhgtVjR09MjeK3lIOS3+hYhRNDdTQipI4S4Cv9tAfAIAHGLCe6B3W5HT3c3xlekv9lWEjF4YxHFDY1SOH36NCxmM/7lztbR00+MuNBeZUB7lQG/cbQenxgpTXThO3eisFosOH36dKXLlYRHH30ULAXGPcpET8NxCo+X4tFHH1M8fZWPuKilzVc6DZhMRtU5NbbCbrfjkz//SUQWKCIbpIzHfRQhD/Cxj/2M6nvtCaEYFQumRB+bhDOaSNccGhwEUSKtN5oAm0yhv1++thPl0NjYCL3BABoU34BnC2NuG+M0s/Y+Mh99ArqaJuhqmmB96tMwH31izd/TfA40l90Wab3A+6JFvIhRpQQWb8HhdBafU2rG7/eDEAKbWZlyD7vFBb9f2bZYpcJHOUWJnBbG4PU3tEBVVRXa29pxKzBb8jGfHH4MHc5GdDgb8ZtHfhGfHH6s5GNvB2cxNDwkm0aGkN/qMQDvEkJuEUKuEEKuEkK2aqDQBOD1wvcugKs5/U65iy2Hffv3405gRfK60/FlLhVi7969ks4jBg6HA0+dPo0LC0ksRsU/L/ORLN5dSOKp06dVm47X3t6OocFBXPEoI7xzdZoFCFTRQsTtdgMAkkmFF1IgmYRmjbfHH38cVVVOLF1GMU2I/yxdprBYzDhz5ozSyxSVYjplUNwLiKZyYBMZTWwq+/v7wcYSoHF5byI+lVgtgnObodPp0NbWVjQkxYQGArA7nXC5tK1Wq9frYTKZBdfl8t9Xc1q3EGpqatDQ2IjQ4h1Rxgsv3cHoyIiqM9p4otEoLCa7Yg5ri8mBiJTCZSKipRRcqRgeGcbd8BxYic9FPJvCXGRZVl0DIXfAKQA7ADwK4EkAHyn8uSmU0iuU0j0FZd8RSun/Vf5Sy2Pv3r3Is6zk0dPL3gXUuN2a8d4+++yzMBqN+Ofb4teefudOBCaTCc8++6zoY4vJYydPwhdmsRCQ9yFHKcVVD7Br125VqAfytd5xlbQVTSSA+np19rXcCpPJhCeffArhWYp3/nrtJzAJPP74E9umNozHYrGgtq5O/MhpgDP0tPBMLdadeuWNOlBvAAajEV1dXbLOWw493d1gpGgnEwigq6NDE8bHVlhsViAt0MGR4b6/XdJ6AWB0ZATh5bsVGyDpRATxsFfVYmGricfjMBnL+z2mMwmYzZzz02w2I50R/kI3GayIx7eUklEVYtz3/BhaM3gHBweRyKSwFJf2vTNZqGuV8z7aUuaKEOKklEYASK+gIwEjIyOwWCx4b2kO+5qk8cDnWBZXVxbxwCMPa+YF6XK58ORTT+Eb3/g6nh5wot4mjjT0UiyL8/NJfPRjH1O9Eunx48fx53/+FVyZyqOlRr55Z1YoQjEWjz1WekqFlFitVlRVORGNqsNjGo0y2LdPvt5dYvPss8/CYDD8lDS9Xq9XTb9fsenq7ERgalzUMamGjNPe3l4YDAbkl3xAt4zCPF4/Bvr7NdGXsKurC6+99hp0qRSISOrklFLQYAA9Rw6LMp7S2G12xNLCnDy08H21ZimVw8DAAF577TUkoz5YneXXX4a9dwGoW8l6Ndlstmz12VQ2gVOnTuG5557j2vi99qbgMXQ6PbIZlYhPyAhvlGpl/87DZ8xMhubRbK+VbB5edEnO8pFS7oJ/ABclfRec7MXq3x4F0C3BukTDYDBg7969uHTxEiceIcHFd8vvRTKbwdjYmOhjS8mzzz6Lb73yCr57N4pf2iVOGuV370ZhMBjw0Y9+VJTxpMRms+G++47jzX9/HSf2UOh18jyYrk5zvU2PHj0qy3yl0N7egcXFa5CxDfGGpFJAKsVqos5wM2w2G37u535O6WXISmdnJ965+C4oS0EYce4jGkjCbLFAbBV3KTAYDBgYGMD40pxsc9JMFqwviJ2PnpJtzkrgo7s04AdpFknRPhIBzWY1ETkuBafDgcWQsIgXLURat0taL/C+MRlenqjIOA0tT0Cn02HHjh1iLU1SuD1qeSm9ZoMVr776KiilOHv2LKoswrOPGMIgr5G6S34vz1JWlLpTLdLW1gazyYzJ0CKOtUqndzMVWkRLc4usWV9b/kYppR8p/NlFKe0u/Ml/VG2Y8hw5cgTBZAJTIWlC3+8uzsJYMIK1RE1NDR47eRJvzSYQTlWuUhZK5fGTuSROnjqlmZrBEydOIJVhcWdBHqMsm6O4NQ/cf/8DivY2XU9PTw9CIUDp91JBeFM2RbgPEYfOzk6ulUxIxNTeQAqdndpJ19yzZw/YlSBoSh5lMbqwDFCqCRE+AOju5rYLrP/erd2YmhrAaASMRpCmZu7nTeDH4sfWOk6nA0Rw5HT7GaednZ0wGIwIeScqGie8PInOzi6YTCaRViYtOp0Oeba8FjomoxWpVAqvvPIKUqlUWenBeTaviSwMQNwoJ0u5jY9cYj9iodPp0NPTDU9E2vY/nugS+vr7JJ1jPSW7GwghPyjl/6mRgwcPgmEYvLNYuqpVqVBK8e7SHPbu26dJtbxnnnkGOZbi9elYxWP923QMLIWmBF927doFd7VLNtXeOwsUmSxVXe/Xvr4+5HIUkbCy6/AV/Ee9vb3KLuRDBNHZ2Qng/VTcSqGUggRS6O7SjtGxb98+AACd9coyH51dgslk0kzKotvthtPlAt1CDVR/5BhITS1ITS2MT56G/sixTb9LA5y6KX/9aR273V6sIS0V3jjdTmm9er0ePT09iPimyx6DUoqobxp9fdqImgKA2WxGNqecbH42l9LMPpYXjeINy0pgwY2hFUfoanp6ezEbXZZMFCmWScKfCMseMNjSOCWEmAkhbgC1hJBqQoi78OkEoInCMKfTidHRUUmM0+lwAL54TFUpmkJobW3F2NgYfjSTRI4t/+LOsRQ/8iRxcGwMLS0ipWzJgE6nw/0PPIjJJSCVkT56emOWhdtdrbqea/wGd2WlvOOrqwGDgfvU13M/l8PKCtDW1rqtogAfBDoKgjTUL5JabTwLNqWtdM2+vj44q6rAehYkn4tSCswsYf/+/ZpqudTb0wMExMtgoj4fWlpbNRMZ2wqn0wk2JTCtt/D97fbM3LGjFzHfDGiZxkcy6kMmFddMSi/AORhSGeUEiZLpGOx2bQj28VHOPN046y+ZS64RiErmNn83sZQFIUTxtn7l0N3djWQ2DV8iJMn4s1HO2Sr3u7iU38SvgKs3HSj8yX++BeDPpVuauBw9ehTzkRAWouKGht5ZmAFDCA4dOiTquHLyxBNPIJzK4epy+Sl5l70pRNM5fOTJewo4q5IHHngAeZbi7ga9KcUknaWYXAKOH79fdQ/BpqYm1NS4sVRm0Gfffs4gra4GHjnB/SyUfB7wrRDs2rW7vEV8iGIYjUY0tzQXFXYrhY/Aask41el0OHrkCDCzBJqTtpk79QbAxhKac4r29vSABgKgIjW7J4EAdmyjLAun0wk2mwHNl57aSdNcbbbBYJBwZfLT09ODbCaJZKQ8j2nU5ymOoxXcbjcy2TTSWWX6uiXSYdTcI41eTfDG6WaR00TufYGokydPIpHb3OmTp3nNpfTy8Fkjc9FlScafi3Djqs44pZT+KaW0C8B/XVdzuotS+hUZ1igKR44cAcDVh4rJhcVZjIyOql6Z9l6MjY2h2lWFN2fK99i9NRuHu9pVTG3TEv39/ahxV+PWnLSpvZNLFLk8xbFjm6epKQUhBPv3H4DXSxSrO/X5gGyWaq52+0M4ent6QQIi1Zz6uY2EloxTgHN00WwWVOLoKZ2Ygd5gwOHD2lKp7enpAWVZ0GDlLWVoKoV8LKop42MrnE4ngPejoaVAUwnY7dsragq8X0cc8c+UdXzUP6u5lG9e/C0cu3ddthSwbB6ReBC1tdKpvooJb0zmNqnRteo5gaiXX34ZZ8+ehVW/eQ1uns1Dx2jTOOXV7OdiZaa9bcFcbAU2q1V2p0XJ4RtK6Z8RQkYIIT9DCPkP/EfKxYlJfX09+nbswAURjdPFaATzkZDmvNfr0el0uO/4/bi2kkE6J9wySeVYXFtJ4/j9D2jS+8QwDA4fOYppL8vprA0AACAASURBVJDLSxc9vbPAwuGwY3h4WLI5KuHAgQPIpCl80jzjtmR+nrsW9+zZo8wCPqQiurq6wIZToJmNo2Kk1goYGe7TbOd+3gTqS6KmtkZzqYo7d+5EtdsN9ranpO+TGhdgNABGA0hTHffzFtA8C0zM4fChQ5rrmcsbkjRQ+eabFsSQPjROE5p2jm8Gv+mOB8tz9MSC82hoaNRMDSWAYklUMCo8hanB3YH2hsHip8EtrAVXOO4Dy+bR3KyJar1ipsBmab0WvWWNQJRFv/l1kKfaEYJaj81mQ21NLRai0mzc5qMr6OjolL0eV4gg0n8H8GeFz4MAXgDwlETrkoSjx45hIrCCYFJ4c+KNeGeR8+hp3TgFuH9DJs9ifEV4Mf74chrZPNX0eTh48CAyOYqZFWmMU5almFoiGBs7qFoDfv/+/TAY9JgRvzR7SygF5uYI9uzZo7kN94dwFKOcm9Sd6o61A7VWoNYK/dMD3M+bQAIp9HRrz+jQ6XR49MQJ0Nkl0PjWqXm6o7tBalwgNS7on3oAuqNbp7RTzwLYZEo1fZKF0NLSAoPRCOqr3DhlC8JK20WpFyjPOEU6DleVU6IVKYfFYkF9fQNigfmyjo8H59HZqf4eyatpa2sDAKyEhL+ETxz4BXzysS8UPycO/IKg41eC3Jxa6CsNoGhMbhY5FUKOzWnWOAWA9o52LMSlibYvxv1o75C/tZ+QwrePAngYwBKl9FMAdgHQlAoBnwJ1SaRedBeX5tDT04P6+npRxlOS0dFRmE0mXF8RnpY3vpKCxWxWbUSwFHbt2gWDQY+pJWmM06UQRSLNYv/+MooxZcJqteLAgTHMzTKyp/YGAkAsSnH8+HF5J/4Q0eCNhEoVe2meBRtMatboOHnyJEAp2JtTkozP3phETW2tJksodDodurq6tlTsLQXq98FVXa2ZtmWlwEdAhRinJJUsGrXbjba2ViTCS4KPoyyLeHi5aOxpBYvFgqbGJngDpWVeiMlSwKOpNGg+ciqGcZpls5qu2W5vb8dCzM8J5YlILJNEJB1XpO+8EOM0RTnZtBwhxAlgGYCmdg8dHR1oamgUpe40kk7hjn9ZczU/m6HX6zE8MoKbgazgY28GshjdOappz5O5YFx7SsiMqHcRmAyAyQC01xHUu7ZOd/B4uYeG2uspH3roISQSLLzydMMoMjUF6PU6Vdbjfkhp1NfXw2yxgPoqzEwJpgCWaq7elKelpYW7z29OgYrs5aGhKOicFx954gnVZmBsRW9PDxAMVL6RCvi5sbYR70dOS9d/YFNxuFxbp4NrkdbWViTCXsHXSjLmB5vPorW1VaKVScfA4AAW/ZX1dy2HBd9dtLd3aCYNmlcpz1Lhe9b15Nicpo3TtrY2pHMZBFIRUcddKNSxKuHkKck4JVyy8RVCiAvAX4FT670I4LyEaxMdQggOHj6E634vMgLU8DbiincBFNC0Su96du7ciYVIBols6RuqWCaPpWgGIyPqao1SDrt27YY3yCKRvveL8MQeHRpcBA0ugp9/UI8Te7beJHpWKDra21Tv5T906BBsNismJ+WbM58HPNMEhw8f2Va9+j5oEELQ3dVVsWIvH3nVigd/I06fPg02lgCdFlcYiR2/C51Oh1OnTok6rpx0d3eDTaWAePkCfDSfBw0Gt1W9KbA6clrauaH5HNhMettGTpuampDNJJFNRQUdlywojDY1NUmxLEkZGhpCJB5ASCKBm41gWRbzvjsYHtZGz2TgfeNUrLReLbej4iObCyILaS3EuAwX1RqnlHNb7aaUhiil/wPACQC/WEjv1RRjY2PI5HK4viI8VWQ173nnUO1yoXcbydj39fUBAKZCmZKPmQ5xXqv+/n5J1iQnfO/Reb+4qREsSzHvB0Z37hJ1XCkwGo145JETmJslSMvUC3x2FkinqaY33B/C0d3dDeJPVRQVo/4kdDqd5lLyVjM2Noa6+nrQa3dFG5NmsqC3PTh+/Djcbrdo48oNb1CyFfQ7paEgKMtqNvV7M/R6Paw2G2iJuhh8+u92FEQC3jcuEwLbyfDfb2xsFH1NUrNz504AgGfpumxzLgWmkM4ki3NrgWLklK08cpplszAYtR05BcQ3ThdjPhgMBjQ0NIg6bikISet9mxByAAAopdOU0isSrUlSdu7cCaPBgKvLi2WPwVKKaytL2Ld/v+r6VVYCb5x6BBmnmTXHapn+/n7odDrM+cQ1TlfCQCZLNVOT+/jjjyOfp5iUKbPo7h2gsbFB9SnPH7I1nZ2dYDM5IFbBhsGfRFt7m6bLBHQ6Hc48/TTYxRVQnzjN0dnb06CZLM6cOSPKeErBR8QrqTulgQCA7SWGxOOsqio9cprkvrdd03p5PY9UTNi1kor5QAijmbYoq+ns7ES1qxqT85dlm3NygdvOa0kpnzdO0/nKvegZNqPpyGl1dTXsNhsWBd4nW7EQ86G1pVWREhIhltWDAH5CCJkghFwhhFwlhGjOQDUajRgdHcXVlfKNU08ogGg6te020w6HA3W1tZiNlL6xnI1k0dhQvy0UVk0mE3p6urEgcuR0IcClSQ8ODoo6rlR0dXVhZGQEd+5IL4wUDALLy8CTTz61rRw9H1T4OlEaKL/ulART6O7SvtHx2GOPwWgyIX/tTsVjUUqB8Qn09fdjYGBAhNUph81mQ21dHWglkdOAHzq9vth6YztR7XIVjc6t4I3Y7Wqc8n0/U7GAoONSsSBc1dWadHARQjB2cAxTS9eQFyFltRQm5t/Djt4dqi87Wk0xcpqvPHKqdeOUEIL29g7MRZdFHXc+voIOhRSvhewGTwHoAfAQgCcBfKTwp+bYs3cv5iMhBIXIta9ivGDYasnLVCrdPT2Yi27cN2oj5qJ5dGmw5cNmDAwMYinEpeKKxUKAwuGwa6r+5ZlnnkEsxmJOHGHrTbl5EzCZjJzC6YdonmKdaJl1pzSdAxtNa7relMfhcODEI48AE7OgqdKzUTaCznnBhqJ4+vRpkVanLN1dXSChYNnH00AAbW3ajq5vhquqCiRdYlpvwYjdrmm9DocDBoMRqbiwayWVCKKutkaiVUnPoUOHkErHMeu9JflcsWQICysTOHxEW+KevDGZZsWJnPLGrlbp6OzAfGxFNMXeVC6DlXhIsdZCJRunlFLPRh8pFycVu3ZxtX83feVJkt7wedHW2oqaGu0+/Dajq6sLS7EsciUYZ9k8hTeW1ayq5kb09fUhk6UIxMQbcylI0NfXL3sT40o4fPgwGhsbcPMGQanPuupq7lMqiQQnhHTy5Ck4HI7yFvohqsLhcMBV7QINCG9JBYBT6oV2eu1txZNPPgmay4O9PV3ROOz1CTiczm3TaqmrqwtsMAjKlu4IXQ0JBdG1DRwYG+FyuQSn9W5X45QQgurqamSSYUHHZZNhTe/P9u3bB5PJhJse6TVHb3kugEJ7ferNZjMAIJOvzPEHcAYuP55W6ezsRCyTRDh9781rR1UjOqq2rsXmo7BKOYo/kHl0vb29sFgsZRmnLGVxK7CCnbvUL25TDl1dXcizFIvRrVMlFmJZsFS7LR82YseOHQCApaA43qdcnsIXZjVXk6vT6fDRj34MPh/FSolaFPv2c59SuXUToJSL0n7I9qGrswskWJ5xyiv1bhfjtLu7G4NDg8CNqbI92jSeBPUs4vFTpzTv3efp6OgAZVnQsPDWBzSTQT4a3RbR9Y1wuVxgk/GSrheaioNhmG2tcu52u5FOCDNO04mwplOdzWYzxsbGcGv2PNgyHTilcsNzDq2tbZq7n4qRUxFqTtN57RunfP39TOTeds0nhx/DJ4cf23K82YLitVJ1/R9I41Sn02FwcBB3gj+tbNVR5YZVb4BVb8BgbQM6qtaqIs5FwkhmM5oRtxEKfyGWUnc6G+a+s52M0/b2dhgMBnhFMk5XwhQshSZVnR999FE4nQ5cHxd/7EwauHuX4P7779dUuvOHbE1HRwdosDzFXhpMwWA0KqIOKBWPn3ocbCgC6i2vxpK97QEo3Vap73zrAxoUVksIADQUWjPGdqOqqorrj5veOjWeJuOwO53bul7f5apCNlV6KhOlLDLJmKaNUwB48MEHEU9GML0owQu4QCTux6z3Jh566EFNZXYBqyKnbOWR00w+84ExTkvFE1mCxWxR7F28fZ9oWzA0NISZcBDJ7Foj7Bd2HkCHy40OlxtfuO8x/MLOA2v+/k6ACyNpRdxGKK2trTAaDKUZp5EsjAbDthKl0Ol06OrqxHJIHOPUWyiV0WI/PrPZjDNnnsHCAlDGHvKe3L4NZLMUH//4x8Ud+EMUp729HTSbB2JlbBqCSbS2tiiiDigVx48fh9FkAr0tvAqGUgrc8WBoeHhbPWf51ge8oSkEWqhV3c7GKVBar1OaisO1TVN6eaqqqpBNlR5hz2WSoJTVfO/XsbEx2Gx2XJ18Q7I5xqfeAgXFgw8+KNkcUmE0GsEQpuLIKaUU6Zz2I6cOhwP1dfWYDpcv9roaT2QJPb09ijm+PrDG6cDAACilmA4L23VPBn1w2O1obm6WaGXKotPp0NnViZnw1sbpTDiL7u7ubbWRBICenl4sh4koheXeEIXFYtZkvzUAOH36NCwWM8ZFdN5ms8Dt2wzGxsY0abR/yL15PyomPLWXCWfQ0b49Unp5LBYLjh45AkzNg+YFyl8HwmCDETzy8MPSLE4hLBYL3DU1oOHyjFNGp9u2GRd8xI8tQbGXJuNwa0hhtRzsdjuyJQpEAShGWbWuY2A0GvHggw/g9uwFpDPlq59vBqUUVyf/HUNDQ5p0fBFCYDKZKjZOczQHlrKaN04BoK+/D9ORpYrHybMsZiLeYpmbEnxgjVP+pE8FhaVaTYUD2NHXp7kUCCHs2NEHTyR3T+OMpRQzkRx6Fbx4paKnpweJNItoeYKja1gOA11d3ZpNu7Lb7Xj66TOYmQHCwsp+NuXuXSCVYvGJT3xCnAE/RFXwUTGEhBmnNMciH06htbVVglUpy/333w82lQZdECb1z07MgRCCY8eOSbQy5WhvawPKMk5DaGxs3JZKvcD7xmkp7WRIOrFtxZB47HY7ctk02HxpbVWyBUNO68YpAJw4cQLZXAY3POdEH3vBdxe+0Dwee2zr+kO1YrFYkMqXKb5XIJ1LF8fSOn19ffDGA4hW6MyYiy4jk8+iv79fpJUJR5s7ZhGorq5GjdstKHKaY/OYi4QU9SbIwY4dO5DI5LEc37wQ3xvPIZnNa07opxT43P1KU3sppVgJa7PedDVnzpyByWQUJXqazwM3bzDYvXsXhoaGKh/wQ1SHy+WC2WIBDQv0aBe+XzRutxH79u3jUnunF4Qd6FnAyOio5uvnNqK1tRWICBdEIpEI2rahA4NHSFovuw1qK7eCF3vKlbjhzhWirNuh9/rAwADa29px+e7roo99+e6PYDKZNK0AbjFbkMpVZpzyxu12iJzyPbAngvMVjTMR4o5XsnzxA2ucAkBXdzfmoqV7bheiEeRZVjH1KrngvSVToc1rxqaDmTXf3U7wAk/eCo3TUBxIZ7V/vbhcLjzxxEfgmQZi0crGmpgAkkkWn/jEz4uytg9RH4QQzvAQmNZLw9z3tZhithUmkwl79+wB5rwllwvQWAJsIIxDBw9KvDplaGlpAZtKgaZKv04opWCjkW15jfDwtZJbGac0nwObTm37yKnVagXA1ZKWAv+97WCcEkJw8tRJzK9MYDk4K9q4mWwK16d/gvvvv1/T58lsMVec1ssbp/x1pmX6+/vBMAzuhiprUH8nOIsqZ5Wi5WgfbOO0qwvzkTDybGl1QLMRTohBa5LbQuno6IDRYMD0PYzTqVAWJuP/396dx0l2l4X+/zzVay1d1dvsk5npySSTWTOTTAghC4FgTII/BQ0EjGhwQa8XUVxwuaiov3tVFEERROQKLoAXQbmKbIoggmwhC9m3WTKTmczae+1Vz/3jnFN1urq27unuqq7zvF+veU3VqVNV3zp9lu9znu/S25GDUkSjUdavW3vRmVPv/Z3Qr/JVr3oVXV1dPPrY4j+jWITHHg2xa9eu0lzDpjNt3rSJ0PQCB0RyM6edGngcOnSI4tQMTDU5h+WJ06X3dSJv3AadWkB/gVQSzeU6dswHcPoa9ocjaKp+plDTThDW6cGp19wyn2vuJoa3Xic00wSnaW9XVzcPPv2lJfvMR49+jWwuze23375kn9kK4YhlTv3C4TDbx7bz5IVnL+pznhw/zp69e1rafTHQwenWrVvJFwucTTY3TPnJ6UlCoVDHVp483d3d7Nixg8MTtQdFOjKR47LLL+u4wZA8Oy67nDOTF3d4PD+uhEKhjriZMTIywnd/920cOSykFtkX9+hRmJ11+pp2cp9tAxs2bKAwlV7QAEA6mSYWH1jVd/LrOXDgAEDT/U6LJ88yEI93xPmjGm9AI11A015v3dU6wFyzEolE48yp+3qnB6deRqvQZHBa6LDgNJFIcMMN1/Pw4f8kX7j4aVMAHnjq39m6Zeuq71oTDodJFy8yOM131v6yb/8+npk4SX6R8+NeSE1xZnacvXv3LnHJFibQwanXt+m56ebu3J6cnmLDuvUdMxF6PTuvuIJnJ3MUivOzh/mi8uxUjp07r2hByVbGpZdeyoXpApnc4rOnpyeUSzZvKk0WvdrdeeedFIvw+OMLf68qPPaYsG3bVl7wghcsfeFMW9m4cSMoC5tOZirDxg4dgRWc681APE7x+fnza1cjp8+zf9++jr2R482fp9MLD047daRez2Azwak7YFKn9zn1Mlr5bLOZ08yc93WCO+64g1RmlsePffOiP+v0hWOcPHeYO15+x6o/t0QikYseEKmTmvUC7Nu3j2whx+GJxfU7ffyCM+XZ/v37l7JYCxbo4NTLgD4/09zF8XRymk2XdO5ADH6XX3452UKRUzPzR8g7OZ0jVyh25GBIHm/Qq9Pjiw9Oz0wIl13eOX1yN23axI033sgzTwu5xjMNzXHyJExOKK9+9V2r/oJoGvMyWzrVfH+g0HSeDes7N+gQEfbs3o2caTwInybTFKdm2LNnzwqUrDXC4TDxRAKdbr4ju7qd3ls1MfxKGRxMIOlGzXqd11f7fJ6NeEFmId/cucTLnHbKTWGAK6+8kg0bNvLAUxc/MNIDT32Rnp4ebumA6amWJDjNd1Zwun//fkSER88dXdT7Hzt/jGgk0vLuaIEOTuPxOLFolDOzjS+Oqsrp2emOv2PrKU21U6Xf6VG3uW8nB6feCLvPLzI4nUkp06lix43s/OpXv5psVnnm6YW97/HHYGRkmJtvvnlZymXaS6nZZZP9Tp2BbtId31xz165dFCem0Uz97aJnnQC2Ewec81u3bh0601y3GgCdniYxONjxrZcSiQRk6vefCEqz3lJwmmsyOM1n6e3rW7XTt1UTCoW4447befb045yfXOCI3z65fIZHjnyVG2+8sSNuaoTDF9/nNFVIlT6rE8TjcbaPjfHo+SOLev+j54+wb//+lnfZ65yjd5HWr9/AmdnGF8eZbIZ0LheY4HTTpk309fZyfGp+iuz4VJZwf19Hb4vh4WFGhoc4tcjg1HtfpwXwl19+OXv27uHJJ4UmxxFjfBxOn4ZXvvL7O3ZuQjPX6OgooVAIbXZQpNkcWtSOz4h5N6v0fP1R4vXcBCLS8rvXy239unWEFhKczsx0/D4CTgWz0GCe08BlTgvNNdcp5DP093VOk17PrbfeSldX10UNjPT4sW+SziZX/UBInkgkQjqfpqjNj21QqdMypwAHr7qKp8ZPkGnymPGcSY5zZnacgwcPLlPJmhf44HTd+nWcb9B8BuBc0rlQBOHCCNDV1cX27ds5Pjl/5352Ks/Y9u0ddWeymp1X7OL58cU1QT11QRGRVT+NTDWvfMUrmZlRTjZ5A/fJJ6Cvr7djLoimsa6uLgaHh5rvc+qut3bt2mUsVet5waaeaxycrt+4oaMqTNWsWbOG4uxM09PrhGZnWdfh+wg4Aafmc2iu9vGj6SSRaLTlGY7l5jXPXVjmtPMy60NDQ1x77bU8dPg/KRTnd7dqxoNPf4kNGza2vD/hUvHOjxcznUyqkKK7q7ujWmMcPHiQfLHAE+ePLeh9j5x1sq1XXXXVchRrQTo7umjC2rVrOZdsfHE8547oG5TgFGDb2BgnZ+aO+KWqnJzOMzbWeUFXpZ07d3Jhukgqs/Ds6cnzyratWzumqYjf9ddfz/DwEE891XjdbBaOHRNe+tJbGBgYWP7Cmbaxds3apoNTdddbs2bNchap5YaGhogPJtDz9Qfhk/Epdmzv7KwpOH9vzeUg07hyqaoUZ2cYHR1dgZK1ltdUVzO1b5xrJsnAQGdnTaEcnBabHKm2mM921GBIfrfddhuzqSmeOfHAgt97Yep5nj39OLffflvHjPvgjeyezDdOMNWSzqc7rp62b98+uru7efjc4QW976FzzzAyPNIWU0QGPjhds2YNmXyeZJ07lEApuxqEC6Nn69atTGfy5H0tJqYyRWazhbbYeZfbrl27ADh5YWHBqapyalzYtcqHaa+lq6uLO+54OadOwmyDKRuPHYV8Xnn5y1++ImUz7WPtmjWEkk3e4XeD0yCcXy8d246M1x6ET3N5ipPTjI2NrWCpWsP7e2uyiblfs1k0l+v4GxhA6Uae1mnVpekkiUTnB6fd3d10dXWXRuFtpJDL0NfbOYMh+V1zzTUMDQ7xnWf+c8HvfeiZ/0QkxMte9rJlKFlreJnTi+l3msqniEY6a/qy/v5+9u7dy8Pnmu93WtQij54/yqFrDrXFzYvAB6fexfF8gwmvx1NJuru7O37wAb/Nm52RiXOFcnD2/KxT2fSm4elkO3fuRER47vzCgtPzU5DOFkvBbSe69dZbATjc4Mbc4cPC2Ni2jhsYyjQ2MjJCcSbbVJNNnc3R09tLLBZbgZK11tjYGDo+hVaZpgtA3cC1U+c39SvdjGh0lwtQd52RkZHlLFJb8PqR1gtOJZMi0eH9TT19fX0U881lTgv5LOFwZ2ZOu7q6uOVlt/D0c/eTTC9glGst8vCRr3Dw4MGOugHoBaep/CInX3ffG4l2XveJQ4cOcXzqNBNN7ieHJ04ym021RZNesOCU4eFhACYbjIw3kU4xPDTUFncUVoo31U7WF5yecYNT77VOFg6H2b59jBPnFhaceut38jQQ69evZ//+fRw7KtSKPSYn4fx55dZbvztQx41xjIyMoLkC5JoYrCKZY2g4GOfXbdu2ofk8TNcIyC5MltbrdF6gqU0Ep7jZ1U6qXNdSCk7r1UsyyY4fDMnT19+/gD6nmY5t1gtwyy23UCwWePTo15p+z/EzTzI5c47v+q7OyZqCLzgtzD9OtgxsIdwVJtwV5orBK9gyUL21XyqfIhrrrMwpwNVXXw3AQ2eba9r78NnDiEgwglMRuUREvigij4nIIyLys8v5fYvhXRwn0/WbBUxkUqVANijWrl2LiJDz3eE/m8wTCoU6fuASz759+zl5AQo1shzVHD9XJBEf6PgA/pZbXsbUlDJeY9rGY8ecuR1t+phgKp0vk02MGJjMMTrc+RkxcLpLAOiF6v1OdXyKnp6ejh4N3ePtI5ps3GfMa/obhOtwqVlvneC0mE4FJjjt7+8n3+Q8p8UOD063b9/O1i1bFxScPnLkv+jr6+NFL3rRMpZs5dXLnN698262Dmxl68BWfvXQr3L3zrurfkaqmOrIgefGxsYYSgzy0Nlnmlr/oXPPsOPSHW3TOnS5M6d54BdUdRfwQuC/i0hbdcQbHBwEGmdOpzIZBoeGVqJIbaOnp4fhocE5wen5ZIGR4eGOHyHQs3fvXnJ5XdB8pyfOC/v2X9nxWaDrr7+eUCjEs8erv37iuLBnz55ANMMz8y0kOA2lCoEIOsAXnI7XCE4vTHLJli2BOMf29vYSiUab6nPqBbBB2E8a9TnVYoFiNh2IZvAA4bBlTj0iwkte+hJOuNnQRorFAk88+02uu+66jhv4ZykGRErlOzM4DYVCXHXoah45f4Rig641qVyGp8ef4+pDV69Q6Rpb1uBUVU+p6n3u42ngMaCt0kmRSITu7m6mMvUzp1PZdCmQDZK169aT9zXrHU8XApM1BWfUM4BnzzQXnE7MKhMzxY4Zqr2eeDzO/v37eO7E/CB8ehomJpQbb7yxBSUz7cA7X2qq8aBImsoF5vwaiURYs24deqH6oEgyPsX2AAyG5BkeHoYmMqckk/T193dcBbua3t5eenp7a2ZO1a2vBGUE9Eg4TCHX3KA3+Wznjb5a6cUvfjEATzz7rYbrHjv9GMn0NDfddNNyF2vFecHpxfQ5TeaTpc/pNFdddRVTmVmOT52uu96j549S1GKpKXA7WLE+pyKyDTgIfKNi+RtE5F4Ruffs2bMrVRz/95OIx5muE5yqKtOZdNuku1fSyMgIeV9cNpFRRgMwWqJnaGiIrVsu4VhFcLp2UFg7OD8o84LYK6+8ckXK12rXXfciJieVmZm5y08+5/z/whe+cOULZdpCKdhM1c+calEpBig4Bbh0bKw0Yq+MDCIjbiCfzlCcTQWiv6lndGQEUo0rl5pMMRSArKknFhuo3azXXR6Y4DQSWUBw2pmZML9Nmzaxbdu2poLTJ459i76+Pg4dOrQCJVtZ4XAYEVl0cKqqTp/TDg5OgYZTyjxy7jB9vX1tNYjnigSnIhIDPgH8nKrOuV2squ9X1UOqeqhVQ8TH43Gms7WbjKTyOQrFYmD6d/iNjIyQ9zXrnUgHp/md58DBqzhxHgq+DPJ3Heziuw7Ob3Z39IzT3zQolUvvgnfq1Nzlp07Bxo0bAtFvzlRXOl+mG2RO3deDFJyOjY1RHJ9CCwW6rj9A1/UHAErZ1CBMI+MZGhpC0k0Ep6kkIwHqWjMwECsFoZU0YMFpOBym0EQAUizkKBYLHR+cAtxwww2cOPsks+k601Kp8tRz93Ho0KGObOocCoWIhCOLbtabLWYpOCWHeAAAIABJREFUFAsdG5yOjIyw5ZItPNIoOD1/lH3799Hb27tCJWts2YNTEenBCUw/rKr/sNzftxjxRIKZOvOczmad14JyIfAbHBykqKDqjNqbzhUCVYkEOHjwILm8NpxSRlU5dkY4eNXVHd/f1LN582aGh4c47Ws1UizC2bPCVVe1TxMRs/K6u7sJRyKNm/W6wWmQzq/btm1zTqoTc4f51wsTgDPoSVAMDQ1RbKJZbyidYihAwWl8YADNVs8WesFpUPqcRqNR8jW2hV8+myqt3+le+MIXoqocfu7BmuucvnCM6dkLXHfddStYspUViUQWnTn13tfJ+8vBqw7y5Phx8sVC1dcn0tOcnD7LgQMHVrhk9S33aL0C/G/gMVX9o+X8rosxMDBAMl+76dms2xE/SJUnjxeI5lWZzhTmLAuKK6+8klAoxJHT9YPTc1Mwkypy8ODBFSpZ64kIV155gHNnQ6UpZSbGIZfTUn9dE1wD8YGmM6dB6jZx6aWXAqDnJ+Ys1/OTxAYGAtU6ZWhoCM3l0FyD5t+pYI2YH4vFkBrdjTQbrOA0EomQyzQeNCvnbpcgZE537NjB0OAQTz/3QM11nnFfu+aaa1aqWCsuGo0uOnPqva+Tj6Mrr7ySTD7H4Ynnqr7+2PljAMEKToHrgdcBLxWRB9x/dyzzdy5YLBYjWS9z6l40gxicek3zCkWYcecrDFrz5mg0ys6dl3P0TP31jjzvbJ92mSdqpezZs4dksuhNQ8jZc+XlJtgS8XjD4FQzzuudXEGotGnTJrp7etDzc0fslQuTbB8bC0zLC6CcDa3T71QLBYrpYA1KGIvFIFt/QKROzvj4xWIxCvkcxUL9Gxh5N4ANwrkkFApx6JpDHHv+EVSrzyV95NRD7Lh0R0e3OIgNxC46OO3k48hLEjzuBqGVHj9/jHA4XLph2i6We7Ter6iqqOp+VT3g/vv0cn7nYkSjUWbr9Dn1AtdO3oFr8bIZBVVmss4JMIhB+tVXH+LUhSKpbO3s6ZHTyuZNG1m3bt0Klqz1Lr/8cgDOu/OdXrgAg4NxWtWH3LSP+EAcstWbE5WkndeDdNOrq6uLLVu2zJnrVFXR8alANekF/6jOdSqYbuDayZXsSrFYjGKtZr3ZYI3W6wWb2XT97GkuQMEpODfCk+lpTl+YH3hkc2lOnH2Kq67u7JvlsViMZGFxwWkQmvUmEgm2XLKFJy5Un/PvyYnj7N69u+2mLlux0XrbWTQaJZPPUyhWv/vkBadBaCpSyTvJF4swG+jg9GpUmTdqrydfUI6fg6sPdW7zmVq2b99OKBRifNx5PjEu7NhxeaCyP6a6WCxGKFP9vFqSCV6fU8DJkI77BjOZmkVz+UANhgTlgFPrZU7dAZOClDmNRqMUM5nqWbFsmu7u7rYawGQ5eeeGfIOmvTk3eA3KjS5vVoBjzz8677Xnzj5FsVhou+aaSy0WW3zmdDbn7C+dfu3Zu28vT0+cmDff6Ww2xYmpM+zdu7dFJavNglPKQWeqRr9Tb3mQg9OCKslccIPTnTt3Egn3c/T56sHpc+eVXF4D16QXnDn5Nm7cwOSEcxNjclIDl/0x1Q0MDKANMqeaKSAiHT83YaWxsTGKsyk07bTa8bKoQRnp21OecqhOcOoOmBSkzKmTzVGo0qpLM2nCkc7N9lTygs1seqbuetmM83pQ6iijo6NsWL+B42eenPfas2eeIBQKsXv37haUbOVEo1GSOWvWW8/u3btJ5tKcnJk7Xeczbj/UdtxHLDiFUqUoXSM4TQc4OPUO2qJSCk47/UCupru7m/1XHqjZ7/ToaSUUCrF///6VLVib2LZtjKmpEDMzToC6ZcuWVhfJtIFoNIqmc6jWGUwsWyAcCRMKBety5GVIveljgh6cWrPeubzrrFYLTrNporHgXIe97kW59HTd9XLpYAWnAHv27uG5c0/OO8eeOPMkY2PbO77eGovFSOVSFGv0u60nCAMiAVxxxRUAPDM+d1CkpyeeQ0TYuXNnK4pVV7BqAzV4B286X33gjnQuR3dXFz09PStZrLbgBe4FhVTeCcA6cb6sZlx11VWMzxSZmJlf0T56Bq7YuTOQgTs4A7zMzBSZmio/NyYajaJFdU4gtWQKRAJ43GzduhUAHZ90/59izdq1gcsg9/T0EIlG0WSdzGkqmM16garTyWg2QyxAx4wXnGYbBKfZ1BSRaJTu7u6VKFZb2LVrF7OpKSZ9WbFiscjz5w+za9cVLSzZyojFYii6qOlkZnOzdHd309fXtwwlax+bN28m3B/myOTcCemPTJzkks2XtOUNDAtOaSJzWsgHrsLgCYVChEIhiqqkckUi4f7A9iX0poip7HeaziqnLhQ5EKApZCpt2LCBYhGOHC4/N6Z0syZTZ8TebJ5YtLPvXFczOjpKfzgM406FWyam2eYGrEEzNDQEdTOnSfr6+gJ1Ha4XnJJLBzM4TU3VXS+bmgrUDQyglPU6df5IadmF6efJ5FKljFkn87Lki+l3OpufZSDW+Vn2UCjEjh07ODb1/Jzlx6af57LLL2tRqeqz4BRKmcCamdN8nnBAs4WAG5xCpqCBqhxU2rJlC4lEnGNn5zYfOXFOUW2/eaJWktdE8fhxSCTigWp+Z2orBad1+p1qttDxzaqqERE2b96MTk47I/VOznDJJZe0ulgtMTI8PCc4DY2MEBoZKT3XZJLBgJ1TStmM3PxmvZLLBqqVTm9vL+FwhEyD4DSTmmIoYMHptm3b6Orq4vkL5eD0tBuo7tixo1XFWjHetWMmV78/cjXJXDIw154dl+3g+PTp0qBIU5lZxlPTbTeFjCc4bR/qKAen1TOnmXzeucMdUF2hEEUtks5rW6b/V4qIsH//lTx431fnLD9+Vunq6mLXrl0tKlnr7dq1iw9/+MNkMhkGBwcDm103czUTnIayGqgskN8lmzdz5NvfgtkUms+zefPmVhepJYaGhpBjz5aed7/ohjmvayrF8PDwSherper1OSWXCdy1eGh4iGxysu46udQUQwEbjK+3t5dLLrmEM+PlqULOjB+nq6s7EGM/lDKnixgUaTY3y8Bo52dOwbmJkcnnOJscZ110mOPTzgAq7Tp4pWVOKQenmUKNzGkhF+jgNBQKoapk8kX6+4O7HQD27t3L1GyRqWS5ae+J83DZZTs6vt9CI2vWrGHz5s2BuRNpGitVsDO1g1PJFQJX0fZs2rSJwvQs6k4ps3HjxhaXqDWGh4frDogUSqec7GqAeMdE9T6n6cAdMyPDw2QaBKfZ5AQjvox7UIyNjXFu8kTp+dmJ42zevDkQfW+94HQmv/DM6Ux+JjDTDnkD7Z2YdvomP+f+v7VNu5JYcEq5z2mmRrPeTD4f2EGAAEJdXRTVSX70BXg7QHnI7efOO8FpoaA8P67s2dN+80QZ02rlpom1R1LUbDFQTRT91q9fD6roKaeiENS+2kNDQxSzWTRXvfWSJpOB60tYPnayc5arKoVM8DKnw8PDZFMTNV8v5LNkM8nAZdjB6XI0OXOOv/u33+f/fOHtPHvmCbZta8+gY6l5wak3Z+lCJAvBadbrZdFPzZwD4OTMOaKRSNseLxac0jhzmikWAt3XUkRQIFvUwGcHt2/fTk9PNyfd4PTMJOQLGoiBB4xZqFIFum6f03zgKtqetWvXAqCnziEijI6OtrhErVGqIFWZ61QLBYrpdNtWopaLVy+Z16w3nwMtBq5OMjIyQma2dnCaSU6U1guaF73oRezevYfuWJquaIqxsS289KUvbXWxVsTFBKcz2eBkTqPRKEODQ5yaPQ/A87Pn2bR5c9t2wer8nH8TyqP1Wua0Gm9ApGyRwAenPT09jI2NcXriaQCeH3eC1Msvv7yVxTKmLTXqc6r5IlooBjY4XbNmDQB65jyJRCKQ05VBOTjV5CxSWVl0m/sGLTgNhUL09fdTrBgQSd1MatCOmeHhYfK5DPlsiu7e+YF5Zna8tF7QjI2N8a53vbPVxWiJ3t5e+nr7FjwgUr6YJ51PB2pO3I2bNnL61AUAzqTG2bupfWeYsMwp0N3dTXd3d+0+p/ngTiUD5cxpvkhgK09+l166gzOTgqpyZkIJh/ud5nnGmDkaZk7d5UFt1luqSBeV0dHgZXw83ujempzf79RbFsSgIxwOzx+t130etDqJ16ogPXuh6utpNzgNauuDIIsPxBccnHrrByVzCk63kXPpSQrFIueTU21db7Xg1BXu7689z2k+F7gLgZ+IoAr5otLb29vq4rTc2NgYqUyR2TScnVLGxsbatmmEMa3U1dVFb18f2iA4DVoWyBMOh0uD7Y0MBzc4LQWeFpzOEQ5HSplSjwY9OJ0Zr/q6t9yC0+CJJ+JM56YX9J4gBqfr1q3jfHKSt/zHeyhqkXXr1rW6SDVZs15XOByu2qxXVUnnsoFv1utkTjUQo7814o1udm5KuTAt7Lk6GAMPGLMY4UiYnGVOa4rH46RTKRKJRKuL0jKJRIJQVxearNJvzF0WxL6EkUhkXp/ToDbr9ZrAp2fOV309PXuBvr7+QJ9LgiqeiDN1vv4cuJWms04wG6Tg9CUveQmnT5+mUChwsLeXa6+9ttVFqskiDVckEiGVnZ85zRTyKMGuPIE7QqAFp4Az/QM4/U1n08XAzk1oTDOi0ShT2erThKgFpyQSCc6cPh2oSlKlUCjE4OAgE9Uyp7NJRCRwo/UCRCJhmK2odLvBadAyp97NCa/5bqX0zAVGR0etFVMAJRIJTuZPLug9XuY0SDcFt2zZwlve8pZWF6Mp1qzXFYlGSeWz85an3KHtg3Yh8PNO9gV1KhFBNzo6Snd3F0dPO4MhBXX6B2OaMRCNWZ/TOsJuq5wgbwNwzqvV+5zOEk8k6OrqakGpWisSDiPWrBdwBr5JJAZrZk4zM+dZu3bNCpfKtINEIlHKhDbLawYcpOB0NbFIwxWNRklVadabdC8MQZkLqZqhoSFEoGiZU8AJ0NeMjnL8nBOctnO7fWNaLRYrB6cyGkFGfc0RA97nFMo3/IJ8jQEYGR4mlKqSYU8mA9uPMBwOQz5LaGQDoRH3JmhAM6fgNO2t1ec0kxwvTc1kgiWRSJDMJckX59bhtwxsYcvAlqrvCWKz3tXEIg1XNBrleJXMadIdJCnId7UPHjzIsSOHKaha5tQ1umYNp54/7TwOaMXJmGZEo1FC2SIAXTdUVBQyljn9nu/5HgCuvPLKFpektUZGRtDZ+X1OJZVkdGMwW6eEw2E0lyV8/ctLyzTAwenatWs4/fixecuLhTypmYlSv1QTLF6AOZObYbCv3Pz/7p1313zPdG6aSDhiM1C0KQtOXbFYjNns/ODUWxbku9rOPKdK0YLTkpGRckBqzUKMqS0ajaLZAtV6gmkmX1onqG666SZuuummVhej5UZGRiim02ihgPib8CaTgRwMCaC/v7/maL1BHKRx7dq1pL99/7zlmeQEoBacBpTXH306Nz0nOK1nOjvNYCJ4/dhXC4s0XAMDA8xmM6jqnOWz7oXAglNFrc9pyZYtTgZoyyWbA9kXyphmxWIxNF19DmmyBfrD/XYMmXIA6suearFAIZkM5DQy4ASgxVxFvSSXJRQKBTLjs2bNGnKZFLnM3ObfqenzpddN8HjB6VSm+RF7p3JTJIYssdCuLHPqGhgYoKhKqmKuUy9zOjAw0IpitYVQKIR3bbTg1HH33Xdz++23B/qmhTHNiMViaKGI5otId8X5I1MgaseQoRycajKJeP3AkikguF0nwuEwqEIhD91OMKr5HH39/YEcldbrU5qeuUBPX7mfujdIkvU5DaZScJprPjidzk2zedBmWmhXFmm4Sm3WK+YUm3afB7nTtP8iGMQLYjUiwsjICH19fa0uijFtrXRjL1Mle5rJMxAL7o0/U1YOTn2ZU/dxkDOnwNymvbkM/f3B628Ktec69Z5b5jSYSs16FzBi71R2KpDTU60WFpy6vOBzOjM3OJ3JZohGIoFudubPllrm1BizEOXgtMp0MpkCiQDf+DNlVYNTt4lvUDOnpX6lvsEaNZcNZH9TKAefqXnB6QWi0VggB4kyzjWmq6uLyexkU+sXtWjBaZuzSMPlDWozPS9zmg78gDeWOTXGLFap6XuVfqeSKQa6y4Qpi8fjzk3gWV9/Qnfe06BmTr1gy5851XyOSECDsOHhYUKhEOmZC3OWp2YuMLommDcwjFMvHYwPMpVtrlnvTG4GVQ3seWU1sODUVcqcZtNzlk9nMoEPTi1zaoxZLC/41FrNei04NbgVzOHhec16Q6FQYK/BpQypv1lvPks4HMzMaVdXF0NDw6Rn5wanmdkLrLP+poE2ODTYdObUW29oaGg5i2QugkUarlKb9YpmvdO5DImAp/4tc2qMWaxSf/303Ga9qoqm84Huz2/mGh0eRpPlzKkmk8QHBwPbrabU59Q/B3s+F9hmveDMdVqZOc3MXghs02/jGBoeanpAJG9UX2vW274sOHVFIhG6u7uZzKTmLJ+yzKllTo0xi1bKjFY2680X0YI16zVlIyMjhFK+a3AyyUiAm96VM6flWQQkH9w+p+D0O80my8FpIZ8lk5q2wZACbnh4mInsRFPreutZ5rR9WaThEhES8TjTmXKzXlVlKpMK/N0Vy5waYxYrEokQ6uqaP9ep+9yCU+MZrsicSirJqDf/aQBVzZwGeEAkcG5gpGfGS3O/ZpJOoGGZ02AbGhpiKjNFUYsN1/Wa9Vqf0/ZlwanP4OAgU74BkZK5HIViMfCZU39AaplTY8xCiIgTgNYITq1Zr/EMDw9TTKfQgtsEPJUKdAWyWnCqAc+cjo6Oks9lyGedDHtmdhwoj/Zsgml4eJhCscBsbrbhupOZSfp6+4hEIg3XNa1hkYZPYnCQKV/m1Hsc9Mypv7+PBafGmIWKx+PzglNNOc+DfvPPlJWutekUWixSSCYDff2t1qy3mAt2n1MvCPUypunZiTnLTTB5N7GaGRRpMjvJ0OCQtQRsYxZp+AwODjKdK2dOp7IWnIIFp8aYizM4mLDMqWnI6wOmqRRk0qAa6H5hlZlT1SIa8AGRSsGpmzG1zKmBcnA6kWnc73QiM8HwaHBbZKwGFmn4JBKJOZlTr/9p0O/s+wPSoI6aaIxZvEQ8gWQq+gKlLXNq5ioFoqmUE6AS7EFLent7EQmVp5LJOxnUIAenXhDiZU4zyQm6e3qs73rALSQ4ncxNBrq7wGrQ3eoCtJNEIkE6l6OoSkikFKgGvfJkmVNjzMUYHBys2qxXRIjFYi0qlWk33rVWU0nEvdYEueWSiNDb10vRDUrVgtNycDrrBaeTDFoTzcBbSLPeicyEZdrbnEUaPt6FsVB07vBPu4MjWXBaDk67u+1+hjFmYeLxOMVUtjTCJgDpPNGBmLXGMCVeIKqpNJq2m8MAff394A2I5GZQgxycRiIRenp6yaScuSozyUmGh4ObXTeOcDhMJBxhPDNed71MIUMyl7TMaZtb1uBURP5SRM6IyMPL+T1LpRScajk47e/ro6+vr5XFajl/QGoVSWPMQiUSCVDmZk/T+cAHHmauSCRCV3c3pFPgNusN+j7S19eH5ixz6hERBgcHyaacDFkuNclwgJt+m7Lh4eGGzXq91y1z2t6WO3P6IeC2Zf6OJeMNzFEoOnf3ZzJp4taPYU5w2tPT08KSGGNWo1KA4Q9OU3mGBq1SacpEhIF4HE2XM6dBHzAr3N9f6mtqmVPH0PAQ2dQ0ANn0dKD7JZuykdERxrP1M6cWnK4OyxqcquqXgQvL+R1LybsI5n2Z04GAXxjBMqfGmItTCk5T5eA0lC4wGPCsmJnPmXYojWbSRKLRwF9zwv3hUsbU+z/orbmGBgfJpqdQLZJJTgU+u24cIyMjDfucesGpNettby3vcyoibxCRe0Xk3rNnz7a0LN5ob4N9YbYmhpnNZS04xTKnxpiLU+pL6MucqjXrNVUMxuOQyUAmYyOwAuGwr8+p+3/Qg9NEIkEuNU0+m0K1aOcRAzjB6UR6Yu7YBhW8Pqmjo6MrVSyzCC0PTlX1/ap6SFUPrVmzpqVl8S6Ee9au53X7r2Emlw18kyKYG5BacGqMWajSedTNnKoqxZSdX8188XgcyWbQTNqCU5xAVArW59QvHo+TTc+UmvZacGrACThzxRwzuZma60xkJ+jt6bVR4ttcy4PTdtLb20tfXx+zWefu5GwuaxdHLDg1xlyceX1OMwXQYE8TYqqLxWKQySKZLAm7eeEEol6fU2vWCzjBaSGfJT1zofTcGK8f6US29qBI4+lxhoeHbeqhNmfBaYVYNMpsNoOqMpvN2N0VLDg1xlyc3t5e+vr70ZRbyXYzqFapNJVisRjFTBrJZu36y9zgVG1AJKDcyi05dXrOcxNsXnA6nq49KNJEZoKRURsMqd0t91QyHwW+BuwUkRMi8mPL+X1LIRaLkcznyBYKFIpFO+nhVCw9FpwaYxYjnoiXM6dpp7JtzfFMpUgkgubzaDpFNBptdXFarq+vDy31ObVmveAPTs8A2E0MA5QHOaqXOZ3ITVh/01Wgu/Eqi6eqr13Oz18OsViM5NkLJN07lHZxnBuQ+gNVY4xpViKe4FzKyXR4AyNZ5tRU8q65xXSaSCTS4tK0Xl9fH8WKeU6Dfh32gtHU9Lk5z02wNcqcqirj6XGbRmYVsGa9FaKxGLO5HLMWnJZY5tQYc7EGEwkkU3CeWLNeU4P/mmvXXy9zmkO1CPkc3T09gZ9epxScTp2d89wEW29vL/FYvGbmNFVIkSlkLHO6ClhwWiEajZLK50i5dyrt4miZU2PMxYvH4+XgNOMEp9as11TyZ0vD4XALS9IeSk1483k0n6OvL9hNeqFcL0vPnKenp9dumpuS4ZHh0lymlbzlljltfxacVohEIk5w6vbxsGZFljk1xly8eDyOelPJpAt0dXcHvu+cmc+/T1hwWh6ZV/M5yGcDP1IvlOtlmeSk1dHMHCOjI6W5TCt5y72+qaZ9WXBaIRKJkMplSeXzpedBZ5lTY8zFisfjFLN5tFCEdJ6BgZgN52/m8QekFpz6po3JZ9FCnt4+uwb7W7RFrHWb8RkdHWUiVz9zas16258FpxXC4TC5QoGZbAaw4BSgu7s8blbQ+7oYYxanNPJ5pgDpvPU3NVX5M6eWWfdnTvOQz9FvzXrp6elh3759xOMJrjp4oNXFMW1keHiYyfQkRS3Oe80LTi1z2v6WdbTe1cgLRifSKcDu3AKEQuV7GJbpMMYsRikYzRQgUyA+Yv1NzXwWnM5Vzpzm0HyW/gFr1isivOMd72h1MUwbGhkZoahFprPTJPrmXmMmMhOE+8NWr18FLHNawdtpLTg1xpilUxpRM51HMgUGbIRNU4U/ILX+lf7MadbNnNo2MaaW0lynVQZFGs+MW9Z0lbDMaQXvwjiZSdHV1WUDABljzBLwglPN5pFsodzM1xif4eFhXv/615NKpdixY0eri9NypQC9kEfyeQvYjanDG4l3IjvBVrbOeW0yO8nIehupdzWw4LSClymdyqQJW5MiY4xZEqXMaaZAMZO3abpMVSLCa1/72lYXo234m/VSyFlwakwdXma02oi9E9kJ9ozsWekimUWwZr0V/JlTuwgYY8zS8DfrVcucGtMU/1Qyms/ZiPnG1DE0NATMb9arqkxmJq1Z7yphwWkF70IwncnYYAzGGLNEvEypTmfnPDfG1FYKRvM5yFvm1Jh6ent7GYgNMJWdmrM8XUiTKWQsOF0lLDit4AWkqXzOglNjjFkivb29dHV3w4wFp8Y0q5Q5LeTRfN4yp8Y0MDQ0NC9zOpmZLL1m2p8FpxX8dyX7LDg1xpglE4mEUTc4tTmkjWnMnzlV63NqTEPDI8NM5ibnLJvIOsGqN2CSaW8WnFaYE5zaRcAYY5ZMJBKB2Vz5sTGmrlLmNJdBi0XLnBrTwNDQEJPZucGp93xwcLAVRTILZMFpBQtOjTFmeTjBqZM5tTmkjWmsu7ubUCiEZtKA1UuMaWRoaIipzNw+p9asd3WxqWQq+O9K2h3Ksng8jrS6EMaYVS0aiYI6jy1zakxzenp6KWRSgNVLjGlkaGiITCFDOp+mv9vpnjeVnSIUChGPx1tcOtMMC04r9PT0ICKoql0EfN797ne3ugjGmFXOny21zKkxzenp6yWfdTKnVi8xpj4vOzqZnZwTnCbiCUIhazC6GlhwWkFE6O3pIZPN2kXAZ8OGDa0ugjFmlfMHpDYaujHN6e3pJWWZU2Oa4vUrnc5Ns451gBOcWpPe1cNuIVTR09MD2EXAGGOWkr+/nAWnxjSnt7cHdTOnXv3EGFOdF5z6+51O5aZIDCZaVSSzQBacVmHBqTHGLD0vIBURq2Qb06Te3t5ScGoDIhlTXyk4zZWD0+nctI3Uu4pYcFqFV2myypMxxiwdr2Ld29eHiA2xZkwzent7S6P12k1zY+pLJJwM6XR2urRsKjtlwekqYsFpFV6HaQtOjTFm6XgV655eO7ca06zenl7IZQCrlxjTSF9fH/19/czkZgDIFXOk8+lS0GranwWnVXh39O0iYIwxS8fLnIbELj3GNKvXdzPHMqfGNBaPx5nKOs16vSDVppFZPayGUIW68/B1d9tgxsYYs1Q2bdoEwNatW1pcEmNWD39AajfNjWksHo+XglKvea8Fp6uHRV/VuF2h7CJgjDFL56abbuKTn/ykDepizAL46yJWLzGmscRggvFz4wDM5madZdasd9WwzGkdljk1xpilFYlE6OrqanUxjFk1/HURC06NaSwejzOTdzKnXgZ1YGCglUUyC2DBaR12ETDGGGNMK1nm1JiFGRgYIJlLAtbndDWy4LQOy5waY4wxppX8AanVS4xpLBaLMZubpahFkvlkaZlZHSw4rcIbrdcuAsYYY4xpJcucGrMwsVgMVSWVTzGTm6G7u9vGOlhFLDitw4JTY4wxxrSS9Tk1ZmG8/qXJfJJkPslAdKCUeDLtz4LTOmzQDmMK1uUYAAAaFElEQVSMMca0kj8gtXqJMY15TXi94NSa9K4uFpxWoe5Ep3YRMMYYY0wreZnT7u5uy/4Y04RoNApAMpckmUsSjUVbXCKzEBacVuGd/C04NcYYY0wrecGpiFXZjGlGKTjNJ0kVUhacrjJ2pqvCy5xan1NjjDHGtJJXF1G0xSUxZnXwgtNUPuUEp1ELTlcTC07rCIVs8xhjjDGmdcqZU2vSa0wz5gSn+RThcLjFJTILsezRl4jcJiJPiMjTIvIry/19S8kyp8YYY4xpJauLGLMwXjCaLqRJ5S1zutosa3AqIl3Ae4Dbgd3Aa0Vk93J+51KyzKkxxhhjWqk0/oVas15jmtHb20t3VzfJfJJ0Pk0kEml1kcwCLHf09QLgaVU9rKpZ4O+A71vm71wyFpwaY4wxppUsc2rMwoXDYSazk6XHZvVY7uhrE3Dc9/yEu6xERN4gIveKyL1nz55d5uIsjAWnxhhjjGklmznAmIXr7+9nMmPB6Wq03NFXtd77c9qlqOr7VfWQqh5as2bNMhenOd6gAxacGmOMMaaVSqP1WrNeY5oWiURKmdP+/v4Wl8YsxHJHXyeAS3zPNwMnl/k7l4yNjGeMMcaYVvIyp1YnMaZ5/f39TGWnAMucrjbLHZx+C7hMRMZEpBd4DfBPy/ydxhhjjDEdwZr1GrNw/eFycGqZ09VlWXvZq2peRN4IfA7oAv5SVR9Zzu9cSnaX0hhjjDGt5AWn1qjXmOb19/ej7lFjwenqsuxDwKnqp4FPL/f3LKVYLNbqIhhjjDHGlJv1trgcxqwm/oDUgtPVxcYnr+KNb3wjn/jEJ1i7dm2ri2KMMcaYAPMGZ7TmvcY076677mL9+vUMDAywbdu2VhfHLIAFp1Xs2rWLt771ra0uhjHGGGMCbtu2bezavZubX/ziVhfFmFVjx44d7Nixo9XFMItgwakxxhhjTJsaGRnhj9/1rlYXwxhjVoRN5GmMMcYYY4wxpuUsODXGGGOMMcYY03IWnBpjjDHGGGOMaTkLTo0xxhhjjDHGtJwFp8YYY4wxxhhjWs6CU2OMMcYYY4wxLWfBqTHGGGOMMcaYlrPg1BhjjDHGGGNMy1lwaowxxhhjjDGm5Sw4NcYYY4wxxhjTchacGmOMMcYYY4xpOQtOjTHGGGOMMca0nKhqq8tQIiJngWOtLodrFDjX6kK0Edse89k2mc+2yXy2Teay7TGfbZP5bJvMZdtjPtsm89k2mcu2x3ztsk22quqaai+0VXDaTkTkXlU91OpytAvbHvPZNpnPtsl8tk3msu0xn22T+WybzGXbYz7bJvPZNpnLtsd8q2GbWLNeY4wxxhhjjDEtZ8GpMcYYY4wxxpiWs+C0tve3ugBtxrbHfLZN5rNtMp9tk7lse8xn22Q+2yZz2faYz7bJfLZN5rLtMV/bbxPrc2qMMcYYY4wxpuW6W10AY4wxxpjVTkR+BXgUOAR8WVX/rcVFMsaYVccyp8YYY4wxxpiOJSI3A2lV/Xqry2Lq67g+pyKiIvI3vufdInJWRD7lPv9e9+7mQj/3v5aynEvJ/c3v8D3/RRF52wI/46iIjC7wPV8SkSUZjnox378E31kQkQd8/7at5Pe3K992eVBE7hORFzXxnpkm1vmAiOx2H+92nx8SkT9ZinIvhZX47a0iIttE5OGKZW8TkV9s8L7S30hEbm5mm1T5jKrHt4j8qIg8JCLfEZGHReT73OX3iMjGJj63qfWWU5XzyIKvLw0+/3oR+S8R+b8ics8SfWY77gul5SJytYgcEZGDi71m1/jum726wFITkc3u3+gpEXlGRP5YRHprrDsoIj+9HOXwfcdvi8jL6rx+j4j86RJ8j7f/P+KeN39eRBZdtxSRnxORyCLeV/Nvu5rrKt71xT1mf7CJ9UvHdrtdXxeq1rlVRP6Xe73YWrH+X4vI10UkUeWz1gO/BjzoW3a5iHzTvdbfKyLXLfdvWgrN1DmqvGfJ9tvFfP9CdWKz3llgr4iEVTUFfBfwnPeiqv4T8E8L/VBVXfBFeAVlgO8Xkd9V1RWZWFdEulbie5ZZSlUP1HpRRLpVNb+SBWoTpe0iIt8N/C7w4ov9UFX9cd/jRwHv+b0X+9lLaNl/+2qjqvdS/hvdDMwAF32zTkQ2A/8DuEpVJ0UkBngTct8DPAycbPAxza63nOqeRy6Wqn4VaIvrz3LtC34ish/4OHCXqt4P3M8irtkrSUQE+Afgz1T1+9zr4/uB/wn8UpW3DAI/Dbx3gd8hqlr0LetS1UK19VX1NxbwEy6G/5y5FvgIkAB+c5Gf93PA3wLJpSlex9gG/CDO9m1KxfG6GlU9t6rqr4nIaeDNOPuLt/yHReSdwOuBd1W8bS/wE25c4LkNeAj4cVVVEelf8l+wQuqdC1ajjsucuj4DvNx9/Frgo94L/ruFIvIq9+7LgyLyZXfZHvdOygPu3fzL3OXe3aub3btwHxeRx0Xkw+5FAxG5w132FRH5k+W6Q1tFHudC+ObKF0RkjYh8QkS+5f673l0+IiKfF5H7ReTPAfG954d82+DPvUBURGbcu7HfAK6r+J4/c+88PSIiv+VbflREfsu9M/WQiFzR6Ptbyd0//l5E/hn4vIjEROQLvvJ7mZ1tIvKYiPyF+5s/LyJh97UdIvJvUs6+Xeou/yX3b/Ad/zZqc3Fg3HvS6DeISEhE3utuk0+JyKdF5E73tdLdaxG5VUS+5m6fv3cDk3azXL/99SLypIj8h7v/eOejD3nru89nmv3upeCW8ffdY/9JEbnRXX6z+3u2AT8FvNk9N9y4mPOLz1pgGifAQVVnVPWIuw0OAR92vycsIr/hfv7DIvJ+cVRb72p3u35bRD4nIhvc8rxJRB51t9/fLcf2qyQiv+f7zj90l31IRN4nIv/pbuPvcZdvc5fdJ76MvdS/3szbJktY9pXeFzy7gE8Cr1PVb7rv91+zPyTOtfW/ROSw7/iqd+zd5m67rwDf7/uNwyLySffv83VxgmIvg/xXbpmPisj3i8jbxTn/f1ZEeqqU+6U4zQU/COBWEt8M/KhUr1P8HnCpu+wP3O+dd4xL+TrzXuA+4BKpuA7X2edL5xMRucbdZg+6ZRlwy73R/U1Picjbm9o56lDVM8AbgDe6x2iXiPyB73f9pFueOVlOEflT9+/8JmAj8EUR+aL7/g+5+/hDIvJmd/2q11ggVu1Y8ZPVW1f5PeBGd595s9Q4Z/j5t7OIvMDdB+53/9+54r9gaX0GeI3MT5R8Brjbv0BE3gq8E/ikiNwgTv0OnNjgFuDLIvJngL8O92V3P/ptcerBS5J5X0ru3/eLIvIRnCC7Zv294n2fdM8Xj4jIG3zLZ0Tkf7rH1ddFZJ27fEyc+tq3ROR3VuTHqWpH/cOp6Hh3XvuBB3Du8H7Kff0e4E/dxw8Bm9zHg+7/7wbudh/3AmHvc93/bwYmgc04wf3XgBvc7zoOjLnrfdT7zhX6zXHgKM4dy18E3ua+9hHgBvfxFuAx9/GfAL/hPn45oMAoTuXgn4Ee97X3Aj/sPlbg1b7v/RJwyH087P7f5S7f7z4/CvyM+/ingQ/U+/4V3lcK7v7xAPCPvv3jhO/3dANx9/Eo8DTOxWkbzk2BA+5rHwN+yH38DeCV7uN+IALcinMDQdz95lPATa0+Xhpsl8fdff1qd3nN3+A7Pu4EPu2+vh4nuLvTv7+42/HLQNRd/svevtDqfyvw2zcAz+JkCHuBr1I+H33IW7/ic5dk33H32Ycrlr0N+EVfGd/hPr4D+Df38c2Uz5+l9d3nCzq/VHx3F/A5d3t8EPj/fK99Cffc4j4f9j3+G29d5p6DenCyeGvc53cBf+k+Pgn0uY8Hl2mf8f7dBQwDT0BpXAfv+vIh4LPu3/EynHONd47od9e5DLjXt+3nXW/qbZPVuC+4rx0FLgB3VCy/h7nHyN+722I38HS9Y4/ydfkynOPnY77yvxv4TffxS4EHfL/rK+7+dCVOBu9297V/BF5RpexvAt5ZZfn9wBepqFNUbn9qHOPuekXghb51S9dh6u/zH3K3QS9wGLjGXR7Hua7d4y5PuNvpGHDJIvb/mSrLxoF1OIHqW91lfThZvDH/fuS+9qfAPb79YNR9fDXwr771vOOo2jX2ZmofK19ildZVmFv39G+zWueM0r7F3OM1DnS7j18GfGKlfsNF/PZ551bfa7+B02rw1or3/KW7/HL3+QtxjsNu4DXA88BPuq+9C7jNffwzwEfcx5/FOXa6cJoBfwf3XN4O/yr2iVnKcUe9+rv/uPKOgTBOy6MR97lSvra+nfKx+0++z/nvVDnml/pfJzbrRVW/I86d3dfiXLBq+SrwIRH5GE6THHBOaP9DnCZn/6CqT1V53zdV9QSAiDyAczKYAQ6r6hF3nY/inJhXhKpOichf41wk/c0WXgbs9t1AjLt3TW/CvYusqv8iIl6G6BacC8K33PeEgTPuawXgEzWK8Gr3Dkw3TgV8N84BDeVt+23Kd65rff9KqtUc719V9YL7WID/JSI34VQSNuFcdAGOqOoD7uNvA9vcbbtJVf8RQFXT4GQKcSog97vrx3AuKF9e4t+0FPzNtK4D/lpE9uKUv9FvuAH4e3Wanj0vIl+s8vkvxNk/vuruY704x107WO7ffi3wJVU9637H/wEub1Cmpdp3tInl/mN1WxOfudDzS/lLVQsichtwDc55550icrWqvq3K97xERN6CUyEbBh7BuQj77cRpuvWvbnm6gFPua9/BybB+Eiczt5TmnUdEpBtIAx8QkX/BCTY8H3P3kadE5DBwBXAE+FMROYBznvXvE9WuN1+huW1SS1vtCz7/Bvy4iHxOazdR+6S7/R717uxT+9i7Auc8/RSAiPwt5evyDcAPuOX6dzdD5vVV+4yq5kTkIZz96LPu8oeovi2E6ttUcAKgX/PXKaok9God488Cx3TuAC7+63C9fR7fOqdU9Vvub51ytwXAF1R10n3+KLAVJ5i/WN4PvBXYL+UWIQn3d2Wb/JzDwHYReTfwLzitmWpdY6H2seK3Gusq1fRQ+5xRTQL4K3Ey9+q+v91VraOJ05f79TiJmLuBz7vL1wAvAf5/d/lv4rTy+xdVzYvIZ3Fa7Hjn49cAv+A+fh/w6+7ja4Hvda9RXwQi6kZmbeibvrijXv3d700i8kr38SU4x+R5nOPS2zbfxukWCXA97rkS50bo7y/lD6imI4NT1z8Bf4hzZ2Gk2gqq+lMici3O3bAHROSAqn5EnOYyLwc+JyI/rqr/XvHWjO9xAWc7tkOz1HfhNP35oG9ZCLhO57az907ktS6mf6Wqv1rltXS1CoOIjOGcJK5R1XER+RDO3UyPt728beVp14N91vf4bpws19VuZeUo5d9WuR+Eqb0fCPC7qvrnS1zWZaWqXxOnE/0amvsNzRwHgnMD4LVLUcblsky/HWrv93ncrhbiHKDeYCpLte+cB4Yqlg3jBEaeWsdqLQs9v8zhXvC/CXxTRP4V59z1torP6se5A3xIVY+LM9hbtb5BAjyiqtUGtXg5TiXze4FfF5E9uoz9yd2K0AtwKguvAd6Ik52D+dtFcZqAnsbJ1IVwAlvPvOvNArZJLW23L7jeiFNJfC/wkzXW8W8Pqfi/mlrfXe093roZAFUtikjOVzEtUn1bPEK58uZ8uEgcp+L3Bzh9KEt1Cpygq7Is845x9yb7bMW6/utwvX3e/9m1tkG1usxFEZHt7medcb/7Z1T1cxXr3MDcbmVV9123PnEl8N04GZtX4+tjWEXd39NhdZV654xqfgf4oqq+0t2vvrSchVtmdwFfAD4A/LKUx5j5KeB/47Qa+ALlfs8Z3//Pqao3Dk03zrYrUG61AM5xnnMfT+O0gmlX/vNDvfq7s4IzYvHLcM7VSRH5EuVjwH+ua+kx0Kl9TsFJ7f+2qj5UawURuVRVv6HOwAHncPpzbMfJgP4JToC7v8nvexznDt829/ldiy75IrnZvo8BP+Zb/HmcCz4A7l02cLIud7vLbqdcUfkCcKc4Axt4/XK2NvjqOM4BMuneyb69ieLW+v52kwDOuIHpS3DuLNfk3pU+ISKvABCRPnFGHvwcTv+jmLt8k7eN25k4/W66cCqzzfyGrwA/IE4fsHU4N4cqfR24XkR2uJ8TEZFGd31X3DL99m8AN7tZmh7gVb7XjuLc9QT4Psp3tpdk31HVGeCUiNzifs4wzoAQlZmFeqaBAd/zhZ5f8K27UUSu8i06gNO0sPJ7vAvnOXcb3Ol7j3+9J4A14o64KCI94vT3C+E0V/wi8BacwWiWtY+zW86Eqn4apzLtv/v/KncfuRTY7pY7gZPdKgKvw9nv6qm3TRpqt33Bp4jT4mmniPz2AspS69h7HBiTcp9E/w0xf7luBs55WcVF+AIQEZEfdj+vC3gHTiV5PfPrFJXbbrHHeNV9vmKdx3H6ll7jrjMgTmZ/ybmZq/fhNMNWnN/139xzHeKMjhrFOc53u9fHBM5NHE9p27g3B0Oq+gmcrNZVda6xzVjNdZXKfWah54wE5cFB71ny0q0srxl9CvhX4Hvdfewe4H1u9vykm3y6FyfzB87NyY3ufgrO39YbAfmHKLdGegC41r12vAKndcJq0Ez9PQGMu4HpFTgt2Rr5Ks5NVqjoz7tcOjZz6u6cf9xgtT8Qp4mD4PxRHwR+BfghEcnhtE1v6gKpqilxhob/rIicw8kGtMI78FUQcA7i94jId3D+3l/Gubv0W8BHReQ+4D9wmg+hqo+K03n88+6BmcO5Y3mMGlT1QRG5H+fu8WGcHbmRqt/fhj4M/LOI3Eu5L2IjrwP+3K1c5YBXqernRWQX8DU3kzCDczKs1uSi1cLiNIkC59j4EfdOfTO/4RM4FY2HgSdxgrFJ/4er6llxpsT4qIj0uYvf6q7fasv920+Jk+X6Gk7zu/soVyr+Avi/IvJNnPPRrPuepdx3fhjnfOBNPfVbqvrMAt7/z8DHxRkY7GdY4PmlQg/wh+JMBZMGzrrvBadS/z4RSeE0y/oLnCaVR4Fv+T6jcr07gT9xK7zdOK1JngT+1l0mOJWaiQX85kb8+ww4TUD/GOdv2e9+p3+wuidwtsk64KdUNS3OgDefEJFX4fRRrMyWzaGqEyJSa5s0q532hRJVzbif+R/ijMhZd1u4qh577rZ9A/Av7nX5K5Qrmm8DPuiWNwn8SNO/fH6ZVZxmcu8VkV/HufH/aZypK36eijqFql4Qka+KM+XHZ1T1l2oc43VH31TVrDhNZiv3+Ucq1rkLeLc4g/alcDInS8Xb/3twWn/8DfBH7msfwGlae584P+wsTp/d4+J0p/oO8BTl5szg9L39jIicwrmx80EpT03jZYTmXWObKegqr6t8B8iLyIM4570FnTNw+hD+lYj8PFDZGrBdVTu3fgo4r6rePv43OPtJD06LrHO+5Xer6pvEGfjn00AU57z3D+KMxv8mnP3rF3Cup/e47/05nL7GArwHeIWIvF7dAc/aVZP1988CP+We957ASRY08rPAR0TkZ6ndtW9JeYM1mCUgIjFVnXFPwu8BnlLVd7a6XMasJN9xMIJzk+Z6VX2+1eVaCQv97W6QfkhV31hrHdM5xGlG+ClV/Xiry9JpgnzeMcaYTtKxmdMW+QkR+RGcvmL3A6uqf6ExS+RTIjKIcxz8TsAqiEH+7ca0kh17xhjTASxzaowxxhhjjDGm5Tp5QCRjjDHGGGOMMauEBafGGGOMMcYYY1rOglNjjDHGGGOMMS1nwakxxhjToUTk11pdBmOMMaZZNiCSMcYY06FEZEZVY1WWC04doNiCYhljjDFVWebUGGNMYIjI74vIT/uev01EfkFEfklEviUi3xGR33Jf2yYij4nIX7gTuX9eRMLua18SkUPu41EROeo+7hKRP/B91k82KM9bROQhEXlQRH7PXXZARL7uvv8fRWSowXfeIyL/ICKfFZGnROTt7vLfw53IXkQ+7Ps97wXuA35dRN7pK8tPiMgfLcmGNsYYYxbBglNjjDFB8nfAXb7nrwbOApcBLwAOAFeLyE3u65cB71HVPcAE8AMNPv/HgElVvQa4Bmf+67FqK4rI7cArgGtV9Urg7e5Lfw38sqruBx4CfrOJ33XA/V37gLtE5BJV/RUgpaoHVPVud72dwF+r6kHgD4HvFZEe97XXAx9s4ruMMcaYZdHd6gIYY4wxK0VV7xeRtSKyEVgDjAP7gVuB+93VYjhB6bPAEVV9wF3+bWBbg6+4FdgvIne6zxPuZx2psu7LgA+qatIt2wURSQCDqvof7jp/Bfx9Ez/tC6o6CSAijwJbgeNV1jumql93v29WRP4d+B4ReQzoUdWHmvguY4wxZllYcGqMMSZoPg7cCazHyaRuA35XVf/cv5KIbAMyvkUFIOw+zlNufdTvfxvwM6r6uSbKIcBCBn6o9Z1UKWet6/tsxfMPAL8GPI5lTY0xxrSYNes1xhgTNH8HvAYnQP048DngR0UkBiAim0RkbYPPOApc7T6+07f8c8B/85rKisjlIhKt8Rmfd7834q477GY/x0XkRned1wFeFrXWd9aT8zXbnUdVvwFcAvwg8NEmP9MYY4xZFpY5NcYYEyiq+oiIDADPqeop4JSI7AK+5gxiywzwQzgZyFr+EPiYiLwO+Hff8g/gZGLvc0fEPYvTr7RaOT4rIgeAe0UkC3waJ4v5I8D73KD1ME5f0HrfWc/7ge+IyH3A/6ixzseAA6o63uRnGmOMMcvCppIxxhhjAkxEPgW8U1W/0OqyGGOMCTZr1muMMcYEkIgMisiTOCP6WmBqjDGm5SxzaowxxiwjEdkH/E3F4oyqXtuK8hhjjDHtyoJTY4wxxhhjjDEtZ816jTHGGGOMMca0nAWnxhhjjDHGGGNazoJTY4wxxhhjjDEtZ8GpMcYYY4wxxpiWs+DUGGOMMcYYY0zL/T9y50GHn9bc/gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1152x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,6))\n",
    "sns.violinplot(beers.venue_country, beers.rating_score)\n",
    "plt.title('Ratings per venue country', fontsize=16)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Modelling\n",
    "\n",
    "For sake of simplicity, we'll start with a linear regression model to predict rating_score."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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>beer_abv</th>\n",
       "      <th>beer_ibu</th>\n",
       "      <th>rating_score</th>\n",
       "      <th>bid</th>\n",
       "      <th>global_rating_score</th>\n",
       "      <th>global_weighted_rating_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>3.840000e+02</td>\n",
       "      <td>384.000000</td>\n",
       "      <td>384.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>mean</td>\n",
       "      <td>6.519297</td>\n",
       "      <td>26.028646</td>\n",
       "      <td>3.228516</td>\n",
       "      <td>7.494015e+05</td>\n",
       "      <td>3.447656</td>\n",
       "      <td>3.473880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>std</td>\n",
       "      <td>1.751411</td>\n",
       "      <td>25.874909</td>\n",
       "      <td>0.737180</td>\n",
       "      <td>9.641024e+05</td>\n",
       "      <td>0.343740</td>\n",
       "      <td>0.331587</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>min</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>2.000000e+00</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>25%</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.309225e+04</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>3.377500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>50%</td>\n",
       "      <td>6.200000</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>3.250000</td>\n",
       "      <td>1.138620e+05</td>\n",
       "      <td>3.480000</td>\n",
       "      <td>3.505000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>75%</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>40.000000</td>\n",
       "      <td>3.750000</td>\n",
       "      <td>1.443948e+06</td>\n",
       "      <td>3.640000</td>\n",
       "      <td>3.640000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>max</td>\n",
       "      <td>11.900000</td>\n",
       "      <td>250.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>3.537592e+06</td>\n",
       "      <td>4.530000</td>\n",
       "      <td>4.530000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         beer_abv    beer_ibu  rating_score           bid  \\\n",
       "count  384.000000  384.000000    384.000000  3.840000e+02   \n",
       "mean     6.519297   26.028646      3.228516  7.494015e+05   \n",
       "std      1.751411   25.874909      0.737180  9.641024e+05   \n",
       "min      0.000000    0.000000      0.500000  2.000000e+00   \n",
       "25%      5.100000    0.000000      3.000000  1.309225e+04   \n",
       "50%      6.200000   22.000000      3.250000  1.138620e+05   \n",
       "75%      8.000000   40.000000      3.750000  1.443948e+06   \n",
       "max     11.900000  250.000000      5.000000  3.537592e+06   \n",
       "\n",
       "       global_rating_score  global_weighted_rating_score  \n",
       "count           384.000000                    384.000000  \n",
       "mean              3.447656                      3.473880  \n",
       "std               0.343740                      0.331587  \n",
       "min               0.000000                      0.000000  \n",
       "25%               3.300000                      3.377500  \n",
       "50%               3.480000                      3.505000  \n",
       "75%               3.640000                      3.640000  \n",
       "max               4.530000                      4.530000  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's see how the model does with numerical variables only\n",
    "num_beers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "beers.drop(columns=['bid','global_weighted_rating_score'], axis=1, inplace=True)\n",
    "num_beers = beers.select_dtypes(include=['float64','int64']).copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2-score:  0.08340682122766696\n",
      "MSE-score:  0.5124627784589202\n"
     ]
    }
   ],
   "source": [
    "# Split into explanatory and response variables\n",
    "X = num_beers.drop('rating_score', axis=1)\n",
    "y = num_beers['rating_score']\n",
    "\n",
    "# Split into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size = .2, random_state=1337) \n",
    "\n",
    "lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "lm_model.fit(X_train, y_train) #Fit\n",
    "        \n",
    "# Predict and score the model\n",
    "y_test_preds = lm_model.predict(X_test) \n",
    "\n",
    "\n",
    "print(\"R2-score: \", metrics.r2_score(y_test, y_test_preds)) #Rsquared\n",
    "print(\"MSE-score: \", metrics.mean_squared_error(y_test, y_test_preds)) #MSE."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let's see if we can increase that score by adding categorical variables to our model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "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>beer_type</th>\n",
       "      <th>brewery_country</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>count</td>\n",
       "      <td>383</td>\n",
       "      <td>383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>unique</td>\n",
       "      <td>11</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>top</td>\n",
       "      <td>IPA</td>\n",
       "      <td>Western Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>freq</td>\n",
       "      <td>110</td>\n",
       "      <td>280</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       beer_type brewery_country\n",
       "count        383             383\n",
       "unique        11               9\n",
       "top          IPA  Western Europe\n",
       "freq         110             280"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping irrelevant columns\n",
    "beers.drop(columns=['beer_name','brewery_name','created_at','brewery_city','brewery_state','venue_country','serving_type'], axis=1, inplace=True)\n",
    "cat_beers = beers.select_dtypes(include=['object']).copy()\n",
    "cat_beers.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating dummy columns with encoded values\n",
    "\n",
    "cat_cols_lst = cat_beers.columns\n",
    "\n",
    "def create_dummy_df(df, cat_cols, dummy_na):\n",
    "    '''\n",
    "    INPUT:\n",
    "    df - pandas dataframe with categorical variables you want to dummy\n",
    "    cat_cols - list of strings that are associated with names of the categorical columns\n",
    "    dummy_na - Bool holding whether you want to dummy NA vals of categorical columns or not\n",
    "    \n",
    "    OUTPUT:\n",
    "    df - a new dataframe that has the following characteristics:\n",
    "            1. contains all columns that were not specified as categorical\n",
    "            2. removes all the original columns in cat_cols\n",
    "            3. dummy columns for each of the categorical columns in cat_cols\n",
    "            4. if dummy_na is True - it also contains dummy columns for the NaN values\n",
    "            5. Use a prefix of the column name with an underscore (_) for separating \n",
    "    '''\n",
    "    for col in  cat_cols:\n",
    "        try:\n",
    "            # for each cat add dummy var, drop original column\n",
    "            df = pd.concat([df.drop(col, axis=1), pd.get_dummies(df[col], prefix=col, prefix_sep='_', drop_first=True, dummy_na=dummy_na)], axis=1)\n",
    "        except:\n",
    "            continue\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(383, 22)\n"
     ]
    }
   ],
   "source": [
    "beers_new = create_dummy_df(beers, cat_cols_lst, dummy_na=False) \n",
    "print(beers_new.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "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>beer_abv</th>\n",
       "      <th>beer_ibu</th>\n",
       "      <th>rating_score</th>\n",
       "      <th>global_rating_score</th>\n",
       "      <th>beer_type_Belgian Quadrupel</th>\n",
       "      <th>beer_type_Belgian Strong Golden Ale</th>\n",
       "      <th>beer_type_Belgian Tripel</th>\n",
       "      <th>beer_type_Hefeweizen</th>\n",
       "      <th>beer_type_IPA</th>\n",
       "      <th>beer_type_Lager</th>\n",
       "      <th>beer_type_Other</th>\n",
       "      <th>beer_type_Pale Ale</th>\n",
       "      <th>beer_type_Pilsner</th>\n",
       "      <th>beer_type_Witbier</th>\n",
       "      <th>brewery_country_Asia</th>\n",
       "      <th>brewery_country_Eastern Europe</th>\n",
       "      <th>brewery_country_Mexico</th>\n",
       "      <th>brewery_country_Scandinavia</th>\n",
       "      <th>brewery_country_Southern Europe</th>\n",
       "      <th>brewery_country_UK</th>\n",
       "      <th>brewery_country_United States</th>\n",
       "      <th>brewery_country_Western Europe</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>9.0</td>\n",
       "      <td>47</td>\n",
       "      <td>4.0</td>\n",
       "      <td>3.72</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>8.0</td>\n",
       "      <td>20</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.67</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>10.2</td>\n",
       "      <td>38</td>\n",
       "      <td>4.0</td>\n",
       "      <td>4.53</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   beer_abv  beer_ibu  rating_score  global_rating_score  \\\n",
       "4       9.0        47           4.0                 3.72   \n",
       "5       8.0        20           3.0                 3.67   \n",
       "6      10.2        38           4.0                 4.53   \n",
       "\n",
       "   beer_type_Belgian Quadrupel  beer_type_Belgian Strong Golden Ale  \\\n",
       "4                            0                                    0   \n",
       "5                            0                                    0   \n",
       "6                            1                                    0   \n",
       "\n",
       "   beer_type_Belgian Tripel  beer_type_Hefeweizen  beer_type_IPA  \\\n",
       "4                         0                     0              1   \n",
       "5                         0                     0              0   \n",
       "6                         0                     0              0   \n",
       "\n",
       "   beer_type_Lager  beer_type_Other  beer_type_Pale Ale  beer_type_Pilsner  \\\n",
       "4                0                0                   0                  0   \n",
       "5                0                1                   0                  0   \n",
       "6                0                0                   0                  0   \n",
       "\n",
       "   beer_type_Witbier  brewery_country_Asia  brewery_country_Eastern Europe  \\\n",
       "4                  0                     0                               0   \n",
       "5                  0                     0                               0   \n",
       "6                  0                     0                               0   \n",
       "\n",
       "   brewery_country_Mexico  brewery_country_Scandinavia  \\\n",
       "4                       0                            0   \n",
       "5                       0                            0   \n",
       "6                       0                            0   \n",
       "\n",
       "   brewery_country_Southern Europe  brewery_country_UK  \\\n",
       "4                                0                   0   \n",
       "5                                0                   0   \n",
       "6                                0                   0   \n",
       "\n",
       "   brewery_country_United States  brewery_country_Western Europe  \n",
       "4                              0                               1  \n",
       "5                              0                               1  \n",
       "6                              0                               1  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "beers_new.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2-score test data:  0.12368942930062865\n",
      "MAE test data:  0.5384205399423837\n",
      "MSE test data:  0.4899409686367143\n",
      "RMSE test data:  0.6999578334704987\n"
     ]
    }
   ],
   "source": [
    "#Split into explanatory and response variables\n",
    "X = beers_new.drop('rating_score', axis=1)\n",
    "y = beers_new['rating_score']\n",
    "\n",
    "#Split into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=1337)\n",
    "\n",
    "lm_model = LinearRegression(normalize=True) # Instantiate\n",
    "lm_model.fit(X_train, y_train) #Fit\n",
    "\n",
    "#Predict using your model\n",
    "y_test_preds = lm_model.predict(X_test)\n",
    "y_train_preds = lm_model.predict(X_train)\n",
    "\n",
    "#Score using your model\n",
    "test_score = metrics.r2_score(y_test, y_test_preds)\n",
    "train_score = metrics.r2_score(y_train, y_train_preds)\n",
    "\n",
    "MSE_train = metrics.mean_squared_error(y_train, y_train_preds)\n",
    "MSE_test = metrics.mean_squared_error(y_test, y_test_preds)\n",
    "\n",
    "MAE_train = metrics.mean_absolute_error(y_train, y_train_preds)\n",
    "MAE_test = metrics.mean_absolute_error(y_test, y_test_preds)\n",
    "\n",
    "RMSE_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_preds))\n",
    "RMSE_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_preds))\n",
    "\n",
    "\n",
    "#Rsquared score\n",
    "print(\"R2-score test data: \", test_score)\n",
    "\n",
    "#MAE score\n",
    "print(\"MAE test data: \", MAE_test)\n",
    "\n",
    "#MSE score\n",
    "print(\"MSE test data: \", MSE_test)\n",
    "\n",
    "#RMSE score\n",
    "print(\"RMSE test data: \", RMSE_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Still not a very good score. Let's use a different algorithm: Random Forest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "R2-score training data: 0.47114975323045405. R2-score test data: 0.13232270202829965.\n",
      "MAE training data: 0.4007456819805041. MAE test data: 0.5142215742695998.\n",
      "MSE training data: 0.2845575484963804. MSE test data: 0.4851141479362356.\n",
      "RMSE training data: 0.5334393578434014. RMSE test data: 0.6965013624798128.\n"
     ]
    }
   ],
   "source": [
    "#Split into explanatory and response variables\n",
    "X = beers_new.drop('rating_score', axis=1)\n",
    "y = beers_new['rating_score']\n",
    "\n",
    "#Split into train and test\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.2, random_state=1337)\n",
    "\n",
    "# Instantiate & fit\n",
    "regressor = RandomForestRegressor(n_estimators=1000, max_depth=5) \n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "#Predict using your model\n",
    "y_test_preds = regressor.predict(X_test)\n",
    "y_train_preds = regressor.predict(X_train)\n",
    "\n",
    "#Score using your model\n",
    "test_score = metrics.r2_score(y_test, y_test_preds)\n",
    "train_score = metrics.r2_score(y_train, y_train_preds)\n",
    "\n",
    "MSE_train = metrics.mean_squared_error(y_train, y_train_preds)\n",
    "MSE_test = metrics.mean_squared_error(y_test, y_test_preds)\n",
    "\n",
    "MAE_train = metrics.mean_absolute_error(y_train, y_train_preds)\n",
    "MAE_test = metrics.mean_absolute_error(y_test, y_test_preds)\n",
    "\n",
    "RMSE_train = np.sqrt(metrics.mean_squared_error(y_train, y_train_preds))\n",
    "RMSE_test = np.sqrt(metrics.mean_squared_error(y_test, y_test_preds))\n",
    "\n",
    "#Rsquared score\n",
    "print(\"R2-score training data: {}. R2-score test data: {}.\".format(train_score, test_score))\n",
    "\n",
    "#MAE score\n",
    "print(\"MAE training data: {}. MAE test data: {}.\".format(MAE_train, MAE_test))\n",
    "\n",
    "#MSE score\n",
    "print(\"MSE training data: {}. MSE test data: {}.\".format(MSE_train, MSE_test))\n",
    "\n",
    "#RMSE score\n",
    "print(\"RMSE training data: {}. RMSE test data: {}.\".format(RMSE_train, RMSE_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
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