BookmakersProjectFinal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Comparing and Analysing Bookmaker's Odds and Underlying Statistics to the Results of the English Premier League's (EPL) 2016/17 - 2018/2019 Seasons. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import display_html\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# Changes number of decimal points displayed for floats in dataframes\n",
    "pd.set_option('precision', 3)\n",
    "\n",
    "import warnings\n",
    "# Remove warning messages\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def team_data(team_name):\n",
    "# Returns dataframe containing all games played by a single team, home and away\n",
    "    return matches.loc[matches[['home_team_name', 'away_team_name']].eq(team).any(1), :]\n",
    "\n",
    "# Source: https://stackoverflow.com/questions/38783027/jupyter-notebook-display-two-pandas-tables-side-by-side\n",
    "def display_side_by_side(*args):\n",
    "# Displays tables side by side inline\n",
    "    html_str=''\n",
    "    for df in args:\n",
    "        html_str+=df.to_html()\n",
    "    display_html(html_str.replace('table','table style=\"display:inline\"'),raw=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data Preprocessing\n",
    "## Initial Wrangling of the Match Results and Bookmaker's Odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "season_16 = pd.read_csv('EPL16-17.csv')\n",
    "season_17 = pd.read_csv('EPL17-18.csv')\n",
    "season_18 = pd.read_csv('EPL18-19.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(True, True)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checks if all datasets have the same columns\n",
    "list(season_16.columns.values) == list(season_17.columns.values), list(season_16.columns.values) == list(season_18.columns.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches = pd.concat([season_16, season_17, season_18])\n",
    "# Reset index after concatenation so it doesn't repeat\n",
    "matches.reset_index(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1140 entries, 0 to 1139\n",
      "Data columns (total 55 columns):\n",
      "index                                  1140 non-null int64\n",
      "timestamp                              1140 non-null int64\n",
      "date_GMT                               1140 non-null object\n",
      "status                                 1140 non-null object\n",
      "attendance                             1130 non-null float64\n",
      "home_team_name                         1140 non-null object\n",
      "away_team_name                         1140 non-null object\n",
      "Pre-Match PPG (Home)                   1140 non-null float64\n",
      "Pre-Match PPG (Away)                   1140 non-null float64\n",
      "home_ppg                               1140 non-null float64\n",
      "away_ppg                               1140 non-null float64\n",
      "home_team_goal_count                   1140 non-null int64\n",
      "away_team_goal_count                   1140 non-null int64\n",
      "total_goal_count                       1140 non-null int64\n",
      "total_goals_at_half_time               1140 non-null int64\n",
      "home_team_goal_count_half_time         1140 non-null int64\n",
      "away_team_goal_count_half_time         1140 non-null int64\n",
      "home_team_goal_timings                 879 non-null object\n",
      "away_team_goal_timings                 754 non-null object\n",
      "home_team_corner_count                 1140 non-null int64\n",
      "away_team_corner_count                 1140 non-null int64\n",
      "home_team_yellow_cards                 1140 non-null int64\n",
      "home_team_red_cards                    1140 non-null int64\n",
      "away_team_yellow_cards                 1140 non-null int64\n",
      "away_team_red_cards                    1140 non-null int64\n",
      "home_team_shots                        1140 non-null int64\n",
      "away_team_shots                        1140 non-null int64\n",
      "home_team_shots_on_target              1140 non-null int64\n",
      "away_team_shots_on_target              1140 non-null int64\n",
      "home_team_shots_off_target             1140 non-null int64\n",
      "away_team_shots_off_target             1140 non-null int64\n",
      "home_team_fouls                        1140 non-null int64\n",
      "away_team_fouls                        1140 non-null int64\n",
      "home_team_possession                   1140 non-null int64\n",
      "away_team_possession                   1140 non-null int64\n",
      "average_goals_per_match_pre_match      1140 non-null float64\n",
      "btts_percentage_pre_match              1140 non-null int64\n",
      "over_15_percentage_pre_match           1140 non-null int64\n",
      "over_25_percentage_pre_match           1140 non-null int64\n",
      "over_35_percentage_pre_match           1140 non-null int64\n",
      "over_45_percentage_pre_match           1140 non-null int64\n",
      "over_15_HT_FHG_percentage_pre_match    1140 non-null int64\n",
      "over_05_HT_FHG_percentage_pre_match    1140 non-null int64\n",
      "average_corners_per_match_pre_match    1140 non-null float64\n",
      "average_cards_per_match_pre_match      1140 non-null float64\n",
      "odds_ft_home_team_win                  1140 non-null float64\n",
      "odds_ft_draw                           1140 non-null float64\n",
      "odds_ft_away_team_win                  1140 non-null float64\n",
      "odds_ft_over15                         1140 non-null float64\n",
      "odds_ft_over25                         1140 non-null float64\n",
      "odds_ft_over35                         1140 non-null float64\n",
      "odds_ft_over45                         1140 non-null float64\n",
      "odds_btts_yes                          1140 non-null float64\n",
      "odds_btts_no                           1140 non-null float64\n",
      "stadium_name                           1140 non-null object\n",
      "dtypes: float64(17), int64(31), object(7)\n",
      "memory usage: 490.0+ KB\n"
     ]
    }
   ],
   "source": [
    "matches.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are missing values in the attendance and goal timing columns. Goal timing would be due to 0-0 results, attendance due to lack of reporting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches.rename(columns={'date_GMT': 'Date'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check if Date is in str or datetime format\n",
    "type(matches['Date'].iloc[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converts date column to 'datetime' format\n",
    "matches.loc[:, 'Date'] = pd.to_datetime(matches['Date'], infer_datetime_format=True)\n",
    "\n",
    "# All matches are taken from the English Premier League, rendering 'Div' redundant\n",
    "cols = list(matches.columns)\n",
    "# Result cols contains a list of column names related to the match statistics\n",
    "result_cols = cols[1:33]\n",
    "# Odds cols contains a list of column names related to the bookmaker's odds \n",
    "odds_cols = cols[45:48]\n",
    "# Removes unnecessary columns\n",
    "matches = matches[result_cols + odds_cols]\n",
    "matches.drop(['status', 'Pre-Match PPG (Home)', 'Pre-Match PPG (Away)', 'timestamp'], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>attendance</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "      <th>home_ppg</th>\n",
       "      <th>away_ppg</th>\n",
       "      <th>home_team_goal_count</th>\n",
       "      <th>away_team_goal_count</th>\n",
       "      <th>total_goal_count</th>\n",
       "      <th>total_goals_at_half_time</th>\n",
       "      <th>...</th>\n",
       "      <th>away_team_shots</th>\n",
       "      <th>home_team_shots_on_target</th>\n",
       "      <th>away_team_shots_on_target</th>\n",
       "      <th>home_team_shots_off_target</th>\n",
       "      <th>away_team_shots_off_target</th>\n",
       "      <th>home_team_fouls</th>\n",
       "      <th>away_team_fouls</th>\n",
       "      <th>odds_ft_home_team_win</th>\n",
       "      <th>odds_ft_draw</th>\n",
       "      <th>odds_ft_away_team_win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2016-08-13 11:30:00</td>\n",
       "      <td>21037.0</td>\n",
       "      <td>Hull City</td>\n",
       "      <td>Leicester City</td>\n",
       "      <td>1.47</td>\n",
       "      <td>0.53</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>17</td>\n",
       "      <td>3.41</td>\n",
       "      <td>3.19</td>\n",
       "      <td>2.39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2016-08-13 14:00:00</td>\n",
       "      <td>19126.0</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>Swansea City</td>\n",
       "      <td>1.74</td>\n",
       "      <td>0.74</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>15</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>2.45</td>\n",
       "      <td>3.22</td>\n",
       "      <td>3.26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2016-08-13 14:00:00</td>\n",
       "      <td>24490.0</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>West Bromwich Albion</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.84</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>12</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
       "      <td>14</td>\n",
       "      <td>2.20</td>\n",
       "      <td>3.25</td>\n",
       "      <td>3.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2016-08-13 14:00:00</td>\n",
       "      <td>39494.0</td>\n",
       "      <td>Everton</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.74</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>10</td>\n",
       "      <td>14</td>\n",
       "      <td>3.13</td>\n",
       "      <td>3.36</td>\n",
       "      <td>2.45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2016-08-13 14:00:00</td>\n",
       "      <td>32110.0</td>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>Stoke City</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.89</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>18</td>\n",
       "      <td>13</td>\n",
       "      <td>2.49</td>\n",
       "      <td>3.20</td>\n",
       "      <td>3.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Date  attendance  home_team_name        away_team_name  \\\n",
       "0 2016-08-13 11:30:00     21037.0       Hull City        Leicester City   \n",
       "1 2016-08-13 14:00:00     19126.0         Burnley          Swansea City   \n",
       "2 2016-08-13 14:00:00     24490.0  Crystal Palace  West Bromwich Albion   \n",
       "3 2016-08-13 14:00:00     39494.0         Everton     Tottenham Hotspur   \n",
       "4 2016-08-13 14:00:00     32110.0   Middlesbrough            Stoke City   \n",
       "\n",
       "   home_ppg  away_ppg  home_team_goal_count  away_team_goal_count  \\\n",
       "0      1.47      0.53                     2                     1   \n",
       "1      1.74      0.74                     0                     1   \n",
       "2      1.05      0.84                     0                     1   \n",
       "3      2.26      1.74                     1                     1   \n",
       "4      0.95      0.89                     1                     1   \n",
       "\n",
       "   total_goal_count  total_goals_at_half_time  ...  away_team_shots  \\\n",
       "0                 3                         1  ...               15   \n",
       "1                 1                         0  ...               15   \n",
       "2                 1                         0  ...               12   \n",
       "3                 2                         1  ...                6   \n",
       "4                 2                         1  ...               10   \n",
       "\n",
       "   home_team_shots_on_target away_team_shots_on_target  \\\n",
       "0                          4                         6   \n",
       "1                          6                         8   \n",
       "2                          7                         6   \n",
       "3                          7                         4   \n",
       "4                          3                         2   \n",
       "\n",
       "  home_team_shots_off_target  away_team_shots_off_target  home_team_fouls  \\\n",
       "0                          7                           9                7   \n",
       "1                          6                           7               10   \n",
       "2                          5                           6               12   \n",
       "3                          3                           2               10   \n",
       "4                          5                           8               18   \n",
       "\n",
       "   away_team_fouls  odds_ft_home_team_win  odds_ft_draw  odds_ft_away_team_win  \n",
       "0               17                   3.41          3.19                   2.39  \n",
       "1               14                   2.45          3.22                   3.26  \n",
       "2               14                   2.20          3.25                   3.80  \n",
       "3               14                   3.13          3.36                   2.45  \n",
       "4               13                   2.49          3.20                   3.21  \n",
       "\n",
       "[5 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>Date</th>\n",
       "      <th>attendance</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "      <th>home_ppg</th>\n",
       "      <th>away_ppg</th>\n",
       "      <th>home_team_goal_count</th>\n",
       "      <th>away_team_goal_count</th>\n",
       "      <th>total_goal_count</th>\n",
       "      <th>total_goals_at_half_time</th>\n",
       "      <th>...</th>\n",
       "      <th>away_team_shots</th>\n",
       "      <th>home_team_shots_on_target</th>\n",
       "      <th>away_team_shots_on_target</th>\n",
       "      <th>home_team_shots_off_target</th>\n",
       "      <th>away_team_shots_off_target</th>\n",
       "      <th>home_team_fouls</th>\n",
       "      <th>away_team_fouls</th>\n",
       "      <th>odds_ft_home_team_win</th>\n",
       "      <th>odds_ft_draw</th>\n",
       "      <th>odds_ft_away_team_win</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>2016-10-22 14:00:00</td>\n",
       "      <td>31969.0</td>\n",
       "      <td>Leicester City</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.11</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>8</td>\n",
       "      <td>11</td>\n",
       "      <td>16</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>245</th>\n",
       "      <td>2017-02-11 15:00:00</td>\n",
       "      <td>56983.0</td>\n",
       "      <td>West Ham United</td>\n",
       "      <td>West Bromwich Albion</td>\n",
       "      <td>1.32</td>\n",
       "      <td>0.84</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>618</th>\n",
       "      <td>2018-01-20 15:00:00</td>\n",
       "      <td>20886.0</td>\n",
       "      <td>Swansea City</td>\n",
       "      <td>Liverpool</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>24</td>\n",
       "      <td>4</td>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>9</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Date  attendance   home_team_name        away_team_name  \\\n",
       "84  2016-10-22 14:00:00     31969.0   Leicester City        Crystal Palace   \n",
       "245 2017-02-11 15:00:00     56983.0  West Ham United  West Bromwich Albion   \n",
       "618 2018-01-20 15:00:00     20886.0     Swansea City             Liverpool   \n",
       "\n",
       "     home_ppg  away_ppg  home_team_goal_count  away_team_goal_count  \\\n",
       "84       1.79      1.11                     3                     1   \n",
       "245      1.32      0.84                     2                     2   \n",
       "618      1.11      1.68                     1                     0   \n",
       "\n",
       "     total_goal_count  total_goals_at_half_time  ...  away_team_shots  \\\n",
       "84                  4                         1  ...               24   \n",
       "245                 4                         1  ...                7   \n",
       "618                 1                         1  ...               24   \n",
       "\n",
       "     home_team_shots_on_target away_team_shots_on_target  \\\n",
       "84                           4                         8   \n",
       "245                          6                         4   \n",
       "618                          4                        13   \n",
       "\n",
       "    home_team_shots_off_target  away_team_shots_off_target  home_team_fouls  \\\n",
       "84                          11                          16               10   \n",
       "245                          6                           3                7   \n",
       "618                          2                          11                5   \n",
       "\n",
       "     away_team_fouls  odds_ft_home_team_win  odds_ft_draw  \\\n",
       "84                 7                    0.0           0.0   \n",
       "245               13                    0.0           0.0   \n",
       "618                9                    0.0           0.0   \n",
       "\n",
       "     odds_ft_away_team_win  \n",
       "84                     0.0  \n",
       "245                    0.0  \n",
       "618                    0.0  \n",
       "\n",
       "[3 rows x 31 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches[matches[odds_cols].eq(0).all(axis=1)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3 columns have a '0' value on their odds, suggesting NaN values (suggests £0 return per £1 placed). Convert all values to '1' "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "matches.loc[[84, 245, 618], odds_cols] = 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Converting Bookmaker's Odds to 'Implied Likelihood'\n",
    "\n",
    "As betting odds represent the potential return on investment, the implied favourite will have lower odds. Calculate likelihoods by the following equation:\n",
    "\n",
    "Event Odds = A / B\n",
    "\n",
    "P(event) = B / (A + B)\n"
   ]
  },
  {
   "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>odds_ft_home_team_win</th>\n",
       "      <th>odds_ft_draw</th>\n",
       "      <th>odds_ft_away_team_win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>22.676</td>\n",
       "      <td>23.866</td>\n",
       "      <td>29.499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>28.986</td>\n",
       "      <td>23.697</td>\n",
       "      <td>23.474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>31.250</td>\n",
       "      <td>23.529</td>\n",
       "      <td>20.833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>24.213</td>\n",
       "      <td>22.936</td>\n",
       "      <td>28.986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>28.653</td>\n",
       "      <td>23.810</td>\n",
       "      <td>23.753</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   odds_ft_home_team_win  odds_ft_draw  odds_ft_away_team_win\n",
       "0                 22.676        23.866                 29.499\n",
       "1                 28.986        23.697                 23.474\n",
       "2                 31.250        23.529                 20.833\n",
       "3                 24.213        22.936                 28.986\n",
       "4                 28.653        23.810                 23.753"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert bettings odds to a probability, and then *100 to return a percentage\n",
    "matches.loc[:, odds_cols] = 1/(matches[odds_cols] + 1) * 100\n",
    "matches.loc[:, odds_cols].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Shorten column names for ease as they will be used frequently in this project\n",
    "matches.rename(columns={'odds_ft_home_team_win': 'odds_home_win', 'odds_ft_draw': 'odds_draw', 'odds_ft_away_team_win': 'odds_away_win'}, inplace=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Feature engineering\n",
    "\n",
    "### Home favourite and W/D/L dummies\n",
    "\n",
    "These will provide clear binary classifications for whether the bookmaker's favourite and the actual result. While these can be derived from the data, having them as separate columns improves clarity of the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "      <th>home_team_goal_count</th>\n",
       "      <th>away_team_goal_count</th>\n",
       "      <th>goal_diff</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Hull City</td>\n",
       "      <td>Leicester City</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Burnley</td>\n",
       "      <td>Swansea City</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>West Bromwich Albion</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>-1</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Everton</td>\n",
       "      <td>Tottenham Hotspur</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Middlesbrough</td>\n",
       "      <td>Stoke City</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>D</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1135</th>\n",
       "      <td>Manchester United</td>\n",
       "      <td>Cardiff City</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>-2</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1136</th>\n",
       "      <td>Liverpool</td>\n",
       "      <td>Wolverhampton Wanderers</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>H</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1137</th>\n",
       "      <td>Watford</td>\n",
       "      <td>West Ham United</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1138</th>\n",
       "      <td>Fulham</td>\n",
       "      <td>Newcastle United</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>-4</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>Brighton &amp; Hove Albion</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-3</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1140 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              home_team_name           away_team_name  home_team_goal_count  \\\n",
       "0                  Hull City           Leicester City                     2   \n",
       "1                    Burnley             Swansea City                     0   \n",
       "2             Crystal Palace     West Bromwich Albion                     0   \n",
       "3                    Everton        Tottenham Hotspur                     1   \n",
       "4              Middlesbrough               Stoke City                     1   \n",
       "...                      ...                      ...                   ...   \n",
       "1135       Manchester United             Cardiff City                     0   \n",
       "1136               Liverpool  Wolverhampton Wanderers                     2   \n",
       "1137                 Watford          West Ham United                     1   \n",
       "1138                  Fulham         Newcastle United                     0   \n",
       "1139  Brighton & Hove Albion          Manchester City                     1   \n",
       "\n",
       "      away_team_goal_count  goal_diff result  \n",
       "0                        1          1      H  \n",
       "1                        1         -1      A  \n",
       "2                        1         -1      A  \n",
       "3                        1          0      D  \n",
       "4                        1          0      D  \n",
       "...                    ...        ...    ...  \n",
       "1135                     2         -2      A  \n",
       "1136                     0          2      H  \n",
       "1137                     4         -3      A  \n",
       "1138                     4         -4      A  \n",
       "1139                     4         -3      A  \n",
       "\n",
       "[1140 rows x 6 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creates new column containing Boolean values for whether Home Team is favoured by Bookmakers.\n",
    "matches.loc[:, 'home_fav'] = matches['odds_home_win'] > matches['odds_away_win'] # Why does this raise warning when below does not?\n",
    "\n",
    "matches.loc[:, 'away_fav'] = matches['odds_away_win'] > matches['odds_home_win']\n",
    " \n",
    "# Creates new column containing Boolean values for whether Home Team wins the game.\n",
    "matches.loc[:, 'home_win'] = matches['home_team_goal_count'] > matches['away_team_goal_count']\n",
    "\n",
    "# Creates new column containing Boolean values for whether Away Team wins the game\n",
    "matches.loc[:, 'away_win'] = matches['home_team_goal_count'] < matches['away_team_goal_count']\n",
    "\n",
    "# For a 'draw' both Home and Away win will be False\n",
    "\n",
    "# Full Time Result\n",
    "matches.loc[:, 'goal_diff'] = matches['home_team_goal_count'] - matches['away_team_goal_count']\n",
    "matches.loc[:, 'result'] = 0\n",
    "matches.loc[:, 'result'].where(matches['goal_diff'] < 0.5, 'H', inplace=True)\n",
    "matches.loc[:, 'result'].where(matches['goal_diff'] > -0.5, 'A', inplace=True)\n",
    "matches.loc[:, 'result'].where(matches['goal_diff'] != 0, 'D', inplace=True)\n",
    "\n",
    "\n",
    "matches[['home_team_name', 'away_team_name', 'home_team_goal_count', 'away_team_goal_count', 'goal_diff', 'result']]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prior Performance\n",
    "\n",
    "### Points Per Game\n",
    "\n",
    "Create a 'Points Per Game' (PPG) column, displaying a cumulative mean of the teams prior results. The current PPG columns include performances dating further back than the data available for this project. An additional PPG column that displays only the 'recent' PPG will be more indicative of the current team's performance. As teams change their players and tactics often, PPG from 5 seasons ago is less relevant than more recent results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "26"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creates list of all teams in DF\n",
    "teams = list(matches['home_team_name'].unique())\n",
    "len(teams)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There are 20 individual teams per EPL season. Each season, 3 teams are relegated to the Championship and 3 are promoted. Over three seasons there have been 26 teams, meaning no team that has been relegated has been re-promoted "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Home vs Away points mapping for final results\n",
    "home_ppm = {'H': 3, 'D': 1, 'A': 0}\n",
    "away_ppm = {'H': 0, 'D': 1, 'A': 3}\n",
    "\n",
    "# Create 'empty' columns for filling\n",
    "matches.loc[:, 'HPPG'] = 0\n",
    "matches.loc[:, 'APPG'] = 0\n",
    "\n",
    "# Iterates through list of all teams in database\n",
    "for i, team in enumerate(teams):\n",
    "    # Returns home games for selected team and gives numerical value to win/loss/draw\n",
    "    home_games = matches[matches['home_team_name'] == team]\n",
    "    home_games.loc[:, 'home_pts'] = home_games['result'].map(home_ppm)\n",
    "    # Returns away games for selected team\n",
    "    away_games = matches[matches['away_team_name'] == team]\n",
    "    away_games.loc[:, 'away_pts'] = away_games['result'].map(away_ppm)\n",
    "    \n",
    "    # Combines home and away games for selected team and sorts by date \n",
    "    overall_games = pd.concat([home_games, away_games])\n",
    "    overall_games.sort_index(inplace=True)\n",
    "\n",
    "    # Creates 'Points Per Match' column. A cumulative mean for each team\n",
    "    team_pts = overall_games['home_pts'].fillna(0) + overall_games['away_pts'].fillna(0)\n",
    "    rolling_count = overall_games.reset_index().index.values\n",
    "    ppg = team_pts.cumsum() / (rolling_count + 1)\n",
    "    overall_games.loc[:, 'HPPG'] = ppg.where(overall_games['home_team_name'] == team)\n",
    "    overall_games.loc[:, 'APPG'] = ppg.where(overall_games['away_team_name'] == team)\n",
    "\n",
    "    # Converts back into separate home and away DFs so only the correct rows are updated\n",
    "    home_games = overall_games[overall_games['home_team_name'] == team]\n",
    "    away_games = overall_games[overall_games['away_team_name'] == team]\n",
    "    \n",
    "    # Updates the PPM columns for selected teams index only\n",
    "    matches.loc[home_games.index.values, 'HPPG'] = home_games.loc[:, 'HPPG']\n",
    "    matches.loc[away_games.index.values, 'APPG'] = away_games.loc[:, 'APPG']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Avg Goals Scored / Conceded / Difference"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Add further 'lagged' features for more in-depth performance stats."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Date', 'attendance', 'home_team_name', 'away_team_name',\n",
       "       'home_ppg', 'away_ppg', 'home_team_goal_count',\n",
       "       'away_team_goal_count', 'total_goal_count',\n",
       "       'total_goals_at_half_time', 'home_team_goal_count_half_time',\n",
       "       'away_team_goal_count_half_time', 'home_team_goal_timings',\n",
       "       'away_team_goal_timings', 'home_team_corner_count',\n",
       "       'away_team_corner_count', 'home_team_yellow_cards',\n",
       "       'home_team_red_cards', 'away_team_yellow_cards',\n",
       "       'away_team_red_cards', 'home_team_shots', 'away_team_shots',\n",
       "       'home_team_shots_on_target', 'away_team_shots_on_target',\n",
       "       'home_team_shots_off_target', 'away_team_shots_off_target',\n",
       "       'home_team_fouls', 'away_team_fouls', 'odds_home_win', 'odds_draw',\n",
       "       'odds_away_win', 'home_fav', 'away_fav', 'home_win', 'away_win',\n",
       "       'goal_diff', 'result', 'HPPG', 'APPG'], dtype=object)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creates Columns for average goals scored, conceded and difference\n",
    "# Uses the cumulative sum of the previous games divided by the number of games played to get the mean average\n",
    "for team in teams:\n",
    "    # Returns home games for selected team and ignores home vs away, merging all goals scored for vs against\n",
    "    home_games = matches[matches['home_team_name'] == team]\n",
    "    home_games.loc[:, 'teams_goals'] = home_games['home_team_goal_count']\n",
    "    home_games.loc[:, 'opponents_goals'] = home_games['away_team_goal_count']\n",
    "    home_games.loc[:, 'goal_diff'] = home_games['teams_goals'] - home_games['opponents_goals']\n",
    "    home_games.loc[:, 'shots_on_target'] = home_games['home_team_shots_on_target']\n",
    "    # Returns away games for selected team\n",
    "    away_games = matches[matches['away_team_name'] == team]\n",
    "    away_games.loc[:, 'teams_goals'] = away_games['away_team_goal_count']\n",
    "    away_games.loc[:, 'opponents_goals'] = away_games['home_team_goal_count']\n",
    "    away_games.loc[:, 'goal_diff'] = away_games['teams_goals'] - away_games['opponents_goals']\n",
    "    away_games.loc[:, 'shots_on_target'] = away_games['away_team_shots_on_target']\n",
    "    \n",
    "    # Combines home and away games for selected team and sorts by date \n",
    "    overall_games = pd.concat([home_games, away_games])\n",
    "    overall_games.sort_index(inplace=True)    \n",
    "\n",
    "    # Rolling count provides a count for each match to then divide the sum of all previous games for a mean average\n",
    "    rolling_count = overall_games.reset_index().index.values\n",
    "    avg_team_goals = overall_games['teams_goals'].cumsum() / (rolling_count + 1)\n",
    "    avg_opp_goals = overall_games['opponents_goals'].cumsum() / (rolling_count + 1)\n",
    "    avg_goal_diff = overall_games['goal_diff'].cumsum() / (rolling_count + 1)\n",
    "    avg_shots_on_target = overall_games['shots_on_target'].cumsum() / (rolling_count + 1)\n",
    "\n",
    "    # Splits each of the above series to home and away games\n",
    "    overall_games.loc[:, 'avg_home_goals_scored'] = avg_team_goals.where(overall_games['home_team_name'] == team)\n",
    "    overall_games.loc[:, 'avg_away_goals_scored'] = avg_team_goals.where(overall_games['away_team_name'] == team)\n",
    "    \n",
    "    overall_games.loc[:, 'avg_home_goals_conceded'] = avg_opp_goals.where(overall_games['home_team_name'] == team)\n",
    "    overall_games.loc[:, 'avg_away_goals_conceded'] = avg_opp_goals.where(overall_games['away_team_name'] == team)\n",
    "    \n",
    "    overall_games.loc[:, 'avg_home_goal_diff'] = avg_goal_diff.where(overall_games['home_team_name'] == team)\n",
    "    overall_games.loc[:, 'avg_away_goal_diff'] = avg_goal_diff.where(overall_games['away_team_name'] == team)\n",
    "    \n",
    "    overall_games.loc[:, 'avg_home_shots_on_target'] = avg_shots_on_target.where(overall_games['home_team_name'] == team)\n",
    "    overall_games.loc[:, 'avg_away_shots_on_target'] = avg_shots_on_target.where(overall_games['away_team_name'] == team)\n",
    "    \n",
    "    # Converts back into separate home and away DFs so only the correct rows are updated\n",
    "    home_games = overall_games[overall_games['home_team_name'] == team]\n",
    "    away_games = overall_games[overall_games['away_team_name'] == team]\n",
    "    \n",
    "    # Update columns in the dataframe\n",
    "    matches.loc[home_games.index.values, 'avg_home_goals_scored'] = home_games.loc[:, 'avg_home_goals_scored']\n",
    "    matches.loc[home_games.index.values, 'avg_home_goals_conceded'] = home_games.loc[:, 'avg_home_goals_conceded']\n",
    "    matches.loc[home_games.index.values, 'avg_home_goal_diff'] = home_games.loc[:, 'avg_home_goal_diff']\n",
    "    matches.loc[home_games.index.values, 'avg_home_shots_on_target'] = home_games.loc[:, 'avg_home_shots_on_target']\n",
    "\n",
    "\n",
    "    matches.loc[away_games.index.values, 'avg_away_goals_scored'] = away_games.loc[:, 'avg_away_goals_scored']\n",
    "    matches.loc[away_games.index.values, 'avg_away_goals_conceded'] = away_games.loc[:, 'avg_away_goals_conceded']\n",
    "    matches.loc[away_games.index.values, 'avg_away_goal_diff'] = away_games.loc[:, 'avg_away_goal_diff']\n",
    "    matches.loc[away_games.index.values, 'avg_away_shots_on_target'] = away_games.loc[:, 'avg_away_shots_on_target']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Team Form (Last 5 Games)\n",
    "\n",
    "Team form over the last 5 games (points gained) highlights recent performance even more than PPG."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "for team in teams:\n",
    "    # Returns home games for selected team and gives numerical value to win/loss/draw\n",
    "    home_games = matches[matches['home_team_name'] == team]\n",
    "    home_games.loc[:, 'team_pts'] = home_games['result'].map(home_ppm)\n",
    "    # Returns away games for selected team\n",
    "    away_games = matches[matches['away_team_name'] == team]\n",
    "    away_games.loc[:, 'team_pts'] = away_games['result'].map(away_ppm)\n",
    "    \n",
    "    # Combines home and away games for selected team and sorts by date \n",
    "    overall_games = pd.concat([home_games, away_games])\n",
    "    overall_games.sort_index(inplace=True)\n",
    "    \n",
    "    # Sum of team's points from the last 5 games\n",
    "    form = overall_games['team_pts'].shift(1) + overall_games['team_pts'].shift(2) + overall_games['team_pts'].shift(3) + overall_games['team_pts'].shift(4) + overall_games['team_pts'].shift(5)\n",
    "    overall_games.loc[:, 'form'] = form\n",
    "    \n",
    "    home_games = overall_games[overall_games['home_team_name'] == team]\n",
    "    away_games = overall_games[overall_games['away_team_name'] == team]\n",
    "    \n",
    "    matches.loc[home_games.index.values, 'home_form'] = home_games.loc[:, 'form']\n",
    "    matches.loc[away_games.index.values, 'away_form'] = away_games.loc[:, 'form']\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Team Ratings\n",
    "\n",
    "Elo is a rating system for a variety of games that provides a 'score' for win/loss depending on the opponent's ELO. To simplify the system, as this data is only covering a short time period, the mean average of each teams ELO is taken and applied to them for every match to give a general indication to the team's strength."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>country</th>\n",
       "      <th>team</th>\n",
       "      <th>rating</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>17351</th>\n",
       "      <td>13/08/2016</td>\n",
       "      <td>England</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>993.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17352</th>\n",
       "      <td>13/08/2016</td>\n",
       "      <td>England</td>\n",
       "      <td>Sunderland</td>\n",
       "      <td>755.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17357</th>\n",
       "      <td>13/08/2016</td>\n",
       "      <td>England</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>894.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17358</th>\n",
       "      <td>13/08/2016</td>\n",
       "      <td>England</td>\n",
       "      <td>Watford</td>\n",
       "      <td>692.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17359</th>\n",
       "      <td>13/08/2016</td>\n",
       "      <td>England</td>\n",
       "      <td>Hull City</td>\n",
       "      <td>681.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  country             team  rating\n",
       "17351  13/08/2016  England  Manchester City   993.0\n",
       "17352  13/08/2016  England       Sunderland   755.0\n",
       "17357  13/08/2016  England      Southampton   894.0\n",
       "17358  13/08/2016  England          Watford   692.0\n",
       "17359  13/08/2016  England        Hull City   681.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "elo = pd.read_csv('ELO_Ratings.csv')\n",
    "# Returns only English games from elo csv\n",
    "elo = elo[elo['country'] == 'England']\n",
    "elo.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>attendance</th>\n",
       "      <th>home_team_name</th>\n",
       "      <th>away_team_name</th>\n",
       "      <th>home_ppg</th>\n",
       "      <th>away_ppg</th>\n",
       "      <th>home_team_goal_count</th>\n",
       "      <th>away_team_goal_count</th>\n",
       "      <th>total_goal_count</th>\n",
       "      <th>total_goals_at_half_time</th>\n",
       "      <th>...</th>\n",
       "      <th>avg_home_goal_diff</th>\n",
       "      <th>avg_home_shots_on_target</th>\n",
       "      <th>avg_away_goals_scored</th>\n",
       "      <th>avg_away_goals_conceded</th>\n",
       "      <th>avg_away_goal_diff</th>\n",
       "      <th>avg_away_shots_on_target</th>\n",
       "      <th>home_form</th>\n",
       "      <th>away_form</th>\n",
       "      <th>home_elo</th>\n",
       "      <th>away_elo</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>764</th>\n",
       "      <td>2018-08-11 14:00:00</td>\n",
       "      <td>24821.0</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>1.11</td>\n",
       "      <td>1.53</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.000</td>\n",
       "      <td>7.000</td>\n",
       "      <td>1.260</td>\n",
       "      <td>1.532</td>\n",
       "      <td>-0.273</td>\n",
       "      <td>4.831</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>713.216</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>766</th>\n",
       "      <td>2018-08-11 16:30:00</td>\n",
       "      <td>31231.0</td>\n",
       "      <td>Wolverhampton Wanderers</td>\n",
       "      <td>Everton</td>\n",
       "      <td>1.79</td>\n",
       "      <td>1.05</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.000</td>\n",
       "      <td>1.403</td>\n",
       "      <td>1.351</td>\n",
       "      <td>0.052</td>\n",
       "      <td>4.948</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>817.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>770</th>\n",
       "      <td>2018-08-18 11:30:00</td>\n",
       "      <td>30720.0</td>\n",
       "      <td>Cardiff City</td>\n",
       "      <td>Newcastle United</td>\n",
       "      <td>1.05</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-1.000</td>\n",
       "      <td>2.000</td>\n",
       "      <td>1.000</td>\n",
       "      <td>1.225</td>\n",
       "      <td>-0.225</td>\n",
       "      <td>4.950</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>713.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>780</th>\n",
       "      <td>2018-08-25 11:30:00</td>\n",
       "      <td>31322.0</td>\n",
       "      <td>Wolverhampton Wanderers</td>\n",
       "      <td>Manchester City</td>\n",
       "      <td>1.79</td>\n",
       "      <td>2.32</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>3.667</td>\n",
       "      <td>2.468</td>\n",
       "      <td>0.861</td>\n",
       "      <td>1.608</td>\n",
       "      <td>7.595</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1077.341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>788</th>\n",
       "      <td>2018-08-26 15:00:00</td>\n",
       "      <td>23438.0</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>1.11</td>\n",
       "      <td>0.89</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.667</td>\n",
       "      <td>8.000</td>\n",
       "      <td>0.987</td>\n",
       "      <td>1.278</td>\n",
       "      <td>-0.291</td>\n",
       "      <td>4.203</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>735.459</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 51 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Date  attendance           home_team_name  \\\n",
       "764 2018-08-11 14:00:00     24821.0                   Fulham   \n",
       "766 2018-08-11 16:30:00     31231.0  Wolverhampton Wanderers   \n",
       "770 2018-08-18 11:30:00     30720.0             Cardiff City   \n",
       "780 2018-08-25 11:30:00     31322.0  Wolverhampton Wanderers   \n",
       "788 2018-08-26 15:00:00     23438.0                   Fulham   \n",
       "\n",
       "       away_team_name  home_ppg  away_ppg  home_team_goal_count  \\\n",
       "764    Crystal Palace      1.11      1.53                     0   \n",
       "766           Everton      1.79      1.05                     2   \n",
       "770  Newcastle United      1.05      1.05                     0   \n",
       "780   Manchester City      1.79      2.32                     1   \n",
       "788           Burnley      1.11      0.89                     4   \n",
       "\n",
       "     away_team_goal_count  total_goal_count  total_goals_at_half_time  ...  \\\n",
       "764                     2                 2                         1  ...   \n",
       "766                     2                 4                         2  ...   \n",
       "770                     0                 0                         0  ...   \n",
       "780                     1                 2                         0  ...   \n",
       "788                     2                 6                         5  ...   \n",
       "\n",
       "     avg_home_goal_diff  avg_home_shots_on_target avg_away_goals_scored  \\\n",
       "764              -2.000                     7.000                 1.260   \n",
       "766               0.000                     5.000                 1.403   \n",
       "770              -1.000                     2.000                 1.000   \n",
       "780              -0.667                     3.667                 2.468   \n",
       "788              -0.667                     8.000                 0.987   \n",
       "\n",
       "    avg_away_goals_conceded  avg_away_goal_diff  avg_away_shots_on_target  \\\n",
       "764                   1.532              -0.273                     4.831   \n",
       "766                   1.351               0.052                     4.948   \n",
       "770                   1.225              -0.225                     4.950   \n",
       "780                   0.861               1.608                     7.595   \n",
       "788                   1.278              -0.291                     4.203   \n",
       "\n",
       "     home_form  away_form  home_elo  away_elo  \n",
       "764        NaN       13.0       NaN   713.216  \n",
       "766        NaN        8.0       NaN   817.709  \n",
       "770        NaN        3.0       NaN   713.200  \n",
       "780        NaN       13.0       NaN  1077.341  \n",
       "788        NaN        2.0       NaN   735.459  \n",
       "\n",
       "[5 rows x 51 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for team in teams:\n",
    "    elo_rating = elo[elo['team'] == team]['rating'].mean()\n",
    "    # Returns home games for selected team and fills column with elo_rating\n",
    "    home_games = matches[matches['home_team_name'] == team]\n",
    "    # Returns away games for selected team\n",
    "    away_games = matches[matches['away_team_name'] == team]\n",
    "    \n",
    "    matches.loc[home_games.index.values, 'home_elo'] = elo_rating\n",
    "    matches.loc[away_games.index.values, 'away_elo'] = elo_rating\n",
    "\n",
    "matches[matches['home_elo'].isna()].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Elo ratings are unavailable for newly promoted teams. Fill missing values with min"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "elo_min = matches['home_elo'].min()\n",
    "matches['home_elo'].fillna(elo_min, inplace=True)\n",
    "matches['away_elo'].fillna(elo_min, inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What feature is most correlated to winning?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "As is seen below, bookmaker's odds are more correlated to a win than almost all features in this dataset. The only statistic more correlated with winning is the number of goals the team scored at half time, information that is unknown before the start of the game, when the bookmaker's have to make their predictions. This indicated the bookmaker's use additional data in their predictions than what is available in this dataset.\n",
    "\n",
    "Despite the success of the bookmaker's predictions, other features are still highly correlated such as points per game, average home goals scored and conceded, as well as ELO.\n",
    "\n",
    "Attendance provides an interesting correlation. Stadium attendance is positively correlated to a home win, negatively correlated to an away win. Suggesting the '12th man' phenomenon may hold true: more home fans attending a match increases the teams chance of winning."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates correlation matrix between each feature\n",
    "corr_matches = matches.drop(['home_team_goal_count', 'away_team_goal_count', 'goal_diff'], axis=1)\n",
    "corr_matches = corr_matches.corr()\n",
    "plt.figure(figsize=(12, 12))\n",
    "\n",
    "# Create plots for home and away wins compared to just home or away wins (1 column of the matrix)\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Feature correlation to home_win')\n",
    "sns.heatmap(corr_matches[['home_win']].sort_values(by='home_win', ascending=False), annot=True)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('Feature correlation to away_win')\n",
    "sns.heatmap(corr_matches[['away_win']].sort_values(by='away_win', ascending=False), annot=True)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### What feature is most correlated with bookmaker's odds?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bookmaker's odds have similar correlations with features as the wins do, suggesting these features are indicative of a teams chance of winning. The correlations with the actual results are more modest than the correlations with the bookmaker's odds. These features are likely to be very indicative of how the bookmaker's set their odds. Match results have more variance than how bookmaker's set their odds, which these statistics explain. \n",
    "\n",
    "Recent points per game (HPPG/APPG) have greater correlation to the result than more long term points per game (home/away_ppg)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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OOZeU5yq3L/FBmXNup5175qk8/sffpjoM55xzrlSeq9y+xAdl+xBJd0q6MUl5G0nzdrKtnd4mFSSNlNQp1XG44np0O4r6GfVSHYZzrgrbkzmrqpN0t6QBqY7DFee5yu1L/I9HuyrNzH6S6hicc865spjZHamOwTm3b/MrZVWIpBskzYuvX8ayX0v6RNIbQMeEut0lzZb0PnBtQnlnSR9KmiVpjqT2ZXRZTdJfJc2X9Jqk9NhGN0lT4/ZjJTWM5RMlPShpsqSPJfWUNEbSZ5K2zQ+QdFFCDCMkVStlf38o6Y9x+ReSFsXltpLeTeizR1zOk3Rv3O+pkrJ26UA755zbbZWdsyS9KGlGzFlXxrKK5JE7JE2LcT6hoK2kmQltt5c0o5R+j5U0Ji4PkpQv6SBJtRL6GyXpB3H5K0l3SZopaa6kw3ftCDvnDiQ+KKsiJHUHLgeOA3oBV8SyHwFHA+cBPRM2eQq4zsx6l2jqauBhM+sG9AC+LqPb9sAjZtYZWAN8P5Y/A9xiZl2AucBvErbZbGYnAo8DLxGS65HAZZIaSToCGAKcEGMoAIaW0v9koG9c7gusktQc6AO8k6R+HWCqmXWN216RrFFJV0qaLmn6yGdGl7H7zjnndkWKctYwM+se610nqREVyyN/MbOeZnYkkA6cbWZfAGsldYt1LgdGldLvzLhPRX3Mi/t2HPBBKdusNLNjgMeAHaZwgucq51xxPn2x6ugDjDWz9QDxrNxZsWxDLBsX/60PNDCzSXHbvwNnxOX3gV9LagGMMbPPyujzSzObFZdnAG2StP008HzCNuPiv3OB+Wb2bYxpEdAy7kd3YJokCAlwebLOzWyZpLqS6sVt/wWcSEh6Y5Jsshn4T0K8p5bS7hPAEwBbVi6yUvfeOefcrkpFzrpO0uC43BJob2ZTK5BHTpJ0M1AbyATmAy8DI4HLJd1AOJl4bLJOzWyrpM/jScdjgT/GPqqR/AQiCX3PIAxQk7Xruco5t41fKas6VEp5sg9qlVKOmf0L+B6QD0yQdHIZfW5KWC6gYoP0om0KS2xfGLcX8LSZdYuvjmZ2ZxntvU84Q/kJIbn1BXoDU5LU3WJmRftd0XjdXnDTb4Yz9Krr+Wrx15xy7kW88PKEVIfknKtclZqzJPUHBgC942yJj4BacXWpeURSLeBR4AdmdhTw14TtXiAMDs8GZpjZqtJ2NrZ7BrAFeIMwKO1DuFKXTFF+9FyVQp6r3L7EPyiqjsnAKEnDCQlsMHAp8FQsqw6cA4wwszWS1krqY2bvkjA9UNJhwCIz+1Nc7gK8VdEgzGytpNWS+prZO8DFwKTytkvwJvCSpAfNbLmkTKCemWWXsd93x9dHwElAvpmt3Yk+XSX7/V23pjoE51xqVXbOqg+sNrMN8R6tXiViSZpHJDWIdVZKqgv8APg3gJltlDSBMMXwxxXY32eAZ8xsRZw62ZRw1c1VUZ6r3L7EB2VVhJnNlDQK+DAWjTSzGZKeBWYB2RSfJnE58KSkDUDiqZ8hwEWStgDLCElqZ10KPC6pNrAo9lXR/Vgg6TbgNUlphLOK18b4k3mHMOVkspkVSFoCLNyFmJ1zzlWSFOSs8cDVkuYQrohNTVhXah6JA8K/EqbcfwVMK9HuPwnTC18rZ5c/ALLYfmVsDrA8YfaGc87tFvnniduf+Tx9t6+r0fiw0qaJOed2k8LfUatvZrenMg7PVW5f5nlqz/ArZc4555w74EgaC7QFyrr32jnnKoUPyvZzcd77m0lWnVLOTc17Oo4PgJolii82s7mVFYNzzrmqrTJzlpkNLlkWB2qHlii+xcz8CRHOub3Kpy+6/ZpPCXH7Op8W4tz+z3OV25d5ntoz/EqZ269tnfBUqkNwbrfUGHpPqkNwzu1lW6f8O9UhOLfLagy6OdUh7Bf875Q555xzzjnnXAr5oMw555xzzjnnUsgHZc4555xzzjmXQj4oc84555xzzrkU8kGZc84555xzzqWQD8qcc84555xzLoX8kfjOuaSmfP4tD0yYRWGhMfjoQxnW54gd6kyYv4QRk+aDoENWA4af12vburxNWxj86HhOPrw5vzrjmMoM3Tnn3AFoyidf88BLUym0QgYf25FhJ3Uttv7346Yy7YtvAdi4ZSs5eRt59+6LUxGqczvwQZkDQFIb4D9mdmSKQ9mBpInAjWY2PdWxHCgKCgu5/9WZPH5RP7Iy0hk68g36dWxG2yb1t9XJXrWOJ6d8zKjLTyYj/SBy1m8s1sYjb8+je+smlR26c87tEklfAT3MbGUF6t4J5JnZHyTdDUw2szck9QUeB7YAvYG7gTOB/5rZTXsteBfy1tj3ePyK08mqX4ehfx5Hv06taJvVcFudm763/cTh6CnzWbh0VSpCdS4pn77onNvBvKU5tGxYlxYN61KjWjUGdm7FxE++KVZnzMxFDOnRjoz0gwDIrFNr27oF3+SQs34jvQ/LqtS4nXOuspnZHWb2Rnw7FPiDmXUzs3zgKuAYH5DtffOWrKBl4wxaNMqgRvVqDOx6GBPnLy61/quzFnF6t7aVGKFzZfNBmUtUTdJfJc2X9JqkdEndJE2VNEfSWEkNIVy9kvSgpMmSPpbUU9IYSZ9J+m1Rg5IukvShpFmSRkiqVlrnkk6T9L6kmZKel1Q3SZ0LJM2VNE/S7/bOYXDL1+XTtH7tbe+zMtJZvi6/WJ3snHVkr1rHpU++ycV/e4Mpn4cpIYVm/O/rs7l+QPFpI8651JL0oqQZ8TP+SknXSHogYf1lkv4cl2+XtFDS65JGS7qxjHavkDRN0mxJL0iqLamapEUKGkgqlHRirP+OpHaSjpX0nqSP4r8dE9Z3S2h/iqQupfTdJMY4M+aYbEmN47obYq6YJ+mXpR2HnTh+v5b0iaQ3gI4J5aMk/UDST4AfAndI+qekcUAd4ANJQyraj9s1y9duoGn9OtveZ9WvzfLc9UnrfrN6Hd/krOPYdodUVnjOlcsHZS5Re+ARM+sMrAG+DzwD3GJmXYC5wG8S6m82sxMJUzVeAq4FjgQuk9RI0hHAEOAEM+sGFBDOIu4gJtHbgAFmdgwwHbihRJ1mwO+Ak4FuQE9J5yZp60pJ0yVN/9tbM3fxUBzYLEmZSrwvKDQW5+Qx8tKTGH5eL+56eTq5Gzfz3LTP6dPukGKDOudclTDMzLoDPYDrgDHAeQnrhwDPSupB+Pw/Oq7vUU67Y8ysp5l1BT4GfmxmBcCnQCegDzAD6CupJtDCzD4HFgInmtnRwB3AfbG9kcBlAJI6ADXNbE4pff8GeCvmjbFAq7hdd+By4DigF3CFpKOTHQdJjcrZv6L2fpRwTHqWrGNmI4FxwE1mNtTMvgfkx6tmzyZpc3uumvBBeSG4ciTPWyUzVzBh1iIGHHUo1dL8a7CrOvyeMpfoSzObFZdnAG2BBmY2KZY9DTyfUH9c/HcuMN/MvgWQtAhoSUjE3YFpkgDSgeWl9N2LkLynxLoHAe+XqNMTmGhmK2I//wROBF5MrGRmTwBPAOT/8/Zkn9OuHFn10lm2dsO299/l5tOkXnrxOhnpHNW8ETWqpdG8YV3aNKrH4lV5zP56FR8tXslz0z8nf/NWthQUUrtGdX4xIOmJbudc5blO0uC43BI4FFgkqRfwGeHqzxTgF8BLcfodkl4up90j4wyJBkBdYEIsf4fwGX0ocD9wBTAJmBbX1weeltSe8J26Rix/Hrhd0k3AMGBUGX33AQYDmNl4SasTysea2fq4D2OAvsBHSY5De6C8m4v6xvY2xPbGlVO/XMVy1UsPeK7aTVn1a7Ns7fYrY9+t3UCTjOQnB8fPXsSvzj2+skJzrkJ8UOYSbUpYLiAk2IrULyyxbSHhd0vA02b2qwr0LeB1M7ugnDquEnRunsninDyWrs7j4Ix0JsxfzH2DexWrc1LH5rw6bzGDuh3K6g2byM5ZR4uGdbg/4QmML836kgXfrvYBmXMpJqk/MADobWYbFB6gVAt4ljDlbiFh0GGKZ8Z2wijgXDObLekyoH8sfwe4GmhGuBJ2U1w3Oa6/B3jbzAYrPGxqIkCM73VgUIytrCt1pcWatLyM41ARPnCqwjq3aMLilbkszVnHwRm1mTB7Efdd0H+Hel8tX0Nu/ma6tj648oN0rgx+3daVZS2wWuFpUgAXE85yVtSbwA8kHQwgKVNS61LqTgVOkNQu1q0dp60k+gDoJ6lxvDftgp2Mx1VQ9bQ0bj3jGK7552QGPzqeUzu1pN3B9Xn07XlM/GQpAMe3bUqD9Jqc9+h4rnhmItcP6EqD2jVTHLlzrhT1gdVxIHI4YXYChCmM5xI+T4um2L0LnCOpVry396xy2q4HfCupBsWnqH8AHA8UmtlGYBbhwRfvJMS0NC5fVqLNkcCfgGlmllNG3+8SBm5IOg0oetTeZODcmEvqEK6mvVPGcSjPZGCwwr3W9YBzKridqyTVq6Vx66DeXDNyPIP/8AKndjmUdk0b8uiEGUycn72t3quzFnF618PY+XMPzu1dfqXMledS4HFJtYFFhDn6FWJmCyTdBrwmKY3wiOBrgewkdVfEM6yj4z0HEO4x+zShzreSfgW8TTgL+l8ze2nXdsuVp2/7Q+jbvvhN0D89aftfTJDEjQO7ldysmEHdDmVQt0P3SnzOuZ0yHrha0hzgE8KJMMxstaQFQCcz+zCWTYvT82YTPq+nE07SleZ2wgAsmzCdvV5sZ5OkJUV9EQZFF8Q6AA8Qpi/eALyV2KCZzZCUCzxVzn7dRcgbQwgn6b4F1pnZTEmjgA9jvZFm9lHc1x2OQ3lie88SBpbZbB9Yuiqk7xEt6XtEy2JlPx3Yvdj7a07zv5vpqiaZ+dV4t//ye8rcvi596D1+OtdVOkl1zSwvnpCbDFxpZpX25KT4YKeJwOFmVlhGvZpAgZltldQbeCw+WGqf4veUuX1Z+qCbPU/tAX6lzDnnnHMlPSGpE+F+q6creUB2CXAvcENZA7KoFfBcnI2xmfAwEeec2+f4oMxVOkkfACVvPrrYzOYmq++cc65ymdmFJcskPQKcUKL4YTMrb4rhzvb9DOHPsST2fTnhqZCJppjZtYTH1O8R8fH4byZZdYqZlfeERuec22U+KHOVzsyOS3UMzjnndk4cAKWq76co//6yPdHPKsLfwXTOuUrlgzK3fyt35otzzjmXYoWeq5w70Pkj8Z1zzjnnnHMuhXxQ5pxzzjnnnHMp5IMy55xzzjnnnEshH5Q555xzzjnnXAr5oMw555xzzjnnUsgHZc4555xzzjmXQv5IfOdcUlM+X8YDE2ZRaMbgow9l2AmH71BnwvwljJi8ABAdsuoz/Lztf4Iub9MWBj82gZM7NudXZ+yxv+3qnHPObTPlk6954OUPQq7q2YFh/bvsUGfCnC8Z8cZHgOhwSCbDL+gHwEOvTuOdhV8DcOXJXRnY9bDKDN25YnxQ5pzbQUGhcf/4j3h8aF+yMmozdOSb9OvQjLZNMrbVyV61jienfMKoy04iI/0gctZvLNbGIxPn071Vk8oO3Tnn3AGioLCQ+1+ayuM/HkhW/doM/cvL9DuiFW2zGmyrk71yLU++PYdRV59FRu2a5OTlAzB54RI+XprDs9cNYktBAT8e8SondGxB3VoHpWp33AHOpy9WQZImSuqR6jj2FEl5O1F3lKQfxOWRkjrF5fMlfSzp7fh+tKQ5kq7fO1Ef2OZ9k0PLhnVp0bAuNaqlMbBzSyZ+8k2xOmM++pIhPduSkR4SWGadWtvWLfh2NTl5G+ndNqtS43bOuVRIzF2u8sxbspKWjerRolE9alSvxsCuhzFxweJidcZ8+ClDeh9BRu2aAGTWTQdg0Xdr6HFoFtWrpZF+UA06HJLJlE+XVvo+OFfEB2WuyjKzn5jZgvj2x8BPzewkSU2B482si5k9mMIQ91vLc/NpmpG+7X1WRjrL1+UXq5O9Ko/sVeu49Km3ufjJt5jy+TIACs3439dnc/2AHaeQOOecc3vK8twNNK1fZ9v7rPq1WZ67vlid7JW5ZK9cy6WPvcLFj/yHKZ+E6YodDsnk3U+Xkr95K6vXb2Taom/5bk3xbZ2rTD4o2w2SXpQ0Q9J8SVdKukbSAwnrL5P057h8u6SFkl6PV3luLKf58yV9KOlTSX1jG7V8XyFeAAAgAElEQVQkPSVprqSPJJ2U0M+Lkl6W9KWkn0m6IdaZKikz1msraXyM+R1JO94ktD32tnHbaZLuLrrapeD3kubFOIbE8rqS3pQ0M5YPquAxlKS/SFog6RXg4IR1EyX1kHQH0Ad4XNLvgdeAgyXNKjo2Jdq8UtJ0SdP/9tZHFQnDlWBJyqTi7wuskMU5eYy8pB/DBx/HXf+ZQe7GzTw3/Qv6tDuEpvVrV0qszrkDT5L8+0NJf4zrfiFpUVxuK+nduHxHzGnzJD0R809bSTMT2m0vaUYZ/XaXNCn2PUHSIUnqnBLz71xJT0qqWUpb23PVax/u7iE5IJntmK1E8WRVUFjI4pW5jLzyDIZf0I+7XphCbv4mju/QnD4dW3DpY69w6+hJdGl1MNXStEN7zlUWv6ds9wwzsxxJ6cA04BRgCnBzXD8EuDdORfw+cDThmM8ESv3Qj6qb2bGSzgR+AwwArgUws6PigOo1SR1i/SNj+7WAz4FbzOxoSQ8ClwAPAU8AV5vZZ5KOAx4FTi6l/4eBh81stKSrE8rPA7oBXYHGwDRJk4EVwGAzy5XUGJgqaZwl+8QsbjDQETgKyAIWAE8mVjCzuyWdDNxoZtMlPQL8x8y6JWvQzJ6I+0r+P35dXv8uiayMdJblbr8y9l1uPk3qphevU682R7XIpEa1NJo3rEObRnVZnJPH7K9X8dHilTw3/QvyN29lS0EhtQ+qzi9OOaqyd8M5t/8qmX8HAjfFdX2BVZKaE07ovRPL/2JmdwNI+jtwtpm9LGmtpG5mNgu4HBiVrENJNYA/A4PMbEU8KXkvMCyhTq24/Slm9qmkZ4BrCDm4mGK5auxwz1W7IKt+HZat3X5167u1G2iSUXuHOke1bBJyVWY92jSpz+KVuRzZsglXnNyVK07uCsCtoyfRqnEGzqWKXynbPddJmg1MBVoChwKLJPWS1Igw2JhCSAovmVm+ma0DXq5A22PivzOANnG5D/B3ADNbCGQDRYOyt81snZmtANYm9DEXaCOpLnA88LykWcAIYIczfAl6A8/H5X8llPcBRptZgZl9B0wCegIC7pM0B3gDaE4YZJXnxIT2vgHeqsA2bi/r3Kwhi3PyWLp6PVsKCpkwfwn9OhT/dTmpYzOmfbUCgNUbNpGdk0eLBnW4f/BxjP/FWbx63Zlcf2oXzu7S2gdkzrk9rWT+bQnUlVQvLv+LkF/6sn1QdpKkDyTNJZyQ7BzLRwKXS6pGOJmamPMSdSScAH095tHbgBZJ6nxpZp/G90/HONxe0LlFYxavymVpzjq2bC1gwuxF9OvUslidkzq1YtqibwFYvX4j2SvX0iKzHgWFhayJD6j69NscPluWQ+/2zSt9H5wr4lfKdpGk/oSrV73NbIOkiYSrVM8CPwQWAmPNzKSSE78qZFP8t4DtP6ey2tmUsFyY8L4wbp8GrCnt6tJOKC2GoUAToLuZbZH0FeF4VISfIaxiqqelcevp3bjmX+9QaMagrm1od3B9Hp04n06HNKR/x2Yc3zaL9xd9x3mPTSBN4vpTutCgdtJZOs45t8eUkX/fJ1zp+oQwEBtGOMH4/+IVrEeBHma2RNKdbM9RLxBmpLwFzDCzVaV1Dcw3s95lhbcbu+Z2UvVqadz6vV5c8+RrFBYag3q0p11WQx59bSadWjSmf6dWHN+hOe9/9g3n/XFMyFVn9qRBnVps2rKVYSP+C0Cdmgdx75ATqV7Nr1W41PFB2a6rD6yOCeFwoFcsHwP8mnAV65ZY9i4wQtL9hGN+FvDXXehzMmHw81acttiKkHyOKW/DOK3wS0nnm9nzcaDYxcxml7LJVMKUy2eBH5WI4SpJTwOZhDOANxHOLi6PA7KTgNY7sU9XxSkeBwMnUfpZSleJ+rY/hL7ti18d+2n/ztuWJXHjaV0JM1mTG9S1DYO6ttlLETrnDlCl5d/JwN3x9REhn+Sb2VpJRc9IXxlnjvwA+DeAmW2UNAF4jPBQqdJ8AjSR1NvM3o/TGTuY2fyEOgsJs1PamdnnwMWEGSVuL+l7eEv6Hl786thPT9v+tUgSN559LHBssTo1a1RnzA3nVUaIzlWInxLYdeOB6nG63j2EQQxmtppwX1RrM/swlk0DxgGzCYO26YQphjvrUaBanHrxLHCZmW0qZ5tEQ4Efxykf84GyHsbxS+AGSR8SpjkWxTsWmEPYl7eAm81sGfBPoIek6bGfhRWMaSzwGWGa5WN48nLOOVe2pPmXcHWsJTDZzAqAJYSTopjZGsLJ0LnAi4T70BL9kzBr47XSOjWzzYTB3O9iHp1FuC0gsc5GwtW652OuLgQe3+U9dc4dMFT+cxjcniCprpnlSapNOJt3pZnNLG+7VIlx5sfplz8CLjCzCj1RsSrxB324fV36Rff6dCjn9jKFJyLXN7PbU9G/P+jD7cvSB9/qeWoP8OmLlecJhT+EXAt4uioPyKLuwF/iNMc1JDxdyjnnnNtfSBoLtKX0pxE759xe54OySmJmF5Ysi492P6FE8cNm9lTlRAWSfg2cX6L4eTO7l7JuFtr5fo4iPjkywSYzO25P9eGcc87tLDMbXLIsDtQOLVF8i5lNqJyonHMHGh+UpZCZXVsFYriX8HdW9nY/cwl/38w555yr0pIN1Jxzbm/yQZnbvxUWpjoC55xzrmwFW1MdgXMuxfzpi84555xzzjmXQj4oc84555xzzrkU8kGZc84555xzzqWQD8qcc84555xzLoV8UOacc84555xzKeRPX3TOJTXli2U88NocCs0Y3K0Nw47vuEOdCQu+ZsQ7HwPQIas+w889loXL1nDf+FnkbdpCtTTxkxMOZ2CnFpUdvnPOuQPAlE+X8sAr0yksNAb3aMewfkfuUGfC3K8Y8eYcEHRo2pDhQ/oC8OD4GbzzyVLMjF7tDuHms3oiqbJ3wTnAB2VuL5KUZ2Z1E95fBvQws59JuhO4AlhB+D38HzMbV1p53P4i4GagGrAVmAbcaGZrKm2nDhAFhcb942fz+IV9yMpIZ+iTb9Ov/SG0bZKxrU52Th5PvvcJoy7pR0b6QeSs3whAeo1q3PO9HrTOrMvydflc+Le36H3YwWTUOihVu+Occ24/VFBYyP0vf8jjlw8gK6M2Qx97lX5HtKDtwQ221clemcuTk+Yx6qqBZKTXJCcvH4BZ2cuZlb2C539+NgCXPzGB6V9+R8/DmqZkX5zz6YsulR40s27A+cCTktJKK5d0OnA9cIaZdQaOAd4DslIR+P5u3jc5tMysQ4uGdahRLY2BnVow8dNvi9UZ89GXDOl+GBnpYbCVWacWAK0b1aN1ZhiLH1wvncw6tVi9YXPl7oBzbr8l6StJjVMdR0VI6i/pP6mOY3817+tVtMysR4vMetSoXo2BXVoz8eMlxeqMmf4ZQ47rSEZ6TQAy66YDIInNWwvYUlDI5q2FbC0opFFc51wq+JUyl3Jm9rGkrUDjMsp/TbgqtjSuKwCerPRgDxDL122kab3tySkrI525S3OK1cnOyQPg0qcnUlhoXH3iEZzQtvgZxrlLc9hSUEjLhnX2ftDOOVcJFOa3ycwKUx3LgW557gaa1t+eX7Iy6jB3ycpidbJX5gJw6YjxFJpx9cldOKFDc7q2akLPw5oyYPi/wWBIr44cdnD9So3fuUR+pcztTemSZhW9gLuTVZJ0HFBImLJYWnlnYGZFOpV0paTpkqb/7e1Zu7UDByrDdigrOc2+oNBYnJPHyItOZPjgY7nrlZnkbtx+RWzFunxuGzedu87pTprP0XeuwiS9KGmGpPnx8+waSQ8krL9M0p/j8u2SFkp6XdJoSTeW0e4VkqZJmi3pBUm1JVWTtEhBA0mFkk6M9d+R1E7SsZLek/RR/LdjwvpuCe1PkdSllL6bxBhnShohKbvoapekGyTNi69flnYckrRZR9IrcX/mSRpSxr4Pl7RA0hxJf4hlWZLGxu1nSzq+tHgktZH0saRHCbmopaTTJL0f9+l5SXVj3dPjz+Rd4LwyYtqeq16fVlo1VwbbMVUlz1Ur1zHyJ6cx/Id9uGvsVHLzN7N4VS6Llq/ltZu/z2u3fJ9pi5Yx48vvKidw55LwQZnbm/LNrFvRC7ijxPrr42DtD8AQs20fr6WVAyDpqDjQ+yJZEjazJ8ysh5n1+PFJ3UqudhWQVS+dZevyt73/LjefJiWmdWTVS6d/h2bUqJZG8wZ1aNOoHovj1bO8TVv4+bPvcW3/TnRpnlmpsTu3HxhmZt2BHsB1wBiKf7kfAjwrqQfwfeDouL5HOe2OMbOeZtYV+Bj4cZx18CnQCegDzAD6SqoJtDCzz4GFwIlmdjThc/y+2N5I4DIASR2AmmY2p5S+fwO8ZWbHAGOBVnG77sDlwHFAL+AKSUcnOw6SGpVo83TgGzPramZHAuOTdSwpExgMdDazLsBv46o/AZPi8TgGmF9OPB2BZ+JxWA/cBgyI+zQduEFSLeCvwDlAX6DUG5SK5apTe5ZWzZUhq35tlq1dv+39d7nraZJRIldl1KZ/p5YhV2XWo03jDBavyuWtBUvo0rIxtWvWoHbNGpzQoTlzSlxlc64y+fRFl0oPmtkfKlg+n5A03zazuUA3SX8BfAL4XtC5WUMW5+SxdM16Dq6XzoQFX3PfucW/NJzU8RBenf81g7q2ZvWGTWSvyqNFgzpsKSjkhn9P5ewurTntCH/qYmXasnJRkvPGlatG48P8sujuu07S4LjcEjgUWCSpF/AZYXAwBfgF8JKZ5QNIermcdo+U9FugAVAXmBDL3wFOjP3cT3jY0iTCw5QA6gNPS2oPGFAjlj8P3C7pJmAYMKqMvvsQBkaY2XhJqxPKx5rZ+rgPYwiDmY+SHIf2wKqENucCf5D0O+A/ZvZOKX3nAhuBkZJeAYru8ToZuCTGVACslVRaPOOAbDObGrftRRjITgmzGTkIeB84HPjSzD6L2/8D2OEqn9szOjdvxOJV61ias46DM2ozYU429/2wT7E6J3VqyatzvmLQMW1ZvX4j2atyaZFZj6U5eYyZ/hlbCwoxYMaX3zH0+MNTsyMHkKqQp6Bq5ioflLl9xf2E5DvIzL6OZT4g20uqp6Vx68BuXDN6CoWFxqCurWnXJINHJy2g0yEN6N+hGccflsX7i5Zz3ojXSZO4/pQjaVC7Jq/MXczMxStZs2Ez42ZnA3D3Od05vGmDcnp1zknqDwwAepvZBkkTgVrAs8APCVetxpqZSTs9L3gUcK6ZzVZ4Gm7/WP4OcDXQjHAl7Ka4bnJcfw/hhNhgSW2AiQAxvteBQTG2sq7UlRZr0vIyjsM2ZvZpvLJ1JnC/pNfMbIdp8ma2VdKxwCnAj4CfEQZkOxMnhKtjifVeN7MLSsTdDZLM/3Z7RfVqadx6zrFcM+pNCs0YdEw72mU14NE3ZtGpeSP6H9GS49s34/3Pv+W8h8aRliauP/0YGtSuyYAjW/HhomWc/+eXEeL4Ds3od0TLVO+SO4D5oMztE8zsv5KaAK9KqgasAeax/Uyv28P6tmtK33bFZ978tF+nbcuSuPHUHW8fOeuoVpx1VKu9Hp9z+6n6wOo4EDmccEUGwhTGXwPZwC2x7F1ghKT7Cfn8LMLUudLUA76VVAMYCiyN5R8AzwCLzGxjnD5+FXB2QkxFdS8r0eZI4GXgHTPLoXTvEgZuv5N0GtAwlk8GRkkaThjoDAYuJkxvTHYctpHUDMgxs39IyksSW1G9ukDtmEemAp/HVW8C1wAPxbxSp4x4SpoKPCKpnZl9Lqk20IIwaD5UUlsz+wK4IMm2bg/q27E5fTs2L1b20wHbb12QxI1n9ghD9wTV0tK4/dwdfq2cSxkflLm9JvFvlMX3o4jTW8zszlK2SVoe1z0NPL2n4nNuv1NYkOoI3O4bD1wtaQ7wCeHLP2a2WtICoJOZfRjLpkkaB8wmDNamA2vLaPt2wgAsmzD1r15sZ5OkJUV9Ea6cXRDrADxAmL54A/BWYoNmNkNSLvBUOft1FzA63gc8CfgWWGdmMyWNAj6M9Uaa2UdxX3c4DiUcBfxeUiGwhTDASqYe8FK830uEP68CYfrnE5J+DBQA15jZ+6XE06bEfq+IVxtHx/vvAG6LV++uBF6RtJIwGN3xrxk7d6DyPFUqWbJH1zi3n8h/5lf+C+72aemX3F/hKWpbln+W8t/3Gge3r3Lz9PdnkuqaWV68UjMZuNLMKvSk2j3UfzPCdMbDy3pEfBy4FMSphL2Bx+IDoByQ/+/fpvz/rnO7Kv0Ht+1TeQqqZq7yK2XOOefcvusJSZ0I91s9XckDskuAe4EbKvA3u1oBz0lKAzYTHibinHMu8kGZc87tL/xv2R5wzOzCkmWSHgFOKFH8sJmVN8VwZ/t+hnAvWmLflxOmBSaaYmbXEh7dv1dJGkt4imSiW8zM7z92rirwPFUqH5Q555xz+5E4AEpV309R/v1le7P/weXXcs65qsf/eLRzzjnnnHPOpZBfKXPOuf1FoU8Lcc45V4V5niqVXylzzjnnnHPOuRTyK2XOObefKP8BeM4551zqeJ4qnV8pc84555xzzrkU8kGZc84555xzzqWQT190ziU15YtlPPDaHArNGNytDcOO77hDnQkLvmbEOx8D0CGrPsPPPZaFy9Zw3/hZ5G3aQrU08ZMTDmdgpxaVHf6ByW+gds4dYKZ8upQHXplOYaExuEc7hvU7coc6E+Z+xYg354CgQ9OGDB/Sl2mLlvH7V6Zvq/PVyrUMH9KXkzu1qszwDzyep0rlgzLn3A4KCo37x8/m8Qv7kJWRztAn36Zf+0No2yRjW53snDyefO8TRl3Sj4z0g8hZvxGA9BrVuOd7PWidWZfl6/K58G9v0fuwg8modVCqdsc558okaTAwBjjCzBZKagN8DHwCHARMBn4KtEpWbmaFktoDDwJHAGuAXOA3Zja5cvfmwFFQWMj9L3/I45cPICujNkMfe5V+R7Sg7cENttXJXpnLk5PmMeqqgWSk1yQnLx+Anoc15bmfnw3A2g2bOOePL9K7XbOU7Idz4NMXd4ukiZJ6pDqOPUVSXqpjqChJl0n6S6rj2F/N+yaHlpl1aNGwDjWqpTGwUwsmfvptsTpjPvqSId0PIyM9DLYy69QCoHWjerTOrAvAwfXSyaxTi9UbNlfuDhyorDD1L+f2TRcA7wI/Sij7wsy6AV2ATsC5pZVLqgW8AjxhZm3NrDvwc+CwytqBA9G8r1fRMrMeLTLrUaN6NQZ2ac3Ej5cUqzNm+mcMOa4jGek1Acism75DO6/Py+aEDs1IP8ivVex1qc5RVThX+aDMVRmSqqU6BhcsX7eRpvW2J66sjHSWr8svVic7J4/snDwufXoiFz/1NlO+WLZDO3OX5rCloJCWDevs9Zidc25XSKoLnAD8mOKDMgDMbCvwHtCujPKhwPtmNi5h/TwzG7X3InfLczfQtP72/JKVUYfla0vkqpW5ZK/K5dIR47n48VeZ8unSHdqZMPcrzuhy6F6P17myHBCDMkkvSpohab6kKyVdI+mBhPWXSfpzXL5d0kJJr0saLenGcpo/X9KHkj6V1De2UUvSU5LmSvpI0kkJ/bwo6WVJX0r6maQbYp2pkjJjvbaSxseY35F0eBn71jZuO03S3UVXuxT8XtK8GMeQWF5X0puSZsbyQUnaPETSZEmz4vZ9S+m7mqRRCX1cH8vbSXpD0uzYT9sy4ukv6W1J/wLmxrKL4jGdJWlE0WBN0uXxOE8iJNDSjsmVkqZLmv63t2eV8+NzyRi2Q5lU/H1BobE4J4+RF53I8MHHctcrM8nduP2K2Ip1+dw2bjp3ndOdtJIbO+dc1XEuMN7MPgVyJB2TuFJSbeAUYo4qpbwzMHNnOi2Wq16ftjvxH7Bsx1SVPFetXMfIn5zG8B/24a6xU8nNT8hVuRv4fNkaerf3qYsutQ6U67TDzCxHUjowjfAhOgW4Oa4fAtwbpyJ+HziacGxmAjPKabu6mR0r6UzgN8AA4FoAMzsqDqhek9Qh1j8ytl8L+By4xcyOlvQgcAnwEPAEcLWZfSbpOOBR4ORS+n8YeNjMRku6OqH8PKAb0BVoDEyTNBlYAQw2s1xJjYGpksaZFftouxCYYGb3xgFR7VL67gY0N7MjASQVTeL+JzDczMbGKR1pZcQDcCxwpJl9KekIws/jBDPbIulRYKik14G7gO7AWuBt4KNkQZnZE/EYkv/Mr5J8ZLvyZNVLZ1nClbHvcvNpUmLKR1a9dI5qnkmNamk0b1CHNo3qsTgnjyObZZK3aQs/f/Y9ru3fiS7NMys7/ANXYUGqI3BuX3QBIfcC/F98/wjQVtIswICXzOzVeK9ZsvJTExuUNBZoD3xqZucl67RYrvr3bz1X7YKs+rVZtnb9tvff5a6nSUaJXJVRm6NaNQm5KrMebRpnsHhVLke2aAzAa/OyOalTS2pUOyCuU6Se56lSHSi/gddJmg1MBVoChwKLJPWS1AjoSBik9SF8wOab2Trg5Qq0PSb+OwNoE5f7AH8HMLOFQDZQNCh728zWmdkKwuCiqI+5QJs4jeJ44Pn4oT8COKSM/nsDz8flfyWU9wFGm1mBmX0HTAJ6AgLukzQHeANoDmSVaHMacLmkO4Gj4rFIZhFwmKQ/SzodyJVUjzBQGxv3f6OZbSgjHoAPzezLuHwKYeA1Le7/KYQ5+ccBE81shZltBp4t45i43dS5WUMW5+SxdM16thQUMmHB1/TrUPzX8KSOhzAtewUAqzdsIntVHi0a1GFLQSE3/HsqZ3dpzWlH+FMXnXNVV/wOcDIwUtJXwE2EE4Mi3jtmZkeb2Z0JmyUrnw9su8JmZoOBywA/K7UXdW7eiMWr1rE0Zx1bthYwYU42/Q5vWazOSZ1aMm1RmF6/ev1Gslfl0iKz3rb14+d8xRldfeqiS739/kqZpP6Eq1e9zWyDpImEq1TPAj8EFgJjzcykXZpjtSn+W8D241lWO5sSlgsT3hfG7dOANfEm4t1RWgxDgSZA93gl6ivC8djGzCZLOhE4C/i7pN+b2TMlGzKz1ZK6AgMJVwd/CPxyJ+MBWJ+wLOBpM/tVsY2lcyHJnDq3V1RPS+PWgd24ZvQUCguNQV1b065JBo9OWkCnQxrQv0Mzjj8si/cXLee8Ea+TJnH9KUfSoHZNXpm7mJmLV7Jmw2bGzc4G4O5zunN40wbl9Oqcc5XuB8AzZnZVUUGcIr+zZ5T+BfxK0vcS7isrbZaJ20OqV0vj1nOO5ZpRb1JoxqBj2tEuqwGPvjGLTs0b0f+Ilhzfvhnvf/4t5z00jrQ0cf3px9Cgdnjox9LVeSxbs57ubUqem3au8u33gzKgPrA6DsgOB3rF8jHArwlXsW6JZe8CIyTdTzg2ZwF/3YU+JxMGP2/FaYutCI/PPabMrYA4rfBLSeeb2fNxoNjFzGaXsslUwpTLZyl+g/Jk4CpJTxPO1J3I9jOAy+OA7CSgdckGJbUGlprZXyXViXHvMCiL0x83m9kLkr4ARsX4v5Z0rpm9KKkmUK2MeEreL/cm8JKkB81sucJ9dvWAD4CH41nNXOB8oLRj4vaAvu2a0rdd02JlP+3XaduyJG48tcsO2511VCvOOsr/zktKVNEnSjlXhV0ADC9R9gLwP/+fvTuPr6q4/z/+eicQEkjYFJHNgkGk4IKKCioKStXWtq6VKlURq3VprVr9auvSamu19dfa1rrhvi/UvVbRIrsgiywBxIXNDVkDJEAgyf38/jiTcJPcm4WS3EA+z8cjD+6dM2dmzkk4nztn5sytSyFmtkXS94G/SvobsBIoAP6wU1rpkhq0fxcG7d+lQtrlQ7ff15bEtd/rD9+rum+Xdtm8e8NZ9d1EF8/jVFJNoVP2NnBpmK73MVEnpmyUZyHQx8ymh7QZkl4n+rC/HJhJNMWwru4DHpCUB5QAI8xsax0G4oYD90u6CWhONMc9WQfkKuBpSb8iWo63rL2vEE1tnEs0wvR/ZvaNpGeANyTNBOYQjRRWNhi4TlIxUEj0rFsiXYDHJJVNgy0b3TqPqHN7G1BM1IFK1p4KnTIzWxiO+51QbjFwhZlNC9MppwIriJ7389UanXPO7TAzG5wg7R/AP5LkX0b0bHiibYtI+NHfOedqJku0dE0TJinbzArDqkoTgUvMrE4rKjWk0M4tYfrlj4FzzKzKiopNlS/04XZ1WeffUeu7OduWTE/533vGvkf4UpvO1ZEv9OF2ZVln3bRLxSlonLGqKYyU1dUoSX2InrN6ojF3yILDgH+GaY7rgZEpbo9zzjnnnHOuDrxTVomZnVs5TdK9VP1erL+b2WMN0yqQdCPRNMB4o83sdqJl5uu7/g+AFpWSzzOzvET5nXPOOeecc7XjnbJaMLMrGkEbbgduT2H9R6aqbudc7Zg/QO2cc64R8ziVnHfK3O6tuDjVLXDOOeeqF/MPqs41dd4pc8653YV/sHPOOdeYeZxKKq3mLM4555xzzjnn6ot3ypxzzjnnnHMuhXz6onPO7S78AWrnnHONmceppHykzDnnnHPOOedSyEfKnHM1mrJkFX8eO5+YGacftA8jB+xXJc+YRV/z4JSPAei1Vxvu/MGhDd1M55xzTcyUT7/mz2/OjOLTYT0ZeWzfKnnG5C3nwXHzANFr77bcefYxAKxYv4lbX53Gyg2bkeCe84bQpV12Ax+BcxHvlDnnqlUaM+74bx4PnD2AjjlZDH9yEsf13JvcPXPK8yxfV8ij0z7l8eFH0zozg3WbtqawxU1YrDTVLXDOuQZTGotxxxszeGDE8XRs3ZLhD7zNcb27krtXm/I8y9du5NGJC3j84hNpndWCdYVF5dtueul9fnrcAQzs2YnNW4uRlIrDaFo8TiXl0xcBSeMl9U91OyqTNELSP+u4z2/qqz21qHuwpKMaqK6rJLVsiLqauvkr8unWthVd27aieXoaJ327M+M/+6ZCnpfnfc6wQ7rTOjMDgPatWqSiqc4555qQ+V+updseOXRtn3wZ7l0AACAASURBVEPzZumcdOC3GP/RFxXyvDzzM4Yd2YvWWVFcap+dCcDiVRsojRkDe3YCoGWL5mRl+FiFSx3/69v9/Ab4Y4rqHgwUAu/XdgdJzcysZAfqugp4Gti8A/u6OlhVWMTeOVnl7zvmZJL39foKeZavKwTggmcmE4sZlx69P0fvu1eDttPhD1A755qUVRu3sHeb7fdnO7ZpSd6XayvkWb6mAIALHhoTxafjD+Lo/TqzfM1GcjKbc82zE/kqv5Ajc/fmlyf2Iz3NxyvqlceppHapvzxJr0qaJWmBpEskXSbpz3HbR0i6J7y+WdIiSe9Kek7StTUU/yNJ0yV9ImlQKCNT0mOS8iTNljQkrp5XJb0haamkn0u6JuSZJql9yJcr6e3Q5kmSeldzbD+SNF/SXEkT4zZ1DmV8WulYzwntmi/pTyHtTiBL0hxJz0hqJenNUOZ8ScOqqf+E0P48SY9KahHSl0m6VdKHYVvCY5DUHbgUuDrUP0jSDyR9EMr9r6SOIe/vJI2S9A7wpKSWkl6UNE/SC2Gf/iHviZKmhvpHS8qWdCXQGRgnaVwNv1f3PzKrmlZ5hkdpzPg8fxMP//go7vzBYdz69lw2FhU3TAOdc7u9BPH/bEl/Ddt+KWlJeJ0raXJ4fYukGSH+jVIkV9KHceXuJ2lWNfUuk/Sn8PlguqSeIf1xSQ+E2P6JpO+H9KTxzO18RtUAVXkCYmksxudrC3h45He48+xjuPXVaWzcso3SmDF7+WquOfkQnrn0ZL7KL+T12UsapuHOJbBLdcqAkWZ2GNAfuBJ4GTgjbvsw4IVwATwTOCRsr80FsZmZHUE0AvPbkHYFgJkdCJwDPCEpM2w7ADgXOAK4HdhsZocAU4HzQ55RwC9Cm68F7qum/luAk8zsYOCHcen9wnEdCAyT1E1SZ+BPwPFh++GSTjOzG4AtZtbPzIYDJwNfm9nBZnYA8HaiisMxPQ4MC8faDLgsLssaMzsUuD8cRxVmtgx4ALg71D8JmAwMCOfleeD/4nY5DDjVzM4FLgfyzewg4PdhG5L2BG4Chob6ZwLXmNk/gK+BIWY2JMHxXCJppqSZj0yYl6i5rg465mTyTcGW8vcrC4rokJ1ZKU8Wg3vuTfP0NLq0bUn39tl8nr+poZvqnNt9VY7/U4BBYdsgYK2kLsAxwKSQ/k8zOzzEvyzg+2a2GNggqV/IcyFR/KvOxvD54J/A3+LSuwPHAacAD4RYmjCeJVIhVv13Zo0nwFXVsXVLvtmwfcLMyg2b6RA3swOi0bPBvbtG8aldNt33bM3nawvo2KYl+3dqR9f2OTRLT2PIt7vy0dfrGvoQnCu3q3XKrpQ0F5gGdAN6AEskDZC0B7A/0YX6GOA1M9tiZgXAG7Uo++Xw7yyiCy2hnKcAzGwRsBzoFbaNM7MCM1sNbIirIw/oLikbOAoYLWkO8CDQqZr6pwCPS7oYSI9LH2tmG8ysCFgIfAs4HBhvZqvD1L9ngGMTlJkHDA13+QaZ2YYkde8PLDWzT8L7JyqVl+jc1EZXYIykPOA6IH5JpNfNrOyT/jFEnTbMbD5Q1pMaAPQBpoRzeAHR8VfLzEaZWX8z63/RcQfVobkukb6d2vJ5/ia+Wr+Z4tIYYz76muN67l0hz5D99mbG52sAyN+8leX5hXRt64/8NbhYLPU/ztWPyvG/G5AtKSe8fpYobg1ie6dsSBipyiO6iVkWgx4GLpSUTnTT89ka6n4u7t+BcekvmlnMzD4FlgC9SR7PqqgQq4b6YNqO6NtlDz5fW8BX+YUUl5QyJm85x/XuWiHPkG93Y8bSlQDkbypi+ZoCurbPpm+X9hRs2ca6TdHCH9OXrGTfuAVCXD1JdYxqxLFql3mmTNJgYCgw0Mw2SxoPZAIvAGcDi4BXzMykHVo+p2y5uFK2n5fqyolfXi4W9z4W9k8D1ptZv8o7JmJml0o6kuiO25y4u3jx9ZS1rVbHZ2afSDoM+B5wh6R3zOy2BFlrKi/RuamNe4C/mtnr4ff3u7ht8cMoyeoX8K6ZnVOHOt1O1iwtjRuGHsBlo6cRM+PUA7vRc88c7pu0iD57t2XwfntzVI8OTF22mjMeGUeaxNWD+9A2KyPVTXfO7Qaqif9TiUa6PibqiI0k6jT9Koxa3Qf0N7MvJP0u7APwEtGMmPeAWWZW8SGkqqwWr8ve+/J9DahZeho3fL8/lz3xHrGYceqhufTs2Jb7xs6lT+c9GPztrhzVsxNTP1vBGf94I4pPJx1C25bRoh9Xn3woP3tsLGbGt7vswZmH9UzxEbmmbJfplAFtiKYEbA7PNQ0I6S8DNxKNYl0f0iYDD0q6g+gYTwEe2oE6JwLDgfck9QL2Ibr41/gFTGa2UdHzZj8ys9Gho3iQmc1NlF9Srpl9AHwg6QdEd/6S+QD4e5jel080tfKesK1YUnMzKw7THNeZ2dOSCoERScpbRDS619PMPgPOAybUdIwJFACt4963Ab4Kry+oZr/JRB3rcZL6EE3VhOiO6L1l7VK02mLXMKJXAOQAa3agna6OBuV2ZFBuxwpplw/a/nihJK49vi8VB0Ndg/MHqN3uKVn8nwjcFn5mA0OIpvBvkNQ25FkTZq6cBfwLwMyKJI0hmpJ/US3qHwbcGf6dGpf+I0lPEM3a2Zfo80GyeObqyaBeXRjUq0uFtMtPOLj8tSSu/e5h8N2qM0kH9uzE6J+fUu9tdHE8TiW1K01ffBtoJmke0TztaQBmlk+Y1mdm00PaDOB1YC5Rp20m0RTDuroPSA9TH14ARphZXb6AaThwUZhysQA4tZq8dyks3EEUaBJ23gDMbAXwa2BcyPehmb0WNo8C5kl6higYTA9T/24E/pCkvCKiu42jw7HGiJ4Pq6s3gNMVFvogGhkbLWkS1Xee7gM6hN/t9UTTPTaEqaEjgOfCtmlE00PKjvMtX+jDOed2ewnjP9HoWDdgopmVAl8QdYows/VEN2PzgFeBGZXKfIZoZOudWtTfQtIHwC+Bq+PSPya6gfkWcGmIpQnjWZ2O1jnXJMkSLa22G5CUbWaFYXRlInCJmX1Y036u4YV5/c3D3ctcYCzQy8y2/a9lb3nk2t3zD9w1GVkX/b9aT4faOv/dlP+9tzjgOz59yzV6ilZkbmNmN9eQbxnRFMg1ldIfB/5tZv+qlL5D8WzLi7el/P+uczsq6+xbdqk4BY0zVu1K0xfralSYOpAJPOEdskatJdFUj+ZE8/Ev2xkdMueanEb68LJzjYmkV4BcosU/djaPZ85Vx+NUUrttpywstV6BpHuBoysl/93MHmuYVoGkG4EfVUoebWa3N1D9rxDNf493vZmNqUMZFxJN44g3xcyu2JE2hRUyfekp55xz9c7MTq+cVk1s7J6kjBFJ0j2eOed2yG7bKUtkRzsNO7kNtxN9r1mq6q8SjHagjMeABuvIOudqJ3qsxjlXVzsjNjrnauZxKrkm1SlzTY+V+n9+55xzjdy2uqwh5pzbHe1Kqy8655xzzjnn3G7HR8qcc2534d//4pxzrjHzOJWUj5Q555xzzjnnXAp5p8w555xzzjnnUsinLzrn3O7Cv//FOedcY+ZxKikfKXPOOeecc865FPKRMudcQlOWruaucQuJmXHaAd0YeWRuhe2vz/+SuycuYq/sFgAM69edMw7qBsDfJy5i0pJVAFw8oCcn9e7csI1vqvwBaudcEzPls2/485g5xMw4/ZAejDy6d5U8YxZ8wYMTFwKiV8c23HnGkeXbCrcWc/r9Yzh+/y78+ruHNGDLmyiPU0l5p6yJktQd+LeZHZDiprhGqDRm3Dl2AfefdQQdczIZ/swUjuu5F7l75FTId9L+nbjhhL4V0iYtWcVHKzfw/PnHUFwa46IXpnF0jw5kt2jekIfgnGsAkgYD15rZ9+uwz1XAKDPbXG8NS153P6Czmf2nAeoaAbxjZl/Xd11NVWnMuOPt2TwwfBAdW7dk+MNjOa5XZ3I7tC7Ps3xtAY9O+ZjHRwyhdVYG6zYVVSjj3vELOGyfDg3ddOeq8OmLzrkq5n+znm5tW9K1bUuap6dx0v6dGP/Zylrtu2RtIYd124NmaWlkNW9Grw6teX/ZmnpusXNuF3IV0DJFdfcDvleXHSTt6A3sEYBPE6hH879eR7d22XRtlx3Fqr7dGP9xxT7wy7OXMuzwXFpnZQDQvlVm+baFK/JZV1jEwNyODdpu5xLxTlnTli7pIUkLJL0jKUtSP0nTJM2T9IqkdgCSxku6W9JESR9JOlzSy5I+lfSHsgIl/UTSdElzJD0oKT1Z5ZIKJf1F0oeSxkrqEFfX3yS9L2m+pCNCegdJ74b8D0paLmnP+j5JTdGqwiI65mwPXB1zslhduLVKvrGffsPZT0zi2tc/5JuNWwDo1SGHKUtXs6W4lPzN25j5xVq+KdjSYG1v0mKlqf9xAEh6VdKscH29RNJlkv4ct32EpHvC65slLQrXt+ckXVtNuRdLmiFprqSXJLWUlC5piSJtJcUkHRvyT5LUU9IR4Zo6O/y7f9z2fnHlT5F0UJK6jwvX9jmhnLKh82xJ/wrH8IwkhfwnhHx5kh6V1ELSlUQdlXGSxoW2Px6u9XmSrq7m2KuLT38KsecTSYOS7J8B3AYMC8cwrJrzMkLSaElvAO9ISpN0X/h9/lvSfySdFfIeJmlC+H2PkdQpbOsPPBPqykp2XG7Hrdq4hb1bbz+1HVtnsapSvFm+tpDlawu44LFxnPfoe0z57BsAYmb85d25XD004Z+7qy+pjlGNOFZ5p6xp2w+418z6AuuBM4EngevN7CAgD/htXP5tZnYs8ADwGnAFcAAwQtIekr4NDAOONrN+QCkwvJr6WwEfmtmhwIRKdbUys6OAy4FHQ9pvgfdC/leAfRIVGj4AzZQ089GJebU9Fy6e1Zzl2Ny9ePOng3nxgkEc+a09uOXteQAM7N6BY3p0YMRz7/PrN2dzUKd2NEtTPTfYuUZnpJkdRvTB/ErgZeCMuO3DgBck9Se69h4StvevodyXzexwMzsY+Ai4yMxKgU+APsAxwCxgkKQWQFcz+wxYBBxrZocAtwB/DOU9TDSig6ReQAszm5ek7muBK8L1fRBQ9un3EKLRrz7AvsDRkjKBx4FhZnYg0eMSl5nZP4CvgSFmNoRo5KqLmR0Q8j1WzbFXF5+amdkRoR2/TbSzmW0Lx/6CmfUzsxeqOS8AA4ELzOx4ot9Nd+BA4KdhG5KaA/cAZ4Xf96PA7Wb2L2AmMDzUVeXOVHyseuS92dUctksmUahSpXBTajE+X1fIw+cfx52nH8mt/57FxqJtvDhzMcf07MTebVI1aOtcRf5MWdO21MzmhNezgFygrZlNCGlPAKPj8r8e/s0DFpjZCgBJS4BuRB8GDgNmhBulWcCqauqPAS+E108TfWgp8xyAmU2U1FpS21D+6SH9bUn5iQo1s1HAKIDNo66uRffCVbZXTiYrC7bPu19ZsIUOYUGPMm3DVBCAMw7ch39M/Lj8/U8H9OSnA3oC8Os3Z9Otbat6brED/AHqxuVKSaeH192AHsASSQOAT4H9gSnAL4HXyj60h5GZ6hygaHZCWyAbGBPSJwHHhnruAC4mutk1I2xvAzwhaT+iz7JlD3mOBm6WdB0wkqgjlcwU4K+SniHqHH4ZrvXTzezL0P45RJ2XAqIY80nY9wmiG3l/q1TmEmDfMGr4JvBOoooltaH6+FQWP2aF+msr2XkBeNfM1oXXxwCjzSwGfCNpXEjfn+jm5LvhXKQDK2pTcXys2vL0jR6rdkDH1lnlszQAVm7cQofsioOSHXNacmDX9jRPT6NLu1Z03yObz9cVMvfLtcz+fA0vzlzMlm0lFJfGaJnRjF+ecGBDH0bT4nEqKe+UNW3x89FKiYJ8bfLHKu0bI/pbEvCEmf16B9tjSV6XvffhlgbSd+82fL5+E19t2Mxe2ZmM+XgFd3yvX4U8qwuL6JAdTXGcsHglPfbIBqIHrwu2FtM2K4NPVm/k09UFDPyuzzJ1TYeixS+GAgPNbLOk8UAm0U2os4lGZ14xMyub6lcHjwOnmdlcRQtJDA7pk4BLiaYG3gJcF7ZNDNt/D4wzs9MVLfQ0HiC0713g1NC2pCN1ZnanpDeJnsmaJmlo2FQ5lpTFgxqZWb6kg4GTiDptZxN1DuuqrA1l9ddWwvMSbIp7nex4RHSTcmAd6nQ7Sd/O7fh8XSFf5W9ir9ZZjFnwBX88/YgKeYbs35m3FnzBqQd3J3/zVpavK6Rr21bccfr2FRhfm7uMhV/ne4fMpZR3yly8DUC+pEFmNgk4j+hOa22NBV6TdLeZrZLUHsgxs+VJ8qcBZwHPA+cCk+O2DSN65uAYYIOZbZA0mShg/0nSiUC7Oh2dq7VmaWlcf3xfLn9pOrEYnHpAV3L3zOG+KZ/Qp2MbBvfsyHOzlzFh8SrS00SbzObcelI0L78kFmPk89MAyG7RjNu/149maT5T2jUpbYD80OHpDQwI6S8DNwLLgetD2mTgQUl3EMXkU4CHqik7B1gRps0NB74K6R8QTe9bYmZFYcTqZ0DZqoht4vKOqFTmw8AbwKS4kaEqJOWaWR6QJ2kg0Jto6nsii4DuknqG6ZPx8aQgHMcaRc8FbzOzlyQtJslIXYgB/0t8KlNWd5nqzku8ycAFkp4AOhB1eJ8FPgY6SBpoZlPD76WXmS1IUJfbyZqlpXHDyf247NlJxMw49eDu9NyrDfeNX0CfTu0YvH9njsrtyNQlKznj/jGkSVx9wkG0bdmi5sKda2DeKXOVXQA8IKkl0bSSC2u7o5ktlHQT4aFooJjozmeyTtkmoK+kWUQdwmFx2/IlvQ+0Zvtd01uB5yQNIwrGK4iCnqsHg/bdi0H77lUh7fKje5W/vnJQb64cVPX7YFo0S+flC4+t9/a5BGI+LaSReBu4VNI8og/t06B8VGgh0MfMpoe0GZJeB+YSXStnEl0Pk7mZqAO2nGgqeU4oZ6ukL8rqIho5OyfkAfgz0TS9a4D34gs0s1mSNlL981wAV0kaQjQatRB4i/BsVWWhY3ghMFrR6oUziJ5HhmjK3luSVhA9A/ZYiBkA1c202OH4FGcccEPotN5BNeelkpeAE4D5RM/vfUB0w3BbWNTjH2GKZTOiKZoLiDqYD0jaQjRq6ise1YNB+3Vi0H6dKqRdPnj7V7VI4toTDwYOTlrGqQd359SDu9dTC10FHqeSkplPY3apIanQzLITpI8n+t6bmZXSWwClZlYS7tLeHx44T8qfKXO7upaX3F3r6W1F015I+d975oBhNbZX0snA34mev3nYzO6stH0fomeG2oY8NzTE90qliqRsMysMnY2JwCVm9mED1t+ZaNpe7/DMlEsg7ve0BzCdaFGrb3ZG2f5MmduVZf3k9l0qTkHNsSoVccpHytyuZB/gxXBHdRvRg+zOuV2Ioq/JuBf4DvAl0cJAr5vZwrhsNwEvmtn9kvoA/6FuizfsakaF48wkei63ITtk5wO3A9d4h6xG/w6LTmUAv99ZHTLnXOOSqjjlnTJX7yR9AFSewH1eolEyADMbnCT9U6Kll51ziewan6mPAD4zsyUAkp4nWmQiPtgZ0dRliJ75qfhtsLsZMzu3cpqke4GjKyX/3cxqmmJY17qfJHoWLb7uC4lWhYw3xcyu2Jl1J7Mzjl3SScCfKiUvNbPTE+WvjWSxyTlXBx6nkvJOmat3ZnZkzbmcc01EF+CLuPdfApWvEb8jejb1F0TfZziUJqahOkBJ6n6Mmp8vq8/6/+djN7MxbP+6AOecq4uUxCnvlLnd29atNedxbnfRCB6glnQJcElc0qjwfUzlWRLsVvkZg3OAx83sL+H50ackHeDT69zuylZV95Wezu1GGkGcghpjVUrilHfKnHPO7TTxX4ibxJdEX6ZcpitVp31cBJwcypsqKRPYk+q/jN4555yrlRpiVUrilH95kHPOuYY0A9hPUg9JGcCPgdcr5fmcaPlxJH2baAGM1Q3aSuecc01VSuKUj5Q559zuopFMC6lO+EqLnxM975MOPGpmCyTdBsw0s9eBXwEPSbqaaMrICPPvb3HOuV2fx6mkvFPmnHOuQYXvcvlPpbRb4l4vpOrqe84551yDSEWc8k6Zc87tJsxKU90E55xzLimPU8n5M2XOOeecc845l0I+UuacS2jK8rXcNekTYmac1qczIw/rXmH76x99zd1TPmOv7Oh7wYcd2JUz+nYBYEVBEbe99xErC4sA+OcP+tG5dVaDtt8559zuL617XzIG/xjS0ijJm0TJjLcrbE/vcxQZx56FFa4HoHjOe5TOn4xy2tPih5eD0iAtnZI571Eyb0IqDsE5wDtlzrkESmPGnRM+5v5TD6FjdguGvziD43rsSW777Ar5TtqvIzcct3+V/W9+dwE/7d+dAfvsweZtJUiJvvLD7XS7wAPUzjm300hkHH8uW1+6GyvIJ3P4jZQunoutW1EhW8knMyh+77kKabZpA0XP3wmlJdC8BZnn/47SxXOwTRsa8giaHo9TSfn0xXokqbuk+Sluw29SWX9lkgrrmP80SX3qqz0usfkrN9KtTRZd22TRPD2Nk/bryPgla2q17+J1hZSaMWCfPQBomdGMrObp9dlc59wuTtJVklqmuh1lJC2TtGcd8g+WdFR9tslVlbZ3D2z9amzDGoiVUrJoBum5/Wq3c6w06pABpDcDv3noUsxHynZ/vwH+mOpG/A9OA/4NLEx1Q5qSVZuK6JiTWf6+Y3YL5q/cWCXf2MWr+PDr9ezTNotrj+nF3jmZfL5+CzkZzfjVf+bx1cYtHNmtPVcO7El6mgc851xSVwFPA5tT3ZAdNBgoBN5PcTuaFGW3xQrWlb+3wnzSOvWokq9Zz0NJ79KLWP5Kise/gBXmh/3b0eL0K1HbDhRP/JePkrmU8pGy+pcu6SFJCyS9IylLUj9J0yTNk/SKpHYAksZLulvSREkfSTpc0suSPpX0h7ICJf1E0nRJcyQ9KCnhMISkO4GskO+Z6vaVdL+kmaGdt8aVsUzSHyVNDdsPlTRG0mJJlyY7aEmdwnHMkTRf0qC4bbdLmhvOQceQ9i1JY8M5GStpn3DX8YfAXaGcXElXSloY8j2fpO5LQltnPjrF+3L15djuHXjzgqN58ZwjObJbe275b3SuS2IxZq9Yz9VH78fTZx/Olxu28PqiFTWU5nYKi6X+x+3SJL0qaVaIBZdIOlvSX8O2X0paEl7nSpocXt8iaUa41o9SJFfSh3Hl7idpVpI6rwQ6A+MkjQtpJ4a486Gk0ZKyk9UV0msVPxPU3UrSmyEmzZc0LG7zL0L9eZJ6h/ztwzmaF2LYQZK6A5cCV4dYNUjSj0J5cyVNTFL39lg1dVGtfj+usgQ3+yp9U1TpkrlseeTXFD11K7HPPyLj5JHbsxbmU/TUrRQ9eiPpfY+Cljn13F6X8hjViGOVd8rq337AvWbWF1gPnAk8CVxvZgcBecBv4/JvM7NjgQeA14ArgAOAEZL2UPSt4cOAo82sH1AKDE9UsZndAGwxs35mNryGfW80s/7AQcBxkg6KK+oLMxsITAIeB84CBgC3VXPc5wJjQj0HA3NCeitgmpkdDEwELg7p/wSeDOfkGeAfZvY+0TeoXxeOYTFwA3BIyJewU2hmo8ysv5n1H3m0z3zcEXu1ymRlQVH5+5WFW+nQqkWFPG2zmpORHl1CzujThY9WRyNpHbMz2X/PHLq2yaJZWhpD9u3AotVVR9mcc43SSDM7DOgPXAlMAcpuqg0C1krqAhxDFBMA/mlmh5vZAUAW8P1wvd4gqWwu2YVE8aMKM/sH8DUwxMyGKJo2eBMw1MwOBWYC1ySrK66oauNnkuM9GfjazA4OZcavErEm1H8/cG1IuxWYHWLQb4ji1rJQ590hVk0CbgFOCrHuh0mOe3usGtg7SfNcdawwH+W0L3+v7HblC3qUK9pUPk2xJG8iaR33qVrOpg3Ymq9J77JfvbbXuep4p6z+LTWzsg7JLCAXaGtmZUv8PAEcG5f/9fBvHrDAzFaY2VZgCdANOAE4DJghaU54v28t21LdvmeHu5qzgb5AfG8mvk0fmFmBma0GiiS1TVLXDOBCSb8DDjSzgpC+jWg6IkTno3t4PRB4Nrx+iijgJzIPeEbST4CS6g/X7ai+HXP4fMNmvtq4heLSGGM+XcngHhUfr1i9aWv56wlLV9OjXato371as3FrCeu2bANgxpf57Bu2uXoWi6X+x+3qrpQ0F5hGFHO6AdmScsLrZ4li1iC2d8qGSPpAUh5wPFEMAXiYKA6kE90QfJbaGUAUg6aEWHUB8K0a6oKa42ciecBQSX+SNMjM4uevvRz+jY9VxxDFKMzsPWAPSW0SlDsFeFzSxYA/VFtPYt8sQ233Qq33hLR0mvU+nNIlcytmarX915Oe24/Yum+AqANHs+bRhhYtSevSk1j+yoZqetOV6hjViGOVP1NW/7bGvS4FknViKuePVdo3RvT7EvCEmf16B9qScF9JPYjuAh5uZvmSHgcy47LU1KYqzGyipGOBU4CnJN1lZk8CxWZWNrmgNNn+VJmAUO4Uog8EPwRultTXzLxztpM1S0vj+mP35/LXZhMzOLVPJ3L3yOa+DxbTZ6/WDO7RgefmfsGEZWtIl2iT2Yxbh0b9+PQ0cc3RPbn01dmYGd/eq3X5UvnOucZL0mBgKDDQzDZLGk8UC6YSjXR9TNQRG0l0I+1XkjKB+4D+ZvZFuBFXFj9eIpoJ8h4wy8zW1rYpwLtmdk6l9lVXF+xYrPpE0mHA94A7JL1jZmWzQMrKiI9ViR6OrRKvzOxSSUcSxaw5kvrV4fhdbVmMbeOepcWZV4FEyfwp2NqvaX7UD4l9s5zSJXNpfsjxpO/bD6wUK9rEtrcfA0Dt96bFcWcT/fpE8cwx2JqvUno4rmnzTlnD2wDkhztyk4DzgLp8McZY4DVJd5vZKkntgRwzW54kf7Gk5mZWAtECaQAAIABJREFUnGxfoDWwiWiqSUfgu8D4HTu8iKRvAV+Z2UOSWgGHEk3bTOZ94MdEdyCHA5NDekFoI5LSgG5mNi48y3AukE00LdTtZIO678mg7hVHxy4/Mrf89ZVH9eTKo3om3HfAPnvw4j7JZgs55xqpNkB+6JD1Jhqxgmiq+W3hZzYwhGhq/Ia42RJrwnNfZwH/AjCzIkljiKb/XVRD3WXX+jVEo3T3SuppZp8pWpWxK7AqWV07SlJnYJ2ZPa1odeARNewykShG/T50YteY2UZJBUSxtKzcXDP7APhA0g+IRuq8U1YPYkvnU7T0pgppxe+/vv315FconvxK1f0+/4iip26tku5cqninLDUuAB4IgWYJ0R3IWjGzhZJuAt4JnZRionnzyTplo4B5kj4Mz5VV2dfMpkmaDSwI7Zmyw0e23WDgOknFRCtSnV9D/iuBRyVdB6xm+zl5HngoPAj+Y+CRMFVERPP3vUPmXJlG+vCy22W8DVwqaR7RqNi0kD6JqFMx0cxKJX0BLAIws/WSHiKaBriMaOp6vGeAM4B3aqh7FPCWpBXhubIRwHOSyh5mvSmMalVX1444kGgxqRhRTLyshvy/Ax4L52gzUTwHeAP4l6RTgV8QLfqxH1GsGgvMTVCWc02Px6mktH0mmXO7n833XO5/4G6X1vIX99X6uwS2/PeBlP+9Zw291L/7wJWTdC3QxsxuTnVbGrPNf7045f93ndtRLa95aJeKU9A4Y5WPlDnn3O6ikT687JomSa8QLW51fKrb4pxrJDxOJeWdst2EpA+AFpWSzzOzvHqu90DCSlRxtprZkfVZr3POucbNzE6vnBY6apW/3fd6MxtTn20JS+KPTbDpBF+AwznXGHinbDeRqk5Q6PT1qzGjc865Ji9RR62B6l2LxyrnXCPmnTK3eysuTnULnGs4/gC1c7um0tJUt8C5huFxKin/8mjnnHPOOeecSyEfKXPOud2FP0DtnHOuMfM4lZSPlDnnnHPOOedcCnmnzDnnnHPOOedSyKcvOufc7sKnhTjnnGvMPE4l5Z0y51xCad37kjH4x5CWRkneJEpmvF1he3qfo8g49iyscD0AxXPeo3T+5O0ZMjLJHHEbpZ/Npvi95xqy6c4555qItO4HkHHCuSBRMm8SJdP/U2F7et+jyRh8NlaYD0Dxh2MpzZuE9upGxnfOQxlZYDGKp/6b0o9npOIQnAO8U+acS0Qi4/hz2frS3VhBPpnDb6R08Vxs3YoK2Uo+mZG0w9X8qFOJfflJQ7TWOedcUySR8Z2fsPXFv2AF68g87xZKF8/B1n5dIVvJoukUj32m4r7F29j25sPY+lWoVVsyz7+FLcvmw9YtDXgAzm3nz5TtZiQtk7RnqttRV5LGS+pfh/yDJf27PtvUlKXt3QNbvxrbsAZipZQsmkF6bu2/d1V77YNatqZ02cJ6bKWrwmKp/3FuFyZpkKQFkuZIykp1e1z10jrti+WvwjasDrHqA9J71i5WWf5KbP2q6PWm9djmApSVU5/NdZD6GNWIY5WPlDnnqlB2W6xgXfl7K8wnrVOPKvma9TyU9C69iOWvpHj8C2F6iMg47my2vfUIaft8uwFb7Zxz/7PhwP8zs8dqk1lSupn5Nz+nSJVYVZBPWqd9q+Rr1usw0rv1IrZuJcXjnsMK8itsT9u7B6SnY+tX13ubnUtmlxspk/SqpFnhTtYlki6T9Oe47SMk3RNe3yxpkaR3JT0n6dpqyr1Y0gxJcyW9JKmlpHRJSxRpKykm6diQf5KknpKOkPS+pNnh3/3jtveLK3+KpIOS1N0htPFDSQ9KWl422iXpGknzw89Vyc5DgjJbSXozHM98ScOqOfbDQ9vnSpouKUdSpqTHJOWFYxsSd35flvS2pE8rnfuTwzHMlTQ2rh2PhnM7W9KpIT1L0vOS5kl6AciKK+dESVNDWaMlZceVv0jSZOCMao7nEkkzJc18dOqiZNlctVQ1ySq+LV0yly2P/Jqip24l9vlHZJw8EoBm/QZTujSvfP6+a0CxWOp/nNsJEsT6syX9NWz7paQl4XVuiAlIuiXEmvmSRoXYnSvpw7hy95M0K0mdPwXOBm6R9EzY/65QXl5ZHFU0U2OcpGeBPEndQ2x6OOR9RtLQEPc/lXREkvq2x6ppH+/U89d0JIhVlYJV6eI5bBn1fxQ9/ltiyxeS8d2fVszeqg0Zp1zMtrcerbKvqwepjlGNOFbtiiNlI81snaJpBTOAE4ApwP+F7cOA2xVNhTsTOIToOD8EEl6Ig5fN7CEASX8ALjKzeyR9AvQBeoT9B0n6AOhqZp9Jag0ca2YlkoYCfwz1PgyMAK6S1AtoYWbzktT9W+A9M7tD0snAJaEdhwEXAkcSXXk+kDTBzGZXPg+SXjKztXFlngx8bWanhLLaJKpYUgbwAjDMzGaE49kC/BLAzA6U1Bt4JxwHQL9wXrcCHyvqBBcBD4VzsVRS+5D3xnBsIyW1BaZL+i/wM2CzmR2kqLP6YWjPnsBNwFAz2yTpeuCa0Pl7CDge+Cy0OSEzGwWMAtj814v9CrsDrDAf5bQvf6/sduULepQr2lT+siRvIs0HRf3ktE65pHXpSbODB6OMFpDWDLZtpXjyyw3SdufcbqFyrD8JuC5sGwSsldQFOAaYFNL/aWa3AUh6Cvi+mb0haYOkfmY2hyimPp6oQjN7WNIxwL/N7F+SziSKdwcDexLF2okh+xHAASHedQd6Aj8iit8zgHND234I/AY4LUF922PVXSM9Vu2AKrEqp4ZYNW8CzY87a/u2jEwyz7yK4kkvE1uxpL6b61y1drmRMuBKSXOBaUA3os7SEkkDJO0B7E/USTsGeM3MtphZAfBGDeUeoGh0K49o+kLfkD4JODb83BHKPZzoogvQBhgtaT5wd9x+o4HvS2oOjCRJEAiOAZ4HMLO3gfy49FfMbJOZFQIvEwWjROdhv0pl5gFDJf1J0iAz25Ck7v2BFWY2I9S/0cxKQt1PhbRFwHKgrFM21sw2mFkRsBD4FjAAmGhmS8M+ZfMJTgRukDQHGA9kAvsQnc+nQ955QFmHdQBRJ3hK2OeCUH5vYKmZfWpmVravqx+xb5ahtnuh1ntCWjrNeh9O6ZK5FTO12t7PT8/tR2zdNwBse+thih6+gaJHfs22Cf+i5KOp3iFzztVV5RjXDciWlBNeP0sURwaxvVM2RNIHIY4fz/Z4/DBwoaR0ohu3z9ayDccAz5lZqZmtBCYQxX+A6WXxLlhqZnlmFgMWEMVJI4rF3et47K6WYiuWonYdUZuyWHUkpZ/NqZgpPlb1PITY2rBgVVo6LU77OSUL3qf0k5kN2GrnEtulRsokDQaGAgPNbLOk8UQf8l8gmnKwiKgTY5ISjWlX53HgNDObK2kEMDikTwIuBToDtxDdqRsMlN0t+z0wzsxOD3fLxgOE9r0LnBraVt0iFsnamjC9mvNQzsw+CSNt3wPukPRO2R3EBHUkukNX3fnbGve6lOjvqLpyzjSzCnMzwq8nWf53zeycSvn7Jcnv6oPF2DbuWVqceVW0zPD8Kdjar2l+1A+JfbOc0iVzaX7I8aTv2w+sFCvaxLa3a/UIhqtPjfThZefqopoYN5VopOtjotg8EhgI/EpSJnAf0N/MvpD0O7bHxZcIM1KAWZVmlVTblGq2bar0Pj4uxuLex9jFPmvtUizGtv8+TYuzrglf3zI5ilVHn0bsm2WULp5D80OHRot/xGJYUSHb3noEgPTeh5PWtRfKyqbZAUcDsPWtR7BVX6TyiHZ/HqeS2tUuFG2A/HCR7k00qgLRCNKNRKM514e0ycCDku4gOs5TiKa/JZMDrAgjW8OBr0L6B8CTwBIzKwqjNz8Dvh/XprK8IyqV+TDRCN2kuJGjRCYTddz+JOlEoF1Inwg8LulOouBwOnAe0UhTovNQTlJnYJ2ZPS2pMEHbyiwCOks6PExfzCGavjgxnIf3wrTFfYgC4aFJypkK3CupR9n0xXDMY4BfSPpF6CwfEqZflpU/TtIBQNnzdtNCOT3D9NCWQNfQzh6Scs1sMXBOlRa4nSq2dD5FS2+qkFb8/uvbX09+heLJr1RbRunC9yld+H69tM85t9tKFusnAreFn9nAEGCLmW0I0+MB1ih6Dvks4F8AIXaPAe4HLqpDOyYCP5P0BNCeaGTuOqKZG66RiC3No+iRvAppxVNe3f560ksUT3qpyn6lC6exZeG0em+fc7W1q3XK3gYulTSPqIMwDcDM8iUtBPqY2fSQNkPS68Bcos7aTCDZFD6Am4k6YMuJphvkhHK2SvqirC6iu3PnhDwAfwaekHQN0V24cmY2S9JGoKYhhFuB58JDxBOAFUCBmX0o6XFgesj3sJnNDsda5TxUciBwl6QYUAxclqhiM9sW6r0nzN3fQnSH8j7ggTANpAQYEc5FwgMws9WKFhx5WVIasAr4DtFI4t+AeWH0chlRh/Z+4LFwDHPKjjGUMyKcjxah+JvCyN8lwJuS1hB1ZA+o5pw61/Q00oeXnaujhLGeKP52I5oqXxpi8yIAM1sv6SGi2LyM7Y8YlHmGaIGod+rQjleIRuLmEs3U+D8z+yZ0FJ1zO8LjVFKKpjzvniRlm1lhGG2ZCFxiZh/WtN9OrL8z0XTG3mGeebJ8LYDSsFjIQOB+M6v9l0K5pHyhD7era3nNQ7Weir3llTtT/veedfoNdZ067ly9U7T6chszuznVbUnEF/pwu7KW1z26S8UpaJyxalcbKaurUZL6EM0rf6KBO2TnA7cD11TXIQv2AV4MI0zbgIvru33OOedcUyDpFSCXaPEP55xrlHbrTpmZnVs5TdK9wNGVkv9e2y+KrEPdTxI9ixZf94WEpebjTDGzK4iWmK9XITBV/gbg681sTH3X7ZxrAP4AtXNVmNnpldM8HjqXIh6nktqtO2WJhA5Qqup+jJqfL6vP+qsEJuecc66p8XjonGtsmlynzDUttnVbqpvgnHPOVcu2laS6Cc65FPNOmXPO7S58VSvnnHONmceppNJS3QDnnHPOOeeca8p8pMw553YXfgfSOedcY+ZxKikfKXPOOeecc865FPJOmXPOOeecc86lkE9fdM4llL7vgWQMHQ5paZTMmUDxtDcrbG924DFkHD+MWEE+ACWzxlIydwIALYb9ivTOuZR++SlbR9/d4G1vssxS3QLnnGtQ6fseSMaJPwGFWDX134nz9T6czDN/wZZHf0tsxVJISyfjexeS3qkHZsa2d54m9vmiBm59E+RxKinvlDnnqpLIOPF8ip7/M7ZxHZkjfkfJp7OxtV9XyFby0XS2vfNUld2Lp71FSfMMmh0ypKFa7JxzrqmRyDj5fIqeDbFq5K2UfPohtqZirCIjk+aHf4fSrz4rT2p2yGAAtjx0I7TMIfPH11L06O8A7zS41Gg00xcldZc0P8Vt+E0q669MUmEd858mqU99taeGuttKuryB6hos6aiGqKupSuu8L7H8ldj61RArpfSjD2jW69Ba7x9bvhDbVlSPLXQJxWKp/3H1QtJ4Sf1T3Y6dpa7xrbGQtEzSnnXIP0LSP+uzTU1ZWudcYutWbY9VC6cljFUZx51J8dT/QEnx9n337ELpsoXRm80FULSZtM49GqrpTVeqY1QjjlWNplPWSDSqTtkOOA1ISacMaAvUqVOmyI78DQ4GvFNWj5TdDtu4rvy9FaxDOe2q5Evfvz9ZF/2BFqf/HOW0b8gmOueca+KU0w4rWFv+3jZWjVVpHb+FWren9LM5FdJjqz6POnBKQ232JK1Td49jLqUaW6csXdJDkhZIekdSlqR+kqZJmifpFUntoPyu4d2SJkr6SNLhkl6W9KmkP5QVKOknkqZLmiPpQUnpiSqWdCeQFfI9U92+ku6XNDO089a4MpZJ+qOkqWH7oZLGSFos6dJkBy2pUziOOZLmSxoUt+12SXPDOegY0r4laWw4J2Ml7RNGjn4I3BXKyZV0paSFId/z1dTfXtKrId80SQeF9N9JejSc6yWSrqzmd3cnkBvqvktSdmjbh5LyJJ0ayuwefl/3AR8C3SRdJOmTUM9DZXcVJXWQ9JKkGeHnaEndgUuBq0Ndgyo3RNIl4fzPfHT6J9U02SUlVU2rNA+85LPZbLnvV2x55CZKly2gxfcvbqDGOdd4hWvprBAfLpF0maQ/x20fIeme8PpmSYskvSvpOUnX1lD8j0JM+qTs2icpU9Jj4To7W9KQuHpelfSGpKWSfi7pmpBnmqT2IV+upLdDmydJ6l3NseWGfWdIuk1htEuRu0L8ypM0LKQnjAOVykwa/xLkPTmUNVfS2JBW5/gl6fyQf66kp0JalXgT0vdQ9HlktqQHAcWVk+wzwoXhdzQBOLqa49keq2Z4rNppKoQqkfGdc9n23+eqZCuZMzGa8njRrWSc+BNKv/wMrHGOoLimobF1yvYD7jWzvsB64EzgSeB6MzsIyAN+G5d/m5kdCzwAvAZcARwAjAgX0m8Dw4CjzawfUAoMT1Sxmd0AbDGzfmY2vIZ9bzSz/sBBwHFlQSD4wswGApOAx4GzgAHAbdUc97nAmFDPwUDZ7ZxWwDQzOxiYCJR96v0n8GQ4J88A/zCz94HXgevCMSwGbgAOCfmSdgqBW4HZId9viM55md7AScARwG8lNU9Sxg3A4lD3dUARcLqZHQoMAf4ilX/S3z+0/xCgGLg5nKPvhPrK/B2428wOJ/pbeNjMlhH9vu8OdU2q3BAzG2Vm/c2s/8gjelVz2C4ZK1iHWm+/Y6ic9ljh+oqZtmyC0hIASuaMJ23v7g3YQpdQqqeDNNIpIQ1spJkdBvQHrgReBs6I2z4MeEHRVMQzgUPC9tpMTWxmZkcAV7E9Fl4BYGYHAucAT0jKDNsOIIovRwC3A5vDdXcqcH7IMwr4RWjztcB91dT/d+Dv4Zoc/9DOGUBZ/BpKdHOwE9XHgTLJ4l8FkjoADwFnhpj4o7CpTvFLUl/gRuD4UM4v446tQrwJ6b8FJofz9jqwT2hPws8I4bhvJeqMfYdqZq9UiFWHe6zaEVaQj3L2KH+v1u2xwvztGVpkktahK5k/+TVZV/yFtC65tPjRVaR16gEWY9t/n6Xo4ZvZOvpvKLMlsXXfpOAomphUx6hGHKsa20IfS82s7II8C8gF2prZhJD2BDA6Lv/r4d88YIGZrQCQtAToBhwDHAbMCHEgC1hVy7acUM2+Z0u6hOj8dSK66M5L0KZsMysACiQVSWprZpU+2QIwA3g0dHhejTsH24CyZYRmEV3gAQayPcg/BZTfha1kHvCMpFeBV6s51mOIghBm9l7o0LYJ2940s63AVkmrgI7Al9WUVUbAHyUdC8SALmFfgOVmNi28PgKYYGbrACSNBsqi01CgT1wMby0ppxZ1u/9R7OulpLXriNrsiRXkk/7tI9n6+gMV8qhVG2zTBgDS9zuUWKVFQJxroq6UdHp43Q3oASyRNAD4lOim1BSizsBrZrYFQNIbtSj75fDvLKB7eH0McA+AmS2StJzt19BxcTFoA1BWRx5wkKRsoqngo+Ousy2qqX8g0TR5gGeB/xfXhufMrBRYGUaIDgfeInEciP/kmyz+VTYAmGhmS8Oxls2vrmv8Oh74l5mtqVROsnhzLCHemtmbkso+8Sf7jHAkMN7MVgNIeoHtvw+3k8W+XkJa+7hY1WcAW1+9f3uGrVvYfPcV5W8zf/Jrto19Plp9sVlG9EmleBtpPfpCrLTqAiHONaDG1inbGve6lOg5pdrkj1XaN0Z0bAKeMLNf70BbEu4rqQfR3cTDzSxf0uNAZlyWmtpUhZlNDEHrFOApSXeZ2ZNAsVn5nLHSZPuTfKmgU4gCyg+BmyX1NbOSBPkSzFUrL7Py76S2fzPDgQ7AYWZWLGkZ28/TphrqLpMGDCz70FK+Q6KpdW7nshjb3n2KzB9fFy0zPG8ituYrmg86ndiKZZR+Nptm/U+k2X6HYLFSKNrE1n8/XL575k9+Q9oenaB5JllX3M22/zxC6dKUruPjXL2TNJjow/1AM9ssaTzRde8F4GxgEfCKmVmCEaPaKLsex1+LqyuncgyKj0/NiK6x68NIz/8iWRuqiwNAtfEvUR2JYl1d41eycqqLN8nqTfQZ4bQk+V19sBjbxjxJ5jn/B2miZG6IVceeQWzFUko/nZ10V7VqTeY514EZVpDP1tcebMCGO1dVY+uUVbYByJc0KExTOw+YUMM+8cYCr0m628xWKZpDn2Nmy5PkL5bU3MyKk+0LtCbqVGxQ9IzXd4HxO3Z4EUnfAr4ys4cktQIOpeIUjMreB35MNEo2HJgc0gtCG1G0gEY3MxsnaTLRFJFsommhlU0M5fw+fKhYY2Yb6/iZobzuoA2wKgTiIcC3kuw3Hbhb0bOCBUR3PPPCtneAnwN3hWPqF+6iFhD9Hlw9Kl08jy2L51VIK570yvbXE0ZTPGF05d0AKHr6j/XaNpeEPw+Ram2A/NAh6000ugPRCNeNwHLg+pA2GXhQ0h1EsfgUoul5dVV2/X5PUi+i6XUfE8WRaoXr/FJJPzKz0aGjeJCZzU2yyzSia/QLRDEovg0/k/QE0J7oZuB1RNP7qo0DdYh/U4F7JfUws6WS2odRrrrGr7HAKyG2r40rJ1m8KSv/D5L+P3v3HSdVdf5x/PMdYAuCIGAXg0GMXSyoKBpQoz+j0Ri7RgWMxsTYW+yIPSYhdkSjqLHF3hIlghSJ0hEEQQU1gEoVELbPPL8/7tnd2dmZLcDuLLvP+/XaF3fOnHvOuXeX+8wp987RwGZJ5aT7jDABuFdSZ2A10TLLTOfTbQBRrLq6Slrp2FfS5i36x50V27ZqGYVDr0mbzzUgj1MZNfVOGcA5wFBJbYH5wIC67mhmsyXdAIwInZRSovX3mTplw4AZkqaG+8qq7WtmH0maBswK7Rm/zkdWqS9wlaRSYA2Va/0zuZhoucdVwFIqz8nzwKOKbmg+Dfh7WMYhorXy6TpkAIOAJyTNAAqIznm9hOA2XtHXGvwbuBt4U9JkonsE0n4jo5ktknQHUSD7BphN1BkvP84HQ7taEwXHC4iW4Lyk6Kbxi9LdV+acc1nwDnBBuGbNJerEEFZVzAZ2NbOJIW2SpDeIPrB/DUym8tpXHw8RxciZQBnQ38yK6zGodibwcIh3bYjiSKZOxKXAPyRdAbyd1N5XiZY2fkw0S3S1mX2n6KFZtcWBvtQh/pnZ0nDbwCshJi8hWtI/iHrELzObJel2YIykODAN6E/meHML8JykqUSDwv8L5aT9fBE+Iwwi6kR+S/RAq7QPGHPOuWQy/2Ztl2WS2pnZGkmtiYL742b2am371cXaO8/xP3C3Udvk2ifr/Om68Klrs/73nn/2nb6+uI6Srn1tiToB55vZ1Gy3K5PQzsKw/PI04HQzq/ZERVd/a28/O+v/d51bV5tc/9RGFaegacaqjWGmzDV/gyQdQXSvwQhqfiiJc841F8Mk7Up07XuyKXfIgn2BB8Iyx5XAwCy3xznnmo0W2SmTNIHqT5g6y8xmpsu/Aevdg+g+sGTFZnZAQ9abVP8AKh//W268mV2YLn+GMjoTraVPdbiZLU+TXiszq+27eZxzrtkxszNS0yQ9SPXvtrrXzJ5onFaBpOupfOR8uRfN7Haix9Y3dP1ZidHOOZdNLbJT1lidoDT1ziT6LpesCEF9vQJ76Hhl7RicczXw5egbvfoMkjVgG24n+l6zbNWflRjtnGsEHqcyapGdMteCFBbXnsc555zLpqKibLfAOZdl3ilzzrnmIuGPGnbOOdeEeZzKKJbtBjjnnHPOOedcS+adMuecc84555zLIl++6JxzzYUvC3HOOdeUeZzKyGfKnHPOOeeccy6LfKbMOZdWqx33Iufn54BilE0dRem4N9Ln2/UA8k67jMKh15H4Zj6t9jyYNgf/ouL92JbbUzT0WhLffd1YTW+5zEcgnXMtS6sd9yLnmAFRrJoyktJxr6fPt9sB5J12BYUP/5HEN/NRx83Jv3gIiWXfAJBY8Dklbz7amE1vmTxOZeSdMudcdRI5xw6k6MnbsdXLyfvtHZTNmYItXVQ1X04ebQ78P+ILPq9Iis8YT3zG+KiYLbqSd8aV3iFzzjm34Unk/OJciobfFsWqC+6kbM7kDLHqaOILPquSbCu+o+ihqxuxwc5l5ssXWwBJ3SR9kuU2nCzpU0nvZ7Mdrm5i2+1IYsV32PdLIB4nPvO/tN55v2r5cg4/hdIP3oSy0rTltN7zYMpm/rehm+ucc1klabCkI7LdjpYmtt2OJJanxKpdelXLl3P4qdFqjwyxyrmmwDtlrrGcC/zezPrVJbMkn8XNIrXvhK1aXvHaVq9Am3aqkie2VTe0aWfin03NWE7r3XtTNnN8g7XTVWUJy/qPcy2Rmd1kZu9lux0tjTZNiVWrlqP2KbFq626oQ5e0sUqbbUHe7+8mb+AgYj/aucHb65pGnGqqsco7ZS1HK0mPSpolaYSkfEk9JX0kaYakVyVtBiBptKQhksaG2a1ekl6R9Lmk28oLlPRrSRMlTZf0iKRW6SqWdBPQBxgq6R5JeZKekDRT0jRJ/UK+/pJelPQmMEJSX0ljJP1T0meS7pJ0ZqhzpqTujXDeWialSbOki5hEztFnU/LuPzIWEdtuRygtxpYs3PDtc861SJJekzQlxLLzJZ0i6a/hvUskzQ/b3SV9ELZvkjRJ0ieShinSXdLUpHJ7SJqSoc79Jb0Sto+XVCgpJ8Sy8vqGSzopbH8l6RZJU0Os8k/7DSZtsEp6W+QcfQ4l7zxVPdcP31Pw599T9NA1lLzzJLknXwy5+Q3XVOdq4Z2ylqMH8KCZ7QasBE4EngKuMbM9gZnAzUn5S8zsUGAo8DpwIbA70F9SZ0m7AKcCB5tZTyAOnJmuYjMbDEwGzjSzq0JZmNkewOnAk5LyQvbewDlmdlh4vRdwCbAHcBawk5ntDzwGXJSuvhCoJ0ua/PjUefWmhUqNAAAgAElEQVQ6SS5iq1egDp0rXmvTTtgP31dmyMkjtsV25A24ifzL7ie23Y7knnElsW1+XJGl9e4H+dJF59yGNtDM9gX2Ay4GxgOHhPcOAZZL2pZoIHBcSH/AzHqZ2e5APnCsmc0DVknqGfIMAIZnqHMqsHdSHZ8AvYADgAkZ9llmZvsADwNXpstQNVbNr+WwXTq2ennVWNWhc5pY1ZW8gTeTf/kDxLbrQe6ZV0exKl4GhWsASHzzJbZiMbHOWzf2IThXwZeItRxfmtn0sD0F6A50NLMxIe1J4MWk/OWP2psJzDKzbwHCqGBXooC3LzBJEkSBbkkd29IHuB/AzOZI+hrYKbz3HzNbkZR3UlLd84ARSe1KuxTSzIYBwwDW3nRa05yjbuISi+YR67QV6rg59sMKWu1xEMUv3l+ZobiQgrvPr3iZN+AmSt79B4lvwgcLiVa7HUDR47c0cstbOP/+F9f8XSzphLDdNfy0k9Q+bD8LHErUeXol5Osn6WqgLdAJmAW8STS4N0DS5USDjPunq9DMyiR9EQYj9wf+GupoRWXHL1V53VOAX2UotzJW3XiKx6p1kFg0j1jnrVNi1X2VGYoLKbjrNxUv8wbeTMk7T0exqm37qFNmhjbbAnXemsT3i7NwFC2Mx6mMvFPWchQnbceBjnXMn0jZN0H0dyPgSTO7dh3akm69Qbm1GdqR2pbydriGkEhQ8vYT5J19HcRilE19H1u6kDaHnUxi0Xzic9Ou8qkQ+9Eu2OoV0c3Xzjm3AUjqCxwB9DazAkmjgTzgQ6KZrrlEnaSBRKsurgirMB4C9jOzBZIGhX0AXiZaITIKmGJmlTcnVTcOOBooBd4jmlVrRYZZMCpjVRyPVQ0nkaDkrcfJO+f6yli1ZCFtDjuFxDfziM/JHKtadduVnMNPwRLxqJw3HoXC1I8gzjUev1C0XKuA7yUdYmbjiJYGjqlln2QjgdclDTGzJZI6Ae3NrC7PPh9LtNRxlKSdgO2Jguk+9TsE15Din0+n8PPpVdJKR72YNm/RE4OrvE58NZuiR29ssLa5DPz7X1zz1gH4PnTIdgYODOljgcHhZxrRKopCM1slqXwAcpmkdsBJwEsAZlYk6V2iJYbn1lL3WKIl/0+Z2VJJnYGtiGbdXBbFP59G4b3TqqSVjvpn2rzJqzfisydQODvT6lPXYDxOZeSdspbtHKKHb7QF5hONNNaJmc2WdAPRAzliRKOHFwJ16ZQ9FOqdCZQB/c2sOCyDdM4559J5B7hA0gyigbyPQvo4oqWLY80sLmkBMAfAzFZKepRoyftXwKSUMp8hWl44gppNALYk6pwBzACWmJkvO3TObRDy64lrzvyeMrex22Tw83UerSh4+KKs/723/d39PrriNhqSrgQ6mFlWp/b9njK3Mdvk1n9uVHEKmmas8pky55xrLprod6841xRJepXooVeH1ZbXObeBeJzKyDtlboOSNAHITUk+y8xmZqM9zjnnXDpmdkJqWuio7ZCSfI2Zvds4rXLOtVTeKXMblJkdkO02ONdi+aOGnVsv6TpqzrkNyONURt4pc82alZZmuwnOOedcjazIY5VzLV0s2w1wzjnnnHPOuZbMZ8qcc6658GUhzjnnmjKPUxn5TJlzzjnnnHPOZZF3ypxzzjnnnHMui3z5onPONRfm3//inHOuCfM4lZHPlDnnnHPOOedcFvlMmXMurVY79ST32IEQi1E6aSSlY15Nn2/3A8k/8yoKHriaxKJ5qOPmtL38XhJLvwEgseAzil8b1phNb7n8BmrnXAvT6id7k3vcuVGsmvgepe+/kj7fHr3JP/tqCu69ksTCecS69iD3pN9VvF/ynxeIfzKhsZrdcnmcysg7ZS2YpOvM7I6w3RE4w8we2kBldwPeMrPdN0R5rpEpRu5x51H498HY6uXkX3g3ZZ9OwpYsrJovJ4+cg44h/r/PqiQnli+m8P4rG7HBzjnnWhzFyD3hfAqHDcJWLSf/4j9RNmti9ViVm0dOn2OIfz23Iinx3dcU3nslJBKo/WbkXz6EgtmTvNPgssaXL7Zs1yVtdwR+n62GuKYl1nVHEsu/w75fDPEyyj7+gNa79KqWL+fI0ykZ+xqUlWShlc61TJIuldQ22+0oJ+krSV3qkb+vpIMask211H9d7bk2SD09Jf28MepqqWLb9yCx7FtsRYhV0z+g9W77V8uXc9QZlIx+DcqSvqS7tKSyA9a6jd/r5LLOO2UthKTXJE2RNEvS+ZLuAvIlTZf0DHAX0D28vifsc5WkSZJmSLolpHWT9KmkR0NZIyTlh/f2lfSxpA+BC5Pq7iZpnKSp4eegkN5X0mhJL0maI+kZSQrv9ZL031DeREntJbWSdE9Sm37buGex5dCmnbBVyype2+oVqEPnKnliW+9ArEMX4nOmVNs/1mkL8i+6h/zzBhPrtkuDt9cFCcv+j2sMlwJNplO2DvoCWeuUUXVAsk4ktVqHenoC3ilrQNq0E7YyKVatWl49Vm2zA7GOXYh/Orna/rGuPci/4l7aXvE3il8Z6rNkjSHbMaoJxyrvlLUcA81sX2A/4GLgHqDQzHqa2ZnAH4F54fVVko4EegD7EwWWfSUdGsrqATxoZrsBK4ETQ/oTwMVm1jul7iXAz8xsH+BU4L6k9/Ym+oCxK/Bj4GBJOcALwCVmthdwBFAInAusMrNeQC/gPEk7pB5o6HROljT58elfruPpaulUPSl5FFEi99j+FL89vHq2H75n7d2/pfD+qyh+ezh5p14KufkN11TnsiTNYNcpkv4a3rtE0vyw3V3SB2H7pjCw9ImkYYp0lzQ1qdwekqqPdkTvXQxsA7wv6f2QdqSkD8Og14uS2mWqK6SPljRE0tgwyNZL0iuSPpd0Ww3Hu4mkt8Ng2SeSTk16+6JQ/0xJO4f8ncI5miHpI0l7hqXtFwCXhUHAQySdHMr7WNLYGurPk/REqGOapH4hvX9o/zvhGP5UQxmpA5LVfo9JeddIGixpAtBb0s/DAOIHku6T9FbSeXk8nOtpko4PcWwwcGqo69Q0bamMVR9/lanJriaqQ6w6biDFbz6RdvfEgs8p/MslFNx3NTn9ToxmzJzLEr+nrOW4WNIJYbsrUceqJkeGn2nhdbuwz/+AL81sekifAnST1AHoaGZjQvrTwNFhuw3wgKSeQBzYKameiWa2EEDSdKAbsAr41swmAZjZ6vD+kcCekk4K+3YIbarS8zKzYcAwgDXXntg0h0OaOFu9HHWoXI2kTTthq1dUZsjJJ7bl9uSfPzh6v11H8s7+I0VP3UVi0TwoWANA4pv52IrviHXZJkp3Dct8lLeRDTSzFYpWC0wCjgKuCu8dAiyXtC3QBxgX0h8ws8EAkp4GjjWzNyWtktQzXFsHAMPTVWhm90m6HOhnZssULRu8ATjCzNZKuga4nKhDUK0u4M1QVImZHSrpEuB1YF9gBTBP0hAzW56m+v8DvjGzY0KZHZLeW2Zm+0j6PXAl8BvgFmCamf1S0mHAU2bWU9JQYI2Z/TmUMxM4yswWKbq/OZMLwznYI3T8Rkgqjyc9iQb5ioG5ku43swVpzt8fJf3BzHomJVf5PUp6ORz/JsAnZnaTpDzgc+BQM/tS0nNJ+18PjDKzgaH9E4H3gJuA/czsD+kOpkqsuuoEj1XrwFYtRx2TYlWHzlVjVW4+sa22J/+CaKxB7TuS1/86iobfQWJhZUyyJQuxkiJiW21fJd01AI9TGXmnrAWQ1Jdotqm3mRVIGg3k1bYbcKeZPZJSVjeioFcuDuSH/JmCymXAYmAvotnZoqT3UstqXUNZAi4ys3drabtbT4mFXxDrsjXabAts9Qpa79WH4uf/VpmhuIC1tw2oeJl/3i0U/+upqOO1yaZRp8wSaLMtUeetSaxYnIWjcK7BpQ52dQXaSWoftp8FDiXqoJU/Eq6fpKuJlh92AmYRdZQeAwaEDtepRKsU6uJAopUG48NEWA7wYS11AbwR/p0JzDKzbwEUze51BdJ1ymYCf5Z0N9GDnMYlvVd+fFOAX4XtPoSVFGY2SlLnlI5cufHAcEn/TConnT7A/aG8OZK+pnKQb6SZrQrHMBv4EVCtU5ZBukHL5UQx6eWQvjMw38zKBwGfA8pn1Y4EjpNU/nSjPGD7Otbt1kNiwedVY1XPPhQ/O6QyQ1EBawedU/Ey/4JbKX5rOImF86J9Vi2LHvTRcXNim29LYsWSLByFcxHvlLUMHYDvQ4dsZ6IgDlAqqY2ZlQI/AO2T9nkXuFXSM2a2Joz2lpKBma0MI719zOwD4MyU+heaWULSOUBta/PnANtI6mVmk8IHnMLQpt9JGmVmpWGEdJGZra37qXB1kkhQ/MZj5A+8ERSjdPIoEksWkHPEacQXfZF2bX65Vt12Jednp0EiHpXz2jAoXNOIjXeu4dUw2PUh0UzXXKLZsYFAb+CKMNvyENHsyQJJg6gcIHsZuBkYBUzJMFOVtinAf8zs9JT21VQXVA6IJag6OJYgw2cDM/tM0r5E90ndKWlE+UxcUhnxpP3TrC2rPuBmZhdIOgA4BpgeZgzTHX+68lKPJ7UNNapl0LLIzOJ1qFvAiWY2t0pidEyuISUSFL/2KPnn3RweiT+SxOIF5Bx5OvGFXxCfPSnjrq122IU2/X5VGatefQQKfmjExjtXlXfKWoZ3gAskzSD6oPBRSB8GzJA01czOlDRe0ifAv8N9ZbsAH4bR1zXAr4mCXSYDgMclFRB1oMo9BLws6WTgfaDGTpSZlYT19/eH5SSFREHzMaLljVMVNWop8Ms6nwVXL/G5UymYO7VKWsl7z6fNW/jozZX7zfqIwlkfpc3nGlgTvXm5mco02DWWaOngYKLl3/2I7t9dlbQ0b5mi+75OAl4CMLMiSe8CDxPdP1uT8kG0ZUTX8wcl7WhmXyh6KuN2RPfypq1rXUnaBlhhZv+QtAboX8suY4kG6G4NnZ9lZrZa0g/ApknldjezCcAESb8g80xdeXmjwqDc9kQxbZ96HkrygGSm32OqOcCPJXUzs6+IZjPLvUt0T91FZmaS9jazaVQf7HQNID5nKgVzUmLViOfS5i0cemPFdtnUMZRNHZM2n2tAHqcy8k5ZC2BmxVTe35VsNHBNUr4zUva7F7g3zX67J+X5c9L2FKIliuUGhfTPgT2T0q8N6aNDG8r3/0PS9iTSB8frWIcnZznn3AaWabBrHFGnYqyZxSUtIPpAX76i4FGiZYBfEd2HluwZoqV/I2qpexjwb0nfmlk/Sf2B5yTlhvdvCLNaNdW1LvYA7pGUIFo58bta8g8CngjnqAAoX0f2JvCSpOOBi4ge+tGDaMZpJPBxhvIeAoaGe9DKgP5mVqx0D3uoWcWAJNFMZrrfYxVmVhjul3tH0jKi+8bK3Qr8LZQpovN9LNEg5B/D/dJ3mtkL9W2oc67lkPn3MrhmzB/04TZ27e58uc6fOAvuHpD1v/e21zxR70/ILhLuSepgZjfWmtk1OkntwnJ+AQ8Cn5vZkNr2qwt/0IfbmLW759WNKk5B04xVPlPmnHPNhPl37Gy0JL0KdAcOy3ZbXEbnhfuic4iWpj5SS37nXAqPU5l5p8w555zLMjM7ITUtdNRSv4vxmoZ+Aq2kzkTLCFMdXo8HkKxP/UcBd6ckf5nuHNVSzgQgNyX5LDObuS7tCrNiG2RmzDnnUnmnzDnnmgu/gbpZqW8nZAPWu5zoe7+yInQ617vjaWb+9EPnmhqPUxnFst0A55xzzjnnnGvJvFPmnHPOOeecc1nkyxedc665ML+B2jnnXBPmcSojnylzzjnnnHPOuSzymTLnnGsu/AZq55xzTZnHqYx8psw555xzzjnnsshnypxzabXaqSe5xw6EWIzSSSMpHfNq+ny7H0j+mVdR8MDVJBbNq0hXhy60vexvlIz8J6Xj3misZjvnnGtBWv1kb3KPOzeKVRPfo/T9V9Ln26M3+WdfTcG9V5JYOI9Y1x7knvS7ivdL/vMC8U8mNFaznavGO2XOueoUI/e48yj8+2Bs9XLyL7ybsk8nYUsWVs2Xk0fOQccQ/99n1YrIPXYA8c+mNVKDHQAJv4HaOdeCKEbuCedTOGwQtmo5+Rf/ibJZE6vHqtw8cvocQ/zruRVJie++pvDeKyGRQO03I//yIRTMnuTX0Ybm5zejZrF8UVI3SZ9kuQ3XZbP+lkZSf0nbZLsdzVWs644kln+Hfb8Y4mWUffwBrXfpVS1fzpGnUzL2NSgrqZLeatf9SaxYTGLxgsZqsnMtgqRLJbXNdjvKSfpKUpd65O8r6aAN3IbnJM2QdJmknSVNlzRNUvc0eTtK+v2GrL+Gdm3wY3VVxbbvQWLZt9iKEKumf0Dr3favli/nqDMoGf0alJVWJpaWVHYQWrcB83udXHY1i05ZE7HRdcokrddMqSLZ+hvqD3inrIFo007YqmUVr231CtShc5U8sa13INahC/E5U6ru3CaXnJ/+kpKR/2yMprpkCcv+j2tolwJNplO2DvoCG6yjImkr4CAz29PMhgC/BF43s73NbF6aXToC9eqUrUes68sGPFZXnTbthK1MilWrllePVdvsQKxjF+KfTq62f6xrD/KvuJe2V/yN4leG+ixOY8h2jGrCsao5dcpaSXpU0ixJIyTlS+op6aMwgvaqpM0AJI2WNETSWEmfSuol6RVJn0u6rbxASb+WNDGMuj0iqVW6iiXdBeSHfM/UtK+khyVNDu28JamMryTdIenD8P4+kt6VNE/SBTUduKSrJc2U9HFoC7Uc+x2SxgCXSBou6T5J/5U0X9JJSeVeJWlSKOOWkNYtnLOHgKlA1wxt+j9JU0ObRoa0TpJeC+V9JGnPkD5I0pVJ+34S6imvK/X3ehKwH/BMOL/5KXWfH87h5Menf1nTqXMZqXpS8iiiRO6x/Sl+e3i1bDlHnErpB29BSVHDNc+5LAvXsinh2nS+pFMk/TW8d4mk+WG7u6QPwvZN4Zr6iaRh4cN+d0lTk8rtIWlKhjovJhqMel/S+yHtyBA3pkp6UVK7THWF9DrFvzR1byLp7XBN/0TSqUlvXxTqnylp55C/2vVeUjfgAuCycO0+RNLJobyPJY2tof48SU+EOqZJ6hfeGgFsEcq7majT+pvy85PGXUD3kP8eSe0kjUxq//GhvmqxTtK5kj4L5/BRSQ+EvJtLejmc70mSDk53rGmOqTJWffxVpkN3NVEdYtVxAyl+84m0uycWfE7hXy6h4L6ryel3YjRj5lyWNKdOWQ/gQTPbDVgJnAg8BVxjZnsCM4Gbk/KXmNmhwFDgdeBCYHegv6TOknYBTgUONrOeQBw4M13FZvZHoNDMeprZmbXse72Z7QfsCfy0vGMSLDCz3sA4YDhwEnAgMDjTQUs6mmhk8AAz2wv4U3irpmPvaGY/NbO/hNdbA32AY4kCFpKODOd0f6AnsK+kQ0P+nwBPhZHIr9O0aXPgUeDE0KaTw1u3ANNCm64LbaxNtd+rmb0ETAbODOe8MHkHMxtmZvuZ2X4De+5QhypcKlu9HHWoXJGkTTthq1dUZsjJJ7bl9uSfP5i2Vz9MrOtO5J39R2LbdqdV1x7kHH0Wba9+mDYHH0tO31/RpvfRWTgK5xrUQDPbl2iA6GJgPFD+wfsQYLmkbYmureNC+gNm1svMdgfygWPDbM4qST1DngFE1/9qzOw+4Bugn5n1U7Rs8AbgCDPbh+i6eHmmupKKqjH+ZTje/wO+MbO9QpnvJL23LNT/MFA+wFbtem9mX4U6h4Rr9zjgJuCoECuOy1A3oY2Y2R7A6cCTkvLCPvNCebckld8vQzl/TMp/FVAEnBDa3w/4S3kHlqRYB5QCNxLF5J8BOyeVeW+osxfRZ4/HMhxrFVVi1V7dajh0l4mtWo46JsWqDp2rxqrcfGJbbU/+BbfR9tpHiG2/E3n9ryO2XdWVrbZkIVZSRGyr7Rur6c5V05we9PGlmU0P21OA7kSdjzEh7UngxaT85Y+DmwnMMrNvAcLoZleiQLovMClcn/OBJXVsy+E17HuKpPOJzv3WwK7AjDRtamdmPwA/SCqS1NHMVqap6wjgCTMrADCzFZI61HLsL6SU8ZqZJYDZkrYMaUeGn/InNbQj6iD9D/jazD6q4fgPBMaa2ZflbQrpfYgCFmY2KnR+O9RQDlT/vXarJb/bABILvyDWZWu02RbY6hW03qsPxc//rTJDcQFrbxtQ8TL/vFso/tdTJBbNo3DYjRXpOYefgpUUUfrhvxuz+S2X+dKbRnSxpBPCdtfw005S+7D9LHAoUQet/HFw/SRdTbT8sBMwC3gTeAwYIOlyogG96jfFpHcgUQwZH2JNDvBhLXVB7fFveZq6ZgJ/lnQ38FZKJ6P8+KYAvwrbdb3ejweGS/pnUjnp9AHuD+XNkfQ1sBOwuoZ96kLAHWHQMQFsC5THweRYtz8wpjyeSXox1A9RHN61si/HpuHvwDWwxILPq8aqnn0ofnZIZYaiAtYOOqfiZf4Ft1L81nASC+dF+6xaFj3oo+PmxDbflsSKun7Mc+vM41RGzalTVpy0HSdaN16X/ImUfRNE50XAk2Z27Tq0Je2+knYgGkXsZWbfSxoO5NWjTZnqqu/i2LUpr5PrUtK/d5rZI1Uqi5ZkpO5f1zalWWeAAWVUnbVNd04g+r1WWaroGkgiQfEbj5E/8EZQjNLJo0gsWUDOEacRX/RF2rX5zrUUkvoSfRDvbWYFkkYTXbc+JJrpmks0OzYQ6A1cEWZ1HgL2M7MFkgZRea17mWg1wyhgipml6xSlbQrwHzM7PaV9NdUF6xBrzOwzSfsCPwfulDTCzMpXcZSXEU/aP9P1PrXcCyQdABwDTJfUM8PxpytvQzgT2BzY18xKJX1F5blKjnU11R8j+luosmpD6ZbWuQ0rkaD4tUfJP+/m8Ej8kSQWLyDnyNOJL/yC+OxJGXdttcMutOn3K0jEo3JefQQKfmjExjtXVXPqlKVaBXwv6ZAwoncWMKaWfZKNBF6XNMTMlkjqBLRPt1wvKJXUxsxKM+0LbEp0kV8VZqSOBkav2+FVGAHcJOnZ8OGgU5gtW59jB3gXuFXSM2a2JizDKa1tp+BD4EFJO5jZl+VtAsYSBcBbw4eaZWa2OgTBYwEk7QPUZc3hD0Tn1DWQ+NypFMydWiWt5L3n0+YtfPTmtOn+sA/XTHUAvg/X3J2JZqwgusYNDj/TiJbDFZrZKknlA4XLFN33dRLwEoCZFUl6l2j537m11F1+7VsGfER0rd3RzL5Q9FTG7ahcmVGtrnWl6Gm3K8zsH5LWED1sqSaZrvc/EMXC8nK7m9kEYIKkX5B5pq68vFGSdgK2J+r8bl3PQ0mNHR2AJaFD1g/4UYb9JgJDFN2f/QPRLODM8N4I4A/APeGYeoYVHlWO1TWM+JypFMxJiVUjnkubt3Bo5UqOsqljKJta349GzjWc5twpAzgHGBoC1XyiEcw6MbPZkm4ARih66lIp0Zr2TJ2yYcAMSVPDfWXV9jWzjyRNI1pGMp9o2cZ6MbN3wr0IkyWVAP8iWr+/zsceyh0R7o37MIz2rQF+TTQSWtu+S8MSzVfC8S8hWoM/CHhC0gygILQRolHisyVNByYB1b/0qrrhRMdXSJoRSudapCb6RKlm6B3ggnAtm0vUOYJodqwr0fLtuKQFwBwAM1sp6VGiD/JfEV3rkj1DtPRvRC11DwP+LenbcF9Zf+A5Sbnh/RvCrFZNda2LPYB7JCWIYtrvask/iPTX+zeBlxQ9UOMiogdh9CCaiRoJfJyhvIeIrvkziVZX9Dez4vrORpnZcknjFX2Nzr+Bu4E3JU0GphN+X2n2WyTpDmAC0X19s4kGfyG6p/DBcKytiTqQF6Qea7r7ypxrcTxOZSTz72Vwzdiaa0/0P3C3UWt358t1/tS59vqTs/73vsntL/qarXWg6Am0Hczsxlozu6yQ1C6sHGkNvAo8bmavboiy11x1Qtb/7zq3rtrd8+pGFaegacaq5j5T5pxzLYb5d+xslCS9SvRwqsOy3RZXo0GSjiC652wE8FqW2+PcRsfjVGbeKasnSROA3JTks8xsZrr8G7DePYCnU5KLzeyAhqy3Ntk6H84511yY2QmpaaGjlnp/7TVm9m5DtkXRI/FHpnnr8Ho8gGR96j+KaElhsi/TnaNaytngx2FmV9aeyznn1o13yuopW52g0MnpWWvGRpbtTqFzzjVH9e2EbMB6l5PFWBM6nevd8cz2cTjnXH15p8w1b35DqWtJ/O/duY1SYsWabDfBucbhcSqjWO1ZnHPOOeecc841FJ8pc8655sJHIJ1zzjVlHqcy8pky55xzzjnnnMsi75Q555xzzjnnXBb58kXnnGsuzL//xTnnXBPmcSoj75Q559JqtdPe5B43EBSjdNJ7lI5+NX2+PXqT/+urKLjvKhKL5lWkq2MX2l5+LyXv/ZPSsa83VrOdc861IK1234+803+PFKNk3L8p+fcLVd5v89NjyTnsOEgksOJCip4cQuLb/6HOW9Lutr+T+G4hAPH5n1L09L3ZOATnAO+UOefSUYzcX55H4WO3YKuWk/+HP1E2exK2ZGHVfDl55Bz0c+L/+6xaEbnHDiA+d1ojNdg5VxtJlwLDzKwg221pCST9EvjMzGZnuy3NlmLkn3kRa/9yDfb9Mja58QHKpn9I4tv/VWQpnTCK0jFvAdB6r97knXoBBX+7DoDE0m9Ye8sFWWm6c6mazD1lkrpJ+iTLbbgum/WnklSvLy6R9EtJu27A+nMlvSdpuqRTJR0iaVZ4nZ8mfzdJZ2yo+mtp2wY9VldVrOuOJJZ/i61YDPEyyj7+gNa77l8tX85RZ1Ay5jUoLamS3mrX/UmsWExi8YLGarKD6KlW2f5xTdmlQNtsN6I+JK334LGkVhuiLevgl4DHqQbU6sc/IbHkG2zZdxAvo3TiaFrvfVDVTEVJYxC5eYBfp7Iq2zGqCceqJtMpayKaVNyCibIAACAASURBVKdsHWzoALA30MbMeprZC8CZwJ/D68I0+bsB9eqUrUew9GDXgNShM7ZyecVrW7UcdehUJU9smx2IdehMfM6Uqju3ySWn7wmUvPfPxmiq2whJ+j9JcyV9IemPNeQ7SZJJ2q8x29fQJL0maUoY5Dpf0imS/hreu0TS/LDdXdIHYfsmSZMkfSJpmCLdJU1NKreHpCkZ6rwY2AZ4X9L7Ie1ISR9KmirpRUntMtUV0kdLGiJprKRPJfWS9IqkzyXdVssxny1phqSPJT0d0n4kaWRIHylp+5A+XNJfQzvvljRI0uOh/vnhWMrL/bWkiWGw8JHymCJpjaTBkiYAvTO0qZek/4Y2TZTUXlKepCckzZQ0TVK/kLe/pAeS9n1LUt+kum4P5XwkaUtJBwHHAfeEtnWv6fy4daOOXUisWFrx2r5fRqxjl2r52vQ7jnZ3Pkneyb+h6NmHKtJjXbZik5sfpu3Vf6FVj90bpc1u45CNONXUOmWtJD0aAtUISfmSeoaL3AxJr0raDOoeHDJdsFNJugvID/meqWlfSQ9LmhzaeUtSGV9JuiMEucmS9pH0rqR5kjLOj0vaOhzH9BAED0l6r8qFPqRVC2TpAoCkiyXNDvmer6H+Too+JMwI9ewpaQvgH0DPUN5vgVOAm8rPTxp3AYeE/JcpmjkbpyjgTw1tRFJfSe9LehaYGdJulDRH0n8kPSfpypDeXdI7ij7AjJO0c23BTtGHnMmSJj8+/ctMh+3qK3lgSSL32AEUvz28WracI0+j9IM3oaSo0ZrmIpawrP/UJlxHHwSOJhpYOV1pZr0ltQcuBiZs4NPUFAw0s32B/YiOcTxQft0/BFguaVugDzAupD9gZr3MbHcgHzjWzOYBqyT1DHkGAMPTVWhm9wHfAP3MrJ+kLsANwBFmtg8wGbg8U11JRZWY2aHAUOB14EJgd6C/pM7p6pa0G3A9cJiZ7QVcUl4P8JSZ7Qk8A9yXtNtOoW1XhNc7A0cB+wM3S2ojaRfgVOBgM+sJxIkGDwE2AT4xswPM7IM0bcoBXgAuCW06AigMx4OZ7QGcDjwpKS/dcSXZBPgolDMWOM/M/gu8AVwVBjLnpe6UHKuemLMw9W1XF9F4QYrq16HS999gzbXnUPTSY+QcG40d26oVrLnqTNbe8juKXhhK/vnXQt5GNZG8Ucp2jKpLrMpWnGpqnbIewINmthuwEjgReAq4Jly0ZwI3J+WvMTjUcsGuwsz+CBSGi+eZtex7vZntB+wJ/FTSnklFLTCz3kSBdDhwEnAgMLiG4z4DeDfUsxcwPaRXu9CH9GqBLEMA+COwd8hX06LpW4BpId91oewlwG+AcaG8R5LKT3sOQ33l+YcAS4CfhYB/KlUD7v5E53HXMLpwItHM3K+IPqiUGwZcFD7AXAk8VFuwM7NhZrafme03sOcONRy2y8RWLUcdKz9fqUNnbPWKygy5+cS22p7882+l7TVDiW2/E3n9ryW2bXdade1BztFn0/aaobTpcyw5/X5Fm95HZ+EoXBO1P/CFmc03sxLgeeD4NPluBf4ENMfe/cWSPgY+ArqGn3YhwHcFngUOJeqglXfK+kmaIGkmcBiwW0h/DBgQPkScGvatiwOJPmyMlzQdOAf4US11QXTthSgezzKzb82sGJgf2p7OYcBLZrYMwMzKLya9k9r7NFEntNyLZhZPev22mRWHMpYAWwKHA/sCk8IxHA78OOSPAy/XcPw/Ab41s0mhTavNrCy04emQNgf4mqiDWJMS4K2wPYVo1UitkmPVgJ23q8suLoV9v5RYp80rXmuzLiSSVnmkKps4mjZ7HxxelGJrfwAg8fXnJJZ8S2xL/z04IEtxqqk96ONLMyvvkEwBugMdzWxMSHsSeDEpf7XgAKBo6UdXootr+QUbohG/JXVsS/LFPnXfUySdT3T+tiYKbDPStKmdmf0A/CCpSFJHM1uZpq5JwOOS2gCvJZ2D1Av9z8J2b6LOC0TB408ZjmEG8Iyk14DXajjWPkSdIsxsVOjQdqghf121AR4Io7hxqga2iWZWPo3VB3i9fEmkpDfDv+2Ag4AXVTkalrsB2uVqkVj4BbHOW6PNtsBWr6D1Xn0ofn5IZYaiAtYO7l/xMv/8wRS//SSJRfMoHHpDRXrOEadiJUWUfvjvRmy9y6ZwbTw/KWmYmQ1Ler0tkHyz4ULggJQy9ga6mtlb5bPmzYWiJW9HAL3NrEDSaCAP+JBopmsuUUdsING1/oowU/MQsJ+ZLZA0KOwDUcfjZmAUMMXMMn8iTWkK8B8zOz2lfTXVBVAc/k0kbZe/zvSZQtTtRp7kPGtT3kuuKx7qEvCkmV2bpqyilE5dXduUbuoFoIyqA9nJ56TUzMrLKm+bawTxL+cS23Jb1GUr7PtltNm/L4XD7qySJ7bFtiSWLAKg9Z4HVGyrXYeoU2YJ1GUrYltuS2LZt41+DC47aolVWYlTTe3CkXrR7VjH/JmCQ00X7Nqk3VfSDkQzNr3M7HtJw1nPgGVmYyUdChwDPC3pHjN7irpf6DMFu2OIRluPA26UtFsYCUxVt/n/+rsMWEw0+xej6khCcsDNFARjwMowg+gaUyJB8euPkX/uTRCLUTppJInFC8j52WnEF84j/umkbLfQpdMEbl4OQW1YDVlqvN5IigFDgP4btmVNRgfg+9Ah25loxgqi1RCDw880oB/R6o1Vkspj4bIwWHUS8BKAmRVJehd4GDi3lrp/ANoDy4hm6R6UtKOZfSGpLbAdlYOP1epaDyOBVyUNMbPlkjqF2bL/AqcRDS6eCVRbZliHcl8P5S6R1Alob2Zf12HfOcA2knqZ2aQwS1lI9Hs4ExglaSdge6KO8qbA78Pf57ZEI+m1KT/frqEkEhQ98wBtL7sTxWKUfPAuiW++Jvf4c4h/9RllH39Im8OPp/Uue0M8jhX8QOHfo3HsVj/Zg9zjz4FEPCrn6XshzJy5BtQE4hTUGquyEqeaWqcs1Srge0mHmNk44CxgTC37JKvvBbtUUhszK820L9GFeS3ROv4tidabjl63w4tI+hGwyMwelbQJsA/Rss1MMgWyigAQ/mC6mtn7im4UPwNoR7QsNFV5ELo1jOIuM7PVSrtWu0apAagDsNDMEpLOATI91OMD4BFJdxL9TR4DPBra8KWkk83sRUUN2tPMPk5Tl9vA4nOnUjB3apW0kv+kvzWxcNhNadNL3nshbbpr0RZSdZnbdkT3OpVrT7QMfXS4Bm0FvCHpODOb3GitbDjvABdImkH0Yf+jkD6O6LyMNbO4pAVEHQfMbKWkR4lWYHxFtLoi2TNEqydG1FL3MODfkr4N95X1B56TVL4C4QYz+6yWuurNzGZJuh0YIylO1OnsT3QvxuOSrgKWEs0U1qfc2ZJuAEaEmFdKdBtDrZ0yMyuRdCpwv6KnCRcSzWA+BAwNSzfLgP5mVixpPPAl0Xn5BJiaoehkzwOPKnowyUnp7itz669s5kTKZk6sklb8+pOV2889VGWEvGK/KR9QNqW+4wCuhchKnGrqnTKI1rkPDaN486nHRXsdLtjDgBmSpob7yqrta2YfSZoGzArtGb/OR1apL3CVpFJgDXB2LfkzBbLkAHAa8PewDFHAkAxLJwEGAU+EDwkFROd8XcwAysK9EsOJgtvLkk4G3qf6chQAwijlG8DHRL+byUQdcog6iw+H30WbcIwf48HOueoSiWy3oC4mAT3CqoNFRNeqiqe2mtkqoOLxaWF535XNpENGuP8q002WSsp3ZMp+NxA9mCOdPsDjtSzXw8zuB+5Pej0K6JUmX9q6zKxv0vZokgYkk9/LUPeTRLcgJKd9RXS/WWre/imvB6W83j1p+wWiB3akltGupvaEPJOonKlM1j81IaxayXRPeruk7ZeonMUcjz8l2LmqPE5lpMrVcc5lj6R2ZrYmdL7HAuebWV1GImu05ppf+R+426i1u/uVOk9Z//CHn2f97739A/+qtb2Sfg78jWj2/HEzu13SYGCymb2Rknc0zahTtqFJepXo/uvDyh+k4TY+q8/9Wdb/7zq3rjb9+382qjgFtceqbMSpjWGmzLUMwxQ9bjSP6F6+9e6QOeeaJjP7F/CvlLS0a2Brm4Fp6czshNS00FFLffTsNWb2bkO2RdEj8UemeevwejyAZIPL1vlwzm28shGnWmSnTNGXSaY+xe8sM5vZwPXuQXjUbpJiMzsgXf4GqH8Ald8PU268mV1Yz3I2+HGYWb2+dNo5l0YTuYHaZVe6jloj1bscaHIPZsrW+XDOpeFxKqMW2SlrrE5QmnpnksWAZWZPAE9sgHKyehzOOeecc841Jy2yU+ZakHiN970755xzWWc+e+Bci+edMuecay78g51zzrmmzONURrHaszjnnHPOOeecayg+U+acc82Ef8WJc865pszjVGY+U+acc84555xzWeSdMuecc84555zLIl++6JxLq9VP9ib3+PMgFqN0wn8off/l9Pn2PIj8s6+h4G9XkFj4Ba167EXOMWdDq9YQL6PkreHEv2jQrwB05fwGaudcC9N6917knfH7KFaN/TfF/3q+yvs5fY8l5/DjIRHHiooofPKvJL75X8X76rQF7W//O0WvP0XJOy82dvNbHo9TGXmnzDlXnWLknvBbCofdjK1aTv4lf6Zs9kRs8YKq+XLzyelzLPGv51Yk2drVFD1+O7Z6BbGttifvvEEU3DqwkQ/AOedcs6cYeWddxNo/X4OtWEq7mx6kdPp/q3S6Sj4aRcnotwBo3bM3eaf9joK/Xlvxfv7pv6Ns5sRGb7pzqXz5YgsmqZukT7LdjppIekzSrtluR0sT274HieXfYSsWQ7yMsunjaL3b/tXy5Rx1BiXvvwJlJRVpiW++xFaviLa/+x9q3SaaNXMNL2HZ/3GuEUgaJOnKNOnrHdcyle2anlY//gmJJd9gS7+FeBmlE0fTZu+Dq2YqKqjYVG4eJD1oovXeB5FY+i3xRV83VpNdtmNUE45V3ilzTZqZ/cbMZme7HS2NOnTGVi6reG0rl6MOnavkiW2zA7GOXYh/OjljOa32PIj4oi8hXtZgbXXOucYgyUeXmhht1gVbsaTidWLFUrRZ52r5cg47jnZ3P0XeKedR9OyDITGP3J+fRtHrTzVWc52rkXfKXCtJj0qaJWmEpHxJPSV9JGmGpFclbQYgabSkIZLGSvpUUi9Jr0j6XNJt5QVK+rWkiZKmS3pEUqt0FUs6RdJfw/YlkuaH7e6SPkiqc7+wvUbS7ZI+Du3bMkO550uaLGny4zO+2qAnq0VLfoytRO7x51L85hMZs8e27Eruz8+m+OWHGqFxzrnmRNLlkj4JP5eGtOslzZX0HvCTpLz7hrjwIXBhUvpuSbFohqQeNdSXqezRku6QNAa4RNIvJE2QNE3Se+VxSNJMSR0VWS7p7JD+tKQjMtRZEauGz120fiesxVL1pDSTICWj3mDNNWdT9OJj5P7iTADyTjibkhEvQ3FRA7fRubrxTpnrATxoZrsBK4ETgaeAa8xsT2AmcHNS/hIzOxQYCrxOFAB3B/pL6ixpF+BU4GAz6wnEgTMz1D0WOCRsHwIsl7Qt0AcYlyb/JsBHZrZX2Pe8dIWa2TAz28/M9hu4Z7e6nAOXwlYtRx27VLxWx84VSxIByM0nttWPyP/dbbS9bhix7X9C3oDriW23Y5S/Q2fy+l9L0fN/w5Z/19jNb7EsYVn/cW59SdoXGAAcABwInBfSTgP2Bn4F9Era5QngYjPrnVLUBcC9IRbtByysob5MZQN0NLOfmtlfgA+AA81sb+B54OqQZzxwMLAbMJ/K2HYg8FG6epNjVf+fbJvpdLga2PdLUactKl7HOm2OrVyeMX/phPcrlje2+vEu5J1yHu3v+Qe5R/6K3GNOjx4I4hpUtmNUU45VPhXvvjSz6WF7CtCdKACNCWlPAsmPI3oj/DsTmGVm3wKEWa6uRB2qfYFJkgDygSWkYWbfSWonqX3Y91ngUKJg9kqaXUqAt5La+rP6Haqrq8SCz4l12Rp12gJbtYLWPQ+h+Jm/VGYoKmDtzWdVvMz/3W0UvzmcxMIvIG8T8s69keJ/PU3iqzlZaL1zbiPXB3jVzNYCSHoFOCakFYS0N8K/Hagas54Gjg7bHwLXS9oOeMXMPs9Q3yHpyk7yQtL2dsALkrYGcoAvQ/o4ovj1NfAwcH4YZFxhZmvqewJc3cS/nEurLbZFXbbCvl9Gm/37UvDIHVXyxLbclsTiaCay9Z4HEF8c9c3X3nlZRZ7c48/GigspGfl64zXeuRTeKXPFSdtxoGMd8ydS9k0Q/T0JeNLMrk3dMYMPiUZE5xIFtYFAb+CKNHlLrfKr4OP432/DSSQofnUY+ecNAsUonTSSxOIF5Bx1BvEFXxCfnflJVW0O/jmxLluTc8QpcMQpABQ9Oghbs6qRGt+CNdHRP+fqKc2aNCDtwjSUIR0ze1bSBKIO3buSfmNmo+pRdrm1Sdv3A381szck9QUGhfSxRCtHtgeuB04ATiL9qg+3oSQSFD5zP5tccVf0SPxx75D45mtyf3kO8a8+o2z6h+Qcfjytd90H4mXY2jUUPvanbLe6ZfM4lZF/qHWpVgHfSzrEzMYBZwFjatkn2UjgdUlDzGyJpE5AezPL9GijscDg8DMN6AcUmpl/gs+y+JwpFMyZUiWt5N1n0+YtfPiGiu3SkS9SOtK/68U5t87GAsMl3UXU6ToBOAd4IqS1Bn4BPGJmKyWtktTHzD4gabm8pB8D883svrC9J5CuU5ZcX0XZGdrWASi/Aeyc8kQzWyCpC5BjZvPDfdFXAn9Yx3Pg6qhsxkTWzKg6UFj82pMV20XP1n5fc7E/7MM1Ad4pc+mcAwyV1JZobfyAuu5oZrMl3QCMkBQDSolGDzN1ysYRLV0ca2ZxSQsAX/PmnHMtlJlNlTQcKP+k/ZiZTZH0AjCdKJ4kz0ANAB6XVAC8m5R+KvBrSaXAd0SDf5nqy1R2qkHAi5IWEd0rtkPSexOA8gdbjQPuJLoHzTnnaiUzn0Z0zdeaK4/3P3C3UWv359czLeWqZtVZh2f9773D0yPr3F7nXGTVgCOy/n/XuXXV4Yn3Nqo4BU0zVvnTF51zzjnnnHMui3z5omsU4Wbr3JTks8xsZjba45xzrmWR1JnovudUh5tZ5ueoO+dcI/BOmWsUZnZAttvgXHPXVL97xbmmIHS8ema7Hc61ZB6nMvNOmWvWimatzHYTnFsv7bLdAOdcg0usLst2E5xzWeadMuecay58BNI551xT5nEqI3/Qh3POOeecc85lkXfKnHPOOeeccy6LfPmic841F4lsN8A555yrgcepjHymzDnnnHPOOeeyyGfKnHNptdl3fza54CIUi1H0ztsUvvhslffzTjiFvP87BuJxEqtWsmbI3SSWLAag81ujiH81H4D40iX8cMt1jd7+lsgfNeyca2la99yftgP/ALFWFI98m+JXq8aq3F+cTO7hx2CJOLZqJQUP/YnE0ihW5f/6fNrs2xuAwhefovS/7zd6+1saj1OZeafMOVddLEa7Cy9l1XVXkFi2lI73PkLJhPHE//d1RZb4vM9ZefH5UFxM3jHHs8nAC/jhrluiN0uKWfmH32Sp8c4551qEWIy2513CmsFXkli+lPZ3D6V00ngSC5Ni1Zefs/rq30JJMTlHHUf+Wb9l7V8H03qfA2n1451YfcVvoE0b2g++l9JpE6CwIIsH5FqyWpcvSuom6ZPGaEwNbdjoh9klDZd0Uhbr/6WkXWvJM1rSfvUos8rfhqTnJM2QdFmG/P0lbZP0+rHa2uSyo/VOuxD/ZhGJ776FsjKKx4wi58A+VfKUzpgGxcXR9pzZxLpsno2mOtckSLpUUttst2N9SBok6cos1t9X0kG15Kl3LJW0Jmn7HkmzJN2TIW+VWClpsKQj6lOfazytdtyZxHeLSCyOYlXpB6PI6XVwlTxln0yHkihWxT+bTaxzFKtadf0RZbM+hkQciouIf/0Fbfbev9GPwblyG8s9ZRt9p6wJ+CXQYB0gSVsBB5nZnmY2JEO2/kBFp8zMfmNmsxuqTW7dxbp0IbF0ScXrxLKlxDp3yZg/78ifUzJ5QmVCTg4d7n2EDkMeIqd3n4z7uQ0s0QR+Wq5LgY26U9YE9AVq7JRtAL8F9jGzqzK8XyVWmtlNZvZeA7fJraNYp81JLFv6/+zdd3xV9f3H8dc7Iew9ZImCjFoVQQQnKKg4qj/3LA7Uuqp1V1tHFbd1VetAsC1qrXvgFgfIEJC9HGxBCTtAwsr6/P4434Sb5N4kIMkN8Hk+HvfBybnfeRLO536/53vOLfw5f/UK1CTxBGH1o08kZ/K3AOQtnEdat4Ogeg1UrwHV9juAlCa7VXibd3nJjlFVOFaVd1CWKmlwmF0aJqmWpK6SxoUrI+9KagSFV1uekDRS0veSekh6R9IcSfcVFCjpfEnfSpoq6XlJqfEqlvQQUCuke6W0vJKekzQxtHNATBkLJT0gaWx4v5ukzyTNk3Rlok5LSpH0bCjvQ0kfF8zQSTpa0hRJMyT9W1KNsP9vkiZImilpkCSV5wCH4/SNpGmhb/Uk1ZT0n1DHFEl9Qtr+kp6OyfuhpN5hO0vS/aGccZKah5nHk4FHwjFrX0pTzgr1z5bUK5TZVtIoSZPDK17QHAbsFsrvFad/ZwLdgVdCmlqxV+ZCux+WNEnSF5IOCu/Pl3RySJMaZjknhL+7K8pzbN22KNefLQA1+vSlWqffsPHt1wr3ZVx4Nmuvu4LMh++lzhXXkNKyVSklOLf9SHovnEdmSbpc0tmSHg/vXSdpfthuL2l02C5x3g7vT44pt6OkSQnqvJZowmm4pOFh37Eh5kyW9KakuonqCvvLFTsT1H+npB8kfa5oxcLNYX+iOH1ZaMM0SW+rnFf4JHUI5+dpoV/tw7F6JPRnhqRzQtrekj6Myfu0pP5he6GkAaGMGZL2ltQWuBK4IVEciXGEong5X1ticl1JX8aUeUqc9r8P1AHGF7Sz2PslYqVirsypnJ8lJP05Jk4NKF6P247ihSqLf89S9SP6Uq39b9g0NIpVudMmkjN5PPUeeIY6N9xJ7o+zoqtmziVJeQdlHYFnzGxfYA1wBvAScKuZ7Q/MAO6KSZ9tZkcAA4GhwNXAfkB/SU0k/RY4BzjczLoCeUC/eBWb2V+AjWbW1cz6lZH3djPrDuwPHClp/5iiFpvZocAoYAhwJnAIcE8p/T4daAt0Bv4AHAogqWYo4xwz60x0b95VIc/TZtbDzPYDagEnlVI+obzqwOvAdWbWBTgG2Eh03Ah1nAe8GOouTR1gXChnJHCZmX0DvA/8ORzHeaXkr2ZmBxHN+hb8TpcDfc2sG9GxfypOvpOBeaH8UcXfNLO3gIlAv5BmY5x2jzCzA4FM4D6gL3AaW35HlwJrzawH0AO4TFK74nWFD2ITJU18aXF6KV11ieSvXEFKsy0zhilNm5G/amWJdGldD6TWuRew7u7bICdnS/7Vq6J/l6aTM30q1dp3rPhGOxe5JJxHugPXAmOAgg/4vYBVkloDPYniAcQ5b4fz5FpJXUOai4nO+yWY2VPAEqCPmfWR1BS4AzgmnDcnAjcmqiumqFJjZ7y6FU1snQEcQBSzYpegJ4rT74Q2dAG+Jzq3lscrRJ8FuhBd0UoPdXYFCmLXI5JalqOsleHYPAfcbGYLQ7+fSBRHYrQk+v2dBDwU9m0CTgtl9gEeKxjwFjCzk9nyeeL14oWWM1aW+llC0rFEn5kOIjouB0o6Il4nYmPVkAVLSumuSyR/1YoiS+dTGjfDVpeMVdX2P5CaZ5xP1oO3Qe6WWLXp7f+SefMfyLrnZiSRl/5zpbTbuXjKOyhbYGZTw/YkoD3Q0My+DvteBGJPOu+Hf2cAs8ws3cw2A/OBNsDRwIHABElTw897lbMtpeU9O8xsTgH2pehyvdg2jTezTDNbAWyS1DBBXT2BN80s38yWAgWP5fkN0TGZHaf/fSSNlzQDOCq0oyy/AdLNbAKAma0zs9xQ/8th3w/AT0CnMsrKBgpmJycRDSq3xjtx8qYBg0Of3qRilkFmA5+G7RnA12aWE7YL2nEscGH4vY8HmhAFvyLMbJCZdTez7he2Kc9nA1dc7uwfSG21OynNW0C1atQ48iiyx40pkia1fUfqXnsT6wb8FVu7pnC/6taFtLRou34D0vbpTN6ihZXZ/F2W5VvSX1XAtZKmAeOI4k0boK6kemH7f0Tn615sGZQlOm+/AFysaDXGOSFveRxCdJ4cE85XFwF7llEXlB074+kJDDWzjWaWCXwAIKkBieP0fopWP8wgmtQsM06F49fazN4FMLNNZrYh1P+qmeWZ2TLga6JJs7LEizXl9V6Iy98BzQuaCDwgaTrwBdA65r3tqazPEseG1xRgMrA3ceIUFI1V/dv5aoJtkTf3R1Ja7k7KblGsSut5FNkTvymSJrVdB2pfcSNZD92GrdsSq0hJQXXrR2n23IvUPduTO3ViZTZ/l5TsGFWFYlUJ5X364uaY7Twg0SCmePr8YnnzQ50CXjSzv5az/lhx84YrJjcDPcwsQ9IQIPaqUlltSlRXufeHq1jPAt3NbLGku4u1IREB8f5CEtWfS9EBdWwdOWaF1+7z2PonbBYcm9i8NwDLiGZCU4hmJLe32HYX/o7MLF9SQTsE/MnMPquA+l2s/DyynvsHDe57FFJT2DTsY/IWLaT2BZeQO/sHssd/Q51Lr0Q1a1H/tmh1TsGj71Pb7EndP90Mlg9KYcMbrxR5aqNzFUXRMu5jgEPNbIOkEUTnx7FEV7p+JBqIXUK08uGmMs7bbxNdXfoKmGRmq8rbFOBzMzuvWPvKihHbM06VZghwqplNC0sKe5cjz1bFQ0qPUxA/1pRX7LEpqL8f0Aw40MxyJC2MU+f2UJ7PNw+a2fMVULcrLj+PDS88Sd07H4GUFLK/+oT8xQupee7F5M39wjBBoQAAIABJREFUkZyJ31DrwqtQzVrUuSmKVfkrl7H+odshtRr17osW/tjGDax/8n5fvuiSalsfib8WyJDUKywxuIBodqy8vgSGSnrCzJZLagzUM7NEn9xyJKWFKydx8wL1gfVEy02aAycAI7ate4VGAxdJepHoZN+baKb0B6CtpA5mNpct/S8IACsV3T9wJvBWOer5AWglqYeZTQgzkhuJlh/2A76S1AnYg+hDRX3gj5JSiGYDy/O4oEyi47QtGgA/hwHSRUDc+/8quA0AnwFXSfoqBN1OwC9mtv5XlOkSyJkwnowJ44vs2/Dyvwu31912U9x8ud/PYs0fL67QtrkEqujNy5WoAZARBmR7E12xguhcek94TSFa3rbRzNbGrJQocd42s02SPiNaYlfWEr+C89tKoqt0zxTECEX3bO1OtBQ8bl2/wmjgeUkPEsX0E4HBoW+J4nQ9IF1SGlGM+aWsSsxsnaSfJZ1qZu8puo86lejYXhHiZGOiq3F/JlphsU9IV5NoVcvoMqrJJIpv26IBsDzEhj5suTK5tbZHnLpX0itmlhWWyuaY2fKyMrptkzt5POsmF41Vm177T+F21oD4sYqcbNZd378CW+bi8jiV0K/5nrKLgIEh2MwnmoUsFzP7TtIdwLAwsMghWjufaFA2CJguaXK4r6xEXjMbJ2kKMCu0Z0yCsrbG20SBZCYwm2jJ3NoQqC8G3gxXcSYAA81ss6TBRMsaFob9ZTKzbEU3Hf9TUi2iAdkxRDOqA8MSk1ygf6hjDLAg1DOTaIlEWV4jWoJ4LXBmGfeVFfcs8Laks4iWcG7rIGgIUX82Eu7P20ovEC1zmRzuFVhB9KQs55yDaAn0lWEJ249EgyOIro61AUaaWZ6kxUSTYZjZmjLO268Q3Tc1rIy6BwGfSEoP95X1B14NgxKAO8xs9rbEiNKEibz3gWlEMXQi0cQpJI7TdxLFs59CW8o7CLmAaAB4D1HsPQt4l+h8Po1oxcctYbk/kt4ApgNziAbDZfkAeEvRQzr+VMZ9ZcW9AnwgaSIwlfD73QZFYuXWZjazYYrufR8bbmnLAs5ny4DcOefikiV4So2LSKobZruaAN8SPWBkabLb5cpn5QlH+h+426E1/eTrci9PW31K8v/eGw8tf3t3BIqeZNjAzO5MdlsSiYlTtYmuXF1uZuWZrHNVRMYZvZP+f9e5bdXo7RE7VJyCqhmrfs2Vsl3Fh2F5S3XgXh+QOeeqKvNlIduVpHeJHmx1VLLbUoZBir7wuCbRPdc+IHPOVUkepxKrUoMySeOBGsV2X2BmMyq43s6EpxzG2GxmB5tZ7+1c17tA8ce431qZD6+Q9AxweLHdT5rZf+Klr2rlO+dcZTCz04rvS9Y5PKzW+DLOW0eb2e+3c11JP4dLup1oeWSsN83s/h2hfOec21pValBmZgcnqd4ZRN8nUhl1lQjylc3Mrt6Ry3fOJeAzkBUuWefw8OTHyopTST+Hh8FRhQ2QKrp851wCHqcSqlKDMue2t/QftvVBXs5VDU2T3QDnXIXLWrStDzV2LvkaJbsBO4nyfnm0c84555xzzrkK4FfKnHNuJ+E3UDvnnKvKPE4l5lfKnHPOOeeccy6JfFDmnHPOOeecc0nkyxedc25n4ctCnHPOVWUepxLyK2XOOeecc845l0R+pcw5F1fdI7rR6q7LICWFjNc/Z8XAt4q8r+rV2P2xG6m1X3vy1mSy6Jq/k/PLcgBq7t2W1vdfTUrd2pCfz9xTbsSyc5LRjV2K30DtnNvV1Dy0Bw1vuhpSUlg/9GMyX3ytyPs1DuhMwxuvJq3DXqy6/T42fjUy2n9gVxreeFVhurQ994je/3pMpbZ/V+NxKjEflDnnSkpJodU9V7LggjvJXbqK9kMfZ90X49k8d3FhkkZnH0ve2ixm97mCBif1osVf+rP4T3+H1BR2f+JGfr7xcTZ9v5DUhvWw3LwkdsY559xOKSWFRrdcy/JrbiFv2Qqav/gsG0eOJXfBT4VJcpcuZ/WAv1Pv/LOKZN08aSrL+l0RFVO/Hi3eeYlN4yZWavOdi+XLF3chkq6XVDtJdV8r6XtJr2xj/qzt3SaXWO0uHcn+KZ2cxcuwnFzWfjCS+n0PLpKmft+DWfP2lwCs/WQMdQ/rAkC9Xgew6YeFbPp+IQB5azIh36fGnJPUVtLMJLfhtmTWX16xx0pSb0kfJrtNZfE4Vfmq77s3OYt/Ie+XdMjNZcPnw6l15GFF0uSlLyNn7nwwS1hOraOPYNPYb7HNmyu6yc4l5IOyXcv1QFIGZcAfgd+ZWb8k1e+2QrUWTchJX1n4c87SVaS1aFIkTVrzJmQXpMnLJy9zPamN6lO9XWswaPviADp88A+aXnF6ZTZ9l2b5yX+5Km+HGJRVdZJ8pVEVkNqsKXnLVhT+nLdsBanNmm51ObX79mHDZ8O3Z9NcAsmOUVU5VvmgLAkkvSdpkqRZki6XdLakx8N710maH7bbSxodtv8maYKkmZIGKdJe0uSYcjtKmpSgzmuBVsBwScPDvmMljZU0WdKbkuomqivsHyHpCUkjw1WvHpLekTRH0n2l9HcgsBfwvqQbJDUOx2C6pHGS9g/p7pZ0c0y+mZLaFiurZah/ani/V5z6Lpc0UdLEtzJ/Kv62K4/oV16EFZ9ljJMGM1QtlTrd92Hx9Y8x76xbaXDsodQ5bP8KaqhzO5xUSYPD+X+YpFqSuoZz4XRJ70pqBOU/50o6X9K34bz4vKTUeBVLegioFdK9UlpeSc+F8+gsSQNiylgo6YEQOyZK6ibpM0nzJF2ZqNOSXpZ0SszPr0g6WVKqpEdCzJku6YrSDl4p8WOGpIYhNq6SdGFMvcckKCtV0qMh73RJfwr7S4uBD0j6GrhOUrtwHCZIujem3DLjVEhXGKteWfFLad12icQJQ6VdEYsnpUlj0jq0Y9PYCdunTc5tIx+UJcclZnYg0B24FhgDFJy0ewGrJLUGegKjwv6nzayHme0H1AJOMrN5wFpJXUOai4Eh8So0s6eAJUAfM+sjqSlwB3CMmXUDJgI3JqorpqhsMzsCGAgMBa4G9gP6Syp6KWVL3VfG1P0EMACYYmb7E83avlT2ISv0e+AzM+sKdAGmxqlvkJl1N7PuZ9bbcyuKdgVy01eS1nLLbGNaiybkLltdJE3O0pVUL0iTmkJqvTrkrckkJ30l68fPJC9jHbZpM5kjJlJrv/aV2fxdVrJnHqvq7GMV0xF4xsz2BdYAZxCdA28N58QZwF0x6Us950r6LXAOcHg4L+YBcVckmNlfgI1m1tXM+pWR93Yz6w7sDxxZMPgJFpvZoUTxaQhwJnAIcE8p/X6BKEYhqQFwGPAxcCmw1sx6AD2AyyS1K6WcRPFjDHA4sC8wny0x9RBgXIKyLgfaAQeE8gqW15cWAxua2ZFm9hjwJPBcaPvSmDRlxikoGqv6NWtdSpddInnLV5LavFnhz6nNm5G3ctVWlVG7b282jhgNeX7vc2VIdoyqyrHKB2XJca2kaUSBok141ZVUL2z/DziCKKgUDMr6SBovaQZwFFHggRDowuzmOSFveRwC7AOMkTQVuAgoGMEkqgvg/fDvDGCWmaWb2WaiINimnHX3BF4GMLOvgCYhSJfHBKL+3g10NrPMcuZzW2HD9DnUaNuKtN2bo7RqNPi/I1j3xbdF0qz7YjwNzzgagAYnHE7W2OkAZI6cTM2926KaNSA1hToH7cfmOYtL1OHcLmqBmRV8SJ8EtCf6oP912Pci0fm/QFnn3KOBA4EJ4Vx+NNHKhPIoLe/ZilZiTCGKAfskaNN4M8s0sxXAJkkN41UU+tdB0m7AecDbZpYLHAtcGOofDzQhGrgmkih+jCI6bkcAzwGdw+TmajNLdK/XMcDA0A7MrGDmqbQY+HrM9uHAq2H75Zj9HqcqSfZ3P5C2R2tSW7WAatWo3bcPG0d+s1Vl1D7Wly66qsHXRFcySb2JAsGhZrZB0gigJjCWaBbxR6LgcglwKHCTpJrAs0B3M1scTvQ1Q5FvE82qfgVMMrPyThEJ+NzMzivWvtLqAii4CzY/Zrvg5/L+PcVdcADkUnSioGaJRGYjJR0BnAi8LOkRM9uaK22uPPLyWXLXQNq9NCB6JP6bX7B5ziJ2u6EfG2fMIfOLb8l4/XPaPHEjnYY/T97aLBb96e8A5K9bz8p/vUeHoY+DGZkjJpI53J9o5VwQe97MA+IOYuKkT3TOFfCimf11G9oSN2+4UnUz0MPMMiQNYfvEgZeJrsSdSxTjCtrwJzP7rFgb2pbS5uIMGEl0FXEP4HbgNKIreKPipI8tq8hat3LEwPVx6i66w+NU5cnLJ+Pv/6TZUw+j1BSy3v+E3Pk/Uf+K/mR//yObRo6l+j6/ocnfB5BSvy41ex5KgysuYuk5lwKQ2rI5qc13Y/PkaUnuiHM+KEuGBkBGGJDtTXTFCqKAck94TQH6EC0zWRsz87hS0X1fZwJvAZjZJkmfEc0MXlpG3ZlAPWAl0VW6ZyR1MLO5ip7KuDuwPFFd29FIosB8bxikrjSzdZIWEpaJSOpGtKykCEl7Ar+Y2WBJdYBubN3yR1dOmSMmkTmi6C2Ky5/Y8vBMy85h0dUPx8275r0RrHlvREU2z8Vj8T6vuipuLZAhqZeZjQIuAL4uI0+sL4Ghkp4ws+WSGgP1zCzRDbU5ktLMLCdRXqA+0eBjraTmwAnAiG3rXhFDgG+BpWY2K+z7DLhK0ldmliOpE1DaDVZx4wewLizLr25m8xXdj30zcE0pZQ0DrpQ0wsxyQ/8LFjaVJwaOIRpg/peYJaMepyrXpm++Zek3xVZyPD+kcDv7ux9JP+ncuHnz0peRfuI5Fdk8V5zHqYR8UFb5PiUKAtOJrooVrHUfRbQUZaSZ5UlaDPwAYGZrJA0mWiqykGhpRKxXgNOJAkxpBgGfSEoP95X1B16VVCO8f4eZzS6jru3hbuA/4RhsIFo6CdFVv4JlLBOA2XHy9gb+LCkHyAIurID2OedcZboIGBgmx+YT7r0qDzP7TtIdwDBJKUAO0RWjRIOyQcB0SZPDfWUl8prZOElTgFmhPdvl23TNbJmk74H3Yna/ALQFJocHaqwATi2lmLuJHz8gWv5Y8JCTUcCDwOhSynoB6ER0PHKAwWb29FbEwOuA/0m6jih+FeiNxynn3FZSiSequR2OoicWNjCzO5PdlqpmRrv/8z9wt0PrvOCDck8rLj2id9L/3luMHOHToC6uMOicAXQzs7XJbk9VsrjH0Un/v+vctmoz4csdKk5B1YxVfqVsByfpXaIbxY9Kdlucc865eBQ9lv7fwOM+IHPOuZJ8ULaDM7PTiu8LA7Xi92PdWvxG6u0tPBL/yzhvHb0VDyBxzjn3K0kaD9QotvsCM5tRwfV2puiTCAE2m9nBRA/hqHSSjgOK3wC7IF78dM65ZPFB2U4oWYEmDLy6lpnQOVchLL/KrcZwSRIGQcmodwZVLA6ECckKnZR0zpWPx6nEfFDmnHPOOZdEm9f7xzHndnX+5dHOOeecc845l0Q+NeOcczsJyy87jXPOOZcsHqcS8ytlzjnnnHPOOZdEfqXMOed2EmZ+A7Vzzrmqy+NUYn6lzDnnnHPOOeeSyAdlzrlCdY/oRqcvn6PT8OdpduWZJd5X9Wq0+ectdBr+PO3ffZS01rsBkNZ6N/b9/i06fPQkHT56klb3/bFE3j0H30HHT5+u8D4455zbddTu2Z09PnqBPT79Dw3/cHaJ92seuB+7v/U07ad/TJ1jexZ5r+Xz99Nu3Nu0fPaeymqucwn58kXnXCQlhVb3XMmCC+4kd+kq2g99nHVfjGfz3MWFSRqdfSx5a7OY3ecKGpzUixZ/6c/iP/0dgOyfljL3xOviFl3/uEPJX7+pUrqxK/MbqJ1zu5SUFJrdcTW//OGv5C5bSZvX/8n64ePImbeoMElu+gqW3/YYDS8uOdG45j9vopo1aHD2iZXZ6l2ax6nE/ErZdiLpekm1k92O8pCUFf5tK2lmsttTFkkLJTVNdjt2drW7dCT7p3RyFi/DcnJZ+8FI6vct+v2z9fsezJq3vwRg7SdjqHtYlzLLTaldk6aXnsryp1+vkHY757afqhAXJN2WxLofkTRL0iPbkDfpx25XU7Pzb8hZtITcn5dCTi5Zn4yg7lGHFkmTu2QZ2bMXQH7J0cDGcVOx9Rsrq7nOlcqvlG0/1wP/BTYkuyE7MknVzCw32e3YFVVr0YSc9JWFP+csXUXtrp2KpElr3oTsgjR5+eRlrie1UX0AqrdpTocP/0Fe1kaWPfYyGyZ8B0DzG89n5Qvvkr9xc+V0ZBdm+X4Dtdsp3AY8kKS6rwCamZmfsHYAqc2bkLN0ReHPuUtXUmP/vZPYIlcWj1OJ7XRXyiS9J2lSmOm6XNLZkh4P710naX7Ybi9pdNj+m6QJkmZKGqRIe0mTY8rtKGlSgjqvBVoBwyUND/uOlTRW0mRJb0qqm6iusH+EpCckjZT0vaQekt6RNEfSfaX0915J18X8fH9oD5L+HOqaLmlAGcetpqT/SJohaYqkPmH/x5L2D9tTJP0tpt4/lFLeLaGsaZIeCvsuC+2ZJuntgiuLkoZIejwcu4clNZE0LNT3PFBwjOpI+ijknynpnAR1Xy5poqSJb2X+VFq3XSyVPFGaWZlpMCN3xWp+OPwS5p50Pen3vUCbf9xMSt1a1PxtO6q3bcm6YeMqqNHOuQqQKmlwiKPDJNWS1FXSuBBP3pXUCMofuySdL+lbSVMlPS8pNV7FIV7UCuleKS2vpOfCuX5WbIxTtLrigRCDJ0rqJukzSfMkXZmo05LeB+oA4yWdI2lPSV+GPn8paY+QboikM2PyZcUpa9+YNk+X1DFOmsJY9VrGz2X+Ulwc8WISFmefc1XfTjcoAy4xswOB7sC1wBigV3ivF7BKUmugJzAq7H/azHqY2X5ALeAkM5sHrJXUNaS5GBgSr0IzewpYAvQxsz6KltrdARxjZt2AicCNieqKKSrbzI4ABgJDgauB/YD+kpok6O+/gIsAJKUA5wKvSDoW6AgcBHQFDpR0RCnH7erQl87AecCLkmoCI4FekuoDucDhIX3s8StC0gnAqcDBZtYF+Ht4653Q9y7A98ClMdk6ER2vm4C7gNFmdgDwPrBHSHM8sMTMuoTj92m8+s1skJl1N7PuZ9bbs5Quu1i56StJa7lllWhaiybkLltdJE3O0pVUL0iTmkJqvTrkrcnEsnPJW5MJwKaZ88hetJQa7VpTu9ve1NqvPb8Z9QLt33yY6u1a0e7VZE2AO+fKqSPwjJntC6wBzgBeAm41s/2BGUTn6QKlxi5JvwXOAQ43s65AHtAvXsVm9hdgo5l1NbN+ZeS93cy6A/sDRxZMIAaLzexQojg1BDgTOARI+EQHMzs5pu7XgaeBl0KfXwGeKvPIbXEl8GRoc3egxKgrNlad22j3rSjaFchbupK0Fs0Kf67Woil5y1clsUXObbudcVB2raRpwDigTXjVlVQvbP8POIJogFYwqOgjabykGcBRwL5h/wvAxWFW7pyQtzwOAfYBxkiaSjRoKhgdJKoLogEIRAFvlpmlhyUU80PbSzCzhUQDzQOAY4EpZrYqbB8LTAEmA3sTBdpEegIvhzJ/AH4iGiiNIjpePYGPiI5lbaCtmf2YoKxjgP+Y2YZQXsEn+/0kjQp971es72+aWV7YPoJoKShm9hGQEXNcjpH0sKReZra2lP64rbRh+hxqtG1F2u7NUVo1GvzfEaz74tsiadZ9MZ6GZxwNQIMTDidr7HQAUhvXh5TodJLWpjk12rYie9FSVr/yCT8c0p8fe/2BeWfdSvaCJSw4L2m3i+z0zJL/cjuFBWY2NWxPAtoDDc3s67DvRaLzdIGyYtfRwIHAhBATjwb2KmdbSst7tqIVLVOI4sk+Cdo03swyzWwFsElSw3LWfShb4v7LRHGwvMYCt0m6FdjTzPzGpQqwaeaPpO3Zmmqtm0NaNeqe0Jv1w31lRlWW7BhVlWPVTnVPmaTeRAOCQ81sg6QRQE2ik+PFwI9Eg4xLiE62N4WrQc8C3c1ssaS7Qx6At4lmA78CJoXBTrmaAnxuZucVa19pdQEUrGHPj9ku+Lm039ULQH+gBfDvmDY8aGbPb0Wb45lANMs3H/gcaApcRhSoSysr3p/8EOBUM5smqT/QO+a99cXSlshvZrMlHQj8DnhQ0jAz8+fYbi95+Sy5ayDtXhoAKSlkvPkFm+csYrcb+rFxxhwyv/iWjNc/p80TN9Jp+PPkrc1iUXjyYp2D9qP5Df2wvDzIy+eXO54hb22JFT3OuR1DbPzJA8oaxJQVuwS8aGZ/3Ya2xM0rqR1wM9DDzDIkDWH7xNPSFMSlXMKktiQB1UskNPufpPHAicBnkv5gZl9tY70ukbx8Vtz/DK0GP4BSUlj37jCy5/5E42suZNOs2WwYPo4a+3Wi5VN/I6V+Per0OYTcay5k8cmXA9D65ceo3m53VLsWbb/6L8vvfIINY0r7eONcxdmpBmVAAyAjDMj2JrpiBdESvHvCawrQh2iJwtqYGbOViu77OhN4C8DMNkn6DHiOokvt4skE6gEria7SPSOpg5nNDVeWdgeWJ6rrV3o39C0N+H3Y9xlwr6RXzCwrLNnMMbPlCcoYSXT16itJnYiWDP5oZtmSFgNnA/cCzYBHwyuRYcDfJP0v/C4ah6tl9YB0SWmhrl/KaMt9YSlkwb0LrYDVZvbfsIa/fxnHxW2lzBGTyBxRNCAtf+KVwm3LzmHR1Q+XyLfu029Y9+k3pZad88ty5hx/zfZpqHOuMq0FMsIKhVHABcDXZeSJ9SUwVNITZrZcUmOgnpkluuk3R1KameUkygvUJ5rMWyupOXACMGLbupfQN0S3BLxMFJNGh/0Lia7evQGcQhR7i5C0FzDfzJ4K2/sTTfC67WzDyAksGjmhyL7VT79UuL155mwWHnV+3Ly/XHBThbbNua2xsw3KPgWulDSd6KpYwTXsUURLKEaaWV4YZPwAYGZrJA0mWuKwkOjKUKxXgNOJBhqlGQR8Iik93FfWH3hVUo3w/h3hSk9pdW2TMHAaDqwpWAJoZsPCWvyx0UQeWcD5bBkYFvcsMDAsLcwF+sc8fWoUcHQYYI0iGmDGvZ8s1P1puBdvoqRs4GOip2ndCYwnWho5gyiwxjOA6NhNJgr8BV840hl4RFI+kANcVdpxcW5X40+1chXoIqIYUZto5cTF5c1oZt9JugMYFu59ziG67yzRoGwQMF3S5HBfWYm8ZjZO0hRgVmjPmG3uWWLXAv+W9GdgBVv6PJhooPgt0aCx+EoPiG55OF9SDrCUUu5lc25X4nEqMZV4uporQtLNQAMzuzPZbUkkBKrJwFlmNifZ7alKZrT7P/8Ddzu0zgs+KHcE+6nbMUn/e99z8hcecZ3bSnP3OS7p/3ed21Ydvvtsh4pTUDVj1c52pWy7kvQu0Q3ORyW7LYlI2gf4EHjXB2TO7dp8BtI551xV5nEqMR+UlcLMTiu+LwzU2hXbfauZfVaRbQmPxP8yzltHm1l5n2K1XUnqTHhiY4zNZnZwMtrjnHNuxxEehFGj2O4LzGxGBdfrscs5V+X4oGwrxRuoVVK9q4i+b6zKCIGzSrXJOefcjiFZgyCPXc65qsgHZW6nlpO7M34Vn3Px+S3Czu2YMtbWTnYTnKsUHqcS80+szjnnnHPOOZdEfqXMOed2En4DtXPOuarM41RifqXMOeecc84555LIB2XOOeecc845l0S+fNE553YSZr4sxDnnXNXlcSoxH5Q55wrV730Au999GaSmsOrVz1n27NtF3lf1arT9xw3U6tyevIxMFvzxEbJ/Xk69Xl1o/ZcLUfVqWHYuP98/hKxvZpBSpxad3n6gMH/1lk1Z/c4Ifh7wr8rtmHPOuZ1C/d4HsMc9l6KUFFa8+gVLn3mnyPuqXo29nryO2p3bk5uRybyrHiX75xWkNqpHh0F/pk6XDqx8YziL7hhcmKf1rf1oemZvUhvUYXKn31d2l5wDfPmic65ASgpt7ruCuRcO4PujrqHRKb2o2bFNkSRNzu1L7posvut1JctfeJ/Wt10EQO7qdcy75H6+73sdC298krZP3gBA/vqN/HD8DYWv7J+Xs+bTsZXeNeecczuBlBT2vP9y5px/LzP7XEuTU3tSs+PuRZI0Pe8YcteuZ0bPP7Js8Ae0uf1CAGxTNr/8/VUW3/tiiWLXfD6B7068pVK64FwiPihLIkkNJf2xjDRtJZU5bRPSzdx+rSu1rhGSuldGXQnq7y+pVbLq31nV6dqRzQuXkr1oGZaTS8b7o2hw7EFF0jQ89mBWv/UVABkfjaHe4fsDsHHWAnKWrQZg04+LSKmRhqoXvRBfo21LqjVtSNb47yqhN7smy0/+y20bjwcg6VRJ+2zP+oofC0mvSpou6YYE6YvEF0kvlNUmV3nqHNCRzQvT2Rzi1Oqho2l0XNE41ejYg1j55nAAVn/0DfV6RnEqf+NmsiZ8T/7m7BLlrp88m5zlGRXfAZf0GFWVY5UPypKrIVBqEAbaAn4tvaj+gA/KtrO0Fk3IXrKy8Oec9FWktWhSLE3jLWny8snLXE9qo3pF0jT83WFsmLkAy84tsr/RKUeQ8cGoimm8czs+jwdwKlBhAyBJLYDDzGx/M3siQbL+xMQXM/uDmflMUhVRPTYGAdlx41STonFq3QaqFYtTzlVFPihLroeA9pKmSnokvGZKmiHpnJg0vUKaG8Ks3yhJk8PrsPJUJKm2pDfCDOHrksYXzDZKOi/UOVPSwzF5npM0UdIsSQPK2ylJx4e2TZP0ZdjXWNJ7of5xkvYP+++WdHNM3pmhj20lfS9pcKh/mKRaks4EugOvhGNSK079l4d2T3wna2F5m+3i3XtrVnaimCQ1O7Wh9W0Xsuivz5ZI1ujkXqwe6oOyipRvSvrLbbMdOh5ISpU0JKbNca9EhbQGgIP/AAAgAElEQVSXSZoQYsTboT2HAScDj4T+tS+lC2dJ+lbSbEm9QpnlORbDgN1C+b3itKtEfIm9MicpS9LDkiZJ+kLSQeH9+ZJOjjkOj4T+TZd0RSnHoTBWvbt+YSnddYUULwZZ2UkqqDlu6yU7RlXlWOWDsuT6CzDPzLoC44CuQBfgGKLA1DKkGWVmXcPM3nKgr5l1A84BnipnXX8EMsxsf+Be4ECAsEzjYeCoUH8PSaeGPLebWXdgf+DIgoFUaSQ1AwYDZ5hZF+Cs8NYAYEqo/zbgpXK0uSPwjJntC6wJZb4FTAT6hWOysXgmMxtkZt3NrPvpdduWoxoH0ZWx6q2aFv6c1rJJ4ZLEwjRLY9KkppBarw55azKj9C2asNfgv7Lw+n+Q/dPSIvlq/bYtqpbCxhnzKrYTzu24dvR40BVobWb7mVln4D+l1P+OmfUIMeJ74FIz+wZ4H/hz6F9pJ4tqZnYQcD1wV9hXnmNxMuEYm1mJGaJyxJc6wAgzOxDIBO4D+gKnAfeENJcCa82sB9ADuExSu3idiI1Vp9VpW0p3XYHsYnGqepw4VSRNagqp9WuTl5FZmc10bpv4oKzq6Am8amZ5ZrYM+JrohF5cGjBY0gzgTcq/1KMn8BqAmc0Epof9PYiCzAozywVeAY4I750taTIwBdi3nHUdAow0swWhroKzZU/g5bDvK6CJpAZllLXAzKaG7UlES3dcBVk/bQ412rakepvdUFo1Gp3ci7Wff1skzZrPv6XxmUcB0OjEw8kcE/0ZpdavQ/sX72TJQy+zfuIPJcpudMoRZPhVMufKa0eMB/OBvST9U9LxwLpS6t8vXNWaAfQL5W2NgsftxcaFbT0WWyMb+DRszwC+NrOcsF3QjmOBCyVNBcYDTYgmGN12sH7qHGq02xKnGp/Sk4xhE4qkWTNsAk3P6gNA4xMPI3PMjGQ01bmt5o/ErzrKey31BmAZ0QxqCrDpV5Yfd3+Y2bsZ6GFmGZKGADXLWU+8lQJxF8cBuRSdHIitY3PMdh5QYqmi247y8ll85yA6/PdulJrCqte/ZNPsxbS86fdsmD6XtZ9/y6rXPqftP25gn1EDyVuTyYKrHwWgWf/fUaNtS1pcdzYtrjsbgLn97iZ31VoAGp10OHMvuidRzW478e9/2WnscPEg7O8CHAdcDZwNXJKgniHAqWY2TVJ/oHc5212gIDbkseVzzLYei62RY1a4Vi6/oB1mli+poB0C/mRmn1VA/S4vn0V3DOY3/7sLUlJYGeJUq5vPY8O0uaz5fAIrXvuCvZ66ns6jnyV3TRbz//hYYfb9xz1Pat1aqHo1Gh1/ED+eN4BNc35m99svpMlpvUipVYMuEwez4n9fsOTx15PY0Z2Xx6nEfFCWXJlAwd2nI4ErJL0INCaanfwz0DomDUAD4OcQBC4CUstZ12iiIDlc0ZOkOof944EnJTUFMoDzgH8C9YH1wFpJzYETgBHlqGcs8Iykdma2QFLjcLVsJNGM6L2SegMrzWydpIXASQCSugFxl3kUE3vc3Ha0bvgkvhs+qci+9Mf+V7htm3NYcNXfS+Rb+tSbLH3qzYTlzuqZ8LYK51xkh44HIU+2mb0taR7RwCuRekC6pDSiuPBLnGOwtbb1WBT3a+PLZ8BVkr4ysxxJnYBfzGz9ryjTxVj71WRmfDW5yL4lj75auG2bc5h3xSNx804/JH4s+vn+l/j5/vLcVeFcxfFBWRKZ2SpJYxQ9rvcToiUk04iuIN1iZkslrQJyJU0jCnLPAm9LOgsYThQoy+NZ4EVJ04mWn0wnWveeLumvoSwBH5vZUABJU4BZRMtSxpSzTyskXQ68IymFsM4fuBv4T6h/A3BRyPI2W5Z6TABml6OaIcBASRuBQ+PdV+bcrsjyfQZyR7UTxIPWROf4gpUPfy2l/juJBoA/ES39KxgEvUa0BPFa4Mwy7iuL16dtORbFDSEmvmxD/heIljJOliRgBdFTJZ1zeJwqjazE09XczkhSKpBmZpvCU62+BDqZWckv7NiJTG5ziv+Bux1at8VDyx3Bfuj0u6T/ve89+2OPuFXcrhoPqrIJrU9L+v9d57ZVj1/e3aHiFFTNWOVXynYdtYmWqqQRzYBe5QHYOed2SR4PnHOuivFB2U5G0nFEjzSOtcDMTiP6/pXtWdd4oEax3ReYmT/qyLkk8IUPLlZlxoME9T8DHF5s95NmVtrj8n913opum3Nu23mcSswHZTuZ8MSnSnnqk5kdXBn1OOec23qVGQ8S1H91MvJWhfKdc25r+aDM7dSy87b1AVzO7Xj8Bmrndkz5/phwt4vwOJWYf3m0c84555xzziWRD8qcc84555xzLol8+aJzzu0kfAmUc865qszjVGJ+pcw555xzzjnnksgHZc4555xzzjmXRL580TnHnvdeSqOjupG3cTPzbniaDTPml0hTp/NetP/Hn0ipWZ2Mrybz053/AiC1YV06DryJGrs3Y/PPK5hzxaPkrV2/JV+XDuz34YPMufJxVn80tnB/at1adPn6KVZ/Op6Ft79Q8Z3cBZgvC3HO7YQa9D6APe+9BKWksPzVL0h/+t0i76t6Ndo/dR11Ou9FbkYmc658jOyfVwDQ6prTaXbe0Vh+Pj/d8S/Wfj0VgK7jB5KXtRHLz8dy85h1wi0AdBh4EzXbtwKgWv065K5bz8y+N1Vib3duHqcS80GZc7u4hkd1o1a7lkw9/GrqduvEXg9ezsyT/lIiXbuHrmD+Lc+RNWk2e//3Dhr2OYA1w6fQ+prTWDd6OkuefpdW15xG62tOZ9H9L0eZUlLY4/YLWDNiaonydr/lPNaNm1XR3XPOObcjS0mh7QOX8cO5A8hOX8W+H/+dNZ9NYOOcnwuTNDvvGHLXZDHt8KtpfMrh7HHHhcy98jFqddydxqf0ZHqf66jevDF7v34303peA/n5AHx/1t/IXZ1ZpLq5Vz5WuL3H3/qTl7ke5yqDL18EJLWVNDPJbbgtmfVvLUn9JT1dSXX1lvRhZdS1K2p03EGseGsEAFmTZ5PaoA5puzUqkiZtt0ak1qtF1qTZAKx4awSNjj94S/43ovwr3hhBo+MPKszX4pLfsfrjseSuXFukvDqd9yKtWUPWfj2tgnq1azJL/stVHI9VW89j1Y6v7gEd2LQwnc2LlmE5uaweOppGxx1UJE2j43qw8s3hAKz+cCz1e3YO+w9i9dDRWHYumxcvZ9PCdOoe0KHcdTc++TBWvjd6+3XGJT1GVeVY5YOyqiPpgU5Slfim5arSjl1F9RaNyV6ysvDn7CWrqN6icck06avipklr2pCc5RkA5CzPIK1Jg2h/i8Y0PuFglr00rGiFEnve1Z9F975YEd1xzlUsj1VBVWnHzq56iyZkL4mJP+mrSGtZPEbFpMnLJ2/dBqo1rkday8Zsjo1v6auo3qIJAGbG3q/exX6fPkKzfn1L1Fvv4H3IWbGGzQvSK6BXzpXkg7ItUiUNljRL0jBJtSR1lTRO0nRJ70pqBCBphKQnJI2U9L2kHpLekTRH0n0FBUo6X9K3kqZKej7RCVzSQ0CtkO6V0vJKek7SxNDOATFlLJT0gKSx4f1ukj6TNE/SlYk6HWb2hkv6HzCjjLovljRb0tfA4aUdTEnNwzGbFl6Hhf3vSZoU2n95TPosSfdIGg8cKul4ST9IGg2cHpPuyNCuqZKmSKpXWjtcOSjO+u7i00jx0lD6VFPbAZdEyxjDMpECzfsfT8ZXk4sEWedcuXms8li1aylP+ImbxlCc2GUhvn13ym3MPO5mfuh3H837n0C9g/cpkq7JqT1Z5VfJXCXyQdkWHYFnzGxfYA1wBvAScKuZ7U8UBO6KSZ9tZkcAA4GhwNXAfkB/SU0k/RY4BzjczLoCeUC/eBWb2V+AjWbW1cz6lZH3djPrDuwPHClp/5iiFpvZocAoYAhwJnAIcE8ZfT8olLtPoroltQQGEAW4vsA+CUuLPAV8bWZdgG5Awc1Dl5jZgUB34FpJTcL+OsBMMzsYmAgMBv4P6AW0iCn3ZuDq0LZewMbiFUu6PAT7ie9tWFBGM3dNzfsfT+fPH6Pz54+RvWw11Vs1LXyveqsmZC/LKJI+O30V1Vs2KZpmabg6tnJN4XLHtN0akbMqWqpYp0t7Oj53IweMH0jjkw6l3YOX0+j4g6h34G9ocfEJHDB+IHv87SKantmbNredX9Fd3iXkm5L+chXOY5XHql1KdvoqqreKiT8tm5CzdHXiNKkppNavTW5GFtlLVlEjNr61bELOsihvTohzuavWkvHpeOoc0HFLgakpNP7dIax+f0wF9WrXlewYVZVjlT/oY4sFZlbwNIJJQHugoZl9Hfa9CLwZk/798O8MYJaZpQNImg+0AXoCBwITwkxNLWB5OdtydCl5zw6zdtWAlkQBZ3qcNtU1s0wgU9ImSQ3NbE2C+r41s4KIkKjug4ERZrYi9PN1oFMpfTgKuBDAzPKAgpuKrpV0WthuQ/QBYxVRQH077N+b6PcxJ9T1X6BgpnIM8HiYpX3HzLbc6RuY2SBgEMC4VqdX0ZXDybVsyKcsG/IpAA2PPpAWF5/AqvdGU7dbJ/LWbShcjlggZ3kGeVmbqNutE1mTZ9PszN4s/ffHAGQMm0Czs3uz5Ol3aXZ2bzI++xaAqYdcVZi//RPXkPHFJDI+/ZaMT78t3N/s7D7U6dKexQ/8t6K77NzOwmNV6XXvkLFqvMeqhLKmzqVmu5bUaLMb2UtX0/iUnsy7+okiadYMm0DTs/qQNWk2jU86lHWjZwBRfGr/zA2kD3qf6s0bU7NdS7KmzCWlVg1IEfnrN5FSqwYNjuzCL49v+W/ToFcXNs79pciyfecqmg/Kttgcs50HNCxn+vxiefOJjquAF83sr9vQlrh5JbUjmn3rYWYZkoYANbeiTYnEPlooUd2nUtZ6tTJI6g0cAxxqZhskjWBL+zeFgFggbl1m9pCkj4DfAeMkHWNmP/yadu3q1nw5iYZHd6PrN8+SHx6JX6Dz548xIzwKeMFfni98JP6a4ZNZ89VkAJY8/Q4dB95Ms3OPJvuXlcy+4tGk9MP5o4Z3ER6rSq/bY9XOJi+fhbe/wG/+9zeUmsKK175k4+zFtP7zuayfNo81wyaw/NUvaf/UdXQZ8wy5a7KYe9XjAGycvZjVH4xh/xFPYXl5LLxtMOTnk9asIR3/dSsAqpbCqndHsXbElMIqm5xyOKveG5WU7u7sPE4l5oOyxNYCGZJ6mdko4ALg6zLyxPoSGCrpCTNbLqkxUM/MfkqQPkdSmpnlJMoL1CcKSmslNQdOAEZsW/e2rt3AeODJsIRjHXAWUNqj874ErgL+Edb51wEaABkhyO1NtFwlnh+AdpLam9k84LyCN8K+GcAMSYcSzVR6oPuVFt42OO7+GTHfzbJ++jymH3V9iTS5GVl8f87dpZYfO9CLteKN4ax4Y3j5G+qcK85jlceqnd7aryYzPUwEFvjlkdcKt21zDnMTTAgueeptljz1dpF9mxctY2bfGxPWNz9BzHKuIvmgrHQXAQMl1QbmAxeXN6OZfSfpDmCYpBQgh2gtf6JANwiYLmlyWKtfIq+ZjZM0hWjN+3yi5RHbVaJ2h7rvBsYC6cBkoLQnT10HDJJ0KdFs7lXAp8CVkqYDPwLjErRhU1j28pGklcBoonsgAK6X1CeU+R3wya/qsHPO7fg8Vnmscs7t4GRV9WH9zm0Hfk+Z29EdsuSdcq/1mNzmlKT/vXdbPNTXpji3lfyeMrcjO3gHi1NQNWOVP33ROeecc84555LIly9WMkXfbVKj2O4Lwtrziqy3M/Bysd2bw2N9f025txOt2Y/1ppnd/2vKdc45lzweq5xzrnL5oKyS/drA8ivqnQF0rYBy7wc8qDlXBVTV715xOx6PVc65iuBxKjFfvuicc84555xzSeRXypxzbifh3//inHOuKvM4lZhfKXPOOeecc865JPJBmXPOuUol6XhJP0qaK+kvcd6vIen18P54SW0rv5XOOed2VcmIU7580TnndhI7wg3UklKBZ4C+wM/ABEnvm9l3MckuBTLMrIOkc4GHgXMqv7XOOee2J49TifmVMuecc5XpIGCumc03s2zgNeCUYmlOAV4M228BR0uq+pHcOefcziApccoHZc45t5OwKvCSdLmkiTGvy4s1szWwOObnn8O+uGnMLBdYCzTZxsPinHOuikh2jCpnrEpKnPLli8459rz3Uhod1Y28jZuZd8PTbJgxv0SaOp33ov0//kRKzepkfDWZn+78FwCpDevSceBN1Ni9GZt/XsGcKx4lb+16AOofui973nMJqpZK7upMvjvjTgBaXHoiu/XrC4Llr3zB0hc+rLzOugplZoOAQaUkiTeTaNuQxjm3i2jQ+wD2vPcSlJLC8le/IP3pd4u8r+rVaP/UddTpvBe5GZnMufIxsn9eAUCra06n2XlHY/n5/HTHv1j79VRUI4193rkPVU9D1VJY/dFYfnn09cLydr/19zQ+6TDIz2fZS5+y7F8fV2p/XcUrI1YlJU75lbIqQtL1kmonux3lJam3pEr5JC2praSZlVHXrqjhUd2o1a4lUw+/mgW3DGSvB4tf2Ii0e+gK5t/yHFMPv5pa7VrSsM8BALS+5jTWjZ7OtJ7XsG70dFpfczoAqfVr0/bBy/mx/4NM73M9sy9/FIBav9mD3fr1ZeaJtzD9mBtp1PdAarZrWTmddVXBz0CbmJ93B5YkSiOpGtAAWF0prXMJeZwqtS6PUxUlJYW2D1zGj/3uY3rv62hySi9qddy9SJJm5x1D7posph1+NemDP2CPOy4EoFbH3Wl8Sk+m97mOH39/L20fvBxSUrDNOXx/1l3M7HsjM/veRMPeB1C3WycAmp5zFNVbNWX6EX9i+pHXsuq9MZXeZZd0SYlTPiirOq4HkhrsFEn630S4wdJVkkbHHcSKt0YAkDV5NqkN6pC2W6MiadJ2a0RqvVpkTZoNwIq3RtDo+IO35H8jyr/ijRE0Ov4gAJqedgSrPx5H9i8rAchdtRaAWh1bkzV5NvkbsyEvn3Vjv6PRCQdXdDd3CfmmpL/KYQLQUVI7SdWBc4H3i6V5H7gobJ8JfGVmfqUs+TxObWmHx6lKUveADmxamM7mRcuwnFxWDx1No+MOKpKm0XE9WPnmcABWfziW+j07h/0HsXroaCw7l82Ll7NpYTp1D+gAQP6GTQAoLRWlVaPgFNP8wuP45Yk3IPxcELvc9pHsGFXOWJWUOJX0E1tVI+k9SZMkzQrrTc+W9Hh47zpJ88N2e0mjw/bfJE2QNFPSoBA02kuaHFNuR0mTEtR5LdAKGC5peNh3rKSxkiZLelNS3UR1hf0jJD0haaSk7yX1kPSOpDmS7iulv21D+meByUCbUuo+XtIPod+nl3Ec60r6j6QZkqZLOiPsfy6s3Z0laUBM+oWhb6OBsyQdKGmapLHA1THp9pX0raT/Z+++47Os7v+Pv96MsCFsgqgM0dbBRkVFQcDR4WhRa60VZ221uFBbV91frVV/daFgFdviqNtqlSkyZBMg4GIqskfYIYHk8/vjOgl3kvtOApJB8nk+Hnlw5VznOudcV8L1uc91znUyN5Tbsah2uOIltWpC1qoNed9nrdpIUqsmhfOs3hg3T81myexelw7A7nXp1GzaCIDa7VtTI7k+R791P8d+8hjNBvYBYOdX39HghKOp0bg+1eokkXx6N2q1blaap+gqkDD3/npgFPAl8B8zWyjpfknnhGz/AJpKWgzcDBRajrgq8zjlcaoqSWrVlKxVMfFn9UZqphSMUTF5snPI3rqTGk0aUDOlCZmx8W31RpJahdd+qlXj2DGP023+y2yZOI8dqYsAqHV4K5qeczLHfPxXjvr3XdTymRxVTnnFKe+UFXaFmXUHegCDgSlA77CvN7BR0iHAKcCkkP6MmfU0s2OBOsDPzGwJsEVSl5DncmBEvArN7CmiYdG+ZtZXUjPgLqC/mXUDZhH9wOPWFVNUlpmdCjwPvE8UJI4FBkkq6uXDo4B/mllXYEe8uiXVBoYDPw/XoVUR5QHcDWwxs+PMrBMwPqTfaWY9gE7AaZI6xRyzy8xOMbPXgZeBwWbWq0C51wJ/N7MuRD+j7wtWrJiXN9/buayYZjriLRZU8GFP3AWFin4gpBrVqHdcB7669CG++vX9HHLjQGq3T2HX4pWseu5dfvz6vfxo5N3s/GI5tid7/9vv8pip3L9K1k77n5kdaWYdzOyhkHaPmX0QtneZ2QVmdoSZHW9mhV9yrNo8Th3kcQo8VpXYfr/dYyhO7MobzMjJYcGAW0jtfjX1uxxBnaMOA6BarRrkZO5m4dm3sW7kGNo/cV2hMtz+K+8YVdJYVR5xyjtlhQ2WNA+YRjRX9FCgvqQGYftV4FSiG35usOur6A/HpQGnA8eE9BeByxVNc7goHFsSJwJHA1MkzSUaHj28mLpg79BqGrDQzFabWSawlPxzYwv61symFVP3j4BlZrYoDM/+u5hz6E/0Nx4AMLP0sHlheDKbGtp+dMwxbwBIagQkm9lnIf1fMXmmAndIuh043MwyClZsZsPMrIeZ9Tivbrtimlk1tRx0FseNeZzjxjxO1tpNJMWMVCW1bkrW2vR8+bNWbyQppWn+PGvC6NiGzXnTHWu2aMzuMNUja/VGtkxIJScjkz2btrFt+hfUPbotAOtfG0famUP44hd3s2fzdnYtW12ap+tcZeNx6iCPU6E+j1UlkLV6I0mtY+JPSlN2r9mUOE/1alRvWJc96dvJWrUx30yMpJSm7F6b/9jsrTvZOnUhjcJ70lmrN7Lpo6kApH88nbo/PhznyoJ3ymJI6kN0k+5lZp2Jbsi1iW6wlwNfEwW43kAvooBQG3gOGGhmxxE9pasdinwbOJvoKeFsM9s7/l5MU4AxZtYlfB1tZlcWUxdAZvg3J2Y79/uiVtrcUVzdYd++zJVVwfyS2gFDgH7hqeRHBdq/I9GxuczsVeAcIAMYJen0fWiTC9aO+IS0AbeQNuAW0j+ZQfMwtbB+tyPJ3rozbzpirt3r0snevivvRejmA/uQPmoGAOmjZ9L8wuj45hfuTd/0yQwaHP9jqF6NanWSqN/1SDIWrQSgRpjimHRIM5r85AQ2vDcJ51zxPE4lrjvs8zhVyWyfu5ja7VKodWgLVLMGTc49hfTRM/Pl2Tx6Js0u6AtAk5/1YuvkNCCKT03OPQUl1aDWoS2o3S6F7amLqdGkIdUbRq9HqnYSDXt3YtfiaEAz/ZMZNArvpDXodQy7lvpDQ1c2fEn8/BoR/XXunZJ+RPQ0DmAicH/4SgX6AhlmtkVScsizIcxpH0j0R+Qws12SRgFDif7yd1G2AQ2ADURPP5+VdISZLVa02lUbYF2iug6gRHV/BbST1CFMebm4mHJGE83HvRFAUmOgIVFA2yKpJdEHgQkFDzSzzZK2SDrFzCYDl+Tuk9QeWGpmT4Xt2Cknbj9sHjeb5H7d6PL5c+SEJfFzHTfmcdIG3ALAsj+9kLck/uZP57B5fPQqyqpn3qHj80No/qt+ZK3cwDe/i1ZZ3LV4JZsnpNJp3JOQY6x7dSwZX38HwJEv3kqNxg2w3dksu2N43hL67ofJKe8GuLLgccrjVNWSncPyO1/kqFfvQdWrsf71cWR8s4JDbv0VO+YtYfPomax7bRwdnrqBzlOeZc/m7Sz+/RMAZHyzgk3/nUKnCU9h2dksv2M45ORQs2VjOvz9j6haNahWjU3/ncLmsdHrlKueeYcOz9xEq6t/TvaOXSwb8lx5nn2l43EqMe+U5fcJcK2k+URPG3OnSkwimlYx0cyyJa0guvnn3piHE03FWE60YkuskUQvG48upu5hwMeSVof5+oOA1yTVCvvvMrNviqnrBzOz9UXUfQ3wkaQNwGSi9wASeZAoaC4AsoH7zOwdSanAQqKpKkWtM3s58JKknUQvWua6CPiNpN3AGqIPIO4HWn7H8LjpuR0ygB3zlzD/9BsL5dmTvp0vL7o37vGrh77P6qHvF0r/4vy79q+hzjmPUx6nqpwt4+cwf/ycfGkrH3s9b9syd7M4PBAsaNVTb7PqqbfzpWV8+S0LzhgSN3/21p1889uHfmCLndt3Ml9luFRJGgI0MrO7y7stVdG01r/wX3B3UDtx1TslWz0DmNRqYLn/vvde81aJ2+sqBo9T5W+6xyp3EDvhIItTUDFjlY+UlSJJ7wIdiF50ds65UmVxlyBzLjGPU865suRxKjHvlJUiMzu/YFoIgAWXWbrdzEYVzHsghaWGx8XZ1W8fXuyOV+7lwA0FkqeYma8h65xzFZzHKeecqxi8U1bG4gXAMqp3I9Cl2Iz7Xu7LRH+rxTlXznIqxKQQd7DzOOWcKy0epxLzTpmr1HJK+MdsnXPOufKyJ8f/QpFzVZ3fBZxzzjnnnHOuHPlImXPOVRI5/gK1c865CszjVGI+Uuacc84555xz5chHypxzrpLwpYadc85VZB6nEvORMuecc84555wrR94pc84555xzzrly5NMXnXO0e+AKkvt1Iycji8U3Ps2OtGWF8tTr1J4j/t/1VKudxOZxc1h290sA1Eiuz5HP30ytQ1uQuWIdX//ucbK37KB6o3oc8eR11D68FTmZWSy56Vl2fr2C2h1ac9TzN+eVW+vwlqx47HVWD/+ozM63ssop7wY451w5aPfgFTTu15WcjCwW3fBMwhjW8e/XUa12EunjUll2VxTD2t5zKY0H9MB272HX8jUsuvFZsrfuLOtTqDI8TiXmI2XOVXHJp3ejdvsUUk+6niW3DqX9I9fEzdf+kWtYcuvzpJ50PbXbp5B8elcADrn+fLZMTiP15OvZMjmNNtdHf3e2zeBfsmPBMub1u5nFg5+m7QNXALBrySrmDRgSfZ15GzkZmWz6eEbZnKxzzrlKpXG/rtRpn8KcXn9k8ZDn6fBo/BjW4dGrWTLkBeb0+iN1YmLY5s/mk9rnJgcBlyYAACAASURBVOaefgsZS1fTZvAvyrL5zuWp9J0yScmS/lBMnraSfl2CstpKWnDgWldkXRMk9ThAZZ0n6egDWV/BayHpNUnzJd2UIP8gSa1jvn+xuDa5stHkrJ6sf/MzALbPWUSNhvWo2SI5X56aLZKp3qAu22d/A8D6Nz+jyVnHR8ef2ZN1//kUgHX/+TQvve6RbdgyOQ2AjMUrqX1oC2o2a5Sv3Ea9j2PX8rVkfr++9E7QOfeDeBzd7/rzxT1XOqIYNAHIjWF148ew+nXZFmLYuv9MoOlZPQHY/Nk8yI7Gb7bN/oZaKU3LrvHOxaj0nTIgGSgymABtgWKDyUHsPKDUOkCSWgEnmVknM3syQbZBQF5wMrOrzOyL0mqTK7mkVk3IXLUh7/vM1RtJKhCUklKakrVqY/48rZoAULN5MrvXbQZg97rNeR2vHV8sp+lPTgSgfpcjqNWmOUmt85fb7NyT2fDe5AN/UlWUoXL/cpWSx9H9M4iYuOdKR1JKUzLzxadNhTpWtVKakrV6b56s1ZsKxTmAlhefTvr4OaXXWFfuMaoix6qq0Cl7BOggaa6kx8LXAklpki6KydM75LkpPMmbJGlO+DqpJBVJqivpP2HE6A1J03Of0km6ONS5QNKjMccMlTRL0kJJ98Ups7qkETFtjjsSFfJeLWmmpHmS3g7tOQk4B3gsnF+HIk7hAkkzJH0jqXcosyTXYjTQIpTfO067BgI9gJEhT53YJ5iStkt6VNJsSWMlHR/2L5V0Tsx1eCyc33xJvyviOlwTrums93cWnlfu8pPi3JzM8ueJd6DFS9xr5dPvUqNRPTqP+RutrvwJOxYsw/Zk7y2zZg2anNmTjf/9fN8b7ZwrSwd1HC2irrNC2+ZJGhfSmkh6L9Q/TVKnkH6vpCExxy4I59hW0peShof6R4cYVyjuxak/JlYtLWmzXUFxQ5gVm6dgnGtzwy+wPdmsf3vSgWubc/ugKiz08SfgWDPrIumXwLVAZ6AZMFPSxJBniJn9DKKgAAwws12SOgKvEd1ci/MHIN3MOkk6FpgbymsNPAp0B9KB0ZLOM7P3gDvNbJOk6sA4SZ3MbH5MmV2AQ8zs2FBW/jH5/N4xs+Eh34PAlWb2tKQPgA/N7K1i2l/DzI6X9BPgL0B/YF0JrsU5ofwu8Qo1s7ckXU90jWeF9sVmqQdMMLPbJb0LPAgMIBrdewX4ALgS2GJmPSXVAqZIGm1mhXpdZjYMGAbwecovi+k6VE2tBp1Fy0v6A7B93mJqtW7GtrCvVkpTstZsypc/c/XGfKNctVKakrU2yrN7/WZqtohGy2q2SGb3hi0AZG/PYPFNz+Yd023GUDK/W5f3ffLpXdmRtjQvv/vh/AVqV0oO9jhaiKTmwHDgVDNbJqlJ2HUfkGpm50k6HfgnURwuSkfgYjO7WtJ/gF+a2b8Lxr2CYmPVlFYDPVbtg1aXn0XLS/oBsH3uEmq1bhoTw5oUjmGr8s8ASSqQp/mFp9F4QHcWXlDiPr3bTx6nEqsKI2WxTgFeM7NsM1sLfAb0jJOvJjBcUhrwJiWf+ncK8DqAmS0AcoNCT6JOx3oz2wOMBE4N+y6UNAdIBY6JU9dSoL2kpyWdBWwtov5jw5PJNOCSUN6+eCf8O5toKgrs/7XYF1nAJ2E7DfjMzHaH7dx2nAH8VtJcYDrQlCgQuv2wZsQneYttbPp4Bs0vOA2A+t06smfbzrzpiLl2r9tMzvYM6neLLnnzC05j0yczAdg0ehYtLuwLQIsL+7JpVJRevWFdVDN67tPikv5snfYF2dsz8spsft4pbHjXpy46d5A5GONoPCcCE3Mf7JlZ7if0U4B/hbTxQFNJjeIXkWeZmc0N27Hx05WSNS9/wrz+tzKv/61s+mQGLS7sAxQdw7J37I1hLS7skxerkvt2oc315/HlZY+Sk5FVpufhXKyqMFIWq6STSG8C1hI9CawG7PqB5cdNl9QOGAL0NLN0SSOA2rF5Qnpn4EzgOuBC4IoE9YwAzjOzeZIGAX1K2O5cmeHfbPb+buzvtdgXu23vXIOc3HaYWY6k3HYI+KOZjSqF+qu09HFzSO7XjW5TnyU7IzPf6FbnMX9j3oBoxs6SPw2jY1gSP318KpvDvPuVz7zDkS/cQouL+5G5cj3fXPM4AHU6tqHjU4OxnBwyvlnB4pufyyu3Wp0kGp3amSW3vVCGZ+qcOwAOujhaRHnxRqfiTnQD9pD/QXZsHZkx29lAoamKrvSkj51D437d6DbtGXIyMll8495Y03nsY8zrfysAS28fzhFhSfzN41NJH5cKQPuHr6RaUk2OeeNuALbPXsSS24eV/Ym4Kq8qdMq2AQ3C9kTgd5JeAZoQPWW7FTgkJg9AI+D70Cm4DKhewromE3WaPlW0suBxIX068HdJzYimXVwMPA00BHYAWyS1BM4GJsQWGI7JMrO3JS0h6ngl0gBYLakm0UjZyjjXYF/t77Uo6Ie0AWAU8HtJ481st6QjgZVmtuMHlOmCZXe8SLy373I7ZAA75i1hbt/CrzTuSd/OFxcWnvKxffY3pJ58fdz6cjKymHnMoP1trkvAp4W4UnJQx9EEpgLPSmqXO30xjJZNJIqfD0jqA2wws62SlgO5UzO7Ae1KUMcPjXuuhJb++cW46bkdMoDt85Ywt8/NhfLM6fXHUmuXK8zjVGKVvlNmZhslTVG0BO/HRFMh5hE9+brNzNZI2gjskTSPqNPzHPC2pAuAT4lu+CXxHPCKpPlE0yjmE70HtVrSn0NZAv5nZu8DSEoFFhJNU5wSp8xDgJcl5T6h+3MR9d9NFLi+JZr6lxsMXieaRjIYGGhmS0p4PrnntD/XoqARwPOSMoBe+3H8i0RTQuYoeiFtPdGqks4550pRJYij8c5pvaRrgHdCfF1H9C7zvUQxdz6wE7gsHPI2e6fQzwS+KUE1I4iJe2aWUUx+51wVpkIr1Lj9Fl4yrhlebO4AjAOONDOfpFxOfKEPd7A7afXbJV6796OWF5f77/tP175WMdcadgeFqhpHfaEPdzA7ec1bB1WcgooZqyr9SFkZq0s05aIm0ZO831f2QOKcc84dQB5HnXNVknfK9oOkM4mW5o21zMzOp2RL/v7Q+p8FTi6Q/Hcze7k0jy3ttjnnnKsayjKOSpoO1CqQfKmZpR3Iepxz7ofwTtl+CCsAltsqgGZ2XXkcWxHKd84lllPhJmM4F19ZxlEzO6Es6nHOFc/jVGLeKXOV2jor+HDUOeecq1h22f4ubOycqyyq2h+Pds4555xzzrkKxUfKnHOuksgp8d/1dc4558qex6nEfKTMOeecc84558qRj5Q551wlUSH++ItzzjmXgMepxHykzDnnnHPOOefKkY+UOecSOu7B39KyXxeyM7KYc8PzbElbXijPj/90IYde0Juk5Hp82OGKvPQOv/sJh1/SB9uTQ9bGrcy5aRgZ328ow9Y755yrjI58aBBN+3UlOyOTLwcPZVvaskJ5GnRqx9FP/YFqtZPYOC6Vb+4cAUC7IQNp/Zt+7N64FYAlD7/GxnFzqX1oc06c9AQ7l6wCYMvsRXx924tldk7OeafMORdXy35dqN++FWN73UzjbkfQ+dErmPiTewrlWzN6DktfGs2AqU/kS9+yYDmfnXkX2RlZtL2sP8fcfTGzfvd0WTW/Ssop7wY451wpa9qvC3XatWLqiTfQsHtHjvrrlcw6+65C+Y7661V8OWQYW2ctovOrf6Lp6V3YOH4uACte+Ijvhn5Y6JiMb9cyo9/tpX4OVZnHqcR8+mIZkdRW0oJybsMd5Vj3Y5IWSnpsP44t92tXFbU6szvf/WcSAOlzFlOzYV1qtUgulC99zmIy120ulL5hyhdkZ2RFeWYvok5Kk9JtsHOuWJJulFS3vNtREpK2h38PihggabmkZuXdjsqu+Vk9WfPmRAC2zl5EjYb1SCoQm5JaJFOjfh22zloEwJo3J9L87J5l3lbn9oWPlFUtdwAPl1PdvwOam1lmOdXv9lGdlMZkrNqU9/2u1Zuok9I4bgesOIf/ui9rx887kM1zceTIlxp2xboR+Dews7wbcjCTVMPM9pR3O6qiWimN2bVyY973mas3UiulCVkxsalWShMyV++NX5mrNlErpXHe922uOJNWF57KtnlLWfSXf7Fnyw4A6hzWnOPHPsKebRksfeQNNk//qgzOqGrxOJWYj5SVreqShocRo9GS6kjqImmapPmS3pXUGEDSBElPSpoo6UtJPSW9I2mRpAdzC5T0G0kzJM2V9IKk6vEqlvQIUCfkG1nUsZKGSpoV2nlfTBnLJT0saWrY303SKElLJF2b6KQlfQDUA6ZLukjS4ZLGhXMeJ+mwkG+EpIExx22PU9YxMW2eL6ljnDzXhPbNGr1zcbE/FJdAnBun7ceySW1+eTLJndux+LnCU0Wcq6okvSdpdrjPXiPpQklPhH03SFoatjtImhy275E0U9ICScMU6SBpTky5HSXNTlDnYKA18KmkT0PaGeGePkfSm5LqJ6orpJcoNsWp+wFJN8R8/1BoD5JuDXXNj405CcqpLellSWmSUiX1Den/k9QpbKdKuiem3quKKO+2UNa8ECeRdHVozzxJbyuMLIYY9US4do9KaqoolqdKegHIvUb1JH0Ujl8g6aIEdefFqg8zlhR12i6fOB/qCwanIuLXylfG8PkJg5lx+u1krk2n432XApC5Np3J3a5jRv8/segv/+SYoX+kev06B7rxziXknbKy1RF41syOATYDvwT+CdxuZp2ANOAvMfmzzOxU4HngfeA64FhgUAgGPwYuAk42sy5ANnBJvIrN7E9Ahpl1MbNLijn2TjPrAXQCTssNdMEKM+sFTAJGAAOBE4H7E520mZ0TU/cbwDPAP8M5jwSeKvbK7XUt8PfQ5h7A93HqG2ZmPcysxxl1j9iHol27ywfQd+zD9B37MLvWpFOn9d4ph7VTmrBrTfo+lde897EcdcN5TLvscXKy/KGyczGuMLPuRPexwcAUoHfY1xvYKOkQ4BSi+y3AM2bW08yOBeoAPzOzJcAWSV1CnsuJ7s2FmNlTwCqgr5n1VTTV7i6gv5l1A2YBNyeqK6aoImNTgvP9B3AZgKRqwK+AkZLOIIqNxwNdgO6STi3iul0XzuU44GLgFUm1gYlAb0kNgT3AySF/7PXLR9LZwHnACWbWGfhr2PVOOPfOwJfAlTGHHUl0vW4hiteTzawr8AFwWMhzFrDKzDqH6/dJvPpjY9XP6nQo4pRdm8vP4Phxj3L8uEfJXJtO7UP2/prVSmlKZoHYlLkqGj3Ly9O6CVkhT9b6LZBjYMaqf4+nYdfoc4Jl7WFPevQseNv8ZWQsX0vdDimlfWrO5fHpi2VrmZnNDduzgQ5Aspl9FtJeAd6Myf9B+DcNWGhmqwHCE9RDiYJNd2BmeIhZB1hXwrb0K+LYCyVdQ/T7kQIcDcyP06b6ZrYN2CZpl6RkMyvJ3LZewC/C9r/YGwhLYipwp6Q2RIFz0T4c64qx7OUxLHt5DAAt+3eh/RVnsPK9qTTudgR7tmXs09TFRsceTpfHruTzix8la8PW0mqyi+F//+WgMljS+WH70PBVX1KDsP0qcCpRB+2dkK+vpNuAukATYCHwX+BF4HJJNxM9bDu+hG04kej+PiXEgSSie2xRdUHxsWnv3LLAzJZL2iipK9ASSDWzjaFTdgaQGrLWJ+qkTUzQ5lOAp0OZX0n6lqijNImoc7sM+AgYEEa42prZ1wnK6g+8bGY7Q3m5892ODaN+yaE9o2KOedPMssP2qYRYZmYfScrtGaQBf5P0KPChmcXtFLqS+/7l0Xz/8mgAmvbvSpsrzmTtu5/TsHtH9mzbmW/qIkDWus1kb99Fw+4d2Tp7Ea0uOJXv/xH1jZNaJOflb/6Tnuz4agUANZs2YHf6dsgxah/egjrtU8j4dm0ZnmXV4HEqMe+Ula3Y96myiW74JcmfU+DYHKKfnYBXzOzP+9GWuMdKagcMAXqaWbqkEUDtfWjT/sj9P7qHMHobpsokFcpo9qqk6cBPgVGSrjKz8ftZryvC2rFzadmvCwOmPcmejExSb3whb1/fsQ/zaf9o3Zhj7r6YNuefRPU6SZw552m+fXUCX/3tbY655xKq16vN8cMHA7Bz5UamX/Z4eZyKcxWKpD5EHYJeZrZT0gSi++xUopGur4k6GVcQPcS6JYwGPQf0MLMVku5l7735baJRm/HAbDMr1ClK1BRgjJldXKB9RdUF+x8HXgQGAa2Al2La8H9m9kKig+K0OZ6ZRKOOS4ExQDPgaqIHoEWVFe8z4gjgPDObJ2kQ0Cdm344CeQsdb2bfSOoO/AT4P0mjzSzhbBK3bzaOTaVZv670mv53cjKy+OKGoXn7jh/3aN7qiV/d/mJYEr8mG8fNZeO46Jn4EfdcQoNj22Jm7Fqxnq+GDAcg+cQf0/62C7HsHCw7h69vG86ezQV/3M6VHu+Ula8tQLqk3uFJ2qXAZ8UcE2sc8L6kJ81snaQmQAMz+zZB/t2SaprZ7kTHAg2Jgs4WSS2Bs4EJ+3d6CX1ONHXlX0RTJieH9OVEo3f/Ac4FahY8UFJ7YKmZPRW2OxF9EHGlYP6fR8RNz+2QASx84DUWPvBaoTyfX1hea8pUXb7U8EGjEZAeOmQ/Ihqxgmh06P7wlQr0JZr6vUVS7kO8DeG9r4HAWwBmtkvSKGAo+afaxbON6F6/AZgGPCvpCDNbHEaW2rB31kShun6gd8O51QR+HdJGAQ9IGmlm28OUzd1mlmjWx0SiuDFe0pFEUwa/NrMsSSuAC4EHgObA38JXIqOBeyS9Gn4WTcJoWQNgtaSaoa6VxbTlwTAVMved8NbAJjP7t6J3owcVc13cPvr6zy/FTY9dzn7bvKVMP21IoTxfXP9s3GPXfzSD9R/NODANdAl5nErMO2Xl7zLg+RAMlxI9JS0RM/tC0l3A6DBHfzfRfPtEnbJhwHxJc8J7ZYWONbNpklKJpqosJXrP4UAbDLwk6VZgPXvPeThRR3EGUacx3iOqi4DfSNoNrKGId9mcc66C+gS4VtJ8olGxaSF9EtH0v4lmlh06GV8BmNlmScOJpsYtJxoZijWSaCrd6GLqHgZ8LGl1eK9sEPCapFph/11hpKeouvZL6Dh9CmzOnQJoZqPDO85TwxTK7cBvSDwV/zmimJlGNLtiUMyqvpOAfqGDNYmog5lw6qCZfRLexZslKQv4H9EqxXcD04liaRpRJy2e+4iu3RyiB6rfhfTjgMck5RDF1t8XdV2ccw5Atj/LqTl3kHiv1a/9F9wd1M5b82qJ1w9+I+WScv99v2j1SF/vuBxIGgI0MrO7y7stiYQHgHOAC/x94PzGtbyo3P/vOre/+q1946CKU1AxY5WPlDnnXCWRU+FCjCsLkt4lWjjq9PJuSyKSjgY+BN71DplzVZfHqcS8U1YJhYUwahVIvtTM0kq53uOI3hOLlWlmJ5Rmvc45V5WZ2fkF00JHrV2B5NvNbFTBvAdSWBJ/XJxd/cysfWnWnYjHJufcwcA7ZZVQeQWa0OnrUmxG55xzpSpeR62M6t1IBYsDHpuccwcD75S5Sm1HNf/76K7qyEm4WrhzriLzFelcVeFxKjH/xOqcc84555xz5chHypxzrpKoEEtaOeeccwl4nErMR8qcc84555xzrhx5p8w555xzzjnnypFPX3TOuUrC//6Lc865iszjVGLeKXPOJdT9gUs55PQu7MnIZOpNw0hPW14oT+fbL6DdBaeQ1Kge/+l4VV56t3svoeXJRwNQo3YStZs15M0f/66smu6cc66SOuqhQTTr15XsjEwWDh7KtrRlhfI06NSOY576A9VrJ7FhXCpf3zkib9+hV57FoVecie3JZsPYVBY9MJKGXTtw9N+uiTJILHnsTdZ/PLOMzsg575Q55xJofXpnGrZrxQcn30LTbh04/v8GMepn9xbK9/2YOXz98hjOmfK3fOlz7h2Zt33kFQNocmzbUm6x82W1nXOVXbN+XajbrhVTTryBRt078uO/XsmMs+8qlO/Hf72KL4cMY8usRXR99U80Pb0LG8fPpfHJx9D8rB5M7XsrlrWHms0aArD9qxVMP+PPWHYOSS2S6fXpX5k4ejaW7XfWA8mvZmKV5p0ySW0lLSjnNtxRnvWXVOy1ktRH0ofl3abiSNpe3m2oatqc2Z2lb00GYOOcJSQ1qkftFsmF8m2cs4Rd6zYXWVbb83qx/L2ppdJO51zlIelGSXXLqe7Bkr6UNLL43HGP9zhVBpqf1ZPVb04EYMvsRdRoWI+kArEpqUUyNerXYcusRQCsfnMiLc7uCUCbywaw/On3saw9AOzesBWAnIysvA5Ytdo1MfN1Al3ZqjSdsgrioOiUVXSSfAS3AqjbqjE7V23M+37nqk3UbdV4n8upd0hT6h/agrWTFx7I5jnnKqcbgXLplAF/AH5iZpeUU/2uBGqlNGbXyr2xadfqjdROaZIvT+2UJuxavWlvnlWbqJUSxa96HVJIPuFHHP/xg/R49y807NIhL1/DbkfQ67O/0WvC3/jy1hd9lMyVqcrWKasuabikhZJGS6ojqYukaZLmS3pXUmMASRMkPSlpYngy1lPSO5IWSXowt0BJv5E0Q9JcSS9Iqh6vYkmPAHVCvpFFHStpqKRZoZ33xZSxXNLDkqaG/d0kjZK0RNK1iU5a0r8knRvz/UhJ50iqLukxSTPD+Rf5Qo+kJpLeC3mnSeoU0tMkJSuyUdJvY+rtn6Cs6pL+Fo6dL+mPIf2e0J4FkoZJUszP42FJnwE3SGoXrsNMSQ/ElJsSfmZzQxm949R9Tbh+s8bvXFTUKbuiKM7buPvx5PDw83rx3UczsBx/6ljarAJ8ucohxILZIU5dI+lCSU+EfTdIWhq2O0iaHLYL3d/D/jkx5XaUNDtBnYOB1sCnkj4NaWeEWDBH0puS6ieqK6SXKLbHqft5oD3wgaSbioiH90oaEnPcAkltC5RVbJwK+fJi1UcZS4r+gbgYhWNToVGtuPEr7KpRnZrJ9Zhx9l18c/+/6TT8xrwsW+csZuppQ5hx5h20u+E8qtWqeSAb7ij/GFWRY1Vl65R1BJ41s2OAzcAvgX8Ct5tZJyAN+EtM/iwzOxV4HngfuA44FhgkqamkHwMXASebWRcgG4j7BM3M/gRkmFkXM7ukmGPvNLMeQCfgtNybfbDCzHoBk4ARwEDgROD+Is77ReByAEmNgJOA/wFXAlvMrCfQE7haUrsiyrkPSA3X6o5w7QCmACcDxwBLgdwAcyIwLUFZ1wDtgK6hvNzpIM+YWU8zOxaoA/ws5phkMzvNzB4H/g4MDW1fE5Pn18CocE07A3MLVmxmw8ysh5n1OL1uxyJO1xV05KD+nD3mIc4e8xAZa9Op27pp3r66rZuwc23R0xTjOfzcE33qonMHnyvMrDvQAxhMFAdy7/29gY2SDgFOIYpXEOf+bmZLgC2SuoQ8lxPFtkLM7ClgFdDXzPpKagbcBfQ3s27ALODmRHXFFFVkbE9Q97UxdT9J4nhYEsXGqVBnXqz6aZ0O8bK4oM3lZ3DiuEc5cdyjZK5Np/Yhe3+MtVOakrkmPV/+Xavyj57Vbt0kL8+uVRtZ99EMALamLsFycqjZtEG+43csWkn2zkzq/+jQ0jol5wqpbJ2yZWaWe/ObDXQg+qD/WUh7BTg1Jv8H4d80YKGZrTazTKKOx6FAP6A7MFPS3PB9+xK2pahjLwxPDlOJOjpHJ2jTdDPbZmbrgV2SCr/QA4TzO0JSC+Bi4G0z2wOcAfw21D8daErUcU3kFOBfoczxQNPQyZtEdN1OBYYCx4VgvMnMEs2h7w88H9qBmeXOI+grabqkNOD0cP653ojZPhl4LWz/KyZ9JnC5pHuB48xsWxHn4/bRNyPG8vGAO/l4wJ2s+GQ27QeeAkDTbh3I2rqz2HfHCmrQIYWkRvXYMMtHLJ07yAyWNI/owduh4au+pAZh+1WimNCbvZ2yRPf3F4nu29WJHla+WsI2nEgUH6eEOHYZcHgxdUHxsb0kEsXDkvA4dYB9//JopvW7nWn9bmf9xzNJuSD6KNeoe0f2bNtJVoHYlLVuM3u276JR9+gjT8oFp7L+k2glxfUfz6TJKdGvS932KVSrWYPdG7dR+7DmqHr0sbh2m2bU65BCxor1ZXWKzlW61RczY7azgbidmDj5cwocm0N0bQS8YmZ/3o+2xD02jFQNAXqaWbqkEUDtfWhTIv8iGon7FXBFTBv+aGajCrShbRFtLsiAiURPGg8D7gTOJxrBmxQnf2xZ+UaIJdUGngN6mNmKELBiz31HnLrzJ5hNlHQq8FPgX5IeM7N9eYLpSmjVuLkc0q8z53z+ONkZWUy9aVjevrPHPMTHA+4EoOtdv6LteSdRo04S5896isWvTSDt8XeAaIGPb99PNJjqDjT/+y/uQJDUh+jBWi8z2ylpAtG9eirRSNfXRPf/K4BewC3F3N/fJpqlMh6YbWZ7XwgqpinAGDO7uED7iosl+xtHC9ZdkAF7yP9Au3ahTB6nStWGsak069eVk6f/neyMLL64YWjevhPHPcq0frcD8NXtL3LMU3+gWu2abBg3lw3jomf2K1/7lGP+3+/p9dnfyMnaw4LBzwHQ+Pgf0faP52J7srEc48s//YPdm7w/faB5nEqssnXKCtoCpEvqbWaTgEuBz4o5JtY44H1JT5rZOklNgAZm9m2C/Lsl1TSz3YmOBRoSdT62SGoJnA1M2L/Ty2cEMANYY2a5KyqMAn4vabyZ7ZZ0JLCyiDImEnXsHghBeYOZbQW2hmkkSWa2NLw/MAS4voiyRgPXSppgZnvC+ee+MbshvBcwEHgrwfFTiDqY/yZmyqikw4GVZjZcUj2gG/s2rcTtg5l3vBI3PbdDBpD64OukPvh63Hy5nTPn3EGlEZAeOmQ/IhqxgihG3B++UoG+RNP2t8TM5Ch0fzezXZJGLGuXLgAAIABJREFUEc20uLKYurcRxcoNRKN0z0o6wswWK1qVsQ2wLlFdB1DceChpOWGqpKRuRNP08/E4Vfq++vNLcdNzO2QAW+ctZeppQwrlsd3ZLLjumULpq9+axOq3inrW7FzpquydMoimOzwfbuZLCe9elYSZfSHpLmC0pGrAbqIRo0SdsmHAfElzwntlhY41s2mSUoGFoT1T9vvM8rd1raQvgfdikl8E2gJzwkvQ64HziijmXuBlSfOBnUTXLtd0IHeRk0nA/wGTiyjrReBIouuxGxhuZs9IGk40pWQ50RSPRG4AXpV0A9FT1lx9gFtDmduB3xZRhnNViq8T5g6QT4geqs0nGhXLHe6eRDT9b6KZZUtaAXwFYGabi7m/jwR+QfTArijDgI8lrQ7vlQ0CXpNUK+y/y8y+2YdYsr/uJX48fJu9rwXMBL6Jc2wfPE45F5fHqcTkf4ehcgidzjSgm5ltKe/2VBQjW//Gf8HdQe2SVf8u8WSP4W3K//f96u9L3l5XdYQVCxuZ2d3l3ZaKaEzLi8r9/65z+2vA2jcOqjgFFTNWVYWRskpP0bL0LwFPeIfMOedcRSLpXaKFt04v77Y451xF5Z2y/SBpOlCrQPKlZpZWyvUeR/6VCAEyzewEokU4ypykM4FHCyQvM7Pzy6M9zlVlPi3EVUTx4kHoqBV8H+v2ggtTHWhhSfxxcXb124cFSJxz+8njVGLeKdsPoRNUHvWmAV2KzViGQgAt1SDqnHOucimvB3eh41Wh4qhzzoF3ylwlt85/w10VYhVuhrxzriS+TqpZ3k1wbr8N2Ie8HqcSq2x/PNo555xzzjnnDireKXPOOeecc865cuSTu5xzrpLwF6idc85VZB6nEvORMuecc84555wrR94pc84555xzzrly5NMXnXMJ9bnvUtr17cLujExG3zKMdQuW59tfo3YSPx06mOTDW2A5OSwdm8rkR94A4LR7LqFNr6MBqFkniTpNGzL0uN+V9SlUKT4txDlXFfW+71IOP70LezIyGXfzMNbHiVVnPT+YRoe3ICc7h+VjU5kaYtWPLujNyXdezPY16QCkjRjDF69PKOMzqDo8TiXmnTLnXFxt+3YmuW0rXj71Flp17cDpDw3i9XPvLZRv9rCP+H7ql1SrWZ2Br91B2z6dWD5hPp/dPzIvT5dBA2h+TNuya7xzzrkq4fC+nUlu14p/976Fll07cNrDg3jrnHsL5Ut94SNWhlh13ut3cFifTnw3YT4Ai/47jYl3/7OMW+5cfj590RUi6V5JQ+Kkt5W0oDTKdhVPhzO68+XbkwFYk7qEWg3rUa9Fcr48e3Zl8f3ULwHI2Z3NugXLqZ/SpFBZR53Ti68/mFr6ja7irAJ8lZcDcX86AG24ozzrPxAkjZA0sBzrP0/S0cXkmSCpxz6Ume93Q9JrkuZLuilB/kGSWsd8/2JxbXLlp90Z3fkqxKq1IVbVjROrVsbEqvVp8WOVK33lHaPKO1YVxTtlrkKQ5KO2FUz9Vo3Ztnpj3vfb12yifqvGCfPXaliX9v27smLKwnzpDQ5pSqPDWhRKd64SOug7ZRXAeUCpdYAktQJOMrNOZvZkgmyDgLxOmZldZWZflFab3A9Tv1Vjtq+KiVWri45VSQ3r0rZ/V76PiUkdzj6eX41+mLOeH+ydNVduvFNWBUm6WdKC8HVjSLtT0teSxgJHxeTtLmmepKnAdTHpx0iaIWlueOLYsYj6EpU9QdLDkj4DbpD0c0nTJaVKGiupZciXJilZkY2SfhvS/yWp/4G+Pi6XCqWYxX++pOrVOPvp60h9eRRbvlufb99R5/Tim49mYDkV9dmUq0SqSxouaaGk0ZLqSOoiaVq4T70rqTHk3X+elDRR0peSekp6R9IiSQ/mFijpNzH3uhckVY9XsaRHgDoh38iijpU0VNKs0M77YspYHu6JU8P+bpJGSVoi6dpEJy2pmqTnQnkfSvpf7miXpH7hnpom6SVJtUL6PZJmhjgwTFLh//Dx6+op6fMQF2ZIaiCptqSXQx2pkvqGvIMkPRNz7IeS+oTt7ZIeCuVMk9RS0knAOcBj4Zp1KKIpF4T6v5HUO5TZVtIkSXPC10lxjhsNtAjl945zfgOBHsDIkKeOYkbmQrsflTQ7xKnjw/6lks4JeapLeixc3/mS/GXa0hTnV7eoWHXmM9cx/+VRbA2xavmYVF456UZeP+MOVkxeQP8n/cflyod3yqoYSd2By4ETgBOBq0Par4CuwC+AnjGHvAwMNrNeBYq6Fvi7mXUhCmDfF1FforIBks3sNDN7HJgMnGhmXYHXgdtCninAycAxwFIgN5CeCEyLU+c14QPNrKnbFxV1OVwBnX/bn0s+fohLPn6IHevSaZDSNG9f/VZN2LF2c9zj+j9yJZuXryH1H6MK7Tvq5yf61MUykqPy/ypnHYFnzewYYDPwS+CfwO1m1glIA/4Skz/LzE4FngfeJ3rwdCwwSFJTST8GLgJODve6bOCSeBWb2Z+ADDPrYmaXFHPsnWbWA+gEnCapU0xRK8L9dhIwAhhIdK+7v4jz/gXQFjgOuAroBSCpdijjIjM7jug98t+HY54xs55mdixQB/hZEeUTyksC3gBuMLPOQH8gg/DALtRxMfBKqLso9YBpoZyJwNVm9jnwAXBruI5Liji+hpkdD9zI3p/pOmCAmXUjuvZPxTnuHGBJKH9SwZ1m9hYwC7gk5MmI0+4JZtYd2AY8CAwAzmfvz+hKYIuZ9SSKeVdLalewrthYNcVj1T457rL+XPTJQ1z0yUPsWJtO/dYxsSolcazq++iVbF62hnkxsWrX5u3kZO0B4ItXP6X5cYV+VO4AKu8YVUFiVVw+ZazqOQV418x2AEh6B/hpSNsZ0j4I/zYi6jR9Fo79F3B22J4K3CmpDfCOmSWKKL3jlR3jjZjtNsAbklKAJGBZSJ8EnAp8CwwFrpF0CLDJzLYXrNDMhgHDAJ487Dc+PLMP5v1zLPP+ORaAdqd3ofNlA/j6g6m06tqBrG072bGucKA7achAajWow5jbXiy0r3H7FGo1qsfq2f6Bw5WJZWY2N2zPBjqQ/x72CvBmTP7c+1EasNDMVgNIWgocSnS/7A7MDANJdYg++JdEvyKOvVDSNUQxOIVout78OG2qb2bbgG2SdklKNrN4nzZPAd40sxxgjaRPQ/pR4Zp8E3P+1wH/D+gr6TagLtAEWAj8t5hzOgpYbWYzAcxsK4CkU4CnQ9pXkr4FjiymrCzgw7A9m6hjsy/eiTm2bdiuCTwjKbcTXFwb9kcW8EnYTgMyzWy3pLSYdpwBdNLed/MaET0wWBZbUGyseuZQj1X7Iu2VsaS9EsWqw0/vQqdBA1j0/lRahli1M06sOuHWKFaNvzV/rKrbIjkvf7szupO+eFXpn4BzcXinrOpJ9HwgXkBQgnTM7FVJ04k6dKMkXWVm4/eh7Fw7YrafBp4wsw/CFJd7Q/pEog8ShwF3Ej2RHEjUWXOlZNn4ubTt25nLJz3OnowsRg8Zlrfvko8fYuTZd1K/VRNOGHweGxet5JL/RTO+5r0yhgVhOeGjzu3FN/8tNJjpSokvNUxmzHY2kJwoY4H8OQWOzSGKjwJeMbM/70db4h4bRkyGAD3NLF3SCCB2VKm4NiWqq8TpYRTrOaCHma2QdG+BNiSSKCYkqn8P+WfkxNax2/bOMctm3z+P5F6b2GNvAtYCnUO9u/axzJKIbXfez8jMcrT33WgBfzSzwlMH3AH37fi5HH56Zy6dHMWqcbfsjVUXffIQb5x1J/VaNaHn4PPYtGglF30cxarcpe87X34GbQd0w7Kz2bV5B2NvfqG8TqVK8DiVmHfKqp6JwAhF7z+IqINzGfBySKsB/Bx4wcw2S9oi6RQzm0zMtB1J7YGlZvZU2O4ExOuUxdaXV3aCtjUCVobty3ITw4eGZkCSmS2VNJnoQ831+3kNXAl9evcrcdNHnn0nEC3+8eRhv0l4/LQn30m4z7kysAVIl9Q7TFW7FPismGNijQPel/Skma2T1ARoYGbfJsi/W1JNM9ud6FigIdHDqC2K3ps9G5iwf6eXZzJwmaRXgOZAH+BV4CugraQjzGwxe88/t3O0QVJ9oodcb5Wgnq+A1pJ6mtlMSQ2Ipi9OJIoP4yUdSfQA7etwrn+QVA04BDi+BHVsI7pO+6MR8H3oIF0GxH3/r5TbADAK+L2k8WEU7UhgZe4MFXfgTbwrfqx646woVu1Ys4lnDo0fq6Y++h+mPvqfUmubcyXlnbIqxszmhCezM0LSi2Y2W9IbwFyiKYKxI1CXAy9J2kkUaHJdBPxG0m5gDQnedwj1JSq7oHuBNyWtJHpXLHZi93T2BthJwP8RfRBxzrmiXAY8L6ku0Tupl5f0QDP7QtJdwOjQsdhNNGqfqFM2DJgvaU54r6zQsWY2TVIq0XTBpUTvzP5QbxNNl1wAfEN0v9xiZrskXU50X60BzASeN7NMScOJpt8tD+nFMrMsSRcBT0uqQ9Qh60806vZ8mMK3BxgU6phCNGUvLbRtTgmqeR0YLmkwMLCY98oKeg54W9IFwKfkn4mxL0YQnU8G4f28ffQi0VTGOYrmrq4nWlXSOecSUqIVapyrDPydMnewu+m7f5f4leTHK8Dv+y370F534Eiqb2bbJTUleuh2spmtKe92uZLxd8rcwez6FQdXnIKKGat8pMw555w7+H0oKZlokaQHvEPmnHMHF++UuQMiPJ0dF2dXPzPbGCfdOXeAVYjHj1VAWOSoVoHkS80srZTrPY5oFdxYmWZ2gpn1OcB1vUv+KeQQ/WmBMlu8QtKzRH8OJdbfzezlg6F851xhHqcS806ZOyBCx6tLebfDOedKm5mdUE71plFG91kzO78s6immDdcdzOU759y+8E6Zq9R2V7gZw84551x+i6rvKe8mOOfKmXfKnHOuksjxhxDOOecqMI9TiVUrPotzzjnnnHPOudLinTLnnHPOOeecK0c+fdE55yqJnPJugHPOOVcEj1OJ+UiZc84555xzzpUjHylzzrlKwv/+i3POuYrM41Ri3ilzziXU795Lad+3C7szMvl4yDDWLlieb3+N2kmcO3QwyYe1wHJyWDw2lYmPvgFAw0OacvZj11CnSQN2bd7BhzcOZfuaTeVwFs455yqzX/zlMo7u25XdGZmMHDKU7xcuT5j3quFDaHZYSx4581YAuvzkBM66cSAtjziEJ869ixVpS8uo1c7l59MXnXNxte/bmcbtWjH8tFsY9ed/MODBQXHzzRz2Ef/odxsjfnInh/Q4knZ9OgHQ585fs+DtyYw46w4+f+pdTrv9wjJsvXPOuarg6D5daN4uhQf73MjrdwzngoeuSpi305k9ydqZmS9t9dcreOnaJ1gy46vSbqpzRTooOmWSbpRUt7zb8UNIulfSkHKsv4+kk4rJM0LSwH0sd3vM9mOSFkp6LEHe8yQdHfP9/ZL670t9ruwcMaA7C9+eDMDq1CXUbliPei2S8+XZsyuL76Z+CUDO7mzWLlhOg1ZNAGjW8RC+nbIQgO8+/4IjBnQvw9ZXTTlYuX85d6BJaitpQXm3oyiSXoyNb67sHHtGD2a+MxGAb1MXU6dBXRo2Ty6UL6luLfpe9VNGPf1OvvS1S1axbunqMmmrqxhxqqLGqoOiUwbcCBzUnbIKoA9QZKfsAPgd0M3Mbk2w/zwgL2iZ2T1mNraU2+T2U4NWjdm6amPe99vWbKJBy8YJ89dqWJcj+nfN64it+/I7jjq7JwAdz+pBrQZ1qJ1cv3Qb7Zxz5cDMrjKzL8q7HVVRcssmbI6JVVvWbKJReDgY66e3XMSnL37E7l1ZZdk850psnzplkt6TNDuMhlwj6UJJT4R9N0haGrY7SJoctu+RNFPSAknDFOkgaU5MuR0lzU5Q52CgNfCppE9D2hmSpkqaI+lNSfUT1RXSJ0h6UtJESV9K6inpHUmLJD1YzDnfLekrSWMkvZY72iWpi6RpkuZLeldS45B+dWjDPElvl3SET9IRksaG4+aEa6Qw+rRAUpqki0LePpI+jDn2GUmDwvZySfeFMtIk/UhSW+Ba4CZJcyX1LqIpp0r6XNLS3FEzSfUljYsp89w47f8AqAdMz21ngf0nAecAj4U2dIgdmQvtfjj8XGdJ6iZplKQlkq6NKefWcH3nS7ovwbW8JpQxa/r2RUVfeJdY9N8nH7P4T5dUvRo/f/o6Zr88ii0r1gMw4cFXOfTEH3HZ/x7k0BN+zLbVm8jJzi7VJld1ORXgy7lSUl3S8PD5Y7SkOkXE4RLFfEm/kTQjxKQXJFWPV7FK9llngqQeYXu7pIdCPJ8mqWWCcvNi1YJtSw7s1apKCoeqQrHqkKMPp9nhLZk/amYZNcolUt4xqiLHqn0dKbvCzLoDPYDBwBQg9wN+b2CjpEOAU4BJIf0ZM+tpZscCdYCfmdkSYIukLiHP5cCIeBWa2VPAKqCvmfWV1Ay4C+hvZt2AWcDNieqKKSrLzE4FngfeB64DjgUGSWoar+5wg/0l0BX4RTjvXP8EbjezTkAa8JeQ/k5oQ2fgS+DKeGXHMRJ4Nhx3ErA61NkF6Az0J+rQpJSgrA3h2gwFhpjZ8nDeT5pZFzObVMSxKUQ/v58Bj4S0XcD5ocy+wOO5Hd5cZnYOkBHKf6NgoWb2OfABcGvIEy8CrTCzXkS/OyOAgcCJwP0QdcaBjsDxRNelu6RT49Q1zMx6mFmPE+p3LOJUXUFdf9ufy/73EJf97yG2r02nYeu9/zUatGrC9nWb4x535iNXkr5sDbNfGpWXtn3dZt773d955Sf/n73zjpOqyNrw84pKkMwiGBCQxVWSRMMKLKMIxjWLigi6pjXgqgRFkphQXP0URAUzYMKMi4KLRBUlJ+OSVldFsiBhCOf741ZDz0z3BJyZHpjz8Ovf3K5b4dTt5r59qk7V7c3UQa8DkL5hc8F2wHGcfZW6RBpZH1hHpM3JdBhy0HxJxwAdgJPMrDGwA+iYpO0p5PxbJ56DgOlBz6cA1ySqNF6rGpSrk6uL4ES07NSO7mMH0n3sQNavWEvFOK2qUL0yv65YmyF/raZHUaNhbfpOG8wto/tTtfYh3PRq38I223GyJa+7L3aVdF44rhFeZSWVC8cvA62JblqxoN00ST2Iwg8rA4uAMcAzwJWSbiO6MR6XSxtOIAqB+yT4BQcCn+XQFkQOAUQ37kVm9hNAGPGqAeye+95NS+BdM9sc8o4JfysAFc1scsj3IjA6HDcII3EVgbLAOHIgXL/DzOxtADPbEtJbAq+Y2Q5ghaTJQAvg1xyqjF37WUSOXV54x8x2Al/Gje4JuD84QDuBw4BqwM95rDsn4j+jsma2AdggaYukikC78JoT8pUlEuop+WxHsWXOS/9mzktRROmRJzemaedT+eq9zzikSR22btjEbwmcspbdLqRkudJ82OOZDOmlK5Vl87rfwIwTbvwrC16fnKWs4zhOLllqZnPD8SygDsl1GHLW/JZAM2BG+C1RGvglUcNm9nOIGMnut0486UAsmmUWcGreuurkxLQR45k2YjwA9dKa0Kpze2a/9yk1m/yRLRs28evKjFr1yciP+GTkRwBUPrwq1z7bgyGXDCh0ux0nO3LtlElqQzRbc6KZbZI0CShF5BBdCXxDNGJ0FXAicLukUsBQoLmZfS+pfygD8CbRqNbHwCwzS+QUJTQF+MjMLs1kX3ZtAcS229kZdxx7n+w6JJgUz5EXgHPNbF4IKWyTizLJ2kmWvp2Ms5ylMp2P9W8HeXe8469NrP2OQFWgmZltk7QsQZv5QU6fkYAHzOzpAmjbycSSj+dyZNqxXDPln2zfnM4H3YbtOtd57H28eMZdlK1emT/ffC6r//M/Ov8rigqa89JHzH91EjVOPIa/9OiAmfHDF9/wUZ8XUtST4kPRXLrsOPlCvCbsIBr4zE3+7PTkRTO7M5ftJ/2tkyDvNtsdP7cnOuzkgS8nzqFeWmP6TH6M9M1bebn7U7vOdR87kEFn3JFt+UbtW3BB/y6UrVye657rwQ9fLeepKx4oaLOLLa5TycnLjaICsDY4ZEcTzVhBNEsxILzmEIW3bTaz9WF2A2CVonVfFwJvQDQbJGkcUYhdTiF+G4BywCpgOvCEpD+a2X8Urdk6nN0jXFna+h1MA56W9ADRtToTGB76tlZSqxAK2AmIjdaVA36SdACRM/O/nBoxs18l/SDpXDN7R1JJoATRtb1O0otEM3+tge7AAUC9kK8UcEqwNTs2AOXz1PvdVAB+CQ5ZGlBzD+uJfY57yjjgHkmjzGxjCB/ZZmYJRzed38+/+7yYMP3FM+4CYOPPa3io5uUJ83w7dgbfjvX4fcdxCoT1QDIdzg0TgHclPWpmv0iqDJQzs+VJ8if9rbPnXXDyizf6Pp8wPZFDtuaHlbueUQYwf9wMX2vmFAny4pR9CFwvaT7RSNH0kD6VaDp/ipntkPQ98DWAma2TNJwofGAZkPlbP4oovG58Dm0PAz6Q9FNYV9YFeCU4JQC9zezbHNrKM2Y2Q9EGFvOA5UTr12I34M7AU8EpXEI0ggbQB/g85F9A7p2QTkQO4ABgG3AR8DbRSNw8osGFHmb2M4Ck14H5wHfsDufLjjHAG4o26bg5h3VlmRkFjJE0E5hL+Hz3gFeB4Yo2b8nT1vsAZjY+rAP4LISbbAQuJ0nIieM4jrNPk0yHc8TMvpTUGxgvaT8i3b2RSLsTkfS3juM4Tn6gZLupFUrj0U6GFcysT8qMyAFJZcOsTBmikbJrzWx2TuWcosFDNS/3mXJnr6bH8pG5DqPuX7Njyr/v/ZeP2pOwb8cp1txS65KU/991nD3lsWWv7lU6BUVTq1IW5yzpbaKFuienyoZcMkzRAyFLEcWfu0PmOI7jOI7jOE6+kTKnzMzOy5wWHLXamZJ7mlmOOxj+HsKW+BMSnDrFzC7L57aeAE7KlPyYmSUOiC4AJN1FFB4Zz2gzu29vqN9xnMTsLHLjfo6zdyHpc6BkpuROZrYgFfY4zr6G61RyitSOQIkctUJqdzXRc68Ko60bC6OdHGy4DygwB6mg688LG1UkZskdx3GcvQAzOz4V7e7wPekcp9iT14dHO47jOI7jOI7jOPlIkZopcxzHcfacnT7a7jiO4xRhXKeS4zNljuM4juM4juM4KcRnyhzHcfYRfPzRcRzHKcq4TiXHZ8ocx3Ecx3Ecx3FSiDtljuM4juM4juM4KcTDF/MBSf8AhpnZplTbUhyQdC7wrZl9mWpb9nXO6HcFddOOZdvmdN7u9jQ/LVqWJU+nF3tQ7uCK7FeiBMtnfMP7fZ7Hdu4OUDjpmjNof1dHBja5jk1rNxai9cWPnak2wHEcJwVc2K8L9dOakL55KyO6PckPi5YmzXvd8O5UOaIa97fvBsCZt11Mo1ObY2ZsWLWekd2eZP0vawvL9GKH61RyfKYsf/gHUCbVRuQFSb/bIZdUIj9s2QPOBeqlqO1iQ902x1KldnUea3M77/V6lrPvuzJhvtdvHMzQ03sxpF1PylQuR/0zdz/mp/whlanTqiHrflhVWGY7jrMPIalX3HFFSTfkY921JC3Mr/qc1FCvTWOq1q7O3W1u4ZVew7nkvr8lzXts++PYumlLhrQJw8bwwOk9GHhGTxZ+PJvTb7mgoE12nITsU06ZpHckzZK0SNK1ki6W9Eg4d4ukJeG4jqRp4bivpBmSFkoapog6kmbH1VtX0qwkbXYFDgUmSpoY0tpJ+kzSbEmjJZVN1lZInyTpUUlTJH0lqYWktyR9J+neHPp8haT5kuZJGhHSakqaENInSDoipL8g6ZFg54OS+kt6LrS/JPQlVu/lkr6QNFfS0zEHTNJGSQMkfQ6cmMSmFpI+DTZ9IamcpFKSnpe0QNIcSWkhbxdJQ+LKvi+pTVxb94V6pkuqJunPwF+BQcG2OtldH2fPObpdM+a+NRWAH+b8h1LlylC2asUs+bZu3AzAfvuXYP8D9s+wivf0Pp0Y98ArmC/tdRxnz+gVd1wRyDenzNk3aNSuBV+8NQWAZXO+o3S5gyifQKsOLFOSk68+kw8Hv5UhfUvQMICSZUph5nrlpIZ9yikDrjKzZkBzoCvwCdAqnGsFrJZ0GNASmBrSh5hZCzNrAJQGzjKzxcB6SY1DniuBFxI1aGaPAz8CaWaWJukPQG+grZk1BWYCtyVrK66qdDNrDTwFvAvcCDQAukiqkqhtSfWBu4CTzexY4JZYO8BLZtYIGAU8HlfsqGDb7eH90UB74Dign6QDJB0DdABOMrPGwA6gY8h/ELDQzI43s2kJbDoQeA24JdjUFtgc+oOZNQQuBV6UVCpRv+I4CJge6pkCXGNmnwLvAd3NrHH4rJwCoHy1yqz/cfWu97/+vIby1SslzHvFSz3pOetJtv62hUVjPwfgT22b8uuKNaz46r+FYq8TPf8l1S/H2VMSDKwOBEqHAbhRwECgTng/KJTpHgY750u6O6TVCgOcw0Nd4yWVDueahYG+zwi6FFdmahhMnR0GAJHUJgxcviHpa0mj4gZUEw1AlpA0KM6m6wr3KhY/KlarxNo4rVr382oqVq+cJd9Zt3dgwjPvk74lPcu5s7t14J5Pn6D5OS351yOvF6i9xZ1Ua1RR1qp9zSnrKmkeMB2oEV5lJZULxy8DrYkctJhTlibpc0kLgJOB+iH9GeDKMEPUIZTNDScQhdZ9Imku0BmomUNbEDkaAAuARWb2k5ltBZYE2xNxMvCGma0CMLM1If3EOHtHEDmhMUab2Y649/8ys62hjl+AasApQDNgRujDKcCRIf8O4M1s+v8n4CczmxFs+tXMtgcbRoS0r4HlRA5idqQD74fjWUCtHPIDEMR8pqSZszf8JzdFnAREPzsykmwE8aUrHmTQcTdS4sD9OfLP9Tmg1IH85aZz+PiRNwrYSsdx9iEyD6wOAjaHAbiOwB3A4vC+u6R2QF2iQcXGQDNJrUNddYFkrh/0AAAgAElEQVQnzKw+sA6IxaQ9D3Q1s8yRHr8Ap4bB1A5kHMxsQrRMoR6RFp6UzQDk34D1ZtYCaAFcI6l2os7Ga9WiDT6+uMckEKvMWnVYvZpUrVmd+eNmJKxizMOv0efPNzLz3Wm07nxagZjpODmxz2z0EULe2gInmtkmSZOAUsBnRDNd3xA5YlcROS23h5maoUBzM/teUv9QBiLHox/wMTDLzHYPw+RgCvCRmV2ayb7s2gLYGv7ujDuOvU/2OYncPfIhPs9vmc7Ft7UjtCXgRTO7M0FdWzI5dbm1KcFPfAC2k3FwIP6abLPdd9aYbTliZsOAYQB9a3UsmsMhRZTjOp1Ks0vTAPjfvCVUOHT3JG356pXZsGJd0rLbt27jm3/P5uhTm7Fh5XoqHl6VGz54YFfZ69+/j2Hn9mXjyvUF24lijH/Znb2crpLOC8c1iByr7GgXXnPC+7KhzH+BpWY2N6TPAmpJqgBUNLPJIX0EcHo4PgAYEiJkdpBx0PALM/sBIAxU1gLWk2kAMpxvBzSSdGEoWyHYlGXniXituqlWB//vmwdad2rHny89BYDl8xZTKU6rKlavwvoVGTfqqN30KI5oWJu7pw1mvxIlKFelAre82pfHLhmQId+Md6fx9+fuYOyjowu+E8UU/6InZ59xyohufGuDQ3Y00YwVRGFvA8JrDpBGNPK2XlIs6HiVonVfFwJvAJjZFknjgCeJRr6yYwNQDlhFNEv3hKQ/mtl/JJUBDicahUvY1u9gAvC2pEfNbLWkymG27FPgEiLB6QhkCTPMRb3vhnp/kVQZKGdmy3NR9mvgUEktzGxGmKXcTPQ5dAQ+lnQUcASRo1weuEHSfsBhRCOeORG73k4+88WIj/hixEcAHJXWmOM7t2PBe59xeJM/smXDZjauzOiUHVimJAceVJqNK9exX4n9qJvWmOVffM0v33zPQ813L/24ddr/8fTZvX33RcdxEpLNwGq2xYAHzOzpTHXVIuuAY2myH8i8FVgBHEs0UBi/G0SywctkA5A3m9m4HGx3fgdTRoxnyojxANRPa0Lrzu2Z9d6n1GpSl80bNvFrJq2aNvIjpo2MtK3y4VW5/tmeuxyyqrWqs3LZzwA0atucFYv/V4g9cZzd7EtO2YfA9ZLmE/3Ynx7SpxKNuE0xsx2SvidyHDCzdZKGE4UMLgMyz2uPAs4HxufQ9jDgA0k/hXVlXYBXJJUM53ub2bc5tJVnzGyRpPuAyZJ2EDmdXYjCPp6T1B1YSTRTmJd6v5TUGxgfnKVtRLH3OTplZpYuqQMwOMTwbyYS2qHAUyF0czvQxcy2SvqEaARxAbAQmJ2k6nheBYYr2pjkQl9XVjB8O3EuddMa84/Jj0Rb4nff/bvn72Pv58kzenFAmZJ0fOY2Shx4APuV2I8lny5i5qgJKbTacZy9lGQDq9skHWBm28g6IDcOuEfSKDPbqGjN+LZkDQTNXy+pZVgT3THudAXgBzPbKakzkNPuwskGIMcBf5f0sZltC4OQ/zOzzFEqTj6xaOIc6qc1od/kx9i2OZ2R3Z/cde6OsQ8y8Iye2ZY/p+dlHHzkodjOnaz53ypevWt4QZvsOAmR7zKTHEndgApm1ifVtjh7hocvOns7A5aNShb6m4VutS5N+ff94WWv5Npex4kRBjHfIYqY+AaoCvQnCi/8KzDbzDpKehloBHwQ1pXdAlwdqtkIXE40m/V+2FQrpuVlzay/pGbAc8AmIgfqQjNrIKku0bKFTcBEotmusmEGr5uZnRXqGgLMNLMXJLUABhPNwsUGIDcB9wJnE82arQTONbNs47Y9fNHZmxmy7LW9SqegaGrVvjRTlq9IehuoQ7SZhuM4jlPAhFDp14jW7CwDLjazhE9xlVQe+Ap428xuKiwbnYIhbGx1eoJTk4Cecfkuy1TuMeCxBOUaxOV5OO54FlGIYoz+If07Imcvxp0hfVKwIVb+prjjGeye0YunFxm38nccZx+ioLRqX9t9Md8ws/PMrFFsZ0OIHDVFW/HGv9oXtC2SqiRod66SbJVfWKTqejiOk5hUbzGcD9sM3wFMMLO6RGtb78gm7z3A5GzOO47jOEWMVGtUUdYqnynLA2Z2Xs65CqTd1UTb/RYpUnU9HMfZZzkHaBOOXyTTLEmMEIJWjWgtcfNCss1xHMdxoIC0ymfKHMdxnHwj/tlL4XVtHopXM7OfAMLfgxPUvx/wT6B7/ljsOI7jFDeKolb5TJnjOM4+QlFYPR3/7KVESPo3UD3Bqbty2cQNwNjwvMc9sNBxHMdJFUVBp6BoapU7Zc4+zXqye8614ziFjZm1TXZO0gpJh5jZT5IOYffzHeM5EWgl6QaihwUfKGmjmWUX0+84RZp8WOPiOE4+kgqtcqfMcRxnH2Fnqg34/bwHdAYGhr/vZs5gZrueLRWeCdncHTLHcZy9g31Ap6CAtMrXlDmO4zhFhYHAqZK+A04N75HUXNIzKbXMcRzHcSIKRKt8psxxHMcpEoSdZk9JkD6T3Q8Ijk9/AXihwA1zHMdxnEBBaZU7ZY7jOPsI5utSHMdxnCKM61RyPHzRcRzHcRzHcRwnhbhT5jhOUs7v15nek/6Pnh88yOH1a2Wb9+rh3bhj3KBd7xufcTx3jB/Eo0tepkbDIwvYUsdxHKe4clG/K+k/6XHu+mAQNerXzjbv9cN70Hvcw1nS215zNkOXvc5BlcoVlJmOky3ulDmOk5B6bRpTtfYh3NvmH7zaazgX3ZclTHoXjdq3IH3T1gxpP33zPc9d/wiLv/i6oE11AjuLwMtxHKcwqd+mCQfXrk7/Nl0Z1WsYl2SjVY3bH8fWTVuypFc6pApHt2rI6h9WFqSpDqnXqKKsVe6UxSHpH5LKpNqOGJKWSfpDHvK3kfTngrQph/Z7FVI7jSWdURhtFWcatGvOjLemALB8zn8oXa4M5atWzJLvwDIlSbv6TMYNfitD+orFP/LLkp8KxVbHKepIqiVpYYptKJR7tBMhqYukQ1Ntx75Oo3bN+Txo1bI531Gm3EEJtapkmZKcfPVZfDD4zSznLujTmbcfGEXRebSxUxxxpywj/wCKjFO2B7QBUuaUAXkWfEkl9qCdxoA7ZQVMxWqVWffj6l3v1/+8hgrVK2fJd+btHZj4zL/YtiW9MM1zErATS/nLKdLsdU6ZpN+1IZkiUvVbpwvgTlkBU7FaZdb+uGrX+7U/r6ZiAq066/ZLmPDMGNIzaVXDts1Yv2IN//tqeYHb6hQNnSqqWlUknTJJ70iaJWmRpGslXSzpkXDuFklLwnEdSdPCcV9JMyQtlDQs3IjrSJodV29dSbOStNmV6OY5UdLEkNZO0meSZksaLalssrZC+iRJj0qaIukrSS0kvSXpO0n3ZtPfgyT9S9K8UGeHuNM3h/YXSDo65K8crtF8SdMlNZJUC7geuFXSXEmtJF0U6psnaUo27ZeS9HxoY46ktJDeJdj/YejDQ9nUMRAoHdoelehzjMu7UdIASZ8DJ0o6Q9LXkqZJelzS+3HX5blwredIOkfSgcAAoENoq0MCW66VNFPSzIUbFicz2ckJZU0yy3gjO6xeTf5Qsxrzx80oJKMcZ6+mhKTh4Z44XlLpMPM/PdzP35ZUCXKvJ5Iul/RFuB8+nWygK8k9OmFZSU+Ge+giSXfH1bFM0v1BF2dKaippnKTFkq7PruOSegSNmRdsiUU9JOv7/ZImA7dIeiFow6eSlki6MK7e7kEj5sdsVTQr+ZWkocBsoEYSm04L+jpP0oSQlkVfQ3p/Sd3iyi4M7cTayvy5Xgg0B0aF61s6Qfu7tOrLDUuyu3xONkhZxSqzVh1eryYH16zOvExadUCpAzntpvMZ88hrBWqj4+SGIumUAVeZWTOiG1pX4BOgVTjXClgt6TCgJTA1pA8xsxZm1gAoDZxlZouB9ZIahzxXkuQ5AWb2OPAjkGZmaYrCBnsDbc2sKTATuC1ZW3FVpZtZa+Apoid83wg0ALpIqpKkv6cBP5rZsaHOD+POrQrtPwnEBOFuYI6ZNSIa+XzJzJaFNh81s8ZmNhXoC7Q3s2OBvyZpm2AjZtYQuBR4UVKpcK4x0AFoSOQIJRS38JTyzaHt2FPMM3yOcf0/CFhoZscTXdengdPNrCVQNa7au4CPzawFkAYMAg4I/XottJXlTmpmw8ysuZk1b1CuTjbddjLTslM7uo8dSPexA1m/Yi0VD939la1QvTK/rlibIX+tpkdRo2Ft+k4bzC2j+1O19iHc9GrfwjbbcfYW6gJPmFl9YB1wAfAS0DPczxcA/eLyZ6snko4huj+fZGaNgR1ARxKQ+R6dQ9m7zKw50Aj4S8wxCXxvZicSae8LwIXACUSDZQmRdDpwLnB80KPYAF92fa9oZn8xs3+G94cQaf5Z7H5Qa7twTY8j0qpmklqH/H8i0sYmZpZlCkRSVWA4cEGw6aJwKou+JutXHFk+VzN7g0jfOoZrvjlzoXitqlfON0PKC607tefOsQ9x59iHWLdiLZUO3b3So1L1KqzPpFW1g1bdM20It48ewMG1D+Ufr/ajas1q/OHwg7nrg0HcM20IFatX4c73H6R81QqF3SXHKbLPKesq6bxwXCO8ykoqF45fBloTOWixhSxpknoQhR9WBhYBY4BngCsl3UYkQMfl0oYTgHrAJ2EU5kDgsxzaAngv/F0ALDKznwAUze7VAHbHg+1mAfCwpAeB94NDFSPWv1nA+eG4JZGYY2YfB3FOdAf5BHhB0utx9SSiJTA41Pe1pOXAUeHcBDNbH/rwJVAT+D6buuLJ/DnWJer/DiAW1H00sMTMlob3rwCxWbV2wF/jRidLAUfksm1nD5g2YjzTRowHoF5aE1p1bs/s9z6lZpM/smXDJn5duS5D/k9GfsQnIz8CoPLhVbn22R4MuSTpbzOngCmaARlOHEvNbG44ngXUIXI+Joe0F4HRcflz0pOWQDNgRtCp0sAvubTllGzKXqwoumF/ImeoHjA/gU1lzWwDsEHSFkkVzSzjTSKiLfC8mW0CMLM1QbOy63vmAbd3zGwn8KWkaiGtXXjNCe/LEunMf4HlZjY9m/6fAEyJaY+ZrQnpudXXeDJ/rrVyyO/8TqaMGMeUEeMAaJDWhL90Po2Z731CrSZ12ZxAq6aO/IipcVp1w7M9+b9Lokngns2v2ZXvnmlDGHj2nfy2dkMh9aT44TqVnCLnlElqQ3QDP9HMNkmaRPRj/DOima5viEborgJOBG4PszpDgeZm9r2k/qEMRD/++wEfA7PCU7hzZQrwkZldmsm+7NoCiG1BtzPuOPY+4fU2s28lNSNaJ/WApPFmFvtlG6tjR1z5BIFlWb/nZna9pOOBM4G5khon6X+i+jL3J7MN2ZLN5wiwxcx25KJtEY04fpOp7uNzY4Pz+/hy4hzqpTWmz+THSN+8lZe7P7XrXPexAxl0xh3Zlm/UvgUX9O9C2crlue65Hvzw1XKeuuKBgjbbcYoyme+nWXcjSJw/mZ4IeNHM7twDWxKWlVSbKCqjhZmtlfQCv1PjQlt5/S32W6b38W0p7u8DZvZ0hsaicP7M5XNrUzJ93U7G6KJE1wSizzVLqKJTcCycOIf6aU25e/LjpG9OZ0T3obvO3Tn2IR44o0cKrXOc3FPknDKgArA2/JA/mmg0C2AKUXjEAKJRsTSiUIz1kmLCtkrRuq8LgTcAzGyLpHFE4X9/y6HtDUA5YBUwHXhC0h/N7D+KdmU8nN0jiVna2lMU7c60xsxGStpItDg4O6YQhZncE5yfVWb2q6QNQPm4euuY2efA55LOJvlMXay+jyUdRTQb9Q3QNI9d2SbpADPbRvLPMTNfA0dKqhVCMOPXiI0jWlN3s5mZpCZmNofdn5NTwLzR9/mE6YkcsjU/rGRg++673s8fN8PXmhUyRXXxspOU9cBaSa1ChEQnYHIOZeKZALwr6VEz+0VSZaBconC9QPw9OmFZIg35jSj0vxpwOjBpz7q3i/FAX0kvB02oHGbLfk/fIdKIeySNMrONYVnDtlyW/YxI42ub2dKYTSTX12WEpQqSmgLZPwwrwrWqkHit77MJ0xM5ZGt+WMm97bslyA19Wt6Ur3Y5WXGdSk5RdMo+BK6XNJ/IMYiFH0wlciqmmNkOSd8T/aDHzNZJGk4UTrEMyPxLcBRR6N/4HNoeBnwg6aewrqwL8IqkkuF87zCrlV1be0JDYJCknUSC8vcc8vcHng/XaBPQOaSPAd6QdA5wM9GmH3WJRv4mAPOS1DcUeErSAqLRwC5mtjXR4tkcGAbMV7S5ylUk/hwzYGabJd0AfChpFfBF3Ol7gP8LdYroep8FTATukDSXaJTUV+g6jrO30pno/lsGWEIUEZIrzOxLSb2B8Yp2GNxGtO4smVO26x4d1pVlKWtm0yXNIQrLX0IUBv+7MLMPw9rumZLSgbFE67X2uO+h3vFhbdxnQa82ApcTzVblVHZlCNF8K/T/F+BUkuvrm8AVQXdmAN/mwsQXiPq3mShqJMu6MsdxnBgy2/c91rAmqYKZ9Um1LU5WJJUNo5wCngC+M7NH86PuW2pdsu9/wZ19mseWvZrr0ZHral2U8u/708tG53k0x3GKOzfUujjl/3cdZ08Zuuz1vUqnoGhqVVGcKctXJL1NtJj65FTb4iTlGkmdiTZTmUO0G6PjOHlkZ6oNcBzHcZxscJ1Kzj7vlJnZeZnTgqOWOR68p5mNK0hbFG0JPyHBqVPysAHJ72m/PfBgpuSlia5RDvV8DpTMlNzJzBbsiV1hVixfZsYcx3GKO/l9j85Duw2BEZmSt4bHn6SMVF0Px3GcvLDPO2WJyKsTko/triZ6lkpKCE7n73Y8Uy2wjuM4TnJSdY8OTk7KNC4ZrlmO4+wNFEunzCk+mO/y4xQj/PvuOHsn/j/XKS64TiVnv5yzOI7jOI7jOI7jOAWFz5Q5juPsI/gCasdxHKco4zqVHJ8pcxzHcRzHcRzHSSHulDmO4ziO4ziO46QQD190HMfZR/AF1I7jOE5RxnUqOe6UOY6TlAv6daFeWhPSN29lVLcn+WHR0qR5rxnenSpHVGNg+24AnHNnRxq0bcb29O2s+u8KXu7+JJt/3VRYpjuO4zjFhIv7XUn9oFUvdRvK99lo1d+H9+APRxzMPUGrYrS95mwuuKsT3Zr8jd/Wbihokx0nCx6+6DhOQuq1aUzV2tW5p80tvNZrOBff97ekeRu1P46tm7ZkSPtm2gIeaNeNB0/vwcqlP3HqDecWtMnFnp1F4OU4jlOY1G/ThINrV6dfm6683GsYl953ddK8jRNoFUClQ6pwTKuGrP5hZUGa6pB6jSrKWuVOWUDSPySVSbUdMSQtk/SHPORvI+nP+WzDK5LmS7pV0tGS5kqaI6lOgrwVJd2Qn+1nY1e+99XJSsN2LfjirSkALJvzHaXLHUT5qhWz5DuwTEnSrj6T8YPfypD+9dT57Nyxc1f5itWrFLzRjlOASKolaWGKbeiVyvYzI2ljHvOfK6lePrZfUtK/gz51kNRK0qLwvnSC/LUkXZZf7edgW7721UnMse2aMz1o1dI531EmiVaVLFOSU64+i7GD38xy7sI+nXnrgVH4E+OcVOJO2W7+ARQZp2wPaAPkm6MiqTrwZzNrZGaPAucC75pZEzNbnKBIRSBPTpki9uQ72IZ87KuTmArVKrHux9W73q/7eTUVqlfOku/M2zsw8Zn3Sd+SnrSuEy5K48tJcwrETscpZhQpp2wPOBfIT0elCXCAmTU2s9eAjsDD4f3mBPlrAXlyyiSV2EPb8ruvTgIqVqvM2h9X7Xq/9ufVVEygVWfffgn/fmZMFq1q1LYZ61as4X9fLS9wWx0nO4qcUybpHUmzwkjXtZIulvRIOHeLpCXhuI6kaeG4r6QZkhZKGhZ+7NeRNDuu3rqSZiVpsytwKDBR0sSQ1k7SZ5JmSxotqWyytkL6JEmPSpoi6StJLSS9Jek7Sfdm09+DJP1L0rxQZ4e40zeH9hdIOjrkrxyu0XxJ0yU1klQLuB64NYwOtpJ0UahvnqQp2bRfStLzoY05ktLCqfHAwaG+fkRO69Wx65OAgUCdkH+QpLKSJsTZf05or1a4PkOB2UANSX+T9G24hsMlDQl5q0p6M1zvGZJOStTXBH26VtJMSTMXbkjkPzq5IXy1M2IZRxEPq1eTqjWrM3/cjKT1tLvxPHbs2MHMd6blt4lOJnaapfxVDCgR7lOLJI2XVFpS43A/ni/pbUmVIPe6IOlySV+Ee9rTyZwASQOB0iHfqOzKSnoy3AcXSbo7ro5lku4P+jZTUlNJ4yQtlnR9sk5LOiT0Y27QllZx5+4LWjNdUrWQVjNowPzw9whFEQ5/BQaFeupI6irpy5Dv1WzaT6R9BwMjgcahvuuAi4G+seuTgIFAq5D/1qBJU4NWzQ42xiIyJkp6GVgQ0vpI+lrSR4oiSbqF9DqSPlT022WqosiSLH1N0KddWvXlhiXJuu7kRC606vCgVfMyadUBpQ7ktJvOZ8wjrxWkhU4cqdaooqxVRXGjj6vMbI2isIMZQHugezjXClgt6TCgJTA1pA8xswEAkkYAZ5nZGEnrJTU2s7nAlcALiRo0s8cl3QakmdkqRWGDvYG2ZvabpJ7AbcCARG0BY0JV6WbWWtItwLtAM2ANsFjSo2a2mqycBvxoZmeGOivEnVtlZk0VhQV2A64G7gbmmNm5kk4GXjKzxpKeAjaa2cOhngVAezP7n6Ss8/i7uTFcg4aKHL/xko4iEpP3zaxxqE/x9SfgDqBBXP79gfPM7NdwPadLei/k/RNwpZndIOlQoA/QFNgAfAzMC/keAx41s2mSjgDGmdkxmfuaGTMbBgwD6FqrQ9H8n1dEadWpHSdeegoA/523mIqH7g45rFi9CutXrM2Qv3bTo6jRsDb9pg2mRIkSlK1SgZtf7cvgSwYAcNwFral/SlOGXHZP4XXCcQqWusClZnaNpNeBC4AewM1mNlnSACA2kAU56AJwMNABOMnMtikasOoIvJS5YTO7Q9JNcffZY7Ipe1fQ0hLABEmNzGx+qOp7MzsxtP8CcBJQClgEPJWk35cR3YPvC3XGIksOAqab2V2SHgKuAe4FhhDp04uSrgIeD7r1HpG2vBH6cAdQ28y25qBVybTvaqCbmZ0V6jsxvv4E3JEpfxngVDPbIqku8ArQPOQ9jkjXlkpqTvRZNyH67TQbiA30DgOuN7PvJB0PDDWzkzP3NTPxWvX3Whe7VuWBv3Rqz0lBq5bPW0ylQ/8AfANApepVWJdJq45sehRHNKzNvdOGsF+JEpSrUoFbX+3Ha/2e4w+HH0zvDwYBkc71ev9BHjz3Tn5dub5Q++Q4RdEp6yrpvHBcI7zKSioXjl8GWhM5aLFFLGmSehCJRGUiYRkDPANcGRyuDkQ32NxwAlHIwSeRL8KBwGc5tAUQczoWAIvM7CcARbN7NYBETtkC4GFJDxLdvKfGnYv1bxZwfjhuSSQMmNnHkqpkcuRifAK8EH40vJXgfIyWwOBQ39eSlgNHAb9mUyY3CLhfUmuiNZWHAdXCueVmNj0cHwdMNrM1AJJGh/YB2gL1tHsUrHz4HjgFxNQR45k6YjwA9dKa0Lpze2a/9ym1mtRly4ZN/LpyXYb800Z+xLSRHwFQ+fCqXPtsz10O2TF/OZa215/D4x36sy2b0EbH2ctYGgb6ILo31wEqmtnkkPYiMDouf0660JLIUZsR7nWlgV9yacsp2ZS9WNK1RDp/CJGmxZyyeJvKmtkGYIOkLZIqmlnG/+gRM4DnJB0AvBN3DdKB9+Oux6nh+ER269YI4KEkfZgPjJL0DvBONn3NrfbllQOAIZIaAzvYrT8AX5hZbBu/lkQh/JsBJI0Jf8sShdOPjtOqkvlgl5MNk0eMY/KIcQA0SGtCm86nMfO9T6jdpC6bE2jVlJEfMSVOq258tiePXhJNIPdofs2ufPdOG8IDZ9/puy86KaFIOWWS2hD9ED/RzDZJmkQ0evcZ0UzXN0SzY1cR3fBvl1QKGAo0N7PvJfUPZQDeJBqx/BiYlWSmKqEpwEdmdmkm+7JrC2Br+Lsz7jj2PuG1NrNvJTUDzgAekDQ+NhMXV8eOuPIJ5umzrkw1s+vDiN2ZwNwwY5io/4nqyw86AlWBZmEEdxm7r9VvuWx/P6LvQoZ1AUoUquDkO19OnEP9tCb0nfwY6ZvTGdX9yV3neox9kIfO6Jlt+Qvvvor9D9yfG0b2BqLNPl6/65kCtbm440PthUL8vX0H0Xra3ORPpgsCXjSzO/fAloRlJdUmiq5oYWZrJb3A79eqKWGQ7UxghKRBZvYSsM1sVyzQjmTlSf71PJNooPWvQB9J9c1se4J8udK+PeBWYAVwLJHmxG/Nlxut2g9YF5u9dAqfhRPn0CCtKQMmP0765nRe6j5017leYx/i/jN6pNA6JzOuU8kpUk4ZUAFYGxyyo4lmrACmEIUODgDmAGnAZjNbHxfusCqMWF0IvAEQwhHGAU8CyffzjtgAlANWAdOBJyT90cz+E8IbDmf3CGSWtvaUEL63xsxGKtrFqksORaYQOTz3BCd2VQgR3ACUj6u3jpl9Dnwu6WySz9TF6vs4hC0eQeT8HpLHrsSuX4wKwC/BIUsDaiYp9wXwqKI1GBuIRkIXhHPjgZuAQaFPsVDUDH11Co7RfZ9LmJ7IIVvzw8pdzygDuKfNLQVml+MUIdYDayW1CpEOnYDJOZSJZwLwbghx/0VSZaCcmSXbdWCbpAPMbFuyskT3x9+A9YrWeJ0OTNqz7kVIqgn8z8yGSzqIKOQ8S4hlHJ8ClxDNknUEYotKd2mFoo2eapjZREVrxC8DygKJZuqSaV9eu5JIq34ws52SOgPJNvWYBjwt6QGi305nAsODDUslXWRmo0OofyMzm5egLaeAeLXvswnTEzlka35YmeUZZTF6t7wpX+1ynLxQ1JyyD4HrJc0ncgxiIW5TiZyKKWa2Q2LG2asAACAASURBVNL3wNcAZrZO0nCiH/LLiEIs4hlFFEIxPoe2hwEfSPrJzNIkdQFekRQLQ+gdZrWya2tPaEi0EHgnsA34ew75+wPPh2u0Cegc0scAbyjaUONmoo0w6hKN7k1g9zqtzAwFnlK0Bm070CXE9uepE2a2WtIniraL/gB4EBgjaSYwl/B5JSj3P0n3A58DPwJfEv3IAehK5BzPJ/quTiHa5CNDXzOFfDpOsWWnj0Gmis5E99EywBKiyI5cYWZfSupNtJ53PyIduBFI5pQNA+ZLmm1mHROVNbPpkuYQhdcvIQpn/720AbpL2gZsBK7IIX9XonDH7sBKdl+TV4HhijbYugR4NoQhimgNcSKHDJJrX16ZD2yXNI9oPd1Q4E1JFwETyTg7tgszmxHWiM0j+mxmslurOgJPhs/igNDHeZn6emGSnYsdp1jhOpUcWRHdgSS/ULQ7UgUz65NqW5zESCprZhsVbQ7yNvCcmb2dH3X7Rh/O3s7jy17L9QjJZTXPS/n3/eXlb3t8sbNPEqdVZYgGCa81s9k5lcsNvtGHszfz5LLX9yqdgqKpVUVtpixfkfQ20SLsk1Nti5Mt/SW1JVrzMJ7sF3s7juM4TioYpuhh0KWI1vLli0PmOI4D+7hTZmbnZU4LjlrtTMk9zWxcQdoiqQpRGGFmTsnDBiS/p/32RCGF8SxNdI1yqCff+2FmiYO7HcfJE+ZhIfsMkj4n6y5+ncxsQaL8+dhuQ6J1YPFsNbPjC7LduPavBDIvSP3EzG7MYz353g8zy9NDpx3HyYrrVHL2aacsEXl1QvKx3dVAynZnCk7n73Y8U90Px3Gc4kBhOUEJ2l1AarXqeeD5fKgnpf1wHMfJK8XOKXOKF5vYkWoTHKfQ2JlqAxzH2SN88wOnuOA6lZz9Um2A4ziO4ziO4zhOccadMsdxHMdxHMdxnBTi4YuO4zj7CB4C5TiO4xRlXKeS4zNljuM4juM4juM4KcRnyhzHcfYRfKthx3EcpyjjOpUcd8ocx0nKpf2uomFaE9I3p/NctyH8d9HSpHlvGt6TqkdUo1/723alndz5dE6+4jR27NjJgo9n8cbAkYVhtuM4jlOM6NDvShqkNSV981Ze6PYE32ejVTcM78kfjjiYAe1vB+Csf1xEy0vasnHNrwC889DLLJw0p1Dsdpx43ClzHCchDds04eDah9Crzc0c2aQul993Lfefe2fCvE3bH8/WTVsypP3pxPo0PrUF/U+/ne3p2ylXpXxhmO04juMUIxoErerT5mZqN6lLx/uuYeC5vRLmbdL+uCxaBTDh2ff5aPiYgjbVcbLF15Q5RRpJAyS1TbUdxZHG7Vrw2VuTAFgy5zvKlCtDhaoVs+QrWaYUp159Fu8PfjNDepuO7fngybfZnr4dgA2rfy1wm4s7O4vAy3H2FEm1JC1MsQ0XSfpK0sRU2uHknmPbtWD6W5MBWDrnO0qXO4jySbSq7dVnMzaTVjmFS6o1qihrlc+UOUUaM+ubahuKKxWrVWHNj6t3vV/78xoqVq/C+pXrMuQ79/ZLGP/MGNK3bM2QXu3IQ6h73DGc1/0ytm1NZ/R9L7Fs/uJCsd1xHGcP+Rtwg5nlyimTtL+ZbS9gm5xsqFitcgatWvfzaipVr8yvmbTqr7d34KMEWgXQpvNpnHD+X1i+YDFv3PsSm379rcDtdpzM+EyZA4CkdyTNkrRI0rWSLpb0SDh3i6Ql4biOpGnhuK+kGZIWShqmiDqSZsfVW1fSrCRtHifprXB8jqTNkg6UVCquvRckXRiOl0m6W9JsSQskHV2wV6V4IyVItIwLdGvUq8XBNaszZ9wXWbKWKFGCg8qX5f5z7+SN+0dw3RO3ZcnjOI6TiRKShgctGi+ptKTGkqZLmi/pbUmVACRNkvSopClhdquFpLckfSfp3liFki6X9IWkuZKellQiUcOS+gItgackDQpa9HzQmzmS0kK+LpJGSxoDjJfURtJkSa9L+lbSQEkdQ5sLJNUphOtWbFECscokVRwetGpuAq2aPHI8vVvfzL1ndGf9L+u4sPcVBWWq42SLO2VOjKvMrBnQHOgKfAK0CudaAaslHUYkWFND+hAza2FmDYDSwFlmthhYL6lxyHMl8EKSNmcDTeLaWAi0AI4HPk9SZpWZNQWeBLolyhCcypmSZn69YUkO3XbiSet0Gn3HDqLv2EGsW7GWyodW2XWuUvXKrFuxJkP+Ok2PombDIxk4bSg9R99LtdqH0P3VuwFY+/NqZo+LPsal8/6D7TTKVvZ1ZQWJmaX85Ti/k7rAE2ZWH1gHXAC8BPQ0s0bAAqBfXP50M2sNPAW8C9wINAC6SKoi6RigA3CSmTUGdgAdEzVsZgOAmUBHM+se6sLMGgKXAi9KKhWynwh0NrOTw/tjgVuAhkAn4CgzOw54Brg5UXvxWvWVa1WeaNOpPb3HDqL32EGsW7Emg1ZVrF4li1Yd2fQojmh4JPdNe4Luo++hWu1Due3V/gBsWLUe27kTM2Paq/+m1rF/LMyuFDtSrVFFWas8fNGJ0VXSeeG4RniVlVQuHL8MtCZynt4K+dIk9QDKAJWBRcAYIhG6UtJtRGJ4XKIGzWy7pP8E0TwOeCS0UYLdjl9mYm3PAs5PUu8wYBjA1bUuLJr/84ooE0d8yMQRHwLQMK0pJ3c+nS/e+4Qjm9Rl84ZNWUIXJ40cz6SR4wGocnhVuj57J4MuiX4vzRk/g6NPbMA30xdRrfYh7H/A/rt2t3Icx0nCUjObG45nAXWAimY2OaS9CIyOy/9e+LsAWGRmPwGEaIsaRAOJzYAZYUalNPBLLm1pCQwGMLOvJS0HjgrnPjKz+F/+M+LaXgyMj7MrLVHl8Vp1Xa2LXKvywKQR45g0YhwADdKaktb5NGa89wm1g1ZlDl2cMnI8U+K06sZn7+CRS/oDUL5qxV35G7c/jh+//b7wOuI4cbhT5iCpDdAWONHMNkmaBJQCPiOa6fqGyEm6imh08PYwWjgUaG5m30vqH8oAvEk0kvkxMMvMdgd7Z2UqcDqwDfg30axaCZLMggGxYPAd+Pe3QFkwcTYN05py/+QhpG/eyvPdh+4613fsIAac0T3b8tNe/5grH7qBu8c9wvZt23nu9iEFbXKxZ6c//8XZ+4lf8LMDyLpjQ+L8OzOV3UmkEQJeNLPEW8dmT6Ig7hiZFx1lbjveLteqAmThxNk0TGvCvZMHk745nRe7P7HrXO+xg7g3B6264M5O1KhXCzNj9Q8rGdnr6YI2uVjjOpUcv1E4ABWAtcEhOxo4IaRPAQaE1xyi0b7NZrZeUkwoV0kqC1wIvAFgZlskjSMKMfxbDm1PIQpNecnMVkqqAlQnmnVzUszLfZ9JmJ7IIVv9w8oMzyjbsW07z9z6eIHZ5jhOsWA9sFZSKzObShQaODmHMvFMAN6V9KiZ/SKpMlDOzJbnouwUolDHjyUdBRxBNEjZNG9dcAqaV/o+mzA9kUO2+oeVu55RBvD8bYMLzC7HyQvulDkAHwLXS5pPJDjTQ/pUovCPKWa2Q9L3wNcAZrZO0nCi0IxlwIxMdY4iCi8cT/Z8DlQjEj+A+cAvVlQDfh3HcZzCpjPR5htlgCVEERy5wsy+lNSbaEOO/YiiMm4EcuOUDQ3tLgC2A13MbGuijSUcx3F+L/Lfvk5BIKkbUMHM+qTSDl9T5uztPLPsjVz/Ajz7iLNS/n0f89/3/Rer4+QRX1Pm7M08vWz0XqVTUDS1ymfKnHxH0ttEi7NPzimv4ziO4ziO4xR33Clz8h0zOy9zWnDUamdK7mlm4wrHKsfZ9zFfQO04uULS50DJTMmdzGxBKuxxnOKC61Ry3ClzCoVEjprjOI7jpAIzOz7VNjiO48TjTpmzT7PDR2Qcx3GcIs4m25FqExzHSTHulDmO4+wj+PNfHMdxnKKM61Ry9ku1AY7jOI7jOI7jOMUZd8ocx3Ecx3Ecx3FSiIcvOo7j7CP4cycdx3GcoozrVHJ8psxxHMdxHMdxHCeF+EyZ4zjOPsLOVBvgOI7jONngOpUcd8ocx0nKZf2uolFaU9I3p/Nst8EsX7Q0ad6uw++g6hHV6NP+VgBq1KtF5/uu44CSB7Bj+w5G9BnO0nn/KSzTHcdxnGJIp/5/49i0pmzdvJVh3YawfOGSLHl6vTqAigdXIn1LOgAPdRrAr6vXF7apjpMBd8ocx0lIozZNqVb7EO5ocxNHNqlLp/uu5d5z70yYt1n749m6aUuGtIvv6MS7j73OgklzaNSmKRff2YkHL+lXGKY7juM4xZBj0yLd6vaXG6nT5CiuvPda+p97R8K8T97yfyxdsLiQLXSc5BT7NWWSJklqnmo78gtJG1Ntw54gaZmkP+QhfxdJQwrSpuJOk3Yt+PStyQAsmfMdZcodRIWqFbPkK1mmFO2uPpsxg9/Icq502dLR3/JlWLdibcEa7GBF4N++jqRakham2IZeqWw/M3nVHUnnSqpXUPbk0HZFSTcUUlttJP25MNpyIpqeehzT3pwEwOI531Km/EFUOLhSao1yMpBqjSrKWlXsnTLHcRJTsVpl1vy4atf7tT+vplL1KlnynXf7JYx75j22btmaIf3lu5/j4juv4J+fPk2HXlfwxkOjCtxmxykmFCmnbA84F0iJUwZUBPLklCliT34vtQHcKStEKlXPqFtrfl5N5WqVE+a95uGbuHfsPzmn60WFZZ7jZMte45RJekfSLEmLJF0r6e+SHoo730XS4HDcR9LXkj6S9IqkbjlUf5GkLyR9K6lVqKOUpOclLZA0R1JaXDvvSBojaamkmyTdFvJMl1Q55Ksj6cNg81RJR2fTtzqh7AxJA2KjjkEIBklaGOzoENLLSpogaXZIPydBnYdImiJpbijfKpv2Twt1zZM0IaRVDv2cH2xrFNL7S3ouzDAukdQ1rp4rQv55kkaEtKqS3gx9myHppJBeRdL4cN2eBhRXz+Xh85gr6WlJJUL6leEzmgyclE1/rpU0U9LMbzYkXwPlZI+kLGmZt7KtUa8W1WoewuxxX2TJm3Z5e1655wVu//N1vHLPC1z5YKEMThdrdmIpfxUTSkgaHvRovKTSkhqHe+V8SW9LqgS7ojEeDffjryS1kPSWpO8k3RurMNl9LzOSBgKlQ75R2ZWV9GS4Fy6SdHdcHcsk3S/ps3C+qaRxkhZLuj5Zp7PTFUn3hXv/dEnVQlrNoFXzw98jFM0c/RUYFOqpI6mrpC9DvlezaT/PupSAgUCd0PagZHqqaEb0K0lDgdlADUl/Cxo0KXz+Q0LeLDonqRZwPXBraCuLBsdr1XcbXavyg9zoFkShi73a38q9F93Fn1ocw0nntykE6xwoGjpVVLVqr3HKgKvMrBnQHOgKvAWcH3e+A/CaolDEC4Am4XxuQhP3N7PjgH8AsUUvNwKYWUPgUuBFSaXCuQbAZcBxwH3AJjNrAnwGXBHyDANuDjZ3A4Zm0/5jwGNm1gL4MS79fKAxcCzQlkjEDgG2AOeZWVMgDfinst6JLgPGmVms/NxEDUuqCgwHLjCzY4HYkNHdwBwza0Q0KvtSXLGjgfah//0kHSCpPnAXcHKo55a4vj0a+nYB8ExI7wdMC9ftPeCIYM8xRJ/lScH2HUDH0O+7iZyxU8lmlNXMhplZczNr/qdytZNlcxJwcqfTuHvsw9w99mHWrVhD5UN3R5RWql6FdSvWZMj/x6Z/ombDIxk07Ul6jb6P6rUPoeer0W+/ky5ow6wPpwMw41+fcuSxfyy8jjhOwVIXeMLM6gPriO5tLwE9wz1zAbu1BCDdzFoDTwHvEulLA6BLGKBKeN9L1LCZ3QFsNrPGZtYxh7J3mVlzoBHwl5gTE/jezE4EpgIvABcCJwADsul3Ml05CJge7v1TgGtC+hDgpXBNRgGPm9mnRPf87qEPi4E7gCYhX1KnkDzqUpI67gAWh7a7k72e/inY3wTYBvQJ1+jU0F6MLDpnZsuIPu9HQ1tTMxsSr1V1y7pW7SltrziNe8f+k3vH/pO1mXSrcvUqrP0la+j82qBlW37bwmfvTqVOY9cnJ/XsTRt9dJV0XjiuAdQGlkg6AfiO6Ob5CZEz8K6ZbQaQNCYXdb8V/s4CaoXjlsBgADP7WtJy4KhwbqKZbQA2SFoPxNpYADSSVJYoZGF0nK9UMpv2TyQK5wB4GXg4zoZXzGwHsELRDFEL4APgfkmtiXYXPQyoBvwcV+cM4LkgTO+YWUKnjEhgppjZ0tDX2K/ulkTigpl9HH44VAjn/mVmW4Gtkn4JbZ8MvGFmqzLV0xaoF3cdyksqB7QmONVm9i9JsbvmKUAzYEYoUxr4BTgemGRmKwEkvcbuz8PJJz4e8SEfj/gQgEZpTTml8+l8/t40jmxSl80b/p+9+46volj/OP75ErBRlKaAqHBRsdMRFRAUEVCvvV8F7A0QxA4I1quiXARRsQFWrr38qCJdUZpUsaHoVRQIvQUlz++PnYRDck4KkpxAnjevvHIyOzszuwn7nNmZ2bORNctXb5d//KujGf/qaAAqVq/MrS/ek/kwj9XLVlG7ydF8M20BR554LH/8tLRwD8a5gvNjzDV1JlAL2M/MJoa0ocBbMfk/DN/nAQvMbCmApMVE8awp8a97eZHomglwkaTriGJ9VaKbWXPjtKlMTEzbLGk/M9v+P3skUVzZAnwccz5OC69PYNvN01eAzNktWcwFXpP0PvB+Dsea37j0vxzKyiDix1OAJWY2LbxuDEzMiG2S3mJbDEoU51wh+GTYKD4ZFsWtOqc04LT2bZn24RRq1Tucjes2siZLp6xESgn2KVea9avWkVIyhbqnNmTBlDnJaLpz29klOmWSWhBd9E4ws42SJgB7AcOBi4BFwHtmZnFGjPIiYzHMVradk5zKiV08kx7zc3rYvwSwOtxN/DsSteFyoDLQwMz+lPQT0fnIZGaTQpA5A3hF0uNmNix7UQjijuPGqzsjX+zxZ5yzROWUIPq9bdqu8OjXlKjeoWZ2d5b85yTI7wrI3PGzOK5lfR6d+DRbNqXx4u1PZ27rM6Iv97XLeVbwkLue4bL7rqJEyRT+TNvCkLufLegmF3vxpum4ApH1Gpj9CTjx88fGi4yfM66f2a57eZTomlmTaJZGIzNbJWkI28eJ3NqUTQ5x5U/b9se3NdH+JL6Gn0F0o+6fQE9JR5vZX3Hy5Tcu5UVO8XRDLnVnyCnOuUI059OZ1G1Zn76TBrFlUxrPd9/2PLAHRzxBj3a3UWqPUtzxSi9SSqZQIqUEC6bMZfwbnySx1cWLx6nEdolOGbAvsCp0yI4gGt2BaITrXmAJcGdImwI8J+kRouM7g2h6Xn5NIrpYfyrpcKLpdd8A9XPb0czWKlpvdqGZvRU6iseZWaJbMdOI7v4NBy7J0obrJQ0FKhAFrduJpqosCwGkJXBI1gIlHQL8ambPSyod2h2vU/Y58LSkmmb2o6QK4U5gxvE/EDrFK8JxJTrsccB7kvqZWWpMOWOAW4DHQ7vqhrurGeU/KKktUD6mnA9COcsUrdErC3wB9JdUEVhLNM3Sb20VsFd7vRA3PV6HLPV/yzM/owzguxmL6HPWHQXWNueKkDXAKknNwjS1K4CJuewTK+51z8yWJMj/p6RSZvZnon2BckSdijWK1ni1BSbs2OFF8hFXMnxGFNNeIbreTwnp60IbUfQAjYPMbLykKURTJMsQTQvNKr9xKZ7MuoN9ySWeBl8C/RStFVxHFLPnhW2J4tw6ot+DK0RDe8Z/y9ej3W0ApG1Ko9eZtxdmk5zLk12lUzYKuEHSXKKO0TSAcPdvIXCUmX0Z0qZL+pDoDfsSYAZRwMyvQcCzkuYBfwEdzCwtHxf/y4FnJPUASgFvkrgTcSvwqqTbgP+Lae97RNM/5hDdDbzDzH5XtLj7I0kziOb0L4pTZgvgdkl/AuvZttZtO2a2PExveTcEx2VEU096Ay+Hc74RaJ/TwZrZAkkPARMlbQVmAx2I1v89HcopSRRUbyBaG/CGpFlEb15+DuUsDOdsTGjPn8DNZjZNUm+iTuRSooXXcRfCO+dcErQnihn7AIuBjnndMdF1jyiGxTMYmCtpVlhXluiaORtYENozdYePbJsW5CGuxOhMNN3xdmA5287Jm8Dzih7IcQnwYpiGKKI1WPE6ZJDPuBRPuGk4VdHHGowEHiX3eIqZ/SrpYaIbhL8BC9kWqxPFuY+AtxU9PKRTvHVlzjmXQbvjMKKkMma2PgTHScB1ZjYr2e1KJLRzU5h+eQlwqZlle6Kiy7+ONc7f/f7AXbHy8k/v5PlOUMvqpyX97338/8b6nC23W4p5b1GS6KbpS2b23s4o+4pDzkv6/13ndtQrS97dpeIUFM1YtauMlOXXYEUfTLkX0Vz7ItshCxoAA8M0x9XAVUluj3POOee211tSK6L3FmPI+aEkzjmXL7tlp8zMLsuaJulpsn+2VX8ze7lwWgWS7mXbI+czvGVmDxE9Xrig6/+C7E+BvMLM5sXL75zbtZg/C2e3kqxrtqRjidaBxUozs+MLst6Y+juy7WNVMkw1s5vzUUZFovV2WZ1qZqk70i4zy+0zT51zufA4ldhu2SmLJz8X8wJsw0NEn2uWrPoLJaAWJX/thtNznXPFQ7Ku2aHT93efHvx36n8Z+Fs3TEPHK2nHkF+b2ZrsJjjnkmxX+vBo55xzzjnnnNvtFJuRMuec292l+8iwc865IszjVGI+Uuacc84555xzSeQjZc45t5vw+4/OOeeKMo9TiflImXPOOeecc84lkXfKnHPOOeeccy6JfPqicy5Pruh9NXVa1idtUxqDuw9kyfzF2fLc8+b97Ld/ebZs3gLAY1fcz9rUNYXd1GIr3SeGOOeKoY69r6V+ywakbUrj6e79+TFOfOr95oOU378CWzanAfDAFb1Zm7qG0y5vQ5sr25K+NZ3NGzfz3N2D+N93vxT2IRQbHqcS806Zcy5XdVrW54CaVel+8s3Uqnc4HR+8jt7n3BU37zNd/sOP834o5BY655wrjuq1bEDVmlXpdPINHFbvcK598EbuOef2uHn7d3mSxfO+3y5tygcTGfvaKAAatmpM+x5X8VD7PgXebuey8umLrlBIaiZpgaSvJO2d7Pa4/Kl/WmOmvDMBgB9mf8s+5Uqz7/7lk9sol006lvQv5/JC0k+SKiW7HfklaYKkhvnI30LSxwXZpuKu0WmNmfjOeAC+m/0tpcuVZr98xKdN6zdlvt5znz0xv44VqGTHqKIcq3ykzBWWy4G+ZvZyXjJLSjGzrQXcJpdH5atUYOVvKzJ/Xvl7KhUOqMCaZauy5b227y2kb01n+qhpfPDUW4XZTOecc8VMhSoVSY2JT6m/r6DCARVZHSc+3dy3E+lb05k26nPeeeq/memnX9mOM6/5JyVLlaLPpT0Kpd3OZeUjZcWMpPclzQyjVtdJukjSk2FbF0mLw+takqaE170kTZc0X9JgRWpJmhVT7mGSZiao8xrgIqCXpNfC/o+H8uZJujjkayFpvKTXgXmSakhaJOmFkPc1Sa0kTZX0naTGCeq7TtIMSTO+W//jTj1/xZWkbGkW5wMgn+nyH+45vSsPXngvtRsdyUnntSiE1jlX9MS51t4o6bGY7R0kDQive4Zr3VhJb0jqnkO514br8RxJ70jaR1KKpMXh2rqfpHRJzUP+yZIOldRY0meSZofvtWO2140pf6qk4xLUXTm0cZak5yQtyRjtktQtXKfnS7o10XmIU2ZpSf8Xjmd+RjxIUH+j0PY5kr6UVFbSXpJeDrFktqSWMef3XUmjQryIPfdtwjHMkTQuph0vhXM7W9LZIX1vSW9KmitpOLB3TDmtJX0eynpLUpmY8hcpiqHn5XA8mbFq8fqfEmVzuYgXn4gTn57q8iS3nd6Fnhfew5GNjqL5eS0zt40eNoJOzW/gtX8P5fxOFxVkc51LyDtlxc9VZtYAaAh0BqYCzcK2ZkCqpAOBpsDkkD7QzBqZ2TFEAelMM/sBWBMTzDsCQ+JVaGYvAB8Ct5vZ5URBqi5QB2gFPC6pasjeGLjXzI4KPx8K9AeOA44ALgtt6w7ck6C+wWbW0MwaHlamZt7PjNtOqyvb8OCIJ3hwxBOs+mMlFaptm2lUoUpFVsW5C7nqj5UAbN6wmc8/mEytuocWWntd1FFO9pfLlPVa+y7bv0G/GBiuaCrc+UC9sD23qXHvhutxHeBr4Oowq+Bb4Cii6+NMoJmkPYHqZvY9sAhobmb1gF7Aw6G8F4AOAJIOB/Y0s7kJ6r4P+NTM6gPvAQeH/RoQxYDjgSbAtZLqxTsPkipmKbMN8JuZ1QkxZlS8iiXtAQwHuoRjbwVsAm4GMLNjgUuBoZL2CrvVJTrPxwIXSzpIUmXgeeD8UM6FIe+94dgaAS2J4lJp4EZgo5kdBzwENAjtqQT0AFqF8zED6Bbqfh44iyimVklwLreLVf8oUyNRNhfH6Ve24/ER/Xh8RD9W/rGSijHxqWKVSqxctjLbPisz49MmpnwwicPqHpYtz9QPJ9O49fEF13CX9BhVlGOVd8qKn86S5gDTgIPCVxlJZcPr14HmRMEko1PWUtIXkuYBpwBHh/QXgI6SUogC3+t5bENT4A0z22pmfwATgUZh25dmFju89aOZzTOzdGABMM6i/03zgBr5PHaXD58MG0WPdrfRo91tzBzzJU3PbwFArXqHs3HdxmxTF0uklKBM+bIApJRMoe6pDfnfNz8XdrOdKyqyXmtrAoslNQkdk9pEN8WaAh+Y2SYzWwd8lEu5x4TRrXlE08IzrseTia7dzYFHQrmNgOlh+77AW5LmA/1i9nsLOFNSKeAqEtxcC5oCbwKY2ShgVUz6e2a2wczWE3VAM272ZT0PWd8JzwNaSXpUUjMzS/S41trAUjObHupfa2Z/hbpfCWmLgCXA4WGfcWa2xsw2AwuBQ4g6jZMy4oyZZbx7bw3cJekrYAKwF1Gnsznwasg7F8jo+cf0KQAAIABJREFUsDYh6gRPDfu0D+UfQRS3vgux6tUczqfbQaOHjeD2dl25vV1Xpo+ZxsnnR6Neh9U7nI3rNmSbulgipQRlY+JTg1Mb8XOIT1VqVM3MV/+Uhiz9aWkhHYVz2/M1ZcWIpBZEdxdPMLONkiYQBZ7Pie5yfkMU2K8CTgBuC3f9BgENzewXSb3DPgDvEO6cAjPNLDWvTclh24YsP6fFvE6P+Tkd//stNHM+nUndlvXpO2kQWzal8Xz3gZnbHhzxBD3a3UapPUpxxyu9SCmZQomUEiyYMpfxb3ySxFY7lxw5XGuHE03lXkTUiTHFnXuVoyHAOWY2R1IHoEVInwzcAFQjGgm7PWybFLY/AIw3s3Ml1SDqeBDaNxY4O7Qtp5G6RG2Nm57DechkZt+GkbZ2wCOSxpjZ/QnqiHd7O6fzFxs/thLFjJzKOd/MvslyDOSQf6yZXZolf90E+V0BmfXpTOq1bMiASc+yZVMaT3cfkLnt8RH9uL1dV0rtUYoer/QmpWRJSqSUYN6UOYx7YwwAbdufwbFN67D1z79Yv3YDA7v9J1mH4oo5f1NbvOwLrArB8QiiO30QBe37w9dsoqkbm8xsjaT9Qp4VYb78BcDbAGa2WdJo4Bng6ny0YxJwvaShQAWiO5G3E91hdEXU0J7Px03v0e42ANI2pdHrzPiPIXaFo6g+UaoYSnStfZdomtwS4M6QNgV4TtIjRDH5DKLpb4mUBZaGka3LgV9D+hfAMGBxuDZ/BVwPnBnTpoy8HbKU+QLRCN3kmJGjeKYQddweldQayHjE3SRgiKR/E3VWzgWuIBppinceMkmqBqw0s1clrY/TtgyLgGqSGpnZ9DC7Y1Oo+3Lg0zD98mCiG4z1E5TzOfC0pJpm9qOkCuGYRwOdJHUKneV6ZjY7pvzxko4hmkoP0cjf05IONbPvJe0DVA/trCmplkXT/C/N1gK3073Y87m46be36wpE8enOM2+Lm+flPi8UWLtcdh6nEvPpi8XLKKCkpLlEd02nhfTJRNNKJoW1Cb8QBV/MbDXRG4R5wPtsmwqT4TWiu4Jj8tGO94imgMwhGmW7w8x+35EDcs65IijutdbMVhGm0ZnZlyFtOtGa2zlEnbYZQE6fuN6TqAM2lqgDQCgnjejaHXtdL0t07QZ4jGgkaiqQElugmc0E1gK5PR23D9Ba0UOe2gJLgXVmNotoBO/L0LYXQocmUcyJdSzwZehE3gs8GK9iM9tCNE1+QJgOOZZo1G0QkBKmcw4HOoRzEZeZLQeuA94N5QwPmx4ASgFzwxTPB0L6M0RT/OcCd4RjzCinA/BG2DYNOCJMlbwO+L/woI8lidrinHOxVFQXu7ldg6KnhO1rZj2T3ZZ4rjjkPP8Dd7u0V5a8m+fpbY2qNU/63/v03ybldzpesSepjJmtD6Mtk4DrQkensOqvRjSd8YiwfjdRvj2BrWb2l6QTgGfMrG6i/C7vLjzk7KT/33VuR7215INdKk5B0YxVPn3R7TBJ7wG1iB7+4ZxzbscMlnQU0cjP0ELukF1J9FTBbjl1yIKDgf9KKgFsAa4t6PY551xx4Z0yt8PM7NysaaGjlvU59Hea2ejCaZVzzu1azOyyrGmSngZOypLc38xym2KY37qHEa1Fi627I9AlS9apZnYz0aP7C5THEedcceSdMrdTxeuoOecKh09H332EDlCy6n6Z3NeXFWT9Hkec2015nErMO2Vut/YXuc3Gcc4555Lrr1xnjjrndnfeKXPOud2EP2rYOedcUeZxKjF/JL5zzjnnnHPOJZF3ypxzzjnnnHMuiXz6onPO7SZ8AbVzzrmizONUYj5S5pxzzjnnnHNJ5CNlzrmE2ve+hrotG7BlUxrPdH+Kn+Yvzpan55sPst/+5dmyeQsAj1zRm7Wpa2h3zT9peclppP+1lbUr1/Lc7QNY8evywj4E55xzu7mr+1xHg5YNSNuUxoDb+rN4/g/Z8jww/GHKx8SqPv/qxZrUNQCceGZTLul6KWbw08If6de5b6G23znwTplzLoG6LRtQpWZVup58I4fWO5yrH7yBnufcETfv012eZPG87YPgTwsWc++Zt7Fl8xZa/asNl93dnqdu8UBXkPypVs654qZ+ywZUq1GNm5pfz+H1anP9Qzdy59nd4+bt1+UJfpj7/XZpVWtU5fybLuDu8+5gw5oN7Ftx38JodrHlcSoxn764m5N0q6R9kt2ODJJ+klQpH/lbSDqxINvk4mtwWmMmvzMBgO9nf8s+5Uqz3/7l87z/ws/nZ96R/H72N1SoWrEgmumc+5sk1ZA0P8ltuCeZ9WclaX0+858j6aiCao9LrHHrJox/51MAvp39DaXLlaZ8PmLVaZedzshhI9iwZgNA5uiZc4XNR8p2f7cCrwIbk92QHdQCWA98luR2FDsVqlQg9bcVmT+v/D2VCgdUYPWyVdnyXt+3M+lb0/ly1Oe899R/s21vcXEr5kyYVaDtdWB+B9Ltuu4BHk52I/6Gc4CPgYXJbkhxU7FKRVKXbotVqb+nUqFKRVbFiVWd+nYhfWs6n4/8jLeeGg5AtZoHAvDwu49SokQJhvd7g9kTPV4VFI9TiflIWSGR9L6kmZIWSLpO0kWSngzbukhaHF7XkjQlvO4labqk+ZIGK1JL0qyYcg+TNDNBnZ2BasB4SeNDWmtJn0uaJektSWUS1RXSJ0jqJ2mSpK8lNZL0rqTvJD2Yw/GWlvR/kuaEMi+O2dwp1D9P0hEhf4VwjuZKmibpOEk1gBuArpK+ktRM0oWhvDmSJiWo+zpJMyTN+H79T3n59bg4wp/AduI9NGlglye58/Qu9Lnwbo5odBTNzmux3fam557MP449lI+ee6+AWuqc2wlSJD0fYtQYSXtLqhuux3MlvSepPOQ9Lkj6l6Qvw/X7OUkp8SqW9G9g75DvtZz2lfRMuL4vkNQnpoyfJD0c4tsMSfUljZb0g6QbEh20pKrhOL4KsaVZzLaHQqyZJumAkHaIpHHhnIyTdLCi2Rz/BB4P5dSS1FnSwpDvzQR1Z8aqn9YvycevyuUm3hP++nXuy62tO3HPBXdxVOOjaXF+SwBSSqZQtUY1el50D0926stNj3Vin3KlC7vJznmnrBBdZWYNgIZAZ2AqkHHxbwakSjoQaApMDukDzayRmR0D7A2caWY/AGsk1Q15OgJD4lVoZk8BvwEtzaylommDPYBWZlYfmAF0S1RXTFFbzKw58CzwAXAzcAzQQVKiOWltgN/MrE4oc1TMthWh/meAjInffYDZZnYc0R3TYWb2U6izn5nVNbPJQC/gdDOrQxQE4x33YDNraGYNDy1TI0HzXDynXdmWR0b045ER/Vj1x0oqVts20zS687gy2z6r/ojSNm/YzNQPJlGr7mGZ24456TjOueUC+l7zMH9t+avgD8A5t6MOA542s6OB1cD5wDDgznBdngfcF5M/x7gg6UjgYuAkM6sLbAUuj1exmd0FbArX+ctz2fdeM2sIHAecLOm4mKJ+MbMTiGLoEOACoAlwfw7HfRkwOtRTB/gqpJcGpoVYMwm4NqQPJIpPxwGvAU+Z2WfAh8Dt4Rh+AO4C6oV8cTuFsbGqRplDcmiiy6rtle14cmR/nhzZn1XLVlKx6rZYVbFKxcy4FGtlZqzaxKT3J3JYncMBSF26gi/HfMHWv7ay7Jc/+G3xr1SrUa1wDsS5GN4pKzydJc0BpgEHha8yksqG168DzYk6aBmdspaSvpA0DzgFODqkvwB0DHcOLw775kUT4ChgqqSvgPZARiRIVBdEwQaioLzAzJaaWRqwOLQ9nnlAK0mPSmpmZrGTtN8N32cCNcLrpsArAGb2KVBRUrzVtlOBIZKuBeLedXU7buywkdzdrit3t+vKjDFf0Oz8FgAcWu9wNq7bkG3qYomUEpQtXxaI7jbWP7Uh//vmZwBqHF2Tax65ib5XP8xan6NfKNLNkv7ldlk/mllGh2QmUAvYz8wmhrShRDEqQ25x4VSgATA9xJtTgX/ksS057XuRotkis4niVOw6rtg2fWFm68xsObBZ0n4J6ppOFE97A8ea2bqQvoVoOiJsH6tOYFvMfYUodsUzF3hN0r8AvyO1k40cNoJubbvQrW0Xvhg9jZbnnwLA4fVqs3HdxmxTF6NYVQ6IYlXDVo34+dtodPKL0dM49sRjAShbvhzValbjj59/L8SjKV6SHaOKcqzyNWWFQFILoBVwgpltlDQB2Av4nGik6xuijthVRBf82yTtBQwCGprZLyFg7BWKfIfojuWnwEwzS81rU4CxZnZplvblVBdAWvieHvM64+e4f0Nm9q2kBkA74BFJY8ws425lRhlbY/bPPleO7BOPzewGSccDZwBfSaqbj+N3+TD705nUbdmA/0x6lrRNaTzX/anMbY+M6Mfd7bpSao9S3PVKb0qWTKFESgnmTZnDuDfGAnDZPR3Ya5+96DIoemJj6m/L6XvNrrxkxLndWuy1fSuQqBOTNX+iuCBgqJndvQNtibuvpJpEsysamdkqSUP4+7FqkqTmRDHlFUmPm9kw4E/bNgdua6L9iROngjOIOrH/BHpKOtrMvHNWAGZ+OoMGLRvyzOTB0SPxu/fP3PbkyP50a9uFUnuU4r5X+5BSMoUSKSnMnfIVY18fA8DsibOo27weT417mvSt6Qx96GXWrV6XqDrnCox3ygrHvsCq0CE7gmjECqIpEfeHr9lAS6IpHGti7uqtULTu6wLgbQAz2yxpNNH0v6tzqXsdUBZYQTRK97SkQ83se0VPZawOLEtU146SVA1YaWavKnqKVYdcdplEND3lgdCJXWFmayWtA8rFlFvLzL4AvpB0FtEdWe+UFZCXew6Om353u64ApG1K494zb4ub5+HL74ub7gqOL6B2O9EaYFWY6TAZuAKYmMs+scYBH0jqZ2bLJFUAyppZosVTf0oqZWZ/JtqXKBZsIJrCfwDQFpiwY4cXkXQI8KuZPS+pNFCfaNpmIp8BlxCNkl0OTAnpGbEWSSWAg8xsvKI14pcBZYimhboCMLjns3HTu7XtAkSxqvsZXRPu//IDL/LyAy8WSNvc9jxOJeadssIxCrhB0lyiUbFpIX0yUadikpltlfQLsAjAzFZLep5oGsZPRFMsYr0GnAeMyaXuwcBISUvDurIOwBuS9gzbe4RRrZzq2hHHEi16Tgf+BG7MJX9v4OVwjjYSTa0E+Ah4W9LZQCeih34cRnQndRwwZye01TnnXHbtgWfDDbzFRDM78sTMFkrqAYwJnZQ/idadJeqUDQbmSpoV1pVl29fMpkmaDSwI7Zm6w0e2TQvgdkl/Ej3p98pc8ncGXpJ0O7CcbefkTeB5RQ/YugR4MUzBF9G6aO+QOedypHhPqHFFn6TuwL5m1jPZbSnKLj3kHP8Dd7u0N5a8H29qb1xHH3B80v/eF/zxRZ7b65yLnHvwWUn/v+vcjnrv5492qTgFRTNW+UjZLkjSe0SLsE9Jdlucc0VHUV287JxzzoHHqZx4p2wXZGbnZk0LHbWaWZLvNLPRBdmW8Ej8cXE2neoP4HDOORdL0hfAnlmSrzCzeQVc77GEJ/zGSDOz4wuyXuecyyvvlO0m4nXUCqneVKBurhmdc84Ve8nqBIVOn8cq51yR5Z0yt1tbnr4p2U1wrtD4U62c2zX95f93XTHhcSox//Bo55xzzjnnnEsiHylzzrndhC+gds45V5R5nErMR8qcc84555xzLom8U+acc84555xzSeTTF51zbjfhC6idc84VZR6nEvNOmXMuoZv73EjjUxqTtmkzj3V7gu/nf58tT8lSJen0wM3UOeE40tONlx8bwuSRUzj/2vNod0kbtm7dyurUNfTt/iTLfl2WhKNwzjm3O7uuz3U0aNmQtE1p9L/tP/ww/4dseR4e/gjl9y/Pls1bAOj1r56sSV1D5WqVufXJrpQuV5oSKSUY+u+hzBw/o7APwTnvlDnn4mvcshEH1jyQ9s06cmS9I+jycCc6/bNLtnyXdbqU1amr6XDy1Uii7H5lAfh+/g/cdEYn0jancdYVZ3Ldvdfw4E0PF/ZhFCu+gNo5V9w0aNmQajWqcX3z66hdrzY3PnQT3c++LW7eJ7r05fu5299cvKjzxUz5eDIjXx3JQYcdxH1DenPNSVcXRtOLJY9TifmasiJKUgtJH+dzn1sl7VNQbcql7rqS2hVSXR0kVSuMuoqzE1ufwNh3PgHg69mLKFOuNBX2r5AtX5uLT+eNgW8CYGasXbUWgDmfzyFtc1q0/6yvqVSlUiG13Dm3u5BUQ9L8ZLfDFV1NWh/Pp+98CsA3s7+hdLnSlN+/fN4LMGOfstFbp33KlmblHysLopnO5co7ZbuXW4GkdMqAukC+OmWSdnSktgPgnbICVqlKJZb/tjzz5+VLV1CpSsXt8pQuVxqADre355kRA+n5zL3sV2m/bGW1uaQN0ydML9gGu12epAqSxkr6LnyP+85K0mOSFkj6WtJTklTYbXXOFQ0Vq1RkxdIVmT+n/p5KxSyxKkOXvrfSf+RTXNz5ksy01/u9TotzW/LyF0PoPbQ3z933bIG32e3aCipW7XKdMknvS5oZDvI6STdKeixmewdJA8LrnpIWhRP2hqTuOZR7raTpkuZIekfSPpJSJC1WZD9J6ZKah/yTJR0qqbGkzyTNDt9rx2yvG1P+VEnHJaj7ZElfha/ZksqGTWUkvR2O4bWMX6akU0O+eZJekrSnpM5EHZXxksaHtg+RND/k65rDsdeVNE3SXEnvZfxxSZog6VFJX0r6VlKzBPvvAdwPXByO4eIczksHSW9J+ggYI6mEpEHh9/mxpBGSLgh5G0iaGH7foyVVDdsaAq+FuvZOdFzu74l36bAs0w5SUlLYv1plFkxfyI3tbmHhrK+5vse12+U59dxTqH3cYfz32bcLsrmOaAF1sv/9TXcB48zsMGBc+Hk7kk4ETgKOA44BGgEn/92KXZGWIun5ECfGSNo7l7jVT9Kk8EaokaR3w5unBzMKlPSvENu+kvScpJRElUtaL+kJSbMkjZNUOaau/4QYN19S45BeObzvmBXKXiLJpwoUmOzBKmusAujbuS+dWt/CXRfcydGNj6Ll+acA0PyfJzPurXF0PL4Dvdv3ptt/bkN+n6fAJDtGFeVYtct1yoCrzKwB0RvzzsC7wHkx2y8GhktqCJwP1AvbG+ZS7rtm1sjM6gBfA1eb2VbgW+AooCkwE2gmaU+gupl9DywCmptZPaAXkLFo5gWiER0kHQ7saWZzE9TdHbjZzOoCzYBNIb0e0ejXUcA/gJMk7QUMAS42s2OJ1gXeaGZPAb8BLc2sJdHI1YFmdkzI93IOxz4MuNPMjgPmAffFbCtpZo1DO+6Lt7OZbQnHPtzM6prZ8BzOC8AJQHszO4Xod1MDOBa4JmxDUilgAHBB+H2/BDxkZm8DM4DLQ12byEJRZ32GpBm/rv9fDoftsvpn+7N4dtQgnh01iNQ/VlK5WuXMbZWrViI1y7SOtavWsmnjZqaMmgrApI8nc9gxh2Vur9+0Hpd1upSeV93Hn1v+LJyDcLuys4Gh4fVQ4Jw4eQzYC9gD2BMoBfxRKK1zyXIY8LSZHQ2sJortOcWtLWbWHHgW+AC4mehNUQdJFSUdSfRe4aQQd7cCl+dQf2lglpnVByZmqau0mZ0I3EQUpwjbPw353wMOjldobKxasv7nvJ4LB7S78gz6j3yK/iOfYuWylVSquq3PW7FKxbhTEFf+kQrApg2bmPj+RA6vczgArS85jSkfTwbgm1mL2GPPPShXoVwhHIXbhRVIrNoVO2WdJc0BpgEHATWBxZKaSKoI1AamEnWiPjCzTWa2Dvgol3KPCaNb84guzkeH9MlA8/D1SCi3EZAxF2tf4C1Fc977xez3FnBm6FxcRdSRSmQq8GQY7drPzP4K6V+a2f/MLB34iqjzUhv40cy+DXmGhrZltRj4h6QBktoAa+NVLGnfUOfEBOW9G77PDPXnVaLzAjDWzDKumE2Bt8ws3cx+B8aH9NpEQXSspK+AHkD1vFRsZoPNrKGZNTywTJ52ccGHQz/ihjY3cUObm5g6+jNOO78VAEfWO4IN6zaycln2QDftk2nUOSEaBK7XtC5LvlsCwKFH1+LWf3em11X3sTp1TeEdRDFmlp70r9g3muHrunwcwgFmtjQ6FlsK7J/9GO1zouvE0vA12sy+3hnnzxVZP5rZV+H1TKAWOcetD8P3ecACM1tqZmlEcfEg4FSgATA9xJdTiW58JpIODA+vXyWKWxneADCzSUA5SfuF7W+G9FHAqniFxsaqQ8rE7be5BEYM+z+6tO1Ml7admTb6c04Jo16169Vm47qNrFq2/SkvkVKCcuWjjlZKyRQatWrMkm+jWLX81+XUOakOANUPrU6pPUuxxmNWgUl2jCrKsWqXevqipBZAK+AEM9soaQJRL3Q4cBHR6Mx7ZmbK/9jzEOAcM5sjqQPQIqRPBm4gmhrYC7g9bJsUtj8AjDezcyXVACYAhPaNJepNX0QOI3Vm9m9J/0e0JmuapFZhU1pMtq1Ev688HZeZrZJUBzid6C7hRUSdw/zKaENG/XkV97wEG2JeJzoeEQXTE/JRp9uJvvj0Sxqf0ohhU14mbVMaj9/2ROa2Z0cN4oY2NwHw/MMvclf/O7ip9w3Ro+9DvuvuvZa999mbns/2AGDZb8vodVXvQj8OV7jMbDAwONF2SZ8AVeJsujcv5Us6FDiSbTdpxkpqHt4Uu91T1liYfeFq/PzpWfZNZ1scHWpmd+9geyzB64yffe5bIZrx6QwatmzI4MnPR4/E7/6fzG39Rz5Fl7adKbVHKfq8ej8pJVNISSnBV1PmMOb10QC8+OCL3PJoJ86+5hzMjP7d/pOoKrcbKYqxapfqlBGNvqwKHZ4jgCYh/V2ik7QEuDOkTQGek/QI0XGeATyfQ9llgaVhZOty4NeQ/gXRNInFZrY53FW7Hjgzpk0ZeTtkKfMFohG6yTEjQ9lIqmVm84B5kk4AjiCaohHPIqCGpEPD9MkriKZTAKwLx7EizF/fYmbvSPqBBCN1ZrZG0ipJzcxscpby8iOj7gw5nZdYU4D2koYClYk6vK8D3wCVJZ1gZp+H38vhZrYgTl2ugAzo8XTc9IwOGcCyX5fR7YLsyzXvuCzbFGvnMLNWibZJ+kNSVTNbKqkqEO+D7c4FppnZ+rDPSKJY4J2y4mMN8Hfi1jjgA0n9zGyZpApAWTNbkiB/CeACotGvy4jiVoaLidZyNwXWhJg6hehG6KOSWgP5eBSg2xHP9oz/cI4ubTsDkLYpja5n3Bo3zy/f/cKd591RYG1zu6ZkxKpdbfriKKCkpLlEIzHTIBoVAhYCh5jZlyFtOtEUhjlEnbYZRBfyRHoSdcDGEnV8COWkAb9k1EU0claWaFoEwGPAI5KmAtstFDazmUTTBnNazwVwa1gkPIdoPdnIRBnNbDPQkWhq4DyiO38ZV6PBwEhJ44EDgQmhEzkEyOmOYHvg8XBe6xI9tCO/xgNHhUXTF5PDecniHeB/wHzgOaLfwZqwTu0CoqA2h2j65olhnyHAs/IHfTi3nXQs6V9/04dE1yPC9w/i5PkZOFlSyXCz5mSidcCueNnhuGVmC4mmxI8J+48FquawywbgaEkzgVOy1LVK0mdEcTjjw636AK0lzQLaEk1dWpfX9jm3O0t2jCrKsUrxnlCzu5BUxszWK/rsrknAdWY2qxDrr0Y0be+IsC7MxRHze6oIfEm0+Pr3nVF2q4NO333/wF2x8Mkvo/M8FeqQiscl/e99SercHZ66Fa4B/yV6MMLPwIVmtjI8uOkGM7tG0VPyBhGtITJglJl12wlNdy4uSevNrEyc9AlAdzObkSV9T2Crmf0VZr88Ex4oktBZB5+Z9P+7zu2oj37+eJeKU1A0Y9WuNn0xvwZLOopo3dnQQu6QXQk8BHTzDlmuPg6Lo/cAHthZHTLn3K7FzFKJHrqQNX0G0dNZseipuNcXctOcy4+Dgf9KKgFsAa7NJb9zbhdSULFqt+6UmdllWdMkPU30uQGx+ptZblMM81v3MKK1aLF1dwS6ZMk61cxu3pl1J7Izjl3S6cCjWZJ/NLNzd7RdZtZiR/d1zm2zO898cK6gSfqC6NHVsa6IN0oGiWOXmX1H9JE2zrksPE4ltlt3yuIprA5QgrpfJvf1ZQVZ/98+djMbDYzeCc1xzjnnigwzOz7ZbXDOFV/FrlPmipe09L9yz+TcbmInLF52ziXBVnyVgysePE4ltqs9fdE555xzzjnndiveKXPOOeecc865JPLpi845t5vwBdTOOeeKMo9TiflImXPOOeecc84lkY+UOefcbiLd70A655wrwjxOJeYjZc4555xzzjmXRD5S5pxLqMv9N9PklONJ25TGw10f49v532XLU7JUSbo+2Il6J9YlPT2d5x99iYkjJgPQ8qyTuapbe8yM7xf+wP23PFzYh+Ccc243d32f62nUshFpm9J48rYn+WH+D9ny/Hv4v6mwfwXSNqcB0ONfPViTuob9D9yfW/veyr4V9mXd6nU83uVxUn9PLexDcM47Zc65+Jqc0pjqNatzadMrOar+kdz2SBeuP+uWbPmu7Hw5q1JXc1mz9kii3H5lAahe80D+dcul3HhOZ9avWc9+Ffcr7EModsw//2W3JWkC0N3MZiS7LbEkdQAamln2i0Pife4xs6TcoZHUAthiZp8VQl23AoPNbGNB11WcNWzZkANrHMg1za+hdr3a3PLQLXQ9u2vcvI93eZzv5m5/c/HqHlcz7p1xjHt7HHVOrEPHuzrS99a+hdH0YsnjVGI+fdE5F1fT009i1NtjAFg462vK7FuGivtXyJav3SVteHXAG0D0VKU1q9YCcNZlZ/DekA9Zv2Y9AKtTVxdSy51zRdw9Say7BXBifnaQtKM3sG8F9tnBfV0eNWndhHHvjAPgm9nfULpcacrvXz7P+x+S+4o5AAAgAElEQVR82MF8NeUrAOZ8NocmpzUpkHY6lxvvlDkkvS9ppqQFkq6TdJGkJ8O2LpIWh9e1JE0Jr3tJmi5pvqTBitSSNCum3MMkzcyh3p8kPSrpy/B1aEgfIulZSZMlfSvpzJC+j6T/SporabikLyQ1LMhzU5xVrlKJZb8tz/x5+dLlVKpSabs8ZcqVBuCaOzry4qhnuf+5XpSvFAXDg/5RnYP+UZ1B7/fn2Y8G0LhFo8JrvHNJEud6eqOkx2K2d5A0ILzuKWmRpLGS3pDUPZfiLwzXym8lNQtl7CXpZUnzJM2W1DKmnvclfSTpR0m3SOoW8kyTVCHkqyVpVGjzZElH5HBsF4Zr/hxJk2I2VQtlfJflWC8N7Zov6dGQ9m9gb0lfSXpNUmlJ/xfKnC/p4hzqPzW0f56klyTtGdJ/ktRH0qywLe4xSKoB3AB0DfU3k3RWiCWzJX0i6YCQt3eIbWOAYTnFH0mtJX0e6n9LUhlJnYFqwHhJ43P5vbq/oVKVSixfui1Wrfh9RbZYlaFr364MGDmASztfmpn248IfadquKQAntjmRfcruQ9kw48O5wuSdMgdwlZk1ABoCnYGpQLOwrRmQKulAoCkwOaQPNLNGZnYMsDdwppn9AKyRVDfk6QgMyaXutWbWGBgI/CcmvQZwMnAG8KykvYCbgFVmdhzwANAgXoHhjdAMSTN+3/Brnk6Ay07Knpb180VSUlI4oNr+zJs+n6vb3MCCmQu5udf10baSKVSveSCdLuhGn5se4s6+t2V24lzBMLOkf7ls19N3gfNitl8MDA9v6M8H6oXtebnBVDJcL28F7gtpNwOY2bHApcDQcL0EOAa4DGgMPARsNLN6wOfAlSHPYKBTaHN3YFAO9fcCTjezOsA/Y9LrhuM6FrhY0kGSqgGPAqeE7Y0knWNmdwGbzKyumV0OtAF+M7M6IZ6MildxOKYhwMXhWEsCN8ZkWWFm9YFnwnFkY2Y/Ac8C/UL9k4EpQJNwXt4E7ojZpQFwtpldRoL4I6kS0ANoFeqfAXQzs6eA34CWZtYywTFlxqqf1/8cL4vbQfGuRY93fpybWt/EHRfcwdGNj+aU808B4IWHXuCY449hwIgBHNvkWFYsXcHWrVsLu8nFRrJjVFGOVb6mzAF0lnRueH1Q+CojqWx4/TrQnKiD9m7I11LSHURTMyoAC4CPgBeAjpK6EQXpxrnU/UbM934x6f81s3TgO0UjdUcQdQr7A5jZfElz4xVoZoOJ3mjQ7MBTi+b/vCLq3PZnc9bl7QBY9NU37F+tcua2ylUrk/rH9ouf16xay6aNm5g0cgoA4z+eyBmXtAVg2dLlLJz1NVv/2srSX37nlx9+oXrN6iya800hHY1zSZH1eloTWCypCfAdUJvoxlcX4AMz2wQg6aM8lJ1x/Z1JdOMKouviAAAzWyRpCXB42DbezNYB6yStIbpGA8wDjpNUhmgq31vadhdmzxzqnwoMkfTfmLYAjDOzNeE4FgKHABWBCWa2PKS/RhRH3s9S5jygbxhJ+zh0lOKpDfxoZt+Gn4cSdUgzbubFnpvzyLvqRJ3kqsAewI8x2z7M+P2QOP40AY4CpoZzuAdRpzdXsbGq3cHtPFblw5lXnsnpl54OwHdzv6Ny1W2xqlKVStliFZCZtmnDJia8P4HadWrz6TufsvKPlTx0/UMA7LXPXpzU9iQ2rvNlgK7w+UhZMado0XMr4IRw93M2sBdRUOkIfEM0OtYMOIEo8OxFdDf1gnDH8vmwD8A7QFvgTGCmmeX2CCPLw+uMn+OM3bid6b2hH3BV6+u5qvX1TB49lTYXtAbgqPpHsn7tBlKXrcy2z2djp1HvxDoANGhan5++WwLA5FFTqXdiNGi6b/lyVP9HdX77eWkhHUnxlI4l/as4y+F6Ohy4iGhk7D2LbtPuyPUsLXzfyrabqjmVkxbzOj3m5/SwfwlgdRg1yvg6MlFhZnYD0ajQQcBXkirGqSejbXk6vtDJakDUOXtEUq8EWXMrL965yYsBRDM/jgWuZ1ssA9iQh/oFjI05f0eZ2dX5qN/tgI+HfUyntp3o1LYTn4/+nFPPPxWA2vVqs2HdBlYtW7Vd/hIpJShXvhwQzeJo3KoxS76NYlW58uXIuClx0c0XMWb4mEI8kuIn2TGqKMcq75S5fYmmZGwM8/AzVrhOIpoCMonojUVLIC3cDc0IWivCndYLMgozs83AaKIpJC/nof6LY77H3l28UFIJSbWAfxB1DqcQvbFB0lFEU2VcAfl83Bf89vNS3pz6Cnc81o0n7+mfue2lMc9lvn7mocF07NaeIWOf5/TzWzGwz7MAfDlhOmtXreWV8S/R/60neOaBwawNDwFxbjeV6Hr6LnAO0fTC4SFtCnBWWBNWhmiq9o6YBFwOIOlw4GCi62WuzGwt8KOkC8P+klQnUX5JtczsCzPrBawg6pwl8gVwsqRKklKIjn1i2PanpFKhzGpE0ypfBfoC9ROUtwioobD2GLgiprz8WAfELhjaF8iY594+h/0SxZ9pwEnatiZ6n/B7iFeXKwDTP53O7z//zouTX6TLo10Y1GPbDNwBIwcAUGqPUjzw6gM8PfppBo4aSOrvqYx6PZope+wJxzJ4wmCen/A85SuV582BbyblOJzz6YtuFHBDmIrxDVGAgWh07CBgkpltlfQLUVDEzFZLep7ozuZPwPQsZb5GNH0kL7eb9pT0BdENgktj0r8hCrgHADeY2WZJg4jWS8wl6ijOBdbk83hdPvS796m46Ve1vj7z9R+/LqPT+fEfPzywzzMM7PNMgbTNuSIo7vXUzFaFaX1HmdmXIW26pA+BOcASorVIO3I9G0S07nYe8BfQwczSFG9RaHyXA89I6gGUIlpXNSdB3sclHUY0OjQu5KsbL6OZLZV0NzA+5B9hZh+EzYOBuYoeDDUslJsO/Mn268Riy9ssqSPRVMuSRHHn2bweZIyPgLclnQ10AnqHMn8l+n3VTLBf3PhjZssVfSzAGxkPHiEaTfw2HOdISUsTrStzO8egnvGXQnZq2wmAtE1pdDmjS9w8U0dMZeqIqQXWNufySkV1sZvbdSl6gti+ZtYzl3w/EX2+zYos6UOI1ha8nSU9BSgVgnMtojcFh5vZlkR1+Joyt6ub/Ou4PL+7rlTu8KT/va9Y+61PM84jSWXMbL2kfYhGvK4zs1m57ecK347En/zwNWVuVzbi5xG7VJyCohmrfKTM7VSS3gNqET1xa2fbh+jxwqWI7rzeuLMConPOJcHgMBVuL2Cod8iKNI8/zrkC5Z0yt1OZ2blZ00JHLeuUkDvNrEaCMjokSF9H3h4b7VyxlO4zH3Yp4VHr25H0NHBSluT+ZpaXNbo7haR7gQuzJL9lZg8VUv2JYsbofJTRkegJl7GmmtnNO9Imjz/O7RwepxLzTpkrcPE6as4557Lb0U7DTm7DQ0Sfa5as+v92zAid2ELryDrn3N/lnTLnnHPOuSTa6qMHzhV73ilzzrndhD+4yTnnXFHmcSox/5wy55xzzjnnnEsi75Q555xzzjnnXBL59EXnnNtNpOPTQpxzzhVdHqcS85Ey55xzzjnnnEsiHylzzrndhC+gds45V5R5nErMR8qccwl1uf9m3pgyjCFjn+fwYw6Lm6dkqZLc/mhXXp88lFcnvszJ7Zplbmt51sm8Mv4lhn36Ir0G3lNYzXbOOVeM3NjnBl6e/CLPjBnEocfUipunZKmSdPl3Z16c+DwvjB9M07bRZ7Qfc/wxDBwxgBE/fkzTdk0Ls9nObcdHypxzcTU5pTHVa1bn0qZXclT9I7ntkS5cf9Yt2fJd2flyVqWu5rJm7ZFEuf3KAlC95oH865ZLufGczqxfs579Ku5X2IfgnHNuN9eoZSMOrFmNjs2u5oh6R9Dp4Vvo8s+u2fJd2ukSVqeu5uqTr0USZUOsWv7rMp7o9gQXXH9+YTfdue34SJnLJOknSZWS3Y68kNRC0sfJbsfurOnpJzHq7TEALJz1NWX2LUPF/Stky9fukja8OuANIJqWsGbVWgDOuuwM3hvyIevXrAdgderqQmp58ZVulvQvV/RImiCpYbLbsbNIWp/sNuSVpA6SBia7HbuzE1o34ZN3xgGwaPYiSpcrQ4X9y2fLd/rFrXlz4HAgilVrQ6z643/L+HHRT379KiTJjlFFOVb5SJkrMiQJkJmlJ7stDipXqcSy35Zn/rx86XIqValE6rKVmWllypUG4Jo7OlLvhDr8uuQ3+t07gFUrVnHQP6oDMOj9/pRIKcFLTwzjywnTC/cgnHNuJ5OUYmZbk90OF6lUpSLLf1uR+fOKpSuoWKUSK5etykwrHWJV+9uv5Lgmx7F0yVKe7jmI1Sv8ZqErOorVSJmk9yXNlLRA0nWSbpT0WMz2DpIGhNc9JS2SNFbSG5K651DutZKmS5oj6R1J+0hKkbRYkf0kpUtqHvJPlnSopMaSPpM0O3yvHbO9bkz5UyUdl6DuyqGNsyQ9J2lJxmiXpG6S5oevWxOdhzhllpb0f+F45ku6OIdj/7ekhZLmSuob0g6Q9F7Yf46kExO1R1INSV9LGgTMAg6S1FrS5+GY3pJUJuRtE34nU4DzcmjTdZJmSJrx+4ZfE2VzuZCyp2VdoJuSksIB1fZn3vT5XN3mBhbMXMjNva6PtpVMoXrNA+l0QTf63PQQd/a9LbMT5wqGFYF/bscVVIwKLpT0paRvJTULZewl6WVJ80IcahlTz/uSPpL0o6RbwvV7tqRpkiqEfLUkjQptnizpiByOrVbYd7qk+xVGu0KMfDzEhXkZ8UZSGUnjQhyYJ+nsOGVWlTRJ0ldh/2ZZ84R8KZKGxNTRNaQfKumTEKdmhTYmak8LSeMlvQ7MC2n/Cuf0qxB/U0J6x3CeJwIn5XBOMmPV/9b/ksuvzyUUJ1jFi1WVq1Vm4fSF3NKuE1/P+ppre1xTWC10MZIdo4pyrCpWnTLgKjNrADQEOgPvsv2b+4uB4YqmeZwP1Avbc5v28a6ZNTKzOsDXwNXhLtq3wFFAU2Am0EzSnkB1M/seWAQ0N7N6QC/g4VDeC0AHAEmHA3ua2dwEdd8HfGpm9YH3gIPDfg2AjsDxQBPgWkn14p0HSRWzlNkG+M3M6pjZMcCoeBWHwHwucLSZHQc8GDY9BUwM56M+sCCX9tQGhoXzsAHoAbQKxzQD6CZpL+B54CygGVAlwfnAzAabWUMza1il9IGJsrk4zm1/Ni+NeY6XxjzHit9T2b9a5cxtlatWJvWP1O3yr1m1lk0bNzFp5BQAxn88MfOBIMuWLmfKmM/Y+tdWlv7yO7/88AvVa1YvvINxbtdTUDEKoKSZNQZuJYobADcDmNmxwKXA0HCtBTgGuAxoDDwEbAzX6M+BK0OewUCn0ObuwKAc6u8P9DezRsBvMennAXWBOkAr4HFJVYHNwLkhDrQEnpCyvfu+DBhtZhn7f5Wg7rrAgWZ2TDjWl0P6a8DTIVadCCzNoT2Ec3GvmR0l6Uii38dJof6twOUhbx+izthpRO8B4oqNVdXLHJQom4vjrPZnMmjUQAaNGsjKP1KpXG3byotKVSuxMkusWrtqLZs3bmbqqM8AmPzxZA475tBCbbNzuSlunbLOkuYA04CDgJrAYklNQsekNjCVqBP1gZltMrN1wEe5lHtMuEs4D7gcODqkTwaah69HQrmNgIw5XPsC/9/efYdZUaRvH//egooCoiLoAioGFDMSRBT8KSjmgKLoKgKuIiZU1vCuOac1rCuIIqtgWNd1FbMECRIkCQbAnFABSRJEggrP+0f3jIeZM0OQmQMz9+e65po+1dVdVe3IM091dc/zkiYDD2Qc9zxwnKSNgXOAPsW03Rz4D0BE9AfmZZT3i4ifI2IRSXDPm0UseB0KvlZvEnC4pLsltYiIBUW0vZAkcPaWdDKwOC1vCfRM+7Q8Pb64/kyNiDHp9oEkQWyUpPeBDsCOQH3g64j4PJIpsKeLuSa2lvr1fZlzWp/POa3PZ8SAURzVtjUAezbcg0ULf15p6WKedwaNYf+D9gOgUfOGfPP5VABG9B/F/gclN3yrbbUFdXauw/RvZ5TSSMw2SCUVoyD5NxeSCcK66XZz4CmAiPgEmArslu4bGhE/RcRsYEFGG5OAukpWMBxEEsPeBx4F8pKXbJqRxDaAf2eUNweeTWPFTOBtkjgp4A5JHwJvAbWBbQucczzQSdJNwD7ptcjmK2BnSQ9JOgpYKKkqSaLWLx3/0ohYXEx/AMZFxNfpdiugETA+HX8rYGeSicdhETE7In4BnivmmthaerXva1x41MVceNTFvDNgNIef0gqA+vvXZ/FPP6+0dDHPmLfGsm+zZNFRg+YNmPr5t6XaZ7NVKTfPlEk6lGTWq1lELJY0DKhE8g/maSR3rfpFRGSZjVuVPsBJEfGBpI7AoWn5CKALUIvkTtiV6b7h6f5bSQJfG0l1gWEAaf8GASemfStuFrSovmYtL+Y65IuIz9I7W8cAd0oaGBG3FDxXRPwm6QCSYHQ6cDFJQrYm/YTk7lhmvUERcUaBfjeA9fR+cxk1evBYDmzZlP+MeoqlS5ZyZ7e/5+97fOCjnNM6WabY8/ZeXPfPv9H1pouY/+N87rg8qTdu2HgO+L/GPDX0cZYvX07PW3vlP1htJWN9fXjZVq2EYxTAsvT7cn6P/cWdZ1nG9oqMzyvS4zcC5qd3if6IovpwJlADaBQRv0r6hsKxariSxwKOBZ6S9PeIeLLgiSJinqT9gCNJ7g6eRnLHcE36A4VjVd+I+NtKB0sn4VhVqsYNGU+Tlk14YuTjLFuylPv++kD+vof7d+fCo5K3Bv/rjse56sEr6HLT+SyYu4D7/no/ALvttxs3PHY9VatV4cDDm3J2t7PofHiXnIylPHCcKlq5ScpI7krNS4NdfZI7MpDMHl5LMkN4dVo2EnhU0p0k1+hYkqVzRakKzEjvbJ0J5D3INBZ4EvgqIpams2nnA8dl9CmvbscC5+xNMjM5IiIK35743UiSAHO3pNZA3iuHhgN9JN1FEjzaAO1Jljdmuw75JNUCfoyIp9N1/wX7llevCrB5RLwhaQzwRbprMHAB8I90jX3lYvpT0Bigh6RdI+ILSZsDdUh+IdlJ0i4R8SXJUhsrYQ9c+8+s5XkJGcDMabO45JTCrx8G6H5zT7rf3LNE+mZWxpRkjCrKcJKYNSRdKr8D8CnJsvNiRcRCJc+bnRoRz6eJ4r4R8UERh4whWXL5HMkkXmYfzpfUF9iaZGXJlSRLA2elCdlhJCsmViJpR2BaRDwmqXLa70JJmZLnrH+JiBckfQn0Sfv/vaSTIuKl9NGCCsX0p+DzcoOBlyU9EBGz0uX8VUni/oPpnc2FwKlAUdfE1pEe12VfOZuXkAHMmjaLK9peVajOZx98xlkHZPt1xKx0laekrD/QJV0K8SlJgMibQfsI2DMixqVl4yW9QvIP6VSS55qKWsIHcD3JP8RTSZZ2VE3Ps0zSd3ltkdw5OyOtA3APyRr+bsCQzBNGxARJC/l97XtRbgaeTR9GfptkTfxPETFRUh9gXFqvd0S8l4610HUoYB+SdfQrgF9JEqxsqpIEpUokiVbeb+aXAr0k/YVkVvaCiBhdRH/qFhj37PRu47NpkAS4Lr171xl4XdIckl9K9l7FtTErVwo+3G4blJKMUUV5GHgkXXr/G9AxjVure/yZQE9J1wEbkyylLyoBuQx4WtJfgdcz+tuPZGnjByR3mK6KiB8kPQO8KuldkmfFPslyzkOBKyX9Cizi92fdCqoNPCEp75GNvLtb7UmS21tIYt2pxfRnpaQsIj5Kxz0wPe+vwEURMSZdTjmaJB5PJEn2zAzHqeLIFyc7SVUiYlF6p2Y40DkiJpZi+7VIljPWj2JeEZ8mLsvTpYTNgJ7rYDlJmdGidiv/gNsGbcS0wav9G3KlSjvk/Od96dJv12Zpna2hXMeoNZX2c0m6/PJ04IyIKPRGxfLqyO2Pzvn/u2Zra8B3b25QcQrWz1hVnu6UralekvYkWcPet5QTsrNJ3nbVrbiELLUD8N90pu4X4LyS7p+ZmeVczmLUWmoEdE+XOc4neYmVmZmlnJQVISL+XLBMUg8K/82RByNiVUsM17TtJymwLl5SJ5JlgZlGRcRFJK9FLlGS+pG8CSzT1RExoKTbNrPVs77+7RVb93IZo4oj6VqSZYCZno+I20leM1/S7Y8FNi1Q3D4iJmWrb2aly3GqaE7K1kCaAOWq7SdY9fNlJdl+m1y1bWZmq5bLGJXRh9tJVnrkqv2muWrbzOyPcFJmZdoKz8iYmdl6zncPzMxJmZlZGeEXN5mZ2frMcapoG626ipmZmZmZmZUU3ykzMysjPANpZmbrM8epovlOmZmZmZmZWQ45KTMzMzMzM8shJ2VmVqTLbrmY50Y+Rd9Bj7Hb3vWy1qm4cUWuursbz47oy7/f7sOhx7TI39fy+P/j6aGP8/SQx7mx+7Wl1e1yK9aDLzOz0nbhzRfwxIjHeWRgT3bde9esdSpuXJHL7urK42/35l9DH6P50cmf9Nun6d70eKM7b379Oi2OaV6a3S6Xch2j1udY5WfKrMRJagO8COwREZ9Iqgt8DHwKbAIMBy4EdshWHhErJNUDHgD2AOYDC4EbI2J46Y6m/GjWsil1dqpNu+bt2avhHlxx52V0Pr7wn0Hq0PVM5s2dzxktOiCJLbasCkCdnWrT/uI/c8FJXflpwSK2rL5laQ/BzKxYkhZFRJWMzx2BxhFxsaSbgPOA2SS/L10TEa8UVZ4efxZwFVAB+A0YD1wREfNLbVDlTJPDmlB7p1p0anEO9fevT9c7LqbrCZcVqnfGJaczf+4Czvm/c5FE1TRWzZo2m3u73Ufb808p7a6brcRJmZWGM4CRwOnATWnZlxHRQFJFYAhwEjAxW7mkN4DXSQJbXuDbG2hMkrhZCWh+5EH0/98gAKZM/Jiq1apQvebWzJ3140r1jj39aP58SEcgeYB3wbyFAJzw52N5sc/L/LRgEQDz5/p3kpL22y/TlOs+mJUxD0TEvZL2AEZIqllMeWvgcuDoiJgmqQLQAdiWZDLRSsBBrZsx6IXBAHzy3idU3qIKW9fcmh8LxKqj2h3JXw49F0hi1cI0Vs38fmZ+mZU8x6miOSmzEiWpCnAwcBjwCr8nZQBExG+S3gF2JUnKspWfCYzOS8jS/ZOBySU+gHKsxnbbMGv6rPzPs2bMpsZ226yUlFXZojIA513Vif2bNWDa1Oncf+0/mTdnHtvvXAeAni/9kwoVNuJf9/Vl7LDxpTsIM7N1ICI+lvQbsE0x5deSTB5OS/ctBx4v9c6WM9W3q87s6bPzP8+ZMZvq21VfKSmrnMaqDld2YN8D92XG1Bl0v74H8+c4V7b1h58ps5J2EtA/Ij4DfpTUMHOnpM2BVsCkYsr3IiNhWxVJnSW9K+ndH36e/kf7X25JhSezCs4kVqhQgW1r1WTS+Mmcc9T5TJ4whYtv6JLsq1iBOjvV4eK2l3Pjhbfx/+69Ij+JMzNbT2wm6f28L+CWbJUkNQVWkCxZLKp8rWPV94u+W+sBlHfZYhVZYlWNWjWYMn4KFx1zMR9P/JjO151XSj00Wz1OyqyknQH8J93+T/oZYJc0AI4CXo+IN1dRnk9SP0mTJb2YrcGI6BURjSOi8XaVa63TwZR1J3c4kT4De9FnYC/m/DCXmrVq5u+r+acazJk5d6X6C+YtZMniJbz95kgAhr72NrunLwSZPWM2IweOYvlvy5nx3Q98++V31NmpTukNxsxs1ZZERIO8L+CGAvsvT2PSvUC7+H1mqqhyACTtkyZ6X0pql63hzFhVp8r263hYZdvxHY6nZ/8e9Ozfg7kz51KjVo38fdv8qQZzZ668dHHhvIUsXbyUUf3fAWD4a8OLfCGIWa54+aKVGEnVgZbA3pKC5MHnAB4mfXYsy2HZyqcAh+R9iIg2khqTBENbh17s+zIv9n0ZgGatmnJKx5N46+Uh7NVwDxYt/LnQ82QAowaNZv+DGjBx1Hs0bt6Qrz+fCsDw/qM44qSWvPHfAVTbagu237kO07+dUarjMTP7gx6IiGyxJlv5FKAhMDQiJgENJHUHNivpTpY3r/Z9lVf7vgrAAS0P4MSOxzPs5WHU378+P//0c6HnyQDGvDWG/Zrty/vvfECD5vvz7efflna3zYrlO2VWktoCT0bEjhFRNyK2B74G1vR2yb+BgyWdkFG2+brqpGU3evBYpn87g/+Oepqr7/kr913zj/x9fQb2yt9++PbH+Eu3DvQd9BhHnnIE3W/uCcDYYeNZMG8hTw99nIeev58etz6a/2C1mVkZdCdwr6TMGOeErISNGzKOGd/+QJ+Rj3P5PZfy0LXd8/f17N8jf7v3HY/TvttZPDKwJ4ef3JJHb03i2G777cYz457ikGNbcOldXen11qOlPgYzAPltM1ZSJA0D7oqI/hllXYGjge0jYu8C9esCrxUsT/fVB+4H6gMzgZ+AeyLireL6cHDtlv4Btw3aqGlD/KYqsxKyGq/EX1TwjlhR5em+DsAVJCtD5pO8kOrGiCh2mUDr7Y9yrLIN1sDv+jtOrQNOyqxMc1JmGzonZWZln5My25A5KVs3vHzRzMzMzMwsh5yUmZmZmZmZ5ZCTMjMzMzMzsxxyUmZmZmZmZpZD/jtlVqYtjxW57oKZmVmxVuD3fJiVd75TZmZmZmZmlkNOyszMzMzMzHLISZmZmZmZmVkOOSkzMzMzMzPLISdlZmZmZmZmOeS3L5pZkbrdegnNWh7IsiVLufXyu/h00ueF6lTcuCJX3H4pDZs1YEUEj97Vm6FvDGfb2jW54R9/o0q1KlTYaCN63NGL0UPG5mAUZmZWll108wUc0PIAli1Zyj3d7uOLyV8UqlNx44pccutF7NdsX1asCJ64pw8j3hzJKeedzDGnH8Xy5cuZP3cB915xP7OmzcrBKKy8c1JmZh9kaSAAACAASURBVFk1a9mU7Xeqw6kHn8leDffkqjsv5y/HXVioXsdLz2LenPmc1qI9kthiqy0A6HRpewa/OpQXn3yFuvV25IGn76ZN09NLexhmZlaGHXBYE2rvVJsOLTqxx/71ufSOS7jkhEsL1fvzJWcwf+58Ov7fX5BE1S2rAvDF5C+58NhLWLZ0Gce3P47O157LbRfeUdrDMPPyRVu/SeojqW2u+1EeHXLkwbzxvwEATJn4EVWqVaF6za0L1Tv+9GPo+9AzAEQEC35ckL9duWplAKpsUZnZM+eUUs/NrCySVFfS5Fz3IxtJwyQ1znU/yqODWjdj0AtvAfDxe59QZYvKbJ0lVh3V7kie7f4fIIlPC+ctBOCD0R+wbOmy5PiJH7PNdtuUUs/NVuY7ZWaWVY3tajBr+uz8z7Omz6bGdjWYO+vH/LIqW1QB4PyrzqHhQQ34/pvp3Hftg/w4Zx697+vDg8/ey6mdTqbS5pW4pN1fS30MZmZWtm2z3TbMzohVs2fMYZvtqvNjRqyqvEUyQdjxyg7sd+C+TJ86g4eu78H8OfNXOtdRpx/F+GHjS6fjZgX4TpmtRNJLkiZImiKps6TTJN2f7rtU0lfp9i6SRqbbN0gaL2mypF5K7CJpYsZ560maUEy7jSS9nbY9QNKfstRpJek9SZMkPS5p0yLO1VnSu5LenbV4+h+9JOWWVLgsIlb6XKFiBbatVZMPx0+mw5GdmTxhCpfccAEArU9qxRv/7c8JjU+lW/uruemha1C2k5qZrb4Kkh5LY9RASZtJaiBpjKQPJfWTtBXk3716QNJwSR9LaiLpRUmfS7ot74SSzpI0TtL7kh6VVKGoxiW1ljRa0kRJz0uqkqXOGWmcmizp7mLOlR+rpi36/o9el3JrtWJVhQrUrFWDKeM/4oJjLuajiR9z/nXnrVSnVZuW7L5vPf77yP9KsrtmRXJSZgWdExGNgMZAV2AU0CLd1wKYK6k20BwYkZZ3j4gmEbE3sBlwXER8CSyQ1CCt0wnok61BSRsDDwFt07YfB24vUKdSeny7iNiH5C7vBdnOFxG9IqJxRDSuuXmtNR1/uXZKx5N4clBvnhzUmzkz51KzVo38fTVr1WBOgSWIC35cwJLFSxj2ZvKjMPi1Yey+Tz0Ajj/jGN56dSgAkyd8xCabbsKWW1crpZGYWRlVD+gREXsB84FTgCeBqyNiX2AScGNG/V8i4hDgEeBl4CJgb6CjpOqS9gDaAQdHRANgOXBmtoYlbQNcBxweEQ2Bd4FuBerUAu4GWgINgCaSTsp2vsxYVbtKnbW4FOXXCR2O55H+D/NI/4eZO/NHamTEqhp/2oa5M39cqf7CeQtZsngpI/uPAmD4ayOot3e9/P0Nm+/Pny85g+vPuZFff/m1dAZhVoCTMiuoq6QPgDHA9ulXFUlV0+1/A4eQJGh5SdlhksZKmkQSiPZKy3sDndJZx3bpsdnsThIkB0l6nyToFYxQuwNfR8Rn6ee+aT9sHXqhz0ucfcS5nH3EubzdfyTHtD0SgL0a7smihT+vtHQxz8hBo2l4UJJ7N2neiK8/mwrAzGmzaNK8EQB1d92BTTbdhHlz5xc63sxsDXwdEe+n2xOAXYAtI+LttKxgbHgl/T4JmBIRMyJiGfAVSUxrBTQCxqfxpxWwcxFtHwjsCYxK63YAdixQpwkwLCJmR8RvwDM4Vq1zr/R9lS5HXUiXoy5k1IB3OOKUwwHYY//6/PzT4pWWLuYZ89YY9mu2LwD7N2/A1M+TWLXrXrtw2V1dueGcG5k/d0HpDcKsAD9TZvkkHQocDjSLiMWShgGVgNEkd7o+JUnEzgGaAX9N72A9DDSOiO8k3ZQeA/ACyYzlEGBCRMwtqmmSYNmsuO79gaHZWnhn8BgOatWU/73zDEuXLOO2y39fhfPkoN6cfcS5APS47VFufOgaLr/5YubNnc9t3ZJ6D978MNfcewWnn9eWAG69/K5cDMPMypZlGdvLgS1Xs/6KAseuIPkdSEDfiPjbarQtYFBEnLGKOlaKxg4ZxwEtm/DkyCdYtmQZf//rffn7Hun/MF2OSt4a/Ngd/+L/PXgVF97UJXn1fVqv87Xnsdnmm3H9I9cBMGv6LG4456ZSH4eZkzLLVA2YlyZk9UlmBQGGA7ekX+8BhwFLImKBpLyAOCddW98W+B9ARCyVNADoCfylmHY/BWpIahYRo9PljLtFxJSMOp8AdSXtGhFfAO2Bt7OdzNade695MGt5XkIG8MO0mVxwcuHXD3/z+VQ6n3hJifXNzAxYAMyT1CIiRrDmsWEw8LKkByJilqStgaoRMTVL3TFAj7w4JGlzoE7GCg6AscCD6VLHecAZJMvzrQQ9dF2PrOV5CRnArGmz6Nb2ikJ1rvrz/yuxfpmtCSdllqk/0EXShySJ0pi0fATJMo/hEbFc0nckSRIRMV/SYyRLQ74BCr626BngZGBgUY1GxC/pa+//Kakayc/lP4ApGXWWSuoEPC+pYtrOI39wvGZmtuHrADySJklfkazsWC0R8ZGk64CBkjYCfiV57qxQUhYRsyV1BJ7NeNHUdcBnGXVmSPobMJTkrtkbEfHy2g3LzMoTFXxDjdm6JOkKoFpEXJ+L9g+sdah/wG2DNmb6MC+HMivjDt/+SMcq22C99d0Ax6l1wHfKrMRI6kfyEHbLXPfFzMzMzGx95aTMSkxEtClYliZqOxUovjoiBpROr8zMzIonaSxQ8G9hto+ISbnoj5mVfU7KrFRlS9TMzMzWJxHRNNd9MLPyxUmZmZmZWQ75+X4z8x+PNjMzMzMzyyEnZWZmZmZmZjnkpMzMzMzMzCyHnJSZmZmZmZnlkJMyMzMzMzOzHPLbF82sSN1uvYRmLQ9k2ZKl3Hr5XXw66fNCdSpuXJErbr+Uhs0asCKCR+/qzdA3hrNt7Zrc8I+/UaVaFSpstBE97ujF6CFjczAKMzMryy665UKatmzCsiXLuOfye/l88heF6lTcuCKX3HYxDZrty4oVweP3PMGIN0Zy3FnHcmLHE1ixfAVLfl7CA1f/g6mff5uDUVh556TMzLJq1rIp2+9Uh1MPPpO9Gu7JVXdezl+Ou7BQvY6XnsW8OfM5rUV7JLHFVlsA0OnS9gx+dSgvPvkKdevtyANP302bpqeX9jDMzKwMO6BlE+rsVJuzm3dij4b1ufTOrlx8fNdC9c7segbz58ynwyHnIImqW1YFYMhLQ3nt6dcBaHbEgXS58Xz+dta1pToGM/DyRSsFkr6RtM1q1r1J0hXp9i2SDk+3W0iaIul9SZtJ+nv6+e8l2ffy7JAjD+aN/w0AYMrEj6hSrQrVa25dqN7xpx9D34eeAZK/tbPgxwX525WrVgagyhaVmT1zTin13MxKg6Rhkhrnuh/riqRFa1C3j6S26XZvSXum26dK+ljS0PTzs5I+lHR5yfTaDm59EAP/NwiAjyd+QpUtKrN1llh1VLujeLb7f4AkPi2ctxCAxYsW59eptHkl8J+MsxzxnTJbb0XEDRkfzwTujYgnACSdD9SIiGU56Vw5UGO7GsyaPjv/86zps6mxXQ3mzvoxv6zKFlUAOP+qc2h4UAO+/2Y69137ID/OmUfv+/rw4LP3cmqnk6m0eSUuaffXUh+DmVlJi4hzMz7+BbgwIoZK2g44KCJ2zFHXyoVttqvO7IxYNXvGHLbZrjo/ZsSqylskE4SdruzAfs32ZfrUGTx0XXfmzZkPwIkdjqfteadQcZONuaLdlaU7ALOU75StA5JekjQhvXPTWdIFku7J2N9R0kPp9vWSPpE0KJ1Bu6KY854nabykDyS9IGlzSRUkfaXElpJWSDokrT9C0q6SDpD0jqT30u+7Z+xvkHH+UZL2LaLtGmkfJ0p6VNLUvLtdkrpJmpx+XVbUdViD63etpE8lvQXsnlHeR1JbSecCpwE3SHpG0itAZWCspHar246tGalwWcTKU4gVKlZg21o1+XD8ZDoc2ZnJE6ZwyQ0XAND6pFa88d/+nND4VLq1v5qbHroGZTupmZWokopRqVMljZP0maQW6TkqSXpC0qQ0Dh2W0c5Lkl6V9LWki9N48p6kMZK2TuvtIql/2ucRkuoXM7Zd0mPHp6srFqXlSldUTE770S4tryJpcBrbJkk6cTWvoSR1l/SRpNeBmhn7hklqLOkGoDnwiJJVHAOBmkpWeLRYnXZsLWSJKwVCFRUqVKBmrRpMfncKXY6+iI8mfMz51//+a8rLfV+lffOOPHZHb87qemZJ99gsKydl68Y5EdEIaAx0BV4ETs7Y3w54Tskyj1OA/dP9q1r28WJENImI/YCPgb9ExHLgM2BPkn/8JwAtJG0K1ImIL4BPgEMiYn/gBuCO9Hy9gY4AknYDNo2ID4to+0ZgSEQ0BPoBO6THNQI6AU2BA4HzJO2f7TpIqr6K8eWd7/SMa9KkYJ2I6A28AlwZEWdGxAnAkohoEBHPZTlnZ0nvSnp31uLpq+qCZTil40k8Oag3Tw7qzZyZc6lZq0b+vpq1ajCnwBLEBT8uYMniJQx7cwQAg18bxu771APg+DOO4a1XhwIwecJHbLLpJmy5dbVSGomZZSipGAVQMSIOAC4jiRsAFwFExD7AGUBfSZXSfXsDfwYOAG4HFqexajRwdlqnF3BJ2ucrgIeLaf9B4MGIaAJk/oN/MtAA2A84HPi7pD8BS4E2aWw7DLhPqzdb1IZk0nAf4DzgoIIVIuIW4F3gzIi4EjgB+DKNVSMK1s+MVdN+/n41umB5TuxwPI8O6MmjA3oyd+ZcamTEqhp/2oa5M+euVH/hvIUsWbyUkW+OAuDt14ZTb+9dC5136MvDOOjIQv9pzUqFk7J1o6ukD4AxwPbATsBXkg5ME5PdgVEkSdTLEbEkIn4CXl3FefdOZwknkSzf2ystHwEckn7dmZ63CTA+3V8NeF7SZOCBjOOeB46TtDFwDtCnmLabA/8BiIj+wLyM8n4R8XNELCIJ7nkzgAWvQ71VjI/02H4RsTgiFpIkX39IRPSKiMYR0bjm5rX+6OnKlRf6vMTZR5zL2Uecy9v9R3JM2yMB2Kvhnixa+PNKSxfzjBw0moYHJTdgmzRvxNefTQVg5rRZNGneCIC6u+7AJptuwry580tpJGaWoaRiFCQxAJIJwrrpdnPgKYCI+ASYCuyW7hsaET9FxGxgQUYbk4C6kqqQJDzPS3ofeBT4UzHtNyOJbQD/zihvDjwbEcsjYibwNkmcFHCHpA+Bt4DawLarMc5DMs43HRiyGscUKzNW1a5c54+erlx5ue+rnH/kBZx/5AWM6v8OrdseAcAeDevz808/r7R0Mc+YQWPYr9l+ADRs3iD/DYu1d/r994QDWzVl2tfTSmEEZoX5mbI/SNKhJLNwzSJisaRhQCXgOZIld5+QJB2xmrNxmfoAJ0XEB5I6Aoem5SOALkAtkjthV6b7hqf7byUJfG0k1QWGAaT9GwScmPatuFnQovqatbyY67A6/FjteuidwWM4qFVT/vfOMyxdsozbLr87f9+Tg3pz9hHJYxQ9bnuUGx+6hstvvph5c+dzW7ek3oM3P8w1917B6ee1JYBbL78rF8MwK9dKOEYB5D3Xu5zff6co7jyZzwGvyPi8Ij1+I2B+RDQoeOAaKqoPZwI1gEYR8aukb3Cs2qCNHTKOpi0P4KmRfVi6dBl/73Zv/r5HB/Tk/COTJfW97ujN3x68motu7sL8uQvy653U8UQaNt+f335bzqIFP3H35X5/mOWGk7I/rhowLw129UmW9EEye3gtyQzh1WnZSOBRSXeSXPtjgceKOXdVYEZ6Z+tMIG/6ZizwJPBVRCxNZxPPB47L6FNe3Y4FztmbZGZyREQUnkr63UiSgH23pNbAVmn5cKCPpLtIgl4boD3J8sZs12FVMs9XETieZGbU1gP3XvNg1vK8hAzgh2kzueDkSwvV+ebzqXQ+8ZIS65uZrZaSjFFFGU4Ss4akS+V3AD4FGq7qwIhYmD5vdmpEPJ8mivtGxAdFHDKGZMnlcyRL4TP7cL6kvsDWJHe6riRZqjkrTcgOA1b3JRx553uS5Hmyw1j5zpzl0D+v6561PC8hA5g1bRaXty38wqkeN/YssX6ZrQkvX/zj+gMV06UQt5IECCJiHvARsGNEjEvLxpMsz/uAJCC+S7J8oyjXkyRgg0hmM0nPswz4Lq8tkjtnVUmWfwDcA9wpaRRQIfOEETEBWAg8sYpx3Qy0ljQROBqYAfwUERNJ7uCNS/vWOyLeK+o6rEp6vueA94EX0rGYmdm6UZIxqigPAxXSpffPAR3X8E25ZwJ/SZdcTiFZ3VGUy4BuksaRLHPM628/4EOSsQwBroqIH4BngMaS3k3b+aTwKbPqB3xOEmd7kiyHNDNbZ1TwbWpWsiRViYhFkjYnmXnrnCYmpdV+LZLljPUjYkUx9TYFlkfEb5KaAT3XwXKSUndgrUP9A24btDHTh/mVlVZqch2j1lTazyXp8svTgTMiYrXeqLg+aVWntWOVbbAGfz/QcWod8PLF0tdLyR+ZrAT0LeWE7GySt111Ky4hS+0A/FfSRsAvJG+bMjOzsi1nMWotNQK6p8sc55O8xMrMbIPjpKyURcSfC5ZJ6gEcXKD4wbw/lLwO236S5Fm0zLY7AQUfCBoVEReRvBZ5nUjf8DU4y65WETE3S7mZmZWyXMao4ki6Fji1QPHzEXE7yWvv11U7+5C+OTLDsohouq7aMDPLxssXrUzz8kXb0Hn5olnZ5+WLtiHz8sV1w0mZlWkVN6ntH3DboP32yzQHO7MyzrHKNmSOU+uG375oZmZmZmaWQ07KzMzMzMzMcshJmZmZmZmZWQ45KTMzMzMzM8shJ2VmZmZmZmY55KTMzMzMzMwsh5yUmVmRHrj/Fj75aCQTJwxi/wZ7Z61z6qknMHHCID54fwh33Xltfvl9f7+Jd8cP5N3xA/loygjmzPqotLptZmbliGOVlQVOyjZAkm6SdEWW8rqSJueiTyVF0i2SDs91P8qjo49qSb1dd6L+ns254IKr6dH9zkJ1tt56K+6+8zpaH9mO/Rq0pGbNGrQ8rDkAf73yJho3aU3jJq3p0eNx+r30ZmkPwcxyYF3GqA0lrknqLWnPXPejPHKssrLCSZmt1yLihoh4K9f9KI+OP/5InnrmfwCMHTeRaltWY7vtaq5UZ+edduDzz79izpwfARg8ZARt2hxT6FyntzuJ5557qeQ7bWaWAxFxbkT4FksOOFZZWeGkbD0kqZukyenXZWnZtZI+lfQWsHtG3UaSPpA0Grgoo3wvSeMkvS/pQ0n1imnvJUkTJE2R1DktO03S/en2pZK+Srd3kTQy3b5B0vi0n72U2EXSxIxz15M0oYh2D5D0Yrp9oqQlkjaRVCmjvT6S2qbb30i6WdJESZMk1V+7K2yro3at7fj+u+n5n6d9P4PatbZbqc4XX37D7rvvyo471qFChQqceMKRbL99rZXq7LBDberW3Z4hQ0eVSr/NrGSVdowCKkh6LI1RAyVtlp6jgaQx6fH9JG2Vlg+T9ICk4ZI+ltRE0ouSPpd0W0Yfzsrow6OSKhQx3tWJh8MkNU63F0m6PR33GEnbrtWFttXiWGVlhZOy9YykRkAnoClwIHBeWnY6sD9wMtAk45AngK4R0azAqboAD0ZEA6Ax8H0xzZ4TEY3Sel0lVQeGAy3S/S2AuZJqA82BEWl594hoEhF7A5sBx0XEl8ACSQ3SOp2APkW0OzEdU14bk9OxNQXGFnHMnIhoCPQECi2PAZDUWdK7kt5dseLnYoZtxZFUqCwiVvo8f/4CLr7kbzz7TE/eHtqPqd98z2+//bZSnXanncgLL77OihUrSrS/ZlbychSj6gE9ImIvYD5wSlr+JHB1ROwLTAJuzDjml4g4BHgEeJkkIdwb6CipuqQ9gHbAwWkflgNnFtH+6sTDTJWBMRGxX3rsedlO6li1bjhWWVnhpGz90xzoFxE/R8Qi4EXg2LRscUQsBF4BkFQN2DIi3k6PfSrjPKOBayRdDewYEUuKabOrpA+AMcD2QL2I+AGoIqlqWvZv4BCSgJQXhA6TNFbSJKAlsFda3hvolM46tkuPLSQifgO+SIPjAcD9Wdoo6MX0+wSgbhHn7RURjSOi8UYbVS5m2FbQBV065D/wPH3GD9TJmEmsXedPTJ8xs9Axr70+iIOaH0/zQ07g08++5Isvvl5p/2mnnchzz71c4n03s1KRixj1dUS8n25PAOpmOXdfkviR55X0+yRgSkTMiIhlwFckMa0V0AgYL+n99PPO2RpfzXiY6Rfgtcz+FnFex6q15FhlZZGTsvVP4SmfRGQpUxHlRMS/gROAJcAASS2zNiYdChwONEtn9d4DKqW7R5PMiH5KEnhaAM2AUZIqAQ8DbSNiH+CxjONeAI4GjgMmRMTcogabnvdo4FfgLZKA35xkdjGbZen35UDFYs5ra6HnI33zH3h+5ZUBtD+zLQBND2jIwgUL+eGHWYWOqVGjOgBbblmNLl068K/Hn83ft9tuu7DVltUYPebd0hmAmZW0Uo1RqWUZ26v7b3/eMSsKHL8iPV5A34hokH7tHhE3FXO+IuNhlrq/xu+3ahyrSoBjlZVFTsrWP8OBkyRtLqky0AZ4HWgjabN0pu54gIiYT7JUsHl6bP7SC0k7A19FxD9JZgz3LaK9asC8iFicPqN1YIG+XJF+fw84DFgWEQv4PQGbI6kK0DbvoIhYCgwgWWL4xGqM9zJgdETMBqoD9YEpqzjOStgbbw7mq6+/5dOPR/HII/dw8SXX5O97d/zA/O0H7r+FDz8YyvC3X+Kev3fn88+/yt93ersT+e/znnk0K0NKO0ZllcaheZLylhW2B94u5pCCBgNtJdVM+7O1pB2LqV9cPLQccqyyssKzN+uZiJgoqQ8wLi3qHRETJD0HvA9MZeXlEp2AxyUtJkmE8rQDzpL0K/ADcEsRTfYHukj6kGQGcEzGvhEkSzWGR8RySd8Bn6T9nC/pMZKlId8A4wuc9xmSZwsGUryxwLb8fmfsQ2BWxiyj5VDXS6/NWt64Sev87bPaX5S1DsAtt96/zvtkZrmTgxhVnA7AI5I2J1mW2GkNxvGRpOuAgZI2IlmtcVHa/2yKjIeWe45VVhbIv/taSVDyN2qqRcT1uexHxU1q+wfcNmi//TKtqOViZlZGOFbZhsxxat3wnTJb5yT1A3YhefmHmZmZmZkVw0lZOZG+5n5wll2tVvEijjUWEW2ytN8P2KlA8dURMaBgXTMzK19KM0atoh9jgU0LFLePiEml1QczK5+8fNHKNC8JsQ2dl4WYlX2OVbYhc5xaN/z2RTMzMzMzsxzynTIzW2uSOkdEr1z3w8zMrCiOVbYh8J0yM/sjOue6A2ZmZqvgWGXrPSdlZmZmZmZmOeSkzMzMzMzMLIeclJnZH+E1+mZmtr5zrLL1nl/0YWZmZmZmlkO+U2ZmZmZmZpZDTsrMzMzMzMxyyEmZmZmZmZlZDjkpMysDJN0k6Yos5XUlTV7Dc63xMbkgqbekPXPdDzMzW33rMl6t7yTdIunwXPfDNgwVc90BM7O1ERHn5roPZmZmRYmIG3LdB9tw+E6Z2QZAUjdJk9Ovy9KyayV9KuktYPeMuo0kfSBpNHBRRvleksZJel/Sh5LqFdNkBUmPSZoiaaCkzdJzNJA0Jj2+n6St0vJhkh6QNFzSx5KaSHpR0ueSbsvow1kZfXhUUoUixnuapPvT7UslfZVu7yJpZEabjdPtRZJuT8c9RtK2a3WhzczsDynteCXpJUkT0njVOS1bnRhyg6TxaT97KbGLpIkZ564naUIR7R4g6cV0+0RJSyRtIqlSRnt9JLVNt7+RdLOkiZImSaq/dlfYyionZWbrOUmNgE5AU+BA4Ly07HRgf+BkoEnGIU8AXSOiWYFTdQEejIgGQGPg+2KarQf0iIi9gPnAKWn5k8DVEbEvMAm4MeOYXyLiEOAR4GWSALs30FFSdUl7AO2Ag9M+LAfOLKL94UCLdLsFMFdSbaA5MCJL/crAmIjYLz32vGLGZmZmJSBH8eqciGiU1usqqTqrF0O6R0STiNgb2Aw4LiK+BBZIapDW6QT0KaLdiemY8tqYnI6tKTC2iGPmRERDoCdQaAmnlW9OyszWf82BfhHxc0QsAl4Ejk3LFkfEQuAVAEnVgC0j4u302KcyzjMauEbS1cCOEbGkmDa/joj30+0JQN0s5+4LHJJxzCvp90nAlIiYERHLgK+A7YFWQCNgvKT30887Z2s8In4Aqkiqmh7777StFmRPyn4BXsvsbzFjMzOzkpGLeNVV0gfAGJJ4UW81Y8hhksZKmgS0BPZKy3sDndKVHO3SYwuJiN+AL9IJxwOA+7O0UdCL6XfHKSvESZnZ+k9FlGf7y+8qopyI+DdwArAEGCCpZTFtLsvYXs7qPX+ad8yKAsevSI8X0DciGqRfu0fETcWcbzTJLOWnJAGuBdAMGJWl7q8RkTfu1e2vmZmtW6UaryQdChwONEtXSrwHVEp3FxlDJFUCHgbaRsQ+wGMZx70AHA0cB0yIiLlFDTY979HAr8BbJElpc5I7ddnkxUbHKSvESZnZ+m84cJKkzSVVBtoArwNtJG2WzgQeDxAR80mWXjRPj81fHihpZ+CriPgnyUzlvmvSiYhYAMyTlLckpD3wdjGHFDQYaCupZtqfrSXtWEz94STLO4aTBNrDgGVpP8zMbP1T2vGqGjAvIhanz2gdWKAvRcWQvARsjqQqQNu8gyJiKTCAZInhE6sx3suA0RExG6gO1AemrOI4s0KcpZut5yJioqQ+wLi0qHdETJD0HPA+MJWVl0p0Ah6XtJgksORpB5wl6VfgB+CWtehOB+ARSZuTLEvstAbj+EjSdcBASRuRzCxelPY/mxEky06GR8RySd8Bn6xFn83MrBTkIF71B7pI+pDkjtiYjH1FxpCImC/pMZLl9t8A4wuc9xmS598GGwwtsAAAAKdJREFUrmLIY4Ft+f3O2IfArIyVG2arTf65MTMzMzNLKPk7atUi4vpc98XKD98pMzMzMzMDJPUDdiF5+YdZqfGdMrNyKn1t8OAsu1qt4sHmdd2PscCmBYrbR8Sk0uqDmZmtv3Idr9JEbacCxVdHxIBs9c3WhpMyMzMzMzOzHPLbF83MzMzMzHLISZmZmZmZmVkOOSkzMzMzMzPLISdlZmZmZmZmOfT/AQOehBBByJZIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 864x864 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates correlation matrix between each feature\n",
    "corr_matches = matches.drop(['home_team_goal_count', 'away_team_goal_count', 'goal_diff', 'home_fav', 'away_fav'], axis=1)\n",
    "corr_matches = corr_matches.corr()\n",
    "plt.figure(figsize=(12, 12))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Feature correlation to odds_home_win')\n",
    "sns.heatmap(corr_matches[['odds_home_win']].sort_values(by='odds_home_win', ascending=False), annot=True)\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('Feature correlation to odds_away_win')\n",
    "sns.heatmap(corr_matches[['odds_away_win']].sort_values(by='odds_away_win', ascending=False), annot=True)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring Home Bias in Bookmaker Predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Home advantages are a well documented phenomenon in sports, with the crowd in football often being nicknamed 'the twelfth man'. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>odds_home_win</th>\n",
       "      <th>odds_draw</th>\n",
       "      <th>odds_away_win</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1140.000</td>\n",
       "      <td>1140.000</td>\n",
       "      <td>1140.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>30.032</td>\n",
       "      <td>19.809</td>\n",
       "      <td>22.999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10.699</td>\n",
       "      <td>4.354</td>\n",
       "      <td>10.698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.348</td>\n",
       "      <td>5.970</td>\n",
       "      <td>2.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>23.767</td>\n",
       "      <td>17.279</td>\n",
       "      <td>14.169</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>30.769</td>\n",
       "      <td>21.097</td>\n",
       "      <td>22.297</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>38.314</td>\n",
       "      <td>22.883</td>\n",
       "      <td>29.963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000</td>\n",
       "      <td>50.000</td>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       odds_home_win  odds_draw  odds_away_win\n",
       "count       1140.000   1140.000       1140.000\n",
       "mean          30.032     19.809         22.999\n",
       "std           10.699      4.354         10.698\n",
       "min            4.348      5.970          2.286\n",
       "25%           23.767     17.279         14.169\n",
       "50%           30.769     21.097         22.297\n",
       "75%           38.314     22.883         29.963\n",
       "max           50.000     50.000         50.000"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches[['odds_home_win','odds_draw', 'odds_away_win']].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "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": [
    "# Creates figure with two frequency histograms \n",
    "fig, (ax1, ax2) = plt.subplots(1, 2, sharex=True, sharey=True)\n",
    "ax1.hist(matches['odds_home_win'], bins=8, color='blue')\n",
    "ax2.hist(matches['odds_away_win'], bins=8, color='orange')\n",
    "# Title for entire figure\n",
    "fig.suptitle('Comparing Bookmakers Probabilities between Home and Away Wins', y=1, fontsize=14)\n",
    "fig.text(0.42, 0.03, 'Probability (%)', fontsize=10)\n",
    "fig.text(0.03, 0.6, 'Frequency', fontsize=10, rotation='vertical')\n",
    "ax1.set_title('Home Games', fontsize=10)\n",
    "ax2.set_title('Away Games', fontsize=10);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bookmakers, on average, give the home team a 30% probability of a win, while giving away teams a mean of of 23%. This suggests playing at home is assumed to have a larger advantage. This is highlighted further by the right skew in the distribution of Home Team probabilities, while Away Team probability distribution is left skewed. Generally, bookmakers give teams a higher chance of winning when playing at home."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bookmaker Predictions Compared to Actual Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates variable evaluating whether Bookmakers favoured the Winning Team.\n",
    "correct_fav = matches['home_fav'] == matches['home_win']\n",
    "\n",
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "# Creates 'count plot' visualising the correct predictions from the Bookmaker's\n",
    "plt.subplot(1, 3, 1)\n",
    "sns.countplot(correct_fav, palette='RdBu_r', order=correct_fav.value_counts().index, saturation=1) # Orders descendingly\n",
    "plt.xlabel('Correct Prediction')\n",
    "plt.title('Bookmakers Correctly Predicting Winner in EPL');\n",
    "\n",
    "# Creates 'count plot' visualising the ratio of Home vs Away wins\n",
    "plt.subplot(1, 3, 2)\n",
    "sns.countplot(matches['result'] == 'H', palette='RdBu_r', order=matches['home_win'].value_counts(ascending=True).index, saturation=1) # Orders descendingly\n",
    "plt.title('Ratio of Home Wins in EPL')\n",
    "plt.xlabel('Home Win');\n",
    "\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.countplot(matches['result'] == 'A', palette='RdBu_r', order=matches['away_win'].value_counts(ascending=True).index, saturation=1) # Orders descendingly\n",
    "plt.title('Ratio of Away Wins in EPL')\n",
    "plt.xlabel('Away Win');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bookmakers correctly predicted the winner ~65% of the time. Home teams won just less than half the games, while away teams lost 70% of the time, suggesting Home Teams do have a significant advantage."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Comparing PPG and ELO to Bookmakers favourites"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((0.8258559724676863, 2.0329382394074134e-285),\n",
       " (0.83220149271562, 9.76271044698753e-294))"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Correlation coeffiecient for both home and away elo and ppg\n",
    "stats.pearsonr(matches['home_elo'], matches['home_ppg']), stats.pearsonr(matches['away_elo'], matches['away_ppg'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates two regression plots on the same figure for both home and away\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 6), sharex=True, sharey=True)\n",
    "sns.regplot(matches['home_elo'], matches['home_ppg'], ax=ax1, marker='o', color='b')\n",
    "sns.regplot(matches['away_elo'], matches['away_ppg'], ax=ax1, marker='s', color='orange')\n",
    "ax1.set_title('Comparing ELO to PPG for Home vs Away')\n",
    "ax1.set_xlabel('ELO')\n",
    "ax1.set_ylabel('Points Per Game')\n",
    "ax1.legend(['home_ppg', 'away_ppg'])\n",
    "\n",
    "all_elo = pd.concat([matches['home_elo'], matches['away_elo']])\n",
    "all_ppg = pd.concat([matches['home_ppg'], matches['away_ppg']])\n",
    "\n",
    "# Combines the above into one regression plot\n",
    "sns.regplot(all_elo, all_ppg, ax=ax2, marker='o', color='g')\n",
    "ax2.set_title('Comparing ELO to PPG for all games')\n",
    "ax2.set_xlabel('ELO')\n",
    "ax2.set_ylabel('Points Per Game')\n",
    "\n",
    "fig.tight_layout()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph shows ELO, a team's ability rating, compared to PPG. While both are indicators of a team's performance, it demonstrates that teams of similar ability perform better at their home ground. As both regression lines have the same gradient, teams with a higher ELO generally outperform weaker teams regardless of home advantage.\n",
    "\n",
    "As both home and away games have a correlation coefficient of ~0.83, it suggests there is a strong correlation between teams strength and the points they score per game."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How Do Teams Perform Against Different Opponents?\n",
    "How do teams perform with against different elo ranges? Do poor elo teams play well against high elo teams? do mid elo perform better against high elo or low?\n",
    "\n",
    "Organising elo ratings into categories is easier for comparison to bookmaker's odds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2280.000\n",
       "mean      816.041\n",
       "std       145.279\n",
       "min       601.257\n",
       "25%       715.953\n",
       "50%       769.202\n",
       "75%       973.430\n",
       "max      1077.341\n",
       "dtype: float64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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/DT8XyiuXtCIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8, 2))\n",
    "sns.boxplot(all_elo)\n",
    "plt.title('All ELO Ratings')\n",
    "plt.xlabel('ELO')\n",
    "all_elo.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Team Strength will be categorised as follows:\n",
    "\n",
    "0-25% = Weak teams\n",
    "\n",
    "25-75% = Average teams\n",
    "\n",
    "75-100% = Strong teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "weak = all_elo.quantile(0.25)\n",
    "strong = all_elo.quantile(0.75)\n",
    "\n",
    "# Fills home and away strength based on whether ELO is greater than the quartile\n",
    "matches.loc[matches['home_elo'] <= weak, 'home_strength'] = 'weak'\n",
    "matches.loc[matches['home_elo'] >= strong, 'home_strength'] = 'strong'\n",
    "matches.loc[matches['away_elo'] <= weak, 'away_strength'] = 'weak'\n",
    "matches.loc[matches['away_elo'] >= strong, 'away_strength'] = 'strong'\n",
    "\n",
    "# Fills all values within interquartile range\n",
    "matches.loc[:, 'home_strength'].fillna('average', inplace=True)\n",
    "matches.loc[:, 'away_strength'].fillna('average', inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Combine home and away strength / odds to cover all games\n",
    "strength = pd.concat([matches['home_strength'], matches['away_strength']])\n",
    "odds = pd.concat([matches['odds_home_win'], matches['odds_away_win']])\n",
    "\n",
    "plt.figure(figsize=(8, 6))\n",
    "sns.violinplot(strength, odds)\n",
    "plt.title('Comparing Team Strength to Implied Probability')\n",
    "plt.xlabel('Team Strength')\n",
    "plt.ylabel('Probability of Win (%)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data suggests that Bookmaker's use team strength as a metric, whether some form of ELO or PPG (as they are highly correlated). A team with an exceptionally high ELO (the upper quartile) is highly correlated to a greater win probability, with less regard for opposition strength. The wider spread for the weak and average teams could be explained by the greater range in opponents they can face. An average team can face much weaker opponents (lowest ELO) and much stronger opponents (highest ELO). A team with an ELO in the upper quartile, won't face drastically better teams, leading to a higher median win percentage."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create Violin Plot for both weak and strong team performances against all opponents\n",
    "\n",
    "# Concatenates the feeatures looked at to ignore whether team is home or away\n",
    "weak_home_teams = matches[matches['home_strength'] == 'weak']\n",
    "weak_away_teams = matches[matches['away_strength'] == 'weak']\n",
    "weak_opponent_strength = pd.concat([weak_home_teams['away_strength'], weak_away_teams['home_strength']], ignore_index=True)\n",
    "# Inverse of away goal difference to show relative goal difference of target teams\n",
    "weak_goal_diff = pd.concat([weak_home_teams['goal_diff'], - weak_away_teams['goal_diff']], ignore_index=True)\n",
    "\n",
    "# Concatenates the feeatures looked at to ignore whether team is home or away\n",
    "strong_home_teams = matches[matches['home_strength'] == 'strong']\n",
    "strong_away_teams = matches[matches['away_strength'] == 'strong']\n",
    "strong_opponent_strength = pd.concat([strong_home_teams['away_strength'], strong_away_teams['home_strength']], ignore_index=True)\n",
    "# Inverse of away goal difference to show relative goal difference of target teams\n",
    "strong_goal_diff = pd.concat([strong_home_teams['goal_diff'], - strong_away_teams['goal_diff']], ignore_index=True)\n",
    "\n",
    "# Create violinplots next to each other for comparison\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(18, 6), sharex=True, sharey=True)\n",
    "ax1.set_title('Weak Teams Performance Against Opponent\\'s of Varying Difficiulty')\n",
    "sns.violinplot(weak_opponent_strength, weak_goal_diff, ax=ax1, order=['weak', 'average', 'strong'])\n",
    "\n",
    "ax2.set_title('Strong Teams Performance Against Opponent\\'s of Varying Difficiulty')\n",
    "sns.violinplot(strong_opponent_strength, strong_goal_diff, ax=ax2)\n",
    "# Shows y labels on both plots even with sharey True\n",
    "ax2.yaxis.set_tick_params(which='both', labelleft=True)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The above graph shows that weak teams perform almost equally well against weak and average teams, while they suffer against strong teams. Strong teams have an all round better performance against all opponents, with a much greater likelihood of winning against weaker opponents."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creates graphs as above but comparing varying opponent strengths to bookmaker implied likelihoods\n",
    "\n",
    "weak_win_odds = pd.concat([weak_home_teams['odds_home_win'], weak_away_teams['odds_away_win']], ignore_index=True)\n",
    "strong_win_odds = pd.concat([strong_home_teams['odds_home_win'], strong_away_teams['odds_away_win']], ignore_index=True)\n",
    "\n",
    "# Create violinplots next to each other for comparison\n",
    "fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(15, 6), sharex=True, sharey=True)\n",
    "ax1.set_title('Weak Teams Odds Against Opponent\\'s of Varying Difficiulty')\n",
    "sns.violinplot(weak_opponent_strength, weak_win_odds, ax=ax1, order=['weak', 'average', 'strong'])\n",
    "\n",
    "ax2.set_title('Strong Teams Odds Against Opponent\\'s of Varying Difficiulty')\n",
    "sns.violinplot(strong_opponent_strength, strong_win_odds, ax=ax2)\n",
    "# Shows y labels on both plots even with sharey True\n",
    "ax2.yaxis.set_tick_params(which='both', labelleft=True)\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>570.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>20.180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.630</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>12.903</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>20.284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26.936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds_vs_weak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>132.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>11.429</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26.631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.274</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>40.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds_vs_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>288.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>22.440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>6.942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>6.557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>17.094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>22.702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>27.397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds_vs_strong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>10.543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>2.286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>9.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>9.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>12.575</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>26.178</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows overall win probabilites for weak teams, as well as win probabilities against varying opponents\n",
    "weak_odds = pd.DataFrame({'odds': weak_win_odds, 'opponent_strength': weak_opponent_strength})\n",
    "display_side_by_side(weak_odds.describe(),\n",
    "                     weak_odds[weak_odds['opponent_strength'] == 'weak'].rename(columns={'odds': 'odds_vs_weak'}).describe(),\n",
    "                     weak_odds[weak_odds['opponent_strength'] == 'average'].rename(columns={'odds': 'odds_vs_average'}).describe(), \n",
    "                     weak_odds[weak_odds['opponent_strength'] == 'strong'].rename(columns={'odds': 'odds_vs_strong'}).describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>570.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>37.604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>33.223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>39.526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds_vs_weak</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>42.261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>27.174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>39.841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>42.918</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>45.249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>48.309</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds_vs_average</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>300.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>39.609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>5.687</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>9.099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>37.037</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>40.323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>43.860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>50.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>odds_vs_strong</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>120.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26.771</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>7.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>21.540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>27.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>31.924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>44.444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Shows overall win probabilites for strong teams, as well as win probabilities against varying opponents\n",
    "strong_odds = pd.DataFrame({'odds': strong_win_odds, 'opponent_strength': strong_opponent_strength})\n",
    "display_side_by_side(strong_odds.describe(),\n",
    "                     strong_odds[strong_odds['opponent_strength'] == 'weak'].rename(columns={'odds': 'odds_vs_weak'}).describe(),\n",
    "                     strong_odds[strong_odds['opponent_strength'] == 'average'].rename(columns={'odds': 'odds_vs_average'}).describe(), \n",
    "                     strong_odds[strong_odds['opponent_strength'] == 'strong'].rename(columns={'odds': 'odds_vs_strong'}).describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bookmaker's implied likelihood against various opponents mirrors the actual goal difference of the games, albeit more extreme. The similarities suggest that the bookmakers are accurate in their predictions, but skew the odds in favour of the better teams, potentially to protect against losses."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Recent Form as an Indicator of Future Performance\n",
    "\n",
    "Form acts as an indicator of how a team is playing over a shorter period. While it does not guarantee results, teams often go on streaks of either winning or losing. There should be a positive correlation between a teams form and winning future matches. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Date', 'attendance', 'home_team_name', 'away_team_name',\n",
       "       'home_ppg', 'away_ppg', 'home_team_goal_count',\n",
       "       'away_team_goal_count', 'total_goal_count',\n",
       "       'total_goals_at_half_time', 'home_team_goal_count_half_time',\n",
       "       'away_team_goal_count_half_time', 'home_team_goal_timings',\n",
       "       'away_team_goal_timings', 'home_team_corner_count',\n",
       "       'away_team_corner_count', 'home_team_yellow_cards',\n",
       "       'home_team_red_cards', 'away_team_yellow_cards',\n",
       "       'away_team_red_cards', 'home_team_shots', 'away_team_shots',\n",
       "       'home_team_shots_on_target', 'away_team_shots_on_target',\n",
       "       'home_team_shots_off_target', 'away_team_shots_off_target',\n",
       "       'home_team_fouls', 'away_team_fouls', 'odds_home_win', 'odds_draw',\n",
       "       'odds_away_win', 'home_fav', 'away_fav', 'home_win', 'away_win',\n",
       "       'goal_diff', 'result', 'HPPG', 'APPG', 'avg_home_goals_scored',\n",
       "       'avg_home_goals_conceded', 'avg_home_goal_diff',\n",
       "       'avg_home_shots_on_target', 'avg_away_goals_scored',\n",
       "       'avg_away_goals_conceded', 'avg_away_goal_diff',\n",
       "       'avg_away_shots_on_target', 'home_form', 'away_form', 'home_elo',\n",
       "       'away_elo', 'home_strength', 'away_strength'], dtype=object)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "form = pd.concat([matches['home_form'], matches['away_form']], ignore_index=True)\n",
    "win = pd.concat([matches['home_win'], matches['away_win']], ignore_index=True)\n",
    "favourites = pd.concat([matches['home_fav'], matches['away_fav']], ignore_index=True)\n",
    "\n",
    "# Returns index positions for games won\n",
    "plt.figure(figsize=(20, 8))\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.title('Form where a Team Won or Lost a Match')\n",
    "sns.distplot(form.iloc[win[win == True].index.values].dropna(), bins=8, label='Win', color='blue')\n",
    "sns.distplot(form.iloc[win[win==False].index.values].dropna(), bins=8, label='Loss', color='orange')\n",
    "plt.legend()\n",
    "plt.xlabel('Form over Last 5 Games (Points)')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.title('Form where a Team was Bookmaker\\'s Favourite or Not')\n",
    "sns.distplot(form.iloc[favourites[favourites == True].index.values].dropna(), bins=8, label='Favourites', color='blue')\n",
    "sns.distplot(form.iloc[favourites[favourites==False].index.values].dropna(), bins=8, label='Underdogs', color='orange')\n",
    "plt.legend()\n",
    "plt.xlabel('Form over Last 5 Games (Points)');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bookmaker's favourites has an almost identical distribution to the win/loss distirbution. This indicates they use form as an indicator for which team will win without being too dependent on the feature. The bookmaker's favourites have a form slightly skewed either side; the favourites have a higher average form, while the underdog has a slightly lower form than the actual winners and losers respectively."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Winner's Form</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>837.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.769</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Loser's Form (Including Draws)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1313.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.446</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>9.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Favourite's Form</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1072.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>8.212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>8.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>11.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\"><table style=\"display:inline\" border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Underdog's Form</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1078.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>4.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>15.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table style=\"display:inline\">"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Returns the describe() function for each of the above distributions\n",
    "form_df = pd.DataFrame({'form': form, 'win': win, 'fav': favourites}).reset_index()\n",
    "form_df.drop(['index'], axis=1, inplace=True)\n",
    "\n",
    "display_side_by_side(form_df[form_df['win'] == True].rename(columns={'form': 'Winner\\'s Form'}).describe(), \n",
    "                     form_df[form_df['win'] == False].rename(columns={'form': 'Loser\\'s Form (Including Draws)'}).describe(), \n",
    "                     form_df[form_df['fav'] == True].rename(columns={'form': 'Favourite\\'s Form'}).describe(), \n",
    "                     form_df[form_df['fav'] == False].rename(columns={'form': 'Underdog\\'s Form'}).describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Predictions\n",
    "\n",
    "## Base Predictions\n",
    "\n",
    "Bookmakers odds are continuous from a scale of 0 - 100. Linear regression can be used on features that indicate prior performance to estimate how bookmaker's will set their odds. Match statistics cannot be used as they would not be available before the game starts. Forecasting requires 'lagged' features that show previous performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split \n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n",
    "# All lagged features except bookmaker's odds which will be used as the target variable\n",
    "X = matches[['attendance', 'home_ppg', 'away_ppg', 'HPPG', 'APPG', 'avg_home_goals_scored',\n",
    "       'avg_home_goals_conceded', 'avg_home_goal_diff',\n",
    "       'avg_home_shots_on_target', 'avg_away_goals_scored',\n",
    "       'avg_away_goals_conceded', 'avg_away_goal_diff',\n",
    "       'avg_away_shots_on_target', 'home_form', 'away_form', 'home_elo',\n",
    "       'away_elo']]\n",
    "# Fill form at the start of the \n",
    "X.fillna(0, inplace=True)\n",
    "\n",
    "y1 = matches['odds_home_win']\n",
    "y2 = matches['odds_away_win']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create train test splits for both target variables\n",
    "# Random state for reproducibility\n",
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X, y1, test_size=0.2, random_state=1)\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X, y2, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(29.700161120730144,\n",
       " array([ 3.07677817e-05,  2.13045290e+00, -3.83648042e+00, -1.96227429e+00,\n",
       "        -6.55461181e-01,  1.49743986e+00,  4.49120922e-02,  1.45252776e+00,\n",
       "         8.42642863e-01, -1.44345334e+00, -2.24002245e-01, -1.21945109e+00,\n",
       "        -5.23307243e-01,  2.35500209e-01, -8.11010304e-02,  2.04802015e-02,\n",
       "        -1.89306942e-02]))"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit linear regression and returns interecept and coefficients\n",
    "reg1 = LinearRegression()\n",
    "reg1.fit(X1_train, y1_train)\n",
    "reg1.intercept_, reg1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "y1_pred = reg1.predict(X1_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.11659660691482333"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "# Returns RMSE normalised by the mean\n",
    "np.sqrt(metrics.mean_squared_error(y1_test, y1_pred)) / y1_test.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8894155473783136"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Returns r2 score\n",
    "metrics.r2_score(y1_test, y1_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# Comparing Predictions to Actual odds\n",
    "sns.scatterplot(y1_test, y1_pred)\n",
    "plt.title('Home Win Odds compared to Predictions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model shows high accuracy for predicting what odds the bookmakers will give each team based on their past results and the opponent they are facing. A normalised RMSE of 10% suggests a low error rate. The graph contains some outliers. As football is a game of high variance, outliers could be affecting the accuracy, even if outlying games are part of the sport."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'LinearRegression' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-6a0d6660e85a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mreg2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mreg2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mX2_train\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my2_train\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mreg2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintercept_\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mreg2\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcoef_\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'LinearRegression' is not defined"
     ]
    }
   ],
   "source": [
    "reg2 = LinearRegression()\n",
    "reg2.fit(X2_train, y2_train)\n",
    "reg2.intercept_, reg2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2_pred = reg2.predict(X2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.15786121230789837"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(metrics.mean_squared_error(y2_test, y2_pred)) / y2.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8781199962887409"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y2_test, y2_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.scatterplot(y2_test, y2_pred)\n",
    "plt.title('Away Win Odds compared to Predictions');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Odds for the away team are slightly harder to predict than the home games. This could be due to the bookmaker's home bias which leads to home odds having a slightly shorter interquartile range, as shown below. A smaller range in values decreases the error in prediction, as values are less likely to vary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(14.5468679034343, 15.793226009198682)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matches['odds_home_win'].quantile(0.75) - matches['odds_home_win'].quantile(0.25), matches['odds_away_win'].quantile(0.75) - matches['odds_away_win'].quantile(0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model with Outliers Removed\n",
    "\n",
    "Removing Outliers may improve the regression model, as larger outliers greatly affect RMSE. While outliers are part of the game and still need to be predicted, it is interesting to see how much they affect accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Bookmaker's odds included for outliers\n",
    "X = matches[['attendance', 'home_ppg', 'away_ppg', 'HPPG', 'APPG', 'avg_home_goals_scored',\n",
    "       'avg_home_goals_conceded', 'avg_home_goal_diff',\n",
    "       'avg_home_shots_on_target', 'avg_away_goals_scored',\n",
    "       'avg_away_goals_conceded', 'avg_away_goal_diff',\n",
    "       'avg_away_shots_on_target', 'home_form', 'away_form', 'home_elo',\n",
    "       'away_elo', 'odds_home_win', 'odds_away_win']]\n",
    "X.fillna(0, inplace=True)\n",
    "\n",
    "y1 = matches['odds_home_win']\n",
    "y2 = matches['odds_away_win']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "features = list(X.columns.values)\n",
    "\n",
    "# Create Z score for each feature in X\n",
    "for feature in features:\n",
    "    X.loc[:, feature + ' zScore'] = (X[feature] - X[feature].mean()) / X[feature].std()\n",
    "\n",
    "# Returns outliers as value '1'\n",
    "# Anything over 2.5 z score considered an outlier. Using a z score above 2 removes too much data\n",
    "X.loc[:, 'isOutlier'] = 0\n",
    "for feature in features:\n",
    "    i = X[X['isOutlier'] == 0].index.values\n",
    "    X.loc[i, 'isOutlier'] = (X[feature + ' zScore'].abs() > 2.5).astype(int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1058\n",
       "1      82\n",
       "Name: isOutlier, dtype: int64"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.isOutlier.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3       24.213\n",
       "15      23.981\n",
       "17      25.445\n",
       "20      29.326\n",
       "22      28.090\n",
       "         ...  \n",
       "1135    43.860\n",
       "1136    43.103\n",
       "1137    31.250\n",
       "1138    27.778\n",
       "1139     6.154\n",
       "Name: odds_home_win, Length: 1058, dtype: float64"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = X[X['isOutlier'] != 1]\n",
    "X.iloc[:, 17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# First y variable = odds of home win\n",
    "y1 = X.iloc[:, 17]\n",
    "# Second y variable = odds of away win\n",
    "y2 = X.iloc[:, 18]\n",
    "X = X.iloc[:, 0:17]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Random state parameter keeps the train and test splits at the same indices\n",
    "X1_train, X1_test, y1_train, y1_test = train_test_split(X, y1, test_size=0.2, random_state=1)\n",
    "X2_train, X2_test, y2_train, y2_test = train_test_split(X, y2, test_size=0.2, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30.457908177247177,\n",
       " array([ 3.33786224e-05,  1.26097948e+00, -3.69472412e+00, -2.84092580e-01,\n",
       "        -3.04499100e+00,  1.94673575e+00,  1.56034809e-01,  1.79070095e+00,\n",
       "         1.10310256e+00, -1.33761005e+00, -2.45905217e-01, -1.09170483e+00,\n",
       "        -3.86347869e-01,  1.92385315e-01, -2.76428334e-02,  1.44507147e-02,\n",
       "        -1.52433284e-02]))"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg1 = LinearRegression()\n",
    "reg1.fit(X1_train, y1_train)\n",
    "reg1.intercept_, reg1.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.10385838272672787"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1_pred = reg1.predict(X1_test)\n",
    "np.sqrt(metrics.mean_squared_error(y1_test, y1_pred)) / y1_pred.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Home Win Odds compared to Predictions with Outliers Removed')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "sns.scatterplot(y1_test, y1_pred)\n",
    "plt.title('Home Win Odds compared to Predictions with Outliers Removed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9162290559932128"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y1_test, y1_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(17.35193618627331,\n",
       " array([-3.25223663e-05, -1.32528782e+00,  3.37007406e+00,  2.44664856e-01,\n",
       "         2.92769537e+00, -1.56714263e+00,  4.20610459e-01, -1.98775308e+00,\n",
       "        -9.86626391e-01,  1.84788526e+00,  7.20083526e-01,  1.12780174e+00,\n",
       "         4.11707146e-01, -1.79148248e-01,  4.19143142e-02, -1.28023549e-02,\n",
       "         1.62416391e-02]))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reg2 = LinearRegression()\n",
    "reg2.fit(X2_train, y2_train)\n",
    "reg2.intercept_, reg2.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "y2_pred = reg2.predict(X2_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.142069791668605"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(metrics.mean_squared_error(y2_test, y2_pred)) / y2_pred.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "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": [
    "sns.scatterplot(y2_test, y2_pred)\n",
    "plt.title('Away Win Odds compared to Predictions with Outliers Removed');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9046945674605188"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.r2_score(y2_test, y2_pred)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The accuracy improves slightly with outliers being removed, however outliers are part of football. Accuracy including outliers could be improved by using a larger dataset. If the model can be trained with more matches, it will have more experience with matches that are currently classed as 'outliers' and should more accurately predict the bookmaker's odds. \n",
    "\n",
    "The accuracy is still high considering most variables are just taking the average prior performance. This suggests that bookmaker's use quite generalisable odds. As this data does not include player information, it is possible this could improve the accuracy even further. A team may perform much better when their players are all fit and in form. If their main goalscorer is injured, that is likely to affect odds set by the bookmaker's. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Predicting Match Outcomes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf = RandomForestClassifier(n_estimators=2000, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "X1 = matches[['attendance', 'home_ppg', 'away_ppg', 'HPPG', 'APPG', 'avg_home_goals_scored',\n",
    "       'avg_home_goals_conceded', 'avg_home_goal_diff',\n",
    "       'avg_home_shots_on_target', 'avg_away_goals_scored',\n",
    "       'avg_away_goals_conceded', 'avg_away_goal_diff',\n",
    "       'avg_away_shots_on_target', 'home_form', 'away_form', 'home_elo',\n",
    "       'away_elo']]\n",
    "X1.fillna(0, inplace=True)\n",
    "y = matches['result']\n",
    "#y = matches['home_win']\n",
    "X_train, X_test, y_train, y_test = train_test_split(X1, y, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "rf.fit(X_train, y_train)\n",
    "preds = rf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 60,  13,  24],\n",
       "       [ 16,   7,  38],\n",
       "       [ 11,  11, 105]], dtype=int64)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6035087719298246"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(y_test, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "mapping = {'odds_home_win': 'H', 'odds_away_win': 'A', 'odds_draw': 'D'}\n",
    "# Maps the column name with the highest value for each row\n",
    "matches.loc[:, 'Favourite'] =  matches[['odds_home_win', 'odds_away_win', 'odds_draw']].idxmax(axis=1)\n",
    "# Maps bookmakers highest odds as H/A/D for classification\n",
    "y_bookies = matches.loc[y_test.index.values, 'Favourite'].map(mapping)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5964912280701754"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Actual results compared to bookmaker's favourites\n",
    "metrics.accuracy_score(y_test, y_bookies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7473684210526316"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Bookmakers predictions compared to model predictions\n",
    "metrics.accuracy_score(y_bookies, preds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.4456140350877193"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Accuracy if every event was classified as a Home win\n",
    "home_only = ['H'] * len(y_test)\n",
    "metrics.accuracy_score(home_only, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model slightly beats bookmaker's favourites in a classification problem, however it is only by <1%. Bookmakers do not usually favour draws and are more focused on their own profit margins than their predictions. Comparing bookmaker's predictions to the models suggests that this model is more closely linked to bookmakers than it is to the actual results. This makes sense as both bookmaker's and this model's predictions are based off statistics. Football matches have high variability as it is a low scoring game; often, the result won't match the performance. \n",
    "\n",
    "Regardless, the model's accuracy is better than classifying every problem as a home win (the most frequent result) as well as being greater than the bookmaker's odds. Although it beats the bookmaker's odds, that does not mean it is profitable. The bookmakers set their odds to increase profitability, not perfectly correlated to likelihood. Further experiments would be needed to prove this."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuned Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv='warn', error_score='raise-deprecating',\n",
       "                   estimator=RandomForestClassifier(bootstrap=True,\n",
       "                                                    class_weight=None,\n",
       "                                                    criterion='gini',\n",
       "                                                    max_depth=None,\n",
       "                                                    max_features='auto',\n",
       "                                                    max_leaf_nodes=None,\n",
       "                                                    min_impurity_decrease=0.0,\n",
       "                                                    min_impurity_split=None,\n",
       "                                                    min_samples_leaf=1,\n",
       "                                                    min_samples_split=2,\n",
       "                                                    min_weight_fraction_leaf=0.0,\n",
       "                                                    n_estimators=2000,\n",
       "                                                    n_jobs=None,\n",
       "                                                    oob...\n",
       "                   iid='warn', n_iter=100, n_jobs=None,\n",
       "                   param_distributions={'bootstrap': [True, False],\n",
       "                                        'max_depth': [10, 20, 30, 40, 50, 60,\n",
       "                                                      70, 80, 90, 100, 110,\n",
       "                                                      None],\n",
       "                                        'max_features': ['auto', 'sqrt', None],\n",
       "                                        'min_samples_leaf': [1, 5, 10, 30],\n",
       "                                        'min_samples_split': [2, 5, 10, 20],\n",
       "                                        'n_estimators': [200, 500, 1000]},\n",
       "                   pre_dispatch='2*n_jobs', random_state=1, refit=True,\n",
       "                   return_train_score=False, scoring=None, verbose=0)"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "\n",
    "random_grid = {'n_estimators': [200, 500, 1000],\n",
    "               'max_features': ['auto', 'sqrt', None],\n",
    "               'max_depth': [10, 20, 30, 40, 50, 60, 70, 80, 90, 100, 110, None],\n",
    "               'min_samples_split': [2, 5, 10, 20],\n",
    "               'min_samples_leaf': [1, 5, 10, 30],\n",
    "               'bootstrap': [True, False],   \n",
    "               \n",
    "              }\n",
    "rf_random = RandomizedSearchCV(estimator=rf, param_distributions=random_grid, n_iter=100, random_state=1)\n",
    "rf_random.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'n_estimators': 500,\n",
       " 'min_samples_split': 2,\n",
       " 'min_samples_leaf': 5,\n",
       " 'max_features': 'sqrt',\n",
       " 'max_depth': 70,\n",
       " 'bootstrap': True}"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rf_random.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "tuned_preds = rf_random.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 61,  18,  10],\n",
       "       [ 10,   5,   8],\n",
       "       [ 26,  38, 109]], dtype=int64)"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.confusion_matrix(tuned_preds, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6140350877192983"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(tuned_preds, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With tuning, the model reaches an accuracy of 61.4%. Almost 2% better than the bookmaker's implied predictions."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accuracy with High Certainty\n",
    "\n",
    "Random Forest returns probabilities (each tree's classification). High probability classifications are likely to be more accurate than ones that are close to a coinflip (0.51 : 0.49)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stores probabilities for each classification for each row\n",
    "probs = rf_random.predict_proba(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "decisions = []\n",
    "for prob in probs:\n",
    "    prob = prob >= 0.69\n",
    "    prob = sum(prob)\n",
    "    decisions.append(prob)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Returns indicies of values with high certainty\n",
    "indices = [i for i, x in enumerate(decisions) if x == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['H', 'H', 'A', 'A', 'H', 'H', 'H', 'A', 'H', 'H', 'H', 'A', 'H',\n",
       "       'A', 'H', 'H', 'H', 'A', 'H', 'H', 'A', 'A', 'H', 'A', 'H', 'H',\n",
       "       'A', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'A', 'H', 'H', 'H',\n",
       "       'H', 'H', 'H', 'H', 'H', 'A', 'A', 'H', 'H', 'H', 'H', 'H', 'A',\n",
       "       'A', 'H', 'A', 'H', 'H', 'H', 'H', 'H', 'H', 'H', 'A', 'A', 'H',\n",
       "       'H', 'A', 'A', 'A', 'A', 'H', 'H', 'H', 'H', 'H', 'H', 'A', 'H',\n",
       "       'H'], dtype=object)"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "preds[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "546    H\n",
       "841    H\n",
       "111    A\n",
       "204    A\n",
       "304    H\n",
       "      ..\n",
       "768    H\n",
       "815    H\n",
       "948    A\n",
       "754    H\n",
       "487    H\n",
       "Name: result, Length: 79, dtype: object"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test.iloc[indices]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8987341772151899"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.accuracy_score(preds[indices], y_test.iloc[indices])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2771929824561403"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Percentage of classifications that have ~90% accuracy\n",
    "len(indices) / len(y_test)"
   ]
  }
 ],
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