NHLSalaryAnalysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Daniel Mccue\n",
    "\n",
    "## NHL Player Salaries Analysis & Prediction (Stats from 2016-17 regular season)\n",
    "\n",
    "Last Updated: January 2025\n",
    "\n",
    "- A comprehensive analysis of NHL player salaries using machine learning to predict \n",
    "compensation and identify market inefficiencies. \n",
    "- Features XGBoost and Lasso regression models trained on 15+ performance metrics, achieving an RSME of $1.7m\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Imports\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import mean_squared_error, r2_score\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.model_selection import RandomizedSearchCV\n",
    "from scipy.stats import uniform, randint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exploratory Data Analysis\n",
    "\n",
    "This section examines the NHL player dataset (789 players) from 2017, analyzing key statistics and their relationships with player salaries. \n",
    "The dataset includes 154 features covering performance metrics, physical attributes, and career statistics. \n",
    "Initial findings show:\n",
    "- Salary range: $575K to $14M with mean of $2.48M\n",
    "- Average player age: 26 years (born 1991)\n",
    "- Key performance indicators: Points (PTS), Goals Scored (GS), and Fenwick For (FF)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "      <th>Born</th>\n",
       "      <th>City</th>\n",
       "      <th>Pr/St</th>\n",
       "      <th>Cntry</th>\n",
       "      <th>Nat</th>\n",
       "      <th>Ht</th>\n",
       "      <th>Wt</th>\n",
       "      <th>DftYr</th>\n",
       "      <th>DftRd</th>\n",
       "      <th>...</th>\n",
       "      <th>PEND</th>\n",
       "      <th>OPS</th>\n",
       "      <th>DPS</th>\n",
       "      <th>PS</th>\n",
       "      <th>OTOI</th>\n",
       "      <th>Grit</th>\n",
       "      <th>DAP</th>\n",
       "      <th>Pace</th>\n",
       "      <th>GS</th>\n",
       "      <th>GS/G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14000000.0</td>\n",
       "      <td>1991-08-25</td>\n",
       "      <td>Jesenice</td>\n",
       "      <td>NaN</td>\n",
       "      <td>SVN</td>\n",
       "      <td>SVN</td>\n",
       "      <td>75.0</td>\n",
       "      <td>224.0</td>\n",
       "      <td>2005.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>92.0</td>\n",
       "      <td>2.6</td>\n",
       "      <td>1.9</td>\n",
       "      <td>4.6</td>\n",
       "      <td>2991.58</td>\n",
       "      <td>165.0</td>\n",
       "      <td>8.2</td>\n",
       "      <td>113.9</td>\n",
       "      <td>60.0</td>\n",
       "      <td>0.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13800000.0</td>\n",
       "      <td>1988-11-19</td>\n",
       "      <td>Buffalo</td>\n",
       "      <td>NY</td>\n",
       "      <td>USA</td>\n",
       "      <td>USA</td>\n",
       "      <td>71.0</td>\n",
       "      <td>177.0</td>\n",
       "      <td>2007.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>91.0</td>\n",
       "      <td>8.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>10.8</td>\n",
       "      <td>3057.71</td>\n",
       "      <td>75.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>114.0</td>\n",
       "      <td>89.2</td>\n",
       "      <td>1.09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13800000.0</td>\n",
       "      <td>1988-04-29</td>\n",
       "      <td>Winnipeg</td>\n",
       "      <td>MB</td>\n",
       "      <td>CAN</td>\n",
       "      <td>CAN</td>\n",
       "      <td>74.0</td>\n",
       "      <td>201.0</td>\n",
       "      <td>2006.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>...</td>\n",
       "      <td>78.0</td>\n",
       "      <td>4.7</td>\n",
       "      <td>1.6</td>\n",
       "      <td>6.3</td>\n",
       "      <td>2866.62</td>\n",
       "      <td>111.0</td>\n",
       "      <td>5.3</td>\n",
       "      <td>116.8</td>\n",
       "      <td>62.4</td>\n",
       "      <td>0.87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>12000000.0</td>\n",
       "      <td>1985-08-14</td>\n",
       "      <td>Sicamous</td>\n",
       "      <td>BC</td>\n",
       "      <td>CAN</td>\n",
       "      <td>CAN</td>\n",
       "      <td>76.0</td>\n",
       "      <td>232.0</td>\n",
       "      <td>2003.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>115.0</td>\n",
       "      <td>4.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>10.1</td>\n",
       "      <td>2759.46</td>\n",
       "      <td>335.0</td>\n",
       "      <td>7.9</td>\n",
       "      <td>110.3</td>\n",
       "      <td>53.9</td>\n",
       "      <td>0.69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>11000000.0</td>\n",
       "      <td>1995-02-08</td>\n",
       "      <td>Clinton</td>\n",
       "      <td>ON</td>\n",
       "      <td>CAN</td>\n",
       "      <td>CAN</td>\n",
       "      <td>73.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>2009.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>...</td>\n",
       "      <td>87.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2809.55</td>\n",
       "      <td>76.0</td>\n",
       "      <td>19.8</td>\n",
       "      <td>117.7</td>\n",
       "      <td>57.7</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 154 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       Salary        Born      City Pr/St Cntry  Nat    Ht     Wt   DftYr  \\\n",
       "0  14000000.0  1991-08-25  Jesenice   NaN   SVN  SVN  75.0  224.0  2005.0   \n",
       "1  13800000.0  1988-11-19   Buffalo    NY   USA  USA  71.0  177.0  2007.0   \n",
       "2  13800000.0  1988-04-29  Winnipeg    MB   CAN  CAN  74.0  201.0  2006.0   \n",
       "3  12000000.0  1985-08-14  Sicamous    BC   CAN  CAN  76.0  232.0  2003.0   \n",
       "4  11000000.0  1995-02-08   Clinton    ON   CAN  CAN  73.0  210.0  2009.0   \n",
       "\n",
       "   DftRd  ...   PEND  OPS  DPS    PS     OTOI   Grit   DAP   Pace    GS  GS/G  \n",
       "0    1.0  ...   92.0  2.6  1.9   4.6  2991.58  165.0   8.2  113.9  60.0  0.79  \n",
       "1    1.0  ...   91.0  8.7  2.1  10.8  3057.71   75.0   4.8  114.0  89.2  1.09  \n",
       "2    1.0  ...   78.0  4.7  1.6   6.3  2866.62  111.0   5.3  116.8  62.4  0.87  \n",
       "3    2.0  ...  115.0  4.1  6.0  10.1  2759.46  335.0   7.9  110.3  53.9  0.69  \n",
       "4    2.0  ...   87.0  4.0  1.9   6.0  2809.55   76.0  19.8  117.7  57.7  0.80  \n",
       "\n",
       "[5 rows x 154 columns]"
      ]
     },
     "execution_count": 301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('NHL_Salaries_ELC_Removed.csv', encoding='iso-8859-1')\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows (players): 874\n",
      "Number of columns (attributes): 154\n"
     ]
    },
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Salary</th>\n",
       "      <th>Ht</th>\n",
       "      <th>Wt</th>\n",
       "      <th>DftYr</th>\n",
       "      <th>DftRd</th>\n",
       "      <th>Ovrl</th>\n",
       "      <th>GP</th>\n",
       "      <th>G</th>\n",
       "      <th>A</th>\n",
       "      <th>A1</th>\n",
       "      <th>...</th>\n",
       "      <th>PEND</th>\n",
       "      <th>OPS</th>\n",
       "      <th>DPS</th>\n",
       "      <th>PS</th>\n",
       "      <th>OTOI</th>\n",
       "      <th>Grit</th>\n",
       "      <th>DAP</th>\n",
       "      <th>Pace</th>\n",
       "      <th>GS</th>\n",
       "      <th>GS/G</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7.890000e+02</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>664.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>789.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>788.000000</td>\n",
       "      <td>787.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.483054e+06</td>\n",
       "      <td>73.082383</td>\n",
       "      <td>201.566540</td>\n",
       "      <td>2007.951807</td>\n",
       "      <td>2.864458</td>\n",
       "      <td>72.417169</td>\n",
       "      <td>51.757921</td>\n",
       "      <td>7.683143</td>\n",
       "      <td>13.051965</td>\n",
       "      <td>7.228137</td>\n",
       "      <td>...</td>\n",
       "      <td>48.177665</td>\n",
       "      <td>1.297592</td>\n",
       "      <td>1.327250</td>\n",
       "      <td>2.626869</td>\n",
       "      <td>2059.012132</td>\n",
       "      <td>132.666667</td>\n",
       "      <td>9.434094</td>\n",
       "      <td>108.841117</td>\n",
       "      <td>22.172843</td>\n",
       "      <td>0.343011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.365233e+06</td>\n",
       "      <td>2.122410</td>\n",
       "      <td>14.704179</td>\n",
       "      <td>4.067763</td>\n",
       "      <td>2.016449</td>\n",
       "      <td>64.012176</td>\n",
       "      <td>28.702101</td>\n",
       "      <td>8.497595</td>\n",
       "      <td>12.620931</td>\n",
       "      <td>7.469985</td>\n",
       "      <td>...</td>\n",
       "      <td>33.337692</td>\n",
       "      <td>1.979311</td>\n",
       "      <td>1.349113</td>\n",
       "      <td>2.805594</td>\n",
       "      <td>1138.084201</td>\n",
       "      <td>101.899381</td>\n",
       "      <td>7.766916</td>\n",
       "      <td>8.598547</td>\n",
       "      <td>21.681661</td>\n",
       "      <td>0.287410</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>5.750000e+05</td>\n",
       "      <td>66.000000</td>\n",
       "      <td>157.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-1.700000</td>\n",
       "      <td>-0.200000</td>\n",
       "      <td>-1.200000</td>\n",
       "      <td>33.510000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>-4.300000</td>\n",
       "      <td>-0.590000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.250000e+05</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>191.000000</td>\n",
       "      <td>2005.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.300000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1019.362500</td>\n",
       "      <td>53.000000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>104.700000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.150000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.100000e+06</td>\n",
       "      <td>73.000000</td>\n",
       "      <td>201.000000</td>\n",
       "      <td>2009.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>54.000000</td>\n",
       "      <td>62.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>47.000000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.800000</td>\n",
       "      <td>2471.875000</td>\n",
       "      <td>121.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>109.150000</td>\n",
       "      <td>16.200000</td>\n",
       "      <td>0.310000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>4.000000e+06</td>\n",
       "      <td>75.000000</td>\n",
       "      <td>211.000000</td>\n",
       "      <td>2011.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>112.000000</td>\n",
       "      <td>78.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>74.000000</td>\n",
       "      <td>2.300000</td>\n",
       "      <td>1.900000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>3030.462500</td>\n",
       "      <td>197.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>113.900000</td>\n",
       "      <td>36.300000</td>\n",
       "      <td>0.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.400000e+07</td>\n",
       "      <td>81.000000</td>\n",
       "      <td>260.000000</td>\n",
       "      <td>2013.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>291.000000</td>\n",
       "      <td>82.000000</td>\n",
       "      <td>44.000000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>38.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>137.000000</td>\n",
       "      <td>10.500000</td>\n",
       "      <td>7.200000</td>\n",
       "      <td>15.300000</td>\n",
       "      <td>3527.100000</td>\n",
       "      <td>622.000000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>156.600000</td>\n",
       "      <td>99.200000</td>\n",
       "      <td>1.260000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 144 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             Salary          Ht          Wt        DftYr       DftRd  \\\n",
       "count  7.890000e+02  789.000000  789.000000   664.000000  664.000000   \n",
       "mean   2.483054e+06   73.082383  201.566540  2007.951807    2.864458   \n",
       "std    2.365233e+06    2.122410   14.704179     4.067763    2.016449   \n",
       "min    5.750000e+05   66.000000  157.000000  1990.000000    1.000000   \n",
       "25%    7.250000e+05   72.000000  191.000000  2005.000000    1.000000   \n",
       "50%    1.100000e+06   73.000000  201.000000  2009.000000    2.000000   \n",
       "75%    4.000000e+06   75.000000  211.000000  2011.000000    4.000000   \n",
       "max    1.400000e+07   81.000000  260.000000  2013.000000    9.000000   \n",
       "\n",
       "             Ovrl          GP           G           A          A1  ...  \\\n",
       "count  664.000000  789.000000  789.000000  789.000000  789.000000  ...   \n",
       "mean    72.417169   51.757921    7.683143   13.051965    7.228137  ...   \n",
       "std     64.012176   28.702101    8.497595   12.620931    7.469985  ...   \n",
       "min      1.000000    1.000000    0.000000    0.000000    0.000000  ...   \n",
       "25%     19.000000   25.000000    1.000000    2.000000    1.000000  ...   \n",
       "50%     54.000000   62.000000    5.000000   10.000000    5.000000  ...   \n",
       "75%    112.000000   78.000000   12.000000   20.000000   11.000000  ...   \n",
       "max    291.000000   82.000000   44.000000   63.000000   38.000000  ...   \n",
       "\n",
       "             PEND         OPS         DPS          PS         OTOI  \\\n",
       "count  788.000000  789.000000  789.000000  789.000000   788.000000   \n",
       "mean    48.177665    1.297592    1.327250    2.626869  2059.012132   \n",
       "std     33.337692    1.979311    1.349113    2.805594  1138.084201   \n",
       "min      0.000000   -1.700000   -0.200000   -1.200000    33.510000   \n",
       "25%     17.000000    0.000000    0.300000    0.200000  1019.362500   \n",
       "50%     47.000000    0.400000    1.000000    1.800000  2471.875000   \n",
       "75%     74.000000    2.300000    1.900000    4.300000  3030.462500   \n",
       "max    137.000000   10.500000    7.200000   15.300000  3527.100000   \n",
       "\n",
       "             Grit         DAP        Pace          GS        GS/G  \n",
       "count  789.000000  789.000000  788.000000  788.000000  787.000000  \n",
       "mean   132.666667    9.434094  108.841117   22.172843    0.343011  \n",
       "std    101.899381    7.766916    8.598547   21.681661    0.287410  \n",
       "min      0.000000    0.000000   75.000000   -4.300000   -0.590000  \n",
       "25%     53.000000    4.800000  104.700000    3.000000    0.150000  \n",
       "50%    121.000000    7.900000  109.150000   16.200000    0.310000  \n",
       "75%    197.000000   12.000000  113.900000   36.300000    0.530000  \n",
       "max    622.000000   61.000000  156.600000   99.200000    1.260000  \n",
       "\n",
       "[8 rows x 144 columns]"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Print the number of rows\n",
    "print(\"Number of rows (players):\", df.shape[0])\n",
    "\n",
    "# number of columns\n",
    "print(\"Number of columns (attributes):\", df.shape[1])\n",
    "\n",
    "df.describe()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Preprocessing & Feature Engineering\n",
    "\n",
    "Transforming raw NHL statistics into meaningful predictive features through:\n",
    "- Age calculation from birth dates\n",
    "- Experience derivation from draft years\n",
    "- Position standardization (Defense/Center/Wing)\n",
    "- Advanced metric computation (e.g., teamNetRebound)\n",
    "- Handling of undrafted players (220 per year average)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 303,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Preliminary Data Analysis\n",
    "\n",
    "\n",
    "# Convert the Born column to datetime using the correct format\n",
    "df['Born'] = pd.to_datetime(df['Born'])\n",
    "\n",
    "# Calculate age\n",
    "df['Age'] = 2017 - df['Born'].dt.year\n",
    "\n",
    "# Experience\n",
    "df['Xp'] = 2017 - df['DftYr']\n",
    "\n",
    "# Draft pos to single value \n",
    "df['Ovrl'] += 30 * df['DftRd']\n",
    "df = df.drop('DftRd', axis=1)\n",
    "\n",
    "# Undrafted to numerical representation (about 220 players drafted each year)\n",
    "df['Ovrl'] = df['Ovrl'].fillna(220)\n",
    "\n",
    "#More over-arching rebound stat\n",
    "    # The team's rebounds while this player was on the ice - Rebounds allowed while this player was on the ice\n",
    "df['teamNetRebound'] = df['RBF'] - df['RBA']\n",
    "\n",
    "# Position adjustments\n",
    "    # Players who play defense and offense are generally defensemen\n",
    "    # Players who play center and wing are generally centers\n",
    "    # Wing is wing\n",
    "df['Position'] = df['Position'].apply(lambda x: \n",
    "    'D' if isinstance(x, str) and 'd' in x.lower() else\n",
    "    'C' if isinstance(x, str) and 'c' in x.lower() else\n",
    "    'W' if isinstance(x, str) and 'w' in x.lower() else\n",
    "    x\n",
    ")\n",
    "\n",
    "#Players listed for multiple teams messes with team salary visualizations, for this sake just keep team started year with\n",
    "df['Team'] = df['Team'].str.split('/').str[0]\n",
    "\n",
    "# More rookie contracts\n",
    "players_to_remove = ['Wennberg', 'Horvat', 'Sheary', 'Teravainen']\n",
    "df = df[~df['Last Name'].isin(players_to_remove)]\n",
    "\n",
    "# Handle the two icf collumns by creating one out of an avg of both\n",
    "    # Merge the two iCF columns by averaging their values\n",
    "df['iCF'] = df[['iCF', 'iCF.1']].mean(axis=1)\n",
    "    # Remove the duplicate column\n",
    "df = df.drop('iCF.1', axis=1)\n",
    "\n",
    "# Same with isf\n",
    "df['iSF'] = df[['iSF', 'iSF.1', 'iSF.2']].mean(axis=1)\n",
    "df = df.drop('iSF.1', axis=1).drop('iSF.2', axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Salary by age\n",
    "\n",
    "plt.figure(figsize=(12, 6))\n",
    "ax = sns.barplot(data=df, x='Age', y='Salary', errorbar=None)\n",
    "\n",
    "# Player count annotations\n",
    "for i in ax.containers:\n",
    "    counts = df['Age'].value_counts().sort_index()\n",
    "    ax.bar_label(i, labels=counts, padding=3)\n",
    "\n",
    "plt.title('Average Salary by Age')\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Average Salary ($)')\n",
    "plt.xticks(rotation=45)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Young players are typically paid far less, aside from the few highly skilled young stars that always seem to arise.\n",
    "- Gernerally highest paid during player's late 20s to early 30s (generally considered a players 'prime' age for preformance)\n",
    "- Fewer older players in the league, although the few are generally paid very high (especially when compared to current production) likely due to factors like experience, past-season production, or even just reputation. It's also very likely that older players who could play for lower salaries choose not to, as they've already made their money and may not find the physical risk worth it anymore."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 305,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Salary by position \n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "sns.barplot(data=df, x='Position', y='Salary')\n",
    "plt.title('Average Salary by Position')\n",
    "plt.xlabel('Position')\n",
    "plt.ylabel('Avg Salary ($ millions)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Center is the highest paying position on average. This makes sense as it is likely the most vital skater position, responsible for the strongest combination of both offense and defensive play. \n",
    "- Defense is second, likely due to the smaller number of them per roster, NHL caliber defenseman are typically more rare than a winger, just due to the added difficulty demanded by the nature of the position.\n",
    "- Left/Right wingers are generally paid the least, likely due to the relative ease of the position (not much responsibility demanded in the defensive zone, generally simpler more straightforward position)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get only numerical columns\n",
    "numerical_cols = df.select_dtypes(include=[np.number]).columns\n",
    "\n",
    "# Get correlations with salary and sort by absolute value\n",
    "salary_correlations = df[numerical_cols].corr()['Salary']\n",
    "salary_correlations_abs = salary_correlations.abs().sort_values(ascending=False)\n",
    "\n",
    "# Get top 15 features by absolute correlation value, excluding salary self-correlation\n",
    "top_15_abs = salary_correlations[salary_correlations_abs.index[1:17]]\n",
    "top_15_abs = top_15_abs[top_15_abs.index != 'FOL']  # Team face-off losses shouldnt be a factor\n",
    "\n",
    "# Create a horizontal bar plot\n",
    "plt.figure(figsize=(12, 8))\n",
    "colors = ['red' if x < 0 else 'green' for x in top_15_abs.values]\n",
    "plt.barh(top_15_abs.index, top_15_abs.values, color=colors)\n",
    "plt.title('Top 15 Features Correlated with NHL Player Salary')\n",
    "plt.xlabel('Correlation Coefficient')\n",
    "plt.ylabel('Features')\n",
    "\n",
    "# Add correlation values on the bars\n",
    "for i, v in enumerate(top_15_abs.values):\n",
    "    plt.text(v, i, f' {v:.2f}', va='center')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. xGF: The team's expected goals (weighted shots) while this player was on the ice, which is shot attempts weighted by location.\n",
    "\n",
    "2. GF: Team's goals for with player on ice\n",
    "\n",
    "3. SCF: Team's scoring chances with this player on ice\n",
    "\n",
    "4. SF: The team's shots on goal with this player on ice   CF: The team's shot attempts (Corsi) while this player was on the ice\n",
    "\n",
    "5. CF: The team's shot attempts (Corsi) while this player was on the ice\n",
    "\n",
    "6. FF: The team's unblocked shot attempts (Fenwick) while this player was on the ice\n",
    "\n",
    "7. A: Assists \n",
    "\n",
    "8. PTS: Player points\n",
    "\n",
    "9. GS: Player's combined game score. Game score weights each of the actions of each player to give us a single number representing their overall performance in that game. Game Score includes goals, assists, shots on goal, penalties drawn and taken, faceoffs won and lost, blocked shots, 5v5 goal differential, and 5v5 Corsi differential. Each action is weighted differently in the formula. For example, one goal scored is worth more than one blocked shot.\n",
    "\n",
    "10. iCF: Shot attempts taken by individual (Individual Corsi)\n",
    "\n",
    "11. FOW: Team face-off wins with player on ice \n",
    "\n",
    "12. iFF: Unblocked shot attempts taken by this individual (Fenwick individual)\n",
    "\n",
    "13. iSF: Individual shots on goal\n",
    "\n",
    "14. A1: Primary Assists only\n",
    "\n",
    "15. PS: Point share. This is a catch-all stat which measures a player's contribution towards points in the standings\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Development"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 307,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Common Data Preparation \n",
    "\n",
    "# Get features from top correlations (excluding FOL)\n",
    "features = top_15_abs.index.tolist()\n",
    "\n",
    "# Prepare data with null removal\n",
    "df = df.dropna(subset=features)\n",
    "X = df[features].dropna()  # Remove rows with nulls in any of these columns\n",
    "y = df['Salary'].loc[X.index]  # Get corresponding salary values\n",
    "\n",
    "# Split data\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Scale features\n",
    "scaler = StandardScaler()\n",
    "X_train_scaled = scaler.fit_transform(X_train)\n",
    "X_test_scaled = scaler.transform(X_test)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## XGBoost Model\n",
    "\n",
    "- Utilize XGBoost to determine the most important factors in player compensation, based on optimal parameters discovered through repeated model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n",
      "Best XGBoost Parameters: {'learning_rate': 0.07030308223177476, 'max_depth': 6, 'n_estimators': 103, 'subsample': 0.6125253169822235}\n"
     ]
    }
   ],
   "source": [
    "# XGBoost parameter grid\n",
    "xgb_params = {\n",
    "    'n_estimators': randint(50, 200),\n",
    "    'learning_rate': uniform(0.01, 0.29),  # uniform between 0.01 and 0.3\n",
    "    'max_depth': randint(3, 10),\n",
    "    'subsample': uniform(0.6, 0.4)  # uniform between 0.6 and 1.0\n",
    "}\n",
    "\n",
    "# Initialize and fit RandomizedSearchCV for XGBoost\n",
    "xgb_random = RandomizedSearchCV(\n",
    "    XGBRegressor(random_state=42),\n",
    "    param_distributions=xgb_params,\n",
    "    n_iter=20,  # number of parameter settings sampled\n",
    "    scoring='neg_mean_squared_error',\n",
    "    cv=5,\n",
    "    random_state=42,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "xgb_random.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Get XGBoost results\n",
    "xgb_best_params = xgb_random.best_params_\n",
    "\n",
    "print(\"Best XGBoost Parameters:\", xgb_best_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 309,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "XGBoost Cross-Validation Results: Mean (std)\n",
      "Average RMSE: $1,730,462.92 (+/- $151,173.26)\n",
      "Average R²: 0.445 (+/- 0.058)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize XGBoost model using optimal params\n",
    "xgb_model = XGBRegressor(random_state=42, n_estimators = 103, learning_rate = .0703, max_depth = 6, subsample = .6125)\n",
    "\n",
    "# First fit the model\n",
    "xgb_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Then do cross-validation\n",
    "xgb_rmse_scores = np.sqrt(-cross_val_score(xgb_model, X_train_scaled, y_train, \n",
    "                                         scoring='neg_mean_squared_error', \n",
    "                                         cv=5))\n",
    "xgb_r2_scores = cross_val_score(xgb_model, X_train_scaled, y_train, \n",
    "                              scoring='r2', \n",
    "                              cv=5)\n",
    "\n",
    "# Now you can get feature importance\n",
    "feature_importance = pd.DataFrame({\n",
    "    'feature': features,\n",
    "    'importance': xgb_model.feature_importances_\n",
    "})\n",
    "feature_importance = feature_importance.sort_values('importance', ascending=False)\n",
    "\n",
    "# Print results\n",
    "print(\"XGBoost Cross-Validation Results: Mean (std)\")\n",
    "print(f\"Average RMSE: ${np.mean(xgb_rmse_scores):,.2f} (+/- ${np.std(xgb_rmse_scores):,.2f})\")\n",
    "print(f\"Average R²: {np.mean(xgb_r2_scores):.3f} (+/- {np.std(xgb_r2_scores):.3f})\")\n",
    "\n",
    "# Plot feature importance\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(feature_importance['feature'], feature_importance['importance'])\n",
    "plt.title('XGBoost Feature Importance')\n",
    "plt.xlabel('Importance Score')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the XGBoost Cross-Validation results, the Average RMSE is $1,730,462.92 (+/- $151,173.26). This indicates the average prediction error in dollars for the salary predictions using the XGBoost model.\n",
    "\n",
    "- An R² value of 0.445 (+/- 0.058), indicating it explains about 44% of the variance in salary predictions\n",
    "- An RMSE of approximately $1.73M, the average prediction error\n",
    "- A clear feature importance ranking with FF, GS, and iSF as the strongest predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Lasso Model\n",
    "\n",
    "- Utilize Lasso Regression to determine the most important factors in player compensation, based on optimal parameters discovered through repeated model training."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 20 candidates, totalling 100 fits\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.192e+12, tolerance: 5.103e+11\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "/opt/anaconda3/lib/python3.12/site-packages/sklearn/linear_model/_coordinate_descent.py:678: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.080e+12, tolerance: 4.989e+11\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Best Lasso Parameters: {'alpha': 9.423017556848526, 'max_iter': 21253, 'tol': 0.0009083973481164612}\n"
     ]
    }
   ],
   "source": [
    "#Find Optimal Parameters\n",
    "\n",
    "# Lasso parameter grid\n",
    "lasso_params = {\n",
    "    'alpha': uniform(0.001, 10),\n",
    "    'max_iter': randint(11000, 30000),\n",
    "    'tol': uniform(0.0001, 0.001)\n",
    "}\n",
    "\n",
    "# Initialize and fit RandomizedSearchCV for Lasso\n",
    "lasso_random = RandomizedSearchCV(\n",
    "    Lasso(random_state=42),\n",
    "    param_distributions=lasso_params,\n",
    "    n_iter=20,\n",
    "    scoring='neg_mean_squared_error',\n",
    "    cv=5,\n",
    "    random_state=42,\n",
    "    verbose=1\n",
    ")\n",
    "\n",
    "lasso_random.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Get Lasso results\n",
    "lasso_best_params = lasso_random.best_params_\n",
    "\n",
    "# Print results\n",
    "print(\"\\nBest Lasso Parameters:\", lasso_best_params)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 311,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Lasso Cross-Validation Results: Mean (std)\n",
      "Average RMSE: $1,696,769.89 (+/- $176,358.98)\n",
      "Average R²: 0.470 (+/- 0.030)\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize Lasso using optimal params\n",
    "lasso_model = Lasso(alpha=9.423017556848526, random_state=42, max_iter=21253, tol=0.0009083973481164612)\n",
    "\n",
    "# First fit the model\n",
    "lasso_model = lasso_model.fit(X_train_scaled, y_train)\n",
    "\n",
    "# Then do cross-validation\n",
    "lasso_rmse_scores = np.sqrt(-cross_val_score(lasso_model, X_train_scaled, y_train, \n",
    "                                           scoring='neg_mean_squared_error', \n",
    "                                           cv=5))\n",
    "lasso_r2_scores = cross_val_score(lasso_model, X_train_scaled, y_train, \n",
    "                                scoring='r2', \n",
    "                                cv=5)\n",
    "\n",
    "# Lasso Feature Importance\n",
    "lasso_importance = pd.DataFrame({\n",
    "    'feature': features,\n",
    "    'importance': np.abs(lasso_model.coef_)\n",
    "})\n",
    "lasso_importance = lasso_importance.sort_values('importance', ascending=False)\n",
    "\n",
    "# Print Lasso results\n",
    "print(\"\\nLasso Cross-Validation Results: Mean (std)\")\n",
    "print(f\"Average RMSE: ${np.mean(lasso_rmse_scores):,.2f} (+/- ${np.std(lasso_rmse_scores):,.2f})\")\n",
    "print(f\"Average R²: {np.mean(lasso_r2_scores):.3f} (+/- {np.std(lasso_r2_scores):.3f})\")\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.barh(lasso_importance['feature'], lasso_importance['importance'])\n",
    "plt.title('Lasso Feature Importance')\n",
    "plt.xlabel('|Coefficient Value|')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Based on the Lasso Cross-Validation results, the Average RMSE is $1,696,769.89 (+/- $176,358.98). This indicates the average prediction error in dollars for the salary predictions using the Lasso model.\n",
    "\n",
    "- An R² value of 0.470 (+/- 0.030), indicating it explains about 47% of the variance in salary predictions\n",
    "- An RMSE of approximately $1.69M, the average prediction error\n",
    "- A clear feature importance ranking with FF, CF, and xGF as the strongest predictors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Performance Comparison\n",
    "\n",
    "Accuracy Metrics:\n",
    "\n",
    "- XGBoost achieved 44.5% accuracy (R² = 0.445 ± 0.058) with RMSE of $1.73M\n",
    "- Lasso achieved 47.0% accuracy (R² = 0.470 ± 0.030) with RMSE of $1.69M\n",
    "\n",
    "Feature Importance Analysis:\n",
    "\n",
    "- XGBoost identified Fenwick For (FF) as the strongest predictor, followed by Goals Scored (GS) and Individual Shot Frequency (iSF)\n",
    "- Lasso model emphasized similar metrics but with different weights, showing FF, xGF (Expected Goals For), and CF (Corsi For) as top predictors\n",
    "\n",
    "\n",
    "While both models achieved similar performance metrics, Lasso showed slightly better accuracy with lower variance in predictions. The models agree on the importance of shot flow statistics (FF, CF) and goal-scoring metrics, suggesting these are robust predictors of player salaries regardless of modeling approach. For more in depth predictions we will continue with soley the Lasso Regression model, due to it's slightly increased accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prediction Analysis\n",
    "\n",
    "- Continuation with the Lasso model to predict player salaries, identify over/under paid players through comparison of their actual contracts, determine which contracts + players the model finds best, visualization of salary descrepancies across teams"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Difference = Predicted - Actual\n",
      "\n",
      "Top 10 Most Overpaid Players (Actual > Predicted):\n",
      "  Player Team  Actual  Predicted  Difference\n",
      " Stamkos  T.B     9.5   1.671265   -7.828735\n",
      " Kopitar  L.A    14.0   6.478144   -7.521856\n",
      "   Toews  CHI    13.8   6.591322   -7.208678\n",
      "   Weber  MTL    12.0   5.020616   -6.979384\n",
      "    Kane  CHI    13.8   6.857871   -6.942129\n",
      "  Subban  NSH    11.0   4.330033   -6.669967\n",
      "Callahan  T.B     6.5   0.846184   -5.653816\n",
      "    Ladd  NYI     8.0   2.591465   -5.408535\n",
      "Ovechkin  WSH    10.0   4.946087   -5.053913\n",
      "    Ryan  OTT     7.0   2.363650   -4.636350\n",
      "\n",
      "Top 10 Most Underpaid Players (Actual < Predicted):\n",
      "      Player Team  Actual  Predicted  Difference\n",
      "     Panarin  CHI  0.9250   5.292501    4.367501\n",
      "      Slavin  CAR  0.7425   5.084022    4.341522\n",
      "Gostisbehere  PHI  0.9250   5.196522    4.271522\n",
      "      Gagner  CBJ  0.6500   4.888606    4.238606\n",
      "     Danault  MTL  0.8750   5.062865    4.187865\n",
      "      Pageau  OTT  1.1000   5.090147    3.990147\n",
      "     Schultz  PIT  1.4000   5.287934    3.887934\n",
      "      Vrbata  ARI  1.0000   4.883693    3.883693\n",
      "     Zaitsev  TOR  0.9250   4.537823    3.612823\n",
      "    Severson  N.J  0.6000   4.179166    3.579166\n"
     ]
    }
   ],
   "source": [
    "# First get predictions for all players\n",
    "all_predictions = lasso_model.predict(scaler.transform(df[features]))\n",
    "\n",
    "# Then create the prediction DataFrame with all required columns\n",
    "prediction_df = pd.DataFrame({\n",
    "    'Actual': df['Salary'] / 1_000_000,  # Convert to millions\n",
    "    'Predicted': all_predictions / 1_000_000,  # Convert to millions\n",
    "    'Difference': (all_predictions - df['Salary']) / 1_000_000,  # Convert to millions\n",
    "    'Player': df['Last Name'],\n",
    "    'Team': df['Team']\n",
    "})\n",
    "\n",
    "# Add absolute difference column\n",
    "prediction_df['Abs_Difference'] = abs(prediction_df['Difference'])\n",
    "\n",
    "\n",
    "# Create prediction vs actual plot with outlier labels\n",
    "plt.figure(figsize=(12, 7))\n",
    "\n",
    "# Scatter plot of all players\n",
    "plt.scatter(prediction_df['Actual'], prediction_df['Predicted'], alpha=0.4, color='blue')\n",
    "\n",
    "# Perfect prediction line\n",
    "plt.plot([prediction_df['Actual'].min(), prediction_df['Actual'].max()], \n",
    "         [prediction_df['Actual'].min(), prediction_df['Actual'].max()], \n",
    "         'r--', label='Perfect Prediction')\n",
    "\n",
    "# Find outliers (players with large differences between actual and predicted)\n",
    "# Using 1.5 standard deviations as threshold\n",
    "diff_std = prediction_df['Difference'].std()\n",
    "outlier_threshold = 1.5 * diff_std\n",
    "outliers = prediction_df[abs(prediction_df['Difference']) > outlier_threshold]\n",
    "\n",
    "# Label outliers\n",
    "for _, player in outliers.iterrows():\n",
    "    plt.annotate(f\"{player['Player']} ({player['Team']})\", \n",
    "                (player['Actual'], player['Predicted']),\n",
    "                xytext=(5, 5), \n",
    "                textcoords='offset points',\n",
    "                fontsize=8,\n",
    "                alpha=0.7)\n",
    "\n",
    "# Formatting\n",
    "plt.xlabel('Actual Salary ($M)')\n",
    "plt.ylabel('Predicted Salary ($M)')\n",
    "plt.title('2016-17 NHL Player Salary: Predicted vs Actual\\nOutliers Labeled (>1.5σ from perfect prediction)')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"Difference = Predicted - Actual\")\n",
    "\n",
    "print(\"\\nTop 10 Most Overpaid Players (Actual > Predicted):\")\n",
    "print(prediction_df[prediction_df['Difference'] < 0]\n",
    "      .nlargest(10, 'Abs_Difference')[['Player', 'Team', 'Actual', 'Predicted', 'Difference']]\n",
    "      .to_string(index=False))\n",
    "\n",
    "print(\"\\nTop 10 Most Underpaid Players (Actual < Predicted):\")\n",
    "print(prediction_df[prediction_df['Difference'] > 0]\n",
    "      .nlargest(10, 'Abs_Difference')[['Player', 'Team', 'Actual', 'Predicted', 'Difference']]\n",
    "      .to_string(index=False))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Stamkos jumps out as a burglar here, mainly because he only played 17 games due to a meniscus tear and slew of other conditions. It's clear in other seasons why he deserves that paycheck, it's just the case that the data from this season alone does not support it.\n",
    "\n",
    "- The high-salary outliers (Kopitar $14M, Kane $13.8M, Toews $13.8M) represent franchise cornerstone players whose compensation includes factors beyond pure statistical performance, such as leadership, playoff success, and market value. While their statistical output may not fully align with their salaries, these contracts often reflect their broader value to their organizations. \n",
    "    - This data also relies on actual salary (not cap hit). This is an issue that will be fully addressed later on, but effects mainly higher end players (as they will have the leaway and higher numbers, allowing their contracts to be unequally disperesed among the years remaining on their deal). * Their true cap-hits this year was: Koptar $10m, Kane & Toews $10.5m, a couple million dollars closer to the model's prediction.\n",
    "    - It's more the guys like Callahan, Ladd and Ryan that are veiwed as bad contracts for their teams here.\n",
    "\n",
    "- There were definitely some leftover Entry Level Contracts (ELC) in this dataset, where rookies/newer players are capped at a certain salary (usually under or around $1m). However I'll keep these couple players in just to show how often it is some of the leagues youngest that are the most severely underpaid. (Dataset was supposed to contain non-ELC only)\n",
    "    - For example panarin shouldnt be here, but he's my favorite player. He came over to the NHL the year prior, from Russia at an older age than most rookies, yet was still subject to an ELC, signing a deal clearly not suited for his skill level. Subsequently on 26 December of 2016, Panarin signed a new two-year $12 million contract with Chicago effective for the 2017–18 and 2018–19 seasons ($6m AAV). Meaning his then yearly salary would be right around our prediction of $5-6m\n",
    "\n",
    "- Most underpaid players are making about $1m or less (these are likely 2-way contracts, meant for players who may split time in the minor leagues). May indicate the underappreciation of some of these journey-men players, as the model thinks they should be making more or equal to amounts made by other, older NHL staples.\n",
    "\n",
    "- Ryan Callahan (TB): Contract was traded to Ottawa Senators in 2019 for cap relief. Placed on long-term injured reserve due to degenerative disc disease, effectively ending his career.\n",
    "\n",
    "- Andrew Ladd (NYI); Model predicted much lower than his $5.5M salary. Was traded to Arizona Coyotes in 2021 with additional draft picks as sweeteners. Retired in September 2023 after knee issues.\n",
    "\n",
    "- Justin Schultz’s Breakout Year: 2016-17 was his career-best season with 51 points (12 goals, 39 assists) in 78 games, as he spent most of the year filling in for star defensemen Kris Letang, who suffered a herniated disk in his neck half-way through the season. Significant improvement from previous year (18 points in 63 games). Earned NHL All-Star votes and finished 10th in Norris Trophy voting all while playing on a $1.4M contract, providing excellent value for Pittsburgh and clearly recognized by the model.\n",
    "\n",
    "\n",
    "Contract Outcomes - The model appears to have correctly identified several problematic contracts:\n",
    "\n",
    "- Teams had to attach assets to move Ladd’s contract\n",
    "\n",
    "- Callahan’s contract required a trade for cap relief\n",
    "\n",
    "- Multiple ended 'over-payments' ended in buyouts, trades, or placement on the LTIR (Long Term Injured Reserve), all likely for the purpose of salary cap relief\n",
    "    \n",
    "- High-salary players like Kane and Toews eventually took significant pay cuts in subsequent contracts\n",
    "\n",
    "The analysis shows the model was effective at identifying overvalued contracts that teams later needed to move or modify, though it may have underestimated the market value of certain elite players like Stamkos and Ovechkin."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Perfect Predictions\n",
    "\n",
    "- Here we can more clearly see every scenario where the model near-perfectly predicted a player's salary for that year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 313,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 15 Most Accurate Predictions:\n",
      "    Player Team  Actual  Predicted  Difference\n",
      "  Koekkoek  T.B  $0.83M     $0.83M      $0.00M\n",
      "     Koivu  MIN  $5.40M     $5.39M     $-0.01M\n",
      "   Kampfer  FLA  $0.62M     $0.62M     $-0.01M\n",
      "    Hanley  MTL  $0.60M     $0.59M     $-0.01M\n",
      "    Strait  WPG  $0.60M     $0.59M     $-0.01M\n",
      "    Kuraly  BOS  $0.69M     $0.68M     $-0.02M\n",
      "  Oesterle  EDM  $0.58M     $0.56M     $-0.02M\n",
      "     Hudon  MTL  $0.57M     $0.55M     $-0.02M\n",
      "  Mitchell  MIN  $0.64M     $0.61M     $-0.03M\n",
      "    Dillon  S.J  $2.75M     $2.78M      $0.03M\n",
      "  Pavelski  S.J  $6.00M     $6.03M      $0.03M\n",
      "Pacioretty  MTL  $5.00M     $5.03M      $0.03M\n",
      "    Barrie  COL  $5.00M     $4.97M     $-0.03M\n",
      "     Brown  CAR  $0.65M     $0.62M     $-0.03M\n",
      "    Nelson  BUF  $0.65M     $0.61M     $-0.04M\n"
     ]
    }
   ],
   "source": [
    "# Create prediction vs actual plot focusing on accurate predictions\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Scatter plot of all players\n",
    "plt.scatter(prediction_df['Actual'], prediction_df['Predicted'], alpha=0.4, color='blue')\n",
    "\n",
    "# Perfect prediction line\n",
    "plt.plot([prediction_df['Actual'].min(), prediction_df['Actual'].max()], \n",
    "         [prediction_df['Actual'].min(), prediction_df['Actual'].max()], \n",
    "         'r--', label='Perfect Prediction')\n",
    "\n",
    "# Find players with very accurate predictions\n",
    "# Using 0.2 standard deviations as threshold for \"close to perfect\"\n",
    "diff_std = prediction_df['Difference'].std()\n",
    "threshold = 0.2 * diff_std\n",
    "accurate_predictions = prediction_df[abs(prediction_df['Difference']) < threshold]\n",
    "\n",
    "# Label accurate predictions\n",
    "for _, player in accurate_predictions.iterrows():\n",
    "    plt.annotate(f\"{player['Player']}\", \n",
    "                (player['Actual'], player['Predicted']),\n",
    "                xytext=(5, 5), \n",
    "                textcoords='offset points',\n",
    "                fontsize=8,\n",
    "                alpha=0.7)\n",
    "\n",
    "# Formatting\n",
    "plt.xlabel('Actual Salary ($M)')\n",
    "plt.ylabel('Predicted Salary ($M)')\n",
    "plt.title('NHL Player Salary: Predicted vs Actual\\nPlayers with Most Accurate Predictions')\n",
    "plt.legend()\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Sort by smallest absolute difference\n",
    "best_predictions = prediction_df.nsmallest(15, 'Abs_Difference')\n",
    "\n",
    "# Format output\n",
    "pd.set_option('display.float_format', lambda x: '${:.2f}M'.format(x))\n",
    "print(\"\\nTop 15 Most Accurate Predictions:\")\n",
    "print(best_predictions[['Player', 'Team', 'Actual', 'Predicted', 'Difference']]\n",
    "      .to_string(index=False))\n",
    "\n",
    "# Reset display format\n",
    "pd.reset_option('display.float_format')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- While this list does contain some very good players, not many of them were big point-getters, indicating the model may be more accurate when judging players who are clearly valued by their all-around metrics, rather than for example Steven Stamkos scoring 60 goals in a season\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Highest Predicted Salaries\n",
    "\n",
    "- Here we can basically see which players the model finds best (in terms of how much they 'should' make)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Top 15 Players by Predicted Salary:\n",
      "  Player Team  Predicted  Actual  Difference\n",
      "  Kesler  ANA   7.424719   7.875   -0.450281\n",
      " Getzlaf  ANA   7.185571   9.250   -2.064429\n",
      " Wheeler  WPG   7.159082   5.800    1.359082\n",
      "  Crosby  PIT   7.145012  10.900   -3.754988\n",
      "Bergeron  BOS   6.924249   8.750   -1.825751\n",
      "    Kane  CHI   6.857871  13.800   -6.942129\n",
      "  Giroux  PHI   6.709216   9.000   -2.290784\n",
      "  Seguin  DAL   6.672329   6.000    0.672329\n",
      "   Keith  CHI   6.670471   6.000    0.670471\n",
      "   Toews  CHI   6.591322  13.800   -7.208678\n",
      "   Burns  S.J   6.569253   5.760    0.809253\n",
      "Karlsson  OTT   6.551317   7.000   -0.448683\n",
      " Doughty  L.A   6.521551   7.450   -0.928449\n",
      " Kopitar  L.A   6.478144  14.000   -7.521856\n",
      "O'Reilly  BUF   6.445357  11.000   -4.554643\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sort players by predicted salary\n",
    "prediction_df['Predicted'] = prediction_df['Predicted'].astype(float)\n",
    "top_predicted = prediction_df.nlargest(15, 'Predicted')\n",
    "\n",
    "# Display results\n",
    "print(\"\\nTop 15 Players by Predicted Salary:\")\n",
    "print(top_predicted[['Player', 'Team', 'Predicted', 'Actual', 'Difference']]\n",
    "      .to_string(index=False))\n",
    "\n",
    "# Optional visualization\n",
    "plt.figure(figsize=(12, 8))\n",
    "plt.barh(top_predicted['Player'], top_predicted['Predicted'])\n",
    "plt.xlabel('Predicted Salary ($M)')\n",
    "plt.ylabel('Player')\n",
    "plt.title('2016-17 Top 15 Players by Model-Predicted Salary')\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Kesler was often underappreciated due to lack of shockingly high point totals, can see here how the league values good all-around players, whose defensive metrics may be stronger than their flashy offense\n",
    "- Strong trend of the model preferring more all around good players, not necessarily the highest point-scorers\n",
    "- A lot of these guys are also on the 'biggest overpay list' despite the model still classifying them among the elites. Assumption can be made that the model dislikes teams forfeiting large percentages of their cap-space on one player, regardless of their talent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## New Signings Analysis\n",
    "\n",
    "- Seeing that there are many players getting over or underpaid (and that a lot of these contracts were signed years before), lets continue examining the accuracy of the model with some specific case studies of players whose deals expired after this season, and had to sign a brand new contract in the summer of 2017."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function that accepts last name and team as input, expected salary as output\n",
    "def predict_player_salary(lastName, team):\n",
    "    \n",
    "    # Get player stats using both last name and team\n",
    "    player_features = df[(df['Last Name']==lastName) & (df['Team']==team)][features]\n",
    "    \n",
    "    # Check if player exists\n",
    "    if len(player_features) == 0:\n",
    "        print(f\"\\nNo player found with last name {lastName} on team {team}\")\n",
    "        return None\n",
    "    \n",
    "    # Scale the features using the same scaler used in training\n",
    "    player_features_scaled = scaler.transform(player_features)\n",
    "    \n",
    "    # Make prediction\n",
    "    predicted_salary = lasso_model.predict(player_features_scaled)[0]\n",
    "    actual_salary = df[(df['Last Name'] == lastName) & (df['Team'] == team)]['Salary'].values[0]\n",
    "    \n",
    "    # Print results\n",
    "    print(f\"\\n 2016-17 Salary Analysis for {lastName}:\")\n",
    "    print(f\"Predicted Salary: ${predicted_salary/1_000_000:.2f}M\")\n",
    "    print(f\"Actual Salary: ${actual_salary/1_000_000:.2f}M\")\n",
    "    print(f\"Difference: ${(predicted_salary - actual_salary)/1_000_000:.2f}M\")\n",
    "    \n",
    "    return predicted_salary\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " 2016-17 Salary Analysis for Boyle:\n",
      "Predicted Salary: $2.73M\n",
      "Actual Salary: $2.00M\n",
      "Difference: $0.73M\n",
      "\n",
      " 2016-17 Salary Analysis for Shattenkirk:\n",
      "Predicted Salary: $4.71M\n",
      "Actual Salary: $5.20M\n",
      "Difference: $-0.49M\n",
      "\n",
      " 2016-17 Salary Analysis for Marleau:\n",
      "Predicted Salary: $4.61M\n",
      "Actual Salary: $6.50M\n",
      "Difference: $-1.89M\n",
      "\n",
      " 2016-17 Salary Analysis for Cousins:\n",
      "Predicted Salary: $2.34M\n",
      "Actual Salary: $0.84M\n",
      "Difference: $1.50M\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "2335548.044682572"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predict_player_salary('Boyle', 'T.B')\n",
    "predict_player_salary('Shattenkirk', 'STL')\n",
    "predict_player_salary('Marleau', 'S.J')\n",
    "predict_player_salary('Cousins', 'PHI')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Free Agency Signings (July 1, 2017): (These contracts began in the 2017-2018 season)\n",
    "\n",
    "- NJD signed Brian Boyle: 2yr 5.1M     ======> 2.55m AAV (Predicted $2.73m, Difference 0.18m)\n",
    "\n",
    "- NYR signed Kevin Shattenkirk: 4yr 26.6M ===> 6.65m AAV (Predicted $4.71m, Difference -1.94m) *Turned out to be the worst contract here (Team bought-out player before full term of deal)\n",
    "\n",
    "- TOR signed Patrick Marleau: 3yr 18.75M  ===> 6.25m AAV (Predicted $4.61m, Difference -1.64m)\n",
    "\n",
    "- ARI signed Nick Cousins: 2yr 4M       =====> 2.00m AAV (Predicted $2.34m, Difference 0.34m)\n",
    "\n",
    "*Our predictions appear to be closer for contracts signed right AFTER this season, indicating that past season player preformance plays a large role in future contracts (not something accounted for in this model, due to the dataset only containing single season statistics, except for years of 'experience' column)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Team Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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S5+da++b13gH4+vrSrVs3pk+fTrdu3fj000+ZMGECFStWvMYV2jMMg88++wwXFxfuuOOOfNu5uLgwYcIEJkyYwO+//27b/dSrVy9+/PFHateuDcBvv/1WqPvmXGetWrUwmUxs3Lgxz6BJzrLff/89V5uUlBTg75+hwv7MXYvatWsXao333nsvAQEBTJs2jSpVqvDf//6Xjz76qND3yVpLzjInJyeqVKliK3vqqaeYPHkyCQkJ/PXXX1y+fJknn3yy0PfJS2E/ix9++IFdu3aRlJTEkCFDbPUHDhy4rvuLiIiUJAWeRERErtOVK1eYNWsWPj4+fPjhh7nqly9fzhtvvMGXX35Jz549qV69On369GH27Nm0bduWlJQUu2N2YD0eM2nSJGrWrIm3t3eR5mUymXBwcLALlJw/f545c+bYtevQoQMACxYssNsdsmjRolxPquvZsydffPEFPj4+eR7tK8iZM2cwmUx2/4jPknVEKCtwZTKZcHJysguWpKSkFOqpdtfTN6dRo0bRpUsXhgwZQsWKFXn88ceveYycJkyYwN69e3nppZcKdUQOrLuNLBYLu3btIi4ujnPnztG4cWN8fHxISEggMjLymnfc9OzZk8mTJ/O///0v1zHLvJw5c4ZPP/3U7rjd3LlzqVChgu1nqLA/c9eiW7duzJkzh59++okmTZoU2PaZZ57hySefJDU1lbp169KvX79C32fJkiW89tprts/kzJkzfPbZZ9xzzz1266lXrx79+vXj3Xff5eLFi/Tq1cvuuGBRFPazyPqZzvlZv//++9d1fxERkZKkwJOIiMh1+vLLLzl69CixsbEEBwfnqs/ahREfH2/LtxIeHs6CBQt4+umnadCgAZ06dbLrM3r0aBYvXkyHDh2IiIigRYsWZGRkcPjwYVauXMmzzz5LmzZtCpxXjx49mDp1KgMGDOCJJ57g5MmTvP7667n+0dq8eXMeeeQR3njjDSpWrMi9997Lnj17eOONN3Bzc6NChb9P5r/88susWrWKdu3a8cwzz9CkSRP++usvDh06xBdffMGMGTPyPRb1008/0bVrVx5++GGCgoKoV68ep06d4vPPP+eDDz4gODiYdu3aAdZ/iC9ZsoThw4fz0EMPceTIEV555RXq1atnO2qWn+vpm1Pnzp3x9/dn3bp1DBo0iDp16hS67+nTp21HoNLT0/npp5+YP38+GzduJDQ0lAkTJhTYv02bNvTs2ZMWLVpQvXp19u3bx5w5c2jbti2VK1cGrMfJevXqxV133UVERAReXl4cPnyYr776ynZEMD/t27fniSee4NFHH+Xbb7+lQ4cOuLi4cOzYMb7++mtuu+02nnrqKVv7mjVr8tRTT3H48GEaN27MF198wcyZM3nqqadsgZfC/sxdi5dffpkvv/ySDh068NJLL3Hbbbdx+vRpVqxYQWRkJE2bNrW1HTRoEFFRUWzYsIGxY8fi5ORU6PtUrFiRzp07ExkZSUZGBrGxsaSlpeX5OY0aNcr25y97vraiKuxn0bRpU3x8fHjxxRcxDIMaNWrw2Wef2Y4XioiIlEmlltZcRETkJtGnTx/DycnJOH78eL5tHn74YcPBwcH2WPQrV64Ynp6eBmCMGTMmzz5nz541xo4dazRp0sRwcnIy3NzcjNtuu82IiIiwe7w6eTzSPktCQoLRpEkTw2w2G//4xz+MmJgYIz4+3gCMgwcP2tr99ddfRmRkpFGnTh2jUqVKxl133WVs2bLFcHNzMyIiIuzG/OOPP4xnnnnG8Pb2NhwdHY0aNWoYrVu3NsaMGWOcPXs23/fg1KlTxquvvmrce++9Rv369Q0nJyfDxcXFCAwMNF599VXj3Llzdu0nT55sNGrUyDCbzUazZs2MmTNn2p7UlV1eT7UrbN+C3rss0dHRBmBs3bq1wHY550Tmk/tMJpNRpUoVo0mTJkZYWJjx1Vdf5dmHHE+1e/HFF4077rjDqF69uu3zi4iIME6cOGHXb8uWLUa3bt0MNzc3w2w2Gz4+PnafWc4nqOWUkJBgtGnTxnBxcTGcnZ0NHx8fY/Dgwca3335raxMUFGQ0b97cWL9+vXHHHXcYZrPZqFevnvHSSy8Zly5dyjVeYX7m8nvyX1BQkBEUFGRXduTIESM8PNxwd3c3HB0dDQ8PDyM0NNT4/fffc/W3WCyGg4OD8dtvv+W53pyynmoXGxtrTJgwwWjQoIHh5ORktGrVKt/PyjAMo1GjRkazZs0KdY+c8vtMCvNZ7N271+jcubPh6upqVK9e3ejXr59x+PDhXD8/+d1jyJAhhouLS645ZX3GIiIixc1kGIV4rIqIiIjccjZv3kz79u35z3/+w4ABA0p7OqXmjjvuwGQysX379tKeSqkJDg7mxIkT/PDDD6U9lQJdvHiRRo0acffdd7Nw4cISu8/3339Py5YtmT59OsOHDy+x+4iIiNwMdNROREREWLVqFVu2bKF169Y4Ozuza9cuJk+ejJ+fH3379i3t6d1waWlp/PDDDyxfvpzvvvuOpUuXlvaUpAB//PEHP/30E4mJifz++++8+OKLJXKfX375hV9//ZWXXnqJevXqYbFYSuQ+IiIiNxMFnkRERISqVauycuVK4uLiOHPmDLVq1aJbt27ExMQUOgH2zeS///0vISEh1KxZk/Hjx9OnT5/SnpIU4PPPP+fRRx+lXr16vPvuu3ZJ8ovTK6+8wpw5c2jWrBkff/yxLdeWiIiI5E9H7UREREREREREpERUuHoTERERERERERGRa6fAk4iIiIiIiIiIlAgFnkREREREREREpEQouXgBMjIyOHr0KK6urphMptKejoiIiIiIiIhIqTEMgzNnzuDh4UGFCoXby6TAUwGOHj2Kp6dnaU9DRERERERERKTMOHLkCA0aNChUWwWeCuDq6gpY39CqVauW8mxEREREREREREpPWloanp6etnhJYSjwVICs43VVq1ZV4ElEREREREREBK4pHZGSi4uIiIiIiIiISIlQ4ElEREREREREREqEAk8iIiIiIiIiIlIilONJRERERERE5CZ25coVLl26VNrTkHLCycmJChWKb5+SAk8iIiIiIiIiNyHDMEhJSeH06dOlPRUpRypUqIC3tzdOTk7FMp4CTyIiIiIiIiI3oaygU506dahcufI1PYlMbk0ZGRkcPXqUY8eO4eXlVSw/Mwo8iYiIiIiIiNxkrly5Ygs61axZs7SnI+VI7dq1OXr0KJcvX8bR0fG6x1NycREREREREZGbTFZOp8qVK5fyTKS8yTpid+XKlWIZT4EnERERERERkZuUjtfJtSrunxkFnkREREREREREpEQo8CQiIiIiIiIit4Tg4GBGjx5d2tMoskaNGhEXF1fa07gmSi4uIiIiIiIicgsxTbhxx++M8cY19wkODiYwMDBXgGXZsmU88MADGMa1j3mz2L59Oy4uLqU9jWuiwJOIiIiIiIiISCFcuXIFk8lEhQqlc4Csdu3apXLf66GjdiIiIiIiIiJSrkRHRxMYGMicOXNo1KgRbm5uPPzww5w5c8bWJj09ncGDB1OlShXq1avHG2+8kWucixcv8sILL1C/fn1cXFxo06YN69evt9UnJSVRrVo1li9fjr+/P2azmV9//TXXOBaLhT59+jBhwgTq1KlD1apVGTZsGBcvXrS1WbFiBXfffTfVqlWjZs2a9OzZk19++cVWf++99/L000/bjXvy5EnMZjNr164Fch+1M5lMfPjhhzzwwANUrlwZPz8/Pv30U7sxPv30U/z8/HB2diYkJIRZs2ZhMpk4ffp0od7r66XAk4iIiIiIiIiUO7/88gvLli1j+fLlLF++nOTkZCZPnmyrf/7551m3bh1Lly5l5cqVrF+/nu+++85ujEcffZRNmzYxf/58vv/+e/r168d9993H/v37bW3OnTtHTEwMH374IXv27KFOnTp5zmfNmjXs27ePdevWMW/ePJYuXcqECRNs9enp6URGRrJ9+3bWrFlDhQoVeOCBB8jIyADgscceY+7cuVy4cMHW5z//+Q8eHh6EhITk+z5MmDCB0NBQvv/+e7p3787AgQP5888/ATh06BAPPfQQffr0YefOnQwbNowxY8Zcw7t8/RR4EhEREREREZFyJyMjg6SkJAICArjnnnsICwtjzZo1AJw9e5b4+Hhef/11OnfuzG233casWbO4cuWKrf8vv/zCvHnz+Pjjj7nnnnvw8fHhueee4+677yYxMdHW7tKlS7z77ru0a9eOJk2a5JtjycnJiYSEBJo3b06PHj14+eWXefvtt22BpQcffJC+ffvi5+dHYGAg8fHx7N69m71799rqTSYTn3zyiW3MxMRELBYLJlP+ebksFguPPPIIvr6+TJo0ifT0dL755hsAZsyYQZMmTXjttddo0qQJDz/8MBaLpWhveBEp8CQiIiIiIiIi5U6jRo1wdXW1va5Xrx7Hjx8HrEGlixcv0rZtW1t9jRo1aNKkie31f//7XwzDoHHjxlSpUsV2JScn2x2Bc3JyokWLFgAcPnzYru2kSZNs7Vq2bEnlypVtr9u2bcvZs2c5cuSIbU4DBgzgH//4B1WrVsXb29s2JoDZbGbQoEEkJCQAsHPnTnbt2nXVQFHW3ABcXFxwdXW1vQ8//fQTd955p137f/7znwWOV9yUXFxEREREREREyoyqVauSmpqaq/z06dNUrVrV9trR0dGu3mQy2XYXFebJdxkZGVSsWJHvvvuOihUr2tVVqVLF9v/Ozs62HUceHh7s3LnTVlejRo2r3ierb69evfD09GTmzJl4eHiQkZFBQECAXR6oxx57jMDAQH777TcSEhLo2LEjDRs2LHD8q70POXdL3einAirwJCIiIiIiIiJlRtOmTfnyyy9zlW/fvt1ux1JBfH19cXR0ZOvWrXh5eQFw6tQpfv75Z4KCggBo1aoVV65c4fjx49xzzz2FGtfBwQFfX98863bt2sX58+dxdnYGYOvWrVSpUoUGDRpw8uRJ9u3bx/vvv2+719dff51rjNtuu4077riDmTNnMnfuXN55551CzSs/TZs25YsvvrAr+/bbb69rzGulo3YiIiIiIiIiUmYMHz6cX375hREjRrBr1y5+/vlnpk+fTnx8PM8//3yhxqhSpQpDhw7l+eefZ82aNfzwww9YLBYqVPg7DNK4cWMGDhzI4MGDWbJkCQcPHmT79u3ExsbmCtYUxsWLFxk6dCh79+7lyy+/ZPz48Tz99NNUqFCB6tWrU7NmTT744AMOHDjA2rVriYyMzHOcxx57jMmTJ3PlyhUeeOCBa55HdsOGDePHH3/kX//6Fz///DMLFy4kKSkJoMC8UcVJgScRERERERERKTMaNWrExo0b+eWXX+jSpQt33nknSUlJJCUl0a9fv0KP89prr9GhQwfuv/9+OnXqxN13303r1q3t2iQmJjJ48GCeffZZmjRpwv3338+2bdvw9PS85nl37NgRPz8/OnToQGhoKL169SI6OhqAChUqMH/+fL777jsCAgKIiIjgtddey3OcRx55BAcHBwYMGEClSpWueR7ZeXt7s2jRIpYsWUKLFi147733bE+1M5vN1zV2YZmMG324rxxJS0vDzc2N1NRUu3OkIiIiIiIiImXZX3/9xcGDB/H29r7u4IVcncVi4fTp0yxbtuy6xzpy5AiNGjVi+/bt3H777dc/uRwmTpzIjBkzbEnPcyroZ6cocRLleBIRERERERERKWWXLl3i2LFjvPjii9x1113FFnR69913ufPOO6lZsyabNm3itdde4+mnny6WsQtDgScRERERERERkVK2adMmQkJCaNy4MYsWLSq2cffv38+rr77Kn3/+iZeXF88++yxRUVHFNv7V6KhdAXTUTkRERERERMojHbWToiruo3ZKLi4iIiIiIiIiIiVCgScRERERERERESkRCjyJiIiIiIiI3KQyMjJKewpSzhR3RiYlFxcRERERERG5yTg5OVGhQgWOHj1K7dq1cXJywmQylfa0pIwzDIM//vgDk8mEo6NjsYypwJOIiIgAYJqQ/5dRY7yeRSIiIlKeVKhQAW9vb44dO8bRo0dLezpSjphMJho0aEDFihWLZTwFnkRERERERERuQk5OTnh5eXH58mWuXLlS2tORcsLR0bHYgk6gwJOIiIgUkcViISkpqbSnISIiIgXIOjJVXMemRK6VkouLiIhIsTEMg+joaDw8PHB2diY4OJg9e/Zctd/ixYvx9/fHbDbj7+/P0qVL7eo3bNhAr1698PDwwGQysWzZslxjBAcHYzKZmDx5cq667t27YzKZiI6OLurSRERERKQIFHgSERGRQjtx4gRDhgzBy8uLefPm4evrS2hoKBcvXgRgypQpTJ06lWnTprF9+3bc3d3p3LkzZ86cyXfMLVu20L9/f8LCwti1axdhYWGEhoaybds2W5v09HRatmzJtGnTCpyfp6cniYmJdmVHjx5l7dq11KtX7zpWLiIiIiJFUeYCT5s3b6ZixYrcd999duWHDh3CZDJRp06dXF9eAwMD8/wN5ty5c6lYsSJPPvlkSU5ZRETklhEREcG2bduYM2cO3bt3Z+bMmXh7e5ORkYFhGMTFxTFmzBj69u1LQEAAs2bN4ty5c8ydOzffMePi4ujcuTNRUVE0bdqUqKgoOnbsSFxcnK1Nt27dePXVV+nbt2+B8+vZsycnT55k06ZNtrKkpCS6dOlCnTp1rnv9IiIiInJtylzgKSEhgZEjR/L1119z+PDhXPVnzpzh9ddfL/RYL7zwAvPnz+fcuXPFPVUREZFbzo4dOwgLCyMoKAg3NzdCQkKIjY2lUqVKHDx4kJSUFLp06WJrbzabCQoKYvPmzfmOuWXLFrs+AF27di2wT36cnJwYOHCg3a6npKQkwsPDr3ksEREREbl+ZSrwlJ6ezsKFC3nqqafo2bNnnglLR44cydSpUzl+/HiBYx06dIjNmzfz4osv0rRpUxYtWlRCsxYREbl1tG/fnsTERJYvX56rLiUlBYC6devaldetW9dWl5eUlJRr7lOQoUOHsnDhQtLT09mwYQOpqan06NGjSGOJiIiIyPUpU4GnBQsW0KRJE5o0acKgQYNITEzEMAy7No888gi+vr68/PLLBY6VkJBAjx49cHNzY9CgQcTHx1/1/hcuXCAtLc3uEhERkb9NnTqV/v37ExERwezZswkMDGTGjBl2bUwmk91rwzByleVUlD75adGiBX5+fixatIiEhATCwsL0JB8RERGRUlKmAk/x8fEMGjQIgPvuu4+zZ8+yZs0auzZZT6v54IMP+OWXX/IcJyMjg6SkJNtYDz/8MFu2bOHAgQMF3j8mJgY3Nzfb5enpWQyrEhERuXm4uLgwceJE9u/fz/33389TTz1FZGQkH3zwAe7u7gC5diodP348146m7Nzd3a+5z9WEh4czffp0Fi1apGN2IiIiIqWozASefvrpJ7755hsefvhhABwcHOjfvz8JCQm52nbt2pW7776bcePG5TnWypUrSU9Pp1u3bgDUqlWLLl265DlWdlFRUaSmptquI0eOXOeqREREbl7VqlVj2LBhdOvWjY0bN+Lt7Y27uzurVq2ytbl48SLJycm0a9cu33Hatm1r1wesf5cX1OdqBgwYwO7duwkICMDf37/I44iIiIjI9XEo7QlkiY+P5/Lly9SvX99WZhgGjo6OnDp1Klf7yZMn07ZtW55//vlcdQkJCfz5559UrlzZVpaRkcGOHTt45ZVXqFixYp5zMJvNmM3mYliNiIjIzSkiIoI+ffoQGBjIlStXWLduHcnJyYwdOxaTycTo0aOZNGkSfn5++Pn5MWnSJCpXrsyAAQNsYwwePJj69esTExMDwKhRo+jQoQOxsbH07t2bTz75hNWrV/P111/b+pw9e9Zu5/LBgwfZuXMnNWrUwMvLK9c8q1evzrFjx3TETkRERKSUlYnA0+XLl5k9ezZvvPFGrqfaPPjgg/znP/+hZ8+eduX//Oc/6du3Ly+++KJd+cmTJ/nkk0+YP38+zZs3t5VnZGRwzz338OWXX+YaS0RERArHy8uLyMhI9u/fT3p6OuvXryc8PJyRI0cC8MILL3D+/HmGDx/OqVOnaNOmDStXrsTV1dU2xuHDh6lQ4e9N1+3atWP+/PmMHTuWcePG4ePjw4IFC2jTpo2tzbfffktISIjtdWRkJABDhgzJ82EkYN2RJSIiIiKly2TkzN5dCpYtW0b//v05fvw4bm5udnVjxozhiy++YOnSpXh7e7Njxw4CAwMB+Pnnn2nevDkODg7861//Ijo6mri4OKZMmcJvv/1m96UWYODAgZw7d46lS5cWal5paWm4ubmRmppK1apVi2WtIiIiZZVpQv7JvI3xub8uWCyWfIM+IiIiInLzKUqcpEzkeIqPj6dTp065gk5g3fG0c+dO/vzzz1x1jRs3Jjw8nL/++stWlpCQwAMPPJAr6JQ11vLly/n999+LdwEiIiIiIiIiIpJLmdjxVFZpx5OIiNxKrnXHk4iIiIjcWsrtjicREREREREREbn5KPAkIiIiIiIiIiIlokw81U5ERERKn47TiYiIiEhx044nEREREREREREpEQo8iYiIiIiIiIhIiVDgSURERERERERESoRyPImIiAgApgmmfOuU/0lEREREikI7nkREREREREREpEQo8CQiIiJFYrFYSnsKIiIiIlLGKfAkIiIixcYwDKKjo/Hw8MDZ2Zng4GD27Nlz1X6LFy/G398fs9mMv78/S5cutavfsGEDvXr1wsPDA5PJxLJly3KNERwcjMlkYvLkybnqunfvjslkIjo6uqhLExEREZEiKJOBp5SUFEaOHMk//vEPzGYznp6e9OrVizVr1tjabN68me7du1O9enUqVarEbbfdxhtvvMGVK1fsxsrvy6mIiIhcuxMnTjBkyBC8vLyYN28evr6+hIaGcvHiRQCmTJnC1KlTmTZtGtu3b8fd3Z3OnTtz5syZfMfcsmUL/fv3JywsjF27dhEWFkZoaCjbtm2ztUlPT6dly5ZMmzatwPl5enqSmJhoV3b06FHWrl1LvXr1rmPlIiIiIlIUZS7wdOjQIVq3bs3atWuZMmUKu3fvZsWKFYSEhDBixAgAli5dSlBQEA0aNGDdunX8+OOPjBo1iokTJ/Lwww9jGEqAKiIiUhIiIiLYtm0bc+bMoXv37sycORNvb28yMjIwDIO4uDjGjBlD3759CQgIYNasWZw7d465c+fmO2ZcXBydO3cmKiqKpk2bEhUVRceOHYmLi7O16datG6+++ip9+/YtcH49e/bk5MmTbNq0yVaWlJREly5dqFOnznWvX0RERESuTZkLPA0fPhyTycQ333zDQw89ROPGjWnevDmRkZFs3bqV9PR0Hn/8ce6//34++OADAgMDadSoEY899hizZs1i0aJFLFy4sLSXISIiclPasWMHYWFhBAUF4ebmRkhICLGxsVSqVImDBw+SkpJCly5dbO3NZjNBQUFs3rw53zG3bNli1wega9euBfbJj5OTEwMHDrTb9ZSUlER4ePg1jyUiIiIi169MBZ7+/PNPVqxYwYgRI3BxcclVX61aNVauXMnJkyd57rnnctX36tWLxo0bM2/evBsxXRERkVtO+/btSUxMZPny5bnqUlJSAKhbt65ded26dW11eUlJSbnmPgUZOnQoCxcuJD09nQ0bNpCamkqPHj2KNJaIiIiIXJ8yFXg6cOAAhmHQtGnTfNv8/PPPADRr1izP+qZNm9raXKsLFy6QlpZmd4mIiMjfpk6dSv/+/YmIiGD27NkEBgYyY8YMuzYmk8nutWEYucpyKkqf/LRo0QI/Pz8WLVpEQkICYWFhODo6FmksEREREbk+DqU9geyycjMV5otmfnmcrueLakxMDBMmTChSXxERkVuBi4sLEydOZOLEifTp04du3boRERFBhQoV6NSpE2DdwZQ9kffx48dz7WjKzt3dPdfupqv1uZrw8HCmT5/O3r17+eabb4o8joiIiIhcnzK148nPzw+TycS+ffvybdO4cWOAfNv8+OOP+Pn5Fen+UVFRpKam2q4jR44UaRwREZFbQbVq1Rg2bBjdunVj48aNeHt74+7uzqpVq2xtLl68SHJyMu3atct3nLZt29r1AVi5cmWBfa5mwIAB7N69m4CAAPz9/Ys8joiIiIhcnzIVeKpRowZdu3Zl+vTppKen56o/ffo0Xbp0oUaNGrzxxhu56j/99FP279/PI488UqT7m81mqlataneJiIjI3yIiIkhOTiY1NZUrV66wbt06kpOTad26NSaTidGjRzNp0iSWLl3KDz/8gMVioXLlygwYMMA2xuDBg4mKirK9HjVqFCtXriQ2NpYff/yR2NhYVq9ezejRo21tzp49y86dO9m5cycABw8eZOfOnRw+fDjPeVavXp1jx46xZs2aEnkfRERERKRwytRRO4B3332Xdu3a8c9//pOXX36ZFi1acPnyZVatWsV7773Hvn37eP/993n44Yd54oknePrpp6latSpr1qzh+eef56GHHiI0NNRuzKwvp9n5+vpSpUqVG7gyERGR8s/Ly4vIyEj2799Peno669evJzw8nJEjRwLwwgsvcP78eYYPH86pU6do06YNK1euxNXV1TbG4cOHqVDh7999tWvXjvnz5zN27FjGjRuHj48PCxYsoE2bNrY23377LSEhIbbXkZGRAAwZMoSkpKQ851qtWrViXLmIiIiIFIXJyC9ZUik6duwYEydOZPny5Rw7dozatWvTunVrIiIiCA4OBmDjxo1MmjSJLVu2cP78eXx9fQkPD2f06NFUrFjRNlZ++Z7WrVtnGys/aWlpuLm5kZqaqt1PIiJy0zNNyD9HojE+99cFi8WSb9BHRERERG4+RYmTlLkdTwD16tVj2rRpTJs2Ld8299xzD19++eVVxyqDcTURERERERERkVtCmcrxJCIiIuWHdjuJiIiIyNUo8CQiIiIiIiIiIiWiTB61ExERkRsvrzxOIiIiIiLXQzueRERERERERESkRCjwJCIiIiIiIiIiJUKBJxERERERERERKREKPImIiIiIiIiISIlQcnEREREBwDTBVOS+SkwuIiIiInnRjicREREpdhaLpbSnICIiIiJlgAJPIiIickMYhkF0dDQeHh44OzsTHBzMnj17rtpv8eLF+Pv7Yzab8ff3Z+nSpXb1MTEx3Hnnnbi6ulKnTh369OnDTz/9ZNcmODgYk8nE5MmTc43fvXt3TCYT0dHR17U+EREREclNgScREREpFidOnGDIkCF4eXkxb948fH19CQ0N5eLFiwBMmTKFqVOnMm3aNLZv3467uzudO3fmzJkz+Y65ZcsW+vfvT1hYGLt27SIsLIzQ0FC2bdtma5OcnMyIESPYunUrq1at4vLly3Tp0oX09HS7sTw9PUlMTLQrO3r0KGvXrqVevXrF+E6IiIiISJZSCzzNmDEDV1dXLl++bCs7e/Ysjo6O3HPPPXZtN27ciMlk4ueff2bHjh307NmTOnXqUKlSJRo1akT//v05ceIEAIcOHcJkMrFz585c9wwODmb06NEluSwREZFbVkREBNu2bWPOnDl0796dmTNn4u3tTUZGBoZhEBcXx5gxY+jbty8BAQHMmjWLc+fOMXfu3HzHjIuLo3PnzkRFRdG0aVOioqLo2LEjcXFxtjYrVqzAYrHQvHlzWrZsSWJiIocPH+a7776zG6tnz56cPHmSTZs22cqSkpLo0qULderUKfb3Q0RERERKMfAUEhLC2bNn+fbbb21lGzduxN3dne3bt3Pu3Dlb+fr16/Hw8KBatWp06tSJWrVq8dVXX7Fv3z4SEhKoV6+eXXsRERG58Xbs2EFYWBhBQUG4ubkREhJCbGwslSpV4uDBg6SkpNClSxdbe7PZTFBQEJs3b853zC1bttj1AejatWuBfVJTUwGoUaOGXbmTkxMDBw602/WUlJREeHj4Na1TRERERAqv1AJPTZo0wcPDg/Xr19vK1q9fT+/evfHx8bH7Qrl+/XpCQkLYvHkzaWlpfPjhh7Rq1Qpvb2/uvfde4uLi8PLyKoVViIiISJb27duTmJjI8uXLc9WlpKQAULduXbvyunXr2urykpKSck19DMMgMjKSu+++m4CAgFz1Q4cOZeHChaSnp7NhwwZSU1Pp0aPHVdcmIiIiIkVTqjmegoODWbdune31unXrCA4OJigoyFZ+8eJFtmzZQkhICO7u7ly+fJmlS5diGMX/2OYLFy6QlpZmd4mIiEjhTJ06lf79+xMREcHs2bMJDAxkxowZdm1MJpPda8MwcpXldC19nn76ab7//nvmzZuXZ32LFi3w8/Nj0aJFJCQkEBYWhqOj49WWJiIiIiJFVOqBp02bNnH58mXOnDnDjh076NChA0FBQbadUFu3buX8+fOEhIRw11138dJLLzFgwABq1apFt27deO211/j9999zjd2uXTuqVKlid23cuLHA+cTExODm5ma7PD09S2LZIiIiNyUXFxcmTpzI/v37uf/++3nqqaeIjIzkgw8+wN3dHSDXTqXjx4/n2tGUnbu7e6H7jBw5kk8//ZR169bRoEGDfMcMDw9n+vTpLFq0SMfsREREREpYqQaeQkJCSE9PZ/v27WzcuJHGjRtTp04dgoKC2L59O+np6axfvx4vLy/+8Y9/ADBx4kRSUlKYMWMG/v7+zJgxg6ZNm7J79267sRcsWMDOnTvtrjvuuKPA+URFRZGammq7jhw5UmJrFxERuZlVq1aNYcOG0a1bNzZu3Ii3tzfu7u6sWrXK1ubixYskJyfTrl27fMdp27atXR+AlStX2vUxDIOnn36aJUuWsHbtWry9vQuc24ABA9i9ezcBAQH4+/sXcYUiIiIiUhgOpXlzX19fGjRowLp16zh16hRBQUGA9beb3t7ebNq0iXXr1nHvvffa9atZsyb9+vWjX79+xMTE0KpVK15//XVmzZpla+Pp6Ymvr69dP2dn5wLnYzabMZvNxbQ6ERGRW0tERAR9+vQhMDCQK1eusG7dOpKTkxk7diwmk4nRo0czadIk/Pz88PPzY9KkSVSuXJkBAwbYxhg8eDD169cnJiYGgFGjRtGhQwdiY2Pp3bs3n3zyCatXr+brr7+29RkxYgRz587lk08+wdXV1bZDys3NLc+/+6tXr86xY8d0xE5ERETkBijVwBNYdz2tX7+eU6dO8fzzz9vKg4KC+Oqrr9i6dSuPPvpovv2dnJzw8fEhPT39RkxXRERE8uHl5UVkZCT79++37VoODw9n5MiRALzwwgucP3+e4cOHc+rUKdq0acPKlStxdXW1jXH48GEqVPh7Q3a7du2YP38+Y8eOZdy4cfj4+LBgwQLatGlja/Pee+8B1iP82SUmJmKxWPKca7Vq1Ypn0SIiIiJSIJNRElm6r0FiYiIjRozg0qVL/Pbbb7acDf/5z3946qmnOHPmDIcPH8bT05Ply5czf/58Hn74YRo3boxhGHz22We8+OKLJCYmEhYWxqFDh/D29mbHjh0EBgba3Ss4OJjAwEDi4uIKNbe0tDTc3NxITU2latWqxbxyERGRssU0oeAk3wUxxtt/nbBYLCQlJV3njERERESkLClKnKRM7Hg6f/48TZs2tUsUGhQUxJkzZ/Dx8bEl+fb396dy5co8++yzHDlyBLPZjJ+fHx9++CFhYWGltQQREREREREREclDqe94Ksu040lERG4lxbnjSURERERuPkWJk5TqU+1EREREREREROTmpcCTiIiIiIiIiIiUiFLP8SQiIiJlg47LiYiIiEhx044nEREREREREREpEQo8iYiIiIiIiIhIiVDgSURERERERERESoRyPImIiAgApgmmIvdVfigRERERyYt2PImIiIiIiIiISIlQ4ElERESKncViKe0piIiIiEgZoMCTiIiI3BCGYRAdHY2HhwfOzs4EBwezZ8+eq/ZbvHgx/v7+mM1m/P39Wbp0qV19TEwMd955J66urtSpU4c+ffrw008/2bUJDg7GZDIxefLkXON3794dk8lEdHT0da1PRERERHIrc4Eni8WCyWTiySefzFU3fPhwTCaTrU1BV9ZvWk0mE8uWLbuxixAREbkFnThxgiFDhuDl5cW8efPw9fUlNDSUixcvAjBlyhSmTp3KtGnT2L59O+7u7nTu3JkzZ87kO+aWLVvo378/YWFh7Nq1i7CwMEJDQ9m2bZutTXJyMiNGjGDr1q2sWrWKy5cv06VLF9LT0+3G8vT0JDEx0a7s6NGjrF27lnr16hXjOyEiIiIiWcpc4AmsXwznz5/P+fPnbWV//fUX8+bNw8vLC4Bjx47Zrri4OKpWrWpX9tZbb5XW9EVERG5JERERbNu2jTlz5tC9e3dmzpyJt7c3GRkZGIZBXFwcY8aMoW/fvgQEBDBr1izOnTvH3Llz8x0zLi6Ozp07ExUVRdOmTYmKiqJjx47ExcXZ2qxYsQKLxULz5s1p2bIliYmJHD58mO+++85urJ49e3Ly5Ek2bdpkK0tKSqJLly7UqVOn2N8PERERESmjgafbb78dLy8vlixZYitbsmQJnp6etGrVCgB3d3fb5ebmhslkylUmIiIiN86OHTsICwsjKCgINzc3QkJCiI2NpVKlShw8eJCUlBS6dOlia282mwkKCmLz5s35jrllyxa7PgBdu3YtsE9qaioANWrUsCt3cnJi4MCBdruekpKSCA8Pv6Z1ioiIiEjhlcnAE8Cjjz5q98UwISFBXwxFRETKsPbt25OYmMjy5ctz1aWkpABQt25du/K6deva6vKSkpJyTX0MwyAyMpK7776bgICAXPVDhw5l4cKFpKens2HDBlJTU+nRo8dV1yYiIiIiRVNmA09hYWF8/fXXHDp0iF9//ZVNmzYxaNCgEr3nhQsXSEtLs7tERESkcKZOnUr//v2JiIhg9uzZBAYGMmPGDLs2JpPJ7rVhGLnKcrqWPk8//TTff/898+bNy7O+RYsW+Pn5sWjRIhISEggLC8PR0fFqSxMRERGRInIo7Qnkp1atWvTo0YNZs2ZhGAY9evSgVq1aJXrPmJgYJkyYUKL3EBERuVm5uLgwceJEJk6cSJ8+fejWrRsRERFUqFCBTp06AdYdTNkTeR8/fjzXjqbs3N3dc+1uyq/PyJEj+fTTT9mwYQMNGjTId8zw8HCmT5/O3r17+eabb651mSIiIiJyDcrsjiewfjFMSkpi1qxZN+SYXVRUFKmpqbbryJEjJX5PERGRm1G1atUYNmwY3bp1Y+PGjXh7e+Pu7s6qVatsbS5evEhycjLt2rXLd5y2bdva9QFYuXKlXR/DMHj66adZsmQJa9euxdvbu8C5DRgwgN27dxMQEIC/v38RVygiIiIihVFmdzwB3HfffbZHMHft2rXE72c2mzGbzSV+HxERkZtRREQEffr0ITAwkCtXrrBu3TqSk5MZO3YsJpOJ0aNHM2nSJPz8/PDz82PSpElUrlyZAQMG2MYYPHgw9evXJyYmBoBRo0bRoUMHYmNj6d27N5988gmrV6/m66+/tvUZMWIEc+fO5ZNPPsHV1dW2Q8rNzQ1nZ+dc86xevTrHjh3TETsRERGRG6BMB54qVqzIvn37bP9fVAcPHmTnzp12Zb6+vlSpUuV6piciIiLZeHl5ERkZyf79+0lPT2f9+vWEh4czcuRIAF544QXOnz/P8OHDOXXqFG3atGHlypW4urraxjh8+DAVKvy9Ibtdu3bMnz+fsWPHMm7cOHx8fFiwYAFt2rSxtXnvvfcACA4OtptPYmIiFoslz7lWq1ateBYtIiIiIgUyGYZhlPYksrNYLJw+fZply5blWd+nTx+qVatGUlKSrSwpKYnRo0dz+vTpXO3zSz66bt26XF9Qc0pLS8PNzY3U1FSqVq1ayBWIiIiUT6YJBSf5Logx3v7rhMVisfu7WkRERETKv6LEScrcjqerfUnNKyBlsVjy/Y1mGYuriYiIiIiIiIjcMsp0cnEREREpn7TbSURERERAgScRERERERERESkhZe6onYiIiJSOnHmaRERERESul3Y8iYiIiIiIiIhIiVDgSURERERERERESoQCTyIiIiIiIiIiUiIUeBIRERERERERkRKh5OIiIiICgGmCqbSnYEfJzkVERETKP+14EhERkXLBYrGU9hRERERE5Bop8CQiIiLllmEYREdH4+HhgbOzM8HBwezZs+eq/RYvXoy/vz9msxl/f3+WLl1qVx8dHY3JZLK73N3d7doEBwdjMpmYPHlyrvG7d++OyWQiOjr6utYnIiIiUt4p8CQiIiJl1okTJxgyZAheXl7MmzcPX19fQkNDuXjxIgBTpkxh6tSpTJs2je3bt+Pu7k7nzp05c+ZMvmNu2bKF/v37ExYWxq5duwgLCyM0NJRt27bZtWvevDnHjh2zXbt37841lqenJ4mJiXZlR48eZe3atdSrV68Y3gERERGR8q3MBp4sFkuu3zSaTCYOHDiAxWKhT58+Vx3jt99+w8nJiaZNm5b8hEVERKTYRUREsG3bNubMmUP37t2ZOXMm3t7eZGRkYBgGcXFxjBkzhr59+xIQEMCsWbM4d+4cc+fOzXfMuLg4OnfuTFRUFE2bNiUqKoqOHTsSFxdn187BwQF3d3fbVbt27Vxj9ezZk5MnT7Jp0yZbWVJSEl26dKFOnTrF9j6IiIiIlFdlNvAEcN9999n9pvHYsWN4e3sXun9SUhKhoaGcO3fO7guhiIiIlA87duwgLCyMoKAg3NzcCAkJITY2lkqVKnHw4EFSUlLo0qWLrb3ZbCYoKIjNmzfnO+aWLVvs+gB07do1V5/9+/fj4eGBt7c3Dz/8MP/3f/+XaywnJycGDhxot+spKSmJ8PDwoi5ZRERE5KZSpgNPZrPZ7jeN7u7uVKxYsVB9DcMgMTGRsLAwBgwYQHx8fAnPVkRERIpb+/btSUxMZPny5bnqUlJSAKhbt65ded26dW11eUlJSblqnzZt2jB79my++uorZs6cSUpKCu3atePkyZO5xhs6dCgLFy4kPT2dDRs2kJqaSo8ePa5pnSIiIiI3qzIdeLoe69at49y5c3Tq1ImwsDAWLlxYYL4HgAsXLpCWlmZ3iYiISOmZOnUq/fv3JyIigtmzZxMYGMiMGTPs2phMJrvXhmHkKsvpan26devGgw8+yG233UanTp34/PPPAZg1a1ausVq0aIGfnx+LFi0iISGBsLAwHB0dr2mdIiIiIjerMh14Wr58OVWqVLFd/fr1K3Tf+Ph4Hn74YSpWrEjz5s3x9fVlwYIFBfaJiYnBzc3Ndnl6el7vEkREROQ6uLi4MHHiRPbv38/999/PU089RWRkJB988IHtKXM5dzcdP348146m7Nzd3a+5j4uLC7fddhv79+/Psz48PJzp06ezaNEiHbMTERERyaZMB55CQkLYuXOn7Xr77bcL1e/06dMsWbKEQYMG2coGDRpEQkJCgf2ioqJITU21XUeOHLmu+YuIiEjxqVatGsOGDaNbt25s3LgRb29v3N3dWbVqla3NxYsXSU5Opl27dvmO07ZtW7s+ACtXriywz4ULF9i3b1++T6obMGAAu3fvJiAgAH9//2tcmYiIiMjNy6G0J1AQFxcXfH19r7nf3Llz+euvv2jTpo2tzDAMMjIy2Lt3b75fCM1mM2azucjzFRERkeIVERFBnz59CAwM5MqVK6xbt47k5GTGjh2LyWRi9OjRTJo0CT8/P/z8/Jg0aRKVK1dmwIABtjEGDx5M/fr1iYmJAWDUqFF06NCB2NhYevfuzSeffMLq1av5+uuvbX2ee+45evXqhZeXF8ePH+fVV18lLS2NIUOG5DnP6tWrc+zYMR2xExEREcmhTAeeiio+Pp5nn30Wi8ViV/7MM8+QkJDA66+/XjoTExERkWvi5eVFZGQk+/fvJz09nfXr1xMeHs7IkSMBeOGFFzh//jzDhw/n1KlTtGnThpUrV+Lq6mob4/Dhw1So8Pcm73bt2jF//nzGjh3LuHHj8PHxYcGCBXa/sPrtt9945JFHOHHiBLVr1+auu+5i69atNGzYMN+5VqtWrfjfABEREZFyzmQYhlHak8iLxWLh9OnTLFu2LM+6X3/9lTfffNOuvEaNGvz555+0atWKffv20bRpU7v6mTNnMmbMGP73v/8V6jeSaWlpuLm5kZqaStWqVa9rPSIiImWdaULBCblvNGO8/VcUi8VCUlJS6UxGRERERIoUJym3O57Wr19Pq1at7MqGDBmCq6sr/v7+uYJOAH369OGpp57is88+o2/fvjdqqiIiIiIiIiIit6Qyu+OpLNCOJxERuZWU9R1PIiIiIlK6ihInKdNPtRMRERERERERkfJLgScRERERERERESkR5TbHk4iIiBQvHW0TERERkeKmHU8iIiIiIiIiIlIiFHgSEREREREREZESocCTiIiIiIiIiIiUCOV4EhERKUkmU2nPoPAM5XgSERERkeKlHU8iIiIiIiIiIlIiFHgSERG5gSylPQERERERkRtIgScREZEy6AIwEqgFuAD3A78Vot+7gDdQCWgNbMyjzb7M8dwAV+Au4HC2+kaNGmEymZg/f36uvs2bN8dkMpGUlFTotYiIiIjIratMBZ4sFgsmk4nJkyfblS9btgyTycScOXNwcXHhwIEDdvVHjx6levXqvPXWW8DfX5hNJhPOzs40bdqU1157DUO5K0REpBScAIYAXsA8wBcIBS4W0Gc0sBSYD3wNnAV6AlcK6LMgs98YYAdwD9AN+6DSL8DdQFNgPbALGIc1UJWdp6cniYmJdmVbt24lJSUFFxeXAmYhIiIiIvK3MhV4AqhUqRKxsbGcOnUqV11YWBhdu3ZlyJAhZGRk2MqfeOIJWrVqxTPPPGMre/nllzl27Bj79u3jueee46WXXuKDDz64IWsQERHJLgLYBswBugMzse5KysinfSoQD7wBdAJaAR8Bu4HVBdxnKjAUeAxoBsQBnsB72dqMyZzDlMxx/wH0AOrkGGvgwIEkJydz5MgRW1lCQgIDBw7EwUHPJhERERGRwilzgadOnTrh7u5OTExMnvXvv/8+Bw4cYOrUqQAkJSWxceNGEhMTMWV7cpCrqyvu7u40atSIxx57jBYtWrBy5cobsgYREZHsdgBhQBDW420hQCy5dxll+Q64BHTJVuYBBACb8+lzMbNflxzlXbL1yQA+BxoDXbEGm9oAy/IYr27dunTt2pVZs2YBcO7cORYsWEB4eHg+MxARERERya3MBZ4qVqzIpEmTeOedd/jtt9zZLGrXrs3777/PuHHjWLVqFREREbz11ls0bNgwz/EMw2D9+vXs27cPR0fHkp6+iIhILu2BRGB5IdunAE5A9RzldTPr8nIC6zG8ugX0OY71yN5k4D5gJfAA0BdIzmPM8PBwkpKSMAyDRYsW4ePjQ2BgYCFXISIiIiJSBgNPAA888ACBgYGMHz8+z/o+ffoQGhrKfffdR4cOHbBYLLna/Otf/6JKlSqYzWZCQkIwDMPuKF5eLly4QFpamt0lIiJyvaYC/bEeuZsNBAIzMusmAVWyXYfz6J/FAEwF1JNHffY+WUf7emfOJRB4EWvuqBnk1qNHD86ePcuGDRtISEjQbicRERERuWZlMvAEEBsby6xZs9i7d2+e9ePGjSMjI4Nx48blWf/888+zc+dOkpOTCQkJYcyYMbRr167Ae8bExODm5ma7PD09r3sdIiIiLsBEYD/Wp8k9BUQCHwBPAjuzXR6AO9ajczmzHR4n946mLLWAiuTeEZW9Ty3AAfDP0aYZeQe8HBwcCAsLY/z48Wzbto2BAwfmt0QRERERkTyV2cBThw4d6Nq1Ky+99FKe9VmJTfNLcFqrVi18fX1p27Ytixcv5s0332T16oJSskJUVBSpqam2K3tCVRERkeJQDRiG9WlzG4EaWJ9yl3U5AK0BR2BVtn7HgB+A/H6F4pTZb1WO8lXZ+jgBdwI/5WjzM5D3gXXrcbvk5GR69+5N9eo5D/+JiIiIiBSsTD+WZvLkyQQGBtK4cePrGqd69eqMHDmS5557jh07dtglIc/ObDZjNpuv614iIiI5RQB9sB5tuwKsw5pTaWw+7d2wPp3uWaAm1uDUc8BtWJ9yl6Uj1hxNT2e+jsSaxPwOoC3WHVWHse6qyvI81mN/HbAmOV8BfAasz2cuzZo148SJE1SuXLkQKxURERERsVdmdzwB3HbbbQwcOJB33nnnuscaMWIEP/30E4sXLy6GmYmIiBSeF9agkCcwFxgMhAMjC+jzJtZgVSjW5OSVsQaIKmZr8wvWpOJZ+gNxwMtYg1wbgC+w3830ANZ8TlOwBrI+BBYDdxcwl5o1a+Ls7FxACxERERGRvJXpHU8Ar7zyCgsXLrzucWrXrk1YWBjR0dH07duXChXKdMxNRERuIhGZF4AFSCpEn0rAO5lXfg7lUTY88ypIeOaV77iH8hr5b6dPn77KHURERERErEyGYRilPYmyKi0tDTc3N1JTU6latWppT0dERMqjHMe7LRQu8FQq9JVARERERApQlDiJtv2IiIjcQEmlPQERERERkRtIgScRERERERERESkRZT7Hk4iISLmm42siIiIicgvTjicRERERERERESkRCjyJiIiIiIiIiEiJUOBJRERERERERERKhAJPIiIiIiIiIiJSIpRcXERERAAwTTCV9hSKhTFeCd1FREREygrteBIREZGbnsViKe0piIiIiNySFHgSERGRW5JhGERHR+Ph4YGzszPBwcHs2bPnqv0WL16Mv78/ZrMZf39/li5dalffqFEjTCZTrmvEiBG2NsHBwZhMJiZPnpxr/O7du2MymYiOjr7uNYqIiIiUNgWeRERE5KZ04sQJhgwZgpeXF/PmzcPX15fQ0FAuXrwIwJQpU5g6dSrTpk1j+/btuLu707lzZ86cOZPvmFu2bKF///6EhYWxa9cuwsLCCA0NZdu2bbY227dv59ixY7Zr1apVAPTr189uLE9PTxITE+3Kjh49ytq1a6lXr15xvQ0iIiIipapMBp6OHDnC0KFD8fDwwMnJiYYNGzJq1ChOnjzJoUOH8vwtYvYr67eI+V2NGjUq7SWKiIhICYuIiGDbtm3MmTOH7t27M3PmTLy9vcnIyMAwDOLi4hgzZgx9+/YlICCAWbNmce7cOebOnZvvmHFxcXTu3JmoqCiaNm1KVFQUHTt2JC4uztamdu3auLu7267ly5fj4+NDUFCQ3Vg9e/bk5MmTbNq0yVaWlJREly5dqFOnTrG/HyIiIiKlocwFnv7v//6PO+64g59//pl58+Zx4MABZsyYwZo1a2jbti2urq52v0V89tlnad68uV3Z7Nmzbf//zTffALB69Wpb2fbt20t5lSIiIlLSduzYQVhYGEFBQbi5uRESEkJsbCyVKlXi4MGDpKSk0KVLF1t7s9lMUFAQmzdvznfMLVu22PUB6Nq1a759Ll68yEcffUR4eDgmk33ydicnJwYOHGi36ykpKYnw8PCiLFdERESkTCpzT7UbMWIETk5OrFy5EmdnZwC8vLxo1aoVPj4+jB07lvfee8/WvkqVKjg4OODu7p7neH/99RcANWvWzLeNiIiI3Hzat29PYmIiLVu2zFWXkpICQN26de3K69aty6+//prvmCkpKXn2yRovp2XLlnH69Ol8k5sPHTqUu+++m7feeovvvvuO1NRUevToofxOIiIictMoUzue/vzzT7766iuGDx9uCzplcXd3Z+DAgSxYsADDKJnHJF+4cIG0tDS7S0RERMqnqVOn0r9/fyIiIpg9ezaBgYHMmDHDrk3OXUiGYeQqy+la+sTHx9OtWzc8PDzyrG/RogV+fn4sWrSIhIQEwsLCcHR0vNrSRERERMqNMrXjaf/+/RiGQbNmzfKsb9asGadOneKPP/4okdwHMTExTJgwodjHFRERkRvPxcWFiRMnMnHiRPr06UO3bt2IiIigQoUKdOrUCbDuYMqeyPv48eO5djRl5+7unmt3U359fv31V1avXs2SJUsKnGd4eDjTp09n7969thQBIiIiIjeLMrXj6Wqydjpd7TeRRRUVFUVqaqrtOnLkSIncR0RERG6satWqMWzYMLp168bGjRvx9vbG3d3d9sQ5sOZjSk5Opl27dvmO07ZtW7s+ACtXrsyzT2JiInXq1KFHjx4Fzm3AgAHs3r2bgIAA/P39r3FlIiIiImVbmQo8+fr6YjKZ2Lt3b571P/74I9WrV6dWrVolcn+z2UzVqlXtLhERESmfIiIiSE5OJjU1lStXrrBu3TqSk5Np3bo1JpOJ0aNHM2nSJJYuXcoPP/yAxWKhcuXKDBgwwDbG4MGDiYqKsr0eNWoUK1euJDY2lh9//JHY2FhWr17N6NGj7e6dkZFBYmIiQ4YMwcGh4A3m1atX59ixY6xZs6ZY1y8iIiJSFpSpo3Y1a9akc+fOvPvuu0RERNjleUpJSeE///kPgwcPLrEdTyIiInLz8PLyIjIykv3795Oens769esJDw9n5MiRALzwwgucP3+e4cOHc+rUKdq0acPKlStxdXW1jXH48GEqVPj793Tt2rVj/vz5jB07lnHjxuHj48OCBQto06aN3b1Xr17N4cOHC/2EumrVql3/gkVERETKIJNRUpm6i2j//v20a9eOZs2a8eqrr+Lt7c2ePXt4/vnnuXDhAlu3bqVGjRq29tHR0SxbtoydO3fmOd6hQ4fw9vZmx44dBAYGXtNc0tLScHNzIzU1VbufRETkpmeacHP8YscYn/urjcViISkp6cZPRkREROQmUpQ4SZk6agfg5+fHt99+i4+PD/3798fHx4cnnniCkJAQtmzZYhd0EhERERERERGRsqvM7XgqS7TjSUREbiU3844nEREREbl+N8WOJxERERERERERuTko8CQiIiIiIiIiIiWiTD3VTkREREqPjqiJiIiISHHTjicRERERERERESkRCjyJiIiIiIiIiEiJUOBJRERERERERERKhHI8iYiIFIbJVNozKHmGcjyJiIiISPHSjicRERERERERESkRCjyJiIgUgaW0JyAiIiIiUg4o8CQiIlKCLgAjgVqAC3A/8Fsh+r0LeAOVgNbAxhz1Z4GngQaAM9AMeC9Hm0aACZifx/jNM+uSCjEXEREREZGiKpOBp5SUFEaOHMk//vEPzGYznp6e9OrVizVr1tja7Nixg/79+1OvXj3MZjMNGzakZ8+efPbZZ/zxxx+4u7szadKkXGOHhoZy5513cvny5Ru5JBERuQmcAIYAXsA8wBcIBS4W0Gc0sBRr8OdrrAGjnsCVAvosyOw3BtgB3AN0Aw5naxMBrAA+AvZlvh4JfJJjLE8gMUfZViAFayBMRERERKQklbnA06FDh2jdujVr165lypQp7N69mxUrVhASEsKIESMA+OSTT7jrrrs4e/Yss2bNYu/evXz88cf06dOHsWPH4ujoyAcffMCECRPYvXu3bexFixbx2WefMXv2bBwclFddRESuTQSwDZgDdAdmYt2VlJFP+1QgHngD6AS0whoo2g2sLuA+U4GhwGNYdzLFYQ0gZd/RtAVrECwY686mJ4CWwLc5xhoIJANHspUlZJbrb0IRERERKWkmwyhbj7Dp3r0733//PT/99BMuLva/iz19+jSOjo40bNiQDh06sGTJkjzHMAwDk8nEo48+ys6dO/nmm284ffo0zZs356WXXmL06NGFmktaWhpubm6kpqZStWrV612aiIiUZyYTAcAjWHciWbj6MbW1QEfgT6B6tvKWQB9gQh59LgKVgY+BB7KVjwJ2Yg0iATwJfAcsAzyA9ViP8X0J3J3ZphHWnVPrgDuBscA5oF7mOMFYg1qWrJuUra8EIiIiIlLGFCVOUqZ2PP3555+sWLGCESNG5Ao6AVSrVo2VK1dy8uRJXnjhhXzHMWU+8vqtt97izz//5JVXXmH48OEEBAQwatSoEpu/iIjc3NpjPba2vJDtUwAn7INOAHUz6/JyAusxvLpX6fM24I81x5MTcB/WvFB3k1s41iCZASwCfIDAwi1BREREROS6lKld9gcOHMAwDJo2bZpvm59//hmAJk2a2Mq2b99OSEiI7fX8+fPp2bMnVatWJTExkS5duuDi4sL3339vC0rl5cKFC1y4cMH2Oi0t7XqWIyIiN5mpwCSsR+5+wboD6cnMa1LmlWVvAeMYWBN7FyRnfc4+b2PN1fQp0BDYAAzHupupU46+PYBhmW0SsAaiRERERERuhDIVeMo69VdQcCgvLVq0YOfOnQD4+fnZJQ6/9957ueuuuwgMDKRhw4YFjhMTE8OECXkdfBAREbEm456YefXBmvA7Auv24SexJhrP4gG4Yz06dwr7XU/HgXb53KMWUJHcO6KO8/cuqPPAS1iTlvfILGuBNRD2OrkDTw5AGDAea46qpQWsUURERESkOJWpo3Z+fn6YTCb27dtXYBuAn376yVZmNpvx9fXF19c3zz4ODg6FSiYeFRVFamqq7Tpy5MhV+4iIyK2pGtZdRN2AjUANrE+5y7ocgNaAI7AqW79jwA/kH3hyyuy3Kkf5qmx9LmVeOf8Sr0j+ic7DseZ16k3uo38iIiIiIiWlTAWeatSoQdeuXZk+fTrp6em56k+fPk2XLl2oUaMGsbGxxX5/s9lM1apV7S4REZEsEViDN6lY8zCty3zdOp/2blifTvcssAbYAQwCbsN+V1JHYFq215HAh1iPxe3LvO9hrLuqAKoCQcDzWJOKH8Saw2k29gnJs2uGNX9U4tWXKSIiIiJSbMrUUTuAd999l3bt2vHPf/6Tl19+mRYtWnD58mVWrVrFe++9x759+/jwww/p378/PXr04JlnnsHPz4+zZ8+yYsUKACpWrFjKqxARkZuRF9ag0H4gHWvQJxwYWUCfN7H+ZRuK9YhcR6xBoux/U/2CNSiUpT9wEngZ6w6pAOALrLmcsswHooCBWJ+a1xDrEcAnyV/NAupEREREREqCyTDK3rOTjx07xsSJE1m+fDnHjh2jdu3atG7dmoiICIKDgwH49ttviY2NZcOGDfz555+4ublxxx138OijjxIaGmqXJyo4OJjAwEDi4uKuaR5FeUygiIjcpHLkH7RgDSDdVMreVwIRERERKUOKEicpk4GnskKBJxERsVHgSURERERucUWJk5SpHE8iIiLlRVJpT0BEREREpBxQ4ElEREREREREREqEAk8iIiIiIiIiIlIiytxT7URERMok5T8SEREREblm2vEkIiIiIiIiIiIlQoEnEREREREREREpEQo8iYiIiIiIiIhIiVCOJxERkcIwmUp7BiVPeaxEREREpJhpx5OIiEgRWEp7AiIiIiIi5YACTyIiIiXoAjASqAW4APcDvxWi37uAN1AJaA1szFF/FngaaAA4A82A93K0aQSYgPl5jN88sy6pEHMRERERESkqBZ5EREQK6QQwBPAC5gG+QChwsYA+o4GlWIM/X2MNGPUErhTQZ0FmvzHADuAeoBtwOFubCGAF8BGwL/P1SOCTHGN5Aok5yrYCKVgDYSIiIiIiJancBJ4sFgt9+vS5pj6//fYbTk5ONG3atGQmJSIit5QIYBswB+gOzMS6Kykjn/apQDzwBtAJaIU1ULQbWF3AfaYCQ4HHsO5kisMaQMq+o2kL1iBYMNadTU8ALYFvc4w1EEgGjmQrS8gsV6JHERERESlp5SbwVBRJSUmEhoZy7tw5Nm3aVNrTERGRcm4HEAYEAW5ACBCL9ThcXr4DLgFdspV5AAHA5nz6XMzs1yVHeZccfe4GPgX+BxjAOuBnoGuOfnUzy2Zlvj6HdUdVeD73FxEREREpTjdt4MkwDBITEwkLC2PAgAHEx8eX9pRERKSca4/12NryQrZPAZyA6jnK62bW5eUE1mN4da/S523AH2uOJyfgPqx5oe7OY8xwrLmcDGAR4AMEFm4JIiIiIiLX5aYNPK1bt45z587RqVMnwsLCWLhwIWfOnCmwz4ULF0hLS7O7REREskwF+mM9cjcba/BmRmbdJKBKtutwHv2zGFgTexckZ33OPm9jzdX0KdYdUm8Aw8n7CF8PrLmlNmA9ZqfdTiIiIiJyo9y0gaf4+HgefvhhKlasSPPmzfH19WXBggUF9omJicHNzc12eXp63qDZiohIeeACTAT2Y3063VNAJPAB8CSwM9vlAbhjPTp3Ksc4x8m9oylLLaAiuXdEZe9zHngJayCsF9AC6xPu+gOv5zGmA9YjguOx5qgaWOAqRURERESKz00ZeDp9+jRLlixh0KBBtrJBgwaRkJBQYL+oqChSU1Nt15EjRwpsLyIit65qwDCsT5vbCNTA+pS7rMsBaA04Aquy9TsG/AC0y2dcp8x+q3KUr8rW51LmlfMv8Yrkn+g8HGuS8d7kPvonIiIiIlJSbsoH2sydO5e//vqLNm3a2MoMwyAjI4O9e/fi7++fZz+z2YzZbL5R0xQRkXImAuiD9YjdFawJvZOBsfm0d8P6dLpngZpYg1PPAbdhfcpdlo7AA1h3LYF1F1UYcAfQFuuOqsNYd1UBVMWa4Px5wBlomDmP2Vh3QeWlGdb8UZULtVIRERERkeJxUwae4uPjefbZZ7FYLHblzzzzDAkJCbz+el4HEURERArmhTUotB9IB9Zj3Uk0soA+b2L9yzYU6xG5jlgTfVfM1uYXrEGhLP2Bk8DLWHdIBQBfYA0wZZkPRGE9NvdnZt1E/g5O5aVmAXUiIiIiIiXBZBiGUdqTKAyLxcKvv/7Km2++aVdeo0YNxo4dS/369YmJiWHnzp20atWKffv20bRpU7u2M2fOZMyYMfzvf//D0dHxqvdMS0vDzc2N1NRUqlatWqzrERGRcsZkn+7bgjWAdFMpH18JRERERKSUFCVOUq52PK1fv55WrVrZlQ0ZMoTDhw9ToYI100V8fDz+/v65gk4Affr04amnnuKzzz6jb9++N2TOIiIiIiIiIiK3qnKz46k0aMeTiIjY5NjxdFPSVwIRERERKUBR4iQ35VPtRERERERERESk9CnwJCIiIiIiIiIiJaJc5XgSEREpNTqGJiIiIiJyzbTjSURERERERERESoQCTyIiIiIiIiIiUiIUeBIRERERERERkRKhHE8iIiICgGmCqbSnUOqM8crlJSIiIlKctONJRERERERERERKhAJPIiIiIvmwWCylPQURERGRck2BJxEREZFrYBgG0dHReHh44OzsTHBwMHv27Llqv8WLF+Pv74/ZbMbf35+lS5fa1Z85c4bRo0fTsGFDnJ2dadeuHdu3b7drExwcjMlkYvLkybnG7969OyaTiejo6Otan4iIiEhxUuBJREREJJsTJ04wZMgQvLy8mDdvHr6+voSGhnLx4kUApkyZwtSpU5k2bRrbt2/H3d2dzp07c+bMmXzH3LJlC/379ycsLIxdu3YRFhZGaGgo27Zts7V57LHHWLVqFXPmzGH37t106dKFTp068b///c9uLE9PTxITE+3Kjh49ytq1a6lXr14xvhMiIiIi16/MBJ4sFkuev8FbtmwZJtPfyU7ff/99WrZsiYuLC9WqVaNVq1bExsba6qOjowkMDMw1/qFDhzCZTOzcubOkliAiIiI3gYiICLZt28acOXPo3r07M2fOxNvbm4yMDAzDIC4ujjFjxtC3b18CAgKYNWsW586dY+7cufmOGRcXR+fOnYmKiqJp06ZERUXRsWNH4uLiADh//jyLFy9mypQpdOjQAV9fX6Kjo/H29ua9996zG6tnz56cPHmSTZs22cqSkpLo0qULderUKZH3RERERKSoykzgCaBSpUrExsZy6tSpPOvj4+OJjIzkmWeeYdeuXWzatIkXXniBs2fP3uCZioiIyM1qx44dhIWFERQUhJubGyEhIcTGxlKpUiUOHjxISkoKXbp0sbU3m80EBQWxefPmfMfcsmWLXR+Arl272vpcvnyZK1euUKlSJbs2zs7OfP3113ZlTk5ODBw40G7XU1JSEuHh4UVes4iIiEhJKVOBp06dOuHu7k5MTEye9Z999hmhoaEMHToUX19fmjdvziOPPMIrr7xyg2cqIiIiN6v27duTmJjI8uXLc9WlpKQAULduXbvyunXr2urykpKSUmAfV1dX2rZtyyuvvMLRo0e5cuUKH330Edu2bePYsWO5xhs6dCgLFy4kPT2dDRs2kJqaSo8ePa55rSIiIiIlrUwFnipWrMikSZN45513+O2333LVu7u7s3XrVn799dcSuf+FCxdIS0uzu0REROTWMnXqVPr3709ERASzZ88mMDCQGTNm2LXJngYArAnHc5bldLU+c+bMwTAM6tevj9ls5u2332bAgAFUrFgx11gtWrTAz8+PRYsWkZCQQFhYGI6Ojte6VBEREZESV6YCTwAPPPAAgYGBjB8/Plfd+PHjqVatGo0aNaJJkyZYLBYWLlxIRkaGXbvdu3dTpUoVu6t58+ZXvXdMTAxubm62y9PTs9jWJSIiIuWDi4sLEydOZP/+/dx///089dRTREZG8sEHH+Du7g6Qa3fT8ePHc+1oys7d3f2qfXx8fEhOTubs2bMcOXKEb775hkuXLuHt7Z3nmOHh4UyfPp1FixbpmJ2IiIiUWWUu8AQQGxvLrFmz2Lt3r115vXr12LJlC7t37+aZZ57h0qVLDBkyhPvuu88u+NSkSRN27txpd33xxRdXvW9UVBSpqam268iRI8W+NhERESk/qlWrxrBhw+jWrRsbN27E29sbd3d3Vq1aZWtz8eJFkpOTadeuXb7jtG3b1q4PwMqVK/Ps4+LiQr169Th16hRfffUVvXv3znPMAQMGsHv3bgICAvD39y/iCkVERERKlkNpTyAvHTp0oGvXrrz00ktYLJZc9QEBAQQEBDBixAi+/vpr7rnnHpKTkwkJCQGsSTd9fX3t+jg4XH2pZrMZs9lcLGsQERGR8ikiIoI+ffoQGBjIlStXWLduHcnJyYwdOxaTycTo0aOZNGkSfn5++Pn5MWnSJCpXrsyAAQNsYwwePJj69evb8laOGjWKDh06EBsbS+/evfnkk09YvXq1XeLwr776CsMwaNKkCQcOHOD555+nSZMmPProo3nOs3r16hw7dkxH7ERERKRMK5OBJ4DJkycTGBhI48aNC2yX9Ru+9PT0GzEtERERucl5eXkRGRnJ/v37SU9PZ/369YSHhzNy5EgAXnjhBc6fP8/w4cM5deoUbdq0YeXKlbi6utrGOHz4MBUq/L2xvF27dsyfP5+xY8cybtw4fHx8WLBgAW3atLG1SU1NJSoqit9++40aNWrw4IMPMnHixAIDS9WqVSv+N0BERESkGJkMwzBKexIAFouF06dPs2zZMlvZ4MGD+fjjj/nrr78wDIOnnnoKDw8P7r33Xho0aMCxY8d49dVX+eabb9i3bx81a9YkOjqaZcuWsXPnTrvxDx06hLe3Nzt27CAwMLBQc0pLS8PNzY3U1FSqVq1afIsVEREpg0wTCk6OfSswxtt/LbJYLCQlJZXOZERERETKmKLEScpkjqcsr7zyCtnjYp06dWLr1q3069ePxo0b8+CDD1KpUiXWrFlDzZo1S3GmIiIiIiIiIiKSU5nZ8VQWaceTiIjcSrTjKfeOJxERERH5202340lERERERERERMovBZ5ERERERERERKRElNmn2omIiMiNpWNmIiIiIlLctONJRERERERERERKhAJPIiIiIiIiIiJSIhR4EhERERERERGREqEcTyIicuswmUp7BmWboRxPIiIiIlK8tONJRERERERERERKhAJPIiJyS7OU9gRERERERG5iCjyJiIgUwgVgJFALcAHuB367Sp8NQC/AAzABy/JoYwDRmW2cgWBgT442jTL7z8+jf/PMuqSrzEVEREREpDSUmcBTr1696NSpU551W7ZswWQy8d///heAJ554gooVKzJ/fu6v4NHR0ZhMJp588km78p07d2IymTh06FCxz11ERMqXE8AQwAuYB/gCocDFAvqMBpZiDf58DZwFegJXCuiTDrQEphXQZgowNbPNdsAd6AycydHOE0jMUbYVSMEaCBMRERERKYvKTOBp6NChrF27ll9//TVXXUJCAoGBgdx+++2cO3eOBQsW8PzzzxMfH5/nWJUqVSI+Pp6ff/65pKctIiLlUASwDZgDdAdmAt5ARj7tU4F44A2gE9AK+AjYDawu4D7dgFeBvvnUG0AcMCazTQAwCzgHzM3RdiCQDBzJVpaQWa4nhYiIiIhIWVVmAk89e/akTp06JCUl2ZVnBZqGDh0KwMcff4y/vz9RUVFs2rQpzx1MTZo0ISQkhLFjx96AmYuISHmzAwgDggA3IASIBSrl0/474BLQJVuZB9ZA0ebrmMdBrDuWso9rzpxXznHrAl2xBqbAGpxaAIRfx/1FREREREpamQk8OTg4MHjwYJKSkjCyPc75448/5uLFiwwcOBCA+Ph4Bg0ahJubG927dycxMefBA6vJkyezePFitm/ffkPmLyIi5Ud7rMfWlheyfQrgBFTPUV43s66osvrWLeS44VhzORnAIsAHCLyO+4uIiIiIlLQyE3gCCA8P59ChQ6xfv95WlpCQQN++falevTr79+9n69at9O/fH4BBgwaRmJhIRkbuwxG33347oaGhvPjii4W+/4ULF0hLS7O7RETk5jMV6I/1yN1srMGbGZl1k4Aq2a7DBYxjYE3sfb1yjpHfuD2w5pbagPWYnXY7iYiIiEhZV6YCT02bNqVdu3YkJCQA8Msvv7Bx40bCw61frePj4+natSu1atUCoHv37qSnp7N6dd4ZNl599VU2btzIypUrC3X/mJgY3NzcbJenp2cxrEpERMoaF2AisB/r0+meAiKBD4AngZ3ZLg+sCb8vAqdyjHOc3LuVroV75n9z7m7Kb1wHrEcEx2PNUTXwOu4tIiIiInIjlKnAE1iTjC9evJi0tDQSExNp2LAhHTt25MqVK8yePZvPP/8cBwcHHBwcqFy5Mn/++We+ScZ9fHx4/PHHefHFF+2O7+UnKiqK1NRU23XkyJGr9hERkfKtGjAMayLwjUANrE+5y7ocgNaAI7AqW79jwA9Au+u4tzfW4FP2cS9iTSKe37jhmfW9yX30T0RERESkrClzD8IJDQ1l1KhRzJ07l1mzZvH4449jMpn44osvOHPmDDt27KBixYq29j/++CMDBw7k5MmT1KxZM9d4//73v/Hx8WH+/PlXvbfZbMZsNhfrekREpOyJAPpgPWJ3BViHNZiT3yMp3IChwLNATazBqeeA27A+5S5LR+AB4OnM12eBA9nqD2LdRVUD8MJ6nG401uN9fpnXJKAyMCCfuTQDTmS2EREREREp68pc4KlKlSr079+fl156idTUVCwWC2A9ZtejRw9atmxp17558+aMHj2ajz76iFGjRuUar27dukRGRvLaa6/diOmLiEg54IX1aN1+IB1Yj3Un0cgC+ryJ9S/NUOA81iBTElAxW5tfsAaFsnyL9Yl5WSIz/zsksy/AC5njDcd6lK8NsBJwLWAuuX/NIiIiIiJSNpW5o3ZgPW536tQpOnXqhJeXF7///juff/45Dz74YK62JpOJvn375nvcDuD555+nSpUqJTllEREpRyKA74A0rDmTjgBTsA8i5VQJeAc4CZwDPgNyZgI8BERnex2MNVF4zispWxtTZp9jwF9Yd14F5DHu6ALmdhqwFFAvIiIiIlJaTEZhkh/dotLS0nBzcyM1NZWqVauW9nREROR6mXI/K86CfSDolqavBCIiIiJSgKLEScrkjicREZEbJam0JyAiIiIichNT4ElEREREREREREpEmUsuLiIiUmJ0lExERERE5IbSjicRERERERERESkRCjyJiIiIiIiIiEiJUOBJRERERERERERKhHI8iYhI+WMylfYMbk7KgSUiIiIixUw7nkREREREREREpEQo8CQiIuWepbQnICIiIiIieVLgSUREbhkXgJFALcAFuB/47Sp9NgC9AA/ABCzLUX8J+BdwW+aYHsBg4GiOdo0y+8/P4x7NM+uSCrMIEREREZFyRIEnEREpl04AQwAvYB7gC4QCFwvoMxpYijX48zVwFugJXCmgTzrQEpiWT/054L/AuMz/LgF+xhrUyskTSMxRthVIwRq0EhERERG52ZSLwJPFYsFkMjF58mS78mXLlmEymZgzZw4uLi4cOHDArv7o0aNUr16dt956C4BGjRoRFxd3o6YtIiIlKALYBswBugMzAW8gI5/2qUA88AbQCWgFfATsBlYXcJ9uwKtA33zq3YBVWINeTYC7gHeA74DDOdoOBJKBI9nKEjLL9bQPEREREbkZlYvAE0ClSpWIjY3l1KlTuerCwsLo2rUrQ4YMISPj739yPPHEE7Rq1YpnnnnmRk5VRERugB1AGBCENfgTAsQClfJp/x3WY3FdspV5AAHA5mKeWyrWo3PVcpTXBboCszJfnwMWAOHFfH8RERERkbKi3ASeOnXqhLu7OzExMXnWv//++xw4cICpU6cCkJSUxMaNG0lMTMSkx26LiNx02mM9tra8kO1TACegeo7yupl1xeUv4EVgAFA1j/pwrLmcDGAR4AMEFuP9RURERETKknITeKpYsSKTJk3inXfe4bffcqeCrV27Nu+//z7jxo1j1apVRERE8NZbb9GwYcNC3+PChQukpaXZXSIiUjZNBfpjPXI3G2vwZkZm3SSgSrYr55G37Aysu5OKwyXgYazH/d7Np00PrLmlNmA9ZqfdTiIiIiJyMys3gSeABx54gMDAQMaPH59nfZ8+fQgNDeW+++6jQ4cOWCyWaxo/JiYGNzc32+Xp6VkMsxYRkZLgAkwE9mNN5P0UEAl8ADwJ7Mx2eQDuWBOP5zywfRzrrqfrdQlrnqeDWHM+5bXbCay5nMKA8VhzVA0shnuLiIiIiJRV5SrwBBAbG8usWbPYu3dvnvXjxo0jIyODcePGXfPYUVFRpKam2q4jR45cvZOIiJS6asAwrInANwI1sD7lLutyAFoDjliDQlmOAT8A7a7z/llBp/1YE5XXvEr7cKxJxnuT++ifiIiIiMjNpNwFnjp06EDXrl156aWX8qx3cHCw+++1MJvNVK1a1e4SEZGyKQJr8CYVuAKsy3zdOp/2bsBQ4FlgDdbk5IOA27A+5S5LR2Battdn+XvnFFh3NO3k7+N7l4GHgG+B/2TOJSXzupjPXJoBJ7DmqBIRERERuZmVy6c3T548mcDAQBo3blzaUxERkVLihfVo3X4gHViPdSfRyAL6vIn1L75Q4DzWIFMSUDFbm1+wBoWyfIv1iXlZIjP/OySz72/Ap5llgTnutw4IzmcuV9sVJSIiIiJyMyiXgafbbruNgQMH8s4775T2VEREpJREZF4AFqxBoKupBLyTeeXnUI7XwVgTkOen0VXq8xs3p9OFGENEREREpLwpd0ftsrzyyisYRmG+6ouIiIiIiIiISGkwGYre5CstLQ03NzdSU1OV70lEpCwxmUp7BjcnfSUQERERkQIUJU5Sbnc8iYiIiIiIiIhI2abAk4iIiIiIiIiIlIhymVxcRERucToSJiIiIiJSLmjHk4iIiIiIiIiIlAgFnkREREREREREpEQo8CQiIiIiIiIiIiVCOZ5EROT6mEylPQMpLsqdJSIiIiLFTDueRERERERERESkRCjwJCIixcpS2hMQEREREZEyQ4EnEREpFReAkUAtwAW4H/jtKn02AL0AD8AELMun3b7M8dwAV+Au4HC2+kaZ/efn0bd5Zl3SVVcgIiIiIiJXU+YCTxaLBZPJhMlkwtHRkbp169K5c2cSEhLIyMiwtWvUqJGtXfZr8uTJREdH51mX/Tp06FDpLVJE5CZzAhgCeAHzAF8gFLhYQJ/RwFKswZ+vgbNAT+BKAX3SgZbAtALa/ALcDTQF1gO7gHFApRztPIHEHGVbgRSsgTAREREREbl+ZTK5+H333UdiYiJXrlzh999/Z8WKFYwaNYpFixbx6aef4uBgnfbLL7/M448/btfX1dUVwzB48sknbWV33nknTzzxhF3b2rVr35jFiIjcAiKA7cAcIA54BlgBZOTTPhWIz2zfKbPsI6zBoNVA13z6dcu8CjIG6A5MyVb2jzzaDQTeBI5k3hcgIbN89lXuISIiIiIihVMmA09msxl3d3cA6tevz+23385dd91Fx44dSUpK4rHHHgOsQaasdjlVqVLF9v8VK1YssK2IiFyfHUAYEIR1F1FI5pWf74BLQJdsZR5AALCZ/ANPV5MBfA68kDnGDsAbiAL65GhbN7PNLGAscA5YACSjwJOIiIiISHEpc0ft8nPvvffSsmVLlixZUtpTERGRHNpjDTgtL2T7FMAJqJ6jvG5mXVEdx3pkbzJwH7ASeADoizWglFM41lxOBrAI8AECr+P+IiIiIiJir9wEngCaNm1ql5vpX//6F1WqVLG71q9fX+TxL1y4QFpamt0lIiJXNxXoj/XI3WyswZsZmXWTgCrZrsN59M9iYE3sXVRZR/t6Z84lEHgRa+6oGXm074E1ULUB6zG78Ou4t4iIiIiI5FYmj9rlxzAMTKa//0ny/PPPY7FY7NrUr1+/yOPHxMQwYcKEIvcXEblVuQATM68+WPMwRWD97caTWBONZ/EA3LEmHj+F/a6n40C765hHLax/sfnnKG+GNYF5Tg5YjwiOB7ZhTXYuIiIiIiLFp1zteNq3bx/e3t6217Vq1cLX19fucnZ2LvL4UVFRpKam2q4jR44Ux7RFRG4p1YBhWINPG4EaWJ9yl3U5AK0BR2BVtn7HgB+4vsCTE3An8FOO8p+Bhvn0Ccd6DK83uY/+iYiIiIjI9Sk3O57Wrl3L7t27iYiIKLF7mM1mzGZziY0vInKzisC60ykQuAKswxrMGZtPezdgKPAsUBNrcOo54Db+fsodQEesOZqeznx9FjiQrf4gsDOzv1dm2fNYj/11wJrgfAXwGbA+n7k0A04AlQtcoYiIiIiIFEWZDDxduHCBlJQUrly5wu+//86KFSuIiYmhZ8+eDB482NbuzJkzpKTYp6GtXLkyVatWvdFTFhG5pXkBkcB+IB1rkCccGFlAnzex/iUUCpzHGmRKAipma/ML1qBQlm+xf1peZOZ/h2T2BWugagYQAzwDNAEWA3cXMJeaBdSJiIiIiEjRmQzDMEp7EtlZLBZmzZoFgIODA9WrV6dly5YMGDCAIUOGUKGC9XRgo0aN+PXXX3P1HzZsGDNm2KeQbdSoEaNHj2b06NHXNJe0tDTc3NxITU1VMEtEJD8m+3TgFv4OAkk5U7a+EoiIiIhIGVOUOEmZCzyVJQo8iYgUggJPNw99JRARERGRAhQlTlKukouLiEjZl1TaExARERERkTJDgScRERERERERESkRZTK5uIiIlCM6niUiIiIiIvnQjicRERERERERESkRCjyJiIiIiIiIiEiJUOBJRERERERERERKhAJPIiIiIiIiIiJSIpRcXEREro/JVNozkOKiRPEiIiIiUsy040lERIqVpbQnICIiIiIiZYYCTyIiUiouACOBWoALcD/w21X6bAB6AR6ACViWT7t9meO5Aa7AXcDhbPWNMvvPz6Nv88y6pKuuQERERERErkaBJxERuW4ngCGAFzAP8AVCgYsF9BkNLMUa/PkaOAv0BK4U0CcdaAlMK6DNL8DdQFNgPbALGAdUytHOE0jMUbYVSMEaCBMRERERketXrgNPx48fZ9iwYXh5eWE2m3F3d6dr165s2bIlz/bR0dEEBgbe2EmKiNwCIoBtwBygOzAT8AYy8mmfCsQDbwCdgFbAR8BuYHUB9+kGvAr0LaDNmMw5TMkc9x9AD6BOjnYDgWTgSLayhMxyJUAUERERESke5Trw9OCDD7Jr1y5mzZrFzz//zKeffkpwcDB//vlnaU9NROSWsgMIA4KwHm8LAWLJvcsoy3fAJaBLtjIPIADYfB3zyAA+BxoDXbEGm9qQ95G8upltZmW+PgcsAMKv4/4iIiIiImKv3P5S9/Tp03z99desX7+eoKAgABo2bMg///nPUp6ZiMitpz3WY2stC9k+BXACqucor5tZV1THsR7Zm4x1Z1QssALrDql1WANj2YUDz2LdJbUI8AECr+P+IiIiIiJir9zueKpSpQpVqlRh2bJlXLhwoVjGvHDhAmlpaXaXiIhc3VSgP9Yjd7OxBm9mZNZNAqpkuw7n0T+LgTWxd1FlHe3rnTmXQOBFrLmjZuTRvgfWQNUGrMfstNtJRERERKR4ldvAk4ODA0lJScyaNYtq1arRvn17XnrpJb7//vsijxkTE4Obm5vt8vT0LMYZi4jcvFyAicB+rE+TewqIBD4AngR2Zrs8AHesicdP5RjnONZdT0VVC+tWXv8c5c3IO+DlgPWI4HisOaoGXse9RUREREQkt3IbeAJrjqejR4/y6aef0rVrV9avX8/tt99OUlJSkcaLiooiNTXVdh05cuTqnURExE41YBjWROAbgRpYn3KXdTkArQFHYFW2fseAH4B213FvJ+BO4Kcc5T8DDfPpE441yXhvch/9ExERERGR61OuA08AlSpVonPnzvz73/9m8+bNWCwWxo8fX6SxzGYzVatWtbtEROTqIrAGb1KBK1jzKSVjDTDlxQ0YijW/0hqsyckHAbdhfcpdlo7AtGyvz/L3zimAg5n/n3030/NYk4TPBA5k9v8MGJ7PXJoBJ7DmqBIRERERkeJV7gNPOfn7+5Oenl7a0xARuaV4YT1a5wnMBQZj3Uk0soA+bwJ9gFCsyckrYw0QVczW5hesQaEs3wKtMi8y79kK+He2Ng9gzec0BWsg60NgMXB3AXOpCTgXUC8iIiIiIkVTbp9qd/LkSfr160d4eDgtWrTA1dWVb7/9lilTptC7d28ABg8eTP369YmJiSnl2YqI3NwiMi8AC5BUiD6VgHcyr/wcyvE6GGsC8qsJp+BE4TnHzel0Ie4hIiIiIiJXV24DT1WqVKFNmza8+eab/PLLL1y6dAlPT08e/3/27j1O5zJ//PjrZhjnoQODZkJDkqRUii2UQ06ltlQks2P3S1qtUdumE9o1Dm2ymw6/LIY2OtjqW/abrY1BJ6nMrg5btohqJkvMaGiI+/fHPe41R2bMmHt4PR+P68F9nT7vz/R5TOM913V9fvEL7rrrLgA2bdpEtWr/XdS1f/9+oqKq7C1LkiRJkiRVKYFgMHg4vzw+JowaNYqvvvqKJUuWHFb/7OxsYmJiyMrK8rwnSSpOIFDZEai8HD8/EkiSJKkMypInOebOeCrKzp07WblyJc8//zw9e/Y89ABJkiRJkiQdseMi8XTfffdxzTXXcNVVVzFq1KjKDkeSJEmSJOm4cFxttSstt9pJkiRJkiSFuNVOkiRJkiRJEcPEkyRJkiRJkiqEiSdJkiRJkiRViKjKDkCSJEWGwKRAZYegchKc4BGekiQpMrjiSZIkSZIkSRXCxJMkSdIxLjExsbJDkCRJx6lSJ56CwSDPPfcco0eP5pprruHqq6/OVyRJkhT5gsEgEydOpFmzZtSuXZvu3bvz0UcflTjmo48+4qc//SktWrQgEAgwc+bMIvt9/fXX3HjjjZx44onUqVOHjh078v7774fbu3fvTiAQYOrUqYXG9uvXj0AgwMSJE4/k9iRJUoQodeLpV7/6FcOGDWPDhg3Uq1ePmJiYfKW8JCYmEggEGDVqVKG20aNHEwgEwr+9S0xMZNCgQYXGFvxh5sUXXyQQ8PwKSZJ07Nu6dSvDhw8nPj6eRYsWkZCQwODBg9mzZw8A06dPZ8aMGcyaNYs1a9YQGxtLr1692LlzZ7Fz7tq1i1atWjF16lRiY2OL7LN9+3a6du1KjRo1eOWVV/j444958MEHadiwYb5+cXFxzJs3L1/dN998w7Jly2jatOmR3bwkSYoYpT5c/M9//jPPP/88/fr1q4h48omLi+Ppp5/moYceonbt2gD88MMPLFq0iPj4+BLH1qpVi2nTpjFy5EgaNWpU4bFKkiRFkuTkZNasWcOTTz7JzJkzufXWW1m6dCn79+8nGAwyc+ZM7r777vCK9fnz59OkSRMWLlzIyJEji5zz/PPP5/zzzwfgzjvvLLLPtGnTCiWVWrRoUajfgAEDePbZZ3nzzTfp2rUrAKmpqfTu3ZtNmzYdya1LkqQIUuoVTzExMbRq1aoiYink3HPPJT4+nueffz5c9/zzzxMXF8c555xT4tiePXsSGxvLlClTKjpMSZKkiLN27VqGDRtGt27diImJoUePHkybNo1atWqxYcMGMjMz6d27d7h/dHQ03bp146233jqi67700kucd955XHvttTRu3JhzzjmH2bNnF+pXs2ZNhg4dmi9BlZqaSlJS0hFdX5IkRZZSJ54mTpzIpEmT2L17d0XEU8jPfvazfD+QzJ0797B+IKlevTopKSk8/PDDfPXVVxUZoiRJUsTp2rUr8+bNY8mSJYXaMjMzAWjSpEm++iZNmoTbyuqLL77gscceo3Xr1vztb39j1KhR3HrrrSxYsKBQ3xEjRvDss8+Sk5PDypUrycrKon///kd0fUmSFFlKnXi69tpr2b59O40bN+ass87i3HPPzVfK27Bhw3jjjTfYuHEjX375JW+++SY33njjYY296qqr6NixIxMmTDis/rm5uWRnZ+crkiRJVdGMGTO47rrrSE5OZsGCBXTs2JHHH388X5+CZ18Gg8EjPg9z//79nHvuuaSkpHDOOecwcuRIfvGLX/DYY48V6tuhQwdat27N4sWLmTt3LsOGDaNGjRpHdH1JkhRZSn3GU2JiIu+//z433ngjTZo0qfDDuk866ST69+/P/PnzCQaD9O/fn5NOOumwx0+bNo1LL72U22677ZB9p0yZwqRJk44kXEmSpIhQt25dJk+ezOTJkxk0aBB9+/YlOTmZatWq0bNnTyC08ungg7y3bNlSaBVUaTVt2pR27drlqzvjjDP4y1/+UmT/pKQkHnnkET7++GPefffdI7q2JEmKPKVOPP31r3/lb3/7Gz/5yU8qIp4iJSUl8ctf/hKARx55pFRjL7nkEvr06cNdd90VfgteccaPH8+4cePCn7Ozs4mLiyt1vJIkSZGkYcOGjBw5kldffZVVq1bxi1/8gtjYWF577bXwuZl79uxhxYoVTJs27Yiu1bVrVz799NN8dZ999hmnnnpqkf2HDBnC7bffztlnn10oYSVJkqq+Uiee4uLiaNCgQUXEUqzLL788/OrfPn36lHr81KlT6dixI23atCmxX3R0NNHR0WWKUZIkKZIkJyczaNAgOnbsyL59+1i+fDkrVqzgnnvuIRAIMHbsWFJSUmjdujWtW7cmJSWFOnXqMGTIkPAcN910E82bNw+/rGXPnj18/PHH4b9//fXXpKenU69ePRISEsLX7dKlCykpKQwePJh3332XJ554gieeeKLIOBs1akRGRoZb7CRJOkaVOvH04IMPcscdd/D4448X+WrcilC9enU++eST8N9L66yzzmLo0KE8/PDD5R2aJElSRIqPj2fcuHGsX7+enJwc0tLSSEpKYsyYMQDccccd7N69m9GjR7N9+3Y6d+7Mq6++Sv369cNzbNq0iWrV/nsk6DfffJPvzcK///3v+f3vf0+3bt1IS0sD4Pzzz+eFF15g/Pjx3H///bRs2ZKZM2cydOjQYmNt2LBh+d68JEmKGIFgMBgszYBGjRqxa9cufvzxR+rUqVPot1PfffdduQSWmJjIjh07ePHFF4tsHzRoEA0bNiQ1NbVQ36LGfvnll5x++unk5uZyuLecnZ1NTEwMWVlZR32VlyRJR1tgUsWe26ijJzgh/886iYmJpKamVk4wkiTpmFGWPEmpVzzNnDmztEPK5FA/HB2cVCrYt6ixp556Kj/88MORByZJkiRJkqTDUurE0/DhwysiDkmSJFUQVztJkqTKUurE08F2797N3r1789W5JU2SJEmSJElQhsRTTk4Ov/nNb3j22WfZtm1bofZ9+/aVS2CSJOnoKngukCRJknSkqh26S3533HEHy5Yt49FHHyU6Opo//elPTJo0iWbNmrFgwYKKiFGSJEmSJElVUKlXPL388sssWLCA7t27k5SUxMUXX0xCQgKnnnoqTz31VImvypUkSZIkSdLxo9Qrnr777jtatmwJhM5z+u677wD4yU9+wsqVK8s3OkmSJEmSJFVZpU48tWrVio0bNwLQrl07nn32WSC0Eqphw4blGZskSZIkSZKqsFJvtfvZz37GP/7xD7p168b48ePp378/Dz/8MD/++CMzZsyoiBglSZEsEKjsCFRegh4uLkmSpPIVCAaP7KfMTZs28d5773Haaadx9tlnl1dcESE7O5uYmBiysrJo0KBBZYcjSZGpQOIpEUitjDh05Ew8SZIkqQRlyZOUeqvdwX744Qfi4+O5+uqrj7mkkySpYuUCY4CTgLrAFcBXhxizEhgINAMCwIvF9Pskb74YoD5wIbDpoPYWeeOfLmLsmXltqYe8A0mSJEmHUurE0759+/jtb39L8+bNqVevHl988QUA9957L3PmzCn3ACVJkW8rMByIBxYBCcBgYE8JY8YCLxBK/rwBfA8MAPaVMCYHOBuYVUKfz4GfAG2BNOAfwL1ArQL94oB5BereATIJJcIkSZIkHblSJ54mT55Mamoq06dPp2bNmuH6s846iz/96U+lDiAxMZFAIFCoXH755QC0aNEiXFe7dm1atGjB4MGDWbZsWb55Nm7cSCAQICoqiq+//jpfW0ZGBlFRUQQCgfDB6JKk8pMMrAaeBPoBs4GWwP5i+mcBc4AHgZ7AOcCfgXXA30u4Tl/gd8DVJfS5Oy+G6XnztgL6A40L9BsKrAA2H1Q3N6++1AcgSpIkSSpSqRNPCxYs4IknnmDo0KFUr149XN+hQwf+9a9/lSmIyy+/nIyMjHxl0aJF4fb777+fjIwMPv30UxYsWEDDhg3p2bMnkydPLjRXs2bNWLBgQb66+fPn07x58zLFJkk6tLXAMKAboe1tPYBpFF5ldMD7wF6g90F1zYD2wFtHEMd+4K9AG6APoWRTZ4rektckr8/8vM+7gGeApCO4viRJkqT8Sp14+vrrr0lISChUv3//fvbu3VumIKKjo4mNjc1XGjVqFG6vX78+sbGxxMfHc8kll/DEE09w7733ct999/Hpp5/mm2v48OHMm5d/80RqairDhw8vU2ySpEPrSmjb2pLD7J8J1AQaFahvktdWVlsIbdmbClwOvApcRWiF1Ioi+icROsspCCwGTgM6HsH1JUmSJOVX6sTTmWeeyapVqwrVP/fcc5xzzjnlEtTh+NWvfkUwGOR///d/89VfccUVbN++nTfeeAOAN954g++++46BAwcecs7c3Fyys7PzFUnSoc0AriO05W4BoeTN43ltKUC9g8qmIsYfECR0sHdZHdjad2VeLB2BOwmdHfV4Ef37E0pUrSS0zc7VTpIkSVL5OuzEU1JSEjt37mTChAn88pe/ZNq0aezfv5/nn3+eX/ziF6SkpHDfffeVKYglS5ZQr169fOW3v/1tiWNOOOEEGjduXOjMpho1anDjjTcyd+5cAObOncuNN95IjRo1DhnHlClTiImJCZe4uLgy3Y8kHW/qApOB9YTeJnczMA54AhgFpB9UmgGxhA4e315gni2EVj2V1UmEzmdqV6D+DIpOeEUR2iI4gdAZVUOP4NqSJEmSCjvsxNP8+fPZvXs3AwcO5JlnnuH//u//CAQC3HfffXzyySe8/PLL9OrVq0xB9OjRg/T09HzllltuOeS4YDBIIFD4d+MjRozgueeeIzMzk+eee46kpMP7Hfb48ePJysoKl82bNx96kCQpn4bASEIHga8CTiD0lrsDJQroBNQAXjtoXAbwIdDlCK5dEzgf+LRA/WfAqcWMSSK0De9KCm/9kyRJknRkDvvFPcFgMPz3Pn360KdPn3ILom7dukWeG1WSbdu28Z///IeWLVsWamvfvj1t27blhhtu4IwzzqB9+/akp6cfcs7o6Giio6NLFYckKbStbRChrW37gOWEkjn3FNM/BhgB3AacSCg5dTtwFqG33B1wGaEzmn6Z9/l74N8HtW8gtIrqBCA+r+7XhLb9XULokPOlwMtAWjGxnAFsBeqUeIeSJEmSyqJUb4wuanVRZfnDH/5AtWrVGDRoUJHtSUlJjB49mscee+zoBiZJx6F4Qlvr1gM5hJI8ScCYEsY8ROh/QoOB3YSSTKlA9YP6fE4oKXTAe4SSSQeMy/tzeN5YCCWqHgemALcCpwN/AX5SQiwnltAmSZIkqexKlXhq06bNIZNP3333XamDyM3NJTMz/3uMoqKiOOmkkwDYuXMnmZmZ7N27lw0bNvDnP/+ZP/3pT0yZMqXYlVK/+MUvuPbaa2nYsGGp45EklU5yXgFI5L9JoJLUAh7OK8XZWOBzd0IHkB9KEiUfFF5w3oJ2HMY1JEmSJB1aqRJPkyZNIiYmptyDWLp0KU2bNs1Xd/rpp/Ovf/0LgPvuu4/77ruPmjVrEhsby4UXXsjrr79Ojx49ipoOyJ+4kiRJkiRJ0tEXCB58eFMJqlWrRmZmJo0bN67omCJGdnY2MTExZGVl0aBBg8oOR5IiUwRtw9YROrwfCSRJknScKkue5LDfahdJ5ztJkiRJkiQp8h124ukwF0ZJkiRJkiRJQCnOeNq/f39FxiFJqqr8xYQkSZKkYhz2iidJkiRJkiSpNEw8SZIkSZIkqUKYeJIkSZIkSVKFOOwzniRJ0rEtMMk32B4PghM8l02SJB09rniSJEmSJElShTDxJEmSdBxLTEys7BAkSdIxzMSTJEmS8gkGg0ycOJFmzZpRu3ZtunfvzkcffVTimI8++oif/vSntGjRgkAgwMyZMwv1OdBWsNxyyy3hPt27dycQCDB16tRC4/v160cgEGDixIlHeouSJOkoqTKJp7feeovq1atz+eWX56vfuHFjvh9cYmJiuPDCC3n55Zfz9UtNTaVhw4ZHMWJJkqTItHXrVoYPH058fDyLFi0iISGBwYMHs2fPHgCmT5/OjBkzmDVrFmvWrCE2NpZevXqxc+fOYufctWsXrVq1YurUqcTGxhbZZ82aNWRkZITLa6+9BsC1116br19cXBzz5s3LV/fNN9+wbNkymjZteiS3LkmSjrIqk3iaO3cuY8aM4Y033mDTpk2F2v/+97+TkZHB6tWrueCCC/jpT3/Khx9+WAmRSpIkRbbk5GRWr17Nk08+Sb9+/Zg9ezYtW7Zk//79BINBZs6cyd13383VV19N+/btmT9/Prt27WLhwoXFznn++efzwAMPcP311xMdHV1kn5NPPpnY2NhwWbJkCaeddhrdunXL12/AgAFs27aNN998M1yXmppK7969ady4cfl8ESRJ0lFRJRJPOTk5PPvss9x8880MGDCA1NTUQn1OPPFEYmNjadu2LZMnT2bv3r0sX7786AcrSZIU4dauXcuwYcPo1q0bMTEx9OjRg2nTplGrVi02bNhAZmYmvXv3DvePjo6mW7duvPXWW+UWw549e/jzn/9MUlISgUD+NyrWrFmToUOH5lv1lJqaSlJSUrldX5IkHR1VIvH0zDPPcPrpp3P66adz4403Mm/ePILBol8FvHfvXmbPng1AjRo1jmaYkiRJVULXrl2ZN28eS5YsKdSWmZkJQJMmTfLVN2nSJNxWHl588UV27NhR7OHmI0aM4NlnnyUnJ4eVK1eSlZVF//79y+36kiTp6Iiq7AAOx5w5c7jxxhsBuPzyy/n+++95/fXX6dmzZ7hPly5dqFatGrt372b//v20aNGCwYMHl+o6ubm55Obmhj9nZ2eXzw1IkiRFkBkzZpCSkkJycjKff/456enpjBo1ilGjRoX7FFyFFAwGC9UdiTlz5tC3b1+aNWtWZHuHDh1o3bo1ixcvZvny5QwbNsxfKkqSVAVF/IqnTz/9lHfffZfrr78egKioKK677jrmzp2br98zzzzD2rVreemll0hISOBPf/oTJ5xwQqmuNWXKFGJiYsIlLi6u3O5DkiQpUtStW5fJkyezfv16rrjiCm6++WbGjRvHE088ET4YvODqpi1bthRaBVVWX375JX//+9/5+c9/XmK/pKQkHnnkERYvXuw2O0mSqqiITzzNmTOHH3/8kebNmxMVFUVUVBSPPfYYzz//PNu3bw/3i4uLo3Xr1vTv358//elPXHfddWzZsqVU1xo/fjxZWVnhsnnz5vK+HUmSpIjSsGFDRo4cSd++fVm1ahUtW7YkNjY2/MY5CJ3HtGLFCrp06VIu15w3bx6NGzc+5Na5IUOGsG7dOtq3b0+7du3K5dqSJOnoiujE048//siCBQt48MEHSU9PD5d//OMfnHrqqTz11FNFjuvWrRvt27dn8uTJpbpedHQ0DRo0yFckSZKONcnJyaxYsYKsrCz27dvH8uXLWbFiBZ06dSIQCDB27FhSUlJ44YUX+PDDD0lMTKROnToMGTIkPMdNN93E+PHjw5/37NkT/lltz549fP3116Snp/Pvf/8737X379/PvHnzGD58OFFRJZ/60KhRIzIyMnj99dfL9wsgSZKOmog+42nJkiVs376dESNGEBMTk6/tmmuuYc6cOQwYMKDIsbfddhvXXnstd9xxB82bNz8a4UqSJFUJ8fHxjBs3jvXr15OTk0NaWhpJSUmMGTMGgDvuuIPdu3czevRotm/fTufOnXn11VepX79+eI5NmzZRrdp/f4f5zTffcM4554Q///73v+f3v/893bp1Iy0tLVz/97//nU2bNh321rmGDRse2c1KkqRKFQgW93q4CDBw4ED279/PX//610JtH3zwAZ06deL999+nU6dOrF27lo4dO4bbg8EgZ5xxBpdeeimPPvooqampjB07lh07dhz29bOzs4mJiSErK8vVT5KkY15gUvkdHK3IFZyQ/0e/xMREUlNTKycYSZJUpZQlTxLRK55efvnlYtvOPfdcDuTMisqdBQIB/vWvf4U/JyYmFvu6XkmSJEmSJJW/iD7jSZIkSRXL1U6SJKkimXiSJEmSJElShYjorXaSJOnoKXj2jyRJknSkXPEkSZIkSZKkCmHiSZIkSZIkSRXCxJMkSZIkSZIqhIknSZIkSZIkVQgPF5ek40kgUNkRKJIFPVxckiRJ5csVT5J0HEus7AAkSZIkHdNMPEmSDikXGAOcBNQFrgC+OsSYlcBAoBkQAF4sos+3hJJfzYA6wOXA+gJ9WuSNf7qI8WfmtaUe6gYkSZIkVQoTT5J0nNkKDAfigUVAAjAY2FPCmLHAC4SSP28A3wMDgH0ljMkBzgZmFdMeBAYBXwD/C6wFTgV65o09WBwwr0DdO0AmoUSYJEmSpMhUZRJPgUCgxJKYmJivf1paWr722rVrc+aZZ/LEE09Uzg1IUoRIBlYDTwL9gNlAS2B/Mf2zgDnAg4SSQucAfwbWAX8v4Tp9gd8BVxfTvp5Q8ugx4HzgdOBRQkmtRQX6DgVWAJsPqpubV+9hhZIkSVLkqjI/r2dkZIT//swzz3Dffffx6aefhutq165d5LhPP/2UBg0asHv3bl5++WVuvvlmTjvtNC677LIKj1mSItFaYBjQjdAqoh55pTjvA3uB3gfVNQPaA28BfcoYR27en7UOqqsO1CS0qurnB9U3ybvOfOAeYBfwDKFk1IIyXl+SJElSxasyK55iY2PDJSYmhkAgUKiuKI0bNyY2NpaWLVty66230qJFCz744IOjHL0kRY6uhBJOSw6zfyahZFCjAvVN8trKqi2hrXXjge2EtvpNzZszo4j+SYTOcgoCi4HTgI5HcH1JkiRJFa/KJJ6OVDAYZOnSpWzevJnOnTsX2Sc3N5fs7Ox8RZKONTOA6whtuVtAKHnzeF5bClDvoLKphHmChA72LqsawF+Az4ATCB0unkZoi171Ivr3J7QNbyWhbXZJR3BtSZIkSUdHldlqV1annHIKEEoq7d+/n/vvv59LLrmkyL5Tpkxh0qRJRzM8STrq6gKT88ogQomeZEK/iRhF6KDxA5oBsYRWI20n/6qnLUCXI4ylE5BO6BypPcDJQGfgvCL6RhHaIjiB0BlVLxzhtSVJkiRVvGN+xdOqVatIT08nPT2dP/3pT6SkpPDYY48V2Xf8+PFkZWWFy+bNm4vsJ0nHiobASELJp1WEVh4lHFSiCCWHagCvHTQuA/iQI088HRBDKOm0HngPuLKYfkmEznW6ksJb/yRJkiRFnmN+xVPLli1p2LAhAGeeeSarV69m8uTJ3HzzzYX6RkdHEx0dfZQjlKSjK5nQSqeOwD5gOaFkzj3F9I8BRgC3AScSSk7dDpxF6C13B1wGXAX8Mu/z98C/D2rfQGh10wlAfF7dc4QSTvGE3pL3q7zYDj7I/GBnAFsJbcuTJEmSFPmO+cRTQdWrV2f37t2VHYYkVZp4YByh1UU5hM5VSgLGlDDmIUL/wxgM7CaUZEol/1lMnxNKCh3wHvnfljcu78/heWMhtHJqHPAt0BS4Cbj3EPGfeIh2SZIkSZHjmEk8zZo1ixdeeIHXX389X/2WLVv44YcfyM3N5d133+XJJ5/kmmuuqaQoJanyJecVgET+mwQqSS3g4bxSnI0FPncndAB5SW7NKyUpOG9BOw7RLkmSJKnyHDOJp61bt/L5558Xqj/99NMBiIqKIi4ujpEjRzJx4sSjHJ0kSZIkSdLxJxAMBg/1C+njVnZ2NjExMWRlZdGgQYPKDkeSjlwgUNkRKJL5I4EkSZJKUJY8yTH/VjtJkiRJkiRVDhNPkiRJkiRJqhDHzBlPkqTD4FYqSZIkSUeRK54kSZIkSZJUIUw8SZIkSZIkqUKYeJIkSZIkSVKF8IwnSapqAoHKjkDHKs8AkyRJUjlzxZMkSZIkSZIqhIknSToGJFZ2AJIkSZJUBBNPknQcyQXGACcBdYErgK8OMWYKcD5QH2gMDAI+LdDneaBP3rwBIL2IeVrktT1dRNuZeW2ph7oBSZIkSVVKlUg8JSYmEggECAQC1KhRg1atWnH77beTk5PDxo0bCQQCpKenFxrXvXt3xo4dW+xnSarKtgLDgXhgEZAADAb2lDBmLPACoeTPG8D3wABgXwljVgC3AO8ArwE/Ar2BnIP65ABdgamHiDkOmFeg7h0gk1AiTJIkSdKxpcocLn755Zczb9489u7dy6pVq/j5z39OTk4Ov/nNbyo7NEmqFMnAGuBJYCZwK7AU2F9M/yxgTl7/nnl1fyaUDPo7oRVLRVla4PM8Qiuf3gcuyasblvfnxkPEPBR4CNicd12AuXn1Cw4xVpIkSVLVUyVWPAFER0cTGxtLXFwcQ4YMYejQobz44ouVHZYkVZq1hBI+3YAYoAcwDahVTP/3gb2EVisd0AxoD7xViutm5f15QmmCzdOEUIJrft7nXcAzQFIZ5pIkSZIU+apM4qmg2rVrs3fv3soOQ5IqTVdCq4+WHGb/TKAm0KhAfZO8tsMRBMYBPyGUsCqLJEJnOQWBxcBpQMcyziVJkiQpslWZrXYHe/fdd1m4cCGXXXZZuK5Lly5Uq5Y/j7Z79246dux42PPm5uaSm5sb/pydnX3EsUpSRZkBpBDacvc5oQO9R+WVlLxywMclzBMkdLD34fgl8E9C50OVVX9gJLCS0DY7VztJkiRJx64qk3hasmQJ9erV48cff2Tv3r1ceeWVPPzww+zatQuAZ555hjPOOCPfmKFDh5bqGlOmTGHSpEnlFrMkVaS6wOS8MgjoSygJVY1Q8mnwQX2bAbGEDh7fTv5VT1uALodxvTHAS4QSRqccQdxRhLYITgBWEzrsXJIkSdKxqcpstevRowfp6el8+umn/PDDDzz//PM0btw43B4XF0dCQkK+Urt27VJdY/z48WRlZYXL5s2by/s2JKlCNCS0iqgvsIrQ+UsJB5UooBNQg9Cb6Q7IAD6k5MRTkNBKp+eBZUDLcog3idDb8q6k8NY/SZIkSceOKrPiqW7duiQkJFToNaKjo4mOjq7Qa0hSeUkmtNKpI7APWE4omXNPMf1jgBHAbcCJhJJTtwNn8d+33AFcBlxFKNkEcAuwEPhfoD7/PQ8qBjiQ3v8O2AR8k/f507w/Y/NKQWcAW4E6h7hHSZIkSVVblUk8SZLyiyd00Pd6IAdII7SSaEwJYx4i9I1/MLCbUJIpFah+UJ/PCSWFDngs78/uBeaaByTm/f0l4GcHtV2f9+cEYGIxsZxYQpySJEmSjg0mniSpikrOKxBKAKUexphawMN5pTgbC3wOHsa8ifw3CXW48xa04zCuI0mSJKlqCQSDwcP5N8VxKTs7m5iYGLKysmjQoEFlhyNJIYHC76BL5PAST1KJ/JFAkiRJJShLnqTKHC4uSSpeamUHIEmSJElFMPEkSZIkSZKkCmHiSZIkSZIkSRXCw8UlqarxHB5JkiRJVYQrniRJkiRJklQhTDxJkiRJkiSpQph4kiRJkiRJUoXwjCdJkgRAYFKgskPQMSo4wbPpJEk6XrniSZIkSZIkSRXCxJMkSZKOqsTExMoOQZIkHSUmniRJklTpgsEgEydOpFmzZtSuXZvu3bvz0UcflThm9uzZXHzxxTRq1IhGjRrRs2dP3n333Xx9Vq5cycCBA2nWrBmBQIAXX3yx0Dzdu3cnEAgwderUQm39+vUjEAgwceLEI7k9SZKOW1Uq8ZSYmEggECAQCFCjRg1atWrF7bffTk5ODhs3biQQCJCens7EiRPD/YorGzdurOzbkSRJOm5s3bqV4cOHEx8fz6JFi0hISGDw4MHs2bMHgOnTpzNjxgxmzZrFmjVriI2NpVevXuzcubPYOdPS0rjhhhtYvnw5b7/9NvHx8fTu3Zuvv/463CcnJ4ezzz6bWbNmlRhfXFwc8+bNy1f3zTffsGzZMpo2bXoEdy5J0vGtSiWeAC6//HIyMjL44osv+N3vfsejjz7K7bffnq/P7bffTkZGRriccsop3H///fnq4uLiKukOJEmSjj/JycmsXr2aJ598kn79+jF79mxatmzJ/v37CQaDzJw5k7vvvpurr76a9u3bM3/+fHbt2sXChQuLnfOpp55i9OjRdOzYkbZt2zJ79mz279/P66+/Hu7Tt29ffve733H11VeXGN+AAQPYtm0bb775ZrguNTWV3r1707hx4yP/AkiSdJyqcomn6OhoYmNjiYuLY8iQIQwdOrTQkul69eoRGxsbLtWrV6d+/fqF6iRJknR0rF27lmHDhtGtWzdiYmLo0aMH06ZNo1atWmzYsIHMzEx69+4d7h8dHU23bt146623Dvsau3btYu/evZxwwgmljq9mzZoMHTo036qn1NRUkpKSSj2XJEn6ryqXeCqodu3a7N27t7LDkCRJUgm6du3KvHnzWLJkSaG2zMxMAJo0aZKvvkmTJuG2w3HnnXfSvHlzevbsWaYYR4wYwbPPPktOTg4rV64kKyuL/v37l2kuSZIUElXZARyJd999l4ULF3LZZZeVy3y5ubnk5uaGP2dnZ5fLvJIkSce7GTNmkJKSQnJyMp9//jnp6emMGjWKUaNGhfsEAoF8Y4LBYKG64kyfPp1FixaRlpZGrVq1yhRjhw4daN26NYsXL2b58uUMGzaMGjVqlGkuSZIUUuUST0uWLKFevXr8+OOP7N27lyuvvJKHH36YXbt2HfHcU6ZMYdKkSeUQpSRJkg5Wt25dJk+ezOTJkxk0aBB9+/YlOTmZatWqhVcoZWZm5jvIe8uWLYVWQRXl97//PSkpKfz973+nQ4cORxRnUlISjzzyCB9//HGhN+RJkqTSq3Jb7Xr06EF6ejqffvopP/zwA88//3y5Hfg4fvx4srKywmXz5s3lMq8kSZL+q2HDhowcOZK+ffuyatUqWrZsSWxsLK+99lq4z549e1ixYgVdunQpca4HHniA3/72tyxdupTzzjvviGMbMmQI69ato3379rRr1+6I55Mk6XhX5VY81a1bl4SEhAqZOzo6mujo6AqZW5Ik6XiWnJzMoEGD6NixI/v27WP58uWsWLGCe+65h0AgwNixY0lJSaF169a0bt2alJQU6tSpw5AhQ8Jz3HTTTTRv3pwpU6YAoe119957LwsXLqRFixbh86Dq1atHvXr1APj+++/597//HZ5jw4YNpKenc8IJJxAfH18ozkaNGpGRkeEWO0mSykmVSzxJkiSp6omPj2fcuHGsX7+enJwc0tLSSEpKYsyYMQDccccd7N69m9GjR7N9+3Y6d+7Mq6++Sv369cNzbNq0iWrV/rtg/9FHH2XPnj1cc801+a41YcIEJk6cCMB7771Hjx49wm3jxo0DYPjw4aSmphYZa8OGDcvhjiVJEph4kiRJ0lGQnJxMcnIyAImJiYWSPoFAgIkTJ4YTRkVJS0vL93njxo2HvG737t0JBoMl9ik4b0Hp6emHvI4kSSpalUo8FfdbKYAWLVoU+0PF4fxQIkmSJEmSpPJV5Q4XlyRJUtVW0i8TJUnSscXEkyRJkiRJkipEldpqJ0mSKk5wQsnn4EiSJEml5YonSZIkSZIkVQgTT5IkSZIkSaoQJp4kSZIkSZJUITzjSZIkARCYFKjsEKR8PHdMkqSqzxVPkiRJkiRJqhAmniRJklRlJCYmVnYIkiSpFEw8SZIkqUoLBoNMnDiRZs2aUbt2bbp3785HH31U4pjZs2dz8cUX06hRIxo1akTPnj1599138/V57LHH6NChAw0aNKBBgwZcdNFFvPLKK/n6dO/enUAgwNSpUwtdo1+/fgQCASZOnHjE9yhJUlVl4kmSJEkRbevWrQwfPpz4+HgWLVpEQkICgwcPZs+ePQBMnz6dGTNmMGvWLNasWUNsbCy9evVi586dxc6ZlpbGDTfcwPLly3n77beJj4+nd+/efP311+E+p5xyClOnTuW9997jvffe49JLL+XKK68slNSKi4tj3rx5+eq++eYbli1bRtOmTcvxKyFJUtVTaYmnxx9/nPr16/Pjjz+G677//ntq1KjBxRdfnK/vqlWrCAQCfPbZZ7Ro0YJAIEAgEKBOnTq0b9+e//f//l++/nv27OGBBx7g3HPPpW7dusTExHD22Wdzzz338M033xyV+5MkSVL5SE5OZvXq1Tz55JP069eP2bNn07JlS/bv308wGGTmzJncfffdXH311bRv35758+eza9cuFi5cWOycTz31FKNHj6Zjx460bduW2bNns3//fl5//fVwn4EDB9KvXz/atGlDmzZtmDx5MvXq1eOdd97JN9eAAQPYtm0bb775ZrguNTWV3r1707hx4/L/gkiSVIVUWuKpR48efP/997z33nvhulWrVhEbG8uaNWvYtWtXuD4tLY1mzZrRpk0bAO6//34yMjL45z//yaBBgxg1ahTPPPMMALm5ufTq1YuUlBQSExNZuXIl77//PtOnT2fbtm08/PDDR/dGJUmSdETWrl3LsGHD6NatGzExMfTo0YNp06ZRq1YtNmzYQGZmJr179w73j46Oplu3brz11luHfY1du3axd+9eTjjhhCLb9+3bx9NPP01OTg4XXXRRvraaNWsydOjQfKueUlNTSUpKKuWdSpJ07Km0xNPpp59Os2bNSEtLC9elpaVx5ZVXctppp+X7QSEtLY0ePXqEP9evX5/Y2FgSEhL43e9+R+vWrXnxxRcBeOihh3jjjTdYtmwZt956K506dSIhIYE+ffrw2GOPkZKScrRuUZIkSeWga9euzJs3jyVLlhRqy8zMBKBJkyb56ps0aRJuOxx33nknzZs3p2fPnvnq161bR7169YiOjmbUqFG88MILtGvXrtD4ESNG8Oyzz5KTk8PKlSvJysqif//+h319SZKOVZV6xlP37t1Zvnx5+PPy5cvp3r073bp1C9fv2bOHt99+O1/iqaBatWqxd+9eABYtWkSvXr0455xziuwbCASKnSc3N5fs7Ox8RZIkSZVrxowZXHfddSQnJ7NgwQI6duzI448/nq9PwZ/xgsFgiT/3HWz69OksWrSI559/nlq1auVrO/3000lPT+edd97h5ptvZvjw4Xz88ceF5ujQoQOtW7dm8eLFzJ07l2HDhlGjRo1S3qkkSceeSk88vfnmm/z444/s3LmTtWvXcskll9CtW7fwSqh33nmH3bt3F5l4+vHHH0lNTWXdunVcdtllAHz22Wecfvrp+fpdddVV1KtXj3r16tGlS5di45kyZQoxMTHhEhcXV343K0mSpDKpW7cukydPZv369VxxxRXcfPPNjBs3jieeeILY2FiAQqubtmzZUmgVVFF+//vfk5KSwquvvkqHDh0KtdesWZOEhATOO+88pkyZwtlnn80f/vCHIudKSkrikUceYfHixW6zkyQpT6Umnnr06EFOTg5r1qxh1apVtGnThsaNG9OtWzfWrFlDTk4OaWlpxMfH06pVq/C43/zmN9SrV4/atWtzyy238Otf/5qRI0eG2wv+duvRRx8lPT2dpKSkfGdHFTR+/HiysrLCZfPmzeV/05IkSSqzhg0bMnLkSPr27cuqVato2bIlsbGxvPbaa+E+e/bsYcWKFSX+whHggQce4Le//S1Lly7lvPPOO6zrB4NBcnNzi2wbMmQI69ato3379kVux5Mk6XgUVZkXT0hI4JRTTmH58uVs376dbt26ARAbG0vLli158803Wb58OZdeemm+cb/+9a9JTEykTp06NG3aNF+iqXXr1vzrX//K1//Aa2yLOyzygOjoaKKjo8vj1iRJklROkpOTGTRoEB07dmTfvn0sX76cFStWcM899xAIBBg7diwpKSm0bt2a1q1bk5KSQp06dRgyZEh4jptuuonmzZszZcoUILS97t5772XhwoW0aNEivGLqwCp5gLvuuou+ffsSFxfHzp07efrpp0lLS2Pp0qVFxtmoUSMyMjLcYidJ0kEqNfEEoVVPaWlpbN++nV//+tfh+m7duvG3v/2Nd955h5/97Gf5xpx00kkkJCQUOd8NN9zAPffcw9q1a4s950mSJElVR3x8POPGjWP9+vXhFfFJSUmMGTMGgDvuuIPdu3czevRotm/fTufOnXn11VepX79+eI5NmzZRrdp/F/s/+uij7Nmzh2uuuSbftSZMmMDEiRMB+Pbbbxk2bBgZGRnExMTQoUMHli5dSq9evYqNtWHDhuV345IkHQMCwWAwWJkBzJs3j1tuuYW9e/fy1VdfhffiP/XUU9x8883s3LmTTZs2hc9batGiBWPHjmXs2LFFzvfDDz/Qs2dPPvroIyZOnMjFF19Mo0aN+Oyzz7jvvvv48ccfef/99w8rtuzsbGJiYsjKyqJBgwblcr+SJEWqwKTDO4hZOlqCEwr/mJqYmEhqaurRD0aSJJUpTxIRK552795N27Zt8x0A2a1bN3bu3Mlpp51WqkO+a9Wqxeuvv87MmTOZN28e48ePZ//+/bRs2ZK+ffuSnJxcEbchSZIkSZKkAip9xVMkc8WTJOl44oonRZqiVjxJkqTKU5Y8SaW+1U6SJEmSJEnHLhNPkiRJkiRJqhCVfsaTJEmKDG5rkiRJUnlzxZMkSZIkSZIqhIknSZIkSZIkVQgTT5IkSZIkSaoQnvEkSZUl4KvrFWGCnvEkSZKk8uWKJ0mSJEmSJFUIE0+SFCESKzsASZIkSSpnJp4kqYrJBcYAJwF1gSuArw4xZgpwPlAfaAwMAj4t0Gci0DZvzkZAT2B1gT4tgADwdBHXODOvLfUw7kGSJEnS8aFKJJ4yMzMZM2YMrVq1Ijo6mri4OAYOHMjrr78OQIsWLZg5c2ahcRMnTqRjx47FfpakyrYVGA7EA4uABGAwsKeEMWOBFwglf94AvgcGAPtKGLMCuAV4B3gN+BHoDeQc1KcNMAtYlzdvi7w+/ykwVxwwr0DdO0AmoaSVJEmSJB0Q8YeLb9y4ka5du9KwYUOmT59Ohw4d2Lt3L3/729+45ZZb+Ne//lXZIUpSmSUDa4AngZnArcBSYH8x/bOAOXn9e+bV/ZlQMujvQJ9ixi0t8HkeoZVP7wOX5NUNKdBnRt61/glcdlD9UOAhYHPedQHm5tUvKOb6kiRJko5PEZ94Gj16NIFAgHfffZe6df/7u/QzzzyTpKSkSoxMko7cWmAY0I1QMqhHXinO+8BeQiuRDmgGtAfeovjEU0FZeX+eUEz7HuAJIAY4u0Bbk7zrzAfuAXYBzxBaVWXiSZIkSdLBInqr3XfffcfSpUu55ZZb8iWdDmjYsOHRD0qSylFXQgmnJYfZPxOoSegMpoM1yWs7HEFgHPATQgmrgy0B6gG1CK1qeo3QWVIFJRE6yykILAZOAzoe5vUlSZIkHT8iOvH073//m2AwSNu2bQ/Z9ze/+Q316tXLV1JSUkp1vdzcXLKzs/MVSapIM4DrCG25W0AoefN4XlsKoSTQgbKphHmChA72Phy/JLR9blERbT2AdEKrpy4ndN7UliL69Sd0ttRKQtvsXH8qSZIkqSgRnXgKBoMABAKH/ufUr3/9a9LT0/OVUaNGlep6U6ZMISYmJlzi4uIOPUiSjkBdYDKwntDb6W4mtBrpCWAUoSTQgdIMiCW0DW57gXm2EFr1dChjgJeA5cApxcSTAFxI6HynqLw/C4oitEVwAqE33w09jGtLkiRJOv5EdOKpdevWBAIBPvnkk0P2Pemkk0hISMhXTjihuNNLijZ+/HiysrLCZfPmzWUNXZJKrSEwEugLrCJ0/lLCQSUK6ATUILQF7oAM4EOgSwlzBwmtdHoeWAa0PMyYgkBuMW1JhM51upLCW/8kSZIkCSI88XTCCSfQp08fHnnkEXJycgq179ixo1yvFx0dTYMGDfIVSapIyYSSN1nAPkIrkVYQSjAVJQYYAdwGvE7ocPIbgbP471vuIPQWulkHfb6F0NvvFgL1CZ0HlQnszmvPAe4C3gG+BD4Afg58BVxbTCxnAFsJnVElSZIkSUWJ6MQTwKOPPsq+ffu44IIL+Mtf/sL69ev55JNP+OMf/8hFF11U2eFJ0hGJJ7S1Lo5QUugmQiuJxpQw5iFgEKHzl7oCdYCXgeoH9fmcUFLogMcIJbe6A00PKs/ktVcH/gX8FGgDDAD+Q2jl1ZklxHIiULukG5QkSZJ0XIuq7AAOpWXLlnzwwQdMnjyZ2267jYyMDE4++WQ6derEY489VtnhSdIRSc4rAImE3hR3KLWAh/NKcTYW+Bw8jDmfP4xrF5y3oB2HMYckSZKk40cgeOAEbxWSnZ1NTEwMWVlZbruTVP4KvDghkcNLPEkVxh8JJEmSVIKy5EkifqudJB0vUis7AEmSJEkqZyaeJEmSJEmSVCEi/ownSTpmua1JkiRJ0jHOFU+SJEmSJEmqECaeJEmSJEmSVCFMPEmSJEmSJKlCmHiSJEmSJElShfBwcUmSBEBgUqCyQ5COiuAEX+4gSdLR4oonSZIkHdcSExMrOwRJko5ZJp4kSZKkAoLBIBMnTqRZs2bUrl2b7t2789FHH5U4Zvbs2Vx88cU0atSIRo0a0bNnT959991C/b7++mtuvPFGTjzxROrUqUPHjh15//33w+3du3cnEAgwderUQmP79etHIBBg4sSJR3yPkiQdDSaeJEmSdNzZunUrw4cPJz4+nkWLFpGQkMDgwYPZs2cPANOnT2fGjBnMmjWLNWvWEBsbS69evdi5c2exc6alpXHDDTewfPly3n77beLj4+nduzdff/11uM/27dvp2rUrNWrU4JVXXuHjjz/mwQcfpGHDhvnmiouLY968efnqvvnmG5YtW0bTpk3L7wshSVIFq5KJp8TExCJ/C/Tiiy8SCBR/PkWLFi2YOXNmBUcnSZKkSJecnMzq1at58skn6devH7Nnz6Zly5bs37+fYDDIzJkzufvuu7n66qtp37498+fPZ9euXSxcuLDYOZ966ilGjx5Nx44dadu2LbNnz2b//v28/vrr4T7Tpk0LJ5UuuOACWrRowWWXXcZpp52Wb64BAwawbds23nzzzXBdamoqvXv3pnHjxuX/BZEkqYJUycQTQK1atZg2bRrbt2+v7FAkSZJUxaxdu5Zhw4bRrVs3YmJi6NGjB9OmTaNWrVps2LCBzMxMevfuHe4fHR1Nt27deOuttw77Grt27WLv3r2ccMIJ4bqXXnqJ8847j2uvvZbGjRtzzjnnMHv27EJja9asydChQ/OtekpNTSUpKamMdyxJUuWosomnnj17Ehsby5QpUyo7FEmSJFUxXbt2Zd68eSxZsqRQW2ZmJgBNmjTJV9+kSZNw2+G48847ad68OT179gzXffHFFzz22GO0bt2av/3tb4waNYpbb72VBQsWFBo/YsQInn32WXJycli5ciVZWVn079//sK8vSVIkqLKJp+rVq5OSksLDDz/MV199VS5z5ubmkp2dna9IkiTp2DNjxgyuu+46kpOTWbBgAR07duTxxx/P16fgEQ7BYLDEYx0ONn36dBYtWsTzzz9PrVq1wvX79+/n3HPPJSUlhXPOOYeRI0fyi1/8gscee6zQHB06dKB169YsXryYuXPnMmzYMGrUqFGGu5UkqfJU2cQTwFVXXUXHjh2ZMGFCucw3ZcoUYmJiwiUuLq5c5pUkSVJkqVu3LpMnT2b9+vVcccUV3HzzzYwbN44nnniC2NhYgEKrm7Zs2VJoFVRRfv/735OSksKrr75Khw4d8rU1bdqUdu3a5as744wz2LRpU5FzJSUl8cgjj7B48WK32UmSqqQqnXiC0AGN8+fP5+OPPz7iucaPH09WVla4bN68uRwilCRJUiRr2LAhI0eOpG/fvqxatYqWLVsSGxvLa6+9Fu6zZ88eVqxYQZcuXUqc64EHHuC3v/0tS5cu5bzzzivU3rVrVz799NN8dZ999hmnnnpqkfMNGTKEdevW0b59+0IJK0mSqoIqn3i65JJL6NOnD3fdddcRzxUdHU2DBg3yFUmSJB17kpOTWbFiBVlZWezbt4/ly5ezYsUKOnXqRCAQYOzYsaSkpPDCCy/w4YcfkpiYSJ06dRgyZEh4jptuuonx48eHP0+fPp177rmHuXPn0qJFCzIzM8nMzOT777/Pd9133nmHlJQU/v3vf7Nw4UKeeOIJbrnlliLjbNSoERkZGfnejCdJUlUSVdkBlIepU6fSsWNH2rRpU9mhSJIkqQqIj49n3LhxrF+/npycHNLS0khKSmLMmDEA3HHHHezevZvRo0ezfft2OnfuzKuvvkr9+vXDc2zatIlq1f77e9xHH32UPXv2cM011+S71oQJE5g4cSIA559/Pi+88ALjx4/n/vvvp2XLlsycOZOhQ4cWG2vDhg3L78YlSTrKAsFgMFjZQZRWYmIiO3bs4MUXXwzX3XTTTTz33HP88MMPBINBvv76ay677DIWLFjABRdcAECLFi0YO3YsY8eOPazrZGdnExMTQ1ZWlqufJEnHvMCkwzs0WarqghPy//ibmJhIampq5QQjSVIVUpY8SZXfanfAb3/7Ww7Ooe3du5dPP/2UXbt2hev2799PVNQxschLkiRJkiQp4lXJLExRv5E69dRT+eGHH8KfW7RokS8RtW/fPrZt2xZ+S4kkSZIERf9sKUmSykeVTDyV1ldffcWCBQvYt28fP/nJTyo7HEmSJEmSpOPCcZF46tixIyeeeCJPPvmkK54kSZIkSZKOkuMi8bR169bKDkGSpIhX8MBlSZIk6UgdM4eLS5IkSZIkKbKYeJIkSZIkSVKFMPEkSZIkSZKkCnFcnPEk6TAEApUdgaTKFvSMJ0mSJJUvVzxJkiRJkiSpQph4klSkxMoOQJIkSZJU5Zl4knREcoExwElAXeAK4KtDjHkM6AA0yCsXAa8U0e+TvPligPrAhcCmg9pbAAHg6SLGnpnXlnpYdyFJkiRJqggRnXjasmULI0eOJD4+nujoaGJjY+nTpw9TpkwhEAiUWFJTU0lLSyMQCLBjx47KvhWpStgKDAfigUVAAjAY2FPCmLHAC4SSP28A3wMDgH0ljDkFmAq8l1cuBa4EPjqoz+fAT4C2QBrwD+BeoFaBueKAeQXq3gEyCSXCJEmSJEmVJ6IPF//pT3/K3r17mT9/Pq1ateLbb7/l9ddfp127dmRkZIT7/epXvyI7O5t58/77z8+YmBhWr15dGWFLVVYysAZ4EpgJ3AosBfYX0z8LmJPXv2de3Z8JJYP+DvQpZtzAAp8nE1oF9Q6hlUoAdwP9gOkH9WtVxFxDgYeAzXnXBZibV7+gmOtLkiRJko6OiE087dixgzfeeIO0tDS6desGwKmnnsoFF1xQqG/t2rXJzc0lNjb2aIcpHVPWAsOAboRWEfXIK8V5H9gL9D6orhnQHniL4hNPB9sHPAfkENpyB6FE11+BO/LmWAu0BMYDgwqMb5LXZz5wD7ALeAZYgYknSZIkSapsEbvVrl69etSrV48XX3yR3Nzcyg5HOi50JZRwWnKY/TOBmkCjAvVN8tpKsg6oB0QDowht12uX17aF0Ja9qcDlwKvAVcDVhBJKBSUROsspCCwGTgM6HuY9SJIkSZIqTsQmnqKiokhNTWX+/Pk0bNiQrl27ctddd/HPf/6zwq6Zm5tLdnZ2viIdT2YA1xHacreAUPLm8by2FEKJogNlUxHjDwgSOti7JKcD6YS2191M6Gypj/PaDmztuzIvlo7AnYTOjnqcwvoTSlStJLTNLukQ15YkSZIkHR0Rm3iC0BlP33zzDS+99BJ9+vQhLS2Nc889l9TU1Aq53pQpU4iJiQmXuLi4Qw+SjiF1CZ23tJ7Q2+RuBsYBTxBalZR+UGkGxBI6eHx7gXm2EFr1VJKahA4vPw+YApwN/CGv7SRC+4DbFRhzBkUnvKIIbRGcAKwmdL6TJEmSJKnyRXTiCaBWrVr06tWL++67j7feeovExEQmTJhQIdcaP348WVlZ4bJ58+YKuY5UFTQERgJ9gVXACYQSRQdKFNAJqAG8dtC4DOBDoEsprxcEDmyqrQmcD3xaoM9nwKnFjE8itA3vSgpv/ZMkSZIkVY6IPVy8OO3atePFF1+skLmjo6OJjo6ukLmlqiCZ0OHdHQkd+r2cUDLnnmL6xwAjgNuAEwklp24HzuK/b7kDuIzQGU2/zPt8F6GEVhywE3gaSCP0Br0Dfk1o298lhA44Xwq8nNevKGcAW4E6h7pJSZIkSdJRE7GJp23btnHttdeSlJREhw4dqF+/Pu+99x7Tp0/nyiuvLNVc69ato379+vnqOnbsWI7RSseGeEJb69YTestcGqGVRGNKGPMQoW8kg4HdhJJMqUD1g/p8TigpdMC3hLbGZRBKXnUglFjqdVCfqwid5zQFuJXQmVB/AX5SQiwnltAmSZIkSTr6IjbxVK9ePTp37sxDDz3E559/zt69e4mLi+MXv/gFd911V6nmuuSSSwrVBYPB8gpVOmYk5xWAREIJpEOpBTycV4qzscDnOYcZTxIlHxRecN6CdhzmdSRJkiRJFSMQNANTrOzsbGJiYsjKyqJBgwaVHY5UsQL530OXyOElniQdQ/yRQJIkSSUoS54k4g8Xl1Q5Uis7AEmSJElSlWfiSZIkSZIkSRUiYs94knSUucVGkiRJklTOXPEkSZIkSZKkCmHiSZIkSZIkSRXCxJMkSZIkSZIqhIknSZIkSZIkVQgPF5ciUSBQ2RFIOh75kgFJkiSVM1c8SVVAYmUHIEmSJElSGZh4ko4hucAY4CSgLnAF8FUpxk8BAsDYAvVBYCLQDKgNdAc+KtCnRd7Yp4uY98y8ttRSxCJJkiRJqvpMPEkRaiswHIgHFgEJwGBgTwljxgIvEEr+vAF8DwwA9h3G9dYATwAdimibDswAZuX1iwV6ATsL9IsD5hWoewfIJJQIkyRJkiQdXyIq8ZSYmEggEAiXE088kcsvv5x//vOf4T779u3joYceokOHDtSqVYuGDRvSt29f3nzzzXxz7du3jylTptC2bVtq167NCSecwIUXXsi8eQX/WSxFpmRgNfAk0A+YDbQE9hfTPwuYAzwI9ATOAf4MrAP+fohrfQ8MzbtGowJtQWAmcDdwNdAemA/sAhYW6DsUWAFsPqhubl69B8pJkiRJ0vEnohJPAJdffjkZGRlkZGTw+uuvExUVxYABAwAIBoNcf/313H///dx666188sknrFixgri4OLp3786LL74YnmfixInMnDmT3/72t3z88ccsX76cX/ziF2zfvr2S7kwqnbXAMKAbEAP0AKYBtYrp/z6wF+h9UF0zQomitw5xrVuA/oQSVgVtILRi6eB5o/PiKjhvE6APocQUhJJTzwBJh7i+JEmSJOnYFHGLEKKjo4mNjQUgNjaW3/zmN1xyySX85z//YdmyZSxevJiXXnqJgQMHhsc88cQTbNu2jZ///Of06tWLunXr8vLLLzN69GiuvfbacL+zzz77qN+PVFZdCW1bO9ynNhOoSeEVS03y2orzNPABoS10xc17YJ6C835ZRP8k4DZCK6QWA6cBHUu4viRJkiTp2BVxK54O9v333/PUU0+RkJDAiSeeyMKFC2nTpk2+pNMBt912G9u2beO1114DQkmrZcuW8Z///Oewr5ebm0t2dna+IlWWGcB1hLbcLSCUvHk8ry0FqHdQ2VTCPEFCB3sXZTPwK0Jb8opbSXVAwTmKm7c/oa17Kwlts3O1kyRJkiQdvyIu8bRkyRLq1atHvXr1qF+/Pi+99BLPPPMM1apV47PPPuOMM84octyB+s8++wyAGTNm8J///IfY2Fg6dOjAqFGjeOWVV0q89pQpU4iJiQmXuLi48r05qRTqApOB9YTeTnczMI7QAeCjgPSDSjNCB37vAQpuJt1C4dVKB7yf196J0PLHKEJnNP0x7+/78uaFwqumips3itAWwQmEzqgaWuJdSpIkSZKOZRGXeOrRowfp6emkp6ezevVqevfuTd++ffnyy6I29RQWCITWYLRr144PP/yQd955h5/97Gd8++23DBw4kJ///OfFjh0/fjxZWVnhsnnz5mL7SkdTQ2Ak0BdYBZxA6C13B0oUoeRRDeC1g8ZlAB8CXYqZ9zJCh4+nH1TOI5QsSgeqEzrQPLbAvHsIJaiKmzcpr/1KCm/9kyRJkiQdPyLujKe6deuSkJAQ/typUydiYmKYPXs2bdq04eOPPy5y3CeffAJA69atw3XVqlXj/PPP5/zzzyc5OZk///nPDBs2jLvvvpuWLVsWmiM6Opro6OhyviOpbJKBQYS22O0DlhNK5txTTP8YYASh85VOJJScuh04i/yHhl8GXAX8EqhP6PDxg9XNG3+gPgCMJbS9r3VeSQHqAEOKieUMYGteH0mSJEnS8SviVjwVFAgEqFatGrt37+b6669n/fr1vPzyy4X6Pfjgg5x44on06tWr2LnatWsHQE5OToXFK5WXeEJb6+KAhcBNhFYSjSlhzEOEklWDCR1OXgd4mdDKpQM+J5QUKo07CCWfRhNaEfU18CqhxFVxTgRql/I6kiRJkqRjS8SteMrNzSUzM3SazPbt25k1axbff/89AwcOpFu3bjz33HMMHz6cBx54gMsuu4zs7GweeeQRXnrpJZ577jnq1q0LwDXXXEPXrl3p0qULsbGxbNiwgfHjx9OmTRvatm1bmbcoHZbkvAKQCKQexphawMN5pTgbDzFHWhF1AWBiXinrvDsO0S5JkiRJOvZEXOJp6dKlNG3aFID69evTtm1bnnvuObp37w7As88+yx/+8AceeughbrnlFqKjo7noootYvnw5P/nJT8Lz9OnTh0WLFjFlyhSysrKIjY3l0ksvZeLEiURFRdxtS5IkSZIkHXMCwWAwWNlBRKrs7GxiYmLIysqiQYMGlR2Ojid5h+RL0lHljwSSJEkqQVnyJBF/xpMkSZIkSZKqJhNPkiRJkiRJqhAediRFIre7SJIkSZKOAa54kiRJkiRJUoUw8SRJkiRJkqQKYeJJkiRJkiRJFcIzniRJEgCBSYHKDkGSyk1wgmdmSlIkcMWTJEmSJEmSKoSJJ0mSJEnHvMTExMoOQZKOSyaeJEmSJB2XgsEgEydOpFmzZtSuXZvu3bvz0UcfHfb4p59+mkAgwKBBg/LVP/bYY3To0IEGDRrQoEEDLrroIl555ZV8fbp3704gEGDq1KmF5u3Xrx+BQICJEyeW5bYkKaJUmcRTZmYmY8aMoVWrVkRHRxMXF8fAgQN5/fXXAWjRogWBQIBAIED16tVp1qwZI0aMYPv27eE50tLSCAQC7Nixo5LuQpIkSdLRsnXrVoYPH058fDyLFi0iISGBwYMHs2fPHgCmT5/OjBkzmDVrFmvWrCE2NpZevXqxc+fOQ8795Zdfcvvtt3PxxRcXajvllFOYOnUq7733Hu+99x6XXnopV155ZaGkVlxcHPPmzctX980337Bs2TKaNm16BHcuSZGjSiSeNm7cSKdOnVi2bBnTp09n3bp1LF26lB49enDLLbeE+91///1kZGSwadMmnnrqKVauXMmtt95aiZFLkiRJqizJycmsXr2aJ598kn79+jF79mxatmzJ/v37CQaDzJw5k7vvvpurr76a9u3bM3/+fHbt2sXChQtLnHffvn0MHTqUSZMm0apVq0LtAwcOpF+/frRp04Y2bdowefJk6tWrxzvvvJOv34ABA9i2bRtvvvlmuC41NZXevXvTuHHj8vkiSFIlqxKJp9GjRxMIBHj33Xe55ppraNOmDWeeeSbjxo3L9827fv36xMbG0rx5c3r06MFNN93EBx98UImRS5IkSaosa9euZdiwYXTr1o2YmBh69OjBtGnTqFWrFhs2bCAzM5PevXuH+0dHR9OtWzfeeuutEue9//77OfnkkxkxYsQhY9i3bx9PP/00OTk5XHTRRfnaatasydChQ/OtekpNTSUpKamUdypJkSviE0/fffcdS5cu5ZZbbqFu3bqF2hs2bFjkuK+//polS5bQuXPnCo5QkiRJUiTq2rUr8+bNY8mSJYXaMjMzAWjSpEm++iZNmoTbivLmm28yZ84cZs+eXeK1161bR7169YiOjmbUqFG88MILtGvXrlC/ESNG8Oyzz5KTk8PKlSvJysqif//+h3N7klQlRHzi6d///jfBYJC2bdsesu9vfvMb6tWrR+3atTnllFMIBALMmDHjsK+Vm5tLdnZ2viJJkiSpapoxYwbXXXcdycnJLFiwgI4dO/L444/n6xMIBPJ9DgaDheoO2LlzJzfeeCOzZ8/mpJNOKvHap59+Ounp6bzzzjvcfPPNDB8+nI8//rhQvw4dOtC6dWsWL17M3LlzGTZsGDVq1CjlnUpS5Iqq7AAOJRgMAoX/h1CUX//61yQmJhIMBtm8eTN33XUX/fv3Z+XKlVSvXv2Q46dMmcKkSZOOOGZJkiRJla9u3bpMnjyZyZMnM2jQIPr27UtycjLVqlWjZ8+eQGjl08EHeW/ZsqXQKqgDPv/8czZu3MjAgQPDdfv37wcgKiqKTz/9lNNOOw0IbaNLSEgA4LzzzmPNmjX84Q9/4P/9v/9XaN6kpCQeeeQRPv74Y959993yuXlJihARv+KpdevWBAIBPvnkk0P2Pemkk0hISKB169ZceumlzJw5k7feeovly5cf1rXGjx9PVlZWuGzevPlIw5ckSZIUARo2bMjIkSPp27cvq1atomXLlsTGxvLaa6+F++zZs4cVK1bQpUuXIudo27Yt69atIz09PVyuuOIKevToQXp6OnFxccVePxgMkpubW2TbkCFDWLduHe3bty9yO54kVWURv+LphBNOoE+fPjzyyCPceuuthc552rFjR7HnPB1Y5bR79+7DulZ0dDTR0dFHFK8kSZKkyJCcnMygQYPo2LEj+/btY/ny5axYsYJ77rmHQCDA2LFjSUlJoXXr1rRu3ZqUlBTq1KnDkCFDwnPcdNNNNG/enClTplCrVi3at2+f7xoH/i1ycP1dd91F3759iYuLY+fOnTz99NOkpaWxdOnSIuNs1KgRGRkZbrGTdEyK+MQTwKOPPkqXLl244IILuP/+++nQoQM//vgjr732Go899lh4NdTOnTvJzMwMb7W74447OOmkk4r9jYUkSZKkY1d8fDzjxo1j/fr15OTkkJaWRlJSEmPGjAHgjjvuYPfu3YwePZrt27fTuXNnXn31VerXrx+eY9OmTVSrVrqNIt9++y3Dhg0jIyODmJgYOnTowNKlS+nVq1exY4r7ZbokVXWB4IFDlCJcRkYGkydPZsmSJWRkZHDyySfTqVMnkpOT6d69Oy1atODLL78M9z/55JM5//zzmTx5Mh07dgRg2bJlXHbZZezcuZN69eod8prZ2dnExMSQlZVFgwYNKurWJEmKCIFJhz5PUZKqiuCE/P/MSUxMJDU1tXKCkaRjRFnyJFVixRNA06ZNmTVrFrNmzSqyfePGjYecY8uWLdStW/ewkk6SJEmSJEk6MlUm8XQkcnNz+fzzz5k1a1b47RWSJEmSjh+udpKkyhHxb7UrD6+88gqdO3embt26/PGPf6zscCRJkiRJko4LVeaMp8rgGU+SJEmSJEkhZcmTHBcrniRJkiRJknT0mXiSJEmSJElShTDxJEmSJEmSpAph4kmSJEmSJEkVIqqyA5AkSZEhMClQ2SFIUqULTvDdS5JUnlzxJEmSJEklSExMrOwQJKnKMvEkSZIkSaUUDAaZOHEizZo1o3bt2nTv3p2PPvrosMc//fTTBAIBBg0alK9+4sSJBAKBfCU2NjZfn+7duxMIBJg6dWqhefv160cgEGDixIlluS1JKncmniRJkiSpgK1btzJ8+HDi4+NZtGgRCQkJDB48mD179gAwffp0ZsyYwaxZs1izZg2xsbH06tWLnTt3HnLuL7/8kttvv52LL764yPYzzzyTjIyMcFm3bl2hPnFxccybNy9f3TfffMOyZcto2rRpGe5YkipGxCWeEhMTw5n9GjVq0KRJE3r16sXcuXPZv39/of69e/emevXqvPPOO0XOVfA3CJIkSZJ0KMnJyaxevZonn3ySfv36MXv2bFq2bMn+/fsJBoPMnDmTu+++m6uvvpr27dszf/58du3axcKFC0ucd9++fQwdOpRJkybRqlWrIvtERUURGxsbLieffHKhPgMGDGDbtm28+eab4brU1FR69+5N48aNj+zmJakcRVziCeDyyy8nIyODjRs38sorr9CjRw9+9atfMWDAAH788cdwv02bNvH222/zy1/+kjlz5lRixJIkSZKOJWvXrmXYsGF069aNmJgYevTowbRp06hVqxYbNmwgMzOT3r17h/tHR0fTrVs33nrrrRLnvf/++zn55JMZMWJEsX3Wr19Ps2bNaNmyJddffz1ffPFFoT41a9Zk6NCh+VY9paamkpSUVIa7laSKE5GJp+joaGJjY2nevDnnnnsud911F//7v//LK6+8QmpqarjfvHnzGDBgADfffDPPPPMMOTk5lRe0JEmSpGNG165dmTdvHkuWLCnUlpmZCUCTJk3y1Tdp0iTcVpQ333yTOXPmMHv27GL7dO7cmQULFvC3v/2N2bNnk5mZSZcuXdi2bVuhviNGjODZZ58lJyeHlStXkpWVRf/+/Q/3FiXpqIjIxFNRLr30Us4++2yef/55IHSY37x587jxxhtp27Ytbdq04dlnnz2ia+Tm5pKdnZ2vSJIkSTr+zJgxg+uuu47k5GQWLFhAx44defzxx/P1CQQC+T4Hg8FCdQfs3LmTG2+8kdmzZ3PSSScVe92+ffvy05/+lLPOOouePXvy17/+FYD58+cX6tuhQwdat27N4sWLmTt3LsOGDaNGjRqlvVVJqlBRlR1AabRt25Z//vOfAPz9739n165d9OnTB4Abb7yROXPm8LOf/azM80+ZMoVJkyaVS6ySJEmSqq66desyefJkJk+ezKBBg+jbty/JyclUq1aNnj17AqGVTwcf5L1ly5ZCq6AO+Pzzz9m4cSMDBw4M1x04wzYqKopPP/2U0047rcg4zjrrLNavX1/kvElJSTzyyCN8/PHHvPvuu2W+X0mqKFVmxRPk/w3CnDlzuO6664iKCuXObrjhBlavXs2nn35a5vnHjx9PVlZWuGzevLlc4pYkSZJUdTVs2JCRI0fSt29fVq1aRcuWLYmNjeW1114L99mzZw8rVqygS5cuRc7Rtm1b1q1bR3p6erhcccUV9OjRg/T0dOLi4oocl5ubyyeffFLsm+qGDBnCunXraN++Pe3atTvym5WkclalVjx98skntGzZku+++44XX3yRvXv38thjj4Xb9+3bx9y5c5k2bVqZ5o+OjiY6Orq8wpUkSZJURSUnJzNo0CA6duzIvn37WL58OStWrOCee+4hEAgwduxYUlJSaN26Na1btyYlJYU6deowZMiQ8Bw33XQTzZs3Z8qUKdSqVYv27dvnu0bDhg0B8tXffvvtDBw4kPj4eLZs2cLvfvc7srOzGT58eJFxNmrUiIyMDLfYSYpYVSbxtGzZMtatW0dycjJPPfUUp5xyCi+++GK+Pq+//jpTpkxh8uTJ4ZVQkiRJklRa8fHxjBs3jvXr15OTk0NaWhpJSUmMGTMGgDvuuIPdu3czevRotm/fTufOnXn11VepX79+eI5NmzZRrVrpNpl89dVX3HDDDWzdupWTTz6ZCy+8kHfeeYdTTz212DEHEliSFIkCwWAwWNlBHCwxMZFvv/2WefPmsW/fPr799luWLl3KlClT6N69Oy+++CKdOnXi8ssvZ+rUqfnG7ty5k5NPPplnnnmGK6+8ksTERL788kseeuihfP1OOOEE4uPjDxlLdnY2MTExZGVl0aBBg3K9T0mSIk1gUtEH4krS8SQ4ofA/jxITE/O9XVuSjldlyZNE5LKgpUuX0rRpU6KiomjUqBFnn302f/zjHxk+fDhr167lH//4R5GvIK1fvz69e/dmzpw5XHnllQCkpaVxzjnn5Os3fPhw/8chSZIkSZJUwSJuxVMkccWTJOl44oonSSp6xZMkKaQseZIq9VY7SZIkSZIkVR0mniRJkiRJklQhIvKMJ0mSdPS5vUSSJEnlzRVPkiRJkiRJqhAmniRJkiRJklQhTDxJkiRJkiSpQnjGkyRJAiAwKVDZIUiSjhLP9ZN0tLjiSZIkSZIkSRXCxJMkSZIkHecSExMrOwRJxygTT5IkSZKkQoLBIBMnTqRZs2bUrl2b7t2789FHH5U4JjU1lUAgUKj88MMP4T6PPfYYHTp0oEGDBjRo0ICLLrqIV155Jd883bt3JxAIMHXq1ELX6NevH4FAgIkTJ5bLfUqqWBGReCrqG9PB5eDs+5IlS+jevTv169enTp06nH/++aSmpuabb+PGjfnGx8TEcOGFF/Lyyy8f3RuTJEmSpAi1detWhg8fTnx8PIsWLSIhIYHBgwezZ88eAKZPn86MGTOYNWsWa9asITY2ll69erFz584S523QoAEZGRn5Sq1atcLtp5xyClOnTuW9997jvffe49JLL+XKK68slNSKi4tj3rx5+eq++eYbli1bRtOmTcvpqyCpokVE4ungb0gzZ84s9I3qD3/4AwAPP/wwV155JV26dGH16tX885//5Prrr2fUqFHcfvvtheb9+9//TkZGBqtXr+aCCy7gpz/9KR9++OHRvj1JkiRJijjJycmsXr2aJ598kn79+jF79mxatmzJ/v37CQaDzJw5k7vvvpurr76a9u3bM3/+fHbt2sXChQtLnDcQCBAbG5uvHGzgwIH069ePNm3a0KZNGyZPnky9evV455138vUbMGAA27Zt48033wzXpaam0rt3bxo3blx+XwhJFSoiEk8Hf0OKiYkp9I0qJiaGzZs3c9tttzF27FhSUlJo164dCQkJ3HbbbTzwwAM8+OCDrF69Ot+8J554IrGxsbRt25bJkyezd+9eli9fXkl3KUmSJEmRY+3atQwbNoxu3boRExNDjx49mDZtGrVq1WLDhg1kZmbSu3fvcP/o6Gi6devGW2+9VeK833//PaeeeiqnnHIKAwYMYO3atcX23bdvH08//TQ5OTlcdNFF+dpq1qzJ0KFD8616Sk1NJSkpqYx3LKkyRETi6XAsXryYvXv3FrmyaeTIkdSrV49FixYVOXbv3r3Mnj0bgBo1alRonJIkSZJUFXTt2pV58+axZMmSQm2ZmZkANGnSJF99kyZNwm1Fadu2Lampqbz00kssWrSIWrVq0bVrV9avX5+v37p166hXrx7R0dGMGjWKF154gXbt2hWab8SIETz77LPk5OSwcuVKsrKy6N+/f1luV1IliarsAA7XZ599RkxMTJF7eWvWrEmrVq347LPP8tV36dKFatWqsXv3bvbv30+LFi0YPHhwsdfIzc0lNzc3/Dk7O7v8bkCSJEmSIsiMGTNISUkhOTmZzz//nPT0dEaNGsWoUaPCfQKBQL4xwWCwUN3BLrzwQi688MLw565du3Luuefy8MMP88c//jFcf/rpp5Oens6OHTv4y1/+wvDhw1mxYkWh5FOHDh1o3bo1ixcvZvny5QwbNszFBFIVU2VWPB1KUd8An3nmGdauXctLL71EQkICf/rTnzjhhBOKnWPKlCnExMSES1xcXEWHLUmSJEmVom7dukyePJn169dzxRVXcPPNNzNu3DieeOKJ8LlMBVc3bdmypdAqqJJUq1aN888/v9CKp5o1a5KQkMB5553HlClTOPvss8Nn+xaUlJTEI488wuLFi91mJ1VBVSbx1KZNG7Kysvjmm28Kte3Zs4cvvviC1q1b56uPi4ujdevW9O/fnz/96U9cd911bNmypdhrjB8/nqysrHDZvHlzud+HJEmSJEWahg0bMnLkSPr27cuqVato2bIlsbGxvPbaa+E+e/bsYcWKFXTp0uWw5w0Gg6Snpx/yLXTBYDDf7pODDRkyhHXr1tG+ffsit+NJimxVJvH005/+lKioKB588MFCbY8//jg5OTnccMMNxY7v1q0b7du3Z/LkycX2iY6OpkGDBvmKJEmSJB2LkpOTWbFiBVlZWezbt4/ly5ezYsUKOnXqRCAQCL/Y6YUXXuDDDz8kMTGROnXqMGTIkPAcN910E+PHjw9/njRpEn/729/44osvSE9PZ8SIEeEtfAfcddddrFq1io0bN7Ju3Truvvtu0tLSGDp0aJFxNmrUiIyMDF5//fWK+2JIqjBV5oyn+Ph4pk+fzu23306tWrXCe3v/93//l7vuuovbbruNzp07lzjHbbfdxrXXXssdd9xB8+bNj1LkkiRJkhR54uPjGTduHOvXrycnJ4e0tDSSkpIYM2YMAHfccQe7d+9m9OjRbN++nc6dO/Pqq69Sv3798BybNm2iWrX/rmfYsWMH//M//0NmZiYxMTGcc845rFy5kgsuuCDc59tvv2XYsGFkZGQQExNDhw4dWLp0Kb169So21oYNG5b/F0DSUREIBoPByg7iYKmpqYwdO5YdO3YU2f7SSy/x+9//ng8++IB9+/Zx5plncsstt/Czn/0s3Gfjxo20bNmStWvX0rFjx3B9MBjkjDPO4NJLL+XRRx89ZCzZ2dnExMSQlZXl6idJ0jEvMKn4w2IlSceW4IT8/wxMTEwkNTW1coKRVGWUJU8ScYmnSGLiSZJ0PDHxJEnHDxNPksqiLHmSKnPGkyRJkiSpYph0klRRTDxJkiRJkiSpQph4kiRJkiRJUoWoMm+1kyRJFavgeR+SJEnSkXLFkyRJkiRJkiqEiSdJkiRJkiRVCBNPkiRJkiRJqhCe8XS8CAQqOwJJUqQLesaTJEmSypcrno5TiZUdgCRJkiRJOuaZeFKJcoExwElAXeAK4KtDjJkIBAqU2AJ9Eovoc2GBPi3y6p8u4hpn5rWlHsY9SJIkSZKkymHi6TiyFRgOxAOLgARgMLCnhDFjgRcIJX/eAL4HBgD7DnGtM4GMg8q6IvpcXqDP/xXRJw6YV6DuHSCTUCJMkiRJkiRFrohLPCUmJhIIBMLlxBNP5PLLL+ef//wnABs3biQQCJCenl5o7KBBg0hMTAx/7t69e765DpQff/zxKN1NZEkGVgNPAv2A2UBLYH8x/bOAOcCDQE/gHODPhJJIfz/EtaIIrXI6UE4uok90gT4nFNFnKLAC2HxQ3dy8eg8okyRJkiQpskVc4gng8ssvJyMjg4yMDF5//XWioqIYMGBAmeb6xS9+EZ7rQImKOj5TFmuBYUA3IAboAUwDahXT/31gL9D7oLpmQHvgrUNca31e35bA9cAXRfRJAxoDbYBfAFuK6NME6APMz/u8C3gGSDrE9SVJkiRJUuWLyAxMdHQ0sbGhU4FiY2P5zW9+wyWXXMJ//vOfUs9Vp06d8FzHu66Etq2dfZj9M4GaQKMC9U3y2orTGVhAKKH0LfA7oAvwEXBiXp++wLXAqcAG4F7gUkLJrugC8yUBtwF3A4uB04COh3kPkiRJkiSp8kTkiqeDff/99zz11FMkJCRw4oknHnrAEcjNzSU7OztfOZbMAK4jtOVuAaHkzeN5bSlAvYPKphLmCRI62Ls4fYGfAmcR2qL317z6+Qf1uQ7oT2j11EDgFeCzg/oerD+hs6VWEtpm52onSZIkSZKqhohMPC1ZsoR69epRr1496tevz0svvcQzzzxDtWqlD/fRRx8Nz1WvXj1uu+22YvtOmTKFmJiYcImLizuS24g4dYHJhLbBXQHcDIwDngBGAekHlWaEzl3aA2wvMM8WQqueSnPds/KuW5ymhFY/FdUnitAWwQmEzqgaWoprS5IkSZKkyhORiacePXqQnp5Oeno6q1evpnfv3vTt25cvv/yy1HMNHTo0PFd6ejrjx48vtu/48ePJysoKl82bNxfbt6prCIwktDppFaGDvRMOKlFAJ6AG8NpB4zKADwltnTtcucAnhJJLxdlG6ADx4vokETpk/EoKb/2TJEmSJEmRKSLPeKpbty4JCQnhz506dSImJobZs2eHVyxlZWUVGrdjxw5OPfXUfHUxMTH55ipJdHQ00dEFTxg6diQDgwhtsdsHLCeUzLmnmP4xwAhC5yudSCg5dTv/3UJ3wGXAVcAv8z7fTmj7XDyh1VG/A7KB4Xnt3wMTCW3HawpsBO4CTsqbpyhnAFuBOodxn5IkSZIkKTJE5IqnggKBANWqVWP37t00atSIk08+mTVr1uTrs3v3bj766CNOP/30Sooy8sUT2loXBywEbiK0kmhMCWMeIpSsGkzocPI6wMtA9YP6fE4oKXTAV8ANwOnA1YQOKH+H0FY68sauI7R6qQ2hhFQb4G2gfgmxnAjULvEOJUmSJElSJInIFU+5ublkZobem7Z9+3ZmzZrF999/z8CBAwG4/fbbSUlJoUmTJnTp0oXt27czbdo0oqKiuPHGGysz9IiWnFcAEoHUwxhTC3g4rxRnY4HPTx9iztrA3w7j2gXnLWjHYcwhSZIkSZIqT0QmnpYuXUrTpqHTfurXr0/btm157rnn6N69OxBKPNWrV4/f//73fP755zRs2JALL7yQVatW0aBBg0qMXJIkSZIkSQcEgsFgsLKDiFTZ2dnExMSQlZVV9RNagUBlRyBJinT+SCBJkqQSlCVPUiXOeJIkSZIkSVLVY+JJkiRJkiRJFSIiz3hSBXD7hCRJkiRJOspc8SRJkiRJkqQKYeJJkiRJkiRJFcLEkyRJkiRJkiqEZzxJkiQAApMClR2CJEnSMSs44fg8e9kVT5IkSZIkSaoQJp4kSZIkSZIqQWJiYmWHUOFMPEmSJEmSJEWIYDDIxIkTadasGbVr16Z79+589NFHhxw3c+ZMTj/9dGrXrk1cXBzJycn88MMPRfadMmUKgUCAsWPH5qvv3r07gUCAqVOnFhrTr18/YmJiSn0/EZd4yszMZMyYMbRq1Yro6Gji4uIYOHAgr7/+er5+KSkpVK9evcgvRmpqKoFAIFyaNGnCwIEDD+s/lCRJkiRJUkXZunUrw4cPJz4+nkWLFpGQkMDgwYPZs2cPANOnT2fGjBnMmjWLNWvWEBsbS69evdi5c2excz711FPceeedTJgwgU8++YQ5c+bwzDPPMH78+EJ916xZwxNPPEGHDh2KnCsuLo558+blq/vmm29YtmwZsbGxpb7fiEo8bdy4kU6dOrFs2TKmT5/OunXrWLp0KT169OCWW27J13fevHnccccdzJ07t8i5GjRoQEZGBt988w1//etfycnJoX///uH/kJIkSZIkSUdbcnIyq1ev5sknn6Rfv37Mnj2bli1bsn//foLBIDNnzuTuu+/m6quvpn379syfP59du3axcOHCYud8++236dq1K0OGDKFFixb07t2bG264gffeey9fv++//56hQ4cye/ZsGjVqVORcAwYMYNu2bbz55pvhutTUVHr37s3JJ59c6vuNqMTT6NGjCQQCvPvuu1xzzTW0adOGM888k3HjxvHOO++E+61YsYLdu3dz//33k5OTw8qVKwvNFQgEiI2NpWnTppx33nkkJyfz5Zdf8umnnx7NW5IkSZIkSQpbu3Ytw4YNo1u3bsTExNCjRw+mTZtGrVq12LBhA5mZmfTu3TvcPzo6mm7duvHWW28VO+dPfvIT3n//fd59910AvvjiC/7v//6P/v375+t3yy230L9/f3r27FnsXDVr1mTo0KH5Vj2lpqaSlJRUpvuNmMTTd999x9KlS7nllluoW7duofaGDRuG/z5nzhxuuOEGatSowQ033MCcOXNKnHvHjh3hzGCNGjXKNW5JkiRJkqTD1bVrV+bNm8eSJUsKtWVmZgLQpEmTfPVNmjQJtxXl+uuv57e//S0/+clPqFGjBqeddho9evTgzjvvDPd5+umn+eCDD5gyZcohYxwxYgTPPvtseLFPVlZWoSTW4Yoq06gK8O9//5tgMEjbtm1L7Jednc1f/vKXcKbvxhtvpGvXrjz88MM0aNAg3C8rK4t69eoRDAbZtWsXAFdccUWJ8+fm5pKbm5vvWpIkSZIkSeVlxowZpKSkkJyczOeff056ejqjRo1i1KhR4T6BQCDfmGAwWKjuYGlpaUyePJlHH32Uzp078+9//5tf/epXNG3alHvvvZfNmzfzq1/9ildffZVatWodMsYOHTrQunVrFi9ezPLlyxk2bFiZF/JETOIpGAwChb+4BS1cuJBWrVpx9tlnA9CxY0datWrF008/zf/8z/+E+9WvX58PPviAH3/8kRUrVvDAAw/w+OOPlzj3lClTmDRp0hHeiSRJkiRJUtHq1q3L5MmTmTx5MoMGDaJv374kJydTrVq18Ba4zMxMmjZtGh6zZcuWQqugDnbvvfcybNgwfv7znwNw1llnkZOTw//8z/9w99138/7777NlyxY6deoUHrNv3z5WrlzJrFmzyM3NpXr16vnmTEpK4pFHHuHjjz8Ob+Eri4jZate6dWsCgQCffPJJif3mzp3LRx99RFRUVLh89NFHhbbbVatWjYSEBNq2bcvIkSMZNmwY1113XYlzjx8/nqysrHDZvHnzEd+XJEmSJElSURo2bMjIkSPp27cvq1atomXLlsTGxvLaa6+F++zZs4cVK1bQpUuXYufZtWsX1arlT/FUr16dYDBIMBjksssuY926daSnp4fLeeedx9ChQ0lPTy+UdAIYMmQI69ato3379rRr167M9xgxK55OOOEE+vTpwyOPPMKtt95a6JynHTt2sHnzZt577z3S0tI44YQT8rVdcsklfPjhh7Rv377I+ZOTk5kxYwYvvPACV111VZF9oqOjiY6OLr+bkiRJkiRJOkhycjKDBg2iY8eO7Nu3j+XLl7NixQruueceAoEAY8eOJSUlhdatW9O6dWtSUlKoU6cOQ4YMCc9x00030bx58/B5TQMHDmTGjBmcc8454a129957L1dccQXVq1enfv36hfIldevW5cQTTyw2j9KoUSMyMjKO+KzsiEk8ATz66KN06dKFCy64gPvvv58OHTrw448/8tprr/HYY4/Rp08fLrjgAi655JJCYy+66CLmzJnDQw89VOTcDRo04Oc//zkTJkxg0KBBh9zSJ0mSJEmSVN7i4+MZN24c69evJycnh7S0NJKSkhgzZgwAd9xxB7t372b06NFs376dzp078+qrr1K/fv3wHJs2bcq3wulA0uqee+7h66+/5uSTT2bgwIFMnjz5iGI9+EVvZRUIHjhcKUJkZGQwefJklixZQkZGBieffDKdOnXilltuYciQIfzmN7/h17/+daFxM2bMYMqUKXz99dcsXLiQsWPHsmPHjnx9Nm3axGmnncZTTz3F4MGDDxlLdnY2MTExZGVl5Tu4XJKkY1Fgkr+UkSRJqijBCYXTL4mJiaSmph79YMqoLHmSiEs8RRITT5Kk44mJJ0mSpIpzvCaeIuZwcUmSJEmSpONJVUo6lZWJJ0mSJEmSJFUIE0+SJEmSJEmqEBH1VjtJklR5ijp3QJIkSToSrniSJEmSJElShTDxJEmSJEmSpAph4kmSJEmSJEkVwsSTJEmSJEmSKoSJJ0mSJEmSJFUIE0+SJEmSJEmqECaeJEmSJEmSVCFMPEmSJEmSJKlCmHiSJEmSJElShTDxJEmSJEmSpAph4kmSJEmSJEkVwsSTJEmSJEmSKoSJJ0mSJEmSJFUIE0+SJEmSJEmqECaeJEmSJEmSVCFMPEmSJEmSJKlCmHiSJEmSJElShTDxJEmSJEmSpAph4kmSJEmSJEkVwsSTJEmSJEmSKoSJJ0mSJEmSJFUIE0+SJEmSJEmqECaeJEmSJEmSVCFMPEmSJEmSJKlCmHiSJEmSJElShTDxJEmSJEmSpAph4kmSJEmSJEkVwsSTJEmSJEmSKoSJJ0mSJEmSJFUIE0+SJEmSJEmqEFGVHUAkCwaDAGRnZ1dyJJIkSZIkSZXrQH7kQL7kcJh4KsHOnTsBiIuLq+RIJEmSJEmSIsPOnTuJiYk5rL6BYGnSVMeZ/fv3880331C/fn0CgcBhj8vOziYuLo7NmzfToEGDCoxQxyKfHx0Jnx+Vlc+OjoTPj8rKZ0dHwudHR8Lnp2yCwSA7d+6kWbNmVKt2eKc3ueKpBNWqVeOUU04p8/gGDRr4AKvMfH50JHx+VFY+OzoSPj8qK58dHQmfHx0Jn5/SO9yVTgd4uLgkSZIkSZIqhIknSZIkSZIkVQgTTxUgOjqaCRMmEB0dXdmhqAry+dGR8PlRWfns6Ej4/KisfHZ0JHx+dCR8fo4eDxeXJEmSJElShXDFkyRJkiRJkiqEiSdJkiRJkiRVCBNPkiRJkiRJqhAmnsrJ9u3bGTZsGDExMcTExDBs2DB27NhxyHGffPIJV1xxBTExMdSvX58LL7yQTZs2VXzAiihlfX4OGDlyJIFAgJkzZ1ZYjIpMpX129u7dy29+8xvOOuss6tatS7Nmzbjpppv45ptvjl7QqjSPPvooLVu2pFatWnTq1IlVq1aV2H/FihV06tSJWrVq0apVKx5//PGjFKkiUWmen+eff55evXpx8skn06BBAy666CL+9re/HcVoFUlK+73ngDfffJOoqCg6duxYsQEqopX2+cnNzeXuu+/m1FNPJTo6mtNOO425c+cepWgVaUr7/Dz11FOcffbZ1KlTh6ZNm/Kzn/2Mbdu2HaVoj10mnsrJkCFDSE9PZ+nSpSxdupT09HSGDRtW4pjPP/+cn/zkJ7Rt25a0tDT+8Y9/cO+991KrVq2jFLUiRVmenwNefPFFVq9eTbNmzSo4SkWi0j47u3bt4oMPPuDee+/lgw8+4Pnnn+ezzz7jiiuuOIpRqzI888wzjB07lrvvvpu1a9dy8cUX07dv32J/2bFhwwb69evHxRdfzNq1a7nrrru49dZb+ctf/nKUI1ckKO3zs3LlSnr16sX//d//8f7779OjRw8GDhzI2rVrj3LkqmylfXYOyMrK4qabbuKyyy47SpEqEpXl+Rk8eDCvv/46c+bM4dNPP2XRokW0bdv2KEatSFHa5+eNN97gpptuYsSIEXz00Uc899xzrFmzhp///OdHOfJjUFBH7OOPPw4CwXfeeSdc9/bbbweB4L/+9a9ix1133XXBG2+88WiEqAhW1ucnGAwGv/rqq2Dz5s2DH374YfDUU08NPvTQQxUcrSLJkTw7B3v33XeDQPDLL7+siDAVIS644ILgqFGj8tW1bds2eOeddxbZ/4477gi2bds2X93IkSODF154YYXFqMhV2uenKO3atQtOmjSpvENThCvrs3PdddcF77nnnuCECROCZ599dgVGqEhW2ufnlVdeCcbExAS3bdt2NMJThCvt8/PAAw8EW7Vqla/uj3/8Y/CUU06psBiPF654Kgdvv/02MTExdO7cOVx34YUXEhMTw1tvvVXkmP379/PXv/6VNm3a0KdPHxo3bkznzp158cUXj1LUihRleX4g9AwNGzaMX//615x55plHI1RFmLI+OwVlZWURCARo2LBhBUSpSLBnzx7ef/99evfuna++d+/exT4rb7/9dqH+ffr04b333mPv3r0VFqsiT1men4L279/Pzp07OeGEEyoiREWosj478+bN4/PPP2fChAkVHaIiWFmen5deeonzzjuP6dOn07x5c9q0acPtt9/O7t27j0bIiiBleX66dOnCV199xf/93/8RDAb59ttvWbx4Mf379z8aIR/TTDyVg8zMTBo3blyovnHjxmRmZhY5ZsuWLXz//fdMnTqVyy+/nFdffZWrrrqKq6++mhUrVlR0yIogZXl+AKZNm0ZUVBS33nprRYanCFbWZ+dgP/zwA3feeSdDhgyhQYMG5R2iIsTWrVvZt28fTZo0yVffpEmTYp+VzMzMIvv/+OOPbN26tcJiVeQpy/NT0IMPPkhOTg6DBw+uiBAVocry7Kxfv54777yTp556iqioqKMRpiJUWZ6fL774gjfeeIMPP/yQF154gZkzZ7J48WJuueWWoxGyIkhZnp8uXbrw1FNPcd1111GzZk1iY2Np2LAhDz/88NEI+Zhm4qkEEydOJBAIlFjee+89AAKBQKHxwWCwyHoI/eYP4MorryQ5OZmOHTty5513MmDAAA9vPUZU5PPz/vvv84c//IHU1NRi+6jqqshn52B79+7l+uuvZ//+/Tz66KPlfh+KPAWfi0M9K0X1L6pex4fSPj8HLFq0iIkTJ/LMM88UmSzXse9wn519+/YxZMgQJk2aRJs2bY5WeIpwpfnes3//fgKBAE899RQXXHAB/fr1Y8aMGaSmprrq6ThVmufn448/5tZbb+W+++7j/fffZ+nSpWzYsIFRo0YdjVCPaf4aoQS//OUvuf7660vs06JFC/75z3/y7bffFmr7z3/+UyjDesBJJ51EVFQU7dq1y1d/xhln8MYbb5Q9aEWMinx+Vq1axZYtW4iPjw/X7du3j9tuu42ZM2eycePGI4pdlasin50D9u7dy+DBg9mwYQPLli1ztdMx7qSTTqJ69eqFfsO3ZcuWYp+V2NjYIvtHRUVx4oknVlisijxleX4OeOaZZxgxYgTPPfccPXv2rMgwFYFK++zs3LmT9957j7Vr1/LLX/4SCCUSgsEgUVFRvPrqq1x66aVHJXZVvrJ872natCnNmzcnJiYmXHfGGWcQDAb56quvaN26dYXGrMhRludnypQpdO3alV//+tcAdOjQgbp163LxxRfzu9/9jqZNm1Z43McqE08lOOmkkzjppJMO2e+iiy4iKyuLd999lwsuuACA1atXk5WVRZcuXYocU7NmTc4//3w+/fTTfPWfffYZp5566pEHr0pXkc/PsGHDCv0A36dPH4YNG8bPfvazIw9elaoinx34b9Jp/fr1LF++3CTCcaBmzZp06tSJ1157jauuuipc/9prr3HllVcWOeaiiy7i5Zdfzlf36quvct5551GjRo0KjVeRpSzPD4RWOiUlJbFo0SLPxzhOlfbZadCgAevWrctX9+ijj7Js2TIWL15My5YtKzxmRY6yfO/p2rUrzz33HN9//z316tUDQv++qlatGqeccspRiVuRoSzPz65duwpt8a1evTrw31XfKqNKOdL8GHT55ZcHO3ToEHz77beDb7/9dvCss84KDhgwIF+f008/Pfj888+HPz///PPBGjVqBJ944ong+vXrgw8//HCwevXqwVWrVh3t8FXJyvL8FORb7Y5PpX129u7dG7ziiiuCp5xySjA9PT2YkZERLrm5uZVxCzpKnn766WCNGjWCc+bMCX788cfBsWPHBuvWrRvcuHFjMBgMBu+8887gsGHDwv2/+OKLYJ06dYLJycnBjz/+ODhnzpxgjRo1gosXL66sW1AlKu3zs3DhwmBUVFTwkUceyfd9ZseOHZV1C6okpX12CvKtdse30j4/O3fuDJ5yyinBa665JvjRRx8FV6xYEWzdunXw5z//eWXdgipRaZ+fefPmBaOiooKPPvpo8PPPPw++8cYbwfPOOy94wQUXVNYtHDNMPJWTbdu2BYcOHRqsX79+sH79+sGhQ4cGt2/fnq8PEJw3b16+ujlz5gQTEhKCtWrVCp599tnBF1988egFrYhR1ufnYCaejk+lfXY2bNgQBIosy5cvP+rx6+h65JFHgqeeemqwZs2awXPPPTe4YsWKcNvw4cOD3bp1y9c/LS0teM455wRr1qwZbNGiRfCxxx47yhErkpTm+enWrVuR32eGDx9+9ANXpSvt956DmXhSaZ+fTz75JNizZ89g7dq1g6ecckpw3LhxwV27dh3lqBUpSvv8/PGPfwy2a9cuWLt27WDTpk2DQ4cODX711VdHOepjTyAYdM2YJEmSJEmSyp9vtZMkSZIkSVKFMPEkSZIk/f/27j0oqvP8A/h35Q6LIBdZCwZEuSiOSGRU0EjSBEisSp04Q5RqFSYWYxrTqaaa1Iq1SSqNVWOrTRtYL9F4iZcKeGcEKeFiuIQacBFcAlq8oGiBJl7Y5/cHw/m5sqyArrbJ9zOzM573fc953+dwlpFnznkOERERWQQTT0REREREREREZBFMPBERERERERERkUUw8URERERERERERBbBxBMREREREREREVkEE09ERERERERERGQRTDwREREREREREZFFMPFEREREFpGSkoLRo0d3afPy8oJKpcKBAwe6bfuu0el00Gg0aGlpeexz19XVQaVSoby8HACQk5MDlUqFGzduPPa1PPvss3jzzTcf+7yP0/3X/eLFi/HGG288cD8/Pz+sW7fukayhrq4OKSkpj+RYRERED4uJJyIioof0+eefw8rKCi+++OKTXorFdSYxOj/Ozs4ICQnBwoULce7cOaOxixcvRnZ2trJdVVWFlStX4qOPPkJjYyNeeuklk23fRe+88w4WLlwIZ2dnAP+f/On8eHp64qWXXsKXX35p8bVERkaisbERLi4uPRr/35wsiomJgZWVFQoLC3u13+bNm+Hq6mqZRd3nrbfeglarhV6vNzvu9OnTmD9/vsXX4+fnB5VKhZ07d3bpCwkJgUqlwubNmy2+DiIi+v5g4omIiOghpaen4+c//zn+8Y9/oL6+3qJztbe3w2AwWHSOnjhx4gQaGxvx5Zdf4r333kNVVRVCQ0ONEk1qtRru7u7Kdm1tLQAgLi4OGo0GdnZ2Jtv64s6dOw8RjWVduHABBw8exLx587r06XQ6NDY2IisrC83NzXjxxRdx8+ZNk8d5VDHa2tpCo9FApVI9kuM9KfX19SgoKMDrr7+OtLS0J72cbg0cOBAxMTH4y1/+Ynacp6cnHB0dH2ouvV6P6dOnY/z48UhNTUVwcDCSk5O7jBs8eDC0Wq1RW2FhIS5dugQnJ6eHWgMREdH9mHgiIiJ6CG1tbdi9ezcWLFiAKVOmGN0pEBERgaVLlxqNv3r1KmxsbHDy5EkAwO3bt/HWW2/B29sbTk5OGDduHHJycpTxnXdmZGZmYsSIEbCzs8PXX3+N06dPIzo6Gh4eHnBxcUFUVBRKS0uN5jp79iwmTpwIe3t7jBgxAidOnOjyONvFixcRHx+PAQMGwN3dHXFxcairq3tg3O7u7tBoNPD390dcXBxOnDiBcePGISkpCe3t7QCMHzlKSUnB1KlTAQD9+vWDSqUy2dZJq9Vi+PDhsLe3R3BwMDZu3Kj0dd51tXv3bjz77LOwt7fHJ5980uP99u3bh+eeew6Ojo4IDQ1FQUGBUWz5+fmIioqCo6MjBgwYgNjYWDQ3NwMARASpqanw9/eHg4MDQkND8dlnn5k9V7t370ZoaCh8fHy69A0cOBAajQZjx47FmjVrcOnSJRQWFvY5RgAoLi5GWFgY7O3tER4ejrKyMqN+U4/adRfz3LlzkZubi/Xr1yt3Z3VeH5WVlZg8eTLUajW8vLwwe/ZsNDU1Kcdsa2vDnDlzoFarMWjQIKxZs8bseeotrVaLKVOmYMGCBdi1axfa2tqM+m/cuIH58+fDy8sL9vb2GDlyJDIzM5GTk4N58+bh5s2bSkydj6WZetzT1dXV6Hv9q1/9CoGBgXB0dIS/vz+WL1/+wKTgtGnT8Omnn5odc/+jdiqVCh9//DGmT58OR0dHBAQE4ODBg2aPMWfOHFy+fBmbNm3C3LlzsX79eqPkb6eEhATk5uaioaFBaUtPT0dCQgKsra3NzkFERNRrQkRERH2WlpYm4eHhIiKSkZEhfn5+YjAYRERkw4YN8tRTTynbnW3e3t7S3t4uIiKzZs2SyMhIOXXqlNTU1Mgf/vAHsbOzk+rqahER0Wq1YmNjI5GRkZKfny9nz56V1tZWyc7Olm3btkllZaVUVlZKUlKSeHl5yb///W8REWlvb5egoCCJjo6W8vJyycvLk7FjxwoA2b9/v4iItLW1SUBAgCQmJkpFRYVUVlbKrFmzJCgoSG7dumUyXr1eLwCkrKysS9/+/fsFgBQVFYmIyIoVKyQ0NFRERFpaWkSr1QoAaWxslMbGRpNtIiJ//etfZdCgQbJ37145f/687N27V9zc3GTz5s1Ga/Dz81PGXLx4scf7BQcHS2Zmpuh0OpkxY4b4+vrKnTt3RESkrKxM7OzsZMGCBVJeXi5nzpyRDRs2yNWrV0VE5O2335bg4GA5cuSI1NbWilarFTs7O8nJyen2GomLi5Pk5GSjtpMnTwoAaW5uVtpKSkoEgGRkZPQ5xtbWVvH09JT4+Hg5c+aMZGRkiL+/v9HP7P65zcV848YNiYiIkFdffVX5Gd29e1f+9a9/iYeHhyxbtkyqqqqktLRUoqOj5bnnnlPiWbBggfj4+MixY8ekoqJCpkyZImq1WhYtWtTtueopg8Egvr6+kpmZKSIiY8aMkfT0dKW/vb1dxo8fLyEhIXLs2DGpra2VjIwMOXTokNy6dUvWrVsn/fv3N7oWRcTo+9HJxcVFtFqtsr1q1SrJz88XvV4vBw8eFC8vL1m9erXSf+9136myslIASF1dXbcx+fr6ytq1a5VtAOLj4yM7duyQc+fOyRtvvCFqtVquXbvW7TGcnJzkk08+Eb1eLytWrDA7z7Rp02TVqlUi0vG7oH///lJWVtYlXiIioofFxBMREdFDiIyMlHXr1omIyJ07d8TDw0OOHz8uIiJXrlwRa2trOXXqlDI+IiJClixZIiIiNTU1olKp5OLFi0bHfP7552XZsmUiIkpipry83Ow67t69K87OzpKRkSEiIocPHxZra2slmSMicvz4caM/rNPS0iQoKMgoMXbr1i1xcHCQo0ePmpzHXOKpqqpKAMiuXbtEpOsf4J2JqXuZahs8eLDs2LHDqG3VqlUSERFhtIbO897b/T7++GOl/6uvvhIAUlVVJSIiM2fOlAkTJpiMvbW1Vezt7eXzzz83ak9KSpKZM2ea3EdEJDQ0VH77298atd2f/GlqapJp06aJs7OzXL58uc8xfvTRR+Lm5iZtbW1K/6ZNm8wmnszFLCISFRXVJVm0fPlyiYmJMWpraGgQAKLT6aSlpUVsbW1l586dSv+1a9fEwcHhkSSejh07Jp6enkrCcO3atUYxHD16VPr16yc6nc7k/lqtVlxcXLq09yTxdL/U1FQZM2aMsm0q8XTz5k0BYDZBaSrx9Otf/1rZbm1tFZVKJYcPH+72GDExMRIeHi5bt259YOLpwIEDMnToUDEYDLJlyxYJCwvrUbxERES9xUftiIiI+kin06G4uBivvPIKAMDa2hrx8fFIT08H0FGzJTo6Gtu3bwfQUX+loKAACQkJAIDS0lKICAIDA6FWq5VPbm6uUvsI6KjJM2rUKKO5r1y5guTkZAQGBsLFxQUuLi5obW1VakzpdDoMHjwYGo1G2Wfs2LFGxygpKUFNTQ2cnZ2Vud3c3PDtt98azd9TIgIAD1U76OrVq2hoaEBSUpLROfnd737XZU3h4eF92u/eczlo0CAAHecTAMrLy/H888+bXFtlZSW+/fZbREdHG82xdetWs+frm2++gb29vck+Hx8fqNVqeHh4oKqqCnv27MHAgQP7HGNnra17awVFRER0u7YHxdydkpISnDx50mgdwcHBADpqedXW1uL27dtGc7u5uSEoKKjbY9bX1xsd77333ut2bFpaGuLj45XHwmbOnImioiLodDolJh8fHwQGBvYqrp747LPPMHHiRGg0GqjVaixfvvyBtd0cHBwAAP/5z396Nde916qTkxOcnZ2Va9WU7du3Y/z48Xj77bfx7rvvIiIiottHQX/0ox+htbUVp06dQnp6OhITE3u1NiIiop7iQ9xERER9lJaWhrt378Lb21tpExHY2NigubkZAwYMQEJCAhYtWoQNGzZgx44dCAkJQWhoKADAYDDAysoKJSUlsLKyMjq2Wq1W/u3g4NAlmTN37lxcvXoV69atg6+vL+zs7BAREYHbt28r63hQAshgMGDMmDFKYuxenp6evTsZ6Eh6AMCQIUN6ve+9awKATtrdzAAAB+dJREFUv/3tbxg3bpxR3/3n6N4iyL3Zz8bGRvl35znq3L8zQWBubVlZWUY/cwBmi6J7eHgoNaLul5eXh/79+8PT0xP9+/fv0t/bGDuTf71hLubuGAwGTJ06FatXr+7SN2jQoC5vOOyJH/zgBygvL1e23dzcTI67fv06Dhw4gDt37mDTpk1Ke3t7O9LT07F69eo+xQR0XA/3n8N76zcVFhbilVdewcqVKxEbGwsXFxfs3LnzgfWrrl+/DqD336t7r9XO9Zl7uYCHhwc2bNiAX/7yl/j9738PPz8/xMfH4/Dhw4iJiTEaa21tjdmzZ2PFihUoKirC/v37e7U2IiKinmLiiYiIqA/u3r2LrVu3Ys2aNV3+oHv55Zexfft2vP766/jxj3+Mn/3sZzhy5Ah27NiB2bNnK+PCwsLQ3t6OK1eu4JlnnunV/Hl5edi4cSMmT54MAGhoaDAq7BwcHIz6+npcvnwZXl5eADpe136vp59+Grt27cLAgQNNJj16w2Aw4MMPP8SQIUMQFhbW5+N4eXnB29sb58+fV+4Ms+R+9xs1ahSys7OxcuXKLn2dxd3r6+sRFRXV42OGhYWhsrLSZN+QIUPg6urao+P0JMYRI0Zg27Zt+Oabb5TkS2FhodnjmosZ6LjjrrNgfKenn34ae/fuhZ+fn8li1MOGDYONjQ0KCwvx1FNPAQCam5tRXV3d7bmztrbGsGHDzK4V6Lirx8fHp0sR8OzsbLz//vt49913MWrUKFy4cAHV1dUm73oyFRPQkRhqbGxUts+dO2d0l1J+fj58fX3xzjvvKG1ff/31A9d85swZ2NjYICQk5IFjHxWNRoOlS5diz549yMvL6/J7CgASExPxwQcfKC8YICIisgQ+akdERNQHmZmZaG5uRlJSEkaOHGn0mTFjhvJ6dycnJ8TFxWH58uWoqqrCrFmzlGMEBgYiISEBc+bMwb59+6DX63H69GmsXr0ahw4dMjv/sGHDsG3bNlRVVaGoqAgJCQlGd3lER0dj6NCh+OlPf4qKigrk5+crfyx33uWTkJAADw8PxMXFIS8vD3q9Hrm5uVi0aBEuXLhgdv5r167h0qVLOH/+PA4ePIgXXngBxcXFSEtL63KHUW+lpKTg/fffx/r161FdXY1//vOf0Gq1+OMf/2iR/e61bNkynD59Gq+99hoqKipw9uxZbNq0CU1NTXB2dsbixYvxi1/8Alu2bEFtbS3Kysrw5z//GVu2bOn2mLGxsSgoKDCZ6OitB8U4a9Ys9OvXD0lJSaisrMShQ4fwwQcf9DlmoONta0VFRairq0NTUxMMBgMWLlyI69evY+bMmSguLsb58+dx7NgxJCYmor29HWq1GklJSViyZAmys7Nx5swZzJ07F/36Pfx/PdPS0jBjxowu37vExETcuHEDWVlZiIqKwqRJk/Dyyy/j+PHj0Ov1OHz4MI4cOaLE1NraiuzsbDQ1NSnJpR/+8If405/+hNLSUnzxxRdITk42uuto2LBhqK+vx86dO1FbW4sPP/ywR3cK5eXl4ZlnnunznVg9lZSUhOLiYrS1teHWrVvYt28fvvrqK4wZM8bk+OHDh6OpqQlardai6yIiou+5J1lgioiI6H/VlClTZPLkySb7Ot9OVlJSIiIiWVlZAkAmTZrUZezt27flN7/5jfj5+YmNjY1oNBqZPn26VFRUiEj3RZBLS0slPDxc7OzsJCAgQPbs2dOlOHFVVZVMmDBBbG1tJTg4WDIyMgSAHDlyRBnT2Ngoc+bMEQ8PD7GzsxN/f3959dVX5ebNmyZj6yx63flxdHSU4cOHy2uvvSbnzp0zGtvX4uIiItu3b5fRo0eLra2tDBgwQCZNmiT79u0zWoOpAue93a+5uVkAyMmTJ5W2nJwciYyMFDs7O3F1dZXY2FilELfBYJD169dLUFCQ2NjYiKenp8TGxkpubq7J8yXSUfjd29vb6Lybeqvdvfoao4hIQUGBhIaGiq2trYwePVr27t1rtrj4g2LW6XQyfvx4cXBwEACi1+tFRKS6ulqmT58urq6u4uDgIMHBwfLmm28qxepbWlrkJz/5iTg6OoqXl5ekpqaaLFTeG1988YUAkOLiYpP9U6dOlalTp4pIRzHzefPmibu7u9jb28vIkSOVt+CJiCQnJ4u7u7sAUApxX7x4UWJiYsTJyUkCAgLk0KFDXYptL1myRNzd3UWtVkt8fLysXbvW6Dtqqrh4YGCgfPrpp2ZjM1VcvLeFzpcuXSohISHi6OgoVlZWMmTIEElNTTU7z/1YXJyIiB41lUgfigEQERHR/5z8/HxMnDgRNTU1GDp06JNezvfKxo0b8fe//x1Hjx590kuhxywrKwtLlixBRUWFyccSLaGurg6bN29GSkrKY5mPiIjIHNZ4IiIi+o7av38/1Go1AgICUFNTg0WLFmHChAlMOj0B8+fPR3NzM1paWuDs7Pykl0OPUVtbG7Ra7WNLOhEREf234R1PRERE31Fbt27FqlWr0NDQAA8PD7zwwgtYs2YN3N3dn/TSiIiIiOh7goknIiIiIiIiIiKyCL7VjoiIiIiIiIiILIKJJyIiIiIiIiIisggmnoiIiIiIiIiIyCKYeCIiIiIiIiIiIotg4omIiIiIiIiIiCyCiSciIiIiIiIiIrIIJp6IiIiIiIiIiMgimHgiIiIiIiIiIiKLYOKJiIiIiIiIiIgs4v8A11VE300sC40AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Team Salary Analysis:\n",
      "      Avg Actual ($M)  Avg Predicted ($M)  Avg Difference ($M)\n",
      "Team                                                          \n",
      "CAR              1.75                2.59                 0.84\n",
      "BUF              2.49                1.94                -0.55\n",
      "TOR              2.06                2.58                 0.53\n",
      "DAL              2.16                2.61                 0.45\n",
      "CBJ              2.57                3.00                 0.43\n",
      "BOS              2.87                2.46                -0.40\n",
      "STL              2.92                2.55                -0.36\n",
      "N.J              1.80                2.06                 0.26\n",
      "CHI              3.17                2.94                -0.23\n",
      "WPG              2.39                2.61                 0.23\n",
      "PIT              2.42                2.63                 0.21\n",
      "PHI              2.85                2.63                -0.21\n",
      "T.B              2.29                2.09                -0.19\n",
      "ARI              2.02                2.19                 0.17\n",
      "EDM              2.41                2.25                -0.16\n",
      "MIN              2.65                2.81                 0.16\n",
      "S.J              3.00                2.84                -0.16\n",
      "DET              2.49                2.33                -0.16\n",
      "NYI              2.76                2.63                -0.12\n",
      "VAN              2.21                2.11                -0.10\n",
      "NSH              2.37                2.46                 0.09\n",
      "L.A              2.96                2.88                -0.08\n",
      "CGY              2.70                2.62                -0.08\n",
      "OTT              2.45                2.52                 0.07\n",
      "NYR              2.69                2.62                -0.06\n",
      "FLA              2.24                2.29                 0.05\n",
      "MTL              2.74                2.76                 0.02\n",
      "WSH              2.79                2.81                 0.02\n",
      "COL              2.26                2.27                 0.01\n",
      "ANA              2.48                2.49                 0.01\n"
     ]
    }
   ],
   "source": [
    "# Calculate average discrepancy by team\n",
    "team_discrepancy = prediction_df.groupby('Team').agg({\n",
    "    'Actual': 'mean',\n",
    "    'Predicted': 'mean',\n",
    "    'Difference': 'mean'\n",
    "}).round(2)\n",
    "\n",
    "# Rename columns for clarity\n",
    "team_discrepancy.columns = ['Avg Actual ($M)', 'Avg Predicted ($M)', 'Avg Difference ($M)']\n",
    "\n",
    "# Sort by absolute average difference\n",
    "team_discrepancy = team_discrepancy.sort_values(by='Avg Difference ($M)', key=abs, ascending=False)\n",
    "\n",
    "# Create visualization\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Color bars based on difference (green for underpaying, red for overpaying)\n",
    "colors = ['green' if x > 0 else 'red' for x in team_discrepancy['Avg Difference ($M)']]\n",
    "bars = plt.barh(team_discrepancy.index, team_discrepancy['Avg Difference ($M)'], color=colors)\n",
    "plt.title('Average Salary Discrepancy by Team')\n",
    "plt.legend(['Under-paying'])\n",
    "plt.xlabel('Average Difference (Predicted - Actual) in $M')\n",
    "plt.ylabel('Team')\n",
    "\n",
    "# Add value labels on bars\n",
    "for i, v in enumerate(team_discrepancy['Avg Difference ($M)']):\n",
    "    text_color = 'white' if abs(v) > 2 else 'black'\n",
    "    plt.text(v, i, f'${v:.2f}M', va='center', color=text_color)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "\n",
    "# Print detailed team analysis\n",
    "print(\"\\nTeam Salary Analysis:\")\n",
    "print(team_discrepancy)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the team-by-team salary discrepancies, several interesting patterns emerge:\n",
    "\n",
    "\n",
    "Most Efficient Teams:\n",
    "- Carolina Hurricanes show remarkable efficiency, underpaying by an average of $0.84M while maintaining competitive performance, however it didn't show in their preformance as their efficient salary cap strategies lead to a mediocre to bad season, as they failed to make the playoffs.\n",
    "\n",
    "- Toronto Maple Leafs and Dallas Stars also demonstrate strong value, underpaying by $0.53M and $0.45M respectively, neither team dominated the league, but also didn't stand out as bad teams.\n",
    "\n",
    "- These teams appear to be getting strong performance relative to their salary expenditure, yet you wouldn't have guessed by looking at the standings at the end of the year.\n",
    "\n",
    "Overpaying Teams:\n",
    "- Buffalo stands out as significantly overpaying, with a -$0.55M average discrepancy. Predictively, they finished dead last in their division with only 33 wins in 82 games. Also notably bad the following season, with only 25 wins in 2017-18, good for last in the entire league. Here is a case where the model may be very accurately depicting a team that struggles in efficiently managing their salary cap (giving too much money to players who just weren't good enough), with these issues spreading into team preformance, as they couldn't pay the right players the right money.\n",
    "\n",
    "- Boston (-$0.40M) and St. Louis (-$0.36M) also show notable overpayment patterns, however both teams had decent/good seasons\n",
    "\n",
    "- These discrepancies often reflect veteran contracts and loyalty premiums, and sometimes maybe even just poor management\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Applications\n",
    "\n",
    "This analysis provides valuable insights for NHL team management and salary cap planning. The model’s ability to identify market inefficiencies through statistical performance metrics offers teams a data-driven approach to player evaluation, potentially saving millions in salary negotiations. For example, the Carolina Hurricanes’ efficient salary structure (underpaying by $0.84M on average) demonstrates how teams can maintain competitiveness while optimizing payroll.\n",
    "\n",
    "\n",
    "However, the model’s 46.4% accuracy highlights the complexity of NHL salary determination. Factors like leadership qualities, playoff experience, and market value significantly influence contracts but remain difficult to quantify. The case of Steven Stamkos illustrates this limitation - while injury limited his 2016-17 statistics, his overall value to the Tampa Bay Lightning extended far beyond measurable performance metrics for this season.\n",
    "\n",
    "\n",
    "The analysis also reveals systematic patterns in team salary management. Teams like Buffalo showing consistent overpayment trends, while Toronto and Dallas demonstrated more efficient salary structures. These insights could help teams benchmark their salary decisions against league-wide patterns and identify opportunities for optimization.\n",
    "\n",
    "\n",
    "The project’s focus on actual salary rather than cap hit presents both a strength and limitation. While actual salary reflects true team expenditure, cap hit analysis would provide more relevant insights for long-term roster planning and salary cap compliance. Additionally, expanding the dataset to include multiple seasons would likely improve prediction accuracy by accounting for performance consistency and career trajectories."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Future Improvements\n",
    "\n",
    "- Incorporating salary Cap Hit rather than actual salary would better reflect team management decisions (more in terms of the roster that management can build while adhearing to that specified cap)\n",
    "\n",
    "- Multiple seasons of performance data could improve prediction accuracy (The model's biggest faults appearred to occur when a player had a major outlier season, including their stats from past years would help smooth that out)\n",
    "\n",
    "- Additional features like playoff performance, marketing value, and leadership metrics could also enhance the model\n",
    "\n",
    "Overall, this analysis demonstrates both the power and limitations of statistical approaches to player evaluation in the NHL, while highlighting contracts and teams that display efficient/inefficient salary management."
   ]
  }
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