NBA Sentiment Analysis (3).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "815c2ead",
   "metadata": {},
   "source": [
    "# Web Scraping NBA Stats & Sentiment Analysis\n",
    "This Notebook is created by Michael Gaynor\n",
    "\n",
    "   A Couple weeks after the 2022 NBA Finals I wanted to see if this current 2022 Golden State Warriors team was a statically a better team, then the 2018 championship team. So, I decided to scrape the web for the data using Python libraries Beautiful Soup & Requests to obtain the most current and accurate dataset for my analysis. Once I began scrapping the data, I decided to make a second part to this project, with doing a sentiment analysis as well. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8dff6b31",
   "metadata": {},
   "source": [
    "# # Import Libraries\n",
    "\n",
    "Import all of the libraries that are going to be used for this project. Also imported additional libraries along the project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4f302a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ## Importing the packages I will need for my analysis/ Project is comparing 2022 to the 2018 regular and playoff season \n",
    "from bs4 import BeautifulSoup \n",
    "import requests \n",
    "import pandas as pd \n",
    "import numpy as np \n",
    "import csv  \n",
    "import tweepy\n",
    "import json\n",
    "import matplotlib.pyplot as plt \n",
    "import re\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "78f6c83b",
   "metadata": {},
   "source": [
    "# # Web Scrapping \n",
    "\n",
    "I used requests & BeautifulSoup libraries to scrape the data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "c0f831e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a request to GET data from html for 2022 playoff and regular season\n",
    "url= 'https://www.basketball-reference.com/teams/GSW/2022.html#all_per_game-playoffs_per_game'\n",
    "gsw_data = requests.get(url)\n",
    "html_doc= gsw_data.text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d18680a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Extracting structruded data from html with BeautifulSoup\n",
    "soup = BeautifulSoup(html_doc, 'html.parser')\n",
    "# Pulling only the table Im working with \n",
    "gsw22_table = soup.find(id='playoffs_per_game')\n",
    "gsw22_table_r = soup.find(id='per_game')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7d9f3ccd",
   "metadata": {},
   "source": [
    "## Data Cleaning \n",
    "I created a dataframe for both years to access the data that was scraped fro the url. So it can be cleaned renamming a column to keep everything uniform. Also created two new columns to help distingush the different dataframes before I combined them into one. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6e1a9d8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS/G</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>33</td>\n",
       "      <td>64</td>\n",
       "      <td>64</td>\n",
       "      <td>34.5</td>\n",
       "      <td>8.4</td>\n",
       "      <td>19.1</td>\n",
       "      <td>0.437</td>\n",
       "      <td>4.5</td>\n",
       "      <td>...</td>\n",
       "      <td>4.7</td>\n",
       "      <td>5.2</td>\n",
       "      <td>6.3</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>3.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>25.5</td>\n",
       "      <td>2022</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Andrew Wiggins</td>\n",
       "      <td>26</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>31.9</td>\n",
       "      <td>6.5</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0.466</td>\n",
       "      <td>2.2</td>\n",
       "      <td>...</td>\n",
       "      <td>3.3</td>\n",
       "      <td>4.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>2.2</td>\n",
       "      <td>17.2</td>\n",
       "      <td>2022</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Jordan Poole</td>\n",
       "      <td>22</td>\n",
       "      <td>76</td>\n",
       "      <td>51</td>\n",
       "      <td>30.0</td>\n",
       "      <td>6.2</td>\n",
       "      <td>13.9</td>\n",
       "      <td>0.448</td>\n",
       "      <td>2.8</td>\n",
       "      <td>...</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>2.7</td>\n",
       "      <td>18.5</td>\n",
       "      <td>2022</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Klay Thompson</td>\n",
       "      <td>31</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>29.4</td>\n",
       "      <td>7.7</td>\n",
       "      <td>17.9</td>\n",
       "      <td>0.429</td>\n",
       "      <td>3.6</td>\n",
       "      <td>...</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>2.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>20.4</td>\n",
       "      <td>2022</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Draymond Green</td>\n",
       "      <td>31</td>\n",
       "      <td>46</td>\n",
       "      <td>44</td>\n",
       "      <td>28.9</td>\n",
       "      <td>2.9</td>\n",
       "      <td>5.6</td>\n",
       "      <td>0.525</td>\n",
       "      <td>0.3</td>\n",
       "      <td>...</td>\n",
       "      <td>6.3</td>\n",
       "      <td>7.3</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.3</td>\n",
       "      <td>1.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>7.5</td>\n",
       "      <td>2022</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk            Name  Age   G  GS    MP   FG   FGA    FG%   3P  ...  DRB  \\\n",
       "0   1   Stephen Curry   33  64  64  34.5  8.4  19.1  0.437  4.5  ...  4.7   \n",
       "1   2  Andrew Wiggins   26  73  73  31.9  6.5  14.0  0.466  2.2  ...  3.3   \n",
       "2   3    Jordan Poole   22  76  51  30.0  6.2  13.9  0.448  2.8  ...  3.0   \n",
       "3   4   Klay Thompson   31  32  32  29.4  7.7  17.9  0.429  3.6  ...  3.4   \n",
       "4   5  Draymond Green   31  46  44  28.9  2.9   5.6  0.525  0.3  ...  6.3   \n",
       "\n",
       "   TRB  AST  STL  BLK  TOV   PF  PTS/G  Year          Season  \n",
       "0  5.2  6.3  1.3  0.4  3.2  2.0   25.5  2022  Regular Season  \n",
       "1  4.5  2.2  1.0  0.7  1.5  2.2   17.2  2022  Regular Season  \n",
       "2  3.4  4.0  0.8  0.3  2.5  2.7   18.5  2022  Regular Season  \n",
       "3  3.9  2.8  0.5  0.5  1.3  1.7   20.4  2022  Regular Season  \n",
       "4  7.3  7.0  1.3  1.1  3.0  3.0    7.5  2022  Regular Season  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Importing the html into a pandas dataframe. [0] only taking the first element \n",
    "gsw22_playoff = pd.read_html(str(gsw22_table))[0]\n",
    "gsw22_per_game = pd.read_html(str(gsw22_table_r))[0]\n",
    "\n",
    "# Renaming a column name to match the column \n",
    "gsw22_playoff.rename(columns={'Unnamed: 1':'Name'}, inplace = True)\n",
    "gsw22_per_game.rename(columns={'Unnamed: 1':'Name'}, inplace = True)\n",
    "\n",
    "# Creating a column year and season type to identify what year and season type the data falls in. \n",
    "gsw22_playoff['Year']= 2022\n",
    "gsw22_per_game['Year']=2022\n",
    "\n",
    "gsw22_playoff['Season']='Playoffs'\n",
    "gsw22_per_game['Season']= 'Regular Season' \n",
    "gsw22_per_game.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ad578f86",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Making a request to GET data from html for 2018 playoff \n",
    "url= 'https://www.basketball-reference.com/teams/GSW/2018.html#all_per_game-playoffs_per_game'\n",
    "gsw_data_18 = requests.get(url)\n",
    "html_doc18= gsw_data_18.text\n",
    "\n",
    "# Extracting structruded data from html with BeautifulSoup\n",
    "soup = BeautifulSoup(html_doc18, 'html.parser')\n",
    "\n",
    "# Pulling only the table Im working with \n",
    "gsw18_table = soup.find(id='playoffs_per_game')\n",
    "gsw18_table_r = soup.find(id ='per_game')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "19b0ef8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Rk</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>G</th>\n",
       "      <th>GS</th>\n",
       "      <th>MP</th>\n",
       "      <th>FG</th>\n",
       "      <th>FGA</th>\n",
       "      <th>FG%</th>\n",
       "      <th>3P</th>\n",
       "      <th>...</th>\n",
       "      <th>DRB</th>\n",
       "      <th>TRB</th>\n",
       "      <th>AST</th>\n",
       "      <th>STL</th>\n",
       "      <th>BLK</th>\n",
       "      <th>TOV</th>\n",
       "      <th>PF</th>\n",
       "      <th>PTS/G</th>\n",
       "      <th>Year</th>\n",
       "      <th>Season</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Klay Thompson</td>\n",
       "      <td>27</td>\n",
       "      <td>73</td>\n",
       "      <td>73</td>\n",
       "      <td>34.3</td>\n",
       "      <td>7.9</td>\n",
       "      <td>16.2</td>\n",
       "      <td>0.488</td>\n",
       "      <td>3.1</td>\n",
       "      <td>...</td>\n",
       "      <td>3.4</td>\n",
       "      <td>3.8</td>\n",
       "      <td>2.5</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>20.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Kevin Durant</td>\n",
       "      <td>29</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>34.2</td>\n",
       "      <td>9.3</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0.516</td>\n",
       "      <td>2.5</td>\n",
       "      <td>...</td>\n",
       "      <td>6.4</td>\n",
       "      <td>6.8</td>\n",
       "      <td>5.4</td>\n",
       "      <td>0.7</td>\n",
       "      <td>1.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>26.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Draymond Green</td>\n",
       "      <td>27</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>32.7</td>\n",
       "      <td>4.0</td>\n",
       "      <td>8.8</td>\n",
       "      <td>0.454</td>\n",
       "      <td>1.1</td>\n",
       "      <td>...</td>\n",
       "      <td>6.6</td>\n",
       "      <td>7.6</td>\n",
       "      <td>7.3</td>\n",
       "      <td>1.4</td>\n",
       "      <td>1.3</td>\n",
       "      <td>2.9</td>\n",
       "      <td>2.6</td>\n",
       "      <td>11.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Stephen Curry</td>\n",
       "      <td>29</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>32.0</td>\n",
       "      <td>8.4</td>\n",
       "      <td>16.9</td>\n",
       "      <td>0.495</td>\n",
       "      <td>4.2</td>\n",
       "      <td>...</td>\n",
       "      <td>4.4</td>\n",
       "      <td>5.1</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.2</td>\n",
       "      <td>26.4</td>\n",
       "      <td>2018</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Andre Iguodala</td>\n",
       "      <td>34</td>\n",
       "      <td>64</td>\n",
       "      <td>7</td>\n",
       "      <td>25.3</td>\n",
       "      <td>2.3</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0.463</td>\n",
       "      <td>0.5</td>\n",
       "      <td>...</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>0.8</td>\n",
       "      <td>0.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>6.0</td>\n",
       "      <td>2018</td>\n",
       "      <td>Regular Season</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 30 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rk            Name  Age   G  GS    MP   FG   FGA    FG%   3P  ...  DRB  \\\n",
       "0   1   Klay Thompson   27  73  73  34.3  7.9  16.2  0.488  3.1  ...  3.4   \n",
       "1   2    Kevin Durant   29  68  68  34.2  9.3  18.0  0.516  2.5  ...  6.4   \n",
       "2   3  Draymond Green   27  70  70  32.7  4.0   8.8  0.454  1.1  ...  6.6   \n",
       "3   4   Stephen Curry   29  51  51  32.0  8.4  16.9  0.495  4.2  ...  4.4   \n",
       "4   5  Andre Iguodala   34  64   7  25.3  2.3   5.0  0.463  0.5  ...  3.1   \n",
       "\n",
       "   TRB  AST  STL  BLK  TOV   PF  PTS/G  Year          Season  \n",
       "0  3.8  2.5  0.8  0.5  1.8  1.6   20.0  2018  Regular Season  \n",
       "1  6.8  5.4  0.7  1.8  3.0  2.0   26.4  2018  Regular Season  \n",
       "2  7.6  7.3  1.4  1.3  2.9  2.6   11.0  2018  Regular Season  \n",
       "3  5.1  6.1  1.6  0.2  3.0  2.2   26.4  2018  Regular Season  \n",
       "4  3.8  3.3  0.8  0.6  1.0  1.5    6.0  2018  Regular Season  \n",
       "\n",
       "[5 rows x 30 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Importing the html into a pandas dataframe. [0] only taking the first element \n",
    "gsw18_playoffs = pd.read_html(str(gsw18_table))[0]\n",
    "gsw18_per_game = pd.read_html(str(gsw18_table_r))[0]\n",
    "\n",
    "# Renaming a column name to match the column \n",
    "gsw18_playoffs.rename(columns={'Unnamed: 1':'Name'}, inplace=True)\n",
    "gsw18_per_game.rename(columns={'Unnamed: 1':'Name'}, inplace=True)\n",
    "\n",
    "# Creating a column 'year' to identify what year the data falls in.\n",
    "gsw18_playoffs['Year']= 2018\n",
    "gsw18_per_game['Year']=2018\n",
    "\n",
    "gsw18_playoffs['Season']='Playoffs'\n",
    "gsw18_per_game['Season']= 'Regular Season' \n",
    "\n",
    "gsw18_per_game.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1927db7",
   "metadata": {},
   "source": [
    "# Combining All of the dataframes \n",
    "Inspected the data for missing values and filled the in with zeros."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "440fd102",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(63, 30)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Merging both dataframes together and addining a year column \n",
    "gsw = pd.concat([gsw18_per_game,gsw18_playoffs, gsw22_per_game,gsw22_playoff],ignore_index= False)\n",
    "\n",
    "gsw.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "6c03abc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rk        0\n",
       "Name      0\n",
       "Age       0\n",
       "G         0\n",
       "GS        0\n",
       "MP        0\n",
       "FG        0\n",
       "FGA       0\n",
       "FG%       0\n",
       "3P        0\n",
       "3PA       0\n",
       "3P%       4\n",
       "2P        0\n",
       "2PA       0\n",
       "2P%       1\n",
       "eFG%      0\n",
       "FT        0\n",
       "FTA       0\n",
       "FT%       2\n",
       "ORB       0\n",
       "DRB       0\n",
       "TRB       0\n",
       "AST       0\n",
       "STL       0\n",
       "BLK       0\n",
       "TOV       0\n",
       "PF        0\n",
       "PTS/G     0\n",
       "Year      0\n",
       "Season    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see if there are any missing values \n",
    "gsw.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4bd6a492",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Replacing the NAN with 0.000 players are not know for shooting 3s\n",
    "gsw['3P%'] = gsw['3P%'].fillna(0.000)\n",
    "gsw['2P%'] = gsw['2P%'].fillna(0.000)\n",
    "gsw['FT%'] = gsw['FT%'].fillna(0.000)\n",
    "\n",
    "# Counting how many duplicates I have\n",
    "duplicates = gsw.duplicated().sum()\n",
    "duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a13774e",
   "metadata": {},
   "source": [
    "# Data Manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "848c9bed",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Season          Name          \n",
       "2018  Playoffs        Draymond Green    21.0\n",
       "                      Klay Thompson     21.0\n",
       "                      Stephen Curry     15.0\n",
       "      Regular Season  Draymond Green    70.0\n",
       "                      Klay Thompson     73.0\n",
       "                      Stephen Curry     51.0\n",
       "2022  Playoffs        Draymond Green    22.0\n",
       "                      Klay Thompson     22.0\n",
       "                      Stephen Curry     22.0\n",
       "      Regular Season  Draymond Green    46.0\n",
       "                      Klay Thompson     32.0\n",
       "                      Stephen Curry     64.0\n",
       "Name: G, dtype: float64"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# seeing the average games the three players played in by season an year\n",
    "name = gsw['Name'].isin(['Draymond Green','Klay Thompson','Stephen Curry'])\n",
    "gsw[name].groupby(['Year','Season','Name'])['G'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4eccd215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Season          Name          \n",
       "2018  Playoffs        Draymond Green    0.432\n",
       "                      Klay Thompson     0.465\n",
       "                      Stephen Curry     0.451\n",
       "      Regular Season  Draymond Green    0.454\n",
       "                      Klay Thompson     0.488\n",
       "                      Stephen Curry     0.495\n",
       "2022  Playoffs        Draymond Green    0.479\n",
       "                      Klay Thompson     0.429\n",
       "                      Stephen Curry     0.459\n",
       "      Regular Season  Draymond Green    0.525\n",
       "                      Klay Thompson     0.429\n",
       "                      Stephen Curry     0.437\n",
       "Name: FG%, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Seeing the field goal percentage for the playofs and the regular season \n",
    "gsw[name].groupby(['Year','Season','Name'])['FG%'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ce06c714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Season          Name          \n",
       "2018  Playoffs        Draymond Green     3.8\n",
       "                      Klay Thompson      7.5\n",
       "                      Stephen Curry     10.8\n",
       "      Regular Season  Draymond Green     3.7\n",
       "                      Klay Thompson      7.1\n",
       "                      Stephen Curry      9.8\n",
       "2022  Playoffs        Draymond Green     1.8\n",
       "                      Klay Thompson      9.1\n",
       "                      Stephen Curry     10.4\n",
       "      Regular Season  Draymond Green     1.2\n",
       "                      Klay Thompson      9.3\n",
       "                      Stephen Curry     11.7\n",
       "Name: 3PA, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking to see the 3PA for playoffs and regular season \n",
    "gsw[name].groupby(['Year','Season','Name'])['3PA'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "94acfdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Season          Name          \n",
       "2018  Playoffs        Draymond Green    0.266\n",
       "                      Klay Thompson     0.427\n",
       "                      Stephen Curry     0.395\n",
       "      Regular Season  Draymond Green    0.301\n",
       "                      Klay Thompson     0.440\n",
       "                      Stephen Curry     0.423\n",
       "2022  Playoffs        Draymond Green    0.205\n",
       "                      Klay Thompson     0.385\n",
       "                      Stephen Curry     0.397\n",
       "      Regular Season  Draymond Green    0.296\n",
       "                      Klay Thompson     0.385\n",
       "                      Stephen Curry     0.380\n",
       "Name: 3P%, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see the 3p% for the playoffs and regular season \n",
    "gsw[name].groupby(['Year','Season','Name'])['3P%'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b8fd7dc8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Year  Season          Name          \n",
       "2018  Playoffs        Draymond Green    10.8\n",
       "                      Klay Thompson     19.6\n",
       "                      Stephen Curry     25.5\n",
       "      Regular Season  Draymond Green    11.0\n",
       "                      Klay Thompson     20.0\n",
       "                      Stephen Curry     26.4\n",
       "2022  Playoffs        Draymond Green     8.0\n",
       "                      Klay Thompson     19.0\n",
       "                      Stephen Curry     27.4\n",
       "      Regular Season  Draymond Green     7.5\n",
       "                      Klay Thompson     20.4\n",
       "                      Stephen Curry     25.5\n",
       "Name: PTS/G, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see the pts/G for the playoffs and regular season \n",
    "gsw[name].groupby(['Year','Season','Name'])['PTS/G'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "ecd9df49",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving the dataframe to a csv file.\n",
    "gsw.to_csv('gsw_games.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4a8a9472",
   "metadata": {},
   "source": [
    "# Tweets Mining \n",
    "Created a twitter developer account to get the private keys. Then setting up the keys as variables to gain access to the twitter API. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "06afc12e",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# Setting up my keys to access twitter api\n",
    "access_token = '1464967868928434183-BQpmO1x9B6ae1Q5dMDqbIoLK7KBlCu'\n",
    "access_secret = 'CfRY83qMxgwNexvvDGCmURUEMSux8cIthAl0uxQQHMRCq'\n",
    "consumer_key ='1UGQIS0kbAxJPqJXxSQ2AADLa'\n",
    "consumer_secret = 'pfU5oQd0YAnwLhZwjE8mecvvcBqoZGVHNUYQNuT3aOmpQr4gGC'\n",
    "\n",
    "auth = tweepy.OAuthHandler(consumer_key,consumer_secret)\n",
    "auth.set_access_token(access_token,access_secret)\n",
    "api = tweepy.API(auth,wait_on_rate_limit=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "27842cb0",
   "metadata": {},
   "source": [
    "# Creating seach queries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "3e08e1fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the keyword to search for and filtering out retweets. \n",
    "keywords= 'Steph Curry-filter:retweets AND -filter:replies AND -filter:links'\n",
    "\n",
    "\n",
    "# Creating a list to store the twitter data in. \n",
    "id_str = []\n",
    "user = []\n",
    "coordinates  = []\n",
    "time=[]\n",
    "retweet_count = []\n",
    "favorite_count = []\n",
    "text= []\n",
    "\n",
    "\n",
    "# Created a loop to pull tweets to get the full_text of the tweet. \n",
    "for tweet in tweepy.Cursor(api.search_tweets,q=keywords,lang='en',count=833, \n",
    "                           tweet_mode = \"extended\", until = '2022-07-11').items(625):\n",
    "    id_str.append(tweet.id_str)\n",
    "    user.append(tweet.user)\n",
    "    coordinates .append(tweet.coordinates )\n",
    "    time.append(tweet.created_at)\n",
    "    retweet_count.append(tweet.retweet_count)\n",
    "    favorite_count.append(tweet.favorite_count)\n",
    "    text.append(tweet.full_text)\n",
    "    \n",
    "# Created a dataframe \n",
    "steph_tweets = pd.DataFrame({'id_str': id_str,'user': user,'coordinates':coordinates ,\n",
    "                           'time':time,'retweet_count':retweet_count,'favorite_count':favorite_count, 'text':text})\n",
    "steph_tweets.to_csv('steph_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f1514726",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the keyword to search for and filtering out retweets. \n",
    "keywords= 'klay Thompson-filter:retweets AND -filter:replies AND -filter:links'\n",
    "\n",
    "# Creating a list to store the twitter data in. \n",
    "id_str = []\n",
    "user = []\n",
    "coordinates  = []\n",
    "time=[]\n",
    "retweet_count = []\n",
    "favorite_count = []\n",
    "text= []\n",
    "\n",
    "# Created a loop to pull tweets to get the full_text of the tweet. \n",
    "for tweet in tweepy.Cursor(api.search_tweets,q=keywords,lang='en',count=833, \n",
    "                           tweet_mode = \"extended\", until = '2022-07-11').items(625):\n",
    "    id_str.append(tweet.id_str)\n",
    "    user.append(tweet.user)\n",
    "    coordinates .append(tweet.coordinates )\n",
    "    time.append(tweet.created_at)\n",
    "    retweet_count.append(tweet.retweet_count)\n",
    "    favorite_count.append(tweet.favorite_count)\n",
    "    text.append(tweet.full_text)\n",
    "    \n",
    "# Created a dataframe \n",
    "klay_tweets = pd.DataFrame({'id_str': id_str,'user': user,'coordinates':coordinates ,\n",
    "                           'time':time,'retweet_count':retweet_count,'favorite_count':favorite_count, 'text':text})\n",
    "# Saving tweets to a csv file\n",
    "klay_tweets.to_csv('klay_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "6125693d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the keyword to search for and filtering out retweets. \n",
    "keywords= 'Draymond Green-filter:retweets AND -filter:replies AND -filter:links'\n",
    "\n",
    "# Creating a list to store the twitter data in. \n",
    "id_str = []\n",
    "user = []\n",
    "coordinates  = []\n",
    "time=[]\n",
    "retweet_count = []\n",
    "favorite_count = []\n",
    "text= []\n",
    "\n",
    "# Created a loop to pull tweets to get the full_text of the tweet. \n",
    "for tweet in tweepy.Cursor(api.search_tweets,q=keywords,lang='en',\n",
    "                            count=833, tweet_mode = \"extended\", until = '2022-07-11').items(625):\n",
    "    \n",
    "    id_str.append(tweet.id_str)\n",
    "    user.append(tweet.user)\n",
    "    coordinates .append(tweet.coordinates )\n",
    "    time.append(tweet.created_at)\n",
    "    retweet_count.append(tweet.retweet_count)\n",
    "    favorite_count.append(tweet.favorite_count)\n",
    "    text.append(tweet.full_text)\n",
    "    \n",
    "# Created a dataframe \n",
    "draymond_tweets = pd.DataFrame({'id_str': id_str,'user': user,'coordinates':coordinates ,\n",
    "                           'time':time,'retweet_count':retweet_count,'favorite_count':favorite_count, 'text':text})\n",
    "# Saving tweets to a csv file\n",
    "draymond_tweets.to_csv('draymond_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "f3626c3f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting up the keyword to search for and filtering out retweets. \n",
    "keywords= '#GoldenStateWarriors-filter:retweets AND -filter:replies AND -filter:links'\n",
    "\n",
    "# Creating a list to store the twitter data in. \n",
    "id_str = []\n",
    "user = []\n",
    "coordinates  = []\n",
    "time=[]\n",
    "retweet_count = []\n",
    "favorite_count = []\n",
    "text= []\n",
    "\n",
    "# Created a loop to pull tweets to get the full_text of the tweet. \n",
    "for tweet in tweepy.Cursor(api.search_tweets,q=keywords,lang='en',\n",
    "                            count=833, tweet_mode = \"extended\", until = '2022-07-11').items(625):\n",
    "    \n",
    "    id_str.append(tweet.id_str)\n",
    "    user.append(tweet.user)\n",
    "    coordinates .append(tweet.coordinates )\n",
    "    time.append(tweet.created_at)\n",
    "    retweet_count.append(tweet.retweet_count)\n",
    "    favorite_count.append(tweet.favorite_count)\n",
    "    text.append(tweet.full_text)\n",
    "    \n",
    "# Created a dataframe \n",
    "GoldenState_tweets = pd.DataFrame({'id_str': id_str,'user': user,'coordinates':coordinates ,\n",
    "                           'time':time,'retweet_count':retweet_count,'favorite_count':favorite_count, 'text':text})\n",
    "# Saving tweets to a csv file\n",
    "GoldenState_tweets.to_csv('GoldenState_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "26ead37a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Bringing all three dataframes together under one dataframe. \n",
    "# Creating a column to identify the key word used for the search \n",
    "\n",
    "steph_tweets['key_word']= 'Steph Curry'\n",
    "klay_tweets['key_word']= 'Klay Thompson'\n",
    "draymond_tweets['key_word']= 'Draymond Green' \n",
    "GoldenState_tweets['key_word']= 'Team Tweets'\n",
    "gsw_tweets = pd.concat([steph_tweets,klay_tweets,draymond_tweets,GoldenState_tweets],axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "300c5a51",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1300, 8)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Clean the tweets and drop colunns that have missing data \n",
    "gsw_tweets.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "877d2956",
   "metadata": {},
   "source": [
    "# Data Cleaning "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d4a53e14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id_str            False\n",
       "user              False\n",
       "coordinates        True\n",
       "time              False\n",
       "retweet_count     False\n",
       "favorite_count    False\n",
       "text              False\n",
       "key_word          False\n",
       "dtype: bool"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see if there are any missing values. \n",
    "gsw_tweets.isna().any()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7c342372",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       No Location\n",
       "1       No Location\n",
       "2       No Location\n",
       "3       No Location\n",
       "4       No Location\n",
       "           ...     \n",
       "1295    No Location\n",
       "1296    No Location\n",
       "1297    No Location\n",
       "1298    No Location\n",
       "1299    No Location\n",
       "Name: coordinates, Length: 1300, dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsw_tweets['coordinates'].fillna('No Location')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8ada0fe0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see how man duplicates I have in my dataframe\n",
    "gsw_tweets.duplicated('id_str').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "82071e54",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Droping the duplicated data \n",
    "gsw_tweets = gsw_tweets.drop_duplicates('id_str')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "169025b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see if the duplicates were droped\n",
    "gsw_tweets.duplicated('id_str').sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "698ca275",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# joing the text together so it can just be one big text \n",
    "tweets = gsw_tweets['text'].str.cat(sep='')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ecc09fb",
   "metadata": {},
   "source": [
    "# Tweets Processing \n",
    "Turned the tweets in 'tokens 'in NLTK library and removed all non-alphanumeric characters within the tokens. Then used WordNetLemmatizer to get the base root of each word along with adjectives. Placed the lemmatized tokens into a word cloud using library WordCloud. To get the sentiment score for every row I used the library Textblob along with a function to retrieve and store the scores. Made a function to count all of the positive, negative, and neutral scores and created scatter and bar plots to visualize the data. I further manipulated the data to gain additional insights. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "008d1fe1",
   "metadata": {},
   "outputs": [],
   "source": [
    "from nltk import word_tokenize \n",
    "from nltk.stem import WordNetLemmatizer\n",
    "tweets = re.sub(r'[^\\w\\s]', '',str(tweets))\n",
    "# Removing all non-alphnumric characters \n",
    "word_token = word_tokenize(tweets)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "347fced7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lemmatization the tweets to get the root words along with adjectives \n",
    "from nltk.stem import WordNetLemmatizer\n",
    "wnl = WordNetLemmatizer()\n",
    "root_tokens =wnl.lemmatize(str(word_token),pos='a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8ad8bf55",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Setting my stopwords to apply to the word cloud \n",
    "from wordcloud import WordCloud,STOPWORDS\n",
    "stopwords = set(STOPWORDS)\n",
    "stopwords.update([\"Golden\",\"State\",\"really\",\"amp\",\"kendrick\",\"Perkins\",\"time\",\"one\",\"Im\",\"people\",\"got\",\"think\",\"year\",\"na\",\"wa\",\"dont\",\"yall\",\"steph\",\"stephn\",\"curry\",\"draymond\",\"green\",\"shit\",\"klay\",\"Thompson\",\"man\",\"talk\",\n",
    "                \"basketball\",\"stephen\",\"didnt\",\"thing\",\"guy\",\"bro\", \"warriors\",\"warrior\",\"player\",\"game\",\"i\", \"me\", \"my\", \"myself\",\n",
    "                  \"we\", \"our\", \"ours\", \"ourselves\", \"you\", \"your\", \"yours\", \"yourself\", \"yourselves\", \"he\", \"him\", \"his\", \"himself\",\n",
    "                  \"she\", \"her\", \"hers\", \"herself\", \"it\", \"its\", \"itself\", \"they\", \"them\", \"their\", \"theirs\", \"themselves\", \"what\", \"which\",\n",
    "                  \"who\", \"whom\", \"this\", \"that\", \"these\", \"those\", \"am\", \"is\", \"are\", \"was\", \"were\", \"be\", \"been\", \"being\", \"have\", \"has\", \"had\",\n",
    "                  \"having\", \"do\", \"does\", \"did\", \"doing\", \"a\", \"an\",\n",
    "                  \"the\", \"and\", \"but\", \"if\", \"or\", \"because\", \"as\", \"until\", \"while\", \"of\", \"at\", \"by\", \"for\", \"with\", \"about\", \"against\", \"between\", \n",
    "                  \"into\", \"through\", \"during\", \"before\", \"after\", \"above\", \"below\", \"to\", \"from\", \"up\", \"down\", \"in\", \"out\", \"on\", \"off\", \"over\", \"under\", \n",
    "                  \"again\", \"further\", \"then\", \"once\",\n",
    "                  \"here\", \"there\", \"when\", \"where\", \"why\", \"how\", \"all\", \"any\", \"both\", \"each\", \"few\", \"more\", \"most\", \"other\", \"some\", \"such\", \"no\", \"nor\", \"not\", \n",
    "                  \"only\", \"own\", \"same\", \"so\", \"than\", \"too\", \"very\", \"s\", \"t\", \"can\", \"will\", \"just\", \"don\", \"should\", \"now\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "92abc618",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+jMYXNxPz+fHMmc2kj30U74J5CE3Dd/QYFT//JS273iBcXcOZf/4KZd/8NgC5d9zGlC9+Ac0+8poNQgh0i5UTf3mcV7/6TdrKKwYWPpESX3UNJx55nPbyCjb++zfIX7xoSC+VlJL2ikq2fPaLnN/6SsoXptd9DRNfdQ1nnn6Oqj17ufa732Lm224f0NAv6vNz+pnnOPL7hzCjoztZVnfgIG/+8EGMSGRYw1eXCikl7ZWVbPncP3H+pW2DCnFI08RfU8uZZ56jas+bbPrut5j19kufTYf4fa+qxp2XSywUYt+Dv2D393+Iv7Z2EI1IbF4v6SUlKVdrNhvW0slYi0twX3U1saoq2p56ktZHH8FsacH/8jaE1ULul76MtTBxUt7w+Wj50x+JVVcjnE4y7r2PrA99GOFyJZ1/rLpmwLFXzePBe8edNP3gfwmfOE7k5AlsU6cqfvHzz4GUOObNxzZ5yrCFdUbU6AbLznPqC/+ErSCfwvfci4zGaD98BP/JU+TdflvndpHaWi7+6Md4lywifdVKml7ayun/9yV0h4Psazdi+gMc+/gnaT90mLy33YktL5fmV17l0H33M++XPyNj5QpsuTnkv+0O3LNnU/adf6fg3e8iY/VqABwTihCW3jV9hxUSag8eYs///pi2i4oqgxBYXS4sTgeaxaI0g02TWDhM1O9PMFBmLEb56zt59Wvf5trvfZvMqVMGZXiladJ8rowtn/t/nN+yLWmiwuJwYHE50a3Wzg+ajBlEg0FlnOMGSpom/uoaXvyHfyQWiTD7nW9D70716eXkVXaZQEuxrZSmmtjogVTb9gYt3u/+IE0TaZpoPVPJVUcwDSPJGAtdH5QBG+wzJU2TlrLzvPSPX+LcC1sGfm9CQWWcu9+bmlpe/PTnMcIRZr/r7QO4N/2j9WI5uXNmc/RPj7Lru/9NsLGxc51mtWKx2xGW+DUyJWYsRiwc7uQ663Y7hUsWYk/vnWcrhABdR09LQ/N6yfn8P5J2551UffQjRCsqCO7ZQ/jw4SSjGzl7hlhNDSCxZGeT/vZ3oLndKQ2h0dyU8jnrDWl3vo3mX/0S0+cjsGcPrquuJlpZSejAAdA0XKtXKz7wlWB0jWAQIxAg54bryX/7nWhOZ+e6hNQ8m5W8O25j4kc+hNA0Cu56B3s2XEvza6+Tfe1G6p55lpY39jDrv/6D3FtuBqDovns4/MAHOPeNb7Po0T9hy8vDlpeHJSMDYbXimT+fnOuvHcnTS4mI38+eH/yk0+C6cnMoWr6UkmvWU7B0MZ6CfCxOJ6HmZhpPnubc85u58PKrtJVXJLyEZZtf4uAvfsPaL38Rq8s1YFGOQH09u/7zf7iw9ZWE9qweD3nzZlOyYR0T16wmvbQYm8eDEY3gr6mj+s29nHtxKzX79hOo7yocGmxoZNd3v4+3qJDiq9Yi9N6NksXhpHjdWizO1CmS7ZVVnH3uRUUTjMOe5mXWO982YMNrdTrJmpE6RbQ7MqdNZeEHHki5LtLWzsVXX6e9sosxo9ttTLp6LZlTpwyoHwATV68c8Lbq3jSw63v/S9nmrT3ujZvcuXMo2bCOSWtXkV5aknhv9u5T92bvvoR7E2pq7rw3k9atRetFr2CgaLtwkYpdb7Dru9/vNLjOnGxyZs0kZ/ZMcubOwZ2Xi83rIRoI4quuoeH4CZpOnqa1vJxYIMikq9cO2DiJeHaqbfoMMh54L/Xf+iZGczNGc7P6wHRrx/T7kR2JLFYbem5uSoMrDYPQoUPIUHDAfbDk5OC97nqVobb7DYzGRtqfeRqkxFJYiGP+gktK++2JETW69vx8MtasoebPjxA4c5b05UtJW7oUe0FisNySlk7G2jWdXobu8WLLyyPa3AxA2779aDYr2Zs2dtsnjaxr1lP+058TqqjENYiXZSQhDYO2CxcByJoxnWWf/Ahz3vXOpK+/pyCfnNmzmHL9Js5vfZnt3/outQcOdXlfUnLw179j8nUbKblmfc/DpIQZiXLisac48ZcnEriwzuwsFn7gARY8cB/pk0uTXs704mIKly9lzt13cezPj/LmD/+PlrLzneubTp7mwM9/Tda0qXgn9J6NY/N6mHffu5l337tTrj+/9WXKtmzF6GZ0XXm5XPPtf8Oe5h3QOQ4EQgiKViyjaMWylOubz5XR/onqBKNrdblZ8MB9IyY+b0ajnHz8KU48+ljCvXFkZbHwA/ez4L3vIaO/e/Pwo+z94YMJySVNp8+w/+e/InP6VNImTuBSULlzN2eee4GWc2UIXad4/dXMedc7mHLDJjz5+amH11ISbGqi9tARmk6conDZ0m6rZOdkVn9GS8bj/cJmSxk71dzuzqQKGYkQq6nBNiXxnZdSEj5+DP8rL/c7idYdwm7Hc8ONtL/4ApGyMsKnT+HfuhUA+8yZ2GbPHnBbA8GIEkqtOdmUfOZTTHj/e4k2NnH+e//Nyc99gcbNLyVK+1ktWDMzunYUgK530j4Mnx/N4UxSHbOkp4MpibUnzoiPBXiKilj1uU8z//57+xxuWV0upt50Axu++TXc+XkJ68Itrez+7x+B2X9MUkpJa0UFe37wk4RJGd1uY/FHPsjKz3yKzGlTe/WGhBA4s7OY/977WPyRD+LI6sr+k6bJuRe3ULHzjTGR2HClQUpJW0UVu//3x0Tau+6NZrOy+MMfYOVn/56s/u5NViYL7r+PJR/9O5zZifembMtWKl7fecn3pmzrNspf3Y5msTD7rrez6bvfZP7996hJsd68VyFwZmdTsmEdiz/6dzizMhNWh48eoe4rX6b10UcJnz2jvNVu777R1kb7s8/Q+vDDAEp7YdLEpOPZikuwZKtMVqOpibbHHsPoJlAko1ECO3fQ8F/fI3L27KDOW2ga9hkzcC5YCIZB+5NPEr14AeFy4Vi0GMswU09HzNOV8eGBvbCQvNtvJeua9YSrayn7j+9S8ctf4Zo+DWepCrgL6DPwbcvLJdbWihEOY+kQO5eScFW1mhnNze3auPNmjeLkiRCUblrP9NtvHlCtNE3XKb56DQvffz87vv2fCevKX99O3eHD5C/qPxPm8K9/T2s3DxWgaMVyVnz6E9j7yGfvDpvbzfz77+Xciy9xYdsrnS9IpK2dk48/xeRrr8Genj6uOztIHPpNinuzbCkr/+GTfX6Uu8PqdjHvvndz7oUtlL20LfHePPlXJt+wCccl0JoicTrfhNUrWfm5vydn1swBx7h7O6bR1ETb03/Ft20rmtuD5vVgyc1FOByY/gBGfR2xxkbMlhaE04nn2utwzJ2X1I6WkYH3ttsJHTmCDAZo/sPvCOx+Q2WfaRqRsnNEzp/HbG8n4z0P0P7Cc8QqKgZ87pa8PFxr1xLY/Qb+V19BRqNYCwpwrV3bq8zkUDFkT1fGokh/O9LXivS1qUoRPWAGgkQbGjBCITS7A8fEIjLXXU20qXlQ3mnWNRvQHQ4qf/ErYu3tGP4AwbIy6p99jvSVy7AXdQXdrZkZCAThixUY/gBGIIAZDg9YQHk44M7LZfJGZZwGCs1iYenHP4IzOzthuRGOcPLxv/a7f6Tdx8Ff/y5hmdA0Vn3+H7B7Bzd0d2RmMOP2W7B2i8EDnHth83jq7BAQ8fk5lOre/ONnBh1WsWekM/32W7C6EnWlyza/RLC+sZe9Bg5nTjYL338/uXPnDAsrQsvIQM/KQkajxBrqiZw5Q2DHDvxbtxLc/QaR8xeQoRB6bi5ZH/8EWR/7eEqZRSEE6e+6m6wPfRjN60VGIkoO8vHHaH3sL4QOqdBc5vveT9aHP4xz+YrBsQ2sVpUsUVqqQh1CYJs2PeUH4FIxZE9Xnj6M+eQvVUUHpxv9E99I1F6Qkta9+zj79W/gmjoFa1YWht9P+6HDZF+7Cefk0gEfK/OqNUx433upfujPtO0/gDUjA9+x41gzM5ny5X9O+MraCwvJuErFkYMXL6A5nKQvX07ubTcni6KPEFx5uRQsWzxoj8OensaUG67l6B//3LlMmibnt77C2i9/sc8Z6nMvbE5Ka82aPo3idWsHTXURQlC6cT0WpyOBbhb1B6jYsYuMKZMH1d7fOspe3EKoqTlhWeb0qUO+NyXXrMPqchL1d/Guo/4AFTt3kTnt0qhNmVOnMOWG64ZlJCOEwLl0GcWPPEpg507CJ08Rq65S6b+xWFzAPA/7rDm4N2xIlGBM1Z6uk/WJT+Jauxbf5s1Ezp1DRsJoHo8SL796HY4FC9VE+oYNxCorsc+cOaDr0aHZYC2dTOTsWYTVhvfWW4fdy4WRnEgTAvesmUx44D2EKioww2Fs2ZPIufF6MtauweJRWVdpS5Rx0j2ebrsK8u+8HWs8liI0jeJPfQLP/Lm07TuAEQpS8K53kn3tJhzFkxKPq2lM/7evUf/sc4SrqxG6BVtO1uUbDguBOy+P9J79Gsiumkbppg0JRhfAV1NDe0UlGX18qMq2bE0SAy/duH7ID016STH2jIwkz7Zqz141UTYeXhgwylJQ90qvWZ+azjYApBcX48jISGAyQPzevOcehnpnNIuF4qvX4srJ7n/jAUJoGtaiCaS/453D1p5zyVKcS5b2uZ33ppvx3nTzoNo229ow29oAsOTn4b563ZD72RdGzOgKIbDn5VL0wHv63C77mg1kX7MhcV9dZ9JHP5y4zKKTfe0msq/tu5yKEAJ7USET/+4DQ+j1pUPTdTJKSwac1todQtPInTMbzWJJmBQxwmGaz57r1ega4XAi8yGO/EULhzxE1G02vAUFtPSQYWw6dRop5ZBf7L81GOEwtQcPJRnd/IULhvxB1G1WPIUFNJ1OrMzSfOrMJU1laBYLRSuXD72BKxjSNAkfPUrk9CkAPDfciDaI8OBg8JZLAx5tCF3H0wetqs99hcDm9eLKz8PXjc5kRCIJ9KaeaK+qJtSSXGomY0ppJ8l+KLBnJD90vupBZCiNg/bqmpRqZhmTL/HepDAI7dUqeWCoELpG1rSpCcvMaJQj3/0vqp5/EYDsZUtY9PWvYvUMnz7IWIDR0oJ/5w6M1laE00naO+8asWONG91hhtC0JNrMYKBZLbhyshOMrlII630Cy1dTm7ICwsN33IWmDT0mFU1BMI/0ohEwjtTw19RiRJLvzSNvu/uSkhlioVDSsku9N0JoOLrR0UA5EVPufTc5y5Zx6qc/o/1sWUJyy5WOjqKZwX178cXTftNuvwPrhAkjFpIcN7rDDCEENs/QK9tqup7EGpCGQaQXsRqAUHNLSo5mLDCwrJzBIJUBGUfvCLW0IFPoUMSCw39vLlnvQggl69l9kabhKS3BWVRI5Qsv0n5mcBzYsQYpJWZrK9HqahXeMQwCe3bT9NMHMdvbsZaUkH7vvcMmbpMK40Z3uCHEkCdIAKW030MZX0qJGU1W6OpALBS6fGVnpBxVCvSVhlgofHnvzaXirS7Ab5r4X3uVun/5CsLlQgYCmHEWiKWggKwPfwTb5MHpnQwW40Z3rKHXF6ePh2AMqWiNIxHSNJF/A18pMxKhcvNLNO8/gBEK4S4uZsJNN+CaOAFMk+ptL9N6/ART778PW0ZG537+ikrO/e4P5F+1lryr1wLQfq6M6pe24Tt/XoleLVtC/lVrsQ6Sb94bNKcT4XAojQdAz8nBMX8B3ltvxb3hmmHVWUiF4TO6Qn0hZVMd8tRB5MUzSH8LGCbCk4YoKEbMXQ45hYP7ihgxZNUFVc69thwZ8KkS7t50RNFkxKzFibXaBgjZ0oA8fQhZWYZsbYRwuLMApsjMReRPRJTMUsU2BxN7kxLjEirMStNMitcJLbVqVwc0qzU5bdLrYf2/fbVPrYShQLdZ+xS9GUcidJtVVbPuBpvHw7qvf4W0SX3zUod0rFHwVE3D4NC3/4PyJ/9Kxtw52DIyqHvoz1x84ilW/eQHuCdNJNLSytnf/J78q9aSuWhhpw2ofeVVzvzqN0y8VdG7mo8c5fC3vkO0tY20mTMJVtdQ+ewLTL7v3Ux7/wNY3UMP3QGqou/yFUz42S/ieg8SYbOjZ2dhyclNLJo5Qhgeoxu/gOb+1zC3Pq4SJmIxkPE6RUJDHtkNrz2NWHMD2rrbEBZb71xPGfcNmmoxtz6OPLEPwiEwjK42NQ15cCdsewKxchPa2pvB6e7ToEspIRTA3LMNufMF8LWqNk2TzjGzEKptTQeLDSZORX/nhxFpWQPipkopibT3Hn/td3/TTIr3CU3vs5qEPc2bNCljRKJMXLuK3DnDK9YxjsHB7vWi9ZCANKIRJq5ZRd78ucN/wFHgT9ds2UrZH/7E7E99nKnvfwChaQRrannl7vs4+LVvsOYXD5K9dAmOvDwqnnmWjPnzEBYLsUCQ2te2kzFvLt6pU4j6fFz8y+PEAiGW/ee/4y4pxoxGOfe7hzj7699SsO4qte8lnKMQAj09HX2E6GADwfAYXdNEnj6E+fxDEAmpUuour/IcTQNCAWU0Y1HkK3/FNEy0a+5EWFMHqyVA1XnMJ36BLI9zEe1O1aauq+F0LKrKvftakS8/idlYh3bDuyEju/cHLxzEfPkp5OvPgBED3QIuj6pkLDRl0I0YRMLqJxqBQJvaboCQpkGwsaH/DVPuLDEjUfx1dQmLNYslQeSkJ9z5+UmesBEOE233q1HBeCLDqMGVn4/WY1LGCEeU8M1b5N5Ub3sZMxZj2gffj8WlJoE9k0uZdOvNlD30Z4LV1XhKS8hZtoTyp55h9t9/Ei0tjZZjx2g9cYIZH/4Qmt1OoLyCup27yFqwADMaxR+XR7WmeYi0t+G/UE7GvLlXfGLO8BjdcAhz62PqIZq7HDF7KaKwBGwOCLQjy45j7nsNasshEkYeeQM5fT5ici9eWEsj5pO/VAbXYkUUT0PMXYGYNE0ZXtNANtWqMMbhN6C9BXnkDczMXLQNtyPszqQmpZRQfRG55yVlWN1exMK1iJkLERm5YLWpj0JbE9RVqrBDXSXaoquUwR/gjTZjBm3llUO6jBIIt7UR6JFDr9uspE3oXbbPO7EopSfccPwEhSuWjkgq4zgGBu+EoiRGAEDjiZNMWLX8sqWmjyRCDQ3YMtITKrMIIXAWFSENk1BdPa6iIgpvuI7zjz5GzauvM+HG62k+cAiBIGfFMoSmYYTDRJqaqXjmOerf2J1wDGdBAcJmeUsk5gzPHZcmhIKI5degrb8d4c3oMlLZ+YiJU6CwBPOh/4WADxprobIMimckx0ulifnaX5EXT4OmIWYuQrvhbkRuUWfcGEDkTUBOX4DMn4j59O8hGka+uQ05cxGUzEj2IEwTWXlOHR8Q81ag3XiP8ra7C6rnTYBp85GxqOqnJw1hGXicRxoGrRcuEg0EkkRJ+t3XNKk7fDSJB6nb7WT0oRds93rJnT2bxhOnEibVKnbsYt573j24mPQ4hhV2r4ecObOoP3Y88d7s3MW8+++5NKbLGIEtLQ3DH1DUuG7PWqS5GTTROQGWvWQx6bNnUfbHh8hZtoT6N3aTs3oVjjxVl02zWNBdTvI3rGPy3Xcljd5ckyaOSsx6uDF8Z5BbhLb2RkRaZrJXKDS0qfO6PFsjhmysRUaSCd6yphx5dI/6IyMHseyaJIPb1ayOmL8aMSXebnuLiv+mpOhICHeLlbrTVPijFw9WWKxqMs2dNujhTKCunuazZYPaB1SdqvMvbUta7p1Q1O+E2OQbNiU9kGVbthJqbhl0P0YKQtMQPfyUy0an6t6PVMN6KUdMiW7ydRuTYu5lW15OEsG5UpG7aiVGNELVi5s7l0VaW6nZ+jLeqVMUgwGwOJ2UvP1OmvYfou71HbSfO0fe6pWd2W22zAyyFi7AV3YeZ34+mfPnkblgPpkL5pM+exa2t4ik6PAYXd2CmDoXkZnX+zaahiie0fV30Kfisj1gnj7c5Y1m5yNKZqQ0uGoDATZ7QphCnj6i4shJ22oJKmhy7yvI8jOK0jPML5uvtpaqPW8Out1QSwtlW7YmLBOaRunG9ejWvj2i6bfejKOHLqu/to6jf3rksspa9gWbx5PEfOte++tyQbfZkrQxjGgEMxIZkWs17ZabkjRzA3V1HH1o7Nyb3hALBmk+cpSGXW8QqKwk3NhI3fadNB04RLBGpYRPvPVm8jesY9//+zL7v/KvHP/hj9n+/g/RdvYcC7/ypQTe+aTblHLXqZ/9Elt6OhndJCRtGRmUvPPthOrq2PHhj3Psf37IqZ/+gr1f+Cf2fPYLnTHeKx3DZ3QnTe3fI3R1o3tEo8keqZRQcVYZYyHA5oRwENlc3+sPrU2KadCBplpSsveFQEycCsXTVdutTZg//ybmIz+GsmNIf5vSCB6GlyDU1Mz5LS/jr6sbcHumYbD3xw8meaa6zcaMO2/vd39HZgbzH7gvyai98b3/oXb/wVHxKHvCkZmZRJ/y19UP6joNByxOZ5K4fNQfwFedOp36UuHIzGDBe9+T9H688V9j5970huZDh3ntvvfxxqc+Q+ux40RbW9n3T19m+wc+xMn/+ykAutPJ6h//gOkf/iD1u3ZT9oeHsGVmsuHhP5CzckVCexavh9K730moro68tWvxlHZVDhaaRt5Va1n1ox+QMWc2Fx59jNO//DW+i+Xkrl6JvY/J5CsJwxNQ0jREZm7/2yU8dJKexlFGI+Br6ap6enQ3xtHEgHq/CKX2nIQQkJGDvuntioZWWaYmzva/jnFktzLIc5aiFU9HZhci3N5LmiW98MqrnHj0CRa+/36V1tsXlc0wqXh9B4d+/fukdcUbriZv3pwBHXPxhz/A6aefpflMlzJYsLGJrf/vy1zz7X8jb8G8QVeNlVIS9ftpr6zCU1gw4AoUqeCdUIg9PT2xJLyUHHv4L6z9p3+8bLPS9jQvrrw8hK4nxM8rdu5i9t3vIG3SxGEfxi7+8Ac49dTTifemqZmXvvAlNn7nG+QtmI9uG+q9qcZdkIdjBGhQuStXcPvBPX1uI4RAdziY8w+fYs4/fKrfbRd+5Uss/MqXel2fMW8Oy/7zO0Pu81jH8Hi6QiimwqUiEr50MQ1p9hLTBXQdMX0B2h0fRFt/O0yaBpoO0Qiy7Djymd9j/OmHmM/9AfPwLqR/6LXXQk3N7P7vH3LgZ78i0NC7on80GOTc5i1s++ev4q+rT1jnyMxgxac/MWARZu+EIlZ+9u9xdBPckaZJxY432PyZL3DoN3+g9WL5gDyrUEsrNfsOcOxPj/Da17/Ni5/+RxqOneh3v76g2+1MXLs6afmhX/2Ocy++dNnqr2kWC3nz5mDrkeFU/toOjj70MFF/YFg9byEEnqJCVn3u04liSFJSuWsPmz/zRVXO58LFgd2b1vi9+fOj8XvzeeqPHBu2/o5jZDF8U6fDMqvY7UHXNETpLMScvsWKkyEU/as3aDqiqASRW4SYt0JRw07uR549qmLJzfXIva8iTx1CzF6s2BhZvVRCTXV0XcdTkE97ZRXtFZXs+M73OP/yq5Res57CZUvwFBZidTkJtbbSfOYsZZu3cm7zS7SWXUjw0IWmseC991O0fNmAKTK61cqMO26l6eRp9v/sl0TjgjfSMKjes5eWc2Uc+9Mj5MyeRd6Cebhyc7F7PcpjCgaJtLfTXllF24Vy2ior8VXV4KuuJtDQiNXlTKlsNVgseO99nHz8yYSPa3tVNS994UvMvPN2Jl97DRmTS7G63ZixGNFAkEhbG8HmZoINjUR8fko3rr/kbK6Sjevx/N/PCXeTXYz4fOz535/QdPIUM+64jZw5s3BkZgCCWChMxNdOuLmFQGMT/rp6ChYvJH/RggF5xbrFwvTbb6Xp1Bn2PfiLTm9fGgbVb+6l+VwZx/78KDmzZ5K3YD6u3BzsXi9SSmLBIOF2n7o3Fy/SVlGJr7oGX1UNgYYGdW9GQEBnHCODscVXsTkQmh43vQJyChCrrh98OwORM7RaEQXFkDcRMXsJsqUReXQPcs9WaG+B9mbk3lcxo1G0298HDteAXq7CeLHBF//h8/hr6wi1tHDuhS1UbN+JzeNBs1rVsDYWIxYKEW5tw4gki9lMvm4jCz/4gCKbD3SoKwSOzEyW/8MnCbW2cvShRzC6laIONjZRsX0nVW/swep2oVmtalZdgmmaSMPAiEQwwuER8zqLVixj6o3XceaZ57sWSknz6bO8+cOfcPh3f8RitytusZSqX7EYZszAjEVxZGSQNX3qJRvdjMmlzH7n29jxnf9MiOOGmpo49vBjnNu8FavT2UnpktJEGiZmLIYZjWFEo6z/t6+Qv3D+wO6PEDgyM1j+6Y8Tbmvj8O//lHBvQk2jf2/GcXkwpoyusNqgg+NrGtDWAqaJsNn723WIBxTq5XangcuLKCyGtTdhbnkEuXurorYd2I5csg4xbWAF6qbfehNTb7qeda1f5rWvfxtfdQ1ISaTdl1B+u9cu6ToTVi7n6n/5EplTB692JITAnZ/Hpu9+G3d+Pvse/Dnh1rYEL9qMxdSyQUCzWC6ZI6lE2j2s+9cv01ZZRf2hIwnD6VgwRCzYtzctdB1zGPRcNV1n1ec+Td2Ro5x5+rkEwysNg2BDI/35joPV2BBC4MrL45rvfANXXi77/u/nSnx+DNybcVw+jK07JYSiiFlUeEC2NKgststyaKF4v24v2rV3IebFZ12liSw7xkD0DIXFwuTrN6Hbbcy99242/fs3mbBy+cBEOuLGctbb7+CGH32fgiWLhjyZ06Hpe/VXv8Ttv/k5kzddg6ewcNBEfKFpOLOzyJ0/lznvvov0kuIh9adn37Jnz+S67/07k6/bhCNFdYqBtDEc0GxWbvzRf7Pog+/FO6Fo8NdnCLlRHffmqq/8E7f/9hdMvnYjnqJLuzez735nn/XzLgVSSqJtbdRv30n99p0EyivGNNviSsCY8nQBxIwFsP05aKqDxlrMo3vQcosQjsFld10SrFZEdkGXme1lOGdx2Jn59jvJX7xI/e10kjNLcZF1q5VZ77yT/EXzOfnk01Tu2k3z2TL8NbVEfD7MWAyL3YY9I4O0CRPImTOL0k3rmXbLzZckgt4dQggmX7eRouVLufDKa1Rs30njyVO0lVfE46M+JUouQLfa0B12HBnpOLOz8RTkkzG5lNx5cylasYys6VOHLXtK03UmrlnJ9f/zXc69sIWLr22n5VwZvuoawm3taghtGOhWKxaXE7vXizM7C3d+HtkzZwybcpoQAmdmBuu/8VVKN22gbMs2Go4dp628klBTM7FQCDMWQ2gausOOzePBmZmJKy8XT1EheQvmDZlxIYSgdNMGCpct4cLLr3a7N5UEGxpS35v0dJw5HfemJH5vlg/p3jizslj6iY8kLLM4HGgpFORaj5/glVvvBGDuP32BGX//CfQRlj98K0P0M0vb60rz+L6kEuyiewn2VPu8uQ3z0QfVgecuR7v9fYj0HpVHpcTc9gTmi/GKuGmZaKuvR6y8FuH0pHzIZSwG9VXIugrE9AUIV3KuuzRiyMNvIHIKEQWTlMhNyjOWyPoqzL/8FHnhJADauz6OWHz1kD0saZq0V1XTVl5BoL6BaCCAGTPQbVbsad5OA2dLSxvRjJtYMIivuhZfdQ2hlmYi/oAaVguBbrGi223Y0rw4MjNw5eTgzs9TQ9cRpnL56+ppr6gkUF9PxOfHCEeQ0kSzWLA4HFg9bhwZGZ2Gt2dljeFCOD6R6KuuIdzaihGOdBldmw2r2409PQ1nVhbu/FzsaWnDNqyPBUP4amrUvWnu5d544/cm9/LdGykljbv38PKNtwF/W0ZXmiaBikr8ZefJXLwQ6+Dokr3emDHn6SIEYtV1iOoLyMO7oK0Z85W/wrG9iGnzEfkTwe5Q3megHVlfhay+AM0N4PaiT5qmlMN6wjCQu1/CbGlQmW7F06GwRInd2Owqhtzeiiw/jXn4DaitUPvlFPUuzDPQU9I00iZOIG1i76I1lwMWp5OMKaVkTCkd1X70hDsvF3feAHjeIwy714t91kxyZs0cmQPI3uXMdYedjMmlIxYmGMfgEfMHuPDQnyn/y+Os/MWDZMwf2LxOfxh7RhfA6Ua77b2YFqsyvKEAlJ9R9K7unoWUibzcvqhiSJV80VSnhNbPHuumvSAAqdozTWWAhYC0LPQb7oa0rLdEzvc4RglSIqUJ/hYI+VKnqXtzwDX0xJNxDD+iLS007NhFsLoGcxhrA45JoyuEUGGF29+HLJmuZCFbGpTxjcWUYRQoapjNoaQXvRmqMoWzl3ioZkGbtRgzElJ83EhYpRsbMWVshYi3Z1f0sPyJaFfdgpgyZ1ylaxyXBAnga4JAK1gdaqK450d8EJrNo4q/EedDSkm4qYmWo0eHve0h32mRkY1YsBoC7cpQ2Qcw0ZVTiFi2Qe1fNBmsfVPBhNONWHU9Yu5yZEUZ1FxE+tuUwdQ0pZubng25RYjC4j4VwYTFgrjmbYgl66DqvNLjbW9VouuGoQyu3aFizIXFaBMmK2N+JUCagPibeSGuSIR84EpDeHN6F3C6EtA5MnxrQ5omrUePEWlswpJCD/lSMHSjW1iCXljS/4bdoJXOgtJZgz+WNxMxOxNmLxn0vokdiGtEZOa+pR4b6W9R3rrDjbDYx43vWISUYLFf2QYXxZNGQKiujvrXd9J++hTRtnZ0lwvv1CnkrFqJu3TgdsEIhWg5dISm/QcI1dZihsNY3G7cpSVkLVuGd/rUAYf2oj4fLQcO0nrsOMHqGoxgCKFrWNPScBYW4Jk2jYz587CmEPw3YzGC1dW0nzpD4OJFfOcvUP/q66qP4TCnfvAj7LnJ8w7CorPg61/ts4ZhT1whY5px9ImgX3lSFivS5gBXOsLuGlhm3jguD2wOiIZADl6feSxBd9hp3L2HY9/5T3xnzxJpaVVZmxYL1rQ0vNOnMf3jH6Hwhuv6NUTtZ85y6kf/R/3r2wnX1RMLBpGGodryenEU5DPpHXcy7UMf7NPblFLSfuo0x77zXZoPHCTS1EwsEFCp5kKg2axYXC6s6em4i4uZ9dlPk7fuqoQ2oi2tnPm/n1H512eItvuI+XyKFQXIaJSKJ/+a8tiazcb8r34Zxo3u3xgEKsQQDaufQDvSYgVXOngyQLMoz77rn3FcbrizobUG2VYHdk/yPIFuQWhj/3VsP3uO0z9+kGBNLbrTiXvSJKQ0CVZVE25qIhzX3dVsNgo2XZOyVJSUEt+ZM+z/x3+ifvtOkBLNasU1cSIWp4NQfT2RllbCjY34z18gWF3L3H/+ItZeKJXhunp2vOf9+MvKkFKiO+y4iydh8XiItrURqq8n2u4j0tJKtLUNa4qEHGGx4Jo4gayli1UfTZOWw0fxl51HWCzkrl2NLTMj5X6DpQ0Omac7ViGlBF8bsqVJLRACkVc4cqnEYwAy5Ad/M4QDCRWTAeVVOb3KANscygCL8fjv5YSUEurPp2YtdCAtF+EavQq1vaEnTxfAmp7OjE98hMnvfQB7bg4Awcoqjn7zO5Q/8SRmKEzOqpWs+PlPcBYVJRhKKSXRllb2ff6LVDz2BLbMTErvv5fpH/k7HIWK52+Gw9RsfZmj//Yt2k6cxOL1Mv+rX2by/fehpZC/PPSVf+XUD3+C7nRScs+7mPUPn8I5YYJ6xqUk0tpK05t7qX7uRWI+P8sf/GGS8e5pB81IhINf/irnfv4rLB4P6554hMwli3u9Tik+BlcQT3cYYBzeS+y5R5HNTcj6auzfehB96uBjyVcKhMMNdhfSiKqKHGG/8nhjERVLDLTFJzwd4PQgbS6lc6Fbx43v5YI3hz59GOsVkmwgBDM++TFmfe4fEgyNc0IRC771dYxIhIrHnqBh1xvUvfIaJe9+V9IzVrv1ZSqeeArd5WL6Jz7K9I9+CEu3VHnd4WDCzTfizM/j9bvuIdLcQsWTT5G/aQOe0tKkLjXGi1h6pkym9L57cE2alNBfe2YmhdddS8G1m5CxWEpvuf+48fBVbr6yo/q9QF+yGtsnv4zlxreB4wphIFwqhEBYbAhvFiKrCLKKICNfebgWKyAhEoTWemisRDbXINsalJdsmlyBg5orBkIIhNOLcKYhHB6EzYmwOhB2t1rmTENY+uKYjx04iwqVIe0BIQS29HSK3/l2rBkZAJQ/9kSyToOUnPnZL8A0SZs1k8LrNiUY3O5InzuX3KtV7LVp7378Fy6m1jmOD+9jfj/hxqZetZCFEIOa8BopvOWMrhAC4XCi5RchsnL/NieTNB1hdyE8WZCZD9kTIbMAHPE0ajMGwXZoq4emKmRjBdLXojzlcYwMpERGgsiWGmRTFbK5CtlUiWxvVCGhKwQZC+ZjTfOm9hY1DXdJMZ4pkwFo3LMX2UOJLVBRScvhw4DyTBO80p7t6Rrpc1Q2qBEI4D9/ARlLvlZ5665WbVdWcfK/f0DlX5/BCIUve+29geKKCS/EXnmB2HOPYv3Y/yP2wuOYxw6Cy43l2tuwbLhpSDnw5rmTxF5+DvP0caSvDZGehb56A5Yb34bQLcholMhPv4usq8b+L/+dMCkgTZPw1/4BkZuP/aNfBIsFKU3MI/uJPfMIZtVFhNuDvvxqLLfcpWLK8QfV2LuDyB9/iv1fvo95YDexLX9FtjUjCiZg+7vPouUWqGNIE+PNHcS2PIWsqwZNQyuchL5mI/rqDUn1xpIgBEK3gmYBmwPpygAjogxuoK0rBBGLQMgPrTrS6VWTb/Hhroi3M46hQ0qprnVzNWgCbC5AU/fC34w0Y+DNQVwBDoK7uBjRh7iONV3Rs5qBmM9HsLqm0wgDtB49hhlVrICqZ5+nYcfO3p8vqdroQLihEWka9DRb0z70Qaqee4HWo8do2PUGrUeP4p0+jdL77qXolptUbTUhxsyzfMV4ujIUxLxwlsiD3wVfuzI6DifRX/+A2DMPD0luLvbK85gnj6DNnIdlw03g8RL940+J/TUutmOxoE+fi1l2GvPEoYR9zTPHMc+dRJsyA3QdaZoYr20m/F//AkJgWXc9omQasWcfJfI/X1cpyB3nEg4h62uIPfMIsRefQJs1H33VBohEEN6uyRTjlReJ/OibCIsVy7ob0JesQba3IS+eHVw0QAgQGkLXEFY7eLIgawJ4s7tGAtIEI6oyp2rLoLFClU8aDzsMD3xNKhkouxjS8hBpOZBZBBkFKuwTufSqHJcDVq+nTwdHs9nQXfFEqfikWXeEGxo6PVAjFCJc30C4rj71T309RjCoBN2t1l5Ledlysln7p99Tcvdd2DIziPn8NL25j32f/Uc2r13PwS/9Cy2HjihK2hjwfq8YT7cD+rTZWO//GMJqQ4ZChL/1eYzXNqOvXI/I61vlrCesd/8dWG2IeJzHEo0Q/vLHMd54Besd96oY0PKrEE//mdjWp9FmL1DeiJQYr76gPNmla0AIZEMd0b/8Fsuajdg++JnOOFP06T8T/fMvMQ7sxrLi6q6Dx6KYJ49g+8y/ouXkq2U9Z1BPH0XoFmwf+QIiLW6MTVOlLg/miy2l8hBiEcVwCLarF73jeJreqWFMLKJm2QNtyvtNz1OaAFdKmupYRTQEnqwEb1YA0mpXE5rmFVINop8RpdKl7tqmZ3VlIxSiw2NInzOb3DWrB8xizFy8KCUFTQiBa0IRS77/Xep37KL8kb/QcugwvgsXCdc3cObBn1H+l8cpfc+9TH7gPjyjLCp0Zb1Jdjva3MVq5j3+t75qA7Gn/qjShAdpdLHaMM8eR9ZUIH0+VaAyHE8LjusxaJnZ6Cuuxnh9C7K6AjGhBNnciHH4TbQ5i9DylLarefIwsq0Z6fcRfey3nYcw62vAiGGeOwndja6uoy9b22VwIcmQarPmY+x6mejDv0RfuBxt2mxERmY/wj7dICUyGlaGtuOne9zWEk/fdrjUbwlEAsrYBn3KELTUIqWB8GZf8dlUo45UXpbsZfkYhREK9ektmtEYZrcyRJYe2V9KHlE951nLljL/619Ftw/PJKLucFCwcQO5V62h7egx6ne+QePOXTS8sZtwfQMn//t/aT99mkX//i1cw6TJPBRcWUZX6AhPYgVXkVeIjESV0RwEZCRM9KGfYR7djyiYiMjNV5KRnRt0PViWTbdibH0G441X0d5+P8ahPciWZiwbb+k0lLK5EaIxZMCHWXkh4Vj66g1oE3pUXRAaIr/vj4S+bC0Asa3PYhx4A1EwAX3ZVVg23KB0J3rzdk0D2d2jjUa6cXeF0pRwpoHdqdKGO9XWUEwHhwfpTIOWGuX5+tuQdo865jiGBpsTQu3q2lvinHFpqmWmobzdKwCh2ro+K3YbgQCR5mZApcg68vMT1ruLixFCqO97YyORlmacPba5VOg2G5mLF5GxcAET77iNxjd2c/K/f0DLkaPUvrSN8kcfY+anPzmsxxwMriyji0yu4mCaanIiheJ9XzD27iD28nNYNt2K9ZZ3gd0Omo556iiytiphW5FXiDZvMcaRfVg23IRxcA8iOw9t+pyujSwWEALrjW9Dmz43+YA9kzME/Q7ZhdOtJs3mLsEsLyO25Slij/4a2ViH9Z4Pdc0gd+i0SgP8rV2TZN3J+EIDpwfcmWCNF35M5bkKobKjnB6kzFOx3VhEDY/f4kZ3qPG+AfE3PVkQZyygWdS1N+OKee50RAqe7oj2Z4hoO36iV5nDDmUu/4WLALhLSrCmJTpJGQvmobtdmC2ttJ85S+BCOY68vBHps9A0XBOKcNx2C0YwxMEvf5VoaysNO3Yx8+8/0W+IrnOiWipKpZRyWPp5ZRndWFSFAhYsi5+8RJ47iXC4FD1sEDDLyyAWxbLqGkSmql4hQ0FkbSVJQSZNx3LtHUQe/C6xl59FVpzHsu76hGG+NqEY4XBgnjuFtmhVZ1xLSqXTO+ibJdVNRtMRmdlomdnYZi0g8tP/wHjpaazv/iAd86AyGlYZaf7WHoZWqBfcnQ7uDLDYus6sv/4IAS4vNGtdGsNvcRhPP4Tx3MNKxW4gEAJt0Wqsn/hK35sB0mJTk5eBFggH1fW02BSP2p6ap2o88yeMZ/88uP4sXIn1k18d2PZDQOvRY7SdOEHO6lVJmWZmJErzvgP4L6q6hkU3XJc06aa7XEx6+52c++VvaD91itpt20ibMwuL293rO9LxDiGSExQ6J9BTrOuAZrXimTYV3WEn2iIHVE1ZCNEpjBPzB4h0FBD9mzO6kTCxN19HlE5HZGRBSxOxV19AK52OVjINiN+EUDwdNuBXXylfm5Jx1C1KvlHX0QpUmqB55hikZ0A0irH9JaTfh3D3CGEIgVYyFZFXQOz1LRCLoS9bk3ADtJnz0WbOJ7b5KbQpMxFF8XBCOIRsrEObswgxyEQN89gB1V9PmsrVD/qRfp/qb/cPQ2s9BDsqyCpPFasdnF6EO/3SuMqa3iUS/1aHaahQTGwQfGVjABNgHXQliw3S8gbRHxOi0eHvzyVAGgZH/vUbLP3f7+MuLUF3qBGcGQ7TuGcvZ372SzBNbFlZTHjbHSkn3qZ9+O+o2fwSgfIKzjz4C2zZ2RRefx2O3Fy0eHsyFiMWCBBtaSVYXYPudJA2a1ZS/DfS1EzT/gO4iydhz8lW+gzdyhhJwyDc2ETNi5uJtrWjWa2kzZzev5er63inTVP9N02qnn6WjHlzsHf3yqUy4GKQZZMGbXRlNIp5bG/SELxXaBpCt6jhtdON8KYjMrIhI6trQmygsNoRaZlEH/5lJ2NAK5yE5fZ7EK64t+BvJ/qX3yADfmR1OQSDRJ/+MyIzF5GVg2X9DYjCSegLV6AvXk30ub8gDu5GSMDpQl92Febxg8nHdrnRl60l+rufoK/aoPSEu11oYXdgvfcjRP/yG6J/+jl442pSkQhYbdhnzAUGYXSlxNixFfP0MSXMbrdDKAi6jvWu9yc/zEKouKHNCY54/HU4eJ+m2Uk5u5yQDbUY+7YnLRc5BehL1lzWvlwqFE83pJgLV0jmWUoIQdaSxbQcPsKej3+KwhuvxxUvQeW/cJHyx57Ad/YsutPJlPe+h7SZM5KMkRACz5TJzP3SFzn6rf8gUF7OoS//KzUvbiFj4QLsWVkglHcZqq2l/fRZWo8fp/idb2fOFz+fZHRD9XW8+clP48zPJ33BfLxTJ2PPzkZ3OJCmSaSpmeYDB6nZshUjEMAzbSqT7npH/+eqaWQsWoB32lTaT53mwp8fwTQMspYsRnc5kdEYRiCAEY0y/WMfHtRlHLynGw1jvvYC5puvDWx7oSkvzWoDh1N5kWmZKk46dTba/OWQPsByODYblquuA6sV2dQAVita8RS0om6TVBYr2uQZyljMXggbb+1aZ3d0epsiPRPrAx/HPHsSQgElmF46DWIxzKWrk4yasFjVcRxO9IXLwZks2i4KJ2J77ycxy04j29RkAk43Wl5h10cB0KbNxvZ3n0MrndbHdRNYbn0XsqpcebdIcLrQCiYiJhQnXi+nV4UCbA6VTjpsBlJAWo4yugMRqR9GGLtfxnj4Z8k9mjQVbeYChHt4haUBFYvfeDvS1woBPzLo7/od9KuKI0P1+tsakQ43wjNwo6tNmwMbb1P9CQaQAd/w9WcIsOfkMPMzn6Zx927O/uwXNB88hMWl3qeYPwBSYnG7mXTXO5j8vgd6Te/VrFYm3HoLwmLhzIM/U0Zx80vUbH6pywPtFs/WbDasXm9KuphmsyE0nZbDR2g5fASEQHc40Ow2pGFiBIOdEo0ZCxcw54ufJ3Phgn7PVQiBu7SE6R/7MMf+43uEqms4/7s/cPHhR9GdDmQshhEMYcvOYvpHPzSosMPIhxekCTFTDZGCfmRzA1CGPKFhvvka4sXH0NfdhHb1DWo2vb/OOxzosxf2ulo4XVjW3zigrmnZeWjZycM9rXBi8mnEopjlZWh5hWgl01JmD6kyQxnKKPd13LxCtP7obUIoI19U3Pd2gHDFvephnowQAN6s7n9dFkjTxNzzSup1LQ3Is8cQC1YM+3HFjPnopTNUaMqIdf6Whgo7GI/9CvPArqE1boRBG1wNNDFjHnrp9NT9iUUxHvs15v4dQ+vPEOCdNoW0WdPJvWo1OatWcuHhR2navYdwUxP27Cwylyxm0tvvpGDTRmxZmX2+yxaPmwm33UrWsiXUbn2Fmpe20nLwEJGmJmTMwJqehru0hMzFi8lbfxXZy5alrEDsmjiJVb/+OdXPv0DT3n34z58n3NhMrK0dYbXiLMgnfe4c8q/dRP76q/FMmTzg7FWLw0HxXe/AXTyJi488Rv32HYRq6zCCQawZGaTNnk32imWDfu9GL6ZrxmOtvjZiD/8M7dg+9Ls+BIUT+09vvVyQEmnEwJTIxlpir7yAvnAFYkLxmEgn7MQwlQFPwiiVZpEXzvQevgoGMI7uQ8xfPuwz3sJijYsD9ViOig3SI9Y/KGgWxS4ZxGRMn/0xL7E/g0D2sqXccfEMQreoGK4QFN5wHfnXrEcaBtJUE8XCoqNZrSk90lTQ7TbcJSVMfuA+Su+9W7UlJci4A6Npqs04MyjV/dbtNrJXLCNr8UJMwwDT7Jp4QyA0gdB1FXfV9cE9M0JgcbvJ27CenLVrkLEY0pTxVV39GyyGz+harCkego7quqaKbUZ7mYWNRjD370RGo1ju+SgUlYyR6ruS2HN/waw4j3nuJMKbhn7VtQj7FSLDdyVCSsyDu3qfsY9FkRdOQ0sTxFknYx0CkO5MCPmQdrcSK08lvzomnvlECCHUPILX23NFSs9zKO0Li0VRLi+lDZttxDQNhKah2+1qXmUYMGxGV0woRb/tvsSF0lQvTziEbG1Wpc/rqtQEV0fMs/vmR/ZiPPswlns/Bi5PIjtgQjGWq65DpGcl7TdyEAinKnujL7tK/UyefhmPf4kYCs9zlF98GQxgnjzc54y9bKjFvHgG/Qoxup13QRrQUoO0OZPj7g53Sq7uON56GD5PNz0TfdlVfW4ijRiyrhp5/hTmzq2Yx/crik7XFpi7X8Gctwxt1TUJzoA+bwn6vEssTDlYCIHl2tuxXHt5DzskxIdUMuxX4ilmbJBGV0BmwagX85EXzyAbahIXutzqOenI429pRJ4/jZy7RA3BrwSEOjImRWpxG6sdrpBTGcel4bLGdIVuQRROQuYVoU2dTezRXyoWRHfifTSM8eKjaCs3jLrXdSVBmgY01yh9hUEbXNS1ziwYmc4NFFIq3nRrt1GQxYI2fR5m1QWojxvjjhBDazOkmAgdk0jrJ3nnCpB1HMfwYFRmrISuI3ILsdz9kZRxOVl+Dll+bhR6doVCSmiphUCrErTpNLgD+GgJTem7ekd/qC7bW5EXzyR6gp50tPnLEWmZCdua508jG+vGhFRfn5ASQdzhsFjVj25R3PX4b2GxDkkPehxXJkbvTgsBWTno19yWvM4wkacOX/4+XaGQkWDX8FW3KAOaWwz5pZ1aCmQWQm4J5ExS9bostniqbxrkTID0kcl/H9R51FYiK84nLBMZWYjp8xC5hYksjaY6zItnRzwD61IgTRMZiyDDAWSHZKY0kdGgqhjRVocMtatRylj/eIxj2DCqacBCCLT5KzAe/UWPNRKz8gKXNOCSEhkJQ3sr0qcKM8pIPMVTmsrDs1gVE8HlUeLhaZlxIZgrLKwRDnRljqXlIjyZKmNPShBK2EbYHF0JDk4v0psZ947b1LXIyAO0UTt3aRjIqgvI+upuSwUiO19Vc54xH/bvgHCXFyyP7oVV14BnDAZDTROCrUq8PE4Tk840VZHD10Tn9FrQB84QeLKTy7JfCZASGQxASwOyrRUZCnS9Y5quMiPdHpWFmpahPPzL0i0JLU3IlkalkhcOqw90PFFL2J2I9AzIyB5YfsAwYtS1F0R+kcpW6z6hJiW0NQ2pPRmLIivPI8+fxqwog/pqZHMjsrVJvbCRuPqWpoPNrtgJaZmI7DxEYTFixjy0mQsRg6GHSBPjzddVnLGbxyIKJqItuxrhTJ2ZMyiYBuaRvZgnEytYiJwCxOQpCE2CblVaC90foHgZ6p5l2YVuRablqBfE36zqpzmHP8trwAj6ME8fTXwObDbEpCkIpxtt9kIMmyPB6Jqnj6j76k5ds2s0oSozt6vQjcOtjHDHJKctLq0piGsXtykqmT4Mz8nlgpTIlkbMo/swzxxDVl9ENtaBvy3+jplqhOXyINIzEXlFiJJpaHOWIKbMGnI4xTx1RFEK4xAZ2WjrbuqicZomZtUFzCNvIs8cR9ZVIluaVAZfLKqorTY7uL1o2flQMAFtzhK02YuUxsllwKgbXTRd0cNaE41sz4J2/UGGApjH9mO+8bIyus2Nqux4b8M2IwbBmEqpbKpHnj+lvoK7tqLNmI9+411oxVMGeHSBbG7AeOlJCAa6FmflYp08CzHBdckepGxrwXj9RczdLycs1666Hn1yvJ+6NXlCRtPBjCFNMzHCKwTC6kDaXUpzN9CKdHpGjb0gfe2YpxI/KDjdaNOUTKbIK1KTsO0tXet9bchjB+LiQmPL6NIRMnClI2wqVVYKwNcMjmw18gCk0JQxvlIqRxAPm+zfgfHSk5jl58DXlvo9i0WhrVmJ+5efg0O7MXdtRVu0Gv36dyjRqsEe+/xJjGf+1LUgLQPbivVgdyDDIczdr2C8/LQqahBOwRKJRtSPvx2zrgqO78c8sAtt+lz0m9+NVjrylNDRN7rIlJzMwSQgyLZmYk/8FnPXNqUwNtR8dMOAhhrMpnpkQw2Wez+GKJnevxclBPqSNRhbnkg0uk0NyKN7kYWTBpylkwpSSkW1KzuZuMLtRZs2V+lZBNsgVT0zXVeTa6mkGYVQX36hKcM7SpCmibxwpoudEIfwpCGmqmqwQregL1xBrEes33jzFbSNt3FpsagRQEfmmaZ3jTb0+LXu7uV1bHMlxHSlVJoDT/4WY+tflYrfYGroRSPIqosY9TWY509jueejiImTL22U0taKbKoHpxvjlecwnvqd+ggMBs0NmG++hmyoVe/81DkjOnIa/SnTuCZDAuIaBgOGy4PIzldftlQGV7eAw6ky5rzpqm1vulLvSpUJYxrIU4cx/vpHaKof2AuRnY8+b3kPj1ZibH9x4HqovcGIYV441SPeCSK3EG3GvK5YYCqqmG5V1yQW6eU84v0dTb1cI4a5f3ti/4SGNnthghymtnhtUtxTnj6GbKy9XD0dNBL1i0Vc5rGbOt1Y89B7g5TIgJ/YH36E8fyjSmynu8EVQlVecXvBm6F+PGnqvesZSohGkMf2Efv9D5EXzw6pqGy3jiHPncDctx3j0Z8nGlxNU6WoPGnxPqWr/qVSNzRN5LkTyotuGllWzKh7uvLsiRSGUqCVDNzNFxYr2qyFmEXFXVQzhxORmQPZeUqZq6hEzYA73WC1QiSCbKhRN+zkIWR1RVKasnloN+bi1WirN/U7ASCEQLv6emVkw11eo7x4Fnn2OGLe0gGfTxICfuTB3YnLdF3pChdMQvqalAdlmuocbF3l06XNAf4WiASRsWiinKaMCxENkzjzkBHwYR7dl7hM0xALVyf2K78IMXGy8oo7IE3MPa+i3fLuy9PXwcA0FLOkoy5dvJqHjHSrVWcaY5qB0QEZCmK8+BfMXVsTR6aaBll5SgRq+ly0ohJl5IRQE2w15Srme/Y4sqE24VzlyUMYzzyE5d0fRWbmDNm7NN58DVlT0eXcWCyIgkmI0ulo0+chcgrA7QHDUJmx505gnjiILD+bFIIwD+zCnLUI7do7RqwY6+gaXdPE2LU1ebnNhjZj/qCaEpOmIGYuQIbDaJPjF7t4GmJiqZrISnVDp89FrtiAVnkeY8sTKl4a6jbMjkYwdqoYFAMIsov4gyePvJmw3Nj2V7Q5i4ckTCOlRNZXq6SB7nC40Jdepdq0OZTRlQYy5OuMFwKqKoEQ6kPga0J6MlUWl2mqOmqhuMdiHZ688qHAPLof2lsTF6ZnKmnD7hAa2pK1GAlGV2Lu24G8/u2D12ceSQihPoLtjcnr2lNMEo8VkacUkEYM8+hejNeeV+G7DlhtaHOXom24BW1OvGBsz/dszmK0q25AnjmGsflxzMN7Eoy2uW87RvE09JvuGrKRk8cPdP3hSUNfuQHt6ht7Dw0uWoWsryK27WnMl59NPCcpMV55Fm3NtUhP2oiEGUbV6BrH92Me3p20XMxaBHmDq9YpbHb0a+9EX3kNIrdAaTQM4IIJXVcG+477iba1IA+9keB5yzNH1ZDF7e2/PaGhb7qdWA+ja548hFl1EW1i6aDOSXVAGZWEBwPFjBDT56n/25xIiwUiMTUb7snsiiVabEpvN9CmJnHCAaRu6fJyY3G2gDt98H0bJhhvJH94tblL1dC0O4RAm78c4+k/dqUEg5qhvnAG0dNIjyasdsWNHijGsrh5Wwvmtr+qUFsHNA1tzmIsd38I8if2yUYQNjti9iLIyoWgH/NEtyIBsRjG5seVA1E46dL66XSjb7gV/YZ3KCZCb++rpiHyJ2K55R5iba2YO7ckhLZk9UXkueOIBSsvrT+94LJ/XmW89pd59jjGo79M9nDsDvSb7x6SV6jlT0CbNkdxAgcp4SaycrFcc0ty8cVwCPPimQFPF2hT5yAmTU1cGAxg7n55aHGiWAyzp1ESAm3lhq54p6aDK0PRvrxZCV6TEEIts9jiAkRBRWUK+bsMrsMDzrRRiS7Kxjrk2eNJy/Ula5OWCUBk5SIm9RB/DwUxj+wdU9lpQtMRdlfXT0dVD5tDTV5abN2WOVPqM48FSNPEPH4A80QPZklGNvqd71XD+IG8q0Kg5U9Av+uDyXKVrU3Ki76U+ycEYtpc9Gvv6Nvgdt/Fm45+y90q3NgdUiaf7zBiZI1u3MBK00BGo8hQEFoaMbc8QfTH31Cz8d0vtG5Bv+5tQ6dtXIqQtxCIuctUsL3nafSYVe+9CQEuN9rqa0igMBkxzINvJH9gBgDz+D7Ff+wOhxN99abEZd4syC1GOBLV2RAormhWoXrBhdb1o+mqYGVWUZdnfJlhHHwjIQYOKN50nLWQACHA4ULrKWIfjWCeOpzIHBlL6CgyGmiBuvNQW6bi7KA+fL4m5FiljMWiGC8/nRh3FgJtxXpEaf+1xhIgBGLyLLTZi5JWGTu3IBPErwYJdxr6xlthgCPczi4VlaQMZZpnjg69L/1g+MIL/vbkr4M0VVZY0I9sqEVWnldDi+aG5P1tdrTl69A33j5qerXCYkGbNFnx97qjg4c4oHCFBW3OEozsp6CbsZTN9ZhH96Kv2jjwh8I0MXdsSfIAtOXrwJ0YY+499iTU4RweyItzco2Y6oPV3pUOPAqQsSjy8G7oUZ1Vm7cU7PaU5yTsDsSUWWqmvPskSGMt8sJpNYwdY5CgRhftTfEPX7hjqUIkoO6FPvYy62RNBbLnfILFhr7u5qHFO+MG2zy8J3G53488fRQxd2hKgiInXyVeDKFPYupsOLI3cWFzilj8MGHYjK48e5zodz47tJ09aeirNqJd9zbFOBhNpCUTtuVgPCihUle1uUsxX32ua7mvDfPEQbRFq1UW3AAg62uUtmx3WG3oqzYNzVBqmsqOGiOQ1eVKW1kmUo+0OYt7j3EKgcgtRBQVI8tOdbXV0oh5/hRi5oKxKR4TaFHVmdNykS3dRk5afNQxRkvcmwd2JbOLCiciCpJLWg0UonQGcW5NtwPFMM+fRBuK0Y3H+ofqrInc5Ni7DMYriYvhD/uM7tMpNMTsRVje8yn029+Dlj9hdKlLqKB/EuQgeYRuL9rMBYmMB9NElp1EVl0YcDPGwZ1xAnq3/k2ZpQjl3bsXaEP6mhUbwYgxKML6KEKeOoxsa0lcmJWLmNB3HSuRnYeY2CNuHg4pKtkQQjiXBWZMebO9Pd9j9JYlTHrFoU2ZNeT3VAAiPVOl4iYcyETWVA6pzY547pCRKk3fMBIma4cTo8NeEEIVAdx0h7qBWTkjN5Eg5eCe52Gw+ULTlHEsKklQS5PVFzEvnEaUzug3Q02G1eRQz8QKbcla9ZB0f+gDbYr6JTSkpqlhqs2hxFVsjk6vsWca8GhChoKYZSeTEmO00unqpeyrfy43oniKYjeEunOilQC6SM/sfd/RgqaropI9J4tMM64FMva8cxmLIqvOJy0XBYplMOSJS6GB05ko4WlKpZHQb6dStScQkyYPrS/Qu9DQYJ2tAWL4jG6HhGAqGEbiCUipxGBmL4IR4MLJDoGXmKqgKhtVPFnWVqlqxP42FWcOh5VRi4SRkbgYTsDX/wEGABFnUhjnTnTxEqNR5JG9sHiNUjfq47xl2SmoraT7UyZyC9VHKlUWnYzXojOkIuGHfKgMKOLiPk5VJqZjBl1o8dU9xHEuE2RtBbLyQlJoQZTO7DcbUQgNbdJUzMwcFZ7oaLOmUtF9BvBRu+xwpYGvJa6FoYysjASU4pjQRpUn3StaGlNmUxqP/gLjid8MvV1JCl0EmTyhmgqpHlGhDUnHYbQwfEa3qAR94+0pVkjMg28oGT6jK25lvvk6cslaxPyeqbOXBhkOIeMCNubBXSom2tJ42XPbhaahLVyF8ca2hAk188RBxYbIyO7VqZamgXn6aLxcfbc2p89Twi89rpfILEB6MpWxjYY6M58wTWWMDUNN5ATbO/ZQL7nNoTLWrA71wbTYLotal5QmsvKCyiLqDqcbNKFE7PtrJBbpzLzrhGlgHj8w4GSWywUhBNKZoZwAX2O8UGucumexgTcbMQZ5urK1CcxehGz6qGE3ZMQGwOBI9WA4XAzLEPUyYfgKU2bnYdmUyuiCWTyNaF0VdH/J/G3EXnwM6+QZSG/Gpb/spoFZXY65fwfmrm1KZWiUISbPREyaogQ5Oox+wId5YCf65Bmpc8ABWpoVna67l+Fyo02fo3R/e6KjEkHHJJmUaigbiyjjFIuqtFMjFjfGhvrbH+6iLllsUDgtue2RQCiIPH86WXMj4MN45BcY9NRXHjjMk4cGnsxyGSE0DdJykA4PxMJxARyL4uqOQdYCoEI3Y4j7DKS0raJnEs0Yx2WJ6WpTZqIvX4/x7J8Tc6+PH8DcuRXt+rdfUvsyFkMe30/suYeRp470/RW22cGTrhgELne8IKANYbWC1YY8dwJZO8SAfg8Imw19zXXEjuxN6JOx+xX0G96BTMtM/thIiaytwLxwOrGtvCI1WTBAIjrx0jDQYYhV7FDGoiqWFg3Ff4fj3vDITBqkgmxrwTx9ZGQab6zDLDuJlmJEMOoQGsLu6hKTH+OQsStkUnYMxsP7wuWZSNMt6NfegbnnVWRNVwwO0yD2/CNYF64cMgVFxtWBYn96EFl1MTn4rWmIiZPR5i5VudjZuZ1ZQcJiUTE2LS63p+nEHvrxsBldAG3BCkRuIbL6YtfChhrMQ7vRrroh+XyiEcyyE9DULbSg66rvg02TjFcIJhqKx6xDysvqHnrovF6Xx0BJaSLrq0duJCIl5puvo61Yz2iTcwYE00AG2sBqVwZ5DKG3uLh23dvi7+vwPjM96+ClxBXwDegPl429INIy0e+4j9hP/z1xyNLSiPHkbxF/98VBT36oLB8fscd+iaw83+OAAlE8Ff22+xR9y+5Q6kOin5I0wy2aYnegXX0DxsM/S1hsbHsGbe31iX2REvw+NdnW/ePhdKMtXNl7ufFOhobsiheGg6qMT4cni4w/sBLiEoNYbWBLV55Xz/TnkUIshjz8ZmKFiA4M1TPtMQQ2j+xRVLtUoZgxBilNiPjVuY8xo4stNcVNmz5PsWiGGwO5/2Ns8DIUXD7KmBBoS69Gm7tZUaE6IKUqQ7NvO9qyq5RRHATM/duRPTPhhEDMWYz1/r+H/AnxRQO8W8McwxKAvnqT0un0t3cul+VnkWeOIWbM61oGyIaapKG3yM5Dm9M7aVyG2hXrIhKMy1N2qwbczYvHYlPsBbtLTZ7Fh2WJmq8jByklRMKKcN8d8Q9kItdSMqA3LBLG3LE5YZKWcAhz/w70dTcNR7dHHmPUe1OiUSneR3+bGn2NtfDNFYLLy9O1WNFvfBdmTxK7rw3j1ecUHSo7b+DtSYnxyvPJy9Mzsbz9A0MLWfSc3LlUCKHywlesx9j2dNdyI4axY4uSL+yISZkG5t7XE0nZQqCt2ND3ZIGvNV45grhxtSs2gtUGFruS3LPak0v5jALkxbPI2h6sBd2Cfsu70VdsGHx7hkH0/KkuHeU4jD2voq+9/sos9jhWkJGtvN0e78SQkxjGAVzmoJfQNMTkGWjLNyQNq+XZ45j7tg9O9MLXpoSIe0CbswRtyswh9VG2NA9pvz5hsaAtvVpN3HXAVGXmE8R0wiGMva8l7ut0D84Yxasc43CB3a2qsY4RgwsoCl1PeNNV6u9QoAmlRdED8sJpZE8NjXEMCsLuQBQWJy0fsUnQvxFc/pkGlwd9+brOrJZOBHwYO7aoF2WAQ3zZWJeSqaDNWjikobJsb0E21fW/4SAhNA1RVIyYNi9huWxtxDzeVTHBPHUEGhJLz2gLVvTv/TvcSqxc0xQLIdAKrfXQVIVsqEA2lCObqpC+ZhXrleboUIFCAaW21gPa3KVJAj4Dh0BbuCo5Fh8KYqTQar5ckNEwsrmq3x9a65IqlowlpPoYyqoL0DAw5b1xJOOyG10hBGLqLLSFK5Myq+SF06qab7S3el49EOpFiCYzd/Adi2v89trmpSI9U6lndR/uBvyYpw4jwyF1/D2vJGVo6etu7PcDItwZkDMRCqZBbgmk56qYrTQUNSzkV9lQzTXI+gvI6jPKGLc3IMMBxQDpkB8cQZjHDqTURtBXXjPkNjs1dntKQUYjmEf3KYrcaEDGSyf19xOLqPs7RmlP2pI1yc9fOIyxfcuY0i++kjAq2gvCakNbswnz6N7EWJxpYmx+XM2MlkzrfxqlN7WuIRSClNEI5vbNEEpRtnkYIDQdbeoszKKSrnOWElldoTKzcvMTJxgBMXlGsiB6KmgaAq2TcC8dbkijky5GONDFZDDjGWpGe2Ic2OaE3OSh5HBBSlMVnzR6qGnlFCCmzh76pIwQ4HCizVuG0V2cRUqoq0ZePIcYYqjpkmB1QG7p5T/uMEPkT0DMXZpYgkqaGDu3oK26BjEGRKquNIzO51UItElT0VZsSFaRD/pVXneqmvU9m0nLTBmrHCwHVMaimHu3qzpkIyRygRCIolLE5JkJXo2sr1Z6AScPJ+aex9keOJwDN0hxEXfR8aMpMr5Iy0HkFiMKpiByJ0F6ntIC6FC9Mg0ID/MEYk80NyLPnUi6vvri1cnK/YOEsFjVJGyPEIVsbcI8d2JUQimi+30Y4M+YhG5Bv+bWZEphQy3GU79XqcKXen07+OR/Ixi9MU186CyKkz058/AeVSSyP6RnIrKS453m/h3JkoG9QMaimMcOYLzwaGpx9WGEcLoU9cvTjT/qa0fWVGAefjORtZCVizZzfu/c3P4gJRhRZDiIDLQj25uQ7Y1If4sysB1VgC8TzJOHkT1DC1Yb2rxll151tUPDuLRHGnPAj7xwChkY4Q/KWxhC09BmzFejz+4fBiOGuX8nsaf+gFlbOaQy6jIUwDx9FOPlZ5BXQEXk4cKoFqYU6Vnot91L7AdfSxRxNgwVZpg+N+XsaSc0HW3RSowXEilIsuIcxjMPod96b2qtgo7t/D6M7S9ivvxMglrVSEKbuxiRV4hs62BJSCXwUlORkCKtTZ+nxJUH4gF1ai2ElTGNhuNaC0aX1oLZQ+mNeNFKhyeeCj2CKlexGPL0kSQFNzGhVBXYHI54Zma2Esc+tr/bx0Qiy8uQ9dUI9xBLQI0D3F70a25VSn0Xu1ViDvoxX38RWXEOfcV6tIWrETn5fWgGS2R7C2b5OeS5k4phUl2O9LdhW70pedT7FsXolmAnTu9asgbzzUSqlKwux9j2DPrdH1JiLr3tv+4mjNdeSHyhYzH19ay6gLb2esTMhUpjVUol6Vh9EfPEQcw9ryHrq7t4iBYL2oZbMbc8MQJnqiA86WgLV2KcP9WpqmSeOADhbnFouxMxfS54MwbUpmyuVfHZDklLKUli3Gu6YjhYHSp+a7V3Vi3oN0vvEmE21GBWnU+K54oZ8wZ8jv1BWG1q1JSWCa1duqyy6gKythJZMi3lEF6aJrKuSqV+B/3KOw761XMS8CcsS6Uta544SORbnwGnW+l5ON0Il1vNNzjdiI7lnnS06f0LbUsZ709NH/2J/z+VIL558lByf+K/O/omHC5F0xtAfyAu1jNlFvpN7yL28E8TR4SREPLUEWLl5+CFxxA5BWgTisGTATabml8JBpCtTeo++NuVkE44qNZJmVpE/C2MUTe62Ozot7xb1VfzdRt+xqIYB3chFixXugmp6mUB5Bai3/AOjKf+kFg8LxzCPPwm5tH9oIl47Dcev+wwTt2HRHYH+u33o2+4mcibryk5yJGAEOhrrsN44bGu8/X38AALJqBNnT1wD9CIq4gJQWcWmsWmNCY6NHS71UK7XBlogPJuKs8nE+odLrTJM3ufDB0shEArnobILVBxxg5EwsrLnr8MXJ7k/YwY5ubHVfHFzjRpevy/jzBMwNcpVC87r6fofpHV/9OzsP/XQ/2fh2Fgbn4CY9tfh9gf/8D6k5aB/ft/7r8/HXtZLGgrN2ABYn9+MPn9CAaUca2vxjhxoOs4A+333xBGnacihEAUTFLUqJ6TYrVVmLu29V6CRQiEzY6+7ma0q65XlQR6wjSURxkJxwVfOiQOzc42yMhGv/09asLA4Ro6UX+AENl5aItXp16paYhJUxETSgfeoM2hqvqm5yNyJyEKpyHyJyOyihCeTITNoSbVOiZsLqVq8iAhoxHkxbMJ3ieAKJykuMvD2A+RV4QoKkl6jowOucfeXvoORkeHEFCnGNAgJ3g6tu/4oHdUhTCMZNZGXxhr/YlDaBr66o1YP/bPaiTW2wez+zH767cQKgQ4VicSRwCjbnRB6WFqy9alMDRSCZEf399noF1kZmO5/T3oN7xTtTHQG2h3oM1bhuXuj6BvukMNC4XoU+dguKCvvzm1uI7Lq4rsDUJ4R6TnILKLEGnZKkarW8bOQ9zarPjPPStETChF5E0Y3mPpukqM6WkMqi4ohshYFTm4wiBmzMf6sS+j33afqr7sGkJ4wOlGlM5AW3cT+tve9zcTzwUQ/RCck1bKWBR5+iiyMTFzSmTmDq2SZ0e7kTDy7PGkdgHl+U2c3H9dsVAAWV6GefIQ8twJzOqL0NbcJcZss6vyQNn5Su5x2hy0KbMQeYWdwh5SSmhrweyWzSTyJyKmzRlWr0yGQ0T/96uqokb3cy2ZhvXz/97nBOCVBOlvV8Li3WPuQkNMKEErmT7sHwfZ2ox58mASV1tMmYUoTPasO6RBEyRHRwJWO/rKDf1upvpzEllzsd9tL60/tktKSgFlC6ivwawoU5VaKsqgvhrZ2qKSjExDGVObHVweRGaOSmQpLFajnNwCxQN2uAb8HJiVF5BlJxIXOlxoS68a0vsppYTmesxj+xNX6Bb0FesvhVnTa2cGbXTHOqSUSjYw4EOGgvFwQnwopWlK3tHmUNoELpeqETYKXqGMRon99n8wX+sm2KNp6Lfdi+XO944dT3Uc4+gPUiLDQRXTDcdDeB1zJyKucqdb1OjNZlfcc6vtrf6M93pyoz+RNswQQqgba7OPbenNYADzwM7EZRarUsZ6az+M43irQQjlrTpcY/udGwDMaBRME2EbuXqBbzmje6XA3PVS0gShNm8p5BaOUo/+NiFD/tRVaDsmGzVdDZGt9hGn1o1j9FH32BMEzp6l9HOfQdhHhrs+bnRHATLox3ithw6wrqNdfYWIbr9FIKVEXjyGPH80eaUQqmCk3Ylwp0NGHmQVIjyZY1acZhyXjrYDB4m1to2omM+40R0FmPt3JpUfF6UzVAbeKPVpHHTxnEFRneLVlKW/FeouIjPy0IpnI/JKxg3vWxSazYolzTuiWhjjRvcyQ7Y1Y7z6XKLOAqCvvU5RncaHr6MDoSFK5ypBIFCTQJEQsq0BWV0GIR+01GEaMTS7C5GZP7r9HceIIH3lSip/+WuMQHDE4rojZnTNWEyR8cfLpShIiTQNjNdeQJ4/TXdiiJg8EzFr4eCrO0jZpVmh6eMG+1Lh9CIy4gJK8WKfImciYsJ0zEMvQ1sTtDch68shLbvP9PRxXJnI2rCeYFkZxz7x90x8/wPYJ05MsGGa3Y6z5NIkUEfkqZFScvIHPyZz0QIKrtkwEoe4otBZkHH/TsxtTycKpdvsaKs2qmyqwRhNKcGMKZ1cTY9Xkh03usMGIdTV1HVwetGmL8Pc+yIAsr0JEQ6Cy5u8X0dlZiMWp07FujKyuk/MWWyq/Z68YSlVWKNjcs+uUrj7ezZkLKqehbimsnC4xz/CQ8DFH/yI6of+hBEMcWzvvqT17pnTWfLU45d0jBExukYoxIVHHsU1YZgzjsYwpK8N2lvixSCtKuZnmhAOIVsaMU8cxHjlOeheDkhoiJkL+i6v3heGIKc3jiFACMjIVUR5I6aqLhupK1LIoA/Z3gQt9ci2Bgi0qfRzpJqYc7qVvnHORGR2Ucr7LhurkIdfA2kipixETF7Qf4HNmnOYJ98EI4YomIyYu7ZzHzMSIbD9dWSwi6XhmL8A66RJvbXW1RfTJHz8ONEL5wHQ8/JxLlyI6KGBHHjjDYzGfqRRNU2NDmxWdI8HLT0dS14+msdzSUpzZiiEf9u2ThU94XDgWrMGra9irr0ga9MGnFNKe11vSb/0pKVhNbqBqmrqXn2NlsNH8ZddoPzxJ2g9fhyAzEULmXDzjWhWK9I0aT99hvrtOwlUV2P1uMlasoTsZUvRnQ6kaVL1/IsYoRBpM6ZT++prRFta8ZSWUnDdRhy5QyjHM8Iwz53AfOlJNSS12dUDbxjIgE8Vn6yvTs4/z8pF33QHWv4QP07m344G6ahDaKDFja7Zu5aArDyNLD/eVbuvgwWhaWpZezOyvRlZX44ono2YPJ/uZc4FQFoO0psFbQ3I2vOI4lmgOXr3XI0osrFa9c1igdyJCUba9Pmo+/rXiFV2iQ6lvfMu8r/xzf6NnWHQ9pdHafn97wBwX7MR+39+D72H0W388Q8J7tyZqoUuaBrCakU4HOgZGVhy87CWluJYsADPxo1Y8oYWJw8dOkj1Zz7d6YQIt5tJv/sD9rlzBx2TTV+2jPRly4bUj4FiWI2uGY1iBIPEAmr4LDS9Mx7ScXOllDTt3c+Rb30HIxLBO3kyrc3NXHz8SaZ94H2UvOudCKuV6i1bqXv1NbKWLEazWpX3/OdHady3j3lf+iL2rKzh7Pqlw+9TGgO+toGl8dnsWN72gEqdHtIwsFs8dxwjDyPW5d1abL3H372Z6r5k5CGyCxHe7E6FNxmLQH0FsqZMTdJdPI7MzEdkFnTtL4SiqeVOVJ6yvw2aapD5pb0Gj2R7ixKnR4Ldhcgp6vd0/Nu2Ej56BMf8BYO5CpcG00SGw8hwGLO1leiFCwTf3INv84v4nnuOnM9+DsfChYNutv2ZZxJGfdLvx//qq9jnzBlyiEVKSaylBRmNYcnMQLvE6ibdMaxG1z1xAqX33E3GvLlUPPEURTffwKS33QmAsOgIi4VIczPnfvcHhK6z+DvfxFNSghEKUfbHP3H6wZ+TuWgh6XPnABIjEKDgmg0U3XwDSKh6/gUOfe2b5K5Zw6S33T52S5z0B3calvs/ibbs6oGFFVJ5VR1plp3bdP6TGqLzn8EfK6mtQcaeB4L++tdDMKcv1ao+jznU/PyGis6PnHCnKWW3VM3nFSOW3ajKIekWRPcJTikhQ4l8y4pTEA0j68oTjS7KWSEjD+nwQMiHrDiFyC/ttW+yrR6CSttC5BYjLP2T+o2WFloeeoj8ufOGR0S+G4Tbja10MporcXgvYzFMf4BYdRWm398Z6zZbWwns2kndN75Owb//B7bJUwZ8n4z2dvyvv5a0vP25Z8j68IcHTe2TUuI/dpxz3/p32g8fwbtgPlO/+mUckyZy8Uc/IefGG/DOnTOoNntiWI2u0HV0XUezq9pbmtWGpceFjzQ107BzF1M/+D4y5sxG6DoWr4e8q9dy8ZG/0HLkGGkzZwDgmjSRnJXLsaWnI6Uk/5r1WP/rf2k7fhzuuLX/ONflhMWipCXDIeUVJUyexPVtHQ606fOUOtOkKYN72I1Y3NAayQbXNCDU3vu+mq7KtPeFjvak2W0CyOw6B6GrmKYe/0j091J0thef8DO7SRWqIEzXtdEtcWGRPjK+wgHVjm4Bmyvez3BcQ1mooX/36hemES/EaShbrlvAYlfn0R/ifZcdffe1YJ6JC6LYnIjsol4rbQihqWrMqfSfhVDx/rxiZN1FCAeQ7c1d17hrQ/BkQnqOMrottQhfM3hTjO4iIWRLvboOmo4omjYwg2UYBHfvJrR/P84lQx1tpYZ9+nTyv/4NbNN7qdZhmoRPnqTtL4/S/tyzGE2qzlr42DFaH32UnM9+DmEZmGkK7tiB2dKi/rBYEJqGjESInD5N+MRxHPPmD6rvgVOnOf7Jv8eMxvDMnkW0uRlpxNDsdtr37ifW3IL3G18bVJs9cdk5L2YkQswfwJqe3hV6EAKrx4M1PZ1QbS0yLlCjO5xYvJ7ObTSLBVtWBlGfDzMaRR9DRlebOgvL296nyo+0NKrZ51hUTay5vYiiErRpc5Ra2iBkGwFlYCIjVBq+8xiyMxkgyWOWEmRMGaBYWFWf0C0JsciUiHZ8gFJN+MU/SoapthECLA5llPoyAB0arZFQt5i2BCMCmGB1qvXhQNd5SLqEj+zuXrwfCb5mZH1F53FkJKjiqvXl6nhWmzJqORN7H2X1Z7yEUF5yR3iilwk5YXciMvORjVUQiyIrzyBmLk9sX0oItkObEhQXmfngTsGo6N6uw4Gw2zFbW4nVVOPbvBn73LlojtSe+9Ag4pNmvbyfuo5j3jzsM2ZgLSmh8Yc/wGxrQ0ajhE+cIFZTg3XixH6PIg0D/+uvYcYnCG2TJ6NnZBDcswekpP2ZZwdtdBueex7N6WLBn35GuKqa01/+qjojIfAuXkjLzjcG1V4qXHajKywWNJsNMxhCmmant2eEIxihEFZv10ymGYtihiOd+0rTJOYPoNnsY47/K7LyVILDiDQer2fWHdLsVikjPlnT2/ven3Hs8Aq7G4B4KR865P+7C1JHgsrTs9j7MDKyh8GNe7Wdqm4ykWcsJcRC8YmnPvSAOz4O0lDeLXQZXyMGWiR+XWS39Ub8ePEss1ShASlVjPXi8eR1mqZitAVTEIUD+2hK04BAO4T8KowQi8ZHKaaiDMbCXefT09PtuGLZhcjK04ob3FiJDM1BOLuqX0hpItsaleEF9UHoJ4Skeb14b7yRloceQkYiBHbuwHPzzTgXXMbYbhzCZiPtjjtoe/IJwkdVKnasvp5Yff2AjG6suorQsWOqSIHVinPpMjSXi+DevWCa+F97layPfQw9La3ftjoQrq7Gu2A+1owMwlXVCet0p0uFRS4RI2J0LW43IIm2JVd8sKalkT5nNo379jHx9luw5+RgxmK0nTpFtLUV7/TpnUOLUE0tbadP45pQhATaz5wj3NCIu3jSgIcfbw0I5V12hxHtMrqa1lVOva82UkHKRIMrhDKmuo4a7qM8xQ6D1bFdNKKMst6bZxr/UBhRZfx0vcuIdy/jYsTU8Ts932j82P0Yc4u9K9QRC8c9dOIGWXZ544i4Bx83ch282VR97l5Ro+OcAdwZaDOWQ3q2Ch/0ASkl1Fdg1pbF6WLdioR2VlEYGNVPuNIQGXlIXwuEg8iGSsSkmV0bxKLIhkrVZlwfor8ggRkI4L3pFgK7dhE5fZrI2TMEd2zHPn06mnPwFKtLhZ6ZhaWoiPCxY+rDFwoqSdYBIHToELGaGgA0lwvXypWYoRBaWjpmSzOx2hpC+/fjXr9+4P3xphEsK0uubiwl7YePYJ946TTYEbFcrokTcOTnc/a3v0dKie5w4C4pJnfNauw52ZTe/U4Of+Pb7PviP5N31RoClVWUP/lX8q9ZT8b8uZ0Pfqi+gZM//AktBw8hdAvn/vAQ7uJJ5G9Yd+VOog0FPc811USRSLFdf+gwct09Zqsj2ZAKgLguajge50SqIbvWh1dqsSW2ldQmcUMcN/zQTYe1j34LLR6G0NS2FlucohU33JoloSac4tdGurxKaSbHdoWGmLqwa8IqGlZx3KZq8LcioyFEPyZNmgbyzAFk1em4iLpUH0N3Bri8SsdZtyg9h6qzXefcx3mKginI6rPqw9FYhSycjLDYlHEPB6BZGR2RM3EAH16QwSCa10vG/Q9Q9y9fQUYitD/3HJ6bbsZaPLzlkwYCKWXidbVY1E9/+0WjhI4cwWhSoRXN7ca5ZCmxhnos+XlEWpoxAwH8r72Ga93A7UXG6lXUP/0M5T/9OZ6ZM0FKInX1XNiylebXXmfmf3x7SOfZHSNidDWbjVU//TFHvv1dTv/052hWG9M+8F5yV69CWCwU3XITFq+XMz//JSf+50fYsjKY8sB9TL73HmyZmZ1fmcwF85l4+61c+POjBKuqyV6+lNmf+wc8UyaPRLf/NtExFAcVZ+zVc0UZOasdwnEjbcbUkFmmEIJPZWhTtilAt3UZoM5qxn2gI0zR2X48dNERquiZnqt1C5NA7+3b4opixI3BpJlq+B6LIE/uUSnCvZWqlxJZcQpZcUJ9AOwuRMkcRNHUeGhIdH1I2pvURFp/RhdUUoY3C5prkb5mRFsjxNkOsvZi3Ou3IbILB1blwDSRoSDeG2+i5fe/I3LqFOHjxwi8+irp99wzIIM3nJDBINHKys57Ys3Lw5Kb1+9+0cpK5R0bBgiBY9489IICtMxMrBMmEDl5EqJRQkcOE6urw5o/MA5w1jXrmfCB93HxBz/G8PsxIxGOvO+DCKuVCe99gJybbrik84URMrpCCDLmz+OqP/4m9XqLhYKNGyjYuCHl+o5XQrNamHjrLUx97/0j0c1xdDAKOtDdO+wNHVWVO+6SaV56pb2OYX2CMYwzHFJu35Pl0MPoJzEUBu+9CSEUvSt3ErLmHATakGWHEdMWp+ToymhYGdJYVFHFSuehFc9OeT2llH2y+3r2Q5s0C7O5FoI+ZGu96pc0kLXn1UbpOeBKH7A3ZwYCaC4XGffcR923vgHRKC1//AOem27EknP5Eo/MSATf5heJ1ca9dYcDx5KlWPvJZJVSEr1wgfCpk2qBEHg2Xau0XhwOnEuWEti5ExkMEquuInz48ICNrtA0Jn7og2SuXUPj1peJ1tdjSfOSsXYN6cuXDctc0hUQGL3iKgZdOehZpXWggjvdvcphS0XuZsj7u+cD8Z57/Vv2337HbnYnFE5BttRBsB1ZfRaZVYjISWEUgr54ui+gWxH5Jf3Q3waR2JI9QfF+A23IljpEQUAdz9+iQhAZeXHtjYHBDIXBYsG1Zg2OOXMIHTxI5OwZ2p95howH3jtyIQYpFVfX5yNaXUVw335a//RHRRnTNNzr1pF2551JKcZJzYTDhI4ewWhQacea241r7VWd612r19D8q1+qRK2GBkJHDuNetw5h638CNFRZie7x4J4zG88l8nF7wxVgdMcxcuhhgKKhAe7W3dB2tNFPYkPPktzdjzuIyaUBea1DiW/3hsx8RN4k5MUTihNbcRI8GUpQpjtkB/9YHb9XBTLTUEY8Fkm9PhUsFkTRNOSZfdDaACE/srZMrXO4Eem5g/LAZLxop6WwEM91NxA+cQIZDtPyhz/gveWWS/Z2oxcv0PD9/0pmDUiJNAzMgJ9YXT3RCxcw21rRMzLw3n4H6ffcg62ktN/2O5IpOuBYvAQ9J6fzb9uMGViLSzAaGyEaJXzsKLG6WqwT+9eaqHn4UXxHjpF51Royr1mPs6Rk2D9CgzK6srFCTRDoVkjPQQzi6zqOMYaeXi70yhntp6He2+9oMxbtMkrDosh/+SZ7hG6B4jnIunLl7TbVQH05YuKMRCqexdaNomYq4fP03IQXVlWerVW830GlcAuVFnzxOESCyNYGZEOVWuVOV+GFQUDG1LE1ux3XmtW0P/cs4aNHiFZW0PbkE2R+4O8uydAYTU34X9rS90ZCYJ85E+9HPopr1SqsJSUDonZJKYnV1hI6eLBzmfe66xI+sprdjnvdOkIH9tORdBEtrxiQ0U1bvIj2A4co+973qfzVb0hfuYKCd70T78IFnR74pRrhwUXjohHlDUVDI6pwJTSNxd/6N9b+4bfYxprGwjh6oA8qWjigOL3ds9s69hGaCmdoloFNAI0ihMONNnUhHfQzWXUW6WtNLOni9CJcXrVNNIJ57iAEWpFGFGnEVMy36izm8V2KpzuIF1cIoTzaXBXWkNVnIexXseOsAkRPDne/MDsaxj5rNq5Vq8BqhWiUtqee6qRhXRI0TVH/ev5oWmf8Pnz6NK1//hPtzz5DrKEBGY32/1E2DPyvbOtUTBMuF85uoYUOeK6/vjNjNVZXR+jYUcxw/xOXmVdfxZwHf8TSvz5B7i030bLrDQ69530cfNe91Pz5EWJtbYO/Fj0wJp92IQT6sGbIjCMJ8Un/BNj7SRVO2U6KhjoSKMzuyRtxZoRmSc4Ik1JVZhhwiOEyQwgomIKoPqcyxFrrkbVlCNeCTvF4oeswcYaSdfS3QkMlZku90tzVNAj41DWxORDTlyBrzkNLXb+H7uyCxQbZE5E1F6C9SS10uBA5ky4plCIsFrw33YTvpS1Ez58nVlGB7/lnyXjgfUOeNNJzcnCuWImeJIMokcEQscZGYjXVGI2NRCvKaf7Fz2n585/I+sAHSb/nXixZWb2ek4xEaH9xc+ffrhUr0FOU17FNmYp9+nTCcZXDwPbtpN1xp5Io6AMdUgbOyaVM/uI/MunjH6X59e3UPvIXznzlX6n67e9Z+uxTg78o3TAmje44LgcESQOd4ao+EYskGlyrrZ/sNRjzE6ZCICYvUFoJkaASockqRGYWdFGOswoR05cp2lh7s6KEtTXFk1cckFWktG4LSpTOcmv9oI6PNxPSsju5uWTkKPGdS4R97jxcq9fQWl6O2d6O/5VXcV97HbZJxV3HHgSsEyeR/fFPYJ8xI+V6KSVGYwOB7dtpe+Jxgnv2IH0+mn72U0y/j6yPfBQ9IzPlvuHTp4mcPtX5t2t1L7q5QuC57vpOoxs6eIBYTTV6dvaAwwMyFiPa1IzQNFwzphMqr8A2DLKy40b3bxkiPszv8DCNmEo6uFR0j1d28HD7M7gjWH21L4i8YnB4lFHNSOaHytYmzMO7kVVxTqzLgTZnMdgdXRlx3dvLnYhIy1S6ueGguraarhIj3OkqjVdoUDhZearWPnRye8LmAKcbmgEh0IqmD8tHUug6Gffeh+/55zGamwgdOULwjTewFsYlIoc55V4IgSUnF+/td2CfM4e6f/kKwX37kMEgbU8/jWPJUkUBS6GR0f5sooxjYMd2IhcvJg3aJBAtv9j5t9nejv/117HPntPv+RjBIG1799H8+nba9x8kUl+Pa8oUCu65m/SlSy7l1IFxo3sFowcNqh8CQeom4llmRofRjcTJ8ZfwIvdkJnRPre0NnRlxlxcdXFyRkZrDKcMhjJefxnxjG9q0uUrwxuFVBSx70V9Q8VcPODx9J9V5MlU598EgHFB6DgCudBjG4pi26dPx3HQTrX/8A2ZbK76XXsJ11dVYcrIHRLUaCoQQ2KdNJ/3udxM6dBgZjWDU1RF6801cq1ajexPFe8xAAP/WlxKW+V95ZcDH8z3/HFkf/Ls+jW7d089S/bs/ECgrw+J2k33jDWStvxpnaYnycodBBnPc6F6p6G7IzG7MgMF4PkKAZlVZPUj1OxrpEtdJ1VbKFOSe26XyO3prSw4sM2sUIBvrkCcPoa3aiL7pzk4FtCGVVrrUvkipNBja44pihVMHX8i0LwhB1gc+SNuTTyD9fgKvv0bkxHEsa9Yi+omDXupxXVddDRYdoqi027NnMH2+JKMb3LO7k5vbff8+0e15DZ88Sfj0aRxzeufftu7cBUIw9Sv/TObVa9GdzmGvCjxChSnNrnIloIZX3eKF0jS6vJuOonvxarlJs9yapvaFgRmUXtsRCKGn+FJJlXbc2R+rygVPoPp0Px8RF6dObEcaRmccU3SUZxlJdKiAdYQGouFkURzVM/UrlVBLh76r2VEVIW4ApVTLpaCnQ02HWlfH9bLaSdioQyO3I8Jgxq+dxUpSY6apmDBjdQItFED6fWiFJaq22Ujf097QMTFZe159GG1OpXY2jIcQQmApKCD9nXfR8ptfK97uQ3/EsWjxMMs+JkPPykrwPo12n2IydIM0TXzbtnUyEITLhX36DPSMjD7bNtrbCR8/ptgOhkH7s0/3aXSn/PM/odmsoOvqne5DBW6oGH6ja5pKXam5Wr1MFhtk5IEzTqcBJUXXWKlOpHA6Ugg1ex1ojxf9i7/Qug42J9LpVZScviZ6pIxXRPWrbJ1ot3aEDjYH0uFWw76ewiAhn+qPlJBXmpzdEw5A3YV4nyyQPTFZFNzfDB0TI3klg8oQGjSEUAZQt3apZxlx6UCtIzzQTWdAaL1WOkAIZTg7BLuRXapdnfKO0OlJd4qQkzKm2bnciHZt2yEm3j2FuPPDGxeoEYxamKEnZF0VsvoisqIMAu3IshOY0kToFkRRMaJQTTDJ1iZkXRX425GGgXC61LoMNVkj21uR5ecQJdOQ9dXQ3KCud1YuorBYecwDeJllJISsLlNav0IgJkxX93O4M8esVtJuuw3f888Rq60l8PrrhA4dQgyhwONgYPr9CXFaYbUmfeBi9fWEDx9SMo6AfdZsCr79HWyTJ/fZdqSsjNqvfJngbqWD63/5ZbI/9gk0d2qmju5yEm1pof3AIYIXLmBJSyNr/dVY0tPxHT+BY+IErP0Y+v4wvEbXNFWtprb6BIMrnN7UnpaUytMJ+RTNpmMCpiMP34gpAx1SdBuZka+81RTtdAhOK+pRNw8XQMbU8pAf7D5kWjYiPnlCR9UB3aoMTTSU2uh2wIh1VYbolAGUcUlBM17BYGRiYEmwWONeZ9wrME0wU2Q69ceD1dTHrbMSQ0cig2nQ5bKmQKfebor2LI5uXmyHmlmK5Avdqox+99HPKEPWVmIe3oNsrIVQEHn+FLQ2Ia02NIul0+garz+PPLZflUk3TWhtQkwoQb/n4+D2IqsvYjzyU8TcpciqC+p6+VVMVr/xXbBgRfKwVUpkoE1RynQLmDGkr7WrXFBGnmJADGdoIQ4hBNaSUjzXX0/L73+PjEZp+vnP8N5007AfqzvCJ0/GQ1wKelZWUkgjdGA/0do4xc5iwT5z5oA0dy35+Tjmzyf45h4wTWLV1QT378N91dUpt482t3DxRz+h7omnkLEY9glFuGfPRE9L4+IPf0zWuqspvPfdQz9ZhtPomiYy0KoMrhFTL1NmoUqX7OuL3FqnPCHNAmlZ6gEWmvLaAu3Ka5Ym+FqUYUgx+SBjEdVOKC4wbHepiYYO42fElPcbaFOecEsMmaUjbE46RbPjkntEQskRyI52Nb1L8Lu70TW7hR+sfRQtHE6IOOXL6lDXzoz2KOMjutgJ2gBusxBxWcd4mMToEMNJ0Z6uq9FDX+epW0A4423FkhkNHUkRHZq3ozRyTwUxfR568VTkhTPE6mvQ116HmLtMef72Lq9Pm70EZi1CeNJBCMzj+zGe+C3a2eOIBSvURrEY5qE9WO75GCI7DxnwYTz+a4yX/4plzmKwpYiX+luR5w52hY86wi+eDLTJC1QW2gjpI2heL+51G/Bt3UassoLQ/n1Y8vpX/RoqpGHQ9sTjCeEE27RpaJ5uYu3RKKEDBzplHHWvF9eKlf1qNAAIpxP7nLnoWVkYDQ2YwSD+bdt6NbpNW7fR8NzzTP6nf4SYQeVvfw+AZrFgTU+n8aWto2R0E0JzUr2WwXZoqVUvl25VJU06jFpfiISUx5Y1QcWqhOgcgUqbS72c7Y1qga9ZaZN2timRplQGucMwxmd1RXedVylVkT+bQxHSo2FFcM+ZiEBDaBZkx1A50kN/wIh1DbWdXiUyEg0hpYmIWwrZYfBAfRguV5pqBzNAWAErnSyGzvVJ/+m7LYhnielgSaGE1bO9vu6tEN3ain/8EgYgPdvQwOHtvV27s9v+Kdbb+lnvcCetl1JCSx2y8hSieA4iLVutdjjB4US6vaBr4PIoGliPIa8ojfNQ4x86be4yjGf+hKy+CB1GF9CXrEXMmKfCE1Jilk7HfGNb77Fsq109a+GAuk4OLyJ3EmLidKUmNoKxZaFpOBYtwrl0Ke1VlchwGN/zzw3rMToz+WIx2h5/TKUMx8MLlvx8HPPmIbrFkaPV1YRPnugMLWgZGThXrRzY+QiBY85srBMmqkm4WIzQYSX3mOpj0rb/AGlLF5N70434T55KWOcoLaHtwMGhnHIChujpdk2wSFAeaVOVeoisdsgqUi/BQL/G3lyl49BD5FpoOtLlVUUXo+F4nato4ktsxlQ8FRQBPz0vWVhbCNWWO0MZ1UCrCjeEg0hHfIKkY0Y6lujFykgw/reuqEDBdtVG9wB7LNbD6F5mdJ6rGJB9HZW2uv3qdds+jbjW+/4dYaI+2+9lf8OI11sbXDl7acSQZacwt7+ArC5Xcd1wEFqbksIkomhSV3itI4Yei/WeD5KRh7b2jp4nEP9WjfwHXUtLw3PttQR27cSoq0NGBiHOA8hYFKOlmVhPpgEoB8iIEauopPUvj+J74XlMn6pkjG7BdfU6nEuXdYZdpJREys4RPn063jkN18pV6FnZA+6PtXQytmnTCB05DIZBrKaa0KGDeK5NLq8lo1E0lys5G09KYq2t6MPA5Biy0RUdQ5/uBtfmgIyCgXm4HdAs4E5Lvb0QCN2G7BzGyk7R5k6E/F0vjNOrLtb/b+/Mo+Oqrzv++b33Zh/NSBptI8nyJm8RYAP2KXESFtcYSiBhL9tpSAgFetpzutAknBTOSds0oSlpe06a0kKbhq1lMTscGvbFUAyEYGMZLGNLxtqQJY22Wd+8X//4vTcz2izZFrIh73OOjzUzv3mL9N59v9+993vvdNvSdGTANpzSUsbacX8Y3qL7wMwUjWc2U/RPe+wWMU7DRcdXaplFBdbRMLou847cvQPzjlvRVpyAfs7vQ0U1JEcx7/jx5MEe3wSDf/AVgphpzKeMEILg+i/hW/UFkn19U6cJHoTsRx/Re/PNU0purVwWK5FQFcBK0XWC69dTcfXV4+TDMpsl+8EH5Ht71RuGQfissw4phUvoOsFTTmH0uWdVQ87+ftLbthE67fRJLorgsmX0PvooY21tqgQmYGUyjLXtZuCFl6g847RZ73c6jsi9IJPDyqUgLWVsojWTZ6wz4fVPHWRz0ATjLsCSJZkE1XW3sC3fwbclhDKajv+2NEBmeIp1YrNpe7kqVRaElEozr+lIw6uMrq2jV0E0O1Kve4648n52aIj2Rx6n8vgWqmapfhls3Un3Cy+x5LJL8VfNfgZwdFDl/RgZUH72ie3ky2sQ4XI10sor11FyWM1IPT6IxBD+8YFOaVnK7TM2ZI/z2uPGR6iltFTnXEdgEChDHqb8WO7eAekk+gVXI6pUJwfrvTePmWDgkaJHIkQvvoTU1jcLxWVmi0ylyO3dM/t9VdcQPnMj5VdciW/5inGfWYkEya1bC6899Q0ETjr5kI4HULUgKiqxhoZUucedrZi9vZOCcbFNv8vgli20ff8WfPE4ma4uOu/8Ban2DrSAn/hllx7yvidy2BZCZpLKx+q0zw6WzRw0m4rp0o5KOdgm8/bSx/EfzrR/TSsGgJx8XqGrvlNCB3JFv27eLAbIvAEVQHLcEJkkMlyBsEqyB7z+GQ52ZrKJIT684z9ovuqK2Rvd7e+z/bZ/on7jhmPe6EpLQs9eVVPWkcQO9Ki/RXVjybLSgt4O5IH9xaBbNqVWVY3LC2ou1Qhyn+ra4LQbyqZUCmDjCkRZZXHcgU7Vm0w31AN6oOewZc+iKg6ajvX6c4hlLcgDvVitv7Z7o30+CH3lK/hXryFVUrt2LhCBAHo0infRYuU/XrcO/wmrJxXIkVKS6+1RJRptwmeeOc7fO1uM2loCJ64h19EOUpL58EOyHR2TjG6gqYml37+JT554isHXtmBEo6T2thNdt5aaC8/Hv2jR4Zzy+GM5rG+ZOTVTcYyNlCqf0V82OQd2Jo40KODk981ylwKh8oIL388Xb1bdUKoYOztBmtmi68JnB8ichohZNUuWMj/eMH9O6Hr+RSRQv+H0uVPj2I0wZW87VDUg4s3qWimrRLa/r6SxQbuAy9gQsms3VMZVnzFNV63Md7+rDOfiE5QUNzmM3L9LBU/rl6kHo5lV4zrbYOkadU2aWVWAPFCGWHS8GpdJIdvemeGgpz53seYUtE+6sN58EfnGc2h1jeinn0t+JDEpNjHlJufBN+ugR6M0/vLuwnWq19TOav9aMEj8H27DGh0pvCeCoSlzXOt+fOssZ8RCCZ50HeH3o4VCCL9/2uCgb9lymh7cjOMA12OHN6kQmkbVd75H5XU3qG1p2rS92ILNS1lw/bXEr7gMaZoIXUMPh9FDoTm5Fw7P6EoLLGlHhEvqpg73IcvrVOHnWR/cEZ6E406Y5SpRiapKawOUBji8kBa20MDOvbXyyhg7VbIc36+ZK6ZC5YtGd25upaPnzwO1XN9z/0NEli+jfpo+dodNLgOWhQiUFeoXyLJKCi3enZlu3351c9YuLMQIpOGFuiXQuUu5E8pr4EAnAKJukWqv44yLN8O+HWpyUBmHRB+YpsoCKB0Xixe2MRGxeAWem3825QpK+ALoX70Mzr4UsAOquo7RsrYwVjS3YNzy8xJRiEI/+1L0TRfPWxNIoet4m5oO67tGTQ3MImWsUBxnDhFCIAIBvEuWzMn2jFgMZjLaUmJls1iZLMLQEYZaFVuZDFYmg9D1oySOEBqEKxGRmDJiBz4uFuPQvchIlXpyzcfT3HFPFNrBzCDZK817FCWuBlB5qkIUDUDeTgULFMvnCd2D1HX1WTalvp/P2alRs1MYFQ5FSnLDw4x27MMcS6IHg2iGPulpapkmY/s7SfcdQOZNPOEw4UUL8ZTkMjrjBlt3khseRvP6CC9cgK+ysrC93NgYqe4esokh8rksutdHoK6WQG0NmmFg5XKM7vuYkfYO+rdtQxgGvVveUM+jSJRI8xKMgJrN59MZxvbvJ90/AFLiLS8n3LQAIzjDbN/jA91AJu0sECGUqMURaDgkh5V8vCBisc1WKKKUh1k1q5LJYRDaNONMZCalXmfGVIJDyTgA4S+b9nktNO2gKzHhuLRKMbSSz6f+vnAKerscc+SGh+m+7356Nz+MOTBQ6EzuEFzWzJoH/+eI9nF4RlfT1SzXzjuUFXHla8umYHRA3VRllZ/6fE0A0heAZKJEFXYQo+sEvZxgxwRprPD6letBSmQuXXQb+IPFczG8tqFPqyCeo0byBg4exJt0KJLsYILWn/8bXc+/gO7z4Y1GCdTVkS9J0ZGWxf7/fZbd99xHbmgYEOQzGRrO3MCKa6/BH7M7a0joePQJEjt3kk0MkUkkqF57Mmtu+g6BOlWNquflV2m7617MZAqZz2Mmk4SbFrD6pr8kumI5ubExPn76GXq3vE6ys4u+bI50n5I2V7R8gZXXXYMRCGCm07RvfoSORx7HTKYKx7nwgq+x9PJL8U7XdsUOZIrahcgD+5FOjnZqRM1AIyWzEGlLVCb+KYWTxF0YOPVy3TF2zqrG/n+S8OVo1VNwOSbp/9VzdP7iv6g87VQCixcXZroOnjnoZHPk6xshEB4fMloNg70q3Wq4Tz3JQ+VHvPkZ8YeKqV7pEWSofPrkcWmN78QaGO+4Vy6EkgyGgtugJFquaXbKmlB+XSdtzDND5sRELIvul1+l/eFHab7ycuo3biCfTrPnvx8g2dlVGDayZy+/+eGt1H55PUv+/CJ0v4/B91t555Yf4C2vYNX131anBiQ++IBVN1yHNxKh/71t/OZvf0SwPs7q794IQKixgearLidYV4ceDJBo3cmOf/4Xup57gciSJXjCYRZffCFVJ5/EaPs+4mecxqrrrwUh0H0+fJUVSCkZePc9Prj9Thac+3s0bjoToWv0vLKFD/713wnG61j4tXOnP28hlGJQ90AoolwHlXGlsioNavnDMNKPzKYLvfgkqBRB3SimDfpDKp6QSRWyFZRYZ1S5Jzx22pLHrz7IplSKosNEMYzLbzUj27ZTdlwLS//qJoxJnS/mhrlxKgmhLv5IrKhKS3yC1D2HnkJ2qPvVPUqFNjoAmTSMDiCjNeMTyZ1ZTnpUCSNA3bTB8ORZj8enlqK5TFHOPLF2gRMszGXAkym8J7TZn6dlmnT+6jlCjQ0svvgCwgubkFKi+3x0PFZsB9Lx+FNkEwlWXvstIs1LAYiuWM6Hd/4nXc+/wLJvXFX4VSy+6ALqvrzeHrOMfU8+zd6HHuaEG/8MoetUHNdCxfHHFbYdjNfx8dPPMLSrDStvYngDBON15DMZNK8HbzRKqGnBOHeHzOdpf+Qx9GCA5isvJ9igfHn+6mp233sffW++RePZm9Cnq8EqLeTQAVVdrqrRrlJmfyRlYV+iqh452IMc6Ia6xYBQnXR796lVhW04RawB2dep2ujUL1XjpIXs7VArGTv9jGgM9km1vWhV4eEqB7pn/Tdz+S1ASjzV1Z9qZbU58+QLoSFDUbXEH+lXs8REDzLWqPo7fUqGVwiBDFeoGYxT9MbMIstixRs6b6oczpF+O0VMU0GYico1UO6C9Gix+aY/VGym5+DxF2fEmWQxwHYIDhVpWYy2d+CvrcFvtwARQuAtjxZeAwy+v4NUTy/PnHWuEqTYmKkU1etOxhwbLbwXaW4u/Kz7fEQWL+LA2++QHR7GG40y1tnFngce4pNXX2espwdzZJR0fz8Lzz9v1gnwUsLgjlb633mXJ0/dOE45lE+lMJNJrExmaqNruwyEna0gt72EE80mEEbULUGW16htRqqgZgF0faRa5HgDKiiWyyCaVhU7UZdVqC4MPXtVsSVvUKUyZpJqnJOr6w+r7R3Yj8xl1OvRhOtecBlHZM1quu66h2z/AN7qqsmrZiGOWIY9p+FTITSIVCHzOWXksmlI9Ko+UvqhBZkOYafKvVFerSLU2ZQKwiSHKchDSzXuugFlsUkBlQJOMCdfVJiJCcZUeHxITVMqtFzG9vMeSsYG6rh0TfmPSw2etMUDNpqu44tVsvaHP0D3jX/6+mOVeEoKPVsTapBauZyKAOs6o/v2s/Uvvku6v5/l13yD8pUrAdj+k58ewjGrwxa6TnTlCo770z9Gm1BRLbxwwbRNRSWomhfde1TKWDBi1941kUN9yP0fqoyGcIW6sBtXIAMRGOhWBjJYhqhpKeTegn3NNSxTtTWccf6QMrglPmIhBDStAl9Q5QinRhCxOFTGkXu3HfMdiV3mh7KTTkR76GG2X/kH1FzwdXzxOrQS1ZoeiRDbcMYR7WPurzRNh2itUgalR9W/kQFktMouy/gpGV5/GFlhKGlvNlVUiWHPbHVDzVBDEYS/bPoZTkHgYIdcpso7dhoNmnbAS/cccmUxoWtEly8n0dpKsrOT6PJlSClJ9vQUglcAVetOpvvlVwg1NFC7/pRiSpVtnLWStKP+d35N1YmrQQhyIyMM7Woj2FCPJxymb+vbDO3axdof/Q0LzjkboWkMte3GTCbxVlaMPz1DFWl3gmTjf9WC6rUn0/ns85SvXElFy6riMdnZIwfrIiv374JACG1hy/hgV1kMuestFZy0hQ9CNxA1C9QM9WC/S01HVDdC9cFL/QlNh/gSRHx8CpJYObviKS6ff/qeeIpsXx8S6H3ksUmfB5csnmejG4nZCjR9nC9uIkI3kBV1yuDCZIPkDUBFHJDgmSHFSAgIV6qqS0JTwa6pB6r8S49PLR+d+rbYRbx1j+pxNZMCTjNUYEdaat/T1ZGIxOxC5lL5J2ejrBu3G4MFXz2b7pdeZuftd9C4aSNmOs3eBzeP04M3nXcOHY8/ybaf/JQll1xEoD6OOTbGyN52Ko8/jvjppxbOv+OJp9C8XgLxOvre3MrgjlZOvPkmhKbhLY/iiUboeWUL/qoqcqOj9LzyGiPtHQQnqHJ8lRUEamvpfvllPn56LZ6yMjyhIOUrV6IHAyy68Hx6XtvC9tv+kQXnnoM/FiMzOEiqu4ea9acQW7N6+hM3syDGrzIKVeoQ81MW08VlGmq+fh7lX/ritJ/rM6VEzgIhD+7LOzxhusuMSCkxk0n2PriZtrvuJZcYItTUSMOmjXS/+Cr1G05j1Q1/iJSSROtO2n55D5/831bM5BhGMEikuZmV136Lmi/+Dnvuf5DWn93O6u/dSNvd9zG8+yM0j8HiSy5ixbe/iTcSwUwm2fPAZnbfdS+5sTH8VTEaNm1ktL0DKSXr/u6vMYLBwrF98sabbP/72xjr6kJoOvUbN9DyJ39EoLYGyzTpe+ttdt99HwPvbSefSeMti1C5+gRWXPtNKlqmbocipUTua4UDXUryG4oqv/jYMHKwB8LliIUtqnOui8tnm2mX9K7RPYpIKZE5UwWf8iaabqAHA0r5YhgFIYKUknw6TT6dRloWQmhoHg96wI9mGOTTGfLpNEZZGHNsDCtnIjQNIxhAK2mql89mySdTWHkToekYAT/SspCWhTFB4ujk8eaztnTU58UIBgtBBGlZmKkUViaragtrGprXi+H3T+tekHY3ENnTrto5ZTN2ClkAUVGrDLExhTvHxeWzh2t0XY4NDna9jUvzc3H5bDPtheyGbF3mlblsZe3i8lnETVJ0cXFxmUdco+vi4uIyj7hG18XFxWUemcmn6zrgXFxcXOYQd6br4uLiMo+4RtfFxcVlHnGNrouLi8s84hpdFxcXl3nENbouLi4u84hrdF1cXFzmkf8H788Shet7JrAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Making a word cloud to see the ost frequent words \n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "wordcloud = WordCloud(background_color = 'white', stopwords=stopwords,max_words = 20,colormap = 'Reds'). generate(root_tokens.replace(\"'\",''))\n",
    "plt.imshow(wordcloud)\n",
    "plt.axis('off')\n",
    "plt.savefig('WordCloud.jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "d7774f4b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                                   (0.0, 0.0)\n",
      "1                                   (0.5, 0.5)\n",
      "2                                  (0.15, 0.1)\n",
      "3                    (0.6000000000000001, 0.9)\n",
      "4    (0.12976190476190474, 0.5642857142857144)\n",
      "Name: sentiment, dtype: object\n"
     ]
    }
   ],
   "source": [
    "# Created a function to get the sentiment score for each row in the dataframe\n",
    "from textblob import TextBlob \n",
    "\n",
    "def senti(x):\n",
    "    return TextBlob(x).sentiment     \n",
    "\n",
    "gsw_tweets['sentiment'] = gsw_tweets['text'].apply(senti)\n",
    "print(gsw_tweets['sentiment'].head())\n",
    "\n",
    "def polarity(y):\n",
    "    return TextBlob(y).sentiment.polarity\n",
    "\n",
    "gsw_tweets['polarity'] = gsw_tweets['text'].apply(polarity)\n",
    "\n",
    "def subjectivity(q):\n",
    "    return TextBlob(q).sentiment.subjectivity\n",
    "\n",
    "gsw_tweets['subjectivity'] = gsw_tweets['text'].apply(subjectivity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "636a53eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot with a scatter plot the distrubtion of the sentiment scores\n",
    "x = gsw_tweets['polarity']\n",
    "y = gsw_tweets['subjectivity']\n",
    "plt.scatter(x,y,s=10,alpha = 0.5)\n",
    "plt.ylabel('< Objective ---------- Subjective >', fontsize=12)\n",
    "plt.xlabel('< Negative ------------Postive >',fontsize=12)\n",
    "plt.title('Sentiment Score')\n",
    "plt.savefig('sentiment_scatter.jpeg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "39cf08c5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating a function to count the postive, negative, and neutral values\n",
    "\n",
    "def value_count(x):\n",
    "    if x > 0:\n",
    "        return \"Postive\"\n",
    "    elif x==0:\n",
    "        return \"Neutral\"\n",
    "    else:\n",
    "        return \"Negative\"\n",
    "    \n",
    "gsw_tweets['analysis'] = gsw_tweets['polarity'].apply(value_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "20981362",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Postive     610\n",
       "Neutral     391\n",
       "Negative    282\n",
       "Name: analysis, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gsw_tweets['analysis'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "83c7598a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating a bar chart to compare the distribution of postive, netural, and negative tweets\n",
    "\n",
    "gsw_tweets['analysis'].value_counts().plot(kind=\"bar\")\n",
    "plt.savefig('Sentiment Count.jpeg')\n",
    "plt.title('Sentiment Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "db3f861a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>favorite_count</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>302</th>\n",
       "      <td>19779</td>\n",
       "      <td>what’s some of the most overused rap bars/line...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     favorite_count                                               text\n",
       "302           19779  what’s some of the most overused rap bars/line..."
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# See which tweet was liked the most likes\n",
    "sort = gsw_tweets.sort_values(['favorite_count'], ascending = False)\n",
    "sort.iloc[[0],[5,6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "e9c1379d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>retweet_count</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>805</td>\n",
       "      <td>Maturing is realizing Ray Allen is better than...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     retweet_count                                               text\n",
       "996            805  Maturing is realizing Ray Allen is better than..."
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see which tweet was retewwte the most \n",
    "retweet = gsw_tweets.sort_values(['retweet_count'], ascending = False)\n",
    "retweet.iloc[[0],[4,6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bab1b490",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Saving dataframe to a csv file.\n",
    "gsw_tweets.to_csv('gsw_tweets.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bddc874e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "94956"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Getting the sum of likes\n",
    "gsw_tweets['favorite_count'].sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "5dd5061b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5488"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#  Getting the sum of retweets\n",
    "gsw_tweets['retweet_count'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "adf48414",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "key_word        analysis\n",
       "Draymond Green  Negative     88\n",
       "                Postive      53\n",
       "                Neutral       6\n",
       "Klay Thompson   Postive     249\n",
       "                Neutral     165\n",
       "                Negative     85\n",
       "Steph Curry     Postive     302\n",
       "                Neutral     215\n",
       "                Negative    108\n",
       "Team Tweets     Postive       6\n",
       "                Neutral       5\n",
       "                Negative      1\n",
       "Name: analysis, dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking to see how many postive, neutral, and negative each player got.\n",
    "gsw_tweets.groupby('key_word')['analysis'].value_counts()"
   ]
  }
 ],
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