FinBERT sentiment analysis and stock prices.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c8edc9fd",
   "metadata": {},
   "source": [
    "# Deep learning sentiment analysis on financial news headlines \n",
    "See [README](https://github.com/quarterpastten/EDA-on-sentiment-analysis-and-stock-prices/blob/main/README.md) for project description "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b2cc0a5d",
   "metadata": {},
   "source": [
    "## Import libraries "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4baa9d4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Stock data gathering. \n",
    "import pandas_datareader as pdr\n",
    "import os\n",
    "\n",
    "# Headline data gathering\n",
    "import pandas as pd\n",
    "\n",
    "# Visualisation \n",
    "from matplotlib import pyplot as plt \n",
    "from matplotlib import dates\n",
    "from matplotlib import gridspec\n",
    "\n",
    "# Preprocessing\n",
    "from pandas.tseries.offsets import BDay\n",
    "\n",
    "# Sentiment analysis \n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification  \n",
    "import torch\n",
    "from transformers import pipeline\n",
    "\n",
    "# Statistical analysis \n",
    "import numpy as np\n",
    "import math\n",
    "from statsmodels.graphics.tsaplots import plot_acf\n",
    "from statsmodels.tsa.stattools import acf\n",
    "from statsmodels.tsa.stattools import adfuller \n",
    "import statsmodels.tsa.stattools as stattools\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b28ff14f",
   "metadata": {},
   "source": [
    "## Step 1: retrieve financial headlines\n",
    "We need our financial headlines. We'll the use a large dataset that can be downloaded from [Kaggle](https://www.kaggle.com/miguelaenlle/massive-stock-news-analysis-db-for-nlpbacktests?select=analyst_ratings_processed.csv). Here it loaded as a .csv file from a local drive:  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6dbc0eb1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1400469, 4)\n"
     ]
    },
    {
     "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>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>date</th>\n",
       "      <th>stock</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>Stocks That Hit 52-Week Highs On Friday</td>\n",
       "      <td>2020-06-05 10:30:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Stocks That Hit 52-Week Highs On Wednesday</td>\n",
       "      <td>2020-06-03 10:45:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>71 Biggest Movers From Friday</td>\n",
       "      <td>2020-05-26 04:30:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>46 Stocks Moving In Friday's Mid-Day Session</td>\n",
       "      <td>2020-05-22 12:45:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>B of A Securities Maintains Neutral on Agilent...</td>\n",
       "      <td>2020-05-22 11:38:00-04:00</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0         0.0            Stocks That Hit 52-Week Highs On Friday   \n",
       "1         1.0         Stocks That Hit 52-Week Highs On Wednesday   \n",
       "2         2.0                      71 Biggest Movers From Friday   \n",
       "3         3.0       46 Stocks Moving In Friday's Mid-Day Session   \n",
       "4         4.0  B of A Securities Maintains Neutral on Agilent...   \n",
       "\n",
       "                        date stock  \n",
       "0  2020-06-05 10:30:00-04:00     A  \n",
       "1  2020-06-03 10:45:00-04:00     A  \n",
       "2  2020-05-26 04:30:00-04:00     A  \n",
       "3  2020-05-22 12:45:00-04:00     A  \n",
       "4  2020-05-22 11:38:00-04:00     A  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "headlines_df = pd.read_csv('analyst_ratings_processed.csv')\n",
    "print(headlines_df.shape)\n",
    "headlines_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "8275d357",
   "metadata": {},
   "outputs": [],
   "source": [
    "# We'll need the title, date and stock columns, but we can drop the Unnamed: 0 column.\n",
    "headlines_df = headlines_df.drop(['Unnamed: 0'], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "05e57b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title       0\n",
       "date     1289\n",
       "stock    2578\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check for null values\n",
    "headlines_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "8c049b98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "title    0\n",
       "date     0\n",
       "stock    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coerce NaNs in date column to Pandas datetime objects, and then get rid of them as follows:\n",
    "headlines_df['date'] = pd.to_datetime(headlines_df['date'], utc=True, errors='coerce')\n",
    "headlines_df = headlines_df[headlines_df['date'].notna()]\n",
    "headlines_df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f021f11c",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "TSLA     550\n",
       "NFLX     242\n",
       "GOOGL    213\n",
       "PCG      195\n",
       "NIO      180\n",
       "GOOG     174\n",
       "NVDA     158\n",
       "BABA     151\n",
       "MU       146\n",
       "FDX      138\n",
       "MCD      127\n",
       "FCEL     120\n",
       "TGT      119\n",
       "CLVS     111\n",
       "NVAX     104\n",
       "M        102\n",
       "INO      100\n",
       "JNJ      100\n",
       "RSX       99\n",
       "JD        96\n",
       "Name: stock, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter data to a 3-month period\n",
    "a = headlines_df.loc[headlines_df['date']>='2019-11-01']\n",
    "a = a.loc[headlines_df['date']<='2020-02-01']\n",
    "# Check which tickers have a sufficient number of headlines\n",
    "a['stock'].value_counts()[0:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f8428ecb",
   "metadata": {},
   "source": [
    "We'll choose two from the top 5: *Tesla (TLSA)* and *Pacific Gas and Electric Company (PCG)*. \n",
    "\n",
    "Let's plot a histogram for each and examine the distribution of headlines over the specified time period. Here are some functions to help us: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "cd037a75",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to returns the rows from headlines_df for a particular ticker passed in as a string (e.g. 'TLSA')  \n",
    "def select_ticker(strTicker):    \n",
    "    return headlines_df[headlines_df['stock']==strTicker]\n",
    "\n",
    "# Accepts the headline data for the chosen tickers and filters for the specified date range  \n",
    "def set_date_range(ticker_data, start, end):\n",
    "    ls = []\n",
    "    \n",
    "    for i in range(len(ticker_data)):\n",
    "        date1=ticker_data[i]['date']>=start\n",
    "        date2=ticker_data[i]['date']<=end\n",
    "        date_range = date1 & date2\n",
    "        ls.append(ticker_data[i].loc[date_range])\n",
    "    return ls\n",
    "\n",
    "# Function to create multiple histograms: \n",
    "def ticker_histogram(ticker_data, ticker_list, start=\"\", end=\"\"):\n",
    "    plt.clf() \n",
    "    fig = plt.figure(figsize=(14, 8), dpi=80, facecolor='white')\n",
    "    plt.subplots_adjust(hspace=.5)\n",
    "    \n",
    "    for i in range(len(ticker_data)):\n",
    "        curr_ticker=ticker_data[i]['date']   \n",
    "        curr_name = ticker_list[i]\n",
    "        # Get start and end dates \n",
    "        if (start==\"\" or end==\"\"):\n",
    "            start=curr_ticker.min()\n",
    "            end=curr_ticker.max()\n",
    "        # Get headlines within these dates\n",
    "        mask = (curr_ticker>=start) & (curr_ticker<=end)\n",
    "        masked_data = curr_ticker.loc[mask]\n",
    "        # Create subplot\n",
    "        do_subplot(fig, masked_data, curr_name, i+1)   \n",
    "    plt.show()\n",
    "\n",
    "# Create the subplots\n",
    "def do_subplot(fig, masked_data, curr_name, num):\n",
    "    # Add a subplot  \n",
    "    ax = fig.add_subplot(2,2,num,) \n",
    "    \n",
    "    # Create histogram\n",
    "    n, bins, patches = ax.hist(pd.to_datetime(masked_data, format='%Y-%m-%d'), bins=50, facecolor\n",
    "                               ='indianred', alpha=0.75, edgecolor='white') # histogram \n",
    "    \n",
    "    # Decorative\n",
    "    plt.xticks(rotation='60', fontsize=10) \n",
    "    plt.yticks(fontsize=10)\n",
    "    plt.xlabel('YEAR', family='serif', fontsize=10)\n",
    "    plt.ylabel('FREQUENCY', family='serif', fontsize=10)\n",
    "    plt.grid(True, linestyle=':', color='gray', linewidth=1)\n",
    "    plt.title(curr_name, family='serif', fontsize=14, fontweight='bold')   \n",
    "    plt.text(0.05, 0.90, \"headline count: {num}\".format(num=masked_data.count()), fontsize=9,\n",
    "             transform=ax.transAxes, bbox=dict(boxstyle='square', facecolor='beige', alpha=0.5, \n",
    "                                               edgecolor='black'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "f0d05f5f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1120x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Place our chosen ticker names in a list\n",
    "ticker_list = ['PCG', 'TSLA']\n",
    "\n",
    "# Create a list with 2 dataframes, each with the headlines for the 2 tickers\n",
    "headlines_data = [select_ticker(x) for x in ticker_list]\n",
    "\n",
    "# Place the specified time-period in variables\n",
    "startDate = '2019-11-01'\n",
    "endDate = '2020-02-01'\n",
    "\n",
    "# Plot a histogram for each ticker for the given period\n",
    "ticker_histogram(headlines_data, ticker_list, startDate, endDate)\n",
    "\n",
    "# Drop 'stock' column - not needed now:\n",
    "headlines_data = [headlines_data[x].drop(['stock'], axis=1) for x in range(len(headlines_data))]\n",
    "\n",
    "# Filter for start and end dates\n",
    "headlines_data=set_date_range(headlines_data, startDate, endDate)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9292766b",
   "metadata": {},
   "source": [
    "## Step 2: retrieve stock data\n",
    "Now that we have our financial news headlines prepared, it is time to get hold of the stock price data for these two stocks for the same time period. Here we use Pandas Datareader's ```get_yahoo_data()```. We then have a look at them: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e311d734",
   "metadata": {
    "scrolled": false
   },
   "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>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Open</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>6.75</td>\n",
       "      <td>6.24</td>\n",
       "      <td>6.42</td>\n",
       "      <td>6.43</td>\n",
       "      <td>25562100</td>\n",
       "      <td>6.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>7.83</td>\n",
       "      <td>6.60</td>\n",
       "      <td>6.65</td>\n",
       "      <td>7.27</td>\n",
       "      <td>46564100</td>\n",
       "      <td>7.27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>8.20</td>\n",
       "      <td>7.52</td>\n",
       "      <td>7.75</td>\n",
       "      <td>8.00</td>\n",
       "      <td>37076800</td>\n",
       "      <td>8.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-06</th>\n",
       "      <td>7.70</td>\n",
       "      <td>6.49</td>\n",
       "      <td>7.65</td>\n",
       "      <td>6.92</td>\n",
       "      <td>24278500</td>\n",
       "      <td>6.92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-07</th>\n",
       "      <td>6.89</td>\n",
       "      <td>5.83</td>\n",
       "      <td>6.49</td>\n",
       "      <td>6.02</td>\n",
       "      <td>25419000</td>\n",
       "      <td>6.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            High   Low  Open  Close    Volume  Adj Close\n",
       "Date                                                    \n",
       "2019-11-01  6.75  6.24  6.42   6.43  25562100       6.43\n",
       "2019-11-04  7.83  6.60  6.65   7.27  46564100       7.27\n",
       "2019-11-05  8.20  7.52  7.75   8.00  37076800       8.00\n",
       "2019-11-06  7.70  6.49  7.65   6.92  24278500       6.92\n",
       "2019-11-07  6.89  5.83  6.49   6.02  25419000       6.02"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Function to retrieve stock data for both tickers\n",
    "def getStockData(ticker_list, start, end):\n",
    "    stock_data = [] \n",
    "    for ticker in ticker_list:\n",
    "        x = pdr.get_data_yahoo(ticker, start, end) \n",
    "        stock_data.append(x)\n",
    "    return stock_data\n",
    "\n",
    "# Grab stock data\n",
    "stock_data = getStockData(ticker_list, startDate, endDate)\n",
    "\n",
    "# View data for one of the tickers\n",
    "stock_data[0].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c4b6d71e",
   "metadata": {},
   "source": [
    "We're going to use the *close* column only. Therefore let's drop the other columns: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7154028b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop columns, leaving only 'Close':\n",
    "stock_data = [stock_data[x].drop(['High', 'Low', 'Open', 'Volume', 'Adj Close'], \n",
    "                                 axis=1) for x in range(len(stock_data))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07f585a2",
   "metadata": {},
   "source": [
    "Let's visualise the stock data for the two tickers (we view them as % change rather than absolute price so that we can view them together on the same scale): "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "99b14f58",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1600x800 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualise the stock data for the two tickers. \n",
    "def plot_stock_data(data, tickers):\n",
    "    plt.clf()\n",
    "    plt.figure(figsize=(20, 10), dpi=80)\n",
    "    # Divide each stock by its first closing price to look at the changes (Cumulative sum)         \n",
    "    plt.plot(data[0]['Close']/data[0]['Close'].iloc[0], color='green')\n",
    "    plt.plot(data[1]['Close']/data[1]['Close'].iloc[0], color='red')\n",
    "    \n",
    "    # Decorative\n",
    "    plt.xlabel('Date', fontsize=14)\n",
    "    plt.ylabel('% change', fontsize=14)\n",
    "    plt.xticks(rotation='60', fontsize=12)\n",
    "    plt.yticks(fontsize=12)    \n",
    "    plt.title('Stock Data for {ticker0} and {ticker1}.'.format(ticker0=tickers[0], \n",
    "                                                                 ticker1=tickers[1]), fontsize=16)\n",
    "    plt.legend([tickers[0], tickers[1]], fontsize=14)    \n",
    "    plt.grid()\n",
    "    \n",
    "    plt.show()\n",
    "\n",
    "plot_stock_data(stock_data, ticker_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62a17aba",
   "metadata": {},
   "source": [
    "## Step 3: Pre-processing\n",
    "Stock markets operate between certain times e.g. our chosen tickers are listed on NASDAQ or NYSE, which trade between 9.30 a.m. and 4 p.m. Thus, a headline that is published at, say, 9 p.m. on a Friday could have no effect on trading till Monday at the earliest. Here we do a little pre-processing and assign the headlines to the correct day. Since the published times of the headlines have been set to UTC (GMT), rather than  US Eastern Time (ET), we adjust the open/close times in our ```get_trade_days()``` function to UTC-5hrs, that is, 13:30/20:00. (We ignore  daylight saving times). Pandas allows us to find the next business day with its module ```Bday```.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8a145b06",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_trade_days(date):       \n",
    "    # Get the business day the article was published. I.e. take today's date, set the time to the open/close times\n",
    "    # of the stock exchange. Then use BDay to ensure it is a business day. If not change the date to the next one.   \n",
    "    today_open = date.floor('d').replace(hour=13,minute=30) - BDay(0)\n",
    "    today_close = date.floor('d').replace(hour=20,minute=0) - BDay(0)  \n",
    "    # Establish the previous and next business days accordingly: \n",
    "    yesterday_close = today_open - BDay()\n",
    "    tomorrow_open = today_close + BDay()\n",
    "    \n",
    "    # If the article was published between yesterday's close and today's close, we assign it today's date\n",
    "    if ((date>=yesterday_close) & (date<today_close)):\n",
    "        return today_open \n",
    "    else: # Otherwise it is assigned the next business day\n",
    "        return tomorrow_open\n",
    "       \n",
    "# Intermediary function     \n",
    "def set_trade_days(data):\n",
    "    data['date'] = data['date'].apply(get_trade_days)\n",
    "    data['date'] = pd.to_datetime(pd.to_datetime(data['date']).dt.date)\n",
    "    return data\n",
    "\n",
    "headlines_data = [set_trade_days(x) for x in headlines_data]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be565a55",
   "metadata": {},
   "source": [
    "## Step 4: Sentiment analysis on headlines\n",
    "It is now time to find the sentiment of the headlines using [FinBERT](https://huggingface.co/ProsusAI/finbert). We implement it here using the ```pipeline``` module that is included in the ```Transformers``` library. \n",
    "\n",
    "The below two functions will allow us to send each headline to FinBERT and it will return us three scores for each headline: a positive, a negative and neutral, each representing a probability between 0 and 1, and all summing up to 1. In order to create a positive/negative polarity score, we simply subtract negative from positive. This results in a range of possible scores for each headline of -1 (most negative) to +1 (most positive). We store the algorithms output (which is the three scores) in a new column of each ticker's dataframe, called *sentiment*, and store the polarity score, which we derive in the first function below, in another column called *sentiment_f*:   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3b433077",
   "metadata": {},
   "outputs": [],
   "source": [
    "def parse_sentiment_score(lst):\n",
    "    dicarray = np.squeeze(np.array(lst))\n",
    "    pos_dic = next(filter(lambda x: x['label'] == 'positive', dicarray))\n",
    "    pos = pos_dic['score']\n",
    "    neg_dic = next(filter(lambda x: x['label'] == 'negative', dicarray))\n",
    "    neg = neg_dic['score']\n",
    "    neu_dic = next(filter(lambda x: x['label'] == 'neutral', dicarray))\n",
    "    neu = neu_dic['score'] \n",
    "    result = pos-neg\n",
    "    return result\n",
    "\n",
    "\n",
    "def get_sentiment_finbert(data):\n",
    "    model_name = \"ProsusAI/finbert\"\n",
    "    model = AutoModelForSequenceClassification.from_pretrained(model_name)\n",
    "    tokenizer = AutoTokenizer.from_pretrained(model_name)\n",
    "    classifier = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer, return_all_scores=True) # 'sentiment-analysis' is the pipeline type\n",
    "    data = (\n",
    "        data\n",
    "        .assign(sentiment = lambda x: x['title'].apply(lambda s: classifier(s)))\n",
    "        .assign(sentiment_f = lambda x: x['sentiment'].apply(parse_sentiment_score))\n",
    "    )\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "85c45b2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Perform sentiment analysis\n",
    "headlines_data = [get_sentiment_finbert(x) for x in headlines_data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6ed0adf6",
   "metadata": {
    "scrolled": false
   },
   "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>title</th>\n",
       "      <th>date</th>\n",
       "      <th>sentiment</th>\n",
       "      <th>sentiment_f</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>973384</th>\n",
       "      <td>30 Stocks Moving in Wednesday's Pre-Market Ses...</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>[[{'label': 'positive', 'score': 0.08981822431...</td>\n",
       "      <td>-0.022129</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973385</th>\n",
       "      <td>PG&amp;E Says Secured 'More Than Two-Thirds' Suppo...</td>\n",
       "      <td>2020-01-29</td>\n",
       "      <td>[[{'label': 'positive', 'score': 0.91707378625...</td>\n",
       "      <td>0.905721</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973386</th>\n",
       "      <td>Mizuho Upgrades PG&amp;E, Expects Bankruptcy Deal ...</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>[[{'label': 'positive', 'score': 0.01478441152...</td>\n",
       "      <td>-0.723882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973387</th>\n",
       "      <td>UPDATE: Mizuho Upgrades PG&amp;E to Buy, Raises Pr...</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>[[{'label': 'positive', 'score': 0.92189252376...</td>\n",
       "      <td>0.897619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>973388</th>\n",
       "      <td>Benzinga's Top Upgrades, Downgrades For Januar...</td>\n",
       "      <td>2020-01-28</td>\n",
       "      <td>[[{'label': 'positive', 'score': 0.02427338436...</td>\n",
       "      <td>-0.630857</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    title       date  \\\n",
       "973384  30 Stocks Moving in Wednesday's Pre-Market Ses... 2020-01-29   \n",
       "973385  PG&E Says Secured 'More Than Two-Thirds' Suppo... 2020-01-29   \n",
       "973386  Mizuho Upgrades PG&E, Expects Bankruptcy Deal ... 2020-01-28   \n",
       "973387  UPDATE: Mizuho Upgrades PG&E to Buy, Raises Pr... 2020-01-28   \n",
       "973388  Benzinga's Top Upgrades, Downgrades For Januar... 2020-01-28   \n",
       "\n",
       "                                                sentiment  sentiment_f  \n",
       "973384  [[{'label': 'positive', 'score': 0.08981822431...    -0.022129  \n",
       "973385  [[{'label': 'positive', 'score': 0.91707378625...     0.905721  \n",
       "973386  [[{'label': 'positive', 'score': 0.01478441152...    -0.723882  \n",
       "973387  [[{'label': 'positive', 'score': 0.92189252376...     0.897619  \n",
       "973388  [[{'label': 'positive', 'score': 0.02427338436...    -0.630857  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sanity check: view data for one ticker\n",
    "headlines_data[0].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8ce50c7d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores:  [[{'label': 'positive', 'score': 0.9170737862586975}, {'label': 'negative', 'score': 0.011352918110787868}, {'label': 'neutral', 'score': 0.07157319784164429}]]\n",
      "polarity (pos-neg):  0.9057208681479096\n"
     ]
    }
   ],
   "source": [
    "# View the results for one headline\n",
    "print(\"scores: \", headlines_data[0].iloc[1]['sentiment'])\n",
    "print(\"polarity (pos-neg): \", headlines_data[0].iloc[1]['sentiment_f'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b300d6e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Drop the sentiment column (no longer needed)\n",
    "headlines_data = [headlines_data[x].drop(['sentiment'], axis=1) for x in range(len(headlines_data))]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79e9aac5",
   "metadata": {},
   "source": [
    "All this gives us a sentiment score for each headline. However, there may be multiple headlines (or none) on any given day. Therefore, we group headlines by day and take the *mean* sentiment score for each day: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "74e1953b",
   "metadata": {},
   "outputs": [],
   "source": [
    "grouped_dates = [headlines_data[x].groupby(['date']) for x in range(len(headlines_data))]\n",
    "sentiments_data = [x.mean() for x in grouped_dates]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bed19ba",
   "metadata": {},
   "source": [
    "For days with no headlines, we will get NaNs. So we impute these with a score of 0. As we do this, it will be helpful to merge our stock and sentiment data for each ticker into a single dataframe, which will then be stored in a list called *two_time_series*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "05ba4269",
   "metadata": {},
   "outputs": [],
   "source": [
    "two_time_series = [pd.merge(stock_data[x], sentiments_data[x], left_index=True, right_index=True, how='left')\n",
    "                   .fillna(0) for x in range( len(stock_data ))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "47ea4b2b",
   "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>Close</th>\n",
       "      <th>sentiment_f</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-11-01</th>\n",
       "      <td>6.43</td>\n",
       "      <td>0.559309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-04</th>\n",
       "      <td>7.27</td>\n",
       "      <td>0.309682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-05</th>\n",
       "      <td>8.00</td>\n",
       "      <td>-0.037692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-06</th>\n",
       "      <td>6.92</td>\n",
       "      <td>-0.565755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-11-07</th>\n",
       "      <td>6.02</td>\n",
       "      <td>-0.248996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-27</th>\n",
       "      <td>14.04</td>\n",
       "      <td>-0.103882</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-28</th>\n",
       "      <td>14.95</td>\n",
       "      <td>0.248716</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-29</th>\n",
       "      <td>15.05</td>\n",
       "      <td>0.441796</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-30</th>\n",
       "      <td>14.91</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-01-31</th>\n",
       "      <td>15.21</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>62 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            Close  sentiment_f\n",
       "Date                          \n",
       "2019-11-01   6.43     0.559309\n",
       "2019-11-04   7.27     0.309682\n",
       "2019-11-05   8.00    -0.037692\n",
       "2019-11-06   6.92    -0.565755\n",
       "2019-11-07   6.02    -0.248996\n",
       "...           ...          ...\n",
       "2020-01-27  14.04    -0.103882\n",
       "2020-01-28  14.95     0.248716\n",
       "2020-01-29  15.05     0.441796\n",
       "2020-01-30  14.91     0.000000\n",
       "2020-01-31  15.21     0.000000\n",
       "\n",
       "[62 rows x 2 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# View merged data (for one ticker)\n",
    "two_time_series[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f20ea64",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Function to plot the sentiment polarity scores for each ticker over the time-period\n",
    "def plot_sentiment(lst):\n",
    "    plt.clf()\n",
    "    fig = plt.figure(figsize=(16, 8), dpi=80, facecolor='white')\n",
    "    spec = gridspec.GridSpec(ncols = 1, nrows = 2)\n",
    "    ax=[]\n",
    "    counter = 0\n",
    "    \n",
    "    for df in lst:\n",
    "        ticker_name = ticker_list[counter]\n",
    "        # Create axis\n",
    "        if counter==0:\n",
    "            ax.append(fig.add_subplot(spec[counter])) \n",
    "            plt.tick_params('x', labelbottom=False)\n",
    "        else:\n",
    "            ax.append(fig.add_subplot(spec[counter], sharex=ax[0]))\n",
    "            ax[counter].set_xlabel('Date', fontsize=14)            \n",
    "        \n",
    "        # Sentiment bars: make the negative values red, and the positive/neutrals green:\n",
    "        mask1=df['sentiment_f'][df['sentiment_f']<0]\n",
    "        mask2=df['sentiment_f'][df['sentiment_f']>=0]\n",
    "        ax[counter].bar(mask1[:].index, mask1[:], facecolor='red', alpha=0.75)\n",
    "        ax[counter].bar(mask2[:].index, mask2[:], facecolor='blue', alpha=0.75)        \n",
    "        \n",
    "        # Axis labels and title\n",
    "        ax[counter].set_title(\"Sentiment score for {ticker_name}\".format(ticker_name=ticker_name), fontsize=16)\n",
    "        ax[counter].set_ylabel('Score', fontsize=14)\n",
    "        ax[counter].tick_params(axis='x', labelrotation=60)\n",
    "        ax[counter].xaxis.set_major_locator(plt.MaxNLocator(10))\n",
    "        plt.xticks(fontsize=12)\n",
    "        plt.yticks(fontsize=12)    \n",
    "\n",
    "        ax[counter].grid(b=True, which='major', color='gray', alpha=1.0, linestyle='--')\n",
    "        ax[counter].minorticks_on()\n",
    "        # Scaling \n",
    "        ax[counter].set_ylim(min(df['sentiment_f'])-.1, max(df['sentiment_f'])+.1)\n",
    "\n",
    "        counter = counter+1 \n",
    "            \n",
    "    fig.tight_layout()\n",
    "    plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "73241984",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABPQAAAJ3CAYAAAATTQ8jAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAxOAAAMTgF/d4wjAADpQElEQVR4nOzdeViVdf7/8edRA1FUTFESSUQLCRcwRclx1Nw1XPgOMpppEoNjai7NNNm0WdbPpgn3LMcktXTwFLnkkktqZaSWW5piBppSIpKEKO7n94d6RsIFWTz3x16P6+Iqzrnv+7zP4fW5b3xz3/fH5nA4HIiIiIiIiIiIiIgRyri6ABERERERERERESk8NfREREREREREREQMooaeiIiIiIiIiIiIQdTQExERERERERERMYgaeiIiIiIiIiIiIgZRQ09ERERERERERMQgauiJiIiIiIiIiIgYRA09ERERkWLy9/enbdu2ri5DiiAtLY0ePXpQvXp1bDYbI0eOdHVJIiIiIjekhp6IiIhYXmpqKrGxsQQGBuLh4UG1atVo1KgRcXFxbN269ZbUMHHiRN56661b8lqlbd26dbz44oscPnzY1aW43MCBA9mwYQNjxoxh7ty5PPLII6X+mvv378dms+X7qlixIqGhocTHx3Pu3LkC62RlZfHCCy/QtGlTqlSpgru7O3Xq1OHhhx9m9erVBZZ3OBx89NFH9OrVC19fX9zd3alUqRKNGzdm+PDhfPPNN6X+PkVERKT02BwOh8PVRYiIiIhcyzfffEObNm1wc3Nj4MCBBAUFcfz4cfbs2cOiRYt46qmn+Nvf/lbqdfj7++Pl5cW2bdsKPHf69GlsNhtubm6lXkdJePHFFxk7dixbt24lJCTE1eW4zKlTp6hQoQLDhw9n0qRJt+x19+/fT926dencuTP9+/cH4Oeff2b27Nns2rWLmJgY3nnnHefymzZtokePHhw9epRevXrRunVrKlWqxIEDB1i0aBHbt29n6dKldOvWDYCTJ0/y5z//mSVLlhAcHEzv3r3x9/fnzJkzfPfddyxevJgff/yRjRs3EhYWdsvet4iIiJSccq4uQEREROR6xo4dy4kTJ/jyyy9p3LhxvuemT59OVlaWiyr7H3d3d1eX8Lty4sQJKlasWOztHDlyBIfDQdWqVUugqv9xOBzk5eVRoUKF6y7XoEEDZ0MPYMiQITRo0ICEhAReeeUVfHx8OHz4MBEREZw+fZovvviCli1b5tvG2LFjmTdvHuXLl3c+NnjwYJYsWcIzzzzDuHHjsNls+daZOHEi77zzjnIrIiJiMF1yKyIiIpa2d+9eqlWrVqCZB1CuXDlq1qxZ4PH333+f8PBwPD09qVixIn/84x9ZtWpVvmUuX/b44osv8uGHHxISEkL58uXx8/Pj1VdfzbeszWbjwIEDbN++Pd9lkvv37weufg89m83Go48+yurVq2nRogUVKlTA39+fGTNmAHDgwAF69+6Nl5cXXl5eDB48mNOnTxd4L3v27KFv377UrFkTd3d36tevz9ixYzl79my+5dq2bYu/vz8//vgjkZGRVKlShUqVKtG7d29++ukn53KPPvooY8eOBSA0NNT5Xl588cWr/wAu+eKLL+jcuTM1atTA3d2du+66i4ceeohvv/0233Lp6ekMGTKEOnXqOJeLiIgocIlnUlIS4eHhVKxYkUqVKtGmTRtWrlxZ4HUvf46ffPKJc/mHH37Y+fyKFSto3749lStXxsPDg2bNmjF//vzrvpfLn0OdOnWAi02x3/5Mz507x/jx4wkKCsLd3R1vb2+io6P5/vvv821n3bp12Gw2EhISmDRpEg0aNMDd3d35c74Znp6etGzZEofDQWpqKgCvv/46R44c4fXXXy/QzLusX79+PPjggwBs376d9957j9atW/PKK68UaOYBlC1blri4OJo0aXLTNYqIiIg16Aw9ERERsbSAgABSUlJISkoiMjLyhss//fTTvPbaa0RERPDqq69y/vx55s2bR5cuXfjggw/o3bt3vuWXLl3KW2+9xZAhQ6hevTrz5s3jn//8J35+fs77qc2dO5dRo0ZRoUIFXnnlFee63t7e161ly5YtLF26lL/+9a8MGDCAhIQEBg8ejIeHB8899xydO3dm/PjxfPrpp8yYMQMfHx9nsw1g8+bNtG/fHm9vb4YNG0aNGjXYtGkTL730Etu3bycpKSnf6504cYJ27drxxz/+kddee42dO3cyffp0cnJyWLNmDXDx7K2cnBw++ugjXnnlFe6++26AqzZML9uzZw+dOnXinnvu4amnnqJq1ar8/PPPrF27lj179tCoUSMAfvjhBx544AGOHTtGTEwMISEhZGdn89lnn/Hll19y//33A/Dmm28ydOhQ7rvvPl544QXOnz/PO++8Q9euXZk3bx7R0dH5Xn/z5s0kJSUxePBgYmJinGeWTZ8+nccff5w//vGPvPDCC7i7u7Nw4UL69etHZmYmTzzxxDXf0+DBgwkJCWHUqFH07t3bma3LP9OHH36YBQsW0KVLF4YOHcqhQ4eYNm0aK1euZOPGjdx77735tjdx4kR+/fVXYmNj8fb2LvB8Yf3www8AVK9eHYAPP/wQd3f3Qt/b73ImYmJiivT6IiIiYgiHiIiIiIV98cUXjjvuuMMBOO69915HTEyM4+2333Z8//33BZbdvHmzA3C88MIL+R4/e/aso1mzZo46deo4Lly44HA4HI60tDQH4KhYsaLjwIEDzmVPnjzp8Pb2drRo0SLfNurUqeNo0qTJVWusU6eOo02bNvkeAxxlypRxbNmyxfnY0aNHHeXLl3fYbDbHlClT8i3frFkzR40aNfI91qhRI8d9993nyM3Nzff4pEmTHIDj008/dT7Wpk0bB+B444038i07bNgwB+DYvXu387EXXnjBATi2bt161ffzW5dfLyMj47rLderUyWGz2RyfffZZgefOnz/vcDgcjl9++cVRsWJFx7333uvIyclxPp+VleWoVauWo3r16o68vDzn40CB9+pwOBzp6ekONzc3x8CBAwu8Vu/evR2enp6OX3/99br1Xs7Ab/OycuVKB+Do16+fMy8Oh8ORnJzssNlsjoceesj52Nq1ax2Ao1q1ao7MzMzrvt5vX3fw4MGOzMxMR2ZmpmPHjh2OwYMHOwBHs2bNHA6Hw5GTk+MAHI0bNy7Udh0OhyMyMtIB5MvdZZdf6/LXb3MlIiIi5tAltyIiImJprVq1YtOmTfTt25fMzExmzZrF4MGDueeee+jevTtHjhxxLjtv3jxsNhv9+/fn6NGjzq/s7GweeughDhw4UOCSyV69ejnPUgPw8PCgZcuW7Nu3r9i1P/DAA4SGhjq/r1atGoGBgZQpU4a4uLgC7/PIkSMcP34cgB07dvDtt9/y8MMPk5eXl+/9dOnSBaDAZcRly5Zl6NCh+R5r164dQLHej5eXFwAffPDBVWdghYuzsK5atYoePXrQunXrAs+XKVPGWfOJEycYMWIElSpVcj5/5513MmTIEI4ePcoXX3yRb93Q0FDn+7jsgw8+4MyZMwwaNCjfZ3P06FEiIiLIzc3lq6++KtL7/eijjwD45z//me+S1ZYtW/Lggw+yYsUK8vLy8q0zYMAA51l1hfX222/j7e2Nt7c3jRs3ZsaMGXTq1Mn5+jk5OQBUrly50Nu83jqXX+vy1z//+c+bqldERESsQ5fcioiIiOWFhIQwb948573F1q1bx5QpU1i2bBkPP/yws7G1Z88eHA4H99xzzzW3lZGRke9yyLp16xZY5s477yyRyTb8/f0LPObl5cVdd91VYEbcy02zX375hUqVKrFnzx7gYlPpWo2XjIyMfN/fddddBSY6uPPOOwGK9X7+/Oc/8/777zN06FCefvppWrVqRZcuXfjzn//svIfhvn37cDgcN7wvW1paGgDBwcEFnmvYsCGA8/5xl9WvX7/Aspc/n9/eu/BKv/18CistLY2yZcsSGBh41RrXrFnDwYMH8+XoajXeSGRkJEOGDMFms+Hh4cG9996bryl4uSl3uclbGJfXudzYu9LlcZKRkZFvMg4RERExjxp6IiIiYgybzUa9evWoV68eDz/8MMHBwaxevZqDBw/i5+eHw+GgbNmyLF++/KqTAcD/mkaXlS1bttTqvda2r/eaDocj33+feuopOnbseNVla9WqddPbLQo3Nzc++eQTNm7cyIoVK1i/fj1/+9vfeP7551m0aNF1m2olUcfVZou9vJ3333+fGjVqXHW9qzUNC8PhcFwzP9dyoxltr8bPz48OHTpc8/lKlSpx9913s2fPHk6dOpVvJttrue+++0hKSmL79u35zg4FnK91eeIPERERMZcaeiIiImKk8uXLExISQmpqKj/99BN+fn7cc889rFixgrp16xbpjKnrudkGT3FdPsvwjjvuuG7TpyiK+l5atGhBixYtAPj+++8JDQ3lhRdeYP369dxzzz3YbDa2bdt23W3Uq1cPgO+++442bdrke+67774DLk6EciOXP58aNWqU+OdTr149PvnkE/bu3UtQUFCBGu+44w78/PxK9DWvJTIykokTJ/Lee+8RGxt7w+V79+7NuHHjSEhI4NFHHy39AkVERMQldA89ERERsbRVq1Zx/vz5Ao8fPXqUL7/8knLlyjmbO5cvI/znP//JhQsXCqxz5f32bpanpye//PJLkde/WaGhoQQHB/Pmm29y8ODBAs+fOnXqpi7FvJKnpydAod/P0aNHCzwWEBBAlSpVnNu488476dy5M0uWLOHzzz8vsPzlM+o6duxIxYoVmTJlCidPnnQ+n52dzZtvvkn16tX5wx/+cMOaoqKicHNz48UXX+T06dMFni/Oz7pXr14AvPbaa/ke37RpE6tXr6Zz5854eHgUefs346mnnqJ69er8/e9/Z9OmTVdd5r///S+ffvopAE2bNiU6OprPPvuM559//qpnRBbnbE0RERGxBp2hJyIiIpY2atQofvnlF3r27EmjRo1wd3cnLS2NOXPmcPjwYZ599lnnfeLCwsJ47rnnePnll9m7dy//93//h4+PD+np6Xz55Zfs27ePH374oUh1tGzZkpkzZ/L3v/+d0NBQypQpQ0REBBUrVizJt+tks9mYM2cODz74IA0bNiQmJoagoCCOHz/Onj17+PDDD0lKSrqpy10va9myJQDPP/88Bw4cwMPDg4YNGxa4HPmycePGsXLlSiIiIqhbty7nzp0jKSmJn376iZEjRzqXmzp1Kg888ADt27cnJiaGkJAQjh8/zmeffUbnzp0ZNmwYXl5e/Otf/2Lo0KG0bNmSAQMGcO7cOd555x1+/vln5s2bV6hLS/38/Jg2bRqDBw8mODiY/v37U7t2bTIyMtiyZQtLlizhzJkzN/3ZwMWmY58+fZg9ezZHjx6la9euHDp0iGnTplGlShXeeOONIm23KO666y4WL15Mz549eeCBB4iMjOQPf/gDnp6eHDx4kMWLF7NlyxaWL1/uXGfmzJlkZ2fz8ssvk5SURO/evfH39+fUqVOkpqby3//+F7j6PR5FRETEDGroiYiIiKXFx8ezcOFCNmzYwIIFC8jJycHLy4umTZvyxhtvEBUVlW/5l156ifvvv5/Jkyfz73//m1OnTuHj40NISAivvvpqket45ZVX+OWXX3jnnXfIzs7G4XCQlpZWag09uHi21datW3nllVf48MMPOXz4MF5eXgQEBDBq1CgaN25cpO22atWK1157jbfeeou4uDjOnTvHCy+8cM2GXs+ePfnpp5/473//y5EjR6hQoQKBgYHMnTs33+QK9erV4+uvv+all15iyZIlzJo1i2rVqtGiRQseeOAB53KPP/44Pj4+vP766zz//POUKVOGpk2bMm3aNDp16lTo9xEbG8u9997L66+/ztSpU8nJyaFGjRoEBwczadKkIn02l73//vuEhIQwe/ZsRo8ejaenJ126dGHcuHH5JsO4FcLDw/nuu++YNGkSH3/8McuXL+f06dPcddddtG7dmjfeeCNfY9fT05Nly5bx4YcfMmfOHGbOnElWVhbu7u7UrVuX3r17M2jQIO6///5b+j5ERESk5NgcOudeRERERERERETEGLqHnoiIiIiIiIiIiEHU0BMRERERERERETGIGnoiIiIiIiIiIiIGUUNPRERERERERETEIGroiYiIiIiIiIiIGKScqwuwGnd3d7y9vV1dhtzAmTNncHNzc3UZIkWi/IrplGExnTIsplOGxXTKsJiuNDOcmZnJ6dOnb7icGnq/4e3tzaFDh1xdhtxAfHw8o0ePdnUZIkWi/IrplGExnTIsplOGxXTKsJiuNDNcu3btQi2nS27FSF26dHF1CSJFpvyK6ZRhMZ0yLKZThsV0yrCYzgoZVkNPjFS1alVXlyBSZMqvmE4ZFtMpw2I6ZVhMpwyL6ayQYTX0xEjz5893dQkiRab8iumUYTGdMiymU4bFdMqwmM4KGdY99C6x2+3Y7Xby8vJcXYqIiIiIiIiIiMg16Qy9S6KioliwYAEeHh6uLkVEREREREREROSabA6Hw+HqIqykdu3amuXWAHl5eWq+irGUXzGdMiymU4bFdMqwmE4ZFtOVZoYL25fSGXpipG3btrm6BJEiU37FdMqwmE4ZFtMpw2I6ZVhMZ4UMq6EnRkpOTnZ1CSJFpvyK6ZRhMZ0yLKZThsV0yrCYzgoZVkNPRERERERERETEIGroiZF8fHxcXYJIkSm/YjplWEynDIvplGExnTIsprNChjUpxiV2ux273c6aNWvIyspydTkiIiIiIiIiIvI7o0kxblJUVBQLFizQTDuGmDdvnqtLECky5VdMpwyL6ZRhMZ0ybD3t2hXuSy5ShsV0VsiwGnpipMOHD7u6BJEiU37FdMqwmE4ZFtMpw2I6ZVhMZ4UMq6EnIiIiIiIiIiJiEDX0xEjh4eGuLkGkyJRfMZ0yLKZThsV0yrCYThkW01khw5oU4zcKe/NBca28vDzd71CMpfyK6ZRhMZ0yLKZThq2nsPfHW7u2dOswhTIspivNDGtSjJtkt9vp06cPeXl5ri5FCmH69OmuLkGkyJRfMZ0yLKZThsV0yrCYThkW01khw2roXaJZbkVERERERERExARq6ImIiIiIiIiIiBjEiIbe+PHjqVWrFhUqVKBHjx7XnR54165ddO7cGS8vL6pVq0ZkZCQ//vjjLaxWboW+ffu6ugSRIlN+xXTKsJhOGRbTKcNiOmVYTGeFDFu+oZeQkMC4ceOYOnUqX375JTk5OURHR19z+R49euDl5cVXX33Fp59+SnZ2Nv369buFFcutcOzYMVeXIFJkyq+YThkW0ynDYjplWEynDIvprJBhyzf0pkyZwogRI4iMjCQkJIRZs2bx2WefsW3btgLLZmZmkpqaypgxY2jQoAFNmjRh1KhRfPPNN7e+cClVK1ascHUJIkWm/IrplGExnTIsplOGxXTKsJjOChm2dEPv9OnTbN++nQcffND5WEBAAP7+/mzcuLHA8tWqVeOee+5h7ty5nD59mtzcXObPn0/Hjh1vZdkiIiIiIiIiIiKlppyrC7ierKwsLly4QI0aNfI97u3tzZEjRwosX6ZMGVauXElERAQTJ07E4XDQvHlzVq5cec3XiI+PJz4+3vl9dna283sfHx/69evHvHnznPftCw8PJyQkJN8UxX379uXYsWPODq2HhwdDhgxh6dKlpKSkABAUFETXrl2ZOnUqZ86cAaBbt25UqlSJxMRE57aGDh3K5s2b2bRpEwC+vr5ER0czd+5cMjMzAWjVqhXBwcHMmDHDuV7//v3JyMhg1apVAHh6ehIXF8fixYvZt28fAA0bNqRTp05MnjyZc+fOARAREUH58uWx2+3ObQ0fPpzk5GS+/vprAPz8/IiKimL27NlkZWUB0Lp1awIDA5k5c6ZzvQEDBpCens6aNWsAqFy5MrGxsSxcuJDU1FQAGjduTIcOHZgwYQIOhwOAnj17UrZsWZKSkpzbGjlyJOvXr2fr1q0A+Pv7ExkZyaxZs8jOzubEiRNs2bKFunXrkpCQ4Fxv0KBBpKWlsW7dOgC8vLyIiYkhKSmJ/fv3AxAaGkqbNm2YOHGic73IyEjOnz/PokWLALDZbIwaNYrVq1ezY8cO4GIzuVevXsycOZOcnBwA2rdvj6+vL3PmzHFuKzY2lpSUFD7//HPgYqN54MCB2O12Dh48CECzZs0IDw9nypQpzvWioqI4deoUS5YsAaBcuXI88cQTrFy5kp07dwJQv359evTowYwZM8jNzQWgY8eO1KxZk/fee8+5rbi4OHbt2sWGDRuAi2PmkUceITExkfT0dADCwsJo3rw506ZNc64XHR3N8ePHWbZsGQBubm4MGzaM5cuXs3v3bgACAwPp3r0706dPJy8vD4AuXbpQtWpV5s+f79zWkCFD2LZtG8nJyYDG05XjycPDw1LjCaBt27YaT2g8FXY8nThxgsTEREuMJ6sdn0DjyYTx5HA49PuexpPR4+nEiRPk5uZaYjxZ6fgErhtPdeokU6vWxfH0669+fPddFE2azKZChYvj6cCB1mRlBRIfr/Hk6+vLiRMnnPthV48nsM7xSePJnOMTXLzstjTGU2HZHJffhQWlp6dTu3ZtduzYQaNGjZyPh4WFERERwXPPPZdv+QsXLvDQQw9RqVIl/va3v3HmzBleeOEF3N3dWbp0aaFes3bt2hw6dKhE34eIiIiIiIjcvtq1K9xya9eWbh0iYr7C9qUsfclt9erVKVOmTIGz8TIzMwuctQfw6aefsnbtWubMmUPz5s1p1aoVc+bMYdmyZXz77be3qmy5BQrboBWxIuVXTKcMi+mUYTGdMiymU4bFdFbIsKUbeu7u7jRp0oS1V/wZIy0tjf3799OiRYsCy588eRKbzUaZMv97W5f//8KFC6VfsNwyl08dFjGR8iumU4bFdMqwmE4ZFtMpw2I6K2TY0g09gGHDhjFp0iQ++ugjtm/fzmOPPUbr1q0JCQkhPT2dBg0aOK/vDg8Px93dnbi4OPbs2cOOHTv4y1/+Qr169QgKCnLxOxERERERERERESk+yzf0YmJieOaZZ3j88cdp2bIlFStWZMGCBQCcPXuWlJQUTp48CVy8ceWyZctITU2lRYsWtGvXDofDwccff4ybm5sr34aUMDVoxWTKr5hOGRbTKcNiOmVYTKcMi+mskGFLT4pxK9ntdux2O2vWrHHOnCIiIiIiIiJyI5oUQ0RKym0xKcatFBUVxYIFC/Dw8HB1KVIIU6dOdXUJIkWm/IrplGExnTIsplOGxXTKsJjOChlWQ0+MdObMGVeXIFJkyq+YThkW0ynDYjplWEynDIvprJBhNfREREREREREREQMooaeGKlbt26uLkGkyJRfMZ0yLKZThsV0yrCYThkW01khw2roiZEqVark6hJEikz5FdMpw2I6ZVhMpwyL6ZRhMZ0VMqyGnhgpMTHR1SWIFJnyK6ZThsV0yrCYThkW0ynDYjorZLicqwuwCrvdjt1uJy8vz9WliIiIiIiIiIiIXJPO0LskKiqKBQsW4OHh4epSRERERERERERErsnmcDgcri7CSmrXrs2hQ4dcXYbcwOnTp3F3d3d1GSJFovyK6ZRhMZ0yLKZThq2nXbvCLbd2benWYQplWExXmhkubF9KZ+iJkTZv3uzqEkSKTPkV0ynDYjplWEynDIvplGExnRUyrIaeGGnTpk2uLkGkyJRfMZ0yLKZThsV0yrCYThkW01khw2roiYiIiIiIiIiIGESz3F6iWW7N4uvr6+oSRIpM+RXTKcNiOmVYTKcMi+mUYTGdFTKsSTF+Q5NiiIiIiIiIyM3QpBgiUlI0KYbc1ubOnevqEkSKTPkV0ynDYjplWEynDIvplGExnRUybERDb/z48dSqVYsKFSrQo0cPDh8+fN3l58+fT+PGjXF3d6dWrVq8/vrrt6hSuVUyMzNdXYJIkSm/YjplWEynDIvplGExnTIsprNChi3f0EtISGDcuHFMnTqVL7/8kpycHKKjo6+5/Ny5cxk2bBhPPvkk3333HR9//DHNmjW7hRWLiIiIiIiIiIiUHstPijFlyhRGjBhBZGQkALNmzaJevXps27aNkJCQfMuePXuWv//978THxzNw4EAXVCu3SqtWrVxdgkiRKb9iOmVYTKcMi+mUYTGdMiyms0KGLX2G3unTp9m+fTsPPvig87GAgAD8/f3ZuHFjgeW/+eYbMjIyOHv2LA0bNsTPz4+BAweSlZV1K8uWWyA4ONjVJYgUmfIrplOGxXTKsJhOGRbTKcNiOitk2NJn6GVlZXHhwgVq1KiR73Fvb2+OHDlSYPn9+/cDF++5N3nyZLy8vBg1ahR9+/Zl5cqVV32N+Ph44uPjnd9nZ2c7v/fx8aFfv37MmzfPed++8PBwQkJCmD59unOdvn37cuzYMVasWAGAh4cHQ4YMYenSpaSkpAAQFBRE165dmTp1KmfOnAGgW7duVKpUicTEROe2hg4dyubNm9m0aRNwcSrk6Oho5s6d67xGu1WrVgQHBzNjxgznev379ycjI4NVq1YB4OnpSVxcHIsXL2bfvn0ANGzYkE6dOjF58mTOnTsHQEREBOXLl8dutzu3NXz4cJKTk/n6668B8PPzIyoqitmzZzubo61btyYwMJCZM2c61xswYADp6emsWbMGgMqVKxMbG8vChQtJTU0FoHHjxnTo0IEJEyZweYLlnj17UrZsWZKSkpzbGjlyJOvXr2fr1q0A+Pv7ExkZyaxZs8jOziY3N5eHHnqIunXrkpCQ4Fxv0KBBpKWlsW7dOgC8vLyIiYkhKSnJmY/Q0FDatGnDxIkTnetFRkZy/vx5Fi1aBIDNZmPUqFGsXr2aHTt2ABebyb169WLmzJnk5OQA0L59e3x9fZkzZ45zW7GxsaSkpPD5558DUK1aNQYOHIjdbufgwYMANGvWjPDwcKZMmeJcLyoqilOnTrFkyRIAypUrxxNPPMHKlSvZuXMnAPXr16dHjx7MmDGD3NxcADp27EjNmjV57733nNuKi4tj165dbNiwAbg4Zh555BESExNJT08HICwsjObNmzNt2jTnetHR0Rw/fpxly5YB4ObmxrBhw1i+fDm7d+8GIDAwkO7duzN9+nTy8vIA6NKlC1WrVmX+/PnObQ0ZMoRt27aRnJwMaDxdOZ4OHjxItWrVLDOeANq2bavxhMZTYcdTbm4ugYGBlhhPVjs+gcaTCeNp6tSpuLm5Aa4fT2Cd45PGkznjKTc3l9GjR1tiPFnp+ASuG0916iRTq9bF8fTrr358910UTZrMpkKFi+PpwIHWZGUFEh+v8eTr68ubb76Jp6cn4PrxBNY5Pmk8mXN8OnPmDIMHDy6V8VRYNsfld2FB6enp1K5dmx07dtCoUSPn42FhYURERPDcc8/lW37evHk8/PDDvPvuu85Lbrdv305ISAg//vgjfn5+N3zNwk4PLK4VHx/P6NGjXV2GSJEov2I6ZVhMpwyL6ZRh62nXrnDLrV1bunWYQhkW05Vmhgvbl7L0JbfVq1enTJkyBc7Gy8zMLHDWHkDNmjWBix3wyy7//+XOvoiIiIiIiIiIiMks3dBzd3enSZMmrL3izxhpaWns37+fFi1aFFj+/vvv54477nCe0gk4///uu+8u/YLllunfv7+rSxApMuVXTKcMi+mUYTGdMiymU4bFdFbIsKXvoQcwbNgwRowYwf33309AQACjRo2idevWhISEkJ6eTvv27ZkzZw5hYWF4eXnx6KOP8vzzz1OnTh2qVKnCsGHD6Nq1K7Vr13b1W5ESlJGRcdWzNEVMoPyK6ZRhMcnVLoOrUSODI0fyZ1iXwYlJtB8W0ynDYjorZNjyDb2YmBgyMjJ4/PHHyc7OpkOHDvznP/8B4OzZs6SkpHDy5Enn8pMmTWLUqFH06NGDcuXK0aVLFyZNmuSq8i2jMPd0MOkX2VWrVuW7r6KISZRfMZ0yLKarV28VR44ow2Iu7YfFdMqwmM4KGbZ8Qw9gzJgxjBkzpsDj/v7+/HZODw8PD9566y3eeuutm3oNu92O3W53zjojIiIiIiIiIiJiRZa+h96tFBUVxYIFC/Dw8HB1KVIIl6c4FzGR8iumU4bFdGfOKMNiNu2HxXTKsJjOChm2OX57itvvXGGnBzbN7XbJrYiIiEhhFOZ3INDvQSJSPNrXiEhJKWxfSmfoiZEWL17s6hJEikz5FdMpw2K6e+9VhsVs2g+L6ZRhMZ0VMqyGnhhp3759ri5BpMiUXzGdMiymq1ZNGRazaT8splOGxXRWyLAaeiIiIiIiIiIiIgZRQ0+M1LBhQ1eXIFJkyq+YThkW02VkKMNiNu2HxXTKsJjOChnWpBiX2O127HY7a9asISsry9XllDhNiiEiIiK/R7pRvYjcCtrXiEhJ0aQYNykqKooFCxbg4eHh6lKkECZPnuzqEkSKTPkV0ynDYroWLZRhMZv2w2I6ZVhMZ4UMl3N1ASJFce7cOVeXIFJkyq+YThkW05UpowyL2bQflhux+hVayrCYzgoZ1hl6IiIiIiIiIiIiBlFDT4wUERHh6hJEikz5FdMpw2K6lBRlWMym/bCYThkW01khw7rkVoxUvnx5V5cgUmTKr5hOGRbTnTunDIvZtB823+99Eg1lWExnhQzrDL1L7HY7ffr0IS8vz9WlSCHY7XZXlyBSZMqvmE4ZFtMFByvDYjbth8V0yrCYzgoZ1hl6l0RFRREVFUXt2rVdXYqIiIiIiIiIiGVZfeKV3wOdoSciIiIiIiIiImIQm8PhcLi6iBsZP348kydPJjs7mw4dOjBjxgx8fHyuu05OTg6NGjXixx9/5OzZs5QrV7iTEWvXrs2hQ4dKomxLud2652fPnuWOO+5wdRkiRaL8yo1Y/b46yrCY5GrjqUyZs1y4kD/DJv0eJKL9sPXc7LG7tI/1Vv/3nykZtvrvZK5k9YyVttLMcGH7UpY/Qy8hIYFx48YxdepUvvzyS3JycoiOjr7hesOHDycoKOgWVCiukJyc7OoSRIpM+RXTKcNiOj8/ZVjMpv2wmE4ZFtNZIcOWb+hNmTKFESNGEBkZSUhICLNmzeKzzz5j27Zt11zno48+Ys+ePfz973+/dYXKLfX111+7ugSRIlN+xdXatbvx1/Uow2K6WrWUYTGb9sNiOmVYTGeFDFu6oXf69Gm2b9/Ogw8+6HwsICAAf39/Nm7ceNV1MjIyGDFiBO+++y5ly5a9VaWKiIiIiIiIiIjcEpae5TYrK4sLFy5Qo0aNfI97e3tz5MiRq67zl7/8hSeeeIKgoCAyMjJu+Brx8fHEx8c7v8/OznZ+7+PjQ79+/Zg3bx6HDx8GIDw8nJCQEKZPn+5cp2/fvhw7dowVK1YA4OHhwZAhQ1i6dCkpKSkABAUF0bVrV6ZOncqZM2cA6NatG5UqVSIxMdG5raFDh7J582Y2bdoEgK+vL9HR0cydO5fMzEwAWrVqRXBwMDNmzHCu179/fzIyMli1ahUAnp6exMXFsXjxYvbt20d4OGRkNCQ1tRMtWkymTJlzAKSkRHDuXHmCg+1c/hiGDx9OcnKys+Ps5+dHVFQUs2fPJisrC4DWrVsTGBjIzJkznTUMGDCA9PR01qxZA0DlypWJjY1l4cKFpKamAtC4cWM6dOjAhAkTuHz7xp49e1K2bFmSkpKc2xo5ciTr169n69atAPj7+xMZGcmsWbPIzs4mLy+PLVu2ULduXRISEpzrDRo0iLS0NNatWweAl5cXMTExJCUlsX//fgBCQ0Np06YNEydOdK4XGRnJ+fPnWbRoEQA2m41Ro0axevVqduzYAVxsJvfq1YuZM2eSk5MDQPv27fH19WXOnDnObcXGxpKSksLnn38OQLVq1Rg4cCB2u52DBw8C0KxZM8LDw5kyZYpzvaioKE6dOsWSJUsAKFeuHE888QQrV65k586dANSvX58ePXowY8YMcnNzAejYsSM1a9bkvffec24rLi6OXbt2sWHDBuDimHnkkUdITEwkPT0dgLCwMJo3b860adOc60VHR3P8+HGWLVsGgJubG8OGDWP58uXs3r0bgMDAQLp378706dPJy8sDoEuXLlStWpX58+c7tzVkyBC2bdvmPBX5dhpPAA0bNqRTp05MnjyZc+cujqeIiAjKly+fbwrzq40nPz8/S40ngLZt22o8YZ3x5OlZlUaN/jeeNm8ego/PNudlgrm5Pnz7bdHH0z33LKV69YvjKTMziH37uhIWNpWyZS+Op717u5Gefu3xlJeXR2JioiXGk9WOT6DxZMJ4OnOmAuHhF3/xKe54up2OTxpP5oynvLw8cnNz9fse1hlPdeokO8/+/fVXP777LoomTWZTocLF8XTgQGuysgKJj5956XOBbdsGULlyOgEBF8fT6dOV2bIllgYNFlK16sXxtHp10cZTUJA/u3dHEho6i/LlswFIS2tLdnZdQkMvjqf4eNeNp7y8POe/u109nuDax6fg4EQqV744ntLTw0hPb05Y2P/G086d0bi5HSc+/vc3nsLD/zeetm/vj6dnBvXqXRxPZ8548s03t/fxydPTk2PHjpXK8amwLD0pRnp6OrVr12bHjh00atTI+XhYWBgRERE899xz+ZZPSEjgP//5D1988QVlypRh3bp1tGvXTpNioBtWioiY5Pd+o2yRkqQbmovIraBJMW5POoZcmzJWem6LSTGqV69OmTJlCpyNl5mZWeCsPYD169ezceNG3NzcKFeuHO3btwegfPny+f4aI+abPXu2q0sQKTLlV0ynDIvpmjRRhsVs2g+L6ZRhMZ0VMmzphp67uztNmjRh7RVt3bS0NPbv30+LFi0KLP/KK6+wfft2tm3bxrZt25ynX37zzTdERUXdsrql9F0+1VbERMqvmE4ZFtNdvgROxFTaD4vplGExnRUybOl76AEMGzaMESNGcP/99xMQEMCoUaNo3bo1ISEhpKen0759e+bMmUNYWBi+vr74+vo61z169CgAwcHBhb7kVkRERERERERExMos3+WKiYkhIyODxx9/nOzsbDp06MB//vMfAM6ePUtKSgonT550cZVyq7Vu3drVJYgUmfIrplOGxXQHDijDYjbth8V0yrCYzgoZtnxDD2DMmDGMGTOmwOP+/v5cb06Ptm3bXvf5K9ntdux2u3PWGbG2wMBAV5cgUmTKr5hOGRbTZWUpw2I27YfFdMqwmM4KGbb0PfRupaioKBYsWICHh4erS5FCuHJ6ahHTKL9iOmVYTNe0qTIsZtN+WEynDIvprJBhNfREREREREREREQMooaeiIiIiIiIiIiIQdTQEyMNGDDA1SWIFJnyK6ZThsV027Ypw2I27YfFdMqwmM4KGTZiUgyR30pPT6d69equLkNuE+3aFW65tWtL5vWUXzGdMiymq1w5nbw8ZVjMpf2wmE4ZFtNZIcNq6MktVxLNkzVr1tCkSZOSKUjkFlN+xXTKsJguIGANGRnKsJhL+2ExnTIsprNChtXQu8Rut2O328nLy3N1KSIiIiIiIiIiIteke+hdEhUVxYIFC/Dw8HB1KVIIlStXdnUJIkWm/IrplGEx3enTyrCYTfthMZ0yLKazQoZ1hp4YKTY21tUlSDEV5tLrkrpnndUov2I6ZVhMt2WLMixm035YTKcMi+mskGGdoSdGWrhwoatLECky5VdMpwyL6Ro0WOjqEkSKRfthMZ0yLKazQobV0BMjpaamuroEkSJTfsV0yrCYrmpVZVjMpv2wmE4ZFtNZIcO65FZERERuW7/ny/tFRERE5PZ102fo7d69m379+lG/fn3Kly/Pli1bAHjuuef45JNPSrzAW8Vut9OnTx/NcmuIxo0bu7oEkSJTfsV0yrCY7vBhZVjMpv2wmE4ZFtNZIcM3dYbe2rVr6dKlC8HBwURGRvLvf//b+ZzNZuPNN9+kc+fOJV7krRAVFUVUVBS1a9d2dSlSCB06dHB1CSJFpvyK6ZRhMV1amjIsZtN+WEynDFtPYa5qAF3ZcJkVMnxTZ+j94x//oHfv3mzZsoVXX30133NNmzZ1nq0nUtomTJjg6hJEikz5FdMpw2K6li2VYTGb9sNiOmVYTGeFDN9UQ+/bb7/lscceAy6ekXelO++8k8zMzJKr7Arjx4+nVq1aVKhQgR49enD48OGrLvfLL78wdOhQ6tevj4eHB/Xq1ePll1/m/PnzpVKXuI7D4XB1CSJFpvyK6ZRhMZ3NpgyL2bQfFtMpw2I6K2T4php6lStXvmbTLjU1lRo1apRIUVdKSEhg3LhxTJ06lS+//JKcnByio6OvuuxPP/1EZmYmkydPZufOnUycOJEpU6bwyiuvlHhdIiIiIiIiIiIirnBTDb2uXbsybtw4MjIynI/ZbDZyc3OZNGkSERERJV7glClTGDFiBJGRkYSEhDBr1iw+++wztm3bVmDZhg0bsmDBArp160a9evWIiIhg1KhRLFy4sMTrEtfq2bOnq0sQKTLlV0ynDIvp9uxRhsVs2g+L6ZRhMZ0VMnxTDb3x48dz6tQp7r33Xnr37o3NZuPpp58mKCiIkydP8tJLL5VocadPn2b79u08+OCDzscCAgLw9/dn48aNhdrG0aNHufPOO0u0LnG9smXLuroEkSJTfsV0yrCYzuFQhsVs2g+L6ZRhMZ0VMnxTs9z6+PiwdetWJkyYwKpVq6hXrx45OTnExMQwatQovLy8SrS4rKwsLly4UOBSXm9vb44cOXLD9VNTU5k5cyZvv/32NZeJj48nPj7e+X12drbzex8fH/r168e8efOc9+0LDw8nJCSE6dOnO9fp27cvx44dY8WKFQB4eHgwZMgQli5dSkpKCgBBQUF07dqVqVOncubMGQC6detGpUqVSExMdG5r6NChbN68mU2bNgHg6+tLdHQ0c+fOdV7u3KpVK4KDg5kxY4Zzvf79+5ORkcGqVasA8PT0JC4ujsWLF7Nv3z7CwyEjoyGpqZ1o0WIyZcqcAyAlJYJz58oTHGzn8scwfPhwkpOT+frrrwHw8/MjKiqK2bNnk5WVBUDr1q0JDAxk5syZzhoGDBhAeno6a9asAS5eoh0bG8vChQtJTU0FLk/t3IGWLSc471+zZ09PHI6yBAUlObeVnDyStWvXs3XrVgD8/f2JjIxk1qxZZGdnk5uby0MPPUTdunVJSEhwrjdo0CDS0tJYt24dAF5eXsTExJCUlMT+/fsBCA0NpU2bNkycONG5XmRkJOfPn2fRokXAxTNPR40axerVq9mxYwdwsZncq1cvZs6cSU5ODgDt27fH19eXOXPmOLcVGxtLSkoKn3/+OQDVqlVj4MCB2O12Dh48CECzZs0IDw9nypQpzvWioqI4deoUS5YsAaBcuXI88cQTrFy5kp07dwJQv359evTowYwZM8jNzQWgY8eO1KxZk/fee8+5rbi4OHbt2sWGDRuAi2PmkUceITExkfT0dADCwsJo3rw506ZNc64XHR3N8ePHWbZsGQBubm4MGzaM5cuXs3v3bgACAwPp3r0706dPJy8vD4AuXbpQtWpV5s+f79zWkCFD2LZtG8nJyUDB8RQeDgcPhnP4cAjNm/9vPH37bV88PI5Rv/4K4uNvzXhycwvm/vv/N562b++Pp2cG9epdHE9nznjyzTf/G09w8YzgTp06MXnyZM6duzieIiIiKF++PHa73bmtq42ngwcPUq1atRIZTx06dGDChAnOezj07NmTsmXLkpT0v/E0cuRI1q+/9ngCaNu2rcYT1hlPnp5VadTof+Np8+Yh+Phsw8/v4njKzfXh22+Lfny6556lVK9+cTxlZgaxb19XwsKmUrbsxfG0d2830tOvPZ5yc3MJDAwskeMTFG883ej4FB4O27YNoHLldAICLo6n06crs2VLLA0aLKRq1VTi4/83nsaMufHx6fnnNZ5MH08NG/6X8+fdgOKPJyv9vgelO55Ax6fLXD2ecnNzGT16tOV/34Pfz3iqUyeZWrUujqdff/Xju++iaNJkNhUqXBxPBw60JisrkPj4wh+fAFavLtp4CgryZ/fuSEJDZ1G+fDYAaWltyc6uS2joxfEUH+/a8eTp6Qm4fjzBtY9PwcGJVK58cTylp4eRnt6csLD/jaedO6NxcztOfLz54yk8HDZtGoqv72Z8fS+Op5wcX3btiqZx47lUrHhxPG3ceHE8hYffun8/WfH4dObMGQYPHlwqx6fCsjkKeSe/U6dO8a9//YsePXoQEhJyUy9SVOnp6dSuXZsdO3bQqFEj5+NhYWFERETw3HPPXXPdI0eO8Mc//pG2bdvy1ltvFfo1a9euzaFDh4pVtxUVZgrqWzX9dElMhx0fH8/o0aNLpiBxidstkzdD+ZUbKe1MFnf8mZThm32vVto3Scm42s80PDye5OT8GdbPVUxi0n64NFlpn32zx26rH+tLmykZvtX/TnCl0sjw7fC5XEtpZriwfalCX3Jbvnx5xo8f7+ys3wrVq1enTJkyBc7Gy8zMvO4EHFlZWXTo0IFmzZrx5ptvlnaZIiIiIiIiIiIit8xN3UOvWbNmfPvtt6VVSwHu7u40adKEtVe0ddPS0ti/fz8tWrS46jrHjh2jY8eOBAQE8O6771KmzE29RTHEyJEjXV2CSJEpv2I6ZVhMl5w80tUliBSL9sNiOmVYTGeFDN9Ut2vy5MlMnjyZ+fPn37Iz9YYNG8akSZP46KOP2L59O4899hitW7cmJCSE9PR0GjRo4LxfQk5ODp07d+aOO+5g8uTJHD16lMOHDzvvnSC3j/Xr17u6BJEiU37FdMqwmM7fXxkWs2k/LKZThsV0VsjwTTX0wsPDSU1NpX///lStWhUPDw8qVKjg/KpYsWKJFxgTE8MzzzzD448/TsuWLalYsSILFiwA4OzZs6SkpHDy5EkAtmzZ4rwhap06dbjrrru46667aN68eYnXJa51+eaUIiZSfsV0yrCY7q67lGExm/bDYjplWExnhQzf1Cy3//jHP7DZbKVVyzWNGTOGMWPGFHjc39+fK+f0aNu2LYWc40NERERELOL3fmNtERERkZt1Uw29F198sZTKcD273Y7dbndOIy3W5u/v7+oSRIpM+RXTKcNiuuxsf1eXIFIs2g+L6ZRhMZ0VMlzkGSMOHDjA1q1bOXDgQEnW4zJRUVEsWLAADw8PV5cihRAZGenqEkSKTPkV0ynDYrrdu5VhMZv2w2I6ZVhMZ4UM33RD77333sPf35+AgACaNWtGQEAA/v7+vP/++6VRn8hVzZo1y9UliBSZ8iumU4bFdKGhyrCYTfthMZ0yLKazQoZv6pLbxMREBgwYQKdOnRg7diw+Pj4cPnyY//73vwwYMIA77riDPn36lFatIk7Z2dmuLkGkyJRfMZ0yLKYrXz7b1SWIFIv2w2I6ZVhMZ4UM31RD7//9v//HwIEDSUhIyPf4wIEDGTRoEK+++qoaenJbK8xNu0E37hYRERERERGR0nNTDb2UlBT+9a9/XfW5vn378t///rdEihK5kbZt27q6BJEiU37FdMqwmC4tra2rSxAplpLcD2uWaXEF/S4hprNChm+qoVelShXS0tKu+lxqaipVqlQpkaJcQbPcmqVu3bquLkGkyJRfMZ0yLKbLzlaGxWzaD4vplGExnRUyfFOTYvTq1YtnnnmGjz/+ON/jS5Ys4Z///Ce9e/cu0eJuJc1ya5bfXvYtYhLlV0ynDIvpQkOVYTGb9sNiOmVYTGeFDN/UGXqvvfYaO3bsoEePHlSsWJEaNWqQkZFBXl4eLVu25LXXXiutOkVERERERERERIQiXHL7xRdfsHTpUr744gt++eUX7rzzTlq3bk23bt0oU+amTvgTERERERERERGRm3RTDT2AMmXKEBERQURERGnUI1IogwYNcnUJIkWm/IrplGEx3datyrCYTfthMZ0yLKazQoZv6pS6Tz/99JrXCb/77rus1fRHcotca3IWERMov2I6ZVhM5+WlDIvZtB8W0ynDYjorZPimGnrPPfccP//881Wfy8jI4LnnniuRokRuZN26da4uQaTIlF8xnTIspqtbd52rSxApFu2HxXTKsJjOChm+qYbezp07ad68+VWfu//++9m1a1eJFOUKdrudPn36kJeX5+pSRERERERERERErummGnoXLlwgNzf3qs8dP36cs2fPlkhRrhAVFcWCBQvw8PBwdSlSCF5eXq4uQaTIlF8xnTIspjt1ysvVJYgUi/bDYjplWExnhQzfVEPv/vvv5913373qc7NmzSI0NLQkahK5oZiYGFeXIFJkyq+YThkW023dqgyL2bQfFtMpw2I6K2T4php6zzzzDB9//DGdOnVi/vz5fPbZZ8yfP5+OHTuyfPlynn322VIpcvz48dSqVYsKFSrQo0cPDh8+fM1lc3NzGTRoEJUrV6ZatWqMGjWKc+fOlUpd4jpJSUmuLkGkyJRfKWnt2t34qyQpw2K6oCBlWMym/bCYThkW01khw+VuZuFOnTqRmJjIk08+ycMPP4zNZsPhcHD33XeTmJhI586dS7zAhIQExo0bx5w5cwgICGDkyJFER0ezfv36qy4/dOhQNm3axKpVqzhx4gT9+/enUqVKvPTSSyVem7jO/v37XV3Cba+wDQBNbn3zlF8xnTIsrlaYY9T1jk9eXvtLrBYRV9B+WEynDIvprJDhm2roAfzpT3/iT3/6EykpKWRlZVG9enXuvffe0qgNgClTpjBixAgiIyOBi5f21qtXj23bthESEpJv2WPHjvH++++zfPlyWrRoAcC4ceN46qmneOGFFyhbtmyp1SkiIiIiFxW34SYiIiIi12dzOBwOVxdxLadPn6ZChQqsXLmS9u3bOx+vW7cuTz/9NIMHD863/OrVq+ncuTOnTp3ijjvuAODAgQP4+/uzZ88eAgMDC7xGfHw88fHxzu+zs7OdZ/P5+PjQr18/5s2b57zMNzw8nJCQEKZPn+5cp2/fvhw7dowVK1YA4OHhwZAhQ1i6dCkpKSkABAUF0bVrV6ZOncqZM2cA6NatG5UqVSIxMdG5raFDh7J582Y2bdoEgK+vL9HR0cydO5fMzEwAWrVqRXBwMDNmzHCu179/fzIyMli1ahUAnp6exMXFsXjxYvbt2wdAw4YN6dSpE5MnT3ZehhwREUH58uWx2+3ObQ0fPpzk5GS+/vprAPz8/IiKimL27NlkZWUB0Lp1awIDA5k5c6ZzvQEDBpCens6aNWsAqFy5MrGxsSxcuJDU1FQAGjduTIcOHZgwYQKXo9ezZ0/Kli2b75TVkSNHsn79erZu3QpHj+KfnU3k7t3MCg0lu3x5TpctS+d9+6ibnU1CaChUrw7AoEGDSHv8cdbVrQuA16lTxGzdSlJQEPsv3bQy9OefafPOO0ycOBGOHgUgcvduzttsLGrQAACbw8Gor75idd267PDxgerVCQgIoFevXsycOZOcnBwA2rdvj6+vL3PmzHHWHhsbS0pKCp9//jkA1apVY+DAgdjtdg4ePAhAs2bNCA8PZ8qUKc71oqKiOHXqFEuWLAGgXLlyPPHEE6xcuZKdO3cCUL9+fXr06MGMGTMuTlBz9Cgdf/iBmrm5vNekiXNbcd98wy5vbzbcfTdUr463tzePzJpFYnAw6ZUrAxCWnk7z9HSmhYU514veuZPj//oXy5YtA8DNzY1hw4axfPlydu/eDUBgYCDdu3dn+vTpzlmhu3TpQtV//IP5jRo5tzVk82a2+fiQ7OcHgE9uLv0mT749xtPRozTMyKBTaiqTW7TgXJmLdy+ISEmh/Llz2IODnZkcPnw4yX/5C1/XqgWA36+/Uv3kSX6sUoWsChUAaH3gAIHTp/9vPB09yoBt20ivXJk1AQEAVD59mtgtW1jYoAGpVatC9eoXx9MrrzChZUscNhsAPffsoazDQVJQkPP9jExOZv3zz18cT4C/vz+RkZHMmjWL7OxsANq2bUvdunVJSEhwrjdo0CDS0tKcU7J7eXkRExNDUlKS8y9SoaGhtGnT5uJ4ulR7qY2no0epdvIkA7dvx37ffRysUgWAZj/9RPjBg0y59Iccqlcv/fH0yCMkJiaSnp4OQFhYGM2bN2fatGnO9aKjozl+/HjRxlPVqsyfP9+5rSFDhrBt2zaSk5MBFxyfjh5l6KZNbPb1ZZOvL6fLliXg2DGid+1ibuPGZFasCNWrXxxPzz/PjPvvd9bQf/t2Mjw9WVWvHgCeZ84Q9803LB416rY8PgG0TUv73/HpUiYLfXzav5+JERHOz90yx6eRI1ly6feochcu8MTGjawMCGBnzZoA1M/Kosfevczo29c5gVrHjh2pWbMm7733nnNbcXFx7Nq1iw0bNgC4bDx98MEHzuPA7/r3vd8cn6K++47ZTZrkPz5lZTGzUyfneqU2nrjJ49PNjicgMjKS8+fPs2jRIgBsNhujRo1i9erV7NixA8Cav+9RcDydPn2a4cOHW2I8ufT4hIXGU2kfn8aMsc7vexR/PP3nP//B3d0dcP14ghI8PmVkMGzTJpbXr89ub28AAo8epfv33zO9WTPy7rgDqle/NeMpLo6US/8mCcrMpOu+fUwNC+PMpROduu3dS6UzZ0hs29a5rd/NeCqB45OPjw9du3YtlfEUHx/PoUOHuJEbNvR++OEHvvvuOyKuOBjCxebZ008/ze7du/Hx8eHJJ5/k8ccfv+EL3oyffvoJX19fduzYQaMrGgVhYWFERETw3HPP5Vt+3rx5DB8+3PlDBjh58iQVK1bks88+o3Xr1jd8zdq1axfqg5Nb6Cp/5r/Ab24AeeWf+W/mtADTryu10nv9PZ2OcbPv9TfLF8jvDZa/7vatlmGTc2By7aXtZjJstUyWppt9r6WxvJXGNhjzc71w4QJlytzUraRvTyb/XE2uvQQow79DVjomlIDbNsNW+jn9zveTpa00M1zYvtQNX33s2LH861//yvfY5Qbf3r176dKlC56engwfPjzfX0pKws2ePHi15W2X/ooht5eJ4eGuLkGkyG7r/K5de+MvMd5tnWH5Xbjyr+IiJlKGxXTKsJjOChm+4T30vvrqK4YPH57vsSlTpnDu3Dk2bNhA06ZNcTgcPPTQQ0ybNo3o6OgSK6569eqUKVOGI0eO5Hs8MzOTGjVqFFi+Zs2aZGdnc/bsWeclt5fXvdryIiIiUsLUtBURERERKXU3PEPv559/JuiKa/MBli5dSvPmzWnatClw8Sy4xx57zHmtfElxd3enSZMmrL3iHwdpaWns37/fOenFlZo2bYrNZss3A+6nn35KtWrVqF+/fonWJiIiIiIiIiIi4go3PEPvjjvucN6wECA9PZ1Dhw7x5z//Od9yNWrU4PTp0yVe4LBhwxgxYgT3338/AQEBjBo1itatWxMSEkJ6ejrt27dnzpw5hIWFceedd9KvXz9GjBhBQkICJ06c4Nlnn+Xxxx/XDLcmu8rZHpH798Orr976WkRKQOSlG06LmEoZFtNFRka6ugSRYlGGxXTKsJjOChm+YUOvQYMGfPLJJ3Tp0gWARYsWYbPZ6HTFbFdwsdFXGpe1xsTEkJGRweOPP052djYdOnTgP//5DwBnz54lJSWFkydPOpd/8803GTZsGB06dKBcuXIMGDCA559/vsTrEtc6f/68q0sQKbLzurenGE4ZFtPp9wgxnTIsprttM6xbj/xuWCHDN7zkdsSIEUyePJlBgwbx9NNP88wzzxAYGEi738yYsnz5cho3blwqRY4ZM4aff/6ZvLw8lixZgo+PD3Bx6mCHw0HbK6ZZ9vT05N133yUnJ4dffvmFiRMnUq7cDfuW2O12+vTp45yWXazt8nTOIiZa1KCBq0sQKRZlWEyn3yPEdMqwmE4ZFtNZIcM37HRFR0dz6NAhJk2axC+//ELz5s158803813CmpGRwbJly3j55ZdLtdjSFBUVRVRUFLVr13Z1KSIiIiIiIiIiItd041PXgCeffJInn3zyms/XrFmzwEy0IqXJpsu9xGA2h8PVJYgUizIsptPvEWI6ZVhMpwyL6ayQ4RtecitiRaNGjXJ1CSJFNuqrr1xdgkixKMNiOv0eIaZThsV0yrCYzgoZVkNPjLR69WpXlyBSZKvr1nV1CSLFogyL6fR7hJhOGRbTKcNiOitkWA09MdKOHTtcXYJIke24NLGPiKmUYTGdfo8Q0ynDYjplWExnhQwX6h56vwd2ux273a5ZbkVERERERERExNLU0LtEs9yaJSAgwNUliBRZwLFjri5BpFiUYTGdfo8Q0ynDYjpl+BZYu9bVFdzWrJBhXXIrRurVq5erSxApsl579ri6BJFiUYbFdPo9QkynDIvplGExnRUyrDP0xEgzZ84kNjbW1WWIFMnMpk2J3bLF1WWIFN5v/sKrffDv0G32V35lWEynDIvplGExnRUyrIaeGCknJ8fVJYgUWY67u6tLECkW7YPFdMqwmE4Z/h26zf6wogyL6ayQYV1yKyIiIiIiIiIiYhA19MRI7du3d3UJIkXWPjXV1SWIFIv2wWI6ZVhMpwyL6ZRhMZ0VMqxLbi+x2+3Y7Xby8vJcXYoUgq+vr6tLECkyXwucni1SHNoHi+mUYTGdMiymU4bFdFbIsM7QuyQqKooFCxbg4eHh6lKkEObMmePqEkSKbE5IiKtLECkW7YPFdMqwmE4ZFtMpw2I6K2RYDT0RERERERERERGDWL6hl5CQQEBAAB4eHrRp04a9e/dec9mzZ8/y1FNPERwcTIUKFbj77rsZNWoUJ06cuIUVi4iIiIiIiIiIlB5LN/Q+/fRT4uLiGDNmDJs3b8bHx4fu3btz5syZqy5/8uRJduzYwUsvvcT27duZPXs2S5cuZfjw4be4ciltsbGxri5BpMhit2xxdQkixaJ9sJhOGRbTKcNiOmVYTGeFDFu6oTd16lT69OnDX/7yFxo2bMisWbNIT09n2bJlV12+SpUqrFixgv/7v//jnnvuoV27dowdO5aFCxfe2sKl1KWkpLi6BJEiS6lWzdUliBSL9sFiOmVYTKcMi+mUYTGdFTJs6VluN23axNixY53fV6xYkRYtWrBx40Z69epVqG0cPXqUO++885rPx8fHEx8f7/w+Ozvb+b2Pjw/9+vVj3rx5HD58GIDw8HBCQkKYPn26c52+ffty7NgxVqxYAYCHhwdDhgxh6dKlzh9yUFAQXbt2ZerUqc4zDLt160alSpVITEx0bmvo0KFs3ryZTZs2ARdnTomOjmbu3LlkZmYC0KpVK4KDg5kxY4Zzvf79+5ORkcGqVasA8PT0JC4ujsWLF7Nv3z4AGjZsSKdOnZg8eTLnzp0DICIigvLly2O3253bGj58OMnJyXz99dcA+Pn5ERUVxezZs8nKygKgdevWBAYGMnPmTOd6AwYMID09nTVr1gBQuXJlYmNjWbhwIampqQA0btyYDh06MGHCBBwOBwA9e/akbNmyJCUlObc1cuRI1q9fz9atWwHw9/cnMjKSWbNmkZ2dTW5uLmXLlqVu3bokJCQ41xs0aBBpPj6sq1sXAK9Tp4jZupWkoCD2e3kBEPrzz7S5cIGJEydCeDgAkbt3c95mY1GDBgDYHA5GffUVq+vWZYePD8THExAQQK9evZg5cyY5l2Ypbd++Pb6+vvluiBkbG0tKSgqff/45ANWqVWPgwIHY7XYOHjwIQLNmzQgPD2fKlCnO9aKiojh16hRLliwBoFy5cjzxxBOsXLmSnTt3AlC/fn169OjBjBkzyM3NhfBwOv7wAzVzc3mvSRPntuK++YZd3t5suPtuiI/H29ubR4DE4GDSK1cGICw9nebp6UwLC3OuF71zJ8f37HE2zd3c3Bg2bBjLly9n9+7dAAQGBtK9e3emT5/unBW6S5cuVPX0ZH6jRs5tDdm8mW0+PiT7+QHgk5tLP7g9xlN4OA0zMuiUmsrkFi04V+bi30YiUlIof+4c9uBguLQfGT58OMl16vB1rVoA+P36KwerVOE7b2+yKlQAoPWBAwTm5PxvPIWHM2DbNtIrV2ZNQAAAlU+fJnbLFhY2aEBq1aoQH39xPAETWrbEYbMB0HPPHso6HCQFBTnfz8jkZNavXXvN8QTQtm3bq4+ntDTWrVsHgJeXFzExMSQlJbF//34AQkNDadOmzcXxdElkZCTnz59n0aJFANhsNkaNGsXq1avZsWMHgPnj6ZFHSExMJD09HYCwsDCaN2/OtGnTnOtFR0dz/Pjxoo2nqlWZP3++c1tDhgxh27ZtJCcnA64/PuXm5pKamqrjU3g4/tnZRO7ezazQULLLlwegbVoadbOzSQgNvbhSfHzhj0/79zPx8u8l4eHmHp+Ajh07UrNmTd577z3ntuLi4ti1axcbNmwAcNl4WrNmjfNzcPV4Ahf+vveb41PUd98xu0mT/MenrCxmXvG78q38fQ+uc3y62fGEwccnCo6n3NxcgoKCLDGerHR8Av37yZTf95YvX+4cP64eT2Cd45PGkznj6cyZM9SvX79UxlNh2RyX34UFubm58eGHHxIREeF8rE+fPlSqVIl33nnnhuv/8ssvNG3alL/+9a88/fTThXrN2rVrc+jQoSLXLLdGfHw8o0ePvvqT7drdeANr1xZ+2SuXtxorvdebqcV0N/tef7N8fHg4oy8dqAuz/HW3b3qGreT3lOFiuu4++PfkZsdfaSyvTBaJMnyJyccQk2svAcqwmE4ZFtOVZoYL25dyyRl6f/3rX3n77bev+XybNm2cHc2iOnnyJD179qRhw4b8/e9/L9a2xHqq6ZLFm3eb/kJromonT7q6BJFi0T5YTKcMi+mUYTGdMiyms0KGXdLQGz9+PM8+++w1n3d3dwegRo0aHDlyJN9zmZmZ1KtX77rbP3XqFBEREbi5ufHBBx9QtmzZ4hctljJw4EBXlyBSZAO3b3d1CSLFon2wmE4ZFtMpw2I6ZVhMZ4UMu2RSDC8vL2rXrn3NL29vb+DiNeprrzir6OTJk2zcuJEWLVpcc9unT5+mV69enDx5kkWLFlH+0v1s5PZy5TX2Iqax33efq0sQKRbtg8V0yrCYThkW0ynDYjorZNjSs9wOHTqUxMRE3nnnHXbt2kVMTAy1atWiW7duzmUaNGjARx99BMDZs2f505/+xL59+0hISCA3N5fDhw87byApt4/LN0cVMdHBKlVcXYJIsWgfLKZThsV0yrCYThkW01khw5ae5bZ9+/a8/fbbvPTSSxw+fJgWLVqwdOlS3NzcnMukpKTw66+/ApCens7HH38MXJzF5UoWnvtDRERERERERESk0Czd0AOIiYkhJibmms9f2ajz9/cvcuPObrdjt9ud00iLtTVr1qxkNqSJIsQFmv30k6tLECmWEtsHm07HEGMpw2I6ZVhMpwyL6ayQYUtfcnsrRUVFsWDBAjw8PFxdihRCeHi4q0sQKbJwC5yeLVIc2geL6ZRhMZ0yLKZThsV0VsiwGnpipClTpri6BJEim3KdiX1ETKB9sJhOGRbTKcNiOmVYTGeFDKuhJyIiIiIiIiIiYhA19ERERERERERERAxi+UkxRK4mKirK1SVYg27IbqSoXbtcXYJIsWgfLKZThsV0yrCYThkW01khw2roXaJZbs1y6tQpV5cgUmSnymnXK2bTPlhMpwyL6ZRhMZ0yLKazQoZ1ye0lmuXWLEuWLHF1CfJ7tnbtjb+uY0lg4C0qVKR0aB8splOGxXTKsJhOGRbTWSHDOk1ERERExIp0WwURERERuQadoSdGKqdLFsVg5S5ccHUJIsWifbCYThkW0ynDYjplWExnhQyroSdGeuKJJ1xdgkiRPbFxo6tLECkW7YPFdMqwmE4ZFtMpw2I6K2RYDT0x0sqVK11dgkiRrQwIcHUJcjXFvDfi74n2wWI6ZVhMpwyL6ZRhMZ0VMqyGnhhp586dri5BpMh21qzp6hJEikX7YDGdMiymU4bFdMqwmM4KGXb9Rb8WYbfbsdvt5OXluboUERERuR3pLE8RERERKSE6Q++SqKgoFixYgIeHh6tLkUKoX7++q0sQKbL6WVmuLkGkWLQPFtMpw2I6ZVhMpwyL6ayQYTX0xEg9evRwdQkiRdZj715XlyBSLNoHi+mUYTGdMiymU4bFdFbIsOUbegkJCQQEBODh4UGbNm3YW8h/CJ87d47mzZtjs9nYt29fKVcpt9qMGTNcXYJIkc24//7rL6DJGcTitA8W0ynDYjplWEynDIvprJBhSzf0Pv30U+Li4hgzZgybN2/Gx8eH7t27c+bMmRuu+/LLL1OtWrVbUKW4Qm5urqtLECmyXDc3V5cgUizaB4vplGExnTIsplOGxXRWyLClG3pTp06lT58+/OUvf6Fhw4bMmjWL9PR0li1bdt31Nm/ezLx583j99ddvUaUiIiIiIiIiIiK3hqVnud20aRNjx451fl+xYkVatGjBxo0b6dWr11XXycvLY8CAAbz99ttUqlTphq8RHx9PfHy88/vs7Gzn9z4+PvTr14958+Zx+PBhAMLDwwkJCWH69OnOdfr27cuxY8dYsWIFAB4eHgwZMoSlS5eSkpICQFBQEF27dmXq1KnOMwy7detGpUqVSExMdG5r6NChbN68mU2bNgHg6+tLdHQ0c+fOJTMzE4BWrVoRHByc7xTP/v37k5GRwapVqwDw9PQkLi6OxYsXOy85btiwIZ06dWLy5MmcO3cOgIiICMqXL4/dbndua/jw4SQnJ/P1118D4OfnR1RUFLNnzybr0s38W7duTWBgIDNnznSuN2DAANLT01mzZg0AlStXJjY2loULF5KamgpA48aN6dChAxMmTMDhcADQs2dPypYtS1JSknNbI0eOZP369WzduhUAf39/IiMjmTVrFtnZ2Zw9e5YtW7ZQt25dEhISnOsNGjSINB8f1tWtC4DXqVPEbN1KUlAQ+728AAj9+WfaXLjAxIkTnetFRkZy/vx5Fi1aBIDNZmPUqFGsXr2aHTt2ABAQEECvXr2YOXMmOTk5ALRv3x5fX1/mzJnj3FZsbCwpKSl8/vnnAFSrVo2BAwdit9s5ePAgAM2aNSM8PJwpU6Y414uKiuLUqVMsWbIEgHLlyvHEE0+wcuVK55TY9evXp0ePHsyYMcP5F4GOHTtSs2ZN3nvvPee24uLi2LVrFxs2bADA29ubRx55hMTERNLT0wEICwujefPmTJs2zbledHQ0x48fdzbN3dzcGDZsGMuXL2f37t0ABAYG0r17d6ZPn+6cFbpLly5U9fRkfqNGzm0N2byZbT4+JPv5AeCTm0s/+H2Opzp1+LpWLQD8fv2Vjj/8wOwmTciqUAGA1gcOEJiTU7TxBExo2RKHzQZAzz17KOtwkBQU5NzWyORk1q9de83xBNC2bdurj6e0NNatWweAl5cXMTExJCUlsX//fgBCQ0Np06aNxlNJj6eqVZk/f75zW0OGDGHbtm0kJycDrj8+nT17lsTERB2f0HgydTz5+/vr972rHJ+ivvuu4PEpK4uZV/yubJnxVJjf9/bvZ+IVtd9O4+ns2bPk5uZaYjxZ6fgE+veTKcens2fPOvfDrh5PYJ3jk8aTOePpnnvu4dixY6UyngrL5rj8LizIzc2NDz/8kIiICOdjffr0oVKlSrzzzjtXXWf48OE4HA6mTp3K/v37qVu3Lt9//32hZyCpXbs2hw4dKpH6pfQcOXKEGjVqXP3Jdu1uvAHdg6zk6XO/tt98NkcqVKDGyZP5lynqZ1OYz7042xe5iuvug0UMoAxfYvIxxOTaS4AyLKZThsV0pZnhwvalXHLJ7V//+ldsNts1v9q2bVuk7X766aesWLGC1157rWQLFsu58q8pIqZ5r0kTV5cgUizaB4vplOFLCjMJ023aEDOdMiymU4bFdFbIsEsuuR0/fjzPPvvsNZ93d3cHoEaNGhw5ciTfc5mZmdSrV++q63322Wf88MMPVKlSJd/jDRo04B//+AevvPJKMSsXERERERERERFxLZc09Ly8vPC6dH+L6wkLC2Pt2rU89thjAJw8eZKNGzcyYsSIqy7/+OOP86c//cn5/U8//UTnzp1ZsmQJTZs2LZHaRUREREREREREXMnSk2IMHTqULl260K5dO1q2bMnLL79MrVq16Natm3OZBg0a8P/+3/+jd+/e1KhRI981zJ6ensDFmxXWrFnzltcvpScuLs7VJYgUWdw337i6BJFi0T5YTKcMi+mUYTGdMiyms0KGXXIPvcJq3749b7/9Ni+//DL3338/P//8M0uXLsXNzc25TEpKCr/++qsLqxRX2LVrl6tLECmyXd7eri5BpFi0DxbTKcNiOmVYTKcMi+mskGFLN/QAYmJi2L9/P6dOnWL9+vUEBgbme97hcPDoo49edV1/f38cDkehZri12+306dPHOY20WNvl6cRFTLTh7rtdXYJIsWgfLKZThsV0yrCYThkW01khw5Zv6N0qUVFRLFiwAA8PD1eXIiIiIiIiIiIick1q6ImRvHXJohjM+8QJV5cgUizaB4vplGExnTIsplOGxXRWyLDN4XA4XF2EldSuXZtDhw65ugwpjnbtbrzM2rWlX8fvjT73ayvNz6Yw2y7O9kVERKxIxz8REblNFbYvpTP0xEiJiYmuLkGkyBKDg11dgkixaB8splOGbwNr1xbu6zalDIvplGExnRUyrIaeGCk9Pd3VJYgUWXrlyq4uQaRYtA8W0ynDYjplWEynDIvprJDhcq4uwCrsdjt2u12z3IqIiIiIiIiIiKXpDL1LNMutWcLCwlxdgkiRhVngrzkixaF9sJhOGRbTKcNiOmVYTGeFDKuhJ0Zq3ry5q0sQKbLmauiJ4bQPFtMpw2I6ZVhMpwyL6ayQYTX0xEjTpk1zdQnyW7/jG1PfrGkW+GuOSHFoHyymU4bFdMqwmE4ZFtNZIcNq6ImIiIiIiIiIiBhEk2KIiJhMZz6KiIiIiIj87ugMPTFSdHS0q0sQKbLonTtdXYJIsWgfLKZThsV0yrCYThkW01khwzpD7xK73Y7dbicvL8/VpUghHD9+3NUliBTZcTc3V5cgUizaB4vplGExnTIsplOGxXRWyLDO0LskKiqKBQsW4OHh4epSpBCWLVvm6hJEimzZvfe6ugSRYtE+WEynDIvplGExnTIsprNChnWGntx+dE8xEREREREREbmNWf4MvYSEBAICAvDw8KBNmzbs3bv3huusXr2aFi1aUL58eby9vRk1atQtqFRuJTddsigGczt/3tUliBSL9sFiOmVYTKcMi+mUYTGdFTJsczgcDlcXcS2ffvopnTt35s033yQ8PJyXX36ZLVu2sGvXrmt+eGvXrqVnz568/PLLdO3aldOnT/Pjjz/SvXv3Qr1m7dq1OXToUEm+DRH5vWvX7sbL6MxSERERERGR373C9qUsfYbe1KlT6dOnD3/5y19o2LAhs2bNIj09/brXKv/tb3/jySefZMSIEdx77700atSo0M08Mcfy5ctdXYJIkS2vX9/VJYgUi/bBYjplWEynDIvplGExnRUybOl76G3atImxY8c6v69YsSItWrRg48aN9OrVq8Dyhw8fZsuWLQwYMIDmzZtz6NAhWrZsycSJE6lTp85VXyM+Pp74+Hjn99nZ2c7vfXx86NevH/PmzePw4cMAhIeHExISwvTp053r9O3bl2PHjrFixQoAPDw8GDJkCEuXLiUlJQWAoKAgunbtytSpUzlz5gwA3bp1o1KlSiQmJjq3NXToUDZv3symTZsA8PX1JTo6mrlz55KZmQlAq1atCA4OZsaMGc71+vfvT0ZGBqtWrQLA09OTuLg4Fi9ezL59+wBo2LAhnTp1YvLkyZw7dw6AiIgIypcvj91ud25r+PDhJCcn8/XXXwPg5+dHVFQUs2fPJisrC4DWrVsTGBjIzJkznesNGDCA9PR01qxZA0DlypWJjY1l4cKFpKamAtC4cWM6dOjAhAkTuHxyaM+ePSlbtixJSUnObY0cOZL169ezdetWAPz9/YmMjGTWrFlkZ2eTm5tLzZo1qVu3LgkJCc71Bg0aRFpaGuvWrQPAy8uLmJgYkpKS2L9/PwChoaG0adOGiRMnOteLjIzk/PnzLFq0CACbzcaoUaNYvXo1O3bsACAgIIBevXoxc+ZMcnJyAGjfvj2+vr7MmTPHua3Y2FhSUlL4/PPPAahWrRoDBw7Ebrdz8OBBAJo1a0Z4eDhTpkxxrhcVFcWpU6dYsmQJAOXKleOJJ55g5cqV7Ny5E4D69evTo0cPZsyYQW5uLgAdO3akZs2avPfee85txcXFsWvXLjZs2ACAt7c3jzzyCImJiaSnpwMQFhZG8+bNmTZtmnO96Ohojh8/7myau7m5MWzYMJYvX87u3bsBCAwMpHv37kyfPt05K3SXLl2oWrUq8+fPd25ryJAhbNu2jeTkZOB3Pp7q1OHrWrUA8Pv1Vw5WqcKRihXJqlABgNYHDhCYk+Oy8QTQtm1bjSc0ngo7nnJzc8nJydHxCY0nU8fT9u3bneu5ejyBft/TeLr58ZSbm0vr1q0tMZ6sdHwCjSdTxtPmzZudeXP1eALrHJ80nswZT2fOnKFly5alMp4Ky9KX3Lq5ufHhhx8SERHhfKxPnz5UqlSJd955p8DyX331FeHh4dSoUYNJkyZRr149xo4dS2pqKjt27KBcuRv3L3XJrRni4+MZPXq0q8sQKZzfXHIbHx7O6EsHaiddcisG0T5YTKcMi+mUYTGdMiymK80MW/qS27/+9a/YbLZrfrVt27ZI271w4QIAjz/+OH/+859p3rw5s2fPZu/evc4ut9weAgMDXV2CSJEFHj3q6hJEikX7YDGdMiymU4bFdMqwmM4KGXbJJbfjx4/n2Wefvebz7u7uANSoUYMjR47key4zM5N69epddb2aNWsC+T/YatWqUb16deepunJ70H0RxWTdv//e1SWIFIv2wWI6ZVhMpwyL6ZRhMZ0VMuySM/S8vLyoXbv2Nb+8vb2Bi9eor73iMrSTJ0+yceNGWrRocdXt1q1blxo1ajiv0YaL98Q7evQod999d+m+KbmlrrxngIhppjdr5uoSRIpF+2AxnTIsplOGxXTKsJjOChm29Cy3Q4cOJTExkXfeeYddu3YRExNDrVq16Natm3OZBg0a8NFHHwFQpkwZRowYwcSJE1m6dCl79uwhNjaW++67j/DwcFe9DSkFl28mKmKivDvucHUJIsWifbCYThkW0ynDYjplWExnhQxbepbb9u3b8/bbb/PSSy9x+PBhWrRowdKlS3Fzc3Muk5KSwq+//ur8/umnn+bEiRPExMRw+vRp/vjHP/Lxxx9TtmxZV7wFERERERERERGREmXpWW5vJbvdjt1uZ82aNc6pkMW6vvvuO+677z5XlyFSOL+Z5fY7b2/uuzTtu5NmuRWDaB8splOGxXTKsJhOGRbTlWaGCzvLraXP0LuVoqKiiIqKonbt2q4uRQqhatWqri5BpPB+06yr+vPPcNddLipGpPi0DxbTKcNiOmVYTKcMi+mskGFL30NP5Frmz5/v6hJEikz5FdMpw2I6ZVhMpwyL6ZRhMZ0VMqyGnoiIiIiIiIiIiEHU0BMRERERERERETGIJsX4jcLefFBcKy8vDw8PD1eXIVIkyq+YThkW0ynDYjplWEynDIvpSjPDhe1L6Qw9MdK2bdtcXYJIkSm/YjplWEynDIvplGExnTIsprNChtXQu8Rut9OnTx/y8vJcXYoUQnJysqtLECky5VdMpwyL6ZRhMZ0yLKZThsV0VsiwGnqXREVFsWDBAp32KyIiIiIiIiIilqaGnhjJx8fH1SWIFJnyK6ZThsV0yrCYThkW0ynDYjorZFiTYvyGJsUQERERERERERFX0KQYclubN2+eq0sQKTLlV0ynDIvplGExnTIsplOGxXRWyLAaemKkw4cPu7oEkSJTfsV0yrCYThkW0ynDYjplWExnhQyXc3UBVmG327Hb7ZrlVkRERERERERELE330PsNd3d3vL29i7WNvLw8o2bLNa1egNzcXDw9PV1dRqGZ9hmr3tJlWn7BvM9Y9ZYu0zJs2ucL5tVsWr3KcOkzrWbT6lWGS5dp9YJ5NSvDpUv1lr7SzHBmZianT5++8YIOKXFRUVGuLuGmmFavw+Fw+Pr6urqEm2LaZ6x6S5dp+XU4zPuMVW/pMi3Dpn2+Dod5NZtWrzJc+kyr2bR6leHSZVq9Dod5NSvDpUv1lj4rZFj30CsFUVFRri7hpphWr4lM+4xVr/yWaZ+x6pUrmfj5mlazafWaxsTP17SaTavXNKZ9vqbVC2bWbBLTPl/V+/ugS27FSIWdxlnEipRfMZ0yLKZThsV0yrCYThkW01khwzpDT4w0evRoV5cgUmTKr5hOGRbTKcNiOmVYTKcMi+mskGGdoSciIiIiIiIiImIQnaEnIiIiIiIiIiJiEDX0REREREREREREDKKGnoiIiIiIiIiIiEHU0BMRERERERERETGIGnoiIiIiIiIiIiIGUUNPRERERERERETEIGroiYiIiIiIiIiIGEQNPREREREREREREYOooSciIiIiIiIiImIQNfREREREREREREQMooaeiIiIiIiIiIiIQdTQExEREblJ/v7+tG3b1tVlSBGkpaXRo0cPqlevjs1mY+TIka4uSUREROSmqaEnIiIilpOamkpsbCyBgYF4eHhQrVo1GjVqRFxcHFu3br0lNUycOJG33nrrlrxWaVu3bh0vvvgihw8fdnUpLjdw4EA2bNjAmDFjmDt3Lo888kipvp6/vz82m61QX/v37wcgIyOD0aNHExwcjKenJ15eXjRo0IABAwawdu3aAtsPCQm5qZr+3//7f9hsNurUqcOFCxdK6J2KiIjIrVTO1QWIiIiIXOmbb76hTZs2uLm5MXDgQIKCgjh+/Dh79uxh4cKF3HvvvYSGhpZ6HRMnTsTLy4u//vWvBZ5LSUnBZrOVeg0lZd26dYwdO5ZevXrh4+Pj6nJc5tSpU3zxxRcMHz6cJ5988pa85sSJE8nNzXV+v3v3bl599VV69+5NZGRkvmW9vb358ccfad68Obm5ufTv359hw4Zx9uxZ9u7dy+LFi6lcuTLt2rUrVk3vvvsu9erV44cffmDNmjV07NixWNsTERGRW08NPREREbGUsWPHcuLECb788ksaN26c77np06eTlZXlosr+x93d3dUl/K6cOHGCihUrFns7R44cweFwULVq1RKo6n8cDgd5eXlUqFChwHO9evXK9/26det49dVXady4Mf379y+w/JgxYzhy5AiLFy8mIiIi33NTpkzh559/LlatX3zxBXv37mXJkiUMHTqUhIQENfREREQMpEtuRURExFL27t1LtWrVCjTzAMqVK0fNmjULPP7+++8THh6Op6cnFStW5I9//COrVq3Kt8z+/fux2Wy8+OKLfPjhh4SEhFC+fHn8/Px49dVX8y1rs9k4cOAA27dvv+olkVe7h57NZuPRRx9l9erVtGjRggoVKuDv78+MGTMAOHDgAL1798bLywsvLy8GDx7M6dOnC7yXPXv20LdvX2rWrIm7uzv169dn7NixnD17Nt9ybdu2xd/fnx9//JHIyEiqVKlCpUqV6N27Nz/99JNzuUcffZSxY8cCEBoa6nwvL7744tV/AJd88cUXdO7cmRo1auDu7s5dd93FQw89xLfffptvufT0dIYMGUKdOnWcy0VERPDNN9/kWy4pKYnw8HAqVqxIpUqVaNOmDStXrizwupc/x08++cS5/MMPP+x8fsWKFbRv357KlSvj4eFBs2bNmD9//nXfy+XPoU6dOsDFpvFvf6bnzp1j/PjxBAUF4e7ujre3N9HR0Xz//ff5trNu3TpsNhsJCQlMmjSJBg0a4O7u7vw5F9fevXsBrnoWns1mo1atWsXa/qxZs6hRowZdunShf//+fPTRR2RnZxdrmyIiInLrqaEnIiIilhIQEEBWVhZJSUmFWv7pp5+mf//+eHt78+qrrzJu3Djy8vLo0qULH330UYHlly5dytChQ+nduzdvvPEGd999N//85z+ZO3euc5m5c+dSvXp17r77bubOnev88vb2vm4tW7ZsoW/fvnTq1InXX3+d6tWrM3jwYObOnUubNm2oUaMG48ePp1OnTsyYMaNAI3Hz5s2EhYWxadMmhg0bxuTJk2nTpg0vvfQS0dHRBV7vxIkTtGvXjipVqvDaa68xcOBAFi9enO++cIMHD6Z3794AvPLKK8738tvLPa+0Z88eOnXqxOHDh3nqqad48803GTp0KHl5eezZs8e53A8//EDTpk1555136Nq1K5MmTWLEiBGcP3+eL7/80rncm2++yf/93/+Rk5PDCy+8wDPPPEN6ejpdu3YlMTGxwOtv3ryZqKgo/vCHPzBx4kRnrdOnT6dr166cO3eOF154gddffx0vLy/69evH5MmTr/uzGTx4MBMmTACgd+/eBX6mDz/8MGPGjMHf35833niDxx57jGXLlhEWFuZssl1p4sSJTJgwgf79+zNlyhSaNGly3dcvrICAAABmzpyJw+EokW1eduLECex2O/369aNcuXIMHDiQU6dOFaohKiIiIhbjEBEREbGQL774wnHHHXc4AMe9997riImJcbz99tuO77//vsCymzdvdgCOF154Id/jZ8+edTRr1sxRp04dx4ULFxwOh8ORlpbmABwVK1Z0HDhwwLnsyZMnHd7e3o4WLVrk20adOnUcTZo0uWqNderUcbRp0ybfY4CjTJkyji1btjgfO3r0qKN8+fIOm83mmDJlSr7lmzVr5qhRo0a+xxo1auS47777HLm5ufkenzRpkgNwfPrpp87H2rRp4wAcb7zxRr5lhw0b5gAcu3fvdj72wgsvOADH1q1br/p+fuvy62VkZFx3uU6dOjlsNpvjs88+K/Dc+fPnHQ6Hw/HLL784Klas6Lj33nsdOTk5zuezsrIctWrVclSvXt2Rl5fnfBwo8F4dDocjPT3d4ebm5hg4cGCB1+rdu7fD09PT8euvv1633ssZ+G1eVq5c6QAc/fr1c+bF4XA4kpOTHTabzfHQQw85H1u7dq0DcFSrVs2RmZl53de7msvr/7aGy77//ntHpUqVHIDj7rvvdvTv398xefJkx44dO666/PVy+luzZs1yAPky2rJlS0fz5s1v9m2IiIiIi+kMPREREbGUVq1asWnTJvr27UtmZiazZs1i8ODB3HPPPXTv3p0jR444l503bx42m43+/ftz9OhR51d2djYPPfQQBw4cKHDJZK9evbj77rud33t4eNCyZUv27dtX7NofeOCBfBN2VKtWjcDAQMqUKUNcXFyB93nkyBGOHz8OwI4dO/j22295+OGHycvLy/d+unTpAlDgMuKyZcsydOjQfI9dvlSzOO/Hy8sLgA8++IBz585ddZmsrCxWrVpFjx49aN26dYHny5Qp46z5xIkTjBgxgkqVKjmfv/POOxkyZAhHjx7liy++yLduaGhogUtOP/jgA86cOcOgQYPyfTZHjx4lIiKC3NxcvvrqqyK938tncv7zn//MN9lJy5YtefDBB1mxYgV5eXn51hkwYADVq1cv0utdT/369dm6dSt/+ctfOH36NO+99x5PPPEEjRs3Jjw8vFg/14SEBBo2bJgvowMGDGDz5s3s2rWrJMoXERGRW0QNPREREbGckJAQ5s2bR1ZWFvv27WPmzJk0adKEZcuW5buf2p49e3A4HNxzzz14e3vn+7p8j7iMjIx8265bt26B17vzzjtLZLINf3//Ao95eXlx11134ebmVuBxgF9++cX5XuBiU+m37yUwMBAo+F7uuuuuAhN03HnnnQDFej9//vOf6dSpE0OHDuXOO+90Xk575evv27cPh8Nxw0tN09LSAAgODi7wXMOGDQFITU3N93j9+vULLHv582nbtm2BzycmJgYo+PkUVlpaGmXLlnV+zr+t8dy5cxw8ePCGNZaUevXqMWPGDA4fPsyBAweYN28ef/zjH/nqq6/o1asXZ86cuelt7tu3j88//5yOHTuyb98+51ezZs0oU6YMCQkJpfBOREREpLRollsRERGxLJvNRr169ahXrx4PP/wwwcHBrF69moMHD+Ln54fD4aBs2bIsX74835lVV7rcNLqsbNmypVbvtbZ9vdd0XLpP2uX/PvXUU9ecdfS3EyIUZrtF4ebmxieffMLGjRtZsWIF69ev529/+xvPP/88ixYtKjAhyPUUpY6rzRZ7eTvvv/8+NWrUuOp6V2saFobD4bhmfq7lajWWhrvvvpu7776bP//5z7Ru3ZoNGzawcePGq54VeT2XG3YTJkxw3kvwSnPnzmX8+PGUK6d/HoiIiJhAR2wRERExQvny5QkJCSE1NZWffvoJPz8/7rnnHlasWEHdunVL/Iypm23wFNc999wDwB133EGHDh1KdNtFfS8tWrSgRYsWAHz//feEhobywgsvsH79eu655x5sNhvbtm277jbq1asHwHfffUebNm3yPffdd98B/5sI4noufz41atQo8c+nXr16fPLJJ+zdu5egoKACNd5xxx34+fmV6GveLJvNRosWLdiwYUO+WYwL48KFC8yZM4fmzZvz1FNPFXh+165dvPjiiyxdupSePXuWVMkiIiJSinTJrYiIiFjKqlWrOH/+fIHHjx49ypdffkm5cuWczZ3+/fsDFy9TvXDhQoF1rrzf3s3y9PR0Xg57K4SGhhIcHMybb75Z4PJOgFOnTjnvt3ezPD09AQr9fo4ePVrgsYCAAKpUqeLcxp133knnzp1ZsmQJn3/+eYHlL59R17FjRypWrMiUKVM4efKk8/ns7GzefPNNqlevzh/+8Icb1hQVFYWbmxsvvvgip0+fLvB8cX7WvXr1AuC1117L9/imTZtYvXo1nTt3xsPDo8jbvxnr1q3j1KlTBR4/deqU8x6Kv2063sjKlSs5dOgQjz76KH/6058KfP3jH/+gYsWKuuxWRETEIDpDT0RERCxl1KhR/PLLL/Ts2ZNGjRrh7u5OWloac+bM4fDhwzz77LPO+8SFhYXx3HPP8fLLL7N3717+7//+Dx8fH9LT0/nyyy/Zt28fP/zwQ5HqaNmyJTNnzuTvf/87oaGhlClThoiICCpWrFiSb9fJZrMxZ84cHnzwQRo2bEhMTAxBQUEcP36cPXv28OGHH5KUlHRTl7te1rJlSwCef/55Dhw4gIeHBw0bNixwOfJl48aNY+XKlURERFC3bl3OnTtHUlISP/30EyNHjnQuN3XqVB544AHat29PTEwMISEhHD9+nM8++4zOnTszbNgwvLy8+Ne//sXQoUNp2bIlAwYM4Ny5c7zzzjv8/PPPzJs3j/Lly9/wPfj5+TFt2jQGDx5McHAw/fv3p3bt2mRkZLBlyxaWLFlSpHvLwcWmY58+fZg9ezZHjx6la9euHDp0iGnTplGlShXeeOONIm23KP7973+TnJxMjx49CA0NxdPTk/T0dObNm8eePXvo378/jRs3zrdORkYG48aNu+r2hg8fzqxZs7DZbPTu3fuqy5QvX56uXbuycOFCjhw5cs1LmkVERMQ61NATERERS4mPj2fhwoVs2LCBBQsWkJOTg5eXF02bNuWNN94gKioq3/IvvfQS999/P5MnT+bf//43p06dwsfHh5CQEF599dUi1/HKK6/wyy+/8M4775CdnY3D4SAtLa3UGnoATZs2ZevWrbzyyit8+OGHHD58GC8vLwICAhg1alSBRk5htWrVitdee4233nqLuLg4zp07xwsvvHDNhl7Pnj356aef+O9//8uRI0eoUKECgYGBzJ0713lWJFy8VPXrr7/mpZdeYsmSJcyaNYtq1arRokULHnjgAedyjz/+OD4+Prz++us8//zzlClThqZNmzJt2jQ6depU6PcRGxvLvffey+uvv87UqVPJycmhRo0aBAcHM2nSpCJ9Npe9//77hISEMHv2bEaPHo2npyddunRh3Lhx3HvvvcXa9s149tlnsdvtrF+/nqVLl/LLL79QuXJlGjZsyOjRo50TgFzp8OHDPPfcc1fdXrdu3Vi8eDHh4eHcdddd13zdyMhIPvjgA+bOncuTTz5ZYu9HRERESofNUZw7JouIiIiIiIiIiMgtpXvoiYiIiIiIiIiIGEQNPREREREREREREYOooSciIiIiIiIiImIQNfREREREREREREQMooaeiIiIiIiIiIiIQcq5ugCrcXd3x9vb29VlyA2cOXMGNzc3V5chUiTKr5hOGRbTKcNiOmVYTKcMi+lKM8OZmZmcPn36hsupofcb3t7eHDp0yNVlyA3Ex8czevRoV5chUiTKr5hOGRbTKcNiOmVYTKcMi+lKM8O1a9cu1HK65FZERERERERERMQgauiJkbp06eLqEkSKTPkV0ynDYjplWEynDIvplGExnRUyrIaeGKlq1aquLkGkyJRfMZ0yLKZThsV0yrCYThkW01khw2roXWK32+nTpw95eXmuLkUKYf78+a4uQaTIlF8xnTIsplOGxXTKsJhOGRbTWSHDauhdEhUVxYIFC/Dw8HB1KSIiIiIiIiIiItekhp6IiIiIiIiIiIhBbA6Hw+HqIqykdu3aHDp0yNVlyA3k5eXpbEoxlvIrplOGxXTKsJhOGRbTKcNiutLMcGH7UjpDT4y0bds2V5cgUmTKr5hOGRbTKcNiOmVYTKcMi+mskGE19MRIycnJri5BpMiUXzGdMiymU4bFdMqwmE4ZFtNZIcNq6ImIiIiIiIiIiBiknKsLsAq73Y7dbicvL8/VpUgh+Pj4uLoEkSJTfsV0yrCYThkW0ynDYjplWExnhQxrUozf0KQYIiIiIiIiIiLiCpoUQ25r8+bNc3UJIkV2o/y2a3fjLxFX0j5YTKcMi+mUYTGdMiyms0KG1dATIx0+fNjVJYgUmfIrplOGxXTKsJhOGRbTKcNiOitk2IiG3vjx46lVqxYVKlSgR48e1/3gdu3aRefOnfHy8qJatWpERkby448/3sJqRURERERERERESo/lG3oJCQmMGzeOqVOn8uWXX5KTk0N0dPQ1l+/RowdeXl589dVXfPrpp2RnZ9OvX79bWLHcCuHh4a4uQaTIlF8xnTIsplOGxXTKsJhOGRbTWSHDlp8Uo2nTpnTt2pVXXnkFgNTUVOrVq8fWrVsJCQnJt2xmZiY1atTI99ySJUvo06dPoWev1aQYZsjLy8PDw8PVZYgUyY3yW5h75K1dW4IFidwk7YPFdMqwmE4ZFtMpw2K60sxwYftS5Url1UvI6dOn2b59O6+//rrzsYCAAPz9/dm4cWOBhl61atW45557mDt3LkFBQZw9e5b58+fTsWPHa75GfHw88fHxzu+zs7Od3/v4+NCvXz/mzZvnvMw3PDyckJAQpk+f7lynb9++HDt2jBUrVgDg4eHBkCFDWLp0KSkpKQAEBQXRtWtXpk6dypkzZwDo1q0blSpVIjEx0bmtoUOHsnnzZjZt2gSAr68v0dHRzJ07l8zMTABatWpFcHAwM2bMcK7Xv39/MjIyWLVqFQCenp7ExcWxePFi9u3bB0DDhg3p1KkTkydP5ty5cwBERERQvnx57Ha7c1vDhw8nOTmZr7/+GgA/Pz+ioqKYPXs2WVlZALRu3ZrAwEBmzpzpXG/AgAGkp6ezZs0aACpXrkxsbCwLFy4kNTUVgMaNG9OhQwcmTJjA5V5yz549KVu2LElJSc5tjRw5kvXr17N161YA/P39iYyMZNasWWRnZ5Obm8tDDz1E3bp1SUhIcK43aNAg0tLSWLduHQBeXl7ExMSQlJTE/v37AQgNDaVNmzZMnDjRuV5kZCTnz59n0aJFANhsNkaNGsXq1avZsWMHcDF7vXr1YubMmeTk5ADQvn17fH19mTNnjnNbsbGxpKSk8PnnnwMXczlw4EDsdjsHDx4EoFmzZoSHhzNlyhTnelFRUZw6dYolS5YAUK5cOZ544glWrlzJzp07Aahfvz49evRgxowZ5ObmAtCxY0dq1qzJe++959xWXFwcu3btYsOGDQB4e3vzyCOPkJiYSHp6OgBhYWE0b96cadOmOdeLjo7m+PHjLFu2DAA3NzeGDRvG8uXL2b17NwCBgYF0796d6dOnOxvlXbp0oWrVqsyfP9+5rSFDhrBt2zaSk5MBjacrx9PBgwepVq3aNcdTeDhs2zaAypXTCQi4OJ5On67Mli2xNGiwkKpVU4mPL7nxBNC2bVuNJzSeCjuecnNzCQwMtMR4strxCTSeTBhPkyZNws3NDXD9eALrHJ80nswZT7m5uYwePdoS48lKxyfQeDJlPL355pt4enoCrh9PYJ3jk8aTOePpzJkzDB48uFTGU2FZ+gy9n376CV9fX3bs2EGjRo2cj4eFhREREcFzzz1XYJ39+/cTERHBd999h8PhoHnz5qxcuZIqVaoU6jV1hp4Z4uPjGT16tKvLECmSG+VXZ+iJ1WkfLKZThsV0yrCYThkW05Vmhgvbl7L0PfRuttd44cIFHn/8ce677z6++uorPv/8cypVqqR76ImIiIiIiIiIyG3D0pfcVq9enTJlynDkyJF8j1++V95vffrpp6xdu5bs7Gzc3d0BmDNnDr6+vnz77bf5zvITs/Xt29fVJYgUmfIrplOGxXTKsJhOGRbTKcNiOitk2NJn6Lm7u9OkSRPWXnFtWVpaGvv376dFixYFlj958iQ2m40yZf73ti7//4ULF0q/YLlljh075uoSRIpM+RXTKcNiOmVYTKcMi+mUYTGdFTJs6YYewLBhw5g0aRIfffQR27dv57HHHqN169aEhISQnp5OgwYNnDdsDA8Px93dnbi4OPbs2cOOHTv4y1/+Qr169QgKCnLxO5GSdPmGnyImUn7FdMqwmE4ZFtMpw2I6ZVhMZ4UMW76hFxMTwzPPPMPjjz9Oy5YtqVixIgsWLADg7NmzpKSkcPLkSeDiTDTLli0jNTWVFi1a0K5dOxwOBx9//LFzJjMRERERERERERGTWfoeepeNGTOGMWPGFHjc39+/wMQZ4eHhrF+//laVJi7i4eHh6hJEikz5FdMpw2I6ZVhMpwyL6ZRhMZ0VMmxz3OxUsrcpu92O3W5nzZo1ZGVlubocEfkda9fuxstccWtRERERERERuU3Url2bQ4cO3XA5y19ye6tERUWxYMECS3RZ5caWLl3q6hJEikz5FdMpw2I6ZVhMpwyL6ZRhMZ0VMqyGnhgpJSXF1SWIFJnyK6ZThsV0yrCYThkW0ynDYjorZFgNPREREREREREREYOooSdGCgoKcnUJIkWm/IrplGExnTIsplOGxXTKsJjOChnWpBi/UdibD4qIlBZNiiEiIiIiIvL7pEkx5LY2depUV5cgUmTKr5hOGRbTKcNiOmVYTKcMi+mskOFyri7AKux2O3a7nby8PFeXIoVw5swZV5cgUmTKr5hOGRbTKcNiOmVYTKcMi+mskGGdoXdJVFQUCxYswMPDw9WliIiIiIiIiIiIXJMaemKkbt26uboEkSJTfsV0yrCYThkW0ynDYjplWExnhQyroSdGqlSpkqtLECky5VdMpwyL6ZRhMZ0yLKZThsV0VsiwGnpipMTERFeXIFJkyq+YThkW0ynDYjplWEynDIvprJBhNfREREREREREREQMollu5XetXbvCLbd2benWISIiIiIiIiJSWDaHw+FwdRFWYLfbsdvtrFmzhqysLFeXIzdw+vRp3N3di70dNfTEFW6U38LkUpkUVyqpfbCIqyjDYjplWEynDIvpSjPDtWvX5tChQzdcTpfcXhIVFcWCBQvw8PBwdSlSCJs3b3Z1CSJFpvyK6ZRhMZ0yLKZThsV0yrCYzgoZVkNPjLRp0yZXlyBSZMqvmE4ZFtMpw2I6ZVhMpwyL6ayQYSMaeuPHj6dWrVpUqFCBHj16cPjw4esuP3/+fBo3boy7uzu1atXi9ddfv0WVioiIiIiIiIiIlC7LT4qRkJDAuHHjmDNnDgEBAYwcOZLo6GjWr19/1eXnzp3LyJEjiY+P5w9/+AO//vorv/766y2uWkqbr6/vNZ/T/cfE6q6XXxETKMNiOmVYTKcMi+mUYTGdFTJs+UkxmjZtSteuXXnllVcASE1NpV69emzdupWQkJB8y549exY/Pz9ee+01Bg4cWKTXK+zNB8W6bqahp0kxxIrUlBYREREREfl9ui0mxTh9+jTbt2/nwQcfdD4WEBCAv78/GzduLLD8N998Q0ZGBmfPnqVhw4b4+fkxcOBAzVp7G5o7d66rSxApMuVXTKcMi+mUYTGdMiymU4bFdFbIsKUvuc3KyuLChQvUqFEj3+Pe3t4cOXKkwPL79+8HLt5zb/LkyXh5eTFq1Cj69u3LypUrr/oa8fHxxMfHO7/Pzs52fu/j40O/fv2YN2+e87594eHhhISEMH36dOc6ffv25dixY6xYsQIADw8PhgwZwtKlS0lJSQEgKCiIrl27MnXqVM6cOQNAt27dqFSpEomJic5tDR06lM2bNztvsOjr60t0dDRz584lMzMTgFatWhEcHMyMGTOc6/Xv35+MjAxWrVoFgKenJ3FxcSxevJh9+/YB0LBhQzp16sTkyZM5d+4cABEREZQvXx673e7c1vDhw0lOTubrr78GwM/Pj6ioKGbPnu1sjrZu3ZrAwEBmzpzpXG/AgAGkp6ezZs0aACpXrkxsbCwLFy4kNTUVgMaNG9OhQwcmTJjA5ZNDe/bsSdmyZUlKSnJua+TIkaxfv56tW7cC4O/vT2RkJLNmzSI7O5vc3Fy2bNlC3bp1SUhIcK43aNAgfHzSqFt3HQCnTnmxdWsMQUFJeHntB+Dnn0O5cKENEydOJDz84nq7d0dis52nQYNFADgcNr76ahR1667Gx2cH8fEXm8m9evVi5syZ5OTkANC+fXt8fX2ZM2eOs4bY2FhSUlL4/PPPAahWrRoDBw7Ebrdz8OBBAJo1a0Z4eDhTpkxxrhcVFcWpU6dYsmQJAOXKleOJJ55g5cqV7Ny5E4D69evTo0cPZsyYQW5uLgAdO3akZs2avPfee85txcXFsWvXLjZs2ABcHDOPPPIIiYmJpKenAxAWFkbz5s2ZNm2ac73o6GiOHz/OsmXLAHBzc2PYsGEsX76c3bt3AxAYGEj37t2ZPn06eXl5AHTp0oWqVasyf/5857aGDBnCtm3bSE5OBjSerhxPmZmZ1x1P4eGwbdsAKldOJyDg4ng6fboyW7bE0qDBQqpWTSU+vuTGE0Dbtm2vOp7S0tJYt24dAF5eXsTExJCUlOTc34aGhtKmzcXxdFlkZCTnz59n0aKL48lmszFq1ChWr17Njh07AI0n08dTbm4uiYmJlhhPVjs+gcaTCeMpPT1dv+9pPBk9nnJzc8nNzbXEeLLS8Qk0nkwZT2lpac79sKvHE1jn+KTxZM54OnPmDMeOHSuV8VRYlr7kNj09ndq1a7Njxw4aNWrkfDwsLIyIiAiee+65fMvPmzePhx9+mHfffdd5ye327dsJCQnhxx9/xM/P74avqUtuzRAfH8/o0aOv+pwuuRWru15+QZfcivXdKMMiVqcMi+mUYTGdMiymK80MF7YvZekz9KpXr06ZMmUKnI2XmZlZ4Kw9gJo1awIXO+CXXf7/gwcPFqqhJ2Zo1aqVq0sQKTLlV0ynDIurFfcPH8qwmE4ZFtMpw2I6K2TY0g09d3d3mjRpwtq1a2nfvj0AaWlp7N+/nxYtWhRY/v777+eOO+5g3759tGzZEsB5eufdd9996wqXUhccHOzqEkSKTPkV093OGdYZsr8Pt3OG5fdBGRbTKcNiOitk2NKTYgAMGzaMSZMm8dFHH7F9+3Yee+wxWrduTUhICOnp6TRo0MB5fbeXlxePPvoozz//PJ9//jk7duzg8ccfp2vXrv+/vTuNq7LO/z/+OoAorrg7galoIbmkpRi/MjWXXAY08miay0gOk7lX/5lxmppKK1sGS03TXHLJwjOZaWa5m6m55JamlAmOkiuJSmwC53+DPKOJigRc1xffz8ejG8A5xw/0ui4uv17nuggMDLT4O5HCdOn79UVMo37FdGq4eLRrd/3/pGDUsJhODYvp1LCYzg4N2/oMPYCoqChOnDjBE088QXJyMh06dODdd98F4MKFC8TFxZGamup5/FtvvcXo0aOJiIjAx8eHzp0789Zbb1k1voiIiIiIiIiISKGy/YIewJgxYxgzZswVn69bty6/vaeHn58f77zzDu+8805xjSciIiIiIiIiIlJsjFjQKw4ulwuXy+W5jbTYW79+/aweQaTA1K+YTg2L6dSwmE4Ni+nUsJjODg3b/hp6xcXpdLJw4UL8/PysHkXy4cSJE1aPIFJg6ldMp4bFdGpYTKeGxXRqWExnh4a1oCdGWrlypdUjiBSY+hXTqWExnRoW06lhMZ0aFtPZoWEt6ImIiIiIiIiIiBhEC3pipPLly1s9gkiBqV8xnRoW06lhMZ0aFtOpYTGdHRp2uH97m9ibXGBgIEePHrV6DPkd2rW7/mPWrs3/Yy99vEhxuJGGRaRw2Wn7s9MsdqOfjYiIiJRU+V2X0hl6YqQlS5ZYPYJIgalfMZ0aFtOpYTGdGhbTqWExnR0a9rF6ALtwuVy4XC7S0tKsHkXy4eDBg1aPIFJg6ldMp4bFdGpYTKeGxXRqWExnh4Z1ht6vnE4nCxcuxM/Pz+pRRERERERERERErkoLemKkxo0bWz2CSIGpXzGdGhbTqWExnRoW06lhMZ0dGtaCnhipU6dOVo8gUmDqV0ynhsV0alhMp4bFdGpYTGeHhrWgJ0aaOHGi1SOIFJj6FdOpYTGdGhbTqWExnRoW09mhYS3oiZGysrKsHkGkwNSvmE4Ni+nUsJhODYvp1LCYzg4Na0FPRERERERERETEID5WD2AXLpcLl8tFWlqa1aNIPoSHh1s9gkiBqV8xnRoW06lhMZ0aFtOpYTGdHRrWgt6vnE4nTqeTwMBAq0eRfChTpozVI4gUmPoV06lhMZ0alsLWrt31H7N2beH9eWpYTKeGxXR2aFhvuRUjuVwuq0cQKTD1K6ZTw2I6NSymU8NiOjUsprNDwzpDT0RExHBFfWbIb18/LOzKzxXmmSciIiIiInJtRpyhN378eG655RbKli1LREQEx48fv+5zzp07R506dXA4HLa4+4iIiIiIiIiIiEhhsP2C3uzZsxk3bhyTJ09m06ZNnDt3jt69e1/3ecOHDyckJKQYJhQrDB8+3OoRRApM/YrptmxRw2I27YfFdGpYTKeGxXR2aNj2C3qTJk1i5MiRREZG0qxZM2bNmsWXX37Jrl27rvqcjz/+mAMHDvD//t//K75BpVht3rzZ6hFECkz9iulq11bDYjbth8V0alhMp4bFdHZo2NbX0MvIyGD37t28/vrrns8FBQVRt25dtmzZQrNmza54zokTJxg5ciRffPEFJ06cuO6fERMTQ0xMjOfj5ORkz8e1atWib9++LFiwwPM237CwMJo1a8bUqVM9z+nTpw9nzpzh888/B8DPz48hQ4awbNky4uLiAAgJCaFLly5MnjyZzMxMALp27UqFChWIjY31vNbQoUPZtm0bW7duBSAgIIDevXszb948Tp06BcC9995Lo0aNmD59uud5/fr148SJE6xcuRKA8uXLEx0dzZIlSzh48CAAjRs3plOnTkycONHzNuTw8HDKlClz2QUdhw8fzubNm9m+fTsAtWvXxul0MmfOHJKSkgBo3bo1wcHBzJgxw/O8AQMGkJiYyOrVqwGoWLEigwcPZvHixRw6dAiApk2b0qFDByZMmIDb7Qage/fueHt7s2jRIs9rjRo1ivXr17Nz504A6tatS2RkJLNmzSI5OZmUlBTKly9PvXr1mD17tud5gwYNolateOrVWwdAero/O3dGERKyCH//BACOHWtOTk4b3nzzTcLCcp+3f38kDkc2DRt+AoDb7eDrr0dTr94qatXaQ0xMbns9evRgxowZnDt3DoD27dsTEBDA3LlzPTMMHjyYuLg4NmzYAEDVqlUZOHAgLpeLI0eOANCiRQvCwsKYNGmS53lOp5P09HSWLl0KgI+PDyNGjGDFihXs3bsXgAYNGhAREcH06dNJSUkBoGPHjtSsWZP58+d7Xis6Opp9+/axceNGAKpXr07//v2JjY0lMTERgNDQUFq2bMnbb7/teV7v3r05f/48n332GQC+vr4MGzaM5cuXs3//fgCCg4Pp1q0bU6dOJS0tDYDOnTvzt79VpkmTDzyvtW3bEGrV2uX5i39KSi0mTtT2VLt2bY4cOUJ8fPxVt6ewMNi1awAVKyYSFJS7PWVkVGTHjsE0bLiYypUPERNTeNsTQNu2bfPcnuLj41m3bh0A/v7+REVFsWjRIhISEgBo3rw5bdrkbk8XRUZGkp2dzSef5G5PDoeD0aNHs2rVKvbs2QNoe7re9lS5cmU++OB/29OQIUPYtWuX58Dht7+fwsLgyJEwjh9vRsuW/9uevv22D35+Z2jQ4HNiYgq+PYWFwdatQwkI2EZAwFZ8fVMoX/4Y+/b1pmnTeZQrd4qYmJLx++meeybgcORuTwcOdMft9iYk5H/b0+bNo1i7tni2p7Aw/X666LfbU2ioL1u3DqNBg+VUr567PZ0+HcwPP3SjRYuplCqVRkzM1benTZs2eTrS8Z59j/fAnN9Pd95Zld27B3LHHS4qVcrdnn76qQVHjoTRqlXu9hQTU3jbU0pKCnfddZftfz+BtidtT3lvT+vWrfP8vErS7ydtTzfP9pSZmUmTJk2KZHvKL4f74ndhQz/99BMBAQHs2bOHJk2aeD4fGhpKeHg4zz777BXPiYiI4P777+fpp59m3bp1tGvXjgsXLuDjk7+1y8DAQI4ePVpo34MUjZiYGJ588sk8v3YjF4fPz2MvfbxcXVFflL8kuVa/oJ+l3LjivylGDJs3X95wSWnSTtufnWaxm9/7s7nefljkRhX39qqGxXRqWExXlA3nd13K1m+5vdG1xtmzZ3P69GntGG4CtWvXtnoEkQJTv2K6s2fVsJhN+2ExnRoW06lhMZ0dGrb1gl61atXw8vLi5MmTl33+1KlT1KhR44rHr1+/ni1btuDr64uPjw/t27cHoEyZMpedDirmczqdVo8gUmDqV0z33XdqWMym/bCYTg2L6dSwmM4ODdt6Qa906dLceeedrL3k/PT4+HgSEhJo1arVFY9/6aWX2L17N7t27WLXrl2e91N/8803tvhhS+GZM2eO1SOIFJj6FdPdeacaFrNpPyymU8NiOjUsprNDw7a+KQbAsGHDGDlyJHfffTdBQUGMHj2a1q1b06xZMxITE2nfvj1z584lNDSUgIAAAgICPM89ffo0AI0aNcr3NfTEDBcvhiliIvUrpitbVg2L2bQfFtOpYTGdGhbT2aFh269yRUVFceLECZ544gmSk5Pp0KED7777LgAXLlwgLi6O1NRUi6cUEREREREREREpHrZf0AMYM2YMY8aMueLzdevWveaNM9q2bZvvG2u4XC5cLpfnNtJib61bt7Z6BJECU79iusOH1bCYTfthMZ0aFtOpYTGdHRo2YkGvODidTpxOJ4GBgVaPIvkQHBxs9QgiBaZ+xXRJSWpYzKb9sJiuMBtu1+76j7nkkuYihUL7YTGdHRq29U0xRK7m4g1PREykfsV0d92lhsVs2g+L6dSwmE4Ni+ns0LAW9ERERERERERERAyit9yKGExvkRARERERERG5+egMPTHSgAEDrB5BpMDUr5hu1y41LGbTflhMp4bFdGpYTGeHhnWGnhgpMTGRatWqWT2GSIGoXzFdxYqJpKWpYTGX9sNipfy8wwKu/S4LNSymU8NiOjs0rDP0xEirV6+2egSRAlO/YrqgIDUsZtN+WEynhsV0alhMZ4eGdYber1wuFy6Xi7S0NKtHERERKVK6/qaIiIiIiNm0oPcrp9OJ0+kkMDDQ6lEkHypWrGj1CCIFVpL71ULRzSEjo+Q2LDcHk/bD2q9KXkxqWCQvpjRcGG+Rl5LJDg1rQU+MNHjwYKtHECkw9Sum27Hj6g3rwFdMoP2wmM7KhrXILIWhpO6HtX3cPOzQsBb0xEiLFy+mR48eVo8hUiDqV0zXsOFiDhzoYfUYIgWm/bCYTg2L6dSw/RT1P8qWtMVOOzSsBT0x0qFDh6weocTTWTZFR/2K6SpXVsNiNu2HzXezH6eoYTGdGhbT2aHhG17Q279/P2PHjmXr1q0cPXqUTZs2cdddd/Hss89y33338eCDDxbFnCIiIiIiIiK2dLMvMotI8fO6kQevXbuWZs2aceDAASIjI8nMzPR8zeFwMGXKlEIfUCQvTZs2tXoEkQJTv2K648fVsJhN+2ExnRoW06lhMZ0dGr6hM/T+9re/8dBDD/Hhhx+SlZXFG2+84fnaXXfdxezZswt9wOLicrlwuVykpaVZPYrkQ4cOHaweQW5iv/f6D+pXTBcfr4bFbNoPi+nUsJhODYvp7NDwDZ2h9+233/LYY48BuWfkXapKlSqcOnWq8CYrZk6nk4ULF+Ln52f1KJIPEyZMsHoEkQJTv2K6e+5Rw2I27YfFdGpYTKeGxXR2aPiGFvQqVqx41UW7Q4cOUaNGjUIZSuR63G631SOIFJj6FdM5HGpYzKb9sJhODYvp1LCYzg4N39CCXpcuXRg3bhwnTpzwfM7hcJCSksJbb71FeHh4oQ8IMH78eG655RbKli1LREQEx48fz/NxP//8M0OHDqVBgwb4+flRv359xo4dS3Z2dpHMJSIiIiIiIiIiUtxuaEFv/PjxpKenc/vtt/PQQw/hcDj4+9//TkhICKmpqbz44ouFPuDs2bMZN24ckydPZtOmTZw7d47evXvn+diffvqJU6dOMXHiRPbu3cubb77JpEmTeOmllwp9LrFW9+7drR5BpMDUr5juwAE1LGbTflhMp4bFdGpYTGeHhm/ophi1atVi586dTJgwgZUrV1K/fn3OnTtHVFQUo0ePxt/fv9AHnDRpEiNHjiQyMhKAWbNmUb9+fXbt2kWzZs0ue2zjxo1ZuHCh5+P69eszevRoXC4Xzz33XKHPJtbx9va2egSRAlO/Yjq325yGf+9NbKRk0n5YTKeGzZef309Qcn9HqWExnR0azvcZeunp6bz44ovEx8fz/PPPs3HjRr7//ns2b97MCy+8UCSLeRkZGezevZsHHnjA87mgoCDq1q3Lli1b8vUap0+fpkqVKoU+m1hr0aJFVo8gUmDqV0wXEqKGxWzaD4vp1LCYTg2L6ezQcL7P0CtTpgzjx4+nbdu2RTjO5ZKSksjJybniZhvVq1fn5MmT133+oUOHmDFjBtOmTbvqY2JiYoiJifF8nJyc7Pm4Vq1a9O3blwULFniu2xcWFkazZs2YOnWq5zl9+vThzJkzfP755wD4+fkxZMgQli1bRlxcHAAhISF06dKFyZMnk5mZCUDXrl2pUKECsbGxntcaOnQo27ZtY+vWrQAEBATQu3dv5s2b57khyb333kujRo2YPn2653n9+vXjxIkTrFy5EoDy5csTHR3NkiVLOHjwIJB7BmOnTp2YOHEiWVlZAISHh1OmTBlcLpfntYYPH87mzZvZvn07ALVr18bpdDJnzhySkpIAaN26NcHBwcyYMcPzvAEDBpCYmMjq1auB3JuoDB48mMWLF3Po0CEAmjZtSocOHZgwYYLnIpLdu3fH29v7sg1i1KhRrF+/np07dwJQt25dIiMjmTVrFsnJyaSkpLBjxw7q1avH7NmzPc8bNGgQtWrFU6/eOgDS0/3ZuTOKkJBF+PsnAHDsWHNyctrw5ptvEhaW+7z9+yNxOLJp2PATANxuB19/PZp69VZRq9YeYmJyF5N79OjBjBkzOHfuHADt27cnICCAuXPnemYYPHgwcXFxbNiwAYCqVasycOBAXC4XR44cAaBFixaEhYUxadIkz/OcTifp6eksXboUAB8fH0aMGMGKFSvYu3cvAA0aNCAiIoLp06eTkpJCWBj8+GNHUlJqcued8z2v9c030VSvvo9bb91ITEzuNtO/f39iY2NJTEwEIDQ0lJYtW/L22297nte7d2/Onz/PZ599RlgYZGf7snXrMBo0WE716vsBOH06mB9+6EaLFlMpVSqNmBjo3Lkz5ctXpkmTDzyvtW3bEGrV2kXt2psBSEmpBZSM7SksDE6caMyhQ51o1WoiXl6521NcXDhZWWVo1MjFxd1KXtsTcM3tKSwMdu0aQMWKiQQF5W5PGRkV2bFjMA0bLqZy5UPExBTe9gTQtm3bPLen+Ph41q1bB4C/vz9RUVEsWrSIhIQEAJo3b06bNrnb08XZTd2eADp27EjNmjWZP/9/21N0dDT79u1j48aNQMG2JwBfX1+GDRvG8uXL2b8/d3sKDg6mW7duTJ06lbS0NCB3e6pcuTIffPC/7WnIkCHs2rWLzZtzt6ff/n4KC4MjR8I4frwZLVv+b3v69ts++PmdoUGDz4mJ+d/2dNtty6hWLXd7OnUqhIMHuxAaOhlv79zt6fvvu5KY+L/tKSwMtm4dSkDANgICtuLrm0KjRrHs29ebpk3nUa7cKWJicrcnX99G3H33/7an3bv7Ub78CerXz92eMjPL8803xff76Ua3p3vumeC56ceBA91xu70vW8DcvHkUa9dqe7J6ewoNzf/vp7y2p8zMTGOO98LC7LM9QfEe78HVt6f8HO8lJLQhJuZNz/MiIyPJzs7mk09ytyeHw8Ho0aNZtWoVe/bsAQq+Pd15Z1V27x7IHXe4qFQpd3v66acWHDkSRqtWudtTTEzu9lSlSjrBwbnbU06OD1u2jCAoaAU1a+ZuT0lJDfj++2tvTykpKaSkpFiyPYWFXft479tv7bs9FeXfn+rU2cwtt+RuT2fP1ua775zceeccypbN3Z4OH25NUlIwMTH5//0EsGqV/Y734PdvTykpKZ79sJ1/PzVqFEvFirnbU2JiKImJLQkN/d/2tHdvb3x9zxMTUzi/n651vJfbTdFtT7893gM4dy7gsuM9gC1bCrY9BQVd++9PkLufNOX3U2ZmJmfOnCmS7Sm/HO4buDXH/fffT+/evRk6dOgN/SEFlZiYSGBgIHv27KFJkyaez4eGhhIeHs6zzz571eeePHmS+++/n7Zt2/LOO+/k+88MDAzk6NGjv2tuKXoxMTE8+eSTeX7tRt5eZfqp7kX5VrIb/dmY/La24v5er9VvYby+lUye3WQ3+nP/vY8PC4th8+Yn83y83farRf2zKUp2msVuino/bCfqIG8m72sKY/bCbLgo95N2+/9UlIriePLSx5c0puyH9XeiKxXl3y/t+rPJS1E2nN91qRu6ht7EiRPp3bs3VapUoVu3blSsWLHAA+ZHtWrV8PLyuuJsvFOnTl1x1t6lkpKS6NChAy1atGDKlClFOqNYY9SoUVaPIJJvV/7yGsWv/5DoYdIvL5HNm0dZPYIt3Ox/GTOZjiPEdGpYTKeGxXR2aPiG7nIbFhbGoUOH6NevH5UrV8bPz4+yZct6/itXrlyhDle6dGnuvPNO1l5yJBwfH09CQgKtWrXK8zlnzpyhY8eOBAUF8d577+HldUPfohhi/fr1Vo8gUmB166pfMZsaFtPpOEJMp4bFdGpYTGeHhm/oDL2//e1vOByOopolT8OGDWPkyJHcfffdBAUFMXr0aFq3bk2zZs1ITEykffv2zJ07l9DQUM6dO8eDDz5IqVKlmDhxIqdPnwZy7z5SvXr1Yp1bitbOnTtpl99TI0Rs5g9/2ElCgvoVc6lhMZ2OI8R0alhMp4bFdHZo+IYW9J5//vkiGuPqoqKiOHHiBE888QTJycl06NCBd999F4ALFy4QFxdHamoqADt27GDbtm0A1KlTx/MaderU8VyAUERERERERERuPrpcRuEpadfEM9ENLehd6vDhw/z8889UqVLlssWzojBmzBjGjBlzxefr1q3Lpff0aNu2LTdwjw8xWN26da0eQaTAkpPrWj2CyO+ihsV0Oo4Q06lhMZ0aFtPZoeEbvsDc/PnzqVu3LkFBQbRo0YKgoCDq1q3L+++/XxTzFRuXy0WvXr1IS0uzehTJh8jISKtHECmw/fvVr5hNDYvpdBwhplPDYjo1LKazQ8M3tKAXGxvLgAEDaNiwIbNmzeKzzz5j1qxZhISEMGDAABYuXFhUcxY5p9PJwoUL8fPzs3oUyYdZs2ZZPYJIgTVvrn7FbGpYClu7dtf/rzBZeRxR3N+rlEw6FhbTqWExnR0avqG33L7yyisMHDiQ2bNnX/b5gQMHMmjQIF5++WV69epVqAOK5CU5OdnqEUQKrEyZZKtHEPld1LCYTscRYjo1LKZTw2I6OzR8Qwt6cXFxvPbaa3l+rU+fPnz44YeFMpSIiIiImEsXyhYREREpWje0oFepUiXi4+Pz/NqhQ4eoVKlSoQwlcj1t27a1egSRAouPb2v1CCK/ixoW0+k4QkynhnPpHw/MpYbFdHZo+IauodejRw/+8Y9/8Omnn172+aVLl/LMM8/w0EMPFepwIldTr149q0cQKbDkZPUrZlPDYjodR4jp1LCYTg2L6ezQ8A0t6L366qsEBwcTERFBhQoVqF+/PuXLl6dHjx40bNiQV199tajmFLnMb6/jKGKS5s3Vr5hNDYvpdBwhplPDYjo1LKazQ8M3/Jbbr776imXLlvHVV1/x888/U6VKFVq3bk3Xrl3x8rqh9UFbcblcuFwu0tLSrB5FRERERERERETkqm5oQQ/Ay8uL8PBwwsPDi2IeyzidTpxOJ4GBgVaPIiIiIiJiO7pemYiIiH3c0Cl1a9asuepphe+99x5r9RtcismgQYOsHkGkwHbuVL9iNjUsptNxhJhODYvp1LCYzg4N39AZes8++yzdunXL82snTpxgxowZfPXVV4UymMi1xMfHU7lyZavHECkQf/94jh9Xv2IuNVww+Tm7CXSGU3HQcUQuNWkuNSymU8NiOjs0fENn6O3du5eWLVvm+bW7776bffv2FcpQItezbt06q0cQKbB69dZZPYLkoV276/8nudSwmE7HEWI6NSymU8NiOjs0fEMLejk5OaSkpOT5tfPnz3PhwoVCGUpERERERERERETydkMLenfffTfvvfdenl+bNWsWzZs3L4yZRK7L39/f6hFECiw93d/qEUR+FzUsptNxhJhODYvp1LCYzg4N39A19P7xj3/QpUsXOnXqxKBBgwgICCAxMZFZs2axdu1ali1bVlRzFjmXy4XL5SItLc3qUSQfoqKirB5BpMB27lS/YjY1LKbTcYSYTg2L6dSwmM4ODd/QGXqdOnUiNjaWuLg4Hn30Udq1a8ejjz7KDz/8QGxsLA8++GBRzVnknE4nCxcuxM/Pz+pRJB8WLVpk9QgiBRYSon7FbGpYTJLX9TCfeGKRrpEpRtOxsJhODYvp7NDwDZ2hB9CzZ0969uxJXFwcSUlJVKtWjdtvv70oZhO5qoSEBKtHECkwf/8Eq0cQ+V3UsJhODYvpdCwsplPDYjo7NHzDC3oXBQcHF+YcIiIiIiIiIiIikg/Xfcvtjz/+yNKlS6/4/KpVq2jRogXlypWjfv36TJkypUgGBBg/fjy33HILZcuWJSIiguPHj1/1sSkpKQwaNIiKFStStWpVRo8eTVZWVpHNZoq83m5i8ltNdAMWMdmxY+pXzKaGxXRqWEynY2ExnRoW09mh4eueoffCCy8QHx9PeHi453Pfffcd4eHhlCpVis6dO3Pw4EGGDx9O1apV6d27d6EOOHv2bMaNG8fcuXMJCgpi1KhR9O7dm/Xr1+f5+KFDh7J161ZWrlzJL7/8Qr9+/ahQoQIvvvhioc4l1mrTpo3VI8jvlJ9F5LVri34OKyQkqF8xmxq++eT3H/5M2W+rYTGdjoXFdGpYTGeHhq97ht7XX39Nr169LvvcpEmTyMrKYt26dXz00Ufs2rWLzp078/bbbxf6gJMmTWLkyJFERkbSrFkzZs2axZdffsmuXbuueOyZM2d4//33mThxIq1ateKBBx5g3LhxTJkyhezs7EKfTazz5ptvWj2CSIGFhb1p9Qgiv4saFtOpYTGdjoXFdGpYTGeHhh1ut9t9rQdUqFCBjz/+mA4dOng+d+uttxIYGMimTZs8n1u0aBFRUVEkJycX2nAZGRmULVuWFStW0L59e8/n69Wrx9///nf+8pe/XPb4VatW8eCDD5Kenk6pUqUAOHz4MHXr1uXAgQN5XvcvJiaGmJgYz8fJycmes/lq1apF3759WbBggedtvmFhYTRr1oypU6d6ntOnTx/OnDnD559/DoCfnx9Dhgxh2bJlxMXFARASEkKXLl2YPHkymZmZAHTt2pUKFSoQGxvrea2hQ4eybds2tm7dCkBAQAC9e/dm3rx5nDp1CoB7772XRo0aMX36dM/z+vXrx4kTJ1i5ciUA5cuXJzo6miVLlnDw4EEAGjduTKdOnZg4caLnbcjh4eGUKVMGl8vlea3hw4ezefNmtm/fDkDt2rVxOp3MmTOHpKQkAFq3bk1wcDAzZszwPG/AgAEkJiayevVqACpWrMjgwYNZvHgxhw4dAqBp06Z0eOklJtxzD26HA4DuBw7g7XazKCTE81qjNm9m/XPPsXPnTjh9mrrJyUTu38+s5s1JLlOGFF9f/hgXR73kZGY3bw7VqgEwaNAg4uPjWbduHQD+/v5ERUWxaNEiz0UrmzdvTps2bS7bACMjI8nOzuaTTz4BwOFwMHr0aFatWsWePXsACAoKosdbbzHjrrs4V7o0AO0PHSLg3DnmNmvmea3BO3YQN3YsGzZsAKBq1aoMHDgQl8vFkSNHAGjRogVhYWFMmjTJ8zyn00l6errnLe4+Pj6MGDGCFStWsHfvXgAaNGhAREQE06dPJyUlBYCOHTtSs2ZN5s+f73mt6Oho9u3bx8aNGwGoXr06/WfNIrZRIxIrVgQgNDGRlomJvB0a6nle7717Of/aa3z22WcA+Pr6MmzYMJYvX87+/fuB3OtnduvWjalTp5KWlgZA586dqVy5Mh988IHntYYMGcKuXbvYvHkzoO3p0u3pyJEjVK1a1ZrtCahbty6RkZHMmjXLs89u27Yt9erVY/bs2Z7n2W57On2aqqmpDNy9G9cdd3CkUiUAWvz0E2FHjjCpVavcJ1WrVvTbU//+xMbGkpiYCEBoaCgtW7a87B+2evfuzfnz50vk9pSSkkJwcLAttqdC/f3UoQMTJkzg4qFR9+7d8fb2vuwuZqNGjWL9+vX225569GDGjBmcO3cOgPbt2xMQEMDcuXM9rzV48GDi4uLs9/vJgu3prbfewtfXF7B+ewL7/H4q0dvT6dNE7t9PtsPBJw0bAuBwuxn99desqlePPbVqARAUGmrE9pSSksKTTz5pi+2pUH8/RUcT9+txfcipU3Q5eJDJoaFkensD0PX776mQmUls27ae17pptqcxY4r2eO8//wHAPz2dqJ07WRQSQoK/PwDNjx2jTUICb17yrr3f+/tpypQplC9fHiiE7enX78cnJ4cRW7awIiiIvTVrAtAgKYmI779nep8+Rvx+0vZ0yfb06/dT++xZnN99x5w77ySpbFkAWh8+THBSEjM6dfI8r7h/P2VmZvKXv/ylSI73YmJiOHr0KNdz3QW9KlWqsGDBAjp37gxAYmIitWvX5umnn+a1117zPO6rr76iY8eOnkALw08//URAQAB79uyhSZMmns+HhoYSHh7Os88+e9njFyxYwPDhwz07TYDU1FTKlSvHl19+SevWra/7ZwYGBubrBye/w42+byePx8eEhfHkrzu6yx5b1Ex+z5HJs5cwMTExPPnkk4XzYib/fy2EfcE1Hy9FplAbFrGAGr4JlbDfISW24RL2/6lQFfX1aor5ejjFfjysZq7Orj8bm/9/Lcr9cH7Xpa57Db2GDRvyxRdfeBb0PvnkExwOB50uWQmF3IW+GjVqFHDcvF1nrTFfj3f8+q8YUrJE/vqvHSImioyMtHoEe7DrwYNclxoW06lhMZ0aFtOpYTGdHRq+7oLeyJEj6du3L8nJydSsWZN33nmH4OBg2v1mtXT58uU0bdq0UIerVq0aXl5enDx58rLPnzp1Ks/Fw5o1a5KcnMyFCxc8b7m9+NzCXmwUa2VroVYMpmt6iunUsJhODYvp1LCYTg2L6ezQ8HVvitG7d29ee+01Vq9ezeTJk2nevDkfffQR3r++7xrgxIkTfPbZZ/zxj38s1OFKly7NnXfeydpLzuKIj48nISGBVhevlXSJu+66C4fDcdkdcNesWUPVqlVp0KBBoc4m1rp47RMRE128NoKIqdSwmE4Ni+nUsJhODYvp7NDwdc/QA3jqqad46qmnrvr1mjVrXnEWXWEZNmwYI0eO5O677yYoKIjRo0fTunVrmjVrRmJiIu3bt2fu3LmEhoZSpUoV+vbty8iRI5k9eza//PIL//znP3niiScuW4AUw+T1tryYGL1dT0RERERERERuSvla0LNSVFQUJ06c4IknniA5OZkOHTrw7rvvAnDhwgXi4uJITU31PH7KlCkMGzaMDh064OPjw4ABA3juueesGl+KiK6NKCZTv2I6NSymU8NiuhLbsP7B/qZRYhuWm4YdGr7uXW5vFi6XC5fLxerVqy+7S64UAZPvtqPZxW5upv+vN9P3KiIihUu/Q8R0Jewut4XK5NmLkun7vZv4/2t+73J73Wvo3SycTicLFy7Ez8/P6lEkH1atWmX1CCIFpn7FdGpYTKeGxXRqWEynhsV0dmhYC3pipD179lg9gkiBqV8xnRoW06lhMZ0aFtOpYTGdHRrWgp6IiIiIiIiIiIhBtKAnRgoKCrJ6BJECU79iOjUsplPDYjo1LKZTw2I6OzSsBT0xUo8ePaweQaTA1K+YTg2L6dSwmE4Ni+nUsJjODg1rQU+MNGPGDKtHECmwQu137dr8/SdSiLQPFtOpYTGdGhbTqWExnR0a9rF6ALtwuVy4XC7S0tKsHkXy4dy5c1aPIFJg6ldMp4bFdGpYTKeGxXSF2rD+8VosYIf9sM7Q+5XT6WThwoX4+flZPYqIiIiIiIiIiMhVaUFPjNS+fXurRxApMPUrplPDYjo1LKZTw2I6NSyms0PDesutGCkgIMDqEUQKTP2K6dSwmE4Ni+nUsJhODct12fyt1HZoWGfoiZHmzp1r9QgiBaZ+xXRqWEynhsV0alhMp4bFdHZoWAt6IiIiIiIiIiIiBtFbbkVERERERETszOZvPxSR4qcz9MRIgwcPtnoEkQJTv2I6NSymU8NiOjUsplPDYjo7NKwFvV+5XC569epFWlqa1aNIPsTFxVk9gkiBqV8xnRoW06lhMZ0aFtOpYTGdHRrWgt6vnE4nCxcuxM/Pz+pRJB82bNhg9QgiBaZ+xXRqWEynhsV0alhMp4bFdHZoWAt6IiIiIiIiIiIiBrH9TTFmz57N2LFjOXbsGKGhobz77rvcfvvteT72woULPPPMMyxbtoz4+HiqVavGww8/zLhx4yhXrlwxTy5FqWrVqtb8wboYrRQCy/oVKSRqWEynhsV0alhMp4bFdHZo2NZn6K1Zs4bo6GjGjBnDtm3bqFWrFt26dSMzMzPPx6emprJnzx5efPFFdu/ezZw5c1i2bBnDhw8v5smlqA0cONDqEUQKTP2K6dSwmE4Ni+nUsJhODYvp7NCwrRf0Jk+eTK9evfjzn/9M48aNmTVrFomJiXz22Wd5Pr5SpUp8/vnnPPzww9x22220a9eOF154gcWLFxfv4FLkXC6X1SOIFJj6FdOpYTGdGhbTqWExnRoW09mhYVu/5Xbr1q288MILno/LlStHq1at2LJlCz169MjXa5w+fZoqVaoU0YRilSNHjlg9gkiBqV8xnRoW06lhMZ0aFtOp4WKgy0UVKTs0bOsFvZMnT1KjRo3LPle9enVOnjyZr+f//PPP/Pvf/+bxxx+/6mNiYmKIiYnxfJycnOz5uFatWvTt25cFCxZw/PhxAMLCwmjWrBlTp071PKdPnz6cOXOGzz//HAA/Pz+GDBnCsmXLPLcyDgkJoUuXLkyePNnzluGuXbtSoUIFYmNjPa81dOhQtm3bxtatWwEICAigd+/ezJs3j1OnTgFw77330qhRI6ZPn+55Xr9+/Thx4gQrV64EoHz58kRHR7NkyRIOHjwIQOPGjenUqRMTJ04kKysLgPDwcMqUKXPZ6vLw4cPZvHkz27dvB6B27do4nU7mzJlDUlISAK1btyY4OJgZM2Z4njdgwAASExNZvXo1ABUrVmTw4MEsXryYQ4cOAdC0aVM6ABPuuQe3wwFA9wMH8Ha7WRQS4nmtUZs3s37tWnbu3AlA3bp1iYyMZNasWSQnJ5OSksKOHTuoV68es2fP9jxv0KBBxMfHs27dOgD8/f2Jiopi0aJFJCQkANC8eXPatGnDm2++6XleZGQk2dnZfPLJJwA4HA5Gjx7NqlWr2LNnDwBBQUH06NGDGTNmcO7cOQDat29PQEAAc+fO9bzW4MGDiYuL89z1pmrVqgwcOBCXy+XZ6Fu0aEFYWBiTJk3yPM/pdJKens7SpUsB8PHxYcSIEaxYsYK9e/cC0KBBAyIiIpg+fTopKSkAdOzYkZo1azJ//nzPa0VHR7Nv3z42btwI5G43/YHYRo1IrFgRgNDERFomJvJ2aKjneb337uX8gQOes2B9fX0ZNmwYy5cvZ//+/QAEBwfTrVs3pk6dSlpaGgCdO3emcuXKfPDBB57XGjJkCLt27WLz5s2AtqdLtyeg8LanDh2YMGECbrcbgO7du+Pt7c2iRYs8rzVq1CjWr19/1e0JoG3btvbfnsLCqJqaysDdu3HdcQdHKlUCoMVPPxF25AiTWrXKfVJMTNFvT/37ExsbS2JiIgChoaG0bNmSt99+2/O83r17c/78+RK5PaWkpBAbG2uL7alQfz/dTNsTNvr9ZMH2lJmZqeO9m217Cgsjcv9+sh0OPmnYEACH283or79mVb167KlVC4CgxYuN2J5SUlJISUmxxfZkp99PoO2pwNtTWBgA/unpRO3cyaKQEBL8/QFofuwYbRISePOSvzf/3t9PKSkpnv2w1dsT2Of3k7Ync7anzMxMzpw5UyTHe/nlcF/8LorR448/zrRp06769TZt2rBu3Tp8fX356KOPCA8P93ytV69eVKhQgZkzZ17zz0hNTeXBBx+kUqVKfPLJJ3h7e+drtsDAQI4ePZq/b0QKpl27/D3uGv+i8OWXX3L//fcX0kA3iUL4uUvhUL8FpIZtQw2L6dTwTaiE/Q5Rw1Lo8rONFOL2oYbFdEXZcH7XpSw5Q2/8+PH885//vOrXS5cuDUCNGjWuOBvv1KlT1K9f/5qvn56eTnh4OL6+vvznP//J92KemCPs139BEjGR+hXTqWExnRoW06lhMZ0aFtPZoWFLborh7+9PYGDgVf+rXr06kHtK69pL/hUgNTWVLVu20Ori26rykJGRQY8ePUhNTeWTTz6hTJkyRf79SPG79FRrEdOoXzGdGhbTqWExnRoW06lhMZ0dGrb1XW6HDh1KbGwsM2fOZN++fURFRXHLLbfQtWtXz2MaNmzIxx9/DMCFCxfo2bMnBw8eZPbs2aSkpHD8+HHP+81FRERERERERERMZ+ubYrRv355p06bx4osvcvz4cVq1asWyZcvw9fX1PCYuLo6zZ88CkJiYyKeffgrkXvTxUhZcKlBERERERERERKTQ2XpBDyAqKoqoqKirfv3Shbq6detq4e4m4XQ6rR5BpMDUr5hODYvp1LCYTg2L6dSwmM4ODdt+Qa+4uFwuXC6X5zbSYm/p6elWjyBSYOpXTKeGxXRqWEynhsV0alhMZ4eGbX0NveLkdDpZuHAhfn5+Vo8i+bB06VKrRxApMPUrplPDYjo1LKZTw2I6NSyms0PDWtATERERERERERExiBb0xEg+Pnq3uJhL/Yrp1LCYTg2L6dSwmE4Ni+ns0LDDrbtIXCYwMJCjR49aPUbJ1q5d/h63dm3RznGz0c9dTKeGRUSkoPQ7ROTa8rONaPsQKRb5XZfSGXpipBUrVlg9gnnWrs3ff1Lk1K+YTg2L6dSwmE4Ni+nUsJjODg1rQU+MtHfvXqtHECkw9SumU8NiOjUsplPDYjo1LKazQ8PWv+nXJlwuFy6Xi7S0NKtHERERERERERERuSqdofcrp9PJwoUL8fPzs3oUyYcGDRpYPYJIgalfMZ0aFtOpYTGdGhbTqWExnR0a1k0xfkM3xSgGuiixiBSE9h0iIlJQ+h0icm26KYaIbeimGFKiTZ8+3eoRRApM/Yrp1LCYTg2L6dSwmE4Ni+ns0LAW9MRIKSkpVo8gUmDqV0ynhsV0alhMp4bFdGpYTGeHhrWgJyIiIiIiIiIiYhDd5VaKXyFce6Fjx46FMIiINdSvmE4Ni+nUsJhODYvp1LCYzg4Na0HvVy6XC5fLRVpamtWjSD7UrFnT6hFECkz9iunUsJhODYvp1LCYTg2L6ezQsN5y+yun08nChQvx8/OzehTJh/nz51s9gkiBqV8xnRoW06lhMZ0aFtOpYTGdHRrWgp6IiIiIiIiIiIhBtKAnIiIiIiIiIiJiENsv6M2ePZugoCD8/Pxo06YN33//fb6el5WVRcuWLXE4HBw8eLCIp5TiFh0dbfUIIgWmfsV0alhMp4bFdGpYTKeGxXR2aNjWC3pr1qwhOjqaMWPGsG3bNmrVqkW3bt3IzMy87nPHjh1L1apVi2FKscK+ffusHkGkwNSvmE4Ni+nUsJhODYvp1LCYzg4N23pBb/LkyfTq1Ys///nPNG7cmFmzZpGYmMhnn312zedt27aNBQsW8PrrrxfTpFLcNm7caPUIIgWmfsV0alhMp4bFdGpYTKeGxXR2aNjH6gGuZevWrbzwwguej8uVK0erVq3YsmULPXr0yPM5aWlpDBgwgGnTplGhQoXr/hkxMTHExMR4Pk5OTvZ8XKtWLfr27cuCBQs4fvw4AGFhYTRr1oypU6d6ntOnTx/OnDnD559/DoCfnx9Dhgxh2bJlxMXFARASEkKXLl2YPHmy5wzDrl27UqFCBWJjYz2vNXToULZt28bWrVsBCAgIoHfv3sybN49Tp04BcO+999KoUSOmT5/ueV6/fv04ceIEK1euBKB8+fJER0ezZMkSz1uOGzduTKdOnZg4cSJZWVkAhIeHU6ZMGVwul+e1hg8fzubNm9m+fTsAtWvXxul0MmfOHJKSkgBo3bo1wcHBzJgxw/O8AQMGkJiYyOrVqwGoWLEigwcPZvHixRw6dAiApk2b0qFDByZMmIDb7Qage/fueHt7s2jRIs9rjRo1ivXr17Nz504A6tatS2RkJLNmzSI5OZmUlBR27NhBvXr1mD17tud5gwYNIj4+nnXr1gHg7+9PVFQUixYtIiEhAYDmzZvTpk0b3nzzTc/zIiMjyc7O5pNPPgHA4XAwevRoVq1axZ49ewAICgqiR48ezJgxg3PnzgHQvn17AgICmDt3rue1Bg8eTFxcHBs2bACgatWqDBw4EJfLxZEjRwBo0aIFYWFhTJo0yfM8p9NJeno6S5cuBcDHx4cRI0awYsUK9u7dC0CDBg2IiIhg+vTppKSkANCxY0dq1qx52V12oqOj2bdvn2cnU716dfr3709sbCyJiYkAhIaG0rJlS95++23P83r37s358+c9i+a+vr4MGzaM5cuXs3//fgCCg4Pp1q0bU6dOJS0tDYDOnTtTuXJlPvjgA89rDRkyhF27drF582ZA29Ol2xNgq+0JoG3btvbfnsLCqJqaysDdu3HdcQdHKlUCoMVPPxF25AiTWrXKfVJMjLanXxXV9pSSkkJsbKwttie7/X4CQ7Ynbu7fT5mZmTreu9m2p/Dw/G1PixcbsT2lpKSQkpJii+3JTr+fQNtTgbensDAA/NPTidq5k0UhIST4+wPQ/Ngx2iQk8OYlf2/+vb+fUlJSPPthq7cnsM/vJ21P5mxPmZmZnDlzpkiO9/LL4b74XdiQr68vH330EeHh4Z7P9erViwoVKjBz5sw8nzN8+HDcbjeTJ08mISGBevXq8cMPP9CgQYN8/ZmBgYEcPXq0UOaXojNv3jz69+9v9RgiBaJ+C6hdu/w9bu3aop1D1LAYTw2L6dSwFLr8HGcV4jGWGhbTFWXD+V2XsuQtt48//jgOh+Oq/7Vt27ZAr7tmzRo+//xzXn311cIdWGxHO38xmfoV06lhMZ0aFtOpYTGdGhbT2aFhSxb0xo8fz5EjR67638XTLWvUqMHJkycve+6pU6eoUaNGnq/75Zdf8uOPP1KpUiV8fHw8Z+U1bNiQZ555pmi/KSlWl54WLGIa9SumU8NiOjUsplPDYjo1LKazQ8OWXEPP398f/1/fj38toaGhrF27lsceewyA1NRUtmzZwsiRI/N8/BNPPEHPnj09H//00088+OCDLF26lLvuuqtQZhd7uHgdAxETqV8xnRoW06lhMZ0aFtOpYTGdHRq29U0xhg4dSufOnWnXrh333HMPY8eO5ZZbbqFr166exzRs2JBXXnmFhx56iBo1alx29l758uUBuO2226hZs2axzy8iIiIiIiIiIlLYLHnLbX61b9+eadOmMXbsWO6++26OHTvGsmXL8PX19TwmLi6Os2fPWjilWCE0NNTqEUQKTP2K6dSwmE4Ni+nUsJhODYvp7NCwre9yW5xcLhcul4vVq1d7boUs9pWRkUHp0qWtHkOkQNRvAekut7ahhsV0alhMp4al0BXzXW7VsJiuKBu29V1u7cjpdLJw4UL8/PysHkXy4e2337Z6BJECU79iOjUsplPDYjo1LKZTw2I6OzSsBT0RERERERERERGDaEFPRERERERERETEILqG3m/k973KYq3ExEQCAgKsHkOkQNRvAekaerahhsV0alhMp4bFdGpYTFeUDesaelKinT9/3uoRRApM/Yrp1LCYTg2L6dSwmE4Ni+ns0LAW9MRIn332mdUjiBSY+hXTqWExnRoW06lhMZ0aFtPZoWEfqwewC5fLhcvlIi0tzepRRERERERERERErkpn6P3K6XSycOFC/Pz8rB5F8sHX19fqEUQKTP2K6dSwmE4Ni+nUsJhODYvp7NCwborxG7ophoiITemmGCIiIiIiUsLpphhSoi1fvtzqEUQKTP2K6dSwmE4Ni+nUsJhODYvp7NCwFvTESPv377d6BJECU79iOjUsplPDYjo1LKZTw2I6OzSsBT0RERERERERERGDaEFPjBQcHGz1CCIFpn7FdGpYTKeGxXRqWEynhsV0dmhYN8X4lcvlwuVysXr1apKSkqweR0REfks3xRARERERkRJON8W4QU6nk4ULF+Ln52f1KJIPU6dOtXoEkQJTv2I6NSymU8NiOjUsplPDYjo7NKwFPTFSWlqa1SOIFJj6FdOpYTGdGhbTqWExnRoW09mhYR+rBxAREckXvZVWREREREQEMOAMvdmzZxMUFISfnx9t2rTh+++/v+5zVq1aRatWrShTpgzVq1dn9OjRxTCpFKfOnTtbPYJIgalfMZ0aFtOpYTGdGhbTqWExnR0atvWC3po1a4iOjmbMmDFs27aNWrVq0a1bNzIzM6/6nLVr1xIZGUnfvn3Zs2cPa9asoUOHDsU4tRSHypUrWz2CSIGpXzGdGhbTqWExnRoW06lhMZ0dGrb1gt7kyZPp1asXf/7zn2ncuDGzZs0iMTGRzz777KrPefrpp3nqqacYOXIkt99+O02aNKFbt27FOLUUhw8++MDqEUQKTP2K6dSwmE4Ni+nUsJhODYvp7NCwrRf0tm7dygMPPOD5uFy5crRq1YotW7bk+fjjx4+zY8cO/P39admyJX/4wx946KGHOHz4cHGNLCIiIiIiIiIiUqRsfVOMkydPUqNGjcs+V716dU6ePJnn4xMSEgB4+eWXeeutt6hfvz4vvPACXbp0Yc+ePfj4XPntxsTEEBMT4/k4OTnZ83GtWrXo27cvCxYs4Pjx4wCEhYXRrFmzy25R3KdPH86cOcPnn38OgJ+fH0OGDGHZsmXExcUBEBISQpcuXZg8ebLnLcNdu3alQoUKxMbGel5r6NChbNu2ja1btwIQEBBA7969mTdvHqdOnQLg3nvvpVGjRkyfPt3zvH79+nHixAlWrlwJQPny5YmOjmbJkiUcPHgQgMaNG9OpUycmTpxIVlYWAOHh4ZQpUwaXy+V5reHDh7N582a2b98OQO3atXE6ncyZM4ekpCQAWrduTXBwMDNmzPA8b8CAASQmJrJ69WoAKlasyODBg1m8eDGHDh0CoGnTpnTo0IEJEybgdrsB6N69O97e3ixatMjzWqNGjWL9+vXs3LkTgLp16xIZGcmsWbNITk4mJSWFHTt2UK9ePWbPnu153qBBg4iPj2fdunUA+Pv7ExUVxaJFizx9NG/enDZt2vDmm296nhcZGUl2djaffPIJAA6Hg9GjR7Nq1Sr27NkDQFBQED169GDGjBmcO3cOgPbt2xMQEMDcuXM9rzV48GDi4uLYsGEDAFWrVmXgwIG4XC6OHDkCQIsWLQgLC2PSpEme5zmdTtLT01m6dCkAPj4+jBgxghUrVrB3714AGjRoQEREBNOnTyclJQWAjh07UrNmTebPn+95rejoaPbt28fGjRuB3O2mf//+xMbGkpiYCEBoaCgtW7bk7bff9jyvd+/enD9/3nMWrK+vL8OGDWP58uXs378fgODgYLp168bUqVM9d/bp3LkzlStXvuxfKYYMGcKuXbvYvHkzoO3p0u0JsNX2BNC2bVttT2h7yu/2lJKSQmxsrC22J7v9fgJtTyZsT5mZmTre0/Zk9PaUkpJCSkqKLbYnO/1+Am1PpmxPKSkpnv2w1dsT2Of3k7Ync7anzMxMzpw5UyTbU3453Be/i2L0+OOPM23atKt+vU2bNqxbtw5fX18++ugjwsPDPV/r1asXFSpUYObMmVc8b9OmTdx77708//zz/Otf/wIgKSmJmjVrsnbtWlq3bn3d2QIDAzl69GgBvispTmlpafj5+Vk9hkiBqF8xnRoW06lhMZ0aFtOpYTFdUTac33UpS95yO378eI4cOXLV/y6uztaoUeOKs/FOnTp1xVl7F9WsWRPIXQG/qGrVqlSrVs2zsi8lw65du6weQaTA1K+YTg2L6dSwmE4Ni+nUsJjODg1bsqDn7+9PYGDgVf+rXr06kHtK69q1az3PS01NZcuWLbRq1SrP161Xrx41atTwnNIJuW+hPX36NLfeemvRflNSrC6ehixiIvUrplPDYjo1LKZTw2I6NSyms0PDtr4pxtChQ4mNjWXmzJns27ePqKgobrnlFrp27ep5TMOGDfn4448B8PLyYuTIkbz55pssW7aMAwcOMHjwYO644w7CwsKs+jZEREREREREREQKja1vitG+fXumTZvGiy++yPHjx2nVqhXLli3D19fX85i4uDjOnj3r+fjvf/87v/zyC1FRUWRkZHD//ffz6aef4u3tbcW3IEWkVq1aVo8gUmDqV0ynhsV0alhMp4bFdGpYTGeHhi25KYad6aYYIiIiIiIiIiJiBVvfFMOOXC4XvXr18txGWuxtwYIFVo8gUmDqV0ynhsV0alhMp4bFdGpYTGeHhrWg9yun08nChQt162xDHD9+3OoRRApM/Yrp1LCYTg2L6dSwmE4Ni+ns0LAW9ERERERERERERAyia+j9RunSpalevfrveo20tDSjzvQzbV6AlJQUypcvb/UY+Wbaz1jzFi3T+gXzfsaat2iZ1rBpP18wb2bT5lXDRc+0mU2bVw0XLdPmBfNmVsNFS/MWvaJs+NSpU2RkZFz/gW4pdE6n0+oRbohp87rdbndAQIDVI9wQ037Gmrdomdav223ez1jzFi3TGjbt5+t2mzezafOq4aJn2symzauGi5Zp87rd5s2shouW5i16dmhYb7ktAk6n0+oRbohp85rItJ+x5pXfMu1nrHnlUib+fE2b2bR5TWPiz9e0mU2b1zSm/XxNmxfMnNkkpv18Ne/NQW+5FSPl9zbOInakfsV0alhMp4bFdGpYTKeGxXR2aFhn6ImRnnzySatHECkw9SumU8NiOjUsplPDYjo1LKazQ8M6Q09ERERERERERMQgOkNPRERERERERETEIFrQExERERERERERMYgW9ERERERERERERAyiBT0RERERERERERGDaEFPbiq6B4yIiHW0DxYRERERKRxa0JObQmZmJidOnMDhcFg9iojITUf7YBER6+kfVURE7KMw9skOt/bschMYNGgQt9xyCy+99JLVo4gUyJ49e9i2bRsHDhygU6dOBAYGEhwcjJeX/l1G7E/7YDGd9sFiurfffpsaNWrQqVMnKlWq5Pm82+3WP7aIEXbu3MmGDRvYt28f9957L8HBwbRo0QJvb2+rRxPJl23btrFy5Ur++9//8re//Y169eoBv28/rAU9KfG++eYbWrZsSVxcHLfddhsA//3vf/nDH/4AQKlSpawcT+S6du/eTUREBNWqVcPb25uDBw8SGhpKp06dCA8P93QtYkfaB4vptA8W0x05coQ6depQqlQpevTowdNPP02jRo0oW7Ys58+fp0KFClaPKHJNu3fvpnv37tx+++1kZ2ezdu1a6tevT0REBH369KFFixZWjyhyTd9++y0PP/wwAQEBpKSk8P3337Nv3z4CAwN/1+vqnxWlxHvssccYPXo0t912Gzt37mTYsGH83//9H3/4wx94+umn+eGHHwC9DUHsa/jw4URERLB69Wq2bt3Kpk2bqFOnDnPmzGHcuHFqWGxN+2AxnfbBYjK3203VqlUZOHAgI0aM4MSJE9x///28+OKLnDp1in79+rFq1SqrxxS5phEjRuB0Olm6dCmrV6/miy++4NSpU6xZs4axY8fy448/Wj2iyDUNHjyYP/7xjyxevJgNGzZw3333sX37dkaMGMELL7zA9u3bgRs/ltAZelKiffrpp0RERBAfH0+dOnW47777qFGjBn/84x/x9fXl+eefx8/Pj5UrV1KrVi2rxxW5wpkzZ4iMjKRfv3489thjl31t5cqVjBo1ilKlSrFixQpq1Khh0ZQiedM+WEynfbCUFAsWLODTTz9lwYIFzJo1izFjxpCRkUF2djbbt28nODjY6hFF8nT48GF69uzJG2+8QZs2bcjIyMDX15eIiAhatGjBvHnzqFmzJp9//rnONhVbWrFiBdHR0Xz99dee49277rqLX375hSZNmrBz5058fHxYvHgxISEhN/TaOkNPSrTDhw/TrFkznnjiCbp168Yvv/zCjBkziIqKol+/fnz//fekpKTw0UcfWT2qSJ4qV67M7bffznvvvcexY8cAyMnJAaBjx47s2LGD9PR0/vOf/1g5pkietA8W02kfLCVF3759+eWXX9iwYQNRUVEcO3aMjIwMUlNTefzxx1myZAkZGRlWjylyhTp16lC5cmVWr14NQOnSpTly5Aiff/45zzzzDNu2beO///0vW7dutXhSkbwlJCQwYMAAypcvD8DSpUvZtWsXCxYs4D//+Q8//vgjZcqUYd68eTf82lrQkxLtscce47XXXqNBgwbEx8czYMAAKleuDEBWVhY5OTm0bNmSw4cP660yYjsXm+zXrx8nTpzgb3/7G4mJiZddhL106dK0bt2aLVu2kJ2dbdWoInnSPlhMpn2wlBRZWVkA3HPPPfzjH/8AIDo6mhYtWnDo0CHKlClDjx49SEtLs3JMkStc3A+3adOGV155hWHDhjFmzBjatGnDY489ho+PDw6Hg1tuuYX9+/dbPK1I3gYMGEDv3r09C3oZGRnMnz+fu+++mwsXLgDQrl07kpKSbvhYwqfQpxWxkTJlytChQwcaN25Mu3btqF27tucOMj4+ufkfO3aMVq1a6Q5fYjsXm2zdujUzZ85k4MCBtGjRgmeeeYaIiAgqVqyIj48Pu3btomPHjrrLl9iO9sFiMu2DpaS4uL8dMWIEe/fu5Z133mHevHl8+umn1KlTh+XLl3P48GH8/f2tHVTkNy7uh5955hmaNGnC2LFjqVKlCo888givvPIKAJUqVfKcOS1iR2XKlKFRo0aeu9l2797ds1++eHO4HTt20KVLlxs+ltA19KREOn/+PMePHyclJYU6depQpUoVIHc1vHTp0gCkpaUxf/58/v73v5OUlGTluCJXyMjI4MiRI2RmZlK7dm0qVKjAhQsXePnll3n99depXbs2lSpV4uzZs5QtW5ZvvvnG6pFFPLQPFtNpHyymu7gfTk1N5dZbb/WcHR0TE8PTTz9N//79mTNnDtnZ2TgcjsvOPBWxk6ysLLy9vT2Le2lpafj5+QFw9uxZZs6cybhx4/j555+tHFPkChf3w7/88ovnrePAZfvd9PR03n//ff72t79x+vTpG/4ztKAnJc7OnTv517/+xYoVK2jatClnz57l4Ycf5uWXX/Y8Jjs7m/Hjx/Pee+/xr3/9i379+lk4scjl9u7dy+uvv05sbCyhoaHcc889PPPMM1SqVAmAzMxMpk+fTlZWFoGBgYSGhnLrrbdaPLVILu2DxXTaB4vp8toP9+zZk5deegnIvUHGfffdp27Fto4dO8bRo0dp2bKl53MXLlzwnM100Zo1a5g+fTrdu3enT58+xT2myFXltR92Op2MGzfO85js7GxeffVV5s6dy7PPPsujjz56w3+OFvSkxGnSpAn33XcfjzzyCOnp6Xz77bdMmjSJ9PR0Jk2aRK9evQA4ffo0X331FT169LB2YJHfuOuuu2jRogU9evTghx9+YMKECTz11FMMHz6crKwszynaInakfbCYTvtgMd219sPTpk3Tfldsr127dmRmZvLoo4/SuXNngoKCgP9dU+/i2XqpqakcPXqU22+/3bJZRfJyI8fDGzdupHv37gX6c7SgJyXKzp07iYiIYMWKFZ5bPl+4cIGEhATeeecdFi1axIgRIxgxYoSudSO2FBsbyzPPPMOuXbs8F06dNGkSLpeLVatW4evrC+T+i44aFrvRPlhMp32wmO5a++GpU6eyaNEiRo4cyejRoy2eVCRvS5YsYfDgwYSFhXHs2DEaNmxI165d6dSpk+cSHgDp6emUKVPGwklF8lacx8O6WIKUKEFBQVSsWJG9e/d6PleqVCluu+02xowZw5/+9CdmzpxJYmKihVOKXN3XX39N27ZtKVWqlOeudF26dGHv3r18++23nse98cYbbNy40aoxRfKkfbCYTvtgMd219sP/+Mc/GDRoEDNnzuS///2vhVOKXN3F/fCcOXP417/+RVJSEhMnTmTs2LGsXr3as29+8sknWbZsmcXTilypOI+HtaAnJYbb7aZSpUrcd999/OUvf+HDDz+87OvVqlVjyJAh5OTksGXLFoumFLm6nJwc7rvvPu68805Kly7teVtXgwYNaNmyJUuXLgVg5cqVPPvsszRs2NDKcUUuo32wmE77YDGd9sNSEvz1r39lwIAB+Pv7061bNz7++GP69OnD9u3beeWVV3jjjTd45ZVXePfdd2nVqpXV44pcprj3w7oIiJQYF6+l8Prrr+Pr68vkyZPZsmULAwYMoHnz5gBUqVKFnJwczp07Z+WoInny8vKic+fOnn95vHhrc4CePXuyaNEiIPdAZ+TIkVStWtWyWUV+S/tgMZ32wWI67YfFdG63G39/f/74xz8CuXe49fX1Zfjw4Tz00EO89dZbLFq0iO3bt/Pcc89RrVo1iycWuVxx74d1DT0pkRISEpg3bx5r1qwhMzOTW2+9lRYtWvDll1+ye/duEhISrB5RJF8uXqdp69atREdH0759e9577z2SkpKsHk3kqrQPlpJC+2AxlfbDUlJcuHABLy8vz7XGHnvsMVavXq2GxfaKYz+sBT0pcS69UPV3333H8uXLWb9+Pd999x09e/YkMjKS0NBQi6cUubqLDefk5ODllXtlhMzMTO644w4OHTpEbGwsTqfT4ilF8qZ9sJhO+2AxnfbDYrq89sM5OTkcPXqU4OBgZsyYwaOPPmrxlCJXV1z7YS3oSYlycad/7NgxHnnkEZYvX06ZMmXw8vLSHenECBcbPnnyJJGRkXz00UfUrFkTgDfffJNvv/2WmTNnWjylSN60DxbTaR8sptN+WEx3rf1wRkYGs2fP5vHHH7d4SpGrK879sBb0pES5eL2byMhIcnJyWLx4McBl/7ojYmeXNux2u/n44489X0tOTsbhcFCpUiULJxS5uov7Wu2DxVSXNqx9sJhIx8JiumsdC4uYoDiPh3VTDClRHA4Hu3fv5quvvmL9+vWez+sARkxxtYYvXiRYxM68vLy0DxajXa1h7YPFFDoWFtNdrWG4/GZFInZVnMfDWtATY2VkZFC6dOk8Pz916lRCQkIsmEok/26kYR28iN188803LFu2jB9//JHu3bsTHh5OqVKlSEtL0z5YjHAjDWsfLHakY2Ex3Y02rH2x2I3Vx8N6y60YKSEhgZiYGKKiomjcuDE+Pldfm9a/5IgdqWEx2ffff0+3bt0ICgoiMzOTTZs28fzzzzNmzBirRxPJFzUsptNxhJhODYvp7HAsoQU9MVLnzp1ZsWIF1atXZ9iwYQwaNIiAgADt6MUYalhM9sADD3DHHXcQExODr68v8+fP55///CebNm3illtusXo8ketSw2I6HUeI6dSwmM4OxxK6mIIYZ+/evRw7dowVK1bw7LPP8vrrr9OhQwcWLFjAmTNnPI87efIk69atQ2vWYjdqWEz2zTff8NNPPzF48GB8fX3JysrioYceokKFCnzwwQeex2VkZHDgwAELJxXJmxoW0+k4QkynhsV0djmW0IKeGMfPz48ePXrg7+/PsGHDSEpKol27dvTv35/evXvz5ZdfkpqaytChQ1mwYIH+lUdsRw2LyVJSUmjatCl+fn4AeHt7U65cOf70pz+xadMmz+OGDh3KjBkzrBpT5KrUsJhOxxFiOjUsprPNsYRbxDA5OTnukydPut1utzs7O9vz+R9++MHdrl07t6+vr7tz585uh8Ph3r9/v1VjilyVGhaTpaSkuD/44APPxzk5OW632+3+6quv3Lfddps7JyfHvWfPHrfD4XDv27fPqjFFrkoNi+lycnLcx44dc7vdOo4QM6lhMV1KSor7/fff93xs1bGErqEnRnHncUHUnJwc3G433t7eAHz00Uc4nU7++te/Mn78eCvGFLkqNSwm+22/l3589uxZQkJCmDt3Li+99BL16tVj1qxZVo0qkic1LCVRdnY2brfbc1MBHUeIadSwmOzikprD4Sj2Y4mr30pGxIYcDgfZ2dmehQ8AL6/cd45f/PzPP/+Mr68v48aNs2pMkatSw2Iyh8NBVlaW54D74kKI2+2mUqVK9OzZkz/96U+cPXuWlStXWjmqSJ7UsJjs6NGjHD58mPj4eB588EGqV68O4Dmm0HGE2J0aFtNdbPjHH3+kS5cuVK9e3dJjCV1DT4yQkJDAxx9/DPxvh5+Tk3PZY7y9vcnOzmbXrl28/PLL17z1uUhxU8Niskv7vdjlpf1ePJBp27YtP/30E1OmTFG/YitqWEy3e/duHnroIR5++GFefvllbrnlFvr27cv27dsvOztExxFiV2pYTHdpw+PHj7+iYSuOJfSWWzFCmzZt2LNnD3/84x8ZNmwYrVq1AnIPxh0Ox2Vvn8nJyfGc8SRiF2pYTJbffpOSkpgzZw5PPvmkleOKXEENi+nuvvtu7rvvPv7yl79QvXp1du7cyZgxYzhw4AD//Oc/GTNmjNUjilyTGhbT5bfhn3/+mXnz5jFy5Mgin0kLemJ769atY+DAgTzyyCMcPHiQhIQE2rVrx+jRowkICAByT2/NzMykdOnSFk8rciU1LCbLb78ZGRmUKVPG4mlFrqSGxXTx8fF07dqVKVOm0K5du8u+NmPGDEaPHk14eDjTp0+nXLlyuiOo2I4aFtPlt+Fp06ZRoUKFYptLp4CI7f3www+0bduWJ554gnHjxtGzZ0927tyJ0+lk4sSJZGdn43A4CA8P591337V6XJErqGExWX77jYiIYNq0aVaPK3IFNSymq1evHnXq1GHJkiWez2VkZAAwePBgPv30U7Zu3cquXbu0ECK2pIbFdPltePfu3cU6l96ULrbXv39/6tevT506dQCoX78+rVq14tNPP2XBggWsXLmSxo0bs2rVKi2GiC2pYTGZ+hXTqWEx2cXrMnXu3Jmnn36agIAAnn76aUqXLu25gUDLli0JDAxk9erV3HfffVaPLHIZNSyms3PDWtATW3O73ZQpU4YHHngAyL3zka+vLw888ADNmjVj9erVfPLJJ7z66qs8++yznoN1EbtQw2Iy9SumU8NiuotnK40aNYqyZcvy7LPP8tFHH/Hqq69y//33A1C2bFnOnTunt4yLLalhMZ2dG9Y19MQ4v72A9VNPPcXcuXM5deqUxZOJ5I8aFpOpXzGdGhbTXDw75MKFC2zYsIHp06ezZMkSGjVqRNOmTYmLi+PEiRP88MMPVo8qkic1LKaza8O6hp4YITs7G4Bz587h5eWFw+HA7XaTlJTE6tWrGT9+vMUTilybGhaTqV8xnRoWkzkcDrKzsylVqhQtWrTgnXfeYfXq1bRq1Yrk5GScTicfffSR1WOKXJUaFtPZtWGdoSe2l5OTg5eXF8nJybRs2ZIVK1ZQr149IPdClF988QUREREWTylydWpYTKZ+xXRqWEx3seGff/6ZsLAwVq1aRe3atQHIysrCx0dXURJ7U8NiOrs2rAU9sbWcnBxycnLw8fGhb9++nDp1ipUrV1o9lki+qWExmfoV06lhMd1vGz558iSrVq3i4l/hdEdQsTs1LKazc8NaChdbOn/+PD4+Pvj5+eHl5cWhQ4dYs2YNn3/+udWjieSLGhaTqV8xnRoW02VlZZGdnU3p0qXx8vLi+++/Z/Xq1XzxxReAFkHE/tSwmM6EhnWGntjK4sWLiY2NJTExkdTUVCIiIhgxYgQA+/fvJywszOIJRa5NDYvJ1K+YTg2L6T799FM+/PBDjh07hp+fH+3ateOpp54CYPPmzWpYbE8Ni+lMalgLemIb8fHxNG/enOHDh1OxYkUSExOZOHEit956K88//zx/+tOfrB5R5JrUsJhM/Yrp1LCY7tChQ9x5550MHDiQMmXK4Ha7WbRoERkZGUyZMoUePXpYPaLINalhMZ1pDWtBT2yjT58++Pj4MG/ePAASExPp27cv3t7ebN68mUcffZSJEyfi5+dni9NbRX5LDYvJ1K+YTg2L6f785z9z6tQpFi9eDEBKSgpHjhzhnXfeYf78+URGRvLGG29QqVIlawcVuQo1LKYzrWEvqwcQgdz3p1eoUIHGjRsDuReeDAgIoH79+rRu3ZpJkyaxefNmvvvuOx2Eiy2pYTGZ+hXTqWEpCW699dbLPi5fvjwhISGMGzeOiRMnsmfPHlatWmXRdCLXp4bFdKY1rAU9sQUfHx+qV6/OhAkT2LhxI15eXsTFxXlWwQcPHky5cuWYMWOG1aOK5EkNi8nUr5hODUtJcMcdd7BkyRJmzZp12ecrVKjAo48+Sv369Zk0aRLp6ekWTShybWpYTGdaw1rQE9sYNWoUnTp14h//+Ad169alW7du9O/fnzvvvBOABx98kJSUFLKysiyeVCRvalhMpn7FdGpYTPfwww8zduxY3nrrLUaNGsW+ffsu+/qAAQNITk62zV8kRX5LDYvpTGtY19ATW9m/fz/btm3j+PHjNGzYkIiICM/X2rVrR5MmTZg4caKFE4pcmxoWk6lfMZ0aFtP99NNPzJs3j5UrVwLQokULoqOjOXToEM899xwNGjRg7ty5Fk8pcnVqWExnUsNa0BNbuHDhAqVKlcrza0ePHmXKlClMnz6d//73v5QtW7aYpxO5PjUsJlO/Yjo1LKaKj49ny5YtHDp0iL/85S9UrVoVgFWrVvHFF1+wZcsWvvrqK+rXr0+jRo348MMPKVOmjMVTi/yPGhbTmdywFvTEMocOHWL16tX8+c9/BnIvYJ2dnU2pUqVwu92ei1b/8ssvTJgwgaCgIPr27WvlyCKXUcNiMvUrplPDYrq4uDj69+9PSkoK2dnZxMfHs2DBAnr27AlAZmYmSUlJZGRkkJmZSVBQED4+PhZPLfI/alhMZ3rDWtATy9x///189dVXNGzYkDfffJNOnToBkJ2djZeXl+5CJ7anhsVk6ldMp4bFdPfddx/NmjXjr3/9K4GBgTz11FPs27ePFStWXPHYSxepRexCDYvpTG9YN8UQS2zfvp2TJ08ybdo0/u///o+uXbvSuXNnDh8+jLe3Nw6Hg5ycHM6dO8fJkyetHlfkCmpYTKZ+xXRqWEy3YcMGjh8/ztNPP82tt96Kl5cX0dHR7Nu3jy+++MLzuOzsbADb/SVSRA2L6UpCw1rQE0ucPXuWTp06ce+99zJjxgxWrlxJRkYGQUFBjBo1CgAvLy8GDhzIu+++a+2wInlQw2Iy9SumU8Niuh9//JFbb73Vcx0mt9tNSEgIrVq1Yv369Z7HPfbYY7qRi9iSGhbTlYiG3SIWSE9Pd+/YseOyz505c8b93nvvuYOCgtxVq1Z1Dxs2zO1wONxxcXEWTSlydWpYTKZ+xXRqWEx35swZd0xMjOfjnJwct9vtdk+fPt39f//3f2632+3evn272+FwuA8cOGDJjCLXoobFdCWhYV1DTyyVk5OD2+3G29vb87mjR48ydepUXnnlFf7+97/z8ssvWzihyLWpYTGZ+hXTqWEpCbKysjwXWf/hhx+499572bFjB3379iUkJIRp06ZZPKHItalhMZ2pDWtBT4rVL7/8wunTp0lJSaFu3bqUK1cOgAsXLlCqVCnP41577TXGjh3L2bNn8fLSO8PFPtSwmEz9iunUsJjuYsPnz5+nXr16noazs7O5+NeyLl26kJmZyTfffMPZs2cvW7AWsZoaFtOVpIZ1hCPFZu/evQwYMICQkBCioqJo2bIlL730EoDnIDw7O5vU1FQ2btzISy+9pINwsRU1LCZTv2I6NSymu7Thxx577LKGvb298fHxwcfHh7vvvpsNGzbwzjvv2PYvkXJzUsNiupLWsM7Qk2LTvHlz7r77bpxOJ5mZmezZs4cpU6bgcDiYMmUKERERQO7prt9//z133HGHxROLXE4Ni8nUr5hODYvp8tvwkSNHeP311+17EXa5aalhMV2Ja9iSK/fJTWfnzp3ugIAA93fffef5XHp6uvu7775zP/HEE+46deq4J06c6LkQpYjdqGExmfoV06lhMV1+G87KyrJwSpGrU8NiupLYsN6HIMWiXr16+Pv7s3//fs/nSpcuTUhICP/6178YMGAAM2fOJDEx0cIpRa5ODYvJ1K+YTg2L6fLb8LFjxyycUuTq1LCYriQ2rAU9KXJut5tKlSrRsmVLoqOjWbRo0WVfr1GjBkOGDCE1NZWvv/7aoilFrk4Ni8nUr5hODYvp1LCYTg2L6Upqwz5WDyAln8PhAODf//43pUuXJiYmhi1btjBgwAAaNWoEQM2aNSlVqhRnzpyxclSRPKlhMZn6FdOpYTGdGhbTqWExXUltWDfFkCJ34cIFz93nDhw4wPvvv8+mTZu4cOECDRo0IDQ0lDVr1rBp0yaOHj1q8bQiV1LDYjL1K6ZTw2I6NSymU8NiupLasBb0pMgcPnyYTz/9lI8++oigoCDeeustypUrB8C6dev4+uuvWbt2Lfv37+fhhx+mZ8+e3HvvvRZPLfI/alhMpn7FdGpYTKeGxXRqWExX0hvWgp4UmbZt2+J2u7njjjvYsmUL58+fZ/HixZ5TWrOzs/H29iYjI4PSpUtbPK3IldSwmEz9iunUsJhODYvp1LCYrqQ3rGvoSZF45513OHLkCDt37qRixYocO3aMHj16sHbtWho1akRWVhY+Pj643W4jNxwp+dSwmEz9iunUsJhODYvp1LCY7mZoWHe5lULndrtZtGgRI0eOpGLFigD84Q9/oE+fPsyZMwcAH5/cteSDBw9aNqfI1ahhMZn6FdOpYTGdGhbTqWEx3c3SsBb0pNClpqZStWpVMjIyLvv8gw8+yPHjx9mzZw8An376KY0bNyYtLc2KMUWuSg2LydSvmE4Ni+nUsJhODYvpbpaG9ZZbKXTlypVj/vz5nD17FshdHXc4HISEhFCnTh22b99O06ZN+etf/8qTTz6Jn5+fxROLXE4Ni8nUr5hODYvp1LCYTg2L6W6WhrWgJ0XC29ubKlWqAOBwODwbUKtWrdi0aRNeXl6cPn2aV155xeJJRfKmhsVk6ldMp4bFdGpYTKeGxXQ3Q8O6y60Ui4sbz5o1a+jduzdJSUnExsbidDqtHk0kX9SwmEz9iunUsJhODYvp1LCYriQ2rAU9KVbnzp0jMDCQJk2asHHjRqvHEblhalhMpn7FdGpYTKeGxXRqWExXkhrWgp4Uu8zMTM6fP0/VqlWtHkWkQNSwmEz9iunUsJhODYvp1LCYrqQ0rAU9ERERERERERERg3hZPYCIiIiIiIiIiIjknxb0REREREREREREDKIFPREREREREREREYNoQU9ERERERERERMQgWtATEREREQCef/55HA4HDocDLy8vKlasSKNGjYiOjmbnzp03/HoJCQk8//zzHD16tAimFREREbl5aUFPRERERDx8fX3ZvHkzmzZt4uOPP+bxxx9n+/bttGjRggkTJtzQayUkJPDCCy9oQU9ERESkkPlYPYCIiIiI2IfD4eCee+7xfNy+fXueeOIJ+vfvz1NPPcU999xDWFiYhROKiIiIiM7QExEREZFr8vb2ZtKkSfj6+jJ58mQAli9fTufOnalZsybly5enefPmzJ071/OcdevW0a5dOwDCwsI8b+W96Ny5c4wYMYLAwEBKly5N48aNWbhwYfF+YyIiIiKG0hl6IiIiInJdVatWpWXLlmzatAmA+Ph4HnzwQUaNGkWpUqXYsGEDjz32GFlZWURFRXHXXXfx9ttvM3ToUGbMmEGjRo08r3XhwgU6derEkSNHeO655wgKCmLx4sU88sgj+Pv706lTJ6u+TREREREjaEFPRERERPIlMDCQ7du3A/DEE094Pp+Tk0ObNm346aefeOedd4iKiqJixYrccccdADRq1Oiyt/G+//77bNu2jW+++YZmzZoB0LFjRxITE3nuuee0oCciIiJyHXrLrYiIiIjki9vt9rxt9ujRowwaNIjAwEBKlSpFqVKlePfdd/n++++v+zorVqzgjjvuoHHjxmRlZXn+69ixIzt27CA7O7uovxURERERo+kMPRERERHJlyNHjlCrVi1ycnIIDw8nKSmJf/7znwQHB1OhQgWmTp3K+++/f93XOXXqFHv37qVUqVJ5fv3YsWMEBgYW9vgiIiIiJYYW9ERERETkuk6fPs0333xDz549+fHHH9m1axcLFy7E6XR6HpPfM+uqVq1Ko0aNeO+99/L8eo0aNQpjZBEREZESSwt6IiIiInJN2dnZjBgxgszMTIYNG0ZqaioAZcqU8Tzm3LlzLFmy5LLn+fr6ApCenn7Z5zt16sTSpUupWbMmtWvXLuLpRUREREoeLeiJiIiIiIfb7ebrr78GIDU1le+++47Zs2eza9cu/v3vf3PPPfeQmZlJ7dq1+fvf/47b7ebChQu88sorVK5c2bPYB3D77bfj7e3NzJkzKV26NKVKlaJFixb069ePGTNm0LZtW5566ilCQkI4f/48e/fuJSEhgenTp1v17YuIiIgYQQt6IiIiIuKRmZlJWFgYAOXLl6d27drcd999vPvuu9x1111A7pl3ixcvZujQoTzyyCPUqFGDESNGcO7cOcaPH+95rWrVqvH222/z2muv8eGHH5KVlYXb7cbX15eVK1cyduxY3njjDY4ePUrlypVp0qQJgwYNsuT7FhERETGJw+12u60eQkRERERERERERPLHy+oBREREREREREREJP+0oCciIiIiIiIiImIQLeiJiIiIiIiIiIgYRAt6IiIiIiIiIiIiBtGCnoiIiIiIiIiIiEG0oCciIiIiIiIiImIQLeiJiIiIiIiIiIgYRAt6IiIiIiIiIiIiBtGCnoiIiIiIiIiIiEH+P0Ma8zqyWmm+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1280x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_sentiment(two_time_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ab8d491c",
   "metadata": {},
   "source": [
    "## Step 5: Statistical analysis\n",
    "\n",
    "### Tests for stationarity\n",
    "\n",
    "We now want to see if there is any correlation between sentiment and stock price over the time period we have selected. It's not obvious from visually comparing sentiment scores with stock price changes whether or not they correlate. Furthermore, we need to avoid 'spurious' correlations i.e. mistakenly seeing correlations in time series that do not exist.  \n",
    "\n",
    "Hence we will ensure our time series are *stationary*. That is, we will need to ensure each has a consistent mean and variance, and have removed any trend or seasonality. In what follows we shall make use the Python library ```statsmodels.tsa.stattools```. One test for stationarity is the *Augmented Dickey-Fuller (ADF) test*, which we shall run our two time series by:    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "e5f0f28a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-0.3153094492982783, 0.9233078948430622, 0, 61, {'1%': -3.542412746661615, '5%': -2.910236235808284, '10%': -2.5927445767266866}, 86.00766715869301)\n",
      "(-6.146422952636766, 7.747481063956256e-08, 0, 61, {'1%': -3.542412746661615, '5%': -2.910236235808284, '10%': -2.5927445767266866}, 20.149084624842402)\n",
      "(2.5291660858731784, 0.9990586559508666, 3, 58, {'1%': -3.548493559596539, '5%': -2.912836594776334, '10%': -2.594129155766944}, 248.09364042208762)\n",
      "(-7.697903715582589, 1.3636980611351953e-11, 0, 61, {'1%': -3.542412746661615, '5%': -2.910236235808284, '10%': -2.5927445767266866}, 9.922157635318271)\n"
     ]
    }
   ],
   "source": [
    "def ADF_test(df):\n",
    "    print(adfuller(df))\n",
    "    \n",
    "ADF_test(two_time_series[0]['Close'])\n",
    "ADF_test(two_time_series[0]['sentiment_f']) \n",
    "\n",
    "ADF_test(two_time_series[1]['Close'])\n",
    "ADF_test(two_time_series[1]['sentiment_f']) "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dd9024f6",
   "metadata": {},
   "source": [
    "The first value in each output is the *test statistic*, which is what we are interested in. The more negative this is, the more likely the series is stationary. We can compare this value to value listed as '5%' in the dictionary that is also returned for each time series above. Looking closely, we see that 2 of the 4 time series have a test stastic that is larger than the associated 5% level. The two series in question are the stock price data for each ticker. Hence, they fail the test and are not stationary. \n",
    "\n",
    "Another test we can do is a test to see if the time series are autocorrelated i.e. correlated with themselves. This test compares each series to itself offset by a lag k, for various values of k. To do this we use the autocorrelation function (ACF): ```stattools.graphics.tsaplots.plot_acf```: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "d0d5b9a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_autocorrelation(df):\n",
    "    plt.clf()\n",
    "    fig = plt.figure(figsize=(9, 12), dpi=80, facecolor='white')\n",
    "    spec = gridspec.GridSpec(ncols = 2, nrows = 4) # create 2 rows x 4 column figure \n",
    "    global ax\n",
    "    ax=[]\n",
    "    counter = 0\n",
    "    ticker=0\n",
    "    \n",
    "    for i in df:        \n",
    "        ticker_name = ticker_list[ticker]\n",
    "        for j in range(0,2):\n",
    "            # Create axes \n",
    "            if counter==0:\n",
    "                ax.append(fig.add_subplot(spec[counter]))\t\n",
    "                \n",
    "            else:\n",
    "                ax.append(fig.add_subplot(spec[counter], sharex=ax[0]))\n",
    "            # Create plot\n",
    "            if j==0: # alpha=0.05 means 95% CI\n",
    "                plot_acf(i['Close'], lags=20, alpha=0.05, zero=True, title=None, ax=ax[counter], \n",
    "                         color=\"navy\", markersize=3, linewidth=1, vlines_kwargs={\"colors\": \"black\", \"linewidth\": .8})    \n",
    "            else:\n",
    "                plot_acf(i['sentiment_f'], lags=20, alpha=0.05, zero=True, title=None, ax=ax[counter], \n",
    "                         color=\"navy\", markersize=3, linewidth=1, vlines_kwargs={\"colors\": \"black\", \"linewidth\": .8})\n",
    "            # Decoration \n",
    "            ax[counter].legend([ticker_name], loc=\"upper right\", fontsize=10, framealpha=1, edgecolor=\"black\", shadow=None)\n",
    "            ax[counter].grid(which=\"major\", color=\"grey\", linestyle=\"--\", linewidth=0.5)\n",
    "            if (counter==0):\n",
    "                ax[counter].set_title(\"Stocks autocorrelation\", family='serif', fontsize=12, \n",
    "                                      fontweight='bold')\n",
    "            elif (counter==1):\n",
    "                ax[counter].set_title(\"Sentiment autocorrelation\", family='serif', fontsize=12, \n",
    "                                      fontweight='bold')\n",
    "                \n",
    "            counter=counter+1\n",
    "            \n",
    "        ticker=ticker+1\n",
    "            \n",
    "    fig.tight_layout()\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d6c5a2e0",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x960 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_autocorrelation(two_time_series)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a9f1dd0a",
   "metadata": {},
   "source": [
    "If the ACF values (listed on the y-axis) lie outside of the shaded areas then they are deemed significant. We see that stock prices are indeed correlated with themselves up to about a lag of 5 days. The sentiment scores are, by contrast, stationary. (We discount the lag of 0 since all series will be perfectly correlated with themselves at the same point in time). \n",
    "\n",
    "How do we make the stock data stationary? A common approach is to do a little feature engineering on the non-stationary series by taking the difference between a data point in the series and the previous data point (i.e. xdiff<sub>t</sub> = x<sub>t</sub> - x<sub>(t-1)</sub>). We'll do this for the two stock price series, and then plot the result: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "926ed510",
   "metadata": {},
   "outputs": [],
   "source": [
    "def take_difference(df):\n",
    "    df_tmp = df.diff().dropna()\n",
    "    return df_tmp\n",
    "\n",
    "\n",
    "def plot_take_difference(df):\n",
    "    plt.clf()\n",
    "    fig = plt.figure(figsize=(14, 20), dpi=80, facecolor='white')\n",
    "    spec = gridspec.GridSpec(ncols = 2, nrows = 4) # create 2 rows x 4 column figure \n",
    "    ax=[]\n",
    "    fig.tight_layout()\n",
    "    counter = 0\n",
    "    ticker=0\n",
    "    \n",
    "    for i in df:        \n",
    "        ticker_name = ticker_list[ticker]\n",
    "        for j in range(0,2):\n",
    "  \n",
    "            if counter==0:\n",
    "                ax.append(fig.add_subplot(spec[counter]))\t                \n",
    "            else:\n",
    "                ax.append(fig.add_subplot(spec[counter], sharex=ax[0]))            \n",
    "            if j==0:\n",
    "                z = i['Close'].dropna()\n",
    "                z.plot(linewidth=1, color='darkorange')       \n",
    "\n",
    "            else:                 \n",
    "                z = i['Close'].diff().dropna()                \n",
    "                z.plot(linewidth=1, color='limegreen')         \n",
    "           \n",
    "            ax[counter].legend([ticker_name], loc=\"upper left\", fontsize=10, framealpha=1, edgecolor=\"black\", shadow=None)\n",
    "            ax[counter].grid(which=\"major\", color=\"grey\", linestyle=\"--\", linewidth=0.5)\n",
    "            if (counter==0):\n",
    "                ax[counter].set_title(\"Stocks: BEFORE (non-stationary)\", family='serif', fontsize=12, \n",
    "                                      fontweight='bold', pad=20)\n",
    "            elif (counter==1):\n",
    "                ax[counter].set_title(\"Stocks: AFTER (stationary)\", family='serif', fontsize=12, \n",
    "                                      fontweight='bold', pad=20)\n",
    "            plt.xlabel('Time', family='serif', fontsize=12)\n",
    "            plt.xticks(rotation='60', fontsize=10)\n",
    "            plt.yticks(fontsize=10)\n",
    "            counter=counter+1\n",
    "            \n",
    "        ticker=ticker+1\n",
    "            \n",
    "    \n",
    "    plt.show()\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9380a635",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1120x1600 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_take_difference(two_time_series)\n",
    "two_time_series_stationary=[take_difference(x) for x in two_time_series]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3112df47",
   "metadata": {},
   "source": [
    "The right-hand side plots show the result of taking the difference. We see that the trend has been removed and that the mean and variance are consistent over time now. \n",
    "\n",
    "We also see the two stock time series, after differencing, now pass the ADF test if we run it again: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "77af7ec9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(-7.340909886097775, 1.0658304621949105e-10, 0, 60, {'1%': -3.5443688564814813, '5%': -2.9110731481481484, '10%': -2.5931902777777776}, 84.84479198462247)\n",
      "(-6.611586355314544, 6.357080919714013e-09, 0, 60, {'1%': -3.5443688564814813, '5%': -2.9110731481481484, '10%': -2.5931902777777776}, 247.36002719824717)\n"
     ]
    }
   ],
   "source": [
    "# ---------------------------------\n",
    "# 2b. Re-test for stationarity\n",
    "# ---------------------------------\n",
    "ADF_test(two_time_series_stationary[0]['Close'])\n",
    "ADF_test(two_time_series_stationary[1]['Close'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7657f4fb",
   "metadata": {},
   "source": [
    "As can be seen, the test statistics are now smaller than the 5% critical value. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "69b170d6",
   "metadata": {},
   "source": [
    "### Tests for cross correlation\n",
    " \n",
    "At last, we can now see if they correlate at all (at least over this time period) by using the ```stattools``` function ```ccf```. We can then create a plot to show this. Python does have a function to plot cross correlation, namely ```matplotlib.pyplot.xcorr```, but it does not plot cross-correlation in quite the way R does: it does not allow us to view negative lags, nor the 95% confidence interval lines. We therefore do a bit of tinkering to manually recreate R's visuals in a ```stem``` plot from ```matplotlib``` in the below function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2e255fc4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_cross_correlation(df):\n",
    "    plt.clf()\n",
    "    fig = plt.figure(figsize=(12, 8), dpi=80, facecolor='white')\n",
    "    spec = gridspec.GridSpec(ncols = 2, nrows = 2)\n",
    "    ax=[]\n",
    "    ticker=0\n",
    "    counter = 0\n",
    "    \n",
    "    for i in df:\n",
    "        # Create axis\n",
    "        ax.append(fig.add_subplot(spec[counter]))\n",
    "        \n",
    "        # Get data\n",
    "        x=i['Close']\n",
    "        y=i['sentiment_f']\n",
    "        \n",
    "        # Some variables to help \n",
    "        n = len(x) # Number of samples         \n",
    "        lags = round(10*math.log10(n/2)) # Number of lags (same formula R uses apparently). \n",
    "                                            # 2 is the number of series we are comparing, which is 2. \n",
    "        slice_value = n-lags\n",
    "        \n",
    "        # Calculate positive and negative values for cross-correlation:  \n",
    "        backwards = stattools.ccf(x[::-1], y[::-1], adjusted=False)[::-1]\n",
    "        forwards = stattools.ccf(x, y, adjusted=False)\n",
    "        cross_cor_result = np.r_[backwards[:-1], forwards][slice_value:][:-slice_value]\n",
    "        \n",
    "        # Draw plot and CI lines: \n",
    "        _, stemlines, _ = ax[counter].stem(range(-len(cross_cor_result)//2+1, len(cross_cor_result)//2+1), \n",
    "                         cross_cor_result, linefmt='-', markerfmt=\" \", basefmt=\" \")\n",
    "        ax[counter].axhline(-1.96/np.sqrt(n), color='b', ls='--', linewidth=1., alpha=0.75) \n",
    "        ax[counter].axhline(1.96/np.sqrt(n), color='b', ls='--', linewidth=1., alpha=0.75)\n",
    "        ax[counter].axhline(0, color='k', ls='-')\n",
    "        plt.setp(stemlines, 'linewidth', 1., color='k')\n",
    "        \n",
    "        # Subplot text: \n",
    "        ticker_name = ticker_list[counter] \n",
    "        ax[counter].set_title(ticker_name)\n",
    "        ax[counter].set_xlabel('Lags')\n",
    "        ax[counter].set_ylabel('Cross correlation')\n",
    "        \n",
    "        counter = counter+1  \n",
    "    \n",
    "    # Figure text: \n",
    "    plt.suptitle(\"Stock vs Sentiment cross-correlations\")\n",
    "    fig.tight_layout()\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "81edf68a",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 960x640 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cross_correlation(two_time_series_stationary)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6f0f48f7",
   "metadata": {},
   "source": [
    "### Conclusion \n",
    "\n",
    "The blue lines are the confidence interval. Where the stems pertrude outside of the confidence interval, the correlation is held to be significent. Here the plot suggests some negative correlation for PCG for this time period, but mainly at a significant lag. We find none for TSLA. Further analysis would be required to form any firmer conclusions, including whether this pattern holds for these tickers at different time periods or different window sizes in time, or whether different tickers have any correlation. \n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}