arima.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n",
    "\n",
    "# Step 1: Fetch data using yfinance\n",
    "ticker = 'AAPL'  # Example: Apple Inc.\n",
    "data = yf.download(ticker, start='2020-01-01', end='2023-12-31')\n",
    "data = data['Close']\n",
    "data = data.dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 2: Visualize and check for stationarity\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.plot(data, label='Close Price')\n",
    "plt.title(f'{ticker} Close Price')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### => Stock price data is usually non-stationarity (Can't have independence but still want consistency). Distribution depends only on difference in time not location in time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 3: Differencing for stationarity (First-order differencing)\n",
    "diff_data = data.diff().dropna()\n",
    "\n",
    "# Plot the original data and the differenced data\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# Original data\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(data, label='Original Close Price')\n",
    "plt.title(f'{ticker} Close Price (Original)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Price')\n",
    "plt.legend()\n",
    "\n",
    "# Differenced data\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(diff_data, label='Differenced Close Price', color='orange')\n",
    "plt.title(f'{ticker} Close Price (Differenced)')\n",
    "plt.xlabel('Date')\n",
    "plt.ylabel('Differenced Price')\n",
    "plt.legend()\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best ARIMA order: (5, 1, 2) with AIC: 4803.54260655204\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    }
   ],
   "source": [
    "# MINIC (Minimum Information Criterion)\n",
    "\n",
    "from statsmodels.tsa.arima.model import ARIMA\n",
    "import numpy as np\n",
    "\n",
    "# Define a function for MINIC to determine the best (p, q) for ARIMA\n",
    "def minic(data, max_p=5, max_q=5):\n",
    "    best_aic = np.inf\n",
    "    best_order = None\n",
    "    for p in range(max_p + 1):\n",
    "        for q in range(max_q + 1):\n",
    "            try:\n",
    "                model = ARIMA(data, order=(p, 1, q))  # Assuming d=1 for differencing\n",
    "                model_fit = model.fit()\n",
    "                if model_fit.aic < best_aic:\n",
    "                    best_aic = model_fit.aic\n",
    "                    best_order = (p, 1, q)\n",
    "            except:\n",
    "                continue\n",
    "    return best_order, best_aic\n",
    "\n",
    "# Example usage:\n",
    "best_order, best_aic = minic(data)\n",
    "print(f\"Best ARIMA order: {best_order} with AIC: {best_aic}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Step 4: Plot the ACF and PACF for ESACF-like analysis\n",
    "plt.figure(figsize=(12, 6))\n",
    "\n",
    "# ACF plot for differenced data\n",
    "plt.subplot(2, 1, 1)\n",
    "plot_acf(diff_data, lags=20, ax=plt.gca(), title=\"ACF (Differenced Data)\")\n",
    "\n",
    "# PACF plot for differenced data\n",
    "plt.subplot(2, 1, 2)\n",
    "plot_pacf(diff_data, lags=20, ax=plt.gca(), title=\"PACF (Differenced Data)\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                               SARIMAX Results                                \n",
      "==============================================================================\n",
      "Dep. Variable:                   AAPL   No. Observations:                 1006\n",
      "Model:                 ARIMA(5, 1, 2)   Log Likelihood               -2393.771\n",
      "Date:                Tue, 03 Dec 2024   AIC                           4803.543\n",
      "Time:                        14:13:38   BIC                           4842.845\n",
      "Sample:                             0   HQIC                          4818.476\n",
      "                               - 1006                                         \n",
      "Covariance Type:                  opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -1.8700      0.036    -51.723      0.000      -1.941      -1.799\n",
      "ar.L2         -1.0370      0.064    -16.179      0.000      -1.163      -0.911\n",
      "ar.L3         -0.0928      0.067     -1.385      0.166      -0.224       0.039\n",
      "ar.L4         -0.0546      0.063     -0.870      0.384      -0.178       0.068\n",
      "ar.L5         -0.0076      0.032     -0.240      0.811      -0.070       0.054\n",
      "ma.L1          1.8471      0.022     85.668      0.000       1.805       1.889\n",
      "ma.L2          0.9575      0.021     46.017      0.000       0.917       0.998\n",
      "sigma2         6.8612      0.237     29.009      0.000       6.398       7.325\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               101.96\n",
      "Prob(Q):                              0.96   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               1.04   Skew:                            -0.10\n",
      "Prob(H) (two-sided):                  0.69   Kurtosis:                         4.55\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "# Step 4: Train ARIMA model (example order (1,1,1))\n",
    "model = ARIMA(data, order=(5, 1, 2))\n",
    "fit_model = model.fit()\n",
    "\n",
    "# Print summary\n",
    "print(fit_model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  return get_prediction_index(\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
      "  return get_prediction_index(\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "# Step 3: Fetch new data\n",
    "new_data = yf.download(ticker, start='2024-01-01', end=datetime.datetime.today().strftime('%Y-%m-%d'))['Close'].dropna()\n",
    "# new_data = yf.download(ticker, start='2024-01-01', end='2024-01-05')['Close'].dropna()\n",
    "\n",
    "# Step 4: Forecast future values based on the trained model\n",
    "# Forecast the same length as new_data to compare\n",
    "forecast = fit_model.get_forecast(steps=len(new_data))\n",
    "forecast_values = forecast.predicted_mean\n",
    "forecast_conf_int = forecast.conf_int()\n",
    "\n",
    "# Step 5: Plot actual vs predicted values\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(new_data.index, new_data, label='Actual New Data', color='blue')\n",
    "plt.plot(new_data.index, forecast_values, label='Forecast', color='orange')\n",
    "plt.fill_between(new_data.index, forecast_conf_int.iloc[:, 0], forecast_conf_int.iloc[:, 1], color='orange', alpha=0.2, label='Confidence Interval')\n",
    "plt.title(f'{ticker} Actual vs Forecast')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:966: UserWarning: Non-stationary starting autoregressive parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-stationary starting autoregressive parameters'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\statespace\\sarimax.py:978: UserWarning: Non-invertible starting MA parameters found. Using zeros as starting parameters.\n",
      "  warn('Non-invertible starting MA parameters found.'\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\base\\model.py:607: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  warnings.warn(\"Maximum Likelihood optimization failed to \"\n",
      "[*********************100%***********************]  1 of 1 completed\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  return get_prediction_index(\n",
      "c:\\Study\\DS_502\\S-P_500_Stock_Prediction\\S-P_500_Stock_Prediction\\.venv\\Lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:837: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
      "  return get_prediction_index(\n"
     ]
    },
    {
     "data": {
      "image/png": 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p+U42C1elShVSU1ML7HM6nRw8eLDAvhkzZtC1a1c+/vjjAvtTU1NLHHwMGDCAsWPHMnPmTKpVq0Z6ejo33HDDccdVr16de+65h3vuuYekpCRat27NCy+8cFqBdt26dQHYsmXLcddt3ryZ6OhoQkJCTnqOBg0aYBgG9erV47zzzjvpcQAbNmw44YczR48eZd68eYwdO5ZnnnnGt3/btm2FHn+q56Q4M69+fn40aNCgWBXz84Pj/NdJVlYW33zzDQMGDOC666477vj77ruPKVOmHBdo/5PNZmPcuHF07dqVt99+m8cff7zI4wGzoNnzzz9PkyZNTtrCrSS/7wkJCVit1pP+nEVE5MQUaIuIVHA5OTl8/fXXXH/99YW+4a9RowZffPEF3377LQMGDGDv3r0sXLiQsWPHFnq80+lk4MCBrFixgosvvviMj7+44+nbty+ff/45q1evPm6dtmEYWCwWX8D474AazCBw4cKFBfZ98MEHx81o22y241KLv/rqK/bv3+9b51pcTZo0oXnz5kyfPp1q1apRvXp1OnXq5Lve4/GQmZlJRESEb19sbCw1atTA4XCU6D7zVa9enVatWvHpp58yatQo39rpDRs28Msvv3DzzTef8hzXXnsto0aNYuzYsXz++ecFglvDMEhJSaFq1aq0bt2aevXq8cYbbzB48OAC67Tzf0b5M+L/fo7feOONApeL+pyEhIQUK6W/ffv2vpZv/zRv3jwuv/zy4/bnr2HPT72fNWsWWVlZDB8+vNC10b/88gtfffUV77zzzklTw7t06UK7du144403GDlyJIGBgUV+DEOHDsVms53097Skv+9//PEHF1xwQYHnXUREik6BtohIBfftt9+SkZHBf//730Kv/89//kNMTAxTpkxhwIABTJ06FcMwTnh8r1698PPzY8qUKSUKtFevXs3zzz9/3P4uXboU6MWcr7jjefHFF/nll1/o3Lkzw4YNo0mTJhw8eJCvvvqKxYsXExkZSatWrbDZbLz88sukpaVht9t9RaqGDh3KXXfdRb9+/bjiiitYt24dP//883Gz1FdddRXPPvsst912Gx06dGD9+vVMmTKF+vXrF/s5+acBAwbwzDPPEBgYyJAhQ3wF18DsF12rVi2uu+46WrZsSWhoKL/++iurVq0qNOW3uF599VWuvPJK2rdvz5AhQ3ztvSIiIgrtLf5vDRo04Pnnn2fUqFHs2rWLvn37EhYWRkJCArNmzWLYsGE8/PDDWK1WJk6cSJ8+fWjVqhW33XYb1atXZ/Pmzfz999/8/PPPhIeH06lTJ1555RVcLhc1a9bkl19+OW6WuajPSZs2bZg+fToPPvggbdu2JTQ0lD59+pzwsVx99dX873//Y+vWrQVmba+++mrq1atHnz59aNCgAVlZWfz666989913tG3b1nfOKVOmULVqVTp06FDo+f/73//y4Ycf8v3335+yYOAjjzzC9ddfz+TJk48rQHcydevWPeXPrSS/7y6XiwULFnDPPfcUeSwiIvIvZVLrXERESk2fPn2MwMBAIysr64THDB482PD39zcOHz5sNG/e3KhTp85Jz9mlSxcjNjbWcLlcvvZVr7766inHwgnaSwG+VkT/VtzxGIZh7N6927j11lt9rZHq169vDB8+3HA4HL7bfPjhh0b9+vUNm81WoNWXx+MxHnvsMSM6OtoIDg42evToYWzfvr3Q9l4PPfSQUb16dSMoKMjo2LGjsWzZMqNz585G586dfccVtb1Xvm3btvmek8WLFxe4zuFwGI888ojRsmVLIywszAgJCTFatmxpvPvuu6c874naOv3br7/+anTs2NEICgoywsPDjT59+hgbN24scEx+e6/k5ORCzzFz5kzjkksuMUJCQoyQkBCjcePGxvDhw40tW7YUOG7x4sXGFVdc4XssLVq0MN566y3f9fv27TOuueYaIzIy0oiIiDCuv/5648CBAwVasBX1OcnMzDRuuukmIzIy0gBO2erL4XAY0dHRx70uv/jiC+OGG24wGjRoYAQFBRmBgYFG06ZNjSeffNJIT083DMMwDh06ZPj5+Rm33HLLCc+fnZ1tBAcHG9dcc41hGCf/+Xg8HqNBgwZGgwYNDLfbbRjGydt7ncy/76ckv18//vhjgVZgIiJSfBbDOEHJTREREZFK7LnnnmPSpEls27at0OJu56q+fftisViYNWtWWQ9FRKTCUqAtIiIi56TMzEzq16/P+PHjGThwYFkPp1zYtGkTzZs3Z+3atWeljZ6ISGWlQFtERERERESkFFlPfYiIiIiIiIiIFJUCbREREREREZFSpEBbREREREREpBQp0BYREREREREpRX5lPYCS8Hq9HDhwgLCwMCwWS1kPR0RERERERCo5wzDIyMigRo0aWK0nn7OukIH2gQMHqF27dlkPQ0RERERERM4xe/fupVatWic9pkIG2mFhYYD5AMPDw8t4NCIiIiIiIlLZpaenU7t2bV88ejIVMtDOTxcPDw9XoC0iIiIiIiJnTVGWL6sYmoiIiIiIiEgpUqAtIiIiIiIiUooUaIuIiIiIiIiUogq5RruoPB4PLperrIchUqr8/f2x2WxlPQwRERERETmBShloG4ZBYmIiqampZT0UkTMiMjKSuLg49ZEXERERESmHKmWgnR9kx8bGEhwcrGBEKg3DMMjOziYpKQmA6tWrl/GIRERERETk3ypdoO3xeHxBdtWqVct6OCKlLigoCICkpCRiY2OVRi4iIiIiUs5UumJo+Wuyg4ODy3gkImdO/utbNQhERERERMqfShdo51O6uFRmen2LiIiIiJRflTbQFhERERERESkLCrSlyCwWC7Nnzy7rYYiIiIiIiJRrCrTLoWXLlmGz2ejdu3exbxsfH88bb7xR+oMqgsGDB2OxWHjppZcK7J89e/ZZT3W2WCy+LSQkhEaNGjF48GD++OOPYp+rS5cujBw5svQHKSIiIiIilZIC7XLo448/5t5772XhwoUcOHCgrIdTLIGBgbz88sscPXq0rIfCpEmTOHjwIH///TfvvPMOmZmZXHzxxXz22WdlPTQREREREanEihVoT5w4kRYtWhAeHk54eDjt27fnxx9/9F2fm5vL8OHDqVq1KqGhofTr149Dhw4VOMeePXvo3bs3wcHBxMbG8sgjj+B2u0vn0VQCmZmZTJ8+nbvvvpvevXszefLk44757rvvaNu2LYGBgURHR3PNNdcA5szr7t27eeCBB3yzuQBjxoyhVatWBc7xxhtvEB8f77u8atUqrrjiCqKjo4mIiKBz5878+eefxR5/t27diIuLY9y4cSc9bvHixVx66aUEBQVRu3Zt7rvvPrKysgB4++23adasme/Y/Bnx9957r8D9PPXUUye9j8jISOLi4oiPj6d79+7MmDGDgQMHMmLECN8HAUeOHOHGG2+kZs2aBAcH07x5c7744gvfOQYPHsyCBQuYMGGC7zndtWsXHo+HIUOGUK9ePYKCgjj//POZMGFCsZ8vERERERGpfIoVaNeqVYuXXnqJP/74g9WrV3PZZZdx9dVX8/fffwPwwAMP8N133/HVV1+xYMECDhw4wLXXXuu7vcfjoXfv3jidTpYuXcqnn37K5MmTeeaZZ0r3Uf2LYUBWVtlshlG8sX755Zc0btyY888/n5tvvplPPvkE4x8n+f7777nmmmvo1asXa9asYd68ebRr1w6Ar7/+mlq1avHss89y8OBBDh48WOT7zcjIYNCgQSxevJjly5fTqFEjevXqRUZGRrHGb7PZePHFF3nrrbfYt29focfs2LGDnj170q9fP/766y+mT5/O4sWLGTFiBACdO3dm48aNJCcnA7BgwQKio6OZP38+YLa0WrZsGV26dCnW2MB8jWZkZDB37lzA/HCoTZs2fP/992zYsIFhw4Zxyy23sHLlSgAmTJhA+/btueOOO3zPae3atfF6vdSqVYuvvvqKjRs38swzz/DEE0/w5ZdfFntMIiIiIiJSyRinqUqVKsZHH31kpKamGv7+/sZXX33lu27Tpk0GYCxbtswwDMP44YcfDKvVaiQmJvqOmThxohEeHm44HI4i32daWpoBGGlpacddl5OTY2zcuNHIycnx7cvMNAwz5D37W2Zm8Z7PDh06GG+88YZhGIbhcrmM6Oho4/fff/dd3759e2PgwIEnvH3dunWN8ePHF9g3evRoo2XLlgX2jR8/3qhbt+4Jz+PxeIywsDDju+++8+0DjFmzZp3wNoMGDTKuvvpqwzAM4z//+Y9x++23G4ZhGLNmzTL++VIbMmSIMWzYsAK3XbRokWG1Wo2cnBzD6/UaVatW9b2WWrVqZYwbN86Ii4szDMMwFi9ebPj7+xtZWVknHMuJxpqTk2MAxssvv3zC2/bu3dt46KGHfJc7d+5s3H///Sc8Pt/w4cONfv36nfK40lDY61xERERERM6ck8Wh/1biNdoej4dp06aRlZVF+/bt+eOPP3C5XHTr1s13TOPGjalTpw7Lli0DzCJfzZs3p1q1ar5jevToQXp6um9W/Fy2ZcsWVq5cyY033giAn58fAwYM4OOPP/Yds3btWi6//PJSv+9Dhw5xxx130KhRIyIiIggPDyczM5M9e/aU6Hwvv/wyn376KZs2bTruunXr1jF58mRCQ0N9W48ePfB6vSQkJGCxWOjUqRPz588nNTWVjRs3cs899+BwONi8eTMLFiygbdu2BAcHF3tcRl52QH5avcfj4bnnnqN58+ZERUURGhrKzz//XKTH/c4779CmTRtiYmIIDQ3lgw8+KPHzJSIiIiIilYdfcW+wfv162rdvT25uLqGhocyaNYumTZuydu1aAgICiIyMLHB8tWrVSExMBCAxMbFAkJ1/ff51J+JwOHA4HL7L6enpxRpzcDBkZhbrJqWmOLHgxx9/jNvtpkaNGr59hmFgt9t5++23iYiIICgoqNhjsFqtBdLPwUy//qdBgwZx5MgRJkyYQN26dbHb7bRv3x6n01ns+wPo1KkTPXr0YNSoUQwePLjAdZmZmdx5553cd999x92uTp06gLne/IMPPmDRokVceOGFhIeH+4LvBQsW0Llz5xKNKz/wr1evHgCvvvoqEyZM4I033qB58+aEhIQwcuTIUz7uadOm8fDDD/P666/Tvn17wsLCePXVV1mxYkWJxiUiIiIiIpVHsQPt888/n7Vr15KWlsaMGTMYNGgQCxYsOBNj8xk3bhxjx44t8e0tFggJKcUBnQFut5vPPvuM119/ne7duxe4rm/fvnzxxRfcddddtGjRgnnz5nHbbbcVep6AgAA8Hk+BfTExMSQmJmIYhm8md+3atQWOWbJkCe+++y69evUCYO/evRw+fPi0HtNLL71Eq1atOP/88wvsb926NRs3bqRhw4YnvG3nzp0ZOXIkX331lW8tdpcuXfj1119ZsmQJDz30UInG9MYbbxAeHu7LvFiyZAlXX301N998MwBer5etW7fStGlT320Ke06XLFlChw4duOeee3z7duzYUaIxiYiIiIicc7we8GSBKxPcGeBxQMQFYLWV9chKRbFTxwMCAmjYsCFt2rRh3LhxtGzZkgkTJhAXF4fT6SQ1NbXA8YcOHSIuLg6AuLi446qQ51/OP6Ywo0aNIi0tzbft3bu3uMMu9+bMmcPRo0cZMmQIzZo1K7D169fPlz4+evRovvjiC0aPHs2mTZtYv349L7/8su888fHxLFy4kP379/sC5S5dupCcnMwrr7zCjh07eOeddwpUiwdo1KgR//vf/9i0aRMrVqxg4MCBJZo9/6fmzZszcOBA3nzzzQL7H3vsMZYuXcqIESNYu3Yt27Zt45tvvvEVQwNo0aIFVapUYerUqQUC7dmzZ+NwOOjYseMp7z81NZXExER2797N3Llzue6665g6dSoTJ070ZV40atSIuXPnsnTpUjZt2sSdd9553Gs0Pj6eFStWsGvXLg4fPozX66VRo0asXr2an3/+ma1bt/L000+zatWq03q+REREREQqNI8DnGmQmwQ5hyD3MDhSzH2uDPNy1m5IWQtJCyBpERxZCakbICcRjMrTjeq0+2h7vV4cDgdt2rTB39+fefPm+a7bsmULe/bsoX379gC0b9+e9evXk5SU5Dtm7ty5hIeHF5hB/De73e5rKZa/VTYff/wx3bp1IyIi4rjr+vXrx+rVq/nrr7/o0qULX331Fd9++y2tWrXisssu81XIBnj22WfZtWsXDRo0ICYmBoAmTZrw7rvv8s4779CyZUtWrlzJww8/fNz9Hz16lNatW3PLLbdw3333ERsbe9qP69lnn8Xr9RbY16JFCxYsWMDWrVu59NJLufDCC3nmmWcKpMxbLBYuvfRSLBYLl1xyie924eHhXHTRRYQUIUXhtttuo3r16jRu3Ji7776b0NBQVq5cyU033eQ75qmnnqJ169b06NGDLl26EBcXR9++fQuc5+GHH8Zms9G0aVNiYmLYs2cPd955J9deey0DBgzg4osv5siRIwVmt0VEREREzhleD6RvNwPn5MWQvAwOL8v7fjEkL8rblsCRPyFnP2BAQBUIqQ2BMWX9CEqdxfj34t2TGDVqFFdeeSV16tQhIyODqVOn8vLLL/Pzzz9zxRVXcPfdd/PDDz8wefJkwsPDuffeewFYunQpYBaeatWqFTVq1OCVV14hMTGRW265haFDh/Liiy8WedDp6elERESQlpZ2XNCdm5tLQkIC9erVIzAwsMjnFKlI9DoXERERkTKXn/6dvg2y9kBAOPiFgNX/2DGG19zwgsVmbv/mzgJ3LlTrDDb7WRt+cZ0sDv23Yq3RTkpK4tZbb+XgwYNERETQokULX5ANMH78eKxWK/369cPhcNCjRw/effdd3+1tNhtz5szh7rvvpn379oSEhDBo0CCeffbZEjxMEREREREROaMMAzzZZlq41wGe3LzAOBPc2eB1gicHgqsXDLDzWazmdo4p1ox2eaEZbTnX6XUuIiIiImeUxwmOw5C9D5xHzSDbyFsSarGBNQBsAeZXq92sQF1S5/qMtoiIiIiIiFQyXpc5K+3JNWepHUfAmWpWA7f6g38E2KoUnvYthVKgLSIiIiIicq7KOQTpm81UcK/L3Gf1B79gCK55TqZ9lwYF2iIiIiIiIucKwzDbaHndkL0fMraYad/2KLD4n14KuPgo0BYREREREakMDMNM//bm5hUvc+alhecVMssvZub1mMG2O8sMsP3DynrklY4CbRERERERkYrMnWUWLXMcAVdmXuEyz7HrLVaw+uW11/IDqw0sdgiINPdLqdOzKiIiIiIiUl7l96E2vHnBc973lrzA2ZEE6VvAlWb2sPYLAmuEAugypmdfRERERESkvPA4IWMHuFLMddR4wevFF2DjBQwgb3banQl+gRBcR+uryxEF2iIiIiIiIuWBMw3SNkLOQfAPzav4bc1L9fbDDK4tgCVvhtsNQbFmL2spV1SrvRwZPHgwFovluG379u1lPbQSmTx5MpGRkWU9DBERERGR8s2ZBunb4MhKyE2CkFpgrwoBVSAgwixW5hdqpoXbAsFmN7/3D1OQXU5pRruc6dmzJ5MmTSqwLyYmptjncTqdBATol05EREREpFzyesBxGLIPgCPRrAbuH24G2VLhaUa7nLHb7cTFxRXYbDYbCxYsoF27dtjtdqpXr87jjz+O2+323a5Lly6MGDGCkSNHEh0dTY8ePQDYsGEDV155JaGhoVSrVo1bbrmFw4cP+27n9Xp55ZVXaNiwIXa7nTp16vDCCy/4rn/sscc477zzCA4Opn79+jz99NO4XC7f9evWraNr166EhYURHh5OmzZtWL16NfPnz+e2224jLS3NNzM/ZsyYM/8EioiIiIiUB4ZhBtMepxlEu7PMmeucRHP2+vBSOLwccvaCXxiE1DGrgEulcG7MaBsGeLLL5r5twaddlGD//v306tWLwYMH89lnn7F582buuOMOAgMDCwSvn376KXfffTdLliwBIDU1lcsuu4yhQ4cyfvx4cnJyeOyxx+jfvz+//fYbAKNGjeLDDz9k/PjxXHLJJRw8eJDNmzf7zhkWFsbkyZOpUaMG69ev54477iAsLIxHH30UgIEDB3LhhRcyceJEbDYba9euxd/fnw4dOvDGG2/wzDPPsGXLFgBCQ0NP63kQERERESm3DMNcW529z+xfbbgLrxbuzZu08guCoDhVB6+kLIZhGGU9iOJKT08nIiKCtLQ0wsPDC1yXm5tLQkIC9erVIzAw0NzpzoIvyyjI659pltkvgsGDB/P5558fGzdw5ZVXct555zFz5kw2bdqEJS9of/fdd3nsscdIS0vDarXSpUsX0tPT+fPPP323ff7551m0aBE///yzb9++ffuoXbs2W7ZsoXr16sTExPD2228zdOjQIo3xtddeY9q0aaxevRqA8PBw3nrrLQYNGnTcsZMnT2bkyJGkpqYW6dxSdIW+zkVERESkbHhdZqXwjG15Pav983pWW48VNLNYj/WxVnXwgtxZ4M6Fap3N9efl1Mni0H/TxyflTNeuXZk4caLvckhICMOHD6d9+/a+IBugY8eOZGZmsm/fPurUqQNAmzZtCpxr3bp1/P7774XOJO/YsYPU1FQcDgeXX375Ccczffp03nzzTXbs2EFmZiZut7vAi+rBBx9k6NCh/O9//6Nbt25cf/31NGjQoMSPX0RERESk3PO68tLBs8GVYfayzj1kFjAr4iSbVG7nRqBtCzZnlsvqvoshJCSEhg0bluiuQkIK/lJnZmbSp08fXn755eOOrV69Ojt37jzp+ZYtW8bAgQMZO3YsPXr0ICIigmnTpvH666/7jhkzZgw33XQT33//PT/++COjR49m2rRpXHPNNSV6DCIiIiIi5YrHCZ6svDXW6eBKNQNswwkeF2CYlcCDaigNXHzOjVeCxVKhP1lq0qQJM2fOxDAM36z2kiVLCAsLo1atE1clbN26NTNnziQ+Ph4/v+N/1I0aNSIoKIh58+YVmjq+dOlS6taty5NPPunbt3v37uOOO++88zjvvPN44IEHuPHGG5k0aRLXXHMNAQEBeDyekjxkEREREZGzzzDAkwPuTDOwzj0MrnSz3pPXDVarGVRb7WANBnuA0sClUKo6XgHcc8897N27l3vvvZfNmzfzzTffMHr0aB588EGs1hP/CIcPH05KSgo33ngjq1atYseOHfz888/cdttteDweAgMDeeyxx3j00Uf57LPP2LFjB8uXL+fjjz8GzEB8z549TJs2jR07dvDmm28ya9Ys3/lzcnIYMWIE8+fPZ/fu3SxZsoRVq1bRpEkTAOLj48nMzGTevHkcPnyY7OwyKkgnIiIiIpLP64KsPZC2CQ6vgOSlkLIGUjdA8hJIWgTJy+DoOnAmg9UGgbEQWgeCa4E92uxfbbMryJYTOjdmtCu4mjVr8sMPP/DII4/QsmVLoqKiGDJkCE899dRJb1ejRg2WLFnCY489Rvfu3XE4HNStW5eePXv6AvSnn34aPz8/nnnmGQ4cOED16tW56667APjvf//LAw88wIgRI3A4HPTu3Zunn37aV+ncZrNx5MgRbr31Vg4dOkR0dDTXXnstY8eOBaBDhw7cddddDBgwgCNHjjB69Gi1+BIRERGRsuNKNwPsnP1mYTJrXrDsPGpWBLfZwS/YXGutIFpOw7lRdVykktHrXERERKQYPA7ITYL0rWZauNpqlS+qOi4iIiIiIlKOeT3gzTWDa6/DrAqevdcsZOYfDCEnrnEkUloUaIuIiIiISMVjeM3q3+7MvFZbmXlbNnid5mZ48wojh5oBtkUlquTsUKAtIiIiIiIVgyPFXGftTDPbbHmyzZlrDLD4gTUArP5msTKrv7kOW6QMKNAWEREREZHyzeuBzATI3Gr2tbb6mW22/MPBrurfUv4o0BYRERERkfLJk2vOYGfth6xdYK8CgdXKelQip6RAW0REREREzh7Dm1dlOssMpPHmFTBzgeEG4x/fu7PMNdcAwXFmarhIBaBAW0REREREzo7cw5CxLW99dS5gMTcLeV9teWngVvOrLRACIlXETCocBdoiIiIiIlJ68quBe3LN9loWq1mozHkUMreb1wdUAXuM1lZLpaVAW0RERERESs7wmm21XBngTAXH4bxq4P9or5V/nL0q+IeW6XBFzoZzKwfD48zrtXeWNo+zrB/xCRmGwbBhw4iKisJisbB27Vq6dOnCyJEjT3q7+Ph43njjjbMyxnOdnmsREREplwzDLFCWvR9SN0DSInM7shIyd4DhMquBB9eA0DoQUhuCa0FIHQXZcs44d2a0PU7zl9+defbu0y8UqrYDW9GLNiQmJvLCCy/w/fffs3//fmJjY2nVqhUjR47k8ssvL7Wh/fTTT0yePJn58+dTv359oqOj+frrr/H39y+1+ygru3btol69eqxZs4ZWrVoV6TZjxoxh9uzZrF279oyOTURERKTC8nrAeQSy9oHjkJkabrGBX4iZCm6zn/i2ShGXc8y5E2gbbjPItgac/I9AafE4zPsz3EDRAu1du3bRsWNHIiMjefXVV2nevDkul4uff/6Z4cOHs3nz5lIb3o4dO6hevTodOnTw7YuKiiq185+rnE4nAQGqhikiIiKViDsHHMmQtQecKYDFDKwDY8t6ZCLl1rmVOg5mkG0LPAtb8YP5e+65B4vFwsqVK+nXrx/nnXceF1xwAQ8++CDLly/3Hbdnzx6uvvpqQkNDCQ8Pp3///hw6dMh3/ZgxY2jVqhX/+9//iI+PJyIightuuIGMjAwABg8ezL333suePXuwWCzEx8cDHJc6npSURJ8+fQgKCqJevXpMmTLluDGnpqYydOhQYmJiCA8P57LLLmPdunVFHguA1+vllVdeoWHDhtjtdurUqcMLL7zgu37v3r3079+fyMhIoqKiuPrqq9m1a1eRn9f58+djsViYN28eF110EcHBwXTo0IEtW7YAMHnyZMaOHcu6deuwWCxYLBYmT55crMf30UcfUa9ePQIDA/nggw+oUaMGXq+3wDiuvvpqbr/9dsD8oOPqq6+mWrVqhIaG0rZtW3799dciPyYRERGRM8LrBlcm5CZD5i44shqSF0PKH+YkUmCsmRLuF1TWIxUp1869QLucSklJ4aeffmL48OGEhIQcd31kZCRgBqVXX301KSkpLFiwgLlz57Jz504GDBhQ4PgdO3Ywe/Zs5syZw5w5c1iwYAEvvfQSABMmTODZZ5+lVq1aHDx4kFWrVhU6psGDB7N3715+//13ZsyYwbvvvktSUlKBY66//nqSkpL48ccf+eOPP2jdujWXX345KSkpRRoLwKhRo3jppZd4+umn2bhxI1OnTqVatWoAuFwuevToQVhYGIsWLWLJkiWEhobSs2dPnM7irYF/8sknef3111m9ejV+fn6+oHfAgAE89NBDXHDBBRw8eJCDBw/6ns+iPL7t27czc+ZMvv76a9auXcv111/PkSNH+P33333H5P98Bw4cCEBmZia9evVi3rx5rFmzhp49e9KnTx/27NlTrMckIiIiclq8brOAWdZeSFkLSQsheREkL4Wja8GRZGaEBteGwBiwVvxlhiJnw7mTOl7Obd++HcMwaNy48UmPmzdvHuvXrychIYHatWsD8Nlnn3HBBRewatUq2rZtC5gB+eTJkwkLCwPglltuYd68ebzwwgtEREQQFhaGzWYjLi6u0PvZunUrP/74IytXrvSd8+OPP6ZJkya+YxYvXszKlStJSkrCbjdn8F977TVmz57NjBkzGDZs2CnHkpGRwYQJE3j77bcZNGgQAA0aNOCSSy4BYPr06Xi9Xj766CMseWt7Jk2aRGRkJPPnz6d79+5Ffo5feOEFOnfuDMDjjz9O7969yc3NJSgoiNDQUPz8/Ao8H0V9fE6nk88++4yYmBjfba+88kqmTp3qW1c/Y8YMoqOj6dq1KwAtW7akZcuWvuOfe+45Zs2axbfffsuIESOK/JhEREREisXrMWem3RngSDlWIdzrMoNoW5BZyMxu17pqkdOgQLucMAyjSMdt2rSJ2rVr+4JsgKZNmxIZGcmmTZt8QXF8fLwvsAWoXr36cbPRp7ofPz8/2rRp49vXuHFj38w6wLp168jMzKRq1aoFbpuTk8OOHTt8l082lk2bNuFwOE5Y6G3dunVs3769wO0BcnNzC9xHUbRo0aLAGMBMj69Tp84J77soj69u3boFgmyAgQMHcscdd/Duu+9it9uZMmUKN9xwA1armUSSmZnJmDFj+P777zl48CBut5ucnBzNaIuIiEjp8XrAm2vWDvLkHGu95c40CwVb/cAvGOzRmqkWKWUKtMuJRo0aYbFYSq3g2b+rh1ssluPWDJ+uzMxMqlevzvz584+77p8B+cnGEhR08vU9mZmZtGnTptD14f8Obk/ln+PInx0/2XNS1MdXWKp/nz59MAyD77//nrZt27Jo0SLGjx/vu/7hhx9m7ty5vPbaazRs2JCgoCCuu+66YqfDi4iIiBTgyYXcJHAeNQuXeRzg/Uc/a79QsEeZ6eAicsYo0C4noqKi6NGjB++88w733XffccFbamoqkZGRNGnShL1797J3717frPbGjRtJTU2ladOmpTaexo0b43a7+eOPP3yz5Fu2bCE1NdV3TOvWrUlMTMTPz89XUK24GjVqRFBQEPPmzWPo0KHHXd+6dWumT59ObGws4eHhJbqPoggICMDj8Rx33yV9fIGBgVx77bVMmTKF7du3c/7559O6dWvf9UuWLGHw4MFcc801gBnUF6fAm4iIiEgBhmEG2OmbwXHEbC9rCwL/MHO22mIr6xGKnFNUDK0ceeedd/B4PLRr146ZM2eybds2Nm3axJtvvkn79u0B6NatG82bN2fgwIH8+eefrFy5kltvvZXOnTtz0UUXldpYzj//fHr27Mmdd97JihUr+OOPPxg6dGiBGehu3brRvn17+vbtyy+//MKuXbtYunQpTz75JKtXry7S/QQGBvLYY4/x6KOP8tlnn7Fjxw6WL1/Oxx9/DJgp2NHR0Vx99dUsWrSIhIQE5s+fz3333ce+fftK7fHGx8eTkJDA2rVrOXz4MA6H47Qf38CBA/n+++/55JNPfEXQ8jVq1MhXPG3dunXcdNNNpZ5xICIiIhWc12MWKUvdCIeXw6EFZrGypMVmsbLDy+HwCrMyeMpqOLISPFkQUhuCqkNApNkNR0G2yFl37gXaHoeZUnPGN0exh1a/fn3+/PNPunbtykMPPUSzZs244oormDdvHhMnTgTMlOdvvvmGKlWq0KlTJ7p160b9+vWZPn16aT9TTJo0iRo1atC5c2euvfZahg0bRmzssX6JFouFH374gU6dOnHbbbdx3nnnccMNN7B7925f1fCiePrpp3nooYd45plnaNKkCQMGDPCt4Q4ODmbhwoXUqVOHa6+9liZNmjBkyBByc3NLdYa7X79+9OzZk65duxITE8MXX3xx2o/vsssuIyoqii1btnDTTTcVuO7//u//qFKlCh06dKBPnz706NGjwIy3iIiInOPcOXD0L7OtVuZ2cKaB4TbTwD055jprZ5qZIu5INreASAisBpZz7y2+SHljMYpahascSU9PJyIigrS0tOOCrdzcXBISEnw9jX08TvNTPnfm2RuoXyhUbWem7oiUohO+zkVERKRi87rNgmUZ281U8ODqWk8tlZ87C9y5UK0z2OxlPZoTOlkc+m/nzhptW4AZ9Brus3efFj8F2SIiIiJyau4cs2d11h6z7ZbVz0wB1+y0SIV07gTakBf0KvAVERERkTLg9ZiTPobb7FvtzfvemQrZ+8CVDv4hEBRnBtoiUmEV6yOycePG0bZtW8LCwoiNjaVv375s2bKlwDGJiYnccsstxMXFERISQuvWrZk5c2aBY1JSUhg4cCDh4eFERkYyZMgQMjPPYkq3iIiIiMiZ5M4yg+f0rXB4VV4Rs/xiZgsheTEkL4EjK8xK4VYbhNTJ62mtIFukoitWoL1gwQKGDx/O8uXLmTt3Li6Xi+7du5OVleU75tZbb2XLli18++23rF+/nmuvvZb+/fuzZs0a3zEDBw7k77//Zu7cucyZM4eFCxcybNiw0ntUIiIiIiJlJecQHF5pBtjpm8B5+FihXJsd/MPBHgPBNSG4lpki7h9u9rkWkUrhtIqhJScnExsby4IFC+jUqRMAoaGhTJw4kVtuucV3XNWqVXn55ZcZOnQomzZtomnTpqxatcrXjuqnn36iV69e7Nu3jxo1apzyfotSDC0+Pr5AKyqRyiQnJ4ddu3apGJqIiEh5YXjNWeycQ5CxxVxbbY9R8CxSFJWwGNppVVdIS0sDICoqyrevQ4cOTJ8+nZSUFLxeL9OmTSM3N5cuXboAsGzZMiIjIwv0fO7WrRtWq5UVK1YUej8Oh4P09PQC24n4+/sDkJ2dfToPTaRcy39957/eRURE5AwyDHNNtTsHXJnmmmrHEchJhMxdcHQ9JC2C5EWQug78QiAwVkG2yDmsxAtAvF4vI0eOpGPHjjRr1sy3/8svv2TAgAFUrVoVPz8/goODmTVrFg0bNgTMNdz/7MUM4OfnR1RUFImJiYXe17hx4xg7dmyRxmWz2YiMjCzQh9miP3JSSRiGQXZ2NklJSURGRmKz2cp6SCIiIpWTxwEZO8F5BAwXeL153Ws85uy11wPkJYZabGALBP8Is4+1iJzzShxoDx8+nA0bNrB48eIC+59++mlSU1P59ddfiY6OZvbs2fTv359FixbRvHnzEt3XqFGjePDBB32X09PTqV279gmPj4uLA/AF2yKVTWRkpO91LiIiIqfBMMDrAE+uednqD+5sSN9ittvyCzUDaasfWALM732bJnNEpHAlCrRHjBjhK2JWq1Yt3/4dO3bw9ttvs2HDBi644AIAWrZsyaJFi3jnnXd47733iIuLOy4AdrvdpKSknDBwsNvt2O1Fz9W3WCxUr16d2NhYXC5XCR6hSPnl7++vmWwREZGSMLzgyTEDaU82ONPAlWquDTWc5jEWGxge8/tg9bEWkZIpVqBtGAb33nsvs2bNYv78+dSrV6/A9fnrRq3Wgn+QbDYbXq8XgPbt25Oamsoff/xBmzZtAPjtt9/wer1cfPHFJX4ghbHZbApIRERERM5FXve/gupUcKWZM9ceB2AcS/n2CwRrhHk7ww1YynVBJhEp/4oVaA8fPpypU6fyzTffEBYW5ltTHRERQVBQEI0bN6Zhw4bceeedvPbaa1StWpXZs2f72ngBNGnShJ49e3LHHXfw3nvv4XK5GDFiBDfccEORKo6LiIiIiBTKMMxgOvsA5CaCNxc8eTPVVv+8ddRhZq/qE6Z9q4e1iJy+YrX3OlFRsUmTJjF48GAAtm3bxuOPP87ixYvJzMykYcOGPPzwwwXafaWkpDBixAi+++47rFYr/fr148033yQ0NLRI4yhOWXURERERqeS8HnAchux9eQG2ywyobUFgDdBaapHyrhK29zqtPtplRYG2iIiIyDnMMMwUcHemueUchNzkvN7VUebMtYhUHJUw0FZujIiIiIiUb15PXlCdAc50cKbkrb3OAQwzsA6KMyuDi4iUA/prJCIiIiLli+E1A2tXBjiPguOIednrNGetbUFmATN7FVUFF5FySYG2iIiIiJQtwzArg7vSwZFirrf2BdY28AuBgCrlOqVUROSfFGiLiIiIyNljGHnrMTPBlZcO7s4Ad14rLqsN/IIVWItIhaZAW0RERETODlcmpG8DR6JZzAyL2XbLGmAG1/aqqhAuIpWCAm0RERERKTmP0yxK5sk211ZjyVs3bckLmq3mV08OpG8x113bo8Eeo6BaRCotBdoiIiIiUnyGF7L3QsZ2c3ba6zIDZ1/nWAuQ973FYh7vFwzBtRRgi0ilp0BbRERERIrHkwvpWyEzAfxDzJRvq/+Jjzf+EXCLiJwDFGiLiIiISNG4cyD3EGTtMquDB1Uze1ifigJsETnHKNAWERERkRMzDLPtVm4iZO0zv/cPgZDa6mEtInICCrRFREREzmUeB3gd5hprrxsMV973TnDn5vW3Pmoe5x+eF2BrhlpE5GQUaIuIiIicazxOyEoAxxGzp7XXCYY7by11XhEziwUsfubmFw6BQWU9ahGRCkOBtoiIiMi5xJUOaRshez/4h4I10Pxq8VMquIhIKVGgLSIiIlLZeZxmgO1Oh4wEcGeabbaseisoInIm6K+riIiISEXldZvrqY389dX5l93mZXeWGWB7csGTZaaG+4VASK2yHrmISKWmQFtERESkojEMyDkAGdvMImWGGwwPGN68A/KKlVltYA0Aqx384zSDLSJyluivrYiIiEh5ZRjgyTFnpL1OwGIWKcs5CJm7zB7WfiFmAG2xaY21iEg5oUBbREREpDxy50DaZnAkme23fLPVAAYExpqBtoiIlDsKtEVERETKG+dRSP0bcpMgMAZsVcwZaxERqRAUaIuIiIiUBa/LXF/tyTVnrD254Mo0K4O7s8zrQ2orHVxEpAJSoC0iIiJythhecByGrH3mrLXhNFtvYQAWc621NQD8gs211yIiUiEp0BYRERE5kzxOcGeYbbZyDpqBNhbwDwNrONgDNGstIlLJKNAWERERKW2uDMjeD84UcGeDJ9tsv2ULMouYWf3LeoQiInIGKdAWERERKU05hyDtb3CmgX8wWAPBv5p6WIuInEP0F19ERESkOLweMNzm5nWB1w1G3ld3NmTtACx5hcwsZT1aEREpAwq0RURERE7E6zYrgOdvzpS8iuBuwGOmg3vdBW8TEAH+4WUyXBERKR8UaIuIiIj8m9cDWbsgazd4csDrBKxgCwCrHfwCzb7WFr+8r5q5FhGRYxRoi4iIyLnH8OalfHuObRhm4Gx4IGM7ZO2BgDCwR5ktt0RERIpIgbaIiIhUfq5MyNxhrqH2OvOCa29egJ33PeS12bKAxwHB1VUdXERESkSBtoiIiFRuuUmQthEcR80q4BYbZhq4PxBoBtcWm3ls/sy23a50cBERKTEF2iIiIlI5uTIg5yBkbjcvF6kKuN4aiYjI6dN/ExEREak8DANcqZC1D3IOmKni9irgH1bWIxMRkXOIAm0RERGp2AzDrAzuyoDsA5B7wCx0Zo+EwOiyHp2IiJyDFGiLiIhI+ebKBE8uGG5z8+Z99eSahc3c2WZva0+umRpurwq2wLIetYiInMMUaIuIiEj543WB4whkHwRHUl4QjTl7nc9iA2teL2u/YDPAVgEzEREpBxRoi4iISPng9YA7HXIPQ/ZeMxXcYoWACAXRIiJSoSjQFhERkbPLMPJSwF3gdZiz1a7MvOJlGWZquH+o2cc6v+2WiIhIBaJAW0RERM4Md5aZ/u11gccB3txj66q9brNntdeZ17vaciz92xpQ1iMXERE5LQq0RUREpHQZBuQmQtpmcB0FrP9YT523ptrqD5ZAsEaCVW9HRESkctF/NhERESk5Z6q5ltqVZlb/tgYABmTvM4Pp4DpaWy0iIuccBdoiIiJSfF4PZCZA5ra8iuA2M8g2PIABAZFmKriIiMg5yFqcg8eNG0fbtm0JCwsjNjaWvn37smXLluOOW7ZsGZdddhkhISGEh4fTqVMncnJyfNenpKQwcOBAwsPDiYyMZMiQIWRmZp7+oxEREZEzx/CCOwccKXD0L0hdD7YgCKkDwTUhMAaC4iCouoJsERE5pxVrRnvBggUMHz6ctm3b4na7eeKJJ+jevTsbN24kJCQEMIPsnj17MmrUKN566y38/PxYt24dVuuxmH7gwIEcPHiQuXPn4nK5uO222xg2bBhTp04t3UcnIiIiJePOAU/+lguudDM93OMwK4UbXgiOU+EyERGRQlgMwzBKeuPk5GRiY2NZsGABnTp1AuA///kPV1xxBc8991yht9m0aRNNmzZl1apVXHTRRQD89NNP9OrVi3379lGjRo1T3m96ejoRERGkpaURHh5e0uGLiIjIPxlec811zgHIOWgG2YYHsJoFy2yBZmBts6vtloiIlB53FrhzoVpn839MOVWcOLRYqeP/lpaWBkBUVBQASUlJrFixgtjYWDp06EC1atXo3Lkzixcv9t1m2bJlREZG+oJsgG7dumG1WlmxYsXpDEdERESKwusx+1bnJkPWXkjfBkf+gKSFcHgZZOwwA+qgODMtPKSW+X3+umsF2SIiIidV4mJoXq+XkSNH0rFjR5o1awbAzp07ARgzZgyvvfYarVq14rPPPuPyyy9nw4YNNGrUiMTERGJjYwsOws+PqKgoEhMTC70vh8OBw+HwXU5PTy/psEVERM49Xg+4M8z0b0eK2XLLkwseJ2AAFrD5g9UOAVXK9WyCiIhIRVDiQHv48OFs2LChwGy11+sF4M477+S2224D4MILL2TevHl88sknjBs3rkT3NW7cOMaOHVvSoYqIiJxbDMNMw3Olg/MoOJLMy15XXup3IPiHg92u1lsiIiJnQIkC7REjRjBnzhwWLlxIrVq1fPurV68OQNOmTQsc36RJE/bs2QNAXFwcSUlJBa53u92kpKQQFxdX6P2NGjWKBx980Hc5PT2d2rVrl2ToIiIilZPXBc40M7DOPWTOYHscZiDtFwr2aLOvtYiIiJxxxQq0DcPg3nvvZdasWcyfP5969eoVuD4+Pp4aNWoc1/Jr69atXHnllQC0b9+e1NRU/vjjD9q0aQPAb7/9htfr5eKLLy70fu12O3a70thERESAgjPW7iyzgJk7HdyZ5nV+QeaMdWBgWY9URETknFSsQHv48OFMnTqVb775hrCwMN+a6oiICIKCgrBYLDzyyCOMHj2ali1b0qpVKz799FM2b97MjBkzAHN2u2fPntxxxx289957uFwuRowYwQ033FCkiuMiIiLnLMOA3CTI3JnXaisHsIItwFxfHRhnVgcXERGRMlWs/8YTJ04EoEuXLgX2T5o0icGDBwMwcuRIcnNzeeCBB0hJSaFly5bMnTuXBg0a+I6fMmUKI0aM4PLLL8dqtdKvXz/efPPN03skIiIiFZnhNdO/j5O3htrwQNYuyNxh7vOPMNPBtcZaRESk3DmtPtplRX20RUSkUvC6zfZazhRzTXWhgfY/uDIgMBr8Qs7O+ERERM6GSthHW/llIiIiZcGTC6mbIHuX+abCaj/Bm4v8z8MtEBChHtYiIiIVgAJtERGRs8kwwHEEMrZBTiIEx5ktt0RERKTSUKAtIiJyNng94DgM2XvM9luGASE1NUMtIiJSCSnQFhEROVO8bvA6zfZbWbvNiuFWGwREles1aCIiInJ6FGiLiIiUlNcF7mzwZJvfe13m9+5scw221w2G2/ze6gdB1cDqX9ajFhERkTNMgbaIiEhxeT1mm63svWYQ7XHmtdmymDPWFj8zsLb6gcUO9ipKERcRETmHKNAWEREpDo8D0jdBxk6zCrg9SsXMREREpAAF2iIiIidiGGB4zM2TDa50yD4AOQchKE7rrEVERKRQCrRFROTcY3jBnQnuLLNYmeE5tsba6zL3eV3m+mrDYx7vdZj7bHYIqaVUcBERETkhBdoiInLuMAzITTR7WLszzbXV+SwWM3i2WIG8rxaruc/qD/5hKmQmIiIiRaJAW0REKi/DMGensZiXsxIgfatZpMw/AgIDy3R4IiIiUjkp0BYRkcrD8IIrw1xL7UoDxxGzeJnFas5Yu9LAXhX8Qst6pCIiIlKJKdAWEZGKzZ1XpMyZCrmHjqWEW21gCwK/QMAwZ7eDapqz2SIiIiJnkN5tiIhIxeNKN2erc5PMANuTA1jALxgCqqgauIiIiJQpBdoiIlJxOI9C1j7I2Q/uHHO22i/ETAe3WMp6dCIiIiKAAm0REakInGmQtRty9plp4fYqEBhT1qMSERERKZQCbRERKX+8rmNFzXKTwJlX1MxeFQKDy3p0IiIiIielQFtERMqe4TWLmLnSwZECjmSzyJnXDX528AuDwNiyHqWIiIhIkSjQFhGRs88wwJNfLTzNrBbuycprxWUz228FxqpCuIiIiFRIegcjIiJnh+E1K4Q7UsCRZAbZnlyziJktGPwjIVDVwkVERKTiU6AtIiJnhtcN7gwzBdydDbmJZqBteMAvyJy1tkerWriIiIhUOgq0RUSkdDmOQM4hcBwCd5ZZ2Ayr2YorMAas/mU9QhEREZEzSoG2iIiUDsMLmbsgY6u51to/b8ZagbWIiIicYxRoi4jIyXly84qWpYPXCRZrXrp33pb/vSvd7HXtH64e1yIiInJOU6AtIiKFM7yQvRfSt5kp4BhmkG1e+Y/j8r63WMxK4TYVNBMREZFzmwJtERE5nscJ6Vshczv4h0FwjX8E2SIiIiJyMgq0RUTkGMMLuUmQuRNyEiGoGtgCy3pUIiIiIhWKAm0RETEDbMdhc4119gGw+kFwTfOriIiIiBSL3kGJiJxrvB6zqFn+5smFnP3mTDYWCIoFa0BZj1JERESkwlKgLSJSWXk94M7M27LNgmbuTPA6wOsGw2V+BbMFV2CMAmwRERGRUqBAW0SkMnKmQtpGcB41Z62xmmngVn9z8wvJ+17/BkRERERKm95hiYhUNjmHIHUDeDLNdluapRYRERE5qxRoi4hUBl63OXudmwzZuwALBNcq61GJiIiInJMUaIuIVBReT966aqfZ59rrNNdbO1LAlQauDPO4gAiz97WIiIiIlAkF2iIi5ZUnF7L2mUXMPFnmZa8LDHdeETPDPM7qD7YgCIrTmmsRERGRckDvyEREyqPcw5C+GXIPgS3QDKAt/uAXbAbWFj+wWMp6lCIiIiJSCAXaIiJlyesCV/qx9lteh7nPkQyGB0LqgMVa1qMUERERkWJQoC0iUlbcWWZ18NxEMAxzhtriZwbWtmDwDy3rEYqIiIhICSjQFhEpC840SF0PjsMQVF1rq0VEREQqEb2zExE5kwyvWcTMk2N+dWebqeLOFPBkQ3BNpYaLiIiIVDIKtEVESpvHATkHwXEE3Ol5rbhyzfRwLGDzB2sgBNVUQTMRERGRSkiBtohIafHkQm4SZO40e1v7BYLVDn4hYKsCFltZj1BEREREzoJi5SuOGzeOtm3bEhYWRmxsLH379mXLli2FHmsYBldeeSUWi4XZs2cXuG7Pnj307t2b4OBgYmNjeeSRR3C73SV+ECIiZcIwwJVpzl4fXQdJiyDlD7NyeEhtCKwGAZHgF6QgW0REROQcUqwZ7QULFjB8+HDatm2L2+3miSeeoHv37mzcuJGQkJACx77xxhtYCkmJ9Hg89O7dm7i4OJYuXcrBgwe59dZb8ff358UXXzy9RyMicrbkJkH6trzUcIe5zto/HIJrKx1cRERE5BxnMQzDKOmNk5OTiY2NZcGCBXTq1Mm3f+3atVx11VWsXr2a6tWrM2vWLPr27QvAjz/+yFVXXcWBAweoVq0aAO+99x6PPfYYycnJBAQEnPJ+09PTiYiIIC0tjfDw8JIOX0SkZLL3m225DLc5Y221K7gWERERKSl3FrhzoVpnsNnLejQnVJw49LRK3aalpQEQFRXl25ednc1NN93EO++8Q1xc3HG3WbZsGc2bN/cF2QA9evQgPT2dv//+u9D7cTgcpKenF9hERM4owwCvy/zD70yF3GTIPmDOYh9dawbWQXFgC1SQLSIiInIa0jNs7D9YfgPskihxMTSv18vIkSPp2LEjzZo18+1/4IEH6NChA1dffXWht0tMTCwQZAO+y4mJiYXeZty4cYwdO7akQxUROTWvG1xp4DgKziNmYTOvy5y1Ntzm9fn8wyEgouzGKiIiIlJJ/L05kGtvbUJYiIvFSyEw5NS3qQhKHGgPHz6cDRs2sHjxYt++b7/9lt9++401a9aUyuDyjRo1igcffNB3OT09ndq1a5fqfYjIOcjjBEeyWSHckQzuTDA8YAsCq3/eFgQWP7CqSYOIiIhIaZo+qwpD7q9LVpaN2jVy2LvXQqPGZT2q0lGi1PERI0YwZ84cfv/9d2rVquXb/9tvv7Fjxw4iIyPx8/PDz898Y9qvXz+6dOkCQFxcHIcOHSpwvvzLhaWaA9jtdsLDwwtsIiKnxZUBR/+EIyshKwEwzCrhIXUgMMZce+0faqaGK8gWERERKTUuFzz4VC1uGFqfrCwbl196lD9+Wk6jRiUuH1buFOvdo2EY3HvvvcyaNYv58+dTr169Atc//vjjDB06tMC+5s2bM378ePr06QNA+/bteeGFF0hKSiI2NhaAuXPnEh4eTtOmTU/nsYiIFOT15KV+53/1mvs9uZC+2Vx7HVRDgbSIiIjIWZJ4yI/+Q+qzaFkYAKNGHuS5R7dhM1xlPLLSVax3l8OHD2fq1Kl88803hIWF+dZUR0REEBQURFxcXKGz0nXq1PEF5d27d6dp06bccsstvPLKKyQmJvLUU08xfPhw7PbKtQBeRM4C51EzYPa6zP7VHid487b84Nrw5G15gTZeMx08uJYKmYmIiIicJUtWhHD9bfU5eCiAsFAPn76zi2uuSgU35laJFCvQnjhxIoAvDTzfpEmTGDx4cJHOYbPZmDNnDnfffTft27cnJCSEQYMG8eyzzxZnKCIikHMQjq4311ZbbWDJ36xmIG2xmuus8/eTF1RbLHmXRURERORMMwx4+8MYHny6Nm63hQsa5/D1pzs4r6GjrId2xhQ7dby4CrtN3bp1+eGHH4p9LhERwJyZzt5r9rK2+EFo3bIekYiIiIicwKNjavLa22bm84BrUvjojd2EhnpPcauKTQsTRaTiMAyzOnjmLsg5AP5hZtEyERERESmXPpsW5Quy/+/5vYy8K+mcWLlXoqrjIiJnnTMVUtbC4eWQewiCqinIFhERkRNa+Ucw/QbV57YRdUk8pPnFsrB6TTDDHjQzD5955AAP3H1uBNmgGW0RKW+8brOwmeEyC5sZLnClm7PYnlyz9ZZNhRNFRESkcGvXB/HMSzX47qdI375vfozk9Wf3MfimI+dMoFfWDiX5cc2tDXA4rPTpmcroRw+W9ZDOKgXaIlJ2DK85U+1IAVcqeLLzqoe7zYrhXjeQV+chIBICo8turCIiIlKu/b05kNEv1WDmd1UAsFoNBl6Xwt9bAvlzXQi33xePzWZw6w0pZTzSys/lgutvr8++AwGc3zCX/01MwHqO5VIr0BaRspF7GDK2gjPFDK5tgWZhM6sf+IWYXy1+ar8lIiIiJ7Vlm52xr9Zg2tdVMAwLFovBDdceZfQjBzi/kQO3G+5+uA4f/S+G7+dGKNA+QxwO8z2b3W7wwJO1WbQsjLBQD7P/t52I8Mpd+KwwCrRF5OwyDMjZD6l/m2nh9qpgDSjrUYmI4PXCbwvD8Hgt9LgsvayHIyJF8L/pUQweEY/XawZ5/focZcxjB2jWJNd3jJ8fXH/1UT76Xwxr1weX1VArtR0JAbTu2pSMTCs14lzsP2i+t/v8vQQan1d5W3idjAJtETl7XOmQfcCcybYFQWD1sh6RiAgeD3z0v2jGT6zGlu2BWCwGG5f+fc6+ORSpKAwDnn2tOl6vhR6XpTHu6f1c2CKn0GNbXmDu37bTTmamtdK3ljrbxk+sRnqGDcAXZI957AD/vTKtLIdVphRoi8iZ4/WAJwtcGWZbrpxE8OSAPQr8Qst6dCIiADz+7LH+rgCGYeHPv4IVaIuUc2v+CmL7zkCCgrzMmLTzpMFztVg31as5OXgogPWbgmjfNussjrRyO5pqY9IXVQH4atIOalZ34XBY6Nwxs4xHVrYUaItIyRmGGTh7neBxmF+9TnBngTvTrBLuzTWvs9ggoIpZNVxEpJz47qcIX5A97ul9bNgUxJQZVdm4JQg4WraDExFSjtr478CGdO6QwQtPHShw3ZezowDofUVakWaoL2yRw8G5Aaz5K7hEgXZaupXfFoYTHOzV8pJ/+Oh/0WRn22jeNJt+fVJVXiePAm0RKRmvGzK2Q9ZuM7g2PMeus/qB1R8s/uAfCYFqxyUi5c+eff4MGh4PwMi7DvH4yENMeD+WKTOqsmlrYNkOTkQA+GRKNEtWhLJ0ZQiDbjjCeQ3NTBPDgOmzzeriA64pWnGzVs2y+WFuBGs3BBVrDMtXhTDq+ZosWhaKx2NGkesWbqTFBYWnqZ9L3G5468NYAEbede70yC6Kc6zIuoicNsMAdw4cXQ/pG81q4YGxEFL72BZUHezREBChntcictb9b3oUn02LOukxLhcMGFKfo6l+tL0wi5dH7wegyXnmG+eNWxRoy7nFMGDc+Dg+/Kz8tNI0DJicl5JsGBZee6ea77rVa4LZtcdOSIiHXt2Ktg64VfNsgCIXRHM6LTz1Qg069jqf+YvD8Hgs2O3mzPkPc8OL81Aqra/nVGHv/gBiY1zc1E/V3P9JgbaInJzXBTmHzMD60AJImg/JSyFrlxlQ+4eaM9giIuXAoSQ/Bg2PZ/CIeJIPn/hv0xPP12T56lAiwt1M/3gnAQEGAE3PMysVb08IxOnU1IycO2Z+F8kTz9fk7ofrkJNTPl77q9cE8/fmIGw28/fz02lVOZho/l7nz2b36ZFGcLBRpPNd2Nz8IG39piDc7pMf+/fmQP7TozEv/J9ZbO3m64+w88/1vDpmHwBz5yvQ3rPPn0dG1wTg7tuSCQws2s/hXKFAW0ROzJECh5ebW9ZOsx2X4QWrFUJqqi2XiJQ7y1aFYBgWDMPC+o2Fp4fO+fnYuuxJb+2mXl2n77qaNVyEhXpwuy1s36mMHDk3OJ0WHhtbCwCPx8LO3eXjtT9pqjmbPeCaFDpenInTaWXC+9VITbP51mf3v7rotRTqxzsIDfGQm2tly/bCs1a8Xnj9nVjaXNaENX8FUzXKzVeTdvC/93ZRr66T7l3NtdmLV4SSnV0+PpAoCwcO+nP5NeexZ5+dRg1yuW9YUlkPqdxRoC0ihcs+ACl/gDMNgqtDcC2zmFlAJPiHm8XNRETKmWWrjnU0+KuQQPuf67Lvv/MQ11yVWuB6iwWa5M1qb9Q6bTlHvPtJDDt3HQuuy8OHTLm5Fr742gymb7vxCI/dlwjAhA9iqd60BXv3BxAW6qHn5UVvH2W1Qstm5qx2Yenju/YEcFnf83j4mdo4HFZ6d09l/aK/ue6/qb5jzmvooHZNJ06nlUXLw07jEVZcyYf96HZtI7bvDCS+joN5s7YSVcVz6hueYxRoi0jeuutsyE2GzF1w5E84ugbwmkG2gmoRqSCWrgrxff/X3wUDbZcLbhhan5SjflzUKotXxuwv9Bz567RVEE3OBUdTbTz7anUAIiPMfOptO8v+tf/Nj5GkpvlRu6aTrpdm0Lt7Ghc0ziE310purpVmTXL4atJOgoKKl658Yf467X8VRPtsWhQtLm3KgiVhhIR4+GD8br6buoPqcQVzzC0WuKKLOas9d/65F2inHLVxRb9GbNoaRM3qTn6bvZXaNV1lPaxySQsrRc5FhgGuVHBlgisdnEfMAmeeXMAwK4b7h4FfyKnOJCJSbjidFlavPfZ369+p40++UJNlq45fl/1vTc/Pm9HeUrzKxCIV0Qv/F8fRVD+aNcmhT49Uxr1Rne0JZTuj7XRaePUts/DZoBuOYMv7vP/Lj3fy1bdV6NUtjYsuzC5Rhev8gmhr/jJntL1eePL5Grw0wfyw4ZL/ZPDpO7uoH+884Tmu6JLOJ1Oi89ZpF/6BXWWUlm6l5/WNWLchmGqxLn6bvbXA0hspSIG2yLnEMMB5FDJ3Q+4Bs9CZxQq2IDOotkeZl0VEKqB1G4LIzbXi52fgdlvYsDkIjwdsNvj+l3Befctcl/3Jm7tP+iY6P3VcM9pS2SXsDvC1Znp17D6Sks3QoKxTxx96uhZ/rAshMsLNXYOTffubNs5ldOODp3Xu/Bnt5X+E8OBTtdi9N4Cv55iF1Z555ADPPHLQF9ifyOWdMgD46+9gDiX5US32FJXVKoGsLCu9b2jEqjUhVI1y8+vXW32t1qRwekctci4wvGZa+NF1cHgZ5Owx11qH1IbgmmaA7RekIFtEKrSleeuzu3VOJyjIS26ulR0JdlwuGHJ/PAD33pHEtX1ST3qepuebqeObtwXi0bJDqcSeeL4mTqeVK7qk0+OydBrWNwOnbWUYaE/7ugpvf2QG/5+/l0DNGqWblnxB41yqRrnJyrIxfmI1vp5TBT8/g8lvJzD28VMH2QAx0W4ubGEG7L8uqPzVx3NyLPx3YAOWrDAzgubO3EqzJrllPaxyTzPaIpWV4c1LC0+F3ETIzasGGZAXVIuIVDLL8tZnd2yXyZEUP1atCeGvjUEk7LFzKMmf2BgXr47dd8rzxNdxYrd7cTis7NoTQIN6So2sCFKO2jh4yJ8LGisAKIoVq4OZ9nUUFovBq2P3YbFAw3pmoL13fwC5uZaz3q5p4+ZAho6sC8CTDx6kd/f0Ur8Pu91g3YKN/LogjD//CiYxyZ+7b0umyyWZxTrPFZ3TWfNXMHPnhzHw+srbP9rptNBvcAN+WxROaIiHn77czoUtcsp6WBWCpq9EKiOPE1L/guQlZlEz51EIjDFnrxVki0gllR9od2iXRYsL8vrlbgxi5neRAFzTOxW7/dSBg80GjRtpnXZF0/uGhjTreAFPPl8Dr7esR1O+GQY8PNps5zX4xiO+Stwx0W7CQj0YhoWEUmzxlZtrYfRL1fnqm8gT/mwyMqz0G1yfrCwbl3dKZ+zjB0rt/v+tZg0Xg25MYcK4fUz/OKHYQTbAZXnp45W98vhTL9bgx18jCAry8v207fynbVZZD6nCUKAtUtl4HJC6HjJ25qWH14HAWPW8FpFKbf8Bf/bss2O1GrRrnUXzpmbgsGZ9MLN/iASg31VF77erddoVy559/ixfbS4deHF8dfre0oD0dL3NPZHZ30eyeHkYQUFenht1LKC1WKBRA/O1X5oF0T74NJpnX61B/9sb0LprE36aVzDd2jDgjgfqsnmbWcl66gcJRUrhLkv/uSgTi8Vg5y47iYcqT5LwjgQzmwFg3oIwX22LKe8l0KlD8T+QOJfpL5BIRWZ4zWrhzqOQk2i25jq6FrJ2Q3ANsOkNooicG5atNmezmzfNITTUS4um5vrJH3+NIPmwP1Ui3XS5JKPI58tfp/33Zs1oVwQ//hoBQM3qZtr/dz9F0m9wA4yzm/lc6l6eUI1OV53HitXH93wuKafTwqNjawLw8PDE49ZA56ePl+Y67XkLjwXW6zYEc2X/Rnz/y7F9b38Yw/RZUfj5GXz5yU5iY8p/cbGIcK/vA72lK0PLeDSnLzvbwpD76tLwouY0aNOM/3s3llvviQfgzsHJXHNVapmOryJSoC1SUeSvuc7aC2mb4fAKSFoASQshaTEkLzODbEeymSJu9S/rEYuInBUHE/14/nWzNU+HtuaMS/4bYLfbnJm5+spU/IvxZ7FZ3jrfdX8r0K4Ivp9rBtp335bMwu+2EBjo5dcF4UydEVXGIzs9E96PZdGyMDr2asyYl6vjKoW6YO9Pjmb7zkCqxbp4ZMSh467PD7RLq/K4xwMLlpqB6I9fbuOm644AcNdDdUlPt7JsVQgPPWOmsb/27D46tKs4qckd8tKol5SzQPtIiq3IHzJ5veaym4u7N+GTKdEAHEgM4KGna3MgMYDzG+by+rOnrm0hx1OgLVLeeXLNGerkpZC0CI6showt5iy24TXbcgXGQmgds4p4UHWwVp4UJhGRk9myzU6HKxuzbkMwsTEuRt5lFn6MruqherVjRcz6naLS+L9ddKH5BnrDpiByckrQrFfOGMOAT7+I4tlXzcAzN9fCvIXmOtne3dNo1yabZx42W0A9+HQtjqaW8xzkE3A4LBw8ZC778ngsjH2lBm27NWHJipBT3PLEUtNsjH21BgDPPn6AsLDjF0w3yqs8vj2hdLLi1vwVTFq6H+FhHrp1TufD8bupH+9g34EA7nmkDv1vr4/LZaV/3xTuG5ZUKvd5tnS82Pxg73R+JqXts2lRRDdqxfuTo0963P4D/tx8Zzyx57ekQ8/GbNgURLVYFz9+uY2Jr+2mdk0n4WEepn6wk5AQFT0oCQXaIuWV1wMZOyBpCRz5A9xZEFDFDKiDa5nFzQIizeJmCqxF5ByTmmbj6RdrcNHlTdi1x07D+rks+2lzgb6u+bPaYaEeruhSvOrFtWq4iI1x4fFYWLuh9NJ25fR4PHDf47UZPKIeo1+qwRvvVWP+kjCys23UrO70FfV6aPghmpyXQ1KyP6Oeq1nGoy6ZfQfMFIzAQC9TP9hJlUg36zYEc0mvxgy6J55DSUX/3790ZQgjn6jFRZc35kiKH03Pz+H2gYcLPbZhfTObo7RSx39fbH4I0rljBn5+EBxs8OH43QBMmVGVfQfMWdOP3tiNpYJ9ppUfaP/5V3CpfCCXlOx32rUF3v80BoCP/nfiQPtQkh+X9T2PKTOqciTFj7BQDzf2S2Ht/I30vDydu247TMKa9RzcuI7WLVVhvKQUaIuUR+4cOPqXuVnIK2gWDbay62spIlIeZGRYef61OOJbNeP516uTmWWj48WZLP1xC/XjC7bhap3X57ZPz6JVG/8niwXa5s1qr16jQLusGIYZFKz6M5iZ30Zy7a0NfD2WAUa/XIOJn5iBRa8r0nyBWkCAwXuv7wHg/ckxvor0FcmefeZsdp2aTm7sd5StKzcw9JZkLBaDz6ZX5bx2zXjz/Rjc/1jOvH5jIK27NmHYA3V8+6fPqkLHKxsz4f1q7EgIJDjYwzuv7MHvBHF6fur4nn0BOBynHzzmB9pd/1Ej4bJOGQy52Qz0g4M9zPx0R6Gz6+VdfB0n1as5cbmsrFpzeq+xAwf9adS2GTWbteC51+LIzCx+mHYoyc/3Wv9jXQgHDh6/XiblqI0r+jVi645A6tRysOC7LRzZvpapHyQQV+3Yi8lmMz8UkZLTNJhIWTK84HWD4QKvy0wT9+RA9n6z73VwdVULFxEBsrKsvPNxDK+8FceRFPPtywWNc3j28QNcc1VqoTNhD484hJ8fDB9SsnTUi1pl8/0vkaxeGwIkn8bopahmzYnk258i2LMvgD37A9i7PwCHo2DAYbd7+ezdXbz7SQwLloTx7U+RAPTqllbguE4dMhl842EmfxHNXQ/VYfW8TcVap1/WfIF2LfMDpOiqHj58Yw933HKY4Y/WYfXaEO5/og4fT4nm7Zf34udn0GtAQ1LT/FjzVzBZ2VbuHZrM4BHxgFmn4NYBR7i8czoR4ScOaqvFugkN8ZCZZWPXngCOpvrh72/QplW275ilK0NwOCx0vfTkVahdLli4zFy/3PVfxQj/77m9hIV66H1FWoXtfW6xQMeLs5jxbQBLVoSeVlXuBUtDSc8wlzk8M64m73wcy5cf7yzWOb/7OQLDOPbH8Pu5Edxx67HMhfR0Kz2vb8T6jcFUr+Zk3qxtNKzvKOxUUgoUaIucLe4sM3h254I3xwyqvY68QNsNhsf8HsAWYK63tijpRETObU6nhXc/iWHcG3EkJZtR0nkNchnz2AH69z160hZAVaM8PPdEyXvx5s9or9KMdqmZtyCMSVOrEh7moXZNF10vyfD15d1/wJ/+Q+r7Ctjls1gMasS5qFPLSXwdJ/fdkcR/2mbRomk2LTs3xem0EhDgpVvn46vKvzp2H9/+FMlffwfz5gexPDS84qwB3rPfDLTr1i6YqdGuTTbLf9nMx59HM+q5mvz1dzCdrjqfgAAvTqeVZk1y2LwtkKkzqvLl7CjcbgtX9Uhl5qc7itQyy2KBhvUdrF0fzPW312f9xmBsNoPvp22nx2Xp/Do/jJ79G+H1wsq5m7nowuwTnmv12hCysmxEVXH7etvnCw/3Mv6Fil9kq0O7TGZ8W4UlK09vRtv8QA8u+U8GBw/5syMhkB7XN2LWZzvoeXnRlr5882MkANViXRxK8ue7n48F2llZVnrf2JBVa0KIruri168VZJ9pCrRFzgZXJhxdB7mJZjVwqx9Y/MBiM4NqS7B5WWutRUQKePCpWrzzsZkqXD/ewTMPH2Dg9SknTHstTW1amgHE5m2BZGRYK2Rq69myd78/X8yMItDuJTzMS0S4h/CwY5ufn8EL/1edSVMLrhv19/eyZcXf1Kvr5IPPonG7LbRsls1D9xyiTi0ndWs7qRHnIiDg+BTWxuc5GDUykbGv1OCKLumEhh7/84mu6uHVsfsYcl88o1+uwfVXH6VOrVIo3X0W/HtG+59sNhg26DD9+hzliedr8uFn0TidVrp3TePrT3fy/dwIbryjHm63hWZNcpj6fvH6UjesZwba6zeaHzJ5PBauG1yfjybs5s4H6+DxmB+GPPBULRbO2XrCtdW/LzLTxrt0zMBaSecOOrYzZ5yXrgzF66XEj3P1WvO5HnrzYQZcc5T+Q+rz3U+R/HdgA6Z+kMB1/0096e0zM63MnW+2THv92X3cfFc9fl0QTk6OBYsF+t7SgMXLw4gId/PLjG00raBZBBWJ3tWLnGnuLEj9y2y7FVJHs9QiIkWUmmbjk7zAbPwLexk+JOmspv7GVXNTq4aTfQcC+POvYDp3LHlaaGWWk2Phyv6NitRz3GIxGHrLYarFuJn1fSR/bw7iudeq8/7/7eaDz8y11qNGJjLgmqNFuu+nHz5I8yY5dGh34p/N4BuPMGlqVRYvD+O+x+sw+/MdRXtgZeyfa7RPpGqUh/f/bw93Dkrmz7+CuaV/Cna7Qf++Rwnw9/L1nCo8/+T+Yn9I1KVjBjO+rcJll6bz2rP7eOjpWvy+OJwbhtYHoF3rLNZvCmLx8jC+/i6SficIAn/LC7Qvu7ToPewrmgtbZBMU5OVoqh+btgaWKA3e64U/15mB9kUXZhMYaDBz8g5uvace076OYsCQ+nw8YTeDbzpywnP88ns4DoeV+vEObrouhVHP1WTv/gB+/i2cT6ZG8+uCcEJCPPz45XYubKECZ2eD3vGLnCmGATmJkPJn3nrrmgqyRUSK4fMvo8jJMVNh77/z7AbZ+XwF0dYqffxEHnq6Fn9vDiI2xsWAa1LoeXkaHdpl0qxJDnVqOYgId2OxGLRsls3iH7bwwfg9PPfEAT6esAuAT6dV5eUJcSQe8ieumotreqcW+b5tNuj331Sqx7lPeIzVCu+9vgc/P4Nvfozkmx8iTvMRnx0nm9H+t9Ytcxh6y5ECRf/69k7js4m7SjSDP3xoMgf+Xsevs7ZxYYscZn66kybnmcFZg3q5fD9tG4+MSATg0bG1Ci2a5nRaWLrKXJ/d5ZLKG2j7+8Ol/zEf33c/ley1tXW7ncwsG8HBHho3yvWd9/P3Ehh6SzJer4Xb7o3nrQ9iTniO2T9EAtC3l1mz4qruqQDccnc9vvspksBAL3Ombqd924rTp7yi07t+kTPBlQGp6+DISnBnKMgWESkmwzjWpmbYrcll1vbnorwCUMWpKHw01cbt99bls2lRZ2pY5cbs7yOYOMlM7f98YgLTPkrgxy+3s+THLaxfvJHd6zaQmrAOT/KfrF2wiQ7tjr3Jv/iibPr0TMXrtfD0OLMF17BbkwtNEz9dFzTO5eHhZmB47+N1SlTR+WwyDNi9z+w0UpRA+0yoHuf2/d5VifTw69fbeP6J/fw2eyvRVT08MuIQ1as52bnLzpsfxB53+zV/BZGTY6VqlJum51fuNOVrr0oF4Os5VUp0+/z12Rc2zymQ4m+zwQfj9/DA3YcAuG9UHV78vziMf/2KbN9p59u8IL9vL3MsfXqaxQEzs2z4+3uZ9dkOulyirJyzqXz/lRGpaLweyNwFh5ebX+1VIbCagmwRkWJatiqEDZuCCArycsuAlDIbR3FntLOzLfS5qSGTpkbz+LMVs39zUR1NtTHk/ngAHr03kSu6nnjW8kQflDz7+LFidTabwbBbC+/tXBqefvgg8XUc7N0fwNhXq5+x+ykNR1Js5OSY7x1q1SibQPvfalR38eRDib4Z8tBQLy8+Zf78nn+9OknJBVekLl5hzmZ3bJdZ4fpjF5c5i2ywak0Ie/YVP/Um/+/LRa2On222WOD15/Yx5jHzuX7yhZo8PramL9hetyGIS3qfT1q6Hxc0PraMouslGVSNcmOzGUz/KKHIBdWk9GiNtkhxGIZZIdzrNDeP89j37kxzJjs3GQLCzarhIiJSIu9PNmezB/RNITLCU2bjyG9ptCMhkA49z2f33gDCQr3Ure2gbm0ndfMKdtWt7aR2TScjHqvNkrwA4+ChgEpdRG3W95GkHPWjcaOcEld3b9U8h+v+e5QZ31ahb69UatY4c4XKgoMN3nllD71vaMT4idW4pX/KcZWwy4v8tPFqsS4CA8tvL+NbbzjCWx/F8Oe6EMa8XJ13X9vru27RMnN99qXtK/8sarVYN5f8J5NFy8KY/X0k991ZvHaA+TPaF7UqvIK7xQKjHz1IWKiHh56uzStvxbFgaSiRER6WrTLbgrVsls3PX23zzYgHBhos/3kzuQ4LzZpU7oyC8kqBtsjJeN3gTAFHihlEe7LNftf5fa+N/DdPFrNiuNUPgmuoeriIyGk4mmrjy2/MFMw7B525Gc6iiKriocl5OWzaGsSyvPWmAFu2B57wNkFBXqxWg6wsG9t22mndsnwGc6fr6zmRAAy8LuW00r0nvrab5k1zGHbrme9V3uuKdPr1OcrM76pw54N1WPLjlnJZDTs/0K5bRmnjRWW1wvjn99G5z/m8/2kMw4cmc0HjXAzj2Iz2JRdX/kAbzPTxRcvC+Pr7KsUKtD0eWLPeLCRY2Iz2Pz14TxJhoV7ufLAOK/449vfo0vYZfDtlx3EfSqp9V9lSNCByIll7IXMHOM01LtgC8lpz+YM1yPxqKUavDBERKZLPplclN9dKiwuyufiisi/cM/3jncydH262m6rlJCPTxu69AezeF2B+3RvA7n129u73x2aDrz7ZwUsT4li8PIytOwIrZaCdnn6slVD++tSSiq7q4ZlHDpbCqIrmjRf28vNv4SxfHcpH/4tmWBl/mFOY4hRCK2udOmRy7VVH+XpOFR56uhY/fbWdzVsDOZLiR1CQl9YtT9xnuzK5pncqDzxZm0XLQklK9iM25sQF+nJzLTz/enXOa5BL65bZZGfbCA3xcF7DUwfGd9x6mHats1i3wQzOw0K99LoirUAhPCkfFGiLFCZrr1nMzOoPQXGaoRYROUsMA96fbLb0unPQ4XKxtrN501yaNz116qXHA263Bbvd4Os5VXyBdmX0w68ROJ1WGjfKqXD9eGvVdPHcEwd44MnaPDa2Jn17pZ40KCoLe/ZXnEAb4OXR+/nu5wh+/i2CH38NZ98Bc/wXt8k6I8XtyqO6tZ1c1CqL1WtDmPV9JHcOPvEHOM+8VINX34oD4LwG5u9P65bZRc6uaNksh5bNKt8HeJVNOUyWESlj2fvNvte2QLBHK8gWETmLFi8PZdPWIIKDPQy8/sQ9Y8sjmw3frFL+m+et2+1lOaQzZuZ3Zmr/6c5ml5URQ5O4sEU2qWl+PDK6Vqmd95MpVblucH0uvqIxDS+6gAFD6jFpSlUOHyleBlxFmtEGM0X5vmFJgNnubf6ScyttPF/+78M9j9Shc5/zGD8xlp27Agocs3xVCK+/Uw0wCwDmfxh3ovXZUnEp0BYBs1p4ziE4sgaOrjVnsgNK1qJBRERKLn82+8ZrjxIRXnGLiJ3XwEwBrYwz2jk5Fn74tXTSxsuKnx+899puAP73ZRQbN5s/p7R0K71vaMjjY4tfMX77TjtD7otn5ndVWPlnCDsSAvlydhS33xdPy05NOZJS9GDbF2jXrBiBNsBTDyUSXdXFpq1BfDHTbG13afvK2z+7MLfdZKZ1e70WFi4N48GnatOgTXNaXNqEZ8ZVZ/mqEAaPiMfrtXDz9UdY9P0WX1X5rpW41/i5SoG2nNs8TsjeB4eXmS25cvaBf7jZlktERM6qIyk2ZnyXXwTtzBfGOpPOa2jOaG/ZHnhcz9vicrmgzWWNad21Ce5ykOH8y+/hZGfbqFPLUaHX37Zrk801vY9iGBaee91s9/XEczX5YW4EL78Zxw9zw4t1vslfmO8d2rXO4utPdzB35laeeeQAtWo4OZAYwNsfHd9r+kTKuod2SURGeBj7mLnW3jAsWK0G/2lT9jUWzqa4am5WzN1Mwpr1THhxD10vScdmM1i/MZjnXqtB+56N2bI9kLhqLiaM20v7tln8veRvlv60md7d08p6+FLKFGhL5edxgisTHEcg+4DZ3zp1IxxeBcmLza/uTHMtdnAN8Ctar1QRESldk7+oisNh5cIW2Vx0YcUN4AAa1nNgsRikZ9iO6y9cXEtXhvLnuhDW/BXM6jUhpTTC4nM4LHz4WTQjnzTbV157VWq5WEN/OkY/agaG02dV4ZMpVZk4KcZ33YjH6pCdXbQH6PHAp9PMQPvBew5xzVWpdOuSwdjHD/L6c/sAePODWLKyTv3W2+GwkHjI7MVckQJtgGGDkmlynrl2uFXzbMIrcFbK6Yiv4+S+O5P57ZttJG1Zx2fvJnDtVUcJDvZgsxl89MYuoqqYFcLDw720b5tV4X+X5HjFCrTHjRtH27ZtCQsLIzY2lr59+7Jlyxbf9SkpKdx7772cf/75BAUFUadOHe677z7S0gp+QrNnzx569+5NcHAwsbGxPPLII7jLw0e0Url4XXD0L0haCMmLzKD6yEozNTxzOziPAAaE1ITAGK3FFhEpQ9t32hn7Sg0A7hqcXOHfdNrtBvF1zCDpdNPHv58b4fv+t0Vhp3WukjqaaqPFpU0Z9kBddu2xExPtYviQip11AGZRqWuvMme1h9wXj2FY6N83hdo1nSTstvPC/1U/4W29/4ghf1sYxr4DAURGuLn6ytQCx/Xrc5QG9XJJOerHR59Hn3JM+w6YQXZQkJfoqhXr/bGfH7z3+h5iY1zccUv5q+ZeFqKqeLhlQAozP93J4a3r2Lf+L3p3Ty/rYclZUKxAe8GCBQwfPpzly5czd+5cXC4X3bt3JyvLTAs5cOAABw4c4LXXXmPDhg1MnjyZn376iSFDhvjO4fF46N27N06nk6VLl/Lpp58yefJknnnmmdJ9ZHJu87og7W/I2G4G0P4REFQDQmqbW3BecB0QqRZdIiJlLDfXQv8h9cnItHHJfzK4fWDleIPuK4i24/QKon3/y7FAe97Csgm0v/0xgq07Aqka5Wb8C3vZ+ceGStOjN39WG6BqlJt3XtnDm+P2APDKW3G0uLQJ57W7gDotmhN7fgsi4lthr34htpg2XHxFY3buCmDSF2YAfVO/FAIDC64VsNng0XsPAfD6O9VwuU4+nn+uz66IHzh16pDJoc1/cddtleP3uDQFBRnEVatYH55IyRVrCu+nn34qcHny5MnExsbyxx9/0KlTJ5o1a8bMmTN91zdo0IAXXniBm2++GbfbjZ+fH7/88gsbN27k119/pVq1arRq1YrnnnuOxx57jDFjxhAQEPDvuxUpGq8bPDngcZhrrTN2mungtspZ8VVEpLJ48KlarPkrmOiqLqZ9mIBfJUkwOq+Bg59/g63bSz6jvWtPABu3BPkuL1kZSm6u5bhg7kz7bZG5XvnOQcmMvCvprN73mdbighxuvv4In39VlTfH7SG6qoere6Vx9ZWpfPNjJOs3nnhJ2co/Q2h3RROyss25q9tuKrxS/q0DjvDMSzXYuz+AL2ZGcesNKSc85/YE8/VS0dLGRaSg0/pXlp8SHhUVddJjwsPD8cv7r7ls2TKaN29OtWrVfMf06NGDu+++m7///psLL7zwuHM4HA4cjmOfmqanK91C/sGdA7mHIGs3eLLA4wLDqyBbRKQCWPNXEBMnmUWiPn9vFzVrnGK6rwIpjRnt/NnsS9tnsH2nnYOHAli6MpTLOmWQlOyHy2U548+ZYRxLWb/s0spZGfnjN3fz3BMHfOn+FgtM/WAni5aHYbMaBAZ6CbT/46vdS1a2lYF31uOPdea6+Qsa59DmBC2aAgMNHrjrEI8/W4uX34zj5v4pJ+yZvGiZ2Rqr7YXnViExOcd5nZCTWNajKFUlDrS9Xi8jR46kY8eONGvWrNBjDh8+zHPPPcewYcN8+xITEwsE2YDvcmJi4U/uuHHjGDt2bEmHKpVZTiKk/g2uNPAPBf9IsAdQIXOtRETOQfmB5NVXptLjssr1QXp+5fHTWaOdvz679xVprN8YxJQZVfltURgXNM6hRaemOJ0Wdv65gSqRnlIZc2G277Sz70AAAQFeOrSrnH2RAwKOranPFxxsnPI1uXDOFm6/L57ps6K4/86kk779uOu2ZF4cX52NW4KY83ME/72y8CrTC5aagXbnjpXzQw2phAwvJM6FXVPMAsT+4Wab3JC6EBIPofHmV/8IcCSbk2P/3nIOmgWJ6/Yv4wdTekocaA8fPpwNGzawePHiQq9PT0+nd+/eNG3alDFjxpT0bgAYNWoUDz74YIFz165d+7TOKZVAbhIcXYdZ0KyOgmsRkQrol/lmSvKV3Spfa5v8XtrbE+x4POZa3eLIyrL6ZpKv6pFGbLTbF2jv2GUnKdksmjVvYRjX/Te1NIdeQP4Y2rfNIijo7Kasl3fBwQbTPkrgrZf2nrJwWUS4l7tvS+blN+N4aUIcfXqmHffWZdeeAPbss+PnZ9ChrWa0pZzwus1JLVd63tc0cP7jcvJiyNh2/O2SFxW8bPEH4yQZOIYXXKngF1eqwy8rJQq0R4wYwZw5c1i4cCG1atU67vqMjAx69uxJWFgYs2bNwt/f33ddXFwcK1euLHD8oUOHfNcVxm63Y7crBVjyeF3gSIHUvwAPBFY75U1ERKT8SU+3smyVOXvXvWvlms0Gc42t3e7F4bCyZ18A9eoWb83t3PlhOBxW6tZ20PT8XEJDzDLXy1eH+J43MPtan41Au7KmjZeGmOiiFbi6/85DvPF+LMtWhbJ4eSiXti+YIbBgiflzvahVFqGh52ZrLDnLvC7Y/x2kbwZbiDmrbHjBcIPjsLk/cycYp8ia8QuB+Fsg6kJwZZi3zdplttXNSjCXeRouswhxUI282e5/bPYYsAabM+GVRLECbcMwuPfee5k1axbz58+nXr16xx2Tnp5Ojx49sNvtfPvttwQGFkyXat++PS+88AJJSUnExpprsubOnUt4eDhNmzY9jYcilZJhmAXO3JngzjJ7YTvTwJMNFqu5DltERCqk+UvCcLstNKyfW+wgtCKwWqFRfQcbNgWxYVNQkR9jdraFl9+M45W3zP9xV3U3Zz7r1nZSP97Bzl3m5MPFbTJZ8Ucov/wejmGcmcQurxd+X5wfaFe+D0POtupxbgbfeIT3J8fw0oQ4Lm2/vcD1C5aaz3XnDpUzRV+KyOOA9C3mL7XVH6wBeZs/WO3mV1vwsV96w4CUPyB7r3nZYjGXUwZUyQucPXnBs/fY93jNdO0dH0H2viIMygL+YWZauH/EP7ZwCKoONftAQMSJb+7OBmeKOUFm9S/k+ixw5xbziSrfihVoDx8+nKlTp/LNN98QFhbmW1MdERFBUFAQ6enpdO/enezsbD7//HPS09N9hctiYmKw2Wx0796dpk2bcsstt/DKK6+QmJjIU089xfDhwzVrLcc408xffsdh8xfPkwt4zeJmtiDwj1XfaxGRCu6X38208e5dKm8A1651Fhs2BTH80Tq0ar6Z2jVdZGZamb8kjP0H/Tl4yNwSD/3j+yR/3G7zDXSXSzJ45pFj7acuuzSdnbtiqB/vYM4X26nZrAW799rZtsPOeQ1Lv93W35sDST7sT3Cwh3atCy/0JcXz8PBDfPhZND/MjeCvv4NocUGO77r5S/ICba3PPjcZHtj/PWx/35wBPpmAKhBzCYQ2gP3fmrPOJRVQFWpeZQbg7kxz1tnqD36hEH6+uQXGnl5LXL9gczuHFCtSmThxIgBdunQpsH/SpEkMHjyYP//8kxUrVgDQsGHDAsckJCQQHx+PzWZjzpw53H333bRv356QkBAGDRrEs88+exoPQyqVnIOQuvFYgTO/YLBHmTPYIiJSaeSvz66MaeP5Xh69j6UrQ9i8LYju/Rpxw7VHefODWFKOnvwtWHwdB6+O3Ue/PqkFZqofvfcQGZk2HrsvkeiqHjq2y+T3xeH88ns45zVMLvXx57f1uvQ/mQQEaH12aWhY38F1/z3Kl7OjeOXNanz+/i4A9u73J2G3HavVoGMlLTon/+LOgYwtkLYR0jbB0bWQm/fBmn+EGeh6nQW3/BRu51Ez5TufLRii2pjvlw2POWnlTDEnqyzWvM0GWMyvFqs5S16tK8QPPOeC4LPBYhhGhfurmZ6eTkREhK91mFQS7mwzdSVjq1ksITC6rEckIiJnSMLuAOq3bo7NZpCyfS3h4ZV3Peqeff50vLIx+w4E+PbF13HQomkOcdVcVP/HFhfronqcixpxriIVT3vpjWqMeq4WfXqm8u2UHaU25qOpNj78LJr/m1iNQ0n+vDx6H4/ed4oZNimyP9cF0eaypthsBttWbaBeXSeffxnFLXfXo+2FWaz8dXNZD1GKy+uE3V/C7i/MImFYjwW4vuDWcmw/BuQmA//62+cfAfVvgzrXF96m1vCaqeVpGyBpIWRsh5iOUKuvOUFVUeWnjlfrXK7b8xYnDlXurZQ9d5YZYGftAXeGmb5Skf9QiIjIKc3Nm81u3zazUgfZAHVqufhlxjauHNCQiHAPj9+fSP++R4tdhbww3bumM+o5+H1RGC4X+Bey9LE4diQEMOH9anwytSpZWeYA69Z2cEv/I6c/WPFp3TKHK7qkM3d+OK+/U423X9mrtl5niycX9s+B7P3mmuKASIi6CIL/UeDZ6zYzLLN2mwW9snbnrWP2mpNBNvuxtcpgBtnJiyHnQPHHY4+FiMYQ3hQimkCVVmZhsROxWMEvCKq2NTcptxRoS9nxusw/chnbzU/+AiIhWG26RETOhJwcCwOG1icq0s2kt3eX+Z/a/P7ZlTlt/J+anJ9LwpoNpf68t2qeQ3RVF4eP+LN89fFVrIvCMGDpyhD+791qzPo+EsMwB9m8aTYP3XOIG649it1e4RIgy73H709k7vxwPp4STUCAwZQZVQEVQjtjXOmQ+Bvs+LDw9c+hDSGkdl5wvffkbahOxB4Dje6EqLaAkVd4LO/rPy+T9zUwThmclZgCbSkb7iw4+hfkJpoVDNUHW0TkjBr1XE2++ykSgDtuPUzHi8uuR++2HXbm5AXa1/ROLbNxnG1n4t+c1QpXdMngi5lR/PJ7eLED7bR0K/8d2JCFedWuwexp/uDdh7i8c4b+NZ9BXS/NoO2FWaxaE8L4iWar0i6XZHBFJS4OeNrcWeZaZqsf2ALNdOz8VlSujIK9np3px77P2nWsIjeYla+rXWYW/sreD6nrIHO7ueWz2s33p/ntp4JrmwXCvG6zI44r3bxPiwUsfmaxsJpXmeMSQYG2lAVfkH3I7KOn6uEiImfUvAVhTHi/mu/ye5NiyjTQfuWtani9Fnp3T6VZk8rVzqUsdO+S7gu0n3uieKmrH3waw8KlYdjtXm7pf4QH7kqiaWP9TM4GiwVefGo/vW5oSOsW2Tz98EF6XZFe+T/cMLx5a5gB/tm+6iTvBz25sGcG7JxkBs4lFVzLXPtc+7qC64CdqZC8xAycQ+pASLzZQlaFeOU0qBianD0eh/nJYfpWcyY7uObptQkQEZFTSk2z0fySpuw7EECXSzKYv9gMqvZv+IuqUZ6zPp79B/yp17oZLpeVxT9sLtOAv7LYf8CfWs1bYLEYHN62jqgqRf+5XnxFY1b+GcK7r+7m7tsPn8FRyol4PJTKev0yY3jMVOuMrZCxzez/nLXbDJz9wswey7GXmunUh36HXVMgZ38hJ7KCLeAfPaMDzEDX4zBr+HjyPgCyR5utXj05gGHezmLLWzMd/o8+z//4PigOwpucvM+zlC0VQxMpIsNrVhF3Z5mfDjqPmF892YChIFtE5Cy597Ha7DsQQIN6uXw3ZTud+pzPmr+C+XRaVR68J+msj2f8e7G4XFYubZ+hILuU1Kzhoun5OWzcEsS8hWFcf3VqkW63e28AK/8MwWIxuPaqot1GSl+FCrLdWWZtnYyt5sRJxjYz3dpzkiyItA2QOLcIJ/ea5znRuQKrQcNhUKO3siGlQtCrVEqPYZg9/XISwZFsftKY/8fSZjf7+/lX0x9HEZGzZMa3kXz+VVWsVoP/TdxFaKiXOwclc9dDdXl/cgwP3J10VtNUU47aeG9yDGAWgpLS071rOhu3BDF3fniRA+2v/7+9+w6PqkzfOP49U9N7o4WOgFhQUUBEURR7d63YCy7qKpZV92fbdRd772tv69rrWhEVBRuC0nsnAZKQnkw9vz/eSYMASUjn/lzXuWYyc+bMmTAkued93+f5OAmA0SNLycwIttzJSfsWrIDSZSZAV4VmV5wZNbaDpnhtxXoTqmuvc67N4YX4/mZL2A3i+gK2mSJevMCMZJcuh+hupmdzt+PM34Z22Bx/y17RtXtGO6PMuUR309+Q0qHo3SrNozLPTBOqzDVFIlwxpsiZN01FzkREWpBtwwuvp9K3l4+DD6wphJWT6+KyST0BE2pHDDOjx2edUsB1t3Zn8bIopk6L59DRzdtKKBiE4hJnvdOXH38unbIyJ3sNKeeosSr41JyOGFPMQ09l8sXUBGy7Yb963/4wGYBTj9vcwmcnTeYvgrIV4EmOTJmO2f4/btgPG78Hf4H52nKax3pSwOE007ADRVCyzATn6vDciJWk3vSaQB3fH+IHmGrd25qpmHkI9L/cvBZ3XN39LEckPEc3/PlFOggFbdk5wXIoXWGqOdoh8Kaq2qKISCv65/1Z3DK5GzExIZb+MpcuWUFsGy76Sy8KNrsYumc5t92QU71/fHyYs08r4OkX03nyhfRmDdorV3s45ox+LFvpZd4P8+jb2199X1mZo7og241/ydVnsM1s9IhSPJ4wq9Z4WbLMy4B+vu3uv269m+k/x2naeHsVLDNrmVe8Gll2F+GMMoHbk2ouvWnmby9vmikyu+Zt8DWh57knFeL7meDsjoNAmXley2XWSntTTKBOGGCCe1NofbTsYhS0pen8hbD5d/AVmB6Arpi2PiMRkV3Kex8nccvkbgCUlzu57e6uPPPgap55KY1Pv0rE6w3zypMr8Hjqjlb9+cJNPP1iOu99ksS69W66dW1Cv9gt/PZ7NMec2Z/cDW5zbp8kc90VNb1qn301jfwCF316+Tj1eI2gNrfY2DCjDijl62kJfDE1gQH9Nm13/3c+SgLgwANK6dpl5//9ZSf5CswU66IFUDzf/H1VVV3bm2qmd4fKzbTu8rVm2xZvBiTtDlhmWrZ/sxnhtsORpXyxENe7Zqp3fD/zHCLSrBS0pWkCJTW/BGJ7aHq4iEgrmzM/ivF/7gXAkYcV8dmURJ57NY1jjyhi0i3dAdM6aPd6WjXtuXsFo4aX8P2P8Tzzchp33Jiz1T6NsXipl4OP243SMifR0WEqKhx8/nVCddD2+y3uf9yMZt9wZS4u/fXRIg4/pNgE7W8SuOKSHQTtjyPTxo8vbIUz28X4C2HZs7DuEzM12hVXsx7ZDmOmadtm3YcdNtO9/fWMQsdkw4A/Q+Zh5u+sYLkZrfblmf0r82qu+yIV47seA1ljtZZZpB3Q/0JpvEAJFM4xv0hiuilki4i0gVsnd6WszMlho4v56PWlnH5RH979OJkTzukHwCGjSrh6wrarik+8aFMkaKfzt0m5W416N8Yb76VQWuZk2NAyHr1rNcPHDWLaj3GUl1vExNi8/nYKa9Z5yMoMcN4ZTZjWKg1yxJhibvoHTJ0Wj99vbfPftKjYwQ8/xQFwwlGFrXiG7UCoEipyoHwdVKyF8vU1raG8aZA2wszSC5TA6rcgb7oJw5YD00bKqnvp9JrHumJNcA77TIXtYK2K+g3q+2xBbE9IHGTaUCUOhsQhdQOzK8ZssT2a93siIi1CQVt2LFQZ6W1XChUbzBSkUDnEdFfIFhFpI7/PM8t1brkuB5cLJt+yjg8/SyIYtEiID/HiYytxOLb9+JOPLSQzI0DuBjfvfZLE6Sc1fTr3Dz/FAnDeGfnsv285Pbr5WbPOw7fT4xl3aDF3P2JGs6+ZsIGoqKYHetm+vfeoIC01QF6+mx9/jWX0yNJ69/v6uwRCIYvd+lXSK9tf7z4dWqiypshX+Tozzbpinbnu2/5IP1gm7JaurLs2urHiB8CAiaaHdKDEjFpvFdYj/0EtF8R2NyPfItJpKGhLXeEQhMoi/a9LzXQkfzGEKyAcBpfX/CLwpipki4i0kbIyBytXewAYvJuZGj6gn4/rJuZyz6NZPHnfKnr22H6A8nhsLj13E/+4rytPPJ/e5KAdCsGMX0xAOPCAUiwLxh1axLOvpPPF1AQqKx0sXBJNUmKQCefvKOTIznA44PBDSvjPOyl8+U3CNoP251MTAPPv1GHYtgnJZatM8VXLZUZ7Lae5r3QZFM2Honnmur111ftqzlgzIy+mm2kZBWbUuXS5eXzRfHNbXD/oebop/mWHgXDd6d92yIT6QIn5u8lymPOJ7wuZh9YEaRHZJSloi2Hbpn9ixVrT/zroM0HaGQ2uKHAmbbttg4iItKpFS73YtkVaaoD0tJr+x/+6ZT03XZ1LQkK4Qce57Lw8/vVgF76bHs+c+VHsMXjr9dw7MndBNCWlTuLjQuwxuAKAcYcW8+wr6Xz+dUL1FOWJF21q8HlJ0x1xSDH/eSeFL6Ym8I+b1291v23D51+boH3EmDZqseYvNP2ZsUy7KtcWrZ0CJbX6Ote6DDTifD2ppuBXVZiO6WZm4kV3A3fitgcLKjdC3o8QlQGpB2hQQUSaTEFbjNIV5hNcdyy4kyDK29ZnJCIi27BgsQkmgwbUDcaWRaPCbLeuAU48upB3PkrmiecyePL+1Y0+l6ogPXy/MpyRz2MPG12Cw2FXn2dUVJirLt32enFpPocfYsLoL7NiKNi8dT/zpcu9rFztxe0Oc8iB9Y94N4gdNmuRV7wGgUIz280VZwJqVDp4M81lVOTSk2rWO694BTbPqnssZ7T528P2Q7DSzKyrj+U0QdnpNaPJ4aC5tMNm3XLC4Mja5sHmeZsSkqMyoPvxjX+ciMgWFLTFrFkqng+eBFPQQ0RE2rX5i6KAmmnjO2PiRRt556NkXnkrhbtuW0tiI0edf/jZrM8+8ICa0JacFOKAfcuqp5RffE4eGenBeh8vzatb1wCDd6tg/qJopnwXz2knFNa5v2o0e9QBpcTGNnKGQaDEfChfvADWf2pGmZvMAUSeP1RhttqisswU7Lh+NZexPU3IFhHpABS0d3WVG6FwLjg8CtkiIh1EddAeULGDPXfskFGl1cHs5TdSufLSxq2jrhrRPnD/uqOj4w4tZsYvcTiddp1+2tLyjhhTzPxF0XwxNWHroD01AbfTz19Pehz++MFUy3bFmfXEVaPDddYjh8xU7+KFUL7FjAdXHPQ6B9IOMHVdAkVmHXXlRrP5NkHlBtN6yg6Z5+pxCvQ8w4wc27YpOOYrMKPiDo8Z3fakgFuFwUSkY1PQ3pVV5sHmP4CQmeIlIiIdwramjjeFZcGfL9zEFX/N5onn07nikk0NnnG7br2bVWu8OBw2B+xbd7rvOafl8/SLaZx3Rv4OC7PJNoT95sNwZ7QpQupNramXYttQ8KtZ+kVVJXcbbLjk4CjCK9JI8oWw15VjRaWCM4aAz0+38Gzm3zOZflnLYOsl3DsW3c1MzU7aA7od27AP6e0w+Asi/aSjam63rEjQjwXUskpEOhfLtu0O12ejuLiYxMREioqKSEjQKGyT+ArMGqlQJURntfXZiIhIA/l8FrE9hhIKWayd8wfdugZ2+pjFxQ66DdmT0jInX727mMMOLmnQ4958P5nTL+rD0D3L+W3qgp0+D4mww5DzOSx+Aipzam53xUHacIjvD+s/g7IVTX8KTypWj5MA20wJx46E+EjlbMuKXDpM0I/fzbS98iTt5IsTEalHsMzUaMg8uF0vEWlMDtWI9q6ociMUzoFgBcR0aeuzERGRRliy3EsoZBEfF6Jrl50P2WAKqJ17ej5PPJ/BI89kNDhoV/XP3nLauGxHoNSscS5dbqpoB4oj7aGKI9Ovi8G/2bTXBFMh2+ExI8LBUsj9ymxgKnanDjOtrqqnIZjLqd/HsynPyYi919EjLReCFeTkxbFuYwKrA4dz8sSjwRXT+q9fRGQXoaC9K7HDpvBZ0TwgrJAtItIBLVhcVQitolk7D115yUaeeD6Djz5PZOlyL/36+La7f2mpg8+mJAJ1C6Ht8sJBWPM2LH/RBGh3vAnEdgjCPrNeuSFcsdD7fOh1pplubYdMIbJNP0DxIhOwux2/zbXMP3+byY2Pdmfk/qX88OkiKiosBg3ek6JiF1PeWwyuhn2YIiIiTaOg3ZmFfJFpGKWmkIm/APxFpoWXJ7mtz05ERJpg/iKzPrs5Ko7XNnCAj6PGFvHpV4k88kwGj9y1Zpv7FhU7OOaM/ixeFkVCfIjDRrfz0BaqhLXvQ8kycLjNZkUuHa7IpReS9oSEgSbU5n4Baz8066Srej07I5vlMMe1bcz6aDtyHSiYWbdomK+eDyyiu0L8APO72B1v1jm7IpdVX8dmm6niVSynWRedtEeDXvL4PxVw611dmf5zHN//GMu6HA9FxS6yu/s4ZFQ7//cSEekEFLQ7CzsMwXITrAMlZspZoMRU87TD5he0KwaiM8wUNBER6ZCqRrSboxDalq6ZsIFPv0rk+ddT+ftN60lKrNuDed16NzN+jeXuh7P4dXYsiQlBPntzKelp7bR1l78INk6Fpf821a8bIioy26v22ujG8iRDv8vMeupgqVmq5XCZcB+d1SrrnLt2CXD+mfk881I6kx/qUv05wLmnF+BwtPjTi4js8hS0Ozrbhop1Zq1XsNx8ag+miIAzBtyZ5pe7iIh0CjU9tHe+tdeWxh5Swu4DK5i3MJqnXkhj7MElTP85lhm/xjH951hWr60pUJOaEuSLtxezz17Nfx51VOZByRJz3bIio8mOyJrk2pcOMzW7eKGZYl00D8rX1hwnKhO6HmP2DwfMZgfNiHU4YNZGF/xaE7A9KaYNVWyvmj7PoXITmrGpWgtdd220BZ5E6HpU3dHoNnL9FRt49pU0/vdlIg6HSdrnnp7fxmclIrJrUALryEI+KF5sQrYzykw386bRrIv2RESk3QgGYdHSlhvRtiy4esIGLrm6Fzf9ozs3/aPu/Q6HzZ67VzByWCl/uWwjA/ptfx03gWLzIfCWT2KumCnb7sSaqdhgljoVzDShN/8XKFu5cy8qJht6nAzZp+24km2oEvJ+NIE94+C6rag6oH59fJx2wmb++14K4bDFyP1L6d93B/9mIiLSLBS0O6pwADbPNqPZUZkd/o8BERHZsRWrvPj9DqKjwy3Wm/rsUwv4x31dWL3WS3JSkBHDyhg5rJQRw8rYf58y4uLCZsfCuTDzOShZbH4HueLM+uHMw8DpgRWvQu4UILz9J7ScNVOpg+Vm5LjuDhDX24Ry2zbLoQhH1kTXvgybwB7X1/R5TtzdXDakz3MVZxRkHtLw/TuAG/+Sy3/fSwHgvDM0mi0i0loUtDuq0hWmgnhMN00NFxHZRaxZZ2ps9Orhw+lsmeeIjraZNXUehZtK6NVlA45gYaSgZiFsKIQ1hVC6DPJ/3vrBRfNg1Rt1b3N4agqFscWlHTKbb4sAGNcHUoaZytop+zQuLEsde+9RwXVX5DJ7TgxnnlzQ1qcjIrLLUELriCrzoHQpeFMVskVEdiEFhSZdpySHdrBnLYFis8a5ZImpuh2uBFeCKZBp22adsh00balCFVC6gpTSZaSEfbBiO8e1nND1aOh+QiQsF8Cm72Hjt+Y4XcZB7/EQ33/bxwgHTc9oX76ZUu6KjVTeVrBuTvfesa6tT0FEZJejlNbRhPxmml44BFFtX2hFRER2QlWhL0+y+fDUk7zdD1CLC4Ocuv9bHD5iOazINeHUk2rqczhcZo2xv9CMOFeF64ZW266PO9FsniRzbp4kcEeuZxwMsT3q7t/lcBOe7WDDljQ5XBCVbjYREZFOREG7IwkHoXgBVORCbPe2PhsREWmqQCksfxFWvW6qXlezIqE7zQRvb1pNkPZt4oyUD7nwL5vNrosa8XxRXSChvxlddsWZ9o/BMrOm2eECK7I5PCY8x/WDmO5NmzXlcKE/L0REZFen34QdRTho1r6VLDM9OK0WWpwnIiINU1V8a4f72WZUuXih+bC0aAEUzoFgibk/upupcu0rAMLgLzBbydaHinHB6rwe5NtDGbpnpD2VvwAqNwE2OKPN9Ou43iZUx/c3odmtGVAiIiKtSUG7IwgHIiF7uQnZO2pPIiIiLceXD0uegnUfm6DtTjQ/l+0wEKmKXbsydtgHwdKtjxPbC3a7EtJHmyngdshM+/blgy+v5tIfubTDPPHZeK564BxuvWEjQ8fntO7rFhERkQZT0G7vQn4onAdlKxSyRUS2xQ5Dxfpa65I3giveFNVKGGiqV1tOyPsBVr5hRoGd0WYdseWM9HZ21Fy3nIBV677IpW3DhqkQKos8L+DbuOPzs5ymknbCQNNyKmGQuV57arbljEwXTwUG1HuYrx/uQyjsIiUpuHPfLxEREWlRCtrtWbDcTDEsXwUxXczaORGRXV2wFEqWRrbFNddD5dt+jDvBrHUu214Z7UZIGAy7XQXRXUxV77APE9QjG46awG45ILprw4qD7cDm6qrjCtoiIiLtmYJ2exMOmVYnlblQkWMK1sR0BYe7rc9MRKTlVK1jLl8dWfvsrNmwzfrmzb9D0Xyo2EarIssN8X0gfoAJwMFyM/06/yczgh0oNqPYPU6F1P1Mhe6wr2aqd/V073q+rr49ZKZ8Z46ptT67W2t8hwAoKDS/tpOTGtHeS0RERFqdgnZ7ESwza/DK1pg/CG0bPIkQmx2Ztigi0gHYtplKHQ4ClunV7E6s+TlWdX/JctOCqmyFuV623PwcbChvhqmiHdcf4vtBwgCIya6/SrYdMiG9fB1kHGRaVHVQ1SPamjouIiLSrilot7XKTWZdYUWu+SPTFQtRGRrBFpG2FQ5C7hew8j+R9cxR4IyJ9DzO3GLLMO2nNk2HFS+Z4o21WS7TsirsMz/n7G2MxlrOSEspr6mmbYciW9iMIifvBUl7mkrajQnLlhNS9jFbB1ewWSPaIiIiHYGCdlvy5UPBbxCuNH80elM1ei0ibSfkg6K5kP8r5HwK5Wu33qd4wY6PYznNh4VVFbftIPg21b0/Jtu0oIrra4qExfUxM3j0IeM2BQJQUqo12iIiIh2BgnZbCYdM8Z6w34zgiIg0l5AP1rxjejW74sAdDzgio8O1R4pDZuQ6WAKlK8za59qjze4k6HU2pO1vjhksM+uoKzeYqt61r4d9psp39mnQ8/RI5WxMe0Jfvqk94Ywy+3gSFaiboLCo5ld2UqJGtEVERNozBe22UrHeFDuL7tLWZyIibSVYDgUzTRiOzjJTsC0zYokdNjNeSpdvXYwLu+bSk2Ie54ozxb3K18LyF01BxabwpkLKfpAyDLqMA1f0jh9j2xAoMuuxt+yO4HCb1xad1bTzkWpV67MT4kO49NtbRESkXWvUr+rJkyfz7rvvsnDhQqKjoxk5ciR33303u+22W/U+lZWVXHvttbzxxhv4fD7GjRvHE088QWZmZvU+q1ev5vLLL2fq1KnExcVx3nnnMXnyZFy7yl8OwQrT59UVU3/hHhHp3OwQrPsYljxhRnurOGPNOuK4vrBhCpSvafpzRGVCj5NNOA8Um9ssV2Rat7PmuuU0I82xvcxUbm9645ewWFaHLjDWUVStz9a0cRERkfavUSnv22+/ZeLEiQwbNoxgMMjNN9/MEUccwfz584mNjQXgmmuu4ZNPPuGtt94iMTGRK664gpNPPpkffvgBgFAoxDHHHENWVhbTp08nJyeHc889F7fbzb/+9a/mf4XtjW1DyTLwF5r1iCLSMdkh8/95yw/LfAWmBVXZctOeL1BipmYHSkz/50CJmUYdKDT7eyPFDys3QKgMNk0zG5jiiCn7mVHiOv2ZHTWtpXwFpop3sMy0rnLGQObBkP2nZunbLO1HQWREW4XQRERE2j/Ltm27qQ/etGkTGRkZfPvtt4wePZqioiLS09N5/fXXOfXUUwFYuHAhgwYNYsaMGQwfPpxPP/2UY489lvXr11ePcj/11FP89a9/ZdOmTXg8nu09JQDFxcUkJiZSVFREQkJCU0+/bZSvhfyZZnpmQ6Zkikj7EvbDqjdg2fMmOLsTzbTtcMAUNqwaPd4RVxz0vRh6/skEaTsExYtNz+eSZZCyL3Q5wsx8EQFeeyuFcyb05rDRxXz13pK2Ph0REZHmEyyDYKUZLHB62/pstqkxOXSn5i0XFRUBkJKSAsDMmTMJBAKMHTu2ep+BAweSnZ1dHbRnzJjBHnvsUWcq+bhx47j88suZN28eQ4cO3ZlTat8CxVC00ARshWyRnbf5dzMFOxzYYjq0q9bXkesOj2kNlbSnKQi29kNY+4Ep4uWMrrVFRUapLczosWWuWw5zWTATylfXnEOgyGzVLDMNO2GAWT9dVYzMFR+5jHwd06NuiLackDjIbCL1KNisiuMiIiIdRZODdjgc5uqrr+bAAw9kyJAhAOTm5uLxeEhKSqqzb2ZmJrm5udX71A7ZVfdX3Vcfn8+Hz+er/rq4uIEjRu1JOAhFC8ynNbGqMi7SZKFKU7F/xUuwYWrjH+/NMJe+jU0/B08qDLgC0g8008CDpab3s9Nj1ka7Ypt+bJFt2FykHtoiIiIdRZOD9sSJE5k7dy7ff/99c55PvSZPnswdd9zR4s/Toio3qMq4SG0lS83/C2ywqVl3bLm2uG5B6Uozklz4O5StAcKRgzig2zGmiFc4WNO+Kly7jVWwpoVV3oyagO1Nh97nQnxfCFWYIoWhSnO9qqI3dqTStx15TtuMTHc7xoxMA3hTWvXbJruu6hHtJI1oi4iItHdNCtpXXHEFH3/8Md999x3du9eMzmZlZeH3+yksLKwzqr1hwwaysrKq9/n555/rHG/Dhg3V99XnpptuYtKkSdVfFxcX06NHj6acetsIB01QcHpVZVw6F18eBErN9epK1bUuHW5Tj6CqZ7Idhs2zYNlzkP/zlkdrOHeiWcPcfwLE9Wn448J+yPvJTBdPP6hdrwES2dLmQo1oi4iIdBSNSn22bXPllVfy3nvv8c0339C7d+869++777643W6mTJnCKaecAsCiRYtYvXo1I0aMAGDEiBH885//ZOPGjWRkmCmcX375JQkJCQwePLje5/V6vXi9HfgP4sqN4Nuk0WzpWDbPhhWvQPHCSEsot1nf3GWcqWy94iXY+G3DjuVJrtVmKlJ/0XJCfD8g0jeaUE2f6DqXQfCmmWCdsg8kDDRTtxvbggrMOu2Mgxr/OJF2oKrquNZoi4iItH+NCtoTJ07k9ddf54MPPiA+Pr56TXViYiLR0dEkJiZy0UUXMWnSJFJSUkhISODKK69kxIgRDB8+HIAjjjiCwYMHM378eO655x5yc3P5v//7PyZOnNixw/S2hINQttKs3dRotrQXdtgU8PLlg78g0iIqr+Z66XIonr/148pWwbqPat1gmcJe1c0LtrgM+cEOmHXMVRwe6HY89DlPHz6JNELViHaKRrRFRETavUYlvyeffBKAQw45pM7tL7zwAueffz4ADz74IA6Hg1NOOQWfz8e4ceN44oknqvd1Op18/PHHXH755YwYMYLY2FjOO+88/v73v+/cK2mvKjdC5SaIrn9avEizs20z0pzzmQm17iQzehwsBn8RlK8xbebCvu0fx3JDt2PNemTLZfo/b/wWcr80xb+6Hg29z4O4Xts/l0CRmdGBAzyJ5nz0oZNIo1Wt0U7WGm0REZF2b6f6aLeVDtVHu2QpFM6F2A60plyaXzgIedMhWG5auzmjzfRrZ7Rp8eSMNsW1agfQqgJgwVJToMsVA9FdzTTqqmPatYt+hcx+q9+Gwj8adl7uRLOG2pNiNm9q5OtUSBsBUWn1vxbCJsSLSKvJGrQnGza6mf3tfPYaUtHWpyMiItJ81EdbmqQpa0ml88j/BRbcB6XLtr+f5YSEQZC0BxTNNxW2m8oZBdmnmbXR/s1mZNkdD+4EE9Zjss0si6oiZY2h0WiRFhEKgcNR/68M24bNhRrRFhER6Sj0F7PItgTLzahtY4JlsAxKlpgCYsWLzFay2NznToSE3cxxQxWRllKR62GfGZEumms2AByQup9pQ+WKMVO3K9abddXVra+c5vwsZ83XCQOg9/n1j0aLSLs0d0EUI8YN5ORjC3nx8ZVbhe3ycgd+vwPQGm0REZGOQEFbZEuVm2DRQ5DzOWCZUWFXbKQCdhioqohtR/ot1/o6VEFNQbAIywk9ToV+l5o1yvUJB01P6c2zTdCOyoSux0BUeku+UhFpJx58MpPSMicv/zeVvfco55rLN9a5v2o02+0OExsbru8QIiIi0o4oaEvHVZELK18za5jdiWbtcvooU5yrciMsfwE2fofp5+wx06Qd7prr1hZfO9xm39wpECqLPIltKnH7Cxp+Xt4MM3KdMNBcJu6+48DscEFMN7N1O6Zp3w8R6RDCYfjki0SG71dGelqQomIHb7yXXH3/Dbd3Z/99yjjwgLLq2wpq9dDWaiQREZH2T0Fb2r9w0EzJDpVHCiWUw6ZpsPL1rStnL3oI4vpEqmr7m/6ciUNg8F/NOubKTea5LaeZso0jMnXbCVg1t1sOU9DMk9T05xWRTu/eRzO58e/d2XP3cn7+ciGvv51CebmTQQMq2GtIBW+8m8LpF/Xht6kLyEg367GrRrRTtD5bRESkQ1DQlkZZt97Ns6+mcfqJBQwcsIP2UNvj32zWL4d9pteyM8oU6goUQ94MKPjV9HMOlW8/MCfvY6pjB4qhdCnk/2x6QAMk723WKnuSTGXusB/Cgci2xXW71vXYXpA1NhKqMVPHRUSawdLlXm6/pysAf8yL4ZbJXfliqqlaetn5eVx0dh6z50SzcEk0Z13am8/fXoLTCQWba0a0RUREpP1T0JYGq6iwOObMfvw+N4a7H8ni0X/M5cJT5mNZlinE5U0Dd9zWD7TDkWDrN1O6V/0X1n/S+BFnhzfSCivGTMXuPR7SR9ct0esvhLwfzf3J+6jiu4i0G7YNl03KprLSQb8+lSxdHsW9j2YB4PWGGf+nfOLiwrzz4nKGHT6QKd8lcMc9Xfj7TTlsLoqMaCdrRFtERKQjUNAW06959X9h03Qz8gvmL8Lqol7mcm1uXw7IuIBg91FcfMi/OTPlGazpW/RydcaasB32m5HqsK/mmFuKyTaj2A4PhCohUGSmYacOg7ThENPDFCFzxpjLhlT/9iRB1yOb8E0QEWk5wSA8/lwGX09LICoqzGdvLuFfD3bh+ddMd4DTjt9MSrIZrR48sJJ/P7iKsy/rwz/u68qIYWXVI9qqOC4iItIxKGjvqvybYcO3kPsl5P/UoIf0T87j6Yvq7ptfkkIo7MTj9pMUU2SKiIXKtnEETJBOGwl9zoOkvTTiLCKd2uq1bu56KIu3P0pmU57pW3/7Devp29vPg3eu4Zsf4lmxysPEizbVedxZp27m+x838uQLGZwzoTdHjy0C1ENbRESko1DQ7ojCAVjzHhT+Ae54cCeZ9cR20BQOCwfMdbvW9XDka1+BaSNVuRGoahFjQcZB0ONk8KTU3IYFFnz1bTy33ZXFiD7fcf3Jz5IZvQiS9yEn7jKe/vgY3ng3lUVLo+jdvZA5X35NrLcsUsnbC87IpcNjNsupcC0inV4gAI88k8Ftd3elrMxM+05NCXL+mXlM+vMGABISwvz4+ULW5bjZe4+KrY7x4D/X8susWH6dHcurb6UCVI96i4iISPumoN2SAsUQ3PqPp2r+zVC2euswXB2QQyb4RmeZqdOBElPoa8lTUL56588vYRBkjoEuR0BM963uDoXg1sld+deDXQBI6rEbqcceAlYluKLpAty+ey43X7OB3Q8czNLlSdz7wnBu/2vOzp+biEgHNf3nWCZcm82c+TEAjBpewi3X5TBmVAlud91909OCpKfVP0rt9dq89cJy9hkziM1V7b0SNaItIiLSEShot6SFD8Gc20yP55huZk1yfD9TNCx3CuRNN2G6KTwpkH2qeby/ELBNQTKHO3Lpqvt11W2eJIjKhOgu5jy2obDIyTkTevHJF0kAXDsxl7tuXYfLZQHRdU/FYzP5lnWcdkFf7n0sk8vO20SXLP0xKCK7loLNTm78ezf+/XI6YEaw7719LeedmY/D0bRj9sr288qTKzj2zP6ARrRFREQ6CgXtllSx3lwGiqCoCIrmw5aDvVFZprVVnZDsjhT+ssBfABW5ps2VO8FsmYeaNc6ueip8N4MFi6I4cXxfFi+LIioqzLMPreLs0wq2+5hTjitkxLBSZvwSxw23d+exe1aTmBDe7mNERFpafoGT2JgwUVH2jnduItuGl99I4brbupOXb4asLzw7j7tvW0ta6s4H42OOKOaRyav5z7spHDGmeKePJyIiIi3Psm275f76aCHFxcUkJiZSVFREQkJCW5/Ottk2FPwGm74zLa5KV0DJYqjIgZSh0O14iOvd1mdZx0efJXL2Zb0pKXXSo5uf915exr57lzfosdN/juXAowYCYFk2ew2p4KDhpRw0ooSDhpeSlalRbhFpPdN/juWwkwaw5+AKvv/fwq2mbTeHsjIHp5zfh8+/TgRg94EVPHX/KkYN305RSBEREakrWAbBSsg8GJzetj6bbWpMDlXQbmklS6FoXr1roNuTcBj++UAWt07uBsDokSW89fxyMtIbF44ffjqDx55NZ+nyqK3u69enktEjSvnrVbkM6OdrlvMWEanP5kInex88iNVrzS/rv9+0jluuy23W5ygvtzjurH58PS2B6Ogwt9+wnmsu39AigV5ERKRTU9BuHxS0m9+d92VxSyRkT7xoIw/+c81O/bGYk+ti2o/xTJsRx7Qf4/hjXjS2baqNHzW2iP/9d2lznLaIyFZsG049vw/vfpxMUmKQwiIXbneYX6csZM/dt1OgshEqKy2OP7sfX36TQFxsiC/eWcKIYRrFFhERaZJOGLSbWJ5FOhPbhmcixXvuvWMtj92zcyEboEtWkD+duJlH717D7G8XULDsd156fAUA306Pw+9Xiy8RaX5r1rm5+ubuvPtxMm53mC/fWcKJR28mEHBwwZU9CQR2/jl8PouTzu3Ll98kEBsb4tM3FbJFRESkLgVt4ZffYlizzkNcbIiJF21skedISgwx/vQCMtIDlJc7+fHX2BZ5HhHZ9YRC8MkXCRx/dl967b0HjzyTCcDdt61jv6HlPHnfapKTgvz2eyxvvp+yU8/l91ucekEfPpuSSHR0mE/+s1TrsUVERGQrCtrCWx8mA3DsuCKio1tuJYFlwaEHlQAw5bv4FnseEdk15OS6uPO+LPrsM4Rjz+zPR58lEQ5bHDKqhLdfWMbVE8wHh1mZQS4ZnwfA19Oa9rNnc6GTV99MYezJ/fn48ySiosJ8/PpSDj6wtNlej4iIiHQeau+1i7NteDsStE89bnOLP99ho4t5490UpnyXwB03btnrTESkLtuGGb/EMm9hNCtXe9iwyU1lpUVegYsp3yUQDJplKMlJQc4/M59Lz93EwAFbF1scPbKEex7NYtqPNW0RX3gtlb/c3ANfZCmLbVNdS8JcN5v5uma5i9cb5oNXl3Lo6JKWetkiIiLSwSlo7+J++z2Glau9xMSEOGpsUYs/39iDzR+mP82MpaTEQXz8zvXa3pTnYvacaNas9+D3W1xwVj5eb4er7yci2/DOR0mcdkHfbd4/cv9SJpy/iVOP37zdGTkHHlCGZdksWRZF7gYXWZlB7n0sk5JSZ4PPZcigCk48upCzT82vN8yLiIiIVFHQ3sW9/WESAMccXkRMTMsH1F7Zfvr08rF8pZfvZsRxzBHFTT7W7DnRHHzcbhSX1PyhPOW7BP773HIckUURtm2mrItIx/Tgk2a99X57l3HAvmV0yQwQHR0mOirMgQeUNbiKeFJiiD13r+D3uTFM+zGOvXavYMHiaFwumznT5hEbG6bqR4Vl1fzcsCwbywK32yY1JdQCr1BEREQ6IwXtXZhtw9sfRaaNH1/Yas972Ohilq9MZ8p3CU0O2rYNE2/IprjESfeufgbvVsE3P8Tz9ofJXHtLd664eCM339mNL6YmcPkFm7j5mlzi4nZu9FxEWtesP6KZ/nMcLpfNh68tpUtWcKeOd9DwUhO0Z8SzcrVpHXLIgSUanRYREZFmp2Jou7DFS70sXR6F1xvm6FaYNl7lsNE7XxDt1TdTmP5zHLGxIWZ8tpDP317KS4+vBOChpzLZ7YAhvPl+CoVFLiY/1IWBw3fnP+8k0/G6xovsuh5/LgOAU4/fvNMhG+CgEaZw2bQf4/jg0yQATjy6cKePKyIiIrIlBe1d2NwF0QDstXtFq472VlUe/2NeDBs3NX5SRXGxg+tv7w7ALdfm0L2baYx7xsmbufeOtQCEQhbjDi3i3w+tpHdPH+tyPJx1aR8OPm4Av8+NbqZXIiItpWCzk9feNq24rri4edoOHjTc/Oz5fW400382LQaPP6qwWY4tIiIiUpuCdmtop8Oo8xdHATBoQMPWODaX9LQgew0pBxrfaie/wMl5E3uxYaObAX0rq9v3VLl24gbef2Up33y4iM/eWsrF4/OZ98M8/nHTOqKjw0ybEc8+YwYx8foeFGxueBEkEWldL7yeSmWlg72GlDNy/+bpU90lK0i/PpXYtoVtW+y7Vxk9Ih/UiYiIiDQnBe2W5oyMntrtr4jOgsXm3AbvVtnqz93Y6eO2Da+9lcLA4bvz/v+ScThsHrt79VYVxi0LTji6qE5v2+hom/+7LpeFP87lTycWEA5bPPF8Bv2HDeH1t5Ob70WJSLNYn+PmvsezADOa3ZwFDUePqPnZoGnjIiIi0lIUtFuaNx08SeBvvTXQDTV/UdWIdlsEbVMEbcp3CdW3PftKKqdf1Hur6eTLV3o48rR+nDOhN3n5boYMquCH/y3i8DGN62Gb3T3Af59bwdfvL2LIoAoKNrsYf3lvlq/07PwLEpFmUV5ucfzZfcnd4GbQgArOPrWgWY9/0PCaoH2CgraIiIi0EAXtlub0QEw2BEuaZQp5c81CD4Vg0dK2C9qjR5TictmsWOVl+UoPxcUO/nJzD958P4XDT+lPwWYnwSDc+2gmQ0btzhdTE/F6w/zzb+uY+fUChg9r+lTSMQeVMuub+Rx6UDHhsFXdPkhE2lY4DOdN7M3M32NJTQny0etLt9sbuykOP6SY+LgQ++1dxpBBrf+zT0RERHYNCtqtIToTnHEQLN3xvtvx6VcJpPXfi2PP7MuKVWYU1uezmP5zLJvyGldUbOVqD5WVDrzeML17tn5rm7i4MMP3M9+PKd8l8J93UygvN2um/5gXw7hT+zNs7CBuuL07FRUOxowqZs60+dw8KRePZ+f/8Ha54KarcwF4/vVU8gu0Xlukrd38j268/WEybneYd19aRt/e/mZ/jm5dAyz+eS5T3lvcrFPSRURERGpT0G5B8+fDQQfB4uWxENMd/IUNfmw4bIL16rVuAKb/HMsp5/elYLOLT75IYvcDd+fkc/uQOXBPDjxqIH33HcL9j2cQaGBdn6r12bv1q8TZRhmz9jrtZ19JA+CSczeRmhLk19mxzJ4TQ3JSkOcfWcmU95fQv2/zfiBw2MEl7L1HOeXlTp58Pr1Zjy0ijfPoM+nc/YhZl/3MA6sZPXLnPpjcnqzMIAkJrddpQURERHY9Ctot6IYb4PvvYfx4CHq6mGnkwfIGPfaOe7pw9On96bPPHpxyXh+OOaMfFRUODj+kmENGlVBR4eC9T5IpKnYRExOipNTJdbf2YK/Rg5ny7Y4LjFWtz26LQmhVqoL2B58m8evsWNzuMP/823o+f2sJQwZVMP5P+Sz8cR4XnJ3fIiNPlgXXX2FGtR99NoPKSg1vibSFtz5I4i839wDgzpvXcf5Z+W18RiIiIiI7R0G7BT35JCQmws8/w+T7kiC6B/jydvi42XOi+deDXQDTD/rdj5MpLHIxYlgp7728jK/fX8wbzy7nb5Ny+O7jRRStmM2zD68kLTXAgsXRjD15AH+6sDdr1rm3+RwLFrfd+uwqB+xbRkxMiMpK8zY86ZhC0tOC7Lt3OXO+n8/LT64kIz3Youdw2gmbye7uY+MmN/98oEuLPpeIbO3bH+I4Z0JvbNvi8gs2cvOk3LY+JREREZGdpqDdgnr0gMcfN9fv+LvFr4v6gituu1PIAwG44MpeBIMWpxy3mT+mzePS8zZx2gkFfPyfpcTGhrEsOP2kzdz5t/UcNKIUlwsuOiefxT/P44qLN+Jw2Lz1gWmF9a8HsvD5th6pnb+oqrVX6/bQrs3jsTm41vTQi8/Z8YcQzc3thluuywHgzvu7cMPt3Zq17XlxsUNVzWWXN2d+FEef3o8Zv8RudfsJ5/TF73dw4tGbefTuNVo3LSIiIp2CgnYLO+ssOO00U+V7/AUxBKP6mFZfdv3rA+96OIvZc2JITQny+D2r2WNwJU8/sJo3n19BSvL2e3EnJ4V49O41/DZ1AaOGl1Be7uRv/+zGkFGD+fSrmjZatt0+RrShps1Xr2wfhx3cuHZdzeXi8fnce8daAO59NItTzuvD/75M2Omp5LYNR5zanwH7D2H6z7E7foBIJ2TbcNmknnz6VSJnXNyb0lLza2fNOjdH/ak/RcUuDjyglNefWdFm9SJEREREmpuCdguzLDOFPDUVFi6Ej6dmQ1Q6VG7cat+5C6L4x31m+vIjk1eTmdG0adN7Dangu48X8+pTK8jKDLB0eRRHn96fE87uy8rVHtatd1NS6sTptOnfp/Urjtd2yfg8Ljgrj38/uApHG74br7tiA88/shKHw+a9T5I55oz+pA/Yi/c/SWzyMT/9KoGfZsYRClnc+PfmHSkX6Sg+/jyRGb/EAbB6rZdb7+rK5kInR57Wn3U5HgYNqODD15q/jZeIiIhIW1LQbgWpqXDxxeb6k0+7IX4AWC6o3FC9TzAIF1zRi0DAwfFHFnLmKZt36jktC84+rYBFP87luitycblsPvwsiWFjB/LmB8kA9Ovta5ZWWTsjISHM84+uYuwhbTOaXdsFZ+fzw/8WMeH8TXTr4qe0zMlFf+nV6NZpVe56OKv6+rQZ8XwxNWE7e4u0b7PnRHP5tdn8OiumwY8Jh+Fv/+wKwCGjzP/xh5/OYMwJA5i/KJquWX4+e2vJDmfriIiIiHQ0Ctqt5LLLTPj94gtYui4TUoaC5YXy9RAOcv/jmfw6O5akxCBP3re62dYpJiSEufeOdfzx3XyG7llOXr6ba28x1X3bcn12ezV8WBlP3r+albPnsNeQcgo2u7j+tm6NPs4PP8UybUY8bneYM08pAOBvd3bVqLZ0WHc9nMVTL6az/+EDufDKnuRu2PEHUP95J4U582NITAjy7kvLOOvUfMJhi9/nxpAQH+Kzt5aQ3b2BPQlFREREOhAF7VbSuzcceaS5/tRTQFQGpO4DniQWzinltrvNlPEH71xN1y7N/4fnoN0q+fr9xQwbWlZzWxuvz27PXC54+v5VWJbNS2+k8c33cY16fFU/4PPOyOehf64hNjbEzN9j+e97ydt9XGWlxdU3d+el/6Q0+dxFWsK6HNPFwLYtXng9jQH7D+HeRzPrLbY4d0EUN/+ja3XLrhuu3EByUogH71xLZkYArzfMB68uZY/B+hkkIiIinZOCdiv685/N5QsvQEUF4EkmlDycC24cjc/n5KhD8zjv+D+gfB0ESmnu4c+kxBBfvruY4fuZSt+ja1X8lq0dsF85E87fBMAZl/ThkquzefL5NH76NYby8m1POfj86wQ++iwJy7K5/ooNZKQHufoysyb/zEv6cPzZfbeqvlzl9ru78vDTmVxwZS8+/rzp68NFmlvuRhO0771jLcOGllFS6uSG27szZNRgPvosEds2VfbHT+jFHqN2Z/JDXcgvcDGwfwVXXWre/xnpQeb9MI9Vs+dwyCj9/BEREZHOy7LtjjeZtbi4mMTERIqKikhI6DjrXkMh6NsXVq2Ce+6B66+HBx6Aa6+F+HiY94ePHpmboTIXKjZCqAycMRCV1qznEQzC8pVe+vf1qZXODhQWORl6yCBWrvbWud3hsBk0oJJ99ixnn73KGbpHOXvvUc7TL6Zz0z+6EQ5bnHVqPq89vRKAsjIHF1/dk/++l4xtm2/66JEl3HR1LuMOLcay4NdZMQwfN5BQyNyfmBDkl68W0r9v2xasEwFI6Lk3JaVOFv88l769fbzy3xRu/Ed3cjeYAD724GKWrfSyYpUXh8PmuHFFnH1aPsceUaRCZyIiIrJ9wTIIVkLmweD07nj/NtKYHKqg3cruvx+uu85cP/tseOcdqKyEZ56BSy6ptWOwAnx5UDTfLO72prbJ+QoUFTuY8m0Cv/0Rw29/xDDz9xg2bnJv9zHnn5nHE/eu3ipgLFri5Z5Hs3jlzRQCATOhZO89yvnrVbn868Es5syP4dTjN5Ozwc0PP8UxeLcKpn2ySMWipE2VlTmIyx4KQPHKWcTHm/aEJSUO/vVgFg88mYnfb97PvbJ9vPb0CkbuX7bN44mIiIjUoaAN3333Hffeey8zZ84kJyeH9957jxNPPLH6/tLSUm688Ubef/998vPz6d27N1dddRUTJkyo3qeyspJrr72WN954A5/Px7hx43jiiSfIzMxs9hfY3oRCcOedcMcdNTPDDzsMvvyS+keXy9dBwW/gTgB349YJS8uwbcjJdTNrTrQJ37+bAL56rRe3O8zD/1rDhAvytjtbYO06Nw88mckzL6dRVlbTPDgtNcD86fMJBmG/wwaxPtdD1yw/Lz62ksPHtH1ldtk1LV/poe++exATE6J09eyt3tvLVni4456uxMWGmHzrOhITwm1zoiIiItIxKWjDp59+yg8//MC+++7LySefvFXQvvTSS/n666959tln6dWrF1988QV//vOfeffddzn++OMBuPzyy/nkk0948cUXSUxM5IorrsDhcPDDDz80+wtsr7780oxo2zb88gv06rWdnYuXQtFc8KaBK7q1TlEaKS/fiddjV4/2NUR+gZPHns3gkWcy2Fzo5L/PLee0EwoB+GNeNH+6sA+LlkYBcONfcvjXLes13V9a3fSfYznwqIH07ulj+W9z2/p0REREpLNR0N7iwZa1VdAeMmQIp59+Orfcckv1bfvuuy9HHXUUd955J0VFRaSnp/P6669z6qmnArBw4UIGDRrEjBkzGD58+A6ftzMEbQC/30wb3+FLsMNQtBBKloAnwYxuS6dSXm6xMc9Nr2z/VrffcHt3Hn8uA4DJt6zlxqs31HcIkRbz7kdJnHJ+X0YMK2X6Z4va+nRERESks+mEQbvZq46PHDmSDz/8kHXr1mHbNlOnTmXx4sUcccQRAMycOZNAIMDYsWOrHzNw4ECys7OZMWNGvcf0+XwUFxfX2ToDj6cBIRvAckDiQEgaYt6ElZsgHGzx85PWExNjbxWyq25/7J41PDJ5NQA3/aM7r76p1l/SujZsMj2zszLU81pERESkIZo9aD/66KMMHjyY7t274/F4OPLII3n88ccZPXo0ALm5uXg8HpKSkuo8LjMzk9zc3HqPOXnyZBITE6u3Hj16NPdpt3+WA+L7QvI+4Iw2lcnL1kJIfWh3BVdeuolrJ5r/Hxde1ZMffqq/PZhIS6hq7aWgLSIiItIwLRK0f/zxRz788ENmzpzJ/fffz8SJE/nqq6+afMybbrqJoqKi6m3NmjXNeMYdTExXSD8Q0kZCXC/TBiyo6r67gntuX8epx28mEHBw0VW9qKzUYm1pHdVBO1MzaUREREQawtWcB6uoqODmm2/mvffe45hjjgFgzz33ZPbs2dx3332MHTuWrKws/H4/hYWFdUa1N2zYQFZWVr3H9Xq9eL3td65+q3O4ICodPCngjDLrt8N+cMWBY/ttp6TjcjjgmQdXMe3HOBYtjWLyQ1nccWNOW59Wo5SXW8xdEM2wfcpV1K0DqeqVnZmuEW0RERGRhmjWEe1AIEAgEMDhqHtYp9NJOGwqMe+777643W6mTJlSff+iRYtYvXo1I0aMaM7T6fwcTogfAEl7gm2Zvttla8xWkQv+QjO1vOO1SpdtSE4K8chkM6Nj8kNZzF8Y1cZn1DiXXNOTA44YxGdTVNCvI9HUcREREZHGafSIdmlpKUuXLq3+esWKFcyePZuUlBSys7M5+OCDuf7664mOjqZnz558++23vPzyyzzwwAMAJCYmctFFFzFp0iRSUlJISEjgyiuvZMSIEQ2qOC5bsCyI7w0x3SBUDqEKs/k2Q7AYAiUQygNss87bFQPupG007ZaO4LQTNvPKm4V8/HkSl07qyXcfL8LR7ItAmt+mPBdvvm8Kuf00M5ajxnaOooa7gtyNKoYmIiIi0hiNDtq//vorY8aMqf560qRJAJx33nm8+OKLvPHGG9x0002cffbZFBQU0LNnT/75z38yYcKE6sc8+OCDOBwOTjnlFHw+H+PGjeOJJ55ohpezC3N6zEaS+ToO0xYsVAHBSAAPFJuK5WWrzdRzV0wbnrA0lWXB4/esZur38fzwUxz/fjmNy87Pa+vT2qH/vJNMMGg+4Fm+UktBOgrbhg2btEZbREREpDF2qo92W+ksfbTbRLACSpdD2QoTxD1J4FIF647o4aczuPrmHiTEh1gwYx5du7Tv0cZ9xgxi1h/mw51Rw0uY9sniNj4jaYjCIifJffYGoGLdb0RFdbhfGSIiItLeqY+2dHiuaEgcDKkHQGy2Ge0uW2XWdAdKwQ619RlKA11x8UaGDS2juMTJVTe175Z3c+ZHVYds0Ih2R5K7wUx8SkwIKmSLiIiINJCC9q7IsszU8eS9TauwlH0hKhPCAajIMVPLy9eDf7OKqbVjTif8+6FVOJ0273yUzAf/S2zrU9qml95IBWD0yBIA1ud6qKjovHUCvvomnhPP6cuSZR3/A4WaQmiaNi4iIiLSUArauzp3PMT2hNT9IOMgSB9lgndsL7DcZl13+VpTybxyo5luLu3GXkMquG7iBgAm3pBNcXH7+y8dDMKrb5mgPenyDSTEm1kTK1d3/BBan7x8J2de2psPPk3ipHP7Ul7esT9QqOmh3b6XJoiIiIi0J+3vr3JpO04veFPNlPLkPSLB+yBIGwHJe4Iz1ox0K2y3K7dev54+vXysy/Hwf//q1tans5X/fZnIho1u0tMCHH14EX16+QBYsdrTxmfWMq69pQd5+SaczlsYzVU3ZbfxGe2c6kJoqjguIiIi0mAK2rJtlgPccRCdCXF9IGUoeFMiYVvTyduLmBibp+5bBcBjz6bz4y/tq7jdUy+mA3D+mfm43dA72wTtzrhO+8up8bz831Qsy+bu29bicNg892oar/w3pa1Prcmq1mgraIuIiIg0nIK2NJw7HpL3MpXKy9aYrSIHQr62PrNd3uFjShj/p3xs22LCddnt5nOQFas8fDbFVGS89FzTgqxqRHv5qs4VtP1+i8uu7QnAlZds5IarNnDr9TkAXH59NgsXd8zXWzV1PDNda7RFREREGkpBWxrHnWDWcKcNM9PJo7qY3tyVeRrlbmMP3LkGp9Pm97kxrFvvbuvTAeCZl9KwbYsjxhTRr48J2H16+oHON6L92+8xrFjlJSU5yJ03rwfg/67N4dCDiikrc3LahR1zvXZNMTSNaIuIiIg0lIK2NJ47DmK610wnT93PrO8uW22qlEubSEsNMXi3CgBmzYnZwd4tz++3eO61NAAmnJ9XfXvNiHbnWqNd9Xr2GFxBfLypY+B0wmtPryAzI8DcBdH85eb23YatPiqGJiIiItJ4CtqycywHxHQzfbnj+5mRbV8ehP1tfWa7pKF7mKD92x9tH7TffD+ZTXluumb5Oe7Iwurb+/SMFENb5e1UkyCqRuirXl+VrMwgrz21AsuyefaVdF57q2Ot19aItoiIiEjjKWhL83BFQ9IeZnTb4QFfQc067vJ1JnwHSkwA70zpqp3ZZ69yAGY1Y9BuzD9XebnFK/9NYcwJAxh/eW8ALjk3D5erZp+ePfxYlk1pmZO8fNc2jrR9ObmudjM9vkrVmvMtgzbAYQeXVK/XvuzabBYtaT/T5t/6IInTLujD5kLnVveFQrApT8XQRERERBqraX/litTHsszodlQGhCogWAHhSgiUQaDQfB0sgHCtP9hdMeBJNiPjstOG7mGC9s6OaAeD8NpbKfzrwS6Uljn4+cuFdOtq/t0WLvby82+xHDuuiJTkELYNP/4aywuvp/LGuymUlJrAZlk2x44r4prLN9Q5ttdr061LgLXrPSxf6SE9rXFFtiorLYaOGYzPZ7FgxjyyMttHka7qoN2r/uKAt1yXw3fT45j6fQKnXdiHn75YSHR0237oNG9hFOMv743P52D/fcq4/sq6/1b5BS5CIQvLshv97yQiIiKyK1PQlubncJvNnVD39nDABPCQH+yAWc9dtsqMervjI4G74xWLak/2HmKC9pp1HvLynaSlhhp9jCnfxjPhumyWLo+qvu3BpzK47+/rqKiwOOykAazP9eD1hjluXBFzF0SxcEl09b59evm44Mw8zj0jn+zu9Y+C9unlM0F7lZcD9itv1Pl9/2McGyLTme+8vwuP3bOm0a+xJdRMHa9/2UTVeu29DxnMnPkxXPN/PXjq/tWteYp1+P1WdcgG+OjzxK2CdtVrykgP1pmVICIiIiLbp2FEaT1V4TsqDaK7QFxvSBsBKfsADlNMLdi40CV1JSSE6dfHFKRr7PTxykqLSf/XnbEnD2Dp8ijSUgNceLYpYvbMS+kUFTt48oV01ud6cDptfD4Hb3+YzMIl0cTEhDj39Hy++XARS36Zy/9dl7vNkA1112nPWxjFrZO7MH9h1Db3r+2rb2s+wHnm5TRWtIOiaj6fxdrIVPZtjWgDdMky67UBnn4xnQ0b2y693nl/FrP+iCExwYxU//BTHHn5daePf/NDHAAjh5W2+vmJiIiIdGQK2tK2nF6I6wXpwyF+AASKoXQ1VOSCv9AE73BA67obYZ89I+u0G1F5/Pe50ex32CAefDITgAnnb2LFb3N59uFV7D6wgpJSJ/c/nsnkh7IAePqBVfzy1QL+NimHfz+0kpx5f/DSEys5+MBSHA34qVIVRj/8LJGRRw7kH/d1ZciowZx+UW/mzN9+4P7y23gAEhOCBAIObr+7a4NfZ0tZtcaDbVvExoZ2OMV67CEl7LNXGUB1j/HW9tOvMfzrwS4A/PuhVew1pJxw2OJ/XybW2e/raeb8Dj2opNXPUURERKQjU9CW9sEVC0m7R0a4h0J0V7NuO1gOvnxTUK18baS42vpI7+5NULkBKjdCWOtHqzRmnXYoBPc8ksmwsQOZtzCajPQAH/9nCU/ev5q4uDCWBddNNNOJ/3FfV/Ly3fTrU8l5Z+Sz39By7vzbei4en09CQrhR59g720yv/mlmHMUlTnp082PbFm++n8KeB+3Oqef34Y950Vs9Li/fWT1S/+pTKwF45c0Uvvk+rk0/i6ldCK0hqx+OObwIgE+2CLatobzcYvyfexMKWZx1aj6nnVDI8ZGq8B9+llS9n89n8f1PZkRbQVtERESkcRS0pf2wLPCmmBHulKGQcTBkHgzpoyB9JKTsB8l7minnnmSzRXUBd6IJ374CjXzT8Mrjq9Z4OPTEAfz1ju4EAg5OOKqQud/P55gjiuvsd9apBXTNqll3fMdf1+/0et3a06uPPKyIhT/O5Y9p8zjthAIsy+adj5LZa/RgThrfh1l/1ATur6clYNsWQwZVcOy4Ik4+djO2bTHmhN3Y++BBPPhkBhs3tf507B2tz97S0WPN9/iLqQkEWrmY91/v6M6SZVF0zfLz2N1mffvxR5rg//nXCfh85pOCH3+NpaLCQWZGgEG7VbbuSYqIiIh0cAra0n5ZDnBGgScRotJNRfO4PpA0BNIOMFvKUEjdH1L3NfuXr93lR7eremkvXhZFSUn9/8Xf+iCJPQ8azHfT44mNDfHswyt575Vl9U579nhsrrl8IwC7D6zg9JM27/Q57rd3OUceVsRl52/ig1eXERNjs8fgSt58fgVzvp/P6SeZwP3+/5LZZ8xgxk/oRTAIX0WmjR9+iAmqT963mjNPKcDrDfPHvBgm/V8Pug3ZkxPP6cv7nyTi97dOcb3lkXXi21ufXduwfcpISw1QVOxi+s9xLXlqdXw5NZ7Hns0A4IXHVpKcZIrl7bNXOV2z/JSWOfnmB/M9/nqauTz0oBLVKBQRERFpJAVt6fgcLojNhtQDTBgvX2daidkhE7p3sVHu9LQg3buakdXf65l+vWSZl7Mu7UNxiZMRw0r5/dv5XHRO/nbD1NUTNvDU/av48LWlOLdut9xoHo/Np28u5an7V+Px1P332X1gJW88u4K538/nzFNM4H71rVSuvrlHdSG0sQeboJ2RHuT1Z1aQM/8PnrxvFfvvU0YwaPHBp0mcdG4/ug3Zg2v+1p3f5279fWhONSPaDQvaTicceah5DVuui24pmwudXHBlLwD+fOFGjhhTMx3c4YBjx5lR7Q8/NedTE7TrznAQERERkR1T0JbOwx0HSXtBfH8IFEHFBvBtqru225cPwbJOP+pdVRDtm+/jt7rvr3d0Ixi0OGJMEd99vIi+vXc83dnlgsvOz6NPr4ZNjW4OgwdW8vozK3j7heUAPP5cBitWeXG7w4weUbcKdnJSiAkX5PHTlwuZ98M8brgyl6zMAHn5bh56KpO9Dx7M0EMGMW1Gy4we76iHdn2OOaJl12lvynPxy28x1Z8zXXVjD9bleOjft5J7bl+31f5V67RffCOVL6fG8+OvWp8tIiIi0lQK2tK5OD2mqFr6gZAxqtb67qFmbbczGoKVpoBa2WoTwgOdr3XREWPMKOTt93Tl3Y+Sqm+fNiOO9z5JxuGweeDOtR2iN/LJxxVy161rq78eMayMuLhtF18bPLCSu29fx5o//uCTN5Zw2gkFeDxhZs+J4biz+rJ4qQnFq9e6+fN1Pfjki5rK35WVFk88l85nUxIIbacFeWmpg0uvyeaF11Kx7caPaIP5N3I4bOYtjGbVmuZtUTZzdgy7HziY/Q8fxHFn9eWRp9N59a1UHA6blx9fSWzs1t+/Iw8r5sjDiigvd3LU6f0JBi169vDRu4HrzkVERESkhoK2dD6WAzxJkYJpSRCVAbE9zdru9FGQOdqE8NT9Ia4fhCqhdBX4i9r6zJvN5Rdu4rwz8giFLM64pDcvv5HCilUerr2lOwAXj89j94Edp8DVDVdt4KJzTE/v045v2BpxlwuOPryYN59fwfp5fzBqeAlFxS5OOKcvX06NZ//DB/HkCxkcd1Y/7nssk1VrPIw6ejcm3pDNUX/qT++he3D73V1YvdZd57i2DRde1ZN/v5zOZddmM+uPaEpKzXz6XtkND6UpySFG7m8+5Hn346QGP25HvpwazyHHD2BTnjnvT75I4i83ZwNw09W5DB9WVu/jnE5458VlHHhAKaGQWUeg9dkiIiIiTWPZdsdbwFpcXExiYiJFRUUkJLRNH1rpRAIlULHejHAHSsGTANgQ8pn1354UE947mFAIzr6sN/99L6XO7XGxIZb8MpeszI41fd62zfryfn18DerVvaUNG13sd9gg1q6vGT3OSA+wcZMJpFFRYSorHSQnme/L5kIz3G9ZNuMOLebic/I47sgiHn46gxtu7159jH33KmPm77F0zfKzbt6cRp3T48+mc8Vfs4mPC/HHtPmNCur1+eW3GA48ejcCAQeHHlTMXbeu4693dGPq9wnss1cZMz5btNWa+C0VFjkZc8IAZs+J4b2Xl3LiMZ3nAygRERFpp4JlZtZp5sHg9Lb12WxTY3KogrZIlWCZmUpevhYsF7gTIFBs1nV7Es20c4cLrGaoBtZKAgG4/rbufPpVIivXePD7HTz4zzVcPWFjW59am/h1VgwHHbsblZUOjh1XyOtPr+D519O45m/dsW2Lffcq452XlpOZHuC9T5J49pU0vp5W8zMmPS1AfoGLcNjionPyeO7VtOr7Rg0vYdonixt1PsEgHHzcbkz/OY6DRpQw9YPFO1Vs7oSz+/LhZ0kcc0Qh77y4HK/XxrbN6x40oHK7U+5rKy+3mDM/mv33LdeItoiIiLQ8Be32QUFbWlQ4YIK2ZZlR7fJ1ULYCwv5IFfNai3ctK7KvKxLCXaYlmaP9LX4Oh6GszEF8fMPCVmf1y28xLFgcxdmnFVSH2qnT4pg1J4bLL9hEdHTdH4nLVnh4/rU0Xng9lZwNZjT8/DPzeP7RVRz1p358/rUpZnbu6fm89MTKRp/PshUe9j54MKVlTu6+bS03XLWhSa9r4WIvg0YMwbJsFv44jwH9Gr5eXERERKRNKWi3Dwra0upCfhO07YAJ4tWb37QSC1eYtd7hIIQra8K6K8YEb9s2+2KDw2t+gHTA6ei7smDQtOJasdrLZedtIirK5ueZMRxwxCAAbv/rem67IadJx37u1VQu/ksvnE6bN55dzqnHFzb6GJdek82/X07nxKM3894ry5t0HiIiIiJtohMG7fY37CbSHjk9Ztse2zYBO1Rm1nr7C8Gfb1qN4QSH24TvYJm53Q6bsO2IMmHc6TX7SLvkcsHxR9Vdr7z/vuWcctxm3vkomRHDml69/sKz8/n+xzhe/E8aZ1zch//8ezmnnVDY4Mdv2Oji5f+mAnDdFU0bERcRERGR5qOgLdJcLKsmkHuSIbYHhEMQKjfruh2RUexQhdmC5ZHQXRC5XmL2j85U4O5AXn9mBQsW57DXkIomH8Oy4NmHV2Hb8NIbaZx5SR/iYpdy1NjiBj3+sWcz8PkcDN+vlJH7119VXERERERaj4K2SEtyOMERX/c2V4zZvKk1t4X8ECyF0mWmGJsrBiy3CebOKAXvdszjsXcqZFdxOuG5R1YRDlu88mYq19/WnXGHzt9hhfVpM+K47/FMAK6buEHFy0RERETaAS0SFWkPnB7wpkDyPpCyjwnXYEa4fXmm9Zh/s5luLp2W0wmP3r2ahPgQ8xZG8/Hnidvdf878KI47qy+VlQ6OP7KQk44tbJ0TFREREZHtUtAWaU8cTojtCemjIfMQUxAibSQkDgGcULbGrP3ueDUMpYESE8JcfsEmACY/lFXvP/XylR4e+3c6407tT1Gxi1HDS3jj2eVN6i8uIiIiIs1PVcdFOoqQz0wrL11h1nYT+a9b1WLM4TLTzR3OSPVzv5ly7k5UhfMOJneDi15D98DnczD1g0WM3L+MaTPi+N9XifzvywQWLomu3nf3gRVM+2QRyUmh7RxRREREpB1T1XERaTNOL8T3heiu5oeRHWkvFvKZYmqhctNiLBQJ2J40UwG9bC24okzgbsc/uKRGVmaQC8/K48kXMjj7st4UlzgpLXNW3+9y2YwaXsrRY4u4eHyeQraIiIhIO6OgLdLRuKLNVp9wCOwgODxmpDvkB9/GmjXeIZ95rDPS39uhHwHt1fVXbuCZl9NZn2vaymWkBzh6bBFHH17EEWOKSUzQen0RERGR9kp/ZYt0Jg4nUDPyidMDMd3NKHig2KzvrtxorgeKI8XVqlaPWLWub8kCTwK44lFZ69bRu6efd15cxvxF0Rx+SDH77FWuNdgiIiIiHYSCtsiuwHKAJ8lscb3MSHeozIyAY0cCd7gmeFdVN3e4AQt8+VCxHspWQVSGaT8mLe6Eo4s44eiitj4NEREREWkkBW2RXZHTY7aGis6CuD6mEFvpMrMm3BVliq7ZYRPIqzbLuePjiYiIiIh0YgraItIwrmhIHGRGxUsW1RRdc7ghFIBQBYQDNaPhlmXuc8WBK7ZNT11EREREpDUpaItIw1kWxHQFbxpg1xRdC9eqgB72Q9hnKqAHSsCXZ6aeuxPBHacRbxERERHp9BS0RaTxtpx2XjWyXd/IdaAYytdD+ToozzEj3rULqjlc4I43ldDV71tEREREOgEFbRFpWe4ESEwwa7wDReAvMkHb6QUsqNhoWpD5Cqmuem45zGh59dpvjxkJVxAXERERkQ5AQVtEWofTA850iEqve3tMN1NcLVQRmXrug2AFBEvMbcFKsEtMf3C7dhB3m2nq9hYtySynGSV3uMGqdWk51ZpMRERERFpFo4eHvvvuO4477ji6du2KZVm8//77W+2zYMECjj/+eBITE4mNjWXYsGGsXr26+v7KykomTpxIamoqcXFxnHLKKWzYsGGnXoiIdGCuGPCmmvXfcb0haTCkHQAZoyFzNGQcBOmjIG0EpOwDcf3Akwrxu0HqMEgbDin7QvKe5vGeNLDcpn1ZoBR8m8zU9bI1Zu24iIiIiEgLavSIdllZGXvttRcXXnghJ5988lb3L1u2jFGjRnHRRRdxxx13kJCQwLx584iKiqre55prruGTTz7hrbfeIjExkSuuuIKTTz6ZH374YedejYh0LpYDnFFma4pwoKZQWzgAFeugdJU5nsNlbgdTqM3pbb7zFhEREZFdmmXbW867bMSDLYv33nuPE088sfq2M844A7fbzSuvvFLvY4qKikhPT+f111/n1FNPBWDhwoUMGjSIGTNmMHz48B0+b3FxMYmJiRQVFZGQkNDU0xeRXY0dhoocKFkC2OCMA0KmKno4YIqyueNVGV1ERESkNQXLzHLBzIPb9eBHY3Jos1YWCofDfPLJJwwYMIBx48aRkZHBAQccUGd6+cyZMwkEAowdO7b6toEDB5Kdnc2MGTOa83REROqyHGZNePooyDgY0oZB6gGQPtL0CMcyldHL1kBFrmlN5i8ya8jDgZr14HYo0rqsAMLBNn1JIiIiItL+NGvQ3rhxI6Wlpdx1110ceeSRfPHFF5x00kmcfPLJfPvttwDk5ubi8XhISkqq89jMzExyc3PrPa7P56O4uLjOJiLSZA5XTQVzywJPMiTsZtaCpw2HxN0huju44s0+wXIz6l2+NhLCc0zAdnhMIC9fZ4K3HWq71yQiIiIi7UazVh0Ph8MAnHDCCVxzzTUA7L333kyfPp2nnnqKgw8+uEnHnTx5MnfccUeznaeISL0cbojONFuVcAjsyDrvkN9cOjymbZnlBH8+VKyHyjwzGo4dqXzurtWSzDKXlgU4akJ+qMKEeMtpwn47niolIiIiIg3XrEE7LS0Nl8vF4MGD69w+aNAgvv/+ewCysrLw+/0UFhbWGdXesGEDWVlZ9R73pptuYtKkSdVfFxcX06NHj+Y8dRGR+jmcgNMUUHPXc39UhtlCPggUQ7A0ss6oLDLdPGS2cAgIm3XiVf3CndGmv3iw3PQSD4cgKq3pxd9EREREpF1o1qDt8XgYNmwYixYtqnP74sWL6dmzJwD77rsvbrebKVOmcMoppwCwaNEiVq9ezYgRI+o9rtfrxevVSI+ItGNOr+kTTvrW99nhmo1wzRRzZ7QZ3bZt8BdA+RozNd2yTCV0O2geYzlqeoE7XCrWJiIiItLONTpol5aWsnTp0uqvV6xYwezZs0lJSSE7O5vrr7+e008/ndGjRzNmzBg+++wzPvroI7755hsAEhMTueiii5g0aRIpKSkkJCRw5ZVXMmLEiAZVHBcR6XCsWtPF673fMn3EPSkQ3RVKlpnRcYfLBGw7aKaZEzRrw+1wzWMdrsi0c42Ci4iIiLQXjW7v9c033zBmzJitbj/vvPN48cUXAXj++eeZPHkya9euZbfdduOOO+7ghBNOqN63srKSa6+9lv/85z/4fD7GjRvHE088sc2p41tSey8R6dSq1oVbrki/76p14gETuqv7gwdMkbbKXLN23BVrNssFoXIIVZoA7orbftAXERERaUudsL3XTvXRbisK2iIiEbYNgSKo3Gi2YKT6uSPa9AQPFJuK6I5a084t19ZT0RXERUREpK10wqDdrGu0RUSklVkWeJLMFt/f/KKyA2YU2+E2o9q+fNMPPFQBYZ8Z/Q4HwK6MrAMP1vQIr10Z3Q5COAwWJoy74yMj5lojLiIiIrI9CtoiIp2FZYE7ru5tziiI6Wa2KrZdMwW9eiq631ROD/siX4fAFVUTrCvzzIh5RU6kQJsLXNGmoJvDGwnoIiIiIgIK2iIiux7LAsttRrwbKrqLCeJV7cv8RaZSeqAEQpsw/cNdNRXVsSP9xuPBGaMgLiIiIrsUBW0REWkYp9ds3lSIxYxshypq+oYHSsyIujPajIpXbgBfAVTmm9Fvd8K2113Zdk3PccvRuA8BRERERNoZBW0REWkay1FT6bw+sdkmgPvyoGwtBAqh0o9Z9L1lHU7LFGyzHJEAH4hMXY83IV1ERESkA1HQFhGRllMVxGN6mGnngcjUc4ezpvJ5VRuzqut2wExNr8wFfyH4NkV6kTsxgbyqarrDjICHQzVF3aCmsBuRNevueBVwExERkValoC0iIi3Pcpip4+4GtmT0JENsz8iU9EIT0O0AhIOmYFuoIlKUzQ2eeHBEmRFwh6cmwAeKoWIdlK0zgduTrLXiIiIi0ioUtEVEpH2qqqK+ZSV1MCHbts3I+DZ1hbg+ZmS8ZAmUrTaj4XbIzFx3OE0bNFd0ZDRdo94iIiLSPBS0RUSk47EcZqn3jjg9Zq24NxVKVwMhM6puuUx/8cqNpmCbHTLhHUzAt+2a6epVU9sdHlPorer+UHnkOVRVXUREROpS0BYRkc7PFQtJg+reFtMVQv6a3uF20ExNr1rvHayM3OeLtDYrN+EcANsEbDC3OTyRaevOLdafRwq8bU/IZ6qs72g/ERER6TAUtEVEZNfl9JhtR2zbrAsPFJnA7YqNrDe3wb8ZKjaaEW47YMK7XREZJQ/WKs4GYJmR+Kqe43bItDwL+c2ouTsBnFGaxi4iItLBKWiLiIjsiGWBK8ZsW3LFQkx3c90O14yKbzVKHgneVaPolhM8KWYNeqAUKnJMKzR/oamk7nCAw2uCt9NrRsxtG8J+8zzOKE1ZFxERaacUtEVERJqL5YiMkDdglLw2dwJEd4FQpRk5D5Wbiuv+zRAog2CBCe5Y4HQDzsg0drtmdN2hX+kiIiLthX4ri4iItAeWZSqgu6KBlJrbwwETvoMVkSAfZS4DpZEWZuuhcoMZ7XZFRYqzOequNw8H6xZ8c3prpqmLiIhIs1PQFhERac8cbrNt2YPcFQvRmRDX24x8+/LN1PNgGWDXVEt3RpvNEQUuL2CZYO4vMFXXq8q3O1ymL7mjViE3rG1MT7fMevRghRmFd3rBFR/5kEBEREQUtEVERDoyhwui0s1m2yb4Vgdtd/1BObYXBEtMUA77zRYsi0xbrzBrxG1frQfY9Vx3gicJ3MkQ2GzCfuVG00qtvt7nIiIiuxAFbRERkc6iavp5Q/ZzJ2w9Sg4mrNepmF5PyLbtyGh5VM3XwVIoXwdlK0xBN8uqmape+3lr9ya3nGaqu9aXi4hIJ6PfbCIiIlLDskwQbsyfCJYF7nhIHGims5etNiHanRiplh6pwl613jxUUdO/vHKDCeTuuMgUd685ZvVj/GaNOUTCeSSgV19X5XUREWl/FLRFRESk+XiSzdYQ4aDpTe7LM+3NAsWm/Rl2ZNq7y1Rx9ySa/UOVkcJulTVF3iwgHDb7uWJMULcswGGKwlkOE9iDZaYHuuWoWfdePb1egV1ERJqXgraIiIi0DYfLrOn2pkJ8fxOEQ+WR+7w1/cMth7nNtuuOjldf90FlvlknHiqqmbJuhyLT3J2meFxsT3NbsMyMlIfKawI7tnkeb5p5XhERkZ2goC0iIiJtz3KY6ePbK6RmWZHK6O6t74vtaUbDw5ERcTscCdohE+hdCSZwVwmHzL52oOZxFRugYk2k4jqRY2HCvivOjJhXhX4REZHtUNAWERGRzsHpMVtDOJzgiAaioSq3R3eByi5QttKMqHuSTbCu3GTaoZUXmqnqVQXcqtqgVa0br5p+Hg6a4nBhvwnnzlhNTRcR2cUoaIuIiIiACcPRWWarLTbbrA/3F5l+5f68SGG3ABAZNQ+HqK7KbjlMcThPinlMZQE4HKZKuzMKiIRyy0F1H/Oqy6pAXlXJPVhm7nMnmOnvCuwiIh2CgraIiIjIjjijIDrKVFWHSK/xYK114sGar53RkYrrLrPu3F9oCr358kzFdTsMhDFT3Kvap21xHcsE67g+5piVm6AsH9yxJsDXXreu8C0i0u4oaIuIiIg0lsMJOIEdFE5zxZiNriYUh/0168ep1ae8umd5rb7lrviaHuPBChPUS5dD2Rrz/LZNvSxHzZR2pycyEu6sf18REWkRCtoiIiIircGyml7R3BUNrh4QlQHl683IuDs+MhXdrjuiHvRF+pRXQqAMynNq1pY7o815BErNMRyuWoXeFMZFRJqLgraIiIhIR+H0Qnzvhu8f8ptCbr48s748WAaEzdT2uD4mcPvzoCK3pi1alap+5FWj91V9yauPXVlTmR1Hzeh9dS9zEZFdl4K2iIiISGfl9NQUeLPtmjXitQurhSrNWnI7GCnsVnUZMEG6egvUmvIORGVCVLoJ1oEiE9YDxRDymfsdrkg/9CjzAYHlNMcOVWDWoEdrFF1EOi0FbREREZFdgWVF1otvoboaegNVrQ2vPWodnQXx/U1gD5WbMO0vgkBhpCBcgXmcwxVpq2ZDRaG5zRlVaxr89p43UuEdq/5e6iIi7YiCtoiIiIg03LamhVsOcMeZDSCWyCh6pQnfYb9ZI+6KjbQvKzEj4BU5JpBXbgQcZhTeDkdG2O26x3e4zH3hADg8kT7lMTVF47alqgCdHakWHw4B4cgl5pxc0Tv3fRERqUVBW0RERERahhWZIl5fiHWmgjcVYnuZwB0sNZu/OBKio8zUc4cLLLcZxXa4TVgOFENlHgQ2g2+TmZKORZ2q7XXOo6oSe63N4QGX14TwQJE5jjMGvCl116KLiDSBgraIiIiItB3LAk+i2RrKkwSx2WZkO1hmRs2re5HbNYXdLGckqLtqLqtanzki68Nt2xzDXwClK0z7NFdMZF16qNYIvmWu1z6m5dJacxGpl4K2iIiIiHRMDrcJ3TvDsmqmvEdlQflaM53dk2TCv+WMFIKrap1WadqnVVVdLy80x3FFm6nxDo8J7sESM8Dujo0Un3M3rRp72B8J9RplF+lIFLRFRERERMCsD4/vY7YdsW0TggNF4N8MlZtMwA4XmhHxuH4mpFduAF9BZHo7gG0Ct23XjJBbDszUd4e5wKqp+u7wmOuWwwR2Z5RaqIl0AAraIiIiIiKNZVmmbZkzA6IyIH6AqbYe9oErrqYyenx/s/bcDkZapwVrFWULmpHx2gXasM39Dq85rjvBHLcyz6wjDxSb/uiETfh2RoEjynxIUH28gLnNFaMK7SJtREFbRERERGRnVbdP26KFmsPZuPXn9UqC6C4mjIcqarZAaU0LtWBpZM14DDiTIwXe8iPV3qNM+HdG1Z2Cbtu1PgAIRC7DkWnwMRo1F9kJCtoiIiIiIh2BwwmOWi3UqoT8Zu245TLrxC3LhOZgqQncFTmmr7mvgLqV2a26xeKcXrOWPFAc2Rdzm8MbKR5nRbaI2iPzVZc1d5pp7+44cMYqtMsuR0FbRERERKQjc3rMVpvDFSnolgQx2aaXebDcjISDmVJuuc3jqtunRaJBsNyE7UCJWX8eLIVQIFLNvXZvc1ekIF28CfjO6Jrj2UHTG91XAJUFpm2aOw5CPnMODnfkQwEVeZPOSUFbRERERKQzsyxTSM0V27D9XTFmi84yX4dDZvSaqvZpVYXcXDXhvD6x2aZAXNkaKFtlWqg5vSZgByvBtzlyTMuMmFcF99rt2Gof3w6bDwxwRqbBa5Rc2i8FbRERERER2TaHE2hir3BXLCQOhJiuZqTcFWvWf4f9kZHyilr90CsibdMC5rodNCPpVtWUdds8lrCp8u6IFINzRptQXttWITxyjFCled5wwIR4V7z5UEEj69LMFLRFRERERKRluRPMVsURbYqubam6QJu/JnCHKsw6dE8SuBPN6HqwJLLuPN+EdIJbH6v2Mc0VsDwQ1QW8yeArBH8elBeAOx7cSXUDeriqQnzViL5lztlq4ocOsktR0BYRERERkfbBsmrWjAOQVP9+7jhTid22TUu18JZB2976aztSoK0q4MdhRrgrcqF0mZniXjtoW86a0XyHy0xdryiEcBhc3kgl9y2qs4d85kMCK/IYy6lgvotq9ByJ7777juOOO46uXbtiWRbvv//+NvedMGEClmXx0EMP1bm9oKCAs88+m4SEBJKSkrjooosoLS1t7KmIiIiIiMiuzLLM9HF33BZb/BZbgmmztuUoujMK4npB2khI3RdS9oW0AyB9FGQcBOmjIfNgyDgYMkab21OGgicVghVQvhbKVpvK7qWrTfE4O2Smyfs3mxBftqZmK19risQFiiPT5P01o/dVfdAr87Z4zPqawnKBYvO8W32wIO1No0e0y8rK2Guvvbjwwgs5+eSTt7nfe++9x48//kjXrl23uu/ss88mJyeHL7/8kkAgwAUXXMCll17K66+/3tjTERERERER2TmuaHBl72gnU8zNm2rCebDcVGYPRaq0uxPAk2zWoduhSHgORKbCR66H/eAvNFPfA8VUj7RXsRzgSoCE/pHq7QETrIPlEI6sZw+VmVF8AG+aOaeq0O7wmHZsKhTX5hodtI866iiOOuqo7e6zbt06rrzySj7//HOOOeaYOvctWLCAzz77jF9++YX99tsPgEcffZSjjz6a++67r95gLiIiIiIi0q5UVWevl9uMlm9LOGCmmVdVcodINXeHmZK+raAcDtYUjytbBRXrzJR2ZyRgB4ojx7XAHRs5lmv7wTscNMdzeFTNvRk1+xrtcDjM+PHjuf7669l99923un/GjBkkJSVVh2yAsWPH4nA4+OmnnzjppJO2eozP58Pn81V/XVxc3NynLSIiIiIi0joctdehN+ZxkZZnrmgzsu7LNiPmrjhTfT1UYUKzvxAqN5hiceEgW69Zx4R6OxwJ97GmGntVNXdHlBkpt5yYiu3bY0f6tFeY65azpp1cU15jJ9HsQfvuu+/G5XJx1VVX1Xt/bm4uGRkZdU/C5SIlJYXc3Nx6HzN58mTuuOOO5j5VERERERGRjsmyICq97m2OyJr06CyI72+mqG9ZPb1qC/nMKLY32Ux7D/nMiHigGAKFECgD20+9IX1LrhiI7Ws+APAXgm8j+PIibdQ8keAdY0J41fNX9We3I2vTrc4Vyps1aM+cOZOHH36Y3377DasZpxzcdNNNTJo0qfrr4uJievTo0WzHFxERERER6VQcTtMSrcH7u00hOSJLecOBSBBuQNB2emt6kcd0g/BuNevQK/PBX2AKxlVNj7eckSntkersnuSaae6dRLO+kmnTprFx40ays2sKCYRCIa699loeeughVq5cSVZWFhs3bqzzuGAwSEFBAVlZWfUe1+v14vV6m/NURUREREREZFscbqCJo8xV4dmTDLE9I6PlJUDYhGmHO3Lp2vEa8g6qWYP2+PHjGTt2bJ3bxo0bx/jx47ngggsAGDFiBIWFhcycOZN9990XgK+//ppwOMwBBxzQnKcjIiIiIiIibc3pNdsupNFBu7S0lKVLl1Z/vWLFCmbPnk1KSgrZ2dmkpqbW2d/tdpOVlcVuu+0GwKBBgzjyyCO55JJLeOqppwgEAlxxxRWcccYZqjguIiIiIiIiHZ6jsQ/49ddfGTp0KEOHDgVg0qRJDB06lFtvvbXBx3jttdcYOHAghx12GEcffTSjRo3imWeeaeypiIiIiIiIiLQ7lm03ZHV7+1JcXExiYiJFRUUkJCS09emIiIiIiIhIJ9eYHNroEW0RERERERER2TYFbREREREREZFmpKAtIiIiIiIi0owUtEVERERERESakYK2iIiIiIiISDNS0BYRERERERFpRgraIiIiIiIiIs1IQVtERERERESkGSloi4iIiIiIiDQjBW0RERERERGRZqSgLSIiIiIiItKMFLRFREREREREmpGCtoiIiIiIiEgzUtAWERERERERaUautj6BprBtG4Di4uI2PhMRERERERHZFVTlz6o8uj0dMmiXlJQA0KNHjzY+ExEREREREdmVlJSUkJiYuN19LLshcbydCYfDrF+/nvj4eCzLauvT2UpxcTE9evRgzZo1JCQktPXpSCei95a0Br3PpDXofSatTe85aQ16n3Vutm1TUlJC165dcTi2vwq7Q45oOxwOunfv3tansUMJCQn6DyYtQu8taQ16n0lr0PtMWpvec9Ia9D7rvHY0kl1FxdBEREREREREmpGCtoiIiIiIiEgzUtBuAV6vl9tuuw2v19vWpyKdjN5b0hr0PpPWoPeZtDa956Q16H0mVTpkMTQRERERERGR9koj2iIiIiIiIiLNSEFbREREREREpBkpaIuIiIiIiIg0IwVtERERERERkWa0ywTtyZMnM2zYMOLj48nIyODEE09k0aJFdfaprKxk4sSJpKamEhcXxymnnMKGDRuq7//9998588wz6dGjB9HR0QwaNIiHH354m8/5ww8/4HK52HvvvXd4frZtc+utt9KlSxeio6MZO3YsS5YsqbPPP//5T0aOHElMTAxJSUmNev3SsjrD++v4448nOzubqKgounTpwvjx41m/fn3jvhHSojrD+6xXr15YllVnu+uuuxr3jZAW1dHfZ998881W77Gq7Zdffmn8N0RaVEd/vwH89ttvHH744SQlJZGamsqll15KaWlp474R0qLa+/vs3Xff5YgjjiA1NRXLspg9e/ZW+zzzzDMccsghJCQkYFkWhYWFDX350kZ2maD97bffMnHiRH788Ue+/PJLAoEARxxxBGVlZdX7XHPNNXz00Ue89dZbfPvtt6xfv56TTz65+v6ZM2eSkZHBq6++yrx58/jb3/7GTTfdxGOPPbbV8xUWFnLuuedy2GGHNej87rnnHh555BGeeuopfvrpJ2JjYxk3bhyVlZXV+/j9fk477TQuv/zynfhOSEvoDO+vMWPG8Oabb7Jo0SLeeecdli1bxqmnnroT3xVpbp3hfQbw97//nZycnOrtyiuvbOJ3RFpCR3+fjRw5ss77Kycnh4svvpjevXuz33777eR3R5pbR3+/rV+/nrFjx9KvXz9++uknPvvsM+bNm8f555+/c98YaVbt/X1WVlbGqFGjuPvuu7e5T3l5OUceeSQ333xzI165tCl7F7Vx40YbsL/99lvbtm27sLDQdrvd9ltvvVW9z4IFC2zAnjFjxjaP8+c//9keM2bMVreffvrp9v/93//Zt912m73XXntt91zC4bCdlZVl33vvvdW3FRYW2l6v1/7Pf/6z1f4vvPCCnZiYuINXKG2pI7+/qnzwwQe2ZVm23+/f7vGl7XTE91nPnj3tBx98sIGvUNqDjvg+q83v99vp6en23//+9+0eW9qHjvZ+e/rpp+2MjAw7FApV7/PHH3/YgL1kyZIGvWZpfe3pfVbbihUrbMCeNWvWNveZOnWqDdibN29u8HGlbewyI9pbKioqAiAlJQUwn1IFAgHGjh1bvc/AgQPJzs5mxowZ2z1O1TGqvPDCCyxfvpzbbrutQeeyYsUKcnNz6zx3YmIiBxxwwHafW9qvjv7+Kigo4LXXXmPkyJG43e4GPY+0vo76PrvrrrtITU1l6NCh3HvvvQSDwQY9h7SNjvo+q/Lhhx+Sn5/PBRdc0KDnkLbV0d5vPp8Pj8eDw1HzJ3V0dDQA33//fYOeR1pfe3qfSeflausTaAvhcJirr76aAw88kCFDhgCQm5uLx+PZau1zZmYmubm59R5n+vTp/Pe//+WTTz6pvm3JkiXceOONTJs2DZerYd/equNnZmY2+Lml/erI76+//vWvPPbYY5SXlzN8+HA+/vjjBj2HtL6O+j676qqr2GeffUhJSWH69OncdNNN5OTk8MADDzToeaR1ddT3WW3PPfcc48aNo3v37g16Dmk7HfH9duihhzJp0iTuvfde/vKXv1BWVsaNN94IQE5OToOeR1pXe3ufSee1S45oT5w4kblz5/LGG280+Rhz587lhBNO4LbbbuOII44AIBQKcdZZZ3HHHXcwYMCAeh/32muvERcXV71Nmzatyecg7VNHfn9df/31zJo1iy+++AKn08m5556LbdtNfh3Scjrq+2zSpEkccsgh7LnnnkyYMIH777+fRx99FJ/P1+TXIS2no77Pqqxdu5bPP/+ciy66qMnnL62nI77fdt99d1566SXuv/9+YmJiyMrKonfv3mRmZtYZ5Zb2oyO+z6SDauu5661t4sSJdvfu3e3ly5fXuX3KlCn1rnfIzs62H3jggTq3zZs3z87IyLBvvvnmOrdv3rzZBmyn01m9WZZVfduUKVPs4uJie8mSJdVbeXm5vWzZsnrXY4wePdq+6qqrtnoNWqPdfnWG91eVNWvW2IA9ffr0xn8jpEV1pvfZ3LlzbcBeuHBh478R0qI6w/vs73//u52enq5aEx1AZ3i/5ebm2iUlJXZpaantcDjsN998s+nfEGkR7fF9VpvWaHcuu0zQDofD9sSJE+2uXbvaixcv3ur+qiIIb7/9dvVtCxcu3KoIwty5c+2MjAz7+uuv3+oYoVDInjNnTp3t8ssvt3fbbTd7zpw5dmlp6TbPLSsry77vvvuqbysqKlIxtA6kM72/qqxatcoG7KlTpzbkWyCtoDO+z1599VXb4XDYBQUFDfoeSMvrLO+zcDhs9+7d27722msb/T2Q1tNZ3m+1Pffcc3ZMTIyCUDvSnt9ntSlody67TNC+/PLL7cTERPubb76xc3JyqrfanyRNmDDBzs7Otr/++mv7119/tUeMGGGPGDGi+v45c+bY6enp9jnnnFPnGBs3btzm8za02uBdd91lJyUl2R988IH9xx9/2CeccILdu3dvu6KionqfVatW2bNmzbLvuOMOOy4uzp41a5Y9a9Ysu6SkpGnfFGk2Hf399eOPP9qPPvqoPWvWLHvlypX2lClT7JEjR9p9+/a1Kysrm/6NkWbV0d9n06dPtx988EF79uzZ9rJly+xXX33VTk9Pt88999ymf1Ok2XX091mVr776ygbsBQsWNP6bIK2mM7zfHn30UXvmzJn2okWL7Mcee8yOjo62H3744aZ9Q6RFtPf3WX5+vj1r1iz7k08+sQH7jTfesGfNmmXn5ORU75OTk2PPmjXL/ve//20D9nfffWfPmjXLzs/Pb9o3RVrcLhO0gXq3F154oXqfiooK+89//rOdnJxsx8TE2CeddFKdN/htt91W7zF69uy5zedt6H+wcDhs33LLLXZmZqbt9Xrtww47zF60aFGdfc4777x6n18jjm2vo7+//vjjD3vMmDF2SkqK7fV67V69etkTJkyw165d25Rvh7SQjv4+mzlzpn3AAQfYiYmJdlRUlD1o0CD7X//6lz7MaWc6+vusyplnnmmPHDmyMS9d2kBneL+NHz/eTklJsT0ej73nnnvaL7/8cmO/DdLC2vv77IUXXqj32LfddtsOn7/2a5D2xbJtVToSERERERERaS4qhygiIiIiIiLSjBS0RURERERERJqRgraIiIiIiIhIM1LQFhEREREREWlGCtoiIiIiIiIizUhBW0RERERERKQZKWiLiIiIiIiINCMFbREREREREZFmpKAtIiIiIiIi0owUtEVERERERESakYK2iIiIiIiISDNS0BYRERERERFpRv8PTnR6be4ar14AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import yfinance as yf\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "import datetime\n",
    "\n",
    "# Step 1: Fetch data using yfinance\n",
    "ticker = 'AAPL'  # Example: Apple Inc.\n",
    "data = yf.download(ticker, start='2020-01-01', end='2023-12-31')['Close'].dropna()\n",
    "\n",
    "# Step 2: Define SARIMA parameters\n",
    "# Example parameters: (p, d, q)(P, D, Q, s)\n",
    "# Adjust based on ACF/PACF analysis or grid search\n",
    "p, d, q = 5, 1, 2  # Non-seasonal orders\n",
    "P, D, Q, s = 1, 1, 1, 12  # Seasonal orders with yearly seasonality (s=12 for monthly data)\n",
    "\n",
    "# Step 3: Fit the SARIMA model\n",
    "sarima_model = SARIMAX(data, order=(p, d, q), seasonal_order=(P, D, Q, s))\n",
    "fit_sarima = sarima_model.fit(disp=False)\n",
    "\n",
    "# Step 4: Fetch new data for 2024\n",
    "new_data = yf.download(ticker, start='2024-01-01', end=datetime.datetime.today().strftime('%Y-%m-%d'))['Close'].dropna()\n",
    "\n",
    "# Step 5: Forecast future values based on the trained model\n",
    "# Forecast for the same length as the new data\n",
    "forecast = fit_sarima.get_forecast(steps=len(new_data))\n",
    "forecast_values = forecast.predicted_mean\n",
    "forecast_conf_int = forecast.conf_int()\n",
    "\n",
    "# Step 6: Plot actual vs predicted values\n",
    "plt.figure(figsize=(12, 6))\n",
    "plt.plot(new_data.index, new_data, label='Actual New Data', color='blue')\n",
    "plt.plot(new_data.index, forecast_values, label='Forecast', color='orange')\n",
    "plt.fill_between(new_data.index, forecast_conf_int.iloc[:, 0], forecast_conf_int.iloc[:, 1], color='orange', alpha=0.2, label='Confidence Interval')\n",
    "plt.title(f'{ticker} Actual vs Forecast (SARIMA)')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "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": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}