code/notebook/bot_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "code/notebook/bot_notebook.ipynb\n",
    "\n",
    "2048-intelligent-bot: Analysis of the bot performance.\n",
    "\n",
    "Author: Filip J. Cierkosz 2022 (updated: 2023)\n",
    "'''\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import sqlite3\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<style>\n",
       ".output_png {\n",
       "    display: table-cell;\n",
       "    text-align: center;\n",
       "    vertical-align: middle;\n",
       "}\n",
       "</style>\n"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Centering plots.\n",
    "from IPython.core.display import HTML\n",
    "HTML(\"\"\"\n",
    "<style>\n",
    ".output_png {\n",
    "    display: table-cell;\n",
    "    text-align: center;\n",
    "    vertical-align: middle;\n",
    "}\n",
    "</style>\n",
    "\"\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create the dataframe for the database records.\n",
    "db = sqlite3.connect('../db/bot_records_2023.db')\n",
    "df = pd.read_sql_query(\"SELECT * FROM bot_records_2023\", db)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>score</th>\n",
       "      <th>win</th>\n",
       "      <th>time_played_sec</th>\n",
       "      <th>date_played</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>298</td>\n",
       "      <td>2048</td>\n",
       "      <td>1</td>\n",
       "      <td>101.329781</td>\n",
       "      <td>21 Aug 2023 06:53:39 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>297</th>\n",
       "      <td>299</td>\n",
       "      <td>2048</td>\n",
       "      <td>1</td>\n",
       "      <td>99.210868</td>\n",
       "      <td>21 Aug 2023 06:55:19 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>298</th>\n",
       "      <td>300</td>\n",
       "      <td>1024</td>\n",
       "      <td>0</td>\n",
       "      <td>47.986255</td>\n",
       "      <td>21 Aug 2023 08:19:30 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>299</th>\n",
       "      <td>301</td>\n",
       "      <td>2048</td>\n",
       "      <td>1</td>\n",
       "      <td>93.459925</td>\n",
       "      <td>21 Aug 2023 08:21:04 AM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>300</th>\n",
       "      <td>302</td>\n",
       "      <td>2048</td>\n",
       "      <td>1</td>\n",
       "      <td>101.825676</td>\n",
       "      <td>21 Aug 2023 08:25:31 AM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      id  score  win  time_played_sec              date_played\n",
       "296  298   2048    1       101.329781  21 Aug 2023 06:53:39 AM\n",
       "297  299   2048    1        99.210868  21 Aug 2023 06:55:19 AM\n",
       "298  300   1024    0        47.986255  21 Aug 2023 08:19:30 AM\n",
       "299  301   2048    1        93.459925  21 Aug 2023 08:21:04 AM\n",
       "300  302   2048    1       101.825676  21 Aug 2023 08:25:31 AM"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the latest 5 records.\n",
    "df.tail(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>score</th>\n",
       "      <th>win</th>\n",
       "      <th>time_played_sec</th>\n",
       "      <th>date_played</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2048</td>\n",
       "      <td>1</td>\n",
       "      <td>105.685521</td>\n",
       "      <td>20 Aug 2023 11:13:32 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2048</td>\n",
       "      <td>1</td>\n",
       "      <td>99.815401</td>\n",
       "      <td>20 Aug 2023 11:15:13 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1024</td>\n",
       "      <td>0</td>\n",
       "      <td>77.247424</td>\n",
       "      <td>20 Aug 2023 11:16:31 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1024</td>\n",
       "      <td>0</td>\n",
       "      <td>98.692646</td>\n",
       "      <td>20 Aug 2023 11:18:11 PM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>1024</td>\n",
       "      <td>0</td>\n",
       "      <td>98.114124</td>\n",
       "      <td>20 Aug 2023 11:19:50 PM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  score  win  time_played_sec              date_played\n",
       "0   1   2048    1       105.685521  20 Aug 2023 11:13:32 PM\n",
       "1   2   2048    1        99.815401  20 Aug 2023 11:15:13 PM\n",
       "2   3   1024    0        77.247424  20 Aug 2023 11:16:31 PM\n",
       "3   4   1024    0        98.692646  20 Aug 2023 11:18:11 PM\n",
       "4   5   1024    0        98.114124  20 Aug 2023 11:19:50 PM"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Show the initial 5 records.\n",
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All sample runs of the bot : 301\n",
      "Bot wins : 211\n",
      "Bot wins : 90\n",
      "Probability for a bot win : 0.7009966777408638\n"
     ]
    }
   ],
   "source": [
    "# Calculate the probability of bot winning a game.\n",
    "df_win = df[df['win']==1]\n",
    "df_loss = df[df['win']==0]\n",
    "win_prob = len(df_win)/len(df)\n",
    "print(f'All sample runs of the bot : {len(df)}')\n",
    "print(f'Bot wins : {len(df_win)}')\n",
    "print(f'Bot wins : {len(df_loss)}')\n",
    "print(f'Probability for a bot win : {win_prob}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Pie chart for win-loss relation.\n",
    "labels = ('Win', 'Loss')\n",
    "results = [len(df_win), len(df_loss)]\n",
    "colors = ['green', 'red']\n",
    "explode = (0, 0.1)\n",
    "fig1, ax = plt.subplots()\n",
    "ax.pie(results, labels=labels, explode=explode, autopct='%1.1f%%', startangle=90, colors=colors, textprops={'fontsize':16})\n",
    "ax.axis('equal')\n",
    "plt.title('Win-Loss Relation', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average time to lose (sec) : 73.32\n",
      "Average time to win (sec) : 100.16\n",
      "Max time to win (sec) : 117.67\n",
      "Min time to win (sec) : 89.43\n"
     ]
    }
   ],
   "source": [
    "# Runtime analysis in seconds (reference: tests were run on a device with M1 chip).\n",
    "avg_time_l = df_loss['time_played_sec'].mean()\n",
    "avg_time_w = df_win['time_played_sec'].mean()\n",
    "max_time_w = df_win['time_played_sec'].max()\n",
    "min_time_w = df_win['time_played_sec'].min()\n",
    "print(f'Average time to lose (sec) : {avg_time_l:.2f}')\n",
    "print(f'Average time to win (sec) : {avg_time_w:.2f}')\n",
    "print(f'Max time to win (sec) : {max_time_w:.2f}')\n",
    "print(f'Min time to win (sec) : {min_time_w:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VpmbNmvFGBBL7elyPPnj00qVLOnLkiBYuXKiBAwemKuxI0scff6y1a9fK09NTX331lc6dO6eoqCjduHHDfo6aNWtKUqI1x43yTJkyxd4n7s/ly5dXrVq1HPo/uhTzsmXLUvSzSqtb1NKSr6+v/YG3a9euTdUtUM7q7t27On36tCSpWLFiDm1vvPGGDhw4oBw5cmjatGm6cuWKIiMjde3aNfu1kjdvXkmJXyvJSc9zPBrgkgomBQsW1J49e7R8+XK99tprqlatmmJjY7V582YNHTpUxYsXdwi3T3o9P84tbH/36O2Hfw+qANIHgQfAP06ePHkkPdmtamb4+eefJUkffvihBg8erIIFC8Z7Axb3CXRiXnjhBXl5eenUqVNas2aNYmJi7M+K+fvojvTwDVncSNnj/Lxy5colScmO3qTH6M7fPfqp/NmzZ+1/jqsxuduu4trj+ptt+fLl9jfwjz4fJyYmRgsWLJAkffvtt+rTp4/9mo/z4MEDXb9+/bHPnd7neHTeTnLzk1xcXNSsWTONHz9eu3bt0s2bNzV79mwVLFhQt27dUvfu3e23uT3p9ZwWvzsefT2JzU8CkLYIPAD+cZ555hlJD8NBZpmPIUkXLlyQJPsDJ//u7NmzOnnyZJLHyJYtm7p16ybp4eIFcfN5PD09E3yavZubm30e1eLFi1Ndc/Xq1SU9nPeS2AM/T5w48UTzSFLq0XM8OroWV+PFixd1/PjxBPd98OCB/ba/v88Zi5s4/yQjgakVHR2tTz75RNLDv9N27drZ265du2a/VSqxa2XTpk2J3k4V93qkxF/Tk54jOdmzZ7eHi7hRrJTy9fVV9+7d7YsTXL161X7b3ZNez7Vr15b08Hp+3FHCM2fOSJKeeuoph/lyANIPgQfAP06DBg1UvHhxSQ9vy0lqkrOU+hWw0kvcilL79u1LsD1uTk5y4m5r+/333/X5559Lir9YwaP69+8vSfrf//6n//3vf0ke++8/q44dO8rV1VWRkZH68ssvE9zno48+SlHdTyIqKso+IuHj46NSpUrZ25o0aWK/tSix2/EmT55sn/sVFxjjxC3GEBoamsZVJywyMlK9e/fWn3/+KUl69913Hf7u/Pz87CN/CV0r9+/f1/vvv5/o8R9dXCKx1/Sk50iJunXrSpJ9IY6/S+7f7aNzZR4NcU9yPXfu3Fl+fn66f/++3njjjccKudu3b5f0f68PQPoj8AD4x8mSJYsmTZqkLFmyaNOmTapbt65Wr17tsLjB6dOnNWnSJNWoUUMTJ040sdr/07x5c0nS6NGjtWDBAt2/f1/Sw0+Mu3fvrl9++UXZs2dP9jjVq1dXtWrVFB0dbX/zldDtbHF69uypxo0byzAMtW/fXqNHj7a/+Zek8PBwrV27Vq+88oqKFi3qsG++fPn0yiuvSJJGjRqlTz/9VGFhYZIejhK8+uqrmjVrVqLLA6eFw4cP67nnnrOvuvXKK684TIT39PS0B52ffvpJAwYMsC9mERERoW+++UaDBw+WJHXt2lXVqlVzOH758uUlSYcOHYq3HHhaiY2N1cGDBzVu3DiVK1dOP/30kyTp+eef19ChQx36+vj42EcxhwwZojVr1ig2NlaSdPDgQbVo0UK7du1KdA6Zv7+/fQGH6dOn26+ztDxHSsTdphd3jf7dli1bVLFiRX311Vc6cuSI/fxxy7IPHDhQ0sPFJipWrGjf70mu52zZstk/JJg7d67at2/vsOR2RESEli5dqrZt2+rOnTsJ1h33eurVq5eKnwaAJ5I+j/cB8E+V1g8eTezBh2vXrk32PNOnTzckGYUKFUqw/bfffrM/RFD//8GVOXLkMLJmzWrfJskYPXp0sq8lsfoTe/BoSvb9+2s/e/askTt3bnt7lixZjGzZstm//+STT+wP4UzuAZ5Tpkyx71e+fPlk67p9+7bRqlUrh5+Ln5+f4e/vb9hsNoea/i4yMtJo3LixvY+rq6uRPXt2+35vv/12iutOSGIPHs2dO7fh5eXlUHPXrl2N6OjoBI/zxhtv2PvZbDYje/bs9oemSjIaNGhg3LlzJ95+MTExRqlSpez9smfPbhQqVMgoVKiQ8euvv6b4dTz6b+fR1+Dv72+4uLg4vI7AwEBj0qRJiR5r165dhre3t71/1qxZ7dd6lixZjB9++MEoVKhQotfoqFGjHPYtUKCAUahQIaNr165pdo7kXL161XBzczMkGcePH4/X/ujvgEf//T76d+bn52ds2LAh3r5Pcj0bhmF88sknDn8nnp6eRkBAgMO2W7duxdvv2LFjhiTD3d3dCAkJSfXPBMDjYYQHwD9Wu3btdPLkSQ0fPlxBQUHy8fFRaGiosmbNqkqVKumll17Sb7/9pn//+99mlyrp4aT7Xbt26cUXX7SvfuXh4aFWrVppxYoVevfdd1N8rE6dOtlvSUpqdCeOn5+fFi9erP/973/q2rWrChYsqHv37ikiIkL58uVT06ZN9emnn9pHUR7l4eGhZcuWafz48apcubLc3d1lGIaCg4P1yy+/aMyYMSmuOyl/f/Do1atX7c87eu6557Rs2TL9/PPP9iW8/27cuHFas2aNOnbsqNy5c+vu3bvy9fVVgwYNNG3aNK1cuTLBJdCzZMmi1atX66WXXlKRIkUUHh6uc+fO6dy5c4nOW0pOXP0hISG6f/++8uTJo6effloDBw7UvHnzdOnSpST/3qpVq6YdO3aoS5cuCgwMVGxsrHx9fdWlSxdt2bIl3sNl/+69997T+PHjVb16dbm5uenixYs6d+6cw6IYT3qO5OTKlUvt27eXJM2ePTtee40aNfTLL79o4MCBqlatmgIDA3Xnzh15eHiocuXKGjp0qI4cOaLg4OB4+z7J9Sw9vI1w37596tevn/322OjoaJUoUULdunXTggULEnzuVNzraN++PQsWABnIZhgZOMsSAOAU5s+fr06dOsnT01OXL19m8jSc0oYNG1SvXj0VK1ZMJ06cSJNloc1iGIZKlCihU6dOaf369czhATIQIzwA8A80YcIESQ8n4BN24Kzq1q2rpk2b6tSpU/r111/NLueJ/PLLLzp16pSaNWtG2AEyGCM8APAP89133+nll1+Wi4uL9u/fr3LlypldEpCoAwcOqHLlyipTpoz279/vsOJaZhEbG6sKFSro6NGj2rt3rypUqGB2ScA/ShazCwAApL9t27bpueee0+3bt+1LDQ8aNIiwA6dXoUIFTZ06VWfPntWVK1fsK8hlJpcvX1bnzp1VpEgRwg5gAkZ4AOAfYN26dWrQoIFcXV1VqFAh9erVS++99579qfMAAFgVgQcAAACAZWW+G2EBAAAAIIUIPAAAAAAsi8ADAAAAwLIIPAAAAAAsi8ADAAAAwLJYj/Rv8uTJo/DwcBUsWNDsUgAAAIB/vPPnz8vb21t//fXXY+3PCM/fhIeHKyYmxuwyAAAAAEiKiYlReHj4Y+/PCM/fxI3sHDp0yORKAAAAAJQrV+6J9meEBwAAAIBlEXgAAAAAWBaBBwAAAIBlEXgAAAAAWBaBBwAAC9u9e7fGjBmjDh06KH/+/LLZbLLZbIn2v3DhgiZOnKjevXurTJkycnFxkc1m07p16xLdp379+vbjJvbl4pL6txyGYWjGjBmqW7euAgIC5OnpqaJFi6p79+7xFhcaMWJEsjXYbDZt2LAh1XUAyNxYpQ0AAAsbNWqUFi5cmOL+8+fP1xtvvJGqczRv3lyFCxdOsG337t06ePCggoODU3XMqKgotW/fXsuXL1dAQICeeeYZeXp66vTp0/rll1/UokULh5WbKleurF69eiV4rEuXLmnVqlXy8vJS1apVU1UHgMwvTQLP7t27tXLlSu3YsUM7duzQpUuXJD38ZObvYmNjtXnzZi1evFirV6/W8ePHFR0drfz586tJkyZ6++23VaRIkUTPtXnzZn388cfatm2boqOjVbZsWb366qt64YUX0uKlAABgKbVq1VLFihVVo0YN1ahRQ4ULF9a9e/cS7V+0aFENHjzY3v/VV1/VH3/8keQ53nnnnUTbatasKUl6/vnnU1X3gAEDtHz5cvXr10/jx4+Xp6enve3KlSvxnpnXrl07tWvXLsFjvf3221q1apXat28vHx+fVNUBIPOzGQmlklRq165dgp8eJXTokydPqkSJEpKkPHnyKCgoSK6urvag5Ovrq//973+qU6dOvH3nz5+vrl27KjY2VnXr1lVgYKBWr16t0NBQvfnmm/ryyy+f9KXYPy3iOTwAACvy8PDQvXv3Evw/OiHNmzfXihUrtHbtWtWvXz9V5zpx4oRKliwpDw8P/fXXX8qWLVuK9tuxY4dq1qypoKAgbdu2Lclb8JJjGIYKFSqkCxcuaPny5WrWrNljHwuAOZ70/XmazOGpVauWPvjgAy1atEhXrlxR1qxZE+1rs9nUpEkTrV69WpcvX9bChQu1YMECnTp1Sr1791ZYWJh69OgR75Obmzdvqm/fvnrw4IHmzZundevWad68eTp69KiKFy+usWPHJnl/MQAAyFizZs2SJLVu3TrFYUeSvv/+e0nSq6+++kRhR5LWrVunCxcuKE+ePGrcuPETHQtA5pQmt7S9/fbbKe5brFixBIfGs2bNqokTJ+q3337T+fPntWXLFtWrV8/ePmXKFN25c0dt27ZVhw4d7Ntz586tzz//XB06dNDYsWNT/ekTAABIH7Nnz5Yk9ezZM1X7rVmzRpJUu3ZtnTp1Sj/99JMuXLignDlzqnnz5gneBZKYuNDVrVs3ubq6pqoOANbgVIsWeHp6qmTJktq5c6cuX77s0LZ06VJJUqdOneLt17JlS3l4eGjVqlWKioqSh4dHhtQLAAAStnXrVp06dUo5cuTQs88+m+L9oqKidPr0aUkPg8+//vUvhzlHH3/8sbp27aoffvhB7u7uyR5r/vz5klI/hwiAdTjVstSxsbE6d+6cpIfzex61b98+SUpwdRV3d3eVL19eUVFROn78ePoXCgAAkvTjjz9Kkp577jm5ubmleL/Q0FD7nwcNGqSWLVvqyJEjCg0N1YIFCxQYGKi5c+fq/fffT/ZYixYt0u3bt1WuXDlVqVIl1a8BgDU4VeD56aefFBISopw5c6p27dr27Xfu3NHt27clSfnz509w37jtcYEJAACYIyYmRr/88ouk1I+sxMbG2v9cunRp/frrrypdurSyZcum9u3ba+bMmZKkb7/9Vnfu3EnyWHG3szG6A/yzOc0tbRcuXNDgwYMlSR999JHDwgd37961/9nLyyvB/b29vSVJYWFhKTrfo2v3P+rUqVMqVqxYio4BAADiW7ZsmW7cuKESJUrYl6VOqUeXjX7hhRfiPbC0RYsWypUrl0JCQrRjx45EFyK4ceOGli9fLhcXF/Xo0SP1LwKAZTjFCE94eLg6dOig69evq127dhowYIDZJQEAgMcUN7KS2sUKJMnPz0/Zs2eXpEQfZhq3PSQkJNHjzJ07VzExMapXr16id4cA+GcwfYQnJiZGnTt31q5du1SnTh3NmTMnXp9HP+2JiIiQn59fvD7h4eGSJF9f3xSdN7F1vBMb+QEAAMm7c+eOFi9eLOnxAo8kVa5cWWvXrtWtW7cSbL9586YkJfkQUW5nAxDH1BGe2NhY9erVS8uWLVPlypW1ePFihycpx/Hz87Ov33/x4sUEjxW3vVChQulXMAAASNK8efMUFRWlZ555RkWLFn2sY7Rp00aSEny+3vnz53X27FlJSnQhgtOnT2vr1q3y9PRUx44dH6sGANZhauD517/+pZ9++kklS5bUihUr5O/vn2jfSpUqSZL27NkTry0mJkYHDx6Uh4eHSpYsmV7lAgCAZKRmZKV06dIqXbq0Ll265LC9b9++9tXYFi1aZN8eERGhQYMG6f79+2rRooUKFCiQZA1t27ZN8K4QAP8spt3SNmzYME2cOFEFCxbUypUrlStXriT7t2zZUhs2bNC8efPiDZEvWbJEUVFRatWqFc/gAQDgEUuXLtWoUaPs30dHR0uSnn76afu2Dz74QC1btpQkXblyRe3bt7e3HT16VNLDJaLjwkPLli31wQcfxDvXxYsXtX79erm7u6tLly7J1nbs2DFJDz+4fJSfn59mzZql1q1bq127dqpZs6aeeuopbd++XZcvX1bhwoX13XffJXrcuAeecjsbAMmkwPPVV1/p448/Vp48ebRq1SoVLFgw2X1eeuklffzxx1q4cKEWLFigDh06SHo4YXHo0KGSpDfffDNd6wYAILO5du2atm/fHm/7o9uuXbtm//O9e/cS7H/kyBH7n0uXLp3guebMmaPY2Fi1bNnSvvDA42rWrJl27typjz76SBs2bNDu3btVoEABvfHGG3rvvfcUGBiY4H47duzQ8ePHlStXLjVt2vSJagBgDTbDMIwnPcjfPz3asWOHDMNwWIoy7tOjvXv3qmrVqjIMQ7Vq1Ur0FrSXXnpJderUcdg2f/58denSRYZhqH79+sqRI4dWrVql0NBQDRkyRGPHjn3Sl2JftCCxRQ0AAAAAZJwnfX+eJiM8qfn0KDQ0VHEZa+vWrdq6dWuCx6xfv368wNOxY0dt2LBBo0eP1rZt2xQdHa2yZcvq1VdfVa9evdLipQAAAACwkDQZ4bESRngAAAAA5/Gk78+d4sGjAAAAAJAeCDwAAAAALMu0ZakBAMg05tjMrgCZVXdmDgBmY4QHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAgEbt379aYMWPUoUMH5c+fXzabTTabLdn9ZsyYoaCgIPn4+CggIEAtWrTQli1bktxn8+bNatGihQICAuTj46OgoCD98MMPqa75+vXrmjp1qvr376/KlSsrS5YsstlsmjFjRor2X79+vTp27Kg8efIoa9asyps3r5599lktWrQo1bUAAOAMsphdAAA4q1GjRmnhwoWp2mfw4MEaP368PD091bRpU0VFRWnlypX6448/NG/ePLVr1y7ePvPnz1fXrl0VGxurunXrKjAwUKtXr1avXr20f/9+ffnllyk+/6ZNm/TSSy+lquY4I0aM0MiRI5U1a1Y988wzypUrly5duqSNGzcqX758atOmzWMdFwAAMxF4ACARtWrVUsWKFVWjRg3VqFFDhQsX1r179xLtv2rVKo0fP145cuTQ1q1bVaJECUnS1q1bVb9+ffXp00f169eXv7+/fZ+bN2+qb9++evDggebPn68OHTpIkq5evao6depo7NixatWqlerXr5+imnPnzq1BgwapevXqqlGjhr755ht9//33ye43Y8YMjRw5UjVr1tS8efOUP39+e1tERIROnz6dovMDAOBsCDwAkIi33347Vf3HjRsnSRo2bJg97EgPg9OAAQP0zTffaOrUqXrzzTftbVOmTNGdO3fUtm1be9iRHgaXzz//XB06dNDYsWNTHHhq1aqlWrVq2b93cUn+zuXIyEi99dZb8vX11e+//648efI4tHt5eal8+fIpOj8AAM6GOTwAkAYiIyO1Zs0aSVKnTp3itcdtW7x4scP2pUuXJrpPy5Yt5eHhoVWrVikqKiqtS7ZbsGCBbty4oc6dO8cLOwAAZHaM8ABAGjh27Jju3bunnDlzOtwOFqdq1aqSpP379zts37dvn0P7o9zd3VW+fHnt2rVLx48fV8WKFdOhctmDWu3atRUaGqrZs2fr4MGD8vT0VO3atdWuXTtlycJ/FwCAzIn/wQAgDZw/f16SEgw7kuTt7S1/f3/dunVLYWFh8vX11Z07d3T79u0k98ufP7927dqlc+fOpVvgOXz4sCTp2rVrKlu2rK5cuWJv++qrr1ShQgUtXbpUBQoUSJfzAwCQnrilDQDSwN27dyU9nO+SGG9vb0lSWFiYwz5J7ff3fdLDrVu3JEkffPCBAgICtHHjRt25c0fbt29X1apVdeDAAXXs2FGGYaRbDQAApBcCDwD8w8XGxkqSXF1dtWzZMtWpU0e+vr4KCgrSsmXL5O3trZ07d2rVqlUmVwoAQOoReAAgDfj4+Eh6uIRzYsLDwyVJvr6+Dvsktd/f90kPcXU0atQo3m1ruXLlUsuWLSU9fCgpAACZDYEHANJAwYIFJUkXL15MsD08PFyhoaHKnj27Pbz4+fkpW7ZsSe4Xt71QoUJpXbJd3LELFy6cYHvc9pCQkHSrAQCA9ELgAYA0UKpUKWXNmlXXrl3TpUuX4rXv2bNHkuItPFCpUiWH9kfFxMTo4MGD8vDwUMmSJdOh6oeqVKki6f/m8vzdzZs3JTmOSAEAkFkQeAAgDXh6eqphw4aSpF9//TVe+7x58yRJrVu3dtged7tYXPujlixZoqioKDVu3FgeHh5pXbJdmzZtJElbtmxRTEyMQ1tsbKw2bdok6f+CEQAAmUmaBJ7du3drzJgx6tChg/Lnzy+bzSabzZbsfjNmzFBQUJB8fHwUEBCgFi1aaMuWLUnus3nzZrVo0UIBAQHy8fFRUFCQfvjhh7R4GQDwRIYMGSJJGj16tE6cOGHfvnXrVk2ePFn+/v568cUXHfZ56aWX5Ofnp4ULF2rBggX27SEhIRo6dKgk6c0334x3rtKlS6t06dIJjialVuXKldWkSROdO3dOw4YNc1iNbfTo0Tp69Khy5cqlDh06PPG5AADIaDYjDdYZbdeunRYuXBhve1KHHjx4sMaPHy9PT081bdpUUVFRWr16tQzD0Lx589SuXbt4+8yfP19du3ZVbGys6tatq8DAQK1evVqhoaF688039eWXXz7pS1G5cuUkSYcOHXriYwHI3JYuXapRo0bZv9+xY4cMw1DNmjXt2z744AP7KI30f7/bvLy81KRJE0VHR2vlypXJ/m7r0qWLDMNQ/fr1lSNHDq1atUqhoaEaMmSIxo4dG2+fuA+Vzpw5E2/uzdNPP23/85kzZxQSEqKiRYsqZ86ckh4+5HTixIkO+1y4cEG1atXSpUuXVLJkSVWoUEFHjhzR4cOH5enpqUWLFqlx48Yp/+FZzZzkP8QDEtSd5dyBJ/Wk78/TJPB89tlnCg8PV40aNVSjRg0VLlxY9+7dSzTwrFq1Sk2aNFGOHDm0detWlShRQtLDT0Hr168vLy8vnTlzRv7+/vZ9bt68qSJFiujOnTuaP3++/ZPGq1evqk6dOjp58qTWrl2r+vXrP9FrIfAAiDNjxgz16dMnyT7Tp09X79694+337bff6siRI3J3d9fTTz+tDz74QLVr1070OJs3b9bo0aO1bds2RUdHq2zZsnr11VfVq1evBPsnFXiSG2GvV6+e1q1bF2/7tWvXNHLkSC1evFhXrlxR9uzZ1bBhQ73//vsqX758kse0PAIPHheBB3hiThF4/s7DwyPJwNOiRQstW7ZMX331lQYPHuzQ9vrrr+ubb77Rl19+6XAbx+eff663335bbdu21e+//+6wz2+//aYOHTqoVatWWrx48RPVTuABAMRD4MHjIvAAT+xJ359n+KIFkZGRWrNmjSSpU6dO8drjtv09uCxdujTRfVq2bCkPDw+tWrVKUVFRaV0yAAAAgEwqwwPPsWPHdO/ePeXMmVP58+eP1161alVJ0v79+x2279u3z6H9Ue7u7ipfvryioqJ0/PjxdKgaAAAAQGaU4YHn/PnzkpRg2JEkb29v+fv769atWwoLC5Mk3blzR7dv305yv7jt586dS+uSAQAAAGRSWTL6hHfv3pUkeXl5JdrH29tboaGhCgsLk6+vr32fpPbz9vaWJHtISk7cvYB/d+rUKRUrVixFxwCQPNtI5j7g8RjDmfsAAHhyPHgUAAAAgGVl+AiPj4+PJCkiIiLRPuHh4ZIkX19fh33i9vPz80t2n+QktspDYiM/AAAAADKfDB/hKViwoCTp4sWLCbaHh4crNDRU2bNnt4cXPz8/ZcuWLcn94rYXKlQorUsGAAAAkElleOApVaqUsmbNqmvXrunSpUvx2vfs2SNJqlixosP2SpUqObQ/KiYmRgcPHpSHh4dKliyZDlUDAAAAyIwyPPB4enqqYcOGkqRff/01Xvu8efMkSa1bt3bY3rJlS4f2Ry1ZskRRUVFq3LixPDw80rpkAAAAAJmUKYsWDBkyRJI0evRonThxwr5969atmjx5svz9/fXiiy867PPSSy/Jz89PCxcu1IIFC+zbQ0JCNHToUEnSm2++mQHVAwAAAMgs0iTwLF26VE8//bT9Kzo6WpIcti1dutTev3Hjxnr99dd148YNVa5cWe3atVOLFi1Ut25d3b9/X9OnT5e/v7/DOQICAjRt2jS5uLioU6dOatiwoTp37qxSpUrp5MmTGjJkiOrXr58WLwcAAACARaTJKm3Xrl3T9u3b421/dNu1a9cc2r7++mtVrlxZ3377rVauXCl3d3c1btxYH3zwgWrXrp3geTp27KgNGzZo9OjR2rZtm6Kjo1W2bFm9+uqr6tWrV1q8FAAAAAAWYjMMgye7PSJuWerElq0GkDo8eBSPy6kePDqH6xiPqbsTXcdAJvWk78958CgAAAAAyyLwAAAAALAsAg8AAAAAyyLwAAAAALAsAg8AAAAAyyLwAAAAALAsAg8AAAAAyyLwAAAAALAsAg8AAAAAyyLwAAAAALAsAg8AAAAAyyLwAAAAALAsAg8AAAAAyyLwAAAAALAsAg8AAAAAyyLwAAAAALAsAg8AAAAAyyLwAAAAALAsAg8AAACc3s6dO9WlSxflzZtXbm5u8vf3V3BwsKZPny7DMOL1v3jxogYMGKCCBQsqa9asyps3r3r37q0zZ848cS3R0dEqW7asbDabsmTJkmCfDRs2qF+/fqpatapy584td3d3BQQEqEGDBvrxxx8TrBnpg8ADAAAApzZ//nzVqlVLv/76q5566il16NBBVatW1bZt29S3b1/17NnTof/BgwdVpUoVTZ48Wa6urmrVqpVy5cqlmTNnqnLlytq3b98T1fPJJ5/o6NGjSfZZtGiRpkyZovDwcFWpUkUdO3ZU+fLltXHjRr3wwgvq0aPHE9WAlLMZxEsH5cqVkyQdOnTI5EoAa7CNtJldAjIpY7gT/fc0h+sYj6m7E13HmdT9+/eVL18+hYSEaPbs2erevbu97ciRI6pTp45u3rypNWvWqEGDBjIMQ5UqVdKBAwfUt29fTZ482T4KM2HCBL322msqW7as9u/fL1dX11TXc+TIEVWuXFm9e/fWd999J1dXV92/fz9ev8OHD8vf31958+Z12H7y5EnVrVtXV65c0eLFi9WqVatU1/BP86TvzxnhAQAAgNM6evSoQkJCVKpUKYewI0llypSxj+7s3LlTkrR582YdOHBAAQEBGj9+vMMtZ//6179Uu3ZtHT58WEuWLEl1LYZhqH///vL399eYMWOS7Fu2bNl4YUeSihcvrkGDBkmS1qxZk+oakHoEHgAAADitrFmzpqhfjhw5JEm7d++WJFWrVk0+Pj7x+jVo0ECStHDhwlTXMnnyZG3atEljx45V9uzZU71/HDc3N0mSu7v7Yx8DKUfgAQAAgNMqWrSoihUrpmPHjmnOnDkObUeOHNGsWbOUPXt2tW/fXpIUHh4uSYkGkrhglNp5PFeuXNE777yjRo0axZszlBoXLlzQpEmTJEktWrR47OMg5RJeVgIAAABwAq6urpo5c6ZatWqlHj16aOzYsSpRooRCQkK0ceNGlS1bVjNmzFBAQIAkKWfOnJKkc+fOJXi8uFXaEmtPzKuvvqqoqChNnDgxVftt3bpVkydP1oMHD3T58mVt2rRJ9+/f1+jRo1W3bt1UHQuPh8ADAAAAp/bMM89o/fr1at++vfbs2aM9e/ZIenhLWJMmTVS0aFF737gQsXPnTh0+fFhly5a1t0VEROiXX36RJIWFhaX4/AsXLtSCBQs0fPhwlSxZMlW1nzp1SjNnzrR/7+rqqo8++khvvfVWqo6Dx8ctbQAAAHBqP/30k4KCglSgQAFt375dd+/e1fHjx9W7d2+NHTtWDRs21L179yRJpUqVUvv27RUbG6s2bdpozZo1CgsL0759+9SyZUvduHFDkuTikrK3wWFhYXr11VdVsmRJvfvuu6muvWfPnjIMQ/fu3dOxY8f0zjvv6KOPPlK9evV069atVB8PqUfgAQAAgNM6ceKEevXqpcDAQC1ZskRBQUHy9vZWiRIlNHnyZLVq1Up79uzRtGnT7PtMnTpVdevW1alTp9SoUSP5+fmpcuXK2r17tz7//HNJic/x+bv33ntPFy9e1MSJE1O8gEJC3N3dVbJkSY0ePVqffvqptm/frg8//PCxj4eUI/AAAADAaf3888+KiYlR8+bNE1x1rUuXLpKkDRs22Ldlz55d69at09KlS/Xvf/9b/fv312effaYjR46oQoUKkv7v2S7JWbx4sTw8PDRq1CjVr1/f4UuSHjx4YP9+7969KTrm888/L+nxVopD6jGHBwAAAE7r4sWLkqRs2bIl2B63/e+3h9lsNrVo0SLeSmhTp06VJHtgSYmoqCitX78+0fa4ttDQ0BQdLyAgQC4uLrp27VqKa8DjI/AAAADAaeXJk0eStGvXrgTb4x44Wrhw4WSPFRERoalTp8rd3V29evVK0fnPnj2baJvNZpOrq6vu37+fomPF2bhxo2JjY1WsWLFU7YfHwy1tAAAAcFpt27aV9PCWtf/+978Obdu2bdNXX30lSerUqZN9+/Hjx3Xnzh2Hvjdv3lTXrl11/vx5vffee8qfP79D+44dO1S6dGk1atQoTer+4osvElyUYOfOnerXr58kqU+fPmlyLiSNER4AAAA4rapVq+qtt97Sl19+qUGDBuk///mPypYtq8uXL2vr1q2KjY1V//791bhxY/s+c+bM0WeffaYaNWooX758un37tjZu3Ki7d++qd+/e+uCDD+KdJyIiQseOHVNUVFSa1D106FANGzZMVapUUeHChRUdHa3Tp0/bH3japUsXvf7662lyLiSNwAMAAACn9sUXX6h27dqaNGmSdu/erWPHjsnX11f16tVTv3791K1bN4f+DRs21N69e7V7925t375dPj4+ql27tgYMGKD27dtnSM0TJkzQ2rVrtXfvXh08eFAxMTHKmTOn2rZtq969e6tdu3YZUgckm2EYhtlFOJO4FTsOHTpkciWANdhG2swuAZmUMdyJ/nuaw3WMx9Tdia5jIJN60vfnzOEBAAAAYFkEHgAAAACWReABAAAAYFksWgAAAPAPMNI20uwSkEkNN4abXcITYYQHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYlumBZ+fOnerSpYvy5s0rNzc3+fv7Kzg4WNOnT5dhGPH6P3jwQF999ZUqVKggT09P5cyZU126dNGRI0dMqB4AAACAM8ti5snnz5+vrl276sGDB6pataqCg4N17do1bdy4UZs2bdKqVas0e/Zse//Y2Fh17txZv/32m/z9/dWyZUtdv35d8+bN09KlS7V27VoFBQWZ+IoAAAAAOBPTRnju37+vQYMG6cGDB5o9e7Z2796tuXPnas2aNdq/f78CAgI0Z84crV271r7PtGnT9Ntvv6lEiRI6evSo5s2bp3Xr1unXX39VRESEevToofv375v1kgAAAAA4GdMCz9GjRxUSEqJSpUqpe/fuDm1lypRRz549JT285S3OuHHjJEmff/65cufObd/esWNHtWnTRidPntTChQszoHoAAAAAmYFpgSdr1qwp6pcjRw5J0pkzZ3TkyBF5enqqZcuW8fp16tRJkrR48eK0KxIAAABApmZa4ClatKiKFSumY8eOac6cOQ5tR44c0axZs5Q9e3a1b99ekrRv3z5JUvny5eXm5hbveFWrVpUk7d+/P50rBwAAAJBZmBZ4XF1dNXPmTPn7+6tHjx6qVq2annvuOTVs2FAVK1ZU/vz5tXr1agUEBEiSzp8/L0nKnz9/gseL237u3LmMeQEAAAAAnJ6pq7Q988wzWr9+vdq3b689e/Zoz549kiR3d3c1adJERYsWtfe9e/euJMnLyyvBY3l7e0uSwsLCUnTucuXKJbj91KlTKlasWIpfAwAAAADnZepzeH766ScFBQWpQIEC2r59u+7evavjx4+rd+/eGjt2rBo2bKh79+6ZWSIAAACATMy0EZ4TJ06oV69eypUrl5YsWSIfHx9JUokSJTR58mRdvnxZS5Ys0bRp0zRw4EB7e0RERILHCw8PlyT5+vqm6PyHDh1KcHtiIz8AAAAAMh/TRnh+/vlnxcTEqHnz5vYw86guXbpIkjZs2CBJKliwoCTp4sWLCR4vbnuhQoXSo1wAAAAAmZBpgScuoGTLli3B9rjtt27dkiRVqlRJknTw4EHFxMTE6x83/6dixYppXisAAACAzMm0wJMnTx5J0q5duxJsj3vgaOHChSVJRYoUUZkyZRQZGamlS5fG6z9v3jxJUuvWrdOhWgAAAACZkWmBp23btpIe3rL23//+16Ft27Zt+uqrryT93wNFJWnIkCGSpKFDhyokJMS+fcGCBVq0aJGKFy9uPy4AAAAAmBZ4qlatqrfeekuSNGjQIJUvX15dunRRnTp19Mwzzyg8PFz9+/dX48aN7fv07dtX7du314kTJ1S6dGl17txZDRo0UKdOneTp6alZs2YpSxZTV9oGAAAA4ERMXZb6iy++0IIFC9S0aVP99ddf+u2333T48GHVq1dPc+bM0eTJkx36u7i46Ndff9XYsWOVN29eLVmyRAcOHFDHjh21a9cu1axZ06RXAgAAAMAZmT4c0r59e7Vv3z7F/V1dXTVkyBD77W0AAAAAkBhTR3hgTevWrZPNZkv266OPPrLvs2jRIvXq1UsVKlRQYGCg3NzclCtXLrVo0UJLlixJdQ0zZszQc889pzJlyiggIEDu7u7KmzevOnXqpM2bNye4z+7duzVixAjVrl1b/v7+cnd3V4ECBdSzZ0/t37//sX8eAAAAMI/pIzywnjx58qhXr14Jtj148ECzZs2SJAUHB9u3//DDD1qwYIHKlSunmjVrytfXV2fPntWyZcu0bNkyvfvuu/rkk09SXMO3336rffv2qUKFCqpTp448PDx07NgxzZ8/XwsWLNDEiRM1YMAAe//79++revXqkqSAgADVrl1b3t7e+vPPPzV79mz9+uuvmj17tsMiGgAAAHB+NsMwDLOLcCblypWTJB06dMjkSqxp2bJlatGihQoUKKBz587JZrNJkv78808VLFhQOXLkcOi/fft2NW7cWOHh4fYAkxLbt29X2bJl5evr67B90aJF6tChg9zc3HThwgUFBgZKehh4ateurffff1+tWrWSq6urJCk2NlYffvihPv74Y/n6+ur06dP2fZAytpE2s0tAJmUMd6L/nuZwHeMxdXee63ikbaTZJSCTGm4MN/X8T/r+nFvakKHiRnd69OhhDzuSVKVKlXhhR5Jq1qyprl27yjAMrV27NsXniRsl+rs2bdqofv36ioqK0pYtW+zbs2TJoh07dqht27b2sCM9XChj1KhRKlWqlMLCwhJ8BhQAAACcF4EHGSY8PFwLFy6UJD3//PMp3s/NzU2S5O7uniZ1pPZ4NptNFStWlCRdvnw5TWoAAABAxiDwIMMsWLBA4eHhqlKlisqWLZuifQ4cOKC5c+fKzc1NTZo0eeIaVq9erTVr1ih79ux6+umnU7zf6dOnJT2cnwQAAIDMg0ULkGHibmdLanRn8eLFmj9/vmJiYnT+/Hlt2bJFbm5u+v7771WsWLFUn3P69Olav369oqKidOrUKe3atUvZsmXTTz/9JH9//xQdY9OmTdq9e7fc3d3VvHnzVNcAAAAA8xB4kCGuXLmi1atXy9XVVd26dUu03759+zRz5kz7956enho/fnyqboF71ObNmx2OFxAQoO+//17NmjVL0f537txR3759JUlvvPGGnnrqqceqAwAAAObgljZkiJ9++kkPHjxQkyZNkrwtbNiwYTIMQ5GRkTpw4ID69Omj/v37q23btoqOjk71eadMmSLDMBQWFqZdu3apcePG6tixo/r375/svg8ePFCPHj104sQJBQUFOTw3CAAAAJkDgQcZIiW3sz3Kw8ND5cuX13/+8x/961//0pIlSzRhwoTHPr+Pj4+qVaumuXPnqk2bNvr+++81f/78JPcZOHCglixZolKlSmnp0qVptmgCAAAAMg6BB+nuyJEj+vPPP+Xj46N27dqlev+4kBS3wtuT6tmzZ7LHe+edd/T999+rQIECWrlyJc/eAQAAyKQIPEh3P/74oySpQ4cO8vLySvX+cWHj2rVraVJPcsf7/PPP9dlnnylXrlxauXKlChQokCbnBQAAQMYj8CBdGYahOXPmSErds3cetX79ekl6rFXaUnu877//Xm+//bb8/f21YsUKlSpVKk3OCQAAAHMQeJCuNm7cqHPnzilfvnxq2LBhgn2uXbum77//XhEREfHaVq5cqaFDh0qS+vTp49C2Y8cOlS5dWo0aNXLYfuTIEf3yyy/xFjkwDEM///yzPv/8c9lsNvXq1cuhfd68eRowYIB8fHz0v//9T5UrV07tywUAAICTYVlqpKu4xQq6d+8uF5eE83V4eLj69++vwYMHq1q1asqfP7/Cw8N1/PhxHT16VNLDJaE7duzosF9ERISOHTumqKgoh+1Xr15V165dlS1bNlWrVk158uRRaGioDh8+rLNnz8rFxUXjxo1TjRo17PuEhISoR48eio2NVZEiRTR58mRNnjw5Xq3t2rV7rHlIAAAAMAeBB+nm3r17mjdvnqT/WyggIbly5dLnn3+udevW6dChQ9q1a5diY2P11FNP6bnnntPLL7+s+vXrp/i85cqV00cffaR169bp+PHj2rx5s1xcXJQ/f3717dtXr7zyiqpWreqwT0REhH1E6MCBAzpw4ECCxy5cuDCBBwAAIBOxGYZhmF2EMylXrpwk6dChQyZXAliDbaTN7BKQSRnDnei/pzlcx3hM3Z3nOh5pG2l2CcikhhvDTT3/k74/Zw4PAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLFZpc2Y2JsniMbEWCQAAgCRGeAAAAABYGIEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYFoEHAAAAgGUReAAAAABYllMEnmvXrumtt95SqVKl5OnpqYCAAFWtWlX//ve/E+y/ePFi1atXT35+fvLz81P9+vW1dOnSDK4aAAAAgLMzPfDs3r1bZcqU0dixY+Xm5qa2bdvq6aef1s2bN/XVV1/F6//111+rTZs22rJli5555hk1bNhQO3bsUKtWrfTtt9+a8AoAAAAAOKssZp782rVrat68uSIjI7Vw4UK1adPGoX3Hjh0O3x87dkxvvfWWsmbNqrVr16pWrVqSpOPHj6t27dp644031Lx5cxUvXjzDXgMAAAAA52XqCM/w4cN1/fp1ffHFF/HCjiQFBQU5fD9+/Hg9ePBAAwYMsIcdSSpZsqTef/993b9/X+PHj0/3ugEAAABkDqYFnsjISM2aNUve3t7q06dPivaJm6fTqVOneG1x2xYvXpx2RQIAAADI1Ey7pW3Xrl0KCwtTnTp15OnpqWXLlmnlypWKiopSyZIl1aVLF+XNm9fePzQ0VOfPn5ckValSJd7xChQooMDAQJ07d0537tyRn59fhr0WAAAAAM7JtMBz+PBhSVKuXLnUrl07LVy40KH9vffe09SpU9WtWzdJsoed7Nmzy9vbO8Fj5s+fX9evX9e5c+dUoUKFJM9frly5BLefOnVKxYoVS9VrAQAAAOCcTLul7datW5KkRYsWafny5frPf/6jkJAQnT17Vm+99ZYiIyPVq1cv7d27V5J09+5dSZKXl1eix4wLQmFhYelbPAAAAIBMwbQRntjYWEnS/fv39fHHH2vQoEH2ti+++ELnzp3Tr7/+qi+++EKzZ89O8/MfOnQowe2JjfwAAAAAyHxMG+Hx8fGx/zmhRQvitq1fv96hf0RERKLHDA8PlyT5+vqmWZ0AAAAAMi/TAk+hQoUkPbxFLWfOnPHaCxcuLEkKCQmRJBUsWFDSw1vh4oLN3128eNHh2AAAAAD+2UwLPHErrUVGRurevXvx2m/evCnp/0Z2/P397aHnzz//jNf/woULun79ugoVKsQKbQAAAAAkmRh4ChYsqEqVKskwDPtta4+K2/boEtQtW7aUJM2bNy9e/7htrVu3To9yAQAAAGRCpgUeSRo6dKgk6a233tKVK1fs2/fu3auxY8dKkgYMGGDf/vrrr8vV1VWTJk3Stm3b7NtPnDihjz/+WFmyZNHrr7+eQdUDAAAAcHamrdImSd27d9cff/yhmTNnqmzZsqpdu7YiIyO1ZcsW3bt3T/369VPnzp3t/UuVKqUvvvhCQ4YMUXBwsJo0aSJ3d3f98ccfioyM1DfffKPixYub+IoAAAAAOBNTA48kTZ8+Xc8884wmT56sdevWyWazqWrVqnr55ZfVq1eveP3feOMNFS9eXF988YU2btwoSapevbqGDh2qVq1aZXT5AAAAAJyY6YHHZrOpX79+6tevX4r3ad26NXN1AAAAACTL1Dk8AAAAAJCeCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALIvAAwAAAMCyCDwAAAAALMtpAs+NGzeUK1cu2Ww2FS9ePMm+M2bMUFBQkHx8fBQQEKAWLVpoy5YtGVQpAAAAgMzCaQLPm2++qevXryfbb/DgwerTp48OHjyoxo0bKygoSCtXrlTdunX1+++/p3+hAAAAADINpwg8q1ev1syZM9WvX78k+61atUrjx49Xjhw5tG/fPv3+++9avny5NmzYIFdXV/Xp00ehoaEZUzQAAAAAp2d64ImMjNTLL7+ssmXL6q233kqy77hx4yRJw4YNU4kSJezba9WqpQEDBig0NFRTp05N13oBAAAAZB6mB56RI0fq9OnTmjRpktzc3BLtFxkZqTVr1kiSOnXqFK89btvixYvTp1AAAAAAmY6pgWf//v0aO3as+vTpo+Dg4CT7Hjt2TPfu3VPOnDmVP3/+eO1Vq1a1HxMAAAAAJBMDT2xsrF566SX5+/vr888/T7b/+fPnJSnBsCNJ3t7e8vf3161btxQWFpamtQIAAADInLKYdeIJEyZo586dmj59unLkyJFs/7t370qSvLy8Eu3j7e2t0NBQhYWFydfXN8njlStXLsHtp06dUrFixZKtBwAAAIDzM2WE5/z58xo2bJjq1aun3r17m1ECAAAAgH8AU0Z4XnnlFUVHR2vSpEkp3sfHx0eSFBERkWif8PBwSUp2dEeSDh06lOD2xEZ+AAAAAGQ+pgSeJUuWyN/fXwMGDHDYHhUVJUm6dOmS6tevL0n6+eeflSdPHhUsWFCSdPHixQSPGR4ertDQUGXPnj1FgQcAAACA9Zk2hyc0NFTr169PsC0qKsreFheCSpUqpaxZs+ratWu6dOmS8uXL57DPnj17JEkVK1ZMx6oBAAAAZCamzOExDCPBrzNnzkiSihUrZt9WuHBhSZKnp6caNmwoSfr111/jHXPevHmSpNatW2fMiwAAAADg9Ex/8GhqDBkyRJI0evRonThxwr5969atmjx5svz9/fXiiy+aVR4AAAAAJ5OpAk/jxo31+uuv68aNG6pcubLatWunFi1aqG7durp//76mT58uf39/s8sEAAAA4CQyVeCRpK+//lrTp09XmTJltHLlSm3dulWNGzfWhg0b1K5dO7PLAwAAAOBETFu0ICGFCxeWYRjJ9uvduzfP7wEAAACQrEw3wgMAAAAAKUXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlkXgAQAAAGBZBB4AAAAAlmVa4ImIiNDvv/+uF198UaVKlZKHh4e8vb1VqVIlffTRR7p7926i+86YMUNBQUHy8fFRQECAWrRooS1btmRg9QAAAAAyA9MCz5w5c9S+fXtNmzZNrq6uatOmjYKDg3XmzBkNHz5cNWrUUEhISLz9Bg8erD59+ujgwYNq3LixgoKCtHLlStWtW1e///57xr8QAAAAAE7LtMDj5uam/v376/Dhwzp8+LB++eUXLV++XMeOHVOVKlV09OhRDR482GGfVatWafz48cqRI4f27dun33//XcuXL9eGDRvk6uqqPn36KDQ01JTXAwAAAMD5mBZ4evXqpcmTJ6tMmTIO25966in95z//kSQtWLBA0dHR9rZx48ZJkoYNG6YSJUrYt9eqVUsDBgxQaGiopk6dmgHVAwAAAMgMnHLRgkqVKkmS7t27pxs3bkiSIiMjtWbNGklSp06d4u0Tt23x4sUZVCUAAAAAZ+eUgef06dOSHt72FhAQIEk6duyY7t27p5w5cyp//vzx9qlataokaf/+/RlXKAAAAACn5pSBZ/z48ZKk5s2bK2vWrJKk8+fPS1KCYUeSvL295e/vr1u3biksLCxjCgUAAADg1LKYXcDf/e9//9PUqVPl5uamUaNG2bfHLVPt5eWV6L7e3t4KDQ1VWFiYfH19kzxPuXLlEtx+6tQpFStW7DEqBwAAAOBsnGqE5+jRo+rZs6cMw9AXX3xhn8sDAAAAAI/DaUZ4Ll26pObNm+vWrVsaMmSIXn/9dYd2Hx8fSQ8fWJqY8PBwSUp2dEeSDh06lOD2xEZ+AAAAAGQ+TjHCc/PmTTVt2lTnzp1Tnz599OWXX8brU7BgQUnSxYsXEzxGeHi4QkNDlT179hQFHgAAAADWZ3rguXv3rp599lkdPnxYHTp00Pfffy+bzRavX6lSpZQ1a1Zdu3ZNly5dite+Z88eSVLFihXTvWYAAAAAmYOpgefevXtq27atduzYoWbNmumnn36Sq6trgn09PT3VsGFDSdKvv/4ar33evHmSpNatW6dfwQAAAAAyFdMCz4MHD9StWzetWbNGwcHBWrBggdzd3ZPcZ8iQIZKk0aNH68SJE/btW7du1eTJk+Xv768XX3wxXesGAAAAkHmYtmjBt99+q99++02SFBgYqEGDBiXY78svv1RgYKAkqXHjxnr99dc1fvx4Va5cWU2aNFF0dLRWrlwpwzA0ffp0+fv7Z9RLAAAAAODkTAs8t27dsv85LvgkZMSIEfbAI0lff/21KleurG+//VYrV66Uu7u7GjdurA8++EC1a9dO15oBAAAAZC6mBZ4RI0ZoxIgRj7Vv79691bt37zStBwAAAID1mL5KGwAAAACkFwIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwLAIPAAAAAMsi8AAAAACwrEwZeCIjI/Xhhx+qZMmS8vDwUN68edW3b19dunTJ7NIAAAAAOJFMF3iioqLUsGFDjRo1Snfv3lXbtm1VoEABTZ8+XVWqVNHp06fNLhEAAACAk8h0gWf06NHatm2batWqpePHj2vu3Lnavn27xo4dq2vXrqlv375mlwgAAADASWSqwBMdHa1vv/1WkvSf//xHPj4+9rYhQ4aoYsWKWr9+vXbv3m1WiQAAAACcSKYKPJs3b9bt27dVrFgxValSJV57p06dJEmLFy/O6NIAAAAAOKFMFXj27dsnSapatWqC7XHb9+/fn2E1AQAAAHBemSrwnD9/XpKUP3/+BNvjtp87dy7DagIAAADgvLKYXUBq3L17V5Lk5eWVYLu3t7ckKSwsLNljlStXLsHtR48elZubW6LtQKbgTNfvNbMLQGZV7hcnuo5vm10AMq2Pnec6vsYvZDymX8r9Yur5T506JTc3t8feP1MFnoxgs9me6AeapsqWNbsCp3Tq1ClJUrFixUyuBClRNifXcUK4jjOZbFzHCeE6zlxyls1pdglOievY+bm5udkHNh5Hpgo8cauyRUREJNgeHh4uSfL19U32WIcOHUq7wpCh4kbf+DtEZsZ1DCvgOoYVcB1bX6aaw1OwYEFJ0sWLFxNsj9teqFChDKsJAAAAgPPKVIGnUqVKkqQ9e/Yk2B63vWLFihlWEwAAAADnlakCzzPPPKNs2bLp1KlT2rt3b7z2efPmSZJat26dwZUBAAAAcEaZKvC4u7vr1VdflSS98sor9jk7kjRu3Djt379f9erVU7Vq1cwqEQAAAIATyVSLFkjSsGHDtGrVKm3ZskUlSpRQcHCwzp07p+3btytnzpyaNm2a2SUCAAAAcBI2wzAMs4tIrcjISH366aeaM2eOLly4oICAADVv3lyjRo1K9KGkAAAAAP55MmXgAQAAAICUyFRzeAAAAAAgNQg8AAAAACyLwAMAAADAsgg8AAAAACyLwAMAAADAsgg8AAAAACyLwAMAAADAsrKYXQCQnDt37mjixIlau3atQkJCNHnyZAUFBenmzZuaMWOG2rRpo+LFi5tdJpCsw4cP6/Tp07p165YSegTaCy+8YEJVQMrdunVLP/30U6LXsc1m09SpU02qDgASRuCBU7t48aLq1aunCxcuqESJEjp69Kju3r0rSQoICNDkyZN17tw5jR8/3uRKgcSdOnVKPXv21I4dOxIMOtLDN4oEHjizFStWqFOnTgoPD5efn5+yZ88er4/NZjOhMiB1jhw5ounTpycZ3FevXm1SdUgPBB44tX//+98KCwvT3r17lStXLuXKlcuhvV27dlqyZIlJ1QEp8/LLL+vAgQP6+uuvFRwcnOAbRcDZvfnmm8qTJ48WLFigChUqmF0O8Fh+/PFH9enTR25ubipVqlSCv48T+2AKmReBB07tjz/+0BtvvKGyZcvqxo0b8dqLFi2qCxcumFAZkHKbN2/We++9p3/9619mlwI8tpMnT+qLL74g7CBTGzFihKpUqaJly5YpMDDQ7HKQQVi0AE4tMjJSOXPmTLQ9LCwsA6sBHk9gYKCyZctmdhnAEylRogS/c5HpXb58WX379iXs/MMQeODUypYtqw0bNiTa/vvvv6tKlSoZWBGQegMGDNCsWbP04MEDs0sBHtvo0aM1ceJEnT171uxSgMdWsWJFXb582ewykMG4pQ1ObfDgwerVq5cqVqyozp07S5JiY2N18uRJjRw5Ulu3btX8+fNNrhJIWsmSJfXgwQNVqlRJffv2VYECBeTq6hqvX4cOHUyoDkiZ1atXK2fOnCpTpoyaNGmS4HVss9lYRAZObdy4cercubOeffZZ1a5d2+xykEFsBjOz4OQ+/vhjjRgxQoZhKDY2Vi4uLjIMQy4uLho9erTefvtts0sEkuTikvxgus1mYwQITo3rGFbQpk0bnThxQsePH1fZsmVVsGDBBIP7woULTaoQ6YHAg0zh/Pnzmj9/vk6ePKnY2FgVK1ZMHTp0UNGiRc0uDUjW+vXrU9SvXr166VwJAPyzFS5cONnl0202m06fPp1BFSEjEHgAAAAAWBZzeODUwsLCFBoaqgIFCti3Xb58WZMmTdK9e/fUsWNHBQUFmVghAAAAnBkjPHBq3bp105kzZ7Rt2zZJ0p07d1SuXDldunRJLi4uypIli5YvX6769eubWyjwiAYNGsjFxUUrVqxQlixZ1LBhw2T34cnecDYuLi5ycXFRRESE3N3d5eLikqJbge7fv59BFQLJO3/+vCSpYMGCDt8nJ64/rIERHji1TZs26eWXX7Z/P2vWLF25ckVbtmxRuXLl1KhRI40ePZrAA6cSt8BGnNjY2GTfKPLZE5zNhx9+KJvNpixZsjh8D2QmcXN2IiMj5e7unqI5PJJYfMNiGOGBU/P09NTEiRPVp08fSVLz5s0VFRWldevWSZK+/fZbjRw5UteuXTOxSgAA4IxmzJghm82mF154QTabzf59cnr16pUB1SGjMMIDp+bv76+//vpLkhQZGamNGzfq/ffft7dnyZJFERERZpUHAP8YERER8vLyMrsMIFV69+6d5Pf4ZyDwwKnVrl1bEydOVOnSpbV8+XJFRUWpbdu29vbjx48rX758JlYIJK9WrVqqV6+e6tSpozp16sjf39/skoBUy5Ytm6pWrarg4GAFBwerTp06ypEjh9llAaly7NgxlSpVyuwykMG4pQ1O7eTJk2ratKnOnj0rSXrzzTf1xRdfSHp4f23hwoXVvHlzff/99yZWCSTt2Wef1datW3Xnzh25uLiobNmyCg4OVt26dRUcHKy8efOaXSKQrM8++0ybNm3S5s2bFRoaKpvNptKlS9uv4+DgYIcVNQFn5OLioly5cqlOnTr267ZKlSrMT7M4Ag+cXkxMjA4fPqxs2bKpcOHC9u1hYWFas2aNKlWq5LAdcEaGYWjv3r3auHGjNm3apI0bN+rq1auy2WwqXLiw6tatq+nTp5tdJpAswzB04MABh2v5ypUrkh6ubHXmzBmTKwQSN3fuXPt1e/DgQRmGIR8fH9WuXdsegGrWrCl3d3ezS0UaIvAAgAmio6M1e/ZsffbZZzp+/LhsNhurAiFTiY6O1vbt27Vhwwb9+OOPXMfIdG7fvq1NmzbZA9Du3bsVHR2trFmzMj/YYpjDA6f34MEDzZo1S0uXLtW5c+ckSYUKFVKrVq3Uo0cPubq6mlwhkLy7d+9qy5Yt2rhxozZu3KgdO3bo3r17Kl26tF5++WUFBwebXSKQpDt37mjz5s32a3jXrl2KiYmxPyJg5MiRXMfIVLJly6Zy5crp5s2bun79ui5fvqyzZ8/KxcXF7NKQxhjhgVO7ffu2mjVrpp07d8rX11dFixaVJJ05c0Z37txRUFCQVqxYIT8/P5MrBRJXvXp17du3TzabTZUqVXKY88Ckb2QWbm5ukh5ez48uXJA9e3aTKwNS7uDBg/bQvnHjRl26dEn+/v72OT1169ZVtWrV7M+fgjXwtwmn9v7772v37t2aMGGC+vXrZ/8PNyYmRlOmTNFrr72m999/XxMmTDC5UiBxe/bskYuLi9q1a6cWLVooODhYxYsXN7ssIFW8vb11584dXb161f517do1Ag8ylYoVK8rV1VWtWrXSu+++q+DgYJUvX55FCyyOER44tXz58qlTp04aP358gu2vvfaa5s2bp8uXL2dwZUDK7dmzx/5p4qZNm3Tt2jXlypXL/il5cHCwKlWqxH+4cGqGYWj//v0On47/9ddfypkzp/3T8Tp16qh69epmlwokqkKFCjp8+LDc3d0VFBRkH9WpVauWfH19zS4P6YTAA6fm4eGhcePGadCgQQm2T5w4UUOGDFFUVFQGVwY8vuPHj9vfMK5bt04XLlyQn5+fbt26ZXZpQKqcOnVKq1at0tdff21ftOD+/ftmlwUk6datWw7z0Xbv3q3Y2FhVrFjRHtw7depkdplIQwQeOLXy5csrf/78Wr58eYLtzZs314ULF3To0KEMrgx4PJGRkdq6das2bNigDRs2aPv27YqMjJSrq6tiYmLMLg9IVnh4uH0Bjg0bNmjHjh2KiopSlixZVKVKFW3fvt3sEoFUiYqK0s8//2xfNVMSqw1aDHN44NQGDRqkV199VS1atNDgwYNVsmRJSQ+flPzNN99o5cqV+vbbb02uEkjakiVLtGHDBm3cuFF79uxRTEyMPDw8FBQUpCFDhig4OFi1a9c2u0wgSW+99ZY2bNigvXv36v79+/Ly8lLNmjU1dOhQBQcHq1atWvLy8jK7TCBFjh07Zv+9vHHjRp0/f16GYShPnjysNmhBjPDA6Y0YMUJjxoyJ9+m3m5ub3n33XQ0fPtykyoCUcXFxkb+/v5555hn7nJ3q1avbF+EAMoOcOXM6XMNVq1ZlJStkOp06dbLPpTQMQyVKlLDPQQsODlaxYsXMLhHpgMCDTOH69etatWqVw3N4GjdurMDAQJMrA5J34MABVgECACdQrVo1hwVjcuXKZXZJyAAEHgAAAACWxVg0nMr58+cfa7+CBQumcSUAAACwAkZ44FRcXFwe67YfVlMBAABAQhjhgVOZNm0a8xwAAACQZhjhAQAAAGBZLmYXAAAAMp/IyEhFRkaaXQYAJIvAAwAZ4MGDB5o5c6a6dOmimjVrqmbNmurSpYt++OEH5qAh0zh//rz69Omj3Llzy8fHRz4+PsqdO7f69u1rf2wAkFncvXtXR44c0ZEjR3T37l2zy0E64pY2AEhnt2/fVrNmzbRz5075+vqqaNGikqQzZ87ozp07CgoK0ooVK+Tn52dypUDijh49qjp16ig0NFRNmjRRmTJl7Nv/+OMPZc+eXZs2bVKpUqVMrhRI2s6dOzV06FBt2rRJsbGxkh4umhQcHKzPP/9c1atXN7lCpDUCDwCks1dffVWTJ0/W+PHj1a9fP7m5uUmSYmJiNGXKFL322msaMGCAJkyYYHKlQOLatWunLVu2aPXq1apQoYJD28GDB9WoUSPVrl1bv/32m0kVAsnbvn276tevL3d3d3Xv3t0e3I8cOaKffvpJ0dHRWrdunYKCgkyuFGmJwAMA6Sxfvnzq1KmTxo8fn2D7a6+9pnnz5uny5csZXBmQctmzZ9ebb76pYcOGJdg+atQojRs3Trdu3crgyoCUa9y4sc6ePatNmzYpT548Dm1Xr17VM888oyJFimjlypUmVYj0wBweAEhnN27cSPI2n9KlS+vmzZsZWBGQejExMfL09Ey03cvLSzExMRlYEZB627dv18svvxwv7EhS7ty51b9/f23bts2EypCeCDxwekz2RmZXvHhxLVq0KNH2RYsWqVixYhlYEZB6VapU0ZQpU3T79u14bXfu3NHUqVNVtWpVEyoDUs7FxUX3799PtP3BgwdyceHtsdVwSxucGpO9YQUTJ07Uq6++qubNm2vw4MEqWbKkJOnYsWP65ptvtHz5cn377bcaOHCgyZUCiVuzZo2aN2+uHDlyqE+fPg7X8cyZM3Xjxg0tX75cDRo0MLlSIHHPPvusDhw4oM2bN6tQoUIObefPn9czzzyjChUq6H//+59JFSI9EHjg1JjsDasYMWKExowZE++WHzc3N7377rsaPny4SZUBKbdq1Sr9+9//1r59+xy2V65cWV988YUaNWpkUmVAyvz555+qW7eu7t+/r/bt2zsE94ULFypLlizauHGjKlWqZHKlSEsEHjg1JnvDSq5fv65Vq1bZn1dSqFAhNW7cWIGBgSZXBqTOX3/95XAdJzQfAnBWhw4d0rBhw7Ry5UpFRERIejgHrWnTpho9erTKli1rcoVIa1nMLgBICpO9YQUbNmxQmTJllDNnTj333HPx2q9fv67Dhw+rbt26JlQHpMxHH32kDh06qHz58sqTJ0+8kHPo0CHNnz9fH374oUkVAilTrlw5/fbbb4qNjdW1a9ckSTlz5mTujoXxNwunxmRvWEGDBg2SXOJ09erVzHuA0xsxYoT279+faPvBgwc1cuTIDKwISL2+fftq+/btkh4uYJA7d27lzp3bHnZ27Nihvn37mlki0gGBB05t0KBB+uOPP9SiRQv98ccfOnv2rM6ePasVK1aoZcuWWrlypV599VWzywSSlNydw/fu3ZOrq2sGVQOkj5s3b8rd3d3sMoAkzZgxQ6dOnUq0/cyZM5o5c2YGVoSMwC1tcGqDBg1SSEiIxowZoxUrVji0ubm56cMPP2RlKzil8+fP6+zZs/bvjx49qg0bNsTrFxoaqsmTJ8dbLQhwBhs2bNC6devs3y9YsEAnT56M1y80NFRz585VhQoVMrA6IO1dvnw5yedNIXNi0QJkCkz2RmYzcuRIjRw5UjabLcl+hmHI1dVVkydP5jYKOJ2461iSbDZbkqOVZcuW1dSpU1WzZs2MKg9IkYULF2rhwoWSHo7w1K1b1/6Yi0eFhoZq1apVqlatmtauXZvRZSIdEXjg1B6d7J0QJnvDWR05ckSHDx+WYRjq0qWLXnvtNQUHBzv0sdls8vb2VuXKlZU7d26TKgUSFxkZqYiICBmGoVy5cmnSpEnq2LGjQx+bzSYvLy95eHiYVCWQtE8//VSffPKJJCkiIkLu7u7KksXxJqe438fVqlXTuHHj7MtVwxoIPHBqrq6u+vHHH9W9e/cE2+fOnavu3bvrwYMHGVwZkHIzZ85UvXr1VLhwYbNLAR7buXPnlDNnTnl5eZldCvDYXFxcNGvWrETfV8CamMMDp8Zkb1hBr169zC4BeGLMM4MVxMbGml0CTEDggdNhsjcAAADSCre0wekw2RsAAABphcADp8NkbwAAAKQVAg+cGpO9AQAA8CQIPAAAIFWuXLmikJAQFS9eXN7e3maXAwBJcjG7AAD4Jzh//rwGDBigUqVKKSAgwL4Qx/Xr1/Xaa6/pzz//NLlCIHkLFy5U6dKllT9/flWtWlXbt2+X9PA6rlKlin7//XdzCwRS6N69e9q6dasWLlyo69evm10O0hmBBwDS2eHDh1WlShXNnTtXRYoU0e3bt3X//n1JUmBgoDZt2qRvv/3W5CqBpC1evFgdOnRQYGCghg8f7vDYgMDAQOXLl0/Tp083sUIgZb755hs99dRTqlOnjjp06KD9+/dLehjcAwMDNW3aNJMrRFoj8ABAOhs6dKj8/f11/PhxzZo1K97zpVq2bKmNGzeaVB2QMh999JHq1q2rTZs26ZVXXonXXqtWLUYq4fSmT5+uwYMHq3nz5po6dWq84N6wYUP9/PPPJlaI9EDgAYB0tmHDBg0cOFA5c+ZMcLn1ggUL6tKlSyZUBqTcwYMH1aVLl0Tbc+fOrZCQkAysCEi9sWPHqm3btpozZ45at24dr71atWo6dOiQCZUhPRF4ACCdxcbGysvLK9H2a9euKWvWrBlYEZB6Xl5eCg8PT7T99OnTypEjRwZWBKTeyZMn9eyzzybaHhAQoBs3bmRgRcgIBB44PSZ7I7OrWrWqli5dmmDb/fv39fPPP+vpp5/O4KqA1GnQoIFmzpxpn3/2qL/++kvff/+9mjZtakJlQMr5+/snuUjB4cOHlSdPngysCBmBwAOnxmRvWMG7776r5cuXa+DAgTp48KAk6erVq1q1apWaNm2qI0eO6J133jG5SiBpH3/8sS5evKgaNWpo8uTJstlsWrFihYYNG6YKFSrIMAwNHz7c7DKBJLVo0ULfffedQkND47UdOnRI33//vdq0aZPxhSFd8RweOLVWrVrpyJEj2rZtm2w2m3LlyqVVq1apYcOGkqQPPvhAc+fO1fHjx02uFEjajz/+qNdff123b9+WYRiy2WwyDEN+fn7673//q27dupldIpCsQ4cO6fXXX9fatWsdJnvXr19f//nPf1SmTBkTqwOSd/nyZdWsWVOGYah169b67rvv1LNnTz148EDz58/XU089pR07digwMNDsUpGGCDxwan5+fvrwww/11ltv6caNG8qZM6dD4Pn+++81ePDgJO8rB5xFeHi4/vjjD508eVKxsbEqVqyYmjVrJl9fX7NLA1Ll1q1b9uu4aNGiypkzp9klASkWEhKi9957TwsWLLCP9Pj6+qpjx44aM2aMcuXKZW6BSHNZzC4ASAqTvWEl3t7eat++vdllAE8se/bsqlGjhtllAI8lV65cmjJliqZMmaJr164pNjZWOXPmlIsLMz2sisADpxY32XvQoEHx2pjsjcwmJiZGly5d0q1bt+I9i0d6eL0Dzm7Dhg06ffp0gtexzWbTG2+8YVJlQOoxOvnPwC1tcGrLli1Tq1at1L9/fz333HNq0KCBZs+erZw5c+qTTz7Rxo0btXr1atWtW9fsUoFEhYaG6q233tLs2bMVHR0drz1uTs+DBw9MqA5Imb1796pr1646efJkgoFdEtcxMoXw8HDNnz8/yeA+fvx4k6pDeiDwwOkx2RuZXbt27bR48WI999xzqlmzprJly5Zgv169emVwZUDKVa9eXadOndLnn3+e5HVcqFChDK4MSLnVq1erc+fOCa7SFofgbj0EHmQKTPZGZubl5aWXX35ZX331ldmlAI/N09NTH330kf7973+bXQrw2EqXLq3w8HBNmzZNNWvWlJ+fn9klIQMwhweZApO9kZnlyJFDxYsXN7sM4ImUKFFCNpvN7DKAJ3L+/Hl99tlnatKkidmlIAMReJApMNkbmVn//v31888/a+DAgawChExrxIgRevPNN9WtWzfly5fP7HKAx1KxYkXdvn3b7DKQwQg8cGpM9oYVfPDBB7p3756qV6+u559/Xvnz55erq2u8fh06dDChOiBlOnTooKioKJUqVUqNGjVK8Dpmsjec3WeffaZu3bqpefPmql69utnlIIMwhwdOjcnesIJLly6pc+fO2rZtW6J9CO5wduvXr1ebNm0UFhaWaB+uY2QGv/zyi3r06KEyZcqoQIECCQb3hQsXmlQd0gOBB06Nyd6wgmbNmmn9+vV68803kwzu9erVy+DKgJSrWLGibt26xWRvZGrz589Xt27ddP/+ffn7+yf4+9hms+n06dMmVIf0wi1tcGpM9oYVbNq0SW+//bZGjhxpdinAYzt58qTGjBnDZG9kau+8845KlSql+fPnq2TJkmaXgwzC7Fk4tbjJ3rGxsWaXAjy2PHnyKCAgwOwygCdSrlw5Jnsj07t8+bIGDhxI2PmHYYQHTo3J3rCCN998U//973/14osvysfHx+xygMfy5ZdfqkePHmrWrJmCgoLMLgd4LDVq1ND58+fNLgMZjMADp3bp0iWtWbNGe/fu1d69exPswyRZOLuoqCi5ubmpePHi6tKlS6KTZN944w2TKgSSN3bsWPn6+qpWrVoqW7asChYsyGRvZDoTJkxQ69atVbVqVXXp0sXscpBBWLQATo3J3rCClDx7h+AOZ1e4cOFkHzzKZG84u4oVK+rmzZu6cuWKfHx8El1efd++fSZViPTACA+cGpO9YQVnzpwxuwTgiZ09e9bsEoAnFhAQoBw5cqhEiRJml4IMROCBU2OyN6ygUKFCZpcAAJC0bt06s0uACbilDU5t4sSJ+u9//6utW7cy2RsAMlDcxO6CBQs6fJ+cuP4A4CwY4YFTY7I3MqMiRYrIxcVFR48elZubm4oUKZKiuQ+nTp3KoAqB5MXN2YmMjJS7u3uK5vBIYi4anMqGDRskSXXr1nX4Pjlx/WENjPDAqTHZG5lR7969ZbPZNGXKFLm6utq/T8706dMzoDogZWbMmCGbzaYXXnhBNpvN/n1yevXqlQHVASnj4uLiENzjvk+MYRi8r7AgAg+c2rlz51LUjzkSAADg79avXy/p/1Zzjfs+Oaz+ai0EHgBIZz/88IPq1q2rwoULJ9h+7tw5rV+/Xi+88ELGFgakQt++ffXyyy+rZs2aCbbv2LFDkyZN0rRp0zK4MgBIWvL3CwEAnkifPn20ZcuWRNu3bdumPn36ZGBFQOrNmDEjyXlmZ86c0cyZMzOwIiD1GjZsqNWrVyfavnbtWjVs2DADK0JGYNECOBUme8OKkhtIDw8PV5Ys/DpG5nb58mV5enqaXQaQpHXr1umll15KtD0kJCTFt70h8+B/WDiVevXqyWaz2RcriPseyGz279+vvXv32r/fuHGj7t+/H69faGioJk2apJIlS2ZgdUDKLFy4UAsXLrR//91332nVqlXx+oWGhmrVqlWqUaNGRpYHPJak3lecPHlSvr6+GVgNMgJzeAAgHYwcOVIjR46U9PA/16R+1fr7++uHH35Qq1atMqo8IEU+/fRTffLJJ5KkiIgIubu7xxuNtNls8vb2VrVq1TRu3DjCO5zOzJkz7bdbrlu3TmXKlFHu3Lnj9QsNDdX+/fvVokULLVq0KKPLRDoi8MCpMdkbmdWVK1d0+fJlGYahoKAgffTRR3r22Wcd+sS9USxWrBi3tMHpubi4aNasWerevbvZpQCp8t///lcTJ06UJB06dEj58+dXtmzZHPo8Gtw//PBD5cqVy4xSkU4IPHBqrq6u+vHHHxP9D3bu3Lnq3r076+XDqa1fv15lypThP1AAMFmRIkU0fvx4tWnTxuxSkIH4SBFOjcnesAKe5wAAzuHMmTNmlwAT8E4RTofJ3gAAAEgr3NIGp8NkbwAAAKQVAg+cDpO9AQAAkFYIPHBqTPYGAADAk3AxuwAgKfXq1SPswHJu377NyoLIdA4cOJBsn3nz5mVAJQCQOgQeAMgAu3btUvPmzeXl5aUcOXJo/fr1kqTr16+rbdu2WrdunbkFAsmoXr26Pv30U8XGxsZru3nzprp27aquXbuaUBkAJI3AAwDpbMuWLapTp45OnDihnj17OrxhDAwM1O3btzV58mQTKwSS16tXL73//vuqXbu2jh07Zt/++++/q1y5clq6dKm+/vpr8woEUsAwDE2ePFlBQUEKDAyUq6trvC/mBlsPf6MAkM7ee+89lSlTRtu2bVNYWJimTJni0N6gQQPNnDnTpOqAlPnuu+/UsWNHvfjii6pSpYqGDx+uAwcOaM6cOapdu7ZmzJih4sWLm10mkKShQ4dq3Lhxqly5snr27Kns2bObXRIyAIEHANLZzp079emnnypr1qy6e/duvPZ8+fLpr7/+MqEyIHWaNWumQ4cOqVmzZnrvvfckSe+//74++ugj2Ww2k6sDkjdz5kx17NhRv/zyi9mlIANxSxsyFSZ7IzNyc3NLcN5DnEuXLsnHxycDKwIeT3h4uIYOHaodO3aoYsWK8vT01LRp07Rs2TKzSwNSJDIyUo0bNza7DGQwAg+cHpO9kdk9/fTTia5eFR4erunTp6tevXoZXBWQOmvXrlWFChU0c+ZMffrpp9q9e7f+/PNPFS5cWK1bt9ZLL72ksLAws8sEktSoUSPt3LnT7DKQwQg8cGpM9oYVjBw5Urt27VLLli3tn4Tv27dPU6ZMUbVq1XTt2jV98MEHJlcJJK1x48bKnj27du/erbffflsuLi4qUaKENm3apM8++0xz5sxRhQoVzC4TSNLEiRO1bds2ffLJJ7px44bZ5SCD8OBROLX69evr9u3b9sneuXLl0qpVq9SwYUNJD99Izpw5U6dPnza5UiBpa9as0cCBA3XixAmH7cWKFdOUKVMY4YHTGzFihIYNG5boClZHjhxR7969tX379gyuDEg5X19fxcbGKioqSpLk4eEhV1dXhz42m023b982ozykExYtgFNjsjesomHDhjp27Jj27t2rEydOKDY2VsWKFVO1atWY7I1MYcSIEUm2lylTRlu3bs2YYoDH1LFjR37n/gMReODUmOwNq6lcubIqV65sdhlAunBx4U55OLcZM2aYXQJMQOCBU4ub7D148OB4bUz2RmaxYcOGJNttNps8PDyUP39+PfXUUxlUFZB6+/fv14QJE7Rnzx7dvn07wQ+kuMUYgLMh8MCpjRw5UvXq1VPLli3VrVs3SQ8ne58+fVpffvklk72RKdSvXz/Ft1CUKFFCI0eOVNeuXdO5KiB11q1bp+bNmyt79uyqXr26/vzzTzVs2FBRUVHaunWrypUrp2rVqpldJuDghx9+kCQ9//zzstls9u+T88ILL6RnWchgLFoAp8dkb2R2f/zxh95++23du3dP/fr1sz+N/sSJE5oyZYo8PT01bNgwnTt3TpMnT9bx48c1d+5cderUyeTKgf9Tt25dXb9+Xdu2bVN0dLTDIjLbt2/Xs88+q9mzZ+vZZ581u1TAzsXFRTabTZGRkXJ3d0/RbZc2m41n/lkMgQeZBpO9kVkNGTJEW7du1fr16+Xu7u7QFhUVpfr166tevXr67LPPFBUVperVq8vT05NnRcCp+Pj4aOTIkXrzzTd169Yt5ciRQytWrFCTJk0kSe+++65WrlypXbt2mVwp8H/OnTsnSSpUqJDD98mJ6w9r4JY2ZBpM9kZmNXv2bA0bNixe2JEeLonao0cPffzxx/rss8/k4eGhnj17atSoUSZUCiQuS5Ys8vX1lST5+/vLzc1NISEh9vaiRYvq8OHDZpUHJOjvwYUg889E4IFTY7I3rCA8PFxXr15NtP3KlSsOy677+/vHey4EYLbixYvbby222WwqXbq0fvvtN/Xo0UOStHTpUuXJk8fMEgEgQQQeODUme8MKGjZsqK+//lpPP/20WrVq5dC2ePFijR8/Xo0aNbJv27t3rwoXLpzBVQJJa9GihaZNm6ZPP/1UWbJk0ZAhQ9SnTx+VKFFCknTq1Cl9+umnJlcJJG/Tpk2aNm2aTp8+rVu3bunvsztsNpv27dtnUnVID8zhgVNjsjes4Pz582rQoIHOnj2rfPnyqVixYpIevkG8dOmSChUqpLVr16pQoUKKiopShw4d1Lx5c7322msmVw78n5iYGN25c0cBAQH2D6JmzZql+fPny9XVVa1atVLv3r3NLRJIxrhx4/Tvf/9bHh4eKlWqlLJly5Zgv7Vr12ZwZUhPBB44NSZ7wyoiIiI0adIkrVixwmESbbNmzfTyyy/L29vb5AoBwPqeeuoplShRQosXL0407MB6CDxwarlz59awYcP0r3/9K8H2CRMm6OOPP9Zff/0lSRozZoxGjRql8PDwjCwTAABkAtmyZdPnn3+ul19+2exSkIGYwwOnxmRvAHAezH1AZtegQQMdOHDA7DKQwQg8cGpM9oZV/PXXX5o6dar27Nmj27dvKzY21qHdZrNp9erVJlUHJO/vcx8CAgLMLglItQkTJqhp06b68ssv1bdvX67jfwhuaYNTY7I3rGD//v2qX7++IiMjVapUKR04cEBly5ZVaGioLl26pGLFiqlAgQJas2bN/2vvDkOivh84jn9OM120ErnOXJtUY+Aoe2K0RY3RNSLatYZrXrEYYlAoYxM0G+Y02KyIou3Wo5aLTRazMCyZMj3XxlxFYURbDsZoK8Ygs8ujLeyc99uDP/NfmCZFfX/39f2CHv188IEOzs997/PVdFRgRGwfYIsPP/xQ5eXlchxHaWlpw74Z4vF4FI1GDaXDw8AJD1wtOztbP/7447Cx97PPPqvS0tI7xt5paWlqaWkxGRe4q3fffVeTJ0/WuXPnNGnSJPl8Pn300Ufy+/06fPiwiouL9cUXX5iOCYzq5s2beuONNyg7SGjV1dWqra3VjBkzNH/+fF7P4wQnPADwkE2dOlUVFRXasmWLIpGIvF6v2tra9NJLL0mS3nnnHZ07d07fffed4aTAyF599VU9+eST2rt3r+kowH3z+Xx6/vnn1dTUpKSkJNNx8IjwPw0AD1k8HldmZqak/1+sEYlEhp7n5uaqq6vLVDxgTD7++GN1dHRo165dd7x+gUQSi8X08ssvU3bGGb7SBtdj7I1EN2vWLP3222+SpKSkJM2aNUvhcFgFBQWSpBMnTig9Pd1gQuDennrqKW3cuFHl5eXavHkz2wckpEAgoO+//55rqccZCg9cbaxjb8DNli1bpsOHD6u2tlaSVFxcrLKyMl28eFGO4+jbb79VWVmZ4ZTA6Ng+wAY1NTUKBoMqKSnR+vXrlZ2dfdc/Z8HtbXZhwwNXW7FihX766Sd1dnYOjb3D4fAdY++WlhYtWLDAdFRgRNevX9fFixc1b948paSkyHEc1dbWqrGxUcnJyQoEAqqsrNTEiRNNRwVGxPYBNrj9tevxeEb8ucHBwUcRB48IJzxwtR9++EEVFRXKzs4e+s74f19pe/3119XZ2alNmzYx9oZrOY6j5ORkzZkzRykpKZL+9yZbVVWlqqoqw+mAsWP7ABtUV1ePWnRgJwoPXG0sY++6ujpT8YB7isViysjI0LZt21RRUWE6DnDf2D7ABlu3bjUdAQbwMQ1cbaSx938Ye8PtUlNTNX36dKWmppqOAjyQmpoadXd3q6SkRF1dXbp69aoikciwfwDgNmx44Grl5eU6duyYfvnlF0nSnj17VFZWJr/ff8fYe+fOnYaTAiOrqqpSa2urTp48yU4HCYvtA4BExVfa4GpbtmzR2rVrNTAwoJSUFJWWlurvv/8eGnu/9957qqysNB0TGFVubq6ampo0Z84cFRYWaubMmXrssceG/Vx+fr6BdMDYsH0AkKg44YFrOY6jGzduaOLEiUpLSzMdB7hvYxl5ezwePhkHAOAh4IQHrsXYG7Y4fvy46QgAAIxbFB64FmNv2OLFF180HQEAgHGLW9rgaoWFhfr8888Vi8VMRwEe2K1bt3Ty5EkdPXpUvb29puMAADAucMIDV2PsDVuEQiFt3bpV0WhUktTe3i6/36/e3l7l5ORo586dKioqMpwSAAD7cGkBXI2xN2xw4MABrV+/XmvWrNGyZctUVFSkcDgsv98vSSooKFBfX5/a2toMJwUAwD6c8MDVGHvDBrt379aqVat08OBBXbt2bdjzvLw8hUIhA8kAALAfhQeuxtgbNvj111/19ttvj/g8IyPjrkUIAAA8OAoPEsKtW7d09uxZ9fT0aNGiRfJ6vaYjAWOWnp4+6iUF3d3dmj59+iNMBADA+MEtbXC9UCikrKwsLV68WPn5+Tp//rwkqbe3V16vV59++qnhhMDoVqxYoX379qmvr2/YswsXLuiTTz7RK6+88uiDAQAwDlB44GoHDhxQaWmpli9frrq6Ot1+x4bX65Xf79eXX35pMCFwbx988IEGBwc1d+5cVVVVyePx6LPPPtO6des0f/58+Xw+VVdXm44JAICVKDxwtdvH3itXrhz2PC8vTxcuXDCQDBi7J554Ql1dXVq+fLkaGhrkOI7q6+vV3NystWvX6tSpU3xNEwCAh4QND1yNsTds4fP5tH//fu3fv19Xr15VPB7XtGnTxnT1OgAAuH+808LVGHvDBi0tLXf8rahp06YpMzOTsgMAwCPAuy1cjbE3bBAIBJSZmakNGzaoo6ND8XjcdCQAAMYNj3P7ChxwmT///FPPPfecHMfRypUrtW/fPq1bt06Dg4NqbGxUVlaWTp8+zf4Brvb111+roaFBTU1Nikaj8nq9Wr16tdasWaMXXnjBdDwAAKxG4YHr9fT0qLKyUkeOHBk66Xn88cf12muvaceOHfL5fGYDAmM0MDAwVH6am5t148YNZWVlafXq1QoGg1q4cKHpiAAAWIfCg4TC2Bu2iMViam1tVUNDg44dO6b+/n79888/pmMBAGAdfmOEqzH2hq3++usv9fT06MqVK+rv7xefPQEA8HBwwgNXS0pKUkZGhvLz8xUMBrVkyRLKDhJWNBrVkSNH1NDQoOPHj2tgYEC5ubkKBoMKBoN6+umnTUcEAMA6FB64GmNv2KC+vl6HDh1Se3u7YrGYcnJyhkpOTk6O6XgAAFiNwoOEwNgbiSwpKUmzZ88eKjnz5s0zHQkAgHGDwoOEw9gbiaarq0t5eXmmYwAAMC4xhkDCYeyNREPZAQDAHE54kBAYeyPR9ff3q7GxUWfPnlU0GlU8Hr/jucfjUV1dnaF0AADYa4LpAMBo7jb2rqysZOyNhHLp0iUtWbJEv//+u9LT0xWNRpWRkaG+vj4NDg7K6/Vq8uTJpmMCAGAlTnjgaoy9YYOCggJ1dHSotbVVs2fPls/nUzgc1qJFixQKhbR3716Fw2E988wzpqMCAGAdTnjgamfOnGH/gIT3zTffqKSkRAsWLFAkEpEkOY6j1NRUbdq0ST///LNKS0v11VdfGU4KAIB9uLQArkbZgQ1u3rypmTNnSpKmTJkij8ejaDQ69HzhwoXq7Ow0lA4AALtxwgPXY+yNRJedna0//vhDkjRhwgTNmDFDp06dUn5+viSpu7tbaWlpJiMCAGAtCg9cjbE3bOD3+3X06FHV1NRIkgoLC7V9+3Zdv35d8Xhc9fX1evPNNw2nBADATlxaAFdj7A0bXL58WWfOnFEgEFBqaqr6+/v11ltvqbGxUcnJyQoEAgqFQpoyZYrpqAAAWIfCA1fzer0qLi7W+++/r0gkIq/Xq/b2di1dulSSVFRUpCtXrjD2BgAAwF1xaQFcjbE3AAAAHgSFB6420tj7P4y9AQAAMBouLYCrMfYGAADAg2DDA1dj7A0AAIAHQeEBAAAAYC02PAAAAACsReEBAAAAYC0KDwAAAABrUXgAAAAAWIvCAwAAAMBaFB4AAAAA1qLwAAAAALAWhQcAAACAtSg8AAAAAKxF4QEAAABgLQoPAAAAAGtReAAAAABYi8IDAAAAwFoUHgAAAADW+he1MxVIX0SigwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar charts for the recorded times played.\n",
    "plt.figure(dpi=150)\n",
    "plt.title('Time Played Bot Data (sec)')\n",
    "plt.xticks(rotation=90, fontsize=8)\n",
    "colors = ['red', 'green', 'orange', 'purple']\n",
    "labels = ['average time to lose', 'average time to win', 'max time to win', 'min time to win']\n",
    "res_times = [avg_time_l, avg_time_w, max_time_w, min_time_w]\n",
    "plt.bar(labels, res_times, color=colors)\n",
    "# Add labels at the top of each bar.\n",
    "for i in range(len(labels)):\n",
    "    plt.text(i-0.15, res_times[i]+1, f'{res_times[i]:.2f}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "score\n",
       "2048     211\n",
       "1024      71\n",
       "512       19\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count occurrences of different scores.\n",
    "scores_count = df.value_counts(['score'])\n",
    "scores_count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 960x720 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Bar chart for the scores.\n",
    "plt.figure(dpi=150)\n",
    "plt.title(\"AI Bot's Scores with Numbers of Occurrences\")\n",
    "colors = ['red', 'purple', 'orange', 'green']\n",
    "score, counter = np.unique(np.array(df['score']), return_counts=True)\n",
    "scores_dict = dict(zip(score, counter))\n",
    "labels = [str(i) for i in scores_dict.keys()]\n",
    "plt.bar(labels, scores_dict.values(), color=colors)\n",
    "# Add labels at the top of each bar.\n",
    "for i in range(len(labels)):\n",
    "    plt.text(i-0.1, list(scores_dict.values())[i]+2.5, list(scores_dict.values())[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probability of a score greater or equal to 1024 : 0.9368770764119602\n",
      "Probability of a score lower than 1024 : 0.06312292358803984\n"
     ]
    }
   ],
   "source": [
    "# Calculate probability of a score greater or equal to 1024, or lower.\n",
    "prob_score_upper_bound = len(df[df['score']>=1024])/len(df)\n",
    "print(f'Probability of a score greater or equal to 1024 : {prob_score_upper_bound}')\n",
    "prob_score_lower_bound = 1-prob_score_upper_bound\n",
    "print(f'Probability of a score lower than 1024 : {prob_score_lower_bound}')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x14a1465f0>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1280x960 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Scatter diagram showing the time played and obtained score.\n",
    "plt.figure(dpi=200)\n",
    "plt.scatter(x=df['time_played_sec'], y=df['score'], color='purple')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.0"
  },
  "vscode": {
   "interpreter": {
    "hash": "7ee941c6eee7abd58bca1e8d62d0125231cec6a322dbdce97177f426017c7712"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}