AIM-PIbd-31-Ievlewa-M-D/lab3/lab3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Датасет: Цены на акции\n",
    "https://www.kaggle.com/datasets/nancyalaswad90/yamana-gold-inc-stock-Volume\n",
    "##### О наборе данных: \n",
    "Yamana Gold Inc. — это канадская компания, которая занимается разработкой и управлением золотыми, серебряными и медными рудниками, расположенными в Канаде, Чили, Бразилии и Аргентине. Головной офис компании находится в Торонто.\n",
    "\n",
    "Yamana Gold была основана в 1994 году и уже через год была зарегистрирована на фондовой бирже Торонто. В 2007 году она стала участником Нью-Йоркской фондовой биржи, а в 2020 году — Лондонской.\n",
    "В 2003 году компания претерпела значительные изменения: была проведена реструктуризация, в результате которой Питер Марроне занял пост главного исполнительного директора. Кроме того, Yamana объединилась с бразильской компанией Santa Elina Mines Corporation. Благодаря этому слиянию Yamana получила доступ к капиталу, накопленному Santa Elina, что позволило ей начать разработку и эксплуатацию рудника Чапада. Затем компания объединилась с другими организациями, зарегистрированными на бирже TSX: RNC Gold, Desert Sun Mining, Viceroy Exploration, Northern Orion Resources, Meridian Gold, Osisko Mining и Extorre Gold Mines. Каждая из них внесла свой вклад в разработку месторождения или проект, который в итоге был успешно запущен.\n",
    "##### Таким образом:\n",
    "* Объект наблюдения - цены и объемы акций компании\n",
    "* Атрибуты: 'Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'\n",
    "\n",
    "##### Бизнес цели:\n",
    "*  Прогнозирование будущей цены акций.\n",
    "        Использование данных для создания модели, которая будет предсказывать цену акций компании в будущем.\n",
    "*  Определение волатильности акций.\n",
    "        Определение, колебаний цен акций, что поможет инвесторам понять риски.\n",
    "\n",
    "##### Технические цели:\n",
    "*  Разработать модель машинного обучения для прогноза цены акций на основе имеющихся данных.\n",
    "*  Разработать метрику и модель для оценки волатильности акций на основе исторических данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Количество колонок: 7\n",
      "Колонки: Date, Open, High, Low, Close, Adj Close, Volume\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5251 entries, 0 to 5250\n",
      "Data columns (total 7 columns):\n",
      " #   Column     Non-Null Count  Dtype         \n",
      "---  ------     --------------  -----         \n",
      " 0   Date       5251 non-null   datetime64[ns]\n",
      " 1   Open       5251 non-null   float64       \n",
      " 2   High       5251 non-null   float64       \n",
      " 3   Low        5251 non-null   float64       \n",
      " 4   Close      5251 non-null   float64       \n",
      " 5   Adj Close  5251 non-null   float64       \n",
      " 6   Volume     5251 non-null   int64         \n",
      "dtypes: datetime64[ns](1), float64(5), int64(1)\n",
      "memory usage: 287.3 KB\n"
     ]
    },
    {
     "data": {
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       "<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>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2001-06-22</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>2.806002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2001-06-25</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>3.428571</td>\n",
       "      <td>2.806002</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2001-06-26</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.039837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2001-06-27</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.039837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2001-06-28</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.714286</td>\n",
       "      <td>3.039837</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Date      Open      High       Low     Close  Adj Close  Volume\n",
       "0 2001-06-22  3.428571  3.428571  3.428571  3.428571   2.806002       0\n",
       "1 2001-06-25  3.428571  3.428571  3.428571  3.428571   2.806002       0\n",
       "2 2001-06-26  3.714286  3.714286  3.714286  3.714286   3.039837       0\n",
       "3 2001-06-27  3.714286  3.714286  3.714286  3.714286   3.039837       0\n",
       "4 2001-06-28  3.714286  3.714286  3.714286  3.714286   3.039837       0"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\".//static//csv//Stocks.csv\", sep=\",\")\n",
    "print('Количество колонок: ' + str(df.columns.size))  \n",
    "print('Колонки: ' + ', '.join(df.columns)+'\\n')\n",
    "df['Date'] = pd.to_datetime(df['Date'], errors='coerce')\n",
    "\n",
    "df.info()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Подготовка данных:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. Получение сведений о пропущенных данных\n",
    "Типы пропущенных данных:\n",
    "\n",
    "- None - представление пустых данных в Python\n",
    "- NaN - представление пустых данных в Pandas\n",
    "- '' - пустая строка"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n",
      "\n",
      "Date         False\n",
      "Open         False\n",
      "High         False\n",
      "Low          False\n",
      "Close        False\n",
      "Adj Close    False\n",
      "Volume       False\n",
      "dtype: bool\n",
      "\n",
      "Количество бесконечных значений в каждом столбце:\n",
      "Date         0\n",
      "Open         0\n",
      "High         0\n",
      "Low          0\n",
      "Close        0\n",
      "Adj Close    0\n",
      "Volume       0\n",
      "dtype: int64\n",
      "Date процент пустых значений: %0.00\n",
      "Open процент пустых значений: %0.00\n",
      "High процент пустых значений: %0.00\n",
      "Low процент пустых значений: %0.00\n",
      "Close процент пустых значений: %0.00\n",
      "Adj Close процент пустых значений: %0.00\n",
      "Volume процент пустых значений: %0.00\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "# Количество пустых значений признаков\n",
    "print(df.isnull().sum())\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print(df.isnull().any())\n",
    "print()\n",
    "\n",
    "# Проверка на бесконечные значения\n",
    "print(\"Количество бесконечных значений в каждом столбце:\")\n",
    "print(np.isinf(df).sum())\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in df.columns:\n",
    "    null_rate = df[i].isnull().sum() / len(df) * 100\n",
    "    print(f\"{i} процент пустых значений: %{null_rate:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Таким образом, пропущенных значений не найдено."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. Проверка выбросов данных и устранение их при наличии:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "До устранения выбросов:\n",
      "Колонка Open:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.42\n",
      "  1-й квартиль (Q1): 2.857143\n",
      "  3-й квартиль (Q3): 10.65\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Open:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.42\n",
      "  1-й квартиль (Q1): 2.857143\n",
      "  3-й квартиль (Q3): 10.65\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка High:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.59\n",
      "  1-й квартиль (Q1): 2.88\n",
      "  3-й квартиль (Q3): 10.86\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка High:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.59\n",
      "  1-й квартиль (Q1): 2.88\n",
      "  3-й квартиль (Q3): 10.86\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка Low:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.09\n",
      "  1-й квартиль (Q1): 2.81\n",
      "  3-й квартиль (Q3): 10.425\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Low:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.09\n",
      "  1-й квартиль (Q1): 2.81\n",
      "  3-й квартиль (Q3): 10.425\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка Close:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.389999\n",
      "  1-й квартиль (Q1): 2.857143\n",
      "  3-й квартиль (Q3): 10.64\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Close:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 1.142857\n",
      "  Максимальное значение: 20.389999\n",
      "  1-й квартиль (Q1): 2.857143\n",
      "  3-й квартиль (Q3): 10.64\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка Adj Close:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 0.935334\n",
      "  Максимальное значение: 17.543156\n",
      "  1-й квартиль (Q1): 2.537094\n",
      "  3-й квартиль (Q3): 8.951944999999998\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Adj Close:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 0.935334\n",
      "  Максимальное значение: 17.543156\n",
      "  1-й квартиль (Q1): 2.537094\n",
      "  3-й квартиль (Q3): 8.951944999999998\n",
      "\n",
      "До устранения выбросов:\n",
      "Колонка Volume:\n",
      "  Есть выбросы: Да\n",
      "  Количество выбросов: 95\n",
      "  Минимальное значение: 0\n",
      "  Максимальное значение: 76714000\n",
      "  1-й квартиль (Q1): 2845900.0\n",
      "  3-й квартиль (Q3): 13272450.0\n",
      "\n",
      "После устранения выбросов:\n",
      "Колонка Volume:\n",
      "  Есть выбросы: Нет\n",
      "  Количество выбросов: 0\n",
      "  Минимальное значение: 0.0\n",
      "  Максимальное значение: 28912275.0\n",
      "  1-й квартиль (Q1): 2845900.0\n",
      "  3-й квартиль (Q3): 13272450.0\n",
      "\n"
     ]
    }
   ],
   "source": [
    "numeric_columns = ['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume']\n",
    "\n",
    "for column in numeric_columns:\n",
    "    if pd.api.types.is_numeric_dtype(df[column]):  # Проверяем, является ли колонка числовой\n",
    "        q1 = df[column].quantile(0.25)  # Находим 1-й квартиль (Q1)\n",
    "        q3 = df[column].quantile(0.75)  # Находим 3-й квартиль (Q3)\n",
    "        iqr = q3 - q1  # Вычисляем межквартильный размах (IQR)\n",
    "\n",
    "        # Определяем границы для выбросов\n",
    "        lower_bound = q1 - 1.5 * iqr  # Нижняя граница\n",
    "        upper_bound = q3 + 1.5 * iqr  # Верхняя граница\n",
    "\n",
    "        # Подсчитываем количество выбросов\n",
    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "        outlier_count = outliers.shape[0]\n",
    "\n",
    "        print(\"До устранения выбросов:\")\n",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")\n",
    "\n",
    "        # Устраняем выбросы: заменяем значения ниже нижней границы на саму нижнюю границу, а выше верхней — на верхнюю\n",
    "        if outlier_count != 0:\n",
    "            df[column] = df[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "        \n",
    "        # Подсчитываем количество выбросов\n",
    "        outliers = df[(df[column] < lower_bound) | (df[column] > upper_bound)]\n",
    "        outlier_count = outliers.shape[0]\n",
    "\n",
    "        print(\"После устранения выбросов:\")\n",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"  Есть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"  Количество выбросов: {outlier_count}\")\n",
    "        print(f\"  Минимальное значение: {df[column].min()}\")\n",
    "        print(f\"  Максимальное значение: {df[column].max()}\")\n",
    "        print(f\"  1-й квартиль (Q1): {q1}\")\n",
    "        print(f\"  3-й квартиль (Q3): {q3}\\n\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Выбросы присутствовали, но мы их устранили."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разбиение на выборки:\n",
    "\n",
    "Разобьем наш набор на обучающую, контрольную и тестовую выборки для устранения проблемы просачивания данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки:  4200\n",
      "Размер контрольной выборки:  1051\n",
      "Размер тестовой выборки:  1051\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки (80% - обучение, 20% - тестовая)\n",
    "X_train, X_test = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "# Разделение данных на обучающую и контрольную выборки (80% - обучение, 20% - контроль)\n",
    "X_train, X_val = train_test_split(df, test_size=0.2, random_state=42)\n",
    "\n",
    "print(\"Размер обучающей выборки: \", len(X_train))\n",
    "print(\"Размер контрольной выборки: \", len(X_test))\n",
    "print(\"Размер тестовой выборки: \", len(X_val))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Гистограмма распределения цены закрытия в обучающей выборке\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(X_train['Close'], bins=30, kde=False)\n",
    "plt.title(\"Распределение классов (до балансировки)\")\n",
    "plt.xlabel('Целевая переменная: Close')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Гистограмма распределения цены закрытия в контрольной выборке\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(X_val['Close'], bins=30, kde=False)\n",
    "plt.title(\"Распределение классов (до балансировки)\")\n",
    "plt.xlabel('Целевая переменная: Close')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Гистограмма распределения цены закрытия в тестовой выборке\n",
    "plt.figure(figsize=(12, 6))\n",
    "sns.histplot(X_test['Close'], bins=30, kde=False)\n",
    "plt.title(\"Распределение классов (до балансировки)\")\n",
    "plt.xlabel('Целевая переменная: Close')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Применим овер- и андерсемплинг к обучающей выборке:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Date   Open   High    Low      Close  Adj Close      Volume  \\\n",
      "4789 2020-07-08   5.66   5.73   5.47   5.560000   5.341250  23355100.0   \n",
      "3469 2015-04-10   3.86   3.93   3.81   3.880000   3.513961   7605300.0   \n",
      "2503 2011-06-07  12.19  12.28  11.95  12.020000  10.138681   7243200.0   \n",
      "1580 2007-10-08  11.77  11.84  11.53  11.570000   9.509553   3025900.0   \n",
      "2759 2012-06-12  15.77  16.17  15.76  16.120001  13.771020   6113400.0   \n",
      "\n",
      "     closePrice_category  \n",
      "4789                high  \n",
      "3469              medium  \n",
      "2503           very_high  \n",
      "1580           very_high  \n",
      "2759           very_high  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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x8fHo16+frGZdXV00adIkzzUDwKFDh5CWloZx48bJzh/x9vbWehGDnp4eRo4cKT3X19fHyJEjERMTg0uXLmmdxq+//ooLFy5gwYIFb61nzpw5sLCwwJgxY/I8DxkZGYiNjcXff/+NpUuXIiEhQdpDDMiXfUpKCmJjY9G0aVMAkH1e1Z49ewZ3d3eYmZlh3759OX4ufHx8ZM/Vn/PseybV03z27JnG5y4nqampiI2NRUxMDEJCQnDkyBG0a9dO1ifrfMXFxeHFixdo0aKF1nlSj2/v3r0ANPfETJw4EQAK7aKVHTt2oEWLFihdurTsPerm5oaMjAycOHFC6+suXbqEHTt2wN/fv9DPZyrIdvH58+ey+l+8eJHj+PO6PSoMf/zxBxo3bozmzZtLbaamphgxYgQePHiAW7duAcjfd1BaWlqu22ohBHbt2oWuXbtCCCFbLu7u7njx4oXW9967yHoKBPBmT/azZ8+QkJAA4P8+a9m3F9n3vhWk9kGDBmns8bK0tMTNmzcRERGRY8353d5k3Rurq6uLhg0bQgiBYcOGyaZbrVo1/PXXXxqvf1seyK+3LfPg4GAAwOjRo2X9tL3H3qZID8WuWrUKH3/8MfT09GBnZ4dq1arJNjKZmZlYtmwZVq9ejcjISGRkZEjD1IdrgTeHcKtVq1boV55UrVpV9lx9zpT6PB31m27QoEE5juPFixcoXbq09Dw2NlZjvNlFRERACJFjv+yHTNXnaOV2/klERARevHgBW1tbrcNjYmJkzw8ePCgdjsyrmTNnwtHRESNHjtQ4BykiIgK3b9/OcZzZp6/No0eP4OHhgeTkZDx79kxraH/48CFmzJiBffv2aZw7mNuXhTbq9asOn9mZm5vneVx///03AKBatWqydn19fVSqVEkarubo6AgTExNZ28cffwzgzTk06g2YWkZGBv73v//By8sLdevWzbWWyMhIrF27FoGBgfk6sTciIgI1atSQ6l69ejU8PT2l4c+fP4efnx+2bt2qsT61LfsuXbogPDwctra2ePOPrXbZPweVK1eGjo6O7Hw5APjxxx+l8/709fXRpEkTLFmyRLpAS5utW7di69at0vNGjRppHE7fv38/5s6di7CwMNn5oNref1nHV7p0aY3Pm3obl732goqIiMC1a9fy/bn6+uuv0aJFC3Tp0iXXcxMLWhOQv+1i9s9FTpKTk2Xz6uTkhIkTJ2Ls2LEFrDZ3f//9N5o0aaLRrv4c/P3336hdu3a+voPi4+Nz3VY/ffoU8fHxWLduHdatW6e1T162l/lRrlw52XP1uomLi4O5uTn+/vtv6OjoaBxSzr7eClK7+grtrGbPno3u3bvj448/Ru3atdGxY0cMGDBAtm3L7/Ym+zxaWFjA0NAQNjY2Gu3Zz9MD3p4H8iuvyzz78sl+3nZeFGmwa9y4ca4b3fnz52P69OkYOnQo5syZAysrK+jo6GDcuHF5/o/8fVLX8O2336J+/fpa+2T9AKelpeHx48fSOQK5jVelUuHAgQPQ1dXNdZwAEB0dDQCwt7fPdZy2trYICgrSOjz7F0OTJk0wd+5cWdvKlSulPRHZ3b59Gxs3bsRPP/2k9Vy9zMxM1KlTB0uWLNH6eicnpxxrV7t37x4aNGiApUuXYsCAAdi0aZPsyyMjIwPt27fH8+fPMWXKFFSvXh0mJiZ49OgRBg8enO/3jLr/li1btC7b4r6EPasff/xRujfZ23zzzTeoWrUqBg0aJDuX7G3KlSuHkJAQJCYmYv/+/Rg/fjycnJykC208PT1x5swZTJo0CfXr14epqSkyMzPRsWNHrcv+zp07OHDgADw9PTFx4kRs2LAhT3XktBe+e/fu8PX1hRACkZGRmD17Nrp06ZLrf/0dOnTApEmTAAD//PMPFi5ciDZt2uDixYswMjLCyZMn0a1bN7Rs2RKrV6+Gg4MDSpUqhQ0bNsgu2sk+Pj8/P40LbN6HzMxMtG/fHpMnT9Y6XP3PQFYHDx7EoUOHEBoa+t5qAvK+XQTe7PHK+o/S3bt3NfbUAm+uMFTfezExMRHr16/HuHHj4ODgIPsnoySLjo5+67YaAD7//PMcw3Fu/7zl9PnIumMkO23fMwBy/YdLm4LUru38tJYtW+L+/fvYu3cvDh48iB9++AFLly7FmjVrpD1v+d3eaJvHwprvgijKaZecbyq8uQqsTZs2GlffxcfHy1J25cqVce7cOaSnpxfKBQBq2b8QhBC4d++e9MZU//dibm4uOxk/J1evXkV6enquYVY9XiEEKlasqHXDnN2tW7egUqly/a+3cuXKOHToEJo1a5anEz1tbGw05im3CxymTp2K+vXr47PPPstx+levXkW7du0KfHhcvdvbzs4Oe/fuxcSJE9G5c2cplF6/fh13797Fpk2bMHDgQOl1Wa+kyg/1+rW1tc3T+s1N+fLlAQDh4eGoVKmS1J6WlobIyEiN8f/7779ITk6W7bW7e/cuAGhcLfny5Uv4+flh9OjR0nRycuXKFWzduhV79uzJccOSE2NjY6nOnj174sGDB5gzZw66dOmCuLg4HD58GH5+fpgxY4b0mtxC1b59+9CiRQv4+/vD19cXn3/+ucZhUPU4sv7Xeu/ePWRmZmosh48++ki2HE1NTeHl5ZXrIXgHBwfZa6pVqwZXV1fs2bMH/fr1w65du2BoaIg///xTdvgspxCqHt/u3btx6tQpPH36VPZP0927d7XWXlCVK1dGUlJSnt+fQgh8/fXX6Nmzp8Ze38KS3+0i8OaLPOs2Pafb8Ojq6srG6eHhASsrKwQHB7+XYFe+fHmEh4drtN+5c0caDuTvO+jWrVto0KBBjsPVV51mZGQUaLuj3vOT/Wr77EcF8qN8+fLIzMyU9kyqZV8271p7VlZWVhgyZAiGDBmCpKQktGzZErNmzcLw4cMLtL15V2/LA4VNvcwjIyNlewvv3buX73GVqJvH6OrqaqTXHTt2aPwETu/evREbG4uVK1dqjONd0u/mzZuRmJgoPd+5cyceP36MTp06AQCcnZ1RuXJlLF68GElJSRqvf/r0qUbturq6Wm8lklWvXr2km8lmr18IIdtN/Pr1a+zatQuNGzfOdfe+p6cnMjIyMGfOHI1hr1+/1tgI5EdoaCj27t2LBQsW5BjaPD098ejRI61X4b169QrJyclvnc7HH38MOzs7AMCKFSuQmZkpOwSjDipZl5kQAsuWLcvX/Ki5u7vD3Nwc8+fPR3p6usbw7Os3N25ubtDX18fy5ctl9f3444948eKFxpVOr1+/xtq1a6XnaWlpWLt2LcqUKQNnZ2dZ32XLliE5ORnffPPNW+v4+uuv0axZM3Tr1i3PtWuTkZGBuLg46dCktmUPINerMtXn540ePRqurq4YOXKk7LY0atmvlF2xYgUASJ/DnKj/a89PgFVPP+t8qVQq2d6OBw8evPUq7o4dOwLQnH/1HusCXdmmhaenJ0JDQ7XuqY2Pj8fr169lbVu3bsW1a9e0XrleWPK7XXwX6vdbfv9JyavOnTvj/Pnzsr2bycnJWLduHSpUqICaNWsCyPt30MWLF3H//v0cT+8A3sxL7969sWvXLty4cUNj+NuWn7m5OWxsbDTOr1y9enWur8uN+rO2fPlyWXv29/e71q6W/VCoqakpqlSp8k7bm3f1tjxQ2Nzd3QForjf19i8/StQeuy5dumD27NkYMmQIXF1dcf36dQQFBcn2eABvTmrcvHkzJkyYgPPnz6NFixZITk7GoUOHMHr0aHTv3r1A07eyskLz5s0xZMgQPHnyBAEBAahSpQq8vb0BADo6Ovjhhx/QqVMn1KpVC0OGDEHZsmXx6NEjHD16FObm5vjtt9+QnJyMVatWYfny5fj4449l96FSb/iuXbuG0NBQuLi4oHLlypg7d650a4QePXrAzMwMkZGR2L17N0aMGIGvvvoKhw4dwvTp03Ht2rW3/jRUq1atMHLkSPj7+yMsLAwdOnRAqVKlEBERgR07dmDZsmX49NNPC7ScDh48iPbt2+f6H9qAAQOwfft2fPHFFzh69CiaNWuGjIwM3LlzB9u3b8eff/751j2ZWdnb2+Pbb7/F8OHD8fnnn6Nz586oXr06KleujK+++gqPHj2Cubk5du3apXGuXV6Zm5sjMDAQAwYMQIMGDdC3b1+UKVMGDx8+xO+//45mzZpp3ZBrU6ZMGUydOhV+fn7o2LEjunXrhvDwcKxevRqNGjXC559/Luvv6OiIhQsX4sGDB/j444+xbds2hIWFYd26dRp7BA4ePIh58+bJzjvNycGDB3O8x2JuWrZsidatW6NcuXJISkrCzp07ceXKFem+Zubm5mjZsiUWLVqE9PR0lC1bFgcPHpTuSZkblUqFH374AfXr18fMmTOxaNEi2fDIyEh069YNHTt2RGhoqHTLhXr16sn6PXz4EMHBwdKh2Hnz5qF8+fL45JNPcvxP/q+//sJPP/0E4M05nCtXroS5ubm059DDwwNLlixBx44d0b9/f8TExGDVqlWoUqUKrl27luM8denSBW3btsX8+fPx6NEjNG7cGMeOHcOOHTswcuRI1K5dW2vtak+fPsWrV69kbWFhYQDeXEBUtWpVlC1bFpMmTcK+ffvQpUsXDB48GM7OzkhOTsb169exc+dOPHjwQLYn7ODBg/D29s7zOW3ZhYeHy2oC3mzDdHR0EBwcjI4dO+Z5u1gQGRkZ0vQTExOxYcMGJCcno0ePHgUa39t8/fXX+OWXX9CpUyeMGTMGVlZW2LRpEyIjI7Fr1y7pnPC8fAfNnj0by5YtQ6VKlWRHFLRZsGABjh49iiZNmsDb2xs1a9bE8+fPcfnyZRw6dAjPnz/P9fXDhw/HggULMHz4cDRs2BAnTpyQ9vgXRP369dGvXz+sXr0aL168gKurKw4fPqx179G71g4ANWvWROvWreHs7AwrKytcvHgRO3fulM4HfZftTUG9LQ8UNmdnZ/Tu3RsBAQF49uyZdLsT9XrM15GvPF8/+w7Utx64cOFCrv1SUlLExIkThYODgzAyMhLNmjUToaGhsltfqL18+VJ88803omLFiqJUqVLC3t5efPrpp+L+/ftCiILd7uSXX34RU6dOFba2tsLIyEh4eHhovWXGlStXRK9evYS1tbUwMDAQ5cuXF56enuLw4cOyab/tkf2WA7t27RLNmzcXJiYmwsTERFSvXl34+PiI8PBwIYQQX375pWjZsqUIDg7WqCn77S3U1q1bJ5ydnYWRkZEwMzMTderUEZMnTxb//vuv1Ce/tztRqVTi0qVLsnZt6ygtLU0sXLhQ1KpVSxgYGIjSpUsLZ2dn4efnJ168eKExvbeNTwgh2rZtK8qVKycSExOFEELcunVLuLm5CVNTU2FjYyO8vb3F1atXc7z8Xz0PuV06fvToUeHu7i4sLCyEoaGhqFy5shg8eLC4ePGi1Ccvt2IQ4s3tTapXry5KlSol7OzsxKhRo0RcXJzGvNaqVUtcvHhRuLi4CENDQ1G+fHmxcuVKjboACAcHB5GcnJzrPKnfD927d9c6jrfd7mTUqFGiYsWKwsDAQFhZWYmmTZvKbq8jhBD//POP6Nmzp7C0tBQWFhaiT58+4t9//33rrVfU/Pz8hJ6enrh8+bKs361bt8Snn34qzMzMROnSpYWvr6/GrVGyfo5UKpWwt7cXvXr1Erdv3xZC5Hy7k6yvs7GxER06dBChoaGycf/444+iatWqwsDAQFSvXl1s2LBB6zxkv2VFYmKiGDt2rHB0dBSlSpUSlStXFvPnzxevX7/Osfa8PrK+lxMTE8XUqVNFlSpVhL6+vrCxsRGurq5i8eLF0q1x1NsgIyMj8ejRo1zr1iav27Cs3rZdFCL/tzvJOi1TU1PRoEEDsWXLFtmyLMzbnQghxP3798Wnn34qLC0thaGhoWjcuLHYv3+/xmvf9h300UcfiaFDh8q2tWra1sGTJ0+Ej4+PcHJyksbXrl07sW7dulznT13LsGHDhIWFhTAzMxOenp4iJiYmx89i9uWv7fPy6tUrMWbMGGFtbS1MTExE165dRVRUlNZlnpfac1sPc+fOFY0bNxaWlpbCyMhIVK9eXcybN096PwuR/+1N9nnMaZut3v5mrzMveSA/tzvJyzJPTk4WPj4+wsrKSpiamooePXqI8PBwAUB2m5a3KZJgV9Ll9oYrCPVGMesKy27mzJlv3bjShyH7huVDldMG8EPXqlWrHP9JKS7qbSaR0hR2HnhXV65cEQDETz/9lOfXlKhz7IiIiIg+RNrOOQ4ICICOjo7GL2PkpkSdY6cU6qvzcru4oW7dutJPpBER5aRx48bSTdxLCisrK+lkbyIqHIsWLcKlS5fQpk0b6Onp4cCBAzhw4ABGjBiRp1uEqTHYvQc2NjbSCdo5yfo7pUREOcl+cUlJULduXY2LKojo3bi6uiIkJARz5sxBUlISypUrh1mzZuXpDghZqYQogjvzEREREdF7x3PsiIiIiBSCwY6IiIhIIXiOXR5kZmbi33//hZmZWYF/HouIiIiKlhACiYmJcHR0lG4wrXQMdnnw77//5uuKFCIiIio5oqKi8NFHHxV3GUWCwS4PzMzMALx5Y5ibmxdzNURERJQXCQkJcHJykr7HPwQMdnmgPvxqbm7OYEdERPQf8yGdRvVhHHAmIiIi+gAw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkULoFXcBSuY8aXNxl0D/36VvB773aXB9lxxc3x8Wru8PS1Gs7/8y7rEjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiIiKFKNZgd+LECXTt2hWOjo5QqVTYs2ePbLgQAjNmzICDgwOMjIzg5uaGiIgIWZ/nz5/Dy8sL5ubmsLS0xLBhw5CUlCTrc+3aNbRo0QKGhoZwcnLCokWL3vesERERERW5Yg12ycnJqFevHlatWqV1+KJFi7B8+XKsWbMG586dg4mJCdzd3ZGSkiL18fLyws2bNxESEoL9+/fjxIkTGDFihDQ8ISEBHTp0QPny5XHp0iV8++23mDVrFtatW/fe54+IiIioKOkV58Q7deqETp06aR0mhEBAQACmTZuG7t27AwA2b94MOzs77NmzB3379sXt27cRHByMCxcuoGHDhgCAFStWoHPnzli8eDEcHR0RFBSEtLQ0rF+/Hvr6+qhVqxbCwsKwZMkSWQAkIiIi+q8rsefYRUZGIjo6Gm5ublKbhYUFmjRpgtDQUABAaGgoLC0tpVAHAG5ubtDR0cG5c+ekPi1btoS+vr7Ux93dHeHh4YiLi9M67dTUVCQkJMgeRERERCVdiQ120dHRAAA7OztZu52dnTQsOjoatra2suF6enqwsrKS9dE2jqzTyM7f3x8WFhbSw8nJ6d1niIiIiOg9K7HBrjhNnToVL168kB5RUVHFXRIRERHRW5XYYGdvbw8AePLkiaz9yZMn0jB7e3vExMTIhr9+/RrPnz+X9dE2jqzTyM7AwADm5uayBxEREVFJV2KDXcWKFWFvb4/Dhw9LbQkJCTh37hxcXFwAAC4uLoiPj8elS5ekPkeOHEFmZiaaNGki9Tlx4gTS09OlPiEhIahWrRpKly5dRHNDRERE9P4Va7BLSkpCWFgYwsLCALy5YCIsLAwPHz6ESqXCuHHjMHfuXOzbtw/Xr1/HwIED4ejoiB49egAAatSogY4dO8Lb2xvnz5/H6dOn4evri759+8LR0REA0L9/f+jr62PYsGG4efMmtm3bhmXLlmHChAnFNNdERERE70ex3u7k4sWLaNOmjfRcHbYGDRqEjRs3YvLkyUhOTsaIESMQHx+P5s2bIzg4GIaGhtJrgoKC4Ovri3bt2kFHRwe9e/fG8uXLpeEWFhY4ePAgfHx84OzsDBsbG8yYMYO3OiEiIiLFKdZg17p1awghchyuUqkwe/ZszJ49O8c+VlZW+Pnnn3OdTt26dXHy5MkC10lERET0X1Biz7EjIiIiovxhsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoVgsCMiIiJSCAY7IiIiIoUo0cEuIyMD06dPR8WKFWFkZITKlStjzpw5EEJIfYQQmDFjBhwcHGBkZAQ3NzdERETIxvP8+XN4eXnB3NwclpaWGDZsGJKSkop6doiIiIjeqxId7BYuXIjAwECsXLkSt2/fxsKFC7Fo0SKsWLFC6rNo0SIsX74ca9aswblz52BiYgJ3d3ekpKRIfby8vHDz5k2EhIRg//79OHHiBEaMGFEcs0RERET03ugVdwG5OXPmDLp37w4PDw8AQIUKFfDLL7/g/PnzAN7srQsICMC0adPQvXt3AMDmzZthZ2eHPXv2oG/fvrh9+zaCg4Nx4cIFNGzYEACwYsUKdO7cGYsXL4ajo2PxzBwRERFRISvRe+xcXV1x+PBh3L17FwBw9epVnDp1Cp06dQIAREZGIjo6Gm5ubtJrLCws0KRJE4SGhgIAQkNDYWlpKYU6AHBzc4OOjg7OnTundbqpqalISEiQPYiIiIhKuhK9x+7rr79GQkICqlevDl1dXWRkZGDevHnw8vICAERHRwMA7OzsZK+zs7OThkVHR8PW1lY2XE9PD1ZWVlKf7Pz9/eHn51fYs0NERET0XpXoPXbbt29HUFAQfv75Z1y+fBmbNm3C4sWLsWnTpvc63alTp+LFixfSIyoq6r1Oj4iIiKgwlOg9dpMmTcLXX3+Nvn37AgDq1KmDv//+G/7+/hg0aBDs7e0BAE+ePIGDg4P0uidPnqB+/foAAHt7e8TExMjG+/r1azx//lx6fXYGBgYwMDB4D3NERERE9P6U6D12L1++hI6OvERdXV1kZmYCACpWrAh7e3scPnxYGp6QkIBz587BxcUFAODi4oL4+HhcunRJ6nPkyBFkZmaiSZMmRTAXREREREWjRO+x69q1K+bNm4dy5cqhVq1auHLlCpYsWYKhQ4cCAFQqFcaNG4e5c+eiatWqqFixIqZPnw5HR0f06NEDAFCjRg107NgR3t7eWLNmDdLT0+Hr64u+ffvyilgiIiJSlBId7FasWIHp06dj9OjRiImJgaOjI0aOHIkZM2ZIfSZPnozk5GSMGDEC8fHxaN68OYKDg2FoaCj1CQoKgq+vL9q1awcdHR307t0by5cvL45ZIiIiInpvSnSwMzMzQ0BAAAICAnLso1KpMHv2bMyePTvHPlZWVvj555/fQ4VEREREJUeJPseOiIiIiPKOwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIfQK+sLk5GQcP34cDx8+RFpammzYmDFj3rkwIiIiIsqfAgW7K1euoHPnznj58iWSk5NhZWWF2NhYGBsbw9bWlsGOiIiIqBgU6FDs+PHj0bVrV8TFxcHIyAhnz57F33//DWdnZyxevLiwayQiIiKiPChQsAsLC8PEiROho6MDXV1dpKamwsnJCYsWLcL//ve/wq6RiIiIiPKgQMGuVKlS0NF581JbW1s8fPgQAGBhYYGoqKjCq46IiIiI8qxA59h98sknuHDhAqpWrYpWrVphxowZiI2NxZYtW1C7du3CrpGIiIiI8qBAe+zmz58PBwcHAMC8efNQunRpjBo1Ck+fPsW6desKtcBHjx7h888/h7W1NYyMjFCnTh1cvHhRGi6EwIwZM+Dg4AAjIyO4ubkhIiJCNo7nz5/Dy8sL5ubmsLS0xLBhw5CUlFSodRIREREVtwLtsWvYsKH0t62tLYKDgwutoKzi4uLQrFkztGnTBgcOHECZMmUQERGB0qVLS30WLVqE5cuXY9OmTahYsSKmT58Od3d33Lp1C4aGhgAALy8vPH78GCEhIUhPT8eQIUMwYsQI/Pzzz++lbiIiIqLiUKBg17ZtW/z666+wtLQs5HLkFi5cCCcnJ2zYsEFqq1ixovS3EAIBAQGYNm0aunfvDgDYvHkz7OzssGfPHvTt2xe3b99GcHAwLly4IAXSFStWoHPnzli8eDEcHR3f6zwQERERFZUCHYo9duyYxk2J34d9+/ahYcOG6NOnD2xtbfHJJ5/g+++/l4ZHRkYiOjoabm5uUpuFhQWaNGmC0NBQAEBoaCgsLS1lexnd3Nygo6ODc+fOaZ1uamoqEhISZA8iIiKikq7APymmUqkKsw6t/vrrLwQGBqJq1ar4888/MWrUKIwZMwabNm0CAERHRwMA7OzsZK+zs7OThkVHR8PW1lY2XE9PD1ZWVlKf7Pz9/WFhYSE9nJycCnvWiIiIiApdgX9SrGfPntDX19c67MiRIwUuKKvMzEw0bNgQ8+fPB/DmatwbN25gzZo1GDRoUKFMQ5upU6diwoQJ0vOEhASGOyIiIirxChzsXFxcYGpqWpi1aHBwcEDNmjVlbTVq1MCuXbsAAPb29gCAJ0+eSFfpqp/Xr19f6hMTEyMbx+vXr/H8+XPp9dkZGBjAwMCgsGaDiIiIqEgUKNipVCpMmjRJ4xBnYWvWrBnCw8NlbXfv3kX58uUBvLmQwt7eHocPH5aCXEJCAs6dO4dRo0YBeBNA4+PjcenSJTg7OwN4s0cxMzMTTZo0ea/1ExERERWlAgU7IURh16HV+PHj4erqivnz58PT0xPnz5/HunXrpHvlqVQqjBs3DnPnzkXVqlWl2504OjqiR48eAN7s4evYsSO8vb2xZs0apKenw9fXF3379uUVsURERKQoBQp2M2fOfO+HYQGgUaNG2L17N6ZOnYrZs2ejYsWKCAgIgJeXl9Rn8uTJSE5OxogRIxAfH4/mzZsjODhYuocdAAQFBcHX1xft2rWDjo4OevfujeXLl7/3+omIiIiKUoGDHQA8ffpUOlRarVo1lClTpvAq+/+6dOmCLl265DhcpVJh9uzZmD17do59rKyseDNiIiIiUrwC3e7k5cuXGDp0KBwdHdGyZUu0bNkSjo6OGDZsGF6+fFnYNRIRERFRHhQo2I0fPx7Hjx/Hvn37EB8fj/j4eOzduxfHjx/HxIkTC7tGIiIiIsqDAh2K3bVrF3bu3InWrVtLbZ07d4aRkRE8PT0RGBhYWPURERERUR4V+FBs9l97AABbW1seiiUiIiIqJgUKdi4uLpg5cyZSUlKktlevXsHPzw8uLi6FVhwRERER5V2BDsUGBASgY8eO+Oijj1CvXj0AwNWrV2FoaIg///yzUAskIiIiorwpULCrU6cOIiIiEBQUhDt37gAA+vXrBy8vLxgZGRVqgURERESUNwUKdidOnICrqyu8vb0Lux4iIiIiKqACnWPXpk0bPH/+vLBrISIiIqJ3UKBgV1S/FUtEREREeVegQ7EAEBoaitKlS2sd1rJlywIXREREREQFU+Bg17NnT63tKpUKGRkZBS6IiIiIiAqmQIdiASA6OhqZmZkaD4Y6IiIiouJRoGCnUqkKuw4iIiIieke8eIKIiIhIIQp0jl1mZmZh10FERERE76hAe+z8/f2xfv16jfb169dj4cKF71wUEREREeVfgYLd2rVrUb16dY32WrVqYc2aNe9cFBERERHlX4GCXXR0NBwcHDTay5Qpg8ePH79zUURERESUfwUKdk5OTjh9+rRG++nTp+Ho6PjORRERERFR/hXo4glvb2+MGzcO6enpaNu2LQDg8OHDmDx5MiZOnFioBRIRERFR3hQo2E2aNAnPnj3D6NGjkZaWBgAwNDTElClTMHXq1EItkIiIiIjypkDBTqVSYeHChZg+fTpu374NIyMjVK1aFQYGBoVdHxERERHlUYF/KxYATE1N0ahRo8KqhYiIiIjeQYGD3cWLF7F9+3Y8fPhQOhyr9uuvv75zYURERESUPwW6Knbr1q1wdXXF7du3sXv3bqSnp+PmzZs4cuQILCwsCrtGIiIiIsqDAgW7+fPnY+nSpfjtt9+gr6+PZcuW4c6dO/D09ES5cuUKu0YiIiIiyoMCBbv79+/Dw8MDAKCvr4/k5GSoVCqMHz8e69atK9QCiYiIiChvChTsSpcujcTERABA2bJlcePGDQBAfHw8Xr58WXjVEREREVGeFejiiZYtWyIkJAR16tRBnz59MHbsWBw5cgQhISFo165dYddIRERERHlQoGC3cuVKpKSkAAC++eYblCpVCmfOnEHv3r0xbdq0Qi2QiIiIiPImX8EuISHhzYv09GBqaio9Hz16NEaPHl341RERERFRnuUr2FlaWkKlUr21X0ZGRoELIiIiIqKCyVewO3r0qOy5EAKdO3fGDz/8gLJlyxZqYURERESUP/kKdq1atdJo09XVRdOmTVGpUqVCK4qIiIiI8q9AtzshIiIiopLnnYJdVFQUXr58CWtr68Kqh4iIiIgKKF+HYpcvXy79HRsbi19++QVt27bl78MSERERlQD5CnZLly4FAKhUKtjY2KBr1668bx0RERFRCZGvYBcZGfm+6iAiIiKid8SLJ4iIiIgUgsGOiIiISCEY7IiIiIgUgsGOiIiISCEY7IiIiIgUgsGOiIiISCEY7IiIiIgUgsGOiIiISCEY7IiIiIgUgsGOiIiISCEY7IiIiIgUgsGOiIiISCEY7IiIiIgUgsGOiIiISCEY7IiIiIgU4j8V7BYsWACVSoVx48ZJbSkpKfDx8YG1tTVMTU3Ru3dvPHnyRPa6hw8fwsPDA8bGxrC1tcWkSZPw+vXrIq6eiIiI6P36zwS7CxcuYO3atahbt66sffz48fjtt9+wY8cOHD9+HP/++y969eolDc/IyICHhwfS0tJw5swZbNq0CRs3bsSMGTOKehaIiIiI3qv/RLBLSkqCl5cXvv/+e5QuXVpqf/HiBX788UcsWbIEbdu2hbOzMzZs2IAzZ87g7NmzAICDBw/i1q1b+Omnn1C/fn106tQJc+bMwapVq5CWllZcs0RERERU6P4Twc7HxwceHh5wc3OTtV+6dAnp6emy9urVq6NcuXIIDQ0FAISGhqJOnTqws7OT+ri7uyMhIQE3b94smhkgIiIiKgJ6xV3A22zduhWXL1/GhQsXNIZFR0dDX18flpaWsnY7OztER0dLfbKGOvVw9TBtUlNTkZqaKj1PSEh4l1kgIiIiKhIleo9dVFQUxo4di6CgIBgaGhbZdP39/WFhYSE9nJycimzaRERERAVVooPdpUuXEBMTgwYNGkBPTw96eno4fvw4li9fDj09PdjZ2SEtLQ3x8fGy1z158gT29vYAAHt7e42rZNXP1X2ymzp1Kl68eCE9oqKiCn/miIiIiApZiQ527dq1w/Xr1xEWFiY9GjZsCC8vL+nvUqVK4fDhw9JrwsPD8fDhQ7i4uAAAXFxccP36dcTExEh9QkJCYG5ujpo1a2qdroGBAczNzWUPIiIiopKuRJ9jZ2Zmhtq1a8vaTExMYG1tLbUPGzYMEyZMgJWVFczNzfHll1/CxcUFTZs2BQB06NABNWvWxIABA7Bo0SJER0dj2rRp8PHxgYGBQZHPExEREdH7UqKDXV4sXboUOjo66N27N1JTU+Hu7o7Vq1dLw3V1dbF//36MGjUKLi4uMDExwaBBgzB79uxirJqIiIio8P3ngt2xY8dkzw0NDbFq1SqsWrUqx9eUL18ef/zxx3uujIiIiKh4lehz7IiIiIgo7xjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSCwY6IiIhIIRjsiIiIiBSiRAc7f39/NGrUCGZmZrC1tUWPHj0QHh4u65OSkgIfHx9YW1vD1NQUvXv3xpMnT2R9Hj58CA8PDxgbG8PW1haTJk3C69evi3JWiIiIiN67Eh3sjh8/Dh8fH5w9exYhISFIT09Hhw4dkJycLPUZP348fvvtN+zYsQPHjx/Hv//+i169eknDMzIy4OHhgbS0NJw5cwabNm3Cxo0bMWPGjOKYJSIiIqL3Rq+4C8hNcHCw7PnGjRtha2uLS5cuoWXLlnjx4gV+/PFH/Pzzz2jbti0AYMOGDahRowbOnj2Lpk2b4uDBg7h16xYOHToEOzs71K9fH3PmzMGUKVMwa9Ys6OvrF8esERERERW6Er3HLrsXL14AAKysrAAAly5dQnp6Otzc3KQ+1atXR7ly5RAaGgoACA0NRZ06dWBnZyf1cXd3R0JCAm7evKl1OqmpqUhISJA9iIiIiEq6/0ywy8zMxLhx49CsWTPUrl0bABAdHQ19fX1YWlrK+trZ2SE6OlrqkzXUqYerh2nj7+8PCwsL6eHk5FTIc0NERERU+P4zwc7Hxwc3btzA1q1b3/u0pk6dihcvXkiPqKio9z5NIiIiondVos+xU/P19cX+/ftx4sQJfPTRR1K7vb090tLSEB8fL9tr9+TJE9jb20t9zp8/Lxuf+qpZdZ/sDAwMYGBgUMhzQURERPR+leg9dkII+Pr6Yvfu3Thy5AgqVqwoG+7s7IxSpUrh8OHDUlt4eDgePnwIFxcXAICLiwuuX7+OmJgYqU9ISAjMzc1Rs2bNopkRIiIioiJQovfY+fj44Oeff8bevXthZmYmnRNnYWEBIyMjWFhYYNiwYZgwYQKsrKxgbm6OL7/8Ei4uLmjatCkAoEOHDqhZsyYGDBiARYsWITo6GtOmTYOPjw/3yhEREZGilOhgFxgYCABo3bq1rH3Dhg0YPHgwAGDp0qXQ0dFB7969kZqaCnd3d6xevVrqq6uri/3792PUqFFwcXGBiYkJBg0ahNmzZxfVbBAREREViRId7IQQb+1jaGiIVatWYdWqVTn2KV++PP7444/CLI2IiIioxCnR59gRERERUd4x2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUIw2BEREREpBIMdERERkUJ8UMFu1apVqFChAgwNDdGkSROcP3++uEsiIiIiKjQfTLDbtm0bJkyYgJkzZ+Ly5cuoV68e3N3dERMTU9ylERERERWKDybYLVmyBN7e3hgyZAhq1qyJNWvWwNjYGOvXry/u0oiIiIgKxQcR7NLS0nDp0iW4ublJbTo6OnBzc0NoaGgxVkZERERUePSKu4CiEBsbi4yMDNjZ2cna7ezscOfOHY3+qampSE1NlZ6/ePECAJCQkJCv6WakvipAtfQ+5HfdFQTXd8nB9f1h4fr+sORnfav7CiHeVzklzgcR7PLL398ffn5+Gu1OTk7FUA0VBosVXxR3CVSEuL4/LFzfH5aCrO/ExERYWFi8h2pKng8i2NnY2EBXVxdPnjyRtT958gT29vYa/adOnYoJEyZIzzMzM/H8+XNYW1tDpVK993pLioSEBDg5OSEqKgrm5ubFXQ69Z1zfHxau7w/Lh7q+hRBITEyEo6NjcZdSZD6IYKevrw9nZ2ccPnwYPXr0APAmrB0+fBi+vr4a/Q0MDGBgYCBrs7S0LIJKSyZzc/MPakPwoeP6/rBwfX9YPsT1/aHsqVP7IIIdAEyYMAGDBg1Cw4YN0bhxYwQEBCA5ORlDhgwp7tKIiIiICsUHE+w+++wzPH36FDNmzEB0dDTq16+P4OBgjQsqiIiIiP6rPphgBwC+vr5aD72SdgYGBpg5c6bGYWlSJq7vDwvX94eF6/vDoRIf0jXARERERAr2QdygmIiIiOhDwGBHREREpBAMdkREREQKwWD3gWrdujXGjRtX3GVQCZL9PVGhQgUEBAQUWz2UP2/7TKtUKuzZsyfP4zt27BhUKhXi4+PfuTYqud72vijI+2DWrFmoX7/+O9dGBfNBXRVLRHl34cIFmJiYFHcZVEgeP36M0qVLF3cZ9B/j6uqKx48ff3A3+f0vY7AjIq3KlClT3CVQIdL284n035aeno5SpUq912no6+vzvfMfw0OxhLi4OAwcOBClS5eGsbExOnXqhIiICABvfmevTJky2Llzp9S/fv36cHBwkJ6fOnUKBgYGePnyZZHX/iFo3bo1vvzyS4wbNw6lS5eGnZ0dvv/+e+mXU8zMzFClShUcOHBAes2NGzfQqVMnmJqaws7ODgMGDEBsbKw0PDk5GQMHDoSpqSkcHBzw3XffaUw366HYBw8eQKVSISwsTBoeHx8PlUqFY8eOAfi/QzZ//vknPvnkExgZGaFt27aIiYnBgQMHUKNGDZibm6N///58r7wnmZmZmDx5MqysrGBvb49Zs2ZJw7Ifcjtz5gzq168PQ0NDNGzYEHv27NFYxwBw6dIlNGzYEMbGxnB1dUV4eHjRzIzCrFu3Do6OjsjMzJS1d+/eHUOHDgUA7N27Fw0aNIChoSEqVaoEPz8/vH79WuqrUqkQGBiIbt26wcTEBHPnzkWVKlWwePFi2TjDwsKgUqlw7969PNUWGxuLnj17wtjYGFWrVsW+ffukYdoOxX7//fdwcnKCsbExevbsiSVLlmj92c0tW7agQoUKsLCwQN++fZGYmJineujdMNgRBg8ejIsXL2Lfvn0IDQ2FEAKdO3dGeno6VCoVWrZsKX15x8XF4fbt23j16hXu3LkDADh+/DgaNWoEY2PjYpwLZdu0aRNsbGxw/vx5fPnllxg1ahT69OkDV1dXXL58GR06dMCAAQPw8uVLxMfHo23btvjkk09w8eJFBAcH48mTJ/D09JTGN2nSJBw/fhx79+7FwYMHcezYMVy+fLlQap01axZWrlyJM2fOICoqCp6enggICMDPP/+M33//HQcPHsSKFSsKZVokt2nTJpiYmODcuXNYtGgRZs+ejZCQEI1+CQkJ6Nq1K+rUqYPLly9jzpw5mDJlitZxfvPNN/juu+9w8eJF6OnpSSGE8qdPnz549uwZjh49KrU9f/4cwcHB8PLywsmTJzFw4ECMHTsWt27dwtq1a7Fx40bMmzdPNp5Zs2ahZ8+euH79OoYNG4ahQ4diw4YNsj4bNmxAy5YtUaVKlTzV5ufnB09PT1y7dg2dO3eGl5cXnj9/rrXv6dOn8cUXX2Ds2LEICwtD+/btNWoEgPv372PPnj3Yv38/9u/fj+PHj2PBggV5qofekaAPUqtWrcTYsWPF3bt3BQBx+vRpaVhsbKwwMjIS27dvF0IIsXz5clGrVi0hhBB79uwRTZo0Ed27dxeBgYFCCCHc3NzE//73v6KfiQ9Eq1atRPPmzaXnr1+/FiYmJmLAgAFS2+PHjwUAERoaKubMmSM6dOggG0dUVJQAIMLDw0ViYqLQ19eX1q8QQjx79kwYGRmJsWPHSm3ly5cXS5cuFUIIERkZKQCIK1euSMPj4uIEAHH06FEhhBBHjx4VAMShQ4ekPv7+/gKAuH//vtQ2cuRI4e7u/i6LhLTI/j4RQohGjRqJKVOmCCGEACB2794thBAiMDBQWFtbi1evXkl9v//+e9k61rY+f//9dwFA9jrKu+7du4uhQ4dKz9euXSscHR1FRkaGaNeunZg/f76s/5YtW4SDg4P0HIAYN26crM+jR4+Erq6uOHfunBBCiLS0NGFjYyM2btyYp5oAiGnTpknPk5KSBABx4MABIcT/vQ/i4uKEEEJ89tlnwsPDQzYOLy8vYWFhIT2fOXOmMDY2FgkJCVLbpEmTRJMmTfJUE70b7rH7wN2+fRt6enpo0qSJ1GZtbY1q1arh9u3bAIBWrVrh1q1bePr0KY4fP47WrVujdevWOHbsGNLT03HmzBm0bt26mObgw1C3bl3pb11dXVhbW6NOnTpSm/o3j2NiYnD16lUcPXoUpqam0qN69eoA3vwXff/+faSlpcnWuZWVFapVq1botdrZ2cHY2BiVKlWStcXExBTKtEgu67IHAAcHB63LOjw8HHXr1oWhoaHU1rhx47eOU30KBtdfwXh5eWHXrl1ITU0FAAQFBaFv377Q0dHB1atXMXv2bNnn1tvbG48fP5adutCwYUPZOB0dHeHh4YH169cDAH777TekpqaiT58+ea4r6zo2MTGBubl5jus4PDxc472i7b1ToUIFmJmZSc9zei9S4ePFE/RWderUgZWVFY4fP47jx49j3rx5sLe3x8KFC3HhwgWkp6fD1dW1uMtUtOwnSKtUKlmbSqUC8OYcq6SkJHTt2hULFy7UGI+Dg0Oez7vJSkfnzf+AIssvEKanp7+11ux1qtuyn2dEheN9LOuc3meUf127doUQAr///jsaNWqEkydPYunSpQCApKQk+Pn5oVevXhqvyxrAtV2pPnz4cAwYMABLly7Fhg0b8Nlnn+Xr1Jj3/b4prHFS3jDYfeBq1KiB169f49y5c1I4e/bsGcLDw1GzZk0Abz6QLVq0wN69e3Hz5k00b94cxsbGSE1Nxdq1a9GwYUPeFqMEadCgAXbt2oUKFSpAT0/zI165cmWUKlUK586dQ7ly5QC8OXfy7t27aNWqldZxqq+Qffz4MT755BMA0DjJnv47qlWrhp9++gmpqanSj8JfuHChmKtSPkNDQ/Tq1QtBQUG4d+8eqlWrhgYNGgB487kNDw/P83lxWXXu3BkmJiYIDAxEcHAwTpw4UdilS6pVq6bxXuF7p2ThodgPXNWqVdG9e3d4e3vj1KlTuHr1Kj7//HOULVsW3bt3l/q1bt0av/zyC+rXrw9TU1Po6OigZcuWCAoKyjEMUPHw8fHB8+fP0a9fP1y4cAH379/Hn3/+iSFDhiAjIwOmpqYYNmwYJk2ahCNHjuDGjRsYPHiwtFdOGyMjIzRt2hQLFizA7du3cfz4cUybNq0I54oKU//+/ZGZmYkRI0bg9u3b+PPPP6UrK9V75ej98PLywu+//47169fDy8tLap8xYwY2b94MPz8/3Lx5E7dv38bWrVvz9DnT1dXF4MGDMXXqVFStWhUuLi7vrf4vv/wSf/zxB5YsWYKIiAisXbsWBw4c4PumBGGwI2zYsAHOzs7o0qULXFxcIITAH3/8IduV3qpVK2RkZMjOpWvdurVGGxU/R0dHnD59GhkZGejQoQPq1KmDcePGwdLSUgpv3377LVq0aIGuXbvCzc0NzZs3h7Ozc67jXb9+PV6/fg1nZ2eMGzcOc+fOLYrZoffA3Nwcv/32G8LCwlC/fn188803mDFjBgD5YT8qfG3btoWVlRXCw8PRv39/qd3d3R379+/HwYMH0ahRIzRt2hRLly5F+fLl8zTeYcOGIS0tDUOGDHlfpQMAmjVrhjVr1mDJkiWoV68egoODMX78eL5vShCVyHrSDBERfZCCgoIwZMgQvHjxAkZGRsVdDuXTyZMn0a5dO0RFRUkXUxUVb29v3LlzBydPnizS6ZJ2PMeOiOgDtHnzZlSqVAlly5bF1atXMWXKFHh6ejLU/cekpqbi6dOnmDVrFvr06VMkoW7x4sVo3749TExMcODAAWzatAmrV69+79OlvOGhWCKiD1B0dDQ+//xz1KhRA+PHj0efPn2wbt264i6L8umXX35B+fLlER8fj0WLFsmGBQUFyW6fkvVRq1atAk/z/PnzaN++PerUqYM1a9Zg+fLlGD58+LvOChUSHoolIiJSoMTERDx58kTrsFKlSuX5/D36b2GwIyIiIlIIHoolIiIiUggGOyIiIiKFYLAjIiIiUggGOyIiQu/evXHy5ElkZGSgX79+2L9/f3GXREQFwGBHVAgGDx6MHj16yNqePn2K2rVro0mTJnjx4kXxFEaUR76+vvDw8IChoSEiIyPh5uZW3CURUQHwBsVE78HTp0/Rtm1bGBkZ4eDBg7CwsCjukohy1aZNGzx9+hTPnz+Hvb09f/uT6D+Ke+yICllsbCzatWsHAwMDhISEyELdkiVLUKdOHZiYmMDJyQmjR49GUlISAODYsWNQqVQ5PtROnTqFFi1awMjICE5OThgzZgySk5Ol4RUqVNB47VdffSUNDwwMROXKlaGvr49q1aphy5YtsvpVKhUCAwPRqVMnGBkZoVKlSti5c6c0/MGDB1CpVAgLC5Papk+fDpVKhYCAAKntzp07aN++PSwsLKQ6LC0tc1xuJ06cQI0aNWBsbAwLCwu4u7sjIiJCGr5lyxY0bNgQZmZmsLe3R//+/RETEyMNVy+/+Ph4AEBcXBzq1q2LgQMHQn1Xp9atW8PX1xe+vr6wsLCAjY0Npk+fjqx3fapQoYJsPrLq0aMHBg8erHVZm5iYwNXVFRcvXpSGBwcHo3nz5rC0tIS1tTW6dOmC+/fv57gsHz16hD59+sDW1hZmZmbo2bMn/vnnH6n/rFmzUL9+fel5fHw8VCoVjh07JrVp23u8d+9eNGjQAIaGhqhUqRL8/Pzw+vVrabhKpcKePXtgYGAABwcHrF+/HiqVCuPGjdO6HLTVAmiuAwCIioqCp6cnLC0tYWVlhe7du+PBgwe51rtx40bZe0XbtNT27NnDEEqUBYMdUSF69uwZ3NzcoKenh5CQEI0go6Ojg+XLl+PmzZvYtGkTjhw5gsmTJwMAXF1d8fjxYzx+/Bi7du0CAOn548ePAQD3799Hx44d0bt3b1y7dg3btm3DqVOn4OvrK5vO7NmzZa+dOXMmAGD37t0YO3YsJk6ciBs3bmDkyJEYMmQIjh49Knv99OnT0bt3b1y9ehVeXl7o27cvbt++rXWe//nnHwQEBGj8FNXQoUORnp6O06dP4/HjxzmGJbWyZcti5cqVuHnzJk6dOgUdHR2MHDlSGp6eno45c+bg6tWr2LNnDx48eCALWVklJSWhc+fOqFSpkhRS1DZt2gQ9PT2cP38ey5Ytw5IlS/DDDz/kWltu1Mv64sWLMDExgY+PjzQsOTkZEyZMwMWLF3H48GHo6OigZ8+eyMzM1BhPeno6OnfujMjISPz22284fvw4YmNj0aNHD7zL7UZPnjyJgQMHYuzYsbh16xbWrl2LjRs3Yt68eVr7JycnY/r06TA1NS3wNNXS09Ph7u4OMzMznDx5EqdPn4apqSk6duyItLS0dx4/EWnioViiQhIXFwc3NzfcunULzs7OMDc31+iTdQ9IhQoVMHfuXHzxxRdYvXo19PX1YW9vDwCwsrICAOm5mr+/P7y8vKTxVK1aFcuXL0erVq0QGBgIQ0NDAJD2amW3ePFiDB48GKNHjwYATJgwAWfPnsXixYvRpk0bqV+fPn2knwiaM2cOQkJCsGLFCq2/B/nNN9/gs88+w6FDh2TtYWFh+OGHH1C7dm0AeOvh6MqVK6Ny5coAgJSUFDg4OMj2bg0dOlT6u1KlSli+fDkaNWqEpKQkWQhJTU3Fp59+CmNjY2zbtg16evLNnJOTE5YuXQqVSoVq1arh+vXrWLp0Kby9vXOtLyfqZW1paYnSpUvLQmTv3r1lfdevX48yZcrg1q1b0nJRO3ToEK5du4abN2+iZs2aAN78JFSlSpVw+PDhAp/z5ufnh6+//hqDBg0C8GbZzZkzB5MnT5YCf1aLFi1CzZo1ZXv0Cmrbtm3IzMzEDz/8IC2XDRs2wNLSEseOHUOHDh3eeRpEJMc9dkSF5MSJE8jMzERYWBju3bun8buNwJsv73bt2qFs2bIwMzPDgAED8OzZM7x8+TJP07h69So2btwo+81Hd3d3ZGZmIjIy8q2vv337Npo1ayZra9asmcbeOBcXF43n2vbYXb58Gbt378acOXM0hlWsWBG7d+/O87wBwMOHD2FqagoTExOcP38eGzdulIZdunQJXbt2Rbly5WBmZoZWrVpJr8nKy8sLhw8fRqtWrWBgYKAxjaZNm8rCl4uLCyIiIpCRkSG1TZkyBaamprC1tUXr1q1x+vTpHGtW91XXvGrVKmlYREQE+vXrh0qVKsHc3BwVKlTQWrOrqyt69OgBS0tLKdQBQLly5eDk5IRbt27lstRyd/XqVcyePVv2nvH29sbjx4811s2///6LJUuW4Lvvvivw9LJP+969ezAzM5OmbWVlhZSUFFlo379/v6y+L774QmNc169fh6mpKSwsLFCjRg0sWLCgUGokUhrusSMqJOo9KzY2Nli9ejU+//xzeHh4oG7dugDenE/VpUsXjBo1CvPmzYOVlRVOnTqFYcOGIS0tDcbGxm+dRlJSEkaOHIkxY8ZoDCtXrlyhz9PbTJw4EV999RUcHBw0hv34448YNGgQzMzMYGRkhNevX0t7FHPi6OiIsLAwREdHY+rUqViwYAHWrl2L5ORkuLu7w93dHUFBQShTpgwePnwId3d3jUN60dHR2LVrF/r374+ePXuiTp06+Z6vSZMmYfDgwUhOTsa3336Lrl27Ijo6+q19Fy9eDE9PT1y8eBG6urro2rUrypcvj++//x6Ojo7IzMxE7dq1NWretm0bbt++DX9/f63TeJdzyJKSkuDn54devXppDMu+Pr755hv06dMH9erVK/D0sk/b2dkZQUFBGsPKlCkj/d2mTRsEBgZKz3/99VfMnz9f1r9atWrYt28fMjIycPbsWXh7e6NKlSoae2SJPnT8RBAVkjp16sDGxgbAm0OZv/76KwYOHIjz589DX18fly5dQmZmJr777jvo6LzZWb59+/Z8TaNBgwa4desWqlSpUqAaa9SogdOnT0uH5QDg9OnTsr1EAHD27FkMHDhQ9vyTTz6R9dm3bx/u3r2L33//Xeu0mjZtim7duuHEiRP46aefsHv3bo0v6+z09PRQpUoVVKlSBV999RW8vLywdu1a3LlzB8+ePcOCBQvg5OQEALKLFLLXValSJXh7e2PIkCE4e/as7Mv/3LlzGvNatWpV6OrqSm02NjbSMp46dSqCgoI09rJp6ztlyhTUqVMHkZGRKF26NMLDw/H999+jRYsWAN5c+KKNk5MTrK2tER8fj1u3bknrIyoqClFRURrrJz8aNGiA8PDwt75nwsLCsHPnToSHhxd4WtqmvW3bNtja2mo9NUHNxMREVp+tra1GH319falPtWrVsHLlSoSFhaFhw4aFVi+REvBQLNF7smrVKsTExMDPzw8AUKVKFaSnp2PFihX466+/sGXLFqxZsyZf45wyZQrOnDkDX19fhIWFISIiAnv37tW4eCInkyZNwsaNGxEYGIiIiAgsWbIEv/76q+yqWQDYsWMH1q9fj7t372LmzJk4f/68xjQWLVqEuXPn5rincdeuXdi4cSN27NiBqlWrav2yzmr//v04ffo0Hj58iDNnzmDRokVSmCxXrhz09fWlZbdv3z6th3+B/zs/ccGCBYiLi9M4ZPfw4UNMmDAB4eHh+OWXX7BixQqMHTtW1uf169dISUnBs2fPsH79elhYWEiBMrvExERER0fjr7/+wsqVK2FmZoayZcuidOnSsLa2xrp163Dv3j0cOXIEEyZMyHH+XV1d0aRJE+mfgcuXL8PLywv169dH27ZtpX5CCKSkpCAlJQWpqakAgLS0NKktIyMDmZmZSE9PBwDMmDEDmzdvhp+fH27evInbt29j69atmDZtmmz6ixcvxoQJE+Do6JhjjdllrSUlJUXaE6muy8vLCzY2NujevTtOnjyJyMhIHDt2DGPGjJFd7ZufaSUnJ+PIkSNaz1MkIgCCiN7ZoEGDRPfu3TXa9+/fL3R1dcXZs2eFEEIsWbJEODg4CCMjI+Hu7i42b94sAIi4uDjZ644ePSpy+nieP39etG/fXpiamgoTExNRt25dMW/ePGl4+fLlxdKlS3OsdfXq1aJSpUqiVKlS4uOPPxabN2+WDQcgVq1aJdq3by8MDAxEhQoVxLZt26ThkZGRAoCoV6+eyMjI0Drd8PBwYWlpKQ4ePCgN37Bhg7CwsMixrsDAQFG5cmWhr68vbG1tRZ8+fcTff/8tDf/5559FhQoVhIGBgXBxcRH79u0TAMSVK1dkyyzrsjx27JgwNDQU169fF0II0apVKzF69GjxxRdfCHNzc1G6dGnxv//9T2RmZsrmA4AAIIyMjESjRo3EkSNHhBBCdO/eXQwaNCjXvocPH5aGh4SEiBo1aggDAwNRt25dcezYMQFA7N69W7Ys1fPwzz//iB49eghTU1NhamoqevbsKaKioqTxzZw5U5re2x5Z6wwODhaurq7CyMhImJubi8aNG4t169bJ1rm9vb1ISkqS2lq1aiXGjh2b4/p6Wy1qjx8/FgMHDhQ2NjbCwMBAVKpUSXh7e4sXL14IIbR/drK/V7JOS0dHRzg5OYnp06cLIYTYvXt3jp8Vog+RSoh3uI6eiBRHpVJh9+7dGvcWU4LWrVujfv36b731yn/dnj17sGfPHtnFJ0XJ0tJSdi87Iio6PBRLRKQwurq6KFWqVLFN/20XyRDR+8OLJ4iIFKZr167o2rVrsU0/pyuIiej946FYIiIiIoXgoVgiIiIihWCwIyIiIlIIBjsiIiIihWCwIyIiIlIIBjsiIiIihWCwIyIiIlIIBjsiIiIihWCwIyIiIlIIBjsiIiIihfh/ZguaOEESSkQAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер обучающей выборки до oversampling и undersampling:  4200\n",
      "Размер обучающей выборки после oversampling и undersampling:  4232\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020-07-08</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.73</td>\n",
       "      <td>5.47</td>\n",
       "      <td>5.56</td>\n",
       "      <td>5.341250</td>\n",
       "      <td>23355100.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2021-01-19</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.15</td>\n",
       "      <td>5.02</td>\n",
       "      <td>5.13</td>\n",
       "      <td>4.966732</td>\n",
       "      <td>15906300.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2010-04-08</td>\n",
       "      <td>10.60</td>\n",
       "      <td>10.65</td>\n",
       "      <td>10.48</td>\n",
       "      <td>10.52</td>\n",
       "      <td>8.794909</td>\n",
       "      <td>10456400.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2020-12-07</td>\n",
       "      <td>5.47</td>\n",
       "      <td>5.80</td>\n",
       "      <td>5.47</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.541336</td>\n",
       "      <td>12929600.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2021-01-05</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.16</td>\n",
       "      <td>5.98</td>\n",
       "      <td>6.04</td>\n",
       "      <td>5.847770</td>\n",
       "      <td>15080900.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date   Open   High    Low  Close  Adj Close      Volume\n",
       "0  2020-07-08   5.66   5.73   5.47   5.56   5.341250  23355100.0\n",
       "20 2021-01-19   5.15   5.15   5.02   5.13   4.966732  15906300.0\n",
       "21 2010-04-08  10.60  10.65  10.48  10.52   8.794909  10456400.0\n",
       "24 2020-12-07   5.47   5.80   5.47   5.75   5.541336  12929600.0\n",
       "28 2021-01-05   6.15   6.16   5.98   6.04   5.847770  15080900.0"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import RandomOverSampler\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Преобразование целевой переменной (цены) в категориальные диапазоны с использованием квантилей\n",
    "X_train['closePrice_category'] = pd.qcut(X_train['Close'], q=4, labels=['low', 'medium', 'high', 'very_high'])\n",
    "print(X_train.head())\n",
    "\n",
    "# Визуализация распределения цен после преобразования в категории\n",
    "sns.countplot(x=X_train['closePrice_category'])\n",
    "plt.title('Распределение категорий закрывающей цены в обучающей выборке')\n",
    "plt.xlabel('Категория закрывающей цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Балансировка категорий с помощью RandomOverSampler (увеличение меньшинств)\n",
    "ros = RandomOverSampler(random_state=42)\n",
    "y_train = X_train['closePrice_category']\n",
    "X_train = X_train.drop(columns=['closePrice_category'])\n",
    "\n",
    "\n",
    "# Применяем oversampling. Здесь важно, что мы используем X_train как DataFrame и y_train_categories как целевую переменную\n",
    "X_resampled, y_resampled = ros.fit_resample(X_train, y_train)\n",
    "\n",
    "# Визуализация распределения цен после oversampling\n",
    "sns.countplot(x=y_resampled)\n",
    "plt.title('Распределение категорий закрывающей цены после oversampling')\n",
    "plt.xlabel('Категория закрывающей цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "# Применение RandomUnderSampler для уменьшения большего класса\n",
    "rus = RandomUnderSampler(random_state=42)\n",
    "X_resampled, y_resampled = rus.fit_resample(X_resampled, y_resampled)\n",
    "\n",
    "# Визуализация распределения цен после undersampling\n",
    "sns.countplot(x=y_resampled)\n",
    "plt.title('Распределение категорий закрывающей цены после undersampling')\n",
    "plt.xlabel('Категория закрывающей цены')\n",
    "plt.ylabel('Частота')\n",
    "plt.show()\n",
    "\n",
    "\n",
    "print(\"Размер обучающей выборки до oversampling и undersampling: \", len(X_train))\n",
    "\n",
    "\n",
    "print(\"Размер обучающей выборки после oversampling и undersampling: \", len(X_resampled))\n",
    "X_resampled.head()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "По сути, балансировка так то  не требовалась, но все же мы ее провели, добавив в обучающую выборку 5 значений (ーー;)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование признаков\n",
    "1. **Унитарное кодирование категориальных признаков. Преобразование категориальных признаков в бинарные векторы.**\n",
    "* В данном датасете категориальные признаки отсутствуют, так что пропустим этот пункт.\n",
    "2. **Дискретизация числовых признаков. Преобразование непрерывных числовых значений в дискретные категории или интервалы (бины).**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Названия столбцов в датасете:\n",
      "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n",
      "Статистические параметры:\n",
      "                                Date         Open         High          Low  \\\n",
      "count                           5251  5251.000000  5251.000000  5251.000000   \n",
      "mean   2011-12-01 11:59:51.772995840     6.863639     6.986071     6.720615   \n",
      "min              2001-06-22 00:00:00     1.142857     1.142857     1.142857   \n",
      "25%              2006-09-13 12:00:00     2.857143     2.880000     2.810000   \n",
      "50%              2011-11-29 00:00:00     4.600000     4.710000     4.490000   \n",
      "75%              2017-02-16 12:00:00    10.650000    10.860000    10.425000   \n",
      "max              2022-05-05 00:00:00    20.420000    20.590000    20.090000   \n",
      "std                              NaN     4.753836     4.832010     4.662891   \n",
      "\n",
      "             Close    Adj Close        Volume  \n",
      "count  5251.000000  5251.000000  5.251000e+03  \n",
      "mean      6.850606     5.895644  8.976705e+06  \n",
      "min       1.142857     0.935334  0.000000e+00  \n",
      "25%       2.857143     2.537094  2.845900e+06  \n",
      "50%       4.600000     4.337419  8.216200e+06  \n",
      "75%      10.640000     8.951945  1.327245e+07  \n",
      "max      20.389999    17.543156  2.891228e+07  \n",
      "std       4.746055     3.941634  7.251098e+06  \n",
      "После дискретизации 'Close':\n",
      "        Date      Open      High       Low     Close  Adj Close  Volume  \\\n",
      "0 2001-06-22  3.428571  3.428571  3.428571  3.428571   2.806002     0.0   \n",
      "1 2001-06-25  3.428571  3.428571  3.428571  3.428571   2.806002     0.0   \n",
      "2 2001-06-26  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
      "3 2001-06-27  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
      "4 2001-06-28  3.714286  3.714286  3.714286  3.714286   3.039837     0.0   \n",
      "\n",
      "  Close_Disc  \n",
      "0        2-4  \n",
      "1        2-4  \n",
      "2        2-4  \n",
      "3        2-4  \n",
      "4        2-4  \n",
      "           Date       Open       High    Low      Close  Adj Close     Volume  \\\n",
      "2623 2011-11-25  14.730000  15.050000  14.65  14.650000  12.429751  2433000.0   \n",
      "2624 2011-11-28  15.150000  15.370000  15.04  15.200000  12.896397  4348600.0   \n",
      "2625 2011-11-29  15.270000  15.710000  15.21  15.600000  13.235776  4576500.0   \n",
      "2626 2011-11-30  16.120001  16.850000  16.07  16.830000  14.279361  9537100.0   \n",
      "2627 2011-12-01  16.770000  16.940001  16.58  16.809999  14.262395  5111500.0   \n",
      "\n",
      "     Close_Disc  \n",
      "2623      14-16  \n",
      "2624      14-16  \n",
      "2625      14-16  \n",
      "2626        16+  \n",
      "2627        16+  \n",
      "           Date  Open  High   Low  Close  Adj Close      Volume Close_Disc\n",
      "5246 2022-04-29  5.66  5.69  5.50   5.51       5.51  16613300.0        4-6\n",
      "5247 2022-05-02  5.33  5.39  5.18   5.30       5.30  27106700.0        4-6\n",
      "5248 2022-05-03  5.32  5.53  5.32   5.47       5.47  18914200.0        4-6\n",
      "5249 2022-05-04  5.47  5.61  5.37   5.60       5.60  20530700.0        4-6\n",
      "5250 2022-05-05  5.63  5.66  5.34   5.44       5.44  19879200.0        4-6\n"
     ]
    }
   ],
   "source": [
    "#Пример дискретизации по цене закрытия\n",
    "# Проверка на наличие числовых признаков\n",
    "print(\"Названия столбцов в датасете:\")\n",
    "print(df.columns)\n",
    "\n",
    "# Выводим основные статистические параметры для количественных признаков\n",
    "print(\"Статистические параметры:\")\n",
    "print(df.describe())\n",
    "\n",
    "# Дискретизация столбца 'Close' на группы\n",
    "bins = [0, 2, 4, 6, 8, 10, 12, 14, 16, 30]  # Определяем границы корзин\n",
    "labels = ['0-2', '2-4', '4-6', '6-8', '8-10', '10-12', '12-14', '14-16', '16+']  # Названия категорий\n",
    "\n",
    "# Создание нового столбца 'Close_Disc' на основе дискретизации\n",
    "df['Close_Disc'] = pd.cut(df['Close'], bins=bins, labels=labels, include_lowest=True) #pd.cut выполняет дискретизацию переменной\n",
    "#include_lowest=True: Этот параметр гарантирует, что самое нижнее значение (в данном случае 0), будет входить в первую категорию.\n",
    "\n",
    "\n",
    "\n",
    "# Проверка результата\n",
    "print(\"После дискретизации 'Close':\")\n",
    "print(df.head())\n",
    "n = len(df)\n",
    "middle_index = n // 2\n",
    "print(df.iloc[middle_index - 2: middle_index + 3])\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Конструирование новых признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Исходный датасет: \n",
      "           Date  Open  High   Low  Close  Adj Close      Volume Close_Disc\n",
      "5246 2022-04-29  5.66  5.69  5.50   5.51       5.51  16613300.0        4-6\n",
      "5247 2022-05-02  5.33  5.39  5.18   5.30       5.30  27106700.0        4-6\n",
      "5248 2022-05-03  5.32  5.53  5.32   5.47       5.47  18914200.0        4-6\n",
      "5249 2022-05-04  5.47  5.61  5.37   5.60       5.60  20530700.0        4-6\n",
      "5250 2022-05-05  5.63  5.66  5.34   5.44       5.44  19879200.0        4-6\n",
      "\n",
      "Обучающая выборка: \n",
      "           Date       Open   High        Low      Close  Adj Close      Volume\n",
      "2435 2011-04-14  12.530000  12.84  12.480000  12.750000  10.754427  10527200.0\n",
      "1756 2013-05-30  11.510000  11.76  11.480000  11.720000  10.166282   9028100.0\n",
      "3296 2009-11-20  13.100000  13.28  12.870000  13.220000  11.031483  17024900.0\n",
      "1243 2012-09-17  18.870001  19.00  18.469999  18.870001  16.178450   6652400.0\n",
      "343  2006-12-12  12.920000  13.00  12.580000  12.800000  10.487218   3981100.0\n",
      "\n",
      "Тестовая выборка: \n",
      "           Date       Open       High        Low      Close  Adj Close  \\\n",
      "3095 2013-10-14   9.290000   9.350000   9.070000   9.130000   8.025586   \n",
      "859  2004-11-24   3.090000   3.160000   3.040000   3.100000   2.537094   \n",
      "3134 2013-12-09   8.550000   8.770000   8.550000   8.770000   7.709136   \n",
      "2577 2011-09-21  16.709999  17.070000  16.379999  16.400000  13.869872   \n",
      "378  2002-12-27   2.571429   2.571429   2.571429   2.571429   2.104502   \n",
      "\n",
      "          Volume  \n",
      "3095   5861400.0  \n",
      "859     211300.0  \n",
      "3134   5335400.0  \n",
      "2577  14524400.0  \n",
      "378          0.0  \n",
      "\n",
      "Контрольная выборка: \n",
      "           Date       Open       High        Low      Close  Adj Close  \\\n",
      "3095 2013-10-14   9.290000   9.350000   9.070000   9.130000   8.025586   \n",
      "859  2004-11-24   3.090000   3.160000   3.040000   3.100000   2.537094   \n",
      "3134 2013-12-09   8.550000   8.770000   8.550000   8.770000   7.709136   \n",
      "2577 2011-09-21  16.709999  17.070000  16.379999  16.400000  13.869872   \n",
      "378  2002-12-27   2.571429   2.571429   2.571429   2.571429   2.104502   \n",
      "\n",
      "          Volume  \n",
      "3095   5861400.0  \n",
      "859     211300.0  \n",
      "3134   5335400.0  \n",
      "2577  14524400.0  \n",
      "378          0.0  \n",
      "\n",
      "Новые признаки в обучающей выборке:\n",
      "      Volume_Change\n",
      "2435       0.977868\n",
      "1756      -0.142403\n",
      "3296       0.885768\n",
      "1243      -0.609255\n",
      "343       -0.401554\n",
      "\n",
      "Новые признаки в тестовой выборке:\n",
      "      Volume_Change\n",
      "3095            inf\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n",
      "\n",
      "Новые признаки в контрольной выборке:\n",
      "      Volume_Change\n",
      "3095            inf\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n",
      "\n",
      "Новые признаки в датасете:\n",
      "      Volume_Change\n",
      "5246      -0.218393\n",
      "5247       0.631626\n",
      "5248      -0.302232\n",
      "5249       0.085465\n",
      "5250      -0.031733\n"
     ]
    }
   ],
   "source": [
    "print('\\nИсходный датасет: ')\n",
    "print(df.tail())\n",
    "print('\\nОбучающая выборка: ')\n",
    "print(X_resampled.tail())\n",
    "print('\\nТестовая выборка: ')\n",
    "print(X_test.tail())\n",
    "print('\\nКонтрольная выборка: ')\n",
    "print(X_val.tail())\n",
    "\n",
    "#Объем изменений\n",
    "df['Volume_Change'] = df['Volume'].pct_change()\n",
    "X_resampled['Volume_Change'] = X_resampled['Volume'].pct_change()\n",
    "X_test['Volume_Change'] = X_test['Volume'].pct_change()\n",
    "X_val['Volume_Change'] = X_val['Volume'].pct_change()\n",
    "# Результатом работы pct_change() является серия, где каждое значение представляет собой \n",
    "# процентное изменение относительно предыдущего значения. Первое значение всегда будет NaN, \n",
    "# так как для него нет предшествующего значения для сравнения.\n",
    "\n",
    "# Проверка создания новых признаков\n",
    "print(\"\\nНовые признаки в обучающей выборке:\")\n",
    "print(X_resampled[['Volume_Change']].tail())\n",
    "\n",
    "print(\"\\nНовые признаки в тестовой выборке:\")\n",
    "print(X_test[['Volume_Change']].tail())\n",
    "\n",
    "print(\"\\nНовые признаки в контрольной выборке:\")\n",
    "print(X_val[['Volume_Change']].tail())\n",
    "\n",
    "print(\"\\nНовые признаки в датасете:\")\n",
    "print(df[['Volume_Change']].tail())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Проверим новые признаки:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Исходный датасет: \n",
      "Volume_Change    501\n",
      "dtype: int64\n",
      "\n",
      "Обучающая выборка: \n",
      "Volume_Change    102\n",
      "dtype: int64\n",
      "\n",
      "Тестовая выборка: \n",
      "Volume_Change    16\n",
      "dtype: int64\n",
      "\n",
      "Контрольная выборка: \n",
      "Volume_Change    16\n",
      "dtype: int64\n",
      "\n",
      "Есть ли пустые значения признаков: \n",
      "\n",
      "Исходный датасет: \n",
      "Volume_Change    True\n",
      "dtype: bool\n",
      "\n",
      "Обучающая выорка: \n",
      "Volume_Change    True\n",
      "dtype: bool\n",
      "\n",
      "Тестовая выборка: \n",
      "Volume_Change    True\n",
      "dtype: bool\n",
      "\n",
      "Контрольная выборка: \n",
      "Volume_Change    True\n",
      "dtype: bool\n",
      "\n",
      "Количество бесконечных значений в каждом столбце:\n",
      "\n",
      "Исходный датасет: \n",
      "Volume_Change    32\n",
      "dtype: int64\n",
      "\n",
      "Обучающая выборка: \n",
      "Volume_Change    310\n",
      "dtype: int64\n",
      "\n",
      "Тестовая выборка: \n",
      "Volume_Change    107\n",
      "dtype: int64\n",
      "\n",
      "Контрольная выборка: \n",
      "Volume_Change    107\n",
      "dtype: int64\n",
      "Volume_Change процент пустых значений в датасете: %9.54\n",
      "Volume_Change процент пустых значений в обучающей выборке: %2.41\n",
      "Volume_Change процент пустых значений в тестовой выборке: %1.52\n",
      "Volume_Change процент пустых значений в контрольной выборке: %1.52\n"
     ]
    }
   ],
   "source": [
    "print('\\nИсходный датасет: ')\n",
    "print(df[['Volume_Change']].isnull().sum())\n",
    "print('\\nОбучающая выборка: ')\n",
    "print(X_resampled[['Volume_Change']].isnull().sum())\n",
    "print('\\nТестовая выборка: ')\n",
    "print(X_test[['Volume_Change']].isnull().sum())\n",
    "print('\\nКонтрольная выборка: ')\n",
    "print(X_val[['Volume_Change']].isnull().sum())\n",
    "print()\n",
    "\n",
    "# Есть ли пустые значения признаков\n",
    "print('Есть ли пустые значения признаков: ')\n",
    "print('\\nИсходный датасет: ')\n",
    "print(df[['Volume_Change']].isnull().any())\n",
    "print('\\nОбучающая выорка: ')\n",
    "print(X_resampled[['Volume_Change']].isnull().any())\n",
    "print('\\nТестовая выборка: ')\n",
    "print(X_test[['Volume_Change']].isnull().any())\n",
    "print('\\nКонтрольная выборка: ')\n",
    "print(X_val[['Volume_Change']].isnull().any())\n",
    "print()\n",
    "\n",
    "# Проверка на бесконечные значения\n",
    "print(\"Количество бесконечных значений в каждом столбце:\")\n",
    "print('\\nИсходный датасет: ')\n",
    "print(np.isinf(df[['Volume_Change']]).sum())\n",
    "print('\\nОбучающая выборка: ')\n",
    "print(np.isinf(X_resampled[['Volume_Change']]).sum())\n",
    "print('\\nТестовая выборка: ')\n",
    "print(np.isinf(X_test[['Volume_Change']]).sum())\n",
    "print('\\nКонтрольная выборка: ')\n",
    "print(np.isinf(X_val[['Volume_Change']]).sum())\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in df[['Volume_Change']].columns:\n",
    "    null_rate = df[['Volume_Change']][i].isnull().sum() / len(df[['Volume_Change']]) * 100\n",
    "    print(f\"{i} процент пустых значений в датасете: %{null_rate:.2f}\")\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in X_resampled[['Volume_Change']].columns:\n",
    "    null_rate = X_resampled[['Volume_Change']][i].isnull().sum() / len(X_resampled[['Volume_Change']]) * 100\n",
    "    print(f\"{i} процент пустых значений в обучающей выборке: %{null_rate:.2f}\")\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in X_test[['Volume_Change']].columns:\n",
    "    null_rate = X_test[['Volume_Change']][i].isnull().sum() / len(X_test[['Volume_Change']]) * 100\n",
    "    print(f\"{i} процент пустых значений в тестовой выборке: %{null_rate:.2f}\")\n",
    "\n",
    "# Процент пустых значений признаков\n",
    "for i in X_val[['Volume_Change']].columns:\n",
    "    null_rate = X_val[['Volume_Change']][i].isnull().sum() / len(X_val[['Volume_Change']]) * 100\n",
    "    print(f\"{i} процент пустых значений в контрольной выборке: %{null_rate:.2f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Заполним пустые данные"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5251, 1)\n",
      "(4232, 1)\n",
      "(1051, 1)\n",
      "(1051, 1)\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "5246      -0.218393\n",
      "5247       0.631626\n",
      "5248      -0.302232\n",
      "5249       0.085465\n",
      "5250      -0.031733\n",
      "      Volume_Change\n",
      "2435       0.977868\n",
      "1756      -0.142403\n",
      "3296       0.885768\n",
      "1243      -0.609255\n",
      "343       -0.401554\n",
      "      Volume_Change\n",
      "3095       0.000000\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n",
      "      Volume_Change\n",
      "3095       0.000000\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_26220\\4038454119.py:39: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  df[['Volume_Change']].loc[df[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_df\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_26220\\4038454119.py:40: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  X_resampled[['Volume_Change']].loc[X_resampled[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_train\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_26220\\4038454119.py:41: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  X_test[['Volume_Change']].loc[X_test[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_test\n",
      "C:\\Users\\K\\AppData\\Local\\Temp\\ipykernel_26220\\4038454119.py:42: FutureWarning: ChainedAssignmentError: behaviour will change in pandas 3.0!\n",
      "You are setting values through chained assignment. Currently this works in certain cases, but when using Copy-on-Write (which will become the default behaviour in pandas 3.0) this will never work to update the original DataFrame or Series, because the intermediate object on which we are setting values will behave as a copy.\n",
      "A typical example is when you are setting values in a column of a DataFrame, like:\n",
      "\n",
      "df[\"col\"][row_indexer] = value\n",
      "\n",
      "Use `df.loc[row_indexer, \"col\"] = values` instead, to perform the assignment in a single step and ensure this keeps updating the original `df`.\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "  X_val[['Volume_Change']].loc[X_val[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_val\n"
     ]
    }
   ],
   "source": [
    "# Заменяем бесконечные значения на NaN\n",
    "df.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "X_resampled.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "X_test.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "X_val.replace([np.inf, -np.inf], np.nan, inplace=True)\n",
    "\n",
    "fillna_df = df[['Volume_Change']].fillna(0)\n",
    "fillna_X_resampled = X_resampled[['Volume_Change']].fillna(0)\n",
    "fillna_X_test = X_test[['Volume_Change']].fillna(0)\n",
    "fillna_X_val = X_val[['Volume_Change']].fillna(0)\n",
    "# используется для заполнения всех значений NaN \n",
    "# (Not a Number) в DataFrame или Series указанным значением. \n",
    "# В данном случае, fillna(0) заполняет все ячейки, содержащие NaN, значением 0.\n",
    "\n",
    "\n",
    "print(fillna_df.shape)\n",
    "print(fillna_X_resampled.shape)\n",
    "print(fillna_X_test.shape)\n",
    "print(fillna_X_val.shape)\n",
    "\n",
    "print(fillna_df.isnull().any())\n",
    "print(fillna_X_resampled.isnull().any())\n",
    "print(fillna_X_test.isnull().any())\n",
    "print(fillna_X_val.isnull().any())\n",
    "\n",
    "# Замена пустых данных на 0\n",
    "df[\"Volume_Change\"] = df[\"Volume_Change\"].fillna(0)\n",
    "X_resampled[\"Volume_Change\"] = X_resampled[\"Volume_Change\"].fillna(0)\n",
    "X_test[\"Volume_Change\"] = X_test[\"Volume_Change\"].fillna(0)\n",
    "X_val[\"Volume_Change\"] = X_val[\"Volume_Change\"].fillna(0)\n",
    "\n",
    "# Вычисляем медиану для колонки \"Volume_Change\"\n",
    "median_Volume_Change_df = df[\"Volume_Change\"].median()\n",
    "median_Volume_Change_train = X_resampled[\"Volume_Change\"].median()\n",
    "median_Volume_Change_test = X_test[\"Volume_Change\"].median()\n",
    "median_Volume_Change_val = X_val[\"Volume_Change\"].median()\n",
    "\n",
    "# Заменяем значения 0 на медиану\n",
    "df[['Volume_Change']].loc[df[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_df\n",
    "X_resampled[['Volume_Change']].loc[X_resampled[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_train\n",
    "X_test[['Volume_Change']].loc[X_test[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_test\n",
    "X_val[['Volume_Change']].loc[X_val[\"Volume_Change\"] == 0, \"Volume_Change\"] = median_Volume_Change_val\n",
    "\n",
    "print(df[['Volume_Change']].tail())\n",
    "print(X_resampled[['Volume_Change']].tail())\n",
    "print(X_test[['Volume_Change']].tail())\n",
    "print(X_val[['Volume_Change']].tail())\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Удалим наблюдения с пропусками"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(5251, 1)\n",
      "(4232, 1)\n",
      "(1051, 1)\n",
      "(1051, 1)\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "5246      -0.218393\n",
      "5247       0.631626\n",
      "5248      -0.302232\n",
      "5249       0.085465\n",
      "5250      -0.031733\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "2435       0.977868\n",
      "1756      -0.142403\n",
      "3296       0.885768\n",
      "1243      -0.609255\n",
      "343       -0.401554\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "3095       0.000000\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n",
      "Volume_Change    False\n",
      "dtype: bool\n",
      "      Volume_Change\n",
      "3095       0.000000\n",
      "859       -0.963951\n",
      "3134      24.250355\n",
      "2577       1.722270\n",
      "378       -1.000000\n"
     ]
    }
   ],
   "source": [
    "dropna_df = df[['Volume_Change']].dropna()\n",
    "dropna_X_resampled = X_resampled[['Volume_Change']].dropna()\n",
    "dropna_X_test = X_test[['Volume_Change']].dropna()\n",
    "dropna_X_val = X_val[['Volume_Change']].dropna()\n",
    "\n",
    "print(dropna_df.shape)\n",
    "print(dropna_X_resampled.shape)\n",
    "print(dropna_X_test.shape)\n",
    "print(dropna_X_val.shape)\n",
    "\n",
    "print(dropna_df.isnull().any())\n",
    "print(df[['Volume_Change']].tail())\n",
    "print(dropna_X_resampled.isnull().any())\n",
    "print(X_resampled[['Volume_Change']].tail())\n",
    "print(dropna_X_test.isnull().any())\n",
    "print(X_test[['Volume_Change']].tail())\n",
    "print(dropna_X_val.isnull().any())\n",
    "print(X_val[['Volume_Change']].tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Масштабируем новые признаки:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Результаты после масштабирования:\n",
      "\n",
      " Датафрейм:\n",
      "      Volume_Change\n",
      "5246      -0.176620\n",
      "5247       0.224373\n",
      "5248      -0.216171\n",
      "5249      -0.033276\n",
      "5250      -0.088564\n",
      "\n",
      " Обучающая:\n",
      "      Volume_Change\n",
      "2435      -0.033736\n",
      "1756      -0.033805\n",
      "3296      -0.033742\n",
      "1243      -0.033834\n",
      "343       -0.033821\n",
      "\n",
      " Тестовая:\n",
      "      Volume_Change\n",
      "3095      -0.033796\n",
      "859       -0.033856\n",
      "3134      -0.032301\n",
      "2577      -0.033690\n",
      "378       -0.033858\n",
      "\n",
      " Контрольная:\n",
      "      Volume_Change\n",
      "3095      -0.033796\n",
      "859       -0.033856\n",
      "3134      -0.032301\n",
      "2577      -0.033690\n",
      "378       -0.033858\n"
     ]
    }
   ],
   "source": [
    "from sklearn.preprocessing import StandardScaler, MinMaxScaler\n",
    "\n",
    "# Пример масштабирования числовых признаков\n",
    "numerical_features = ['Volume_Change']\n",
    "\n",
    "scaler = StandardScaler()\n",
    "df[numerical_features] = scaler.fit_transform(df[numerical_features])\n",
    "X_resampled[numerical_features] = scaler.fit_transform(X_resampled[numerical_features])\n",
    "X_val[numerical_features] = scaler.transform(X_val[numerical_features])\n",
    "X_test[numerical_features] = scaler.transform(X_test[numerical_features])\n",
    "#  fit() - вычисляет среднее и стандартное отклонение для каждого признака в наборе данных.\n",
    "# transform() - применяет расчеты, чтобы стандартизировать данные по приведенной выше формуле.\n",
    "\n",
    "# Вывод результатов после масштабирования\n",
    "print(\"Результаты после масштабирования:\")\n",
    "print(\"\\n Датафрейм:\")\n",
    "print(df[numerical_features].tail())\n",
    "print(\"\\n Обучающая:\")\n",
    "print(X_resampled[numerical_features].tail())\n",
    "print(\"\\n Тестовая:\")\n",
    "print(X_val[numerical_features].tail())\n",
    "print(\"\\n Контрольная:\")\n",
    "print(X_test[numerical_features].tail())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Данные признаки предоставляют важную информацию о текущем тренде и возможных изменениях в будущих ценах. Положительные значения Price_Change и Percentage_Change, наряду с высоким Volume_Change, могут поддерживать гипотезу о росте цен на акции.\n",
    "\n",
    "Также, эти признаки помогают понять уровень рискованности инвестиций. Высокие значения Price_Range и резкие изменения в Volume_Change могут указывать на склонность к большим колебаниям, что требует внимательного управления рисками."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Применим featuretools для конструирования признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         Date   Open   High    Low  Close  Adj Close      Volume  \\\n",
      "0  2020-07-08   5.66   5.73   5.47   5.56   5.341250  23355100.0   \n",
      "20 2021-01-19   5.15   5.15   5.02   5.13   4.966732  15906300.0   \n",
      "21 2010-04-08  10.60  10.65  10.48  10.52   8.794909  10456400.0   \n",
      "24 2020-12-07   5.47   5.80   5.47   5.75   5.541336  12929600.0   \n",
      "28 2021-01-05   6.15   6.16   5.98   6.04   5.847770  15080900.0   \n",
      "\n",
      "    Volume_Change  id  \n",
      "0       -0.033796   0  \n",
      "20      -0.033816  20  \n",
      "21      -0.033817  21  \n",
      "24      -0.033782  24  \n",
      "28      -0.033786  28  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\woodwork\\type_sys\\utils.py:33: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n",
      "  pd.to_datetime(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "        Open      High       Low     Close  Volume Close_Disc  Volume_Change  \\\n",
      "id                                                                             \n",
      "0   3.428571  3.428571  3.428571  3.428571     0.0        2-4      -0.073594   \n",
      "1   3.428571  3.428571  3.428571  3.428571     0.0        2-4      -0.073594   \n",
      "2   3.714286  3.714286  3.714286  3.714286     0.0        2-4      -0.073594   \n",
      "3   3.714286  3.714286  3.714286  3.714286     0.0        2-4      -0.073594   \n",
      "4   3.714286  3.714286  3.714286  3.714286     0.0        2-4      -0.073594   \n",
      "\n",
      "   DAY(Date) MONTH(Date) WEEKDAY(Date) YEAR(Date)  \n",
      "id                                                 \n",
      "0         22           6             4       2001  \n",
      "1         25           6             0       2001  \n",
      "2         26           6             1       2001  \n",
      "3         27           6             2       2001  \n",
      "4         28           6             3       2001  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\synthesis\\deep_feature_synthesis.py:169: UserWarning: Only one dataframe in entityset, changing max_depth to 1 since deeper features cannot be created\n",
      "  warnings.warn(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\featuretools\\computational_backends\\feature_set_calculator.py:143: FutureWarning: The behavior of DataFrame concatenation with empty or all-NA entries is deprecated. In a future version, this will no longer exclude empty or all-NA columns when determining the result dtypes. To retain the old behavior, exclude the relevant entries before the concat operation.\n",
      "  df = pd.concat([df, default_df], sort=True)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Open   High    Low  Close      Volume  Volume_Change DAY(Date)  \\\n",
      "id                                                                    \n",
      "0    5.66   5.73   5.47   5.56  23355100.0      -0.033796         8   \n",
      "20   5.15   5.15   5.02   5.13  15906300.0      -0.033816        19   \n",
      "21  10.60  10.65  10.48  10.52  10456400.0      -0.033817         8   \n",
      "24   5.47   5.80   5.47   5.75  12929600.0      -0.033782         7   \n",
      "28   6.15   6.16   5.98   6.04  15080900.0      -0.033786         5   \n",
      "\n",
      "   MONTH(Date) WEEKDAY(Date) YEAR(Date)  \n",
      "id                                       \n",
      "0            7             2       2020  \n",
      "20           1             1       2021  \n",
      "21           4             3       2010  \n",
      "24          12             0       2020  \n",
      "28           1             1       2021  \n",
      "       Open   High    Low      Close      Volume  Volume_Change DAY(Date)  \\\n",
      "id                                                                          \n",
      "1437  18.25  18.68  17.90  18.559999   6853000.0      -0.033812        26   \n",
      "2700   3.09   3.11   2.98   3.100000  10015600.0      -0.031587         5   \n",
      "3647  12.89  12.98  12.54  12.650000   3031800.0      -0.033849        26   \n",
      "2512   2.93   2.93   2.87   2.910000   6872900.0      -0.033823         7   \n",
      "2902  15.22  15.63  15.18  15.520000   8104600.0      -0.033743        17   \n",
      "\n",
      "     MONTH(Date) WEEKDAY(Date) YEAR(Date)  \n",
      "id                                         \n",
      "1437           9             2       2012  \n",
      "2700           4             1       2016  \n",
      "3647          12             1       2006  \n",
      "2512           6             3       2018  \n",
      "2902           8             2       2011  \n",
      "       Open   High    Low      Close      Volume  Volume_Change DAY(Date)  \\\n",
      "id                                                                          \n",
      "1437  18.25  18.68  17.90  18.559999   6853000.0      -0.033812        26   \n",
      "2700   3.09   3.11   2.98   3.100000  10015600.0      -0.031587         5   \n",
      "3647  12.89  12.98  12.54  12.650000   3031800.0      -0.033849        26   \n",
      "2512   2.93   2.93   2.87   2.910000   6872900.0      -0.033823         7   \n",
      "2902  15.22  15.63  15.18  15.520000   8104600.0      -0.033743        17   \n",
      "\n",
      "     MONTH(Date) WEEKDAY(Date) YEAR(Date)  \n",
      "id                                         \n",
      "1437           9             2       2012  \n",
      "2700           4             1       2016  \n",
      "3647          12             1       2006  \n",
      "2512           6             3       2018  \n",
      "2902           8             2       2011  \n"
     ]
    }
   ],
   "source": [
    "import featuretools as ft\n",
    "\n",
    "# Добавляем уникальный идентификатор\n",
    "df['id'] = df.index \n",
    "X_resampled['id'] = X_resampled.index\n",
    "X_val['id'] = X_val.index\n",
    "X_test['id'] = X_test.index\n",
    "\n",
    "# Удаление дубликатов по идентификатору\n",
    "df = df.drop_duplicates(subset='id')\n",
    "\n",
    "# Удаление дубликатов из столбца \"id\", сохранив первое вхождение\n",
    "df = df.drop_duplicates(subset='id', keep='first')\n",
    "\n",
    "# Удаляем столбец 'Adj Close' из оригинального датафрейма\n",
    "df = df.drop(columns=['Adj Close'], errors='ignore')\n",
    "\n",
    "print(X_resampled.head())  # Убедитесь, что датафреймы содержат корректные данные перед удалением\n",
    "\n",
    "# Создание EntitySet\n",
    "es = ft.EntitySet(id='stock_data')\n",
    "\n",
    "# Добавление датафрейма с акциями\n",
    "es = es.add_dataframe(dataframe_name='stocks', dataframe=df, index='id')\n",
    "\n",
    "# Генерация признаков с помощью глубокой синтезы признаков\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='stocks', max_depth=2)\n",
    "\n",
    "# Выводим первые 5 строк сгенерированного набора признаков\n",
    "print(feature_matrix.head())\n",
    "\n",
    "# Удаляем 'Adj Close' из X_resampled\n",
    "X_resampled = X_resampled.drop(columns=['Adj Close'], errors='ignore')\n",
    "X_resampled = X_resampled.drop_duplicates(subset='id')\n",
    "X_resampled = X_resampled.drop_duplicates(subset='id', keep='first')  # or keep='last'\n",
    "\n",
    "# Определение сущностей (Создание EntitySet)\n",
    "es = ft.EntitySet(id='stock_data')\n",
    "\n",
    "# Добавление датафрейма X_resampled\n",
    "es = es.add_dataframe(dataframe_name='stocks', dataframe=X_resampled, index='id')\n",
    "\n",
    "# Генерация признаков для X_resampled\n",
    "feature_matrix, feature_defs = ft.dfs(entityset=es, target_dataframe_name='stocks', max_depth=2)\n",
    "\n",
    "# Преобразование признаков для контрольной и тестовой выборок\n",
    "val_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_val.index)\n",
    "test_feature_matrix = ft.calculate_feature_matrix(features=feature_defs, entityset=es, instance_ids=X_test.index)\n",
    "\n",
    "# Удаляем 'Adj Close' из X_val и X_test (если необходимо)\n",
    "X_val = X_val.drop(columns=['Adj Close'], errors='ignore')\n",
    "X_test = X_test.drop(columns=['Adj Close'], errors='ignore')\n",
    "\n",
    "print(feature_matrix.head())\n",
    "print(val_feature_matrix.head())\n",
    "print(test_feature_matrix.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Система сгенерировала следующие признаки:\n",
    "1. **Open, High, Low, Close, Adj Close**: Это стандартные финансовые параметры акций, отражающие цены открытия, максимальные, минимальные и закрытия за определенный период.\n",
    "**Volume**: Объем торгов акциями, который показывает, сколько акций было куплено/продано за определенный период.\n",
    "\n",
    "2. Сложные признаки:\n",
    "**Close_Disc**: Это диапазон цены закрытия.\n",
    "**Price_Change**: Изменение цены, т.е. разница между ценой закрытия и ценой открытия акций.\n",
    "**Percentage_Change**: Процентное изменение цен, которое позволяет оценить относительное изменение стоимости акций.\n",
    "**Average_Price**: Средняя цена акций за указанный период. Этот показатель может быть использован для оценки общей тенденции рынка.\n",
    "\n",
    "3. Также произошло разбиение даты на месяц, день недели и год, что может помочь в анализе сезонных и временных закономерностей."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Оценим качество каждого набора признаков:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     Open   High    Low      Volume  Volume_Change DAY(Date) MONTH(Date)  \\\n",
      "id                                                                         \n",
      "0    5.66   5.73   5.47  23355100.0      -0.033796         8           7   \n",
      "20   5.15   5.15   5.02  15906300.0      -0.033816        19           1   \n",
      "21  10.60  10.65  10.48  10456400.0      -0.033817         8           4   \n",
      "24   5.47   5.80   5.47  12929600.0      -0.033782         7          12   \n",
      "28   6.15   6.16   5.98  15080900.0      -0.033786         5           1   \n",
      "\n",
      "   WEEKDAY(Date) YEAR(Date)  \n",
      "id                           \n",
      "0              2       2020  \n",
      "20             1       2021  \n",
      "21             3       2010  \n",
      "24             0       2020  \n",
      "28             1       2021  \n",
      "\n",
      "LinearRegression:\n",
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 0.05 секунд\n",
      "Среднеквадратичная ошибка: 0.09\n",
      "Средняя абсолютная ошибка: 0.06\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Кросс-валидация RMSE: 0.08938664892927554 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "DecisionTreeRegressor:\n",
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 0.05 секунд\n",
      "Среднеквадратичная ошибка: 0.00\n",
      "Средняя абсолютная ошибка: 0.00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Кросс-валидация RMSE: 0.1802140124251679 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "RandomForestRegressor:\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 3.28 секунд\n",
      "Среднеквадратичная ошибка: 0.04\n",
      "Средняя абсолютная ошибка: 0.03\n",
      "Кросс-валидация RMSE: 0.13929243803791755 \n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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/XAOxqW3br9doyWvj9ttv5/bbb489njhxIn/729+a1NuV+7127VqWLVtGRkZGs8d/+j288cYbufHGG5vU29E03J97zTUYOnRo7P+9evVqcrxPnz68+uqrlJaW7vC5CgoK6NWrFw7Hnv8VokePHk3KevbsSW1tbWzdT21t7Q6vxTRNNm3aRL9+/Xb5Of1+/w6/Tw0afm6be97evXvzySefNCqbP38+kyZNAiApKYlXXnmFww47rNm2n332Wb7//nteeuklnnvuuWbr/Nx9yc7Opnfv3hxyyCE8++yzzJo1K9b2YYcdRvfu3YGWvc82yMjIYPLkyTz++OMMHz6cxx9/nBkzZsT+WLO796jBJ598wptvvsn8+fPZuHFjs3VE9mUKrkT2Uccffzz3338/jz32WOyD8aeKiop46qmnyMjIiH1Qf/zxx5xwwgkceeSRPPDAA+Tk5OB0OnniiSea/aBesmQJ5557Ll6vl1tvvZVTTjml2Q/DlnjsscfYsGED7733XrPHzzvvPN577z0uu+yyJsk4GjQs8r/yyiuZMGFCs3UafkFoic8//5wnn3yS++67j/POO4+lS5fidrtjx5955hnOPvtsTjzxRK666ioyMzOx2+3cdtttTYLdF198kTPPPLNJ/1ozw1XDffjNb37DjBkzmq3z01+mb7jhBkaPHt2obMqUKY0e/+Uvf+H6669n5syZ3HLLLaSlpWGz2bj00ksbJVg4kLT0tXHWWWcxffp0TNNk3bp13HLLLRx//PHMmzevUTC2K/fbNE2OPfZYrr766mb79tM1Tuedd16Tv9qfe+65O7y2n3vNNXjmmWeora3lvPPO22m9g5nH4+HNN99sVPbxxx83WrvXUocccghz586loqKCZ555hpkzZ5KXl8fw4cMb1QuFQlx//fXMmjVrp+vedtX06dO55JJL2Lx5M8FgkM8++4z77rsvdnx332dnzpzJ9OnTueiii1i4cCGPPvpoq+1J9Yc//IEJEyZwzDHHNElwJLI/UHAlso+67rrreP311/n973/PqlWrOOOMM2KjCxs3bmT+/PnccMMNVFRU8Nxzz8UChFdffRWPx8N7773XKGh44oknmn2eY489lgcffJBAIMDrr7/OeeedF8tCtjtqa2u5+eabueCCC+jUqVOzdTweD2+//TZr1qxh06ZNWJZFUVFRbKoKEBuFcDqdjBs3bqfP2a1bt13OcnbzzTczY8YMBg8ezPDhw7n11lu55ZZbYsdfeeUVunbtyn/+859G96C5UYQhQ4bwr3/9i9GjR/OnP/2Jww47jNtvv51FixbtUl92RUZGBomJiUSj0Z+9Dw0GDBjQpK7dbm/0+JVXXmHMmDE89thjjcorKyubTQCyuzp16sS8efPw+XyNRq9WrVoVO74jDVM2ly9f/rPX3hAAbf8L6Zo1awBiWcha+tro2rVro+dNTk7mjDPO4LPPPoslA4Bdu9/dunWjpqZml7+HPXr0aFJ3R0H7rrzmGhxxxBF4vV7OO+88Vq9e3eT4qlWr8Hq9O/0Z6NatG59//jnhcBin07kLV7P71q5d26RszZo1xMfHx0aX4uPjd3gtNpuNvLy8Fj2n3W5vcu+3zxIJP/7crl69mmOOOabRsdWrVzf5PqSnp8fanDp1Kr169eL222/nxRdfbFTvgQceoLi4uFGmx+bsyn0BOP3007n88st5/vnnqaurw+l0ctppp8WOt+R9dnuTJk3C4/Fw+umnM2rUKLp169YkuGrpPQJ4/fXXWbx4casnBRLZm7TmSmQflZ2dzeLFi5k0aRL/+Mc/GDp0KM888wx+v59OnToxc+ZM4uLiePPNN5k2bVrsvIaU4NtP89qwYUOT1L8NDj/8cOx2O16vl4ceeoiFCxfyr3/9a7f7fc899+D3+/njH//4s3V79uzJ2LFjGTduXJP1B5mZmRx99NE8/PDDbNu2rcm526cCnjp1Kt9++22zaYetn6TibhhhGDRoEFdeeSV/+9vfGgVmDb8Ub3/e559/3ihtcYPq6mrOOussTjjhBK677jrGjRtHTk7Oz153S9jtdqZOncqrr77abAD505TILWn3p/fm5ZdfbrK+4pc67rjjiEajjf5aDnDXXXdhGEZsqlRzhg4dSpcuXbj77rub/HL7074DjZ7Dsizuu+8+nE5nbC1IS18bP1VXVwewW1M/Tz31VBYvXtzsyFJlZSWRSKTFbTZoyWsO6gP2oUOH8txzzzX6+SkoKOCNN95g0qRJTYLD7U2dOpXS0tIm31No/vvyS/z0F+1Nmzbx3//+l/Hjx2O327Hb7YwfP57//ve/jaZ/FhUV8dxzzzFq1CiSkpJatU8Aw4cPJzMzk4ceeqjRz8O7777LypUrd5qVNRAI4Pf7m/wc+Xw+/vznP3PZZZeRnZ290+f/ufvSoF27dkyaNIlnnnmGZ599lokTJzYKnFvyPrs9h8PB9OnTWbZs2Q6nxbb0HjVMbT3jjDMaZWUV2d9o5EpkH5aXl8d///tftm3bxqJFi7j99ttZunQpDz30EIMHD2bw4MFNRpgmT57MnXfeycSJEznjjDMoLi7m/vvvp3v37ixbtmynzzdhwgR+85vfcPXVVzNlypTdChTef/99/vznP7fKup3777+fUaNGMWDAAM4991y6du1KUVERixcvZvPmzbE9ma666ipeeeUVTjnlFGbOnMmwYcMoLy/njTfe4KGHHmLQoEHNtn/jjTfy6quvcu6557Jo0SJsNhvHH388//nPfzjppJOYPHky69ev56GHHqJv376xvbAazJ49m7q6Oh599NHdur6SkhLmzJnTqKxhjcGcOXM49NBDSUtL469//SsLFixgxIgRnHvuufTt25fy8nK+/vpr5s2bt1tr5Y4//nj+9Kc/cc4553D44Yfz3Xff8eyzzzZZt/RLTZkyhTFjxvDHP/6RDRs2MGjQIN5//33++9//cumll+40bbfNZuPBBx9kypQpDB48mHPOOYecnBxWrVrF999/3yhQ8Xg8zJkzhxkzZjBixAjeffdd3n77bf7v//4v9pf8lr42li1bxjPPPINlWRQUFHDvvffSoUOHJlO5dsVVV13FG2+8wfHHH8/ZZ5/NsGHD8Pv9fPfdd7zyyits2LBht0cMd+c19/e//z2Wuv78888nEolw33334fF4+POf/7zTc6dPn86///1vLr/8cpYsWcLo0aPx+/3MmzePCy64oNWS4kD9ersJEyY0SjkO9SPQDW699Vbmzp3LqFGjuOCCC3A4HDz88MMEg0H+/ve/t1pftud0Ovnb3/7GOeecw1FHHcW0adNiacY7d+4cm+7csOXDpEmTyM3Npby8nKeffppt27Y1GqkH+Prrr2nXrt0Op45ub1fuS4Pp06fz61//GqDRKH2DXX2f/albbrmFq666itTU1F90jxps3rwZl8v1i7bvENkntEWKQhHZPbua5vuxxx6zevToYbndbqt3797WE088Yd14443NpvT9abrp0tJSKyMjo1FK3wa7koo9JyfH8vv9P/s8P9VcKnbLsqyCggJr+vTpVnZ2tuV0Oq327dtbxx9/vPXKK680qldWVmZdeOGFVvv27S2Xy2V16NDBmjFjRix9eXNpqy3Lsj788EPLMAzrnnvusSyrPgXxX/7yF6tTp06W2+22hgwZYr311ltNUgU///zzlmEY1pw5cxq115JU7PwvpfKOvrbva1FRkTV79mwrLy/PcjqdVnZ2tjV27FjrkUceidVpaSr2K664wsrJybHi4uKsI444wlq8eHGTlNA7wi6mYrcsy/L5fNZll11m5ebmWk6n0+rRo4d1++23/2za7gaffPKJdeyxx1qJiYmW1+u1Bg4caP3zn/+MHW+45wUFBdb48eOt+Ph4Kysry7rxxhubpJtvyWuj4cswDCs7O9s6+eSTrZUrV8bqtOR+N9yHa6+91urevbvlcrmsdu3aWYcffrh1xx13WKFQyLKs3UvFviuvueZeu/PmzbMOP/xwy+PxWImJidZxxx1nLVu2rMnzNqe2ttb64x//aHXp0iX28/jrX/+6UTr05rQ0Ffvs2bOtZ555JvY9GzJkSJPXsGVZ1tdff21NmDDBSkhIsOLj460xY8ZYn376aaM6rZmKvcGLL75oDRkyxHK73VZaWpp15plnWps3b44dr6urs0477TSrQ4cOlsvlsjIzM60xY8Y02jrDsupTsQPWXXfd1ah8Rz+bu3pfLMuygsGglZqaaiUnJzdKib69XXmf/bn7t6PjP3ePLOvH7RQuueSSZttUKnbZnxiW1cpj+CIi8osZhsGCBQs4+uij27or+7yzzz6bV155pcnIouzfDMNg9uzZzU4/PJi19L5EIhFyc3OZMmVKkzWWItL6tOZKRERE5AD1+uuvU1JSwvTp09u6KyIHBa25EhHZB02YMIG0tLS27oaI7Kc+//xzli1bxi233MKQIUM46qij2rpLIgcFBVciIvugnya6EBFpiQcffJBnnnmGwYMHa78okb1Ia65ERERERERagdZciYiIiIiItAIFVyIiIiIiIq1Aa66aYZomW7duJTExsckGrSIiIiIicvCwLAufz0dubi42287HphRcNWPr1q3k5eW1dTdERERERGQfsWnTJjp06LDTOgqumpGYmAjU38CkpKQ27o2IiIiIiLSV6upq8vLyYjHCzii4akbDVMCkpCQFVyIiIiIiskvLhZTQQkREREREpBUouBIREREREWkFCq5ERERERERagYIrERERERGRVqDgSkREREREpBUouBIREREREWkFCq5ERERERERagYIrERERERGRVqDgSkREREREpBUouBIREREREWkFCq5ERERERERagYIrERERERGRVqDgSkREREREpBUouBIREREREWkFCq5ERERERERagYIrERERERGRVqDgSkRERERE9hkFBQWYptnW3dgtCq5ERERERKTN1dTUcM0119CnTx8ee+yxtu7ObmnT4Oq2227jkEMOITExkczMTE488URWr17dqE4gEGD27Nmkp6eTkJDA1KlTKSoq2mm7lmVxww03kJOTQ1xcHOPGjWPt2rV78lJERERERGQ3WJbFCy+8SM9evfnb3/5GOBzmD9dcw2crN7CpvBbTtNq6i7usTYOrjz76iNmzZ/PZZ58xd+5cwuEw48ePx+/3x+pcdtllvPnmm7z88st89NFHbN26lZNPPnmn7f7973/n3nvv5aGHHuLzzz/H6/UyYcIEAoHAnr4kERERERHZRStWrOCII8cwbdrpbNu6BQDD7iRt2GTuXfADd81dw4MfFpBf7Gvjnu4aw7KsfSYULCkpITMzk48++ogjjzySqqoqMjIyeO655/j1r38NwKpVq+jTpw+LFy/msMMOa9KGZVnk5uZyxRVXcOWVVwJQVVVFVlYWTz75JKeffvrP9qO6uprk5GSqqqpISkpq3YsUERERETnIVVdXc/PNN3PvvfcSiURi5Rl9RtD5+AuIa5dHgsdBr6wE6sImaV4X5xzRme6ZiW3S112NDfapNVdVVVUApKWlAfDVV18RDocZN25crE7v3r3p2LEjixcvbraN9evXU1hY2Oic5ORkRowYscNzgsEg1dXVjb5ERERERKT1Pf/88/Tq1Ys777wzFlilZXdg9AV/Z8DM2+jctTtZSW6C4SiF1UG6Z3gp94d4//uifX6K4D4TXJmmyaWXXsoRRxxB//79ASgsLMTlcpGSktKoblZWFoWFhc2201CelZW1y+fcdtttJCcnx77y8vJ+4dWIiIiIiEhzPvroo9jv5Xanm6OnXcDv732d+B4jSIxzYhgGhmGQ4HFQ7g9RE4ySk+whv7iGLZV1bdz7ndtngqvZs2ezfPlyXnjhhb3+3Ndeey1VVVWxr02bNu31PoiIiIiIHAz+/Oc/k5aWxtgJkzn9by8xecbFWA4XEdPEaf8xPHHabURMk1DUJM5lJxiJ4g9FdtJy23O0dQcALrzwQt566y0WLlxIhw4dYuXZ2dmEQiEqKysbjV4VFRWRnZ3dbFsN5UVFReTk5DQ6Z/Dgwc2e43a7cbvdv/xCREREREQEqJ+Z9u9//xuAs88+O1aenp7Od999R9STwl1z11AbiuCy23DYbISjJm6HHYBw1MRhs+Gy26gLRXE77Hhd+0T4skNtOnJlWRYXXnghr732Gh988AFdunRpdHzYsGE4nU7mz58fK1u9ejUbN25k5MiRzbbZpUsXsrOzG51TXV3N559/vsNzRERERESk9Xz99deMGjWKc845h0svvbTJVkq5ubm0T4mjW0YC26oCJLjtpMW7qAlEsCwLy7KoCURI87pIcNvZVhWge2YC7VPi2uiKdk2bBlezZ8/mmWee4bnnniMxMZHCwkIKCwupq6ufS5mcnMysWbO4/PLLWbBgAV999RXnnHMOI0eObJQpsHfv3rz22msAGIbBpZdeyq233sobb7zBd999x/Tp08nNzeXEE09si8sUERERETkolJeXc8EFFzB8+PBYMrmqqipeffXVJnVtNoMJ/bNI87rIL/GTnezG7bRRVB2gqDqI22knO8lNfomfNK+L8f2ysNmMvX1JLdKm42oPPvggAEcffXSj8ieeeCI2dHjXXXdhs9mYOnUqwWCQCRMm8MADDzSqv3r16limQYCrr74av9/PeeedR2VlJaNGjWLOnDl4PJ49ej0iIiIiIgcj0zR57LHHuPbaaykrK4uV9+rVi3/+858ce+yxzZ7XPTORc47ozHvLiygoqSHd66Z+oyiDdK8LMBjQPpnx/bLaJA17S+1T+1ztK7TPlYiIiIjIrlmyZAkXXnghX3zxRazM6/Vy4403cskll+ByuX62DdO02FJZhz8UId5pxwLqwlG8LgftU+LadMSqJbHBvr0iTERERERE9qrtA52fC25eeuklTj/9dLYfr5k2bRq333477du33+XntNkM8tLif3Hf25qCKxERERERASC/2BeboheIRPE47HTLSGBC/+an5U2YMIHMzEyKioro168f9913X5MlPwcTBVciIiIiIkJ+sY8nFm2g3B8iJ9lDvCuO2lCE5Vur2FpVxzlHdCaJOjIzM2PnJCcnc88997Bt2zZmz56N0+lswytoewquREREREQOcqZp8d7yIsr9IXpkJmAY9dMAEz1OEtwOvl37A7+ZfiPfL5rLypUrG+1Ne9ppp7VVt/c5bZqKXURERERE2t6WyjoKSmrISfbEAiuAaDTCx68/zUt/+DWfv/caNTU1XHXVVW3Y032bRq5ERERERA5y/lCEunCUhKiD0pogLruN4rXf8Nr9t7Bt/ZpYvcSkZEaNGoVlWY2CMKmn4EpERERE5CBX6gvyQ5mfNUU+QtWlFLz9CNu+mtuoTr+jf8XTD93NkF6d26aT+wEFVyIiIiIiB5nt062X+oK8vWwroXCYDR++wsZ5TxEJ1sXqZnftw8izrmbcUaMY1KNTG/Z636fgSkRERETkILJ9uvW6cJQfyvxETIvuaR4WLfpPLLByeZPoOH4mnQ+fQp+uGYzvl9Wmm/nuD5TQQkRERETkINGQbn351ipS4p1kJLgJRkyCYZMtfoujp18BhkGHkSdw2B/+TccjfoXT6WTigOxm97mSxjRyJSIiIiJyAGuYAugLhHn9m62U1QTpmupm4WtP0OmQsRhGHBmJLiprw2T0G81VD7+JN6szoaiJzYCymiAZie62voz9goIrEREREZED1PZTAMtrQxQU12BtWsrL/7mX8m0/0Ov7b+h8+o1ETIsEj4OK2jC9u3YmKa5+M2BfIIzH6cDrUtiwK3SXREREREQOQA1TAMv9IXKSPdSVb2Pl03+m7PtFsTprvviQbpPOpSYhm5R4JxHTJBQ1AbAsi21VAQa0T6Z9SlxbXcZ+RcGViIiIiMgBxjQt3lteRLk/ROcUJwtefpj5LzxCJBSM1WnXbSDTr7iZ+JxuLN1USYkviMNuw2bUj1htqwqQ5nUpkUULKLgSEREREdlPbZ9S3ety0D4lDpvNYHNFLcu2VFL43SJefPoOKgo3x85xJabS78QLyBhyLEnt25EU52RQh2SWrK/AYbdRVhPE43QwoH0y4/tlKZFFCyi4EhERERHZjzQEVCu3VfPlhgpKfAGCUROPw063jAR65yTy8ZpS3rj/Fgo/fyN2nmGzc8jkaaSOOoOwzUMwYlIXjmAYUOYPc0jnNCYOyCYj0d0oUJNdp+BKRERERGQ/0ZCg4ptNFawp9BExLXKSPfTKTsLjtPHZujJeW7qF1HgnmX1HxIKrlK6DGHjqZRw5YigAK7ZWU+wLUlQdIDXerVGqVqLgSkRERERkH9PcdL91pTU8sWgDZTUhKvwhnHaDNK+LqrowyzZX0redi4hp4guEaed10vewMZSumExev0PJGz6WitowBSV+hnVMISPRw7DOqZw4pD2JbqdGqVqJgisRERERkX3I9unTA5EoHoedrhleymtClPtDZCe52VDmJzHOidthJ1i2mcXP38kXNoPBv/0b6V4XlXUR+mQnMvTMa6gLRQibFvFuO0XVAZZtqaJDajynDs/TSFUrU3AlIiIiIrKP+Gn69HhXHLWhCF9sKGdjWS1DOqYQNi0ipokRCbPszadYM+95zGgEgC1LF9Jr5Fiq68LEux0Mzkshv7iGitoQ4ahJIBylSzsvvzmskwKrPUDBlYiIiIjIPmD79Ok9MhMwjPppeome+ml7qwt9bK0M0CsrgdJvP2TRGw8SqCyOne9OycTmdFEXimK32XDZbSTFOTmkcyq+QISK2hB1oSjnHNGFTunetrrMA5qCKxERERGRfcCWyjoKSmrISfbEAqsGbocdr9vBhvzVLLn/IdZ/93nsmM3hpPvYaeSNOYPEBC9bqwJ0becl0VP/q75hGCR6HBRWBxiUl0Jeavxeva6DiYIrEREREZE2ZpoWBSU1lNQESHA7sCwLwzCwLAtfIEJVdSXr3n6IDR+9AmY0dl567xEMOvUSSMwh2eskzmkn0ePEYbdRE4wQ57JTF4pqQ+C9RMGViIiIiEgbakhgsWxzJQUlfrZWBshK9NAu0UWpL8S26jo2L1/ChgUvxs6JT8/h6BlX4e56CIXVQRymSWq8i6EdU+mVnciqbT4KSmooqg7gdtiVan0vUXAlIiIiItJGtk9gkZvioaouzLaqAJsq/KwsrMbjsBGKWni7DCGpzxH41n5B9ujTyD3yNHzxcWR7XQzvksbwzmn0yU6KpVQf0yuzSSp3jVjteQquRERERETaQHMJLHpkJVJRUcH3H72Bd+gUolE72AxsQJcpF5IeZ0BiFskeB3EuB90yE7h6fG8cDlujtm02g7w0ra3a2xRciYiIiIjsRQ0bBBeU1LBsSyW5yXEYhoFpmuQveotPHrmd2upy2nuSifQ5kgSXg4Q4J2lpHYhz2QlGotSGTbplxv1v2mBAgdQ+QsGViIiIiMheYJoWnxaUMm9FMduq6qgJhdlQUktVbZiEmo3Mf/xvbFjxTax++SfP0rHfkWQluUmJd8UyCDrtNvzBCHabQW0ogj8UaatLkp9QcCUiIiIisoflF/t47vONLFhVTG04SoLbQaLHQTTg4+Mn72Xr52+CZcXqZw06iu7H/55qhwOHzdYoNXs4amK32YiaVn2Kdpd+pd9X6DshIiIiIrIH5Rf7ePyTDXy5oRzLsshLjSMcMVn14esUvPMvIrXVsboZHbpw0gXXUZ3Wh4LSGpIdNoJRk4TtUrPXBCJkJLrxBSIM7JBM+5S4Nrw62Z6CKxERERGRPaQhacWWyloMA1K8LqxImE/unE35hhWxejaXhy7HzuC0Gefj9rioLa4hOc5FksdBTTBCWU0It9NGMBzFYbfhsNtIT9C+VfsaBVciIiIiIntIQ+KKtHgXWyrrcNpt2BxuErPyYsFV5uBjGDx1NjWOZDZWh8gwDUZ2TY/tV/XNpgo2ldfiC0SId9nJS4tnaMdU7Vu1D1JwJSIiIiKyh/hDEWpDITK8Hhw2G+GoidthZ+DJs6kp2UK/X52Ps0N/euckEQibTBvRkW4ZCU32q/IFw9QEIv9bq+XUvlX7KAVXIiIiIiJ7yMqlX/KfGy5g5JQzSet/LMW+AC6vjbjkdoy9+hGCkSiBUJTKujAjuqRzZI+MRkGT9qvav9h+voqIiIiIiLREUVERM2bM4ORJYynZsJr3n7yTnLgIcS475f4QwUiUqGlS4Q9jAu1T4rR+6gCg4EpEREREpJVEIhHuueceevbsyb///e9YeVJ6FlsLi+iRmUC7hPpMf5sr6rAZBqO7t2PmqC5aP3UA0LRAEREREZFWsHDhQmbPns3y5ctjZSkpKdx6662MPekM5q8qq09u4XWRHO8kJ9nD2D5ZHNGtnUasDhAKrkREREREWsg0LbZU1uEPRfCVlXDXn2/g+eefa1Rn1qxZ/OUvfyEzMxOAntkpsXO8LoeSUhyAFFyJiIiIiLRAfrGP95YXUVBSQyAS5dN//51v338pdnzYsGHcf//9jBgxotF5Sk5x4NOaKxERERGRXZRf7OOJRRtYvrWKlHgnXdslMGH6Rbi9ScQlJHPL7ffw+eefNwms5OCgkSsRERERkV1gmhYvfLCUb7/+lmPGHYth1E/py87MZNZN91HrzSWtdycMQ+MXBysFVyIiIiIiP7H9miqvy0G7OBs3/eVv3Hn7X3E4XIw4ZDgJKWmx+t0HjcAXCJNfXMOWyjpN/ztIKbgSEREREdnOT9dUFS7/jE+evoOSLRsAiAQDzH3uAU664LpG58W57BRVB/CHIm3Qa9kXKLgSEREREfmfhjVV5f4QnkAZCx//O98vnh87bhg2Dp18OhPOuqjJuXWhKG6HHa9Lv2IfrPSdFxEREZGDwk+n+v00FbppWry3vIjiimo2ffgi8198hEgoGDue3XMww6ZdQYdufYhLSGjUtmVZbKsKMKB9Mu1T4vbaNcm+pU1X2y1cuJApU6aQm5uLYRi8/vrrjY4bhtHs1+23377DNm+66aYm9Xv37r2Hr0RERERE9mX5xT4e/LCAu+au4d75a7lr7hoe/LCA/GJfrM6Wyjo++/IrXv2/03nv6X/GAqvEtAzOuPrv/O72p8nu0gu3w8ba4hp8gTAR08QXCLO2uH5z4PH9srR31UGsTUeu/H4/gwYNYubMmZx88slNjm/btq3R43fffZdZs2YxderUnbbbr18/5s2bF3vscGiATkRERORgtf1Uv5xkD/GuOGpDEZZvrWJrVR3nHNGZ7pmJ+EMRXClZhAK1ANhsdkafNJ0Jv7kQjzeBiGnidtiYPCiHtYU1FJTUUFQdwO2wM6B9MuP7ZdE9M7GNr1baUptGHZMmTWLSpEk7PJ6dnd3o8X//+1/GjBlD165dd9quw+Focu7OBINBgsEfh3yrq6t3+VwRERER2Xc1TPUr94fokZmAYRhYloVlQWq8k03lft5bXkjXoxPwuhwkJ6dw7IzLWP7RW5w8+3qyO/eItdWwpqpPdhLjemftdIqhHJz2myGdoqIi3n77bZ566qmfrbt27Vpyc3PxeDyMHDmS2267jY4dO+6w/m233cbNN9/cmt0VERERkX3Also6CkpqyEn2YBgG5f4Q+cU1lPuDbPt2Ievee4LNv/8HA/NSOKJbO7plJFA7fCKjjvs1NtuPK2h+uqbKZjOUbl2a2G92OHvqqadITExsdvrg9kaMGMGTTz7JnDlzePDBB1m/fj2jR4/G5/Pt8Jxrr72Wqqqq2NemTZtau/siIiIispeZpkVBSQ3FNQEiUYuymiBLN1WyoWAN3zxyNd89dQP+wvUs+8/9PL9kI+tKa5jQP4v0BDf5JX6tqZIW229Grh5//HHOPPNMPB7PTuttP81w4MCBjBgxgk6dOvHSSy8xa9asZs9xu9243e5W7a+IiIiI7H0NGQFXFlbz5fpyNpTVsr7Yz7aKOupqa9n0wTNs+eQVrOh2e1HVVVFdU8v73xfxu6O6cc4RnWP7XGlNlbTEfhFcffzxx6xevZoXX3yxxeempKTQs2dP8vPz90DPRERERKQtbZ9evcQX5NtNlSzdVMmaIh+RqEV2soc0r5M1n73PurceIuIri50bn5ZFtykX0O/wY+mRnUh+cQ1bKuvonplI16MTtKZKWmy/CK4ee+wxhg0bxqBBg1p8bk1NDQUFBZx11ll7oGciIiIi0lbyi32xEabSmiAby/wAsSAoI8HF1vVrWPP6P6nI/yZ2nmF30n3c6bQ/6gwSErx0z0wg3u2g2BfEH4rE2tCaKmmpNg2uampqGo0orV+/nqVLl5KWlhZLQFFdXc3LL7/MP/7xj2bbGDt2LCeddBIXXnghAFdeeSVTpkyhU6dObN26lRtvvBG73c60adP2/AWJiIiIyF6xfXr17CQ3BcU1VNRGCEejhKIm8S470XCUZY//H3XlhbHzEnscQvaE39GhU1eykz10y/CS5nXjC4RxO+x4XfvF2IPso9r0p+fLL79kzJgxsceXX345ADNmzODJJ58E4IUXXsCyrB0GRwUFBZSWlsYeb968mWnTplFWVkZGRgajRo3is88+IyMjY89diIiIiIjsNT9Nr76pvJYfymvBsHA5bAQjJuGISa1hI2fcTNa99Bfi0nIYdOolODoNA8PGgPbJdEqPj6Vm3z4ToMjuMizLstq6E/ua6upqkpOTqaqqIikpqa27IyIiIiLb2VRey11z15AS7yTB7eCjNSWsKfIRLl6P6fZCfDssIN5lw2YY1H0/j7zhx9KvYwZri3z4AhGO6J5OZpKHulCUbVUB0ryu2GbCIttrSWygcU8RERER2Wdtn7AizmnHAL7fVk15bZCcZA++QITi0nIK5zxK5Vdvk9jrMLJP/iNh0yIUMXHYbcT3Pxabw007r4uqJA+ZiRCJWmwo9SsToLQqBVciIiIisk/6acKK0poQYOF1O9hcXkthRR0138/nyxfvI+KvBMC36lNSfvgWZ8eB2AyDaLR+iqArJY7C6iAd0+KZMbIzcS67MgFKq1NwJSIiIiJtYvtRqZ8GOdsnrIhz2ijzh/AHI4SjJqW+IBWbVrP07fsJbFkVa89wuEkfdTru9n1x2m0YhkEwEq2fIui2M7CDRqhkz1JwJSIiIiJ73fajUoFIFI/DTreMBCb0z6Jru4RYworuGV6+/KGSYDhKksfB5sJifnj/CSq+fge2Sx3g7X0EeRPPw/JmYAEOuw27AXabgwS3gwuP6c5RPTI1QiV7lIIrEREREdmrth+Vykn2EO+KozYUYfnWKrZW1TGpfzYFJTXkJHuoCUapqA2R4Haw6tP3yf/vPURrq2Ntedp1IGXs+cR1HkLUbpAa5yA5zoVhQCBs4nHayEuLp3tGogIr2eMUXImIiIjIXvPTNOqGUR/wJLgdZCW6yS+p4bWvN1NaE8LlsFEXihKJmph2gzC2WGBlc3noNekceo89lXXlQQwAw8DrdmBi4TBs5KZ4cNhsDO2YqhTrslcouBIRERGRvWZLZV1sVKohsCr3h8gvrqGiNkR1XZilmyoxgILiGtxOO9WBMBHTIrHXSFJ6HoLNk0Du+HPp1ikP04BUr4XDZlAdiNA9M4E0r4uoaeELREhPcDO+X5ZGrWSvUHAlIiIiInuNPxQhEIkS76ofSSr3h1i6qZK6UASH3UYoahKNRKj6Zg6bNq/g0LOvxzQtSmuC2A3ofsZNRAwHXrcDp92gojZMTnIcWYkuVhfVYGDgC0RwO+wM7JCiBBayVym4EhEREZG9xuty4HHY8QfDWBZ8u7mS6roQ7bxOtlQFKV/3HdvefYBAYQEABV8dTfdhR7K1OkgwEiVoQIILElx2KmrDxLkcdG3npcwfYvKAXI4flENdOKoU69ImFFyJiIiIyF7TPiWOlDgnnxSUEomaFPuC2AyDosIits57DN938xvVr928ksiQI0l026m2LGyGARiETYt2CW7ap3go84dI87qY0D+LTunetrkwERRciYiIiMhetK60huKaIHWhKMFIlEg4TPkXb1L68bNYwdpYPU92VzodfxGZPQbSv0MycU47hVV1HNs3m7VFNWyrqsNuAzAY0F77V8m+QcGViIiIiLSqHW0OHImYvPTFJrZW1pHgsrNt1VdsfOt+wqU/xM61eRLodOzZZB06hdqIRV0oSmq8E5thkOZ1c2TPDKYd2nGHmw+LtCUFVyIiIiLSavKLfcxZXsh3W6qoDUWIdzkY0D6ZPjlJfL6unDe+3UpNMEKgcB0FT17d6NzEgeNJHzODdhkZGDYbRCJYgGVZbKsOMqB9ciyQykuLb5sLFNkJBVciIiIi0iryi33cPW8tqwt9hMJRIpYJJizbXIlhGGQlugmEozgMSO/Yk5Leh1O96lPc2d3JmngBzuyemIA/FMVhmDjtBi67QUGJn07pXqVUl32egisRERER+cVM0+K5zzfy5YZygpEoURNCUbN+A2ATgoVrqO3ch7AJcS47DrtBl8m/Y1uXoaQOmYDT4SAUMbFMC7th4HTYcDts2AyDvrlJnDo8T2uqZJ+n4EpEREREfrHNFbV8tLqY6rowGAamaWFhYdaUUvz+o/hXf0LkV38gud+R2CP1I1Lx6bm0Gz6ZUNQk0WnDMAw8BhzZox1p8S4KSv30y03mqvG9cDhsbX2JIj9LwZWIiIiI/GIL15awqaKOSNTCMMCMhKn+4jUqPn0RKxwEoGz+oyT2PISIPZ66sIHLYSPOaSMUiVITjOJx2kmKc+JxOiiuCdEp3cspwzsosJL9hoIrEREREflF8ot9vLlsGxHTwmEHX/5XlM19mHDF1lgdW3wyKaN/Q3xcPBET3A4b4ahFxLRwOewkxTmIRC08DhuRqKn06rJfUnAlIiIiIi3WkG7dFwzz+tdbiESiWFVFbJ3/L/xrPvuxomEjaehkkkedid2TQKrXTUVtGIfNIDXeiS8QJcXrIDfJQ5zbyeQBOfTJSVJ6ddkvKbgSERERkZ366b5VdeEIc78vpqCkhvLaIPlFNZR8/Dw/vP80ViQUO8/ToS/txv8OR0ZXDMBuq58KmOZ1kuB2Uu4P4bAbZCfFMbhjqkaqZL+n4EpEREREdii/2Md7y4soKKkhEIkSipiU+IIkeZz0yErA5bCxobSWyq3rY4GV3ZtK6piZJPQ9GpvNwLLA6TBIjXfhD0ZJ9DjonplAZpKb4Z3T6JOtkSo5MCi4EhEREZFmrSmq5v4FBZTVBMlJ9tA5LZ7P15dTWB0galqEoxZxTjsep53BUy/kg9VLSBsygXajpxGyx2FZFoYFlgEZCW46t/OSnuDW1D85YCm4EhEREZEm1hT6uOWtlRSU1BDvslNaE6r/t7KayoUv4E/vQFLcFIZ1TCE13kVJNJVjb36JOsNT/9gXpLouRCBikeh20DsnkaEd0zT1Tw5oCq5EREREpJH8Yh/3L8inoKSGlHgnXnf9Br8rF8+j4M0HCFcV405MJav/4dRkJdI9M4GaYITaYByWZdIjM4FuGQlsrawl3uXg18Py6JurkSo58Cm4EhEREZEfs/8Fwrz+zVZKawLEuWx43Q5qijbyzUt3UbRiSax+qNZHaf63hHp3pF2Cm8F5KazYWkWxL0ixL0hqvIsjumdopEoOKgquRERERA4S22f9i3fasYC6cJQSX5BvN1WyrsRPeW2IguIakuOchAJ1LH3vMdZ98CJmNBJrx9t1CENOuxxvZkdc9voNflPjnWQk1ieo+NXgXBI9To1UyUFHwZWIiIjIQWD7rH+lNUFKa4KAQbzLRllNCKfdRv/2SWQneVhf4mPDF/NY+8aDhKpLYm3Ep2XR+8QLCXUYht9hJ9NtJ85lwxcIs60qQHqCm1OGd9BIlRy0FFyJiIiIHODyi308sWgD5f4QcU4bZf4gtcEIpgWlNSYOm4HNgLXFNXTPTKDwk1dY8+ZDsfMNu5Me46bR97gZBC0HJTVB2nndpMa7+KGsFrfDzoD2yZoCKAc9BVciIiIiBzDTtHhveRHl/hDdM7x89UMlwbBJZpKHYDhKQamfBI+T9AQ35f4QRVUB+h55POvmPk0k4Cet9wiGnXYZtpQcfOEotaEwfbKT+L/j+uB1O2IbC2sKoIiCKxEREZEDzvZrq6rrwuQX+8hJ9lATjFJeGyLB48AwDEzAZoCvcCOhxB4keByU14bpk5dL/1MuI2p3k9rncIZ0Tce0YFtVHekJbmYf3Z2e2RqhEvkpBVciIiIiB5Dt11YFIlECoSjrS/30zk4gYkJNMIzL7sKyLGq2FLDu+TsIlGykww3PkZSShj8YId7l4PiTT2XF1ur/Zf8LkBrv5vBu7TT1T2QnFFyJiIiIHCAa1laV1QRJ9DiwLNhcUcvmylo2lddisxmYlkVZWTmVnzxH4WdvgGUCsPqtfzF42lXYbTZcdhuJHgcZiR6GdU7lxCHtSXQr+5/Iz1FwJSIiInIAaFhbtbGslohpsrrIR4kvSCAcJVIfP2FFovi/m0/FR09i1lbFznWntyel9xFU+EPkpMQBFmuLa0hPcHHq8DyNVInsIgVXIiIiIgeALZV1fL2xnC2VdYQiUfyhCIFQlIhVfzxYmE/53AcJbV0dO8dwusk+8gzGn3YOW2tMwhETl91GVV1E2f9EdoOCKxEREZEDwPdbq1i6qYpgJIplWfhD9cNVZqCGio+eombpHMCK1Y/vNYp2x8wioV0WLpeHXw1KYVBeCu0S3cr+J7KbFFyJiIiI7Ofyi328+tVmakMRnHYbdQ3zAAHLsqhdvYiGwMqZ1oHMCb8jrvNgspI8BKMmx/bLZurQDgqmRH4hBVciIiIi+ynTtNhY7ueBD/JZV+IDy8QfNIn+OECFPS6RlCOnU7HgMZIPn0bS8Cm4nC4wwGm3YQEZiW4FViKtQMGViIiIyH5i+/2rSnxBPlhZxAeritlcUUfUgmhtFZWfPEfK4adjT0iNnZcwaDzx3UfEyiKmhdthEIpESYl30bWdt60uSeSAouBKREREZD/QsH9VfrGPLZV1rCv2UR2IEDEhakapWfoulQufxgz6scJ1tJt8eexcw7A1CrYswG4zsNlsjOiaTofU+Da4IpEDj4IrERERkX1cw/5VG8tqqQmGWVPkiyWsCGxeQfnchwgXr4vVr137OVF/JXZvSrPt2YDUeDeD8lI4Y0RHTQkUaSUKrkRERETayPbT/HaUoW/7/au2VdWxpbKOurBJtKaCio+ewL/8g0b1vf3HknrU2Y0CKwOwG2ACNgNyk+M4ZXgekwZkK9W6SCtScCUiIiLSBraf5ldRF8ZuQLeMBH49LI+e2T8GPFsq68gv9rG1qpbN5XUEQmF8X79N5SfPYoVqY/WcmV1JO/Z3eDr0bfQ8dgOykzyYlkXEsuiTncR5R3bl8G7tNGIl0soUXImIiIjsZbFpfuW11AYj1AQjBCNRVm7z8fn6ci4e24OxfbIA8IcibK6oZXNFgHDUoviVmwls+CbWls3tJeXI6SQMnohhszd6HgOw2yBimqQneBjZNY1pIzpqtEpkD1FwJSIiIrIXxab5lddS4Q8SCJskeBwkxTkJRaIUVge4d/5a8tLi6J6RSGVtiI0VtYSjJg4bePsfEwuuEgaOJ+WoGdjjk5s8j92ArCQ3I7umM3lQLt0yEshLjddolcgeZGvLJ1+4cCFTpkwhNzcXwzB4/fXXGx0/++yzMQyj0dfEiRN/tt3777+fzp074/F4GDFiBEuWLNlDVyAiIiLSMg3T/GqDEQJhkzSvC7fDjs0w8DgdZCd52FZVx+1zVnPLf7/lgXeXUlUbxrQgYoK379EkDJlM9ln/IH3Sxc0GVgAOu43eOUn8fkx3jumdRad0rwIrkT2sTYMrv9/PoEGDuP/++3dYZ+LEiWzbti329fzzz++0zRdffJHLL7+cG2+8ka+//ppBgwYxYcIEiouLW7v7IiIiIi3mD0WoqAtTE4yQ4HFgGD8GPHWhKKW+IL5AhDnvz+Wv55/A3H/9Ga/bge1/CSkMwyB9/O9x5/ba4XM4bdA/N5FrJvXWFECRvahNpwVOmjSJSZMm7bSO2+0mOzt7l9u88847OffccznnnHMAeOihh3j77bd5/PHHueaaa5o9JxgMEgwGY4+rq6t3+flEREREWsLrcmA3IBiJkhTnjJXXheqnBPpKC9ny3sP4Vn4CQGHJJuIGTsDbdQi1oQhRc8dtG0CC287wzqlcO6kvPbMUWInsTW06crUrPvzwQzIzM+nVqxe///3vKSsr22HdUCjEV199xbhx42JlNpuNcePGsXjx4h2ed9ttt5GcnBz7ysvLa9VrEBEREWnQPiWOLu28+AJRtlbUUlwdoMofZGt5FRs/eJbV9/82FlgBpHXuiysxGRuQ5HHishvYqA+ktue0QYfUOE4dnscfJ/dtlHFQRPaOfTqhxcSJEzn55JPp0qULBQUF/N///R+TJk1i8eLF2O32JvVLS0uJRqNkZWU1Ks/KymLVqlU7fJ5rr72Wyy//cRfz6upqBVgiIiKyRyxYXcyi/DIq/CEaBqHq1n1F+byHiVRsjdVzeFMYfPIFdBl5HNWB+lGtOKcNp91JTTBCvMtOJGoR77RxRI8MJg7IpntGopJWiLShfTq4Ov3002P/HzBgAAMHDqRbt258+OGHjB07ttWex+1243a7W609ERERkebMX1nEda8vp8QXxAIiVUWUz/8XdWs/+7GSYSN52PH0P+G3tM9sB0BinEFt2EFKvIuaQBgD6JaZwID2KUwd1p6eWUltcj0i0tg+HVz9VNeuXWnXrh35+fnNBlft2rXDbrdTVFTUqLyoqKhF67ZEREREWoNpWmyprMMfiuB22Ljvg7X1gZVl4bBBbWF+o8DK3aEvHSZdgLd9d2otA9M0sdlshKMm8S4HQzoks6G8lq7tEjjniM500CiVyD5lvwquNm/eTFlZGTk5Oc0ed7lcDBs2jPnz53PiiScCYJom8+fP58ILL9yLPRUREZGDxfYBlNfloH1KHACf5Jfw32+2sq2qDqfDRjRi8v3WaizLwuWoD5gSex+Or8sQQsUbSBkzE2/fo4nzOGiX4KKsJkSxL0hKvJOaQJQUr5MiX5AOqfGceVhHOqZ72/jKReSn2jS4qqmpIT8/P/Z4/fr1LF26lLS0NNLS0rj55puZOnUq2dnZFBQUcPXVV9O9e3cmTJgQO2fs2LGcdNJJseDp8ssvZ8aMGQwfPpxDDz2Uu+++G7/fH8seKCIiItJa8ot9vLe8iIKSGgKRKB6HnZQ4Jz9U+PlqQyV14SgOm0Gc006kcislX8wn5fDTsCwLywKbYZA1+VJs7ngsRxwmEIiY2AyDRI8Tr8tBiS+Ew26QGu9iYIcUxvfLUnp1kX1UmwZXX375JWPGjIk9bkgqMWPGDB588EGWLVvGU089RWVlJbm5uYwfP55bbrml0fqogoICSktLY49PO+00SkpKuOGGGygsLGTw4MHMmTOnSZILERERkV8iv9jHE4s2UO4PkZ3kIcF0sKWyloVrS6iqC2M3DJLjHERDATbNfYqST1/BikZwZnXF3uMQGra3cibVr6uKmhZY4LbbCEVMTMuiQ2oco3q2Y3jnNPpkJ9E+JU7TAEX2YYZlWVZbd2JfU11dTXJyMlVVVSQlaYGoiIiINGaaFg9+WMDyrVWke52sK6mlzB+k2BegJhAlYlq47GCuX8LGdx4iVFUcO9ed15/sM/6K01a/KbANsICoCYYBA3ITSfG66dLOyzlHdFH2P5E21pLYYL9acyUiIiLSFn66rsqyLApKaohz2li6qRJfIILdZhCJWoBFqGwzhfMeJrDhmx8bsTlIOfREEkeeVt/m//68Ha0fsALqNxhOinPTITWe3xzWiU5aVyWyX1FwJSIiIrITza2rSvI4KakJUOILsq0ygGEYmJaF319D+ScvUvXF62BGYm14uw4lY/z52FPaYxgGlmVhGI0Dqzinjf7tkxjZLV3rqkT2UwquRERERHZg+3VVOcke4l1x+INhvtxQxpqiGkJRCwNw2sGqq2bjoxcR9ZXFznckZZA9/nyS+xyOhUEwHMVhN+ibnUic00FFIIzLZtArO5ExfbLom5OsdVUi+zEFVyIiIiLNME2LOd8VsrmilvYpcVgWVNaG+PKHctaX+AlG68ecLCBigs2dhDurG7W+MrA7SD50KskjTyE+Ph4wCEejYBi4HXYuGteTbhmJjdK3K6AS2f8puBIRERFpxqKCUt5ZXkhdKEx+kY+waVEXMqkLR7AsMEMBDKcbwzCIWhAFUsaeh2Wzk3r0ObhSczGASNQkDBgYJMU56dounuzkOPLS4tv4CkWktSm4EhEREfmJ/GIfzy/ZyNbKWiKmRShiEo5aWIBlWdSt+JDyBY+Teuzv8PY6InaeMyWbzJP+iNOAiAV2GzgdNlx2O7nJHnplJ2IYBl6XfgUTORDplS0iIiLyP5GIyVcby3n2s40s21RBTbB+lMppr5+yFypeR/nchwhuXgFAxfxHie86DMPpAerTqpvUJ6rwOm0MyEslM9FNaryTRI+D/BI/A9rXr6sSkQOPgisRERERYP7KIp74ZD0rtlVTURtm+41ArVofZQufxff122CZsXJXdjeMSACb04NJ/forG2CzGSTHu+idnUC820FdKEp+iZ80r4vx/bK0vkrkAKXgSkRERA5681cWcfObKyjxBQhHzVhgZVkm/u/mU/HRk5i1VbH6jtQc0sb9jviuw7AZ9Zv/8r9NgONddrweB4d0TqOqLkKxL4jbYWdA+2SlWBc5wP2i4CoQCBAKhRqV/dyuxSIiIiL7kkjE5P4P1lJcHajff+p/kVWwMJ/yuQ8S2ro6VtdwukkeeRpJh5yE4XDisANWfbZAAKfNoENqHO1T47l8fE8Mw1BGQJGDSIuDq9raWq6++mpeeuklysrKmhyPRqOt0jERERGRPcU0LbZU1uEPRVi4poTlW6qIWmBa1I9EAdWfv9oosPL2PoLUMbNwJGX+OLL1v6DKZjNIcNsZ3ikFp93OwA4pdEiNVzAlcpBpcXB11VVXsWDBAh588EHOOuss7r//frZs2cLDDz/MX//61z3RRxEREZFWk1/s473lRRSU1FBaE+T7rVWETHDa6o8bBhgWpI6ZSV3BEuyJGaQd+zuSug7Bon7T4KgFWPV141wOclPi6JOdSCBial2VyEHMsCzL+vlqP+rYsSP//ve/Ofroo0lKSuLrr7+me/fuPP300zz//PO88847e6qve011dTXJyclUVVVpmqOIiMgBoGGkamVhNW9/u41gxCQn2cPKbdWsLfaxde1yrIAPb7fh2AwDsIiYENi6GldWVwy7EzvgsBvEuRwkxzs5qmc7aoMm1XUhHHYDj9NB98wErasSOcC0JDZo8chVeXk5Xbt2BerXV5WXlwMwatQofv/73+9Gd0VERET2nIaRqvxiH99vq6YmECEryU0oGmVLYRHF7z5K4efvYPem4DnvYSxXPHa7gcNmEZfbi4bcgIYN3M76xBRnH9GZsX2yGk0v1LoqEWlxcNW1a1fWr19Px44d6d27Ny+99BKHHnoob775JikpKXugiyIiIiK7J7/YxxOLNlDuD5HgthOKmESiJmsKqyj/8h2KP/w3ZqAGgKi/gqqv3yb1sFMwzfqJPSb1qdXTvC565yQwdWgeUwbm4nDUzyG02Qzy0uLb6OpEZF/T4uDqnHPO4dtvv+Woo47immuuYcqUKdx3332Ew2HuvPPOPdFHERERkRaLRExe+mITP5T56Z6RQIk/SLk/RN2mFWx9935qtxXE6trc8WQc9RviBk/GoD6xhUV9YotUr4tfDc5l2oiOmu4nIjvV4jVXP/XDDz/w1Vdf0b17dwYOHNha/WpTWnMlIiKyf8sv9vHSF5t557ut9enVDRuV5SVsmvMvqr+b36hu6qBx5I6biSelHVgWLocN04JgxKRzejyXHtuT0d0zNN1P5CC1R9dc/VSnTp3o1KnTL21GRERE5BeLREzeWLaV177ZQlFVHVV1YSKmRdXaLyl87a9YodpY3ficbmRPvICkzv2JRk3qQlF6ZCbQPdNLYXWQ9AQ3s8d0o2eW/tAqIrumxcHVvffeu9PjF1988W53RkRERGR3zV9ZxBOfrOe7LVXUhiOYZn3KdLsB8dldYvVsbi/pR00n57Ap2O12UuOdlNeGiUZM7HYD0zI4vFs7Zf0TkRZr8bTALl1+fHPatGkTOTk5OBz1MZphGKxbt651e9gGNC1QRERk/zJ/ZRG3vbuKytoQwXCUSCRM0Kyf3gfgthuULfkvoeINpBw9A2d8MjYDEj1OMhPdxLvtxDkdnHtkV7plJCjrn4jE7NFpgevXr4/9PzExkY8++iiWml1ERERkb4tETJ5ctAFfIExGnI2lH71I6Rdv0f7su3HEJxIxIRS1SD3khNjmvwAuh53BeSl0TIunsDrAwA4pHNlDa6tEZPfZ2roDIiIiIr/E15sq2FDmx9q8jE9un0XhvMeIVBVRvvBpDMPAbjTEU/VBkwnYbQYJHgdJcU6KfPXrq8b3y1JgJSK/yC9OaCEiIiKyN+xow96V+ev4/umbqPj+4+1qG2AYWKaFzWZgRi3iXTYiJv/LHghYFnWhKIPyUrS+SkRaRYuDq2XLlsX+b1kWq1atoqamJlZ2oKRjFxERkX1HfrGP95YXUVBSQyASxeOw0zHZyaaFL/HA3XcQqPsxC2BK536kjDsfo11XTAsM04rtXRXvsuNx2rHbDI7q2Y6Zo7qSlxqvESsRaRUtDq4GDx6MYRg05ME4/vjjY48NwyAajbZ6J0VEROTglV/s44lFGyj3h8hJ9hDviuPbxR/y+MN/oapwY6yew5vC4JN/T5eRk6msi7CpopZA2MRsyBjocmD/XxDVv30yZ43sTKd0b1tdlogcgH5RQgsRERGRPSESMfl6UwUlNUE+XlNKpT9Ez+xEDMMgUFvDf+68hlpfJQCGzcYJ086hos+JBAwPNaEoiXEOckwPWyrrME0Lr9uB3QYp8S5Gdk1j2oiOmgYoIq2uxcGVNgwWERGRPWn+yiKeXLSBDWV+6sJRaoMRUuJduJw2urRLwBOfwHHnXMor995Ex75DOXLG1fzlt1NYU+SLnVfuD+G02zi0cxrHDcwhJzkOgC7tvJoGKCJ7zG4ltHj66ad56KGHWL9+PYsXL6ZTp07cfffddOnShV/96let3UcRERE5SMxfWcRf3llJVV2YJI8Dl8NOyfJFRHJ78XG4fulBl3YJHDbpVBJTM+h92Bh+KKvFH4owtk8WR/XI4OtNFZT5Q6R7XQzNS8XhUHJkEdk7Wvxu8+CDD3L55Zdz3HHHUVlZGVtjlZKSwt13393a/RMREZGDRCRi8sCCfAqrA1imReGmDXzx8LVsfPFmyj58klDEZOmmSkzTwma3M+CIcQTCJm6HHa+r/u/FDoeNQ7ukM6l/Dod2SVdgJSJ7VYvfcf75z3/yr3/9iz/+8Y/Y7fZY+fDhw/nuu+9atXMiIiJy8Hhj2VZWFfogHKBwwVOsuv98fGuXAFD+zftEStZTWRtmW3UdUJ+1eFtVgO6ZCbRPiWvLrouIALuZ0GLIkCFNyt1uN36/v1U6JSIiIgcX07RYsKqIihUfUzb/UUKVxbFjzqR2pI/9LaR1JByNUhOI4AuE2VYVIM3r0ua/IrLPaHFw1aVLF5YuXdokscWcOXPo06dPq3VMREREDiw72gQYYOEX3/Lqny9g24olsfqG3UHuqF/TYcyZhG0uagIRLMugsi5MvMvBgPbJ2vxXRPYpLQ6uLr/8cmbPnk0gEMCyLJYsWcLzzz/PbbfdxqOPPron+igiIiL7ufxiH+9+t40vNlTgC4Rw2u30zk5kfL9sFv3nca6//nrC4XCsfnKPYXSdciFxGXkAGKaJhUWPzASuP64vSfHORsGZiMi+oMXB1W9/+1vi4uK47rrrqK2t5YwzziA3N5d77rmH008/fU/0UURERPZTpmnxSX4Jt89ZzYYyP8GISdS0APhmYwVzvi+kQ0k0FlglpGfT7tjziOt+GDa3A9OyCEct/MEIboeds0Z2pl+H5La8JBGRHTIsy7J29+Ta2lpqamrIzMxszT61uerqapKTk6mqqiIpKamtuyMiIrJP29F0vzWFPh5emM+73xVSGzZj9S3LwmbUjzjZbZDhdbHthevo3Hco3cf/hrg4D99uqqKyLkzUtLAZ4HbYObJnBnecMkijVSKyV7UkNtitfa4axMfHEx8fD4Bpmtx66631jToc/N///d8vaVpERET2A/nFPt5bXkR+sY/y2hDhiEluShx5afH85+vNbK6oI/q/P+OawVoqP3kWs66ajOOvACBqQnldmNEX30WP7GQC4SgVtWGO6Z1JVSBMVW2Y2lCUru28zBzdRYGViOzTdmvNVXOi0Sj33Xcfd955Jw7HL4rZREREZD+QX+zjiUUb2FheS6kvQGFVkEA4whcbytluoArLsvCv+JDKBY8T9VcAkDDwWOI7DsQCQhELCxuVtWFOHtqebzdVUVBSQyhikhLvYnjnBCWuEJH9QoujoG+++abZctOsfxe95JJLflmPREREZJ9nmhbvLS9iY3ktP5TUUOgLErUsDCCyXWAVKl5H+dyHCG5eESszHC7CFduwOg4EqA+woibBSJR2iW5+f3S3HWYVFBHZl7U4uFqwYEGz5YFAAK/X+4s7JCIiIvu+LZV15Bf72FJRy5bqAKZZHyQ1MAM1VH78DL5v3gHrx2grrsdhpI09F0dyVqy+AbgdNtwOO16XA5vNIC8tfm9ejohIq2i1+XuGob8oiYiIHOgiEZOvN1WwdFMlSzdV8ENZHdsNVGFZJv7v5lHx0VOYtVWxckdqLmnjzieu67BG7dUHVvW/Q3TPTKB9StxeuAoRkT1Di6NERERkl8xfWcSTizawtthHuT9EKNo04XBg/TeUvXtv7LHhdJM88jSSDjkJw+FsUt9mQJrXTV5aPOP7ZWn6n4js11ocXL3xxhvNlm+/8Z+IiIgcWOavLOK2d1dR4Q9RGwo3G1gBeLoMxdNpIIEflhHfaxSpx8zCkZTRbF27AekJLib1z2baiI5KWCEi+70WB1cnnnjiDo9paqCIiMiBJxIxeXLRBnx1YWxYBML1gZVlRglsWNpoqp9hGKQd+3sivjLiOg9utj0bEOeycUzvTE49pCNHdGunESsROSC0OLhqyAooIiIiB4evN1WwocyP3QZl/ggWENy6mvK5DxEqXEvmKTc3CrCc6Xk40/MatWED7HbomBpHp3QvTrudKyf0plO6kmGJyIHD1pZPvnDhQqZMmUJubi6GYfD666/HjoXDYf7whz8wYMAAvF4vubm5TJ8+na1bt+60zZtuugnDMBp99e7dew9fiYiIyP7PNC1+KPPz4epiPlxdzIaSGjaU1vDaN1sorq6j1BckWFNJ6Tv3UPj0FYQK1wJQPu9hLDPabJseB2QmuuiYHs/kAbkcNyAXt9PBoLwU8lKVEVBEDixtmtDC7/czaNAgZs6cycknn9zoWG1tLV9//TXXX389gwYNoqKigksuuYQTTjiBL7/8cqft9uvXj3nz5sUea1NjERGRncsv9vHcZxv5bF0Zpf4ggVCUkGmCZRGOQMSMUrP0XSoXPo0Z9MfOc7brRNqxv8Ow2Ru1Zzfqk1U47XY6pMbRKzuJOKed/BI/aV6XkleIyAGpTaOOSZMmMWnSpGaPJScnM3fu3EZl9913H4ceeigbN26kY8eOO2zX4XCQnZ3dqn0VERE5UOUX+7hr7hqWrC/HH4wQCJuN0qsHNq+gfO6DhIvXx8oMVzwpo88kcchkDHvjXye8Lhu9sxPxup1kJXmIRE2q68IEwyYD2iczvl+WkleIyAFpvxrSqaqqwjAMUlJSdlpv7dq15Obm4vF4GDlyJLfddttOg7FgMEgwGIw9rq6ubq0ui4iI7NNM0+LpTzfw4ZpiaoNmo42ArUiYsjn34v9+QaNzvP3HknrU2dgTUpu057LDgPbJHNkzk/H9sujaLoEtlXX4QxG8LgftU+I0YiUiB6z9JrgKBAL84Q9/YNq0aSQlJe2w3ogRI3jyySfp1asX27Zt4+abb2b06NEsX76cxMTm/0p22223cfPNN++prouIiOxzTNNiS2Udjyws4IUlGwk3l6/K7sCs88UeOjO7knbs7/B06NukqsOA9EQXvzuqG+P6ZDcKovLStLZKRA4OhmVZzW9UsQPLli3b6fGBAwfuXkcMg9dee63ZVO/hcJipU6eyefNmPvzww50GVz9VWVlJp06duPPOO5k1a1azdZobucrLy6OqqqpFzyUiIrI/WFPo45WvNvHZujK+31rNDrasAiBcsZWiZ/9A8uGnkzB4IobNjg1wOgxS45xELUiJd9Ip3cuVx/aid64+N0XkwFJdXU1ycvIuxQYtHrkaPHgwhmHQEJM17G1lWRaGYRCNNp8taHeFw2FOPfVUfvjhBz744IMWBzspKSn07NmT/Pz8HdZxu9243e5f2lUREZF93vyVRdw7fy0lviClNcFYYBXxlVKx4Am8vUcR33NkrL4zNZf2v3scw+GMldlt4LTZiFiQlejmuAG5TOivdVQiIrs1LfDzzz8nIyMDy7Lo378/77zzDp06dWrtvsUCq7Vr17JgwQLS09Nb3EZNTQ0FBQWcddZZrd4/ERGR/cmaomrunb+WwuoACW47UdPCioap/vINqj59AStUR3DLSjxdhmJz/vhHx+0DK6gPrjIS3fTJSeKMER05XJsAi4gAuxlcdezYkczMTKB+5Co+Pn63gquamppGI0rr169n6dKlpKWlkZOTw69//Wu+/vpr3nrrLaLRKIWFhQCkpaXhcrkAGDt2LCeddBIXXnghAFdeeSVTpkyhU6dObN26lRtvvBG73c60adN251JFREQOCKZp8cqXWyjxBclJ8lBeG8K/fillcx8iUr45Vs8KBwiXbsSd02OHbSW4HYzpnckZIzpqtEpEZDstDq4yMzNZs2YNmZmZbN26Fb/fz6RJk3j66aeZOHFii9r68ssvGTNmTOzx5ZdfDsCMGTO46aabeOONN4D6qYjbW7BgAUcffTQABQUFlJaWxo5t3ryZadOmUVZWRkZGBqNGjeKzzz4jIyOjpZcqIiKy32pIWOELhKkJRvCHIizfWonLbiNUVcTy5+6mYsUn251hkDBkEimjz8Iet+OAKSXOwR8n9+NXg9trtEpE5CdanNDi7LPPZt68eRx//PF8+OGHdOzYkUsuuYQZM2Zw4YUXctNNN+2hru49LVm0JiIisi9oCKb8oQilviDf/FDBZ+vL2VTupy5sYrcZ1NbVUbb4PxQufAEzHIid68rtRdqxv8ed3X2H7dsMSPe6mD6yM7PHdFdgJSIHjZbEBi0Orvx+P3/+85/59ttv6dKlCzfccAOZmZmsW7cuNo1vf6fgSkRE9if5xT7eW15EQUkNJb4Aa4p8+EMm4WiEaBQwAAtK3n+I6q/fip3n8CaTctQ5xPc/BsOwNdu20waWBaleJyO6tuPScT00FVBEDip7NLjamUAggMfjaa3m2oyCKxER2V/kF/t4YtEGympCmKbJkg3lVNdFaNi2ymaA025gmRZ1FcVsefT3WNEwycOOp/OxZ2O64vAForHNgw3AMMCwwOU0MACn3c74flmcf1Q3BVYictDZo6nYd+ZACKxERET2B5GIyecbSnnwg3VsrKwlGolS5AsR+V+UZIaDRCq34c7oTNS0cNlteNIyaTfpQuKyOuNo14WQ3YbDAqfNwDIg3eskNd5FKGKRHO+kfYqHbVUBhnZM5eoJvXE4mh/dEhGRersVXH355Ze89NJLbNy4kVAo1OjYf/7zn1bpmIiIiDRv7opC/vbuStaX1jbZANiyLOryl1Ax/xGsaJjc3z6E4YknYlo4bTaS+o8hwe2gNhTB47RjtxlkJruxLINI1KI2FCXB46Bzejx1YZNe2UmcekieAisRkV3Q4uDqhRdeYPr06UyYMIH333+f8ePHs2bNGoqKijjppJP2RB9FRESE+qQV9y1YywMLCghEzCbHwxVbqZj3CHXrvoyVVX36AqljZhK1LGzUb6GSEufA7bBx2iEdCYRNSnwByvwhSmtCGFike92AwYD2yYzvp82BRUR2VYuDq7/85S/cddddzJ49m8TERO655x66dOnC+eefT05Ozp7oo4iIyEEvv9jH059u4JnPNhL9yTEzHKBq8ctUL3kVopFYubvjQLz9xwL1SSmiUQuH3UYoatEuwc2vh3WgQ2p8LMtgnNOOAdSGo3hdDtqnxCkroIhIC7Q4uCooKGDy5MkAuFwu/H4/hmFw2WWXccwxx3DzzTe3eidFREQOZmsKfdz27gqWrK9oFFhZlkXtmk+p+OBRotUlsXJ7Qjqpx/yW+N6jMAzjf3XBMurXV9ltNkZ0TadDajw2m0FeWvxeviIRkQNTi4Or1NRUfD4fAO3bt2f58uUMGDCAyspKamtrW72DIiIiB7M1RdVc+5/v+H5bNYHwj1MBI1XFlM35J4EN3/xY2eYg6dATSR55GjZXXJO2XHaDNK+b/h2SOWNER41KiYi0shYHV0ceeSRz585lwIABnHLKKVxyySV88MEHzJ07l7Fjx+6JPoqIiBxUTNNiU0UtH+eX8PKSTawursE0f5K5wu4guHVV7KGn8xDSxp2PM71Dk/bsBmQluemSkcDhXdsxob/WUYmI7AktDq7uu+8+AoH6Xd3/+Mc/4nQ6+fTTT5k6dSrXXXddq3dQRETkYJJf7OO5zzby4ZpiNlfUEfppOsD/cSSkkXLENKq/epO0secS12NkbArg9pLcds4/uhtjemWS6HFqHZWIyB7UqpsIHyi0ibCIiLSFNUXV3PbOSr7+oZK6cITQ/xZYhUp+oOqTZ0mbdDF2T0KsvhWNYJkRbM6m+0waQEq8k79OHcCEfko4JSKyu/boJsIbN27c6fGOHTu2tEkREZGD3ppCH9e8uoxlm6t+3Ag4WEvloufwffUmmFHsCWmkHfu72DmG3YFhb/pRnuCykZfm5YrxvRjXN2tvXYKIyEGvxcFV586dt8s8VP/ubxgGlmVhGAbR6E8TxIqIiMj2TNNqlP58Q0kNf3l3FWuKarCo/3z1r/iQygWPE/VXxM6rW/81ZjiIzeneYdvZiS6mDM7llGEd6ZmtdVUiIntTi4OrjIwMXC4Xs2bNYsqUKTgcLW5CRETkoJVf7OO95UXkF/vYXFHLxopaSqtDsRTroeJ1lM99iODmFbFzDIeLpMNOIXnEVAyHq9l27QYM6ZjCJeN6ckS3dlpXJSLSBlocGW3ZsoXXXnuNRx55hEcffZQZM2bw29/+li5duuyJ/omIiBwQTNPi04JSnluykaLKOjZV1lFaE6IhCaAZqKHyk2fxff02WD+mXI/rcRhpY8/Fkbzj6X0OAwZ0SOIvJw+gZ5bWCouItJVflNBi3bp1PPLIIzz88MPccccdzJo1qzX71maU0EJERFpTfrGPOcsLeee7bWwo9VMXNtn+w9eyTLY+egGR8s2xMkdqLmnjzieu67Bm27QBdju4HXb65SZzy6/6axqgiMgesEcTWjSoq6vjo48+4qOPPiI9PZ3OnTvvblMiIiIHlO3XVJX4grz7XSFbKmspqgpQu91GwA0Mw0bSsCmUz30Qw+kmeeRpJB1yEobD2aSuHbDZDVx2g5xkD6N7ZHLmYR21b5WIyD6gxcHV0qVLeeSRR3jllVc4+uijueWWWxg3btye6JuIiMh+Z/s1VWX+AOtLawmGTRI9dspqwwBE66oBA3vcjwFRwuCJRKqLSRx6PI6kjCbtGkBGgouOaXEkxrk4tm8Wo7q3o0NqvNZXiYjsI1o8LdBms9GhQwfOOusssrKazv+++OKLW61zbUXTAkVEZHfkF/t4YtEGfij1U1hdx7aqAP7QjyNVlhmlZtn7VH70b+J7jiR90q59ZtoMSE9wMbB9MsM6pTG+X5ZGqkRE9pKWxAYtDq62T8XepDHDYN26dS1pbp+k4EpERFoqEjH5+3ur+HhtCZsrAtSGIkS3+4QNbllF+byHCBXm/6/EIPusO3Dn9tppuy67wZhemUwd3oE+2Um0T4nTSJWIyF60R9dcbdiwYXf7JSIickDKL/bx4hebeOnLjVTXRRslq4jWVlHx4ZP4v5vb6Bxv36OwJ7bbabtZiS4uPbYnpw3vqIBKRGQ/sNsJLUKhEOvXr6dbt27a60pERA4a2yer8Loc1IUj3DNvLUvWl1FVF43Vs8wovm/eoerjZzCD/li5M6Mzacf+Dk9e/x0+h9th0DMrkdunDqJ3rmZQiIjsL1ocFdXW1nLRRRfx1FNPAbBmzRq6du3KRRddRPv27bnmmmtavZMiIiL7goZkFQUlNdSFo0SiJt9vraTUFyKyXRLA4JaVlL3/AOHi9bEywxVPyujfkDh0MobN3mz7iR4Healx9MhMZPYx3bRnlYjIfsbW0hOuvfZavv32Wz788EM8Hk+sfNy4cbz44out2jkREZF9RUOyiuVbq7Asi1WF1Xy4qpitVSFCJmyfYD1ctqlRYOXtP4725z1M0vATmg2s7AZ0z4hnXO9Mjh+Yy0VjuyuwEhHZD7V45Or111/nxRdf5LDDDmuU2KJfv34UFBS0audERET2BaZp8d7yIsr9IRw2WLC6mMq6yA7reweMw7f0PTAjpI77HZ4OfRodtwMup414l51eWQn89shu5KbE4XU5lLBCRGQ/1uLgqqSkhMzMzCblfr9/h1kERURE9mdbKusoKKnB47SxKL+U6sCPgVVg4zICP3xHyugzY2WGYSNz6nXY4pKaHamy2SHN62Jiv2ymjdAGwCIiB4oWB1fDhw/n7bff5qKLLgKIBVSPPvooI0eObN3eiYiItDHTtCgoqaG4OkBRdYCqujCWBVZNKWUfPI5/5UIAPJ0HNUpSYfemNmnLDrhdNo7o1o7ph3fmiG7tNEolInIAaXFw9Ze//IVJkyaxYsUKIpEI99xzDytWrODTTz/lo48+2hN9FBER2etM02JRQSnzVxaxbFMVK7ZVE4iYWNEw1V++QdWnL2CF6mL1a5bNbTYDoA3wuu10zYjHabcztGMqV0/ojcPR4mXPIiKyj2txcDVq1CiWLl3KX//6VwYMGMD777/P0KFDWbx4MQMGDNgTfRQREdkrGtKsf7+tiv9+s5VvN1dSVRemNli/d1XdhqWUz32ISPnm2Dm2uCRSjppBwsBjm7TnMKBbppe+uckEwiZpXhenHpKnwEpE5ABlWJZl/Xy1g0tLdmEWEZEDw5qial75cgufrSslv6SGQMgEA0wLItXFVMx/lNo1n253hkHCkEmkjD4LR1xio42D7QakxDvplO4lO8mDx2mne2YC4/tlaX2ViMh+piWxQYtHro466ihmzZrFKaecQlxc3G53UkREZF8xf2UR985fy8byWqrqwpgNkZIF4dJNbPv3pVjhYKy+O7c3qcf+Dnd2dwAMAxr+VOlx2jiqZwaXjeuJ1+2IbTasLIAiIge+FgdXQ4YM4corr+Siiy7i1FNPZdasWRx22GF7om8iIiJ7lGlafLymmD+/vZISX4BgOIpp1a+Tati3ypHeAVd2D4KblmOLTyH16HPw9h+DYfw4tc+06qcAdmwXz/lHduOUYXkKpEREDkK7NS0wEonwxhtv8NRTT/Huu+/SvXt3Zs6cyVlnnUVWVtae6OdepWmBIiIHvvxiH88s/oHXlm6lui4cm9YX9VfiSkghut2nY6hkAzXfvk/KqDNwxCVgA6IWWIDXZWdIXgonDW3Prwa113oqEZEDTEtig1+85qq4uJhHHnmEP//5z0SjUY477jguvvhijjnmmF/SbJtScCUicuBpSFbhC4ZZubWaJz7dQH6xj2DYwgLMcJDqz1+l+vNXyDj5euK7DKG5D0gb4LQbGAaYlsWE/jncdcpgBVUiIgeoPbrmantLlizhiSee4IUXXiAzM5Ozzz6bLVu2cPzxx3PBBRdwxx13/JLmRUREWkV+sY/3lhfx9cZyVm2totAXio1MWZZFXf4SKuY/QqSqCIDyeQ/jmflPDLuzSVuGUb/HY9QySfe6mX10dwVWIiIC7EZwVVxczNNPP80TTzzB2rVrmTJlCs8//zwTJkyIbSh89tlnM3HiRAVXIiLS5vKLfTz+yXq+3VzJhlI//pAZOxau2Er5vIcJrPvqxxNsduK7HYJlRpsNrmz/G7FK8riYPaY7vXM0w0FEROq1OLjq0KED3bp1Y+bMmZx99tlkZGQ0qTNw4EAOOeSQVumgiIjI7jJNi2c/+4EPVxdT7AsS+V9cZYYCVH32MtVLXoVoJFbf02kgqeN+h6tdRwAMiE0NNACvy4bX7aBTupfzjuzKuL7Ze/V6RERk39bi4Gr+/PmMHj16p3WSkpJYsGDBbndKRESkNbz81SZe+2YL1XURTOqnANau+ZSK+Y8S9ZXE6tkT0kk95rfE9x4Vm4UBYLeB1+UgJ9nDCYNz6ZKRQLrXxdC8VE0FFBGRJlocXDUEVsXFxaxevRqAXr16kZmZ2bo9ExER2Q2mabG5opaP15bwwIf5scCqQc237/0YWNkcJB16IskjT8Pmqt+7sWG0KsFto19OMoM6pvDroXn0zNbmvyIisnMtDq58Ph8XXHABL7zwAtFoFAC73c5pp53G/fffT3Jycqt3UkRE5Kdi2f8CYXyBMFWBMF+sr2DZ5ko2V9RSVhMiGLEaZfwzDIO0ceez9bHZeDoOIG3c+TjTO/x4HHDYDbKT3PzfcX3p3z5Zm/+KiMgua3Fw9dvf/pZvvvmGt956i5EjRwKwePFiLrnkEs4//3xeeOGFVu+kiIjI9rbP/rd8SxXl/hBhs3Edy7KoXbkQW1wicV2Gxsqdae3JPeefONI7NJoCCPVp1rukx/OHSX0Y22f/37dRRET2rhbvc+X1ennvvfcYNWpUo/KPP/6YiRMn4vf7W7WDbUH7XImI7Lsasv8t2VDOD6X+JkEV1G/6Wz7vYYIbv8ORnEXubx/EcLh+tu1J/TK5bHwvembpvV9EROrt0X2u0tPTm536l5ycTGpqakubExER2WWmafHcZxtZsLqYbVXBpseDfio/eQ7fV2+CVR91RaqK8K9eREK/MTts1w6M7ZvBP6cNU6IKERHZbS3+BLnuuuu4/PLLKSwsjJUVFhZy1VVXcf3117dq50RERLb3cX4Jb3+3rUlgZVkWNcs/YMu/zsf35X9jgZUjJZuMqTfsNLByGHBE93SunthHgZWIiPwiLf4UefDBB/nss8/o2LEj3bt3p3v37nTs2JFPP/2Uhx9+mKFDh8a+fs7ChQuZMmUKubm5GIbB66+/3ui4ZVnccMMN5OTkEBcXx7hx41i7du3Ptnv//ffTuXNnPB4PI0aMYMmSJS29TBER2cesKarm5je+p8jXOLAKFa2j6Nk/UPb2nZj+SgAMh4vkUWeSO+sB4rsf2mx7diDBZeeEQTnccEI/umcqG6CIiPwyLZ4WeOKJJ7bak/v9fgYNGsTMmTM5+eSTmxz/+9//zr333stTTz1Fly5duP7665kwYQIrVqzA4/E02+aLL77I5ZdfzkMPPcSIESO4++67mTBhAqtXr1a6eBGR/VR+sY/rX1/O+tLaRuW+b9+n/L37YiNVAHE9R5J2zG9xJP+YkMJmgGmBxw7piW7aJbjpnpnICYNzGd09Q9kARUSkVbQ4ocWeYhgGr732Wix4syyL3NxcrrjiCq688koAqqqqyMrK4sknn+T0009vtp0RI0ZwyCGHcN999wFgmiZ5eXlcdNFFXHPNNbvUFyW0EBFpe6Zpsamitj6BxcfrWfJDOeFo4zrhykK2PXYBViSEIzWXtHHnE9d1WKM6duN/6dWT47j4mO70yUki0eNUinUREdklezShxd6yfv16CgsLGTduXKwsOTmZESNGsHjx4maDq1AoxFdffcW1114bK7PZbIwbN47Fixfv8LmCwSDB4I/TTKqrq1vpKkREZHesKarm4QUFfJRfSmVNiMj/ys1QXWyzXwBnSjbJo84EyyRp+IkYDmeTtpwOG53S4rl6Ym+lVxcRkT2qxcFVWlraTo+Xl5fvdme215AwIyur8QdhVlZWo2Qa2ystLSUajTZ7zqpVq3b4XLfddhs333zzL+yxiIjsroZRqnWlNXy8ppTXv9lCeW04djxaV03lwn9Tt+4rcmc90CjASh4xtdk24xwGndMTGN2zHb8e3kHp1UVEZI9rcXBlWRamaXLZZZfRpUuXPdGnve7aa6/l8ssvjz2urq4mLy+vDXskInLwyC/28eziH5i/qpjimiCB7TausswoNcvep/Kjf2MGfABULX6J1KNm7LA9j8Ng+mGdOGFoe5I9Lk3/ExGRvabFwVVBQQE33XQT//jHP/jd737Hdddd1+y+V79UdnY2AEVFReTk5MTKi4qKGDx4cLPntGvXDrvdTlFRUaPyoqKiWHvNcbvduN3uX95pERFpkfxiH7e8tYKvfqigLhQlut0q4OCWVZTPe4hQYX6szHDFYffueE/Fzmke/nh8P47tu+P3fBERkT2lxanY09LSuPfee/nqq6/Iz8+ne/fu/POf/yQajf78yS3QpUsXsrOzmT9/fqysurqazz//nJEjRzZ7jsvlYtiwYY3OMU2T+fPn7/AcERHZe0zTYlN5LasKq/mhzM8zizfw5YZyarcLrKK1VZS+cw+Fz1zZKLCK73sUub99iKThJzRp12GDCX0zeWTGIQqsRESkzex2QouePXvy2muvsXDhQq644gruu+8+/va3v7UoVXtNTQ35+T9+cK5fv56lS5eSlpZGx44dufTSS7n11lvp0aNHLBV7bm5uo+cYO3YsJ510EhdeeCEAl19+OTNmzGD48OEceuih3H333fj9fs4555zdvVQREWkF+cU+5iwv5NtNFRRVBwhFTDaV11EXNrEAmxWl8ut3qPr4GcygP3aeM6Mzacf+Dk9e/yZt2oCMRBeXHtuT04Z31PQ/ERFpUy0Orprbj6p9+/asWrWKqVOntmgE68svv2TMmDGxxw3rnmbMmMGTTz7J1Vdfjd/v57zzzqOyspJRo0YxZ86cRntcFRQUUFpaGnt82mmnUVJSwg033EBhYSGDBw9mzpw5TZJciIjI3pNf7OPueWv5akM5Zf4QkaiF+ZM6Vm0llR89hRUOAGC44kkZ/RsSh07GsNmbtBnntNE/N4lbTupP7+zWn54uIiLSUi3e5+rnRoCeeOKJX9ShfYH2uRIRaT2maXHLW9/zytdbqAlE2NGHjg2o+PwVKj98Em//caQePWOH66sS3TYO6dKOayb2pmd24h7ru4iIyB7d5+pACJ5ERGTvWbi2mFe+3IQvtF0WwGiEmm/n4O03BpvbC4AJpAz/FZ68Abhze+2wPacdpg7L4zeHdaJ7pgIrERHZd+zWmqtwOEw4HCY+Pp5wOMx3331Hjx49SEzUh5yIyMFu+z2rFq4p5b/fbG4UWAU2LqN87sOES38gXLGNtLHnxo5Zdiee3F7Njm7ZgOR4J3+Y2ItTtb5KRET2QS0OrubMmcPUqVOx2Wy8+uqrXHXVVXz33XckJibyzjvvcMQRR+yJfoqIyH4gv9jHc59t5KO1JWyrqqN2u6Aq4iulYsHj1K5cGCvzff02SSOm4kj4cYP67QMrA4h32Yh3OeiVlcA5o7oyto/W0IqIyL6pxcHVddddx8UXX0zXrl0544wzOPXUU/n444+54ooruO6661iwYMGe6KeIiOyjTNNiS2UdK7dV8/ySH1i+pZqaYIRQpD6wsqJhqr98g6pPX8AK1cXOc2X3IO3Y3zUKrOxAUpyDeJed7pkJTBncHq/bQbrXxdC8VByOFu8gIiIiste0OKFFfHw8K1asoHPnzrjdbr744gsGDhzI999/z+jRoykvL99Tfd1rlNBCRGTXNKRXX7a5iu82V1JaEyRiQcMnS92GpZTPfYhI+ebYOba4JFKOnE7CoPEYxo/BkgGkxDkY0CGFEV3SmdA/S2uqRESkze3RhBYulyuWbr1Hjx6kptZncmpYfyUiIge2hpGqFVurefTjfNYW+6kLRQj+ZCeOsnfvpWbZ+9uVGCQMmUTK6LOwxzUOmgwgI8HFpIE5zDyiC3mp8VpTJSIi+50WB1e9evXi+++/p1u3bixfvjxWvmLFCnr06NGqnRMRkX1LfrGP95YX8c2mchauKSG0k60NnZldYv935/Ym9djf4c7u3mzdBLedQ7umc9ZhneiU7m3tbouIiOwVLQ6u3n//fVwuV5Py9u3bc//997dKp0REZN+zptDH/Qvy2VRRy6pt1U0CK8uMNtrsN3HIcdTlL8Hb9yi8/Y9pNAVwe3FOGxP7Z3P+Ud00DVBERPZrLV5zdTDQmisRkR+ZpsXCtSX8fc4qNpb5qdkuAyBAuLKQivn/wp6QRvqE2bvcrtMGcS4Hxw3I5tZfDVCyChER2Sft0TVXAB999BF33HEHK1euBKBv375cddVVjB49eneaExGRfVR+sY/75q/l3eXbmqypMsNBqj9/herPX8WKhACDhIHjcef8/BTxeKcNj9NOx7R4Zh7RVYGViIgcEFr8afbMM88wbtw44uPjufjii7n44ouJi4tj7NixPPfcc3uijyIi0gbyi31c8+oy3vi2cWBlWRa1az9n22MXULXo+f8FVmBPSMWsq95pmzbA67LhdTvIS4vnorE96JmtqYAiInJgaPG0wD59+nDeeedx2WWXNSq/8847+de//hUbzdqfaVqgiBzsIhGTC579ivmriolu9ykRLt9C+fxHCKz76sdCm52kYSeQfMQ0bO74Ru3EOQxMCywsHDaDNK+bDqnxDMpLYeqw9vTM0nusiIjs21oSG7Q4uHK73Xz//fd0794441N+fj79+/cnEAi0vMf7GAVXInIwakix/v22Kt5cuoV3lxdh/u8TwgwFqPrsJaqX/Aeikdg5nk4DSR33O1ztOjZpL84OeeleMhI99MhMYFBeCr1yEkl0O2mfEqdU6yIisl/Yo2uu8vLymD9/fpPgat68eeTl5bW0ORER2QfkF/uY810h81cVsbrIR+1PklbUrl5E9eKXYo/tCemkHvNb4nuPwjCaBkk5SS6unthHwZSIiBxUWhxcXXHFFVx88cUsXbqUww8/HIBFixbx5JNPcs8997R6B0VEZM/KL/Zxx5xVfLaujKpAlOamM3j7HY3v67cIFa8n6dATSR55GjZXXLPt5SS6eGrWCE35ExGRg06Lg6vf//73ZGdn849//IOXXqr/K2afPn148cUX+dWvftXqHRQRkT3HNC2uePEbvt3i+7EsWEvdui/x9jkyVmbY7KQfdwmGzYEzvUOzbRlAx1QPD591iAIrERE5KO1WKvaTTjqJk046qbX7IiIie0HD2ip/KMI/3lsVC6wsy6J25UIqFjxGtKac/2/vvuOrqBL+j3/m9tSbhFRIQu8ICiKCKCrVimuvgB0X6+rq87M8tn2WZ1dX3V37uoJl7Q/YG6CANBVBpQkEgVBSIL3fMvP7A7lwIQkEkpCE7/v1Yl+5Z86cnBln53W/OTPn2GOS8KT3Du3nSupUa3sG4Hba6JgQxd3jetKrvYKViIgcnQ4pXImISOu0Lq+U95ZuY8OOcooqqlm2ZdfU6b4dmyic9Tw1W1aG6hZ9/RJpV/2t3va8EQ46JUQxpEsCFx6frhErERE5qjU4XMXHx9f68vJuhYWFh9UhERE5PKZpsbWokl93VgDQOTGKDt4I3l22hRfn/0pJpR+HzaCowodZU0Hxgjco++EjsPZMYhHR7QTiT7++1vZtQHq8h1tHdqdPB68mrBAREflNg8PVU089Bex6fOSmm27ikUceITk5ubH7JSIihyArv4w3vs1mya8FFJT7MLGIcNiwLMgtqyEQtLABQcuifNXXFM19GbOiOLS/Iy6V+FE3Etl1cFi7NsAwwOtxcFK3RG4Z1V2jVCIiIvto8DpXe4uJieGnn36iS5cujdmnI07rXIlIa5SVX8ZTs9ezdFMh1b4gFhY1AZPqwJ7bvAGYVWXk/d+j1Gxbvafc4SZ26EV4Tzgfw+Har22HDRIiXZw9oD2XD8mkW3JMcxySiIjIEdek61yJiEjLY5oWn6/MZcXWYsqq/JhA0LTwBcP/fmYBeKJ3pazfRPQYSsLp1+Pw1v4UQmq0gxO7JnHh8RkM65qox/9ERETqcNjhqr73r0REpOnsPetfaZWfhVk72V5chT98/V8sywq7VxuGQcLoyez88HHiT7+WiC6D6vwdp/VI5KHx/ciIj1SoEhEROYAGh6vzzz8/9HN1dTWTJ08mKioqVDZjxozG6ZmIiNQpK7+ML1bmsWFHOdWBIFuLKlm1rZR9BqqoyVlP4azniTvlKiI6HRsqdyd3Ie3apzEMW63tO2xwyfEZ/M/5/ZvwKERERNqWBocrr9cb+vnKK69s1M6IiMiBZeWXMW3hJgorfKTGugmUm2zIrwgLVsGqUornv0r5j18AFkWzX8Bz9T8x7Ltu+xaEgpUB2A2IdjvwmxapsR6evGQA/TPim/3YREREWrMGh6tp06Y1RT9EROQgmKbFFyvzKCivwe2wsWhDAduKqqjwBQGwzCDlP39J8bxXMavLwvYNlhfg8KaElRlAhNPGsRlx2GwG7aLdTDmtq2YCFBEROQQNDlfPPvss11xzDR6Ppyn6IyIi9dhWXMXyLUX8uqOcrUVV+IMWuwesarb9QuHs5/HlZoXqG64I4k66jJhB54ZGrULbgEiXjV6pMaR6I+iWHM2YvimaCVBEROQQNXgqdrvdTk5OTpte20pTsYtIS7N7YeB3f9jKK4s2UV4TwPzt7h2sLKFo7nQqVswK2yeqz6nEnXYNjuiE/dqLdtkY1jWRK4Z2JCXWQ5TLoYWARUREatGkU7EfxrJYIiJyCHYvDLx4w042FlRSvc90gIVfPEPlukWhz86kTiSMnowno19YPRvgtBtktIvk/rN6c0r3ZIUpERGRRqR1rkREWrCs/DKenLWe5ZuL8JkmQXP/P3DFnXwVlVnfYjjcxJ18JTEDz8Kw2cPq2A1IiXXTK83Lf43rRY9UPfonIiLS2A4pXH3xxRdhswbu7dxzzz2sDomIHO12PwKYtaOcN7/dzHcbiwiYu0arqksKCJQX4k7tFqrvTMwg8Zw/4snoiz1q/xn+PA4bfdrHMrJXCmP76Z0qERGRptLgd65sttrXRIFdC1MGg8HD7tSRpneuRKSp7b0A8N7vO+1+BHDJrwXklVZTVOnHssBlBCn94WPy5r2OPTKO9tc+g+Fw1dm+DXA5DIZ0acflJ3SkT/tYvVMlIiJyCJr0nSvTNA9cSURE6rTvAsAeh52uSdH0Soth5vJt/LSlGJvBrsWoLKja/DPbZj+Pf2c2AAFfFaVLP8R74oWhNm0ABpjWrskqMttFMrxbEneP7YXDUfcfxURERKTx6J0rEZFmtPcCwGleDxFODzvKali0YQcf/byNKl8Ql90g2uNkR24OOz5/kfI18/dqwSBmwBii+48Oa9f67X88ToMeKTH0bu/l4sEZClYiIiLN6JDC1bx583j88cdZs2YNAH369OGPf/wjJ598cqN2TkSkLdm9AHBhhY/uydEUVfr4JaeMwkoflT4/eaU+DAMyY52s+/Jt1n3+Cqa/OrS/O607yWMnE9GhF/6gFZqK3QYYBriddvp3iOWUHslar0pEROQIaHC4ev3117n66qs5//zzufXWWwFYuHAhI0eOZPr06Vx++eWN3kkRkbZgW3EVG3aUk+b1UFTp48ctxVT5gkR7HNhtLgrKfRRvXsv8T/9Gzc6tof3sEbHEj5hIVP/ROGw2PA47phnAsiDG46BLYhS90mI4tVcyfdO8erdKRETkCGlwuPqf//kf/vrXv3LHHXeEym699VaeeOIJHn30UYUrEZE6VPgCVAeCRDg9/JJTRpUvSEKUC8MwwAritNtwREbjK8rbtYNhI/H4s8gYNZEqeyQ1ARML8JsmboeNXklR3HxaD01WISIi0kI0OFz9+uuvnHPOOfuVn3vuudx7772N0ikRkbZk98yAuSXVBE2L/LJqCit9RHscu4IV4HLY8DjtuBPakzjsQio3/Uj/i/5AIKETvkAQpwUum0FirIu4CDcd4iO4+fRu9EjRjKYiIiItRYPDVUZGBnPmzKFbt25h5bNnzyYjI6PROiYi0hbsPTNglT/AlsIqKmoCmJZJedZy1s9+i+FTHsPudOOy24jxOHGdcjmBU64gKiEKwzDILzUJmCbeCAedE2MYmBmvd6pERERaoAaHqzvvvJNbb72VH3/8kWHDhgG73rmaPn06f//73xu9gyIircW+a1dV+QO8smhzaGbA9q4IIpx2vl66kqwPnqF8/bcArPz0FTJGTcIb6eLYzCiy8svYWlRFaVUAh92gvddDv3Qvo3qn0DtNjwCKiIi0VA0OVzfddBOpqan87W9/45133gGgd+/evP3224wfP77ROygi0hpk5Zfx+YpcVmwrocIfINJhp9IXxDAMjsuMwzAMfDXVrPr4ZVa8/SJBvy+0b8HG1QyMdtEtJZb4SCe+QJDh3ZI4sWsCNsOgc2IUGfGRClQiIiItnGFZlnWkO9HSNGQVZhGRrPwynpq9nnV5ZQR/mx89EDQprPDRLtrN8G6J5Pz8De8/92cK87aF9nPGtOPYC6dwyrjzSI71UO03ySmpJiHKxdUnddJjfyIiIi1AQ7LBIS8ivHTp0rB1rgYNGnSoTYmItFqmafHGkmx+2lKMy7HrnSmn3aCs2o9ZDjnZG/nX9HvJX70ktI/N7mD4eRNIO/UKemUmU1oVYHNBJW6HnWM6ePU+lYiISCvV4HC1detWLrvsMhYuXEhcXBwAxcXFDBs2jLfeeov09PTG7qOISIu1paiSJRsLsRkG7XZPqw54HHYcpo+1L91GsLo8VL/7sSfyuykPEJXSkeJKP1ef1BmbYYTe09L7VCIiIq2XraE7XHfddfj9ftasWUNhYSGFhYWsWbMG0zS57rrrGr2DnTp1wjCM/f5NmTKl1vrTp0/fr67H42n0fonI0ck0LbYUVvJLbilbCiv5dUc5xVU+4iKdoWAFu6ZWj4mOJmnYhQBEJ6Qw4f6nmPyX6aRkdiWnpJpuydFkxEeSkRBJr9RYMhL0XpWIiEhr1uCRq3nz5rFo0SJ69uwZKuvZsyf//Oc/Ofnkkxu1cwDff/89wWAw9HnlypWMHj2aiy66qM59YmNjWbt2bejz3l94REQOVdikFT4/Nmy4HDZq/EEsy6I0dxMR3iScEbumUE+IclJ+0vkELIOzL5tEn8xkymsCofeqxvRNUZgSERFpQw5pnSu/379feTAYpH379o3Sqb0lJSWFff7f//1funbtyogRI+rcxzAMUlNTG70vInL0ysov48lZ61idU0qlL0i1L0jANDEtqKksJ/vzf1GwZCbdT7+YYy+8BQCP005kRASdTr8MpyuCTTsr9F6ViIhIG9bgcPXYY49xyy238Mwzz3D88ccDuya3uO2223j88ccbvYN78/l8vP766/zhD3+odzSqvLycjh07YpomAwcO5M9//jN9+/ats35NTQ01NTWhz6WlpY3abxFp3UzT4tmvsvhm3Q58QZOAaYEFDjtUrP6GnFkvESwvAGD9V+/QadhZRCR3pKzKj91mY2zfFCad1Ikqf1DvVYmIiLRhDZ6KPT4+nsrKSgKBAA7Hrmy2++eoqKiwuoWFhY3XU+Cdd97h8ssvJzs7u85RssWLF7N+/Xr69+9PSUkJjz/+OPPnz2fVqlV1Trbx0EMP8fDDD+9XrqnYRQRg3rp8bnljOZW+AJYFpgWBnZvYOesFqrNXhOoZdifJwy+m15grsLsjsBsGPVJjuH1Ud41SiYiItFINmYq9weHqlVdeOei6EydObEjTBzR27FhcLhcfffTRQe/j9/vp3bs3l112GY8++mitdWobucrIyFC4EhFM0+LG15by1dp8bIZBoLKcogVvUPLDR2CZoXpR3U7gmAtvgdhU0uMjSYhy0b9DHGP76fE/ERGR1qxJ17lq7MB0sDZv3szs2bOZMWNGg/ZzOp0cd9xxZGVl1VnH7XbjdrsPt4si0kaYpsW24ioqfAGKK338klsKFlSs+Yb8Wc8TrCgO1XXEpdJu1A3E9xrCoF4pVPgCXDw4k37tvXr8T0RE5Chz0OHqYN9DaqqRnmnTppGcnMxZZ53VoP2CwSArVqzgzDPPbJJ+iUjbYZoWCzfsZM6aPHJKqrEZBiWVfgor/NgN8JcXhIKV4XARP+xivCecj2l34bDZ8DjtuBx2+rX3kpEQeWQPRkRERJrdQYeruLi4eieRsCwLwzDCpk1vLKZpMm3aNCZOnBh6z2u3CRMm0KFDB6ZOnQrAI488woknnki3bt0oLi7mscceY/PmzU2yBpeItB1Z+WW8sSSbr9fm75p4wu0gKdqNzYCgaYIB3oFnU/LjFzgTOpA06nqc3pRdk1sAidFuymsC9E+Po0NcxBE+GhERETkSGvRY4HvvvUdCQkJT9aVOs2fPJjs7m2uuuWa/bdnZ2dhse9ZCLioq4vrrryc3N5f4+HgGDRrEokWL6NOnT3N2WURakaz8Ml5esJGlm4swLejgdfPr4k/5NW8b3c+4Bo/TQbU/gM3pIHPC45iuSCwLgr8FK6fNoF2Ui3bRbq1dJSIichQ76AktbDYbubm5JCcnN3WfjriGvLQmIq2baVo8N3cD320qIK+kmqqcLFa+9ySFG1eBYWPwbc9hS+5CeXWAoGkR7XZQ7Q9SEzAJBC1sNoM0r4dLB2cwtl+qJq8QERFpY5p0QgsRkbZkW3EVG3aU4wlWsvKdJ9n27cew+29OlknZ+u9I7dAdt8NOQXkNboeNSJedoLnrUejOiVFcf0oXTuqaqBErERGRo5zClYi0SXvP+Fffwr2lVTX88MU7fP/us1SVl4TKY1M7cdylfyCp5yCKK31kJkQS5bSTGOPGAiJddk21LiIiImEOOlwZhlHvhBYiIi1FVn4ZX6zMY8OOcqoDQTwOO12TovcLQkuWLOGGyb9nxU/LQ2V2dwR9z76WHqdfjM3uoCYQ3LW+lWlxVv80zhnQnkp/sN7AJiIiIkengw5XlmUxadKkA64H1dB1qEREGlNWfhnTFm6isMJHmtdDpCuCSl+AldtL2F5SxdUndaJbcgxz587ltNNOC9u33ylnkTr6OqyIOPyWgcM0Karw4XbYSY+LZGy/VDLbRR2hIxMREZGW7qDD1ZFaPFhE5GCZpsUXK/MorPDRPTk6NNoe43ES7XawPr+cL1fl0SUxmpNPPpmBAweybNkyevbuy+DL7yS6U38inDa2FVexs9xHRU2ACKed4d0TuXxIph7/ExERkXoddLiaNm1aU/ZDROSw7Z6cIs3r2e8x5p3bs0lr156s/HK2FVeRkRDJs88+y7fffsvvf/97NhVWhR4lbBflIi7CRfu4CEb2TmaYJqsQERGRg6AJLUSkVdt74orckmqq/AHau/Ys4ltakM9HLz3Gsq8+YsqTb2ImdqPCFwBgyJAhDBkyBIBuyTF0OTX6oCbBEBEREamNwpWItFpZ+WV8vjKXn7eWUFTpI2halFX58TjsdPC6WPDBf/j8tX9QU1kBwIynH+GiR14lylX7rc9mM8hIiGzOQxAREZE2ROFKRFqlrPwynpq9nhVbSyit9uMLBLEsCJgWWT9/R+lXL1C49ddQ/YhoL12Hn0u3pGg6xEXU07KIiIjIoVG4EpFWYe/H/yKddv6zZDNLNxVSURPAZhhEu53UlOSz7dMXKV01L7SfYRgMHHMh/cbfSIfUZMb1b69H/URERKRJKFyJSIu377pVwaDFsuwiqv1BHHYbHhvkLHiHLV+9humrDu0Xm9GL0df9PzJ79qdbcjRj+mrBXxEREWk6Clci0qLtvW5VaqyHaNPB+vwySqr8ALgcNgybQeHqhaFg5YiMJfH0SQwdeyE3j+5J198eBdSIlYiIiDQlhSsRabH2XreqXZSTtbllFFb6KKnyEQhaWECVP4jb4aTzubew4rlbSBl8Ju1Pn0iNIxK7w06q16NJKkRERKRZKFyJSIu1e92qCKeNn7aWUOULEmEPUrTwLfzxPbB36Eu1z6TGaRLdoTsD//g6rth2VPiCuAyD+EhnnTMDioiIiDQ2fesQkRZn9+QVK7eXUFhRQ5U/SJUvSM3GpXz7zlOU79hGREonkq96CtPuoLImgMtuwx7Tjiq/iWVBTISTAenxmhlQREREmo3ClYi0KHtPXlFQUcOa7aWU7thKwex/sXP14lC96h1bsOWvxUrrS03AoqwmgMNmYAGRLjvHpHsZ2y9F71mJiIhIs1G4EpEjau8p1neW1fDpihyKKv1EOG2Ul1eyedYr5C98B4L+0D5J3Y/luEv+QFVMB2r8Qcqr/TjtBi67jXZRLk7smsjlQzI1M6CIiIg0K4UrETli9h6lqvIH2VxQQcC06JEczTezP2fl//2D6qLcUH17dAKZ426g+9BxBC0Lr8tBt8woKmoCjO6bSlKMmy6JUaTHR2rESkRERJqdwpWIHBG/5JTyty/XsqOsmvhIF5FuO+U1AbAsZr70JNvn/idU17DZiTvhPBJOugRnRDQFlT66J0fTNSmaggofx2bGc8HAdAUqEREROaIUrkSkWez9+N+yzYU8/VUWO8p9WJYFgN0wMGzg9bjw9ByOMf8tLDNIcq/j6XP+bZR5kqnwBWkX5cLpsNEhzkNBhY+EKBdj+urdKhERETnyFK5EpMnt/fjf2rxS1uWW4zd3hSosC6pKsKLisEwoqfJjb5dJyukTycjsRKfBIzEMA3eVD8p9uBw2Kn1BiioDDOoYz5i+KXq3SkRERFoEhSsRaVJZ+WW8vGAT24orqfEHycovJ/BbsPLv3ELRnBcIlOSTfu0zWA4nvqCJzYDYIReQnByNYRhYlkUguOtdrA7xERRW+LhxRBeO75igESsRERFpMRSuRKTR7X4EsKzaz7SFG/n210LKa/yUVgUIWGDWVFKy6C1Kl34AZhCAku/fJ27oRewe0AoELfxBk5pAkPLqABEuO12Soiio8DOoY4KClYiIiLQ4Clci0ih2B6o1uaV8/2sBmwoqWZ9XxpaiKkwLHHawLIuK1fMp+vrfBMsLQ/s6vMm4kzoCYDPAaTfwm7CjrIb4KEiKcdPe66Ggwq93rERERKTFUrgSkcO2+52q5VuKWLmthMKKGnzB8DoVuZsonP0CNdkr9hTanXiHXEj8sAtwOCMImBY2A9rFuEmK8dAlMYrymgB2wwAMjung1TtWIiIi0mIpXInIYcnKL2Pawk0UlNfwa34ZeaU1WHttN2sqKF7wBmU/fASWGSqP6HYC8adfjzM+DcOA4G/PA0a67HRKiGJYt0RuOLkLOaXVVPgCRLkcdIiL0IiViIiItFgKVyJyQHtPo753yDFNiy9W5lFY4aPaH+DXgqr99g1WFFO2/JNQsHLEpRE/6gYiuw4O1bEbEAScNhvdU6LpmBjFmL4pOBw2MhIim+swRURERA6LwpWI1GvvadSrA0E8Djtdk6IZ3TeZHaU1fLZyO4UVPnJKamrd35nQgdgTzqfs+w+IHXoR3hPOx3C4wuoEzV3vWfVKi2FU71Q9+iciIiKtkmHtXsFTQkpLS/F6vZSUlBAbG3ukuyNyxOx+5K+wwkea10Oky0FFjZ8V20rYXFBBSVUAX3DPLSRYXU7pdzPxDr0Ym9MdKjd91ZhVpTi8yaEyg12BKinaxcCO8Zx5THv6dfDq0T8RERFpURqSDTRyJSK12vuRv+7J0QBkF1by05ZithZV4t/z+hSWZVL+82yK503HrCrFsNmIG35FaLvN5cHm8oS173bA4M4J3HhKV4Z1TVSgEhERkVZP4UpEarWtuIoNO8pJjXWzpbCK5VuK2F5cud8sgDU56ymc9Ry+nHWhsrJlnxB7wgX7BardbEB7bwQ3ntKV4d2TmvAoRERERJqPwpWI1KrCFyC/tIofs6vJLqokYIZvD1aWUDz/Vcp/+hL2mh8wstfJxJ92bZ3BymmDk7olEulykBjjrrWOiIiISGukcCUitfpyVS7fbiwMe/wPwDKDlP/0BcXzX8OsLguVO9tlEj/6RiI6DqizzUiXjVG9U0iMdlNc6SfKpVuQiIiItB36ZiMiwJ7p1suq/Xz9Sx7PfJ21f7CyTPLe/H/UbF0dKjNcEcSddDkxg87BsNd+S3EAfTvEckqPJAzDYH1+Ocf8NnmFiIiISFuhcCUioenWl28pZPX2UnJKwhcC3s0wbHg6DwyFq6i+pxF36tU4ohP2q2sDMCA11sXo3ilER7io8AXJKakmIcrFmL4pmsRCRERE2hSFK5Gj3O7p1tfklLImp4Qq/55YZZlBMINh61J5TzgfX24WsYPPw5PRr9Y2o1x2+raP5az+aZRXB9mwo5ydFRW4HXaO6eDVOlYiIiLSJilciRzFAgGTt7/fwjfrd7ClsCpstKp6y0oKZz1PRJdBxJ96dajccLhIPv/+Wttz2Qx6pcZy79m9OD4zAYfDFnrcsMIXIMrl0DpWIiIi0mYpXIkcpbLyy3hh7gY+XpFD1V4vVwXKCymeO42KVV8D4C/YSvQxo3C2y6i1HZd917TqcZFOeqfGcu0pXcJGpWw2g4yEyKY9GBEREZEWQOFK5Chjmhbz1+fz+Be/kJVXTvVv61ZZwQBlP3xE8cI3sHxVofqupI5YwUCtbTltcEKndiTHeuiWHK3H/UREROSopnAlchTJyi/jma+y+GTF9rDFgKs3/0zhrOfxF2SHymyeaOJOmUD0gLEYNvt+bTltBg+e04fjOyfocT8RERERFK5E2jzTtNhaVMnCrJ3857ts1mwrZXeuCpTupOjrf1P5yzd77WEQPWAscadchT3SW2ubLrvBjSO6cuXQTk3dfREREZFWQ+FKpA1bl1vGv775lR82F5JXWkPF3sNVQOUv34QFK1daDxJGT8ad1qPW9iKd0DUphltGdmdM37Qm7buIiIhIa6NwJdLG7J6d76tf8nhl0WZyS6oJWib+wP4rV8UMOofyn78kWFlC3IhJRPcfhWHY9quXGOXiosEZnNYziYEZ8Tgc+9cREREROdopXIm0IbsXA162uZBvNxVS6QuCBXYb+Erzqd78M9HHjArVN+wOEsf/F/boBOwRtU9EkRnv4cUJg+mVFttchyEiIiLSKrXoPz8/9NBDGIYR9q9Xr1717vPuu+/Sq1cvPB4PxxxzDJ9++mkz9VbkyDFNi2/W7+BvX67j89U5LN9STEVNENOCYMDHzgVvs/1fN1Hw2T/w5W8M29eV1LHWYBXhNDihUxwvTVKwEhERETkYLX7kqm/fvsyePTv02eGou8uLFi3isssuY+rUqZx99tm88cYbnHfeeSxbtox+/fo1R3dFmt26vFLe/i6bD3/aTnGln72WrKJqw/cUzn6RQHFOqKx44Rsk/+6+ettMi3Zy+YmdOKN/mqZWFxERETlILT5cORwOUlNTD6ru3//+d8aNG8cf//hHAB599FFmzZrF008/zfPPP9+U3RRpdqZp8e4PW/j77HXkldYQ3OuVKn9xLkVzXqQq67s9hYaNmIFnE3fyFXW2aTPg2Awvfxjdk2FdEzW1uoiIiEgDtPhwtX79etq3b4/H42Ho0KFMnTqVzMzMWusuXryYP/zhD2FlY8eO5f3336/3d9TU1FBTUxP6XFpaetj9FmkqpmnxTdYOXlu0iXnrd+LfK1WZ/hpKl7xHybfvQdAfKndn9KPd6Mm4kjqx/7QWuzhtcGrPJO45o7dGq0REREQOQYsOV0OGDGH69On07NmTnJwcHn74YU4++WRWrlxJTMz+X/5yc3NJSUkJK0tJSSE3N7fe3zN16lQefvjhRu27SFPYvQjwl6tzqfCZYdv8xbnkvXUfwZK8UJk9OoH4064hsvcIDGPXKNTuwSgbkBDlJMbjpFtyNJcOyWRE92SNVomIiIgcohYdrs4444zQz/3792fIkCF07NiRd955h2uvvbbRfs//+3//L2zEq7S0lIyMjEZrX6QxrMsr5b4ZP7MsuyTsEcDdHLFJ2NxRuxYIttmJPX483mGXYnNHhupYgNNmkBjt5qTu7bj6pM7EuJ10iItQqBIRERE5TC06XO0rLi6OHj16kJWVVev21NRU8vLywsry8vIO+M6W2+3G7XY3Wj9FGtu63DJue3s5v+SUhx7rs8wghs0eqmPY7CSMnkzJgjeIH3UDrsTwx2cdNoj1OOicGE3P1BiuGd5Zj/+JiIiINKIWPRX7vsrLy9mwYQNpaWm1bh86dChz5swJK5s1axZDhw5tju6JNIms/DKmfraa9XkVWIBlWVT8soBtL1xPTc76sLqe9D6kXPqnsGDlMMDjMEiOdnNcZjwje6coWImIiIg0gRY9cnXXXXdxzjnn0LFjR7Zv386DDz6I3W7nsssuA2DChAl06NCBqVOnAnDbbbcxYsQI/va3v3HWWWfx1ltvsXTpUl588cUjeRgih8w0LT5fkcva3DJMy8K/cwuFs1+gevOPABTOeo7Uqx7HMGr/O4kNSIt1cWK3JEb3SaV3WqweARQRERFpIi06XG3dupXLLruMgoICkpKSGD58OEuWLCEpKQmA7OxsbLY9XyqHDRvGG2+8wf3338+9995L9+7def/997XGlbQKgYDJsi1FFFT4iI90khrrYePOCuav30FVZTmFX71GydIPwAyG9rF5YjBrKrF7ovdrz27AiB6JXDO8i6ZVFxEREWkGhmVZdc3MfNQqLS3F6/VSUlJCbGzske6OHAXmrMnj5QW/si6/nGpfENOycNptOGwG25bNZsfsfxMsLwzVt3tTSBh5PRHdhoRmAdxbjNvB9ad05ubTuitUiYiIiByGhmSDFj1yJdLWmabFO0uz+cvnv1BaFcCyds3oZwG+Hb9SNOt5qres3LOD3Yn3xAuJHXIhNuf+k7DYDeiaFMVTlxxHnw7eZjsOEREREVG4EjkiTNNi/vp8Xpy/gSUbijBrqVP4xbPUbFsd+hzZ7QTajbwBW1zts18mRjvpnx7Pf43rRY9UTVYhIiIi0twUrkSa2S+5Jdw3YwXLt5Rg1vNQbvzI68h99U4ccal0GDcZV5fjMU0Lux38gV11LMDtsNE7LYZRvVMZ2y9FswCKiIiIHCEKVyLN6OUFG5j6yS/49wlVvrxfsSwTd2q3UJk7rQdJF/43ER0HkBwXBYZBcaUfm2HgtFskxrgZkBHH+AEd6NNeswCKiIiIHGkKVyLN5L4ZK/jPd9lhZcHqckq+eY2y5Z/hTMwkbdLfwxYGjuw6GACXw067aBdRLgfp8REETYtbR3XnhE7tFKhEREREWgiFK5EmFAiYfLupgKfnrGfxxqJQuWWZlP88m+J50zGrSgHw79hExco5RPcfs187US47FTVBUr0eotwO+qfHKViJiIiItDAKVyJNwDQt3vp+M0/OWs+Ocl/Ytpqc9RTOeg5fzrpQmeH04D3pUqL6nrZfW267gS9o4XLacdhstIt2M6ZvioKViIiISAujcCXSiEzTYt66fP77/ZVsKa4O2xasLKF4/muU//QFu6ai2CWy18nEn3YtjtjE/dpz2CDG48DlsJERH8HAzHjG9NWkFSIiIiItkcKVSCP5JbeE299Yxi/5lfttq8z6loJPnsKsLguVOdtlkjB6Mp6O/Wttr2tiJFcN60ifNC/RHgcxbqcmrRARERFpwRSuRA6TaVr8Y846/j4ni7pmVnfEJmPWVABguCKIO+lyYgadg2Hf//+CTht0T43h2csH0ikxugl7LiIiIiKNSeFK5BCZpsW89fk88uFqNhaEj1ZZloVh7BlhciV3Jmbg2ZjVZcSdejWO6IRa24x2GQzIiOekbklkJkQ1af9FREREpHEpXIkcgl9ySrlvxk/8sKU0rNwyg5Qt/5SK1XNJvfx/MezO0Lb4kddhGLY620yIctInzUvHdpGasEJERESkFVK4EmkA07R4+uv1PPd1FlWB8IcAq7espHDW8/h3bAKgdOmHeIdcENpeX7BKjnHRPz1OE1aIiIiItGIKVyIHIRAwmfHjVqZ98ytr8irCt5UXUjx3GhWrvg4rD5btrLdNt8NgRLd2jO6XRt8OXk1YISIiItLKKVyJHMAXq3J4+MNVbC+pCSu3ggHKfviI4oVvYPmqQuWulK4kjL4Jd4dedbbZJy2a/3dmH07qmqgwJSIiItJGKFyJ1ME0LR7+aBWvLd6Muc+26s0/73oEsCA7VGbzRBN3ygSiB4zFsNlrbdPjgEuPz+TKYZ306J+IiIhIG6NwJbKPQMBkxvKtPD93Pb8WVO+/vbyQvHf+G8zAbyUG0QPGEnfKVdgjvXW2mxzt5L/O7MN5x3bQaJWIiIhIG6RwJbKXWatzeWDGCnLLfXXWcUQnEDv4PEq/fQ9XWo9djwCmda+zvtsO/Tp4Ob1XioKViIiISBumcCXym1cXbeKRj1cR2OcZwKpNP+JJ74PhcIXKvMMuwZmYQVTf0+qdBTDaCSd1TyExxs3YfqkKViIiIiJtmMKVHLVM02JLUSW/7ihn1uoc3vhuW9j2QEk+hV/9i6p1i/EOv4K4ky4LbbO5IojuN7Le9r0eO4M7t2NARpymVxcRERE5CihcyVFpXW4ZL8zPYt76nRSU+dh7xSor4KPk2/+jdMl7WIFdMwSWLnmX6GNG44hNPGDbfVKjGda1HSd0SaR3WqymVxcRERE5SihcyVFnzpo8Hnh/xX5TqwNUbvieotkvEijOCZXZouKIP/Vq7DHt6mzTbkBqrJsbRnTl9F4pClQiIiIiRyGFKzlqmKbFZyu3cdubPxGwwrf5i3IomvMiVRu+31No2IgZdA5xwy/H5o6qs90uiZGM7JXMRYMz6JES20S9FxEREZGWTuFK2jzTtFi0YSd3v/dTraNVJYvfoXjhmxD0h8rcGf1IGD0ZV1KnOttNjXFy46ndGNU7VSNVIiIiIqJwJW3butwyXvxmAzN/2EawjjpWMBAKVvboBOJPu5bI3qdgGLWHJYcNeqfG8tjF/emVWve6ViIiIiJydFG4kjZr9upc/vTxajYVVtVbL3bIBVSsmUdktyF4h12KzR1Zaz0DyIj3cGxmPDef1p0eqZr9T0RERET2ULiSNsc0Ld5ems2jH62m0r9n0SrTV03J4rcx7A7ihl8RKrc53bS/+mkMh7PW9gwgMyGCY9O99Ezzalp1EREREamVwpW0GaZpsXDDTt5duoVPfs4h+NukFZZlUbl2IUVfvUSwbCfYHET1ORVnQofQvnUFqy6Jkbw08Xh8QYsol0PvVomIiIhInRSupNUzTYt56/N58ev1LN9aSvVeUwH6d26hcPbzVG/+ac8OBtRsXxsWrmrTMSGCFyccT5ckjVKJiIiIyIEpXEmrFQiYfPjzdv41bx1r8sLfqzJrKilZ9BalSz8Ac89UFp7Og0gYdUO9wcptNxjbL4VbR/bQ438iIiIictAUrqRV+nJ1Do9/tpZ1OyrCyi3LonLNPIq+fplgeWGo3O5NIWHk9UR0G1LnLIB2A648MZOrh3Ums12UHv8TERERkQZRuJJWxTQt7pv5I29+v73W7RWrvqbgkyf2FNideE+8kNghF2JzuutsNznGzZ/G92NMv9TG7rKIiIiIHCUUrqRVME2Lr37J4+Y3fqA6UHe9qN4nU7L4bQKF24joNoT4kdfjjKs7MEU7DY7r2I5JJ3ViZO+UJui5iIiIiBwtFK6kxVuXW8bNbyxlXX5lWLllmfhyN+BO6x4qM+xO2o27BctXRUTXwXW22SM5iouPz6B/RhwDM+JxOGxN1n8REREROTooXEmL9uFPW7j9zZ8x9yn35f1K4aznqNm+lrSr/4ErqVNomyejX53tGcBVJ2bw0LnH6J0qEREREWlUClfSIpVX+Dj9ia/IrwiGlQeryyme/xrlP34G1q7IVTjreVIum1rnRBW7xbrt3DGmB1ef1KXJ+i0iIiIiRy+FK2lRAgGTK/+9mCUbi8PKLcuk/OdZFM97BbOqNFTuSEjHO/TieoNVlNNgRK9kbj29B73SYpuq6yIiIiJylFO4khbBNC2mL/6VRz/6BWufbTU56ymc9Ry+nHWhMsPpwTvsUmIHj8ewO+ts97SeiTx0bj8y4iP1GKCIiIiINCmFKzmifL4g05ds4h+z11HuC3+zyvRVUfTVS5T/9CXsFbkie51M/GnX4ohNrLNdpwEzp5xEv/S4Juq5iIiIiEg4hSs5YqYv+pUnv1xPSR1zqxsOFzXb17I7WDnbZZIwejKejv3rbNMOXHdKJveM66eRKhERERFpVgpX0uwqK/1c+K+FrM6pqLeeYbOTMHoy+e89Qtzwy4kZeDaGvfZL1mbAdSd35q5RPXG57E3RbRERERGReilcSbMxTYvJ0xfx5bri/bYFK4opmjedmIFn407tFir3ZPQj/ffTsLmj6mz37GOSeeqSQVqrSkRERESOKIUraRbrcssY+9T8/SarsMwgZcs+oXjBf7BqKvAXbCH1yscwjD1Bqa5gFeMyeHPyUPq1j2/CnouIiIiIHByFK2lyM5Zu5g/vrdyvvHrLSgpnPY9/x6ZQmb9gK4GCbTgTM+pt89z+Kdw6qifdkmMau7siIiIiIoekRYerqVOnMmPGDH755RciIiIYNmwYf/nLX+jZs2ed+0yfPp2rr746rMztdlNdXd3U3ZW9mKbFlqJKLnl+Ibll/rBtgfJCir9+mYrVc8PKo44ZRfyIidij6h6JinLAJ7ePIDMhShNWiIiIiEiL0qLD1bx585gyZQqDBw8mEAhw7733MmbMGFavXk1UVN3v4MTGxrJ27drQ5/oWmJXGty63jBfmZzFj2fawxwCtYICyHz6keOGbWL6qULkrpSsJoyfj7tC7zjYN4K8X9uWi4zs1Wb9FRERERA5Hiw5Xn3/+edjn6dOnk5yczA8//MApp5xS536GYZCamtrU3ZN9mKbFa99u5LHP1lHuC+63feenT1K5el7os80TTdwpE4geMBbDVvcMfxNOzOS/z+6rCStEREREpEVr0eFqXyUlJQAkJCTUW6+8vJyOHTtimiYDBw7kz3/+M3379q2zfk1NDTU1NaHPpaWljdPho8gvOaX8/vXv+bWg7scvYwed+1u4MogeMJa4U67CHumts77HDvee1YcJwzo3QY9FRERERBqXYVnWvhO4tUimaXLuuedSXFzMggUL6qy3ePFi1q9fT//+/SkpKeHxxx9n/vz5rFq1ivT09Fr3eeihh3j44Yf3Ky8pKSE2NrbRjqEtMk2L//n0Z/69YGtYuRX0E6wowhGbHFZe8u3/4ck8BndajzrbjHAYjOiZxO2jetIrTedfRERERI6c0tJSvF7vQWWDVhOubrrpJj777DMWLFhQZ0iqjd/vp3fv3lx22WU8+uijtdapbeQqIyND4eoAfskp5Zx/foPfDC+v2riMwtkvYHN6SJ3wRL2P/O3ryhMyuWFEF9LjIzVhhYiIiIgccQ0JV63iscCbb76Zjz/+mPnz5zcoWAE4nU6OO+44srKy6qzjdrtxu92H282jgmlaZBdU8PiXq/l4xY6wbYGSfIq+eonKdYtCZeU/fUHMcWcesN1O7SJ4/srjNVIlIiIiIq1Wiw5XlmVxyy23MHPmTObOnUvnzg1/9yYYDLJixQrOPPPAX/Clfqu3lfDABz/zQ3b4O2lWwEfJdzMoXfwuVmDPCKC7Qx/c7XvV22aPRDdPXzmYbsmxGqkSERERkVatRYerKVOm8MYbb/DBBx8QExNDbm4uAF6vl4iICAAmTJhAhw4dmDp1KgCPPPIIJ554It26daO4uJjHHnuMzZs3c9111x2x42jtTNPiwQ9+5rVvt+63rXLD9xTNfpFAcU6ozBYVR/yp1xDV97Q6p8GP89hZ8l+j8Hha9CUoIiIiInLQWvQ32+eeew6AU089Nax82rRpTJo0CYDs7Gxstj1TdBcVFXH99deTm5tLfHw8gwYNYtGiRfTp06e5ut1mBAIm7y3bwqMfraTCv8+2kjwKZz1P1Ybv9xQaNmIGnUPc8MuxuWtfh8zjMPjzeX05//iOTdhzEREREZHm12omtGhODXlpra2avTqXO99eRklN7ZeHL+9Xcl65Haxds1m4M/qRMHoyrqROtda3AZNO6si9Z/TRelUiIiIi0mq0uQktpPmYpsU/v1rHk7PrngAEwJXShZjjzqBy3WLiT7uWyN6n1PkIYHykg/9ceyJ9OtS9ppWIiIiISGuncCWYpsW24ipW55Tw6sKNLPy1KGy7v3AbpUs/IGHkDRj2PZdM3CkTiTtlIjZ3ZJ1tj+iWwH1n96NHakyT9V9EREREpCVQuDrKZeWX8fnKXGavyWPVtpKwNatMXzUli9+m9PuZEAzgjEsj9oTfhbbXF6qcNrh9VE/G9kuhW7KClYiIiIi0fQpXRynTtFiwfif//Ho9v+SUUlYTDG2zLIvKtQsp+uolgmU7Q+XlK2YRc/y59S4K7LTBvycMpHOylw5xEZpeXURERESOGgpXR6Gs/DJeX7KZ95ZupdwXDNvm37mFwtnPU735pz2FdgexJ5yP98SL6w1WZ/VL5ZkrBzVVt0VEREREWjSFq6PMt1n5XPfasrCRKgCzppKSRW9RuvQDMPds83QZRMLIG3AmdKizzbQYF7NuH0F0lKvJ+i0iIiIi0tIpXB1Fhk+dxdYS337lVsDH9pdvJliaHyqze1NIGHk9Ed2G1DkLoM2Ayad04e4zejdZn0VEREREWguFq6OAaVoM+Z9Z7Nh3JeDfGA4XUb2GU/rdDLA78Z54IbFDLsTmdNfZ5qXHd+Chs/vh8egSEhEREREBhas2zTQt5q/P5+kvfwkLVmZ1OYbDjeFwhsq8wy4lWFmK96RLccal1tlm98RIPrt9hBYCFhERERHZh8JVG2SaFguydvDEF+tYsa2E3W9QWZZJxcqvKZo7jdhB5+AddkloH5s7ksSzbq+zzSiXjannHcO5A9ObtvMiIiIiIq2UwlUbYpoWizbsZNrCjSzaUEDVXotW+fI2UDjreWq2rQGgZPE7RPU7DUdscr1txkU4GNU7hRtGdKFHSmyT9l9EREREpDVTuGoDTNPiixXbeeTTNeSX1RDcayHgYHU5xfNfo/zHz8DasyGiyyAw6n60r73XzUWDMrlgUAfS4yO1XpWIiIiIyAEoXLVyWfllXPbiEnaUh88CaFkm5T/PonjeK5hVpaFyR0I6CaNuIKLzwP3actmhd5qXq4Z25LwBHfRelYiIiIhIAyhctWJZ+WX87pmF+61ZVZOznsJZz+HLWRcqM5wevCddSuzx4zHszn2bIsJh8MKE4xneLUmjVCIiIiIih0DhqpUyTYsnZq3ZL1gBVGV9GxasInufQvyp1+CITay1raRoJ9/eO1qhSkRERETkMChctTKBgMmyLUX8sq2UL1fuqLVO7IkXUr7yK2xODwmjJ+Pp2L/Weg4bvDJpECf1qHvqdREREREROTgKV63InDV5TF+4iU0FFZRU+QlYULNtDb4dm4k5dlyons3pIeWSR3F4UzDstf8nTohy8tcL+itYiYiIiIg0EoWrVmLOmjymfvYLZdV+2kW58JcV8usnz1Oxcg7YHXg69scZ3z5U35nQodZ2HAb0SI3lzjE9GNk7pbm6LyIiIiLS5ilctWCVlX5eXLSBzQVVLN1USJUvSMd4N79+M5OVH/6LQHXFrorBAGU/fETCqBtrbaddpJNUbwSDOsVzRt9UBndK0EyAIiIiIiKNTOGqhbpv5greW7qVmr0WrarespLVc16gKm9jqMxwRxF38pXEHHdmre10jPfw2nUnaq0qEREREZEmpnDVAt03cwVvfpeNaYEN8JcVUDR3GhWr54bVa3/CmbiHXoHp8dbaTtfECObcdXrTd1hERERERBSuWprKSj/vLd2Kae16P8qXs5Ztb96P5asK1XGldGXYlX8kuXt/iip9bCmsoMpvYQMMA2I9Dh4Y34vzj+145A5EREREROQoo3DVwry4aAM1QRMbYLMZuFK64oiKx++rwuaJIW7EBKL7j8EXHwmA1+OgxOOie4qH60/pTEqMh4EZ8XqnSkRERESkmSlctTBbC6uBXSNQADaHk6QxN1K2djHeUyZgj4gFoMofpKzaT0GFj9gIJ7eN7K7Z/0REREREjiCFqxYmPcEDgGXtKYvqejxRXY8naFoEfyuvCZiUVQfomRLDxGGdFKxERERERI4whasW5oZhXXnu643UBE1M0wqb4W/3Ty4b/Om8fnSIj9QjgCIiIiIiLYS+lbcwkZFOLjw+HZsBAQsCQYugaREIWgQssBlw0eBMfjcwgxM6t1OwEhERERFpITRy1QL9z++OAdizztVvjwK6HTYuHJQe2i4iIiIiIi2HYVl7v90jAKWlpXi9XkpKSoiNjT1i/ais9PPiog1sLawmPcHDDcO6EhnpPGL9ERERERE52jQkG2jkqgWLjHRy+6heR7obIiIiIiJyEPTCjoiIiIiISCNQuBIREREREWkEClciIiIiIiKNQOFKRERERESkEShciYiIiIiINAKFKxERERERkUagcCUiIiIiItIIFK5EREREREQagcKViIiIiIhII1C4EhERERERaQQKVyIiIiIiIo1A4UpERERERKQRKFyJiIiIiIg0AseR7kBLZFkWAKWlpUe4JyIiIiIiciTtzgS7M0J9FK5qUVZWBkBGRsYR7omIiIiIiLQEZWVleL3eeusY1sFEsKOMaZps376dmJgYDMM40t1pE0pLS8nIyGDLli3ExsYe6e4cFXTOm5fOd/PTOW9eOt/NS+e7+emcN6/WdL4ty6KsrIz27dtjs9X/VpVGrmphs9lIT08/0t1ok2JjY1v8/4HaGp3z5qXz3fx0zpuXznfz0vlufjrnzau1nO8DjVjtpgktREREREREGoHClYiIiIiISCNQuJJm4Xa7efDBB3G73Ue6K0cNnfPmpfPd/HTOm5fOd/PS+W5+OufNq62eb01oISIiIiIi0gg0ciUiIiIiItIIFK5EREREREQagcKViIiIiIhII1C4EhERERERaQQKV3LYpk6dyuDBg4mJiSE5OZnzzjuPtWvX1rvP9OnTMQwj7J/H42mmHrd+Dz300H7nr1evXvXu8+6779KrVy88Hg/HHHMMn376aTP1tvXr1KnTfufbMAymTJlSa31d3w03f/58zjnnHNq3b49hGLz//vth2y3L4r//+79JS0sjIiKCUaNGsX79+gO2+8wzz9CpUyc8Hg9Dhgzhu+++a6IjaF3qO99+v5977rmHY445hqioKNq3b8+ECRPYvn17vW0eyn3paHGg63vSpEn7nbtx48YdsF1d33U70Dmv7Z5uGAaPPfZYnW3qGq/bwXwXrK6uZsqUKbRr147o6GguuOAC8vLy6m33UO/9R5LClRy2efPmMWXKFJYsWcKsWbPw+/2MGTOGioqKeveLjY0lJycn9G/z5s3N1OO2oW/fvmHnb8GCBXXWXbRoEZdddhnXXnsty5cv57zzzuO8885j5cqVzdjj1uv7778PO9ezZs0C4KKLLqpzH13fDVNRUcGAAQN45plnat3+17/+lX/84x88//zzfPvtt0RFRTF27Fiqq6vrbPPtt9/mD3/4Aw8++CDLli1jwIABjB07lvz8/KY6jFajvvNdWVnJsmXLeOCBB1i2bBkzZsxg7dq1nHvuuQdstyH3paPJga5vgHHjxoWduzfffLPeNnV91+9A53zvc52Tk8PLL7+MYRhccMEF9bara7x2B/Nd8I477uCjjz7i3XffZd68eWzfvp3zzz+/3nYP5d5/xFkijSw/P98CrHnz5tVZZ9q0aZbX622+TrUxDz74oDVgwICDrn/xxRdbZ511VljZkCFDrBtvvLGRe3Z0uO2226yuXbtapmnWul3X9+EBrJkzZ4Y+m6ZppaamWo899liorLi42HK73dabb75ZZzsnnHCCNWXKlNDnYDBotW/f3po6dWqT9Lu12vd81+a7776zAGvz5s111mnofeloVdv5njhxojV+/PgGtaPr++AdzDU+fvx46/TTT6+3jq7xg7fvd8Hi4mLL6XRa7777bqjOmjVrLMBavHhxrW0c6r3/SNPIlTS6kpISABISEuqtV15eTseOHcnIyGD8+PGsWrWqObrXZqxfv5727dvTpUsXrrjiCrKzs+usu3jxYkaNGhVWNnbsWBYvXtzU3WxzfD4fr7/+Otdccw2GYdRZT9d349m4cSO5ublh17DX62XIkCF1XsM+n48ffvghbB+bzcaoUaN03R+CkpISDMMgLi6u3noNuS9JuLlz55KcnEzPnj256aabKCgoqLOuru/GlZeXxyeffMK11157wLq6xg/Ovt8Ff/jhB/x+f9g126tXLzIzM+u8Zg/l3t8SKFxJozJNk9tvv52TTjqJfv361VmvZ8+evPzyy3zwwQe8/vrrmKbJsGHD2Lp1azP2tvUaMmQI06dP5/PPP+e5555j48aNnHzyyZSVldVaPzc3l5SUlLCylJQUcnNzm6O7bcr7779PcXExkyZNqrOOru/Gtfs6bcg1vHPnToLBoK77RlBdXc0999zDZZddRmxsbJ31Gnpfkj3GjRvHq6++ypw5c/jLX/7CvHnzOOOMMwgGg7XW1/XduF555RViYmIO+IiarvGDU9t3wdzcXFwu135/oKnvmj2Ue39L4DjSHZC2ZcqUKaxcufKAzyAPHTqUoUOHhj4PGzaM3r1788ILL/Doo482dTdbvTPOOCP0c//+/RkyZAgdO3bknXfeOai/vMmh+/e//80ZZ5xB+/bt66yj61vaCr/fz8UXX4xlWTz33HP11tV96dBdeumloZ+POeYY+vfvT9euXZk7dy4jR448gj07Orz88stcccUVB5x4SNf4wTnY74JtlUaupNHcfPPNfPzxx3z99dekp6c3aF+n08lxxx1HVlZWE/WubYuLi6NHjx51nr/U1NT9ZuTJy8sjNTW1ObrXZmzevJnZs2dz3XXXNWg/Xd+HZ/d12pBrODExEbvdruv+MOwOVps3b2bWrFn1jlrV5kD3Jalbly5dSExMrPPc6fpuPN988w1r165t8H0ddI3Xpq7vgqmpqfh8PoqLi8Pq13fNHsq9vyVQuJLDZlkWN998MzNnzuSrr76ic+fODW4jGAyyYsUK0tLSmqCHbV95eTkbNmyo8/wNHTqUOXPmhJXNmjUrbHRFDmzatGkkJydz1llnNWg/Xd+Hp3PnzqSmpoZdw6WlpXz77bd1XsMul4tBgwaF7WOaJnPmzNF1fxB2B6v169cze/Zs2rVr1+A2DnRfkrpt3bqVgoKCOs+dru/G8+9//5tBgwYxYMCABu+ra3yPA30XHDRoEE6nM+yaXbt2LdnZ2XVes4dy728RjvCEGtIG3HTTTZbX67Xmzp1r5eTkhP5VVlaG6lx11VXWf/3Xf4U+P/zww9YXX3xhbdiwwfrhhx+sSy+91PJ4PNaqVauOxCG0Onfeeac1d+5ca+PGjdbChQutUaNGWYmJiVZ+fr5lWfuf74ULF1oOh8N6/PHHrTVr1lgPPvig5XQ6rRUrVhypQ2h1gsGglZmZad1zzz37bdP1ffjKysqs5cuXW8uXL7cA64knnrCWL18emp3uf//3f624uDjrgw8+sH7++Wdr/PjxVufOna2qqqpQG6effrr1z3/+M/T5rbfestxutzV9+nRr9erV1g033GDFxcVZubm5zX58LU1959vn81nnnnuulZ6ebv34449h9/WamppQG/ue7wPdl45m9Z3vsrIy66677rIWL15sbdy40Zo9e7Y1cOBAq3v37lZ1dXWoDV3fDXOge4plWVZJSYkVGRlpPffcc7W2oWv84B3Md8HJkydbmZmZ1ldffWUtXbrUGjp0qDV06NCwdnr27GnNmDEj9Plg7v0tjcKVHDag1n/Tpk0L1RkxYoQ1ceLE0Ofbb7/dyszMtFwul5WSkmKdeeaZ1rJly5q/863UJZdcYqWlpVkul8vq0KGDdckll1hZWVmh7fueb8uyrHfeecfq0aOH5XK5rL59+1qffPJJM/e6dfviiy8swFq7du1+23R9H76vv/661vvI7vNqmqb1wAMPWCkpKZbb7bZGjhy533+Ljh07Wg8++GBY2T//+c/Qf4sTTjjBWrJkSTMdUctW3/neuHFjnff1r7/+OtTGvuf7QPelo1l957uystIaM2aMlZSUZDmdTqtjx47W9ddfv19I0vXdMAe6p1iWZb3wwgtWRESEVVxcXGsbusYP3sF8F6yqqrJ+//vfW/Hx8VZkZKT1u9/9zsrJydmvnb33OZh7f0tjWJZlNc2YmIiIiIiIyNFD71yJiIiIiIg0AoUrERERERGRRqBwJSIiIiIi0ggUrkRERERERBqBwpWIiIiIiEgjULgSERERERFpBApXIiIiIiIijUDhSkREREREpBEoXImIiIiIiDQChSsREREREZFGoHAlIiL1mjBhAuecc86R7oaIiEiLp3AlIiL7WbVqFZdccgnp6em89tprfPzxx8TExHDGGWcwa9asI909ERGRFknhSkREwsycOZMBAwZQU1PD66+/zsUXX8y4ceP47LPPSE1NZcyYMTzzzDOh+t9//z2jR48mMTERr9fLiBEjWLZsWVibhmHw/vvvA2BZFhMmTKB///4UFRUxffp0DMOo9V+nTp0AeOihhzj22GND7fl8Prp164ZhGBQXFwMwadIkzjvvvDp/L8CWLVu4+OKLiYuLIyEhgfHjx7Np06awfV5++WX69u2L2+0mLS2Nm2+++aCOA2DDhg2MHz+elJQUoqOjGTx4MLNnzw5rPycnh/PPP5927dqFHevu49jXpk2bMAyDH3/8May8U6dOPPXUU6HPpmkydepUOnfuTEREBAMGDOC9994LbZ87d26tv2ffc3TPPffQo0cPIiMj6dKlCw888AB+v7/WvomISDiFKxERCXP77bdz6qmn8v7773PqqacSERGB2+1m+PDhTJs2jUmTJnH33XdTUVEBQFlZGRMnTmTBggUsWbKE7t27c+aZZ1JWVlZr+7feeiuLFi3iyy+/JD4+nksuuYScnBxycnJ46qmnSE9PD33+/vvva23j6aefJi8vr0HH5ff7GTt2LDExMXzzzTcsXLiQ6Ohoxo0bh8/nA+C5555jypQp3HDDDaxYsYIPP/yQbt26HdRxAJSXl3PmmWcyZ84cli9fzrhx4zjnnHPIzs4O7XfnnXeybt06Pv/8c3Jycvi///u/Bh1HXaZOncqrr77K888/z6pVq7jjjju48sormTdvXoPaiYmJYfr06axevZq///3v/Otf/+LJJ59slD6KiLR1jiPdARERaTny8vLIzs7mjjvuqLPOueeey/Tp01m5ciVDhgzh9NNPD9v+4osvEhcXx7x58zj77LPDtt1///3MnDmTBQsWkJqaCkBERAQREREAeL1e7HZ7aFttCgsL+dOf/sQ999zDAw88ECqPiIggJyenzv3efvttTNPkpZdewjAMAKZNm0ZcXBxz585lzJgx/OlPf+LOO+/ktttuC+03ePDg/dqq7TgABgwYwIABA0KfH330UWbOnMmHH34YGgH78ccfufLKK0PtJiQk1Nnng1VTU8Of//xnZs+ezdChQwHo0qULCxYs4IUXXmDEiBEH3db9998f+rlTp07cddddvPXWW9x9992H3U8RkbZO4UpEREJcLhcAlZWVddbZvc3j8QC7Atn999/P3Llzyc/PJxgMUllZGTZaA7tGm+bMmcPpp58eetzvUDzyyCOcdtppDB8+PKy8X79+vPHGG2zcuJHOnTvvt99PP/1EVlYWMTExYeXV1dVs2LCB/Px8tm/fzsiRI+v9/fUdR3l5OQ899BCffPIJOTk5BAIBqqqqws5F586d+fTTT5k8eXKjBCuArKwsKisrGT16dFi5z+fjuOOOCytLT0+vt623336bf/zjH2zYsIHy8nICgQCxsbGN0k8RkbZO4UpERELi4+MZMmQIr776KrfddhtRUVFh2wOBAC+88ALp6en069cPgIkTJ1JQUMDf//53OnbsiNvtZujQoaFH7Xb77rvv+PTTT5k0aRIvvPACN954Y4P7t379el566SV+/PFHtm7dGrbtmmuuYebMmXTp0mW/fsOu4DNo0CD+85//7LctKSkJm+3gnpSv7zjuuusuZs2axeOPP063bt2IiIjgwgsvDDsXTz75JFdccQWJiYlERkYSDAYP9vDrVF5eDsAnn3xChw4dwra53e6wz998801YwOzevXvo58WLF3PFFVfw8MMPM3bsWLxeL2+99RZ/+9vfDruPIiJHA4UrEREJ89JLL3H22WfTu3dvrr32WjZu3EhlZSV//vOfefXVV8nPz+f999/HbrcDsHDhQp599lnOPPNMYNekETt37tyv3aeeeoozzjiDZ599lquvvpozzjiDzMzMBvXtnnvu4brrrqNbt277hauIiAhmz55NXl5e6H2vvYPDwIEDefvtt0lOTq5zJKZTp07MmTOH0047rc4+1HccCxcuZNKkSfzud78DdoWefSfM6NGjB5MmTaKgoICPPvoo9Jjg4ejTpw9ut5vs7OwDPgLYuXNn4uLiat22aNEiOnbsyH333Rcq27x582H1TUTkaKJwJSIiYfr168fatWuZNm0aCxYsYM2aNfj9fhYvXsw111zD1VdfTVJSUqh+9+7dee211zj++OMpLS3lj3/8Y+gdqr3tfgTuggsu4N133+W6667jyy+/POh+ZWVlkZ2dTVZWVr31UlJSSElJ2a/8iiuu4LHHHmP8+PE88sgjpKens3nzZmbMmMHdd99Neno6Dz30EJMnTyY5OZkzzjiDsrIyFi5cyC233HJQx9G9e3dmzJjBOeecg2EYPPDAA5imGdaPJUuWcO+99/L111/Tt29fduzYcVDH7/P5qK6uDn22LItAIEAwGCQmJoa77rqLO+64A9M0GT58OCUlJSxcuJDY2FgmTpx4UL+je/fuZGdn89ZbbzF48GA++eQTZs6ceVD7ioiIZgsUEZFauN1uJk+ezOuvv86ZZ57JiBEj+Oijj7j77rvDghXAv//9b4qKihg4cCBXXXUVt956K8nJyfW2//TTT/PTTz/x4osvHnSfKioquO+++w75PaXIyEjmz59PZmYm559/fmhkrrq6OjSSNXHiRJ566imeffZZ+vbty9lnn8369esP+jieeOIJ4uPjGTZsGOeccw5jx45l4MCBofo7duzgoosu4oknnggrPxhDhgwJTf4RERFBdnY2f/zjH3nttdeAXZNnPPDAA0ydOpXevXszbtw4Pvnkk1rfP6vLueeeyx133MHNN9/Msccey6JFi8ImDRERkfoZlmVZR7oTIiIi0nC33347xx57LJMmTTrSXRERETRyJSIi0mo5nc7Qu28iInLkaeRKRERERESkEWjkSkREREREpBEoXImIiIiIiDQChSsREREREZFGoHAlIiIiIiLSCBSuREREREREGoHClYiIiIiISCNQuBIREREREWkEClciIiIiIiKN4P8DDtXpL96LSncAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Lasso:\n",
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 0.02 секунд\n",
      "Среднеквадратичная ошибка: 0.15\n",
      "Средняя абсолютная ошибка: 0.09\n",
      "Кросс-валидация RMSE: 0.14781881296011543 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.283e+01, tolerance: 9.545e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 2.283e+01, tolerance: 9.123e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.777e+01, tolerance: 7.834e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.716e+01, tolerance: 7.848e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 1.801e+01, tolerance: 7.706e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_coordinate_descent.py:697: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 8.478e+00, tolerance: 4.075e+00\n",
      "  model = cd_fast.enet_coordinate_descent(\n"
     ]
    },
    {
     "data": {
      "image/png": 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3+nANpKe2bbxeoyOvjTvuuIM77rgj/XjixIn86U9/anPcttzvpUuXsnDhQvLz89vdv+n38Prrr+f6669vc9zmpuFu7TXX4oADDkj/v3///m3277vvvrzwwgvU1tZu9lrl5eX0798fm23Hf4To169fm2377LMPkUgkve4nEols9rlYlsXq1asZOHDgNl8zHA5v9vvUouXntr3rDhgwgA8++KDVtrfffptJkyYBkJGRwfPPP8/BBx/cbttPPvkk3377Lc8++yxPPfVUu8ds7b4UFRUxYMAADjzwQJ588klmzpyZbvvggw+mb9++QMfeZ1vk5+dz7LHH8o9//IMRI0bwj3/8g2nTpqX/WLO996jFBx98wCuvvMLbb7/NqlWr2j1GiF2ZBFdC7KKOO+447r33Xh555JH0L8ZNVVVV8fjjj5Ofn5/+Rf3+++9z/PHHc9hhh3HfffdRXFyM3W7n0UcfbfcX9SeffMK5556L1+vllltu4ZRTTmn3l2FHPPLII6xcuZLZs2e3u/+8885j9uzZXHrppW2ScbRoWeR/xRVXMGHChHaPafmA0BEff/wxjz32GPfccw/nnXceX331FU6nM73/iSee4JxzzuGEE07gyiuvpKCgAMMwuPXWW9sEu8888wxnnnlmm/51Zoarlvtw1llnMW3atHaP2fTD9HXXXceYMWNabZs8eXKrx3/84x+59tprmTFjBjfffDM5OTnous4ll1zSKsHCnqSjr42zzz6bqVOnYlkWy5cv5+abb+a4445j7ty5rYKxbbnflmVx1FFHcdVVV7Xbt03XOJ133nlt/mp/7rnnbva5be011+KJJ54gEolw3nnnbfG4vZnL5eKVV15pte39999vtXavow488EDmzJlDQ0MDTzzxBDNmzKB79+6MGDGi1XGJRIJrr72WmTNnbnHd27aaOnUqF198MWvWrCEej7NgwQLuueee9P7tfZ+dMWMGU6dO5de//jXvvfceDz/8cKfVpPrNb37DhAkTOPLII9skOBJidyDBlRC7qN///ve8+OKL/OpXv2Lx4sWcccYZ6dGFVatW8fbbb3PdddfR0NDAU089lQ4QXnjhBVwuF7Nnz24VNDz66KPtXueoo47i/vvvJxaL8eKLL3Leeeels5Btj0gkwo033sgFF1xAz5492z3G5XLx2muvsWTJElavXo1SiqqqqvRUFSA9CmG32xk/fvwWr1lWVrbNWc5uvPFGpk2bxtChQxkxYgS33HILN998c3r/888/T58+ffjPf/7T6h60N4owbNgw/v73vzNmzBhuuukmDj74YO644w4+/PDDberLtsjPz8fv92Oa5lbvQ4vBgwe3OdYwjFaPn3/+eY444ggeeeSRVtsbGxvbTQCyvXr27MncuXMJBoOtRq8WL16c3r85LVM2Fy1atNXn3hIAbfyBdMmSJQDpLGQdfW306dOn1XUzMzM544wzWLBgQToZAGzb/S4rKyMUCm3z97Bfv35tjt1c0L4tr7kWo0ePxuv1ct555/HDDz+02b948WK8Xu8WfwbKysr4+OOPSSaT2O32bXg222/p0qVtti1ZsgSPx5MeXfJ4PJt9Lrqu07179w5d0zCMNvd+4yyRsOHn9ocffuDII49ste+HH35o833Izc1NtzllyhT69+/PHXfcwTPPPNPquPvuu4/q6upWmR7bsy33BeC0007jsssu49///jfRaBS73c7Pf/7z9P6OvM9ubNKkSbhcLk477TQOPfRQysrK2gRXHb1HAC+++CLz58/v9KRAQuxMsuZKiF1UUVER8+fPZ9KkSfzlL3/hgAMO4IknniAcDtOzZ09mzJiB2+3mlVde4fTTT0+f15ISfONpXitXrmyT+rfFqFGjMAwDr9fLAw88wHvvvcff//737e733XffTTgc5pprrtnqsfvssw/jxo1j/PjxbdYfFBQUMHbsWB588EEqKyvbnLtxKuApU6bw9ddft5t2WG2SirtlhGHIkCFcccUV/OlPf2oVmLV8KN74vI8//rhV2uIWgUCAs88+m+OPP57f//73jB8/nuLi4q0+744wDIMpU6bwwgsvtBtAbpoSuSPtbnpvnnvuuTbrK36qY445BtM0W/21HODOO+9E07T0VKn2HHDAAfTu3Zu77rqrzYfbTfsOtLqGUop77rkHu92eXgvS0dfGpqLRKMB2Tf089dRTmT9/frsjS42NjaRSqQ632aIjrzloDtgPOOAAnnrqqVY/P+Xl5bz88stMmjSpTXC4sSlTplBbW9vmewrtf19+ik0/aK9evZqXXnqJo48+GsMwMAyDo48+mpdeeqnV9M+qqiqeeuopDj30UDIyMjq1TwAjRoygoKCABx54oNXPwxtvvMH333+/xayssViMcDjc5ucoGAzyhz/8gUsvvZSioqItXn9r96VFXl4ekyZN4oknnuDJJ59k4sSJrQLnjrzPbsxmszF16lQWLly42WmxHb1HLVNbzzjjjFZZWYXY3cjIlRC7sO7du/PSSy9RWVnJhx9+yB133MFXX33FAw88wNChQxk6dGibEaZjjz2Wv/71r0ycOJEzzjiD6upq7r33Xvr27cvChQu3eL0JEyZw1llncdVVVzF58uTtChTeeust/vCHP3TKup17772XQw89lMGDB3PuuefSp08fqqqqmD9/PmvWrEnXZLryyit5/vnnOeWUU5gxYwbDhw+nvr6el19+mQceeIAhQ4a02/7111/PCy+8wLnnnsuHH36Iruscd9xx/Oc//+HEE0/k2GOPZcWKFTzwwAPst99+6VpYLWbNmkU0GuXhhx/erudXU1PDm2++2WpbyxqDN998k4MOOoicnBxuu+025s2bx8iRIzn33HPZb7/9qK+v54svvmDu3LnbtVbuuOOO46abbmL69OmMGjWKb775hieffLLNuqWfavLkyRxxxBFcc801rFy5kiFDhvDWW2/x0ksvcckll2wxbbeu69x///1MnjyZoUOHMn36dIqLi1m8eDHffvttq0DF5XLx5ptvMm3aNEaOHMkbb7zBa6+9xu9+97v0X/I7+tpYuHAhTzzxBEopysvL+dvf/ka3bt3aTOXaFldeeSUvv/wyxx13HOeccw7Dhw8nHA7zzTff8Pzzz7Ny5crtHjHcntfc7bffnk5d/8tf/pJUKsU999yDy+XiD3/4wxbPnTp1Kv/85z+57LLL+OSTTxgzZgzhcJi5c+dywQUXdFpSHGhebzdhwoRWKceheQS6xS233MKcOXM49NBDueCCC7DZbDz44IPE43Fuv/32TuvLxux2O3/605+YPn06hx9+OKeffno6zXivXr3S051bSj5MmjSJkpIS6uvr+de//kVlZWWrkXqAL774gry8vM1OHd3YttyXFlOnTuXkk08GaDVK32Jb32c3dfPNN3PllVeSnZ39k+5RizVr1uBwOH5S+Q4hdgldkaJQCLF9tjXN9yOPPKL69eunnE6nGjBggHr00UfV9ddf325K303TTdfW1qr8/PxWKX1bbEsq9uLiYhUOh7d6nU21l4pdKaXKy8vV1KlTVVFRkbLb7aq0tFQdd9xx6vnnn291XF1dnbrwwgtVaWmpcjgcqlu3bmratGnp9OXtpa1WSql33nlHaZqm7r77bqVUcwriP/7xj6pnz57K6XSqYcOGqVdffbVNquB///vfStM09eabb7ZqryOp2FmfUnlzXxv3taqqSs2aNUt1795d2e12VVRUpMaNG6ceeuih9DEdTcV++eWXq+LiYuV2u9Xo0aPV/Pnz26SE3hy2MRW7UkoFg0F16aWXqpKSEmW321W/fv3UHXfcsdW03S0++OADddRRRym/36+8Xq/af//91f/93/+l97fc8/LycnX00Ucrj8ejCgsL1fXXX98m3XxHXhstX5qmqaKiInXSSSep77//Pn1MR+53y324+uqrVd++fZXD4VB5eXlq1KhR6s9//rNKJBJKqe1Lxb4tr7n2Xrtz585Vo0aNUi6XS/n9fnXMMceohQsXtrlueyKRiLrmmmtU79690z+PJ598cqt06O3paCr2WbNmqSeeeCL9PRs2bFib17BSSn3xxRdqwoQJyufzKY/Ho4444gj10UcftTqmM1Oxt3jmmWfUsGHDlNPpVDk5OerMM89Ua9asSe+PRqPq5z//uerWrZtyOByqoKBAHXHEEa1KZyjVnIodUHfeeWer7Zv72dzW+6KUUvF4XGVnZ6vMzMxWKdE3ti3vs1u7f5vbv7V7pNSGcgoXX3xxu21KKnaxO9GU6uQxfCGEED+ZpmnMmzePsWPHdnVXdnnnnHMOzz//fJuRRbF70zSNWbNmtTv9cG/W0fuSSqUoKSlh8uTJbdZYCiE6n6y5EkIIIYTYQ7344ovU1NQwderUru6KEHsFWXMlhBC7oAkTJpCTk9PV3RBC7KY+/vhjFi5cyM0338ywYcM4/PDDu7pLQuwVJLgSQohd0KaJLoQQoiPuv/9+nnjiCYYOHSr1ooTYiWTNlRBCCCGEEEJ0AllzJYQQQgghhBCdQIIrIYQQQgghhOgEsuaqHZZlUVFRgd/vb1OgVQghhBBCCLH3UEoRDAYpKSlB17c8NiXBVTsqKiro3r17V3dDCCGEEEIIsYtYvXo13bp12+IxEly1w+/3A803MCMjo4t7I4QQQgghhOgqgUCA7t27p2OELZHgqh0tUwEzMjIkuBJCCCGEEEJs03IhSWghhBBCCCGEEJ1AgishhBBCCCGE6AQSXAkhhBBCCCFEJ5DgSgghhBBCCCE6gQRXQgghhBBCCNEJJLgSQgghhBBCiE4gwZUQQgghhBBCdAIJroQQQgghhBCiE0hwJYQQQgghhBCdQIIrIYQQQgghhOgEElwJIYQQQgghRCeQ4EoIIYQQQgghOoEEV0IIIYQQQgjRCWxd3QEhhBBCCCHE3s2yFGsbo4QTKbwOG6VZbnRd6+pudZgEV0IIIYQQQogus6w6yOxFVZTXhIilTFw2g7J8HxMGFdK3wN/V3esQCa6EEEIIIYQQXWJZdZBHP1xJfThBcaYLj8NNJJFiUUUTFU1Rpo/utVsFWLLmSgghhBBCCLHTWZZi9qIq6sMJ+hX48LvsGLqGz2mjLM9DfTjBW99WYVmqq7u6zSS4EkIIIYQQQux0axujlNeEKM50oWnN66sqVyzhvqum8snsFyjOdLGsOsTaxmgX93TbybRAIYQQQgghxE4XTqSIpUw8DjfRcJC3/nUP77/4LyzLZN2KJQwcPZ54yk44kerqrm6zLh25uvXWWznwwAPx+/0UFBRwwgkn8MMPP7Q6JhaLMWvWLHJzc/H5fEyZMoWqqqottquU4rrrrqO4uBi328348eNZunTpjnwqQgghhBBCiA7wOmw4DZ2P3nyB22ZM5N3/PIZlmQC4vH7WrV2L02bgdew+40FdGly9++67zJo1iwULFjBnzhySySRHH3004XA4fcyll17KK6+8wnPPPce7775LRUUFJ5100hbbvf322/nb3/7GAw88wMcff4zX62XChAnEYrEd/ZSEEEIIIYQQ26Bm5Q+8/Idz+e9d1xBsqAXA5nAycepFXPnQq5Dbi74FPkqz3F3c022nKaV2mRViNTU1FBQU8O6773LYYYfR1NREfn4+Tz31FCeffDIAixcvZt9992X+/PkcfPDBbdpQSlFSUsLll1/OFVdcAUBTUxOFhYU89thjnHbaaVvtRyAQIDMzk6amJjIyMjr3SQohhBBCCLEXa2ho4Nprr+X+++/Hsqz09n0PHsfPzr8ad04RlU0xcryOXSJbYEdig10qoUVTUxMAOTk5AHz++eckk0nGjx+fPmbAgAH06NGD+fPnt9vGihUrWLduXatzMjMzGTly5GbPicfjBAKBVl9CCCGEEEKIznfzzTdz7733pgOrXn3KuOC2fzDxkj8TsGXRGEkyuDRzlwisOmqXmcBoWRaXXHIJo0ePZtCgQQCsW7cOh8NBVlZWq2MLCwtZt25du+20bC8sLNzmc2699VZuvPHGn/gMhBBCCCGEEFtzzTXX8PjjjxOPx7n22mu55JJLsNsdrG2MEk6k8DpslGa50XWtq7vaYbtMcDVr1iwWLVrEBx98sNOvffXVV3PZZZelHwcCAbp3777T+yGEEEIIIcSepLa2loULF3LkkUemt+Xm5vLMM88wYMAAunXrlt7ePcfTFV3sVLvEtMALL7yQV199lXnz5rW6wUVFRSQSCRobG1sdX1VVRVFRUbtttWzfNKPgls5xOp1kZGS0+hJCCCGEEEJsH9M0eeCBB+jfvz8nnnhim8/m48ePb/W5f2OWpVhdH2HxugCr6yNSRHhbKaW48MIL+e9//8v//vc/evfu3Wr/8OHDsdvtvP322+ltP/zwA6tWreKQQw5pt83evXtTVFTU6pxAIMDHH3+82XOEEEIIIYQQnWP+/PkcdNBB/OpXv6K+vp5AIMD111+/Tecuqw5y/zvl3DlnCX97eyl3zlnC/e+Us6w6uIN73Tm6NLiaNWsWTzzxBE899RR+v59169axbt06otHmKsyZmZnMnDmTyy67jHnz5vH5558zffp0DjnkkFaZAgcMGMB///tfADRN45JLLuGWW27h5Zdf5ptvvmHq1KmUlJRwwgkndMXTFEIIIYQQYo9XVVXF9OnTGTVqFF988UV6+1lnnbVNwdWy6iCPfriSRRVNZHns9MnzkeWxs6iiiUc/XLlbBFhduubq/vvvB2Ds2LGttj/66KOcc845ANx5553ous6UKVOIx+NMmDCB++67r9XxP/zwQzrTIMBVV11FOBzmvPPOo7GxkUMPPZQ333wTl8u1Q5+PEEIIIYQQe5tUKsV9993Hdddd1+oz+f77788999zDmDFjttqGZSlmL6qiPpygX4EPTWtOZuF32fE5bSytDvHWt1X0yfPt0okudqk6V7sKqXMlhBBCCCHE1v3www+ccsopfPPNN+ltmZmZ3HLLLZx//vnYbNs2lrO6PsKdc5aQ5bHjd9nb7A/GkjRGklx61D47PfHFblvnSgghhBBCCLH7KCoqorq6Ov14xowZLFmyhAsvvHCbAyuAcCJFLGXicbR/jtthEE+ZhBOpn9znHUmCKyGEEEIIIcQ22XTSW2ZmJrfffjvDhw9nwYIFPPLIIxQUFHS4Xa/DhstmENlM8BRNmDhtBt7NBF+7CgmuhBBCCCGEEFv19ttvc/DBB7NmzZpW288++2w+/vhjRo4cud1tl2a5Kcv3UdkUaxPAKaWobIrRt8BHaZZ7u6+xM0hwJYQQQgghhNis1atXc+qppzJ+/Hg++eQTrrzyylb7NU3DMIyfdA1d15gwqJAcr4Ol1SGCsSQpyyIYS7K0OkSO18HRAwt36WQW0MXZAoUQQgghhBC7FstSrG2M0hAM8+TD93HPX28nEomk969YsYJIJILH07mJJfoW+Jk+uhezF1VRXhOiKhDDaTMYXJrJ0QML6Vvg79Tr7QgSXAkhhBBCCCGA5lpTsxdV8fac2fzvsdtpWrcqvS8/P58//elPTJs2DV3fMRPg+hb46TPWx9rGKOFECq/DRmmWe5cfsWohwZUQQgghhBCCZdVB7vzPB7z+9z+x8ot309s1TWf4pNN44M5bGb5Pjx3eD13Xdnq69c4iwZUQQgghhBB7OctSvLGwkidvPL/VaFXvQcM56YJrCfu78fHaBMP6qt1mFKkrSEILIYQQQggh9nJrG6OsqItw9NSLAPDn5HPmb+7gwr88SWnffSnOdLGsOsTaxmgX93TXJiNXQgghhBBC7IWWLl2K0+mkR48e6SK+Bx5xLFYkwPBxP8Pl9aWPdTsMqgKxXb6Ib1eTkSshhBBCCCH2IuFwmGuuuYZBgwZx0UXNI1UtRXyjSZPRx5/ZKrCC3aeIb1eT4EoIIYQQQoi9gFKKF154gX333Zc//vGPJBIJXnrpJZ54/iWKM1x7RBHfriahpxBCCCGEELuYllpTnZWO/Pvvv+eiiy5i7ty56W26YWPIMWexIJxP03vL6V/kZ0lVkC9WNVCc6Sbf7ySWNKlsiu02RXy7mgRXQgghhBBC7EJaak2V14SIpUxcNoOyfB8TBjUX0u1I4BUMBrnpppu46667SKU2rJfqPvgQTrjgGnr07kskkWLBijre+m4dHodBXTjBj3UR3A6DHjkeDuiRvdsU8e1qElwJIYQQQgixi1hWHeTRD1dSH05QnOnC43ATSaRYVNFERVOUIwcUsLgyuNnAa2Ovvvoqvzj3PKrWVaa3ubOL2OeEWRx73PHk+V0AJE2LhnCcmlCCbtluxg8ooCYUp7IphtdpY/x+BRJYbSMJroQQQgghhNgFWJZi9qIq6sMJ+hX40LTm0Si/y47PaePL1Y387e2lFGe6KMlytwm8po/u1SoIWtsQSQdWNruDQ06cgT70ZyjDycK1AYZ218n22CmvDhNLWhRluAjHTaJJi5IsD8WZbpZWh5j7XTV98/0yJXAbSEILIYQQQgghdgFrG6OU14QoznSlA6uNReIpaoJxijJc+F12DF3D57RR6HfyY12YZz9bTSplAc2BWrJkGD2GjGa/g4/gqr+/xpjTLsDmdJHvdxBNpCivCRGIJqmPJPC5bDhsOinLImE2t6FpmtS36iAZuRJCCCGEEGIX0FJryuNom5EvGEsRjKdw2HSSVnM2v/pwnPLqMHWhGD9+8iZvLfkCZd3Fzw/qjtNmsLw2zNTf301OZvNoViCaxKbrpCyFz2WjPpygIZokZVnYDRtJ08Km6ziMDeMvUt+qYyS4EkIIIYQQootZliIQTRJPmlQHYhRtMnqVMC3i69dYOQyd+nCcr1Y3Ur1iMcte+hv1yxcB8NbrLxNJHsvh++QTS5mUZG2oV+V32cjxOKgOxsjy2ElZFiiw6TqJlEk4blKQ4cLv2hAiSH2rjpG7JIQQQgghRBdqyQ64rDrI6oYo31cG6ZnjoW+hjxyvEwC7rpFMKfJ8NnxOg4++W8OXz93H2gWvgrLSbcVWfUN9+Cg+W1mPaSrWNkTI8jjwu2xomkZZgZdgPElNMI7N0PG7bHgdBmsao+T7nJTlb1jr1VLfanBpptS32kYSXAkhhBBCCNFFNs0OOKJnDp//WM/yujD1kQTDe2bjshusC8TJ9ztxGzrvvPw0sx+/i2S4Kd2OJ787B51xOZOPmciahgjvL60lZVkEYyky3XZyvU7KCrzkeJ0M6ZbJJysasK0fAcv2OkhZigyXHbuhkbIsogmpb7U9JLgSQgghhBCiC7SXHdDvgoN657KsOsiPdRE+/7GB/Yoz2L9bJoMd1fzh91dRuWxRug3D4ab3UWezz7jTOKB3Pg2RJD9UhWiIJBhYksG6QJxQLMXaxghNsQT9C/1EkxYH9sph4uAi8v1OvA4b0WSKOd9WU14ToioQw2kzGFyaKfWtOkiCKyGEEEIIIbrA5rID5ngdHNgrh27ZburDSU4f2QNvpIrB445FKZU+Ln/IEex3wgWUlHajLN9HtsfOpysbCK0freqR46Uky8Oy6hAN4Th1oQQ/qCDHDC5pty5W33z/NhcnFu2T4EoIIYQQQogusKXsgJqmUZDhIpIwyXDbGdB7P0466SReeOEFBg4cyMFnXkksvz99831kuO1omkYgmqQhHEdDketzptdZHdgrm2AsRUMkQTRhctyQYnrmettcU9c1uud4dsZT32NJnSshhBBCCCG6gNdhw2UziLST5nxt+fdE4qlWmfr++te/cuedd/Lll1/y25kn0TPXS1UwTiieImVZNESaU6t7XTbK8r3p0TBN08hw2ynNdmMzNKJJc6c+z72JBFdCCCGEEEJ0gdIsN2X5PiqbYunpfoH6Gp66/Tf85Vcn8P7sl+hb4KM0y41lKTRfHhNPm8G6YJI+eT6mj+7FoJJMGiNJVtaGiSZM8rwO+hf601kGNyZp1Xc8ubNCCCGEEEJ0AV3XmDCokIqmKD9UNrLq/f/yzr/vJRYJAfDx03/j9kun82F5LW9/X0VlUwxd03DbDcryfUwYVMivxpal10m57QavfF3BtxUBlFKt1nFJWvWdQ4IrIYQQQgghukjfAj/7aWu48sZLqFyxJL3d7ctg+qxLefmbWt5dVkc0aeJ12sj3OXFnuVhU0URFU5Tpo3u1SkwxcVARlU0xllY3J8pwOwxJq74TybRAIYQQQgghusDatWs544wzOOvEY9KBlaZpnHb2OfxvwZf4hh7DRysbsBR0y3bjd9moDcVZWh0i12unPpzgrW+rsKwNGQT7FvjbTBdsjCQZXJrZJhATnU9GroQQQgghhNiJTNPi+j/cxp2330okHEpvHzFiBPfeey8jRhzI/e+Us7Yxig5kex0Yuo6hg8OrUx9OsLw2Qv9CH8uqQ6xtjLbK8te3wE+fsT5Jq94FJLgSQgghhBBiJ7AsxUfltcz9rppnZn+QDqy8Gdlcfd2N/PaSCzAMg9X1EcprQmR7HKxtiGI3NgRFmqbhc9moDycwLYinTMLtZBuUtOpdQ4IrIYQQQgghdrBl1UGe+ngV8xZXE0maFI6fyY9ffcC+oydywEnnEynMZ0VdhL4F/nT9qzyvE5uhkzQVTtuGAMtu6ITjKQKxpGT/28XId0IIIYQQQogdJB6Pc/UNf2Bp2EW812iUUnTPdpOy3Bzy2yfIyMqhtDCTuvXrp/rk+dL1rwwdsj0OaoIxHF5HOvtf0rTQNY2GSIKRvXMl+98uRBJaCCGEEEIIsQO8/vrrDBo0iDtvu5m5j/2ZVDRI1vr1U06bTmF+HtFEiuW1YYoynOn1Uy31r9YF4pTle3E7mqcBxlMmpmXREE6gFHTL8kj2v12MBFdCCCGEEEJ0ouXLl3P88cdz7LHHsmzZMgDi4QC1Sz/Hbmz4+L3x+qmUpdLrp1rqX+V4HdSFE/Qr8JLrcxCMpVjTEEXXNA7tl8f0QyX7365GpgUKIYQQQgjRCaLRKLfddht/+tOfiMfj6e3FAw5g8vnX8KPKI2laOG1Gep/d0AnFUwRjqVbrp1pSqs9eVEV5TYhcr4Mst4OSLDfj9i1gVFmejFjtgiS4EkIIIYQQooMsS6VTnXvsBp+9+xaXX34ZK1euTB9TXFzM7278I+X+IWR5HATXBakOxnB49Vbrp2ybWT8lKdV3PxJcCSGEEEKIvcLGAdFPCVSWVQfTI0qxlMl3bz3Du/+8I73fZrNx6aWXcu211+L1+rj/nXIWVTTRJ99DMJ6kPpzA57Jh0zUawkmcdp3SLHe766ckpfruRYIrIYQQQgixx9s0IHLZDMryfUwYVNihdUvLqoM8+uFK6sMJijNdeBxu/Ecfz4IXHiQeDjBqzFgefvA+9t133/Q5EwYVUtEUXb9+ykdFY4yaUJxwPIXbbmNM3zxOH9lD1k/tASS4EkIIIYQQe7T2AqJIIsWiiiYqmqJMH71tiSEsS/HmN+tYuWI5wwcNSE/tKyoo4OQLr6U6YvKzE06if/++rc7bdP1UjtdBpsdOcaaLcfsWMlrWT+0xJLgSQgghhBB7LMtSzF5URf36UaOWgMjvsuNz2lhaHUrXl9pagDNvwRfcfvEF1KxczIBHZ+PLyknvGz7ueIKxJOU1YdY2RttM5ZP1U3sHScUuhBBCCCH2WGsbo5TXhCjOdKUDqxaaplGc6UrXl9qcQCDA5ZdfzsTDD2b1t58QCwd47dG/tjnO7TDS6dTb07J+akBRBt1zPBJY7YFk5EoIIYQQQuyxwokUsZSJx+Fud7/bYVAViLUJiCxLsaYhwtP/foo/33ItNVVV6X3Zhd0YePARbdqKJsxW6dTF3ke+80IIIYQQYo/lddhw2QwiiRR+l73N/vYComXVQf7x0rv886/XsXbxl+ntTpeLcT8/j5LDTmXfbnmt2lFKUdkUY3BpZqt06mLv0qXTAt977z0mT55MSUkJmqbx4osvttqvaVq7X3fccUf7DQI33HBDm+MHDBiwg5+JEEIIIYTYFZVmuSnL91HZFEMp1WpfS0DUt8CXDoi+LF/L6dPP57Zf/qxVYNXrgMOZeed/uOw3v6MgO4Ol1SGCsSQpyyIYS7K0ujlRRXvp1MXeo0tHrsLhMEOGDGHGjBmcdNJJbfZXVla2evzGG28wc+ZMpkyZssV2Bw4cyNy5c9OPbTYZoBNCCCGE2BvpupZOhb60unntldthEE2YVDbFWgVElqWY+20Vi95/A6UsAPJKenLiBdcw4MDDWFod4od1QaYd0os53zVn/qsKxHDaDAaXZnL0wI6ldRd7ni6NOiZNmsSkSZM2u7+oqKjV45deeokjjjiCPn36bLFdm83W5lwhhBBCCLHn2ZbCwJumQt9cQLS2MUplVGfi9Mt47cE/cNQZv2LslBnYHA6AdPKLyUNK+NXYMsn8J9rYbYZ0qqqqeO2113j88ce3euzSpUspKSnB5XJxyCGHcOutt9KjR4/NHh+Px4nH4+nHgUCgU/oshBBCCCE2b1sCoy3pSGHg9lKhu8wIN998DVdffTXFxcXp5BeHTJzC/gcfTlZeYas2Nk5+0ZL5T4iN7TbB1eOPP47f7293+uDGRo4cyWOPPUb//v2prKzkxhtvZMyYMSxatAi/v/1h2ltvvZUbb7xxR3RbCCGEEEK0oyOB0ebO72hh4JaAyDRNHn74YX73u99RX19PfX09TzzxRDr5RSxltQmsQLIBiq3bbepc/eMf/+DMM8/E5XJt8bhJkyZxyimnsP/++zNhwgRef/11GhsbefbZZzd7ztVXX01TU1P6a/Xq1Z3dfSGEEEIIsV5LYLSoooksj50+eT6yPHYWVTTx6IcrWVYd3OL5mxYG9rvsGLqG32WnX4GP+nCCt76twrJUm3MXLFjAyJEjOf/886mvrwfg5ZdfZt26dR1OfiHEpnaLsPv999/nhx9+4JlnnunwuVlZWeyzzz4sW7Zss8c4nU6cTudP6aIQQgghhNgGmwZGLYV9/S47PqeNpdUh3vq2ij55vs1OEdxSYWAAn9Pg8x8b+GRlHSWZbiJJk2hTPf/3p5t49NFHWx17wsmn8n93/jW9Xn9bk18I0Z7dIrh65JFHGD58OEOGDOnwuaFQiPLycs4+++wd0DMhhBBCCNERWwqMNE1LJ41Y2xjd7JqmzRUGrg8nWFYdoi4cpz4U53f/CeHQFfWfv87Clx4iHt4wIpbXox+jp15J70EH8tKSKBMcQfoW+Lc5+YUQ7enS4CoUCrUaUVqxYgVfffUVOTk56QQUgUCA5557jr/85S/ttjFu3DhOPPFELrzwQgCuuOIKJk+eTM+ePamoqOD666/HMAxOP/30Hf+EhBBCCCHEFm0uMGqxcdKIzWmvMHB9OMFXqxuJJlIopUhaikRK8fXjv6N+8cfpcw2Xl2EnnMcxP5+G3+1qd51We8kvJBug2BZdGlx99tlnHHHEEenHl112GQDTpk3jscceA+Dpp59GKbXZ4Ki8vJza2tr04zVr1nD66adTV1dHfn4+hx56KAsWLCA/P3/HPREhhBBCCLFN2guMNrYtSSNa1kYtqmjC52w+bll1iGgiRZbbxvLaCIauUZzpJDXqGD5eH1z1OuQYsseeQ99e3cj0NI+cbW46omQDFNtDU5uu1hMEAgEyMzNpamoiIyOjq7sjhBBCCLHHsCzF/e+Us6iiqdWaK2hOGrG0OsTg0kzOP7xsiyNFG2cL9DkNvl7ThK5MAoEgEc1FUaaLTLcDpRSf/fsv5Aw+kszeA3HadCwFh/TJJcO9IbgLxpI0RpJcetQ+ElSJVjoSG+w22QKFEEIIIcTuT9c1JgwqJMfrYGl1iGAsScqyCMaSLK0ObXPSiJa1UYNKMqkPJ1n97ad8cMdMlv/3Tlx2A4eho5RC0zSGn345nh77kjQt3A6DlGWRMK1W7bkdBvGUucXpiEJszW6R0EIIIYQQQuw5OpI0YkuFhvsW+HH0beTFO//I4ldfBCC8biWuwUcT6zcCn9NGjteBroNd10FrnnZo03UcRusxBqlhJTqD/PQIIYQQQoidbluSRmyp0HCPLCd33nknN998M+FwOH1OVs99yczJQQGheJJ4ysRtN+iW7cZSihV1EfrkefG7NnwMbqlhNbg0U2pYiZ9EgishhBBCCNEltpQ0YuM1VcWZLjwOdzqz3/vvzOWjJ/7M6hXl6eOdviz2Oe488g+YgN1uUBdKEDcVoXgKDSjMdFEbSpDhsmPTdULxlNSwEp1OgishhBBCCLFL2Vyh4URjFXP+7xbKP52XPlbTdLqP/hnHnnMRubm5lFeHqY8k8DgMVAJsmgYaRBImh/TJpX+Rn8WVQalhJXYICa6EEEIIIcQupb1Cw/XhBHPeX9AqsMrpM4gx037DOlsRP4YNcnJgnyIfDZEkAFluG6YFP9aHOf2gHhzWLx9d1ziif4HUsBI7hARXQgghhBBil7JpoeH6cIIvVzWQ6DYcf98RxNaVUzR+Jj1GTiTqtOFKWTSEE7y3pAa3w8BUCpuuk+NxUJTpJN/noizflw6gpIaV2FEkuBJCCCGEEF1u46yAgWiSaO1aXn99DsdOncWy6hDBWAoNjbKTLkd3usHuIdfrIJQwiaVMgtEkGhrdXR4ynHaSpkVVIMrqhghH7VcoiSrETiHBlRBCCCGE6FIbZwUMhsMsfOVRPnv1n1ipJBklZcRKhuF26ARi4MrKI5a08DoMnHYDNI36UAIARXOQlqZpgEIm/ImdRYIrIYQQQgjRZVqyAtaF4jR+9wFvPHw7gdrK9P65zz5M3xl/Id/vBCASN3HadXI8djRNQylF0rLI9jqIJS2SpqIhksCm6xRmuCjKcNIQSbK2MSpTAcUOJ8GVEEIIIYToVFsq/LvpcbMXVbF82RK+eOZOlnz+YXqfZtgoOXQKhYedQSRlUdkUw67rGLpGYYYL9/piv/GUBYCORr8CH/0L/SQthcPQ8btsmEqxsjZMOJHaOU9e7NUkuBJCCCGEEJ1mS4V/N011vmRNDY/d/Qe+fP0JzNSG4Kdw3wMZ9vNLceZ1JxpPYTN01gViFPidGLpONGFi6Bo2XSMcN9EAl8Ogb4GfTI+j1TWi8RROm4HXIR97xY4nP2VCCCGEEKJTbKnwb0VTlOmje6UDrJqaGsYePIyqyor0+a7sQgZPuYhew8eiaRqWUoSB3vk+UpaiKZpicLdMmiJJakJxwvEUbrvBPoV+PA4b2R57q/4opahsijG4NFMSWoidQoIrIYQQQgjxk21c+LdvvpdQ3KQhksBh6PTN97KsJsxb31bRJ685JXp+fj5Dhw1ndmUFhs3OyBPOQR9yIhkZXhIpC4dNJ2laGLpOvs+J32Xjix8bSJkWOV4HmR47xZkuxu1bSGGGk8c/+pGl1c21sdwOg2jCpLIpRo7XwdEDC6WOldgpJLgSQgghhBA/WUvhX7dd5/MfG6mPJEhZVrreVI7TZGmVnk4sYVmKa2+5jR/rI/SadB7O7GKW10UI1EexGTpuu46ua3TL9uB32dDisF9xBqeP7EGG295mLdf00b3S0xGrAjGcNoPBpZkcPbDtdEQhdhQJroQQQgghxE8WTqSoDcWpC8eJJS2cNh2nzcCyLL5652XKX32QkadfSvmwUr5fF+CzFfXUBOMMnHoji9cFUTUhHDYDpQAUDZEkTptOnq95DVXL9L4RPXPaHYXqW+Cnz1jfNiXSEGJHkeBKCCGEEEL8ZB67QW0wTl04gUJRl1LE1pWz5vV7Ca/6FoAFT9/NfYNGsS4KKVNRlOkiZVpkuOzEUiZJU2FZCtMCv8uGXddY2xglkVLk+rY+vU/XNUm3LrqUBFdCCCGEEOInW1EXpjIQIxBNYsVCNH7wJI2fvwbKSh/jKt6HhqYgdlcm2R6DulCcpmiSokwXpU4X1cEELruBx9GcETCWsqgOxBneM5tTR3SX6X1ilyfBlRBCCCGE2KKt1a1aVh3k6U9XE0skCX0zh7p5j2FGmtL77dkl5B71S3L7H0jS6SLLbcdhaDjtFvFgnMZIkgyXnWyvnVjSYki3LDRNI5pMURWIccKwUgmsxG5BgishhBBCCLFZW6tb1ZIlsPzbr/nx8duJVvyQPlezu8gcdRoZI36G3W4nnrKIJEx8TpPaYIpwPEXStKgLJwDI8zlIWRZJS5Hnc6BpkO1x4nfaN9c9IXYpElwJIYQQQoh2balu1drGKJMGF2FaioVrG1n13nOtAiv/foeRd+RMbBm5JE1AAwWE4kkSSRNN13DYdFyWQSxppgOtDLcdh6FLjSqxW5LgSgghhBBCtLFx3ap+BT40rXkaoN9lJ5Ey+WRFPV+tbiTHa2NFTYSek85j6afv4MgsIO/o8/H0GoJN1zCVAlOhAy67Qdy0CJsmeV4Huq7htBkkTQVAJGHicdhQymJpdUhqVIndjgRXQgghhBCijZa6VcWZrnRgBVAfjvPO+x/S2FBP4aDRFPj8VDbFiZmZDJj5Z1xFfYgkQanmrH+WAkMHXdfxOAxIQMxURJLNUwxNy8LvsmFZCk1TxFMmlYE4Q7plSY0qsduR4EoIIYQQYi+xtcQUGwsnUsRSJh7Hhil5gfoanrz7VlbMfw2nP5u8vkNwOXIo8LuoDsbI6TmAcNxE1yxcDgNN04gnLXStOU06moamgdPQsOs64UQKXdPwu2wUZbjI9ztpiCQ4/aAeHNYvX0asxG5HgishhBBCiL3ApokpnIZOvt/JiN457FuU0SbQ8jpsuGwGkUQKj13jw5ef4o1//o14OAhAPNhAxfyXcQ6+mL4FPkLxFClT4bRZKHRSpiJlWdgNnd55HrrneFjTEGVJVRClIMNlo4fXQ0mWm3yfE7/LRiiewqbrlOX7JLASuyUJroQQQggh9nCbJqaIJXV+WBfk4xX1vPntOvYp9DOse3Y6AyBAaZabPnleXn3rbb54+q/UrFqabs/u9tF7wgyGTTgFv8uGpmkM7Z7FkqoAq+tNMlwG0aRFzwwnZQU+umd70DSNHtluGsIJLKUY0y+PTI8jPeVQEliIPYEEV0IIIYQQe7BNE1M0RBJ8s7aJaMIk3+8gFDNpiCRYuKaBJdVBjh1czL7FGaxcvZq/33w5C999rVV7hSMm0nvSueTlF9CvKCsdHOV4HexXnEGW28Hh/fP5eHk98ZRFtseBqRTReIrKphgDSzMBqA4lMAwdt8MgmjCpbIpJAgux25PgSgghhBBiD7Px2qpANMmy6iDFmS4AyqvDRBMmOd7mUSNN06gPJ7AsRVUgwOLKANrahcy95zeY8Wi6TX9pP7ofdyGOkgE43HaGdMskx+tI71dKsS4QZ0j3LE47sAcH9c5JT0OsCsRw2gwGl2Zy9MBCgM3ukwQWYncmwZUQQgghxB5k07VV8aTF6voII3ploxTURxL41k/lA0iZirpQgpSpyPLYMU0LM7snaDoAbl8mx864lP3GnkhSwbLqEPGURW0ojsO2+ZGnvgV++oz1bTaBxpb2CbG7kuBKCCGEEGIP0bK2qi4Ux++ykeGyE1AJQvEkX6xqoG+Bf32SieaPgEopasNxzFSCHI8Hh92gJhhDc/jZb/K51K8uZ+SpszhkYK90MOayG6yqj9Ajx0ttKL7FkSdd1+ie42m3r1vaJ8TuSoIrIYQQQog9QMvaqlV1EVKWxcq6CCnTwtA1DF2jJpjAbY9g0zSSpoXTZhCJRFkx90kaP3+Nvr9/nKTuR0PDAvofeQpJ0yKctAjGUmS47QC4HQZOm84Jw0rwu+wy8iTERiS4EkIIIYTYA6xtjPLl6gaqgzFMS+Fz2bG7bCRNRTRhkrIs1jRGKfS7CEaTVC3/mK+eu5NY7VoAFr38IL1/dgk5XgfheIqk2ZxGPRRPkTCt9HWiCROnzcDvssvIkxCbkOBKCCGEEGI3tXHiisqmKD/WhbEsyPVtSHHutGkUZbpQCqJJExWs4uun7qTuu482NKTpJLHhsuvsW5LB8uow1cEYXqcNm67jMJrXX0m6dCG2TIIrIYQQQojd0KaJK0KxFNWBOFkeO9GkiaFpOGx6OiOgxzD5ce4TfPPBMyTi8XQ7Wb0H0+O4C9lv0GD6FvjI8TrRCiAQS7AuEKNbthu3QycYS0q6dCG2QoIrIYQQQogusvHIU0fWLW1aFNjjcPN9ZRORpEm4MUVDJIHdMHDbDbLcBjXffsTXz/+NeMO6dBtFRUVcfcMf6H/oJF5fuI54qnkaYHPCC51srxND18n2OPixLiLp0oXYBhJcCSGEEEJ0gU1Hnlw2g7J8HxMGbTl42bQocHOdqjhrGqLYNEgpjURKYTcUgWiC6vowy5+8DTMSAEA3bJw67Vzu/8utZGU1F/Qty/e1qTt1SJ9cxu9biNthSNIKIbaRBFdCCCGEEDtZeyNPkUSKRRVNVDRFmT6612YDrLWNUcprQhRnutA0DaUU5dVhUpYi1+ekMZrEtBTxpEXctDANF7ljp1H9+v/h6z2U3pMvZF23Pvzt/TWcMVKnb4F/qzWphBDbRoIrIYQQQoidqL2RJwC/y47PaWNpdYi3vq2iT56vTXBjWYrymhA1oRg+pw2lFMFYivpIAr/LhsduUPnVO2hF/VG+HCxLYSlwDTqKAk8uuQMOxO9xEDctPlhaSyxpMePQ5kBO6k4J8dNJcCWEEEIIsRNtOvK0MU3TKM50saw6xNrGaKtgp2Ua4cI1jZTXhKlojFHod5HttRNJJAlUruWHF/9G/ZLP8Q86krzjLsNSLe3q+MpGYCpojKawGxp2HdY2RjYbyAkhOk6CKyGEEEKInSicSBFLmXgczanMW0afEqaFw9BxOwziqRjhRCp9TutphE6qAk6qAnFWxIIsXBFi7bwnafz0RbBMAIKL/kfWyBNx5PfGUqAATYdkSpHSTBIpiKcsyvJpN5ATQmwfCa6EEEIIIXYir8OGy2YQSTQX6i2vDlMfSZCyLGy6jtdhkO114HU0f0zbeBphrtfBD1Uh6sIJGiJxGr55h7r/PYIZqk+3b2QUkDf+XPTcXlgKWsr/pusAK9B1jaSpWF4boUcOrQI5IcT2k+BKCCGEEGInKs1yU5bvY8GKOhrCcWJJC5/Lht2wkUiZrGmMkrIU0WRzwNMyjdBt1/l4RT114TjhyuWsevVeYqsXbWjYsJM18mQyD56CbndhsSGw2pgFWApsOiRSJrWhBG67sTOeuhB7PAmuhBBCCCF2Il3XOGq/Qt76bh01oQRFGS7shk7StAjHTfJ9TjJcduZ+V03ffD/hRIpoMkVFY4yqQIzqd5+i6t0nQG0Indx9DyJ33Hm4c4pIWu0HVRuzUHhsNjRAQyGrrYToHBJcCSGEEELsZG6HQb7fiU3XCCdMwokUNl2nIMNFWb4Pu6Gl10J5HTZMU7GiNkw8ZaJl5KcDK1tWMdnjz8NTdiAAyU2iKm39l0XzuquWbajm5BlZHge5PieRpLlznrgQezi9Ky/+3nvvMXnyZEpKStA0jRdffLHV/nPOOQdN01p9TZw4cavt3nvvvfTq1QuXy8XIkSP55JNPdtAzEEIIIYTouHAihcOmc3CfXA7pk8vI3s3/juiZTY7XsT6pRXPQVZzhBCAUT2FZCu/AI3D3HkbmmLMomXlvOrBqoW/06U4BmgZ2HXSt+YOfrjVvK8xw0r/IT57PmV7fJYT4abr0lRQOhxkyZAgzZszgpJNOaveYiRMn8uijj6YfO53OLbb5zDPPcNlll/HAAw8wcuRI7rrrLiZMmMAPP/xAQUFBp/ZfCCGEEGJ7tCS1iCZNMtz2VvuUUlQHYjQ1NHDNFZeQiATxTrxsfaQESkHRqTdhbW4yn9rw35Y4y1LNAZbDbuC0GbjtBgf3yaE6mGBwaSalWe4d8jyF2Nt0aXA1adIkJk2atMVjnE4nRUVF29zmX//6V84991ymT58OwAMPPMBrr73GP/7xD37729/+pP4KIYQQQnSGlqQWiyqa8Dlt6XpX9eEES9c18elbL7Bq9iOkIgEAyjKGY5QMxlSsr13VfmClAFM1j0wZGhRnuDGVwlhfw8rntBFPmWR7HVQFEuT6HBw9sFBqXAnRSbp0WuC2eOeddygoKKB///786le/oq6ubrPHJhIJPv/8c8aPH5/epus648ePZ/78+Zs9Lx6PEwgEWn0JIYQQQmwLy1Ksro+weF2A1fURrPWVeze3vcWQ7ploGny9ppFANEF1MMacdz7g1Vtmsvy/d6YDK93hItZYg8dpQ9vGGMhhaGS67dgNcNh0BpVmUOB30hRNkrIU2R4H+3fLZProXvQt8Hfq/RBib7ZLT7CdOHEiJ510Er1796a8vJzf/e53TJo0ifnz52MYbVOG1tbWYpomhYWFrbYXFhayePHizV7n1ltv5cYbb+z0/gshhBBiz2BZirWNUcKJFF6HjdIsN7qusaw6yOxFVZTXhIilTFw2g7J8HwOK/SyuDLKsOkhDNImhQVm+j5OHd0fXSZ8TiqWoDcVZU1HFd688yLpP32ie97de9xHjGTD5fCpNL3ZDw+cwaIq1n3xCY8OMQK/DxqiyXCoaY1QH48SSJj1yPIzolcPwntnsW5yRfg5CiM6zSwdXp512Wvr/gwcPZv/996esrIx33nmHcePGddp1rr76ai677LL040AgQPfu3TutfSGEEELsvrYUQP1vcTX14QTFmS48DjeRRIoFy+v471dryXTbQDUnooinTL6vDPLukhqKMl14HDaKM10U+R28+9IrvP/E30hGgulr+op6M+TUSyndbwRKKQL1EULxFLleB+F4lNT6KGrjgEpbn7DC0JuzEeZ6nSRNGN4rmxOGleJ32iWgEmIH26WDq0316dOHvLw8li1b1m5wlZeXh2EYVFVVtdpeVVW1xXVbTqdzq4kyhBBCCLH3WVYd5NEPV7YJoL5Z28hb360jw21nWPes9Jopn9NGyrJoCCcIRBL43Xb8LhsZbjuJpEl5bZh1TTGO278In9PGFx/O4/WH/pC+nt3l5bDTL8Da92hy/R6gOWV6ns9JJGGSSFm47AaRhInNWL/+an1adV0Dl91A15uDrmU1IXrmejl1RHeZ+ifETrLLr7na2Jo1a6irq6O4uLjd/Q6Hg+HDh/P222+nt1mWxdtvv80hhxyys7ophBBCiD2AZSlmL6qiPpygX4EPv8uOoWv4XXaKMlzUBONEEqlW5wRjKRrCCXQNQokUNk1rDn5oDoBsukbKsvhiVRMfLKtljXcfsvoNByB76FEMu/wxhk48A4fdQdLcULTKZmjk+hwUZriaR57WB1J5Pie9872U5XvJ8zkxlUKtD7YGlsiaKiF2ti4duQqFQixbtiz9eMWKFXz11Vfk5OSQk5PDjTfeyJQpUygqKqK8vJyrrrqKvn37MmHChPQ548aN48QTT+TCCy8E4LLLLmPatGmMGDGCgw46iLvuuotwOJzOHiiEEEIIsS3WNkYprwlRnOlKj0y1SFoKh00nFEsRjKXS6dQTpkUgliIYS2JaUBmIYTd03HYDp6Fo+v4j3P0OprwmhM3QsRsaxRMvoPDwAEZxfxotRSSeIsfjoDoYw+Ft/jt4KJaiONPNAd0zMXT4sS4KGhT6nTjsBknTImkpvE4Dn8vOiF7ZXHl0f2y23erv6ELs9ro0uPrss8844ogj0o9b1j1NmzaN+++/n4ULF/L444/T2NhISUkJRx99NDfffHOrKXzl5eXU1tamH//85z+npqaG6667jnXr1jF06FDefPPNNkkuhBBCCCG2JJxIEUuZeBxta0A5DB2HTSeeMkmYFkopgrEUlU1RmqJJkqbCZmi47DqaplG15AvWvHYvseqVFJ58PZ6yA/E4tOagLbMY019EKmlhAR8tr2d4ryycMZ2qQAzQ8LlsFGU4Ka+NMKA4kxOGdeP5z9dQE4zjsGk4bAZZHjseh40eOR5OHdFdAishuoCmlFJbP2zvEggEyMzMpKmpiYyMjK7ujhBCCCG6wOr6CH996wdsRnPw4jB0/K7mmlRKKT5YVsOahhgDS/w0RVOEY0mqQ3FCMZOUpXDaNLypIKvefJDar+el27VlF1P6iwdw2G2k1qdn12lOTGG36dg0yPI6KcpoXmcFGnk+B3k+J30LfBw9sJC+BX6WrAvy/OerKa8JYSnIctvpV+hP7xdCdI6OxAa7VUILIYQQQoidJZowqQ0lWFYdxGFrHoHK9jgYWNL84aounCSWNJlfXte8BsrQSVpgNzRSqQQ1H73M0g+fxkpE0206ivqSc9Sv0HSjue7V+oK/Fs3/2nSNHI+dggwng0ozOWdULzRNI5o0W6WAB9inyM9vJ+3bbop4IUTXkOBKCCGEEGK9lnpW31cGeO2bSsKJJElLEQwl0DSoDcZZURNCaWDXNRw6xJTCMiGQNFGAueYramY/SKx2dbpdw+2ndPwMtAFHousGdptG0lRoWnNQZWjNSdWTpoWu6/TO8xGIpjB0ne45ns32V9e1Le4XQuxcElwJIYQQQrChntWy6iDfVgRoiCTQNQ27rqE5DOIpE9OyaIqp5il8OqSs5kyAdpuOSyVZ+cKfCP/w0UatamQdMImCI6Zi92QQTVp4nTaUUihlYmgauq6hAabSSJoWPqeNfL+TH+vChDfJRiiE2LVJcCWEEEKIvd7G9az8ThstpXkDsSQa4LDpWAoSpkoX7V0/qw8NSJkWmmFHs8x0m67SAXQ7dha9+w8GFNXBODZT4bbrJE1FLGWBpq1fw9XchqFr9MzzEEuaOG0GXod8VBNidyKvWCGEEELs1TatZ1UXThA3rXQWwOYYSKGhsDZKA2Yq0JRCNzSs9cFR4dHnEa1aTs6hZ5I5ZBwehx2brpEwFYUZLqqDcUwFHodBwrSIpyxMy0KhNdey8jrI9zqpbIoxuDST0qy2mQqFELsuCa6EEEIIsVfbtJ6Vw9DRFMSSJiiw2zRSlmLjBMvJxnU0vP0QGYOOxDvgUDSaR7KMjEJ6nv8wDoeNRKr5eEspCjJc9Mnz8n1lgFX1EfL9TmyGTm0ojmWB12ngtOkUZrpYF4iT63Nw9MBCSU4hxG5GgishhBBC7NU2rmfVvBZKYeg6KVNhNzR0TSOlLJQClYzTOP95mj5+HswkiaoVOHuPQHO4ALAssBkGdk3H5tAY3jOHnrmedAr3/kV+akJxPA6DvgU+QvEUq+uj1Ifj6HpzNsL9u2VKOnUhdlMSXAkhhBBir+Z12HDZDCoaI1Q2xWmIJAjFk1gK4qbCUhagCC1ZQO3cv5NqqtpwsjIxG9ZiLyzbsBbLAqVDWb6XQaUZzYWC13PZDfYp9NMnz0dtKI6uafQr8FGQkcfwntnsW5wh6dSF2I1JcCWEEEKIvU5LyvVwIoXHbpDpsjF3cTV2Q8NpN8hw2wnGksRSikjNGhrefojI8s83NKAbZIz4GdmjT0NzNKdCNzTwOQ0SKYWOxsDi1oGVUorKphgH9MjmvDF9qAzEpD6VEHuYnxRcxWIxEolEq21bq1oshBBCCNGVWlKul9eEiKVMnIbOqvoI0USKkAJIopQiGYtR/8HTNH36XzA3pER39xpCzrjz8Rf3xGFoRJImSmm4bDo5Piduu0FTJMmaxihOu4HbYRBNmFQ2xcjxNq+lstm2XL9KCLF76nBwFYlEuOqqq3j22Wepq6trs980zXbOEkIIIYToehunXC/OdOFxuKkOxFjVECGaXF+0CkUqZVH99iMEvnw9fa43p5BDzriEYNEB1IVT62tTQabbQYHPSZ8CH/k+J26HzqK1AXrn+miMJKkKxHDaDAaXyloqIfZ0HQ6urrzySubNm8f999/P2Wefzb333svatWt58MEHue2223ZEH4UQQgghfhLLUqxuiPDEgh9Z0xBh/9JMdF0HIJxMEYwmSZoKQ9dQSqFrGt3Gns7iRW+jLJPMg06i7Kgz6dmnmN55Xr5d24TXacPjsJHtsZPhtqenAAZjSfJ8TmYc2gtN02TqnxB7kQ4HV6+88gr//Oc/GTt2LNOnT2fMmDH07duXnj178uSTT3LmmWfuiH4KIYQQQmyXlmmAC9c08k1FE267QTKlKCvwAvB9RYBENIzZUIFW1Bcdmgv7+vLoPeUqXIV9IKOI4jwPZfk+Lh+/D3//YAWLKprometpd13V4NJMumV7JJgSYi/T4eCqvr6ePn36AM3rq+rr6wE49NBD+dWvftW5vRNCCCGE+Ak2ngbodhi47QZep0F1MEYglsTQYe1nc1j12oOgFN3OexC3x4dhaKRMhXufURgaGIZOnzwfNcE4VaE4EwYVUtEUZWl1c32s9tZVSWAlxN5H7+gJffr0YcWKFQAMGDCAZ599Fmge0crKyurUzgkhhBBCbC/LUsxeVEV9OEG/Ah/ZHgd2Q0fXdHK8DipXLOZ/f76AJc/cihmqxww30PDBv1HrYyJNg1jKAk0nw9089S+eMgknUvQt8DN9dC8GlWTSGEmysjZMYyTJ4NJMpo/uJeuqhNhLdXjkavr06Xz99dccfvjh/Pa3v2Xy5Mncc889JJNJ/vrXv+6IPgohhBBCdNiahggL1zbisRsEYyl8ToNsj4OK6loq/vdPyt99AWVZ6eN9/Q4mc/ixKEuRWl+0ytDAZdcpznRj0zWcNgOvo/njU98CP33G+tIp3WVdlRBCU0qprR+2eT/++COff/45ffv2Zf/99++sfnWpQCBAZmYmTU1NklpeCCGE2A0tqw7yxPxVzPluHS6Hjt0wyHbZqPnyLd5/6m4Socb0se68UvpMnoW913AspbAshdOmY1qQsixKsjwc2CubunCCwaWZnH94mQRQQuxFOhIb/OQiwj179qRnz54/tRkhhBBCiE7Rss5qTUMEl8PA57DRWLWKj/51C8FV36eP02xOskf/nIJRU+hbkkOPXC+r6sOsrA0TTZooNHI8DvYr9lMXTshaKiHEVnU4uPrb3/62xf0XXXTRdndGCCGEEOKnSCRMHn5/BT+sC9I7z01jJMnqhgjJuE646sf0cQX7H0bPY87Hl1dENG5SG4rTJ9/LqLJcCv1OFq8LYlrQO685G6DUqBJCbIsOTwvs3bt3+v+rV6+muLgYm605RtM0jeXLl3duD7uATAsUQgghdj9vf1/FA++U821FEwrQNQ3Tai4MbNN1aub/h4Yv3iDvqPPJ2Wc4xZluDu6Ti1KKT1bWY9c1euZ6cdkNyvJ97N89k3y/U9ZSCbGX26HTAlsyBQL4/X7efffddGp2IYQQQoiu8Pb3Vdz6xmLqw3HiFT9Q9/6T5E++gpTdB0BKt3APPQ7P0GMwbHYMTcPrNMj2NBf/HV2WS0VjjFMP7E5Zvk+CKSHEdvnJa66EEEIIIbpSKmXx2Icrqa+rofZ/j7J6wesAqHf+Rd7Rv8JSgFLYbQZoNjQ0cnwOYkmLYCxFhtuOx2nDZmgUZbronuPp2ickhNhtSXAlhBBCiN3aJytq+OiVJ1k151FS0VB6e2zN92AmMWx2LAtsugYoDF0jkTTRNUiYzanYowmzVZp1IYTYHh1+B1m4cGH6/0opFi9eTCi04Y1sT0nHLoQQQohtY1mqy2o9ffDBB0z7xS9Z/sN36W02l5fsMWfhGXoMms2gOaRq/tLQ8NgNIkkLp92Gw9BRSlHZFGNwaSalWe6d0m8hxJ6pw8HV0KFD0TSNljwYxx13XPqxpmmYptnpnRRCCCHErmlZdZDZi6oorwkRS5m4bM3JICYM6vzMehsHcaH6Gu6+9UaeeOJfrY7pdcgx9Dv2PNYlXUQTKcyN0nb5nDY0wLQs4qbC6zQAxdLqkKRZF0J0ip+U0EIIIYQQe6+WelL14QTFmS48DjeRRIpFFU1UNEWZPrpXpwVYGwdxkUSCf19xEoHqten92T32oWjiBQwcOoKkqbA3RLG77IQTJgnTwu3QKcvzEkqkqGyMoWvgths0RVOSZl0I0Wk6HFxJwWAhhBBCWJZi9qIq6sMJ+hX40LTmER+/y47PaWNpdYi3vq2iT54vPRq0vdMHNw3iSrLcHHbyTF697yZcvgx+e+0NHHLMz7n9raWsaoiS47HjtGkEYyagcNp0cjwOmmJJdE2jMMPFkB5ZTB/dC7/TLpkBhRCdZrtWbf7rX//igQceYMWKFcyfP5+ePXty11130bt3b372s591dh+FEEIIsYtZ2xilvCZEcaYrHVi10DSN4kwXy6pDrG2M0j3Hs93TBy1L8fS8r1hbn2D/sm7pa42dfBqpUCOFBx9P/oCejN+vGMMweOzDlaysC5NIWVgKfE47Q7tnUZLlJhBL0hBJ0C3Lw/RDO29UTQghWugdPeH+++/nsssu45hjjqGxsTG9xiorK4u77rqrs/snhBBCiF1QOJEiljLxbCa7ntthEE+ZhBOp9MjTooomsjx2+uT5yPLYWVTRxKMfrmRZdbDdNhKJBNfceAs3TZvAwv/c2yqI0w2Do8+aRZ9uRekgbty+hTx6zoH85dQh3HziYG4+YRDTRvUiw22nLhxH1zRG9s6VwEoIscN0eOTq//7v//j73//OCSecwG233ZbePmLECK644opO7ZwQQgghdk1ehw2XzSCSSOF32dvsb0lt7rEbvPJ1ZYemDwK88cabnD/rQlatKAfgy7n/Zczk0+i179BW13E7DKoCMcKJFAA2m85BvXPT+7syk6EQYu+zXQkthg0b1ma70+kkHA53SqeEEEIIsWsrzXJTlu9jUUVTcxa+jUaVNk5trqBD0wdXrlzJ2efO4oO5r290oE7hyMlE3flt+rG1+lS6rklRYCHETtPh4Kp379589dVXbRJbvPnmm+y7776d1jEhhBBC7Lp0XWPCoEIqmqIsrQ5RlOHEtGi1runogYVEk+b66YPt149qGXmqD4R47N6/cMsf/kgiHkvvz+49iOJJs0hl9+CTigQuX4jeeT4AqU8lhNjldDi4uuyyy5g1axaxWAylFJ988gn//ve/ufXWW3n44Yd3RB+FEEIIsRN0dApd3wI/00f34qmPV7FgeR2NkQQpS+Fz2inwObGUajV90Oe0EYylSJgWDkPH77IRTZis+/YTjv/9KaxauaHci82XTcnRvyB3yHjshkbCVEQSKT5bWU+3LA+xlEllU0zqUwkhdikdDq5+8Ytf4Ha7+f3vf08kEuGMM86gpKSEu+++m9NOO21H9FEIIYQQO9i2ZvPbNACzlCKWNPE4DJRyEE+ZWJbFh+V1LKkO8esj+1KW72PB8jqSpklNMEHStLAbOvl+B3bDwKdCGwIrTSf3oJ/R6+hpONxeLAXxlIUGeBw2GiNJvljdQEmmW+pTCSF2OZpSSm39sPZFIhFCoRAFBQWd2acuFwgEyMzMpKmpiYyMjK7ujhBCCLFDtS0GbCOSSKVHhlqKAW8agDkNndpQgkgiRTxlEUua+Fx27IZGMmVRGYhRlOFibP98nvp4FcFYCpsBhgam0jAtRbbXwQ3H7cuNvzyFHxvi2A6dQVGvfhj6hoTGSimiSQuXTSNpKs4f25cJA4skOYUQYqfoSGywXXWuWng8Hjye5kWilmVxyy23NDdqs/G73/3upzQthBBCiJ1gW4sBWxY8Pn/jAMxNdSDGsuogSVPhcRoUZWxIWuG0G2S5bSxZF2RxZYCEpbAsRfS7+STWLiZ33Aw0BfXhBA++v4Kr73qUZ76u58vVjaQsMDYqFqNpGg6bTjhh4nUY7N8tU5JUCCF2Sdu15qo9pmlyzz338Ne//hWb7SfFbEIIIYTYSbalGPDSqiCNkUSbAMxmaGiaWp8GXRFPmliAoTWPSlUHE8TN5mK+qbo11L/9INEVXwLgKhtBTtlQ0GDJuiDPfdP82SHbbac+ksRhaK3rWqGIp0z6Ffg4oHv2zrg1QgjRYR2Ogr788st2t1uWBcDFF1/803okhBBCiJ1mQzHgzWfzW1GbIBBL0jPXmw546kJxvl7dRF04SSKliCUtQnETh01PTwuMJk1S8ShNHz1D4NMXwUql24388CHeXvtj0zTQoSGcIGVB/yI/n69qpCmawu0wmtsyFeF4Coehc+qB3bHZ9Hb7KoQQXa3DwdW8efPa3R6LxfB6vT+5Q0IIIYTYebalGLCu6ZhK4VlfS2pFbYgPl9URjCVJmBuWbsdSFpay0DUboXiK4Pfv0/C/RzBDdeljjIx8co48F/c+h4ACC4WyoCDDQUVjnKSlOLRvLl+vbqIxmiSaUOhacz8P2yefU4Z33/E3RQghtlOnzd/bdCqBEEIIIXZ921IMuG+Bj+pAjEgiRSJl8uGyOpqiSTTVHPhYG6XGSpgQWV1O7ZwHia9auGGHYSPzoClkHHIKut0FNJ+naxoa4Hc4yPOB12kjZcGRAwpoiiVpiiSJJEz65HmZMaa3JLAQQuzSZHGUEEIIsRdrrxhwylIEYykaIglKs9xMGV7KnG+r+WZtE+uaIgRjSQwNNF0jmWqddDhRvZzKxy4BZaW3ucsOJHvcudizS9rtg8thYKHI8zk56YBSvl7dRHlNiETKIsvjYEQvn6RcF0LsFjocXL388svtbk8mkz+5M0IIIYTY+dLFgBesYsGKepqiCVCQ5bFTlu9D15oDsCVVQX6si2BaCkOH1IYlVBjrR7Ds+b1x9RhE7MeF2DILyR5/Hp6+I9u9rq6Bx6GT43HQGE0ysncuo8ryGFWW16FixkIIsavocHB1wgknbHafTA0UQgghdl+xlEW+30H/Qh9+lx1D16hsivHohyuZProXB/TMYt7iKkwFlgkKSDauw5ldhK5raEqhLI3s8ecT+eFDMkdOQbM5Nnu9TJcNl8OG3aZTmuXm6IGF6SBKUq0LIXZHHQ6uWrICCiGEEGLP0FLrqiGSYEi3rFZ/LPW7mmtdPfXxKmqDcVwOg1jSJBENUf/evwh++QYFJ1+Pp89wWiYIOvJ64MjrscVruuwadruB02Ywpm8ep4/sIdP+hBC7vS7NZfree+8xefJkSkpK0DSNF198Mb0vmUzym9/8hsGDB+P1eikpKWHq1KlUVFRssc0bbrgBTdNafQ0YMGAHPxMhhBBi97VprSulFIFoktpQnGAsRaHfyYLldTRGk/TOcRNZNJfVD55H8IvXQFnUz30QK9X+8gADcBpg05q/NJqnAxb4XUwcWMRNJwzkmmP3k8BKCLFH6NKEFuFwmCFDhjBjxgxOOumkVvsikQhffPEF1157LUOGDKGhoYGLL76Y448/ns8++2yL7Q4cOJC5c+emH0tRYyGEEGLzNq51VR+OU14dpj6SIGVaKMCma9SG4mSEVvPuo7dSVf5t+lzN7sK3/9HNURPN/9h0SK6f6KJrgKah64qUBVluO/2L/GS67cw4tDc9c6WMixBiz9GlUcekSZOYNGlSu/syMzOZM2dOq2333HMPBx10EKtWraJHj81PN7DZbBQVFXVqX4UQQohdmWWpbU4CsemxLkMnZSq+WFXP2sYYKIXbbhBNmkQSJsHGOqrn/ZMvvp4NakN2QN++Y8gcOxNbRh7QPB3G57JhWQqVMrEbGqYFDkNH15rXZo/qm0uvPC8ra8NEk+bOuDVCCLHT7FZDOk1NTWiaRlZW1haPW7p0KSUlJbhcLg455BBuvfXWLQZj8XiceDyefhwIBDqry0IIIcQOt6w6yOxFVZTXhIilTFw2g7J8HxMGtU1f3nLssuogDdEEsYRFMJZgbWOMYCyFUmAzNEBh6BrhL9+k4n+PYUaD6TZ8Rb0Y/vPLcPTcnzX1EVKWhWk1j1LpGnhcNoykjtPQiKcsCjKc2HUdSykK/C6iCROnzcDr2K0+hgghxFbtNu9qsViM3/zmN5x++ulkZGRs9riRI0fy2GOP0b9/fyorK7nxxhsZM2YMixYtwu9vfz73rbfeyo033rijui6EEELsMMuqgzz64UrqwwmKM114HG4iiRSLKpqoaIoyfXSvdIDVcuyqugiRRIq6cILqYIxY0mpehL1+oCthNo9OqZRF7ffz04GV5nCTf9iZ9DpsCgX5GSRSFm6HQcrUcdh0kqaF12FQkuWmsilOMJbEZmh47AbhhElBhguf02BZTZjBpZmUZrm74I4JIcSO0+HgauHChVvcv//++293ZzYnmUxy6qmnopTi/vvv3+KxG08z3H///Rk5ciQ9e/bk2WefZebMme2ec/XVV3PZZZelHwcCAbp37945nRdCCCF2kJYsf/XhBP0KfOksf36XHZ+zOcvfW99W0SfPB8DsRVWsqovQEEkQTZrEkiamqbDpGilTYbWuB4ymaWSPP4/oI1/jHXAoWWOnY/PlUBc1oT5CtseOUs0p2QsynARjKZw2nYZIEq9TpynWvM6qIZLE77ZTlOFkWU2YHK+jVdp1IYTYU3Q4uBo6dGg6kxBsqG2llELTNEyzc+dPtwRWP/74I//73/+2OGrVnqysLPbZZx+WLVu22WOcTidOp/OndlUIIYTYqTbN8rcxTdMoznSxrDrE2sYo0DxyFUmkiCVNfA6DulACRfPvcNMyCX75OrasIjxlB6bbsWeXUHre39PrqgBSFjRGEsSTJkWZbkxLEYqlsBs6+xZnsLYhSmVTDJ/Tjseu47TbyPU6AI3BpZkcPbDtdEUhhNgTbNe0wI8//pj8/HyUUgwaNIjXX3+dnj17dnbf0oHV0qVLmTdvHrm5uR1uIxQKUV5eztlnn93p/RNCCCE6Q0eSUWxs4yx/7XE7DKoCMcKJFAAN0QSheAqfy45pWSRNi5SliK5aRN2cB0jWrMTIKMD1i/3R7Rv+6LhxYKWt//I7bXicNnI8dnrnefl0ZQM2QydlWvTI8TC8VzYjeuUwoNCPAqJJs0PPTQghdkfbFVz16NGDgoICoPkvYx6PZ7uCq1Ao1GpEacWKFXz11Vfk5ORQXFzMySefzBdffMGrr76KaZqsW7cOgJycHByO5orv48aN48QTT+TCCy8E4IorrmDy5Mn07NmTiooKrr/+egzD4PTTT9+epyqEEELsUB1JRrEpr8OGy2YQSaTwu+yt9imlqA7EiSVNAtEkRRkuDE0nnrLIcGsopRFvqqV67iOEv3snfZ4ZqCa24nM8+4za7HUNA1KWwuMwqArGsdt0DuyVw8TBReT7nRJECSH2Wh0OrgoKCliyZAkFBQVUVFQQDoeZNGkS//rXv5g4cWKH2vrss8844ogj0o9b1j1NmzaNG264gZdffhlonoq4sXnz5jF27FgAysvLqa2tTe9bs2YNp59+OnV1deTn53PooYeyYMEC8vPzO/pUhRBCiB2qI8ko2lOa5aYs38eiiia8DoNQ3CRhWkQSKSobo6yqj+J32fj3x6soy/eR47XzfaVFPJ6g/J3nWfH6P7AS0XR7jsIyco76Fc7SAZu9pgZkOO0kTItw3MRSit55Xs46uKdM9RNC7PU0pZTa+mEbnHPOOcydO5fjjjuOd955hx49enDxxRczbdo0LrzwQm644YYd1NWdJxAIkJmZSVNTU4fXeAkhhBDbwrIU979TzqKKplbJKKB51GlpdYjBpZmcf3jZFkeAllUHuWvuUpasC2IqRSJl0hRJYgH5PieHlOXhsutUNsXQNfj4g3f59oW7idWsSrehu/xkHT4V3/5Ho+nGFvttaNA330vSgr4FPjTgd8fuK8WAhRB7rI7EBnpHG7/33nuZOnUqq1evZvz48TzxxBMce+yxfPLJJ+mRJiGEEEJsWUeTUWxVc2kqwnETUykcho5tffFepSDbY+eTV5/g8wcu3yiw0vAPnUjpeQ/iHzppq4FVi0AsRWGGE13TGNI9i+7Znm1/4kIIsQfr8LRAr9fLH//4xzbb+/Tpw0cffdQpnRJCCCH2dB1NRtGeVMri2U/X0BhJcEjvHELxFJ+vasTtcOFbvx7qvSU1uB1Gc8BVdjC63YmVjJPTaz8yxp2Hs6gfCkikFNsylUUp0HWwGTq5PkmpLoQQG+vUIsIul6szmxNCCCH2WFtPRtFc3DcQTWJZqk0As7giwD3vLOXDpXXoOlQ0RvG77FhK4XPaiCVN6mpqUK4MirPcZHvseIpK6D7hF5TmZ9H/0OOw2wx+qApSFYjhtGnENwmw1g+GtWLoGj1zvRzSJ1dSqgshxCa2K7j67LPPePbZZ1m1ahWJRKLVvv/85z+d0jEhhBBiT7ZxMgqf05aeGlgfTrCsOsiPdZF0MopPVzQwYVAhffJ8rG2M8synq3jy41UEoknM9dFPYySJw9Y827+pag0rXr2fSMUSepz3ANW6TiiWxOu00f+Ik/G57axtijN+30LsNp1ANLU+NbuZbg9A05rXDygFhg4Om8G4fQu54uh96JbtkRErIYTYRIeDq6effpqpU6cyYcIE3nrrLY4++miWLFlCVVUVJ5544o7ooxBCCLHH0XWNCYMKqWiKsrS6ee1VNGnyxY8NNEaTZHvsHNAjG5fdYFFFE9+vC5Dvc/DV6kYWrmlKB0EtdadMBeFIlMDHLxD4+HlUqvmPn/UfPo1t3Mzmkaxwgjy/k565XqoDcWqCcfJ9Lgr8zeunwgmTdYEo8VRz40qtbxxw2Q0O6JnNReP60kOSVwghRLs6HFz98Y9/5M4772TWrFn4/X7uvvtuevfuzS9/+UuKi4t3RB+FEEKIPVLfAj/TR/di9qIqllUH+a4yQDCWok+el74FPnK8zYV8EymLud9XkzRNgrFkq9ElBVhKEV32CQ1vP0SqqSq9z/BmYy8oQ0Ol5/hFEyZrGiIYukZFU5Rh3bPI9jqpCcYozHDidxn8WBshYVmAwlKQ4bIzef9izjpky6nhhRBib9fhVOxer5dvv/2WXr16kZubyzvvvMPgwYP5/vvvOfLII6msrNxRfd1pJBW7EEKIncmyFJ/9WM+D75aT63VStFEGQaUU7y2tYVl1iJRptVkXlWyooGHuQ0SXf7Zho6bjH3E8WaPPQHd60AGv08BhM1Cqufiv02YwsDSDpKlw23V+qAoRiqWA5jVbpdke6sNx/C4700f34tC++TINUAixV+pIbNDhkavs7GyCwSAApaWlLFq0iMGDB9PY2EgkEtm+HgshhBB7MV3XyHDbcdoNCjJap2YPRJOsaYhiaBpK12gJraxkjKb5zxH45AUwN2QUdPbYn5zxv8SR33PDBTRwGDpOm0402Zyq3WXXOXl4N75ZE6C8JkSu14FSoKHI9TnxOW0M7Z4lSSuEEKIDOhxcHXbYYcyZM4fBgwdzyimncPHFF/O///2POXPmMG7cuB3RRyGEEGKPt7nsgQ2RJLGkiV3XSJlWersVjxD8/OV0YGX4csk+8hd4BhyaDs5asv0pBdGUCRokTYs8n4Ncn5OCDBe/GpvP2sYo4UQKt91AAyJJE6/DRmmWW0arhBCiAzocXN1zzz3EYjEArrnmGux2Ox999BFTpkzh97//fad3UAghhNgbbJw90OswCMZSNESSrKwNEUuYRAFro/mANl8OWaNPp+Hdf5Jx0AlkHvJz9E1qZmkb5VJPppoDszyfk0GlmWhoeB02dF2je44UARZCiM7Q4TVXewNZcyWEEKIrLKsOctfcpXyzpolgLEk0aRJPWqTiEQKf/Af/gSdguHzp45WZItVUhT2ndLNt6oDDpqFpGoV+J6P75lEfSTK4NJPzDy+TkSkhhNiKHbrmatWqVVvc36NHj442KYQQQoj1grEUDdEEiZRJPGER+v49GuY9ghmqx4qHyRn/y/SxmmHbbGCVHrTSwGZouOw2+hf5qY8kyfE6OHpgoQRWQgjRyTocXPXq1atVBiMATdNQSqFpGqZpdm4PhRBCiD2MZan0OqeWtU0Aby5aR0VjFK/dILpuBeteu5fY6kXp80IL3yJz1GkYnsytXsNhaNgMjTyfk2jSIttjx+Ow0a/QL0kqhBBiB+lwcJWfn4/D4WDmzJlMnjwZm63DTQghhBB7nZaA6vvKAJ+tbKA6EKUxlsTQdMryfYzqm8O8H6qpqK6l4n//ovGzV0BtSGDh7nsQ2Ueeu02BVYbLRnGmk5Sp6JbjIcvj4Nj9i9m3KEOSVAghxA7U4TVXqVSK//73vzz00EN8//33TJs2jV/84hf07t17R/Vxp5M1V0IIITrTsuogsxdV8eXqBpasCxJNmtgNDYfNAKUIxFLEUyY1X8yhbt6jWJHG9Lm2rGKyx5+Hp+zArV7HroOu6+T7HKQsRa7XwTGDS5gwSEaqhBBie+3QNVc2m41TTjmFU045heXLl/PQQw9xwAEH8Oc//5mZM2dud6eFEEKIPcXG0/5qgnHe+GYd9eEEDeEESiksBU3RFHbdRKEIx00qnruRaPmn6TY0m5OMQ04h86CT0GyObbqu22HQLctDnt+Bz2XnjIN6MKosT0aqhBBiJ9nuOX3RaJR3332Xd999l9zcXHr16tWJ3RJCCCF2L+lpf+sCfLq8jh/rIoSTKaqb4qBpDCzxE4qnSCqFpRQOQyMQS2Gunz/i7jMiHVx59hlF9pG/wJZZsMVrOgywLDBVc0bATLeDkmw3B/TIlnVVQgjRBTocXH311Vc89NBDPP/884wdO5abb76Z8ePH74i+CSGEELuFjaf9fVvRRCiWwm7oOG06wXgKm67xxSqTlGk115syTRpDEZTdlW7DN3QisdWL8O1/FO7eB2z1mrrWHFgpwOe0MXFQIUcNLJJ1VUII0YU6vOZK13W6devG2WefTWFhYZv9F110Uad1rqvImishhBDball1kEc/XEltME55TYjKphi6tr6AL5AyFTZdw6K55lTTqu9ZN/t+HPm9yZ207b8zNcCmg641ZwE0LXDZDQr8TmaO6c0pw7tLQCWEEDvADl1z1aNHDzRN46mnnmqzT9O0PSK4EkIIIbaFZSlmL6piVX2EYCTB6voIpqXSgZW5fmTJ0MCMNFH7zuOEFs4BFInKZfiGTMBZ0n+z7bfUqtIAn9Mgy+PAZmhYliKaMDmsfz7njSljnyKZ/ieEELuCDgdXK1eu3AHdEEIIIXYPGyerCESTfLGqnupAlPpwkqSlQIG10ZwQZZkEvnqTxvf/hRULpbfb87pv0/XshobTpnNIWR79C5vXbTVEEkSTJhcd2Y8eud7OfopCCCG203YntEgkEqxYsYKysjKpdSWEEGKv0LK2qrwmRCxlEk2YfLayAUtZWJsEVQCxNd9TP+d+ktXL09s0h4esQ8/Ef8CxaMaWf38qQClFt2w3A4r8aJqG32VjXSDGkG5ZdMv27IBnKYQQYnt1OCqKRCL8+te/5vHHHwdgyZIl9OnTh1//+teUlpby29/+ttM7KYQQQnS1lrVV9eEExZkuYkmdbyuaCMZTbLrSyQw30PDOY4QXvd1qu3fQkWQfPh3Dl73V6xka2A0dTQND00hZilgyRWVTjByvg6MHFsoaKyGE2MXoHT3h6quv5uuvv+add97B5dqQ5Wj8+PE888wzndo5IYQQYmeyLMXq+giL1wVYXR/BWj8U1bK2qj6coF+Bj0TK4sNltVQ2xID1I0wbtRNd/nmrwMpe0JvCM28n79jLtimwchga2V4H/Yv89M7zEoil+L4yQGMkyeDSTKaP7iVp1oUQYhfU4ZGrF198kWeeeYaDDz4YTdvwF7OBAwdSXl7eqZ0TQgghdpZNp/y5bAZl+T6OGlhAY6R5bVWu10ldOM7/vq+mOhhP16jalHfQkQS/fINU/RqyDjsb39BJaLqxTf3I99nJ8jgZVJJJ9xw3Kcvi+8oApx7YnUElmZJmXQghdmEdDq5qamooKGhb1DAcDrcKtoQQQojdxaZT/jwON5FEigXL63jru3W47QYr68K47DrBWIqmaBKN5lpTiUAt0WWf4B92TLo9TdPJO+4ydJcPw5O52eu2ZANskeG00bfAT98CPzleBwCxpEV2OtiSNVZCCLEr6/C0wBEjRvDaa6+lH7cEVA8//DCHHHJI5/VMCCGE2Ak2nfLnd9kxdI2kqWiIJFgXiBFKJMlw2YnETUJxE9MC00zSsOAFKh7+FfVv3Uds9aJW7dpzSrcYWMGGNO0OXSPTbePIffM5sFdOOrBSSlHZFKNvgY/SLPeOugVCCCE6SYdHrv74xz8yadIkvvvuO1KpFHfffTffffcdH330Ee++++6O6KMQQgixw6xtjFJeE6I405X+g6FlWXxb0URTNEmOx04yaWG3GUSTJnYdGld8Sf3cB0nVr0m30/jBUxSd/sfNXkfXQK0fplKATYNsr50eOV72L82gJpwknlKE4incDoNowpTkFUIIsZvpcHB16KGH8tVXX3HbbbcxePBg3nrrLQ444ADmz5/P4MGDd0QfhRBCiJ9s4/pUXoctvXYpnEgRS5l4HM0jQ/XhBIvWNrGsOghAIKqhadAt2020oYp1b/2d8A8fbtSyhn/YJHIOO6vV9ZxGc2p2i42miWgaNkOje7aLkw7ozmH98/E77ZRmuVleG0qv+aoKxHDaDAaXZnL0wEJJXiGEELsJTSm1meW4e69AIEBmZiZNTU1kZGR0dXeEEEL8RJtLVjFhUCFOm8Gdc5aQ5bGTNBVfrW6kJhijIZJAA0xLkUgkCH76Xxo/ehaViqfbdZb0J//oX+Eq7ou5vs6VBmS4bZTl+cjzO6kOxKgNxYkkm697aL88fjGmN/sUtv39srkAUAghRNfpSGzQ4ZGrww8/nJkzZ3LKKafgdsv8byGEELuWTQOUaDLF4x/92CZZxaKKJiqaokw7pBdl+T4WrmmgOhinJhgjmjAxLYVN04gF6ln35G9INVamr6F7ssgeOx3foCOwGzq61hxYOQzIdDu44IgyInGL8poQtiw3hZkuijNdjNu3kNFleZsNmHRdk6QVQgixG+twcDVs2DCuuOIKfv3rX3Pqqacyc+ZMDj744B3RNyGEEGKbWJZidUOED5fV8vmPDYRiKXRdw2XTqQ0lUCj65vuIpyyUSuF32ehX4GNpdYi531fRv8jHKwsrWFkbxlIKpRQaEDcVujcbw5/bHFxpOv4DjiNrzJkYTi+6BrrWPG3Q49Dpnu2hJNvN+H2LKM1yyyiUEELsZbZrWmAqleLll1/m8ccf54033qBv377MmDGDs88+m8LCwh3Rz51KpgUKIcTuY1l1kKcWrOLdJTVUBmJYlsLtMOie46Yk080XqxqxlMLvsmFoGjZDJ9tjpyjDhQJqQnFyvQ7WNERZVh0kEo2hG3ZMpWGu/xWZqFlJw9sPkT3uPBz5vYDm6X+6Dk5Dp2eul/5FPmJJxf7dMjn/8DIJpIQQYg/RkdjgJ6+5qq6u5qGHHuIPf/gDpmlyzDHHcNFFF3HkkUf+lGa7lARXQgixe1hWHeSuuUv5//buPD6q6v7/+Ove2TPZyUZIWAOIbO4I4goC7lutVVtFtC51r7Z+26+22vZXv61dtK11aSu0tVarVduqVQF3wH0DVCQIhCUL2SYzmX3u/f0RMhBIQiIBAryfjwePMnfuPXPmdhzyzjnncz6saiIYTWEbNh6HQTxpYwN+j5OWSBwwyPI5Kcv1EYolqQvGsCzI9JgEIkmyfS5GFvl565UXWfWf3zP0jBvwDzuY5kgyvQ9V+55UJuB0GFg2mNjkZ7qZNGwA8ZRNvt/NJUcNVQEKEZF9yC5dc7W1t99+m7lz5/Loo49SVFTE7Nmz2bBhA6eeeirf+ta3+MUvfrEzzYuIiHTJsmyeX1rD57VBTNPA6QCXw4HDNHA5IBxP0tgax7ZtsrwOEkmbUDRJYziOZbWFr1DMIppI0Vq/jo//9CChyncBqHrmXkZddR9tkwPBweY9qUxwmkbbWq5kiljSJpmCaNJiYlmuKvuJiOzneh2u6urq+Otf/8rcuXNZuXIlp512Gn//+9+ZOXNmen+Q2bNnM2vWLIUrERHZZTY0R1i6IUDKsvG5nASjSdpn4hkGOB0mVjyFaUAiaeEwTeqCUeJJG7fTJGXZNLcECbz5GIG3n8ZOJdJtG5kDaAk0Q0YeACna9qUyDQOXw4HHZZJMpcBhctSIAm6aOYqyvAxNBRQR2c/1OlyVlZUxYsQI5syZw+zZsyksLNzunAkTJnD44Yf3SQdFREQ60xpP0ppIAuBxGphG21Q9R3vAAmzbxulwYAHhaILU5jl+oViS1hWLaHrpj6SC9ek2ndmFFJ/4TVwVkzEwsLZ6PZu2an4up0FrLEUKgzy/i3MPL2PwAP/ueMsiItLP9TpcLVy4kKOPPrrbc7Kzs3n55Ze/dKdERER2xO924nc5wbZJJC2cDoNIIkWGy4FpGtjQNqPCtolbNhZtASlRX0XjggeIrv1oS2MOJ9lHnE3OkV8lN9uPy2ESjCawbAPbtrHttrZM2kbBPE6TXI+baWOKmDKiYM/cABER6Xd6Ha7ag1VdXR0rVqwAYPTo0RQVFfVtz0RERLoxKNdHaY6Xd9c2EU+mAIinbOJJiwy3g2TKwsAmnGgbrto8c52mV+Z2CFbe4YeSP+1yXPmDAAhFU2R526b/5WS4cJomKcvCYcDIoiwcDpNYMkVZbgYXTBqsqYAiIpLW63AVDAb51re+xaOPPkoq1faPmcPh4LzzzuPee+8lJyenzzspIiKyNcuyeaNyE8uqA8STKeLJFC6HiddlEo2naI4kcBgGhmFgGDbY0F4bN+/4S4ms/gBH1gDyp12Or+KI9JphaBvdao0nyfW5mDQ0n0yvi89rW1jfFCFu2RRmODmoXMUrRERke70OV5dddhkffPABzzzzDJMnTwZgyZIlXH/99VxxxRU8+uijfd5JERHZt1mW3eMNdyvrgvztzSr+9eEGApEEVlt2IpaycBoWThO8TpMMtwNPcAONDY3ECg/A2hyuXAPKKDr3djyDxmC6PB3adjvaRrgSKSjK9lCen4FhGBw4MJtcn5vzJw1mRGGmNgQWEZFO9TpcPfPMM7zwwgtMnTo1fWzmzJn84Q9/YNasWX3aORER2bd0FqK+qA/xwrJaVm0KEU2m8DodjCjMZOa47UeG2ve1emt1A83hRIeCEwBJGxwYlPksPvr3g9S9/R/cOYUMnHMvOLcEKd/Qgzrtn20DNpgGFGR6MYy2NVc1LTEmludyzMhChSoREelSr8PVgAEDOp36l5OTQ15eXp90SkRE9j2VdcHtQlSuz0VdKEbKshmY48Xn8lIXjLLki3pW1LbwlUPLKMr24nc7GZjt5fllNayoCRKKbB+sAGzbouGDhVS+Og8rHAAg1lRD4IP/kn34mTvsY3sRDLdpUOD3EIwmqA5Eyfe7mTG2WMFKRES61etwdeutt/Ltb3+bv/71r5SUlABQU1PDd77zHW677bY+76CIiOz9KuuCzF20hsbWOANzvGS4fbTGEryxqp5IPMWxowpIpGw+rW6mKRwnHEvy0bpmlnzRwAEl2RRmeijI9LC8OkAwmiSStLd7jVhNJY3z7yO+cUX6mOH0UHTM+XgOPqVH/RzgdxGKW/jdDiKJJIRh/KAcra8SEZEeMXt7wX333cebb77J4MGDqaiooKKigsGDB7N48WIeeOABDjnkkPSfHXnttdc47bTTKC0txTAMnn766Q7P27bND37wAwYOHIjP52P69OmsXLlyh+3ee++9DB06FK/Xy6RJk3j77bd7+zZFRKSPWJbNC8tqaWyNM7IokyyvC4dpAG2lzR0GLNvYwgdVTWwKRgGIJFJYQDCaZH1jK7Zt80l1gM+qg9RvPqddKtJCwwv3UvPnGzsEq4zRR1H+zfvImXwuTqdrh/00gGjCYmCOl5tmjOb6E0dx44mjuPLYEQpWIiLSI70euTrzzDP77MVbW1uZOHEic+bM4eyzz97u+Z///Of85je/4c9//jPDhg3jtttuY+bMmXzyySd4vd5O23zsscf49re/zf3338+kSZO4++67mTlzJitWrFC5eBGRPWBDc4RVm0IMzPF2qMoXT1kkLZssn5P1TRH8bidFWW6qmiKEYkks2yaVsqkOxIglGxhVnEUgEmfrQavI6vep//ddWNFg+pgzv4z8E6/EP+wgnI62cOd0wOYCt0DbbxZNgw5tmQaMKs7iyuMqmDameBfeERER2VcZtm1vP7diDzAMg6eeeiod3mzbprS0lJtuuombb74ZgEAgQHFxMfPmzeNrX/tap+1MmjSJww8/nN/97ncAWJZFeXk51157Lf/zP//To760tLSQk5NDIBAgOzt759+ciMh+7LOaFn6zcCXDCzI3j1i1aYkkWPJFAwawvinMoDwf2AarG1qxLAsbsOy2kS3LbhtZ2nY2YKK5huo/fQs7Gcdw+8iZcj7Zh52G4XDhdhhkehyE4xZuh0FrLEWKtmqA5ub/dZgGTtPE5TCYUjGAu889GLfbsftujoiI9Hu9yQa9HrnaXVavXk1NTQ3Tp09PH8vJyWHSpEksWbKk03AVj8d57733+N73vpc+Zpom06dPZ8mSJV2+ViwWIxaLpR+3tLT00bsQERG/24nX6SAcT5Ll3TI9L8vrJC/DzZr6VmzA5TCpa4mRsizszftSORwGbodBNGGRtNsKVhjGlhntrtwSso88l0TjevKOm4MzawDQFsQ8ThOnw8TvMSjM9NIYjpFM2fhcJim7LVy5TBOPy2R0STY3TB+pYCUiIjul1+EqPz+/2+cbGxu/dGe2VlNTA0BxccepGcXFxenntlVfX08qler0ms8++6zL17rzzju54447drLHIiLSmUG5PkYUZrJsYwC/20EoliKesnA7TIYXZLCmPoQBtITjhKIJUlZb1T6nAS6HQcqCVCpF8MP/EvzweUq+fhem25duP2fK1zpMNwRwOgy8LpNEysbvduJ2gtM0OW50AeW5PpZtbCGcSJLhcjKhLIeZ40q0rkpERHZar8OVbdtYlsWNN97IsGHDdkWfdrvvfe97fPvb304/bmlpoby8fA/2SERk32GaBjPHFfNpTQvPL6shlrKwbBvTMPA4TEpzvVQ1hlnfHCW11bQ/y4ZYwiK87hMa5t9Hom41AIEljzHg2Nls3pJqu2BlAC7TIJ6ycTtN/B4HtcE4A7O9XDZ1OBVFmT3esFhERKQ3eh2uVq1axe23384vf/lLrrzySm699dZO973aWe1l3mtraxk4cGD6eG1tLQcddFCn1xQUFOBwOKitre1wvLa2Nt1eZzweDx6Pp8vnRURk5wWjCRrDCWLJFLbdvubJpCmSAAzcTpNYwkrvXxUPNdL8ylxal7/coR0rEiRl27gdJmCT2HyBCTjMtnVUHpcD0zBwmSbBaJKSbC/XThvJqJK20any/Izd9bZFRGQ/0utS7Pn5+fzmN7/hvffeo7KykoqKCn7729+S2roMUx8YNmwYJSUlLFy4MH2spaWFt956i8mTJ3d6jdvt5tBDD+1wjWVZLFy4sMtrRERk17Ism0ferOKLTa3k+lwMzstgaEEG5XkZmNg0h+PEkxbleT68LhM7laTlnafZ+IcrOgQrd/EIii+8iwGzrsUwDBKWTcpqG6kyDSjOdjN1ZAGzxg3k8CF5jB+UxZiB2Zw2sZS7zp2gCoAiIrLLfemCFqNGjeKpp57itdde46abbuJ3v/sdP/vZz3pVqj0UClFZWZl+vHr1aj788EPy8/MZPHgwN9xwAz/5yU8YOXJkuhR7aWlph9eYNm0aZ511Ftdccw0A3/72t7n44os57LDDOOKII7j77rtpbW3lkksu+bJvVUREumFZdrfT7NY1hXnziwZs28bnNnFsnsYXtS2SKRvbhtZYkrogRNd+TPV/f0+ivip9venNJPeYi8icOBPD3FJwov0VTLNtbdaRwwu4+oQKhhdo2p+IiOwZvQ5Xne1HNWjQID777DPOOeecXo1gvfvuuxx//PHpx+3rni6++GLmzZvHd7/7XVpbW7n88stpbm5m6tSpPP/88x32uFq1ahX19fXpx+eddx6bNm3iBz/4ATU1NRx00EE8//zz2xW5EBGRnVdZF+SFZbWs2hQimkzhdToYUZjJzHHF6ZDzz/fXs7axFadp0BJNkkhZ6WmBsaSFtXmdVTISZO1jd2DFI5tbN8icOIPcYy7CkbH99HMbcDsNSnO8lOZlcOOJoxg8wA9o2p+IiOwZvd7nakcjQHPnzt2pDvUH2udKRGTHKuuCzF20hsbWOANzvGS4nYTjSaoDURymQVGWh6rGMB+ta6YhFMflNLBtSFl2OlBt/Q9Qjs/Jpjcep3rBn3APHEX+iVfiGTiq09d2GOByGgzwexhRmMmRwwdw5bEjNEIlIiJ9bpfuc7UvhCcREdk5lmXzwrJaGlvjjCzKTFfsy/K6iCVSLPysDocBPpcDp9EWhuJJOx2mnCaEVn2Au3QUpqdttCmWSDHgyDNJ+PLIGHNMh/2sDDoGMYdh4HWYxJIWmV4nM8YWK1iJiMge96XWXCUSCRKJBBkZGSQSCZYuXcrIkSPJytIeISIi+4MNzRFWbQoxMMfboRR6QyjKS5/V0RCKY9n25lLpbRsCt4ejZKCOTS/9kfDni8k69HTyp18O0FY63e3Ef+Bx272esTldpQOW0bZJfFGWhwuOGKw9qkREpF/odbXA559/ntzcXIqLi3nxxRc57LDDOOywwygrK2PRokW7oo8iItLPtMaTRJMpMtxObNumJZJg1aYQr6zYxKZQHIy20SbLBmvzpsB2Mk7zor+z8Y9XEf58MQDB958h0bAeNp8bT9md/sNkbRWsXCaU5/moKMrklPGlTBlRsDvesoiIyA71euTq1ltv5brrrmP48OFccMEFfPWrX+X111/npptu4tZbb+Xll1/ecSMiIrJL7aiC386053M5CITjxBIWK2tbCEQS1AVj1IditMZSWHbbtD+btt/gmSYEP3+bxoUPkmyuSbdp+nPJO+4SXPmD0tP+Rhf78bqdLN/QQjiewmFAyia995XDgMEDMijNzWBwfgYzx2k6oIiI9B+9LmiRkZHBJ598wtChQ/F4PLzzzjtMmDCB5cuXc/TRR9PY2Lir+rrbqKCFiOzNuqvg92Wmz23dXn0oRn0oDrZNcyRBY2scsHGYJtFEitQ2/6Ikm6ppXPggkVXvbDlomGQdehq5Uy/A5W1bb9Ve4OKA4iyOHJHPxxsCbGiKkLJs4imLZMrC7XRQluejojCTkcVZzBj75d6PiIhIb+zSghZutztdbn3kyJHk5eUBpNdfiYjInrN9BT8f4XiSZRsDbAxEuOSoob0KJFu353OZNLTGaY0lwbZpicSJJtvGlBzG9sEq8NY/aX79YUht+bfBUz6O/BOvxF04FADDBAcGScvGAOpbY2xsjjLtgGJOOKCIlmiChtY4eRkuSrK8RFOW9q4SEZF+q9fhavTo0SxfvpwRI0awbNmy9PFPPvmEkSNH9mnnRESk57qr4JfpcbKyLsSLy2sZXpDZo2CydXsVhX7eXdtMLJEi2+tkYyBCKG6lz902WAEYDlc6WDky88k7/tLNVQC3vHYqBaYTMr1OsjxOBub6OH/SYI4ZWajwJCIie51eh6sXX3wRt9u93fFBgwZx77339kmnRESk97qq4AdgGAYDc7xU1oXY0Bzp0Sa7W7cXiqVoCsdxOkw2NEdoDu94pkLWIafQuvwlvEMmkjP5PEzP9q9pA16HSbbXSVG2l7JcHyMKexb+RERE+pteh6ucnJxOjx900EE72xcREdkJWyr4+Tp93ud2UNsSpTWe7HV7TeE4yZRFMJqgJZrA2uo8Kx4hsOQx7ESM/OlXpI8bpoOSb/wSw3R0+RomkLRtfC4HGW4nI4uzGJTbef9FRET6u16XYgd49dVXOe2006ioqKCiooLTTz+d119/va/7JiIiveB3O/E6HYS7CE+ReAqP04Hf3bPfq7W31xpLUBeIUhOIUheMs3mZFbZt0/rpa2z8w5W0vPkEwfeeIVZT2aGN7oKVQVsVQAODTK+LwfkZ2gxYRET2ar0OVw8//DDTp08nIyOD6667juuuuw6fz8e0adN45JFHdkUfRUSkBwZtnlJXHYiybSFY27apDkSpKMrs8cjQoFwfuT4Xzy+r4dXP6wjFU+m9puKb1lL76P9S/++fkwo1tB10OEjUfZG+ftuI5DTBNMDtaCup7jTb/hHyuR0cPjS/18U2RERE+ptel2IfM2YMl19+OTfeeGOH47/61a/4wx/+wKefftqnHdwTVIpdRPZWlXVBHnpjNRuaI+RluMnyOnGaBjUtMfL97m4DzLZ7Y0XiKb7/1Me8X9WcLpVuxcI0v/E3gu/9B+wtkwN9ww8jb/rluPJK08dcDoNkysamLUwZBtg2OE0Dt9PEsiwwTE6bOJCfnDEep/NLTaYQERHZpXZpKfYvvviC0047bbvjp59+Ot///vd725yIiPQxr8vBpmCMytoQGJDjczN5eD7nTxrcZbCqrAvy/NIalm4IEIonMG2DTaEoaxoiWPbmKYDLX6bplYewWpvT1zlzismbfjkZFZO2a9PtMDANSKbszW20hywD27KxMBicn8Gco4YrWImIyD6h1+GqvLychQsXUlFR0eH4ggULKC8v77OOiYhI72y9J9WkYQNIWTbBaILGcJxIwur2ursXrOTz2iCReIpQLEkkliCa2nJOZOUSGp79Vfqx4XSTfeS55Ew6B8O5fQVZ04CDy3NpiSZZtamVSCKFYbcdNwwwTIPBuRl8d+ZoRpVoKqCIiOwbeh2ubrrpJq677jo+/PBDpkyZAsCiRYuYN28e99xzT593UEREdmzbPa4AgtEkGR4nOT4XNYEo/3h3HWcePIgsjyu9Ca9l2TzyZhUfrWsGIBxPEU+miKU6tu8beSTugSOJV6/EN/JI8qd9E2dOcad9cZowwO/G43JQ5nOR5XURiafAsDGNtimB40pz+cphgxhVrKnXIiKy7+h1uLrqqqsoKSnhl7/8Jf/4xz+AtnVYjz32GGeccUafd1BERHZs6z2pmsJxVtW10hiOk7QsUpZNLJHiw/XNfF4bJD/Dw4jCTGaOK8blMHlzdSPYNpGkRSiWIJZIEV23HO/g8en2DcMkf8bVWOEAvuGHdtkPtwnlAzIYX5rDBUcOIdvnwu92MjDbS/XmMvB+tzMd7kRERPYlvQ5XAGeddRZnnXVWX/dFRES+pPY9qaIJk6UbAkTiKTK9TpIpg+pAhEg8hWkaZLgc5Ga4WLYxwMZAhLGl2dQEIkQSSSIJm1j1Shrn30e8+nOKL/w53rID06/hKanopgfgcxkMG+BnQKaXUSXZHDYkv0OA6snGxSIiInszrSAWEdkH+N1OPA6TFTVt66by/W7cDpPmcALLhixv2+/Salpi+N0OirM8rG1o5Z/vraM5nKC1JUDD87+j5i/fJl79OQCN8+/Htrteq7U1t8Ng6OZgNXiA9qsSEZH9U69HrvLy8jCMrv/BbGxs3KkOiYhI7w3K9VGY5eGt1Y0UZrkxDINYIkUkkcLlMIinbDK9ThpCUV5aUUdrLEU8kaQ2ECHw4Qs0v/ZXrGgw3Z5rwGDyTrgUw+j6d3AmbcUpnA6TUUV+KoqyGFmcxYyxxdqvSkRE9ku9Dld333030FaW96qrruJHP/oRRUVFfd0vERHpBdM0OGxYPs8vryEUTWEYBvGURTzZVpnCYZpg2VSH4jiDcVxOk+Da5Wx47vfEa1el2zHcPnKPuoCsQ0/DcHT9T4TDgGyfi5JsLxdNGcLBg/O0lkpERPZ7vd5EeGtZWVl89NFHDB8+vC/7tMdpE2ER2dW23bD3y4SSbduwbJsfP/MJTa1xGsNxmlrjBKPJtpNtsNr+Bw8x6l98gMYPXuzQnn/s8eQedwnOzPxuX9fvMhhRnMXk4QWcc6gq/omIyL5tl24iLCIiO6eyLsgLy2pZtSlENJnC63Skq/f1dDpdZ20ML/QzOC+DaDyFyzTxuBxEEhbJpAVG2ya+AEnDTeum9em2XIVDyT/xSrzl4zp9LQdgOgxcDpPDhuRxwZGDGTswR6NUIiIi29jpcNXd+isREelo641+B+Z4yXD7CMeT6ep9lxw1dIcBq6s2lm9swTRgUyhGQ2sM27ZxmJAyIblVXYqUbZB/4pXU/v1/yZl6AVkHn4xhOrp8vSyvg+FFWZx3eDnnHlquQCUiItKFXoers88+O/33aDTKlVdeid/vTx978skn+6ZnIiL7mG03+m3/5VSW10Wmx8nKuhAvLq9leEFmlwGmuzb8bgevfl7HhqYICStFLAnJYAPNr8zFP+FEfEMmpttxFw1n0LfmYrq8Hdo3aJs62C7H5+RHZ47l5LGlOJ0qMCsiItKdXoernJyc9N+//vWv92lnRET2ZVtv9LvtqL9hGAzM8VJZF2JDc6TLPaG6aqOxNc47axpYVRciYYGdStLy7r8JLP47djxCvPYLvJf8Jl2kwmEA2wQr2BKszM1VAGeOLeHU8YM0WiUiItIDvQ5Xc+fO3RX9EBHZ57Vv9Jvh9nX6vM/toLYlSms82eM2bNtmXWOY96qaqG6OkrAgsuZDmhY8QKJhXfq6VGsTiYZ1uIuGAWDZbaNUsCVQtQ9M2Ta4nSaD8zO47OhhClYiIiI91Otw9fvf/545c+bg9W7/G08REema3+3E63QQjifJ8rq2ez4ST+FxOvC7u/5qbm+jNZagOZxk1aYg6xojBCIJ4i2baHrpT4RXvLHVFQaZB80k95iLcPqy00HKpi1cGZvnAfrdJg7TIJq0cJoGIwozuX76KFUCFBER6YVeh6trr72Wr3zlKwpXIiK9NCjXx4jCTJZtDJDpcXaY1mfbNtWBKOMHtVXh21Z72fVgNIHDNHjp0zqaowliiRSxaJzmd54isOQx7EQsfY174GjyT7wSz8CRba9B28a/0BaqXA4Tz+bhqhyfi3Aihd/rYmpFAZdNHc6oEm0ELCIi0hu9Dlc7sS2WiMh+zTQNZo4rZmMgwsq6tnVTPreDSDxFdSBKvt/NjLHF203D27rsen0o1va/wRgpy8a2oX7+fYQ+3rJnlZmRQ96xs8kcPw2MLUUoDMDnNnE7HYwqziSesKhpiZLldTJ0gJ9BeRlMG1PElBEFmgooIiLyJWifKxGR3aiiKItLjhqaDku1LVE8TgfjB+UwY2zbPleWZbO+KcwX9a1sDER4s7KBeMpmYI6Hjc0RkimbhGVjbf5dV/YRZxFathBsm6xDTiFn6oU4vJkAOEywrLZRK6/TIDfDTVmejwMHZlMdiDG4wM8pEwYypiRb+1aJiIjspC8Vrl544YUOVQO3dvrpp+9Uh0RE9nUVRVkMPy6TDc0RWuNJ/G5nOthU1gV55K0q3vyigeZwgmA0AcDwgky8LpOqTc3Ub1yPnV2abs81oJz8E6/CUzoKd9HwDq/VHqxcBuRnenA7HHicDgKRJBPKtgQ6ERER2XmG3ct5fqbZ9T4nhmGQSqV2ulN7WktLCzk5OQQCAbKztZhbRHaPyrogdy9YyUfrmnEYbdUDqwNRUpZN0rJo/fwtql94AEyT0jm/x3C6MGgrq57s5pvcYUBpro+vHlbOQYNzKczydAh0IiIi0rXeZINej1xZlvWlOyYisi9rLzqx7WhUT699flkNn9cEcTsMBmR6iCRSWDaEN62n+sUHiKx6N31+y9tPkjPlPGzaRqYcBqS2CVgOIM/v4sDSHC4/ZrjWUomIiOxiWnMlItIHti46EU2m8DodjCjMZOa4YoYXdD4FcGsbmiMs3RAgZdtk+VwYhkE0HGHDgrk0vvlPSG3Z+8ozeAK+kUemH1t229oq04b2X39luEwG5vg46+BSZo0fqKl/IiIiu8GXClevvvoqv/jFL/j0008BOPDAA/nOd77D0Ucf3aedExHZG1TWBZm7aA2NrXEG5njJcPsIx5Ms2xjg05oWijI9NEcS24Wu9uIVG5ojLNsYoLE1DraN0zRY995LvPf4b4g316Vfx5E1gLzjLyXjgKM7lnEHUtbm8uoGZLidHDIklzlTh3OURqtERER2m16Hq4cffphLLrmEs88+m+uuuw6ARYsWMW3aNObNm8cFF1zQ550UEemvLMvmhWW1NLbGqSj0E4qlaArHcTtM8v0uXvu8Hp/bwVEjBlDq2RK6NgYinHBAEZ9VB1m1KURjOMb6pjCB2g189N/f0fD5limAmE5yjjiT3MnnYbrb9sBqH6Fybq4G6DTBMA3cDpMJZTl87+Qx2gBYRERkN+t1QYsxY8Zw+eWXc+ONN3Y4/qtf/Yo//OEP6dGsvZkKWohIT61rDPPr+Z8DNjWBGI3hOEnLwmkYhOMpkpZFttfF5BEFZPtcQNt+gR9UNdMSTTAwx0dprhefy8GSLxpY9ukKKu+7EjvVViUwu+JQ8qZdjiNvUNvaKhMMDJKbF1hluE0MDDI8DgzD4MDSbP5n1hhtACwiItJHdmlBiy+++ILTTjttu+Onn3463//+93vbnIjIXq01nqQ+FKOhNUYsYZHpdeJyOGmNJWkMx3E7DJwOk3iqYzGgcDzJpmCMg8tzyfK2ha6xpTkEo8NpnPwVWpYuZNDMy8kbO5XWeIqUZZPpdlCU7cVhGNSHYrREk8RTNh6nwQC/h8kjBnD+pMFaXyUiIrKH9DpclZeXs3DhQioqKjocX7BgAeXl5X3WMRGRvUGGy0F9KEY4lqQo25teC+UwDVwOg1QKwvEUrq3WPQWjSYKxJIn6NTz+y19y0XfuxO31ke93c+TwAszTL2HDMV/FdngJxy18LicOE/weJ36PE6dpEI4nyfQ4GVaQyYljizmqooDyvAytrxIREdmDeh2ubrrpJq677jo+/PBDpkyZArStuZo3bx733HNPn3dQRKQ/a5ucZ2DTMdQ4DAMDA4vtt69oaQnwyZO/YePip7Eti4XlQzlp9g0A5PvdTBtfxicbW5hQlsvHG5qxLJsBfg8bAxHqQ3FaY0l8LgfHH1DEBRqpEhER6Td6Ha6uuuoqSkpK+OUvf8k//vEPoG0d1mOPPcYZZ5zR5x0UEenPIokUBZluDAMaW+ObpwWabaHLaNtcPcPlIGHZWJbFewv+xb//8HNaA43pNj5eNJ8ZX78ah7NtemA0YZHv93DuYeWcflBpusT7AL+bXJ+b0lwf08YUad8qERGRfqbXBS32BypoISI9tXVBi+pAjKb2ghamiddl0hxOEE9aVDg28dqff87aTz5IX+twezjx/Cs5/txLcbk9QFuxi5V1IcYPyuHKY0dgmsZObU4sIiIiO2eXFrRo9+6773bY5+rQQw/9sk2JiOy1BuX6GFGYybKNAQ4bkksoliKesnA7TDI9Dt76rIqPnnuAN199GtveMkXw6BmnMOHsa0n5C4haJoZlEYmnqA5Eyfe7mTG2OB2gTNOgPD9jT71FERER6aFeh6v169dz/vnns2jRInJzcwFobm5mypQpPProo5SVlfV1H0VE+o3ORpFmjitmYyBC5aZWBuZ4yc1wEYmnWFEd4MX/dwmBTRvT14+oGMXvfvsbZs2aSWVdMD3lr7YlisfpYPygHGaMLdY6KhERkb1Qr6cFzpo1i+bmZv785z8zevRoAFasWMEll1xCdnY2zz//fJ92cOjQoaxdu3a749/61re49957tzs+b948Lrnkkg7HPB4P0Wi0x6+paYEi0pmtw1A0mcLrdDCiMJOZ44oB0s/Fkik8TgcVRZl8seBhfvnTO/D7/fzgBz/ghhtuwO12p9vUlD8REZH+bZdOC3z11VdZvHhxOlgBjB49mt/+9rccffTRve/tDrzzzjukUqn042XLlnHiiSdy7rnndnlNdnY2K1asSD9uL40sIvJlVdYFeeiN1WxojpCX4abA78FhwrKNATYGIlxy1FDOHZdDQyQLw+NLB6XElO+RCge46aabOh3Z15Q/ERGRfceX2ucqkUhsdzyVSlFaWtonndpaYWFhh8f/93//x4gRIzj22GO7vMYwDEpKSvq8LyKyf7Ism0ferOLdtU0YwNr6MIYBeX43Y0qyqA9GuPWnv+LFv9zDnDlz+MUvfpG+1uPx8Otf/3rPdV5ERER2G7O3F9x1111ce+21vPvuu+lj7777Ltdff32HHyh2hXg8zsMPP8ycOXO6HY0KhUIMGTKE8vJyzjjjDJYvX95tu7FYjJaWlg5/RETaLVpVz8sr6ghFkwQiSVqiCRpb46yoCfLEf1/i8Vu/zmP33E5TUxP33HMPn3zyyZ7usoiIiOwBvV5zlZeXRzgcJplM4nS2DXy1/93v93c4t7GxsbMmvrR//OMfXHDBBVRVVXU5SrZkyRJWrlzJhAkTCAQC/OIXv+C1115j+fLlXRbbuP3227njjju2O641VyJ7r75ay2RZNj96ZjnPfFSNbVskLDAMiAYaqF7wEMGlCzuc/41vfIOf//znGj0XERHZR/RmzVWvw9Wf//znHp978cUX96bpHZo5cyZut5v//Oc/Pb4mkUgwZswYzj//fH784x93ek4sFiMWi6Uft7S0UF5ernAlspfqrvBET6rwbR3MWiIJ/vD6F7z1RSOxRArbStHwzn9oev1v2PFw+hpP8XBOveL7/N/VX1WlPxERkX3ILi1o0deBqafWrl3LggULePLJJ3t1ncvl4uCDD6aysrLLczweDx6PZ2e7KCL9QGVdkLmL1tDYGmdgjpcMt49wPNmh8ER34WfbYBZLWHxW3UI0kaK1ahn1L/ye+KYtFUxNr5+8o7/BoCNPI2PwAF5cXsvwgkxV/BMREdkP9Thc9XQd0q4a6Zk7dy5FRUWccsopvboulUqxdOlSTj755F3SLxHpPyzL5oVltTS2xhlZlJlem5nldZHpcbKyLtRt+OksmNUEwjRF4iQtm3h1ZYdglTVhBrnHXozLn0OG10W+301lXYgNzRFVABQREdkP9Thc5ebmdltEwrZtDMPoUDa9r1iWxdy5c7n44ovT67zaXXTRRQwaNIg777wTgB/96EcceeSRVFRU0NzczF133cXatWu57LLL+rxfItK/bGiOsGpTiIE53u2+rwzDYGCOt8vwY1k2zy+rYX1TmEG5PmwbTAMyPS48TgfRuEXeYacS+OgFTKebATOuwlvatiWFwzTwuZ1keZ00tsZpjSd323sWERGR/qNX0wKfeOIJ8vPzd1VfurRgwQKqqqqYM2fOds9VVVVhmluKHjY1NfHNb36Tmpoa8vLyOPTQQ1m8eDEHHnjg7uyyiOwBrfEk0WSKDLev0+d9bge1LdFOw8/iVfU8t7SaWNJiQ3OEwMr3SdRWMvUrl5LlcRKJJ0mkHJSd/2PcWfkYpgPLtttKrhoGWV4XTtPA43Tgd/d6xrWIiIjsA3r1E8BRRx1FUVHRrupLl2bMmEFXdTdeeeWVDo9//etfa08Zkf2U3+3E63QQjifJ8rq2ez4ST3UafirrgjzydhUNrXGyks189vS9bPjgFTBMfMMOwTtwBCU5Xqqbo6QyB5C0wWmD02FiAE7TZFiBn5qWGOMH5TAot/NwJyIiIvu2Xu9zJSLSXw3K9TGiMJPqQHS7X8jYtk11IEpFUWaH8NO+TqslFKbxjUd55SdfbwtWALbFujefIZWysW0YmONl6AA/PpcDwwCnAS6HSWmul2giRb7fzYyxxSpmISIisp/S3BUR2WeYpsHMccVsDERYWde29srndhCJp6gORDsNPxuaIyx48Xle+8tdNFZXpY97svKYcNa3GHjYDALRFLGkRZbHyZQRA2gMx1lZG6KhNY7XaVKa62NCWS4zxvas1LuIiIjsm3ocrgzD6LaghYhIf1BRlMUlRw1Nl1OvbYnicToYPyhnu/DzxRdf8M1vXctLLzyXPmaYDsqOOpPxp11KRlYOsaRFNBFnVHEWFUWZNIcTpCybA0qyKMz2cNjQfMaUZH/pTYpFRERk39HjcGXbNrNnz97hflC93YdKRKSvVRRlMfy4zPRGwH63c7vw895773HUUUd12EB8+PjDmTbnFoIZg2gKx2kKx7FtyPd7+OYxwzlqREG3bYqIiMj+rcfhak9tHiwi0h3LsjsNPKZpdLvX1EEHHcTYsWN5//33yR5QxOFfvY6TzjgH0zSxbZtgNEksmWJDc4QjhuZz1IiCHbYpIiIi+zfD7qoM336spaWFnJwcAoHALtsUWUR2XmVdMD39L5pM4XU6GFGYycxx26992rRpE4WFhR2OLVmyhCeffJILrryBJz5uTG8evO06rUuOGqq1VCIiIvup3mQDhatOKFyJ9D/bjlBFEkn+vHgtDaE4WV4H0YRFLGkRS6YYlJvBnKltgai1tZX/9//+H7/61a945ZVXOPLIIzttf+ugFku2lWyvKMpUkQoREZH9XG+ygaoFiki/t+0IlcdhUh+KE04kSSYt3g1EiSZSGIDbabKmPozHaXBA5BNuvvkm1q9fD8A111zDW2+9hcPh2O41erJOS0RERKQ7ClciskNdrWvaHSrrgsxdtCY9ZS/D7aOuJcqK2iDheArbtnGaBn63ExubWMJi45qV/PrBmwis+iDdjsvlYsaMGSSTyU7DFaA1VSIiIrJTFK5EpFu9WdfU19o3+G1sjTOyKDO9HYTTYZBIpoglUzhMg2y3E9M0SMUibHrpr1QvehKsVLqdWbNmcc899zBq1Khd2l8RERHZvylciUiXOhs1CseTLNsYYGMgsssLPWxojrBqU9tmwFvvsxdPWSRSNoZtY9sGKcui4eNXWPPcgySCDenz/AUDuefXdzPnwnO1T5+IiIjscgpXItKprkaNsrwuMj1OVtaFeHF5LcMLMnfZFMHWeJJoMkWG2weQLpEejqfAAMsGBza2bVPz5r/Twcpwuimeeh7HnjuHo6YfomAlIiIiu4XClYh0qqtRIwDDMBiY46WyLsSG5sguW6fkdzvxOh2E40kSKYtVda00huNEEklSlo1lA5aNbZgMO+0aPr73arJHT6L8pCvJKy4jJysTv1tfcyIiIrJ76KcOEenUtqNG2/K5HdS2RGmNJ3dZHwbl+hhRmMmbqxtoao0RTVj43SYN7y/E8AyAgtHYNiSSKRyFwxlz9QPkDxqGAfhcDiYMymVQbuf9FxEREelrClci0qmtR42yvK7tno/E2/aC2hUjQ1tXJxxXls2Ln9SwKRTH27yWN574NY2rl5NRMozhl/2WmAWWbVCU6cGRPZJwLImNwaiSLGaOK1YpdREREdltFK5EpFPto0bLNgbI9Dg7TA20bZvqQJTxg3J6PDLU03Lu21YnTKVsmhobqH7hIdYt+Q9s3vc8XLOaoZGVpMoPYUNThJZIAqfDINfnYtLwAVwwabA2/xUREZHdSuFKRDplmgYzxxWzMRBhZV3b2iuf20EknqI6ECXf72bG2J6NDPW0nPu21Qk9potnnvgbS/5xL6lwS/q8grJhnHP1bYw+9CgSqRSfbAww/cASyvIyGF7gpywvQyNWIiIistspXIlIlyqKsrjkqKHpYFTbEsXjdDB+UA4zxvZsn6sdlXO/eMoQfC4nwWiCpz/YSEMoxqjiLJZ9+B7/+v1PaFz7aboth9vHcV+7ipnnXYLT5QYgmrDI93s5dlSRNgAWERGRPUrhSkS6VVGUxfDjMns0pW9b3ZVz97sdLPmigVv++TGFmV7iyRSrNrVSlOXhk/mP8vpff9mhrZxxx1E8/VIcg8tpiUO+68tNTxQRERHZVRSuRGSHTNPo1ahQ+/qqVZtCfLy+mdLcjuXcG1tjLF0f4Iv6VuIpi8bMONkZLlK2TXM4QVPuAWCaYFnklA7nwHNuIFF0AK2xJMFoks9rgxw4MIuallivpieKiIiI7EoKVyLSp7ZeX7UpFGXVplYCkQQji7PI97tpbI3xQVUTG5ujYIDXaeK0Y7REIBxL4nUYkFfGoOMuoHDAACqOOwfT4SQQiWMDLofB+qYwuT4XE8tzezw9UURERGRXU7gSkT5hWTaLVtXz97eraI0lGV6QSabHycbmKNWBKKFYkoqiTFbXt9IYipGyLOItjdQseIiVNZ9z9C0P0QzUh+KYhkHxcd+gPC8D0+HAtm2SKZtRRZmMKMpkTUOY8ycN5piRhRqxEhERkX5D4UpEdlplXZDnl9bw3LIaGltj5PhcJJI2wwv9FGd5WdfUyrqmMFUNrcRSKVKJJI3vPkPzG3/DjkcAeO+Zhyk6+jwSKRvDsHHbDhKWDckUoWgSn9vBiKJMXA6TwkwPIwozFaxERESkX1G4EpGd0l4NcH1TmFgyRXG2F2ybtQ0hVje04nWZ1AaiJC3AgPCaj2lccD+J+qp0G6Y3E3w5JC0wDDAwiacsQtE4PreLomwvIwr95GW4WVkXUgELERER6ZcUrkTkS9u6GuCgXB8bmiK0xhJsaI4QTdjYW52bbKmn6eU/Ef7s9a2OGmRNnEHBcReDN5tkysLlMPA4TdxOk0G5Pgbl+SnM8hBNpFhZF1IBCxEREem3FK5EpFfaKwG2xpM0h+N8tK6JDI+TeNKiJZqgPhjH2up8O5Wg5Z1/EVj8KHYimj7uHjiK/BOvxDNwFKbZdixpgWnZDMh0U5ztZdygXOpDMdY2tPZ6fy0RERGR3U3hSkR6bOtKgPWhGOubImwKRsnNcGMaNnXB+HbXJFs20fzGw5BKAmD6ssk9djaZE6ZjGCYGYNlgbx7m8rgcHFCShWEYnHlwKVleV6/31xIRERHZExSuRKRH2tdWNbbG8blMGkJxwvEkiZRFSyROImV1ep0rr5TsI86m5c0nyDr4JHKmfh2HLwsD0tMGnaaBY/OfoiwPfo+TlNW22XBv9tcSERER2ZMUrkSkW5Zls74pzMNLqljfFGZ8aTZvrGqguiWCbdlYNgSiKQDsZJzgB8+RedBJmC5Puo2cI7+Kf/RU3MXD08dswKBtn6tsn5NY0sI0DYqzvASjSSaU5apohYiIiOxVFK5EBOi4lqp9Ct4X9SFeWFbLxxuaWbY+gNdlsqExwsaWKJZtk0pZJDYPWIVXvUPTggdJNldjxVrJnXphum3T7e0QrNoZgGlCOJ4CA/K8bpwOkwGZHhWtEBERkb2OwpWIdFhLFU2m8Dod5Ga4qG1p2/zXBOLJFKFYglA0RWqraxNN1TQtfJDIqnfSx1reeZrsw85oK7HeBQPIcDswDAPTgDy/mxFFmRwyOE9FK0RERGSvpHAlsp/bei3VwBwvGW4foWiCF5bVEIgm8LsdBCIJokm7w3VWIkrLm08QeOufkEqkj3vKx5F/4pXdBiu3CWNLs7ngyKEMyHTjdzvI8rnI8rhUtEJERET2WgpXIvuxrfepGlnUFobWNYb5aH0zNcEolgWhWKrDNbZtE1m5hMaFfyTVUpc+7sjMJ+/4S8kYcwyG0XU48jkNirJ9TD9wIOccUqYgJSIiIvsMhSuR/diG5girNoUYmOOlKRxn6foAqxtaCUWTpOztz7dtm01P/5TI50u2HDQdZB9+JjmTz8P0dF/ZL8Nlku1zMb4sh5njtKZKRERE9i0KVyL7sdZ4kmgyRTRh8vH6ZtY1RogkUp0GKwDDMHAXDU+HK++Qg8g/8QpcA8q3P5etSq0bkO1zUZLt4cgRBVwwabDWVImIiMg+R+FKZD/mdzvxOExW1ARpbI23BStrS7KybRusFIZjy1dFzqRziFYtJeuQU8gYNaXTKYCm0VZi3TQNDi7P5eKjhuI0TYYX+CnLy9CIlYiIiOyTFK5E9mMDs714XA6+qG8lHEsQ32p5VXzTWhoXPIBn4Ejyjrskfdxwuik5/6fdtpvjdZLrd3PgwGxuPHGURqlERERkv6BwJbKfad/P6tPqFt5d08TyDc0Ewgms9udjYZoXPULwvf+AlSK2fjmZ46d3OvWvncsE224bscrNcHFQeR4Ty3OZOa5EwUpERET2GwpXIvuR9v2sPljXxIrqFlrjKVrjbcHKtm1aP3mF5pcfItXalL7GmVVAKhLE1Ul7JjCxPIe8DDet8STF2V5OnVjKmJJslVQXERGR/Y7Clcg+rH2UqjWeZFMwxn+X1tDYGmdDU4SG1hjRhI0FxOu+oHH+/cTWf5K+1nC6yT7yXLKPOBvT5em0/bwMJwNzfHhdDg4bmq/Nf0VERGS/pnAlso9qH6VatSlEJJFkbUOEZMrC6zL5vDZIygYrGqL59YcJfvAc2Fb6Wt/II8mf9k2cOcVdtu8y4YrjKjhmVCF+t1MjVSIiIrLfU7gS2cdYls3iVfU88nYVrbEkwwv8ZHqcfF4boiUSpz6USJdIDy1bSPD9Z9LXOvNKyZ9+Bb7hh3batgkYRltJ9lHFmcyZMgyn09z1b0pERERkL6BwJbIPqawL8vyyGp5bWk1Da5w8n4tYwsLnNgnHkgTCW4IVQNbBpxD68HmSLXXkTPka2YedieHsbHVV20iVy2EAJgVZbr49Y7SClYiIiMhWFK5E9gGWZbNoVT1/f7uKxlCcWCJFcbaHeNLm0+oWwokUsVCAyJqP8I85On2d4XBScNrNmL5snNmF27VrGm1VAF0OyHA78bkcVBRlMvuoYUwb0/WUQREREZH9Ub8OV7fffjt33HFHh2OjR4/ms88+6/Kaxx9/nNtuu401a9YwcuRIfvazn3HyySfv6q6K7DGVdUGeX1rDc8tqaGyN4XWatESTpGyoD8VpjcZo+fAFml/7K1asFdeAMtxFw9LXu4tHdNquYYDP5aA4y8MFRw5hUJ6PAX43h5TnacRKREREpBP9OlwBjB07lgULFqQfO51dd3nx4sWcf/753HnnnZx66qk88sgjnHnmmbz//vuMGzdud3RXZLeqrAsyd9Ea1jWGCcUSZPtcROMpWiIJmsIJohs+pXH+/cRrV6WvaXp1HsXn3tFNq2AAQ/MzGJDp5vjRRcw5apiKVYiIiIjsQL8PV06nk5KSkh6de8899zBr1iy+853vAPDjH/+Y+fPn87vf/Y77779/V3ZTZLfYurR6hsvB80trWFvfSn0oxqaWGDY2yZRNItRMw6vzaF26oMP1/gOPI/e4S7p9DQcwtDCDisIsCrI8zBxXomAlIiIi0gP9PlytXLmS0tJSvF4vkydP5s4772Tw4MGdnrtkyRK+/e1vdzg2c+ZMnn766W5fIxaLEYvF0o9bWlp2ut8ifa29tHplXZCmSIJ40mL1phChWJJIIkXSAttKEXz/WZrf+Bt2rDV9ratwKPknXom3vPsRXAPI87sYVpDJxPJc7VslIiIi0gv9OlxNmjSJefPmMXr0aKqrq7njjjs4+uijWbZsGVlZ2//AV1NTQ3Fxx0X2xcXF1NTUdPs6d95553Zru0R2t61HpbbdN6p9+l9VY5hwLEkolqQhFKO+NZG+PtlST90Tt5PYtCZ9zPD4yT3662QdfDKm6QDailRYNh2qBhqA22kwqiiLq0+oYGxpjvatEhEREemlfh2uTjrppPTfJ0yYwKRJkxgyZAj/+Mc/uPTSS/vsdb73ve91GPFqaWmhvLy8z9oX2ZGtN/yNJlN4nQ5GFGYyc1wxwwsyeWFZLVWNrdQGokQSFolkioatghWAIzOvrQrFZv7x08k79mIc/jwMwOM0sWwbA0habdGqJNtNSa4P24ayvAyuOX4ko0o0UiUiIiLyZfTrcLWt3NxcRo0aRWVlZafPl5SUUFtb2+FYbW3tDtdseTwePB5Pn/VTpDfaR6UaW+MMzPGS4fYRjidZtjHAxkCEk8aV8EFVE6vqWmmOxIknrbYpgLaFYWyp2meYDvJPvIqmhQ+SP/1KPIMOSD9nA/bmYOV0mKRsiwy3gxGFWRRkeakoytQUQBEREZGdtFeFq1AoxKpVq/jGN77R6fOTJ09m4cKF3HDDDelj8+fPZ/LkybuphyK9Y1k2LyyrpbE1zsiiTIzNI09ZXheZHicr60L88731LK8O0BxOkLJskhZE1nxI08IHGXDS9XhKR6fb85YdSMlFv063A+DYvFdVwrJxGG3TAkcU+rlk6jAOKs/dbgqiiIiIiHw5/Tpc3XzzzZx22mkMGTKEjRs38sMf/hCHw8H5558PwEUXXcSgQYO48847Abj++us59thj+eUvf8kpp5zCo48+yrvvvsuDDz64J9+GSJc2NEdYtSnEwBwvAC2RBPGUhdthkuV1UpLt4fWVDcQSFpZlEW3aRMNLfyK84g0AGhfcT8k3ftlxBGurYOU0IdPtIJxIkelxMjjfzxHD8vnKYWWMKs7evW9WREREZB/Xr8PV+vXrOf/882loaKCwsJCpU6fy5ptvUlhYCEBVVRWmueWHyilTpvDII49w66238v3vf5+RI0fy9NNPa48r6beCsQSN4RjRRJLqQJRAJEHSsnE5TIqyPBRmeQjHE/idFmtfepymxY9iJ2JbtWBihVtw+HM7bd/rMPG6HIwqyebSqcM5sDRbo1QiIiIiu4hh27a949P2Ly0tLeTk5BAIBMjO1m/3ZdeorAvyj3fW8e+PNtKyOVSZpoHbYWBuHn1yOQwaPnuHTfPvJ1S3Pn2t6csm77jZ+MdP7zBqtTW/y8GoEj+DB2Ry9fEjNFIlIiIi8iX0Jhv065ErkX1VexGLhmAMy7KJJS0cBlgWRC0bw4RwfQ0NC/9AZOWbWy40TLIOPpmco7+Ow5vZZfsZLoMJ5TkcPbJQhSpEREREdhOFK5HdyLJsqhpb+f1LlSyvbsHrNAjGkgAkbbAtu23/qRRseu5uYlVL09f6yseSN/0KXEXDu32NgkwXN5w4imNHFmkKoIiIiMhupHAlsgt0tiHwF/UhHl6yhn9/VE1jOLHDNvKOm0PNX76Nw59L6YzLyDjwOCzLJm51fr7bAUMG+DnzoDIuOHyIQpWIiIjIbqZwJdLHPq8J8sR761i1KUTKhjyfizy/m7dWN/J5TZCEtf0yx0TTRux4FHfxllEp78CRFJ15C75hhzCkZACxlEUwksRhW8QSNoYBDtPA7TApyHJTlpfBkAF+Zo4rVrASERER2QMUrkT6iGXZPP7eOv70xmqawwmcDgPDBp/bQWM4TlNrgm1jlZWIEljyOC1v/xNXfhkDZ9+DYTrSz2eMnophQIbbSaHHwSZnjAOKs6kOhCnM8hJJtJVtz8twMbI4S+urRERERPYghSuRPlBZF+Rvb67lqQ820hpPgm2ng5Rlt/3Zmm3bhD9fTNNLfyTVsgmAxKY1hD6eT9ZBs9rOAQzaKgY6TIOkZeNzOcn2OfF7srl++khMw+gw9VAjViIiIiJ7jsKVyE6qrAvy0BtrWLKqnlgyhWHbJK0t4WjbJVKJhnU0LniQ6JoPthw0nWQfcSb+A4/tcK7TBJ/LgcOAUDRJYZaHYDTJhLJcyvMyFKZERERE+hGFK5GdYFk2LyyrZUNzmGgiSTxpkdpqlGrrASsrFiaw+FFa3v0XWKn0ce/Qg8mffgWuAWUd2jagbd8rp0kwmsTlcuA0TQZkepgxVuuqRERERPobhSuRnbCuKczH65uJxi0aWuMdgtXWIms+pOHZX5EKNaaPObKLyJ92Gb6RkzGMjkHJALwuE7fDxDQMXE6T8jwfhwzO07oqERERkX5K4UrkS/q8JsgDr63i/aomwvEk8VTX5zr8uaRamzc/cJEz6StkH3kOpsvb6fluh8Ep4wcy/cBi8v1uMr1OsjwurasSERER6ccUrkR6oX3/qoWf1vLwm2tpbI0TjCZIdLH3VDt34VCyDjmVZKCGvBO+iStvYJfn+t0mo4qzuHbaSIYM8PfxOxARERGRXUXhSqQHLMtm0ap6Fn5ay4frmvl0YwvxlL1daXXbtmhd9jKhZQso/uqPMByu9HN5J1zaocx6Zwr8LqaPKSIYSxFJdDMUJiIiIiL9jsKVyA5U1gV55M0qXl5RR0s0QVNrYrsKgADx2lU0zr+f2IZPAWh599/kTDon/Xx3wco0oCTbywkHFOF2msRT4HfrP08RERGRvYl+ehPpgmXZLF5Vz8NL1vDhumZCsSSRuLVdsEpFgjS//jChD/8L9pZnE/VVPXodr8tk6AA/hw/NJy/Dxcq6EOMH5TAo19eH70ZEREREdjWFK5FOVNYFeX5ZDY+9s46NzZFOqwDatkXo4/k0v/pnrEhL+rgzv4z86ZfjG3ZIl+0bQI7XyaA8HweUZFGc4yWasFhZFyLf71apdREREZG9kMKVyDY+29jCL+ev4LOaIOubItutqwKIVX9O4/z7iFevTB8zXF5yjvoa2Yed0WGt1bYyXCaHD8vnoslD+aw6yKpNIdY2hPE4HYwflKNS6yIiIiJ7KYUr2a+1V/9rjSfJcDl4rXITv3upkoZgrMsKgKlwgJq/3QKpRPpYxgFHk3f8pTizC7p8LScwujSLk8YOZNb4EiqKsjh+dFH69f1up0qti4iIiOzFFK5kv1VZF+SFZbVU1gXZ0ByhqjFMXUus02IVW3Nk5JB92Bm0vPUErgGDyT/xSrxDJnR7TXGWi5PGlTJn6jDK8jLSAco0DcrzM/roHYmIiIjInqRwJfulyrogcxetoaoxTEMwxrrGEMF4ZxMAIbbhM9zFwzGc7vSxnCnn4cwuIHPiLAxH9/8ZeR0w7YASvj55CIO1b5WIiIjIPkvhSvY7lmXz/LIaVtS0sLEpQkNrnFgnFStSrU00vTKP1mULyZl6IblHnZ9+znT7yDrk1B693nEHFDPn6GFaRyUiIiKyj1O4kv3O4lX13VcBtFIE33+G5tf/hh0PA9Dy5uNkjp+GM7uox6+T4TI5emQBvzv/EJxOs6+6LyIiIiL9lMKV7Fcq64L8esHnbGiOYHUSrKJVS2mcfz+J+rXpY6bHT+4x38CROWCH7ZsGDMn3UZjloaIwizlHD1OwEhEREdlPKFzJfiOZtPj7W1V8siGwXbBKButpenku4U9f7XA8c8IMco+9GEdGzg7bdzsMRhZnMro4m4qiTJVUFxEREdnPKFzJPq291PqnNS08/f4GFnxSQ3ybcoAt7/6b5tf/ih2PpI+5S0aSf+KVeEpHd9t+lttBWX4GE8pyOGl8CSU5PpVUFxEREdlPKVzJPqu91Pr7VY18tL6Z+lCi0/OsaCgdrExfNrnHXETmhBMxTEen5zsM8DhNDh+ax4VHDmXMwGyFKRERERFRuJJ9x9YbAm8Kxnju42pW1rVQWRemOdJ5sALInnQOrZ+8jHfoweQe/XUcvuwuz3UakOl1Me2AQq46vkLT/kREREQkTeFK9nqWZbNoVT0LP62lOhAF2+bT6iCN4TjReIrk5vVVdjJByztPYdsWuVO+lr7edHkYeMlvMV3eTtt3m5Dpc1Ge66WiOJvTDyrl6IpCjVSJiIiISAcKV7LXsiybxavqeeLd9by7tpFYIgmmSSSWIhRPdTg3supdGhc+QLKpGhxO/AccjSt/UPr5roJVhtvk9ImlnDCmmDElmv4nIiIiIl1TuJK9UmVdkEfequKlz+qoD8WIJy1Slr3dvlWJ5hqaFv6BSOVbWw5aFtGqpR3CVWe8TpNTxpfykzPGq5y6iIiIiOyQwpXsdT6vCfLbl1fy/pomWuNJorEUyW3OsRIxWt56gsCbT0Bqy3orT9lY8k+8EnfRsC7bN4EMj4NDh+RxxbHDFaxEREREpEcUrmSv0F6sYnl1gN8uWMmqTSGiye13AbZtm0jlWzQu/AOpQG36uCMzn7zj55Ax5lgMo+tpfR4HlOf7OXpkARceOUQFK0RERESkxxSupF9rX1f14rIa3qtqonJTkNi2w1RbCX/2OvX//vmWA6aD7ENPJ+eo8zE9Gd2+1rEjC7hk6jCGF/gpy8vQ2ioRERER6RWFK+l3tt7498VlNbz8WR3NkcR266k6kzFqMs78QSQbN+AdMoG86VfiLhjc7TUuA249bQwXTxneR+9ARERERPZHClfSr7Rv/PvBuiaWbQhQH4zRyew/oG0KYGLTatxFW0KR4XAxYObVpMItZIw+aodTAM8/opzvnzQWt7vzDYNFRERERHpK4Ur6jcq6IHMXraEhFGNDYyv1oa6DVaJ+HY0L7idatZSBl/wGd+HQ9HPewRO6fR2HARdNHsz/njxWxSpEREREpM8oXEm/YFk2LyyrpbE1TjyR4rPaVjrLVVYsTGDxo7S8+y+w2vayapx/P8Xn39ntKFW7bI+Tm2eO5qIpQ/v2DYiIiIjIfk/hSvao9vVVK+uCLFlVT2Moxqe1oe2ClW3bhD99laaXHyIVakwfd+QUk334mTt8nTyfkyOHD+D6E0dyQElO374JEREREREUrmQ3aw9TrfEkdS1RXv+8nqUbAnxeG6QxnOj0mvimNTTOv5/YumVbDjpc5Bz5FbInfQXT5eny9fxuk6kjC/jeSWMYnO9XBUARERER2WUUrmS3aS9WsWpTiKrGViprQ0STSRIpOq0EaCWiNL/6Z4LvPwu2lT7uq5hE3rRv4sot6fb1cnxOhg3wc9OJBzC0ILOv346IiIiISAcKV7JbbClWEce2LT6vCdISS3V7jeFwEa1amg5WzryB5E+7HN+Iw3f4etleB0MH+Ll22khGlWgjYBERERHZ9RSuZJdrL1ZR1RgmkUjx8YYmupgB2IFhOsg/8UrqHv8hOZPPI/vwszCcri7PdwB+j5OhhRlMHl7AOYcOYlRxdt+9ERERERGRbihcyS7Tvr5q1aYQi1fVU9cSZuWmSKfnpiJBml//K5kTZuApqUgf95aPY9BV83D4uh59GpTjZvaUYQzM8zEsP5PsDBeDcn1aXyUiIiIiu5XClfQ5y7J5vXIT//5wI9WBKJF4gqXrWzrds8q2UoQ+nk/za3/BirQQr11FydfvwjC27D/VVbByGnDqxIFcc8JIKoo09U9ERERE9iyFK+lTlXVBfv9SJS9/volIIkUyaXW5EXBs4woa599PvGZl+lhi01oS9etwFw7p9nVOHFPIN44cxtSRBRqhEhEREZF+oV+HqzvvvJMnn3ySzz77DJ/Px5QpU/jZz37G6NGju7xm3rx5XHLJJR2OeTweotHoru7ufmnr0ur1wRiPvLWWRZUNxFM2qVTnwSoVDtD86p8Jffxih+MZY44l7/hLcGYVdPl6uV4H/772aMryMhSqRERERKRf6dfh6tVXX+Xqq6/m8MMPJ5lM8v3vf58ZM2bwySef4Pf7u7wuOzubFStWpB8bhn4I3xW2Lq0eiSdZWReiNhAladuY2CSsjufbVorQh/+l+bW/YsVa08ddBUPIP/FKvIPHd/t63z2pgm8d23WwFhERERHZk/p1uHr++ec7PJ43bx5FRUW89957HHPMMV1eZxgGJSXd74EkO6e9tHpjaxyfy2RjIML6pjDRruYAAg3//S2tyxakHxvuDHKPvpCsg0/BcHT9UZw8PI8/z56E2+3o0/cgIiIiItKX+nW42lYgEAAgPz+/2/NCoRBDhgzBsiwOOeQQfvrTnzJ27Nguz4/FYsRisfTjlpaWvunwPqq9tHpja5wBfhevr6ilqjlO17GqTdbBJ6XDlX/cNPKOm43Dn9fl+S4Trjh2ODfPHNOHvRcRERER2TX2mnBlWRY33HADRx11FOPGjevyvNGjR/PQQw8xYcIEAoEAv/jFL5gyZQrLly+nrKys02vuvPNO7rjjjl3V9b3W1uup/G5nurz5uqYwH69vxut08Og760ha219rp5KkWptxZm9ZP+UpHU3usRfjKRuHt6zrwJTvczKlYgDfOn4kB5bm7Iq3JiIiIiLS5wzbtnc04NAvXHXVVfz3v//ljTfe6DIkdSaRSDBmzBjOP/98fvzjH3d6TmcjV+Xl5QQCAbKz989NaLdeTxVNpvA6HQwv9JOf6WbhJzUsXllPJNX5tdG1H9O44H4Mh4uSi36FYfZ8Ot9VxwzlgiOHaZ8qEREREekXWlpayMnJ6VE22CtGrq655hqeeeYZXnvttV4FKwCXy8XBBx9MZWVll+d4PB48Hs/OdnOfsfV6qoE5XrxOL0s3NPPi8mpC8U6GqTZLttTT9PKfCH/2evpY6KMXyDr45B2+ps9l8j8nHcDFU4b1yXsQEREREdnd+nW4sm2ba6+9lqeeeopXXnmFYcN6/4N3KpVi6dKlnHzyjn/Al47rqUYWZbK6vpU3KutpCie6vMZOJWh5518EFj+KndhS8t49cBTukpHdvp7XAePLcrni2AqmH1jcZ+9DRERERGR369fh6uqrr+aRRx7hX//6F1lZWdTU1ACQk5ODz+cD4KKLLmLQoEHceeedAPzoRz/iyCOPpKKigubmZu666y7Wrl3LZZddtsfex95kQ3OEVZtClGR7eXt1A++sbqKL2X8ARFa/T+OCB0g2bkgfM33Z5B57MZkTTsQwzE6vK/C7uPzYERxUnssh5Xk4nZ2fJyIiIiKyt+jX4eq+++4D4LjjjutwfO7cucyePRuAqqoqTHPLD+ZNTU1885vfpKamhry8PA499FAWL17MgQceuLu6vVdrjSepD8VYtqGZz+tauzwv2bKJpoV/IPz54i0HDZPMg04i9+iv4/BldXrd4DwP5xxSzikTS6ko6vwcEREREZG90V5T0GJ36s2itX3B1lUBP93Ywl0vfMbGQKzba+K1X1D95xvAbluD5Rk0hvwTr8RdPKLT8zNcJt+ZNZrpY0pUrEJERERE9hr7XEEL2XXaqwJW1gVpDMf5oi60w2AF4C4e3rZv1YpF5B13Cf6xJ2AYnQemXJ+TRy47kgMHqay6iIiIiOy7FK72U5Zls3hVPY+8XcWmYIxQNEFVY5jWTqoBJpqqCb77L/JOuAzDseUjk3vMReQecxGmx9/pazgNGDrAx/dOGatgJSIiIiL7PIWr/VBlXZDnl9Xw3NJqaltixJIpIvEUqW0miFqJKC1vPkHgrX9CKoEzp5jsI85KP99VqPI6YGhBJseMKuQrh5Uxqnjfn1opIiIiIqJwtZ9p38NqfVOYUDRBPJkkFOs4WmXbNpGVS2hc+EdSLXXp48GPXiDrsNO73BS4NMfDnKnDOXJEPjlet9ZWiYiIiMh+ReFqP9K+h1VVY5jaQIQNzdHtRqsSDetpXPAA0TUfbDloOsg+/ExyJp/XabByGfCbCw5i5thShSkRERER2W8pXO1H1jeFeWVFLR+saya5zdIqKx4hsPgxWt55Gqxk+rh3yEHkn3gFrgHlnbZZ6Hfxf1+ZyLQx2gBYRERERPZvClf7AcuyWbSqnsv//DaR5PbP28k41Q9dQzJQmz7myC4k/4Rv4hs1udMqgB6nwbEjC7l55gGMKtF+VSIiIiIiClf7MMuyeW1lHX98fTVvVDZ0eZ7hdJMx+iha3n4SHE5yjjiH7MnnYrq8nZ7/lUMGcc0JFQzO92saoIiIiIjIZgpX+6D2Muu/W1jJ++saiae2eT4WxnC4MJyu9LGcKV8jFW4mZ8rXcOWVdtpulsfBOYeW8YNTxypUiYiIiIhsQ+FqH9Ieqp54bz2vfb6JxnCiw/O2bdO6/GWaXnmI7ENOI2fKeennTE8GBad8u9N2vS6DLK+LI4YO4OtHDlGwEhERERHphMLVPqB9TdU/31vPu2saaWiNE0l0rFgRr/uCxvn3E1v/CQCBJf/AP+54nNlFXbbrNCHH66I4x8uRwwdwwaTBVBRpfZWIiIiISGcUrvZylXVBHl6ylueWVrMpFGebyuqkoiECrz9M8IPnwN4SuLzDDgbM7dozgEy3yeQRAzjugGJKc30ML/BTlpehESsRERERkW4oXO3FKuuC/Og/n/D6yvrtQpVtW7QuXUDTq3/GCgfSx515peRPvwLf8EO3a68ww8lBQ/L5xuQhTK0oVJgSEREREekFhau9lGXZ3PtyJa+trN/uuVhNJY0v3ke8ekX6mOHykDPla2QfdmaHQhbtCvxO/vmtqRqhEhERERH5khSu9lJVDa28+mltp89FPl/SIVhlHHA0ecfPwZld2On5g/O8vHbLtF3STxERERGR/YXC1V4mmbR4f10Tz3y0kcZoqtNzsiefS2j5S5guL3nTr8A39KBOz/O7TebfMJXSfBWpEBERERHZWQpXe5GFn9Yyb9Ea1jS00hyOAxDb8CnxTWvJOmhW+jzT5aX4vJ/gzCnGcHT8v9hhQKbHSVG2l/856QAFKxERERGRPqJw1Y+FwwkeXLyK9Y1R4imLpeubCScsBvjdOKLNrH7uPlqXLgCHE++QCR02/3XlD+rQltMAh8MkL8PFqOIsLp4ylGljinf3WxIRERER2WcpXPVT//vUUp54dz2xVMf9qgZ4Tao/epbl//kjiUio7WAqSfC9/5A//YpO2zppbDG5fjcjizMZV5rDIeV5OJ3bl2EXEREREZEvT+GqH/rfp5by97ersOwtO1FZQHTdMpbOv5/EpjXpcw2Pn9yjv07WwSd32taBA7P4/ikHMijXpyqAIiIiIiK7kMJVPxMOJ3ji3fVYdttUPtM0iAXqaXh5Lq2fvNLh3KFTTmHwzEtZG3Z32tZhQ3J54qqjdkOvRURERERE4aqfeXDxKmIpC5O2YBXd8Bnr/34rdjySPsddPILRZ13HmIMOByAjkqC2JUKe341t2YwqzuL/zhpPbpZ3D70LEREREZH9j8JVP7O+MQqAsXkGn7t4BE5/Hol4BNObRe6xF5E5YQauzcHJsiwaw3HGDcrloYsP11oqEREREZE9ROGqnynLbwtNtt322HS6KJxxBcEVS8g55iIcvmwAXKZBMJqgoTVOttfFxVOGKliJiIiIiOxB+mm8n7l8ygg8DhMLsKy2hOUfcRglJ1+Lc3OwAkjZNsFoktHFWfzPSQeorLqIiIiIyB6mkat+JiPDxVcOK+Pvb1eRtMFM2RhG20iWTVsanjamkLMPLWeA362y6iIiIiIi/YTCVT/0/84aD7Bln6vNUwQ9TpOvHFqWfl5ERERERPoPw7bbV/dIu5aWFnJycggEAmRnZ+/4gl0kHE7w4OJVrG+MUpbv5fIpI8jIcO2x/oiIiIiI7G96kw00ctWPZWS4uGH6AXu6GyIiIiIi0gNarCMiIiIiItIHFK5ERERERET6gMKViIiIiIhIH1C4EhERERER6QMKVyIiIiIiIn1A4UpERERERKQPKFyJiIiIiIj0AYUrERERERGRPqBwJSIiIiIi0gcUrkRERERERPqAwpWIiIiIiEgfULgSERERERHpAwpXIiIiIiIifUDhSkREREREpA8493QH+iPbtgFoaWnZwz0REREREZE9qT0TtGeE7ihcdSIYDAJQXl6+h3siIiIiIiL9QTAYJCcnp9tzDLsnEWw/Y1kWGzduJCsrC8Mw9nR39gktLS2Ul5ezbt06srOz93R39gu657uX7vfup3u+e+l+716637uf7vnutTfdb9u2CQaDlJaWYprdr6rSyFUnTNOkrKxsT3djn5Sdnd3v/wPa1+ie716637uf7vnupfu9e+l+736657vX3nK/dzRi1U4FLURERERERPqAwpWIiIiIiEgfULiS3cLj8fDDH/4Qj8ezp7uy39A93710v3c/3fPdS/d799L93v10z3evffV+q6CFiIiIiIhIH9DIlYiIiIiISB9QuBIREREREekDClciIiIiIiJ9QOFKRERERESkDyhcyU678847Ofzww8nKyqKoqIgzzzyTFStWdHvNvHnzMAyjwx+v17uberz3u/3227e7fwcccEC31zz++OMccMABeL1exo8fz3PPPbeberv3Gzp06Hb32zAMrr766k7P1+e791577TVOO+00SktLMQyDp59+usPztm3zgx/8gIEDB+Lz+Zg+fTorV67cYbv33nsvQ4cOxev1MmnSJN5+++1d9A72Lt3d70QiwS233ML48ePx+/2UlpZy0UUXsXHjxm7b/DLfS/uLHX2+Z8+evd29mzVr1g7b1ee7azu65519pxuGwV133dVlm/qMd60nPwtGo1GuvvpqBgwYQGZmJueccw61tbXdtvtlv/v3JIUr2WmvvvoqV199NW+++Sbz588nkUgwY8YMWltbu70uOzub6urq9J+1a9fuph7vG8aOHdvh/r3xxhtdnrt48WLOP/98Lr30Uj744APOPPNMzjzzTJYtW7Ybe7z3eueddzrc6/nz5wNw7rnndnmNPt+909raysSJE7n33ns7ff7nP/85v/nNb7j//vt566238Pv9zJw5k2g02mWbjz32GN/+9rf54Q9/yPvvv8/EiROZOXMmdXV1u+pt7DW6u9/hcJj333+f2267jffff58nn3ySFStWcPrpp++w3d58L+1PdvT5Bpg1a1aHe/f3v/+92zb1+e7eju751ve6urqahx56CMMwOOecc7ptV5/xzvXkZ8Ebb7yR//znPzz++OO8+uqrbNy4kbPPPrvbdr/Md/8eZ4v0sbq6OhuwX3311S7PmTt3rp2Tk7P7OrWP+eEPf2hPnDixx+d/9atftU855ZQOxyZNmmRfccUVfdyz/cP1119vjxgxwrYsq9Pn9fneOYD91FNPpR9blmWXlJTYd911V/pYc3Oz7fF47L///e9dtnPEEUfYV199dfpxKpWyS0tL7TvvvHOX9Htvte397szbb79tA/batWu7PKe330v7q87u98UXX2yfccYZvWpHn++e68ln/IwzzrBPOOGEbs/RZ7zntv1ZsLm52Xa5XPbjjz+ePufTTz+1AXvJkiWdtvFlv/v3NI1cSZ8LBAIA5Ofnd3teKBRiyJAhlJeXc8YZZ7B8+fLd0b19xsqVKyktLWX48OFceOGFVFVVdXnukiVLmD59eodjM2fOZMmSJbu6m/uceDzOww8/zJw5czAMo8vz9PnuO6tXr6ampqbDZzgnJ4dJkyZ1+RmOx+O89957Ha4xTZPp06frc/8lBAIBDMMgNze32/N6870kHb3yyisUFRUxevRorrrqKhoaGro8V5/vvlVbW8uzzz7LpZdeusNz9RnvmW1/FnzvvfdIJBIdPrMHHHAAgwcP7vIz+2W++/sDhSvpU5ZlccMNN3DUUUcxbty4Ls8bPXo0Dz30EP/61794+OGHsSyLKVOmsH79+t3Y273XpEmTmDdvHs8//zz33Xcfq1ev5uijjyYYDHZ6fk1NDcXFxR2OFRcXU1NTszu6u095+umnaW5uZvbs2V2eo89332r/nPbmM1xfX08qldLnvg9Eo1FuueUWzj//fLKzs7s8r7ffS7LFrFmz+Mtf/sLChQv52c9+xquvvspJJ51EKpXq9Hx9vvvWn//8Z7KysnY4RU2f8Z7p7GfBmpoa3G73dr+g6e4z+2W++/sD557ugOxbrr76apYtW7bDOciTJ09m8uTJ6cdTpkxhzJgxPPDAA/z4xz/e1d3c65100knpv0+YMIFJkyYxZMgQ/vGPf/ToN2/y5f3pT3/ipJNOorS0tMtz9PmWfUUikeCrX/0qtm1z3333dXuuvpe+vK997Wvpv48fP54JEyYwYsQIXnnlFaZNm7YHe7Z/eOihh7jwwgt3WHhIn/Ge6enPgvsqjVxJn7nmmmt45plnePnllykrK+vVtS6Xi4MPPpjKyspd1Lt9W25uLqNGjery/pWUlGxXkae2tpaSkpLd0b19xtq1a1mwYAGXXXZZr67T53vntH9Oe/MZLigowOFw6HO/E9qD1dq1a5k/f363o1ad2dH3knRt+PDhFBQUdHnv9PnuO6+//jorVqzo9fc66DPema5+FiwpKSEej9Pc3Nzh/O4+s1/mu78/ULiSnWbbNtdccw1PPfUUL730EsOGDet1G6lUiqVLlzJw4MBd0MN9XygUYtWqVV3ev8mTJ7Nw4cIOx+bPn99hdEV2bO7cuRQVFXHKKaf06jp9vnfOsGHDKCkp6fAZbmlp4a233uryM+x2uzn00EM7XGNZFgsXLtTnvgfag9XKlStZsGABAwYM6HUbO/pekq6tX7+ehoaGLu+dPt99509/+hOHHnooEydO7PW1+oxvsaOfBQ899FBcLleHz+yKFSuoqqrq8jP7Zb77+4U9XFBD9gFXXXWVnZOTY7/yyit2dXV1+k84HE6f841vfMP+n//5n/TjO+64w37hhRfsVatW2e+99579ta99zfZ6vfby5cv3xFvY69x00032K6+8Yq9evdpetGiRPX36dLugoMCuq6uzbXv7+71o0SLb6XTav/jFL+xPP/3U/uEPf2i7XC576dKle+ot7HVSqZQ9ePBg+5ZbbtnuOX2+d14wGLQ/+OAD+4MPPrAB+1e/+pX9wQcfpKvT/d///Z+dm5tr/+tf/7I//vhj+4wzzrCHDRtmRyKRdBsnnHCC/dvf/jb9+NFHH7U9Ho89b948+5NPPrEvv/xyOzc3166pqdnt76+/6e5+x+Nx+/TTT7fLysrsDz/8sMP3eiwWS7ex7f3e0ffS/qy7+x0MBu2bb77ZXrJkib169Wp7wYIF9iGHHGKPHDnSjkaj6Tb0+e6dHX2n2LZtBwIBOyMjw77vvvs6bUOf8Z7ryc+CV155pT148GD7pZdest9991178uTJ9uTJkzu0M3r0aPvJJ59MP+7Jd39/o3AlOw3o9M/cuXPT5xx77LH2xRdfnH58ww032IMHD7bdbrddXFxsn3zyyfb777+/+zu/lzrvvPPsgQMH2m632x40aJB93nnn2ZWVlennt73ftm3b//jHP+xRo0bZbrfbHjt2rP3ss8/u5l7v3V544QUbsFesWLHdc/p877yXX3650++R9vtqWZZ922232cXFxbbH47GnTZu23f8XQ4YMsX/4wx92OPbb3/42/f/FEUccYb/55pu76R31b93d79WrV3f5vf7yyy+n29j2fu/oe2l/1t39DofD9owZM+zCwkLb5XLZQ4YMsb/5zW9uF5L0+e6dHX2n2LZtP/DAA7bP57Obm5s7bUOf8Z7ryc+CkUjE/ta3vmXn5eXZGRkZ9llnnWVXV1dv187W1/Tku7+/MWzbtnfNmJiIiIiIiMj+Q2uuRERERERE+oDClYiIiIiISB9QuBIREREREekDClciIiIiIiJ9QOFKRERERESkDyhciYiIiIiI9AGFKxERERERkT6gcCUiIiIiItIHFK5ERERERET6gMKViIiIiIhIH1C4EhGRbl100UWcdtppe7obIiIi/Z7ClYiIbGf58uWcd955lJWV8de//pVnnnmGrKwsTjrpJObPn7+nuyciItIvKVyJiEgHTz31FBMnTiQWi/Hwww/z1a9+lVmzZvHf//6XkpISZsyYwb333ps+/5133uHEE0+koKCAnJwcjj32WN5///0ObRqGwdNPPw2AbdtcdNFFTJgwgaamJubNm4dhGJ3+GTp0KAC33347Bx10ULq9eDxORUUFhmHQ3NwMwOzZsznzzDO7fF2AdevW8dWvfpXc3Fzy8/M544wzWLNmTYdrHnroIcaOHYvH42HgwIFcc801PXofAKtWreKMM86guLiYzMxMDj/8cBYsWNCh/erqas4++2wGDBjQ4b22v49trVmzBsMw+PDDDzscHzp0KHfffXf6sWVZ3HnnnQwbNgyfz8fEiRN54okn0s+/8sornb7OtvfolltuYdSoUWRkZDB8+HBuu+02EolEp30TEZGOFK5ERKSDG264geOOO46nn36a4447Dp/Ph8fjYerUqcydO5fZs2fz3e9+l9bWVgCCwSAXX3wxb7zxBm+++SYjR47k5JNPJhgMdtr+ddddx+LFi3nxxRfJy8vjvPPOo7q6murqau6++27KysrSj995551O2/jd735HbW1tr95XIpFg5syZZGVl8frrr7No0SIyMzOZNWsW8XgcgPvuu4+rr76ayy+/nKVLl/Lvf/+bioqKHr0PgFAoxMknn8zChQv54IMPmDVrFqeddhpVVVXp62666SY+//xznn/+eaqrq/nnP//Zq/fRlTvvvJO//OUv3H///Sxfvpwbb7yRr3/967z66qu9aicrK4t58+bxySefcM899/CHP/yBX//6133SRxGRfZ1zT3dARET6j9raWqqqqrjxxhu7POf0009n3rx5LFu2jEmTJnHCCSd0eP7BBx8kNzeXV199lVNPPbXDc7feeitPPfUUb7zxBiUlJQD4fD58Ph8AOTk5OByO9HOdaWxs5Cc/+Qm33HILt912W/q4z+ejurq6y+see+wxLMvij3/8I4ZhADB37lxyc3N55ZVXmDFjBj/5yU+46aabuP7669PXHX744du11dn7AJg4cSITJ05MP/7xj3/MU089xb///e/0CNiHH37I17/+9XS7+fn5Xfa5p2KxGD/96U9ZsGABkydPBmD48OG88cYbPPDAAxx77LE9buvWW29N/33o0KHcfPPNPProo3z3u9/d6X6KiOzrFK5ERCTN7XYDEA6Huzyn/Tmv1wu0BbJbb72VV155hbq6OlKpFOFwuMNoDbSNNi1cuJATTjghPd3vy/jRj37E8ccfz9SpUzscHzduHI888girV69m2LBh21330UcfUVlZSVZWVofj0WiUVatWUVdXx8aNG5k2bVq3r9/d+wiFQtx+++08++yzVFdXk0wmiUQiHe7FsGHDeO6557jyyiv7JFgBVFZWEg6HOfHEEzscj8fjHHzwwR2OlZWVddvWY489xm9+8xtWrVpFKBQimUySnZ3dJ/0UEdnXKVyJiEhaXl4ekyZN4i9/+QvXX389fr+/w/PJZJIHHniAsrIyxo0bB8DFF19MQ0MD99xzD0OGDMHj8TB58uT0VLt2b7/9Ns899xyzZ8/mgQce4Iorruh1/1auXMkf//hHPvzwQ9avX9/huTlz5vDUU08xfPjw7foNbcHn0EMP5W9/+9t2zxUWFmKaPZsp3937uPnmm5k/fz6/+MUvqKiowOfz8ZWvfKXDvfj1r3/NhRdeSEFBARkZGaRSqZ6+/S6FQiEAnn32WQYNGtThOY/H0+Hx66+/3iFgjhw5Mv33JUuWcOGFF3LHHXcwc+ZMcnJyePTRR/nlL3+5030UEdkfKFyJiEgHf/zjHzn11FMZM2YMl156KatXryYcDvPTn/6Uv/zlL9TV1fH000/jcDgAWLRoEb///e85+eSTgbaiEfX19du1e/fdd3PSSSfx+9//nksuuYSTTjqJwYMH96pvt9xyC5dddhkVFRXbhSufz8eCBQuora1Nr/faOjgccsghPPbYYxQVFXU5EjN06FAWLlzI8ccf32UfunsfixYtYvbs2Zx11llAW+jZtmDGqFGjmD17Ng0NDfznP/9JTxPcGQceeCAej4eqqqodTgEcNmwYubm5nT63ePFihgwZwv/+7/+mj61du3an+iYisj9RuBIRkQ7GjRvHihUrmDt3Lm+88QaffvopiUSCJUuWMGfOHC655BIKCwvT548cOZK//vWvHHbYYbS0tPCd73wnvYZqa+1T4M455xwef/xxLrvsMl588cUe96uyspKqqioqKyu7Pa+4uJji4uLtjl944YXcddddnHHGGfzoRz+irKyMtWvX8uSTT/Ld736XsrIybr/9dq688kqKioo46aSTCAaDLFq0iGuvvbZH72PkyJE8+eSTnHbaaRiGwW233YZlWR368eabb/L973+fl19+mbFjx7Jp06Yevf94PE40Gk0/tm2bZDJJKpUiKyuLm2++mRtvvBHLspg6dSqBQIBFixaRnZ3NxRdf3KPXGDlyJFVVVTz66KMcfvjhPPvsszz11FM9ulZERFQtUEREOuHxeLjyyit5+OGHOfnkkzn22GP5z3/+w3e/+90OwQrgT3/6E01NTRxyyCF84xvf4LrrrqOoqKjb9n/3u9/x0Ucf8eCDD/a4T62trfzv//7vl16nlJGRwWuvvcbgwYM5++yz0yNz0Wg0PZJ18cUXc/fdd/P73/+esWPHcuqpp7Jy5coev49f/epX5OXlMWXKFE477TRmzpzJIYcckj5/06ZNnHvuufzqV7/qcLwnJk2alC7+4fP5qKqq4jvf+Q5//etfgbbiGbfddht33nknY8aMYdasWTz77LOdrj/ryumnn86NN97INddcw0EHHcTixYs7FA0REZHuGbZt23u6EyIiItJ7N9xwAwcddBCzZ8/e010RERE0ciUiIrLXcrlc6bVvIiKy52nkSkREREREpA9o5EpERERERKQPKFyJiIiIiIj0AYUrERERERGRPqBwJSIiIiIi0gcUrkRERERERPqAwpWIiIiIiEgfULgSERERERHpAwpXIiIiIiIifeD/A+UHK6S3v5JLAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Ridge:\n",
      "[18.32722123  3.0107936  12.6847593   2.87696207 15.52981848  3.48194544\n",
      "  4.53098976  4.58990992  8.55918054  1.71694281  9.10822415  8.40752737\n",
      "  2.55937943 17.00035263  8.5683403  15.2286672  16.48759661  2.64328782\n",
      " 14.35748458 12.31290367  9.18390836 12.55374573 11.94125633  5.63035597\n",
      "  2.42612661 12.21701737  5.89097154  2.70327352  2.71025583  5.88467691\n",
      "  6.18495597 10.41194006 11.74058835  5.66495824  8.63860471  2.32680261\n",
      "  4.25343744  5.2432205   1.14830738  1.72497365 16.34541382 15.46871272\n",
      "  2.85962121  2.30344579  5.65496708  1.92032975 11.16954884  9.45732656\n",
      "  3.02702328  8.7593053  16.29196128  9.14135635 11.67985565  2.93153544\n",
      "  5.25600956  2.04094627 11.01252369 15.56616586  9.17103507  8.37553299\n",
      "  2.00616581  2.1215906   2.9696912   4.12823341  4.16293466  4.06749422\n",
      " 13.4970923   1.41292202  2.46751547  6.27820521  2.81149324  3.19217976\n",
      " 15.48506179  5.80541842  9.63517226 10.44588766  1.72088075 19.0229673\n",
      "  7.97420514  3.54378077  3.84646413 12.44758703  5.96056526 12.07860988\n",
      "  1.72342917  5.51538196  2.28724266  3.70750799  6.35093254 11.23979499\n",
      "  5.44158532  3.14961345  1.44146004 12.3629413   2.0369365   2.653985\n",
      "  4.38821694  2.86791114  4.36303521  7.77605572  1.6223479   2.74020297\n",
      "  2.92511993  3.96494258  1.44296481  6.5874516  10.2366303  12.2951812\n",
      "  5.76265943  8.84323802  2.61169285  2.8650806   1.71448408  3.84116443\n",
      " 10.27423177  2.13438867  9.71588695  5.87075642  6.55998543  4.01717677\n",
      "  2.49161305  4.92076646 14.71785247 13.03487059  4.19637943  4.12280758\n",
      "  9.53833488  4.36984794  2.30435943  2.35286733  9.83632877  1.71672432\n",
      "  3.0790716  10.17767211  8.49247673 12.90571486 11.46719152 15.61927112\n",
      " 13.87151069 12.22130308  1.60054527  2.2925814   8.77186654  3.14831016\n",
      "  3.43659687  3.69635824  3.98611212  1.43750507  1.72273422 16.09455883\n",
      "  5.52081983 12.01068013  2.91301752 14.32208531  3.15080861  8.2657655\n",
      " 12.93407876  1.82653829  6.83263136 13.79486528  3.26175027  5.79487874\n",
      " 11.94414316 12.98191364 14.47550566  5.32553162 10.28129845 17.37413521\n",
      "  2.49622968  4.0074591  18.25192405  9.90052442  1.41478482 10.68641859\n",
      "  9.11578983 17.58319025  3.5779263   2.80777855  3.72334889  9.48123556\n",
      "  4.00253311  2.50851055  3.14725992  1.88480232  5.66582446  4.70190489\n",
      "  2.29057556  3.4425988  16.47818856  2.61829716  3.04047372  3.65158322\n",
      "  9.9697805   2.59514454  3.4319669  12.70570616  2.09688785  2.2249029\n",
      "  5.64255812 10.51420545  9.81358192  5.25856628  9.47732466  9.10028075\n",
      "  3.3194552  14.63525856  2.96062829  2.54155985 11.96331849 10.44282616\n",
      "  3.70419454 14.53535116  1.43558882  5.48292316 14.79396409  5.17119502\n",
      " 11.8387389   4.7281767   2.74742947  3.89148373  9.15393279 13.16546132\n",
      "  9.74668684  3.00362808 17.70317768 11.05084012  9.46590928  4.65493814\n",
      "  2.29413516 12.8961292   4.21372965 13.24823607  1.15054761  3.52127164\n",
      "  4.00415437 14.2052063   1.88592957 18.87168748  2.35635132 13.21057174\n",
      "  3.85142815  9.30387653  6.0170587   2.13101031 18.52228946  3.90643655\n",
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      "  2.92780012  9.77211671  2.12647041  8.37581821  2.58051123  5.77277617\n",
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      "  9.32414072  2.14939107  2.91632385  8.52646224  2.61797119  4.98654586\n",
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      "  2.51840058 15.18609256  4.41520206  8.17249811  3.50445626 15.83736599\n",
      " 12.39381531 18.78524005  2.51074177  1.72609665 11.90800331  2.29281582\n",
      "  4.37636745  8.77104754  8.85386525  3.71534887  9.0174119   3.83050688\n",
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      " 11.60215407  9.62647822  3.18537771  4.38413336  3.38858381 11.07807859\n",
      "  4.57342873  3.62605304 14.13067537  2.97358525  2.80824007  2.60264335\n",
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      "  6.4900012   2.70668023  5.41869081  9.4076443   3.35718526  3.88477761\n",
      " 12.59921378 12.45690744  1.72196407  5.42504719  1.59211472 18.38656896\n",
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      "  7.34653952  3.0257806   1.26915484  3.90973225  1.14757191 15.30102494\n",
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      " 12.75370589 11.83447587  2.2282411   1.14611468  2.44167877  2.58179507\n",
      "  4.21622913  4.01778848 15.37576704  2.77757243  4.02608739  3.06976841\n",
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      "  2.29713549 12.29220855  2.11357083  2.64336908 17.1744381  12.47864336\n",
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      "  5.86877613  2.2468947   2.46717077 10.30357811  5.86081991 16.33232632\n",
      "  8.25391682 16.93006673  2.04444389  2.76610558  4.33679861  2.98011323\n",
      " 10.28818282  3.71219997  3.76573281  4.27291475 11.05404335 11.35363208\n",
      " 12.65547427 15.35903285  3.10943919  4.11863028  2.04139352  3.44169724\n",
      " 14.15719058  3.71007754  2.60839298  9.40803854  7.965787    5.72961304\n",
      " 11.32356401  4.03859843 11.26824897 10.81440803 11.2887788   2.85992\n",
      "  4.76882445 10.52593601  9.29548328  1.9204114   3.34590612  1.71958594\n",
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      "  2.81527421  9.10469504  3.59359429  3.09393291  2.14025116 10.12501543\n",
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      "  3.01206209  3.72110863  4.81275181  4.17754395  3.37907416 16.67083867\n",
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      " 16.10939211  9.55281074  4.89147294  3.77039723  4.00053916 11.89179237\n",
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      "  3.15457744  4.18287376 11.24267304  3.14165207  4.17388857  8.72188417\n",
      "  1.85582758  2.20106313  4.05432171  8.87105029  8.25713815  7.4982045\n",
      "  5.31099181  2.03252885  2.35380106  2.90833172  5.27744197  2.17862018\n",
      " 10.59979178 11.67659365  2.86590528  2.09763582  9.82317744  2.90241731\n",
      " 16.46282977  5.26638038  3.05806445  6.09927815 10.13120605  9.52575222\n",
      "  4.25604601  3.14696785  2.00463723  5.04334856  3.84137804 14.25459319\n",
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      "  8.71833373 12.93418185  9.28551261 17.96015086  2.19455615  3.91631872\n",
      "  9.59075942 15.12231599 16.98194811 11.24424247  5.95905356  2.50041082\n",
      "  4.47796826 19.95913906 12.81342182  5.63047224  2.69892087  5.43626143\n",
      " 12.46475017 14.56422072  1.69619261  9.7081645  10.36442387  2.00400579\n",
      " 11.90183282 11.45347863  3.04380204  4.39388834 18.2592019  10.16132727\n",
      " 11.81919909 11.7150185   6.10742334  5.70336903 10.81056606  2.67050571\n",
      " 14.3015793   5.20797712  4.19400473  2.44345517 12.926497    2.82576306\n",
      "  3.52995951  9.08843842 10.79776541  2.1054964  11.8111336   5.53884535\n",
      "  8.42241261  2.13193761 17.25708399  5.26646335 12.92020654  3.15086709\n",
      " 14.42222551 11.24062861 15.18238417  8.29062296 19.18811532 10.75429813\n",
      "  6.09171082  5.51060657  3.38282565 15.16024941  1.72118892  1.8841125\n",
      " 15.75713605 10.00156083  1.82383918 15.17370047  2.76597514 12.97800436\n",
      "  2.00911775 14.65916569  1.80074335  4.1277356   3.18348842  3.13512011\n",
      "  2.86072911 14.03376005  5.90289278  1.41908883 15.16040243  3.21897769\n",
      "  2.86368881  2.28948292  2.86396836  3.61071167 15.61242608 11.64838829\n",
      "  4.85794237  3.15039497  2.57324047 11.66309006  3.67598898  2.53054004\n",
      "  4.72814742  3.22933023  2.18218159  4.0139289   2.57884396 10.38283196\n",
      "  3.43718286  3.36874742  4.27099997  2.10418287  2.29338051 20.29371949\n",
      "  2.56748636 15.58927323  2.49890628  9.75547704 11.41633048  8.99062199\n",
      "  2.91620698  2.00690127  9.84708901  2.20853016  1.6000834   2.36301124\n",
      " 14.13997031 13.40623928 14.37827182  1.43777928  2.77851552  2.80842233\n",
      "  4.06616654  3.85792485  9.37734454  3.89462333  4.04386877  4.12924573\n",
      "  9.80364686  5.27666885  9.49792776  2.04498024  8.39342772 12.43929111\n",
      "  3.33066377 10.10553764 11.50080194 14.15645551  3.53308783  7.23569727\n",
      "  4.24063666 11.93546104  1.4341425   4.73309817  3.39423613  3.15689465\n",
      "  2.48179631  3.90472832  4.1746958  18.57109419  9.08110507  2.97701524\n",
      "  2.51687935  5.91769028  2.08750654  1.95767093  2.72859734  2.57603073\n",
      " 11.05087488  2.83452257 12.74139073 10.27409885  2.09043978 15.38054965\n",
      " 11.11194565 12.44357901 11.21301652 14.62181777 12.86121086  3.03239726\n",
      "  1.14981215 10.95406398  8.09016551 11.30190626  8.72749802 11.48987906\n",
      "  2.73349591  1.81491824  2.75723715 11.00142742  9.26644724 10.80686206\n",
      "  5.47401715  1.5233777   2.68723787 12.21653822 11.67249167 11.73429027\n",
      " 12.65422229  3.29490981 10.73473701  1.58175851  3.36260784 10.43117767\n",
      "  4.74239496  9.31321539  1.53214498  9.36787631  3.17960608 11.33815015\n",
      "  1.4944268   2.55231257  3.4338913   4.88079947  5.54352484  9.44755779\n",
      "  5.47553143  2.2867321   3.35788652  3.03689751 13.69364835  2.16172887\n",
      "  8.82221604  7.2449377   5.91869505  1.9524711   5.08923505  2.29223848\n",
      " 15.42311306  4.14145758 11.86010012  3.02606217  3.87950275  5.41448186\n",
      "  8.92537914  3.43315584  8.60694749 15.05033692  2.28146164 18.8219829\n",
      "  4.04083324 11.22431607  4.54687533  5.77312967 16.68722862 12.3673102\n",
      "  2.77424166  5.0751549   2.60859918]\n",
      "id\n",
      "1437    18.559999\n",
      "2700     3.100000\n",
      "3647    12.650000\n",
      "2512     2.910000\n",
      "2902    15.520000\n",
      "Name: Close, dtype: float64\n",
      "Коэффициент детерминации R²: 1.00\n",
      "Время обучения модели: 0.00 секунд\n",
      "Среднеквадратичная ошибка: 0.09\n",
      "Средняя абсолютная ошибка: 0.06\n",
      "Кросс-валидация RMSE: 0.08939608015848853 \n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\metrics\\_regression.py:492: FutureWarning: 'squared' is deprecated in version 1.4 and will be removed in 1.6. To calculate the root mean squared error, use the function'root_mean_squared_error'.\n",
      "  warnings.warn(\n",
      "c:\\Users\\K\\source\\repos\\AIM-PIbd-31-Ievlewa-M-D\\aimenv\\Lib\\site-packages\\sklearn\\linear_model\\_ridge.py:216: LinAlgWarning: Ill-conditioned matrix (rcond=7.51858e-17): result may not be accurate.\n",
      "  return linalg.solve(A, Xy, assume_a=\"pos\", overwrite_a=True).T\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import time\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import r2_score, mean_absolute_error\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.linear_model import Lasso\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.metrics import mean_squared_error\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки. Удаляем целевую переменную\n",
    "#y = feature_matrix['Close'] #- целевая переменная\n",
    "#X = feature_matrix.drop('Close', axis=1)\n",
    "\n",
    "# Удаление строк с NaN\n",
    "feature_matrix = feature_matrix.dropna()\n",
    "val_feature_matrix = val_feature_matrix.dropna()\n",
    "test_feature_matrix = test_feature_matrix.dropna()\n",
    "\n",
    "# Разделение данных на обучающую и тестовую выборки\n",
    "y_train = feature_matrix['Close']\n",
    "X_train = feature_matrix.drop('Close', axis=1)\n",
    "y_test = test_feature_matrix['Close']\n",
    "X_test = test_feature_matrix.drop('Close', axis=1)\n",
    "\n",
    "print(X_train.head())\n",
    "\n",
    "# Обучение модели\n",
    "model1 = LinearRegression()\n",
    "#Линейная регрессия — это простая модель, которая пытается установить связь между двумя переменными, рисуя прямую линию на графике. \n",
    "# Она прогнозирует значение зависимой переменной (Y) на основе одной или нескольких независимых переменных (X).\n",
    "model2 = DecisionTreeRegressor()\n",
    "#Это модель, которая принимает решения, дробя данные на «ветви», как дерево. На каждом уровне дерева модель выбирает, \n",
    "# какой признак (фактор) использовать для разделения данных.\n",
    "model3 = RandomForestRegressor(n_estimators=100) \n",
    "#Случайный лес — это ансамблевая модель, которая использует множество деревьев решений. \n",
    "# Вместо того чтобы полагаться на одно дерево, она комбинирует результаты нескольких деревьев, чтобы получить более точные предсказания.\n",
    "model4 = Lasso(alpha=0.1)\n",
    "#Lasso регрессия — это разновидность линейной регрессии с добавлением регуляризации. \n",
    "# Она помогает избежать переобучения модели, уменьшая влияние некоторых признаков.\n",
    "model5 = Ridge(alpha=0.1)\n",
    "#Ridge регрессия похожа на Lasso, но вместо полного исключения некоторых переменных она уменьшает значения всех коэффициентов.\n",
    "\n",
    "print('\\nLinearRegression:')\n",
    "start_time = time.time()\n",
    "model1.fit(X_train, y_train)\n",
    "#Метод fit обучает модель на обучающем наборе данных, состоящем из X_train (набор данных) и y_train (целевая переменная).\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model1.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "# Этот показатель показывает, насколько в среднем наши предсказания отклоняются от фактических значений. Чем меньше RMSE, тем лучше модель.\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "# Коффициент детерминации - показывает, насколько модель объясняет разброс значений в наборе данных\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "# Измеряет среднее расстояние между предсказанными значениями и фактическими значениями, игнорируя направление ошибок.\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model1, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "# Здесь мы используем метод cross_val_score для оценки модели с помощью кросс-валидации. \n",
    "# cv=5 означает, что мы будем разбивать наш обучающий набор на 5 частей (фолдов) и \n",
    "# использовать каждую часть для тестирования модели, обученной на остальных частях. \n",
    "# (по сути разбивка на выборки но несколько раз с использованием разных разбиений, чтобы получить норм оценку)\n",
    "\n",
    "# Параметр scoring='neg_mean_squared_error' говорит о том, что мы хотим получать отрицательные значения среднеквадратичной ошибки, \n",
    "# так как cross_val_score возвращает лучшие результаты как положительные значения. Таким образом, использование отрицательного \n",
    "# значения MSE позволяет \"перевернуть\" метрику так, чтобы более низкие значения (более точные предсказания) приводили \n",
    "# к более высоким (в терминах абсолютного значения) результатам.\n",
    "\n",
    "# После этого мы берем среднее значение отрицательной MSE и берем его корень (RMSE) \n",
    "# для получения усредненной оценки ошибки модели через кросс-валидацию.\n",
    "\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "#//////////////////////////\n",
    "\n",
    "print('\\nDecisionTreeRegressor:')\n",
    "start_time = time.time()\n",
    "model2.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model2.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model2, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model2.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "#//////////////////////////\n",
    "\n",
    "print('\\nRandomForestRegressor:')\n",
    "start_time = time.time()\n",
    "model3.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model3.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model3, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Анализ важности признаков\n",
    "feature_importances = model3.feature_importances_\n",
    "feature_names = X_train.columns\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "#//////////////////////////\n",
    "\n",
    "print('\\nLasso:')\n",
    "start_time = time.time()\n",
    "model4.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model4.predict(X_test)\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model4, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()\n",
    "\n",
    "#//////////////////////////\n",
    "\n",
    "print('\\nRidge:')\n",
    "start_time = time.time()\n",
    "model5.fit(X_train, y_train)\n",
    "\n",
    "# Время обучения модели\n",
    "train_time = time.time() - start_time\n",
    "\n",
    "y_predict = model5.predict(X_test)\n",
    "print(y_predict)\n",
    "print(y_test.head())\n",
    "\n",
    "mse = mean_squared_error(y_test, y_predict, squared=False)\n",
    "r2 = r2_score(y_test, y_predict)\n",
    "mae = mean_absolute_error(y_test, y_predict)\n",
    "print(f'Коэффициент детерминации R²: {r2:.2f}')\n",
    "print(f'Время обучения модели: {train_time:.2f} секунд')\n",
    "print(f'Среднеквадратичная ошибка: {mse:.2f}')\n",
    "print(f'Средняя абсолютная ошибка: {mae:.2f}')\n",
    "# Кросс-валидация\n",
    "scores = cross_val_score(model5, X_train, y_train, cv=5, scoring='neg_mean_squared_error')\n",
    "rmse_cv = (-scores.mean())**0.5\n",
    "print(f\"Кросс-валидация RMSE: {rmse_cv} \\n\")\n",
    "\n",
    "# Визуализация результатов\n",
    "plt.figure(figsize=(10, 6))\n",
    "plt.scatter(y_test, y_predict, alpha=0.5)\n",
    "plt.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], 'k--', lw=2)\n",
    "plt.xlabel('Фактическая цена')\n",
    "plt.ylabel('Прогнозируемая цена')\n",
    "plt.title('Фактическая цена по сравнению с прогнозируемой')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На основании представленных данных можно сделать несколько выводов:\n",
    "\n",
    "1. **Общие выводы по точности**\n",
    "В данном случае среднеквадратичные ошибки близки или равны нулю, к тому же коэффициент детерминации 1.00 - это говорит либо о том, что модель обучается идеально, либо о том, что модель запомнила значения. Поэтому я проверила ее на нескольких моделях и отдельно вывела для сравнения список предсказанной целевой переменной и тестовую(с которой сравниваем) целевую переменную - результаты оказались весьма близки к тестовым показателям, но не точь в точь, что, скорее всего, говорит о том, что модель все же обучается идеально...\n",
    "**Среднеквадратичная ошибка (RMSE) и Средняя абсолютная ошибка (MAE)**\n",
    "* LinearRegression: MAE = 0.04 и RMSE = 0.05 указывает на весьма точные предсказания.\n",
    "* DecisionTreeRegressor: MAE и RMSE равны 0.00, что может указывать на чрезмерное подстраивание модели к обучающим данным.\n",
    "* RandomForestRegressor: MAE = 0.02 и RMSE = 0.03 показывают высокую точность прогнозов, но не столь идеальные результаты, как у дерева решений.\n",
    "* Lasso и Ridge: Обе модели имеют MAE = 0.10 и 0.04 соответственно, что также предполагает приемлемую точность, но с возможностью недопущения переобучения.\n",
    "2. **Переобучение модели**\n",
    "Высокие значения R² и нулевые ошибки (MAE и RMSE) у DecisionTreeRegressor могут указывать на переобучение модели. Это значит, что модель отлично работает на обучающих данных, но может быть неэффективной на новых, невидимых данных.\n",
    "Для линейной регрессии и других регуляризованных моделей (например, Lasso и Ridge) результаты более сбалансированы, что делает их менее подверженными переобучению.\n",
    "3. **Производительность модели**\n",
    "Время обучения у моделей достаточно быстрое, что свидетельствует об их высокой производительности.\n",
    "4. **Соответствие бизнес-целям**\n",
    "Учитывая высокую точность модели и ее способность к обучению на исторических данных, можно использовать ее для прогнозирования цен на акции. Однако рекомендуется дополнительно проверять результаты на тестовых данных, чтобы избежать проблем с переобучением."
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