{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Датасет №1: [Объекты вокруг Земли](https://www.kaggle.com/datasets/sameepvani/nasa-nearest-earth-objects).\n",
    "\n",
    "### Описание датасета:\n",
    "Данный набор данных представляет собой коллекцию сведений о ближайших к Земле объектах (астероидах), сертифицированных NASA. Он содержит данные, которые могут помочь идентифицировать потенциально опасные астероиды, которые могут оказать влияние на Землю или на космические миссии. Набор данных включает в себя такие ключевые характеристики астероидов, как их размер, скорость, расстояние до Земли и информация о возможной опасности столкновения.\n",
    "\n",
    "---\n",
    "\n",
    "### Анализ сведений:\n",
    "**Проблемная область:**\n",
    "Основной проблемной областью является отслеживание и оценка рисков, связанных с приближением астероидов к Земле. С помощью данных о движении и характеристиках астероидов можно предсказать возможные столкновения и минимизировать угрозу для Земли, планируя превентивные действия.\n",
    "\n",
    "**Актуальность:**\n",
    "Набор данных высокоактуален для задач оценки рисков от космических объектов, мониторинга космического пространства и разработки превентивных мер по защите Земли. Также он важен для научных исследований в области астрономии и планетарной безопасности.\n",
    "\n",
    "**Объекты наблюдения:**\n",
    "Объектами наблюдения в данном наборе данных являются астероиды, классифицированные NASA как \"ближайшие к Земле объекты\" (Near-Earth Objects, NEO). Эти объекты могут проходить в непосредственной близости от Земли, что потенциально представляет опасность.\n",
    "\n",
    "**Атрибуты объектов:**\n",
    "- id: Уникальный идентификатор астероида.\n",
    "- name: Название, присвоенное астероиду NASA.\n",
    "- est_diameter_min: Минимальный оценочные диаметры астероида в километрах.\n",
    "- est_diameter_max: Максимальный оценочные диаметры астероида в километрах.\n",
    "- relative_velocity: Скорость астероида относительно Земли (в км/с).\n",
    "- miss_distance: Расстояние, на котором астероид пролетел мимо Земли, в километрах.\n",
    "- orbiting_body: Планета, вокруг которой вращается астероид.\n",
    "- sentry_object: Признак, указывающий на наличие астероида в системе автоматического мониторинга столкновений (система Sentry).\n",
    "- absolute_magnitude: Абсолютная величина, описывающая яркость объекта.\n",
    "- hazardous: Булев признак, указывающий, является ли астероид потенциально опасным.\n",
    "\n",
    "**Связь между объектами:**\n",
    "В данном наборе данных отсутствует явная связь между астероидами, однако на основе орбитальных параметров можно исследовать группы объектов, имеющие схожие орбиты или величины риска столкновения с Землей.\n",
    "\n",
    "---\n",
    "\n",
    "### Качество набора данных:\n",
    "**Информативность:**\n",
    "Датасет предоставляет важные сведения о ключевых характеристиках астероидов, такие как размер, скорость и расстояние от Земли, что позволяет проводить качественный анализ их потенциальной опасности.\n",
    "\n",
    "**Степень покрытия:**\n",
    "Набор данных включает данные о большом количестве астероидов (>90000 записей), что позволяет охватить значительную часть ближайших к Земле объектов. Однако не все астероиды могут быть обнаружены, так как данные зависят от возможности их наблюдения.\n",
    "\n",
    "**Соответствие реальным данным:**\n",
    "Данные в наборе предоставлены NASA, что указывает на высокую достоверность и актуальность информации. Тем не менее, параметры, такие как диаметр и расстояние, могут быть оценочными и подвергаться уточнению с новыми наблюдениями.\n",
    "\n",
    "**Согласованность меток:**\n",
    "Метрики в датасете четко обозначены, а булевы признаки, такие как \"hazardous\" (опасен или нет), соответствуют конкретным параметрам астероидов и легко интерпретируются.\n",
    "\n",
    "---\n",
    "\n",
    "### Бизес-цели:\n",
    "1. **Мониторинг космических угроз:**\n",
    "Создание системы, которая анализирует астероиды и предсказывает риски столкновения с Землей, помогая государственным агентствам и частным компаниям разрабатывать превентивные меры.\n",
    "2. **Поддержка космических миссий:**\n",
    "Предоставление точных данных для планирования и безопасного проведения космических миссий, минимизация рисков столкновения с космическими объектами.\n",
    "3. **Образовательные и научные исследования:**\n",
    "Использование данных для поддержки образовательных программ и научных исследований в области астрономии и космической безопасности.\n",
    "\n",
    "**Эффект для бизнеса:**\n",
    "Набор данных способствует развитию технологий космической безопасности, минимизирует финансовые риски от потенциальных катастроф и поддерживает стратегическое планирование космических миссий.\n",
    "\n",
    "---\n",
    "\n",
    "### Технические цели:\n",
    "1. **Моделирование риска столкновения:**\n",
    "Построение алгоритмов машинного обучения для прогнозирования вероятности столкновения астероидов с Землей.\n",
    "2. **Анализ и кластеризация астероидов:**\n",
    "Исследование взаимосвязей между астероидами, анализ орбитальных данных и выделение групп астероидов, имеющих схожие характеристики.\n",
    "3. **Оптимизация системы предупреждения угроз:**\n",
    "Создание системы раннего оповещения, которая будет автоматически анализировать данные и предупреждать о потенциальных угрозах в реальном времени.\n",
    "\n",
    "**Входные данные:**\n",
    "Диаметр, скорость, расстояние, орбитальные параметры астероидов.\n",
    "\n",
    "**Целевой признак:**\n",
    "Признак \"hazardous\" – бинарная метка, указывающая на потенциальную опасность астероида."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Выгрузка данных из файла в DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Any\n",
    "\n",
    "import pandas as pd\n",
    "from pandas import DataFrame, Series\n",
    "from sklearn.model_selection import train_test_split\n",
    "from imblearn.over_sampling import ADASYN\n",
    "from imblearn.under_sampling import RandomUnderSampler\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "\n",
    "df: DataFrame = pd.read_csv('..//static//csv//neo.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Краткая информация о DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 90836 entries, 0 to 90835\n",
      "Data columns (total 10 columns):\n",
      " #   Column              Non-Null Count  Dtype  \n",
      "---  ------              --------------  -----  \n",
      " 0   id                  90836 non-null  int64  \n",
      " 1   name                90836 non-null  object \n",
      " 2   est_diameter_min    90836 non-null  float64\n",
      " 3   est_diameter_max    90836 non-null  float64\n",
      " 4   relative_velocity   90836 non-null  float64\n",
      " 5   miss_distance       90836 non-null  float64\n",
      " 6   orbiting_body       90836 non-null  object \n",
      " 7   sentry_object       90836 non-null  bool   \n",
      " 8   absolute_magnitude  90836 non-null  float64\n",
      " 9   hazardous           90836 non-null  bool   \n",
      "dtypes: bool(2), float64(5), int64(1), object(2)\n",
      "memory usage: 5.7+ MB\n",
      "\n",
      "                       count          mean           std           min  \\\n",
      "id                  90836.0  1.438288e+07  2.087202e+07  2.000433e+06   \n",
      "est_diameter_min    90836.0  1.274321e-01  2.985112e-01  6.089126e-04   \n",
      "est_diameter_max    90836.0  2.849469e-01  6.674914e-01  1.361570e-03   \n",
      "relative_velocity   90836.0  4.806692e+04  2.529330e+04  2.033464e+02   \n",
      "miss_distance       90836.0  3.706655e+07  2.235204e+07  6.745533e+03   \n",
      "absolute_magnitude  90836.0  2.352710e+01  2.894086e+00  9.230000e+00   \n",
      "\n",
      "                             25%           50%           75%           max  \n",
      "id                  3.448110e+06  3.748362e+06  3.884023e+06  5.427591e+07  \n",
      "est_diameter_min    1.925551e-02  4.836765e-02  1.434019e-01  3.789265e+01  \n",
      "est_diameter_max    4.305662e-02  1.081534e-01  3.206564e-01  8.473054e+01  \n",
      "relative_velocity   2.861902e+04  4.419012e+04  6.292360e+04  2.369901e+05  \n",
      "miss_distance       1.721082e+07  3.784658e+07  5.654900e+07  7.479865e+07  \n",
      "absolute_magnitude  2.134000e+01  2.370000e+01  2.570000e+01  3.320000e+01  \n"
     ]
    }
   ],
   "source": [
    "# Краткая информация о DataFrame\n",
    "df.info()\n",
    "\n",
    "# Статистическое описание числовых столбцов\n",
    "print('\\n', df.describe().transpose())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проблема пропущенных данных:\n",
    "\n",
    "**Проблема пропущенных данных** — это отсутствие значений в наборе данных, что может искажать результаты анализа и статистические выводы.\n",
    "\n",
    "Проверка на отсутствие значений, представленная ниже, показала, что DataFrame не имеет пустых значений признаков. Нет необходимости использовать методы заполнения пропущенных данных."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id                    False\n",
      "name                  False\n",
      "est_diameter_min      False\n",
      "est_diameter_max      False\n",
      "relative_velocity     False\n",
      "miss_distance         False\n",
      "orbiting_body         False\n",
      "sentry_object         False\n",
      "absolute_magnitude    False\n",
      "hazardous             False\n",
      "dtype: bool \n",
      "\n",
      "id                    0\n",
      "name                  0\n",
      "est_diameter_min      0\n",
      "est_diameter_max      0\n",
      "relative_velocity     0\n",
      "miss_distance         0\n",
      "orbiting_body         0\n",
      "sentry_object         0\n",
      "absolute_magnitude    0\n",
      "hazardous             0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Проверка пропущенных данных\n",
    "def check_null_columns(dataframe: DataFrame) -> None:\n",
    "    # Присутствуют ли пустые значения признаков\n",
    "    print(dataframe.isnull().any(), '\\n')\n",
    "\n",
    "    # Количество пустых значений признаков\n",
    "    print(dataframe.isnull().sum())\n",
    "\n",
    "    # Процент пустых значений признаков\n",
    "    for i in dataframe.columns:\n",
    "        null_rate: float = dataframe[i].isnull().sum() / len(dataframe) * 100\n",
    "        if null_rate > 0:\n",
    "            print(f\"{i} процент пустых значений: %{null_rate:.2f}\")\n",
    "            \n",
    "\n",
    "# Проверка пропущенных данных\n",
    "check_null_columns(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Проблема зашумленности данных:\n",
    "\n",
    "**Зашумленность** – это наличие случайных ошибок или вариаций в данных, которые могут затруднить выявление истинных закономерностей. Шум может возникать из-за ошибок измерений, неправильных записей или других факторов.\n",
    "\n",
    "**Выбросы** – это значения, которые значительно отличаются от остальных наблюдений в наборе данных. Выбросы могут указывать на ошибки в данных или на редкие, но важные события. Их наличие может повлиять на статистические методы анализа.\n",
    "\n",
    "Представленный ниже код помогает определить наличие выбросов в наборе данных и устранить их (при наличии), заменив значения ниже нижней границы (рассматриваемого минимума) на значения нижней границы, а значения выше верхней границы (рассматриваемого максимума) – на значения верхней границы."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Колонка est_diameter_min:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 8306\n",
      "\tМинимальное значение: 0.0006089126\n",
      "\tМаксимальное значение: 0.32962154705\n",
      "\t1-й квартиль (Q1): 0.0192555078\n",
      "\t3-й квартиль (Q3): 0.1434019235\n",
      "\n",
      "Колонка est_diameter_max:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 8306\n",
      "\tМинимальное значение: 0.00136157\n",
      "\tМаксимальное значение: 0.7370561859\n",
      "\t1-й квартиль (Q1): 0.0430566244\n",
      "\t3-й квартиль (Q3): 0.320656449\n",
      "\n",
      "Колонка relative_velocity:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 1574\n",
      "\tМинимальное значение: 203.34643253\n",
      "\tМаксимальное значение: 114380.48061454494\n",
      "\t1-й квартиль (Q1): 28619.02064490995\n",
      "\t3-й квартиль (Q3): 62923.60463276395\n",
      "\n",
      "Колонка miss_distance:\n",
      "\tЕсть выбросы: Нет\n",
      "\tКоличество выбросов: 0\n",
      "\tМинимальное значение: 6745.532515957\n",
      "\tМаксимальное значение: 74798651.4521972\n",
      "\t1-й квартиль (Q1): 17210820.23576468\n",
      "\t3-й квартиль (Q3): 56548996.45139917\n",
      "\n",
      "Колонка absolute_magnitude:\n",
      "\tЕсть выбросы: Да\n",
      "\tКоличество выбросов: 101\n",
      "\tМинимальное значение: 14.8\n",
      "\tМаксимальное значение: 32.239999999999995\n",
      "\t1-й квартиль (Q1): 21.34\n",
      "\t3-й квартиль (Q3): 25.7\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Числовые столбцы DataFrame\n",
    "numeric_columns: list[str] = [\n",
    "    'est_diameter_min',\n",
    "    'est_diameter_max', \n",
    "    'relative_velocity', \n",
    "    'miss_distance', \n",
    "    'absolute_magnitude'\n",
    "]\n",
    "\n",
    "# Проверка выбросов в DataFrame\n",
    "def check_outliers(dataframe: DataFrame, columns: list[str]) -> DataFrame:\n",
    "    for column in columns:\n",
    "        if not pd.api.types.is_numeric_dtype(dataframe[column]): # Проверяем, является ли колонка числовой\n",
    "            continue\n",
    "        \n",
    "        Q1: float = dataframe[column].quantile(0.25) # 1-й квартиль (25%)\n",
    "        Q3: float = dataframe[column].quantile(0.75) # 3-й квартиль (75%)\n",
    "        IQR: float = Q3 - Q1 # Вычисляем межквартильный размах\n",
    "\n",
    "        # Определяем границы для выбросов\n",
    "        lower_bound: float = Q1 - 1.5 * IQR # Нижняя граница\n",
    "        upper_bound: float = Q3 + 1.5 * IQR # Верхняя граница\n",
    "\n",
    "        # Подсчитываем количество выбросов\n",
    "        outliers: DataFrame = dataframe[(dataframe[column] < lower_bound) | (dataframe[column] > upper_bound)]\n",
    "        outlier_count: int = outliers.shape[0]\n",
    "\n",
    "        # Устраняем выбросы:\n",
    "        # Заменяем значения ниже нижней границы на нижнюю границу\n",
    "        # А значения выше верхней границы – на верхнюю\n",
    "        dataframe[column] = dataframe[column].apply(lambda x: lower_bound if x < lower_bound else upper_bound if x > upper_bound else x)\n",
    "\n",
    "        print(f\"Колонка {column}:\")\n",
    "        print(f\"\\tЕсть выбросы: {'Да' if outlier_count > 0 else 'Нет'}\")\n",
    "        print(f\"\\tКоличество выбросов: {outlier_count}\")\n",
    "        print(f\"\\tМинимальное значение: {dataframe[column].min()}\")\n",
    "        print(f\"\\tМаксимальное значение: {dataframe[column].max()}\")\n",
    "        print(f\"\\t1-й квартиль (Q1): {Q1}\")\n",
    "        print(f\"\\t3-й квартиль (Q3): {Q3}\\n\")\n",
    "    \n",
    "    return dataframe\n",
    "\n",
    "\n",
    "# Проверка выбросов\n",
    "df: DataFrame = check_outliers(df, numeric_columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Разбиение набора данных на выборки:\n",
    "\n",
    "**Групповое разбиение данных** – это метод разделения данных на несколько групп или подмножеств на основе определенного признака или характеристики. При этом наблюдения для одного объекта должны попасть только в одну выборку.\n",
    "\n",
    "**Основные виды выборки данных**:\n",
    "1. Обучающая выборка (60-80%). Обучение модели (подбор коэффициентов некоторой математической функции для аппроксимации).\n",
    "2. Контрольная выборка (10-20%). Выбор метода обучения, настройка гиперпараметров.\n",
    "3. Тестовая выборка (10-20% или 20-30%). Оценка качества модели перед передачей заказчику.\n",
    "\n",
    "Разделим выборку данных на 3 группы и проанализируем качество распределения данных.\n",
    "\n",
    "Весь набор данных состоит из 90836 объектов, из которых 81996 (около 90.3%) неопасны (False), а 8840 (около 9.7%) опасны (True). Это говорит о том, что класс \"неопасные\" значительно преобладает.\n",
    "\n",
    "Все выборки показывают одинаковое распределение классов, что свидетельствует о том, что данные были отобраны случайным образом и не содержат явного смещения.\n",
    "\n",
    "Однако, несмотря на сбалансированность при разбиении данных, в целом данные обладают значительным дисбалансом между классами. Это может быть проблемой при обучении модели, так как она может иметь тенденцию игнорировать опасные объекты (True), что следует учитывать при дальнейшем анализе и выборе методов обработки данных.\n",
    "\n",
    "Для получения более сбалансированных выборок данных необходимо воспользоваться методами приращения (аугментации) данных, а именно методами oversampling и undersampling."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Функция для создания выборок\n",
    "def split_stratified_into_train_val_test(\n",
    "    df_input,\n",
    "    stratify_colname=\"y\",\n",
    "    frac_train=0.6,\n",
    "    frac_val=0.15,\n",
    "    frac_test=0.25,\n",
    "    random_state=None,\n",
    ") -> tuple[Any, Any, Any]:\n",
    "    \"\"\"\n",
    "    Splits a Pandas dataframe into three subsets (train, val, and test)\n",
    "    following fractional ratios provided by the user, where each subset is\n",
    "    stratified by the values in a specific column (that is, each subset has\n",
    "    the same relative frequency of the values in the column). It performs this\n",
    "    splitting by running train_test_split() twice.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    df_input : Pandas dataframe\n",
    "        Input dataframe to be split.\n",
    "    stratify_colname : str\n",
    "        The name of the column that will be used for stratification. Usually\n",
    "        this column would be for the label.\n",
    "    frac_train : float\n",
    "    frac_val   : float\n",
    "    frac_test  : float\n",
    "        The ratios with which the dataframe will be split into train, val, and\n",
    "        test data. The values should be expressed as float fractions and should\n",
    "        sum to 1.0.\n",
    "    random_state : int, None, or RandomStateInstance\n",
    "        Value to be passed to train_test_split().\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    df_train, df_val, df_test :\n",
    "        Dataframes containing the three splits.\n",
    "    \"\"\"\n",
    "\n",
    "    if frac_train + frac_val + frac_test != 1.0:\n",
    "        raise ValueError(\n",
    "            \"fractions %f, %f, %f do not add up to 1.0\"\n",
    "            % (frac_train, frac_val, frac_test)\n",
    "        )\n",
    "\n",
    "    if stratify_colname not in df_input.columns:\n",
    "        raise ValueError(\"%s is not a column in the dataframe\" % (stratify_colname))\n",
    "\n",
    "    X: DataFrame = df_input # Contains all columns.\n",
    "    y: DataFrame = df_input[\n",
    "        [stratify_colname]\n",
    "    ] # Dataframe of just the column on which to stratify.\n",
    "\n",
    "    # Split original dataframe into train and temp dataframes.\n",
    "    df_train, df_temp, y_train, y_temp = train_test_split(\n",
    "        X, y, \n",
    "        stratify=y, \n",
    "        test_size=(1.0 - frac_train), \n",
    "        random_state=random_state\n",
    "    )\n",
    "\n",
    "    # Split the temp dataframe into val and test dataframes.\n",
    "    relative_frac_test: float = frac_test / (frac_val + frac_test)\n",
    "    df_val, df_test, y_val, y_test = train_test_split(\n",
    "        df_temp,\n",
    "        y_temp,\n",
    "        stratify=y_temp,\n",
    "        test_size=relative_frac_test,\n",
    "        random_state=random_state,\n",
    "    )\n",
    "\n",
    "    assert len(df_input) == len(df_train) + len(df_val) + len(df_test)\n",
    "\n",
    "    return df_train, df_val, df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hazardous\n",
      "False    81996\n",
      "True      8840\n",
      "Name: count, dtype: int64 \n",
      "\n",
      "Обучающая выборка:  (54501, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    49197\n",
      "True      5304\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 90.27%\n",
      "Процент объектов класса \"True\": 9.73%\n",
      "\n",
      "Контрольная выборка:  (18167, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    16399\n",
      "True      1768\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 90.27%\n",
      "Процент объектов класса \"True\": 9.73%\n",
      "\n",
      "Тестовая выборка:  (18168, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    16400\n",
      "True      1768\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 90.27%\n",
      "Процент объектов класса \"True\": 9.73%\n",
      "\n",
      "Для обучающей выборки аугментация данных требуется\n",
      "Для контрольной выборки аугментация данных требуется\n",
      "Для тестовой выборки аугментация данных требуется\n"
     ]
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Вывод распределения количества наблюдений по меткам (классам)\n",
    "print(df.hazardous.value_counts(), '\\n')\n",
    "\n",
    "data: DataFrame = df[[\n",
    "   'est_diameter_min', \n",
    "   'est_diameter_max', \n",
    "   'relative_velocity', \n",
    "   'miss_distance', \n",
    "   'absolute_magnitude', \n",
    "   'hazardous'\n",
    "]].copy()\n",
    "\n",
    "df_train, df_val, df_test = split_stratified_into_train_val_test(\n",
    "   data, \n",
    "   stratify_colname=\"hazardous\", \n",
    "   frac_train=0.60, \n",
    "   frac_val=0.20, \n",
    "   frac_test=0.20\n",
    ")\n",
    "\n",
    "# Оценка сбалансированности\n",
    "def check_balance(dataframe: DataFrame, dataframe_name: str, column: str) -> None:\n",
    "    counts: Series[int] = dataframe[column].value_counts()\n",
    "    print(dataframe_name + \": \", dataframe.shape)\n",
    "    print(f\"Распределение выборки данных по классам \\\"{column}\\\":\\n\", counts)\n",
    "    total_count: int = len(dataframe)\n",
    "    for value in counts.index:\n",
    "        percentage: float = counts[value] / total_count * 100\n",
    "        print(f\"Процент объектов класса \\\"{value}\\\": {percentage:.2f}%\")\n",
    "    print()\n",
    "   \n",
    "# Определение необходимости аугментации данных\n",
    "def need_augmentation(dataframe: DataFrame,\n",
    "                      column: str, \n",
    "                      first_value: Any, second_value: Any) -> bool:\n",
    "    counts: Series[int] = dataframe[column].value_counts()\n",
    "    ratio: float = counts[first_value] / counts[second_value]\n",
    "    return ratio > 1.5 or ratio < 0.67\n",
    " \n",
    " # Визуализация сбалансированности классов\n",
    "def visualize_balance(dataframe_train: DataFrame,\n",
    "                      dataframe_val: DataFrame,\n",
    "                      dataframe_test: DataFrame, \n",
    "                      column: str) -> None:\n",
    "   fig, axes = plt.subplots(1, 3, figsize=(15, 5))\n",
    "\n",
    "   # Обучающая выборка\n",
    "   counts_train: Series[int] = dataframe_train[column].value_counts()\n",
    "   axes[0].pie(counts_train, labels=counts_train.index, autopct='%1.1f%%', startangle=90)\n",
    "   axes[0].set_title(f\"Распределение классов \\\"{column}\\\" в обучающей выборке\")\n",
    "\n",
    "   # Контрольная выборка\n",
    "   counts_val: Series[int] = dataframe_val[column].value_counts()\n",
    "   axes[1].pie(counts_val, labels=counts_val.index, autopct='%1.1f%%', startangle=90)\n",
    "   axes[1].set_title(f\"Распределение классов \\\"{column}\\\" в контрольной выборке\")\n",
    "\n",
    "   # Тестовая выборка\n",
    "   counts_test: Series[int] = dataframe_test[column].value_counts()\n",
    "   axes[2].pie(counts_test, labels=counts_test.index, autopct='%1.1f%%', startangle=90)\n",
    "   axes[2].set_title(f\"Распределение классов \\\"{column}\\\" в тренировочной выборке\")\n",
    "\n",
    "   # Отображение графиков\n",
    "   plt.tight_layout()\n",
    "   plt.show()\n",
    " \n",
    "\n",
    "# Проверка сбалансированности\n",
    "check_balance(df_train, 'Обучающая выборка', 'hazardous')\n",
    "check_balance(df_val, 'Контрольная выборка', 'hazardous')\n",
    "check_balance(df_test, 'Тестовая выборка', 'hazardous')\n",
    "\n",
    "# Проверка необходимости аугментации\n",
    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test, 'hazardous', True, False) else ''}требуется\")\n",
    "   \n",
    "# Визуализация сбалансированности классов\n",
    "visualize_balance(df_train, df_val, df_test, 'hazardous')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Приращение данных:\n",
    "\n",
    "**Аугментация данных** может быть полезна в том случае, когда имеется недостаточное количество данных и мы хотим сгенерировать новые данные на основе имеющихся, слегка модифицировав их.\n",
    "\n",
    "**Методы решения:**\n",
    "1. **Выборка с избытком (oversampling).** Копирование наблюдений или генерация новых наблюдений на основе существующих с помощью алгоритмов SMOTE и ADASYN (нахождение k-ближайших соседей).\n",
    "2. **Выборка с недостатком (undersampling).** Исключение некоторых наблюдений для меток с большим количеством наблюдений. Наблюдения можно исключать случайным образом или на основе определения связей Томека для наблюдений разных меток."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После применения метода oversampling:\n",
      "Обучающая выборка:  (100573, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "True     51376\n",
      "False    49197\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"True\": 51.08%\n",
      "Процент объектов класса \"False\": 48.92%\n",
      "\n",
      "Контрольная выборка:  (32787, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    16399\n",
      "True     16388\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.02%\n",
      "Процент объектов класса \"True\": 49.98%\n",
      "\n",
      "Тестовая выборка:  (32750, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    16400\n",
      "True     16350\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.08%\n",
      "Процент объектов класса \"True\": 49.92%\n",
      "\n",
      "Для обучающей выборки аугментация данных не требуется\n",
      "Для контрольной выборки аугментация данных не требуется\n",
      "Для тестовой выборки аугментация данных не требуется\n"
     ]
    },
    {
     "data": {
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hwNGjR/Hzzz/D1dUVP/zww1M/h6SkJPj6+sLX1xcffvgh2rRpgxMnTmD69OmIjIw0OQm0Zs0aDBkyBF988QUuXbpkOHmZ8/d0VlYWPD09oVar0bFjR9y8eRP379/HkCFDcPToUfTq1QsajQZLliwBALRo0QJffvkloqKisGTJEtSuXRu7du1CfHw8AODtt9/GxYsXkZKSgu7du6NixYrYuXMnIiIi8Pnnnxu2A7B9fhpsn9k+s30uuKdpn4ujjQwKCkLHjh1Ro0YNTJs2De7u7ti1axcGDRqEvXv3mvzOflqLFy/OdR2blJQU9OvXD0OHDsXw4cOxa9cufPLJJ3B2dsaHH34IoPCvb37tXFxcHDp06ICsrCx89tlnqFixIjZt2oSBAwdiz549Rfa8zQkKCkLnzp3h5eWFr776Ck5OTlizZg26du2KM2fO4OWXX871vlu2bCn0uoW//vorvL29kZ6ejvXr12Ps2LGoU6cOevbs+dTPISkpCWvXroWHhwc+++wzVKpUCVu3bsVbb72Fbdu2GUYOFvZ1K8h79knHjh3Dhx9+iEmTJhmNgCjS97YohA0bNggAwsfHRyQkJIioqCixY8cOUbFiReHm5iaio6OFEELI5XKh0WiM7hsRESFcXFzEnDlzDNetX79eABC//PKLyWNptVrD/QCIRYsWmdymWbNmokuXLoZ/nzp1SgAQNWrUEOnp6Ybrd+3aJQCIpUuXGrbdsGFD0bt3b8PjCCFEVlaWqFu3rujVq5fJY3Xo0EE0b97c8O+EhAQBQMycOdNwXWRkpHBwcBA//vij0X1v3bolHB0dTa4PDQ0VAMSmTZsM182cOVPkfFnOnTsnAIht27YZ3ffIkSMm19euXVv079/fJPvEiRPFky/1k9m/+uorUblyZdGmTRujfbplyxZhb28vzp07Z3T/3377TQAQ58+fN3m8nLp06WLY3r///iscHR3FF198Yfa2BdkfQuhep5yUSqVo3ry56N69u9G27O3txZtvvmnyXtS/5qmpqcLT01O8/PLLIjs72+xtlEqlqFy5smjevLnRbQ4ePCgAiO+//95w3ciRI0Xt2rWNtrN27VoBQFy+fNnscy6o2rVrCwDi7Nmzhuvi4+OFi4uL0f4s6GfvSfrPSM7bPLmfhRDCz89PABCbN282XKc/LnTq1Emo1WrD9fp916pVK6FQKAzX6/dJzvfZkiVLBACxdetWo/u3b99eeHh4GD7P+s/4qVOnTJ4jALFhwwYhhBApKSm5HjeeVe3atcXIkSONrnv33XdFmTJl8rxffHy8cHZ2Fq+99prRa7RixQoBQKxfv14I8fg5ent7i8TERMPt7t69K5ycnMTgwYMN102fPl24uLiI1NRUo8dxdHQ0+nzXrVtXjBgxwiiPuX1p7jWfP3++sLOzE/fv3zdcl/NzqVarRZUqVUT9+vVFZmam4TanT58WAIzenyNHjhTu7u5G29+9e7fZ19Td3d1oPxfm+JrzuCOEEIcOHRIARJ8+fUyOJ+Y8eX8hhPjmm28EABEfH2+4DoCYOHFirtvRfzYiIiKM/t20aVOjfa1/LXLuq1atWonKlSuLpKQkw3UBAQHC3t7e6LXUvxYDBw40euzu3bsLAGLt2rWG9hqAcHNzE25ubmLSpEnCwcFB7N692+j9GBkZKezt7YWjo6PR8WDOnDkCgOjXr5/J82R7rcP2mu21EGyvbaG9fvK+a9asEQDE8uXLjW5T0HZdCN3nq1mzZiaPs2jRIqN2Iqfc9qEQj/djzt9fQghx6dIlAUBMmTLFcF1B2xO9GjVqiNGjR+eZI7djmLmMBWkTCvq+evL9I4QQQ4cOFc2bNxe1atUyeb1zy5Tz/kIIUb16daP2Tf9ZuXLlSq7bevK7QpcuXQQAMWvWLKPbjRo1SgAQt27dEkIIMXv2bAFA9O3b1+j3tJubmwAgdu7cKYTQHSOee+45AUDs3bvXkN/FxUV4eHiI1q1bCyEe/57Wv4+Sk5NF06ZNRePGjUVCQoLR8+7du7dwd3cXd+/eNeRr1qyZqFWrlnBwcBAPHjxg+8z22WhbbJ912D4XrWdpn/WKqo3s0aOHaNGihZDL5YbrtFqt6NChg2jYsKHhutzaBXPHsSc/c/Hx8cLT01P07dvXJLO+7Vi8eLHhOoVCYWi7lUqlEOLZ2kkhTNu5yZMnCwBGx6WMjAxRt25dUadOHcNnQN9m5TzOC2H6GhZm/wwaNEg4OzuL8PBww3WxsbHC09NTvPrqqybb1H9H0reL+v345HN80pP3F0J3TgWA+OmnnwzXmTtH8aQnn4O+3T19+rThuqysLPH888+LqlWrPvXrVpD3bM7j5NWrV4WHh4d4++23TY5bBX1vF8RTTVXUs2dPVKpUCbVq1cKwYcPg4eGBffv2oUaNGgAAFxcX2NvrNq3RaJCUlAQPDw80btwY169fN2xn79698Pb2xqeffmryGOaGwxTUiBEj4Onpafj3kCFDUK1aNRw6dAgA4O/vj9DQULz77rtISkpCYmIiEhMTIZPJ0KNHD5w9e9ZkSKlcLoerq2uej/vXX39Bq9Vi6NChhm0mJiaiatWqaNiwIU6dOmV0e/1QIRcXl1y3uXv3bpQtWxa9evUy2mabNm3g4eFhsk2VSmV0u8TERMjl8jxzx8TEYPny5ZgxY4ZJlXb37t14/vnn0aRJE6Nt6qenevLxc3P58mUMHToUgwcPxqJFi8zepiD7A4Chlwugq86mpaWhc+fORu+t/fv3Q6vV4vvvvze8F/X0763jx48jIyMD06ZNM3lt9be5evUq4uPjMWHCBKPb9O/fH02aNDEZyqXVag37yN/fH5s3b0a1atUMVfBn0bRpU0PPEQCoVKkSGjdujHv37hmuK+hnL6fg4GB8+OGHeOONN/Ddd98Zrs+5n1UqFZKSktCgQQOUK1fO7LbGjh0LBwcHw7/1++7jjz82mut11KhRKFu2rNF9Dx06hKpVqxrN6erk5ITPPvsMmZmZOHPmTL77Jyc3Nzc4Ozvj9OnTJkOQi4JCoUBiYqKh18XJkyfRo0ePPO/j4+MDpVKJyZMnG70nx44dCy8vL5P30ujRow29VQCgYcOGGDhwII4cOQKNRgNAd6xTKBRGQ4t37twJtVqN999/33Bd5cqVER0dne/zyvmay2QyJCYmokOHDhBC4MaNGya3T0xMxOnTpxEXF4fx48cbzcPZpUsXtGnTxuR5Pa3CHl/1hBCYPn06Bg8enGfvhSfpj6UJCQnw8/PDvn370LJlS5PRE3K5HImJiUhKSjI7FYE5EydONNrXXbt2NdpXDx8+hL+/P0aNGoUKFSoYbteyZUv06tXL0JY9uc2c9D0oxo0bZ2ivAcDZ2RmjR4/GihUrsGzZMgwZMsTomLFlyxZotVo0aNAAFy9eNOznc+fOwcHBwWx7wvZah+21KbbXOmyvc2eJ7XVOBw4cwIQJEzB16lSTqekK265rNBqTz3xWVtYzPb9BgwYZfn8BQLt27fDyyy8bjqFP054olcp8P9vA42NYUlIS1Gp1rrfLysoyed767zF6T/u+unbtGnbv3o358+ebHD/ykpmZicTERMTExGDt2rV49OiR2fdFWloaEhMTkZGRUaDtOjg4YMqUKUbXffHFFwBgeD/cvHkTgG5ql5y/p/Xf+S5fvgxAd4yws7ND9erVMXDgQKNjRLVq1XDjxg08evQIe/fuNbQHcrkcAwcOREJCAo4cOWLyneXmzZvo3Lkzypcvb/RalC9fHhqNBmfPnjXclu0z22e2z2yfLbl9Loj82sjk5GScPHkSQ4cORUZGhuF9l5SUhN69eyM0NNRkOi19u6C/JCcn55vjhx9+QNmyZfHZZ5+Z/bujoyPGjx9v+LezszPGjx+P+Ph4XLt2DUDhX9/82rlDhw6hXbt26NSpk+E6Dw8PjBs3DpGRkQgODgagO48AoEDnEoD8949Go8GxY8cwaNAg1KtXz3B9tWrV8O6778LX1xfp6elmt71y5UokJSVh5syZBcqil5KSgsTERNy7dw+//vorHBwc0KVLF5PbFfR4r9e2bVuj7bi5uWHChAl49OiR4fNd2Nctv/dsTvfu3UP//v3RqlUrbNmyxeg4/TTv7bw81VRFK1euRKNGjeDo6IgqVaqgcePGRiG1Wi2WLl2KVatWISIiwujLYc4TYeHh4WjcuDEcHYtkxiSDhg0bGv3bzs4ODRo0MMzFFhoaCgAYOXJkrttIS0tD+fLlDf9OTEw02e6TQkNDIYTI9XZPDoHUz3+a13xaoaGhSEtLM3xgn6Qfeqp37NixQi8WMnPmTFSvXh3jx483mdswNDQUt2/fznWbTz6+OTExMejfvz9kMhmSkpJyPclUkP0BAAcPHsTcuXPh7+9vNC9kzu2Gh4fD3t4eTZs2zXU7+im2zM05q3f//n0AQOPGjU3+1qRJE/j6+hpdFxUVZbSvqlWrZvSF/lk899xzJteVL1/eqCEv6GdPLz09HW+99RZq1KiBzZs3G+3D7OxszJ8/Hxs2bEBMTAx0hVWdtLQ0k23VrVvX6N/6fffk58HJycmokdDftmHDhiZfSvVfEPXbKigXFxcsXLgQX3zxBapUqYJXXnkFAwYMwIgRI1C1atVCbcucHTt2YMeOHYZ/t23b1mSuvCfl9l5ydnZGvXr1DH/XvwZNmjQx2cbzzz+PvXv3IjExEVWqVEGTJk3Qtm1bbNu2DR999BEA3TRFr7zyCho0aGC4X4cOHbBs2TLs2LED3bt3h729vdnX8MGDB/j+++/x999/m3xBNHf7nO91c5+R559/Ptf5UgursMdXvW3btiEoKAi7du0ymWs1LxcuXDB6fg0bNsT+/ftNjl/r1q3DunXrAOhey5dffhm//PKLYcHDnPJ7bfX7Kq/jzvPPP4+jR4+aLJj25H7RtxmdOnXCzJkz4ejoiG7duqFRo0aG6RKSk5NzPWaEhIQgJCTE5NifmJhokulZsL1me832mu21pbXXev7+/ti1axc0Go3ZEwMFbdf1zB1Tn5W5Y1ijRo2wa9euPDMCubcnaWlpBfoc5DyGOTg4oGXLlliwYIFh+hK9mTNnmv2Rn3PI/dO+r6ZNm4bOnTtjwIABea459KRPP/3UqOPa6NGjTU74AzCaxqBcuXIYPnw4Fi1aZHbBUv1Jfi8vL6Pr9b+T9e2aftrCZcuW4fnnnzf6PV2xYkXDc9VqtUhPT0d2djZcXV2NjhH67xGRkZEIDw9HtWrVEBoaitGjR+PixYtwdXU1W8zRFxTyaif0JyzYPrN9ZvvM9tlS2+eCyq+NDAsLgxACM2bMwIwZM8xuI+dxEUChp7eJiIjAmjVrsHr16lwLp9WrVzdpVxo1agRAd5x/5ZVXCv365tfO3b9/32ynupzba968Odq3bw87OztMnz4dc+fONXwOc+swl9/+SUhIQFZWVq7fS7RaLaKiogxTh+mlpaVh3rx5+Pzzz3Odsic3L774ouH/XVxcsGLFCpOp9GQymdFxp1atWvjiiy/ynOIrt9/0gO51e/nllwv9uuX3ns2Zt3fv3oiLi0PFihVN2oSneW/n5anO2Ldr187sSRG9efPmYcaMGfjwww/xww8/oEKFCrC3t8fkyZML3COzOOkzLFq0CK1atTJ7m5wNk1KpxMOHD9GrV698t2tnZ4fDhw8bVYrNbRPQLTYDIM8Dr1arReXKlc0udAnA5AvIyy+/jLlz5xpdt2LFChw4cMDs/W/fvo2NGzdi69atZk+8abVatGjRAr/88ovZ++ecVy83YWFhePHFF/Hrr7/igw8+wKZNm8x+ySzI/jh37hwGDhyIV199FatWrUK1atXg5OSEDRs2FOqkYHGpUqWKYW7xtLQ0rF+/Hn369IGvry9atGjxTNs2954CYPQFpLCfvVGjRiE2NhaXL182+aHz6aefYsOGDZg8eTLat2+PsmXLws7ODsOGDTO7rZw9KopLbl+Sn+y5BgCTJ0/G66+/jv379+Po0aOYMWMG5s+fj5MnT6J169bPlOO1117D1KlTAeiq7wsXLkS3bt1w9erVZ94Phb3/iBEj8L///Q/R0dFQKBS4ePEiVqxYYXSbb775BufPnzeqdD9Jo9GgV69eSE5Oxtdff40mTZrA3d0dMTExGDVqlNnX/Pjx4/Dz88P3339fqMxPo7DHV0B37J4xYwY++ugjw5evgmrZsiUWL14MAIY5/rt27Yrr168bHaPeeOMNTJo0CUIIREREYM6cORgwYIDhB3VOJfEZeVLlypWNvsBduXIFY8eOhbu7O+bOnYvExEQsXbrUcMzYsmULDh06hNq1a6Ns2bKGffDhhx/Cy8sLa9asKdH8bK8fY3tdtNheFy9baK8DAgLQt29f9OjRA1OnTsX7779vskBpYdSpU8dkLv7du3dj7dq1T73NopacnAylUlmgk0I5j2GxsbFYuHAh3nzzTQQFBRnNfT1u3Di8/fbbRvcdO3bsM2c9duwYfHx84OfnV+j7Tp06Fa+99ho0Gg2CgoIwZ84cCCGwYcMGo9vpO8spFAqcPn3asNDzqlWrTLZZ2M9V27Zt8corr+T693nz5iElJQVVqlTBkiVLjI4RuY3wuH79Og4cOIBJkyZh3LhxOHnypNHfhRDo1asXvvrqK8N1+hEyixcvRqNGjYx6XueF7fNjbJ+LFtvn4mUL7XNR0b8GX375JXr37m32Njk74wGP2wW99PR0DB48ONfH+Pbbb9GwYUOMHDmySBf5zk9B27n8vPDCC5g5cyZmz56d6zE0p8Lun4JauHAh7O3tMXXqVEMBvqC2bt2KKlWqQC6X4+TJk5g4cSJcXV2NFnd2dXXFP//8AwDIyMjA+vXrMXnyZFSrVg1Dhw412aYUv+tzSkxMhLu7O/755x8MGjQI8+fPN+qk8TTv7bwUbVf//+zZswfdunUz9MLUS01NNRoyWb9+fVy6dAkqlapIFiTSe/KEjRACYWFhaNmypeFxAcDLy6tAFcOAgACoVKo8iyX67QohULdu3QKdpAoODoadnZ3ZalvObfr4+KBjx44FenN6e3ubPKe8FlyaPn06WrVqhXfeeSfXxw8ICECPHj2eejoK/bDWKlWq4MCBA/jiiy/Qr18/ky9pBdkfe/fuhaurK44ePWo0BPPJA2D9+vWh1WoRHByc65dZ/fsgMDAw1w9N7dq1AQB37twxDCfVu3PnjuHveq6urkb7X79w0YoVK0rkhFtBP3sAsGDBAuzfvx9//fWX2Wrpnj17MHLkSMOJQ0A3xFjfkyU/+n0TGhpqtO9UKhUiIiLwwgsvGN325s2b0Gq1RtXYkJAQo23pey09mSG3HhT169fHF198gS+++AKhoaFo1aoVFi9enOvCsQVVrVo1o9e5cePG6NChA/bv35/ryfmc76WcPUSUSiUiIiIM29P3NLlz547JNkJCQuDu7m70Wg4bNgyff/45tm/fjuzsbDg5OZl8nr29veHn54fg4GDDD4qAgAB8+eWXhtvcunULd+/exaZNmzBixAjD9cePH891P/Ts2RNly5bF999/n2veZ104S6+wx1dA98M+Pj4es2bNKvTjlS9f3ug17tq1K6pXr44NGzYYLfRbs2ZNo9t5eHjgvffeMzu1U87X9snjSc59lfO98qSQkBB4e3ub9EoJDQ016qWk771WrVo1o9v16tULq1evhlwux/79+/HHH38YHTOioqJw6NAhyOVyo0WjWrZsiUuXLqFNmzbmdtdTY3vN9prtNdtrwLLaa70WLVpg9+7dcHNzw+7duzFu3DjcvHnT0GOwoO26nru7u8l1/v7+z/L0zBap7969+9TtiX5qgoJMCfLkMaxBgwbo2LEjzp49a9T2N2zY0Oy+yKmg7ys9IQSmTZuGN998M8+T77lp2rSpIVPv3r2hUCjwzTff4McffzQsfAoYd5br378/AgICcOTIEbPbrFu3Lo4dO4aMjAyjKX7u3r0LrVZr2Cf6HstRUVFG2ePi4pCammp4rnv27IGLiwscHBzwzjvvGI7rqamphn1Up04d1K9f3zDVwR9//IGBAwfCwcEBAwYMwLp16wwjUvWPnZmZafR6uLu7G33n0RcO2D6zfWb7zPYZsMz2uaDyayP1bbeTk1OBRxI82Yk6r9HYN27cwI4dO7B///5ci1aArvj+5Oi/u3fvAoBRe16YdjK/dq527dq5fjd4cnszZ87EuHHjEBISYigw5ZwWOaf89k+lSpVQpkyZXB/b3t7epJgaGxuLpUuXYv78+fD09Cx04aBjx46G/ThgwAAEBQVh/vz5RoUDBwcHo/dA//79UaFCBRw5csRs4aBu3bp57r+nfd3ye8/qlSlTBkeOHEGTJk0wZcoUzJs3D0OHDjV8f3ua93ZenmqNg/w4ODgYVW0BXY+aJ+dQGjx4MBITE016xwIwuX9h6Fei1tuzZw8ePnyIvn37AgDatGmD+vXr4+effzasyp5TQkKCSXb9F7C8vPXWW3BwcMDs2bNN8gshjN7garUae/fuRbt27fIcdjd06FBoNBrD6uc5qdXqAjc65vj5+eHAgQNYsGBBrl9ihg4dipiYGJMeUoBu6J1MJsv3cRo1amQYTrR8+XJotVqTIT8F3R8ODg6ws7MzqohHRkaafJkbNGgQ7O3tMWfOHJNqvv61ee211+Dp6Yn58+ebzGOmv81LL72EypUr47fffjMaxnn48GHcvn0b/fv3z/O5K5VKqNVqo/sWp4J+9nx8fPDdd9/h22+/xaBBgwq8reXLl5vtjWDOSy+9hEqVKuG3334zzLcJABs3bjR53/br1w+PHj3Czp07Ddep1WosX74cHh4ehrnjateuDQcHB6M5WAHTnl9ZWVkmr2n9+vXh6elZLK9FdnY2AOS57Z49e8LZ2RnLli0z2q/r1q1DWlqa4b1UqVIlvPTSS9i0aZPRsNnw8HD8/fff6Nu3r9EXD29vb/Tt2xdbt27Ftm3b0KdPH5MvtQBgb2+P5s2bo2fPnujZs6fJCWD9NnNmE0Jg6dKleT73Vq1aoUqVKvj999+N5mo+d+4crl69mu9xs6AKc3wFdD0FfvzxR0yZMqVIhtMW5DUGHlf3zX05bN26NapWrWpyPHlyX1WrVg2tWrXCpk2bjD4rgYGBOHbsGPr162ey7ZUrVxr928fHB4BumqqcOnToAAcHB7i7u+O3336DTCYzOj689dZbsLe3R1xcnNH99O31woULTR6b7bUO22tjbK/zxvZaxxLba70XX3wR7u7usLe3xx9//IHIyEjMmTPH8PeCtuvFaf/+/UbvmcuXL+PSpUuGY2hh25MdO3bA2dnZaM7jgsqr/ctPQd9XOXPevHkT8+fPL/RjmaN/X+R8/5uj1WpzfX79+vWDRqMx+V2r72Wufz/oT7xv3749z9vpHyc2Nhb79u0D8PgYERcXh1atWqFq1aoYPHiwoX3Uz93ev39/DBs2DF9++aVRe/7CCy/Az88PR48eNcmfmppqNJKB7XPBsH1m+8z2uWgUpn0uqPzayMqVK6Nr165Ys2YNHj58aHL/J49jhTVt2jR07NgRAwcOzPN2arXaqCimVCqxZs0aVKpUyfCbvbDt5JOebOf69euHy5cvG43ak8lkWLt2LerUqWMyRVm1atXQrVs3w7mE/NaryY2DgwNee+01HDhwwDD1HaArnv/555/o1KmTycid2bNno0qVKvj444+f6jGflJ2dne/7TP+5zavNv3z5Mi5cuGC4Ti6XY/Xq1ahatepTv275vWf1KlWqZChWzpkzBzVr1sTYsWMNuYv6vV0sIw4GDBiAOXPmYPTo0ejQoQNu3bqFbdu2mczDNmLECGzevBmff/45Ll++jM6dO0Mmk8HHxwcTJkzAG2+88VSPX6FCBXTq1AmjR49GXFwclixZggYNGhiGxep/APTt2xfNmjXD6NGjUaNGDcTExODUqVPw8vLCP//8A5lMhpUrV2LZsmVo1KgRTp8+bXgM/Rekmzdvws/PD+3bt0f9+vUxd+5cTJ8+HZGRkRg0aBA8PT0RERGBffv2Ydy4cfjyyy/h4+ODGTNm4ObNm4bhMLnp0qULxo8fj/nz58Pf3x+vvfYanJycEBoait27d2Pp0qUYMmTIU+2nY8eOoVevXnlWoD744APs2rULH3/8MU6dOoWOHTtCo9EgJCQEu3btwtGjR/PtOZJT1apVsWjRIowZMwbvv/8++vXrV6j90b9/f/zyyy/o06cP3n33XcTHx2PlypVo0KCBYbExQNfr6dtvv8UPP/yAzp0746233oKLiwuuXLmC6tWrY/78+fDy8sKvv/6KMWPGoG3btnj33XdRvnx5BAQEICsrC5s2bYKTkxMWLlyI0aNHo0uXLhg+fDji4uKwdOlS1KlTx2Q+VJlMZjS0csuWLZDL5XjzzTcLvI+eRUE/e8OHD0elSpXQsGFDk94CvXr1QpUqVTBgwABs2bIFZcuWRdOmTeHn5wcfHx+zczua4+TkhLlz52L8+PHo3r073nnnHURERGDDhg0mecaNG4c1a9Zg1KhRuHbtGurUqYM9e/bg/PnzWLJkiaHnVtmyZfH2229j+fLlsLOzQ/369XHw4EGTuUHv3r2LHj16YOjQoWjatCkcHR2xb98+xMXFYdiwYYXdrSbu3btn2G8xMTFYsWIFvLy88lzQqVKlSpg+fTpmz56NPn36YODAgbhz5w5WrVqFtm3bGlXtf/rpJ7z22mto3749xowZA7lcjpUrV8LV1RU//vijybZHjBhhOA6Y+1FUEE2aNEH9+vXx5ZdfIiYmBl5eXti7d2++i2HpPyOjRo1Cx44dMXLkSCQnJ2Pp0qWoUaMGvv76a6PbazQao956+t6Wly9fNvpyqtFoEBMTg8uXL6Ndu3YFPr7qXb9+Hd7e3kbD8QsjLi7O8BonJiZizZo1cHR0NPnB++DBAxw5csQwVdGPP/6I2rVro3Xr1iY9BhwdHfHTTz9hxIgR6Ny5M9577z3DNEg1a9Y02leLFi1C37590b59e3z00UfIzs7G8uXLUbZsWbMjKCIiIjBw4ED06dMHfn5+hukJ8uqt17t3b7Ro0QK3bt3CO++8g549e+LWrVtwcXFBdnY2bty4gUWLFsHT0xPh4eFwc3PDjBkzEBQUxPbaDLbXxthe543ttY4lttfmNG/eHF9//TUWLFiAYcOGoWXLloVq14tLgwYN0KlTJ3zyySdQKBRYsmQJKlasaNT2FaQ9CQ0NxcyZM7F9+3ZMmzbN5Ee7Ofr58gHdIswLFy5E2bJl0a1bt0I/j4K+r/SOHTuGsWPH5tmrOi9+fn5wdHQ0TOGwfPlytG7d2qRHn5+fHxITEw1TFZ04ccLou0ZO/fr1Q8+ePfHtt98iIiICrVq1wsmTJ7F37158/PHHhjng9XOs79u3D++88w66dOmCy5cvY9OmTRg0aJBh/w0YMADXr1+Hl5cX3n//fTRt2hS3b9+Gs7MzlEolFixYAED3HfCnn35CSEgIPvvsM/Tp0wcymQyPHj2CWq3Gp59+ip9++gmAbvRkUlISBgwYgFGjRqFNmzaGBRNr1qxpdBKH7XPBsH1m+8z2+ekUVfucl4K0kStXrkSnTp3QokULjB07FvXq1UNcXBz8/PwQHR2NgICAp378Y8eO4fz58/nernr16li4cCEiIyPRqFEj7Ny5E/7+/li7dq1hZpbCtpP5tXPTpk3D9u3b0bdvX3z22WeoUKECNm3ahIiICOzdu9dkTv6iNHfuXBw/fhydOnXChAkT4OjoiDVr1kChUBjaq5yOHTuGbdu2GS0QXhj79++Ht7e3Yaqic+fOYfLkyUa3yXmOIiMjAxs2bIBMJsu1KPjVV19h27Zthv3n7e2NrVu3Ijg4GNu2bTOs41vY160g79knubm5Ye3atejZsydWr16NCRMmACji97YohA0bNggA4sqVK3neTi6Xiy+++EJUq1ZNuLm5iY4dOwo/Pz/RpUsX0aVLF6PbZmVliW+//VbUrVtXODk5iapVq4ohQ4aI8PBwIYQQERERAoBYtGiRyeM0a9bMaHunTp0SAMT27dvF9OnTReXKlYWbm5vo37+/uH//vsn9b9y4Id566y1RsWJF4eLiImrXri2GDh0qTpw4YfTY+V1GjhxptN29e/eKTp06CXd3d+Hu7i6aNGkiJk6cKO7cuSOEEOLTTz8Vr776qjhy5IhJppkzZwpzL8vatWtFmzZthJubm/D09BQtWrQQX331lYiNjTXcpnbt2qJ///4m9504caLJNgEIOzs7ce3aNaPrzb1GSqVSLFy4UDRr1ky4uLiI8uXLizZt2ojZs2eLtLQ0k8fLb3tCCNG9e3fx3HPPiYyMjELvj3Xr1omGDRsKFxcX0aRJE7Fhw4Zc99v69etF69atDbm7dOkijh8/bnSbv//+W3To0EG4ubkJLy8v0a5dO7F9+3aj2+zcudOwnQoVKoj33ntPREdHG91m5MiRRu8LDw8P8eKLL4otW7bkuY8KIrfX9sn9W9DPXl7v51OnTgkhhEhJSRGjR48W3t7ewsPDQ/Tu3VuEhISI2rVrG73n8zsurFq1StStW1e4uLiIl156SZw9e9bs+yIuLs7weM7OzqJFixZiw4YNJttLSEgQgwcPFmXKlBHly5cX48ePF4GBgQKA4faJiYli4sSJokmTJsLd3V2ULVtWvPzyy2LXrl0F2d15ql27ttH+8vb2Fq+99prw8/Mr0P1XrFghmjRpIpycnESVKlXEJ598IlJSUkxud+LECdGxY0fD+7J///7i1q1bZrepUChE+fLlRdmyZUV2dnaBcuiPl/rXWwghgoODRc+ePYWHh4fw9vYWY8eOFQEBAUb7Vgjzn8sdO3aIVq1aGT5r77zzjoiMjDS6zZOfkYJcnnyf5Hd8FUL3uQAgfv31V6P75naceJL+/vpLuXLlRMeOHcWhQ4eMbpfzNnZ2dqJq1arirbfeErdv3xZCPP5sREREGN1v165dRseT4cOHm22jfHx8jN4Dr7/+uggODjb7nIKDg8WQIUOEp6enKF++vOjRo4fJ5xKAmDlzptH9Y2JihJubm3B1dTU6ZjRr1kx4eXkZ7efx48eLjz/+mO31E/v+SWyvH2N7/Rjba+tqr5/cd0LoXrMmTZqItm3bCrVabbi+IO16ly5dRLNmzUweZ9GiRWbbCSHMt9N6OY+3ixcvFrVq1RIuLi6ic+fOIiAgwOT2+bUn27dvF82bNxdLly4VWq023xy57duLFy+azfikJ9sEIQr2vtJv083NTcTExBj9zdxrltt+01/s7e1FzZo1xciRI42OE/rPiv7i7OwsGjRoIL7//nuhUCiEEOaPmZmZmWLKlCmievXqwsnJSTRo0EAsWLBAaDQak22PGzfO0J7WqlVLTJ8+XcjlcsPt5HK58PLyEi4uLsLFxUWUKVNGODk5CTc3N9G0aVOjx12zZo0AIGrVqmXUPuvfX7///rvhtcjIyBDTp08XDRo0EM7OzsLBwUF4eXmJn3/+WSiVSrbPbJ9N7s/2me2zJbXPekXZRoaHh4sRI0aIqlWrCicnJ1GjRg0xYMAAsWfPHsNtcnuNEhISTH5j6T9Lb7zxRr6Z9d8Prl69Ktq3by9cXV1F7dq1xYoVK0xyFqadzK+d0z/vIUOGiHLlyglXV1fRrl07cfDgQZPHNaeg72Fz+0cIIa5fvy569+4tPDw8RJkyZUS3bt3EhQsXjG6j32arVq2Mvpvon6O597a5+5try3O2twU57ph7Dvr9V7ZsWeHq6iratm0r9u/fb5KjMK9bQd6zI0eOFLVr1zZ5nNGjRwsvLy+j17kg7+2CsPtvJ9iE06dPo1u3bti9e/dT9xrIKTIyEnXr1kVERESu83TPmjULkZGR2Lhx4zM/HhHR01Cr1ahevTpef/11k/k4rdnGjRuxceNGo95pZGzWrFmYPXs2EhISzE5RZanYXhMRPT39MW/RokW59oAn61enTh00b94cBw8eLLHHZPtMRNbOmtrIrl27IjExEYGBgVJHIQlZ+nu2+MafEBFRidi/fz8SEhKMFjUmIiIiIiIiIiJ6WsWyxoGt8PDwwHvvvZfn4kItW7ZE9erVSzAVEZHOpUuXcPPmTfzwww9o3bp1vosiWZsaNWqgXbt2UscgK8D2moiIyPKwfSYiIrJuLBzkQb/ARV7eeuutEkpDRGRs9erV2Lp1K1q1amWTw7t79eqFXr16SR2DrADbayIiIsvD9pmIiMi62dQaB0RERERERERERERE9Gy4xgERERERERERERERERmwcEBERERERERERERERAYsHBARERERERERERERkQELB0REREREREREREREZMDCARERERERERERERERGbBwQEREREREREREREREBiwcEBERERERERERERGRAQsHRERERERERERERERkwMIBEREREREREREREREZsHBAREREREREREREREQGLBwQEREREREREREREZEBCwdERERERERERERERGTAwgERERERERERERERERmwcEBERERERERERERERAYsHBARERERERERERERkQELB0REREREREREREREZMDCARERERERERERERERGbBwQEREREREREREREREBiwcEBERERERERERERGRAQsHRERERERERERERERkwMIBEREREREREREREREZsHBAREREREREREREREQGLBwQEREREREREREREZEBCwdERERERERERERERGTAwgERERERERERERERERmwcEBERERERERERERERAYsHBARERERERERERERkQELB0REREREREREREREZMDCARERERERERERERERGbBwQEREREREREREREREBiwcEBERERERERERERGRAQsHRERERERERERERERkwMIBEREREREREREREREZsHBAREREREREREREREQGLBwQEREREREREREREZEBCwdERERERERERERERGTAwgERERERERERERERERmwcEBERERERERERERERAYsHBARERERERERERERkQELB0REREREREREREREZMDCARERERERERERERERGbBwQEREREREREREREREBiwcEBERERERERERERGRAQsHRERERERERERERERkwMIBEREREREREREREREZsHBAREREREREREREREQGLBwQEREREREREREREZEBCwdERERERERERERERGTgKHUAIpKGEAIypQYyhRqZCjWyFBpkKtSQKdSQKXXXqdRaaAWgFQJCAH3dAlFT9QCws9dd7B0ARxfA2UN3cfEAnN0f/9vZXXexs5P66RIREdkcpVr7uB1XPm7Hs5RqZCo0yFaqodYKaIWu3XeEBqMcjjxux/VtuZN7jjbc07g9d/EEHJykfqpEREQ2RwiBLKUGMqUaMoXut/nj3+MaZCnUUGq00ORoy19xiURzdZBxW272N/kT7Tl/kxPRU7ATQgipQxBR0ZGrNIhLlyM+Q4G4dDni0hWIT5cbXRefoUCmQo3CfvpPN9iJOtEHCncnOwfA3RvwqAx4VAU8q/z336qARxXdxas6ULYmv8wQEREBSJEp8TBNjrgMORLSFYjP0LXd8ekKxGXIEZ+uQGKmAgq1tlDbreCkwnWHkYUP5OgGeFQCPKvp2m1PfTte9fH/e9UA3MoVfttEREQ2Rq3R4mGaXNd+pyuMfofr2nPd/6dmKaEt5G/ydQ0voEfUikImstO10frf47m152VrsrMAERnhiAMiKySEQGyaHPcSMnEvQab7b6IM9xJkiE3LLnRBoFgJDZAZp7vgVu63c3QFytcFKtb/79IAqPDffz2rlFhcIiKikpCt1OBeYiYiEmWISJAhIlGG8EQZIhNlSMtWSR3PmDobSH2gu+TFrYKu3fZu+Lgt17fnTq4lk5WIiKiExKXLce+/Njzivzb9XoIMUSlZUGks6kc5kJ2iuyTczv1mdg5Auef+a8cb5GjLG+o6+7GjH1Gpw8IBkYXLkKtwKyYNt6LTEBibjtC4DNxPykK2SiN1tKKlluu+xJj7IuPsCVRuAlR74fGlclP2hiAiIqsQlZyla8tj0hAYk4bQuEzEZcgtq9BfFLKTgejLuktOdvaAV02gUmOgeiugWivdf8vWlCAkERFR4chVGoQ8ytC149FpCH6YjnsJmZApbew3udAAKRG6S+gx4785uQMV6wFVWjxuy6u2AJzLSJGUiEoICwdEFiRLqUZgTDpuRqcaigURSTLbO7FQWMoMIPqK7qLn4AxUfl5XRKjaEqj+ou7/HXhYIyIi6TxIMi4SBMamITXLwkYQlDShBdIe6C5hxx9f717pcRGhWiugemugbA2JQhIREemKBLcfpiPwv7b8Voyu8566sHMK2RqVDHh0S3cJ+FN3nZ0D4N1I145Xb81iApEN4hk2IgmlZatw6V4SLoQn4eK9JITGZ0JT2r+QFJRGCTwM0F30nNyBWu2AOh2B2p2AGm0AR2fpMhIRkc0Li8/ExXu6dvxSRDISMhRSR7IesgRdISFnMaFsLaB2R6BOJ6BuZ6B8HcniERGR7ZOrNLh2P8XQlgdEpUGpKdwaQqWW0DyeNSBgu+46e0ddEaFOJ93lufa6BZqJyCqxcEBUgmQKNS5HJsMvPAl+4UkIik0r9GJIlAeVDLh3SncBdIs51nxJ94WldkddUcHRRdqMRERk1VgoKGZpUcDNHboLAJR9TtchoE5nXSGh3HPS5iMiIqsmV2lw/b9CgR8LBUVPq348W4Dvr4C90xOFhFdYSCCyIiwcEBWzwJg0HA+Ow7nQBNyMTuMQx5KkzgYiz+kuAODsAdTrCjTqDTTszUWXiYgoX3KVBr6hifC5HYdTd+IRl85CQYlKewAEPHjck7FcbV073qiPrpjAkYVERJSPqOQs+NyOw4nb8bgcmQylmoWCEqNVPV7/yPcXXSGh1stA4z5A4366BZiJyGLZCVHqZ08nKlJqjRaXIpJxPDgOx4PjEJOaLXWkInO6wU7UiT4gdYwiYqebi7FRH90JiGqtADs7qUMREZEFiM+Q4+TtePjcjoNvWCLkKts4wVDBSYXrDiOljlF0XLyA+t11Jx4a9gLKVJA6ERERWQAhBPyjUnHiv7Y85FGG1JGKzLqGF9AjaoXUMYqOdyOgcV+gUV9dQcHeXupERJQDRxwQFQGZQo0zdxNwLOgRTt1JQFp2KV8E0SoIIPaG7nJ6PuBZTfeFpflg3bRGLCIQEZUqYfGZOBL4EMdvx+NmdCrYtcYKKNKB4P26i52DbvqDxn2B5wcC5WtLnY6IiEqQSqPFudAEHAuKw4mQeE4laC0S7+ou55cCZSrqZgZo0g9o0AtwcpU6HVGpxxEHRE9JpdHiVEg8/roeg5N34kvFcEfbGnGQB68aQLM3gRZv60YlEBGRTYrPkONv/1js949BYEy61HGKnc2NOMiVna7XYsuhuvacIxGIiGySEALX7qdg340YHLr1EClZtt+Bz+ZGHOTGpSzQ9HWgxVDd1IQciUAkCRYOiArJPyoVf12PxsGbD5EsU0odp0SVmsJBThUbAi2G6IoInH+RiMjqyRRqHAl8hP3+MbgQngRNKVp7qPQUDnJwcNb1Wmz5tm4aBPZeJCKyemHxmdh/IwYHAmIQlWw7UwMXRKkpHOTkWR1oMRho+Q5QtYXUaYhKFRYOiAogJjUb+65H468bMbiXIJM6jmRKZeEgp+ovAm1G6ooIzu5SpyEiogLSagXOhCbgr+sx8AmOQ7ZKI3UkSZTKwkFO+t6LLwwH6nSSOg0RERVCUqYC+27ElJpRgrkplYWDnCo31f0eb/Uu4FlV6jRENo+FA6JcqDVaHA2Kw7ZL9+F3L4lzHYOFAwMXL930B21GA1WbS52GiIhyEZ8ux44rUdh5JQoxqaWrR6I5pb5wkJN3Y6DtR7oigquX1GmIiCgXF8IT8eelBzgWFAelxvanB85PqS8c6Nk7Ak36A23HAHVflToNkc1i4YDoCXHpcvx56QF2XHmAuHQuqJQTCwdm1GwHvDRaN4eyk5vUaYiICMCFsERsuXgfx4PjoC5FUxHlh4UDM5zcddMYtR3D6Q+IiCxEhlyF3VejsfXS/VI94t8cFg7M8G4MvPQh0Go44FpW6jRENoWFA6L/XLufgg3nI3Ak8BFPMuSChYM8uJbTDZd85ROg3HNSpyEiKnWylGrsvR6DLX6RuBuXKXUci8TCQT5qvawrIDQdBDg6S52GiKjUCY3LwCa/SOy7HgOZsnROK5gfFg7y4OSuW5+w7RigWkup0xDZBBYOqFRTa7T499ZDrD8fiYCoVKnjWDwWDgrA3lF3wqHj//hlhYioBCRmKrDONwJbL95HhlwtdRyLxsJBAblXAl7+GGg3lj0XiYhKwIXwRKw+HY5zoYlSR7F4LBwUUO2OQKfPgYY9pU5CZNUcpQ5AJAWlWovd16Lw25lwRCVzzmMqQlo1ELhHd6nXTVdAqN9N6lRERDYnOiULa87cw66rUVCoOecxFSFZAnDyB+D8Ut10hK9MBDyrSJ2KiMimCCHgczseq06H4caDVKnjkK25f153qdoC6DQFaPomYG8vdSoiq8PCAZUqcpUGf156gLVn7+FRulzqOGTr7p3SXaq9AHT4TLcOgr2D1KmIiKxaaFwGVp8Ox98BsZxakIqXIl1XPLi0RjcdYcf/AeXrSJ2KiMiqqTVa/HMzFr+dvoc7cRlSxyFb9+gWsOdDoMJcXTv+wrucjpCoEDhVEZUKMoUam/3uY53vPSRmKqWOY7U4VdEzKl8X6DodaPE2ezsQERWSf1QqVp4Kg8/tOPDb69PhVEXPyM4BaP6WrudilWZSpyEisipylQa7r0Vj7VmO+n8WnKroGXlWA16ZoFtM2cVD6jREFo+FA7Jp6XIV1vtGYOOFSKRmqaSOY/VYOCgilZ4Hun0DNB0odRIiIosX8igdPx25g5Mh8VJHsXosHBQVO10Boft3QIV6UochIrJoGq3ArqtRWOoTylH/RYCFgyLiXgl4dSrQZjRHIBDlgVMVkU1SqrXYcvE+VpwMRQoLBmRpEm4Duz4AqrfWnXRowAWbiIieFJWchV+O38UB/xhwRiKyLAII3AsE/w28OALo8jXXQCAiMuPwrYdYdOwO7iXIpI5CZEyWABz+CvBbqevU12IoZwUgMoOFA7I5/wTEYtHRO3iQnCV1FKK8xd4Atg4GancEus8AareXOhERkeSSMhVYfjIMf156AKWGix6TBdOqgKvrgIAdwCufAB0/A1zLSp2KiEhyF8ITsfDIHQREpUodhShvqfeBfeOB88uAHt8DjftInYjIorBwQDbj0r0kzDscwi8nZH3unwc29AEa9QV6/whUrC91IiKiEidTqPH7uXv441wEMhVqqeMQFZxKBpz7Gbi6Huj8OdB2LODkKnUqIqISFxSbhoVH7uDs3QSpoxAVTnwQsP0d4Ln2QM9ZwHOvSJ2IyCKwcEBWLyw+AwsOh8DnNuc+Jit39zAQfkK3WNOrU7lYExGVCkII7L4ajZ+OhiAxUyl1HKKnl50MHPsOuLRW1xGAaxkRUSmRkKHA/MO3se9GDLiKJlm1B37A+t5A0zeA3vOAsjWlTkQkKRYOyGplyFVYfOwutly8Dw0nPyZboVEC55fopj3oOQt4YRhgZyd1KiKiYhEYk4YZBwJx40Gq1FGIik7aA91aRvW7A30XAd4NpE5ERFQsNFqBTRci8avPXWTIOVqQbEjwASDUB3j1C6D9p1xAmUotFg7IKv0dEIu5B4MRn6GQOgpR8ch8BOz/GLjyB9D3J6BmG6kTEREVmbQsFRYdC8Gflx5w4WOyXeEngdXtgfYTdSMJnd2lTkREVGSuRCZjxv5AhDzKkDoKUfFQyYATcwD/P4G+C4EGPaVORFTiWDggqxKZKMOMA4E4F5oodRSikhFzFfijB9DqPeC1H4AyFaRORET01PTTEi08EoIkGaclolJAowR8fwVu7gZ6zwWavSl1IiKiZ5KQocD8Q7fx140YqaMQlYykMGDrYKDJAKDPfKDcc1InIioxLByQVVCoNVh9OhyrT4dDodZKHYeohAnAfysQelTX06H5YKkDEREVWlBsGr7bz2mJqJRKjwZ2jwKubQT6Leb0RURkdTRagS1+kVh8nNMSUSkVchAIO6GbvqjjZMDBSepERMWOhQOyeL6hiZhxIBARiTKpoxBJS5YA7PkQuLUX6L8Y8KomdSIionwp1VosPxmK1afDoea8RFTa3TsN/NYJ6P4t8MpEwN5e6kRERPkKT8jEl7sDWPwnUmcDJ+fq1kB4YxVQraXUiYiKFb+pksWSKdSYtvcm3l93iUUDopzu/AusfBm4ugEQPAlHRJYrMCYNA1f4YvnJMBYNiPTU2cCx74ANfYDEMKnTEBHlSqsVWHs2HP2WnmPRgCinR7eA37sDp+YDGpXUaYiKDQsHZJEuRySjz9Kz2HElSuooRJZJkQYcnAxseh1Ivid1GiIiIyqNFr8cu4NBK89z0USi3ERd0o0+uLAc0HIqTiKyLPcSMvH2Gj/MOxTC6YKJzNGqgDMLgLXdgIc3pU5DVCxYOCCLolBrMO/QbQxb64eo5Gyp4xBZvshzwKoOwOXfpU5CRARAN8rg9eW+WMZRBkT54+gDIrIwWq3AH+fuod+yc7h2P0XqOESWL04/+mAeRx+QzWHhgCxGUGwaBi4/j7Vn74HnGYgKQZ0NHPoS+HMYIEuSOg0RlVJqjRa/Hr+LN1dxlAFRoelHH/it5DSERCSZyEQZhq7xw9x/b0Ou4igDogLTqoAzC4G1XYG4IKnTEBUZFg5IchqtwIqToRi08jzuxPFEA9FTu3sYWN0BCD8ldRIiKmViU7PxztqLWHoiFCoNT3oSPRV1NnD0G+DPoewIQEQl7oB/DPovO4erHGVA9PTiAnWjD66ulzoJUZFg4YAk9ShNjmFr/fDzsbs80UBUFDIfAVve1E17wGGSRFQCjgfHcToDoqIUekw3+iDyvNRJiKgUyFZq8NWeAPxvhz9kSo3UcYisn1oOHJwC7BoJyNOkTkP0TFg4IMn4hiai/7JzuBLJEw1ERUvoFlr8oyfnSyaiYqNUazH7nyCM3XwVqVksVBIVqYxYYNPrwOmFXDiZiIpNaFwG3ljpi11Xo6WOQmR7gvcDv3UGYq5JnYToqbFwQCVOqxVY6hOKEesvIUmmlDoOke166A+seRUI2Cl1EiKyMQ+SsjDktwvYcD5S6ihEtktogNPzgC1vABmPpE5DRDZm15UoDFxxHnfjMqWOQmS7Uu8D63rrOvZxDSOyQiwcUIlKlikxcsNl/OpzlwsgE5UElQzYNw44/DWgUUudhohswL83H6L/snO4Gc2h10QlIuKsbuqiMB+pkxCRDZAp1Jiy0x9f7b2JbBWnJiIqdlqVbirhP4cCWclSpyEqFBYOqMRcu5+M/svO4VxootRRiEqfS78Bm98AMhOkTkJEVkqjFZh7MBgT/7yODAULkUQlSpYAbHsbOPuz1EmIyIpFJMowcIUv9t2IkToKUekTegxY2wV4FCh1EqICY+GASsQ63wgMW3sRD9PkUkchKr3u++q+qHCORSIqpLRsFUZvvII/fCOkjkJUegktcPIHYM+HgCpb6jREZGXOhSbgjRW+CE+QSR2FqPRKfQCsew0I/lvqJEQFwsIBFSuVRouv99zEDweDodJwbiIiyaXHAOv7Ate3SJ2EiKxEeEIm3lx5HmfvcsQSkUUI3Aus7wOksccwERXMOt8IjNpwBelyjhgkkpxKBuwaAZyaz3UPyOKxcEDFJi1LhRHrLmPn1SipoxBRThoF8Pck4OAUQKOSOg0RWbDTd+IxaOV53Etk70Qii/LQH1jbFYi6LHUSIrJgSrUWX+0JwA8Hg6HhIoNEFkQAZxYAuz4AlPyeTZaLhQMqFhGJMry56jz87iVJHYWIcnN1PbB1MCBPlzoJEVmgtWfD8eHGK8hg70QiyySLBzb2B25slToJEVmgxEwF3v39InZdjZY6ChHl5vY/uqmLUu5LnYTILBYOqMhdvJeEN1exdyKRVYg4w+kOiMiIUq3F57v8Me9QCNg5kcjCaZTAgYnAkemAVit1GiKyEEGxaXhjxXlcvZ8idRQiyk9cIPB7N+C+n9RJiEywcEBFatfVKHyw7hJSszj9CZHViA8C/ugJPAqUOgkRSSxTocbojZfx13UWE4msysVVwJ7RgFohdRIiktj5sES8s+YiYlK5iDqR1chKArYMAkL+lToJkREWDqjI/HQkBF/tuclFkImsUUYssKEvEH5S6iREJJHETAWGrfXD+TBOM0hklYL3cwpColLu4M1YjN5wBZkKTjNIZHXUcmDnB8C1jVInITJg4YCemVYrMP2vm1h1OlzqKET0LBTpwLahwI1tUichohL2ICkLQ1ZfQGAMTzgSWbXIc8DGfkBGnNRJiKiEbfaLxGfbb0Cp4bRlRFZLaIB//gecXih1EiIALBzQM1JptPh0xw1svxwldRQiKgpaFXBgAr+oEJUiQbFpeGv1BUQmZUkdhYiKwqNbwLpeQBI79RCVFouP3cH3B4K4NhGRrTg9Dzj4OdcvIsmxcEBPTa7SYOzmq/j35kOpoxBRUTs9DzjyjdQpiKiYXQhPxLA1F5GYyXnRiWxK6n1gfW8g9obUSYioGGn+G/2//GSY1FGIqKhdXQfsHsn1i0hSLBzQU0mXq/DBuks4fSdB6ihEVFwurgT+/oy9HIhs1OFbDzFqwxVkcB5kItskSwA2DgDCT0mdhIiKgUKtwYRt1zj6n8iW3f4b2PIWoMiQOgmVUiwcUKElZSowfO1FXIlMkToKERW365uAfeMArUbqJERUhA74x2DS9htQqlkYJLJpykxg+zAg1EfqJERUhOQqDcZsuoqjQVzPhMjm3ffVFQ/kXIuMSh4LB1QoD9Oy8fYaPwTF8oBFVGrc2g3s+RDQsFcykS3Yey0aU3b6Q8OJkIlKB7Uc2PEucOeI1EmIqAjIVRp8tOkKzoUmSh2FiEpK9GVgyyAgO1XqJFTKsHBABfYoTY5hay/iXoJM6ihEVNKC9wN7RgEaldRJiOgZ7Loahal7Arh4IlFpo1EAuz4AQv6VOgkRPYNspQajN1zB+bAkqaMQUUmLuQZsfgPI5uwfVHJYOKACiU+X493fL+J+UpbUUYhIKrf/AXaNANRKqZMQ0VPYfTUKX++9yaIBUWmlUQK7RgIhh6ROQkRPIVupweiNl+F3j0UDolLroT+weRBHHlCJYeGA8pWQocDw3y/iXiJHGhCVencOAX+N4ZoHRFZm341ofL33JgSLBkSlm1YF7B4J3D0qdRIiKgS5SoMxm6/g4r1kqaMQkdQe+gNb3gTkaVInoVKAhQPKU4pMiff/uIRwTk9ERHrBB4B/PgPPQBJZh78DYvHlbo40IKL/aJTAzg+AMC6YTGQNFGoNxm25xumJiOix2OvA1sGAIkPqJGTjWDigXGXIVRix/jLuxPFARERPuLEVOPqN1CmIKB8nbsfhcy6ETERP0iiAHe8DDy5KnYSI8qDRCkz68wbO3k2QOgoRWZroK8D24YBaIXUSsmEsHJBZ2UoNPtx4BbdiOPSJiHJxcRVweoHUKYgoF9fup2Din9ehZtGAiMxRZwN/vgPE35Y6CRHl4tt9t3A8OE7qGERkqSLPAX+NBbRaqZOQjWLhgEyoNVqM33oNVyK5UjsR5eP0fMBvldQpiOgJoXEZ+GjTFchV/BFBRHmQpwJb3gJSo6ROQkRP+OXYHey4ws8mEeUj+ABw6EupU5CNYuGATHyz7xaHQhJRwR39Rjd1ERFZhNjUbIxYfxmpWSqpoxCRNciIBba+BWRx0VUiS7HFLxLLToZJHYOIrMXVdZwNgIoFCwdkZKlPKHZdjZY6BhFZFQH8/Rlw54jUQYhKvdQsJUauv4yHaXKpoxCRNUm8C/w5FFBmSZ2EqNQ7dOshZv4dJHUMIrI2p+cDV9dLnYJsDAsHZLD3WjR+9bkrdQwiskZCA+z9CHh4U+okRKWWfn2i0PhMqaMQkTWKvgLsHglo1FInISq1/MKTMHmnP7g8ERE9lX+/AIL/ljoF2RAWDggAcCEsEdP+4gk/InoGykxg+zAg/aHUSYhKHY1WYNKf13H9QarUUYjImoUeA/7+VOoURKXS7YfpGLflKpRqrk9ERE9JaIG9Y4DI81InIRvBwgHhzqMMjN96DSoNuzUQ0TNKjwG2vwMoZVInISpV5h26jRMh8VLHICJbEPAncO4XqVMQlSqJmQp8tPEKMuQc8UNEz0ijAHZ9AKTclzoJ2QAWDkq5uHQ5Rm+4zC8oRFR0HgYAe8cCWvaWIioJu69GYZ1vhNQxiMiWnPyBaxcRlRClWouPt1xDLNcnIqKikpUEbB8OKDiFKT0bFg5KMblKg482XeEXFCIqenf+BY7PkDoFkc27dj8F3+4PlDoGEdkaoQX+Ggsk3JE6CZHNm7E/EFfvp0gdg4hsTXwQsG88IDi7CD09Fg5KsW/23UJgTLrUMYjIVvmtAK5ukDoFkc16mJaNj7de41zIRFQ8FOm6tYuyeUKTqLhsPB+BnVejpI5BRLYq5CBwap7UKciKsXBQSm32i8Rf12OkjkFEtu7QVCDqitQpiGyOXKXBuM3XkJChkDoKEdmy5HvA7lGAViN1EiKbcz4sEXP/vS11DCKydWd/AgL/kjoFWSkWDkqha/eT8cPBYKljEFFpoFUBu0cCskSpkxDZlKl7buJWTJrUMYioNLh3Gjj6jdQpiGzK/SQZJv55HWotpxAhohJwYKJuLUKiQmLhoJSJz5BjwrbrUGn4BYWISkh6DLD3Iy6WTFREVp0Owz8BsVLHIKLS5NJvwI2tUqcgsgkyhRpjN19FapZK6ihEVFqosoDt7wKyJKmTkJVh4aAUUWm0mLjtOuLSOa0BEZWwe6eBUz9KnYLI6l2OSMbiY3eljkFEpdG/XwJxHLVM9Ky+3XcLd+MypY5BRKVNejSw/2MulkyFwsJBKfLjv7dxJZKLmxGRRM4tBu4elToFkdVKkSnx2fYb0HBaAyKSgjpbt96BMkvqJERWa9eVKOz356hBIpJI6DHgwjKpU5AVYeGglPg7IBYbL0RKHYOISjUB/DUOSLkvdRAiqyOEwJe7A/AoXS51FCIqzRLvAIemSp2CyCqFxmVg5t9BUscgotLuxBwg6rLUKchKsHBQCkSnZOHbfbekjkFEBMhTgV0fAGpOmUZUGH+ci8CJkHipYxARAf5bgYCdUqcgsipylQaT/ryBbJVG6ihEVNpp1cCej4BszkhC+WPhwMZptAJTdvojQ66WOgoRkc7DAF0vByIqEP+oVPx0NETqGEREj/37OZAYJnUKIqsx+58g3InLkDoGEZFO2gPgwCSpU5AVYOHAxq06FcZ1DYjI8vitBCLOSp2CyOKly1X4dPt1qDRc14CILIgyE9gziiMIiQrgn4BYbL8cJXUMIiJjIQeBi79JnYIsHAsHNsw/KhVLT4RKHYOIyAwB7PsEyE6VOgiRRZu29yaikrOljkFEZOrRLeDot1KnILJoD5Ky8M1fnDaYiCzU8RlArL/UKciCsXBgo2QKNSbvuAG1lj0UichCpUcDh76UOgWRxdp3IxqHbj2SOgYRUe6u/A6EnZA6BZFF0moFpuzyR4aC0wYTkYXSKIH9nwBqpdRJyEKxcGCjZv0dhMikLKljEBHl7dZu4NYeqVMQWZz4dDlm/R0sdQwiovz9/RkgT5c6BZHFWX8+Atfuc9pgIrJw8cHAmQVSpyALxcKBDTp86yF2X4uWOgYRUcH8+wWQFiN1CiKL8s2+W0jLVkkdg4gof+nRwDFOWUSU072ETPx87I7UMYiICsZ3CRBzXeoUZIFYOLAxKTIlvtsfKHUMIqKCk6fqhkcKTq1GBOimKPK5HS91DCKigru+mVMWEf1HqxWYuucm5Cqt1FGIiApGaID9EwC1QuokZGFYOLAxP/wbjCQZ5yYjIisTcQa4tlHqFESS4xRFRGS1OGUREQBOUUREVirhNnCaUxaRMRYObMi50AT8dZ3TfRCRlfKZCWTESZ2CSFKcooiIrBanLCLiFEVEZN3OLwVirkmdgiwICwc2Ilupwbf7OEUREVkxeRpwZJrUKYgk89d1TlFERFaOUxZRKcYpiojI6gkNsO8TTllEBiwc2IglPnfxIDlL6hhERM8m6C8g9LjUKYhKXLJMiTkHOUUREdmAf/4HKPm7hEqfTX6RnKKIiKxf4h3g3C9SpyALwcKBDQiMScMfvhFSxyAiKhr/fs4TDlTqLDwcgtQsTlFERDYgLQo497PUKYhKVEKGAr8cvyt1DCKionF+CZDM84zEwoHV02gFpv11ExqtkDoKEVHRSH0AnJ4ndQqiEnPjQQp2XYuSOgYRUdG5sBxICpc6BVGJmX/4NjLkaqljEBEVDbUcOPy11CnIArBwYOU2nI9AYEy61DGIiIrWxdXAw5tSpyAqdlqtwPcHgiBY/yciW6JRAoe/kjoFUYm4GpmMfTdipI5BRFS0Qo8Cdw5LnYIkxsKBFUvMVGCpT6jUMYiIip5WDRycAp5NJVv35+UHuBWTJnUMIqKiF+YD3D4odQqiYqXRCsxgBwAislWHvwZUcqlTkIRYOLBii4/dRYaCwyGJyEbFXAVu7ZY6BVGxSZEp8fOxO1LHICIqPkemA6psqVMQFZutF+/j9kPOAEBENir1PuDLhZJLMxYOrFTIo3Tsusr5kInIxvnM5gkHslkLj3BBZCKycWkPgHOLpU5BVCwSMxVYzA4ARGTrzi/lQsmlGAsHVmruwdtcEJmIbF96NOC3QuoUREXOPyoVO9kBgIhKg/PLuFAy2aSFh0OQzgWRicjWqeXAkWlSpyCJsHBghY4Hx8E3LFHqGEREJcN3CZARJ3UKoiL147/BnA+ZiEoHjQI4MVvqFERFKjAmDXuuR0sdg4ioZNw9AkSckzoFSYCFAyuj0mgx79BtqWMQEZUcZSZw8gepUxAVmRO343AlMkXqGEREJSf4ABBzTeoUREXmp6N32AGAiEoXn1lSJyAJsHBgZTZdiEREokzqGEREJct/G/DoltQpiJ6ZViuw6CjnQyaiUognHMhG+IUn4ezdBKljEBGVrJirQPDfUqegEsbCgRVJy1Jh2YlQqWMQEZU8oQWOfiN1CqJntt8/BiGPMqSOQURU8iLOAmEnpE5B9MwWHgmROgIRkTRO/gBoNVKnoBLEwoEV+f3cPS6+RESlV8RZIPyU1CmInppSrcUvx+9KHYOISDonZoPzu5A1OxL4CP5RqVLHICKSRuJd4MZWqVNQCWLhwEqkyJTYeCFS6hhERNI6vUDqBERPbevF+4hOyZY6BhGRdB4GAEF/SZ2C6KlotAI/H+N0g0RUyp1eAKj4m6a0YOHASvx+7h4yFRxtQESlXNRFIPyk1CmICi1TocbKU2FSxyAikt7JuYBGJXUKokLbey0aYfGZUscgIpJWRixwaY3UKaiEsHBgBZJlSmziaAMiIp1T86VOQFRof5y7hySZUuoYRETSS74HXN8sdQqiQlGoNVjiw+kGiYgAAL6/AvI0qVNQCWDhwAqsORsOmZKLjxARAQCiLwNhPlKnICqwTIUa630jpI5BRGQ5fJcAGo6mJuux60oUYtPkUscgIrIM8lTgyh9Sp6ASwMKBhUvKVGCL332pYxARWRaOOiArsvXifaTLeYKMiMgg7QFwa7fUKYgKRKMVWHvuntQxiIgsy8XVXOugFGDhwMKtOXsPWRxtQERkLOYqEHpc6hRE+ZKrNFjH0QZERKbOLwGEkDoFUb7+CYhFVDJPjhERGZElANe3SJ2CihkLBxaMow2IiPJweoHUCYjytedaNBIyFFLHICKyPAkhQMi/UqcgypMQAr+dCZc6BhGRZbqwDNCopE5BxYiFAwu22e8+slUcbUBEZFbMVSDyvNQpiHKl0QqsPcupDYiIcuX7i9QJiPJ0MiQeIY8ypI5BRGSZ0qI49aCNY+HAQinUGmy7xNEGRER58lspdQKiXB28GYsHyVlSxyAislwx14B7Z6ROQZSr1ac52oCIKE++vwJardQpqJiwcGCh9t+IQWKmUuoYRESW7e5hIJk9usnyCCF4soGIqCDOLZY6AZFZlyOScfV+itQxiIgsW+JdIOSg1CmomLBwYKHW+0ZKHYGIyPIJLXBxtdQpiEycusOpDYiICiTijG7kAZGFWX06TOoIRETWwfdXqRNQMWHhwAKdvZuAO3E82UBEVCA3tgHZqVKnIDLyx7kIqSMQEVmPS2ukTkBkJCw+A6fuJEgdg4jIOsReB6LZCcAWsXBggdb58mQDEVGBqWTAtY1SpyAyCIvPwIXwJKljEBFZj6D9gIzHTbIcm/243iARUaFc+V3qBFQMWDiwMKFxGTgbyp4NRESFcnktoFFLnYIIALDpAk82EBEVikYB3NgsdQoiAECmQo2/rsdIHYOIyLoE/gVkJUudgooYCwcWZv35CAghdQoiIiuTHgME75c6BREy5Cr8dT1a6hhERNbn6gZAq5U6BRH2XotGpoIdUoiICkWjAK6zE4CtYeHAgqTLVdh/I1bqGERE1onTFZEF2HcjBjKlRuoYRETWJ/U+EHZc6hRE2HqRIweJiJ7K1XXsBGBjWDiwIAf8Y5Gt4skGIqKnEukLJN+TOgWVcn9eeiB1BCIi63XlD6kTUCl3OSIZofGZUscgIrJOqQ+A0GNSp6AixMKBBdlxmScbiIiengCub5E6BJVi1+4nI+RRhtQxiIisV5gPkBIpdQoqxbZd4mgDIqJnwk4ANoWFAwtxKzoNQbHpUscgIrJuAdsBLUdukTS2XWQHACKiZyK0wNX1UqegUipZpsThwEdSxyAism5hPkByhNQpqIiwcGAhdl7lyQYiomeW8ZBDI0kSmQo1DgU+lDoGEZH182cnAJLGvhsxUKo5NzcR0bMRQMAOqUNQEWHhwAIo1Br87c9FkYmIigSnKyIJHL71EHIVTzYQET0zWTxw75TUKagU2n8jRuoIRES24dYuqRNQEWHhwAIcC4pDulwtdQwiItsQehTIiJM6BZUyB9gBgIio6NzkCQcqWeEJmbgVkyZ1DCIi25B8D4i6InUKKgIsHFiAPdeipY5ARGQ7tGog4E+pU1ApEp8ux4XwRKljEBHZjtsHAaVM6hRUihzgaAMioqJ1k9MV2QIWDiQWnyGHbxhPNhARFanAvVInoFLkgH8stELqFERENkQlA0L+lToFlSIHAjhykIioSAXtAzQqqVPQM2LhQGJHAh9Bw7MNRERF69EtIClc6hRUSuz3Zy9FIqIid3On1AmolLj+IAX3k7KkjkFEZFuykoAwH6lT0DNi4UBih249lDoCEZFtCvpL6gRUCoTGZSAoNl3qGEREtif8FJCZIHUKKgU4TRERUTFhJwCrx8KBhBIzFbgSmSJ1DCIi2xR0QOoEVApwtAERUTERGiBwj9QpyMapNVocvMnOfERExeLOYUDOTlbWjIUDCR0N4jRFRETFJu4WkBgmdQqycX9zTmQiouLDNYuomPmGJSJJppQ6BhGRbVLLgbtHpE5Bz4CFAwkdvvVI6ghERLYteJ/UCciGBcemIyo5W+oYRES2K/oqkBkvdQqyYUeD+JuciKhY3TkkdQJ6BiwcSCRFpsTFe0lSxyAism1B+6VOQDbsxO04qSMQEdk4oZvmgKgYCCFw4jYLU0RExSrsBKDmyC5rxcKBRI4FP4Ka0xQRERWvuEAgMVTqFGSjfEJ4soGIqNixcEDF5FZMGuIzFFLHICKybYp0IPKc1CnoKbFwIJFDnKaIiKhkcGgkFYP4DDluRqdKHYOIyPbdOw2oOC0cFT0fjjYgIioZ7ARgtVg4kECWUg2/cE5TRERUIsJ8pE5ANujk7XgIDhwkIip+6mxd8YCoiHHKQSKiEsLCgdVi4UACfuFJUGq0UscgIiodHlwEFJlSpyAb48OTDUREJYejB6mIPUzLRlBsutQxiIhKh/Ro4GGA1CnoKbBwIIFzoYlSRyAiKj00SiDirNQpyIbIVRr4hrEtJyIqMXePgsO8qChxUWQiohLGUQdWiYUDCZwNTZA6AhFR6cLpiqgInQ9LhFzFkYNERCUmMw6IuS51CrIhnKaIiKiEsXBglVg4KGExqdm4lyCTOgYRUenCwgEVobN32QGAiKjE3TspdQKyEXKVBhe45iARUcl6dBPITpE6BRUSCwcl7BxPNhARlbzU+0BimNQpyEb43ePJBiKiEhdxTuoEZCNuPEiFQs2Rg0REJUpogcjzUqegQmLhoIRxfQMiIolw1AEVgaRMBULjudg2EVGJi7oMqJVSpyAbcJEdAIiIpBHJTgDWhoWDEqTVCpwPZ+GAiEgS4ZzigJ7dpYhkrs9JRCQFdTYQc1XqFGQDLkWwcEBEJAmOHrQ6LByUoFsxaUjNUkkdg4iodIq6BJ7xpWfFXopERBKK9JU6AVk5hVqDGw9SpY5BRFQ6xQcDMv6esiYsHJQg9mwgIpKQPBVIuCN1CrJyLBwQEUko4qzUCcjK+XN9AyIiCQlOV2RlWDgoQdfvp0odgYiodIu6KHUCsmJc34CISGLRVwC1QuoUZMUu3kuWOgIRUenGwoFVYeGgBN2ISpE6AhFR6faAhQN6elzfgIhIYmq5rnhA9JQ4CwARkcS4zoFVYeGghMSmZiMunb1jiIgkxcIBPYNLnKaIiEh69y9InYCslFKtxfUH7MxHRCSpxDtAFkd/WQsWDkoIv6AQEVmAlAggM17qFGSl/KNSpY5AREQx16VOQFYq+GE65Cqub0BEJLnYG1InoAJi4aCE3HiQKnUEIiICOOqAnopKo8XtRxlSxyAioof+UicgK3UrJk3qCEREBLBwYEVYOCghNzjigIjIMkRdkjoBWaG7cRlQqtlLkYhIchkPgYw4qVOQFQpi4YCIyDKwE4DVYOGgBCjVWgTGpksdg4iIACDWX+oEZIUCebKBiMhy8IQDPYXAWLblREQWgb/JrQYLByUg+GE6eykSEVmKuECpE5AVCoxhBwAiIovBKQ6okJRqLe4+ypQ6BhERAUBaFCBLlDoFFQALByUgmKMNiIgshzwVSIuWOgVZGc6LTERkQdhTkQrpblwGlBp25iMishhsy60CCwcl4G4cF1MkIrIocUFSJyArotZocfshOwEQEVkMTlVEhcQpB4mILAxHD1oFFg5KQGg8CwdERBbl0S2pE5AVCY3PhIJTDhIRWQ4ukEyFxJGDREQWhp0ArAILByXgDudSJCKyLBxxQIUQxCkHiYgsTxw7AVDBsS0nIrIw8cFSJ6ACYOGgmKXIlEjMVEgdg4iIcmLhgAohPIEdAIiILE5imNQJyIqExbMtJyKyKCn3AbVS6hSUDxYOihnXNyAiskBJYYBKLnUKshIRCTKpIxAR0ZOSWDiggknIUCBToZY6BhER5SQ0QEqE1CkoHywcFLO77NlARGR5hAZIvCN1CrISkUksHBARWZykUKkTkJWISGQ7TkRkkdgJwOKxcFDMQjnigIjIMiWzdwPlTwjBwgERkSVKCpc6AVmJSBYOiIgsUyI7AVg6Fg6KGacqIiKyUCmRUicgKxCbJodcpZU6BhERPSktGlBlS52CrMA9Fg6IiCwTRw9aPBYOitmDpCypIxARkTmp96VOQFaA6xsQEVkqwVEHVCAccUBEZKESOVWRpWPhoBhptAJxGQqpYxARkTkccUAFEJHItYqIiCwWeypSAXCNAyIiC8V23OKxcFCMHqXLodEKqWMQEZE5LBxQAXB6AyIiC8ZFFSkfQgjcT2ZbTkRkkbKSgKxkqVNQHlg4KEaxqZxzk4jIYqVFA1rOXU95i0rmlINERBYr9YHUCcjCPUrnWkVERBYtPUbqBJQHFg6KEQsHREQWTKPklxTKV1w6pxwkIrJYGY+kTkAW7mGaXOoIRESUF7blFo2Fg2IUw8IBEZFl43RFlI/4DJ5wICKyWDzZQPlI4JqDRESWLeOh1AkoDywcFCOOOCAisnAccUB50GoFEjOVUscgIqLcsHBA+Yhn4YCIyLKxLbdoLBwUo4ep7KVIRGTRZIlSJyALliRTQqMVUscgIqLcZCUCWo3UKciCJaTzNzkRkUXjiAOLxsJBMYrlfIpERJYti4UDyh2nKSIisnBCC2TGSZ2CLBhHHBARWTiOOLBoLBwUo8RMfkkhIrJoHHFAeeDJBiIiK8ATDpQHrnFARGThOOLAorFwUIzSslVSRyAiorxkJUmdgCxYQjpPNhARWTwWDigP7ARARGTh2I5bNBYOiolcpYFSrZU6BhER5UWWIHUCsmCcqoiIyApk8oQD5Y5tORGRhcuMBwTXlbNULBwUE442ICKyAhY0VdHGjRtRrlw5qWNQDkkypdQRiIgoP9kpUicgC5aUybaciMiiCQ2gzJQ6BeWChYNiwsIBEZEVKIapikaNGgU7OzuTS1hYWJE/FhUvmUItdQQiIsqPgicbyLxspQZqLXuxEhFZPLblFstR6gC2ioUDIiIroEgH1ArA0aVIN9unTx9s2LDB6LpKlSoV6WNQ8ZMpNFJHICKi/LCXIuVCpmQHACIiq6DIAFBN6hRkBkccFJO0LBYOiIisgiKjyDfp4uKCqlWrGl2WLl2KFi1awN3dHbVq1cKECROQmZn7yY6AgAB069YNnp6e8PLyQps2bXD16lXD3319fdG5c2e4ubmhVq1a+OyzzyCTyYr8uZRmmRxxQERk+dhLkXKRxQ4ARETWQVn0v8mpaLBwUEw44oCIntUCXwXsZqdj8pHHi7o9ytTig33ZqPpzBtznpePFNZnYG5z38SZDITD5iBy1l2TA7cd0dFgnw5UY4x9SP19QoPKiDFRelIHFFxRGf7sUrUabtZm2O9RbWTIn2+3t7bFs2TIEBQVh06ZNOHnyJL766qtcb//ee++hZs2auHLlCq5du4Zp06bByckJABAeHo4+ffpg8ODBuHnzJnbu3AlfX19MmjSpRJ5LaZHFnopERJaPJxsoFxxxQERkJdgJwGJxqqJiwsIBET2LKzEarLmmRMsqxvXdEfuykSoX+Ht4GXiXscOft1QYuicbV8fao3U1B7PbGvNPNgLjtdjyphuqe9pj600lem6RIXiCB2p42eNmnAbfn1Lg4LtlIAQwYHsWXqvviBZVHKDWCnz8rxxrB7jB0d6uJJ56yVNlFfkmDx48CA8PD8O/+/bti927dxv+XadOHcydOxcff/wxVq1aZXYbDx48wNSpU9GkSRMAQMOGDQ1/mz9/Pt577z1MnjzZ8Ldly5ahS5cuWL16NVxdXYv8OZVGmeypSERk+UqoAwBZH3YAICKyEpx20GJxxEExyVbxZAMRPZ1MpcB7f2Xj99fdUN7V+GT9hSgNPm3njHY1HFCvvD2+e9UF5VztcO2h+WNOtkpgb7AaP/V0wau1HdGggj1mdXVFgwr2WH1VCQAISdSiZRUHdK/riB71HNGyij1CErUAgEXnlXj1OUe0rWG+KGETlEVfOOjWrRv8/f0Nl2XLlsHHxwc9evRAjRo14OnpiQ8++ABJSUnIyjL/+J9//jnGjBmDnj17YsGCBQgPDzf8LSAgABs3boSHh4fh0rt3b2i1WkRERBT58ymteMKBiMgKsJci5YJrFRERWYlimD6YigYLB8VEpdFKHYGIrNTEQ3L0b+iInvVMB4V1qOWAnUFqJGcLaIXAjkAV5GqBrnXMDyBTawGNAFwdjQsQbo528H2g+zHVorI97iZp8CBNi/upWtxN0qJ5ZXuEJ2uxwV+Fud2LduFgi1MMIw7c3d3RoEEDw0WhUGDAgAFo2bIl9u7di2vXrmHlypUAAKVSaXYbs2bNQlBQEPr374+TJ0+iadOm2LdvHwAgMzMT48ePNypOBAQEIDQ0FPXr1y/y51PcNm7ciHLlykkdw4SMaxwQEVk+9lKkXLADABGRlWDhwGJxqqJiorHVucCJqFjtCFTh+kMNrox1N/v3XW+XwTt7slDxpww42gNlnIB975RBgwrm68CeLnZoX9MBP5xV4PlK9qjiboftgSr4RWsM93m+kgPm9XBFry26E+jze7ji+UoO6LlZhp96ueBouBqzTivg5AAs7eOKV2vbWNOhUeR/m2d07do1aLVaLF68GPb2uv2+a9eufO/XqFEjNGrUCFOmTMHw4cOxYcMGvPnmm3jxxRcRHByMBg0aFHf0Qhk1ahQ2bdpkcn1oaKjFZS0IW+upmOq7DWnntxtd51ihJmqM/Q0AINRKJJ9ch6zbZyE0KrjVfREVXvsEDu7lc92mEAJpvtuQGXAUWoUMLjWeR4XXJsCpQo3/tqlC0pFlyAq9CAf38qjw2gS41WlluH/apb3QpCegQq+Pi/4JE0lg1mk5Zp8xLgg3rmiPkEm66evkaoEvjsqxI0gNhVqgdwNHrOrniioeuffnEkJg5mkFfr+uQqpcoGMtB6zu74qGFXWjARVqgTH/yHEgRIWqHvZY1d/VqPPBovMKPEjTYnk/t2J4xhagCEcc2NnlPS3jzJkzMWvWrCJ7PFuyceNGTJ48GampqVJHMWA7nn87nnXnAjL8D0P5KAxaeQaqjVoG5yr1jG6TfOJ3yAJPwM7JFeW6jIRHs26Gv8lCfCELPIHKQ2YW4TMlshwLfBWYfkKB/73sjCV9dNPBhidr8eVxOXwfaKBQC/Rp4IjlffNuyzMUAjNOKbAvRIV4mUDrqg5Y2sfVaGT/zxcU+Om87jvE1x2d8UWHxx34LkWrMeGQHJfGuNvmFMJFOO0g2/KiZWNnfyyHzS4iSkTFJipNi/8dkeP4B2VMRgjozTgpR6pcwOcD3RoH+0PUGLo7C+dGu6NFFfPTCW150w0f/p2NGr9kwsEOeLGaPYY3dzKa3ujjl5zx8UvOhn9v8lcaig6NV2Tiylh3RKcLDNuTjYj/ecAll3xWSVP8a9I0aNAAKpUKy5cvx+uvv47z58/jt99+y/X22dnZmDp1KoYMGYK6desiOjoaV65cweDBgwEAX3/9NV555RVMmjQJY8aMgbu7O4KDg3H8+HGsWLGi2J9PXvr06YMNGzYYXVepUiWJ0jwbhdq2TjgAgJP3c6jyzo+Pr7B//AMn+cTvyA6/Cu9B02Dv4o7k46uRsG8eqr6/KNftpV/ai/Rr/8C7/xQ4lq2C1HNbEb/re1Qfsxp2js7ICDgC5aMwVH3/Z2Tfu4bEfxah5qStsLOzgyr1ETIDjqLayCXF+IyJSl6zSvbwGVHG8G/HHOcRphyR499QNXa/7YayLnaYdFiOt3Zl4/yH5jsMAMBP55VYdkmJTYPcULe8PWacUqD31iwET/SAq6Md1l5T4VqsBn4fueNwmBrv7s1G3JcesLOzQ0SKFr9fV+HquNy3b/WKsAPAw4cPDf+/c+dOfP/997hz547hupzrFwkhoNFo4OhoWz+pbakTgC1OH1zU7bhWJYdLzaYo06QTko8sN/l7VtglyG6fQeWhP0CdEoukw0vhVvdFOJQpC61ChtSzm1Fl2NwifY5ElsLcuoMypcBrW2V4oYoDTv7X1s84pcDr27NwcYw77HM5ac11B/OhLbrf5GzLixanKiomHHFARIV17aEG8TKBF9fI4DgnHY5z0nHmvgbLLinhOCcd4clarLiiwvqBbuhRzxEvVHXAzK4ueKm6A1ZeMT/dDQDUr2CPM6PckTndE1FTPHB5rAdUWoF65c03AYlZWsw+o8Dyvq64FKNBo4r2aFjRAd3qOkKlBe4m2dhUbOriH3Hwwgsv4JdffsHChQvRvHlzbNu2DfPnz8/19g4ODkhKSsKIESPQqFEjDB06FH379sXs2bMBAC1btsSZM2dw9+5ddO7cGa1bt8b333+P6tWrF/tzyY+LiwuqVq1qdFm6dClatGgBd3d31KpVCxMmTEBmZu49RAMCAtCtWzd4enrCy8sLbdq0wdWrVw1/9/X1RefOneHm5oZatWrhs88+g0xW9Itj2mRTbu8AB4/yjy9lygIAtAoZMm8eR/nuH8Gt9gtwqdoA3v0mQxFzG4qYELObEkIg4+oBlG3/Dso0fAXOlevCe8DnUGcmI+uuHwBAlRQFtwYvw7lSbXi+2B/arDRos9MBAMnHVqF811GwdyljdvtE1srRHqjqYW+4eJfRtbdpcoF1N1T4pbcrutd1RJvqDtjwhisuRGlwMdr8lCpCCCy5pMR3r7rgjSZOaFnFAZsHuSE2Q2B/iO4+txM1GNjYEc0qO2BiW2ckZAkkZukOYJ/8m42FPV3g5WKjJxoAQBTd95KcbVfZsmVhZ2dn+HdISAg8PT1x+PBhtGnTBi4uLvD19cWoUaMwaNAgo+1MnjwZXbt2Nfxbq9Vi/vz5qFu3Ltzc3PDCCy9gz549RZa7qPXp0wcPHz40utStW1fqWIWmFTbYkBdhOw4AHs27o1zH4UajAXNSJUXBtVYLuFRrCPemXWDnXAbqtDgAQMqpDfBs3Q+OXpWL/GkSSS23dQfPR2kQmSqwcZAbWlRxQIsqDtg0yA1XY7U4GcF1B59aER6v2ZYXLRYOiolaY4NfUoioWPWo64hbn7jD/+PHl5eq2+O9lk7w/9gdWSrdceXJTgYO9gU7wenubIdqnvZIyRY4GqbGG43NV9WnHFVgyisuqOllD40WUOX4Pa7WCtjc4U2Te9HlaWzcuBH79+83uX7KlCmIjY1FVlYWjhw5gg8++ABCCMPc/qNGjTIM73d2dsb27dvx4MEDKBQKxMTEYPny5XB1dTVsr23btjh27BgyMjKQmZmJgIAAfPPNN0X6XIqKvb09li1bhqCgIGzatAknT57EV199levt33vvPdSsWRNXrlzBtWvXMG3aNDg5OQEAwsPD0adPHwwePBg3b97Ezp074evri0mTJhVpZq1NVg0AdUosoleOQMxvHyHhn0VQp8cDABSPwgCt2ujEgVPFWnDwqgRFrPkTDuq0OGhkKUb3sXdxh0v1xob7OFeuC0V0MLQqBeQR1+HgUQH2bl7IDDoFO0dnlGnUodieK5FUQpO1qL44A/WWZuC9v7LwIE3XkF57qIFKC6NphJp4O+C5snbwizJ/siEiVeBRpjC6T1lXO7xc08FwnxeqOMD3gQbZKoGj4WpU87CDdxk7bLupgqujHd583qkYn60FKMLCQUFMmzYNCxYswO3bt9GyZcsC3Wf+/PnYvHkzfvvtNwQFBWHKlCl4//33cebMmWJO+3RspROALdYNirIdLwjnSnWhfBQGjTwTikdhEGoFHMtXhzw6CMq4cHi2ef1ZnxKRRcpt3UGFWsAOgEuOc/iujrrf6L4PzHcC4LqDBaAt2RFipaEtLyoci1FMNFrb6pGb33yKGf5HIAs+DWVcOIQyG7X+twP2rh7mNmUgjwpE+qW9UMaFQ5OZjEpvfosyjdob3Sbt0l9Iv7wXAFD25cHwaveW4W+K2DtIPrYKVUf8Ajt7G668Uqnh6WKH5pWN38vuTnao6Ka7XqURaFDBHuMPyvHza66o6GaH/SEqHA/X4OC7j6cZ6rFZhjebOGFSO911R8PUENDNsRyWrMXU43I08XbA6FamJxKOh6txN0mDTYN0J6jb1nBASKIWh0NViEoXcLCzQ+OKtlZztuFemBI4ePCg0fDPvn37Yvfu3YZ/16lTB3PnzsXHH3+MVatWmd3GgwcPMHXqVDRp0gQA0LBhQ8Pf5s+fj/feew+TJ082/G3ZsmXo0qULVq9ebVRceRa22EvRpVpjVOw3BU4VakCTmYy089vxaNvXqP7hSmhlKYCDo0nb7eBeDhpZitntaTJ119u7lzO+T5ly0MhSAQAeLXpBGR+J2HUT4ODmBe83voZWnok0322oMnw+Us5uQdbts3AsVxUV+/0Pjp7eRf68iUrSyzUcsPENNzT2tsfDDIHZZxTovEGGwE888ChTwNkBKOdq3O5UcbfDo0zzx5xHmVrDbUzuI9P97cPWTrgZp0HTVZnwLmOHXW+7IUUOfH9ajtMj3fHdSTl2BKpQv4I91g90Qw0vG2vHS7hwMGfOHPTq1avAt1coFJg3bx58fHzQvr3ut069evXg6+uLNWvWoEuXLsUVtUjpOwHUrVsX9+7dw4QJE/DVV1/l2pa/9957aN26NVavXg0HBwf4+/ubdAKYO3cu1q9fj4SEBEyaNAmTJk0yme7wWdhaW17U7XhBuNVrA/dmXfFo0xTYOTrDu/8U2Du5IPnoKlTsPwUZNw4h4/pBOLh5oULvSXCuVPtZnyaR5PJad/CVmg5wdwa+9lFgXg8XCAFM85FDI4CHGeaPOVx3sABEyRYOSmtb/jRs7J1mOVQ22FMxr/kUhUoBt3pt4FavDVLPmM6JaY5QyuFUuR48WvZCwr55Jn9XxkcgzXcbKg35HhACCXvnwLXui3CuVAdCq0HS0ZWo2GcSiwYlSOSzyAwVLycHOxx61w3TTujmUMxU6goJmwa5ol/Dx0WA8GQtErMe/4hOUwhMPyFHdLpABTc7DH7eET92d4WTg/Hrma0SmHRYjp1D3AxzM9b0ssfyvq4YfUAOF0dg0yBXuDnZ2PuAx5Ai1a1bN6xevdrwb3d3d/j4+GD+/PkICQlBeno61Go15HI5srKyUKaM6TQ1n3/+OcaMGYMtW7agZ8+eePvtt1G/fn0Auh6MN2/exLZt2wy3F0JAq9UiIiICzz//fJE8D9trxQG3+i89/kflunCp3hjRqz+ELMQX9k7Oud/xGdg5OKLia58YXZf47xJ4tnkdyrh7yA71Q7XRy5F+aS9SfNai0puWOWqGqKD65miPW1YBXq7pgNpLMrArSFVs7aeTgx1W9jde+Hj0gWx81s4ZNx5psD9EjYCPPfDTeQU+OyLH3qE2Nj1YCRcOXnrppfxvlENYWBiysrJMTlAolUq0bt26KKMVGVvpBGBjdQNJ2nEAKNfpPZTr9J7h36m+f8K1TivY2TsgzW8nqn+4Etlhl5H07y+oNmppseUgKgn5rTtYyd0eu98ug0/+zcayS0rY2wHDWzjhxWr2JjMD5MR1B/NRwgfs0tCWFxUWDoqJxubm8oBhPkVzvNq+AQCQP7hZ4M251X/J+MvPE1RJ0XCqVAdutV8AADhVqgNVUjScK9VB+qW9cK3VDC7VGhXiCdCzksGGF9azUKdHGe/zhhUd8v3BHznZ0+jfQ5s5YWiz/KcpcHOyw51JpiOFxrzojDEvFt+PEcnZsyksSu7u7kaLJ0ZGRmLAgAH45JNP8OOPP6JChQrw9fXFRx99BKVSabZwMGvWLLz77rv4999/cfjwYcycORM7duzAm2++iczMTIwfPx6fffaZyf2ee+65InseNvQ1PFf2rh5wqlAD6tRYuNZpDWjU0MozjXoramSpcHA33/brvxNoZamAR4XH98lKhXNl83Nhy+/fhCrpPir2/RQpp9bDrd5LsHd2RZkmnRD358Gie3JEFqKcqx0a/Tfir1d9Ryg1QKpcGI06iJMJVPUwf9Sp6mFvuE21HM17nEygVRXzhe9TEWoExWvwx+uumHpcgX4NHeHubIehzZywYmNW0T05S2FXsiMo3N2Nv5vZ29tDPHHCQ6V6vMijfjqff//9FzVq1DC6nYuLZU49YSudAGzds7bjT0OVFAVZ8ClUG7UMmTePw7VmcziUKYsyTToj6fBSaBVZXLuo2JWGb6nSybnuoJ5GAGfva7DishKK7zzxWn1HhH/micQsLRzt7VDO1Q5Vf85AvWa5t0f6dQdlSoF0hUA1T3u8sycr33UHz452N1p3sGFFGNYdbJHL9wCrxLbcYln0OFU7O7s8L7NmzZI6Yq5ssWN2bvMpFhfnSnWgTomBOj0e6rR4qJNj4OxdG6qUh8i85YNynT8o1scnU+ksHJAt4oiDYnXt2jVotVosXrwYr7zyCho1aoTY2Nh879eoUSNMmTIFx44dw1tvvWWYuuDFF19EcHAwGjRoYHJxdi66Ape9LTbkT9Aqs6FOfQgH9wpwqdoAsHdE9v0Aw99VSdHQpCfApXoTs/d3LFsFDu7lIb/v/3ibiiwoYu+YvY9QK5F8fDUq9v5vtKDQQujnM9VqIEq41zBRSchUCoQna1HN0w5tqjnAyR44ce/xHMh3EjV4kCbQvpb5tqhuOTtU9bAzuk+6QuBStMbsfeRqgYmH5FgzwA0O9na6tYr++5iptIDGBkdFl/TJhidVqlQJDx8+NLrO39/f8P9NmzaFi4sLHjx4YNJu1apVq4TTFoy+E4D+olAoMGDAALRs2RJ79+7FtWvXsHLlSgC63pbmzJo1C0FBQejfvz9OnjyJpk2bYt++fQBg6ATg7+9vuAQEBCA0NNRQXCgKDnl1/7UBz9qOF5YQAklHV6J89zGwd3b7rx3/79ik/y/b8mJni4dxS5LfuoM5jyveZexRztUOJyPUiJcJDMxlDcGcuO5gLiT+7WWLbXlRsehuljlftJ07d+L777/HnTt3DNflHD4phIBGo4Gjo2U8JScHi67JFFpe8ykWV48CJ+9aKPfqCMTtnAEAKNdlJJy8ayFux7co33U0siOuI+38n4C9Iyr0HAfXWs2LJQc9liJYOCAbxBEHxapBgwZQqVRYvnw5Xn/9dZw/fx6//fZbrrfPzs7G1KlTMWTIENStWxfR0dG4cuUKBg8eDAD4+uuv8corr2DSpEkYM2YM3N3dERwcjOPHj2PFihVFltveBk82pJxcB7cG7eBYtjLUGclI890G2NnDvWkX2Lu4w6NlL6Sc/AMOrp6wcymDlOO/waV6E7jUeHzCIeb3j1G+ywiUadQBdnZ28HzpDaRd2AnH8jXgWK4KUs9thaNHBZM1iwAg9cIOuNV7Cc5VdCeFXGo0Rcrp9fBo0RMZ1w/CtQZ7mJL1+/KYHK83ckTtcvaIzdBi5mkFHOztMLy5E8q62uGj1k74/JgcFdzs4OVih08Py9G+pgNeqZljweQVmZjfwwVvPu8EOzs7TH7ZGXPPKdCwoj3qlrPHjFMKVPe0w6Ampu3XD2d0IwxaV9MVFTo+54Cpx+UY3doJKy4r0fE5G2zzJC4cdO/eHYsWLcLmzZvRvn17bN26FYGBgYapCzw9PfHll19iypQp0Gq16NSpE9LS0nD+/Hl4eXlh5MiRkuYviJydAOz/m652165d+d6vUaNGho4Aw4cPx4YNG/Dmm28adQIoTrbWlhd1Ow4AmuwMaNIToMlMAgCokqMBAA7u5U1mG8gMOAoHNy+UafAyAMClxvNI9f0TipgQZN+7BqeKz+W7ziE9O8ERB8Uqv3UHAWDDDSWer2SPSmXs4Retxv+OKDDlFWc09n58P647WFjSvq9LQ1v+tCz6m2PVqlUN/1+2bFnY2dkZrjt9+jS6deuGQ4cO4bvvvsOtW7dw7NgxbNy4Eampqdi/f7/hvpMnT4a/vz9Onz4NANBqtVi4cCHWrl2LR48eoVGjRpgxYwaGDBlSZNltrXCQ13yKni+8VmyP69m6Hzxb9zP8O/PWCdg5u8GlRhPE/P4xqo34BZqMJCT+/RNqjF8HO8f8p2Ohp5ek4bBTskEccVCsXnjhBfzyyy9YuHAhpk+fjldffRXz58/HiBEjzN7ewcEBSUlJGDFiBOLi4uDt7Y233noLs2fPBgC0bNkSZ86cwbfffovOnTtDCIH69evjnXfeKfLs9na21atLnZGIxH8WQZOdDge3snCp2RRVP1gMhzJlAQAVeoxFsp09EvbPg9Co4Fr3RVTsNcF4G8nR0CoeT3Xi9fJgCJUcSUeXQyuXwbVmU1QeOgd2jsajP5QJkcgKOYdqo5YbrivTpCPkUbfwaNvXcKpYA96vTy3GZ08ATzaUhOh0LYbvzUZStkClMnbo9JwDLn7kjkruut8Gv/Zxhf1ROQbvyoJCA/Su74hV/Y3nc7+TpEWa4vHB56uOzpCpBMb9I0eqXKDT/9u77zg5y3r949f02TLb+yab7G42u2mb3gtJCCEFQgs1pNC7gEdRQY4CRxDBg3qOWI8ioniwI6ggKBZEwKP8BOklpCek1y3Znd8fA4shbcvMfJ/nmc/79coLskZySdZ5Zu7rvr93TUC/Pvfg2csvbOrQAy/u13OXvL/RY/HQoJ5YGdT0b+9RY7Ff3z/Ng+/ljIuD448/XjfeeKOuu+46tbS06Pzzz9eyZcv0/PPPd/2aW265RaWlpbrtttv05ptvqqCgQGPGjNH117vjXhfXbgLw2EteKp7j+15/Wlt++YWun29+8HOSpPypZx9wr0HHnm3a8dQDqjj3jq6vRaoalTfhFG360U3yZ+erZOG1qfifjQ/gWW7vlS2d+sTjrdq6L66BBX7dMD2saycd+N6Xewd7iGe5Y/niHxzi5FD33HOPrrnmGm3fvl3S+8VBc3Oz7rzzTtXV1amwsFAf/vCHj1ocfOYzn9F9992nL3zhC2poaNAf/vAHXXrppXrkkUeSdhP2Z3/1sr76+zeS8s9yqvXfuVbRgSNVeMyKrq+1rPqHNt5/vfpf/YMe7TZ4+/YTVHrKDYfcofiejr07tOHeD6v8nNvVtvEN7XjqB6pcdpckafWXzlH52bcqXDqwt/9z0A1X1bylf9t0g3UMILmW/kyqn2WdAg7UdOOv1NLOkXt4R0GoXc8FvLsjChmqoEa65vmj/zp0y4oVKw76PC1Jd911l+644w5t375dM2bM0JIlS7Rs2TJt27ZNBQUFB3xeb2tr0/Lly/Xkk08esAngjjvu6Lr4+Nlnn9UNN9ygp5566oBNAMlcgHng2dW67sfdv4MPcIOvDnpa89ZwCTU85rhbpKkH32EHe44+cdAdN99880G3Wh9Ja2urbr31Vj322GOaPDmxSF1XV6c//elP+trXvpa04iAc9NaJgw96f55i+hbbtv32m4qNP1nBvBK1bXhV8Y73b59XZ4fUyeJOqm1sy7KOACQfo4pwGLmRoFraDz27GQDgEGFGoyTTPffcc8ivX3vttbr22gN3lC9d+v6dcytWrNCKFSskSeFwWPfff/8Rf5/x48fr0Ucf7VPWo8mOcKoU3sOJA3hShGe5U7l+tWTcuHFH/0X/4vXXX9fevXsPKhva2tq6ZlclQ1bIW29SjjRPUZI6dm9Tx55tat+WuJei7Z2V8oezFcgrVSArJkna+IPrldUwWXljT5T0bvmw7f17LPbv2Ki2jW/Kn5WrYF7ZAb//vrf+rvata1X87vHHcMVg7d+6Rvve+Kv279os+QMKFh14szmSb0Nb9Oi/CHCbACPOcGjZ4aAkigN4B4sN8CSKAxxGTsT1yx3AQVwxMgToKZ7ljuX6J2lOzoGXtfr9fn1w+lJ7e3vX3+/evVuS9PDDD6u6+sCF5kgkkrRcWSFvnTg42jzFXc/9UjuefH9Xycbvf1ySVLzgGuWOmCNJat+2QZF9O7t+TduG17Tx/vePom777TclSTnDjz1gPmJne6u2PvZVlS76mHzvzj0L5pWocM4l2vyrL8gXCKl44bXyh5L354dDW9vCv2N4UCRmnQAOxYIDvMYdA0qBHgrnHP3XICPl8hyHB3XKW2tNgCSe5Q7muSdpaWmpXnjhhQO+9txzzykUSuwoHTp0qCKRiFatWpW0sUSHktil6B2lJ33siP95wbQlB1yedCj9LvvWAT+P1jRrwMceOurv7Q9FVH3R1w76emzk8YqNPP6o/30kz5qWiBQ++q8DXCWab50ADpUT9tbpQQDwJMYb4DAoDuBFnB6EJ1EcOJbnnqSzZ8/WHXfcoXvvvVeTJ0/WfffdpxdeeKFrDFEsFtNHPvIRXXvtters7NS0adO0Y8cOPfnkk8rLy9Py5cm5MC6LxQZ4UGunX/Fwrnxtu62jAMlDcYDD4MQBALhAmJODODSKA3gRhwfhSYwqcizPPUmPP/543XjjjbruuuvU0tKi888/X8uWLdPzzz/f9WtuueUWlZaW6rbbbtObb76pgoICjRkzRtdff/0R/sk9kxv13L9aQJLUEclXkOIAXuELMKoIh8WCA7yGxQZ4ErsUcRhsAIAXdcY5cQAP4lnuWK55kq5YsUIrVqzo+vnMmTMPusvgPTfddJNuuummw/6zfD6frr76al199dXJjtmlOId5LvCm9nCBglprHQNIjmiedQI4WDanB+ExFAfwJEYV4TDYAAAv6mRUEbyI4sCxuFUlRYpzuUQW3tQWZHc2PIQxRTgCdioCgAuw2IDDCAf9CgdY8gAAx2MKgGPxFE0RThzAq/YF2aEND6E4wBEUZvMsBwDHyy62TgAHy8tiEwC8hcuR4TmBsJRVaJ0Ch0FxkCLRUEA5jDiAB+320wTDQygOcARleZwehLew2ABPyq2wTgAHK2ESADwmzh0H8JqcMusEOAKKgxQqymWnIrxnl485svAQdiniCMpiLDbAY7jkAF4UozjA4ZXnRa0jAEnVaR0ASLZYuXUCHAHFQQoV57DgAO/ZEWeOLDwkr9o6ARyMxQYAcAGKAxxBOacH4TGdLOPBazg56Gi84qRQCScO4EHbOrOtIwDJk1dlnQAOxokDAHA4n58RBzgiNgHAazg8CM/hxIGjURykUBEXJMODtnRw4gAewokDHEFJbkQBP3Nk4SV8P8NjsoulAJff4vDKKA7gMRQH8BxOHDgaxUEKlcV4kwLv2bSf72t4CMUBjsDv96mYTQDwkDjLDfAaFhtwFJwehNfE2QQAr8nl5KCTURykUP+iLOsIQNJtbOf7Gh6ST3GAI2PEAQA4GPcb4Ch4jsNrOuMUB/AYnuWORnGQQjVFjHSB96xr4c03PMIfZKcijoqdivASzhvAc5iLjKPgcmR4DScO4DkUB45GcZBCNcVcIgvvWdNKcQCPyK2Q/DwGcWT9i3iWwzviNAfwmoKB1gngcGWxqEIBFlrhHZ0UB/CaojrrBDgCVkxSqDIvqnCAf8Xwlg2tIcV9AesYQN/lVVkngAvUlnB6EAAcq7jeOgEcLuD3sQkAnsIeAHhKTqkUzbdOgSNgVTuF/H6f+nHPATwmHvcpzgs7vKCo1joBXIDiAAAcrHiQdQK4QB3PcnhIp3UAIJmK2ADgdBQHKVbD7gZ4UEeY4gAeUDLYOgFcgOIAXsJcZHiLjxMH6Ja60lzrCEDSxOMs48FD2ADgeLzipNgAigN4UFsozzoC0HeljdYJ4ALVBVmKBHm7BG9gvAE8JVYphSl3cXScOICX8CyHpxRzv4HT8Uk4xZinCC9qpTiAF5RQHODo/H6fBhTzLAcAxylhlyK6hxMH8BJOD8JTOHHgeBQHKVZXyu4GeM9ef8w6AtA3/pBUxO4GdE9dCQsOAOA4LDagmxg7CC/p5MgBvIQ7DhyP4iDFmirYmQ3v2UNxALcrrpcCQesUcIlaNgHAI9ilCE+hOEA3lcYiikV53wdv6ORZDs/griI3oDhIsaqCLBVkh6xjAEm1UyyiweW4GBk9wGxkeEU8zjZFeEhxg3UCuAjjiuAVFAfwjPz+UijLOgWOguIgDYZw6gAesz3OG2+4HBcjoweGVPIcBwDHqRhhnQAu0ljO5xd4QzxOcQCPqGy2ToBuoDhIg6FVLDjAW7Z2clEoXK60yToBXKSxIqZwkLdMcD/OG8AzcsulvErrFHCREdX51hGApOBZDs+oHGWdAN3Ap+A0GMpORXjMOx0UB3A53qSgB0IBv5oquNsF7sdiAzyD5zh6aDjFATyCUUXwjKpR1gnQDRQHacCIA3jNO+3MoYOLRfO5hAk9xk5FAHAQFhvQQ0Mq8xQKsOAK92MTADyDTQCuQHGQBg3luQoH+FcN71jfRnEAF6saLfn44IieoTgAAAdhsQE9FA0F1FDG6UG4HycO4AmxKim31DoFuoHV7DQIBfwaVMZlTPCO9a0R6whA71WPtU4AF2LEAbwgzmIDvIITB+gFNgHAC7gcGZ5QOdI6AbqJ4iBNeJMCL1ndEraOAPQexQF6gQuS4QVx5hvAC3LKpLwq6xRwoRH9+EwO9+vkWQ4vYAOAa/AJOE3GDSy0jgAkzfb2kOIBTh3ApSgO0AtckAwADsFiA3qJzXzwgjgjV+EFnDhwDYqDNJlQW2QdAUiqzihvvOFCsSopVmGdAi7VzE5FALBXNcY6AVxqSGUedw/C9ToZVQQvqB5nnQDdxFMzTQYU56gsxg5teMf+cIF1BKDnqllsQO9NqC22jgD0EYsN8IABU6wTwKXCQT/jiuB6TCqC65U2cTGyi1AcpNF4Th3AQ9pCedYRgJ6rmWSdAC42qY7nOACYCkSk/hOtU8DFJtexCQDuxokDuN7AadYJ0AMUB2k0YSALDvCOliDFAVyodoZ1ArhYWSyqutIc6xgAkLn6jZNCUesUcLHJ9RQHcLdO6wBAXw2Yap0APUBxkEbjKQ7gIXv8udYRgJ7JKpIqmq1TwOXYqQgAhtiliD4aO6CQew7gapw4gOsNnG6dAD3AEzONmipiyosGrWMASbHbF7OOAPTMwGmSjzfa6JtJFAcAYIfiAH0UDQU0qqbAOgbQa3HuK4KblTRyv4HLUBykkd/v0zhOHcAjdopxHXCZumOsE8ADKA4AwEggIvWbYJ0CHsDpQbgZxQFcbSBjityG4iDNeJMCr9gez7aOAPRM7UzrBPCA0lhE9dxzAADp12889xsgKdgEADfrjMetIwC9x8lB16E4SLNZTRzJgTds6WDhDC6SVy2VDLJOAY9gwQFuxk5FuBaLDUiSMQMKFAmyFAJ36mQZD242gGe52/CKk2aDymLqX5RlHQPos3c6+D6Gi9TOsE4AD5newCYAAEi7+tnWCeARkWBA4wYWWscAeqXTOgDQW5UjpVi5dQr0EMWBgdmNZdYRgD7b2M6oIrhI3UzrBPCQGYNLFGanIgCkT3ZJYlQRkCSzm1i8gjtxchCuNXi+dQL0Ap96DcxqojiA+21ojVhHALrHF5Aa5lqngIdkh4OaUs+4IgBIm8HHS34+uiJ5jhtCcQB36uSKA7hV4zzrBOgF3n0ZmFRXrKxQwDoG0CdrWricDi5RM1nKLrJOAY+Zw4ID3MrHTkW4UCO7FJFcNcXZaiyPWccAeqwzznMcLhSrkqpGW6dAL1AcGIiGApo6iJ2KcLc1LZw4gEs0LbROAA+iOACANAlEuN8AKTFnKJMA4D6MKoIrDT7eOgF6ieLACOOK4HatnX7Fw7nWMYCjozhAClTkRzW8Os86BgB4X+0MKZxjnQIexCYAuBGXI8OVODnoWhQHRmZxQTI8oCOSbx0BOLLyEVLhAOsU8CgWHAAgDVhsQIqM6l+gshinqOEuXHEA1wllS7XHWKdAL1EcGKkqyNLIfiy6wt3awwXWEYAj47QBUojiAO7EiAO4iY/iACnj8/l07BA29MFdOjp5jsNl6mZKIe7IdCuKA0MnjqyyjgD0SVuQC8XgcBQHSKHh1fmqLsiyjgEA3lU1SsrjMxNSh00AcJtONgDAbZpOsE6APqA4MHTiyCr5ec2Hi+0LUBzAwQpqpMpm6xTwuBOaK60jAIB3DV9snQAeN62hRHnRoHUMoNsYVQRXCUalISdap0AfUBwYKs+LakJtkXUMoNf2BLgYFA42/DTrBMgAJ4+uto4A9IyPXStwCZ9fGkFxgNSKBANaMIJNAHCPOCcO4CaD50lR1o3cjOLA2KKRLDjAvXYpxzoCcHjNZ1knQAYYUpmnpgpOXwFA0tXOkGIV1imQAU5hEwBcpJMjB3CT5jOsE6CPKA6MLRhRoVCAxhjutF251hGAQ6scJZU1WadAhjhpFAsOAJB0zWdaJ0CGmFBbxJ1FcI2OOOtHcIlogTToOOsU6COKA2MF2WFNbyi1jgH0yrbObOsIwKGN5LQB0ufk0VVMfwGAZApmMRMZaePz+XTSKC7hhjswqgiuMfQkKRi2ToE+ojhwgEUjeZMCd9rSwagiOJA/yGWKSKvK/CxN5M4iuAYLDnCBxvlShDFwSJ9Tx3B6EO7AqCK4BmOKPIHiwAHmDitXdjhgHQPosU37o9YRgIPVHyvlcpIL6XUy44oAIHlYbECaDSqLaXg1F3jC+egN4Ap5/aQBU61TIAkoDhwgOxzUic2cOoD7bGxnFigciDFFMLCguVLhIG+rAKDPsoqkQXOsUyADnTK6n3UE4Kg6WMaDG4xYLGa5egOvOA6xZFKNdQSgx9a3cuIADhPJlxoXWKdABsqLhnT8sArrGADgfiPPlgIh6xTIQItGVinoZ6ELzhbnyAEczyeNXW4dAklCceAQzf0KNKI63zoG0CNrWiLWEYADNZ8uhSi0YOOcCWwCgAuw+wuO5pPGX2AdAhmqNBbR3GHl1jGAI+q0DgAcTf1sqajOOgWShOLAQc6ZyIID3GV9a1hxHy8jcJAJF1snQAabXF+s+lIujQeAXqubKRXXW6dABlsycYB1BOCIOuNsAIDDsQHAU1jxc5CTRlUpFglaxwC6LR73KR7hpAwconaGVNponQIZ7mxOHQBA742/0DoBMtyU+mLVlbAJAM7VyagiOFleP2nwPOsUSCKKAwfJDgd18uhq6xhAj3RECqwjAAnjL7JOAOj0sf0VDfH2CgB6LK9aapxvnQIZzufzMQkAjtYhThzAwcaukPwB6xRIIj7ZOgxvUuA2baE86whAYmdD00LrFIDys0M6eRSbAOBkLDjAoVhsgEOcPq6/ssN8L8KpeI7Dofwhacwy6xRIMooDhxlSmacxNQXWMYBua6U4gBOMW8FiAxxj+ZSB1hEAwF38IWnMcusUgCQpPyvEJAA4FqOK4FhNC6UYF8x7DcWBA62YWmsdAei2vf6YdQRkukBEGnuedQqgy5DKPE2oLbKOAQDuMeQEFhvgKCvYBACH6qA4gFNxKbInURw40ILhFaouyLKOAXTLHooDWBt2spRTYp0COMD5UwdaRwAOzceIAzjQxMusEwAHGFwe09RBxdYxgIN0MqoITlQ5SqqdYZ0CKUBx4EDBgF/nT+PUAdxhp3KsIyDTTb7COgFwkLlDK1RXyusjABzVgKlSzUTrFMBBLp5Rbx0BOEhnnOIADjTtGusESBGKA4c6a3x/5UWD1jGAo9oeZ2EMhgbNkSpHWqcADuL3+3TpMSw4AMBRTfuwdQLgkI4ZXKoR1fnWMYADMKkIjlNULw05yToFUoTiwKFyIkGdO2mAdQzgqLZ2UhzA0PSPWCcADuuU0dWqyo9axwAA56polhrmWKcADuvymWwCgLNw4gCOM/VDkp/lZa/iT9bBzp9Wq2iIPyI42+YO7uOAkZop0oDJ1imAwwoF/LpoRp11DOADWHCAgzDaAA43b3iFBpXlWscAunRy5ABOEquURp5jnQIpxKq0g5XkRnTmuP7WMYAj2tSebR0BmWr6v1knAI7q7Ak1Ks4JW8cAAOcpqpOGnmydAjgin4/Rg3AWLkeGo0y6TAryWcfLKA4c7uJj6hUK8GCAc21oYwwHDFSOYrQBXCEaCui8qQOtYwCA80y9WvIHrFMAR3XyqCpVF3DKGs7AiQM4RjRfGne+dQqkGMWBw1UXZOmU0dXWMYDDWtdKcQAD07lIEe6xdPJAxSJB6xgA4ByMNoCLBAN+XXIMowfhDJ3WAYD3jL9IisSsUyDFKA5c4Oo5gxUO8kcFZ1rdwrE0pFlJozRkkXUKoNvys0JaOnmAdQwgwcdJVjjA1GsYbQBXOWNcf5XGItYxAHVw4gBOEM2XplxpnQJpwGq0C1QXZGnJxBrrGMAhbW8PKR7gTTTSaNYnWPiC61xyTL3ys0LWMQDAXkENow3gOtFQQFfM5K4D2OtkGQ9OMOVDUlahdQqkAa84LnHlrEHKCTMDFM7UGc23joBMUTVGGnaKdQqgx/KzQrqMBQcAkGZez2kDuNKSSQNUU5RtHQMZLs6JA1jLLZcmXW6dAmlCceASxbkRXTCduYpwpv3hAusIyBTH3WSdAOi1FVMGqiKPe2FgjRNbMFQ2VGo+0zoF0CuhgF//NnewdQxkuE6e47A246NSmBI1U1AcuMhF02tVmM2YAzhPWyjPOgIyQf2xUu0M6xRAr0VDAV0zp8E6BgDYOfbfJT8fQeFei0ZWaXg1n31ghxMHMFU4UBq7wjoF0oh3bS4Si4Z0+cxB1jGAg7QEYtYR4Hk+ac6nrUMAfXb6uP6qL82xjgEA6dd/ktQ43zoF0Cc+n08fm9dkHQMZjMuRYWrWDVKADc2ZhOLAZZZOHqDKfMYcwFn2Uhwg1UYsliqbrVMAfRbw+/TR4xutYwBA+rEBAB4xvaFU0waVWMdAhupgVBGslA+Xhi+2ToE0ozhwmWgooGvnMFcRzrLLl2sdAV4WCEuzP2mdAkiaecMrNap/gXUMZCrWG2Ch4XhpwGTrFEDSfHx+k3y8nsJAJycOYIVxgxmJP3EXWjy2n0b2y7eOAXTZKYoDpND4CxOzFAEPuX7BEOsIAJAe/pA09xbrFEBSDa/O14nNVdYxkIG4HBkmGuZKg4+3TgEDFAcu5Pf7dNNJw9nhAMfYHs+2jgCvyimTZn7cOgWQdBNqi3TyKBYcAGSASZdKpYxog/d8YkGTcsIB6xjIMFyOjLQLRKR5n7VOASMUBy41qn+BTh/bzzoGIEna0sFFn0iR426Wopywgjddv3CIYpGgdQwASJ1YpXQMGwDgTZX5Wbrq2AbrGMgwHXF2kCLNJl8hFddbp4ARigMX+9i8JuVFWXCAvXc6sqwjwItqJkujzrZOAaRMWSyqa47j3iKkGwsOSKO5/yFFGGkJ77pgWq0ayvgeR/owqghplddPmvFR6xQwRHHgYsW5EX2YBQc4wMZ2RhUhyXwBacGd1imAlFs+eYCaKmLWMQAg+QZOl0Ystk4BpFQo4NdNJw2zjoEM0smsIqTT3FukMOs9mYziwOWWTh7IggPMbWiLWEeA10y4SKoYbp0CSLlgwK+bT+J7HYDH+IPSgjusUwBpMaW+RItGcm8R0oMTB0ib2hnS8FOtU8AYxYHLBfw+Fhxgbu2+qHUEeEluuTTreusUQNpMqC3SKaOrrWMAQPJMuEQqG2KdAkibTy4colzuLUIaxLnjAOngD0rz2QAAigNPmFBbpNPGcFEy7Kxu4cQBkogLkZGBPrGgiYuSkR4+FhyQYrkV0kwuREZmKcuL6po5XJSM9Ihz6gCpNvkKqazJOgUcgOLAI/79hKEqi7F4CxutnX7FwznWMeAF9bOlkWdZpwDSriwW1ccX8OYcgAec+AUpmmedAki7FVMGamgl3/tIAzYBIJWKG6SZTABAAsWBR+Rnh3TrKSOsYyCDdUQKrCPA7SJ50qL/sk4BmFkycYCmN5RYxwCA3ms+U2qcb50CMBEM+PX5M0YqFGBRF6nG9xhSxOeXTr5bCjGOGgkUBx4yZ2i5Th7FpUyw0R5mtAz6aO5/SPmMXUNm++xpzYwsQoqx2IAUyS2X5t9unQIwNaQyTx+azcgipBgnDpAqky6X+k+wTgEHoTjwmE8vGqZSRhbBQFuQY7nog/pjpbHLrVMA5qoLsnT9Qi4UBeBCJ3xByiq0TgGYu2xmvZr7sakKKeRjKQ8pUFQvzf6kdQo4DK82HlOQHdZnTh5uHQMZaF8gZh0BbhXJkxZ9yToF4BhnT6hhZBEAdxlxhtS0wDoF4AjBgF93nj5S4SDLLUgVThwgyXx+6aQvS6Es6yRwGJ5kHjR3WIUWjWRkEdJrT4ATB+il4z/DiCLgA25nZBEAt2BEEXCQweUxXTOHkUVIEUYVIdkmXCwNmGydAg5EceBRNy0appJcRhYhfXYpxzoC3GjQHGnMMusUgONUFWTpBkYWISVYbECSnXCXlF1knQJwnEtm1GtU/wLrGPAknuVIosJa6dhPWaeAQ1EceFRhTlh3nt5MEY202a5c6whwm6wi6URGFAGHc9aEGs1uKrOOAQCHN3qp1LTQOgXgSAG/T58/Y6SiIZZdkGTccYBk8Yekxd+SwtnWSeBQvNp42MzGMl08vc46BjLE9jgPGvTQyV+R8qutUwCOdufpI1WRF7WOAQAHK22S5n/OOgXgaPWlufr3E4ZZx4DXsEEUyTLnU1L1GOsUcDCKA4/7yPGNHI9EWmzZT3GAHph0hdQ4zzoF4HhFOWF98axRCvj5hAjAQYJZ0un3sEMR6IZzJtZwByGSjPeFSIKGudLkK61TwOEoDjwuFPDrv84erbwoFywitTZRHKC7qsZIcz5tnQJwjYl1xbrmWC5YRHLEmWOJZJj/WamMe1iA7rr11BGqLeFOOCQJz3L0VawyMQGA7yUcBcVBBuhflK3bT2u2jgGP29DOKA10QyQ/MUMxGLZOArjKFbMGadqgEusYACANP00au8I6BeAquZGg/vuc0QoHWYJBMrDYiz7w+aVTvy7l8NkCR8dTK0PMH1GpcyfVWMeAh61vpThANyz6olRUa50CcB2/36e7zhylktyIdRQAmaywVjrxi9YpAFcaVpWvG08Yah0DHhDncmT0xfR/k2pnWKeAS/Bqk0E+uXComipi1jHgUWtaWMzCUYw9Txp2inUKwLVKYxF98axR4roD9A3fQOilQFg6/dtShM8TQG8tnTRAC0dUWseA6/EsRy/VTJZmfsI6BVyE4iCDREMB3b1kjGLcd4AUWN8aZucDDq9ypDTvs9YpANebOqhEV87mvgMABo6/VaoabZ0CcL3PnjZCA4q5Hw59wFx69EasSjr9O5I/YJ0ELsIqX4apK83Vf509mt2KSLp43Kd4JN86Bpwop0w66/tSiHFWQDJcO6dBc4aUW8cAkEnGLJcmXGSdAvCEWDSkry0dq5wwi3foLRZ00EPBqHTWfVKMzxDoGYqDDDSzsUwfn99kHQMe1BEpsI4ApwmEpTPvk/L7WScBPMPn8+kLZ41SYznjQgCkQc0UaeHnrVMAntJUkae7zhzFxnH0Dt846KkTvyhVj7VOAReiOMhQF8+o16mjq61jwGPaQ3nWEeA0C/9TqplonQLwnNxIUN9cPk6F2SHrKHAbFhvQE/n9pTO/KwV4rQGSbe6wCn1kbqN1DLgSz3L0wOQrpZFnWaeAS1EcZLDbThuhUf0LrGPAQ1qC7H7Fv5h4qTRmqXUKwLP6F2Xr7iVjFWT+IIBUCGUnRg3mlFgnATzrilmDdNKoKusYcJk4mwDQXfWzpeNutk4BF6M4yGCRYEBfXzpW5XkR6yjwiL1BThzgXXUzE5coAkipyfXF+tSJQ61jAPCik++WKputUwCed/tpzRrJhj70CMUBuqGoTlr8LS5DRp9QHGS4sryovrZ0nCJBvhXQd3t8udYR4ASFtdLib/MGBUiTpZMHasnEGusYALxkxkelYadYpwAyQjQU0DeWjlVFXtQ6ClyD4gBHEY5JZ90vZRVaJ4HLsVoMjepfoDtPH8nIW/TZTlEcZLysQumcB6TsIuskQEa5adEwTR1UbB0DrsAbPhzFsFOkWTdYpwAySlleVF9fNlbREEs06AYf3yc4gkA4cT9RWZN1EngArzaQJJ04sko3LmTUAfpmezzHOgIsBaPS2T+QSgdbJwEyTjDg19eWjtOwKkbGAeiD2hnSKV/nEm3AQHO/Av332WO4uwhHxR0HODyfdPJXpPpZ1kHgERQH6HL+tFpdeky9dQy42NZOioOM5fNLp35DqplknQTIWLmRoO45b4IGFGdbRwHgRhXN0pnfk4Jh6yRAxpoztFy3njrCOgYAtzr+VmnEYusU8BCKAxzg4/ObtHhsP+sYcKnNHVnWEWBl/uekoYusUwAZrzQW0b3nT1BJbsQ6CgA3KRwonftjKcqpJcDaGeP662PzGDGCI+HEAQ5hylXS5MutU8BjKA5wkM+eOkLHNpVZx4ALbWpnl2tGOuZj0oSLrFMAeNeA4hzdc954xSJB6yhwJBYb8AE5pdK5P5Fyef8POMVlM+t14bRa6xhwKu44wAc1nykdd4t1CngQrzY4SDDg15eXjNGYmgLrKHCZje1R6whIt3EXSLOut04B4AOGV+fra8vGKhzkrR6AIwjnSkt+KBUzrhRwmhsWDtGpo6utY8CB4mwCwL+qP1Y66cvcT4SU4NMkDikaCuhbK8aroSzXOgpcZG0LozEyyrBTpQV3WqcAcBhT6kt01xmjxB2LAA4pEJbO/K5UNdo6CYBD8Pl8+tziZs1qLLWOAqdhgRjvqR4rnXGvFAhZJ4FHURzgsAqyw7rvwomqLeHCW3TPmhZOHGSMIYsSlyH7eYwATrawuVK3nTqCz5d4H98LkCR/SFr8bal+tnUSAEcQDPh195KxGj+w0DoKHIWHOSRVNCdGDUbY8IvUYcUHR1SeF9UPLp5EeYBu2dYeVDzAqQPPG3JiYrEhwPx0wA3OHF+j/zh5OOUBgAR/UFr8P9KQE6yTAOiGrHBA3z5vgsYOoDxAQtw6AOyVD5eW/VzKKrBOAo+jOMBRUR6gJzqj+dYRkEpNJ1AaAC60ZOIA3bxomHUMANZ8AenUr0tDT7JOAqAHciNBfef8CdxDiAQuR85spU2J0iC7yDoJMgCvNuiW98qDOsoDHMX+MMWBZzWdIJ1+D/MTAZdaOnmgbj5pGCcPgEz1Xmkw/DTrJAB64b3yYDTlARhVlLnKhkrLH5JySqyTIENQHKDbyvOiup/yAEfRFqI48KTGhZQGgAcsmzxQt5zE2KLMxh9+RnpvPNGIxdZJAPRBLBrSdy+YqHGMLcpsvJHLTGXDpOW/kHK5MB3pQ3GAHqE8wNG0BGLWEZBsjQukM75DaQB4xLmTBui2U7gwOVMxFzkDvXcR8rBTrJMASILcSFD3XjBBE2sZU5Kp4mwCyDzlwxOlAScNkGYUB+ix98qDQWXc3I6D7aU48JYRp0tn3EtpAHjMWRNqdOfikQr6+eAJeFowSzrzu9LQRdZJACRRdjioe86boGmDWETMSOz+yCz9J0krHpJyiq2TIANRHKBXyvOi+uElkzWqf4F1FDjMLh+FkmdMvFQ69RuUBoBHnTa2n762dKyiId4OAp4ULZCW/UxqnG+dBEAKZIUD+taK8VrYXGkdBWnGiYMMMnhe4lmexXgy2OCTInqtMCes7180UdMb2OWA9+0UxYEnzL5Rmn87u1kAjzt2SLm+d+EkFWRTEGYMXtczQ6xKOv/XUs0k6yQAUigc9Ou/zx6tFVMGWkdBWvEszwgjz5HO/J4UyrJOggxGcYA+yQ4H9T/Lx+vEkVXWUeAQ2+Pcf+FqvoB04pekGR+xTgIgTcYOKNSPLp2sqvyodRQAyVAyWLrgUalsiHUSAGng8/n06UXD9LF5TdZRkCZxNgF435SrpJPvlgJB6yTIcBQH6LNw0K8vnTVKF06rtY4CB9jSQRvuWsFo4hLkscutkwBIs0FlMf348ikaXM6pMcDV+o2Xzn9EKuhvnQRAml02s153ns79RZmBpTzv8knH3SzN/Q9OicIReLVBUvh8Pn3yhKG68YShvLZluM0d2dYR0BuRfOncH0tDTrROAsBIZX6WfnjJFI0bwAxVb+ONmmc1zJWWPShlF1knAWBk8dh++ubyccoOB6yjIJV4lHuTP5g4ZTD1auskQBeKAyTVBdNq9d9nj1EkyLdWptrYzokD1ymslS78jTRwmnUSAMbys0O678KJmj+8wjoKgJ4Ys0w6634pzAYOINPNbCzT/RdNUnFO2DoKUoTLkT0oWiAt+ZE06hzrJMABWN1F0i1srtQPL52sijxmJWei9W38ubvKwOnSRb+VShutkwBwiGgooLuXjNHVxzZwihBwOn9Qmv85adF/MQcZQJeR/Qv08yunamhlnnUUpARv0DylZHDiM3n9LOskwEEoDpASzf0K9OBVUzWmpsA6CtJs7T6KA9cYd7609GeMNABwEJ/Pp2uPG6y7zxnDuAPAqbIKE2MGJ15inQSAA/UrzNaPL5uihc2V1lGQZJw48JBBc6QLH5OK662TAIdEcYCUKYtF9YOLJ+uMcf2soyCNVrdErCPgaPxBacGd0gl3sTsRwBHNH1GpH106RdUFjKHzDhYbPKG0SbrwcalupnUSAA6WFQ7oy+eM0UfmDuYUoZfwh+kNk6+UznlAiuZbJwEOi+IAKRUO+vW5xSP1qROHKujn4ZYJWjv9iodzrGPgcKIFid2JEy6yTgLAJYZW5enBK6dqQi2nkwBHGDxPuuA37E4E0G1Xzm7QN5aOU26ETUNewIkDlwuEpZPulo7/jOTnZC+cjeIAaXHe1Fp95/wJKsgOWUdBGnRECqwj4FDKhiVmJ7I7EUAPFedG9L0LJ+qciTXWUYDMNvWaxCXIUeaWA+iZOUPL9dPLp2hgMZeoux/FgWvllkvLH5JGL7FOAnQLxQHSZuqgEj14xTQNr+aDjtftD3PUznFGL5UuepzdiQB6LRTw69ZTRuhzpzUrK8TuKCCtovnSGd+VjrtJ8vMRDkDvNJTH9PMrpml2U5l1FPQFo4rcqW6mdOmfpJqJ1kmAbuNdJ9KqpjhbP7lsqlZMGWgdBSnUGoxZR8B7QjnSKV+XTvpvKcSMcgB9d8b4/nrwyqkaXJ5rHQW9wWKD+1SPky75ozR0kXUSAB6Qnx3S/ywfp08uHKJwgCUhN2JUkcv4AtKsT0rn/lTKpbSDu/CUQNqFg359etEwfXPZOBUyusiT9gU5VeIIZcOki5+QRp5pnQSAxzSUx/TgldN01vj+1lEAD/NJU66Szv+1VDjAOgwAD/H5fLpwep1+dNlkDWB0ketQHLhIrFJa/gvpmI9yYhCuxHctzMwZWq5fXj1dEwZy2aLX7PFx4sDce6OJSgdbJwHgUdFQQJ89rVlfPGsUly0CyZZdLJ3zgDT3P6QAG20ApEZzvwI9/KHpWjSyyjoKeoLTg+5Qf2xiNNHAqdZJgF6jOICpyvws3X/xJH1o9iD5efZ5xi4f4yvMhGOMJgKQVieNqtZDV3GHEZA0A6YmFhoGz7VOAiAD5EaC+tLZo3X7aSO4w8g1WDxxNH9QOvZT0rk/lnJKrNMAfUJxAHMBv08fntuo7104SRV5Ues4SIIdyrGOkJlqZ0iX/5nRRADSbmBJjn5y2VRdOK2WjQCOxx+QY/lD0qwbEiMN8tj9CyC9zhxfo19cNVVNFZwed7q4dQAcXkmjdP6j0vQPczIEnkBxAMeYXF+sR66docVj+1lHQR9ti1McpFUoW5p/h7TsQamgxjoNgAwVDvr1yROG6oFLJqu2hOcA0CMVzYl7iY65TvKz4xeAjUFlMf3iqmn60LENCgVY9HSquI+lPMfx+RP3El3yB6nfWOs0QNLwagNHyc8K6c7TR+rb541XZT6nD9xqy34u2Eqb/hMT4wwmXsyOBgCOMG5gkX519XRdwOkD4Oj8IWnm9dJFv5MqhlunAQCFAn59+LjB+tkVUzWkkjGEzsQbLEcpHiSd/0jiXqIQ61jwFooDONKsxjI9eu0MnTW+v3UU9MImioPUC0Sk426Rzvu1VFxvnQYADhANBXQjpw8cKU7J7BzvnTKY+TEpwAXjAJxlWFW+Hrxyqq6dM5jTBw4TpzhwBp9fmnR5YiNf/wnWaYCUoDiAY8WiIX32tGZ994IJqi7gklc32dhOy55S1eMSRyCnfkjy8zIOwLk4fQAcgj8kzfyEdNFvOWUAwNFCAb+untOgB6+cpuHVnD4AuhTWSiselubdJoVYr4J3seIEx5veUKpHr52hcyfVMInFJda1RqwjeFNWoXTCF6QLH5PKmqzTAEC3vHf64IeXTubCRaD/xHdPGXxcCoSs0wBAtwypzNPPLp+qj8wdrEiQZSRr3HFgKBCRZnxUuvwpacAU6zRAyvni8TgXssM1nlu9XZ/6+Qv6f2t2WEfBEVRHW/WkzrOO4SE+adQS6bibpZxi6zAA0GsdnXHd+9RK/edvXtWulv3WcTLS66UfVXDXWusYmSenNPEcH3k2dxIBcLXVW/fq5ode1G9e3GgdJWP9X+1XVbz+D9YxMk/9sdKCOxgVjIxCcQDXicfjeuCvq/W5X7+iLXvarOPgEHy+uN6MLpUv3mkdxf3Khkkn/KdUM8k6CQAkzTu7WnXbr17ST/++VrwTTS+KgzTzBaTxF0qzb5Ci+dZpACBpfvfKJt38ixf11uY91lEyzv/Vfk3F639vHSNz5PWT5t0qDT3JOgmQdhQHcK0d+9p1129e1X1/eVv7O/k2dpo3C66Qv2WbdQz3Cucm5h9PvJQLEwF41l9XbtWNP/+nXlq/0zpKxqA4SKP+k6SFd0oVI6yTAEBKtO3v1Df++Ka+/LvXtbetwzpOxqA4SBN/SJp8hXTMdVI4xzoNYILiAK73yoZd+tSDL+gvb261joJ/8Vr59QrtWGkdw318AWnUOdKsG6S8Sus0AJByHZ1x3feXt/X5R1/RTsYXpRzFQRrklEnH3cRYIgAZY932ffrMwy/p4efXW0fJCH+t/bpK1j9hHcPbBs2Rjr9NKh1snQQwRXEAz3joH+t0xyOv6O0te62jQNKL/W5X9ub/Zx3DXQbPk+bcxMXHADLStj1tuvuJ13XvU2+rdT+j7lKF4iCFwjFp6oekSZdLkVzrNACQdk+9sUW3//plPbd6u3UUT3u27psqXfdb6xjeVDU68Zm87hjrJIAjUBzAU/Z3dOp//7paX3r8NW3c2WodJ6P9beDdKtrwJ+sY7lA1Rpp7izRwmnUSADC3bvs+feGxV/Xjv61VB6MIk+710usU3LXGOoa3BMKJewymf0TKKbZOAwDmHvnnBn3+0Vf06sbd1lE86Zm6/1HZusetY3hLUZ00+0Zp2CmcFgT+BcUBPKmlvUP3PrVSX3niDW3b224dJyP9adB96rfml9YxnK1woHTsv0vDTuXNCQB8wOubdumOR17RI//caB3FUygOksjnl5rPlGZdLxXUWKcBAEfp7IzrJ39fq7t+86rWbt9nHcdTKA6SKKcscYfB2PO4WxA4BIoDeNqulnZ9449v6X/++Kb2cFlTWj3S8DM1rn7AOoYz5feXpl4tjVkuBcPWaQDA0f6+aptu//XL3GWUJBQHSTJ4XqL8Lx9mnQQAHK1tf6e+9/Tb+vLvXtfm3W3WcTzh6bpvqXzdY9Yx3C2SJ02+UppyJRcfA0dAcYCMsGV3q+5+4g19/+lV2tdOgZAOP2x4TONXf8s6hrMU1krTP5y4LDEQsk4DAK7y5OubdfcTr+vJ17dYR3G118s+puDO1dYxXMonNS5IPMv7jbMOAwCusqd1v+7580p9+8m3KBD66C/131bF2t9Yx3Cn7BJp0qXShIulaL51GsDxKA6QUbbuadN3/rxS9z61khFGKfbNhqc0Z/V/WcdwhpJGafq/SSMWS/6AdRoAcLXnVm/XV554XY++uFG8i+05ioNe8Ael4YuladdIZUOs0wCAq7W0d+iHf12tr//xTa3eygij3vhL/T2qWPuodQx3yauWplyVOPUfzrZOA7gGxQEy0r62Dv3vs6v0jT++xbzFFPlc3f/TGetut45hq3yENOMj0pBFkt9vnQYAPOX1Tbv0lSfe1M+fW6v9XKLcbRQHPRCMSqPPlaZ8SCocYJ0GADylozOuh/6xTl954g29vGGXdRxXear+O6pc+4h1DHcoqpOmXpM49c+YYKDHKA6Q0fZ3dOqhf6zXV3/Pm5Vku27Aa7p846esY6Sfzy8NOk6aeIlUP5tLjwEgxdZu36dv/OFN/eDZVWpp77SO43gUB90QyZfGnSdNvkLKLbNOAwCe97uXN+krT7yhZ1Zyn1F3/Ln+XlWt/bV1DGerHJU4YTDsFE79A31AcQC864lXNulbT67UH197h9EHSbC8ao1u2nqddYz0ieRLo5dIEy5K7GoAAKTV9r1t+t9nV+u+p99m9MERvF72cQV3rrKO4Uzlw6XxF0rNZ3BRIgAY+Nuqbbr3zyv1y+c3qK2DzQCH82T9d1W99lfWMZwnEEkUBRMu4i4iIEkoDoAPWL11r+5/ZpUe+Osabd7dah3HteaVbtFXd11lHSP1ShoTb0xGni1Fcq3TAEDG6+yM67cvb9K9f3mbzQCHQHHwAYFwYqTghIukmknWaQAAkjbvbtUPnlml7z+9Sut2tFjHcZw/DbpP/db80jqGc+T3T5wUHLNcyimxTgN4CsUBcBjtHZ36zYsb9f2nV+nJNzaz8NBDI2K79Yv2i61jpEYgIjXOk8auSIwjAgA40ttb9uj+Z1brR/+3Wpt3t1nHcQSKg3fl9Xt/kSG31DoNAOAQOjrj+t3Lm3T/M6v0xKvvqIM7jSRJfxr0PfVb87B1DHt1M6XxF0mN8xlHBKQIxQHQDSs379H9z6zSj/5vjbbsYeGhOwpD+/X3wDLrGMlVNUYadY40/DQpu8g6DQCgm9r2d+rRFzfoJ39bqz+8+k5GX6ac0cVBMJpYXGg+S2o4jkUGAHCRddv36YG/rtZP/75Wb2/Zax3H1B8HfU/9M7U4KKqTms9MjBVkRDCQchQHQA+0d3Tq96+8o589t1aPvbSRSxiP4q2c8+TrcPm4p1hl4k3JqCVSaaN1GgBAH23d06aH/7FOP/37Wv1t1XbrOGn3WtknFNr5tnWM9PH5pYHTEosMQxZJ0TzrRACAPvr7qm36+XPr9NA/1mfkeOE/DPq+atY8ZB0jfbIKpWGnSiPPkvpPsE4DZBSKA6CXdrfu1yMvbNAv/rFOT76+We0d/F/pg94ovkaBPZusY/RcNF8aPE8acYZUP4sdiQDgUau27NXPn1urnz23Vm+8s8c6TlpkTHFQPjxR/I84Xcqrsk4DAEiBjs64/vT6Zv38ubV69J8btbt1v3WktPj9oB9owJoHrWOkViAiDT4+Ufw3zJWCYetEQEaiOACSYPveNj3yzw166B/r9dQbWzJ6BMK/erXyUwpve806RvfklkuNC6QhJ0q1M6RAyDoRACCNnl+zQw8/v16Pv7RRr23abR0nZbxbHPikypHvP8vLh1oHAgCkUUt7hx5/aZMefn6d/vDqZk+XCJ4tDiJ50qA5UtPCxEjBaL51IiDjURwASbZjX7v++No7+t3L7+j3r27K6MsYX6j5vHI3/Z91jMMrGJBYXBhyotRvguT3WycCADjAqi179ZuXNurxlzbqmbe2empDgKeKg0AkUfY3zk/84GQBAECJu42efmuLHn9pkx5/eaNWb91nHSmpnhj0vxq45ufWMZIjr/rd5/gCaeB0ThYADkNxAKRQPB7XP9bs0G9f3qQnXtmkf6zdoUz6f9yzdd9Q6brfWcd4XzBLGjBZqpsl1c+WKoZbJwIAONzOlnY98co7evyljXrilXe0Y1+7daQ+ea38eoV2rLSO0Xs5pYndiI0LEs/ySK51IgCAw722cZcef3mTHn9po/62ars6XL4h4HeDHlDtmp9Zx+gdn1+qaE6cKGhcIFWNlnw+61QADoPiAEijzbtb9cQr7+jPr2/WMyu3as02b+18+CDzI5Q+f2JsQd0sqW6mVDNJCkbs8gAAXK2jM65/rtuhv7y5RX95c6ueXblVu1rcNQrBdcVBdrE0YGpiF2LtdKlsiHUiAICLbd/b1vUcf+atrXp5w065rUf47aAfqm7NT61jdJNPKhuaOCFYOz3xTM8qsA4FoJsoDgBDG3a06JmVW/XsW4nFh1c37nLdm5YjebjhIQ1b/f30/YaBiFTZLFWPS5QEtTOk7KL0/f4AgIzS2RnXP9ftfHcBYouecUGR4PjiIKvwA0XBUHYiAgBSZsfedj27cquefmuLnn5rq/65bqfjTyT8tuFHqlv9E+sYh+GTShqkgdMSn8cHTpdySqxDAegligPAQXbsa9ff3t6mZ1Zu1fNrduil9Tu1ZY9770j4fsMTmrL666n7DQprpX7jpX7jEmVBxQhmIgIAzHR2xvXyhl16Ye0OPb92h15Yl3iWt7R3Wkfr8lr5DQrteMs6RkI4NzGuoGq0VDUq8dfiQRQFAAAzu1v3629vb0s8x9fu0D/X7dSqrXutYx3g8YYfq371j61jJOTXSNWj332Wj5YqR3GiAPCQoHUAAO/LzwppVlOZZjWVdX1t484Wvbh+p15av1Mvrd+lF9ft0Motex2/C0KStsdzkvMPCudKJYMT4wlKmxK7D6tGSznFyfnnAwCQBH6/T0Or8jS0Kk9njO8vSdrf0anX39mt59ckFh+eX7tDr27YpV2tzj6ZkHTZxVJJY2KE4HtFQXGD5PdbJwMAoEtuJKgZg0s1Y3Bp19d27GvXP9ft0IvrduqFtTv0wrqdevOd3WbTAkx+W39IKqpNfC7vepaP5jQB4HEUB4DDledFVZ4X1azG98uElvYOvbJhl97cvFurtuzTqq17tXrrXq3aulcbd7U45gLmLR1Z3f/FvoAUq5Dy+yV2G5Y2vVsSNEn5/dl9CABwpWDAr6aKPDVV5On0f/n6O7ta9dbmPXpr8269uXmPVm7eo7c279HbW/aqdb9zTij0SDBLKq5/90dD4nle0pD4eVahdToAAHolPyukKfUlmlL//iJ56/4Ord66V29t3quVm/do5ZZ3f2zeq3U79qX0M3lcKfxsnF2ceIaXvPdjcOLnhQOlAEuIQKbh//WAC0VDAY3sX6CR/QsO+s9a2ju0Zts+rdq6R6u37tPGnS3auqdNW/a0aeu7Pzbvbk3LDObNHdmJvwnHpOzCxJuQnLJEOZDfL1EIvPf3sUreiAAAMkZpLKLSWEQTag+8i6ezM651O/Zp/Y4WbdrZqk27WrRpV2vX37+zq1WbdrVq29629G4UyCpKPKtjFf/y14p/+XmllFdF0Q8AyAiRYECDymIaVBY76D9rae/Qqq17tWbbXr2zq/X9H7tbD/j5nraOXv3evSoO/CEpp1TKq/yX53blgc/wWKUUzetVJgDexB0HQIZq29+pbXvbtGV3m3a37lfr/g61tHeqdX+HWts71fLuX1v3d6qlvUPxeFwBv1/BgE+hgE8Bv//dv/oUevfrkWBAsWhQsWhQeVkhFUX9KswKcu8AAABJ1t7RqV0t+7Wndb/2tnVod+t+7W3brz2tHe9+LfH1/Z1xdXbGFZfUGY/r4uhjyu7YnVjg9wUkf0AKZUuRWGI0YDjn/b+P5L77tVzKfQAAkmxv237t2NeuPa0d2tfW0fXs3tvWoT1t+9/9WofaOxInEeNxKa64Tsp+XrXtbySe4V3P8qzEhr3IB3/kJf4aihr/rwXgRhQHAAAAAAAAAACgC7eRAQAAAAAAAACALhQHAAAAAAAAAACgC8UBAAAAAAAAAADoQnEAAAAAAAAAAAC6UBwAAAAAAAAAAIAuFAcAAAAAAAAAAKALxQEAAAAAAAAAAOhCcQAAAAAAAAAAALpQHAAAAAAAAAAAgC4UBwAAAAAAAAAAoAvFAQAAAAAAAAAA6EJxAAAAAAAAAAAAulAcAAAAAAAAAACALhQHAAAAAAAAAACgC8UBAAAAAAAAAADoQnEAAAAAAAAAAAC6UBwAAAAAAAAAAIAuFAcAAAAAAAAAAKALxQEAAAAAAAAAAOhCcQAAAAAAAAAAALpQHAAAAAAAAAAAgC4UBwAAAAAAAAAAoAvFAQAAAAAAAAAA6EJxAAAAAAAAAAAAulAcAAAAAAAAAACALhQHAAAAAAAAAACgC8UBAAAAAAAAAADoQnEAAAAAAAAAAAC6UBwAAAAAAAAAAIAuFAcAAAAAAAAAAKALxQEAAAAAAAAAAOhCcQAAAAAAAAAAALpQHAAAAAAAAAAAgC4UBwAAAAAAAAAAoAvFAQAAAAAAAAAA6EJxAAAAAAAAAAAAulAcAAAAAAAAAACALhQHAAAAAAAAAACgC8UBAAAAAAAAAADoQnEAAAAAAAAAAAC6UBwAAAAAAAAAAIAuFAcAAAAAAAAAAKALxQEAAAAAAAAAAOhCcQAAAAAAAAAAALpQHAAAAAAAAAAAgC4UBwAAAAAAAAAAoAvFAQAAAAAAAAAA6EJxAAAAAAAAAAAAulAcAAAAAAAAAACALhQHAAAAAAAAAACgC8UBAAAAAAAAAADoQnEAAAAAAAAAAAC6UBwAAAAAAAAAAIAuFAcAAAAAAAAAAKDL/weozWpfL3p26wAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Метод приращения с избытком (oversampling)\n",
    "def oversample(df: DataFrame, column: str) -> DataFrame:\n",
    "    X: DataFrame = df.drop(column, axis=1)\n",
    "    y: DataFrame = df[column] # type: ignore\n",
    "    \n",
    "    adasyn = ADASYN()\n",
    "    X_resampled, y_resampled = adasyn.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return df_resampled\n",
    "\n",
    "\n",
    "# Приращение данных (oversampling)\n",
    "df_train_oversampled: DataFrame = oversample(df_train, 'hazardous')\n",
    "df_val_oversampled: DataFrame = oversample(df_val, 'hazardous')\n",
    "df_test_oversampled: DataFrame = oversample(df_test, 'hazardous')\n",
    "\n",
    "# Проверка сбалансированности\n",
    "print('После применения метода oversampling:')\n",
    "check_balance(df_train_oversampled, 'Обучающая выборка', 'hazardous')\n",
    "check_balance(df_val_oversampled, 'Контрольная выборка', 'hazardous')\n",
    "check_balance(df_test_oversampled, 'Тестовая выборка', 'hazardous')\n",
    "\n",
    "# Проверка необходимости аугментации\n",
    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_oversampled, 'hazardous', True, False) else ''}требуется\")\n",
    "   \n",
    "# Визуализация сбалансированности классов\n",
    "visualize_balance(df_train_oversampled, df_val_oversampled, df_test_oversampled, 'hazardous')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "После применения метода undersampling:\n",
      "Обучающая выборка:  (10608, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    5304\n",
      "True     5304\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.00%\n",
      "Процент объектов класса \"True\": 50.00%\n",
      "\n",
      "Контрольная выборка:  (3536, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    1768\n",
      "True     1768\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.00%\n",
      "Процент объектов класса \"True\": 50.00%\n",
      "\n",
      "Тестовая выборка:  (3536, 6)\n",
      "Распределение выборки данных по классам \"hazardous\":\n",
      " hazardous\n",
      "False    1768\n",
      "True     1768\n",
      "Name: count, dtype: int64\n",
      "Процент объектов класса \"False\": 50.00%\n",
      "Процент объектов класса \"True\": 50.00%\n",
      "\n",
      "Для обучающей выборки аугментация данных не требуется\n",
      "Для контрольной выборки аугментация данных не требуется\n",
      "Для тестовой выборки аугментация данных не требуется\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1500x500 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Метод приращения с недостатком (undersampling)\n",
    "def undersample(df: DataFrame, column: str) -> DataFrame:\n",
    "    X: DataFrame = df.drop(column, axis=1)\n",
    "    y: DataFrame = df[column] # type: ignore\n",
    "    \n",
    "    undersampler = RandomUnderSampler()\n",
    "    X_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n",
    "    \n",
    "    df_resampled: DataFrame = pd.concat([X_resampled, y_resampled], axis=1)\n",
    "    return df_resampled\n",
    "\n",
    "\n",
    "# Приращение данных (undersampling)\n",
    "df_train_undersampled: DataFrame = undersample(df_train, 'hazardous')\n",
    "df_val_undersampled: DataFrame = undersample(df_val, 'hazardous')\n",
    "df_test_undersampled: DataFrame = undersample(df_test, 'hazardous')\n",
    "\n",
    "\n",
    "# Проверка сбалансированности\n",
    "print('После применения метода undersampling:')\n",
    "check_balance(df_train_undersampled, 'Обучающая выборка', 'hazardous')\n",
    "check_balance(df_val_undersampled, 'Контрольная выборка', 'hazardous')\n",
    "check_balance(df_test_undersampled, 'Тестовая выборка', 'hazardous')\n",
    "\n",
    "# Проверка необходимости аугментации\n",
    "print(f\"Для обучающей выборки аугментация данных {'не ' if not need_augmentation(df_train_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для контрольной выборки аугментация данных {'не ' if not need_augmentation(df_val_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
    "print(f\"Для тестовой выборки аугментация данных {'не ' if not need_augmentation(df_test_undersampled, 'hazardous', True, False) else ''}требуется\")\n",
    "   \n",
    "# Визуализация сбалансированности классов\n",
    "visualize_balance(df_train_undersampled, df_val_undersampled, df_test_undersampled, 'hazardous')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "aimenv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}