diff --git a/lab1.ipynb b/labworks/lab1/lab1.ipynb similarity index 100% rename from lab1.ipynb rename to labworks/lab1/lab1.ipynb diff --git a/labworks/lab2/lab2.ipynb b/labworks/lab2/lab2.ipynb new file mode 100644 index 0000000..fb1fb60 --- /dev/null +++ b/labworks/lab2/lab2.ipynb @@ -0,0 +1,1165 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проблемная область связана с анализом и рекомендацией сериалов.\n", + "Объектами наблюдения являются сериалы, представленные в датасете. \n", + "Атрибутами является наши критерии в датасете.\n", + "Бизнес цель: понимание, анализ предпочтений аудитории и развитие стратегии для создания новых сериалов,которые будут ещё популярнее.\n", + "Цель технического проекта:\n", + "- Входные данные: Атрибуты сериала, включая genre, release_date, popularity.\n", + "- Целевой признак: Популярные жанры или тренды." + ] + }, + { + "cell_type": "code", + "execution_count": 319, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"data/Movies.csv\", index_col=\"id\")\n", + "\n", + "df.to_csv(\"lab2movies.csv\")" + ] + }, + { + "cell_type": "code", + "execution_count": 320, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "df[\"release_date\"] = pd.to_datetime(df[\"release_date\"])\n", + "plt.scatter(df[\"release_date\"], df[\"popularity\"])\n", + "plt.xlabel(\"Дата выпуска\")\n", + "plt.ylabel(\"Популярность\")\n", + "plt.title(\"Популярность сериалов по дате выпуска\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "При проверке на шум можно заметить выброс в 2020-2021 годах. Количество популярности там огромное." + ] + }, + { + "cell_type": "code", + "execution_count": 321, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Выбросы:\n", + " title \\\n", + "id \n", + "49529 John Carter \n", + "60747 Red Dawn \n", + "238 The Godfather \n", + "278 The Shawshank Redemption \n", + "240 The Godfather Part II \n", + "... ... \n", + "225886 Sex Tape \n", + "47964 A Good Day to Die Hard \n", + "980078 Winnie the Pooh: Blood and Honey \n", + "632727 Texas Chainsaw Massacre \n", + "37265 All Ladies Do It \n", + "\n", + " overview popularity \\\n", + "id \n", + "49529 John Carter is a war-weary, former military ca... 75.456 \n", + "60747 A city in Washington state awakens to the surr... 124.651 \n", + "238 Spanning the years 1945 to 1955, a chronicle o... 167.536 \n", + "278 Framed in the 1940s for the double murder of h... 179.959 \n", + "240 In the continuing saga of the Corleone crime f... 106.688 \n", + "... ... ... \n", + "225886 When Jay and Annie first got together, their r... 82.240 \n", + "47964 Iconoclastic, take-no-prisoners cop John McCla... 68.417 \n", + "980078 Christopher Robin is headed off to college and... 68.676 \n", + "632727 After nearly 50 years of hiding, Leatherface r... 70.354 \n", + "37265 After five years of marriage, Diana discovers ... 66.019 \n", + "\n", + " release_date vote_average vote_count \n", + "id \n", + "49529 2012-03-07 6.302 5276 \n", + "60747 2012-03-15 5.683 1482 \n", + "238 1972-03-14 8.708 18991 \n", + "278 1994-09-23 8.705 24985 \n", + "240 1974-12-20 8.589 11467 \n", + "... ... ... ... \n", + "225886 2014-07-17 5.410 3898 \n", + "47964 2013-02-06 5.328 6096 \n", + "980078 2023-01-27 5.281 1038 \n", + "632727 2022-02-18 5.200 1283 \n", + "37265 1992-02-21 4.827 343 \n", + "\n", + "[696 rows x 6 columns]\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df[\"release_date\"] = pd.to_datetime(df[\"release_date\"])\n", + "\n", + "Q1 = df[\"popularity\"].quantile(0.25)\n", + "Q3 = df[\"popularity\"].quantile(0.75)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Определение пределов для выбросов\n", + "upper_line = Q3 + 1.5 * IQR\n", + "lower_line = Q1 - 1.5 * IQR\n", + "\n", + "\n", + "outliers = (df[\"popularity\"] < lower_line) | (df[\"popularity\"] > upper_line)\n", + "print(\"Выбросы:\")\n", + "print(df[outliers])\n", + "\n", + "\n", + "median_review_no = df[\"popularity\"].median()\n", + "df.loc[outliers, \"popularity\"] = median_review_no\n", + "\n", + "\n", + "plt.scatter(df[\"release_date\"], df[\"popularity\"])\n", + "plt.xlabel(\"Дата выпуска\")\n", + "plt.ylabel(\"Популярность\")\n", + "plt.title(\"Популярность сериалов по дате выпуска (после устранения выбросов)\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 322, + "metadata": {}, + "outputs": [], + "source": [ + "# Удаление строк с пропущенными значениями\n", + "df_dropna = df.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 323, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 6181\n", + "Размер контрольной выборки: 1325\n", + "Размер тестовой выборки: 1325\n", + "Распределение vote_average в обучающей выборке:\n", + "vote_average\n", + "6.700 48\n", + "6.200 44\n", + "6.800 43\n", + "7.200 40\n", + "7.000 39\n", + " ..\n", + "7.917 1\n", + "6.057 1\n", + "8.262 1\n", + "7.452 1\n", + "7.698 1\n", + "Name: count, Length: 2580, dtype: int64\n", + "\n", + "Распределение vote_average в контрольной выборке:\n", + "vote_average\n", + "6.700 13\n", + "7.400 11\n", + "6.300 10\n", + "7.000 10\n", + "6.200 8\n", + " ..\n", + "6.326 1\n", + "6.552 1\n", + "7.473 1\n", + "6.370 1\n", + "6.809 1\n", + "Name: count, Length: 987, dtype: int64\n", + "\n", + "Распределение vote_average в тестовой выборке:\n", + "vote_average\n", + "6.100 15\n", + "7.200 10\n", + "7.500 9\n", + "6.700 8\n", + "5.800 7\n", + " ..\n", + "6.759 1\n", + "6.911 1\n", + "5.298 1\n", + "7.376 1\n", + "7.457 1\n", + "Name: count, Length: 990, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "from sklearn.model_selection import train_test_split\n", + "train_data, temp_data = train_test_split(df, test_size=0.3, random_state=42)\n", + "val_data, test_data = train_test_split(temp_data, test_size=0.5, random_state=42)\n", + "print(\"Размер обучающей выборки:\", len(train_data))\n", + "print(\"Размер контрольной выборки:\", len(val_data))\n", + "print(\"Размер тестовой выборки:\", len(test_data))\n", + "# Сбалансированность выборок можно оценить, проверив распределение классов.\n", + "def check_balance(df, name):\n", + " counts = df[\"vote_average\"].value_counts()\n", + " print(f\"Распределение vote_average в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "\n", + "check_balance(train_data, \"обучающей выборке\")\n", + "check_balance(val_data, \"контрольной выборке\")\n", + "check_balance(test_data, \"тестовой выборке\")" + ] + }, + { + "cell_type": "code", + "execution_count": 324, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение title в обучающей выборке после oversampling:\n", + "title\n", + "Aftersun 4\n", + "Alice Through the Looking Glass 4\n", + "London Boulevard 4\n", + "The Unforgivable 4\n", + "Dead Silence 4\n", + " ..\n", + "Sharknado 3: Oh Hell No! 4\n", + "Captain Fantastic 4\n", + "Toy Story 4\n", + "Fear Street: 1978 4\n", + "Samaritan 4\n", + "Name: count, Length: 6022, dtype: int64\n", + "\n", + "Распределение title в контрольной выборке после oversampling:\n", + "title\n", + "Darc 2\n", + "The Queen 2\n", + "Cruising 2\n", + "Veronica Mars 2\n", + "City Island 2\n", + " ..\n", + "My Hero Academia: World Heroes' Mission 2\n", + "Walk of Shame 2\n", + "Leroy & Stitch 2\n", + "The Conjuring: The Devil Made Me Do It 2\n", + "The Messenger: The Story of Joan of Arc 2\n", + "Name: count, Length: 1319, dtype: int64\n", + "\n", + "Распределение title в тестовой выборке после oversampling:\n", + "title\n", + "Dark Water 2\n", + "The Naked Gun 2½: The Smell of Fear 2\n", + "The End? 2\n", + "Ocean's Eleven 2\n", + "Carandiru 2\n", + " ..\n", + "Shut In 2\n", + "The Number 23 2\n", + "The 39 Steps 2\n", + "Final Destination 3 2\n", + "Miller's Crossing 2\n", + "Name: count, Length: 1316, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "\n", + "def check_balance(df, name):\n", + " counts = df[\"title\"].value_counts()\n", + " print(f\"Распределение title в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "\n", + "def oversample(df):\n", + " X = df.drop(\"title\", axis=1)\n", + " y = df[\"title\"]\n", + "\n", + " oversampler = RandomOverSampler(random_state=42)\n", + " X_resampled, y_resampled = oversampler.fit_resample(X, y)\n", + "\n", + " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "\n", + "train_df_oversampled = oversample(train_data)\n", + "val_df_oversampled = oversample(val_data)\n", + "test_df_oversampled = oversample(test_data)\n", + "\n", + "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проблемная область связана с анализом стрельбы в штатах.\n", + "Объектами наблюдения являются граждане которые были ранены, представленные в датасете. \n", + "Атрибутами является наши критерии в датасете.\n", + "Бизнес цель: понимание, анализ и статистика общих данных обо всех жертвах стрельбы в штатах.\n", + "Цель технического проекта:\n", + "- Входные данные: Атрибуты датасета, включая age, race.\n", + "- Целевой признак: Больше всего пострадавших по возрасту и национальности." + ] + }, + { + "cell_type": "code", + "execution_count": 325, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 4895 entries, 3 to 5924\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 name 4895 non-null object \n", + " 1 date 4895 non-null object \n", + " 2 manner_of_death 4895 non-null object \n", + " 3 armed 4895 non-null object \n", + " 4 age 4895 non-null float64\n", + " 5 gender 4895 non-null object \n", + " 6 race 4895 non-null object \n", + " 7 city 4895 non-null object \n", + " 8 state 4895 non-null object \n", + " 9 signs_of_mental_illness 4895 non-null bool \n", + " 10 threat_level 4895 non-null object \n", + " 11 flee 4895 non-null object \n", + " 12 body_camera 4895 non-null bool \n", + " 13 arms_category 4895 non-null object \n", + "dtypes: bool(2), float64(1), object(11)\n", + "memory usage: 506.7+ KB\n", + "(4895, 14)\n" + ] + }, + { + "data": { + "text/html": [ + "
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namedatemanner_of_deatharmedagegenderracecitystatesigns_of_mental_illnessthreat_levelfleebody_cameraarms_category
id
3Tim Elliot2015-01-02shotgun53.0MAsianSheltonWATrueattackNot fleeingFalseGuns
4Lewis Lee Lembke2015-01-02shotgun47.0MWhiteAlohaORFalseattackNot fleeingFalseGuns
5John Paul Quintero2015-01-03shot and Taseredunarmed23.0MHispanicWichitaKSFalseotherNot fleeingFalseUnarmed
8Matthew Hoffman2015-01-04shottoy weapon32.0MWhiteSan FranciscoCATrueattackNot fleeingFalseOther unusual objects
9Michael Rodriguez2015-01-04shotnail gun39.0MHispanicEvansCOFalseattackNot fleeingFalsePiercing objects
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" + ], + "text/plain": [ + " name date manner_of_death armed age gender \\\n", + "id \n", + "3 Tim Elliot 2015-01-02 shot gun 53.0 M \n", + "4 Lewis Lee Lembke 2015-01-02 shot gun 47.0 M \n", + "5 John Paul Quintero 2015-01-03 shot and Tasered unarmed 23.0 M \n", + "8 Matthew Hoffman 2015-01-04 shot toy weapon 32.0 M \n", + "9 Michael Rodriguez 2015-01-04 shot nail gun 39.0 M \n", + "\n", + " race city state signs_of_mental_illness threat_level \\\n", + "id \n", + "3 Asian Shelton WA True attack \n", + "4 White Aloha OR False attack \n", + "5 Hispanic Wichita KS False other \n", + "8 White San Francisco CA True attack \n", + "9 Hispanic Evans CO False attack \n", + "\n", + " flee body_camera arms_category \n", + "id \n", + "3 Not fleeing False Guns \n", + "4 Not fleeing False Guns \n", + "5 Not fleeing False Unarmed \n", + "8 Not fleeing False Other unusual objects \n", + "9 Not fleeing False Piercing objects " + ] + }, + "execution_count": 325, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"data/shootings.csv\", index_col=\"id\")\n", + "\n", + "df.info()\n", + "\n", + "print(df.shape)\n", + "\n", + "df.head()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Box с усами выглядит сбалансированным" + ] + }, + { + "cell_type": "code", + "execution_count": 326, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "# Box plot для столбца 'age'\n", + "plt.figure(figsize=(10, 6))\n", + "df.boxplot(column=\"age\")\n", + "plt.title(\"Box Plot для age\")\n", + "plt.xlabel(\"Age\")\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "При проверке на шум , здесь все данные в порядке" + ] + }, + { + "cell_type": "code", + "execution_count": 327, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "\n", + "plt.figure(figsize=(15, 6))\n", + "plt.scatter(df[\"age\"], df[\"race\"])\n", + "plt.xlabel(\"Возраст\")\n", + "plt.ylabel(\"Национальность\")\n", + "plt.title(\"Анализ возраста и национальности\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 328, + "metadata": {}, + "outputs": [], + "source": [ + "# Удаление строк с пропущенными значениями\n", + "df_dropna = df.dropna()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 329, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 2937\n", + "Размер контрольной выборки: 979\n", + "Размер тестовой выборки: 979\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "df = pd.read_csv(\"data/shootings.csv\", index_col=\"id\")\n", + "# Разделение на обучающую и тестовую выборки\n", + "train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)\n", + "\n", + "# Разделение обучающей выборки на обучающую и контрольную\n", + "train_df, val_df = train_test_split(train_df, test_size=0.25, random_state=42)\n", + "\n", + "\n", + "print(\"Размер обучающей выборки:\", len(train_df))\n", + "print(\"Размер контрольной выборки:\", len(val_df))\n", + "print(\"Размер тестовой выборки:\", len(test_df))" + ] + }, + { + "cell_type": "code", + "execution_count": 330, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение race в обучающей выборке после oversampling:\n", + "race\n", + "White 1481\n", + "Black 1481\n", + "Hispanic 1481\n", + "Native 1481\n", + "Asian 1481\n", + "Other 1481\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение race в контрольной выборке после oversampling:\n", + "race\n", + "White 503\n", + "Black 503\n", + "Other 503\n", + "Hispanic 503\n", + "Asian 503\n", + "Native 503\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение race в тестовой выборке после oversampling:\n", + "race\n", + "Hispanic 492\n", + "Black 492\n", + "Native 492\n", + "White 492\n", + "Asian 492\n", + "Other 492\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "\n", + "def check_balance(df, name):\n", + " counts = df[\"race\"].value_counts()\n", + " print(f\"Распределение race в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "\n", + "def oversample(df):\n", + " X = df.drop(\"race\", axis=1)\n", + " y = df[\"race\"]\n", + "\n", + " oversampler = RandomOverSampler(random_state=42)\n", + " X_resampled, y_resampled = oversampler.fit_resample(X, y)\n", + "\n", + " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "\n", + "train_df_oversampled = oversample(train_df)\n", + "val_df_oversampled = oversample(val_df)\n", + "test_df_oversampled = oversample(test_df)\n", + "\n", + "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Проблемная область связана с анализом выпуска игр стим.\n", + "Объектами наблюдения являются выпускаемые игры, представленные в датасете. \n", + "Атрибутами является наши критерии в датасете.\n", + "Бизнес цель: понимание, анализ и статистика игр.\n", + "Цель технического проекта:\n", + "- Входные данные: Атрибуты датасета.\n", + "- Целевой признак: Популярные игры выпускаемые в стим." + ] + }, + { + "cell_type": "code", + "execution_count": 331, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Index: 168 entries, Aatrox to Zyra\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Nick Name 168 non-null object\n", + " 1 Classes 168 non-null object\n", + " 2 Release Date 168 non-null object\n", + " 3 Last Changed 168 non-null object\n", + " 4 Blue Essence 168 non-null int64 \n", + " 5 RP 168 non-null int64 \n", + " 6 Difficulty 168 non-null object\n", + " 7 Role 168 non-null object\n", + " 8 Range type 168 non-null object\n", + " 9 Resourse type 168 non-null object\n", + " 10 Base HP 168 non-null int64 \n", + " 11 Base mana 168 non-null int64 \n", + "dtypes: int64(4), object(8)\n", + "memory usage: 17.1+ KB\n", + "(168, 12)\n" + ] + }, + { + "data": { + "text/html": [ + "
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Nick NameClassesRelease DateLast ChangedBlue EssenceRPDifficultyRoleRange typeResourse typeBase HPBase mana
Name
AatroxThe darkin bladeJuggernaut2013-06-13V14.144800880AdvancedTopMeleeBlood Well6500
AhriThe nine-tailed foxBurst2011-12-14V14.183150790IntermediateMiddleRangedMana590418
AkaliThe rogue assassinAssassin2010-05-11V14.183150790ExpertTop,MiddleMeleeEnergy600200
AkshanThe rogue sentinelMarksman Assassin2021-07-22V14.144800880Intermediate_PlusMiddleRangedMana630350
AlistarThe minotaurVanguard2009-02-21V14.161350585NoviceSupportMeleeMana685350
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" + ], + "text/plain": [ + " Nick Name Classes Release Date Last Changed \\\n", + "Name \n", + "Aatrox The darkin blade Juggernaut 2013-06-13 V14.14 \n", + "Ahri The nine-tailed fox Burst 2011-12-14 V14.18 \n", + "Akali The rogue assassin Assassin 2010-05-11 V14.18 \n", + "Akshan The rogue sentinel Marksman Assassin 2021-07-22 V14.14 \n", + "Alistar The minotaur Vanguard 2009-02-21 V14.16 \n", + "\n", + " Blue Essence RP Difficulty Role Range type \\\n", + "Name \n", + "Aatrox 4800 880 Advanced Top Melee \n", + "Ahri 3150 790 Intermediate Middle Ranged \n", + "Akali 3150 790 Expert Top,Middle Melee \n", + "Akshan 4800 880 Intermediate_Plus Middle Ranged \n", + "Alistar 1350 585 Novice Support Melee \n", + "\n", + " Resourse type Base HP Base mana \n", + "Name \n", + "Aatrox Blood Well 650 0 \n", + "Ahri Mana 590 418 \n", + "Akali Energy 600 200 \n", + "Akshan Mana 630 350 \n", + "Alistar Mana 685 350 " + ] + }, + "execution_count": 331, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pandas as pd\n", + "\n", + "df = pd.read_csv(\"data/league.csv\", index_col=\"Name\")\n", + "\n", + "df.info()\n", + "\n", + "print(df.shape)\n", + "\n", + "df.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Сильных критических выбросов данных не наблюдается и лишних данных не присутствует" + ] + }, + { + "cell_type": "code", + "execution_count": 332, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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J4OzZsxaf7e3tqVatGoZhkJGRQWZmZo7vvlSpUpQpU8Z8vgX9vgsy1q08+uijeHl5sWTJEpYsWULDhg0tttdfuXKFtLQ0iz4VK1bEzc2tQOPnpmfPnjz00EO6D1hE7ntaGRYRkX+1l156iStXrtCpUyeqVKnCtWvX2LJlC0uWLCEgIIB+/foBN95B+8YbbzB+/HiaNWvGE088gYODAzt27KBMmTJMmjSJxo0bU7x4cfr27cvgwYMxmUzMnz8/12S7Xr16LFmyhFdffZUGDRrg6upKREQEFStWxNPTk48++gg3NzdcXFx4+OGHKV++PHPmzKFNmzZUr16dfv36UbZsWf788082bNiAu7s733zzzb2+fLzzzju0adOGRx55hKefftr8aiUPDw/Gjh1rcb5w47VS3bp1o1ixYkREROR6P3GxYsWYPHky/fr1IyQkhO7du5tfrRQQEMArr7xyW7FWrFiRzz77jK5du1K1alX69OlDcHCw+Tv/4osvbvme4vbt2zNu3Dj69etH48aN2bt3LwsXLsxxb+5jjz2Gj48PTZo0oXTp0iQkJPDBBx/Qrl073NzcuHDhAg899BBPPvkktWrVwtXVle+//54dO3YwdepUAGxsbAr0fV+6dCnfsW6lWLFiPPHEEyxevJjLly/z7rvvWtT/9ttvtGrVii5dulCtWjXs7Oz46quv+Ouvv257VbdYsWK8/PLLvPbaa6xZs4bw8PDbGkdE5K4rwidZi4iI3HWrV682+vfvb1SpUsVwdXU17O3tjcDAQOOll14y/vrrrxztP/nkE6NOnTqGg4ODUbx4cSMkJMRYt26duX7z5s1Go0aNDCcnJ6NMmTLmVzUBxoYNG8ztUlNTjR49ehienp4GYPGapa+//tqoVq2aYWdnl+NVRr/88ovxxBNPGCVKlDAcHBwMf39/o0uXLkZsbKy5TfYrbU6fPl2ga5D9aqUvvvjilu1ye7WSYRjG999/bzRp0sRwcnIy3N3djYiICOPAgQM5+o8fP94oW7asYWNjU6DXLC1ZssR8rb28vIyePXsaf/zxh0Wbgr5a6Wa//fab8eyzzxoBAQGGvb294ebmZjRp0sR4//33LV5/ldurlYYOHWr4+voaTk5ORpMmTYytW7caISEhRkhIiLndxx9/bDRv3tz8HVWsWNF47bXXjJSUFMMwDCM9Pd147bXXjFq1ahlubm6Gi4uLUatWLWPGjBk5Ys3v+y7MWHlZt26dARgmk8k4ceKERd2ZM2eMgQMHGlWqVDFcXFwMDw8P4+GHHzY+//zzfMe91Z/DlJQUw8PDw+K6+fv7G+3atStw3CIid5vJMHL5dbaIiIiIiIjIv5juGRYRERERERGro2RYRERERERErI6SYREREREREbE6SoZFRERERETE6igZFhEREREREaujZFhERERERESsjl1RByBSUFlZWZw8eRI3NzdMJlNRhyMiIiIiIkXEMAwuXbpEmTJlsLG5vTVeJcPywDh58iR+fn5FHYaIiIiIiNwnTpw4wUMPPXRbfZUMywPDzc0NuPEH3t3dvYijERERERGRonLx4kX8/PzMOcLtUDIsD4zsrdHu7u5KhkVERERE5B/dPqkHaImIiIiIiIjVUTIsIiIiIiIiVkfJsIiIiIiIiFgdJcMiIiIiIiJidZQMi4iIiIiIiNVRMiwiIiIiIiJWR8mwiIiIiIiIWB0lwyIiIiIiImJ1lAyLiIiIiIiI1VEyLCIiIiIiIlZHybCIiIiIiIhYHSXDIiIiIiIiYnWUDIuIiIiIiIjVUTIsIiIiIiIiVkfJsIiIiIiIiFgdJcMiIiIiIiJidZQMi4iIiIiIiNVRMiwiIiIiIiJWR8mwiIiIiIiIWB0lwyIiIiIiImJ1lAyLiIiIiIiI1VEyLCIiIiIiIlZHybCIiIiIiIhYHSXDIiIiIiIiYnWUDIuIiIiIiIjVUTIsIiIiIiIiVkfJsIiIiIiIiFgdJcM3OXr0KCaTifj4+Hs+t8lkYvny5XnWBwQEMG3atCKZ+3bbioiIiFibzCyDrUfO8nX8n2w9cpbMLKOoQxKRPNjdzcG3bt1K06ZNCQ8PZ9WqVXdzqjvCz8+PpKQkSpYseUfHPX36NKNHj2bVqlX89ddfFC9enFq1ajF69GiaNGlSoDF27NiBi4vLHY0rW1JSEsWLF78rY4uIiIhYizX7koj65gBJKWnmMl8PR8ZEVCM82LcIIxOR3NzVleG5c+fy0ksv8eOPP3Ly5Mm7OdUdYWtri4+PD3Z2d/Z3BJ07d+aXX35h3rx5/Pbbb6xYsYIWLVpw9uzZAo/h7e2Ns7NznvUZGRm3HZ+Pjw8ODg633f+funbtWpHNLSIiInInrNmXxIAFuywSYYBTKWkMWLCLNfuSiigyEcnLXUuGU1NTWbJkCQMGDKBdu3bExMRY1J8/f56ePXvi7e2Nk5MTQUFBREdHAzeSo0GDBuHr64ujoyP+/v5MmjTJ3Pc///kPNWrUwMXFBT8/P1588UVSU1PN9ceOHSMiIoLixYvj4uJC9erV+fbbb/Od9+/bpOPi4jCZTMTGxlK/fn2cnZ1p3LgxBw8eLPB1uHDhAhs3bmTy5MmEhobi7+9Pw4YNGTlyJI8//nie/caMGYOvry979uwBcm6TNplMzJw5k8cffxwXFxcmTpzI2LFjqV27Np988gnlypXD1dWVF198kczMTKZMmYKPjw+lSpVi4sSJFnPdvPU5v2sPcObMGTp16oSzszNBQUGsWLHCon7fvn20adMGV1dXSpcuTe/evTlz5oy5vkWLFgwaNIghQ4ZQsmRJwsLCCnw9RURERO43mVkGUd8cILcN0dllUd8c0JZpkfvMXUuGP//8c6pUqULlypXp1asXn3zyCYbxv78ARo0axYEDB1i9ejUJCQnMnDnTvD35vffeY8WKFXz++eccPHiQhQsXEhAQ8L+gbWx477332L9/P/PmzWP9+vUMHz7cXD9w4EDS09P58ccf2bt3L5MnT8bV1TXfefPyxhtvMHXqVH7++Wfs7Ozo379/ga+Dq6srrq6uLF++nPT09HzbG4bBSy+9xKeffsrGjRupWbNmnm3Hjh1Lp06d2Lt3rzmmI0eOsHr1atasWcOiRYuYO3cu7dq1448//uCHH35g8uTJvPnmm2zbti3XMfO79gBRUVF06dKFPXv20LZtW3r27Mm5c+eAG8l/y5YtqVOnDj///DNr1qzhr7/+okuXLhZjzJs3D3t7ezZv3sxHH32Uayzp6elcvHjR4hARERG532xPPJdjRfhmBpCUksb2xHP3LigRydddu2d47ty59OrVC4Dw8HBSUlL44YcfaNGiBQDHjx+nTp061K9fH8Ai4Tp+/DhBQUE0bdoUk8mEv7+/xdhDhgwx/xwQEMCECRN44YUXmDFjhrl/586dqVGjBgAVKlSwGDuvefMyceJEQkJCABgxYgTt2rUjLS0NR0fHfPva2dkRExPDs88+y0cffUTdunUJCQmhW7duORLd69ev06tXL3755Rc2bdpE2bJlbzl2jx496Nevn0VZVlYWn3zyCW5ublSrVo3Q0FAOHjzIt99+i42NDZUrV2by5Mls2LCBhx9+OMeY+V17gMjISLp37w7AW2+9xXvvvcf27dsJDw/ngw8+oE6dOrz11lvm9p988gl+fn789ttvVKpUCYCgoCCmTJlyy/ObNGkSUVFRt2wjIiIiUtSSL+WdCN9OOxG5N+7KyvDBgwfZvn27OWGys7Oja9euzJ0719xmwIABLF68mNq1azN8+HC2bNlirouMjCQ+Pp7KlSszePBg1q5dazH+999/T6tWrShbtixubm707t2bs2fPcuXKFQAGDx7MhAkTaNKkCWPGjDFvNc5v3rzcnLT6+t54+EFycnKBr0fnzp05efIkK1asIDw8nLi4OOrWrZtj6/grr7zCtm3b+PHHH/NNhAFzQn+zgIAA3NzczJ9Lly5NtWrVsLGxsSjLK/78rj1YXg8XFxfc3d3N4+3evZsNGzaYV8RdXV2pUqUKcGPVOlu9evXyPb+RI0eSkpJiPk6cOJFvHxEREZF7rZRb/gskhWknIvfGXUmG586dy/Xr1ylTpgx2dnbY2dkxc+ZMli5dSkpKCgBt2rTh2LFjvPLKK5w8eZJWrVoxbNgwAOrWrUtiYiLjx4/n6tWrdOnShSeffBK4cV9v+/btqVmzJkuXLmXnzp18+OGHwP8exPTMM8/w+++/07t3b/bu3Uv9+vV5//338503L8WKFTP/bDKZgBsrsIXh6OhI69atGTVqFFu2bCEyMpIxY8ZYtGndujV//vkn3333XYHGzO3p0jfHmh1vbmV5xX+ra3+rObLHS01NJSIigvj4eIvj0KFDNG/e/Jax/52DgwPu7u4Wh4iIiMj9pmF5L3w9HDHlUW/ixlOlG5b3updhiUg+7ngyfP36dT799FOmTp1qkQzt3r2bMmXKsGjRInNbb29v+vbty4IFC5g2bRqzZs0y17m7u9O1a1dmz57NkiVLWLp0KefOnWPnzp1kZWUxdepUGjVqRKVKlXJ9UrWfnx8vvPACy5YtY+jQocyePbtA894r1apV4/LlyxZljz/+OJ999hnPPPMMixcvvucxZcvr2hdE3bp12b9/PwEBAQQGBlocd+vVUCIiIiJFydbGxJiIagA5EuLsz2MiqmFrk1e6LCJF4Y7fM7xy5UrOnz/P008/jYeHh0Vd586dmTt3Li+88AKjR4+mXr16VK9enfT0dFauXEnVqlWBG0+L9vX1pU6dOtjY2PDFF1/g4+ODp6cngYGBZGRk8P777xMREZHrA5iGDBlCmzZtqFSpEufPn2fDhg3msW81791w9uxZnnrqKfr370/NmjVxc3Pj559/ZsqUKXTo0CFH+06dOjF//nx69+6NnZ1djlXZu+1W174gBg4cyOzZs+nevTvDhw/Hy8uLw4cPs3jxYubMmYOtre3dPQERERGRIhAe7MvMXnVzvGfYR+8ZFrlv3fFkeO7cuTz66KM5EmG4kQxPmTKFPXv2YG9vz8iRIzl69ChOTk40a9bMvBrq5ubGlClTOHToELa2tjRo0MD8AKhatWrxn//8h8mTJzNy5EiaN2/OpEmT6NOnj3mezMxMBg4cyB9//IG7uzvh4eH897//BbjlvHeDq6srDz/8MP/97385cuQIGRkZ+Pn58eyzz/J///d/ufZ58sknycrKonfv3tjY2PDEE0/ctfj+7lbXviDKlCnD5s2bef3113nsscdIT0/H39+f8PDwAo8hIiIi8iAKD/aldTUftieeI/lSGqXcbmyN1oqwyP3JZNz8viOR+9jFixfx8PAgJSVF9w+LiIiIiFixO5EbaKlORERERERErI6SYREREREREbE6SoZFRERERETE6igZFhEREREREaujZFhERERERESsjpJhERERERERsTpKhkVERERERMTqKBkWERERERERq6NkWERERERERKyOkmERERERERGxOkqGRURERERExOooGRYRERERERGro2RYRERERERErI6SYREREREREbE6SoZFRERERETE6vwrk+GYmBg8PT2LOowHRosWLRgyZIj5c0BAANOmTTN/NplMLF++/J7HJSIiIiLyd5lZBluPnOXr+D/ZeuQsmVlGUYckD6h7mgxHRkZiMpl44YUXctQNHDgQk8lEZGTkvQzpgXX16lW8vLwoWbIk6enpBeoTFxeHyWTiwoULFuXLli1j/PjxdyFKEREREZE7Z82+JJpOXk/32T/x8uJ4us/+iaaT17NmX1JRhyYPoHu+Muzn58fixYu5evWquSwtLY3PPvuMcuXK/ePxMzIy/vEYD4KlS5dSvXp1qlSpUqBV21tdFy8vL9zc3O5gdJauXbt218YWEREREeuwZl8SAxbsIiklzaL8VEoaAxbsUkIshXbPk+G6devi5+fHsmXLzGXLli2jXLly1KlTx6LtmjVraNq0KZ6enpQoUYL27dtz5MgRc/3Ro0cxmUwsWbKEkJAQHB0dWbhwYY45T58+Tf369enUqRPp6emcP3+enj174u3tjZOTE0FBQURHR1uM+fnnn9OsWTOcnJxo0KABv/32Gzt27KB+/fq4urrSpk0bTp8+bZ5jx44dtG7dmpIlS+Lh4UFISAi7du2yiMNkMjFnzhw6deqEs7MzQUFBrFix4rau49y5c+nVqxe9evVi7ty5OepNJhMzZ87k8ccfx8XFhWeffZbQ0FAAihcvbrEK//dt0vk5ceIEXbp0wdPTEy8vLzp06MDRo0fN9ZGRkXTs2JGJEydSpkwZKleuzLhx4wgODs4xVu3atRk1alShzl1ERERErEtmlkHUNwfIbUN0dlnUNwe0ZVoKpUjuGe7fv785+QT45JNP6NevX452ly9f5tVXX+Xnn38mNjYWGxsbOnXqRFZWlkW7ESNG8PLLL5OQkEBYWJhF3YkTJ2jWrBnBwcF8+eWXODg4MGrUKA4cOMDq1atJSEhg5syZlCxZ0qLfmDFjePPNN9m1axd2dnb06NGD4cOHM336dDZu3Mjhw4cZPXq0uf2lS5fo27cvmzZt4qeffiIoKIi2bdty6dIli3GjoqLo0qULe/bsoW3btvTs2ZNz584V6vodOXKErVu30qVLF7p06cLGjRs5duxYjnZjx46lU6dO7N27l6ioKJYuXQrAwYMHSUpKYvr06YWaF26sMIeFheHm5sbGjRvZvHkzrq6uhIeHW6wAx8bGcvDgQdatW8fKlSvp378/CQkJ7Nixw9zml19+Yc+ePbl+9wDp6elcvHjR4hARERER67M98VyOFeGbGUBSShrbEwv372qxbnZFMWmvXr0YOXKkOYHbvHkzixcvJi4uzqJd586dLT5/8skneHt7c+DAAYtVxiFDhvDEE0/kmOfgwYO0bt2aTp06MW3aNEwmEwDHjx+nTp061K9fH7jxwKi/GzZsmDmxfvnll+nevTuxsbE0adIEgKeffpqYmBhz+5YtW1r0nzVrFp6envzwww+0b9/eXB4ZGUn37t0BeOutt3jvvffYvn074eHheV6vv/vkk09o06YNxYsXByAsLIzo6GjGjh1r0a5Hjx4WiWZiYiIApUqVuu0HjC1ZsoSsrCzmzJljvp7R0dF4enoSFxfHY489BoCLiwtz5szB3t7e3Dc7zgYNGpj7hYSEUKFChVznmjRpElFRUbcVp4iIiIj8eyRfyjsRvp12IlBEK8Pe3t60a9eOmJgYoqOjadeuXY6VWYBDhw7RvXt3KlSogLu7uzlpPX78uEW77KT2ZlevXqVZs2Y88cQTTJ8+3Zy4AQwYMIDFixdTu3Zthg8fzpYtW3L0r1mzpvnn0qVLA1CjRg2LsuTkZPPnv/76i2effZagoCA8PDxwd3cnNTU1R6w3j+vi4oK7u7vFOPnJzMxk3rx59OrVy1zWq1cvYmJicqyY53Zd/qndu3dz+PBh3NzccHV1xdXVFS8vL9LS0iy2sNeoUcMiEQZ49tlnWbRoEWlpaVy7do3PPvuM/v375znXyJEjSUlJMR8nTpy44+cjIiIiIve/Um6Od7SdCBTRyjDc2Co9aNAgAD788MNc20RERODv78/s2bMpU6YMWVlZBAcH53ggk4uLS46+Dg4OPProo6xcuZLXXnuNsmXLmuvatGnDsWPH+Pbbb1m3bh2tWrVi4MCBvPvuu+Y2xYoVM/+cnUj/vezm5LNv376cPXuW6dOn4+/vj4ODA4888kiOWG8eI7dx8vPdd9/x559/0rVrV4vyzMxMYmNjad26tbkst+vyT6WmplKvXr1c78329va+5dwRERE4ODjw1VdfYW9vT0ZGBk8++WSeczk4OODg4HBnAhcRERGRB1bD8l74ejhyKiUt1/uGTYCPhyMNy3vd69DkAVZk7xnOvsc0+x7Uvzt79iwHDx7kzTffpFWrVlStWpXz588XeHwbGxvmz59PvXr1CA0N5eTJkxb13t7e9O3blwULFjBt2jRmzZr1j85n8+bNDB48mLZt21K9enUcHBw4c+bMPxozN3PnzqVbt27Ex8dbHN26dcv1QVo3y16pzczMvO3569aty6FDhyhVqhSBgYEWh4eHxy372tnZ0bdvX6Kjo4mOjqZbt244OTnddiwiIiIiYh1sbUyMiagG3Eh8b5b9eUxENWxt/l4rkrciS4ZtbW1JSEjgwIED2Nra5qgvXrw4JUqUYNasWRw+fJj169fz6quvFnqOhQsXUqtWLVq2bMmpU6cAGD16NF9//TWHDx9m//79rFy5kqpVq/6j8wkKCmL+/PkkJCSwbds2evbseccTvdOnT/PNN9/Qt29fgoODLY4+ffqwfPnyWz6My9/fH5PJxMqVKzl9+jSpqamFjqFnz56ULFmSDh06sHHjRhITE4mLi2Pw4MH88ccf+fZ/5plnWL9+PWvWrLnlFmkRERERkZuFB/sys1ddfDwst0L7eDgys1ddwoN9iygyeVAVWTIM4O7ujru7e651NjY2LF68mJ07dxIcHMwrr7zCO++8U+g57OzsWLRoEdWrV6dly5YkJydjb2/PyJEjqVmzJs2bN8fW1pbFixf/o3OZO3cu58+fp27duvTu3ZvBgwdTqlSpfzTm33366ae4uLjQqlWrHHWtWrXCycmJBQsW5Nm/bNmyREVFMWLECEqXLm3epl4Yzs7O/Pjjj5QrV44nnniCqlWr8vTTT5OWlpbnd3mzoKAgGjduTJUqVXj44YcLPb+IiIiIWK/wYF82vd6SRc82Ynq32ix6thGbXm+pRFhui8kwDL2MS+4ZwzAICgrixRdfLPRK/8WLF/Hw8CAlJaVAibeIiIiIiPw73YncoMgeoCXW5/Tp0yxevJhTp07l+W5hERERERGRe0HJsNwzpUqVomTJksyaNcv8jmQREREREZGioGRY7hntyBcRERERkftFkT5AS0RERERERKQoKBkWERERERERq6NkWERERERERKyOkmERERERERGxOkqGRURERERExOooGRYRERERERGro2RYRERERERErI6SYREREREREbE6SoZFRERERETE6igZFhEREREREaujZFhERERERESsjpJhERERERERsTr/6mQ4MjKSjh07FnUY/0omk4nly5cXdRgiInIPZWYZbD1ylq/j/2TrkbNkZhlFHZKIiMhtK7JkODIyEpPJlOMIDw8vqpBu29ixY6ldu/Y9m69bt245rtOaNWswmUyMHTs2R2zlypW7Z7GJiMi/05p9STSdvJ7us3/i5cXxdJ/9E00nr2fNvqSiDk1EROS2FOnKcHh4OElJSRbHokWLijKkInXt2rUCtQsNDWXz5s1cv37dXLZhwwb8/PyIi4uzaLthwwZCQ0PvajwiIvLvtmZfEgMW7CIpJc2i/FRKGgMW7FJCLCIiD6QiTYYdHBzw8fGxOIoXL26uN5lMzJkzh06dOuHs7ExQUBArVqywGGP//v20b98ed3d33NzcaNasGUeOHLFo8+677+Lr60uJEiUYOHAgGRkZ5rr58+dTv3593Nzc8PHxoUePHiQnJ5vr4+LiMJlMxMbGUr9+fZydnWncuDEHDx4EICYmhqioKHbv3m1e3Y6JiQHgwoULPPPMM3h7e+Pu7k7Lli3ZvXu3eezsFeU5c+ZQvnx5HB0dC3TdQkNDSU1N5eeff7aIc8SIEWzbto20tBv/WElLS2Pbtm3mZPj111+nUqVKODs7U6FCBUaNGmVxLfKK59ChQzRv3hxHR0eqVavGunXrcsR04sQJunTpgqenJ15eXnTo0IGjR4+a67O3rN/quxARkftPZpZB1DcHyG1DdHZZ1DcHtGVaREQeOPf9PcNRUVF06dKFPXv20LZtW3r27Mm5c+cA+PPPP2nevDkODg6sX7+enTt30r9//xwrpkeOHGHDhg3MmzePmJgYc7IKkJGRwfjx49m9ezfLly/n6NGjREZG5ojjjTfeYOrUqfz888/Y2dnRv39/ALp27crQoUOpXr26eXW7a9euADz11FMkJyezevVqdu7cSd26dWnVqpU5foDDhw+zdOlSli1bRnx8fIGuSaVKlShTpgwbNmwA4NKlS+zatYunnnqKgIAAtm7dCsCWLVtIT083J8Nubm7ExMRw4MABpk+fzuzZs/nvf/9rMfbf48nKyuKJJ57A3t6ebdu28dFHH/H6669b9MnIyCAsLAw3Nzc2btzI5s2bcXV1JTw83GJ1Ob/v4u/S09O5ePGixSEiIvfW9sRzOVaEb2YASSlpbE88l2cbERGR+5JRRPr27WvY2toaLi4uFsfEiRPNbQDjzTffNH9OTU01AGP16tWGYRjGyJEjjfLlyxvXrl3Lcw5/f3/j+vXr5rKnnnrK6Nq1a55x7dixwwCMS5cuGYZhGBs2bDAA4/vvvze3WbVqlQEYV69eNQzDMMaMGWPUqlXLYpyNGzca7u7uRlpamkV5xYoVjY8//tjcr1ixYkZycnKe8eSlZ8+exmOPPWaOp1q1aoZhGMZzzz1njB492jAMwxg1apRRvnz5PMd45513jHr16pk/5xbPd999Z9jZ2Rl//vmnuWz16tUGYHz11VeGYRjG/PnzjcqVKxtZWVnmNunp6YaTk5Px3XffGYZxe9/FmDFjDG78O8viSElJyff6iIjInbH8lz8M/9dX5nss/+WPog5VRESsSEpKyj/ODeyKJgW/ITQ0lJkzZ1qUeXl5WXyuWbOm+WcXFxfc3d3N25jj4+Np1qwZxYoVy3OO6tWrY2tra/7s6+vL3r17zZ937tzJ2LFj2b17N+fPnycrKwuA48ePU61atVzj8PX1BSA5OTnPh1Pt3r2b1NRUSpQoYVF+9epVi23c/v7+eHt75xl/Xlq0aMGQIUPIyMggLi6OFi1aABASEsLHH38M3Ng6ffP9wkuWLOG9997jyJEjpKamcv36ddzd3S3G/Xs8CQkJ+Pn5UaZMGXPZI488kuNcDx8+jJubm0V5Wlqaxbnm91383ciRI3n11VfNny9evIifn1+e7UVE5M4r5VawW3gK2k5EROR+UaTJsIuLC4GBgbds8/dE12QymRNWJyenfOe4Vf/Lly8TFhZGWFgYCxcuxNvbm+PHjxMWFpbj4VE3j2MymQDM4+QmNTUVX1/fHA+0AvD09DT/7OLiku855CY0NJTLly+zY8cONmzYwGuvvQbcSIb79+/PuXPn2LZtG88//zwAW7dupWfPnkRFRREWFoaHhweLFy9m6tSpFuPeTjypqanUq1ePhQsX5qi7ObG+1XeRGwcHBxwcHAodj4iI3DkNy3vh6+HIqZS0XO8bNgE+Ho40LO+VS62IiMj9q0iT4X+qZs2azJs3j4yMjFuuDufl119/5ezZs7z99tvmFcebH0pVUPb29mRmZlqU1a1bl1OnTmFnZ0dAQEChx8xPxYoV8fPzY8WKFcTHxxMSEgJA2bJlKVu2LFOnTuXatWvmleEtW7bg7+/PG2+8YR7j2LFj+c5TtWpVTpw4QVJSknlF/KeffrJoU7duXZYsWUKpUqVyrDSLiMiDzdbGxJiIagxYsAsTWCTEpv//v2MiqmFrY8qlt4iIyP2rSB+glZ6ezqlTpyyOM2fOFLj/oEGDuHjxIt26dePnn3/m0KFDzJ8/3/yk5/yUK1cOe3t73n//fX7//XdWrFjB+PHjC30eAQEBJCYmEh8fz5kzZ0hPT+fRRx/lkUceoWPHjqxdu5ajR4+yZcsW3njjjdtKuHMTGhrKjBkzCAwMpHTp0ubykJAQ3n//ffODtgCCgoI4fvw4ixcv5siRI7z33nt89dVX+c7x6KOPUqlSJfr27cvu3bvZuHGjRUIN0LNnT0qWLEmHDh3YuHEjiYmJxMXFMXjwYP744487cq4iIlJ0woN9mdmrLj4elluhfTwcmdmrLuHBvkUUmYiIyO0r0mR4zZo1+Pr6WhxNmzYtcP8SJUqwfv16UlNTCQkJoV69esyePbvAq8Te3t7ExMTwxRdfUK1aNd5++23efffdQp9H586dCQ8PJzQ0FG9vbxYtWoTJZOLbb7+lefPm9OvXj0qVKtGtWzeOHTtmkbj+E6GhoVy6dMl8v3C2kJAQLl26ZHG/8OOPP84rr7zCoEGDqF27Nlu2bGHUqFH5zmFjY8NXX33F1atXadiwIc888wwTJ060aOPs7MyPP/5IuXLleOKJJ6hatSpPP/00aWlpWikWEfmXCA/2ZdPrLVn0bCOmd6vNomcbsen1lkqERUTkgWUyDEMvBpQHwsWLF/Hw8CAlJUVJtoiIiIiIFbsTucF9/55hERERERERkTtNybCIiIiIiIhYHSXDIiIiIiIiYnWUDIuIiIiIiIjVUTIsIiIiIiIiVkfJsIiIiIiIiFgdJcMiIiIiIiJidZQMi4iIiIiIiNVRMiwiIiIiIiJWR8mwiIiIiIiIWB0lwyIiIiIiImJ1lAyLiIiIiIiI1VEyLCIiIiIiIlZHybCIiIiIiIhYHbuiDkBERERE5N8iM8tge+I5ki+lUcrNkYblvbC1MRV1WCKSC60M3yfGjh1L7dq186yPiYnB09OzSOa+3bYiIiIi1mTNviSaTl5P99k/8fLieLrP/ommk9ezZl9SUYcmIrl44JLhyMhITCaT+ShRogTh4eHs2bPnrs/9TxLBr776ikaNGuHh4YGbmxvVq1dnyJAhBe7ftWtXfvvtt9uaOz/Dhg0jNjb2rowtIiIiYg3W7EtiwIJdJKWkWZSfSkljwIJdSohF7kMPXDIMEB4eTlJSEklJScTGxmJnZ0f79u3/0ZiZmZlkZWXdoQgtxcbG0rVrVzp37sz27dvZuXMnEydOJCMjo8BjODk5UapUqTzrr127dtvxubq6UqJEidvu/08ZhsH169eLbH4RERGRfyIzyyDqmwMYudRll0V9c4DMrNxaiEhReSCTYQcHB3x8fPDx8aF27dqMGDGCEydOcPr0aQDi4uIwmUxcuHDB3Cc+Ph6TycTRo0eB/207XrFiBdWqVcPBwYHjx48TFxdHw4YNcXFxwdPTkyZNmnDs2DFiYmKIiopi9+7d5lXpmJiYAsX7zTff0KRJE1577TUqV65MpUqV6NixIx9++GGefY4cOUKFChUYNGgQhmHk2CadvUo9Z84cypcvj6OjIwAmk4mPP/6Y9u3b4+zsTNWqVdm6dSuHDx+mRYsWuLi40LhxY44cOZJjrGx5XYObzZ8/n4CAADw8POjWrRuXLl0y12VlZTFp0iTKly+Pk5MTtWrV4ssvv7QY32QysXr1aurVq4eDgwObNm3KcQ3S09O5ePGixSEiIiJyv9meeC7HivDNDCApJY3tiefuXVAikq8HMhm+WWpqKgsWLCAwMLDQq5tXrlxh8uTJzJkzh/379+Pl5UXHjh0JCQlhz549bN26leeeew6TyUTXrl0ZOnQo1atXN69Kd+3atUDz+Pj4sH//fvbt21eg9nv27KFp06b06NGDDz74AJMp94cuHD58mKVLl7Js2TLi4+PN5ePHj6dPnz7Ex8dTpUoVevTowfPPP8/IkSP5+eefMQyDQYMG5Trm9evX87wG2Y4cOcLy5ctZuXIlK1eu5IcffuDtt98210+aNIlPP/2Ujz76iP379/PKK6/Qq1cvfvjhB4u5RowYwdtvv01CQgI1a9bMEcukSZPw8PAwH35+fgW6fiIiIiL3UvKlvBPh22knIvfGA/k06ZUrV+Lq6grA5cuX8fX1ZeXKldjYFC63z8jIYMaMGdSqVQuAc+fOkZKSQvv27alYsSIAVatWNbd3dXXFzs4OHx+fQs3z0ksvsXHjRmrUqIG/vz+NGjXiscceo2fPnjg4OFi03bJlC+3bt+eNN95g6NChtxz32rVrfPrpp3h7e1uU9+vXjy5dugDw+uuv88gjjzBq1CjCwsIAePnll+nXr1+uY168ePGW1wBurPzGxMTg5uYGQO/evYmNjWXixImkp6fz1ltv8f333/PII48AUKFCBTZt2sTHH39MSEiIeZxx48bRunXrPM9v5MiRvPrqqxaxKSEWERGR+00pN8c72k5E7o0HcmU4NDSU+Ph44uPj2b59O2FhYbRp0ybHVt782NvbW6xIenl5ERkZSVhYGBEREUyfPp2kpH/+sAMXFxdWrVrF4cOHefPNN3F1dWXo0KE0bNiQK1eumNsdP36c1q1bM3r06HwTYQB/f/8ciTBgcU6lS5cGoEaNGhZlaWlpuW47Lsg1CAgIMCfCAL6+viQnJwM3VquvXLlC69atcXV1NR+ffvqpxdZsgPr169/y/BwcHHB3d7c4RERERO43Dct74evhSF4vUDIBvh43XrMkIvePBzIZdnFxITAwkMDAQBo0aMCcOXO4fPkys2fPBjCvEBvG/x5SkNvDqpycnHJsQY6Ojmbr1q00btyYJUuWUKlSJX766ac7EnfFihV55plnmDNnDrt27eLAgQMsWbLEXO/t7U3Dhg1ZtGhRge6PdXFxybW8WLFi5p+zzy+3srweGJbfNbh5rOzxssdKTU0FYNWqVeZfWMTHx3PgwAGL+4ZvFb+IiIjIg8TWxsSYiGoAORLi7M9jIqrpfcMi95kHMhn+O5PJhI2NDVevXgUwr5bevKJ58z21+alTpw4jR45ky5YtBAcH89lnnwE3VpIzMzPvSMwBAQE4Oztz+fJlc5mTkxMrV67E0dGRsLAwi4dS3Wt5XYP83PwwsuxfWGQf2uIsIiIi/1bhwb7M7FUXHw/LrdA+Ho7M7FWX8GDfIopMRPLyQN4znJ6ezqlTpwA4f/48H3zwAampqURERACYE6+xY8cyceJEfvvtN6ZOnZrvuImJicyaNYvHH3+cMmXKcPDgQQ4dOkSfPn2AGwlsYmIi8fHxPPTQQ7i5ueW45zc3Y8eO5cqVK7Rt2xZ/f38uXLjAe++9R0ZGRo57ZrO3VLdp04Y2bdqwZs0a8/3R90J+1yA/bm5uDBs2jFdeeYWsrCyaNm1KSkoKmzdvxt3dnb59+97lMxAREREpGuHBvrSu5sP2xHMkX0qjlNuNrdFaERa5Pz2QyfCaNWvw9b3x2zU3NzeqVKnCF198QYsWLYAb23gXLVrEgAEDqFmzJg0aNGDChAk89dRTtxzX2dmZX3/9lXnz5nH27Fl8fX0ZOHAgzz//PACdO3dm2bJlhIaGcuHCBaKjo4mMjMw33pCQED788EP69OnDX3/9RfHixalTpw5r166lcuXKOdq7urqyevVqwsLCaNeuHd9++23hLtA/kN81KIjx48fj7e3NpEmT+P333/H09KRu3br83//9312MXERERKTo2dqYeKRi4d5wIiJFw2TcfGOtyH3s4sWLeHh4kJKSoodpiYiIiIhYsTuRG/wr7hkWERERERERKQwlwyIiIiIiImJ1lAyLiIiIiIiI1VEyLCIiIiIiIlZHybCIiIiIiIhYHSXDIiIiIiIiYnWUDIuIiIiIiIjVUTIsIiIiIiIiVkfJsIiIiIiIiFgdJcMiIiIiIiJidZQMi4iIiIiIiNVRMiwiIiIiIiJWR8mwiIiIiIiIWB0lwyIiIiIiImJ17Io6ABERERGRf4vMLIPtiedIvpRGKTdHGpb3wtbGVNRhiUgu7suV4ZiYGDw9PYs6jAeWrp+IiIjIvbdmXxJNJ6+n++yfeHlxPN1n/0TTyetZsy+pqEMTkVwUKhmOjIzEZDLxwgsv5KgbOHAgJpOJyMjIOxXbv1JMTAwmkynH4ejoWNShFVpcXBwmk4kLFy4UdSgiIiIiRWrNviQGLNhFUkqaRfmplDQGLNilhFjkPlTolWE/Pz8WL17M1atXzWVpaWl89tlnlCtX7h8HlJGR8Y/HuN+5u7uTlJRkcRw7dqyowyoyhmFw/fr1og5DRERE5LZkZhlEfXMAI5e67LKobw6QmZVbCxEpKoVOhuvWrYufnx/Lli0zly1btoxy5cpRp04di7Zr1qyhadOmeHp6UqJECdq3b8+RI0fM9UePHsVkMrFkyRJCQkJwdHRk4cKFOeY8ffo09evXp1OnTqSnp3P+/Hl69uyJt7c3Tk5OBAUFER0dbTHm559/TrNmzXBycqJBgwb89ttv7Nixg/r16+Pq6kqbNm04ffq0eY4dO3bQunVrSpYsiYeHByEhIezatcsiDpPJxJw5c+jUqRPOzs4EBQWxYsWKwl5CTCYTPj4+Fkfp0qXN9S1atGDw4MEMHz4cLy8vfHx8GDt2rMUYFy5c4Pnnn6d06dI4OjoSHBzMypUrLdp89913VK1aFVdXV8LDw0lK+t9vJP/p+R49epTQ0FAAihcvbrErICsri0mTJlG+fHmcnJyoVasWX375pXnc7BXl1atXU69ePRwcHNi0aVOO65Sens7FixctDhEREZH7zfbEczlWhG9mAEkpaWxPPHfvghKRfN3WPcP9+/c3J58An3zyCf369cvR7vLly7z66qv8/PPPxMbGYmNjQ6dOncjKyrJoN2LECF5++WUSEhIICwuzqDtx4gTNmjUjODiYL7/8EgcHB0aNGsWBAwdYvXo1CQkJzJw5k5IlS1r0GzNmDG+++Sa7du3Czs6OHj16MHz4cKZPn87GjRs5fPgwo0ePNre/dOkSffv2ZdOmTfz0008EBQXRtm1bLl26ZDFuVFQUXbp0Yc+ePbRt25aePXty7tyd/4tt3rx5uLi4sG3bNqZMmcK4ceNYt24dcCPZbNOmDZs3b2bBggUcOHCAt99+G1tbW3P/K1eu8O677zJ//nx+/PFHjh8/zrBhw+7Y+fr5+bF06VIADh48SFJSEtOnTwdg0qRJfPrpp3z00Ufs37+fV155hV69evHDDz9YjD1ixAjefvttEhISqFmzZo5rMGnSJDw8PMyHn5/fnbm4IiIiIndQ8qW8E+HbaSci94hRCH379jU6dOhgJCcnGw4ODsbRo0eNo0ePGo6Ojsbp06eNDh06GH379s2z/+nTpw3A2Lt3r2EYhpGYmGgAxrRp0yzaRUdHGx4eHsavv/5q+Pn5GYMHDzaysrLM9REREUa/fv1ynSN7zDlz5pjLFi1aZABGbGysuWzSpElG5cqV84w1MzPTcHNzM7755htzGWC8+eab5s+pqakGYKxevTrPcf4uOjraAAwXFxeLIzw83NwmJCTEaNq0qUW/Bg0aGK+//rphGIbx3XffGTY2NsbBgwdvOcfhw4fNZR9++KFRunTpO3q+GzZsMADj/Pnz5jZpaWmGs7OzsWXLFovxn376aaN79+4W/ZYvX55nPNljpaSkmI8TJ04YgJGSknLLfiIiIiL30pbDZwz/11fme2w5fKaoQxX510hJSfnHucFtvVrJ29ubdu3aERMTg2EYtGvXLsfKLMChQ4cYPXo027Zt48yZM+YV4ePHjxMcHGxuV79+/Rx9r169SrNmzejRowfTpk2zqBswYACdO3dm165dPPbYY3Ts2JHGjRtbtLl5pTF7C3KNGjUsypKTk82f//rrL958803i4uJITk4mMzOTK1eucPz48TzHdXFxwd3d3WKcgnBzc8uxJdnJySnPeQB8fX3N88THx/PQQw9RqVKlPOdwdnamYsWKufaHu3e+hw8f5sqVK7Ru3dqi/Nq1azm20ef2vd/MwcEBBweHW7YRERERKWoNy3vh6+HIqZS0XO8bNgE+HjdesyQi94/bfs9w//79GTRoEAAffvhhrm0iIiLw9/dn9uzZlClThqysLIKDg7l27ZpFOxcXlxx9HRwcePTRR1m5ciWvvfYaZcuWNde1adOGY8eO8e2337Ju3TpatWrFwIEDeffdd81tihUrZv7ZZDLlWnbzdu2+ffty9uxZpk+fjr+/Pw4ODjzyyCM5Yr15jNzGKQgbGxsCAwNv2eZW8/w9cS5of8P431/Pd+t8U1NTAVi1apXFdwbkSGxz+95FREREHjS2NibGRFRjwIJdmMAiIc5+w/CYiGp637DIfea23zMcHh7OtWvXyMjIyHGfL8DZs2c5ePAgb775Jq1ataJq1aqcP3++4IHZ2DB//nzq1atHaGgoJ0+etKj39vamb9++LFiwgGnTpjFr1qzbPRUANm/ezODBg2nbti3Vq1fHwcGBM2fO/KMx75aaNWvyxx9/8Ntvv932GHfifO3t7QHIzMw0l1WrVg0HBweOHz9OYGCgxaF7fkVEROTfKjzYl5m96uLjYfm6TB8PR2b2qkt4sG8RRSYiebntlWFbW1sSEhLMP/9d8eLFKVGiBLNmzcLX15fjx48zYsSIQs+xcOFCunfvTsuWLYmLi8PHx4fRo0dTr149qlevTnp6OitXrqRq1aq3eyoABAUFMX/+fOrXr8/Fixd57bXXCrQCezsMw+DUqVM5ykuVKoWNTf6/nwgJCaF58+Z07tyZ//znPwQGBvLrr79iMpkIDw8vUAx34nz9/f0xmUysXLmStm3b4uTkhJubG8OGDeOVV14hKyuLpk2bkpKSwubNm3F3d6dv376FmkNERETkQREe7Evraj5sTzxH8qU0Srnd2BqtFWGR+9NtrwzDjffluru75z6wjQ2LFy9m586dBAcH88orr/DOO+8Ueg47OzsWLVpE9erVadmyJcnJydjb2zNy5Ehq1qxJ8+bNsbW1ZfHixf/kVJg7dy7nz5+nbt269O7dm8GDB1OqVKl/NGZeLl68iK+vb46jMPceL126lAYNGtC9e3eqVavG8OHDLVZo83Mnzrds2bJERUUxYsQISpcubd42P378eEaNGsWkSZOoWrUq4eHhrFq1ivLlyxdqfBEREZEHja2NiUcqlqBD7bI8UrGEEmGR+5jJuPlGUpH72MWLF/Hw8CAlJSXPX8KIiIiIiMi/353IDf7RyrCIiIiIiIjIg0jJsIiIiIiIiFgdJcMiIiIiIiJidZQMi4iIiIiIiNVRMiwiIiIiIiJWR8mwiIiIiIiIWB0lwyIiIiIiImJ1lAyLiIiIiIiI1VEyLCIiIiIiIlZHybCIiIiIiIhYHSXDIiIiIiIiYnWUDIuIiIiIiIjVUTIsIiIiIiIiVkfJsIiIiIiIiFgdJcN30dGjRzGZTMTHxxd1KHdMXFwcJpOJCxcuFHUoIiIiIvedzCyDrUfO8nX8n2w9cpbMLKOoQxKRPDxQyfDWrVuxtbWlXbt2RR1Kgfj5+ZGUlERwcPAdH/vUqVO89NJLVKhQAQcHB/z8/IiIiCA2NrbAY8TExODp6XnHYxMRERGxRmv2JdF08nq6z/6JlxfH0332TzSdvJ41+5KKOjQRycUDlQzPnTuXl156iR9//JGTJ08WdTj5srW1xcfHBzs7uzs67tGjR6lXrx7r16/nnXfeYe/evaxZs4bQ0FAGDhx4R+e6VzIyMoo6BBEREZHbtmZfEgMW7CIpJc2i/FRKGgMW7FJCLHIfemCS4dTUVJYsWcKAAQNo164dMTExFvXnz5+nZ8+eeHt74+TkRFBQENHR0QBcu3aNQYMG4evri6OjI/7+/kyaNMnc9z//+Q81atTAxcUFPz8/XnzxRVJTU831x44dIyIiguLFi+Pi4kL16tX59ttv853379uks7cYx8bGUr9+fZydnWncuDEHDx4s1LV48cUXMZlMbN++nc6dO1OpUiWqV6/Oq6++yk8//VSg84qLi6Nfv36kpKRgMpkwmUyMHTsWgPT0dF5//XX8/PxwcHAgMDCQuXPnWsSwc+fOW57D119/Td26dXF0dKRChQpERUVx/fp1c73JZGLmzJk8/vjjuLi4MHHixEJdAxEREZH7RWaWQdQ3B8htQ3R2WdQ3B7RlWuQ+88Akw59//jlVqlShcuXK9OrVi08++QTD+N9fKKNGjeLAgQOsXr2ahIQEZs6cScmSJQF47733WLFiBZ9//jkHDx5k4cKFBAQEmPva2Njw3nvvsX//fubNm8f69esZPny4uX7gwIGkp6fz448/snfvXiZPnoyrq2u+8+bljTfeYOrUqfz888/Y2dnRv3//Al+Hc+fOsWbNGgYOHIiLi0uO+pu3Pd/qvBo3bsy0adNwd3cnKSmJpKQkhg0bBkCfPn1YtGgR7733HgkJCXz88cfm8y3IOWzcuJE+ffrw8ssvc+DAAT7++GNiYmJyJLxjx46lU6dO7N27N9drkJ6ezsWLFy0OERERkfvN9sRzOVaEb2YASSlpbE88d++CEpF83dn9u3fR3Llz6dWrFwDh4eGkpKTwww8/0KJFCwCOHz9OnTp1qF+/PoBFsnv8+HGCgoJo2rQpJpMJf39/i7GHDBli/jkgIIAJEybwwgsvMGPGDHP/zp07U6NGDQAqVKhgMXZe8+Zl4sSJhISEADBixAjatWtHWloajo6O+fY9fPgwhmFQpUqVfNve6rzs7e3x8PDAZDLh4+Njbvfbb7/x+eefs27dOh599NEc51uQc4iKimLEiBH07dvX3H/8+PEMHz6cMWPGmMfo0aMH/fr1yzP+SZMmERUVle95ioiIiBSl5Et5J8K3005E7o0HYmX44MGDbN++ne7duwNgZ2dH165dLbbuDhgwgMWLF1O7dm2GDx/Oli1bzHWRkZHEx8dTuXJlBg8ezNq1ay3G//7772nVqhVly5bFzc2N3r17c/bsWa5cuQLA4MGDmTBhAk2aNGHMmDHs2bOnQPPmpWbNmuaffX19AUhOTi7Qtbh5NTw/+Z1XbuLj47G1tTUnunm51Tns3r2bcePG4erqaj6effZZkpKSLObO/gVCXkaOHElKSor5OHHiRL7nLCIiInKvlXLLf0GjMO1E5N54IJLhuXPncv36dcqUKYOdnR12dnbMnDmTpUuXkpKSAkCbNm04duwYr7zyCidPnqRVq1bmbb9169YlMTGR8ePHc/XqVbp06cKTTz4J3Livt3379tSsWZOlS5eyc+dOPvzwQ+DGvcYAzzzzDL///ju9e/dm79691K9fn/fffz/fefNSrFgx888mkwmArKysAl2LoKAgTCYTv/766y3bFeS8cuPk5FSgOG51DqmpqURFRREfH28+9u7dy6FDhyxWv3Pb5n0zBwcH3N3dLQ4RERGR+03D8l74ejhiyqPeBPh6ONKwvNe9DEtE8nHfJ8PXr1/n008/ZerUqRbJ1e7duylTpgyLFi0yt/X29qZv374sWLCAadOmMWvWLHOdu7s7Xbt2Zfbs2SxZsoSlS5dy7tw5du7cSVZWFlOnTqVRo0ZUqlQp1ydV+/n58cILL7Bs2TKGDh3K7NmzCzTvnebl5UVYWBgffvghly9fzlGf/f7fgpyXvb09mZmZFmU1atQgKyuLH3744bZjrFu3LgcPHiQwMDDHYWNz3/+RExERESkUWxsTYyKqAeRIiLM/j4mohq1NXumyiBSF+/6e4ZUrV3L+/HmefvppPDw8LOo6d+7M3LlzeeGFFxg9ejT16tWjevXqpKens3LlSqpWrQrceKqyr68vderUwcbGhi+++AIfHx88PT0JDAwkIyOD999/n4iICDZv3sxHH31kMc+QIUNo06YNlSpV4vz582zYsME89q3mvVs+/PBDmjRpQsOGDRk3bhw1a9bk+vXrrFu3jpkzZ5KQkFCg8woICCA1NZXY2Fhq1aqFs7MzAQEB9O3bl/79+/Pee+9Rq1Ytjh07RnJyMl26dClQfKNHj6Z9+/aUK1eOJ598EhsbG3bv3s2+ffuYMGHC3bgkIiIiIkUqPNiXmb3qEvXNAYuHafl4ODImohrhwb5FGJ2I5Oa+X6abO3cujz76aI5EGG4kwz///DN79uzB3t6ekSNHUrNmTZo3b46trS2LFy8GwM3NjSlTplC/fn0aNGjA0aNH+fbbb7GxsaFWrVr85z//YfLkyQQHB7Nw4UKL1y4BZGZmMnDgQKpWrUp4eDiVKlUyP1zrVvPeLRUqVGDXrl2EhoYydOhQgoODad26NbGxscycOROgQOfVuHFjXnjhBbp27Yq3tzdTpkwBYObMmTz55JO8+OKLVKlShWeffTbXVei8hIWFsXLlStauXUuDBg1o1KgR//3vf3M8uExERETk3yQ82JdNr7dk0bONmN6tNouebcSm11sqERa5T5mMwjyRSaQIXbx4EQ8PD1JSUnT/sIiIiIiIFbsTucF9vzIsIiIiIiIicqcpGRYRERERERGro2RYRERERERErI6SYREREREREbE6SoZFRERERETE6igZFhEREREREaujZFhERERERESsjpJhERERERERsTpKhkVERERERMTqKBkWERERERERq6NkWERERERERKyOkmERERERERGxOkqGRURERERExOooGRYRERERERGro2RYRERERERErI6SYSmUFi1aMGTIkKIOQ0RERESsVGaWwdYjZ/k6/k+2HjlLZpZR1CHJA8qqk+HIyEhMJpP5KFGiBOHh4ezZs+euzz127Fhq16592/2XLl1KixYt8PDwwNXVlZo1azJu3DjOnTtX4DECAgKYNm3abccgIiIiInIvrdmXRNPJ6+k++ydeXhxP99k/0XTyetbsSyrq0OQBZNXJMEB4eDhJSUkkJSURGxuLnZ0d7du3/0djZmZmkpWVdYcizOmNN96ga9euNGjQgNWrV7Nv3z6mTp3K7t27mT9//l2b9265du1aUYcgIiIiIve5NfuSGLBgF0kpaRblp1LSGLBglxJiKTSrT4YdHBzw8fHBx8eH2rVrM2LECE6cOMHp06cBiIuLw2QyceHCBXOf+Ph4TCYTR48eBSAmJgZPT09WrFhBtWrVcHBw4Pjx48TFxdGwYUNcXFzw9PSkSZMmHDt2jJiYGKKioti9e7d5VTomJqZA8W7fvp233nqLqVOn8s4779C4cWMCAgJo3bo1S5cupW/fvgAcOXKEDh06ULp0aVxdXWnQoAHff/+9eZwWLVpw7NgxXnnlFXMM2TZv3kyLFi1wdnamePHihIWFcf78eXN9VlYWw4cPx8vLCx8fH8aOHWsR44ULF3jmmWfw9vbG3d2dli1bsnv3bnN99qr4nDlzKF++PI6OjgU6dxERERGxTplZBlHfHCC3DdHZZVHfHNCWaSkUq0+Gb5aamsqCBQsIDAykRIkShep75coVJk+ezJw5c9i/fz9eXl507NiRkJAQ9uzZw9atW3nuuecwmUx07dqVoUOHUr16dfOqdNeuXQs0z8KFC3F1deXFF1/Mtd7T09N8Lm3btiU2NpZffvmF8PBwIiIiOH78OADLli3joYceYty4ceYY4Eai36pVK6pVq8bWrVvZtGkTERERZGZmmueYN28eLi4ubNu2jSlTpjBu3DjWrVtnrn/qqadITk5m9erV7Ny5k7p169KqVSuLLdyHDx9m6dKlLFu2jPj4+FzPJT09nYsXL1ocIiIiImJ9tieey7EifDMDSEpJY3tiwW8ZFLEr6gCK2sqVK3F1dQXg8uXL+Pr6snLlSmxsCvd7goyMDGbMmEGtWrUAOHfuHCkpKbRv356KFSsCULVqVXN7V1dX7Ozs8PHxKdQ8hw4dokKFChQrVuyW7WrVqmWOBWD8+PF89dVXrFixgkGDBuHl5YWtrS1ubm4WMUyZMoX69eszY8YMc1n16tUtxq5ZsyZjxowBICgoiA8++IDY2Fhat27Npk2b2L59O8nJyTg4OADw7rvvsnz5cr788kuee+454MbW6E8//RRvb+88z2HSpElERUUV8MqIiIiIyL9V8qW8E+HbaScCWhkmNDSU+Ph44uPj2b59O2FhYbRp04Zjx44Vahx7e3tq1qxp/uzl5UVkZCRhYWFEREQwffp08+rrP2EYBdv6kZqayrBhw6hatSqenp64urqSkJBgXhnOS/bK8K3cfJ4Avr6+JCcnA7B7925SU1MpUaIErq6u5iMxMZEjR46Y+/j7+98yEQYYOXIkKSkp5uPEiRO3bC8iIiIi/06l3Ap2W11B24mAVoZxcXEhMDDQ/HnOnDl4eHgwe/ZsJkyYYF4hvjkJzcjIyDGOk5OTxX23ANHR0QwePJg1a9awZMkS3nzzTdatW0ejRo1uO95KlSqxadMmMjIybrk6PGzYMNatW8e7775LYGAgTk5OPPnkk/k+rMrJySnfGP4+r8lkMj8wLDU1FV9fX+Li4nL0y97CDTeue34cHBzMq8siIiIiYr0alvfC18ORUylpud43bAJ8PBxpWN7rXocmDzCrXxn+O5PJhI2NDVevXgUwr17evKqb1z2uualTpw4jR45ky5YtBAcH89lnnwE3VpJvvg+3oHr06EFqaqrFNuabZT/oa/PmzURGRtKpUydq1KiBj4+P+YFf2XKLoWbNmsTGxhY6rmx169bl1KlT2NnZERgYaHGULFnytscVEREREetla2NiTEQ14Ebie7Psz2MiqmFr8/dakbxZfTKcnp7OqVOnOHXqFAkJCbz00kukpqYSEREBQGBgIH5+fowdO5ZDhw6xatUqpk6dmu+4iYmJjBw5kq1bt3Ls2DHWrl3LoUOHzPcNBwQEkJiYSHx8PGfOnCE9Pb1A8T788MMMHz6coUOHMnz4cPP4sbGxPPXUU8ybNw+4cS9v9sOpdu/eTY8ePXK87ikgIIAff/yRP//8kzNnzgA3tibv2LGDF198kT179vDrr78yc+ZMc31+Hn30UR555BE6duzI2rVrOXr0KFu2bOGNN97g559/LtAYIiIiIiJ/Fx7sy8xedfHxsNwK7ePhyMxedQkP9i2iyORBZfXbpNesWYOv743/cNzc3KhSpQpffPEFLVq0AG5sCV60aBEDBgygZs2aNGjQgAkTJvDUU0/dclxnZ2d+/fVX5s2bx9mzZ/H19WXgwIE8//zzAHTu3Jlly5YRGhrKhQsXiI6OJjIyskAxT548mXr16vHhhx/y0UcfkZWVRcWKFXnyySfNr1b6z3/+Q//+/WncuDElS5bk9ddfz/E05nHjxvH8889TsWJF0tPTMQyDSpUqsXbtWv7v//6Phg0b4uTkxMMPP0z37t0LFJvJZOLbb7/ljTfeoF+/fpw+fRofHx+aN29O6dKlCzSGiIiIiEhuwoN9aV3Nh+2J50i+lEYptxtbo7UiLLfDZBT0iUwiRezixYt4eHiQkpKCu7t7UYcjIiIiIiJF5E7kBla/TVpERERERESsj5JhERERERERsTpKhkVERERERMTqKBkWERERERERq6NkWERERERERKyOkmERERERERGxOkqGRURERERExOooGRYRERERERGro2RYRERERERErI6SYREREREREbE6SoZFRERERETE6igZFhEREREREaujZFhERERERESsjpJhERERERERsTpKhkVERERERMTqKBkWxo4dS+3atc2fIyMj6dixo/lzixYtGDJkyD2P636VmWWw9chZvo7/k61HzpKZZRR1SCIiIiIiUkhWnQxHRkZiMpnMR4kSJQgPD2fPnj13fe6/J6C3IywsDFtbW3bs2FHgPiaTieXLl1uUDRs2jNjY2H8Ui7VYsy+JppPX0332T7y8OJ7us3+i6eT1rNmXVNShiYiIiIhIIVh1MgwQHh5OUlISSUlJxMbGYmdnR/v27f/RmJmZmWRlZd2hCHN3/PhxtmzZwqBBg/jkk0/ybX/t2rU861xdXSlRosSdDM/Cvbge98KafUkMWLCLpJQ0i/JTKWkMWLBLCbGIiIiIyAPE6pNhBwcHfHx88PHxoXbt2owYMYITJ05w+vRpAOLi4jCZTFy4cMHcJz4+HpPJxNGjRwGIiYnB09OTFStWUK1aNRwcHDh+/DhxcXE0bNgQFxcXPD09adKkCceOHSMmJoaoqCh2795tXpWOiYkpVNzR0dG0b9+eAQMGsGjRIq5evWpR36JFCwYNGsSQIUMoWbIkYWFhBAQEANCpUydMJpP5c2FXqdPT0xk2bBhly5bFxcWFhx9+mLi4OHN9btdj06ZNFCtWjFOnTlmMNWTIEJo1a1aocy8KmVkGUd8cILcN0dllUd8c0JZpEREREZEHhNUnwzdLTU1lwYIFBAYGFnql9MqVK0yePJk5c+awf/9+vLy86NixIyEhIezZs4etW7fy3HPPYTKZ6Nq1K0OHDqV69ermVemuXbsWeC7DMIiOjqZXr15UqVKFwMBAvvzyyxzt5s2bh729PZs3b+ajjz4yb6eOjo4mKSmpUNurbzZo0CC2bt3K4sWL2bNnD0899RTh4eEcOnQoz+tRv359KlSowPz5881tMjIyWLhwIf379891nvT0dC5evGhxFJXtiedyrAjfzACSUtLYnnju3gUlIiIiIiK3za6oAyhqK1euxNXVFYDLly/j6+vLypUrsbEp3O8JMjIymDFjBrVq1QLg3LlzpKSk0L59eypWrAhA1apVze1dXV2xs7PDx8en0DF///33XLlyhbCwMAB69erF3Llz6d27t0W7oKAgpkyZkqO/p6fnbc0LN7ZnR0dHc/z4ccqUKQPcuOd4zZo1REdH89ZbbwE5rwfA008/TXR0NK+99hoA33zzDWlpaXTp0iXXuSZNmkRUVNRtxXmnJV/KOxG+nXYiIiIiIlK0rH5lODQ0lPj4eOLj49m+fTthYWG0adOGY8eOFWoce3t7atasaf7s5eVFZGQkYWFhREREMH36dJKS7sw9pZ988gldu3bFzu7G7zK6d+/O5s2bOXLkiEW7evXq3ZH5brZ3714yMzOpVKkSrq6u5uOHH36wmP/v1wNuPLDs8OHD/PTTT8CN7dRdunTBxcUl17lGjhxJSkqK+Thx4sQdP5+CKuXmeEfbiYiIiIhI0bL6ZNjFxYXAwEACAwNp0KABc+bM4fLly8yePRvAvEJsGP+7FzQjIyPHOE5OTphMJouy6Ohotm7dSuPGjVmyZAmVKlUyJ4K369y5c3z11VfMmDEDOzs77OzsKFu2LNevX8/xIK28ksx/IjU1FVtbW3bu3Gn+JUJ8fDwJCQlMnz7d3C6361GqVCkiIiKIjo7mr7/+YvXq1XlukYYb93O7u7tbHEWlYXkvfD0cMeVRbwJ8PRxpWN7rXoYlIiIiIiK3yeq3Sf+dyWTCxsbG/EAqb29vAJKSkihevDhw4wFaBVWnTh3q1KnDyJEjeeSRR/jss89o1KgR9vb2ZGZmFjq+hQsX8tBDD+V4PdLatWuZOnUq48aNw9bWNs/+xYoVu615s9WpU4fMzEySk5Nv68FXzzzzDN27d+ehhx6iYsWKNGnS5LZjuZdsbUyMiajGgAW7MIHFg7SyE+QxEdWwtckrXRYRERERkfuJ1a8Mp6enc+rUKU6dOkVCQgIvvfQSqampREREABAYGIifnx9jx47l0KFDrFq1iqlTp+Y7bmJiIiNHjmTr1q0cO3aMtWvXcujQIfN9wwEBASQmJhIfH8+ZM2dIT08vULxz587lySefJDg42OJ4+umnOXPmDGvWrLll/4CAAGJjYzl16hTnz58v0Jw3q1SpEj179qRPnz4sW7aMxMREtm/fzqRJk1i1alW+/cPCwnB3d2fChAn069ev0PMXpfBgX2b2qouPh+VWaB8PR2b2qkt4sG8RRSYiIiIiIoVl9cnwmjVr8PX1xdfXl4cffpgdO3bwxRdf0KJFC+DGSuqiRYv49ddfqVmzJpMnT2bChAn5juvs7Myvv/5K586dqVSpEs899xwDBw7k+eefB6Bz586Eh4cTGhqKt7c3ixYtynfMnTt3snv3bjp37pyjzsPDg1atWjF37txbjjF16lTWrVuHn58fderUyXfO3ERHR9OnTx+GDh1K5cqV6dixIzt27KBcuXL59rWxsSEyMpLMzEz69OlzW/MXpfBgXza93pJFzzZierfaLHq2EZteb6lEWERERETkAWMybr4ZVuQeePrppzl9+jQrVqwoVL+LFy/i4eFBSkpKkd4/LCIiIiIiRetO5Aa6Z1jumZSUFPbu3ctnn31W6ERYRERERETkTlIyLPdMhw4d2L59Oy+88AKtW7cu6nBERERERMSKKRmWeyYuLq6oQxAREREREQH0AC0RERERERGxQkqGRURERERExOooGRYRERERERGro2RYRERERERErI6SYREREREREbE6SoZFRERERETE6igZFhEREREREaujZFhERERERESsjpJhERERERERsTpKhkVERERERMTqKBkWERERERERq6NkWERERERERKyOkuF/oaNHj2IymYiPjy/qUERERESsyrXrWczd+Dujv97H3I2/c+16VlGHJCJ5eKCT4cjISEwmk/koUaIE4eHh7Nmz567PPXbsWGrXrl3oftmJam7HTz/9dOcDvctMJhPLly8v6jBEREREitykbw9QZdRqxq9K4NOtxxi/KoEqo1Yz6dsDRR2aiOTigU6GAcLDw0lKSiIpKYnY2Fjs7Oxo3779PxozMzOTrKy7+1u877//3hx39lGvXr27Ouf9LCMjo6hDEBEREbltk749wMc/JpJlWJZnGfDxj4lKiEXuQw98Muzg4ICPjw8+Pj7Url2bESNGcOLECU6fPg1AXFwcJpOJCxcumPvEx8djMpk4evQoADExMXh6erJixQqqVauGg4MDx48fJy4ujoYNG+Li4oKnpydNmjTh2LFjxMTEEBUVxe7du82rujExMYWKu0SJEua4s49ixYoB/1t1nj9/PgEBAXh4eNCtWzcuXbpk7p+VlcWUKVMIDAzEwcGBcuXKMXHiRIs5fv/9d0JDQ3F2dqZWrVps3brVXHf27Fm6d+9O2bJlcXZ2pkaNGixatMiif4sWLRg8eDDDhw/Hy8sLHx8fxo4da64PCAgAoFOnTphMJvNngK+//pq6devi6OhIhQoViIqK4vr16+Z6k8nEzJkzefzxx3FxcckRu4iIiMiD4tr1LGZvTLxlm9kbE7VlWuQ+88AnwzdLTU1lwYIFBAYGUqJEiUL1vXLlCpMnT2bOnDns378fLy8vOnbsSEhICHv27GHr1q0899xzmEwmunbtytChQ6levbp5Vbdr16539FyOHDnC8uXLWblyJStXruSHH37g7bffNtePHDmSt99+m1GjRnHgwAE+++wzSpcubTHGG2+8wbBhw4iPj6dSpUp0797dnJCmpaVRr149Vq1axb59+3juuefo3bs327dvtxhj3rx5uLi4sG3bNqZMmcK4ceNYt24dADt27AAgOjqapKQk8+eNGzfSp08fXn75ZQ4cOMDHH39MTExMjoR37NixdOrUib1799K/f/8c1yA9PZ2LFy9aHCIiIiL3m/lbj+ZYEf67LONGOxG5f9gVdQD/1MqVK3F1dQXg8uXL+Pr6snLlSmxsCpfnZ2RkMGPGDGrVqgXAuXPnSElJoX379lSsWBGAqlWrmtu7urpiZ2eHj4/PbcXduHHjHDGmpqaaf87KyiImJgY3NzcAevfuTWxsLBMnTuTSpUtMnz6dDz74gL59+wJQsWJFmjZtajHesGHDaNeuHQBRUVFUr16dw4cPU6VKFcqWLcuwYcPMbV966SW+++47Pv/8cxo2bGgur1mzJmPGjAEgKCiIDz74gNjYWFq3bo23tzcAnp6eFtchKiqKESNGmGOrUKEC48ePZ/jw4eaxAHr06EG/fv3yvEaTJk0iKioqv0spIiIiUqSOnbtyR9uJyL3xwCfDoaGhzJw5E4Dz588zY8YM2rRpw/bt2/H39y/wOPb29tSsWdP82cvLi8jISMLCwmjdujWPPvooXbp0wdfX947EvWTJEovk+u8CAgLMiTCAr68vycnJACQkJJCenk6rVq1uOcfN55Mdd3JyMlWqVCEzM5O33nqLzz//nD///JNr166Rnp6Os7NznmP8PY687N69m82bN1usBGdmZpKWlsaVK1fMc9SvX/+W44wcOZJXX33V/PnixYv4+fndso+IiIjIvebv5Zx/o0K0E5F744HfJu3i4kJgYCCBgYE0aNCAOXPmcPnyZWbPng1gXn01jP/tXcntYU1OTk6YTCaLsujoaLZu3Urjxo1ZsmQJlSpVumNPfPbz8zPHnX3cLPv+4Wwmk8n8UC8nJ6cCzXHzGNnnlj3GO++8w/Tp03n99dfZsGED8fHxhIWFce3atQLHkZfU1FSioqKIj483H3v37uXQoUM4Ojqa27m4uNxyHAcHB9zd3S0OERERkftN70cCsDHduo2N6UY7Ebl/PPDJ8N+ZTCZsbGy4evUqgHkrb1JSkrlNYd6/W6dOHUaOHMmWLVsIDg7ms88+A26sJGdmZt65wAshKCgIJycnYmNjb3uMzZs306FDB3r16kWtWrWoUKECv/32W6HHKVasWI7rULduXQ4ePJgj2Q8MDCz09nURERGR+529nQ3PNit/yzbPNiuPvZ3+HSRyP3ngt0mnp6dz6tQp4MY26Q8++IDU1FQiIiIACAwMxM/Pj7FjxzJx4kR+++03pk6dmu+4iYmJzJo1i8cff5wyZcpw8OBBDh06RJ8+fYAb25gTExOJj4/noYcews3NDQcHhwLHffbsWXPc2Tw9PS1WTvPi6OjI66+/zvDhw7G3t6dJkyacPn2a/fv38/TTTxdo/qCgIL788ku2bNlC8eLF+c9//sNff/1FtWrVCnwOcOM6xMbG0qRJExwcHChevDijR4+mffv2lCtXjieffBIbGxt2797Nvn37mDBhQqHGFxEREXkQjGx7499Qszdavl7JxnQjEc6uF5H7xwOfDK9Zs8Z8P6ybmxtVqlThiy++oEWLFsCNlctFixYxYMAAatasSYMGDZgwYQJPPfXULcd1dnbm119/Zd68eZw9exZfX18GDhzI888/D0Dnzp1ZtmwZoaGhXLhwgejoaCIjIwsc96OPPpqjbNGiRXTr1q1A/UeNGoWdnR2jR4/m5MmT+Pr68sILLxR4/jfffJPff/+dsLAwnJ2dee655+jYsSMpKSkFHgNg6tSpvPrqq8yePZuyZcty9OhRwsLCWLlyJePGjWPy5MkUK1aMKlWq8MwzzxRqbBEREZEHyci21Rj6WBXmbz3KsXNX8PdypvcjAVoRFrlPmYybb6YVuY9dvHgRDw8PUlJSdP+wiIiIiIgVuxO5gX5NJSIiIiIiIlZHybCIiIiIiIhYHSXDIiIiIiIiYnWUDIuIiIiIiIjVUTIsIiIiIiIiVkfJsIiIiIiIiFgdJcMiIiIiIiJidZQMi4iIiIiIiNVRMiwiIiIiIiJWR8mwiIiIiIiIWB0lwyIiIiIiImJ1lAyLiIiIiIiI1VEyLCIiIiIiIlZHybCIiIiIiIhYHSXDIiIiIiIiYnWsLhlu0aIFQ4YMKeowcjh69Cgmk4n4+Pg825hMJpYvX14kc99OWxERERGROy0zy2DrkbN8Hf8nW4+cJTPLKOqQ5AF1T5Phjz76CDc3N65fv24uS01NpVixYrRo0cKibVxcHCaTiSNHjtzLEO+axMREevToQZkyZXB0dOShhx6iQ4cO/PrrrwUeIykpiTZt2tzx2Pz8/EhKSiI4OPiOjy0iIiIicqes2ZdE08nr6T77J15eHE/32T/RdPJ61uxLKurQ5AF0T5Ph0NBQUlNT+fnnn81lGzduxMfHh23btpGWlmYu37BhA+XKlaNixYqFnscwDIuEu6hlZGTQunVrUlJSWLZsGQcPHmTJkiXUqFGDCxcuFHgcHx8fHBwcbjnP7bC1tcXHxwc7O7vb6n8nXLt2rcjmFhEREZH735p9SQxYsIuklDSL8lMpaQxYsEsJsRTaPU2GK1eujK+vL3FxceayuLg4OnToQPny5fnpp58sykNDQwGYP38+9evXx83NDR8fH3r06EFycrJFW5PJxOrVq6lXrx4ODg5s2rSJy5cv06dPH1xdXfH19WXq1Kk5YkpPT2fYsGGULVsWFxcXHn74YYv4YmJi8PT05LvvvqNq1aq4uroSHh5OUlLB/2Pbv38/R44cYcaMGTRq1Ah/f3+aNGnChAkTaNSoUa59MjMz6d+/P1WqVOH48eOA5Tbp7O3KS5YsISQkBEdHRxYuXEhkZCQdO3bkrbfeonTp0nh6ejJu3DiuX7/Oa6+9hpeXFw899BDR0dHmuf6+9fn8+fP07NkTb29vnJycCAoKsmgP8PvvvxMaGoqzszO1atVi69atFvWbNm2iWbNmODk54efnx+DBg7l8+bK5PiAggPHjx9OnTx/c3d157rnnCnw9RURERMS6ZGYZRH1zgNw2RGeXRX1zQFumpVDu+T3DoaGhbNiwwfx5w4YNtGjRgpCQEHP51atX2bZtmzkZzsjIYPz48ezevZvly5dz9OhRIiMjc4w9YsQI3n77bRISEqhZsyavvfYaP/zwA19//TVr164lLi6OXbt2WfQZNGgQW7duZfHixezZs4ennnqK8PBwDh06ZG5z5coV3n33XebPn8+PP/7I8ePHGTZsWIHP2dvbGxsbG7788ksyMzPzbZ+ens5TTz1FfHw8GzdupFy5cnm2HTFiBC+//DIJCQmEhYUBsH79ek6ePMmPP/7If/7zH8aMGUP79u0pXrw427Zt44UXXuD555/njz/+yHXMUaNGceDAAVavXk1CQgIzZ86kZMmSFm3eeOMNhg0bRnx8PJUqVaJ79+7m1fgjR44QHh5O586d2bNnD0uWLGHTpk0MGjTIYox3332XWrVq8csvvzBq1Khcr8PFixctDhERERGxPtsTz+VYEb6ZASSlpLE98dy9C0oefMY9Nnv2bMPFxcXIyMgwLl68aNjZ2RnJycnGZ599ZjRv3twwDMOIjY01AOPYsWO5jrFjxw4DMC5dumQYhmFs2LDBAIzly5eb21y6dMmwt7c3Pv/8c3PZ2bNnDScnJ+Pll182DMMwjh07Ztja2hp//vmnxfitWrUyRo4caRiGYURHRxuAcfjwYXP9hx9+aJQuXbpQ5/3BBx8Yzs7OhpubmxEaGmqMGzfOOHLkiLk+MTHRAIyNGzcarVq1Mpo2bWpcuHDBYgzA+OqrryzaT5s2zaJN3759DX9/fyMzM9NcVrlyZaNZs2bmz9evXzdcXFyMRYsWWYz1yy+/GIZhGBEREUa/fv1yPY/stnPmzDGX7d+/3wCMhIQEwzAM4+mnnzaee+45i34bN240bGxsjKtXrxqGYRj+/v5Gx44db3nNxowZY3Dj7zaLIyUl5Zb9REREROTfZfkvfxj+r6/M91j+yx9FHarcIykpKf84N7jnK8MtWrTg8uXL7Nixg40bN1KpUiW8vb0JCQkx3zccFxdHhQoVzCuiO3fuJCIignLlyuHm5kZISAiAeftwtvr165t/PnLkCNeuXePhhx82l3l5eVG5cmXz571795KZmUmlSpVwdXU1Hz/88IPFg7ucnZ0t7l329fW12KZdEAMHDuTUqVMsXLiQRx55hC+++ILq1auzbt06i3bdu3fn8uXLrF27Fg8Pj3zHvfmcs1WvXh0bm/99taVLl6ZGjRrmz7a2tpQoUSLPcxgwYACLFy+mdu3aDB8+nC1btuRoU7NmTfPPvr6+AObxdu/eTUxMjMU1DQsLIysri8TExFvGfrORI0eSkpJiPk6cOHHL9iIiIiLy71TKzfGOthMBuOdPTAoMDOShhx5iw4YNnD9/3pzYlilTBj8/P7Zs2cKGDRto2bIlAJcvXyYsLIywsDAWLlyIt7c3x48fJywsLMdDl1xcXAoVS2pqKra2tuzcuRNbW1uLOldXV/PPxYoVs6gzmUwYRuHvR3BzcyMiIoKIiAgmTJhAWFgYEyZMoHXr1uY2bdu2ZcGCBWzdutV8DW4lt3POLd7cyrKysnIds02bNhw7doxvv/2WdevW0apVKwYOHMi7776b6xwmkwnAPF5qairPP/88gwcPzjH2zVu+8/u+HBwcbvnAMBERERGxDg3Le+Hr4ciplLRc7xs2AT4ejjQs73WvQ5MHWJG8Zzg0NJS4uDji4uIsXqnUvHlzVq9ezfbt2833C//666+cPXuWt99+m2bNmlGlSpUCrcpWrFiRYsWKsW3bNnPZ+fPn+e2338yf69SpQ2ZmJsnJyQQGBlocPj4+d+6Ec2EymahSpYrFQ6Xgxqrs22+/zeOPP84PP/xwV2O4FW9vb/r27cuCBQuYNm0as2bNKnDfunXrcuDAgRzXNDAwEHt7+7sYtYiIiIj8G9namBgTUQ24kfjeLPvzmIhq2Nr8vVYkb0WWDG/atIn4+HjzyjBASEgIH3/8MdeuXTMnw+XKlcPe3p7333+f33//nRUrVjB+/Ph853B1deXpp5/mtddeY/369ezbt4/IyEiL7cOVKlWiZ8+e9OnTh2XLlpGYmMj27duZNGkSq1atumPnGx8fT4cOHfjyyy85cOAAhw8fZu7cuXzyySd06NAhR/uXXnqJCRMm0L59ezZt2nTH4iio0aNH8/XXX3P48GH279/PypUrqVq1aoH7v/7662zZsoVBgwYRHx/PoUOH+Prrr3M8QEtEREREpKDCg32Z2asuPh6WW6F9PByZ2asu4cG+RRSZPKiK5MWyoaGhXL16lSpVqlC6dGlzeUhICJcuXTK/gglurFDGxMTwf//3f7z33nvUrVuXd999l8cffzzfed555x1SU1OJiIjAzc2NoUOHkpKSYtEmOjqaCRMmMHToUP78809KlixJo0aNaN++/R0734ceeoiAgACioqLMrzHK/vzKK6/k2mfIkCFkZWXRtm1b1qxZQ+PGje9YPPmxt7dn5MiRHD16FCcnJ5o1a8bixYsL3L9mzZr88MMPvPHGGzRr1gzDMKhYsSJdu3a9i1GLiIiIyL9deLAvrav5sD3xHMmX0ijldmNrtFaE5XaYjNu5+VWkCFy8eBEPDw9SUlJwd3cv6nBERERERKSI3IncoEi2SYuIiIiIiIgUJSXDIiIiIiIiYnWUDIuIiIiIiIjVUTIsIiIiIiIiVkfJsIiIiIiIiFgdJcMiIiIiIiJidZQMi4iIiIiIiNVRMiwiIiIiIiJWR8mwiIiIiIiIWB0lwyIiIiIiImJ1lAyLiIiIiIiI1VEyLCIiIiIiIlZHybCIiIiIiIhYHSXDIiIiIiIiYnWUDIuIiIiIiIjV+VcmwzExMXh6ehZ1GP9KJpOJ5cuXF3UYIiIiImKlMrMMth45y9fxf7L1yFkys4yiDkkeUPc0GY6MjMRkMvHCCy/kqBs4cCAmk4nIyMh7GdID6dq1a0yZMoVatWrh7OxMyZIladKkCdHR0WRkZBRojKNHj2IymYiPj7+7wYqIiIiI3CFr9iXRdPJ6us/+iZcXx9N99k80nbyeNfuSijo0eQDd85VhPz8/Fi9ezNWrV81laWlpfPbZZ5QrV+4fj1/QZPBBde3aNcLCwnj77bd57rnn2LJlC9u3b2fgwIG8//777N+/v6hDLLRr164VdQgiIiIicp9bsy+JAQt2kZSSZlF+KiWNAQt2KSGWQrvnyXDdunXx8/Nj2bJl5rJly5ZRrlw56tSpY9F2zZo1NG3aFE9PT0qUKEH79u05cuSIuT57dXPJkiWEhITg6OjIwoULc8x5+vRp6tevT6dOnUhPT+f8+fP07NkTb29vnJycCAoKIjo62mLMzz//nGbNmuHk5ESDBg347bff2LFjB/Xr18fV1ZU2bdpw+vRp8xw7duygdevWlCxZEg8PD0JCQti1a5dFHCaTiTlz5tCpUyecnZ0JCgpixYoVhbp+06ZN48cffyQ2NpaBAwdSu3ZtKlSoQI8ePdi2bRtBQUEFunbly5cHoE6dOphMJlq0aGGu++STT6hevToODg74+voyaNAgixjOnDlzy3PYt28fbdq0wdXVldKlS9O7d2/OnDljrm/RogWDBg1iyJAhlCxZkrCwsEJdAxERERGxLplZBlHfHCC3DdHZZVHfHNCWaSmUIrlnuH///ubkE24kX/369cvR7vLly7z66qv8/PPPxMbGYmNjQ6dOncjKyrJoN2LECF5++WUSEhJyJFYnTpygWbNmBAcH8+WXX+Lg4MCoUaM4cOAAq1evJiEhgZkzZ1KyZEmLfmPGjOHNN99k165d2NnZ0aNHD4YPH8706dPZuHEjhw8fZvTo0eb2ly5dom/fvmzatImffvqJoKAg2rZty6VLlyzGjYqKokuXLuzZs4e2bdvSs2dPzp07V+Brt3DhQh599NEcvzgAKFasGC4uLgW6dtu3bwfg+++/JykpyfzLiZkzZzJw4ECee+459u7dy4oVKwgMDCzwOVy4cIGWLVtSp04dfv75Z9asWcNff/1Fly5dLMaYN28e9vb2bN68mY8++ijXc01PT+fixYsWh4iIiIhYn+2J53KsCN/MAJJS0tieWPB/V4tg3EN9+/Y1OnToYCQnJxsODg7G0aNHjaNHjxqOjo7G6dOnjQ4dOhh9+/bNs//p06cNwNi7d69hGIaRmJhoAMa0adMs2kVHRxseHh7Gr7/+avj5+RmDBw82srKyzPURERFGv379cp0je8w5c+aYyxYtWmQARmxsrLls0qRJRuXKlfOMNTMz03BzczO++eYbcxlgvPnmm+bPqampBmCsXr06z3H+zsnJyRg8eHCB22fL69r98ssvFu3KlCljvPHGG3mOk985jB8/3njssccs+pw4ccIAjIMHDxqGYRghISFGnTp18o15zJgxBjf+brM4UlJSCnTOIiIiIvLvsPyXPwz/11fmeyz/5Y+iDlXukZSUlH+cGxTJyrC3tzft2rUjJiaG6Oho2rVrl2NlFuDQoUN0796dChUq4O7uTkBAAADHjx+3aFe/fv0cfa9evUqzZs144oknmD59OiaTyVw3YMAAFi9eTO3atRk+fDhbtmzJ0b9mzZrmn0uXLg1AjRo1LMqSk5PNn//66y+effZZgoKC8PDwwN3dndTU1Byx3jyui4sL7u7uFuPkxzAKtvWjoNfuZsnJyZw8eZJWrVrdcuxbncPu3bvZsGEDrq6u5qNKlSoAFtu069Wrl+85jBw5kpSUFPNx4sSJfPuIiIiIyL9PKTfHO9pOBMCuqCbu37+/+V7UDz/8MNc2ERER+Pv7M3v2bMqUKUNWVhbBwcE5HriUvTX4Zg4ODjz66KOsXLmS1157jbJly5rr2rRpw7Fjx/j2229Zt24drVq1YuDAgbz77rvmNsWKFTP/nJ1I/73s5u3affv25ezZs0yfPh1/f38cHBx45JFHcsR68xi5jZOfSpUq8euvv+bbrqDX7mZOTk4FiuFW55CamkpERASTJ0/O0c/X19f8c27f2d85ODjg4OBQoJhERERE5N+rYXkvfD0cOZWSlut9wybAx8ORhuW97nVo8gArsvcMh4eHc+3aNTIyMnJ9gNLZs2c5ePAgb775Jq1ataJq1aqcP3++wOPb2Ngwf/586tWrR2hoKCdPnrSo9/b2pm/fvixYsIBp06Yxa9asf3Q+mzdvZvDgwbRt29b88KmbHxp1p/To0YPvv/+eX375JUddRkYGly9fLtC1s7e3ByAzM9Nc5ubmRkBAALGxsbcdX926ddm/fz8BAQEEBgZaHAVJgEVERERE/s7WxsSYiGrAjcT3Ztmfx0RUw9bm77UieSuyZNjW1paEhAQOHDiAra1tjvrixYtTokQJZs2axeHDh1m/fj2vvvpqoedYuHAhtWrVomXLlpw6dQqA0aNH8/XXX3P48GH279/PypUrqVq16j86n6CgIObPn09CQgLbtm2jZ8+eBV5pLYwhQ4bQpEkTWrVqxYcffsju3bv5/fff+fzzz2nUqBGHDh0q0LUrVaoUTk5O5gdcpaSkADB27FimTp3Ke++9x6FDh9i1axfvv/9+geMbOHAg586do3v37uzYsYMjR47w3Xff0a9fP4vEW0RERESkMMKDfZnZqy4+HpZboX08HJnZqy7hwb559BTJXZElwwDu7u64u7vnWmdjY8PixYvZuXMnwcHBvPLKK7zzzjuFnsPOzo5FixZRvXp1WrZsSXJyMvb29owcOZKaNWvSvHlzbG1tWbx48T86l7lz53L+/Hnq1q1L7969GTx4MKVKlfpHY+bGwcGBdevWMXz4cD7++GMaNWpEgwYNeO+99xg8eDDBwcEFunZ2dna89957fPzxx5QpU4YOHToAN7Z7T5s2jRkzZlC9enXat2/PoUOHChxfmTJl2Lx5M5mZmTz22GPUqFGDIUOG4OnpiY1Nkf5xExEREZEHXHiwL5teb8miZxsxvVttFj3biE2vt1QiLLfFZBT0iUwiRezixYt4eHiQkpKS5y9RRERERETk3+9O5AZaqhMRERERERGro2RYRERERERErI6SYREREREREbE6SoZFRERERETE6igZFhEREREREaujZFhERERERESsjpJhERERERERsTpKhkVERERERMTqKBkWERERERERq6NkWERERERERKyOkmERERERERGxOkqGRURERERExOooGRYRERERERGro2RYRERERERErI6SYREREREREbE6dkUdgNx5kZGRXLhw4f+xd+dxUdX7H8dfA8gOoygGGoILaqiopHZzX/KKC5V508yNLCvLa1a2YJlSJje7Vtq9WZGKS4Z1MzMzyqthpiaa4RJm6gWxxDDQQZRN4PcHD+fniCjqwAjzfj4e5/Fozvmccz4zOcO853sWVq9ebetWaqW8wmJmr0shLessQfXdmTYoBDdnR1u3JSJS5YpLSklKzSbzdD4NvVzp0tQHRweDrdsSuaHofVL1Cs+VsGxbGkeyzxLo486Y24NwdtIYX1Wrjf+2FYZtIDIykiVLlpSbP2DAABISEmzQ0bWZOXMmq1evJjk52datVJsJS3ewPiXT/HjzQVj2Qzr9QxoSO7azDTsTEalaCfsyiP4ihQxTvnmev9GVGREhhLf1t2FnIjcOvU+qXsy6FGI3p1JS+v/zXl23nwk9mhI1KMR2jdVytfXftn5CsZHw8HAyMjIspo8++sjWbdlEYWGhrVuolIuD8IXWp2QyYemOau5IRKR6JOzLYOLyXRZfggCOm/KZuHwXCfsybNSZyI1D75OqF7Muhfe+swzCACWl8N53qcSsS7FNY7Vcbf63rTBsIy4uLvj5+VlM9erVA8BgMPDBBx8wdOhQ3N3dCQ4OZs2aNRbr//zzzwwZMgRvb2+8vLzo0aMHhw8ftqj55z//ib+/P/Xr1+fxxx+nqKjIvGzZsmV06tQJLy8v/Pz8uP/++8nM/P+gl5iYiMFgYMOGDXTq1Al3d3e6du3KgQMHAIiLiyM6Oprdu3djMBgwGAzExcUBcOrUKR566CF8fX3x9vamb9++7N6927ztmTNn0qFDBz744AOaNm2Kq6urVV/bqpBXWFxhED5vfUomeYXF1dSRiEj1KC4pJfqLFEovsez8vOgvUii++NupiB3R+6TqFZ4rIXZz6mVrYjenUniupJo6sg+1/d+2wvANKjo6muHDh7Nnzx4GDRrEqFGjyM7OBuD333+nZ8+euLi4sHHjRn788UfGjx/PuXPnzOt/++23HD58mG+//ZYlS5YQFxdnDqsARUVFvPLKK+zevZvVq1eTlpZGZGRkuT5eeOEF5s6dy86dO3FycmL8+PEAjBgxgqeffpo2bdqYR7ZHjBgBwL333ktmZiZfffUVP/74I2FhYfTr18/cP8ChQ4f49NNPWbVqVYWHWRcUFJCTk2Mx2crsSv7SWNk6EZGaIik1u9xowIVKgQxTPkmp2RXWiNR2ep9UvWXb0sqNCF+spLSsTqyntv/b1jnDNrJ27Vo8PT0t5k2bNo1p06YBZecVjxw5EoDZs2czf/58kpKSCA8P59///jdGo5H4+Hjq1KkDQMuWLS22Va9ePf71r3/h6OhI69atGTx4MBs2bGDChAkA5lAL0KxZM+bPn0/nzp3Jzc216OvVV1+lV69eADz//PMMHjyY/Px83Nzc8PT0xMnJCT8/P3P9999/T1JSEpmZmbi4uABlI9SrV6/mP//5Dw8//DBQdmj00qVL8fX1rfA1iomJITo6+ipe1aqTlnXWqnUiIjVF5umKvwRdS51IbaT3SdU7kl2571iVrZPKqe3/thWGbaRPnz4sWLDAYp6Pj4/5v0NDQ83/7eHhgbe3t/kw5uTkZHr06GEOwpfSpk0bHB3//wrH/v7+7N271/z4xx9/ZObMmezevZuTJ09SUlJ2SEl6ejohIf9/8YEL+/D3Lzs5PjMzkyZNmlxyv7t37yY3N5f69etbzM/Ly7M4jDswMPCyQRggKiqKp556yvw4JyeHgICAy65TVYLqu7P5YOXqRERqk4ZelTuVpbJ1IrWR3idVL9Cnct+xKlsnlVPb/20rDNuIh4cHLVq0qHD5xUHXYDCYA6ubm9sVt3+59c+cOcOAAQMYMGAAH374Ib6+vqSnpzNgwIByF7O6cDsGQ9ml089v51Jyc3Px9/cnMTGx3LK6deua/9vDw+OKz8HFxcU8umxr0waFsOyH9ErViYjUJl2a+uBvdOW4Kf+S54wZAD9j2S02ROyV3idVb8ztQby6bv9lD5V2MJTVifXU9n/bOme4BgoNDWXz5s0WF8S6Gr/88gtZWVn84x//oEePHrRu3dri4lmV5ezsTHGx5QWjwsLCOH78OE5OTrRo0cJiatCgwTX1eyNwc3akf0jDy9b0D2mo+w2LSK3j6GBgRkTZD30X303y/OMZESE1/l6TItdD75Oq5+zkwIQeTS9bM6FHU91v2Mpq+79t/WuxkYKCAo4fP24x/fnnn5Vad9KkSeTk5HDfffexc+dODh48yLJly8xXer6SJk2a4OzszNtvv83//vc/1qxZwyuvvHLVzyEoKIjU1FSSk5P5888/KSgo4I477uD222/n7rvv5ptvviEtLY2tW7fywgsvsHPnzqvex40kdmznCgOx7jMsIrVZeFt/FowOw89oeRicn9GVBaPDavQ9JkWsRe+Tqhc1KIRHejbl4tzlYIBHeuo+w1WlNv/b1mHSNpKQkGA+B/e8Vq1a8csvv1xx3fr167Nx40aeeeYZevXqhaOjIx06dKBbt26V2revry9xcXFMmzaN+fPnExYWxj//+U/uvPPOq3oOw4YNY9WqVfTp04dTp06xePFiIiMjWbduHS+88AIPPPAAJ06cwM/Pj549e3LTTTdd1fZvRLFjO5NXWMzsdSmkZZ0lqL470waFaERYRGq98Lb+9A/xIyk1m8zT+TT0KjssrqaOBohUBb1Pql7UoBCe/mtrlm1L40j2WQJ93Blze5BGhKtYbf23bSgtLa2ZN4USu5OTk4PRaMRkMuHt7W3rdkRERERExEaskQ30E4qIiIiIiIjYHYVhERERERERsTsKwyIiIiIiImJ3FIZFRERERETE7igMi4iIiIiIiN1RGBYRERERERG7ozAsIiIiIiIidkdhWEREREREROyOwrCIiIiIiIjYHYVhERERERERsTsKwyIiIiIiImJ3FIZFRERERETE7igMi4iIiIiIiN1RGBYRERERERG7ozAsIiIiIiIidkdh2M4ZDAZWr14NQFpaGgaDgeTkZAASExMxGAycOnXKZv2JiMiNo7iklG2Hs/g8+Xe2Hc6iuKTU1i2JiB3KKyxm+uq9jFm4nemr95JXWGzrluxCbfwb4GTrBuTabNu2je7duxMeHs6XX355xfqZM2eyevVqc9A9LyMjg3r16lVRlyIiUlsk7Msg+osUMkz55nn+RldmRIQQ3tbfhp2JiD2ZsHQH61MyzY83H4RlP6TTP6QhsWM727Cz2q22/g3QyHANtXDhQv7+97/z3XffcezYsQrrSktLOXfuXIXL/fz8cHFxqYoWASgsLKyybYuISPVI2JfBxOW7LL4EARw35TNx+S4S9mXYqDMRsScXB+ELrU/JZMLSHdXckX2ozX8DFIZroNzcXFauXMnEiRMZPHgwcXFx5mXnD23+6quvuPXWW3FxcWH58uVER0eze/duDAYDBoPBvM6Fh0lXxvfff0+PHj1wc3MjICCAyZMnc+bMGfPyoKAgXnnlFcaOHYu3tzcPP/wwffv2ZdKkSRbbOXHiBM7OzmzYsOF6XgoREalixSWlRH+RwqUOhjs/L/qLlFpxuJyI3LjyCosrDMLnrU/J1CHTVlbb/wYoDNdAH3/8Ma1bt6ZVq1aMHj2aRYsWUVpq+Q/w+eef5x//+Af79++nf//+PP3007Rp04aMjAwyMjIYMWLEVe/38OHDhIeHM2zYMPbs2cPKlSv5/vvvywXdf/7zn7Rv356ffvqJ6dOn89BDD7FixQoKCgrMNcuXL6dx48b07du3wv0VFBSQk5NjMYmISPVKSs0uNxpwoVIgw5RPUmp29TUlInZn9roUq9ZJ5dT2vwEKwzXQwoULGT16NADh4eGYTCY2bdpkUfPyyy/Tv39/mjdvTuPGjfH09MTJyQk/Pz/8/Pxwc3O76v3GxMQwatQopkyZQnBwMF27dmX+/PksXbqU/Pz/f5P07duXp59+mubNm9O8eXPuueceAD7//HNzTVxcHJGRkRgMhsvuz2g0mqeAgICr7llERK5P5umKvwRdS52IyLVIyzpr1TqpnNr+N0BhuIY5cOAASUlJjBw5EgAnJydGjBjBwoULLeo6depk9X3v3r2buLg4PD09zdOAAQMoKSkhNTW1wn27uroyZswYFi1aBMCuXbvYt28fkZGRl91fVFQUJpPJPB09etTqz0lERC6voZerVetERK5FUH13q9ZJ5dT2vwG6mnQNs3DhQs6dO0ejRo3M80pLS3FxceFf//qXeZ6Hh4fV952bm8sjjzzC5MmTyy1r0qTJZff90EMP0aFDB3777TcWL15M3759CQwMvOz+XFxcqvTiXiIicmVdmvrgb3TluCn/kueMGQA/oytdmvpUd2siYkemDQph2Q/plaoT66ntfwM0MlyDnDt3jqVLlzJ37lySk5PN0+7du2nUqBEfffRRhes6OztTXHx9FxQICwsjJSWFFi1alJucnZ0vu267du3o1KkTsbGxrFixgvHjx19XLyIiUj0cHQzMiCj7cnnxiS3nH8+ICMHRoeLTXkRErpebsyP9QxpetqZ/SEPcnB2rqSP7UNv/BigM1yBr167l5MmTPPjgg7Rt29ZiGjZsWLlDpS8UFBREamoqycnJ/PnnnxYXs6qs5557jq1btzJp0iSSk5M5ePAgn3/+ebkLaFXkoYce4h//+AelpaUMHTr0qvcvIiK2Ed7WnwWjw/AzWh4G52d0ZcHosBp9j0kRqTlix3auMBDrPsNVpzb/DdBh0jXIwoULueOOOzAajeWWDRs2jDlz5rBnz55Lrjts2DBWrVpFnz59OHXqFIsXL77iObsXCw0NZdOmTbzwwgv06NGD0tJSmjdvXukrU48cOZIpU6YwcuRIXF1r5nkFIiL2KrytP/1D/EhKzSbzdD4NvcoOi6upowEiUjPFju1MXmExs9elkJZ1lqD67kwbFKIR4SpWW/8GGEovviePSBVJS0ujefPm7Nixg7CwsKtePycnB6PRiMlkwtvbuwo6FBERERGRmsAa2UAjw1LlioqKyMrK4sUXX+Qvf/nLNQVhERERERERa9I5w1LltmzZgr+/Pzt27ODdd9+1dTsiIiIiIiIaGZaq17t3b3Q0voiIiIiI3Eg0MiwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxOwrDIiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbujMCwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxOwrDN7i0tDQMBgPJyck268FgMLB69Wqb7f9GU1xSyrbDWXye/DvbDmdRXFJq65ZERERE7Ia+i4m1ONm6geq2bds2unfvTnh4OF9++aWt27migIAAMjIyaNCggVW3GxkZyZIlSwBwcnLCx8eH0NBQRo4cSWRkJA4O//87SUZGBvXq1bPq/muqhH0ZRH+RQoYp3zzP3+jKjIgQwtv627AzERERkdpP38XEmuxuZHjhwoX8/e9/57vvvuPYsWO2bueKHB0d8fPzw8nJ+r9bhIeHk5GRQVpaGl999RV9+vThiSeeYMiQIZw7d85c5+fnh4uLi9X3f15xcTElJSVVtn1rSdiXwcTluyw+fAGOm/KZuHwXCfsybNSZiIiISO2n72JibXYVhnNzc1m5ciUTJ05k8ODBxMXFWSw/efIko0aNwtfXFzc3N4KDg1m8eDEAhYWFTJo0CX9/f1xdXQkMDCQmJsa87htvvEG7du3w8PAgICCAxx57jNzcXPPyI0eOEBERQb169fDw8KBNmzasW7fuivu9+DDpxMREDAYDGzZsoFOnTri7u9O1a1cOHDhw1a+Hi4sLfn5+NG7cmLCwMKZNm8bnn3/OV199ZfHaXHiYdNeuXXnuuecstnPixAnq1KnDd999B0BBQQFTp06lcePGeHh4cNttt5GYmGiuj4uLo27duqxZs4aQkBBcXFxIT0+/6v6rU3FJKdFfpHCpg3DOz4v+IkWH6YiIiIhUAX0Xk6pgV2H4448/pnXr1rRq1YrRo0ezaNEiSkv//w0zffp0UlJS+Oqrr9i/fz8LFiwwH548f/581qxZw8cff8yBAwf48MMPCQoKMq/r4ODA/Pnz+fnnn1myZAkbN27k2WefNS9//PHHKSgo4LvvvmPv3r289tpreHp6XnG/FXnhhReYO3cuO3fuxMnJifHjx1vlNerbty/t27dn1apVl1w+atQo4uPjLV63lStX0qhRI3r06AHApEmT2LZtG/Hx8ezZs4d7772X8PBwDh48aF7n7NmzvPbaa3zwwQf8/PPPNGzYsNy+CgoKyMnJsZhsJSk1u9yvkBcqBTJM+SSlZldfUyIiIiJ2Qt/FpCrY1TnDCxcuZPTo0UDZIcImk4lNmzbRu3dvANLT0+nYsSOdOnUCsAi76enpBAcH0717dwwGA4GBgRbbnjJlivm/g4KCmDVrFo8++ijvvPOOef1hw4bRrl07AJo1a2ax7Yr2W5FXX32VXr16AfD8888zePBg8vPzcXV1rfwLUoHWrVuzZ8+eSy4bPnw4U6ZM4fvvvzeH3xUrVjBy5EgMBgPp6eksXryY9PR0GjVqBMDUqVNJSEhg8eLFzJ49G4CioiLeeecd2rdvX2EfMTExREdHX/fzsYbM0xV/+F5LnYiIiIhUnr6LSVWwm5HhAwcOkJSUxMiRI4Gyi0aNGDGChQsXmmsmTpxIfHw8HTp04Nlnn2Xr1q3mZZGRkSQnJ9OqVSsmT57MN998Y7H9//73v/Tr14/GjRvj5eXFmDFjyMrK4uzZswBMnjyZWbNm0a1bN2bMmGERNi+334qEhoaa/9vfv+xiAZmZmdfwypRXWlqKwWC45DJfX1/++te/8uGHHwKQmprKtm3bGDVqFAB79+6luLiYli1b4unpaZ42bdrE4cOHzdtxdna2eA6XEhUVhclkMk9Hjx61yvO7Fg29KvcjQ2XrRERERKTy9F1MqoLdhOGFCxdy7tw5GjVqhJOTE05OTixYsIBPP/0Uk8kEwMCBAzly5AhPPvkkx44do1+/fkydOhWAsLAwUlNTeeWVV8jLy2P48OH87W9/A8rO6x0yZAihoaF8+umn/Pjjj/z73/8Gys41BnjooYf43//+x5gxY9i7dy+dOnXi7bffvuJ+K1KnTh3zf58Prta6CNX+/ftp2rRphctHjRrFf/7zH4qKilixYgXt2rUzj3jn5ubi6OjIjz/+SHJysnnav38/8+bNM2/Dzc2twsB9nouLC97e3haTrXRp6oO/0ZWKOjZQdiXDLk19qrMtEREREbug72JSFewiDJ87d46lS5cyd+5ci4C2e/duGjVqxEcffWSu9fX1Zdy4cSxfvpy33nqL999/37zM29ubESNGEBsby8qVK/n000/Jzs7mxx9/pKSkhLlz5/KXv/yFli1bXvJK1QEBATz66KOsWrWKp59+mtjY2Erttzpt3LiRvXv3MmzYsApr7rrrLvLz80lISGDFihXmUWGAjh07UlxcTGZmJi1atLCY/Pz8quMpVAlHBwMzIkIAyn0In388IyIER4fLB3wRERERuXr6LiZVwS7OGV67di0nT57kwQcfxGg0WiwbNmwYCxcu5NFHH+Wll17i1ltvpU2bNhQUFLB27VpuueUWoOxq0f7+/nTs2BEHBwc++eQT/Pz8qFu3Li1atKCoqIi3336biIgItmzZwrvvvmuxnylTpjBw4EBatmzJyZMn+fbbb83bvtx+q1JBQQHHjx+nuLiYP/74g4SEBGJiYhgyZAhjx46tcD0PDw/uvvtupk+fzv79+82HngO0bNmSUaNGMXbsWObOnUvHjh05ceIEGzZsIDQ0lMGDB1f586oq4W39WTA6rNy97fx0bzsRERGRKqfvYmJtdhGGFy5cyB133FEuCENZGJ4zZw579uzB2dmZqKgo0tLScHNzo0ePHsTHxwPg5eXFnDlzOHjwII6OjnTu3Jl169bh4OBA+/bteeONN3jttdeIioqiZ8+exMTEWATK4uJiHn/8cX777Te8vb0JDw/nzTffBLjsfqtSQkIC/v7+ODk5Ua9ePdq3b8/8+fMZN24cDg6XP2hg1KhRDBo0iJ49e9KkSROLZYsXL2bWrFk8/fTT/P777zRo0IC//OUvDBkypCqfTrUIb+tP/xA/klKzyTydT0OvssNx9CukiIiISNXTdzGxJkPphffIEbmB5eTkYDQaMZlMNj1/WEREREREbMsa2cAuzhkWERERERERuZDCsIiIiIiIiNgdhWERERERERGxOwrDIiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbujMCwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxOwrDIiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbvjZOsG5MZmMBj47LPPuPvuu23dyg2juKSUpNRsMk/n09DLlS5NfXB0MNi6LRGRKpebf44nV/5E+sk8mtRz480RHfF01VcJkQvpfVL1TGeLGB+XxDFTPo2MriyK7ILRvY6t26r1auN3YENpaWmprZuQ6hcZGcmSJUsAcHJywsfHh9DQUEaOHElkZCQODmUHDRw/fpx69erh4uJiy3YByMnJwWg0YjKZ8Pb2tkkPCfsyiP4ihQxTvnmev9GVGREhhLf1t0lPIiLV4c5/bWbPbznl5ofe7M2aST1s0JHIjUfvk6rX6/WNHMnKKzc/sL4bm57pa4OO7MON+B3YGtlAh0nbsfDwcDIyMkhLS+Orr76iT58+PPHEEwwZMoRz584B4OfnV6VBuLi4mJKSkirbvjUl7Mtg4vJdFh8CAMdN+UxcvouEfRk26kxEpGpV9AUfYM9vOdz5r83V3JHIjUfvk6pXURAGOJKVR6/XN1ZzR/ahNn8HVhi2Yy4uLvj5+dG4cWPCwsKYNm0an3/+OV999RVxcXFA2WHSq1evBqBr164899xzFts4ceIEderU4bvvvgOgoKCAqVOn0rhxYzw8PLjttttITEw018fFxVG3bl3WrFlDSEgILi4upKenV8fTvS7FJaVEf5HCpQ6jOD8v+osUikt0oIWI1C65+ecq/IJ/3p7fcsjNP1dNHYncePQ+qXqms0UVBuHzjmTlYTpbVE0d2Yfa/h1YYVgs9O3bl/bt27Nq1apyy0aNGkV8fDwXHlm/cuVKGjVqRI8eZYf+TJo0iW3bthEfH8+ePXu49957CQ8P5+DBg+Z1zp49y2uvvcYHH3zAzz//TMOGDS/ZS0FBATk5ORaTrSSlZpf7NexCpUCGKZ+k1Ozqa0pEpBo8ufInq9aJ1EZ6n1S98XFJVq2Tyqnt34EVhqWc1q1bk5aWVm7+8OHDOXbsGN9//7153ooVKxg5ciQGg4H09HQWL17MJ598Qo8ePWjevDlTp06le/fuLF682LxOUVER77zzDl27dqVVq1a4u7tfso+YmBiMRqN5CggIsPpzrazM0xV/CFxLnYhITZF+8vIjMVdbJ1Ib6X1S9Y5dJpBdS51UTm3/DqwwLOWUlpZiMJS/Mpyvry9//etf+fDDDwFITU1l27ZtjBo1CoC9e/dSXFxMy5Yt8fT0NE+bNm3i8OHD5u04OzsTGhp6xT6ioqIwmUzm6ejRo1Z6hlevoZerVetERGqKJvXcrFonUhvpfVL1Ghkr9x2rsnVSObX9O7DCsJSzf/9+mjZtesllo0aN4j//+Q9FRUWsWLGCdu3a0a5dOwByc3NxdHTkxx9/JDk52Tzt37+fefPmmbfh5uZ2ybB9MRcXF7y9vS0mW+nS1Ad/oysVdW2g7Ip6XZr6VGdbIiJV7s0RHa1aJ1Ib6X1S9RZFdrFqnVRObf8OrDAsFjZu3MjevXsZNmzYJZffdddd5Ofnk5CQwIoVK8yjwgAdO3akuLiYzMxMWrRoYTH5+flV11OoEo4OBmZEhACU+zA4/3hGREiNv9eaiMjFPF2dCL358j9Ght7srfuoil3T+6TqGd3rEFj/8iPrgfXddL9hK6vt34EVhu1YQUEBx48f5/fff2fXrl3Mnj2bu+66iyFDhjB27NhLruPh4cHdd9/N9OnT2b9/PyNHjjQva9myJaNGjWLs2LGsWrWK1NRUkpKSiImJ4csvv6yup1Vlwtv6s2B0GH4XHX7jZ3Rlwegw3WdYRGqtNZN6VPhFX/dPFSmj90nV2/RM3woDse4zXHVq83dg/TxlxxISEvD398fJyYl69erRvn175s+fz7hx43BwqPh3klGjRjFo0CB69uxJkyZNLJYtXryYWbNm8fTTT/P777/ToEED/vKXvzBkyJCqfjrVIrytP/1D/EhKzSbzdD4NvcoOC6mpv4aJiFTWmkk9yM0/x5MrfyL9ZB5N6rnx5oiOGukSuYDeJ1Vv0zN9MZ0tYnxcEsdM+TQyurIosotGhKtYbf0ObCi98D45IjewnJwcjEYjJpPJpucPi4iIiIiIbVkjG+gwaREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjpOtG5CaqXfv3nTo0IG33nrL1q1Uu+KSUpJSs8k8nU9DL1e6NPXB0cFg67ZERETkBpBXWMzsdSmkZZ0lqL470waF4ObsaOu2ahV9FxNrURiuod59912eeeYZTp48iZNT2f/G3Nxc6tWrR7du3UhMTDTXJiYm0qdPHw4dOkTz5s1t1HHtkLAvg+gvUsgw5Zvn+RtdmRERQnhbfxt2JiIiIrY2YekO1qdkmh9vPgjLfkinf0hDYsd2tmFntYe+i4k16TDpGqpPnz7k5uayc+dO87zNmzfj5+fH9u3byc///w+Ib7/9liZNmlx1EC4tLeXcuXNW67mmS9iXwcTluyw+fAGOm/KZuHwXCfsybNSZiIiI2NrFQfhC61MymbB0RzV3VPvou5hYm8JwDdWqVSv8/f3LjQDfddddNG3alB9++MFifp8+fVi2bBmdOnXCy8sLPz8/7r//fjIzMy3qDAYDX331FbfeeisuLi58//33nDlzhrFjx+Lp6Ym/vz9z584t109BQQFTp06lcePGeHh4cNttt1n0FhcXR926dfn666+55ZZb8PT0JDw8nIyMmvGhVVxSSvQXKZReYtn5edFfpFBccqkKERERqc3yCosrDMLnrU/JJK+wuJo6qn30XUyqgsJwDdanTx++/fZb8+Nvv/2W3r1706tXL/P8vLw8tm/fTp8+fSgqKuKVV15h9+7drF69mrS0NCIjI8tt9/nnn+cf//gH+/fvJzQ0lGeeeYZNmzbx+eef880335CYmMiuXbss1pk0aRLbtm0jPj6ePXv2cO+99xIeHs7BgwfNNWfPnuWf//wny5Yt47vvviM9PZ2pU6dW+PwKCgrIycmxmGwlKTW73K+QFyoFMkz5JKVmV19TIiIickOYvS7FqnVSnr6LSVXQOcM1WJ8+fZgyZQrnzp0jLy+Pn376iV69elFUVMS7774LwLZt2ygoKKBPnz40adLEvG6zZs2YP38+nTt3Jjc3F09PT/Oyl19+mf79+wNl5yEvXLiQ5cuX069fPwCWLFnCzTffbK5PT09n8eLFpKen06hRIwCmTp1KQkICixcvZvbs2QDmvs4frj1p0iRefvnlCp9fTEwM0dHR1niprlvm6Yo/fK+lTkRERGqPtKyzVq2T8vRdTKqCRoZrsN69e3PmzBl27NjB5s2badmyJb6+vvTq1ct83nBiYiLNmjWjSZMm/Pjjj0RERNCkSRO8vLzo1asXUBZmL9SpUyfzfx8+fJjCwkJuu+028zwfHx9atWplfrx3716Ki4tp2bIlnp6e5mnTpk0cPnzYXOfu7m5x3rK/v7/FYdoXi4qKwmQymaejR49e+4t1nRp6uVq1TkRERGqPoPruVq2T8vRdTKqCRoZrsBYtWnDzzTfz7bffcvLkSXO4bdSoEQEBAWzdupVvv/2Wvn37cubMGQYMGMCAAQP48MMP8fX1JT09nQEDBlBYWGixXQ8Pj6vqIzc3F0dHR3788UccHS1vHXDhiHOdOnUslhkMBkpLKz6vw8XFBRcXl6vqpap0aeqDv9GV46b8S56rYgD8jGWX9hcRERH7Mm1QCMt+SK9UnVwbfReTqqCR4RquT58+JCYmkpiYSO/evc3ze/bsyVdffUVSUhJ9+vThl19+ISsri3/84x/06NGD1q1bX3ZU9rzmzZtTp04dtm/fbp538uRJfv31V/Pjjh07UlxcTGZmJi1atLCY/Pz8rPp8bcXRwcCMiLI/YBffxe784xkRIbrHnYiIiB1yc3akf0jDy9b0D2mo+w1fB30Xk6qgMFzD9enTh++//57k5GTzyDBAr169eO+99ygsLDSfL+zs7Mzbb7/N//73P9asWcMrr7xyxe17enry4IMP8swzz7Bx40b27dtHZGQkDg7//0+nZcuWjBo1irFjx7Jq1SpSU1NJSkoiJiaGL7/8skqety2Et/Vnwegw/IyWh9/4GV1ZMDpM97YTERGxY7FjO1cYiHWfYevQdzGxNh0mXcP16dOHvLw8WrduzU033WSe36tXL06fPm2+BROU3d5o2rRpzJ8/n7CwMP75z39y5513XnEfr7/+Orm5uURERODl5cXTTz+NyWSyqFm8eDGzZs3i6aef5vfff6dBgwb85S9/YciQIdZ9wjYW3taf/iF+JKVmk3k6n4ZeZYfj6FdIERERiR3bmbzCYmavSyEt6yxB9d2ZNihEI8JWpO9iYk2G0sudtClyA8nJycFoNGIymfD29rZ1OyIiIiIiYiPWyAY6TFpERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdcbJ1AyI1TXFJKUmp2WSezqehlytdmvrg6GCwdVsiIlUur7CY2etSSMs6S1B9d6YNCsHN2dHWbYncUArPlbBsWxpHss8S6OPOmNuDcHbS+JM1ncgpYOg735N9pggfjzp89lh3fL1dbN2W1ECG0tLSUls3IWAwGPjss8+4++67L7k8KCiIKVOmMGXKlGrf97XWWltOTg5GoxGTyYS3t3e17x8gYV8G0V+kkGHKN8/zN7oyIyKE8Lb+NulJRKQ6TFi6g/UpmeXm9w9pSOzYzjboSOTGE7MuhdjNqZRc8O3awQATejQlalCI7RqrRUJnfk1O/rly871dndgzc4ANOhJbsUY20M9U1eTEiRNMnDiRJk2a4OLigp+fHwMGDGDLli2VWn/Hjh08/PDDVdJbRkYGAwcOrJJt1yYJ+zKYuHyXRRAGOG7KZ+LyXSTsy7BRZyIiVauiIAywPiWTCUt3VHNHIjeemHUpvPedZRAGKCmF975LJWZdim0aq0UqCsIAOfnnCJ35dTV3JDWdwnA1GTZsGD/99BNLlizh119/Zc2aNfTu3ZusrKxKre/r64u7u3uFy4uKiq65Nz8/P1xcbHdoSWFhoc32XVnFJaVEf5HCpQ6jOD8v+osUii/+CygiUsPlFRZXGITPW5+SSV5hcTV1JHLjKTxXQuzm1MvWxG5OpfBcSTV1VPucyCmoMAifl5N/jhM5BdXUkdQGCsPV4NSpU2zevJnXXnuNPn36EBgYSJcuXYiKiuLOO++85DozZszA39+fPXv2AGWHSb/11lvm5QaDgQULFnDnnXfi4eHBq6++ysyZM+nQoQOLFi2iSZMmeHp68thjj1FcXMycOXPw8/OjYcOGvPrqqxb7MhgMrF69GigLppMmTcLf3x9XV1cCAwOJiYmxqP/zzz8ZOnQo7u7uBAcHs2bNGovl+/btY+DAgXh6enLTTTcxZswY/vzzT/Py3r17M2nSJKZMmUKDBg0YMODSh7QUFBSQk5NjMdlKUmp2uRHhC5UCGaZ8klKzq68pEZFqMLuSo1mVrROpjZZtSys3InyxktKyOrk2Q9/53qp1IqAwXC08PT3x9PRk9erVFBRc/teq0tJS/v73v7N06VI2b95MaGhohbUzZ85k6NCh7N27l/HjxwNw+PBhvvrqKxISEvjoo49YuHAhgwcP5rfffmPTpk289tprvPjii2zfvv2S25w/fz5r1qzh448/5sCBA3z44YcEBQVZ1ERHRzN8+HD27NnDoEGDGDVqFNnZZSHw1KlT9O3bl44dO7Jz504SEhL4448/GD58uMU2lixZgrOzM1u2bOHdd9+9ZC8xMTEYjUbzFBAQcNnXriplnq44CF9LnYhITZGWddaqdSK10ZHsyv37r2ydlJd9pnJHQVa2TgR0Nelq4eTkRFxcHBMmTODdd98lLCyMXr16cd9991mE3XPnzjF69Gh++uknvv/+exo3bnzZ7d5///088MADFvNKSkpYtGgRXl5ehISE0KdPHw4cOMC6detwcHCgVatWvPbaa3z77bfcdttt5baZnp5OcHAw3bt3x2AwEBgYWK4mMjKSkSNHAjB79mzmz59PUlIS4eHh/Otf/6Jjx47Mnj3bXL9o0SICAgL49ddfadmyJQDBwcHMmTPnss8vKiqKp556yvw4JyfHZoG4oZerVetERGqKoPrubD5YuToRexXoU7l//5Wtk/J8POpw9tSVT8fw8ahTDd1IbaGR4WoybNgwjh07xpo1awgPDycxMZGwsDDi4uLMNU8++STbt2/nu+++u2IQBujUqVO5eUFBQXh5eZkf33TTTYSEhODg4GAxLzPz0ud/RUZGkpycTKtWrZg8eTLffPNNuZoLA7yHhwfe3t7m7e3evZtvv/3WPBru6elJ69atgbJR6/NuvfXWKz4/FxcXvL29LSZb6dLUB3+jKxXdQMlA2VWluzT1qc62RESq3LRKXgG3snUitdGY24O40l0WHQxldXJtPnusu1XrREBhuFq5urrSv39/pk+fztatW4mMjGTGjBnm5f379+f333/n668rdyU8Dw+PcvPq1LH8NcxgMFxyXknJpS/gEBYWRmpqKq+88gp5eXkMHz6cv/3tb1fcx/nt5ebmEhERQXJyssV08OBBevbsedneb2SODgZmRJR90bv4b935xzMiQnS/YRGpddycHekf0vCyNf1DGup+w2LXnJ0cmNCj6WVrJvRoqvsNXwdfbxe8XS9/UKu3q5PuNyxXRe9IGwoJCeHMmTPmx3feeScrVqzgoYceIj4+3mZ9eXt7M2LECGJjY1m5ciWffvqp+ZzgKwkLC+Pnn38mKCiIFi1aWEw1LQBfLLytPwtGh+FntDwU2s/oyoLRYbrPsIjUWrFjO1cYiHWfYZEyUYNCeKRn03IjxA4GeKSn7jNsDXtmDqgwEOs+w3ItdM5wNcjKyuLee+9l/PjxhIaG4uXlxc6dO5kzZw533XWXRe3QoUNZtmwZY8aMwcnJqdyobFV744038Pf3p2PHjjg4OPDJJ5/g5+dH3bp1K7X+448/TmxsLCNHjuTZZ5/Fx8eHQ4cOER8fzwcffICjY80eOQhv60//ED+SUrPJPJ1PQ6+yQ6M1IiwitV3s2M7kFRYze10KaVlnCarvzrRBIRoRFrlA1KAQnv5ra5ZtS+NI9lkCfdwZc3uQRoStaM/MAZzIKWDoO9+TfaYIH486fPZYd40IyzVRGK4Gnp6e3Hbbbbz55pscPnyYoqIiAgICmDBhAtOmTStX/7e//Y2SkhLGjBmDg4MD99xzT7X16uXlxZw5czh48CCOjo507tzZfPGtymjUqBFbtmzhueee469//SsFBQUEBgYSHh5e6W3c6BwdDNzevL6t2xARqXZuzo68cnc7W7chckNzdnLgwR7NbN1Grebr7cL3z/ezdRtSCxhKS0uvcFc0kRtDTk4ORqMRk8lk04tpiYiIiIiIbVkjG9SOoToRERERERGRq6AwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJid5xs3YCIiIjUDIXnSli2LY0j2WcJ9HFnzO1BODvpd3WRC+l9UvXyCouZvS6FtKyzBNV3Z9qgENycHW3dltRAhtLS0lJbNyE1Q2JiIn369OHkyZPUrVu32vefk5OD0WjEZDLh7e1d7fsXEbFnMetSiN2cSskF3xocDDChR1OiBoXYrjGRG4jeJ1VvwtIdrE/JLDe/f0hDYsd2tkFHYivWyAb6maqGOn78OH//+99p1qwZLi4uBAQEEBERwYYNGyq1flxcnE0CrYiI1Dwx61J47zvLL/gAJaXw3nepxKxLsU1jIjcQvU+qXkVBGGB9SiYTlu6o5o6kplMYroHS0tK49dZb2bhxI6+//jp79+4lISGBPn368Pjjj9u6vatWVFRk6xZERKQChedKiN2cetma2M2pFJ4rqaaORG48ep9UvbzC4gqD8HnrUzLJKyyupo6kNlAYroEee+wxDAYDSUlJDBs2jJYtW9KmTRueeuopfvjhBwDeeOMN2rVrh4eHBwEBATz22GPk5uYCZYc7P/DAA5hMJgwGAwaDgZkzZwJQUFDAc889R0BAAC4uLrRo0YKFCxda7P/HH3+kU6dOuLu707VrVw4cOGCx/PPPPycsLAxXV1eaNWtGdHQ0586dMy83GAwsWLCAO++8Ew8PD1599dVLPs+CggJycnIsJhERqV7LtqWVG+m6WElpWZ2IvdL7pOrNruTIemXrREBhuMbJzs4mISGBxx9/HA8Pj3LLzx/67ODgwPz58/n5559ZsmQJGzdu5NlnnwWga9euvPXWW3h7e5ORkUFGRgZTp04FYOzYsXz00UfMnz+f/fv389577+Hp6WmxjxdeeIG5c+eyc+dOnJycGD9+vHnZ5s2bGTt2LE888QQpKSm89957xMXFlQu8M2fOZOjQoezdu9di/QvFxMRgNBrNU0BAwDW/biIicm2OZJ+1ap1IbaT3SdVLy6rca1fZOhHQ1aRrnEOHDlFaWkrr1q0vWzdlyhTzfwcFBTFr1iweffRR3nnnHZydnTEajRgMBvz8/Mx1v/76Kx9//DHr16/njjvuAKBZs2bltv3qq6/Sq1cvAJ5//nkGDx5Mfn4+rq6uREdH8/zzzzNu3Djz+q+88grPPvssM2bMMG/j/vvv54EHHrjsc4iKiuKpp54yP87JyVEgFhGpZoE+7latE6mN9D6pekH13dl8sHJ1IpWlkeEaprIX//7vf/9Lv379aNy4MV5eXowZM4asrCzOnq3417Lk5GQcHR3NQbcioaGh5v/29/cHIDOz7ByO3bt38/LLL+Pp6WmeJkyYQEZGhsW+O3XqdMXn4OLigre3t8UkIiLVa8ztQTgYLl/jYCirE7FXep9UvWmVvBp3ZetEQGG4xgkODsZgMPDLL79UWJOWlsaQIUMIDQ3l008/5ccff+Tf//43AIWFhRWu5+bmVqke6tSpY/5vg6Hsk7+kpOyCELm5uURHR5OcnGye9u7dy8GDB3F1dTWvd6lDvEVE5Mbj7OTAhB5NL1szoUdT3UdV7JreJ1XPzdmR/iENL1vTP6Sh7jcsV0XvyBrGx8eHAQMG8O9//5szZ86UW37q1Cl+/PFHSkpKmDt3Ln/5y19o2bIlx44ds6hzdnamuNjyanvt2rWjpKSETZs2XXN/YWFhHDhwgBYtWpSbHBz0z01EpCaKGhTCIz2blhv5cjDAIz11/1QR0PukOsSO7VxhINZ9huVa6JzhGujf//433bp1o0uXLrz88suEhoZy7tw51q9fz4IFC4iPj6eoqIi3336biIgItmzZwrvvvmuxjaCgIHJzc9mwYQPt27fH3d2doKAgxo0bx/jx45k/fz7t27fnyJEjZGZmMnz48Er19tJLLzFkyBCaNGnC3/72NxwcHNi9ezf79u1j1qxZVfFyiIhINYgaFMLTf23Nsm1pHMk+S6CPO2NuD9JIl8gF9D6perFjO5NXWMzsdSmkZZ0lqL470waFaERYronCcA3UrFkzdu3axauvvsrTTz9NRkYGvr6+3HrrrSxYsID27dvzxhtv8NprrxEVFUXPnj2JiYlh7Nix5m107dqVRx99lBEjRpCVlcWMGTOYOXMmCxYsYNq0aTz22GNkZWXRpEkTpk2bVuneBgwYwNq1a3n55Zd57bXXqFOnDq1bt+ahhx6qipdCRESqkbOTAw/2KH9hRRH5f3qfVD03Z0deubudrduQWsBQWtkrMonYWE5ODkajEZPJpItpiYiIiIjYMWtkAx2zISIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO442boBuTEYDAY+++wz7r777krVz5w5k9WrV5OcnAxAZGQkp06dYvXq1VXW440ir7CY2etSSMs6S1B9d6YNCsHN2dHWbYmIVDl9/olcmd4nVc90tojxcUkcM+XTyOjKosguGN3r2LotqYEMpaWlpbZuwt6dOHGCl156iS+//JI//viDevXq0b59e1566SW6detWLT0cP36cevXq4eLiUqn6i8OwyWSitLSUunXrXnHdaw3OOTk5GI1GTCYT3t7eV7WutUxYuoP1KZnl5vcPaUjs2M426EhEpHro80/kyvQ+qXq9Xt/Ikay8cvMD67ux6Zm+NuhIbMUa2UCHSd8Ahg0bxk8//cSSJUv49ddfWbNmDb179yYrK6vaevDz86t0EL4Uo9FYqSBck1X0Bw5gfUomE5buqOaORESqhz7/RK5M75OqV1EQBjiSlUev1zdWc0dS0ykM29ipU6fYvHkzr732Gn369CEwMJAuXboQFRXFnXfeCZQdwrxgwQIGDhyIm5sbzZo14z//+Y/Fdo4ePcrw4cOpW7cuPj4+3HXXXaSlpVnULFq0iDZt2uDi4oK/vz+TJk0yLzMYDBYjtc899xwtW7bE3d2dZs2aMX36dIqKiip8HpGRkRaHWP/nP/+hXbt2uLm5Ub9+fe644w7OnDnDzJkzWbJkCZ9//jkGgwGDwUBiYuI1v37VJa+wuMI/cOetT8kkr7C4mjoSEake+vwTuTK9T6qe6WxRhUH4vCNZeZjOVvx9VeRiCsM25unpiaenJ6tXr6agoKDCuunTpzNs2DB2797NqFGjuO+++9i/fz8ARUVFDBgwAC8vLzZv3syWLVvw9PQkPDycwsJCABYsWMDjjz/Oww8/zN69e1mzZg0tWrSocH9eXl7ExcWRkpLCvHnziI2N5c0336zUc8rIyGDkyJGMHz+e/fv3k5iYyD333ENpaSlTp05l+PDhhIeHk5GRQUZGBl27dr3kdgoKCsjJybGYbGX2uhSr1omI1BT6/BO5Mr1Pqt74uCSr1omALqBlc05OTsTFxTFhwgTeffddwsLC6NWrF/fddx+hoaHmunvvvZeHHnoIgFdeeYX169fz9ttv884777By5UpKSkr44IMPMBgMACxevJi6deuSmJjIX//6V2bNmsXTTz/NE088Yd5m584Vn7vy4osvmv87KCiIqVOnEh8fz7PPPnvF55SRkcG5c+e45557CAwMBKBdu3bm5W5ubhQUFODn53fZ7cTExBAdHX3F/VWHtKyzVq0TEakp9PkncmV6n1S9Y6Z8q9aJgEaGbwjDhg3j2LFjrFmzhvDwcBITEwkLCyMuLs5cc/vtt1usc/vtt5tHhnfv3s2hQ4fw8vIyjzT7+PiQn5/P4cOHyczM5NixY/Tr16/SPa1cuZJu3brh5+eHp6cnL774Iunp6ZVat3379vTr14927dpx7733Ehsby8mTJyu97/OioqIwmUzm6ejRo1e9DWsJqu9u1ToRkZpCn38iV6b3SdVrZHS1ap0IKAzfMFxdXenfvz/Tp09n69atREZGMmPGjEqtm5uby6233kpycrLF9Ouvv3L//ffj5uZ2Vb1s27aNUaNGMWjQINauXctPP/3ECy+8YD7k+kocHR1Zv349X331FSEhIbz99tu0atWK1NTUq+rDxcUFb29vi8lWpg0KsWqdiEhNoc8/kSvT+6TqLYrsYtU6EVAYvmGFhIRw5swZ8+MffvjBYvkPP/zALbfcAkBYWBgHDx6kYcOGtGjRwmIyGo14eXkRFBTEhg0bKrXvrVu3EhgYyAsvvECnTp0IDg7myJEjV9W/wWCgW7duREdH89NPP+Hs7Mxnn30GgLOzM8XFNesCEm7OjvQPaXjZmv4hDXUfQRGpdfT5J3Jlep9UPaN7HQLrX36AJ7C+m+43LFdFYdjGsrKy6Nu3L8uXL2fPnj2kpqbyySefMGfOHO666y5z3SeffMKiRYv49ddfmTFjBklJSearQY8aNYoGDRpw1113sXnzZlJTU0lMTGTy5Mn89ttvQNl9gefOncv8+fM5ePAgu3bt4u23375kT8HBwaSnpxMfH8/hw4eZP3++OchWxvbt25k9ezY7d+4kPT2dVatWceLECXN4DwoKYs+ePRw4cIA///zzslepvpHEju1c4R863T9QRGozff6JXJneJ1Vv0zN9KwzEus+wXAtdQMvGPD09ue2223jzzTc5fPgwRUVFBAQEMGHCBKZNm2aui46OJj4+nsceewx/f38++ugjQkLKDrVxd3fnu+++47nnnuOee+7h9OnTNG7cmH79+pkPLR43bhz5+fm8+eabTJ06lQYNGvC3v/3tkj3deeedPPnkk0yaNImCggIGDx7M9OnTmTlzZqWek7e3N9999x1vvfUWOTk5BAYGMnfuXAYOHAjAhAkTSExMpFOnTuTm5vLtt9/Su3fva38Rq1Hs2M7kFRYze10KaVlnCarvzrRBIfqlV0RqPX3+iVyZ3idVb9MzfTGdLWJ8XBLHTPk0MrqyKLKLRoTlmhhKS0tLbd2EXJ7BYOCzzz6zuI+vPcrJycFoNGIymWx6/rCIiIiIiNiWNbKBDpMWERERERERu6MwLCIiIiIiInZH5wzXADqSXURERERExLo0MiwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxOwrDIiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbujMCwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxOwrDUiXi4uKoW7eurdsQERERERErKC4pZdvhLD5P/p1th7MoLim1dUvXzS7CcGRkJAaDgUcffbTcsscffxyDwUBkZGT1N3YVYmJicHR05PXXX7d1K5UyYsQIfv31V1u3ISIiIiIi1ylhXwbdX9vIyNgfeCI+mZGxP9D9tY0k7MuwdWvXxS7CMEBAQADx8fHk5eWZ5+Xn57NixQqaNGliw84qZ9GiRTz77LMsWrTI1q1UipubGw0bNrR1GyIiIiIich0S9mUwcfkuMkz5FvOPm/KZuHxXjQ7EdhOGw8LCCAgIYNWqVeZ5q1atokmTJnTs2NGiNiEhge7du1O3bl3q16/PkCFDOHz4sEXN1q1b6dChA66urnTq1InVq1djMBhITk421+zbt4+BAwfi6enJTTfdxJgxY/jzzz+vuvdNmzaRl5fHyy+/TE5ODlu3brVYvnv3bvr06YOXlxfe3t7ceuut7Ny5E4AjR44QERFBvXr18PDwoE2bNqxbtw6A4uJiHnzwQZo2bYqbmxutWrVi3rx5FttOTEykS5cueHh4ULduXbp168aRI0euuN+LD5OeOXMmHTp0YNmyZQQFBWE0Grnvvvs4ffr0Vb8eIiIiIiJS9YpLSon+IoVLHRB9fl70Fyk19pBpuwnDAOPHj2fx4sXmx4sWLeKBBx4oV3fmzBmeeuopdu7cyYYNG3BwcGDo0KGUlJQAkJOTQ0REBO3atWPXrl288sorPPfccxbbOHXqFH379qVjx47s3LmThIQE/vjjD4YPH37VfS9cuJCRI0dSp04dRo4cycKFCy2Wjxo1iptvvpkdO3bw448/8vzzz1OnTh2g7DDwgoICvvvuO/bu3ctrr72Gp6cnACUlJdx888188sknpKSk8NJLLzFt2jQ+/vhjAM6dO8fdd99Nr1692LNnD9u2bePhhx/GYDBccb+XcvjwYVavXs3atWtZu3YtmzZt4h//+EeF9QUFBeTk5FhMIiIiIiJSPZJSs8uNCF+oFMgw5ZOUml19TVmRk60bqE6jR48mKirKPLK5ZcsW4uPjSUxMtKgbNmyYxeNFixbh6+tLSkoKbdu2ZcWKFRgMBmJjY3F1dSUkJITff/+dCRMmmNf517/+RceOHZk9e7bFdgICAvj1119p2bJlpXrOycnhP//5D9u2bTM/hx49ejBv3jxzqE1PT+eZZ56hdevWAAQHB5vXT09PZ9iwYbRr1w6AZs2amZfVqVOH6Oho8+OmTZuybds2Pv74Y4YPH05OTg4mk4khQ4bQvHlzAG655RaLbVe030spKSkhLi4OLy8vAMaMGcOGDRt49dVXL1kfExNj0Z+IiIiIiFSfzNMVB+FrqbvR2NXIsK+vL4MHDyYuLo7FixczePBgGjRoUK7u4MGDjBw5kmbNmuHt7U1QUBBQFv4ADhw4QGhoKK6uruZ1unTpYrGN3bt38+233+Lp6WmezofGiw+5vpyPPvqI5s2b0759ewA6dOhAYGAgK1euNNc89dRTPPTQQ9xxxx384x//sNj+5MmTmTVrFt26dWPGjBns2bPHYvv//ve/ufXWW/H19cXT05P333/f/Dx9fHyIjIxkwIABREREMG/ePDIyMiq130sJCgoyB2EAf39/MjMzK6yPiorCZDKZp6NHj1biFRMREREREWto6OV65aKrqLvR2FUYhrJDpePi4liyZAnjx4+/ZE1ERATZ2dnExsayfft2tm/fDkBhYWGl95Obm0tERATJyckW08GDB+nZs2elt7Nw4UJ+/vlnnJyczFNKSorFhbRmzpzJzz//zODBg9m4cSMhISF89tlnADz00EP873//Y8yYMezdu5dOnTrx9ttvAxAfH8/UqVN58MEH+eabb0hOTuaBBx6weJ6LFy9m27ZtdO3alZUrV9KyZUt++OGHK+73Ui4+hNpgMJgPPb8UFxcXvL29LSYREREREakeXZr64G90xVDBcgPgb3SlS1Of6mzLauwuDIeHh1NYWEhRUREDBgwotzwrK4sDBw7w4osv0q9fP2655RZOnjxpUdOqVSv27t1LQUGBed6OHTssasLCwvj5558JCgqiRYsWFpOHh0elet27dy87d+4kMTHRIlAnJiaybds2fvnlF3Nty5YtefLJJ/nmm2+45557LM6NDggI4NFHH2XVqlU8/fTTxMbGAmWHiXft2pXHHnuMjh070qJFi0uO7nbs2JGoqCi2bt1qPky8MvsVEREREZGay9HBwIyIEIBygfj84xkRITg6VBSXb2x2F4YdHR3Zv38/KSkpODo6llter1496tevz/vvv8+hQ4fYuHEjTz31lEXN/fffT0lJCQ8//DD79+/n66+/5p///CeA+eJSjz/+ONnZ2YwcOZIdO3Zw+PBhvv76ax544AGKi4sr1evChQvp0qULPXv2pG3btuapZ8+edO7cmYULF5KXl8ekSZNITEzkyJEjbNmyhR07dpjP7Z0yZQpff/01qamp7Nq1i2+//da8LDg4mJ07d/L111/z66+/Mn36dItQn5qaSlRUFNu2bePIkSN88803HDx4kFtuueWK+xURERERkZovvK0/C0aH4We0PBTaz+jKgtFhhLf1t1Fn18+uLqB13uUOt3VwcCA+Pp7JkyfTtm1bWrVqxfz58+ndu7fF+l988QUTJ06kQ4cOtGvXjpdeeon777/ffB5xo0aN2LJlC8899xx//etfKSgoIDAwkPDwcBwcrvwbRGFhIcuXLy93lerzhg0bxty5c5k1axZZWVmMHTuWP/74gwYNGnDPPfeYLzxVXFzM448/zm+//Ya3tzfh4eG8+eabADzyyCP89NNPjBgxAoPBwMiRI3nsscf46quvAHB3d+eXX35hyZIlZGVl4e/vz+OPP84jjzzCuXPnLrtfERERERGpHcLb+tM/xI+k1GwyT+fT0Kvs0OiaOiJ8nqG0tLRm3hTqBvPhhx/ywAMPYDKZcHNzs3U7tVJOTg5GoxGTyaTzh0VERERE7Jg1soFdjgxbw9KlS2nWrBmNGzdm9+7dPPfccwwfPlxBWEREREREpAZQGL5Gx48f56WXXuL48eP4+/tz7733Vni/XBEREREREbmx6DBpqTF0mLSIiIiIiIB1soHdXU1aRERERERERGFYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxOwrDIiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbujMCwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxO062bkCsKy4ujilTpnDq1Clbt1Jr5eaf48mVP5F+Mo8m9dx4c0RHPF31VhKR2k+ffyJXZjpbxPi4JI6Z8mlkdGVRZBeM7nVs3VatciKngKHvfE/2mSJ8POrw2WPd8fV2sXVbtV5xSSlJqdlkns6noZcrXZr64OhgsHVb18VQWlpaausm7EVkZCRLlizhkUce4d1337VY9vjjj/POO+8wbtw44uLirnkftTkM5+TkYDQaMZlMeHt726SHO/+1mT2/5ZSbH3qzN2sm9bBBRyIi1UOffyJX1uv1jRzJyis3P7C+G5ue6WuDjmqf0Jlfk5N/rtx8b1cn9swcYIOO7EPCvgyiv0ghw5RvnudvdGVGRAjhbf1t0pM1soEOk65mAQEBxMfHk5f3/x+U+fn5rFixgiZNmlzXtouKiq63PbmMir4IAuz5LYc7/7W5mjsSEake+vwTubKKgjDAkaw8er2+sZo7qn0qCsIAOfnnCJ35dTV3ZB8S9mUwcfkuiyAMcNyUz8Tlu0jYl2Gjzq6fwnA1CwsLIyAggFWrVpnnrVq1iiZNmtCxY0fzvISEBLp3707dunWpX78+Q4YM4fDhw+blaWlpGAwGVq5cSa9evXB1deXDDz8st78TJ07QqVMnhg4dSkFBASdPnmTUqFH4+vri5uZGcHAwixcvttjmxx9/TI8ePXBzc6Nz5878+uuv7Nixg06dOuHp6cnAgQM5ceKEeR87duygf//+NGjQAKPRSK9evdi1a5dFHwaDgQ8++IChQ4fi7u5OcHAwa9assdrrWtVy889V+EXwvD2/5ZBbwQe0iEhNpc8/kSsznS2qMAifdyQrD9NZDVxcqxM5BRUG4fNy8s9xIqegmjqyD8UlpUR/kcKlDiU+Py/6ixSKS2rmwcYKwzYwfvx4cwAFWLRoEQ888IBFzZkzZ3jqqafYuXMnGzZswMHBgaFDh1JSUmJR9/zzz/PEE0+wf/9+BgywPDTk6NGj9OjRg7Zt2/Kf//wHFxcXpk+fTkpKCl999RX79+9nwYIFNGjQwGK9GTNm8OKLL7Jr1y6cnJy4//77efbZZ5k3bx6bN2/m0KFDvPTSS+b606dPM27cOL7//nt++OEHgoODGTRoEKdPn7bYbnR0NMOHD2fPnj0MGjSIUaNGkZ2dXeHrVFBQQE5OjsVkK0+u/MmqdSIiNYU+/0SubHxcklXrpLyh73xv1TqpnKTU7HIjwhcqBTJM+SSlVvyd/kamq17YwOjRo4mKiuLIkSMAbNmyhfj4eBITE801w4YNs1hn0aJF+Pr6kpKSQtu2bc3zp0yZwj333FNuHwcOHKB///4MHTqUt956C4Oh7OT29PR0OnbsSKdOnQAICgoqt+7UqVPNwfqJJ55g5MiRbNiwgW7dugHw4IMPWpzX3Lev5Tkw77//PnXr1mXTpk0MGTLEPD8yMpKRI0cCMHv2bObPn09SUhLh4eGXfJ1iYmKIjo6+5LLqln7y8r/2Xm2diEhNoc8/kSs7dpmwcC11Ul72mcqNqle2Tion83Tl/s1Wtu5Go5FhG/D19WXw4MHExcWxePFiBg8eXG509uDBg4wcOZJmzZrh7e1tDq3p6ekWdedD7YXy8vLo0aMH99xzD/PmzTMHYYCJEycSHx9Phw4dePbZZ9m6dWu59UNDQ83/fdNNNwHQrl07i3mZmZnmx3/88QcTJkwgODgYo9GIt7c3ubm55Xq9cLseHh54e3tbbOdiUVFRmEwm83T06NEKa6tak3puVq0TEakp9PkncmWNjK5WrZPyfDwqd0XuytZJ5TT0qty/2crW3WgUhm1k/PjxxMXFsWTJEsaPH19ueUREBNnZ2cTGxrJ9+3a2b98OQGFhoUWdh4dHuXVdXFy44447WLt2Lb///rvFsoEDB3LkyBGefPJJjh07Rr9+/Zg6dapFTZ06//8hcj5IXzzvwsO1x40bR3JyMvPmzWPr1q0kJydTv379cr1euI1LbedSz8Pb29tispU3R3S8ctFV1ImI1BT6/BO5skWRXaxaJ+V99lh3q9ZJ5XRp6oO/0ZWKbqBkoOyq0l2a+lRnW1ajMGwj4eHhFBYWUlRUVO5c36ysLA4cOMCLL75Iv379uOWWWzh58mSlt+3g4MCyZcu49dZb6dOnD8eOHbNY7uvry7hx41i+fDlvvfUW77///nU9ly1btjB58mQGDRpEmzZtcHFx4c8//7yubd5oPF2dCL358mE89GZv3W9TRGodff6JXJnRvQ6B9S9/dERgfTfdb/g6+Hq74H2FzxlvVyfdb9jKHB0MzIgIASgXiM8/nhERUmPvN6wwbCOOjo7s37+flJQUHB0dLZbVq1eP+vXr8/7773Po0CE2btzIU089ddXb//DDD2nfvj19+/bl+PHjALz00kt8/vnnHDp0iJ9//pm1a9dyyy23XNdzCQ4OZtmyZezfv5/t27czatQo3Nxq3+Fyayb1qPALoe6zKSK1mT7/RK5s0zN9KwzEus+wdeyZOaDCQKz7DFed8Lb+LBgdht9Fh/n7GV1ZMDrMZvcZtgb9jGtDFR326+DgQHx8PJMnT6Zt27a0atWK+fPn07t376vavpOTEx999BEjRoygb9++JCYm4uzsTFRUFGlpabi5udGjRw/i4+Ov63ksXLiQhx9+2HzbqNmzZ5c79Lq2WDOpB7n553hy5U+kn8yjST033hzRUSMiIlLr6fNP5Mo2PdMX09kixsclccyUTyOjK4siu2hE2Ir2zBzAiZwChr7zPdlnivDxqMNnj3XXiHAVC2/rT/8QP5JSs8k8nU9Dr7JDo2vqiPB5htLS0pp5UyixOzk5ORiNRkwmk03PHxYREREREduyRjbQYdIiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B0nWzcg12bmzJmsXr2a5ORkW7did4pLSklKzSbzdD4NvVzp0tQHRweDrdsSERGRG0Bu/jmeXPkT6SfzaFLPjTdHdMTTVV+5rSk7t5D73t9K5ulCGno5E/9wV3w8nW3dltRAemdaSWRkJEuWLDE/9vHxoXPnzsyZM4fQ0NBKbaOigGswGPjss8+4++67rdixXIuEfRlEf5FChinfPM/f6MqMiBDC2/rbsDMRERGxtTv/tZk9v+WYHx84fpq2M78m9GZv1kzqYcPOao/Os9ZzIrfQ/PhUXhFhs9bj6+nMjhf727AzqYl0mLQVhYeHk5GRQUZGBhs2bMDJyYkhQ4bYui2xkoR9GUxcvssiCAMcN+UzcfkuEvZl2KgzERERsbWLg/CF9vyWw53/2lzNHdU+FwfhC53ILaTzrPXV3JHUdArDVuTi4oKfnx9+fn506NCB559/nqNHj3LixAkAnnvuOVq2bIm7uzvNmjVj+vTpFBUVARAXF0d0dDS7d+/GYDBgMBiIi4sjKCgIgKFDh2IwGMyPL+WDDz7glltuwdXVldatW/POO++Yl6WlpWEwGPj444/p0aMHbm5udO7cmV9//ZUdO3bQqVMnPD09GThwoLlfgB07dtC/f38aNGiA0WikV69e7Nq1y2K/v/zyC927d8fV1ZWQkBD++9//YjAYWL16tbnm6NGjDB8+nLp16+Lj48Ndd91FWlra9b3g1ai4pJToL1IovcSy8/Oiv0ihuORSFSIiIlKb5eafqzAIn7fntxxy889VU0e1T3ZuYYVB+LwTuYVkX6FG5EIKw1UkNzeX5cuX06JFC+rXrw+Al5cXcXFxpKSkMG/ePGJjY3nzzTcBGDFiBE8//TRt2rQxjy6PGDGCHTt2ALB48WIyMjLMjy/24Ycf8tJLL/Hqq6+yf/9+Zs+ezfTp0y0O3QaYMWMGL774Irt27cLJyYn777+fZ599lnnz5rF582YOHTrESy+9ZK4/ffo048aN4/vvv+eHH34gODiYQYMGcfr0aQCKi4u5++67cXd3Z/v27bz//vu88MILFvssKipiwIABeHl5sXnzZrZs2YKnpyfh4eEUFlb8gVVQUEBOTo7FZCtJqdnlRoQvVApkmPJJSs2uvqZERETkhvDkyp+sWifl3ff+VqvWiYDOGbaqtWvX4unpCcCZM2fw9/dn7dq1ODiU/ebw4osvmmuDgoKYOnUq8fHxPPvss7i5ueHp6YmTkxN+fn7mOjc3NwDq1q1rMf9iM2bMYO7cudxzzz0ANG3alJSUFN577z3GjRtnrps6dSoDBgwA4IknnmDkyJFs2LCBbt26AfDggw8SFxdnru/bt6/Fft5//33q1q3Lpk2bGDJkCOvXr+fw4cMkJiaa+3v11Vfp3///z9lYuXIlJSUlfPDBBxgMZReaWrx4MXXr1iUxMZG//vWvl3xOMTExREdHV/icq1Pm6YqD8LXUiYiISO2RfjLPqnVSXubpyo34VrZOBDQybFV9+vQhOTmZ5ORkkpKSGDBgAAMHDuTIkSNAWSjs1q0bfn5+eHp68uKLL5Kenn7d+z1z5gyHDx/mwQcfxNPT0zzNmjWLw4cPW9ReeDGvm266CYB27dpZzMvMzDQ//uOPP5gwYQLBwcEYjUa8vb3Jzc01933gwAECAgIsgnqXLl0s9rl7924OHTqEl5eXuTcfHx/y8/PL9XehqKgoTCaTeTp69Og1vDrW0dDL1ap1IiIiUns0qedm1Topr6FX5a4WXdk6EdDIsFV5eHjQokUL8+MPPvgAo9FIbGwsgwcPZtSoUURHRzNgwACMRiPx8fHMnTv3uvebm5sLQGxsLLfddpvFMkdHR4vHderUMf/3+VHai+eVlJSYH48bN46srCzmzZtHYGAgLi4u3H777Zc9vPlS/d166618+OGH5Zb5+vpWuJ6LiwsuLi6V3k9V6tLUB3+jK8dN+Zc8b9gA+BnLbrMkIiIi9uXNER1pO/PrStXJtYl/uCthlbhAVvzDXauhG6ktFIarkMFgwMHBgby8PLZu3UpgYKDF+bTnR4zPc3Z2pri4uNx26tSpc8n559100000atSI//3vf4waNcp6TwDYsmUL77zzDoMGDQLKLoT1559/mpe3atWKo0eP8scff5hHmi8+rzksLIyVK1fSsGFDvL29rdpfdXF0MDAjIoSJy3dhAItAfP4OwzMiQnS/YRERETvk6epE6M3el72IVujN3rrf8HXw8XTG19P5shfR8vV01v2G5aroMGkrKigo4Pjx4xw/fpz9+/fz97//ndzcXCIiIggODiY9PZ34+HgOHz7M/Pnz+eyzzyzWDwoKIjU1leTkZP78808KCgrM8zds2MDx48c5efLkJfcdHR1NTEwM8+fP59dff2Xv3r0sXryYN95447qeU3BwMMuWLWP//v1s376dUaNGmc9jBujfvz/Nmzdn3Lhx7Nmzhy1btpjPjT4/8jxq1CgaNGjAXXfdxebNm0lNTSUxMZHJkyfz22+/XVd/1Sm8rT8LRofhZ7Q8FNrP6MqC0WG6z7CIiIgdWzOpB6E3X/pHf91n2Dp2vNgf3wrCru4zLNdCP09ZUUJCAv7+ZYHIy8uL1q1b88knn9C7d28AnnzySSZNmkRBQQGDBw9m+vTpzJw507z+sGHDWLVqFX369OHUqVMsXryYyMhI5s6dy1NPPUVsbCyNGze+5C2JHnroIdzd3Xn99dd55pln8PDwoF27dkyZMuW6ntPChQt5+OGHCQsLIyAggNmzZzN16lTzckdHR1avXs1DDz1E586dadasGa+//joRERG4upaFRnd3d7777juee+457rnnHk6fPk3jxo3p169fjRspDm/rT/8QP5JSs8k8nU9Dr7JDozUiLCIiImsm9SA3/xxPrvyJ9JN5NKnnxpsjOmpE2Ip2vNif7NxC7nt/K5mnC2no5Uz8w101IizXxFBaWqobo4pVbdmyhe7du3Po0CGaN29ute3m5ORgNBoxmUw1LkSLiIiIiIj1WCMb6GcquW6fffYZnp6eBAcHc+jQIZ544gm6detm1SAsIiIiIiJiTQrDct1Onz7Nc889R3p6Og0aNOCOO+6wylWyRUREREREqooOk5YaQ4dJi4iIiIgIWCcb6GrSIiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbujMCwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxOwrDIiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbvjZOsGxD4lJibSp08fTp48Sd26dW3dzlUxnS1ifFwSx0z5NDK6siiyC0b3OrZuS0SkyuUVFjN7XQppWWcJqu/OtEEhuDk72rotkRtKcUkpSanZZJ7Op6GXK12a+uDoYLB1W7VKdm4h972/lczThTT0cib+4a74eDrbui2pgQylpaWltm7CnkVERFBUVERCQkK5ZZs3b6Znz57s3r2b0NBQG3RXda4lDOfk5GA0GjGZTHh7e1dtgxXo9fpGjmTllZsfWN+NTc/0tUFHIiLVY8LSHaxPySw3v39IQ2LHdrZBRyI3noR9GUR/kUKGKd88z9/oyoyIEMLb+tuws9qj86z1nMgtLDff19OZHS/2t0FHYivWyAY6TNrGHnzwQdavX89vv/1WbtnixYvp1KlTjQ7ChYXlP6xqqoqCMMCRrDx6vb6xmjsSEakeFQVhgPUpmUxYuqOaOxK58STsy2Di8l0WQRjguCmfict3kbAvw0ad1R4VBWGAE7mFdJ61vpo7kppOYdjGhgwZgq+vL3FxcRbzc3Nz+eSTT7j77rsZOXIkjRs3xt3dnXbt2vHRRx9Z1Pbu3ZvJkyfz7LPP4uPjg5+fHzNnzrSo+eWXX+jevTuurq6EhITw3//+F4PBwOrVq4GykVqDwcCpU6fM6yQnJ2MwGEhLSwMgKyurUr1MmjSJKVOm0KBBAwYMGADAunXraNmyJW5ubvTp08e8zZrCdLaowiB83pGsPExni6qpIxGR6pFXWFxhED5vfUomeYXF1dSRyI2nuKSU6C9SuNThlufnRX+RQnGJDsi8Vtm5hRUG4fNO5BaSfYUakQspDNuYk5MTY8eOJS4ujguPWP/kk08oLi5m9OjR3HrrrXz55Zfs27ePhx9+mDFjxpCUlGSxnSVLluDh4cH27duZM2cOL7/8MuvXl/06VlxczN133427uzvbt2/n/fff54UXXrjqXvPz8yvdi7OzM1u2bOHdd9/l6NGj3HPPPURERJCcnMxDDz3E888/f8X9FRQUkJOTYzHZyvi4pCsXXUWdiEhNMXtdilXrRGqjpNTsciPCFyoFMkz5JKVmV19Ttcx972+1ap0IKAzfEMaPH8/hw4fZtGmTed7ixYsZNmwYgYGBTJ06lQ4dOtCsWTP+/ve/Ex4ezscff2yxjdDQUGbMmEFwcDBjx46lU6dObNiwAYD169dz+PBhli5dSvv27enevTuvvvrqVffZuHHjSvUSHBzMnDlzaNWqFa1atWLBggU0b96cuXPn0qpVK0aNGkVkZOQV9xcTE4PRaDRPAQEBV92ztRy7zB+4a6kTEakp0rLOWrVOpDbKPF25v/+VrZPyMk9XbsS3snUioDB8Q2jdujVdu3Zl0aJFABw6dIjNmzfz4IMPUlxczCuvvEK7du3w8fHB09OTr7/+mvT0dIttXHxesb+/P5mZZYe1HThwgICAAPz8/MzLu3TpctV9VraXW2+91eLx/v37ue222yzm3X777VfcX1RUFCaTyTwdPXr0qnu2lkZGV6vWiYjUFEH13a1aJ1IbNfSq3N//ytZJeQ29Kne16MrWiYDC8A3jwQcf5NNPP+X06dMsXryY5s2b06tXL15//XXmzZvHc889x7fffktycjIDBgwod2GqOnUsb+1jMBgoKSmp9P4dHMr+KVx4qHZRkeX5r5XtxcPDo9L7vRwXFxe8vb0tJltZFFm5Hw8qWyciUlNMGxRi1TqR2qhLUx/8ja5UdAMlA2VXle7S1Kc626pV4h/uatU6EVAYvmEMHz4cBwcHVqxYwdKlSxk/fjwGg4EtW7Zw1113MXr0aNq3b0+zZs349ddfr2rbrVq14ujRo/zxxx/meTt2WF7509fXF4CMjP+/0mFycrJFzbX2csstt5Q7r/iHH364qudga0b3OgTWd7tsTWB9N91vWERqHTdnR/qHNLxsTf+QhrrfsNg1RwcDMyLKfhC6OBCffzwjIkT3G74OPp7O+F7hXsK+ns6637BcFYXhG4SnpycjRowgKiqKjIwM8zm1wcHBrF+/nq1bt7J//34eeeQRi1BbGf3796d58+aMGzeOPXv2sGXLFl588UWgbAQZoEWLFgQEBDBz5kwOHjzIl19+ydy5cy22c629PProoxw8eJBnnnmGAwcOsGLFinJXz64JNj3Tt8JArPsMi0htFju2c4WBWPcZFikT3tafBaPD8LvolCk/oysLRofpPsNWsOPF/hUGYt1nWK6Fk60bkP/34IMPsnDhQgYNGkSjRo0AePHFF/nf//7HgAEDcHd35+GHH+buu+/GZDJVeruOjo6sXr2ahx56iM6dO9OsWTNef/11IiIicHUt+8CuU6cOH330ERMnTiQ0NJTOnTsza9Ys7r33XvN2rrWXJk2a8Omnn/Lkk0/y9ttv06VLF2bPns348eOv4VWyrU3P9MV0tojxcUkcM+XTyOjKosguGhEWkVovdmxn8gqLmb0uhbSsswTVd2faoBCNCItcILytP/1D/EhKzSbzdD4NvcoOjdaIsPXseLE/2bmF3Pf+VjJPF9LQy5n4h7tqRFiuiaH0wpNExW5s2bKF7t27c+jQIZo3b27rdiolJycHo9GIyWSy6fnDIiIiIiJiW9bIBhoZthOfffYZnp6eBAcHc+jQIZ544gm6detWY4KwiIiIiIiINSkM24nTp0/z3HPPkZ6eToMGDbjjjjvKnRMsIiIiIiJiL3SYtNQYOkxaRERERETAOtlAV5MWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2J1aEYbj4uKoW7eurduQiwQFBfHWW2/Zug0RERERqUWKS0rZdjiLz5N/Z9vhLIpLSm3dktRQVRqGIyMjMRgMPProo+WWPf744xgMBiIjI6uyhVojLy8PHx8fGjRoQEFBga3bqZQdO3bw8MMP27oNEREREaklEvZl0P21jYyM/YEn4pMZGfsD3V/bSMK+DFu3JjVQlY8MBwQEEB8fT15ennlefn4+K1asoEmTJte9/aKiouveRk3w6aef0qZNG1q3bs3q1att3U6l+Pr64u7ubus2RERERKQWSNiXwcTlu8gw5VvMP27KZ+LyXQrEctWqPAyHhYUREBDAqlWrzPNWrVpFkyZN6Nixo0VtQkIC3bt3p27dutSvX58hQ4Zw+PBh8/K0tDQMBgMrV66kV69euLq68uGHH5bb54kTJ+jUqRNDhw6loKCAkydPMmrUKHx9fXFzcyM4OJjFixdbbPPjjz+mR48euLm50blzZ3799Vd27NhBp06d8PT0ZODAgZw4ccK8jx07dtC/f38aNGiA0WikV69e7Nq1y6IPg8HABx98wNChQ3F3dyc4OJg1a9Zc0+u4cOFCRo8ezejRo1m4cKHFstLSUmbOnEmTJk1wcXGhUaNGTJ482bz8nXfeITg4GFdXV2666Sb+9re/Vfo1LywsZNKkSfj7++Pq6kpgYCAxMTGV2u/Fh0lb8/UQEREREftRXFJK9BcpXOqA6PPzor9I0SHTclWq5Zzh8ePHm8MnwKJFi3jggQfK1Z05c4annnqKnTt3smHDBhwcHBg6dCglJSUWdc8//zxPPPEE+/fvZ8CAARbLjh49So8ePWjbti3/+c9/cHFxYfr06aSkpPDVV1+xf/9+FixYQIMGDSzWmzFjBi+++CK7du3CycmJ+++/n2effZZ58+axefNmDh06xEsvvWSuP336NOPGjeP777/nhx9+IDg4mEGDBnH69GmL7UZHRzN8+HD27NnDoEGDGDVqFNnZ2Vf1+h0+fJht27YxfPhwhg8fzubNmzly5Ih5+aeffsqbb77Je++9x8GDB1m9ejXt2rUDYOfOnUyePJmXX36ZAwcOkJCQQM+ePSv9ms+fP581a9bw8ccfc+DAAT788EOCgoKuuN+KXM3rUVBQQE5OjsUkIiIiIvYnKTW73IjwhUqBDFM+SalX9z1b7JtTdexk9OjRREVFmQPcli1biI+PJzEx0aJu2LBhFo8XLVqEr68vKSkptG3b1jx/ypQp3HPPPeX2c+DAAfr378/QoUN56623MBgMAKSnp9OxY0c6deoEYA5zF5o6dao5WD/xxBOMHDmSDRs20K1bNwAefPBB4uLizPV9+/a1WP/999+nbt26bNq0iSFDhpjnR0ZGMnLkSABmz57N/PnzSUpKIjw8vMLX62KLFi1i4MCB1KtXD4ABAwawePFiZs6caX5+fn5+3HHHHdSpU4cmTZrQpUsX8zIPDw+GDBmCl5cXgYGBFiPyV3rN09PTCQ4Opnv37hgMBgIDA821l9tvRa7m9YiJiSE6OrrSr5OIiIiI1E6ZpysOwtdSJwLVNDLs6+vL4MGDiYuLY/HixQwePLjcyCzAwYMHGTlyJM2aNcPb29scWtPT0y3qzofaC+Xl5dGjRw/uuece5s2bZw7CABMnTiQ+Pp4OHTrw7LPPsnXr1nLrh4aGmv/7pptuArAY5bzpppvIzMw0P/7jjz+YMGECwcHBGI1GvL29yc3NLdfrhdv18PDA29vbYjtXUlxczJIlSxg9erR53ujRo4mLizOP3t57773k5eXRrFkzJkyYwGeffca5c+cA6N+/P4GBgTRr1owxY8bw4YcfcvbsWfO2rvSaR0ZGkpycTKtWrZg8eTLffPONed3L7bciV/N6REVFYTKZzNPRo0cr/bqJiIiISO3R0MvVqnUiUI23Vho/fjxxcXEsWbKE8ePHX7ImIiKC7OxsYmNj2b59O9u3bwfKzlu9kIeHR7l1XVxcuOOOO1i7di2///67xbKBAwdy5MgRnnzySY4dO0a/fv2YOnWqRU2dOnXM/30+SF8878LDtceNG0dycjLz5s1j69atJCcnU79+/XK9XriNS23nSr7++mt+//13RowYgZOTE05OTtx3330cOXKEDRs2AGUXKTtw4ADvvPMObm5uPPbYY/Ts2ZOioiK8vLzYtWsXH330Ef7+/rz00ku0b9+eU6dOAVd+zcPCwkhNTeWVV14hLy+P4cOHm885vtx+K3I1r4eLiwve3t4Wk4iIiIjYny5NffA3umKoYLkB8De60qWpT3W2JTVctYXh8PBwCgsLKSoqKneeL0BWVhYHDhzgxRdfpF+/ftxyyy2cPHmy0tt3cHBg2bJl3HrrrfTp04djx45ZLPf19WXcuHEsX76ct956i/fff/+6ns+WLVuYPHkygwYNok2bNri4uPDnn39e1zYvZeHChdx3330kJydbTPfdd5/FhbTc3NyIiIhg/vz5JCYmsm3bNvbu3QuAk5MTd9xxB3PmzGHPnj2kpaWxcePGSr/m3t7ejBgxgtjYWFauXMmnn35qPs/3cvsVEREREbEGRwcDMyJCAMoF4vOPZ0SE4OhQUVwWKa9azhkGcHR0ZP/+/eb/vli9evWoX78+77//Pv7+/qSnp/P8889f9T4+/PBDRo4cSd++fUlMTMTPz4+XXnqJW2+9lTZt2lBQUMDatWu55ZZbruv5BAcHs2zZMjp16kROTg7PPPMMbm5u17XNi504cYIvvviCNWvWWJwzDTB27FiGDh1KdnY2a9asobi4mNtuuw13d3eWL1+Om5sbgYGBrF27lv/973/07NmTevXqsW7dOkpKSmjVqlWlXvM33ngDf39/OnbsiIODA5988gl+fn7UrVuXuLi4CvcrIiIiImJN4W39WTA6jOgvUiwupuVndGVGRAjhbf1t2J3URNUWhoHLHubq4OBAfHw8kydPpm3btrRq1Yr58+fTu3fvq9qHk5MTH330ESNGjDAHYmdnZ6KiokhLS8PNzY0ePXoQHx9/Xc9l4cKFPPzww+ZbR82ePbvcodfXa+nSpXh4eNCvX79yy/r164ebmxvLly+nSZMm/OMf/+Cpp56iuLiYdu3a8cUXX1C/fn3q1q3LqlWrmDlzJvn5+QQHB/PRRx/Rpk0bgCu+5l5eXsyZM4eDBw/i6OhI586dWbduHQ4ODtStW7fC/YqIiIiIWFt4W3/6h/iRlJpN5ul8GnqVHRqtEWG5FobS0lLdjEtqhJycHIxGIyaTSecPi4iIiIjYMWtkg2o7Z1hERERERETkRqEwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO4oDIuIiIiIiIjdURgWERERERERu6MwLCIiIiIiInZHYVhERERERETsjsKwiIiIiIiI2B2FYREREREREbE7CsMiIiIiIiJidxSGRURERERExO442boBqX3S0tJo2rQpP/30Ex06dLB1OyIiYiV5hcXMXpdCWtZZguq7M21QCG7OjrZuS+SGciKngKHvfE/2mSJ8POrw2WPd8fV2sXVbtUrhuRKWbUvjSPZZAn3cGXN7EM5OGuOrasUlpSSlZpN5Op+GXq50aeqDo4PB1m1dF0NpaWmprZuQK9u2bRvdu3cnPDycL7/80tbtXFZxcTEnTpygQYMGODlZ7/eWnJwcjEYjJpMJb29vq21XRESubMLSHaxPySw3v39IQ2LHdrZBRyI3ntCZX5OTf67cfG9XJ/bMHGCDjmqfmHUpxG5OpeSCBONggAk9mhI1KMR2jdVyCfsyiP4ihQxTvnmev9GVGREhhLf1t0lP1sgG+gmlhli4cCF///vf+e677zh27Jit27ksR0dH/Pz8rBqERUTEdioKwgDrUzKZsHRHNXckcuOpKAgD5OSfI3Tm19XcUe0Tsy6F976zDMIAJaXw3nepxKxLsU1jtVzCvgwmLt9lEYQBjpvymbh8Fwn7MmzU2fVTGK4BcnNzWblyJRMnTmTw4MHExcWZl508eZJRo0bh6+uLm5sbwcHBLF68GIDCwkImTZqEv78/rq6uBAYGEhMTY173jTfeoF27dnh4eBAQEMBjjz1Gbm6uefmRI0eIiIigXr16eHh40KZNG9atW3fF/aalpWEwGEhOTgYgMTERg8HAhg0b6NSpE+7u7nTt2pUDBw5U8SsnIiLXK6+wuMIgfN76lEzyCourqSORG8+JnIIKg/B5OfnnOJFTUE0d1T6F50qI3Zx62ZrYzakUniuppo7sQ3FJKdFfpHCpQ4nPz4v+IoXii3+hqCEUhmuAjz/+mNatW9OqVStGjx7NokWLOH90+/Tp00lJSeGrr75i//79LFiwgAYNGgAwf/581qxZw8cff8yBAwf48MMPCQoKMm/XwcGB+fPn8/PPP7NkyRI2btzIs88+a17++OOPU1BQwHfffcfevXt57bXX8PT0vOJ+K/LCCy8wd+5cdu7ciZOTE+PHj79sfUFBATk5ORaTiIhUr9mVHGmpbJ1IbTT0ne+tWiflLduWVm5E+GIlpWV1Yj1JqdnlRoQvVApkmPJJSs2uvqasSMex1gALFy5k9OjRAISHh2Mymdi0aRO9e/cmPT2djh070qlTJwCLsJuenk5wcDDdu3fHYDAQGBhosd0pU6aY/zsoKIhZs2bx6KOP8s4775jXHzZsGO3atQOgWbNmFtuuaL8VefXVV+nVqxcAzz//PIMHDyY/Px9XV9dL1sfExBAdHX3F7YqISNVJyzpr1TqR2ij7TJFV66S8I9mV+4ypbJ1UTubpioPwtdTdaDQyfIM7cOAASUlJjBw5EgAnJydGjBjBwoULAZg4cSLx8fF06NCBZ599lq1bt5rXjYyMJDk5mVatWjF58mS++eYbi23/97//pV+/fjRu3BgvLy/GjBlDVlYWZ8+WfYhMnjyZWbNm0a1bN2bMmMGePXvM615uvxUJDQ01/7e/f9mJ9pmZFR96FxUVhclkMk9Hjx694j5ERMS6guq7W7VOpDby8ahj1TopL9Cncp8xla2TymnodelBq2utu9EoDN/gFi5cyLlz52jUqBFOTk44OTmxYMECPv30U0wmEwMHDuTIkSM8+eSTHDt2jH79+jF16lQAwsLCSE1N5ZVXXiEvL4/hw4fzt7/9DSg7r3fIkCGEhoby6aef8uOPP/Lvf/8bKDvXGOChhx7if//7H2PGjGHv3r106tSJt99+G+Cy+61InTr//wfAYCi7DHtJScXndbi4uODt7W0xiYhI9ZpWyauzVrZOpDb67LHuVq2T8sbcHsSV7uLjYCirE+vp0tQHf6MrFb30BsquKt2lqU91tmU1CsM3sHPnzrF06VLmzp1LcnKyedq9ezeNGjXio48+AsDX15dx48axfPly3nrrLd5//33zNry9vRkxYgSxsbGsXLmSTz/9lOzsbH788UdKSkqYO3cuf/nLX2jZsuUlr1IdEBDAo48+yqpVq3j66aeJjY01L7vcfkVEpHZwc3akf0jDy9b0D2mo+w2LXfP1dsHb9fJnH3q7Oul+w9fB2cmBCT2aXrZmQo+mut+wlTk6GJgRUfZj58WB+PzjGREhNfZ+wzpn+Aa2du1aTp48yYMPPojRaLRYNmzYMBYuXMixY8e49dZbadOmDQUFBaxdu5ZbbrkFKLtatL+/Px07dsTBwYFPPvkEPz8/6tatS4sWLSgqKuLtt98mIiKCLVu28O6771rsY8qUKQwcOJCWLVty8uRJvv32W/O2X3rppQr3KyIitUvs2M66z7DIFeyZOUD3Ga5i5+8jrPsMV6/wtv4sGB1W7j7Dfja+z7A1KAzfwBYuXMgdd9xRLghDWRieM2cOERERREVFkZaWhpubGz169CA+Ph4ALy8v5syZw8GDB3F0dKRz586sW7cOBwcH2rdvzxtvvMFrr71GVFQUPXv2JCYmhrFjx5r3UVxczOOPP85vv/2Gt7c34eHhvPnmmwA4OztXuF8REal9Ysd2Jq+wmNnrUkjLOktQfXemDQrRiLDIBfbMHMCJnAKGvvM92WeK8PGow2ePddeIsBVFDQrh6b+2Ztm2NI5knyXQx50xtwdpRLiKhbf1p3+IH0mp2WSezqehV9mh0TV1RPg8Q+n5e/SI3OBycnIwGo2YTCadPywiIiIiYseskQ30E4qIiIiIiIjYHYVhERERERERsTsKwyIiIiIiImJ3FIZFRERERETE7igMi4iIiIiIiN1RGBYRERERERG7ozAsIiIiIiIidkdhWEREREREROyOwrCIiIiIiIjYHYVhERERERERsTsKwyIiIiIiImJ3FIZFRERERETE7igMi4iIiIiIiN1RGBYRERERERG7ozAsIiIiIiIidsfJ1g2I1DSF50pYti2NI9lnCfRxZ8ztQTg76XclEan99PkncmW5+ed4cuVPpJ/Mo0k9N94c0RFPV33ltqa8wmJmr0shLessQfXdmTYoBDdnR1u3JTWQobS0tNTWTYhtREZGsmTJEvNjHx8fOnfuzJw5cwgNDa2y/c6cOZPVq1eTnJx8Vevl5ORgNBoxmUx4e3tXTXNXELMuhdjNqZRc8K5xMMCEHk2JGhRik55ERKqDPv9EruzOf21mz2855eaH3uzNmkk9bNBR7TNh6Q7Wp2SWm98/pCGxYzvboCOxFWtkA/2ca+fCw8PJyMggIyODDRs24OTkxJAhQ655e8XFxZSUlFixwxtHzLoU3vvO8osgQEkpvPddKjHrUmzTmIhIFdPnn8iVVRSEAfb8lsOd/9pczR3VPhUFYYD1KZlMWLqjmjuSmk5h2M65uLjg5+eHn58fHTp04Pnnn+fo0aOcOHGCxMREDAYDp06dMtcnJydjMBhIS0sDIC4ujrp167JmzRpCQkJwcXEhPT2dxMREunTpgoeHB3Xr1qVbt24cOXKEuLg4oqOj2b17NwaDAYPBQFxcnE2e+9UoPFdC7ObUy9bEbk6l8Fzt/CFAROyXPv9Eriw3/1yFQfi8Pb/lkJt/rpo6qn3yCosrDMLnrU/JJK+wuJo6ktpAYVjMcnNzWb58OS1atKB+/fqVXu/s2bO89tprfPDBB/z888/4+Phw991306tXL/bs2cO2bdt4+OGHMRgMjBgxgqeffpo2bdqYR6RHjBhxye0WFBSQk5NjMdnKsm1p5UZELlZSWlYnIlKb6PNP5MqeXPmTVeukvNmVPAKlsnUioAto2b21a9fi6ekJwJkzZ/D392ft2rU4OFT+d5KioiLeeecd2rdvD0B2djYmk4khQ4bQvHlzAG655RZzvaenJ05OTvj5+V12uzExMURHR1/tU6oSR7LPWrVORKSm0OefyJWln8yzap2Ul5ZVuc+YytaJgEaG7V6fPn1ITk4mOTmZpKQkBgwYwMCBAzly5Eilt+Hs7GxxwS0fHx8iIyMZMGAAERERzJs3j4yMjKvuLSoqCpPJZJ6OHj161duwlkAfd6vWiYjUFPr8E7myJvXcrFon5QXVr9xnTGXrREBh2O55eHjQokULWrRoQefOnfnggw84c+YMsbGx5tHhCy84XlRUVG4bbm5uGAwGi3mLFy9m27ZtdO3alZUrV9KyZUt++OGHq+rNxcUFb29vi8lWxtwehIPh8jUOhrI6EZHaRJ9/Ilf25oiOVq2T8qZV8qr1la0TAYVhuYjBYMDBwYG8vDx8fX0BLEZ1r+Z2SB07diQqKoqtW7fStm1bVqxYAZSNJBcX16yLGzg7OTChR9PL1kzo0VT32xSRWkeffyJX5unqROjNl//RPvRmb91v+Dq4OTvSP6ThZWv6hzTU/Yblqugvl50rKCjg+PHjHD9+nP379/P3v/+d3NxcIiIiaNGiBQEBAcycOZODBw/y5ZdfMnfu3CtuMzU1laioKLZt28aRI0f45ptvOHjwoPm84aCgIFJTU0lOTubPP/+koKCgqp+mVUQNCuGRnk3LjZA4GOCRnrrPpojUXvr8E7myNZN6VBiIdZ9h64gd27nCQKz7DMu10M9Tdi4hIQF/f38AvLy8aN26NZ988gm9e/cG4KOPPmLixImEhobSuXNnZs2axb333nvZbbq7u/PLL7+wZMkSsrKy8Pf35/HHH+eRRx4BYNiwYaxatYo+ffpw6tQpFi9eTGRkZFU+TauJGhTC039tzbJtaRzJPkugjztjbg/SiIiI1Hr6/BO5sjWTepCbf44nV/5E+sk8mtRz480RHTUibEWxYzuTV1jM7HUppGWdJai+O9MGhWhEWK6JofTCE0JFbmA5OTkYjUZMJpNNzx8WERERERHbskY20M+5IiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbujMCwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdhWERERERERGxOwrDIiIiIiIiYncUhkVERERERMTuKAyLiIiIiIiI3VEYFhEREREREbujMCwiIiIiIiJ2R2FYRERERERE7I7CsIiIiIiIiNgdJ1s3IFLT5BUWM3tdCmlZZwmq7860QSG4OTvaui0RkSpnOlvE+LgkjpnyaWR0ZVFkF4zudWzdlsgNRe+TqldcUkpSajaZp/Np6OVKl6Y+ODoYbN2W1ECG0tLSUls3UVtFRkZy6tQpVq9ebetWbhhpaWk0bdqUn376iQ4dOlzVujk5ORiNRkwmE97e3lXT4BVMWLqD9SmZ5eb3D2lI7NjONuhIRKR69Hp9I0ey8srND6zvxqZn+tqgI5Ebj94nVS9hXwbRX6SQYco3z/M3ujIjIoTwtv427EyqmzWygQ6TFgoLC23dQo1QURAGWJ+SyYSlO6q5IxGR6lHRF3yAI1l59Hp9YzV3JHLj0fuk6iXsy2Di8l0WQRjguCmfict3kbAvw0adSU2lMFxNgoKCeOuttyzmdejQgZkzZ5of//LLL3Tv3h1XV1dCQkL473//i8FgsBhZ3rp1Kx06dMDV1ZVOnTqxevVqDAYDycnJ5pp9+/YxcOBAPD09uemmmxgzZgx//vmneXnv3r2ZNGkSU6ZMoUGDBgwYMIDExEQMBgMbNmygU6dOuLu707VrVw4cOGBe7/Dhw9x1113cdNNNeHp60rlzZ/773/9aPKeL+wWoW7cucXFxADRt2hSAjh07YjAY6N2791W/lraQV1hcYRA+b31KJnmFxdXUkYhI9TCdLarwC/55R7LyMJ0tqqaORG48ep9UveKSUqK/SOFSh7Senxf9RQrFJTroVSpPYfgGUVxczN133427uzvbt2/n/fff54UXXrCoycnJISIignbt2rFr1y5eeeUVnnvuOYuaU6dO0bdvXzp27MjOnTtJSEjgjz/+YPjw4RZ1S5YswdnZmS1btvDuu++a57/wwgvMnTuXnTt34uTkxPjx483LcnNzGTRoEBs2bOCnn34iPDyciIgI0tPTK/08k5KSAPjvf/9LRkYGq1atqrC2oKCAnJwci8lWZq9LsWqdiEhNMT4uyap1IrWR3idVLyk1u9yI8IVKgQxTPkmp2dXXlNR4uoDWDWL9+vUcPnyYxMRE/Pz8AHj11Vfp37+/uWbFihUYDAZiY2PNo8e///47EyZMMNf861//omPHjsyePds8b9GiRQQEBPDrr7/SsmVLAIKDg5kzZ465JiMjw7zPXr16AfD8888zePBg8vPzcXV1pX379rRv3968ziuvvMJnn33GmjVrmDRpUqWep6+vLwD169c3P8+KxMTEEB0dXantVrX/a+/eg6Oqzz+OfzYJSRZCEiQkm9AQEkIDEZJyURoudSpRoUiptWgx+gtEp0NLp9AihpuklUGpNwaHNrUWdaoUqh2gSkVMuURJEQIlgSgTQIJQJaQj5AKEW/b5/cGwugI2YNhNsu/XzM7A+T4bvuc8893Jh3P2nIOfnWrROgBoKz79il8+r6UOaI9YJ9dfTUPzjl1z6wCJM8OtRmVlpRITE70C4s0333xJTUZGhsLDw69YU15ero0bNyoiIsLz6tOnj6QLlzlfNGjQoMvOIyMjw/Pn+PgLNyGoqblwefCJEyf08MMPq2/fvoqOjlZERIT27NlzVWeGr8asWbNUV1fneR0+fPi6/DvN0bNrxxatA4C2IiEq/H8XXUUd0B6xTq6/2M7NO3bNrQMkzgz7TFBQkL584+5z51r+eyMnTpzQ2LFj9dvf/vaSsYvhVpI6dep02fd36PD5rf8djgu3qHe73ZKkhx9+WEVFRXr66aeVmpoqp9OpH/3oR1434HI4HC22n2FhYQoLC7um97a02d9L1yvv/+/QP/t76T6YDQD4zosTykI56wAAEohJREFUb1bmY+80qw4IVKyT6+/m5BsUHxWu6rrTl/3esEOSK+rCY5aA5uLMsI9069bNcymydOH7v1VVVZ6/p6Wl6fDhwzp69KhnW2mp992J09LStHv3bp05c+aKNQMHDtQHH3ygnj17KjU11et1pQDcXCUlJZo4caLuuusu9e/fXy6XSwcPHvzK/dy3b59Onfr80uHQ0FBJF74j3ZY4Q4N1W3rsV9bclh7L84YBtDtRHTsoqavzK2uSujp5jioCGuvk+gsOcqhg7IWTDl9+ovDFvxeMTed5w7gqhGEfufXWW/XKK6/ovffe0+7du5Wbm6vg4M+D02233aZevXopNzdXu3btUklJiebOnSvp8zO09913n9xut37yk59oz549WrdunZ5++mmvmilTpujYsWOaMGGCSktL9dFHH2ndunWaNGnS1w6gvXv31sqVK1VWVqby8nLPfL68n0uWLNHOnTu1fft2TZ482etsc2xsrJxOp+fGXnV1dV9rTr70wv/ddMVAzHOGAbRnxTNuveIv+jw/FbiAdXL9jeoXr8L7B8r1pcvNXVHhKrx/IM8ZxlXjMunryO12KyTkwiGeNWuWqqqqdOeddyoqKkrz58/3OjMcHBys1atX66GHHtJNN92klJQUPfXUUxo7dqznO8KRkZF688039dOf/lTf+ta31L9/f82bN0/33XefpyYhIUElJSXKz8/X7bffrjNnzigpKUmjRo1SUNDX+7+PZ599Vnl5eRo6dKhiYmKUn59/yR2en3nmGU2aNEkjRoxQQkKCFi9erB07dnjGQ0JC9Nxzz+mxxx7TvHnzNGLECG3atOlrzcuXXvi/m9R4tkmPv/WhDn52Sj27dtTs76VzRhhAu1c841bVnTqnvJe36dO600qICteLE2/mTBfwBayT629Uv3jdlu7Stqpjqmk4rdjOFy6N5owwroXDvvwFT7SYUaNGKTU1VUuWLLmm95eUlGj48OHav3+/evXqddmaZcuWadKkSaqrq5PT+dWX57R19fX1ioqKUl1dnSIjI/09HQAAAAB+0hLZgDPD18Hx48dVUlKiTZs2afLkyc1+36pVqxQREaHevXtr//79mjp1qoYNG+YVhP/85z8rJSVF3bt3V3l5ufLz83XPPfe0+yAMAAAAAC2JMHwd5OXlqbS0VNOnT9e4ceOa/b6Ghgbl5+fr0KFDiomJUXZ2tp555hmvmurqas2bN0/V1dWKj4/X+PHjtWDBgpbeBQAAAABo17hMGm0Gl0kDAAAAkFomG3A3aQAAAABAwCEMAwAAAAACDmEYAAAAABBwCMMAAAAAgIBDGAYAAAAABBzCMAAAAAAg4BCGAQAAAAABhzAMAAAAAAg4hGEAAAAAQMAhDAMAAAAAAg5hGAAAAAAQcAjDAAAAAICAQxgGAAAAAAQcwjAAAAAAIOAQhgEAAAAAAYcwDAAAAAAIOIRhAAAAAEDAIQwDAAAAAAIOYRgAAAAAEHAIwwAAAACAgEMYBgAAAAAEHMIwAAAAACDgEIYBAAAAAAGHMAwAAAAACDgh/p4A0FxmJkmqr6/380wAAAAA+NPFTHAxI1wLwjDajIaGBklSYmKin2cCAAAAoDVoaGhQVFTUNb3XYV8nSgM+5Ha79emnn6pz585yOBz+nk6rVl9fr8TERB0+fFiRkZH+nk7Aog+tB71oHehD60EvWgf60HrQi9bhavpgZmpoaFBCQoKCgq7t27+cGUabERQUpG984xv+nkabEhkZyQd6K0AfWg960TrQh9aDXrQO9KH1oBetQ3P7cK1nhC/iBloAAAAAgIBDGAYAAAAABBzCMNAOhYWFqaCgQGFhYf6eSkCjD60HvWgd6EPrQS9aB/rQetCL1sHXfeAGWgAAAACAgMOZYQAAAABAwCEMAwAAAAACDmEYAAAAABBwCMMAAAAAgIBDGAbaiMLCQmVkZHgeQp6VlaW1a9d6xk+fPq0pU6aoa9euioiI0N13362jR496/YxDhw5pzJgx6tixo2JjYzVjxgydP3/e17vSrixcuFAOh0PTpk3zbKMXvvHrX/9aDofD69WnTx/POH3wnU8++UT333+/unbtKqfTqf79+2v79u2ecTPTvHnzFB8fL6fTqezsbO3bt8/rZxw7dkw5OTmKjIxUdHS0HnzwQZ04ccLXu9Km9ezZ85I14XA4NGXKFEmsCV9pamrSo48+quTkZDmdTvXq1Uvz58/XF+9Zy5rwnYaGBk2bNk1JSUlyOp0aOnSoSktLPeP0ouW9++67Gjt2rBISEuRwOLR69Wqv8ZY65rt27dKIESMUHh6uxMREPfnkk1c/WQPQJrzxxhv2j3/8w/bu3WuVlZU2e/Zs69Chg1VUVJiZ2eTJky0xMdHWr19v27dvt29/+9s2dOhQz/vPnz9v/fr1s+zsbNu5c6e99dZbFhMTY7NmzfLXLrV527Zts549e1pGRoZNnTrVs51e+EZBQYHdeOONduTIEc/rv//9r2ecPvjGsWPHLCkpySZOnGhbt261AwcO2Lp162z//v2emoULF1pUVJStXr3aysvL7fvf/74lJydbY2Ojp2bUqFGWmZlp77//vr333nuWmppqEyZM8McutVk1NTVe66GoqMgk2caNG82MNeErCxYssK5du9qaNWusqqrKXn/9dYuIiLDFixd7algTvnPPPfdYenq6FRcX2759+6ygoMAiIyPtP//5j5nRi+vhrbfesjlz5tjKlStNkq1atcprvCWOeV1dncXFxVlOTo5VVFTY8uXLzel02vPPP39VcyUMA21Yly5d7E9/+pPV1tZahw4d7PXXX/eM7dmzxyTZli1bzOzCB1NQUJBVV1d7agoLCy0yMtLOnDnj87m3dQ0NDda7d28rKiqyW265xROG6YXvFBQUWGZm5mXH6IPv5Ofn2/Dhw6847na7zeVy2VNPPeXZVltba2FhYbZ8+XIzM/vwww9NkpWWlnpq1q5daw6Hwz755JPrN/l2burUqdarVy9zu92sCR8aM2aM5eXleW374Q9/aDk5OWbGmvClU6dOWXBwsK1Zs8Zr+8CBA23OnDn0wge+HIZb6pj//ve/ty5dunh9NuXn51taWtpVzY/LpIE2qKmpSStWrNDJkyeVlZWlHTt26Ny5c8rOzvbU9OnTRz169NCWLVskSVu2bFH//v0VFxfnqbnjjjtUX1+vDz74wOf70NZNmTJFY8aM8TrmkuiFj+3bt08JCQlKSUlRTk6ODh06JIk++NIbb7yhwYMHa/z48YqNjdWAAQP0wgsveMarqqpUXV3t1YuoqCgNGTLEqxfR0dEaPHiwpyY7O1tBQUHaunWr73amHTl79qxeffVV5eXlyeFwsCZ8aOjQoVq/fr327t0rSSovL9fmzZs1evRoSawJXzp//ryampoUHh7utd3pdGrz5s30wg9a6phv2bJF3/nOdxQaGuqpueOOO1RZWanjx483ez4hX3eHAPjO7t27lZWVpdOnTysiIkKrVq1Senq6ysrKFBoaqujoaK/6uLg4VVdXS5Kqq6u9fsG5OH5xDM23YsUK/fvf//b6ztFF1dXV9MJHhgwZopdffllpaWk6cuSIfvOb32jEiBGqqKigDz504MABFRYW6le/+pVmz56t0tJS/eIXv1BoaKhyc3M9x/Jyx/qLvYiNjfUaDwkJ0Q033EAvrtHq1atVW1uriRMnSuKzyZdmzpyp+vp69enTR8HBwWpqatKCBQuUk5MjSawJH+rcubOysrI0f/589e3bV3FxcVq+fLm2bNmi1NRUeuEHLXXMq6urlZycfMnPuDjWpUuXZs2HMAy0IWlpaSorK1NdXZ3+9re/KTc3V8XFxf6eVkA5fPiwpk6dqqKiokv+pxm+dfEsiyRlZGRoyJAhSkpK0muvvSan0+nHmQUWt9utwYMH6/HHH5ckDRgwQBUVFfrDH/6g3NxcP88ucC1dulSjR49WQkKCv6cScF577TUtW7ZMf/nLX3TjjTeqrKxM06ZNU0JCAmvCD1555RXl5eWpe/fuCg4O1sCBAzVhwgTt2LHD31NDK8Bl0kAbEhoaqtTUVA0aNEhPPPGEMjMztXjxYrlcLp09e1a1tbVe9UePHpXL5ZIkuVyuS+4aevHvF2vwv+3YsUM1NTUaOHCgQkJCFBISouLiYj333HMKCQlRXFwcvfCT6OhoffOb39T+/ftZEz4UHx+v9PR0r219+/b1XLJ+8Vhe7lh/sRc1NTVe4+fPn9exY8foxTX4+OOP9c9//lMPPfSQZxtrwndmzJihmTNn6sc//rH69++vBx54QL/85S/1xBNPSGJN+FqvXr1UXFysEydO6PDhw9q2bZvOnTunlJQUeuEHLXXMW+rzijAMtGFut1tnzpzRoEGD1KFDB61fv94zVllZqUOHDikrK0uSlJWVpd27d3t9uBQVFSkyMvKSX2RxZSNHjtTu3btVVlbmeQ0ePFg5OTmeP9ML/zhx4oQ++ugjxcfHsyZ8aNiwYaqsrPTatnfvXiUlJUmSkpOT5XK5vHpRX1+vrVu3evWitrbW60zNhg0b5Ha7NWTIEB/sRfvy0ksvKTY2VmPGjPFsY034zqlTpxQU5P0rdnBwsNxutyTWhL906tRJ8fHxOn78uNatW6dx48bRCz9oqWOelZWld999V+fOnfPUFBUVKS0trdmXSEvi0UpAWzFz5kwrLi62qqoq27Vrl82cOdMcDoe98847ZnbhkRk9evSwDRs22Pbt2y0rK8uysrI877/4yIzbb7/dysrK7O2337Zu3brxyIwW8MW7SZvRC1+ZPn26bdq0yaqqqqykpMSys7MtJibGampqzIw++Mq2bdssJCTEFixYYPv27bNly5ZZx44d7dVXX/XULFy40KKjo+3vf/+77dq1y8aNG3fZx2gMGDDAtm7daps3b7bevXvz6JJr0NTUZD169LD8/PxLxlgTvpGbm2vdu3f3PFpp5cqVFhMTY4888oinhjXhO2+//batXbvWDhw4YO+8845lZmbakCFD7OzZs2ZGL66HhoYG27lzp+3cudMk2bPPPms7d+60jz/+2Mxa5pjX1tZaXFycPfDAA1ZRUWErVqywjh078mgloL3Ky8uzpKQkCw0NtW7dutnIkSM9QdjMrLGx0X72s59Zly5drGPHjnbXXXfZkSNHvH7GwYMHbfTo0eZ0Oi0mJsamT59u586d8/WutDtfDsP0wjfuvfdei4+Pt9DQUOvevbvde++9Xs+2pQ++8+abb1q/fv0sLCzM+vTpY3/84x+9xt1utz366KMWFxdnYWFhNnLkSKusrPSq+eyzz2zChAkWERFhkZGRNmnSJGtoaPDlbrQL69atM0mXHF8z1oSv1NfX29SpU61Hjx4WHh5uKSkpNmfOHK9HwLAmfOevf/2rpaSkWGhoqLlcLpsyZYrV1tZ6xulFy9u4caNJuuSVm5trZi13zMvLy2348OEWFhZm3bt3t4ULF171XB1mZtd6mhsAAAAAgLaI7wwDAAAAAAIOYRgAAAAAEHAIwwAAAACAgEMYBgAAAAAEHMIwAAAAACDgEIYBAAAAAAGHMAwAAAAACDiEYQAAAABAwCEMAwAAAAACDmEYAAC0OhMnTpTD4ZDD4VCHDh2UnJysRx55RKdPn/bUXBx3OByKiorSsGHDtGHDBj/OGgDQlhCGAQBAqzRq1CgdOXJEBw4c0KJFi/T888+roKDAq+all17SkSNHVFJSopiYGN155506cOCAn2YMAGhLCMMAAKBVCgsLk8vlUmJion7wgx8oOztbRUVFXjXR0dFyuVzq16+fCgsL1djYeEkNAACXQxgGAACtXkVFhf71r38pNDT0ijVOp1OSdPbsWV9NCwDQhoX4ewIAAACXs2bNGkVEROj8+fM6c+aMgoKCtGTJksvWnjp1SnPnzlVwcLBuueUWH88UANAWEYYBAECr9N3vfleFhYU6efKkFi1apJCQEN19991eNRMmTFBwcLAaGxvVrVs3LV26VBkZGX6aMQCgLSEMAwCAVqlTp05KTU2VJL344ovKzMzU0qVL9eCDD3pqFi1apOzsbEVFRalbt27+mioAoA3iO8MAAKDVCwoK0uzZszV37lw1NjZ6trtcLqWmphKEAQBXjTAMAADahPHjxys4OFi/+93v/D0VAEA7QBgGAABtQkhIiH7+85/rySef1MmTJ/09HQBAG+cwM/P3JAAAAAAA8CXODAMAAAAAAg5hGAAAAAAQcAjDAAAAAICAQxgGAAAAAAQcwjAAAAAAIOAQhgEAAAAAAYcwDAAAAAAIOIRhAAAAAEDAIQwDAAAAAAIOYRgAAAAAEHAIwwAAAACAgPP/qc4gzRwuCSQAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Визуализация данных\n", + "plt.figure(figsize=(10, 10))\n", + "plt.scatter(df[\"RP\"], df[\"Classes\"])\n", + "plt.xlabel(\"RP\")\n", + "plt.title(\"Scatter Plot of Classes vs RP\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 333, + "metadata": {}, + "outputs": [], + "source": [ + "# Удаление строк с пропущенными значениями\n", + "df_dropna = df.dropna()" + ] + }, + { + "cell_type": "code", + "execution_count": 334, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 100\n", + "Размер контрольной выборки: 34\n", + "Размер тестовой выборки: 34\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "df = pd.read_csv(\"data/league.csv\", index_col=\"Name\")\n", + "# Разделение на обучающую и тестовую выборки\n", + "trainl_df, testl_df = train_test_split(df, test_size=0.2, random_state=42)\n", + "\n", + "# Разделение обучающей выборки на обучающую и контрольную\n", + "trainl_df, vall_df = train_test_split(trainl_df, test_size=0.25, random_state=42)\n", + "\n", + "\n", + "print(\"Размер обучающей выборки:\", len(trainl_df))\n", + "print(\"Размер контрольной выборки:\", len(vall_df))\n", + "print(\"Размер тестовой выборки:\", len(testl_df))" + ] + }, + { + "cell_type": "code", + "execution_count": 335, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Classes в обучающей выборке после oversampling:\n", + "Classes\n", + "Burst 11\n", + "Vanguard 11\n", + "Marksman 11\n", + "Marksman Catcher 11\n", + "Specialist 11\n", + "Battlemage 11\n", + "Assassin 11\n", + "Artillery 11\n", + "Skirmisher 11\n", + "Diver 11\n", + "Enchanter 11\n", + "Warden 11\n", + "Burst Enchanter 11\n", + "Juggernaut 11\n", + "Catcher 11\n", + "Assassin Diver 11\n", + "Assassin Skirmisher 11\n", + "Burst Skirmisher 11\n", + "Enchanter Warden 11\n", + "Warden Skirmisher 11\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Classes в контрольной выборке после oversampling:\n", + "Classes\n", + "Catcher 4\n", + "Skirmisher 4\n", + "Assassin Diver 4\n", + "Mage Assassin 4\n", + "Vanguard 4\n", + "Specialist 4\n", + "Marksman 4\n", + "Burst Catcher 4\n", + "Diver 4\n", + "Assassin 4\n", + "Marksman Assassin 4\n", + "Enchanter 4\n", + "Warden 4\n", + "Battlemage 4\n", + "Juggernaut 4\n", + "Marksman Enchanter 4\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Classes в тестовой выборке после oversampling:\n", + "Classes\n", + "Skirmisher 6\n", + "Assassin 6\n", + "Specialist 6\n", + "Diver 6\n", + "Marksman Artillery 6\n", + "Vanguard 6\n", + "Artillery 6\n", + "Enchanter 6\n", + "Catcher 6\n", + "Juggernaut 6\n", + "Burst 6\n", + "Battlemage 6\n", + "Burst Artillery 6\n", + "Marksman 6\n", + "Assassin Catcher 6\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "\n", + "def check_balance(df, name):\n", + " counts = df[\"Classes\"].value_counts()\n", + " print(f\"Распределение Classes в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "def oversample(df):\n", + " X = df.drop(\"Classes\", axis=1)\n", + " y = df[\"Classes\"]\n", + "\n", + " oversampler = RandomOverSampler(random_state=42)\n", + " X_resampled, y_resampled = oversampler.fit_resample(X, y)\n", + "\n", + " resampled_df = pd.concat([X_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "train_df_oversampled = oversample(trainl_df)\n", + "val_df_oversampled = oversample(vall_df)\n", + "test_df_oversampled = oversample(testl_df)\n", + "\n", + "check_balance(train_df_oversampled, \"обучающей выборке после oversampling\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке после oversampling\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке после oversampling\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}