From a4bf6795950979b600733eb7eb7554e4d1183cdb Mon Sep 17 00:00:00 2001 From: Kirill Date: Fri, 11 Oct 2024 23:02:19 +0400 Subject: [PATCH] =?UTF-8?q?=D0=9B=D0=B0=D0=B1=D0=BE=D1=80=D0=B0=D1=82?= =?UTF-8?q?=D0=BE=D1=80=D0=BD=D0=B0=D1=8F=20=D1=80=D0=B0=D0=B1=D0=BE=D1=82?= =?UTF-8?q?=D0=B0=20=E2=84=962?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- Lab2/Lab2.ipynb | 1738 +++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1738 insertions(+) create mode 100644 Lab2/Lab2.ipynb diff --git a/Lab2/Lab2.ipynb b/Lab2/Lab2.ipynb new file mode 100644 index 0000000..cc40848 --- /dev/null +++ b/Lab2/Lab2.ipynb @@ -0,0 +1,1738 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Начало лабораторной\n", + "\n", + "https://www.kaggle.com/datasets/nikhil1e9/goodreads-books?resource=download\n", + "Данный набор данных представляет книги с Goodreads\n", + "Примр цели — создание системы рекомендаций для книг, прогнозирование рейтингов для новых книг.\n", + "Входные данные: Название, Автор, Средняя оценка, Общее количество оценок, Количество добавлений на полки, Год публикации, Описание, Изображение" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Столбцы в Popular-Books:\n", + "Index(['Title', 'Author', 'Score', 'Ratings', 'Shelvings', 'Published',\n", + " 'Description', 'Image'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "df_books = pd.read_csv(\".//static//csv//Popular-Books.csv\")\n", + "\n", + "print(\"Столбцы в Popular-Books:\")\n", + "print(df_books.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Посмотрим краткое содержание датасета." + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Информация о датасете Popular-Books:\n", + "\n", + "RangeIndex: 27621 entries, 0 to 27620\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Title 27621 non-null object \n", + " 1 Author 27621 non-null object \n", + " 2 Score 27621 non-null float64\n", + " 3 Ratings 27621 non-null int64 \n", + " 4 Shelvings 27621 non-null int64 \n", + " 5 Published 27621 non-null int64 \n", + " 6 Description 27549 non-null object \n", + " 7 Image 27621 non-null object \n", + "dtypes: float64(1), int64(3), object(4)\n", + "memory usage: 1.7+ MB\n" + ] + }, + { + "data": { + "text/html": [ + "
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TitleAuthorScoreRatingsShelvingsPublishedDescriptionImage
0The English Assassin (Gabriel Allon, #2)Daniel Silva4.1640122446022002The Unlikely Spy, Daniel Silva's extraordinary...https://images-na.ssl-images-amazon.com/images...
1PompeiiRobert Harris3.8646097648402003With his trademark elegance and intelligence R...https://images-na.ssl-images-amazon.com/images...
2Notorious RBG: The Life and Times of Ruth Bade...Irin Carmon4.19596701719592015You can't spell truth without Ruth.Only Ruth B...https://images-na.ssl-images-amazon.com/images...
3The Abolition of ManC.S. Lewis4.1134390527701943Alternative cover for ISBN: 978-0060652944The ...https://images-na.ssl-images-amazon.com/images...
4Portrait of a MurdererAnne Meredith (Pseudonym)3.38112917391933'Adrian Gray was born in May 1862 and met his ...https://images-na.ssl-images-amazon.com/images...
\n", + "
" + ], + "text/plain": [ + " Title \\\n", + "0 The English Assassin (Gabriel Allon, #2) \n", + "1 Pompeii \n", + "2 Notorious RBG: The Life and Times of Ruth Bade... \n", + "3 The Abolition of Man \n", + "4 Portrait of a Murderer \n", + "\n", + " Author Score Ratings Shelvings Published \\\n", + "0 Daniel Silva 4.16 40122 44602 2002 \n", + "1 Robert Harris 3.86 46097 64840 2003 \n", + "2 Irin Carmon 4.19 59670 171959 2015 \n", + "3 C.S. Lewis 4.11 34390 52770 1943 \n", + "4 Anne Meredith (Pseudonym) 3.38 1129 1739 1933 \n", + "\n", + " Description \\\n", + "0 The Unlikely Spy, Daniel Silva's extraordinary... \n", + "1 With his trademark elegance and intelligence R... \n", + "2 You can't spell truth without Ruth.Only Ruth B... \n", + "3 Alternative cover for ISBN: 978-0060652944The ... \n", + "4 'Adrian Gray was born in May 1862 and met his ... \n", + "\n", + " Image \n", + "0 https://images-na.ssl-images-amazon.com/images... \n", + "1 https://images-na.ssl-images-amazon.com/images... \n", + "2 https://images-na.ssl-images-amazon.com/images... \n", + "3 https://images-na.ssl-images-amazon.com/images... \n", + "4 https://images-na.ssl-images-amazon.com/images... " + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"Информация о датасете Popular-Books:\")\n", + "df_books.info()\n", + "df_books.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Анализируем датафрейм при помощи \"ящика с усами\". Проверяет на пустые значения." + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Проверка на пустые значения в наборе данных 'Popular Books':\n", + "Description 72\n", + "dtype: int64\n", + "\n", + "\n" + ] + } + ], + "source": [ + "# Настройка стиля графиков\n", + "sns.set_theme(style=\"whitegrid\")\n", + "\n", + "plt.figure(figsize=(12, 6))\n", + "sns.boxplot(x='Score', data=df_books)\n", + "plt.title('Распределение оценок книг')\n", + "plt.xlabel('Оценка')\n", + "plt.show()\n", + "\n", + "# Проверка на пустые значения для каждого набора данных\n", + "def check_missing_values(dataframe, name):\n", + " missing_values = dataframe.isnull().sum()\n", + " print(f\"Проверка на пустые значения в наборе данных '{name}':\")\n", + " print(missing_values[missing_values > 0]) # Отображаем только столбцы с пропущенными значениями\n", + " print(\"\\n\")\n", + "\n", + "check_missing_values(df_books, \"Popular Books\") " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Удаляем все найденные пустые значения." + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "В наборе данных 'Popular Books' было удалено 72 строк с пустыми значениями.\n" + ] + } + ], + "source": [ + "# Функция для удаления строк с пустыми значениями\n", + "def drop_missing_values(dataframe, name):\n", + " before_shape = dataframe.shape # Размер до удаления\n", + " cleaned_dataframe = dataframe.dropna() # Удаляем строки с пустыми значениями\n", + " after_shape = cleaned_dataframe.shape # Размер после удаления\n", + " print(f\"В наборе данных '{name}' было удалено {before_shape[0] - after_shape[0]} строк с пустыми значениями.\")\n", + " return cleaned_dataframe\n", + "\n", + "cleaned_df_books = drop_missing_values(df_books, \"Popular Books\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Очистка данных от шумов" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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Lovecraft 2.15 \n", + "27267 Murder in the Snow Gladys Mitchell 3.09 \n", + "27282 The End Samuel Beckett 3.18 \n", + "27527 Snuff Chuck Palahniuk 3.22 \n", + "27618 Mosquitoes William Faulkner 3.11 \n", + "\n", + " Ratings Shelvings Published \\\n", + "32 728 747 1926 \n", + "96 59 158 1919 \n", + "156 5197 24819 2023 \n", + "197 972 1142 1919 \n", + "249 4194 6625 1911 \n", + "... ... ... ... \n", + "27183 328 293 1902 \n", + "27267 784 1035 1950 \n", + "27282 1369 3025 1946 \n", + "27527 62483 95559 2008 \n", + "27618 1193 2331 1927 \n", + "\n", + " Description \\\n", + "32 ...the sin of an old man is equal to about two... \n", + "96 This book was converted from its physical edit... \n", + "156 Long-standing tensions between a husband, his ... \n", + "197 With the onset of Prohibition, the Sheehan Bil... \n", + "249 In a tale of ancient evil, Bram Stoker creates... \n", + "... ... \n", + "27183 \"The Mysterious Ship\" is a story story by Amer... \n", + "27267 ‘A delight… An amateur sleuth to rival Miss Ma... \n", + "27282 'They didn't seem to take much interest in my ... \n", + "27527 From the master of literary mayhem and provoca... \n", + "27618 A delightful surprise, Faulkner wrote his seco... \n", + "\n", + " Image \n", + "32 https://images-na.ssl-images-amazon.com/images... \n", + "96 https://images-na.ssl-images-amazon.com/images... \n", + "156 https://images-na.ssl-images-amazon.com/images... \n", + "197 https://dryofg8nmyqjw.cloudfront.net/images/no... \n", + "249 https://images-na.ssl-images-amazon.com/images... \n", + "... ... \n", + "27183 https://images-na.ssl-images-amazon.com/images... \n", + "27267 https://images-na.ssl-images-amazon.com/images... \n", + "27282 https://images-na.ssl-images-amazon.com/images... \n", + "27527 https://images-na.ssl-images-amazon.com/images... \n", + "27618 https://images-na.ssl-images-amazon.com/images... \n", + "\n", + "[437 rows x 8 columns]\n" + ] + }, + { + "data": { + "image/png": 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0bNgQf/zxh+Tn9McffyAxMRENGzbUur1v3764efMmzp07J/lYwN2CFuvWrcO6deuwbNkyJCcn45577sGcOXMwePBgTSxQqVTi888/x0MPPYTr169j7969+Prrr7Fz504A5r0vAJCYmKj1c7169VBcXAzg7vvm4+ODnj17au738vLSqnoYHh6OFi1aYPr06ZgyZQq2bNkClUqFadOm4b777tN7ToVCgb59+2LHjh2a/v73v/9FkyZNEB8fDwBITU3F2rVrMWrUKKxatQqXLl3CuHHj0LVrV6PPp1evXti1axeWLl2KZ599Fs2bN0daWhpeeuklTJgwQVOS/M8//0Tnzp3h6+ur9Vr8/PPPaNWqlVnvb6tWrbTa/P7772jVqhXq1q2rGeteXl7o3Lkz0tLSDPb90qVLCA4O1in8kJubi507d6J3794oKChAQUEB7rvvPjRs2FAnGmhKSUkJjhw5gi5dukAIoelfo0aN0Lx5c/z2229a7as/t+rU13Xqi2JKsXfvXrRt2xZ+fn6avgQGBiI5OVnzWt1///1YtWoVfH19kZmZif/9739YtGgRcnNzdcZ79esK69Wrh5KSEqN9MPaYw4cPo7S0FA888IBWm+r/hqg1bNgQlZWVuHbtmqmnTkQ2xIIWRCS7kJAQPPLII5ovAcePH8err76K9957D3369EFeXh4AICwszOAxcnNzMWPGDOzYsQMKhQKNGzdGcnIyAPP2zVGf6+GHH9Z7f9XrXGrWrCn5uIsWLdJUC/Tx8UGtWrVQt25dg+3V11WFh4fr3BceHo7CwkLJ587Pz0ejRo30HgcACgoKJB8LuDtpio2N1fyclJSEgQMHYtSoUfj222/RtGlTzX179uzB22+/jXPnzqFmzZpo2bIlAgICAJi/n1H1C++9vLw0x7h16xZCQ0Ph5aX9N8CqY0ahUGDZsmVYtGgRfvrpJ2zcuFEzIXvjjTcQEhKi97z9+vXDokWLsGfPHnTq1Anbt2/XquT373//G/Xq1cPmzZvxn//8B//5z3+QmJiImTNnmqwC6ePjg06dOqFTp04A7o6vWbNmYdu2bdi1axe6deuGvLw8o2PfnPdX/dqr5eXl4eLFi4iOjtZ77JKSEvj7++vcXlRUpPf2zZs3o6KiAgsWLNAppX/lyhWcPXsWzZs3N/hcqiooKIBKpcKSJUuwZMkSnfurTjYB3edWXYMGDTT9MCQ3NxeBgYF6/2iSl5eHrVu3YuvWrTr3qaslqlQqfPDBB1i9ejWKi4tRv359xMXF6fQVgM7rV3U8G2LsMerr0KpXbjQ0dtSvlzn/lhCR/Di5IiJZXL9+HQMHDsSLL76Ixx9/XOu+1q1bY+LEiZqLxdV/Ha++l86tW7dw/PhxJCYmYtKkSTh37hyWL1+OxMREKJVKlJSUYO3atWb1S32uFStW6J08qb+gmSsyMlKnWqAx6i/62dnZaNasmdZ9N2/e1Ptl2tixbt68qXO7+rZatWpJPpY+/v7+mDNnDgYNGoRp06bhq6++gkKhwF9//YVx48ahZ8+e+Oyzz9CoUSMoFAqsXr0ae/bsseqc1dWtWxe3bt2CSqXSmmDl5OTotJs5cyZmzJiBkydP4scff8SSJUtQq1Ytg6WvmzZtiri4OPzwww/w8vJCQUEB+vbtq7lfqVTi+eefx/PPP4+srCzs3LkTn3zyCV555RX897//1XvMJ598Ek2bNtUpO163bl289dZb2L59OzIzM9GtWzcEBQXp3Udq9+7daNWqlVXvb1BQENq2bYvJkyfrvd/QymytWrX0filfv369pphIVcXFxRg7diy++uorvPbaawb7U1XNmjWhUCgwfPhwvX/s0De5M6ZVq1YIDw/HL7/8gqefflpvm9deew2HDx/Grl27dO4LCgpChw4d9BavUa+KLV68GMuXL8cbb7yBXr16ISgoCADsUvJcvXVBTk6O1r8Z+sYO8PcfcKz9/BORdRgLJCJZhIeHw9vbG2vWrEFZWZnO/efOnYOvry8aN26MZs2aoVatWpo4mdqmTZswevRoVFRU4M8//0SvXr2Qmpqq+UL4yy+/APg7BqSvrHX1lQ71atetW7cQGxur+S83NxcfffSRZmXL1uLj46FUKvH9999r3X7gwAFkZWUhKSlJb//1SUlJwaFDh3T+Yr9582bUqVNHp0S2JeLi4vDEE0/g0KFD2LhxIwAgIyMDZWVlGD16NO69915NXFA9sVL/xV3KczClbdu2uHPnDn7++WfNbUII7NixQ/PzoUOH0KFDBxw9ehQKhQKtWrXCxIkTERkZabRiI3B39WrPnj3473//i6SkJM3ktrS0FL1798ayZcsA3J18P/3003j44YeNHrNhw4b48ccfdSrNAXdjsMDdCTlwd0z+9ttvWrGy48ePY/To0Th27JhV72/btm1x/vx5NG3aVGu8b9q0CevWrTNYCr5BgwYoLi7WfEEHgPT0dJw+fRoDBgxAamqq1n/dunVDu3btsGnTJq2qeVVVP1dgYCBat26Nc+fOafXtvvvuw4IFC8ze3NfLywvDhw/Hrl27tMaJ2t69e7F792488MADeieV6iqErVq10vQlJiYGy5cv12z8/Oeff6JFixYYOHCgZmJ1/fp1nD592uI4olQtW7ZEUFCQzibU27dv19v++vXrqFGjhtEVdCKyPa5cEZEsatSogZkzZ2LcuHEYOHAgnn76aTRv3hwlJSX47bffsHr1arz44ouaFZwXXngBb775JsLCwtC9e3ecP38e8+fPx9NPP42QkBDExcVhy5YtiI6ORr169XDw4EEsXrwYCoVCc02C+svO77//jubNmyM+Pl6zUvX9998jPj4eUVFR6Nu3L6ZPn44rV64gJiYG58+fx7x58xAREaG1J5EthYaGYvTo0fj444/h4+ODbt264fLly/joo4/QokULPProowD+Xmn76aef0LlzZ72RqxEjRmDz5s0YPnw4xo8fj9DQUGzcuBF79+7F22+/LcvkBgBeeukl/PDDD5g7d67mGjNvb2+89957ePbZZ1FeXo4NGzZoVgXU10tVfw/MWZVTS0lJQceOHfHvf/8b2dnZaNCgAdatW4dTp05pJnWtW7eGn58fJk+ejBdeeAHh4eFIS0vDiRMn8Mwzzxg9/kMPPYQ5c+Zg69atWitcfn5+iI6OxsKFC+Hj44OoqCicP38e3333nd6Nc9UmTpyIffv24bHHHsMzzzyDxMREeHl5IT09XVMuvHPnzgCAsWPHYtCgQRgzZgyeeeYZlJaW4sMPP0RcXBw6duyImJgYi9/f4cOHY9OmTRg+fDieffZZ1KpVC1u3bsXatWuN7vXUsWNHAHcnE+qy6evXr4ePjw969eql9zH9+vVDWlqaZnuB6vR9Pl9++WWMHj0ar7zyCvr27YvKykosW7YMR44cwdixYw32z9jz3b9/P1544QU88cQT6NKlC7y8vLB//36sXLkSrVq1wiuvvKL3sWPHjsWTTz6JMWPG4KmnnoKvry+++eYb7NixA/Pnzwdw948M6v35EhIScPHiRXz22WcoLy83eT2VtQIDAzFy5EjMnz8f/v7+aNu2Lf744w989dVXAHT/iPHnn38iOTnZ7BVAIpKZoyppEJF7ysjIEBMnThSdO3cWMTExIikpSQwZMkSr6pvahg0bxMMPPyyio6NFjx49xCeffCIqKiqEEEJcvnxZjBkzRrRp00a0adNGDBw4UGzatEk899xzYuDAgZpjzJ49WyQkJIiUlBRRXl4url27JgYOHCiio6PFjBkzhBBCVFRUiIULF4oePXqI6Oho0blzZzFjxgxx69YtzXGkVNYTQlo1PzV9VfPWrFkjHnroIREdHS06duwoZs6cKfLy8jT3FxUVieHDh4vo6GgxatQog8f+66+/xIsvviiSk5NFfHy8GDRokNixY4dWG6nVAqtWeKtOXTJfXT3thx9+EA8//LCIjY0V999/vxg/frz4448/RFRUlKbam773QF+1wOrVzqr3Ny8vT0ydOlUkJyeLhIQE8corr4g33nhDJCYmatqcP39ejB8/XrRv315ER0eLhx9+WHz99ddGn7PamDFjRExMjNbrL4QQhYWF4j//+Y/o2rWrZrzMmTNHUyrckBs3boj//Oc/onfv3iI+Pl7ExcWJPn36iCVLluhUoTx06JAYMmSIiIuLEx06dBDTpk3TKpcv5f2t+ppWdfHiRTFhwgSRkpIi4uLiRN++fcW3335r8vV49NFHxeuvvy6EEKK0tFQkJyeL0aNHG2x/+/ZtkZCQIB5//HEhhP6xVP3zKcTd6oyDBw8WcXFxok2bNuKZZ54R+/fv1zzGnM+YEHc/36tWrRKPP/64aNu2rUhISBB9+vQRn332mbh9+7amnb5xl5GRIZ577jmRmJgoEhISxBNPPKH1OpeVlYk33nhDdOzYUcTFxYnevXuL+fPniwULFoiYmBhNdUV970X1z7++aoGmHqNSqcQnn3wiunTpoinf/8UXX4jIyEiRkZGhaVdaWipSUlIkl6UnIttRCGHmFchEREQ2duXKFRw+fBg9evTQKnwxYcIEXLp0Cd99950De+eetm3bhn/961/45ZdfzCruQrZx584dfP/990hNTUX9+vU1t69evRqzZs3Cvn37NKvEGzduxPvvv48dO3boFIohIvtiLJCIiJyOl5cXpk6dih49euCxxx5DjRo1sGfPHmzfvl2naATJo1evXvjiiy/w1VdfYeTIkY7ujsfz9vbGkiVLsGLFCjz//POoVasWTp8+jQ8//BD9+/fXTKxUKhWWLVuG8ePHc2JF5AS4ckVERE5p7969+Pjjj3HixAncuXMHzZs3x4gRIwzu80PW++uvvzBkyBBs3LhRpwQ42d+lS5fwwQcfYN++fSgoKECDBg3Qt29fjBkzBj4+PgCAb7/9Fj/++CM+//xzB/eWiABOroiIiIiIiGTBUuxEREREREQy4OSKiIiIiIhIBpxcERERERERycDjqwUeOnQIQgjNhaFEREREROSZKioqoFAokJiYaNHjPX7lSggBZ6npIYRAeXm50/SHXBfHEsmFY4nkwrFEcuFYIjkYGkfWzg08fuVKvWIVGxvr4J4AxcXFOHHiBFq0aIGAgABHd4dcGMcSyYVjieTCsURy4VgiORgaR+np6VYd1+NXroiIiIiIiOTAyRUREREREZEMOLkiIiIiIiKSASdXREREREREMuDkioiIiIiISAacXBEREREREcmAkysiIiIiIiIZcHJFREREREQkA06uiIiIiIiIZMDJFRERERERkQw4uSIiIiIiIpIBJ1dEREREREQy4OSKiIiIiIhIBpxcERERERERycDb0R2gu26X3sGcL//EsXO5UKkuw9+3BtrHNcCoR+Oh9PbC8XM5yC0oRe1gP7RuFoYaXgpHd5mIiIiIiKrg5MoJvPzhLpy5lK91W1FpJX764xJ++uMSlN5eKL+j0twXFuKH0f1j0SGugb27SkREREREBjAW6GD6JlbVVZ1YAUBOfilmr9iPtKNZtuwaERERERGZgZMrB7pdesfkxMqYJZsyUKkSMvbIuEqVQHpmNnYfvIz0zGy7npuIiIiIyNkxFuhA89YcsOrx2XklOH4uB7EtwmXqkWFpR7OweGM6cvJLNbcxnkhERERE9DeuXDnQtZxiq4+RW1BqupGV0o5mYfaK/VoTK4DxRCIiIiKiqji5cqDwEF+rj1E72M/gfXLE+CpVAos3phttY+94IhERERGRM2Is0JEU1s1tw0P90bpZmN775IrxHT+Xo7NiVZ0944lERERERM6KK1cOlJ1XYtXjR/WL0bvflZwxPqmxQ3vEE4mIiIiInBknVw4UXsvf4scO7t1S7wqU3DE+Y7FDS9oREREREbkrTq4cSVVp0cOCa/rgiZ6Reu8zJ8YnRetmYQgLMT5xMhZPJCIiIiLyFJxcOVB2frlFj+ua1EhvHBCQP8ZXw0uB0f1jjbYxFE8kIiIiIvIknFw50D21Aix6XLuY+gbvkzPGp642WHFHhcG9W+qsYIWH+mPasBTuc0VEREREBFYLdKioprWw/8R1sx4TFKA0GsFTx/iMRQOlxPj0VRusHeyLwb2j0CA8ELWD/dC6WRhXrIiIiIiI/h9XrhzoVn6Z2Y8pLC7HvoyrBu+XI8ZnqNpgbkEZ1mw7BR9vL8S2COfEioiIiIioCk6uHKiWhZsIL/z2MA6fuWmw4l+HuAaYNizFohgfNw12DDk2fCYiIiIix2Is0IFOnb9l0eMKiysw/dM0o5sCd4hrgNSY+jh+Lge5BaWSY3zcNNj+5NrwmYiIiIgciytXDnTjVrFVjze1KXANLwViW4SjS1KE5BgfNw22Lzk3fCYiIiIix+LkyoHqWLGJcFVyxvS4abD9MIJJRERE5F44uXKgWsGWXXNVnTmbApvCTYPtR+4Nn4mIiIjIsTi5cqCbt+SL1skV0+OmwfbDCCYRERGRe+HkyoFCA2vIdqzawX6yVZyzptogSccIJhEREZF7YbVAB/rj+E1ZjhMe6o/8ojI8N2u7bBXnLK02SNLJteEzERERETkHrlw5UFlZpSzH6ZzQAO+sPCB7xTlLqg2SdIxgEhEREbkXTq4cyNvKVGBQgA+e+kcUdh+6YrTdkk3pdqk4544b4dr6OTGCSe7AHT/7RERElmAs0IEUFi5IeNdQ4E6lQGFxBb766ZTJ9tl5pVi74xSe6tXSshNK4I4b4drrOTGCSa7MHT/7REREluLKlQNV3LHscXcqzf+r8Jptp7TigXL+pdkdN8K193NiBJNckTt+9omIiKzBlSsH8vXxQnG5ym7nW7IpA6kx9bEv46psf2mWuhFuakx9l5kwuONzIpIbPydERES6uHLlQHXDAux6vuy8EqzdcUrWvzS740a47viciOTGzwkREZEup5pcnT9/HomJidiwYYPBNhUVFZg7dy46deqEhIQEDBkyBCdOnLBjL+WTV1Ru93Nu2n3W6P1LNmWYFRE0dyNcV7jwnZv7EpnGzwkREZEup4kFVlRUYNKkSSguLjbabubMmdi1axfmzJmDBg0a4KOPPsKoUaPwww8/ICgoyE69lUdRcYXdz3m71PiFXuq/NMe2CJd0vKzsIkntagf7ucyF79zcl8g0fk6IiIh0Oc3K1YIFCxAYGGi0zaVLl7B+/Xq89dZb6NSpE5o3b45Zs2ZBqVQiIyPDTj2Vj4+3c16HIPUvzWlHs7Bmm+lqheGh/si/XeYyF76rN/c1hpv7kqfj54SIiEiXU0yu9u/fj2+++QZz5swx2u63335DUFAQOnfurLktODgYP//8M9q3b2/rbsquVpBz/kVXyl+apVzMrvZcn2gs3WR88mtuHNGWuLmvvFwhCkrm4+eEiIhIl8NjgQUFBZg8eTJee+011K9f32jb8+fPo1GjRti+fTsWL16M69evo3Xr1pg6dSqaN29ucR+EECbjiLZQqbKwFruFwoJ9oRICtwoNX+sVFuKLpvX8Tb4ex87nmryYHQAe794Mvj5C0oXvB09cQXTT2iaPaQ8JLULx8pNxWL71FHILyjS3h4X4YtiDUUhoEeqQMSNVSUmJ1v86yr5j13Vew9rBvhj+UBRSo+s6sGcklbGx5OqfE7IvZ/l3iVwfxxLJwdA4EkJAYelmtHCCydXMmTORmJiIPn36mGxbVFSEixcv4pNPPsHkyZMRHByMRYsWYfDgwdi6dSvCwiyLn1RUVDikKEZ+oX2vueoRfzd2uXaP4epdPeICcerUSZPHOnZB2hcmVWkejp3Mk9T22Mnz8Cq9LqmtPQR7AeMfCsfFm2UoKlEh0N8Ljev4wssrFydO5Dq6e5JcuHDBYec+fqlE71jLLSjDB18fxROdwtC6kb8DekaWMDSW3OFzQvblyH+XyL1wLJEc9I0jpVJp8fEcOrnauHEjDhw4gC1btkhq7+3tjaKiIsybN0+zUjVv3jx06dIF3333HUaOHGlRP3x8fNCiRQuLHmuNoIDrKMm3/QRL/Vdk9UpBREN9qwlK9GgTgXrhAVD5+aJV41rwMhLnUfnlYn2a6S9O0S2bAoDktq2cZOWqqmhHd8ACJSUluHDhApo0aQJ/f/tPYFQqgfnf7zHa5n9Hi/Boz0Sj48wVqVQCJy7eQl5hGUKDTH+WnL1PUseSK35OyL4c/e8SuQ+OJZKDoXGUmZlp1XEdOrlav349cnJy0LVrV63bZ8yYga1bt2Lp0qVat9erVw/e3t5aEUA/Pz80atQIly9ftrgfCoUCAQH23XMKAHJstHIVHuqHlwYlIa+oDLWD/dC6WZjWdQ/dUpqic5smOH4uB7kFpcjKvo1tey/g253nNG1MVfFLauWPsJBjRuN+4aH+SGrV8P+PJ60tr8+Ql7+/v0PGdnpmttbkXZ+c/DKcv1YiuTKlK3DGiphy9clRY4ncD8cSyYVjieRQfRxZEwkEHFzQ4v3338fWrVuxceNGzX8AMGHCBLz11ls67VNSUnDnzh2kp/9dSKG0tBSXLl1C48aN7dVt2VSqbHPcUf1iER9ZB12SIhDbIlzvhKWGlwKxLcLh4+2FNdtOml3Fz5yL2Xnhu+fxxD2Q0o5mOV1FTGfsExERkTtz6OSqbt26aNy4sdZ/ABAWFoa6deuisrISN2/eRGnp3S8GycnJ6NChA6ZMmYIDBw4gMzMTkydPRo0aNdCvXz9HPhWnEBbih8G9o1BxRyWpKpuUin/Gqvh1iGuAacNSdMoxh4f6Y9qwFK2/ipvTllyfp+2BZO1nydxzSam+aM8+ERER0V0OL2hhzNWrV9GjRw/Mnj0bAwYMAHB3P6z3338f48ePR2lpKZKSkvDll1+idm3nu1bHFJ8aClRUWv/Fxt+3BpJb1cXx87la+06Ziv4cP5cjqYqfsU2FO8Q1QGpMfU3EUF8M0ZK25NrUeyCZioK6yx5IcnyWpDAn4mevPhEREdHfnG5yderU35ODiIgIrZ8BIDAwEDNnzsTMmTPt3DP53ZFhYgUAJWWV2HNYN96jjv4YWhnam3FV0vFNRbfUEUMpzGlLrksdBZ29Yr/BNu4UBbVHDFId8avO0OfcE6OZREREjuYUmwh7KqWPfV5+fdGfSpXAroPSioDYO7rFTWfdgydFQW0dg7Qk4udp0UwiIiJn4HQrV54kJKAGbuTbqKpFFfqiP8fP5aDgtuHNhNX8fb3tGt1yxmprZDlPiYLaOgZpScTP06KZREREzoArVw5UYnpuI5vq0R+pUaCSsjvYJzE+aC1WNnNP6iioseqVrs7WFTEtifixSicREZH9cXLlQMoalXY7V/XojzlRoI/XHcHOPy/hyOmbOHzmpk3ieqxsRq7OljFISyN+nhTNJCIicgaMBTpQTpHtI4GA/uiPlMiQWsHtcnyw5qDO7XLG9VjZjNyBrWKQ1kT8PCWaSURE5Ay4cuUB9EV/pESGTJEzrsfKZuQubBGDtDbi5wnRTCIiImfAyZUbCwlUGo3+dIhrgMG9W1p9HnPieoYqAbKyGZFxjPgRERE5P8YC3VRwTSWWTe8Npbfx+fMTPSPx4+/nkVtQZvG5pMb1jFUCTI2pz8pmRCYw4kdEROTcuHLlpsY9Fm9yYgXcjQuNeTTO6vOZiuuZqgS4L+MqK5sRScCIHxERkfPi5MrN1PT3xuDeUUiNqS/5MR3iGmDKM8kIrqm0+LzquJ6+2J/USoCpMfUZeyIiIiIil8VYoJu5XXIHa7adwra9FyVX8ks7moWlmzIkbSqsjzquZyj217tdY8mVABl7IiIiIiJXxcmVm1LH7Uyt+KjjetYY1S8G+zKu6j1OTn4p1mw7Jek46mihOvZERERERORKGAt0c8Yq+UmJ6xmjjuulxtS36jhqhioBGqowSERERETkTLhy5eaMVfKTsnFvdcE1fTCyXyzCQ/w1cb30zGyzj1OdoUqAxioM8hosIiIiInImXLnyAIYq+VmyIe+4xxLQrU0jrSplcmzsq68SoKkKg3JsXkxEREREJBdOrjxA7WA/vdE6czbkrRoBtPQ4g3u3lFwJUGqFQUYEiYiIiMhZMBbo5hQKYP/xq5i75k+daF2nhIbwUgDG5idBAT6YMjQFMS3CsS/jKp6btV3nOCP7xUjaAPiJnpF4omekpEqAUiKLUjcvJiIiIiKyB06u3JwQwHe7z+ncnpNfio27z5p8/PjHExAfWcdgVcGc/FK88+UBDOjaHBt2GT5e1diflMmQ1KihHJFEIiIiIiI5MBZIBnVLjkBy63qSInq/HM7ClKHJsm0ALDVqaE60kdwDq0cSERGRs+LKFRm088Bl7P7zMjrGN5QU0QsJ9MXnr/WSZQPg1s3CJEUN9VUYJPfF6pFERETkzLhyRUapBLDn8BVJbdURPZVK4K/rhThy5ibSz1q2slDDS4HR/WONttFXYZDcF6tHEhERkbPjyhXJJiv7NobM+AFFxRWa277ZcRpBAT4Y/3iCRSsLgQE+WscDgKAAJcY/Hs+VCg8itXpkakx9TriJiIjIYbhyRbIIClBizbaTOhMhACgsrjB7ZUG9SqH/eOVW9ZVcjznVI4mIiIgchZMrkoUQKpNtlmxKlxQRlLZKIe1YZD5nLBjB6pFERETkChgL9BAKxd2y7GpeCiCldV1kXs43uSKg1iOlEQ6fvqnVPjzUH71S78WabadMPj47r1TSvlTSVilKsXbHKTzVq6WkvpM0zlowgtUjiYiIyBVwcuUhRLXFB5UA9h27jilDkxHg74OZi3+HsfUJLwUw9rEE1PBS6FQD/FViwQtA2sqC1NWHNdtOoXG9YF57JRNje5nNXrHfopL6cmH1SCIiInIFjAV6uM+3ZCD+vjp4tGtzo+36dm4OpbcXangpENsiHF2SItC6WRiOn8vBX9cLJZ8vJMjX6P2VKoG8wjLJx1uyKcOmsTVnjMgB8vdLasEIRz1/Z6ge6axjgYiIyJW52+9Xrlx5OHW8bkSfGADAxt1noW9M7zl8Ba2a1NasXOiLj0lx7FwOEu6ro/c+S46pLmJgKmpoCWeNyNmiX+YUjLDFay1Fh7gGmDYsRee5h4f6Y1S/GJu+J846FoiIiFyZO/5+5eSKNPG6EX1i0DwiFO+t+lOnTdVoGAC98TEpvt5+Ck3r60b5DEXSpNibcVX2L/zOGpGzVb9cpWBEh7gGSI2pL8tG1VI561ggIiJyZe76+5WxQAJwN/JVfkeFZVuOGW33ybojmP/NIavPVXXJV0okzZhdBy/LuoTsrBE5W/bLlQpGVI2mxrYIt3kU0BnHAhERkStz59+vnFwRgLuRrx9+O28yGpZ/uxy3S+9Yfa6q+xFJiaQZU3C7XNb9jZx1TyVb9ktdMMIYTywY4axjgYiIyJW58+9XTq5I42rObbudq2q8TI6omZxxNWeNyNmyX85QMMIZOetYICIicmXu/PuVkyvSqB9W027nqhovCw00XkHQ3OPZ61j2jsjZul/qghHVV7DCQ/1dNvdsLWcdC0RERK7MnX+/sqAFAbj7BfrBjk3x3e5MqyJ6wN09sYxFZKvHy4SViyFyx9WcdU8lc/pVVlpi0TkcUTDCmTnrWCAiInJl7vz7lStXBOBu5Evp7WUyGiZF/y7G98yqHi/LN2NfKynHs5azRuTs1S97Foxwds46FoiIiFyZO/9+5eTKjVg6/Ab3jtJEvgxFw6RQx8dG9IkxK14mdck3uKaPpOPJwVkjcob6FRTgg8G9WyI1pr5D+uXOnHUsEBERuTJ3/f3KWKAbsaRYZViIH57oGaV1W9VoWHZ+CZZuSkfB7QqDxwgK8MGUoSmIqbLKYU68TOrS8GfTeuLUhVy7xdWcNSKn7tfaHaew+ZdzKCqpQGFxBdZsO4ltey9gdP9YJLQIdWgf3Y2zjgUiIiJX5o6/Xzm58nCj+8fqHcDqaBgA+PrUMLrB7/jHExAfWcfoMYxRLw0bO4c6tij3ZsGmSH0O9rYv4yrWbDulc7t6472Xn4xDMNelZeWsY4GIiMiVudvvV3798lC+PjXw6tBkSUuuhpdt/WRbtnXXpWFjKlUC6ZnZ2H3wMtIzsyVvlCdl470VP5yCygU33qO7LB0bRERE5FhcufJQZRWVmLvqAM5euoURfWIkPUYI7S94cn95d8elYUPSjmZh8cZ0rShkWIgfRvePNTmRlLLxXk5+GS7eLEO0LL0le7JmbBAREZFjceXKg6kEsGHXWXyxJcNou7SjWZi9Yj9yC7Sr+uUWlGH2iv1IO5olW588oVKd+vWsPkFSR/pMvZ5SN9QrKlFZ3EdyDGvHBhERETkWJ1eEjbvPovyO/i/i5XdU+HjdYaOPX7Ipw6xIm764k77b3TEaJSXSZ+r1lFpdMdCfH29nY2xMyzE2iIiIyLEYCySoBPDDb+fRr9r+VGlHs/DxuiNGKwUCQHZeCY6fyzF5MaKhuFOXxIbYfeiK1u2BAT5QACgsrtBq6+rRKCmRPlOvp5TqimEhvmhcx9eqvpK8TMX95BgbRERE5Fj80zYBAK7m3Nb6WR1PKrhdLunxpqJqxuJOG3ad1bm9qLhCa2Klbuvq0SipkT5j7aRsvDfswSh4uWGk0lVJifvJMTaIiIjIsTi5IgBA3doBmv8vJZ5UXW5BqcH4niXHM8aVo1FSI32m2pmqrpgaXdfiPpK8pMb9QgOlrTRKHUNERERkf4wFEgBg0y9nUbd2gOR4UnXLthzT/P/q8T1LjmeMK0ejpG6Y3LpZmMljGauuWFxcLGe3yQpS435CAdnGBhERETmGU61cnT9/HomJidiwYYOk9ps3b0ZUVBQuX75s4565P0viSVKOBdgmxuSq0Sgpkb5R/WIkV0n0hOqKrk7qWM0vLJN1bBAREZH9Oc3kqqKiApMmTZL8F/crV67gzTfftHGvPI858SRTPl53BOV3VDaJMblyNMoTN0z2ZOZEQTk2iIiIXJvTxAIXLFiAwMBASW1VKhVeffVVREdHY+/evTbumWeRGk+SouB2OUa8uQ3PD4yT5Xhq7hCN8qQNkz2duVFQjg0iIiLX5RQrV/v378c333yDOXPmSGr/6aefoqKiAmPGjLFxzzyTlHiSVAW3y/HOlwfQJbGhLMcD3CcaxUifZ7AkCsqxQURE5JocPrkqKCjA5MmT8dprr6F+/fom2x89ehTLli3De++9hxo1atihh54nJMgXHeIaYHDvlrId85fDWZgyNFlv3GlA1+Y6twcFKBEU4KPTltEoUnPWTaYrVQJHTt/Eyh9OYNUPJ3D4zE2kxtRn3I+IiMgDODwWOHPmTCQmJqJPnz4m2xYXF2PSpEmYNGkSmjRpguvXr8vSByEEq6tV8cHqAxjxcEv06RCBzb9koqjkjtXHzM4rga+PwMKX78eJi7eQV1iG0CBftGpcC15eCjzeranO7QD0tuV7ZVpJSYnW/7qbfceuY/nWU8gtKNPcVjvYF8MfinJoGfp9x65j8abjWp+Zb3acRqC/D0b3a2Vw/DvzmHb3sUT2w7FEcuFYIjkYGkdCCCgUlidGHDq52rhxIw4cOIAtW7ZIaj9r1iw0bdoUTz75pKz9qKiowIkTJ2Q9piu7VViOD74+iic6hSHmXj/sPVUky3GPnTwPr9Lr8AJQ2wdAKXDq1N8TZH23G2pL0ly4cMHRXZDd8UslWLsnR+f23IIyzbht3cjfafoFAEUlFVp9c8Ux7Y5jiRyDY4nkwrFEctA3jpRKpcXHUwghHJalGTp0KA4ePKj1BIqLi6FUKpGamoqlS5dqtY+KioJSqYS39905YWVlJcrKyuDv749//vOf+Oc//2l2H9LT0yGEQIsWLax7MhYYNP0nu5/THIH+3ni0c1Os3HZGluO9/mwbRDetLbm9SiU0f+UPrqmEAkD+7XKtv/hbo+rx5TqmsygpKcGFCxfQpEkT+Pvbf6JhKyqVwLi5e7RWrKoLC/HFwpc72fW9VKkExr7/C24VlhttFxbsi4Wv2Ldv1nLXsUT2x7FEcuFYIjkYGkeZmZlQKBSIjbWs/oBDV67ef/99lJZqV9Dq1asXJkyYgL59++q03759u9bPR44cwauvvorFixcjMjLS4n4oFAoEBARY/Hh3VVRyByu3nYGXArD2cpbwUH8ktWoo+cL8tKNZWLwx3WCFteobFZtL3/GtPaYz8vf3d6uxnZ6ZbXRiBQA5+WU4f63ErptMp2dmm5xYAUBOgf37Jhd3G0vkOBxLJBeOJZJD9XFkTSQQcPDkqm5d/ddGhIWFoW7duqisrERubi6CgoLg5+eHxo0ba7W7du0aAKBBgwYIDQ21dXc9lhx1AnqlNkalSkgqL/3r4St4Z+UBo8dTb1RsSTGAtKNZmL1iv6zHJPuQuiGvvTeZNud8rroBNhEREZnm8IIWxly9ehU9evTA7NmzMWDAAEd3h6ywZttJfL39pNZETd9K0a9HruC9VcYnVlUt2ZSB1Jj6klfEKlUCizemy3pMsh9zNuS1J3PO58obYBMREZFxTje5OnXqlOb/R0REaP1cXWpqqtH7yblUXwGrvlKUdjQL73wpfWIF3K1CePxcjuSY1fFzOSY3Mzb3mGQ/5m7Iay+tm4WhdrCvychieKify2+ATURERIY5fJ8roiWbMlB+R2VyRckQKTEr9Z5Ivx3NMvuYzrqfkieyZENee6jhpcCYR+NMthvVL5YrokRERG7M6VauyPNk55Xgk3WHTa4oGWIqZmWqOIaxY3pK4QtX0iGuAaYNS9F5X8JD/TGqX4zD3hd1vxZ8exhFxRVa9wUFKDH+8XiOGSIiIjfHyRU5hf/tv2TR40xFwAwVr5ByTBa+cF4d4hogNaa+pAIpjuhXRmY2jp7NhgJATItwxDYPd3jfiIiIyPY4uSKXZiwCJqV4haFjAmDhCydXw0vhlNfF1fBSID6yDuIj6zi6K0RERGRnvOaKJLk/vj6caQ4RFKA0uXIkpXhFVeGh/ppjmlP4goiIiIgI4MoVSdQupgE6xjc0u5qfrRQWm96wVep+Qg93bIqOcQ20YmXOup8SERERETkvrlyRJLWD/XB/fENMG5aCsBDtAhLhof54sleURcf197V8fr9kU4bRyn1S9xPqGNcAsS20r4mR+ti/rheygqCHYhVJIiLyFPydJx1XrsikqkUj9BUSyL9dhqWbMiw6dknZHQTXVKLgtumVqOpM7UdlzZ5IUh4LAGt3nMbaHadZQdDDsIokERF5Cv7OMw9Xrsik6kUj1IUEuiRFoLC4HO98ecDiMuoA0DUpwuLHGovlWbMnkpTHVqWuIJgmcR8tcl3qKpLVxzzHABERuRv+zjMfJ1dkVLfkRvBXeuPL/x7H+6v/xPurD+DLrcdx+MxNqzb+rapt63p644ZSmIrvqfce0hdlNFUQIzWmPgb3bonAAB/J/TEVVSTXJqUCpa3HAKMZRERkD87wO88VMRZIRu08cAk7D+juQfXt/85A6eOF8gqV1ecQCu244eEzN7F2x2mTjwsJVBrd40rNkj2R9C2B+/vWQElZpdFzmYoqkmszp4qkLcYAoxlERGQvjv6d56o4uSKLyTGxAoD8wjIAf8cNpVbg65IYIXmPKXP2RDK0ebCpiZUaKwi6L0dWkeSm1kREZE+snGwZxgLJ4apH+0KCfCU9LiW6nux9sXTj4aouuVkFQWeLoTmyP1KrSEptJxWjGUREZG+O+p3n6rhyRTblpQCMfd/TV61PIfH7odR25jB342F9vtlxGt+4SQVBZ4uhObo/1lSgtAajGUREZG+O+p3n6rhyRTbVv0tzo/frq9aXV1Qm6dhS25lDzqVtV6+k42wVgpyhP9ZUoLQGoxlERGRvjvqd5+o4uSKbGdy7JUb0idFbrS+4phKvDmmDoAClTrzL2mVoQ7ExY3GySpXA4TM38eeJ65LOHVxTKakd4JpxLWeLocnRH7nihNZUoLQUoxlEROQIjvid5+oYCySbCAvxwxM9IwHc/WCqhMCi9Uc1mwUX3C7H3NV/akUG1fGu1Jj6JpehvRRA/m3dlStDsbEuiQ2x+9AVvXEyAFj47WEUFldIem7hof74bFpPnLqQK6myoSvGtZwthmZtf+SOE1pSgdIajGYQEZGj2Pt3nqvjyhXZxOj+sZoPXdrRLLzz5QHNxEqt+sKBOt61L+OqyWVolQDe+fKAVhTMWGxsw66zBuNks1fslzyxAu4ugSu9vRDbIhz31g2S9BhXi2s5WwzNmv7YKk5YdTPt2BbhNv0lw2gGERE5kj1/57k6Tq5Idn6+NVBWUYn0zGyLNhpevDEdqTH18crgJJj66KqjYHJU+TMlPNQfU4Yma0UZpVY2dLW4lrPF0Cztj7PFG63BaAYREZHzYyyQZFdaVokP1hwEAATX9EHBbemrQsDdFYVpH+/ByQu3YOorrzoKpn6crYzsG4OwUD8s3ZShdZ7awb4ICvAxuvLlinEtZ4uhWdofZ4s3WovRDCIiIufGlSuyKXMnVmonJEys1HILSm0eT7txqxjvfHlA54t6bkGZyUihK8a1nC2GZml/nC3eKAdGM4iIiJwXJ1dklqTIOo7ugo7awX42j6ftOnjZ6P1BAUq3i2s5WwzNkv44W7yRjHO2DauJiIjMxVggmeXg6ZsmNwa2p6pRMFOxMUvdjTaWG21TWFyOWWM6wMtL4VZxLWeLoZnbH2eLN5Jhjt4gmoiISA5cuSKzOcvECvg7CiYlNmaprkmNJLXLKypzy7iWs8XQzOmPs8UbST9n2CCaiIhIDpxckUuq6eejEwUzFBsLCvBBp4QGBuNk04alICjAR+ccQQFKTBuWgnYx9SX1idEy5+Rs8UbS5k4VHYmIiBgLJJf0zwH6o0Lq2NjaHaexec9ZFBVXoLC4AnsOZ6F2sC8G945Cg/BAnThZakx9pJ/NRkZmNgSAuObhiPn/VZFKlWC0zMU5W7yR/uZuFR2JiMizcXJFLmnumoNQ+tTQO8Hal3EVa7ad1Lk9t6AMa7adwrRhKTpf0mp4KZBwXx0k3KdbsEMdLZu9Yr/B/jBa5vzUcUJyLu5Y0ZGIiDwXY4FklR4pjXTiVlXV9PNBvy7NjUbyqt8XEqhE84bBJs/98bojKL+j0qowdvjMTXz23VGjj7MkYsRoGZFtsKIjERG5E65ckVX+t/8SAv29EeDrjeKyOzr33y6twJZfzqJf52ZIaV1fbyRLHdfam3EVuw5eQn5ROfKLjFfnA4CC2+UY+voP8KqhQJGJvaaqsjRixGgZkfxY0ZGIiNwJJ1dktaIS3UlVVSoBfLf7HBQKBUb0idG5v4aXAoXF5di855zZ59Y3oZPC0ogRo2VE8mLsloiI3AljgWQ33+06i4OnbuhE8qRUC5MbI0by4wawZCnGbh2Pn18iInlw5YrsRgCYsfh3nY1BpVQLkxMjRvLjBrBkLcZuHYefXyIi+XDliuyu+sag9q4CxoiRvLgBLMnF2Tas9gT8/BIRyYuTK3IYddU+e0X0GDGSHzeAJXJd/PwSEcmPsUByGHXVPinVwiwRHuqHlwYlIa+ojBEjG+EGsESui59fIiL5ceWKHCq3oFRTLUxuo/rFIj6yDu5PaAgA+PXwFVku1OaF339z5w1g+T6Tu3Pnzy8RkaNw5YocKiu7CMDdi9kH947Cmm2nzHp8UIASAkJrn6vwUH+M6heDDnENZL9Qmxd+a3PXDWD5PpMncNfPLxGRI3FyRQ61ZtspNK4XjA5xDdAgPFDSY57oGYl76wZpon4A9FYYU1+oXZ36Qm1zr7+S+3juwB03gOX7TJ7CHT+/RESOxlggOZy5hS0S7qujVU2saoWx1s3CcPxcDnYeuISP1x2RdF4pHHnhtzPH06REOl2pOiMv8CdP4m6fXyIiZ8CVK3I4cwpbGPsrqr4ol5TzSrlQ21EXfrtCPE29AeyCbw9rxTMBICjAx0G9sgwv8CdPo/78Vv93pmq8moiIpOPkipxC1cIW+iJZaob+imooyiXlvI5oJ4WrxdOqT6wAoLC4win7aggv8CdPxA2ciYjkw1ggOQV1JFD9V9SwEO2IYHioP6Y8k4ygAKVOPE5KlMvUee3dzhRXiqe5Ul9N4QX+5Km4gTMRkTy4ckUOVz3qp++vqPlFZVi6KUNvPC4oQGnRHlnmXKht7wu/XSme5kp9NYUX+BMREZE1uHJFDqcv6lf1r6iFxeV4Z+UBnS+86njc3oyrsp3XEHtf+O1K8TRX6qspvMCfiIiIrMHJFdmE0tsL99YNhLGvoOGh/iavxZESOdt18LJZfZNyXn2MRRblvqbIleJprtRXKez5PhMREZF7YSyQbKL8jgp/XS/S/OyrrIEOcQ3QNSkChbfLJV8wLSVyVnC7HME1lSi4XW6wTXBNJUb2i0F4iL9VF2rb68JvV4qnuVJfpeIF/kRERGQJrlyRXZSVV2LngUsoLbtj1gXTUqNkXZMijN4/7rF4dGvTSJYLte1x4bcrxdNcqa/m4AX+REREZC6nmlydP38eiYmJ2LBhg8E2Z86cwejRo5Gamor27dtjwoQJyMrKsmMvyRqLN6ajUiUkb4wbGugr6bhtW9dzuyiXI+Np5m5cbG5fnXljZCIiIiJLOU0ssKKiApMmTUJxcbHBNrdu3cKIESOQlJSElStXory8HHPmzMHIkSPx3XffwddX2hdxcpyc/FLMXf0njp/PkbQxrpC4WCAU7hnlcsRzsnTjYql9dYWNkYmIiIgs4TSTqwULFiAwMNBomx07dqC4uBjvvvsu/Pzu/oX8vffeQ9euXXHw4EG0b9/eHl0lK+05fEXnNkMb4+YXlkk6prqdOsrlTuz5nKzduNhUX11tY2QiIiIiczhFLHD//v345ptvMGfOHKPt2rdvj08++UQzsQIAL6+7T6GgoMCmfST7qL7ZrLtVonNmtt4M2J02GybDGPl0DL7uRETOweErVwUFBZg8eTJee+011K9f32jbiIgIRERoFy5YvHgx/Pz8kJKSYnEfhBBG44ieroYXUKmyz7my80pw8MQVRDetDQBoWs8ftYN9kVtgeAUrLMQXTev58z2soqSkROt/pTh2PlfSZsBV3x9z2Pr4ZBvmjKV9x65j+dZTWp/X2sG+GP5QFFKj69qsj57OVV53S/5dItKHY4nkYGgcCSGgUFh++YXDJ1czZ85EYmIi+vTpY/ZjV65ciVWrVuG1115D7dqWfxmrqKjAiRMnLH68u7PXxErt2Mnz8Cq9rvm5Z3wg1u4xPLnqEReIU6dO2qNrLufChQuS2x67IG1yWv39cZbjk22ZGkvHL5Vg7Z4cndtzC8rwwddH8USnMLRu5G+j3nkuV3zdzfl3icgYjiWSg75xpFQqLT6eQydXGzduxIEDB7BlyxazHieEwEcffYRFixbh+eefx9ChQ63qh4+PD1q0aGHVMSxj3ua3niK6ZVO0qrJy0aoVENFQ9y+zYSG+GPagc/1lVk4qlcCJi7eQV1iG0CBftGpcC14SC1mUlJTgwoULaNKkCfz9pX2xUvnlYn1arsl21d8fqf2W6/hkX1LGkkolMP/7PUaP87+jRXi0Z6LkMUymudrrbsm/S0T6cCyRHAyNo8zMTKuO69DJ1fr165GTk4OuXbtq3T5jxgxs3boVS5cu1XlMRUUFpk2bhu+//x7Tpk3D8OHDre6HQqFAQECA1cch64WH+iOpVUOdCnPdUpqic5smblUJ0Bi5Kur5+/tLHttJrfwRFnLM5GbA+t4fKf1OjWlo9fHJcYyNpfTMbKPRXQDIyS/D+WslbldwxpFc9XU3598lImM4lkgO1ceRNZFAwMGTq/fffx+lpdpftHr16oUJEyagb9++eh8zefJk/PTTT5g7dy4efvhhe3ST7MjYZrPuWAlQH0dV1FNvBqzv3GrG3h8p/bbm+OS8pG72LbUdScPXnYjI+Ti0WmDdunXRuHFjrf8AICwsDHXr1kVlZSVu3rypmYBt2LABW7duxcSJE9G2bVvcvHlT81/1SRq5nh7JjVBxRyWp0pW7VsZydEU9Szcultrv1Jj6brfZszncddyyqqdj8HUnInI+Di9oYczVq1fRo0cPzJ49GwMGDMD3338PAHj33Xfx7rvvarVVtyHXpADwvwOX8L8DlwAYj8C58ya0x8/lSKqod/xcjs1W8SzZuNicfrvjZs9SuPO4bd0sDGEhfiYjn62bhdmxV+6PrzsRkfNxusnVqVOnNP8/IiJC6+dly5Y5oktkB9X/fm8oAufum9A6S8zH3Aimuf32lIinmruPW2sjpWQZvu5ERM7HKTYRJjKkagROWvQsHUdO33TZ2JWrxnxctd/msDTS5+iop71YGikl6/B1JyJyLk63ckVUVdUInLToWSle+yxN87Orxa5cNebjqv2WyppInzNEPe3FUyOfjsbXnYjIeXDlipyeOkpmSRROHbtKO5old7dsQh3zMcYZYz6u2m8p1JG+6hMkqWPLWaKe9qKOfHZJikBsi3CXfM9dEV93IiLnwMkVOT11lMyaSJmjYldVo2RHTt/E4TOmI4v2jPnIWb3OHeNJckT6PCEySURERHcxFkiySIqsg+4p9+LKjSJ8veMUhEzzmOCaSk2UTEr0zBBHxK70RcmqMhYrs0fMxxbV69wtniRHpM/dI5NERET0N65ckSwOnr6Jc1fy8NVP8k2sAKBrUoTmi7mU6Jkx9oxdGYqSVWUqVmbLmI+1UTdj3CmeJEekz50jk0RERKSNkyuSzXe7z8p+zHYx9bWia0EBSkx5JlkneiaFvtiVLTZ1lRIlq8rekUVPqV4nB7kife4amXSXDZHd6bkQEZFjMRZIspFzxQq4+8Uzv6gMz83arhNdG9k3BiGBvsgtKEVIkC/mrfkTuQVlRo9VPXZlq01dpUTJqrJ3ZNGTqtdZS85InztFJt1pQ2R3ei5EROR4XLkip9WqSS28s/KA3ujaOysPoLC4HF2SIpBwXx2MeTTO6LGqx65sGYuzJH5oz8ii1HPtzbhq4564ht7tGhu935xInztEJm352bE3d3ouRETkHDi5IqcTFKBEYIAP9hw2/sWmanTNnNiVrWNxllR9s2elOKnn2nXwskfHo9KOZuG5WduxZtspvfe7cqTPUu4UKXWn50JERM6DsUByqJBAJUY8HI2ikgoEBypxLee2wS+z1VWPrkmNXdk6FmduVUN7V4pr3SwMwTWVKLhdbrRdwe1yj40Gqlc0DBncuyWe6BnpkitP1nCnSKk7PRciInIeXLkihxo7MB492t6Lfl2ao3NiBLbtvWjW46tH3KTEruyxqaupKFlV9q4UV8NLga5JEZLausvGtuaQsqKxfZ9549RduNOGyO70XIiIyHlw5YocIjzUH6P6xWhFqswtBAHYNoJnybFN7W1Vlb7XwF7axdTH5j3nTLbzxI1tuaJhmDttiOxOz4WIiJwHJ1fkEM/1idaZVJj7F2JL43S22tTVVJTsqX9EoXXzMOQXljm8Uhw3tjWMKxqGudO4cafnQkREzoOxQHKIRRuOYuefl7T2lDH3L8SWxulssamrlCjZT/v/Qmxz21eKq1QJHDufi/QLxTh2PlfvBfnc2NYwrmgY5k7jxp2eCxEROQ+uXJFDFNwuxwdrDgL4e0+Z1Jj6kgtBBAb4WHV+dXXB6hE+S6N6zhIlqx5LXJ+Wi7CQY3r37JH7NXAXXNEwzp3GjTs9FyIicg6cXJHDqfeUmTYsBaP7xxqN1qkVFVdoHmPpFyA5N3V1hiiZoVhi1ddX3wTLXTa2lYt6RcPYOPT0FQ13Gjfu9FyIiMjxOLkip7FkUwY+mdIDCgUgJG4ts3hjOgL8fRx+HZOjo2RS9+xJjamv8/qoKyzS37iiYZo7jRt3ei5ERORYnFyR08jOK8HyLRmSJ1bA3VWZ6Z+maX5WRwylfPnVV9nPnMdX5egombPEEt0JVzSIiIjIXCxoQU4lK/u2VY9XR+DSjmYZbaeO0FWfkEh9fHWOvjjeGWKJ7kjKvmlEREREapxckVOpH1ZTluMs2ZSht0oeID1CZ+jxVY+TnpmN3QcvIz0zG6kx9TFtWArCQrSjf+Gh/lZdGyaFo2OJ5qj+upl6nYmIiIhcBWOB5FTaRtfDtr0XYO33bWMRODkidMYihZ+/1svuUTJHxxKlkjOKSURERORsuHJFTuV2SQX6d2kuy7EMReCsjdCZihTuy7hq9yiZo2OJUsgdxSQiIiJyNpxckUXCQ/3Rv0tzBNdUynrc2sF+GNEnBp3irV/FMBSBsyZCJ1ek0BRLonPqCneOiCWaYq/XjYiIiMiRGAsks3SMq4+HOzZDflEZlm7OQMHtctmOXTW29sqQZBw68wOKiiusPlZ11kTo7FGVz5ronLrC3cETV3Ds5HlEt2yKpFYNHV6IgdUMiYiIyBNw5YrM8tvRqzhw4hreWXnA5Jdlc3VOaKCZBOzLuGrxxAowHoGzJkJn66p8ckTnangpEN20NmKbBCC6aW2HT6wAVjMkIiIiz8DJFZntu91nbXLcXw5noVIlJEXIDJEagbM0QmfLqnxSo3Pld1QuV23PlaoZEhEREVmKsUAymzmb/AJ3I23x99XBzwcuGW2njoUBMGtVLLimEiP7xSA8xN+synyWbBJry6p8UqNzI97cphXHdIVqe65SzZCIiIjIGly5IpvrktjQ5MRKLbeg1Oxo2LjH4tGtTSOLKvOZu0msLavySX3e1a9zc4Vqe65QzZCIiIjIWpxckc2Eh/pjytBk7D50RfJjagf7SY6GhQQqHVIFz1ZV+ayNxDl7tT1nrmZIREREJAfGAkl2QQE+mPxMCmKbh0uKuqlVjYWZipAF11Ri2fTeUHo75u8DlkQKTZESnTPGFart2eJ1IyIiInIWXLki2Y1/PAEJ99VBDS+FWRE/dbVAKRGycY/FO2xipWZupFDK8Uw9b1Ncodqe3K8bERERkbPg5IpkFRSgRHLrekjPzMbOA5dw/HyO5MeqqwUChiNkwTWVmDI02SUiZHJuBBxc00fSOVltj4iIiMhxGAskWRUWl2PEmz+i4Lb5e1RVj7V1iGsAlRBYtP6opohDwe1yLN2cAS8vhVNPsOTYCFgdncvKLsIPaedNnpPV9oiIiIgciytXJDtLJlZqVWNtaUez8M6XB1yuOp5cGwHHtgiHj7cX1mw7hVuF5SYfw2p7RERERI7FyRU5ldBA37uRwj8v4eN1h422dcbqeFI3ApbSb6mbKYeH+rHaHhEREZETYCyQnEZQgBLzvj4ouVqeM1bHk7oRsJR+S620+NKgJMRH1jGrn0REREQkP06uyGkUFpuOvlXnbNXxpPZHSjupx8orKpPUjoiIiIhsy+LJVXl5OdatW4e0tDTcvHkTb7/9Nv744w9ER0cjLi5Ozj6SmwsL8UN5RSUKi82/VsvZquNJ7Y+UdnIei4iIiIhsz6LJVW5uLoYNG4Zz586hWbNmyMzMRGlpKXbt2oU5c+Zg+fLlSExMlLuv5CbCQvzw0lNJyC8sQ+1gP1QKgemfppl9HGesjidlI2Cp/ZbzWERERERkexYVtHj33Xdx+/ZtbN26Fd999x2EuHtx/vz58xEbG4v58+fL2klyL6P7xyLhvjqaTWTzCy2LtTljdTwpGwFL7becxyIiIiIi27NocrVz5068+OKLaNy4MRSKv7/Y+fr64tlnn8WxY8dk6yC5j/BQf71V7cyNtdlyI2FLNv6tztBGwIaev72OZUtyvG5ERERErs6iWGBZWRlCQ0P13lejRg1UVFi+zxG5F18fLzz/WALu+f/4mr5VFinxN4UC+P8FUpttJGzNxr/VVd8IuHawn8Hnb89j2YKcrxsRERGRK7No5So2NhZr1qzRe9+WLVsQExNjVafIfdxbLxhhIbqTgUqVwJHTN7HyhxNY/eMJxN9nvJS4qLYQIvdGwr8evmL1xr/VqTcCVscfrZkMyXksOcmxYTIRERGRu7Bo5erFF1/E8OHD0a9fP3Tp0gUKhQLff/89FixYgF9//RVLly6Vu5/kos5cysP0T9MQFOCD8Y8noENcA6QdzcKCbw+jyILqgNUt2ZSB1Jj6Vk02fj1yBe+tOmDz87gbqRsm83UjIiIiT2HRylVycjK++OIL+Pv7Y+nSpRBCYPny5bh58yY+++wztGvXTu5+kosrLK7A7BX78cWWDMxesV+WiRXw94a8lko7moV3vjwAU5cIWXsed2TOhslEREREnsCilavff/8diYmJ+Prrr1FaWor8/HwEBgaiZs2aVnXm/PnzGDBgAKZPn44BAwbobXPr1i3MmjULv/zyCxQKBR5++GFMnjwZ/v7+Vp2b7OO73WdlP6alGwlLWXmR4zzuSurrkZ1fgvTMbKe8XoyIiIhIThZNrl544QW8/vrr6Nu3L/z8/ODnZ/0mphUVFZg0aRKKi4uNtpswYQJKSkqwfPlyFBQU4N///jeKi4vxzjvvWN0Hsr3q107JwdJNdKWsvMhxHncl9fVYuikdBbf/XqlksQsiIiJyVxbFAoODg2WZUFW1YMECBAYGGm1z6NAh/PHHH3jnnXcQHR2N9u3b480338SmTZtw/fp1WftDrsGaTXTNWYniZr261FUeTak6sQJY7IKIiIjcl0UrV2PGjMGsWbNw/vx5tGzZEgEBATptUlJSJB9v//79+Oabb7Bx40Z07drVYLsDBw6gTp06aN68uea2tm3bQqFQ4M8//8RDDz1k1vMg12fNJrrmrEQ5YrPeSpVwmvLrhvoyun8sZq/Yb9ExWexCGmcaB0RERGScRZOrGTNmAADmzZsHAFobCQshoFAocOLECUnHKigowOTJk/Haa6+hfv36Rttev35dp41SqURoaCiuXr1qzlPQIoQwGUck5xIW4othD0YhoUWoxe9d03r+qB3si9yCMoNtvBTAhMdjrTqPJfYdu47lW09p9a12sC+GPxSF1Oi6Jh9fUlKi9b+27MvLT8bp3B9c00dnxaq67LwSHDxxBdFNa1vdR3dl7TiQg5xjiTwbxxLJhWOJ5GBoHKnnMpayaHL15ZdfWnzC6mbOnInExET06dPHZNuSkhIolUqd2319fVFWZvgLsikVFRWSJ4PkONH3+qFlRAAC/b3QuI4vvLxyceJErlXH7BkfiLV7DI+dgR1qI9T7Fk6cuGXVecxx/FIJ1u7RrbCXW1CGD74+iic6haF1I2kFXC5cuGCXvox/KBwXb5ahqESFQH8vFBZXYsPvpl+zYyfPw6uUkV595BwHcrB2LBGpcSyRXDiWSA76xpG++YZUFk2u2rZta/EJq9q4cSMOHDiALVu2SGrv5+eH8vJyndvLysr0RhOl8vHxQYsWLSx+vOUuO+CcrutavsBrzyXBS8ZIVKtWQERD3dUB9cqYvVYH1FQqgfnf7zHa5n9Hi/Boz0Sjr0NJSQkuXLiAJk2aWFxJ09y+RFe5/dj5XGz4/U+T54hu2RStuHKlQ65xIAc5xhIRwLFE8uFYIjkYGkeZmZlWHdeiyRVwt2z6/Pnz8ccff6CgoAC1atVCcnIyxo0bp3VNlDHr169HTk6OznVWM2bMwNatW3U2I65Xrx527NihdVt5eTny8vJwzz33WPpUoFAorJqckX3k5Jfh/LUSxLYIl/W43VKaonObJk5xXcvdkuXGV2HNeR38/f0tHtvW9CWplT/CQo4ZrcYYHuqPpFYNef2QHnKPAzlYM5aIquJYIrlwLJEcqo8jayKBgIWTq8zMTDz55JOoUaMGunfvjvDwcNy8eRM7d+7Erl278O2330qaYL3//vsoLdX+8tWrVy9MmDABffv21WmfkpKC999/HxcvXkTjxo0BAH/88QcAoE2bNpY8FXIxttprqoaXwm5fUo2R+vzsseeWNX2RUuzCEUVCXIUzjQMiIiKSzqLJ1fvvv4+IiAisXLkSQUFBmtsLCwsxbNgwzJs3DwsXLjR5nLp19UeuwsLCULduXVRWViI3NxdBQUHw8/NDfHw8kpKSMHHiRMycORPFxcV4/fXX0b9/f4PHIvfi7ntNSX1+IUG+Nt+YV2pf8grLUKkSOufvENcA04alYPHGdK0VrPBQf4zqF8N9royQ+tq7++eBiIjI1Vg0udq/fz/eeustrYkVAAQFBWH06NGaaoLWunr1Knr06IHZs2djwIABUCgUWLhwId544w0MGzYMvr6+eOCBBzBt2jRZzkfOzRP2mlLvHWUsThcUoMS8NX9Wu0ZM/o15pfQFAJZuzsB3uzP1nr9DXAOkxtR3isilK5Hy2nvC54GIiMjVWDS58vb2hq+vr977lEql3qITUp06dUrz/yMiIrR+Bu6uas2fP9/i45Pr8oQYmZQ4XWGx7udLvTHvtGEpsk2wzNnHytj5nSVy6UoYqyQiInJNXpY8KDY2FmvWrIEQQut2IQRWr16NmJgYWTpH7i24po/Wz+Gh/hjQtTnCQvx0bpdz0iCHSpVAemY2dh+8jPTMbFSqhOkHSaSO0+m+Dn4IDPAx8Ki7lmzKkLUvqTH1EWTinJae35avoTswPA6c7/NAREREd1m0cvXiiy/iqaeeQt++ffHAAw+gTp06uHnzJn788UecP38eX3zxhdz9JDcTHuqPz6b1xKkLuTpxsWcejnbqGFna0Syd64jkjuXpi9OpVAKvfZZm9HHZeSU4fi4HzRvIUz3p+LkcFBYb3wxY3/lNrVTZ4zV0B4xVEhERuRaLJlexsbFYunQp5s6di4ULF2p2Mo6JicGSJUuQkpIidz/JzYzqFwOlt5feL+HOHCNLO5qlN6plq1he1ddh90Fp+6LlFpTKNrmypBqdqcfY8zV0B878eSAiIiJtFu9z1a5dO3z99dcoLy9HQUEBgoODcefOHZ0iF0RVeXkBje4JxJlLeQjw90Fs83CX+St8pUpg8cZ0o22WbMpAakx9mzwnW1aQq1QJvasjlhzL2GMc/RoSERER2ZJFk6uKigrMmjULGRkZWL9+Pfz9/ZGWlobRo0dj6NChePXVV+HlZdHlXOTmVCrg4rUiXLx2But+PoOgAB+MfzzBJVYqjp/LMVk5T2oszhLmVJArKy2RfFxjEb3UmPqSKgZWP78hjn4NiYiIiGzJohnQggULsHnzZjz88MOa21q3bo1JkyZh7dq1WLp0qWwdJPdWWFyB2Sv2I+1olqO7YpKjN3ZVV5AzxtwKcuqIXvUJjzqity/jqslzmnN+R7+GRERERLZk0eRqy5YtmDJlCp599lnNbaGhoRg+fDgmTpyIdevWydZB8gzzvzmEReuPYNPusyi/o7J5JTlLju8MG7vKWUHOnIievnNKPX/V1zqvsEzPo3VZ+hqyAiERERE5kkWxwFu3bqFRo0Z672vWrBmuXbtmVafI89wuvYOtaRcAAJ9vzoCfbw2UlFVq7pezkpylleqcZWNXuSrImRPRq37O0EBfCAWQX1hm9Pz6XmsvBWBszmPpa8gKhERERORoFq1cNWvWDNu2bdN7388//4zGjRtb1SnybALQmlgBf8fUrI0PmorBGTu+LWJ5llJXkOuSFIHYFpYVBTE3olf1nPGRdZBwXx2j5zf0WptaTLLkNbTmfSUiIiKSi0UrV8888wymTp2KvLw89OzZE2FhYcjNzcXOnTvxww8/YPbs2XL3kwgA8Mn6I0huXQ9Kb/P/LiBHpTp1LK/6Ckl4qD9G9YtxqRUSW1cfNPVaV1/BsvQ1ZAVCclWGqnQSEZHrsmhy1b9/f9y+fRuffPIJtm/frrm9Vq1amD59Ovr37y9X/8jJDeoZiZgW4Zi35k/kFki7nsYa+UXlGPHmjxj3mPkVBuWqVOcuG7vaMuYo5bVWCWBk3xiEBvla9RqyAiG5IsZYiYjck8X7XD399NMYPHgwzp8/j7y8PKhUKtx3330ICQmRs3/k5BrVDULCfXUw5tE4vRvD2kLB7QqLNpuVs1KdO2zsqo45GnvfLI05Sn2tQ4N80SUpwuzjW3IuViAkZ8GNtImI3JdZ2aqjR4/in//8JzZu3AgAUCgUSEtLw4gRIzB06FB06dIFn3/+uS36SU4qr7AMuw9eRlCAEq8OaQN7Lt4s2ZRhsBqcvqpxzlDtz9nIWX2wKnu+1nxfyZVIjbGy0iURkWuSvHJ18uRJDB06FKGhoRgwYAAAID09HW+99RaaN2+Ol156CefOncO8efPQuHFj9OzZ02adJufgpQCWbs7Q/BxcU2myWIGcDEW9DMVtRvaNcYpqf87GFjFHe1ZWdJYqjkRSMMZKROTeJE+uPvvsM7Rs2RLLly+Hv78/AODLL78EALz//vto2bIlACA7OxsrV67k5MoDVJ9IFdwut3sfqke9jMVt3ll5AAO6NseGXWcNHs9e1f6cjdwxxxpeCnRJbGj0te6c0ECW19qW8UYiuTHGSkTk3iTHAvfv34+hQ4dqJlYA8Ouvv6JRo0aaiRUA3H///Th+/Li8vSSn4kzfUatGvaTEbX45nIUpzyTLHoMjbZUqgd2Hrhht88vhLNmiT4bijUEBPhjcOwqpMfVlOQ+RtRhjJSJyb5JXrvLy8lCvXj3Nz2fPnsWtW7d0Vqj8/f1RXm7/FQyyrYc6NEHLxrVRcLtcKwroSNWjXlLjNiE1ffH5a71cvtqfM3NE9Ekdb1y74zQ27zmLouIKFBZXYM22U9i29yKrsJFTYIyViMi9SV65Cg0NRU5OjubnvXv3QqFQoH379lrtzp49i9q1a8vXQ3IK3jW80C25EUKDfB3dFY3qUS9z4jZybMJLhjkq+rQv4yrWbDuJouIKrdu5mTA5C2fajJyIiOQneXLVtm1brF27FkII3LlzB+vXr4evry86deqkaVNeXo7Vq1cjKSnJJp0lx9l18JJZFfeCa/oYvC8kUGlVXwxF+Bi3cR6OeC9YhY1cha2qdBIRkeNJjgU+//zzGDRoEHr27AkhBLKysjBu3DgEBQUBANavX4/Vq1fj/PnzePfdd23WYXKMgtsVWLvjNBrVDYSXQreYRVXhof4Y8UhrfPZdulaRi0B/H/Tt3AwDu0di9Ns/mYyNVRcU4IPJz6Qgtrn+lSbGbZyHI94LVmEjV+Ium5ETEZE2yStX9913H9auXYvU1FTcd999mDFjBl544QXN/R9++CFu3bqFjz/+GK1atbJJZ8mx1mw7iXe+PGCy3HrnhAZ4b9WfOtUDi0ruXv9y4Pg1k7EYfcYOjEfCfXUMfvmwJm6jb18sspwjok+swkauhvFkIiL3I3nlCgBatGiBt99+W+9969atQ506deDlZda+xORGvBTAK0+3wbItx4y2W/jtEax84wFMG5aisx9VUIASAkLnmhng7p5aXl4Ko5EZddym+nHDQ/0xql+M3sca2heLBRCsY8l7YQ3GQomIiMjRzJpcGVO3bl25DkUuSiWAWwVlJqNZhcXlWLvjNJ7qFaU3FvN7ehbe+fKAzuPURQlMXZNgTtzG2L5YUs5Fxtkz+sRYKBERETmabJMrIgDIyi6S1G7LnrN4omckangp0LpZmObLd0ZmNpZuMl7qfcmmDKTG1Df6BV3KprhSCiB8vO4IyioqER7iz+shLCT3BsXGzmPpZsKVKsFrX4iIiMhqnFyRQxQWV+D4uRwUFpfrxMZMkasogZQCCAW3y/HBmoMAGBV0BYyFEhERkSNxckWyimxUC7/4X0FRie41U9XtzbiKzXvOWXQeOYoSmHsMRgVdA2OhRERE5CisPkGyCg/1R9/OzSS13XXwssXnMVWUQEr1P0sLG3CvJOcnpQob98UiIiIiuXHlimQlFMATPaOwZc85FOqp+KcWXFOpU6pdKlNFCaTGvKQUQNCHeyW5B+6LRURERHLjyhXJKr+wDDW8FBj/eILRdl2TIiw+h7H9kdQxr+pfmtUxr7SjWZrbpOzFZAj3SnJ93BeLiIiI5MbJFclKHbVTFxYIC9GO3gUF+GBw75ZIia5n9rHDQ/0118Doi/1ZEvMy1E9TuFeS6+O+WERERCQ3xgJJNmEhflpxPXVhgbU7TmHzL+dQVFKBwuIKrNl2ErWDfREU4GM0OgjcnYx1a9MI7WLqa4oSGIr99W7XxKKYV9UCCNl5JVi6OcNoZJF7JbkH7otFREREcuPKFclmdP9YnbjevoyrWLPtlE71wNyCMpMTK+BuyfbNe86hsLhcM7EyFPtbs+2kpH7qi3mpCyB0S26EcY/FG328sVgiuQ4psVC+10RERGQOrlyR1RQKYPKQZKTG1Ed6ZjZyC0oRGuiLSiGwYO1ho48NClBC6eNlcsVp8cZ0+Pl6mzyeFKZiXpbsleTMuEGuYe72XhMREZFjcXJFVhMCuHSjCEtnbTe78l5hcTme7RONZVuOGW2Xk1+KGYt/t6abAKTHvMzZK8mZ7Tt2HSt+OM0Nco1wl/eaiIiIHI+TK5KF1EiePtdzi2XsiXHmxLzUUUFXdfxSCdbuOapzOzfI1eXq7zURERE5B15zRQ5XP6ymrMcb3DtKp/pf1UqDnkClEvjxzzyjbbhBLhEREZG8uHJFDhUe6o8HOzbFd7szzY4UGjreEz2j8ETPKI+OeZ24eAsFxZVG23CDXCIiIiJ5ceWKHGpUvxgovb0s3sxX3/FqeCk0Ma8uSRGIbRHuURMrAMgrLJPUjhvkEhEREcmHkytyiOobAgcFKNG3UzME1/Sx+ngEhAb5SmrHDXKJiIiI5MNYINnds32i0bdzc4MbAisASL0SKCjAB1OGpiDGA1enjGnVuBaCA2oYjQZyg1wiIiIieXHliuyudrCf0Q2BzSmxMP7xBMRH1uHEqhovLwUeaBNqtA03yCUiIiKSFydXZHe1g/1QqRJYvDHdquMM7t1SEytMz8zG7oOXkZ6ZzQp4/691I3+8/GScx1dOJCIiIrIXxgLJJIXi7kbBcggLuVu57/i5HKuqA4aF+OGJnpF6Y4XcJPdvqdF10blNE4+unEhERERkL1y5IpMe7dJctmON7h+LGl4Kq6vUje4fi30ZV/XGCtWb5KYdzbLqHO7C0ysnEhEREdkLJ1dkUFiIH576RyR8vGugQ2x9BAVYvtAZFKDUiqJZWqUuuObd46TG1DcZK7TlJrmMIhIRERFRdYwFko4nekbCu4YXtuw5i69+Oq11n6+PF8oqVJKP5e/rjWnPpCCuWtGJ1s3CEBbiZ1Y0MCRQiWXTe0Pp7YX0zGyTj7XVJrmMIhIRERGRPg5fucrJycGrr76Kdu3aITExEaNHj8bZs2eNtn/llVfQrl07pKamYuLEibh+/bode+z+SsvuYM22kygsrtC5z5yJFQC89GQiElveoxNFq+GlMHvj4LED46H0vjtkpcYK5d4k11CFQ0YRiYiIiMjhk6tx48bh4sWLWLx4MdatWwc/Pz8MHz4cJSUletu/9NJLyMrKwhdffIEvvvgCWVlZGDdunJ177d5+3HvB6mOEBCp1KtJVj9KlxtTHtGEpOtXsFNUuCVJXt0uNqa95fF5hmaR+yLlJrpQKh7aMIhJJwcgqERGR4zg0Fpifn4+GDRtizJgxiIyMBACMHTsW/fr1w5kzZxAXF6fVvqCgAH/88QcWLVqEVq1aAQBGjx6NsWPHIi8vD6GhofZ+Cm6p3MzVqeoC/Lw18T01Y1G6z1/rhePncrA34yp2HbyEgtt/r5gF1/TBc32iAQDPzdqu9XgvBWDse6Pcm+RKqXBoqygikRSMrBIRETmWQydXISEhmDt3rubn3NxcLF++HPXq1UOLFi102vv5+aFmzZrYuHEj2rZtCwDYtGkTmjZtiuDgYLv1m4zrmXKvzsRq9or9Ou3UUbppw1IAAJv3nNNpU3C7Au+sPKD3PKb+IC/3JrmOiiISSSHlc8YJFhERkW05TUGL6dOnY+3atVAqlVi0aBECAgJ02iiVSsyZMwevv/46kpOToVAocM8992DVqlXw8nJ4wpH+X7uY+pr/Ly1Klw6VFdGl6itY4aH+GNUvRvYvklIjhnJGEYmkkBpZTY2pz1L8RERENuQ0k6thw4Zh0KBBWL16NcaNG4c1a9YgOjpaq40QAidOnEBiYiJGjhyJyspKzJs3D2PHjsVXX32FwMBAi84thEBxcbEcT8PjhYX4omk9f83reex8roQonXUrPSoBPPNgJEIDlQgN8kWrxrXg5aWQ/T1tWs8ftYN9kVtg+Hqv6s/fUdTXLBq6dpHci7TPWQkOnriC6Ka1zTo2xxLJhWOJ5MKxRHIwNI6EEFBULwBgBoUQwqmudlapVHjkkUcQHx+P2bNna923detWzJgxAzt37tRMpPLz89GtWzdMmDABw4cPN/t86enpKC8vl6PrZpu55rJDzmtLT3QKQ+tG/pqf0y8UY31ars3PO7BDbcQ20V3tlNvxSyVYuyfH4P3Vnz+RPUj9nNnrc0JEROTKlEolYmPNq2qt5tCVq9zcXPz+++/o3bs3vL3vdsXLywstWrTAjRs3dNofOHAATZs21VqhCgkJQdOmTXHx4kWL++Hj46P3Gi/bc5/JVViIL4Y9GIXU6Lpat6v8cu0yuYpu2RStzPyLvCVatQIiGl7H8q2ntFawDD1/RykpKcGFCxfQpEkT+PtzsufupH7OLPmccCyRXDiWSC4cSyQHQ+MoMzPTquM6dHKVnZ2Nl19+GUuXLkWnTp0AABUVFTh+/Di6d++u075evXr473//i7KyMvj6+gIAiouLcfnyZfTt29fifigUCr3XeJFp3ZIi8I/UxmjdLEzvtRxJrfwRFnLMaGQpPNQPKpUwGrczJjzUH0mtGtrtWpJuKU3RuU0THD+Xg9yCUtQO9jP4/B3N39+fY9sDSPucWfc54VgiuXAskVw4lkgO1ceRNZFAwMH7XEVGRqJz586YNWsW9u/fj9OnT2Pq1KkoKCjA8OHDUVlZiZs3b6K09O4Xhv79+wO4u9fVyZMncfLkSbz88svw9fXFgAEDHPhMPNetIuMTohpeCozsF2O0zah+sRjzaJzRNsYfb3lVQEv3BKrhpUBsi3B0SYpAbItwp5xYkeeQsim33NUziYiISJfDC1p88MEHmDt3LiZOnIjCwkIkJydj9erVaNCgAS5fvowePXpg9uzZGDBgAO655x6sWbMG7733HoYNGwYvLy8kJydjzZo1CAoKcvRT8UiHT9/E4dM3De6lk3Y0C0s3Zeh9bPWqftOGpejs0aNuA8DgfZZWBeSeQOROOsQ1MPoZ4pgmIiKyPacraGFv6el3yxdbetGaNfq8ssnu57S1qnvpGNp3R23K0GTcn9BQ67ZKlTAYtzN2n7lM9c3V9wQqLi7GiRMn0KpVK0YmPIycnxOAY4nkw7FEcuFYIjkYGkfWzg0cvnJF7kW9lw4Ak/vuLNpwFG1j6mttOKyO2+lj7D5zcE8gcmdyfU6IiIjIfNx5l2SVnVeC4+dycPxcjsl9dwpul2PEm9uQdjTLTr27S0rf1M+DiIiIiEgqTq5IdrkFpcgtkLYxcMHtcsxesd+uEyypfZPajoiIiIgI4OSKbODS9ULkFZpXVn3JpgzJlfqsVTvYT9Z2REREREQAr7kiG/hmx2kAgJcCkDpfUsfw7HGtSOtmYQgL8TO5J1DrZmE27wsRERERuQ+uXJHNmLsQZa8YHvcEIiIiIiJb4OSKbE7qFMWeMTz1nkBhIdrnDA/1d5ky7JZugExEREREtsFYIJnFx9sLFXdUZj1GAKjp543bpXcMtnFEDK9DXAOkxtSXdU8ge+EGyERERETOhytXZJYH2zex6HE9Uu41er+jYnjqPYG6JEUgtkW4y0ysZq/Yr3PNWE5+qd0rLxIRERHR3zi5Ih1+St1hERSgxLRhKWj3/xsEm6tdTH2TMTxPi7lZ8nylboCscvPXjoiIiMgZMRZIOny8a6C0/O/oX3BNJZ4fGKeZAAXX9EHB7QrJx1NH/mp4KQzG8Dwt5mbp85W6AfKJi7f4lxMiIiIiO+P3L9JRWKw9cSq4XY53vjyAtKNZqOGlQNekRmYdr2rkT18Mz9NibtY8X6kVFc3dZ4yIiIiIrMfJFUmm3ug3JbqepPbBNZUmK+9Jjbm5S0TQ2ucrtaJiaJCv2X0jIiIiIutwckWSZeeVYO2OU5i35k+TbUMClfji9d4mI31SY27Hz+WY1VdnZe3zVW+AbEx4qD9aNa5lcR+JiIiIyDKcXJFZ1mw7hdwC05GzsQPjofQ2PbykxtzstcGwrVn7fKVugOzlAlUPiYiIiNwNJ1ckq7AQPwzu3RIVd1SSKuBJjbnZc4NhW5Lj+brDBshERERE7ojVAklW5RWVWLPtpOZnUxXw1DE3Y1E5R2wwbCtyPV9X3gCZiIiIyF1x5YpkVb3SoKkKeFJjbu4yaZDz+briBshERERE7oyTK9KIaRqGJ3tF6Y2bDe7d0qpjL96YjsNnburdMNfTYm6e9nyJiIiIPAVjgaSRcT4HGedzEOjvjcG9o9AgPFATNwOAbXsvmKx0Z0hOfimmf5qm+bl6XNDTYm6e9nyJiIiIPAFXrkhHUckdrNl2Cj7eXpq4mZQ4mzn0xQU9Lebmac+XiIiIyN1xckUGLdmUrhXfS42pj6AAH5nP4T4bBJP5KlUC6ZnZeuOiRERERK6GsUAyKDuvFMfP5SC2RTiAuxvgVi9YYf05SrTOQZ4j7WgWFm9M14qamqouSUREROTMuHJFRlXdzNZWG/m6ywbBJF3a0SzMXrFf5xo+U9UliYiIiJwZJ1dkVNXNbG21ka+7bBBM0lSqBBZvTDfahnFRIiIickWcXJFB4aF+WpvZqjfANUdwTaWJc7jPBsEkzfFzOSarTqrjokRERESuhJMrMmhUv1itCnaWVAzsmhRh4hzus0EwSSM1Bsq4KBEREbkaTq5Ir8G9o/QWFVBvgGtqRUqtXUx9p9kwl5XpnIPUGCjjokRERORqWC2QdISH+uGJnlEG7+8Q1wDJrethxJs/ouC24eqB6shfDS+FwzfMZWU656GOlxqLBjIuSkRERK6IK1eko3ocUB+ltxfGPZZg4jh/R/4cuWEuK9M5FynxUsZFiYiIyBVxckUaXgpgyjPJOis5huJ06oigM0T+DGFlOudkr7HDKCgRERHZE2OBpKESQEhNX63bTMXpOsQ1cHjkzxhzKtNxI2P7svXYYRSUiIiI7I2TK9KSnVeC9Mxs5BaUIiu7CGu2ndJpo47TqVcY1JE/Z8TKdM7NVmNHHQWtrvrYJSKqVAmn/QMhEbkeTq5Iy9LNGSi4XS6p7ZJNGUiNqe/Uv4RYmc7zSI2COvvYJSLb4wo3EcmN11yRFqkTK8A1NnqVsvExK9O5F25STERSsNgREdkCJ1dkFWeP07EynedhFJSITGGxIyKyFU6uyCquEKdzhaqGJB9GQYnIFK5wE5Gt8JorDxAU4AMBoKjY8Ia/lnClOJ2zVzUk+XCTYiIyhSvcRGQrnFx5gG5tGqGmvze+2n5a1uNaEqdzZFUmW1Y1ZLUp56GOguqrFqjGKCiRZ+MKNxHZCidXHmDznnOyHi881B+j+sWYHadz16pM7vq8XJk6Clr9fbF07BKRe+EKNxHZCidXJMng3i3RILymxasy7rrvkLs+L3fAKCgRGcIVbiKyFU6uyCg5/tLvrvsOuevzcifOvME1ETkWV7iJyBY4uSK9ggJ8MPmZFMQ2D7d6YmBOVSZX+iLsrs+LiMhTcIWbiOTGyRXpNf7xBCTcV0eWY7lrVSZ3fV5ERJ6EK9xEJCdOrkhLcE0lnh8QJ2scwl2rMrnr8yIiIiIiy3ATYYKiSvqh4HY5lm7OQNrRLNmOr67KZIwrVmVy1+dFRERERJbh5IoghPbP6kp3ck2w1FWZjHHFqkzu+ryIiIiIyDIOn1zl5OTg1VdfRbt27ZCYmIjRo0fj7NmzBttXVFRg7ty56NSpExISEjBkyBCcOHHCjj12H6a+8y/ZlIFKlTDeSCJ1VabqKz3hof5OUa68UiWQnpmN3QcvIz0zW/LzdvbnRURERET24/BrrsaNGweVSoXFixejZs2a+OijjzB8+HBs374d/v7+Ou1nzpyJXbt2Yc6cOWjQoAE++ugjjBo1Cj/88AOCgoIc8AxcT1CADx7vEYllW44ZbSd3pTtnrcpk7SbAzvq8iIiIiMi+HLpylZ+fj4YNG2LWrFmIi4tD8+bNMXbsWNy4cQNnzpzRaX/p0iWsX78eb731Fjp16oTmzZtj1qxZUCqVyMjIcMAzcE3jH09Adl6JpLZyV7pTV2XqkhSB2BbWl3m3lnoT4Ool1c2NRjrb8yIiIiIi+3PoylVISAjmzp2r+Tk3NxfLly9HvXr10KJFC532v/32G4KCgtC5c2fNbcHBwfj555/t0l938MrTbZAaUx8frzssqb07V7qz5ybAlSrBlS0iIiIiN+fwWKDa9OnTsXbtWiiVSixatAgBAQE6bc6fP49GjRph+/btWLx4Ma5fv47WrVtj6tSpaN68ucXnFkKguLjYmu67jA/W/Im0I5dRcLvCZNvgmj5oWs/fbV+bY+dzJW0CfPDEFUQ3rW3xefYdu47lW08ht6BMc1vtYF8MfygKqdF1LT6uMSUlJVr/S2QpjiWSC8cSyYVjieRgaBwJIaBQWP4HcIUQ1WvFOUZmZiZKS0uxevVqbN26FWvWrEF0dLRWm3//+9/48ccf0bBhQ0yePBnBwcFYtGgRDh48iK1btyIszPyS1+np6SgvL5fraZhl5prLDjmvVO2iAvFAm1BHd8Nm0i8UY31arsl2AzvURmwT3cm+FMcvlWDtnhyD9z/RKQytG+leW0hEREREjqFUKhEba7witCFOs3KljgG+9dZbOHLkCFatWoXZs2drtfH29kZRURHmzZunWamaN28eunTpgu+++w4jR4606Nw+Pj56Y4i259yTq14do9DKihUbZ6fyy5U0uYpu2dSi10GlEpj//R6jbf53tAiP9kyEl8wRwZKSEly4cAFNmjTRWxiGSCqOJZILxxLJhWOJ5GBoHGVmZlp1XIdOrnJzc/H777+jd+/e8Pa+2xUvLy+0aNECN27c0Glfr149eHt7a0UA/fz80KhRI1y+bPlERaFQ6I0herKQQCWSWjV06+uCklr5IyzkmNFoYHiov8WvQ3pmtlYUUJ+c/DKcv1YiW0XG6vz9/Tm2SRYcSyQXjiWSC8cSyaH6OLImEgg4uFpgdnY2Xn75Zfz++++a2yoqKnD8+HG911ClpKTgzp07SE//uwhBaWkpLl26hMaNG9ulz56iS2KEW0+sANtvAiy10qLcFRmJiIiIyDEcOrmKjIxE586dMWvWLOzfvx+nT5/G1KlTUVBQgOHDh6OyshI3b95EaendL5/Jycno0KEDpkyZggMHDiAzMxOTJ09GjRo10K9fP0c+FbfTLqa+o7tgF7bcBFhqpUV3rshIRERE5Ekcfs3VBx98gLlz52LixIkoLCxEcnIyVq9ejQYNGuDy5cvo0aMHZs+ejQEDBgAAFixYgPfffx/jx49HaWkpkpKS8OWXX6J2bfe9NsjewkP90bqZ+cVBXJWtNgFu3SwMYSF+JmOHnvRaExEREbkzh0+ugoKCMHPmTMycOVPnvoiICJw6dUrrtsDAQIPtSR7WROFclXoTYLmPObp/LGav2G+wjSe+1kRERETuyqGxQLI/BYABXZvbLApH2mwZOyQiIiIi5+LwlSuyHz9lDXz5xoPwV9YAAJtE4UiXrWKHRERERORcOLnyIL1SG2smVoBtonCkH19rIiIiIvfHyZUHMVYBsFIluLJCRERERGQFTq48hLGqdGlHs7B4Y7pWVbuwED+M7h/La4KIiIiIiCRiQQsPYagqXdrRLMxesV+nXHhOfilmr9iPtKNZ9uoiEREREZFL4+TKzYWF+BmsSld+R4WP1x02+vglmzJQqRI6t1eqBNIzs7H74GWkZ2brbUNERERE5EkYC/RQaUez8PG6Iyi4XWG0XXZeCY6fy9EqxsAYIRERERGRLq5cuTl1vO/Xw1c0t6mjgAW3yyUdI7fg70kUY4RERERERPpxcuUh3lt1AL8euYJKlcDijelmPbZ28N0NcKU81lCMkIiIiIjI3XFy5SFUAnjnywNYu+OUzqqTMVWrDB4/l2PyseoYIRERERGRp+HkysNs2XPOrPaj+sUAANIzs/GbxMhf1RghEREREZGnYEELD1NYbLyAhVpwTSXGPRYPAHhu1nazVrvUMUIiIiIiIk/ClSsPFBjgY/T+kEAlvni9NwDoLV5hjLHNiomIiIiI3BknVx6ob6fmRu8fOzAeNbwUZhe+AAxvVkxERERE5O4YC/Qw4aH+eKJnJBrXC9LZqyo81B+j+sWgQ1wDpGdmm71ipX4sEREREZEn4uTKw6hXljrENUBqTH0cP5eD3IJS1A72Q+tmYZpVJ6lFKR7u2BQd4xpoPZaIiIiIyBNxcuVBBveO0lpZquGlQGyLcL1tpRal6BjXwOAxiIiIiIg8Ca+58hDBNX3wRM8oye1bNwtDWIjxCRaLVxARERER/Y2TKw/RNamRWbG9Gl4KjO4fa7QNi1cQEREREf2NkysP0S6mvtmP6RDXANOGpeisYIWH+mPasBSPKV5RqRJIz8zG7oOXkZ6ZjUqVcHSXiIiIiMgJ8ZorDxASqLQ4vmeq8IW7SzuapVNVMSzED6P7x3rM5JKIiIiIpOHkygN0SYywajJkrPCFO0s7moXZK/br3J6TX4rZK/Z71OodEREREZnGWKAHsCQS6OkqVcLkJspLNmUwIkhEREREGpxcubmwED9W9LPA8XM5JjdRzs4rwfFzOXbqERERERE5O06u3Nzo/rEec32UnKRuoiy1HRERERG5P15z5aaCApQY/3g8rwmykNRNlKW2IyIiIiL3x8mVmxj+SGuUlN6BABDXPBwxLcK5YmUF9SbKxqKB3ESZiIiIiKri5MpNhIf4o0u3CEd3w22oN1HWVy1QjZsoExEREVFVvObKTYQE+ZrVnhvjmsZNlImIiIjIHFy5chMKM+ZG3BhXOk/fRJmIiIiIpOPkyk3kFZVJaseNcc3nqZsoExEREZF5GAt0E1Kq1nFjXCIiIiIi2+Hkyg1IrVrHjXGJiIiIiGyHkys3ILVqHTfGJSIiIiKyHU6uXFy35EaSr5HixrhERERERLbDyZWLO3L6BtKOZklqq94Y1xhujEtEREREZBlOrlxcbkEZZq/YL2mCpd4Y1xhujEtEREREZBlOrtzE4o3pOHL6pslNgbkxLhERERGRbXCfKzeRk1+K1z5L0/xsbFNgboxLRERERCQ/rly5KfWmwIbiguqNcbskRSC2RTgnVkREREREVuLkys1xU2AiIiIiIvvg5MrNcVNgIiIiIiL74OTKA3BTYCIiIiIi2+PkygNwU2AiIiIiItvj5MrNcVNgIiIiIiL7cPjkKicnB6+++iratWuHxMREjB49GmfPnpX02M2bNyMqKgqXL1+2cS9dFzcFJiIiIiKyD4dPrsaNG4eLFy9i8eLFWLduHfz8/DB8+HCUlJQYfdyVK1fw5ptv2qmXrie4ppKbAhMRERER2ZFDJ1f5+flo2LAhZs2ahbi4ODRv3hxjx47FjRs3cObMGYOPU6lUePXVVxEdHW3H3rqWkf1iOLEiIiIiIrIjh06uQkJCMHfuXERGRgIAcnNzsXz5ctSrVw8tWrQw+LhPP/0UFRUVGDNmjL266nLCQ/wd3QUiIiIiIo/i7egOqE2fPh1r166FUqnEokWLEBAQoLfd0aNHsWzZMqxbtw7Xr1+X5dxCCBQXF8tyLGcQFuKLpvX83eo5kXnUsVpT8VoiUziWSC4cSyQXjiWSg6FxJISAQmF5vQKnmVwNGzYMgwYNwurVqzFu3DisWbNGJ/ZXXFyMSZMmYdKkSWjSpIlsk6uKigqcOHFClmM5gx5xgTh16qSju0FO4MKFC47uArkJjiWSC8cSyYVjieSgbxwplUqLj+c0kyt1DPCtt97CkSNHsGrVKsyePVurzaxZs9C0aVM8+eSTsp7bx8fHaAzRduStchgW4othD0YhNbqurMcl11NSUoILFy6gSZMm8PdnRJQsx7FEcuFYIrlwLJEcDI2jzMxMq47r0MlVbm4ufv/9d/Tu3Rve3ne74uXlhRYtWuDGjRs67devXw+lUonExEQAQGVlJQDgkUcewT//+U/885//tKgfCoXCYAzRFQzqGYn4++qgdbMwll0nLf7+/i49tsl5cCyRXDiWSC4cSySH6uPImkgg4ODJVXZ2Nl5++WUsXboUnTp1AnA3onf8+HF0795dp/327du1fj5y5AheffVVLF68WFMUwxM1qhuE2Bbhju4GEREREZFHc+jkKjIyEp07d8asWbMwa9YshISE4LPPPkNBQQGGDx+OyspK5ObmIigoCH5+fmjcuLHW469duwYAaNCgAUJDQx3wDJzDpeuFSM/M5soVEREREZEDOXwT4Q8++ADt27fHxIkT8fjjjyMvLw+rV69GgwYNcPXqVdx///3YunWro7vp1L7ZcRr/WvQbnpu1HWlHsxzdHSIiIiIij+TwghZBQUGYOXMmZs6cqXNfREQETp06ZfCxqampRu/3NDn5pZi9Yj+mDUvhBsJERERERHbm8JUrkt+STRmoVAlHd4OIiIiIyKNwcuWGsvNKcPxcjqO7QURERETkUTi5clO5BaWO7gIRERERkUfh5MpN1Q72c3QXiIiIiIg8CidXbigkUInWzcIc3Q0iIiIiIo/CyZUb6pIYwf2uiIiIiIjsjJMrN9Qupr6ju0BERERE5HE4uXIz4aH+jAQSERERETkAJ1duZlS/GEYCiYiIiIgcwNvRHSB5hIf6Y1S/GHSIa+DorhAREREReSROrlxYgF8NjH40Hvf8fxSQK1ZERERERI7DyZULe3FQEleqiIiIiIicBCdXLsjH2wspreqipp8PKlWCK1ZERERERE6AkysXVHFHhbT0q0hLv4rAAB+88HgCV7CIiIiIiByM1QJdXFFxBWav2I+0o1mO7goRERERkUfj5MpNLN6YjkqV0PxcqRJIz8zG7oOXkZ6ZrXUfERERERHJj7FAN5GTX4rj53IQ2yIcaUezsHhjOnLySzX3h4X4YXT/WMYHiYiIiIhshCtXbmRvxlWkHc3C7BX7tSZWwN3JF+ODRERERES2w5UrN7Lr4CX8euSK0TafrD+CsvJKhHNvLCIiIiIiWXFy5UYKbleYbJNfVI4PvjoIgFFBIiIiIiI5MRbowRgVJCIiIiKSDydXhCWbMlhNkIiIiIjISpxcuZHwUD/UDvY1+3HZeSU4fi7HBj0iIiIiIvIcnFy5kef6xmDMo3EWPTa3oNR0IyIiIiIiMoiTKzcSUtMXHeIaYNqwFISF+Jn12NrB5rUnIiIiIiJtrBboRtSrTx3iGiA1pj6On8tBdn4Jlm5KN1pJUF2WnYiIiIiILMfJlRupuvpUw0uB2BbhAABfnxqYvWK/wceN6hfD/a6IiIiIiKzEWKADyTmdCQvxM7j6ZCgqGB7qj2nDUrjPFRERERGRDLhy5SZG9481uvpUNSqYW1CK2sF3J2NcsSIiIiIikgcnVy4uKECJ8Y/HS1p9qhoVJCIiIiIieXFy5UDWbtv7YPsmGDMgjqtPREREREROgNdcuTAvLwUnVkREREREToKTKxdWP6ymo7tARERERET/j5MrF+WlAB7s2NTR3SAiIiIiov/HyZWL6t+lOZTefPuIiIiIiJwFC1q4GC/F3YnViD4xju4KERERERFVwcmVA81/sSsmfLRLUltfHwWefqA1Hr6/GVesiIiIiIicECdXDtT03hDJbV8enCxpLysiIiIiInIMLoE42Ja5/Yze7+PthWnDUjixIiIiIiJycly5cgJb5vbD+b/ytSKCkQ1q4qmHYpEYdQ/3siIiIiIicgGcXDmJpveG4Jv//AMnTpxAq1atEBAQ4OguERERERGRGRgLJCIiIiIikgEnV0RERERERDLg5IqIiIiIiEgGnFwRERERERHJgJMrIiIiIiIiGTh8cpWTk4NXX30V7dq1Q2JiIkaPHo2zZ88abH/mzBmMHj0aqampaN++PSZMmICsrCw79piIiIiIiEiXwydX48aNw8WLF7F48WKsW7cOfn5+GD58OEpKSnTa3rp1CyNGjICfnx9WrlyJJUuWIDc3FyNHjkRZWZkDek9ERERERHSXQydX+fn5aNiwIWbNmoW4uDg0b94cY8eOxY0bN3DmzBmd9jt27EBxcTHeffddREZGIiYmBu+99x7Onj2LgwcPOuAZEBERERER3eXQTYRDQkIwd+5czc+5ublYvnw56tWrhxYtWui0b9++PT755BP4+flpbvPyujs/LCgosH2HiYiIiIiIDHDo5Kqq6dOnY+3atVAqlVi0aBECAgJ02kRERCAiIkLrtsWLF8PPzw8pKSn26ioREREREZEOp5lcDRs2DIMGDcLq1asxbtw4rFmzBtHR0UYfs3LlSqxatQqvvfYaateubfG5hRAoLi62+PFyUV9npu96MyJzcCyRXDiWSC4cSyQXjiWSg6FxJISAQqGw+LgKIYSwqmcyU6lUeOSRRxAfH4/Zs2frbSOEwEcffYRFixbh+eefx0svvWTx+dLT01FeXm7x44mIiIiIyH0olUrExsZa9FiHrlzl5ubi999/R+/eveHtfbcrXl5eaNGiBW7cuKH3MRUVFZg2bRq+//57TJs2DcOHD7e6Hz4+Pnqv8bK3kpISXLhwAU2aNIG/v7+ju0MujGOJ5MKxRHLhWCK5cCyRHAyNo8zMTKuO69DJVXZ2Nl5++WUsXboUnTp1AnB38nT8+HF0795d72MmT56Mn376CXPnzsXDDz9sdR8qKioghDC6t5a9qBcRr1y5YtVyJBHHEsmFY4nkwrFEcuFYIjkYGkcVFRVWjSuHlmKPjIxE586dMWvWLOzfvx+nT5/G1KlTUVBQgOHDh6OyshI3b95EaWkpAGDDhg3YunUrJk6ciLZt2+LmzZua/9RtzKVQKJzmg6lQKKBUKp2mP+S6OJZILhxLJBeOJZILxxLJwdA4snZu4PBrrgoLCzF37lzs2LEDhYWFSE5OxtSpU3Hffffh8uXL6NGjB2bPno0BAwbg2WefxW+//ab3OOo2REREREREjuDwyRUREREREZE7cGgskIiIiIiIyF1wckVERERERCQDTq6IiIiIiIhkwMkVERERERGRDDi5IiIiIiIikgEnV0RERERERDLg5IqIiIiIiEgGnFwRERERERHJgJMrIiIiIiIiGXByRUREREREJANOroiIiIiIiGTAyZUdqVQqzJ8/H506dUJCQgJGjRqFS5cuGWx/69YtvPLKK0hJSUHbtm3xxhtvoKSkxI49Jmdl7lg6c+YMRo8ejdTUVLRv3x4TJkxAVlaWHXtMzsrcsVTV5s2bERUVhcuXL9u4l+QKzB1LFRUVmDt3rqb9kCFDcOLECTv2mJyVuWMpJycHr7zyCtq1a4fU1FRMnDgR169ft2OPyRV89tlnGDp0qNE2cnz35uTKjj755BOsWbMG//nPf/D1119DpVJh5MiRKC8v19t+woQJuHjxIpYvX46PPvoIu3fvxsyZM+3baXJK5oylW7duYcSIEfDz88PKlSuxZMkS5ObmYuTIkSgrK3NA78mZmPvvktqVK1fw5ptv2qmX5ArMHUszZ87Ehg0b8Pbbb2P9+vWoXbs2Ro0ahcLCQjv3nJyNuWPppZdeQlZWFr744gt88cUXyMrKwrhx4+zca3Jmq1evxocffmiynSzfvQXZRVlZmUhMTBSrV6/W3Jafny/i4uLEli1bdNofPHhQREZGiszMTM1te/bsEVFRUeLatWt26TM5J3PH0tq1a0ViYqIoKSnR3JaVlSUiIyNFWlqaXfpMzsncsaRWWVkpnnrqKfHMM8+IyMhIcenSJXt0l5yYuWPpr7/+ElFRUWLnzp1a7bt168Z/lzycuWMpPz9fREZGiv/973+a23bs2CEiIyPFrVu37NFlcmLXrl0TY8aMEQkJCeKBBx4QQ4YMMdhWru/eXLmyk5MnT+L27dto37695rbg4GC0bt0a+/fv12l/4MAB1KlTB82bN9fc1rZtWygUCvz555926TM5J3PHUvv27fHJJ5/Az89Pc5uX192PfkFBge07TE7L3LGk9umnn6KiogJjxoyxRzfJBZg7ln777TcEBQWhc+fOWu1//vlnrWOQ5zF3LPn5+aFmzZrYuHEjioqKUFRUhE2bNqFp06YIDg62Z9fJCR07dgw+Pj7YvHkz4uPjjbaV67u3t8W9JbNcu3YNAFC/fn2t2++55x7NfVVdv35dp61SqURoaCiuXr1qu46S0zN3LEVERCAiIkLrtsWLF8PPzw8pKSm26yg5PXPHEgAcPXoUy5Ytw7p163hNA2mYO5bOnz+PRo0aYfv27Vi8eDGuX7+O1q1bY+rUqVpfbMjzmDuWlEol5syZg9dffx3JyclQKBS45557sGrVKs0fEslzde/eHd27d5fUVq7v3hx1dqK+GE6pVGrd7uvrq/e6l5KSEp22xtqT5zB3LFW3cuVKrFq1CpMmTULt2rVt0kdyDeaOpeLiYkyaNAmTJk1CkyZN7NFFchHmjqWioiJcvHgRn3zyCV5++WUsWrQI3t7eGDx4MHJycuzSZ3JO5o4lIQROnDiBxMRErF69GitWrECDBg0wduxYFBUV2aXP5B7k+u7NyZWdqCNZ1S/GLCsrg7+/v972+i7cLCsrQ0BAgG06SS7B3LGkJoTAhx9+iFmzZuH55583WTGH3J+5Y2nWrFlo2rQpnnzySbv0j1yHuWPJ29sbRUVFmDdvHu6//37ExcVh3rx5AIDvvvvO9h0mp2XuWPrhhx+watUqvPfee2jTpg3atm2LTz/9FFeuXMG6devs0mdyD3J99+bkyk7Uy4w3btzQuv3GjRuoW7euTvt69erptC0vL0deXh7uuece23WUnJ65Ywm4W/L41Vdfxaeffopp06bhpZdesnU3yQWYO5bWr1+PtLQ0JCYmIjExEaNGjQIAPPLII/j0009t32FyWpb8jvP29taKAPr5+aFRo0Ys7e/hzB1LBw4cQNOmTREYGKi5LSQkBE2bNsXFixdt21lyK3J99+bkyk5atmyJwMBA7Nu3T3NbQUEBjh8/rve6l5SUFFy7dk3rH4Y//vgDANCmTRvbd5iclrljCQAmT56MH3/8EXPnzsXw4cPt1FNyduaOpe3bt+P777/Hxo0bsXHjRsyaNQvA3Wv4uJrl2Sz5HXfnzh2kp6drbistLcWlS5fQuHFju/SZnJO5Y6levXq4ePGiVmyruLgYly9fZnyZzCLXd28WtLATpVKJIUOG4P3330ft2rXRsGFDvPfee6hXrx569eqFyspK5ObmIigoCH5+foiPj0dSUhImTpyImTNnori4GK+//jr69+9vcHWCPIO5Y2nDhg3YunUrJk+ejLZt2+LmzZuaY6nbkGcydyxV/9Krvri8QYMGCA0NdcAzIGdh7lhKTk5Ghw4dMGXKFLz55psIDQ3F/PnzUaNGDfTr18/RT4ccyNyx1L9/f3z++ed46aWX8OKLLwIAPvzwQ/j6+mLAgAEOfjbkzGz23duK0vFkpjt37oh3331XtGvXTiQkJIhRo0Zp9oe5dOmSiIyMFOvXr9e0z87OFi+88IJISEgQqampYsaMGaK0tNRR3ScnYs5YGjFihIiMjNT7X9XxRp7J3H+Xqtq7dy/3uSINc8dSYWGhmDFjhkhNTRXx8fFixIgR4syZM47qPjkRc8dSZmamGDNmjGjbtq1o166dGD9+PP9dIh1TpkzR2ufKVt+9FUIIYbs5IRERERERkWfgNVdEREREREQy4OSKiIiIiIhIBpxcERERERERyYCTKyIiIiIiIhlwckVERERERCQDTq6IiIiIiIhkwE2EiYjI4wghoFAoHN0NIiKy0meffYZff/0VK1eulNR+3759eOaZZ/TeFxERgf/9739W9YcrV0RE5NSGDh2KqKgorf9atmyJpKQkDBgwAJs2bTLreH/++SdGjx6t+fny5cuIiorChg0b5O46ERHZ0OrVq/Hhhx+a9ZjExET8+uuvWv8tXLgQCoUCY8eOtbpPXLkiIiKn17p1a8yYMUPzc2VlJa5du4bly5dj8uTJCA0NRZcuXSQd69tvv8XZs2c1P99zzz345ptvcO+998rebyIikt/169cxY8YM7Nu3D02aNDHrsUqlEnXq1NH8XFxcjNmzZ+PRRx/FwIEDre4bJ1dEROT0AgMDkZCQoHN7586d0b59e2zYsEHy5Ko6pVKp99hEROScjh07Bh8fH2zevBkff/wxrly5onX/zp07sWDBAmRmZqJu3bp4+OGHMXbsWCiVSp1jffrppygpKcGUKVNk6RtjgURE5LJ8fX2hVCo110/l5ubijTfeQLdu3RATE4O2bdti3LhxuHz5MgBg6tSp+O6773DlyhVNFLB6LHDDhg1o3bo1jhw5gkGDBiE2NhbdunXD559/rnXuGzduYOLEiWjbti1SUlLw+uuvY968eejevbumTUZGBoYNG4Y2bdogMTERw4cPx+HDh+3z4hARuanu3btjwYIFaNSokc59v/zyC1566SU88cQT+P777zFjxgz88MMPePXVV3Xa5ubmYvny5fjnP/+J0NBQWfrGlSsiInJ6QgjcuXNH83NlZSWuXLmCjz/+GLdv30a/fv0ghMCYMWOQn5+PSZMmITw8HKdOncKHH36IGTNm4PPPP8fYsWORm5uL48ePY+HChbj33ntRXFyscz6VSoWXXnoJw4cPx0svvYR169bh3XffRWRkJDp16oTy8nIMGzYMxcXF+Ne//oXAwEAsXrwYJ06c0MRNioqKMHLkSLRr1w4LFixAeXk5Fi1ahOeeew67du1CUFCQ3V4/IiJP8emnn+KJJ57Ak08+CQC499578cYbb2DYsGG4fPkyIiIiNG3XrFmDoKAgDBo0SLbzc3JFREROb//+/YiOjta6TaFQIDIyEh999BG6deuG69evw9/fH1OmTEFycjIAIDU1FX/99Re++eYbAHd/ydauXVsrCqhvciWEwNixY/H4448DANq0+b/27i4kqjSO4/h3K0KaGkIjw4hgYCitCF2IgfQiSFFIy+oqqIvIXpDwphesEIsCISghIaikEISECpFIoy56gdKohCgzakYiyQsVCSmiyNkL2aHWZdmtExX7/VzNeWbOPM/MzfA7z//853euXbvGjRs3KCgooK2tjUQiwcWLF1m8eDEAsViMlStXpt7jxYsXjIyMsGnTJvLy8gCIRCK0tLTw9u1bw5UkfQc9PT08evSICxcupMaSySQA8Xj8i3DV2trKmjVrSEtLC2x+w5Uk6ae3aNEiDh48CIyX49XX1/Px40fq6+uJRCIAZGZm0tTURDKZpL+/n5cvX5JIJHj48CEfPnz4z3Pm5uamHk+dOpX09PRUEOvs7GTevHmpYAXj94WtWLGCrq4uAKLRKOnp6Wzfvp3i4mIKCgpYvnz535amSJKCMTY2xpYtWygvL5/w3OeNLHp7e3n16hWlpaWBzm+4kiT99EKhEEuWLEkdL126lLKyMjZv3sylS5dIT08HoK2tjWPHjjEwMMDMmTPJzs7+6iuSfz1v0qRJqaufIyMjZGRkTDjn87FQKERzczMnT56kvb2dlpYW0tLSWL16NQcOHPjbG6slSd8mGo3S19fH/PnzU2NdXV00NTVRW1vLtGnTALh//z4ZGRksXLgw0PltaCFJ+uXMmjWLmpoaBgYGOHLkCDD+Q7l3716Kioq4desWXV1dnDt37rt0AszMzGRoaGjC+PDw8BfHkUiEo0eP0tnZyfnz5ykvL6elpYWmpqbA1yRJgoqKCq5evUpDQwN9fX3cvXuX6upqRkdHv9i56unpYcGCBYHPb7iSJP2S/iy1u3z5Mvfu3aO7u5uxsTF27txJZmYmMN744s6dO8B4qQiM70B9q2XLltHf38/Tp09TY+/fv+f27dup446ODmKxGIODg0yePJnc3Fxqa2sJh8O8fv36m9cgSZqouLiY48ePc/36dUpLS9m9ezf5+fk0NDR88brBwcHAOgR+zrJASdIva9++fZSVlXH48GGqq6sBOHToEOvWrePNmzc0NzfT29sLjDeumD59OuFwmKGhIW7evEl2dvZXzbtq1SpOnTpFZWUlVVVVhMNhzp49y/DwMFlZWQDk5eUxNjZGZWUlW7duJRQK0d7ezujoKEVFRcF8AZL0P1dXVzdhrKSkhJKSkn887/Tp099lPe5cSZJ+WZFIhI0bN/Ls2TPi8Tg1NTV0d3dTUVFBXV0dWVlZqauVDx48AGDt2rXMnTuXyspKWltbv2reKVOm0NjYSE5ODrW1tezZs4doNEphYWGqnn/27NmcOXOGGTNmsH//frZt28aTJ084ceIEsVgskM8vSfq5/Jb88+5cSZL0rzx//pxEIkFRUVHqD4wB1q9fz5w5cyaUn0iS/h8sC5Qk6T969+4dVVVVbNiwgcLCQj59+sSVK1d4/Pgxu3bt+tHLkyT9IO5cSZL0FTo6OmhsbCQej5NMJsnJyWHHjh3k5+f/6KVJkn4Qw5UkSZIkBcCGFpIkSZIUAMOVJEmSJAXAcCVJkiRJATBcSZIkSVIADFeSJEmSFADDlSRJkiQFwHAlSZIkSQEwXEmSJElSAAxXkiRJkhSAPwA7k5skjGtEsAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Визуализация перед очисткой\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(cleaned_df_books['Ratings'], cleaned_df_books['Score'])\n", + "plt.xlabel('Ratings')\n", + "plt.ylabel('Score')\n", + "plt.title('Scatter Plot of Ratings vs Score (Before Cleaning)')\n", + "plt.show()\n", + "\n", + "# Рассчитываем квартиль 1 (Q1) и квартиль 3 (Q3) для Score\n", + "Q1 = cleaned_df_books[\"Score\"].quantile(0.25)\n", + "Q3 = cleaned_df_books[\"Score\"].quantile(0.75)\n", + "\n", + "# Рассчитываем межквартильный размах (IQR)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Определяем порог для выбросов\n", + "threshold = 1.5 * IQR\n", + "lower_bound = Q1 - threshold\n", + "upper_bound = Q3 + threshold\n", + "\n", + "# Фильтруем выбросы\n", + "outliers = (cleaned_df_books[\"Score\"] < lower_bound) | (cleaned_df_books[\"Score\"] > upper_bound)\n", + "\n", + "# Вывод выбросов\n", + "print(\"Выбросы в Popular Books:\")\n", + "print(cleaned_df_books[outliers])\n", + "\n", + "# Заменяем выбросы на медианные значения\n", + "median_score = cleaned_df_books[\"Score\"].median()\n", + "cleaned_df_books.loc[outliers, \"Score\"] = median_score\n", + "\n", + "# Визуализация данных после обработки\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(cleaned_df_books['Ratings'], cleaned_df_books['Score'])\n", + "plt.xlabel('Ratings')\n", + "plt.ylabel('Score')\n", + "plt.title('Scatter Plot of Ratings vs Score (After Cleaning)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разбиение набора данных на обучающую, контрольную и тестовую выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 16529\n", + "Размер контрольной выборки: 5510\n", + "Размер тестовой выборки: 5510\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import train_test_split\n", + "\n", + "# Разделение на обучающую и тестовую выборки\n", + "train_df, test_df = train_test_split(cleaned_df_books, 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", + "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": "markdown", + "metadata": {}, + "source": [ + "Видим недостаток баланса:" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Ratings в обучающей выборке:\n", + "Ratings\n", + "70 18\n", + "55 18\n", + "16100 18\n", + "61 18\n", + "162 17\n", + " ..\n", + "15370 1\n", + "4510 1\n", + "50015 1\n", + "24791 1\n", + "16244 1\n", + "Name: count, Length: 10844, dtype: int64\n", + "\n", + "Распределение Ratings в контрольной выборке:\n", + "Ratings\n", + "86 9\n", + "246 8\n", + "66 8\n", + "83 8\n", + "237 8\n", + " ..\n", + "15184 1\n", + "65771 1\n", + "6498 1\n", + "457617 1\n", + "316921 1\n", + "Name: count, Length: 4435, dtype: int64\n", + "\n", + "Распределение Ratings в тестовой выборке:\n", + "Ratings\n", + "136 11\n", + "100 11\n", + "159 8\n", + "55 8\n", + "71 8\n", + " ..\n", + "45669 1\n", + "2055 1\n", + "179534 1\n", + "16031 1\n", + "1108 1\n", + "Name: count, Length: 4428, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "def check_balance(df, name):\n", + " counts = df['Ratings'].value_counts()\n", + " print(f\"Распределение Ratings в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "check_balance(train_df, \"обучающей выборке\")\n", + "check_balance(val_df, \"контрольной выборке\")\n", + "check_balance(test_df, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Используем oversample и undersample" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оверсэмплинг:\n", + "Распределение Ratings в обучающей выборке:\n", + "Ratings\n", + "2906 18\n", + "647 18\n", + "84803 18\n", + "52669 18\n", + "4880 18\n", + " ..\n", + "6093 18\n", + "2341 18\n", + "29423 18\n", + "93667 18\n", + "224935 18\n", + "Name: count, Length: 10844, dtype: int64\n", + "\n", + "Распределение Ratings в контрольной выборке:\n", + "Ratings\n", + "19873 9\n", + "224 9\n", + "1896 9\n", + "39208 9\n", + "9145 9\n", + " ..\n", + "10122 9\n", + "132 9\n", + "53626 9\n", + "17870 9\n", + "88623 9\n", + "Name: count, Length: 4435, dtype: int64\n", + "\n", + "Распределение Ratings в тестовой выборке:\n", + "Ratings\n", + "141477 11\n", + "1441 11\n", + "2471 11\n", + "17264 11\n", + "637349 11\n", + " ..\n", + "556 11\n", + "20224 11\n", + "24353 11\n", + "719 11\n", + "7381 11\n", + "Name: count, Length: 4428, dtype: int64\n", + "\n", + "Андерсэмплинг:\n", + "Распределение Ratings в обучающей выборке:\n", + "Ratings\n", + "9282201 1\n", + "1 1\n", + "2 1\n", + "3 1\n", + "4 1\n", + " ..\n", + "19 1\n", + "18 1\n", + "17 1\n", + "16 1\n", + "15 1\n", + "Name: count, Length: 10844, dtype: int64\n", + "\n", + "Распределение Ratings в контрольной выборке:\n", + "Ratings\n", + "5202524 1\n", + "8 1\n", + "9 1\n", + "2058282 1\n", + "1900499 1\n", + " ..\n", + "15 1\n", + "14 1\n", + "13 1\n", + "12 1\n", + "11 1\n", + "Name: count, Length: 4435, dtype: int64\n", + "\n", + "Распределение Ratings в тестовой выборке:\n", + "Ratings\n", + "9596885 1\n", + "5 1\n", + "9 1\n", + "10 1\n", + "11 1\n", + " ..\n", + "28 1\n", + "27 1\n", + "25 1\n", + "23 1\n", + "22 1\n", + "Name: count, Length: 4428, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "from imblearn.over_sampling import RandomOverSampler\n", + "from imblearn.under_sampling import RandomUnderSampler\n", + "\n", + "def oversample(df, target_column):\n", + " X = df.drop(target_column, axis=1)\n", + " y = df[target_column]\n", + " \n", + " oversampler = RandomOverSampler(random_state=42)\n", + " x_resampled, y_resampled = oversampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df = pd.concat([x_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "def undersample(df, target_column):\n", + " X = df.drop(target_column, axis=1)\n", + " y = df[target_column]\n", + " \n", + " undersampler = RandomUnderSampler(random_state=42)\n", + " x_resampled, y_resampled = undersampler.fit_resample(X, y) # type: ignore\n", + " \n", + " resampled_df = pd.concat([x_resampled, y_resampled], axis=1)\n", + " return resampled_df\n", + "\n", + "train_df_oversampled = oversample(train_df, 'Ratings')\n", + "val_df_oversampled = oversample(val_df, 'Ratings')\n", + "test_df_oversampled = oversample(test_df, 'Ratings')\n", + "\n", + "train_df_undersampled = undersample(train_df, 'Ratings')\n", + "val_df_undersampled = undersample(val_df, 'Ratings')\n", + "test_df_undersampled = undersample(test_df, 'Ratings')\n", + "\n", + "# Проверка сбалансированности после oversampling\n", + "print(\"Оверсэмплинг:\")\n", + "check_balance(train_df_oversampled, \"обучающей выборке\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке\")\n", + "\n", + "# Проверка сбалансированности после undersampling\n", + "print(\"Андерсэмплинг:\")\n", + "check_balance(train_df_undersampled, \"обучающей выборке\")\n", + "check_balance(val_df_undersampled, \"контрольной выборке\")\n", + "check_balance(test_df_undersampled, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://www.kaggle.com/datasets/gallo33henrique/bitcoin-btc-usd-stock-dataset\n", + "Данный набор данных относится к анализу и прогнозированию финансовых временных рядов, связанных с криптовалютами.\n", + "Примр цели — разработка модели машинного обучения для прогнозирования цен на основе временных рядов.\n", + "Входные данные: Дата, Цена открытия на начало торговли, Самая высокая цена, Самая низкая цена, Цена закрытия в конце торговли, Скорректированная цена закрытия, Количество проданных." + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Столбцы в Popular-Books:\n", + "Index(['Date', 'Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')\n" + ] + } + ], + "source": [ + "df_btc = pd.read_csv(\".//static//csv//BTC-USD_stock_data.csv\")\n", + "\n", + "print(\"Столбцы в Popular-Books:\")\n", + "print(df_btc.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Посмотрим краткое содержание датасета" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Date Close Price_Change\n", + "0 2017-01-01 998.325012 up\n", + "1 2017-01-02 1021.750000 up\n", + "2 2017-01-03 1043.839966 up\n", + "3 2017-01-04 1154.729980 down\n", + "4 2017-01-05 1013.380005 down\n", + "\n", + "Информация о датасете BTC-USD:\n", + "\n", + "RangeIndex: 2836 entries, 0 to 2835\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Date 2836 non-null object \n", + " 1 Open 2836 non-null float64\n", + " 2 High 2836 non-null float64\n", + " 3 Low 2836 non-null float64\n", + " 4 Close 2836 non-null float64\n", + " 5 Adj Close 2836 non-null float64\n", + " 6 Volume 2836 non-null int64 \n", + " 7 Price_Change 2836 non-null object \n", + "dtypes: float64(5), int64(1), object(2)\n", + "memory usage: 177.4+ KB\n", + "None\n" + ] + }, + { + "data": { + "text/html": [ + "
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DateOpenHighLowCloseAdj CloseVolumePrice_Change
02017-01-01963.6580201003.080017958.698975998.325012998.325012147775008up
12017-01-02998.6170041031.390015996.7020261021.7500001021.750000222184992up
22017-01-031021.5999761044.0799561021.5999761043.8399661043.839966185168000up
32017-01-041044.4000241159.4200441044.4000241154.7299801154.729980344945984down
42017-01-051156.7299801191.099976910.4169921013.3800051013.380005510199008down
\n", + "
" + ], + "text/plain": [ + " Date Open High Low Close \\\n", + "0 2017-01-01 963.658020 1003.080017 958.698975 998.325012 \n", + "1 2017-01-02 998.617004 1031.390015 996.702026 1021.750000 \n", + "2 2017-01-03 1021.599976 1044.079956 1021.599976 1043.839966 \n", + "3 2017-01-04 1044.400024 1159.420044 1044.400024 1154.729980 \n", + "4 2017-01-05 1156.729980 1191.099976 910.416992 1013.380005 \n", + "\n", + " Adj Close Volume Price_Change \n", + "0 998.325012 147775008 up \n", + "1 1021.750000 222184992 up \n", + "2 1043.839966 185168000 up \n", + "3 1154.729980 344945984 down \n", + "4 1013.380005 510199008 down " + ] + }, + "execution_count": 136, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Добавляем бинарную переменную 'Price_Change': 'up' - цена выросла, 'down' - цена упала\n", + "df_btc['Price_Change'] = df_btc['Close'].diff(-1).apply(lambda x: 'up' if x < 0 else 'down')\n", + "\n", + "# Удаляем строки с NaN значениями, возникшими из-за сдвига\n", + "df_btc.dropna()\n", + "\n", + "# Вывод первых строк для проверки\n", + "print(df_btc[['Date', 'Close', 'Price_Change']].head())\n", + "\n", + "print(\"\\nИнформация о датасете BTC-USD:\")\n", + "print(df_btc.info())\n", + "df_btc.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Анализируем датафрейм при помощи \"ящика с усами\". Проверяет на пустые значения." + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Проверка на пустые значения в наборе данных 'BTC-USD':\n", + "Series([], dtype: int64)\n", + "\n", + "\n" + ] + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "sns.boxplot(x='Close', data=df_btc)\n", + "plt.title('Распределение цен закрытия BTC-USD')\n", + "plt.xlabel('Цена закрытия (USD)')\n", + "plt.show()\n", + "\n", + "check_missing_values(df_btc, \"BTC-USD\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Видно, что выборка относительно сбалансированна, пустых значений нет." + ] + }, + { + "cell_type": "code", + "execution_count": 138, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df_btc['Date'], df_btc['Close'])\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Close Price')\n", + "plt.title('Scatter Plot of Date vs Close Price (Before Cleaning)')\n", + "plt.xticks(rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Убираем шумы" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Выбросы в BTC-USD Stock Data:\n", + "Empty DataFrame\n", + "Columns: [Date, Open, High, Low, Close, Adj Close, Volume, Price_Change]\n", + "Index: []\n" + ] + }, + { + "data": { + "image/png": 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evXuLXkdy6aWXIiMjAxkZGRgxYgSuvvpqlJSU4LnnnuuwQuNPP/0Ek8lkDSosRo0aheTkZOzZs0f0c9qzZw8yMzOtgZnF9OnTUVFRgePHj4s+ljOWK/mWogDLli3DTTfdhOrqauTl5WHDhg3W2Tp778u+fftgMBg6fA8A4IcffuhwP61Wi8mTJ9ukhX322WcYN26cdWZjzJgxWL58OebPn48PP/wQlZWVePDBB61BUUdCQkIcplM6smfPHiQnJ1sDM4vp06fDaDTi559/Rr9+/dC1a1f85S9/waOPPoqvvvoK8fHxWLx4MZKSkqDX6/Hzzz/j4osvhtlstr4eKSkp6NOnj93Xw+LUqVPWz15GRgbGjBmD2267DXFxcVi5cqVNMZCYmBiHxT/y8/PRo0cPm4BEo9Fg69atuPbaayV9f1o7c+YMmpubO7zAsGHDBqSlpaFnz56oq6tDXV0dcnJysGPHDpw4ccLhc29r586dSEhIQEZGhnVszc3N+MMf/oADBw7YzDKJCbrc+Wzs3LkTCoUCF198cbvXqqKiAr/++itUKhXWrl2Lyy+/HGVlZdi1axfef/99/O9//wNg+x3q2rWrzSyo5X3U6/V2x+Bsn127dmHChAnWwAxAh78lABAVFYXo6GjZq0ESBSPOnBGRT3Xp0gVTp061BiIFBQVYvHgxnnvuOUybNs260D8uLs7uMaqrq/HPf/4TX3/9NRQKBVJTU61FAaT0OWpdVKAjZWVl1v+PiIgQfdxXXnnFeoU7LCwMsbGxdlOhAFhPEi0zUa3Fx8db093EOHfuHFJSUjo8DtCS+icXy+tjeW779+/HY489hv3790Oj0aBv37644IILANh/XyzvgWXmpa3y8nK7j3/llVdi8+bNKCwsRHx8PHbv3o2nnnrKev8LL7yA1atX44svvsDWrVuhVCoxfvx4PP744x2ecALABRdc4HBdYGNjI86dO9fhe3Xu3LkOU9pav/YRERF499138corr+CLL77ABx98gPDwcFx55ZV45JFHUFdXB0EQ8O9//xv//ve/2x3LWWXMhIQEvPLKK9Z/q1QqJCUloUuXLu22dfaZrq2tdfg9lPL9ac3yedZqtTa3V1VV4bvvvkNjYyOysrLa7ffBBx9g8eLFDsfcdnwVFRXIyMjo8P6Kigrr69J2LB1x57NRW1sLs9ls98JAeXk5Bg0ahO3bt+Opp57C8ePHERERgYEDB1rH1vo71DqAAn6/QNJ2TW9rzvaprq7u8P3u6PlYjtfQ0GD38YhIHAZnROR1lnLZ9957r3Wtl0V6ejoWLlyIefPmobS0FNHR0QDQrvhBTU0NCgoKkJmZifvvvx/Hjx/Hm2++iczMTKhUKuj1eqxfv17SuCyPtW7dug5PVC2BhVT9+/e3m3bYEcsJYmVlJXr37m1zX0VFRYfBlqNjVVRUtLvdcltHaWau2rFjB1JTU5GYmGhdSzVgwAB89tln6N27N5RKJb777jts3brV7jEs78HSpUvRq1evdvfbOzEEWtb4JCQk4IsvvkBCQgLUarVN9bioqCgsXrwYixcvxvHjx/HNN99g1apVeOyxx+xWBr3ooouwbt06VFRUdBhofffdd5g3bx5WrFjRbna3S5cuNmvgLNq+9r1798Zzzz2H5uZm/PLLL/jkk0/wn//8Bz179sR1110HhUKBm2++ucOgp+0JdlsqlcqacuauqKioDmdGfvzxR3Tp0sXl74/ldWh7oWDz5s1oamrCypUrERUVZXPf8uXLsXHjRtx7772iZ7CjoqLQq1cvLF26tMP7pXxHAfc+G1FRUdBqtdYMgbZSU1Nx4sQJaxruq6++ipSUFCgUCrz77rvYvn27pLG6IikpCZWVle1ur6qqave7BLS8f3L+nhAFK6Y1EpHXxcfHIzQ0FO+99x6MRmO7+48fPw61Wo3U1FT07t0bsbGx1lQei08++QR33HEHGhsbkZ+fjylTpmDMmDHWEzVL1THLVeC2xQ0AtCvZbZltq6mpwZAhQ6z/VVdX46WXXrLODHjasGHDoFKp8Omnn9rcnpeXh9OnT1uvtrcdf0eysrKwb9++dk2eN2/ejISEBKSmpsoy5m+//Rb79++3NvU+fvw4amtrceONN6Jv377WsbZ9X9o+h2HDhiEsLAxlZWU270FoaCief/55h2lTISEhmDZtGv73v/8hNzcXkydPts4ynDp1ChdffDFyc3MBtAREt99+O8aPH+9w9mP27NkICwvDk08+2S6FTafT4eWXX0ZsbCwmTJjQbt+srCycOnWqXS+9zZs3IywsDEOHDkVubi7Gjh2LiooKhISEIDMzE0uWLEF0dDROnz6NyMhIpKen4/jx4zavR79+/bB8+XJrFUJvGDVqFEpLS/Hrr79abzMajbjnnnvw0Ucfufz9SUxMREhICM6ePWtz+8aNGzF8+HBMnjwZY8aMsflv1qxZqK6uxldffWV3vG0/W6NHj8aZM2cQFxdnM74ffvgBa9as6fA3whF3PhujR4+GTqeD2Wy2GcuRI0ewcuVKNDU14cCBAzAajbjjjjvQs2dP68yWJTCTkhXgiqysLGzfvt3mN7qgoKDD7+C5c+eg1+tdvoBFRL/jzBkReV1ISAiWLFmCefPmYebMmZg9ezb69OkDvV6PH374Ae+++y7uvfde6wzSPffcg8cffxxxcXGYNGkSioqK8PLLL2P27Nno0qULhg4dii1btiAjIwNJSUn48ccf8dprr0GhUFjXT1iuvO/cuRN9+vTBsGHDrFf6P/30UwwbNgwDBgzA9OnT8Y9//AOnTp3C4MGDUVRUhBdeeAE9evTocCbHE2JiYnDHHXdg5cqVCAsLwx/+8AecPHkSL730Evr27WutZGkZ/1dffYUJEyagT58+7Y41d+5cbN68GTfffDP++te/IiYmBps2bcKuXbvw1FNPiQrwWjOZTNYy72azGXV1dcjLy8Nbb72FMWPGYM6cOQCAtLQ0REZGYvXq1QgNDUVoaCi2bt1qLSNveV86eg633XYbXnrpJTQ0NGDMmDEoKyvDSy+9BIVC4bRh95VXXonXX38dSqXSJg0wOTkZSUlJ+Ne//oWGhgb07NkTBw4cwHfffYc777zT7vF69OiBJUuW4OGHH8bs2bNx3XXXoXv37jhx4gTeeOMNlJaWYu3atR2mF1599dV47733MG/ePMyfPx89evTAf//7X2zYsAF//etfER0djREjRkAQBMybNw933HEHIiIi8MUXX6C+vt4662cpsHHfffdh+vTpaG5uxuuvv46ff/7ZbpVOT7j66qvx9ttv46677sL8+fMRGxuLt956C42Njbj++uuRkpLi0vdHq9VixIgRyM/Pt1YH/OWXX3DkyBG7FTovvfRSRERE4P3337ebRhkdHY19+/Zh586dSE9Px9VXX4133nkHc+fOxV/+8hd0794dO3bswL///W/MmTMHYWFhkl4Pdz4bF198MbKysnD33Xfj7rvvRp8+ffDLL7/g5ZdfRnZ2Nrp27YqMjAyEhobiueeewy233AKTyYSNGzfi22+/BdASAHrSX/7yF3z++ee47bbbcMstt6Curg4vvfQSlEplu2bTlvWEF110kUfHRBQMGJwRkU9MnDgR69evx9q1a7F69WpUV1dDpVIhPT0dL7zwgk062uzZs6HVarF27Vp88MEHSEpKwu23347bb78dAPDMM8/giSeewBNPPAEA6NWrFx577DFs3rwZeXl5AFoqt82dOxcffPABvvvuO/zwww+YMmUKPvnkEzz00EO45pprsGTJEjz99NN49dVX8f777+Ps2bOIi4vD5ZdfjgULFki+su6Oe+65B/Hx8XjnnXfwwQcfICYmBjk5OViwYIF1NmjMmDEYP348li1bhp07d3aYmpeQkID//Oc/WLZsGf71r3+hsbERAwcOxKpVq6y936SoqKiwqTCp1WqRlpaG+fPn44YbbrCe4EZFRWHVqlV49tlnce+99yIiIgKDBg3CO++8g9tvvx15eXmYNGlSh89hwYIFSEhIwHvvvYc1a9agS5cuGDduHBYtWtQuva2tgQMHon///qipqcG4ceNs7luxYgWef/55vPTSS6ipqUH37t3x17/+1e76NosZM2YgNTUV69atw4svvoiqqiokJCRgxIgRWL58eYdBMdCScvj2229j2bJl1mCzd+/eePLJJ61tHLp164Y1a9bgpZdewsMPPwy9Xm+dFRs7diyAlhPetWvXYsWKFZg/fz7CwsKQkZGBN954w2tNooGW79A777yDZ599Fk888QQEQcDw4cPx1ltvWVNtXf3+XHbZZVi+fDmMRiPUajU2bNiAkJAQ5OTkdLi9RqPBZZddho0bN+LYsWMdbjN79mwcOHAAt99+O55++mlMmzYN7777LpYtW4bnnnsO9fX1SE5Oxn333YdbbrnFpdfE1c+GUqnEa6+9hpdeegmvvvoqqqqqkJiYiLlz51qrjaampmLZsmVYsWIF7rrrLnTp0gXDhw/H22+/jRtuuAF5eXkdFhSSS2pqKtauXYtnn30W8+fPR1xcHO6880688sor7dJWt23bhqFDh9pdu0lE4inMnp4XJyIiInJAr9dj8uTJWLx4Ma666ipfD4fQkmUQFhZmTVcFWtaVjR8/Hg888ABuvPFGAC0zeNnZ2fi///s/a5sKInId15wRERGRT2k0Gtxzzz1Yu3aty+XpSV4HDx7ELbfcgjfffBN79+7FV199hb/85S+IioqyafPx/vvvo1+/fi7NxBNRe5w5IyIiIr9w++2345JLLumw0TF5lyAIWL16NT755BOcOXMGWq0Wo0ePxn333WctJFRdXY2rrroKb7/9tmzFhYiCHYMzIiIiIiIiP8C0RiIiIiIiIj/A4IyIiIiIiMgPMDgjIiIiIiLyA+xz5gH79u2D2WyW3NCSiIiIiIg6l8bGRigUCmRmZjrdlsGZB5jNZvhTnZXm5maWJiYiIiKioBMaGgql0rfJglLiAgZnHmCZMRsyZIiPR9KipKQElZWVvh4GEREREZFXJSQk4IILLvDpGPbv3y96W645IyIiIiIi8gMMzoiIiIiIiPwAgzMiIiIiIiI/wOCMiIiIiIjIDzA4IyIiIiIi8gMMzoiIiIiIiPwAgzMiIiIiIiI/wOCMiIiIiIjIDzA4IyIiIiIi8gMMzoiIiIiIiPwAgzMiIiIiIiI/wOCMiIiIiIjIDzA4IyIiIiIi8gMMzoiIiIiIiPxAqK8HQEREREREviUIZpRUGNGgFxCpUSI1QQ2lUuHrYQUdBmdEREREREGsoFSP3Pxa1OmarbdFa0OQMzIG6SkaH44s+DCtkYiIiIgoSBWU6rF+e5VNYAYAdbpmrN9ehYJSvY9GFpwYnBERERERBSFBMCM3v9bhNrn5tRAEs3cGRAzOiIiIiIiCUUmFsd2MWVt1umaUVBi9NCJicEZEREREFIQa9IKs25H7GJwREREREQWhSI24UEDsduQ+vtJEREREREHovNH5jFi0NgSpCWovjIYAltInIiIiIgo6gmDGlz+ec7rdlOHR7H/mRQzOiIiIiIiCjJhiIADwef456FrNsDnqf8ZG1u5jcEZEREREFGTEFvnQtUl9tPQ/m5UdZxOgsZG1PLjmjIiIiIgoyLhb5KN1/zNnjaxz82tRVGZgvzQROHNGRERERBRkUuJUUCgAs4vxkqX/WWqC2mkj612HG7DrcANn0kTgzBkRERERUZAprTK5HJhZNOgF0WvXgN9n0gpK9e49cCfG4IyIiIiIKMjI0Vg6UqN06TitUyLJFoMzIiIiIqIg4+6aM42qpRqjK8expERSewzOiIiIiIiCTGqCGlq166GA3iSg8JQBqQlqRGtDJO9fLzIVMtgwOCMiIiIiCjJKpQKXZ8W4dQxLIZCckdKPc97I4KwjPg3Odu/ejQEDBnT43yWXXAIAOHnyJO68806MGDECF110EV588UU0N9u+me+++y4uueQSDB06FNdffz0KCgps7pfjGEREREREncngnlqMHxTp8v6W9MT0FI3k40Sopc+2BQOfBmeZmZn4/vvvbf5bsWIFFAoF7r77bjQ2NuLWW28FALz//vtYsmQJ/vOf/2DlypXWY3z88cd49tlnce+992Ljxo3o0aMH5s6di+rqagCQ5RhERERERJ3RlMwYXHthV6jDFC7t36AXUFCqx45DDZL2i3IhFTIY+DQ4U6lUSEhIsP4XERGBp59+GjNmzMDMmTOxdetWnD59Gs8++yz69++PyZMnY9GiRVi3bh1MJhMAYPXq1ZgzZw6mT5+Ovn374qmnnoJGo8GHH34IALIcg4iIiIios8pI1WLxjO4IdSFeClfDaZ+ztrTqlmIi1J5frTlbvXo19Ho9HnzwQQBAXl4eMjIy0KVLF+s2Y8eORUNDAw4dOoSqqioUFxdj3Lhx1vtDQ0MxatQo7N27V7ZjEBERERF1ZoJgRpMLy8AqappE9zmzGJamhVLp2kxdZxfq6wFYVFdX480338R9992HmJgYAMDZs2eRlJRks123bt0AAGfOnEFoaMvwu3fv3m6bwsJC2Y7hCrPZDJ1O5/L+cmpqavL1EIiIiIjITxWU6vHJLteW89Selx7R7SxsQEqCGukpGpceU4rGxkafn5ObzWYoFOKCUb8Jzt577z1ERUXhT3/6k/U2g8GA6Ohom+3U6pYpUKPRCL2+pbu4SqVqt43RaJTtGK5obGzEoUOHXN6fiIiIiMjTCkr1WL+9yuX9Y6NcWzu2ZXcNBiaHe3wGrbKyEpWVlR59DDHaxhr2+E1wtmnTJlx11VUIDw+33hYeHm5dF2ZhCZi0Wq1124620Wg0sh3DFWFhYejbt6/L+8vpzJkzqK2t9fUwiIiIiMiPCIIZW3bXuLx/tDYEWX0jseNQA+r1gqR99SYBReUG9Eny7OxZfHw8EhISPPoYzhw9elT0tn4RnBUWFqK0tBTTpk2zuT0pKQlHjhyxua28vBwAkJiYaE1FLC8vR58+fWy2SUxMlO0YrlAoFNBqtS7vLydL6iYRERERkcW2g/XQm6QFVa2N6KNFaKgSKfEqFJQaJO9fUmbyeHAWFhbm83NysSmNgJ8UBMnLy0NcXBwGDhxoc3tWVhYKCgrQ0PB7ac5du3YhIiICAwcORFxcHNLS0rB7927r/U1NTcjLy0NWVpZsxyAiIiIi6kwEwYzdh+vdOkbXyFAIghlHz7i2FMjs1qN3Tn4RnBUUFGDAgAHtbp88eTISEhKwYMECFBYW4uuvv8bzzz+PW265xZq3ecstt+CNN97Axx9/jKNHj+Lvf/87DAYDrrnmGtmOQURERETUmZRUGKE3uRceNehbmlCbmlw7Tlo3ltNvyy/y3SoqKqwVGltTq9VYs2YNHnvsMcyaNQtdunTB9ddfj7vvvtu6zaxZs1BfX48XX3wRtbW1GDx4MN544w107dpVtmMQEREREXUmh09KT0NsS2cS0CBxrZlFWAjQK/H34EwQzCipMKJe14zzxmZEqEMQpQ1BaoI6qMruK8xmM2cUZbZ//34AwJAhQ3w8khYlJSV+UaWGiIiIiHzP3QqNFhMyopCWpMa6b6SfZ14zPhaDe0VYx5ObX9thv7RobQhyRsa4XHa/e/fuuOCCC1zaVy5SYgO/SGskIiIiIiLPEwQzcvNrZTlWaqIKqQlqqMOkz2wpQ1rCEEugaK+RdZ2uGeu3V6GgVO/WWAMFgzMiIiIioiBRUmG0GwhJoQoF0rq19ClLiRfXw6u13PxaNDUJogPF3PxaCELnT/hjcEZEREQBRxDMKCozYH+xDkVlhqA4aSOSg6trxNoaPyjKuhYsIlx6I+o6XTP2Hm0QHSjW6VqKj3R2flEQhIiIiEisjtanuLsuhShYRGrkmZu5aFAUgJYLJcfOuJZyWFMvbQavXoYZP3/HmTMiIiIKGPbWpwTbuhQiV6UmqKFVux8ClFaZALSkSTYYXJu5jo2SNuN23sjgjIiIiMgviClkECzrUohcpVQqcHlWjNvHsZTidzVNUqNWYmTvCEn7RKilp08GGgZnREREFBDEFDIIlnUpRO4Y3FOL8YMi3TrGrsMN+HZ/HSJcnYUzAyerGiXtEqXt/MEZ15wRERFRQBB7hV6uggdEndmUzBiYzcDOwgaXj/Ht/jqEh7m2r94k4HiZ+EbY0b81pO7sOHNGREREAUEbLq6XktjtiIJZQanercDMwiBt8suGlJL+g1M11uqQnRmDMyIiIgoICrO4EzOx2xEFKzkbUbujtFJ8CvKBEn1QrCdlcEZEREQB4bxRXLqi2O2IgpVcjajdVdMg/rsaLOtJueaMiIiI/JogmFFSYUTFOXH5U3L1cSLqrAJ1XWagjlsKBmdERETktzpqOO1IsBQNIHJHoF7AcDbupiYBe482oKa+GbFRIcjq615FSl9gcEZERER+ydJwWoqckTFBUTSAyB0pcSooFIA5gJZwKRQt47bny3212HHItsDJ1h/rcOmoZsz/8wWeHp5sAjNsJiIiok5NasECrVqJay7qivQUjecGRdRJlFaZAiowA1oCydIqU4f3dRSYWXyVV4Y3thzw5NBkxeCMiIiI/I7UggU6o4AvfzyHglK9B0dF1Dn409otKfPcHY27qUmwG5hZfPztMZia/Oc5O8LgjIiIiPyOKyePdbpmrN9exQCNyAl/WnMWFxUietuOxr3n13qn+5kBfPb9cSnD8hn/eWeIiIiIfuPOyWNufm1Q9EMiclVqghrRWvFBkSdp1eK/6zpD+4s2J8rFVXEtKJK2ftVXGJwRERGR33Hn5DFY+iERuUqpVCBnZIyvhwEAOFEpLrgCgK37zrW78KIKE5cYqVEFRh1EBmdERETkd9w9efSnNTVE/ig9RYOJQ6J9PQxJOrrwMqSnuCJAE0b28MSQZMfgjIiIiPxSeooGs7LjXJpBi5CQKkUUrOKiAmM2qbW2F16UoeJmzsKUgfGbEHjvCBEREQWN9BQN+ndXY+/RBpSUmVB4yiBqP7OCa86InPGnwiBiNRiasL9Yh0iNEqkJaugM4r7rtQ2BkerM4IyIiIj8VkGpHrn5tZLK6gMQfcJGFMwsazulfr98aeuPddb/j9aGYESfCFH7dY0O99SQZBV44TIREREFhYJSPdZvr3LpxFEbLqV7ElFw8qfCIK6o0zXj2/11TreLj9EgvXecF0bkPgZnRERE5HcEwYzc/FqX91eYGZwRiTEwORwTh0RB7mKG0doQTBgcJe9BXXTr9AyEKAPjN4FpjUREREFAEMwoqTCiQS9Y12oo/fhkpaTC6FaqVYMhcNK0iHzF1bRhZ1RhCsyfmgilUoGdh+rR6OOvY5cItW8HIAGDMyIiok6uoxOwaG0IckbGID1FXBlqb3O3FP55I4MzIkcsacOeYGo040SFCb27h6PvBRocKtV75HHEqq4TV0jIHzCtkYiIqBOzt26rTteM9durUODjkyZ73K0iV9vA4IzIHnfThsUoKm+pjpgQ7fu5oEApBgIwOCMiIuq0xJyAbdldA0Hwv8qGlipyrtpfovfL50XkD9xNGxbDkjSdmqjy6OOIURMgZfQBBmdERESdlpgTML1JwLaDzqudeZu7VeR0RgElFYFzQkbkTYdPej7NzxKUpXULh0pko+iOyLEy9rm387Djl9MyHMnzGJwRERF1UmLXbe0+3OCXs0zpKRqMHxTp8v7urlsj6owEwYxfinUef5yjrRrGu1opUaNWYuxA138DWlvx4c9o9sPfubYYnBEREXVSYnt96U1mv5xlEgQzDpS4vibO3XVrRJ1RSYUROqPnL1zsPHweB0t0KKkwQm9y7fGmjY7FZSNiMCDZ/TVj9ToT1n99xO3jeBp/tYiIiDopKb2+/HGWyZ11MdHaEKQmBE75bCJv8eZ3/bO8WtS78B2O1oZgVnYc0lM0EAQzztQ0yjKeLduP+f3sme/LpxAREZFHnJdwddwfZ5ncOYnMGRnj133ciHylqr7Ja4+lMwqS21pMGR6NsQOjrN9fOYuX1OsaUXC8CkP6xstyPE9gcEZERNRJiQ24tGqlX84yuRowXnthV7/t30bkS4JgRv7RBqfbqUKBqVmxiNKGQGcQ8OEP1S4/ZoQ6BNHaENEBlr7RbHNhRe6ZPn/veeZ/l8mIiIhIFmLL0V8xyj9nmVIT1JKrvF08OBIZqVoPjYgosJVUGFEvIthpFoDI8JbU4IxULWZlxyHKxYslUb81vBer7Tde7ll9f+95xuCMiIiokxJTjn78IP8NZgpO6mFqEr8+RKNS4uLBXTw4IqLAJnYWqlkA3vpfJV7cfBYFpXqkp2iw8MruuHiwtMqJlrWf6SkaXDw4StQ+lhL8gmBGUZkB9bpmaNXyhCwadQjSe8fJcixPYVojERFRJ5aeosGs7Djk5tfapBWpwxSYOjoGQ1IjfDg6+wTBjM/31kraZ9qYWL+cASTyF1Jnoep0zVi/vcpanOMPQ2NwuroRv54WV9219drP+C7iwo7UeDUKSvXtfrPkYDA2o1kwu1za3xs4c0ZERNTJpadocFlmF5urz8ZGM77aV4eCUtdL1XuS1HLfSgUwUIZy20SdWWqCGuow6YFJbn4tmpoEFJUZ0D1WJWqflPgw69pPQTDji7xzovb7/lA91m+vEhWYaVTSQhkzgC9+KJK0j7cxOCMiIuoELClA+4t1KCoz2DSVLijV48MfqtsFO5ar4v4YoB0+KW3RvmAGNuyo8tBoiDoHpVKBYb2lpzHX6Zrx/Cdnse6bSmw7WC9qn9LKRny5rxaAtIstOw45LliiCgWy06MwZ2IcwpwvqW3nTNV56Tt5EdMaiYiIAlxHKUDRvy3CH5gcjtz8Wof75+bXYmByuN+kBAqCGT8XST+BOnjCgBlNAkJDee2ZyJ5BPTTYc1j698uVxtU7CxswaUi0pF5nztaZmpqA7QX1yD+mdGlM3eP8M5Xbgr9eREREAaygVN9hCpBlVmzbwXqn6UF1umaUVIhbQ+INJRVG6E2uNYrdK6JMOFEwk9L/0F1mc8t3UmqvMzFcCcyUCuCPF6bJPhY5MTgjIiIKUIJgdjortvuwuBQkuXsJucOdsVTXy38SSNRZCIIZX/4obu2XXGrqmxGhdiH/0AOuurgPVH4+s+7foyMiIiK7SiqMTmfFxM5Ayd1LyB3ujcW1GTeiYCDmN0NusVEhiBLRb9HT+qV0wdxpg309DKe45oyIiChAiZ1hUoW2rNOwR6NSIjVBLdOo3JcSJ64aXEeSu4bJOBKizkXqrLRW7dq6rtay+krrjeYpR0+eg6lJ4MwZEREReYbYGSaFwkmhD/+oA2JVWmVyed8vf6r3y+qTRP5A7G9GdkYUbrokHouuTEK0G7NeiTGhCA1VuvWdlovZDHz2/XFfD8MpvwjONm3ahMsvvxxDhgzBFVdcgS+++MJ638mTJ3HnnXdixIgRuOiii/Diiy+iudl2Ovbdd9/FJZdcgqFDh+L6669HQUGBzf1yHIOIiMjfpCaonZ44adVKGBsdp/rpjYJfFQRxZ82Zzij4bXsAIl8T85sRrQ3BH4ZEIy0xHKGhSuSMjHH58Xr+NiMvtTWGpxQU+X+7DZ8HZ5988gkefvhhzJ49G5999hmmTp2KRYsWYd++fWhsbMStt94KAHj//fexZMkS/Oc//8HKlSut+3/88cd49tlnce+992Ljxo3o0aMH5s6di+rqagCQ5RhERET+SKlUOD1xSo4Tl+bnTwVB5Fj/tmVPjU2vNyIS95uRMzLGpq1GeooGs7LjXJpB6xoV4nJrDE/QqPx/RZdPgzOz2YyXXnoJN954I2bPno2ePXvirrvuwvjx47Fnzx5s3boVp0+fxrPPPov+/ftj8uTJWLRoEdatWweTqWV6dPXq1ZgzZw6mT5+Ovn374qmnnoJGo8GHH34IALIcg4iIyF/ZO3GK1obgmou6ouisuBkxfyoIIubqvjN6o4DiMv+ZDSTyF45+M2ZlxyE9RdPhPgumJ+GyEdGiH0ehaFlv5k5rDLlNHJXi6yE45dPwsaioCKdOncK0adNsbl+7di0AYMmSJcjIyECXLl2s940dOxYNDQ04dOgQevTogeLiYowbN856f2hoKEaNGoW9e/fizjvvRF5entvHICIi8mfpKRoMTA5HSYURDXoBkZqWAh/f7j+HJhETYlq1fxUEsVzdX7/dvRSkonIjencPl2lURJ2Hvd8MR43olUoFxvSPws7C86IqPo4bGInQUKXfzMqHq0IxrF+Cr4fhlM+DMwDQ6XS49dZbUVBQgB49euCuu+7CpEmTcPbsWSQlJdns061bNwDAmTNnEBraMvzu3bu326awsBAAZDmGK8xmM3Q6ncv7y6mpyUGJLiIi6hSUSgVSE9TWk63vDpzDtoPiGjL36qZyeFLmC5ar+5t2VsPU5NpV96q6RplHRdR5KJUKpCVKu3gh9sLJ+EGRmJIZA8B/ZuXvvjodRoNv1qKazWbnhZl+49PgrKGh5Y/Ggw8+iL/+9a+4//77sXXrVtx999144403YDAYEB1tO32qVrdc2TMajdDrW15glUrVbhujsSWVQY5juKKxsRGHDh1yeX8iIiIpCkr1yM2vdamHUVy0f5afT0/RoH/37nju4zNOi5p0pLjcBEEw+13gSRTILBdOvsirQX2rWbGwECC9pxbTsmIQ2qpcfWqCGlEapc22zqjDFC595zsSpVHij6NiEa2sxqFDvqsn0TbWsMenwVlYWMsfg1tvvRUzZswAAAwaNAgFBQV44403EB4ebl0XZmEJmLRaLcLDW6L9jrbRaFryZeU4hqvPrW/fvi7vL6czZ86gtrbW18MgIiIPKSjVu5UCmNbNf1Ia2woNVeLKsV1den6636pQSp0dICLn2l7y0KiUGNBDYxOYAS2zbX8cFSvpOyxXYHbZiGhcMT4NiYndZDmeq44ePSp6W58GZ4mJiQCA/v3729zet29ffPvttxg9ejSOHDlic195ebl1X0sqYnl5Ofr06WOzjeXYSUlJbh/DFQqFAlqt1uX95WRJ3SQios5HEMzIza91eX9L8bL9xTpR6058wXKl3pWZQX9Z70LUWdi7GFSnb2lj0VFREct3eMvuGuhN3vlOalRKjOkfBbVa5fNzcrEpjYCPqzVmZGQgIiICP//8s83tR44cQc+ePZGVlYWCggJr+iMA7Nq1CxERERg4cCDi4uKQlpaG3bt3W+9vampCXl4esrKyAECWYxAREfmrkgqjS6mMFiFKBd76XyU27KjGum8q8eLms37ZI8xSLe6mS+Ixc3xX0VXj/GW9C1FnIOZiUG5+bYdtLNJTNFh8dXfcMCkOqQleSKX2r2tMovn0Fys8PBy33XYbVq5ciU8//RQnTpzAK6+8gh9++AFz587F5MmTkZCQgAULFqCwsBBff/01nn/+edxyyy3WvM1bbrkFb7zxBj7++GMcPXoUf//732EwGHDNNdcAgCzHICIi8lf1bgRmANqVuK7TNQdEE+duMWHQqByffWlU/lWFkijQibkYVKdrttvUXqlUoE+SBqP6RXlieDb0v6U1Bxqf57vdfffd0Gg0eOGFF1BWVoY+ffpg+fLlGDNmDABgzZo1eOyxxzBr1ix06dIF119/Pe6++27r/rNmzUJ9fT1efPFF1NbWYvDgwXjjjTfQtWtXAC2FPdw9BhERkb86b3QvOLMnN78WA5PD/SbF0aWCJ/4xdKJOQ2yasLPtvDWjHYhpzQqz2ewfXeE6kf379wMAhgwZ4uORtCgpKUFlZaWvh0FERB7wS9F5bNxZ45Fj33RJvF8U03Cn4Im/PAeizqCozIB13zg/p3T2vTtwQocNP1TD01HITZfEY/zwNFxwwQWefSAnpMQGTMQmIiIKYFHaEI8d2x+uOrtb8MQfngNRZ5GaoIZG5Th80Dhpal9QqsdH33s+MAvUtGYGZ0RERAGs5WTJM/l72nDf5wW6W/CEBUGIvMxB0OXuxRZJfP/z5RL+YhEREQUYQTCjqMyA/cU6lFQY0TXKM0vIFWbfnt0IghnHz7q3oP+83jNr8oiCUUmF0WkpfL3JfiEOdy+2SMGCIERERORxLhXGcNF5o+9SAuV6nh/tqIEyRNmu7xIRSeduQRBvpxkHYlozZ86IiIgChKUwhreuPPsqJVDu52mv7xIRSSP2N8Hedt7+TQnEtObAGzEREVEQ8upaDQChSvhkMb0nnqejvktEwap1enRRmUHUBYzUBDWinRQhitaG2P3tSE1QQx3mnXTp8DBFQBYEYVojERFRAPDmWg0AaBJaTt683efMU8/z8EkDS+oT/aajtOFobQhyRsY4TAFWKhXIGRnjsLVFzsgYu78bSqUCw3prsefwedcHL9LQNI3f9GmUgjNnREREAcAXayf2Hm3w+mNKeZ5xUeJPY34p1jG1kQj204brdM1Yv70KBaV6h/unp2gwKzuu3QxatDYEs7LjnK7v1Ko81/6jtdjIwJyDCsxRExERBRlfrJ2oqfd+pUMpz7OqXnwgp/utchtnzyiYiUkbzs2vxcDkcIezTukpGgxMDkdJhRENegGRmpaeYs5mqgTBjHwvXfSJUHsnCJQbgzMiIqIAYFnr4c3URlOT92frLGtSjI3yz3IFYuU2IjmJSRu2rNF0diFDqVRIvthRUmFEvZe+h1FO1sb5K6Y1EhERBQDLWg9vOn7W6PVUQKVSgamjYzxy7ECs3EYkJ3dL4burXsLFJY3K9e+rRhWYxUAABmdEREQBIz1Fg/GDIqHw0hr3Or1vmrgOSY3AgGR50w8VAFLiVLIekyjQuFsK313njeKDs9H9tZg4JAoalfQfvDEDIgOyGAjA4IyIiChgFJTqseNQA8xenMzyVSrgsN4RUNlZfJHRU3rgZgZQWmVyb1BEAc7dUvjukrIObM+R8/h2fz30ppYfPI1KgR5xYU7306iUmJAR7fIYfY3BGRERUQDwdp8zC1+kAlqqyZmaOr5/UE8ttGrp4+KaMwp2YtKjHZXCd5eUdWCWoKz1v09WNTrdb2qW58bvDQzOiIiIAoC3+5wBgCrU++s2xAShX/54DkNSHZfr7gjXnBG5XwrfHakJakR5+HuoDQ/s7zmrNRIREQUAKQvpA5nYanIxkdIqsXkyVYso0LhaCt9dSqUCl42MwUffV3vsMQJ9hpzBGRERUQCQspBeLqYms9d7g4k9sYpQhyBKoxRdltuTqVpEgciVUvhyiHAhJVmKQJ8hD+zRExERBQlfNVT19lXoqno7C83aiNKG4I+jYp1up1ErPZ6qRUTiefI3pTPMkHPmjIiIKAD4qqGqN69CC4IZ+UcbnG5nOQFTKhWYlR2HLbtroDfZnvCFhAAXDorCxMHRnDEj8iOe/E3pDDPknDkjIiIKAGJKYMvN21ehSyqMotIUU+LDrCdg6SkaLL66Oy4eHAlV6O8nZc3NwE/HdSg8ZfDYeIlIOnd/ywYkh3fYoNqdptX+pHM8CyIiok5OTAlsuXn7KrTYdKdfTxsgCL+X2S48ZcB3BxpgarItvV2na8b67VUoKNXLOk6izkAQzCgqM2B/sQ5FZbbfKU9y97fsRIWp3Uw5AOhNQqf4vjOtkYiIKEBYSmDn5td6tKy+Vq3E1NGxXl+nJTbdydQEFJUb0CdJI6r0fm5+LQYmhwd8uhORXApK9e1+R6K1IcgZGeOV77293zKtWgmd0fFFmo4Cs9YC/fvO4IyIiCiApKdoYBbM2LynBsZGz1zpHtlH65MCGqkJaqhCYbf5dGslZSb0SdKILr3v7aqTRP7K0uS9LctMs7cK6HRUzr9e14yNO2vcOm6gf9+Z1khERBRACkr1+PCHao8FZgAAhW+uOCuVCvTpLu6k0PLsxaZCBnrvIyI5iJ1p9maKY1piOIb00iItMVy2wkeB/H1ncEZERBQgxJxYySGtm+9KUWf1jRC1nWWMYlMhA733EZEcpMw0+4JchY8C+fseuCMnIiIKMmJOrNylUSvRK9F3wZmu0fkV79ZjFHMy1xl6HxHJwd9nmpVKBQanup9SmRKnkmE0vsHgjIiIKEB444Rp6ijf9QkSBDM+21PrdLvWYxRzMjc4VROwxQGI5OTvM82CYMaBEverLZ6oMMkwGt9gcEZERBQgvHHCVFHX6PHHsKe4zOi0Ehtg28+ooFSPHYccN67ecagh4MtrE8nB32ea5coOKCr3TVqmHBicERERBQhvNKLecajea8UA2hJ7QmXZTsoaPG8WOSDyV2J6jHm7v2FrcmUHBPI8OYMzIiKiACHmxGr8oMh2AZxWLf7PvakJ2HawzpXhuU3sCZVlOylX2X1Z5IDIn1h6jLX9nYjWhnitjL49cmUHpCYG7poz9jkjIiIKIPaat7ZuIDtpSDT2Hm1ATX0zYqNCMLJ3BJZ/VoZ6kVeldx9uwISMaK9fPU9NVAEHRW4H6VfZA7m8NpGcOuoxlpqg9vnaTEt2gDupjRqVEmndArPHGcDgjIiIKOA4OrEqKNW3C9x2Fp7HkF5ap2uzLPQms8ebuAqCud3407qFQ6NSOlx31vrES+pV9kAur00kN0uPMX9iyQ7oqEm2xfhBkQ5/y6aNifV5kOkOBmdEREQBqKMTq4JSfYcnNXW6Zuw41IBxAyOws/C8qON7cpapowDSMvM3bUyswxOz1ideUq6ys5w+UWAQkx3QI16NL/JqbLIBojRK/HFUrE/TMuXA4IyIiCiAdDTjBABF5QZs2V3tcN+DJwyYkBGJbQedz6B5apbJUQC5fnsVZmXHYVZ2nKgTLzFX2S18WeSAyF8IghlF5QaUlJlgRksz916Jvk9nbMtZ2qW/pmXKgcEZERFRgOhoxslSVl5MCfo6XTNSE9XQ/KpzuL2nZpnEVFfMza/FgulJGJjcXdSJl72r7Batr7YTBbOCUj227K6x+e5vP1gPjUqBaWO6+t13xFnapT+mZcqBwRkREVEAsDfjJCYoa01nMDtNHfTULJOY6oqWqoppieGiT7xaX0Wv1zXjvLEZEeoQRP0WZHaGq+lE7rD3+wG0rDG1zFr7W4AWjBicERER+Tkp/byc0YYroFQoMHZAJH4p1kFn/D248/Qsk9h1bK6sd+usV9GJ3CUIZnyRV+N0uy27qzEw+QJezPAxBmdERER+Tko/L0c0KiU+2VmDulbBj1atxNBeWgzoEe7xWSax69giNcoO19bxpJFIupIKo6g2GnqTGcVlRvTuzoscvsTgjIiIyM/JVTlRbxKgb3Obzihg1+EG9Ozm+eAnNUHtvFS+WgmdQcCLm8/ardRGROKdO98kets9vzYwOPMxNvwgIiLyc97oz5WbXwtBMHv8cZxpbjbjwx+q280UWqo5FpS2DS+JyJGTlUbR2/562uAXvwPBjMEZERGRn0tNUEOr9uyfbEshDk8qqTA6LWBianJ8YugvQSRRoKjXiZ95bxaA4jLP/g6QYwzOiIiI/JxSqcDQXlqPP44nG0/LdXxXgsimJgE7Dp3D+99VYuOOKhw9rWeAR0FDrZJ2ul/E4MynuOaMiIgoAAzoEY5dh503j3ZHVX2jR48vV3qmsyCvdTGRwpM6HDxhsLn/l2I9wkKAGeNZOpw6v6G9tPilWHw6cK1O/Bo1kh+DMyIiogCQmqBGtDZElqqN9vx4TIcJGdEeKwwi13NwFOQVlOrxxd5q1Bscz4w1NoO9nSgo9E4KR1hIy2dejBhtiGcHRA4xrZGIiCgAKJUK5IyM8ehjeHrdmZjnoHGSghX9W2Ppjlga7ToLzFrbsruaKY7UqSmVCswYHyd6+15JHX+/yDsYnBEREQWI9BQNZmXHIVrile1obQjGDogUta2n153Zew7R2hDMyo7DtDGxDvfPGRnT4cyeIJixaWe15PHoTWYUlRucb0gUwNJTNJh5oePvlkVqPIMzX/J5cFZWVoYBAwa0+2/jxo0AgEOHDmHOnDkYPnw4Jk2ahLfeestmf0EQ8PLLLyM7OxvDhw/H7bffjtLSUptt5DgGERGRP0hP0WD+1ERoVM5TDzUqBW78QzwWTE/CgB7iehd5o2x/eooGf728G7L6adEnSY2sflr89fJuSE/ROA3e7KUgHj9rcFrp0Z7isyyAQJ1fhErcRZ3SKpOHR0KO+HzNWWFhIdRqNb7++msoFL//oYmKikJNTQ3mzp2LSZMm4bHHHsNPP/2Exx57DBEREZg5cyYAYNWqVXjvvffwzDPPICkpCc899xxuu+02bNmyBSqVSpZjEBER+ZPSKhP0JueByJgBkdaGsmLWe2nVStTrmlFUZkBqgueaUn+5rxY7Cxtg/u0pHDsL5B3VYdzASEzJbGk0PTA53FrUI1KjdDqen4t1Lo/nnAfX8RH5g4JSPTbvEjez7OnZc3LM58HZkSNH0KtXL3Tr1q3dfevWrUNYWBgef/xxhIaGok+fPigpKcFrr72GmTNnwmQy4fXXX8f999+PiRMnAgBeeOEFZGdn48svv8TUqVOxfv16t49BRETkT8SePMVFhVn/X6lUYHCqBjsO2a/4qDMK2LizBkDLTFXOyBjZi2V8ua+2wzGYzbDePiWzJXUxLVHcbB8ANLo4awYAXVgAgToxy1pMsbwxe072+fzVP3z4MPr06dPhfXl5eRg9ejRCQ3+PIceOHYvi4mJUVlaisLAQ58+fx7hx46z3R0dHIz09HXv37pXtGERERP6kql5cqevWJ1mCYMaBEvHltOt0zVi/vQoFpeL3ccZkasZOB8EhAOwsbEBTk/Qr9ykJrme6sAACdVaCYMaW3TWit3dUcIe8wy9mzmJjYzF79mwUFRUhNTUVd911FyZMmICzZ8+if//+NttbZtjOnDmDs2fPAgC6d+/ebhvLfXIcwxVmsxk6nespFnJqamK/CiKizqKgVI9v99c53a7tSVZJhdGlEva5+bUYmBzudorjwRIdNu2qhrP5LbMZ2Hu0AeMGRks6/ph+kfhqn/PXpS2NSom0buJn6IgCSVG5AXqT+Isd9gruBLLGxkafn5ObzWab5VuO+DQ4a2pqwvHjx9G3b1889NBDiIyMxGeffYY77rgDb7zxBgwGQ7s1X2p1yx8ao9EIvb7lal5H25w7dw4AZDmGKxobG3Ho0CGX9yciImpLEMz4Yq+4dSOXZXaxOclydR2Jpby+lBTDtuylMtpTXdeEojKD6PVmABAaqkRGz/B2DaedmTYmttOdjBJZlJSJK+6hDlXgynFdO2XPv8rKSlRWVvp6GKLrWPg0OAsNDcXu3bsREhKC8PCWH/3Bgwfj119/xdq1axEeHg6TyfZDZTS2VFTSarXWfUwmk/X/LdtoNC0fLjmO4YqwsDD07dvX5f3ldObMGdTW1vp6GERE5KZtB+tF9/DShtuuXHBnHYk7BQIOnNBJCswA4JcSPfYe/f1Kt9j1b1JaDERplPjjqNhOeTJKZCF2JWZW/4hO+12Ij49HQkKCT8dw9OhR0dv6PK0xIiKi3W39+vXD999/j6SkJJSXl9vcZ/l3YmKiNV2vvLwcPXv2tNlmwIABACDLMVyhUCig1Wpd3l9OrdfbERFRYBKbzmhx+KTBZrZLTLVGe1wN7ATBjM/2iF/vYmFstD2ltKx/c1RK/8AJHXYWnhd1fK1Kgcs8UOyEyN+kdVNj+8F6p9v1dmNm3N+FhYX5/JxcbEoj4OOCIL/++itGjBiB3bt329x+4MAB9O3bF1lZWcjPz0dz8+9/SHbt2oW0tDTExcVh4MCBiIyMtNm/rq4OBQUFyMrKAgBZjkFERORLgmBGbn6tpH12HW6wKeZhqdYolVatdLlAQEmFUVTJf7Fy82shCO2PJwhmfL63VvRxdCYzPvq+WtZiJ0T+qFeiGhqV49N9jVqJXoksAuIvfBqc9enTB71798bjjz+OvLw8HDt2DE8//TR++ukn3HXXXZg5cyYaGhrw8MMP4+jRo9i4cSPefPNN3HnnnQBacjfnzJmDpUuX4ptvvkFhYSEWLlyIpKQkTJkyBQBkOQYREZEvuVPMwxLMSK3WaJHVT+vymqx6mfuHWda/tVVSYYTOKD310l6wR9RZKJUKTBsT63CbaaO57tKf+DTfTalUYvXq1Vi2bBkWLFiAuro6pKen44033rBWWFyzZg2efPJJzJgxAwkJCXjggQcwY8YM6zHmz5+PpqYmPPLIIzAYDMjKysLatWsRFtbS2yUuLs7tYxAREfmSq0FO62IergZ48dGul6g/b5S/uXNH6998WeyEyN+lp2gwKzsOX+TVoL7VdyVao0QO1136HZ8vRoqPj8fTTz9t9/6hQ4figw8+sHt/SEgIFi9ejMWLF3v0GERERL7iTpBjCVxcDWDcKSQSoZa/uXNH43FnjHLP7hEFCs4Z+yefN6EmIiIix9wJciyBiysBjLsNaaMkVE8UQ2Nn/Zul2IkrPDG7R+RPCkr1WL+9ymbWDADq9YLsjebJfQzOiIiI/JyrQY4qVGENZlwJYNxtSJuaoEZUuHxrWZqbO77Wr1QqkDMyxqVjemJ2j8hfiCkmxLWX/oXBGRERkZ8770KxCwDo2/335s1SAphobYjDsvViFZ4yoEmQLzgzNZlRXNa+IAjw+7oaqQFodUOTHEMj8kti1praK7RDvuHzNWdERERknyCYsVViGX2Lkf1se4laApjc/FqbE7YojRIj+0YiLioUkZqW1EF3q7dZUqnkVlRuRO/uHRfwSE/RwCyY8eEP1aKP9+3+enSLUbkViAqCGSUVRjToBdlePyI5iF1r6k6jeZIXgzMiIiI/VlJhbLdWRAyNSom0bu2DmPQUDQYmh3s0mHClL5toZvvpV4JgRu6P0h93y+4aDEwOd+k1KCjVtwt2o7UhyGGTa/IDEWpxSXJityPP4ztBRETkx1ytJjhtjP3eRUqlAmmJ4RjSS4u0RNeCEkdcLdsvhkZtf6yuBrJ6k4BtB+sk72eZHWz7XOt0zSy0QH7BrBC3lkzsduR5DM6IiIj8mNRqglEapSzrxdzhyRSpyHD7ST/uPO7uww2SiiKw0AL5G0Ewo6jMgP3FOhSVGSAIZugM4j5/Yrcjz2NaIxERkR+TWk3wMhfT6eRcN+VO3zFnHFWu1LpRGVJvMktqSC2l0AKbXJOn2UuvHd5bK2p/pjX6DwZnREREfixCYqDz5Y/nkN5DIymwknvdVGqCGpHhCjTIfDXeWd81hdm99EwpM29158VVeRS7HZGr7BXfqdM1Y9uBelHHYFqj/2CYTERE5MekBhxSy2J7Yt2UUqlAn+7yp1U667vmassBCykzfqUVJlHb/VKkc3U4RE7JVXyHaY3+g8EZERGRHzty2iB5H7EzQJ5cN3XeIF9BEK1a3Do6d9IptWqlw1m5tupFPr9jZSYWBiGPkav4jidTkUkavhNERER+ShDM+KVY+sxLVX2jqO081aC2oFSPo2fkaWqrVSux6MokUemVqQlqaFSupTYOS9NKSgVVh4k/hWJhEPIUV6u5tuYsXZi8i8EZERGRnyqpMELnQqre7sPnRQUDYk/spJwAyt3jbOroWISGijtdUSoV6JXo2knmwRKdpABqSJr4tE1XAlwiMRoM7q9pdJYuTN7F4IyIiMhPuXpVXG8SUFTuPB1SbJl+KeX85UqzClHCpZYA8dFhLj1enV6QFED1SdQgRMJZlCfbC1DwOm8Q97kamKxGdJtKp9HaEJ+33aD2WK2RiIjIT0ntcdZaSZkJfZIcn3SJLdMvpZy/XEFIWAgwMFl6Cfq0bmpsPyiuQl1bUsauVCrQP1mDQyLXk3FND3mC2KbrqjAlFkxPkq1dBnkOfymIiIj8lNQeZ62JSdBz1DPMle0A+YIQQyNcSgXslahGWIhrJ5xS+6TFR4l7XdShCq7pIY/oIvK7Ga1RMjALEJw5IyIi8lNSgqK20ro5DwZSE1pSnRylIUotFiDmmGK5MgunVCpwYXoUvt1fJ3lfqW0LNGpx2yd1DeWJMHlEryQ1thc0ON3ux+N6fH/ovPXf7vQxJM/izBkREZGfsgQ6UmnUSlGFMZRKBXJGxjjcRmqxAKVSgUszo0Vv74irs3ATMqKgUUnfV2qfNK3Imc3y2iZWaySPSOsWLqpCadvCQu70MSTPYnBGRETkp8QETx3J7C2+LHx6igazsuNkLRbQoHd/1kwVCqTEqVzaV6lUYNqYWMn7SQ0G9SZxwZzeZGa1RvKIwlMGAK7PyrLNg/9hWiMREZEfS0/RYPygSOw45Dx1yeJAiR6Th3WRFKANTA6XbU1KTb37wZmpCXh5y1nkjIp1KUC0BJ25+bWiUiw7St8UBLPD18QXhVKILApK9Vi/vcru/WGhQKOTSvuWNg9pidKL75BnMDgjIiLyY4JgxoESaalHrpxwKZUK2U7QYkUWynCmTi9g/fYql2fwWgedh08asOuw/QC3bfpmQam+XWDXdp2OLwqlEAHi+gmGKBRoFFEaiBcO/At/KYiIiPyYq33DfHnCldU3UtbjuZN6ZQk6c0bGiE7fPFiiw/rtVe1ed8s6nW/310EQzKLXqEktqkLkjJjfBUOjuO8MLxz4F86cERER+TFXgyxfnnAdOSPv+iq5Uq/EpG8eOKHDhh+qHR7n2/112H24HnqTuJNfqUVViNoSBDOKyg0oKTO1zIWJvFahUSkcfk554cD/MDgjIiLyY64EWb484RIEM77Iq5H9uHLNBFpm0izryQ6e0FuDtMJTBnz0vePAzEJMYKZRKTBtTFeWKye3FJTqsWV3jegCNK31TlLj4AmD3fsHp2p44cDPMDgjIiLyY670DfPlTE1JhRH1HkiplHMmsKP1ZFEaJYwi08DEmjm+K/pewMCMXOes6Iczv552PIsttXgQeR6TTImIiPyYlHL67pS/FwQzisoM2F+sQ1GZweU1Xp5Y66ZRKWWbCbS3nqxeL8DUJG9wVlJhkvV4FFzkmIV29pm2pAyT/+DMGRERkZ+zVxY+SqPEyL6RiIsKdav8vZjKhGJ5Yq2b3iSg8JTB7fTAAyd02LBDXNqiHMwCq+CR6zw1C90WqzX6FwZnREREAUDuXmQW9tKmLJUJpc7ESWkcrQpViJ6tys2vxcDkcJefb0GpXvR6MrnsParDBfHhXHNGLql3oUqrK1it0b+4/G4IgoDCwkJs27YNDQ0NqK2tlXFYRERE1JalmMWQXlqkJboeqFiI6ZUktYx9aZW4VL70FDUeuLo7tGpxpyLupF+JeZ6eYGw0Y/32KhSUSutTRwQA542eD85YrdH/uDRz9sknn2DZsmUoLy+HUqnEhx9+iOXLlyMsLAzLli2DSiX+qhkRERH5hpheSVLL2ItNkRqUEoHSKhN0InuFSTl2W672ipPLlj01bs36UXCKUMvTzN0RtnnwP5Jnzj7//HM8+OCDGDt2LF544QUIv+VTX3rppfjuu++watUq2QdJRERE8hMb7EgJisSmSEVqlJKDLVfTr3y9pkZvFFBcxqILJE2U1vPBGfkfyb9yq1evxnXXXYdnn30WU6ZMsd4+c+ZM3HPPPfjss89kHSARERF5hthgp6q+UfQxLaX/HbGkUkkJtrRq1ys2+sOamqJyBmckTWqCGuFhnn2M3LwalyuzkmdI/rUqKirCpZde2uF9w4YNQ1lZmduDIiIiIs9LTVAjSkTg8uMxnegTOKVSgcGpjgtgWBrfSikeMrSX1uX0q9QEtei1bZ5SVSc+wCUCWr5Lw3tHevQx6vQCS+n7Gcm/VHFxcTh27FiH9x07dgxxcXFuD4qIiIg8T6lUYGRf5yd/UopxCIIZB4p1Drc5UKKHIJhFFw8BgL7dXS9aoFQqMLSX1uX95VBcbuIMBUk2oIe4tZ7u8HXaL9mSHJxdfvnlePnll5GbmwuTqeVHVaFQ4MCBA1i1ahVycnJkHyQRERF5RlyUuNpgYk/gth2sR52TbS3BnpSTwvJz7jV09sZJriM6I2coSLrUBDVUHm58FeHjWWWyJfntXrBgAY4cOYIFCxZAqWx5M2+44QbodDqMGjUK9957r+yDJCIiIs+QUsDDmYJSPb7dXyfqeJZebWIdP2vC+EGiN2/HcpJranL9GO7iDAVJpVQqMG5gFL47UO+xxzArOKPrTyQHZyqVCmvWrMGOHTuwc+dO1NbWIioqCqNHj8bFF18MhYLlOImIiAKFpYCHo1LzYnohSe0lZmmirQ5TwNjo/OTwZGVLWqCr686USgX6dNfgkA97jmlUPEci6RJiPFsVRGdgcOZPXJrHrKurQ1NTE+677z488cQTuP7663Hq1Ck0NDTIPT4iIiLyIKVSgZyRMQ63EdMLSUovMUuwp1QqMHW048e2MDSa3U4LTOji4fwwJ8rOsSgISVNQqsdH31d79DH8oZop/U7yu3Hs2DFcccUVWLJkifW20tJSPP3005g5cyZOnz4t5/iIiIjIw9JTNJiVHdeuBH60NgSzsuOQnuK4+iIgLWWvdbA3JDUC3WPFzQy4kxYoCGbsLvRcapgYRWe55ozEkzob7Qoxs+LkXZKDs+eeew6JiYn4z3/+Y71t3Lhx+O677xATE4Nnn31W1gESERGR56WnaLBgehJuuiQeM8d3xU2XxGPB9CRRgRkg/ur7xCFR7Y45ZUQXWR+jI8VlRhh9uN4MAI6eMaLAh2mVFFikzEa7SsysOHmX5F+5H3/8Effccw8SExNtbo+Li8Nf/vIX7Nq1S7bBERERkfcolQqkJYZjSC8t0hLDJZ20pcSpIGbZ+UWDolzaV6GApL5obRWV+ces1ZbdbPpL4ni6gExHF0rI9yQHZwqFAnp9x1d9mpqa0NjIfGoiIiKpBMGMojID9hfrUFRm8MkJvDtjKK0ywSxi8456m4nZ12zueF+xanU+njb7jd4koKjc4OthUADw5FowjUqBCRnRHjs+uU7yytisrCysXLkSo0ePRteuXa2319bWYvXq1Rg9erSsAyQiIursCkr1yM2vtUlhitaGIGdkjNeubLs7BrFX+Tvazp19xYpps57Ol0rKTOiTxBkLckxMJVVXTc2KZTqjn5IcnN13332YNWsWLrnkEgwfPhxdu3ZFTU0NfvrpJ6hUKixbtswT4yQiIuqUCkr1WL+9qt3tdbpmrN9eJbogh6/HIPYqf1V9+wybijpxWTfuNMvtlaTG9gL3qkondw3BqWr3T5SZ1EhiWCqpdvTddEd6SjgyUrWyHpPkI/lXLi0tDZ9++imuu+466HQ6HDhwAHV1dZg1axY2bdqEtLQ0T4yTiIio0xFTjS03v9ajKY5ix9DUJDhMeUxNUCNKRID24zGdzb4FpXpsE9lg151muWndwqFRuZcmVlYrzwyGguEZiZSeosGglHDZjheiAK65ME6245H8XGr4kZiYiAcffFDusRAREQUVMdXY6nTNKKkwIi1RvhM0V8bw3MdnbJpFt015VCoVGNk3Et/ur3N6LMvzkVoq3J1muUqlAtPGxLo1C9EkU32GbQcbkNRVzWIMJMqofhE4VCrPOsXswVFMZ/Rzoi4hbdq0CTU1Ndb/d/afq4qKipCZmYmNGzdabzt06BDmzJmD4cOHY9KkSXjrrbds9hEEAS+//DKys7MxfPhw3H777SgtLbXZRo5jEBERyc0ba63kOnbrwAz4PeWxdWn4uChx13wtjym1VLi7BRIs/dw0Kt+fnHp6RpQ6j5ZZX+efWWfbqEPBIiABQNSv6EMPPYT169cjNjYWDz30kMNtFQoFrrrqKskDaWxsxP333w+dTme9raamBnPnzsWkSZPw2GOP4aeffsJjjz2GiIgIzJw5EwCwatUqvPfee3jmmWeQlJSE5557Drfddhu2bNkClUolyzGIiIg8wZ11Wt4egz25+bUYmNxSdr+qXlxFRMtjSgk65WqWm56iQVNTDDburHH7WO7w9IwodR6FpwwAFHC0WnH8oEj0iFc7nBk2NgFf/3wOUzJjZB8jyUdUcPbNN98gISHB+v+esHz5ckRGRtrctn79eoSFheHxxx9HaGgo+vTpg5KSErz22muYOXMmTCYTXn/9ddx///2YOHEiAOCFF15AdnY2vvzyS0ydOlWWYxAREXmCZZ1WvZMg5cdjOkzIiPZIOpK7FeEsQYbeZHaa0gjYBllSAkM5m+VG+UnlRk/3saLAZ69Yj0VYKHDl2K4Y3LOlwMes7Dhs/KHKbgrujkMtRXEYoPkvUb+KycnJ1hmkRx99FCdOnEBycrLd/6Tau3cvPvjgAzzzzDM2t+fl5WH06NEIDf09hhw7diyKi4tRWVmJwsJCnD9/HuPGjbPeHx0djfT0dOzdu1e2YxAREXmCZZ2WM5YAyFNjyBkZ49Yx6nXN+HxvtahtM1J+b25tCQwdUSiAay/sKuv6LDGP6w2enBGlwCdmTWZ4mBLpPX7/bjQLgtO1kTsLG9Ak1wJKkp3kgiA//vgjFAr5rtzV1dXhgQcewCOPPILu3bvb3Hf27Fn079/f5rZu3boBAM6cOYOzZ88CQLv9unXrZr1PjmO4wmw226Ro+lJTk3803iQiovbErtOq1zWjqMyABr2ASI0SqQlq2WaSLGux2vY5E+u8sRkNIot17Duuw6WZLbNgYkqFz7ywKzJ6ylv221MlyqX6dn89usWoWBiEOiRmTWa9XrApsPPpnlqnxzWbgb1HGzBuYHCsP2tsbPT5ObnZbBYdP0kOzrKzs7F582aMHDkSYWFhkgfX1pIlS5CZmYlp06a1u89gMLRb86VWt6RCGI1G6PUti5A72ubcuXOyHcMVjY2NOHTokMv7ExFRcBCb2pf74znojL9f7Za7SXV6igYDk8Ox7WAddh9ugN4kLtjSqpWIUIufhTI0mm3WWtkLDD3dhNve42pUCtHPXQ6t1+wRtXb4pLgKja0L7LQt3GNPTb38ja39VWVlJSorK309DNF1LCQHZ2q1Gps3b8YXX3yBPn36QKu1vZqlUCiwbt06UcfatGkT8vLysGXLlg7vDw8Ph8lksrnNaGxJ69BqtQgPb/lhN5lM1v+3bKPRaGQ7hivCwsLQt29fl/eX05kzZ1BbW+vrYRARUQfOG8WlF+nabOeJJtWFpwz4dr+4nmMWV4yKkdw/rO1aK0tgWFJh9MjMoD0dPa5ZAN76n/dO5FgYhDoiCGb8Uixutsdygadewqx3bJTv03q9JT4+3lo7w1eOHj0qelvJwdnZs2eRmZlp/bfZbBuht/23Ixs2bEBVVZW1EIfFP//5T3z++edISkpCeXm5zX2WfycmJlrT9crLy9GzZ0+bbQYMGAAAshzDFQqFol3g6iut19sREZH/EAQzvvzR9SwNQL6ZF6k9xwBgQHI4MlK1OHZW73zjVjqaLVQqFT4JUFo/riCYUVRu8PrsGQuDUFslFcZ2F2Q6olUrrQV2zhvFB2dZIta6dhZhYWE+PyeXsiRM8ln722+/LXUXu5YuXQqDwXbKdsqUKZg/fz6mT5+OTz75BO+//z6am5sREtIS4e/atQtpaWmIi4tDVFQUIiMjsXv3bmtgVVdXh4KCAsyZMwcAkJWV5fYxiIiIPEFqn6+OyDXz4spYztQ0QhDMkppDKxRASpz/takpKNW7vObOXe62M6DOR2zAPrSX1nphRmx6cb/uaoSG8jPnryS9M7/88gu++OILFBQUyPLgiYmJSE1NtfkPAOLi4pCYmIiZM2eioaEBDz/8MI4ePYqNGzfizTffxJ133gmgJXdzzpw5WLp0Kb755hsUFhZi4cKFSEpKwpQpUwBAlmMQERF5glwzJlLSmexxZSyWwFBKcGE2A6VVJucbepGlXLkvAjO5+rdR5yL2OzWgx+8XZcS2iBifHuXSmMg7RM2c1dXV4c4778RPP/1krTaSmZmJZcuWtatyKKe4uDisWbMGTz75JGbMmIGEhAQ88MADmDFjhnWb+fPno6mpCY888ggMBgOysrKwdu1aa7ESOY5BRETkCXLNmEhJZ7JHbAPpthr0AjJ6aiT1SvOnND5X0jnlJGf/Nuo8UhPU0KiU0Jvsf1c0rVIaLfs4+x7yYoD/ExWcvfjiiygoKMA999yDwYMH4/jx41i9ejUeffRR/Pvf/5Z1QIcPH7b599ChQ/HBBx/Y3T4kJASLFy/G4sWL7W4jxzGIiIjk5m4DaAsp1RI7Ighm7CqUVgjEIlKjlFya3p/S+ORILXVVqBIYmMxCIOSiNtnEYr6HvBjg/0T9Ov7vf//DokWLcPfdd2PChAm4+eab8c9//hM7duzwed8AIiKiQCVHA2hAfDqTPUXlBhhEluBuS2doubKfnqLBNRd1hbN17/525V6OlNDWQiW8FU0CcKxMWjEV6vwEwYzdR+odzpoBgN4ktGtOb2kR0bbJerQ2RNbKruQ5ombOKioqkJGRYXPbmDFj0NzcjDNnzqBPnz4eGRwREVFnNzA53Gn6kiNyBDslZa6vAft0by0GpWigVCowuKcWpypN2FnYYHf7wakav7pyf+ys0flGEvxpQleEKpX4dE8NqkT0ktpfpEe/7v5R3Zl8T2phmo5ShH3VmoLkISo4a2pqatc4rUuXLgB+7xlGRERE0pVUGF0OzADgsswubp90uVM0Xm8SUFRuQJ8kDQTBjIMnHM8EHSjRY/Iw98csB0Ew4/BJ+TKANCoF+iS2zEzUnhd3cm1s8l7JfvJvlsI0UthLEfZVawpyn9tJ31L6mhEREZEtd4tjaMPdX7+V1k2emTcx67csFR79QUmFEYZG+Y7XO6lldqKkwohmkW9reKjvg1TyPVcL0/hjWwpyj9u/6FKaqhEREZEtd4tjyFH5sFeiGmFuLFuzXKY9fNLgcDsLf6nWKPd6s+NnjRAEs6TnV1TWsg8FN1cL05RU+seFDpKP6CbUS5YsQWTk793ELTNm//jHPxAREWG9XaFQYN26dTIOkYiIqPNyt2KjXJUPWy62uhYkpHVTo6BUj12H7a81a81fqjXK0YKgNb3JLLnvW51ekKWJOAU2Vy9YlJSZ0CeJRT46E1G/HllZWYiIiIDZbLb+Z7ldq9Xa3C4I/nE1jIiIKBC4U7FRoZAnrWnbwXqYXFz7pFEp0DNBJToly5+qNbrbgqAjDXrhtx5V4jOL/GUmkXzH1T6DnHPtfETNnL399tueHgcREVHQElxcv202A6VVJrdmXQTBjB8KXOtxBgBjBkSitMokeubPn/osuduCoCOWvm9jBkTh2/11oveh4NXyHRT3WWnL3fWi5H/4a0BERORDBaV6fPR9tcv7uzvrUlxmRGOz69ff46LCRI9h7IBIv+qz1GCQN62x9azghIwohIc5D0L9aSaRfONYmR6NLnwUQ0Na1otS58LgjIiIyEdcrdDWmjbcvVmoonL3CgpEapSiZ34G9PCfdVWCYMbmXTWyHrP1rKBSqcD0sV0l7UPBaX+Ra43I+10Qzs9OJ8TgjIiIyEdcrdDWmsLs3smZO3tH/9bc1lLUxOG2fjZD1DJjKM+xorUhmJUd125WMD1Fg/GDIu3sRdTC2OjazHVWX362OiMGZ0RERD4iRyGI80b3jpGa6HpBkRF9I6BUKkQVNfG3GaK9R8+7fQxVqAI3/iEeC6YndZiuKQhmHChxPCuSm1/LUvpBzpU1hxqVgimNnRSDMyIiIh+RoxCEu8dI6xYOjcq1Y+gMAorKDBAEM9JTNJiVHdduBs3erJIvCYIZx864lkrW2pVjYtG7u/3UskBryk2+0TNe+gWSaWO6+tXFDpKP6D5nbX333XfYsWMHysvLsWjRIhw6dAgZGRlITk6Wc3xERESdlrs9zuRIFVQqFZg2Jhbrt1dJ3nfPr+ex59fziNaGIGdkDNJTNBiYHI6SCiMa9AIif0t79LeTyJIKI0yuVS63Gj8oEhmpWofbiJ0ZZSn94BYdIe10vG93tV9d7CB5Sb5Uptfrccstt+DOO+/Ehg0bkJubi7q6OvznP//B1VdfjV9//dUT4yQiIup03OlxBsiXKmiZ9Yp0sbhIna4Z67dXoaBUD6VSgbTEcAzppUVaon8WLHAnGNKqlbjmoq6YkhnjfFuRr6e7RV0osKUmqBEq4Yw8MpyJb52Z5Hf3+eefx8GDB/Hmm29i165d1obU//d//4fExES89NJLsg+SiIios0pP0WDsAGkL+xWKlpkbOa+ep6do8MeRsW4dIze/Fk1NLamO+4t11pRHfyMlFXR0Py1uuiQeM8d3xU2XxOP+Gd0xuKfjGTMLscVa3C3qQoGt8JQBTRKuFxw/a/TL7xXJQ3Ja4xdffIFFixZh7NixaG7+PQ2jW7duuOuuu/D444/LOkAiIqLOLlzimi+zGdhxqAE94uVLbxIEM3J/rHXrGHW6Zjz/yVnoWhUpaZ3y6C9SE9TQqpU247RnUE+ty02+xRZrOXLagN7d/afNAHmPIJixaae0Pod1egElFUa3ms+T/5I8c1ZXV2d3XVmXLl2g0+ncHhQREVGwEAQz8o82uLSvnJX+SiqMqJdh7VPbgKd1yqO/UCoVuDwrxul27q7pEztD90uxjjMhQerbA3UwNUl/77lOsfOSHJz169cPW7Zs6fC+//73v+jXr5/bgyIiIgoW7gRFclb68/TJnr+VjB/cU+u0B5m7a/osM3TO6IwCKzYGIUEwY1ehaxdm5Kj0Sv5JclrjXXfdhb/+9a+ora3FH/7wBygUCuzduxcbN27E+++/j2XLlnlinERERJ2Su0GRXEGVp0/2LIGkP6Vi9YhXA3Dt5FgMpVKBob202HXY+WNwJiT4tFQNlX7Bwt8aupO8JP8ST548Gc899xwOHz6MJUuWwGw245lnnkFubi6WLFmCnJwcT4yTiIioU3I3KJIrqHK3mbUY/hSACIIZufm1DreRY7ZvQA9xwShnQoJP3XnX+jn4W0N3kpdLfc6mTZuGadOm4fjx46itrUV0dDR69+4NpZI/LERERFK40+tMrivogmDGlz+ec/s4zvhTACKlQbQ7s31i3l/OhASnHYX1krZXKIBrxnf1q+I6JD+XfyV1Oh169+6NESNG4NixY3jrrbdQXFws49CIiIg6P3d6ncl1BV1MoOIufwtAvNUgWsz7y5mQ4HPghA5ltdK+czMv7Oq08TkFPsnB2fHjx3HppZfitddeAwC8+OKLWLBgAZ555hlceeWVyM/Pl32QREREnVl6igbXXtgVGpX4E/QByeGyXUH3RrqhvwUgYmfx5JjtszT5jmpzrCiNErOy4zgTEmQEwYxPd0srnz88TSO6vx4FNsm/OEuXLkVoaCguueQSmEwmvPfee/jjH/+IvLw8ZGdn48UXX/TAMImIiDqvglI9Pt1bC71J/PqmI6cNaJLSudYBT6cbalRKDEz2n0IgwO/pho7IPdvXNjT1n1CVvKmkwghDo7R9pma51yCeAofkX+O8vDzcd999GDJkCPbs2YP6+nr86U9/QmRkJK677jocOHDAE+MkIiLqlApK9Vi/vQp6k7RAy2wG9rrYH60tsSXfXaU3+V+peG+mG1re47o2M5R1esHvesCR59VLTCHO6BmO0FD/Wa9JniX5nW5sbER0dDQAYNu2bdBoNBg5ciQAoLm5GaGhLtUYISIiCjqCYMYXeTUu719TL886MbFNmd3hT5UaLSzphm1n0KK1IbKlG3qrKiQFjnqd+CqNSgUwc3ycB0dD/kZyJNW/f398+eWXSEtLQ25uLi666CKEhoaisbER7777Lvr37++JcRIREXU67jSgBoDYKMdpeVIM7qnF6UEm7Djkmb5f/lSpsbX0FA0GJoejpMKIBr2ASI0SqQlq2dbHeasqJAWOM7XicxpH9dP61VpN8jzJwdn8+fMxb948vPvuu1CpVLj99tsBAJdddhkqKyuxevVq2QdJRETUGbk7m5TVN1KmkbSYkhmD5K4qfLq3RtL6N2cUCiAlTiXb8eSmVCo8Fhh5qyokBY4mCRPeg1JYBCTYSA7OLrzwQmzZsgX79+/HsGHDkJycDAC46aabMHbsWAwYMED2QRIREXVG7swmqUIVHrminpGqhTpMgXe+rZLtmGYzUFplCsqZIW9WhaTAkJKgQuFJg9Pt1GEKv2o/Qd7h0gKxlJQUpKSk4NixY/jpp58QGxuLm266Se6xERERdWqpCWpEaZQupTaamsweS4U7UWGS/ZjBOjPEJtTUlrMqoRbTsvyr/QR5h0uXaT799FNkZ2dj6tSp+POf/4ycnBxkZ2dj06ZNMg+PiIio81IqFRjSy/W0JalV38TyRGmKYJ0ZYhNqak0QzPhqX53T7fonh2NwrwgvjIj8jeSZs//+979YvHgxxo4di0WLFiE+Ph7l5eXYvHkz/va3vyEmJgYTJ070wFCJiIg6l4JSvVsFOM4bPROcpXVTY/vBetmOF+wzQ5aqkLn5tTYzaNHaEOSMjGET6iAipkAMAIwbKO96UgockoOzV155BTk5OXjhhRdsbp85cyYWLlyIV199lcEZERGRE2JKrDsToZavWmNrvRLV0KiUknuv2cOZIc9XhaTAwAIx5IzkHIMjR45gxowZHd43Y8YMFBYWuj0oIiKizk7sFXRHokSuXZFKqVRg2phYWY7Vq5uKM0O/sVSFHNJLi7TEcAZmQYgFYsgZye98bGwszp071+F9tbW1UKn8t1QuERGRv3D3yrhWrfRoqqAlFS8q3L0AorjchIJSvUyjIgpslgIxjgR7GnCwkxycjRs3DitWrMDZs2dtbj9z5gxWrlyJCy+8ULbBERERdVbuXhlP7aby+MxLeooGM8bFuX2cLXtqIAieKDMS+ATBjGNn9fjvz+fwzc/ncPyMga9VJ8YCMeSM5DVnixYtwsyZMzFlyhRkZmYiPj4elZWV2LdvH7p06YL77rvPE+MkIiLqVNxtypwQHSbTSBw7b3R/7YveKKC4zIje3YOvz5kjBaV6bNldY7O2b/vBemhUSkwbE8t00E6KBWLIEcnBWUJCAj7++GO8/vrr2Lt3Lw4cOIAuXbrghhtuwNy5cxEfH++JcRIREXUqJZVGt/ZPTfTOMgK51r4UlTM4a62gVI/12ztu9K03CVi/vQqzsuN4ou4jgmD2aPEWFoghe1xqQh0XF4fFixfLPRYiIqKgUVLmeqPnUCWQ1s07gY67M3wWPOX8XVOTgC27q51ut2V3DQYms3CINwmCGdsO1mH34QboTb+nl3piVstSIIaoNVHB2YoVK0QfUKFQYN68eS4PiIiIKBi4s6rowvRIr52wl1a5HkS25q2ZPn9mOfHfcagepibn2+tNArYdrMPEIV08PzjqMM3Uok7XzNlM8goGZ0RERD7gaqNnVagCFw/23sm6HP2WvDnT568cnfg7suNQPSZkRHP2zMMcpZm2lptfy9lM8ihRwRl7lxEREcnL1UbPV43r6tUTQznWnPUP8pNZsSf+HTE1AUXlBvRJ4myNpzQ1Cfh0T42obet0zSipMDIdkTxG8i+uwWBod9uhQ4dkGQwREVGwkLPRsyelJqihVbsXoI3sFyHTaAKPIJiRm1/r1jHcWZ/Y2QmCGUVlBuwv1qGoTHobgoJSPf5v42noJFQllWM2mcge0b+2hw8fxsyZM/HGG2/Y3F5XV4eZM2fiyiuvRFFRkewDJCIi6qwsJbWjJcxO5ebXerUPllKpwOVZMW4dIzU+eBvqllQYbcqlu4Jdzzp2sESHZzecwrpvKrFhRzXWfVOJZzecwoETOlH7W2Y0G0Ws/2tNrgqmRB0R9ek6efIkbrzxRlRWViItLc3mvrCwMDzwwAOora3F9ddfj7KyMo8MlIiIqDNKT9FgwZXdcdMl8ZiQEeV0e0talTcN7qnF+EGRLu8vV1GRQCTHLEtat+ANbu35cl8tPvyhGoZG29sNjcBH31fjy321DvcXBDM273JeMbMjclUwJeqIqODstddeQ0xMDD7++GPk5OTY3KfRaHDzzTfjo48+glqtxquvvippAFVVVVi8eDHGjh2LzMxM3HHHHTh27Jj1/kOHDmHOnDkYPnw4Jk2ahLfeestmf0EQ8PLLLyM7OxvDhw/H7bffjtLSUptt5DgGERGRp1hKaid0EddY+vDJ9ksMPG1KZgyuvbCrSymOwZwG5u4sS6iyZX0i/e7ACR12HGpwuM2OQw04WGJ/Bq2o3ABDo2tzktsL6lzary13UzKpcxL1i7Fz507cdttt6Nq1q91tEhIScMstt+CHH36QNIB58+ahpKQEr732Gj766COEh4fj5ptvhl6vR01NDebOnYuePXtiw4YNmDdvHpYuXYoNGzZY91+1ahXee+89PPHEE3j//fchCAJuu+02mEwtV+nkOAYREZE3iD2R/6VY55MTuYxULe6f0TLLN3N8V1w2IlrUfsGcBpYSp4LCjVooTQLw9c/n5BtQgBMEMzZ+L27G67O8GrvfE3fW8X13oAH/++WcW0FVQakeL35yxiYl88VPzqCgVO/yuKhzEFWtsby8HL169XK6Xf/+/XH27FnRD37u3DkkJyfjzjvvRP/+/QEAd999N6688kr8+uuv2LlzJ8LCwvD4448jNDQUffr0sQZyM2fOhMlkwuuvv477778fEydOBAC88MILyM7OxpdffompU6di/fr1bh+DiIjIGyzFN5wVJ9AZBZ9VjGvdOFcQzNhZeN7hmqpobQhSE4J35qe0ygSzm3H0zsIGTBoSjdDQ4A1yLY6fNUDsPKzOaLb7PXH30sZ3B35vgyG1QbW96p11eoG91EjczFnXrl1RXl7udLuamhp06SK+90qXLl2wbNkya2BWXV2NN998E0lJSejbty/y8vIwevRohIb+HkOOHTsWxcXFqKysRGFhIc6fP49x48ZZ74+OjkZ6ejr27t0LALIcg4iIyBuUSgWG9tKK2tYfUgWVSgUGpzo+iRycqgnqMvpyvE9mM7D3qOM0vmDxS7G4Yh8WRWeNHc5spSbIt27M0qBazKyXIJixZbfjsv1b9tif8aPOT9TMWVZWFjZu3IgrrrjC4XabNm1Cenq6SwP5xz/+gfXr10OlUuGVV16BVqvF2bNnrYGbRbdu3QAAZ86csc7Sde/evd02lvvkOIYrzGYzdDppPyCe0tQksQwRERF5lSC0XOFv0AvoEiFudsQfUgUFwYwDJY5PSA+U6DF5WJegDdDkep9q6t2r+NhZmCSuE9t2sB4/FenazWyZFfIHP1t21zhtUF1cZnTa21BvFFBcZkTv7uylJofGxkafn5ObzWYoROY3iwrObrjhBvz5z3/GM888g4ULF0Kttk1PMJlMePHFF7Ft2za89tpr0kcM4KabbsKf/vQnvPvuu5g3bx7ee+89GAwGqFS2VzYsj200GqHXt/xB6Gibc+da8rPlOIYrGhsb2f+NiIicKijVIze/1iY1UKGAw1Q4f0kVFFMmPtib9qYmqKFRKaA3uRcMxEaFyDSiwJYSH4bCU9IK4lhmtlqnC5aWNzrZSzq9ScC2g3WYOMR+FllRmbhKq0UMzmRTWVmJyspKXw+jXaxhj6jgbMiQIfjb3/6Gp556Cp988gnGjRuHHj16oLm5GadPn8bu3btRU1ODe++9F9nZ2S4NuG/fvgCAJ598Ej///DPeeecdhIeHtyvKYTS2fKi1Wi3Cw1s+tCaTyfr/lm00mpYvnxzHcEVYWJj1OfnamTNnUFtb6+thEBFRG/bWnjhbo5QzMsYvZqLEpuz5QwqmryiVCowZEIVv97tX4S+rr+utDDqTbl3FVTTtSG5+rXVmy1NJg98frMeEjGi7389anbhsJrHbkXPx8fFISEjw6RiOHj0qeltRwRkAzJ49GwMHDsTatWvxzTffWAOciIgIXHTRRbjlllswbNgwSQOtrq7Gzp07cdlll1nXhCmVSvTt2xfl5eVISkpqt9bN8u/ExERrul55eTl69uxps82AAQMAQJZjuEKhUECrFbduwNNar7cjIiL/IGbtSdsZNKmFBzxNbMqeP6Rg+tJFgyLdCs4SokNZDOQ3Bjda/LWexU3rpsb2g/XOd5KoSQCOlenRr3vH54AxWnEzoGK3I+fCwsJ8fk4uNqURkBCcAcDIkSMxcuRIAC2BVWhoKKKjxZXR7UhlZSUWLVqENWvWWGfcGhsbUVBQgEmTJiE+Ph7vv/8+mpubERLS8iHdtWsX0tLSEBcXh6ioKERGRmL37t3WwKqurg4FBQWYM2cOgJb1cu4eg4iISG7bDtY7XXtiNgOXjYhGZHgoIjVKpCao/WLGzCI1QY1obQirNTrhbhNuY6MAQTD71XvvK+4G+pZZ3F6JamhUSqffQVf8clxnNzjrlaTG9gLnxV16JQX3dyaYufwJ79q1q1uBGdBSen/ChAn417/+hb179+LIkSN46KGHUFdXh5tvvhkzZ85EQ0MDHn74YRw9ehQbN27Em2++iTvvvBNAS+7mnDlzsHTpUnzzzTcoLCzEwoULkZSUhClTpgCALMcgIiKSkyCY8UOBuKv2keGhGNJLi7REx4UGfEGpVCBnZIzDbfwlBdOXzp13L0WtTt/SOoGA5NgwhLkxqaQNb/ksKpUKTBsTK9OobNWet3+xIq1bOFShjr8PGpUSad243ixY+Tzf7fnnn8eyZcuwcOFC1NfXY9SoUXj33XdxwQUXAADWrFmDJ598EjNmzEBCQgIeeOABzJgxw7r//Pnz0dTUhEceeQQGgwFZWVlYu3YtwsJacpLj4uLcPgYREZGcisuMaGwWt+rF31MC01M0mJUd166oib+lYPrSKTdnzoDgXrcHtFzQ2LCjCgdPSCsG0pa51cuYnqLBtRd2xWd5tU57C0rhKIWt8JQBpibH3/1pY2KD/oJGMFOYze62RqS29u/fD6ClkIo/KCkp8YsqNURE1OKbn8+JWu+iDlXgwWsuCIgTtdbtAPwxBdOXPttTjb1H3SvlfdMl8UFb8fJgiQ4f76xGkwzx05BeGvS/QINIjRI6g4Ct+87ZXFTQqBTI6h+JmvpG7C9xLRDUqpW4f0b3dp9/QTDjmY9OOwzOQpXA32cl87sjo+7du1snfXxFSmzg85kzIiKiYCP2tKtP98AJcJRKRdAGD850jXbvdEujUgbtur0v99VixyH5GnDvL9Zjf7H93nx6kxnbDtRj5oWxLgdnOqPQrn2EIJix/VCt01mzJgE4ftaAvhdwxjlY+XeuBBERUSeUmiiu383IfhEeHgl5g9tl8AMjPpfdgRM6WQMzKb7aV4dxA11/3+pbzcYVlOrxzEen8b+fz4va9+cicdtR58TgjIiIyMvSuoVDo3L8J5hFATqP0FAlMnq6/l7qjcFXEEQQzPhsj+NWE55Up2tG/+RwjB/kWoCW++M5FJTqrb0Mnc2YteaooAh1fgzOiIiIvExMpTgWBehcZo6Pc1qlz5FgKwhSUmGE3uTbsggNegFTMmPwyKwLMCUzCjFa8afNOqOA9dur8MnO9k3mnYmJYI+zYMbgjIiIyAfSUzR2r8qPHxTJKoedjFKpwFXjurq8v79X7ZRbvYPeed5iec1DQ5UYP6gLFlx1AR6ZdQGGp4n/bhpd6KIwLI3pzMEsuL7pREREfqKgVG93Pc2OQw0oKLVftIACk6XtQLRW2sxIMDbyPm/0bXBm7zUPDVXiqnFxmJUdB61a/tNoVagCvZOYzhzMWK2RiIjIywTBjNz8Wofb5ObXYmCy/zWeJvekp2gwMDnc2nZAG67AiXITvjtgv7VCMDbydrYm09OcvebpKRo0NQnYuFPedXFXjesadO812WJwRkRE5GUlFUab3kodqdM1tyvHTZ1D27YDfZI0SIxVtWvkrVUrcXlWcDby1pt8s8ZOAWCcyLRiuWfOkuPCgvK9JltMayQiIvIysetp/GHdDXlHeooGl2V2sTnh1xkFfPlb1b9gE6H2TVEMM8SlFReU6rFhR7Wsj322phGC4NsiKOR7DM6IiIi8TOx6Gl+vuyHvKSjV48MfqqEz2s4Y1emasX57VdAFaBE+LoCSm19rN1CylMeXu5pk828NqCm4MTgjIiLyMrGzAr6aPSDvErsGMZhmVRRm3667sqQVtyXmvXLHz0U6jx2bAgODMyIiIi+rbhA3IxYlsaofBSYpaxCDxXmj7/u6dZRWLOa9ckfteRdq71OnwuCMiIjIiwTBjF2F9ivzWQRj+fRgJbbBdDA1ovaHvm71hvZBmKffAzagJt9/8omIiIJIUbkBhkbn6WmZvTUsqR0kxAYi/hCweEtqglp0PzhPld03dDB75+n3YGhvrUePT/6PpfSJiIi8qKTMJGo7s4/X3JD3WAIRR+lywTaTqlQqkDMyBuu3V9ndJlylwPQxXW36xlWcM2LbwfPyDELR/juYHBsmz7E7oApVoE8iS+kHOwZnREREXiS2pEPwlH4gMYFIMDaiTk/RYFZ2XLv+bxqVAmMGRGFCRpT1Nfm9b5wWZbXNOHzK/aqHad3aB8P5x2UK/DrABtQEMDgjIiLyqrRuamw/6HzNWUcnhtR52QtEorUhyBkZnI2ogZbXpfXMWKRGidQEtcMg5s8Xx2Prj7XYWdjg8uOqwxToldj+O1hTL38xkMhwBS7P6hq07zHZYnBGRETkRb0S1dColNCb7BcW0KiVHZ4YUufmSiDS2QmC2aXX47IRMbhkaDTe/rYCJeWNkh93eJq2w8eJjZKvYEdYCPDnCfHolRjc7zHZYnBGRETkRUqlAtPGxDpMYZs2OpYna0FKqVS0StELLm0DMZ1BwNZ951yeSQwNVWLu5ER8ua8WOw5Jm0WLiew4CMvqG4mtP9ZJOpY9M8bHoXf34HyvyT4GZ0RERF5mSWH7Iq8G9a1Kc0drlMgZFcv0Jgo6B0t0+CyvFjon/c3qdM1Yv70Ks7LjRH9PpmTGoHeiGu98a/+CSFv2KkCGhioxflCk5GCvtbCQlsCM33PqCIMzIiIiP8EiIOQOV1MAfc2Vma3c/FoMTA4X/fx6J4U7rYjZmqO04ymZMQDgUoA2eXgUxg+MDoj3hXyDwRkREZGXFZTqO0xrrNcLkmcFiICWz9QXe6tRb/g9xI8KV+CPfl5o4sAJnUtBTp2uGSUVRtEpoGIqYrYWoXa8tmxKZgwmDYnG7iN1+O5AA0xNog6LLtowBmbkUPB0MyQiIvIDgmBGbn6tw21y82shCJxHI3EswX7rwAwA6g1mrN9ehYJSvY9G5pggmLFpR7XL+9eLnAWzSE/RYOKQaFHbRologB0aqsSF6TH4+6wemJARKeq4wdRInFzDTwgREZEXlVQYnaZWWWYFiJwRBDM27XQc4Gz8ocovg/1vD9ShyfESM4fOG6WXtZ+QEYVoJwGSKw2/Jw7p4pHjUvBhcEZERORFDXpxZ6Nit6Pg1dQk4LO9VTA1OQ68mgTg2/3nvDQq5wTBjGNn9fheRL8/R7R2inY4olQqkDMq1uE2rjT89tRxKfhwzRkREZEXVdWLW5zC9Cdy5Mt9LU2WzSInxHYWNmDikC4+Dw4KSvXtGm276rzBtQsYnmr4zUbiJAcGZ0RERF5SUKrHt/ud90jSqJVMfyK7tv7YEphJ0dgMSQU0PMFeIRxX6RxUVHTGUw2/2Uic3MXgjIiIyAsEwYwv8mrEbex/y4PIT+wvOS85MLOQWkBDTmIK4UilVLgX8Hiq4XcwNxIn9zFngoiIyAtKKow2Dacd0ZsEFgShdgpK9djwg8gAvwOuFNCQi5hCOFKlJqpkPR6RP2BwRkRE5AVSC3ywIAi1JghmbNntemAGOO/d5Ulyf541KiXSunF2ijofBmdEREReILXABwuCUGtF5Qbo3VhjBQARPvxMiS2EI9a0MbFcx0WdEtecEREReUFKnPgULHWoggVBgpQgmDssJlF81v00V7OPJmMFwSyqEI4YqhDgqvFxrHxInRaDMyIiIi8orTKJ3rZ3d1Z3C0YFpXrk5tWgrlUKYLRGiZxRsaiVYb1WSbkRISEKr1cR/PD7StmONWFIFAMz6tQYnBEREXmBlEp5I/tEeHAk5I/slZmv0wtYv70KA3u4v75q95Hz2F7we6VHb/TfamoScOikfMVtojU8daXOjQntREREXiClUp6Cf52DiphiH8fOGtx+HFOTbY+GOl0z1m+vQkGp3u1j27PniGtl/+05fMpzYyXyB/z5JyIi8gIplfJ0BjY6CybFZUanxT4a5a2nYSM3vxaC4JnPXEml+HReMQ6eMKCpiZVMqfNicEZEROQFUVrxwRkrNQaX42Xuz4q5o07X7LG+eupQ+de0bd5dLfsxifwFf/2JiIi8IDVBjSgRQVe0NoSVGoOM3M2ZWxMbGnmqr96QNPnXsx0sNXhspo/I1xicEREReYFSqcAfR8U63S5nZAwrNQaZaAmzqlKJDWE8NVvbJ1GDMJmfXrMAj830EfkagzMiIiIvSU/RYFZ2HDSq9sGXRq3ErGz2bwpGvRPdr8Tojujfyup3RBDMKCozYH+xDkVl0meslEoFZoyPk2OYNqRUPyUKJKxHSkRE5GWhIQq0ntPQqBS4IsuzJc3Jf/VKVEOjUjotCuIpvZM67nfmqO+alM/qyUr5Z7mkVD8lCiScOSMiIvISSy+r+jbre/QmMz76vtqjJc3JfymVCkwb4zzl1VPCQtufDh4s0WH99iqbwAz4ve+a2M/ql/tqseOQvOX0AWnVT4kCCYMzIiIiLxAEM3Lzax1u48mS5uTfLCmvcq/PEsf2M3eg+Dw+/MFxRcQte2qcflabmgSPBGaAtOqnRIGEwRkREZEXlFQYnVbl82RJc/J/A5PDRVdXlFOPOJX1/7/cV4uPdjhuiA0AeqOA4jLHn9Ute2vdHVqHWNGUOjOuOSMiIvICsaXKPVXSnPxfSYURJh8spdKZBOwv1qGyziRpput4mQG9u3dczEQQzCg4oZNriDZY0ZQ6MwZnREREXiC2VDkbUAevHYfqffK4W3+sc2k/RzPBJRVGNLJmB5FkPv8LUFtbi0cffRQTJkzAiBEj8Oc//xl5eXnW+3fu3Imrr74aw4YNQ05ODj777DOb/Y1GIx577DGMGzcOmZmZuO+++1BdbZsnLccxiIiI3JGaoHbaz4rpWsGrqUnAr6cDK6XVUVN1V2eAVaGAKtTxrJiY9W5EgcrnwdmiRYuwb98+PP/889iwYQMGDRqEW2+9FcePH8exY8dw5513Ijs7Gxs3bsS1116LBx54ADt37rTuv2TJEnz//fdYvnw51q1bh+PHj2P+/PnW++U4BhERkbuUSgVyRsY43IbpWsFrzxHPFM7wJEcVE6XMAIcpgavHxeKmS+Ix66I4mJocB15i1rsRBSqfpjWWlJTghx9+wHvvvYeRI0cCAP7xj39g+/bt2LJlC6qqqjBgwAAsXLgQANCnTx8UFBRgzZo1GDduHMrKyrBp0yasXr0ao0aNAgA8//zzyMnJwb59+5CZmYl169a5fQwiIiJ3CIIZJRVGNDebMXFIFH48et62d5Q2BDkj2ecsmJVUmnw9BMkiNfaDs9QENTQqBfQm5zNc49OjMDQtAgDw9U+1oh7b0Xo3okDm0+AsNjYWr732GoYMGWK9TaFQQKFQoK6uDnl5eZg8ebLNPmPHjsWTTz4Js9mM/Px8620WaWlpSExMxN69e5GZmSnLMYjaspxoNegFRGqUSE3ouIEnEVFBqR5f5NXY9DaLDFdg4pBoxEWF8jeEAABqJ6l8YgzpFY79xQYZRiOOo3L2SqUCw3trsbPwvMNjqEIVuHhwtPXfziqaSt2OKND4NDiLjo7GxRdfbHPb1q1bUVJSgr///e/4+OOPkZSUZHN/t27doNfrUVNTg7KyMsTGxkKtVrfb5uzZswCAs2fPun0MV5jNZuh0nqlSJFVTU5Ovh9CpFJTqkZtfa/OHgVe9iagjlqbTbTUYzPh2fx0mDolGRs8oBmaEIWka/FLsehPyUCXQr7vGa8FZWKjC4fpIQTDj4AnnY7lyTKzN59/Zukyp2xE1Njb6/JzcbDZDoRD3O+9X1Rp//PFH/O1vf8OUKVMwceJEGAwGqFQqm20s/zaZTNDr9e3uBwC1Wg2jsSUXWY5juKKxsRGHDh1yeX/yT/ZOtOp0zVi/vQqzsuMYoBERgJaT0y27HReX+nZ/HfKPNuCPo2L52xHk+iRqEKIEml3spNA/OdyrjZkzUsIdXlQQ09cPALThtmvTeieG4/sC5+vveicypZHEqaysRGVlpa+H0WG80RG/Cc6+/vpr3H///RgxYgSWLl0KoCVAMplsc7At/9ZoNAgPD293P9BSfVGj0ch2DFeEhYWhb9++Lu8vpzNnzqC2ttbXwwh4gmBGbn6tw2027azGwOQLeBWciPDdgTpR623q9QIv7hCUSgWyM6Lx7X7XytqP7BeB1AQ1QkOAJi9k/E3NinV4v6t9/XolqqFRKaE32d9fo1aiVyKrmpI48fHxSEhI8OkYjh49KnpbvwjO3nnnHTz55JPIycnB//3f/1kjy+7du6O8vNxm2/Lycmi1WkRFRSEpKQm1tbUwmUw20Wh5eTkSExNlO4YrFAoFtFqty/vLKTTUL97mgCfmKqCpyYwNO6pw7UXxXhoVEfmjglI9vjsgrWcVL+7QhIwo7DhU77RaYUdS41vWLfa9IByFpZ5NbRw/KBKhoY6rMbra10+pVGDamNgOs1Qspo2O5feERAsLC/P5ObnYlEbAD0rpv/fee3jiiScwe/ZsPP/88zYB0qhRo7Bnzx6b7Xft2oURI0ZAqVRi5MiREATBWtQDAIqKilBWVoasrCzZjkEEiL8KePCEAU1NLualEFHAEzPL3hFTkxnHz3qvmAP5n8JTBpcCMwAorWrJAsrqFyHnkNrpEReGKZkxTrdzp69feooGs7LjEN0mcIvWhnCGmTo9n06pFBUV4amnnsKll16KO++80yYfNDw8HDfccANmzJiBpUuXYsaMGfjuu++Qm5uLNWvWAAASExNxxRVX4JFHHsFTTz0FjUaDf/7znxg9ejSGDx8OALIcgwiQ1rNl79EGjBsY7XxDIup0xK616cgvxTr0vYAnnsHI1aDewnIBMa1buNO0QHfERoVCEMxOZ66USgUGp2qw45D99WODUzV2j5OeosHA5HBWRqag49PgbOvWrWhsbMRXX32Fr776yua+GTNm4JlnnsGqVavw3HPPYd26dejRoweee+45jBs3zrrdE088gaeeegp//etfAQATJkzAI488Yr2/X79+bh+DCGi5ChgWAjSKOOeqqWeJX6JgteOQtHTG1oyNrs2aUOBzJ6gHfr+AKCYt0B37i/UoKT/rtEKxIJhxoMRx9ckDJXpMHtbFbsClVCqQxsIfFGQUZrOZfwlktn//fgCw6d/mSyUlJX5RpaYz+HhnNX4ucl6O9bIR0Zw5IwpCTU0C/rX+tMv7j+qrwdTRcTKOiALF/mIdNuxwXN3TnmhtCBZMT7IJcv73yznJ6x6lcpRiWFRmwLpvnJ973HRJPAMw8qju3bvjggsu8OkYpMQGPl9zRhRIrhjZxek2CgWQ1TfSC6MhIn+z96jzEuCOpMSzAl2wkpI631bOyJh2s0/x0WHuDsmp3PxaCELH1/hdrdZIFOxYxo9IJEvzaWfGDXRexYqIOid3U5qjI/hnOVhZCmhISW3UqJWYNrrjHnnuBHti1emaUVJhRGqCGkXlBpSUmWAGkNZNjQi1a9UaiYId/woQiWCv+XRbA5LDRVWxIqLOKTbK9SbAYSHosHIdBQelUoGckTGi/taEhykwdmAUJmRE2V2v5Uqw54rDJw1Yv73apgDJ9oP1CA9TQBWqcFh9UqNW8jNP1AaDMyInpFTQOnzKgA+2VyI+Ogxp3dTolcjKUkTBJKtvJLb+6FoT4WhtCH8vgpylhHxufq1NUBWlUWJk30jERYWKrlooJdhzx67DHafyGsQUt2HVA6J2GJwROSG1gtahUgMAA7YfrIdGpcS0MR2nnBARtda9q8r5RtTpyVlC3l6wZ0+oEhg7IALn9AK6aEKw59fzLvddE0NvElBSYWRBEKJWGJwROeHOYmW9ScD67VVsmkkUJPYccb0gyPA0rYwjoUAmZwn51sHe4ZMGuzNdAHD1hb//rRIEM/KP6eDp6S0WBCGyxVWYRE7IsVjZUUUrIuo8SipNLu2nClWgdxJnD8gzLMFezsgYzMqOQ7TWdm1ktDak3UXEkgqjxxpZt8aCIES2OHNG5ERqghpatRI6o+t/pCwVrZi6QdS5qUNdWzN21biuXG9GXiE2bdIbM1paFgQhaoeXK4icUCoVGNrL/XQjpm4QdX5D0qSlL0dplEx7Jq+zzKQN6aVFWmJ4hxcGtOGev1iQmqDiRQmiNhicEYnQ/wL3Z7yYukHU+RkbpW0/bmAEAzPySwqz54Om0BAGZkRt8WyRSISSCqNb+0drQ5i6QdTJCYIZX/54TtI+tQ2cUSf/dN6NVH6xzKylT9QOgzMiJwTB7FYFNgAYnKph6gZRJye17QbgXtNqIk/yRrZHbARLHxC1xeCMyImWilXuXd07UKJntUaiTu7wSYOk7RWKlqbVRP4oNUGNEA+fJfZKYkYJUVsMzoickKOQh6VaIxF1ToJgxs9F5yXtM25gJEJD+WeY/JNSqcAFXcM8dvzQECCtGysYE7XFvwpETsiV2sFqjUSdl5QZdgWA8YMiMSUzxqNjInJXhMZzabdXjYlluj9RBxicETmRmqBu17DTFazWSNR5Sbn4Mn6gloEZ+T1BMKOkzDMZH/26qzC4V4RHjk0U6Hi2SOSEUqlAzsgYt47Bao1Endvuw/Wit60zcP0p+T851lt3JCZCidl/6Cb7cYk6CwZnRCKkp2gwfpDrC/cvy+zC9A2iTspkasbJKvENzmK0rFBH/s8Tqfj9uquw4MoLZD8uUWfCvxBEIgiCGfuO6Vzev6BUh4xUrYwjIiJ/8dXP0nqbpSVyFp38nxyp+AoAEeEK9E/WICezC1Qqto4gcobBGZEIxWVG6E2uX0U8eMKA9BIGaESdUXW9+N5mYSFALwZnFAAs662l9O67eHAkeiWGo0EvIFKjRGqCmlkjRBIxrZFIhKJy9xdFf5ZXy15nRJ1QVwmNpPt2D+fJKgUEqeutozRKXDy4C9ISwzGklxZpifysE7mCwRmRCHL8edEZBfY6I+qELhkaLXrb+C6e6xtFJLf0FA1mZceJqlj8x1EsjU8kB6Y1EomQmqgCDrp/HPY6I+p8ztQ2id42rRtTGimwpKdoMDA5HCUVRhw+acAvxTrojL//LYvWhiBnZAzSUzQ+HCVR58HgjEgEucoJs9cZUecj9qKLKlTB9WYUkJRKBdISw5GWGI4pmV1QUmHkujIiD2FwRuSEIJjx5Y/SqrF1RAEgJU7l/oCIyK9U1YubORs/KJInsRTwLIEaEXkGL+MTOVFSYZRUrcoeM4DSKpP7AyIivyEIZvxQUOd0O41KiQkZ4temERFRcGJwRuSEnOvEdhTUy3YsIvK9j36oQqOIazej+0dw1oyIiJxicEbkhJzrxH49Y0RTE4uCEHUGB07oUFBqELUtu2gQEZEYDM6InLA04pTL3qMNsh2LiHxDEMz4fG+t+B3MjM6IiMg5BmdETkhtxOlMTb3769cocAiCGUVlBuwv1qGozMBG5J1ESYXRppy4M2oVUxqJiMg5VmskEsHSiDM3v9bt4iCxUfLNwpF/KyjVt/vMsCdQ5yB1LapRpnYcRETUuXHmjEik9BQNFkxPwg2T4qBy47JGVDiDs2BQUKrH+u1V7YL5Ol0z1m+vwrf76ziLFsAKSs9L20HBmTMiInKOwRmRBEqlAn2SNLhqXJzLx/jyJ56Ud3aCYEZufq3Dbb7dX4cXPzmDglK9dwZFsmlqEnCo1Chpn7RubD5NRETOMTgjckF6igYTh7jWs6hO14ySCmkndhRYxPbGq9MLWL+9igGaD7myJnDPEWlFfdRhCvRKZHBGRETOcc0ZkYviolz/+sjZO438j9T3Nze/FgOTw9kHy8tcXRNYUimtmfz00bF8b4mISBQGZ0QSCIIZJRVGNOgFNBiaXD6OnL3TyP9ow6WdiNfpmnGsTI+K2kacKG+EKkyBob206J3EgM1TDpbo8OEP1e1ut6wJnDgkGhMyojp8/dWh4t+T9JRwZKRq3RorEREFDwZnRCJ1dJVdoZDevihKo0RqAlOcOjOFWXpA9e7/bAOFX4r1UIUqcNW4rqzsKCNBMOObX2rwQ4HO4Xbf7q9D/tEG/HFUbLvXf0iaBr8UO09FVSiAay50fX0qEREFH16+JxLBXuU9V/rKNjUDhacMtrc1CdhZWIfP99ZgZ2EdmpqY9hjIzkvof+WIqcnMNWkyKijV418fnHIamFnU21kT2CdRAzETmteM78qZTyIikoQzZ0ROCIIZW3bXONym7QxaiBJotnN+rje1nPBdc1FXRKiV2FFQj1/P2BYI+XJfHcYNjMSUzBg3R0++IHfaam5eDdekuclygcUVW/bYvv5f7quFs7ohqlAFBnHGk4iIJGJwRuTEtoP10Jscz4SYzcBlI6IRGR6KPUfqUVrZ6PS4G76vhr3zO7MZ2HGopSIcA7TAkxKnkvV4dXoBJRVGpCWGy3rcYCEIZny2x7XADAD0RgHFZUb07h6OrT/WYNdh5z3OTE1mvmdERCQZ0xqJHBAEM3Yfrhe1bWR4KKAwiwrMANgNzFrbWdjAFMcAVFolrZqfGPUiSvNTx0oqjDjvZveKr38+h//9UoudheKbT7MqKxERScXgjMiBkgoj9CZxC8u04Qps2uk4/VEqsxnYe1RaTyXyPU+clNfpXa8OGuwOnzQ438iJ09WN+O6AtO8iq7ISEZFU/MtB5IDY2QqNSgGzYH+dmTtq6jljEmgq68TNnkpx5BQbl7tCEMzYddj7FzjCQhSsykpERJJxzRmRAw16cYFR/wvCcaJC/lQ2AIiNCvHIcckzBMGMHYfEpcJKcaLChJc+OY3YqFCM6x+JPhdoWCBEhG8PnPPJ48Z3CeH7Q0REkjE4I3JAJ7IkepQmRNQaMldk9Y300JHJE4rLjGj00GRnzXkBNedNOH62pSfa0F7hmD66K0JDfZsEIQhmFJUbUFJmghlAWjc1eiWqfR6cCIIZPxT4Ji04ITrMJ49LRESBjcEZkQPnRM6cndM3IzMtAtsPyjtjEqKEz09wSZqiMu+lH/5SbMAvxacxpr8Gfxzlm2bHBaV6bN5VBUOrTM7tB+uhClVg+thYDO6plf0xBaGlEmKDXkDkb03dO/qelFQYPZJqLMaQNJbRJyIi6fxqzdmrr76KG264wea2Q4cOYc6cORg+fDgmTZqEt956y+Z+QRDw8ssvIzs7G8OHD8ftt9+O0tJS2Y9BwSlGKy6lMEYbgl6JamhU8n6lmoWWE8z/b+++o6M47/3xv2eLtkhaNYQkqpAACUQVVYAbMcXdQHAuLcV2HCf45+sSJ9/7vSb2jQucmMT2sW9+x7hcl5iLiY0dgm1CDxiDAIEpAlElAaqol62zs98/lF2rbNXualbS+3WOjqSd9plnZmfns88zz0O9R4Ox5zvuyL9gwmt/K+/x7TrHDjO7ecTOKjrw6Td12HGiIeTbfG1rJT7YXYPPvq3DB7tr8NrWSrcDdcvVW2KUSkBmCpMzIiIKXMQkZx9//DFee+21Dq/V19fjZz/7GYYNG4bPPvsMq1evxvr16/HZZ5+55vnzn/+MjRs34oUXXsCmTZsgSRIefvhhWK3WkK2D+i9/a63SU9u+ub9nRkLIY/B1gylJDhRXmXG6xIjiKjMkX6PjUlj5m9CHWkOrhLe+ruyx7UmSA59943vssG/PtaCw1Nit9V+uNGHPyUbsPtmIKxVmFJYasflALZo6ddTTZLRj84FanLnacTty9ZZ4f14ia7yJiKhbZG/WWFVVheeeew75+flIT0/vMG3z5s1Qq9X4/e9/D5VKhczMTJSWlmLDhg1YsmQJrFYr3nvvPfz617/GrbfeCgB49dVXcdNNN2HHjh24++67Q7IO6p/axjjz/byKLkqBEQPbBpodO1SHJbMT8NnB0HWp7+4G09ms6/x1M04Wt8DU7nuEGK2AO6clYuxQfnMvh+EDNTgg03NOFfUiThW3YMKI8D+nuPd0I+x+fg/wt/x6jBnqfwcmZ6+Z8Pf8+g6Dv/vTZPjTb+pwIrUVIwdpMG1kDIYnaxCrU6C5B2vQls7me4+IiLpP9pqzwsJCqNVqbN26FRMnTuww7dixY5g+fTpUqu9zyJkzZ6KkpAQ1NTUoKipCa2sr8vLyXNMNBgPGjh2Lo0ePhmwd1D+VVFlgtvm++5w+OrrDTef44dHIyw7dzfHQpKgO/7dv1nX4fMfEDABazA5sPlDrtpkXhZ8g81X170cbIEmOsNaoSpID3xT6n4BaRQf2Fzb5Na+zqWT7xCwQlyst+MfxJrz013LsOtmI8em+n3lLiQ9NbeecMdHIGR76Z+yIiKj/kL3mbO7cuZg7d67baZWVlRg9enSH1wYOHAgAqKioQGVlWxOetLS0LvM4p4ViHd3hcDhgNAbelCccRJGD13bH5Ur/Bq4V3VQfLMiNx7UbFlyvDX68q2u1VoxIaauZc964+mPLwVpkPzCYzat6mNEsb7NSmwjsL2zC8cvGDs3/BLQNy5CbEY2ZWTFB9fC471RDwL2TflPYjJtzDF7PR0ly4Otjoal1djjamlT6olYCQwdoUNUQ3PVaIQBzJ8YHtQ4iIgo9m80m+z25w+GAIPh3PyZ7cuaN2WxGVFTHWgONpm1QT4vFApOprWbA3TyNjY0hW0d32Gw2nDt3rtvLk/zK6/x75tDTfBmp2pAkZ+evmzEiRQtJcmB7QYPfy4kScKnChNGD+U1+T6ptlv/LkH2nuzYBdACoa7Zj18km7DrZhFljYjB/cnzA695xogHfnmsNeDl/zsfSG5YebYIIoG3YgxDk0z+ck8QvQoiIIlBNTQ1qamrkDqNLruFJRCdnWq22S6ccFktbz3V6vR5abVttgtVqdf3tnEen04VsHd2hVqsxcuTIbi8fShUVFWhoaJA7jF7H7GezqiiV+xuy4SlRQGHwcRw+34LbJxpwrdbapSMEX7482sDkrAe1javlX/M9uTlrlQJJ0E4Xt/hVG+XJ5m/qsHiW4PGZLLl6V9Sou59UKQVgyZwkPmdGRBShBgwYgOTkZFljuHTpkt/zRnRylpqaiurq6g6vOf9PSUlxNderrq7GsGHDOsyTlZUVsnV0hyAI0Osj46a4/fN25B9JcvhdAzJsoMbt6yMGahGlEmAVg/9afu2n5Zg2KvDn2BqNEkRRkn2Q4r6s/ZhbjUZb2AagDodvz7Vg7niDX+fH9mN1OHwhuGYhoh3YfKAWD9zkPpmRq3fFJrOEvOxoHCoKrEbwppxo3DY+njVmREQRTK1Wy35P7m+TRiACOgTxZtq0aSgoKIDd/v3dzuHDhzFixAgkJSUhOzsbMTExyM/Pd01vamrC2bNnMW3atJCtg/qf0hsWWP1snTbDS9IUqns2u9RWg9Yd+Rfl6TmwPzh7zYTX/lbhGnNr13ehHYS8J3y074bPeTZsrwo6MWvv70fq3XZQcv66PM8ExOuVWJCbgKzBWt8zAzDolXjgpiT8YGICEzMiIgqpiE7OlixZgpaWFvznf/4nLl26hC1btuD999/HL37xCwBtbTdXrlyJ9evXY/fu3SgqKsKTTz6J1NRUzJ8/P2TroP7H3+ZVowdpPdY6lN7wr7fHcCupDKzXRo6b5h9n5yxNMjXFC5XSapvXcci2F9ShvC74ZyfbM1kklFR1HFz9zFUjDp+XJzlLT22r/V52ywD8cFYCojo1NojRCrh1vAFLZiXiJz8YgCfuTWUzRiIiCouIbu+WlJSEd955By+99BIWLVqE5ORk/OY3v8GiRYtc8zz++OMQRRHPPvsszGYzpk2bhnfffRdqtTpk66D+J1rj3/cWM7M815o1B/h8WLhcrLBhx4kGv54tOnvNhO0FDR2ebTPolVg4JZ43o+1IkgN/zw/dWHZy+/xQndtxyERRClvCdKXKjIy0tpoqSXJgW35dWLbji0oB1ziFADAuPRpjh+ldTVVjdAoMT9awhoyIiHqE4HA4+LV4iJ0+fRoAMH78eJkjaVNaWhoRvdT0JpcrTfhoj+8u61fNTUJmqvuk5VBRE/5xPHI6h/DVO5+vbvo9PSfUH10qN+Ev+/wb0qC7orUKtJp7rlbulnExuG1CfIfXvj3XiB0nwtNUc0K6DotnJQEAiqvM+GC3PNcod/tNRER9R1paGgYNGiRrDIHkBhHdrJFILhfLLL5ngvcxraI1oRnYNlS+PdcCUXR/s+9PN/2fflPLJo5oS2L/d39wiZkuynstTNZgLZ5ZPCikg5n7cvBsS5fje/RC4F3m+8ug//79IVcvjWoVcMu4OFm2TURE5E5EN2skkoMkOXDUz040vDV/jNVHVnIGAEcutGDWWEOX10tvWHx20y85gP/+shL/3z1pXufrywIZBNwTjUrA0/en4VqtFc1GO5qMVlyutMJsAwYnqDF/chyiotrOnQW58fjBBAOOXmpBfbMdNU02XKnyb/y9QIkScLnKhFFpbT1a7TjRgPrW8CVNGSnfNyWUq5fGRXkcm4yIiCILkzOiTq5UmmH3857UIXiuSRqerIFeo4DREjkdRhw424RkgwqZgzo+X+Tv83G1zXaYLSK0mv536ZAkB7YeDv65qBlZ0VCpFBjhSk6iMSfH8/wqlQJ52W0J9e6TjWFLzgBg4946TBhhxh25hqDGM/PH4fPNrmfOhidroFUD5tD2O+KRAGApm+kSEVEEYrNGonZEUcKWQ/7XjLR6aY6lUAiYkB4ZY905mazAx/vr8NInZTh77fteHFst/nde8sGe/vn8YklVaHrfHJ7iflw8f4zwMKZeqDgAnCw2Yt1nlWHdDgBcKLfAam077xQKAdqo7n8cZQ3WYunsRKi9VFYrFUBqvBLLbk7Amn8bzMSMiIgiUv/7+pvIgx0nGgKuLfCV1IwarOn2+GThZHd0HAw4kOfjKupFSJIDoihh58lG1DXbkRirxLyJ3zfH64uKq/x7DtEXb88p+pKeooGAtiSqL9h+vA5KpRJF14xoNndvHVmDtVh2ywAAwJihOhRXm1FSaUGD0Y54vQojUjRIT2Fvi0RE1DswOSNC9xIzwHenH4Ijsm8I/55fh+zBg3DkQmD7/vbOKlTUfj9K9+VK4OhFY4cb5b6mwejnqOQ+BPN8lUIhYHy6FqdKupnJhJlBr4RBr8D1Gv/aJx6/EljCqwCQnhIFQRDcfiGgUAjITNV57EGViIgo0rFZI/V7oih1+/kaX51+tEbQ82bumKwO7D1Vj+u1gT3s0z4xa+98mRn/+8++2ewxPgQdvKhVbc9XBePe6YlBxxEuC6fE48HbByJclVQjB2vw4x8MxKq5ybhrWmKfrqklIqL+ickZ9Xtbj3RvMGGDXunzRjuQWhK56ti+ORvaQYbPl5nxwa5qfHm0zvVMUV8QimZxkzP0Qa9HpVJg1pie62LfHzqNwtVEVqEQcH9efFi2o1XzI4uIiPo2Nmukfk2SHCi8avI9oxsLp8T7vNEenqxBlEqAVfT9lNAt42Ox73T3B/zVRbV1+BGocDy/VFxtRXG1FUcvtiV+agWg0whIjFUhOU6NeRPjoFIpUHrDghaThBidAsOTI/e5IEly4Pjl4Mf8GjM0NB3EOAcTD3ePir4IAFbcmoSMVG2HYzchPQaHi1pRXhfa7hfHj2BzRSIi6tuYnFG/VnrD4ne3+e3NGB3td29v/uQbBr0SN+cYMDA+Cp8drA04pqWzE1FeZ8VBmW/WPbFJgM3kQJPJhpJqmytp62zccC3un5EIlSqyakhKb1jQHORAyf7UtAZi/uR4zB1vwLaj9Si8aoJNhkrKW8bHYuQg9++DRxamYOM/a3ChLDTPxykUQGYKkzMiIurbIusOiKiH+Tu+V2fZfiZmpTf86349N7OtudvYoTqMHar1OX9nGrWAzNTAl4s0Z0rNeHFzOXacaJA7lA5agkzMAP9qWgOlUilwf14S/mPpYKyamwStOqSr90qpAG7O6TqgeXvLbxmA/7MkFaoQPBo2bWR0xNasEhERhQqTM+rXmrqRnBn+1QTPH/7e1CfFfn9X3Z3HtE6VGJGeovE6zlNv8u25lohK0ILpYdGgV7qexwoXZy+F985MCts2Ohs1SOtXslTRIEIMQa2ev1+IEBER9WZs1kj91j+ON+BQUeDNAEcEMGaSvzf17ecbPiAK568H1hTManNAoRBwX14iPv2mLqBlI9Whcy2YO94QEU0chyZF+T3v4EQVxqXrEa1RIvZfTRl7qsZn7FAdHrgpCX/Pr4fJ2vGLAW2UALvdEbLmj9NH+dcpybfnuv8cpZNGLYS0SSgREVGkYnJG/dL//rMG57v5LIzZj849nIYna2DQK73W0HV+Fmn66Bjs+K4poJiGDWyreRs3TI/yMVbZO4oIBQeAo5dakJftvelcT7h6w7+eVlQK4KH5KbI2vxs7VIfswVoUV5tRWmWFA8CIgW0DMReVmbH5QG3Q29BpFEhP8Z0siaKEi+XBD959z7TQNwklIiKKRPJ/JU3Uw06XtnY7MQMAjcr/m0SFQsDCKfFe5+n8LJJKpcDMrMB69Zs+Ktb19/zJ8Vg6OxG6qN5/M1vfLH9X/JLkwOeH/UtoRg32r6lfuDmbOc6dGIcfTIxDRprW9UzjAzclBX1u3DM9wa/9PHop+C8JhiSpMS49Ouj1EBER9QasOaN+RZIc2HKwe+OaOeWkB9bxhvOGeHtBQ4caNINeiYVT4t0+i7RwSiJKq62oqHc/2HN7edkxXZr+5QzXY8xQHf6ytxpXqkLbnXlPSoiV5yE6SXKg9IYF564ZceSC/+PADYiN/EtqW83aIFfNmuRw4PhlI4x+DJju7Zx1J9jkWikAD84bGNQ6iIiIepPIv5MgCqF9pxuDHtdLLQSeMDibmgUyrtcv7kjFxn03cMFLs7CswVosyI13O02hELD8lmS8uLk84HgjxZUKc483azxV3IK/H22ELYDmq046jfy1Zv5w1qxlprYlWYOSNF6bO87MikHWEG3Az89ZulGG7S2ZkxQRNZFEREQ9hckZ9RuS5MCBwuCbWbX6UcPgjkIhYERKYLVuy29NxpmSVvz9aAMs7brk16gF3D09HuOHe2/upVIpMGtMTK99Bu1ihRVWqx1RUT1Tg/bW15V+1VZ6EqPtnZfU7tTu+iJJDhRd87/Wsb1gtktERNSb9c47CSIPnM3R3NVOlVRZgq41A4LrVr07xqVHY+wwfUC1bu3NnxwPSZJw+Hz3bpTltvNkI+6alhj27bz2t3I0tAY3nlmsvveOZdCd2l1viqvNsHQjz12Qa8CM0bGsMSMion6JyRn1GWevmbD9WD2a2o0tZtApsHBqAsYO1aG4Ovhe4zr3rNhTulPr1t7CKYkQIODQ+Va/l1EqgJR4NcrruvfMmlLR9mPtfkUUAKCuBzoF+fpYbdCJWV/o7j3Y86y9Yxf8P9ecDHolEzMiIurXmJxRn3D2msntMzNNJgmbD9TigZuSEIrbvc49K/YmC6YkQFAIPps4DoxTYd5EAzIH6aBQCLBa7fi6oBZF16wQpbaaw+yhOoxK02FYchQuV5uQf64VRqsEg06JMUN0iI9VuWpdzBYRnx+uR32LHQkxStwzPQ57TzWh4LJ/PWYmhrlTEFGUkH/BFPR62N379yTJgQvlgfeI2pvfX0RERKHA5Ix6NavVjh3fNeDYJe8311sO1mLG6MC6p2+vrzwDM39yPOaON2Brfi3OXLVAatfO09M+RkUpcV/eQNyX536dWYOikTXI87NvWo0Ky25J7vDaXdOScKq4DDY/Kqt+MCG8HYIcPh/883jJBiW7e2+n9IYF9gAqIqNUAu7PS+z17y8iIqJgMTmjXiuQgaRFCThYFNgzV0MHqDF9dGzQz95EGpVKgcWzk3F/nufn88JNoRAwO8eAfad9D7b95bF63DcjCddqrd2O1duziOfLgq81+8XClKDX0Ze0mAJrInrr+BgmZkRERGByRr1UIIlZoAQAi2cn+OwJsbcL5fNF3ZHk55hgp0stOF3acTgAhQBkpKjQYgEECBicqMb8yXFue3V019tltAbISNWiptGCiobguomZPlrfZZy5/i6QTnMEdBxEnYiIqD9jcka9jtVqD1tiBgBzxsb0+cQsEgTT66XkAC5Vft/TSEW9DccuG5E1WItltwxwve4piW+1AKdLgz+HorUK3Dk1/D1J9jbDkzWI1SnQ7EcN2kw3g6gTERH1V0zOqNf5xMtguaGQntq7e9zrLYYna6AQ0OG5t2CdLzPjxU3XMT5dhxaTiIsV3etp0h+p8Uo8emda2NbfmykUAu6YmuB1YGvA+yDqRERE/RGTM+pVJMmBy1XWsK1fIQAjBsrX1K8/USgEGPRKNLSGtqt8UQJOXAn+OTJ39BoBQ5KisDgvAVoNL5/eOAe2/nt+PUzWjjVoKgVwX17fbzpMREQUKN5dUK9SUhX8WGXejE/X9pmOP3qDzDQNCi5F/uDYKXEK/OKONJ4bAXIObF1cbUZplRUOACMGapCe0nc62CEiIgolJmfUq+w+2RjW9d8zjc8P9aQFk+J6RXKmVCiZTHSTQiEgM1WHzFT2xkhEROQLkzOSjSQ5AvpG/cxVI8rqwvcMUR47JuhxUVFKaFSARfQ9r5wGJfFSSUREROHHOw6SRWGpEX/Lr4O13U35gcJmRKkE5GZGI2uI1jUWlSQ5cLHSiM++qQ9bPOyYQD7j0vURX3s2f1K83CEQERFRP8DkjHrcjhMN+PZci9tpVtGBw+dbcPh823SNGrCJoe3Rr7PZY/WYN4nNGeUS6U0bswZr3Y6fRkRERBRqbMNFPUaSHNh7ynNi5o7FFt7ETK0EfjAhIXwbIJ+iopTIGhyZPWR2HjeNiIiIKJxYc0Y94uw1E746UouW8Ha2GLBFs5LY0UMEWHbLAI8DRvc0XRSQPUSLO3ITWGNGREREPYrJGYXd2Wsmn4PR9jQBwNKbkjB2KHuQixTLbhkAq9WOV/9WAVP4+n3pIjFWidyMaMzMYocwREREJC8mZxRWkuTAloM9m5jpogSYrJ7bQqoVwG9/OIg34hEoKkqJ3y4dgv//qwpUNfgenFqtAGySz9lcsgapMCMrHq0WCTE6havTGSIiIqJIwOSMwkYUJXy0txJiADfPwUo2qHDbxDivNXWLZicxMYtwv7wzDR/vrcbFCqvHeWaNicHtE+NQesOCplYR12osqG+2obRahNgpNxeEtqES5k+OD2/gREREREFgckZh8Y+Cehw639rj250/xYBRaTo8cFMSthc0oMn4fe2LQa/EwinxbMrYS6y4bSD+cbwBh4q6diAza8z3idaIlLbORCZmxLimi6KEo5daUN9sR0KsEtNGsskiERERRT4mZxRyb22vQkUYB4v2JEoFZKa0JV5jh+qQPViL0hsWtJjYhK23WpAbjx9MMAScaKlUCuRlG3ooSiIiIqLQYHJGbkmSA+crWrH3RCNqmhyQ0Jb8GPRKTErXY2Z2LBQKARcrjTh8tgW1zTYYzejSnKwn3Z/XsedFhUJw1apQ78VEi4iIiPoLJmd9nMlqx6Y9V3GlrBlajYC80THIHKRzJTFmi4hPD9bgeq0IADDogAYjYBW7rssqAjVNduw61Yxdp5p7cje8UgjAD+ew50UiIiIi6t2YnPVhL753GPmFVR1eu1JZ53UZc8+3RgzKzeNicOu4ODZXJCIiIqJej8lZH+UuMYt0cXoFBsQqYLHacb3ed/vI//vDNA4STERERER9BpOzPshktfe6xKx973sAsONEA74917WXvvbzMzEjIiIior6EyVkf9D9bz8gdgt90UQLump6AccP0HV53JmqHilrgaFeJxvGqiIiIiKivYnLWB128Vi93CD4ZdAIWzUry2r39/MnxmDs+8G7UiYiIiIh6IyZn/yJJEt5880389a9/RXNzM6ZNm4bf/e53GDp0qNyhBSxap5Y7BI/UCuDpRanQavw79diNOhERERH1F6yC+Jc///nP2LhxI1544QVs2rQJkiTh4YcfhtVqlTu0gC26eaTcIbgVpxfwn/82xO/EjIiIiIioP2FyBsBqteK9997D448/jltvvRXZ2dl49dVXUVlZiR07dsgdXsAmZQ9EpPUsPyotCk/eP1juMIiIiIiIIhaTMwBFRUVobW1FXl6e6zWDwYCxY8fi6NGjMkbWPUqFgN/+eJrcYUAXBUzO0OL//jANK24bKHc4REREREQRje3LAFRWVgIA0tLSOrw+cOBA17RAORwOGI3GoGPrrkkj4/HUv03Aq5tOwfeIYZ4pACTHKVDbIkG0e54vRisgRqvEkKQozJ8cx27uiYiIiEh2NptN1ntyoC0vEAT/mrUxOQNgMpkAAFFRUR1e12g0aGxs7NY6bTYbzp07F3RswTAogDX/NhibD9ag6JrF43wqBZAYAzgcAprNgEYtYMpIPWZlG1w9I0qSA6U3LGhsseHcdTNsdiApVol5E5mIEREREVFkqqmpQU1NjdxhdMkzPGFyBkCr1QJoe/bM+TcAWCwW6HS6bq1TrVZj5MjI6Jjj0cQK1NTU4VBRA45dMkOUHBiUGIUleQl+d86hUAgYkaIFUrSYlBkb5oiJiIiIiII3YMAAJCcnyxrDpUuX/J6XyRm+b85YXV2NYcOGuV6vrq5GVlZWt9YpCAL0er3vGXuASqWCSqXATeMScdM4uaMhIiIiIuoZarVa9ntyf5s0AuwQBACQnZ2NmJgY5Ofnu15ramrC2bNnMW2a/B1rEBERERFR38eaM7S1AV25ciXWr1+PxMREDB48GK+88gpSU1Mxf/58ucMjIiIiIqJ+gMnZvzz++OMQRRHPPvsszGYzpk2bhnfffRdqtVru0IiIiIiIqB9gcvYvSqUSzzzzDJ555hm5QyEiIiIion6Iz5wRERERERFFACZnREREREREEYDJGRERERERUQRgckZERERERBQBmJwRERERERFFACZnREREREREEYDJGRERERERUQRgckZERERERBQBmJwRERERERFFAJXcAfRFNpsNDocDp0+fljsUAIAoinKHQERERETU46qrq1FbWytrDFarFYIg+DUvk7Mw8Lfwe4pKxcNMRERERCQHQRD8zg8Eh8PhCHM8RERERERE5AOfOSMiIiIiIooATM6IiIiIiIgiAJMzIiIiIiKiCMDkjIiIiIiIKAIwOSMiIiIiIooATM6IiIiIiIgiAJMzIiIiIiKiCMDkjIiIiIiIKAIwOSMiIiIiIooATM6IiIiIiIgiAJMzIiIiIiKiCMDkjKgPczgccocQUuHcH1/rdk5vP58/8fi73u5O7y5P++Hv9gKdrzvr7cmy7qlzi2Xd82UdrnL2tEx3yrJzrN1Zb6D762veYMvSn33qjvbr9jS989+8Vvs/X6RfP/zdRncEW9ahwuSMQqrzBdn5I0mS6zXn396WDXS6r2WDEYp9ci5vsVjgcDggiiLsdjsAwG63d9mGJEkdlvW0Xec6JElyLeNcn8lk8hlzd7cbimPobZ86x2K322E2m/2Oy7mss3w7b6/zNsxms8+ydM7XfhlJkmCxWFBWVgaHwwGbzdYl5s770X4b/my3c7m1X1/7fQ+0LDvve/v9tFqtrn0SRdFjXO2Pa+ffncvNGcfVq1dht9s7rDfQuCRJgiiKPsva3TH299zq6bJ2t0+BlLXRaASADtsrLS3tcgz9jctisaC8vBySJMFms3k8bzvvuyRJrlg8nfO+ytLbtcnf95Pz7/bnXmlpqaucO8ff/hxoX76dy9rbNQAAWltb3R5jZ1k6YzAajSgvLwcA1NfXAwCMRmOXmJ3vl9bWVr+2625Zu93u9vri6zh5K8urV6+6ytJ5npWUlECSJNjtdjQ2NkIQBI/75JzPHedxaH8OFhcXA2j7DG1paYEgCK598/Q+d/eeKCkp6bFrdefztfNPIJ977d87kiSF9Frt7Rh7u1b7E5e/1+pIKWt/Pxed76lwUYVtzdSnORwOlJWV4ciRIxg5ciQOHz6MGTNmYOjQoTh8+DDGjBmDkydPIicnB8nJydi1axfGjBmDq1evYvr06diyZQumTZuGpKQkHD58GFlZWbh8+TLGjx+PESNG4L333sP06dNx5MgRTJs2DTk5Odi2bRsmTZqEXbt2Yfr06UhISEB+fj5Gjx6N4uJijB8/HsOHD8f69esxc+ZMJCYm4siRIxgzZgzOnj2LGTNmwOFwoKCgAKNHj8aBAwcwc+ZMZGZm4siRIxg9ejTy8/Mxffp0JCYmdohr3LhxGDFiBLZu3YrJkycjLi4Oe/bsQVZWFq5fv46ZM2di8+bNyMvLQ3Z2Nt5//33MmDHDFdfly5dRXV2N2NhYFBQUYOLEiUhLS0NZWRmGDBmCMWPG4NChQxg1ahTOnDmDESNGIDEx0bXtAwcOICcnBwMHDnSVZWlpKbKzs2EwGLB//35MnToVgiBgz549KCoqQkNDA9atW4ddu3Zh0qRJSEhI6BDzjBkzYLfbcezYMWRlZeHkyZNIT09HSkoKzp07h8mTJ2Pfvn3IyclBSkoKdu3ahaysLJSXl2PKlClobm7G8ePHkZmZiaNHj2Lq1KnIyMjAwYMHMXbsWOzcudPtMfZVlmPGjIEgCCgsLEROTg7OnDmDr7/+Gnq9HsuWLcOoUaOwc+dOjBkzBqmpqR3imjp1KjZs2IDbbrsNoiiiqqoKQ4YMQUFBAWbPno2MjAwcPnwYGRkZiIqKQmFhIbZt2wabzYYXXngBoigiPz8fkydPhlKpdJVlU1MTFAoFdDod7r77bnz++ecwmUwYMGAAzp8/j5KSErzzzju4du0axo8f3yHm++67DxkZGRAEAd999x1Gjx6NU6dOYdeuXdDr9ViyZAkGDx6M/Px8TJ06FadPn4ZOp8OBAwdw48YNvPzyy664Ro8ejSNHjmDChAlISkpCcXExMjMzcfDgQUycOLHLMfZWlkuWLMHGjRuh0WiQm5uLc+fOuc7R0tJSXLlyBZs3b8aJEyeQm5uLM2fOuOKqqanByy+/jPfffx/3338/ampqUF5ejgkTJmDv3r0YNWoUdu/ejcrKSigUCuj1ejzwwAN47rnncPXqVfz1r3+FXq/HqVOnMHbsWJw9e9avuK5evYrLly/jgw8+QHFxMXJyclBYWNilrE+ePInMzExotdoux7i5uRlFRUUYN24cTp8+je3bt0On02HFihXIzMyE1WqVpaw//PBD1zWw/TlQU1ODl156CR9++CHuu+8+j2VdVlYGjUYDrVaLuXPnYsOGDbh48SI2bdqE+Ph4nDhxIqCyvnLlCq5evYrf/va3mDRpEgwGA7799lvs3Lmzw/vlzJkzyMzMhEKhwO7du3HlyhXcuHEDzz//PJRKJU6dOoXMzEycPn3adc7/5Cc/wZAhQ7Bv3z5kZWW53sfO69q0adMgCAKOHj2KUaNGobCwEMOGDUNKSgqOHz+OCRMmoLCwEKNGjUJ8fDwOHjyInTt3us6zjz76CBaLBWq1GlqtFsuXL8fvfvc7FBcX44svvkB0dDTOnDmD4cOH4/Lly/jyyy+h1+uxcuVKtLS0oKamBhMmTMBXX32FO++8E1arFSUlJfjqq6+g1Wpx11134fPPP4fRaHRdA0pLSzF16lTExsZi8uTJOHv2LG7cuOE6xiUlJRgwYACuXbuGO+64AyUlJSgtLUVqairKy8uRl5cHs9kMjUYDjUbjirmkpAQ33XQTYmJicM899+Czzz6D2WxGcnIyioqKUFJSgunTp8Nut0Oj0UClUmHlypWuZT/99FMMGDAA+/fv73Jdq62txXPPPYfTp09j1KhRiI2NxaFDh/wqy5KSErz99ttYv349zp8/j2HDhuHatWvYtGkTXnjhBVy+fBl5eXmwWCxu49qxYwfq6upQVlaGOXPm4KOPPsI999wDq9WKoqIiHDx4EJcuXcKFCxdcx+n+++9HRUUFzp8/j7/85S8oKirC8OHDodVqcebMGXz11VcQRRErV67Ejh07UFVVBY1GA6vViscffxxr1qzBhQsX8NFHH+H69evdvlYfOXIEubm5XT4jXnrpJdjtdhw9ehSZmZkoLCzs8Fmek5OD/fv3u/089nb9WLFiBd555x3odDpMnDgRFy5cwI0bN6DX63H9+nVcunQJn376KQoKCjBlypQuca1du9bttXrPnj245557sHbtWrS2trqO04oVKzqcP9HR0Th58mSXa+bKlSs9xlVWVoaLFy92uK65u1b7W9adP39efPHFsJS1P9dq5+dXQkICJEmCShX6VEpwtP96iMgPBQUFWLNmDa5evQqbzYaYmBi0tLQgKSkJRqMRJpMJsbGxaG5uxtChQ1FRUQFRFKFQKCBJEvR6PYxGIxITE9Hc3AybzeZ6bdasWSgsLERjYyM0Gg0sFgtyc3NRVlaGqqoq6HQ6mEwmxMfHw2g0wmq1ul6bOHEiLl++jJaWFmg0GthsNkiSBK1WC7PZjJiYGLS2tsLhcECtVru2a7PZYLPZXOtxF9e8efNw6tQpVFVVIS0tDTdu3IAoihAEAQ6Hw/V79OjRqKmpQV1dnWt9zt/tOcsCAPR6vevbosTERNTV1UGlUkEURQBwxdq+LJ3bc07zxFmG7mJuX0bO49V+fc4YUlNTUVNT02VZi8XSYRsDBw5Ec3Nzh31OSEhAS0tLt8rSE6VSCbvd7jYu5+/25eecv/3xb1/+kU6lUrm+qXOWa/v4vR1jX2UZjPbngvPbVecxjgTdOca+yjoqKgpWq7XHy9pJrrL2dZ3pDuf70tN1zfltu8FgQFNTk9sYnMcjHJzbi4uLg8lkCtt2IkU4jrG/2n+e9GYqlcpV0+Pus1yu64dz3VqtFqIodrh+9MR1KxzkLGtBEKDRaPDBBx9g7NixUKlUUChC2xCRyRkF5IknnsDXX38tdxhERERERLIQBAEff/wxJk2aBKVSGdJ185kz8tuGDRuwfft2ucMgIiIiIpKNw+FwNTM2m82uWrtQ4DNn5JfGxkZs3bq1V1Z/ExERERGF0meffYaWlhY89NBDGDt2bMjWy2aN5Jfq6mrMmzcPZrNZ7lCIiIiIiCLGggULsGbNGiQnJwe9LjZrJL8MGDAAw4YNkzsMIiIiIqKI8o9//APvvPMOAATd4RhrzsijyspKHDt2DAcOHMC+ffvw6quv4v3338c///lPuUMjIiIiIpKdQqGAQqGA3W7HoUOHEB8fD0EQur0+Jmfk1qpVq3DkyBG5wyAiIiIiingxMTEoKCgIej1s1khdPPzww0zMiIiIiIj8FBsbC6PRGHSzRiZn1EFLSwvOnj3r+n/w4MEyRkNEREREFPmeffZZ6PX6oAelZnJGHQiCgPr6etf/NTU1MkZDRERERBQ5EhMTXc+UKRQKJCYm4o033sDtt98ekvVznDPqIDo6GtnZ2a7aM4vFAkEQOL4ZEREREfUriYmJuP3227F582YAbcnYu+++i4SEBFy7dg1Dhw6FwWBAdHR0yLbJmjPqYvHixR3+1+l0GD58OKKjo6HX6z0uFxUV5XGaQqGASuX5uwCdTudxmiAIXpeNiYnxuqy3mLVarcdpgPd98tUTz+TJkz1O02g03d4u0Nau2RNv1enOHoU88VUeSqXS47SoqCivx8nbPun1ep/77I238sjIyAjLegFArVZ3a1qwvB1DX2WZmJgYjpCg0Whw++23ezxHBg4c6HX5YOLydt4FS66y9nZ9iYqKwsKFCz2Wta/3UrjOAW/XB394O45jxozp9np9xeWtrH/6059i/vz5Hqf7Kktv216wYIHHaXFxcV7XG4xgjtOgQYO8Tvf1PvfG22emL74+UxMSEjxOC+e12tfnnq+4vfF2bfJ2TguCgHnz5nk8D4K9nsr1uRjMPYa397HZbMbevXtd/0uShDfffBNpaWmYPn060tLSQpqYAeytkTqRJAmSJOG5557Dp59+6no9MTERb731FgYNGoRFixahurq6y7JxcXFobGx0u960tDRUVFS4nSYIgqsLUneSkpJQW1vrMeaEhIQOTTHbUyqVHtcLtN18Nzc3u52mUqngcDg8Lq/X62E0Gj2u21vcGo0GFovF47Lx8fFoaGjwOL27fMXsqzxEUfS4bHZ2NoqKijwuKwgCbDab2+nB1M4qFIqgH77tS1jT3XNY1qETFRUFq9Xqcbqva3m46HQ6mEymHt8u9Q3e7k94/QitnizrkSNH4ssvvwzZ+jpjckYdSJIEo9EIi8WCNWvWYPfu3a5pSqUSzzzzDN5++22vyRIRERERUV8jCAKmT5+ODz/8MGzbYLPGfq6goADl5eWu/61WK0RRxKVLl5CamtqhaYXdbse6deuYmBERERFRv+NwOPDwww+HdRvsEKQf2rhxI3bs2IHa2lpcuXIFycnJMBgMUKlUkCQJtbW1bpstEhERERH1R9HR0Xj55Zdx8803h3U7bNbYj4iiiPz8fDz44INyh0JEREREFHF0Oh0SEhIQHR2NH/3oR7jnnntgtVqh1WphMBjCvn0mZ/2Is7OPnJwcuUMhIiIiIpLdpEmTcOrUKUiShBEjRuCLL75w9V5tt9tdHYo4O7ALNyZn/dD48eO99opFRERERNRf/PznP0dOTg5uvvnmkHeNHyh2CNLP/M///A8TMyIiIiKif3n33Xcxa9YsaLVa2YfnYXLWTxw9ehR79uxBRUVFj1TJEhERERH1BpIkYeXKlT3WdNEb9tbYh3388cfYuHEjSkpK3A4eLAgCBEGQ/RsCIiIiIiI5Xb9+HSaTSfZmjUzO+qilS5fi1KlTXudxOBwcnZ6IiIiI+j2LxYKamhrZkzO2b+uDXn31VZ+JGRERERERtdHpdIiNjZU7DCZnfdHFixflDoGIiIiIqNf4/e9/j8TERLnDYLPGvigSsn4iIiIiokghCAIyMzNxxx13oLKyEjt37oTdbkdWVhZWr16NWbNmyR0iAI5z1idt3LgR//Vf/yV3GEREREREEePtt9/GhAkToNPpoFQqoVJFXj0VmzX2QcuXL8fMmTPlDoOIiIiIKGL86U9/Qnx8PCRJitjeypmc9RFlZWUAAFEUIYoi/vu//xuDBg2SOSoiIiIioshw7tw5/OpXv4JOp0NUVJTc4bjFZo29WEFBAc6fP4/du3fj4sWLePDBB6FUKiGKImw2G6qrq/HFF1+gublZ7lD7hHnz5mHgwIE4efIkRo8eDUEQsHXrVthsNrlD63NmzZqF8vJyNDY2YuDAgTh//rzcIfVZsbGxsNlsMJvN0Gq1MJvNcofUZ+l0OlgsFtf4kvz4DQ9BEFxlrVKpYLFY5A6pT1OpVK6Bex0OB2w2G9RqNURRjNiaid4oOjoara2tAOAqX15DuicmJgZbt27F4MGD5Q7FLSZnvdSyZctw/PhxucMgIiIiIopYWq0Wjz32GIYMGYKrV69i5syZmDhxotxheRR5T8GRT4sXL0ZhYaHcYRARERERRZz4+Hi8/vrrrj4Y7Ha7q4Y30rHmrJe5//77ce7cObnDICIiIiKKOJmZmdi6dWtE9sToj8hPH8ll0aJFTMyIiIiIiAA88cQTOH/+PNLS0qBUKpGVlYVt27b12sQMYM1Zr/Hggw/i4MGDcodBRERERBRRli5diueffx5KpRKCIMgdTlCYnPUSu3btwrZt21BdXQ2dTge73Y5z586hoaFB7tCIiIiIiGSVkZGBbdu29ZpnyzzpvXV+/Ywoirj55puxePFiAIDD4UBpaSkWLFggc2RERERERPIaNWoUlEql3GEEjTVnEWzdunXYsmULjEYj7HY7HA5Hl+pajrFFRERERP2dUqnEs88+i+XLl8sdSlCYnEUgSZJgNBoxZ84cmEwmucMhIiIiIopISqUSdrsdADB48GB88cUXMBgMMkfVfb23QWYfZrVaIYoioqOj5Q6FiIiIiCjiCIKAN998E2fPnsWcOXMwe/ZsrF27tlcnZgBrziLa1KlT0dzcLHcYRERERESyWrhwIcaOHYu3334bsbGx+N3vfodZs2ZBqVT26q7zO+s7e9KHiKKI48ePMzEjIiIiIgJQVVWFu+++G//xH/+BKVOmID09HXa7vdd3nd8Za84izNatW1FcXIz6+nrs27cPtbW1kCQJQFvSRkRERETUXykUCsycORO//vWvkZOTI3c4IcfkLAI8//zz+Pzzz2E2m+UOhYiIiIgooimVSgwbNgybN2/u9c+YdcZmjTL70Y9+hO+++07uMIiIiIiIegW73Y7HHnuszyVmAHtrlNXOnTuZmBERERERBeCVV17B3XffLXcYYcHkTEaVlZVyh0BERERE1GusW7cO9957r9xhhA2bNcqIz5gREREREbmn1Wo73C+//vrrWLhwoYwRhR87BOlh69evxyeffIKmpia5QyEiIiIiikgKhQLr1q3DO++8g6ioKDz99NOYNWuW3GGFHWvOetADDzyAkydPyh0GEREREVFEkyQJR44cwdatW/vcWGbe8JmzHvLiiy8yMSMiIiIi8oNSqcSAAQMgSZJrzN/+gDVnPaSoqEjuEIiIiIiIegVJknDzzTdDqVTKHUqPYs1ZmEmSBFEUUVVVJXcoREREREQRp3OzRaVSiT/+8Y+YMmWKTBHJhzVnYaZQKKBQKLB48WK8/vrrYP8rRERERETfi42NxYoVKzBhwgSoVCqMGjUKaWlpcoclC/bW2INOnTqFs2fPwmKxID4+Hnv27MH27dvlDouIiIiISFbR0dG49dZb8ac//UnuUGTFmrMeNGHCBEyYMMH1/+nTp2WMhoiIiIgoMrS2tuLOO++UOwzZ8ZkzGVksFrlDICIiIiKS3R/+8AfcfvvtcochO9acycDZJWhUVJTcoRARERERyeoPf/gD7rvvPrnDiAisOZOBQqGASqVCbm6u3KH0av2ta1W5qFT8DqensKx7Dq8fPSdcZR3K9Uba+aBQ+Hd7JggC4uLiXP879yPS9qevMRgMcofQp/zpT39iYtYO7wRkNHv2bAwfPhylpaUA2i6mdrvd7bwqlQqiKLqdr/00dxQKBSRJcv12N83TOnyt29+Y/d0nZzxKpRIKhQI2m61DrA8//DB++tOfdlneYrHg5Zdfxs6dOzuszzlP+zjdbVelUkEQBNjtdowYMQIlJSUwGAx46qmnsGHDBly7dq3L/jr/dveaczsAumxLqVRCEIQuZRPqslSpVK4P+OjoaDQ1NSErKwtPPPEEXnjhhS771D4ujUaDJ598Evfee2+XbTc1NeGhhx5CRUUFYmNjYbFYIAgCHnroIWzbtg0mkwkNDQ2w2WxdyvK9996D2WzGr371K3z44Yf48Y9/jA8++AC//OUvkZubiz/+8Y/Ys2cPRFHsEvNLL72EiooKWK1WqNVqxMbGoqmpCcuXL0djYyOOHj2K2tpaWK1WxMTEwGg0Ijs7G//+7//eYX/dHTt/zg9PZbl27VqYTCZkZGQgNjYWS5YswSuvvIJHH30UeXl5uHHjBlasWAGj0Yhhw4ahtLQUmZmZ+M1vfoO1a9fCYrHg6aefRl5entvjXF1djWeeeQaPPvoo9u/fD6vVilWrViE5ORmvv/66x/LyFpc/ZV1eXg6bzYaYmBiIotjlGDc2NsLhcECv13dY9sUXX8TVq1f9Kmt3f3e3rHNzc7F+/Xrs3bsXdrvdFZfzHAikrH/+85/j0KFDMBqN+MlPfoK0tDSv5eUrrrVr12LHjh2IjY2FTqdDXV0dHnjgAXz77bcd3i8ZGRkoLi7u8H5paWlBTU0NVCpVl3P+yJEjqKio8HjeerqeOq93kiTBbrd3iGv58uWoq6vDP//5T/zsZz/DV199hUceeQQHDx50nXtpaWlYt24ddu7cCaVS6Ypr5cqVqKurw4kTJzBixAg8+uijSE9P71LWoiiiqakJTz75ZIdrQF5eHoC26/kbb7yB1tbWLu+nztc9s9nc4UuNpqYmPPbYY11iTk9PhyiKMBqNePzxx7F8+XL85S9/cW1XpVKhuroaTzzxBB577DGMHDkSTz75pNv3cXp6OkpLSzscp+bmZtTU1HQpy8bGRhw/fhypqamorq7G6tWru8TVmSiK0Gq1rjKrq6vzGBcA6HQ6mEymDueB87fzb3eamprw85//HGVlZYiJiUFLSwvi4+Px5JNP4t1334XVasUvf/nLDtfqxx57DBMnTnS914K5Vttsti5l+c4777iW7Xxd8Pez3NP1Y926da7jFxcX5zq3Vq9ejdzcXNTX17uOsbvPrvbXD51Oh+bm5g5l6+k4ebo2+RNXTk6O1+t8IGXd+fOn/bKhLmt/PhdXrVoFrVaLF154Abfeeqvbc7S/Ym+NMjMajV0uqu60n9Z5Pn9u3M1mc4eLPdDxA8DThTyQpCCQaZ72qX2szhidv1UqFZKSkjyuz2azoa6uDiqVqssHlKfyaz/duR2DwYCmpibodDro9XqPx8ifdbffB0/7Fs6ydFKr1TCZTDAYDFCr1V32qXNcKpWqw7exnTmX1+l0rhu+uLg4GI1G17HwVJYAXH+3/+1czjmPu5id61WpVK7pSUlJsNlsrh9RFF03K+72t3MZBXJ+eIrL+brzd/t9AoDGxkZXedTV1SExMbHDsu3n9VTeer3eVdbObfkqL29x+VPWZrMZsbGxHo+xp2XlKmt/ykOusq6rq0NsbCwAuM5bf98vJpPJ4znv69rk7prTvoydx7h9XM59dR5vT+XR1NTkMS61Wu2a11dZd36/ONfvriz94Slmf7bb/jVv72N3x6m5udltWTrLw2azeY3Ln31yF1cw2l/Lnb978lrtriyd155gPss9xeV83fk70GPs7/Wj89/hvlb7W9adP3/kLmu1Wh2yc7kvYXJGREREREQUAfjMGRERERERUQRgckZERERERBQBmJwRERERERFFACZnREREREREEYDJGRERERERUQRgckZERNTD2FEyERG5w+SMiIjIi1WrViErK8v1k52djcmTJ2Px4sX48MMPvY5B6M7FixexbNmyMEVLRES9mfth44mIiMhl7NixeO655wAAdrsdjY2N2L9/P9auXYtjx47htddeg0Lh3/ed27dvx4kTJ8IZLhER9VJMzoiIiHyIiYnBpEmTOrw2d+5cZGRk4KWXXsK2bdtw7733yhMcERH1GWzWSERE1E0rV65ESkoKNm3aBAAwm8344x//iPnz52PcuHHIzc3Fz372M5w7dw4A8MYbb+DNN98EAGRlZeGNN94AAEiShA0bNmDevHkYN24cFixYgI8++kienSIiItmw5oyIiKibFAoF8vLy8OWXX0IURfzmN7/BsWPH8NRTT2HYsGEoLS3F66+/jqeffhpffvklli5disrKSnz66af45JNPkJqaCgB4/vnnsWXLFvziF7/A5MmTcfToUbz88stoamrC6tWrZd5LIiLqKUzOiIiIgjBgwADYbDY0NDSgtbUVzz77LO68804AwPTp09HS0oJ169ahpqYGqamproTM2UyyuLgYmzdvxlNPPYVHHnkEADBnzhwIgoC33noLy5cvR0JCgiz7RkREPYvNGomIiILg7BZfEAS8++67uPPOO1FVVYXDhw9j06ZN2Lt3LwDAarW6Xf7w4cNwOByYO3cuRFF0/cydOxcWiwUFBQU9ti9ERCQv1pwREREFoaqqClqtFvHx8Thw4ABefvllXLlyBdHR0cjOzoZerwfgeWyzhoYGAMBdd93lcf1ERNQ/MDkjIiLqJlEUkZ+fj9zcXJSVlWH16tW4/fbb8dZbb2Ho0KEQBAEff/wxDhw44HEdBoMBAPDBBx8gOjq6y/RBgwaFLX4iIoosbNZIRETUTZ988glu3LiBZcuW4cyZM7BYLHjkkUcwbNgwCIIAAK7EzFlz1nk8tKlTpwIA6uvrMX78eNdPXV0dXn/9dVfNGhER9X2sOSMiIvKhpaUF3333HYC2bu/r6+vxzTff4JNPPsG9996L+fPno7S0FCqVCq+88goefPBBWK1WbNmyBfv27QMAGI1GAN/XlG3btg0TJ05EVlYW7r33XqxZswZlZWUYN24ciouL8eqrr2LIkCFIT0+XYY+JiEgOgsNTI3giIiLCqlWrcOTIEdf/giAgOjoao0ePxqJFi7B06VJXLdn27dvx5ptv4urVq4iLi8OkSZPw4x//GKtWrcKaNWuwYsUKVFVVYfXq1SgqKsIPf/hDPP/88xBFEW+99RY+//xzVFZWIikpCbfddhueeOIJxMfHy7TnRETU05icERERERERRQA+c0ZERERERBQBmJwRERERERFFACZnREREREREEYDJGRERERERUQRgckZERERERBQBmJwRERERERFFACZnREREREREEYDJGRERERERUQRgckZERERERBQBmJwRERERERFFACZnREREREREEeD/ASL4jDTq1zrQAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Рассчитываем квартиль 1 (Q1) и квартиль 3 (Q3) для Close\n", + "Q1 = df_btc[\"Close\"].quantile(0.25)\n", + "Q3 = df_btc[\"Close\"].quantile(0.75)\n", + "\n", + "# Рассчитываем межквартильный размах (IQR)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Определяем порог для выбросов\n", + "threshold = 1.5 * IQR\n", + "lower_bound = Q1 - threshold\n", + "upper_bound = Q3 + threshold\n", + "\n", + "# Фильтруем выбросы\n", + "outliers = (df_btc[\"Close\"] < lower_bound) | (df_btc[\"Close\"] > upper_bound)\n", + "\n", + "# Вывод выбросов\n", + "print(\"Выбросы в BTC-USD Stock Data:\")\n", + "print(df_btc[outliers])\n", + "\n", + "# Заменяем выбросы на медианные значения\n", + "median_close = df_btc[\"Close\"].median()\n", + "df_btc.loc[outliers, \"Close\"] = median_close\n", + "\n", + "# Визуализация данных после обработки\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df_btc['Date'], df_btc['Close'])\n", + "plt.xlabel('Date')\n", + "plt.ylabel('Close Price')\n", + "plt.title('Scatter Plot of Date vs Close Price (After Cleaning)')\n", + "plt.xticks(rotation=45)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разбиение набора данных на обучающую, контрольную и тестовую выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 140, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 1701\n", + "Размер контрольной выборки: 567\n", + "Размер тестовой выборки: 568\n" + ] + } + ], + "source": [ + "# Разделение на обучающую и тестовую выборки\n", + "train_df, test_df = train_test_split(df_btc, 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", + "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": "markdown", + "metadata": {}, + "source": [ + "Видим недостаток баланса:" + ] + }, + { + "cell_type": "code", + "execution_count": 141, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Price_Change в обучающей выборке:\n", + "Price_Change\n", + "up 882\n", + "down 819\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Price_Change в контрольной выборке:\n", + "Price_Change\n", + "up 301\n", + "down 266\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Price_Change в тестовой выборке:\n", + "Price_Change\n", + "up 308\n", + "down 260\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "def check_balance(df, name):\n", + " counts = df['Price_Change'].value_counts()\n", + " print(f\"Распределение Price_Change в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "check_balance(train_df, \"обучающей выборке\")\n", + "check_balance(val_df, \"контрольной выборке\")\n", + "check_balance(test_df, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Используем oversample и undersample" + ] + }, + { + "cell_type": "code", + "execution_count": 142, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оверсэмплинг:\n", + "Распределение Price_Change в обучающей выборке:\n", + "Price_Change\n", + "up 882\n", + "down 882\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Price_Change в контрольной выборке:\n", + "Price_Change\n", + "down 301\n", + "up 301\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Price_Change в тестовой выборке:\n", + "Price_Change\n", + "down 308\n", + "up 308\n", + "Name: count, dtype: int64\n", + "\n", + "Андерсэмплинг:\n", + "Распределение Price_Change в обучающей выборке:\n", + "Price_Change\n", + "down 819\n", + "up 819\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Price_Change в контрольной выборке:\n", + "Price_Change\n", + "down 266\n", + "up 266\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Price_Change в тестовой выборке:\n", + "Price_Change\n", + "down 260\n", + "up 260\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "train_df_oversampled = oversample(train_df, 'Price_Change')\n", + "val_df_oversampled = oversample(val_df, 'Price_Change')\n", + "test_df_oversampled = oversample(test_df, 'Price_Change')\n", + "\n", + "train_df_undersampled = undersample(train_df, 'Price_Change')\n", + "val_df_undersampled = undersample(val_df, 'Price_Change')\n", + "test_df_undersampled = undersample(test_df, 'Price_Change')\n", + "\n", + "# Проверка сбалансированности после oversampling\n", + "print(\"Оверсэмплинг:\")\n", + "check_balance(train_df_oversampled, \"обучающей выборке\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке\")\n", + "\n", + "# Проверка сбалансированности после undersampling\n", + "print(\"Андерсэмплинг:\")\n", + "check_balance(train_df_undersampled, \"обучающей выборке\")\n", + "check_balance(val_df_undersampled, \"контрольной выборке\")\n", + "check_balance(test_df_undersampled, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "https://www.kaggle.com/datasets/junaid512/random-student-data-set-for-education-purpose\n", + "Набор данных включает случайные данные о студентах, которые используются для целей моделирования в сфере образования.\n", + "Примр цели — образовательная аналитика.\n", + "Входные данные: Полные имена студентов, Класс/программа обучения, Возраст, IQ, Совокупный средний балл успеваемости, Навыки" + ] + }, + { + "cell_type": "code", + "execution_count": 143, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['Name', 'Class_or_Program', 'Age', 'Country', 'IQ', 'CGPA', 'Skill'], dtype='object')\n" + ] + } + ], + "source": [ + "df_students = pd.read_csv(\".//static//csv//student_data_01.csv\")\n", + "\n", + "print(df_students.columns)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Посмотрим краткое содержание датасета" + ] + }, + { + "cell_type": "code", + "execution_count": 144, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Информация о датасете BTC-USD:\n", + "\n", + "RangeIndex: 50000 entries, 0 to 49999\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Name 50000 non-null object \n", + " 1 Class_or_Program 50000 non-null object \n", + " 2 Age 50000 non-null int64 \n", + " 3 Country 50000 non-null object \n", + " 4 IQ 50000 non-null int64 \n", + " 5 CGPA 50000 non-null float64\n", + " 6 Skill 50000 non-null object \n", + "dtypes: float64(1), int64(2), object(4)\n", + "memory usage: 2.7+ MB\n", + "None\n" + ] + }, + { + "data": { + "text/html": [ + "
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NameClass_or_ProgramAgeCountryIQCGPASkill
0Catherine OwenArts21Tonga1053.18Communication
1Melissa Wright10th24United Arab Emirates1022.72Leadership
2Laura Shaw12th18Slovakia (Slovak Republic)1363.40Communication
3Rodney Cummings10th17Barbados832.49Problem-solving
4Barbara Hicks12th25Canada1292.39Communication
\n", + "
" + ], + "text/plain": [ + " Name Class_or_Program Age Country IQ \\\n", + "0 Catherine Owen Arts 21 Tonga 105 \n", + "1 Melissa Wright 10th 24 United Arab Emirates 102 \n", + "2 Laura Shaw 12th 18 Slovakia (Slovak Republic) 136 \n", + "3 Rodney Cummings 10th 17 Barbados 83 \n", + "4 Barbara Hicks 12th 25 Canada 129 \n", + "\n", + " CGPA Skill \n", + "0 3.18 Communication \n", + "1 2.72 Leadership \n", + "2 3.40 Communication \n", + "3 2.49 Problem-solving \n", + "4 2.39 Communication " + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "print(\"\\nИнформация о датасете BTC-USD:\")\n", + "print(df_students.info())\n", + "df_students.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Анализируем датафрейм при помощи \"ящика с усами\". Проверяет на пустые значения." + ] + }, + { + "cell_type": "code", + "execution_count": 145, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Проверка на пустые значения в наборе данных 'Student':\n", + "Series([], dtype: int64)\n", + "\n", + "\n" + ] + } + ], + "source": [ + "plt.figure(figsize=(12, 6))\n", + "sns.boxplot(x='Class_or_Program', data=df_students)\n", + "plt.title('Распределение Class_or_Program студентов')\n", + "plt.xlabel('Class_or_Program')\n", + "plt.show()\n", + "\n", + "check_missing_values(df_students, \"Student\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Видно, что выборка относительно сбалансированна, пустых значений нет." + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df_students['Class_or_Program'], df_students['IQ'])\n", + "plt.xlabel('Class_or_Program')\n", + "plt.ylabel('IQ')\n", + "plt.title('Scatter Plot of Class_or_Program vs IQ (Before Cleaning)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Убираем шумы" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Выбросы в Student Data:\n", + "Empty DataFrame\n", + "Columns: [Name, Class_or_Program, Age, Country, IQ, CGPA, Skill]\n", + "Index: []\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Рассчитываем квартиль 1 (Q1) и квартиль 3 (Q3) для IQ\n", + "Q1 = df_students[\"IQ\"].quantile(0.25)\n", + "Q3 = df_students[\"IQ\"].quantile(0.75)\n", + "\n", + "# Рассчитываем межквартильный размах (IQR)\n", + "IQR = Q3 - Q1\n", + "\n", + "# Определяем порог для выбросов\n", + "threshold = 1.5 * IQR\n", + "lower_bound = Q1 - threshold\n", + "upper_bound = Q3 + threshold\n", + "\n", + "# Фильтруем выбросы\n", + "outliers = (df_students[\"IQ\"] < lower_bound) | (df_students[\"IQ\"] > upper_bound)\n", + "\n", + "# Вывод выбросов\n", + "print(\"Выбросы в Student Data:\")\n", + "print(df_students[outliers])\n", + "\n", + "# Заменяем выбросы на медианные значения\n", + "median_iq = df_students[\"IQ\"].median()\n", + "df_students.loc[outliers, \"IQ\"] = median_iq\n", + "\n", + "# Визуализация данных после обработки\n", + "plt.figure(figsize=(10, 6))\n", + "plt.scatter(df_students['Class_or_Program'], df_students['IQ'])\n", + "plt.xlabel('Class_or_Program')\n", + "plt.ylabel('IQ')\n", + "plt.title('Scatter Plot of Class_or_Program vs IQ (After Cleaning)')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Разбиение набора данных на обучающую, контрольную и тестовую выборки" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Размер обучающей выборки: 30000\n", + "Размер контрольной выборки: 10000\n", + "Размер тестовой выборки: 10000\n" + ] + } + ], + "source": [ + "# Разделение на обучающую и тестовую выборки\n", + "train_df, test_df = train_test_split(df_students, 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", + "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": "markdown", + "metadata": {}, + "source": [ + "Видим недостаток баланса" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Распределение Class_or_Program в обучающей выборке:\n", + "Class_or_Program\n", + "12th 5072\n", + "11th 5070\n", + "10th 5067\n", + "Commerce 4988\n", + "Science 4915\n", + "Arts 4888\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Class_or_Program в контрольной выборке:\n", + "Class_or_Program\n", + "10th 1722\n", + "Science 1693\n", + "Arts 1676\n", + "12th 1661\n", + "11th 1637\n", + "Commerce 1611\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Class_or_Program в тестовой выборке:\n", + "Class_or_Program\n", + "10th 1713\n", + "Science 1692\n", + "12th 1669\n", + "11th 1648\n", + "Commerce 1641\n", + "Arts 1637\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "def check_balance(df, name):\n", + " counts = df['Class_or_Program'].value_counts()\n", + " print(f\"Распределение Class_or_Program в {name}:\")\n", + " print(counts)\n", + " print()\n", + "\n", + "check_balance(train_df, \"обучающей выборке\")\n", + "check_balance(val_df, \"контрольной выборке\")\n", + "check_balance(test_df, \"тестовой выборке\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Используем oversample и undersample" + ] + }, + { + "cell_type": "code", + "execution_count": 150, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Оверсэмплинг:\n", + "Распределение Class_or_Program в обучающей выборке:\n", + "Class_or_Program\n", + "10th 5072\n", + "12th 5072\n", + "Science 5072\n", + "11th 5072\n", + "Commerce 5072\n", + "Arts 5072\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Class_or_Program в контрольной выборке:\n", + "Class_or_Program\n", + "Arts 1722\n", + "Science 1722\n", + "10th 1722\n", + "Commerce 1722\n", + "11th 1722\n", + "12th 1722\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Class_or_Program в тестовой выборке:\n", + "Class_or_Program\n", + "Science 1713\n", + "11th 1713\n", + "Commerce 1713\n", + "12th 1713\n", + "10th 1713\n", + "Arts 1713\n", + "Name: count, dtype: int64\n", + "\n", + "Андерсэмплинг:\n", + "Распределение Class_or_Program в обучающей выборке:\n", + "Class_or_Program\n", + "10th 4888\n", + "11th 4888\n", + "12th 4888\n", + "Arts 4888\n", + "Commerce 4888\n", + "Science 4888\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Class_or_Program в контрольной выборке:\n", + "Class_or_Program\n", + "10th 1611\n", + "11th 1611\n", + "12th 1611\n", + "Arts 1611\n", + "Commerce 1611\n", + "Science 1611\n", + "Name: count, dtype: int64\n", + "\n", + "Распределение Class_or_Program в тестовой выборке:\n", + "Class_or_Program\n", + "10th 1637\n", + "11th 1637\n", + "12th 1637\n", + "Arts 1637\n", + "Commerce 1637\n", + "Science 1637\n", + "Name: count, dtype: int64\n", + "\n" + ] + } + ], + "source": [ + "train_df_oversampled = oversample(train_df, 'Class_or_Program')\n", + "val_df_oversampled = oversample(val_df, 'Class_or_Program')\n", + "test_df_oversampled = oversample(test_df, 'Class_or_Program')\n", + "\n", + "train_df_undersampled = undersample(train_df, 'Class_or_Program')\n", + "val_df_undersampled = undersample(val_df, 'Class_or_Program')\n", + "test_df_undersampled = undersample(test_df, 'Class_or_Program')\n", + "\n", + "# Проверка сбалансированности после oversampling\n", + "print(\"Оверсэмплинг:\")\n", + "check_balance(train_df_oversampled, \"обучающей выборке\")\n", + "check_balance(val_df_oversampled, \"контрольной выборке\")\n", + "check_balance(test_df_oversampled, \"тестовой выборке\")\n", + "\n", + "# Проверка сбалансированности после undersampling\n", + "print(\"Андерсэмплинг:\")\n", + "check_balance(train_df_undersampled, \"обучающей выборке\")\n", + "check_balance(val_df_undersampled, \"контрольной выборке\")\n", + "check_balance(test_df_undersampled, \"тестовой выборке\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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 +}