{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "600b7ddb-36e3-47cf-8467-56814329c41f",
   "metadata": {},
   "source": [
    "# Анализ данных Week 2\n",
    "## Изучение библиотек pandas, numpy, matplotlib, seaborn"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "379033f7-37e2-4d9a-bd31-4d2252760df2",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 3. Работа с pandas и numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "07c3d0a7-4592-4728-91e6-ee2c0327637b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы\n",
      "0    Анна       21     89\n",
      "1   Борис       22     76\n",
      "2  Виктор       23     95\n",
      "3  Галина       24     82\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Имя      4 non-null      str  \n",
      " 1   Возраст  4 non-null      int64\n",
      " 2   Баллы    4 non-null      int64\n",
      "dtypes: int64(2), str(1)\n",
      "memory usage: 228.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя        0\n",
      "Возраст    0\n",
      "Баллы      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "id": "41933698-5262-4e09-8924-5cc446a15de8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы\n",
      "0    Анна       21     89\n",
      "1   Борис       22     76\n",
      "2  Виктор       23     95\n",
      "3  Галина       24     82\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Имя      4 non-null      str  \n",
      " 1   Возраст  4 non-null      int64\n",
      " 2   Баллы    4 non-null      int64\n",
      "dtypes: int64(2), str(1)\n",
      "memory usage: 228.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя        0\n",
      "Возраст    0\n",
      "Баллы      0\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e448e9ba-76c2-4d9e-84b4-7cd78609b7a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы\n",
      "0    Анна       21     89\n",
      "1   Борис       22     76\n",
      "2  Виктор       23     95\n",
      "3  Галина       24     82\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Имя      4 non-null      str  \n",
      " 1   Возраст  4 non-null      int64\n",
      " 2   Баллы    4 non-null      int64\n",
      "dtypes: int64(2), str(1)\n",
      "memory usage: 228.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя        0\n",
      "Возраст    0\n",
      "Баллы      0\n",
      "dtype: int64\n",
      "      Имя  Возраст  Баллы  Новый столбец\n",
      "0    Анна       21     89           97.9\n",
      "1   Борис       22     76           83.6\n",
      "2  Виктор       23     95          104.5\n",
      "3  Галина       24     82           90.2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())\n",
    "df[\"Новый столбец\"] = df[\"Баллы\"] * 1.1\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8d7ef57b-395b-467d-a81f-291250cff5f0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы\n",
      "0    Анна       21     89\n",
      "1   Борис       22     76\n",
      "2  Виктор       23     95\n",
      "3  Галина       24     82\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 3 columns):\n",
      " #   Column   Non-Null Count  Dtype\n",
      "---  ------   --------------  -----\n",
      " 0   Имя      4 non-null      str  \n",
      " 1   Возраст  4 non-null      int64\n",
      " 2   Баллы    4 non-null      int64\n",
      "dtypes: int64(2), str(1)\n",
      "memory usage: 228.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя        0\n",
      "Возраст    0\n",
      "Баллы      0\n",
      "dtype: int64\n",
      "------------------------------\n",
      "Возраст\n",
      "21    89.0\n",
      "22    76.0\n",
      "23    95.0\n",
      "24    82.0\n",
      "Name: Баллы, dtype: float64\n",
      "------------------------------\n",
      "Имя\n",
      "Анна      89\n",
      "Борис     76\n",
      "Виктор    95\n",
      "Галина    82\n",
      "Name: Баллы, dtype: int64\n",
      "------------------------------\n",
      "Имя\n",
      "Анна      21\n",
      "Борис     22\n",
      "Виктор    23\n",
      "Галина    24\n",
      "Name: Возраст, dtype: int64\n",
      "      Имя  Возраст  Баллы\n",
      "0    Анна       21     89\n",
      "1   Борис       22     76\n",
      "2  Виктор       23     95\n",
      "3  Галина       24     82\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())\n",
    "print('-'*30)\n",
    "print(df.groupby(\"Возраст\")[\"Баллы\"].mean())\n",
    "print('-'*30)\n",
    "print(df.groupby(\"Имя\")[\"Баллы\"].sum())\n",
    "print('-'*30)\n",
    "print(df.groupby(\"Имя\")[\"Возраст\"].sum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "de50b45d-a8ff-4d0f-bbd2-b528690dcf3a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Первый взгляд на данные:\n",
      "      Имя  Возраст  Баллы Категория\n",
      "0    Анна       21     89         A\n",
      "1   Борис       22     76         B\n",
      "2  Виктор       23     95         A\n",
      "3  Галина       24     82         B\n",
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 4 entries, 0 to 3\n",
      "Data columns (total 4 columns):\n",
      " #   Column     Non-Null Count  Dtype\n",
      "---  ------     --------------  -----\n",
      " 0   Имя        4 non-null      str  \n",
      " 1   Возраст    4 non-null      int64\n",
      " 2   Баллы      4 non-null      int64\n",
      " 3   Категория  4 non-null      str  \n",
      "dtypes: int64(2), str(2)\n",
      "memory usage: 260.0 bytes\n",
      "None\n",
      "         Возраст      Баллы\n",
      "count   4.000000   4.000000\n",
      "mean   22.500000  85.500000\n",
      "std     1.290994   8.266398\n",
      "min    21.000000  76.000000\n",
      "25%    21.750000  80.500000\n",
      "50%    22.500000  85.500000\n",
      "75%    23.250000  90.500000\n",
      "max    24.000000  95.000000\n",
      "Имя          0\n",
      "Возраст      0\n",
      "Баллы        0\n",
      "Категория    0\n",
      "dtype: int64\n",
      "============================================================\n",
      "Категория\n",
      "A    92.0\n",
      "B    79.0\n",
      "Name: Баллы, dtype: float64\n",
      "============================================================\n",
      "           mean  sum  min  max\n",
      "Категория                     \n",
      "A          92.0  184   89   95\n",
      "B          79.0  158   76   82\n",
      "============================================================\n",
      "          Возраст         Баллы               \n",
      "             mean min max  mean  sum       std\n",
      "Категория                                     \n",
      "A            22.0  21  23  92.0  184  4.242641\n",
      "B            23.0  22  24  79.0  158  4.242641\n",
      "============================================================\n",
      "           Среднее  Сумма  Максимум  Минимум\n",
      "Категория                                   \n",
      "A             92.0    184        95       89\n",
      "B             79.0    158        82       76\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "# Создадим DataFrame\n",
    "data = {\n",
    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\"],\n",
    "    \"Возраст\": [21, 22, 23, 24],\n",
    "    \"Баллы\": [89, 76, 95, 82]\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"]\n",
    "\n",
    "print(\"Первый взгляд на данные:\")\n",
    "print(df.head())\n",
    "print(df.info())\n",
    "print(df.describe())\n",
    "print(df.isnull().sum())\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(df.groupby(\"Категория\")[\"Баллы\"].agg(\"mean\"))\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(df.groupby(\"Категория\")[\"Баллы\"].agg([\"mean\", \"sum\", \"min\", \"max\"]))\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(df.groupby(\"Категория\").agg({\n",
    "    \"Возраст\": [\"mean\", \"min\", \"max\"],\n",
    "    \"Баллы\": [\"mean\", \"sum\", \"std\"]\n",
    "}))\n",
    "\n",
    "print(\"=\"*60)\n",
    "print(df.groupby(\"Категория\")[\"Баллы\"].agg(\n",
    "    Среднее=\"mean\",\n",
    "    Сумма=\"sum\",\n",
    "    Максимум=\"max\",\n",
    "    Минимум=\"min\"\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c4ac42af-c3b9-4cbc-b3c7-8f771ec05044",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Имя  Возраст  Баллы Категория\n",
      "1   Борис       22     76         B\n",
      "2  Виктор       23     95         A\n",
      "3  Галина       24     82         B\n"
     ]
    }
   ],
   "source": [
    "print(df[df[\"Возраст\"] > 21])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "660f41ed-b6aa-4b91-a45f-4ab608579f21",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "           Среднее  Сумма  Максимум  Минимум\n",
      "Категория                                   \n",
      "A             92.0    184        95       89\n",
      "B             79.0    158        82       76\n"
     ]
    }
   ],
   "source": [
    "print(df.groupby(\"Категория\")[\"Баллы\"].agg(\n",
    "    Среднее=\"mean\",\n",
    "    Сумма=\"sum\",\n",
    "    Максимум=\"max\",\n",
    "    Минимум=\"min\"\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "ac77752e-cb13-4d9b-9051-f04e02134c0d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      Имя  Возраст  Баллы Категория\n",
      "0    Анна       21     89         A\n",
      "2  Виктор       23     95         A\n"
     ]
    }
   ],
   "source": [
    "print(df[df[\"Категория\"] == \"A\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "199f8580-690f-4420-805c-08ebb1303d4f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сумма элементов массива: 10\n",
      "Среднее значение: 2.5\n",
      "Медиана: 2.5\n",
      "Стандартное отклонение: 1.118033988749895\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "print(\"Сумма элементов массива:\", np.sum(arr))\n",
    "print(\"Среднее значение:\", np.mean(arr))\n",
    "print(\"Медиана:\", np.median(arr))\n",
    "print(\"Стандартное отклонение:\", np.std(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "895167e3-e2c4-417c-826d-62f84d987520",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сумма элементов массива: 10\n",
      "Среднее значение: 2.5\n",
      "Медиана: 2.5\n",
      "Стандартное отклонение: 1.118033988749895\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([[1, 2], [3, 4]])\n",
    "print(\"Сумма элементов массива:\", np.sum(arr))\n",
    "print(\"Среднее значение:\", np.mean(arr))\n",
    "print(\"Медиана:\", np.median(arr))\n",
    "print(\"Стандартное отклонение:\", np.std(arr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "fe451fc3-e043-481d-96fb-0c91f1ee79e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Сумма элементов массива: 15\n",
      "Среднее значение: 3.0\n",
      "Медиана: 3.0\n",
      "Стандартное отклонение: 1.4142135623730951\n",
      "[ 0.   2.5  5.   7.5 10. ]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array([1, 2, 3, 4, 5])\n",
    "print(\"Сумма элементов массива:\", np.sum(arr))\n",
    "print(\"Среднее значение:\", np.mean(arr))\n",
    "print(\"Медиана:\", np.median(arr))\n",
    "print(\"Стандартное отклонение:\", np.std(arr))\n",
    "\n",
    "linear_space = np.linspace(0, 10, 5)\n",
    "print(linear_space)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "ab284249-b61b-426e-ab7d-1ebef55f4afa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0.          1.11111111  2.22222222  3.33333333  4.44444444  5.55555556\n",
      "  6.66666667  7.77777778  8.88888889 10.        ]\n"
     ]
    }
   ],
   "source": [
    "print(np.linspace(0, 10, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "5bf9e49f-40c9-4cd9-9fc6-1d4b246ea29e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-0.62092842 -1.46458049  1.41194612 -0.47673214 -0.78046921]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.randn(5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "b0f7d976-b096-4fc6-bcba-f2d63dd973a0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0.75352168  0.36346459 -0.31471048]\n",
      "  [ 1.37328117 -0.62441716  0.375754  ]\n",
      "  [-0.20041632  0.74303806  0.85736196]]\n",
      "\n",
      " [[-1.50618929 -1.66635217 -0.2189948 ]\n",
      "  [-0.35885843  0.37852769  0.68421537]\n",
      "  [-1.16785607 -0.79321662 -0.0388354 ]]\n",
      "\n",
      " [[ 2.70525499 -1.49138851  0.09597793]\n",
      "  [ 0.52468671  0.8156655   0.0515058 ]\n",
      "  [-0.16455536  0.27819942  0.0871147 ]]\n",
      "\n",
      " [[ 0.03426837  0.74656892 -0.94363758]\n",
      "  [-0.24577667  1.10812096  0.03900789]\n",
      "  [-0.21326669 -0.89095461 -0.27064634]]]\n"
     ]
    }
   ],
   "source": [
    "print(np.random.randn(4, 3, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d912955e-a1f9-458f-912e-379507a00a50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Вектор a: [1 2 3]\n",
      "Вектор b: [4 5 6]\n",
      "Скалярное произведение (1*4 + 2*5 + 3*6) = 32\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])\n",
    "b = np.array([4, 5, 6])\n",
    "dot_product = np.dot(a, b)\n",
    "print(f\"Вектор a: {a}\")\n",
    "print(f\"Вектор b: {b}\")\n",
    "print(f\"Скалярное произведение (1*4 + 2*5 + 3*6) = {dot_product}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "1882ab34-fa93-4bc0-b4dc-9f90c7de3acc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица A:\n",
      "[[1 2]\n",
      " [3 4]]\n",
      "Матрица B:\n",
      "[[5 6]\n",
      " [7 8]]\n",
      "A * B:\n",
      "[[19 22]\n",
      " [43 50]]\n"
     ]
    }
   ],
   "source": [
    "matrix1 = np.array([[1, 2], [3, 4]])\n",
    "matrix2 = np.array([[5, 6], [7, 8]])\n",
    "result = np.dot(matrix1, matrix2)\n",
    "print(f\"Матрица A:\\n{matrix1}\")\n",
    "print(f\"Матрица B:\\n{matrix2}\")\n",
    "print(f\"A * B:\\n{result}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4eb4cb1e-0c55-42bc-9b97-c3fb419dd431",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Матрица (2x3):\n",
      "[[1 2 3]\n",
      " [4 5 6]]\n",
      "Вектор (3): [1 2 3]\n",
      "Результат (2): [14 32]\n"
     ]
    }
   ],
   "source": [
    "matrix = np.array([[1, 2, 3], [4, 5, 6]])\n",
    "vector = np.array([1, 2, 3])\n",
    "result2 = np.dot(matrix, vector)\n",
    "print(f\"Матрица (2x3):\\n{matrix}\")\n",
    "print(f\"Вектор (3): {vector}\")\n",
    "print(f\"Результат (2): {result2}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6fee1ab1-7c16-436b-80c0-a32c118d6fc7",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 4. Визуализация данных"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "2c13c031-d4cd-4a00-9f0a-13c834ae736d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "x = np.linspace(0, 10, 100)\n",
    "y = np.sin(x)\n",
    "\n",
    "plt.plot(x, y, color='red', label='sin(x)')\n",
    "plt.plot(x, np.cos(x), color='black', label='cos(x)')\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.title(\"График синуса\")\n",
    "plt.legend()\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "c221c412-8ee4-47ec-a444-d429e97a5855",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "categories = ['A', 'B', 'C', 'D', 'E']\n",
    "values = [23, 45, 56, 78, 89]\n",
    "# plt.figure(figsize=(10, 4)) #изменение размер диаграммы\n",
    "plt.bar(categories, values, color='skyblue', edgecolor='black', alpha=1)\n",
    "plt.xlabel('Категории')\n",
    "plt.ylabel('Значения')\n",
    "plt.title('Столбчатая диаграмма (bar chart)')\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "6538978d-b73b-4919-84cb-da161b2d23ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "x_scatter = np.random.randn(100)\n",
    "y_scatter = 2 * x_scatter + np.random.randn(100) * 0.5\n",
    "\n",
    "# plt.figure(figsize=(10, 6)) #изменение размер диаграммы\n",
    "plt.scatter(x_scatter, y_scatter, color='green', alpha=0.7, s=50, edgecolors='black')\n",
    "plt.xlabel('X')\n",
    "plt.ylabel('Y')\n",
    "plt.title('Диаграмма рассеяния (scatter plot)')\n",
    "plt.grid(alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "id": "cc8ce3ab-2298-4885-89db-149de2daa165",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1)\n",
    "data_hist = np.random.normal(0, 1, 1000) #(срзнач списка, большинство чисел в этом диапазоне, кол-во чисел)  \n",
    "\n",
    "# plt.figure(figsize=(10, 6))\n",
    "plt.hist(data_hist, bins=40, color='grey', edgecolor='black', alpha=1)\n",
    "plt.xlabel('Значения')\n",
    "plt.ylabel('Частота')\n",
    "plt.title('Гистограмма')\n",
    "plt.grid(axis='y', alpha=0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "e8dabb65-8625-4635-afa8-008039d29a27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "\n",
    "df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\"] # таблица взята из прошлых блоков (данные сохраняются, если не перезаписываются)\n",
    "sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "10c5a86a-ef8a-4095-8f9b-12de19dc4c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Частота')"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 3, 2)\n",
    "sns.histplot(data=df, x=\"Баллы\", bins=10, color='skyblue', edgecolor='black')\n",
    "plt.title(\"Histplot - Распределение баллов\")\n",
    "plt.xlabel(\"Баллы\")\n",
    "plt.ylabel(\"Частота\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "e24788d6-6239-4493-bc8e-ff16d5d1435b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 6))\n",
    "plt.subplot(1, 3, 3)\n",
    "sns.scatterplot(df, x=\"Возраст\", y=\"Баллы\", hue=\"Категория\", \n",
    "                s=100, palette={\"A\": \"red\", \"B\": \"blue\"})\n",
    "plt.title(\"Scatterplot - Возраст vs Баллы\")\n",
    "plt.xlabel(\"Возраст\")\n",
    "plt.ylabel(\"Баллы\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "6b875301-3648-4175-9af6-81733327df53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YMJFmH6/LQWyIL4Ymhtp/kMYa4OByIPFUwHByv5bn9TWhpM6ircSALMzb/xxgy0dAs8acKCIi0h0GTKRJdUMzvt2eh7MGxNi/Zpw4+BPQ0gQkjj7pL+0TYkBamAGfZmoMdNInAbUlwL4F2o5DRES6w4CJNPluWz4amsyYmB6l7UD7frQuiOtvXy/VxGQjluW0oKhWQ/J3eD/rcODmD+0/BhER6RIDJtLki025aiguUkuyd1UeULwbSDjF7kOMSzDB4AV8s78Jmsiw3P4fgZoSbcchIiJdYcBEdjtSXoe12aWYkKaxdylrmTWHKHaQ3YcI8vHCqBgjvtyrMWCS6uJSOXznl9qOQ0REusKAiew2b2sefIwGjOkXruEsWoDspUDMYMDoo+mnMSHJiJ0lZmSVa6j87RcKJJ4CbP9cU1uIiEhfGDCR3b7emoeRfcIQ4GOy/yClB4CKXCB+hOafxMgYI/xNMiynYbac6HcmkLMGKM/R3CYiItIHpwZMzz77LMaMGYPg4GDExMRg+vTpyMzMbPec22+/HampqfD390d0dDSmTZuGPXv2nPC4N954o1prrO02ZcqUHv5uPMvBozXYmVeJ8f0jtR0oewXgE2hN+NbIx+iFU+OM+CZL47Bc8unW3q5dX2luExER6YNTA6Zly5bh7rvvxpo1a7Bw4UI0NTVh0qRJqKmpaX3O6NGj8c4772D37t1YsGCBqiotz2lpOfGwiwRI+fn5rdvHH3/cC9+R5/huR75aaFd6mOxnsZYTkOE4g4b159oYG29EVrkZ+8o0DMv5BFgT0HfNdUibiIjI/WkYS9Fu/vz57e6/++67qqdp48aNmDhxonrstttua93fr18/PP300xgxYgQOHjyoep464+vri7g4LeuaUVflBEYmh8HXpCHQKT0IVOUDAxzX+zcs2jos992BZvxutIa29Z0ArPgnUHEECE10WPuIiMg9uVQOU0VFhbqNiIjocL/0PElvU0pKCpKTk094rKVLl6rga+DAgbjzzjtRUtL5NPGGhgZUVla226hzuWW12JFXiTH9Ov45ddvhVYC3PxDR32GnW4blRsUaMT9b67DcaYDBBOz51lFN8yi8pohIb1wmYDKbzZgxYwYmTJiAoUOHttv32muvISgoSG3ff/+9Gr7z8fE54XDc+++/j0WLFuG5555TQ39Tp07tdBhPcqlCQ0Nbt66CMU+3cFchTAYvjNI0HAfg0CproUijYzs6JY9pd4kZOVUailhKXlXccGD3N45smsfgNUVEeuNlkaQgFyC9QBIMrVixAklJScf1PBUVFalcpBdeeAFHjhzBypUr4efn161jHzhwQA3f/fjjjzjvvPM6/DQsm430MEnQJK8bEhLigO9OX656Yw3qm5rx0BT76yahugD4/BZg5DVA3DBHNg91zRbctqAOj4z1xS3DNRTUzPwOWPtf4IH9QIDG3jQPw2uKiPTGJXqY7rnnHsybNw9Lliw5LlgS0uuTnp6u8po+//xzNUtuzpw53T5+//79ERUVhf3793ea7ySBUduNOlZR24T12aU4pU+4xuG4ddYhr6h0h59qf5MXhkYa8KOWxXhF0mmApQXYv8hRTfMYvKaISG+cGjBJ55YESxL8LF68WOUmdedrZGvbI9SV3NxclcMUHx+vscW0dG8RWiwW7QGT1DmS3CVT93oJT9YpcUasz29BRYOGDtTAKCAy3drTREREHs2pAZOUFPjwww8xa9YsVYupoKBAbXV1da1DaZILIbPmDh8+jFWrVuHyyy9XNZkuvPDC1uNkZGS09jhVV1fjgQceUKUKZCad5DFJ7aa0tDRMnjzZad+rXizeU4SUqABta8c11gCFO4Dogegpp8Qa0WwBludo7WUaY11brkVjEjkREbk1pwZMM2fOVHlCZ599tur9sW2ffPKJ2i85Sj/99JMKjiTgufLKK1VgJYGTzICzkWKXthl2RqMR27Ztw69+9SsMGDAAt9xyi6rlJMeRYQKyX4vZgqWZxRiZrLF3KX8LYG4BojN67McR6W9A3xAvLDnsgICpoRLIWeuophERkRtyah2mrvLNExIS8N13353UcaT3SQpckuNtySlDRV0TRiVrnB2Xux4Iiu3xROoRMUYVMEmgZzR42XeQyFTAPxzYuwDod4ajm0hERG7CJZK+yT1I71KInwmp0UEajmIBcjf0SLL3sUbFGFHWYMGOoxrKC3gZrFW/9y90ZNOIiMjNMGCikwqYhiaGwmBvb42tunddGRDVc/lLNmnhBgSYgGVa85gSRwNFu4HKPEc1jYiI3AwDJuqWkuoGbD9SgRFJGofjjmy0Lmwb3rfHz7wU1xwaJcNyGhO240dae5ok+ZuIiDwSAybqlhX7j6rb4Umh2s7YkU3WcgJG714588NjjNhabNZWXsAvxFqRPGuxI5tGRERuhAETdcvyvUfRNzIAYQGdL0nTpeZ6oHhXr+Qv2QyPNsBsAVbnNWvvZcpaYp3dR0REHocBE3VrFuLyfcUYlqixd6lwp7WeUWRar5316AADEoK8tNdjShgF1JdbSyIQEZHHYcBEXdpXVI3iqgYMTdAYMOVtBvzDgMDoXj3rkse0IldjwCRFNr39gQNLHdUsIiJyIwyYqEsr9x+FyeiFjPhgbWcrfzMQkQp4aZhlZ4ehUQYcrrIgp0pDeQFZ9y52GHBgmSObRkREboIBE3Ur4XtgbDB8TUb7z1ZdubWkgARMvWxwlFH9oq86ojWPaQRweDXQZF26h4iIPAcDJjqh5hYz1mSVYIjW4biCbb9Uzu5lgd5e6B9m0D4sJwFTSyNweI2jmkZERG6CAROdkNReqmlswZCEEG1nqmCrdTkUmaLvBIOjDFiV19LlcjwnFNYX8AsDDv7kyKYREZEbYMBEJ7T6QAn8vY3oHx2o7UzlbwMiUpx2todEGVFSZ0FWuZZlUryAuOFA9nJHNo2IiNwAAyY6oVX7SzAwLhgmg4ZflZpi67IiTshfshkYboDJS/KYNNZRihtmLb7ZUOWophERkRtgwESdamw2Y8OhUgyO1zoct8N668QeJl+Tl1pbTnMBS+lhsrQAh9c6qmlEROQGGDBRp7YfKUd9kxmDNecvbQOC4wEfjcN6Gg2KNGCN1jymkAQgIJJ5TEREHoYBE3VqzYFSlb/ULzJQe8AU3s/pZ3pwpBFlDRbsLdOYxxQ7lHlMREQehgETdWrNAWv+ktHgpS1/qarAuuCuk6WHG2D0AtbmacxjkoApfyvzmIiIPAgDJupQU4sZGw6WYVBcsPb145ycv9Q2jyk1zIC1+VrzmIZa85hy1jmqaURE5OIYMFGHduZVoq6pBYO0JnwXbrfWX3Jy/pJNhuQx5WvNY0oC/MOBQysd2TQiInJhDJioQ+uyS+BrMiBFa/0lmSHnAvlLNoMirPWYsis05jHFDAYOMmAiIvIUDJioQ+uyS5EeE6St/pKsH1eR61IB04AIg/qlX1+gNY9pCJC3CWiqd1TTiIjIhTk1YHr22WcxZswYBAcHIyYmBtOnT0dmZma759x+++1ITU2Fv78/oqOjMW3aNOzZs+eEx5Xhlscffxzx8fHq684//3zs27evh78b/TCbLSpgGhincTiu6Of8JRcKmAK8vdA31Atr8x2Q+C3ryh3Z6KimEVEbFbVN2HK4DLPXH8ZnG3Kw40g5qus15h8SuWvAtGzZMtx9991Ys2YNFi5ciKamJkyaNAk1NTWtzxk9ejTeeecd7N69GwsWLFDBkDynpaXzP3jPP/88XnnlFbz++utYu3YtAgMDMXnyZNTXszegO/YVVaOyvhkZmhO+dwH+EYB/GFzJwAgj1mktYCnryvkEAYdXOapZRPSzkuoGFSh9siEXW3MqsOlwOT5am4O5W46goq6J54mcwgQnmj9/frv77777rupp2rhxIyZOnKgeu+2221r39+vXD08//TRGjBiBgwcPqp6nY0lA9fLLL+PRRx9VvVHi/fffR2xsLL766itcddVVPf59ubv1B0tVKYG0mCBtByqU/KW+cDUZEQbMz25GQY0ZcYF2fmYwGIHoDODQakc3j8jjbTxchoMltcedhx15lUiNCcLp/SM9/hyRh+cwVVRUqNuIiIgO90vPk/Q2paSkIDk5ucPnZGdno6CgQA3D2YSGhmLs2LFYvbrjP24NDQ2orKxst3l6wJQSFQg/b6P9B2mqA0oPuNRwXNseJrFe67CcJH7nrgPMGo+jQ7ymyF6VdU3YfLi80/0bDpaisZnXHHlwwGQ2mzFjxgxMmDABQ4cObbfvtddeQ1BQkNq+//57NXzn4+PT4XEkWBLSo9SW3Lft6yiXSoIq29ZZMOYpJH9pQKzG4bjiPYDF7JIBU5ifF+IDvbC+oFl7wCSL8EpPGrXDa4q01IBrau58FmtDsxnNZg1lQYjcPWCSXKYdO3Zg9uzZx+279tprsXnzZpXzNGDAAFxxxRUOzUd65JFHVO+WbcvJyYGnyiuvQ35FPTK0BkxFu6y1lwKj4IpktpzmmXLRAwCDiQvxdoDXFNkr2M+EuFC/Tvf3jQxQSzYReWTAdM8992DevHlYsmQJkpKSjtsvvT7p6ekqr+nzzz9Xs+TmzJnT4bHi4uLUbWFhYbvH5b5t37F8fX0REhLSbvNUGw6VqdsBjkj4Du0DeLnEr9hxBoQbkVlqRlWjhk+qRh8gMg3IWePIpukCrymyl4/JiAlpkR0uyeRjMuCUPuHwklpoRL3MqX/NJEFbgiUJfhYvXqxyk7rzNbJJjkRH5BgSGC1atKj1MclJktly48aNc2j79UjyA+JD/RDq723/QSSnR4bkXDDh22ZghAHSq7+50AF5TIc4U47IkQbGhuDy0YmICfZV9yU8Sgrzx9VjktE/WuNkFCJ3nCUnw3CzZs3C3LlzVS0mW46R9ChJ/aQDBw7gk08+UWUEpAZTbm4u/v73v6t9F154YetxMjIyVM7EpZdeqj55SC6UzKaTXikJoB577DEkJCSoOk/UdcK35vylsmygud469d5FJQR5IcQH2FDQjInJGi6DmEHAzi+B8hwgzLNz34gcxWDwwojkcKRGB6GkplE9FhPsB38fDsWRhwZMM2fOVLdnn312u8dlJtyNN94IPz8//PTTT6pMQFlZmUrclmG5VatWqfIDNlLs0jbDTjz44INqRp2UJCgvL8cZZ5yhShjI8ahz1Q3NyCyowplp0dpOU9Fu67T70ESXPd0SWKeHG7FRaw9T9CDrbc5aBkxEDhbk5602IrcNmJYvX47x48fDZNIWb3W1AKr0Cn333XcnfRz5Y/jkk0+qjbpvy+FyNUw1wBEJ3yGJgNG13+jSww34en8TWsyWDvMlukWKcsr3KgHTsP9zdBOJiMidc5jOOecclJaWOr415FQbD5UhyNeE+DA/7QGTCw/Htc1jqm0G9pRqWIhXSAHLw0z8JiLSM0NP9AyRe9pwyLrgrkHLDJSaYqDmqFsETP3DDDB5AZs0J34PstZiaqh2VNOIiMjF2D2mJlWzw8PDO9xnW9aE3GvBXamue9GweG0HktlxIrwPXJ2P0Qv9Qg0qYPrNEI15TFKkM28TkMLffSIiPbI7YJIZaR2R/KETLYxLrrvgriR9p8dqnLJbtAcIiAR8NeZB9WIek8yU00Rmx8lCvJLHxICJiEiX7K7DJCUAZDmTYzcGS+6bvyR5zzKNV5NiKVh5fPFRV5UeYUBOlQVFtRrymKQ4Z9RA5jEREemYXQETq6zqM2DqG6lxwd2WRqDkABDm+sNxNgPCrZeA5jym6IFA7noZ23RMw4iIyKUw6ZuUjYdKkRajsXepZD9gbnaLhG+bSH8DIv29tFf8lply9RXWc0BERLpjV8AkQ29tC0eSeyuracTBklo1Q05zwUpZXy1YY+J4L0sPM2Cj5oV4B1oXcMhd56hmERGRuwdMsgzJ22+/fdzj8thzzz3niHZRL9qc8/OCu1oLVsoMOclfMrjmgrsnSvzefrQFjS0aymX4BFp71nIYMBER6ZFdf9n++9//qvXbjjVkyBC8/vrrjmgX9SIpJyCL7doWurSPxdrDFOp+66lJ4ndDC7C7RGsBywHsYSIi0imDvTPk4uOPH3aRBXLz8/Md0S7q5YRvyV/SlMxfXQTUlblV/pJNvxADTAZJ/G7WnsckZRXqKx3VNCIicueAKTk5GStXrjzucXlM1n8j9yHrqG3JKdeev2QrWCk1idyMt9EL/X8uYKl9IV4LcGSjo5pGRETuXLjy1ltvxYwZM9DU1IRzzz1XPbZo0SI8+OCDuP/++x3dRupBewurUNvYgnRH5C8FRgG+GgMvJ0kLN2Cj1oApNNFawFLKC6Se46imERGRuwZMDzzwAEpKSnDXXXehsbFRPebn54eHHnoIjzzyiKPbSD2cvyQFK/tHBWo7kAxFuWH+Utt6TN8daEZRjRkxgQb7C1jKbDkmfhMR6Y7dhStlNlxxcTHWrFmDrVu3orS0FI8//rjjW0g9atNhBxWsLJWCle4bMEkPk9hUpLGXSSp+Sw8TF6gmItIVTfO/g4KCMGbMGAwdOhS+vlpmWJGzbPo54dshBStD3afCd4cFLP0cVcCyHCjJclTTiIjInRff3bBhAz799FMcPny4dVjO5ssvv3RE26iHVdQ24cDRGkweEqftQMWZgNEbCNZ4HCdLDXdE4vcA660UsIxKc0i7iIjITXuYZs+ejfHjx2P37t2YM2eOSv7euXMnFi9ejNDQUMe3knq0YGV6bJCDClZqGNZzlQKWxS1oNmspYBlkXUtPhuWIiMizA6a//e1veOmll/DNN9/Ax8cH//rXv7Bnzx5cccUV6NPHfYdlPDHhO8TPhLgQPwcETO6bv9Q2YKpvAfaUaixgGTWAid9ERDpjV8CUlZWFiy66SP1fAqaamhqVCP773/8eb7zxhqPbSD1k8+EypGotWFlbAtQcdev8JZuUUAOMXnBMHlPRLqCh2lFNIyIidwyYwsPDUVVVpf6fmJiIHTt2qP+Xl5ejtrbWsS2kHmE2W7A5pxxp0Z5bsPJYPkYvFTRtLnJAwGQxA/lbHNU0IiJyx4Bp4sSJWLhwofr/5Zdfjt/97neqmOXVV1+N884776QW8ZVZdsHBwYiJicH06dORmZnZul9KFdx7770YOHAg/P391XDffffdh4qKihMe98Ybb1S9Jm23KVOm2POt6pYke1fVNzumYKV/OOAXAj2QxO+NBRqXSJHhSW9/5jEREXn6LLl///vfqK+vV///85//DG9vb6xatQqXXXYZHn300W4fZ9myZbj77rtV0NTc3Iw//elPmDRpEnbt2oXAwEDk5eWp7YUXXsDgwYNx6NAh3HHHHeqxzz///ITHlgDpnXfeab3PsgfHD8fJQFxqtGcXrDxWepgBC7KbUVZvRrifnVU3JPld8piY+E1E5NkBU0REROv/DQYDHn74YbtefP78+e3uv/vuu6qnaePGjaoXS+o7ffHFF637U1NT8cwzz+C6665TAZbJ1HnzJUCKi3Pvae49SYbjksL9EeBjd2UJa+2lkn1A2iToha2ApeQxndvXoK2A5YEl1gKWWnLEiIjIJdj917KlpUWVFJDSAkJ6gKZNm3bCIKYrtqG2tgFZR88JCQnp8nWWLl2qgi/Jt5L17p5++mlERkZ2+NyGhga12VRW6n+1eYcUrCzLBlqadJG/ZBMT4IVQX2BLkQRM3vYfSJZI2f4pUJFjLTPgYTzxmiIifbPrI7TUXBowYABuuOEGFTTJJv9PT09vTQA/WWazWS3oO2HCBNWz1JGjR4/iqaeewm233dblcNz777+vFgSWJVxk6G/q1KkqyOssl0rqR9m25GT9BAAdqW5oVovupscEax+Ok+GnkAToheS7pYUZtS/EK4nfwkPXlfO0a4qI9M/LYjn5Ra/GjRuH6OhovPfee6oHR5SVlalka1lfTvKZTtadd96J77//HitWrEBSUtJx++UT6gUXXKB6n77++muVN9VdBw4cUMN5P/74Y4dJ6R19GpY3eFtvlt6syjqKa95ci+cvG47kiAD7D7T8BeuyKOPugp58ta8J87KasO3GYBhlZWJ7zbkNGDQNmPp3eBpPu6aISP/sGj/bsmWLWhrFFiwJ+b/kF0kC98m65557MG/ePCxfvrzDYElKGEivkcymk96skwmWRP/+/REVFYX9+/d3GDBJvpMnJYVLwcoAHyMSw/21Hah4NxDRH3ojeUw1TcD+cjMGRhg1FrBcC0/kadcUEemfXUNyMhxXWFh43ONFRUVIS+v++lnSuSXBkgRBsqxKSkrKcc+RT6Yyc04KZErPkp/fyVelzs3NRUlJCeLj40/6a/Vo0+EyVX/JoCUZub4CqCrQZX5OaphBXRiSx6SJJH4XbAearDNKiYjIwwImyU+QekgytV+CEdnk/5KDJDlDEuTYthORkgIffvghZs2apXqPCgoK1FZXV9cuWJJK4m+99Za6b3tO23ykjIwMFXSJ6upqPPDAA1izZg0OHjyo8pgkGV0CucmTJ8PTSZAqPUyaE75bC1bqL2DyN3khOcRL+0K8MYMAcxNQsM1RTSMiIncakrv44ovVrawdZ1tWw5YKdckll7Tel32dJVqLmTNnqtuzzz673eNSP0nyoTZt2oS1a61DGsf2XGVnZ6Nfv37q/1Ls0jbDzmg0Ytu2bSq/SiqPJyQkqKBLksU5RAAcLq1FaU2jYwIm32DALwx6JInfmwo0Bkzh/QCjjzXxO/k0RzWNiIjcJWBasmSJQ168q3xzCaS6k5Pe9jlSEXzBggUOaZ8eSe+S0BwwFWVaC1bqtMZQeoQBiw83o7LBghBfO79HgwmISgdyPXOmHBERPD1gOuussxzfEuq1Ct8JYX4I9tNQY8jSApRkAintewb1JC3MAAnDtxa34Mwkk7Y8ppw1jmwaERE5gd2ljCX3SIa9pHdH6hxJTpNU6ibXtvFQGVK1LrhbdsiayKzD/CWb+CAvBHlDex6T1GOqzLNuRETktuz66CxLobzxxhsICAhQeUazZ89WBScl90hmzz300EOObylpVtfYgj0FVbh+nDX3S1P+kpcBCE3U7U9FZhBKeYFNhbIQr6+2it9C1pUbPM1h7SMiIjfoYZLepY8//hg//fSTWoj35ZdfxpdffqmSuN9++23Ht5IcYvuRCjSbLUiP1ZrwnQmExFsTmnUsLdyo1pQzn3xt118ERAJBMR5b8ZuIyKMDJqnmPWTIEFU3SeoijR49Wj1+5plnIicnx9FtJAfWX/LzNiA5XEN1b1G0y5rwrXPp4QZUNgIHys3aDqTymBgwERF5XMAkVbNlXTfx6KOPqkVuhdRLknpK5LoL7kr+kqblPqRgpeTj6Dh/qW3it5wph+Qx5W8Bmn9ZKoSIiDwgYLr++utVjSPxyCOPICzMWotHkr/tWRqFep4k5288XIZ0zfWXMq23YX2hdwHeXkgO9nJMwNTSCOSzgCURkUclfUul784W0JWNXE9OaR1KqhuRHhOsff04KVjp/8s6gnqWHm7ERq0Bk6y3J/leUo8pmR8oiIg8qqyALZdpxYoVapP/k2vnLwnNCd9Fe6zDcTotWNlRAcv9ZWZVwNJuRm9rAUvmMREReVbAJLlKN998s1p2ZOLEiWqT/99yyy2ora11fCvJIfWXEsP8tRWsNLcAR/d6RP5S28RviyMW4pVhucNrZGzUUU0jIiJXD5j+8Ic/qHylr7/+WuUyyTZ37lz12P333+/4VpJmGw6Val8OpTwbaK4HQj0nYIoP9EKwD7ChQOoxaRA9CKguACpyHdU0IiJy9RymL774Ap9//nm7RXMvvPBCtY6bLMhrW1SXXENNQzMyC6pwRlq09uE4g1HXBSuPJQtISy+TQxK/heQxhem/JAMRkd7Y1cMkw26xsbHHPS7lBTgk53q25pTDbAEGaM5f2g2EJFpzcjyIJH5vLmpBi5xEe/mHWc8d85iIiDwnYBo3bhz+8pe/oL6+vvWxuro6PPHEE2ofuV7+UqCvEQlh/toLVnpg78iAcANqmoC9ZRoLWMoyKZLHREREnjEk969//QuTJ09GUlISRowYoR7bunUrfH198cMPPzi6jaTRhkNSfylYrY9mt9oSoLoISDvf434eqWEGGL0kj6kFgyKN2vKY1r4ONNYAPoGObCIREbliD9PQoUOxb98+VY9p5MiRavv73/+O/fv3qyVTyHWYzRZV4XtAbLD23iURpnHhXjfka/JCSqgBGws05jHFDAIsLcCRjY5qGhERuXLAVFJSgoCAANx666343e9+h8DAQGRmZmLDhg2ObyFpsreoClUNzY7JX5KFZP08c+kbSfzWPFNOyjH4BAGH1zqqWURE5IoB0/bt29GvXz+V3J2RkYEtW7bgtNNOw0svvYQ33ngD55xzDr766queay2dtA0HyyBLx8kacpoU7vSo+kvHGhhhQG61BYU1GvKYvAw/5zGtdmTTiIjI1QKmBx98EMOGDcPy5ctVSYGLL74YF110ESoqKlBWVobbb79dDc2RayV8p0QFws9bQ+5Ncx1QesAj1o/rzIAI6/nTvEyK5DHlrrcWASUiIn0GTOvXr8czzzyDCRMm4IUXXkBeXh7uuusuGAwGtd17773Ys2dPz7WWTtq67FLt+Uuy4K7FDIR7bsAU7ueF2AAvrM/XGOjEDgYaKq1DnEREpM+AqbS0FHFxcer/QUFBKncpPPyXRVjl/1VVVY5vJdmloKIeR8rrMDBOY8BUuBvw9geCYjz6JzEgwoD1WvOYogZYi39yWE6fWpqBhiqgpcnZLSEiZyd9S+XjE90/GTLLbsyYMQgODlZ5UdOnT1fJ420DNOm1GjhwoKoi3qdPH9x3331qCPBELBYLHn/8ccTHx6uvO//889WsPk+z/mCpuh2oeYac5C/1tebgeLCBEUbsKjGjulFDAUuTHxCZznpMetPUAORttpaNWPESsO5N6/3mRme3jIicVYfpxhtvVPWWhBSuvOOOO1RPk2hoaDipY8nac3fffbcKmpqbm/GnP/0JkyZNwq5du9QxZchPNhn+Gzx4MA4dOqReTx6TpVk68/zzz+OVV17Be++9h5SUFDz22GOqbpQc18/PD54UMMWH+iEswMf+g0iuTfFuIOUseLqMCIOqmC7LpExMtquE2S95TIdWWhfi1VIbi1yD2QxkLwUyv5ePa7/ULZOFqjMustYu48+ZyO15WaQ7pptuuummbj3vnXfesasxxcXFqqdJAqmJEyd2+JzPPvsM1113HWpqamAyHf9HS76dhIQEtQjwH//4R/WY9EjJUi7vvvsurrrqqi7bUVlZidDQUPV1ISEhcFdTXl6O2BA/3HFWqv0HKdkHfDMDGHuHR+cw2X637vihDjcO9cEfxmgIvKXa95KngRnbPWbmoV6uqQ6VHwZWvQq0dNCbZPQFJtwHhCY5o2VE5EAn9THZ3kCou2xDbRERESd8jrzhdhQsiezsbBQUFKhhOBt5ox47dixWr17dYcAkPWNte8fkzd3dVdQ1qQV3zxoQrb2cgKwdJ+ugeTgZfpZhubVaE79jBltvD63SbcCkx2uqU5X5HQdLoqXBup8BE5Hbc5mkFLPZjBkzZqgZeFJJvCNHjx7FU089hdtuu63T40iwJI5dHFju2/Z1lEslQZVtS052//XSNh4qVYMDGXEaP80X7LC+2Rs1DEHprB7TlqIWNLRoyGPyC7HmhEnApFN6vKY619XvgobfFSJyGS4TMEku044dOzB79uwO98snVKn5JLlMf/3rXx362o888ojqubJtOTk5cHfrsssQHuCN2BBrvpl9LNYepnDPWw6lMxmRBjS0ADuKHdDLpOOASY/XVKeC4wBDJx8opHc2qP2HNyJyTy4RMN1zzz2YN28elixZohb0PZaUKpgyZYqaTTdnzhx4e3t3eixb2YPCwsJ2j8t9275jSRK7DPO13dzd2uwS1bukZRYjKnKsNYPCUxzZNLfWL8QAfxO0D8vFDrXmh1UXQ4/0eE11KiQJSOk45xIpZ3M4jkgnDM5OopVgSYKgxYsXqxltHfUsycw5Hx8ffP31113OcpNjSGC0aNGidsdYu3Ytxo0bB09Q29iM7bkVyIgP1j4cJ6UEdJpnYw+jwQsDwg1Ym6+xHlPsz4tUy2w5cm8yXJ12ATD8KiAkwbpeoARRI64G0s6z1t0iIrdncvYw3KxZszB37lzVe2TLMZKcB6mfZAuWamtr8eGHH6r7tuTR6OhoGI3WNyJZ105yJi699FLVoyK5UE8//TTS09NbywrIzDmp8+QJNh0qR7PZgkGa85e2Wz8dm7QM6+lPRqQR3+xvUufYJAv12SMwyvrHVQKmIZ7xe6lrPgFA33FA/AigqRbwDrA+RkS64dSAaebMmepW1qU7djae1HvatGmT6hkSaWlpx82Gk4WAhRS7bFvMUta8k7IDkhxeXl6OM844A/Pnz/eYGkwyHBfiZ0JSuL+Go1isAVPcMAe2TB8GRxrwyR5g51EzRsRo6D2IGQIc/MmRTSNnkyCJgRKRLjk1YOqqBJQEUt0pE3Xsc6SX6cknn1SbJ1qd5Yj8pTygrgyIYP7SsfqHGeBrBNbkNWsLmCQY3b8QqCkBAiPtPw4REXlG0jc5Tl1jC7bklGNwgtbhuG0/5y9xhtyxZBhuYLgBq/O05jH9XD6DeUxERC6PAZPObDxUpnJrBsc7IGCSYpXenjGMebIGRRmxvqBFnWu7yWLGwfEcliMicgMMmHRmzYEShPp7OyZ/KaK/A1umvzymmiZgu9Z6TDIsl73cUc0iIqIewoBJZ1ZmHcWg+GDt9ZdU/hIDphPlMUk9pjX5DgiYivfoth4TEZFeMGDSkeqGZmzLqcCQhFBtB8rfZq0dwwrfJ8xjyogwYNURjXlMcSOstwfZy0RE5MoYMOnIuuwStFgsGKI14Ttvi7VYpcnHUU3TpcGSx5SvcV25gAjruT6wzJFNIyIiB2PApCMr95cgMsgHcSEaErUtLdaEbw7HdWlIlBH1LcDmQq3LpAwDDizVdgwiIupRDJh0ZMX+oxiaEKotf6kkC2isASLaFwql4/UN8UKwD7BS67Bcwkig/BBQdpCnmYjIRTFg0oniqgZkFlRhaKLW/KUt1qVQwpId1TTdMnh5qV6mn3K15jENs9a84rAcEZHLYsCkE6uyjqrboVrzl45sAcJTuGBoNw2NMmJbsRkVDRrymGSx1qgBQNZi+49BREQ9igGTTizfexR9IwMQFqAhUbu5HijeBUSlO7JpujY82gCpXam56nf8SGsek1ljPhQREfUIBkw6IGvpLd9XjGFah+MKdwItTUAk85e6KzrAgIQgL+3DcgmjgPpy65AoERG5HAZMOpBZWKVymDQHTEc2Af7hQGC0o5rmMcNySw83d2uh6E5FDwS8A4D9ixzZNCIichAGTDqwLLMYviYDMuK05i9tsPYuaZll54FGxBhxpNqC7Aqz/QcxmID4EcD+Hx3ZNCIichAGTDqwNLNYLbbrY9Lw46wuACpygaiBjmyax6wrJ6d+aY7GYbnE0UDueuuyNERE5FIYMOlgOZT1B0sxPClM24GObLRObY9MdVTTPIafyQuDIgxqWE6ThFMAi5lFLImIXBADJje3cv9RNJstGNVHY8CUs966dpy3hirhHmxkjBFr8lpQ26QhjykoBgjrB+xb6MimERGRAzBgcnNLM4uQGOaPWC3LobQ0AvlbrYnHZJdRsUY0mh1QXiBpNLDvB8CsIR+KiIgcjgGTG5NZWYt2F2FEksbZcbJ2nARNzF+yW1ygl9oWHdKax3QqUFMM5G/WdhwiInIoBkxubMeRShRVNeCUvuHaDnR4LRAQaR0SIrvI+n2jYowqYNJUXiBmMOAbDGTO50+CiMiFODVgevbZZzFmzBgEBwcjJiYG06dPR2ZmZrvnvPHGGzj77LMREhKi/iiVl5d3edy//vWv6rltt4yMDOjNj7sLEehjxMC4YA1HsQC566zDcSwnoMkpcUYU1lqw86iW8gJGa/J35nfaGkNERPoJmJYtW4a7774ba9aswcKFC9HU1IRJkyahpqam9Tm1tbWYMmUK/vSnP53UsYcMGYL8/PzWbcWKFdCbH3YVYERyGEwGDT/Gkiyg5igQPciRTfNIGREGBHoDPxxs0nag5LFA4Q6g/LCjmkZERBqZ4ETz57cfdnj33XdVT9PGjRsxceJE9diMGTPU7dKlS0/q2CaTCXFxcdCrnNJa7M6vwn3nalz3LWcN4O0PRKQ4qmkey2SwDsv9cLAZfxijMY9JCllmfg+Mvd2BLSQiIl3kMFVUVKjbiIgIzcfat28fEhIS0L9/f1x77bU4fFhfn9Z/2FUIb6MXRiRrTPg+tNo6HCdDQaTZ6Dgj9pSacbhSw7CcT4C16vfub/gTISJyES4TMJnNZtWbNGHCBAwdOlTTscaOHat6q6QHa+bMmcjOzsaZZ56JqqqqDp/f0NCAysrKdpur+357vlo7LsBHQydh5RGg7CAQo+18U/t6TD4GYH62xmG5PuOAQyutw6VuyB2vKSIitwiYJJdpx44dmD17tuZjTZ06FZdffjmGDx+OyZMn47vvvlPJ4p9++mmnyeehoaGtW3JyMlxZUVU9Nh4qw5h+GnviDq0CjD5AtMZhPWpX9Xt4tBHfHdCax3S69XbPt255dt3tmiIicouA6Z577sG8efOwZMkSJCUlOfz4YWFhGDBgAPbv39/h/kceeUQNB9q2nJwcuLIFOwrUhLbRWssJHPwJiB5gDZrIYU5LMGJLkRl51RqG5fzDgNihwM6v3PIn427XFBGRSwdMUq9GgqU5c+Zg8eLFSEnpmcTj6upqZGVlIT4+vsP9vr6+qmxB282VzdtmHY4L9vO2/yBVedYZcnHDHdk0kjymWCO8DdDey9T3DCB7GVBT4nbn1d2uKSIilw6YZBjuww8/xKxZs1QtpoKCArXV1dW1Pkfub9mypbV3aPv27ep+aWlp63POO+88/Pvf/269/8c//lGVLDh48CBWrVqFSy+9FEajEVdffTXcXVFlPdZll2JsSqS2Ax1cYe1ZYnVvhwvw9sKIaCO+ydIaMI2z1sna/bWjmkZERO4YMElCtnTXS2FK6f2xbZ988knrc15//XWMGjUKt956q7ov5Qbk/tdf//JHRHqPjh79JTk2NzdXBUcDBw7EFVdcgcjISFXrKTo6Gu7um235MBq8MCZFY/7SgWVAzCDAxOG4njAu0YitRRpny/mHW2fLbf/ckU0jIiI7eFk0reOgTzKjRxJVJZhztaGEX726Aj4mA+6fpGGhXJkZN/du4JTrrUETOVx9swV3/lCHu0/xxb2n+Np/oH0LgVWvAL/fCYQmwl258jVFROQ2Sd/UPVnF1dh2pAIT0qK0nbIDSwCfQCCSs+N6crbcqXFGzNnbpG1tub7jAaM3sIO9TEREzsSAyY3M2XRErR13Sh8Ns+MsLUDWEiBuGGB0aqF33TszyYQDFWZsK9ZSxDLQWmJgy8cyS8KRzSMiopPAgMlNtJgt+HJTLk7vH6mG5OyWtwWoLbEu8Eo9ami0ARF+Xvgss1HbgVLPAYp3A/lbHNU0IiI6SQyY3MSqrKPIq6jH2QM1Jq7v/xEIigVCHV/vitozeHnhzCQj5u5vUjlNdpPgNiAS2PwRTzERkZMwYHITs9flICncH6nRQfYfpL4COLwaSBwNVfmSetxZySZUNcpSKc32H0TW+et/DrDtU6Cx1pHNIyKibmLA5AaOVjdgwc4CnDMwBl5aAp2sxdY8mEQOx/WW+CADhkQZMGuXxmG59MlAQwWwyz0rfxMRuTsGTG7g0w05qkPozHQNs+MsZiDzOyB2iDWRmHrNuX1MWFfQgn1lLfYfJCQeSBgFrH/LkU0jIqJuYsDkBsneH64+hPGpUdqWQsnbDFTm/bKoK/Wa0+KNCPP1wvs7NPYyDbwQOLLB+rMkIqJexYDJxS3cVaiSvS8YHKvtQLu/AUISgPC+jmoadZPJ4IVz+xjx+d4mVDRoSP5OOg0IigHW/pfnnoiolzFgcnFvrTiAgXHB2pK9K44AueuBPuOY7O0k5/fzRlML8OmeRm3J3wMvAnZ8AVQVOrJ5RETUBQZMLmxLTjnWHyzD1KFx2g60aw7gG2xdl4ycItzPC+MTjXh7eyOaWjT0Mg2YbA2c1rGXiYioNzFgcmGvL92P+FA/jOmrYaHdulJr7SXbEhvkNBeneiO/xqLqMtnNJ8g6Y27d/4D6Skc2j4iIToABk4vaW1iFBTsLcfHwBBgMGkoJ7JwDeBmB5LGObB7ZITnEgNGxRry2uVEl89tt8HSgqQbYwBlzRES9hQGTi3p10T5EBflgopZSAnXlwJ5vrblL3v6ObB7ZaXq6dX25bw9oKGQZGAWknQ+sfAVoqObPgoioFzBgckG78ysxb1s+po1MhMmo4Ue0/TMAXkC/MxzZPNIgLdyIUTEGvLihHs1aepmGXQE0VAFrX+fPg4ioFzBgckH/WJCJmBBfnKVl3bjqIiDzW2uw5BPgyOaRRpdn+OBghQWfZWrIZZLyAgOmACv/BdSW8mdCRNTDGDC54CK7i/cU4cpTk2EyaPjxbH4fMPmxd8kFpYQaMCHRiH+ub0BNk4ZepuFXAuZmYNnzjmweERF1gAGTC2luMeOJb3YhPTYIp/ePtP9AxXuArCXWPBeTryObSA5yZYY3Khss+M+mBvsP4h8GDPs/YP2bQHEmfzZERD2IAZMLeX/1IewtqMIN4/rZv8iuuQVY/R8gNBFIPNXRTSQHiQ4w4JI0E97c1oj9WtaYkxlzgTHAt/dbF1YmIqIewYDJReSV1+GFHzJx3qBYbVW993wDlGYDg6YBWob0qMf9Ks0bkf5e+PNP9TDbG+wYfYCxtwMHfwK2fuzoJhIR0c/4F9UFWCwWPPzFNvh7G3HVmGT7D1R5BNj4nrVIZZiG41Cv8DF64ZbhPlib34KPdmlIAE8cDfQ/G5j/MFCZ78gmEhGRKwRMzz77LMaMGYPg4GDExMRg+vTpyMxsn4vxxhtv4Oyzz0ZISIgapiovL+/Wsf/zn/+gX79+8PPzw9ixY7Fu3Tq48lDc8n1H8dszUxDoa7LvIJL8u/wFwC8ESJ/k6CZSDxkaZcT5fU14ZnW9tqG5MbcBBhPw1V2A2ezIJhId9wGvqLIeR8prUV2vIdAncjNODZiWLVuGu+++G2vWrMHChQvR1NSESZMmoaampvU5tbW1mDJlCv70pz91+7iffPIJ/vCHP+Avf/kLNm3ahBEjRmDy5MkoKiqCq9lxpALPfLsbkwbHYmRyuP0H2vguULrfWp/H5OPIJlIPu3awNyL8vXD3wjrU2TtrTgLl8b8DDiwGVr7s6CYSKQWV9fhiYy5mLsvCfxZn4b8/HcCGg6VokJWliXTOyyIfF1xEcXGx6mmSQGrixInt9i1duhTnnHMOysrKEBYWdsLjSI+S9Fz9+9//VvfNZjOSk5Nx77334uGHH+6yHZWVlQgNDUVFRYXq2eopZTWNuPjVFfA1GfCXS4bAx2Rn/Cr5K0v/Dgy8EEg509HNpF5wuNKMx1fUY0qKCS+f629/0v+m94EdnwPXfQGkngtX0VvXFPWcspoGfLD6MPIr69s9Lr+pl4yIx7hUDasSELkBl8phkjdTERFh/2KzjY2N2LhxI84///zWxwwGg7q/evVquIr6phbc9sEGVNU3Ycb5A+wPlmQ6+U8vAvEjWHPJjfUJMeC2ET6Yu78Z/97caP+BRl4LJIwCPr0BKNrjyCaShztwtOa4YEnIJ+5le4+ivFbD7y2RG3CZgEl6gWbMmIEJEyZg6NChdh/n6NGjaGlpQWxsbLvH5X5BQUGHX9PQ0KA+Abfderre0n0fb8bWnArcP2kgooPtrJVUcRj48S9ASAIw9DLA3l4JcgnjE034v4HeqqDlx7vt/ONjMAITHwQCIoEPLgXKD8MZevuaot4JmDpTUdeEkhoGTM524403qlzgY0du5G+qjLzYOiXIzQMmyWXasWMHZs+e7ZTkcxkusG0yfNdTGpvNuG/2ZizaU4TfnZeOAbHB9h1I/hDOfwTwCQJG/QYweju6qeQEv043YVI/E/60vB6f7LHzD5BPIHD+X62f/d+9CCg7BD1fU9Q7JHWgM/JRzWTgBzZXI8HSueeeC39/f/zwww/qWiQ3D5juuecezJs3D0uWLEFSUpKmY0VFRcFoNKKwsLDd43I/Li6uw6955JFHVORt23JyctATZPjtt++tx8JdhSpYOqWvnUnehTuB7x4ATP7AmFu4VpyOSO7SDUO9cX4/Ex5aVo//bG5Qs5JOmvQwTXoGaGkG3poE5G9Db+qta4p6T0ZcsAqMOpIc4Y/YEK4q4EpktOW8886Dr6+vmlRlC5ZefPFFDBs2DIGBgeqDzF133YXq6urWXGF5D+pss1mxYgXOPPNMFYjJMe677752k7VkhvqxX/vHP/6xdf/MmTORmpoKHx8fDBw4EB988AHakufLc6ZOnapeo3///vj8889b9x88eFA9Z8uWLa2PPfbYY+qxl1/+ZdLLnj17cMEFF6jv3daOrnKgXTZgkj8EEizNmTMHixcvRkpKiuZjyg9g9OjRWLRoUbvhPrk/bty4Dr9GfqEkEbXt5mhZxdW49LVV2HCoDA9OzsCYfvbkaVmAzO+BBX+yLr465lZrbwLpisHLCzcN9cavB5jwj3UNmLG4DrX2zJ6T35GpzwG+wcDbk4AdX6C39MY1Rb2rT0Qgxqcev2RTgI8RFwyOg5+3nSVRyOFKSkpU3q7JZFLBUtsgQXJ6X3nlFezcuRPvvfee+tv74IMPqn3jx49Hfn6+2r74wvp+Ybufn2+t8ZaVlaVmrl922WXYtm2bmpUuAZT8LW/rySefbPe1MmtdyN/73/3ud7j//vvVqNLtt9+Om266SXWYtCUBkLzG1q1bce211+Kqq67C7t27O/x+c3NzVaAkwVVbN998s5p9v3LlStWGtsGU282Sk8h21qxZmDt3rooybSQatH3jknck24YNG3Drrbdi+fLlqm5Tnz59WpPDJYq+9NJLW39g8gO84YYb8N///hennXaaOkmffvqpijaPzW3q6Rk9ZrMFH68/jKfn7UZEoA9+f8EAJIa1/6F2S30FsOY14OAKIHkskHExYOQblN6tPNKMN7c2IiHIgJfO9cfIGOPJH6SpHljzb+DAUmtS+JRnAb/e7ZrnLDl9qG9qRlZRDbbklKOqoQmp0cEYHB+MxPAAZzeNfs5hys7OVtebBETSeSDBjIy6dEZ6bu644w7VI9XRzPRjQ4Tf/va36njy99VGXuOss85SvUxS+1B6mCQnWbZjSZ7ykCFDVI1FmyuuuEJ97bfffqvuS0+QtEl6mWxOP/10nHLKKXjttddUD5N0sGzevBkjR45Uf++9vb3x448/tnvdgIAA/O9//8M111yj7r/77rtqX3frObpUD5OcDAlKpDBlfHx86yYBj83rr7+OUaNGqWBJSLkBuf/111+3Pkci3rY/7CuvvBIvvPACHn/8cXUypdtu/vz53QqWHGl7bgWu+O9q/HnODrWY7tPTh558sCQFKTO/A+bcDuRtBkZeAwyZzmDJQ0xINOGZM/0g6SGXzqnBYz/VoaTuJAtTevsBZ9wPjL8P2Pkl8O8xwNbZLHBJJ016kYYkhuLa0/vijrPScMHgWAZLLkY6FWRURf7u7d+/H88//3y7/RJUSCdDYmKi6nz4zW9+o3qkpOZhd2zdulUFHkFBQa2b1DmU15RgrSvSSyRBU1ty/9jeo2NHhOR+Rz1MUmtReq2eeuqp4/ZJUCX7uvu9dcWpXRTd6dz661//qrYTkWjzWNLbdGwXYW/ZfLgM/12Whfk7C5EU7o8/XzgIQxNP8hN9SyNwYAmw7TOgKh9IPAUYMBXw1bDOHLmlxGADnpjgiwXZzfhyXxO+2NuE64f64MahPogL7OZnHsk/kArwUnJg/f+sAfiKl4Ez/2BdwJfFTol0QfJ9JAVF8nmlN+a6667DRRddhOHDh6u/lRdffDHuvPNOPPPMM2qURnqHbrnlFlWSR3pkulJdXa2G0SRv6Vgy8tPbZGhP8qOks+VYb731lup9ksBQRq2am5tVD5i9OKbjIPkVdfh+ewG+2JSLnXmViAv1w+0T++PM9GgYuzt7xNJirauUvRzIWgI01gCxg4FhlwMhx/8ykOeQ36ELU71xZrIJ87Ka8N6ORjVUd24fEy4d4I2zkk0I9O7G71lgNHD2I0DRbmsv05e3WnPiRlwFDPm1NaBieQoityUJ3RIsicsvvxxffvklrr/+erU8mNQolJ6gf/7znyqXSUi6ysk45ZRTsGvXLqSlpdnVvkGDBqmcIglkbOT+4MGD2z1PVgCRdre9L6NLbclI0969e1uH8o4lw3i/+tWvVK/bhx9+qHqb/va3v8FeDJg0WLHvKJZkFmHl/qPYU1ClptWOTA7DA5MGqltDdwKl5jprcFSwHcjfAtRXWpe5kB6lpDFAIKvn0i+Cfbxw9SAfTEvzxorcZizLacFdC+vgYwBOjTNiXKIJF6eakBLaRa5TzCDggiesJQf2zrdWCF/1KhAUC/Q/B+g3ARh+JWDizCcidybrqkodpieeeELlCkkS9KuvvopLLrlEBSqS9nIyHnroIRWIyAiO5DPJbDsJoCS53La6xok88MADqh0S/Ehi+jfffKOCOhkqbOuzzz7DqaeeijPOOAMfffSRCvikx6gtGW6U76WznjFJXJfhQwkUpfdLVhLRggHTCYYKT1Rsr9lswXVvrVX/H58ShtsmJGNYQrCaMSLq67s3ZmrKXgyf9a/DHJIEc8xwmCMHqP+3fsqvP76yLpF8NpwYb92Kar2wpRjYVdqCf65vwca8Bvzn3G5e2r7RwLDfAEOugeHobhjzN8N08CcYts1GLfzQnDblhF8uXd3dWcalO9cUEXX/muouGXZ78803MW3aNNXbImUFnnvuOVX6Q3KCpWZa256crgwfPlwtX/bnP/9ZlRaQa1tKBEjucHdIYc1//etfKs9YZstJntE777yjcpnbkgBP6jLK5DAZbvv444+P64WSXq62PVVtSc+TBHTSg+aooUKXWkvOVcgUxS4L7RmM6PvAXPXfphL7a8yE+QFxQQY0NvPHQNrlGJMwunkznq77u93HCPD2Qt8wAy7/rBaf72o+4XO7O5O0W9cUEXG9RVhnycnw2bFVy52NAVMHZIw3Ly/P4ZG+K5FP+vIHTAoKskYOz6+9unuNeMI15Ui8Pj33HPMagcsGTByS64Akw2mtOO4uWFSQ57c3eNI15Ui8PnmOyXUwYCIiIiKXYXHRTCGXWEuOiIiIyJUxYPJQstaXrO0jt8TzS66F1yfPMbkeJn0TERERdYE9TERERERdYMBERERE1AUGTERERERdYMBERERE1AUGTJ3UgJAqsK5aC4LI3fCaIiItVq9eDaPRiIsuugjOwllyHZBgKTQ0lGv6EDkIrykifaiqb0JuWR2q65sR5GdCUrg/gv28e/x1ZSHdoKAgvPXWW8jMzERCQgJ6Gyt9ExERUZcOl9Tiy825KKxsaH0sNsQXvx6VhD6RAT12Bqurq/HJJ59gw4YNKCgowLvvvos//elPvf4T45AcERERddmz9OUxwZKQ+/K47O8pn376KTIyMjBw4EBcd911ePvtt52SMsOAiYiIiE4ot6zuuGDJRh6X/T1FhuEkUBJTpkxR6TLLli1Db2PARERERCdUXd+sab+9JF9p3bp1uPrqq9V9k8mEK6+8UgVRvc0tA6aqqirMmDEDffv2hb+/P8aPH4/169e37r/xxhvh5eXVbpOolPRJumYLK+txqKQGxVX1zm4OEZHuBPmZNO23lwRGzc3NKslbgiXZZs6ciS+++EL1NPUmt0z6lmz5HTt24IMPPlAn8cMPP8T555+PXbt2ITExUT1HAqR33nmn9Wu4yKw+ldY04qe9xdiSW476JjMCfIw4tW84xqdFIdS/52duEBF5gqRwf5Xg3dGwnDwu+x1NAqX3338f//znPzFp0qR2+6ZPn46PP/4Yd9xxB3qL25UVqKurQ3BwMObOnduuHsPo0aMxdepUPP3006qHqby8HF999ZVdr8Ep0O6hvqkZn6zPxZ6CquP2jeoThktHJcLb6JadqLrDa4pIx7PkTklCnwjHz5KTv+Ey/FZUVKRK/bT10EMPYfHixe1Gl3qa2/01kYizpaUFfn5+7R6XobkVK1a03l+6dCliYmJUVv2dd96JkpISJ7SWepIkGWZ2ECyJbbkVyCvvuSREIiJP0ycyALeckYLrx/XFr0clqlu53xPBkm04TkaPjg2WxGWXXabKDGzbtg29xe16mITkLPn4+GDWrFmIjY1V3XI33HAD0tLSVILY7NmzERAQgJSUFGRlZal6DVLwylYp9FgNDQ1qa/tpODk5mYUrXdyarBLM3ZrX6f4rTk3CqD7hvdomsuI1RUR645Y5TJK7dPPNN6t8JQmATjnlFJVBv3HjRrX/qquuan3usGHDMHz4cKSmpqpep/POO++44z377LN44oknevV7IO28TV4n3s/hOKfhNdVNZQeBFS8BexcA9eVARCow4ipgzK2Ad/tedHe28VAZ3liehXXZpWhqsWBIQgiuO70vLh4eryblELkDt+xhsqmpqVG9QfHx8WqcU6qBfvvttx0+Nzo6WuU33X777cft46dh91RQUYf/Lj+gkr2PFexnwh1npSIi0McpbfN0vKa6YfvnwNy7AZ9AIOUswD8CKNkHHFoBRKYDV88GIlLgzsxmC174IROvLc1Cnwh/jOkXCR+TATuOVGD7kQqclxGDf109CkG+bvnZnTyMW/+WBgYGqq2srAwLFizA888/3+HzcnNzVQ6TBFYdkRl0nEXnfmJD/HDhsHh8vSUPzeZf4n4fo0F9cmWw5Dy8prqw+UNrsJR6LjD2TsC7zQyjssuBpX8D3p4C3PIDEN4X7kg+iz/+9Q58tOYwrj6tj7omDT/3Jv1qRILqdXpt6X5c/9ZafPTb0+Hvc3y6BJErccseJgmOpNmS0L1//3488MADKgn8p59+Up9sZXhNEsLi4uJUDtODDz6oajdt3769W4ERZ/S41yfYQ6U12J1fheKqBsSF+iEjLhh9IwOd3TRqg9dUG4dWAe9eDKRfAJx+N9DRkFRdGfD9Q4BfCHDrYmsvlJt5e0U2npy3C7ed2R/nZMR0+Jz9RdV4+ttdmDQ4Fq9cPYrDc+TS3G6WnJBiVXfffbdaW+b666/HGWecoYIob29vldMkWfO/+tWvMGDAANxyyy2q5IAEU+xF0h+DwQspUUGqp+mG8f0weUgcgyVyXfUVwOe3ADGDrD1LneXv+IcD5z5qzXFa8Ge4m115lfjbd7tx4dC4ToMlkRYThNsnpuKbbfn4bGNur7aRyCN6mHoaPw0T8ZrqEd/eD2z5CPjVf4CgzgOJVnvmAWtfB276Hug73m16faf/ZyXK6xrx9PRh3Zp8MXPpfmzJKcfiP56NqKCuRwGInMEte5iIiNxO4S5gw9vAiGu7FyyJgRcCUQOB7x+USATu4ItNudh2pAI3TUjp9kzVa0/vC0lDfHHh3h5vH5G9GDAREfWGRU8CQXFAxi8rFHTJywCMuQUo2A7s/BKurqG5RQU9Y1MikBEX0u2vC/HzxvSRifhkXQ6yj9b0aBuJ7MWAiYiop+VvBfZ+b62xZDzJNQ5jBgOJpwLLnnf5XqbPN+aioKIel49OPumvvWBwLEL8Tfj34n090jYirRgwERH1tJWvAMFx1npL9hh+BXA0E9i3AK6qxWzBG8sO4LSUCCTasRCr1Ge6aFgC5m7JQ34FlzUi18OAiYioJ1XmATvnAIN+BRjsrDUkvUzRGcCamXBVS/YU4VBpraq3ZK9zM2JU4PThmkMObRu5rxtvvFGVm7BtkZGRmDJlSq+uIWfDgImIqCdtfBcw+QCp52s7TsbFQPYyoNg1E6PfX30QqdGBSIsJtvsYUrxyYno0Pl6Xo/KhyAXVVwIFO4BDq623cr+HSYCUn5+vtkWLFsFkMuHiiy9Gb2PARETUU8wtwKb3rUNxPhpXdO87AfANATa9B1eTU1qLn/YdxXmDYjUf67xBMSitacTCXYUOaRs5UNlBYM1/gPVvAttmW2/lvjzeg6SGohSilm3kyJF4+OGHkZOTg+LiYvQmBkxERD3lwBKgKh9Iu0D7sSRZvP85wNbZQEszXMmXm47Az9uIcf0jNR8rKTwAA2KD8NkGFrJ0KfWVwNaPgaqC9o/LfXm8F3qahKwZ++GHHyItLU0Nz/UmBkxERD1FgpuwPkDUAMccL+08oPYokLUIrkJqH3+5KVcle0vQ5Ahnpkfjp33FKKqqd8jxyAHKDx8fLNnI47K/h8ybNw9BQUFqCw4Oxtdff41PPvkEBkPvhjAMmIiIekJjjbVStwzHdbYEyskKTwHC+gHbP4er2JZboZK9J6RFOeyYp6dEqoV6v9uW77BjkkYNVdr2a3DOOedgy5Ytalu3bh0mT56MqVOn4tCh3p0cwICJiKgn7PsBaKoD+k103DEl8Op3BpD5rfXYLmDetjyE+ntjcHz3C1V2JcjPhOFJofh6a57Djkka+QZr269BYGCgGoKTbcyYMfjf//6HmpoavPnmm+hNDJiIiHrCrrlAZBoQYv80+w5JwCS9V1mL4QrDcd9uz8eYfhEwGhzUi/azsSmR2HS4HIWVHJZzCWF9rLXEOiKPy/5eIuUFZDiurq53PzQwYCIicrSmemsPU59xjj+3oUlAWF9g9zdwtp15lcgrr1f5S452St9wFYT9sLOTvBnqXX4hwIirjw+a5P6Ia6z7e0hDQwMKCgrUtnv3btx7770q+fuSSy5BbzL16qsREXmCgz9Ze4GST++Z4yePBfbOt86WMzrvbVym/gf6GDEo3vHDMUG+JjXMJ6/xm3H9HH58skN4P+D0u60J3pKzJMNw0rPUg8GSmD9/PuLjrT21kvSdkZGBzz77DGeffTZ6EwMmIiJHy/wOCI7vuWEKCZi2fwrkrgP6joezSDAzIjkMph6arXRKnzB8tPYwqhuaVQBFLsAvBIgb2msv9+6776rNFXBIjojIkSwWYO8CIOlUx82OO1ZUOuAXZh32cxLJLdqVX4lT+oT32GuM6hOOZrMFK/cf7bHXIOouBkxERI5UvAeoPAIkntpz59XLACSeYg3MnGRZZjEkz1tms/WU2BA/JIb5q3XqiJyNARMRkSPt/xEw+gBxw3r2vCaMBop2dV5MsIct21uM1OggBPt59+jrDEsKVa8lM/KInIkBExGRI+1fDMQOswZNPSlhpHQ1AVlL0NtazBas2H9UBTM9bXhiKPIr6nHgaE2PvxbRiTBgIiJyZDmBwyuBhBE9f079QoHIVOt6db1sZ14FKuqaMCyx5wOmQfEhMBm8sGIf85jIuRgwERE5Su56oLkBiJfen14QNxw4sNSaaN6LVu4vgZ+3AWnRQT3+WrI+XXpsEBO/yekYMBEROUr2csA3xFqvpjfEjwCqC4Gje9GbVmUdRUZcCEzG3vkTMjg+FKsPlKihQCJnYcBEROTIgpWS7C2z2HpDzGDAYAQOrui9UccWM9YfLHXo2nFdGZoQgqr6ZuzOr+y11yQ6FgMmIiJHaKwFjmwEYnuvqB+8/YHIAb0aMG3LrUB9kxmDE3ovYEqNCYKP0YA1B0p67TWJjsWAiYjIEY5sAFoaezdgErFDrAFTL+Uxrc0ugb+3Ef0iA9FbvI0Glce0Nru0116T6FgMmIiIHOHQauvaWuF9ez9gqikCSg/0ysutyy5VwYssjNubBsYGY312KczMYyInYcBEROQIh1YB0YN6L3/JJmaQtR7T4dU9/lKSdL3xYJkKXnrbwLhglNc1Iau4utdfm0gwYCIi0qqlGTiy3pqE3dt8gqyz8g6v6fGX2ldUhaqGZmTE9X7AlB4TrJZi2XCorNdfm0gwYCIi0qpoJ9BY83NvjxNEZ/RKwLThYJkKWvr3Qv2lY/n7GNE3MlC1gcgZGDAREWmVsw4wmICodOecS+nZKtkH1PZsUvSmQ2XoFxWoikk6Q3pMEDYcYuI3OQcDJiIiRwRMkWk9v37ciXqYhJQ16EEbD5WpoTFnGRAbjEMltSiraXRaG8hzMWAiItIqZy0QNcB55zE4zrq2nARuPaSkugGHSmtVL4+zpP382ptzOCxHvY8BExGRFjVHgfJDQNRA551HLy8geqB1LbsesiWnvF3Q4gwxwb4I8/fG5sPWthD1JgZMRERa2IbBJGBxJgnYpC1mc48cfmtOOUL9vVXQ4ixeXl5IjQ5iwEROwYCJiEiL3A3W4bCgWOeeRxkSbKgESvb3yOE355Sjf3SgClqcSdogwRsLWFJvY8BERKSF9OrI7DgnBxKtM/R6IPHbYrGoIEV6d5xNhgSlFlR2SY2zm0IehgETEZG9ZP22vE3WBXCdTQpYhiYBeZsdfujDpbWorG92iYDJVgNqe26Fs5tCHoYBExGRvSTZu67MefWXjhWR2iM9TFt/Dk5kOMzZgnxNiAvxw9ZcJn5T72LARERkL1tvTqSLBEySx1SwDWhpcuhht+eWq2TvED9vuIKUn/OYiHoTAyYiIi0BU2AM4B/mGucwMhVoaQSK9zj0sNtyK5AS5fzeJZv+UYHYlV+pFgMm6i1uGTBVVVVhxowZ6Nu3L/z9/TF+/HisX7++XYLi448/jvj4eLX//PPPx759+5zaZiLSobwt1iDFVciQHLys7XIQmY2244hrBUzSlvomM7KKq53dFPIgbhkw/fa3v8XChQvxwQcfYPv27Zg0aZIKio4cOaL2P//883jllVfw+uuvY+3atQgMDMTkyZNRX1/v7KYTkZ4SvvO3/BykuAhvfyA02douB5Hq3jWNLS4XMAkmflNvcruAqa6uDl988YUKiiZOnIi0tDT89a9/VbczZ85UvUsvv/wyHn30UUybNg3Dhw/H+++/j7y8PHz11VfObj4R6Snhu77CuoacK4nsD+Rvddjhth+xJny7UsAU4GNCfKgfduZVOrsp5EHcLmBqbm5GS0sL/Pz82j0uQ28rVqxAdnY2CgoKVI+TTWhoKMaOHYvVq1d3eMyGhgZUVla224jIfh5xTdmCElcakhPS41WwHTC3OORwO49UIDrIF8EukvBt0zcyANuPMPGbeo/bBUzBwcEYN24cnnrqKdVrJMHThx9+qIKh/Px8FSyJ2Nj2VXflvm3fsZ599lkVVNm25OTkXvleiPTKI64pCZgCIgH/cLhcwNRcDxzd57AeJglOXE2/yEDsyqtkxW/qNW4XMAnJXZKht8TERPj6+qp8pauvvhoGg33fziOPPIKKiorWLScnx+FtJvIkHnFNScAUkQKXE9HfeivlBTSS91kZ9uob6TrDcW0DJsmtkqKaRL3BLQOm1NRULFu2DNXV1eqNeN26dWhqakL//v0RFxennlNYWNjua+S+bd+xJOgKCQlptxGR/TzimpJhr3AXG44TvkFAUJxDAqb8inpU1DWhX5QL9jD9nFPFPCbqLW4ZMNnI7DcpHVBWVoYFCxaoJO+UlBQVGC1atKj1eZI/IbPlZCiPiEiz6iKgutA1e5iEtCtfe8BkC0ZSXLCHKdTfGxGBPtiZxyVSqHeY4IYkOJKu4oEDB2L//v144IEHkJGRgZtuukmtpC01mp5++mmkp6erAOqxxx5DQkICpk+f7uymE5Ee2HpvXDZg6g9kfmctfaBhUWDJEQr2M6nAxBX1jQhgDxP1GrcMmCQnQnIkcnNzERERgcsuuwzPPPMMvL2tszgefPBB1NTU4LbbbkN5eTnOOOMMzJ8//7iZdUREdinYYa15FBzvmicwPMW6xl1VPhCSYPdhdudXoE9EgPog6or6RAZgdVaJs5tBHsItA6YrrrhCbZ2Ri/vJJ59UGzlefVMzso/Wquq/NQ0tSI0JxIDYYMSGMCAlD6Hyl1IALxfNamhN/N6uKWCSIblhiaFwVX0jAjF3Sx5KqhsQGeTr7OaQzrno1U6uqr6pBT/uKsL7qw9h0+FyZBZW4bvtBXhnZTYOl9Y4u3lEvRgw9XPdsx0YDfgEaUr8rm5oRk5ZHfq4YP6Sja3cwe78Kmc3hTwAAyY6KQeP1mBlB13gFXXNWLSrCE3NZp5R0remeqBkv7WHyVXJEJoEdIU77T5EZoE14dsVazDZxIX4wddkwJ6f20rUkxgw0UkngXYmq7gGRVVcr490rngPYGlx3YRvGwnopCfMTrvyq2A0eCExzB+uymDwQnJEAHblM2CinseAiU5KbVNzp/taLBY0my08o6RvqtfGCwjrC5cmPUylB4BG+wo77smvVMGSt9G1/0wkhwec8IMckaO49pVALic1OrjTfWEB3ggNcK31poh6JGAKibfOknP1gMlitvaI2WF3fiWSw138e5SZchEByCquRlML0wGoZzFgopOSFhOIiA6CIpl0fNaAaIT5u2a9FiKHBkyu3rskVBu9gKJdJ/2lUucus6BKBSOuTkoLNLVYVH4lUU9iwEQnJTrYD9eM7YPB8cEwGay1WSICvTFtVAJGJYfxbJL+Fe5w7RlyNt5+1pICdiR+55bVqXXaJBhxdX3Cf54pV8CZctSz3LIOEzlXYngArj6tD4qqGtBsNqtepRB/DsWRhyyJUnvUPQImEd7XWmTzJO35OfiQ/CBXF+RnQmSgj8q5+tUI+2tOEXWFARPZxWQ0IMGFZ88Q9Qhbb02YmwRMMiy37we7SgoE+hpddkmUYyVH+LcGeUQ9hUNyRETdVbQbMPkCwXHucc6kJ6y2BKguPqkvyyysduklUY6VFB6gcq6IehIDJiKi7iraCYT2AQxG9zhntp4wafdJkOEtCULchQR3R8rrUFXf5OymkI4xYCIi6q7CXUBYH/c5X9ITZvSx9ox1U2OzGdlHa9yipICNFK8U+4qqnd0U0jEGTERE3WH+uaaRJFK7C+kJkwDvJGbKHTharQrQ2oIQd5AQ6g+ZtMthOepJDJiIiLqj/BDQVOseNZjakoDpJGox2YIOdxqS8zEZEB/qz4CJehQDJiKi7rANa7ldwNTP2nbpIeuGvYVVapp+kK97TaJOCudMOepZDJiIiLpDeml8goCASPfrYZKesYqcbvcwSfDhbqTNEuwR9RQGTERE3SG9NJK/5CZT7VvZcq66mfhtDZjcZzjORopsltY0oqS6wdlNIZ1iwEREdDIlBdxNQBTgEwgUdx0w1TY2q2VRpBCku0n6OUl9byFnylHPYMBERNSVlibg6H73KilgIz1iEuh1o4dpf1E1LG6W8G0TF+IHk9GLw3LUY3o1q89gMJywcmxLS0tvNoeIqHtKDwDmJvcqKWDHTDlb70yiGy57ZDR4qXYzj4l0ETDNmTOnN1+OiMgxbMGGu82QaxswZS8FzC0nrFIuwUZsiC/8vN2kkvkxJGBiLSbSRcA0bdq0dve//PJLbNmyBUOHDsUVV1zRm00hIuq+oj2AXxjgF+qeZ00CveYGoOwgEJna6dMk2HDH3iUbGUqcvyMfFovFbdbBI/fhtBym5557DldffTXmz5+PW265BU888YSzmkJEdGKSMO2O+Us2trZ3kceUWeieM+TalhaorG9GcRVnypGOAqb33nsP//vf/7Bu3TrMnTsX77zzjrOaQkR0YhJohCa771nyDwd8g084U04Wri2oqHfLGkw2trZzphzpKmDKy8vD6aefrv4vt0eOHHFWU4iIOtfcaE36dtf8JSHDUyrxe0+nT7EtXOvOPUyxwX7wMRqY+E36CphkRpzJZE2hMhqNMHezbD8RUa8q2Q+Ym917SE5ID9kJZsrtK6yCZP0khPnBXRlkply4P/YVseI3uXnS96hRo1oT8erq6nDJJZfAx8dHJegREbkk2zCWuwdM0kOWtRhoaQaMx7/1yzBWbIgffE3uOUPOJoEz5UgPAdP06dM7nTHXdh8RkcsozrTmAPmFwK1JwNfSaJ0pF5XWYQ+T9M64O8lj+m47Z8qRmwdMf/nLX3rz5YiIHJTw7ea9S217yKTHrIOASXqYTkuJgLtLCvNHVX0ziqoaVI8ZkVsGTJWVlSfcHxLi5p/giEifAVP0QLg9qSPlG2JN/B50yfEz5Crde4acjS1p3VqEkwETuWnAFBYW1mExMVuRMS6NQkQuOUMu/QK4PdtMueI9upwhZxMT7Ktmyu0rrMaZ6dHObg7pSK8GTOLzzz9HRIT7d/sSkYfMkLO06GNIToQmdThTTg8z5Gw4U450EzBNmDABMTExvf2yREQnz9Yb4+4z5LqYKbdPJzPk2s6UY/FKcvs6TLt27cLu3btx+PBhNDY29vbLExGdXMCkhxlyHc2Ua0Pyfdx5DbljSS6WfE8sWUNuHTCdd955GDJkCFJSUhAYGIhhw4bhpZde6u1mEBF1L2DSy3Bcu5ly7fOY9hZV66KkwLEz5bimHLntkFx2draK+JuamtSMOVkeRdaSe+yxx9Dc3IwHHnigN5tDRNT1DLmoAfo5S7aZclJaYNDFullDrvOZctWI4Uw5cseAqW/f9msxjR49WlX7HjBgAJ588kkGTETkejPkUs+HbqiZcsnWYpw/26+jGXLHzZQrqsIZ6VHObg7pRK8nfXfkqquuUsN0REQuozTr5zXkkqErx6wpJwnfepkhd+xMOSZ+ky4LV6alHV95lojIafQ2Q85Gvh+ZKWduAQxG1QsTo6MZcjYJoX4q8ZvILZO+pXBleHj4cZvt8e6Q4paS8yRJ4/7+/khNTcVTTz3VbjbEjTfeqAphtt2mTJnSg98ZEelOkW2GXCh05ZiZctILI0nSeiNDjJwpRx5duPK5557DzJkz8d5776lhvA0bNuCmm25CaGgo7rvvvtbnSYD0zjvvtN739fXV3HYi8rQZcjobjhO2WX+S0B6ZqoKKMf30V0xYhuS4phx5dOHKVatWYdq0abjooovU/X79+uHjjz9Ws+3akgApLi5Oc3uJyEOpgEKHqQLSa+YbrALC6v5TkF9Rr6saTDa2WX+2opxEHle4cvz48Vi0aBH27t2r7m/duhUrVqzA1KlT2z1v6dKlKjAbOHAg7rzzTpSUlDjseyAiT5ghl6W//CXbTDnpOSveo5ZEEXoqKWATG+wHb6MX85jIfXuYpHClbbFdg8GAjIwM3Hzzzfj973/fra9/+OGHVfK4fJ3RaFQ5Tc888wyuvfbadsNxv/71r1WeU1ZWFv70pz+pgGr16tXqa47V0NCgtu4kpxNR19z+mpJyAmqGnA4DJiHfV9FuteiuzJDTU9HKdjPlwvxbFxYm8rjClZ9++ik++ugjzJo1S+UwbdmyBTNmzEBCQgJuuOGG1jIFNlJJfPjw4So5XHqdJGA71rPPPosnnnjCwd8tkedy+2tKCjsKPQdMWYuxr6ASMSG+upshZyMBE2fKkaN4WVxgsZ0PPvhAFa7ct29fl89NTk5WvUx3331362NPP/00PvzwQ+zZ077cf1vR0dHqebfffnu3Pg3L61RUVCAkRCdrSBH1Ire/ppY8C6x9HbjyQ+hS3hZg4aO4IW4uqszeeGByBvRozuYj+H5HPrb9ZZIa1SDyqMKVtbW1aiivLRlmM5vNnX5Nbm6uymGKj4/vcL8kiHMWHZHjuP01JT1Meu1dEj9/b5lF1TgtPRF6JblZnClHbpv03RFvb2+ccsop3XquLKUiOUvffvstDh48iDlz5uDFF1/EpZdeqvZXV1erob01a9ao/ZIgLrPqpDDm5MmTe/g7ISLdzJDTY0kBG/9wVPlEo6DWS5cJ3za2743DcuR2PUySoP3SSy+pPKSOZsmVlpZ2eYxXX31V5TzdddddKCoqUrlLMsz2+OOPt/Y2bdu2TdVpKi8vV/snTZqkilu69SdeIuodelxD7lheXtgXMBKo1Ncach3NlJM15aQ455np0c5uDrm5Xg2YJAn0f//7H+6//348+uij+POf/6x6gb766qvWgKcrwcHBePnll9XWEan+vWDBAge3nIg8Rsl+fc+Q+9k+00B4waKrNeQ6W1POVj6ByG2G5GR225tvvqkCJpPJhKuvvloFUBIsyRAaEZHT6X2G3M/2Wvog1qsMvi6RmNFzEsL8kcmAiRygVy+VgoICNc1fBAUFqRkz4uKLL1Y5SURErrOGnBvM5tNgb2MEElEMVOVDzySPSap9u8CEcHJzvRowJSUlIT/fenFKXaQffvhB/X/9+vXMLyIi16D3GXI/21vtjySvYqD8EPQsOTwA1Q3NagkYIrcJmGQmm8xaE/fee69K3k5PT8f111+vqn0TETld4S7dB0wVDRYU1nkh2bsSKDsMPeNMOXLLpO+///3vrf+/8sor0bdvX7WYrgRNUi6AiMipmuqBsmxgYPu1KfVmX1mLuk0KlOhJ3z1M0cFSydyghuXOHmj/wu9ETkv3k2G4b775RpUGCA0N5U+CiJzv6F7AYgbC+kLPMkvNMHhJQrQPUHoQembwstaaYuI3uUXA1KdPH1Vp22bhwoWYMGECXnnlFcycORPnnnsuPvvss95oChFR54p/Xl5J50Ny+8rMiA/0gndwLFCVB7Q0Qc+k1tTeApYWIDcImGRpEilaafOPf/xDlRSQwpJlZWWq8OTzzz/fG00hIupc0S4gMAbwkbEq/dpT0oKkYAMQFAuYW4DKI9Az6WHaW1QFs5kz5cjNhuR2796tkr6lKresCyf/787Cu0REPR4w6bx3SewtM/8SMAkPmClX32RGblmds5tCbswpAZP0LEVERLTeDw8PR1UVu0uJyMkKd+o+YCquNaO03oLkYC/Axx/wDwXK9J3HlBxhXf6FeUzk8gGTl5eX2o59jIjIZdRXAhW5QLi+E773lprVbXLIz2//gbG672EKD/BGoK+Ri/CS65cVkAqrN954Y2txyvr6etxxxx0IDLTmCTQ0NPRGM4iIupHw3U/XZ2lPaQu8DUBc4M8fWoPjgOJM6Jl8QO8TEYA9TPwmVw+Ybrjhhnb3r7vuuuOeI8UriYicmr/kZQTCkj0gf8lLTbdXJI8peznQVAd4+0OvEsMCsCe/0tnNIDfWKwHTO++80xsvQ0SkrcJ3SCJg9NH1WdxtmyFn0zbxOzoDetUnwh9LM4vQ2GyGj0nnKw5Tj+BvDRGRKNyh+4Rvs8Wiepj62PKXRFAM4GXwiMTvZrMFWcXVzm4KuSkGTEREspK9zJDTecL34UoL6puBPm17mIzeQGCU/gOm8J9nyjGPiezEgImIqKoAqC8HwvWf8N1uhlzbYTlZQ0/HAn1NiA7yxe4C5jGRfRgwERFJ75IIT9H1udhTYkaIDxBmnbDcPmBSa8rpuxJ2UoQ/9uSz5h/ZhwETEZHkL8kMMcnn0fmSKJK/dFwdvOB4oLEaqP1lzU89spYWYA8T2YcBE5GN/MEoyQKO7gPqynlePK7Cdz9r8rOO7SptQXLb/CUbqcUkdJ7HJAFTYWUDymoand0UckO9UlaAyOUVbAf2fGdduV34RwIDJgOJowEjLxOP+PnrPOG7tsmCnEoLpqZ0EDD5hwEmP6D0AJB4KvQcMAnJYxqfGuXs5pCb0ffHKaLukF6lTR/8EiyJuhJg22zrVHPSt+YGoGQfEN4fek/4lgylvscmfAvpWZNeplJ9J37Hh/rD2+iF3cxjIjswYCLPJtPJc9YBLR0sz2MxA9nLgKZ6Z7SMeossC2JuBiL0nfC9u8QMgxeQKIvudsQDAiaj4eclUljxm+zAgIk8W3P9iadTV+UDDUwS1TVbL6LOh+Qk4TsxyAs+xs4Cpnig8kjHHx50RAKmnXm8punkMWAiz2bwBryteQ0dkrwOeQ7pO38pJOHEvwc6sPPnGXKdkoBJelXLDkHP+kQEYl9RFZpazM5uCrkZBkzk2SShu8/pne9POhUICO/NFpFTEr5TdL8kigzJdZi/ZBMca81l0vmwXL/IADS1cIkUOnkMmIhihwBJY44/DxGpQNJYnh+957AVbNN9wCRLotQ1A31DT/CWL4sOB0ZbZ8rpWJ9Ia0/iLg7L0UnifGki32BgyK+BhFFA0W7A3AREZQCR/QG/UJ4fPavIBeorgAh9z5DbedS6JMoJe5haE7+zoGcBPibEhvipgOnXpzi7NeROGDARCZ8AqJ4m2chzSO+S8ICAKdLPC6G+nSR820gu14El1lwmHRfx7BsZgB3sYaKTpN8rgoioK/nbAL8wICBS9wnffUO7CJZEcIK1jEZlPvSsX2QgduZVwCJDskTdxICJiDxXwVZr/aVj11bTEQkKdhztIuHbJiTeelu6H3pP/K6qb0ZuWZ2zm0JuhAETEXmuvC3W5H4dK6q1oKTOgpQTJXzb+AQC/uG6z2NKiQpUt9LLRNRdDJiIyDNVF1sLk0amQc+2F1sTvrsVMNnymI7qu4cpLMAHEQHe2H6EARN1HwMmIvJM+Vs9IuFbhuNCfIBI/24OO0oByxIJmPSd39M3KhDbcxkwUfcxYCIiz5S3GfAJsgYIOratuBn9Qg3w6m6eVmgi0FgDVBVA78Ny0sPExG/qLgZMROSZ8jdbh+N0nPAttheb0b+7w3EiJNF6q3qZ9B0wldU2Ia+Ci2tT9zBgIiLPdGQTEKnvhO/CGjOKJeE7zHByhVz9w4CSfdCz/lFB6nZ7brmzm0JuggETEXmeqsKfE77ToWdbf074Tj2ZgMnWy1Ss7x6miEBr4vdW5jFRNzFgIiLPk7fJehs1AHq2ragFYb5eiPA7yWHHkCSgdJ+14reO9Y8OwtYc9jCRTgOmlpYWPPbYY0hJSYG/vz9SU1Px1FNPtUvck/8//vjjiI+PV885//zzsW+fvruXiegkh+OkwrcsNqvzHqb+YV7dT/hul/hdC1TmQe8Bk8yUM5v1PSOQPDRgeu655zBz5kz8+9//xu7du9X9559/Hq+++mrrc+T+K6+8gtdffx1r165FYGAgJk+ejPp6JvcRkQRMG4GodF0nfMsHxy1FLUgNM578F4cmWW+P7oWepUYHoqqhGdklNc5uCrkBtwuYVq1ahWnTpuGiiy5Cv3798H//93+YNGkS1q1b1/om8fLLL+PRRx9Vzxs+fDjef/995OXl4auvvnJ284nI2aQ3+sgG3Q/HZVeYUdVoR/6S8PYHgmKA4kzoWWq0NfGbw3Kky4Bp/PjxWLRoEfbutX7y2bp1K1asWIGpU6eq+9nZ2SgoKFDDcDahoaEYO3YsVq9e7bR2E5GLKMkC6it0HzBJ75KwK2Cy9TIV74GeBfqakBjmjy3MY6JuMMHNPPzww6isrERGRgaMRqPKaXrmmWdw7bXXqv0SLInY2Nh2Xyf3bfuO1dDQoDYbOT4R2c+lrynpXRJRA6H3gCkxyAtBPnYOO4b2AfK/AVoaAKMv9Dwst+lQmbObQW7A7XqYPv30U3z00UeYNWsWNm3ahPfeew8vvPCCurXXs88+q3qhbFtycrJD20zkaVz6mspdbw0GfK3DMXq1qVDylzS8xYcmA+YWa4+cjqXFBGNPQRXqGq09ckS6CZgeeOAB1ct01VVXYdiwYfjNb36D3//+9+oNWsTFxanbwsLCdl8n9237jvXII4+goqKidcvJyemF74RIv1z6mspZq/vhuLomC/aUmJEWruEtPjgOMPoARbuhZ+mxQWg2W7CNBSxJbwFTbW0tDIb2zZahObPZWi9Eyg1IYCR5Tm2HA2S23Lhx4zo8pq+vL0JCQtptRGQ/l72mGqqBwp1ATAb0Xk6g2QIMiLBjhpyNvM9KL5POA6bk8AD4exux6TDrMZHOcpguueQSlbPUp08fDBkyBJs3b8aLL76Im2++We2XeiMzZszA008/jfT0dBVASd2mhIQETJ8+3dnNJyJn5y9JMcaYwbr+OWwsaEGACUgO1lg2IazPz0U+pU6RPkswGA1eSI0JxIZDpZLR5OzmkAtzu4BJ6i1JAHTXXXehqKhIBUK33367KlRp8+CDD6Kmpga33XYbysvLccYZZ2D+/Pnw8/NzatuJyMkOr7WulWarM6RT6wua1XCcQWudqfA+wIEl1gKWtkV5dWhAbDAW7S5SBSwNBn0GhqSdl6VtiWxqHcKTRFXJvXCZoQQiN+Yy19R704CmWuC8Xz5g6U2L2YKR71ZhSn9v/HqAt7aDNdUBi54CzpgBpP1SqkVvJH/p2e/3YOHvJyI9NtjZzSEX5XY5TEREdmlpBnLX6X44bm+ZGVVNQEaEA97epYBlcDxQuAN6lh4TDOlYWndQhuWIOsaAiYg8Q8FWa+9S7BDo2br8Fpi8NBSs7GhYThLldczfx4iUqECsz2bARJ1jwEREnuHQKsDkC0SmQc/W5jcjNdwAX4maHCGivzWHqU7fwURGXAjWHChtt5A7UVsMmIjIMxxcAUQPAowa83pcmPyxX5PX4pjhOJvwftbbgu3Qs4z4YBRU1iOntM7ZTSEXxYCJiDwjf+nQSiBuGPRsX5kZpfUWDI7SUH/pWDKrMCgWyN8KPRsUF6LymFYfOOrsppCLYsBERJ6Rv9RQBcQNh56tzrPmLw3UUuG7IxEpug+YZCFeyWNalVXi7KaQi2LARET6d2AZYPIHotKhZyuPNCPdkflLNhFpQFUBUN1+ySm9GRwfgpX7jzKPiTrEgImI9O/AUiBuKGBwu1q93Sbroa0+0oyh0Q4cjmub+C2VvnXeyzQ0MRRHqxuxr6ja2U0hF8SAiYj0rbEWOLwaiB8JPdta1KLqLw2N6oG3dR9/a3X0vM3Q+0w5b6MXftrHPCY6HgMmItJ/OYGWRiDhFOjZT7ktCPR2YP2lY0k5BgmYLC3QKx+TQQVNy/cWO7sp5IIYMBGRvmUtAgJjdL9+3LKcJgyJMqrFZHtE1ABr4vzR/dCz4UmhWHOgBPVN+g0MyT4MmIhI3/bOBxJPAbQuROvCSuvM2FJkxsiYHshfsgnrY10q5chG6NnI5DA0NJtV0ETUFgMmItKvkiyg9ACQeCr0bHluC6Q+dY8GTAYDEJluXY9PxxLD/BEd7Isle4qc3RRyMQyYiEjfvUtGHyB+BPTsx4NN6B9qQLhfD/eiRWcAR/fpepkULy8vjEoOw8LdhSwvQO0wYCIi/drznTVYkqEknWpssWBpTjNOievB3iWb6IGAlwE4vBZ6NrpvOPLK67E7v8rZTSEXwoCJiPSppgQ4vApIOg16r+5d3QSMju2FgMknwLq2nJRp0HkBywAfI37YVeDsppALYcBERPqU+Z2sRgv0OR16Nj+7CbEBXugb0ktJ7bFDrAUsG/Vb3NFkNGBUnzB8v50BE/2CARMR6dPOOdY/7v7h0HN17/kHmjEm3qhyb3pFzBDA3Azk6Dv5+7R+kcgsrMKBYv0GhnRyGDARkf7UlgLZy4C+E6Bna/JaUNZgwekJvTAcZ+Mfah2Wy14OvZcX8Pc24ttt+c5uCrkIBkxEpD+7vrIOx/U7A3o2d38T4gK91Ay5XhU3DMjbBDRUQs9VvyX5+6stRzhbjhQGTESkP1s/sa4dp+PhuPpmC74/0ITxib04HGcTN9wakB78CXo2IS0SWcU12Jmn38CQuo8BExHpixSqzFkDpJ4DPVtwsFnNjjszydT7L+4bZF0qZf+P0LNhiWEID/DG5xtznd0UcgEMmIhIXzZ/CPgE6n523Ce7GzEwwoC4QCe9jctyM8V7gfJD0CtZl29CWhTmbD7CteWIARMR6UhLkzVgSjkbMPlBrw5VmLEqrwXn9HFC75JN9CDAJwjYuwB6dvbAGFTUNWHBTpYY8HTsYSIi/djzLVBdCAyYAj37aFcjAr2B0+N7cXbcsYwmay+TDMs110HPa8tJIcsP1+i3J426hwETEenH2tettZciUqBXtU0WzN7TiLOTTfA19XKy97Fk2LOpDshaAj2bNDgW6w+WYceRCmc3hZyIARMR6cORTdYlOwZNg559ntmkkr0npzhxOM5GZiHGDrYWCbWYoVen9otAdLAv/vfTAWc3hZyIARMR6cOKl4DgBCB5LPRc2fu/WxvUUFx0gIu8ffc7E6jM0/X6cpL8PXVoHL7Zmo+c0lpnN4ecxEWuOCIiDQp3Abu/AYb+GjA4Ma+nh83Z14Qj1Rb8Ks0bLiOsDxCRCmydDcACvTpnYAwCfY14bWmWs5tCTsKAiYjc35JngKBYIPU86FVDiwUvb2jAmDgj+vZ2Ze+upJ1nrX91cCX0ys/biIuHJ+DTDTk4VFLj7OaQE7jYVUcepTIf2LcQ+OlFYPVr1sU861lRl07S4TXAnnnAyGsAowv1vDjYBzsbkV9jwZUZLvg9SpJ99ABg47vW0g46NWlILEL9vfH8/ExnN4WcgAETOUf5YWDtf61/6KTw3dFMYMtHwPbPGDRR95lbgO8fBCLTgP5n6/bMFdea8a+NDaruUmKwi75tD5hqLemw+2vola/JiCtOTca32/Ox5kCJs5tDvcxFrzzSNbMZ2L8IqC87fl/BNqCYn96om9a9CeRvBU67HfDS79vZ06vr1e0VA12wd8kmOM5aZmDLLKBav0Uez0yPQnpsEP48Zzsamluc3RzqRfp9hyHXVVMEFO/ufH/u+t5sDbmrkixg0V+BgRcCMYOgVz8cbMLc/c24drAPQnydXHepK2kXAN7+wIpXdFtmwODlhVvP6I9DJbV4+cd9zm4O9SIGTOScYRRzc+f7m62fpok6/x1pAD6/GfALB0bfpNsTlVdtxoNL63FqrBETk9xg9p+3n3WmYsFWYMcX0KvkiABcNjoJry/Nwsr9R53dHOolDJio9wVEACFJne+PG9abrSF3Y7EA394PFO4EznrQ2qOhQ/XNFtzxQy1MBuC2kT7w8nLx3iUbWz7ZpveBIxuhV78anoChiaG4Z9Ym1mbyEAyYqPfJH7jUczuul+MXBsQO5U+FOrf8H8DmD4Bx91j/OOu0QOW9P9Yhs9SMP5zqi2AfNwmW2g7NRaUDS58FSvZDjwwGL9x7bhp8TAZc//Y6lFQ3OLtJ1MMYMJFzxA0HRl0PhPW1Bk4mXyB+JHDqzUBIPH8q1HHPkgRLUnNp5HXW2j86rbckwdLiw824b7QvUsLc8G3aYABGXAUERAEL/gwc3Qs9CvbzxkNTMlBW04hr3lyLoiqmE+iZl8Ui70LUVmVlJUJDQ1FRUYGQkBCenJ7UWAvUlVmDpsBoXVdp9mSarynJWfr+IWDjO8DIa4HhVwHuMkR1EkrqzLhrYR02FbbgvtE+ODXOBdaL00IW5pXaTNVFwMT7gT7joUe5ZbV49vs98Pc24n83nIpB8fy7oUcMmDrAgInIha4pyVWacwdQtBs4/U4gfZIufzyLDzXhoWX1aDRbMGO0LzIidfLhobkR2PEZULADyLgYGH0D4B0AvTla3YB//pCJ/Ip61et0w/h+ag060g8GTB1gwETkAteULOgqVeA3vA2EJgJn/EGXOUu7Slrwz3X1WHS4BSOiDSrBO8LPDYfhTkQGMmRx3r0LAJ8gYNS11mVsdFaZvbHZjFnrDuOHnQXIiA/Gg5MzcPbAaPdJ2KcTYsDUAQZMRE66pprqgQNLgW2zrYvpmvyAYf8HDJqmqz+uFQ0WLDzYhM8ym7A2vwWxAV5qyZPTE4z6/uNaVw7snQ/kbwP8w4C084GUidalVaCf73t/URU+WnsYewqqkBYThCtPTcbUYXFICtdfz5onYcDUAQZMRE66pl45BSjNsk4GkKE3mW3lo68/MoU1Zkz8uBoNLcDgSAPO7WvC2HgjTJ40fCM5TdLjJJX9JY9R1gGU3DQdkfTg3fmVWLi7EBsOlaG5xYJ3bhyDczJinN00spObZxT2DFsevLzJE1HngoODu9Uj0t1rKkSCJRnaSBoP1FUD2+fo7vQX1wWjoeVspPhWY4hXCQoPA18fhgcaAPilwVCXDXO2CS2N2dCjhGBvTBoYhe92FSO7sAyjE/wcck1R72MPUwdyc3ORnJzc+z8NIjfT3Zyk7l5Ti64PwKkJRjS26HfybjmCcL3XMyhHCLxxgor3HkJig5pGC+r1fiq8vFD85dNoyN15wqdxdrbrYsDUAbPZjLy8PF1H+vJJX/6A5eTksHQCz6/dunuNeMI15Ui8Pj33HPMacV0ckuuAwWBAUtIJlu7QEXmjcKU3C73h+fW8a8qR+PvDc0yuQ2dzV4mIiIgcjwETERERURcYMHkoX19f/OUvf1G3xPNLroXXJ88xuR4mfRMRERF1gT1MRERERF1gwERERETUBQZMRERERF1gwERERETUBQZMOvbss89izJgxqnJsTEwMpk+fjszMzHbPqa+vx913343IyEgEBQXhsssuQ2FhodParLfze/bZZ6vK1m23O+64w2ltJtfR0tKCxx57DCkpKfD390dqaiqeeuqp1nX3hPz/8ccfR3x8vHrO+eefj3379jm13a5u+fLluOSSS5CQkKCut6+++qrd/u6c09LSUlx77bWqcGhYWBhuueUWVFdX9/J3Qq6GAZOOLVu2TAVDa9aswcKFC9HU1IRJkyahpqam9Tm///3v8c033+Czzz5Tz5flK3796187td16Or/i1ltvRX5+fuv2/PPPO63N5Dqee+45zJw5E//+97+xe/dudV9+N1599dXW58j9V155Ba+//jrWrl2LwMBATJ48WX3QoY7J9TdixAj85z//6XB/d86pBEs7d+5U1/W8efNUEHbbbbfxlHs6C3mMoqIi+ehqWbZsmbpfXl5u8fb2tnz22Wetz9m9e7d6zurVq53YUn2cX3HWWWdZfve73zm1XeSaLrroIsvNN9/c7rFf//rXlmuvvVb932w2W+Li4iz/+Mc/WvfLNevr62v5+OOPe7297kiuxzlz5rTe78453bVrl/q69evXtz7n+++/t3h5eVmOHDnSy98BuRL2MHkQWQVbREREqNuNGzeqXhHpkrbJyMhAnz59sHr1aqe1Uy/n1+ajjz5CVFQUhg4dikceeQS1tbVOaiG5kvHjx2PRokXYu3evur9161asWLECU6dOVfezs7NRUFDQ7voMDQ3F2LFjeX3aqTvnVG5lGO7UU09tfY48X9ZDlB4p8lxcfNdDyGrxM2bMwIQJE9QfbiFvHD4+PurNoa3Y2Fi1j7SdX3HNNdegb9++Kp9i27ZteOihh1Se05dffsnT6+EefvhhVFZWqg8pRqNR5TQ988wzajhI2K5BuR7b4vVpv+6cU7mVnMS2TCaT+iDE90XPxoDJQ0iuzY4dO9QnWOq989s272HYsGEq0fS8885DVlaWSvIlz/Xpp5+q3sdZs2ZhyJAh2LJliwq6Jbi+4YYbnN08IjoGh+Q8wD333KMSF5csWYKkpKTWx+Pi4tDY2Ijy8vJ2z5dZcrKPtJ3fjkjXv9i/fz9Pr4d74IEHVC/TVVddpYLp3/zmN2oShsy+FLZr8NhZq7w+7dedcyq3RUVF7fY3NzermXN8X/RsDJh0THIe5Y/5nDlzsHjxYjV9ua3Ro0fD29tb5VHYyHDR4cOHMW7cOCe0WF/ntyPSiyCkp4k8m+SySV5MWzI0J8O7Qn6f5A902+tThvAkj4bXp326c07lVj5ESo6njVzf8nOxfeAhD+XsrHPqOXfeeaclNDTUsnTpUkt+fn7rVltb2/qcO+64w9KnTx/L4sWLLRs2bLCMGzdObaT9/O7fv9/y5JNPqvOanZ1tmTt3rqV///6WiRMn8vSS5YYbbrAkJiZa5s2bp34/vvzyS0tUVJTlwQcfbD07f//73y1hYWHqd2fbtm2WadOmWVJSUix1dXU8g52oqqqybN68WW3yJ+7FF19U/z906FC3z+mUKVMso0aNsqxdu9ayYsUKS3p6uuXqq6/mOfdwDJh0TN4sOtreeeed1ufIm8Rdd91lCQ8PtwQEBFguvfRS9UeftJ/fw4cPq+AoIiJCTVtOS0uzPPDAA5aKigqeXrJUVlaqkhPygcXPz08F03/+858tDQ0N7abBP/bYY5bY2Fj1O3TeeedZMjMzefZOYMmSJR1elxKgdveclpSUqAApKCjIEhISYrnppptUIEaezUv+cXYvFxEREZErYw4TERERURcYMBERERF1gQETERERURcYMBERERF1gQETERERURcYMBERERF1gQETERERURcYMBERERF1gQET2e3GG2+El5dX6xYZGYkpU6Zg27ZtPKtEPXSd2bauFnomIsdiwESaSICUn5+vNlnQ0mQy4eKLL+ZZJeqh68y2bd68meeYqBcxYCJNfH191erfso0cORIPP/wwcnJyUFxcrPZv374d5557Lvz9/VUP1G233Ybq6urWr5fnJyQkwMfHB4mJiXjooYdaV2tfunSp+iT97bffYvjw4fDz88Ppp5+OHTt2tH59SUkJrr76avW1AQEBGDZsGD7++ON2bZTjPf/880hLS1Pt7dOnD5555hm1r6NP7rZNXp/I1a4z2xYdHd26X66bAQMGqGugf//+eOyxx9DU1NTuGAcPHuzw97y8vLzbz/nrX/+qrvOOfPXVV+q5RHrFgIkcRgKhDz/8UAUmEhzV1NRg8uTJCA8Px/r16/HZZ5/hxx9/xD333NP6NZMmTcK8efOwf/9+/O9//8Mbb7yhjtHWAw88gH/+85/qGPJH4pJLLmn9Y1BfX4/Ro0eroEoCKQnIfvOb32DdunWtX//II4/g73//u/ojsmvXLsyaNQuxsbFqX9tP7OKLL75ovT9+/Hj+dpBbCA4Oxrvvvqt+v//1r3/hzTffxEsvvdTuObZlQ+UalN9v+V0/VneeQ+SxnL36L7kvWf3baDRaAgMD1Sa/TvHx8ZaNGzeq/W+88YYlPDzcUl1d3fo13377rcVgMFgKCgqOO96BAwfU17/99tvtVh2fPXt2u1XE/f39LZ988kmn7broooss999/f+uK8LIi+Ztvvtnl9yOvJa9J5MrXWWJiouX888+3LFiwoNOv+cc//mEZPXp0u8cyMzPV7/iOHTvaXV9lZWXdfs5f/vIXy4gRIzp8zTlz5qjnEukVe5hIk3POOQdbtmxRm/TqSI/S1KlTcejQIezevRsjRoxAYGBg6/MnTJighsgyMzNbH/vb3/7WOpRw2WWX4frrr2/3GuPGjWv9f0REBAYOHKiOLVpaWvDUU0+poTjZFxQUhAULFuDw4cNqvzyvoaEB5513Hn/SpIvr7Msvv0R8fDwuuugirF27Vu3/5JNP1LUlQ3VyDTz66KOt14BNZWWlum17PR6rO8+RYXZ5jdDQUAwaNEj13hJ5ApOzG0DuTd5YZQjORobV5I1UhgS664477sCvf/1rbNy4ETNmzFD/lz8Q3fGPf/xDDUG8/PLLKmiS9sgxGhsb1X7JnSLS23X29ttvq8BJ8obkA8i1116LJ554Qn1gketv9uzZahi7rby8PBgMBhVUdaY7z5EPLF9//bX6sLJmzRrceuutqm0y4YNIz9jDRA4lSZ/yhltXV6c+fW7dulXlMtmsXLlS7Zc3XRvpGcrIyFBv+mecccZxeRPypmxTVlaGvXv3qmPbjjdt2jRcd911qjdLeqlkv016eroKmmQGH5FeyDUkmwQtq1atQt++ffHnP/8Zp556qvqdlx7eY0kOoFxnMnmiM915jkzQkABJruEbbrhBXXfS80WkdwyYSBMZ7iooKFCbDH/de++9KvlbErMlAJI3XnlTlYTsJUuWqP2SlG1Lun7ttdewc+dONTtHkr0XLlyIUaNGtXuNJ598UgU8cgypSRMVFYXp06erffLHQb5G/mjI699+++0oLCxs/Vp5fZlB9OCDD+L9999HVlaWCsDeeust/uTJ7a+zCy+8UF0DMvwmvUry+/3KK69gzpw5rV8rva0ffPABXnzxRdx0000dHr87z7GRdD+ZbCEfhBYvXqwSzYcOHerw75nI5Tg7iYrcOxlVfoVsW3BwsGXMmDGWzz//vPU527Zts5xzzjkWPz8/S0REhOXWW2+1VFVVte6/8MILLVFRUSoxOy0tzfLMM89YzGZzu4TTb775xjJkyBCLj4+P5bTTTrNs3bq1XRL4tGnTLEFBQZaYmBjLo48+arn++uvVYzYtLS2Wp59+2tK3b1+Lt7e3pU+fPpa//e1vx30/TPomd7nOTjnlFMvHH3/c+pwHHnjAEhkZqa6DK6+80vLSSy9ZQkND1b4NGzZY+vfvb3n22WfVtWDTNqG7O8+xJX3b2iGTN5KTky2PPfaY2sekb9I7L/nH2UEbUUekDpLkMskwXFhYGE8SERE5DYfkiIiIiLrAgImIiIioCxySIyIiIuoCe5iIiIiIusCAiYiIiKgLDJiIiIiIusCAiYiIiKgLDJiIiIiIusCAiYiIiKgLDJiIiIiIusCAiYiIiKgLDJiIiIiIcGL/D+UpZ5kQ8crGAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 588.236x500 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(df, hue='Категория', diag_kind='kde', \n",
    "             plot_kws={'alpha': 0.6}, height=2.5)\n",
    "plt.suptitle(\"Pairplot\", y=1.02)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "eb95aa22-2bd6-41f3-ac8e-87dc0dc0664b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(1, 2, 2) # Берем только числовые столбцы для корреляции\n",
    "numeric_df = df[[\"Возраст\", \"Баллы\"]]\n",
    "corr_matrix = numeric_df.corr()\n",
    "sns.heatmap(corr_matrix, annot=True, cmap='coolwarm', center=0, \n",
    "            square=True, fmt='.2f', cbar_kws={\"shrink\": 0.8})\n",
    "plt.title(\"Heatmap\")\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2f73162e-f331-4bd9-a2c8-53bd3db5cb35",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 5. Прогресс-бар с tqdm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "d46f3605-9d0c-446e-a29c-3e8213457bd9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████████████████████████████████████████████████████████████| 100/100 [00:05<00:00, 19.72it/s]\n"
     ]
    }
   ],
   "source": [
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "for i in tqdm(range(100)):\n",
    "    time.sleep(0.05)  # Симуляция долгого процесса"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "4a14872e-5e86-4e2f-96d6-b06f10d5707f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Загрузка:   0%|                                                                                | 0/100 [00:00<?, ?it/s]\u001b[A\n",
      "Загрузка:  10%|███████                                                                | 10/100 [00:00<00:00, 97.66it/s]\u001b[A\n",
      "Загрузка:  20%|██████████████▏                                                        | 20/100 [00:00<00:00, 95.12it/s]\u001b[A\n",
      "Загрузка:  30%|█████████████████████▎                                                 | 30/100 [00:00<00:00, 94.55it/s]\u001b[A\n",
      "Загрузка:  40%|████████████████████████████▍                                          | 40/100 [00:00<00:00, 94.51it/s]\u001b[A\n",
      "Загрузка:  50%|███████████████████████████████████▌                                   | 50/100 [00:00<00:00, 94.61it/s]\u001b[A\n",
      "Загрузка:  60%|██████████████████████████████████████████▌                            | 60/100 [00:00<00:00, 94.67it/s]\u001b[A\n",
      "Загрузка:  70%|█████████████████████████████████████████████████▋                     | 70/100 [00:00<00:00, 94.27it/s]\u001b[A\n",
      "Загрузка:  80%|████████████████████████████████████████████████████████▊              | 80/100 [00:00<00:00, 93.95it/s]\u001b[A\n",
      "Загрузка:  90%|███████████████████████████████████████████████████████████████▉       | 90/100 [00:00<00:00, 93.65it/s]\u001b[A\n",
      "Загрузка: 100%|██████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 93.91it/s]\u001b[A"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Результат обработки:\n",
      "      Имя  Возраст  Баллы Категория\n",
      "0    Анна       21     89         A\n",
      "1   Борис       22     76         B\n",
      "2  Виктор       23     95         A\n",
      "3  Галина       24     82         B\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "for i in tqdm(range(100), desc=\"Загрузка\"):\n",
    "    time.sleep(0.01)\n",
    "\n",
    "print(\"\\nРезультат обработки:\")\n",
    "print(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c532e8d-abc9-4257-8175-eb606a89dad1",
   "metadata": {
    "jp-MarkdownHeadingCollapsed": true
   },
   "source": [
    "### 6. Самостоятельное задание"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80bd8588-a9aa-4f1c-9333-e30dd2c9f4c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Начинаю загрузку данных...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Загрузка: 100%|██████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 96.92it/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Размер данных: (7538092, 6)\n",
      "Количество строк: 7,538,092\n",
      "Количество столбцов: 6\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from tqdm import tqdm\n",
    "import time\n",
    "\n",
    "print(\"Начинаю загрузку данных...\")\n",
    "file_path = 'C:/Users/Максим/Downloads/btcusd_1-min_data.csv'\n",
    "\n",
    "for i in tqdm(range(100), desc='Загрузка'):\n",
    "    time.sleep(0.01)  # Просто визуализация\n",
    "df = pd.read_csv(file_path)\n",
    "\n",
    "print(f\"Размер данных: {df.shape}\")\n",
    "print(f\"Количество строк: {len(df):,}\")\n",
    "print(f\"Количество столбцов: {len(df.columns)}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "5aa2cba6-35f8-48af-9ab1-384decffc0e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.DataFrame'>\n",
      "RangeIndex: 7538092 entries, 0 to 7538091\n",
      "Data columns (total 6 columns):\n",
      " #   Column     Dtype  \n",
      "---  ------     -----  \n",
      " 0   Timestamp  float64\n",
      " 1   Open       float64\n",
      " 2   High       float64\n",
      " 3   Low        float64\n",
      " 4   Close      float64\n",
      " 5   Volume     float64\n",
      "dtypes: float64(6)\n",
      "memory usage: 345.1 MB\n",
      "None\n",
      "============================================================\n",
      "          Timestamp          Open          High           Low         Close  \\\n",
      "count  7.538092e+06  7.538092e+06  7.538092e+06  7.538092e+06  7.538092e+06   \n",
      "mean   1.551560e+09  2.292484e+04  2.293292e+04  2.291658e+04  2.292486e+04   \n",
      "std    1.305724e+08  3.106732e+04  3.107573e+04  3.105885e+04  3.106734e+04   \n",
      "min    1.325412e+09  3.800000e+00  3.800000e+00  3.800000e+00  3.800000e+00   \n",
      "25%    1.438483e+09  4.527700e+02  4.529000e+02  4.525800e+02  4.527800e+02   \n",
      "50%    1.551555e+09  7.796780e+03  7.801855e+03  7.790430e+03  7.796280e+03   \n",
      "75%    1.664626e+09  3.660800e+04  3.662800e+04  3.658716e+04  3.660800e+04   \n",
      "max    1.777767e+09  1.262020e+05  1.262720e+05  1.261580e+05  1.262020e+05   \n",
      "\n",
      "             Volume  \n",
      "count  7.538092e+06  \n",
      "mean   5.036331e+00  \n",
      "std    2.176448e+01  \n",
      "min    0.000000e+00  \n",
      "25%    2.123590e-02  \n",
      "50%    4.543000e-01  \n",
      "75%    2.838958e+00  \n",
      "max    5.853852e+03  \n"
     ]
    }
   ],
   "source": [
    "print(df.info()) # вывод информации\n",
    "print('='*60) # разделитель\n",
    "print(df.describe()) # вывод стандартных агрегаций"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "e5ce3c48-eb29-49a1-93ed-bb810721fa10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df['Close'], bins=50, color='yellow', edgecolor='black', alpha=0.7) # (столб, ширина, цвет, контур, прозрачность)\n",
    "plt.xlabel('Цена Bitcoin (USD)')\n",
    "plt.ylabel('Количество наблюдений')\n",
    "plt.title('Распределение цены закрытия Bitcoin')\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ae4d2db0-a560-46bf-9d82-039942b6a1af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# сократили данные чтобы ускорить отрисовку графика\n",
    "sample_size = 5000 \n",
    "df_sample = df.sample(n=sample_size, random_state=1) # новый датафрейм с рандомными 5к строк\n",
    "\n",
    "plt.scatter(df_sample['Volume'], df_sample['Close'], alpha=0.5, s=10)\n",
    "plt.xlabel('Объем торгов (BTC)')\n",
    "plt.ylabel('Цена закрытия (USD)')\n",
    "plt.title('Взаимосвязь объема торгов и цены')\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "89d2ac89-1e48-4949-a387-fc6cdadcbe6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot([df['Open'], df['High'], df['Low'], df['Close']], \n",
    "            labels=['Open', 'High', 'Low', 'Close'])\n",
    "plt.ylabel('Цена (USD)')\n",
    "plt.title('Распределение цен: Open, High, Low, Close')\n",
    "plt.grid(True, alpha=0.3)\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}