diff --git a/.idea/KRUK.iml b/.idea/KRUK.iml
index d8b3f6c..ac08d52 100644
--- a/.idea/KRUK.iml
+++ b/.idea/KRUK.iml
@@ -1,8 +1,6 @@
 <?xml version="1.0" encoding="UTF-8"?>
 <module type="PYTHON_MODULE" version="4">
   <component name="NewModuleRootManager">
-    <content url="file://$MODULE_DIR$" />
-    <orderEntry type="jdk" jdkName="Python 3.13" jdkType="Python SDK" />
     <orderEntry type="sourceFolder" forTests="false" />
   </component>
 </module>
\ No newline at end of file
diff --git a/.idea/misc.xml b/.idea/misc.xml
index 1d3ce46..e60fd8a 100644
--- a/.idea/misc.xml
+++ b/.idea/misc.xml
@@ -3,5 +3,4 @@
   <component name="Black">
     <option name="sdkName" value="Python 3.13" />
   </component>
-  <component name="ProjectRootManager" version="2" project-jdk-name="Python 3.13" project-jdk-type="Python SDK" />
 </project>
\ No newline at end of file
diff --git a/.ipynb_checkpoints/Untitled-checkpoint.ipynb b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/Untitled1-checkpoint.ipynb b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/Untitled1-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/matplotlib-checkpoint.ipynb b/.ipynb_checkpoints/matplotlib-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/matplotlib-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/numpy-checkpoint.ipynb b/.ipynb_checkpoints/numpy-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/numpy-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/pandas-checkpoint.ipynb b/.ipynb_checkpoints/pandas-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/pandas-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/seaborn-checkpoint.ipynb b/.ipynb_checkpoints/seaborn-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/seaborn-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/.ipynb_checkpoints/tqdm-checkpoint.ipynb b/.ipynb_checkpoints/tqdm-checkpoint.ipynb
new file mode 100644
index 0000000..363fcab
--- /dev/null
+++ b/.ipynb_checkpoints/tqdm-checkpoint.ipynb
@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Untitled.ipynb b/Untitled.ipynb
new file mode 100644
index 0000000..6e2d004
--- /dev/null
+++ b/Untitled.ipynb
@@ -0,0 +1,13 @@
+{
+ "cells": [
+  {
+   "metadata": {},
+   "cell_type": "raw",
+   "source": "",
+   "id": "6bd3d2c648b71e8d"
+  }
+ ],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/Untitled1.ipynb b/Untitled1.ipynb
new file mode 100644
index 0000000..6ffc922
--- /dev/null
+++ b/Untitled1.ipynb
@@ -0,0 +1,343 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "70531e1f-c2ba-4849-b394-e9cb8aaca4b7",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- Статистика по пропускам ---\n",
+      "title           0\n",
+      "popularity      0\n",
+      "vote_average    0\n",
+      "vote_count      0\n",
+      "release_year    0\n",
+      "dtype: int64\n",
+      "\n",
+      "--- Описание числовых данных ---\n",
+      "       popularity  vote_average    vote_count  release_year\n",
+      "count   25.000000     25.000000     25.000000     25.000000\n",
+      "mean    91.828000      8.268000  18880.000000   2002.560000\n",
+      "std     70.081331      0.268825   5666.568627     13.073765\n",
+      "min     28.400000      7.500000  10000.000000   1972.000000\n",
+      "25%     42.500000      8.200000  14000.000000   1994.000000\n",
+      "50%     70.200000      8.300000  19000.000000   2000.000000\n",
+      "75%    110.100000      8.500000  23000.000000   2014.000000\n",
+      "max    310.500000      8.700000  31000.000000   2021.000000\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Вычисление новых метрик: 100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 25/25 [00:00<00:00, 34066.80it/s]\n",
+      "Загрузка аналитических модулей: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 91.47it/s]\n",
+      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_15564\\224466945.py:75: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(x=\"decade\", y=\"vote_average\", data=df, palette=\"Pastel1\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x1200 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>title</th>\n",
+       "      <th>popularity</th>\n",
+       "      <th>vote_average</th>\n",
+       "      <th>vote_count</th>\n",
+       "      <th>release_year</th>\n",
+       "      <th>pop_per_vote</th>\n",
+       "      <th>decade</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>Spider-Man: No Way Home</td>\n",
+       "      <td>310.5</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>15000</td>\n",
+       "      <td>2021</td>\n",
+       "      <td>0.020700</td>\n",
+       "      <td>2020</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>Avengers: Endgame</td>\n",
+       "      <td>250.7</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>20000</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0.012535</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>Joker</td>\n",
+       "      <td>180.3</td>\n",
+       "      <td>8.2</td>\n",
+       "      <td>19000</td>\n",
+       "      <td>2019</td>\n",
+       "      <td>0.009489</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>Interstellar</td>\n",
+       "      <td>150.2</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>28000</td>\n",
+       "      <td>2014</td>\n",
+       "      <td>0.005364</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>Avatar</td>\n",
+       "      <td>140.8</td>\n",
+       "      <td>7.5</td>\n",
+       "      <td>25000</td>\n",
+       "      <td>2009</td>\n",
+       "      <td>0.005632</td>\n",
+       "      <td>2000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Inception</td>\n",
+       "      <td>120.4</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>31000</td>\n",
+       "      <td>2010</td>\n",
+       "      <td>0.003884</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>17</th>\n",
+       "      <td>Titanic</td>\n",
+       "      <td>110.1</td>\n",
+       "      <td>7.9</td>\n",
+       "      <td>21000</td>\n",
+       "      <td>1997</td>\n",
+       "      <td>0.005243</td>\n",
+       "      <td>1990</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>19</th>\n",
+       "      <td>The Wolf of Wall Street</td>\n",
+       "      <td>95.4</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>18000</td>\n",
+       "      <td>2013</td>\n",
+       "      <td>0.005300</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>The Dark Knight</td>\n",
+       "      <td>92.1</td>\n",
+       "      <td>8.5</td>\n",
+       "      <td>27000</td>\n",
+       "      <td>2008</td>\n",
+       "      <td>0.003411</td>\n",
+       "      <td>2000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>21</th>\n",
+       "      <td>Mad Max: Fury Road</td>\n",
+       "      <td>88.1</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>19000</td>\n",
+       "      <td>2015</td>\n",
+       "      <td>0.004637</td>\n",
+       "      <td>2010</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                      title  popularity  vote_average  vote_count  \\\n",
+       "9   Spider-Man: No Way Home       310.5           8.1       15000   \n",
+       "8         Avengers: Endgame       250.7           8.3       20000   \n",
+       "15                    Joker       180.3           8.2       19000   \n",
+       "5              Interstellar       150.2           8.3       28000   \n",
+       "18                   Avatar       140.8           7.5       25000   \n",
+       "3                 Inception       120.4           8.3       31000   \n",
+       "17                  Titanic       110.1           7.9       21000   \n",
+       "19  The Wolf of Wall Street        95.4           8.0       18000   \n",
+       "2           The Dark Knight        92.1           8.5       27000   \n",
+       "21       Mad Max: Fury Road        88.1           8.1       19000   \n",
+       "\n",
+       "    release_year  pop_per_vote  decade  \n",
+       "9           2021      0.020700    2020  \n",
+       "8           2019      0.012535    2010  \n",
+       "15          2019      0.009489    2010  \n",
+       "5           2014      0.005364    2010  \n",
+       "18          2009      0.005632    2000  \n",
+       "3           2010      0.003884    2010  \n",
+       "17          1997      0.005243    1990  \n",
+       "19          2013      0.005300    2010  \n",
+       "2           2008      0.003411    2000  \n",
+       "21          2015      0.004637    2010  "
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "import numpy as np\n",
+    "\n",
+    "# --- 1. Подготовка данных (создаем top_movies.csv) ---\n",
+    "raw_data = \"\"\"title,popularity,vote_average,vote_count,release_year\n",
+    "The Shawshank Redemption,85.5,8.7,21000,1994\n",
+    "The Godfather,70.2,8.7,16000,1972\n",
+    "The Dark Knight,92.1,8.5,27000,2008\n",
+    "Inception,120.4,8.3,31000,2010\n",
+    "Pulp Fiction,65.8,8.5,23000,1994\n",
+    "Interstellar,150.2,8.3,28000,2014\n",
+    "The Matrix,75.4,8.2,24000,1999\n",
+    "Forrest Gump,55.9,8.2,22000,1994\n",
+    "Avengers: Endgame,250.7,8.3,20000,2019\n",
+    "Spider-Man: No Way Home,310.5,8.1,15000,2021\n",
+    "Parasite,45.3,8.5,12000,2019\n",
+    "The Lion King,35.2,8.2,14000,1994\n",
+    "Fight Club,60.1,8.4,24000,1999\n",
+    "Spirited Away,38.7,8.5,11000,2001\n",
+    "Gladiator,42.5,8.2,15000,2000\n",
+    "Joker,180.3,8.2,19000,2019\n",
+    "The Green Mile,30.2,8.5,13000,1999\n",
+    "Titanic,110.1,7.9,21000,1997\n",
+    "Avatar,140.8,7.5,25000,2009\n",
+    "The Wolf of Wall Street,95.4,8.0,18000,2013\n",
+    "Star Wars: A New Hope,50.2,8.2,17000,1977\n",
+    "Mad Max: Fury Road,88.1,8.1,19000,2015\n",
+    "La La Land,40.5,7.9,14000,2016\n",
+    "The Silence of the Lambs,33.2,8.3,13000,1991\n",
+    "Goodfellas,28.4,8.5,10000,1990\"\"\"\n",
+    "\n",
+    "with open(\"top_movies.csv\", \"w\", encoding=\"utf-8\") as f:\n",
+    "    f.write(raw_data)\n",
+    "\n",
+    "# --- 2. Загрузка и первичный анализ ---\n",
+    "df = pd.read_csv(\"top_movies.csv\")\n",
+    "\n",
+    "print(\"--- Статистика по пропускам ---\")\n",
+    "print(df.isnull().sum())\n",
+    "\n",
+    "print(\"\\n--- Описание числовых данных ---\")\n",
+    "print(df.describe())\n",
+    "\n",
+    "# --- 3. Обработка данных с прогресс-баром ---\n",
+    "# Добавим новый столбец: \"Относительная популярность\" (Popularity / Vote Count)\n",
+    "tqdm.pandas(desc=\"Вычисление новых метрик\")\n",
+    "df['pop_per_vote'] = df.progress_apply(lambda row: row['popularity'] / row['vote_count'], axis=1)\n",
+    "\n",
+    "# Имитация долгой загрузки как в примере\n",
+    "for i in tqdm(range(100), desc=\"Загрузка аналитических модулей\"):\n",
+    "    time.sleep(0.01)\n",
+    "\n",
+    "# --- 4. Визуализация данных ---\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "plt.figure(figsize=(15, 12))\n",
+    "\n",
+    "# График 1: Распределение рейтингов (Histplot)\n",
+    "plt.subplot(2, 2, 1)\n",
+    "sns.histplot(df[\"vote_average\"], bins=10, kde=True, color=\"teal\")\n",
+    "plt.title(\"Распределение средних рейтингов\")\n",
+    "\n",
+    "# График 2: Популярность vs Рейтинг (Scatterplot)\n",
+    "plt.subplot(2, 2, 2)\n",
+    "sns.scatterplot(data=df, x=\"popularity\", y=\"vote_average\", hue=\"release_year\", size=\"vote_count\", palette=\"viridis\")\n",
+    "plt.title(\"Связь популярности и рейтинга\")\n",
+    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2)\n",
+    "\n",
+    "# График 3: Динамика среднего рейтинга по десятилетиям (Boxplot)\n",
+    "plt.subplot(2, 2, 3)\n",
+    "df['decade'] = (df['release_year'] // 10) * 10\n",
+    "sns.boxplot(x=\"decade\", y=\"vote_average\", data=df, palette=\"Pastel1\")\n",
+    "plt.title(\"Рейтинги фильмов по десятилетиям\")\n",
+    "\n",
+    "# График 4: Корреляционная матрица (Heatmap)\n",
+    "plt.subplot(2, 2, 4)\n",
+    "numeric_df = df.select_dtypes(include=[np.number])\n",
+    "sns.heatmap(numeric_df.corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
+    "plt.title(\"Корреляция параметров\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# Итоговый вывод DataFrame\n",
+    "df.sort_values(by=\"popularity\", ascending=False).head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "418e7eaf-a4b7-492b-80a7-99a90f171df4",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/matplotlib.ipynb b/matplotlib.ipynb
new file mode 100644
index 0000000..0620058
--- /dev/null
+++ b/matplotlib.ipynb
@@ -0,0 +1,95 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "4c9e05e8-c336-494e-b71b-70613c44189f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x800 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# Подготовка данных\n",
+    "x = np.linspace(0, 10, 100)\n",
+    "y1 = np.sin(x)\n",
+    "y2 = np.cos(x)\n",
+    "\n",
+    "# Создаем фигуру с несколькими подобластями (2 строки, 2 столбца)\n",
+    "plt.figure(figsize=(12, 8))\n",
+    "\n",
+    "# 1. Линейный график: несколько линий и изменение цвета\n",
+    "plt.subplot(2, 2, 1)\n",
+    "plt.plot(x, y1, color='red', label='sin(x)') # Цвет изменен на красный\n",
+    "plt.plot(x, y2, color='blue', linestyle='--', label='cos(x)') # Добавлен второй график\n",
+    "plt.title(\"Линейные графики (sin и cos)\")\n",
+    "plt.legend()\n",
+    "plt.grid(True)\n",
+    "\n",
+    "# 2. Точечный график (scatter)\n",
+    "plt.subplot(2, 2, 2)\n",
+    "x_scatter = np.random.rand(50) * 10\n",
+    "y_scatter = np.random.rand(50)\n",
+    "plt.scatter(x_scatter, y_scatter, color='green', alpha=0.6)\n",
+    "plt.title(\"Точечный график (scatter)\")\n",
+    "\n",
+    "# 3. Столбчатая диаграмма (bar)\n",
+    "plt.subplot(2, 2, 3)\n",
+    "categories = ['A', 'B', 'C', 'D']\n",
+    "values = [15, 30, 45, 10]\n",
+    "plt.bar(categories, values, color='orange')\n",
+    "plt.title(\"Столбчатая диаграмма (bar)\")\n",
+    "\n",
+    "# 4. Гистограмма (hist)\n",
+    "plt.subplot(2, 2, 4)\n",
+    "data = np.random.randn(1000)\n",
+    "plt.hist(data, bins=30, color='purple', edgecolor='black')\n",
+    "plt.title(\"Гистограмма распределения (hist)\")\n",
+    "\n",
+    "# Автоматическое выравнивание и отображение\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "631822c0-f4bd-4229-bca6-473362a1be8e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/numpy.ipynb b/numpy.ipynb
new file mode 100644
index 0000000..9e5b545
--- /dev/null
+++ b/numpy.ipynb
@@ -0,0 +1,107 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "f75546c6-5eb9-4b1e-be1a-ec2bf162b848",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "--- 1. Двумерный массив ---\n",
+      "[[1 2]\n",
+      " [3 4]]\n",
+      "\n",
+      "--- 2. Равномерные интервалы (linspace) ---\n",
+      "[0.         0.55555556 1.11111111 1.66666667 2.22222222 2.77777778\n",
+      " 3.33333333 3.88888889 4.44444444 5.        ]\n",
+      "\n",
+      "--- 3. Случайные числа (randn) ---\n",
+      "[[-0.54989953 -0.65009822]\n",
+      " [-0.65433968  0.85084421]]\n",
+      "\n",
+      "--- 4. Результат np.dot() ---\n",
+      "[[-1.8585789   1.0515902 ]\n",
+      " [-4.26705733  1.45308217]]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[-1.8585789 ,  1.0515902 ],\n",
+       "       [-4.26705733,  1.45308217]])"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# 1. Создание двумерного массива (матрицы 2x2)\n",
+    "matrix_2x2 = np.array([[1, 2], [3, 4]])\n",
+    "\n",
+    "# 2. Использование np.linspace() \n",
+    "# Создаем 10 равномерно распределенных чисел от 0 до 5\n",
+    "lin_points = np.linspace(0, 5, 10)\n",
+    "\n",
+    "# 3. Использование np.random.randn()\n",
+    "# Генерируем случайную матрицу 2x2 из нормального распределения\n",
+    "random_matrix = np.random.randn(2, 2)\n",
+    "\n",
+    "# 4. Использование np.dot()\n",
+    "# Выполняем умножение двух матриц (нашей первой матрицы и случайной)\n",
+    "matrix_product = np.dot(matrix_2x2, random_matrix)\n",
+    "\n",
+    "# Вывод всех результатов\n",
+    "print(\"--- 1. Двумерный массив ---\")\n",
+    "print(matrix_2x2)\n",
+    "\n",
+    "print(\"\\n--- 2. Равномерные интервалы (linspace) ---\")\n",
+    "print(lin_points)\n",
+    "\n",
+    "print(\"\\n--- 3. Случайные числа (randn) ---\")\n",
+    "print(random_matrix)\n",
+    "\n",
+    "print(\"\\n--- 4. Результат np.dot() ---\")\n",
+    "print(matrix_product)\n",
+    "\n",
+    "# Последняя строчка для отображения в интерактивной среде\n",
+    "matrix_product"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e7ad4d46-abc8-4dea-b290-1f6c73ca06ed",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/pandas.ipynb b/pandas.ipynb
new file mode 100644
index 0000000..a614ea9
--- /dev/null
+++ b/pandas.ipynb
@@ -0,0 +1,31 @@
+{
+ "cells": [
+  {
+   "cell_type": "raw",
+   "id": "d3aefaad2d76b7cf",
+   "metadata": {},
+   "source": []
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/seaborn.ipynb b/seaborn.ipynb
new file mode 100644
index 0000000..ac1a6c8
--- /dev/null
+++ b/seaborn.ipynb
@@ -0,0 +1,131 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "8ddda55a-7940-4f15-8264-09f72c6afc21",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "C:\\Users\\User\\AppData\\Local\\Temp\\ipykernel_13180\\2582512541.py:40: FutureWarning: \n",
+      "\n",
+      "Passing `palette` without assigning `hue` is deprecated and will be removed in v0.14.0. Assign the `x` variable to `hue` and set `legend=False` for the same effect.\n",
+      "\n",
+      "  sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df, palette=\"Set2\")\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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3VDDevFhpg62e49Wq12tS271jYI/3Go/3XhS9r9L2uUUnmBQgVv3+wJKSOhZTiRJlZgeW6AlGmd4aeaETCPqrEzI6fjtR2RuvzWmPgdJ77+g16PguozEALzNesQQF/rWtgo1aOJntdbrvLeFYB7GKgDlwhujHzatJpuFxCnKKzhDrzHhaCg6Lfrh0VlzSTsaoOnkS2DFNu47OemuZguv60dUPvwK2wTo6aQOLx+skKBiqx9GZbnXEFchWO7zSF6dCw0V19l/Dw9JuE3XkFEhXZoMyuXXiQcFVZXlnhmATXaoNaTv96uQo4Kus52BD+kTbWcNHtd9U508lXQKDxDpACuxYB9b3U8kTZUgoY1xDKL0OkIbmpc3aUOdQJy7UOVRGkpcBrfdU2sk2076nPMpsUMker+6iHkvbVkH7YPvfu68C4IFDR08ko+89lU9RVrk6+Mq28jqgqpN/JmjfKLtKNfMVmNc+VTa8hpsGe4+UKVPmuJ8/b9so8wIAACC9fpFGTx6PAn3qn6jfqFFtXuAzWF9b2czqeyqZRX1PJaDor/ojgZPYe7xJRQMDdmn7wl5fXP09L6NVy5QElLZ/6WW3qo1pJ3P3ynEokB9sXiC10etTpW2jl/ijetkZ7a9l1PGOczL6ej1eCU0dm6jWtLKBlWkfrIRhRnjPr+cKTCJScpH2U2BfPhhNVCoKGOu94U1ceTLvqWC8kwA6Zgssc+I5Xv9Xr0lt17FpYNDcO84NfE3pHZcF9sNFo2O1vQNLmAZSSR1NwKokHCV4KREnvWNHlbbUcZDmAtCEnxrxq/ft8QQeF6UVLFit/ekdP2UkBuCd5NBnWa9d66v8UuCx48lsL+95T+e95b1ejnUQiyjJApxBmmREAVAFW73OpM5+60c5MCNVHQkNqVRHOjAoq6yKQAosKtAYeKZZQ8gCh9TpTLk6CQo4KmtFQVZ1OPXY3kX14jRJpTcEyyt14mUIp0c/3pokRHXnVHPtRB3/41FHWfWgVTstWDa12qRaa1pPbVUwVXUEM4tmKdcER4EZEip3kjZQq8C1hu0Fqxv49ddfu9qK2m5eDXQFh9MOh1PHNXD7B3Y49Zx6rcpM8YLlej/oxIS3HTzan5pEUzX/1PHVhDnee0r11ANL9YiC4MqMD+y862SH2qBlCp5rFITqGwbreInXOdJEMCcjo+89nRRQVpS2nRcs17BEfV4CX/uJ3psZpefTAYOe0/us6TMkaUv+pP38ffnll66jGdgZ1YkHvXfSjpAAAAA42T6KkkRuvvlmf2BTfQz107w+ikatqm+qvrP6cqr7rXrEXt9T2bIqExfYJ1RfRWVDVAIlMPCrsilpjzMUVFa/V8ca6id99tlnqdZRoocC215JPGV9q8xe4LGIjhWUDJPeHDlqo9oXWM5O99dzqb+vvmtG+2v6P20Gc1oZOc7J6Ov1eP1a9cGV+KLnSFu25WR4I0+9EjseXddxgUo3Ho/66wMHDnTBfiWHeKVIMvKeOh4vq1/HCYH9efXTdTzoZaCn95rUDvWf0x6fSOBrUjsDj0V0LKD3R9rjMo1KVVBc81wFozZp0kyVMVHGeNpj60A6ltVxprLRlQyTkZMdOlbT+y3YcZFOXmn+gMCkK73PvdEfGY0B6OSESnPqJNjDDz/sEu80V1KgjG6vk3lvpff54FgHsYwMc+AMUmBUAWEFfr3JCBVEV4dPQU91JtUpVG05BT81S3UgzWqvH3AFa9UJ03A6zZ4dOAxMP8Y6c67H0/8adqeOqPfj7E3SojYoO1k/jMp6V33pTp06uSGdXpmR4w0l1Nl6dXg0CWd6nRSPauQFDvXzOgbqRHjBRgWr1SFPW9fOo6wZtVHZAcGGnZ0u/firFIm2pzo+6izpDHxgbXDR9knbwfCo46lJgDR0VJ03dUBV7uREndpAXuesf//+rjOr7ATtZy87SMMn05YdUXBd+0LvJw3BVFaJJv5U5rgmuFQ2hQ6C1Da93wL3qzq4qtOoTrL+1zp6j6WXXaCOsg7K1DFTAPxknOi9571+vc/VGVT9Tb1u1VbUAZsyiTx6Deo86oDwRPUFj0fPp8wUZfyrxqceU9k3aZ9PNJmu3seqP6iTI+rk6nMcSAccGsqozxwAAMDp9FGUTKJ+ifoeGj2out0KJnt9FJVRGDdunMtsVkBZfW5lxXqjDtWXVJ9K/SwF21SCQ3099b3UZwyk/qCCqwq6q/+lhAb1Z72guvpwypzWsYqSLBSQU0BSAVjv+fQ8t956q8sI17GI2qVRg3otCuQHoxGVCppqFKL6tOpPKwtaGbJ6/Rnpr+nYYsmSJa7dgSVI0tLxlTcZ5PGOc5RQkpHXG7jtRG3xEiy8CSxPhfccSrLSYyq4qmMQHZ/omC4j/UwdzyihRu8BZUvrmC0j76njUaa6+vBPPvmkS87RCFcFhkeOHOmONwLr0KeljHu1XVnvOkbS8aOOZzUKV69Vr9mjtmhksY5pdVylx1eiVNqa8sGOlz36LCjYrFr8J5og1Xs/aDvrPnpPZoSOwXXyRMdFaUtu6phB7dexpY559N7R+0ojziUjMQBtW31+9DlU2/Rc+hwoQUt/vaSrjG6vk3lveZ8PnTTSCSTve4BjHcQyAubAGaYfLmWDqLOqDqU6LgpuKrCtsibq+GmZOjZpA4Hq6Kpjofurw6vgcdqZr5XRrB84ldXQj7h+EPVDHTjZizrW+lFUMFU/zup8KhiojpM6FcqcUGc5vY6eOlU6o6/AtX7sT0TB32DUYdMZf6/Drw5UMMpW0PZRgDbYzOiZQZkY6kjq7L06D8poUaA2bd1HZXSrbmIwOouvoXzq+KnDok6MOoe9evXKcDvUUdF+1f5QEFxDZbVM+0sBcHXI0nb6tJ/VcdLtGlGgIYWa6EfBXHXOdBChDBMF1NMOudRBlTfcUO1V51UHB+kNU9WIBr0m3SftyYQTOdF7T7StNCRQnUO9z7yhk+r0KujvDePUyQ29f1TbXnUGT5Xef3ov6yLq6OvgQgduyiIK9Pjjj7uMLB1YaHuqo+lNiOVRx1c1AdNmHgEAAJwMBZ2VNapjAvXN1PdX/1x9NPVFVBZBwVAFyHQsobIUSvZQcE3JCaK+lpITVMZBAXCd1NdIR/XFlFEbSIE8BTLVt1fihPpaaoNHwWz1SxXU07GI+shKdPGOOUR9da8PquVK8lC/VYH49DJ79Ti6jzLl1Q9XvXL1R9U/9CYKPVF/TcHy22+/3ZW70AjY9ChQrGMwHWup5vXxZOT1enRM521vBfT1ejMSpD0e9dt1zKH26thC5WwUXNVJiYyOtNT2UxlQ7XsdI2bkPXWi2uuDBw92606ZMsVlVivRRttS2+V42f3ec6j/rBMiStRRP18JNTq+CqRjYCVS6fhGNKmoks7Svof0nGknwPTo/aH11X/PKI1wVfZ2ejXag9GxgLav3reBCWL6LOg2vR/1udNxotrilWTRcfbxYgBKZtJynQBSmRjR9tVnQMdzzz77rP8YO6Pb62TeWzoBpzmldGym5/M+VxzrIJbF+c7ErBkATosyGhQEVQfFq0kXjH4cNczKC0KHMy+IHA5tVVt0IiLtUFQEt2DBAndgoCzrYLPCRxuVi1EnUh1Yb6RGMPr5VMe4TZs2QSd2BQAACEfKHFagXEFiIJS8hBydRIkESrRSoF1BbW/i3WjdXhzrINZRwxwAcFyqq6cMG2WL4390AkEZ8LfddhubBQAAAIhyGn2rE006LtJxQDTjWAexjoA5gLNCddDSzjCPyKGyMSrLolIpOFqmSEMqvYm3AAAAAEQ/JcuoFI/m3IpWHOsAlGQBAABAmFC9U00yfbyhxqrHOnDgQFdXUzVAVXtUtTuV9QUAAAAAp4tJPwEAABByb7/9tpsAOO0E2GlpEmHVENUkYpqwTJNe7d+/302IBQAAAACni4A5AAAAQmbTpk321FNPuQl3y5Urd9x1f/vtNzdp8/Tp061ixYpuWf/+/a1Dhw72yCOPWNGiRc9SqwEAAABEK2qYAwAAIGT+/PNPS0hIsE8++cRq1ap13HXnzp1rhQsX9gfLpV69eq40y6+//noWWgsAAAAg2pFhDgAAgNPSpEmT494+Y8aMdG9r3Lixu2Q0G7148eKpliUmJlr+/Pltw4YNGWwtwplGEfh8PncSBQAAAMgsycnJLtGmdu3aJ1yXgHkGfZ5Q+XT3C4AoN/jaV0PdBABh7sdPr7Co7Nc0LGVng2qXK0CeVrZs2ezgwYNnpQ04sxQs1wUAAADITCfTxyRgDgAAgNNyvAzyzJQ9e3Y7dOjQMcsVLM+ZM+dZaQPOLC+zvEaNGmxqAAAAZJoFCxZkeF1qmAMAAMSYuIS4TL2cLcWKFbPNmzenWqYA+s6dO61IkSJnrR0AAAAAohcBcwAAAESEunXr2saNG2316tX+ZbNnz3Z/L7roohC2DAAAAEC0IGAOAAAQY+KzxmXq5Uw5cuSIbdmyxQ4cOOCu16pVyy688ELr1q2b/f777/bLL79Y3759rXnz5la0aNEz1g4AAAAAsYOAOQAAQIyJS4jP1MuZsmHDBmvQoIFNnz79aLvj4mz06NFWqlQpu+uuu6xr167WsGFD69ev3xlrAwAAAIDYwqSfAAAACAtDhgxJdV2B8SVLlqRaVrBgQRs1atRZbhkAAACAWEHAHAAAIMacyTIqAAAAABDJCJgDAADEmLgEAuYAAAAAEAw1zAEAAAAAAAAAIMMcAAAg9lCSBQAAAACCoyQLAABAjKEkCwAAAAAER0kWAAAAAAAAAADIMAcAAIg9lGTBydi7d68NGzbMZsyYYYcOHbKGDRta7969rWDBgu72u+++2/7973+nuk+9evVs0qRJbGgAAABEHEqyAAAAAEjXww8/bCtWrLBBgwZZiRIl7Pnnn7c777zTPvroI0tMTLQlS5ZYv3797KqrrvLfJyEhgS0KAACAiETAHAAAIMbEZYkLdRMQIRYtWmQ//vijvfbaay6zXIYOHWqNGjWyzz//3C3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+      "text/plain": [
+       "<Figure size 1500x1000 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Генерация pairplot...\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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/HIUb4XV3vNYdERFFLgZLEU6m7ZdUNqGfRkNwfjILb1RyMxa5BsNrC32ieW9JqQOfx6Vm8hERUXRhsBThNu6oVpdaB0tiOLaiyWvFoq2NCDfmlCxVSLNlxxqtm0JERCHGYCnCrd9epRa1TU6wadoOj6MRiU070TfBg+/WtF8rLhwYDEZVb6l5O4MlIqJow2Apwq0vqEZeVrzms8+cZbuLUk7Mj8Oqnc0oq3Uh3Eg1b8eujWo4joiIogeDpQjm8fqwaacES9oPwTlLt8Noj8eoAcmwWQxh2btkzRoAn9sJR/EWrZtCREQhxGApghWW1qPF6UFepg6CpbLtsKRkwWo2YmxeDL5fWw+3x4dwYknJhsFiQ8uOdVo3hYiIQojBUgTbsKMaMvrWJzNe03Z4XQ64qothTs5Sv0/Kj0NtkweLwyzRW+otWdL7onnnWq2bQkREIcRgKcJnwmWlxsKmYTFK4azYCfh8qmdJZCVZkJdmwcxVtQg31ox+aNm5Hj6fV+umEBFRiDBYivBgqU+Gtr1K/iE4o9UOY2xS63WT8+OwprAFhVXOsEvy9jma4CrfqXVTiIgoRBgsRSiHy4PtJfXoq4t8pR0wJWW2m5E3sk8M4m1GzFwZXr1LMgwHg1H1LhERUXRgsBShtu2qhdfrQ1+N85V8Pg9cFYWwJGe2u95sMmDCgFjMXlePJocH4cJotqqFdVsKGSwREUULzYOlmpoa3HrrrZgxYwYmTpyIc889F4sXL269/bXXXsMxxxyDMWPG4MQTT8QHH3ygaXvDxcad1SogkZwlLbmqSlRdInNS+2BJTBkYB6fbhx/W1iOcWDLy0LKTM+KIiKKFWesGXH311SgvL8fDDz+MtLQ0FRxdeuml+Oijj7Bo0SI8+OCD+L//+z+MHz8e8+bNwy233IKkpCQcddRRWjdd1zbvrEFOejzMJm3jYafk9hiMMCel7XVbQowJo/rG4MsVtTh+XBKMRm0LZ+5PknfT+vlw11fDnJCidXOIek0mLLjrquCuLoansVZ9ITDHp8AUu+91HN1uDyrrHCgsq0dTswsDcpPUigGycgBRpNA0WNq+fTvmzp2LN998E5MmTVLXSTD0888/47PPPkNcXByuueYanHzyyeq2vLw8dV95DIOlrm3aWaP5EJxwlu+AOSkdBmPHb7UDB8fh+R8qsHBrIw4crH17u8MqeUuSF7ZrA8zDD9S6OUS94vN64CjeipK3/w/elobW6+0DxyPzpD/BnJDa6WOdLg9Wb6nE3a8shMP563D6EZPzcMlJI5GSYOfRoYigabdDSkoKnnvuOTXE5idJwPJTV1eHyy67DBdddJG63uVy4csvv8SWLVtw8MEHa9hq/WtqcWFXeYM+ZsKpYCmj09tzU6wYkG7Fp0tq4POFR5FKU1wSTHHJaCncoHVTiHrNXVeJ4jfvaBcoiZaty1Ez72N43Z0v71NR24x/vzi/XaAkvl+8E3OWF4XNOU2k656lxMREHHrooe2umzlzpupxuummm1qvkxymCy+8EF6vF6effjqOPPLIXj2vnMBNTU0IZ83Nze0u21q7rUrKGiE9yYIWRwu04m2uh7epDohLg9PZeYmAKfl2vLeoDisK6jE859cFf50OR7tLPTGm5KBpx1rE9PJ91NVx1JPY2Fjdn1fh8lrqab/ki6mnZBt8zo63Xb/sWyRMOREe296zao1GIxasLlbLKnXk/e834cBRmYizG6PiWPVkn7pzXpE+aJ6z1NbSpUtx4403qoTuww47rPX6/Px8lcO0atUq3H333apH6rrrruvx80gv1bp1kZGgW1BQsNd189bVw2wEmmrLsL1euzwgc9UOxACodvjgKy3t9H7xPiA1xoj35pXh4kl7F3ssKSmB3lgMsbCVLse6NasBoykox1FP/MPk4XBe6f211NN+xcTEIKumuNPbZS3ElsYGbN5auNdtkju6q6x9b1RbVXUtaGxqxo5tO6LqWO3PPnXnvCJ90E2wNGvWLFx77bVqRpwkdbclid/yM3z4cFRVVeGJJ57A3/72N1itPUsgtFgsGDx4MMKZfHuRk3LAgAHqA6+tb1evQk66G/n5A6ClpurNcNrikNm33z7ve4jHgU+X1cMTm4P8DGtrj5IEStnZ2bDafu1x0gN3ggX1O5dgUKod1pzBQTmO4Ubr8yqSXsuQ7le5D51VOzPFp8Aen4ARqTkd9kqNra/EzAUdB0MD+yQhIT4WmakjouJYReI+kc6Cpddffx133XUXjjvuONx3332tQdBPP/2E3Nzcdh/Aw4YNU0M6UnIgM3Pv6ejdISd5pHR/ykm5575sK65Hv6xE2G3aJlfWVxfBnJzRraB2XH8L5m5qwucrGnDdSe0/mCVQstv1FSz5svJQbzLDULkDsYPGBuU4hhu9nFeR8FqGcr/cabmq2KrUQ9tTyqHnwpqU0a6gbFsjBqQiJcGG6vq9h8p/f/IopCXHRd2xisR9Ih3UWZLZbXfeeSfOP/98VT6g7R/WRx99FE899VS7+69YsQLJyclIT0/XoLXhkdxdXN6IPhldf0gFm8/rhquqCJYO6it1RMoGHDIsAYu2NmFbmf5ylPZkMJlhSc1Fy66NWjeFqFekRED2Of9CzJAp8s5W1xlj4pF23OWIGzql00BJZKTE4t4/HYLRg34tDZKaaMcNF03G4L7JPDIU3T1LI0aMwDvvvIOxY3v3jXrbtm0qB+noo4/GFVdcgYqKitbb7Ha7mg0ndZhkaG769OlYsGABXnzxRVx//fUquZD2tnVXLSTdMlfjmXCu6lLA64Gpi5lwexqbF4M5G+rx7oIq3HDy3t3+emNN78MZcRQRLEkZyPztX9WEDJ/LCaMtFqaEFBi6kY8nnzU3XTIVdY1OuD1exNstSE2ydxlkEUVFsBSo6aAy802SQr/99lv109app56Ke++9V93+/PPPq+E5GZKTOkxnnnlmQJ4/Em3ZVQuL2YjMlFh9FKNM3LsYZVe9SzOGJ+CjxTXYWNyCfin6/rC1pOehcd08FqekiGCSAMnWs88NKUDJIpQUyXqcs7R27Vo4upjSPWWKdOl27corr1Q/Xfntb3+rfqh7thTWIDstDiaNq2G7KnepQKk730zbGp0Xg7kbG/DmL5W44YTuB1pa9SwJR9EmmIdN1bo5RESkt2Dpjjvu6LTXSbpf9TCFOFord+ulGKUlJXu/H2c0GHDkqES8Na8KK3e2QM+LiRhjk2CMSYCjaCPiGCwREUWsHgdLMhwW7tPvI02L060qd08ZkaVpOzyOJngaqhEz4NfK7PtjSLYN/dOteHt+LS7fdwelZuRLgcwiYpI3EVFk63GwNHr06F4neFNgFRTVqcrdmid3/28KsqwJ19Mg5JgxiWrNuCW7DMjPh25Z0/qgYe0ctb7W/g45EhFReOCUsghL7pZcpaxUjZO7K3fBaLHDGLPvFcu7WjNudB8bZm0yoMm5d1VvvbCk94HP2aJytIiIKDL1KFhav369qlIqRSP9CgsL8cYbb6C+vj6Q7aP9TO6WQMlsMmres2RKTO/11OHDRsRC1uf8aHEd9EpqLUltGkfRZq2bQkREQdKjv6pbt27FiSeeiNtvv731up07d+Kee+5RC90WFRUFso3UTZsLa5CTrnExSp8PrsrCHg/BtZVgN2Fcrg/frmnAjkp9Fqo0Wu2qSnnLrk1aN4WIiPQULN1///3IysrCW2+91XrdtGnTMHv2bFVdW26n0HK5vdhRWo/cdG3zlSSx2+tohnk/ilF2ZXSWD8mxJrz4Q0XA6nsFo3dJygcQEVFk6lGwtHTpUvzlL39RAVNbstit1E2aP39+oNpH3bSztB4ejw+5Gi9z4s/d2Z9ilF2REcVjRsdhXVELft7Q+QrnWuctSakEr0ufvV9ERKRBsCS5KLLCckfcbrequk2hz1eSDCEpSKklZ0UhjLGJMFoDt+p2foYVo/ra8erPFWho8UBvrGl91dIuztICrZtCRER6CZakOveTTz6JqqqqdtfX1NTgmWeewdSprGasxZpw6SkxsFm0nb7ulHylhMBX3j52TBIcbh/emFsJvTEnZwImM4fiiIgiVI/qLF1zzTU466yzcOSRR2L8+PFITU1FdXU1li9fDqvVioceeijwLaUubdlVgxyNe5V8Pg/cVSWIGTg+4NtOiDHhyJGJ+HJFLQ4dkYDhuYHrueotg8kMS0oOHMVbtG4KERHppWcpPz8fn3/+Oc455xw0NTVh9erVqKurUwHUxx9/rG6n0PF6fdhWVIdcjWfCuWrK4fO4AjITriOTB8aib6oFz35XDrdHX8nelrRcVvImIopQPa7gLcndN9xwQ2BbQz1SXNmIFqcHORrPhHNV7JKENpgClNzdUa7cSROS8dz35fhkSQ1On6qfleMsaX3QtGEBPM0NMMVovzYfERHpIFgqLS3FkiVL4HQ6W6/zer0q8Xvx4sV45JFHAtVG2oethbXqUvOepcpCmOJTYDRZgvYcWUkWTBsSjw8WVWHakDhV6Vsvy54IGYqLHThO6+YQEZHWwdLXX3+Na6+9Vs1881dplho4/v8PHDgwkG2kbuQrJcVbERcTvCCluz1LwUju3tOhw+Oxdlcznv+hHLeemtvrSuGBYEpMhcFih6N4M4MlIqII06OcJZnxNmrUKHz44Yc47bTT8Nvf/hZffPEFrrvuOphMJtx0002Bbyl1uSac1pW7vW4nXLVlMCcGJ1+pLYvZiBPGJ2FNoX5qLxkMRpW3xGVPiIgiT4+CpW3btuEPf/gDRo4ciQMOOECtFTdo0CD8/ve/x0UXXaSCKQoN6dGTsgG5adrmybhrSgGfN2jJ3XsanGVXtZde+Uk/tZdUsFTMNeKIiCJNj4Ilo9GIpKQk9f/+/furteIkX0nMmDEDmzfzD0aoVNc7UNfo1Lxyt1MldxtVzlKoSO0lp8eHt+e1r/el5bInnvoquBuqtW4KERFpHSxJTpIseeL/vyR5S++SkBICbZO+Kbi2FderS62H4VxVkq+UCoMxdEUxpfbSYSMS8O2qOmwtc+hiRpzgUBwRUWTpUbAk9ZUee+wxNeMtISEBBx54IG688Ua89tprqiCl5DNRaBQU1SPWZkZyvE375O4Q5CvtaerAOGQmmfHCD+XwarzQrikuCUZ7HItTEhFFmB4FS2eeeSZuvvnm1h6kf//733A4HLjrrrvUDDm5jUJjW3Gd6lXSckaYLCDrrquEKUT5Sm0ZjQYcNzYJm0sd+Hm9tsnecgyYt0REFHl6XGfp/PPPb/1/v3798NVXX6klT2TpEwrtMNzw/tq+5q6qIkk1hzlIxSj3ZUCGDaP62NW6cQcMioPd2qPvAAHLW2ravKxdKQ0iIorSYEnMmTMHixYtUgvopqenY9q0aQyWQqjZ6UVFTQtyJ2ldjLJIrY9mik3WrA1HjU7Ek9+W4ZOlNTj7wFRN85a8q2bDXVcOS1KmZu0gIiKNg6Xa2lpcccUVauFcs9mM5ORkFTA99dRTajbc448/rhbUpeAqqd49DKr1MifOyt3FKA1G7Xp0kuPMOGBwPD5bUoOjRyciNb5X3wN6TIbh/JW8GSwREUWGHv11u/vuu1WtpSeeeAKrVq1SPUwrV65USd8SQHGpk9AornKpAo3pyTHQkqtyV9DWg9sfhwyLh8VswLvztSslYIpJgDE2iTPiiIiiPVj68ccf1XInRx11VGtehtReOuaYY/CPf/wDn332WaDbSR0oqXYhKyUGJqN2uTEeRxM8DdWazITbk91iVAHTD+vqsatKu/IVu5O8t2j2/EREpINgSZJXJUepIzk5OWhqauptu6gbiqucyE6L1UFyN3QRLInJ+XFIijHhHQ17l6z/C5Z8vt2FWomIKAqDpVNPPRVPP/00Ghsb210vZQNef/11dTsFl8PpQUWdG9lpWg/BFcFgtsIYmwA9MJsMmDE8AfM3N6Kg3KHZjDifownu6hJNnp+IiAKrR1mwMTExKCgowJFHHql+srKyVNmA2bNno6SkRC2FIkUqhQzTSY4TBdaO0gZICUbNe5b+V4xSFpLVi3H9YjBnQz3eW1CF607K0S7Ju2iLCpyIiCgKg6VPP/0U8fG7Z2DNmzev3W3Z2dmtS6EI1poJjoLiOkiqUqbGyd3OykJYswZAT6RQ5fThCfhkSY3qXZI6TCF9flssTAmpalHd+NHTQ/rcRESkk2Dp+++/D3xLaL+LUSbHmWA2a9ej42mqh7e5Xjf5Sm2NzYvBT+vr8cGialxzQnbIn196lFqKuKA0EVEkCNhf2uLiYsycORNFRbsTfim4thXVISVBm1pCfs6qXepSj8GS9C4dMjQeCzc3olCDmXEyFOcs2Qqf1xPy5yYiIh0ES3V1dbjmmmswdepUnHvuuWqpk+OPPx5/+9vfcMIJJ2D+/PkBbia15fZ4Vc5SmsbBktRXMlrtavFYPRrbLxYJMUZ8sqRak0rePrdT5XQREVEUBkt33nknFixYgDPOOAMtLS24+uqrVeXuTz75BOPHj8ejjz4a+JZSq52l9XB7fJpVqfaTQMCUkK7bNdBkZpxU9Z6zvgGV9e6QPrclVRLLDSpviYiIojBY+umnn3D77bfj+uuvxzPPPKPqLl100UUYNmwYLrvsMmzcuDHwLaVWWwprIOFJqoY9S3LMXVW7YE7SvnJ3VyYNiFVVvb9cURPS5zVabDAnZbCSNxFRtAZLJpMJDQ0N6v8ZGRmt5QP868ZxXbjg2lJYi7RkuwoCtOJprIHX0QxzYgb0zGYxYlJ+HGatrlMLD4eSJS0HLUWbQvqcRESkk2BJljW544478M4776hlTp588knk5eVh7dq1aghu8uTJgW8ptdpcWIPsVI3rK1X6k7v13bMkpg6Mg8Plw/dr6kKet+Qs2w6f2xXS5yUiIh0ESzfddBNOP/10bNrU/lvzf//7X3V5ww03BKZ1tBeP16dmwmldudspyd32OFVTSO8SY00Y2ScGX62ohdcrpTxDFyzB64GjbHvInpOIiAKvR0kvMsz2r3/9a6/rr7vuOrVmnPQ2UXDsKquHw+VBTnoc4NRmOY9fK3frewiurQMGx+HFHyuwtKAJkweGZvaeJSULMBhV3pI9d3BInpOIiAKvxxnCTqcTH3zwARYuXKhKCaSkpKjht1NOOQV2uz2wraRWmwtr1WVOWixKi7VZLNbn86gFdGMGjAmbI9M31Yo+KRbMXFkbsmDJYLLAkpoNR7H0wB4XkuckIiKdBEsSHMnst/Xr1yM3N1cleW/btg2ff/453njjDbz55ptISNDHwqqROBMuPckOu9WkWRvctZWqhpA5SX/FKLsyZWAcPl5Sg6JqJ3JTrCGr5O3YxSRvIqJw1qPxsoceekgtmPv666+rpU8k0Vsu5ffKyko89thjgW8pKZt21iAnY/e6fFqRkgFSQ0hqLIWTUX1jEGs14ttVoUv0tqT3hauySM0cJCKiKAqWvvvuO/z973/fa9ab/P7Xv/4V33zzTaDaR3skd28tqkUfyVfSuhhlXBKMltD0zgSySOX4/jH4cV09nG5vyJY9AXxwlGwJyfMREZFOgqXGxkZVKqAjcn1NTWgLAEZVcrfTgz6Z2vYsOSsLw6JkQEek5lKjw4t5mxpD8nySBG8wW1mckogo2oKlgQMH4ocffujwNrm+f//+vW0XdZHc3Sddu2DJ53HDVVWiqlOHI1kiZmCmDd+u2v1aBpvBaFS9SyxOSUQUZQnel156qVpI1+Px4MQTT1TlAioqKlSC97vvvovbbrst8C0lVYwyPTkGdpsZLY7QrnXm56ouAXxemBLDK1+prYkDYvH+wmrsrHQiL80aknpLLYXrg/48RESko2DphBNOQEFBgVoX7u2332693mKx4KqrrsLZZ58dyDbS/2zaUY1cjfOVnBWFqnaQOSE1bI/L8Fw74mxGVdH74hnpIQmWGtfOhbuhGub4lKA/HxER6aTOkgRFF1xwAZYvX46qqirs2rUL5557LlJTw/ePqJ55PF6V3H3UlH6aL3MigZLBqF3pgt4yGQ0Y2y8Gs9fX47yD0oK+xp6q5A2ovCXz0ClBfS4iItI4Z2nLli144IEH8OCDD6qepcTERCxbtkxV837iiScwY8YM3H777Wp4jgJrR2k9nC4v+mhcNkB6lsI1X2nPobiGFi8WbQ1+oreaORgTDwcX1SUiiuyepUWLFqlcJVnKxGazqeKTf/zjH9VQ3BlnnIHRo0djxYoValhOClVefvnlwW15FNZXMhiAXA2DJY+jGZ76Stj7jUK4S0+wIC/Ngu/X1uGgocF9TQ0GAyxpfdGya2NQn4eIiDQOlqTnaOrUqXj88ccRExOjepceeeQRXHzxxfjnP/+p7iO5StLb9NlnnzFYCkKwlJkSC5tFu+EvV2WhurSEWeXuzozvH4vPl9aist6NtIQej0h3i8yIa1w3Dz6fFwYD104kIgon3f7UXrt2rcpJkkBJXHLJJfD5fGrora0jjzwSO3fu7HYDpCbTrbfeqrYzceJE9RyLFy9uvV3Wnzv55JMxfvx4HHPMMXjuueeicphv445q9NW4vpKrohAGsw3G2MhYymZUnxhVqFJyl4LNmt4XPmezquZNREQRGizV19e3S95OTk5Wl9KT1JbVaoXD4eh2A66++mqV9/Twww+rwGjEiBFquG/r1q349NNPVRkCSSSX/0vV8GeffRZPP/00oonT5cH24jqd5CulR0zPiM1ixIg+dvy4tk4F/sFP8jbAwaE42k8G+DAoOw3GuhI4K3bB0xS65XqIaLf9GnswmUzt8jDaXvbE9u3bMXfuXLXw7qRJk9R1t9xyC37++Wc1lDd//nyccsopraUI+vXrpxbsfe+99/DnP/8Z0UJmwclSJ3lZ2vXoSDAhw3C2PsMQScb1i8VrcyqxqcSBoTn2oD2P0WqHOTkDLbs2IWHcEUF7HoosnuZ6ONf9gpof34C3ZfdkBFvuEGT85i+w/m+WJREFX6+7CHoTLKWkpKhhtTFjxrTbnvzU1dXh2muvVb1MbUmCeW1taKov68XG7dVquCgrNVazNngaKtVisJEwE66t/AwrkmJNar24YJPeJceuDUF/HooczQWrUfn1c62BkpBZlcWv3wp3bYWmbSOKJvvVsyRlAeLjdw8F+YctpCcoLu7XQokNDQ3d3p4M4R166KHtrps5c6bqcbrppptae5vaDgW+9dZbmD59OqLJBlWMMh5mk3bDX87y3cndkRYsSWA+Ji8Gv2xswCUz0mA1G4Oat1S7dTm8zmYYrbtz/4g6I0VMq75/rcPbPA01anFmGRYnIh0FS1Om7C6m1za3o6PrJHCaPHlyjxqzdOlS3HjjjSqR+7DDDttr8V4phCn5UNdffz16Q9rb1NSEcLG+oBL5OYlocbS0XufPC9uf/LDeaCktgDE2CW6fAXA6Q/KcLper3WWwjMwxY84GL+ZvrMXUgcHrvfMlZsmbD7Xb1sKWN0Jd19zc3O5Sr2JjY3V/XoXLa9ldJqcD7prSTm9v3rkepv7j4PV6EW4i7Vj1dJ+6c15RmAVLr73W8TecQJk1a5YadpMZcVKWoK3y8nJcccUVKCwsxIsvvoi+ffv26rnkj++6desQDhpbPCirbsGwXIvqcdtTSUlJSNoRW7wNPkscGko7//AOFqkQH2wZcUZ8s7wS2aby4D2Jz4t4kwVFK35Byx4dsFLkVc/27OXV83ml99eyu/KzUmGMTYS3k4RuY3K2KhTc0vLrl6hwEynHqqf71J3zivQhuMVluun111/HXXfdheOOOw733XefmlHnJx8Gl112mfr2JIUwhwwZ0uvnkzXsBg8ejHCwdIP88S7G+BH9kZJoa71eepQkUMrOzlZFQoPJ53aielEt7H2GwpKVhVCRP74SKMksTDlmwTShuRmz1jQiObMvEmOCV8uqfkdf2Dy1yB/xa8+SfLgOGDCgtSxHuNL6vIqk19I/RGycdgqqvnt179vMVsQNHId8e/vZyOEi0o5VpO4T6ShYkplwd955Jy688ELcfPPN7RLGpV6TFL2U3CbpUcrJyQnIc8pzhEv3Z0FJE+JjLMhOT+wwmV4CJbsteLO4hKOmWPWK2NKyYW4TyIbyj3DbADoYxvU3qWBpyQ4Xjh8XvPeGK6sfmjYvVR+mbY+n/B4u70m9n1eR8Fr6WcYcqkp2NKz4vvU6WTon+8wbYU3OCOs1GiPtWEXyPpHGwZKUAbj77rtx9NFHq2G2iopfZ3fY7XaV5O10OlUNJrPZrIbj/DIyIivRuDPrt1ehb1Z8r2Yd9pazfKf6JmuK211bKxLF2kwYkm3HT+vqcfy4pKA9jyUjD95Vs+GuLoYlNTdoz0ORwRyXjMRDz0fMxBNgaKyEyR4Hc2IGzAkpYR8oEYUTTYMlmfkmQy3ffvut+mnr4IMPxsKFC9X/f/vb3+712A0bIn8KttRW2rijBtPHa1tPxVm+Y3cxSmNkFKPsjMyKe39hNYqqnchNCU5PljVtd75dS+EGBkvULV6TFRuLqzBixEjEsMeCKPqCpSuvvFL9UMcKS+vR7HAjL0u7yt2ylpmrfCdsfSOrGGVHhuXYYbMY8POGBpx94K/V6gPJaIuBOSlTBUsJYw8PynMQEVFgRXZXQZhbV1AFo8GAvpnaVe5211WqukCW5NAldmtFCn+OzI3BT+vrg7r8iQzFtezUftYYERF1D4MlnQdLOelxsFlMmg7BwWCAKTE6it+N7ReD8jo3NpYEr36VNSNPLUrsae5+AVciItIOgyWdB0v9srXrVRLOsh0wxafAaAn9LDgt9E/fvfzJz+uDt/yJNbOfuuTSJ0RE4YHBkk7V1DtQXNGIfhouniucZQVRMQTnJ7MOR/eNwdyNDXB7gjMUZ4pPVdO/W3auD8r2iYgosBgs6dS6gkp1OSAnUdMVzz0N1TBHUbAkxubFoNHhxfLtTUELyKwZ/dDMvCUiorDAYEmn1m6rQkqCDUnxwa3Ova8hOGFJzkQ0yUyyIDvJohK9g0WCJVk93ucO7rp3RETUewyWdGrN1kr0z9Z2KQNn+Xa1NpXRHodoIzWXFm9rRJPDE5TtWzP7Ax43XKVbg7J9IiIKHAZLOiS1lbbuqtU+ubukAJak6OpV8hudFwOPB5i/uTEo2zenZMNgscG5K/KLqxIRhTsGSzq0YXuVqt6dnxu8ZTf2xeNshqumFOaU6MpX8pPFdPMzrEEbipNq6FJCwFnIJG8iIr1jsKRDq7dWIi7GgsyUGI3zlXywRGmwJMb0i8W6XS2oqHcHbSjOVbQR8HqDsn0iIgoMBks6tHqL5CslaLt4blkBjLY4GGO0zZvS0ohcu6rqPWdDcHqXrFn58LkcMNWXBGX7REQUGAyWdMbp8mDjjmpNh+BUO0q3qSE4LQM2rdksRrVe3Ox1wVn+xJKWC5gsMFftnnVIRET6xGBJZzZsr4bL7cXAPtoFS7IWnKuqGJaUbEQ7Wf5kV7UL28qdAd+2wWiCOb0vLJXbA75tIiIKHAZLOrNycwVi7WZkpcZq1gaH5Cv5fLCkMlgalGlDvN0YtERvc0Z/mKt3wOcJTl4UERH1HoMlnVmxqVwNwRm1zFcq3Rb1+Up+RuPu5U9krbhgLH9iyegPg8cFV1lBwLdNRESBwWBJZ/WVJF9pkIZDcMJZvAWW1Jyozldqa1y/WNS3BGf5E1NKDnwmC5w71gR820REFBgMlnRWtVvqK2mZr+RpadhdXyk1V7M26E128u7lTyTROxj1ljwJmXAwWCIi0i0GSzobgkuMsyIjWcP6SqXb1KWV+UrtjOsfgyXbGlHfHPjlT9wJ2XAVbYLX5Qj4tomIqPcYLOnIso3lGNw3WdPhL0fxVpjiUqJyPbiujOkbA68PmLuxIeDb9iRlA143WnauC/i2iYio9xgs6UR1XQu2F9epYEkrUkuopWgzLGk5mrVBr+LsJgzJtuP7tXUB37bXngRDTAKat64I+LaJiKj3GCzpqFdJDO6rXb6Su74S3qZaWJiv1KHx/WNQUO5EQXmAh8sMBlgyB6Bp6/LAbpeIiAKCwZJOLF1fhj4ZcYiPtWrWBkfRJsk4jtrFc/dFepak5tIPawOf6G3JGghX+Q6466sCvm0iIuodBks6IDPglm4oxZC8FE3b4ZAhuJRsGM3aBWx6ZjIaMDYvRhWodLoDu/itOStfupjQzN4lIiLdYbCkA5t3VqO+yYWh/bQLlrxuJ5ylBbCk9dGsDeFgwoA4NDq8WLS1MaDbNdpiYUnvg6YtywK6XSIi6j0GSzqwaG0pYm1m5GUlaNYGCZR8Hpf6g02dS08wo3+6FbNWBz7R25Y7WOUtcekTIiJ9YbCkAwvXlmBIv2Q1zKMVx66NMMYkwBSn3Wy8cDFxQCzWFLaguCawi+va+gyFz9GElsINAd0uERH1DoMljVXUNGNbUR2G90/VtGRAc+EGWNP6cImTbhjRJwYxVkPAe5dkiRljTDyaNi8O6HaJiKh3GCxpbMGaErVYq5b5SrK8iZQMsGb006wN4cRiMqj14mRWXCATvQ0Go+pdaty4KGDbJCKi3mOwpLF5q4oxMDcJMTazZm1wFG6AwWyBmUucdNuk/Dg0tHgxf3NgE73tfYbBXVUMZ0VhQLdLREQ9x2BJQw1NTqzeUoER+doNwYmWHWthSesLg9GkaTvCLdF7YKYVM1fUBnS7tpxBMJitaNq4MKDbJSKinmOwpPEQnNfrwygNgyVPYw1c1cWwZnIIbn9NGRiHTaUObCltCdjxkB4+W85gNK5fELBtEhFR7zBY0tDcFUXon5OIxDibZm1o3rFWVe2WniXaP0Nz7EiJM+GrAPcu2fuNgKN4M9x1FTwkREQ6wGBJwyG4pRvLMGpgGrTUvH2Nqq1ktLBq9/4yGgyYnB+HXzY2oKbRHbBjIkneMJrQuH5+wLZJREQ9x2BJw8Rur8eHMYPStWoCPE21cFXshDVzgGZtCHcTBsSq2YzfrApcGQGj1a4KVDasnRuwbRIRUc8xWNLI7GWFyO+ThMQ47Xp0mgtWqx4MlgzouRirEeP7x2Lmylo4XIErIxDTf7QqFOqqKQvYNomIqGcYLGmgsrYZKzdVYPyQDGipuWAVrOl5HILrpQMHx6HB4cXsdfWBG4rrOwwGkwWNa+cEbJtERNQzDJY0MHtpIcxmI0ZrmK/kqi2Hq6oI1mxZ7Z56IyXOjJF97Ph0aQ08Xl9AXkyjxaYCpvpVs1WFdSIi0g6DpRCTP3zfLtyBEQNSYdewEGXz1hUwmG2wZuRp1oZIcvDQBJTVuTFvU0PAthmTPw6uikI4S7cFbJtERLT/GCyF2IYd1Sgsa8DkEVnQis/nUavb27IHsBBlgOQkWzA4y4YPFlbDG6CeIFvuILVWXP3KHwKyPSIi6hkGSyE2c952pCTYMLBPErTiKN4Kb3MdrLlDNGtDJJoxPAG7ql1YEKAlUKSiuvQuNaz+CT63KyDbJCKi/cdgKcS1lX5aXogpI7NVjR6tNG1aDFN8CsyJ2pUtiER5aVYMyrThvQVVqjJ7IMQOmgBvcwMaN7CiNxGRVhgshdCsRTvg8fgwaXgmtOJpqkdL4QbYZbaVhgFbpDpsZAIKq1yYuzEwuUvmpAxVB6tu6cyAbI+IiPYfg6UQkVlSn/28FWMGpyMhVrvaSo2bF8NgNMKaPVCzNkSyvqlWDMux4535VXB7AtS7NGSyWuzYWbYjINsjIqL9w2ApROavKkZZdTMOGpMTqqfci8/jRtPGRWple5maTsFxxKgElNe78c2q2oCtFWeMSUDt4i8Dsj0iIto/DJZCVC7ggx82qaTuvpkJ0Erz9lXwtjTA1m+kZm2IBpmJFlXV+70F1Who8fR6ewaTGbFDp6Bh5Y/wNAVuWRUiIuoeBkshsGJTOTbtrMGM8X2gFZ/Pq9Yas6TnwRyXrFk7osURIxPUMNy7C6oDsr24oVPUZd3irwOyPSIi6j4GSyHoVXrzmw3omxmPIXnaBSktuzbCXVOGmAGjNWtDNIm3m1QpAVkzrqDc0evtGW2xiBk8EbWLvoDX0RyQNhIRUfcwWAqyZRvKsW5bFY6YnKfZ7DMJ2GQIx5KcBUtKtiZtiEYHDI5DRoIZz39fHpBSAvEjD4bX2Yy6JV8FpH1ERNQ9DJaCSP5AvvLFGvTPScSwfinQSkvhOrUOXMygCZq1IRqZjAacOD4Jm0od+Hpl75O9TXFJiB08ETXzPoG3JTCFL4mIaN8YLAXR94t3YmtRHY4/sL+GvUoe1C+bBUtqLiyp2s3Ei1b90m2YMjAWb/5SheIaZ6+3Fz96BryuFtTM/yQg7SMion1jsBQkDc0uvPzFWlVXqV92IrTStGkJ3HUViB0ySbM2RLujRici3mbE4zPLel17yRSbiLjh01A7/1O4a8sD1kYiIuocg6Ugee3LtWhxuHHCtAHQisfRhPoV38GWM4RLm2jIajbi1Ckp2FrmwLvzq3q9vfjR02Gw2lH53asBaR8REek8WKqpqcGtt96KGTNmYOLEiTj33HOxePHive63fft2jB8/HoWFhdC71Vsq8OUvBTjmgP5Iiteu+GPd0m/g83jZq6STyt5SrPLjJTVYvLV3+UZSUDRx4jFoXPcLmjYvDVgbKTRanG5U1DShoqZZfaHSo6raFlRUN6OmvvczOYkigVnrBlx99dUoLy/Hww8/jLS0NLz22mu49NJL8dFHH2HgwN1LcmzZsgWXX345mpv1P2W6sdmFh99cigE5iThgtHYzz1qKN6N5y1LEjTgIRluMZu2gXx00JF6tG/efmaW4+6y+6JvW82Vv7APGwLp1Ocq/fAZ5lz8Coz2OL3UYKK5oxFvfrMfPy4vU74eMy8W5xw5Dbno89KCmwYEFq4vxzqyNKK9uRr+sBFx84kiMyE/VdJkmoqjuWZLeorlz5+L222/H5MmTkZ+fj1tuuQWZmZn47LPP1H2effZZnHHGGUhKSoLeyRT9/7y7DPXNTpx5xBAYNUrq9jgaUfPLRyqp29ZnqCZtoL1Jkv8pk5KRGGPCPZ8Wo6bR3attJR3wG1WRvXzmC3y5w0BpVROue/wn/LCkEG6PV/38uLQQ1/3nZ5RWaT+7sbHFhfdmbcQT761QgZLYUVqPO19agHkri+DxeLVuIlF0BkspKSl47rnnMGbMmHZ/BOSnrm73sg6zZs3CPffcgxtuuAF698EPm/HLymKcftgQpCTaNavUXTP3Q8DtQvyoQzSbhUcds1mMOO+gVDjcXtz1cTEaHT3/A2SOT0bS1JPQuPon1C2fxZdc5wtp/7hkJ2ob9p4RWdfoxHeLdmgejNQ2OPD5nK0d3vbS52tRVdcS8jYR6YWmw3CJiYk49NBD2103c+ZM1eN00003qd/fe+89dblgwYKA9gA1NTUhkOatLsWrX6zF9HHZGNQnDi2O4H6wOByOdpd+Tat+hKNoM2LGHAa30QI4ez9dXSsul6vdZaSIMQNnTUnEG/Nrce/nZTh/LODc4zh2lyFnGGz541Hx1fPwxmfAmjsEwRAbG6vJebU//MP0ehyub3H5MH91Sae3z1tVgmMP6IcYq0Gz/SqpaERntVMlvUCCqThb5B+rUO5Td84r0gfNc5baWrp0KW688UYcc8wxOOyww4L2PPLHd926dQHb3qaiZrw1uxL5WTbkp7lUsBcqJSW/fgBbSjfBvn0hnBlD0OQyAaWliARVVb2fQaZHxwwBZm7w4oVFRlzsLUFSTzsjU4cjpqIIFR8+gPoDLoI3Pi3ALQUmTZoU8vOqpwoKCqA3SSnpiLV3/nEbF2NBdVUlCqrLNdkv6YE2x+d2eR+v14116zrueYqkYxXKferOeUX6oJtgSYbbrr32WjUj7sEHHwzqc1ksFgwePDgg21q4tgzv/LxSrft2xhEDVdXmUJAeJQmUsrOzYbPZ4Ni2Eo3bF8GSOxTxg6cgEkbf5I+vBEqpqanqmEWaLIlzklvw3qJ6PLfQgr8fm4ZBmT376u7tk4OGH19DyrJ3kXrmTTBrsKxNIM+rnpBv9PKHasCAAYiJ0dekBglGfjvDipWbKzq8/bczBqJPTgpys9M126/6Zi8SYi2ob9q7Jzc/NxFpSbHomzEi4o9VT0XiPpHOgqXXX38dd911F4477jjcd999sFqtQf/g6m33pww5fPTjFrz8+RqMHpSGM48cCrMp9Clg8lq5ty5B45KZKpk7bsQ0GAyaV4QI+B/hYL8ntJKdApw0shY/b7fg/z4tx4WHpOG4cUn7PznAboPtqEtQ9d0rqHr3TuScfTNsuaENXAJxXgWC/KHSQzv2NLSfEYdN6osfl7QvfzJjfB+MGJCKmBi7pvtls/tw8++m4pZn58Hl/jV/KjHOimvPn4S05LioOVa9EYn7RDoIlt58803ceeeduPDCC3HzzTeHRUKyjN0/+f4KzFtVjBkT+qh6SprMfPN60LjoCzi3r1RTyWMHTwqL14/ai7UA509LwuwNLXj5p0os2tKIPxyRgdyU/QsQTbEJSD36d6j+8U0UvfovpJ9wJRLGBm84m/ZPcoINl/1mNH5zyED8tHyX+sI1fXxfZKfFalqPzU96xYf1S8VT1x+BxetKsb2kDiMHpGHUwDRkpvKPP0U3TYOlbdu24e6778bRRx+NK664AhUVv3ZR2+12JCQkQG8L4/6wZCde+myNmvZ7/rHD1QeJFtzlOxG36gs4Xc2IGzUd9hD3IlDg/1BJj9LQHDs+X1aDa9/YqX4/ZXKKKjXQ7e3Y45B21CWoXfgFyj97HE2bFiPt2EthjtduIWf6lQRF8jNEw4W1u2I2G5GdFoeTDtld446IdBAsycw3yUv59ttv1U9bp556Ku69917oZdrv/FXFePvbDSgorlPrvZ10cL4mRdpcdeWoX/EDWravhi8mCXETj4M9JSPk7aDgGJhpwx+PysQvGxvwzao69XPkqEQcNy4ROcnde78ZzBYkH3QKbLmDULfoK+x8+s9InnYqkqacAKONPQRERGEVLF155ZXqpzsOOOAAbNiwAaEiXeQ7S+tVd/l3C3egorYFg/ok4YpTxqB/TmgXxvV53Wgp2ozmzYvRUrhJ/cGzDz0QVYZ4GOP0X6yT9o/FZMChIxIwZWAsFmxpxOx19fhqRS2G5dhw0NAETM6PRUbivhPeYwaMgS1nEOpXzUb1z++iZv4nSBh3BBLGHg5rZn8O2RIRhUvOkl5d//jPWL+9GnarCaMHpePso4ehT0Z8yApLumvL4CzfCUfJNlU3yedqgSkhTS1fIj0GLrcnYkoDUMdibSYcPjIR04clYF1RM1buaMYrP1Xgv7OB7CQzRvaNwZAsO/IzbeiTYlEFL/ckgXXS5OMRP/JgNK6fj4aVP6Bu4edqtlzsoImI6T9KTQwwJ6TyMBARdcLgky6UKCK1nGSX9zW76pZXt2JEXhymDE0IWTkAP/O2hTAXr4UPBiAmCZ74NHgTs+Gztw/WPB4PTKbu57OEI+5je043UFgLFNUCpQ1AdRPgP4HzkoE/HbKP96rXC2NtEUxVO2GsKYLR0aCubj74d/Cm9e/0YXK+DBs2rNfnVTDJ88uwvsyejKSJDpG4X9yn7p1XpB9R17PU3Q8bWXlg9fZGbC3RoMKsOwcG7//ykBoMQGuduo6qPOtz1fLA4j52xmIwwenbfRrvrAHsi9/t0StscPau8rYe/ohLGyKxxEQk7hf3KbBkNrmQhej9Fi9ejH//+9/YvHkz7rjjDpx55pkBftboEnU9S0RERJFkz2CpsrISxx57LEaNGqXygqX3Sor7Us9FXc8SERFRJPvvf/+LlpYW3H///cjKkrUCqLciq9QzERFRFKuurlbFnk8++eR2gdKiRYtw6aWXYsqUKRg9ejSOOOIIPP744/B6vfjwww9V71NHP//85z9bl9iS4OvQQw9Vj5ftf/nll+2eW7a55+MLCwtRX1+Pe+65B0cddRTGjBmDk046Ce+///5ej33kkUdU7UVpo8yAv/7661FTU9N6H2mL3K+tt99+Wz2P7Ivfc889hyOPPBIjR45s15a299lf7FkiIiIKc5JRU1xcjP/7v/+D2+1WhZ791q9fj0suuUQtKSYBidz3s88+wxNPPIGBAweqhevfeecddd+nnnoKa9euVbcJGb7z+Xz405/+pCZy/PWvf8WgQYNUbcR//OMfcDqdOOWUU1qfS4Kpq666qvX3pKQknHfeeWpoUB7bp08ftRasrNghhajblg+SIK9///4qsJJ1QR966CG1ML0ERB3lRdbW1uLRRx9td93HH3+sHif7P23atNZ1+s4+++xevb4MloiIiMKc9BxJ0COzJp9//nm1oG/bYOmggw7CAw88AKNx94DSwQcfjO+//x4LFizAiSee2JrTJJcyoWD8+PGtj587dy5+/vlnFWidcMIJ6rrp06erxYNl4XvpKTKbza2Pb/tYCYA2btyoAp4JEya0PlYCOgnMzjnnHCQnJ6vrpW0yhOhfvUO2JUGaPPeMGTP22uf//Oc/yM3NVb1pfitXrlTbu/rqqwP6+nIYjoiIKMzJkJOseiE9OTfeeKMa/vKTnh8JoKQEhQROsnqGBBpSmkWu25d58+apnh3pNZIgx/8jQ2Ll5eXYtGlTp49duHCh6k3yB0p+v/nNb9TQ3ooVK1qvk+21XeZMfpcgTALBPUkAJr1ht9xyS7vrx44dq4bu5DbpnZL9k7b2FnuWiIiIwlx8fLxaJkyG1c4991z8/e9/VwGD1OKTZG9ZsP6TTz5RgUPfvn1V8CKBSHcmxNfU1Kj7TZw4scPby8rKMGLEiA5vk6GyjIy9l+RKT09Xl3V1da3X7ZmMLj1NKSkpaht7kuFG6RHrKAiTdWcffvhh3HrrrQiUqAuW/EumsBAYEc8rokgzbtw4XHzxxXjppZfw1ltv4YILLsBdd92lepMkv0eG42Jjd68RKTk93ZGQkKAe8+qrr3Z4u+QZdUZ6uiTvaE/SIyUkGPJrO5wmpOdLrtuz7MFXX32F1atXq9ykPUmAJflZv/zyixqOk162tLQ0nHHGGeiNqBuGk2Q0+Ql30n25ZMkSdRmpuI/hQw/nVaS+XyJxv7hPwfXnP/9Z9dLIUJsMRcn7R2aXyWw0f6AkwYbcJrPh9mXq1KloampSvUsym83/I0NhTz75ZJfDXDKzbdeuXVi2bFm76z/99FOVXyXDZn4//fRTu8+R7777Tm27bVAnt8usPMll6qjHStx+++0qSf2xxx5TeVzS1t6KumApUkjE3fYyEnEfie+XyDwPuE/BFRcXp6bdy/CV9L5IQDJnzhzV0yQ5RNJD9Ic//EHlIUmS9r4ceuihKuiRWW6SsC1J4ZIDJUGJ9OR0VfDytNNOw+DBg1VwI0ne0g6pLP7BBx+oGWuJib8uTC+z+f74xz9i9uzZ6r7/+te/VDK4BHpte6RsNhsuuuiiDp9Ptv/111+r24cPH45AibphOCIiokgnM9QkZ0mCErmURGcZhpOeGclZkqBElkKRGXH7WoPTaDSq2kXSU/Pss8+qMgDSc/W73/1OBUFdkan7UllcgjZ5fENDg8qrkqHBPYfGJAdJgifJt5IeMMnBkvIEe5KyA9IrtSfZN8nNkrZJ71ogRd1yJ6tWrVKXgeiW05J0ia5bt04l1fm7VSMN9zF86OG8itT3SyTuF/eJ9iQz32S4T2b06ZHmPUsSZUrtBxmblKhQain4E7KERK6SqNWWvKBtFwwk8np98Hi9sJg7/3ZEREQUlsHS3/72N2zZskV1yUlxKekmlLHGjz76SBXGktlrMi4qiWl+HXW/UXRqanGhrLoZX88rQGlVEyYOy8TUUdnITInpsOIrERFRWAVL0rUsyViSKOavzilZ7pK9/sUXX6jrZGxUpkJ2lvVO0cvhdOOXVUV47O3lrdctXleKt75Zj/v+PB15Wb8WNyMiIv36/vvvoWeazoYrKChQl5MnT26XxS81GyRjX3qVpHcgPz9fw1aSXlXXO/D4u79Wf/Wrb3LhqQ9WoL4p/EtEEBFRlPcsZWZmtk4XlIX5hGTll5SUqJwlqeEgxbBkmqGsTSPJjbIQoExflCG6npKcdkkwDGf+6Z7dmfYZifsoQfSmHdUqV6kjq7dUorbBARN6X+Y+mMLlOHYnsVjr8ypcXsv9FYn7xX3aLVIS9qOBpsGSzJyRKYS33XabmlYolT6liJZU7JRpjhIsSfEyqREhid4ybCfDdEVFReqyp2Tbsq1I4O+di2Qd7aPU2Whs3r2adFcfyOt27kQ40PtxnDRpUticV3p/LUO1X6baYlh3rYTB54UjdzQ8KXnQm0g8VvuzT905r0gfNC8dIMndUjxLqolK4vbJJ5+M+vp6VddB1nZpbGxUQZTfl19+qeouSE+Tf22Z/Z3iLLssRbLCmQQCclLKytJSxyIS7WsfK+rc+NMDP3T42L6Z8bjz8gMRa9N3kne4HMd9fQPWw3kVLq9lKParacUs1H3/KoxxSYDBCG9DFeIPPhPxU38DPYjEY9WTfWLPUvjQfDacDL9J0SxZqE8W9ZPFAKVQ1YEHHqh+bxsoiSFDhqhLGarrSbDkH8KJlDepnJSRsi/7u48pcOKEgwbgy1/af5MzGg246oxxSE+JQ7iIhOOol/MqEl7L3uxX05ZlqPv+FcQOOwCJk46VA4OGlT+iYe57iM3og/hR06EXkXisInGfSOMEb6mxJIv8rV+/Xi14J4FSYWGhWtPl4IMPxoUXXqhqLu35DVZ6oCR6p+iWEGvFuccOx3UXTMKAnEQkxlkxdWQWHvn7DAzt9+vijETRwutsRvnnT8GWOxiJk4+DwWiCwWBE/NjDYR8wBhVfPw9P494ruBORjnuWJDiSrnupsXTrrbeipaUFN910k+pVkoXzZIju7rvvVjlLhxxyiAqUJFfp0ksvVY8lSo63YcaEvhg3JANujxcxNjNi7azDRdGpdsFn8DTXIfWoi1WQ1LbXL3Hy8Sj/9D+onvs+0o+5VNN2EvW0g0U6UmTWvKwfF8qai5oPw0lekqzlcu6556oZbscccwyuu+46dZv0OslJLtW6JWiSWkuXXHIJLr/8cq2bTTqTFG/TuglEmvcq1S78HLFDJsMcn7zX7SZ7HOKGT0P90m+RcvAZMEk+E1E31Dc51ezixmYX4mIs6vNWevZDTeovykx5WUz322+/xQknnBA9wZIsePfEE090evv555+vfoiIqHMNq2bD62hC/Ihpnd4nbthUNK6Zg7pl3yLlkPaLmBJ1pLymGY+/uwzLNpS3XjdhWAb+ctYEZCSHNjlf8punT5+uZsS//fbbIQ2WNM1ZIiKiwJAAyNZnGExxe/cq+RltsbAPGI365bPg83n50tM+e5T2DJSE/C7Xh7Lwr6TlrFixQg3DyQjUggULsG3btpA9P4MlIqIw5yzbAWdpAWIHTdjnfeU+7tpytOzQviYW6ZsMve0ZKPnJ9XJ7qLz//vtqlqEsg3b00UerfCXpXQoVBktERGGuYe0cGKwxahbcvlgy8lTvU+PauSFpG4UvyVHqze2B4na78emnn+KII46A3W5Xs+dl0tfHH3+sCleHAoMlIqIw17h+Pux9h8Fg2ncaqsySs+eNQOOGBRyKoy5JMndvbg8UmflWUVGBE088sfU6+b/UZ/zqq68QFQneRETUc66qIrgqdyF+9IxuP8aWNxyN6+fBUbQF9j67C/0S7UlmvUky97IOhuLk+lDNQv7www/V5Z///Oe9bpOhuFNOOSXobWCwREQUxpo2LwWMJliz87v9GGtGnhq2a9q8hMESdUrKA8ist8c7mA3317MmhKR8QGVlpepZOu2009QasW29/PLLaoacrCM7dOjQoLaDwRIRURhr2roc1sz+MFq6/y1fKnvbsgeieety4NBzgto+Cm9SHuC6CyZrVmdJcpUkZ+kPf/gDBg4c2O62K6+8Eh999JHqXZLC1sHEnCUiojDl87jQsmMtbDmD9vuxtpyBcBRvhrelMShto8iREGtF38wEDOufqi5DWZBShuAOOuigvQIl0a9fPxx11FEqoGpqagpqO9izREQUpiTnyOdy7NcQnJ81eyDg86F5x1rEDZ0SlPYR9dZnn33W5e2PP/44QoE9S0REYap5xxoYLDZYUnL2+7Gm+BS15In0TBFR1xgsERGFKQl0VLK2cf8/ymXdTUtGPwZLRN3AYImIKAzJciUthRtUwNNT1sx+cJRug9cVukrMROGIwRIRURhylRfC52yGtTfBkjzW61GJ3kTUOQZLRERhqKVoo4ylwZKW2+NtmJMyYDBb4Ni1KaBtI4o0DJaIiMKQBDjmpMz9qq/UUb0lS1oftDBYIuoSgyUiojAkQ2e96VXys6TmchiOaB8YLBERhRmv2wln+c7ABEtpufDUVcDTWBuQthFFIgZLRERhxlW2QyVmS69Qb/m3IbPiiKhjDJaIiMKMo2SrJBzBkpzV622ZElJUYUunbJOIOsTlToiIwoz0ApmT0tVMtt4ySNCVkg1HaUFA2kYUSEcccQR27drV+rvFYkGfPn1w5pln4rLLLkOoMFgiIgozztICmAPQq+RnTsmCs4TDcNQxT3ODymnzOhphtMfBFJsEU0x8yF6u3//+9+pHtLS0YOXKlfjXv/6FmJgYnH/++SFpA4MlIqIwq9ztLNuB+NHTA7ZNS3I2mjYuUpW8e1OKgCKPu64C5Z8/heZtK1qvixk4Hhkn/hHmxPSQtCE2NhYZGRmtv+fl5WHBggX44IMPQhYsMWeJiCiMuGvL4XO1qN6gQFHb8vngqigM2DYpMnqU9gyURPPW5Sj/4ml1u1bsdntIn4/BEhFRGJFeJRGI5O62lbx3b3t7wLZJ4U+G3vYMlNoGTFqVm5BhuM8//1zlLYUKh+GIiMKIs3wHDNYYGGMSArZNGXozxaeq2k1EfpKj1BWvoykkL9azzz6Ll156Sf3f5XKpn3HjxuHkk09GqDBYIiIKIy4pRilruhkMAd2uzK6TQIzIz2iL6/LFMNpiQ/JinXPOObjwwgvV/91uN7Zv345HHnlE5Su99957sFqtkT8M19DQgNtuuw2HHHIIpk6dimuvvRaVlZWtt8+bNw+nnXaaiiKPO+44fPHFF5q2l4hISxLQmP43bBZI5uRM9ixRO6a4JJXM3RG5Xm4PhaSkJPTv31/9DBo0SJUTuP3227F+/Xr88ssvIWmD5sHS3/72N8yePRt33XUX3njjDTQ3N+Oiiy6C0+nEli1bcMUVV2D69On48MMP1fjk9ddfrwIoIqJo4/N64KosgiU5CMFSUiY89ZXwOpoDvm0KT1IeQGa9xewRMPlnw4WyfMCefD6fuvR6vZE/DLdu3TrMmTMHzz//PGbMmKGuu//++3HYYYepHqRly5Zh2LBh+Mc//qFuk4hy7dq1eOGFFzBt2jQtm05EpM1MOI8L5sRgBEu7p4E7K3fBnjs44Nun8CTlATJP+cf/6iw1qaE36VEKZaDU1NSE8vLy1iBpx44duPvuu5GZmRmyWEDTYKmgYHfF2MmTJ7deFxcXp7raFi5ciBUrVuCoo45q95gDDzxQ9ULJCxboMXsiIj1zVexqN3stkPw1c6R8AIMlassUE69pL5Ikd/sTvI1GI5KTk1Xc8OCDD6rClBEfLElUKIqLi1WvkfB4PCgpKUFaWpq6zM7O3usxMlRXXV2N1NTUHj2vBFoSqYYzeQ3aXkYi7qN+SFE4vZ9Xkfp+abtfXqmybbbCabTB0OII+HMZY5PQVLodpiAfx0g8Vj3Zp+6cV9Hu+++/hx5oGiyNGTMGAwcOVAneDz30kEri+s9//qMCIZkaKGXN98xy9/8uOU09JduWIcBI4O+di2TcR+1NmjQpbM6rSH2/yH7Fbl0Hsy0BO3cEZ9ZajDkWLQXrUZgamuMYicdqf/apO+cV6YOmwZIEPk888YRK2pacJVkgT+omHH744aqrzWaz7RUU+X/vTdebPM/gweE9Ji/fXuSkHDBgQMi6IUON+xhetD6vIvX90na/mlY2AalZSO/fPyjP1VSVC3d1MfqNGIFgisRjFYn7RDqqsyTDb7K+S01NDcxmM+Lj43HGGWeo3KScnByUlZW1u7/8Ll2XCQk9L8gmuU6R0v0pJ2Wk7EtnuI/hQS/nVaS+X2S/6mtKEDt4Iuz24Kzf5knJhKNgBWLsNhiMJgRbJB6rSNwn0rh0gNRYuuCCC1StBEnYkkCpsLBQzXg7+OCDVQKXJHq3NX/+fEycOFH1PFFgeTxelFc3oaC4DsUVjWhqcfElJtIJb0sjvE11QV28VG3b41az7ohIJz1LEhxJUqjMbrv11ltVjtJNN92kepVkOmB6ejpOPfVUlfEul1KP6euvv1alAyiwahsc+GnZLrwxcz0am12QiYZTRmbhilPHIjOF35KItOapKVWX5oS0oD2HKXH3tl1VxbCktJ9cQxTNNO+eefjhh1Vi97nnnqsKUErC2+OPP65uGzJkCJ566ikVJJ1yyimqrPkDDzzAGksB5vX6MG9VMZ77eJUKlITU+1q4phT/fnE+qutaAv2URLSf3DUl7QKaYDDFJgFGkwqWiEhHOUtZWVkqybszkvjtL1hJwVFV14I3vl7f4W3bi+tRXNmIlEQ7X34iDbmrS2CMiVeL3gaLwWiEOSGVwRKR3nqWSHstTjdqGjqv2bKtqDak7SGijofhzPE9qy23P0zxqXBVs2eJqC0GSwSr2QSrufO3AnOWiLTnqS6BKSH4wZI5kT1LRHtisERITrDhqAP6dfhKxNnN6J+TyFeJSGPu2tKQBEvyHGoNOq8n6M9FFC4YLBGsFhPOOnIoJgxtv95UYpwVd155ENKTWGCNSEsGVwt8LY0qnyjY1FCf18PyAUR6SvAmfUhLisE1509SM98KyxqQFG9Ddlqsut5o1O+CxV63C566SjRtWQJXdSliBoyBLXsgzEGcMUQUasam6tZ8omDz917J+cTyAUS7MViiVhIgyc+A3KSweFW8HhdaClah5L171TdhUbfoC7Uie875d8CSkqV1E4kCGiyFomfJFJck0+LU7DtgXNCfjygccBiOwpanvhqlHzzQGij5Sb5FxTcvwdMS3JXTiULF2FwDg9UOoy34Q+KyzIkpPgWu/9V1IiIGSxTGnKXb4HO3X2jZr3nzEnib60LeJqJgMDXVwBiXErIX1xSfDHdN+3U5iaIZe5YobHmaG7q41Qefxx3C1hAFdxjOKMNjIWKWnqUq9iwR+TFYorBlyx3c6W2St2S0cU07iqRgKZQ9SzIMV6rW7iQiBksUxuTbb+zQqR3elnb070OSDEsUbNJDanTUwxSXHNJgyedshrelq95boujBniUKW6bYRKQffzlSDjsPxpgEdZ01sz+yz7sN9v6jtW4eUUB46qtg8PlgDGmwtPu53NWlIXtOIj1j6QAK+96l5GmnIGHsYfB5vTCarbunPhNFCE9duboMdc6ScNWWdTncTRQtGCxR2JOpzuYEFqGkyOSRpUckWIoNXbBksMbAYLFxRhzR/3AYjohI5z1LPmuc+lIQKgaDQeUtsXwA0W4MloiIdB4seW1xIX9eSSiXGXFExGCJiEj/w3BWDYKleAZLRH7sWSIi0jFPXQW8tviQP685Lhme2grWWiJisEREpF8+twvexhp4NepZ8nlc8DTWhvy5ifSGPUtERDrl/l/ZAJ9GOUuqDbVcI46IwRIRkU65ancHS1r1LAn3/9pAFM0YLBER6ZS7RgIVA3zW0K9zaFS1luwMlogYLBER6Zf06qilfEJYY2nP3iX2LBGxZ4mISLckX8gYm6jp+ouuGuYsEXEYjohIxzlLBi2DpTjpWWKwRMRgiYhIp2QIzBTCNeH2JItSu1lriUj7YMntduOxxx7D4YcfjgkTJuD888/H8uXLW2//17/+hWHDhrX7OeKIIzRtMxFRsPm8Hnjqq0K6gG6HtZZcLfC2NGrWBiI9MGvdgKeffhrvvfce7r33XuTl5eH555/HZZddhi+//BKZmZnYsGEDrrzySlxwwQWtjzGZtEl2JCIKFQmU4PPuDpac2rzuv9ZaKocpJvRVxInCOlhatGjRPu8zZcqUbm1r1qxZOOmkk3DIIYeo3//5z3+q4El6l44++mhs3rwZl19+OTIyMnrSVCKisOSuq1CXxrhEwNmk2TCcvy227HxN2kAUtsHShRdeCIPB0O46n8/X+n+5bd26dd3aVlpaGn744QfVc5STk4N33nkHVqsVw4cPx44dO9DU1ISBAwf2pJlERGFfkFL1LFVrEywZ7XGqbAHLB1C061Gw9OqrrwasATfffDP+9re/4cgjj1TDa0ajEY8//jj69euHb7/9Vt3ntddew08//aRumzFjBv7xj38gISGhx88pgZ0EYeGsubm53WUk4j7qR2xsrO7Pq0h7vzRXFMFgjYHLs/uLqNPh0KQdEqw1VxbBEsBjG2nHqqf71J3zisI4WJo6depe173yyitYuHChGn675JJLur0tGWaTwOfJJ59EVlaWGoK79tpr8frrr2Pjxo0qQJLcpWeeeUb1NN1///3YtGmTej65rSdcLle3e770rqCgAJGO+6i9SZMmhc15FSnvl9gdm2E221FSUqJ+91+GWozRipZdBdgVhGMbKceqp/vUnfOKIijBWwKX++67T81Ue/DBB+F0OlWe0b4UFxfjmmuuwcsvv4zJkyer68aMGaMCKOldeuKJJ3DeeechJSVF3TZ06FCVu3TWWWdh1apVGDduXI/aa7FYMHjwYIQz+fYiJ+WAAQMQExODSMR9DC9an1eR9n6pWv8FfMnpSMzOVoFSdnY2rDZbyNvRWJYJn6MR/UaMCNg2I+1YReo+UYCDpffffx9///vfVYD0yCOP4MMPP+xWsLRixQr1bVQCpLYkCPIPu/kDJb8hQ4aoS/nw6GmwJDlVkdL9KSdlpOxLZ7iP4UEv51WkvF8qG6pgSctpDZDk0m4PfbDkSkxB87adQXlNI+VYRfo+UYDqLBUWFrZ2J8oQnfQYdYd8UxJSHqAtGX6T6Pz666/fa0hPepREuPcMERF1RWagaVmQsu2MOE9DDXwel9ZNIQrfYMnr9aruR7vdrn6Pj49Xw3DdMXbsWBVk3XDDDZg/f77qwnz00Ucxb9481TN17LHHqv/LcJzkK82ePRs33XSTKjUwaNCg3jadiEiXpAikz9ncWudIS7vb4INb6j4RRakeDcMVFRW1/t/j8ajLiooKdX15+e7prt0hw2xSlFICpBtvvBG1tbUqL0lymPxDbHLbc889p4pVSiL4ySefrIb8iIgivcaS9Op4NW6Lv3dLlj2xJGdp3BqiMAqWZLmRPessSZVt//ThPW/rSlJSEm677Tb105Hjjz9e/RARRQsJTHQTLElRTBXAdf+LMFGk6VGwdPfdd+9XQERERN2nAhODEUZ7PODUNlfIYLaq4pT+AI4oGvUoWDrttNMC3xIiIlLcdZWqV8nQw1pywRiKkzYRRaseBUsff/zxPu9zyimn9GTTRERRb/dMuN3DX3pgjE3kMBxFtR4FS7LYrQzDtV0Pri25jcESEVHP14VTa8LphPRyuSp3ad0MovArSikVtkcEsKIrERHt5qmrgK3vMN28HNLL1bxtpdbNIAq/YEnWa+vTp09gW0NEFOV8Xo+qaRQbp6eepWRV90nqP0myN1G00Uf2IBERKZ7GWsDr0UX17r3LBzDJm6JTj3uWpPhk2+KUkqckRSalcCTXxSEi6n1BSr1oLUxZVw5rZj+tm0MUPsHSn//85w6vl6BJliphlW0iol4ESzrqWTLGJKi6T+xZomjVo2Dpnnvu6XSduAULFuCVV15hsERE1ANS/FEKQRqsu9fb1AOp92SKTYC7llW8KTr1KFg69dRTO71t8ODB+PLLL3vTJiKi6K6xFJesu1USpJSBu545SxSdejwM1xlZAHf16tWB3iyRJrzOFnia6mSKEoy2GF0NjVDkBkvG2ATojZQPYM8SRaseBUsXXXRRl7fLNyIZiiMKZ66aMlT9+AYa181Ts5Os2flIP/YyWLPyYbTYtG4eRSgJSEzxydAbSTh3FG3SuhlE4VM6YOHChWhoaFAVvDv6kdwlonDmqqtA8eu3onHNHBUoCWfJNhS9egtcFYVaN48ifV04HfZgqp6l+ir4fPx8p+jT42G422+/HWPHjg1sa4h0wlG4oeMhB58XlT+8jqzTroWJxfkowHxuF7xNtboqG+Cn2uRxqzpQ5vgUrZtDFFIsSknUgcaNizp9XRw71sHnbOHrRgHnT6DW0yK6e9daYpI3RR8GS0QdMCemd37SSPKtzmYqUWTQY0FKP+P/2iTr1hFFmx4Pw73//vv46aef2iV1m0wmVcH7hBNOQEoKu2kpfCWMORS18z7q8LakA36jywRciowaS0KPOUtGWyxgMrcGdETRpMfB0rvvvtvpbZ999hnefvvtnm6aSBc9S+kn/hEVXzwjmSSt18cMnID4UYfAYGCnLAWpbIA9DgazRfOX1+3xYWuZA0XVTjS7fIizGhFvyUPC/wI6omjSo2Bp/fr1HV4vM+GkIOUNN9zQ23YRaUpqKsWPPAQx/UahqWAlvC1NiMkfq4Iosw6HSCiCClJq3KskwdHny2oxd2MDmp27Z76ZjYBb/Xc6UipcONm4EScfMhB2W8BL9RHpUkDf6TIUJzPkTjvttEBulkgTRqsdxtQcJKXm8AhQCAtSapPc3dDiwVvzqjBrdR3ibUZMzo/FsBw7MpMssJgMcLq92Lx0KdbV2PHmzPX4Yu42/P2cCRg/NFOT9hKFTbBUWVkJp9OpepSE1FdqaWnBiBEjAtU+IqKoIeUqLGm5IX/eVTub8MQ3ZWhyeHH06ERMGRgHs6n9JAar2YgBaUbkONfjuN/+BR/P3oxbn52Hi08cidMOH6y75VmIdDEMd+2112LLli0d3i4nzbnnntvbthERRRWZlm/PC92XTfmi+8mSGrz1SxXyM6y4ZHo6EmNNnd7faIuHt7kBKfFmXHLSKMxauAMvf7EW9U1OFTQxYKJI1aNg6f7770dtba3KTfrhhx9gtVpx+OGHq9lx8vPqq68GvqVERBHM29IIn7M5ZDlLksD97PdlmL2uAdOHxeOwkQkw7qN3aHchVh88TfWqMOUxB/RHXIwFH/ywWeUvnXP0sJC0nSjUejSlZ8WKFfjb3/6GSy65RJUJaG5uxnnnnYdnnnkGRx11FF577bXAt5SIKIKFssaSw+XFA58XY86GBpw2JRlHjErcZ6AkDDESLAGepprW6w4em4ujpvTDG1+vx+ylXAqIIlOPgiXJUxowYID6v1y2nR0nyd3Lly8PXAuJiKJAqIKlFpcX935ajNWFLTh3WirG5MXuX60lCZYa69pdf/ikvpgwNAP/eXcZCorb30YUtcFSbm4udu7c2RosyaK6hYW7v1HIkJwM0XWX2+3GY489pobxJkyYgPPPP79dsLVu3TpccMEFGD9+PI444ggO8RFR5BakNBhhjIkPao+SBEqbSh244OBUDMqy79fjjWYrDBa7Wh+uLclV+u2MQUhNtOP+VxfB4dq9+DRRVAdLxxxzDB566CHMnDkTWVlZGDhwIB599FFs2LABL730EvLy8rq9raeffhrvvfce7rzzTnz88cfIz8/HZZddhrKyMlRXV+N3v/sd+vXrhw8++AB/+tOf8OCDD6r/ExFFXo2lRBiMnSdY94bL7cMDX5Rgc6kD5x+Uin7pth5tR4pm7hksCavFhLOPGoaSqia8+uXaALSYKMyDpT//+c+YOHGiWvJE3Hjjjfj2229xyimnYP78+fjLX/7S7W3NmjULJ510Eg455BD0798f//znP1FfX696l6RKuMViwb///W8MGjQIp59+usqTeu6553rSbCIifQdLQRqC83h9ePTrEqwtbMY5B/Y8UOoqWBJZqbEqf+mzn7ZifUFVL1pMFAHBks1mw3/+8x889dRT6vekpCQVQF1zzTX46quvVM9Td6WlpakZdTKM5/F48M4776ihvOHDh2Px4sWYOnUqzOZfJ+0deOCBKCgoQEUFS+4TUeRw1ZYHpSCl1+fDM9+VYcm2Jpx5QCryM3seKPlnxHkaf03w3pMkfPfJjMcT7y2Hx7O7AjhR1JQOkAKUycnJarFcv8bGRlx11VVYtmxZ63Vz587FE088gbi43bMm9uXmm29WM+uOPPJItW2j0YjHH39cDb2VlJRg6NCh7e6fmbm7WmxxcTHS0ztfGX5ftUWampoQzmQGYtvLSMR91I/Y2Fjdn1fh/n5x1ZTBkjsULS2Odtc7HY52l/vrrfk1qjzAyePjVVFJmaDTGz5LjAqWWhwtnd7n+APz8MJn6/DhDxtw4kH9I+5YdaQn+9Sd84rCLFiSYTLp9ZHlTPykd0mGy8455xwV1EgytuQTSR2mO+64o1vb3bx5MxISEvDkk0+q/CfJX5KCl6+//rqqBi69THv2aglHDz84hMvlUm2NBNLLFum4j9qbNGlS2JxXYfl+8XmR3FCFphY3KrZv7/Au8uVxf83ZZsDXG404oJ8XGZZalJZ2f/JNZ0zNTtjcTmzfsgkwt/98bmtIrh3vzNqErJh6xNlNkXOs9mF/9qk75xWFWbDkX9KkLSlA+Yc//AH/+Mc/Wq9LTEzE559/3q1tSu+QDN29/PLLmDx5srpuzJgxKoCS3iW73b7XtyB/kNSbiFzyoAYPHoxwJt9e5KSU2YgxMTGIRNzH8KL1eRXO7xdPfRXKfT6k982HJad9T4z0KEmglJ2dDev/vix2x88bG/H1xmocNDgGhw6PC1xbY0xoKl6NPulJMCd1vi5ceqYLT36wBquKzPjdicMj5lh1JhL3iQK0NlxpaSmmTJnS7rpp06ap4Ke7xS3l26gESG2NGzdOBWJSokBmxbXl/116oXpKprlGSvennJSRsi+d4T6GB72cV+H4fmmp2l2KxZ6cDou944BIAiV7J7ftacm2Rrw4uxoTB8TiqDFJAV2GxBufBBlsNTubYbd1XnpAbjt0Yl98u3AHTj18KHLT4yPiWO1LJO4T9TDB279orgQ60pPUVnx8vErU7g75piSk5EBbGzduVNG5BGJLlixptz2ZbSflBSQxnIgoUhbQFYGYDbe+qBkPf1mKoTl2nDghsIGSMFhjVD0odycz4to6aEyOWg7lzZm/Fi4miqpgyT8s1zbhe39J/pOM2coacxIESRem1GuaN28eLr/8clUqQApeShK4DM19+OGHqtfqiiuu6PFzEhHpMVgyWO0wWvevSOSetlc4cO+nJeiTasHpU1K6tYTJ/jIYjTDa49stedIZi9mkqnv/tHQXtpewsjdFYbDk15tvLTLzTYpSSjkAqdUkS6VI0CQBkQzFSe/RCy+8gG3btuHUU09Vs+yuv/569X8iosiqsZTcq22U1Ljwfx8VIynWpGopmU2BD5TalQ9o2HewJCYNz0Jygg3vfNt+BIEoYnOWpIL2nrPTrrzySpXY6be/01KlRtNtt92mfjrrfZJZeEREkcpVW6aqd/dURb0bd3y4C1azAecfnAqbpdffg7tktMd2WphyT2aTEYdN7IuPZ2/BOUfXoV924GtJEekmWGJvDhFRcLhrymFJzenRY2sa3fj3h0WQ+o8XzUhDnC04y6W0JevXuWq2dvv+E4Zl4vslO/H+95tw9XmcLk8RHCzdc889wW0JEVEUkvxPd1057Hntp9d3R22T9CgVocnpwSUz0pEYG/xASUjOkre5AT6vGwajuVu9S9PH9cGXvxTgvGOHIzEmeEOERMEQ3L5aIiLqkrelET5nC0zx+5ezVNfkUT1Kdc0eXHRIOlLielUJZr+Y7FIGwAdPY/eTtiePyEKMzYRPZm8JatuIgoHBEhGRLsoGJO9nj9IuVDd5cOEhaUhLCF2g5F9MV3S1RtyerBYTDhydg28WbkddY++WXCEKNQZLRERhFCxVN7px+wdFqGny4OLpachI/HWCTahIztL+BktCgiWpOvPtwsIgtYwoOBgsERFpyF1bBpjMrb01XSmvc+HW93ahvkUCpXSkJ4Q+UBIGowlGWyzc3Swf4CcFKicOy8TXC3bA5dl7CS0ivWKwRESkIVdtOcxxKfusWVdc48Kt7++C0+NTydyhHnrbkzEmYb97lvxVvesaXVhdIIumEIUHBktERBpy15TBGNd17aGiOuD/Pi2H0WhQgVIok7k7Y9yPwpRtZaTEYkheEuatr+9wgXYiPWKwRESkcbBkjk/p9PZ1RS14YaER8XYjLpmehsSY0JQH6E75AHdDVY8ee8CoTJTVurG2oDrg7SIKBgZLREQa5yx1lty9cEsjHviyAulxwLkHJiI2BAUnu8sUI7WW6uHzdm/h9LbycxKQHGfCV/N2BKVtRIHGYImISMMaS15HU4c1lr5bXYeHvizBkCwbjh7ihc2sr49rVWvJ5+v2sidtSX7W8L52LF5fjrIq5i6R/unr7CMiiiKumjJ1aWozDCd5PB8uqsaz35djUn4sfjMxHiYdflL3tHyA38AcO2wWE778ZVuAW0YUeDo8BYmIoidfSfiH4bw+H179uRJvz6vCocPjccK4JBj3MUtO22DJAHdDz/KOLCYDxg1Jw8z52+Fw7f9QHlEoMVgiItKIq7YUBpNl98wyrw9PzyrDl8trcfy4JBw2MnGf5QS05K+15OlhsCQmD89AQ7MLc5bvCmjbiAKNwRIRkYY9S6aEVHi8wCNfleDn9Q04dUoypg7ad4FKvSR596R8gF9akh1D8pLx+VwOxZG+MVgiItKIq7oUnphk3P95MZZua8JZB6ZiTF5s2BwPGYrr6TBc2yVQNu+swcYdLCNA+sVgiYhII01V5Xi6cDTWFLbg3IPSMCzHHlbHQlXxru9ZrSW/Yf1SkJJgY6I36RqDJSIiDbQ4XXhqxwhsro/FeQelYmCmLeyOgwRLXkcjvG5nz7dhNGDKyGz8tGwX6pt6vh2iYGKwREQUYi63B3e/MA9bXRk4c5QXAzLCL1ASppgEddmbJG8xeXgmvF4fZi1kkUrSJwZLFLLFQhvWz0fVj2+hYd08VV+G60JRNPJ4vLj/tcVYta0aJ8YsRX7O7npF4Rws9TZvKT7WilGD0vDVvAIVNBHpjfarMVLEc5TtQPHrt6qlEdquK5Vz4b9hy+yvaduIQkm+IDz+3nIsXFuKM8fZkLG5Ss0oC1cGWwwMRnOv85bEASOz8fwnq7FycznGD80MSPuIAoU9SxRU8o2z7IMH2gVKwtvSgNL37oM7AB+yROHi1S/X4btFO3HG4YORH1MLoy1O1VkKV1IHyhjb+yRvMSAnEdmpsfiCZQRIhxgsUVB5murgqirq8DZ3Tam6nSgaSBDw/vebcMJBA1TPiae+Oqx7ldomeQfiS48EXlNHZWPhmlJU1jYHpG1EgcJgiYLKt49ZMvu6nSgSLF5Ximc/WomDx+bgkHF91HXu+koVaIQ7U2yi2pdAGD80AxazEV/P2x6Q7REFCoMlCipTTCJg6iQ1zmhSH7REkayguA73vboIw/un4vhp+a3Xy9CVKS783/+S5O1prIXP2/v13exWM8YNScfM+QVwS1lzIp1gsERBZYpPRvK0Uzq8LemAk1sXECWKRLUNDtz54nykJNpx1lFDVU0h4XE0wetshjECviyoLzw+LzyNPV/2pK0DRuWgut6BBatLArI9orCfDbdgwQJcdNFFHd7Wt29ffPfdd3j66afx6KOP7nX7hg0bQtBC6i2jxYbEySfAnJCK6p/fU/VYJEBKPuQMxI84CEZreFUsJtqfEgH3vbYYTS1u/PH0sbBZTL/e9r8cH//U+3AmCd7CXVcJc0Jar7eXkx6H/tkJ+OKXbTh4XG4AWkgU5sHShAkTMGfOnHbXLV++HH/5y19w1VVXtQZFv/3tb3Hddddp1ErqLXNcEhImHIPYIZPhc7thMJlhSkiBwcCOTYpcr3y5Dmu2VOL3vxmFlIT2Xwr8OT6R0LMkM/pgMAZ0ZusBo7Lx7nebsLO0HnlZ4R9QUvjT9K+V1WpFRkZG609cXBzuuecenHrqqTj99NPVfTZu3IiRI0e2u5/8UHiRmS7yrdOSkgVzYhoDJYpoc1cW4aMfN+O4af0xMDdpr9ulF8Zoi4XRbEW4MxiNMMUlwROgJG8xelA64mMsqkglkR7o6qv9M888g+bmZtxwww3qd6fTiYKCAgwcOFDrphERdUtRRQMee3sZRg9Kw8FjOx5GctdVRMQQnJ/0kMk+BYrZZMTkEVlq+ZNmhztg2yUK+wreVVVVePnll3HNNdcgOXl30u/mzZvh8Xgwc+ZM3HXXXXA4HJgyZYoaksvMzOxVFd2mpiaEMwkq215GIu6jfsTGxur+vNLD+8Xp8uDu/y5ErM2EE6flweF0dLr8j5QNkC+E++Jyudpd6pI1Dq6qQrQ4Wrp1d/ksb3vZkXGDUzB7WSG+mbcFR0/Ng9715P3XnfOK9EE3wdKbb76JhIQEnH322a3XyRCciImJwWOPPYbKyko8/PDDKin8448/ht3es+Rg+dBZt24dIoH0vEU67qP2Jk2aFDbnlZbvly8XV6OwrBEnTE5GSXFhx3fyAfF1lXBZElFbWrpfXyj1yuQGrE112L5tqyoJ0l0lJV3PeMtLt+KT2ZvRJ75eDeWHg/15/3XnvCJ90E2wJMHPKaec0i4Akt9nzJiB1NTU1uuGDBmirvv+++9xwgkn9Oi5LBYLBg8ejHAm317kpBwwYIAKJiMR9zG8aH1eaf1+WbyuDAs3FuK4A/MwYWTnPd+y9E+N143EzFxY0rO6FYRKoCSfg/Ia65HHbkRTyVr0SY2HOWnfvf7SoySBUnZ2Nmw2W6f3O9RSh9e+3gSvPRujB/76d0CPtH7/URQES+vXr8fOnTtx8skn73Vb20BJyPCbDNPt6xtJV+QbSqR0f8pJGSn70hnuY3jQy3mlxftFlud45uO1GDEgFdPH53XZC+Ko2qUu7UlpMFm7n+AtgZJMitEjb1IqZADW1FwPe2a/bj9OAiW7rfMRguEDbMhK3YVvF+3C1NF9EQ6i4fMqGukiwXvx4sVIS0vD8OHD213/yCOP4Nhjj1W5EH6FhYWorq4O+54hIooMXq8Pj7y1VAVIpx02eJ/DRS5JhDYYYYyAdeH8jNYYVTPNXVse0O3Ka3ng6BwsWFOC0qrwzjOl8KaLYGnt2rUYNmzYXtcfffTR2LVrF26//XZs27YNixYtUjWYJk6ciOnTp2vSViKitj79eStWbKrAGUcMQVzMvofJPDITLjYJhv3I7QkHsk+uusAGS2LC0Ay1DIosREwU1cFSeXl56wy4tkaPHo3nn39eFaY87bTT8Oc//xkjRoxQJQbCJdmPiCLX9uI6vPrlWlUiYHDf7i3d46opU3WJIo2qtVQT+GDJajFhyogstV4cywhQVOcsSUDUmWnTpqkfIiI9cbk9ePCNJUhLtOOYA/p3+3Hu2jLYciMvjUCWMXIUb4XP54HBENheswPH5GDOiiJ8t2gHTjqEdfcoSnuWiIjCzZszN6jlOM44cggs5u59lKoFdFsaYYpLQSQGSz6vG56GwCyo21ZyvE0V+fzkpy3weH/NYSUKFQZLRET7ad22KnzwwyYcNaUfctPj96tXSZjiuzdkF078+yTDjMFwyLhclFQ2YcHq4qBsn6grDJaIiPZDi8ONh99aohZ4nT6+z369dm4JJAxGmCJgAd09GWyxMJhtu/cxCPpmJiA/NxEf/rC53QxpolBgsEREtB/++/kaVNa24IzDh8Bo3L+JJu7qUpUIHWkz4VoXy45Phqum+1XJ99f0cX2wYUc11m7TbzVzikwMloiIumnZhjJ8+UsBjps2AOnJ+1+l2VlTAlN85OUr+ZniU+Gu7nnB4H0Z2j8FWamxeO+73UthEYUKgyUiom5oaHbhsXeWYXDfJBwwKnu/XzMZOnJXl6nel0hlSkiBu74KXk9wFv01GgyYMb4Plqwvw7ai2qA8B1FHGCwREXXDcx+tRGOzC6cdNkT90d5fnsZq+NwOmBLSIvb1NiWkAj5v0PKWxNjB6UhJtLF3iUKKwRIR0T78srIIPywpxEkHD0RyQucLv3bF9b/hKXNED8MlS/JS674G5TlMRswY3xdzlhep0g1EocBgiYioC9X1LXjivRUYmZ+KCcMyevxauaqK1RpqMmssUhlNFrXsibsquNP7Jw3PRGK8Fe/OYu4ShQaDJSKiLvKMHn93ubo8ZcagXi2zJMGSDFNF+lJNso/OIAdLZuldmtAXs5cVsneJQoLBEhFRJ75ZsB2L1pbi1EMHIT7W2uPXSYItV+UumBPTI/61NiekqRlxsuxJMMl6cYlxVrz1zYagPg+RYLBERNSBovIGPP/JakwekYUR+b1LyvY216llTsyJkZvc7WdOSofP44K7tjy4z2My4vBJefh5+S7OjKOgY7BERLQHt8erFslNiLHgxIPze/36OCt2qcto6FlSM+IMhtZ9DqZJwzKRnmTHK1+sDfpzUXRjsEREtAcZ2tlSWIszjxwKm6X31badlYUw2uJgtMdF/GttNFvVorquisKgP5fMjDtqan9Vd2nV5oqgPx9FLwZLRERtrNxcjvdmbcSRU/LU+m+B4CovhDmp5zPpwo3sqzMEwZIYMygNeZnxeOHT1fB6uWYcBQeDJSKi/6ltcODB15cgv08SDp3QNyCvi8/r2Z3cHU3BUnKmKkzpdTYH/blkduHxB+Vj665afL94Z9Cfj6ITgyUiIknC9vrw8JtL4XJ7cdaR+79IblclAyThWQKIaGFJkn31hax3aUBOIsYMTscrX65FU0twllqh6MZgiYgIUMtnyEK5Zx45BIlxPavS3RFn2XYYjOaomAnnZ4xNVAU4naXbQ/acx08bgKZmF0sJUFAwWCKiqLd8Yxne+Ho9DpvUF0PyArscibOsQPUqGYy9TxQPFzI0Zk7OgqNsW8ieMznehsMn5+HTn7aylAAFHIMlIopqpVVNuO/VxRicl4wjJ/cL6LalMKOjdDvMKdmINpbUbLgqdsHrdoTsOQ8Zm4uMlBj8551l8DDZmwKIwRIRRa0Whxv/99ICWC0mnH3U0IDlKfm5Kovgc7XAkpqDaGNJzZXsdjhLC0L2nFJK4LTDBquyD5/M3hyy56XIx2CJiKI2ofuhN5egqKIBFxw3HLF2S8Cfw1G0GQazNaryldrlLdnj4CjeEtLnlXIPB4/LxWtfrcP2krqQPjdFLgZLRBSVpOrzgtUlOPuoYchOC06xyJZdm1QPSzTlK7XNW7Kk9VGvQagdPbUfUhPtqgyEyx3cNeooOjBYIqKo8/mcrfjwx8044eB8jBiQGpTn8LQ0qPpKlozA1GsKR9b0PHjqK+GqC211bYvZhLOOGorCsnq89NmakD43RSYGS0QUVX5etgvPfbQKB4/NVT/B0rJzA2CQgCF6gyVLWo4qm+DYuT7kz52bHo/jp+Xj8znb1GK7RL3BYImIosbCNSUqT2n80Awcf9CAoD5Xy47VsCRnqXpD0cpgssCc3gfNO7Tp3TlwdDbGDk7HY+8sYzkBCt9gacGCBRg2bFiHP0ceeaS6T2FhIa644gpMnDgRhxxyCB599FF4PByDDpaaegdKqxpRUdOsVl4nihSL1pbgnlcWYviAVJx2+BAYDYGd+daWx9EIR8k2WLOCG5CFA1tWvhqOdNdXaZI3JbPj0hLt+PeLC1Bd1xLyNlBkMGv55BMmTMCcOXPaXbd8+XL85S9/wVVXXQWXy4VLL70UAwYMwNtvv40dO3bg5ptvhtFoxF//+lfN2h2JGltc2Li9Gi98sho7SusRazfjpEMG4sSD81WiJFE4m7uyCA+8vhjD+6WoEgGmAJcI2FPztlXqksESYM3oC4PZgqZtK5A49nCEmpSFuOD4EXjmw5W4/YX5uOeqg4My85Eim6Y9S1arFRkZGa0/cXFxuOeee3Dqqafi9NNPx8yZM1FUVIT7778fQ4cOxVFHHYWrr74ar7zyCpxOp5ZNjzirt1Ti1ufmqUBJNLW48e6sjXjkraVqcVGicPXVvALc9+oijBqYhnOOHgazKbgfez6fD82bl8CSkRfVQ3Bth+Ksmflo2rxUFenUglT3vvjEkapMhPQwSX0torDNWXrmmWfQ3NyMG264Qf2+ePFijBo1CklJSa33OfDAA9HQ0IB169Zp2NLIUlXXguc/3v1NeE/LN5arITmicCMVnF/+fA2een8FDhiVg7OOHKqKFgabq3wnXDWlsPcZGvTnChe2vkPhbarVpIyAX05aHC4+YSQ276xRPUxccJfCZhiuraqqKrz88su45pprkJycrK4rKSlBdnb7ZQIyM3ev3F1cXIxx48b1+JtfU1MTwpkElW0ve6Ox2a2WfOjM+oIq5KbZ1OsWrvuoV+Gyj7Gxsbo/r9q+lg3NLjz+3iqs2FyJY6b2xQGjMuF0hqaHtH7Nz6ogoy8hMyA94JKO0PYyLMUkwZiQjvrVc2DIGACHY/ex8F+GSlaKFecdMxhvfrsZNz75M264cILqddLqXO7OeUX6oJtg6c0330RCQgLOPvvs1utaWlqQmJjY7n42m63XJ5l86ERKz1RBQe+XEkjN6qeWeZCKxh2JtRuxefNmzYY+A7GPeqf3fZw0aVLYnFc/LdqAD+ZWocXlxZHjEpGT0KLyHUPB2FSDuF0b4MwegYay0oB/oQxnpoRc2IpWonD1YngSMlq/EGvh6PEJ+G55Ha5/Yi7OnZGG7BSrJudyd84r0gfdBEsff/wxTjnlFNjtvyYTy//3/APtD5J6E5FbLBYMHjwY4Uy+vchJKcnvMTG9y4twe4GDxuRgzoqivW6T/I7hA9KRFLu7Ry9c91GvImkftT6vausb8ernqzB3XT36ZsTh4sPyA9Zr0F31cxbAY49H6pDxMBgDM+QnQagESqmpqeo1Dle+zCw01e1EYtla2AafjZLS3SMH/i/AodQfwKABTrzz3Ra89G0FLj15BA6dkKNmz/VUJJ3LpNNgaf369di5cydOPvnkdtfLibRx48Z215WVlanLrKysHj+fnBCR0v0pJ2Ug9uWSk0Zh665aFFU0tl4nM4Zu/t1UpCXHwGo2hf0+6lkk7KOW59XS9WV4+oPlKKtpxmETcnHElAFBn/G2p5ZdG+Aq3oz4MYfB1uZLX6BIoCSTYsKZcehU1C2dCVup5C4lqEDJbtNmtq0875WnjsVnc7bi6Y/WYMXmKlx5+likJPSuPZFwLpNOgyVJ5E5LS8Pw4cPbXT9lyhTV4yQJ3fHx8eq6+fPnq1lze96XeicrNRZ3X3UwthfXYdWWSmSkxGDC0EykJdk1DZSIuiLJuq9+uRbLNpZjQE4CTp6ajHEjc0IeKHkcTaiZ/xksabksF9CF3a/PQDQt+waGUcdDa1JW4PTDh2BovxR8+vNW/PHe73D+cSNUwdJgz5qk8KKLYGnt2rWqEOWepFSAFKH8+9//jmuvvVYVqHz44Yfx+9//Puy/YelRWlKM+pk4vOe9dkTBJonkKzaV46Mft2DphjJkJMfgvGOHY1BubMhyk9q1x+tB9Zz34HM7ET/y4F4N5USDuBEHoHbep4jZ9DN8+YMBjXqW2hozKB0Dc5Mwc8F2NTP405+3qHpch07Mg8XMoIl0EiyVl5e3zoBrS7poX3jhBdxxxx0466yzVAmB8847TxWsJKLoIjM2f1pWiG8X7kBxRSNy0uNUOQBZzkImKLQ4Ql+d2edxo3ru+3CWbEPixGNgtO/uAafOGS122EfOgGf5N2iY+z5sR5wPo1n7L79xMRZV7VvyN2ct2oHH3lmOV79ch+OmDcCRU/qp3neKXroIlp5//vlOb+vfvz9eeumlkLaHiLTncnuwcUcNlm0sw6K1pSqnTr7lj8pPw0kH52NATqKmvTiyfEf13A/UUh4JYw+HJTVHs7aEG1NiKpx9x8FQtAqVXz+P5EPOUOvo6UF2WhwuOG6ECs7nrSrChz9sxlvfbMDQfsk4cHQOJg3PUu89CdApeugiWCKi6B5Wq2t0oqi8ETvL6tWCp5t21qjgyOX2ItZmxqC8ZDUsIuu62Sza5tC56yvRuGEhGjcuhNEWi8TJx8OSHPrZouHOE5eK2PHHwLF+Lsq/eBqxgychbthU3QRN0pN0yqGDccJB+Vi7rQqrt1bg7W82qN6m+BiLei8OyUtGfm4S+mUnoJd54aRzBl+oKw1qbOlSKbnvC/ucJ9kHmVIsM2QiNUeC+6gfcr50lFe4v+fVkk31KKtxoqHFg/pmD+oa3ahpdMPp3v0xJO/kpDgz0hLMyEi2qvo3qQnmbi1664MPbrcbZrMZBrWlwDM4G2Fd/L76vydzIDxp+YAx+MGbLB5uMkXWRIvWffJ5YarcDlPZFhh8HjjG/xa+2BTotSp8abUTJdVOlNW6UFHnQotz94Lj8hZNjDEhOd6s3sPJcWYcPi4FNouxx+cV6UfU9SxFSmAh+xHuAd++cB8j67ySXqIP5pbvYztAo8OjfnZU6HBNQp8PBs//FoOtNwBbJMgL1TpjkbiemX+f8gD0Vf/z/dICGLQpVrnf2nQ1SLdDbZNH/QC737sj8uLQP4tdTpEg6nqWiIiIiPYH50QSERERdYHBEhEREVEXGCwRERERdYHBEhEREVEXGCwRERERdYHBEhEREVEXGCwRERERdYHBEhEREVEXGCwRERERdYHBEhEREVEXGCwRERERdYHBEhEREVEXGCzpQE1NDW699VbMmDEDEydOxLnnnovFixe33j5v3jycdtppGDduHI477jh88cUX+9zmG2+8gSOPPBJjx47Feeedh7Vr10Lv+/nBBx/g5JNPxvjx43HMMcfgueeeg8cjK3h3rLS0FMOGDdvr58MPP4Re9/F3v/vdXu298MILw+5Yam1fx37dunW44IIL1HvpiCOOwKuvvgo9W7BgQYf7Iz9y7MXTTz/d4e169eyzz+713t7XcfF6vfjPf/6D6dOnq/v84Q9/wM6dO6Hnffr+++9x+umnY8KECWqf7rvvPrS0tLTevmTJkg6PmxxzCiM+0tzvfvc730knneRbtGiRb+vWrb477rjDN3bsWN+WLVt8mzdv9o0ZM8b38MMPq/+/8MILvpEjR/p++eWXTrf34Ycfqsd/8sknvk2bNvmuu+4639SpU32VlZU+ve6ntHXUqFG+t99+27d9+3bfF1984Zs4caLv8ccf73R7P/74o3ptSktLfWVlZa0/zc3NPj3uo5g2bZrvzTffbNfe6urqsDuWWuvq2FdVVfkOOOAA34033qjOmffff1/dVy71yuFwtNsP+fnmm298w4YNa2333/72N3X897yfHr3++uu+4cOH+y644ILW67pzXOR8l/v88MMPvnXr1vl+//vf+4455hj1+uhxn+Q8HzFihO/pp5/2bdu2Tb0vZ8yY4fvnP//Zep833njDd9RRR+113PSwT9R9DJY0VlBQ4Bs6dKhv8eLFrdd5vV51cj366KO+W265xXfGGWe0e8zVV1+tPkQ6Ix8u999/f+vvLpfLd+ihh/qeeeYZn17385xzzvHdfPPN7R7zxBNPqHZ35rnnnvOdfPLJPr3Y1z5WVFSo29esWdPtberxWOpBV8deXptDDjlEvVZ+Dz30kHotw0VjY6Pv8MMPb/dH9/jjj/f997//9elZSUmJ74orrvCNHz/ed9xxx7ULLPZ1XCR4mDBhggou/Gpra9WXhc8++8ynx3265pprfJdcckm7+3/00Ufqi58/GLrtttt8V155ZcjbTYHFYTiNpaSkqOGmMWPGtF5nMBjUT11dnRrCmTZtWrvHHHjggaprV4LdPVVWVqKgoKDdY8xmMyZPnoxFixZBr/t57bXX4tJLL233GKPRiNra2k63uWHDBgwaNAh6sa99lPbK//Pz87u1Pb0eSz3o6tjLOTN16lT1WrU9Z+S1rKioQDh45pln0NzcjBtuuEH97nQ6VfsHDhwIPVuzZg0sFgs+/fRTlTawP8dl/fr1aGxsbPd+T0xMxMiRIzV9v3e1T7///e9bj1Hbzy2Xy4WGhgZdfk5RzzBY0ph8GBx66KGwWq2t182cORPbt29X4/YlJSXIzs5u95jMzEz1QVpdXb3X9uT+IicnZ6/H+G/T435OmjSpXRBRX1+Pt956S93WmY0bN6Kqqgrnn38+DjroIJUf9NNPP0Gv+yjtTUhIwL///W+V0yT5Z48++qj6Q9gRvR5LPejq2Hd2zoji4mLonezXyy+/jCuvvBLJycnqus2bN6v8PXk/HXvssTjssMNw3XXXoaysDHoiOTuPP/448vLy9rptX8dFr+/3rvZJArnhw4e3/i5Bkhy70aNHIzU1VV23adMmbN26VeWdHnzwwSpvceXKlSHdB+o9Bks6s3TpUtx4440qwVk+ECVRsO0fX+H/vaM/shJEtb2Pn81mg8PhgF73sy35dnnVVVep9l5//fUdPt7tdqsPIOl5+stf/qJ6dCQh9PLLL1cJ8XrcR/kDL/skidovvPAC/vjHP+K9997Dv/71rw4fHy7HMtT2dew7OmfkNRPh8Lq9+eabKqg+++yzW6+T946IiYnBY489hrvuuku9BhdddFG7ZGI929dxCff3u7wv5fNKgqPbbrutNQiUL35NTU3qPH/qqaeQnp6uktwlAKbw8Wt/KGlu1qxZajhKZlE9+OCDrR8UewZF/t/lg3NPdru93X385MOmo/vrZT/9ysvLccUVV6CwsBAvvvgi+vbt2+E2pCtfZpOYTKbWfZZvc/JBJY/bc+hSD/soPUrSZZ+UlKR+Hzp0qOre/8c//qE+ZOVDNNyOpRb2dezluo5eMxEbGwu9+/jjj3HKKae07puQ36U30t9bIYYMGaKuk9lYJ5xwAvRuX8el7fu97b6Hw/tdhtz+/ve/Y+HChXjiiSfUFyJ/L5kMIUr75VwXMkwvM1pfe+013HHHHRq3nLqLPUs68frrr6tvyYcffrjKV/B/45KTbc+udvldPlzk2+ee/F3YHT0mKysLet1PsWXLFpx11lkqV0emy7fN/elIXFxcuw9V/x8QmVaux32UP/L+QKlte0VHwwx6P5Za6urYy1BPR6+Z0PvrJnk7MlVeSmjsqW2g5B+ekmG6cBmS3ddxCdf3u7RPhoOXL1+ugnUZit9zeN4fKPlzmiSHSevPKdo/DJZ00u1+5513qhPu4YcfbtcNLcm88m2lrfnz56seCznp9pSWlqZyf9rW8JDuYUmunDJlCvS6n/IH4uKLL1bfwN5+++3WIKIz0osgr8GetUpWr16NwYMHQ4/7KPVZZFiurVWrVqkP0gEDBoTVsdTSvo69vDYyAaJtjS45Z+S1lNdUz+TYShvb5sGIRx55ROUqtZ3UIb2vkreo5ft9f+zruMg+x8fHtzuuMjFCemH0+n6XoWD53JI8M/mCt2c7JY9O6i+1rRUl57AExeFy3Oh/Ajy7jvaT1OKRaaZ/+tOf9qrDUVdX59u4caO6/YEHHlC1SV588cW96ixJnZ62tXreeecdNd1WavT4a/NI7RIta/Psaz9lOu6UKVNUbZXO6shI++W+wuPx+E4//XTfCSecoGqdyGtz9913+0aPHu3bsGGDLvfxtddeUzVZpM7Sjh07VC0pOS5SQyucjqXW9nXspUSDvJduuOEG9Zp98MEHqp6PvIZ6JzWI9pyKLlatWqXeW7feeqt6ny1cuNB3yimnqJIbUp5Cj+T1bzvNvjvHRc4FqSM2a9asdnWWnE6nT4/7JL/LcZk3b95e57zb7fbV19erEhDnnnuuOobr169XpV/kdSgvL9d0X2j/GOQff+BEoSfDNPKtsSOnnnoq7r33XvXt5IEHHlBTbCWHR4Z42uYo+CvKyhi4n3QHS3VcqSgt+RySXDhixAjocT9lhsjcuXM7faxMvfXPSpGpx/KaCJlu/NBDD+Hnn39W30BlZorkCUlvnF6PpXz7lB/5ppmRkaGGHSUx2d9LGA7HUg/2dexltpEkQUuvhLzOMsVbkmr1TipWS+9KR+8jSV6X5G45H6THUip7t82B05t//vOf2LVrV7v38r6Oi/Q6SY+sVGKXhHDpqZGK+J3lLmq5T9JW6TXqLPn8u+++U+3esWOHyluUHjO5r8z8leMmOYsUPhgsEREREXWBOUtEREREXWCwRERERNQFBktEREREXWCwRERERNQFBktEREREXWCwRERERNQFBktEREREXeBCutQhKY645zIrshadFP/785//rIpDElFgCh1+9NFHnd4+Z84cVcCRiLTDYIk6JYHRbbfdpv4v1WplHaq33noLl156qaqwu6/124ioeyQYktXqOyKL5RKRthgsUadk2YXx48e3u+6ggw7CtGnTVLAkJfuJqPdk+ZI9zzUi0g8GS7RfYmJiYLPZYDAYWq/78ssv8cILL2Dbtm2IjY1Va1Zdc801rWtWyXpI99xzD2bOnAmv14vp06fjlltuab1d1nw7+eST0dzcrIYjZJ20Qw89FDfddFO7b9Xvvfee6tnaunWr2o6sVn7llVfi+OOPb72P3CZrS8kQoix7KKvTX3/99Rg0aBCGDRu2z/XniPRK1kp78skn1XlUVFSkAqxx48ap93fbtQI7e5/L0Ll/nTb5snPjjTd2eD85V0877bTW+/jXONuTrHV20UUXqXULDzjggIDtJ5EeMViiTkmw4Xa7W/8vC7m+8sorcDqdOP3009X1Tz31FP7zn//gvPPOwz/+8Q+1QKws9rl8+XK8++67sNvtahHgjz/+WAVIiYmJuOOOO3D77be3Wyz0zTffRP/+/dUHdVVVlVokdfv27Xj77bdVYCaLz/7f//2fWkRYFqKsra3F888/rxZPlcUss7OzUVpairPPPhtZWVlq+xK4Pf7447j44ovx+eef45133lHPtWbNGvz73/9WC3SOGjWK7wDSBf+5Ju93k8m01+0SFC1evBhXX301+vXrp84POdfki8kXX3zR7gvMGWecgTPPPLP1dznnOiJDf/58qPLycpWPSER7Y7BEnVq0aFGHwYR8WEtPjQQsTz/9NM466ywVePjJatrnn38+PvjgA3UpgZZ80PsDrKVLl6peorakN+m///2vSiIXqamp+NOf/qRWlZ8xY4YKwiRX6qqrrmp9TJ8+fdQ34CVLluDEE0/Eyy+/rAI52Y7/D8Dw4cNx7rnnYsWKFaq3SvhXCR88eDCHPkgXZCX7tueaxWJRQb+89//6178iLi4OjY2N+Ne//oUTTjihtaeooaEB9957LyoqKtolgcuXh7bDejKk3hHpkfL3GhUWFgZxD4nCG4Ml6pR8ePu/kUrAU1dXh59++kn1CDU1NakhLglOTjrppHaPmzx5sgpkZChMgiXpUfInicuH+rx581Sw1ZYMxfkDJf/vZrNZBWzyB0NmDAlpgwy1ybdqGQYQ0gYhQZP8gdjzj8YPP/zAo0y6Ju9Z+eLhP0/kfS7vfRneXrdunephffHFF9Xt0oMqQ94FBQWt723/ORAMMuQtbeqot4soWjBYok7Jt9kxY8a0u+6QQw5RgZJ8iMuwmEhPT9/rsXJdfX19u+tkCE3yH0TbITgh36L37GlKSUlRvVdix44dqvdKAi351j1w4EDVa+QP5IQME3aUW0Gkd5J/tOe5Jl8S5BySPD0Z1pb/33333erLgpyb8v6Xoea250AwHH300a35inl5eaqH+JJLLgna8xHpEYMl2m+jR49Ww2gydCCkt0iCl7Yk/0E+WNuS2XO/+93vVN6TJI5Kcqr0QAkpS9CWv1SBDMfJN9vLL79cBUnvv/++GjqQXqfNmzfjk08+aX2M9ExJvtOeJMCSIGrP9hDpnQxpC+mllfyio446Cs8++6x6L/tz+WSoek9t85e60p37SY+X9HzJ8LX03t53333q+rZJ5USRjhW8ab+tXLlSdcmfc8456huxJE+3JUmoMltHhulkyECSsCWpWhK4p0yZopJIZWaPbMdPhvfaDiVID5QkvEqZAgmaZNhBklbl27cESv7HCAmm/MN/kpvUNmCqrKzEZZddhtmzZ/NIU9hZv3596/8lWJEvDZLc7Q9y/IGSv2fJfy5Iz2xX/PfrztCaBGxy3sn5dcUVV6jZdv4hcKJowZ4l6pQkj0r3v58EM99//71K3JZZZzLUJh/eMp1Zen0OP/xwlSQqM3QkefrUU09VZQZkG5KkKrPlpBSADOHJ9dJD5VdcXIw//vGPaiqy/F+m/0uJAf+UZOmBkm/RkoMkM+rkj4RMWRZSckDI0IDMupPgSD7UpU3yrVgeI6UJiPRKzq2255qce/Pnz1c9qXIOHHvsseq8kpmlv//979X9ZWr/jz/+qO4vQ+MyCcL/BUTOkc7IFwrpqZKAq7PE77YkZ0p6j6VNkke1ceNG/OY3vwnIfhOFCwZL1Km1a9eqoMhPAhz5VitBj8xM8+chSdD0+uuvq6n5Egwdd9xx+Pvf/96aTyGJqdJ1f+edd6oPean8/cwzz7QbFpPZbPIB73+cBFryPH5SouCuu+5Sid7SmyXBmARCksMhPVmyPEtOTo4qQSB/UPz3kz80kh/lr+lEpEcybN32XJPe07S0NJUfJMPXEtRIOQ0ZipMvFfJ+lskMUjdJ3vtyDkgvlHyRGTt2bOuMuY7IpAvZvnzR6U6w5C8nIOdTbm6uOufli4kETkTRwuALZmYgUTfIzDeZBi1ToImIiPSGOUtEREREXWCwRERERNQFDsMRERERdYE9S0RERERdYLBERERE1AUGS0RERERdYLBERERE1AUGS0RERERdYLBERERE1AUGS0RERERdYLBERERE1AUGS0RERETo3P8DGZSOlayVfI8AAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 589.847x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import seaborn as sns\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "# 1. Подготовка данных (воссоздаем df из первого шага)\n",
+    "data = {\n",
+    "    \"Имя\": [\"Анна\", \"Борис\", \"Виктор\", \"Галина\", \"Дмитрий\", \"Елена\"],\n",
+    "    \"Возраст\": [21, 22, 23, 24, 21, 23],\n",
+    "    \"Баллы\": [89, 76, 95, 82, 70, 88]\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "df[\"Категория\"] = [\"A\", \"B\", \"A\", \"B\", \"A\", \"B\"]\n",
+    "\n",
+    "# Настройка стиля Seaborn для красивых графиков\n",
+    "sns.set_theme(style=\"whitegrid\")\n",
+    "\n",
+    "# Создаем большую область для графиков\n",
+    "plt.figure(figsize=(15, 10))\n",
+    "\n",
+    "# --- Попробуйте: histplot ---\n",
+    "plt.subplot(2, 2, 1)\n",
+    "sns.histplot(data=df, x=\"Баллы\", kde=True, color=\"skyblue\")\n",
+    "plt.title(\"Распределение баллов (histplot)\")\n",
+    "\n",
+    "# --- Попробуйте: scatterplot ---\n",
+    "plt.subplot(2, 2, 2)\n",
+    "sns.scatterplot(data=df, x=\"Возраст\", y=\"Баллы\", hue=\"Категория\", s=100)\n",
+    "plt.title(\"Взаимосвязь возраста и баллов (scatterplot)\")\n",
+    "\n",
+    "# --- Попробуйте: heatmap (тепловая карта корреляции) ---\n",
+    "# Для корреляции берем только числовые столбцы\n",
+    "plt.subplot(2, 2, 3)\n",
+    "numeric_df = df.select_dtypes(include=[np.number])\n",
+    "sns.heatmap(numeric_df.corr(), annot=True, cmap=\"coolwarm\", fmt=\".2f\")\n",
+    "plt.title(\"Корреляция признаков (heatmap)\")\n",
+    "\n",
+    "# --- Попробуйте: boxplot (из примера на скриншоте) ---\n",
+    "plt.subplot(2, 2, 4)\n",
+    "sns.boxplot(x=\"Категория\", y=\"Баллы\", data=df, palette=\"Set2\")\n",
+    "plt.title(\"Разброс баллов по категориям (boxplot)\")\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()\n",
+    "\n",
+    "# --- Попробуйте: pairplot ---\n",
+    "# Этот метод создает отдельное окно с матрицей графиков для всех пар признаков\n",
+    "print(\"Генерация pairplot...\")\n",
+    "sns.pairplot(df, hue=\"Категория\")\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "89de5eac-b84d-4196-8e11-3238d9b89137",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/top_movies.csv b/top_movies.csv
new file mode 100644
index 0000000..7c8b932
--- /dev/null
+++ b/top_movies.csv
@@ -0,0 +1,26 @@
+title,popularity,vote_average,vote_count,release_year
+The Shawshank Redemption,85.5,8.7,21000,1994
+The Godfather,70.2,8.7,16000,1972
+The Dark Knight,92.1,8.5,27000,2008
+Inception,120.4,8.3,31000,2010
+Pulp Fiction,65.8,8.5,23000,1994
+Interstellar,150.2,8.3,28000,2014
+The Matrix,75.4,8.2,24000,1999
+Forrest Gump,55.9,8.2,22000,1994
+Avengers: Endgame,250.7,8.3,20000,2019
+Spider-Man: No Way Home,310.5,8.1,15000,2021
+Parasite,45.3,8.5,12000,2019
+The Lion King,35.2,8.2,14000,1994
+Fight Club,60.1,8.4,24000,1999
+Spirited Away,38.7,8.5,11000,2001
+Gladiator,42.5,8.2,15000,2000
+Joker,180.3,8.2,19000,2019
+The Green Mile,30.2,8.5,13000,1999
+Titanic,110.1,7.9,21000,1997
+Avatar,140.8,7.5,25000,2009
+The Wolf of Wall Street,95.4,8.0,18000,2013
+Star Wars: A New Hope,50.2,8.2,17000,1977
+Mad Max: Fury Road,88.1,8.1,19000,2015
+La La Land,40.5,7.9,14000,2016
+The Silence of the Lambs,33.2,8.3,13000,1991
+Goodfellas,28.4,8.5,10000,1990
\ No newline at end of file
diff --git a/tqdm.ipynb b/tqdm.ipynb
new file mode 100644
index 0000000..dad4f88
--- /dev/null
+++ b/tqdm.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "13714da7-099c-4820-9a4c-02bb3a0f2aad",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Запуск обработки данных с tqdm...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Обработка строк: 100%|\u001b[32m███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████\u001b[0m| 1000/1000 [00:05<00:00, 169.78row/s]\u001b[0m\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Обработка завершена!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Общий прогресс:   0%|                                                                                                                                                                                        | 0/5 [00:00<?, ?it/s]\n",
+      "Этап 1:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 1:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 91.48it/s]\u001b[A\n",
+      "Этап 1:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 91.39it/s]\u001b[A\n",
+      "Этап 1:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 91.50it/s]\u001b[A\n",
+      "Этап 1:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 91.38it/s]\u001b[A\n",
+      "Этап 1:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 91.72it/s]\u001b[A\n",
+      "Этап 1:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 91.47it/s]\u001b[A\n",
+      "Этап 1:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 91.24it/s]\u001b[A\n",
+      "Этап 1:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 91.28it/s]\u001b[A\n",
+      "Этап 1:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 91.83it/s]\u001b[A\n",
+      "Этап 1: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 91.82it/s]\u001b[A\n",
+      "Общий прогресс:  20%|███████████████████████████████████▏                                                                                                                                            | 1/5 [00:01<00:04,  1.10s/it]\u001b[A\n",
+      "Этап 2:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 2:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 93.49it/s]\u001b[A\n",
+      "Этап 2:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 92.26it/s]\u001b[A\n",
+      "Этап 2:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 92.55it/s]\u001b[A\n",
+      "Этап 2:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 91.98it/s]\u001b[A\n",
+      "Этап 2:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 91.01it/s]\u001b[A\n",
+      "Этап 2:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 91.48it/s]\u001b[A\n",
+      "Этап 2:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 91.42it/s]\u001b[A\n",
+      "Этап 2:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 91.67it/s]\u001b[A\n",
+      "Этап 2:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 91.75it/s]\u001b[A\n",
+      "Этап 2: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 91.85it/s]\u001b[A\n",
+      "Общий прогресс:  40%|██████████████████████████████████████████████████████████████████████▍                                                                                                         | 2/5 [00:02<00:03,  1.10s/it]\u001b[A\n",
+      "Этап 3:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 3:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 94.04it/s]\u001b[A\n",
+      "Этап 3:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 92.75it/s]\u001b[A\n",
+      "Этап 3:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 92.43it/s]\u001b[A\n",
+      "Этап 3:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 91.80it/s]\u001b[A\n",
+      "Этап 3:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 91.91it/s]\u001b[A\n",
+      "Этап 3:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 91.83it/s]\u001b[A\n",
+      "Этап 3:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 92.02it/s]\u001b[A\n",
+      "Этап 3:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 91.79it/s]\u001b[A\n",
+      "Этап 3:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 92.07it/s]\u001b[A\n",
+      "Этап 3: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 91.84it/s]\u001b[A\n",
+      "Общий прогресс:  60%|█████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                      | 3/5 [00:03<00:02,  1.10s/it]\u001b[A\n",
+      "Этап 4:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 4:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 95.57it/s]\u001b[A\n",
+      "Этап 4:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 92.96it/s]\u001b[A\n",
+      "Этап 4:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 92.42it/s]\u001b[A\n",
+      "Этап 4:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 92.03it/s]\u001b[A\n",
+      "Этап 4:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 91.90it/s]\u001b[A\n",
+      "Этап 4:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 91.83it/s]\u001b[A\n",
+      "Этап 4:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 92.30it/s]\u001b[A\n",
+      "Этап 4:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 92.39it/s]\u001b[A\n",
+      "Этап 4:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 92.15it/s]\u001b[A\n",
+      "Этап 4: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.07it/s]\u001b[A\n",
+      "Общий прогресс:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                   | 4/5 [00:04<00:01,  1.09s/it]\u001b[A\n",
+      "Этап 5:   0%|                                                                                                                                                                                              | 0/100 [00:00<?, ?it/s]\u001b[A\n",
+      "Этап 5:  10%|██████████████████                                                                                                                                                                   | 10/100 [00:00<00:00, 94.20it/s]\u001b[A\n",
+      "Этап 5:  20%|████████████████████████████████████▏                                                                                                                                                | 20/100 [00:00<00:00, 93.14it/s]\u001b[A\n",
+      "Этап 5:  30%|██████████████████████████████████████████████████████▎                                                                                                                              | 30/100 [00:00<00:00, 92.40it/s]\u001b[A\n",
+      "Этап 5:  40%|████████████████████████████████████████████████████████████████████████▍                                                                                                            | 40/100 [00:00<00:00, 92.30it/s]\u001b[A\n",
+      "Этап 5:  50%|██████████████████████████████████████████████████████████████████████████████████████████▌                                                                                          | 50/100 [00:00<00:00, 92.35it/s]\u001b[A\n",
+      "Этап 5:  60%|████████████████████████████████████████████████████████████████████████████████████████████████████████████▌                                                                        | 60/100 [00:00<00:00, 92.47it/s]\u001b[A\n",
+      "Этап 5:  70%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▋                                                      | 70/100 [00:00<00:00, 92.34it/s]\u001b[A\n",
+      "Этап 5:  80%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▊                                    | 80/100 [00:00<00:00, 92.37it/s]\u001b[A\n",
+      "Этап 5:  90%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████▉                  | 90/100 [00:00<00:00, 92.20it/s]\u001b[A\n",
+      "Этап 5: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 92.47it/s]\u001b[A\n",
+      "Общий прогресс: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5/5 [00:05<00:00,  1.09s/it]\u001b[A\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from tqdm import tqdm\n",
+    "import time\n",
+    "\n",
+    "# 1. Подготовка данных (создаем DataFrame на 1000 строк)\n",
+    "df = pd.DataFrame({\n",
+    "    'ID': range(1000),\n",
+    "    'Data': np.random.randn(1000)\n",
+    "})\n",
+    "\n",
+    "print(\"Запуск обработки данных с tqdm...\")\n",
+    "\n",
+    "# 2. Использование tqdm для итерации по DataFrame (iterrows)\n",
+    "# Добавляем кастомную стилизацию через аргументы:\n",
+    "# desc — описание процесса\n",
+    "# unit — единица измерения\n",
+    "# colour — цвет бара (поддерживается в современных терминалах/ноутбуках)\n",
+    "for index, row in tqdm(df.iterrows(), \n",
+    "                       total=df.shape[0], \n",
+    "                       desc=\"Обработка строк\", \n",
+    "                       unit=\"row\", \n",
+    "                       colour=\"green\"):\n",
+    "    \n",
+    "    # Симуляция сложной обработки данных\n",
+    "    time.sleep(0.005) \n",
+    "    _ = row['Data'] ** 2\n",
+    "\n",
+    "print(\"\\nОбработка завершена!\")\n",
+    "\n",
+    "# 3. Пример с вложенным циклом и кастомным описанием\n",
+    "for i in tqdm(range(5), desc=\"Общий прогресс\", position=0):\n",
+    "    for j in tqdm(range(100), desc=f\"Этап {i+1}\", position=1, leave=False):\n",
+    "        time.sleep(0.01)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "834b60ab-68b9-48ed-9223-3b2a1b3b186e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.13.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}