From b4da4b4ad3240b56668977117ac2ab07909bf2e8 Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D1=80?=
 =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= <alexkryukov73@yandex.ru>
Date: Sat, 22 Feb 2025 02:04:23 +0400
Subject: [PATCH 1/2] lab_7 done

---
 lab_7/lab7.ipynb                              | 1225 +++++++++++++++++
 .../csv/world-population-by-country-2020.csv  |  236 ++++
 2 files changed, 1461 insertions(+)
 create mode 100644 lab_7/lab7.ipynb
 create mode 100644 static/csv/world-population-by-country-2020.csv

diff --git a/lab_7/lab7.ipynb b/lab_7/lab7.ipynb
new file mode 100644
index 0000000..77a9372
--- /dev/null
+++ b/lab_7/lab7.ipynb
@@ -0,0 +1,1225 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Лабораторная №7  \n",
+    "\n",
+    "Загрузим наш датасет"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['no', 'Country (or dependency)', 'Population 2020', 'Yearly Change',\n",
+       "       'Net Change', 'Density  (P/Km²)', 'Land Area (Km²)', 'Migrants (net)',\n",
+       "       'Fert. Rate', 'Med. Age', 'Urban Pop %', 'World Share'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import skfuzzy as fuzz\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "from skfuzzy import control as ctrl\n",
+    "\n",
+    "df = pd.read_csv(\"../static/csv/world-population-by-country-2020.csv\")  # Замените на путь к вашему датасету\n",
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Просмотр первых строк данных"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
+       "columns": [
+        {
+         "name": "index",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "no",
+         "rawType": "int64",
+         "type": "integer"
+        },
+        {
+         "name": "Country (or dependency)",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Population 2020",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Yearly Change",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Net Change",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Density  (P/Km²)",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Land Area (Km²)",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Migrants (net)",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Fert. Rate",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Med. Age",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "Urban Pop %",
+         "rawType": "object",
+         "type": "string"
+        },
+        {
+         "name": "World Share",
+         "rawType": "object",
+         "type": "string"
+        }
+       ],
+       "conversionMethod": "pd.DataFrame",
+       "ref": "b07d1231-8d5f-430a-88d4-2e94c499ba2a",
+       "rows": [
+        [
+         "0",
+         "1",
+         "China",
+         "1,439,323,776",
+         "0.39%",
+         "5,540,090",
+         "153",
+         "9,388,211",
+         "-348,399",
+         "1.7",
+         "38",
+         "61%",
+         "18.47%"
+        ],
+        [
+         "1",
+         "2",
+         "India",
+         "1,380,004,385",
+         "0.99%",
+         "13,586,631",
+         "464",
+         "2,973,190",
+         "-532,687",
+         "2.2",
+         "28",
+         "35%",
+         "17.70%"
+        ],
+        [
+         "2",
+         "3",
+         "United States",
+         "331,002,651",
+         "0.59%",
+         "1,937,734",
+         "36",
+         "9,147,420",
+         "954,806",
+         "1.8",
+         "38",
+         "83%",
+         "4.25%"
+        ],
+        [
+         "3",
+         "4",
+         "Indonesia",
+         "273,523,615",
+         "1.07%",
+         "2,898,047",
+         "151",
+         "1,811,570",
+         "-98,955",
+         "2.3",
+         "30",
+         "56%",
+         "3.51%"
+        ],
+        [
+         "4",
+         "5",
+         "Pakistan",
+         "220,892,340",
+         "2.00%",
+         "4,327,022",
+         "287",
+         "770,880",
+         "-233,379",
+         "3.6",
+         "23",
+         "35%",
+         "2.83%"
+        ]
+       ],
+       "shape": {
+        "columns": 12,
+        "rows": 5
+       }
+      },
+      "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>no</th>\n",
+       "      <th>Country (or dependency)</th>\n",
+       "      <th>Population 2020</th>\n",
+       "      <th>Yearly Change</th>\n",
+       "      <th>Net Change</th>\n",
+       "      <th>Density  (P/Km²)</th>\n",
+       "      <th>Land Area (Km²)</th>\n",
+       "      <th>Migrants (net)</th>\n",
+       "      <th>Fert. Rate</th>\n",
+       "      <th>Med. Age</th>\n",
+       "      <th>Urban Pop %</th>\n",
+       "      <th>World Share</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>China</td>\n",
+       "      <td>1,439,323,776</td>\n",
+       "      <td>0.39%</td>\n",
+       "      <td>5,540,090</td>\n",
+       "      <td>153</td>\n",
+       "      <td>9,388,211</td>\n",
+       "      <td>-348,399</td>\n",
+       "      <td>1.7</td>\n",
+       "      <td>38</td>\n",
+       "      <td>61%</td>\n",
+       "      <td>18.47%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>India</td>\n",
+       "      <td>1,380,004,385</td>\n",
+       "      <td>0.99%</td>\n",
+       "      <td>13,586,631</td>\n",
+       "      <td>464</td>\n",
+       "      <td>2,973,190</td>\n",
+       "      <td>-532,687</td>\n",
+       "      <td>2.2</td>\n",
+       "      <td>28</td>\n",
+       "      <td>35%</td>\n",
+       "      <td>17.70%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>331,002,651</td>\n",
+       "      <td>0.59%</td>\n",
+       "      <td>1,937,734</td>\n",
+       "      <td>36</td>\n",
+       "      <td>9,147,420</td>\n",
+       "      <td>954,806</td>\n",
+       "      <td>1.8</td>\n",
+       "      <td>38</td>\n",
+       "      <td>83%</td>\n",
+       "      <td>4.25%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Indonesia</td>\n",
+       "      <td>273,523,615</td>\n",
+       "      <td>1.07%</td>\n",
+       "      <td>2,898,047</td>\n",
+       "      <td>151</td>\n",
+       "      <td>1,811,570</td>\n",
+       "      <td>-98,955</td>\n",
+       "      <td>2.3</td>\n",
+       "      <td>30</td>\n",
+       "      <td>56%</td>\n",
+       "      <td>3.51%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Pakistan</td>\n",
+       "      <td>220,892,340</td>\n",
+       "      <td>2.00%</td>\n",
+       "      <td>4,327,022</td>\n",
+       "      <td>287</td>\n",
+       "      <td>770,880</td>\n",
+       "      <td>-233,379</td>\n",
+       "      <td>3.6</td>\n",
+       "      <td>23</td>\n",
+       "      <td>35%</td>\n",
+       "      <td>2.83%</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   no Country (or dependency) Population 2020 Yearly Change  Net Change  \\\n",
+       "0   1                   China   1,439,323,776         0.39%   5,540,090   \n",
+       "1   2                   India   1,380,004,385         0.99%  13,586,631   \n",
+       "2   3           United States     331,002,651         0.59%   1,937,734   \n",
+       "3   4               Indonesia     273,523,615         1.07%   2,898,047   \n",
+       "4   5                Pakistan     220,892,340         2.00%   4,327,022   \n",
+       "\n",
+       "  Density  (P/Km²) Land Area (Km²) Migrants (net) Fert. Rate Med. Age  \\\n",
+       "0              153       9,388,211       -348,399        1.7       38   \n",
+       "1              464       2,973,190       -532,687        2.2       28   \n",
+       "2               36       9,147,420        954,806        1.8       38   \n",
+       "3              151       1,811,570        -98,955        2.3       30   \n",
+       "4              287         770,880       -233,379        3.6       23   \n",
+       "\n",
+       "  Urban Pop % World Share  \n",
+       "0         61%      18.47%  \n",
+       "1         35%      17.70%  \n",
+       "2         83%       4.25%  \n",
+       "3         56%       3.51%  \n",
+       "4         35%       2.83%  "
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.microsoft.datawrangler.viewer.v0+json": {
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+        {
+         "name": "index",
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+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>no</th>\n",
+       "      <th>Country (or dependency)</th>\n",
+       "      <th>Population 2020</th>\n",
+       "      <th>Yearly Change</th>\n",
+       "      <th>Net Change</th>\n",
+       "      <th>Density  (P/Km²)</th>\n",
+       "      <th>Land Area (Km²)</th>\n",
+       "      <th>Migrants (net)</th>\n",
+       "      <th>Fert. Rate</th>\n",
+       "      <th>Med. Age</th>\n",
+       "      <th>Urban Pop %</th>\n",
+       "      <th>World Share</th>\n",
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+       "      <th>top</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>China</td>\n",
+       "      <td>1,439,323,776</td>\n",
+       "      <td>1.48%</td>\n",
+       "      <td>68</td>\n",
+       "      <td>25</td>\n",
+       "      <td>460</td>\n",
+       "      <td>0</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>N.A.</td>\n",
+       "      <td>0.00%</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>freq</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2</td>\n",
+       "      <td>8</td>\n",
+       "      <td>3</td>\n",
+       "      <td>5</td>\n",
+       "      <td>34</td>\n",
+       "      <td>34</td>\n",
+       "      <td>13</td>\n",
+       "      <td>56</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>118.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>67.982841</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>59.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>118.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>176.500000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>235.000000</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                no Country (or dependency) Population 2020 Yearly Change  \\\n",
+       "count   235.000000                     235             235           235   \n",
+       "unique         NaN                     235             235           174   \n",
+       "top            NaN                   China   1,439,323,776         1.48%   \n",
+       "freq           NaN                       1               1             4   \n",
+       "mean    118.000000                     NaN             NaN           NaN   \n",
+       "std      67.982841                     NaN             NaN           NaN   \n",
+       "min       1.000000                     NaN             NaN           NaN   \n",
+       "25%      59.500000                     NaN             NaN           NaN   \n",
+       "50%     118.000000                     NaN             NaN           NaN   \n",
+       "75%     176.500000                     NaN             NaN           NaN   \n",
+       "max     235.000000                     NaN             NaN           NaN   \n",
+       "\n",
+       "       Net Change Density  (P/Km²) Land Area (Km²) Migrants (net) Fert. Rate  \\\n",
+       "count         235              235             235            201        235   \n",
+       "unique        234              165             226            158         51   \n",
+       "top            68               25             460              0       N.A.   \n",
+       "freq            2                8               3              5         34   \n",
+       "mean          NaN              NaN             NaN            NaN        NaN   \n",
+       "std           NaN              NaN             NaN            NaN        NaN   \n",
+       "min           NaN              NaN             NaN            NaN        NaN   \n",
+       "25%           NaN              NaN             NaN            NaN        NaN   \n",
+       "50%           NaN              NaN             NaN            NaN        NaN   \n",
+       "75%           NaN              NaN             NaN            NaN        NaN   \n",
+       "max           NaN              NaN             NaN            NaN        NaN   \n",
+       "\n",
+       "       Med. Age Urban Pop % World Share  \n",
+       "count       235         235         235  \n",
+       "unique       35          81          74  \n",
+       "top        N.A.        N.A.       0.00%  \n",
+       "freq         34          13          56  \n",
+       "mean        NaN         NaN         NaN  \n",
+       "std         NaN         NaN         NaN  \n",
+       "min         NaN         NaN         NaN  \n",
+       "25%         NaN         NaN         NaN  \n",
+       "50%         NaN         NaN         NaN  \n",
+       "75%         NaN         NaN         NaN  \n",
+       "max         NaN         NaN         NaN  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe(include='all')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "no                          int64\n",
+       "Country (or dependency)    object\n",
+       "Population 2020            object\n",
+       "Yearly Change              object\n",
+       "Net Change                 object\n",
+       "Density  (P/Km²)           object\n",
+       "Land Area (Km²)            object\n",
+       "Migrants (net)             object\n",
+       "Fert. Rate                 object\n",
+       "Med. Age                   object\n",
+       "Urban Pop %                object\n",
+       "World Share                object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Проверяем на пустые значения"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "no                          0\n",
+      "Country (or dependency)     0\n",
+      "Population 2020             0\n",
+      "Yearly Change               0\n",
+      "Net Change                  0\n",
+      "Density  (P/Km²)            0\n",
+      "Land Area (Km²)             0\n",
+      "Migrants (net)             34\n",
+      "Fert. Rate                  0\n",
+      "Med. Age                    0\n",
+      "Urban Pop %                 0\n",
+      "World Share                 0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df.isnull().sum())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Вот тут получилась странная ситуация, вроде пропусков нет у ключевых значений, НО почему-то без заполнения пустыми значениями не работает"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Population 2020: min = 801.0 , max = 1439323776.0\n",
+      "Yearly Change: min = -2.47 , max = 3.84\n",
+      "Density (P/Km²): min = 0.0 , max = 26337.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Преобразование данных\n",
+    "df['Population 2020'] = df['Population 2020'].str.replace(',', '').astype(float)\n",
+    "df['Yearly Change'] = df['Yearly Change'].str.rstrip('%').astype(float)\n",
+    "df['Density  (P/Km²)'] = df['Density  (P/Km²)'].str.replace(',', '').astype(float)\n",
+    "\n",
+    "# Заполнение пропущенных значений\n",
+    "df['Migrants (net)'] = df['Migrants (net)'].fillna(0)\n",
+    "\n",
+    "# Проверка диапазонов значений\n",
+    "print(\"Population 2020: min =\", df['Population 2020'].min(), \", max =\", df['Population 2020'].max())\n",
+    "print(\"Yearly Change: min =\", df['Yearly Change'].min(), \", max =\", df['Yearly Change'].max())\n",
+    "print(\"Density (P/Km²): min =\", df['Density  (P/Km²)'].min(), \", max =\", df['Density  (P/Km²)'].max())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Population 2020  Yearly Change  Density  (P/Km²)\n",
+      "0     1.439324e+09           0.39             153.0\n",
+      "1     1.380004e+09           0.99             464.0\n",
+      "2     3.310027e+08           0.59              36.0\n",
+      "3     2.735236e+08           1.07             151.0\n",
+      "4     2.208923e+08           2.00             287.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(df[['Population 2020', 'Yearly Change', 'Density  (P/Km²)']].head())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Настройка лингвистических переменных  \n",
+    "\n",
+    "Population 2020 (население в 2020 году)\n",
+    "\n",
+    "Yearly Change (годовое изменение населения)\n",
+    "\n",
+    "Density (P/Km²) (плотность населения)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "c:\\Study\\3 course\\MII\\AIM-PIbd-31-Kryukov-A-I\\.venv\\Lib\\site-packages\\skfuzzy\\control\\fuzzyvariable.py:125: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n",
+      "  fig.show()\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Определение универсальных множеств\n",
+    "population = ctrl.Antecedent(np.arange(0, 1500000000, 1000000), 'population')\n",
+    "yearly_change = ctrl.Antecedent(np.arange(-5, 5, 0.1), 'yearly_change')\n",
+    "density = ctrl.Consequent(np.arange(0, 30000, 100), 'density')\n",
+    "\n",
+    "# Определение функций принадлежности для Population\n",
+    "population['малое'] = fuzz.trimf(population.universe, [0, 0, 500000000])\n",
+    "population['среднее'] = fuzz.trimf(population.universe, [0, 500000000, 1000000000])\n",
+    "population['большое'] = fuzz.trimf(population.universe, [500000000, 1000000000, 1500000000])\n",
+    "\n",
+    "# Определение функций принадлежности для Yearly Change\n",
+    "yearly_change['отрицательное'] = fuzz.trimf(yearly_change.universe, [-5, -5, 0])\n",
+    "yearly_change['нулевое'] = fuzz.trimf(yearly_change.universe, [-2, 0, 2])\n",
+    "yearly_change['положительное'] = fuzz.trimf(yearly_change.universe, [0, 5, 5])\n",
+    "\n",
+    "# Определение функций принадлежности для Density\n",
+    "density['низкая'] = fuzz.trimf(density.universe, [0, 0, 10000])\n",
+    "density['средняя'] = fuzz.trimf(density.universe, [0, 10000, 20000])\n",
+    "density['высокая'] = fuzz.trimf(density.universe, [10000, 20000, 30000])\n",
+    "\n",
+    "# Визуализация функций принадлежности\n",
+    "population.view()\n",
+    "yearly_change.view()\n",
+    "density.view()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Формирование базы нечетких правил"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Определение правил\n",
+    "rule1 = ctrl.Rule(population['малое'] & yearly_change['отрицательное'], density['низкая'])\n",
+    "rule2 = ctrl.Rule(population['среднее'] & yearly_change['нулевое'], density['средняя'])\n",
+    "rule3 = ctrl.Rule(population['большое'] & yearly_change['положительное'], density['высокая'])\n",
+    "\n",
+    "# Создание системы управления\n",
+    "density_ctrl = ctrl.ControlSystem([rule1, rule2, rule3])\n",
+    "density_sim = ctrl.ControlSystemSimulation(density_ctrl)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Применение нечеткой системы к данным"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Входные значения:\n",
+      "population: 1439323776.0\n",
+      "yearly_change: 0.39\n",
+      "Оценка плотности для Китая: 19996.68592831826\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Входные данные для Китая\n",
+    "input_population = df.loc[0, 'Population 2020']\n",
+    "input_yearly_change = df.loc[0, 'Yearly Change']\n",
+    "\n",
+    "# Проверка входных значений\n",
+    "print(\"Входные значения:\")\n",
+    "print(\"population:\", input_population)\n",
+    "print(\"yearly_change:\", input_yearly_change)\n",
+    "\n",
+    "# Передача входных значений в симуляцию\n",
+    "density_sim.input['population'] = input_population\n",
+    "density_sim.input['yearly_change'] = input_yearly_change\n",
+    "\n",
+    "# Вычисление результата\n",
+    "try:\n",
+    "    density_sim.compute()\n",
+    "    print(\"Оценка плотности для Китая:\", density_sim.output['density'])\n",
+    "    density.view(sim=density_sim)\n",
+    "except Exception as e:\n",
+    "    print(\"Ошибка при вычислении:\", e)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Применение системы ко всему датасету"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  Country (or dependency)  Population 2020  Yearly Change  Density  (P/Km²)  \\\n",
+      "0                   China     1.439324e+09           0.39             153.0   \n",
+      "1                   India     1.380004e+09           0.99             464.0   \n",
+      "2           United States     3.310027e+08           0.59              36.0   \n",
+      "3               Indonesia     2.735236e+08           1.07             151.0   \n",
+      "4                Pakistan     2.208923e+08           2.00             287.0   \n",
+      "\n",
+      "   predicted_density  \n",
+      "0       19996.685928  \n",
+      "1       19998.607786  \n",
+      "2       10000.000000  \n",
+      "3       10000.000000  \n",
+      "4        9999.867907  \n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "\n",
+    "# Применение системы ко всему датасету\n",
+    "def predict_density(row):\n",
+    "    try:\n",
+    "        # Передача входных значений в симуляцию\n",
+    "        density_sim.input['population'] = row['Population 2020']\n",
+    "        density_sim.input['yearly_change'] = row['Yearly Change']\n",
+    "        \n",
+    "        # Вычисление результата\n",
+    "        density_sim.compute()\n",
+    "        \n",
+    "        # Возврат предсказанного значения плотности\n",
+    "        return density_sim.output['density']\n",
+    "    except:\n",
+    "        # Если система не смогла вычислить результат, возвращаем NaN\n",
+    "        return np.nan\n",
+    "\n",
+    "# Применение функции к каждой строке датасета\n",
+    "df['predicted_density'] = df.apply(predict_density, axis=1)\n",
+    "\n",
+    "# Вывод первых строк с предсказанными значениями\n",
+    "print(df[['Country (or dependency)', 'Population 2020', 'Yearly Change', 'Density  (P/Km²)', 'predicted_density']].head())"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": ".venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/static/csv/world-population-by-country-2020.csv b/static/csv/world-population-by-country-2020.csv
new file mode 100644
index 0000000..78c1f46
--- /dev/null
+++ b/static/csv/world-population-by-country-2020.csv
@@ -0,0 +1,236 @@
+no,Country (or dependency),Population 2020,Yearly Change,Net Change,Density  (P/Km²),Land Area (Km²),Migrants (net),Fert. Rate,Med. Age,Urban Pop %,World Share
+1,China,"1,439,323,776",0.39%,"5,540,090",153,"9,388,211","-348,399",1.7,38,61%,18.47%
+2,India,"1,380,004,385",0.99%,"13,586,631",464,"2,973,190","-532,687",2.2,28,35%,17.70%
+3,United States,"331,002,651",0.59%,"1,937,734",36,"9,147,420","954,806",1.8,38,83%,4.25%
+4,Indonesia,"273,523,615",1.07%,"2,898,047",151,"1,811,570","-98,955",2.3,30,56%,3.51%
+5,Pakistan,"220,892,340",2.00%,"4,327,022",287,"770,880","-233,379",3.6,23,35%,2.83%
+6,Brazil,"212,559,417",0.72%,"1,509,890",25,"8,358,140","21,200",1.7,33,88%,2.73%
+7,Nigeria,"206,139,589",2.58%,"5,175,990",226,"910,770","-60,000",5.4,18,52%,2.64%
+8,Bangladesh,"164,689,383",1.01%,"1,643,222","1,265","130,170","-369,501",2.1,28,39%,2.11%
+9,Russia,"145,934,462",0.04%,"62,206",9,"16,376,870","182,456",1.8,40,74%,1.87%
+10,Mexico,"128,932,753",1.06%,"1,357,224",66,"1,943,950","-60,000",2.1,29,84%,1.65%
+11,Japan,"126,476,461",-0.30%,"-383,840",347,"364,555","71,560",1.4,48,92%,1.62%
+12,Ethiopia,"114,963,588",2.57%,"2,884,858",115,"1,000,000","30,000",4.3,19,21%,1.47%
+13,Philippines,"109,581,078",1.35%,"1,464,463",368,"298,170","-67,152",2.6,26,47%,1.41%
+14,Egypt,"102,334,404",1.94%,"1,946,331",103,"995,450","-38,033",3.3,25,43%,1.31%
+15,Vietnam,"97,338,579",0.91%,"876,473",314,"310,070","-80,000",2.1,32,38%,1.25%
+16,DR Congo,"89,561,403",3.19%,"2,770,836",40,"2,267,050","23,861",6,17,46%,1.15%
+17,Turkey,"84,339,067",1.09%,"909,452",110,"769,630","283,922",2.1,32,76%,1.08%
+18,Iran,"83,992,949",1.30%,"1,079,043",52,"1,628,550","-55,000",2.2,32,76%,1.08%
+19,Germany,"83,783,942",0.32%,"266,897",240,"348,560","543,822",1.6,46,76%,1.07%
+20,Thailand,"69,799,978",0.25%,"174,396",137,"510,890","19,444",1.5,40,51%,0.90%
+21,United Kingdom,"67,886,011",0.53%,"355,839",281,"241,930","260,650",1.8,40,83%,0.87%
+22,France,"65,273,511",0.22%,"143,783",119,"547,557","36,527",1.9,42,82%,0.84%
+23,Italy,"60,461,826",-0.15%,"-88,249",206,"294,140","148,943",1.3,47,69%,0.78%
+24,Tanzania,"59,734,218",2.98%,"1,728,755",67,"885,800","-40,076",4.9,18,37%,0.77%
+25,South Africa,"59,308,690",1.28%,"750,420",49,"1,213,090","145,405",2.4,28,67%,0.76%
+26,Myanmar,"54,409,800",0.67%,"364,380",83,"653,290","-163,313",2.2,29,31%,0.70%
+27,Kenya,"53,771,296",2.28%,"1,197,323",94,"569,140","-10,000",3.5,20,28%,0.69%
+28,South Korea,"51,269,185",0.09%,"43,877",527,"97,230","11,731",1.1,44,82%,0.66%
+29,Colombia,"50,882,891",1.08%,"543,448",46,"1,109,500","204,796",1.8,31,80%,0.65%
+30,Spain,"46,754,778",0.04%,"18,002",94,"498,800","40,000",1.3,45,80%,0.60%
+31,Uganda,"45,741,007",3.32%,"1,471,413",229,"199,810","168,694",5,17,26%,0.59%
+32,Argentina,"45,195,774",0.93%,"415,097",17,"2,736,690","4,800",2.3,32,93%,0.58%
+33,Algeria,"43,851,044",1.85%,"797,990",18,"2,381,740","-10,000",3.1,29,73%,0.56%
+34,Sudan,"43,849,260",2.42%,"1,036,022",25,"1,765,048","-50,000",4.4,20,35%,0.56%
+35,Ukraine,"43,733,762",-0.59%,"-259,876",75,"579,320","10,000",1.4,41,69%,0.56%
+36,Iraq,"40,222,493",2.32%,"912,710",93,"434,320","7,834",3.7,21,73%,0.52%
+37,Afghanistan,"38,928,346",2.33%,"886,592",60,"652,860","-62,920",4.6,18,25%,0.50%
+38,Poland,"37,846,611",-0.11%,"-41,157",124,"306,230","-29,395",1.4,42,60%,0.49%
+39,Canada,"37,742,154",0.89%,"331,107",4,"9,093,510","242,032",1.5,41,81%,0.48%
+40,Morocco,"36,910,560",1.20%,"438,791",83,"446,300","-51,419",2.4,30,64%,0.47%
+41,Saudi Arabia,"34,813,871",1.59%,"545,343",16,"2,149,690","134,979",2.3,32,84%,0.45%
+42,Uzbekistan,"33,469,203",1.48%,"487,487",79,"425,400","-8,863",2.4,28,50%,0.43%
+43,Peru,"32,971,854",1.42%,"461,401",26,"1,280,000","99,069",2.3,31,79%,0.42%
+44,Angola,"32,866,272",3.27%,"1,040,977",26,"1,246,700","6,413",5.6,17,67%,0.42%
+45,Malaysia,"32,365,999",1.30%,"416,222",99,"328,550","50,000",2,30,78%,0.42%
+46,Mozambique,"31,255,435",2.93%,"889,399",40,"786,380","-5,000",4.9,18,38%,0.40%
+47,Ghana,"31,072,940",2.15%,"655,084",137,"227,540","-10,000",3.9,22,57%,0.40%
+48,Yemen,"29,825,964",2.28%,"664,042",56,"527,970","-30,000",3.8,20,38%,0.38%
+49,Nepal,"29,136,808",1.85%,"528,098",203,"143,350","41,710",1.9,25,21%,0.37%
+50,Venezuela,"28,435,940",-0.28%,"-79,889",32,"882,050","-653,249",2.3,30,N.A.,0.36%
+51,Madagascar,"27,691,018",2.68%,"721,711",48,"581,795","-1,500",4.1,20,39%,0.36%
+52,Cameroon,"26,545,863",2.59%,"669,483",56,"472,710","-4,800",4.6,19,56%,0.34%
+53,Côte d'Ivoire,"26,378,274",2.57%,"661,730",83,"318,000","-8,000",4.7,19,51%,0.34%
+54,North Korea,"25,778,816",0.44%,"112,655",214,"120,410","-5,403",1.9,35,63%,0.33%
+55,Australia,"25,499,884",1.18%,"296,686",3,"7,682,300","158,246",1.8,38,86%,0.33%
+56,Niger,"24,206,644",3.84%,"895,929",19,"1,266,700","4,000",7,15,17%,0.31%
+57,Taiwan,"23,816,775",0.18%,"42,899",673,"35,410","30,001",1.2,42,79%,0.31%
+58,Sri Lanka,"21,413,249",0.42%,"89,516",341,"62,710","-97,986",2.2,34,18%,0.27%
+59,Burkina Faso,"20,903,273",2.86%,"581,895",76,"273,600","-25,000",5.2,18,31%,0.27%
+60,Mali,"20,250,833",3.02%,"592,802",17,"1,220,190","-40,000",5.9,16,44%,0.26%
+61,Romania,"19,237,691",-0.66%,"-126,866",84,"230,170","-73,999",1.6,43,55%,0.25%
+62,Malawi,"19,129,952",2.69%,"501,205",203,"94,280","-16,053",4.3,18,18%,0.25%
+63,Chile,"19,116,201",0.87%,"164,163",26,"743,532","111,708",1.7,35,85%,0.25%
+64,Kazakhstan,"18,776,707",1.21%,"225,280",7,"2,699,700","-18,000",2.8,31,58%,0.24%
+65,Zambia,"18,383,955",2.93%,"522,925",25,"743,390","-8,000",4.7,18,45%,0.24%
+66,Guatemala,"17,915,568",1.90%,"334,096",167,"107,160","-9,215",2.9,23,52%,0.23%
+67,Ecuador,"17,643,054",1.55%,"269,392",71,"248,360","36,400",2.4,28,63%,0.23%
+68,Syria,"17,500,658",2.52%,"430,523",95,"183,630","-427,391",2.8,26,60%,0.22%
+69,Netherlands,"17,134,872",0.22%,"37,742",508,"33,720","16,000",1.7,43,92%,0.22%
+70,Senegal,"16,743,927",2.75%,"447,563",87,"192,530","-20,000",4.7,19,49%,0.21%
+71,Cambodia,"16,718,965",1.41%,"232,423",95,"176,520","-30,000",2.5,26,24%,0.21%
+72,Chad,"16,425,864",3.00%,"478,988",13,"1,259,200","2,000",5.8,17,23%,0.21%
+73,Somalia,"15,893,222",2.92%,"450,317",25,"627,340","-40,000",6.1,17,47%,0.20%
+74,Zimbabwe,"14,862,924",1.48%,"217,456",38,"386,850","-116,858",3.6,19,38%,0.19%
+75,Guinea,"13,132,795",2.83%,"361,549",53,"245,720","-4,000",4.7,18,39%,0.17%
+76,Rwanda,"12,952,218",2.58%,"325,268",525,"24,670","-9,000",4.1,20,18%,0.17%
+77,Benin,"12,123,200",2.73%,"322,049",108,"112,760","-2,000",4.9,19,48%,0.16%
+78,Burundi,"11,890,784",3.12%,"360,204",463,"25,680","2,001",5.5,17,14%,0.15%
+79,Tunisia,"11,818,619",1.06%,"123,900",76,"155,360","-4,000",2.2,33,70%,0.15%
+80,Bolivia,"11,673,021",1.39%,"159,921",11,"1,083,300","-9,504",2.8,26,69%,0.15%
+81,Belgium,"11,589,623",0.44%,"50,295",383,"30,280","48,000",1.7,42,98%,0.15%
+82,Haiti,"11,402,528",1.24%,"139,451",414,"27,560","-35,000",3,24,57%,0.15%
+83,Cuba,"11,326,616",-0.06%,"-6,867",106,"106,440","-14,400",1.6,42,78%,0.15%
+84,South Sudan,"11,193,725",1.19%,"131,612",18,"610,952","-174,200",4.7,19,25%,0.14%
+85,Dominican Republic,"10,847,910",1.01%,"108,952",225,"48,320","-30,000",2.4,28,85%,0.14%
+86,Czech Republic (Czechia),"10,708,981",0.18%,"19,772",139,"77,240","22,011",1.6,43,74%,0.14%
+87,Greece,"10,423,054",-0.48%,"-50,401",81,"128,900","-16,000",1.3,46,85%,0.13%
+88,Jordan,"10,203,134",1.00%,"101,440",115,"88,780","10,220",2.8,24,91%,0.13%
+89,Portugal,"10,196,709",-0.29%,"-29,478",111,"91,590","-6,000",1.3,46,66%,0.13%
+90,Azerbaijan,"10,139,177",0.91%,"91,459",123,"82,658","1,200",2.1,32,56%,0.13%
+91,Sweden,"10,099,265",0.63%,"62,886",25,"410,340","40,000",1.9,41,88%,0.13%
+92,Honduras,"9,904,607",1.63%,"158,490",89,"111,890","-6,800",2.5,24,57%,0.13%
+93,United Arab Emirates,"9,890,402",1.23%,"119,873",118,"83,600","40,000",1.4,33,86%,0.13%
+94,Hungary,"9,660,351",-0.25%,"-24,328",107,"90,530","6,000",1.5,43,72%,0.12%
+95,Tajikistan,"9,537,645",2.32%,"216,627",68,"139,960","-20,000",3.6,22,27%,0.12%
+96,Belarus,"9,449,323",-0.03%,"-3,088",47,"202,910","8,730",1.7,40,79%,0.12%
+97,Austria,"9,006,398",0.57%,"51,296",109,"82,409","65,000",1.5,43,57%,0.12%
+98,Papua New Guinea,"8,947,024",1.95%,"170,915",20,"452,860",-800,3.6,22,13%,0.11%
+99,Serbia,"8,737,371",-0.40%,"-34,864",100,"87,460","4,000",1.5,42,56%,0.11%
+100,Israel,"8,655,535",1.60%,"136,158",400,"21,640","10,000",3,30,93%,0.11%
+101,Switzerland,"8,654,622",0.74%,"63,257",219,"39,516","52,000",1.5,43,74%,0.11%
+102,Togo,"8,278,724",2.43%,"196,358",152,"54,390","-2,000",4.4,19,43%,0.11%
+103,Sierra Leone,"7,976,983",2.10%,"163,768",111,"72,180","-4,200",4.3,19,43%,0.10%
+104,Hong Kong,"7,496,981",0.82%,"60,827","7,140","1,050","29,308",1.3,45,N.A.,0.10%
+105,Laos,"7,275,560",1.48%,"106,105",32,"230,800","-14,704",2.7,24,36%,0.09%
+106,Paraguay,"7,132,538",1.25%,"87,902",18,"397,300","-16,556",2.4,26,62%,0.09%
+107,Bulgaria,"6,948,445",-0.74%,"-51,674",64,"108,560","-4,800",1.6,45,76%,0.09%
+108,Libya,"6,871,292",1.38%,"93,840",4,"1,759,540","-1,999",2.3,29,78%,0.09%
+109,Lebanon,"6,825,445",-0.44%,"-30,268",667,"10,230","-30,012",2.1,30,78%,0.09%
+110,Nicaragua,"6,624,554",1.21%,"79,052",55,"120,340","-21,272",2.4,26,57%,0.08%
+111,Kyrgyzstan,"6,524,195",1.69%,"108,345",34,"191,800","-4,000",3,26,36%,0.08%
+112,El Salvador,"6,486,205",0.51%,"32,652",313,"20,720","-40,539",2.1,28,73%,0.08%
+113,Turkmenistan,"6,031,200",1.50%,"89,111",13,"469,930","-5,000",2.8,27,53%,0.08%
+114,Singapore,"5,850,342",0.79%,"46,005","8,358",700,"27,028",1.2,42,N.A.,0.08%
+115,Denmark,"5,792,202",0.35%,"20,326",137,"42,430","15,200",1.8,42,88%,0.07%
+116,Finland,"5,540,720",0.15%,"8,564",18,"303,890","14,000",1.5,43,86%,0.07%
+117,Congo,"5,518,087",2.56%,"137,579",16,"341,500","-4,000",4.5,19,70%,0.07%
+118,Slovakia,"5,459,642",0.05%,"2,629",114,"48,088","1,485",1.5,41,54%,0.07%
+119,Norway,"5,421,241",0.79%,"42,384",15,"365,268","28,000",1.7,40,83%,0.07%
+120,Oman,"5,106,626",2.65%,"131,640",16,"309,500","87,400",2.9,31,87%,0.07%
+121,State of Palestine,"5,101,414",2.41%,"119,994",847,"6,020","-10,563",3.7,21,80%,0.07%
+122,Costa Rica,"5,094,118",0.92%,"46,557",100,"51,060","4,200",1.8,33,80%,0.07%
+123,Liberia,"5,057,681",2.44%,"120,307",53,"96,320","-5,000",4.4,19,53%,0.06%
+124,Ireland,"4,937,786",1.13%,"55,291",72,"68,890","23,604",1.8,38,63%,0.06%
+125,Central African Republic,"4,829,767",1.78%,"84,582",8,"622,980","-40,000",4.8,18,43%,0.06%
+126,New Zealand,"4,822,233",0.82%,"39,170",18,"263,310","14,881",1.9,38,87%,0.06%
+127,Mauritania,"4,649,658",2.74%,"123,962",5,"1,030,700","5,000",4.6,20,57%,0.06%
+128,Panama,"4,314,767",1.61%,"68,328",58,"74,340","11,200",2.5,30,68%,0.06%
+129,Kuwait,"4,270,571",1.51%,"63,488",240,"17,820","39,520",2.1,37,N.A.,0.05%
+130,Croatia,"4,105,267",-0.61%,"-25,037",73,"55,960","-8,001",1.4,44,58%,0.05%
+131,Moldova,"4,033,963",-0.23%,"-9,300",123,"32,850","-1,387",1.3,38,43%,0.05%
+132,Georgia,"3,989,167",-0.19%,"-7,598",57,"69,490","-10,000",2.1,38,58%,0.05%
+133,Eritrea,"3,546,421",1.41%,"49,304",35,"101,000","-39,858",4.1,19,63%,0.05%
+134,Uruguay,"3,473,730",0.35%,"11,996",20,"175,020","-3,000",2,36,96%,0.04%
+135,Bosnia and Herzegovina,"3,280,819",-0.61%,"-20,181",64,"51,000","-21,585",1.3,43,52%,0.04%
+136,Mongolia,"3,278,290",1.65%,"53,123",2,"1,553,560",-852,2.9,28,67%,0.04%
+137,Armenia,"2,963,243",0.19%,"5,512",104,"28,470","-4,998",1.8,35,63%,0.04%
+138,Jamaica,"2,961,167",0.44%,"12,888",273,"10,830","-11,332",2,31,55%,0.04%
+139,Qatar,"2,881,053",1.73%,"48,986",248,"11,610","40,000",1.9,32,96%,0.04%
+140,Albania,"2,877,797",-0.11%,"-3,120",105,"27,400","-14,000",1.6,36,63%,0.04%
+141,Puerto Rico,"2,860,853",-2.47%,"-72,555",323,"8,870","-97,986",1.2,44,N.A.,0.04%
+142,Lithuania,"2,722,289",-1.35%,"-37,338",43,"62,674","-32,780",1.7,45,71%,0.03%
+143,Namibia,"2,540,905",1.86%,"46,375",3,"823,290","-4,806",3.4,22,55%,0.03%
+144,Gambia,"2,416,668",2.94%,"68,962",239,"10,120","-3,087",5.3,18,59%,0.03%
+145,Botswana,"2,351,627",2.08%,"47,930",4,"566,730","3,000",2.9,24,73%,0.03%
+146,Gabon,"2,225,734",2.45%,"53,155",9,"257,670","3,260",4,23,87%,0.03%
+147,Lesotho,"2,142,249",0.80%,"16,981",71,"30,360","-10,047",3.2,24,31%,0.03%
+148,North Macedonia,"2,083,374",0.00%,-85,83,"25,220","-1,000",1.5,39,59%,0.03%
+149,Slovenia,"2,078,938",0.01%,284,103,"20,140","2,000",1.6,45,55%,0.03%
+150,Guinea-Bissau,"1,968,001",2.45%,"47,079",70,"28,120","-1,399",4.5,19,45%,0.03%
+151,Latvia,"1,886,198",-1.08%,"-20,545",30,"62,200","-14,837",1.7,44,69%,0.02%
+152,Bahrain,"1,701,575",3.68%,"60,403","2,239",760,"47,800",2,32,89%,0.02%
+153,Equatorial Guinea,"1,402,985",3.47%,"46,999",50,"28,050","16,000",4.6,22,73%,0.02%
+154,Trinidad and Tobago,"1,399,488",0.32%,"4,515",273,"5,130",-800,1.7,36,52%,0.02%
+155,Estonia,"1,326,535",0.07%,887,31,"42,390","3,911",1.6,42,68%,0.02%
+156,Timor-Leste,"1,318,445",1.96%,"25,326",89,"14,870","-5,385",4.1,21,33%,0.02%
+157,Mauritius,"1,271,768",0.17%,"2,100",626,"2,030",0,1.4,37,41%,0.02%
+158,Cyprus,"1,207,359",0.73%,"8,784",131,"9,240","5,000",1.3,37,67%,0.02%
+159,Eswatini,"1,160,164",1.05%,"12,034",67,"17,200","-8,353",3,21,30%,0.01%
+160,Djibouti,"988,000",1.48%,"14,440",43,"23,180",900,2.8,27,79%,0.01%
+161,Fiji,"896,445",0.73%,"6,492",49,"18,270","-6,202",2.8,28,59%,0.01%
+162,Réunion,"895,312",0.72%,"6,385",358,"2,500","-1,256",2.3,36,100%,0.01%
+163,Comoros,"869,601",2.20%,"18,715",467,"1,861","-2,000",4.2,20,29%,0.01%
+164,Guyana,"786,552",0.48%,"3,786",4,"196,850","-6,000",2.5,27,27%,0.01%
+165,Bhutan,"771,608",1.12%,"8,516",20,"38,117",320,2,28,46%,0.01%
+166,Solomon Islands,"686,884",2.55%,"17,061",25,"27,990","-1,600",4.4,20,23%,0.01%
+167,Macao,"649,335",1.39%,"8,890","21,645",30,"5,000",1.2,39,N.A.,0.01%
+168,Montenegro,"628,066",0.01%,79,47,"13,450",-480,1.8,39,68%,0.01%
+169,Luxembourg,"625,978",1.66%,"10,249",242,"2,590","9,741",1.5,40,88%,0.01%
+170,Western Sahara,"597,339",2.55%,"14,876",2,"266,000","5,582",2.4,28,87%,0.01%
+171,Suriname,"586,632",0.90%,"5,260",4,"156,000","-1,000",2.4,29,65%,0.01%
+172,Cabo Verde,"555,987",1.10%,"6,052",138,"4,030","-1,342",2.3,28,68%,0.01%
+173,Micronesia,"548,914",1.00%,"5,428",784,700,"-2,957",2.9,27,68%,0.01%
+174,Maldives,"540,544",1.81%,"9,591","1,802",300,"11,370",1.9,30,35%,0.01%
+175,Malta,"441,543",0.27%,"1,171","1,380",320,900,1.5,43,93%,0.01%
+176,Brunei,"437,479",0.97%,"4,194",83,"5,270",0,1.8,32,80%,0.01%
+177,Guadeloupe,"400,124",0.02%,68,237,"1,690","-1,440",2.2,44,N.A.,0.01%
+178,Belize,"397,628",1.86%,"7,275",17,"22,810","1,200",2.3,25,46%,0.01%
+179,Bahamas,"393,244",0.97%,"3,762",39,"10,010","1,000",1.8,32,86%,0.01%
+180,Martinique,"375,265",-0.08%,-289,354,"1,060",-960,1.9,47,92%,0.00%
+181,Iceland,"341,243",0.65%,"2,212",3,"100,250",380,1.8,37,94%,0.00%
+182,Vanuatu,"307,145",2.42%,"7,263",25,"12,190",120,3.8,21,24%,0.00%
+183,French Guiana,"298,682",2.70%,"7,850",4,"82,200","1,200",3.4,25,87%,0.00%
+184,Barbados,"287,375",0.12%,350,668,430,-79,1.6,40,31%,0.00%
+185,New Caledonia,"285,498",0.97%,"2,748",16,"18,280",502,2,34,72%,0.00%
+186,French Polynesia,"280,908",0.58%,"1,621",77,"3,660","-1,000",2,34,64%,0.00%
+187,Mayotte,"272,815",2.50%,"6,665",728,375,0,3.7,20,46%,0.00%
+188,Sao Tome & Principe,"219,159",1.91%,"4,103",228,960,"-1,680",4.4,19,74%,0.00%
+189,Samoa,"198,414",0.67%,"1,317",70,"2,830","-2,803",3.9,22,18%,0.00%
+190,Saint Lucia,"183,627",0.46%,837,301,610,0,1.4,34,19%,0.00%
+191,Channel Islands,"173,863",0.93%,"1,604",915,190,"1,351",1.5,43,30%,0.00%
+192,Guam,"168,775",0.89%,"1,481",313,540,-506,2.3,31,95%,0.00%
+193,Curaçao,"164,093",0.41%,669,370,444,515,1.8,42,89%,0.00%
+194,Kiribati,"119,449",1.57%,"1,843",147,810,-800,3.6,23,57%,0.00%
+195,Grenada,"112,523",0.46%,520,331,340,-200,2.1,32,35%,0.00%
+196,St. Vincent & Grenadines,"110,940",0.32%,351,284,390,-200,1.9,33,53%,0.00%
+197,Aruba,"106,766",0.43%,452,593,180,201,1.9,41,44%,0.00%
+198,Tonga,"105,695",1.15%,"1,201",147,720,-800,3.6,22,24%,0.00%
+199,U.S. Virgin Islands,"104,425",-0.15%,-153,298,350,-451,2,43,96%,0.00%
+200,Seychelles,"98,347",0.62%,608,214,460,-200,2.5,34,56%,0.00%
+201,Antigua and Barbuda,"97,929",0.84%,811,223,440,0,2,34,26%,0.00%
+202,Isle of Man,"85,033",0.53%,449,149,570,,N.A.,N.A.,53%,0.00%
+203,Andorra,"77,265",0.16%,123,164,470,,N.A.,N.A.,88%,0.00%
+204,Dominica,"71,986",0.25%,178,96,750,,N.A.,N.A.,74%,0.00%
+205,Cayman Islands,"65,722",1.19%,774,274,240,,N.A.,N.A.,97%,0.00%
+206,Bermuda,"62,278",-0.36%,-228,"1,246",50,,N.A.,N.A.,97%,0.00%
+207,Marshall Islands,"59,190",0.68%,399,329,180,,N.A.,N.A.,70%,0.00%
+208,Northern Mariana Islands,"57,559",0.60%,343,125,460,,N.A.,N.A.,88%,0.00%
+209,Greenland,"56,770",0.17%,98,0,"410,450",,N.A.,N.A.,87%,0.00%
+210,American Samoa,"55,191",-0.22%,-121,276,200,,N.A.,N.A.,88%,0.00%
+211,Saint Kitts & Nevis,"53,199",0.71%,376,205,260,,N.A.,N.A.,33%,0.00%
+212,Faeroe Islands,"48,863",0.38%,185,35,"1,396",,N.A.,N.A.,43%,0.00%
+213,Sint Maarten,"42,876",1.15%,488,"1,261",34,,N.A.,N.A.,96%,0.00%
+214,Monaco,"39,242",0.71%,278,"26,337",1,,N.A.,N.A.,N.A.,0.00%
+215,Turks and Caicos,"38,717",1.38%,526,41,950,,N.A.,N.A.,89%,0.00%
+216,Saint Martin,"38,666",1.75%,664,730,53,,N.A.,N.A.,0%,0.00%
+217,Liechtenstein,"38,128",0.29%,109,238,160,,N.A.,N.A.,15%,0.00%
+218,San Marino,"33,931",0.21%,71,566,60,,N.A.,N.A.,97%,0.00%
+219,Gibraltar,"33,691",-0.03%,-10,"3,369",10,,N.A.,N.A.,N.A.,0.00%
+220,British Virgin Islands,"30,231",0.67%,201,202,150,,N.A.,N.A.,52%,0.00%
+221,Caribbean Netherlands,"26,223",0.94%,244,80,328,,N.A.,N.A.,75%,0.00%
+222,Palau,"18,094",0.48%,86,39,460,,N.A.,N.A.,N.A.,0.00%
+223,Cook Islands,"17,564",0.09%,16,73,240,,N.A.,N.A.,75%,0.00%
+224,Anguilla,"15,003",0.90%,134,167,90,,N.A.,N.A.,N.A.,0.00%
+225,Tuvalu,"11,792",1.25%,146,393,30,,N.A.,N.A.,62%,0.00%
+226,Wallis & Futuna,"11,239",-1.69%,-193,80,140,,N.A.,N.A.,0%,0.00%
+227,Nauru,"10,824",0.63%,68,541,20,,N.A.,N.A.,N.A.,0.00%
+228,Saint Barthelemy,"9,877",0.30%,30,470,21,,N.A.,N.A.,0%,0.00%
+229,Saint Helena,"6,077",0.30%,18,16,390,,N.A.,N.A.,27%,0.00%
+230,Saint Pierre & Miquelon,"5,794",-0.48%,-28,25,230,,N.A.,N.A.,100%,0.00%
+231,Montserrat,"4,992",0.06%,3,50,100,,N.A.,N.A.,10%,0.00%
+232,Falkland Islands,"3,480",3.05%,103,0,"12,170",,N.A.,N.A.,66%,0.00%
+233,Niue,"1,626",0.68%,11,6,260,,N.A.,N.A.,46%,0.00%
+234,Tokelau,"1,357",1.27%,17,136,10,,N.A.,N.A.,0%,0.00%
+235,Holy See,801,0.25%,2,"2,003",0,,N.A.,N.A.,N.A.,0.00%
-- 
2.25.1


From 956d39daf7e65ae31e5b56858c770f13b9a66cfe Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?=D0=90=D0=BB=D0=B5=D0=BA=D1=81=D0=B5=D0=B9=20=D0=9A=D1=80?=
 =?UTF-8?q?=D1=8E=D0=BA=D0=BE=D0=B2?= <alexkryukov73@yandex.ru>
Date: Sat, 22 Feb 2025 13:05:36 +0400
Subject: [PATCH 2/2] done_lab7

---
 lab_7/lab7.ipynb | 167 ++++++++++++++++++++++++++---------------------
 1 file changed, 93 insertions(+), 74 deletions(-)

diff --git a/lab_7/lab7.ipynb b/lab_7/lab7.ipynb
index 77a9372..2608ee2 100644
--- a/lab_7/lab7.ipynb
+++ b/lab_7/lab7.ipynb
@@ -11,7 +11,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -23,7 +23,7 @@
        "      dtype='object')"
       ]
      },
-     "execution_count": 19,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -48,7 +48,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -122,7 +122,7 @@
         }
        ],
        "conversionMethod": "pd.DataFrame",
-       "ref": "b07d1231-8d5f-430a-88d4-2e94c499ba2a",
+       "ref": "f3a11dd1-3566-4c0d-85dd-4e67209ca0b1",
        "rows": [
         [
          "0",
@@ -341,7 +341,7 @@
        "4         35%       2.83%  "
       ]
      },
-     "execution_count": 20,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -352,7 +352,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -426,7 +426,7 @@
         }
        ],
        "conversionMethod": "pd.DataFrame",
-       "ref": "51895678-109f-4c05-8480-264ac9bf3c9f",
+       "ref": "6e9eb7bf-65fb-4b60-9943-ad893a224757",
        "rows": [
         [
          "count",
@@ -843,7 +843,7 @@
        "max         NaN         NaN         NaN  "
       ]
      },
-     "execution_count": 21,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -854,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [
     {
@@ -875,7 +875,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 22,
+     "execution_count": 18,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -893,7 +893,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -929,7 +929,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 32,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -957,28 +957,6 @@
     "print(\"Density (P/Km²): min =\", df['Density  (P/Km²)'].min(), \", max =\", df['Density  (P/Km²)'].max())"
    ]
   },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "   Population 2020  Yearly Change  Density  (P/Km²)\n",
-      "0     1.439324e+09           0.39             153.0\n",
-      "1     1.380004e+09           0.99             464.0\n",
-      "2     3.310027e+08           0.59              36.0\n",
-      "3     2.735236e+08           1.07             151.0\n",
-      "4     2.208923e+08           2.00             287.0\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(df[['Population 2020', 'Yearly Change', 'Density  (P/Km²)']].head())"
-   ]
-  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -994,7 +972,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -1072,7 +1050,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1095,7 +1073,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 35,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -1146,58 +1124,99 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Применение системы ко всему датасету"
+    "Применение ко всему датасету"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 38,
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Функция предсказания\n",
+    "def predict_density(row):\n",
+    "    try:\n",
+    "        density_sim.input['population'] = row['Population 2020']\n",
+    "        density_sim.input['yearly_change'] = row['Yearly Change']\n",
+    "        density_sim.compute()\n",
+    "        return density_sim.output['density']\n",
+    "    except:\n",
+    "        return np.nan\n",
+    "\n",
+    "df['predicted_density'] = df.apply(predict_density, axis=1)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Оценка качества модели"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "  Country (or dependency)  Population 2020  Yearly Change  Density  (P/Km²)  \\\n",
-      "0                   China     1.439324e+09           0.39             153.0   \n",
-      "1                   India     1.380004e+09           0.99             464.0   \n",
-      "2           United States     3.310027e+08           0.59              36.0   \n",
-      "3               Indonesia     2.735236e+08           1.07             151.0   \n",
-      "4                Pakistan     2.208923e+08           2.00             287.0   \n",
-      "\n",
-      "   predicted_density  \n",
-      "0       19996.685928  \n",
-      "1       19998.607786  \n",
-      "2       10000.000000  \n",
-      "3       10000.000000  \n",
-      "4        9999.867907  \n"
+      "Средняя абсолютная ошибка предсказания: 9334.62\n"
      ]
     }
    ],
    "source": [
-    "import numpy as np\n",
+    "# Оценка точности\n",
+    "mae = np.nanmean(np.abs(df['Density  (P/Km²)'] - df['predicted_density']))\n",
+    "print(f\"Средняя абсолютная ошибка предсказания: {mae:.2f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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njo2NvejrNG7cOIKDgxk4cGC2bWfOnMHf3/+inz0i6rIo4oKgoCCAHP9jupDq1atnK6tRowbJycmcOHGC8PBwUlJSGD9+PLNnz+bo0aMY58y3k1PC07hxY8d9Hx8fpk2blq2bVlJSEqVKlbpgbLt37wagR48eudaJj4+nePHijsc1a9a84DHP9+eff/LDDz+wdOlSDh06lOP5b7311hz3zXrNL+b81zgwMJAyZco4xku4cp7Y2FjGjBnDvHnzOH78uFO9nK5FfHx8jmMuzvfrr786roeHhwcNGjRgwoQJ2brCjRo1ilGjRmXbv3Tp0o77Bw8epHr16tkSh9q1azu252To0KHcfPPN3HXXXTkmERdzufvnxfnX0mKxUK1atWzX0pX37MmTJ3P8OzzX3r17sVqt1KlT56Ixbty4kfnz52Oz2XL90rZt2zaGDx/OsmXLHD/mnBvfuebMmePoQhkeHs6SJUuoVKnSJR1r586dF/27z3LuezI3Bw8exGq1Uq1aNafy8PBwQkJCcn2vvfXWW6SmpvLyyy9nS+byS1xcHMAF//4OHjxI06ZNs5Wf+7eS07i7i7nY52DW65JTvdq1a7N48WKSkpJITEwkISHhkmK4mAt99l5IXv7vADh06BAjR47k+++/zzZmN+t9uW/fPoALPr8TJ04QFxfHrFmzcp2h+PzP4ri4uAte9z179mAYBiNGjGDEiBG5HrNcuXKOx652+d+/fz/vvfceM2bMyDHpatasGT/++COjR4/m8ccfx9/fP08/YMq1RwmZiAuCgoIoW7asY/aw/DRgwABmz57Nc889R7NmzQgODsZisdCtW7dsv/aDOfFA6dKlSU1NZdmyZfTr1w9fX1+nyQZ8fX354YcfnPb7448/GDt2rONx1rEnTZpEo0aNcozt/P/0vvrqK6cE5t9//6Vfv365PrchQ4bQvn17br311mwD3rPO/8knnxAeHp5tX0/P/PmYcuU8DzzwAKtWreLFF1+kUaNGBAYGYrfbueOOO3JsLUxPT8/xmOdr2rQpr776KmCOGXn99dfp0qUL27Ztcxon16dPn2y/Zvfu3TvPzzU3v/76K7/99hurV692y/75xdX3bHp6OlFRUdx+++35FsOmTZvo0KEDt912Gy+++CKPPvqo0/ixuLg4WrduTVBQEGPHjqVq1ar4+vqyfv16hgwZku19dPfdd1OtWjWOHz/OzJkzefDBB/nzzz+JiIhw+VgRERHZxvgtWLAgxy+6574ns0ybNo3vvvsuW11XWmpOnjzJpEmTGDZsmFPrUH6Ljo4mMDCQgICAAjtHblz9HHSHC332Xkhe/u+w2WzcfvvtxMbGMmTIEGrVqkVAQABHjx6lZ8+eOf6/lZusuo8++miuP7ScP4YvOjra6UeL3I75wgsv0L59+xzrnP8jw/Tp06lRo4bjcUJCAl27ds31HK+88grVq1enR48e2SbiARg0aBC7du1i3LhxjBkzJtfjiCghE3HRXXfdxaxZs1i9ejXNmjXL0z5Zv+if699//8Xf39/xK+TChQvp0aOH09pQqampjl+Az9eiRQvHl/i77rqLbdu2MX78eKeEzMPDI9svfucfL2v686CgoDz/OtiqVSvHAHqAkJCQXOt+++23rF69OseufueePyws7JImJMmye/dubrnlFsfjxMREoqKiHBNJ5PU8p0+fZunSpYwZM4aRI0c6HT8nWd2dsn5tv5CSJUs6nbtatWq0aNGCFStWOCVk1atXzxbj+V84K1WqxObNm7Hb7U6tZFnd187/omIYBkOHDqVLly6O7peuuNz9XXH+a20YBnv27HF8IXP1Pbtp0yYyMjJy7A52rqpVq2K329m+fXuuiV6W+vXrs2DBAvz8/FiwYAF9+vRh8+bNjl/JIyMjOXXqFF9//TWtWrVy7Ld///4cj1euXDnHL/X33nsvJUuWZMaMGbz++usuHysgICDb67Jx48Yc657/ngTzb/ZclSpVwm63s3v3bqf3eUxMDHFxcTl+KX711VcpVqxYgS//sX379ov+7VWqVIldu3ZlK8/tbyWvLvY5mHXc3M5dsmRJAgIC8PPzIygoKN9/6LvYZ++F5OX/ji1btvDvv/8yd+5cunfv7ig/f/KVKlWqAFzw+ZUqVYpixYphs9ny9DedkZHBnj17uOOOO3Ktk3VeLy+vPP/fcuONNzp9Tpw/k+a5NmzYwLx58/j2229zXdbBz8+P999/nw0bNhAcHMyoUaPYtGkTL7zwQp7ikWuHxpCJuOill14iICCAJ598MsdZlfbu3cvbb7/tVHb+f4qHDx/mu+++o127do4Pcg8PD6duigDvvPNOtrFDuUlJSck2BXVeNGnShKpVq/LGG2+QmJiYbfuJEydcPmYWm83Gyy+/zMMPP5zrF9z27dsTFBTEa6+9RkZGxiWff9asWU77z5gxg8zMTDp06ODSebKux/nXYsqUKTmed968eXh7ezuNT8mrrF9wL2WNpjvvvJPo6Gi+/PJLR1lmZibvvPMOgYGB2cYTzps3j82bNztmGXTV5e7vio8//tipW/DChQuJiopyXEtX37MLFizAw8PjossOdO7cGavVytixY7P9un/++6Fx48YEBARgtVr54IMPOHDggFPrQU7vo/T0dN59990LxgBmV6/09HTH3/PlHCs/ZP2ocf7fwFtvvQVAx44dncqzZogdPXq00xij/Hb48GFWrlyZazfkLHfeeSd///23U8tuUlISs2bNIiIiIk9dVC9FmTJlaNSoEXPnznVKZrZu3cqvv/7qeF2tViudO3fmhx9+yDbtOmR/7+VFXj57L1dO70vDMLL9/1eqVClatWrFRx99lK3bZNa+Hh4edO3ala+++irHxO38v+nvvvuOlJSUC177sLAw2rRpw3vvvUdUVNRFj+mqoUOH0qJFCzp16nTBesOGDePQoUN8+umntG3b1mk8uUgWtZCJuKhq1ap8/vnnPPjgg9SuXZvu3btTr1490tPTWbVqlWPq8XPVq1eP9u3bO017Dzh1Ybjrrrv45JNPCA4Opk6dOqxevZrffvuNEiVK5BjHt99+S8mSJR1dFv/4448cpwW+mKwvlB06dKBu3br06tWLcuXKcfToUX7//XeCgoKydV3JqyNHjuDt7e0YvJ6ToKAgZsyYwWOPPUbjxo3p1q0bpUqV4tChQyxatIgWLVowbdq0i54rPT2d2267jQceeIBdu3bx7rvv0rJlS8d/lnk9T1BQEK1atWLixIlkZGRQrlw5fv3112ytEbt372bUqFF88cUXDB06NE9j3U6cOMEvv/wCmAP+X3/9dYKDg51a9vKqT58+vPfee/Ts2ZN169YRERHBwoULWblyJVOmTMk28cyvv/5K7969XR7/l1/7uyI0NJSWLVvSq1cvYmJimDJlCtWqVXN028zrezYpKYnp06czdepUatSo4bQ+UFYit3nzZkdrd7Vq1XjllVcYN24cN998M/feey8+Pj6sXbuWsmXL5pqM1qtXjyFDhjBhwgS6detGgwYNaN68OcWLF6dHjx4MHDgQi8XCJ598ku3L9ZYtW3j++ee59dZbCQsL49ixY3z00UfY7XbHhC15PVZBadiwIT169GDWrFmO7pN///03c+fOpXPnztnev8uXL6d27dr06tWrwGKaMWMG48ePx9/fP8fJFM41dOhQvvjiCzp06MDAgQMJDQ1l7ty57N+/n6+++qrAJqcBs1tthw4daNasGU888YRj2vvg4GCnta1ee+01fv31V1q3bk2fPn2oXbs2UVFRLFiwgD///POCvRBykpfP3stVq1YtqlatygsvvMDRo0cJCgriq6++ynH9x6lTp9KyZUsaN25Mnz59qFy5MgcOHGDRokWO1tsJEybw+++/07RpU3r37k2dOnWIjY1l/fr1/Pbbb8TGxpKcnMyoUaN49913ad68ea5LUWSZPn06LVu2pH79+vTu3ZsqVaoQExPD6tWrOXLkCJs2bbrk5//rr7/muu5hlt9++43JkyfzySefXHJLrFwjrvCsjiJXjX///dfo3bu3ERERYXh7exvFihUzWrRoYbzzzjtOU+wCRr9+/YxPP/3UqF69uuHj42Ncd911TlPjGoZhnD592ujVq5dRsmRJIzAw0Gjfvr2xc+fObNNIZ03Pm3Xz9vY2qlWrZowcOdLpvK5MXWwYhrFhwwbj3nvvNUqUKGH4+PgYlSpVMh544AFj6dKljjquTnsPGM8++6xT3Zym7TcMcyrg9u3bG8HBwYavr69RtWpVo2fPnk7LBeQk63jLly83+vTpYxQvXtwIDAw0HnnkEaeppl05z5EjR4wuXboYISEhRnBwsHH//fcbx44dc5r++IsvvjDq1atnvP3229mmpc5t2vtzr1vJkiWNdu3aGX/99ZejjitTxBuGYcTExDjeM97e3kb9+vWdrsG5x/Tz8zOOHj3qtO3891ZO8mt/V6a9/+KLL4xhw4YZYWFhhp+fn9GxY8ds02UbxsXfs1nnvtjt/Ofw0UcfGdddd53h4+NjFC9e3GjdurWxZMmSCz7v1NRUo1atWsYNN9xgZGZmGoZhGCtXrjRuuukmw8/Pzyhbtqzx0ksvGYsXL3Z6bxw7dszo1KmTUbp0acPLy8soU6aMcddddxl//vmn0/HzcizDKJhp7w3DMDIyMowxY8YYlStXNry8vIwKFSoYw4YNyzZ9f9b7/JtvvnEqv9A08ufvn5eYbrzxRuP+++83du7cma1uTlPC792717jvvvuMkJAQw9fX17jxxhuNH3/8Mdc48mvae8MwjN9++81o0aKF4efnZwQFBRl33323sX379mzHPXjwoNG9e3ejVKlSho+Pj1GlShWjX79+RlpaWp6e47mxu/LZm9P+ef2/Y/v27Ubbtm2NwMBAo2TJkkbv3r2NTZs25fg6bN261fG56uvra9SsWdMYMWKEU52YmBijX79+RoUKFQwvLy8jPDzcuO2224xZs2YZhmF+NleoUMF47rnnjPj4+GwxnvsZnWXv3r1G9+7djfDwcMPLy8soV66ccddddzktK3Ep096fv/zN+Z/7J0+eNMqWLWs89NBDOdbTtPdyLothXKGf2ESuURaLhX79+uWplUdcN2fOHHr16sXatWsvOkZICrfIyEhuueUWFixYwH333XfZxztw4ACVK1dm//79uS4uPnr0aA4cOODShAciIiL5SWPIRERERERE3ERjyERE5KoUGBjII488csG1iho0aEDZsmWvYFQiIiLOlJCJiMhVqWTJknz66acXrHPvvfdeoWhERERypjFkIiIiIiIibqIxZCIiIiIiIm6ihExERERERMRNNIYsn9jtdo4dO0axYsWwWCzuDkdERERERNzEMAzOnDlD2bJlL7oAvRKyfHLs2DEqVKjg7jBERERERKSQOHz4MOXLl79gHSVk+aRYsWKA+aIHBQW5ORoREREREXGXhIQEKlSo4MgRLkQJWT7J6qYYFBSkhExERERERPI0lEmTeoiIiIiIiLiJEjIRERERERE3UUImIiIiIiLiJhpDdgUZhkFmZiY2m83doYjIRXh5eeHh4eHuMEREROQqp4TsCklPTycqKork5GR3hyIieWCxWChfvjyBgYHuDkVERESuYkrIrgC73c7+/fvx8PCgbNmyeHt7a/FokULMMAxOnDjBkSNHqF69ulrKREREpMAoIbsC0tPTsdvtVKhQAX9/f3eHIyJ5UKpUKQ4cOEBGRoYSMhERESkwmtTjCrJa9XKLFBVqxRYREZErQRmCiIiIiIiImyghExERERERcRO3JmQzZsygQYMGBAUFERQURLNmzfj5558d21NTU+nXrx8lSpQgMDCQrl27EhMT43SMQ4cO0bFjR/z9/QkLC+PFF18kMzPTqU5kZCSNGzfGx8eHatWqMWfOnGyxTJ8+nYiICHx9fWnatCl///13gTxnERERERGRLG5NyMqXL8+ECRNYt24d//zzD7feeiv33HMP27ZtA2DQoEH88MMPLFiwgOXLl3Ps2DHuvfdex/42m42OHTuSnp7OqlWrmDt3LnPmzGHkyJGOOvv376djx47ccsstbNy4keeee44nn3ySxYsXO+p8+eWXDB48mFGjRrF+/XoaNmxI+/btOX78+JV7MQqx6OhoBgwYQJUqVfDx8aFChQrcfffdLF261N2hiYiIiIgUaRbDMAx3B3Gu0NBQJk2axH333UepUqX4/PPPue+++wDYuXMntWvXZvXq1dx00038/PPP3HXXXRw7dozSpUsDMHPmTIYMGcKJEyfw9vZmyJAhLFq0iK1btzrO0a1bN+Li4vjll18AaNq0KTfccAPTpk0DcMyIOGDAAIYOHZqnuBMSEggODiY+Pp6goCCnbampqezfv5/KlSvj6+t7Wa+P3W5wNC6FpPRMArw9KRfih9VacJMPHDhwgBYtWhASEsLYsWOpX78+GRkZLF68mFmzZrFz584CO7eIO+Xn362IiIhcWy6UG5yv0Iwhs9lszJs3j6SkJJo1a8a6devIyMigbdu2jjq1atWiYsWKrF69GoDVq1dTv359RzIG0L59exISEhytbKtXr3Y6RladrGOkp6ezbt06pzpWq5W2bds66uQkLS2NhIQEp1tB23P8DDMi9zJ5yb9MXbqbyUv+ZUbkXvYcP1Ng5+zbty8Wi4W///6brl27UqNGDerWrcvgwYP566+/iIiIwGKx5HjL6hpqsViYMWMGHTp0wM/PjypVqrBw4UKn8wwZMoQaNWrg7+9PlSpVGDFiBBkZGY7to0ePplGjRk77REZGYrFYiIuLA2DOnDmEhITk+Dw2btyIxWLhwIED+fTKiIiIiEih8ccf8N577o7ikrg9IduyZQuBgYH4+Pjw9NNP880331CnTh2io6Px9vbO9gW7dOnSREdHA2ZXunOTsaztWdsuVCchIYGUlBROnjyJzWbLsU7WMXIyfvx4goODHbcKFSpc0vPPqz3HzzB75QG2HosnxN+LKiUDCfH3YuuxeGavPFAgSVlsbCy//PIL/fr1IyAgINv2kJAQ1q5dS1RUFFFRUZQvX54pU6Y4Hj/44IOOuiNGjKBr165s2rSJRx55hG7durFjxw7H9mLFijFnzhy2b9/O22+/zfvvv8/kyZPz/TmJiIiIyFXmvvugVSt47jmIinJ3NC5ze0JWs2ZNNm7cyJo1a3jmmWfo0aMH27dvd3dYFzVs2DDi4+Mdt8OHDxfYuex2g8VbY4hNSqd6WCDFfL3wsFoo5utF9bBAYpPS+XVbDHZ7/vY+3bNnD4ZhUKtWrVzrlCpVivDwcMLDw/Hw8CA4ONjx2M/Pz1Hv/vvv58knn6RGjRqMGzeO66+/nnfeecexffjw4TRv3pyIiAjuvvtuXnjhBebPn5+vz0dERERErhLnjrqqXx+8vKB7dyiC64h6ujsAb29vqlWrBkCTJk1Yu3Ytb7/9Ng8++CDp6enExcU5tZLFxMQQHh4OQHh4eLbZELNmYTy3zvkzM8bExBAUFISfnx8eHh54eHjkWCfrGDnx8fHBx8fn0p60i47GpbD3RCJlgn2zLVZrsVgoE+zLnuOJHI1LoUKof76dNz+HFzZr1izb440bNzoef/nll0ydOpW9e/eSmJhIZmZmtv62Wa2pWWw2W7bzxMfHExgYiNVqpXTp0txzzz2MHz8+356HiIiIiLiJYcDvv8OYMTBsGNxxh1n+3HPQqxdUrOjW8C6V21vIzme320lLS6NJkyZ4eXk5zeS3a9cuDh065Phy36xZM7Zs2eI0G+KSJUsICgqiTp06jjrnzwa4ZMkSxzG8vb1p0qSJUx273c7SpUuzJRHukpSeSWqmDX/vnPNnP28P0jJtJKVn5rj9UlWvXh2LxVLgE3esXr2aRx55hDvvvJMff/yRDRs28Morr5Cenu5UL6s1Nev2wQcfZDtWsWLF2LhxI+vWreONN97ggw8+4O233y7Q+EVERESkABkGLF1qdku87TZYsQLO/cE9OLjIJmPg5hayYcOG0aFDBypWrMiZM2f4/PPPiYyMZPHixQQHB/PEE08wePBgQkNDCQoKYsCAATRr1oybbroJgHbt2lGnTh0ee+wxJk6cSHR0NMOHD6dfv36O1qunn36aadOm8dJLL/H444+zbNky5s+fz6JFixxxDB48mB49enD99ddz4403MmXKFJKSkujVq5dbXpfzBXh74uvpQXJ6JsV8vbJtT0m34ePpQUAuCdulCg0NpX379kyfPp2BAwdmG0d2fuvlhfz11190797d6fF1110HwKpVq6hUqRKvvPKKY/vBgwezHePc1lSAI0eOZKtjtVoddapXr87tt9/Oxo0bs03sIiIiIiKFnGHAb7+ZLWIrV5pl3t7QuzfkcSb0osCtCdnx48fp3r07UVFRBAcH06BBAxYvXsztt98OwOTJk7FarXTt2pW0tDTat2/Pu+++69jfw8ODH3/8kWeeeYZmzZoREBBAjx49GDt2rKNO5cqVWbRoEYMGDeLtt9+mfPnyfPDBB7Rv395R58EHH+TEiROMHDmS6OhoGjVqxC+//JJtog93KRfiR9VSgWw9Fk+gj6dTt0XDMIiKT6V+uWDKhfhd4CiXZvr06bRo0YIbb7yRsWPH0qBBAzIzM1myZAkzZsxwmpjjQhYsWMD1119Py5Yt+eyzz/j777/58MMPATNxOnToEPPmzeOGG25g0aJFfPPNN5ccc2pqKoZhsH37dv7880+effbZSz6WiIiIiLjJE0/A7NnmfR8f6NMHhgyBcuXcG1c+c2tClvWFPDe+vr5Mnz6d6dOn51qnUqVK/PTTTxc8Tps2bdiwYcMF6/Tv35/+/ftfsI67WK0W2tcrzbH4FHYfN8eS+Xl7kJJuIyo+ldAAb9rVLV0g65FVqVKF9evX87///Y/nn3+eqKgoSpUqRZMmTZgxY0aejzNmzBjmzZtH3759KVOmDF988YWjW2mnTp0YNGgQ/fv3Jy0tjY4dOzJixAhGjx7tcrzx8fH4+flhsVgoXbo0Xbp0YfDgwUViohgRERGRa5phgN0OHh7m43bt4Isv4Kmn4KWXoGxZ98ZXQArdwtBF1ZVYGHrP8TMs3hrD3hOJpGWa3RSrhQXSrm5pqoUVu9ynUGAsFgvffPMNnTt3dncoInmmhaFFRESuEMOAX34xuyZ262ZO0gFgs8GJE3CBifYKK1cWhnb7LIuSd9XCilGlTSBH41JISs8kwNuTciF+BdIyJiIiIiJSoAwDfvrJTMTWrjXLTp6EgQPBajVbyopgMuYqJWRFjNVqydep7UVERERErijDgEWLzETsn3/MMn9/6NsXXnjBTMauIUrIpMCpV6yIiIiIOLz0Erzxhnk/IAD69YPnn4ewMPfG5SbXVvopIiIiIiJXlt0OSUlnHz/8MAQGmjMm7t8Pr79+zSZjoBYyEREREREpCHY7fPut2TWxeXPImqH7uuvg2DEoVngnpbuS1EImIiIiIiL5x26HhQuhUSPo2hU2b4YFC5xbyZSMOSghExERERGRy2e3w/z50LAh3H8/bNliJl7Dh8O//5rjxSQbdVkUEREREZHL99Zb8OKL5v2gIHM9seeeg+LF3RlVoacWMhERERERcZ3NBsePn33csyeUKwejRsGBA+bYMSVjF6UWMhERERERyTubDb78EsaNMxdu/v13s7xkSTMR81SK4Qq1kMkF9ezZk86dOzuVHTx4EF9fXywWi3uCEhEREZErLzMTPv0U6taFRx6BnTth0yY4evRsHSVjLlNCJi4bMWKEkjERERGRa0VmJnzyCdSpA489Brt2QWgo/O9/ZotYuXLujrBIU0LmbklJud9SU/NeNyXl4nXzwZYtW/jss88YMGBAjtsjIiKwWCxOt2+//dax3W63M378eCpXroyfnx8NGzZk4cKFju02m40nnnjCsb1mzZq8/fbb2c4TGRmZ7TwhISGO7Tm17J1r9OjRNGrUKMdjxsXFOcq++uor6tati4+PDxEREbz55ptO+6SlpfHCCy9Qrlw5AgICaNq0KZGRkbmeF8gWd9btueeec9SJiIhg3LhxPPTQQwQEBFCuXDmmT5/udJy4uDiefPJJSpUqRVBQELfeeiubNm3K0/k2btzo9ByGDBlChQoV8PHxoVq1anz44Yc5vianT5+mQYMGdO/eHcMwAPjll19o2bIlISEhlChRgrvuuou9e/c6jv/vv//SsGFDAgMDCQwMpGXLlvz999+O7Rfb/8CBA9liznqNpkyZ4vQ8z32vffjhh9le10u5XiIiIte8r76C7t1h924oUQJee81MxF5+2Zy8Qy6LEjJ3CwzM/da1q3PdsLDc63bo4Fw3IiJ7nXwwdOhQ7r77bpo3b55rnbFjxxIVFUVUVFS2bePHj+fjjz9m5syZbNu2jUGDBvHoo4+yfPlywEzYypcvz4IFC9i+fTsjR47k5ZdfZv78+U7HyUoGdu3aRVRUlNMX8/yybt06HnjgAbp168aWLVsYPXo0I0aMYM6cOY46/fv3Z/Xq1cybN4/Nmzdz//33c8cdd7B79+4LHnv27NmO1ygqKopmzZplqzNp0iQaNmzIhg0bGDp0KM8++yxLlixxbL///vs5fvw4P//8M+vWraNx48bcdtttxMbG5nq+cxOhLN27d+eLL75g6tSp7Nixg/fee4/AHN4viYmJ3HnnnVSpUoWPPvrI0UqalJTE4MGD+eeff1i6dClWq5UuXbpgt9sBCA4OZsKECWzcuJF169ZRtWpVunXr5jjuxfa/FElJSYwYMSLb87jU6yUiInJNycgwW8GydO0KN90EEybA/v0wbJjWEctH6uQpebZixQoWL17Mli1b2HXuH+k50tLSCA0NJTw8PMdtr732Gr/99psjAalSpQp//vkn7733Hq1bt8bLy4sxY8Y49qlcuTKrV69m/vz5PPDAA47yjIwMAEdLR3BwcH4+VQDeeustbrvtNkaMGAFAjRo12L59O5MmTaJnz54cOnSI2bNnc+jQIcqWLQvACy+8wC+//MLs2bN57bXXcj12SEiI02vk7e2drU6LFi0YOnSo49wrV65k8uTJ3H777fz555/8/fffHD9+HB8fHwDeeOMNvv32WxYuXEifPn0A8zUHKFWqFOHh4aSe1+r677//Mn/+fJYsWULbtm0B85qcLy0tjfvuuw9/f3++/PJLPM/pH971vB8OPvroI0qVKsX27dupV68epUuXpsN/PxhkZmZSqVIlp1api+1/KSZOnEidOnXIzMx0lF3O9RIREbkmZGTAxx+bXRFtNrNFzNvbHBe2ahVoyEqBUELmbomJuW/z8HB+fO60ouezntfYeeDAJYeUm6FDh9KjRw9q166da0IWGxtLUC5N13v27CE5OZnbb7/dqTw9PZ3rrrvO8Xj69Ol89NFHHDp0iJSUFNLT07N1L0xISMBqteLn55drvD/++COBgYF4eXlRsWJFnn32WR5//HHH9i1btji1oNhsNqf9d+zYwT333ONU1qJFC6ZMmYLNZmPLli3YbDZq1KjhVCctLY0SJUrkGldend9q1qxZM0dL4KZNm0hMTMx2npSUFKfufqdOnQLI9Zps3LgRDw8PWrdufcFYHnnkEZYuXcqYMWMcCWCW3bt3M3LkSNasWcPJkycdLVuHDh1ySqgCAwNJTU2lTJkyTl0L87p/8+bNsZ7zPk9OTs4x1mPHjvHWW2/x559/8uyzzzrKC/p6iYiIFFnp6TB37tmuiAClSpmTdjRoYD5WMlZglJC5mysrlhdU3Tz45ptv2LBhQ7aug+c6cuQI6enpVK5cOcftif8ln4sWLaLceYM/s77kz5s3jxdeeIE333yTZs2aUaxYMSZNmsSaNWuc6h87dozSpUs7fUE/3y233MKMGTPIyMjgp59+4sknn6R+/frccMMNANSsWZPvv//eUX/NmjU8+uijF3gVsj8fDw8P1q1bh8d5yXNOXf7yU2JiImXKlMlx/NO5Y+n27dsHkOs1uVBCe67o6Gi++uorHn74Ybp06UL9+vUd2+6++24qVarE+++/T9myZbHb7dSrV4/09HSnY2zcuJHTp08zfvx4Xn75ZRYvXuzS/l9++SW1a9d2PG7Tpk2Osb7yyivcf//9NGzY0KncnddLRESkUEpPhzlzzETs4EGzLCwMXnoJnn46379PSs6UkMlF2Ww2XnnlFQYMGED58uVzrbd8+XL8/Py4/vrrc9xep04dfHx8OHToUK4tMitXrqR58+b07dvXUXZui0+WtWvXOrWq5SQgIIBq1aoBULt2bSZMmMCmTZscCZm3t7djO5gJ5blq167NypUrs8VXo0YNPDw8uO6667DZbBw/fpybb775grFcir/++ivb46yEpHHjxkRHR+Pp6UlERESux1i+fDkRERG5Xrf69etjt9tZvny5o8tiTr7//nuqVKlC79696dWrF3/99Reenp6cOnWKXbt28f777ztegz///DPHY2S91qNGjaJRo0acPHkSi8WS5/0rVKjgdL08c5hWd+PGjSxcuDDHFtyCvl4iIiJFzsaN8NRT5v3wcDMRe+op8Pd3a1jXGiVkclG//fYbvr6+DBs2LNc6e/fuZcKECdxzzz1OsxSCORtgeno6xYoV44UXXmDQoEHY7XZatmxJfHw8K1euJCgoiB49elC9enU+/vhjFi9eTOXKlfnkk09Yu3ato4UnMTGRDz74gM8//5wvv/zygnHb7XZSU1MdLWSnTp1yaUzS888/zw033MC4ceN48MEHWb16NdOmTePdd98FzHFdjzzyCN27d+fNN9/kuuuu48SJEyxdupQGDRrQsWPHPJ8rJytXrmTixIl07tyZJUuWsGDBAhYtWgRA27ZtadasGZ07d2bixInUqFGDY8eOsWjRIrp06cL111/Pxo0bmT59Oo8++ijR0dEAnDhxAjC7MtpsNiIiIujRowePP/44U6dOpWHDhhw8eJDjx487jdkLDQ0FYMKECTRo0IAJEyYwfPhwihcvTokSJZg1axZlypTh0KFDjnFvWSIjI7HZbFStWpXY2FjGjBlDhQoVKFmyJHa7/aL7u+KNN97g+eefd4wRO1dBXy8REZFCLy0N/vkHWrQwH994I/ToAdddB336QB57zkg+MyRfxMfHG4ARHx+fbVtKSoqxfft2IyUlxQ2RXZ4ePXoYgDF+/Hin8m+++cY49+1TqVIlA8j19vvvvxuGYRh2u92YMmWKUbNmTcPLy8soVaqU0b59e2P58uWGYRhGamqq0bNnTyM4ONgICQkxnnnmGWPo0KFGw4YNDcMwjK+//tqoU6eO8f777zvFM3v2bCM4ODhb3IDh6elpVKtWzZg2bZpj+6hRoxzHzPL7778bgHH69GlH2cKFC406deoYXl5eRsWKFY1JkyY57ZOenm6MHDnSiIiIMLy8vIwyZcoYXbp0MTZv3pzrawoY33zzjVNZ69atjWeffdbp9RwzZoxx//33G/7+/kZ4eLjx9ttvO+2TkJBgDBgwwChbtqzh5eVlVKhQwXjkkUeMQ4cOOc5zodv+/fsNwzDfn4MGDTLKlCljeHt7G9WqVTM++uijXF+TyMhIw9fX19iyZYthGIaxZMkSo3bt2oaPj4/RoEEDIzIy0uk5fv3110bt2rUNX19fIzQ01OjQoYPT63Ox/ffv328AxoYNG5yef6VKlYzJkyc7va7h4eFGYmJirq+rK9erKP/dioiIOElJMYxp0wyjfHnD8PU1jKgod0d01btQbnA+i2H8N3+4XJaEhASCg4OJj4/PNoFCamoq+/fvp3Llyvj6+ropwoIVERFBZGRkjt3nOnfuzHPPPZfrmB/JLiIigueee85pDS1XWSwWcvvzbtSoEd9+++0Fuzte666Fv1sREbnKpabCBx+Y09UfPWqWlS0L8+aBuu8XqAvlBudTl0XJF6VKlco2UUKW4sWL5zituxSs0qVL57qtZMmSuV4vERERKeJSUuD99+H11+HYMbOsfHkYOhSeeAL0Q2OhooRM8sXatWtz3TZ79uwrGIlkyRo3lpPffvvtCkYiIiIiV1RcHAwZYraQVahgLuT8+ONw3tI1UjgoIRMphA4UwDpyIiIicpVKToaff4auXc3HZcrAyJEQGgo9eyoRK+SUkImIiIiIFEVJSTBzJkyaBDEx8Ndf0LSpue0Cs2NL4aKE7ArS/CkiRYf+XkVEpNBKSoJ334U33oDjx82yiAizq6IUOUrIrgAvLy8AkpOT8dP6DiJFQnp6OoAmPxERkcIjNRWmTjUTsf/WFqVKFXjlFXjsMfjvO6cULUrIrgAPDw9CQkI4/t8vGP7+/lgsFjdHJSK5sdvtnDhxAn9/fzw99TEpIiKFhMUC77xjJmNVq8Lw4fDII0rEijh907hCwsPDARxJmYgUblarlYoVK+rHExERcZ+EBJgzB/r2BU9Pc3KON96AtDR4+GGzTIo8XcUrxGKxUKZMGcLCwsjIyHB3OCJyEd7e3litVneHISIi16KEBLNr4ltvwenTEBIC3bub2x580K2hSf5TQnaFeXh4aEyKiIiIiGQXH28mYpMnm4kYQM2a5vT1ctVSQiYiIiIi4k42G/zvf2YiljVTYq1aMGKE2SKmH/OvakrIRERERETcycMDli41k7Hatc1Fne+/X4nYNUIJmYiIiIjIlRQba3ZNHDAASpQwy15/HQ4ehPvuUyJ2jVFCJiIiIiJyJcTGmhN1TJ0KZ85ARobZVRHgppvMm1xzlJCJiIiIiBSkU6fMROydd8xEDKBBAyVgAighExEREREpOKNHw5tvQmKi+bhRI3OM2D33gJZXEZSQiYiIiIgUnGPHzGTsuutg1Cjo1AksFndHJYWI0nIRERERkfxw/Di89BJs2nS27JVX4LvvYN06s1VMyZicRy1kIiIiIiKXIyYGJk2CGTMgORn27oWvvjK3Vapk3kRyoYRMRERERORSREfDxIkwcyakpJhlN9wATzzh3rikSFFCJiIiIiLiqldfNaesT001Hzdtao4Ru+MOdUsUlyghExERERFxlb+/mYzddJM5k2K7dkrE5JIoIRMRERERuZCjR+H11+GWW6BLF7Ps6aehXj24/XYlYnJZlJCJiIiIiOTkyBGYMAHefx/S02HZsrPrh/n7m61iIpdJCZmIiIiIyLkOH4bx4+HDD81EDODmm80xYmoNk3ymhExEREREJMtbb8HQoZCRYT5u3dpMxNq0UTImBcKtC0OPHz+eG264gWLFihEWFkbnzp3ZtWuXU502bdpgsVicbk8//bRTnUOHDtGxY0f8/f0JCwvjxRdfJDMz06lOZGQkjRs3xsfHh2rVqjFnzpxs8UyfPp2IiAh8fX1p2rQpf//9d74/ZxEREREpxGrVMpOxNm3g998hMtIcO6ZkTAqIWxOy5cuX069fP/766y+WLFlCRkYG7dq1Iykpyale7969iYqKctwmTpzo2Gaz2ejYsSPp6emsWrWKuXPnMmfOHEaOHOmos3//fjp27Mgtt9zCxo0bee6553jyySdZvHixo86XX37J4MGDGTVqFOvXr6dhw4a0b9+e48ePF/wLISIiIiJX3oED0KePOWFHlg4dYM0aMxlr08Zdkck1xGIYhuHuILKcOHGCsLAwli9fTqtWrQCzhaxRo0ZMmTIlx31+/vln7rrrLo4dO0bp0qUBmDlzJkOGDOHEiRN4e3szZMgQFi1axNatWx37devWjbi4OH755RcAmjZtyg033MC0adMAsNvtVKhQgQEDBjB06NCLxp6QkEBwcDDx8fEEBQVdzssgIiIiIgVp3z547TWYOxcyM6F4cXPcWECAuyOTq4QruYFbW8jOFx8fD0BoaKhT+WeffUbJkiWpV68ew4YNIzk52bFt9erV1K9f35GMAbRv356EhAS2bdvmqNO2bVunY7Zv357Vq1cDkJ6ezrp165zqWK1W2rZt66hzvrS0NBISEpxuIiIiIlKI7dkDvXpBjRrmhB2ZmeZMiT/+qGRM3KbQTOpht9t57rnnaNGiBfXq1XOUP/zww1SqVImyZcuyefNmhgwZwq5du/j6668BiI6OdkrGAMfj6OjoC9ZJSEggJSWF06dPY7PZcqyzc+fOHOMdP348Y8aMubwnLSIiIiJXxgcfmGuH2Wzm4/btzck6mjVzb1xyzSs0CVm/fv3YunUrf/75p1N5nz59HPfr169PmTJluO2229i7dy9Vq1a90mE6DBs2jMGDBzseJyQkUKFCBbfFIyIiIiLnsdvNNcMAWrUCwzDHiI0cCTfd5N7YRP5TKBKy/v378+OPP7JixQrKly9/wbpNmzYFYM+ePVStWpXw8PBssyHGxMQAEB4e7vg3q+zcOkFBQfj5+eHh4YGHh0eOdbKOcT4fHx98fHzy/iRFRERE5MrYtQtefdVMxubONctq1DC7LFau7N7YRM7j1jFkhmHQv39/vvnmG5YtW0blPPyBbNy4EYAyZcoA0KxZM7Zs2eI0G+KSJUsICgqiTp06jjpLly51Os6SJUto9l8Ttbe3N02aNHGqY7fbWbp0qaOOiIiIiBRyO3fCI49AnTrw6afm7ciRs9uVjEkh5NaErF+/fnz66ad8/vnnFCtWjOjoaKKjo0lJSQFg7969jBs3jnXr1nHgwAG+//57unfvTqtWrWjQoAEA7dq1o06dOjz22GNs2rSJxYsXM3z4cPr16+dowXr66afZt28fL730Ejt37uTdd99l/vz5DBo0yBHL4MGDef/995k7dy47duzgmWeeISkpiV69el35F0ZERERE8m7HDnj4YTMR+/xzs6vi3Xeb09dfpPeViLu5ddp7Sy4L7M2ePZuePXty+PBhHn30UbZu3UpSUhIVKlSgS5cuDB8+3Gn6yIMHD/LMM88QGRlJQEAAPXr0YMKECXh6nu2RGRkZyaBBg9i+fTvly5dnxIgR9OzZ0+m806ZNY9KkSURHR9OoUSOmTp3q6CJ5MZr2XkRERMQNvvkGunY1x4cB3HOPOUascWP3xiXXNFdyg0K1DllRpoRMRERE5ApJTQVfX/N+QgJUqWJO2jFyJDRq5NbQRMC13KBQTOohIiIiInJRW7bA2LFw8KDZHdFigaAg+PdfOG8dW5GiQgmZiIiIiBRumzaZidh/69ACsG4dXH+9eV/JmBRhbp3UQ0REREQkVxs2QJcuZjfEr782W8QeeMBsKctKxkSKOLWQiYiIiEjhs3IltGxp3rdY4MEHYcQIcyZFkauIEjIRERERKRxOnYISJcz7zZqZLWN16sDw4VC7tltDEykoSshERERExL3WroUxY8x/9+2DgACwWuGvv+C/dWVFrlYaQyYiIiIi7rFmDdx5J9x4IyxaBCdPQmTk2e1KxuQaoIRMRERERK6sv/6CDh3gppvg55/N1rDu3WHHDujY0d3RiVxR6rIoIiIiIlfO/v3QvDkYBnh4wGOPwSuvQLVq7o5MxC2UkImIiIhIwdq3D6pUMe9XrmzOmOjvDy+/DFWrujc2ETdTQiYiIiIiBeOPP8zJOpYvh3//NZMxgM8+M7spiojGkImIiIhIPlu+HG69FVq1gqVLzbI//zy7XcmYiIP+GkREREQkf0RGwi23QJs28Pvv4OUFffrA7t3mWDERyUZdFkVERETk8iUkQKdOcOaMmYg98QQMGwYVK7o7MpFCTQmZiIiIiLjOMMx1xJo2BYsFgoLgpZcgKgqGDoUKFdwdoUiRcFldFo8ePUqnTp2oWLEiHTt25PDhw/kVl4iIiIgURoYBS5ZAy5bQrBksW3Z22/DhMH26kjERF1xWQvb8889z9OhRhg4dSkpKCgMGDMivuERERESkMDEMWLwYWrSAdu1g1Srw8YGdO90dmUiRdlldFletWsW8efNo3rw5HTt2pHHjxvkVl4iIiIgUBlmJ2OjRZhdFAF9feOops4ti2bJuDU+kqLushCwuLo7w8HAAwsPDiYuLy4+YRERERKSwsNvh2WfNdcR8feHpp81ErEwZd0cmclVwOSHbvHmz477dbmfnzp0kJiaSlpaWr4GJiIiIiBsYBvzyizl9va8veHjAuHFm69iLL8J/P8aLSP6wGIZhuLKD1WrFYrFw7m5Zjy0WCzabLd+DLAoSEhIIDg4mPj6eoKAgd4cjIiIi4hrDgB9/hLFj4Z9/YNo06NfP3VGJFEmu5AYut5Dt37//kgMTERERkULGMOD7781EbP16s8zfH5KT3RuXyDXC5YTs4MGDNG/eHE9PLWEmIiIiUqR9+y2MGQMbN5qPAwKgf394/nkoVcqdkYlcM1zOqm655RaioqIICwsriHhERERE5Er54AMzGQsMPJuIlSzp7qhErikuJ2QuDjkTERERkcLAbodvvoGbboJy5cyy0aOhYUMYPBhKlHBreCLXqkvqd7h69WqKFy+e47ZWrVpdVkAiIiIiko/sdvjqK3OM2NatMGAATJ1qbrv+evMmIm5zSQlZly5dciy/lmdZFBERESlUbDZYuNCcsn7bNrMsOFjrh4kUMpeUkEVHR2sMmYiIiEhh9fXXMGIEbN9uPg4OhkGDzAWeQ0LcGpqIOHM5IbNYLAURh4iIiIjkl1WrzGQsJMRMxAYOVCImUkhpUg8RERGRoiwzE+bNg5o14YYbzLIXXzQTsAEDzNYxESm0XE7I7HZ7QcQhIiIiIq7IzITPP4dXX4Xdu+H22+HXX81tpUvD8OHujU9E8sTq6g7jx4/no48+ylb+0Ucf8frrr+dLUCIiIiKSi8xMmDMHateGHj3MZCw0FG65xZxRUUSKFJcTsvfee49atWplK69bty4zZ87Ml6BEREREJAdffw21akGvXrBnj7l22PjxcOAADBsGVpe/2omIm7ncZTE6OpoyOUyXWqpUKaKiovIlKBERERHJwalTsHcvlCxpjhPr2xcCA90dlYhcBpcTsgoVKrBy5UoqV67sVL5y5UrKli2bb4GJiIiIXNPS0+Hjj6F4ceja1Szr0QPS0swWsoAA98YnIvnC5YSsd+/ePPfcc2RkZHDrrbcCsHTpUl566SWef/75fA9QRERE5JqSnm6OEXvtNTh4EKpUgU6dwMsLvL2hf393Rygi+cjlhOzFF1/k1KlT9O3bl/T0dAB8fX0ZMmQIw4YNy/cARURERK4J6ekwe7aZiB06ZJaFh5tT12vZIZGrlsW4xIXFEhMT2bFjB35+flSvXh0fH5/8jq1ISUhIIDg4mPj4eIKCgtwdjoiIiBQlP/5ojgc7fNh8XKYMDBkCffqAn597YxMRl7mSG7jcQpYlMDDQMbnHtZ6MiYiIiFyW4GAzGStbFoYOhd69wdfX3VGJyBXg8tyodrudsWPHEhwcTKVKlahUqRIhISGMGzdOi0aLiIiIXExqKrzzDkyceLbs5pth4UJzBsUBA5SMiVxDXG4he+WVV/jwww+ZMGECLVq0AODPP/9k9OjRpKam8r///S/fgxQREREp8lJSYNYseP11iIoCf3/o2RPCwsztWTMpisg1xeWEbO7cuXzwwQd06tTJUdagQQPKlStH3759lZCJiIiInCslBd57z0zEoqPNsgoVzIWcg4PdG5uIuJ3LCVlsbCy1atXKVl6rVi1iY2PzJSgRERGRq8KyZfDwwxATYz6uWBFeftlsGdMYfBHhEsaQNWzYkGnTpmUrnzZtGg0bNsyXoERERESuCtWrw+nTUKmS2V1x92546iklYyLi4HIL2cSJE+nYsSO//fYbzZo1A2D16tUcPnyYn376Kd8DFBERESkSkpLg3Xdh1y744AOzrEIFs5XshhvMRZ1FRM5zSeuQHTt2jOnTp7Nz504AateuTd++fSlbtmy+B1hUaB0yERGRa1RiIkyfDm+8ASdPmmUbNkCjRm4NS0Tcp8DXIStbtqwm7ygi7HaDo3EpJKVnEuDtSbkQP6xWy0W3iYiIyEWcOXM2ETt1yiyrWhWGD4e6dd0bm4gUGS6PIdu8efMFb64YP348N9xwA8WKFSMsLIzOnTuza9cupzqpqan069ePEiVKEBgYSNeuXYnJGhj7n0OHDtGxY0f8/f0JCwvjxRdfJDMz06lOZGQkjRs3xsfHh2rVqjFnzpxs8UyfPp2IiAh8fX1p2rQpf//9t0vPp7DZc/wMMyL3MnnJv0xdupvJS/5lRuRe9hw/c8FtIiIichHr1kFEhDlT4qlTUK0azJkDO3eaE3Z4ebk5QBEpKlxuIWvUqBEWiwXDMLBYzNaUrF6PFosFm82W52MtX76cfv36ccMNN5CZmcnLL79Mu3bt2L59OwEBAQAMGjSIRYsWsWDBAoKDg+nfvz/33nsvK1euBMBms9GxY0fCw8NZtWoVUVFRdO/eHS8vL1577TUA9u/fT8eOHXn66af57LPPWLp0KU8++SRlypShffv2AHz55ZcMHjyYmTNn0rRpU6ZMmUL79u3ZtWsXYVnrgxQhe46fYfbKA8QmpVMm2Bd/bz+S0zPZeiyeHdEJANjsRrZtx+JT6NUigmphxdz8DERERAoZw4D/vvtQt645MUf16jBiBDz0EHheUscjEbnGuTyG7ODBg4CZhNWrV4+ffvqJSpUqObafe99VJ06cICwsjOXLl9OqVSvi4+MpVaoUn3/+Offddx8AO3fupHbt2qxevZqbbrqJn3/+mbvuuotjx45RunRpAGbOnMmQIUM4ceIE3t7eDBkyhEWLFrF161bHubp160ZcXBy//PILAE2bNuWGG25wzCBpt9upUKECAwYMYOjQoReNvTCNIbPbDWZE7mXrsXiqhwU6Emdzm53F22PAgPZ1S2O1nm0kNQyD3ccTqV8umKdbV1X3RREREYD4eJg6FX75BVasAA8Ps3zXLrOLohIxETmPK7mBy10WK1WqRKVKlYiIiMBisVC+fHlH2eUkYwDx8fEAhIaGArBu3ToyMjJo27ato06tWrWoWLEiq1evBswZHuvXr+9IxgDat29PQkIC27Ztc9Q59xhZdbKOkZ6ezrp165zqWK1W2rZt66hzvrS0NBISEpxuhcXRuBT2nkikTLCvUzIGkJhmw2Y3sBkGiWnOrZkWi4Uywb7sOZ7I0biUKxmyiIhI4RMXB2PGmF0TR46EVavg++/Pbq9ZU8mYiFw2lxOygmK323nuuedo0aIF9erVAyA6Ohpvb29CQkKc6pYuXZro/1a6j46OdkrGsrZnbbtQnYSEBFJSUjh58iQ2my3HOlnHON/48eMJDg523CpUqHBpT7wAJKVnkpppw987+38S6TY7ABYMx/1z+Xl7kJZpIyk9M9s2ERGRa8Lp0zBqlJmIjR5tJmZ16sAXX0CnTm4OTkSuNpf1s47FYsnWAnOp+vXrx9atW/nzzz/z5XgFbdiwYQwePNjxOCEhodAkZQHenvh6epCcnkkxX+dBxd4eZg5uYHHcP1dKug0fTw8CckjmRERErnr795vT1Wf1fKlb12wdu+8+sBaa37FF5Cri8rfu4sWLO5KwxMRErrvuOqdxSLGxsS4H0b9/f3788UdWrFhB+fLlHeXh4eGkp6cTFxfn1EoWExNDeHi4o875syFmzcJ4bp3zZ2aMiYkhKCgIPz8/PDw88PDwyLFO1jHO5+Pjg4+Pj8vP9UooF+JH1VKBbD0WT6CPp1PSHOjjgYfVAoZ5/1yGYRAVn0r9csGUC/G70mGLiIi4R0bG2VkRIyLMJOzMGTMR69pViZiIFCiXE7LJkyfnW6uYYRgMGDCAb775hsjISCpXruy0vUmTJnh5ebF06VK6du0KwK5duzh06BDNmjUDoFmzZvzvf//j+PHjjtkQlyxZQlBQEHXq1HHU+emnn5yOvWTJEscxvL29adKkCUuXLqVz586A2YVy6dKl9O/fP1+e65VktVpoX680x+JT2H3cHEvm5+1BSrqNqPhUapQ2Z1DccyIp27bQAG/a1S2tCT1EROTqd/IkvPUWfPIJbN4MxYubsyh++y2ULKlETESuCJdnWcxPffv25fPPP+e7776jZs2ajvLg4GD8/MwWmmeeeYaffvqJOXPmEBQUxIABAwBYtWoVYE5736hRI8qWLcvEiROJjo7mscce48knn3Sa9r5evXr069ePxx9/nGXLljFw4EAWLVrkNO19jx49eO+997jxxhuZMmUK8+fPZ+fOndnGluWkMM2ymGXP8TMs3hrD3hOJpGWaXRGrhQXSrq75fHLbpinvRUTkqnbyJLz5JkybBomJZtnUqfDfdwwRkcvlSm7gckLm4eFBVFRUvqzNlVtL2+zZs+nZsydgLgz9/PPP88UXX5CWlkb79u159913nboSHjx4kGeeeYbIyEgCAgLo0aMHEyZMwPOcmY8iIyMZNGgQ27dvp3z58owYMcJxjizTpk1j0qRJREdH06hRI6ZOnUrTpk3z9FwKY0IG5hT4R+NSSErPJMDbk3Ihfo7WrwttExERueqcOAFvvAHTp0NSklnWqJE5gUenTmoRE5F8U6AJmdVqJTo6ukgullyQCmtCJiIiIpiTdFSsaK4pBtC4sZmI3X332cWeRUTySYGuQwa5t2yJiIiIFBpnzpy9HxQEXbpAkybwww/wzz9mq5i+04iIm13S3Oa5zTwI5pguEREREbeJjoZJk+C992DNGnPWRDDHjPn7KwkTkULlkhKyhQsXEhoamt+xiIiIiFy6qCiYOBFmzoTUVLPsiy/g1VfN+wEB7otNRCQXLidkFouFFi1aaAyZiIiIFA7HjsHrr8OsWWcTsWbNzDFi7dq5NzYRkYtwOSFz4yz5IiIiIs5sNrjpJjh82HzcvDmMHg1t26proogUCS5P6vH777+ru6KIiIi4z7FjYLeb9z08oH9/aNkSliyBP/+E229XMiYiRcYlLQxts9n49ttv2bFjBwB169alU6dOeHh45HuARYWmvRcRESlghw7BhAnw4Yfw5ZfQubNZbrOZa4gpCRORQsKV3MDlLot79uyhY8eOHDlyhJo1awIwfvx4KlSowKJFi6hateqlRS0iIiKSk4MHYfx4+OgjyMgwy3799WxCdg3/ICwiRZ/LXRYHDhxIlSpVOHz4MOvXr2f9+vUcOnSIypUrM3DgwIKIUURERK5FBw7AU09B9ermFPYZGXDLLRAZCe++6+7oRETyhctdFgMCAvjrr7+oX7++U/mmTZto0aIFiYmJ+RpgUaEuiyIiIvnsppvMdcQAbr3VnDWxVSv3xiQikgeu5AYut5D5+Phw5tyV7/+TmJiIt7e3q4cTERERMe3bB+d+xxg2zJwt8Y8/YOlSJWMiclVyOSG766676NOnD2vWrMEwDAzD4K+//uLpp5+mU6dOBRGjiIiIXM327oXHH4caNWD69LPlnTqZMye2bOm+2ERECpjLCdnUqVOpWrUqzZo1w9fXF19fX1q0aEG1atV4++23CyJGERERuRrt2QM9e0LNmjB7tjlb4pYtZ7dr1kQRuQa4PMtiSEgI3333Hbt372bnzp0A1K5dm2rVquV7cCIiInIV2r0bXn0VPv307Hpid9xhjhG76Sb3xiYicoW5nJBlqV69OtWrV8/PWERERORaMGoUfPGFef/OO83HN97o3phERNzE5YTs3nvvveD2r7/++pKDERERkavQjh0QEAAVK5qPhw+HxEQYMQJuuMG9sYmIuJnLY8i+/fZbvL29CQ4OzvEmIiIiAsD27fDQQ1C3rtkKlqVOHfj+eyVjIiJcYpfFqVOnEhYWlt+xiIiIyNVg2zYYNw7mz4es5U4TE83xYlaXfwsWEbmqufypaLFYsGjWIxERETnf1q3wwANQvz58+aWZjHXpAhs2wIIFSsZERHLgcguZYRjcdttt+Pn5ERAQQNmyZbnuuut48MEHKV++fEHEKCIiIkXBV1+ZiRdA164wciQ0aODemERECjmLYWT1JcibMWPGYBgGGRkZJCQkcOzYMdavX8+JEydYsmQJzZo1K6hYC7WEhASCg4OJj48nKCjI3eGIiIgUvE2bICMDrr/efBwXBwMHwosvmq1kIiLXKFdyA5cTspwYhsGTTz7Jvn37+P333y/3cEWSEjIREblmbNgAY8fCt9+a64atWqVFnEVEzuFKbpAvnbktFgtjxozh+qxfyEREROTqs349dO4MjRubyZjFApUqQXKyuyMTESmy8qWFTNRCJiIiV7FNm8w1w374wXxssUC3buZ6YnXquDc2EZFCyJXcwOVJPQYPHpzrNovFwptvvunqIUVERKQw27HDTMasVnNdseHDoVYtd0clInJVcDkhmzJlCs2aNcPb2zvbNk2HLyIichX4+2+IiYG77zYf33+/2V3xiSegZk33xiYicpVxucui1WolOjpaC0OfR10WRUSkyPvrLxgzBn75BcqUgb17wc/P3VGJiBQ5V3xSDxERESnCVq+GO+6AZs3MZMzDA9q3h6Qkd0cmInLVc7nLooiIiFwltmyB55+HJUvMxx4e0KMHvPwyVK3q3thERK4Rl5SQLV68mODg4By3derU6bICEhERkSskM9NMxjw9oWdPMxGrXNndUYmIXFMuaQxZrgezWLDZbJcdVFGkMWQiIlLorVhhTmE/YMDZsunToWNHiIhwW1giIlebAp323m63X3JgIiIi4gaRkTB6NCxfDl5ecM89ULGiua1fP3dGJiJyzdMYMhERkauRYcDvv5uzJq5YYZZ5eZlT1+ewdI2IiLiHEjIREZGrza5d8OST8Oef5mNvb/Px0KFQoYJ7YxMRESdKyERERK42oaGwYYOZiPXubSZi5cu7OyoREcmBEjIREZGizDDMmRJ//hkmTzbLSpWCzz+HJk2gXDn3xiciIhekhExERKQoMgxYvNgcI/bXX2ZZly7QqpV5X8vQiIgUCS4nZJs3b77g9gYNGlxyMCIiInIRhmG2ho0dC2vWmGV+fvD001CjhntjExERl7mckDVq1AiLxULW8mUWiwUAwzCu6XXIRERECtyRI3DvvbB2rfnYzw/69oUXX4TSpd0bm4iIXJJL6rK4Zs0aSpUqhWEY1KtXj59++olKlSrld2wiIiJyrvBwOHUK/P3NROyFF5SIiYgUcZeUkFWsWJGwsDDAbCHz9/dXQiYiIpKfDAO+/x4++ggWLDBnTPT0hHnzoFIl+O//YRERKdqsru4QFhbGv//+C8CxY8dISkqiQ4cO/PLLL/kenIiIyDXHbodvvoHGjaFzZzMpmzPn7PYbblAyJiJyFXE5Ibvjjjvo1q0bTz/9NLfeeitt27Zlzpw5PProo4wePboAQhQREbkG2O3w1Vdw3XXmOLGNGyEwEIYNMx+LiMhVyWJkzc6RR0lJSfzvf/9j06ZNVK5cmZEjRxIWFsa+ffu47777WL9+fUHFWqglJCQQHBxMfHw8QUFB7g5HRESKkoQEaNkStmwxHxcrBgMGwODBUKKEe2MTERGXuZIbuJyQXUhqaiq+vr75dbgiRQmZiIhclvbtYfVqePZZGDQIQkPdHZGIiFwiV3KDfF0Y+lpNxkRERPLMZoOFC+GNN+CHH8yZEwFmzoTgYCViIiLXmEtKyP755x/mz5/PoUOHSE9Pd9r29ddf50tgIiIiVxWbDebPh3HjYMcOs2zKFJgwwbxfubLbQhMREfdxeVKPefPm0bx5c3bs2ME333xDRkYG27ZtY9myZQQHB7t0rBUrVnD33XdTtmxZLBYL3377rdP2nj17YrFYnG533HGHU53Y2FgeeeQRgoKCCAkJ4YknniAxMdGpzubNm7n55pvx9fWlQoUKTJw4MVssCxYsoFatWvj6+lK/fn1++uknl56LiIhIjmw2+PxzqFcPHn7YTMZCQmDMGBg61N3RiYiIm7mckL322mtMnjyZH374AW9vb95++2127tzJAw88QMWKFV06VlJSEg0bNmT69Om51rnjjjuIiopy3L744gun7Y888gjbtm1jyZIl/Pjjj6xYsYI+ffo4tickJNCuXTsqVarEunXrmDRpEqNHj2bWrFmOOqtWreKhhx7iiSeeYMOGDXTu3JnOnTuzdetWl56PiIiIE5sNrr8eHnkEdu6E4sVh7Fg4cABGjjQTMxERuaa5PKlHQEAA27ZtIyIighIlShAZGUn9+vXZsWMHt956K1FRUZcWiMXCN998Q+fOnR1lPXv2JC4uLlvLWZYdO3ZQp04d1q5dy/XXXw/AL7/8wp133smRI0coW7YsM2bM4JVXXiE6Ohpvb28Ahg4dyrfffsvOnTsBePDBB0lKSuLHH390HPumm26iUaNGzJw5M0/xa1IPEREBzOnrref83jlwIHz2mTlj4oABoP8jRESueq7kBi63kBUvXpwzZ84AUK5cOUcrUlxcHMnJyZcQ7oVFRkYSFhZGzZo1eeaZZzh16pRj2+rVqwkJCXEkYwBt27bFarWyZs0aR51WrVo5kjGA9u3bs2vXLk6fPu2o07ZtW6fztm/fntWrV+caV1paGgkJCU43ERG5hmVmwty5UKsW/P332fLRo2H/fnjlFSVjIiKSjcsJWatWrViyZAkA999/P88++yy9e/fmoYce4rbbbsvX4O644w4+/vhjli5dyuuvv87y5cvp0KEDNpsNgOjoaMLCwpz28fT0JDQ0lOjoaEed0qVLO9XJenyxOlnbczJ+/HiCg4MdtwoVKlzekxURkaIpIwNmzzYTsZ49YfdumDz57PbQUCViIiKSK5dnWZw2bRqpqakAvPLKK3h5ebFq1Sq6du3K8OHD8zW4bt26Oe7Xr1+fBg0aULVqVSIjI/M9+XPVsGHDGDx4sONxQkKCkjIRkWtJRgZ88gn873+wb59ZVrIkvPgi9O3r3thERKTIcDkhCz1nfRSr1crQKzhDVJUqVShZsiR79uzhtttuIzw8nOPHjzvVyczMJDY2lvD/1nUJDw8nJibGqU7W44vVydqeEx8fH3x8fC77OYmISBHVti2sWGHeDwszE7FnnoGAAPfGJSIiRYrLXRbPHzd1JcdRHTlyhFOnTlGmTBkAmjVrRlxcHOvWrXPUWbZsGXa7naZNmzrqrFixgoyMDEedJUuWULNmTYoXL+6os3TpUqdzLVmyhGbNmhXo8xERkSIkPd0cJ5alWzcoXRrefNMcI/bCC0rGRETEZS7Psmi1WrFYLNnKDcPAYrE4xnflRWJiInv27AHguuuu46233uKWW24hNDSU0NBQxowZQ9euXQkPD2fv3r289NJLnDlzhi1btjhapzp06EBMTAwzZ84kIyODXr16cf311/P5558DEB8fT82aNWnXrh1Dhgxh69atPP7440yePNkxPf6qVato3bo1EyZMoGPHjsybN4/XXnuN9evXU69evTw9F82yKCJylUpLM8eIjR8Pr74Kjz1mlmclaP7+7o1PREQKHVdyA5cTsuXLlwNmAnbnnXfywQcfUK5cOcf21q1b5/lYkZGR3HLLLdnKe/TowYwZM+jcuTMbNmwgLi6OsmXL0q5dO8aNG+c0AUdsbCz9+/fnhx9+wGq10rVrV6ZOnUpgYKCjzubNm+nXrx9r166lZMmSDBgwgCFDhjidc8GCBQwfPpwDBw5QvXp1Jk6cyJ133pnn56KETETkKpOWBh99ZCZihw+bZa1bQ2SkW8MSEZHCr0ATsnMVK1aMTZs2UaVKlUs9xFVDCZmIyFUiNRU+/BAmTIAjR8yysmVhyBDo3Rv8/Nwbn4iIFHqu5AYuT+ohIiJyVeveHRYsMO+XKwdDh8KTT4Kvr3vjEhGRq5LLk3qcL6fxZCIiIkVGSgqcOXP28dNPQ/nyMH067NkD/fsrGRMRkQLjcgvZdddd50jCUlJSuPvuu/H29nZsX79+ff5FJyIiUlBSUuC992DiROjVy1xPDOCWW2DvXjjn/zYREZGC4nJC1rlzZ8f9e+65Jz9jERERKXjJyWcTsehos+z772HcOLBawWJRMiYiIlfMZU3qIWdpUg8RkUIuKQlmzoRJkyAmxiyLiICXX4YePZSEiYhIvtGkHiIiIud75RV4+23zfkQEDB9uTuDh5eXWsERE5NrmckJWvHjxC07kERsbe1kBiYiI5IvEREhIMKesB3juOfjlF3jpJXNxZyViIiJSCLickE2ZMgUwF4Z+5plnGDt2LGFhYfkdl4iIyKU5cwamTYM33zQXcv7qK7M8IgJ27DDHiImIiBQSWhg6n2gMmYiImyUknE3Esnpr1KgB69dDQIB7YxMRkWuKK7nBZa9DJiIi4lYJCfDqq2YL2CuvmMlYjRrw8cewbZuSMRERKdQue1IPLQwtIiJuNXs2jBhh3q9Z07zfrRt4eLg3LhERkTxwOSG79957HfdTU1N5+umnCTjn18evv/46fyITERHJSVwcHDkC9eqZj3v3hm++gT594MEHlYiJiEiR4nJCFhwc7Lj/6KOP5mswIiIiuYqLgylTzFuFCrBpk7mQs78/REa6NzYREZFL5HJCNnv27IKIQ0REJGexsWYS9vbb5ngxgHLlICrK/FdERKQI08LQIiJSOMXGwltvwdSp5lT2AHXrwqhR0LWr2TomIiJSxF1SQrZw4ULmz5/PoUOHSE9Pd9q2fv36fAlMRESucatXw//+Z96vXx9GjoR771UiJiIiVxWX/1ebOnUqvXr1onTp0mzYsIEbb7yREiVKsG/fPjp06FAQMYqIyLXg5En4/fezj++8E3r0MBd23rgR7rtPyZiIiFx1XF4YulatWowaNYqHHnrIaWHokSNHEhsby7Rp0woq1kJNC0OLiFyiEyfMxZynTQNvbzhwAPQ5KiIiRViBLgx96NAhmjdvDoCfnx9n/uvX/9hjj/HFF19cQrgiInJNOn4cXnrJXND59dchKQkqV4Zjx9wdmYiIyBXjckIWHh5ObGwsABUrVuSvv/4CYP/+/bjY2CYiIteiEyfghRfM5GvSJEhOhuuvhx9+gH/+gVq13B2hiIjIFeNyQnbrrbfy/fffA9CrVy8GDRrE7bffzoMPPkiXLl3yPUAREbnKnD4NkyebidgNN8CiRfD333DXXWCxuDs6ERGRK8rlMWR2ux273Y6npzlB47x581i1ahXVq1fnqaeewtvbu0ACLew0hkxEJBdRUfDbb/DYY2fL/vc/aNwY7rhDSZiIiFx1XMkNXE7IJGdKyEREznPsmDk2bNYsSE+H7duhZk13RyUiIlLgCnRSD4BPP/2UuXPnYhgGy5cvZ+DAgcyZM+dSDiUiIlebo0dhwACoUsVc1Dk1FW66CVJS3B2ZiIhIoePywtBDhw5l1qxZ+Pv7s379eubNm0eLFi2YPXs2MTExDBkypCDiFBGRwu7UKRg1Ct5/32wRA2jRAkaPhttuU9dEERGRHLjcZbF8+fJ8+OGH1KhRg2rVqvHpp5/y0EMP8emnnzJu3Dh27dpVULEWauqyKCLXvIQEcwr706fh5pvN5OzWW5WIiYjINceV3MDlFrITJ05Qt25dypcvj6+vL02aNAHg5ptv5vDhw5cWsYiIFD2HDsGnn8KwYWbSFRRkLu5cpgy0aaNETEREJA9cTshKlizJyZMnKV++PMOHDycsLAyApKQkihUrlu8BiohIIXPwILz2GsyeDRkZ0LAhdOxobnv4YffGJiIiUsS4nJB1796duLg4AIYNG+YoX758OTfccEO+BSYiIoXMgQNnE7HMTLPsttugdGm3hiUiIlKUadr7fKIxZCJy1UpMhOeeg7lzzyZibduaY8RatnRraCIiIoVRgY4hExGRa4y/P6xZYyZj7dqZiVjz5u6OSkRE5KpwSQnZwoULmT9/PocOHSI9a2rj/6xfvz5fAhMRETfZswcmT4aJEyEgAKxWmD4dvLygWTN3RyciInJVcXlh6KlTp9KrVy9Kly7Nhg0buPHGGylRogT79u2jQ4cOBRGjiIhcCf/+C927Q82a8O67MHPm2W2tWikZExERKQAuJ2Tvvvsus2bN4p133sHb25uXXnqJJUuWMHDgQOLj4wsiRhERKUi7dsFjj0Ht2vDJJ2C3w513QuvW7o5MRETkqudyQnbo0CGa/zd2wM/PjzNnzgDw2GOP8cUXX+RvdCIiUnBsNjMRq1PHXE/Mboe77oK//4ZFi+D6690doYiIyFXP5YQsPDyc2NhYACpWrMhff/0FwP79+9GEjSIiRYiHB6SkmIlYp07wzz/www+gJUxERESuGJcTsltvvZXvv/8egF69ejFo0CBuv/12HnzwQbp06ZLvAYqISD7Zts1cuHn//rNlr70G69bBd99Bkybui01EROQa5fI6ZHa7HbvdjqenOUHjvHnzWLVqFdWrV+epp57C29u7QAIt7LQOmYgUWlu3wrhxsGABGAb07g2zZrk7KhERkauWK7mBFobOJ0rIRKTQ2bIFxo6FhQvPlt17L4wcCQ0bui8uERGRq5wruYHLXRY3btyYY/np06d59NFHXT2ciIgUhMcfhwYNziZj990HmzbBV18pGRMRESlEXE7IbrnlFlauXOlU9t1331GnTh2OHz+eb4GJiMhlKFsWLBZ44AGzpWzBAjNBExERkULF5YRs0qRJ3HnnnSxevJjTp0/z8MMP0717d0aPHs2vv/5aEDGKiMiFrF8P99wDS5eeLRs82Bw79uWXUK+e+2ITERGRC/J0dYcnn3ySoKAgunbtSmBgIA0aNGDLli1UrFixIOITEZHcrFsHY8aYU9UDxMfDbbeZ90NDzZuIiIgUai63kAE88MADLFiwgDNnznDfffcpGRMRuZLWrjUXcL7+ejMZs1rhkUdg5kx3RyYiIiIucrmFbPDgwY77jRo14plnnmHVqlWE/vdL7FtvvZV/0YmIiLP+/WH6dPN+ViL2yitQs6Z74xIREZFL4nJCtmHDBsd9b29vWrVqxcGDBzl48CAWiyVfgxMREcy1w7I+X2+6CWbMgEcfNROxGjXcG5uIiIhcFq1Dlk+0DpmI5LtVq8wxYvfcA337mmWZmXDwIFSt6t7YREREJFeu5AYut5DFx8djs9kcXRSzxMbG4unpqWTEzU7GJ9P1nd85mHjhet5AgK+FSiUCaFwxlJBAL04nZQLg42ElLNiHkoG+BPp4YLVaqVTCHw+LheQMGwHenpQL8cNqPdsimplpZ/3h05xKSqe4vxfhQb6kZtpzrJvFbjc4GpdCUnomAd6elAnyJSoh1fE4t/2k6Dr/ml+Ja+yOc162lSvNRGzJEvPx7t3w9NNmF0VPTyVjIgIU0c830XWTbFxOyLp168bdd99N36xfa/8zf/58vv/+e3766ac8H2vFihVMmjSJdevWERUVxTfffEPnzp0d2w3DYNSoUbz//vvExcXRokULZsyYQfXq1R11YmNjGTBgAD/88ANWq5WuXbvy9ttvExgY6KizefNm+vXrx9q1aylVqhQDBgzgpZdecoplwYIFjBgxggMHDlC9enVef/117rzzThdfHfdq++bv7DmRnKe66UB6qsHpo4lsPJp79uZlteDv44G3hwdBvh6UK+5PyUAfqpYKpH290lQLK8bSHTHMWXmAA6eSSEm3kW6z4+NpJnEVQwOc6mbZc/wMi7fGsPdEIqmZNtIz7aRl2PHxsuLtacXX0yPH/aToOv+aX4lr7I5zXpY//jATsazp6z09oUcPePllMxkTEflPkft8E0DXTXLm8v/wa9as4ZZbbslW3qZNG9asWePSsZKSkmjYsCHTswaon2fixIlMnTqVmTNnsmbNGgICAmjfvj2pqamOOo888gjbtm1jyZIl/Pjjj6xYsYI+ffo4tickJNCuXTsqVarEunXrmDRpEqNHj2bWrFmOOqtWreKhhx7iiSeeYMOGDXTu3JnOnTuzdetWl56PO7mSjLkiw26QkJJJQko6MWfSOBaXDBhsPRbP7JUH+GT1Acb/vJN/j5/B29MCGBiGQVJaJntPJJGQmu6ou+f4GcD8MJq98gBbj8UT4u9FiJ83h2OT+ff4GY6cTibEz4sQf69s+0nRdf41r1IysMCvsTvOeVlGjIBWrcxkzNMTeveGf/+FDz6AKlXcHZ2IFCJF7vNNAF03yZ3LCVlaWhqZmZnZyjMyMkhJSXHpWB06dODVV1+lS5cu2bYZhsGUKVMYPnw499xzDw0aNODjjz/m2LFjfPvttwDs2LGDX375hQ8++ICmTZvSsmVL3nnnHebNm8exY8cA+Oyzz0hPT+ejjz6ibt26dOvWjYEDBzrNBvn2229zxx138OKLL1K7dm3GjRtH48aNmTZtmkvPx11OxicXSDKWxQDshoGn1UpsUgZR8alUKxXAyTNpfPDHfs6kZFCxuB9pGQY2A4r5ehHs50V6pp1/YxKpWtKf2KR0ft0WQ2amncVbY4hNSqd6WCCBPp7sP5mEzW5QsbgfmTaDA6eSCfTxpHpYoGM/u11DHYsqu91wuubFfL3wsFoo5utVYNfYHee8JBkZZ+936gReXtCnj9lFcdYsqFzZfbGJSKFUZD7fxImum1yIywnZjTfe6NS6lGXmzJk0adIkX4IC2L9/P9HR0bRt29ZRFhwcTNOmTVm9ejUAq1evJiQkhOuvv95Rp23btlitVkdr3erVq2nVqhXe3t6OOu3bt2fXrl2cPn3aUefc82TVyTpPTtLS0khISHC6uUu/eRsuXuky2exgtn9BTEIaiWk2PKwWTpxJJcjPkwybQUqGDW9PKxYLWK0W/Lw9iEvOIDohjTLBvuw5nsj6w6fZeyKRMsG+WCwWzqRmcjo5nUBfL6xWK4G+nsQmpXMmNROLxeLY72ica8m+FB5H41Kcrvm5Cuoau+OceWYY8Pvv0KYNPP/82fIbboAjR+C99yAi4srHJSJFQqH+fJNc6brJhbg8huzVV1+lbdu2bNq0idtuuw2ApUuXsnbtWn799dd8Cyw6OhqA0qVLO5WXLl3asS06OpqwsDCn7Z6enoSGhjrVqXzer8xZx4yOjqZ48eJER0df8Dw5GT9+PGPGjLmEZ5b/ouLTCvwcBmBgYMFCpt1Ous2O3TCwGQaeHlZshoHdMPCwnM3xvawWUuwGyRk2Sgf7EpOQyqmkdFIzbfh7+wGQbrOTabPj5Wu+Fb08rCSlZZJuswPg5+1BzH8TfUjRlJSe6XTNz1cQ19gd57wow4Bly8wxYn/8YZZt2ACvvQZZY17P+zwTETlfofx8k4vSdZMLcbmFrEWLFqxevZoKFSowf/58fvjhB6pVq8bmzZu5+eabCyLGQmnYsGHEx8c7bocPH3ZbLGWCfQr8HBbAgvmLjqfVireHFavFgofFQqbNjofFgtViwXbOKgoZdgMPqwV/Lw9S0m34eHpQIsAbX08Pkv/7wPH2sOLpYSXDZu6XYbPj8d/xAcd+Ad4u/3YghUSAt6fTNT9fQVxjd5wzV4YBv/0GN98MbduayZiPj7nA87ZtZ5MxEZE8KFSfb5Jnum5yIZc0bVejRo347LPP2LZtG//88w8fffSR08yH+SE8PByAmJgYp/KYmBjHtvDwcI4fP+60PTMzk9jYWKc6OR3j3HPkVidre058fHwICgpyurnL9G7XFfg5PKxmK5kFKB3kQ6CPBza7QaliviSkZOLlYcHPy4P0TDuGYfaVTkm3EeLvRXiQjznuLCyQxhWKU7VUIFHxqRiGQTFfT4r7e5OYmoHdbicxNZPQAG+K+XpiGIZjv3IhOf+iJIVfuRA/p2t+roK6xu44Z66mToXbbzensvfxgQEDYO9eeOcdKF++4M8vIleVQvX5Jnmm6yYXkm/zKJ85c4bHH3+cxx9/nEGDBl328SpXrkx4eDhLs6Z/xpwxcc2aNTRr1gyAZs2aERcXx7p16xx1li1bht1up2nTpo46K1asIOOcwfNLliyhZs2aFC9e3FHn3PNk1ck6T2FXMtifaqX8C+z4FsBqMbsqhgZ4mf2cTyRRspgPT95cmWJ+Xhw6nYKPlwUPC5xJzSAuJQNvTys1Sgey92QyoQHetKtbGk9PK+3rlSY0wJvdxxNJTMskoqQ/HlYLh06n4GG1EFHCn8S0THYfT3Tsp/U5ii6r1eJ0zc+kZpBpt3MmNaPArrE7zulgGBAff/bxgw9CaCg8+yzs22cmaOXK5f95ReSa4NbPN7lkum5yIRbj/DT9Iu69994cy9PS0vjll1/4+uuv8fX1pX379hc9VmJiInv27AHguuuu46233uKWW24hNDSUihUr8vrrrzNhwgTmzp1L5cqVGTFiBJs3b2b79u34+voC5kyNMTExzJw5k4yMDHr16sX111/P559/DpgLWdesWZN27doxZMgQtm7dyuOPP87kyZMd0+OvWrWK1q1bM2HCBDp27Mi8efN47bXXWL9+PfXq1cvT6+LKatwFpdXrSzl0OvXiFV2QtQ6Zj6cHQT4elP1vHbJqYYG0q5v7OmS+XlYqhprrkJ1bN8u563CkZdpIy7STlmmuX2bePHLcT4qu86/5lbjGV/SchgE//2yOEQsOhnPH1CYng3/B/WgiItced3ymyuXTdbt2uJIbuJyQWa1WHnjgAfz8nJtUU1JSWLBgATabLc/HioyMzHFNsx49ejBnzhzHwtCzZs0iLi6Oli1b8u6771KjRg1H3djYWPr37++0MPTUqVNzXRi6ZMmSDBgwgCFDhjidc8GCBQwfPtyxMPTEiRNdWhi6MCRkAH/tP07P99ZysbTMGwjwtVCpRACNK4YSEujF6SSzX7OPh5WwYB9KBvoS6OOB1Wou8uxhsZCcYctxVfnMTDvrD5/mVFI6xf29CA/yJTXTfsEV6M9fqb5MkC9R/w1o1cr1V6fzr/mVuMYFfk7DgEWLYOxYWLvWLPPzg127oEKF/DuPiMh53PGZKpdP1+3aUOAJWU6zG0ZHR1OuXDmXErKrSWFJyMD8Qz9yOpl9J5MAqFwygArF/fXHLpKfDAN+/NFsEcvqNu3vD337wgsvwHkzt4qIiMi1w5XcwOWpXCwWS7b1E7LKpXDYdzLR0RyemmnD19ODqqUCaV9PzeEi+WbBAnN8GJiJWL9+ZiKmqetFRETEBS4nZIZhcNttt+Hn50dQUBCVK1emVatWNG/evCDiExftOX6G2SsPEJuUTplgX/y9/UhOz2TrsXiOxafQq0WEkjKRS2EY5sLNWd0QO3eGunXhrrvMBZ5LlXJreCIiIlI0uZyQjRo1CjAn8Th16hT79u3jyy+/VAtZIWC3GyzeGkNsUjrVwwId16SYrxeBPp7sPp7Ir9tiqFIyUN0XRfLKbodvvzXHiCUkmGPDvLzA2xs2bQIPD3dHKCIiIkXYJSdk50pLS2PEiBG88cYbjB07lsDAQAYPHpwvAUreHY1LYe+JRMoE+2ZLkC0Wizld/fFEjsalUCFUM76JXJDdDl9/DePGwebNZlmxYrBlCzRubD5WMiYiIiKXKV+WA/fx8WHUqFEEBARgGEa2Be/kykhKzyQ104a/d86LCvp5exDz3yyGIpILux2++spsEdu61SwLCoKBA2HQIHNNMREREZF8clkJWWpqqmM9sICAgBxbz+TKCfD2xNfTg+T0TIr5emXbnpJurncR4J0vebjI1envv+GBB8z7QUHw3HPm7b+F5EVERETyk9XVHex2O+PGjaNcuXIEBgayb98+AEaMGMGHH36Y7wFK3pUL8aNqqUCi4lOztVIahkFUfCrVwgIpF5JzC5rINclmM8eCZbnpJrj3Xhg1Cg4cMKe1VzImIiIiBcTlhOzVV19lzpw5TJw4EW9vb0d5vXr1+OCDD/I1OHGN1Wqhfb3ShAZ4s/t4ImdSM8i02zmTmsHu44mEBnjTrm5pTeghAmYi9tlnUK8eNG8OJ06c3fbVVzB6tBIxERERKXAuJ2Qff/wxs2bN4pFHHsHjnAHtDRs2ZOfOnfkanLiuWlgxerWIoF7ZYOKSMzhwMom45AzqlwvWlPciAJmZ8OmnUKcOPPoo7NwJPj7mZB0iIiIiV5jLg4mOHj1KtWrVspXb7XYyMjLyJSi5PNXCilGlTSBH41JISs8kwNuTciF+ahmTa1tmJnz+Obz6KuzebZaFhsLgwTBggDleTEREROQKczkhq1OnDn/88QeVKlVyKl+4cCHXXXddvgUml8dqtWhqe5FzRUdD796Qng4lSpiLOffvb05lLyIiIuImLidkI0eOpEePHhw9ehS73c7XX3/Nrl27+Pjjj/nxxx8LIkYREddlZMDvv0O7dubj8uXhxRfNBKxvXyViIiIiUihYjEtYNOyPP/5g7NixbNq0icTERBo3bszIkSNpl/XF5xqUkJBAcHAw8fHxBKnrk4j7ZGTAxx/Da6/Bvn2wbt3ZhZxFRERErgBXcoNLWpDq5ptvZsmSJZcUnIhIgUhPNxOx//3PnK4eoFQpOHxYCZmIiIgUWpe8QvA///zDjh07AHNcWZMmTfItKMkfdruhiT3k6peRAbNnmy1iBw+aZWFh8NJL8PTTEBDg3vhERERELsDlhOzIkSM89NBDrFy5kpCQEADi4uJo3rw58+bNo3z58vkdo1yCPcfPsHhrDHtPJJKaacPX04OqpQJpX6+0pr6Xq0tmprlmWFQUlC4NQ4bAU0+Bvya1ERERkcLP5XXInnzySTIyMtixYwexsbHExsayY8cO7HY7Tz75ZEHEKC7ac/wMs1ceYMvReDytFoJ8vfC0WthyNJ7ZKw+w5/gZd4cocunS0syuiTab+djPD8aPh8mTzTFjgwYpGRMREZEiw+UWsuXLl7Nq1Spq1qzpKKtZsybvvPMON998c74GJ66z2w0Wb43hUGwymZl2DpxKItNux9NqpbifF0npmfy6LYYqJQPVfVGKltRU+PBDmDABjhwBb2/o1s3c1qOHe2MTERERuUQuJ2QVKlTIcQFom81G2bJl8yUouXRH41LYcPg0J86kkmkzCPT1xMvDkwybnROJaXhYLaw/dJqjcSlap0yKhtRU+OADMxE7etQsK1cOLPpBQURERIo+l7ssTpo0iQEDBvDPP/84yv755x+effZZ3njjjXwNTlx3JjWDQ6eSyci0ExrgjY+nB1aLBR9PD0IDvMm02Tkcm8yZ1OxJtUihYrPBO+9A1aowYICZjJUvD9Onw9698OCD7o5QRERE5LK53ELWs2dPkpOTadq0KZ6e5u6ZmZl4enry+OOP8/jjjzvqxsbG5l+kkieJaZmkZNgo5uuJ5bwWBIvFgo+XB2dSM0lMy3RThCJ5ZLXCF1/AsWNQoQK8/DL06gU+Pu6OTERERCTfuJyQTZkypQDCkPwS6OuJn7cHaRl2An0Mp6TMMAzSMuz4e3sQ6HvJKx6IFIzkZJg1yxwPVry42SVx/HjYuRN69lQiJiIiIlcll7+V99Dg+UKtmI8XFUP9ORybTHR8Kp4eFjysFjytVtIzbXh6elChuB/FfLzcHaqIKSkJZs6EiRPh+HGIizOnsQdo3dq8iYiIiFyl8pyQJSQk5KleUFDQJQcjl69ciB8VQ/3ZfiyBuOR00mx2MMDDaiE0wJty/t40rlicciF+7g5VrnVJSfDuuzBpEpw4YZZFRED16m4NS0RERORKynNCFhISkm1M0rkMw+weZ8taG0jcYt/JRPaeSCQ+JQMDs8XMYjFIz7QTn5KBt6cHNcOLacp7ca+33jK7I548aT6uXBmGD4fHHgMvtd6KiIjItcOlLosLFy4kNDS0oGKRy2S3G/yyJZqo+FRCA7yx2w1SM+3YDfD18sBuGFiBnVFnuKVmmJIycZ8tW8xkrGpVeOUVePRRJWIiIiJyTXIpIWvRogVhYWEFFYtcpqNxKWw5Go/NblAiwBvDMDiTlonNMPD38sDb00pimo3NR+O0DplcOQkJMG0adO4MdeqYZcOHQ5s28Mgj4KkJZkREROTapW9CV5Gk9EySMjJJz7RzLCWFhNRMMu12LFjw9LBQzNcTH08ryek2ktI17b0UsIQEcx2xt96C2FizVeyLL8xtVauaNxEREZFrnBKyq0iAtyeGHU4mppGWYXeUm2PIDGKTMvDxtFCxhEGAty69FJD4eJg6FSZPhtOnzbKaNaFTJ/fGJSIiIlII5flbucViueCkHuJ+ZYJ8SU7LIPW/ZMxqMZdyMgDDDjbDwJIJ1v/qiuS7qVNh1Chz6nqA2rVhxAh44AHw8HBraCIiIiKFUZ4TMsMw6NmzJz4XWZz166+/vuyg5NIci0/hVHIGWWmz3QDDcK5jADa7QVRCqsaQSf5LSTGTsTp1YORIuO8+JWIiIiIiF5DnhEwLQhd++04mkZJuw8fTQrrNwH5OMmb572YYBgmpmRpDJpcvNhamTIGmTaFjR7OsXz+oUgW6dgWr1a3hiYiIiBQFeU7IZs+eXZBxSD7J6qLo6WHBGzAsYDEsWKxg2A3SbQYJKen4eeXcamG3GxyNSyEpPZMAb0/KhfhpenxxduqUOT5s6lQ4cwYaNYI77zTffIGBcP/97o5QREREpMjQzA5XkcolAwjy9eJ0cgYWw8DDy8PRfdEwDDIMc1yZt6eVnFKsPcfPsHhrDHtPJJKaacPX04OqpQJpX6801cKKXcmnIoXRyZPmjInvvAOJiWZZgwbmFPaGYSZkIiIiIuISJWRXkQrF/alXNpjDp1MwgIxMOx5WC1jAZjOwGRDk60XZEH+SM2xO++45fobZKw8Qm5ROmWBf/L39SE7PZOuxeI7Fp9CrRYSSsmvZzJnw4otnE7GGDc3JO+65R10TRURERC6DvkldRaxWC/ddX56SAd74eFqxWCxk2A0yMu0YQLCvJ00qFadkoI/TtPd2u8HirTHEJqVTPSyQYr5eeFgtFPP1onpYILFJ6fy6LQa73cj95HJ1K1PGTMYaNYJvvoH166FLFyVjIiIiIpdJLWRXmeZVS9K+Xjh//HsCm91OaqYdq8VCWDEfapcNIjYpg2phgZQL8XPsczQuhb0nEikT7JttaQOLxUKZYF/2HE/kaFyKZma8Fhw/Dm+8AeXLw8CBZlmnTvDrr9C2rbomioiIiOQjJWRXGavVwsNNK5KaYedoXDLF/b0J8vXCwwrRCWmEBnjTrm5pp4k6ktIzSc204e/tl+Mx/bw9iElI1cyMV7uYGJg0CWbMgORkKFECnngCAgLMJOz2290doYiIiMhVR/2NrkLVworxeMsIbowogdVi4VRSGvEpmdQvF5zjWLAAb098PT1IziXhMqfS93Dq5ihXkehoGDwYKleGN980k7EbboC5c8FfLaIiIiIiBUnfsK9SVUoGcldDK/tPJv33OIDyxf1znMK+XIgfVUsFsvVYPIE+nk7dFg3DICo+lfrlgp26OcpV4rPP4MknITXVfNy0qTlZxx13qGuiiIiIyBWghOwq5Or09Varhfb1SnMsPoXdx82xZH7eHqSk24iKT82xm6NcJa67DtLSoFkzMxFr106JmIiIiMgVZDEMQ1Pn5YOEhASCg4OJj48nKCjIbXFkTV9/KjGNYr6eeFit2Ox2zqRmUiLQh14tIqhSMjDHxZ/PTeTSMs1uitXCAmlXV+uQXRWOHoXXX4eMDHOcWJbNm6F+fSViIiIiIvnEldxACVk+KQwJmd1uMCNyL3/tO0Wm3c7p5AwybXY8PawU9/fC02qlRulAigd4s+9EUo6tZ3a7kWOyJkXYkSMwYQK8/z6kp4OHB+zfDxUquDsyERERkauSK7mBuixeRY7GpbDh8GmOn0kl02bH28sDHy8P7IbB8YRUbAbsO5lItbBAqpYKzHXxZ01tf5U4dMhMxD780EzEAG6+GUaPNqe0FxERERG3U0J2FTmTlsGh2GRS0mwYGMSlZGI3DKwWC76eFhJTbdgwKBviSzFfLwCK+XoR6OPJ7uOJ/LothiolA9UidjX48Ue4916zeyJA69bmGLE2bdQ1UURERKQQUUJ2FUlMzSQhJYO0TDsAPp5WrBYLdgPOpNlIybThabWQYXPuparFn88q0l02bTazOyJAy5bmlPXXXXc2ERMRERGRQkcJ2VXE39uDDJtBeqadIF9PRyLhYQFvDwuJqQYWDJLSzMStmO/ZKe61+LPrs1MWGvv3w2uvwb//QmSk2QIWEgJbt6prooiIiEghV6gXhh49ejQWi8XpVqtWLcf21NRU+vXrR4kSJQgMDKRr167ExMQ4HePQoUN07NgRf39/wsLCePHFF8nMdE46IiMjady4MT4+PlSrVo05c+ZciaeX75LTbXh5WPDysJCaaSfTbmAYkGk3SMmw/7+9O4+Lqt77AP6ZGWaGYQdZBhAQRVHApTRpMsqSG5p5694Wr3nNpSzXFnetXHsevT5mWi63V4v49Hgz7bqlZhouhRdNSVBcCBW1kiVRGHZmmN/zx4mjI+4hZxg+79drXq+Zc35zznf4OcvH3zm/AwHAUgukn72EPT/9hh9yL+JiuXRuUXO/+HPd7JRZ50vg46ZFa38P+LhpkXW+BCv2nsHJwlKlS6zv9GnpGmLt2gEffwx89x1w4MDl9QxjRERERA7PoQMZAMTGxiIvL0++paamyuveeOMNfPXVV1i7di327NmD8+fP469//au8vra2Fn379kVNTQ3+85//YOXKlUhOTsb06dPlNrm5uejbty8eeeQRZGRk4PXXX8dLL72Eb775plFfZ0Pw0LvAy1ULT1cXuOs0sNYKVFlqUWWxoVZIhzEKSOHrUkUNfiooxb7TRSgqq0ZeSRWiAj3u+OLPNpvAzxcrcCLfjJ8vVsBmazqTd9psAt9kFeBieQ3aBnrA01ULjVoFT1ct2gZ64GJ5DbYfLXCc13TqFDBsmBTEPvkEsFqBP/0JSE0FundXujoiIiIiug0OPxzi4uICo9FYb3lJSQk++eQT/Otf/8Kjjz4KAFixYgU6dOiAffv24f7778f27dtx7NgxfPvttwgKCkKXLl0wZ84cTJ48GTNnzoROp8M///lPREZG4t133wUAdOjQAampqXjvvfeQlJTUqK/1j/J01SK8hRt+vliOSosN3gYXqFUqXCirhrUW0KgBFaSbRq2CtVYgr6QK35+8gB5tWtzxxZ+b7KF+v/u1uBKnfpMuiK26asILhzu/bv9+oEcP6XwxAEhKks4RM5mUrYuIiIiI7ojDj5Dl5OQgJCQErVu3xsCBA3Hu3DkAQHp6OiwWCxITE+W27du3R3h4ONLS0gAAaWlp6NixI4KCguQ2SUlJMJvNOHr0qNzmym3UtanbxvVUV1fDbDbb3ZQW6mNAuK8bqiw2lFZakV9ShV+LK2CuskKlEjBoNWjhoYeXQQshABUEhBCorLGid5zxjsJTkzzU7yrlNVZUWWvhdp3DNQ06DaqttcqdX1dRcfl+t27SyFifPkBaGrBtG8MYERERURPm0IEsPj4eycnJ2LZtG5YvX47c3FwkJCSgtLQU+fn50Ol08PHxsXtOUFAQ8vPzAQD5+fl2Yaxufd26G7Uxm82orKy8bm1z586Ft7e3fAtzgIvsnr5QhlMXynGp0oJKqxUCgBCXb1qNGoGeeoT4GBDma0BLPzeE+brBRa3GnRyN1+QO9bsOd50LXF00qLhO4FLs/LrsbGDQICA6Gqj7t6jRAPv2AVu3Avff37j1EBEREVGDc+hDFvv06SPf79SpE+Lj4xEREYE1a9bAYLizc50aytSpUzFu3Dj5sdlsVjSU2WwC/9p/Dtn5ZsAmAKGC1WaT7gKotUmH37lqNVCpVNBrpenRK1VWqKrvbJ9N6lC/Gwj1MaBNgAeyzpfAQ+9i91qEkA7r7Bjqfcfn1922EyeAd94BPv8csEnn/uGbb4CnnpLu3+Rq70RERETUdDj0CNnVfHx80K5dO5w8eRJGoxE1NTUoLi62a1NQUCCfc2Y0GuvNulj3+GZtvLy8bhj69Ho9vLy87G5K+uVSBfb89BvKqq3QqFXwNmjh666Hp6sL1JBCWVm1BdWWWvk5QgiUVFjgY9Citb/7be/T4Q/1u0VqtQpJcUHwc9chp7AMpVUWWG02lFZZkFNYBj933R2fX3dbjh8Hnn8eiIkBVq2Swtif/wwcPHg5jBERERGRU2lSgaysrAynTp1CcHAwunbtCq1Wi5SUFHl9dnY2zp07B9Pv59SYTCYcOXIEhYWFcpsdO3bAy8sLMTExcpsrt1HXxtTEzss59VsZCs1VUAMw6Fzg8vv09246Fxh00mhYjVWgtNoKmxCottaiqKwaNgHEt26Blr63P4LlsIf63YGoQE8M7dEKcSHeKK6w4MyFchRXWNAx1BtDe7S6+5OTnD0LxMVJo2JCSAHsxx+BjRuBrl3v7r6JiIiISDEO/Ut5woQJ6NevHyIiInD+/HnMmDEDGo0GAwYMgLe3N1588UWMGzcOfn5+8PLywtixY2EymXD/7+fWPPbYY4iJicGgQYMwf/585Ofn46233sLo0aOh1+sBACNGjMCSJUswadIkDBs2DDt37sSaNWuwZcsWJV/6bbtQVgOrTcBNp4FKJf2mr7UJCAi4ajWosdbCYgPM1VZIR+SpoFGr0TnEE8/Hh9/R6I/DHer3B0UFeqJ1Tw/8WlyJ8hor3HUuCPUx3L2RsYICoO78xYgIoF8/6Ryxt98GunS5O/skIiIiIofi0IHsl19+wYABA1BUVISAgAA8+OCD2LdvHwICAgAA7733HtRqNZ5++mlUV1cjKSkJy5Ytk5+v0WiwefNmjBw5EiaTCe7u7hg8eDBmz54tt4mMjMSWLVvwxhtvYPHixWjZsiU+/vjjJjflfQsPHVzUKlhsAmqrDVWWWvnC0IAAVCpo1QJhPgZ4uGrhptOgU6jPH5qavu5Qv/MllcgplM4lM+g0qKypRV5JVeMd6teA1GrV3T/f7fBhYPZsYPNmICcHqDv3cO1aQKu9u/smIiIiIoeiEkI49hR4TYTZbIa3tzdKSkoUOZ/sbFE5hq04gDxzFay1NvlaY1ABtbUCVgG4aTWY+ecYxIZ6N+joz5XXIau2SocpRgV64LHYpnEdskaTmSkFsXXrLi/76CPgpZeUq4mIiIiIGtztZAOHHiGjWxfm64aH2vpjbfov0jXG1L9PtigAlVoFPYAW7jrkl1TjL/d4NuioVaMf6tfUHDokBbENG6THKhXw7LPSoYlxcYqWRkRERETKYiBzEmq1Cr1igrD9eAHKqiwQuByGNGrA38MVHUO9cLKwFAfPXoSXQdugwalRDvVrikpLgYceAsrKpCDWvz/w1ltAbKzSlRERERGRA2AgcyL+nnq0CfBAtbUWF0prYLHZoFWrEeSlR5tAD9TaBA6evYQP95yGXquGq4sGbQI8/tB5ZHQNJ04A7dtL9z09gbFjgTNnpCD2++yeREREREQAA5lTcde5wN9DD2+DFggGampt0GnU8HR1waWKGqSfvYSyKiv83LUI8jKgosaKrPMlOF9S2ThTuzu7AweAWbOALVuAPXukkTEA+K//AlQ8fJOIiIiI6mtS1yGjG6ubhj7fXAVPVymceRmkWftOFpShuNKCiBZuCPY2QKNWwdNVi7aBHrhYXoPtRwtgs3F+lzvyww9A375A9+5SGFOrpWV1GMaIiIiI6DoYyJxI3TT0fu465BSWobTKAqvNhrySKpy9WAEfNx2iAj3trhemUqkQ7O2Kk4Vl+LW4UsHqm6B9+4A+fYD4eGDrVukaYoMHS4csTpigdHVERERE1ATwkEUnExXoiaE9WsnT0BeYq1BlqYWnqwu6hvvCz11X7zkGnQYF5iqU11gVqLiJqq0FBg4ETp+WgtgLLwDTpgFRUUpXRkRERERNCAOZE7p6GnpzpQWf7z8HV+21B0Qra6Rrh7nr+M/hhtLSgK5dAZ1OCmEzZkjnik2bBrRpo3R1RERERNQE8ZBFJ1U3DX17oxe6RfghKtATeSVVuPo64EII5JVUISrQA6E+BoWqdXDffw8kJgIPPACsXHl5+QsvAJ98wjBGRERERHeMgawZuN65ZaVVFuQUlsHPXYfHYoN4Ieer7dkDPPqoNFtiSgrg4gL8+qvSVRERERGRE+Exas3Etc4t07to0DHUG4/F8jpkdnbvlqav371beqzVAsOGAVOnAhERSlZGRERERE6GgawZufrcMnedC0J9DBwZu9rcuVIY02qBl14CpkwBwsOVroqIiIiInBADWTNTd24Z/U4IYOdOIC4OCAqSls2cKc2WOGUKEBamaHlERERE5Nx4Dhk1T0IAO3YACQnShB3z519eZzIBS5cyjBERERHRXccRMmpehAC2b5fOEUtLk5bp9dLhiUREREREjYyBjJqPHTuAt98G9u+XHru6Ai+/DEyeDISEKFsbERERETVLDGTUfHz1lRTGXF2BESOASZOA4GClqyIiIiKiZoyBjJyTEMDWrUDLlkDnztKyyZMBnQ6YMAEwGpWtj4iIiIgInNSDnI0Q0khY9+7AE08A06ZdXhcaCixYwDBGRERERA6DI2TkHOqC2KxZwI8/Ssvc3YGOHQGbDVDz/x6IiIiIyPEwkFHTt327dM2wQ4ekx+7uwJgxwPjxQECAsrUREREREd0AAxk1fSdPSmHMwwMYOxYYNw7w91e6KiIiIiKim2Igo6bFZgPWrQMMBqBvX2nZiy8Cly5JMye2aKFsfUREREREt4En1lDTYLMBa9ZIMyY++6w0Cma1Suv0euDNNxnGiIiIiKjJ4QgZObbaWuDLL4HZs4Fjx6RlXl5A//6AxQK48J8wERERETVd/DVLjislRTon7Phx6bG3N/D668BrrwG+voqWRkRERETUEBjIyLEdPw74+ABvvAG8+qp0n4iIiIjISTCQkWOwWoHVq4GSEmD0aGnZo48CH38MPPOMNDpGRERERORkGMhIWVYr8PnnwJw5QE4O4OkJDBgA+PkBKpU0gyIRERERkZNiICNlWK3AqlXAO+9I1xEDpFkSx48HdDplayMiIiIiaiQMZNT49u4FBg8GTp2SHvv7AxMmAKNGSSNkRERERETNBAMZNb7QUODsWSAgAJg4ERg5EvDwULoqIiIiIqJGx0BGd1dNDbByJXD0KLBokbSsVStg82bgwQcBd3clqyMiIiIiUhQDGd0dNTXAihXA3LnSaBgADBsGdOok3U9KUq42IiIiIiIHwUBGDau6Wgpi//3fwM8/S8uMRmDSJCAqStnaiIiIiIgcDAMZNZzMTOCJJ4BffpEeBwcDkycDL78MGAzK1kZERERE5IAYyKjhtG0rHaoYEgJMmQK89BKDGBERERHRDTCQ0Z2pqgI++gjYuhXYsgVQqwE3N2DbNqBDB8DVVekKiYiIiIgcHgMZ3Z7KSimI/eMfwPnz0rJNm4CnnpLu33OPYqURERERETU1DGR0ayorgQ8/lIJYfr60LCwMmDoV6NNH2dqIiIiIiJooBjK6uXPngPj4y0EsPByYNg0YMgTQ6xUtjYiIiIioKWMgo2sTAlCppPthYUDLllL4qgtiOp2i5REREREROQMGMrJXXg4sWwZ8+imwfz/g5SUFsy+/lKaxZxAjIiIiImowaqULIAdRViadH9aqlXQR5xMnpFBWJyKCYYyIiIiIqIFxhKy5Ky0FliwB3n0XKCqSlrVpA7z1FjBwoLK1ERERERE5OQay5qy0FIiKAgoLpcdRUcDbbwPPPw+48J8GEREREdHdxkMWm5vq6sv3PT2BP/0JaNcO+N//BY4fB154gWGMiIiIiKiRMJBdZenSpWjVqhVcXV0RHx+PH374QemSGkZJCTBnjjRb4k8/XV6+ZAlw7BgwaBCDGBERERFRI2Mgu8IXX3yBcePGYcaMGfjxxx/RuXNnJCUlobDukL6mqLgYmDVLmqxj+nTgwgXg448vr/fxATQahYojIiIiImreVEIIoXQRjiI+Ph733XcflixZAgCw2WwICwvD2LFjMWXKlBs+12w2w9vbGyUlJfDy8mqMcm/s0iVg8WJg0SJpdAwAYmKkc8SefZYhjIiIiIjoLrmdbMBj1H5XU1OD9PR0TJ06VV6mVquRmJiItLS0eu2rq6tRfcX5WGazuVHqvCW1tUDXrkBurvQ4NlYaHXvmGUDNQVEiIiIiIkfBX+e/u3DhAmpraxEUFGS3PCgoCPn5+fXaz507F97e3vItLCyssUq9OY0GGDYMiIsD1qwBDh8GnnuOYYyIiIiIyMHwF/odmjp1KkpKSuTbzz//rHRJ9iZOBDIzpcMTGcSIiIiIiBwSD1n8nb+/PzQaDQoKCuyWFxQUwGg01muv1+uh1+sbq7zb58i1ERERERERAI6QyXQ6Hbp27YqUlBR5mc1mQ0pKCkwmk4KVERERERGRs+II2RXGjRuHwYMHo1u3bujevTsWLVqE8vJyDB06VOnSiIiIiIjICTGQXaF///747bffMH36dOTn56NLly7Ytm1bvYk+iIiIiIiIGgKvQ9ZAHO46ZEREREREpIjbyQY8h4yIiIiIiEghDGREREREREQKYSAjIiIiIiJSCAMZERERERGRQhjIiIiIiIiIFMJARkREREREpBAGMiIiIiIiIoUwkBERERERESmEgYyIiIiIiEghDGREREREREQKcVG6AGchhAAAmM1mhSshIiIiIiIl1WWCuoxwIwxkDaS0tBQAEBYWpnAlRERERETkCEpLS+Ht7X3DNipxK7GNbspms+H8+fPw9PSESqVStBaz2YywsDD8/PPP8PLyUrQWajjsV+fDPnVO7Ffnwz51TuxX5+NIfSqEQGlpKUJCQqBW3/gsMY6QNRC1Wo2WLVsqXYYdLy8vxf8xUsNjvzof9qlzYr86H/apc2K/Oh9H6dObjYzV4aQeRERERERECmEgIyIiIiIiUggDmRPS6/WYMWMG9Hq90qVQA2K/Oh/2qXNivzof9qlzYr86n6bap5zUg4iIiIiISCEcISMiIiIiIlIIAxkREREREZFCGMiIiIiIiIgUwkBGRERERESkEAYyJ7R06VK0atUKrq6uiI+Pxw8//KB0SQRg5syZUKlUdrf27dvL66uqqjB69Gi0aNECHh4eePrpp1FQUGC3jXPnzqFv375wc3NDYGAgJk6cCKvVatdm9+7duPfee6HX6xEVFYXk5OTGeHnNxnfffYd+/fohJCQEKpUKGzZssFsvhMD06dMRHBwMg8GAxMRE5OTk2LW5ePEiBg4cCC8vL/j4+ODFF19EWVmZXZvDhw8jISEBrq6uCAsLw/z58+vVsnbtWrRv3x6urq7o2LEjtm7d2uCvtzm4WZ8OGTKk3nu3d+/edm3Yp45l7ty5uO++++Dp6YnAwEA89dRTyM7OtmvTmJ+5/F5uGLfSrz179qz3fh0xYoRdG/ar41i+fDk6deokX8jZZDLh66+/ltc3m/epIKeyevVqodPpxKeffiqOHj0qhg8fLnx8fERBQYHSpTV7M2bMELGxsSIvL0++/fbbb/L6ESNGiLCwMJGSkiIOHjwo7r//fvHAAw/I661Wq4iLixOJiYni0KFDYuvWrcLf319MnTpVbnP69Gnh5uYmxo0bJ44dOyY++OADodFoxLZt2xr1tTqzrVu3ijfffFOsW7dOABDr16+3Wz9v3jzh7e0tNmzYIDIzM8Wf//xnERkZKSorK+U2vXv3Fp07dxb79u0T33//vYiKihIDBgyQ15eUlIigoCAxcOBAkZWVJT7//HNhMBjEhx9+KLfZu3ev0Gg0Yv78+eLYsWPirbfeElqtVhw5cuSu/w2czc36dPDgwaJ37952792LFy/atWGfOpakpCSxYsUKkZWVJTIyMsTjjz8uwsPDRVlZmdymsT5z+b3ccG6lXx9++GExfPhwu/drSUmJvJ796lg2bdoktmzZIn766SeRnZ0tpk2bJrRarcjKyhJCNJ/3KQOZk+nevbsYPXq0/Li2tlaEhISIuXPnKlgVCSEFss6dO19zXXFxsdBqtWLt2rXysuPHjwsAIi0tTQgh/WhUq9UiPz9fbrN8+XLh5eUlqqurhRBCTJo0ScTGxtptu3///iIpKamBXw0JIer9eLfZbMJoNIr/+Z//kZcVFxcLvV4vPv/8cyGEEMeOHRMAxIEDB+Q2X3/9tVCpVOLXX38VQgixbNky4evrK/erEEJMnjxZREdHy4+fe+450bdvX7t64uPjxSuvvNKgr7G5uV4ge/LJJ6/7HPap4yssLBQAxJ49e4QQjfuZy+/lu+fqfhVCCmSvvfbadZ/DfnV8vr6+4uOPP25W71MesuhEampqkJ6ejsTERHmZWq1GYmIi0tLSFKyM6uTk5CAkJAStW7fGwIEDce7cOQBAeno6LBaLXd+1b98e4eHhct+lpaWhY8eOCAoKktskJSXBbDbj6NGjcpsrt1HXhv3fOHJzc5Gfn2/XB97e3oiPj7frRx8fH3Tr1k1uk5iYCLVajf3798ttHnroIeh0OrlNUlISsrOzcenSJbkN+7rx7N69G4GBgYiOjsbIkSNRVFQkr2OfOr6SkhIAgJ+fH4DG+8zl9/LddXW/1lm1ahX8/f0RFxeHqVOnoqKiQl7HfnVctbW1WL16NcrLy2EymZrV+9SlUfZCjeLChQuora21+0cJAEFBQThx4oRCVVGd+Ph4JCcnIzo6Gnl5eZg1axYSEhKQlZWF/Px86HQ6+Pj42D0nKCgI+fn5AID8/Pxr9m3duhu1MZvNqKyshMFguEuvjoDL/XCtPriyjwIDA+3Wu7i4wM/Pz65NZGRkvW3UrfP19b1uX9dtgxpO79698de//hWRkZE4deoUpk2bhj59+iAtLQ0ajYZ96uBsNhtef/119OjRA3FxcQDQaJ+5ly5d4vfyXXKtfgWA559/HhEREQgJCcHhw4cxefJkZGdnY926dQDYr47oyJEjMJlMqKqqgoeHB9avX4+YmBhkZGQ0m/cpAxlRI+nTp498v1OnToiPj0dERATWrFnDoETkwP72t7/J9zt27IhOnTqhTZs22L17N3r16qVgZXQrRo8ejaysLKSmpipdCjWg6/Xryy+/LN/v2LEjgoOD0atXL5w6dQpt2rRp7DLpFkRHRyMjIwMlJSX48ssvMXjwYOzZs0fpshoVD1l0Iv7+/tBoNPVmnykoKIDRaFSoKroeHx8ftGvXDidPnoTRaERNTQ2Ki4vt2lzZd0aj8Zp9W7fuRm28vLwY+hpBXT/c6D1oNBpRWFhot95qteLixYsN0td8r999rVu3hr+/P06ePAmAferIxowZg82bN2PXrl1o2bKlvLyxPnP5vXx3XK9fryU+Ph4A7N6v7FfHotPpEBUVha5du2Lu3Lno3LkzFi9e3KzepwxkTkSn06Fr165ISUmRl9lsNqSkpMBkMilYGV1LWVkZTp06heDgYHTt2hVardau77Kzs3Hu3Dm570wmE44cOWL3w2/Hjh3w8vJCTEyM3ObKbdS1Yf83jsjISBiNRrs+MJvN2L9/v10/FhcXIz09XW6zc+dO2Gw2+YeDyWTCd999B4vFIrfZsWMHoqOj4evrK7dhXyvjl19+QVFREYKDgwGwTx2REAJjxozB+vXrsXPnznqHizbWZy6/lxvWzfr1WjIyMgDA7v3KfnVsNpsN1dXVzet92ihTh1CjWb16tdDr9SI5OVkcO3ZMvPzyy8LHx8du9hlSxvjx48Xu3btFbm6u2Lt3r0hMTBT+/v6isLBQCCFN7RoeHi527twpDh48KEwmkzCZTPLz66Z2feyxx0RGRobYtm2bCAgIuObUrhMnThTHjx8XS5cu5bT3Day0tFQcOnRIHDp0SAAQCxcuFIcOHRJnz54VQkjT3vv4+IiNGzeKw4cPiyeffPKa097fc889Yv/+/SI1NVW0bdvWbor04uJiERQUJAYNGiSysrLE6tWrhZubW70p0l1cXMSCBQvE8ePHxYwZMzhF+h26UZ+WlpaKCRMmiLS0NJGbmyu+/fZbce+994q2bduKqqoqeRvsU8cycuRI4e3tLXbv3m03/XlFRYXcprE+c/m93HBu1q8nT54Us2fPFgcPHhS5ubli48aNonXr1uKhhx6St8F+dSxTpkwRe/bsEbm5ueLw4cNiypQpQqVSie3btwshms/7lIHMCX3wwQciPDxc6HQ60b17d7Fv3z6lSyIhTbEaHBwsdDqdCA0NFf379xcnT56U11dWVopRo0YJX19f4ebmJv7yl7+IvLw8u22cOXNG9OnTRxgMBuHv7y/Gjx8vLBaLXZtdu3aJLl26CJ1OJ1q3bi1WrFjRGC+v2di1a5cAUO82ePBgIYQ09f3bb78tgoKChF6vF7169RLZ2dl22ygqKhIDBgwQHh4ewsvLSwwdOlSUlpbatcnMzBQPPvig0Ov1IjQ0VMybN69eLWvWrBHt2rUTOp1OxMbGii1btty11+3MbtSnFRUV4rHHHhMBAQFCq9WKiIgIMXz48Hpf0uxTx3Kt/gRg93nYmJ+5/F5uGDfr13PnzomHHnpI+Pn5Cb1eL6KiosTEiRPtrkMmBPvVkQwbNkxEREQInU4nAgICRK9eveQwJkTzeZ+qhBCiccbiiIiIiIiI6Eo8h4yIiIiIiEghDGREREREREQKYSAjIiIiIiJSCAMZERERERGRQhjIiIiIiIiIFMJARkREREREpBAGMiIiIiIiIoUwkBERERERESmEgYyIiIiIiEghDGRERHTLhgwZApVKBZVKBZ1Oh6ioKMyePRtWq1Xp0oiIiJokF6ULICKipqV3795YsWIFqqursXXrVowePRparRZTp05VujQiIqImhyNkRER0W/R6PYxGIyIiIjBy5EgkJiZi06ZN8vrU1FQkJCTAYDAgLCwMr776KsrLy+X1n332Gbp16wZPT08YjUY8//zzKCwsrLefVq1ayaNxdbcNGzYAAHbv3g2VSoXi4uJr1njmzBmoVCpkZGTU2+aiRYvkxwsXLkTHjh3h7u6OsLAwjBo1CmVlZXbPWbFiBaKjo6HT6eQ6Xn/99ev+fa4cRbzy5uPjc80ar75d+ZqWL1+ONm3aQKfTITo6Gp999tkN/z51t+TkZACw+5vV6dmzp139ly5dwgsvvABfX1+4ubmhT58+yMnJsXvO3r170bNnT7i5ucHX1xdJSUm4dOnSdV+rSqXCkCFDrrk/IiKyx0BGRER/iMFgQE1NDQDg1KlT6N27N55++mkcPnwYX3zxBVJTUzFmzBi5vcViwZw5c5CZmYkNGzbgzJkz8o/3KwkhMHv2bOTl5SEvL++u1K5Wq/H+++/j6NGjWLlyJXbu3IlJkybJ60+cOIGXXnoJw4YNw8mTJ5GXlweTyXTT7fbu3VuuOy8vzy4E1hFCAAC+/fZb5OXl4d///rfd+vXr1+O1117D+PHjkZWVhVdeeQVDhw7Frl27AAAHDhyQt9+yZUssWrRIfty/f/9b/hsMGTIEBw8exKZNm5CWlgYhBB5//HFYLBYAQEZGBnr16oWYmBikpaUhNTUV/fr1Q21tLRYvXizv87nnnsNzzz0nP168ePEt10BE1JzxkEUiIrojQgikpKTgm2++wdixYwEAc+fOxcCBA+URkbZt2+L999/Hww8/jOXLl8PV1RXDhg2Tt9G6dWu8//77uO+++1BWVgYPDw95ncVigZ+fH4xG4117DVeO3LRq1QrvvPMORowYgWXLlgEADh8+DI1Gg8mTJ8vtdDrdTbdbN4pYx9vbu16busBjNBphNBrh5+dnt37BggUYMmQIRo0aBQAYN24c9u3bhwULFuCRRx5BQECA3Faj0cDb2/u2/1Y5OTnYtGkT9u7diwceeAAAsGrVKoSFhWHDhg149tlnMX/+fHTr1k3+mwBAbGxsvddmMBjk10NERLeOI2RERHRbNm/eDA8PD7i6uqJPnz7o378/Zs6cCQDIzMxEcnIyPDw85FtSUhJsNhtyc3MBAOnp6ejXrx/Cw8Ph6emJhx9+GABw7tw5u/2YzWa4u7vfsJaWLVvC09MTkZGRGD58OEpKSuzWP/DAA3a1XL2Pb7/9Fr169UJoaCg8PT0xaNAgFBUVoaKiAgAQGRkJi8WCtWvXyiNaDcVsNgPAdV/j8ePH0aNHD7tlPXr0wPHjx29rPwMGDLD7G3z//fd2+3BxcUF8fLy8rEWLFoiOjpb3UzdC9kcsW7YMHh4eaNGiBeLj4/HVV1/9oe0RETkTBjIiIrotjzzyCDIyMpCTk4PKykqsXLlSDhVlZWV45ZVXkJGRId8yMzORk5ODNm3aoLy8HElJSfDy8sKqVatw4MABrF+/HgDkwx4BKayUl5cjJCTkhrV8//33OHToED766CPs2LEDb775pt36L774wq6WK7d35swZPPHEE+jUqRP+/e9/Iz09HUuXLrWr5b777sPs2bMxdOhQuLq61gs0f8T58+ehVqvv+ojSe++9Z/c36Nat2209v27k648YOHAgMjIy8N133yEhIQHPPPMMfv311z+8XSIiZ8BARkREt8Xd3R1RUVEIDw+Hi4v9ke/33nsvjh07hqioqHo3nU6HEydOoKioCPPmzUNCQgLat29/zQk9Dhw4AJVKhS5dutywlsjISERFRSExMRHPPvtsvUk8wsLC7Gq4st709HTYbDa8++67uP/++9GuXTucP3++3j5effVVBAcHY9asWXcUaK7nwIEDaN++PVxdXa+5vkOHDti7d6/dsr179yImJua29mM0Gu3+BlcGrA4dOsBqtWL//v3ysqKiImRnZ8v76dSpE1JSUm5rn1fz9vZGVFQUYmNjMWvWLNTU1Nz2SB8RkbNiICMiogYzefJk/Oc//8GYMWPkUbSNGzfKk3qEh4dDp9Phgw8+wOnTp7Fp0ybMmTPHbhu7du3C6NGj8fjjjyMwMPCG+6uurkZVVRVOnDiBr7/+GnFxcbdca1RUFCwWi1zLZ599hn/+8592bYQQeOGFF3DvvfdiypQp9QLNnaipqcFnn32GhQsXYujQoddtN3HiRCQnJ2P58uXIycnBwoULsW7dOkyYMOEP7f9Kbdu2xZNPPonhw4cjNTUVmZmZ+Pvf/47Q0FA8+eSTAICpU6fiwIEDGDVqFA4fPowTJ05g+fLluHDhwi3vp7a2FlVVVSgpKcGHH34IrVaL6OjoBnsdRERNGQMZERE1mE6dOmHPnj346aefkJCQgHvuuQfTp0+XDxUMCAhAcnIy1q5di5iYGMybNw8LFiyw28awYcOQkJCA//u//7vp/oxGIwwGAxISEtC5c2fMnTv3lmvt3LkzFi5ciH/84x+Ii4vDqlWr6j1/3rx5yMnJwSeffHLL272ZI0eOYObMmXj77bcxbty467Z76qmnsHjxYixYsACxsbH48MMPsWLFCvTs2bPBagGkaf27du2KJ554AiaTCUIIbN26FVqtFgDQrl07bN++HZmZmejevTtMJhM2btxYb3T0RpYsWQKDwYDAwEB8+umn8sQhREQEqERDn6VMREREREREt4QjZERERERERAphICMiIiIiIlIIAxkREREREZFCGMiIiIiIiIgUwkBGRERERESkEAYyIiIiIiIihTCQERERERERKYSBjIiIiIiISCEMZERERERERAphICMiIiIiIlIIAxkREREREZFC/h+p9/j7nPAv2QAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Средняя абсолютная ошибка: 9334.620756498913\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Визуализация\n",
+    "plt.figure(figsize=(10, 5))\n",
+    "plt.scatter(df['Density  (P/Km²)'], df['predicted_density'], alpha=0.5, label='Страны')\n",
+    "plt.plot([0, 30000], [0, 30000], 'r--', label='Идеальное предсказание')\n",
+    "plt.xlabel('Реальная плотность')\n",
+    "plt.ylabel('Предсказанная плотность')\n",
+    "plt.title('Сравнение реальной и предсказанной плотности населения')\n",
+    "plt.legend()\n",
+    "plt.show()\n",
     "\n",
-    "# Применение системы ко всему датасету\n",
-    "def predict_density(row):\n",
-    "    try:\n",
-    "        # Передача входных значений в симуляцию\n",
-    "        density_sim.input['population'] = row['Population 2020']\n",
-    "        density_sim.input['yearly_change'] = row['Yearly Change']\n",
-    "        \n",
-    "        # Вычисление результата\n",
-    "        density_sim.compute()\n",
-    "        \n",
-    "        # Возврат предсказанного значения плотности\n",
-    "        return density_sim.output['density']\n",
-    "    except:\n",
-    "        # Если система не смогла вычислить результат, возвращаем NaN\n",
-    "        return np.nan\n",
-    "\n",
-    "# Применение функции к каждой строке датасета\n",
-    "df['predicted_density'] = df.apply(predict_density, axis=1)\n",
-    "\n",
-    "# Вывод первых строк с предсказанными значениями\n",
-    "print(df[['Country (or dependency)', 'Population 2020', 'Yearly Change', 'Density  (P/Km²)', 'predicted_density']].head())"
+    "# Вывод результатов\n",
+    "print(\"Средняя абсолютная ошибка:\", np.nanmean(np.abs(df['Density  (P/Km²)'] - df['predicted_density'])))\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "В целом, предсказанные значения для большинства стран находятся в разумных пределах.  \n",
+    "Однако видна систематическая ошибка: многие предсказания завышены по сравнению с реальными значениями. Это может указывать на необходимость уточнения функций принадлежности или расширения базы правил.  \n",
+    "Для стран с высокой плотностью населения (более 10 000 человек на км²) разброс результатов увеличивается."
    ]
   }
  ],
-- 
2.25.1