PCA - Assignment 8.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "40c0436f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from sklearn.decomposition import PCA\n",
    "from sklearn.preprocessing import scale"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "9a6a46ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type  Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0       1    14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1       1    13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2       1    13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3       1    14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4       1    13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..    ...      ...    ...   ...         ...        ...      ...         ...   \n",
       "173     3    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174     3    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175     3    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176     3    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177     3    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \n",
       "0             0.28             2.29   5.64  1.04      3.92     1065  \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050  \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185  \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480  \n",
       "4             0.39             1.82   4.32  1.04      2.93      735  \n",
       "..             ...              ...    ...   ...       ...      ...  \n",
       "173           0.52             1.06   7.70  0.64      1.74      740  \n",
       "174           0.43             1.41   7.30  0.70      1.56      750  \n",
       "175           0.43             1.35  10.20  0.59      1.56      835  \n",
       "176           0.53             1.46   9.30  0.60      1.62      840  \n",
       "177           0.56             1.35   9.20  0.61      1.60      560  \n",
       "\n",
       "[178 rows x 14 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine=pd.read_csv(\"C:/Users/mewit/Desktop/Data Science Notes/Assignmnets/8 - PCA/wine.csv\")\n",
    "wine"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c7aeb55",
   "metadata": {},
   "source": [
    "# EDA Process"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "508b6509",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 178 entries, 0 to 177\n",
      "Data columns (total 14 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Type             178 non-null    int64  \n",
      " 1   Alcohol          178 non-null    float64\n",
      " 2   Malic            178 non-null    float64\n",
      " 3   Ash              178 non-null    float64\n",
      " 4   Alcalinity       178 non-null    float64\n",
      " 5   Magnesium        178 non-null    int64  \n",
      " 6   Phenols          178 non-null    float64\n",
      " 7   Flavanoids       178 non-null    float64\n",
      " 8   Nonflavanoids    178 non-null    float64\n",
      " 9   Proanthocyanins  178 non-null    float64\n",
      " 10  Color            178 non-null    float64\n",
      " 11  Hue              178 non-null    float64\n",
      " 12  Dilution         178 non-null    float64\n",
      " 13  Proline          178 non-null    int64  \n",
      "dtypes: float64(11), int64(3)\n",
      "memory usage: 19.6 KB\n"
     ]
    }
   ],
   "source": [
    "# Checking the data types\n",
    "wine.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c5f1250b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Type               0\n",
       "Alcohol            0\n",
       "Malic              0\n",
       "Ash                0\n",
       "Alcalinity         0\n",
       "Magnesium          0\n",
       "Phenols            0\n",
       "Flavanoids         0\n",
       "Nonflavanoids      0\n",
       "Proanthocyanins    0\n",
       "Color              0\n",
       "Hue                0\n",
       "Dilution           0\n",
       "Proline            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking the null values\n",
    "wine.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "ce8a92e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicate values\n",
    "wine.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "be238761",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    71\n",
       "1    59\n",
       "3    48\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine['Type'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d3c78bae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 13 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0      14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1      13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2      13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3      14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4      13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..       ...    ...   ...         ...        ...      ...         ...   \n",
       "173    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \n",
       "0             0.28             2.29   5.64  1.04      3.92     1065  \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050  \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185  \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480  \n",
       "4             0.39             1.82   4.32  1.04      2.93      735  \n",
       "..             ...              ...    ...   ...       ...      ...  \n",
       "173           0.52             1.06   7.70  0.64      1.74      740  \n",
       "174           0.43             1.41   7.30  0.70      1.56      750  \n",
       "175           0.43             1.35  10.20  0.59      1.56      835  \n",
       "176           0.53             1.46   9.30  0.60      1.62      840  \n",
       "177           0.56             1.35   9.20  0.61      1.60      560  \n",
       "\n",
       "[178 rows x 13 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Dropping thw Type Column\n",
    "wine2=wine.drop(['Type'],axis=1)\n",
    "wine2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "af403f1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.423e+01, 1.710e+00, 2.430e+00, ..., 1.040e+00, 3.920e+00,\n",
       "        1.065e+03],\n",
       "       [1.320e+01, 1.780e+00, 2.140e+00, ..., 1.050e+00, 3.400e+00,\n",
       "        1.050e+03],\n",
       "       [1.316e+01, 2.360e+00, 2.670e+00, ..., 1.030e+00, 3.170e+00,\n",
       "        1.185e+03],\n",
       "       ...,\n",
       "       [1.327e+01, 4.280e+00, 2.260e+00, ..., 5.900e-01, 1.560e+00,\n",
       "        8.350e+02],\n",
       "       [1.317e+01, 2.590e+00, 2.370e+00, ..., 6.000e-01, 1.620e+00,\n",
       "        8.400e+02],\n",
       "       [1.413e+01, 4.100e+00, 2.740e+00, ..., 6.100e-01, 1.600e+00,\n",
       "        5.600e+02]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converting data to numpy array\n",
    "wine_ary=wine2.values\n",
    "wine_ary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5d8f57f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.51861254, -0.5622498 ,  0.23205254, ...,  0.36217728,\n",
       "         1.84791957,  1.01300893],\n",
       "       [ 0.24628963, -0.49941338, -0.82799632, ...,  0.40605066,\n",
       "         1.1134493 ,  0.96524152],\n",
       "       [ 0.19687903,  0.02123125,  1.10933436, ...,  0.31830389,\n",
       "         0.78858745,  1.39514818],\n",
       "       ...,\n",
       "       [ 0.33275817,  1.74474449, -0.38935541, ..., -1.61212515,\n",
       "        -1.48544548,  0.28057537],\n",
       "       [ 0.20923168,  0.22769377,  0.01273209, ..., -1.56825176,\n",
       "        -1.40069891,  0.29649784],\n",
       "       [ 1.39508604,  1.58316512,  1.36520822, ..., -1.52437837,\n",
       "        -1.42894777, -0.59516041]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Normalizing the numerical data \n",
    "wine_norm=scale(wine_ary)\n",
    "wine_norm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "176a74d0",
   "metadata": {},
   "source": [
    "# PCA Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "41968d09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3.31675081e+00, -1.44346263e+00, -1.65739045e-01, ...,\n",
       "        -4.51563395e-01,  5.40810414e-01, -6.62386309e-02],\n",
       "       [ 2.20946492e+00,  3.33392887e-01, -2.02645737e+00, ...,\n",
       "        -1.42657306e-01,  3.88237741e-01,  3.63650247e-03],\n",
       "       [ 2.51674015e+00, -1.03115130e+00,  9.82818670e-01, ...,\n",
       "        -2.86672847e-01,  5.83573183e-04,  2.17165104e-02],\n",
       "       ...,\n",
       "       [-2.67783946e+00, -2.76089913e+00, -9.40941877e-01, ...,\n",
       "         5.12492025e-01,  6.98766451e-01,  7.20776948e-02],\n",
       "       [-2.38701709e+00, -2.29734668e+00, -5.50696197e-01, ...,\n",
       "         2.99821968e-01,  3.39820654e-01, -2.18657605e-02],\n",
       "       [-3.20875816e+00, -2.76891957e+00,  1.01391366e+00, ...,\n",
       "        -2.29964331e-01, -1.88787963e-01, -3.23964720e-01]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Applying PCA Fit Transform to dataset\n",
    "pca=PCA(n_components=13)\n",
    "\n",
    "wine_pca=pca.fit_transform(wine_norm)\n",
    "wine_pca"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e859bbfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.1443294 , -0.24518758, -0.00205106, -0.23932041,  0.14199204,\n",
       "         0.39466085,  0.4229343 , -0.2985331 ,  0.31342949, -0.0886167 ,\n",
       "         0.29671456,  0.37616741,  0.28675223],\n",
       "       [-0.48365155, -0.22493093, -0.31606881,  0.0105905 , -0.299634  ,\n",
       "        -0.06503951,  0.00335981, -0.02877949, -0.03930172, -0.52999567,\n",
       "         0.27923515,  0.16449619, -0.36490283],\n",
       "       [-0.20738262,  0.08901289,  0.6262239 ,  0.61208035,  0.13075693,\n",
       "         0.14617896,  0.1506819 ,  0.17036816,  0.14945431, -0.13730621,\n",
       "         0.08522192,  0.16600459, -0.12674592],\n",
       "       [-0.0178563 ,  0.53689028, -0.21417556,  0.06085941, -0.35179658,\n",
       "         0.19806835,  0.15229479, -0.20330102,  0.39905653,  0.06592568,\n",
       "        -0.42777141,  0.18412074, -0.23207086],\n",
       "       [-0.26566365,  0.03521363, -0.14302547,  0.06610294,  0.72704851,\n",
       "        -0.14931841, -0.10902584, -0.50070298,  0.13685982, -0.07643678,\n",
       "        -0.17361452, -0.10116099, -0.1578688 ],\n",
       "       [-0.21353865, -0.53681385, -0.15447466,  0.10082451, -0.03814394,\n",
       "         0.0841223 ,  0.01892002,  0.25859401,  0.53379539,  0.41864414,\n",
       "        -0.10598274, -0.26585107, -0.11972557],\n",
       "       [-0.05639636,  0.42052391, -0.14917061, -0.28696914,  0.3228833 ,\n",
       "        -0.02792498, -0.06068521,  0.59544729,  0.37213935, -0.22771214,\n",
       "         0.23207564, -0.0447637 ,  0.0768045 ],\n",
       "       [-0.39613926, -0.06582674,  0.17026002, -0.42797018,  0.15636143,\n",
       "         0.40593409,  0.18724536,  0.23328465, -0.36822675,  0.03379692,\n",
       "        -0.43662362,  0.07810789, -0.12002267],\n",
       "       [ 0.50861912, -0.07528304, -0.30769445,  0.20044931,  0.27140257,\n",
       "         0.28603452,  0.04957849,  0.19550132, -0.20914487,  0.05621752,\n",
       "         0.08582839,  0.1372269 , -0.57578611],\n",
       "       [ 0.21160473, -0.30907994, -0.02712539,  0.05279942,  0.06787022,\n",
       "        -0.32013135, -0.16315051,  0.21553507,  0.1341839 , -0.29077518,\n",
       "        -0.52239889,  0.52370587,  0.162116  ],\n",
       "       [-0.22591696,  0.07648554, -0.49869142,  0.47931378,  0.07128891,\n",
       "         0.30434119, -0.02569409,  0.11689586, -0.23736257,  0.0318388 ,\n",
       "        -0.04821201,  0.0464233 ,  0.53926983],\n",
       "       [-0.26628645,  0.12169604, -0.04962237, -0.05574287,  0.06222011,\n",
       "        -0.30388245, -0.04289883,  0.04235219, -0.09555303,  0.60422163,\n",
       "         0.259214  ,  0.60095872, -0.07940162],\n",
       "       [ 0.01496997,  0.02596375, -0.14121803,  0.09168285,  0.05677422,\n",
       "        -0.46390791,  0.83225706,  0.11403985, -0.11691707, -0.0119928 ,\n",
       "        -0.08988884, -0.15671813,  0.01444734]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# PCA Components matrix or covariance Matrix\n",
    "pca.components_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "13ea7569",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.36198848, 0.1920749 , 0.11123631, 0.0706903 , 0.06563294,\n",
       "       0.04935823, 0.04238679, 0.02680749, 0.02222153, 0.01930019,\n",
       "       0.01736836, 0.01298233, 0.00795215])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The amount of variance that each PCA has\n",
    "var=pca.explained_variance_ratio_\n",
    "var"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "573d21af",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 36.2 ,  55.41,  66.53,  73.6 ,  80.16,  85.1 ,  89.34,  92.02,\n",
       "        94.24,  96.17,  97.91,  99.21, 100.01])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cummulative variance of each PCA\n",
    "var1=np.cumsum(np.round(var,4)*100)\n",
    "var1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "9c4ba6d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1b897b39550>]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAigAAAGdCAYAAAA44ojeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAy2ElEQVR4nO3dfXhU5YH38W9EHAINUVAzpIKNbqoWUFEsNdqCK9C1aPXR+oZWurZdXKwarYJUu6W2Jgut1K1pdXH7KGJp7T6+rq0r8aVRyloRxCq6omsKKKSpLiZRMCA5zx9nEhIIkMBMzpnM93Ndc+XMmZPhx5QmP+/7nPvkBUEQIEmSFCP7RB1AkiRpexYUSZIUOxYUSZIUOxYUSZIUOxYUSZIUOxYUSZIUOxYUSZIUOxYUSZIUO/tGHWBPtLS0sG7dOgoKCsjLy4s6jiRJ6oIgCGhqaqK4uJh99tn1GElWFpR169YxdOjQqGNIkqQ9sHbtWg455JBdHpOVBaWgoAAI/4IDBw6MOI0kSeqKxsZGhg4d2vZ7fFeysqC0TusMHDjQgiJJUpbpyukZniQrSZJix4IiSZJix4IiSZJix4IiSZJix4IiSZJix4IiSZJix4IiSZJix4IiSZJix4IiSZJip9sF5ZlnnuGMM86guLiYvLw8HnrooQ6vB0HArFmzKC4uJj8/n3HjxrFy5coOxzQ3N3PFFVdw4IEHMmDAAL785S/z9ttv79VfRJIk9R7dLigffvghxxxzDFVVVZ2+PmfOHObOnUtVVRVLly4lmUwyYcIEmpqa2o4pLy/nwQcf5Ne//jWLFy/mgw8+4PTTT2fr1q17/jeRJEm9Rl4QBMEef3NeHg8++CBnnXUWEI6eFBcXU15ezowZM4BwtKSoqIjZs2czdepUGhoaOOigg1iwYAHnn38+sO3uxL/73e/44he/uNs/t7GxkcLCQhoaGrwXjyRJWaI7v7/Teg5KbW0tdXV1TJw4sW1fIpFg7NixLFmyBIBly5axZcuWDscUFxczYsSItmMkSVIPaQH+ArwAPATcBkwHfhhhJtJ8N+O6ujoAioqKOuwvKipi9erVbcfst99+HHDAATsc0/r922tubqa5ubnteWNjYzpjS5LUOwXAu8DbwNp2j/bP3wE2d/K9pcCNPROzM2ktKK22v41yEAS7vbXyro6prKzk+9//ftrySZKU9QLgf+lYNrbffhv4qAvvlQckgaGpxyHA36Q/cnektaAkk0kgHCUZMmRI2/76+vq2UZVkMsnmzZvZsGFDh1GU+vp6ysrKOn3fmTNncs0117Q9b2xsZOjQoemMLklSfARAAzsvHq1fN3bx/YroWD623y4G+qYvfjqktaCUlJSQTCaprq5m1KhRAGzevJmamhpmz54NwPHHH0/fvn2prq7mvPPOA2D9+vW88sorzJkzp9P3TSQSJBKJdEaVJClaDcCfgNfZsYSsBT7s4vscxM6LR2v5yMJfod0uKB988AFvvvlm2/Pa2lpWrFjBoEGDGDZsGOXl5VRUVFBaWkppaSkVFRX079+fyZMnA1BYWMjXv/51vv3tbzN48GAGDRrEtddey8iRIxk/fnz6/maSJMVBC/A/wEupx59SX1d34XsHs/PicUjq0S/9keOg2wXlhRde4JRTTml73jr1MmXKFO6++26mT5/Opk2bmDZtGhs2bGDMmDEsWrSIgoKCtu/5yU9+wr777st5553Hpk2bOPXUU7n77rvp06dPGv5KkiRFpAF4mY5l5GV2PhUzFBgODGPHEnII0D/DeWNsr9ZBiYrroEiSItUCvMWOoyJ/3snx/YARwDHA0e2+HrCT43up7vz+zshVPJIk9RqNdD4qsrNzRA4hLCDty0gp4CRBt1hQJEmCbaMiraMhrWWkdifHJ+h8VGRQxpPmBAuKJCn3NBGWj9Yy0joq8sFOjj+EjiWkdVTE36IZ40crSerdNgLPAYuBFwnLyFs7OTZBeNJq+ymaowmvplGPsqBIknqXRuAPwDNADeE9ZrZ0ctwn2TYa0lpGPo2/GWPC/xkkSdntPcLRkRrCUvIi4fkk7X0SGAucwLZRkQN7MKO6zYIiScoudYRFpPXxcifHHEZYSL6Q+vopwvvNKGtYUCRJ8baWbaMjNcCqTo45im1l5POEJ7Uqq1lQJEnxERAuC99aRp5hx8XP8ginaNoXkoN7LqJ6hgVFkhSdAHiNbWXkGWDddsf0AY5j25TNyeTcCqy5yIIiSeo5WwnPGWlfSN7d7pj9gM+ybYTkRKAA5RgLiiQpc7YAy9k2ZbOY8IZ67eUTlpDWQjImtU85zYIiSUqfLcDzwO8JC8kSdrxnTQFwEtumbEYTjppI7VhQJEl7LgDeAKqBRcDThMvIt3cAYRFpfRyLv320W/4TkSR1z3vAk4SFpBpYs93rg4C/JRwhGUu4dPw+PRlQvYEFRZK0a82ES8dXpx7LCUdOWu1HOGUzIfUYRXjljbQXLCiSpI4CYCXbRkieIbzhXnsjCMvIRMJ1SAb0ZEDlAguKJAnWA08QFpInUs/bSwLjCQvJeGBIj6ZTDrKgSFIu2kg4MtI6bbP9/WzyCc8faZ22GYH3slGPsqBIUi5oIbzLb2shWQxsbvd6HuFqra2FpAzo18MZpXYsKJLUW61h2+W/TxJefdPeMLYVklOBA3s0nbRLFhRJ6i0aCRdIaz25dfu7/hYAp7CtlHwap20UWxYUScpWWwlXbW0dJXkuta9VH8J72rRebfNZoG8PZ5T2kAVFkrLJJsKrbB4C/gP463av/w3bCskpQGFPhpPSx4IiSXH3HvBbwlLyOB3XJNmfbZf/TgA+1bPRpEyxoEhSHP0ZeJiwlDxLx6mbocBZqcfncdpGvZIFRZLiIABWsK2UvLTd68cAZxKWkmPx5Fb1ehYUSYrKFsLRkdZS0v6me/sQ3vn3zNSjpKfDSdGyoEhST/qA8DySh4FHgQ3tXssH/o6wkEzCdUmU0ywokpRpfyG84uZhwkuCm9u9diDwZcJSMh7o3+PppFiyoEhSJrxBOG3zMLCE8ByTVocTnktyJuGS8n16OJuUBSwokpQOLcALbCslr273+mi2lZLheJKrtBsWFEnaU5uBpwlLySPAunav7Uu4UNpZhFM4h/RwNinLWVAkqTsagMcIS8ljhPe/aVUAnEZYSk4jXERN0h6xoEjS7rwP/Aa4n3DEZEu714YQjpCcRThikujhbFIvZUGRpM5sJbznzd3Ag3S88uYoti2adgLhmiWS0sqCIknt/TcwH7iHjueUjAAuJiwlR/R8LCnXWFAk6X3CKZy7gOfa7R8EXAR8DRiFV95IPciCIik3bQWeZNsUzkep/X0IT3D9e8LVXD2nRIqEBUVSbnmdbVM477TbP5ywlFwEJCPIJakDC4qk3q8BuI9wtOS/2u0fBEwmnMI5DqdwpBixoEjqnbYCTxGWkgfoOIXzd4SjJafjFI4UUxm5OK6pqYny8nIOPfRQ8vPzKSsrY+nSpW2vB0HArFmzKC4uJj8/n3HjxrFy5cpMRJGUa1YBNwCfAiYCCwnLyWeAHwFrCe8ifA6WEynGMlJQvvGNb1BdXc2CBQt4+eWXmThxIuPHj+edd8IJ3zlz5jB37lyqqqpYunQpyWSSCRMm0NTUlIk4knq7BuDfgJMJLwGuAN4GDgAuB5YCrwDXEi6sJin28oIgCHZ/WNdt2rSJgoICHn74YSZNmtS2/9hjj+X000/nBz/4AcXFxZSXlzNjxgwAmpubKSoqYvbs2UydOnW3f0ZjYyOFhYU0NDQwcODAdMaXlC22Eq7qejfhFM6m1P59CKdwvgacAfSLIJukTnXn93faz0H5+OOP2bp1K/36dfypkJ+fz+LFi6mtraWuro6JEye2vZZIJBg7dixLlizptKA0NzfT3LxtGcfGxsYdjpGUI95g21U4a9vtP4rwvJKLcZRE6gXSPsVTUFDAiSeeyA9+8APWrVvH1q1buffee/njH//I+vXrqaurA6CoqKjD9xUVFbW9tr3KykoKCwvbHkOHDk13bElx1gj8Avg88GngZsJysj/wj8DzwErgOiwnUi+RkXNQFixYQBAEfPKTnySRSPDTn/6UyZMn06dPn7Zj8vI6Xs8XBMEO+1rNnDmThoaGtsfatWs7PU5SL9JCuJDaVwnXJfkGsJjwp9ZphJcNrwd+Tng/HC8RlnqVjFxmfPjhh1NTU8OHH35IY2MjQ4YM4fzzz6ekpIRkMlwBqa6ujiFDtv2nTn19/Q6jKq0SiQSJhKfbSzmhtXTcA6xpt/9Itk3hFEeQS1KPyug9OAcMGMCQIUPYsGEDjz/+OGeeeWZbSamurm47bvPmzdTU1FBWVpbJOJLibDPwY8IpnB8SlpNC4DLC++O8CkzHciLliIyMoDz++OMEQcARRxzBm2++yXXXXccRRxzB3//935OXl0d5eTkVFRWUlpZSWlpKRUUF/fv3Z/LkyZmIIynuqoErCe8kDPBZ4BrgTLwKR8pRGSkoDQ0NzJw5k7fffptBgwZxzjnncPPNN9O3b18Apk+fzqZNm5g2bRobNmxgzJgxLFq0iIKCgkzEkRRXq4FvA/ennh8EzAEuIcPju5LiLu3roPQE10GRstxHhNM5FYTrl/QBvgXMIrwyR1KvFOk6KJK0S48CVwFvpZ5/AbgNODqyRJJiyEFUST3jTcKb851BWE6KCe+T83ssJ5J2YEGRlFkfAjcCw4HfAn0Jr8b5b+BCXL9EUqec4pGUGQHhya/XsG1J+gnATwnXNJGkXbCgSEq/14ArCFeCBRgG3AqchSMmkrrEKR5J6dMIXEt4TsmTQAL4LmFh+T9YTiR1mSMokvZeQHjC63WES9VDeDLsT4DDowolKZtZUCTtnZcIp3OeTT0/HPgXYFJkiST1Ak7xSNozGwiLyXGE5SQfuBl4BcuJpL3mCIqk7mkB7gKuB95N7fsKcAvhybCSlAYWFEldt5RwSfrnU8+PJFwFdnxkiST1Uk7xSNq9d4F/AMYQlpNPEN5L5yUsJ5IywhEUSTu3FfhXwpVgN6T2XUx4x+EhUYWSlAssKJI69wfC6ZwVqedHA1XA56MKJCmXOMUjqaP1wCXAyYTlZH/CYrIMy4mkHuMIiqTQFsITXmcBTal9XwcqgYMiyiQpZ1lQJMFThNM5r6WejwZ+Bnw2skSScpxTPFIuWwucD5xKWE4GA3cCf8RyIilSFhQpFwXAHcBRwG8IfxJcDqwCvoE/GSRFzikeKdesJzy35LHU8zLC6ZxjowokSTvyv5OkXPL/gBGE5SQBzCW8j86xEWaSpE44giLlgvcJb+x3b+r5scACwrIiSTHkCIrU2z1FuMjavYT/j/8O4UmwlhNJMeYIitRbbSIsI7emnh9GOGpSFlUgSeo6C4rUGy0nvGdO67om/wDcQniTP0nKAk7xSL3Jx8DNhHcdfg0oAh4lvOGf5URSFnEEReot3gS+CjyXen42YTE5MLJEkrTHHEGRsl3romvHEJaTgcB8wkuKLSeSspQjKFI2237RtXHA3cChEeWRpDRxBEXKVp0tuvYklhNJvYIjKFK2eZ8dF127FxgeUR5JygBHUKRssrNF1ywnknoZR1CkbLD9omuHA/fgomuSei0LihR32y+6NhX4Ma5rIqlXc4pHiqudLbp2B5YTSb2eIyhSHG2/6No5hMXEdU0k5QhHUKQ46WzRtXuAf8dyIimnOIIixcX2i66dQrjo2rCoAklSdBxBkeLg/wEj6bjo2hNYTiTlLEdQpCi9T8dF10YBC3BdE0k5zxEUKSqdLbr2HJYTScIRFKnnueiaJO1W2kdQPv74Y2688UZKSkrIz8/nsMMO46abbqKlpaXtmCAImDVrFsXFxeTn5zNu3DhWrlyZ7ihS/CwHjmdbOZkKrMByIknbSXtBmT17NnfccQdVVVW89tprzJkzhx/96EfcdtttbcfMmTOHuXPnUlVVxdKlS0kmk0yYMIGmpqZ0x5HiYftF15LAb3HRNUnaibwgCIJ0vuHpp59OUVERv/jFL9r2nXPOOfTv358FCxYQBAHFxcWUl5czY8YMAJqbmykqKmL27NlMnTp1t39GY2MjhYWFNDQ0MHDgwHTGl9KvETgLeDr13EXXJOWo7vz+TvsIysknn8yTTz7JqlWrAHjppZdYvHgxX/rSlwCora2lrq6OiRMntn1PIpFg7NixLFmypNP3bG5uprGxscNDygr1wDjCclKAi65JUhel/STZGTNm0NDQwJFHHkmfPn3YunUrN998MxdeeCEAdXV1ABQVFXX4vqKiIlavXt3pe1ZWVvL9738/3VGlzPozMIFw2fqDgf8kvIxYkrRbaR9Bue+++7j33ntZuHAhy5cvZ/78+fz4xz9m/vz5HY7Ly8vr8DwIgh32tZo5cyYNDQ1tj7Vr16Y7tpRerwAnEZaTTwGLsZxIUjekfQTluuuu4/rrr+eCCy4AYOTIkaxevZrKykqmTJlCMpkEwpGUIUOGtH1ffX39DqMqrRKJBIlEIt1RpcxYApwObABGAI8DxZEmkqSsk/YRlI0bN7LPPh3ftk+fPm2XGZeUlJBMJqmurm57ffPmzdTU1FBW5rWWynKPAeMJy0kZUIPlRJL2QNpHUM444wxuvvlmhg0bxvDhw3nxxReZO3cul156KRBO7ZSXl1NRUUFpaSmlpaVUVFTQv39/Jk+enO44Us9ZCEwhvKT4NMKTYQdEmkiSslbaC8ptt93Gd7/7XaZNm0Z9fT3FxcVMnTqVf/qnf2o7Zvr06WzatIlp06axYcMGxowZw6JFiygoKEh3HKln3AZcmdqeTHgX4r6RpZGkrJf2dVB6guugKDYCYBZwU+r5FYSrxHqXK0naQXd+f3svHmlPbSUsJLennt8E3Ah0fjGaJKkbLCjSntgMXALcR1hIfgb8Y6SJJKlXsaBI3fUB4XL1iwjPM7kXOC/SRJLU61hQpO54D5gE/JHwCp0HgIm7/A5J0h6woEhd9TZhGXkNGAT8jvDuxJKktLOgSF3xOmE5WQMcQrg67GciTSRJvZoXQ0q78wJwMmE5OQL4A5YTScowC4q0K08BpwDvAqOBZ4FhkSaSpJxgQZF25n7CJes/AP6WsKwcFGkiScoZFhSpM3cSXjq8mfCS4t8B3olBknqMBUVqLwAqgX8AWlJf7wMSUYaSpNxjQZFatQDfBr6Tev4d4A6gT2SJJClneZmxBLAF+AZwT+r5XODq6OJIUq6zoEibCM83eZRwtOT/Et5nR5IUGQuKctv7wBnAYqAf8O/A6VEGkiSBBUW5rA74IvAnoJBwBOXkSBNJklIsKMpN/0O4dP1bQJJw6fqjI00kSWrHgqLc8xLhyMlfgMOA6tRXSVJseJmxcsuzwFjCcnIM4X11LCeSFDsWFOWORwmndRoIzzX5PeH0jiQpdiwoyg33AGcBHxFepfM4sH+EeSRJu2RBUe/3E2AKsJVwfZMHgP6RJpIk7YYFRb1XQLhc/TWp59cAdwF9I0skSeoir+JR77QV+EfCuxJDeAPAGUBeZIkkSd1gQVHv0wxcBNxPOEZ4B/DNSBNJkrrJgqLepYnwZNingP2AhcA5UQaSJO0JC4p6j0bgNGAJ8AngYeBvI00kSdpDFhT1Dg2E5eS/CC8fXgScEGUgSdLesKAo+zUQLl3/R+AA4AnguEgTSZL2kpcZK7u9T7g67B+BQcCTWE4kqRdwBEXZawNhOXkBGExYTo6JNJEkKU0sKMpO/wtMAJYDBxKWk6MjTSRJSiMLirLPe4Tl5EXgIMJLikdEmkiSlGYWFGWXd4HxwEvAwYTlZHikiSRJGeBJssoefwVOJSwnRcDTWE4kqZdyBEXZoZ6wnLwCJAnLyZGRJpIkZZAFRfFXT7gi7EpgCGE5OSLSRJKkDHOKR/H2F+AUwnJSDPwey4kk5QALiuJrPTAOeBX4JGE5+XSEeSRJPcYpHsXTesKRk9eBoYTTOodHmkiS1IMcQVH8vEM4cvI6MIxw5MRyIkk5Je0F5VOf+hR5eXk7PC6//HIAgiBg1qxZFBcXk5+fz7hx41i5cmW6YyhbvU1YTlYBhxKWk8MizCNJikTaC8rSpUtZv35926O6uhqAc889F4A5c+Ywd+5cqqqqWLp0KclkkgkTJtDU1JTuKMo2awnLyZvApwjLSUl0cSRJ0Ul7QTnooINIJpNtj0cffZTDDz+csWPHEgQBt956KzfccANnn302I0aMYP78+WzcuJGFCxemO4qyyRrCcvI/hKXk94QlRZKUkzJ6DsrmzZu59957ufTSS8nLy6O2tpa6ujomTpzYdkwikWDs2LEsWbJkp+/T3NxMY2Njh4d6kdWE5eQtwumcGsLpHUlSzspoQXnooYd4//33+drXvgZAXV0dAEVFRR2OKyoqanutM5WVlRQWFrY9hg4dmrHM6mF/JiwntYQnwtYQXrUjScppGS0ov/jFLzjttNMoLi7usD8vL6/D8yAIdtjX3syZM2loaGh7rF27NiN51cNqgbGEJaWUsJwcEmUgSVJcZGwdlNWrV/PEE0/wwAMPtO1LJpNAOJIyZMiQtv319fU7jKq0l0gkSCQSmYqqKPwP4TonawkXX3uacKVYSZLI4AjKXXfdxcEHH8ykSZPa9pWUlJBMJtuu7IHwPJWamhrKysoyFUVx8ybhtM5awhv+/R7LiSSpg4yMoLS0tHDXXXcxZcoU9t132x+Rl5dHeXk5FRUVlJaWUlpaSkVFBf3792fy5MmZiKK4eYNw5OQd4CjgKcK7E0uS1E5GCsoTTzzBmjVruPTSS3d4bfr06WzatIlp06axYcMGxowZw6JFiygoKMhEFMXJ64R3JV4HfIawnOx8Zk+SlMPygiAIog7RXY2NjRQWFtLQ0MDAgQOjjqOu+G/CcrIeGAE8CRwcaSJJUg/rzu9vbxaozHuNcFrnL8BIwnJyUKSJJEkx580ClVmvEp4Q+xfgGMJpHcuJJGk3LCjKnFcIy0k9cCzhyMmBEeaRJGUNC4oy42XCaZ2/AqMIy8ngSBNJkrKIBUXp9xJhOXkXOJ6wnAyKNJEkKctYUJReK4BTgfeAE4AngAOiDCRJykYWFKXPcraVk88Ci4D9owwkScpWFhSlxzJgPPC/wOewnEiS9ooFRXtvKWE52QCcCDwOFEaaSJKU5Swo2jvPAxOA94GTCMuJi/tKkvaSBUV77o+E5aQB+DzwGOAtlSRJaWBB0Z6pA04DGoEvAL/DciJJShvvxaM9cyXhOSfHEpaTAZGmkST1Mo6gqPseBv4d6AP8XywnkqS0s6CoexqAaantawmXsZckKc0sKOqe64F1wN8A34s4iySp17KgqOueBe5Ibc8D8iPMIknq1Swo6pqPgG+ktr9BeDNASZIyxIKirvkhsApIAj+KOIskqdezoGj3/gTMTm3/DO+xI0nKOAuKdm0r4ZTOx8D/Ac6ONo4kKTdYULRrPyW8GWAhUBVxFklSzrCgaOdqgRtT2z8CiiPMIknKKRYUdS4ALgM2AmOBr0cbR5KUWywo6twCYBGQIFzzxH8pkqQe5K8d7ageuDq1PQv4dHRRJEm5yYKiHZUD/wscA3w72iiSpNxkQVFHvwV+Rfgv49+AvtHGkSTlJguKtmkC/jG1fTUwOsIskqScZkHRNt8B1gKHATdFnEWSlNMsKAotIVzGHuBfgf4RZpEk5TwLiqCZcDn7APgaMD7SNJIkWVAEVAKvAQcDt0ScRZIkLChaCVSktm8DBkWYRZKkFAtKLmu9U/EW4Azg3GjjSJLUyoKSy34OPAcUpLbzoo0jSVIrC0quWgPMTG3PBg6JMIskSduxoOSigHBBtg+Bk4Cp0caRJGl7FpRc9Cvgd8B+hMvZ+69AkhQz/mrKNe8CV6W2vwscGWEWSZJ2woKSa64hLCkjgOkRZ5EkaScsKLnkP4EFhFfr/BvhFI8kSTGUkYLyzjvvcPHFFzN48GD69+/Psccey7Jly9peD4KAWbNmUVxcTH5+PuPGjWPlypWZiKJWHwCXpbavBMZEmEWSpN1Ie0HZsGEDJ510En379uWxxx7j1Vdf5ZZbbmH//fdvO2bOnDnMnTuXqqoqli5dSjKZZMKECTQ1NaU7jlp9F1gNHAr8MOIskiTtRl4QBEE63/D666/nD3/4A88++2ynrwdBQHFxMeXl5cyYMQOA5uZmioqKmD17NlOn7v6a18bGRgoLC2loaGDgwIHpjN87/RE4kfDy4seAv4s2jiQpN3Xn93faR1AeeeQRRo8ezbnnnsvBBx/MqFGjuPPOO9ter62tpa6ujokTJ7btSyQSjB07liVLlnT6ns3NzTQ2NnZ4qIs2s+1OxRdjOZEkZYW0F5S33nqL22+/ndLSUh5//HEuu+wyrrzySu655x4A6urqACgqKurwfUVFRW2vba+yspLCwsK2x9ChQ9Mdu/eaA7wCHAj8JOIskiR1UdoLSktLC8cddxwVFRWMGjWKqVOn8s1vfpPbb7+9w3F5eR1v/BIEwQ77Ws2cOZOGhoa2x9q1a9Mdu3f6b+AHqe1/ISwpkiRlgbQXlCFDhvCZz3ymw76jjjqKNWvWAJBMJgF2GC2pr6/fYVSlVSKRYODAgR0e2o0W4JuEUzynARdGG0eSpO5Ie0E56aSTeP311zvsW7VqFYceeigAJSUlJJNJqqur217fvHkzNTU1lJWVpTtO7vpXYDEwALgd71QsScoq+6b7Da+++mrKysqoqKjgvPPO4/nnn2fevHnMmzcPCKd2ysvLqaiooLS0lNLSUioqKujfvz+TJ09Od5zc9DYwI7VdSXhpsSRJWSTtBeWEE07gwQcfZObMmdx0002UlJRw6623ctFFF7UdM336dDZt2sS0adPYsGEDY8aMYdGiRRQUFKQ7Tu4JgGlAE/C51LYkSVkm7eug9ATXQdmF3wDnA32BF4Hh0caRJKlVpOugKEL/C1yR2p6J5USSlLUsKL3JtUA9cBTwnYizSJK0FywovcUTwF1su1NxIto4kiTtDQtKb7AR+IfU9jTAq7UlSVnOgtIbfA+oBQ4hvKxYkqQsZ0HJdsuAuant2wGv1JYk9QIWlGy2Bfg64bL2FwCnRxtHkqR0saBks1uAl4BBhDcDlCSpl7CgZKs3gFmp7Z8AB0cXRZKkdLOgZKPWOxU3AxOAr0YbR5KkdLOgZKNfADVAf8K7FnunYklSL2NByTbrgOtS2z8ESiLMIklShlhQss0VQANwAnBlxFkkScoQC0o2eSD12Be4E+gTbRxJkjLFgpIt3gcuT21PB46JLookSZlmQckW04E64NPAdyPOIklShllQskEN4ZQOqa/9IswiSVIPsKBkg9mpr98EvhBlEEmSeoYFJe7WA4+ntq+NMogkST3HghJ3vyRcOfZEwvNPJEnKARaUOAuAu1PbUyLMIUlSD7OgxNlyYCWQAM6POIskST3IghJn81NfzwL2jy6GJEk9zYISV5uBhaltp3ckSTnGghJXvwPeA5LAhIizSJLUwywocdU6vXMx4b13JEnKIRaUOHoX+G1q2+kdSVIOsqDE0a+ALcBxwIiIs0iSFAELShy1Tu84eiJJylEWlLhZCSwjPO/kwoizSJIUEQtK3LSOnkwCDooyiCRJ0bGgxMnHwILU9tcizCFJUsQsKHFSDdQBg4EvRZxFkqQIWVDipHV6ZzKwX5RBJEmKlgUlLt4HHkpte/WOJCnHWVDi4jdAMzCccP0TSZJymAUlLtqvfZIXZRBJkqJnQYmDN4AlhP9rXBxxFkmSYsCCEgf3pL5OBIZEGUSSpHiwoESthW0FxZNjJUkCLCjRqwHWAIXAmRFnkSQpJiwoUbs79fU8ID/CHJIkxUjaC8qsWbPIy8vr8Egmk22vB0HArFmzKC4uJj8/n3HjxrFy5cp0x8gOHwD3p7a/FmEOSZJiJiMjKMOHD2f9+vVtj5dffrnttTlz5jB37lyqqqpYunQpyWSSCRMm0NTUlIko8XY/8CFQCpwYcRZJkmIkIwVl3333JZlMtj0OOii8LW8QBNx6663ccMMNnH322YwYMYL58+ezceNGFi5cmIko8da69skluPaJJEntZKSgvPHGGxQXF1NSUsIFF1zAW2+9BUBtbS11dXVMnDix7dhEIsHYsWNZsmTJTt+vubmZxsbGDo+stxp4OrX91SiDSJIUP2kvKGPGjOGee+7h8ccf584776Suro6ysjLee+896urqACgqKurwPUVFRW2vdaayspLCwsK2x9ChQ9Mdu+ctSH09BTg0yiCSJMVP2gvKaaedxjnnnMPIkSMZP348v/3tbwGYP39+2zF5eR3nM4Ig2GFfezNnzqShoaHtsXbt2nTH7lkBrn0iSdIuZPwy4wEDBjBy5EjeeOONtqt5th8tqa+v32FUpb1EIsHAgQM7PLLafxEubz8AOCfiLJIkxVDGC0pzczOvvfYaQ4YMoaSkhGQySXV1ddvrmzdvpqamhrKyskxHiY/WwaRzgE9EGUSSpHjaN91veO2113LGGWcwbNgw6uvr+eEPf0hjYyNTpkwhLy+P8vJyKioqKC0tpbS0lIqKCvr378/kyZPTHSWeNgH3pbad3pEkqVNpLyhvv/02F154Ie+++y4HHXQQn/vc53juuec49NDwTNDp06ezadMmpk2bxoYNGxgzZgyLFi2ioKAg3VHi6RGgARgGjIs2iiRJcZUXBEEQdYjuamxspLCwkIaGhuw7H+U04D+BG4EfRJxFkqQe1J3f396LpyetAxalti+JMogkSfFmQelJvwRagDLC5e0lSVKnLCg9JWDb1TueHCtJ0i5ZUHrKcmAlkADOiziLJEkxZ0HpKa2jJ2cB+0cXQ5KkbGBB6QmbgdabNTu9I0nSbllQesLvgPeAJDAh4iySJGUBC0pPaJ3euZgMLI0nSVLvY0HJtL8Cj6a2nd6RJKlLLCiZ9ivgY+A4YETEWSRJyhIWlExrnd75WpQhJEnKLhaUTHqFcP2TvsCFEWeRJCmLWFAyqXX0ZBJwYJRBJEnKLhaUTPkYuDe17cmxkiR1iwUlU6qBOmAw8KWIs0iSlGUsKJnSOr0zGdgvyiCSJGUfC0omvA88lNp2ekeSpG6zoGTCb4BmYDjh+ieSJKlbLCiZ0Dq9MwXIizKIJEnZyYKSbm8ASwg/2YsjziJJUpayoKRb6+jJF4EhUQaRJCl7WVDSqQVYkNr25FhJkvaYBSWdfg+sAQqBM6ONIklSNrOgpFPr9M75QL8og0iSlN0sKOnyAXB/atvpHUmS9ooFJV3uBz4ESoETI84iSVKWs6CkS+v0ziW49okkSXvJgpIOq4GnU9tfjTKIJEm9gwUlHVovLT4FODTKIJIk9Q4WlL0V0HFpe0mStNcsKHtrCfAmMAA4J+IskiT1EhaUvdU6evIV4BNRBpEkqfewoOyNTcB9qW2ndyRJShsLyt54GGgkPDF2bMRZJEnqRSwoe6N1euer+ElKkpRG/lrdU+uARantS6IMIklS72NB2VO/BFqAMsLl7SVJUtpYUPaEa59IkpRRFpQ9sRxYCSSA8yLOIklSL2RB2ROtoydnAftHF0OSpN7KgtJdm4GFqW2ndyRJyoiMF5TKykry8vIoLy9v2xcEAbNmzaK4uJj8/HzGjRvHypUrMx0lPX4LvAcMASZEnEWSpF4qowVl6dKlzJs3j6OPPrrD/jlz5jB37lyqqqpYunQpyWSSCRMm0NTUlMk46dE6vXMxsG+UQSRJ6r0yVlA++OADLrroIu68804OOOCAtv1BEHDrrbdyww03cPbZZzNixAjmz5/Pxo0bWbhw4S7eMQb+SjiCAk7vSJKUQRkrKJdffjmTJk1i/PjxHfbX1tZSV1fHxIkT2/YlEgnGjh3LkiVLOn2v5uZmGhsbOzwi8SvgY+B4YHg0ESRJygUZmaT49a9/zbJly3jhhRd2eK2urg6AoqKiDvuLiopYvXp1p+9XWVnJ97///fQH7S7XPpEkqUekfQRl7dq1XHXVVfzyl7+kX79+Oz0uLy+vw/MgCHbY12rmzJk0NDS0PdauXZvWzF3yCuH6J32BC3v+j5ckKZekfQRl2bJl1NfXc/zxx7ft27p1K8888wxVVVW8/vrrQDiSMmTIkLZj6uvrdxhVaZVIJEgkEumO2j2toyeTgAOjDCJJUu+X9hGUU089lZdffpkVK1a0PUaPHs1FF13EihUrOOyww0gmk1RXV7d9z+bNm6mpqaGsrCzdcdLjY+De1LbTO5IkZVzaR1AKCgoYMWJEh30DBgxg8ODBbfvLy8upqKigtLSU0tJSKioq6N+/P5MnT053nPSoBuqAwcCXIs4iSVIOiGQlj+nTp7Np0yamTZvGhg0bGDNmDIsWLaKgoCCKOLvXOr0zGdgvyiCSJOWGvCAIgqhDdFdjYyOFhYU0NDQwcODAzP5hGwhXjW0GlgHHZfaPkySpt+rO72/vxbM7vyEsJyOAURFnkSQpR1hQdqf92iedXwUtSZLSzIKyK6uA/yL8lC6KOIskSTnEgrIr96S+fpHwPBRJktQjLCg70wIsSG279okkST3KgrIzvwfWAIXAmdFGkSQp11hQdqb15NjzgZ3fUkiSJGWABaUzHwD3p7ad3pEkqcdZUDpzP/AhUAqcGHEWSZJykAWlM63TO5fg2ieSJEXAgrK9PwNPExaTS6KNIklSrrKgbK/10uJTgGFRBpEkKXdZUNoL2LY4myfHSpIUGQtKe0uAN4EBwNkRZ5EkKYftG3WAWDkCmAs0AZ+IOIskSTnMgtLegcDVUYeQJElO8UiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNixoEiSpNjJyrsZB0EAQGNjY8RJJElSV7X+3m79Pb4rWVlQmpqaABg6dGjESSRJUnc1NTVRWFi4y2Pygq7UmJhpaWlh3bp1FBQUkJeXl9b3bmxsZOjQoaxdu5aBAwem9b17Gz+rrvOz6jo/q67zs+oeP6+uy9RnFQQBTU1NFBcXs88+uz7LJCtHUPbZZx8OOeSQjP4ZAwcO9B9wF/lZdZ2fVdf5WXWdn1X3+Hl1XSY+q92NnLTyJFlJkhQ7FhRJkhQ7FpTtJBIJvve975FIJKKOEnt+Vl3nZ9V1flZd52fVPX5eXReHzyorT5KVJEm9myMokiQpdiwokiQpdiwokiQpdiwokiQpdiwo7fz85z+npKSEfv36cfzxx/Pss89GHSmWKisrOeGEEygoKODggw/mrLPO4vXXX486VuxVVlaSl5dHeXl51FFi65133uHiiy9m8ODB9O/fn2OPPZZly5ZFHSt2Pv74Y2688UZKSkrIz8/nsMMO46abbqKlpSXqaJF75plnOOOMMyguLiYvL4+HHnqow+tBEDBr1iyKi4vJz89n3LhxrFy5MpqwEdvVZ7VlyxZmzJjByJEjGTBgAMXFxVxyySWsW7eux/JZUFLuu+8+ysvLueGGG3jxxRf5/Oc/z2mnncaaNWuijhY7NTU1XH755Tz33HNUV1fz8ccfM3HiRD788MOoo8XW0qVLmTdvHkcffXTUUWJrw4YNnHTSSfTt25fHHnuMV199lVtuuYX9998/6mixM3v2bO644w6qqqp47bXXmDNnDj/60Y+47bbboo4WuQ8//JBjjjmGqqqqTl+fM2cOc+fOpaqqiqVLl5JMJpkwYULbPd5yya4+q40bN7J8+XK++93vsnz5ch544AFWrVrFl7/85Z4LGCgIgiD47Gc/G1x22WUd9h155JHB9ddfH1Gi7FFfXx8AQU1NTdRRYqmpqSkoLS0Nqqurg7FjxwZXXXVV1JFiacaMGcHJJ58cdYysMGnSpODSSy/tsO/ss88OLr744ogSxRMQPPjgg23PW1pagmQyGfzzP/9z276PPvooKCwsDO64444IEsbH9p9VZ55//vkACFavXt0jmRxBATZv3syyZcuYOHFih/0TJ05kyZIlEaXKHg0NDQAMGjQo4iTxdPnllzNp0iTGjx8fdZRYe+SRRxg9ejTnnnsuBx98MKNGjeLOO++MOlYsnXzyyTz55JOsWrUKgJdeeonFixfzpS99KeJk8VZbW0tdXV2Hn/WJRIKxY8f6s74LGhoayMvL67FRzay8WWC6vfvuu2zdupWioqIO+4uKiqirq4soVXYIgoBrrrmGk08+mREjRkQdJ3Z+/etfs2zZMl544YWoo8TeW2+9xe23384111zDd77zHZ5//nmuvPJKEokEl1xySdTxYmXGjBk0NDRw5JFH0qdPH7Zu3crNN9/MhRdeGHW0WGv9ed7Zz/rVq1dHESlrfPTRR1x//fVMnjy5x260aEFpJy8vr8PzIAh22KeOvvWtb/GnP/2JxYsXRx0ldtauXctVV13FokWL6NevX9RxYq+lpYXRo0dTUVEBwKhRo1i5ciW33367BWU79913H/feey8LFy5k+PDhrFixgvLycoqLi5kyZUrU8WLPn/Xds2XLFi644AJaWlr4+c9/3mN/rgUFOPDAA+nTp88OoyX19fU7NG1tc8UVV/DII4/wzDPPcMghh0QdJ3aWLVtGfX09xx9/fNu+rVu38swzz1BVVUVzczN9+vSJMGG8DBkyhM985jMd9h111FHcf//9ESWKr+uuu47rr7+eCy64AICRI0eyevVqKisrLSi7kEwmgXAkZciQIW37/Vm/c1u2bOG8886jtraWp556qsdGT8CreADYb7/9OP7446muru6wv7q6mrKysohSxVcQBHzrW9/igQce4KmnnqKkpCTqSLF06qmn8vLLL7NixYq2x+jRo7noootYsWKF5WQ7J5100g6Xq69atYpDDz00okTxtXHjRvbZp+OP7z59+niZ8W6UlJSQTCY7/KzfvHkzNTU1/qzvRGs5eeONN3jiiScYPHhwj/75jqCkXHPNNXz1q19l9OjRnHjiicybN481a9Zw2WWXRR0tdi6//HIWLlzIww8/TEFBQdvIU2FhIfn5+RGni4+CgoIdzssZMGAAgwcP9nydTlx99dWUlZVRUVHBeeedx/PPP8+8efOYN29e1NFi54wzzuDmm29m2LBhDB8+nBdffJG5c+dy6aWXRh0tch988AFvvvlm2/Pa2lpWrFjBoEGDGDZsGOXl5VRUVFBaWkppaSkVFRX079+fyZMnR5g6Grv6rIqLi/nKV77C8uXLefTRR9m6dWvbz/pBgwax3377ZT5gj1wrlCV+9rOfBYceemiw3377Bccdd5yXze4E0Onjrrvuijpa7HmZ8a79x3/8RzBixIggkUgERx55ZDBv3ryoI8VSY2NjcNVVVwXDhg0L+vXrFxx22GHBDTfcEDQ3N0cdLXJPP/10pz+fpkyZEgRBeKnx9773vSCZTAaJRCL4whe+ELz88svRho7Irj6r2tranf6sf/rpp3skX14QBEHma5AkSVLXeQ6KJEmKHQuKJEmKHQuKJEmKHQuKJEmKHQuKJEmKHQuKJEmKHQuKJEmKHQuKJEmKHQuKJEmKHQuKJEmKHQuKJEmKHQuKJEmKnf8PjjTqdb/xRJsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Variance plot for PCA components obtained \n",
    "plt.plot(var1,color='magenta')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7c4f6dbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>PC1</th>\n",
       "      <th>PC2</th>\n",
       "      <th>PC3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3.316751</td>\n",
       "      <td>-1.443463</td>\n",
       "      <td>-0.165739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>2.209465</td>\n",
       "      <td>0.333393</td>\n",
       "      <td>-2.026457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2.516740</td>\n",
       "      <td>-1.031151</td>\n",
       "      <td>0.982819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3.757066</td>\n",
       "      <td>-2.756372</td>\n",
       "      <td>-0.176192</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1.008908</td>\n",
       "      <td>-0.869831</td>\n",
       "      <td>2.026688</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>-3.370524</td>\n",
       "      <td>-2.216289</td>\n",
       "      <td>-0.342570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>-2.601956</td>\n",
       "      <td>-1.757229</td>\n",
       "      <td>0.207581</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>-2.677839</td>\n",
       "      <td>-2.760899</td>\n",
       "      <td>-0.940942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>-2.387017</td>\n",
       "      <td>-2.297347</td>\n",
       "      <td>-0.550696</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>-3.208758</td>\n",
       "      <td>-2.768920</td>\n",
       "      <td>1.013914</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type       PC1       PC2       PC3\n",
       "0       1  3.316751 -1.443463 -0.165739\n",
       "1       1  2.209465  0.333393 -2.026457\n",
       "2       1  2.516740 -1.031151  0.982819\n",
       "3       1  3.757066 -2.756372 -0.176192\n",
       "4       1  1.008908 -0.869831  2.026688\n",
       "..    ...       ...       ...       ...\n",
       "173     3 -3.370524 -2.216289 -0.342570\n",
       "174     3 -2.601956 -1.757229  0.207581\n",
       "175     3 -2.677839 -2.760899 -0.940942\n",
       "176     3 -2.387017 -2.297347 -0.550696\n",
       "177     3 -3.208758 -2.768920  1.013914\n",
       "\n",
       "[178 rows x 4 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Final Dataframe\n",
    "final_df=pd.concat([wine['Type'],pd.DataFrame(wine_pca[:,0:3],columns=['PC1','PC2','PC3'])],axis=1)\n",
    "final_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "73660385",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAzkAAAH5CAYAAACiZfCEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOzdd3hUVfrA8e/0Se8VAgQSei8KFgSxoBQpIooooOhaV1dZt6ir68/u2hW7dOz0pmIBEZAO0luAQHqv0+f3x0lmEgg1Pbyf58lDzr03M3cmc8N573nPezRut9uNEEIIIYQQQjQR2vo+ASGEEEIIIYSoSRLkCCGEEEIIIZoUCXKEEEIIIYQQTYoEOUIIIYQQQogmRYIcIYQQQgghRJMiQY4QQgghhBCiSZEgRwghhBBCCNGk6Ov7BM7E5XKRkpJCQEAAGo2mvk9HCCGEEEIIUU/cbjeFhYXExsai1Z55rKZBBzkpKSnExcXV92kIIYQQQgghGojk5GSaN29+xmMadJATEBAAqBcSGBhYz2cjhBBCCCGEqC8FBQXExcV5YoQzadBBTnmKWmBgoAQ5QgghhBBCiHOaxiKFB4QQQgghhBBNigQ5QgghhBBCiCZFghwhhBBCCCFEk9Kg5+QIIYQQQgjR2DidTux2e32fRqNjMBjQ6XQ18lgS5AghhBBCCFED3G43aWlp5OXl1fepNFrBwcFER0dXe41MCXKEEEIIIYSoAeUBTmRkJL6+vrKY/Xlwu92UlJSQkZEBQExMTLUeT4IcIYQQQgghqsnpdHoCnLCwsPo+nUbJx8cHgIyMDCIjI6uVuiaFB4QQQgghhKim8jk4vr6+9XwmjVv5+1fdOU0S5AghhBBCCFFDJEWtemrq/ZMgRwghhBBCCNGkSJAjhBBCCCGEaFIkyBFCCCGEEEI0KRLkCCGEEEII0YA4XW7WHcpm4bYTrDuUjdPlrrXn0mg0Z/yaOHFirT13bZIS0kIIIYQQQjQQK3am8t/Fu0nNt3i2xQSZeWZYRwZ3rt7aMVVJTU31fP/VV1/xn//8h3379nm2lZd1bmxkJEcIIYQQDZ49MwvLgQPYM7Pq+1SEqDUrdqZy/+wtlQIcgLR8C/fP3sKKnamn+ckLFx0d7fkKCgpCo9EQHR1NVFQUV1xxBZ988kml43fu3IlWq+XQoUOAGgn64IMPuOGGG/Dx8SE+Pp5vvvmm0s+cOHGCsWPHEhISQlhYGDfddBNHjhyp8ddSkQQ5QgghhGjwnHm5JA0bjjMvt75PRYha4XS5+e/i3VSVmFa+7b+Ld9dq6lpFGo2Gu+66i2nTplXa/vnnn3PllVfSpk0bz7ann36a0aNHs337dsaPH89tt93Gnj17ACgpKWHgwIH4+/uzevVq1qxZg7+/P4MHD8Zms9Xa+UuQI4QQQgghRD3bkJRzyghORW4gNd/ChqScOjunSZMmsW/fPjZs2ACoBTpnz57NXXfdVem4MWPGMHnyZNq2bcv//d//0bt3b959910AvvzyS7RaLZ9++ildunShQ4cOTJs2jWPHjvHrr7/W2rnLnBwhhBBCNEj2zCzPyI0jPd3zb3k3UBccgiEivJ7OToialVF4+gDnQo6rCTExMQwZMoTPP/+cSy65hCVLlmCxWBgzZkyl4/r163dKe9u2bQBs3ryZgwcPEhAQUOkYi8XiSXmrDRLkCCGEEKJBKk9Rqyh58j2e7+MXL5IgRzQZkQHmGj2upkyePJk77riDN998k2nTpjF27Fh8fX3P+nMajQYAl8tFr169mDNnzinHRERE1Pj5lpMgRwghhBBCiHp2SXwoMUFm0vItVc7L0QDRQWYuiQ+t0/O68cYb8fPz44MPPmD58uWsXr36lGPWr1/PnXfeWando0cPAHr27MlXX31FZGQkgYGBdXbeMidHCCGEEA2SLjiE+MWLiF+8iLhPVYWnuE8/8WzTBYfU8xkKUXN0Wg3PDOsIqICmovL2M8M6otOevLeWz0unY+LEifzrX/8iISHhlNQ0gG+++YbPP/+c/fv388wzz7BhwwYeeughAG6//XbCw8O56aab+O2330hKSmLVqlU88sgjHD9+vNbOW4IcIYQQQjRIhohwzImJmBMT0UdFAaCPivJsk1Q10dQM7hzDB+N7Eh1UOSUtOsjMB+N71so6Oefi7rvvxmaznVJwoNx///tfvvzyS7p27cqMGTOYM2cOHTuqgM3X15fVq1fTokULRo0aRYcOHbjrrrsoLS2t1ZEdjdvtrps6dBegoKCAoKAg8vPz63R4SwghhBANS3kRAik2IBoqi8VCUlIS8fHxmM3VmzfjdLnZkJRDRqGFyACVolbXIzgV/f777wwYMIDjx48TVXbDoZxGo2H+/PmMGDGiRp7rTO/j+cQGMidHCCGEEA2eISJcghtx0dBpNfRrE1bfp4HVaiU5OZmnn36aW2655ZQApyGTdDUhhBBCCCHEKb744gvatWtHfn4+r776an2fznmRkRwhhBBCCCHEKSZOnMjEiRPPeExDnfkiIzlCCCGEEEKIJkWCHCGEEEIIIUSTIkGOEEIIIYQQokmRIEcIIYQQQgjRpEiQI4QQQgghhGhSJMgRQgghhBBCNCkS5AghhBBCCHERmzhxIhqNBo1Gg8FgoHXr1kyZMoXi4mLPMd999x0DBgwgKCgIf39/unbtynPPPUdOTg4AqampjBs3jnbt2qHVann00Ufr6dUoEuQIIYQQQgjRUDiskLQaytefcbtV22Gt1acdPHgwqampHD58mOeff56pU6cyZcoUAJ588knGjh1Lnz59WL58OTt37uT1119n+/btzJo1CwCr1UpERARPPvkk3bp1q9VzPReyGKgQQgghhBANgcMKX4yDQyvh0vvh+hfh+3/BHx9Cm2vgtrmgN9XKU5tMJqKjowEYN24cv/zyCwsWLGDSpEm8+OKLvPXWWzzyyCOe41u1asW1115LXl6ep/32228D8Pnnn9fKOZ4PGckRQgghhBCivpUHOId/Vu0/PoCPrlQBDqjtX4yr9RGdcj4+PtjtdubMmYO/vz8PPPBAlccFBwfXyfmcLwlyhBBCCCGEqG/Jf6gRHLfLuy19p/d7t0vtT95Q66eyYcMG5s6dy6BBgzhw4ACtW7fGYDDU+vPWJAlyhBBCCCGEqG+troRL7zvzMZfeD62uqJWnX7JkCf7+/pjNZvr160f//v159913cbvdaDSaWnnO2iRBjhBCCCGEEPVNo4HrX4KozlXvj+qs5ujUUsAxcOBAtm3bxr59+7BYLMybN4/IyEjatm3LoUOHsNvttfK8tUWCHCGEEEIIIeqb262KDFRMUasofSd8/29v1bUa5ufnR0JCAi1btqyUmjZu3DiKioqYOnVqlT9XXnigoZHqakIIIYQQQtS3I795iwyczh8fQPshEH9l3ZwTcOmll/LEE0/w+OOPc+LECUaOHElsbCwHDx7kww8/5IorrvBUXdu2bRsARUVFZGZmsm3bNoxGIx07dqyz8y0nQY4QQgghhBD1Le5SVSb68M/e4gNRnb0jOxottL4a4i6p81N75ZVX6NWrF++//z4ffvghLpeLNm3acPPNNzNhwgTPcT169PB8v3nzZubOnUvLli05cuRInZ+zxu2upTGvGlBQUEBQUBD5+fkEBgbW9+kIIYQQQghRJYvFQlJSEvHx8ZjN5gt7kHpcJ6ehONP7eD6xgYzkCCGEEEII0RDoTSqQSd6gqqhpNDD4ZWg/VI3gNPEApyZJkCOEEEIIIURDoTdVnnOj0dTpHJymQqqrCSGEEEIIIZoUCXKEEEIIIYQQTYoEOUIIIYQQQogmRYIcIYQQQgghRJMiQY4QQgghhBCiSZEgRwghhBBCCNGkSJAjhBBCCCGEaFIkyBFCCCGEEEI0KRLkCCGEEEIIcRGbOHEiGo0GjUaDwWCgdevWTJkyheLiYs8x3333HQMGDCAoKAh/f3+6du3Kc889R05ODgDz5s3j2muvJSIigsDAQPr168f3339fXy9JghwhhBBCCCEudoMHDyY1NZXDhw/z/PPPM3XqVKZMmQLAk08+ydixY+nTpw/Lly9n586dvP7662zfvp1Zs2YBsHr1aq699lqWLVvG5s2bGThwIMOGDWPr1q318nr09fKsQgghhBBCiFMcLThKsb34lO1+Bj9aBrastec1mUxER0cDMG7cOH755RcWLFjApEmTePHFF3nrrbd45JFHPMe3atWKa6+9lry8PADeeuutSo/34osvsnDhQhYvXkyPHj1q7bxPR4IcIYQQQgghGoCjBUcZOn/oafcvGbmkVgOdinx8fLDb7cyZMwd/f38eeOCBKo8LDg6ucrvL5aKwsJDQ0NBaPMvTk3S1i53LBQ5r5W0OS/2cixBCCCHERayqEZzz2V9TNmzYwNy5cxk0aBAHDhygdevWGAyG83qM119/neLiYm655ZZaOsszkyDnYuZyQcZuOPIb2EvVttwj8Od3UJpfr6cmhBBCCCHqzpIlS/D398dsNtOvXz/69+/Pu+++i9vtRqPRnNdjffHFFzz77LN89dVXREZG1tIZn5mkq13MsvbB9BvBVgS3fQUR7WDmTSrQsZdA17FgDqzvsxRCCCGEELVs4MCBfPDBBxgMBmJjYz0jN23btmXNmjXY7fZzGs356quvuPvuu/nmm2+45ppravu0T0tGci5mpkAIaQUuJ3wxFj7qrwIcvRliuoPBt55PUAghhBBC1AU/Pz8SEhJo2bJlpWBm3LhxFBUVMXXq1Cp/rrzwAKgRnIkTJzJ37lyGDBlS26d8RhLkXMyCmsGtX0BEBxXolOaCRgsTFkNsD9DJQJ8QQgghxMXs0ksv5YknnuDxxx/niSeeYN26dRw9epSffvqJMWPGMGPGDEAFOHfeeSevv/46ffv2JS0tjbS0NPLz62cKhAQ5FzuXAypOYnO7wFIALnv9nZMQQgghxEXIz+BXrf215ZVXXmHu3Ln88ccfXH/99XTq1InHHnuMrl27MmHCBAA++ugjHA4HDz74IDExMZ6vimWn65LG7Xa76+WZz0FBQQFBQUHk5+cTGChzQ2pc7tGyOThJKkXNPwryjoJWB7d9Da0uB4NPfZ+lEEIIIUSDZ7FYSEpKIj4+HrPZfMGPU1/r5DQUZ3ofzyc2kHyki5nLDo5SFeBMWAxBzeGLWyFtB1gLwO2s7zMUQgghhLioXAyBTF2QIOdiFpYAE5dCaZ4qNKDTqzk6mXsgri8Y/ev7DIUQQgghhDhvEuRc7MISVNEBrU61g5pBQLS3LYQQQgghRCMjhQfEqQGNBDhCCCGEEKIRkyBHCCGEEEII0aRIkCOEEEIIIYRoUiTIEUIIIYQQQjQpdRbkvPTSS2g0Gh599NG6ekohhBBCCCHERahOgpyNGzfy8ccf07Vr17p4OiGEEEIIIcRFrNaDnKKiIm6//XY++eQTQkJCavvphBBCCCGEEBe5Wg9yHnzwQYYMGcI111xz1mOtVisFBQWVvoQQQgghhBC1Z+LEiWg0GjQaDQaDgdatWzNlyhSKi4s9x3z33XcMGDCAoKAg/P396dq1K8899xw5OTkArFmzhssvv5ywsDB8fHxo3749b775Zn29pNoNcr788ks2b97MSy+9dE7Hv/TSSwQFBXm+4uLiavP0hBBCCCGEaJBsR45QumsXtiNH6uT5Bg8eTGpqKocPH+b5559n6tSpTJkyBYAnn3ySsWPH0qdPH5YvX87OnTt5/fXX2b59O7NmzQLAz8+Phx56iNWrV7Nnzx6eeuopnnrqKT7++OM6Of+Tadxut7s2Hjg5OZnevXvzww8/0K1bNwAGDBhA9+7deeutt6r8GavVitVq9bQLCgqIi4sjPz+fwMDA2jhNIYQQQgghqs1isZCUlER8fDxms7laj2U7coRDg2/wtNusWI6xVatqnuHpTZw4kby8PBYsWODZds8997BkyRIWLlzIpZdeyltvvcUjjzxyys/m5eURHBxc5eOOGjUKPz8/TyB0Ls70PhYUFBAUFHROsUGtjeRs3ryZjIwMevXqhV6vR6/Xs2rVKt555x30ej1Op/OUnzGZTAQGBlb6EkIIIYQQ4mLirJAmVlW7Lvj4+GC325kzZw7+/v488MADVR53ugBn69atrF27lquuuqoWz/L09LX1wIMGDeLPP/+stG3SpEm0b9+ef/zjH+h0utp6aiGEEELUkazSLPIseadsDzYHE+4TXvcnJEQjZjtyBGdxMbbDhytvL2vr/PxqdUSn3IYNG5g7dy6DBg3iwIEDtG7dGoPBcE4/27x5czIzM3E4HDz77LNMnjy5ls+2arUW5AQEBNC5c+dK2/z8/AgLCztluxDny56ZhTMvF11wCIYI+U9UCCHqS54lj5GLRp6yff7w+RLkCHEeTk5Rqyjl7094vq+t1LUlS5bg7++Pw+HAbrdz00038e677zJhwgQ0Gs05P85vv/1GUVER69ev55///CcJCQncdtttNX6+Z1NrQY4QtcmZl0vSsOHEL14kQY4QQgghGr1zTUmrrdS1gQMH8sEHH2AwGIiNjfWM3LRt25Y1a9Zgt9vPaTQnPj4egC5dupCens6zzz5bL0FOnSwGWu7XX389bdGBJsNaCMVZ3rbTBiU54HTU3zk1EfbMLCwHDmA5cABHejoAjvR0zzZ7ZtZZHkEIIYQQomHS+fnV6HHny8/Pj4SEBFq2bFkpmBk3bhxFRUVMnTq1yp/Ly8s77WO63e5KRcXqkozk1CRrIexdCiufhYlLIbgFHN8EX46D8d9BdDfQyVt+ocpHbypKnnyP53sZ1RFCCCFEY2Vs1Yo2K5Z75uRUTFGLfe1VjK1b19mcnIouvfRSnnjiCR5//HFOnDjByJEjiY2N5eDBg3z44YdcccUVPPLII7z//vu0aNGC9u3bA2rdnP/97388/PDDdXq+5aTHXZPspbDs72AtgOk3woB/w7IpajRn8aNwx3zwk064EEIIIYQ41ekCGGPr1vh06lS3J1PBK6+8Qq9evXj//ff58MMPcblctGnThptvvpkJEyYA4HK5+Ne//kVSUhJ6vZ42bdrw8ssv85e//KVezrnW1smpCedTC7tBcLkg/U+YPkSN6pQLawN3LFAjO+KClRcbAJWmljz5HuI+/QR9VBSAFCEQQoh6INXVhFAa8zo5DUlNrZMjIzk1SauFiA7Q/wn48Wnv9lGfSIBTAwwR4Z4gxlK2TR8VhTkxsf5OSgghLnLhPuESzAhRwyqmrtVHilpTUKeFB5o8pw1ObIZfnq+8/cvbIftQ/ZyTEEIIIYRodIytWuHTqZMEOBdIgpyaZCmA2aPAYVUpard/C6YAKEyFr8ZXrromqkUXHEL84kXogkPq+1SEEEIIIUQDI0FOTTL6wZgZEN5WzcFpM8hbZe3mz8E3rL7PsMkwRIRjTkyUOThCCCGEEOIUMienJhl8IP5KmLQM/CLUtqgucM+v4BsK57FarBBCCCGEaHwacE2vRqGm3j8ZyalpBh9vgAOqGIFfWNMNcIqzoCjT27YUQFFG/Z2PEEIIIUQ9KF9As6SkpJ7PpHErf/8qLkh6IWQkR1y44ixY/RrYLTDoadAZYdd82PEV3PwZBMTU9xmK+uZ2Vw7wT24LIYQQTYROpyM4OJiMDHWz19fXF438n3fO3G43JSUlZGRkEBwcjE6nq9bjSZAjLozbDSnb4I8Py9ouiO4My8tW5934OVz5NzD41tspinpWlA7pu6FZLzAHQkkOnNgEsb3U6KYQQgjRxERHRwN4Ah1x/oKDgz3vY3VIkNPYFaRCaS5EtAOtDooz1bbwRJU6V5WauLuu0UCznnDVP2HVy7B1pndf28HQZ7IEOBezonRY9CjsXwbD34MOQ2Htu7DmDbj0fug/BfykaIQQQoimRaPREBMTQ2RkJHa7vb5Pp9ExGAzVHsEpJ0FOY1aQCt9MhPSdqthBUBz8+Cxsnwu3faWKIJwc6OQegdI8iOoMOr16jJxDENsTjOcZlPiGQt/74MD3kLJVbTMFwIgP1D5x8XIDbof6ftFDsPETSN2u2i5HvZ2WEEIIURd0Ol2NddbFhZHCA42ZwwJ5R8FWBNNuhK/vgG2zVepY2p9qrkxFuUdg5k0w7QZI2w6F6fDdZJgxFA79DLbi83t+SwHsXuQNcACshbDyv7Im0MUuIEqN4CRcq9rlAU7vyTDgnzKKI4QQQohaJUFOYxYar9bhCYhRgc6RNWr7wKeg913ge9JCmW43uJxgL4HpQ2HmMDha9jNOG+r2+zlyu+HEFlj8V9VOvB4ue0R9v2U6bPr8/IMm0bToDBCWUHlbZAfQmernfIQQQghx0ZB0tcbOJxiiu0Fhqndb2+vBHHTqsaHxMGExTB8CBScgc5/aPvpzSLwOjP7n/rwaDUR1hA43gdMKw95R1dUMPrB7IXQfpxZHFRenkhw1B+ePD1TbFAjWAlj2OOhN0PEmVYxAnDd7ZhbOvFx0wSGyGK4QQghxGjKS05gVZ8IP/4EDK1RbU/brnHYDpP8JLtepP2PwAb9Ib1ujgZBWoDef//P7R8KQ/6kAJyBKjRxd+he4cwEENT//xxNNh9MG+5ap7/vcA49sV8E3wJ/fqlRLcUGcebkkDRuOMy+3vk9FiCbFnpmF5cAB7JmSbi1EUyBBzrk4OVioKnioD5YC2DVPfT/wKdWRLE9d2/ApWPIqH1+QCt/eDalbVXBj9FdpZ9OHQNoOcF7AhHD/SBXglPMNhYDql/0TjVxANNz+LQx6Vs3B8Q2FYe/CVf+CUR+pz41o0LJKsziYe/CUr6xS6QCKpkluIAjRtEi62tkUpqsAIO4SlQJWkgPJf0DzPvU/eTq4pZqTk/Qb9LxTjaRMXAp/fKRK9J5c4cySpwoOaDQw+jNo1lsFOPnJcOgXCG2t0t+EqAnBcWpkr7xqX0AUXPHI6Uubi9MqT1EDcKSne/4tHw+rjdS1PEseIxeNPGX7/OHzCfeRNDkhhBANmwQ5Z1KYDosfVWt93Pg6dB7lXeuj910w8N/gF1F/56fTQ3RXCGvjnYMT1gaufrLqOTnh7VQQlJMECdeAyV/N0dm3HLrdKgGOqHknlyWXAOeClN9hrih58j2e7+MXL5L5OUJcgPq4gSCEqBsS5JyJRgPlNc6XPa6qhqX9WbZPB5znApq1QacH3UkBTVUBDoBWC1FdICzR2/kMjYdeE6RIgBBCiIuO3EAQoumSIOdM/CNhyBtq3sreJd4ApzGv9aHVnnp3XQIcIRo0XXAI8YsXAeouc/Lke4j79BP0UVGe/UIIIYTwkiDnbLR6CG5ReVtYG7UGiBAXM2sR2EvV3C+tDhx2sOaBwe/UQFpUiyEi3HM3uTyNRh8VhTkxsf5OSogmQG4gCNF0SZBzJuVrfayfqtrmYDV5//t/gd4IXcacPjVMiKbMWqTmci15FCYsgahOkLIFZo1URS1aD5BAp5ELNgczf/j8KrcL0VTIDQQhmi4pIX0mThsc+EF932cy/HUrtB+q2rsXgcNaf+cmRH1ylMJPz6py5TOGqBsBM4eDvQR+fBrsxfV9hk1W+Z3n2r7DHO4TTkJIwilfUllNNFV1dW0JIeqGxu12u+v7JE6noKCAoKAg8vPzCQysp9XR80/An99Aj/FqDk5RBmyeAT3vkPVgxMUtJwlmDIX8495toa3VYrDBLWv/+W0lYCsG/7IKh06HGmltjHPlhBBCCHFW5xMbyEjO2QQ1U2t9lHec/CPhsockwDlXOUmQd8zbLkiFrP2qmINo3ILi1CK0FV33AgQ2r/3ntpXAoZ/gq/FQkAJOp1oDatoN6jMnhBBCiIuaBDnn4uS1PWStj3OTk6RSmGaNUIFOQSp8MxE+uw4y90qg05g57HBiEyx5pPL2eZPV4rkuZ+0+vyUPvpkAyevh6wlwZDVMH6oC6IUPQHF27T6/EEIIIRo0CXJE7XA5oTgTitIh+xDMGA5f3Ko6pZY8FQDJnKbqcblUcQynzbutOFMVBahttkL48jb1OwxtDff8CkHNVfrYl7er33FtMgfDmJlqLavjG1QgbS9RlRBvmgp+YbX7/EIIIYRo0CTIqWslOWqeTzlrERSmg8N2+p9pjLQ6iO0OdywAnRFykyB1m+qU3jJLVd8ymOv3HBszlwsydsL7feD4ZnDaVeD4ySDYu7T2Ax1zEIyfD9Fd1RycZj1gwmKI6gy3fwM+obX7/EZfaHM19H3Isynr5k84OP4rDmqcHMw96PnKKs2q3XMRQgghRIMjJaTrUkkOrHsfNn0Gk5arydn7V6j0mvHzoXkfVZq6qdAZIbSNusOfc1htMwdDbA8pL1xdpTmw8GEozoJZN8Gwd+CXF1Ra4JJHoc1AMPnX3vNrdRDdBe6Y752vFtoa7lwIvmEqmK1NTgdk7lHXUpk8oy8jl4495dD5w+dLRTAhhBDiIiNBTl2yl8Lmz6E0V02Q7jUJ1ryh9v32Ooz6GPRNqDNWPgenPMAB9dpnDlcjPCcvsirOnV84jJ2l0rSyD8H8v6jtBl+1bk1tj6SACnROrmRWV5XNitLUHJzyFLVL/lI3zyuEaDDsmVk483LRBYd41rqpC1a7k8wiKxkFVnRaDVEBJuwuN5mFVvQ6DZEBJuxO1TboNET4m7C5XGQV2lQ7wIStbL9RpyUiwIjN4SKzyIpJryPcz4jN6W2H+av9WZ79JqxOJ1lFVswGHWF+RqwOF5mFVnyNOkL9jFgcLrLK2iG+RqzO8raeUD8DFrt6PD+TnhDfU9uldhfZRVb8TXqCyvZnF1kJNOsJ9DFSYnOQU2wraxsotjnJLbYRaDYQ4KOnxOokt8RGkI8Bf5OeYpuTvBIbQb4GAkx6iqyqHeJrwNeo9ueW2AjzM+Jj0FFkdZBXave0C60OCkrthJa1CywOCi1qv7lCO9zfhFGvpbCsHeFvwmjQUlBqp9DiICLAhFGnJd9ip6isbdBpyS+1U2x1EBloQq/1tqMCzei0kF/qoNjqIDrQjFarIa/UTonVQXSQGa0G8krslNicxASZ0QC5pXZKy9qgIa/UhsXuJDrQBGjILbFjdTiJCTTjBm87yIzLDbnFdmxOV1nbTW6xDZvTTWyQGWdZ2+50E122P6fYhsPpJjrYjMvlbccEm3C6ILvIhtPtJibQhNN9ajuryIbLrR7f4XKTVWjFDUQHqufLLLSiASICzEQGqvesMZAgpy4FNYOJy2H6DWpUpzzAadUfRkxtWqVvXU5I/1PNwdFo1PwJvwg16pB9CA6uhG63SRGH6ghuATdPh4+u9G678nGI7gy6Jn5pG/2g/xTYPB3uXKSqHebsre+zEkLUIWdeLknDhhO/eFGdBTn5pXYWb0/h+aW7sdhd/KV/a/xMet7/5SBWh4sHByag18KHqw5jdbh49JpEHE4XH69OwuZ08fh1bSm2OvhsTRJ2p5t/Dm5PdrGV6WuPYHe6eXJIB1LySpm17igOl5tnhnUkKauYuX8cw+Fy89xNndibVshXG5Nxuty8OLIzO47n883m4zhdbl4Z3ZWNR3KYt+U4buC1m7uy9mA2C7apNPn/jenGr/syWbwjBQ3w5tju/LgrnaU7U9FqNLw9tjtL/0xlxa40dBoNb9/ag4XbTvDD7nQMOg3v3NaD7zbvY+WeDEx6Le/c1oMvNxzjl32ZmPRa3hvXg9nrjrLqQBZmg5b3xvXk8zVJrD2Uja9Rx3vjevLx6kOsP5yDv0nPu7f1YOqvB9l4JJcAk553x/Xg3Z8PsvloLoE+et69tQdvrTzA1uQ8gn0NvHNrD/73wz52HM8n1M/IW2O78+r3e9l5ooAIfxOv39KNl5fvZXdqAZEBJv43phsvLtvD3rRCogPNvHpzV/5vyW4OZBTRLNiHl0Z14b+Ld3Mos4i4UB9eGNGFZxft4nBWMS3DfHlueCeeWbSLI9kltA734z/DOvKfhbs4llNCQqQ/T97YgacX7uR4bintogJ4YnA7/rNwFyfySukYE8jj17XlqQU7Sc230KVZEI8MSuSpBTtJK7DQPS6Y+we04ekFO8kotNKrZQj3XNmapxfuJLPQyiXxoUzs14r/LNpJVpGNfm3CuP2SFjyzaBfZxTauTAxnTO/mPLtoNznFNga0i2BE92Y8u3gXeSV2rukQyQ2dY3huyW7yS+1c3ymaQR0ieX7pbgpKHQzpEsOVieG8uGwPBRYHw7vFcmnrUF5etpdCq4PRPZvRPS6Yl5fvpdimign5m/S8MrorA9pF4Gdq+P2Mhn+GTU1wCzWC89vr3m3XP9/0SlJrddCiH4z82Dt/QmdQIzjJG6DjSAlwqiv3CHx1e+Vtq1+FlpdD814qXbCp8gmB3ndB9/EQEKW2Gfzq95yEEE3e7pQCnlqwE4BmwT60jQ7g8a+3A9Ai1JeWYb488e0OAFqH+xEZYObf8/8EIDHSn2AfI6//sB+AjjGBmAxaPvlNlb3v0iwItxum/X4EgJ4tgim1OZm57igAl8SHkltiZ+4falmGy9qEkZpv4cuNyQBc1TaCI1nFfLtZrV02qEMke9MKmbdVBTjXd4pmx/F8Fm1PAWBI1xg2JuWw5M9UAIZ1i+W3g1ks35kGwMiezfh5bzo/7E4H4OZezVn+Zxor92QAcEvvOBZuO8Ev+zIBuP3SFnyz6TirDqh5kHf2a8Wc9cdYe0hVu5x4WSum/Z7E+sM5ANx9RTwfrz7MxiO5ANx7VWveKwtwAB64KoE3Vx5gW3IeAA8NTPAENAAPX53gCWAAHrkmkeeW7OZghpqT+rdr2/LMol0kZanFqR+7ri1PLviT5JxST/tf8/7kRJ5qP35tO574dgdpBRYAplzXjinf7iCz0Oo5/vGvt5NdrOZQP35dW/729TbySuye/X/7ehsFpY6y50/k0S+3UWh1eM7vkS+2egKGvw5K5ME5Wyi1Oz2v7/45m7HYXer1D2jDX2ZtxupwodHAX/q35t6Zm7E5XWg1MPmKeCbP3ITd6Uan1TDxslZMnrEJh8uNQadhfN+W3D1jE06XG5Ney6194rh7xkZcbjAbtIzu1Zy7Z2zE7QZfo47h3WO5Z+Ym3G4VzAzuHM09MzdTUZHVwUNfbGHJw1fQKTaIhq5xjDc1FdYi2L/cO4JTbtYIyNhTL6dUq0wB0P5GaH21uvOuM0Lz3tBrIvjKitLVUpwFX9+p5uAYfGDsbAhro6qdzRoBpXn1fYa1zyfEG+AAaOTPmRBNnT0zC8uBA1gOHMCRrjrfjvR0zzZ7Zu0VGskvsfHmyn2e9qiezZhdFoAAjO7VnJnrjnjaN5/UHtM7julrK7abV2rf0juOGWsr/nwcMyr8/OiezSs936iezZi93tu+qXssczZ428O7xfLVhmRPe2jXGL7e5G3f0DmGb7d4F3O+vnMUC7Z6CyMN6hDlCYgABraLZFlZQATQv204K8oCIoB+bcL5cU+6p31JfCi/7s/wtHu1DOG3A97fT9fmQaw77C333zEmkE1lAQ5AYpS/J8DRaKBVuJ8nwNFpNTQP8fEEOAadhnB/kyfAMem1BPkYPAGOr1GHr0HnCXACTHr0Wo0nwAn2NeB0uz0BTpifEYvd6QlwIgNMFFocngAnNshMdpHNE+C0CPUlJa/UE+C0DvfjaHaJJ8BpG+XPwfQiT4DTKTaQXSn5ngCnW/MgtiXneQKcXi1D2JCUg9Wh2pfGh7L2UDY2p2pf1iacVfszsTvVUhxXJobz054MHC7VHtAuku93peEsa1/dXv3uyppc1zGaRdtSPCt5DO4czYKtJzztG7vE8O3mCkWyKnC74eNVh7HYa3mpiBogIzl1yVYMC+5Xn5BWV8L1L6gOaUkOLHwQxn3T9ErfmgIqt3VG8GnCIwx1xTcMRn4Is0eranUxPSCmG8waCYOeUUHlRSbYHMz84fOr3C6EaBrKU9QqSp58j+f72kxds9hdJGWVeNrRgWaOZBdXah+tsD8q6OT9Jo5WaEcEmDia7T0+1M/o6XQDBPkYSC/wLrXgb9KTWeRt+xj05JZ1sgFMep2nkw2g12o9nWwArUZDia1yx7S8Uw2qaGd5pxrA6XJ7OtEAdqfb04kGsDncVGhic7gqLX9ntVduV3yuk9saDZ4OP6igpajCuZv1OvIrvFY/o84TcAAEmA1kVXhv1Htn8bRDfI2kVmiH+htJqfBeh/mZOJHrbUcEmEjO9f5uIgNNJOdU+N0Gmiv97qICTRzLOfl3f/rPysk/H33KZ+XkxzdX+uxEV/H4R096/EOZRZWO33kiv9L+TUdyKv386gOZFY43seN4HqdzMLOIUpsTs0F32mMaArn1WZd8QlQVtTZXw+hPVad04nJo1gtuntb0AhxRezQaiOgA962BmO6g06lUyLtWQMKgizLICfcJJyEk4ZQvqawmRNOhCw4hfvEi4hcvIu7TTwCI+/QTzzZdcO1lCfgYdSRGeqtWnsgrpU1ExXYJbSruzz15f+V2ar6FNhHev9UZhRZahnkrj+aW2Gge4k3rzi+1Ex3oXXqh2OYgwt/kaVvsTkL9vDcRbU4XQT4GT9vpchFQYR6F2+3Gz+jtpGo0Ko2pnE6rwaT3tvU6DcYKE86Neg16raZCW4uuQttk0FKhidmgrVR406fCc6uUKe+52Z1uAs3ecy+1Own29baLrA7CK7z2glI7kQHedn6pnZgg73uXU2wjtkI7q9BK8xDve51ZZKFFqLedXmChZZj3d5Oeb6VVuLedkl9KfIV2ar6F1uHe321KXmml3+2JvNIqPhve/cer+qxE+p10/Fk+W2f67J1yfOXP6inPf1L7ZB1iAiv9/hoqCXLqkt4IcX1g1CfeOTiR7WHc1xDSsn7PTTQ+Go0a0alYZMAvov4CHFuxSqMrv3XndKhRSln0VQhRQwwR4ZgTEzEnJqKPUumq+qgoz7baLEAQ6GPgsWvbetrfbTnOhMtaedrfbqrc/mZzMnddHu9pf73pOJOu8O7/euPJ+yu3v9qYzKTLKzz+5mQmVmpXfr7vthznzn7evsSCrScY39fbXrg9hXGXequaLt6Ryq2XeNvL/kzllt5xnvaKnWmM7tnc0165O52besR62j/vzWBYN2979f5MbuzsnV+89mAW13Xytv9IymFQ+0hPe9ORHAa0jfC0tyfncXmC92bvzpR8Lo33Vgo9kF5EzxYqiHW54UhWMV2bq3khDpebE3mldIoNBPBUm2sXpbJJSu1O8kvtno57sc1Jqd1Jq7KgsqDUgdPt9gSVuSV2dBoNsUEqqMwssmLWa4kKVIFUeoGVQB+9J8g8nltKmL+RsLIg82h2CbHBPp7A7FBmMa3CfAn0Uf9f70svJDEqwBN07kopoFNsIP5l7W3JefSIC8G3LJDYdDSXS+JDPUHo+qRsLk8I8wShvx/MYkDbCE979YFMru0QiUGnospf9mVwQ+doT1C6cncGQ7vGeILS73elMaJ7M09QuuzPNEb1bFYpSC2n1cDkK+Mb/CgOSJBT93TG+iu7K8TZOJ1nbp+OrRgO/qQWJ83cAy4HpG6Hd7pD8h8S6AghmoR20QG8NbY7gWY96QVWNiTl8PTQjgSY9KTkW/jzeB5P3tgBf5Oe5JxS9qYV8o/B7fEz6kjKKuZYdgl/v74dvkYdBzKKSCuw8Ldr2+Jj0LEntZC8Ujt/HZSA2aDlzxP5WO2qYptJr2XLsTw0wH1Xtcak17IhKQcfo5Z7rlTttYeyCfY1ctflrTDqtKzan0lMkJkJ/Vph0Gn4aU8GLcP8GH9pCww6DT/sTqNddAC39olDr9Ww7M9UujUP5pbezdFpNSzafoI+8aGM7tkMnVbDvK0nuDIhnJE9YtFq4JtNxxnUPpLh3WLQauDLjce4oUsMQ7pEo9XA7PXHGNG9GYM7R6PRwIy1R7ildxzXdYxCo4HPf0/i9r4tPYHPx6sPc9fl8Z7A58NfD/GXq9rQP1H1kd7/5SAPX53A5W1UIPTOzwd47Nq29G2tAqG3fzrAE9e345KywOjNlft5ckgHerVUgdEbP+7jmWEd6REXDMBr3+/juZs6060sUHptxT5eGtmFzs1UoPTqir28PLqrJ3B69ft9vHZzNzrEBHh+/vVbunkCqde+38cbY7uRUDZC8uqKfbw1trtnxOa17/fxzq09PCNAb/ywn7dv6+4JtN5cuZ+3b+3uGVF6a+V+3r61hyfweueng7xT1na71fvx9q09iC0rO/3hqsO8ObY7MUFmnC43n/yWxFtjuxMVqEqaT/v9CG+M7V5WwtzFzHVHeX1MNyL8TVgdLr7YcIz/jelGuL+RUruTbzcf59Wbu1UaHQz3N/LZhD6VRrkaMo3bXTFjsmEpKCggKCiI/Px8AgMD6/t0hGjaijLg8K8q3c03TI3CHP5FVWs7W/W/4myYeikUZ6q0zEHPwPf/VuvYhLaGu3+UYF4IUaPqa50ch9NFeqGVnCIbOi2E+5uwO13kFNvQabVE+BuxOd1kF1sx6LSE+RuxO1xkF9s8bZtDHW/QaQn3U+vY5BTbMJbttzpcZBfZMOm97ZxiG0a9tmxSvGqbDTpCfQ1YHBXaZevg5JbYMOt1hPgZKbWpdWfKjy+xOcktseNr1BFc1s4vteNjqNz2NeoJ8tFTYnOSV2LHz6TaxTYnBRX2F1udFFjs+Bn1BPqodXAKLer4QLOeIquDQosDf5Pes25OocVOgEmPv1lPkUX9fKBZtQstDoqsDgJNenxNegqtap2aQB89vsay/RYHQb4GfMvW0Sm2Ogj2MXjWzSmxOQj2NWDWq3ax1UGon0HNXSrbH+JrwKjXUVBqp9TuJMTXgEmnI9+i1r0J8zNi0Kt1cyx21dbrNOSXOrDYnYT7mdCWraNjsTuJCDCh1Wg8x6s25JXasdpd3naJHavDRWSACU1Z2+Z0eVLuckvs2E/TdrvV49mdLqICTLhQ6+o4Xerx3W6V6uh0QWSgEZfL244KNOIoa7vdEBFgxFmpbcJZts4OQIS/Cafb2w7zMxJVtlZQfTmf2ECCHCGECnCW/wN2zYO+D6o1aDZ9Dj//H7S5Bka8f/ZAJ3OfWuS2xFsth+AWah2b0PjT/5wQoknJKs0iz5J3yvZgc7DMkRNCVMv5xAZSXa0xshaBqcKEMFvxRTnRXNQgjQ4Cy3Kr17+vSp3nHFbtwGi17tHZhLWBa59TlQLLDX0bQlrV+OkKIRquPEseIxeNPGX7/OHzJcgRQtQZmZPT2GTshZ3zwFJWCjAvGda9ryZ8C3G+yufc+IXBlY9B3wdUuzzA6TYOrnlWFTQ44+M4IGU7LPt75e3f3QWZe+FCBoxL86DQu+YC9lL1OXc6TvsjQgghhBAgQU7jkpME02+AxQ/D9q+gIBVmj4RfXoBfX66cJiTE2RRnwu75aj4NgNMOenPlY0wBlRfZtBZW3l/ethbAnNFqDk5wC5iwWM3BKc2Fubeo+T3nozQPts6Cj66ErINgt0DSb/B2N0jdJoGOEEIIIc5IgpzGxOgHLa9Q3y//O7zXG7IOqIptnUaCMeDMPy9EuaIM+P4p+O5uWP2qCpg3fgpr3lD7/VVpVjZ8BKtfV0FKXjL8/Lx3dKUwDX76P8hPVumTt34BYQlqDk58f5i4DELiYewcVYzgfDhKYdWrUJQO02+EDR/DF2PBVgQ/PA1V5PsLIYQQQpSTIKcx8Y+EIa9DwjWqbStbzXb8PGjeR63DIy6c3QLJG1SHH1SKVfouNYLW1Gh13kICf3wIKVvVawfoPAbuXwv9HlLtE5vBYYHZo9Wxi/+qgp5FD6sgaPYYNfLSvBfc9b23yEBEO5j8E0R1Bu15/qnxi4JJy8AUqAKdH58Gt0stfHrzZ1KpTQghhBBnJIUHGhuHFXKPVN6WsQdiukqQUx12CxxbB3Nuhma94ZYZKv1v2g1gDmp6FcJ8w+DyR8DlhHXvwjcTYORHKoDuPFoFEVc+pl5zuyFqFLHfgyrA2b8CXm+r0tsALnsY9D6nWQMq7NTnPhdarRoV6ns/rHrFu/2m97wFEoRoIppaNbJgczDzh8+vcrsQQtQVCXIak7xkNQcn+6DqUEa0g7Q/VeoaQNdbwCe4Xk+x0XI71fwStxOS18PM4Sody5IPepNa3LKp0WjBtyyNzGmD+X+BYW975+X4hkGPO73Bc6eRau7ND095A5wbXoGOw9XcnZpkL4Wk1bD6tcrbZ4+GiUshPLFmn080eE0tEKioqVUjC/cJb5TnLYRoWiRdrTHRaL13zO9YoL46DFf7TP6qDLC4MEY/tQjmmJmqnblPBTh+EfXbqbaVVG5bi2vmcUtzYctM+Ok51faLUIHOgvvVHJ3yYgQVRwftJSrwqOjQr6eeY02wFsJX48tS1HrApOXe1LV590g1wYtQeSBw8ldVgY8QQgghQU5jEtQMbvtSdfia91GpQUNeV5Ws2g8DsxQeqBaDr1rrRVthgNM/SnWu60NRBqx5EwpOqHZJLmydDdmHqv/YTjsc/V1932UsPLQRLn9UtZM3gste+fjCdDUH58APqt16gPp3/3JY/IgKPmqSKVB91pv1gdvmQlxf9bmPaAejL9I5OQ77qaW4nbb6ORchhBCigZN0tcYmqJn6Kucfqe7CazT1d05NQXmRgek3qtQ0g4+abJ++E76eAGOmQ2BM3Z1PcRZ8/yT8+TUc+B5u/1aNvPz8f/B7NNy9EoLjLvzx/SNh2Duwax50vllVP7v8EfWY7W70FiUop9WpSmkAN02F9kNg90I1Ryc0HjQ1/KfEYIaWl8O4L7xr9ER2VBXbLsYAx2lTBSBMARDVSV3vmftUSmXcper9EtVTmlc53bck15vOKYQQotGRIKcm2ErA6Ott20tVJ7muNNYAx+1WC0UafCCkldqWdwwshRDZ4fwrclWHtRAO/6pS1PwjYdIKyNgNX9+h1mUpzqz9IMfpgNJsNaJk9FNzrHZ9B6nb4Z0e3mp6ba6pmU5tQBT0vkvNOQLwDYUed3jbFfmFw1VPqHMKb6dGDTuNVB3ukPgLLzBwJgZz5dep1V6kAY4DTmyFWTep+VITl4HOoIpiWPJh/HfQ4jIpPFId+cfh+3/DdS+oQL8oHVb+Fy77K0S2B5r2nCAhhGiKJMiprrxjsGsB9LhdTdQuTIPNM6DXhFPvhl+IwlQoylR3sXV6dYc/7xiEt1XzcBort1tVhZt+o7o7PWGxmnM0a4R6vZOWQWSn2g90ijJVQGEOhO7jVMCYcI1KWwuIhltmq6AnsmPtnofTAalbYcYwGPIGdLwJwhJh6Duw6EFvgNNpJFz735rr7J8c0FQV4JTzC6/8vOZAaN67Zs5DnJ5Or0YUTIEq2P78ejWyZslXI3D+kSroERemOAu+vF3dzMjYoyopLnkM9i9TlQQfWA/+kbVaHECqkQkhRM2TIKc6ClJVpzz7kLrzd+VjMP9+OPwznNgEN72vOiAXqjAVvr1bpalMWKQ6vT/9H2ydoeYlJF7feAMdl1NNZHdY1CT4aTequTB5R1VH21asjqnNIKcwDebfB5feC/ED1CR33zA4ulb93nxCoM3VqtCDrpYvFUse/PamGgVccL9a5NUceOoE++xDqox4U2UvUSNZnnYdj4o2VOFtVeD/2XXqegF1c2DSMojo0HhHc89DrQUCpkC4/gX1tzxrP7zR3rvv6qe81QZrkVQjE0KImieFB6pDb4TEwer7de/B291VgANqrZFz6Zy5XCr3u6LSPPWv0w4FKSoQmDFc3W3cMr0szetA4550rNOrhR0nLFHvU8EJFeDojHDHQmjWE/S1eHfaWgRr34HDv6j39cAP8OvLqozykkfVHV1QaYi1HeCAGiEZ+ia0GeTdVpKtPleg3iutDtJ2wJe3Qf6J2j+nupZ7FDbPVAuNggpC176jCjAIdd1XLDxwcruJC/cJJyEk4ZSvagcHeiM0vwTGzq28/ap/QJcx6maDEEKIRkeCnOrwDYP+j6t5DaDWEAEY9q6amH22tUNcLjWx/deXVBoKQE4SLH1MjRIFt4A7F6o5Dw4LHFurjrnqn2r0wTe0dl5XXdHpVfUyc5B3mylALfaoq+X5BSZ/6PeQWvjT7VKLYW78RO274jGIaH/mn68NAVEw+CX1vaPUO6LRfTzcMQ9u+1oFOnofldrXlBSkwMybYMU/YP2HagTr27vhlxdh2RSVVngxy9xXNgcnT408+ISoFMZpN6q5YxdRsFMrSnNh07TK23bN895wEkII0ehIulp1Oe2Qfbjytsy95zbKUpiq5qRYC8FerIKXmcPVnJvSXBj1CRj9IaYr5CZ5f67NwPora1yT8o6pFJHCNNV5R6NGL2YMVXN0yosR1JbAWBj3FbzZ0ZsC1qIvXP5X1Ymsa1kH1ZwcgPUfwGUPw5gZENxSBX2tLleTzoNb1m2lt7qgM6iy1JuTYPUrsOFDNedEo1Hzky7mlDWnQ70XtmL1uZy0DLQG9bejNFeNfDntZNkLZGL8hSjKhMWPqjk4AJ1Gw97FKmV09kg1Rycwtl5PUQghxPmTIKc6CtPUHJykX1U7uqtKJ1r/vuqcXfG3M08QN/io6j2/vKDWP9k6u2y7r8oFd7vUnezdC9V2rV6VN555k5qjE9urblKpaoPToYLBnMNqDs4dC1VHd8ZQFfyk/QkBsbVbMao4S6WoVZzjkrwBjq1Tc3QqVsyrbSXZqhxzUboKYO9YoH73J7aoc3p0OwQ2U0FYU+QXoT7zjlLY/qXq1AOM+KjRzD2rtepbOj3E9lSBv8nfOwdn4jKVyte8N+iN5BXW3sT4Jk1vhg5DVZAz9E1VUr3P3eoGTPwAz6iyFAcQQojGpZH2kBuQ8rShER9B2+tg9WuwfipodGefDOwbCpfcq1LTNn/u3X7nAojuBgXHVcoEqFGeXhNUekpuEmz4FAa3abwldXV6tQ7KmJmqg9u8F6BVc3RykqD1wNoNcKxFsOETb4pan8mqTG/KZjVH564fIK5P7T3/yXzDYOSHKm1u2DuqstyI92HRX9Vok08tlGhuaFwOyD1WeVt245l7VpvVtzyBjrbC35XwRJXK2lhvdDQU5gDoMFytN+QfWVY1sA/c/zv4hHr+xkpxACGEaFzkf8fqCIiGEVNVTnzz3mo+Sf8pKu2meZ9zmzNTmguHVlbetmUWhLZWaUkTl8HBldDzTvV4dy5UqUxXPtZ4A5xyRj9IGKRSb8rn4MR0V+tSGGv5zr3JH7qNha2zoOtYNaLmKIWv7gC/SDUfqq4Ft4Bx36iOlVZb9vl6H4wBTX+xx8I0NQfn2FrViY/qrEbzVr8GaKDv/Y1/Dlp1VRXM1GOAU9MjV/W6Do05QH2V0xtVRbtqqOr1SOqgEELUHQlyqisgSt39K7+76hsGidedW0nXvGTvHByDr6qstXex6nhrtDDoP2qxxeAW3go/IS3h6ifPXtTgQjls4LJVDjKshbX3fEa/ym2dHnR1lJoU2hom/6TS5XxDgBAYO1u99wFRdXMOJzs5cPWLqJ/zqC8ajSqP3nog/PQcbJ529p8R9aKmR65qdSSsHlT1ehrraxFCiMZIgpyacHJAc65rVuhNal5NcZZK0wpLUEUGfn0REq9Vc3Y0mlNLmNZWwOG0qTV5ClNVoGbyV1Wd9i2HnhPKAoEm5uQJ/E1tQn9jERANoz+FrH2q4p3JXwXz7QZD80ub5mdPnOL97s8TZlML0mYbrTy47al6PiMhhBCNlQQ59ck/Em58FUr/rUYVdHo1N6TjCAhqduooR23L2AOzblLBzujPVCGFaTeoSfFut5qM29jXjDh5cUmHTY3cyLyG+hcYo4Kd8psEfhGq6MBFsNBlTWgKE+PDbCa0d/xNfT/rzRp97HpNhwNeuOIFbE4bB3MP1vpz1/drFUKIhkB6dvXNP1J9lfMNrb+5B/6R0KwPHF0D396l5sk4bapsbbvra28Eqa5YCmH/crXQaFiCqqqWvEEFPTHdJdBpCC50VLQ+FKSo66Os1Hmw3pf5Q75SVQLxnnddBRmNeWK8PTOLyGwruXlWSsq2+edZ+bLja0SmW7E7sjBEVO+11Wg6XHGWWuMsppv6+2gpgNTtan0t/6pTTE06E2OXjq3+c5+Dmk79k6BJCNEYSa/uQthKKpcXtpd4F25szAJi4ObPYc7NqhS206Y6bJNWQES7ht3hPBtrEexdAgvuUwuQTlquOqmzR6rS3JOWQ2yP+j7Lpq04G6z5ENRcBdDWQtVZ9AtvfAF0QYoqUlGUXlbaOYDwH/9L+L6lMHEpRHVRxSPEOXHm5XJixM2VtpU8/E+0wAkgfvGiagc5NaY4SxXE+ONDuOa/qurl7sWw+GHoMhauf+G0gU5j1dTmSwkhLg4S5JyvvGT48xtV7cwvXFWF2jQdek9UqTaNnSUPCk542067qh4X1LxRrFVyitI8VUTB4AvRndUaNEXp8PEAFZy6HBDaBnzlP+paVZwNv78FGz5SawBFd4G9S1XQOeJDaD+kcQU6JdnqurCXqLWdIjrCwR/UviO/q9LOjT218yyqmx5XcXQgsh7KhL9wxQuYdKbzTyFzu9XfFYCVz6hiMcc3qbY1X61vxqnvj62RlEKvDzJSJISoDRLknI/CNHXnP+uAmpw/4F+w6GE48AMkr4NRn1ROPWtsMverVdRLslUKRmAz6HkHuOxw4Edoey04neC0No7XWZIDGz9VZaqju6mO5x3zYPoQsBaoY0LiYfx3KogTtcdlh31LVYrgrBHQeQxsK1v8dsfX0Obqswc5pXlln72yynf2UhVkmIPV+jF1KaKDGrGZPgTyj6svUHf2u9/W5AMcqH56XMXRgfe7P0/YrDfxz7NS8vA/AYj79BP0Uep3rQuu+cITJp2JKaumnLL9rKMT/hFw/fOAG3Z85Q1wEq+HYW97KjOe/P5UDKREZTJSJISoDZJPcT50Rmg/TH2/4WN4u5sKcAC63AJ6n9P/bGOgN6o1WXxC1Po8t30FKdvVf+RRHdUk/Z3fwsybvJ26hspWDNu/gF9egOlDVfrd8U2Qe1SN3niOKwKHpf7O82IREK1GcMLaqEBn22xodwPc8wuM/EAFzS6XGvFxu0/9+dI82DITPh4IOYfBboHDq+Dt7up363LW7evR6dU6VtFdvNu0eug8Wl0/4rw8uO0pbt39d4qCTZ5t+qgozImJmBMTayRVrXxkpfwrzj/uwh9MZ1KLs1YU01VVzBRCCNEgyEjO+fANhcseVneQ//jAOxow5A21Yra5gabbOKyV//M9uV0upJVabNReoibQHlsP2+eqfdpnoGU/+PE/qr3+Ixjwj4abwmb0g3Y3qrz5vGMqraTnBJUe5XJCUJxKzSvOhGk3wqRlqhiBqD0+YdDtdvj5ORXgdLxJ/T7GfaMCnIxd8OXtMO4r9fmrOAfMXgKrXlbB6/Sh0O9B+OEplRr0w39gzLS6XRy3KBN+eBKS//BuczlUNcIJiyE0vu7O5TQkBaiyGhtZsRTArvmw4h+qbfQDWzFZkW3Jy9kPPsGgUSOLFd/rqtL7LrQoxdl+t02h0p4QQlSXBDnny2mD7AOVt2XsUWk0NMAgJ3OfGr2Iv1JVEctLhqTVag6ET/Cpx4e09H4f1QmGvgVLHlVVyfYvV9vbD4XLH2q4AU650HiVUjS1nyqg4LSpTnFEe5WiVpqrAhyXo+5HAuqCywmWfDUPqbxyXHGW+hzUdXlyayHsWaoCHJ0Rrn0OPrpKBS+zR8INr8E3E9WNg28nqXWjKgYt/lFqdHH6jWrO2Pf/Vttje8Goj+o2wAHIT4Y/v1bfX/NfiO9flrqWDJs+g/5P1HvKWmNMAco2Wum+4FuMOmOtpKjVCIcFtsxS37cdDCM+gO//TZ7Rl5ErJ1c6tOJ7XZPV7872u63pSnsSNAkhGiMJcs5HYZqag3NwpWrHdFNlQzd+otZaueqJuu9snUnWwbI5Njlw61y17s2cmyFzL1hehO63Vx3olDMHQqcRqtDC0d/VNr0Jhr97+jLXOUnqDnxZWV0KUtVk3LC2dV9tyloExzeqlLSDP4HWAKM/V6l3/tEQEKtGcHRGVT2uKXE51Wdzzs0w7mtVOS73CMwYBoNfVvOU6jLQcVjgezXXgpZXgMEPRn4E390N2Ydg9ii1zz8Kxs4+9TrS6tTv6JK/wJo3vNtHvA+BsXXzGiqKaA+3zFbn3utOleY5cSlsmQGXPVLvAU5j9eC2p5g/fD4JIWcfVa23kSr/SBg7EzZ8Apfep/4WXvd/0ITn3DTm8uQgo5pCXKwkyDkvGk8aAje9r9Kh1r4Da94sm/hcAyWWizJVUBDSWgUFJblgyVXlnQ3nOefH6AtBLdXd+y/Hqf+Mi7PUqEZ0l7M/Xmk+7JznDXBApboteBCGveWZYOuRkwQzhwMamLBI5a1/M1FVoZq0HCI71l2gYytRweh3d6t2bE+1rsX6qWo067a5am2cyE5Ns9RvaS4suF8VkZgxFIa9o1L2ClJg4YPw1611G+T4hqvPwKrXYPCLao6OTq9GPH553nvcrXOrThu0l6o5OL+/VXn77NEwcYlaTLe6rEXgKFWLkAI47OparOrGhdEX2gyEVpd75+BEd4VBz9TfOleNTHVHB6o7UlWt5w+Mhf5TvNeQXwRYc8/+c+foYuuU1/ZIUWMc1RRCVJ8EOecjIEpVz8n4CzTrrebgXPZXaHkZNOsFvmGn/VF7ZhbOPPWfoC44pOqJtEWZKs/7wA9qhCG4pUp9+fVFuO1r1aE6n0AnMBZunQ1zboH0P1WAAypVK+7Ss0+SzdgNSx9T37cfCi36qXkI+5fB2jYw4J/elDWXS3WsizNVh3T6UNX5S9uhRnbyjqpJ59o6Ks6gN6uOr9FfVVa7+TN1XjOHq9ftE6rOqzGv/XMmfuFqbsuMYWpO0rx71HajvwoK6npyvEajRj+G/E8FAS4XFGWomwQVzb9XFSgIblF5uyUfvh6v0g1je8E1z8KXt6nUtXn3qiIZfqe//s7KWqTSMQ/+pO7Km4MhZQv89ByM/qTq0SKjH1AhUNTpJcA5D/U9OlDt5z/5JoGm5m6WXGyd8vr+LAghmiYJcs5XQJRKVyjvHPuGQsK1Z+0sO/NySRo2HDjDwnaOUjVqYi1Uc0USr4Od36l9uxdCbPfzH81xOU+tHmYr8azlcEbhidDnHlUue+gbqnqc0Rc2fAp976s8J0erVXeyJyxRIwf5yepLo4FbZkHrged/7tWh1UJUZ5i8UnVYy9cwunOR6oxUnHvUVIW0glGfwufXebdd/XTZiNo5lFy2FqmJ/r4hKqXPYYfSHPUZuJA1bTQabxBQkq0+49YClaI28ElY/neV/vXlOBXoVBxBMQWqz9Hq11WqkH+USg+bNxlGfewJcOxOF1mFVhwuNz5GHUE+ejILbThdbnyNOgLMerKKVNvPpMPfpCe3xE546XF036n5FC63G3f38ejmjgaHFfcPT1NyzSvYDIH4GHXkFKufDzDrMRt0ZBfbcLvd+Jv0mPRackvsuNxuAkx6jAYtOcV2z35j2X63202A2YBBqyG3xIYbCDQb0Os05BZ72zqthryy/UE+BjQayC9xAG4CT2oH+RgByC+1ARpcVVWpK3uPTuSWEOxrxOlyU2CxowFCfI04ytsaDSG+BuxOF4UWBxqNhlA/A1a7iyJrWdvXgNXhbYf5GSm1OSm2OdBqNIT6GSm1OymyONBpNYT5Gym2Oim2qna4v4kiq51iq7OsbaTI6qjULrQ4KLGpdkSAkYJSb9vN6V9fan4pEf4mcktsWOwu9DpNWduOxe5Er9MQ6W8iu8SGtWx/ZICZ7CIrVocLg05LRICpQlvtzzqpnVlkxeZwYdRpqzwbh8tNck4JRp2WiAAjmYU2bE4XRr2WCH8TmYXWSu2MQgt2pxvHaX53DpeL5JySs+43G3SE+BrILL8WDDqCfA3ea+Pk9lmulaqvHQNZRdayth5/k75S28+kI7vseH+zHh+DunZcZdfCKdfOSddKxXaV18pJ7VOuFSC/1I677Jo+/bVQSpCvHrdbHa8Bgn2NuNzuSu2TrxVfk3SfhGjoNG73aa7+GvDSSy8xb9489u7di4+PD5dddhmvvPIK7dqd2/yHgoICgoKCyM/PJzCwcee4Ww4cqBTkmBMTqz4w+6CawFyY5t3WfTxc+6w3jeZc5SV75+DoDCrlLe+Y6uTfOhdaDzh74FGcBW6nd20SS4EKmk63Tk5hOky/QXVWQXVO7//91DvzovZlH1IBZ0GKd5vBVwWiMd28xQiqYi2C/Stg4QMq4GjWU83xmTFcFaPoMLR6i3daC9VioCufUcFKcEtV4vuLW9VIY0z3U8/PVqKCrvLV5F1ONXpYFgylF1iYufYIM9Ydxely89EdvfjtQCZz/zgGwEd39OKXfZl8seEYWo2GD+/oxcrdaXy18ThPDIhivP4XDL/8t/Jzhrbm8A1zmLwwgxdHdWHRthS+23Icf5Oeqbf3ZP7WE8zfeoJAs4H3xvXg283HWbgthRA/A+/e1oOvNiazaHsK4f4mZt/ZmY1J2Ty9/AhRgWY+vr07J9Izuf+7Q8QEm3ljTHdmrDvCip1pxIX68Orobkxbm8T3u9JpFebLy6O68tnvh/lhVzqtI/x5cWQXPltzmB93p9M2MoDnRnTio1WH+WVfBu2jA3l3fALHC7IosTkw6XVEBJjIKbaRU6hnxm/Z/POGDrz/8wHWHMqmd4sQHr+uHe+Vtfu2DuGRQe1456f9rDucw+VtQnnw6kTeXnmAP5JyuCoxnHuvasNbP+5n49Fcrm4XwaQr4nnrxwNsPpbLdR0juaNvK974cT9bk/O4oXMUjw2OJTk/C4vDSYDJQKCPnuwiG/lFBlbvsTC0eyxv/LCfXSkFjOnVnOs7RfPGj/vZnVrAbX3iGNg+kjd+3M/etELG923BzX0N3PH9mFM+Wq/0ncmWgya6xwXz5o/7OZRZzH0DWtMxOpC3Vh7gcFYxD1+dQJsIP95aeYAj2SX87ZpEmof68vbKAxzLKeHv17cjMsDEOz8fIDmnlH/d0J4gHwPv/XKQ47mlPD20Az4GPe/9fICUfAvPDu9Ir3g9qUU5OFwuogLMuHGTnqvjwVkHeXlUF0rsTj789RCZRVZeHd2V/FI7H606TFaxlTfGdCOj0Monvx0mp9jGd39tzZ0/3HLKa/tfv1k8OD2FJY92I9+WT16JDZcbYoLMWB0uMvJ0PP7FYabe3oPtx/OZse4IVruLqbf3ZPPRXGavP4rV4eLD8b1Yn5TN3PXHcLjcfDi+J2sOZjH3j2O4y66VX/dl0j9cS7irlJggHzI1ZkZ8sReDTsMH43vxw640vt50HKNeywfje7JsRyrfbjmOj0HH1Nt7snh7qudaef/2niw46Vr5bstxFmxNIdhXtb/amMzi7amE+Rt5+9buzP3jGEv/TCUywMybY7sxZ71qn3ytNA/14bXR3Zi+9ggrdqXRKsyXl0Z14fPfk/hxdwbx4X68OT6acctvPuX9fLXvTN5dUcQzwzry0Wp17XSJDeTJIR35YNUhVu3PpFvzIP55Qwem/nKQ3w5modNouKFzNFOub0fLsDou4CKEOK/YoFZvRaxatYoHH3yQPn364HA4ePLJJ7nuuuvYvXs3fn5N/49DxRQ1R3q6Z7sjPZ3ysZVTUtf8IiDhetg6w7vtkntU+d3z5XKozqTOAOPnQXhb+GIcpGxWwYvTfvYg5+T5COZA4DQfqoJUNQenPMDRaNSd+ulD1Ryd8mIEovYVZ6mAoSBFpaiN+hhW/FMFuTOHwyM7zpzeZS9VJZsdVrUu0hWPqfkwDgusfhUSrqlekGMKUBX+EgZ5g/fmvdRcoYrV4Coy+qqvclqd5/OZVWTlb19tZe2hHACeHd6Jt1YeYMsxdf09P6Izr32/j+3H8wF4cWQXXlq2h10pqgy8X1A460zDuaztZvT7l3ieImfMfMZ+fpB/39iBJ+f/yaHMYgBeu7kj//huB0eySwB4ZnhH/v7tDo7llLWHdeKxr7dzPLcUgI9v60hI2u9cq7ews1trhvdqTUTxPuKyfua/195IdHQsD8zdQmahFYD/DO3EfbM3k11sQ6OBp4d25N5Zm8gtsav2kA7cM3MT+aV2tBr495AOTJ6xiQKLA71Wwz9vaM9N72yhyOrAoNPw2YQ+3PreZoptTkx6LZ/c2ZtJ0zZSandiNmh5eFAiE6dvwGJ34WvU8cCARCZO24DV4SLApOfe/m2Y+PlGbE4XgT56Jl4ez8RpG7A73YT4Gri9b0smTduIw+Um3N/ImN4tmDh9I06Xm6hAEyN6NOdgdipPrL/zlF/rG5fPZlCHKCbP2ITbDfHhflyRGM7kmWqRzYRIf/rEh3LvrM0AtI8OoFvzYFLys6r8aJkNOlqG+vHQ3K0A9GoZQlSAmb9+uQ2Avq1DCfQx8OhX2wG4PCEMk17H41+r9oC2Ebjcbv7+7Q4ArukQSYnNyUvL9wIwuHM02UU2pv66B4BhXWNIybPw7NuHARjZoxkhvi4+//0IAGN6N2dXSgGz1h8FYNwlLdhyLJcvNiQDcGe/lqw9lM03m9XaY3dfEU+xrepqj063m79c1YY3v0/j+13qRtij1yTy7fp8ftqTAcCU69oxa/0xVu3PBOCfg9vz2Zok1h7KBuCpIR34cNUh/kjKKfusduSdnw+y+ai6Vv7vps68/sN+tiXnMWJoLNrJ40gHHJ/OpdTu5NnhXXlh6R52p6pr5/kRnXl20S72pxcB8NKorjy1YBeHMlX7tZs78q95f5KUVex5vie+28HRsmvn2eGVr5Vnh3fi0S+3kZJvKWt35K9fbCOtoKw9rFOla+WZoZ24f85msopsgLpW7pu9hZyya+c/QzuSkneiyvcT1LUyecYmCsuulSnXt+eu6Rs918pj17bjrukbKSn7nTjcbhbvSGV9Ug4LHricZiGNfH08IZqwWg1yVqxYUak9bdo0IiMj2bx5M/3796/Np24QKqaoVZQ8+R7P95VS10py1RycigEOqLvxk5ZBZOfzmyQfGq/mXxSkQvPeai7KrbMhZSu0urJmK0C5XJC1D47/4U1RC4hR5553FA6shB7j1EiCqH0+oWoe0pxb1NycqM4qTW3WSLjh5crBQlX8I1RgPGukGl1c9bLaHtpajez4n+eoYlVMAZUDJZ3xgue0pOSVegIcf5OeCH+TJ8AJ9NET5GPwBDghvgZ8jTpPgBPubyTKX0c7Zwr6wz9Welzz6hd4Y8hjnLA5PQFOXKgPBRaHJ8CJD/cjs9DqCXASI/1JySv1dNpG9Ygl0X4A85K7wO3m7zd+hF6rw+/bUWAvYdRAPzY4R3g6bd+NbUd0aSavDWrOXYsO0691GGsPZZNbYgegf2IEq/Znkl+q2gPbR/Lj7nQKLGqR20Edoli+M5Uiq2pf3ymaxdtTPB3nGzrHMG/LCUrtqj2sayxfb0rGYlcprDd1j2XOH8ewOlR7RI9mzFx/FJtTtW/u2Zzpa49gd6okgDG94/hsTRIOl2rf0juOT347jLOsfWufFny06hCTB1X9mQv2MfDM0kOeNWBvv7QF7/3srVR2arsl7/58kL9dH80rfWcSFWAmp9iG3aXOL9I3lH/9stdz/LhLWvDycm97bJ8WPLd4l7fdO46nFu70tG/u3Zx/ffenpz2qZ3OmfLPd0x7ZoxmPfLnV0x7ePdYTUAHc2CWGB+Zs9rQHd4rmL7O87Ws6RjF5xkZPe2C7SO6q0L4iIZxCS9Wdcg0aerUM4f1f1Puh12ro0iyIt1aqZQ2MOi3togP43w/7ADDptbSO8OPlFer1+xh0NA/x9QQ4/iY9kQFmT4ATaNZzTbSO7t18oZsvgUW5lJQ9d0BhDsuGNyMoTMM/ygKcyAATLrfbE+DEBJmx2J2eAKd5iLpWygOc+HA/soptngAnIdKf1HzvtdIhJoCkrGJPgNO5WSD70oo8AU6PuGD+PJHvuVb6tAph09FcT4DTr00Y6w5lk1Os2lcmhLNqfyad40y80ncm/kY9ep2WvFK1308fyIrtaRSWXSvXdYxm8Q7vtTK4czTzt57wBDgVZRZaWbknnTv7tUTTVOd2CtHI1WlSaX6+6mSEhlbdkbFarVitVk+7oKCgTs6rwbAXw2+vq++7j1fVe6YNVqlrK/8LIz8+/8nVoa0hqIX3znhgLPhFge4c5mRUxe1WXxWDLadDPX7z3nDzDLWvzdWqutqEJXDkd7USfFMIcKzFYKowCmkrVkUOyue4WAoqB4+WAvW6z5QaVhu0WhUU3/+7mpOk1amgd/JKNWH6XOZHBcSoOTzfTPBuG/Cv+inZfBabjnorW8WH+7HzRL6nnRDhz/bjeZ52YlQA25K97SsSwunln4v/9JvVyFVoa6zx12Da/DG+e77mEp9ANre+33N8x5ggNpR1EgE6xQay8UjFduX9Jr2O49pYWsRdgfHYbwQt+4u6EeB2Q1AcJa1v4KtV2Z7jI90WCsaNIeJTtRBv52anPl/5XXqAzrFBrNjpTW/t3CyQBVtPVNo/d8OxSvs/X5PkfbxmQZ5Oc/nx5Z3k8uev+Hidmp30eLGVH69jbCAfrDrkabeNCuDtnw4AVV//ep3WMyoAEBPkw4GMIu/7EWDmcFknGSDUz8ixnBL+9oUaOXl/XE8enLvFs//9cdGkF3j/HzEbdGQWedtGndYTMALotFoKSh2etgaNp9ML6tdUsZPrcLo9ASGAzeH2BISq7fIEgBoNlNqdngDQoNNQaLFT1vTMUykP8ALNetILLZiMZl7pO1PNTfIzkV6oOvlOhw9H87zvRVRg5fcmJtjMwYxCT7t5iC9707ztlmG+7E71Xhutwn3ZleJtt4nwx5mXh37yOABPgANQ+reH0ADOud/w5m2tMZkt+Bn16LX5TJ2k/ib46QJZvNV7LZ58bXSMCWTjSZ/lyp/typ/1zrFBbEjyXhudmgV5AjQovzayTzq+8v7VBzL5bI36fP11UAI/7Er3vCePXet/0uMF8uXGZM/ri/A3kVdi59ru6vVZLWbP5w5gxa40bu7VHD+ZnyNEg1RnV6bb7eaxxx7jiiuuoHPnzlUe89JLL/Hf//63yn2NkS44hPjFiwCVolY+ghP36Sfoo6I8x3j4R6ugYNscGPgvlcYzcakKcG549cKrR53cwa5OgJO5Vy2qGdVZdZzzkiHnMMRdotKiEgeBW+OtPBTTXRUwMAdd2HM2JCU5sGUmdByugkdrERz4XpX7jumqJuX//i70mqAqyZXmqQUjm/VWRRnqI9A5ueLfua7j5LCr6mLz7628feGDENRcBbQ6Y82cZw0I9/OeS4nNQZCvwdMutjoJ9jFWaDsIrrD/WE4J6fZgSm7/hgKXBXtAc5xuLYYeo9GV5uAX2oEdfzorPX5UoF+lx48JMp/2+fMtdj7eWsydl79J59Lx6hpyu8HoR8rI7/hkm4P7ugZzfyt148CQo9KwAgpzWDw0liBfJzc2i2bEF3llj++sdP7Ftsqvp8TqLCtCUFxp/7Gc8uOdBPsaPXfLS6wOgn0Mnrvj5fvLA4Hy11Pe8S8pez/T7JYKxxs8d9MtdieBZoNnpMnqcOJ/pk6gW404lAcKbtwYdVrPyJFGo4KD8sBBqwWdVuMZKdJpNWg1eAIHvU7jiSFB/WxFRn3dtd1lr62cw+XGbPD+/bU5XfiavG2rw4W/Uc9DZR1pX6OOl0Z14ZEvVTvQrOeZ4Z08x5fYHAT5nPy7N5x2f7HNUelaKD++vFNv0mvxs4I3LDqVTqPBZLbwjypSD9+9ck7l57M6iQ7yqRQUGXQahvQuC4r0QSzdWlTh+JM+y2WfxYqvJ/ikxz95f6Vr33byte886fEdJ11LToJ9DKd9fa/0nVmpHe5nxKBrgksQCNFE1NnV+dBDD7Fjxw6++OKL0x7zr3/9i/z8fM9XcnJyXZ1erTBEhGNOTMScmOgJagD0UVGe7ZXm4+j0akL41U955ymEJaiy1YExdXz2VcjcC9NuUFWx0neqAGfWCPWVtEqtXm/0rzzSodM3jQCnNA82fKwmyk8fohbW3LcMvr0LZgxRKXm/vgpr3y7bfxS2zYVlfy9rJ53tGRoWW4EawSkb2eD+deqz6LSp7daisz9GHereIsTTmT2UWUzbqAD0WtXel15Ip9hAyprsTi2ge1ywpyDituQ8km1+pJrDGblmCrcsv5XbVtzCPmsGhww6MhzF9OvgYOqkWKZOimV0nyD6t/Wm6609lMXA9t5CHL8dyOLaDt7rfdW+TEZ2iybclanmRJWzFWNK24zBUUKsxoZ+8jj0k8eR+5AaNSr920PoJ4+jeNwYmmltnh9bsTONm7o3O2172c5URvao0P7zpPaOVEb19LaX/pnK6J7Nve0dKZXaS3akcnOvCvv/TGF0xfaO1DMev3xnWqXnO1mx1cHw7t7RwZ/3ZjCkq/fv3a/7MhncKdrTXnMgi2s7et/fdYezubrC+78hKYerEr2/ny3Hcrk8wRvsbz+ez6Xx3myC3SkF9Gzhvdm0P72Irs29f7MOZxbTMcY7OpucU0pipLeyZEqehdbh3r95GQVWWoR6R62yi200C1Yjp243FFrsRAWq8v1Ol5tSm5MIf9W2Olw43W5Cy4L2EpsTnUbjCRwKLA58DDoCyoLG3BI7gWYDfkYVKGUWWYkIMGE2qP/aU/MtNAvx8QRayTmltAzzxVjWMT+cpa4Vs9nKP9bfyaNrxvOn/RCuWW/imvUmUR994HkdPm++h+OzuRhCg0+7Ilyp3cXlCd7/0zYk5dA/MdwTNDy0ehx/+eU2/rH+Tv6x/k5KnQVc29H7u1u9P5PrOnp/17/szWBwZ2/7pz0ZDOni/Wys3JPO0AqflR92pTO8m/ez9P3ONG6q8NlasTONEZWujTRGnXRtjKzwWT6biZfHY9RLkCNEQ1UnV+fDDz/MokWL+OWXX2je/PR/QEwmE4GBgZW+LjpVrbXRUNbe0JtUmpOtSAU7n1+n5mto9WqyuKYJD9mbAqDtYBXEFaTA1L7etWea9VEr3vccr/YXpsK7PeH7f6n9bQc3vkDPJxTumK+qqt2xAKI6qn+b91b/1vU6O2cRFWDig/G9PIHNrHVH+b8RndGVteduOMZzN3VGW3aH/6uNyTwzrJNnBOC7zcexuyp33Uw6E1NWTWH8itsYv2KMp2NmNJXy05505j3UhamTYnnzjmj8/LP46qFWTJ0Uy4s3t2T94WweGNAGgGYhPnTWJxM9b7S6ERDYDFdsLwDCVtzPfc0Ow2nKIZdzOF2M6a3+dqYVWMgrsfPKsNZcHh/I8dxSLHYnjw9qzW3dQjmaXYLL7ebGLqpzeCizGKNOy/WdVGCwL70Qf7OeQR1U53JXSgHhAUYGlAVu24/n0yzEhysTVWd189FcWof70a+NChTWH86hY0ygJ1D4/VAW3VsE06ul+kys3p/JpfGh9IgLBlTQcmViBAYCeKXvTF7tO4vZ13/DW1fM4ZW+M8kq0HND5xg6xKj5WUt3pDK8Wyxto1QgsXDbCUb3ak6bCNWet+UEt/WJ8wQW325K5s5+rWgZpgKLrzcmc9cV8cSF+nh+93/p34bmZRPEZ68/yoMDEzyjbzPXHeXRaxI9gcf0348w5bp2RASo9ue/J/GPwe0I91eBxye/HebJIR08gcgnvx3mP8M6ekYEPlp9mGeHdyLQR/09/GjVYZ67qROBZtX+4NfDPD+ii2d068NVh3hxVGdPoPLRqsO8NKoLPmUjPh+uPsTLo7t4ApePVx/mpdFdPIHLx6sP8/Lorp72J78l8fKorp5A5rM1Sbw4qovnJsD0tUd4YWRnz7UyY90RIgO8a6Y9uO0pbt39d27d/Xdc4d7gsDAwlGGLU3ju93RPUHYyN26W/5nKPwarCqo2p4sfd6cT7lf18S63m7WHvNdKsc3J5qO53NtfLe5baHWw80Q+d18RD6gSz/szCrmzn1oCIKfYxpHsEsZdoqp3ZhZZScu3cEvvOEBdKznFNk+QfSKvlBKr0xMIHcspweFyewKnw1nFGLSaM488lnlgQJtKwa0QouGp1RLSbrebhx9+mPnz5/Prr7+SeLqyyafRlEpIn9NioI1BzmH4fDAUlVWL0+pUil0DS1+qFS6nGsH6+CpvLkxsL7jtC7V+ksul9n90pfdnWg+E0Z+ee5pYQ+J2q/VsKp57cZZKgWuAE20tdifpBRZWH8giNa+UaztGEehjYM3BLNLzLVzXKYoAU1m7wMLgzlH4GQ2sPpBJZqGVG3tpuG2Zt8zs/676H1NWTTnleT6/5it+/VPHjb3g1grHV9q/U8fQrjFoNbDuUA7DWtoJm3cLOO3k3TKPUreRqJV/RZu6haLbFpLjiCDYriZfW1LTyH7gPmI+/hhNRCTFVgelPv64g0OxOV2s3JNB5wgd/d2bcAc0Y2l2LC5geEQa5B5ljf4S/kx3cmPXGEpsDlbuycCg03BDlxiKrQ5W7k7HpNdyQ5cYCi0OftqTjo9By+DOMeSX2vlpbwZ+Rh3Xd44mr8TOT3syCDDrua5jFDkldn7em0Gwr4FrOkSSXWTjl72ZhPoZGNQhiswiK7/uzSTM38DV7SNJL7Syel8WkYFGrmobSVqBhdX7M4kOMtE/MYLUfAu/HcgiNsiHKxLDSckrZc3BLJqH+HBZm3BO5JXy+8EsWoT60rd1GMk5Jaw7nE2rMF8uaR3GsewS1h/OpnWEH5cmGMmx5FJqc2LUa/Ex6LA5XdhsPpzI0tO9RTCHMorYdCSXjrEBdGkWzIGMQjYfzaVzsyA6xwaxP72QLcdy6dosiI6xQexLL2TrsVx6xIXQNjqAvWkFbE/Oo1fLEBIiA9iTWsCO43n0bhlCm0h/dqUUsPNEPpe0CiE+IoCdJ/LZlaJGjlqF+bHjRD57Ugu4rE0YcaF+bE/OZW9aIZe3Cad5iA9bk/PYn17IlYnhxAb5sPlYHgczCrmqbQRRgapAwKHMIga0jSQy0MTGIzkkZRVzdftIwv1NbEjK4Wh2CVe3jyDM38T6w9kcyynhmg6RhPqZWHcomxN5pVzTIZJgXyNrD2WTklfKjT013FZFieXfLvmC1JGqTHez+Qv4LsdY5bVS+drQckOXaEx6Hb/uyySvxH6O14qGX/ZmUGh1MLybCjp+2pNBic3JsG4xuN3w094MLHYnw7rG4nS7+WlPOjaHi6HdYnA43fy0JwO708WwbrHYnC5+2pOB0+VmeLcYSu0uft6TgRs3Q7rGYLG7WLknHR0abuwWQ4nVe61c3dXN2KWjTznfTwd9ycb9Rq7rGE1UkLlSap4Qom6cT2xQq0HOAw88wNy5c1m4cGGltXGCgoLw8Tn7xOemFOQ0GXnJKv0qT5VDRWdUk9nL5+g0ZdYilaJWPoIDahL+xKUqpas0T82n+v7f3v0BMWp/WJs6P11xfg7mHqy0yvzpgpz5w+eTEJJwyvEn7z9F7lG1CG+ouitNYZr6zIS3rVTIo3xNrdOup+W0w4Ef1KKpOiPcuVCtfTVzuEovHDtbjR7qLqwDVn5DprHdjDnv34fwON179/t1SzGWFW2o+Hk43/e6sf1uGtv5CnExaTDr5HzwgcrnHTBgQKXt06ZNY+LEibX51KI25B1TJYXzjqoOlMEPLHlqjs7EpRDdpeYDHWuRql5WPmnfYQOXTaWFAeQkqRS6gLK87fzjagQiOK7mz2Pfcm+AE9lRvR8FKWodoIlLYf9yb4DT/BLI3KNS16YPUSXAQ1vX7DmJxiWkZeV2QLT3c3s+dAZ1rYW1UWtSTR+iRtZcTvUZi+l2wQEOeEvfVypvXw+ySrPIs+Sdsj3YHEy4T+MJvhqS880oyDDbSIg5NdAONgczf/j8Krc3BU399QlxsajVIKcWB4nE6eQfV6vAR3ZUAUdROuQeg6hOZ18b5WxKc1WnvfzucUC06mAVpEDWAQhP8AYfNcFSCHuXqM5cbE91F/z4RijOgITroDBFPX9UFxgxFVx2+OI2cJTC+Pk1G+hodWDyV3fMW/WH0R+r9YemD1FzlbQ6CE1Q+zuOgBv/BwXH1X7/KBWoiQbt5I6NzWk7w9G1p7wqY6XKiycLbgF3LIRPB6lr3A34R8Kdi2o+wK8neZa8095NlyDnwlRcu61iEHu+nfpwn/Dz+h00tqDhfF+fEKJhasIzxS9C+cdh7lg1b2biUtXZWfo47F2qUlhaD/CWdr4QUZ3V49otanV6nVG1U3eoletrMsBxWOHQT7DgPhUgTFis0nRmjVDBzF0/qJGUonT19d3dKghL31lW2vqYSiWrqZElg496/yYth5BWKnDxDVdtnyDV6fSLgHt+hsBmqty3Twjc9b36twGuLSMqO7ljk1WaVS8dM0NE+NlHUJx2KDihPvPlSvPU34CAqPOeH1fxDr8jPd3zr6Vsf2NLXRPnp7Y79RI0CCHqgwQ5TYnTrtLH7CVqBCGivVrrBNSog9Nxxh8/K61Orfficno7UaGtIaAZGKqunnPB9CaI7Q4h8ar88rTBZQuRutS6OwExENkBRnwAC+6HI795z3Hct9CsV82nzhl8oEVfb1urU+vjVNwf26PCfq0aQRON0tk6ZvV2d9rpgBNb1Bwcpw2CW6p0tdwjMOsmNZoT2wv0556yVvEOf7nydb2Aek9dExeuqgC2/HsLEsA2Fk2meJEQdUiCnKYkNF5VOps+RN3lLQ9wbngNuo6pmTLGWt2pwUNNBzjlQlrBhEXw8QBV5QtUcHPHArUgJUCbq1WJ7ZKylQ5D4tV8BYOkh4naVW93p7U6MAeCwVeNGt65UG2fNVJdJ6bAul94tgFobClR56M685OqCmDBG8RKANs4nC7VUAhxehff/4RNndEfQtuoIKdc8z41m0pWm0rzy9bjMasiA3nHwFpQYX+OmgPkHw1FaWoOTnmAA2rdnvn3wYj3L2xStxD1Je+YGpEJKptTU5gGtuJTK/NpNGqUdtIKNc8uWK0Rwh3z1fER7c+7xHf5PCBQd/iTJ99D3KefeBYxPuP8oAaiKadEVWd+UlW/W8Dz+20Mv1shhLgQEuQ0JUXpag7OkdWqrTepuS3Ty6qfxXRr2GWeS/Phz6/ViFRcX0jdru5OO+1qhMblgPxkmDEM7l0Nh3+GtB3qNd32lXr9Cx+EQyshZSskXlf911uUAXofMKuFCilMU4ufNsZ1b0TDlXcUZo5QaaDjvwWtAeb9BTJ2wsRlENGu8vEaDUS2r7ytPNi5ABXnAZXPw9FHRVVdwrqONOWRmbpU1e8W6v/3K85NeZpaVamGIKlrQpyJBDlNiaUADv2ivr/xf9D2eph2g5qMvONrFSj4NtC7dk47HPwRlk3xLjBqMKt5LiEtYcwMVbJ61giw5gNu6HqLCjri+0PLy1VBAjTqbnbLy6of4OQeVc93+d+g80j1uPPuVXOB+j+higsIUV0uF+SnqADeaYdZo8AnFJLXq/0Ze1QAYzj72mJNSVMemRHiXMl8uZohc5ouTrW6GGh1yWKg58nlVNXFTmxVnXJzkFpHZtscuPQvqvpXQ5Z/Ar64VY3OgJq0f+1z6u52WCIExqjJ1Q6r9852SY6am1A+B8daqN4Hn+DqnYulAFY+C5s+U+1rn1cLMJaPkt27ShVGEKImOKyQ/AfMHqUCnXKjPoF2N6ry5XWksS4G2pRVe3FKeynYirCXaLwdvQA/DNFS9bGhqziSc3KqIUiH/VyVL7IMnH6hZdEonE9soD3jXtG4aHWqzHOX0d4iA6HxcPkjDT/AAQhqBuO+VsUFANJ3wapXIaytCnBAFSOomLrjG1q5yIApoPoBDqiJ3Vf9Q60cD/DjU94AZ/RnECarXosapDepuTTBFRYM9QmBVlfUaYADKr3JnJgoHaemwl4KSavgm0kYTHbMbVpj9i/BMP9myDlS32cnzqL8eiwPasCbaijXqRBnJulqTY1Wpzr6FZ3cbqjsFsjYrRb7LHdiE2TsUoFLXVdMC4iCm96H19uq0SGAhGsh4Zo673iKJq4wTaVCZh/0bivNVXPSbv+2ySzwWZeqU5GsoanW/KTSPPjydjWn8du7YNDTMGcM2IrUOmRj50jqrWiSzlY+HWQkrKmTdDXRMDhtcHStStdxOSGyo9qesVsFbuPnqXk3unNf+6PaCtPgu3u8Izjlhr0LnUao0Z7qsuRXLu1dmq+CUq0Msl40XC44uBLmjlHtUZ+oAHv2aJW6dsOr0HOClEU/T9VO8WoqbMVwYCV8O0GtNVYuMFYVpAltXX/nJs6ZzCk5fxVT1E5HUtcaH0lXE42PzqjW/PANVwuO3v6t+oruqrYFNa/bAMdSAL+85A1whr3tTV1b/DDkHKr+cxSmw0//500ZKcmBjZ+okSuXq/qPLxoHrVYtMjvsXRj1qZqDE9dXBfb9n4AuYyTAERfO6AeJ10Lfhypvv2OBBDiNSHnamqSoCXHuJF1NNBzhiTBpOejNan4OwG1fgsNy6lohtc0cCP2nqMng/aeoAKftDbD4r2reU8W5ExeiOBu+/zfs/FYVNJi0HDZ9Dr/9D35/G+5bo6rKiYuDOVCNDmq03lTIuEvVZ803tF5PTTRyLidk7YfN0ypvX/wo3Py5d75jEyRFNC5uZ1sjqvwY0XRJkCMalpODmfJgpz4Ex8GERap6m8lffQ1/R61hUt2OpzkI+t4P+5aqNVLe7uKd99P1FnX3VVxcTk5/1JvUVyPVlObENGqFqTB9iJqDExgLvSfDry9A9gFY9DCM/LDJrvtVXn5ZyixfnGSNKCFBjhBn4h95Ujuqcrs4WxVFKF+TpzhbrWdi9D3z4+r0ENMd7lwEn13rDXA6jYSB/wZfmQgsGpbzDVryLHmnnRPTJIOckpzKNz9KshvGdWzwJWvCAvJKs9V6S1o9dLge3C6CjYGE+4SoIheWfFXZshEH1qBGb2y5WThdTtwZWQBYU1OwOW3otDqMIeES8IhGR+ZkXRgJcsRFoVbSFgrTYNnf4crHIbqL6iiseQtaXQ7xV6lApzSvcknr0lwwBqggx1oA+1dUfswTW8BS2DA6R0JU0NiClmpVJDsHlYI+pw2K0ggObUt4YHMoOAGL/gqDX4LwtjXyfBfMN5Q8SyYjV97l2fTCFS9g0pmI0+rIyzuo/k4VZxLsshMe1KJRBzrOvFyODa/8OU259z7P9zKqc3GqmLrWGFPUKi4KK5/hcydBjrgonClt4YLukJTmwcr/wp5FcOhnmLgEdnwN66fC+vfh/rVq1fpfXoDLHlbzjUpyYMMn0GagmvC7/kP47XX1eK2uVOWy847CzOHq8YJb1PC7IOqTpG/VrXCf8Fp9X6sK+uZf+TrhTid8fQekbocZO+G+3+s/HUyjq9Q06UxMWTXllMPmD5xKuDmk3oMcuVZETauYuiYuHhLkiIveBd0h8QmGq/6uFtkrOAEfD/Du6/cwmINh5TOw/QvYvxzu+h62zlGFBda8AQ+sV6M/Gg30uhuufhJyDsOMoWrBU20dVpJrohpaR6m+R0Ia2vvRJDls8HZX9b1WB8Pfa1zz6/wiqhWQ1VRKzVmvFZdTlcauooy/LjiEZgu+JbkwGf88KyUP/xPfd1+mKNhEXEBco7yLfzGS9Kyzr/Nzsb4v50OCHNFkVfUHouIiYFo/P1zFxRe+SFhoa7hrBbzVxbut7fVw5WOqsEC/h2DfMijKgHd6eI/pfrtaCyfxGrj3VwhsrnL5TYEweSX4hDXpikd1pb6DioamNt6P97s/T2S6FUvWAfkP92RD31YjtHVQ/rvGAljt+XUJTu6I1npKjdulApz0nXDkd+h+G/hUDloMEeEc1edx6x9/58uOr6EFioJN3Lr772VrJMlntDGQ9KzK70FF5VXiLtb35XxIkCOarKr+QJT/cQBo9d23HBl982n3n/UPSHE2rJtaeduR3yH3iBqlieqkSkN/cJl3f7sb4eqnvBOUY7p79+n0qmSwEI1EmM3EibHqGjr5eqntOTF1oVp3k9dPVampQc1r6ey8zhbAVvpduJ3YnLaqH8hhVQuGajTn9Lwnd0Rrnb0E0nfBtBtVtTi9GbrdevZCLxf6dFKCuk6dbeQCZPRCnB8JcoS4EKW5sO49+OMD1e48Bo6tValr04fAPT+r1I+d31X+uROboCRL1j4RDY/Toe6U641V7q7YUY60GCG/EHdGFill+0/uiIRH1O6cmLpwQXeTL7lXrXmVsRvmjIE7F55apbGOVZqflJfMweyDVR9YnAG+0eB37oVPYl5+Ga3JiNtmw5mb69leKyk1+cfhyzvVaE5gLLQZUGWAU/5ZjbQYYcG3EBTA/IT55x1gSwnqunW2kQu4uEYvzrbOj6Renp0EOaLJquoPRMVFwLR+fsQvXnRhi4SZAqHjcNjwEfS+G654DCy5MH2oGsEx+FUuLJBwDRzfqFLXpg9Rc3RktXFRDTU6x8bpgJTN4HJB816gOzXQqdhRthw4QNKIaoyCNgGeoM/tUpUSC9MIDk2EawZC+6EwexT0uKPK97Je+UcT7LIxf+AHav6NVl9WHS6D4LDEswY4J99t15qMnPjbY6ccV2spNS4nGP1h4jIIja/ykEpBXVnm76mzd4Ro2GSdn+qTIEc0HQ47pO9Qd02D4tQfCE0uuF1YUIFLVX8cLuiPh1YH0V3h/nXqP1zfEPV113LQmdWcnJb9VAei50RVWKAwFabdABEd1AKjol7V9UT8mk7fqrE5Nk6bCnBmDAfcMGEpNOsFWu0FnVdjd64pM5VGqiwF4BOlggaDD7ToC3/dqm6GVCwh3xDoDYQHtiDcHFy5VL1v9DmNMJ98t73Zm2+c9ymc7dqrfK24VRpd3jGCrUVqk70EMveqdcsuIFXtTM8fVMQZ53I21HSppjBR/2wjF+XHCHGuJMgRTYPDDilbYOYwCImH8d+BvVQFFbhhWC3ki2t1ENKy8rbgCu0W/bzlY31D1QTZyStVxycguubPR1RytqCirgsT1HZJ47Op9H44rapcuctFcPoemHefCnZC4tVNgrMEOOWdkabYEbmglBlzYOVKX3pTwy4BrzeA/qQRmwtMoXVZbZ5Ax2W1kfrPfwJnTqk527XnuVbcbkj7U/0dtxWpxUojO6pUwC/HwW1fQnx/FViehzM9v3+e+4xzORvqKGVTmKgvIxen19jX+akvEuSIJsKtOm5ul7rDN30IWAvVquPmYHQBKjWtqj8OtfbHw2CGyPbetkZT/wsDXkTqO6hoaCq9H243uLTwydXqrjioOQ53Ljw1cK9CeWekKXZEGuPd5Los8nCm98fQwvu+1MjnQaMBk78aGTcHwsSloPeBr8ZD5h4177EG0gHLF0e1OW3YnO5qP15dKh/BkYn6TZus83NhJMgRTYPeCHGXwvh5MGuEqnAG6j/HScswRLbEcJqKQef7x6MppAWIi5zLqeaRuOzebfZScFhO/zMXicZ4N7kuA/ozvT+1IrS1Why5/HuAsbOgNEel/mp1p//Zc1RxcdT3uz9P2Kw3AWhe6kPKvfc16AD3bFVEG+uoTm2PXMj/4xcHCXJE06E3qXQGvY9KbYCyO4BB51wS9VxUKy3AVlx5cUBrkbpTWResRWDJV2lzRl812bww1TunSFwcyosMzBgOTru6ZuylqmLgtBtU2fOIduf0UJJCceGqmhfS2BdmrbXPw8lFWgJj1VcteHDbU57vf7/0S6BhB7hNVW2PXDSF9D5xdhLkiKYja783d1tvVnep847B7NFw+7cQHFeth692WkBxliot2+02dS6WfNi9EJr3gcgO1Tq3s7IWwYHvYd49MGYGtBmkFtSbORyunKIqxEmg06hccIqS21W2HopTzcG5cxHYi9W143KCy3HOa6U09RSK2gziqpoX0tgWqj35/anpz0NDWafG6XZyMPfUstsNIShtyvPjhKguCXJE3SpIBY0WAspSG4oz1Z3lwJjqPa6tBE5sVYGEORgmLVPzcWaPguyDav2awGbVqhhVrbSAkmz49WXY+An8+bXqWO5dCsumqAm/k3+q3ZLS9lLY8LHqxH59B1xyH2yZrrZvna1K3V5kGvtilRecolSe2nnnYnU3PKSFCmomLVefj8iONTry2Zg19SCuui70/TnXa6+21qk5+flPuzhqUADxixdh8zcxctGQU3Y3hKC0Kc+Pq2my2OjFR4IcUXcKUuGbieATCsPeUgHHymfhxBa4/ZvqrQxu9IV2N8CIDyGmq+qoOW1qjo7bBbE96rckrjEAOo2ELTMg6wC818ebUtfySpUyVpv8I+Dm6er9T17vXcQ0JF5NNg+opXz6BuyiLkygN0HcJd75DBoNRLT3fi9EFWpqHsOZrr2qOqI13Qk9+fmrGqUByDDbSIhJ5Php9ovGRRYbvfhIkCPqhtOpgpnk9aq92KVGXHaonGcOrlRpXHrThT+HuWyBToOv6qjpTeqONVTvcctUKy1Ab1RpaePnwYyh3gAn4RoY8r+6WRHdLwKu+rtK3yt3yb31vhq7qFqtr+Nz8oTtswU3RZlqPln5uiTFWWpU9gJLDzc6JbmVUzpLci6e116mLuYx1MdE+sY+qgsyP06IqkiQc47sTidWhwuzXodep/W0ffQ6dDotdqcLq8PpadscLmxOJz460FkLsJlCVNugR2fJwWas0NZqvMef1PY16NGeQ9vqcGJ3uvAz6tFoVNvhdOFb3rY7cbgqt+0u7/GWsv3+JgMAFrsTp8uNn0l/mrYDp4tKbZcLfMvapXYH7grtEqcbfYvLMA59C5Y8CvtXeN/cgU9hbzcMh0tP+WoHpTYnAD5GXZXtEpsDDZpKbQDfskn9JVYHGi34GEyetlYLZoM6n2KrA90pbQ1mg67KdpHVjj44BHMVaQGmhASKbQ40ZSNFbrebEpsDvU6LSa/D7XZTbHNgdhSjz9hT+YOVewS3w0Kx1Y5Rp8Oo1+JyuSmxOzxtp8tNaXXaThdWuw2fzB1ovry98vP/8G/cwXFYWw5AZ/LDoNPicLqwOJyY9DpPu9TuxGxQbbvThcXuxMdQfi2c2rY6nOd/rZzcPsu1cbprpamo63V8zqgwDeb9BS6ZrOZz2Uvg15dU0YKLYT5X7lFY8U+48TU14lyYBt//G676J0RIWfjGrimM6kpq5dk1xvLwDUVDmR93viTIOYtSm4PknFJmrT/KgYxCRvdsToeYQL744xiHsoq4pXcciVEBzP3jKElZxdzWpwXxEX7MWX8Ms97Nv7pb0ax9gxP9XuDdDQU8398X3xWPkjvobZ7+NZ9RPZsTGWBm1h9HSc0r5e7L4wn1NzFr3RHSCizce2VrAn0MzFp3lPRCC3/p3wZ/s56Z646QWWjlgQFtMBt0zFx3lOwiGw8ObINBp2XmuqPkFNt4eFACGmDmuqPkl9j566BEXG43M9cdpcBi55FBidhdbmauPUKRzcHfBrXF4nAyc90RSmxO/nZNW4ptDmatO4rV7uJv17Yl32Jn9vqj2BwuHr+2LTklNmavP4bTpfZnFdmYvf4o4Oaxa9qRVmBh7h/H0Gpg6qihmMPeU/NkAPwiKO0+gQe/OYxWC3+7ti1Hs0v4csMxzAYtj17TjsNZRXy1MZkQXyMvXx9FRrGTp39IIcCk5+UbYrHY7Px9eRr+Rh0PD0pkd2oB3205ToivkYeuTmDniXzmbz1BuJ+JBwa2YfvxfBZsPUFUoIn7rkpg67FcFm1PISbIzF/6t2HT0RwW70glLsTM5Cvb8MfhbJbtTKNlqC93XxFPYIm37K7N4eKLtUf4YVc6CZF+3NmvFasPZLJydwbtogO4/dIW/LovkwCKGa5dg36lWiiP6C6QuQ+yD6KZPYq8IXP4er+bYd2b8f2uNNYczKJnXDAjezZn+c401h7KonerEG7q1oylO1JZn5RN3/hQhnSNZfH2FDYcyaFfmzBu7BzDwm0pbDqaw5UJEVzbKYoFW08wrosfzRc+oEoEh7Si4Oav8F/+CNrj69Esehjr3euYtyWLyxLC+GbzcXaeyGdwp2j6tgnj643J7EopYGjXGHq3CuWrjcnsSS1gePdYesSF8OXGY+xPL2RE92Z0aR7EFxuOcTCjiNE9m9MxJpC55ddKr+a0jQ5kTtm1cmufONpEBjB73VGO5hRz+yUtaRnuy6z1R0nOKeHOvi1pFqLax3NLmHhZK6ICzcxaf5SUvFLuujyecH8Ts9YfJTW/lCsSwhnevRlxIT5oJOXqvJ125MgYQPjadyHpVziyCkZ9Bkd/h02fqQNa9INWl9fpudap4iyYM1qlmeYeUQsNz78PklZB0mp4YL1a8Pc8BZuD+f26pZBfqDYEBWAz+9bsudeAs81jqOlOT1Ud0fPuhDpsoNVXTlF22NSIurhoNcby8A1Fbc2Pq20S5JyB3eHitwNZ3Dd7My43XJ4QhtXhYth7a3C74aq2ERRaHAx/bw2fDWtNqzg/fLX57D5WwMLtJ9j0aDfMH/YCewnNLMX899qX8Z0zDIrSiVp+D0/eOIsF+4u4b/YWAEb3bMafKfm885MKAMb2iWPT0Vym/noIgPGXtmDtoWw++e0wABMva8Uv+zKZ9vsRACZfGc+KXellAQbcd1VrFm1L4cuNyQA8dHUC3245zrebjwPw6DWJfLEhmQXbTgAw5bp2zFh3hCU7UgH45+D2fLomiRU70wB4ckgHPlh1iB93q//onhnWkbd/Osgv+zIAeO6mTrzxw35WH8gC4MWRnXl5xV7WHspGo4HfHuwCP/7HG+AAFGeiWfQwd/V4hix3EE8v2MmWY3kAvHNrd/45bwc7jufjY9Cx9qGOGOdNIiy6D0MSbmZAYhiGX57DVJLNvb2epcQQyl+/3Mr+9CI0Gvj4jt48OGcrhzJV+9M7e3Pf7C0kZRWj1cBnE/pwz8xNHMspQa/V8OmE3tw1YyPHc0sx6DTc1783Ez/fQEq+BZNey/1XteGOzzbw4oBYIj+bS2ywDzsK4b+Ld+Nr1HH/gDaM++QPMous+Jv03D+gDbd+vJ7sYhv394vE0EzNu7EkDuXgJc+RqDmB6YvROLVmSuxu+sSHMfqDtRRYHEQEmLi3fxtGTV1LodVBdKCZe65szYj3f6fY5qRZsA+Trwzmpvd/p8TmJC7Uh44xgQx/73dK7U5ahfmSEOXPsHfXYHW42Hw0hM9v/hK/FY9y6LJXeO77fN4e9jH+3z9OxqX/4PkVx7mhSzOGvrsGu9NN52aBBPsaGfrOGhwuN93jgvE16Rn67hqcLje9WoZg1GkZ+u5vuNxwaXwoaGDYu2twueGyNmHYHC6Gll0r/RPDKbQ6PdfOwHaR5BTbefTdNQBc1zGK1AILD3+5FYAbOkdzNKeEB+aq9vBusRzI8F4ro3o2Y1dKAW//dMDzUVp/OIePVx/m2/svo21UwAVd8xezM44c9XsIjm9S6abf3eXdeeWU2q8MWFtOLt9+unLupgC48XVVxCRjN7xR4fUOfllVcrwA4T7hWI4fIGnEzYBKwwqMaXFBj1WbzjaPoaY7PVV1RM+rE+qwwfGNYA6AyM4q0MnYCwUp0PIytUjzeWoK6WyiMknvuzjU40zshi+j0MpjX2/HVbYA8h19W/HC0j24y9q3X9qCF5epdoTLgvWOseSOHU2Ey4LF7uJEMeQMeg0A47HVBHx2GRSlgymQ1AH/I8Puy9sr9wMqHX5o11je/VkFAFoNDO4U7QlwdFoNV3eI8gQ4eq2GKxPDPQGOUaelb+swT4Bj0mvp1TLEE+D4GHR0aRbkCXD8jDraRQd4ApwAk55W4b6eACfIx0CzEB9PgBPqZyTc3+QJcCL8TQSaDZ4AJzrQjNmg8wQ4zUN8AA1rD2UDML5PcyKz1mPeOReAwr5TsFz7MgDmQyvoVfgTEb4aT4DTKTaQlDwLO47nA/DwgBb4HVqK/vh6Aja9y7CCLwhd9wI+O+diOvw93Z27KCq1sj9dzXW5rE0YG4/kcChTta9KjOD3g1kkZRUDcHX7SH7em8GxHLXa+zUdo/h+VxrHc0sB9d4v2ZFKSr76b/bGLjHM23KCtAILdy06zIxULf/ZXsL4eer3NaJ7M+b8cYzMIiugOuEz1h0hu1hV7bHp/PimpCd5Y+axoeO/WXLQxlt7g8kcs4B9V3/M8mQd7/5ykAKLw/PZeu/nAxRaVXt835a8tfIAxWVpe3f0a8nrP+yjpKw98bJWvLpiH6X2svbl8by8fC9WhwuAYd2aMX5eGmt6vsGtX6cyuHMMo2Yd5rcu/8fdy4sZ2D6a55fuxl622veky+N5fuluHGUf/omXteL5JbtxVmj/39Ldnmvjzn6teH7JnkrtF5Z5r5XxfVvy0nJve9ylLXhlxV7K3dInjte+97bH9I7jfz/s97RH9mjGmz96r5VhXWN552dvgFOuwOLg6QV/kldymmpJ4sIExsBtcyt36Fv0hcseqtV5KfbMLCwHDmA5cAB7ZlbNPXBhGvzygur4ghqtWfse5Bw+9Vi9Sb3WMdMqb7/uRVXspK7WuaIW34+mwumAE5vVgtDThkDGThXgTL8B5t4MR9eqIOg8hfuEkxCScMpXY05xu9g/S4aIcMyJiZgTExvVyERdqvgZqVQEpBF9bmQk5wzSCywUlXUyTXotNofT04n0N+np5OPg28HRAAQW5VJS9nMBhTksG96M4Lw8coMvx9j3cfzXv+553OyRX/DsH24uaV3g6RS2CvNjX3qhpxPYJsKfXSkFnp9pFxXAjuN5nnbH2EBPQADQuVkQG5NyPO3uccGsP+xt92wZzO8HvR/IPq1C+W2/t31p61BW7c/0tPu1CePnvRme9uVtwvh5jzdV4YrEcH6s0L6ybTg/7ErztPsnRlRqa3Q6Nmu70aXrJJx+Edjb3UFpfgHu23+FjW+R0nwIb6444jl+QLtIlu1M9bRtbj2/mwfQp9td+G//HL/NH3r25fd7gqMBvXlnYZJn29XtIz0BIMDA9pF8UBYwlu9/48f9ldovLN1T6finF+z0tttF8Pdvd3jaV7WN4G9fbfO+PwnhPFI2ClH+/j04Z4un3adVKPfP2cz8liFsOXaU98f15L7Zm1l9OJBj2cn875Zunk48QIeYQN5a6e3EJ0b5878f8j3t+HA/9qQWetrNQ3w5kFHkaccEmT0BHUBEgImtx/IZP1c9RqifkcNZJUz6SnXqAn0M/9/eecc3Ve///5mmSbo3pS1bhgg4QRH3REEZigiiCLj39Xq996fXO7xeB1/16p1uBRURFQUHuAfuibgHexe6d5M2ye+Pd9M0adombXbfz8cD7Tk5yTlJzjn5vD7v9/v1Zk+1tXX7NEsipbXuwUCy2UhFS5qewQAmYwLVDXJtJLbUwXhfKy4Blmo2UmttprHJ0bKvRCrqba0CLDfVzN5qa6vA6pthYUdFfaug6p+dzObSutZrZVBOCr+2uVa8+WxzBVUNTWSlxFZqine6WIe2tpGgrhTevUPSHV1s/wy2fCg1OubQpFmFpNC9rkTSzTa9C9s+g3OfhTX/JxbrXz8BF73VvtFkQwV8/ojnunVLYPQ0z+a+ftATG9tINDDsqo4hlLPgrn37vQ9jojhJpuSIkH30FElbs1ZLE+SMQk1Za0GbYSpdEQkTkGCjIqcTmh2O1r8TDIbWWW1ZBlNtNYkXzQFoFTgADb+9CgNQDvR//jnSfnjK43XTP7mTqybcxaduDSGvb3e/vjHB4Ln/BFoHgQBGg4Fmu6PN9ngeX4KBpraPe71+QoKBJkfb53vt3+v1xdzAe/vAXv+fn5Qza/R8yuvtnFpRTc05M2la+gw/Dv896361YfN+P21fz2Bg4Zq9PDbr96RteFkGKgC5w9iz71w+297s8X67/jwNXXye3t+3oXXQDWDAa9kAdq9Rd5uHceLE6YQvtlS0LgOtQtbR7rnRu2yAdu/d43EDtPlo2312vs6Vtt9Nu8cNXuey13fjizYvFzxqiqWJZvYgWa4rBWsNZA0Kij25d7rYbUfdxt3H3s2AtAGYje6BWdhTZJwO+Pxh+LJlkH/YpbB7nYicZ+fCgtdh4Pig7rLHjXc7w5QK4y+VmppdX8G9o+R7BThgVvv0s9q98MIlsj3AgXOk19XeH+GpmTB3BaQX+L37WLOxjWQdQ7eK6XOHwfxV8OhE6U8GknK44NXYTa1UFKVbaLpaJxRmJmNJlI+ooclORrIJk1FmrasbxX2rK0w122VwZMnAfthlAJi3rmHfT29gxki3rfGWsjpGFWW0Lm8sqWVMv8zW5V+LazloQFbr8o+7qxk7yD279cOuag4d7E4b+W5HFeOH5LYur9tRyYSh7uW12yo4apj7x+PLLRUcNdy9/MWWco4Z0ce9vLmc4/Z1L3+6qYzj9833XB7puXxCm+VPNpax8Ph+DMfJ+JQEKBGR0rSnhAJrE9OzrNx78gCP7U/Yz/38o3INPDexD3nlG9wCB6BsA4U/PMyI9CZO3M/d6+XTTWWcuJ/n/k8Y2ddj2ePxTWUez5ft27yfzeUe7+/LrZ6fz9fbKjlyqPvz+25HFYfv4/4+fvL6vtbvqeXA/u7vd0tpHaPbfP87KhrYt01dSXFVI0P7uGeMS2qsDMp1z55X1NlaUgSF6gap43FRb7XTJ919vjU2OchJdQ+cm5odZCabWpftDifpFvcciNMpERkQ8ZZoNJDc4jzXZHeSZEpovVYamxykWRIxG2W5xtpMTqq5NeJTUd9EQWZS6/VTUmNlQHYKrstpd3UjQ/JSWx2Nd1TUe9TYbCmrZ79C92flzeiiDDKSgzx/U1MMyy+ExadB+WYZPL17Gzx4NOz5LiSq6qYPb+L6NddjNpojmyJjSIADZ0PmADj6d3DcjTDzcRhwOOx7GmQPDvouXUKg7cB/+0UXs3nKVDZPmdoaCekW5hQYcizMXCzLLoFz6EVwxDXt0+8Sk2D0dPl7xiPisHbeCxIh2HcSGAOLDLiiE0NefokBjzzcun7AIw+3rm8bufCVMgKxlzYSVuzN0tjWhcMOjqYQzX7EFq7zSc8lpSt83as6uk9FKypyOqFPuoU/neae+Xlh7Q6uOG5Y6/Jup4WMp5fT/MhSku/9b+v65Hv/S/MjSylYvhxDVrbU4Jz5PJ8MuoK60x+AhESqDrqYtzbWM+cwKTS1O5x8vLGUmeOkIWaT3clXWyo44+B+ANjsDr7dUcmUAwoBsDY7+Gl3NaeOkRnEepudjSW1nDxKBuq11ma2V9S3CpPqhmb2VDdydIuQqaxvorzOxhEtwqeszkadtVkKyJF6pCa7o3VgvquqEQMGDm4RWjsqGrCYEti/RYhtKasnPSmxdaC+YW8teWkWRhbI4PTn4hrSrXUkXjSHxIvmUHPNFYBEvRIvmkPzvNlkNdUzuGXg/sWWCg7sn8WAHBm497E3sHPGDJzVkm5VddhvaT7kQgDSv/g3hzSvY+YhhfTNkIH8Wz/tZeKovvRJk+U3fizm9AMKyW0Z2L/6XTHTD+pHdooM7F/+Zhdnje3fOtB/cd0uzjlsIBlJMlhe+fVO5h4+qHXg//xXO5h/xODWgf9zX23nwqOGkNKy/MwX27nkmKEkmeQSe/rz7Vx+3NBWIbD0s21cdcKw1uUln27jmhOHtwqDJz/Zym9OGt4qqp/4ZCu/PXlEq1B44pOt/O7kEa1C4fGPt/K7ifu2CoUnPtnC709xLz/+yRaun7hvq3B40nv5061cP3FfXDz12Taum+i2xn3682389mT38rLPt/Obk9yzuM9+uYOrT3Avv7B2B1ccP7R1+cV1u7j0WPfyK9/u4uKjh7Quv/p9MQuOlGWnE976aQ/nTxgMiKh679cSzh3vvlY+2lDK2ePcotiF2ZjAbdPHkJvW875IHtSXwa61ULUdHp8Cr/wWvnxMIjkb3pL/B0hpQykbKja0/ot0epqruNr7X1ZSFuQMgYvehAlXiV10RqGIhNPu6biRrN3utdzc42MsXLiQfvfeg9Nm69lgrKlenNHasvNraG5ov21SBow5C675Gka01OAMPByu/Lzl8wisJqltLYDLMQzckRHvGgGX4AuZ6AuAtoOeqB3guGpwGishKRNScuX7XjRZanR6udAJ6QSCElf4uld1dJ+KVgxOZ0eZ7ZGnurqazMxMqqqqyMjoeOY2lFQ12Ph5dw3/fns9W8vrufK4ofTLTuH+NRvZXl7PNScMJz/DQv+qYprOnwVA3+UruPyjCnZWNrDknGEUpjj409sVfLKlgvtn7svIbPjXJ2Ws/K6UGyePJDEhgQfXbGRvjZU/nbYfTic89P4mSmut/GXKKGzNTh7+YBPldVb+OmU09TY7D3+wicp6G3+bOobqxiYe+WAz1Q1N3DJtNOX1TTz64SZqGpu5dfoY9tZYeezDzdRZm7ntjP3ZVdnA4o+30NDUzG3T92dbeT2Pf7wVq93ObdP3Z3NpHU98soUmu4Pbz9if9XtqGWaykd1UT36GhWpzKhe/vBkMcPv0MXy3q4qln20jMcHArdPHsG5HFU9/tg1zYgK3Th/DV1srWPbFdu4/MovmebM7/KwTH19GQ9EgPthQwvNf7eTOE/ozPMlOna0Z+9691F5zBQP+cxfGPrnUGtIhI52cH/6Lo7aEtaNv4D+fVXLDpP147ftiXv52FwOzU/h/k0ay+rvdrPpuN4NzU/jDKSN5+dtdrP6umGF90vjdKSN48etdvPZDMSP6pvHbk0ewYu1OXv+xmFGF6Vxz4giWf7WDt37aw5iiTK46YRjPfrmdd37ey4H9M7n8uGEs+3w77/6yl7EDs7jk2KEs/Wwba34t4bDB2Vxw1D4s+XQrH24o5fAhOcw/cghPfLKFjzeWceSwXOYePpjFH2/h001lHDuiD7MPHcCij7fw+eZyTti3DzPHDeDRDzfz5dYKTt4vnzMO6c8jH2xm7bYKThldwNQDi3j4/U2s21HJafsXMmlMAQ+9v4lvd1Yx7aAiTtqvLw+s2cgPu6o58+B+HLtvHx5cs4kfd1czc1x/jhyaxwNrNvJzcQ3nHDqAw4bkcv+ajfy6p4bzxg/kkEHZ3PfuRjaU1DLviMHsX5TJfe9tZFNpLRccNZj9CjK4790NbC6r55Kj92Fofhr3vbuBreX1XH7sUAbmpnDfe3KtXH38MAqykrj/vY3sqGjg2pOGk5tq4YE1G9lZ2cDvTh5BZrKJ+97bSHF1I384ZV9SzIncv2Yje6obuXHSSEzGBB5Ys4m9NY2tExAPrtlESa2VwwbncPlxQxmUm4I50djhedYt7M1Q/A0sPl0GTC6O+QMcfnm3iu83VGzwSE+7+9i7uX7N9e22WzF1BcOyh7VbH9VYa6Rep+AAyOwHdps0A07Nk3SiLmibrta2BsSYnc2WGW43sm6lS9WVwnsL4YuWKMrAw2H755KWVzQWZi9pX5MTIhrXr/eoi/D1ftpu0xHd/iziDYddUikXnwamZJi/GowmWa4vh7krpSlzL67L6ep80nNJ8YXrvImG8yMQbaAix99jaWiisclOisVImsXkXjYbSUsyUf/rr2ydOg2Qm4S1/2CsTXZSLYmkWhKpaim0TrMkkmJJpLLehs3XclIiKWb3cnpyIsmmNstJiSSbE6mos9Fkd5CRbCLJZGy3XF5npdnubLNso9nuIDPFhCVRlu0OBxlJJiwt29sdTjKTTZgTjZTVWnE43ct1v/zKtmnu91dXOBC700l2iqQhldXZcDohK8XksZydYsKYYKCs1kZKXRUJNRKJsRUXs/PiS1qLV+0OJ4bMLCz5Enkqr7OSsmtb6z59MfDFF0npl4PTaaeMLAxAbpoZu8NJRX0TBoMUtTc7nFR6LVfU2zAaDOSmWWiy26mob2pdtjXbqWpoIsFr2ZhgICfVgrXJTnVjE8aEBHJSzVib7VTVN5FolOXGJjvVDU0kGmV717LJmEB2m8ddyw22ZmoamzEnJpCVYqahqZmaBvdyva2Z2jaP11ubqbW2X7YkJpCZYqbO2kydtRmLyUhmsqndcm1jE/U2ae6ZkWyi1tpEvdW9XNPYRIPNTrLZSHpS4MtdXStVDU2e14b3cg+vlY7osA9MUpZ/KWD15fDs+bDlA1lOMMJvvpXmkN0gbkWOtQZ+egVWXiYiZ86zULEZPvq39IY6bznkDhXhaOw8rdBbBABdioIuaaiAtY/Dm3+Fo66DI6+BbZ/Csjkw/FSY8s+Oo1P+4nBAQ7ln/5y6UkjO8ajfamtC0FGdkbdRQUcNDGNhVjUs2JtF6JhT3TU4ZRugtgT6je3VAgc6nkDQcykw/Ll244loagYaiDZQ4wE/yUg2kdGmZsF72ZSd4+G5nplsgjaPZ3o5PXk7PwW6nJ3a+XJOqsVr2fvxzrf3Tvfxrj/KaKjBXlmBHSArmzyvkz7P6/l56RZIz4eCfI/1HRWv9klPorGLmidjggFDWh4GoE+b9YlGg0f9icnHcn56UptlI/np7pl/c6KRPp0sW0xG+pjaLCcayc9wLyeZjCSZ/F9ONotwbV02eQ7WU8wymG9dbhnsd7TsEgsdLaclmUhLcp+baRYTaRb3cnqSifSk7i93da20uzaCfK10RKd9YLoSOXWlUoPjEjggs8aLJsH5L0k6Vw+x2q3RYTTQUxJM4nCVYITib+HRkyTNa9QUSLtYaliabbDzSynYz9mny5csXLgQp82GvcKdSuPLiMCvgUdyNhwyH/qPhz77yvKQY+GCN2gyFWEvrobi6u7/mDscsPcHWHk5zFoiNUsVW+CZ82D6A5A/qlXo+FNYrw0MA8SYCIUHeQro3GGQNbhLUd0V0TTQ6y6u80nPpZ7R29zpumUCEgWoyAkSsXoCdEZnVqdOm80jbSQU7z0ona8VJRhU74SvWvqkHPMH2PdUSV2r3Aaf/A9O/IvUbvSAmz68CYiSyI3dBsXfiwhxpW6V/CLF9rlDO3+uKQkGHSURnCUzREBkDYSVV0DRIXDO0+LMtuQMmiY9in1vIxhNPgeOrntA2/uNC19uZJ0OPBoqITlL/k7Ogr6jxXULxIxgwKHYvSJH3bqvNVZI49DavfDENDFpePZ8qNwq66/4ROpEukGvamDodNJaNAgiHv11MfQlZnoocCB2u777oledSzFIb4sUhQoVOUqHeFudFi5cSILFjKOmhubSstb1rhnVQC5Ef/of9LjzdQzQ4xQqJTzkDqf0ik+otFZJ4b0hAS55E+rLycoZSl4PBU4k6PDcs2SQV7IRnpwGfUaJKLFWS9Qq0QLzXula6CQkQGKyDFIbKtyf2a618OjJULMb7E3Y7Ulsnj4D8C0qWmed17dv/BoQ1bvg9T/ByTeL4KotkbqcQy+UlCZD51Fjf2kdmEx+HmPtr5hWXwgPHSsPmpJh9lKwZHb+Ip0Qrsm0iN+XSn4BR7NEvQwGqNwOVTug6GAR0UqPiceJ2VDTVY+rYIqR3hYpChUqchS/SbCY2fnb69qtd82oBnIh6g1W6FEKlRI+zClUGo2c8fbF7R5aMXUF3fmmXG5mvtaHgw7PvSnPk2etAXsT7P4anpgi7nL15ZLa1ZULnN0G27+QrvNOJ+z4XCJAU/4NL10FldtoOvUh7IYsmq1u2/PO+uC4JkU6qiNISE31bYsL4LBjbNyK6Yfn5f3MXwVv3gzfPQM/PE/TvI+w19S3Psef4+nwrbcdmCx/ClPbB6c/0D6NKkqJ6H2p5BcR1A47LFgtDmlPnSV1Nec9L9blYRQ6vga2QevZpMQUXfW4UjESfUT/3VaJGN7pYo6awG1yg30svT6s7p3C4b2sBB3XICff3sT/DrqVK9f9KSivm5ecF51C1pAAQ46Gc5bB07OgtCWKkpwtg84+Izt/flOjNMq026QW4vyXJJJSubV1E7sjhc0XXOvxtM6aYXZVR+DLMcrj9V54BlOiBco3wT1tGkIe9Vvs1bVsnjq93dvwtzlnh7O7u7bTOHEJAMaEekxv/RWKDgpJX6FYwa8UHEOC/Gssg8dOldTCym3ikmZICOr9zp8am3jo+q7EBuGMFPUWVOQoHeKdLtZYWka/e+/BNHAg9oqKdjOqoRQgGvkB6itkVrzfIZDaB2z1sPMrSR0Kk+VtLNPdyEnbQU7uk/eG4tCiD1MyZA8BowWaW6RFSi4kZXU9yEzKgP3PlnqXQUfKuVq+UdzWwNNxLFyYkmDOc/BEm8Hq4VfC2PkYa5ra1f5Be8epjuhwdveaG1r/HrLoLkwVW+CJ6XDRW5H5DKIAv1Jw8obDglfhsVMkgmitFhOL816AAeMlZTLIx6NCRfEHX3XCELwxkEaKgo+KHMUvjFnZJI0e1fq3wex2f4q3OpmopL4cvlwE79wCB50HE2+BrZ/As+eJS9TMRSp0uiBqIyfRiCtlqLlRBpV2m6QLPX2O1Oh0da4lZ8LoM8Wut6EKir+T9Lc+I+G8FzBW1jDksTtprrGz/Tc3Av6Jio6KpbtKZzOmp8JHSzxf7NdX4fBLMfUZFHr3ssyBYEqBk/8mAlLpHFOyiOT6ltrPBJOI7BbXwXDWDKkBjuIi1E6HoRZRvREVOYpfeEdStCtyBFm3RPpA7P1B0tWUoNNR2kBapZVlo+6izGwNWtpa1OF0iFV2fVlLitprki709Cw556p3+SeoE83uzzH3JIznH40pJxsy+2FKacBk205jtvt1/BkodBTR7TSdra4EXrsJvntWHthvKqx/XVLXnpguRgqZ/fz4YHzT4cDk4QdJLChs2SYLrv1WhI45tdv7ikUCTsGp3C41OBVbpJYrMQlstbBosqRL5o/qUc1QoDU2sWqAo+5csYfaxQcfFTlKt1D7yeDgdwpVSg6Mu0AGoO/eCnu+l/X9D9MoTgjoKG2g/uobSAAOWrm89XuLuT42LXR67o05C5zA4COll0z2IDjnGZlhLzjA7314pCe99CKmrAHygCkZhhwDm7cF4Z248XlfSkyG4SeJ0cDJf4ex86Vm6ImpLelP5s6f3wUdDkwKCmN2YBJMU4yAU3DqSkTgGE2SopZeKFHFuhLY/a3PmqbbjroNi9GCzW5jQ8UGj+P1Fj09qbGJpdpQdecKLToGig1U5CjdQmtkgkNAKVSJFsjz6p+SOUBSOZSwUbhwISY79C91ygxpjKbAdXnuHThbog4GQ4soORoMxu53jPeu5Um0BH2g4PO+ZEmDfSfDlZ9DWl+pGSoaC5d+IJMHqX06f34vJKKpnX33h3kvQbPVXYOz4FXY9bU0lTWnQp3nUyxGC9evub7dSwXbDU7PD8VFqM8FFVHBQUWOosQCtnrY+A4sXyDLSZnQWAU/PC8D0JNv9hisKT2js9xoY3Z2yBvhRgWWNM/lAGpJXKkyXVkyh23QaEmTiJSLRJPncpDQgUl7Aq4zSDRB4SGA0y2o84ZDRj9p2hqC44mnGht/rz3Fk2hL71NBHRxU5ChKLNBUBz+skBqcAYfD7CXw1RNiRLB5jcx6xgnR8GPTWW600jW91XZXBybt6VadQaKP6HQQBE5HxxNPNQ+99drrKZreF5+oyFGUWCC1D5y6UNypDj5XlsddIBGdYSdCZv9IH2HQiMYfm8KFC0mwmHHabNgr3KYb2r9AUVpotnraO3svE7xIl3fNkK2rBrUdEEs1NoqiBI6KHCWqCKc1aLAIW+QhrQ9MuNI9o5mSDQfN8ZzhbKwS4eOioVKa6SndxpiVjWX4sNYUtbZo/wJPXNeCwWxm8MoV2EtLA+47o8Qge3+G6h3SF8mULA5pWz6QWqg2959gRbq8a4ZcZgP/O+hWcm0Wv90P4zHy1qWdul57rXTl/Ac6gRXrqMhRooqeWINGCu/Ig2sduH9QgiaCvFM22i5X74K3/w4n3CSRndq98ME9cOgFkDei+/sMA9H8Y+PaZ7T2L/Cna3u48L4W2qb3xVNKkNKG0vWweJJMqJzzDPQdDUvOhNJfJfp84Dkhn2hxRXby91jZOeusVvfDWHU+7Amd2qkrHnTl/Ac6gRXrqMhRlCDjS/SEPP2qrgSeWwDbP5UeOue/CG/8Gb5dBt8vh8s+hPSC4O83SET7j0009y/Qru1KRDGnSrPT+nLppZSSA3Wl0riz4ADpDRRiXJGdxtL1ckhGM8Oyh7XbLlomBGIxYyHYREPtZSTp7e8/XKjIUZRu0pGLjTE7ArP65nQ4/kZYMkN6gPxjX+mpAzDhqna58YoSTDq6FkwDBjD4+eVgNGqaTKDUV0hKqouGCmnOGm1kFME5T8NTM6V/V12p2IXPXQH9D+2+5bifBNLcM1omBMKRsRDtTn/RUHvZlfOfa5tQEA3vvzegIkdRuol39KFw4UIcNTU0l5a5l+vrsVdWtm4TsvQrU1KL69pTsHSWW+AccY00P4zyupxI/tgESjQMHvwa2KUapBGmK6XRWgP2Zs+Bc1tqimX2PSVHlmtbBEta145y6ugUZEp+kVTTk2+B9L5S4/L6H+GU28HVUDWacNihqcG97HSCrQ6c9pDvWs8930RjvVG0pSVHc4ReCQ4qcpSoIpidtkONd4FngsXMzt9e1/p4gsXM1lmzPZ4T0h9faw18/4Lnug1vwfhLo17kxNKPTTQMHroc2L24AtO6pdJMcZ/jZbD50ysiZMbNbx8RqNoBS86C/abA4VeA3QYvXwtGI5z2D7+EjhIk6kpg0SSoL4OGcpjyb3jqLCj5Gco3w/krITWKBq+V26UGp3wjGE2QVgBV2+Hp2VKjM+SokKasddX3JiE1lcb161sfd/0/0rV+vZFoTksOx+RVVyJPz8XgoyJHiSoi2mk7QHwVeLbFYbXR7957cFht7L7hBiCEkYm6EnjjJvj2GVkefgpsfFtS156aCec9L2klUYDmIvecTgd2TidGYz189C8wJMDZT4gAXnm5PDlnMIycKgIGpNHsN8ug5Cf519woReO/viaPj7sQUvMlBamL44n2KFwgRKxuwmiBk/4GL10F69+Ae0a2rDfBqbeDJT10++4OdpucX0YTzF0JuUNh6WypDawrkeihj7Y3waKrvjeN69drpEfpknBMXnUl8vRcDD4qcgJECwaVjnBYbQx+fjn2igq2X3Qxu2+4gQGPPIxpoHuAF7LIhNECAw8XkXPS32DsAtj9tdToFOwPCdFzqXeVixwN6WDRTpcNDat3Qb9DYecX8Mx57icOPREGTnALHJB0trHzJJrz1SL4+N/ux6b8G/qN7VTgtD2eaI/CBULEnB6TMmD0GRJ1e/dW9/qzl0hkLtrq63KHwvxV4ubYb6zU4JzzNOz6GgYfA0mRFWVdRXr0HhM+YiktWYkPomfkEyPEosVxLBMLotJ7UG4wuwtt21rohpSkDBhzlhT6ZvaH5EwZzF66Rmbh0/LDcxxBIBrSwWKejCI4Zyncf4TMpgPk7ANnPOD7XEjtAyf+RZz4rDWyruhg2G+qnFt+ogI1SDRUyHfRli8fle8kveN7SsSipLlDIWsQGFuGFBlFkNrXU0yHAV/NPbucEIgAsZSWHUxiKS05FHQl8vSeGXxU5ChRTSyISl+Dcu+BXlgGfkkZErVxkWiBvmNCt78ACGbBqaa7eeKza7u1RuqxXAIHoGIL7PgC9jlObH/bUrsHXvqNW+CAzMR/eh8cfrnbjKALVKAGgerd7hocowlGnQHfPSupay9dBdP+1+GkRUQdm4xew4kwCxyInfMvltKyleDR20VeJFCRoyhBxtcPbSz88IaSYBacqvWmJ+3OtyYrbHjbXYMz+Biw1cKutZK6du5yGHKcexDaVA9fPAa/virLk+6EPT/C2sXw/p0w4DAYdlKXKWtKkDCaxSyifCOc94JEb/Y5Fl68UiJriUmRPsKYxeeEgBIRenvUt7e//3ChIkfxic6WK8FEc7HDiMkChQdK2mLevjD9fnFXe+Z8aKyAvBGes+ymFBi3ALZ8CAfMhNFngt0qosZokkG2CpzwkZoLx/weDr1QUsASzSJuBh4uKWBeNS7RZssbzcRKpAfi/zc4lr6LUNDd9x+sFP5oaYwbalTkKD7R2XIlmPQ0TB8TA7n6CnA2S30LUFq3h8rGinamD2GpJ8sZAvNXy6y/q4Zj1hPSz8RXn5X0Api5SLZ31eCccBNgiC674jAS0bqJ1Fz55yIpo8PaqGi25VW6j/4GK74IVgp/tDTGDTUqcgKktxYMKkokifqBXEMFfLUYKreKODClUlm7mzNem9tu07DVk2UP8lzuykLcu86jRaz1VrRuQlEUJbZRkRMg4frhi0SoOpoaVbmOpSgjVUVlnBGXucglP8PbN7uX9zkWTCFsDqIEhXhICepJKmg8vP94w/WdRG3EWolZfI3x4v3cUpETpUQiVB1NjarahlKHqetIXNGdXOSor+nJGwnH/EEK9b9aJP/mPBW541F84j2oj4eUoJ6kgsbD+483fP0OR03EWolpeuO5pSJHUZSoJ+qtN1OyYcIVsPVD2PqxrNNi/ajDe1Afb3hESTO9mnBaa8GSFoGjUgLB9R1G5WSOosQYKnKiiEini0W6UVVvDKUqgROV6W62ehE32z51r3M6g/LSvcUFJ1wULlxIgsWM02bDXlHRuj4e7jWtkwGl6+GnpZA2X3ocVe2ATx+EI66iqdHY4e+M1WzG0dCAwWiM2c8g1nF9h1E5maNEnJ7Uhfsa48W7gFaRE0VEOl0sErPlbe0Q88us7Jx+lsfjgYZSg2WvGC/E4+cRVOtRhwMSEjpe9pe9P0gPGqcD+o2D/uPIstWzYsIdkJIr/wzyuoHWk8WKC060nmvekycJFjM7f3tdu+3iJm2jagcsngy1e6W56+GXw9JZsOd7qNiC/eCb2DytvTvT9osupt+997R+NjH9GXRFfYVYc7ua4trqoNkmEVlFiWJ6Uhfua4wX7wJaRY7ik3DNlre1Q1w26i66Mbzs8PXaEjZHqyjD1+fxv4NuJd8yjEZjL48OVGyFmt1QdIgMeGqKofhbGHB4h3a9HZI1CEaeBtXFYtVsSibvk/vI+2YpzHtFLJ3jnGi99rwnj/rde0/EjiUsJCbBftPgi4fhw3vg0/uguVGszA+9AOw9vcvGOPXl8MWj0HeUNF3FCevfgopNMHY+JEeH0InKiLWixBgqcqKISKeLtSUSjbrKzFZyn7wXgP4Nyey65LK4D6VGglybhZ2zJGIWitnamHBsqtgGT06Hqu0wd4U0zXzhEti8Bqb9T5ovBiJ00vLhtHukD01Goaw7/App6JheEPDhaepm6HBYba1Cx2G1sfuGG4A4qntIzYPjb5QanG+fFoEDMPtpGDgBY2Wtx+9Mc2kZCRYziUVF2CsrW18mEq6aIcfeBL+8Cu/eKpHVs58ARzMsXyDppRn9YPQZ0gQ3wvT2ZplKaHGNN2P6XucHYRE59913H3fddRe7d+9m9OjR/POf/+Too48Ox65jiqgvrg4xV677U+vfH41fBvSe9x5PxIRjk8EACSYZ9Dx5hkRiyjYA0JR1MPZtu8GwJ7ABnnefmR6kvvRGF5xQ0dnkkWmg+zuKq3tNU4NEJduy7RPoP67d74yjpqbT9L24OteMJhh6PPQ/DHZ8LimmLgYeAYOPjgqBoyihpreI6JCLnGeeeYZrr72W++67jyOPPJIHH3yQSZMm8eOPPzJw4MBQ7z5m6fWh6sz0uJ1liESk438H3UquzQJAWqWV+pb1vTY6kDUAznteBE7Z+laBw4xHsDclsnnqdCByA7zeWCDaHfy5ljqbPIrL+2zVTqnB2fuDpKj1OwS2fy6pawBHXCU1Yi24IlumgQOxV1RENIMgLGQUwewl8NBxUL1L1mX2h7Mfbz9RoShKTBNykXPPPfdw4YUXctFFFwHwz3/+k9dff53777+fO+64w2Nbq9WK1WptXa6urg714UUtvUVld+QUYktKIaMwPkVwJCIduTYLCXN/C9AqcMC/6IA/A8munAE7e27EMJpa8++bTn0IuyMFjANp3lPSukmkUnZ6Y4Fod+jptRSX99kEo4iYhEQ49zkoPAjevV1qdDL6gcH9s2/MyiZp9KjWvw1mc+tjcXu+2epg66dSj+eieqcIwaEngDklcsemKEpQCanIsdlsfPXVV9zQkvPsYuLEiXz88cfttr/jjjv429/+FspDUqKMnjiFtMU1yC7KSO22vaIvotUxyl9cIjJ/j5Wd3XwNfwaSXTkDdvbciFBTDM9fLCkrgN2ZzuYLftdus7hM2QkRPbE2DRXeAj0uIzfepBfAmQ+Jy1rfMWBKguNugDFnQt/RkJTZuqm3yHN9VkHH3gx2m6eAsNW53c1CTU2xiL+kTNj8PiyfJzU4/Q8VR8SdX8Gz58Gc52Cf4zRlLQYJdoaE9+sB0V9rqrQjpCKntLQUu91O35YUCxd9+/aluLi43fY33ngj113nzg2urq5mwIABoTxEJU5oa7M7LIizj8F0jHLdNAONdPTk5u0SkU3NpZ2aWrhe29c+YyYyEwi2OknnAZjxKJij1/0sVgpEgzVh4Q/+XkveAj1p+PDYPWcDIb3A0/AiNU/+dUFIRKC9GXavE6Ex9HgRNmUbYP2bcMAs6eMTSmqKYfmFkDMYTr4FcodJRCujH8x4RCJez86D2mLIG64Cp5tE2nAm2BkSvhoHR32tqdKOsBgPGLw6fzudznbrACwWCxaLJRyHpChhp7vF5MG4eQdqahFoZKYrZ0DXNlFD7lBY8CqU/AxDT8RY3ciQlc+DrZbmKivbL74EiI66hLhMqeohaswQGkJyrpX8DI+fLi5vM5+AgjGw+DQRH/ZmGDsvcMt2f7E3iWPi1g/ln8MOqX1g6n+hqQ42fwgHzJR6HHsTZA8KzXH0AmLCcEbpdYRU5OTl5WE0GttFbfbu3dsuuhPtRHqWItYIR5f2cNnsti3aLzNbPVzgAsGYlU2/lcup2L6B+qslhTPlPwupzbIwIH1A2AbSoZitjUlnwD77QtZAMCVj6pMmx9/USOOW7a2bRPXxKz4pXLgQp81G4/r10RuFrCuVdLK84RLZaKyC8k3i8hfqyEa4ScmFggNh+6fw3PmQmAxN9WDJgKHHgjktdPs2mmDYyXD8n8Q2+punZf3XT8L4y+GwS2SbjKKg71rHDKEn2BkH3q9XuHAhCRYzjvr6+LdXj1NCKnLMZjNjx47lzTff5Iwz3Ck/b775JtOmTQvlroNOoLMUvf0GF44u7eGazW1btO/q49MdTH3y2JpYibXG0tr0tDbLwuwff8+KqSsYlu15rN1Nb/PnOLp6nr+RGV81S0UZqbFT92BK9lpOisxxKAHhOj99nZtOm40tM85q95yoifTUlcK7t8FXi+CsxZLC9d1yWHUdHHsjjL8kvoRORqFESpbMgD3fi8BJSIQFqyF/NCSEuDlpSo70q/r6SajcKutMyXDYxT2yee+KnkQ2YqUWNNJpzcGuBfXVOLjX2KvHKSFPV7vuuuuYO3cu48aNY8KECTz00ENs27aNyy67LNS7jigaug090WizG8wfp65EXL+Vy9maWNnu9YMhsP2NzFRWbGhXs7Ri6oqg1kWFm15RnB7juM5PX+dm4/r13XrNsA0sHc2w92cpfF8+H4aeCBveksf2fAeOpuDtK1qw1UKd27UQRzNUbIGcfUJvPlBXAu/c5hY4INbRb/4ZTrpZ0tcCJNTnSjBrQUNJzBnOKL2OkIucWbNmUVZWxi233MLu3bsZM2YMq1evZtCg6M99DXSWItKzGpEm3F3au2OzG8iPU1NJKYUVzTQ3JNPSTYH+Dcl8NH4ZxpJmmppL270ff36cyszW1ohQmdnablt/2V6zndmf/b7d66vA7hnxXgMTjlTSSNLd+rDuDCy7NaGQXgBnPQbLL4BtH7sFzsjT4fR7IC22Urk7xGEXR7OyjVKDU7sHLOmSGlbyCzw7F85+EvY5ASwhEjr2Zvj1DYmaARz9O2kCvGYhfL0E+o2Fg+cGbDbQ2bmSWUtIIvDRSKRrMYO9f+/Xay4to9+995BYVIS9spIdl17m8fo6CRb9hMV44IorruCKK64Ix66CSqCzFL19ViMWioEDGcjYKyvYNtVz212XuCOQ3X0//tb0+ErJKXroAXYkNwCeAim/0dw6gx3sH1eNbMQX4Ugl7Yhgp/H6OjfDWR/W7QkFcyoMP0lEjot9J4e2PiWclG2E+nIoOhgMCWA0Sw3O/FdExD03D3Z8IfU5oUxXMybC8JNh9Jniqnb4FYBT0uWKv4GRpwXdTS0Wfgd90Z1rM9K1mMHev/frtU1LS0hx259rrWbsEBaRoyguQhXmjxWb3UB6ifhKyTHk5zH7s9ntX7iqhs3TQ1OHEO2Rjd5S/xYrefqdEewoY1fnZlQK9MYqqcF5+xZZTjBK1OOlK0XkDD8pfP1jXFTvgtq9UiOTaILaEkkny98PLAEKL1fkpqEC5r0E2YNh+v1Si1O7B3KGSi1S5VYoPLB9XZwPenTup+XDpP8TYeOqwTn0QkmZS8sP7L3FMZoB4In3vSNkPaSUkKIipxMCDYVGOnQbafypkekokvLRxFU07lgf0mL6QOmsuNn1eKCEopfI/w66lcRmZ1BfM5boLT/OPcnTD3cqabQQivtCj9OSm63w0b/k75Gnw5R/wbPnw9aP4P07YdAR4RU51bvgmfNhz7dw/suQNwze/Ct885T0kBpxamBCJyEREpOguZGmXdux76wAnBjNdkxpZoncZBTKPz/pcY2Kt5gJobGDMSubgS+toLl4T2vkv+ihBzDk52FMMMbtGKCnEwo9nawK9oSGr3tH1E2YKF2iIqcTAg2FRjp0G2n8qpGpqPT95JZIRDQMUn11OnYRie+y7c3bkZHaLhKUv8eK7ecN9Lv3HkwDB2KvqAibwPYVmYpkh3vFN5FOoQmVU2Aw8TfK2uO05LR8iXB88j845npp0jnjUVjzf7KcFnghfI9wNENDmYivJ6ZAwQGSSgZQtV36xwRC9iA4/0V4/HTsdgubLxBnyiHPP41pyL5+RW5imVYXzaqGVhfNHckNzP5stk8XTV8EEvGH9pEus9FMra1WRJXB6PH8tqIwmHXEPZ1Q6OlkVagzDqI9o0HxjYocJSREa/qYPz8evjodB/P1u8LXjFbbm+swr+0bS9ez+QbpuzPwpRU48tyfuSMvmx15hpClNIWzy31buvpxjoZBczTRXSfCYKUCRlpk+YO/53JQIvbZg+GEP7ubYGYUwsm3BNYUs7Hac3trjRT2B0rWQJi7Ep6YBhWb3QLnxL/C2PmQ3I17uNEEZq9jMSSAofMhR6ykngbjPt8Zgd5XvSNddx97N9evub7ddt6Rr95eR9ybiHfDmY5QkeMngYZCozIXPIx4D8xdF1h+mqV1Xdsmm869pUDoZ3e7MygP5LsMxqDfnxmtDgf5xXsgyz3wcTmwRZv1aE/p6sdZf5g96Y4TIfgW/LEwCA0lQYvYewuaQARO2UZpbDn+MokEVe+ED/8p7mHpBf6/jgtzKvTZT0SOi4ETwBS4EUJT8Q7s236Cg26kubq5dX3zto0yAdGnEFMf39GqWEk99ec+H4iLZqA1R97b2+y2rg/aB5FOse/tjrThJJKGM5FERY6fBBqq1NCmJ64LrN/K5a3r2jbZdFk0R3IGqaOUGhehvtkGktLT0SB/1yWXUbhwIf3uvQdnvwK+cW4L2fEqvY/uDkJDUd/Wa6neBYsnQ00x1FfA8TfA0+fA7m+gdD2c+XBgKW+1JfDmX+DX1bJsNIPdBk9OkxqdokPEjMBP7JVVbD7/mnbrt1/7RwCGvLSyQ5ETq/gSHW1dNG876jbuPvZubHYbGyo2tK53iZhAa458RW4647ajbsNitLTff1oWeX1EmEcixV4jSaGlt9ZitkVFTpwQjWF+n7M0JWV8NH4ZAIlOA1s7enIEiHRKTbD2v7sldc3x5L1c+aN/dtWxRlczkPE4aA5Wiow/qaTegrtw4UIc9fXYKytbtwnkx7Kz5p2xTEQi9olJcNC58ME/4MtH4OvHpW4mwQgTrgzcDc1WC7+0CJwTb4YDZ4k7WvkmWPukGBEk5vr/ekZz548bPC2j/Uk9zUoLbXpYT/EWHS5RMyBtAGajGZvdxqxVs9o9L1wRdovR4lf6mhJfRHpMEw2oyIkTojHM7+sC23nxpa1/7/P6a92qE1A6HuR31Ecn3uiNJh/Bqn/yJ8rsfe0mWMxsneVpXd6dH8t4S+ONSMQ+JQcmXC1W1F884jYGOPtJGHxU4IX92YNhwWrY/D4cOFtqcOa+KK99xFWQkgtNDWCrk9Q4AIdDLKJT24uf1u/Y6aR57173BMTDD5FYUNDue/cn9TSvz/DOz/2mRjAY3REnhwOaG8Gc0vFzQshNH94E0GI0MIwNFRs80rPLzFa/+6V1B6vd2k5kdUUkrs3upMvFg5W+Ej5U5CgRw2mztQ5IA6kTCBWRTqkJZP8dDfI77KOjKFGApvEGRocR+uZ62P6558Yb3oYB4z1Ejl8RfoMB8kdB5kBIajELyB4Ix/5BokJNDbD5A/hlFZzwJ0jOhT3fw0f/hIm3tbOC9rg3GQyt6xMLCkJzb29qhK0fS11R0SES0dr7I5Sth2EnBx7ZAqgrkUalruc2VIhw8iHq/KVterarVidYeIsaF66Bf9sUtY4I9rXpz7nXncmqHtuJR4hIZNt013AmnlCRE8NEk8OUL+eOWLvAIp1SE4z9GxOMUZ3WEQriLToQDXgLbofVxqBnlmGvrGTHpdL7Y8BDD8i17Or9UblNeqRkFEX46OMHnxH66l1Sg1P8LU2THsaekAtN9RgT6jG9ezsc/8fWiIvfEX6DwS1wXLgG+NU74elZ4HRI09JDL4KqHXDIPPjgHtmf0y6RpcyBHvU7/lybPUo9ddhh55ewdKaIm/NfloaqiyfL8Zz9JAyfCKakjl/DG1eN0tDjYd/J4GiCtUugoRyOuDqkPXa6i3fkKBpQ0xJPIpFt013DmXhCRU4ME00OU76cOwK5wKLVcjoW8B5I+NOHIZyEegZLowPBx1tw777hBoa8/BIJKe70n0RTHUk//QdO/pukMj0xHVLzYebigBo9KgFiMELmANjzPfa0EWyecQ4AQx67E1PecBGawSQlF469Ad67Hb57FoafDJ8/BCW/wPktFvvv3wVfLZb+P0VjwSjH4M+12aPU04SWzyJroNQQPX6avP+mBulHlL9fYAKn2QrfPgPfLJV/0++XqM6bf5HHCw+E/abIfv2kqaSU/DIrFZVW6lvWpVVaWTbqLvL3WGlqLg245qin9XneRgT+9tXpCV0N8nWySgkFKnKUbhNM545oGqhG+mYb6P57+tmFWoREY72YEgScdli3BBorYc8PYj9cvQvq9nZP5DRbIdFtMU+TFUxtlq21nqlHjTWy3CYlKmg0NbT0ncmQQbLDIe/LlAxJmYG/nsPeUsPS5tyvK4XkHEgIrBDfYDbjOPBPGA68gebSSvfjTWk0Fp6AYW8VjoZiDEZjcGx5k7PFqnrvj9JDJykLir+TSMkTUyVF7udXZNv1b0HeiMB76zRUyfnUFofd97beZA+SGqLHJkLNbqlRsmTA/NWQF+CMdaIF9j8LNrwJm96DlZe7H9v/bBh0pE+B05nosO+oYOf0szzW1199AwnATvysOfIi0Po87+PzNkLwt69OV3R27hqzOz8nomkMECyiKdsm0InkeOmroyInhom0w1Qgzh2xFKmJ9M023PtXEaJ0hC/BPeTll8Bhx2iokY1cA9zEJJnJ7zMq8B3Vl8OPKyW1KLO/CJpN70GffWWgWrsXPvkfHHqhzNo3VMA3y2DIsTJbH0yh46pBefY8mL0MBh0Bpb9KCtQJfxH3sUCEjsMOu7+FF6+Ac56WQv/yTZJyNuNRqYdpI3S6itD3u/cedv72uvaPX3pF549311XJ4YDKLbDhLXFie+0Gsal+4WKJcri+/6N+C4dfFrjAaaySCFHtXoyjL2DISy9K+l1TMdTmSESmq+OzVsuxuWhulHOquSkg+2tA+gzNeBTuGy9CFOQcnLRQolo+6Ex0NFIR2P5DgPfx+VOj0x28z93ChQtJsJhx1NTQXFrWur632BhHU7ZNoOOKeOmroyInhoklh6lICwclvGiTt/jA13Xbuly9Szrbu9y9Cg+C3OGBDyrrKyT96b07oO8YOHc5bPsEnr8AUvvAhW/Bmv+DdU/BTy/JDP03T8Pbf5MB9SXviXAIFrZaePc2iSwtPQuO+QN88l+J7Hx4D4yeHtjrNVbBUzOgvgyemAZnPgLPnS+f39KZcNmHHQ6eu4PDamPw88sxmM3BMVEp3wiLJsvnkl4I/cZJfUr/cWJ24OKQ87v3Poq/h9USRTDhxNT/MHj+HHA0w5HXigGCOdX3c51OiTAtnizfT2qeiO2qHfDElG71+aGhAtY97RY4IP2HNrwtNToBGhlE2tAmGHTX0SzBYg6u4CZ4VvrdJZDPItIT0YqKnLghWuwfY+3m3VsJtQgJZpO3iPWAqtwOjdUSKUhIgJo9ULUd+o4O3KY33qjYCk9OdwscgO2fwpt/hpNuFnHiL6ZkGHCY1FLs+R4eOFIG0U4n5I6QFKKD58L3yyUC8u8DRYAAjJgMpg4GwN0ltQ/MXgpPz4bib0V8gQzw56/qOrLgjSUDznlGBt0VW+DRk2S9OQ1mPy3pX23oamBkMJsZvHJFazpaRwPnoJmoJGWKS9n2T+X9m1Pgvf/zFDgAj04UK+q8EYG9fp99YdwF8OVjUtvjou8YOOzijgWOC4MBMMj3Mn81GC3SyLR6l/TkCSTK19wIP70Cb7bYO+83Tc7FLR9I5Orc52DoiQHV5ETa0CYY+Oto5n3uOmpqgn4swbLS7y6BuLvF0kQ0xGfzUBU5ESLYAzfXxdQ6y1BR6fF4KDzk1bkjdumJCAm36IhIOl3ldlgyA2p2ycAtrRBeugo2vg3nPAuDjwya0Im53GdrjUReyjfJrPn5L0p617u3wtdLYOyCAEVOEgw8AuY8B0vOkIgHQP/DYMYjkj6UkgtzV8KiSW6BM/J0mHiLZ51LsEgvFHvkJ6a41x35W0mlCxRjIvQ7GE7/F6y4xL3+zIehYP92A+ZABkZhGTil5cPkO0UAZA0Uw4Gvn5THjvqt1KosmiSpax/+C065DZKz/H/91Dw44c8iKHd8KetMyTB3RdeC0mV/vWC1NCF11eDMXQn1pVBwYKsJgl8kJsE+x0LOPhKxOvV2STd84VJJies7JiCB09vwPncbS8vod+89mAYOxF5REbORrN5CPDYPVZETIUI1cItVD3kldARblPhz7nanyVtUYbeBtUoG9Ismy6Bn9zfyWO0eiWAESeTEXO6zJR2O/I3McI+dD4WHyOy9IUGKwPuMDPw1Hc3Q6FW7YK1xF6PbaiWFrS17f4SmeoKOwyERpWfmeK5/40YZRA86IjDHLhB77bdv9lz3+o0SFcweJPUjjZWQ0R8SzVKTVF+KMSO9RxH6oEX424qNrEGSUrjlQ5hwpaQMLlgtAufEvwQmcED63OxaJ/9a1zXA5w/D4Zd3bdnsEjptIzbZg0SQdadWK2sgzHtFRJNLrJ/5oFho98AePdKGNm3xTvkyG808c9ozPt3VesLuG24A5HfCdd+H3jkZGk3ff29CRY4SFGLJWCCcREN3Zl+ipCMR0u/hB6GP5NXb0izsqNjQrWPtaZg+4jU9uUMl9WXRJBE1LoEz9b9iIZuUEZr9xgpp+XDyrZKKlWiCxBwYt0AGhoE2X7TWwvo34PkLZTl7iKQFlvwES2dJStf3y+HtW+Tx/uPE3at8k6TMzX0RsgYE7701lMPy+SKy0gskwvTilRJpeG4e/GZdYCKnrhQWny7pU+Y06Svzzt8lde3J6ZIC9sWj8PG/JHpReDCsfx2evxDT1P9i2m9qh+dbVwOnkNRCmpJg4ASxU3YJkPxRLYX53eghs+c7qU1yNEukJH+UGBG8f6cI6nEXdH1O+RIzPTGjyOznuZxe0P3XaqEn30Wwf0d6mvLlbUHt63g6NC2hdw7yY6EuOR5LEFTkhJFIDNz8uRkFg0hdwNEgIjojWiNrHYkQ+uRy5GezPbZdMXUFmbWE9dwNZk1Pt7GkQ9ZgETkgg6aig2WgGgC+Iml+5T7nZrevKbA3B5Z+E0q8u793t0miwy4pak4nDDoKZj4Ge36Ep85qccxyutPEDjwXTvm7FII/MRWScoKfPpSaJzU0z18IZy0SwXvOMli+AE69AywBWkibkiWF66WrJULQd7TUID0xDU65AzDATy9LdPDJM+CA2WLN7XSKg9zwiYBvkROxgZMpyVPoGQzd//4zB8CAw8WgYc4zkjJmSYOfV3Wr0D8eibbfEYvR0qXldKemJUpUEo8lCFHya9k7iMTAzZ+bUSzT1c0/1CIoWkVWIIK67eyNLc3i/VJAz87dmAzT1+yRGpwdn8lyokVqQRZNggWrIH9Mu/4mHeErkuZX7nPTdhnw9Rkpg8jyTVBTLLUCiebgvM9oIDkTDjgb0oug3yGQ1leExPkvirjJHiQ1ORe8IWmDKbnQLx0ueF227UEKUYfkDYfzXnDX+2T2EzOCpKzARaY5VYTKNV+39MUxSrTmN9/KY6ZkOO95qQErbVPvMvhoqUkK1Ogg1kgvgLMeFbHriqAcfxMcfZ0IoBgi5urr/MRXn53eiq9Uv1pbLXanPaQTyUr3UJETRmK+TiEGCfUMWLTNsLkIRJS0nb3ZEYL+Cd2ZbY74teJyVAKY9j9pArjoVBEZP7wImQMDrz1oQ5dpAclGWHyapH8teFVE1uLTpSHl3JVSlB9PQicpE0acCsaWqIwpSWb3XYLCkgYDx7u3N5olqhZKvA0NemJwYE71dAkzJnq+XkqOWDC/cZN73eGXiyjqDXing4XCTCIMxFx9nZ+Eq89OLODrs2jbWNVFpMcAPSVeShBU5ISRcNgJ6oxL/OOPkUCwRYK/r+crshWJmp4ekzsc5q2SmfWRp0tNxPxV8N3z0pCyC4HjT6dr1/vwmRaw92cRNvXl8Ngp0o+mrlRS5cxp8enwZPR6T9GSlhdqrLXw62vwxk00nfoQdkcKAMaqekzbP4MB4+NL0CpKGOiu4U60ZmeEm1ioIfKHXvIr0nvQGZf4wtcNN7/Mys7pZwEdp4gFWyT4+3qVFRvaRbZ6OqMVkXQ3Y6IUVvfZ110TkDsMDr8CktK7fHqPO13nj4T5r4rAcX3/pmQRWj5sh2OSuhIwpbgjHDV7RMx1t7YjVmluhNf+HwB2Yx6b518FwJDH7sS06rqWSF7sDzbilXjsLRIPdNfBNlqzM5TuoSInQnR34BZrswyxdrzBpqfdmX3dcJeNugv/qkECx9fxhquTdEdEbEbJmAhGr6JnPwQOIHaz/u6mo7SARLMM+l0YjBLdMYTq2w8j5Zvg8Skw8VapV2msgmfmwrCTYPylvUroNNWDfcpLUFdCc737u22uT6DxpMdhVwXGLC3ajlbC0Vukp78jwSbajkdROkJFToTo7sAt0FmGSN+MQj0r0tX7i/T7D1Z35v8ddCu5NjEFSKu04uoO4s+MYSCC2p/jjUkjgTBjTDYy5LE7AWi2JrH98msAGPDP20gsKMSY6RZLPu8F5ZukJqeuRCI4CSZpRrhokszsu8wI2uJwtDdD8LUu0jRUwju3QdUOcSybdDd88zTs/BJ2rYX9Z/QqkWOvrGBzS2S2LduvchvGxFuNRzwRDtvdYP2O9IS2EavMrGzy+gyL6PH4IuKtB8JIuJtyxyoqcuIc182x3QURJ1GUrm7+of5xCJeIyrVZSJj7WwDatj/0Z8Yw2JGQWMvVjcSPgSk7A9Mnz8OPK2icuKR1fWJ2CkmD+0FWJ303HHaoLXHX4MxfJUJn0SRoqIDK7dJLpq2Fr70Zir+Rxow5+8i6sg1S7xFt6W3JWRLBqdkNWz+C1b+T9YYEmP2UNMRUlBghHm13fRGqBubBJBIOtpGaSG37XvutXM7WxMp2+4+0MI4GVOSEmGhJ14qFG1QsEg0zbErnROTcT8uH0+6Gis0YE+olqmMwYBw0BrK6GMQnGKHfwXD+SyJuXCJlwatQuQ0GH9Ve4OxeB4+fLm5c81+RdLlFk6XPzPxVUHBAdAmdjEI46zH4x77udaPPlD455pTIHVcEiLiToKIo3SYaJpK312xn9me/91inNUSCipwQo0VsSjAoM1vJffJeoCVd7eobgOgbCEW0pqepQSIXaX1k2WGXyEckaG6SSErJT5heu4TWyppT/w8yz5HeMJ1hNEO/sSJMXOKkz76QPVjqctpiMEBCokRCqneKWYHTIZEgS4Y8Rg+6v4eC6l2w7DzPdT88DyNPkxqdXtQAMuJOgkrQiBfbXRexlv4VjAmD7kZmOppMC+ZEd0ffR1qllWWj7qLMbOXKdX8CIL/RTOP69VH1/UQCFTlRjvcFEogltD82tr355I8FfN1ws/e4a3KibSAUschWUwNsfh8+fwSm/YemRiP20mKoKaa50S0Kwnbu7/kOnpgqDURz9oGcobDhTXHRSrTA/jO7Hsj7sg32FjggIqhgf5i/Wnr51O6V9eY0WLAa8kdHV11OQyW89kfY9ZUIs2n3wddLYOuH8PwFcOXnYBkR6aNUlICJtVTerohE+ldPCMaEQSC/Yf6MsSoTgzfR3dH3UX/1DSRA60QoAFU1bJ5+lt/fT7w2slWRE+V4R4JuO+o27j72bgakDcBsdA+CfM0y9NjGNggEO181WtL/woWvG25j6foIHU1oCErNTH0ZLDtHojcrr8Q+9q9sPrN9g7awnftpfSFvX0kXO/9FSTtb9XvY/J70PWnbGDIYJBhFNCUmieADSWkzp0aXwAGpyTn5b1JDdMrtMOQ42Oc4eP4iGH4ypPaJ7PFFEDX1UEJFrBeqR+Px+zPGIi/KougdEK+NbFXkRAn+XsA3fSgdsT+auApzhdW9vY8Bfleh23D8iAZ7Zl/T/+JvIBSUmpmkTJjyH3jxCtj4FgydH9yDDJTMfnDOMnDaIWugrJt8p4ie7CHtndF6StkGqcFpqBBB5XRKutri06VGx2VGEC1kD4IL3wBTKpiT5d/MxyRNLzn2z+nuEm+RACV66M59NprqxQI9/kj/ThYuXIjTZiN/j7ijutLIekJH30fKfxZSm2UhP28gy0bdBYBzb2nrdtGYWhguVORECQHfgFpCkZ1tr7ne8Uk8DISCnuttSYdR02DnWvjyEXexf84+NJdWRETgk9nPczktH8gP/n6abVC+WeymLRmSouZoFoFTWyxmBZkDpe9PNOEdsUnrG5njUBTFJ7E8hgjH72RnItCYnc2WGTJG80gj6wEdfR/ZA4bR1NeCZY+11YV1V8tjnaUW9oZGtlH2qxd/dJau5U8+p35DSjwS9Fxvhx3KN8K3ywDcxf5DT6LxsFtbN4uFH+eASTTDoCMlcpRRJDU4OCWCU18OAw6PPoGjKEqP8Df7w7VdLBgHeBMS4wN7MzRWQmqb59SXQUpuwMfXmQgMNd6RqmHZeQGnsoejkW2k0V++HtJVjUhn6VqNO9YHlM/ZtiGkKxQJ/hVTRzp0qyghpWqH2zI5owgOnAMf3C2pa6OuDuuhRCR33JwCQ46VdC9XDU7BAWBv8rSbVhQlLvA3+yNYA9lIjCGCPhnmstv/4B44/V5I7yupvi9dA2c84E4t7iaFCxeSYDHjtNmwV7idPfs3JPPR+GWQmc7eJDGP6qnrqK9IVTga08YaKnJ6SKhrRNpGgvL3WNk5S8Kfu9ps408xdbhSnGLBGCAaCxh7G8asbPqtXM72mu0eltgp/1lI9oBhmI3mwG7IljQYOx9+eAHmvQJpBZA7FFZfj7HvwLD+OEesJ5W3mGlrP60EBb13KJEmUpGZeEiTpqEcHp8CTfVi0DJpoSzX7oFnz4dzl3tGeALAmJWNZfiw1hS1tuy65DJAfg+GFYYukyDQxrS9QRSpyIkg/hgDtI0ENTWXRtxIoCvCKfq81/uLNkaNPKY+eWxNrGT2Z79n2ai7cPl/1WZZaOprYVj2sMBeMCUXjr4OJlwpkRyAkVNg2ImYknMxabpW7FFXKjbYLvFWu1cstJO66DEUQvTeoQQLb8EM+CWgA43MuMYZkTYO6A5BNz4wJUsEZ8WlsOkd+N9hst6SAVP/I82Uu4nrM4+WMZo/PZsCFUWxiP7yR5BAi/piuQgwWESsD4sS/XjnVCelA+lh2bX2pAoy5Zvgielw+j9h8JGSM//MXDhwNhxwdkSFjqIEA2/BDIREQLvGDbE4Zgj6mMeSDvtNgdL1ks7sYu4LQeknFk1jtEAjb/HWyNaFipxeRiykk4WKWC7ADCfhTskpM1tb3WfKzFaKQrq30BANPanihvpyePUGqNwKS8+C6Q/CZw/Azi9h11oYPjGsIifWur4rsYXLarhtDUdn51YsR2Z6QtBqgmp2w7olnuveuV1qctJ7r8NjXKQj+kBFTpQQ6AXc3Qu+N/eZiUcnkVCI1nCl5PhKPSyi5wWZSmBEXafrlBxJKVk6C/Z8By9cJOsNCTD7qbA3C421ru+9gtL1cj7kDpXlqh3SIyp/VFjr0Lpz7XhPtiVYzO3qODo7t7obmQlkzBCNk6FBGYRX7YTFp0FNsaSoHTRHJlA2vQMvXgVn3N/tmhxv1OwpOlCR00N6WiPSejNJpI2TWiVZDXR6M+nqgu9qNr6tU1uZ2erXsSrRRyyL1nhKPYyGxrvdJSo7XWf2g3Oehn+Oca87eC4MOUac5JTeS+l6WDwZEhLFZCQxSQRxxWaYv0pcBcMkdLpz7XQkmkNNICIhFn5XupVxkJgE+8+CrxZJP7GcfaDoYHjxShh/qdQAetFdwRevkZFYQ0VOD+npQC1UN5OuZuNzbZbWplHBalQFwTEGCBW9NczfGa4beH6jGapqZGVJWevjmpLjH9GUix0XVO+CZ+d5rlu3BEZNh0FHhNUWO5q6vvd6HA5xxbLVi1384skyMC3bIKKnoVJs06PYVdD7d8hhtTHomWXYKyvZcam4cPlzbvX2SEG3Mg5Sc+HI38BhF0NGP6nBGXkaDDkaknN93lfCJfjUuTE0qMiJI6KhuWg0z87HcgFmqHDdwJeNuqtV9LYlmlJyojGFojtEQ3pYVHe6ri+HV66DXV9JStLp98IXj0Lxt1Kjc/VayB4ctsNRARtFJCRA39EyC79osqQdgYiaOc/BwAkhF8A9vXa8f4d233ADQ15+iYQUd4TSn3Mr1JEC72yPK9f9KWT78peg1Mel5gJtTGos6fIvwqhzY2hQkRNHdJU7vs/rr5HfYGXZqLukN0nL42mVVvpWJdKw52cMRmOvmkWwO+1sqNjQbn2sDZzDgc1uY0fFhoh9NrGQQgFdz7BGQ3pYVNenpeTAxFulad/p98I+x8Gwk+Hp2TBmRkSd1Xr77HlUkGCUuonUPInmAJhSIWdwWCJ8UX3tBJFQZXv0hHirj1NXztCjIqcX4aitZeeMs0iAVoEDUH/1DdjuvYedv70OiK2bRHdoO1CxpVk446XT2m0TbQPnUNPW4ax/Q3Jr87Kihx5gR3IDAOusG7jypT/1us8mUHzNsEZ15CQayRsGl30IphTpbZHZD85bDkYzJEdOXPSaPPv6CrDb3G5TtnppoBikouweUbWjpQZni0T6EoxgrZaC8nmvuM0IohxfgjlWBXS8RNnDjbpyhh4VOTFO25tLfpqFfiuXywMlZey8+FLAnd/rtNs9nlu4cCEJFjMADqutdX28zyK0Hajs8BHFiSWCVQPVNhXho/HLWv825Ocx+7PZ3T4+RYi22d+Y6HTtPaBO6732rmGlvgK+fAx+eAHmPCORtY3vwOrfiYjIi2CKnsMB5Zth749SgzPnOUk/WjRZ6ri2fwbphSE1pzBmZTPkxRfAYKB5b6lcOw8/SGJ+PiQY/b52fAnmSP/euiZjCp12mhuS2dWyvn9DMh+NX4axpJmm5tJ2xxmuKLvWxymBoiInwvR0kNrRzaXtQNWV39tUUupxg3DU1LRGb9qiswixQ0hqoDLTPSJdSvwR852uHQ5oKJdIj2tAW7NHIj0pOsjpEY2V8N7t4GiWiMm4C0TgOJ3w1t9g6r9F+ESChAToP06c9xLMYkJhNEmNzs6vpYg8xO57JrMV03uXwqgzaOw7CYDEFCdJ390Np94OabH7m9lRZMEV2YfIjgu6Ux/X0yhTKM2UjFnZ9Fu5nO0126WE4OobAEj5z0KyBwwLSLCpcYFvVOREmHAW6nvfIGw1NWHZrxK9+LqB25JSyCgcCIQ30hXPKQ8xETmJFRwO2PsDLD4dpv0Xhp0EtXvg8alwyPkw7kIVOj0hvVAiJEtnwp7vYVXLRNiAw2HyXZETOC5MyWIlToK7BqfvGLEDDnUBua0e1i2F3d/A7m8wzjmSIU89gLGpGL5/Dg6cDcNOBIOh69eKQuIxUtLTKFMox2imPnlsTaxk9me/F/OflvW1WRaa+loYlu3/ftW4wDcqcuKVNrPxvm5KxqxsLKNGMfj55WA0Yi8tjaneHl0RDQ5WsUA0ueF19WMUzfbkXRHNkRPXwCZmrveGCnjrZok4PDsXTr4VPn8IKrfCB3fDAbNU5PQEUxIMOAwOPAe+ftK9/qzHIKMwcsfVFpNXtCbBGB6HLHMKjJ0vKXPfPIVp6cmYXI+dcrtEmWJU4EBsOQmqCYjiDypy4pS9STaGFXZ8U5IbmXtw27h+fevf0XhDCxR/HaxieeAcDnx9PpH6bKJJkMUTMVdMn5oLU/8Lz82H7Z/CGzfJelOy1IykR8lAPFax1cOmd6UvUVuWni01Opn9I3Nc0UJaHzjlVvjlFWisknWFB8JBcyA5K6KH1puIuftWF7Q1/ykzWyny4znqztY1KnKUXk00DJyjOZc2Gj6feCNikRO7TWpWXDgc4HSAMQZ/BjIKYfr/4D9j3evGLoD8UbH5fqKJ2r0SIXM6YcB4OOxSWHFJS+ra9TD9vsinrEWS2r3w2g1ugQOSvvblYhg3P6Luf8EkkEiJThZ2H1+fXRH+fXbqztY1+mvgB75qBaKlTiBYN5d4CP3Gqk2v5tL65rajbsNitGCz2zx6GUXLtdddIjID2dQA2z6VWfi84SJw9nwPdisUHhx7wqBiCzx5pue6z+6HQUfC0BNCXnwe16Rkw6S74LvnYOZiSMmF5OekNufUO3q3wLE1wFeL4PvnZfnEv0LJr/Dt0/D2zVAwWno6xXDKmouu7lNtf28zs7LJ6zMsXIcWV/RkIrGrGqpYHcsFkxj7ZYsMvmoFoqVXSLBm2uMh9BttNr1d4fqR6Hbn5jjHYrRw/Zrr262PlmsvZmhqhK0fSbpRSh7MXy09TxafBo4mSfEqOljqGmKB+nJ48UqpwTElw6ylsOb/JHXtufPh2u9V5PSEpEw44GwYNQ3S8mXdoCPgoreio09OJDEnw8Fz4dc3YcyZkqJmb5J+PQaDXEdxIHD8IVYm5+I5yhRLNVSRQkWOEjcEw8EqnA5fsSbKQo33j5HNbutka8VvjGZIK5Cu8LV74NGTJHXNVgfpBZJeEysCBySSMPW/Itqm3w+FB0H+SFh+AYy/HJIyIn2EHRLNqakeJGV6LpuS3E5mvZ2MIjjnKTBa3DU4J98MGCC1TwQPTPGFplz3blTkKHFDMByswtXUTGmP94/Rhp7aV1trJWLhmo22N4sjV2+bjU5IkFqVBa/CoyeLOxnI5zB/Vcx0iPcgZ4i8n6QsSbXLKIKzn5SZdnNapI+uQ2Jl9lvpAu/GtK57TJzTVaE7RLl4j2PioeQgFKjIUZQI4bopxUs/gqjCWgvr34C1T8IZ90ua1q618Mq1cM4yyBoY6SOUOhmQlCuQOhlbHST1zAq3Y/t0B+B0Lzqd8i9W8RarCQkyu+6idq9YDVsiK3p0YKjEC10VukNwxHs010FHK/FQchAKVOQocUks9P5w3ZQ0lzYENJTD8xfIIH7FZXDUtdK9vblRhM6Zj0S2gLqpATZ/IH8POVoG53u+l7qS/c/ukRVtO/t0hwP2fg+LJst+k7OljqC+TOpy5q8SM4JYpvRXePIM+V77j5O0vKfOlu9938kREzpNJaU0793DlhlntXusN6emRh22ekhMEqEMEvV1NGuKnhfhahYazXXQXRHPTa1jERU5fhBNvUIU/9BZjdinRwWjyTlwxkPwwsXS82PTu7I+ewhNJ/4L+84yoCwyM+hOJ+xcC8tmy/LsZVIbs3gyWGtE8Ow/M3jF844mqC2V1L30AhE1TY2waJLsz1oNDnts1eW0pb4MXroaqnbAk9NE6Lx3B+z9EV68An7zXcREjr2ygqZt28KzM1sdmFPbLNerAYM/WGthw1uQsw/0HSO26ru+ltTWwUe5I62K34XuMTXQb6yCZqs75bCpEZrqICnbLXoDQFPeowsVOX4QzsK1mLo5KEFBc2l906PrzpImM/iHzIe1i93rz38Re2ljZOsiDAbIHiwDqt3fwNNng8EoM8fphTKwCnBw2qV9euYYTOetEAvp3KES3VnwqkS2Cg6MXYEDYnF85sPwxDQo3yQ9XgASEuHc5WHvW+L9XTisNvrdew+JRUXYKyvZcellQJBnv8s3wy+r4cBzJEJZvQu+XASHXdS+fkRx02yFzWtg+Xyp5Zq/WtY9MUXMOc57AQYdDcYYvj4iQMwM9Bur4Ntn4aN/wbyXpbZv28diYnL+i9B3/24JHSV6UJETZUT7zSHeRVhX0YNQvP94jTpF9FyxN8tM/rfLPNe/fA2MXxjafftDZj845xmJ3pRvAmczWNJhwWqZUQ4Qv5z69jkCjCZZ4TIjiNVmoN5kDYQ5z8B/D3Wvm3C1NLMMc8pRR3ULAP3uvaf176ClplbthMdPl0hWQyWMvwSenQc7PofSX2DyPyBNXb98kmiBPiPFfbBmtxhzOO2Szpk9WP6pwPFJXEzONdXDm3+R/z8+BY76Lbz6B5lwWv0HmP1U7zOqiTPi4NdNCSfRLsJ6SlfRg3h//8Ekop9V1Xb50WpulBS1yY9gL9kNQPP2Ta2buaIdYU9bczigbq8Ux7uw1UHpBonmhCJFxiVwXCQkAHEyS1m1Qwb2bfnsPtj3VOg3tv17jycSzdKA8qtF8P6d0hTVWiO9Ww6Y5ZnCprQnd6ikcD5yott5MK0vnP8SZA+K7LFFMXExOZfSB+a/AotPl9+MVdfJ+r5j4KxHVeDEASpyFA/iPVKj9BKSMuDwy+GHlZKiVtLI5gvmttvMFe0Ia9qa0ykmA4tPA1utDKiSsmTWfdlsifAMPjqgCEQwekTFLHVl8MIlErlLSIQp/4IP74GyjfDEVLjmG8goDNvhdFacbRowgMHPLwejMXjfSWofOOFPYirx7TIROABnLYJ9jte6nK6wN0uD2aZ69zpbrdTkOAZoulIECFsdtDFRItoTrpIJAhdnPiypvUrMoyJH8UAjFUpckJILR1wNh10qA9zS9ZE+IjcGg8yum9Na6gBWSeTm6XPEJSwlVwbrARCMHlExS0oOTL5b6ihmPAoDJ8CQY+DJ6TJ4CXMko7PibMugEEUG7DYo3+i5bs8P8jmoyOkYe7OYDDwxRWpxMvq1GHXsdTsPal1G2AlbHXRTI2z7VCZF2rL0bKnRyRkS8Ev2yDBHCToqchRFiU9Sclv/7Mr6NOyRDleKjOtvgHOeFqewPvvFR51MuDAYIH8/uPJzMKVKnUXWQFjwmtgCJ2VE+ghDS/WulhqcL+SzKDgQdq9zz0wffnlk7dKjnYQEoMUM5PyXpB5j8WkS2TFoPU53iJmBfmOVCBpHs6SonXQzPHu+pK49fyHMeQ5Sc7t8mbaE06hK6Rr9JY0yOro5FNlSaFwvs9HaOE5RAkut9Nf6NKy4xI2LjCL510NioUdU0DEYPEQtEBVd6INWnN1QITVb6YXihGerg8Zq6adkSACjWT6Ds5fAoCPg7VukRseUDBiC8l7iEmOiiMIFq8V23lWDM3+VpP/lj9IoTjeImYG+JR3OegzW3CmTTGmFUqOz/EI448GABY4SfRiczuhteV1dXU1mZiZVVVVkZMT5bFwXNK5f72F7G6qB2YaKDR2mqw3LHtbra3b0/fvfiTrUn1VX52pHtLX4dQ082y7rBIISVTRUwGcPwcf/bmncOgI2vCk2t7Oegn2OlRnpso1QdLCkp9WViD15v7Fht9COSZxOEYm+1jkcUi+XlOmehCjfAji65YSo+IG92TOa7WgOOIXXb2z1MmngciC0N0s9VpyaDsTDGCYQbaCRHCUgYmaGJkTE+vvv6Q0ukE7UUflZ2Zs9XYHszTRu2hzZvjmK0hnNVun1ZKuVNKpxC+CT/8og/ON/i5BJLxADC9dAPbUPDD2x/cBd8Y2vz8klcPb+AIsmi7ic9SQ028TQwtEss/4qdIJLTTFseg+GT5Q0y/oy+PUNGHZCaHo+mVM869aMiXErcKD31V2ryIlifDX4c/0dKtvbmMmljVNCPcvS225wHlRskaaJAw+nqbIOe3kJNDXQXFHXuknELKUVpSPSCySCs/g0qb/5+D+yfuAEMVpwzUB7D9RV4PQcu00+c1st7PwSnjpbZvmrtksqYEOlCCFNaQsONXtg5ZWw8S045vcw4Ur44B4R9SNPh9PvaSd04iEyoYQOFTlRTEdN5UJpexuVs++9iF4tQkJJxVbpm1O1A85Zht05kM3Tzmy3WUQspRWlK9IK4dCL4e2/uded8OeoqDuKa0xJYuc+eyksmwN7vmtZnwzzXoGCA1TgBJMEo7su6v274LvnZHIKxBjC0H7Iqr+ZSmfo1akoSvxjNEFyLjgd8PRsET2KEgvY6mDDG/DOLZ7rl54Nxd+Bwx6Z4+otmFOg72gpUneRXih9VNQFMbik5sHxf4Sx82XZJXDGXw5HX6dGAErA6BUaxUSd7a3S6+lJk7ZgpxUElFqZUQTnLIWls6D4W4xUMeSxOyGjH80VdWy/+BJAr624wruY3FdxeSzQ1AArr5DjH3g4TPm39ACq3gUrL5d+HjFaQ+BtAhKV0dPyLVKD01glTnZOB5RvgqfnwOwlQXFEVNpgMIDR4rku0YK6BCrdQUVOFBOVtrdKTNPTmquepDMGO60g8GMxiNUuYHrtEkwA5yyjsa/bylmvrTihoQJ2fAVFB0oRvq1emj7m7CPNYWOJ5GxJjXrn7zDtf3L881eJwDnjwZgVOOCZkh2VKaK2Btj0LlRudaeo1ZVI6trutVDyC6QVaMpasKgvkxqczx+U5YwiEfMf/VOi8YdfoT2fekhvq7tWkRMjBK3fgtKr6bU1V9W7Yfl8KR42GCA1H2r3yGDlrHcifXRKMKmvgLWPw1t/hYPPg5NvgW2fwTPnQv/DYObi2BI6CUYoPADOfNidrpOzj9SJePcGigEiYajTbczJMHq6iOSB46HwIDEjmL1UXNYGHKYCJ5jYm8T6HGDCVXDM9fD23+HLR2Hn1/J4F9x21G1YjBZsdhsbKja0rlcjAqG3jQFU5MQIHra3StwSi7MsMZFyYrdC5TYRODMfhwHj4elzYNdajGY7Q15cAQlGnUCIC5zuwdDXS2D3t7Dne0kzctjl8Vgjwdi+HiEGBQ5ExlCnRyRni1g2p0oNjjERhhwr6YPm1EgfXXyRXgAzHoFfXoNRU+SzP/6PUDAG9p0M6e0tpL1/M212G7NWzWq3nRoR9E5CJnK2bNnC3//+d9555x2Ki4spKirivPPO46abbsJsNodqt4oS08TiLEvUp5yAOPPMewVKf4Uhx8jgZPZTsOFtTP2HYtKGifFDSg4ceqEMQt+7DYq/lfVFY2HWE1pDoQROcqbnctu+KkpwSS+Ag+ZAYss4MTUPDjrPveyF929m2+iNooRM5Pz88884HA4efPBBhg0bxvfff8/FF19MXV0dd999d6h2GzZiYvZaUUJEVyknEIXXRe5QyBzg/rHMKIIDZnX44+kvei+IQhKTIH+k57rswZLXr0QUNdRRusT7ntzDe7TSewmZyDn11FM59dRTW5f32WcffvnlF+6///64EDkxMXutKCGiq5QTaH9dREUqXgh+PPVeEGXY6mHze/DcPFm2pIO1Bn54XmosTrpZzAiUiKCGOoqihIuw1uRUVVWRk9OxM4bVasVqtbYuV1dXh+OwFEUJA7GYiqfEIE118N3zUoPTbxzMWQZfLoZ3b4VN70GztatXUBQliPhqH9DWCKCrxxWlu4RN5GzcuJH//Oc//OMf/+hwmzvuuIO//e1vHT4eaWIyRUdRQkBXKSeubeKVmHKI6m2k9oFT74C84XDI+bJ86IWQlAHDT5YmjkpUoK6hvQNf7QPaGgF09XggREXGQA8Idj+53k7AIufmm2/uUoh88cUXjBs3rnV5165dnHrqqcycOZOLLrqow+fdeOONXHfdda3L1dXVDBgwINBDDBndSdGJdvSCUrpDb085iTmHqN5GWj4ceQ2YWgrEU3LcDllK1KCuoUqwifWMgWD3k+vtBCxyrrrqKmbPnt3pNoMHD279e9euXRx//PFMmDCBhx56qNPnWSwWLBZLp9tEkmDMXkebqNALSlGUuMTk5YClAkeJBHYb7PlRXMPSC2RdyS9ijpE9KLLHpihxTsAiJy8vj7w8/wa/O3fu5Pjjj2fs2LEsWrSIhBhvmhWM2WsVFUq80RtTTtQhSlGULmm2SQPiJ6bBgMPhrEekWe3iSWDJhPNfVKHjB72qZsdpj/QRxBUhq8nZtWsXxx13HAMHDuTuu++mpKSk9bGCgoJQ7VZRlDDTG1NOenu6nqIofuCwQU0xOJpgy/uw5Cyo3gH15dLHqakh0kfom8ZqqWFz0VDVvldQGAlmzU5U01gln70vHM3hPZY4IWQi54033mDDhg1s2LCB/v09Cz2dzhjsOO1Fb5y9VhSlPXovUBTFJ+Y0GD4RZjwKyy9wN6ZNzoYFr0KffSN7fL6o3g3v3QHH/B6yBkBdCXz8X2nQ2c3j9WUG0NYIoKvHI47TCQZDx8vBomwj1O3x/VhdKaTWQFJ6py8RbSURkSZkImf+/PnMnz8/VC8fcXrj7LWiKO3Re4GiKB1iSYOC/cFolvocgIx+YoYRioFyT6grhZWXw6Z3YftnMO8lePcO+GoRfP0kXPahNFEOkK7MAKLaLKB6J1TugMIDwZQEtXulxqrfwZAU5OhW9mCysLNiwh2e6xMtZGUN7lLgQPuo121H3YbFaGGAfYCH+OktoiesfXKU6CPW7RYVRVEUJWop+QUWTRKBYzSBvQn2fA/LL4IZD7vNCEKJwwFta6K9l11Y0uHo62Drh1DyM9yzHzhaakTGXQiJyaE/1miieicsO08icOcuF7H6+k3w3bNw2j9gzMzgpvGl5JBnSCDv7ZaeXgAJRrjuF0jrXgNji9HC9Wuub7c+LtP9fKAiJ8xEm6iI6hkURVEURYlVGqvh59VQXyYpahe8LgLn+Qth+ydQszv0IqehErZ9Av0OgbS+Uge040vIGdK+Z1SiBQaMh3OegSVnegqcCVdCclZojzXacDpFnDqa4amzIH+UO+XQVg8EufSiqRF2roUtH7jXOezw9t/gpL9Kzy8lIFTkhBkVFYqiKIrSC0jKgLHzACfsOwnyRkiq2ozHZGY+f3Ro999QAV8vgTf+BCNPhyn/kkH007Ogz35w7rPthY61Gn562XPdlg/BWhMckdPcBLZqSG6TrldXCpYMSDT7fErEanYy+8OcZ+CpmbD3R7fAOfkWaTQcbNFX+gssPUuETd/9YfCR8NkDkiqYkgNHX+9pCKF0iYocRVEURVGUUJCSA4deJKlgBoPU6IyYCAnmDgf1wSPB3R/q51egbD2UrgenQ2qEDF4pa3Wl8M5tUoMDMPQEiSqU/iKRjPNegMx+3T+c5ibYvRaenQdzV0CfkXJMT0yHMx6UKJKPzySik8NGC2QPEZHjIn+U9DkKNumFMHIKlG+Cc5aBOUXE4JePwsFzwyJw4s24QEWO0inxdsIriqIoSljxHpw2NYDZ6F6uLQFjoqS0dYeGSs+oQkMFmNOlXmT0DKnBWf07qQ8CKDgQzlnqNhFwWUYbzVB0iIicY/4gKWrF30rqWt5wqSnqCbZqeG6BpOktmgRT/wuvXCsObsvnw5VfQGJOz/YRTGr3Sg3OL6tk2ZwGtlp4ejac+xwMPELMCIJFWj5MvkvS41zfzfhLYex8SO/r3s5hl1odH3hHvWwusws/ibdejipylE6JtxNeURRFUYKGwwFlv0JSlru+pnyzDEKzBrbfvnoXPDtfCvz3ORastfDmnyF/PzhkXuApUDV74O1b4KjfQt4w6cHz2QMw7GQoOlgiI97Rl7Q+kNAy/KsplujNsX8Qy+jhJ0vtUGofcQ8bMB4uflfqedLyA/xwvEjJhbkrKC35kcrEFsE07Z9gSCCrzyjyuivyQkVTA2x+T/4++VY44GwRfHu+h+9fgIIDgityoP1nXFciwio1T86pqh0SjRswXiI9XnhHvUobSgOrA3c09eToow4VOYqiKIqiKIHicMCe72DxadBvLJz5kBSkPz5Foh7nv+gpdBpr4P27YcdnsOwcOOsx+PV1+OZpeXzw0WIQ4C/1ZVJv892zsOFNuOANWLsYPrwXPv43XP4plP4Ky+bI9snZEuXZ8Ba88luJGrx0jTx320cwfzV8cLcYFVTvgss+EoFUsH/QPjLyRlDpaOCM1+Z6rF4xdQV50WapnT0I5q+CzR/AmBkiQOc8I3VO4y4U4REoldsk8tZ3tIiWmmKo2CqfsbdoKV0PiyeLEJ6/SkT00lmw9wc4e4mkE/oQOm0JKNWvcpv884U9NsWPihxFURRFUcJH9S4ZNGUPkuXavVLYnj3Et7VxtNLUAKUb5Ng3vQdPnwu1u2W23ZQiUZW2IicpHY65Hnatg11fwXPz3Y+d9DdxPAuEpGw48jew/nWo3QP/PtD92MHni9Da8KbU4BQeLEYDP70Mq66DnV+JSDv6OtjyvjSivGc/2RbgqOvAFALL6LL1kq7mjd0auiabndBlSn7eCEgvkloqEDOCI67pUlz4pHIbPHmGvP/5q8SEYuWVsOkdmP0UDDnO83UTEqUmqGmvCOnUPHmNhETZzrumqifYm2HPD2Cw+368uRGabWGoIwsuKnIURVHimKaSUuyVFRizsrVpqRJ5qnfB03OgoQzmvSyF8a/9UQbqC1aL41isCB1LKow4Bc58GF64GHZ+IetNKTD/Feg7pv1zMopEbPxjX6m9ANjnOKm7CDRVLSFBiuDnr4YHjnSvHzkFjrsRUrLh2P8HmQNg/7MkFWr/mXJ8Aw6DrP6Qni89YB6f4hY4h5wPR17T/Rqhjqgrk0H+af/X/rHKbZBSICltYcSvlHyXwHHRHYEDUkvT3Ai2OhEt2UMk9Q0kuuP0Ehg5Q+QaWTxZrpvKbSICO6sHqi+X/6e01DbZ6iS62FWfHWMiDD6arOqt7ZuRpheSlV4YcwIHVOQoiqLENfbKCjZPmcqQl19SkaNEnrpSKP1ZoiCPT4G8fSXaALD1E/Czs3vUYEmDgYeLWLPVybrsQSIsjD6GWLUlUoPjEjgAm9+XFLF9jgs8etJYAd8/77lux+eSypaSLbP/h13iHhAnZcDo6e79NFbDjy95Pn/rR5IiFWyRk5QugrCpqv1j6UVgSg3u/qKNVtFyujQadQmcaffBflPEgc8bo9nzczEYJYKX4OPcqi+HLx6Vc2v8pdL3aOM78N1zMPnurmuqLGnkpeST99Q5ULlV1qUXwqUfxGyPnhiZLlEihcupw/tfpJqXKoqiKDFM/iiY94oMsiu3uQXOxJbC7lgSOCAmA4smi8BxpVrt/Qmev1jqLdrSWA0f/8tdg3PkteJm5nRI3czenwPbd305fHIffHiPLA8/WcwCavfI7H/5ZlnvPePvEjh1pfDeQvjiYVkeeqIMjMs2wpPTJO0umBjN0H8c5OzT/jFTSvCL+KMRcxpkePUm6jta3r83VTtg6dmS4pdgFHMLR7N8t8Xfupu1gqT67fwK3r0V1iyET++Xeq9n58KPL4r4aarv/NhqiuGFy9wCByS1bvkF7c/lGEEjOUqnaPNSRYk9XClqAM179rT+v7Hl8ZCkrtWXywDLZW3aUCH55N1N7VDiE2MiZA+Wgd2OL1vWmWH0mcFvrhhqbHVS41K1vSVFbRWUbZDUtS1rpN+Jy3ENJIoy7gKJvBzWYg08/lJYdh7kDm3fmLMrTCkw6Ej46F449GI49gaJECyeLG5tXUWFEi0iOr54GI7+ndSa7P0RnpwOuftCQg8to31hNJOVnBuZ5p6RpqZYanB2fCbLpmSJaC6eDPNWQeEBntbQjVVQsUWiNucul0jQ4tPlfNv9jdh6u6I/BgMUHQRjF4gF+Pt3ul9nwOFyrvkSUi7sTVJXtvEtWZ52n9R0rbhEarZ+eQ0OmhNzKWsGp9PpjPRBdER1dTWZmZlUVVWRkaFdXqMR7aOjKNFH4/r1bJ4ytcPHh7z8EknDhwdvh7V74e1bYdwC+aFurBaXp7x9JQVHhY7iwtV75LtnPddnDYJ5L4kAiiXqy+Czh6Q2p+AAqbn4ZbUMYIee6Pvcr9kjg0VXOlj1bhnIdlU34YumRqjYLOlEqXktltYbRFC1FVgdYa2RiE9mf6njsNug5Fd5vba9WUJFfblEC7IGSeqftVYiCemF7rqSELKhYkOHNTnDsocFd2clv8DDJ4gl9PT7RaAuniwRm6OvhyOu8kwRdNglpa2+QlIiTUnyXW37FEZOlkklb+rLRaTu/kaWLelwzTr/nOBckcHcobDf6YBBauV2rZOoY2p466U6IhBtoCJH6RFhvUEoiuIX3pGc7RddzIBHHiaxrwxaghrJaayG9+6AT++TuoT5qyQP/O1bxP3n4ndlhlFRQKI3j54k6TUTb5XB2+NTZEb7qOvE7ctXbUI001ApaUiuGpzGGjEFMMd5jUlPqS+HT/4nttUzHoXhEyV98fkL5VyYcFXIhU5YJ2oddij+TsTOyMlynpdvgu9XwLj5vk0XHHZJUUu0uNc1NfpO7bPVyb332blyfbk45v/B4Zf591nWl0sEx3UNWmuh2Ro1AgcC0waarqYoihJnmPrktYoYV4paYt++wY3euEjKgMMuhp9ekhnJh45zP3bgHLFJVRQX+fvBzMehcjscfJ6Ig3mvwLqlcPgVsSdwoH2aXazVFUUKexPsaHGke/5CGHk6/LJKBujbPpOUvhDT45T8mmJJK8sdLsK2rlQaeGYPaS9EEozSDydvmJz3IPVJ4y9t7+DW9jltU9jAt8BxOmHnWrfAGTBejuHbZfD+/0mEaOz5MillSHBH6epKxD46o1CWvYWQJa3jY4sBVOQoiqIoPSNnH1jwmvTpcBXDDjwCJt4SdktYJcoxp8Kwk2SA6xIHhQfJORSG9CQlQJqbwFolg3LX4Lp2DyQm+U6XCoT0vnDGg7DiUti8Bn5+RdYPOgrOejQ86XI9oaZYjn3nWrE/z+gP798lNU5znpX34SVISq0VEjmq83ypLIfvyJHfkSaDAXKHyX3XYYeZiyUiY0qGjW/DvpNE4Dx/oUwkTPm3iKd3bhNnv/OWiyNgnKEiR1EUJY4xZmUz5OWXMGYF2Q62LQ0VUmPR1u2n+Buo2CaOQN4zkUrvxjuNy5ioAicaaW6SpqVLzoSzl8DgI6FiKyyeJClQB87qudCxZMCoaSJyXIyeHhsRvaYGcdKzVovD3sDxsN5lh/6xOOd5iRy/+vJ0d/uMQkn7c/0NcMJNcMzvpT5r/ZtiDw7w0lWQkgffLJXlX1+Hg86LO4c7tZBWFEWJY0x98kgaPjx0PXIaq+HrJVKDAzD8VElRs9XB46dBSYC2uIqiRAfWKnjjT3ItLz0LPn9YBE5dKbx7m9SG9Oj1a2H9a7D6d7LssuBefb2YN1hre/b6oSZniERw0vqK0HEJnCOvhQlXRka4ZxS6BQ6IgURmP5loGnQETPmPrF//hgicibfC7GXibmhKkvt5XUn4jztEaCRH6RGuPjq+1iuK0gswp8LgoyWdZdR0OPkWGRw9PkXETrAbCiqKEh5S86R+6unZ0pfljZtkfVIWLHi16+aSXdHUAO/cKjUkg4+GmYtg+YUS1Xn3dnFmjPZ6kOQcsWj+6UX3uv3Pkoad0UZShrimffpfMT84dSEUfy81OYMOF4HzwwqJ9ky8rXtuf1GGihylR2gfHUXp5biKaS99X1JPUnPl3/xV0v8koyjSR6goSnfJ7AdT/gUPH+9ed8Q1YjPsirx0l7Q+MHclrPk/OPEvEnU440F45+9w7P+T5WimrlRqcNoKHJDUtQWrIX+0mBFEC3UlUoNT8gvsN0Xuz+uWyGNOu0xIrbpOlosOlp5ObV3dYpAo+vQVRVGUmCTBKIOetjN/2YNV4ChKrFPyKyyd6bnuvdtgy4c9T1cDyB4Ep9zh7umTUSjL2YPc27S1Q67dK/UurlS2+grY/L5YH4cbazWsfVz+Puq38Jtv3KlrH/4LfBgGRAyHHbZ8JI1CAfqMgsIDpR4KRFi6BM6IUyV9LcYFDmifHEVRFEVRlPjBVudp7mCt7V7aV10pPDFNGlImZcGcZbD6D5K6lpgEv/0+9NGW2r2y/35jpV/Lq/8PfngBpj8gjmFfPArv3AJH/AaO+k143RztzbB7ndS3jL9U9l22AT78J5zwZ5/ucIH25QlqHx9X5CkpS443IVGiO0tnQtlG2SYtH674NKpdMbUZaAQJa2MpRVEURVEUF3Ul8NlDcMj5kDVAGpV+/zwMOhLyRwb+eqXrYdkcqc3J3w+qd8Ez58FJf4WBE3zP9jscnmla3sv+UrtXRNWPK+C0e2DEKfDiFbCpxYltwGGw/XP5+6CWY+ppnVCg2JvBVuNZe9hQ2b53UrRQVwIGo5giNFZTWrmFyuptntuk9iErcyB5aYW+XyPCaDPQCBKoPaCiKIqiKEqPqS+TmouvFokwmPcyfP8CvP5HGdRe9I44ggVC3nDpgZWSIzU4mf3g3OfE4tmXwKkrlQafAw6TaEBjtQiRgv270ffGCY4m+XPVdfDNYTDhipbUqw/dAufAc+HkmyNTw2NMbG+uEq0CB9yfkd0OWz6gMsHJGZ/c2G6zFRMXkZeUQ2lTTUxP3KvIURRFURRFiXXM6bD/TFj3lERg/n0INNXLY0OOa9+fyF9SvVKXUjsY3NaVwnt3wBePSI3KEdfAjyvhld9KnceUfwcmdNL6wun3yN8/vwI7PoeSE6Hv/iJyXAw4FBKTA3pL3cZhB2uNp5CJ5shNRxiNYi5Q9qPvx82pkGihsmZ7TE/cq/GAoiiKoihKrJNohv6HwnkvyLJL4Aw/BSbfGZ5UroSWufMP74UnporAkQe693pGM+SNkL+P+b28p8/ul2VTi7B55VoRQaHuq+OwSz3SJ/+DujJZV74J3vwz1OwJ7b5DQUYRZPT3/ViCKbzHEiJU5CiKoiiKosQDTfUyEG9L+cbgOKF1RWqeCJHDLpHl4u9E9Jz8d5jyT3cUx97k3+vVl0sD0g/vkVS5YSeKmAE4YBZc+wPsc4Isf7lI3nuzVSJKLpxOtyDpKVXbYfFp8P6d8NG/oHI7LD4d1j4hhgj1QdpPOEmI74QuFTmKoiiKoiixTkMlrFsqNTgAhQdJJKRsAyw5QwblocZohtxh8ndCIky/T2p3HHZZV18O3z4D1bu7fi17E/ywUv4+6Dzosx9M/Z+kwY08TXq7nHG/OKvNXARJmbD9M3GEq9wuAqfkJ3jsZNj7c8/fmzkNDj5f/v74X/Cv/aF6p6R2HfUbcS1Togp1Vwsy6q6mKIqiKL2Y2r1gSnHbNteWSC+plJzQ7rexBn56SRzIxsyASXdByc/w5HTIHwWzl4pxQMj2Xw3fL3enqJ35CHz3DKx/Ew46F06+BT57UCIhg46Esx5z98fpiKodItzGLZCi+dq9sPNrKDxAeuqA2zK7rhT+dYAs5+0r9TzPnAcNFZLytuC19vVFgVJfDquvF8c6Fxe/J8eTYJRlp9OzUar3cluqd0FzI+TsI8u1JdJfJ3uI0Vx4RQAAIGhJREFUmBqEmK7GrNE4plV3tQiSl5ynYkZRFEVReiPVu2H5Ajj0Iim2b26At/8m9TATrgqt0ElKh/2mSiPNvH1lQG85FBa8CumFoRU4IIP1H1+Uv0dMhsFHSo+b9W+KGcLmNSJaAPrs61+qVGZ/+dzMKbKclg/7HC0i0oXLUMGSAXOegSfPgNJfJLUMxOVt1pKeCxwQAbLtU891P7wAWQPl9Su3Q+0eET1Gs9TqlPwsfX68exVV74Jn50HlVliwGpKy4c2/wo8viDNe4cEhFzpdjVljfUyrIkdRFEVRFKWn2Orgk/tg2yfyb9p9sO1j+HqJPD70BBh8VGiPISndcx+JZug/znObulJIznH3rqktEUtoU1LP9p2WD2c8KDUqY+eJO9r4S8DpgI//7RY4h8yD42/q2KXNG3OK57Ipxfd2iWYYMB5O+ps7ZQ/gnKdFVPWUiq1Sg+NKURswATa+Je/NlAwHzYGnzobKLTD3RcgbBi9dJc1Cz3gQRp7uKXSs1SKArNWwaLLYbG94Sx7b+olEn4yZPT/uXozW5CiKoiiKovQUc6r0cRlyrCy/eIVb4Bz3R+g7OnLH5qJyKzx1Fuz9UZp01hTDikth68fBMSdIL4AjrhaBA2Lt7B2xcdXnBBunE8o2wvt3ea5/8erg1CMlWiRCZk6F+avgrEdg/OVgSBBXOwBns5gfPDkNHp8qAgekvsjp8Hy93BESwbFkSPTHJXBO/AscMldqjJQeoSJHURRFURQlGGQUShG8qU3flgHjJaLh3TQy3NSVwQuXwq6vYfFk2LVWlje+DUtnQkN5cPbjeu/15fDp/eKOBpLSBbBuCbz9d6grCc7+XNSVwqJJUoOTmgeT7gSjSVLXls3xdF3rDukFYnRw0TtQcIB8n8dcD5d9KDVG2YNh7kqpr2m2ipAEmPofGDUNkrzqRxISxMa58AD3OkOC1FNF+lyJE1TkKIqiKIqiBIO6EnjrZmhqcK/b/hlseLvjPi5ttwWJqDgcvrftCam5MPW/EmVprIJHToTN78lj0x+QiEIwcTRDyS/y97gL4JI1cOwNsly2IfgRnUQLTLxVBM781TB2AcxdIal4k+5sLzK6Q3oB5I90mwyk5kmEzpVSZ7RAhlftU+4w36mAtSXwxl88G5s6HfDoRPl8lB6j7mqKoiiKoig9xVor/VPev1OWj7gGdq2DLe/L8oLXYNAEz+c0VMKPK2Gf48UwoKkBNn8gM/z5o9x1M8Fk789w33j38uFXwgl/al/7Egxq98Kvr8O+k0QQ1JfDr69JfVJXzmrdobEa7FZxYgNotoGtRoSO0Rz8/bWlZg+8fLW8X5B9WmtEfM19EfqP9TyG4u/hoWNE7J34FxhxCjw2SWp0DrsMjv8jJGvKmjfqrqYoiqIoihJOLGlw8Hlio3zgOXDYxRKVWXGpFPrnDvXcvqFKXMde/6OkOs17RdzIls2RAfIFb0jUoC3NVqjYIo5hqXktdSgbZHt/RENNsTSubMvXT8JB50D+6OCLqrR8+SxcLmEpObD/2aFzDfOO1iSaITEIrmr+YKuF7Z/L39P+B0OPh8enSJ3QL6vF/KCtu17uMJjznDRNHTsPLJlSo/PFo3DM71TgBAGN5CiKoiiKogSLmmKZvXfVVdQUS61FWr7ndvZmqY95YopEcJKyZBbf6YCisTB7iUR0XDRbYfunsGSG26GspljqawoOgDMf6lzo1JXCi1dKJAXgmD/A2sel6N2SAZd/DFkDgvpR9CpczUeLv4MRk0RwVW6TnjoHz/XtJtfUKDbjrnPF4ZBUwhStyekIjeQoiqIoiqJEAm+h0ZHwMCZC0cFw/svw2ETpwQLQd0x7gQNiUb3lI3Hq+uIRGUDv+EIK7fd8L493RlIWHH299KuZ8h8YeRoccLb0kznoXHe/md5MXQnY6sUkwWAQwdFYLelvXVlsGwzQZz/IGuT+LLMGwmGXdpwKaEryfN2EBBU4QUSNBxRFURRFUSKB3Qb1ZZ72wtZqETLepOTA+Mvg6N/J8vo3ROCk5ErDT+90OG9couo330qNjDkF8obDJe+J7XMoG5XGAnUl8Pat8OAx4oxmrYFvnoF/HyR9j/yx2DYY2ovFUNQ6KX6hIkdRFEVRFCXc2Oph8/uw7BxZzh4CiUkSoXliqjSf9CY5W2pa2tL/MHehfVcYEyVtrm1TyowiFTggIuaXVRJRWzQZXv8TvPp7cYlb+6TU3PQ2fLn8hcL5L0SoyFEURVEURQk7DmiqB5xSg3PBq2I+YEpuaR7pZbHsdMLen2DRqZ7rf30V3rmt531gejtZA6TwP7WPCJ21i2X9ftNg0kLfNTXhpnKbGBm4qCmGPT9KfVewsTfD7nVQvsm9rmwjFH8TuoauQUZFjqIoiqIoSrgxp8HwiXDeC1KDk14o6WTzVsH8V6SpZFsaKuDj/7ibXV79lTt1bd2Tsl7pGemFMPpMz3XH/M7/SFkoqdwGT04Xo4myjWIYseIyeOQEESPBFDr2ZhEzi0+Tf2Ub5d/iyRLl2h0bQkeNBxRFURRFUSKBJQ2GHOe2bnbVzfiyck7JgZNuFqe2I68RC+IJV0GCCfY5Tmyole7TWCU1OJ8/6Ln+8SnSXLTvaKm5iRSNNdJA1FoNiyZBZn/Y+ZU8Vr5Z7MaNaZ2/hr8YDJCQKOda9S547BTAKdFCS7o8RgQ/Cz9RC2lFURRFUZRYoaHCbTkMUF8hUaFEU+SOKR6o3gX/PhiaGyVF7cQ/SdSirgQGHwUzn4DUMPXc8YXDAXu/l2Oy1rjXn/EgjDzds84qKPuzi2vfY6e2pFUipgoLXhMHwFA0qvWDQLSBpqspiqIoiqLECsleFsMp2SpwgkFKH5j3Euw/C067G/JGSI3O0BPhjIciK3BAREVagWfELtECA8YHX+AAJBhbxLPFc3/m1IgJnEDRSI6iKIqiKIqiNDeBtcrTZKC+TGy6I01NMay4HDa9I8uGBLEeTy+QdLquLMQDpWyj1OPU7BYzDKdTolwZRTB/VfuasTChkRxFURRFURRFCYREU3sXtWgQOABV22HLGvn7jIfg0g/AkiHi54eVYPVhcd1shWabe9npFOvyrrDboGKLmBtY0uGCN+DCNySyU7tH7M199XKKMjSSoyiKoiiKosQnDVWQnOlebqyGpBgcUzY1wJaPJLI08jQwpUiNzk+rYPwl7cVYsxW2fyrGFP3Hyf/3/iSNTkecIuKlq/1t/kAiRX3HAE6p0akrgUFHSnQnAgSiDdRdTVEURVEURYk/StfD2ifEjS61D1TtgDV3wXE3QEZhpI8uMEzJMPhIsXd21eDkj4HMgZCc5bmtwwE718KSGYABzn9RarkWTxbjihmPwcjJnQsVUzIMOQaMZncNTt8xEsExJYXiHQYdFTmKoiiKoihKfFGzWwb1tXuhoRxO/CssnSXRiLL1cPbj0dH/JhBMydDWYyIhob3Aca3P7AdZg6Bsg9hgG00SnUnJhYIx/kVivMVMglH+xQhak6MoiqIoiqLEF0YLHHqJ/P31ErhnpAicBCMcfT2Yu0jXinWyBsLclZA5ABzNInDMaWIB3WffSB9dWFCRoyiKoiiKosQXKTlw2EVw+BWy7LDL/2c91VJTEhspV93G6ZR+Om176jQ3Sk2N3dbx8+IIFTmKoiiKoihK/GGrg83ve6776RWwVUfmeMKF0ykmA4snQ2OlCL6sgRLReXI6bP/C03UtTlGRoyiKoiiKosQX1bvcNTgJidLUE2DdEnjzZolohBpbLdSVupftzZ7LocRgkP+n5kmK2vzVkDtM1hsSAEN4jiOCqPGAoiiKoiiKEl8kJELeCCj5GeY8B/0OobR8A5V1xZDWF6zlYKsCICspi7zkvC5eMEBstbD+bVj/Bpx0s7ib7V4Hb98CZzwgTTVDhcEAfUbCgldF0LhqcOaukD43hQeJEUGcoyJHURRFURRF6R62ejCnuJetdWBJjdzxuEjLh0n/B0ddC3n7gimJyuR0znhrQbtNV0xdEXyRU70Lls+T1DGnEw6ZC0vOEAOA12+C0+/17YwWLAwGyN/Pc13WQDEiMMR/FAc0XU1RFEVRFEXpDjW7Yc3/QU2xLNeVwif/gYqt4TuGxiqv5Tb1Nmn5UHig22TAEEb749Q+cMJf5O9vnoJFp4rAyRoIJ/01tAKnM3qJwAEVOYqiKIqiKEqg1O6F5y+Gj/4JL1wC9WXw7m3w3h3wxDS38Akl5Zvg3duhtqW+pnoXvPEnqNoZ+n13RXI2HHoh7DfVc/2CVyF7cEQOqbehIkdRFEVRFEUJDFMKjFsgkYHNa+DeMfDlY/LY/jOlT00oqd0Di0+Dzx6AV/8gUaRl58Hax+GZ88JjLNAZ9mYoXQ8b3vRc/85tblGmhBQVOYqiKIqiKEpgWNJg+Ckw7T5ZbqqX/x9+BUy4AlKyQ7t/owUmXC1///AC/GME7PpKCu2P/YM0vowk5Zvg8dNbUtQGwaEXy/pvlkqKX0NFz/fR3ATNVs91trqev26coMYDiqIoiqIoSuA0N8L2zz3XFX/XfuAdCpKz4OBzRSy8f6e72eeMR2Cf48CU3O4pWUlZrJi6wuf6kBzfvqfDjs9h3kuSvpZRBB/eC2PngSWzZ6/f3CSirrkRBhwudUclv8r+9psKSRlBeRuxjIocRVEURVEUJTBqS+C92+GrRbLc/1DY8QVs+UBqdWY8DOkFoT0GWx1seNtz3U+vwOBjfIqcvOS84LuodURaPpx6O9htkNlf1o1bAAfOhrQCSOhhMtWeb+GJqSLuzl0u+1g8SdL2nE4YfYZE23oxmq6mKIqiKIqiBEaCUaITAMf8Ac59DmY8KstJGS0NJ0NIzW6pwXGlqA07Wdb/8ILU6ERD3UtavlvggDua01OBA9LrJ2cYOJrhqRnw0LEicJKyoN9YMEeBjXeE0UiOoiiKoiiKEhgpOTDhKtjnWOh7gAzgR5wq7mG5w2SAH0oSEqUPzO6vYfbTMPBwWLcUXr8Rig4CY5wPcTP7w7nPwuNTpP7HVgeJSXDBq9Bnv15lFd0RcX4GKIqiKIqiKCEhJQeGHOtetqTBoCPCs+/UPnDyzTD+EsgbIelpB82R/WcPjlwfmnBiq/fsE2S3Qc0eyN7H3RuoF6PpaoqiKIqiKErskdqnpdlnS/1NcpZEcXqDwCn5VWpw6sskRS17CDgd8NRZsO0TaGqM9BFGHBU5iqIoiqIoihJLGFr+k5wtKYLzV0H+aElTMyRouhqarqYoiqIoiqIosUXeCFiwWowHXDU45z4LVTslmpUY4masMYCKHEVRFEVRFEWJNfJGiF20K2qT2R8y+mkUpwVNV1MURVEURYkWmm1ey2ForKnELt6CRgVOKypyFEVRFEVRooGGSvh+OVRul+WmBij5BUo3uLdprAF7c0QOT1FiCRU5iqIoiqIokaahCr55GlZeDktmSG1F2UZZV7UdyjeLXfD3y6H4WxU6itIFKnIURVEURVEijSkZCsaA0QSlv8APz8MXj8Cn98GLVwBO+PZZeOVaWHyaNIBUFKVDVOQoiqIoiqJEmkQz9B8Pc1fI8nfLYcQpYEmH6l3wv8Ng9fXy2LCTe0cvGEXpAWEROVarlYMOOgiDwcC6devCsUtFURRFUZTYwmkHW4P8vfsbeO8OOPtJWbY3yf8HHwOn/wPS8iNzjIoSI4RF5PzhD3+gqKgoHLtSFEVRFEWJPZrqYfMHsGy2LGcOgEFHwo7PPbcrWw+N1eE/PkWJMUIucl599VXeeOMN7r777lDvSlEURVEUJTaxN0NdCTjsUHgQLHgNsgbBu7fL4/3GgjkNanZrTY6i+EFIm4Hu2bOHiy++mJUrV5KSktLl9larFavV7QdfXa0zFYqiKIqi9AKSMmC/KVJrU3Sw/D97MBgSYOTpcPq9UL0TFk2GlDwwakd7RemMkIkcp9PJ/Pnzueyyyxg3bhxbtmzp8jl33HEHf/vb30J1SIqiKIqiKNFLUgYMPxWMRlne51i46C1JXUvNg+QcuPANSMqCzH4RPVRFiXYCTle7+eabMRgMnf778ssv+c9//kN1dTU33nij36994403UlVV1fpv+/btgR6eoiiKoihK7OISOCC20v3Guk0GEhKg72gVOIriBwan0+kM5AmlpaWUlpZ2us3gwYOZPXs2L7/8MgaDoXW93W7HaDRy7rnn8vjjj3e5r+rqajIzM6mqqiIjIyOQw1QURVEURVEUJY4IRBsELHL8Zdu2bR41Nbt27eKUU05h+fLljB8/nv79+3f5GipyFEVRFEVRFEWBwLRByGpyBg4c6LGclpYGwNChQ/0SOIqiKIqiKIqiKN0hLH1yFEVRFEVRFEVRwkVILaTbMnjwYEKUGacoiqIoiqIoitKKRnIURVEURVGUrmm2QV0ZNDW619XuhUbta6hEHypyFEVRFEVRlM5ptsGOL+Bf+8P2T2V5789w33j4frkKHSXqUJGjKIqiKIqidI61Gl7/I9jqYMkM+PwhWDwJ6svh7VugubHr11CUMKIiR1EURVEURemc1DyY/RTkjwJHM7xxkwic5GxY8Kq7YamiRAkqchRFURRFUZSuyewPU/7pue6o6yBnn4gcjqJ0hoocRVEURVEUpWv2/gxPz/Zc9/bfYOvHnmYEihIFqMhRFEVRFEVROqeuFJ6bJylqKTlw4Rvu1LVlc8BWG+kjVBQPVOQoiqIoiqIonZOaB7OehD77wvzVMGA8nPscFB4E5y4HS0akj1BRPDA4o7hDZ3V1NZmZmVRVVZGRoRePoiiKoihKRKkrFcHTdtmSAYnmyB2T0msIRBskhumYFEVRFEVRlFinrcDxtawoUYKmqymKoiiKoiiKEleoyFEURVEURVEUJa5QkaMoiqIoiqIoSlyhNTmKosQVTSWl2CsrADBmZWPqo/niiqIoitLb0EiOoihxhb2ygs1TprJ5ytRWsaMoiqIoSu9CRY6iKIqiKIqiKHGFpqspihLztE1Ra96zp3V98549NKJpa4qiKIrS21CRoyhKzONKUfNm+0UXAzDk5ZdU5CiKoihKL0LT1RRFURRFURRFiSs0kqMoSsxjzMpmyMsvAZKi5orgDHjkYRL79sWYlR3Jw1MURVEUJcyoyFEUJeYx9clrTUdrbLM+sW9fkoYPj8xBKYqiKIoSMVTkKIoSV7SN6mgER1EURVF6JypyFEWJK9pGdRRFURRF6Z2o8YCiKIqiKIqiKHGFihxFURRFURRFUeIKFTmKoiiKoiiKosQVKnIURVEURVEURYkrVOQoiqIoiqIoihJXqMhRFEVRFEVRFCWuUJGjKIqiKIqiKEpcoSJHURRFURRFUZS4QkWOoiiKoiiKoihxhYocRVEURVEURVHiChU5iqIoiqIoiqLEFSpyFEVRFEVRFEWJK1TkKIqiKIqiKIoSV6jIURRFURRFURQlrkiM9AF0htPpBKC6ujrCR6IoiqIoiqIoSiRxaQKXRuiMqBY5NTU1AAwYMCDCR6IoiqIoiqIoSjRQU1NDZmZmp9sYnP5IoQjhcDjYtWsX6enpGAyGiB5LdXU1AwYMYPv27WRkZET0WBT9PqIJ/S6iC/0+ogf9LqIL/T6iC/0+oodY+i6cTic1NTUUFRWRkNB51U1UR3ISEhLo379/pA/Dg4yMjKg/AXoT+n1ED/pdRBf6fUQP+l1EF/p9RBf6fUQPsfJddBXBcaHGA4qiKIqiKIqixBUqchRFURRFURRFiStU5PiJxWLhr3/9KxaLJdKHoqDfRzSh30V0od9H9KDfRXSh30d0od9H9BCv30VUGw8oiqIoiqIoiqIEikZyFEVRFEVRFEWJK1TkKIqiKIqiKIoSV6jIURRFURRFURQlrlCRoyiKoiiKoihKXKEiR1EURVEURVGUuEJFjp/cd999DBkyhKSkJMaOHcsHH3wQ6UOKe+644w4OPfRQ0tPTyc/PZ/r06fzyyy8e28yfPx+DweDx7/DDD4/QEcc3N998c7vPuqCgoPVxp9PJzTffTFFREcnJyRx33HH88MMPETzi+GXw4MHtvguDwcCVV14J6HURat5//32mTJlCUVERBoOBlStXejzuz7VgtVq5+uqrycvLIzU1lalTp7Jjx44wvov4oLPvoqmpif/3//4f+++/P6mpqRQVFXH++eeza9cuj9c47rjj2l0vs2fPDvM7iQ+6ujb8uTfptREcuvoufP2GGAwG7rrrrtZtYv3aUJHjB8888wzXXnstN910E19//TVHH300kyZNYtu2bZE+tLhmzZo1XHnllXz66ae8+eabNDc3M3HiROrq6jy2O/XUU9m9e3frv9WrV0foiOOf0aNHe3zW3333Xetjd955J/fccw///e9/+eKLLygoKODkk0+mpqYmgkccn3zxxRce38Obb74JwMyZM1u30esidNTV1XHggQfy3//+1+fj/lwL1157LStWrGDZsmV8+OGH1NbWcvrpp2O328P1NuKCzr6L+vp61q5dy5///GfWrl3LCy+8wK+//srUqVPbbXvxxRd7XC8PPvhgOA4/7ujq2oCu7016bQSHrr6Ltt/B7t27eeyxxzAYDMyYMcNju5i+NpxKlxx22GHOyy67zGPdyJEjnTfccEOEjqh3snfvXifgXLNmTeu6efPmOadNmxa5g+pF/PWvf3UeeOCBPh9zOBzOgoIC58KFC1vXNTY2OjMzM50PPPBAmI6w9/Kb3/zGOXToUKfD4XA6nXpdhBPAuWLFitZlf66FyspKp8lkci5btqx1m507dzoTEhKcr732WtiOPd7w/i588fnnnzsB59atW1vXHXvssc7f/OY3oT24Xoiv76Ore5NeG6HBn2tj2rRpzhNOOMFjXaxfGxrJ6QKbzcZXX33FxIkTPdZPnDiRjz/+OEJH1TupqqoCICcnx2P9e++9R35+PiNGjODiiy9m7969kTi8XsH69espKipiyJAhzJ49m02bNgGwefNmiouLPa4Ti8XCscceq9dJiLHZbCxZsoQLLrgAg8HQul6vi8jgz7Xw1Vdf0dTU5LFNUVERY8aM0eslxFRVVWEwGMjKyvJY/9RTT5GXl8fo0aO5/vrrNQIdQjq7N+m1ERn27NnDqlWruPDCC9s9FsvXRmKkDyDaKS0txW6307dvX4/1ffv2pbi4OEJH1ftwOp1cd911HHXUUYwZM6Z1/aRJk5g5cyaDBg1i8+bN/PnPf+aEE07gq6++wmKxRPCI44/x48fzxBNPMGLECPbs2cOtt97KEUccwQ8//NB6Lfi6TrZu3RqJw+01rFy5ksrKSubPn9+6Tq+LyOHPtVBcXIzZbCY7O7vdNvq7EjoaGxu54YYbmDNnDhkZGa3rzz33XIYMGUJBQQHff/89N954I998801rGqgSPLq6N+m1ERkef/xx0tPTOfPMMz3Wx/q1oSLHT9rOkIIMur3XKaHjqquu4ttvv+XDDz/0WD9r1qzWv8eMGcO4ceMYNGgQq1atanexKj1j0qRJrX/vv//+TJgwgaFDh/L444+3Fo7qdRJ+Hn30USZNmkRRUVHrOr0uIk93rgW9XkJHU1MTs2fPxuFwcN9993k8dvHFF7f+PWbMGIYPH864ceNYu3YthxxySLgPNa7p7r1Jr43Q8thjj3HuueeSlJTksT7Wrw1NV+uCvLw8jEZjuxmEvXv3tpupU0LD1VdfzUsvvcS7775L//79O922sLCQQYMGsX79+jAdXe8lNTWV/fffn/Xr17e6rOl1El62bt3KW2+9xUUXXdTpdnpdhA9/roWCggJsNhsVFRUdbqMEj6amJs4++2w2b97Mm2++6RHF8cUhhxyCyWTS6yUMeN+b9NoIPx988AG//PJLl78jEHvXhoqcLjCbzYwdO7ZdaO7NN9/kiCOOiNBR9Q6cTidXXXUVL7zwAu+88w5Dhgzp8jllZWVs376dwsLCMBxh78ZqtfLTTz9RWFjYGs5ue53YbDbWrFmj10kIWbRoEfn5+Zx22mmdbqfXRfjw51oYO3YsJpPJY5vdu3fz/fff6/USZFwCZ/369bz11lvk5uZ2+ZwffviBpqYmvV7CgPe9Sa+N8PPoo48yduxYDjzwwC63jbVrQ9PV/OC6665j7ty5jBs3jgkTJvDQQw+xbds2LrvsskgfWlxz5ZVXsnTpUl588UXS09NbZ0YzMzNJTk6mtraWm2++mRkzZlBYWMiWLVv44x//SF5eHmeccUaEjz7+uP7665kyZQoDBw5k79693HrrrVRXVzNv3jwMBgPXXnstt99+O8OHD2f48OHcfvvtpKSkMGfOnEgfelzicDhYtGgR8+bNIzHRfSvX6yL01NbWsmHDhtblzZs3s27dOnJychg4cGCX10JmZiYXXnghv/vd78jNzSUnJ4frr7+e/fffn5NOOilSbysm6ey7KCoq4qyzzmLt2rW88sor2O321t+RnJwczGYzGzdu5KmnnmLy5Mnk5eXx448/8rvf/Y6DDz6YI488MlJvK2bp7PvIycnp8t6k10bw6Oo+BVBdXc1zzz3HP/7xj3bPj4trI4LObjHF//73P+egQYOcZrPZecghh3jYGCuhAfD5b9GiRU6n0+msr693Tpw40dmnTx+nyWRyDhw40Dlv3jzntm3bInvgccqsWbOchYWFTpPJ5CwqKnKeeeaZzh9++KH1cYfD4fzrX//qLCgocFosFucxxxzj/O677yJ4xPHN66+/7gScv/zyi8d6vS5Cz7vvvuvz3jRv3jyn0+nftdDQ0OC86qqrnDk5Oc7k5GTn6aefrt9RN+jsu9i8eXOHvyPvvvuu0+l0Ordt2+Y85phjnDk5OU6z2ewcOnSo85prrnGWlZVF9o3FKJ19H/7em/TaCA5d3aecTqfzwQcfdCYnJzsrKyvbPT8erg2D0+l0hlxJKYqiKIqiKIqihAmtyVEURVEURVEUJa5QkaMoiqIoiqIoSlyhIkdRFEVRFEVRlLhCRY6iKIqiKIqiKHGFihxFURRFURRFUeIKFTmKoiiKoiiKosQVKnIURVEURVEURYkrVOQoiqIoiqIoihJXqMhRFEVRFEVRFCWuUJGjKIqiKIqiKEpcoSJHURRFURRFUZS44v8D80TtzY1CwGAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualization of PCAs\n",
    "fig=plt.figure(figsize=(10,6))\n",
    "sns.scatterplot(data=final_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15f4921d",
   "metadata": {},
   "source": [
    "# Checking with other Clustering Algorithms"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "889194cf",
   "metadata": {},
   "source": [
    "# 1. Hierarchical Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "79a14b71",
   "metadata": {},
   "outputs": [],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "from sklearn.cluster import AgglomerativeClustering\n",
    "from sklearn.preprocessing import normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "70f57bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 1000x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# As we already have normalized data, create Dendrograms\n",
    "plt.figure(figsize=(10,8))\n",
    "dendrogram=sch.dendrogram(sch.linkage(wine_norm,'complete'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "93a170f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering(n_clusters=3)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create Clusters (y)\n",
    "hclusters=AgglomerativeClustering(n_clusters=3,affinity='euclidean',linkage='ward')\n",
    "hclusters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ec701bd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    64\n",
       "0    58\n",
       "1    56\n",
       "Name: clusters_id, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y=pd.DataFrame(hclusters.fit_predict(wine_norm),columns=['clusters_id'])\n",
    "y['clusters_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "32e2e80f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "      <th>clusters_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type  Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0       1    14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1       1    13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2       1    13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3       1    14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4       1    13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..    ...      ...    ...   ...         ...        ...      ...         ...   \n",
       "173     3    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174     3    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175     3    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176     3    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177     3    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \\\n",
       "0             0.28             2.29   5.64  1.04      3.92     1065   \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050   \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185   \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480   \n",
       "4             0.39             1.82   4.32  1.04      2.93      735   \n",
       "..             ...              ...    ...   ...       ...      ...   \n",
       "173           0.52             1.06   7.70  0.64      1.74      740   \n",
       "174           0.43             1.41   7.30  0.70      1.56      750   \n",
       "175           0.43             1.35  10.20  0.59      1.56      835   \n",
       "176           0.53             1.46   9.30  0.60      1.62      840   \n",
       "177           0.56             1.35   9.20  0.61      1.60      560   \n",
       "\n",
       "     clusters_id  \n",
       "0              2  \n",
       "1              2  \n",
       "2              2  \n",
       "3              2  \n",
       "4              2  \n",
       "..           ...  \n",
       "173            1  \n",
       "174            1  \n",
       "175            1  \n",
       "176            1  \n",
       "177            1  \n",
       "\n",
       "[178 rows x 15 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Adding clusters to dataset\n",
    "wine3=wine.copy()\n",
    "wine3['clusters_id']=hclusters.labels_\n",
    "wine3"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59bb4e3b",
   "metadata": {},
   "source": [
    "# 2. K-Means Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "9a89865e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import KMeans"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "bb32bcca",
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we already have normalized data\n",
    "# Use Elbow Graph to find optimum number of  clusters (K value) from K values range\n",
    "# The K-means algorithm aims to choose centroids that minimise the inertia, or within-cluster sum-of-squares criterion WCSS \n",
    "# random state can be anything from 0 to 42, but the same number to be used everytime,so that the results don't change. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c29435cd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# within-cluster sum-of-squares criterion \n",
    "wcss=[]\n",
    "for i in range (1,6):\n",
    "    kmeans=KMeans(n_clusters=i,random_state=2)\n",
    "    kmeans.fit(wine_norm)\n",
    "    wcss.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "fed28032",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot K values range vs WCSS to get Elbow graph for choosing K (no. of clusters)\n",
    "plt.plot(range(1,6),wcss)\n",
    "plt.title('Elbow Graph')\n",
    "plt.xlabel('Number of clusters')\n",
    "plt.ylabel('WCSS')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "25152dcb",
   "metadata": {},
   "source": [
    "# Build Cluster algorithm using K=3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b53fa9e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans(n_clusters=3, random_state=30)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Cluster algorithm using K=3\n",
    "clusters3=KMeans(3,random_state=30).fit(wine_norm)\n",
    "clusters3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "0cb03e55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 0, 2, 2, 1, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters3.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "6e977eae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "      <th>clusters3_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>14.23</td>\n",
       "      <td>1.71</td>\n",
       "      <td>2.43</td>\n",
       "      <td>15.6</td>\n",
       "      <td>127</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.06</td>\n",
       "      <td>0.28</td>\n",
       "      <td>2.29</td>\n",
       "      <td>5.64</td>\n",
       "      <td>1.04</td>\n",
       "      <td>3.92</td>\n",
       "      <td>1065</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>13.20</td>\n",
       "      <td>1.78</td>\n",
       "      <td>2.14</td>\n",
       "      <td>11.2</td>\n",
       "      <td>100</td>\n",
       "      <td>2.65</td>\n",
       "      <td>2.76</td>\n",
       "      <td>0.26</td>\n",
       "      <td>1.28</td>\n",
       "      <td>4.38</td>\n",
       "      <td>1.05</td>\n",
       "      <td>3.40</td>\n",
       "      <td>1050</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>13.16</td>\n",
       "      <td>2.36</td>\n",
       "      <td>2.67</td>\n",
       "      <td>18.6</td>\n",
       "      <td>101</td>\n",
       "      <td>2.80</td>\n",
       "      <td>3.24</td>\n",
       "      <td>0.30</td>\n",
       "      <td>2.81</td>\n",
       "      <td>5.68</td>\n",
       "      <td>1.03</td>\n",
       "      <td>3.17</td>\n",
       "      <td>1185</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>14.37</td>\n",
       "      <td>1.95</td>\n",
       "      <td>2.50</td>\n",
       "      <td>16.8</td>\n",
       "      <td>113</td>\n",
       "      <td>3.85</td>\n",
       "      <td>3.49</td>\n",
       "      <td>0.24</td>\n",
       "      <td>2.18</td>\n",
       "      <td>7.80</td>\n",
       "      <td>0.86</td>\n",
       "      <td>3.45</td>\n",
       "      <td>1480</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>13.24</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.87</td>\n",
       "      <td>21.0</td>\n",
       "      <td>118</td>\n",
       "      <td>2.80</td>\n",
       "      <td>2.69</td>\n",
       "      <td>0.39</td>\n",
       "      <td>1.82</td>\n",
       "      <td>4.32</td>\n",
       "      <td>1.04</td>\n",
       "      <td>2.93</td>\n",
       "      <td>735</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>173</th>\n",
       "      <td>3</td>\n",
       "      <td>13.71</td>\n",
       "      <td>5.65</td>\n",
       "      <td>2.45</td>\n",
       "      <td>20.5</td>\n",
       "      <td>95</td>\n",
       "      <td>1.68</td>\n",
       "      <td>0.61</td>\n",
       "      <td>0.52</td>\n",
       "      <td>1.06</td>\n",
       "      <td>7.70</td>\n",
       "      <td>0.64</td>\n",
       "      <td>1.74</td>\n",
       "      <td>740</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>3</td>\n",
       "      <td>13.40</td>\n",
       "      <td>3.91</td>\n",
       "      <td>2.48</td>\n",
       "      <td>23.0</td>\n",
       "      <td>102</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.41</td>\n",
       "      <td>7.30</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.56</td>\n",
       "      <td>750</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>175</th>\n",
       "      <td>3</td>\n",
       "      <td>13.27</td>\n",
       "      <td>4.28</td>\n",
       "      <td>2.26</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.59</td>\n",
       "      <td>0.69</td>\n",
       "      <td>0.43</td>\n",
       "      <td>1.35</td>\n",
       "      <td>10.20</td>\n",
       "      <td>0.59</td>\n",
       "      <td>1.56</td>\n",
       "      <td>835</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>176</th>\n",
       "      <td>3</td>\n",
       "      <td>13.17</td>\n",
       "      <td>2.59</td>\n",
       "      <td>2.37</td>\n",
       "      <td>20.0</td>\n",
       "      <td>120</td>\n",
       "      <td>1.65</td>\n",
       "      <td>0.68</td>\n",
       "      <td>0.53</td>\n",
       "      <td>1.46</td>\n",
       "      <td>9.30</td>\n",
       "      <td>0.60</td>\n",
       "      <td>1.62</td>\n",
       "      <td>840</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>3</td>\n",
       "      <td>14.13</td>\n",
       "      <td>4.10</td>\n",
       "      <td>2.74</td>\n",
       "      <td>24.5</td>\n",
       "      <td>96</td>\n",
       "      <td>2.05</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.56</td>\n",
       "      <td>1.35</td>\n",
       "      <td>9.20</td>\n",
       "      <td>0.61</td>\n",
       "      <td>1.60</td>\n",
       "      <td>560</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>178 rows × 15 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Type  Alcohol  Malic   Ash  Alcalinity  Magnesium  Phenols  Flavanoids  \\\n",
       "0       1    14.23   1.71  2.43        15.6        127     2.80        3.06   \n",
       "1       1    13.20   1.78  2.14        11.2        100     2.65        2.76   \n",
       "2       1    13.16   2.36  2.67        18.6        101     2.80        3.24   \n",
       "3       1    14.37   1.95  2.50        16.8        113     3.85        3.49   \n",
       "4       1    13.24   2.59  2.87        21.0        118     2.80        2.69   \n",
       "..    ...      ...    ...   ...         ...        ...      ...         ...   \n",
       "173     3    13.71   5.65  2.45        20.5         95     1.68        0.61   \n",
       "174     3    13.40   3.91  2.48        23.0        102     1.80        0.75   \n",
       "175     3    13.27   4.28  2.26        20.0        120     1.59        0.69   \n",
       "176     3    13.17   2.59  2.37        20.0        120     1.65        0.68   \n",
       "177     3    14.13   4.10  2.74        24.5         96     2.05        0.76   \n",
       "\n",
       "     Nonflavanoids  Proanthocyanins  Color   Hue  Dilution  Proline  \\\n",
       "0             0.28             2.29   5.64  1.04      3.92     1065   \n",
       "1             0.26             1.28   4.38  1.05      3.40     1050   \n",
       "2             0.30             2.81   5.68  1.03      3.17     1185   \n",
       "3             0.24             2.18   7.80  0.86      3.45     1480   \n",
       "4             0.39             1.82   4.32  1.04      2.93      735   \n",
       "..             ...              ...    ...   ...       ...      ...   \n",
       "173           0.52             1.06   7.70  0.64      1.74      740   \n",
       "174           0.43             1.41   7.30  0.70      1.56      750   \n",
       "175           0.43             1.35  10.20  0.59      1.56      835   \n",
       "176           0.53             1.46   9.30  0.60      1.62      840   \n",
       "177           0.56             1.35   9.20  0.61      1.60      560   \n",
       "\n",
       "     clusters3_id  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  \n",
       "..            ...  \n",
       "173             0  \n",
       "174             0  \n",
       "175             0  \n",
       "176             0  \n",
       "177             0  \n",
       "\n",
       "[178 rows x 15 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Assign clusters to the data set\n",
    "wine4=wine.copy()\n",
    "wine4['clusters3_id']=clusters3.labels_\n",
    "wine4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c1f9010c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    65\n",
       "1    62\n",
       "0    51\n",
       "Name: clusters3_id, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine4['clusters3_id'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "bf9db3d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>Type</th>\n",
       "      <th>Alcohol</th>\n",
       "      <th>Malic</th>\n",
       "      <th>Ash</th>\n",
       "      <th>Alcalinity</th>\n",
       "      <th>Magnesium</th>\n",
       "      <th>Phenols</th>\n",
       "      <th>Flavanoids</th>\n",
       "      <th>Nonflavanoids</th>\n",
       "      <th>Proanthocyanins</th>\n",
       "      <th>Color</th>\n",
       "      <th>Hue</th>\n",
       "      <th>Dilution</th>\n",
       "      <th>Proline</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>clusters3_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.941176</td>\n",
       "      <td>13.134118</td>\n",
       "      <td>3.307255</td>\n",
       "      <td>2.417647</td>\n",
       "      <td>21.241176</td>\n",
       "      <td>98.666667</td>\n",
       "      <td>1.683922</td>\n",
       "      <td>0.818824</td>\n",
       "      <td>0.451961</td>\n",
       "      <td>1.145882</td>\n",
       "      <td>7.234706</td>\n",
       "      <td>0.691961</td>\n",
       "      <td>1.696667</td>\n",
       "      <td>619.058824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.048387</td>\n",
       "      <td>13.676774</td>\n",
       "      <td>1.997903</td>\n",
       "      <td>2.466290</td>\n",
       "      <td>17.462903</td>\n",
       "      <td>107.967742</td>\n",
       "      <td>2.847581</td>\n",
       "      <td>3.003226</td>\n",
       "      <td>0.292097</td>\n",
       "      <td>1.922097</td>\n",
       "      <td>5.453548</td>\n",
       "      <td>1.065484</td>\n",
       "      <td>3.163387</td>\n",
       "      <td>1100.225806</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>12.250923</td>\n",
       "      <td>1.897385</td>\n",
       "      <td>2.231231</td>\n",
       "      <td>20.063077</td>\n",
       "      <td>92.738462</td>\n",
       "      <td>2.247692</td>\n",
       "      <td>2.050000</td>\n",
       "      <td>0.357692</td>\n",
       "      <td>1.624154</td>\n",
       "      <td>2.973077</td>\n",
       "      <td>1.062708</td>\n",
       "      <td>2.803385</td>\n",
       "      <td>510.169231</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Type    Alcohol     Malic       Ash Alcalinity   Magnesium  \\\n",
       "                  mean       mean      mean      mean       mean        mean   \n",
       "clusters3_id                                                                   \n",
       "0             2.941176  13.134118  3.307255  2.417647  21.241176   98.666667   \n",
       "1             1.048387  13.676774  1.997903  2.466290  17.462903  107.967742   \n",
       "2             2.000000  12.250923  1.897385  2.231231  20.063077   92.738462   \n",
       "\n",
       "               Phenols Flavanoids Nonflavanoids Proanthocyanins     Color  \\\n",
       "                  mean       mean          mean            mean      mean   \n",
       "clusters3_id                                                                \n",
       "0             1.683922   0.818824      0.451961        1.145882  7.234706   \n",
       "1             2.847581   3.003226      0.292097        1.922097  5.453548   \n",
       "2             2.247692   2.050000      0.357692        1.624154  2.973077   \n",
       "\n",
       "                   Hue  Dilution      Proline  \n",
       "                  mean      mean         mean  \n",
       "clusters3_id                                   \n",
       "0             0.691961  1.696667   619.058824  \n",
       "1             1.065484  3.163387  1100.225806  \n",
       "2             1.062708  2.803385   510.169231  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine4.groupby('clusters3_id').agg(['mean'])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb1c64dc",
   "metadata": {},
   "source": [
    "# t-SNE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6cb00b9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.manifold import TSNE\n",
    "from bioinfokit.visuz import cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "908a12d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -8.227839  ,   7.9088    ],\n",
       "       [ -7.9978027 ,   7.6401386 ],\n",
       "       [-10.22949   ,   9.051615  ],\n",
       "       [-12.296299  ,  11.069634  ],\n",
       "       [ -2.1945798 ,   1.3563098 ],\n",
       "       [-12.429919  ,  10.675212  ],\n",
       "       [-11.605188  ,   9.7914295 ],\n",
       "       [-11.226246  ,  10.327548  ],\n",
       "       [ -8.360497  ,   7.0812287 ],\n",
       "       [ -8.352684  ,   7.086842  ],\n",
       "       [-12.620555  ,  10.963057  ],\n",
       "       [-11.47873   ,   9.602354  ],\n",
       "       [-11.826593  ,  10.066494  ],\n",
       "       [ -9.755122  ,   8.63723   ],\n",
       "       [-12.642809  ,  11.163078  ],\n",
       "       [-11.44037   ,  10.352367  ],\n",
       "       [-11.008008  ,  10.185005  ],\n",
       "       [ -9.4033575 ,   8.437754  ],\n",
       "       [-12.797326  ,  11.372908  ],\n",
       "       [ -4.374584  ,   4.0042186 ],\n",
       "       [ -3.2195191 ,   2.453591  ],\n",
       "       [ -2.762975  ,   2.369012  ],\n",
       "       [ -7.781877  ,   7.354219  ],\n",
       "       [ -7.7923503 ,   6.7316413 ],\n",
       "       [ -4.711453  ,   3.6966736 ],\n",
       "       [ -4.1098113 ,   3.5596826 ],\n",
       "       [-10.40486   ,   9.126109  ],\n",
       "       [-11.569135  ,   9.679678  ],\n",
       "       [ -5.6737847 ,   4.983886  ],\n",
       "       [ -7.7826924 ,   7.357461  ],\n",
       "       [-11.52583   ,   9.732281  ],\n",
       "       [-12.506421  ,  11.117524  ],\n",
       "       [ -7.343043  ,   6.5259376 ],\n",
       "       [-10.644925  ,   9.671656  ],\n",
       "       [ -8.850731  ,   8.0929785 ],\n",
       "       [ -5.76175   ,   5.0687647 ],\n",
       "       [ -4.974506  ,   4.523629  ],\n",
       "       [ -9.207453  ,   7.956756  ],\n",
       "       [ -7.6618156 ,   6.9873004 ],\n",
       "       [ -2.8585467 ,   1.9410856 ],\n",
       "       [ -3.4949172 ,   2.8820484 ],\n",
       "       [ -8.175116  ,   6.885714  ],\n",
       "       [ -9.10763   ,   7.7924094 ],\n",
       "       [ -0.4885674 ,  -0.46196613],\n",
       "       [ -5.136948  ,   4.549906  ],\n",
       "       [ -8.598971  ,   7.9521937 ],\n",
       "       [ -8.324775  ,   7.7783628 ],\n",
       "       [ -7.267296  ,   6.4494905 ],\n",
       "       [ -8.638003  ,   7.305878  ],\n",
       "       [-11.021359  ,   9.631953  ],\n",
       "       [ -9.752744  ,   8.640385  ],\n",
       "       [-11.227494  ,   9.475411  ],\n",
       "       [-10.281324  ,   9.132393  ],\n",
       "       [-11.900045  ,  10.560443  ],\n",
       "       [ -8.654005  ,   7.3071585 ],\n",
       "       [ -9.207445  ,   8.368403  ],\n",
       "       [ -6.912437  ,   6.129668  ],\n",
       "       [-10.932868  ,   9.995865  ],\n",
       "       [-11.108282  ,  10.2068405 ],\n",
       "       [  5.9636936 ,  -6.987195  ],\n",
       "       [ -0.4093785 ,  -0.3990761 ],\n",
       "       [  8.079995  , -10.433325  ],\n",
       "       [  1.5464964 ,  -2.2006462 ],\n",
       "       [  8.995595  , -11.579385  ],\n",
       "       [ 10.700665  , -12.952698  ],\n",
       "       [ -0.0856507 ,  -0.3786801 ],\n",
       "       [  6.725371  ,  -7.7399206 ],\n",
       "       [  6.4985566 ,  -7.433746  ],\n",
       "       [ -2.3843555 ,   1.8422561 ],\n",
       "       [ -2.4338531 ,   0.768967  ],\n",
       "       [ -4.9743357 ,   4.177565  ],\n",
       "       [  9.461604  , -11.758352  ],\n",
       "       [  7.4879003 ,  -9.26107   ],\n",
       "       [ -7.0739    ,   6.3900967 ],\n",
       "       [ -5.2008166 ,   4.5215154 ],\n",
       "       [  8.845463  , -11.15872   ],\n",
       "       [  9.703852  , -12.358026  ],\n",
       "       [  5.891729  ,  -8.546213  ],\n",
       "       [ -2.7772627 ,   1.5989921 ],\n",
       "       [  7.6366673 ,  -9.904491  ],\n",
       "       [ 11.043422  , -14.1474905 ],\n",
       "       [ -1.0972492 ,   0.8801893 ],\n",
       "       [  1.856948  ,  -1.9029436 ],\n",
       "       [  6.113227  ,  -7.393655  ],\n",
       "       [  5.5519185 ,  -7.312779  ],\n",
       "       [  8.083135  , -10.43149   ],\n",
       "       [  6.530573  ,  -8.371655  ],\n",
       "       [  4.221307  ,  -5.0609784 ],\n",
       "       [  0.18290722,  -0.0438314 ],\n",
       "       [  2.195836  ,  -1.9980382 ],\n",
       "       [  7.3140097 ,  -8.738841  ],\n",
       "       [  8.231694  , -10.33164   ],\n",
       "       [  6.8731003 ,  -8.090044  ],\n",
       "       [ 10.978726  , -14.086874  ],\n",
       "       [ 10.680319  , -13.255291  ],\n",
       "       [ -6.239283  ,   5.530637  ],\n",
       "       [  1.1336783 ,  -3.1709125 ],\n",
       "       [  8.661528  , -11.447636  ],\n",
       "       [  0.6604113 ,  -0.948093  ],\n",
       "       [  9.536561  , -11.88708   ],\n",
       "       [ -1.1719439 ,   0.6393283 ],\n",
       "       [  4.222925  ,  -5.066974  ],\n",
       "       [  8.4766035 , -10.873281  ],\n",
       "       [  9.163969  , -11.737086  ],\n",
       "       [  0.41235164,  -0.45108342],\n",
       "       [ 11.035984  , -13.667083  ],\n",
       "       [  6.400094  ,  -7.5102115 ],\n",
       "       [  6.9885507 ,  -8.505042  ],\n",
       "       [ 11.07344   , -13.690299  ],\n",
       "       [ -0.03461465,  -0.15018839],\n",
       "       [  3.8692176 ,  -5.438766  ],\n",
       "       [ 10.5787    , -13.79845   ],\n",
       "       [  2.267075  ,  -3.2936513 ],\n",
       "       [  8.551137  , -11.116417  ],\n",
       "       [  9.637136  , -12.707905  ],\n",
       "       [  9.244748  , -12.105405  ],\n",
       "       [  6.65807   ,  -8.258952  ],\n",
       "       [ 10.904829  , -13.105245  ],\n",
       "       [  9.870529  , -13.03532   ],\n",
       "       [  4.1105504 ,  -4.9579277 ],\n",
       "       [  1.724153  ,  -2.5246396 ],\n",
       "       [  7.190661  , -10.0999975 ],\n",
       "       [ 10.539985  , -12.759576  ],\n",
       "       [ 10.136193  , -12.467855  ],\n",
       "       [ 10.085186  , -12.506255  ],\n",
       "       [  9.825376  , -12.841128  ],\n",
       "       [ 10.236768  , -13.380479  ],\n",
       "       [  7.6521435 ,  -9.636871  ],\n",
       "       [ 10.382748  , -13.547815  ],\n",
       "       [  3.5730164 ,  -4.3941226 ],\n",
       "       [  1.0967848 ,  -2.823429  ],\n",
       "       [  5.140603  ,  -6.7514796 ],\n",
       "       [  4.1271687 ,  -5.4027867 ],\n",
       "       [  2.5443451 ,  -3.6042137 ],\n",
       "       [  1.0509948 ,  -1.3319232 ],\n",
       "       [ -0.6361398 ,   0.28111222],\n",
       "       [ -1.3324438 ,   1.0767297 ],\n",
       "       [  5.6471715 ,  -7.6981483 ],\n",
       "       [  3.493662  ,  -4.457726  ],\n",
       "       [  3.0217025 ,  -4.0374045 ],\n",
       "       [  2.6875012 ,  -3.507014  ],\n",
       "       [ -2.9540448 ,   2.6528018 ],\n",
       "       [  5.4459853 ,  -7.3942823 ],\n",
       "       [  4.639258  ,  -5.7503395 ],\n",
       "       [ -4.5925207 ,   4.1197343 ],\n",
       "       [ -4.19272   ,   3.5156922 ],\n",
       "       [  8.969575  , -11.95996   ],\n",
       "       [  1.9349313 ,  -2.2965066 ],\n",
       "       [  0.87155205,  -1.4871192 ],\n",
       "       [  4.4279523 ,  -5.936457  ],\n",
       "       [  5.716497  ,  -8.773522  ],\n",
       "       [  6.6722574 ,  -9.42919   ],\n",
       "       [  9.169601  , -10.934041  ],\n",
       "       [ -0.08916081,  -0.68483293],\n",
       "       [  1.034613  ,  -2.034085  ],\n",
       "       [ -1.5237089 ,   1.2219458 ],\n",
       "       [  7.1153274 ,  -8.959699  ],\n",
       "       [ -5.17418   ,   4.318211  ],\n",
       "       [  0.31415305,  -1.2631732 ],\n",
       "       [  2.0794306 ,  -2.5932841 ],\n",
       "       [  5.8264904 ,  -7.1063333 ],\n",
       "       [ -0.58616716,  -0.5237097 ],\n",
       "       [  3.6186545 ,  -5.0026145 ],\n",
       "       [ -0.34450927,  -0.81276053],\n",
       "       [  2.2117972 ,  -2.81258   ],\n",
       "       [  5.8066044 ,  -7.125397  ],\n",
       "       [ -0.9835638 ,   0.04503025],\n",
       "       [ -0.16731063,   0.12091088],\n",
       "       [ -2.2699957 ,   1.9259646 ],\n",
       "       [  1.2394611 ,  -2.6209636 ],\n",
       "       [  5.8836246 ,  -7.8760004 ],\n",
       "       [  7.575718  ,  -9.303974  ],\n",
       "       [  0.51971227,  -1.0820308 ],\n",
       "       [ -1.8655314 ,   1.6721799 ],\n",
       "       [ -2.2226818 ,   1.9485306 ],\n",
       "       [ -4.3296576 ,   3.5841784 ],\n",
       "       [ -4.261808  ,   3.8953755 ],\n",
       "       [  4.1560054 ,  -5.382742  ]], dtype=float32)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = TSNE(n_components=2)\n",
    "data_tsne = model.fit_transform(wine2.iloc[:,1:])\n",
    "data_tsne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "45677206",
   "metadata": {},
   "outputs": [],
   "source": [
    "#TSNE visualization\n",
    "#Plot will be stored in the default directory\n",
    "a=cluster.tsneplot(score=data_tsne,\n",
    "                 colorlist = wine['Type'],\n",
    "                 legendpos='upper right')"
   ]
  },
  {
   "attachments": {
    "tsne_2d.png": {
     "image/png": "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"
    }
   },
   "cell_type": "markdown",
   "id": "7cfcfa46",
   "metadata": {},
   "source": [
    "![tsne_2d.png](attachment:tsne_2d.png)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}