project 2 Youtube subscribers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "b6820e91",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "edad88ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"topSubscribed.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3d834bb6",
   "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>Rank</th>\n",
       "      <th>Youtube Channel</th>\n",
       "      <th>Subscribers</th>\n",
       "      <th>Video Views</th>\n",
       "      <th>Video Count</th>\n",
       "      <th>Category</th>\n",
       "      <th>Started</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>T-Series</td>\n",
       "      <td>234,000,000</td>\n",
       "      <td>212,900,271,553</td>\n",
       "      <td>18,515</td>\n",
       "      <td>Music</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>YouTube Movies</td>\n",
       "      <td>161,000,000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>Film &amp; Animation</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Cocomelon - Nursery Rhymes</td>\n",
       "      <td>152,000,000</td>\n",
       "      <td>149,084,178,448</td>\n",
       "      <td>846</td>\n",
       "      <td>Education</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>SET India</td>\n",
       "      <td>150,000,000</td>\n",
       "      <td>137,828,094,104</td>\n",
       "      <td>103,200</td>\n",
       "      <td>Shows</td>\n",
       "      <td>2006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>MrBeast</td>\n",
       "      <td>128,000,000</td>\n",
       "      <td>21,549,128,785</td>\n",
       "      <td>733</td>\n",
       "      <td>Entertainment</td>\n",
       "      <td>2012</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>995</th>\n",
       "      <td>996</td>\n",
       "      <td>XO TEAM</td>\n",
       "      <td>11,500,000</td>\n",
       "      <td>11,471,384,709</td>\n",
       "      <td>2,679</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>2021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>997</td>\n",
       "      <td>Nirahua Music World</td>\n",
       "      <td>11,500,000</td>\n",
       "      <td>5,997,717,755</td>\n",
       "      <td>788</td>\n",
       "      <td>Music</td>\n",
       "      <td>2014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>998</td>\n",
       "      <td>YtCrash</td>\n",
       "      <td>11,400,000</td>\n",
       "      <td>3,825,084</td>\n",
       "      <td>1</td>\n",
       "      <td>Education</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>999</td>\n",
       "      <td>Painzeiro</td>\n",
       "      <td>11,400,000</td>\n",
       "      <td>2,143,444,281</td>\n",
       "      <td>946</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1000</td>\n",
       "      <td>Oum Walid</td>\n",
       "      <td>11,400,000</td>\n",
       "      <td>2,480,064,722</td>\n",
       "      <td>1,598</td>\n",
       "      <td>People &amp; Blogs</td>\n",
       "      <td>2015</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Rank             Youtube Channel  Subscribers      Video Views  \\\n",
       "0       1                    T-Series  234,000,000  212,900,271,553   \n",
       "1       2              YouTube Movies  161,000,000                0   \n",
       "2       3  Cocomelon - Nursery Rhymes  152,000,000  149,084,178,448   \n",
       "3       4                   SET India  150,000,000  137,828,094,104   \n",
       "4       5                     MrBeast  128,000,000   21,549,128,785   \n",
       "..    ...                         ...          ...              ...   \n",
       "995   996                     XO TEAM   11,500,000   11,471,384,709   \n",
       "996   997         Nirahua Music World   11,500,000    5,997,717,755   \n",
       "997   998                     YtCrash   11,400,000        3,825,084   \n",
       "998   999                   Painzeiro   11,400,000    2,143,444,281   \n",
       "999  1000                   Oum Walid   11,400,000    2,480,064,722   \n",
       "\n",
       "    Video Count          Category  Started  \n",
       "0        18,515             Music     2006  \n",
       "1             0  Film & Animation     2015  \n",
       "2           846         Education     2006  \n",
       "3       103,200             Shows     2006  \n",
       "4           733     Entertainment     2012  \n",
       "..          ...               ...      ...  \n",
       "995       2,679    People & Blogs     2021  \n",
       "996         788             Music     2014  \n",
       "997           1         Education     2015  \n",
       "998         946    People & Blogs     2015  \n",
       "999       1,598    People & Blogs     2015  \n",
       "\n",
       "[1000 rows x 7 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3c629cee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1000 entries, 0 to 999\n",
      "Data columns (total 7 columns):\n",
      " #   Column           Non-Null Count  Dtype \n",
      "---  ------           --------------  ----- \n",
      " 0   Rank             1000 non-null   int64 \n",
      " 1   Youtube Channel  1000 non-null   object\n",
      " 2   Subscribers      1000 non-null   object\n",
      " 3   Video Views      1000 non-null   object\n",
      " 4   Video Count      1000 non-null   object\n",
      " 5   Category         1000 non-null   object\n",
      " 6   Started          1000 non-null   int64 \n",
      "dtypes: int64(2), object(5)\n",
      "memory usage: 54.8+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b7f9e295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Youtube Channel    0\n",
       "Subscribers        0\n",
       "Video Views        0\n",
       "Video Count        0\n",
       "Category           0\n",
       "Started            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "47b087e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Subscribers\"]=df[\"Subscribers\"].str.replace(',','')\n",
    "df[\"Video Views\"]=df[\"Video Views\"].str.replace(',','')\n",
    "df[\"Video Count\"]=df[\"Video Count\"].str.replace(',','')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "786d2c4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"Subscribers\"]=df[\"Subscribers\"].astype(\"float64\")\n",
    "df[\"Video Views\"]=df[\"Video Views\"].astype(\"float64\")\n",
    "df[\"Video Count\"]=df[\"Video Count\"].astype(\"float64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "428ee7e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns=df.columns.str.replace(' ','_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1563bfec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entertainment                                                                   238\n",
       "Music                                                                           217\n",
       "People & Blogs                                                                  132\n",
       "Gaming                                                                           94\n",
       "Comedy                                                                           68\n",
       "Film & Animation                                                                 50\n",
       "Education                                                                        45\n",
       "Howto & Style                                                                    43\n",
       "https://us.youtubers.me/global/all/top-1000-most_subscribed-youtube-channels     30\n",
       "News & Politics                                                                  27\n",
       "Science & Technology                                                             18\n",
       "Shows                                                                            14\n",
       "Sports                                                                           10\n",
       "Pets & Animals                                                                    6\n",
       "Trailers                                                                          2\n",
       "Nonprofits & Activism                                                             2\n",
       "Movies                                                                            2\n",
       "Autos & Vehicles                                                                  1\n",
       "Travel & Events                                                                   1\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "322e3229",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entertainment            238\n",
       "Music                    217\n",
       "People & Blogs           132\n",
       "Gaming                    94\n",
       "Comedy                    68\n",
       "Film & Animation          50\n",
       "Education                 45\n",
       "Howto & Style             43\n",
       "News & Politics           27\n",
       "Science & Technology      18\n",
       "Shows                     14\n",
       "Sports                    10\n",
       "Pets & Animals             6\n",
       "Trailers                   2\n",
       "Nonprofits & Activism      2\n",
       "Movies                     2\n",
       "Autos & Vehicles           1\n",
       "Travel & Events            1\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df[df.Category != 'https://us.youtubers.me/global/all/top-1000-most_subscribed-youtube-channels']\n",
    "\n",
    "df['Category'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "549f4610",
   "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>Video_Views</th>\n",
       "      <th>Youtube_Channel</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.129003e+11</td>\n",
       "      <td>T-Series</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.490842e+11</td>\n",
       "      <td>Cocomelon - Nursery Rhymes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.378281e+11</td>\n",
       "      <td>SET India</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9.111729e+10</td>\n",
       "      <td>Sony SAB</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8.720294e+10</td>\n",
       "      <td>Like Nastya</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8.663857e+10</td>\n",
       "      <td>✿ Kids Diana Show</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7.357770e+10</td>\n",
       "      <td>WWE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>7.244412e+10</td>\n",
       "      <td>Vlad and Niki</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.949393e+10</td>\n",
       "      <td>Movieclips</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.782565e+10</td>\n",
       "      <td>Colors TV</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Video_Views             Youtube_Channel\n",
       "0   2.129003e+11                    T-Series\n",
       "2   1.490842e+11  Cocomelon - Nursery Rhymes\n",
       "3   1.378281e+11                   SET India\n",
       "16  9.111729e+10                    Sony SAB\n",
       "8   8.720294e+10                 Like Nastya\n",
       "7   8.663857e+10           ✿ Kids Diana Show\n",
       "10  7.357770e+10                         WWE\n",
       "11  7.244412e+10               Vlad and Niki\n",
       "28  5.949393e+10                  Movieclips\n",
       "26  5.782565e+10                   Colors TV"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"Video_Views\",\"Youtube_Channel\"]].sort_values(by = \"Video_Views\", ascending  = False).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "a14b1f3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rank                            1\n",
       "Youtube_Channel          T-Series\n",
       "Subscribers           234000000.0\n",
       "Video_Views        212900271553.0\n",
       "Video_Count               18515.0\n",
       "Category                    Music\n",
       "Started                      2006\n",
       "Name: 0, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Channel with highest views\n",
    "df.iloc[df['Video_Views'].argmax()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "66da7632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Entertainment            238\n",
       "Music                    217\n",
       "People & Blogs           132\n",
       "Gaming                    94\n",
       "Comedy                    68\n",
       "Film & Animation          50\n",
       "Education                 45\n",
       "Howto & Style             43\n",
       "News & Politics           27\n",
       "Science & Technology      18\n",
       "Shows                     14\n",
       "Sports                    10\n",
       "Pets & Animals             6\n",
       "Trailers                   2\n",
       "Nonprofits & Activism      2\n",
       "Movies                     2\n",
       "Autos & Vehicles           1\n",
       "Travel & Events            1\n",
       "Name: Category, dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"Category\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "479b98f7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "top5 = df[\"Category\"].value_counts().head(5)\n",
    "\n",
    "plt.figure(figsize=(12,10))\n",
    "\n",
    "ax = sns.countplot(x=df[\"Category\"],order=top5.index)\n",
    "ax.bar_label(ax.containers[0])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "35815fc6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WHaSCNH9NWu7tlipRs9dlzqb94ZnNWpcX/tnyg1SQ5j5XZs9Z1cKTVBbX/UClaFKcrK2tzfTp03PxxRfnzjvvzK9//eskydZbb50RI0bk4osvTm1ty/2rW21tbfr3799oW79+/TJ37tyG/UmycOHC9OjRo+GYRYsWrXU3JQAAAABQWZoUJ5OkW7dumTx5curr67No0aLU19enpqbmLe9UbK799tsvzz77bKNtzz77bHr27Jkk6d27d0Mwfd/73pckWbFiRR588MFccsklLT4PAAAAANBymhwn31BVVdXqdyd+4QtfyEc/+tFMmDAhRx99dGbNmpVrr702F154YcMMI0eOzMSJE9O3b9/06dMnEyZMSOfOnXPssce26mwAAAAAwNvT7DjZFt73vvdlypQpueSSS3LFFVekR48eueCCC/K5z32u4Zizzjory5cvz6hRo1JXV5dBgwZl2rRp2XrrrQtODgAAAABsSEXHySQ5/PDDc/jhh693f1VVVcaMGZMxY8a04VQAAAAAwNtV8XESgE3LdXd9svQIrepzR9xaegQAAIB3jM1KDwAAAAAAtE/iJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBEdSw8AAAAAvDP88WerSo/Qat47bIvSI0C75M5JAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoomPpAYBNw0NTjy89Qqva79ibm/V1t//kky08SeX4P8NvLT3CJuXse0aUHqFVfePQW0qPAAAAVCB3TgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEe+oODlx4sRUV1dn1KhRDdvq6+szduzYDBgwIN27d8+RRx6Zp556quCUAAAAAMDGeMfEyUceeSQ33HBD9thjj0bbr7rqqkyaNCnjx4/PPffck5qamgwfPjxLliwpNCkAAAAAsDHeEXHy5Zdfzqmnnpqrr7461dXVDdvr6+szefLknH322Rk2bFh23333TJ48OUuXLs3UqVPLDQwAAAAAbNA7Ik6+ER8PPvjgRtvnzJmTBQsW5NBDD23Y1qlTp+y///6ZOXNmW48JAAAAADRBx9IDbMgNN9yQ5557Ltdcc81a+xYsWJAkqampabS9pqYm8+bNW+9jzp49u2WHpF3ZovQArczPx7pZl7VZk3WzLutmXdZmTdbNuqzNmqybdVm35q1LbYvPUUma/1zp3aJzVBrrsjZ/r0Dr6Nu371vur+g4OXv27FxyySX5xS9+kS22WH8SqqqqavTn+vr6tba92YYWBd7KnEdKT9C6mvvz8dLjLTxIhWnuujz1xxYepII0d03u3cSv+Zr9O+aFlp2j0jRrXea2/ByVpNnPlb893LKDVJhmr8vzT7fsIBWk+Wsyv2UHqTDNXpc5m/aHZzZrXV74Z8sPUkGa+1yZPWdVC09SWZq7Ln/806a7LloBlFHRL+t++OGH89JLL+Xf/u3f0rVr13Tt2jUPPPBArrvuunTt2jXbbrttkmThwoWNvm7RokVr3U0JAAAAAFSWir5z8sgjj8w+++zTaNsZZ5yRXXfdNV/+8pfTp0+f1NbWZvr06Xnf+96XJFmxYkUefPDBXHLJJSVGBgAAAAA2UkXHyerq6kafzp0kW265Zbp06ZLdd989STJy5MhMnDgxffv2TZ8+fTJhwoR07tw5xx57bIGJAQAAAICNVdFxcmOcddZZWb58eUaNGpW6uroMGjQo06ZNy9Zbb116NAAAAADgLbzj4uSdd97Z6M9VVVUZM2ZMxowZU2giAAAAAKA5KvoDcQAAAACATZc4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAU0bH0AAAAAADvVIu/t7z0CK2qyymdSo/AJs6dkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFBEx9IDUNmWT7my9AitqtOJXy49AgAAAEC75c5JAAAAAKAIcRIAAAAAKKKi4+SVV16ZQw45JD179syuu+6aESNG5E9/+lOjY+rr6zN27NgMGDAg3bt3z5FHHpmnnnqq0MQAAAAAwMaq6Dh5//3357Of/Wx+9atf5fbbb0/Hjh1z1FFHZfHixQ3HXHXVVZk0aVLGjx+fe+65JzU1NRk+fHiWLFlScHIAAAAAYEMq+gNxpk2b1ujP11xzTXr16pWHHnooQ4YMSX19fSZPnpyzzz47w4YNS5JMnjw5ffv2zdSpU3PKKaeUGBsAAAAA2AgVHSf/1dKlS7NmzZpUV1cnSebMmZMFCxbk0EMPbTimU6dO2X///TNz5sz1xsnZs2e3xbibhB6lB2hlzXkubNEKc1QSPx/rZl3WZk3Wzbqsm3VZmzVZN+uyNmuybtZl3Zq3LrUtPkclaf5zpXeLzlFprMvamrsm223i/+Xs71verr59+77l/ndUnBw9enQGDhyYfffdN0myYMGCJElNTU2j42pqajJv3rz1Ps6GFoX/tfzh0hO0ruY8F+Y80gqDVJDm/ny89HgLD1JhmrsuT/2xhQepIM1dk3s38WubZv+OeaFl56g0zVqXuS0/RyVp9nPlb5v2L+dmr8vzT7fsIBWk+Wsyv2UHqTDNXpc5m/b70zdrXV74Z8sPUkGa+1yZPWdVC09SWZq7Ln/806a7Ls1dk8X3L2/hSSqLhkJre8fEyQsuuCAPPfRQfvnLX6ZDhw6N9lVVVTX6c319/VrbAAAAAIDKUtEfiPOGMWPG5Mc//nFuv/327LTTTg3ba2tff/nBwoULGx2/aNGite6mBAAAAAAqS8XHyfPPPz9Tp07N7bffnn79+jXa17t379TW1mb69OkN21asWJEHH3wwgwcPbutRAQAAAIAmqOiXdZ977rm55ZZb8sMf/jDV1dUN7zHZuXPnbLXVVqmqqsrIkSMzceLE9O3bN3369MmECRPSuXPnHHvssYWnBwAAAADeSkXHyeuuuy5JMmzYsEbbzz///IwZMyZJctZZZ2X58uUZNWpU6urqMmjQoEybNi1bb711m88LAAAAAGy8io6TdXV1GzymqqoqY8aMaYiVAAAAAMA7Q8W/5yQAAAAAsGkSJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKCIjqUHAAAAAGDTsuK/55QeoVW9+//2Lj3CJsOdkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFCFOAgAAAABFiJMAAAAAQBHiJAAAAABQhDgJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAAAABAEeIkAAAAAFCEOAkAAAAAFNGx9AAAAAAAsKlb+YNHSo/Qqt510gea9XXunAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoAhxEgAAAAAoQpwEAAAAAIoQJwEAAACAIsRJAAAAAKAIcRIAAAAAKKJj6QEqxaqb7ig9Qqva4oSPlx4BAAAAABpx5yQAAAAAUIQ4CQAAAAAUscnEyeuuuy577rlnamtrc/DBB+f3v/996ZEAAAAAgLewScTJadOmZfTo0TnnnHNy3333Zd99980nPvGJvPDCC6VHAwAAAADWY5OIk5MmTcoJJ5yQk08+Of37988VV1yR2tra/Pd//3fp0QAAAACA9aiqq6urLz3E27Fq1apsv/32uf7663PUUUc1bD/33HPzpz/9KXfddVe54QAAAACA9XrH3zn50ksvZfXq1ampqWm0vaamJgsXLiw0FQAAAACwIe/4OPmGqqqqRn+ur69faxsAAAAAUDne8XGya9eu6dChw1p3SS5atGituykBAAAAgMrxjo+TW2yxRfbee+9Mnz690fbp06dn8ODBhaYCAAAAADakY+kBWsIZZ5yR008/PYMGDcrgwYPz3//935k/f35OOeWU0qMBAAAAAOvxjr9zMkmOPvrojB07NldccUUOPPDAPPTQQ7n11lvTq1evVvueDzzwQI477rjstttuqa6uzpQpUxrtX7hwYUaOHJkBAwZk++23zzHHHJO//OUvDfvnzJmT6urqdf7vm9/8ZsNxdXV1Oe2009KrV6/06tUrp512Wurq6lrtvN6OK6+8Moccckh69uyZXXfdNSNGjMif/vSnRsfU19dn7NixGTBgQLp3754jjzwyTz31VKNjVq5cmVGjRmWXXXbJDjvskOOOOy4vvvhio2MGDhy41rr953/+Z2ufYrO05bokyd13352PfOQj2X777dOrV6/8n//zf1r1/JqjrdZkxowZ6/05++lPf9oWp9okbflcefbZZ3PCCSdkl112SY8ePXLYYYflt7/9baufY1O15Zo89thjOeqoo9KrV6/svPPOOeuss7J06dJWP8fmaKl1+f73v5+hQ4emV69eqa6uzpw5c9b6Xu3x99DGrMuECRNy+OGHZ4cddkh1dXVrntbb0lZrMmfOnJx55pnZa6+90r179+y11175r//6ryxfvrzVz7E52mpd1qxZk+OOOy7vfe97U1tbm/79++e0007L3//+91Y/x6Zqy5+fN6xYsSIHHHBAqqur8+ijj7bKeb1dbbku7fH6dmOfL+3p+nZDa9Jer2835rnS3q5vN2ZN2tv17eLFizNq1Kh84AMfSPfu3bPHHnvky1/+cv7xj380epz2dn27setS8vp2k4iTSfK5z30uTzzxRBYuXJh77703BxxwQKt+v2XLlmX33XfPuHHj0qlTp0b76uvrc+KJJ+a5557LlClTct9996Vnz54ZNmxYli1bliTp0aNHnn766Ub/mzhxYqqqqhr9sv3c5z6XWbNm5bbbbsvUqVMza9asnH766a16bs11//3357Of/Wx+9atf5fbbb0/Hjh1z1FFHZfHixQ3HXHXVVZk0aVLGjx+fe+65JzU1NRk+fHiWLFnScMyYMWNyxx135Prrr89dd92VJUuWZMSIEVm9enWj73feeec1Wr9zzz23zc61KdpyXX7+85/n//7f/5sRI0bkvvvuy29+85t86lOfatPz3RhttSaDBw9e6+fsy1/+crbaaqscdthhbX7eG9KWz5URI0Zk5cqV+dnPfpb77rsv++23X0444YT89a9/bdNz3pC2WpN58+blqKOOyk477ZS77747P/7xj/PnP/85X/jCF9r8nDdGS63LK6+8kkMPPTSjR49e7/dqj7+HNmZdVq5cmaFDh2bkyJGtek5vV1utyezZs7N69epceeWVeeihh3L55ZfnRz/60VuuYUlt+Vw56KCD8r3vfS+PPPJIfvCDH+T555/fpH83b8yavOHCCy/Mjjvu2Crn01Lael3a2/XtxqxLe7u+3dCatNfr2415rrS369sNrUl7vL6dN29e5s2bl//6r//K73//+1xzzTX5/e9/n89+9rONvld7u77d2HUpeX1bVVdXV9/m33UTs+OOO+byyy/PiSeemOT1f7F5//vfnxkzZmTgwIFJXv+X8379+uWiiy7KSSedtM7HOeqoo1JVVZWf/OQnSZKnn346gwcPzi9/+cvst99+SZIHH3wwQ4YMySOPPJK+ffu2wdk139KlS9OrV69MmTIlQ4YMSX19fQYMGJBTTz214UJr+fLl6du3by699NKccsopefnll9OnT59MmjQpn/zkJ5Mkc+fOzcCBAzN16tR8+MMfTvL6vyyfdtpp+eIXv1js/JqrtdZl9erV2WuvvTJq1KicfPLJJU+xyVrzufKv3v/+9+eAAw7IVVdd1Wbn11yttS4vvfRSdt1119x+++056KCDkiSvvfZaunXrlu9973sZNmxYsXPekNZak+9///u55JJLMnv27HTo0CFJ8uSTT+aAAw7IH/7wh+yyyy7FznljNGdd3uzRRx/NIYcckscffzy9e/du2N4efw+92frW5c1+9rOf5eSTT67Yf23/V22xJm+47rrrctlll1XcfxSuS1uuy1133ZUTTjgh8+fPz7vf/e5WO6e3q7XX5M4778yll16aG264IYMHD8706dOzzz77tMm5vR2tuS7t7fr2zda3Lu3t+vbNmvL3yqZ+fftm61uX9nZ9+2brW5P2fn37hl//+tcZMWJE5syZk/e85z3t/vr2Df+6Lm9W4vp2k7lzspKsXLkySRpdcG622WZ517velQcffHCdX/P888/n3nvvzWc+85mGbQ8//HC22mqrRh/ss99++6Vz586ZOXNm6wzfgpYuXZo1a9Y03A48Z86cLFiwIIceemjDMZ06dcr+++/fcD6PPfZYXn311UbH9OjRI/3791/rnK+++ursvPPO+eAHP5gJEyZk1apVrX9SLaC11uWxxx7L3Llzs8UWW+Sggw5Kv379Mnz48Dz++ONtd3LN1NrPlTfMmDEjzz77bKOfs0rWWuuy7bbbpn///rnllluydOnSrF69Ot///vfX+vumErXWmqxcuTKbb755w4XbG4+TZL1/b1eS5qzLxmiPv4c2dW25JkuWLKnol7y/WVuty+LFi3Pbbbfl/e9/f0WHyaR11+TFF1/MOeeck2uvvbbi1+FftfZzpT1d326M9nZ92xzt4fp2Y7S369uN4fr2dUuWLMm73vWubLnllklc377hX9elNHGyFfTr1y89e/bMJZdcksWLF2fVqlX5xje+kRdffDELFixY59f84Ac/SNeuXXPEEUc0bFu4cGG6du2aqqqqhm1VVVXZbrvtsnDhwlY/j7dr9OjRGThwYPbdd98kaTj3mpqaRsfV1NQ0nM/ChQvToUOHdO3adb3HJMnpp5+e6667LnfccUdOO+20fPvb384555zTmqfTYlprXZ5//vkkyWWXXZZzzjknt956a3bYYYcMHTo08+bNa81Tetta87nyZjfccEPe+973viPuzEhab13euEP7qaeeSs+ePdOtW7eMGzcuU6dOTffu3Vv7tN6W1lqTgw46KC+99FK+/vWvZ9WqVamrq2t4n6/1/b1dSZqzLhujPf4e2tS11Zq88MILufrqq9d6uVClau11ufjii7PDDjtk5513zty5c3PLLbe8/aFbWWutyerVq3PqqafmjDPOyJ577tlyA7eR1nyutLfr243R3q5vm6M9XN9ujPZ2fbsxXN++/t6Sl112WU466aR07Pj650G7vl33upQmTraCzTffPDfeeGP++te/Zuedd87222+fGTNm5CMf+Uijf7V4w2uvvZabbropJ5xwQjbffPNG+978A/OG+vr6dW6vJBdccEEeeuih3HjjjWud87/OvjHn86/HnHnmmTnooIPy3ve+NyeddFImTpyYG2+8ca03dK00rbkua9asSZKce+65GTZsWPbee+9cddVV2WabbSr6P4Ja+7nyhn/84x+544473jH/qtya61JfX59zzjkn2267bX7xi1/k7rvvzrBhw3LSSSdV5Ic0vKE112S33XbL5MmTM3ny5Gy//fbp169fevfunW7duq3z7+1K0tLr8q/8Htp0tNWaLFy4MMccc0wOOeSQnHHGGc2et620xbr8+7//e+6777785Cc/SYcOHXLaaaelvr5y31mpNddk4sSJ2XzzzXPmmWe2yKxtqbWfK65v1+b69q219+vbfz3e9W1j7f36dtmyZTn++OOz/fbb55JLLnnLx3irx6kkrb0uJYmTrWTvvffO/fffnzlz5uTpp5/Oj3/84/zjH/9Y5/uF/OIXv8j8+fPXei/Kbt26ZdGiRY0uXuvr6/PSSy+tVcUryZgxY/LjH/84t99+e3baaaeG7bW1tUmyVr1ftGhRw/l069Ytq1evzksvvbTeY9Zl0KBBSZLnnnuuJU6hVbT2urzxOP3792/Y37Fjx+yyyy6ZO3dui59PS2jL58rNN9+czTbbLJ/4xCda+CxaXmuvy3333Zdf/vKXue6667Lffvtl7733zsSJE7PllltmypQprXhmzdcWz5VPfOITeeaZZ/LUU0/lueeey+jRo7No0aINvs9TSW9nXTZGe/w9tKlqqzVZsGBBPv7xj2e33XbLNddcU/EX+W21Ll27dk2fPn1yyCGH5Prrr8/dd99dsS+pa+01uffeezNjxoxst9126dq1a973vvclSQ477LCceuqpb/8EWkmJv1c29evbjdHerm+bqr1c326M9nZ9u7Ha6/Xt0qVLc+yxxyZJbrnllkZvIdKer2/fal1KEydb2TbbbJPtttsuf/nLX/Loo482etn2G37wgx/kgAMOSJ8+fRpt33fffbN06dI8/PDDDdsefvjhLFu2rGLfN+P888/P1KlTc/vtt6dfv36N9vXu3Tu1tbWZPn16w7YVK1bkwQcfbDifvffeO5tvvnmjY1588cWGN61dnyeeeCLJ//5gVpq2WJe9994773rXuzJ79uyGY9asWZO//vWv6dmzZ2ueXrO09XPlxhtvzFFHHZVtttmmlc6oZbTFurzyyitJXn8v3DfbbLPNGu5QqCRt/Vzp1q1bttpqq0ybNi3vfve786EPfah1TuxtervrsjHa4++hTVFbrcn8+fMzdOjQ9OvXL9dff33FvExofUo9V974e7YS30uwLdZk0qRJuf/++zNjxozMmDEjt912W5Lku9/9bsPLDStNqefKpn59uzHa2/VtU7WX69uN0d6ub5uqPV3fLlmyJMcee2zWrFmTW2+9NVtttVWjx2mv17cbWpfSKvuqsYItXbq04V8x16xZk7lz52bWrFnp0qVLevbsmZ/+9KfZdttt06tXrzz55JMZPXp0jjzyyEZvUpq8/p5Md999d77zne+s9T369++fww47LF/60pdy1VVXpb6+Pl/60pdy+OGHV+QnSJ177rm55ZZb8sMf/jDV1dUN733QuXPnbLXVVqmqqsrIkSMzceLE9O3bN3369MmECRPSuXPnhnq/zTbb5NOf/nQuuuii1NTUpEuXLvnKV76SPfbYo+Ev0IcffjiPPPJIDjzwwLznPe/Jo48+mgsuuCBDhgypyIuUtlqX97znPTnllFMybty47LjjjunVq1euvfbavPzyyw2fUFwp2mpN3vDggw/mz3/+c77xjW+08Zk2TVuty7777psuXbrkjDPOyHnnnZdOnTrlhhtuyPPPP5/DDz+81OmvU1s+V6699trsu+++2WqrrTJ9+vRcdNFFufjiiyvyAz1aYl2S1+9yW7BgQZ599tkkr38698svv5yePXumS5cu7fL3ULLhdUle//29ePHi/O1vf0uSzJo1K0myyy67VNTFXlutybx58zJ06NB07949Y8eObXSn8nbbbVdxLx9rq3V5+OGH8/jjj2e//fbLNttsk7/+9a/52te+ll69ejV8QmilaKs1efPdHm88fpLsvPPO2XHHHdvmZJugLZ8r7e36NtnwurS369tk434HJe3r+jbZ8Lq0t+vbZOOeK+3t+nbJkiU5+uijs2TJkkyZMiWvvPJKQ7ju0qVLtthii3Z5fbsx65KUvb6tqqurq9w3vKlgM2bMyMc//vG1th9//PGZPHlyvvOd7+Tqq6/OwoULU1tbm+OOOy7nnXdew//T3/C1r30t1157bf785z+v85baxYsX5/zzz88vfvGLJMmQIUNy+eWXV+RfJuub6fzzz8+YMWOSvH679Lhx4/L9738/dXV1GTRoUCZMmJDdd9+94fgVK1bkwgsvzNSpU7NixYocdNBBmThxYnr06JHk9U/tO/fcc/PMM89k1apV6dmzZ44++uicddZZFfNJU2/WVuuSJK+++mouvfTS/OhHP8ry5cuz55575rLLLsvee+/dmqfYZG25Jkny+c9/Po8++mjFf/paW67Lo48+mksvvTSPPvpoXnvttfTr1y/nnXdexV28teWanH766fn1r3+dZcuWpW/fvvniF7+Y4447rlXPr7laal3Gjh2b8ePHr/U4kyZNyoknnpikff4e2ph1GTlyZG6++ea1jrnjjjty4IEHtsDZtIy2WpMpU6as9/0lH3/88Yp7+VhbrcusWbNywQUX5Mknn8yyZcvSvXv3HHbYYTnnnHMqLsS15c/Pm82ZMyd77bVXpk+fXpEf6NFW69Jer2835vnS3q5vN/ZnqL1d327MurS369uNWZP2dn27vk6TNL5Ga2/Xtxu7LiWvb8VJAAAAAKAI7zkJAAAAABQhTgIAAAAARYiTAAAAAEAR4iQAAAAAUIQ4CQAAAAAUIU4CAAAAAEWIkwAA8C8GDhyYkSNHlh4DAGCTJ04CANBkTz75ZD7zmc9k4MCBqa2tzYABA3LEEUdk7NixDcdce+21mTJlSqvNcMUVV+TnP/95qz0+AACtT5wEAKBJHnrooRxyyCH5n//5n5xwwgm54oorcsopp6Rz586ZMGFCw3Hf/e53c9NNN7XaHBMmTMidd97Zao8PAEDr61h6AAAA3lmuvPLKbLnllvnd736Xrl27Nto3b968Vv3e9fX1WblyZd797ne36vcBAKBtuHMSAIAm+etf/5rddtttrTCZJNtvv32S19+zcfbs2XnggQdSXV2d6urqDBw4MEmyatWqXHbZZfnQhz6U3r17p3v37vnwhz+cu+66a63Hq66uzpe+9KX89Kc/zf77759u3brlxz/+caqrq7Ny5crcfPPNDY9/5JFHNnzdP//5z/zHf/xHBg4cmG7duuW9731v/vM//zMrV65s9PirVq3KxRdfnH79+mWHHXbIsGHD8swzz7TkcgEA8BbcOQkAQJP06tUrM2fOzBNPPNEQHP/V2LFjc+655+Y973lPzjnnnCRJ586dkyRLlizJ9773vQwfPjyf+tSnsnz58tx222058cQTM3Xq1Hz4wx9u9FgPPvhgfvazn+XUU09NbW1t+vXrl2uuuSZnnnlm3v/+9+czn/lMkqRbt25JkuXLl2fo0KGZM2dOPvOZz2TnnXfOE088kW9961t55plnGr3U/Oyzz85NN92UYcOG5cADD8wf/vCHDB8+PCtWrGjpZQMAYB2q6urq6ksPAQDAO8e9996b4cOHJ0n22Wef/Nu//VsOPPDAHHzwwY1ebv2BD3wg3bp1W+t9IVevXp3XXnst73rXuxq2rVq1KgceeGC23377/PSnP23YXl1dnaqqqtx7773Zc889Gz1ObW1tjj766EyePLnR9okTJ2bChAn53e9+l/79+zdsv+6663Luuefmrrvuyv77758nn3wyBxxwQD71qU/lW9/6VsNxl1xySa688socf/zxaz02AAAty8u6AQBokoMPPji/+MUv8rGPfSxPP/10vvWtb2XEiBHp169ffvjDH27w6zt06NAQJletWpXFixdnyZIlOeCAA/LYY4+tdfzgwYPXCpNv5Sc/+UkGDx6c7bbbLi+99FLD/z70oQ8lSe67774kya9+9askyciRIxt9/Re+8IWN/l4AALw9XtYNAECTDR48ODfddFNWr16dP/7xj/nVr36Vb33rWznzzDPTs2fPHHzwwW/59T/4wQ/y7W9/O08//XTq6//3hTxVVVVrHbvTTjs1aba//OUv+eMf/5hdd911nfsXLVqUJHnhhRdSVVWVPn36NNq/3Xbbpbq6uknfEwCA5hEnAQBotg4dOmSvvfbKXnvtlcGDB2fYsGG59dZb3zJOTp06Nf/+7/+eIUOG5KyzzkpNTU06duyYKVOm5Lbbblvr+E6dOjVppjVr1uSggw7Kl7/85XXu32GHHZKkURT9V2+1DwCAliNOAgDQIgYNGpQkmT9/fpJ13wWZJNOmTctOO+2Um266qdExU6ZMadL3W9/j77zzzlm6dGnDy7jXp1evXqmvr8+zzz6bPfbYo2H7okWL8vLLLzdpFgAAmsd7TgIA0CT33ntv1qxZs9b23/zmN0mSvn37Jkm23HLL1NXVrXVchw4dkjS+O/H555/Pz3/+8ybNsb7HP/roo/OHP/whd91111r7li9fnqVLlyZJPvrRjybJWh968+1vf7tJcwAA0HzunAQAoElGjx6dpUuXZujQoenfv3/WrFmTxx9/PLfccku23Xbbhg+Y2WefffL9738/48aNS58+fdK5c+cMGTIkQ4YMyR133JHjjz8+Q4YMyd///vdcf/312XXXXfPHP/5xo+fYZ599cu+99+bqq6/ODjvskO222y4HH3xwvvjFL+bXv/51Pv3pT+eTn/xkBg0alJUrV+bZZ5/NT37yk9x22235wAc+kPe+970ZMWJEfvjDH2bJkiU58MAD84c//CG/+93v0rVr19ZaPgAA3qSqrq7OG+oAALDRfvvb3+b222/PzJkz8/e//z0rV65M9+7dc/DBB+ecc85p+ACbBQsW5Kyzzsrvf//7/POf/0zPnj3zxBNPJEm++c1v5vrrr8/8+fOzyy675Oyzz85zzz2X8ePHN7obsrq6Oqecckq+/vWvrzXH7Nmz8+Uvfzl/+MMfsmzZshxwwAG58847kySvvPJKrrrqqkybNi1z5sxJ586ds9NOO+VjH/tYTj/99IYPvFm1alUuvfTS3HzzzXnllVfygQ98IFdccUWOOeaYfPCDH1zrrkoAAFqWOAkAAAAAFOE9JwEAAACAIsRJAAAAAKAIcRIAAAAAKEKcBAAAAACKECcBAAAAgCLESQAAAACgCHESAAAAAChCnAQAAAAAihAnAQAAAIAixEkAAAAAoIj/DyKp6WeVgjDvAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1440x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1008x864 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(x=\"Started\",data=df)\n",
    "plt.figure(figsize=(14,12))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "10381252",
   "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>Subscribers</th>\n",
       "      <th>Video_Views</th>\n",
       "      <th>Video_Count</th>\n",
       "      <th>Started</th>\n",
       "      <th>Total_Views</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Subscribers</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.753648</td>\n",
       "      <td>0.064661</td>\n",
       "      <td>-0.135162</td>\n",
       "      <td>0.753648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Video_Views</th>\n",
       "      <td>0.753648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151196</td>\n",
       "      <td>-0.157634</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Video_Count</th>\n",
       "      <td>0.064661</td>\n",
       "      <td>0.151196</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.141080</td>\n",
       "      <td>0.151194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Started</th>\n",
       "      <td>-0.135162</td>\n",
       "      <td>-0.157634</td>\n",
       "      <td>-0.141080</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.157634</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Total_Views</th>\n",
       "      <td>0.753648</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.151194</td>\n",
       "      <td>-0.157634</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Subscribers  Video_Views  Video_Count   Started  Total_Views\n",
       "Subscribers     1.000000     0.753648     0.064661 -0.135162     0.753648\n",
       "Video_Views     0.753648     1.000000     0.151196 -0.157634     1.000000\n",
       "Video_Count     0.064661     0.151196     1.000000 -0.141080     0.151194\n",
       "Started        -0.135162    -0.157634    -0.141080  1.000000    -0.157634\n",
       "Total_Views     0.753648     1.000000     0.151194 -0.157634     1.000000"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6496f3fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ihNDKKiFhYrGWmwH8CMDnQT8oxryBTVWv/FnnWVlVnEr/C9yu9wwH8MbfUatH1GkT/3ru6VcNWVhdgMOnBPC8UF1/ojra8/b09qav9+HQ2QD22Wy27wL4uc1m02ISFyFxg75hEhIGFmv5JgDHAXwR9O8uLjAwDO1ZBRN1ACAqroEA2wPGRp236u+o1UO8JC2AMofhKfr6iV5jLNvwqCWU158IzllDZeV6fyZq6eHZvWMH9bISElr0TZOQEOqfQPUXAK/Ds4cjiR9XDwWQAEBUHBwAVIYxt0Pyd9TqcXVKYQA1DhPK/VUTeM+xQtRaQnX9ibpwYeF5RdGNutI9hjUA3rXZbHcHuyZCiAeFVUJCxGItXwrgKIB7NC6FaICBgXM+5Il+SQcAkuIEAKgC7F5PBPwetapw1tgDc3KgtQL9/aq5pukTucZYrkW530MOwsXtNhytq529agKXSALwV5vN9ozNZgvpmFpC4hGFVUKCrH9U6tcB7EYQns4m0YkBw9oAGPOEVVH2PAcli6OHVX9HrXYgceJv3wvsLPRi+oSv4w3nvdfj+QUhufYEcQ75eEWZX/3BY7gZQIXNZntfkK5HCAGFVUKCymItzwfwXwA/BT3pH+cYB4aurIr9K6t2AQBcEpyjnenvqNULPG/Cq5Y8RRey/VUn4eIRExyBvMUecp2dObt7ejKD+VBZDoDnbTbbX2w2W0R+zoREGwqrhASJxVr+PgDvAtikdS1Ee/0rq1fCKpP0ACDJjoGwOuo+nf6OWj2mTg2syCGUPLPPbQf+2op/JoXq2hPBOZpPntiwIESXvwfAUZvN5m3PVkKIH2jrKkImyGItNwL4BYB7ta6FRI7+ntUruwFANACAJNtFAHDoIXs/0/9Rq4fU6RNawfPsr2oMSb+qyOVLi3BofiiuPVGXLs0/rSj61SG8RTGAnTab7WcAvmuz2Ub935wQMjpaWSVkAizW8tkA9oOCKrkK4+BDVlY5G9y6yiEBQJ8Bymjn+jtq9V1ePLG9SwV2BnpxwltfebMcey6E4roTJcu6iprquRN5qMpXIoBvAnjTZrPlhuF+hMQcCquEBMhiLd8GYB+AuVrXQiIRA4c6EDoHV0ol2a4DgF4jG3UjeX9GrXKOnlqeOaEQpKboQrO/Kufq7Xg8ZDsMBIpzKMcrNumB0fe5DYE18Ey+CuVKLiExicIqIX6yWMtFi7X85wCeAEYfl0niGwPA+eA+q4MPU0mKoz+sjn6uP6NW+2ConWjoUvPMpomcP5oUdBzNQnN+KK49EV1dWbu7u7NmaHDrPHhWWD+vwb0JiVoUVgnxg8Vang7gZQBf1boWEtn6J1gNrKwOPkwlyg49AHSbRv/668+o1cs8069trq4Wyn7VLXjOEYrrTgTnaD15YuM8DUvQAbjfZrM9arPZzBrWQUjUoLBKiI8s1vL5AA4CeI/WtZDI51lZHVhYZVfaABSnAQC6TWzUr7/+jFo9ySf59TDWCCI7Db3o8/18xnnnZryyKOjXnaCa6rmVsmxI1boOAB8C8I7NZpv4Vg6ExDgKq4T4wGItvwPAHtDIVOKj/pXV/rR6JayKg2F19N1Y/Bm1ekidPqERqWqKrmEi54+mBKff1cMdsu2wAiHLuhOXLs2PpJ7RuQAO2Gy2jVoXQkgko7BKyBj6+1PvA/A4AHrLjviMgfErD1hdCauC4jICQI9plKERfo5aPayWTGi8Z6j6VW/HY5mhuG6gOId64sRGFuaHqnyRDuA1m832Wa0LISRSUVglZBQWa3kqgFcBfEnjUkh0YnywZZUN7q8pqk4zAHSb4PUhKn9GrXIO5TQvKgq0QA6oSo4p6A8a6bjr7CycmBXs605ET0/G7q7OnIiqaQgJwO9tNtsfbTYb7X9OyFUorBLihcVaPgnAbtA0KhIgBgb1ysqqJ6xyLgtclQCgx8S8rp6aVLXT13u4Ida5oPN554ARRHYaOiEl4PNHsRZv1QX7mhPBOdqPV5TN1roOH3wawH9tNpvPbSCExAMKq4RcxWItXwDgHQCRugpDogDz7FzlCatMGFhZ7Rt4vdcIr2+/p6hqn7ePe9OKlMaJ1Kim6Cd0vlecy7fhyYj6t1NbO7tClo3pWtfhow3w9LHO1LoQQiIFhVVChrBYy68B8DY8+yESEjAGxjgG2gCE/mlVfHArpz4DvI5I9WfU6hm1wOdg642aZwp6v2ommg+lomNCfbTBpChS5cULC9doXYefpsAzpnWx1oUQEgkorBLSz2It/ziAcgBJGpdCYgADG7J1lSesMq6OG1b9GbV6lBeLgdYXqn7Vm/GfYF8yYJyDnzyxQQFG3yYsgmXCM0CAdgogcS8a/wETEnQWa/l3AfwNGH07IUL8wQA+2LPKRBUAGFedAMABuyowr0HTn1Grh9TpgfebhqBflXG1eR22R8xqYG9v2u6Ojrw5WtcxAUkAXrbZbDdrXQghWqKwSuKaxVouWazlfwVg07oWEmsYG+hZZYMrq4oLADhDz2hn+TNqtUKdWhBodWpq8PtVZ6HipAg1In7g4xydxys2aTFSNdgMAP5ts9nu1LoQQrRCYZXELYu13AzgeQB3a10LiT0CAA61f09Pz8qqoHr6UVUG+2jn+TpqVeWstQ3JAT80pOaZgr5v8B14JODwHGyXL8886nabIqZ3doJEAA/abLYva10IIVqgsEriksVangzgNQDXaV0LiVGccXVwNwCRA4DA3W4AkEWM+mCUr6NWu2AOeHsoDihKtml6oOd7Y+R9J6fgwrRgXjNQiiKePn9ucbQ9VDUeBuAXNpvtJ1oXQki4UVglccdiLc8AsB1AJI1dJDGGAYxj4AErybOyqrgVAHCLcI52nq+jVqt5ts/7sY4gslPB7lfdhNdagnm9iaisXGcHhIAfPotwVpvN9iebzUbfv0ncoP/YSVyxWMtzALwFIGIeAiGxiYFB5Z42ADawsqp6wqpLB5fXk/wYtVqhTlHHP8o7NVXfHOi5XnHuuAlPzw/qNQPU25uyu72tMCJqCaFPAnjCZrPptS6EkHCgsErihsVaXgDPHqrR/HQwiRL9YbV/GyrPM0ei4lQBwKGH171U/Rm1ekidHnDPqZJnDmq/ah4uH05Ab9AnYfmLc3Qfr9hconUdYbIVwAs2m82nHmdCohmFVRIXLNbyIgA7AAS1T4+Q0TAAg20ATOIAICouFQDseijezvFn1OpRPi0nkLo4oKjZxqA+JX8bngx85GsQNdSXHHa5zNla1xFG1wB4w2azRct0LkICQmGVxDyLtXwSPG/9F2tcCokjDIwNtAGASQwARMUzE6DXwLy+he/rqFXO4bzAcwsDKszTr5oc0LleCFy5vBx7FgbreoFSFPHs2bPL4rEPfTmA1202W6rWhRASKhRWSUyzWMst8KyoTtW4FBJnGBjn/QuorL8NQBoIq6N0pfo6atUBXTWHENDXbzUtuP2qi3DwtACu+feSU1VrugEhIvZ41cBCAK/YbDaavkdikuZfYAgJFYu1vBCeFVWLtpWQeMQApvLBNgAAgCQ7GAB0m7yf4+uo1QaeHvCT90qeOXg9jpzzbXh0StCuF6C+vuQ9ra2TNF/d1dhyAC/abLag759LiNYorJKYZLGWZwL4L4DJWtdC4hMDA+dq/9dYSQAAUfGE1R6T96+9vo5areKTvO8mMA4OyMHsV01Ez7F8XNb03xjn6D1esVnzwBwh1gF41mazRUQPMSHBQmGVxJz+Df9fBTBT61pI/GJgUAfaAPp7ViXZLgJAt4l53QPU11GrR9QSXUBFiewUJCFobxVfi/LuYF0rUI2NxQeczoQ8reuIIO+BZzxrYP+NEBKBKKySmGKxlpsAvABgkda1kPg2vA1AJwCAJDv6wyq87o/p66jVQ2pJQE9/q2n64G3cz3nP9Xhe07feVVW4cPbMinh8qGo8W9LVxF/WWnfS93gSE+g/ZBIzLNZyHYCn4HkrjBBNeWsDkBS7BADdZu9hNV9Wxt2rlHPwSj65KJCagtmvOhkXjxrh1HSPz1NVq9s4F2gF8SpJqumdm1xLPwngAa1rISQYKKySmGCxlgsAHgZwg9a1EAJ49llVMXxlVZQdEgD0GJnXt/uzFTlzvOsqEOr7YPQ7JPb3qwatNWYrHg/a9leBsNsT32lpsdAkuqtkqIm7trpWLhUh6AF8ota680da10TIRFFYJbHi9wDu0LoIQq5gTOVK/7hVSQQASXHqAaDXiJFPbPs4arUdSQ0BlSOxU5CExIDOvfpS3H1xIQ7PC8a1AsE5+o5XbA5odTmW5SmpO252LVstYFhP9DdrrTu/qFVNhAQDhVUS9SzW8h8D+LTWdRAyFOMMHP1DASBJACAqjlHDqq+jVs/zvJ5A6gnm/qrLsedisK4ViOZmywGHI6lAyxoijUXJeusG9+L1DIx5efmXtdadHwl7UYQECYVVEtUs1vLPAviG1nUQcrX+B6w8X2P7V1ZF2WEEgD4DRqxw+jpq9aga2CA2Jc8cnF0AOFe34p9BHdfqD1UVLp0+tWqFVvePRDPlgh2b3fM2jHEIA/BQrXUntUmRqERhlUQti7X8WgC/0boOQrxhYBgYt8rYwMqq08ABtyyN7Fn1ddTqIXWG372iHJDVrOD0q6ai43AWmjXbKurM6ZVNnIu0jygAcKgLZMvONfLM9T4cLQF4qta6c3moyyIk2CiskqhksZbPBvAvAF73qyREawxe2gBUl4kDXt/G93XU6rvq1Hy/i5FYJSQhKE/ub8GzAQ0kCAaHw7y/qWnqUq3uH1E43Cvkkn1L5OK1fpxlAvBMrXWn//8NEaIhCqsk6lis5dkAXgSg6dPIhIzF0wagDLQB6ABAVFwmVUCvt+PzZEUd75qco7MR6dn+1qKmGXzqhx0X5x2b8YomT+BzDkdFxXto838A4LCvd886OkeZtDKAs/MAPF1r3Umr0yRqUFglUcViLTcCeBaARdtKCBkbA2MDbQCApAPnTgYuKALs3o4vkOVx3yXogak2kFqUPFNQ+lWno+qYDr5N2Qq2lpZJ+xz2ZNoBgKP7Gvf80yVq3kRWmJeD9mAlUYTCKok2DwEIZDWBkLBiwGAbAGOiDvCEVFmEw9vxvoxareWZHf7WwQG3mmUMygNR2/BYVjCu4y9VZbWnT61epsW9IwpH2w2uRTWT1Mz5Qbjax2lLKxItKKySqGGxlttAe6mSKNG/stq/WirpAe4AAKcOTm/H+zJq9SS3yH4XIrGqYPSr6rnzzExUlk70OoE4d3Z5napKJi3uHSkYR8PNrmXteTxtVhAv+4ta685NQbweISFBYZVEBYu1/A4A39W6DkJ8xcCgwjMUAEzUM64OhFWvD1L5Mmr1kFoy7tCAqwWrX3Ud3rwcjOv4y+k0HWxoKInrJ9gZZzW3uVa4M3lSYPuWjU4E8K9a686pQb4uIUFFYZVEPIu1fDE8b/8TEjUYAD6wzypEPeOKCwDsenhdHfVl1OoRtcTvt+GV/CD0q3LuvhX/mj3h6/h9W7iOV2we9+8llolcOLfNuUqXyhNC1a+bDuC5WuvOoEw3IyQUKKySiGaxlqcB+Dcw/hhKQiIJAxP6w6rKmCAK/WG1z8D4iIN9GLXKOeQzvMCvwMIBVzD2V81C0+EUdIY9NLa1Fe7p60u1hPu+kULHxZMfcK5OS4QxN8S3mgPg4VrrTm/TrwjRnKR1AYSMxmItZwD+AXryn0QI+/lDaH35N1B62wFwMMkIMAYuOyHozUiYuwl9p/figc7GdQ8AkASBc3DG+StQuTKHAWCnsAZ7BagOFQNrrMZ8g6HiRhEb/t4HpwL85joj7lqoBwDc9EQf/niDERmJ+hoZ0hS/CpbYKYjC3Il+3jfj32EPMZyzy1WVa+P2oSoD1x3d5lxVrIcUnMlj47sVwHcAfC9M9yPEZ7SySiKZFcAWrYsgBAC4qqDttT8CjEFISIOUmg8uu8DdDmTe8i2ISRnoO70XqtOOzLyCjpIcS7deFNWlU4qcyeZ0mPWJLg5ABQS1TwUTGYyTjRDMApQemf1mnwuSyKATgc+97MDhegUvnHJjRoaA25+yY8Zvu3L7Tu8drKfpPz+A3N06Zs1q+sT7VRlXm9firUUTvY6/zp1bcklVJXO47xsJzNxw4A7n6hlhDKoDvltr3XlzmO9JyLgorJKIZLGWbwTwA63rIGSAq/40BHMyxMR06DMnwTh5HpjOAMGQALn5IhJK10E0p0A0JiC30NKpl/R8UnqG06TTyQ5XHzISMhwDb2UxPQN3c8i9MrjCYUgSnC+fVdDt5Dj6KTNK0gV8+sU+/HqfC9kJwMfm63D/XUsPde1/GgDQd3Yf9DnFkJIyxqxZyTNPOOzMwbGTItSwvgvnchkP11+eGZdb1CWrpr0fcK5aIEHUYvcDBuAftdadYe9PJmQsFFZJxLFYy/MA/BM0SpVEELm7FUxnBNMZISVnAYIIMAGQdFB6WiEmZYLLTjDxSq4z6nTq2cZW3VzLSvQ6u3WDI6pkAByQW2SAAZn5hm69CGSZGRb/2Y6LHSoO13PcOF1Cgk6AXeY47JqsA2PgqoLug8/BOHkeLv38RvRW7QIAKH2daHj0a7j84GfRd3rvQL9qacf/fhFKS1PAn/cH8Ghh4H9r/uMc7uMVm1PDec9Ikakm7dzqWrlcgKDTsIwkeB64StOwBkKGobBKIorFWi4BeBJAjta1EDIudvVvhrd2djsdUnNPj7RgylooiizkShIkCTKTGEzFJojJIriL49LBnryaTo68JAadyDElTYDKgYV5It6pVfCTXS7c98irC1NWfxDdh8thnrURHbseh3HKwsF79Z7cgYQ5Zcj98C/Qtf9pQGJVzn1vm6SSUoiZfk9oBQCYeN8JCy4Ee7ukMbW35+/u7U2Lu62UCpT0HTe7lq1lYJHwfbkYwN+1LoKQAZHwj4KQoX4CYK3WRRByNSkpA9ztAHc7IHc1A6oCqCoguyEmpkPpbgGT9OCK56kph9slXmptNZRkZ/U+tfu34FzFj3JzoXImSGkSuJvDWGgE0zEIArgoAKdbVXxtlQH13RwMwGvnZKwsEnHqfxLBE3MEfe402M8dgNrbCSguuFuq4W6rAwAwUfL00CpugDEoKWJ7338eR8K2jwb8OW/Cq0HZo9VXnLOGqsp1S8J5z0gwVcl56zr3wvVa13GVG2utOz+tdRGEABRWSQSxWMtvAvAVresgxBt93nQofZ2Qe9rgaqmG49IxcLcTqrMXUpYFvZVvQ3X0gMsu9HR1Gqpba02SKPALLa0mzoH/WbLtJBgDGKB0KeAqBxjAnZ5gKgmAWwFa+1Q4ZA4OwCACAgMUzlpklUudu/+JxPnXoqfiNZhnbYChYBb6KncAABy1J9H+5kOo++OdSFn9QXQefHqa8ZobwIye1ke1uwsd3/4SWu+5Ha2f+TDkC2c9H+9oQ9vn70TLXe+HY9ebg59v+zc/r6xqfjIYYz19duH8ovOKoouf/T45+Cy5cEeZe84GrUsZxS9rrTs1mVpGyFCM85Fb/hESbhZreS6ACgBxvQE4iWz2cwfQ+spvofR2wLN1lQFcVQDFDQgSjJPmwll7ElweOVFVYAIHV5kKQJejA1c45FYZ4IAogKfowewy4FKAJAPQ5wJqvpSIm5+wo6EXjo7F9xidtScBrkJMyoQuowiO6gq4Gs6g4JN/hqPmOJjOiNbyXyL7gz9zNu/8lS7lh78Sen7/C6g9XYCkh1Q0GYkf+xTk6gvovv+nSLvvT+h7+nFAb4Sx7Fp0fP1epP/273Du2QHd4Teq/3nviUnh+rt1uw1H39l7+4Jw3U9zHMoieereRcqUNVqXMo53ASwv/Olar2OCCQkH2meVRIoHQUGVRDhT8VIU3vuPcY+7Tl/59ofkqcnvXnyywKEql42pn53/0NOfbK5uu5jdyhVwF0f2zdmA4jn+nRJHXaHbXfg/LznwyjkZOoHBbeTIThCw5+4EHFanHVj7rxNrU9d9BI3/+g543Smo9g5AUcD0JvSd3gvz9JWQOxsBAB1v/60r4WOfyHK88Qqk6aUwbroOLR/cAvNNWwEA0qQpUBouQ2lrBUQJcDkAtwsQBHBFRt9/Hof1+5ZWAGEJq5xDPl6xKSEc94oIHK5V8ozDs5TCSA+qADAfnvasL2ldCIlfFFaJ5izW8k8BuF7rOggJJgYwlasSIMgA8P1FN5+eerE8+yNfFnudejYsmGVfrM5kjOH3N3jesr/YoWLL432Drx9Tp/Ksmz8OACj89IODH28p/xVMxUthnn5llyeuuCHb25A4fwnks6cBgwFgDEyvh3PnG9DPXQh35XEojfVQWxph3HQdOn/0TdhfexGJn/wC7M/9C6YNm9vXmMPXAtDZmbO7pycj0no2Q4Ojb6N79sliNXeF1qX44Yu11p2vFv507ataF0LiE/WsEk1ZrOXTANyndR2EBBsDA4cqAIIKAJJsFzigXh1Urx61esd/+rDywV6calVR+MtuPHjYhb/sabV0H3nJp/sqvZ1IuvsLLQBgLHsvHK88j7Z7P4rET3weanc3Wj+xDX3PPAGpZAYgihASk5D2k98i44HHoSsphXPvTqzYkHH8V/c1Cd+zNeLkCUfw/lK84BzNJ09sWDj+kTGAo/Na9/yzxWputD1ExgD8vda6k979IpqglVWiGYu1XIRnnGr8vP1H4gYDBM5VkTFRAQBJcUgAugGkDD1OB7QCSB748z9vGzm06WHnh8UkPnI3t8wb/t+Ij0lJmVxcOHsKAAhp6Uj/3cODr5mu8QyE45yj5YM3QMwtGHZu7z/+jIQP3c2z//uT2enTDSgrS8R3vt2A+36Z7/Pn7a9LFxecUhR9NLwdPjEcLe9zLW7J4anztC4lQLkA/ghgq9aFkPhDK6tES1YAcTmlhsQ+BsY4uASIKgCIskNSGfquPs6kql1jXYdz9FXzbJ/TIldlJ0RmvPrjak83uNsNALCXPwP9vEUQEq48eC/XXoLS2oz0+dOPJamd6YwBjAEuV+gewnW79cdqauasDtkNIgTjqL/Ftawrh6fO1LqWCXp/rXXnHVoXQeIPrawSTVis5YsAfFfrOggJFQYGlasiBIEDgCTbdYowMqymqOqIjw1lh6EGYDPGu1/z8z+Hs7oCir3T0Hz7tUj8+KfBZc+er+Ybt0K+dB5dP/02IIiQJk9F8leH//PrefD3MN+8Dd2f2WZ5rrcTLS0KHv57Oz73+eEjXXt6VPz0J01oapKhKBxbb0/Fe9+bhI4OBd/9bgN6e1TceWc6Vq/xvGHy7W834AtfyERm5vBvN5xDOXG8zNNQG8MEzi6937VCTObmWBl08Lta6863Cn+6tl7rQkj8oLBKws5iLTcCeBSAliMFCQkpBjAOVWSQPD2rikMvixjRAJqpKO6xrnOZp7f6cr+sG78GAHAtTH9XzTYtuPp1/ez5yHzk+VHPT/3uz6E0N/Z884tGw5wZk9DXp+Izn67D5En6Ycc9/1wnJk/W4Yc/ykVHh4I7P16DTZsS8eb2HlxzTRI2bkzEN6z1WL0mAXv39KKkxDAiqAJAV1fW7u7urHW+fG7RSuTCmdudq1ISYAhshFhkSgfwFwBbtC6ExA9qAyBa+CEA2miaxDbPu+cCWH8bgOLUuySM2KsyT1bUsS5TySe7/LilQ80wBvxvqziz58icGYIRAMxmAZMm69DSIg8/iAF9dg7OOex2FUlJAkQRECUGl5PD7eZgjEFROJ5+uhO3354y4j6co+XkiY1hHTgQbjounrjDuSYrxoLqgBtqrTvv1roIEj8orJKw6n/7/4ta10FIqDEw7vn/nlVFUXEYnTqMCJ4FsiyOdZ1D6nSDzzfVCVUQme/HX+V2PD6YLBsa3Dh71omZpcPbX2++OQXVl1zYdns1PnFPLT57byYEgaGsLBEHDvbhG9Z6fPRjaXj+uS685z1JMBpHfpuprp5bJcuGkSk2Rhi57sgHnWsmG6FL1bqWEPpVrXXnZK2LIPGB2gBI2Fis5QKAPwEY85szITHCs7bKRA4AouI0OvQDYwCuKHTLY4bLI2qJz9sFqRmGDv9KvELi7gvzcXQeANjtKr5na8RnP5uJhIThYfPggT4UT9PjF/fl4fJlGV//Wj3mzi1EYqKAH/84DwDQ3a3gySc6YPteDu67rxk93Sq2bk3BrNlGyLLuRPWl+TH7UFUCN+zf6lw5T4I44iG3GJMEz9fz92pdCIl9tLJKwul/AETb/oKEBKr/MXqJAYCouEx9BshXH1Qgy6Nu3cY51CpeVOTrDZU8U7r/ZXqsxK5qAJBlDputEZs2JWLt2pGlvfJqN9auSQBjDAUFOuTmSqipGb5g/Mgj7fjgh1KxfXsPpk834CtfzcKDD7aBc6gnjm8UYvWhqlTVvHubc9WiOAiqA66tte68ResiSOyjsErCwmItL4CnV5WQuMA4719ZlTg456LqNvYa2Yh9oPJlZdS3w2WIdU7ofQo+HLCrGcZxdw3wfjJXtuKfMzjn+MUvmjF5kg7v35rq9dDsbAmHj9gBAO1tMmpq3MjLu/KsZG2tG62tCubPN8Hp4Bi6BVZ3d8burq6cmOxXz1aT377NtWKlACHe3rH8Za11p0nrIkhsi7d/VEQ7v4XnbSNC4kR/zyrTMQB2AOZeL7EzW5FHfZu/FcmNAHxbWdUJpyCyBQEUijS0H8lA65KK4068/t8eTJmix6c+WQsAuOvudDQ1eRaE3/e+ZHz4w2n4v5834557agAOfOIT6UhJudLZ89BDbbjrLs8C78ayRHz3Ow145ulOfPgjaT0njpfNDqS+SFekZLx1rXvBBq3r0IgFnj2zaStCEjIUVknIWazlNwKgt4pInOFD2gC4HYC523TVu1meUavJI8/1OKfm9/p6t4n0q74Pz7gBYO5cI15/Y+ztQDMzJfzs53mjvv6d71yZtJWWJuI3v/VMyaqpmX3k4gXj2kBrjFTTlNy3Nrhnb9C6Do19rda68+HCn649r3UhJDZRGwAJKYu1PBHA77Sug5BwY5wNtAEwxrkDALpNbNjX3P5Rq6M6yqf5/DVayTcH1q/KeXsZXlsU0Lk+UhSp8uKFhbH1UBUHnytPepuCKgDACODXWhdBYheFVRJq34evb2MSElP4wNZVAqA6AaDbNPzdrPFGrR5Up4+66jrsToBdzTAENMpzBiqP6TD2jgQTwTn4yRMbFIDFzvcbDnmpXLxnuVwS00MN/PS+WuvO67UugsSm2PniQSKOxVo+D8Dnta6DEC0w9D/wznSCoCouAOgxY1goHG/U6jF1aqFPN9MLlRCYfvwDR9qGx3LGPypwvb1puzs68uaE8h5hxeFcI888NF+xxNZKcXDcX2vdGbIffEj8orBKQumXoD1VSfzyTKZiksC4J6x2mzAsUI41alXlrK0VKRk+3SjD0B1IgXruODUDVQGtyPqCc3Qer9gUsuuHHUdvmXvOiZlKwXKtS4lQ0wB8WesiSOyhsEpCwmItvwHAJq3rIEQ7A20AOlFQZTcA9BjZsP0Axhq12g1Tna93UvIC61ddj+0NgZznq8t1M4+63SafhxpENI6O97oXnJ+q5oS0vzcGfKvWupNav0hQUVglQWexlksA/k/rOgiJCEwSBdUtA0CvEeahL401arWGZ3f6cnkO9KkZBv/3V+XcfSueCtnb84oinjp/fvGaUF0/rDiab3QtaSpUM+ZqXUoUMAO4T+siSGyhsEpC4RMAYnLjb0J8NWQogCiobgUA+gwYNhJqrFGrx9UpI0azeqUXqgLpV81C46FkdPnUZhCIypPrnYAQ9W1AjLO621zLe7N5ynSta4kiW2utO8u0LoLEDgqrJKgs1vJkAN/Tug5CtDcwrUqSRNWlAIDdgMShRxSOMWr1EC8xj/baUIH2q96Cf4fs639vb8ru9vaCeWMdo6oq/vSnP+Hxxx8f9Zi6ujp8//vfx8mTJ/uv24uHHnoIf/jDH1BVVTV43BNPPIHu7oD+GsYkcHZhq2slS+OJlqBfPPb9tta6k/ZyJ0FBYZUE2zcBZGldBCHa619YZZIoKi7OgV7Ohm/flDfGqNUj6rRsX+4SyP6qjKtNa7Fjsb/n+YJzdB2v2Fwy3nH79u1DZubo7ayqquL1119HcXHx4MeOHz+O+fPn4+6778aePXsAAKdOnUJubi6SkoI7IE/iwqkPOFcnJnNTflAvHD9mgXaDIUFCYZUEjcVaPhnAF7Wug5AIMbCyqhMVJ+cMI6ZRjTZqlXO4zvP8cbet4kCvmu7//qpz8W6lADUkb9HX108/4nKZxwzaXV1dOHPmDBYtGv1Zpf3796O0tBQJCVcWnwVBgCzLUBQFjDGoqop9+/Zh9erg7iKl51LFHc41uWYY6AfviflOrXVnmtZFkOhHYZUE008A0B57hAAAH1haFXWi7IAiXBVWPaNWjd5OdUJXrfrS76kXTkFgOn9LuwOPhORpbUURz547u3Tc5PjKK69g8+bNYIx5fb2rqwtVVVVYsmTJsI/PnTsX586dw6OPPor169fjwIEDmDdvHnQ6v/8KRmXkusN3OFdPNUA36qo38VkKgK9qXQSJfhRWSVBYrOXLAHxA6zoIiRRsYOsqJkmS4oAswDH09bFGrTbytBZf7qFmGsacgOWNifcen4RLU/09zxdVVWt7AGHMPsXTp08jISEB+fmjv7v+6quvYvPmzRCE4d+ijEYjPvjBD+KTn/wk8vLycPr0acyaNQvPP/88/vWvf6GmpmZC9Seqxn13ONfM1kEatZeY+O3ztdadtEJNJoSan0mwfB+A92USQuIQH/ydpBdlB3NLcA59faxRq1W8yDnaa0MpeWa/9zB9D15p9/ccX/T1Je9pay1aNd5x1dXVOHXqFM6cOQNZluF0OvH000/j1ltvHTzm8uXL+Pe//91/3T6cOXMGgiBg5swrHQ87duzA2rVrUVFRgfz8fMydOxdPPPEEPvaxjwVUf5qasPsW17Llwjhhm/gtAYAVNCyATAD9oyQTZrGWrwBwrdZ1EBJZ+uMqE/WSYhedOriGvpqiqvbRzjyiloz7vnZA/aqc29+HZ+f7dY5Pl0XP8YrNU3w5dvPmzdi8eTMA4OLFi9izZ8+woAoAX/jCFwZ//+yzz2L69OnDgmprayt6enpgsVjQ0NAw2AYgy3JA9eeoKW9vcS1eyzBKXwKZqM/UWnfeV/jTtZe1LoREJ2oDIMFg07oAQiLOQM8qRL0kOwSHHsNGq2Ypyqirp4fVkvEfStELlRCYXwsOBag9bEZfsj/n+KKxsfig05mQN5FrHDx4EAcPHvTp2O3bt2Pjxo0APH2sR48exYMPPoiVK1f6fd9JSuZb73MtWUdBNaRMAL6ldREketHKKpkQi7V8OWhVlZAR2GAjgKiXFLvUrcewldRcWeFeTgMAnOCWcR+AUjKNI3YXGM/78YRPe7f6Q1WFC2fPrAjocXyLxQKLxQIAIx6mGnDzzTeP+NjWrVsHf5+QkIC77747kNtjupz31jp51oaATib+uqfWuvNnhT9dW611IST6UFglE2XTuoB4IXc1o6X8l1B62sGYgMQF1yJ5yU2Dr3fuexodbz2Ews89BtE88kHm2j/eBUFvAgQBTBCR97FfAwDa3/ob7OcPQZ89BZlbPG1lPce3Q3V0D7s+8ZsKwM0Y04myQ9eXxIaFy9FGrSpcaOiFKXfci+eb/OpXFbhcuxTvLPDnHF+cqlrdxrngUwtAxOBQ5ymTdy2Tp23QupR44VadZw62vPqprVhLK6zEbxRWScD6dwB4r9Z1xA1BRNrGu2HInQbV2Yf6h78Io2Uh9JmTIHc1w3HxCMTksR+6zbnjx8OCrOrshbOuEvl3/Q7NL/wfXM0XIaXmoff468je+v1Qf0axTeUMgBOATpIduh7T8AcQRxu12o7EegBjhlUO9Khphhn+lLME+88yYNy9W/1htye+09JiWRHMa4Ych7xMnrZ/njJ5ndalxAO36jxxqOU1+6Xek0sAlNy3bcsfv/zki7Va10WiC/WskomwaV1APJES02HInQYAEAxm6DKKoHR7dj9qf+MvSNt4J/zfkIGBKzI45+CyC0wQ0bX/aSQtvhFMpJ9lJ6L/PX43AEiKQ99tGv76aKNWz/O88bejMghVfvWrcs634bFpPh/v0yXRd7xic0j2aw0ZDsc6ufTwPGXyuLsWkIlxq84T7zS9cPDpS7+e3R9UAUAP4Gta1kWiE4VVEpD+VdXrtK4jXsmdjXA1nochfwb6zuyDmJQBffY4W2cyhqZ/fQf1f/8Cuo++AsATes0zVqH+75+HlJIDZkiAq/40zCXRtVgWmTgHmAsARMVh6DHBp1Gr76rF4/7E4W+/ahK6juaiIairqs1NU/Y7HEkFwbxmSHH0bHbPrZyu5C/TupRYNkpIHeqe+7Zt8WmUMCEDaOmEBOq7WhcQr1SXHc3P/Bjpmz4BCAI69z6JnG0/GPe83A/9HFJSBpTeDjQ++b/QZRTCWDQHKcvfj5Tl7wcAtL78G6Su/TC6330VjgtHoMu2IHUVzXoIhCdxMjcAiIrT0G0aPmlqtFGrh9Tp4w65V/3cX/U6vNjnz/Hj3l8VLp0+HcCj91rhaL/evfByvpq+UOtSYpVLdRw/1PKas7q3cvE4h5rg2XP162Eoi8QIWlklfrNYyxcAuF7rOuIRV2Q0P/NjJMzaAPOMVZA7GiB3NuLyQ59D7R/vgtLdgvq/fxFKz8h936WkDACAmJAK8/SVcF4+Pex1V+M5z3FpBeg9vh1ZN1vhbr4Ed1td6D+xGMQ550PCqqnbNGRxYIxRq8fUqWNuAcWBbjVN73u/Kudd70V5UEPa6dMrmzkXo2K0MuNousm1tCVfTZ+tdS2xyKU6ju9tev7QM5fun+NDUB3wmfu2bRl/ezZC+tHKKgnE/9O6gHjEOUfry/dDl1GE5GW3AAD0WRYUfe6xwWNq/3gX8j72qxG7AaguB8BVCAYzVJcDjgtHkLL6jmHHdOx8FOnX/g+gygBXPR9kArjs0zAlMgJnAJMBQFRcpm4TBsNd/6jVEfudco7uy8gceycAg3AKAvO+z5MXU3HuXQOca32ve2wOR8L+5qapUfFWOuOs9jbXciWVJ5RoXUus8WMl1ZskAF8APfdAfERhlfjFYi3PBUDvC2vAWXcSvSfehC7Lgst/+xwAIG3dR2EqXur1eLm7Fa2v/AY5W78Hpa8DzU//0POCqiJh1nqYpl75HtN3ei/0uSWDq6+G/Jm4/OC90GVbxu+FJaPoX1nlXBa4ous1iYMrqaONWu2FsQbArLGu6m+/6u14LNWf48fCORwVFZsntPl/uAicnd/qXGlKgimovbrxboIhdajP37dty31ffvLF7qAURmIahVXir8/C80QnCTNj4WxM/vqLYx5T+JmHBn8vJWUgZ+v3AAC61Fzk3/W7Uc8zT18J8/QrLYhpZXcjDYFttE76cQ4wQQFgB5DUY8TghvyjjVqt45kj+zeuouSbx96fbAiJu87PxbG5vh4/npaWyfsc9uT1wbpeqEhcrNzmXJVtgj5D61piRRBD6oA0AJ8EcF+QrkdiGPWsEp9ZrOVGAJ/Wug5CogAHwABBBrgDAHoNV8LqaKNWT/LJYw6350AX96NfdRV2BW1akKqymlOnVi0P1vVCRc+lY3c4V+dTUA0Ol+KoCKAn1Vefum/bFhpzS8ZFK6vEHx8C4POqDiHxjHOVA4LKuCes2g1IHHhttFGrh9TpYz+0ZBBOgTHvfR8jC1C24p+lvlc8trNnl9dzVYrofVVNXH/wdueqWTqIQR8rG29ciqPiUOtrrhAE1KFKAGwEsD2E9yAxgMIq8ccXtS6AkOjAwKECTFAYV5wccCoiGwyio41aPayWjPnDoJJl9HkLqjS0HU5Hm2/BdhxOp+lgY0NJRD9Ulaya9t7mWrFYhEBtShPgUhwVB1tfddf0Vi0K0y0/BQqrZBwUVolPLNby9wCYo3UdhEQFDq5ylTGICuOqiwPdwJXdALyNWuUc8hleOObKpZJn9nkz9ZvwtOJXzaPgHM6KY++J6HdUMtTEXTe5lq0UwLz+EEDGp0FIHXDLfdu2ZH/5yRebwnxfEkUorBJf0XZVhPiOc6gARM647FYFDFsR9TZq1Q2p1g3JMuoFgU6epp/uy80ZV9s24vWghI621sJ37PaUiH2oKk9J3XG9e9E6Bka9jwHQMKQO0AH4OICfa3R/EgXoASsyLou1fAaA92pdByHRg0HlKsBERVBltyJg2NP/3katNiNl7JUlo3gKzLeVwxmorJAgT/jtcM5ZXVXV2qC0EoSCRcl66wb34vUUVP3nUhwVe5qePfxM9f1zNQyqAz5JD1qRsdDKKvHFpzAwPZIQMi4GgENlgMgF1S27JQx7+t/bqNUzaoHX7awGKJmGMV8f6gN4dOzBAj46d3ZpjapKBcG4VrDNlAt2rJFnbtC6jmjjUhzHDra+Itf0ntI6oA5VDGATgNe1LoREJgqrZEwWa7kOwEe0roOQaMLBuMpVzpjIRNWtOIeGVc47DRwjVlaP8mljrpoq+eYcX+5t4I5TJTjt+zjWUbhcxkP19TNWTPQ6QcehLlQsuxfLxRHbmhCJIjSkDvUpUFglo6A2ADKeGwGMWAUihIyuf2UVgKSKikt16uEeeE0HtHk755A6PXW063Ggk6f61q+6Hm80+FnuyPtxuI9XbI682e0c7hVyyb7FcnHQxsfGOpfiOLa78dkjz1TfPy+CgyoA3HTfti0+/UBG4g+trJLx3KV1AYREGwbPbgBgEhdlp2of0j062qjVY+rU0ceCevpVx986inPXLXhqwhOr2tvz9/T2pkXWyiWHfb171vESNW/l+AcTl+I4dqDlFaW279RCrWvxkQ6e7zc/0boQEnloZZWMymItLwBwrdZ1EBJtOBg4VwEmMVFxos/A1IHXvI1aVTlr6URi6mjXU7KMPvWr5qDhUDK60wMquh/nrL6qcl0oN4L3H0fXNe75p0vUPL8f9lJUBe/92934+L+/7vX1vdVHcO3f7sKmv34U73/8cwCA1r4O3Provdj04Mfwyumdg8fe9Z9voKG7JcBPIjxcin1wJTWKguqAT9CDVsQbWlklY/kYANq3kBA/MQAqVDBIXJQdrMuEwYlV3katdiKhDmO02yh5Jp/eHr0FT034a/qF84suKIpu1USvEzQcbVtcixtzeer8QE5/8OC/MS1jMnpcvSNe63R041uv/RKP3P4LFCTnoKW3HQDw3MnX8f4578WNpZvwkae+gvdOX4v/nt2NuTnTkZsUmV1RLsXev5J6OtoC6lBTAFwD4FWtCyGRhVZWyVju1LoAQqIT49zTBiBIigM9xiu7aXgbtXqJ53htDQAADnT40q/KuNKwGjsn1JPodhuO1tXNipigyjgabnYta8/lqQGNja3vasL283txx/wbvL7+7MnX8d7p61CQ7PlZIDPB06YriRIcshMuxQXGBMiqjAcPPoVPL78jwM8kdDwrqc8ceab6N/OiPKgO+KTWBZDIQ2GVeGWxlq8DME3rOgiJUlzlCgAJkmwXekxXvtZ6G7VaoU5Rr/7YIKN4GoyN+7V6Ho5WCVADfieEc8gVFZsSAz0/2BhnNbe5VrgzeVJxoNewvfFbfHPDZyCM8td3oa0GnY5ubH3887j+7/fg38dfAQDcPGszdlzYjw8/9VV8afWd+MfhZ3Hb7Gth0hkDLSXonIr93RgLqQNuvG/bljytiyCRhdoAyGju1roAQqIVA4MKlTGmY6LSzrrMVzat9zZq9ZA6fcREqwG+9qvegUcnB1atR2dH7u7enoyIeKhK5MLZrc6ViYkwBrxf7Otn9yAjIQ3zcmdgb/URr8fIXEFFw2k88YFfwSE7cdOjn8Gi/NmYml6Eh7d6Bip1OLrxh32P4S+3/BBfe/nn6HR045PLtmFxgTbTp52K/d0DLS/zur4zCzQpIPQkeB60+pHWhZDIQSurZASLtTwJwPu1roOQKMb7H7ASJNku9pigG3jB26jVo7x41FCm5JvHDWxm3nu8CNVTAi8WTSdPro+I1TkdF09+wLk6fSJBFQAO1lXgv2d2Y+Ufb8e9z38Puy8dxudf+MGwY/KSsrBh6jKY9Sakm1OxvHA+TjadHXbMr3f/HZ9b+VE8d/INzM2dgV9cb8XP3v7LREoLiFOxv7ur8emjz1b/Zn4MB9UBn7hv2xbKJ2QQrawSb24EYNa6CEKiFQOYClUADEySHVK38UpYvXrUKuewX+I5+d6uw4EOnqIbt1/1GrzUPpF6L15ccEZR9Ksnco1gMHDd0W3OVcV6SEkTvZZ1/adgXf8pAJ4n/v+0/wn85n3fHnbMNdPW4Nuv/xqyKsOtyDhSX4l7lt4++PqFtho09rRg5aQFONl0BkbJAAYGp+yaaHk+i4OVVG8mA1gLYIfWhZDIQGGVeLNV6wIIiW7M07PKdKKkOHQ9JjbY7Hj1qFUH9DUcgvdA6ulXHXt/Vc77tuC5BYFW6nbrj9XWzNU8qJq54cDtzpVzJIimUN7nkSPPAQA+svAmlGRasGHKclzz0J1gTMAd827AzKypg8f+/O2/4mvr7gEA3FS6Gfc8/U08dPDf+PLa0G8/HachdajbQGGV9GOcj3gwlcSx/haAJgCR8yQBIVHmfboLr17b0Jp0XskyLDnxsul72y4kXM5gk8F5Z8XFmmErq+fVvD1lrvu8PoEvT0rYIZemjtlHWsgv7f4ZvhRQ2OQcyrtH33u2uztrwuNZJyJFNe+5zbV8qQBBN/7RsY1C6qA6AEVffvJFCimEVlbJCFtAQZWQCenfZ5UBkigpTkOvwdNW0z9qdVhYreST3N6uAQBKnnncp6K34omAW3a6urJ3dXdnafpQVaaatPMm19LVDOPveBDLnErf0QMtr4BC6qACACsA7NW6EKI9CqvkavRgFSETxRlUrjAwSRIVh8FuQBLgfdTqYbVEP/ICAAfaeYquZKzbiFyuWYz9CwIqkaPl5IkNAZ0bLAVK+o7r3AsjYgcCrVBIHdNtoLBKQGGVDGGxlicAuE7rOgiJdsyzGwBjkERBcYounadn1duo1cNqSYa3a3CTeAqMrRjrPkuw7zwDigKpsbp6XqUsG9YGcm4wTFVydpS558RtUHUqfUf3t7zMLvedXaB1LRHsNgBf0boIoj0Kq2SoLQBC+nADIXHCsxsA00mC6hrc8P/qUaucg1fySV7DppplHPuRc875NjwW0Ib5sqw7Xn1p3ppAzp0wDj5LKXx7lTwjLoOqU+l7d3/Ly6CQ6hPLfdu2LP7yky8e0roQoi0Kq2QoagEgJAgYGFe5yiCIkqA6FcAzWOrqUasKhMsOGAq8XUPJH7tfNRldR3LQ6Pd4Vc6hnji+UQSuDCoIGw5lkTx17yJlStwF1SEhdb7WtUSZ2wBQWI1zFFYJAMBiLTcDuF7rOgiJDQwcKgMXoQp8cIX06lGrrUhugOdBkmE40MqTdWOOO74ez/s02epq3d2Zu7q6ctYFcu6EcLhWyTMOz1IKtVnR1Uj/2/20khq42wB8U+siiLbi+ulLMsz1oEEAhAQFA6ByRWRMUN0iBkNl0VWjVs+r+b3ezucm8QzYGCufnHdeg5cCWVVtO3F841x/z5swjr6N7jkVs5TCMXtwY4lT6Tu6s/E/R5+t/u0CCqoTMv2+bVvGHYxBYhutrJIBW7QugJDY4WkDYJypLglDV1aHjVo9yr23nI7XrzoVZ48Z4PL74ajamtknZNkY3oeqODqvdS+4VKRmLA7rfTVCK6khsQXAL7UugmiHwioZcI3WBRASKxgADpUxiIpTh8F9VK8etXpQnZ4y4mSM36+6DY+l+1uTokiVFy8uDO+kKo6W97kWt+Tw1Hlhva8GHErf0f3NL7F6+7kFWtcSg24AhdW4RmGVwGItnwdg3M3HCSG+YlC5KgICd+ghD3z06lGrx9Sp3vpVW8bqV9Vx17k5qJjtTzWcg584sUFFGDfeZxz1N7uW2TN40sxw3VMLFFLDYu1927Ykf/nJF0fsU0ziA4VVAgDXal0AIbGkf2UVAqD0GqAAADjvNPAr06tUjo5mpGVefS43iWfB2IiPD1iNt2sA+LVlVW9P+q7Ojrywvf0vcHbp/a4VYjI3Tw3XPcONQmpY6eD5PvWU1oUQbVBYJQCFVUKCioFxzrkiqLLaZ2QcGDlqtQemWgCpV5+rZptG71flXH4/npjlTy2co+P48bJSf86ZCJELZ253rkpJgCE7XPcMJ4fSe2R/80tCvf38Aq1riTNbQGE1btFuAHGuf2pVXG0lQ0joMcbBuaDK7h4TGACYVbV76BE1PLvD25lKvsnrvqsAkI7Ww2lo9ysEXq4rPeZ2m0ZdqQ0mHRdP3OFckxWLQdWh9B55u+Gpd5+r/t3Cevt52is1/K67b9sWyixxilZWyQYAhvEOIoT4jgFc5VwVVLfc3R9WU1R12DZVJ1SLcvV5nn5V/ahv8d+Ep9XRXvNGUcRT588vCstDVUauO7LNuapEBykxHPcLlyErqQu1riXOZQFYDmCv1oWQ8KOwSqgFgJAgY2Dg4KqkutTuRCYCQKaiDHt7/xAvGTHamJvFswC8roIyrrZuwOs+b//EOXjlyfVOQBDHP3piErhx/1bninkSRGOo7xUuDqX3yL7ml8QGCqmRZDMorMYlCquEwiohQcYAcA5FUFxKt8nzzsXVo1aPqCVZV583Vr/qTJw8IUHxefJUX1/qnvb2gpCvqqaq5t23upYvFyDExPcTCqkRbaXWBRBtxMQXFxIYi7XcAoAmgxASbJyBM0BSnLynP6wOHbXKOVzneH7R1acpeaP3q96BR3J9vj1H1/GKTSX+lu2vbDX57fe5lqxhYdwSK1QopEaFFfdt28K+/OSLfPxDSSyhsBrfyrQugJBY5Bm3ylVRcaDbxPTA8FGrLuhqFIjDelM50Dxav6qB2yuLcdbnJ/rr66cfcbnM6wMs3ydFSsZb17oXbAjlPcLBofQe3tdcLjXYL1BIjXxpAGYCqNS6EBJeFFbjW3in2RASJzw9q1BF2YkeI0zA8FGrjTy1BVftldrfrzqiNQAANuL1JgA+hVVFEc+cO7s0pP+2S5TcHevdszeE8h6hNiSkLtK6FuKXlaCwGncorMa3VVoXQEgsYmDgHFxS7KzXCDMwfNTqaV7kuPocNdskX/0xAADnzpvxH5/HlVZVru1FqPpHOfhcZdLO5XJJSFdtQ4lCatRbCeAhrYsg4UVhNU5ZrOXpAGZoXQchMYqrjHNJdgh9RiQAw0etHlGnjfjaq+SZvfar5qDhcBK6fXqwpK8veU9bW1FofgjlkJfKxfvmKxafH/KKJHa55/C+lnJdo/0ihdToRosscYjCavxaCU9rHSEkyBgYA2dcVByCQ4eEq0etHuIlaUOP50ATT9Z5HU16K57U+XJPztFzvGLzlIlVPtrF4Vwjzzw6Uwn97gLBRiE15pTet21L6peffLFD60JI+FBYjV/00ykhIcIAqOAAd6pgjOk4HzZq9bg6pXDo8dwsnQMwYuqTwJX6VdjlU8hqbJh20OlM2DCxyr3g6N3knntqipq9POjXDiEKqTGLwTMc4FWtCyHhQ2E1flFYJSREGBjnnHFwJweGj1pVOGvogXnYNlRqttFrv+p8HD4lgOeNdz9VFc6fPbs8+KueHB3XuRfWFqjpURP4KKTGhVWgsBpXKKzGIYu1XAKwVOs6CIlVDGAqGDicKjB81Go7kuoBDAurSr65EF58AI9afLnfqao1HZwLXtsIAsbRfKNrSXs2T5kT1OuGiF3uObSvuVzf6KCQGgdoOECcobAan+YDSBj3KEJIQPq3ruIqHBwYPmr1Is/tHnosBxp5km5Er6mZ9xwrRO24uwDY7Ul7W1omB/WbN+Os7lbXMncaT4z4oSFDQqrPo2hJ1Ft+37YtwpeffFHVuhASHhRW4xO1ABASUoxzJkBlnh2qho5aPaYOXwDlCdI5ADlXX+FavNQ53l04R1/Fsc2TJ1zuEAJnF97vWmlI5iZLMK8bbBRS41oygFkAjmtdCAkPCqvxaYXWBRASyzjnnEGEwjwrq0NHrR5SpycOPVbNNipeLtB7A55fMN59mpqm7Hc6EzdMuOB+EhdO3e5clW6GwetwgkjgCakvGhodl8IWUn/04nYYdBIExiAwhi++Z82w1w9fqsObVecAAHpJxG2L5yI/NRk9Dif+vucQ7C43rps7A3MKPN0ff9t1ELcunoMUkzFcn0IsWgUKq3GDwmp8mq91AYTENM4BJnFZcAIYPmr1XV487IEpJc9cdPXpk3DpiAn2NVd/fChVFS6eOb0yaO+S6LlUsc25apIBupTxjw4/LULqUJ/ZsAIJBr3X19ITTPjMxpUw63WorG/CUwcr8IXNq3Gk+jKWTC7Egkn5+Ovb+zGnIBcnLjeiIC2ZgurErQTwZ62LIOFBYTXOWKzletAwAEJCjHNAYjJzMuDKqFXO0VPLMwcfruJAA0/SWa4++/34Z+LVH7va6dMrWzgXR5wbCCPXHd7mXDVDBynietm1Dqm+sGSmD/5+ckYaOu12AIAoCHArChRVBWOAoqrYefoC7lpDz7cGAT1kFUcorMafmaD/3QkJPSZyt+QQABF5spIKAH0w1AJs5sAh/f2qw3YGELlcvQgHx3z3w+FI2NfcNDUo+54mqsZ3trpWLhQhGMY/OnzscvfBd5pfNDU5qrUPqQz48459AANWTp2MFcWTRj10//lqzMz1bJm7cFI+Htt3BIcu1eGGeTOx5+wlLLYUQi+Jo55PfDb9vm1bMr785IutWhdCQo9CS/yZq3UBhMQ+DsaZ2meAAAA5smfU6mWe2Tb0KDXHNOJp5mXYe54Bo6YhzuGoOLbZ62hWf6WpCbtucS1fKYBFTHoaElKXaF3LgP8pW4UUkxHdDif+vGMfspITUJyVMeK4s00t2H+hBveWebozTHod7lm7DADQ53Ljzapz+NiqxXjqwDH0ud1YP30qLJlpI65DfDIwHOAlrQshoSdoXQAJu3G3wiGETAzngACm9pjAwHmnHjAAwEk+2TX0OCXPNOmqE9Xb8XjJWNduaZn8jsOR7HVfVn/kqClv3+pavjpSgqpd7j74Zv0/Tzxf84clTY7q2VrXM9RAf2mS0YA5Bbmoae0Ycczlji48daACd65Z4rW39b8nzmBT6TQcqb6MgrQUbFs6Dy9XVIW69Fg3S+sCSHjQymr8oZVVQkKMc5UzztRuMxN1wOCo1UPq9MGnajhQzxN1w7adSkbn0Ww0jbqpvaqy6lOnVk14N49JSuaOa9zz10/0OsHQJ3cf3BdhK6lDOWUZnANGnQSnLON0YzPeM2v4zxPtvXY8vOcQ7lg+H1lJI9uNm7t70eVwoDg7A5c7uqATRQAMskrbhE7QzPEPIbGAwmr8obBKSBgIHLzbBGnoqNXD6rTMgd/zBOk8gGE7A9yA5xxjXfPsmRUNXJVGb5j0wQw5/621cumGiVwjGCI9pA7ocbjw990HAQAq51g4KR8z87Kx5+wlAMCqaZPx35Nn0Od04enDJwBgxPZWL1ecwnVzPc+1LpiUj7/vPoidZy7g2jkRP3Mh0tHDwnGCwmocsVjLUwFM+O1DQsjYODgTONRuM3QDo1Y5h3KaFw1uU6XmmPjwk3jnNXh51FVVp9N0oLFx2rIJFKXOUybvWiZP2xDwNYIgWkLqgIxEM7587boRH1817cqi+O1L5+H2paN3WH101ZX/WZOMBnxu0+rgFhm/KKzGCepZjS+0qkpIOHDOBQ7eY4RhYNSqG2KdC7rBJ+6v7ledhtPv6uH2uvkm53BWHHtPduD1QF4mT3tnmTxtZOoKkz65+8D2+sdPvhCBPakkamXdt21L+viHkWhHK6vxhcIqIeHAOQTO0WNiptL+UautSGlE/1P+/f2qw8LqNjw28vHyfm2thXvt9pQNgdUCxzq59Nh0JV+TMct9cveBd5pfSGh21NDmoiQUZgDYq3URJLQorMYXesuEkDDgnENQOe8xwjQwavWMWtA3+Hri8H5VHXedm4UTXlcbOWd1VVVrA3v7n6P7Pe555yarWYG3DwSIQioJk5mgsBrzKKzGlylaF0BIfOAQVaDPgISiHs+o1aO8eHCLqKv7VddgRy2AYm9XOnd2aY2qSv7vq8rRfr174eV8NX2B3+dOQJ/cdeCd5hcppJJwoUWYOEBhNb5QWCUkHDiHyDnsBiQWdHhGrR5UZyQPvDysX5Vz+TY84XW/SJfLeKi+fobfW1UxjqYbXUs7s3hy2HpDPSH1hYRmRy2FVBJqLWCGBsaSOgUp32ufN4ktFFbji0XrAgiJBxwq51C5KjBxYNTqMXVqEQBwhjqecKVfNQMth9PQMeJtes7hqji22e+HRxhntbe5liupPGHM4QLBQiGVhAAHWCOYoZEJyd1MzFQEMccgSDlpTMzIZ8yQCWBgG7gkLQsl4UFhNU5YrOWZAEbuVk0ICTrOOVTGOeAZtapy1tqBpAwA4AnSRQCDb+vfjP9wb9dob8/f29eX5tfG/SIXzm11rjQnwhjyLeoopJIJUgGhHszQxISUHkHMUpmUYxTE7HQmZhQwpssFkOvDdegdwzhAYTV+WLQugJB4ooic949aTemA+RSADABQhvSrMq62rMP2EXurcs7qqyrXLfbnfhIXK7c5V2WboB91V4Fg6JO7DuxteiGxxUkhlYxLBoQ6MFMzE1N7BTGTMTHXJIjZmUxML2BMLMCQH9wClPT7T2/PvPeBspZgFEwiE4XV+EE/fRISLlxlKuN8YNRqNc/uHHhJzTNbBn4/C8dPSFBGrJ6eP7/4gqLofN5qSs+lY9ucqyYboEuZaOmjoZBKRuEExDowc6sgpvYxMZsJUk4CE7OzmJCaz5gwGcDkca8yMVMBUFiNYRRW44dF6wIIiRecq5AljoFRqxXqFBUAOEMtT5AG36Lfhkfzrz7X7TIcuVxX6nNQNXH9wdudq2bpIJqDUfvVKKQSAH2AVMcEcxsT0h1MyhYEMSeJidnZTEjOZYxNhScwamUKgP0a3p+EGIXV+EErq4SEDYciKHxg1OohdboZGOxXLQQAI7dXFuNc6bCzOOSK45t8fmAkWTXtvc21YrEIQR+82j165c797zS9mEQhNW50AbrLTEhoZ2KGSxCzRSblJAliVi4TknIAhOWBvQBZtC6AhBaF1fhh0boAQuIG58wtKsLAqNWjfFoOACi5JjZwSBleawYwLKx2dOTu7u3J8Omhqgw1cddNrmUrBTBx/KN95wmpLyS1OOvCPkiAhFwboK9nQlInE9NdgpijY1JOiiBm5jEhIQNA8rhXiEw5WhdAQovCavwIdc8QIaQf5wrckspyZQWcw3GB5xYCQ/pVOXfehKfnDT8HTZUn1y/05fp5SuqO692L1jEwNv7RvqGQGitYE5i+gQlJ3UzMlAUxRy+IOalMzMxngjEdgN/boUWBbK0LIKFFYTV++LIFCCEkKBh3SW6p0C0LDuhrOIQSzlDLzZ5+1VxcPpSInmF9qRcvLjyjKPrV413ZomTt2Oye59eWVmOhkBp1OMAawIyNTEjuFsRMlXn2IE337EGqz0b8hbd4+3zjDoXVOGCxlosAUrWug5B4wcHgkuxCoSwbGnh6C4ASnqi7iP5+1dvwr2E9pm63/t3amjnjBtVSuWDHanlmUIIqhdSIpgDCZTBjc/8epJxJOSZBzMnwbPkk5QHI07rICJKldQEktCisxoc0AILWRRASPwTuFO1igSwnVPGiFgBQcozM84pSvwK7B/dW5RzK8eObTGNejkNdqFh2L5aLJxxUe+XO/XubXkhupZCqNZdnyydTCxNT7YKYBUHMNTMpO5MJqQWMiUUAirQuMkrQymqMo7AaHzLHP4QQEjRc4E69Q5cnK6mvqyWdAKDmmScDwAIcOi2AD66KdXVm7+rpzhw9hHK4V8glB+cok9ZOpKRed+f+vc0UUsPMDoh1EBLaBCHNzsQsJkg5iZ4tn1LyGBOmgHZqCQb6HhfjKKzGB/qHTEgYcSbAITmkHFnOPKSWdHOGGm6WigDgA3jUMngcR/OJExsXjH4h2De4Z52YpuatDLQWCqkh1w1Il5mQ0MaEdBeTskVBzEkSxOwcCEk5jLFpWhcYB/S///T21HsfKOvQuhASGhRW4wOFVULCiEOAKjq4jkNfyScX8STdEQBFCbz73QLUzR84rvrSvFOKol8zykW6rnHPvzBJzVwSSA2ekPp8SqvzMoXUievw7EGa2MHEDLcgZktMyk32bPmUmAlghtYFEmQB6NC6CBIaFFbjA4VVQsKJM4jMLisQ6vtgzB/YX/ValHcPHCLLuuPV1fO8P1TF0bbFtbgxl6fO9/r6aLflnPfKnfvfaX4hlUKq31rADA2MJXUyMUMWpBwdE3NSBTEznwmmVNBDqpEuG8AZrYsgoUFhNT5QWCUkrBh0glNpR1IDgHw1zzQFnPdejxcWAADnUE8c3yjCyz6pjKPhJtey3kyeVHr1a6O5KqQuD97nEVM4wBrBDI1MSO5mYqYieLZ8SvVs+WTIBH2tjGb0kFUMo7AaH+gLMCFhxFRFFfUqP+fK7+YM1dwkTZrEL+wywbEGALq7M3d1deWsu/o8gbPq21wrWAo3F/tyHwqpI6iAUA9maGJCSrcgZnIm5RgFMSediRkFjOlyQXtOxyraviqGUViNDxRWCQkjQZEhGlS86ygWeKKuGsCkrfhnEgBwjrYTxzfOvfockQtntzpXJibCOG6YGgipe5ufT21z1sdbSJUBoX/Lp5ReQcwCE3NNgpid6dmDVCwAUKB1kSTsKKzGMAqr8SFN6wIIiSdMUZhJp4iH1OlJSq6pV+TuS4twaD4A1NbMOSHLxmHbUOm4ePJ256pcE/RjjsKMo5Dq9OxBam4VxNQ+JmYzQcpJYGJ2FhNS8xkTJoNGSJPhzFoXQEKHwmp8SNC6AELiCYesZkDRvaJOzVPyTOpq7DkFYLIsSycvXlww7KEqA9cd3eZcVayHlDTq9WIzpPYBUh0TzG1MSHcwKVsQxJwkTyBNzmOMTQUwVesiSdQwal0ACR0Kq/Fh7Ok4hJCgcgsKy5Q56lm6A0axaCseB+fgJ09s5AAbnCaXwA0HtjpXzpUgev1G6wmpHfv2Nr2Q3uaKypDa5dnyKaGdiRkuQcwWmZiTLEhZOUxIygFQonWBJGYYtC6AhA6F1fhAb48QEkZOyQWdnODkSbrqFHS0ZKF5cXdP+s7OztzBt/9TVPOe21zLlwoQdFeff1VIXRHe6v3WBujrmZDUycR0lyDm6JiUk+LZgzQhA0Cy1gWSuEArqzGMwmp8oJVVQsLIJSpcdaeoSoFJ3IL/ODlHx4njm2YNvJ6pJu28ybV0NRuyygpEckhlTWD6BiYkdTMxUxbEHL0g5qQyMTOfCcZ0AGP22hISBrSyGsMorMaBdcK7tU6uU+ww6BzQ6+ww6Jxcp3dAZ3BCr3dCZ/C23yMhJDAOyS3YXbkizzFkbsYrk+rqSve73cZ1AFCgpO+4zr1w/dDjOee8R+7Yt7fp+Yx2V4MWIZUDrAHM2MiE5G5BzFSZZw/SdM8epPps0D6WJLLRymoMo7AaB/6h/9lcjPONhnO4ADg5mFMFc6tgLhWCW4HoliHIMkTZDUlxcUl2Q1Kd0HEndIoTeti5XnVAz+wwcDvXMzuMrA96oY8bmR0GsQ8Gwc4Noh16yQ6DZOcGnR0GyQGd3sENuoHQ7IDOyCEIY9VJSDQQRSc/L0xWi03nG0RF5RfOL1oNAMVKzlsb3XM2DBwX5pCqAMJlMGMzE1J6BDGLMynHJIg5GZ4tn6Q8AHkhroGQUKGV1RhGYTU+jOiJuxpj0APQM/AkAbz/owoA91UHBr22YTiHjP7QzMFcV0KzIMsQ3DJERYbkdkFSXVxSXNCpTs8v7uB6boceDhi4nRtYHwywQy/YuUHog5HZoZf6uFGwwyD1wSDauWeV2QG9zsH1Ogf0Bid0eid0BhnSuH9nhIwmEU4cSZyr3M4fyzh5coMLXBBmKYU7VskzNgAhDaluQKz17EGaahfELAhirplJWZlMSCtgTCwCUBTE+xESKWhlNYZRWI0Peq0L8BVjkABIDDwB4BABeEKzt4NDVwfnUAE4OeDqD85uBYJL9QRmtwxBkSG53Z7QLLsgDaw0cyfXcTv03AED6+N6bodBsMOAPm4Q7DAKfTCInt97QrOD66WB9gwH9Ho71+ucntVmgws6Wi2IQmbBhYtpuXxy7+X2w22LVy6Sp+5epExZH6SQagfEOggJbYKQamdittC/B2k2E1LyGBOmAJgSzM+HkChAXytjGIXV+BA1YTVSMAYBgIkBJoAD4JCgjnJw6OrgHBy4ukVDcClgbgWiW4EguyEpMkTZxSXVBZ3iHFht5nrYMbxFow8GZoeB2blB7P99/2qzXucJzgadHXqdA3r91avN1KLhu06WyBdkH8fxik2WVfKMA6Vywepuuf0dP0Jqj2cP0oR2JqQ7mZQtCmJOkiBm50BIymGMTQv5J0FIdKGV1RhGYTU+0P/OUYoxMHhWDAwMHJ4WjYHQHPYWDTc8odk1EJwVT4tGf2+zqLg9vc2qi+tkJyTuhF51Qqc6uB4O6LkdembnBthh8ARnbhD6YOhfbfaEZjuMAy0akgM6nZPr9fb+BwGd0BsUiBH/33ODPpdvaD8lLOq9vj7HacB/mx4+2+5qvDqkdgK6OiYkdjAxwy2I2RKTcpM9Wz4lZgKYoUXthEQpWlmNYRH/RZ8EBUfIowyJdYxBB0DH+leaRQA6bVo0FAxp0VAhuFUwlwJB7g/Ncn9oVtxcUp3QKS7ouMMTmrknNBuYfXiLhtjf1yzYeX9Ps+dhQMk+sIPGVS0abkijvmORZJTlaRUL5QtNrwuHXC0mxpKaBN2My4KUo2NiToogZuQzwZwGICV0f1OExBUKqzGMwmp8kEGtACRGMAYRgJkBZk9oHiUwA+Fo0XDCs9rsVvuDswLB/UDTLNZbc53BnnFbhtEsWUJXBSGkHx//EBKtKKzGBwqrhARZf4uGEYDx6haNR+e3Ct86/nDtxhMPW7oTCs7VFJXVNmfOz1RE4yww2tOYkBBwal0ACR0Kq/FhjKUnQkgw2RnrczFM+f4HxUl/vV+pSOqtmzur6pFi4BE49KmNtYXrTzXkLDO79CnzwBj9EElIcLi0LoCEDoXV+CBrXQAh8eKw0XAOjM1VRAhfuVvM/P0flWaBIwsAjK6OnGnnn8uZdv45yKKhuz535aG6/DVqnzl3Dhij/lVCAkcrqzGMwmp8oLBKSJjsMhnbB37fmsLy7rtFOPKVp9V0hv5tg/tJijOpqO6tlUV1b0Flgrs5c8Hh2sINPZ3JlhIwkSZJEeIfCqsxjMJqfKCwSkiYHDAah+1He2CGsPCtufytjRV8w2jnCFzV5TQfXpTTfBgA0JFSXFldWNbUlj4rXxX1JaGtmJCYQG0AMYzCanygsEpImFzSSdlXf+yPW8QNs6vlfdmdWO7LNVI7z5Wmdp4rBYA+U3Z1TeHGC43Zi1JlKWEOGBPHO5+QOEQrqzGMwmp8oLBKSBg4GRwOxqZ6e+2rd4mlf71fuahTYfHnmmZ706QZZ56cNOPMk3DpElvr8tdU1eeukhzG9LlgzByUwonfGjtq8NDrPxj8c2tXPW5Y8nFsnHfb4Mc45/j3nt/jRPU+6CUDPrLhayjKmo5uewf+8tp3YXf2YMvSOzF/yhoAwJ9e+Ta2rf0CUhMyw/75xAAKqzGMwmp8oLdHCAmDdw2Gc2BstrfX7EaW/J2Pio0//rvS59kj1n96d0/GlEuvrJ5y6RUogs7emL1kf23BeldPYsFMMIESThjlpBbhG+//MwBAVRV869Ftg6FzwMma/WjurMV3P/APXGyqxBO77sdXb/k9Dp3djuXTr8Hi4o34w0tWzJ+yBhUX96Aos4SCauDo+1wMo7AaH7q0LoCQeLDTZGob6/VzeazksY3C7g+/qa6e6L1E1W3Kb9i7LL9hLziY2po+61hN4cb2jtQSCxekyRO9PvHdqbojyErOR3pSzrCPH7u4G8umXwPGGKbkzILd2YPO3laIggS37ISsuMEYg6IqeLPiaXz6vT/U6DOICbSyGsMorMaHTq0LICQeHDCNP/Hx+RXC6iVn1Ldn1mJdsO7LwIXMthPzMttOAAC6E4vOVheW1bVkzstSREMpDSIIrUPn3sTiaWUjPt7R24K0hKzBP6cmZKGjrwVLppXh79t/jH2n/4ubl38CO088h2XT3wO9zhjOsmMNhdUYRmE1PlBYJSQMzut0Pr2H+/0Piiv+cr9yPMGJOaGoI6mnZtrsqoenAYDDkFZfU7DhTGPO0gSXPnkeGNOF4p7xSlbcqLi0Bzcuu9un4xkYTIZEfOa6HwMA+pzd+O+7T+AT13wPj++4D33ObpTN24qpuV67ScjoqA0ghgnjH0JiAIVVQkLMBbjsjBX7cqwsMv1X7xYzVKAl1HUZne15JeefWbdm7zcXr9311b5pZ/+zx9zXuBecU3tQEJys2Y+izBIkm9NHvJaakIn23ubBP3f0NiPFnDHsmJcPPYJrF34IB89uR1HWdHxow1fxwoEHQ153DOrRugASOhRW40OH1gUQEutOGPTn/Rmf2pLC8n55q1DDwzgOWafYUybVbl+1Yv/3V254+wvG2ScfOpTcef5tcLU+XDXEmoNnt2Nx8cgWAACYO3kV9p9+DZxzXGg8CZM+ASkJV8JqU2ctOntbUZI/Hy7ZAdb/f26ZFgkD0Kp1ASR0qA0gPtDKKiEhtstkagYw059z9s8QFu6Yy9/aMMbAgFARuKLPaTq0OKfpEDjAO1JKTtYUlTW3pc0sUEX9tHDXE41cbgeqag/hjrX/b/BjO0++AABYO+t9mD1pOU5U78P3nvgIdJIRH97w1WHnv7D/Ibxv2V0AgCXTyvDnV7+Dt44/jRuWfDxsn0MMGfPhRhLdGOdc6xpIqNlSvgDg11qXQUgs+1Bezs5jRsNav0/knP/+D8r+rC7fBgaEQ68551JNYdnFpqyFabJkngPG6F04Eumuv/eBspe1LoKEBq2sxgdaWSUkxM7pdSObFn3BGPvq3eLMv96vXJJURMSWUwl9jZNnnv7n5Jmn/wmXLqmltmBtZX3uSoPTkDYXjJm0ro8QL2hlNYZRWI0PFFYJCSEZkHt9fLjKmz4jS/n2R8WmiQwMCBW9uztz6sWX1k69+BIUQd/XkLNsX13BWndPQsEsMBZYQCck+CisxjAKq/Eh5E8cExLPKvX6C2CsZCLXOJfHSh7fIOz+0FsTHxgQKqLqMhfU71peUL8LHExpyZhztLZwY2dHyrSpXBCLtK6PxLXm8Q8h0YrCany4rHUBhMSy3WZjI4AJhVUAeG6lZ2DAjLrgDQwIFQYuZrVWLMhqrQAAdCVNPlNdWFbXmjEnV5GMfj1oRsgEOe99oKxD6yJI6FBYjQ8UVgkJob0moxqsa33vQ+KKv/5aOWF2Iap2hU/uvlQyp/JvJQBgN6Zfri3YeLYhZ0mSW5c0F4zR9xoSSk1aF0BCi3YDiBe2lDYAaVqXQUgsWjWpsKJbFOYG63qZnbz+d39QdALg00SsSOaWTJ2X81afuJy3mtlNWXPAWJLWNZGYc/DeB8qWal0ECR36aTd+XAaFVUKCTgXUboFNDeY1W1JY3q9uEQ5/6Rk1jQFiMK8dbjrZnjK55vVVk2teh8okZ1P2woO1BRvsXUmTZoAJ2VrXR2JCo9YFkNCisBo/6oDoeluRkGhwSq+7gAnsBDCafTOFRW/P4W+tPx7+gQGhInDZkNt4YElu4wFwgLenzjhRU7SxuS1t5iQu6IIa+ElcobAa4yisxg/qWyUkBPaYTA0Agh5WAeD3W4T1s6qV/VldWBaK62uJASy949Ts9I5TAIAec96FmqKy6ubMBemyZJpNgwiIH+q0LoCEFoXV+EH/mAkJgT0moxyyi3sGBsyIpIEBoZLYVz+l9NRjU0pPPQanPrm5Ln9dVX3uCqPTkDoXjBm1ro9EtHNaF0BCi8Jq/KCVVUJC4JRelxrK6/cZWcq3PyI2/vjhyBsYECoGV1fW1IsvZk29+CIUQd9bn7v8nbr8dUpvQt4sMEa99+RqFFZjHIXV+EErq4QEGQd4pyBMCfV9zuWz6f9cL+z+4I7IHRgQKqLqSii8vHNF4eWdUJkgt2bMOVJTuLG7M7l4KhfEQq3rIxGBwmqMo7AaPy5oXQAhseacTncJjFnCca9nVwmrF5+NjoEBoSJwVcpqObYwq+UYAKAzecqpmsKyhpaM2bmqaJihcXlEG333PlBWr3URJLQorMaPMwA4AKZ1IYTEij0m42UAlnDdL1oHBoRKSteFGSknH5wBAHZjZm1N4cZzjdmLU9y6xDk0iCBunNe6ABJ6NBQgnthSqgHQ/G5CguRTOVk79phN68N5z6wOfvm3f1QMApARzvtGE7dk7qjLX3OiPm+VaDdmzgVjCVrXRELmuXsfKLtZ6yJIaNFPnvHlNCisEhI0lQZ9crjv2ZzK8u+/WTj0xWfVNAbQ9k5e6OS+VEv1a6st1a9BESRHU9biA7UF6x3dSUUzwYQsresjQUX9qnGAwmp8OQ1gk9ZFEBIrOsLwcJU3e0uFxUvO8LfWnoidgQGhIqqyMa9x39K8xn3gYGpb2ozjNYVlre1pMyZxQdLkfz8SVBRW4wCF1fhyWusCCIkVFyWphjOm2TsVv32fsL60RtmfGYMDA0KFgQsZ7VVzMtqrAADdCQXnPYMI5mcponEWGKOe/uhDYTUOUFiNLxRWCQmSvSZjHbRsq2GMffUuccZffhP7AwNCJam3buqsqkemAo/AoU9trCtYd7o+d7nJpU+ZC8YMWtdHfEJhNQ5QWI0vFFYJCZLdZqND6xp6TSzlOx8WG370D8XOAJPW9UQzo6sjp/jC8znFF56HLBq663NXHK7LX8v7zLmzwViK1vURrxQAl7QugoQe7QYQT2wpIgA7AJ3WpRAS7TYWFRxqkcTFWtcBALfuVnd94G11jdZ1xCKVCe6WzPnHawo2dHWmTCkBE/O1rokMOnPvA2XTtS6ChB6trMYTW6cCW8oZALO0LoWQaNcqChataxjw9GphzeKz6tsll+N3YECoCFzVZTcfWZjdfAQA0JE8tbKmqKypNX12nirqKShp64jWBZDwoLAaf94FhVVCJqRWEi9zxiJqhe27HxKXP3i/ctLkon/foZTadb409cT5UgDoM2XV1BRuPN+UvTjVLSXMAWOi1vXFGQqrcYL26Is/h7UugJBo947JWKt1DVeTJWb46t1iigq0aV1LvDDbm4tmnPnX+rW7vz5/zZ6vd049/8Juo73lHXDep3VtcYK+n8UJWlmNP/SPm5AJ2mUyRWQYaUplBf0DA1JpYEB46d296ZbqV1Zbql+BIujsjdlL9tcWrHf2JBaUggmZWtcXo+j7WZygB6zijS0lDbTyQsiEbCrKP9AkSUu1rmM0n39OeWvNSRoYEAk4mNqWXnq8prCsvT21xMIFibYZC47aex8oo4mMcYJWVuONrbMdtpSLACwaV0JI1GoRxYgOHL+5UVg/s0bZn9lNAwO0xsCFjLaT8zLaTgIAuhOLzlUXbqxpyZyfrYiGUhpEEDBaVY0jFFbj0xFQWCUkIA2i2KgylqN1HWNijH31bnHGX36jVEsqJmldDrkiqaemeHbVP4oBwGFIbagt2HCmIWeZyaVPngfG9FrXF0Xo4ao4QmE1Ph0GcIvWRRASjd4xGasBRHZYhWdgwHc/LDb8kAYGRCyjsyN32vlnc6edfxayaOy6nLfq0OX81bzPlDMHjCVrXV+Eo5XVOEJhNT7RP3JCArTbZOzVugZfnSlgM/61Vti1bScNDIh0kuJInlS7feWk2u1QmehqzlxwqKZwQ29XsqUETMjTur4IRN/H4giF1fhE/8gJCdC7RoNZ6xr88Z81wprFZ9Wd0+qxVutaiG8Eruhzmg8tzmk+BA7wjpRpJ2uKypra0koLVVE/Tev6IkDzvQ+URdz2cSR0aDeAeGVLuQRQLxsh/lpgKapXGIuqlS5J5s4H71fO0cCA6NdryrlUU7TxYlPWolRZMs8FY/G4Rdlr9z5Qdq3WRZDwicf/yInHTq0LICTatApCS7QFVYAGBsSSBHvj5Jmnn1i/bvfX5q/Z8422KRfKdxkcrfvBuV3r2sJov9YFkPCiNoD4tRPAh7QugpBo8o7JeAlAVG7w3pTKCn5zk3DoC8/RwIBYoXd3Z0659NKaKZdegiLo+xpylu6rK1gn9yQUlIKxdK3rC6EdWhdAwovCavyilVVC/LTLbOrWuoaJ2DNLWLzkDKeBATFIVF3mgvrdywvqd4ODKa0Zc96tKdzY0ZFSPIULUiy1fLkA7Na6CBJe1LMaz2wpzYjSVSJCtHBdYd47tTrdCq3rmBDO+R9/rxzM6EbETuAiwdWVNOlMTWFZXUvG3BxFMpZqXc8E7br3gTJ6WDDO0MpqfNsF4GatiyAkWtRLUqHWNUyYZ2BAyZ9/o9RIKmhcZRxI7q4umV359xIAsBvTL9cWbDjbkLM00a1LmgvGdFrX56e3tC6AhB+F1fi2ExRWCfFJhyC0K4xFf1gF0GNiqbYPiQ0/eERxMMCodT0kfEyOtvySc0/nl5x7Gm7R1Hk5f9WJy3lrmN2UNQeMJWldnw/e1LoAEn4UVuMb9a0S4qP9RsNFAGla1xEspwvZzKfWCLtu30UDA+KVTrGnTK55Y9XkmjegMtHVlLXwYG3hBntX0uTpYEIkTmlzAtirdREk/CisxrfDAHoAJGpdCCGRbpfZ1KV1DcH277XCmkXnaGAA8QwiyG06uCS36SA4wNtTp5+oKSxrbk+fWaQKumKt6+u3/94HyuJpiy7Sj8JqPLN1KrCl7AZAmysTMo7DRkO09fb55DsfFpc9eL9ykgYGkAEMYOkdp2end5wGAPSacy9WF5Zdas5amC5LptkaDiJ4S6P7Eo3RXnvkFa0LICQaXJakAq1rCAVZYoav3SUm08AAMpqEvgZL6enH16/b/dW5q/d8o9Vy8eVdBkf7fnDuCHMp1K8ap2jrqnhnS5kBoErrMgiJZN2Mda2aXJgExpjWtYTK6hPqwc8/ry6igQHEV4qg763PXV5Rl79W6U3InwXGQtnT7QSQeu8DZeEOyCQCUFglgC3lHICpWpdBSKR602x69/M5WfO1riPUvvCs8tbqShoYQPynMkFuzZhzvKZgY2dnSnExF8Rg75zx9r0PlK0P8jVJlKCeVQIALwO4V+siCIlUu0zGDq1rCIf7bxLWz6xRDmT00MAA4h+Bq1JWy7EFWS3HAACdSZZTNUVlDS0Zc3JU0TAzCLd4NQjXIFGKwioBKKwSMqZDRmN8fK1kjH31HhoYQCYupfvijJSTD80AALsxs7amcMP5xuwlSW5d4lwwFsi/pxeCXCKJItQGQABbihmehysMWpdCSCRabCk672Isblplptfyqh88olhoYAAJNrdk7rict/rE5fzVot2YOQeM+bJ14qV7HyizhLo2ErkorBIPW8qrAK7RugxCIk0vYz0rJheaNdyuRxNbdyo7t+7itP8qCRmVSc7G7MXHagvWO7qTimaCCVmjHPq7ex8o+1xYiyMRJT7e2iK+eBkUVgkZ4bDRcAGMzdW6jnB7aq24dtFZeWdxAw0MIKEhcNmQ17hvaV7jPnAwtT1tRkV1YVlbe9r0SVzQTRlyKLUAxDkKq2TASwB+pXURhESaXSZTu9Y1aOXbHxGXPfhrpdLkRqnWtZDYxsCF9Paquentnp0UexLyL9QUll1qzpyXIOsS3tK2OqI1agMgV9hSjgOYrXUZhESS2/Jzd5026NdoXYdWctp47W/+pCQwIJR7aBIymidLqyo/oHURRFtx1YNFxvWU1gUQEmku6aQcrWvQUmM6K/ztjcI5Dqha10Li0n+0LoBoj8IqGepfWhdASCRxMGZ3xtEuAKPZNVtY8s5M9rbWdZC4Y4enRY3EOQqr5ApbZyWAE1qXQUikeNegPw/GRK3riAS/vllY15aIg1rXQeLKq6VVlb1aF0G0R2GVXI1WVwnpt9NsatW6hkjBGRO+crdYLAuo1boWEjeoBYAAoLBKRqK+VUL67TcamdY1RJIeM0v7/gfFbg44ta7FV4+0t+HGC+fxvgvn8Y+2thGvP9jWilsuXsAtFy/gxgvnMedUFToUBW2yjA9XX8KNF87j9e7uwePvratFk+wO56cQr1ygLatIPwqrZDhPK8BxrcsgJBJc1EnZWtcQaaqKWOnTq9h+revwxRmnE091dODJyRY8Y5mCt3p7cNHlGnbM3ekZeMYyBc9YpuD/ZWVhqdmMVFFEeXcXbkpOwT8nT8bf+kPumz3dmGUwIlvSafHpxJs3SqsqO7UugkQGCqvEG1pdJXHPBbjs9HCVV0+uF9eez8FOresYzzmXE/NNJpgEARJjWGoy440hq6RXe6mrG9cnJQMAdGBwcBUulYMxQOYc/2hvx13p6eEqP95RCwAZRGGVeEN9qyTuVRgM58AYLaGN4tsfFZc6dKjUuo6xlOgNONjXhw5FgV1V8XZvD+pHeQvfrqrY2duD9yQlAQBuSE7G7t5efLK2FvdmZOKfHe24KTkFJoG+bYZBH4B/a10EiRz0r46MZOusAuipXxLfdpmNLVrXEMncEjN+7S4xkQMdWtcymmKDAfekZ+Dummp8srYGMwxGSMx7G/JbPT1YZDIhVfRs/pAkinigsAhPWSyYZTRiR48nyH6noR5frKvDUbs9nJ9KvHmKWgDIUBRWyWge0roAQrS0z2ik8X7jaEhnRb/fIpzhQMT+Xd2Wmor/WKbgkUmTkSKKmKzTez3upe4uXJ+c7PW1P7a24FMZmXipqwuzjEb8MDcXv25uDmXZ8e7PWhdAIguFVTKax+HZkJmQuHROr8vUuoZo8PZcYem+GWyH1nWMplWWAQCX3W683tPtNZB2KwoO9PWhLDFpxGsXXS40yTKWms1wcBUCGBgDnJwGeoXIidKqyj1aF0EiC4VV4p2tsxPAM1qXQYgWZEDuY6xY6zqixa9uEda1J0Rm69AXLtdhy4XzuLeuFv+bnYMUUcQTHe14oqN98JjXe7qxOiEBZi/9qPe3NOPzmVkAgOuTkvFsZyc+cOkS7qQHrULlL1oXQCIP4zxi370hWrOlbALwutZlxKqaThUffdaOhh4OgQGfXKTDF1YY8NXXHHjhtAy9CBSnC/jbTSakjrLdp6JyLPlLLwqSBLz4QTMA4Ov/deDlszIW5Ir4xy0mAMAj77rQZuf4wgpD2D6/aHbMoD/1ofzcGVrXEU0S+3j7X36r9IoqCrWuhUQtB4CC0qrKkRvikrhGK6tkLNsBXNS6iFglCcB91xhReW8i3rk7Ab8/4MbJZgXvKZZw/LMJOPaZRExPF/CTnaPvv37/PhdKM6/8M+50cOypVXDsM4lQOEdFowK7m+Pv77rx2aXee/XISLtMJmpI9FOPmaV9/w6xK5oGBpCI8x8KqsQbCqtkdLZODuBvWpcRq/KSBCzK63/y2MBQmiWgrovjmmIJkuBZSV1RKKK223tvXG2XivIzMu5ZdCWECgxwKRycc9jdgE4E/m+PC59fpodOpGFMvnrHZKCGxABUTmKznomSgQEkIlELAPGKwioZz98B0DfuELvYoeJIvYLlheKwjz901I3rpklez/niKw78fLMRwpAMmmRguK1Uh4V/6sWUVAEpBoYDlxXcNJO2C/XHGZ0+TesaotUT68W1F3KwS+s6SNQ5VVpVGbEP6hFtUVglY7N1VoP6VkOqx8Vx27/68Ov3GpFsuJI8f/S2E5IAfGjuyKD54mk3shMYFueLI1772moDjn46Efdda8S333Ti+xsM+OthF25/qg8/fJveoR2PAig9Aj1cNRH/+1FxiUOHKq3rIFHlr1oXQCIXhVXiiz9qXUCsciueoPqhuTrcWnollD581IUXz8h47FYTmJdNzHdXK3j+lAzLr7vxgX/bsf2CjA8/PXynsSP1CgBgeoaAf7zrxr+2mnG8ScGZViW0n1SUO6XXXQBjZq3riGb9AwMSInlgAIkoLnjexSPEKwqrxBfPA7igdRGxhnOOu593oDRTxJdWXnlK/5WzMn6224XnP2CCWee9z/Qnm42o/VISLn4xCU+834SyKRIevdU07Jhvv+nE9zca4FYBpX/TD4EBfd6nTZJ+u02mRq1riAUN6azoDxE+MIBEjGdLqyppYhwZFYVVMj5bpwrgd1qXEWt21yh45Jgb2y/IWPBADxY80IOXzrjxPy/Z0e3ieM8jfVjwQA8+/aJnxfRyt4rrH+vz6drPVrmxNF9EfpKAVCPDykIRc//YA8aA+bkjWwfIFXtNRlp6DpIdc4Wl+6dH7sAAEjHowSoyJtpnlfjGlpICoBZAotalEBJKqycVvNslivO1riNWMM7VB36rHE7rxRKtayER6TyAaaVVlRRGyKhoZZX4xjPR6mGtyyAklDjAuwSBHq4KIs6Y8JV7xKmKgFqtayER6a8UVMl4KKwSf/wG1H9GYthZne4iGKN3D4Ks28zSaWAA8cIF2sub+IDCKvGdrfM0gFe0LoOQUNltMtZrXUOsqpzEZj27kgYGkGH+VlpV2aB1ESTyUVgl/rpf6wIICZU9JiPtlRBC/9wgrr2YTQMDCABABvBTrYsg0YHCKvHXawAqtS6CkFCoMuiTta4h1v3vR8XFDh1OaV0H0dyjpVWVF7UugkQHCqvEP7ZODuBnWpdBSLBxgLcLwlSt64h1Lh0zff1O0cSBTq1rIZpRAPxY6yJI9KCwSgLxGGhIAIkxF3VSDRhL0bqOeFCfwSb9YYtwmgYGxK0nS6sqz2hdBIkeFFaJ/2yd1GtEYs4ek7FO6xriyY65wtIDNDAgHnEAP9K6CBJdKKySQP0dQI3WRRASLLtNJpfWNcSb+24V1rUn4JDWdZCwerq0qvKk1kWQ6EJhlQTG1ukC8HOtyyAkWE7q9bS/apj1DwyYojDQqnb8+KHWBZDoQ2GVTMRfAdAeeSQmtInCFK1riEfdZpb+gzvEThoYEBdeKK2qPKp1EST6UFglgbN1OgD8QusyCJmoGkmq44yla11HvDo5mc16bgXbp3UdJORoVZUEhMIqmagHALRoXQQhE7HXZKT+a409vlFcd4kGBsSy10qrKmmCGQkIhVUyMbbOXtDqKolyu0xGegs6Anzro+Jip0QDA2LUD7QugEQvCqskGH4L4LLWRRASqBMGfYLWNRDPwICv3UUDA2LQW6VVlbRqTgJGYZVMnK2zD8B3tS6DkEA1i+JkrWsgHvUZbNIfbxBO0cCAmEK9qmRCKKySYPkbgEqtiyDEX/Wi2MAZy9K6DnLFW/OEZQdL2Nta10GCYk9pVeUbWhdBohuFVRIctk4FgFXrMgjx1zsmY7XWNZCRfnGbsLbDjMNa10EmhAP4ktZFkOhHYZUEj63zeYCe5iXRZZfZZNe6BjJS/8CAyQqjfvgo9o/SqkrakoxMGIVVEmxf07oAQvxxzKA3aV0D8a4rgWX88A6hnQM0Cjf6dIHebSNBQmGVBJetcy+Ap7UugxBfNYlikdY1kNGdmCzMfmE5e0frOojfflBaVUkTDklQUFglofANALLWRRAynmZRaFYZy9O6DjK2R8vEddVZ2K11HcRnpwDcr3URJHZQWCXBZ+s8DeD3WpdByHj2GY2XtK6B+OabHxMXOSWc1roO4pMvllZVurUugsQOCqskVL4LoFHrIggZy06zqVfrGohvXDpmst4pGmhgQMR7sbSq8hWtiyCxhcIqCQ1bZyfoYSsS4d41GIxa10B8V5fJJj9wPQ0MiGBOAF/UuggSeyisktCxdf4DwE6tyyBkNA2SWKB1DcQ/b84Xlh2axnZoXQfx6lelVZXntC6CxB4KqyTU7gU9bEUiULsgtCmMFWpdB/HfL24T1nbSwIBIcxnAj7QugsQmCqsktGydFaCHrUgE2m8yXtS6BhIYVWDiV+4RJykM9VrXQgZ9vbSqskfrIkhsorBKwuG7AGi/PRJRdpmMXVrXQALXmcAyf/QBoY0GBkSEPaVVlY9qXQSJXRRWSejRw1YkAh02Ggxa10Am5rhFmP3iMhoYoDEVwOe0LoLENgqrJDxsnY8AeEvrMggZcFmS8rWugUzcI5vEdTWZNDBAQw+WVlVS/zAJKQqrJJzuBtCndRGEdAqsU2ZsstZ1kOD4xsfFhU4JZ7SuIw41A/im1kWQ2EdhlYSPrfM8PKNYCdHUQaPxgtY1kOBx6ZjZeqeop4EBYffp0qrKFq2LILGPwioJt9+C9l4lGttlMlKoiTF1mWzyn64TqmhgQNj8s7Sq8mmtiyDxgcIqCS9bJwdwFwC71qWQ+HXIaNRpXQMJvu0LhOWHi2lgQBjUA/gfrYsg8YPCKgk/W+dZAN/SugwSv2p1Up7WNZDQ+L/3C2s7zTiidR0x7lOlVZVtWhdB4geFVaKV+wHs0boIEn96GetxA/RwVYzqHxhQRAMDQubh0qrKF7QugsQXxjm19xCN2FKmA3gXgFHrUkj8eNtkPHZvbvY8resYS+2Dteg+2g0pWULJj0oAAI3/aUTXkS4wxiAmiyi8pxC6tJHdDEqvgrq/1cFR6wBjDAV3F8A8zYyGfzWg+1g3TJNMKPykZ8ps++52KL0KMq/JDOvnFw5zL6jH//cJdToD9FrXEkPqAMwuraqknm8SVrSySrRj6zwNagcgYbbLbGrXuobxpK1Jg+XLlmEfy7w+EyU/LMG0H0xD8oJkND3X5PXc+sfrkTg3EdN/Oh3FPyiGIc8ApU9B39k+lPywBFzlcNQ4oLpUdOzqQEZZRhg+o/CrmCLMKV9KAwOCiAO4i4Iq0QKFVaK1XwF4TesiSPw4aDRIWtcwnoQZCRATxGEfE01X/qw6VTDGRpyn2BX0nupF2ro0AIAgCZ7rMIDLHJxzcDcHExlaXm5BxnsywKSR14kV/9hMAwOC6P7Sqkr6Wk00QWGVaMuzO8BHATRqXQqJD9WSlKN1DYFq/Hcjqr5UhY69Hci+JXvE664mF6QkCXV/rcPZ75xF3UN1UJ0qRJOI5CXJOPedc9Bl6iCYBdjP25G8KFmDzyK8aGBAUBwDYNW6CBK/KKwS7dk6GwF8DLQ/IgkxO2N9TsamaF1HoHLen4OZv5yJ1JWpaH2jdeQBKmC/ZEd6WTqmfX8aBIOA5hebAQBZ12dh2g+mIe+OPDQ93YTsW7PRtqMN1b+vRtPz3lsKYoFLx8zf+Lioo4EBAXMA+GBpVaVT60JI/KKwSiKDrfNVAL/UugwS244aDOfBmDj+kZEtZUUKug52jfi4lCZBl6aDudgMAEhekgz7peFbGg/82ZBrQMfuDky6dxKctU44G2I3i9RmMcuf30sDAwL01dKqyhNaF0HiG4VVEkm+AeCg1kWQ2LXLbIzavSGHhsnuI90w5BlGHKNL1UGXoYOz3nNsz8keGPOHb7bR9HQTsm/JBpc5oPZ/UABUl4pY9sZCYfkRGhjgr/LSqsrfaV0EIbR1FYkstpRpAA4DSNK6FBJ7tubn7qoy6NdoXcd4av5Yg96qXsg9MqRkCdk3Z6PnWI8nsDJAn6FH/sfzoUvTwd3uRt3f6mD5kgWAZ+W07m914DKHPkuPwnsKBx/W6jrUBUeNA9k3e/pd65+oR8/xHhgLjSj6dJFWn27YCCpX/vRb5VhKHxZqXUsUaAIwt7SqMnZ7REjUoLBKIo8t5SMA/qF1GST2LJ1ceNohCNO1roNoJ6WHNz/wO0UWOWiK2egUANeWVlW+oXUhhADUBkAika3zEQAPa10GiS0uwOmI4oerSHB0JrKsH28TWjjg1rqWCPYVCqokklBYJZHqMwCOal0EiR3vGg3nwNjIkU8k7lRMEea+tJTRuGfvHi6tqvy11kUQMhSFVRKZbJ12ALcCiNoHYkhk2W0yetnricSrhzeL62szaGDAVfYD+JTWRRByNQqrJHLZOi8A+BCuPLNMSMD2mYzjH0TiivVOGhgwRD2AW2g/VRKJKKySyGbrfAXAd7Qug0S/8zpdptY1kMgyZGDAyE1r44sTwK2lVZWXtS6EEG8orJLIZ+v8EYCntC6DRC834O5jbKrWdZDIU5vFLH+5VjipdR0a+3RpVeU7WhdByGgorJJocSc886kJ8dsJg/48GBu5iz4hAF5fJKw4MpW9pXUdGvlNaVXl37UugpCxUFgl0cHW2QvgJgD0kAzx226TqVnrGkhk+9lWYW2XKe52INkO4MtaF0HIeCiskuhh6/z/7d19jFzVfcbx752xd2fAZDDIb2DH6xgShnTtgp0UsMEpKSaERISoGNoIBoLbIpRE5EUtNFFyq7RAGylNFfFSUqC0BEhDiKBYaaBJebGBECC2b2CmYLAx1AYcwIMNfsG72z+uEZaF7V3vzJw7M9+PNNrVypr72H94H5055/zWkN4Q4AEAjcgjxYKH9LRXg7ko/7XF+cMHIl4KnaVFVgOLyrXqjtBBpH2xrKq9xPUHSLcEOHpNw7aqZ+yhoTMo+zaOiyZcsSi3oQsGBrwJnFGuVf2kSm3Bsqr2E9dvBb4eOobawwAMbPZwlYZp5Qdy/f81p6MHBgwB55Vr1SR0EGm4LKtqT3H9CuC60DGUfdWenueIomLoHGofNy7ML/i/Q+jUwvq35Vr1jtAhpJGwrKqdXQz8LHQIZduyYuGV0BnUfi69ID97+xhWhc7RYLcC3wodQhopy6raV1wfABYBT4SOoux6pFjwAIlGbFtPdOBllXx+CDaFztIgd5F+/O9+f7Udy6raW1zfDHwKWBs6irLp6Z6xh4TOoPb0wsRoxvULc0+GztEAv8CT/2pjllW1v7i+HjgNeC10FGXLIAy+kcvNCJ1D7eueObnjVsyI7g+dYxQeJj3575V/aluWVXWGuP4UcCrO+NYunhk7dg1RNC50DrW3Kxbl5m9qz4EBy4FPlmvVN0MHkUbDsqrOEdcfA04H3godRdmw7IBCt1zwriYazEX5r7bfwIAasLBcq24MHUQaLcuqOktcXwp8BqdcCXi4WOz0y93VIhvHRROuPCv3SpsMDFgDnFKuVR0zrI5gWVXniev3kt4S4GGCLlfrGXtw6AzqHCtm5ma1wcCAdcDHy7Xqi6GDSI1iWVVniut3AecCzoTvUkMwtDGX6wudQ50l4wMDXiVdUX0udBCpkSyr6lxx/TZgMel4QXWZ1WPHrCWKSqFzqPNcekF+9vY8z4bOsZs3gFPLtepToYNIjWZZVWeL6zcCX8TC2nWWFYvrQmdQZ9rWEx142fn5XIYGBrwFnF6uVR8PHURqBsuqOl9cvwr4C9wS0FUeKha2h86gzpWhgQHbgTPLterS0EGkZrGsqjvE9R8A5wEDoaOoNZ7q7TkodAZ1tnvm5I5b0Rd0YMA20slU9wTMIDWdZVXdI67/EDib9rh6RqP0upOr1AJXnJ2bt6nAigCPfgP4RLlWvTPAs6WWsqyqu8T1nwBnAltDR1HzrB0z5sWhKBofOoc632AuGvO1xfkpgxEvt/CxLwELyrXqfS18phSMZVXdJ64vAT4FOIKwQz1ULHjHpFrm9YOiiVeelXt5qDV3O68CTijXqstb8CwpEyyr6k5x/RfAqaQfpanDLCsWnGCmllo+Mzfr58dGy5r8mMeBeeVadXWTnyNlimVV3SuuLwNOhpZ+fKcWeLK358DQGdR9bjg1v2Bd8wYG/Dfwh+Va9ZUmvb+UWZZVdbe4/jhwPPC/oaOocX6Xz08PnUHd6dIL8rOaMDDgR6T3qGblXleppSyrUlxfDZwANPsjPLXAujH59UNRNCF0DnWnrT3RuL8+Px81cGDA94E/Kdeq3husrmVZlQDi+mvAHwG3h46i0Xm4UHghdAZ1t7UTow/ccErutw14q2+Ua9UvlWtVJ/Cpq1lWpXfE9a3AIuAfQ0fR/lt2QHFL6AzSz+fmjl+5/wMDBoDF5Vr17xqZSWpXllVpV3F9iLj+FeASHM/allb29hRDZ5AALt+/gQFbgM+Wa9Xrm5FJakeWVem9xPV/As4C3godRSOzwcNVyoj9GBjwOrCwXKve1cxcUruxrEp7EtfvAOYBawIn0TC9ks+/MhhFk0LnkN4xgoEBCTC3XKsubUUuqZ1YVqW9ievLgbnALwMn0TA8Uux9PnQGaXfLZ+Zm3XPMXgcG/Ag4vlyrPteqTFI7saxK+xLXXwUWAt8LnET7sLRYdNuGMun6T+QXrB/Pw7v9eAD4y3Ktek65VnX8s7QHllVpOOL6AHH9y8C5wNbQcfTeVvT2FkJnkPbkrz6f799lYMBrwGnlWvU7ITNJ7cCyKo1EXL8ZmA94l2cGvTwmPy10BmlPtvZE475eyUcDEQ+R7k+9N3QmqR1EQ0PeNSyNWFyaAPwYWBA6ilKv5XKvLpg+9dDQOaR9+Ffg4qSSeB+wNEyurEr7I65vAD4OfBvvY82EXxULa0JnkPZiC3BhUkkusKhKI2NZlfZXuo/1m6SldV3oON1uabGwOXQGaQ9WAccnleSG0EGkdmRZlUYrrt8HzAaWBE7S1X5T6O0JnUF6Dz8G5iSVZKSTrCTt5J5VqVHiUkQ6pvVKwOLUYsf0TXthRxR5wEpZUQe+mFSSfw8dRGp3llWp0eLSHOA24IjQUbpFPRfV50+fVgqdQ9rpPqCSVJK1oYNIncBtAFKjxfXHgWOBfwsdpVv8ulBw8o+yYBvwVeBki6rUOGNCB5A6UlzfBFSIS7cD/wxMCZyooz14QPGN0BnU9ZYD5yaV5Lehg0idxpVVqZni+n8CHwZuDh2lkz3R2zs2dAZ1rUHg74E/sKhKzeGeValV4tIZpKusk0JH6TTH9k1b/XYUzQidQ11nNXBeUkmWhg4idTJXVqVWiet3AkcDt4SO0kk2R9Gmt6EvdA51lUHgGmC2RVVqPldWpRDi0pmkv+xcZR2l+4uFFV+YPHF26BzqGsuBi5JK8qvQQaRu4cqqFEJc/ylwFGlhdVzrKDx4QHFj6AzqCpuBrwBzLapSa7myKoUWl+YCVwMfCR2lHZ15+ORlq3p65oXOoY72U+BLSSV5MXQQqRtZVqUsiEs54M+By4HxgdO0lTnTpz27PRfNDJ1DHel54AtJJbk7dBCpm1lWpSyJSxOAfwAqQBQ4TeZtiaK3Pjp9aoEockuTGmkH8F3gb5JK8lboMFK3s6xKWRSX5pNuDegPHSXLlhULyUWTJ/pvpEa6n3Q11TtTpYxwNULKori+FDiGdGvA+sBpMmtpsfB66AzqGE8Bn04qyccsqlK2WFalrIrrA8T1HwBHAt8iPY2sXfy6UPD/MI3WOuDPgFnuTZWyyW0AUruIS5OAGFgMjAkbJhs+Mn3q01tzuQ+GzqG2tIl0f/h33ZcqZZtlVWo3celDpLPIzwgdJaRtEVvnTp82hiiyuGsk3gauIz08tSF0GEn7ZlmV2lV6COsKYH7oKCE8Wuh96sIpk44OnUNt5SfAZUkleSZ0EEnD54qE1K7SQ1gnEpdOBr4JLAicqKWWFou/C51BbWMJ8G0nT0ntyZVVqVPEpQWkpfXk0FFa4ZzDJj34ZG/viaFzKLMGgduBy5NKsiJ0GEn7z7IqdZq4NI+0tC4MHaWZPjp9anVLLlcOnUOZ8zZwM3BlUkmeDh1G0uhZVqVOFZeOIy2tp4WO0mjbYfucvmkQRT2hsygztgL/AnwnqSRrQ4eR1DiWVanTxaXfAy4BPgcUwoZpjN/09tTOO2zyUaFzKBM2AdeQXkH1cugwkhrPsip1i7g0AbgIuBiYHDjNqHz/4NKD140vuV+1u60FrgWuTSqJk8ykDmZZlbpNXOoBzgG+DPx+2DD753NTJj24suDhqi40BNwLXAUsSSrJQOA8klrAsip1s/QGgUuATwP5sGGG77jpU598M5f7cOgcapnXgRuBa5JKsip0GEmtZVmVBHHpMOB84PPAzLBh9m4H7Dimb9oOoqgj9t9qrx4HrgZuTSrJltBhJIVhWZX0rrgUAR8DFgOfJYMHspKenmf+9PDJR4bOoabZCvwHcLWX+EsCy6qkPYlL40lvELiQDO1tvfbg9y29avzBXTlitoMNAP8D/BC4I6kkbwTOIylDLKuS9i0uHUtaXM8CpoWMUpky8YEnCoWTQmZQwzxGWlBvSyrJS6HDSMomy6qk4Uu3CRwPnE1aXKe0OsIJ75+abMrn+lv9XDXMKtKCeosTpiQNh2VV0v6JSzngRGAR8MfAxGY/chAGZ/dN20IUHdjsZ6mh1pPuQ70lqSSPhg4jqb1YViWNXlzKkx7M+gxwOjCjGY+p9ox9dtHhUzJ9W4GA9D7Ux4AlwN3AE0kl8ZeNpP1iWZXUeHHpKNLS+knS1dexjXjb60vvW/a9Qw6e14j3UsNtIr2w/27gZ+5BldQollVJzRWXDgJOIS2upwGH7e9bXTh54v2PFgsLGhVNo7aKtJwuAR5IKsn2wHkkdSDLqqTWikuzgAXASTtfw97rOv/9h6+o5/OzmxVN+7QeuH/n65cekJLUCpZVSWGlWwZO4t0CO/W9/tgQDM3qm7aJKHpfK+N1udXAMnYW1KSSPBM4j6QuZFmVlC1xaQZpaT0OmAvMAnpWjR275sypU/pCRutw24EngIdIC+pD7juVlAWWVUnZFpd6gFl3jjvw6G9MOHQe6TStfqAYNFd7ewlYAazc+XUFUEsqyY6gqSTpPVhWJbWd/pv688CHSIvrLOCDwBHATOCAcMkyZxtQ5d1iuhJYkVSSDUFTSdIIWFYldYz+m/oj0tsGjtj5OnKX748AOm2YwBDpKulqYM0uX9/5/nlXSyW1O8uqpK7Rf1P/FKAPmER6C8E7Xyfu9rPxQBQmJZDuH30VeG23rxuA53m3lD6fVJKtgTJKUktYViVpN/039Y8BJpAW1xLp/tgiUNjH94Wdb/H2MF/bgY3sVkyTSrK5yX9FSWobllVJkiRlVi50AEmSJGlPLKuSJEnKLMuqJEmSMsuyKkmSpMyyrEqSJCmzLKuSJEnKLMuqJEmSMsuyKkmSpMyyrEqSJCmzLKuSJEnKLMuqJEmSMsuyKkmSpMyyrEqSJCmzLKuSJEnKLMuqJEmSMsuyKkmSpMyyrEqSJCmzLKuSJEnKLMuqJEmSMsuyKkmSpMyyrEqSJCmzLKuSJEnKLMuqJEmSMsuyKkmSpMyyrEqSJCmzLKuSJEnKLMuqJEmSMsuyKkmSpMyyrEqSJCmzLKuSJEnKrP8HdH+trs1uwdMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# New dataframe to hold category value count\n",
    "df_byCat = df['Category'].value_counts().to_frame('Count').rename_axis('Category').reset_index()\n",
    "\n",
    "df_byCat \n",
    "labels = df_byCat['Category']\n",
    "plt.figure(figsize=(12,9))\n",
    "plt.pie(df_byCat['Count'], autopct='%1.1f%%',shadow=False, startangle=90)\n",
    "plt.axis('equal')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}