src/data_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "[nltk_data] Downloading package stopwords to /Users/hugo/nltk_data...\n",
      "[nltk_data]   Package stopwords is already up-to-date!\n",
      "[nltk_data] Downloading package wordnet to /Users/hugo/nltk_data...\n",
      "[nltk_data]   Package wordnet is already up-to-date!\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from modules.data_samples import get_data\n",
    "from modules.data_cleaning import data_cleaning_reviews\n",
    "from modules.utility_functions import create_wordcloud\n",
    "from modules.data_cleaning import data_cleaning_books\n",
    "import random\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set_theme(style='darkgrid', context='talk')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get data sample"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books_sample,df_book_authors,df_book_reviews, df_book_genres  = get_data()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Save samples to CSV for better handling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books_sample.to_csv('../data/sample_data/books_sample.csv', index=False)\n",
    "df_book_authors.to_csv('../data/sample_data/authors_sample.csv', index=False)\n",
    "df_book_reviews.to_csv('../data/sample_data/reviews_sample.csv', index=False)\n",
    "df_book_genres.to_csv('../data/sample_data/genres_sample.csv', index=False)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Get data sample for processing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books_sample   = pd.read_csv('../data/sample_data/books_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_authors_sample = pd.read_csv('../data/sample_data/authors_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reviews_sample = pd.read_csv('../data/sample_data/reviews_sample.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_genres_sample = pd.read_csv('../data/sample_data/genres_sample.csv')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Data exploration and cleaning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 23382 entries, 0 to 23381\n",
      "Data columns (total 14 columns):\n",
      " #   Column                Non-Null Count  Dtype  \n",
      "---  ------                --------------  -----  \n",
      " 0   text_reviews_count    23382 non-null  int64  \n",
      " 1   country_code          23382 non-null  object \n",
      " 2   language_code         13621 non-null  object \n",
      " 3   average_rating        23382 non-null  float64\n",
      " 4   description           23382 non-null  object \n",
      " 5   num_pages             23382 non-null  int64  \n",
      " 6   publication_year      23382 non-null  int64  \n",
      " 7   image_url             23382 non-null  object \n",
      " 8   book_id               23382 non-null  int64  \n",
      " 9   ratings_count         23382 non-null  int64  \n",
      " 10  title                 23382 non-null  object \n",
      " 11  title_without_series  23382 non-null  object \n",
      " 12  author                23382 non-null  int64  \n",
      " 13  name                  23382 non-null  object \n",
      "dtypes: float64(1), int64(6), object(7)\n",
      "memory usage: 2.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df_books_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>text_reviews_count</th>\n",
       "      <th>country_code</th>\n",
       "      <th>language_code</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>description</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>publication_year</th>\n",
       "      <th>image_url</th>\n",
       "      <th>book_id</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>title</th>\n",
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       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.33</td>\n",
       "      <td>A love story about a girl who comes back from ...</td>\n",
       "      <td>208</td>\n",
       "      <td>2015</td>\n",
       "      <td>https://images.gr-assets.com/books/1415581655m...</td>\n",
       "      <td>22965343</td>\n",
       "      <td>52</td>\n",
       "      <td>Who Is Mackie Spence?</td>\n",
       "      <td>Who Is Mackie Spence?</td>\n",
       "      <td>8456833</td>\n",
       "      <td>Lin Kaymer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.83</td>\n",
       "      <td>Military service was once taken for granted as...</td>\n",
       "      <td>256</td>\n",
       "      <td>2006</td>\n",
       "      <td>https://s.gr-assets.com/assets/nophoto/book/11...</td>\n",
       "      <td>80118</td>\n",
       "      <td>72</td>\n",
       "      <td>AWOL: The Unexcused Absence of America's Upper...</td>\n",
       "      <td>AWOL: The Unexcused Absence of America's Upper...</td>\n",
       "      <td>45600</td>\n",
       "      <td>Kathy Roth-Douquet</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>US</td>\n",
       "      <td>eng</td>\n",
       "      <td>3.95</td>\n",
       "      <td>Raenef is the black sheep of the demon court, ...</td>\n",
       "      <td>192</td>\n",
       "      <td>2003</td>\n",
       "      <td>https://images.gr-assets.com/books/1288648957m...</td>\n",
       "      <td>29497</td>\n",
       "      <td>4399</td>\n",
       "      <td>Demon Diary, Volume 01</td>\n",
       "      <td>Demon Diary, Volume 01</td>\n",
       "      <td>16589</td>\n",
       "      <td>Kara</td>\n",
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       "      <th>3</th>\n",
       "      <td>207</td>\n",
       "      <td>US</td>\n",
       "      <td>eng</td>\n",
       "      <td>4.02</td>\n",
       "      <td>Set in South Carolina in 1964, The Secret Life...</td>\n",
       "      <td>333</td>\n",
       "      <td>2003</td>\n",
       "      <td>https://images.gr-assets.com/books/1444392146m...</td>\n",
       "      <td>6081433</td>\n",
       "      <td>3607</td>\n",
       "      <td>The Secret Life of Bees</td>\n",
       "      <td>The Secret Life of Bees</td>\n",
       "      <td>4711</td>\n",
       "      <td>Sue Monk Kidd</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>US</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.09</td>\n",
       "      <td>Cathy Cramer is a former lawyer and investigat...</td>\n",
       "      <td>305</td>\n",
       "      <td>2013</td>\n",
       "      <td>https://images.gr-assets.com/books/1356054466m...</td>\n",
       "      <td>16182612</td>\n",
       "      <td>47</td>\n",
       "      <td>Truth Stained Lies</td>\n",
       "      <td>Truth Stained Lies</td>\n",
       "      <td>19811</td>\n",
       "      <td>Terri Blackstock</td>\n",
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      "text/plain": [
       "   text_reviews_count country_code language_code  average_rating   \n",
       "0                  27           US           NaN            3.33  \\\n",
       "1                  10           US           NaN            3.83   \n",
       "2                  61           US           eng            3.95   \n",
       "3                 207           US           eng            4.02   \n",
       "4                   6           US           NaN            4.09   \n",
       "\n",
       "                                         description  num_pages   \n",
       "0  A love story about a girl who comes back from ...        208  \\\n",
       "1  Military service was once taken for granted as...        256   \n",
       "2  Raenef is the black sheep of the demon court, ...        192   \n",
       "3  Set in South Carolina in 1964, The Secret Life...        333   \n",
       "4  Cathy Cramer is a former lawyer and investigat...        305   \n",
       "\n",
       "   publication_year                                          image_url   \n",
       "0              2015  https://images.gr-assets.com/books/1415581655m...  \\\n",
       "1              2006  https://s.gr-assets.com/assets/nophoto/book/11...   \n",
       "2              2003  https://images.gr-assets.com/books/1288648957m...   \n",
       "3              2003  https://images.gr-assets.com/books/1444392146m...   \n",
       "4              2013  https://images.gr-assets.com/books/1356054466m...   \n",
       "\n",
       "    book_id  ratings_count                                              title   \n",
       "0  22965343             52                              Who Is Mackie Spence?  \\\n",
       "1     80118             72  AWOL: The Unexcused Absence of America's Upper...   \n",
       "2     29497           4399                             Demon Diary, Volume 01   \n",
       "3   6081433           3607                            The Secret Life of Bees   \n",
       "4  16182612             47                                 Truth Stained Lies   \n",
       "\n",
       "                                title_without_series   author   \n",
       "0                              Who Is Mackie Spence?  8456833  \\\n",
       "1  AWOL: The Unexcused Absence of America's Upper...    45600   \n",
       "2                             Demon Diary, Volume 01    16589   \n",
       "3                            The Secret Life of Bees     4711   \n",
       "4                                 Truth Stained Lies    19811   \n",
       "\n",
       "                 name  \n",
       "0          Lin Kaymer  \n",
       "1  Kathy Roth-Douquet  \n",
       "2                Kara  \n",
       "3       Sue Monk Kidd  \n",
       "4    Terri Blackstock  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(23382, 12)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove unnecesary columns\n",
    "df_books_sample.drop(columns=['language_code','country_code'], inplace= True)\n",
    "df_books_sample.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text_reviews_count      0\n",
       "average_rating          0\n",
       "description             0\n",
       "num_pages               0\n",
       "publication_year        0\n",
       "image_url               0\n",
       "book_id                 0\n",
       "ratings_count           0\n",
       "title                   0\n",
       "title_without_series    0\n",
       "author                  0\n",
       "name                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books_sample.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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h/UMhv7FmU0V1LRIzih3cIqKW40z9RERkVa1OjwSzYGdQjGPzxwBA5aGA0k2BQT0DcfzGcOXJ5ALcOahrs+rrdAZUVta0ZhOJbMKAjIiIrErJKkOtzjgc6KqQoafZlBOOopBJoa3VY1BMgBiQHTmfg4dH9YC7a+O/0vy83CGXc2CI2icGZEREZNUls96x6K5ekMvaRzBTXF6DrNxyKORS1OoMqNUZ8M32C03mt/3h7ij4e9m+/iZRa2of311ERNTumOdm9Q73cWBL6pNJJehqNnP/Nc7aTx0cAzIiIqpHo9UhLadM3O7VzgIyAOgWVNcjVlyhRVmV1oGtIWoZBmRERFRPcmapuJi4u6sc4UGOn+7iZl5KF3gpXcTta7kVDmwNUcswICMionrM88d6hnlD6oDFxJujW5BKfH0trwJ6A+cko46JARkREdVzqR3nj5nrEqCE7EawqNUZkFVQ5eAWEdmGARkREVmo1NTiqlmSfHsOyFzkMovk/rTsMghccJw6IAZkRERkITmzFKaYRuWuQKhZwNMeRYSoxdellVoUV3DiV+p4Osw8ZNu2bcOmTZuQmJiI6upq+Pn5YdiwYZg5cyaioqIsym7YsAHz5s1r8FzR0dGIj4+32KfT6bB582Zs2LAB6enpkMvl6N+/P2bOnInY2Fir5yktLcXKlSuxe/duZGdnQ61W46677sKcOXMQFhbW8g9NROQAV7LqFhOPCfOGtB0sl9QYL6UL/NSuKCwzBmJp2eXw7cn5xqhjafcBmSAIePXVVxEfHy8GSb6+vkhMTMTWrVuxc+dOfPbZZ7jrrrvEOhcvXgQAxMbGIjAwsN45Q0JC6r3HG2+8gfj4eHh5eSEuLg4lJSU4cuQIDh8+jPfffx+PPPKIRZ2SkhJMnToVKSkp6NatG0aNGoW0tDT8+OOP2L17N9atW4devXq1whUhImpdKZl1AVlUF3UjJduP7iFqFJYZ193MLqxEX60P3Fza/a84IlG7v1u3b9+O+Ph4BAQEYOXKlWKQo9fr8emnn+LLL7/E66+/jt27d0OpNHarmwKyBQsWoHv37k2+x+bNmxEfH4/evXtj1apV8Pb2BgAcPXoUs2bNwoIFCxAXF2cRyC1atAgpKSmYOHEi3nvvPchkMgDAl19+iY8//hivvfYatm3bBqmUo8JE1HHo9AaL+cd6dPFyYGuaL9jXA24uMmi0eggCkHG9Aj27eTu6WUTN1u6jhU2bNgEAXnnlFYseJ5lMhpdeegnR0dEoLCzE4cOHAQC1tbVISkqCp6cnIiIimvUeK1asAADMnTtXDMYAYPjw4Zg+fTpqamqwdu1acX9mZibi4+OhVqvx1ltvicEYAMyePRsDBw5EUlISDh48aOvHJiJyiGt5FdDeWL9SJpU0uRxReyG9qa3p18thMDC5nzqOdh+QqdVqREVFYciQIfWOSSQSsQcsN9e4yGxKSgq0Wi369esHSTPyHlJSUnD16lX4+flh8ODB9Y6PHz8eALBnzx5x3/79+6HX6xEXFweVStWsOkREHYF5/lhEsCcUclkjpduX8CBPmKZLq6nVI6eQU2BQx9Huhyw/++yzBo/p9XpxeNI0nGjaDgoKwpIlS7Bv3z5kZ2fDx8cHo0aNwnPPPYegoCDxHElJSQCMif7WAjjT/qtXr6K6uhru7u5inZiYGKvt6tGjBwAgMTHxVj8uEZFDpWSZ5491jOFKE1cXGUL9lcjMrwQApOWUoUs7f0KUyKTd95A15rvvvkNWVha8vb0xfPhwAHUB2datW7Fx40aEh4dj8ODBqK6uxvfff4+HH34Yly9fFs+Rl5cHABZBmjlXV1eo1WoYDAYUFBQ0q47pQQJTOSKijsK8h6yj5I+Z6242BUZReQ1KOQUGdRDtvoesIUePHsWHH34IAHj11VfFhP6EhAQAxmHDRYsWiUOK5eXleOutt7Br1y688MIL2LFjB+RyOSorjX9Jubk1/Ii0q6srAIhlq6qM3eDu7u5Wy5vOZSpnTwqFDAEBTed0NKcM1cfrZjteO9u0p+tWUFItTh0BAEMHhMLPy/rPOQCQyaRQKl1v+X0kEmPKiS11TRqqq1S6wi+9GIVlGgDA1fxKDAtSi+11cZG3q2ve1pz5s7dUa1+7DtlDtm/fPsyePRtarRaTJ0/GpEmTxGOrV69GfHw8PvroI4v8Lk9PT3zwwQcIDAxEeno6Dhw4AABiQn5z8s1MmluHs0UTUUeSmFEkvg70cW80GGvPos2ersy4Xo4arc5xjSFqpg7XQ7Z27Vp88MEH0Ov1mDp1Kt5++22L425uboiOjrZaV6lUYtiwYdi+fTvOnz+PMWPGiD1rGo2mwfesqTH+xWjqETPVqa6utlredK6GetBaorZWj5KShnveTBF8fn55g2WoPl432/Ha2aY9XrfTl3LF1xHBno22zcvLHXq9AZWVtz4kKAjGP1htqWvSWF0/T1e4KqSoqTXAYBBwKa0Q0V29odcboNXqUFpq/Wd3Z9Ye77eOoqXXztvbAwpF0w/HdJiATKfT4d1338WGDRsgkUjw8ssvY/bs2bd8HlPyvymYMuWB5efnWy1fU1ODsrIySCQSBAQENKuOKXfM2qS0RETthVLpCrm8bqDkWn6F+LpvlD+8Gukhk8tlkErb5wz+MqkE4cGeSLpmzIdLzynvcA8okPPpEAGZRqPBnDlzcOjQIbi7u2Px4sW455576pXLzc3FJ598gsrKSnz88ccW84OZ5OTkAACCg4MBAD179gQAJCcnW33v5ORkCIKAbt26wcPDo1l1UlJSLMoREbVHcrkUAiQoLK2G3mCwSOgP8HZHQWnDIwfBfu376cWIYE9xTc5qrR7XOQUGtXPtPiDT6/ViMObn54cvv/wSAwYMsFrW09MTO3bsQE1NDY4dOyY+eWlSUlKCffv2QSKRYMSIEQCA8PBwREREID09HWfOnMHAgQMt6uzcuRMAMGrUKHHfiBEjIJVKcejQIVRVVYmBmsmuXbsAAKNHj27JRycianWFpdXYcfAKSiu10NYaJ4SVALiYko/EtMIG6027r18btdA2bi5yhPopkVVgmgKDQ3XUvrX7pP4vvvgChw4dgoeHB1avXt1gMAYAHh4eeOihhwAA77zzDrKyssRjpaWlePHFF1FeXo4HHngAkZGR4rFp06YBAN5++21xagvA+CTnmjVroFAo8PTTT4v7g4KCMG7cOJSUlGD+/PnQarXisRUrVuDMmTPo0aMHxo4d2+LPT0TUFkrMpofwVLpAJmv3vx6a1D2k7qm4wjINrhdWOrA1RI1r1z1kpaWl+Ne//gXAmI9lWuLImvvvvx8jR47E66+/jkuXLuHcuXOYMGECbr/9dri5ueH48eMoLy/H4MGDMX/+fIu6kydPxoEDB/DLL79g/PjxiI2NRXl5OU6cOAFBELB48eJ6C5LPnTsXCQkJ2L59O06cOIH+/fsjLS0NSUlJUKvVWLZs2S09uUlE5Egl5XUBmY/KxYEtsR8fT1eoPRQoq6oFABw8k41+kX4ObhWRde06IDt27Jg4l1d6ejrS09MbLNu7d2+MHDkSKpUK69atE6e/OHnyJGQyGbp3744HHngAU6dOhUKhsKgrlUqxfPlyfPvtt9iyZQsOHToElUqFuLg4zJo1C0OHDq33foGBgdi4cSM+//xz7N27F3v37kVgYCAefvhhzJkzB2FhYXa9FkREramkoq6n31tl+/xg7YlEYkzuP59qnM7j6PlsPHN/Hwe3isi6dh2QjRs3zmJW/eZycXHBs88+i2effbbZdRQKBWbMmIEZM2Y0u46vry/mzp2LuXPn3nIbiYjaC73egLJKs4DMs3MEZADQNUCFhPRi6A0CKjU6HL2Qg4HsJaN2qOMnCRARUYuUVmphmsZaJpXA00PRaPmORCGXoot/3ROh//vtqgNbQ9QwBmRERE7OfLjSS+kCaSfLf40IrkvuT8woxvUiToFB7Q8DMiIiJ1ds9oRlZxquNPFSuVj0+h25kOPA1hBZx4CMiMjJmT9h6d1JnrA0J5FIEBZYt7bx0QvXYeBaw9TOMCAjInJiVZpaVGrqFt/26SRPWN6sa4BSHIotLKvB5YxiB7eIyBIDMiIiJ5ZxvW4Ge4VcCg+3dv3wvc3cXOToE+krbh++cN2BrSGqjwEZEZETS8suE197q1w69YTWw/vVTfB98nI+NFpdI6WJ2hYDMiIiJ5aRUxeQddbhSpPbov2hvNEDWFOrx5mUgiZqELUdBmRERE4s3Swg64xPWJpTyGWI7Rcsbh+/lOfA1hBZYkBGROSkCks1FouKd8YnLG92Z/9Q8fX51EJU13DYktoHBmRERE4qJbNEfO3uIoObS+dM6DfXL8oPKnfjnGQ6vYDTyfkObhGREQMyIiInZR6QdfbhShO5TIrBPQPE7WMctqR2ggEZEZGTSsksFV97d/KEfnN39AoUX19MK0KlptaBrSEy6vz900REVI9BEHDFvIfMCfLH1EoXyOUyDO0XAi9VAkortNAbBCReK8WYIWFN1tfpDKisrGmyHJEtGJARETmhvOJqixn6naGHTCGXQac3oLhCi4HRAdh/OgsAcOhsNgZEBzRa18/LHXI5B5Wo9TAgIyJyQuYTwqrcFVA4SbBRVKbBjoNXUGP2dOWF1EJs+yUZclnD1+APd0fB38utLZpITso5vgOJiMhCqllA5uPZ+Ycrb+bv5Qa5zLgqgd4gIL9E4+AWkbNjQEZE5IRSzSeEdYLhyptJpRIE+riL29eLqhzYGiIGZERETqdWZ8C1vLpFxX2cZMqLmwX7eoivc4urIAiCA1tDzo4BGRGRk7mWVwGd3hh8yGVSqD2cb8gSAIJ83GFaS11ba0BROZ+gJMdhQEZE5GTSzIYruwV7QiqVOLA1jqOQy+CnrkvU57AlORIDMiIiJ2Oe0B8RonZgSxzPYtiyqNqBLSFnx4CMiMjJmPeQdXfygCzIty6xv6K6FlUaLjZOjsGAjIjIiVRqai2G5rqHOndApnRTQOlWNyVnXgl7ycgxGJARETmR9Jy6pytV7gr4e7s3Uto5BJlNf5FXzICMHIMBGRGREzGffyw6zBsSiXMm9Jszn48sv6QaBgOnv6C2x4CMiMiJmC+ZFB3m7biGtCN+ajfxSVO9QUBROWftp7bHgIyIyEkIgmDRQ9ajq7fjGtOOyGRS+JtNf8FhS3IEBmRERE6iqKwGZZVacbtHVy8HtqZ9CWQeGTkYAzIiIidh3jsW4O0GLydcw7Ih5gFZWVUtNFpOf0FtiwEZEZGTMM8fc/b5x26mdJPD3VUmbheUMI+M2hYDMiIiJ2HeQxbJgMyCRCJBgJfZ05alDMiobTEgIyJyAnqDAenXzQKyUOaP3czfuy6xv6CkGoLA6S+o7TAgIyJyAtkFVdDWGgAAUokE3YJUDm5R++PvVReQVWv1qOQyStSGGJARETkB8/UruwYq4aKQNVLaObm5yOHpoRC3C7iMErUhBmRERE7gSlap+Jr5Yw0LMOslYx4ZtSWbA7Ls7GwUFhY2q2xSUhL27t1r61sREVELpZgFZFFdmD/WEPO1PQtKNcwjozZjc0A2ZswYvPjii80q++abb+LNN9+09a2IiKgFKqprkVNYJW5zQtiG+andYFrds1ZnQKnZRLpEranZAZnBYBC/9Ho9AOMyHIIgWBy7udy1a9dw7do1VFdzLJ6IyBHMe8fUHgoEmvUCkSWFXApvz7oJcws5bEltRN6cQleuXMGDDz4oBmKAcc6WU6dOoU+fPs16o169etnWQiIiapGUzLqArEdXb0gkkkZKk5/aFcXlNQCAwjINh3ipTTSrhywqKgqPPfaY2CNmGlM3327sy83NDa+88kqrfhAiIrIuJbNEfN2DwUWT/MwS+wvLaphHRm2iWT1kAPB///d/uOeeewAYA7Hp06cjJiYGc+fObbCOVCqFh4cHIiIi4OHh0fLWEhGRBaXSFXJ5w39b1+oMSL9eLm4P6hUIrxsz0svlMkil7C27ma9nXUBWqzOgrKrWga0hZ9HsgEylUmHo0KHidmhoKCIjIy32ERFR25LLpRAgQWGp9Tzd1KxSaHXGCWHlMinUKlcU3MiLCvZTtlk7OxKFXApvlQtKKowJ/cwjo7bQ7IDsZm09jcW2bduwadMmJCYmorq6Gn5+fhg2bBhmzpyJqKioeuWPHDmCr7/+GomJidBoNIiMjMQTTzyBRx991Gr+hE6nw+bNm7Fhwwakp6dDLpejf//+mDlzJmJjY622qbS0FCtXrsTu3buRnZ0NtVqNu+66C3PmzEFYWJjdrwERkTWFpdXYcfCK1WMWCf1KBXYdSRO3p93Xr9Xb1lH5qd3qArIyBmTU+tr9xLCCIOCVV17B66+/jlOnTiEqKgojRoyATCbD1q1bMXHiRBw6dMiizvr16zFjxgwcP34cffr0QWxsLK5cuYK5c+danX5DEAS88cYbmDdvHjIzMxEXF4eYmBgcOXIE06dPx+bNm+vVKSkpwZQpU/DVV19Br9dj1KhR8PHxwY8//oiHHnoIiYmJrXZNiIiaq6isRnzta/b0IDXOIo+M85FRG7C5hwwAcnNz8c033+DUqVMoKyuDXq9v8KaVSCT4+eefb/k9tm/fjvj4eAQEBGDlypXi05p6vR6ffvopvvzyS7z++uvYvXs3lEol0tLSsHDhQqhUKqxdu1Z8CjQ7OxvTp0/Hli1bMHLkSDEfDgA2b96M+Ph49O7dG6tWrYK3tzcA4OjRo5g1axYWLFiAuLg4hISEiHUWLVqElJQUTJw4Ee+99x5kMuMyJF9++SU+/vhjvPbaa9i2bRuk0nYf8xJRJyUIAorK63p3fNVujZQmc37quuBVqzMgp6ASAZwuhFqRzdFCVlYWJk6ciDVr1uD8+fPIyMhAZmYmsrKyGvyyxaZNmwAAr7zyisXUGTKZDC+99BKio6NRWFiIw4cPAwBWrlwJvV6PZ555xmJKjtDQUMybN08sY27FihUAgLlz54rBGAAMHz4c06dPR01NDdauXSvuz8zMRHx8PNRqNd566y0xGAOA2bNnY+DAgUhKSsLBgwdt+sxERPZQqdGJC4oD7CG7FQq5DGqli7iddK3EcY0hp2BzD9kXX3yBwsJCKJVKPPzww4iKioKbm/3/+lKr1YiKisKQIUPqHZNIJOjevTuSk5ORm5sLANi3bx8A4Pe//3298nFxcfD09MT58+eRm5uLoKAgpKSk4OrVq/Dz88PgwYPr1Rk/fjy++uor7NmzB6+//joAYP/+/dDr9YiLi4NKpbJa58yZM9izZw9GjhzZos9PRGSrovK64UqVu4ILit8if7Urym7M1J/CgIxamc0B2cGDByGRSPDNN9/gtttus2ebLHz22WcNHtPr9bh48SIAICQkBAUFBSgsLIRCoUBkZGS98jKZDJGRkTh79iwuX76MoKAgJCUlAQCio6OtJvub9l+9ehXV1dVwd3cX68TExFhtV48ePQCAeWRE5FDFZsno7B27db5qN6TmGKcMSckqcWxjqNOzOSArKipCjx49WjUYa8p3332HrKwseHt7Y/jw4cjIyAAA+Pv7N5i7FRgYCADIy8uz+DcoKMhqeVdXV6jVapSWlqKgoABhYWFN1rn5PexJoZAhIMCzyXLNKUP18brZjtfONva4bjKZFEpl/YDL9JQgAIQEqOqVkUiMIw3W6jbFUXVN2uJ9u8plOHE5HwBQXFaDkkotAjv4fc7vU9u19rWzOYfM19cXtbWOmyzv6NGj+PDDDwEAr776KpRKJaqqjIvnNjZ06upq/EY0la2srGx2HVNZU113d+sJnqZzmcoREbU1ba3eYmFsfyak3zJ3VzmU7gpx+1JakQNbQ52dzT1kcXFx2L59O65evYpu3brZs01N2rdvH1566SVotVpMnjwZkyZNAgCxV6w567SZngY1JeTfytpuza3TGo9J19bqUVLScKBniuDz88sbLEP18brZjtfONva6bl5e7tDrDaisrLHYn1tU93PCRSGFVKhfRhCMP6du3t8cjqpr0lbv66NyQWW1sfPhwpUC9AnrmEtP8fvUdi29dt7eHlA0I3/T5h6yv/zlL3B3d8cbb7yBoqK2+6th7dq1mDNnDjQaDaZOnYr58+eLx5RK46zTGk3Dk/jV1Bi/EU1LOd1KHVOPmKlOdbX1mbFN52qoB42IqLWZJ/T7erpyQXEbmU8Vcjmj2IEtoc7O5h6yQ4cOYcKECdi4cSNGjRqFfv36ISgoCAqFwmp5iUSCJUuW2NxQnU6Hd999Fxs2bIBEIsHLL7+M2bNnW5Qx5XQVFBRAEASrP4BMeV2mPC9Tnfz8fKvvW1NTg7KyMkgkEgQEBDSrzs3vQUTU1oosEvo5/5itzB+GyLhehuoaHdxdWzSFJ5FVNt9V8+fPFwMerVaLU6dOWS0nkUjE4MjWgEyj0WDOnDk4dOgQ3N3dsXjxYouJXU28vb0RFBSE3NxcZGRkICIiwuK4Xq9HamoqAKBnz54W/yYnJ1t97+TkZAiCgG7duom9ak3VSUlJsShHRNSWDAbBIqHfR80nLG3l6aGAQiZFrd4AgwBcyS5Fv+5+jm4WdUI2B2QPPfRQm3SB6/V6MRjz8/PDl19+iQEDBjRYfuTIkdi4cSP+97//YebMmRbHDh8+jPLycvTq1QvBwcEAgPDwcERERCA9PR1nzpzBwIEDLers3LkTADBq1Chx34gRIyCVSnHo0CFUVVWJgZrJrl27AACjR4+29WMTEdmstFILvcGYwyqVAN4qlyZqUEMkEgl81K7IKzamqKRkMiCj1mFzQLZ48WJ7tqNBX3zxBQ4dOgQPDw+sXr0a0dHRjZafOnUqNm/ejBUrVmDo0KFigJWdnY2FCxcCAGbNmmVRZ9q0aXj33Xfx9ttv49///jf8/f0BGJ/kXLNmDRQKBZ5++mmxfFBQEMaNG4ddu3Zh/vz5eP/99+HiYvyBt2LFCpw5cwY9evTA2LFj7XUZiIiazXy40kvlChmXcGsRX8+6gCw5s7SJ0kS2adcD4aWlpfjXv/4FwJiPZVriyJr7778fI0eORK9evfDSSy9h6dKlmDJlCoYOHQo3Nzf89ttvqKqqwqRJkzBhwgSLupMnT8aBAwfwyy+/YPz48YiNjUV5eTlOnDgBQRCwePFii3UsAeMySwkJCdi+fTtOnDiB/v37Iy0tDUlJSVCr1Vi2bBmTaInIISwS+jlc2WLmif1pOWUwGARIpfz5TvbVrgOyY8eOiXN5paenIz09vcGyvXv3FpcpmjlzJqKiorBq1SqcO3cOEokEUVFRmDx5Mh5++OF6daVSKZYvX45vv/0WW7ZswaFDh6BSqRAXF4dZs2Zh6NCh9eoEBgZi48aN+Pzzz7F3717s3bsXgYGBePjhhzFnzhyEhYXZ5yIQEd0CQRBQVGb+hCUT+lvKW+UCicQ4bYZGq0dOYSW6BNRfNo+oJWwOyHr37n1L5SUSCRISEm6pzrhx43D58uVbqmMyduzYWxoyVCgUmDFjBmbMmNHsOr6+vpg7dy7mzp1rSxOJiOyuqkaHmlq9uM0espaTy6To4q9CZn4FACA1p4wBGdmdzYkFgiDc0pfBYLBnu4mIyArz3jGlmxyuXFDcLiJC1eLrtOwyB7aEOiube8jWrFnT4LHq6mrk5eXhf//7Hw4ePIiXX34Zf/rTn2x9KyIiaibL/DEOV9pL91A1Dp3NBgCkMiCjVmBzQGYtr+pmkyZNwtKlS/HJJ5+gX79+uPPOO219OyIiagaLCWE5XGk33UPqlkzKzK9ETa2evY9kV63+LPScOXOgVCrxzTfftPZbERE5tVqdHuVVteK2+Szz1DIh/kq4uRgDMIMgIOM614Qk+2r1gMzNzQ3dunXD+fPnW/utiIicWrHZcKVCLoXK3fpSdnTrpFIJorrU9ZJx2JLsrdUDMr1ej9zcXNTW1jZdmIiIbGY53QUXFLe36DBv8XVaDgMysq9WDch0Oh2WLl2KwsJCREVFteZbERE5PU4I27p6mAVk7CEje7M5qX/q1KkNHhMEAVqtFteuXUNZWRkkEgkmTpxo61sREVETDAbBYsiSE8LaX4xZQFZYpkFppRZeSq4TSvZhc0B28uTJZpd94IEHMHnyZFvfioiImlBWVbeguIQLircKPy93eKtcUFKhBWCcj2xgtL+DW0Wdhc0B2V/+8pdGj8tkMvj4+GDw4MFNLghOREQtY54/5q1yhUzGBcVbQ/cQNU4nFwAAUnNKGZCR3bRaQEZERG3HYv4xTnfRaiJDzQIy5pGRHfFPKCKiDk4QBCb0t5HI0LqpL9JyymEQBAe2hjoTm3vITCoqKvDtt9/i559/RlpaGqqqquDh4YHw8HCMHDkS06dPh7e3tx2aSkRE1hSVaaDR1i0o7sOE/lYTEewJCQABQHWNDrlFVQjxUzq6WdQJtCggS0pKwuzZs5GTkwPB7K+EyspKJCQk4NKlS9i6dSu++OIL9OrVq8WNJSKi+q5kloqvlW5ycUZ5sj93VzlC/ZXIKqgEYBy2ZEBG9mBzQFZeXo5Zs2YhJycH/v7+eOSRR9CvXz+oVCqUlpbiwoUL2Lp1K3JycjBnzhxs27YNKpXKnm0nIiIAKVkl4msf5o+1uu6h6rqALKcMd/YPcXCLqDOwOSBbvXo1cnJyMGjQIKxYsQJqtdri+D333IOZM2di5syZOHv2LL7//nv86U9/anGDiYjIknkPma+aw5WtLTJUjUPncgAwsZ/sx+ak/p9//hkymQx///vf6wVjJmq1Gn//+98hkUiwc+dOmxtJRETWVWlqkZVfIW4zob/1RYbU/c7LzKuAtlbfSGmi5rE5IMvIyEBkZCS6du3aaLmwsDBERUXh6tWrtr4VERE1IOlqCUwpvAqZFJ5cULzVdQlQwkVh/PWpNwi4mlfRRA2iptkckAmCAIWied/4crmci4sTEbWCy1eLxdc+ai4o3hZkUikigjzF7TQOW5Id2ByQdenSBcnJySgqKmq0XFFREZKTkxESwqRHIiJ7S8yoC8g4IWzbiTAbtky/Xu7AllBnYXNANmLECNTW1mLevHnQ6XRWy+h0OsydOxd6vR4jR460uZFERFSf3mBAklkPGRP6205EcF0PWfp19pBRy9n8lOVTTz2FTZs2Yc+ePXjkkUcwefJk9O3bF56enigvL8fFixfx3XffITk5GSqVCk899ZQdm01ERJl5leKEsFxQvG2FmwVk1wurUF2jg7tri+daJydm890TFBSETz/9FHPmzMHly5exYMGCemUEQYBSqcQnn3yCoKCgFjWUiIgspWTVTXfhpXSBnAuKt5kgXw+4ucig0eohALiWV4GYMG9HN4s6sBZ99w4fPhzx8fF47LHHEBgYCEEQxK+AgAA89thj2Lp1K+688057tZeIiG5IziwRX3O4sm1JJRLLYcscDltSy7S4fzU0NBTvvvsuAOOSSRUVFVAqlZyVn4iolZn3kDGhv+2FB3si8WoJACb2U8u1uH+7vLwcGzduBAAolUoEBQVBpVLh3//+Nz777DMUFBS0uJFERGSpqEyDorIacZsTwra9iGA+aUn206KA7ODBgxg1ahTmz5+P3Nxci2MHDhzA8uXLMWHCBBw6dKhFjSQiIkvJZssl+Xu5wc2FCeVtLSLELLG/yJjYT2QrmwOyixcv4s9//jMqKysREREBrVZrcXz8+PHo27cvysrK8PzzzyMjI6PFjSUiIqMUs4Asqqu34xrixAK93S2erMxgLxm1gM0B2cqVK6HT6TBlyhT897//RVhYmMXxJ554Aj/88AOmTp2K6upqfPXVVy1uLBERGZnnj0V19XJgS5yX5ObEfgZk1AI2B2QnTpyAl5cX/vrXvza4VIdEIsHrr78OpVKJw4cP29xIIiKqo9HqcM1s/cQeXbwd1xgnxwliyV5sDsiKi4sRFhYGF5fGJyJ0dXVFeHg4k/uJiOwkNbsMhhsrinu4yRESoHRwi5wXl1Aie7E5IPPz86uXyN+Q4uJiToNBRGQn5vljPbv5QMoFxR3GfMb+vOJqVGpqHdga6shsDsj69OmDgoIC/PTTT42W27dvH3JyctC3b19b34qIiMwkm+WP9Qr3cWBLKMDLDUo3JvZTy9kckE2aNAmCIODNN9/Ehg0b6j1lqdVqsWXLFrz++uuQSCSYNGlSixtLROTsDAYBV8wCsp4MyBzq5sR+BmRkK5snrhkzZgweeOABbN++He+88w7ef/99REREwMPDA5WVlbh69Sq0Wi0EQcCECRNwzz332LPdREROKTO/QlxQXCqRIDrMGxXVnP/KkcKD1biYXgwASGNARjZq0UyCixcvRo8ePfD111+jvLwcSUlJFsc9PDzw1FNPYc6cOS1qJBERGZlPdxEWpIKbi5wBmYNxTUuyhxYFZFKpFDNnzsSMGTNw4sQJZGRkoKSkBO7u7oiIiMCQIUOgVPLpHyIiezFP6I/uwvnH2gPzGfsLSjWoqK6Fyl3hwBZRR2SXtTYUCgWGDx+O4cOH2+N0RETUAPMlk3pwQth2wU/tBpW7AhXVxicsM66Xo293Xwe3ijqaFi8uTkREbaO4vAaFZRpxO5pLJrUL9Wfs57Al3ToGZEREHYR5/pif2g0+nq4ObA2ZMx+2TM9hYj/dOgZkREQdRHJmifg6msOV7UpEMGfsp5ZhQEZE1EGkMH+s3TIfsiws06CsSttIaaL67JLU39bS09Px0EMPYeLEiZg3b1694xs2bLC63yQ6Ohrx8fEW+3Q6HTZv3owNGzYgPT0dcrkc/fv3x8yZMxEbG2v1PKWlpVi5ciV2796N7OxsqNVq3HXXXZgzZw7CwsJa9iGJiMzUaPW4mmu+oDgDsvbEx9MVag8FyqrqEvv7R/o5uFXUkXS4gKygoADPPfccqqurGyxz8eJFAEBsbCwCAwPrHQ8JCbHYFgQBb7zxBuLj4+Hl5YW4uDiUlJTgyJEjOHz4MN5//3088sgjFnVKSkowdepUpKSkoFu3bhg1ahTS0tLw448/Yvfu3Vi3bh169eplh09MRASk5tQtKO7uKkPXAK4P3J5IJBJEhKhx7kohAOOwJQMyuhUdKiC7dOkSXnzxRWRkZDRazhSQLViwAN27d2/yvJs3b0Z8fDx69+6NVatWwdvbGwBw9OhRzJo1CwsWLEBcXJxFILdo0SKkpKRg4sSJeO+99yCTyQAAX375JT7++GO89tpr2LZtG6RSjgoTUculmOWPRYZ6QSrlguLtTXiQZ11Axgli6Ra1SrSQm5uLtWvXYtWqVbhy5UqLz1daWoq///3veOyxx5CRkYGuXbs2WLa2thZJSUnw9PREREREs86/YsUKAMDcuXPFYAwAhg8fjunTp6OmpgZr164V92dmZiI+Ph5qtRpvvfWWGIwBwOzZszFw4EAkJSXh4MGDt/ZBiYgaYL6gOIcr2yeLJy2Z2E+3qEUBWWJiIp599lksWbJE3Hfy5Encc889WLRoEZYsWYL7778f//rXv1rUyDVr1mDlypXw9fXFF198gYceeqjBsikpKdBqtejXrx8kkqb/gkxJScHVq1fh5+eHwYMH1zs+fvx4AMCePXvEffv374der0dcXBxUqvrDBtbqEBHZyiAIuJJV1+PChP62p1a6QC6XwcvLvcGvATF1KTLF5TUQpFLxmFLJKUqocTYPWWZlZeGPf/wjKisrLQKfBQsWoLq6GiqVCiEhIUhOTsZHH32EwYMHY+DAgTa9V3BwMN544w1MmTIFbm5u4pCkNaZjQUFBWLJkCfbt24fs7Gz4+Phg1KhReO655xAUFCSWN62/GR0dbTWAM+2/evUqqqur4e7uLtaJiYmx2oYePXoAMAasREQtlV1Qieoa43qVEgkQGaJuogbZm0Iug05vQEGpptFyaqULyiqNT1ieTs5H/yh/+Hm5Qy5n+go1zuaAbNWqVaioqMCgQYPwwgsvADAGIElJSVAoFNi8eTPCw8Oxbt06LFy4EN9++63NAdmkSZOaXdYUkG3duhUqlQpDhgxBSEgILl68iO+//x67d+/Gv//9b/Ts2RMAkJeXBwAWQZo5V1dXqNVqlJaWoqCgAGFhYU3WMT1IYCpHRNQS5tNdhAWq4O7aodJ/O42iMg12HGw8DcfdRYaySuPr3b9m4Gp2Kf5wdxT8vdzaoIXUkdn8XX3kyBG4urris88+g6+vcc2uAwcOAACGDRuG8PBwAMCUKVOwfPlynDx50g7NbVpCQgIA47DhokWLxCHF8vJyvPXWW9i1axdeeOEF7NixA3K5HJWVxu8cN7eGv1lcXY1dzaayVVVVAAB3d3er5U3nMpWzJ4VChoAAzybLNacM1cfrZjteO9s057pdK6gUXw/oEVCvjkwmtWlITCIxPh3YkeqatNc2B/h6ILfYOAtAhaYWSqUrZDIpXFzk7eJ7pD20oaNq7Wtnc0CWk5ODiIgIMRgDgMOHD0MikVgsMi6RSBAaGioO87W21atX49q1awgPD4eLi4u439PTEx988AFOnz6N9PR0HDhwAGPGjBET8puTb2bS3DrCjUfUiYha4lJ6kfi6Nxetbtd81XV/3BeV1TiwJdTR2ByQ3RyM1NTU4PTp0wCAoUOHWhwrLy+HXN42Xexubm6Ijo62ekypVGLYsGHYvn07zp8/jzFjxkCpVAIANJqG8wJqaozfVKYeMVOdhuZCM52roR60lqit1aOkpOGeN1MEn5/PJ3xuBa+b7XjtbGN+3by83CFAgsLS+j9TSitqcL2w7nveV+2G7Py6CWKD/ZQQBAGVlbf+y18Q0OHqmrTXNrvJ6343VtfoUFhUCb3eAK1Wh1Ir/79thd+ntmvptfP29oBCIWuynM1RUteuXXHt2jUx0f3QoUPQarXw8fFBv379xHKpqanIzMwUE90dzTSXmCmYMuWB5efnWy1fU1ODsrIySCQSBAQENKuOKXfM2qS0RETWFJZWW81PyjYbrnR3keHo2SyL49Pu63dzFXIgNxc53Fxk0Gj1AIDiCi6hRM1j82Mfd9xxB6qqqvDmm29iz549+OijjyCRSDBu3DixzMWLF/HSSy9BEASMGDHCLg1uTG5uLv72t7/hhRdegF6vt1omJycHgPHJTQBicn9ycrLV8snJyRAEAWFhYfDw8GhWnZSUFItyRES2Kiqv65ExHw6j9svHsy7PrKSCw5bUPDYHZM8++yx8fX2xc+dO/OUvf0FaWho8PDwwc+ZMAMak/0cffRRJSUkICQnB008/bbdGN8TT0xM7duzArl27cOzYsXrHS0pKsG/fPkgkEjFADA8PR0REBPLy8nDmzJl6dXbu3AkAGDVqlLhvxIgRkEqlOHTokNXE/V27dgEARo8ebYdPRUTOrKisLp3C/Bc9tV/eqrr/p+JyBmTUPDYHZEFBQdiwYQPuuecedO/eHaNHj8a6devEWfTDw8MhlUoxZswYbNiwwSL5v7V4eHiIk8a+8847yMqq69ovLS3Fiy++iPLycjzwwAOIjIwUj02bNg0A8Pbbb6OgoEDcf/ToUaxZswYKhcIioAwKCsK4ceNQUlKC+fPnQ6ut65JesWIFzpw5gx49emDs2LGt9VGJyAnodAaUmg15+XHqhA7Bx7PugbKSihpxDVKixrQo0z4sLAwff/yx1WNdunTB4cOHLZYiaguvv/46Ll26hHPnzmHChAm4/fbb4ebmhuPHj6O8vByDBw/G/PnzLepMnjwZBw4cwC+//ILx48cjNjYW5eXlOHHiBARBwOLFi+stSD537lwkJCRg+/btOHHiBPr374+0tDQkJSVBrVZj2bJlt/TkJhHRzYrKa2D6Va6QSaH2UDi0PdQ85j1kOr2A64WVCPS2/0Ne1LnY3ENWVFTUZBlTMFZdXY13333X1re6JSqVCuvWrcOrr76KiIgInDx5Er/++iu6deuGv/3tb1i9erX4lKSJVCrF8uXL8de//hWhoaE4dOgQrly5gri4OKxZs8bqUk2BgYHYuHEjnnzySUgkEuzduxeVlZV4+OGHsWXLFuaPEVGLFZoNV/qqXflHXgchl0nhaRY8p2VzoXFqms09ZDNmzMDatWuhVje+hMexY8fw5ptvIisrC/PmzbP17Sw8//zzeP755xs87uLigmeffRbPPvtss8+pUCgwY8YMzJgxo9l1fH19MXfuXMydO7fZdYiImss8IONwZcfio3JFeVUtACA9hwEZNc3mHrLLly9jxowZKC+3Pi9HdXU1Fi5ciKeeegqZmZmNzoRPRESW9HoDSswSwv34hGWH4m32AAZ7yKg5bA7IevTogYsXL+JPf/qTuKSQybFjx/DAAw/gu+++g8FgwNChQ7Ft27YWN5aIyFkUV2hhuJFAJpNK4KV0abwCtSvmT8Rm5VWgptb6VExEJjYHZGvWrEFMTAzOnj2LmTNnorq62qJX7Nq1a1CpVFi4cCHWrFmDbt262bPdRESd2s35Y1Ip88c6Ek8PBWQ3/s8MgoC0rNImapCzszmHzNfXF2vWrMGMGTNw6tQpzJgxA4WFhcjMzIQgCPjd736H+fPni7PbExFR8xWWmuWPcbiyw5FKJPBSuYjrWSZdK0GID5+0pIbZ3EMGGJ+iXL16NXr37o0zZ87g2rVr8Pf3xz/+8Q8sX76cwRgRkQ0MBsFiQlEGZB2Tr9mwZWJG0zMTkHNrUUAGAGq1GmvWrMGAAQMAAJGRkZyhnoioBUortdDfSCCTSgBvT+aPdUS+nnWB9OWMEgicIJYa0awhy02bNjVZZsKECUhKSsKxY8fw3HPP4Z577qlX5tFHH731FhIROZmCUsvlkmTSFv/tTA7go7Zc0zK/pBqBPh4ObBG1Z80KyObOndvsCQkFQcDhw4dx+PDhescYkBERNc18/UoOV3ZcrgoZVO5yVFTrAADJmaUMyKhBzQrIQkNDW7sdREQE4x+1Fk9YckLYDs3X0w0V1RUAgJSsUtzZP6SJGuSsmhWQ7d27t7XbQUREAMoqtdDpjblGElgmhlPH46t2xdW8GwFZJqe+oIYxMYGIqB0pLKt7utJL5QK5jD+mOzLzxP6sgkpUamod2Bpqz2yeh8xcdnY29u3bh7S0NFRWVkKpVCIiIgJ33303wsPD7fEWREROoZD5Y52K0l0OlbsCFdXGQOxKVikGRPk7uFXUHrUoINPr9ViyZAm+++476PXGZSEEQRAfAJBIJHj88cfxt7/9DS4ufGybiKgxN+ePcUHxjk8ikSCqixfOphQAMCb2MyAja1oUkL366qvYuXMnBEFAUFAQ+vTpA5VKhbKyMiQkJCA/Px/ff/89SkpK8PHHH9urzUREnVJFdS20tQZx21fN/LHOIKqrtxiQXeESStQAmwOy3bt346effoJSqcTChQsxYcIEi+OCICA+Ph7z58/Hzp078cADD3DCWCKiRpjPP6ZWKuAilzmwNWQvUV29xNep2WXQ6Q3MDaR6bL4jNm7cCIlEgkWLFtULxgBjN+3999+PDz74AIIgNGtyWSIiZ5ZfUi2+DvDiuoedRXiwpxiAaXUGXLvx1CWROZsDsgsXLiAwMBDjx49vtNz48eMRGBiICxcu2PpWRESdnt5gsOghC/BmQNZZKOQyRHVRi9vJnP6CrLA5ICsvL0dQUFCzygYHB6OoiAurEhE1JD27TJx/TCph/lhn0zPcV3ydklniuIZQu2VzQObt7Y1r1641WU4QBFy7dg1eXl5NliUiclYJ6XV/tPqq3Zhj1Mn0CvcRXydnlXKhcarH5u/4QYMGoaSkBN9//32j5davX4/i4mIMGjTI1rciIur0Es0CsgBvTnfR2ZgHZKUVWovhaSKgBQHZlClTIAgC3nvvPXz99deorKy0OF5ZWYmvvvoKixYtgkQiweTJk1vcWCKizqi6RofU7DJx258J/Z2Ol8oVQT51/69cRoluZvO0F8OHD8fUqVOxbt06LFu2DP/4xz8QEREBlUqFiooKpKenQ6/XQxAETJkyBXFxcfZsNxFRp3ExtRAGg3EISyGXwlvFibQ7ox5dvZBbbHySNiWrFMP7BTu4RdSetGhi2LfffhtdunTBl19+ibKyMqSkpFgc9/LywsyZM/HMM8+0qJFERJ3Z6aR88bW/l5u42gl1LtFdvXH4/HUAfNKS6mvxWpZPP/00/vjHP+LEiRNITU1FRUUFlEolIiMjMXjwYLi5MReCiKghgiDgZGKeuB3ow+HKzqpHl7qH27LyK1ClqYWHm8KBLaL2xC6Li7u4uCAuLo7DkkREtyiroNJiQtggBmSdVoifh7jQuABjL9ltPbiuJRnZJSA7deoUfv75Z6SlpaGyshJKpRIREREYMWIEhg8fbo+3ICLqlExrHAKAt8oFbi52+bFM7ZBEIkFMmDdO3RiiTrpWwoCMRC36zq+oqMAbb7yBvXv3AoDFvCoSiQSrVq3CiBEj8OGHH3IeMiIiK85eKRRfB/l4OLAl1BZuDsiITGwOyAwGA/785z/jxIkTEAQBgwYNQt++faFSqVBWVoYLFy7g3LlzOHDgAF544QWsXr3anu0mIurwSitqcCWrLrk7yJfDlZ1dzzBv8XX69XLU1OrhquAi8tSCgOzHH3/E8ePH4e/vj+XLl2PgwIH1ypw6dQovvPACjh07hu3bt+OBBx5oSVuJiDqVk4l5MA0seKtc4aXkdBedXVigCm4uMmi0eugNAlKzStE7wrfpitTp2Twx7NatWyGRSLBs2TKrwRgA3H777Vi6dCkEQcDWrVttfSsiok7pWMJ18XW/KD9Od+EEpFIJenStS+G5zGFLusHmgCwpKQldunTB0KFDGy0XGxuLLl26ICkpyda3IiLqdDRaHU5eyhW3BzC522mYD1syj4xMbA7IKisr4ePj03RBAL6+vigrK2u6IBGRkzh5KQ8arR4A4OEqR5/uHLZyFjFmAVlqdhl0eoPjGkPths05ZAEBAUhPT0dtbS0UioYnttNqtUhLS4Ofn5+tb0VE1OkcPJMlvr6jTxAUciZ2d1ZqpQvkchm8bqxRepvSBS5yKbQ6A7Q6AwrKtegZ3nAHh05nQGVlTVs1lxzE5h6yIUOGoKKiAitWrGi03IoVK1BRUYEhQ4bY+lZERJ2KRqvDcbPhyrj+IQ5sDbU2hVwGnd6AglINCko1KK2sRUSIWjx+PDFXPHbzlwAJ5HKbf1VTB2JzD9n06dOxY8cOfPbZZygoKMC0adMQGRkpHk9NTcXq1auxceNGSKVSTJs2zS4NJiLq6M6mFEJbWzdceVt0AEortQ5uFbWmojINdhy8UrfDbN7Ow2ezob9xP9zsD3dHwd+LSxA6A5sDsn79+uHll1/G0qVLsWHDBmzYsAFubm5QqVSoqKiARqMBYJws9uWXX0b//v3t1mgioo7smFnv2O0xAVCwB8Tp+Hm5ATcWGC8q00AQBD5l6+RaNFP/s88+i27duuHjjz9Geno6qqurUV1dtyZb9+7d8eKLL+Kee+5pcUOJiDqDSk0tzqcWidtDewc6sDXkKD6erpAAEADo9ALKKrXwUrk6ulnkQC1eNG38+PEYP348MjIykJqaioqKCiiVSkRGRiIiIsIOTSQi6jyOXrguPlXnpXJBr0aSuanzksuk8FK5oKTCOFRdWFbDgMzJ2W0V2/DwcISHh9vrdEREnY4gCDhwNlvcHjukG+QyDlc6Kz+1m1lApkFkqLqJGtSZ2S0gM8nNzUVCQgIEQUDfvn0RFBRk77cgIuqQUnPKkJlfKW7/flg4jINW5Iz8vNxwJds4R2ch88ic3i0HZIcOHUJycjKUSiXuvvtuhIQYH9c2GAx47733sHHjRuj1xqdFJBIJ7rnnHsyfPx9eXl6NnZaIqNM7cKaud6xvpB+6BKiQn1/uwBaRI/l61g1RamsNqKzWQeXR8Lye1Lk1OyBLTk7GCy+8gPT0dHGfVCrFrFmz8MILL2DRokVYv349BLNHeQVBwE8//YRr165hw4YNkErZNU9Ezqm6Rodjl/LE7fHDmOLh7FwUMnh6KFBeVQvA2EvGgMx5NSsgKyoqwrRp01BcXAwXFxdER0dDp9MhOTkZX3zxBXQ6Hb7//nvIZDLMnj0bv/vd7yCRSPDLL7/gyy+/xIULF7B9+3Y89NBDrfxxiIjap0Pnc1BjNvdY3IBQB7eI2gM/tZtFQBYe7OngFpGjNCsgW716NYqLizFq1CgsXrwY3t7eAICrV69i5syZ+PrrrwEA7777LiZNmiTW69mzJ7p3744XXngBu3btsltAlp6ejoceeggTJ07EvHnzrJY5cuQIvv76ayQmJkKj0SAyMhJPPPEEHn30Uatj9DqdDps3b8aGDRuQnp4OuVyO/v37Y+bMmYiNjbX6HqWlpVi5ciV2796N7OxsqNVq3HXXXZgzZw7CwsLs8lmJqOPTGwz437Fr4vZdA0LgquBSSWQMyNKvG4etC8s0Dm4NOVKzxhAPHDgAuVyOJUuWiMEYAHTr1g2vv/46BEGAi4sLHn744Xp1f//738PPzw+XL1+2S4MLCgrw3HPPWcx3drP169djxowZOH78OPr06YPY2FhcuXIFc+fOxZtvvlmvvCAIeOONNzBv3jxkZmYiLi4OMTExOHLkCKZPn47NmzfXq1NSUoIpU6bgq6++gl6vx6hRo+Dj44Mff/wRDz30EBITE+3yeYmo4zuemCf+spVJJRg3hH+wkZGfui6PrLpGjyqNzoGtIUdqVg9ZdnY2wsLCrCbm33777QCMwZlcbv10Xbp0QVJSUguaaXTp0iW8+OKLyMjIaLBMWloaFi5cCJVKhbVr16JPnz7iZ5g+fTq2bNmCkSNHWkxWu3nzZsTHx6N3795YtWqVGHQePXoUs2bNwoIFCxAXFyc+wAAAixYtQkpKCiZOnIj33nsPMpnxr90vv/wSH3/8MV577TVs27aNeXNETk4QBOz87aq4PbR3oHGWdiIAbq5yKN3kqLwRiBWWaeDhpnJwq8gRmhUtlJWVNfiUpFptnDdFqVQ2WF8ul6OmxvaV6ktLS/H3v/8djz32GDIyMtC1a9cGy65cuRJ6vR7PPPOMGIwBQGhoqDi8uXLlSos6pgXS586da9EDOHz4cEyfPh01NTVYu3atuD8zMxPx8fFQq9V46623xGAMAGbPno2BAwciKSkJBw8etPkzE1HncCmjGFdzK8Tt8UO7ObA11B75qesC9CIOWzqtZgVkgiBYBB0WJ7jRA9TQcXtYs2YNVq5cCV9fX3zxxReN5qLt27cPgHGo9GZxcXHw9PTE+fPnkZtrXEsuJSUFV69ehZ+fHwYPHlyvzvjx4wEAe/bsEfft378fer0ecXFxUKnq/yVjrQ4ROR9BELDtUJq43be7L7oFMWmbLPmaDVsWltneeUEdW4cYTwsODsYbb7yBXbt2YcyYMQ2WKygoQGFhIRQKBSIjI+sdl8lk4n5TTptpKDU6Otpqsr9p/9WrV8W8NVOdmJgYq+3o0aMHADCPjMjJnU8tQvKNBaQB4L7hnOqC6jMfwq6orkWNVu/A1pCj2H2m/tZg/uRmY/LyjHP8+Pv7N5i7FRgYaFHW9G9DKwq4urpCrVajtLQUBQUFCAsLa7LOze9hTwqFDAEBTf+F3ZwyVB+vm+147SwZDAK2rz0pbt/eMxB3Da4/XGm6bjKZFErlra9lKJEYJ+F2lromHanNTdX18HCBu6sc1TXGPLJKrR6+Ph4AjPeFi4vcbt9f/D61XWtfu2YHZOXl5Th+/LhNx8vL22Ym6qqqKgCAm1vDCbOurq4WZSsrK5tdx1TWVNfd3d1qedO5TOWIyPkcOZ+N1Ky63rE/3tvLga2h9kwikSDQxx0ZN6a/yCuuRhiHtp3OLc3UP23aNKvHJBJJo8fbiqlXrDlrgZlWFDDlvt3K+mHNrWO+aoG91NbqUVLScKBniuC5HMut4XWzHa9dfXqDAWt2JIjbg2MC4O0mt7hG5tfNy8sder0BlZW3nj8kCMafNc5S16Qjtbk5ddVmM/TnFlaKZfV6A7RaHUpLG57qqTn4fWq7ll47b28PKJox72CzA7KWBhdtsWCq6UlPjabhp1RMT3t6eHjcch1Tj5ipTkNzoZnO1VAPGhF1bkcv5CKn0PiHkwTAQyPq57QSmTPPIyut1KJWZ4BC3iHSvMlOmhWQdZSnBU05XQUFBRAEwWoQaMrrMuV5merk5+dbPWdNTQ3KysogkUgQEBDQrDo3vwcROY9ancHiycrh/YLRxb/haYGIAMDTXQEXuRRanQGAcfqLIF8PB7eK2lKzArIuXbq0djvswtvbG0FBQcjNzUVGRgYiIiIsjuv1eqSmpgIwLutk/m9ycrLVcyYnJ0MQBHTr1k3sVWuqTkpKikU5InIeB85mW8zK/+Bd3R3cIuoIJBIJfNWuuF5kHHkpLKthQOZkOl1/6MiRIwEA//vf/+odO3z4MMrLy9GrVy8EBwcDAMLDwxEREYG8vDycOXOmXp2dO3cCAEaNGiXuGzFiBKRSKQ4dOmQ1cX/Xrl0AgNGjR7f04xBRB1Kj1eM/R9LF7REDQxHgzdQFah7zCWK5rqXz6XQB2dSpUyGTybBixQqLACs7OxsLFy4EAMyaNcuijulhhLfffhsFBQXi/qNHj2LNmjVQKBR4+umnxf1BQUEYN24cSkpKMH/+fGi1WvGY6X179OiBsWPHtsZHJKJ26ueT11BWafx54CKX4v64CMc2iDoU84CspKIGOr3Bga2httYh5iG7Fb169cJLL72EpUuXYsqUKRg6dCjc3Nzw22+/oaqqCpMmTcKECRMs6kyePBkHDhzAL7/8gvHjxyM2Nhbl5eU4ceIEBEHA4sWLLdaxBIzLLCUkJGD79u04ceIE+vfvj7S0NCQlJUGtVmPZsmVt8iADEbUTUqnFmpUT4iIQ3sW7yWpeXu6Qy2WQSvnzwtmpVS6QSSXQGwQIAlBSzln7nUmnC8gAYObMmYiKisKqVatw7tw5SCQSREVFYfLkyXj44YfrlZdKpVi+fDm+/fZbbNmyBYcOHYJKpUJcXBxmzZqFoUOH1qsTGBiIjRs34vPPP8fevXuxd+9eBAYG4uGHH8acOXMQFhbWFh+ViNqJHUfTxQWi3VxluHtQVxSUNjzsJJMZByj0egOC/Zj0T4D0Rh5ZfonxvuEySs6lQwZkzz//PJ5//vlGy4wdO/aWhgwVCgVmzJiBGTNmNLuOr68v5s6di7lz5za7DhF1PmWVWuw4XPdkZXiQJ/afuNpIjbqZ5israzDtvn6t2j7qOPzUbmYBGfPInEmnyyEjImpru45dhebG+oMucimiQtQObhF1VOZ5ZMXlNdAzj8xpMCAjImqBiupa7D2dJW736OIFOSf0JBt5e7rAlE6oNwi4msuZ9Z0Ff2oQEbXAnpOZqLnRO+bhJkdEMNcgJNvJpFJ4e9YtQp50rcRxjaE2xYCMiMhG1TU6/Hzimrg9dkgYe8eoxcyHLZOvljiuIdSm+JODiMhG+89ki09WurvKMXoIn66mljMPyK5klsBgaNla0tQxMCAjIrKBTm/AbrPesfHDwqF0UziwRdRZ+JoNWVbV6JhH5iQYkBER2eBUUj6Kb0zcKZNK8AfOyk92IpdL4a1yEbcT0ooc2BpqKwzIiIhs8L/jdb1jQ3sHwtdsmImopczvp4R0BmTOgAEZEdEtupJVitTsMnF73B3MHSP7Ms8ju5RWBEFgHllnx4CMiOgWmeeOxXT1QkQwJ4Il+/JT1+WRlVTUIK+42oGtobbAgIyI6BYUlmpwIjFf3B53RzcHtoY6KxeFDJ4edQ+JXOZ8ZJ0eAzIioluw91QmDDeGj/y93DAo2t/BLaLOynzYkhPEdn4MyIiImkmj1WH/mWxx+3dDwiA1rXNDZGe+as7Y70zkjm4AEVF7oVS6NjrT/tGj6aiqqZsI9g93dYfHjbnH5HIZgzOyK/MesoJSDYrKNHyatxNjQEZEdINcLoUACQpL6ydQGwQB2w+lidtx/UNQVaNHVY1xHctgP2WbtZOcg7urHP7e7igoMd6PSddKMKxvsINbRa2FARkRkZnC0mrsOHil3v7rRVXILaqq2yEYLMpNu69fWzSPnEx0mDcDMifBHDIiomYwn3cs2NeDyyRRm4ju6i2+5pOWnRsDMiKiJpRWalFQqhG3o0I57xi1jehu3uLrnMIqlFVpHdcYalUMyIiImmDeO+aldLF4+o2oNQV4u8PHbLHx5GulDmwNtSYGZEREjdBodcjKrxC3o0LVkEj4NCW1DYlEgt4RvuL25avFDmwNtSYGZEREjUi/Xg7DjWUE3VxkCPXn05TUtvpH+YmvL3Kh8U6LARkRUQP0BgPSr5eL2xHBnpxrjNrcgOgA8XVOYRWKyjSNlKaOigEZEVEDMvMroa01AABkUgkigj0d3CJyRsG+Hgj0dhe3L6axl6wzYkBGRGSFIAgWyfxdA5RwUcgc2CJyZn271+WRcdiyc2JARkRkRX6pBuVVteJ2JKe6IAcyD8gS0ovFBe6p82BARkRkxZWsuukFAn3c4enh4sDWkLPr1c0H0htP91ZU1+JqbnkTNaijYUBGRHSTkooa5JdwIlhqPzzc5IjsUncfMo+s82FARkR0k+TMut4xb5UL/L3cHNgaIqN+ZvORXUhlQNbZMCAjIjJzvbASOYV1i4hHd/XiRLDULpjnkaVklaJKo3Nga8jeGJAREZnZ+WuG+FrlrkCwr4cDW0NUp3uIGip346L2eoPApy07GQZkREQ35BRU4rcL18Vt9o5ReyKVSjDAbNb+sykFDmwN2RsDMiKiG37YmyxOJ+DhKkcXLpNE7Yx5QHY+tRAGA6e/6CwYkBERAcgprMTBM1nidkw3by6TRO1Ov+6+kN24L8urapGWU9ZEDeooGJAREQHYdihNXERc6SZH1wD2jlH74+GmQHRXL3H77JVCB7aG7IkBGRE5vcy8Chy7lCdu9+zmLU7CSdTeDIjyF1+fYx5Zp8GAjIic3tZDaeLrUH8lc8eoXbutR10e2dW8ChSWahopTR0FAzIicmrp18twKilf3L7/7kg+WUntWrCvB4J83MXtU8n5jZSmjoIBGRE5ta0H63rHuoeqMSgmwIGtIWqaRCLB7Wb36ekkBmSdAQMyInJaKVmlOGeWFP3E72LYO0YdgnlAdvlaCcqqtA5sDdkDAzIickqCIGDTvhRxOzJUjcG9Ah3YIqLm6x6qhrfKBQAgCMDZZCb3d3QMyIjIKZ1JKUCS2SLiE0cwd4w6DqlEYjG8forDlh0eAzIicjp6gwGbfrkibveP9EOfCN9GahC1P4PNArKL6UWoruFi4x2Z3NENaA1paWm45557Gi1z9OhR+PrW/QA+cuQIvv76ayQmJkKj0SAyMhJPPPEEHn30Uat/Net0OmzevBkbNmxAeno65HI5+vfvj5kzZyI2Ntbun4mI7Ofg2RzkFFYBACQAJo2KcmyDiGwQE+YNpZsclRoddHoB564UIrZPkKObRTbqlAFZQkICAKBHjx7o3bu31TKurq7i6/Xr1+Odd96BQqFAbGwsFAoFfv31V8ydOxenTp3CBx98YFFXEAS88cYbiI+Ph5eXF+Li4lBSUoIjR47g8OHDeP/99/HII4+03gckIptptDqLecfu7B+CroEqB7aIyDZymRQDo/1x+Px1AMCxS7kMyDqwThmQXbhwAQAwbdo0PP74442WTUtLw8KFC6FSqbB27Vr06dMHAJCdnY3p06djy5YtGDlypEWP2+bNmxEfH4/evXtj1apV8Pb2BmDsdZs1axYWLFiAuLg4hISEtM4HJCKb7Tp2DWWVxifSFHIpHrq7u4NbRNQwtdIFcrkMXl7uVo+PHhwmBmQX0oqgcJXDw00hHtfpDKisrGmTtlLLdMocMlMPWf/+/Zssu3LlSuj1ejzzzDNiMAYAoaGhmDdvnljG3IoVKwAAc+fOFYMxABg+fDimT5+OmpoarF27tqUfg4jsrLSiBjt/uypu//6OMPiq3RzYIqLGKeQy6PQGFJRqrH6FBqigdDcGYLU6A/aezBSPCZBALu+Uv+Y7pU7ZQ5aQkABXV1dER0c3WXbfvn0AgN///vf1jsXFxcHT0xPnz59Hbm4ugoKCkJKSgqtXr8LPzw+DBw+uV2f8+PH46quvsGfPHrz++ust/zBEZDfbDqWhplYPAFC5K3BvbLiDW0TUtKIyDXYcvNLgcX+1KyqrawEA/z2chsJiY37kH+6Ogr8X/+DoKDpd6Hzt2jWUlZUhIiIC69evx8SJEzFo0CDExsbiueeew7lz58SyBQUFKCwshEKhQGRkZL1zyWQycf/ly5cBAElJSQCA6Ohoq8n+pv1Xr15FdXV1a3xEIrJBZl4F9p/NFrcfuDMCHm6d8m9ScjKhZmuv5pVUQ6vTO7A1ZKtOF5CZ8scuX76MJUuWQKlUYtiwYfDw8MCePXswefJkbN++HQCQl5cHAPD394dUav1SBAYGWpQ1/RsUZD1x0tXVFWq1GgaDAQUFnKiPqD0QBAHf/ZwEQTBuB/q4Y9SgLo5tFJGd+Hu5wVVh/B0mCMD1G08QU8fS6f48NH/C8vPPP0d4uHFIwmAw4Ouvv8ayZcvw1ltv4bbbbkNVlfGmdXNruEvX9DSmqWxlZWWz65jK2pNCIUNAgGeT5ZpThurjdbNde752h89lI/Fqibg9a+IAhAR7WS0rk0mhVLpaPdYYicS4xuCt1lUqXW2u25L37ah1TTpSm9uibrdgNZKvlQAAcoqq0TvSHzKZFC4ucovvzfb8fdretfa163Q9ZC+++CJ2796NtWvXisEYAEilUsyaNQujR4+GVqvF+vXrxV6x5szOLdz401omkzW7DhE5Xk2tHt9svyBu394rEHf05tQA1LmEh6jF17lFVajS1DqwNWSLTtdDJpfL0a1btwaPjx07Fvv27cP58+fx8MMPAwA0Gk2D5WtqjI8Le3h4AACUSmWz67i7W39MuSVqa/UoKWm4O9oUwefnl9v9vTszXjfbtbdr5+XlDgESFJYaczh3HE5DXrHxtVQqwYN3RyKnwHrvdbCfEoIg2DRNgCDgluqaejwqK2tuuW5L3rej1zXpSG1ui7rucgk83OSo0hhn60/KKIY+zgCtVofS0up2933akbT02nl7e0ChkDVZrtMFZE0xzQ1WXV0t5oEVFBRAEASrvV6mnDFTLpmpTn6+9XXDampqUFZWBolEgoCAAKtliKh1FZZWY8fBK6iu0WHvqSxxf0SwJ04mXG+w3rT7+rVF84jsTiKRICxAhcs3hi0z8yrEkR3qGDrdkOX777+Pv/zlL0hOTrZ6PCcnBwAQHBwMb29vBAUFQavVIiMjo15ZvV6P1NRUAEDPnj0t/m3o/MnJyRAEAWFhYWKvGhE5RkJ6MfQG4y8lF4UUPcOs540RdQZdA+qetiyvrsW1XPaGdSSdLiC7ePEidu/ejf/+979Wj2/btg0AMGrUKADAyJEjAQD/+9//6pU9fPgwysvL0atXLwQHBwMAwsPDERERgby8PJw5c6ZenZ07d1qcn4gco7BMgyyzocne3XygkDc9bEDUUSndFfD1rEv+P3qh4d5gan86XUA2depUAMbZ9X/99Vdxv16vx4cffojjx48jIiICDz74oFheJpNhxYoVFgFWdnY2Fi5cCACYNWuWxXtMmzYNAPD2229bTG1x9OhRrFmzBgqFAk8//XSrfD4iappOb8C5lEJx20vpgm5BXK+SOj/zdVmPXbyOWs5J1mF0uhyyP/zhDzh27Bi+//57PPXUU7jtttsQFBSECxcuICsrCwEBAfj888/FqSl69eqFl156CUuXLsWUKVMwdOhQuLm54bfffkNVVRUmTZqECRMmWLzH5MmTceDAAfzyyy8YP348YmNjUV5ejhMnTkAQBCxevJjrWBI50P9+y0B5dd1TZv0jfflkNDmFUH8PXEgrgsEgoKK6FscSctEv3MfRzaJm6HQBGQAsWLAAw4YNw3fffYeEhAQkJCQgJCQEM2bMwMyZM+Hr62tRfubMmYiKisKqVatw7tw5SCQSREVFYfLkyeKTmOakUimWL1+Ob7/9Flu2bMGhQ4egUqkQFxeHWbNmYejQoW31UYnoJtkFldhxOF3cDg/25HqV5DRc5DKE+nkgM984XP/z8WsMyDqIThmQAcC9996Le++9t9nlx44di7Fjxza7vEKhwIwZMzBjxgxbmkdErUAQBHy19Tx0egMAwFUhQ59wb8c2iqiNhQd5igHZuZQC5JVUc0LYDqDT5ZARkfM6cuE6zl+pyx3rH+nLRH5yOr5qV6jcFeL2QbM1XKn9YkBGRJ1CeZUWG/amiNtBPu4I8ePUM+R8JBIJws0eYjl0LkfsNab2iwEZEXUKG/amoOJGIr+rQob+UX5M5Cen1TVQBbnMeP+XVmpx9FyOg1tETWFARkQd3pnkAhwxm3PpgRGR8HDttCmyRE1yVcgwpFfdmq3bDl5xYGuoORiQEVGHVlalxaqfLonbPbp6YczgMAe2iKh9GHNH3ffB5YxiJGYUObA11BQGZETUYQmCgLU7L6OsyjhUqZBL8fyk2yCVcqiSKDxYjd4RddM8bT+Q6sDWUFMYkBFRh3XoXA5OJuWL24+OjELXQD7eT2Ry350R4uvD57JRVKZxXGOoUQzIiKhDysqvwLrdSeJ2r27eGDukqwNbRNT+3NEnGP5exomRDQYBO3+76uAWUUMYkBFRh1NTq8cX2y5CqzM+yq9yV+DZ+/tCyqcqiSzIpBKMM8sl2382GyUVNQ5sETWEARkRdSiCIGDNzkRkF1SK+/50X2/4eLo6sFVE7dfI20LF749anQE//cpesvaIARkRtTtKpSu8vNytfh26mIujF3PFsg/cHYm7bw8Tj8vlMib1E5lxUcjw6JhocfuXM1koZS9Zu8OAjIjaHblcCgESFJRqLL4On8vG6h11U1xEh3njnmHhFmWIqL7xwyMsesl2HM1wcIvoZpw5kYjapcLSauwwm8yyvEqLQ+evwyAIAAB3Fxkigz2x80iaRb1p9/Vr03YSdQSuChkeGRONldsuAAD2nc7CmMFdEezL5cXaC/aQEVG7p9Hq8GtCLmpvJPFLJcAdvQLh6sKFw4kaola6QC6XwcvLHQBw7/AIBPoYX+sNArYcTG0wNcDLyx1KJfMy2xIDMiJq13Q6A35LyEN1jV7cd3tMALyZxE/UKIVcBp3egIJSDXKLqlBcXoOHRkaJx09cysPBs9n1UgMKSjUQIIFczhChLXHIkojaLYNBwPHLeSit1Ir7+nX3Rai/0oGtIuo4iso02HHwitjbVVGhga+nK4rKjUn9/9p+ASNvC633IMwf7o4S5y+jtsHwl4jaJUEQcPZKIfJL6hL1I0PViAxVO7BVRB2bRCJB3+51yymVV9UiJavUgS0iEwZkRNTuCIKAH/Yk41pehbgv1M8DfSN8HNgqos7Bx9MV4UEqcTvpWgkqqmsd2CICGJARUTsjCAK+3ZmIPSeuifv81K4YFOMPCWfiJ7KLPhG+cFUYH4oxCMDZlAIIN55gJsdgQEZE7YYgCPhh3xVsPZAq7vNSumBo70DIpPxxRWQvCrkUA6Lqhi4Ly2qQmlPmwBYRf8IRUbtgMAhY9VMidh6rW9ZFrVRgeN8gKOSc3oLI3kL8lAjxq5uH7FJ6McrMHqChtsWAjIgcTqPV4bMfz+PguRxxX9dAFYb3DYaLgsEYUWsZEOVnMXR5MikfeoPBwa1yTgzIiMihCkqqsWjtSZxOLhD39Y7wwStTbhd/URBR63BVyDAo2l/cLq+qxaX0Esc1yIkxICMihzmTUoB3V59AZn6luO+2KD+8PSMWHm4KB7aMyHkE+rije4inuJ2aU4aEtEIHtsg5cWJYImpztTo9fvjlCn4+kWmx/57Ybnh0ZBRcXWQo52P4RG2mT7gPCko1KK8yft+t3nEJg6IDHNwq58KAjIhahVLpanXplaSrxVi+6SyyzHrFXBRSzHqoP0bd3hUAIJfL6s0cTkStRyaT4vaYABw8mw2DAJRU1OCzzecw+4E+nG6mjTAgI6JWIZdLIUCCwtJqAECtzoD4Q6nY9VsGzKc76hqgwp8e7IcQfyUKSo2z8gf7cWkkorbmpXRBr3AfJKQXAwCOX8pF92BP3BPbzcEtcw4MyIio1RSWVmPHwSsoqajB6aSCesOQkaFq9O7mjVOXrlvsn3Zfv7ZsJhHdEBWqRkGpBnnFxj+kNv1yBVFd1Iju6u3YhjkBJvUTUasxGAQkZ5bg4Lkci2BM6SbHnf2D0a+7L2Qy/hgiai8kEgluj/aHj9q4GLlBEPDltosoq+L8ZK2NPwmJqFXkFVdh2fpTuJRRYjFEGRniiZEDQ+GndnNc44ioQS4KGZ59sD9kN/I4i8tr8PV/EmDg0kqtigEZEdndrwnX8cqnB5F8rUTc5+4iQ1y/IPSL9IOcvWJE7VpUFy88eW8vcftiWhF2HEl3XIOcAHPIiMhuKqpr8e3/LuPYpTyL/V38lRgQ5cslkIg6kPvu7I5zyQU4lZQPANh6KA3hwZ4YEOXfRE2yBf9MJSK7OHelEG//6zeLYMzNVYbbo/0xuGcAgzGiDkYikeDpCb0Q4G1MLxAE4MttF5GVX+HglnVODMiIqEWqa3T4bNNZfPLDWZRW1CX+9o7wxdszYtE1UOXA1hFRS3i4KfCXiQPEZcw0Wj3+sekck/xbAQMyIrKJwSDg0Lkc/HnJHuw8mi7ul8skeGx0Dyx4dhj8vd0d10AisouwQBVmPtAHpulhC0o1+HjDWVRpdA5tV2fDHDIiuiXaWj1+TcjF7uPXkFVQaXGsW5AKz97XB10CVOITWkTU8Q2KDsCjo6Pww74rAICM3HJ8uuksXn58oNh7Ri3DgIyImlRWqUVKVilOJ+XjdHIBqmos/zJWyKW4N7Yb7ouL4BOURJ2AWukCuVwGL6+6Xu7Hx/VEtdaA+MNpAICkzFJ89uMFvPHkYHi4KSzq63QGVFbWtGmbOzoGZERkobi8Bhm55ci4fuMrtxzF5Q3/YB05qCuenNAbUr2+DVtJRK1JIZdBpzeIy5mZ3HdXdxSXa3D4XA4A4EJqId784gief2wgvFTGyWT9vNytrmNLjWNARtTJNbTINwBUamqRlFGMxKvFSM0qRWp2GUoaCb5MXBUyjB7cFfcOj0DkjSVVtFrLXjMuEE7UsRWVabDj4JV6+31VLugaoERmvjFl4VpeBeZ/fRSDewbA19MNf7g7Cv5enPj5VjEgI+rkzBf5rtHqkZBWiMtXi5FyrQSZ+RVo7uTbaqULYrp5Y1BMIPpF+sHN1fjjI7eoCgCg1xssynOBcKLOSSKRYFC0P1wVMlzJLgMAVNfocfj8dfQO94HBwBn9bcGAjKiTq6yuxb5TmfjpSDryS6qhb8YPSzcXGbxVLhh5exgCfNyRlF4IN5cbAVhBBXIL6uYhUiqNwxQ354twgXCizksikaBvd1+4ucqQkFYMAcZ5yhLSi7F4zXHMefQ2+KtcHN3MDoUBGVEnVFapxenkfJxMykdiRjF0+oaDMIVcCl9PV/h4usJb5QK10hVuLsanpiaNjUFxuQZXs0vbqulE1IFEhXrBW+WKk5fzodEa80gzrpfjjc8O4Y5egXjgzu4I9WdveXMwICPqJHKLq3A6qQCnk/ORklmKhkIwuUyCIB8P+Hm5wVftCk93BSQS5noRkW381G4YOTAUF9OKxLwyQQCOXcrD8Ut5uK2HP8bc3gV9uvtCyp81DWJA1kJpaWn47LPPcPLkSRQWFiI4OBj33nsvZs2aBQ8PD0c3jzopQRCQX6rBlcxSpGSV4vK1EmTfNCeYOZW7Ar5qV4T6ecDfy53J9kRkV64KGW6PCUBYoArpuRXIufHzSABwJqUAZ1IK4Kd2xdDeQRjSKxDhwZ4Mzm7CgKwFzp07h+nTp6OqqgoDBgxA//79cerUKXz55ZfYt28fvvvuO6hUXDaGbFOj1aOoXIPCMg2KympQVKZBUXkNiss0yMyvRGll40uX+Hi64vaYAIy8vSv8fdyx88bcQURErSXA2x3T/tAXSVeL8f3uy8gprBKPFZbV4KffruKn365C5a5Anwgf9I3wRd/uvvBV86lMBmQ20ul0+L//+z9UVVXh/fffx6OPPgoA0Gg0ePnll7F3714sW7YM8+bNc3BLqT3S6Q1icFVUVnMj8LoRdJXVoLhcg0obliXpFuSJoX2CMLRvMCJD1ZBIJJDLZcgrrmq6MhGRHXh7umLU4DDE9Q/B2ZQC7Pw1HacS82D+PFFFdS2OXcrDsUt5AAA/LzdEdfFCVFcvRASrEah2hVrp4lTpFAzIbLRjxw5cu3YNw4cPF4MxAHBzc8OiRYswZswYbNy4ES+++CK8vLwc2FJqbYIgQKc3QKszQFtrQE2tHhXVtaioqkVFdS3Kq7UoLq9BWVUtiso0KCjVNGuur+YI8vVAZBcv9OjqhZ7dfBDgUzdMXlhmfA9OP0FEbck0qWxReQ3Cgjzx7IP9UTq2BicT83AiMRdpWWUw3DTfTmGpBoWlGhxLyBX3ebjKEeLngWA/D4T4KRHi64EQfyX81G5QdMKJZxmQ2Wjv3r0AgHHjxtU75uPjg9jYWOzbtw8HDx7Efffd19bNo2YQBAG1OgMqNTpU1BpQVqFF5vVSlN8IpCqqjMFUeVUtamr1qNUZxC+d/sbrG/+2FqlEAm9PVwT5esDTQ4Gi0mq4u8rh4SqHt6eruIZcaZkGxy7kWD0Hp58gorbW0KSyfcN9ENPFCwWl1cgv0SCvpLrBRcqranS4kl0mznVmTq10gZ/aFb6ebvDxdIXKXQGluwJKdzlUbsbXKncF3F3lcFVIIZdJ231vGwMyGyUlJQEAevbsafV4jx49sG/fPiQmJrbrgCw1uwxHL1yHVle37E29p/ME85dCg8es1TX+ESRAEHBjnhpB3C/ceCEAMBhulBEEGG78a/66bp9ZGyzadfN7Wm+V3iCgptaA6hodqjS1jU4H0RZcFTK4u8rg7iqHu8uNf13lcHOVwd1FDjcXGSQSCabd1w/F5Rr8Z3+KQ9tLRNRSCrnU2ON1o/deU6NDSaUWJRU18HB3QVp2KUorGs+RLavUoqxSi7Sc8ma9pwSAi0IGV4UULgqZ8UtufK2QS6GQSaHyUOD3Q8LQNdAxud8SQWjuPN1kbvDgwaioqMDPP/+MsLCwesfXrFmD999/Hw8++CA+/PBDB7SwaYIgIC27jLMq25sEkEklkEklkEqlxtcyCRQyKWQyiTGvS2bc39y/2OQyCQQAehsCSNZlXdZl3Y5UVyKRQH8jDaRWp4e21gCtTo/aWuOoRINz+tiBq4sMYUGerfcGjWAPmY2qqoxJ0m5u1p8MMe03lWuPJBIJIrswv62jkACQym3rcmdd1mVd1u0odQFAJpPCXSaFu6vzhCmdLyuujchkxtydpno42AFJRERETWFAZiOl0jj2XV1dbfW4RqMBALi7u7dZm4iIiKhjYkBmo8DAQABAfn6+1eN5eXkW5YiIiIgawoDMRqanK1NSrD/1Ztrf0FOYRERERCYMyGw0cuRIAMCuXbvqHSsuLsZvv/0GhUKBO++8s62bRkRERB0MAzIbjRs3DqGhoTh06BDWrVsn7tdoNHjrrbdQVVWFRx99FP7+/g5sJREREXUEnIesBX777TfMnDkTGo0Gffv2RdeuXXH69Gnk5eWhT58+WLt2LRcXJyIioiYxIGuhpKQkLF++HMeOHUNVVRW6du2K8ePH45lnnmEwRkRERM3CgIyIiIjIwZhDRkRERORgDMiIiIiIHIwBGREREZGDMSAjIiIicjAGZEREREQOxoCMiIiIyMEYkBERERE5GAMyIiIiIgeTO7oB1LRt27Zh06ZNSExMRHV1Nfz8/DBs2DDMnDkTUVFRzT5Pbm4uPv/8cxw5cgTXr1+Hv78/xowZgzlz5sDX17cVP4Fj2OO6aTQa3H777dDr9Q2W2bRpE/r372+vZjvcTz/9hHXr1uHixYsQBAFhYWGYMGECZsyYATc3t2afx9nuN3tcN2e83272wgsvYNeuXfjggw8wceLEZtdztvvNGluunbPdc2lpabjnnnsaLXP06NFm3TP2vucYkLVjgiDg1VdfRXx8PORyOfr37w9fX18kJiZi69at2LlzJz777DPcddddTZ7r2rVrmDx5MvLz8xETE4PRo0cjISEB3377LX7++Wds2LABwcHBbfCpWp89r1tiYiL0ej1CQ0MxePBgq2W8vb3t/Akc55///CeWL18OmUyGwYMHQ6VS4dy5c/jkk0+wY8cOrFu3Dl5eXk2ex5nuN8B+183Z7reb/fDDD9i1a9ct13O2+80aW6+ds91zCQkJAIAePXqgd+/eVsu4uro2eZ5WuecEare2bt0qxMTECHfeeadw6dIlcb9OpxOWLVsmxMTECMOHDxcqKiqaPNeUKVOEmJgY4R//+IfFeebNmyfExMQIM2fObJXP4Aj2vG7ffvutEBMTIyxdurQ1m9wuHD9+XIiJiRGGDBlicd0qKyuFZ555RoiJiRHeeeedZp3Lme43e143Z7rfbpaamioMHDhQiImJEWJiYoTNmzc3u64z3W/WtOTaOds9t3jxYiEmJkb4/vvvW3Se1rjnmEPWjm3atAkA8Morr6BXr17ifplMhpdeegnR0dEoLCzE4cOHGz3PiRMncOLECUREROAvf/mLxXnmzp2L0NBQ/PLLL0hJSWmdD9LG7HXdAODixYsA0Gm66xvz448/AgCeffZZi+vm4eGBF154AQDwyy+/NHkeZ7vf7HXdAOe638xptVq88sorkEql6NOnzy3Vdbb77WYtuXaA891zph6ylnze1rrnGJC1Y2q1GlFRURgyZEi9YxKJBN27dwdgHMduzN69ewEAY8eOhVRq+V+uUCgwZswYAMCePXvs0WyHs9d1A5zrh9WCBQvw3//+F0888US9Y6b8EplM1uR5nO1+s9d1A5zrfjP38ccf4+LFi5g3bx5CQkJuqa6z3W83a8m1A5zvnktISICrqyuio6NtPkdr3XPMIWvHPvvsswaP6fV68RupqW/CpKQkAEBMTIzV4z169ABgzCXoDOx13bRaLa5cuQJvb28cPXoUGzZsQEpKCgRBwIABA/CnP/0Jd955p13b7khyudzqww45OTlYsmQJADQrUdjZ7jd7XTdnu99Mjhw5gn//+9/4wx/+gAcffPCW86Cc7X4z19Jr52z33LVr11BWVoaePXti/fr12Lp1K9LS0uDi4oLBgwdj9uzZGDBgQJPnaa17jj1kHdR3332HrKwseHt7Y/jw4Y2WzcvLAwAEBQVZPR4YGGhRrjO7leuWmJiI2tpalJSU4G9/+xsAIDY2Fv7+/jhy5AiefvppfPXVV23RbIdYvHgxJk+ejLFjx+L8+fN4+umnMXv27CbrOfv9Zut1c8b7raioCK+//jqCg4Pxzjvv2HQOZ73f7HHtnO2eu3DhAgDg8uXLWLJkCZRKJYYNGwYPDw/s2bMHkydPxvbt25s8T2vdc+wh64COHj2KDz/8EADw6quvQqlUNlq+qqoKAODu7m71uOmRfFO5zupWr5sp1yAwMBCff/65RZf+jz/+iLfeegvLli3DoEGDcMcdd7Rewx1k8+bNKCsrAwC4uLggPz8fBQUF4g+bhjj7/WbrdXPG++2tt95CYWEhVq1aBbVabdM5nPV+s8e1c7Z7zvwJy88//xzh4eEAAIPBgK+//hrLli3DW2+9hdtuu008Zk1r3XPsIetg9u3bh9mzZ0Or1WLy5MmYNGlSk3VM+SsSiaTRcoIg2KWN7ZEt1+2xxx7Dvn37rM7B8/DDD2PKlCkQBAFr1qxprWY71Pbt23H27Fn88MMPuP322/Gf//wHkydPbvKHjLPfb7ZeN2e739atW4e9e/fimWeeQWxsrM3nccb7zV7XztnuuRdffBG7d+/G2rVrLQIuqVSKWbNmYfTo0dBqtVi/fn2j52mte44BWQeydu1azJkzBxqNBlOnTsX8+fObVc/UE1RdXW31uEajAdBwtN/R2XrdpFIpQkNDG+yWHjt2LADg/PnzdmtrexISEgI3NzcMGDAAX3/9NWJiYpCZmYmNGzc2Ws/Z7zdbr5sz3W/Jycn48MMP0bdvX7z44ostOpez3W/2vHbOdM8BxnzPbt26NThpa3M/b2vdcxyy7AB0Oh3effddbNiwARKJBC+//HKzclJMAgMDcfHiReTn51s9bhrnbmpIpaNp6XVrimnSv4a+KTsTFxcX3HvvvUhKShK7/RvirPebNbdy3ZrSme63jz76CBqNBm5ubmLukonpoZuNGzfiyJEjuOOOO/D44483eC5nu9/see2a0pnuueYwPejV1OdtrXuOAVk7p9FoMGfOHBw6dAju7u5YvHhxk8s+3Kxnz57Yt29fg3OimPb37Nmzxe1tL+xx3T7//HMkJiZi8uTJVh8AuH79OgB0mhnAP/30U6SmpuKvf/2r1c/k4uICwBjoNsbZ7jd7XTdnut9Mw7cnT57EyZMnrZY5ffo0Tp8+Dblc3mhQ4Wz3mz2vnTPdcwDw/vvvIycnBy+++KLVaS9ycnIANP15W+ue45BlO6bX68Wgws/PD2vWrLnloAIARo4cCQDYvXs3DAaDxbHa2lpxrpTRo0e3vNHtgL2uW1paGnbt2iVO/Hkz0/5Ro0a1pLntxuHDh/HTTz9hx44dVo/v378fQNPzFTnb/Wav6+ZM99vatWtx+fJlq1+mYaMPPvgAly9fxuLFixs9l7Pdb/a8ds50zwHGHsTdu3fjv//9r9Xj27ZtA9D0522te44BWTv2xRdf4NChQ/Dw8MDq1aubnB+ltrYWV65cwZUrV1BbWyvuv/3229G/f39cuXIFy5YtExMN9Xq9+BfDiBEjGlzXq6Ox13WbMmUKJBIJtm/fXu9R6DVr1mDbtm3w9vbGtGnTWuVztLWpU6cCAJYvX45z586J+2tra/HRRx/h2LFj8PPzwyOPPCLu5/1mv+vmbPfbreL9Zjvec0am79WVK1fi119/Fffr9Xp8+OGHOH78OCIiIvDggw8CaPt7TiJ0pkdPOpHS0lKMGjUKVVVViIiIaPSv6/vvvx8jR45EZmam+BfSnj170LVrV7FMcnIy/vjHP6KkpASRkZGIjo7GpUuXcPXqVXTp0gXr169vMLGzI7H3dfvqq6+wdOlSAECvXr0QHh6O5ORkpKamwsPDA19//bXVFQE6qnnz5mHDhg2QSqUYNGgQ1Go1Ll26hOvXr8Pb2xtfffUVbrvtNgDg/WbGXtfN2e43a5577jns2bMHH3zwgcWEurzfmmbLtXO2e27+/Pn4/vvvIZFIcNtttyEoKAgXLlxAVlYWAgICsHr1anGi57a+55hD1k4dO3ZMzBVIT09Henp6g2V79+4tdqE2JDo6Glu2bMHy5ctx8OBB7Nu3D8HBwXjyyScxe/Zs+Pv727P5DmPv6zZz5kz0798f//73v3H27FlcuXIFAQEBeOyxxzB79mx06dLFns13uHfffRfDhg3D+vXrcfHiRWi1WoSGhmL69Ol45plnmv0DxlnuNxN7XTdnu9/sxdnuN3tytntuwYIFGDZsGL777jskJCQgISEBISEhmDFjBmbOnNngE5g3a417jj1kRERERA7GHDIiIiIiB2NARkRERORgDMiIiIiIHIwBGREREZGDMSAjIiIicjAGZEREREQOxoCMiIiIyMEYkBERERE5GAMyIiIiIgdjQEZErSo+Ph49e/ZEz549MX/+fEc3h+zkypUruHmhlyeffBI9e/bExx9/7KBWEXVcDMiIqFVt3rxZfL19+3ZUVFQ4sDXUUhUVFXj33XfxwAMPQK/XO7o5RJ0GFxcnolaTnZ2NX3/9Fd7e3oiIiMCZM2fwn//8B5MnT3Z008hGFy9exLp166weW7JkCaqrq+Hj49PGrSLq+NhDRkStZsuWLTAYDBg0aBDGjBkDAPj+++8d3CpqLaGhoYiKioKvr6+jm0LU4TAgI6JWIQgCfvzxRwDAiBEjcO+99wIAEhMTcebMGQe2jIio/WFARkSt4tdff0VmZiakUinGjh2Lbt26YcCAAQCA9evXW5TdsmULevbsiWHDhqG2ttbq+XJyctC7d2/07NkTGRkZFseOHz+OF154AXfddRf69euHuLg4PPfcczh69KjVc40ZMwY9e/ZEYmIi3nvvPdxxxx0YNGgQJk6ciJKSEgDGgHLPnj144YUXMHr0aAwYMAADBgzAmDFj8Nprr+H8+fMNfvadO3fiySefxLBhwzBw4EA8/vjjiI+PR2ZmJnr27Cn2Ft7s559/xsyZMzF8+HD069cPd999N1555RVcvHixwfdqiOlBioKCArz66qsYNGgQBg8ejGnTpkGn0wEAdDodtm7ditmzZ+Puu+9G//79MWjQIIwfPx7z5s1DWlpaves2bdo0cbtv377o2bMnMjMzAVhP6jd95jvvvBOCIOCHH37ApEmTMGjQIAwaNAiPP/44Nm/eXO8BAZPU1FS8+eabGDt2LPr3748xY8ZgyZIlKC8vF9/vt99+u+XrQ9TeMCAjolZhSuYfOnQogoKCAAD33XcfAOCnn35CaWmpWPaee+6Bh4cHiouLcfDgQavn+89//gODwYAhQ4YgPDxc3P/RRx/hj3/8I3bt2gWtVouYmBhIpVLs2bMHTz31FP7+97832MYFCxZg7dq1CAwMhI+PD1xcXODt7Q1BEPDqq6/iueeew65du6DX6xEdHY2AgADk5ORg+/bteOKJJ7B//36L8xkMBrz99tt48cUXcezYMbi5uSEyMhKXLl3CK6+8giVLllhth06nw6uvvoo5c+Zg//79kEgk6NmzJ7RaLeLj4zFp0iR8++23zbjq9T3//POIj49HWFgY3N3dERAQALlcDo1Gg6effhpvvPEG9u3bB4VCgZiYGHh5eSE9PR0bNmzAxIkTkZCQIJ6rX79+iImJEbdvv/123H777XB1dW2yHYIg4I033sDcuXORnp6O7t27Qy6X48yZM3jzzTexdOnSenUOHjyIRx55BJs3b0ZRURFiYmKg1WrxzTffYNKkSSguLrbpmhC1SwIRkZ2VlZUJAwYMEGJiYoRNmzaJ+wsKCoQ+ffoIMTExwr///W+LOn/729+EmJgY4aWXXrJ6zgkTJggxMTHCDz/8IO5bv369EBMTIwwZMkTYtm2buN9gMAg7duwQBg4cKMTExAgbN260ONfo0aOFmJgYISYmRtixY4e4v7CwUBAEQdi8ebMQExMjDBgwQPjll18s6qakpAh/+MMfhJiYGGHixIkWx77//nuxnvl5S0pKhOeff158z9GjR1vU++ijj4SYmBhhxIgRwoEDB8T9Op1OWLNmjdCnTx+hZ8+ewqFDh6xeG2tM79WvXz/h2LFjgiAIgl6vF4qLiwVBEIRPP/1UiImJEWJjY4WzZ89a1D179qxw5513CjExMcLzzz9vcezXX38Vz11bW2tx7I9//KMQExMjLFu2TNx37do1sXzv3r2F1atXCzqdThAEQdBoNMKrr74qxMTECH369BGvvyAY75UhQ4YIMTExwhtvvCFUVFQIgmD8v920aZPQt29f8by//vprs68LUXvFHjIisrv4+HhoNBq4urpi/Pjx4n4/Pz8MHz4cQP3k/okTJwIA9u7dW29qjIsXLyIlJQUeHh5iLppWq8U///lPAMCiRYvwwAMPiOUlEgkmTJiA1157DQDwz3/+UxymMzdkyBBMmDBB3DYlox8+fBhyuRxTpkzByJEjLepERUXhT3/6EwAgKSlJ3G8wGMT2vP766xbn9fLywtKlS9GzZ896bSgsLMSqVasAAJ9//jnuvvtu8ZhMJsOTTz6Jp556CoIg4JNPPqlXvyn33nsv7rjjDgCAVCqFt7c3AODIkSOQSqX4y1/+Ig4lmwwYMEB8Etb8M7bUlClTMG3aNMhkMgCAq6sr3nzzTUgkEuh0Opw7d04s+80336CsrAwDBgzAokWLoFQqARj/bx955BG8+OKLdmsXUXvAgIyI7G7Lli0AjDlHKpXK4pgpcEpLS8Ovv/4q7h8yZAgiIiKg0Wiwe/duizpbt24FAIwfP178xXz69GkUFBRAqVRi7NixVtvxwAMPQCqVIjc312LozWTw4MFW6y1duhTnzp3Dyy+/bPW4u7s7AGNQaDAYAABnzpxBfn4+XFxc8Mgjj9Sro1AoMHXq1Hr79+/fD61Wix49eqBv375W3+/BBx8EAJw7dw6FhYVWyzSkoc+4fv16nDt3Dk888YTV46bPqNFobun9GjN69Oh6+3x8fMRAuKysTNz/888/AwAef/xxSKX1f1VNmTIFCoXCbm0jcjTOQ0ZEdpWSkiL2dJj3WpmMGzcOHh4eqKqqwvr16zFs2DDx2MSJE7Fs2TJs374dDz/8MABjftWOHTsAQNwHAMnJyQCA2tpaq4GOiUwmg8FgQGpqar2eoICAgEbrabVaHD16FKmpqbh27RrS09ORmJiInJwcsZzBYIBUKhXbExERATc3N6vn7NevX719pnrXr19vcH42wSzhPTU1FX5+fg22+2aNfUaFQoHy8nKcOnUK6enp4me8dOkSCgoKAEAMOO3BlEt4M9P1Mk00q9VqkZ6eDgDo1auX1TpKpRLdu3e3aw8ekSMxICMiu9q0aZP4+s9//nOjZffs2YOCggL4+/sDAB566CF88skn+PXXX5GXl4fAwEAcPnwYhYWF6Nq1K4YOHSrWLS8vB2D85X3q1Kkm22Xe+2LSUOBUW1uLzz77DOvXrxefugSMQVpMTAwGDBiAXbt2WdQxJZh7eHg02IabewvNP0dFRYXNn6MxDX3GiooKLFu2DD/++COqqqrE/QqFAn379kXv3r0bfMDCVk31aJkCT/Nk/Vu9nkQdFQMyIrKb2tpabN++HQCgVqvFYa+bCYKAvLw81NbWYtOmTZg9ezYAYw/KnXfeiYMHD+K///0vnnrqKWzbtg2AsXdMIpGI5zCdu2/fvuIQqb3MmzcPW7ZsgUwmw+OPP4477rgD0dHRYu/X4cOH6wVkpvY0tjRUZWVlvX2meuPHj8enn35qx0/RuOeeew6//fYb3NzcMGPGDNx2222Ijo5GeHg4FAoFNm7caPeArLnMg7BbvZ5EHRUDMiKym/3794s5Tt988w369+/fYNn7778fSUlJ2LhxI2bOnCnmCU2cOBEHDx7Ezp078dhjj2Hv3r2QSCR46KGHLOp3794dAJCeng6dTge5vP6PM0EQ8NtvvyE4OBihoaFwcXFp8jPk5uaKE9ouXLjQaj7Y9evX6+0zTQeRkZEBjUZjtWcqMTGx3j7T5zANXVpTXV2N8+fPIyQkBKGhoWJSvK3OnDkjzt21YsUKi2FjE2ufsa14enoiJCQEOTk5uHz5cr2hZgCoqakRhzWJOgMm9ROR3ZiGK2NiYhoNxgCIyeRZWVk4cOCAuP93v/sdvL29cebMGWzcuBHV1dWIjY1F165dLerfcccd8PT0RGVlZYM9ZP/5z38wffp03Hvvvc0OMLKzs8WhM2tJ9gaDweL9THlPgwcPhq+vL2pra8VePXOCIGDjxo319o8cORIymQypqak4fPiw1TatWrUKTz75JB588EFUV1c363M0xjSRK2A9r626ulrM27t5AXHzBHuhgclc7WHcuHEA0OCksT/++CNqampa7f2J2hoDMiKyi/z8fHGIy1qv0s0efPBBcWjKfAoMFxcX3HfffRAEQRzCM0/mN/Hw8MDMmTMBAO+//z42b95skYD+888/Y/78+QCMUz9069atWZ8jPDxc7IH6+uuvLQKg7OxsvPjiizhx4oS4z3TcxcVFHHpdvHgx9uzZI5apqqrCvHnzcPr06Xrv16VLF0yaNAkA8H//93/Yu3eveMxgMOCHH37A8uXLAQBTp061S95UZGSk+Pqzzz6zmBIkJSUFzz77rNj7dHMAaD6cmJ2d3eK2NOTpp5+GUqnE6dOn8c4771i0Y/fu3Q1OskvUUXHIkojsYuvWrdDpdFAoFFafrryZSqXC/fffjw0bNmD//v3Izs5GaGgoAOOw5bfffovKykoolUqLuczMPfvss7h27Ro2btyIN998E3//+9/RtWtX5ObmIi8vD4BxNvn33nuv2Z/D19cXM2bMwMqVKxEfH4/9+/ejW7duqKysREZGBgRBQGxsLE6ePAmdTofr16+Lc3s9+eSTOHv2LHbs2IHnnnsOoaGh8PPzw5UrV1BVVYUBAwbg3Llz9YYc33zzTeTm5mLfvn3485//jMDAQAQFBSErKwtFRUUAjDlmL730UrM/R2P69OmDe++9Fz/99BO++eYb/Pjjj+jSpQtKSkrE3rM777wThw8fRmVlJSoqKsRAMCIiQnxK9rHHHkPXrl3x/vvvN/g0pK1CQkLw4Ycf4qWXXsL333+P7du3IyoqCoWFhcjOzkbv3r2RkpKC2traFg/hErUH7CEjIrsw5V2NGTNGnFeqKaZpHgwGg8VwXt++fcVf8Pfee2+DDwdIJBIsXLgQ//rXvzBu3DjI5XJcunQJlZWVGDhwIObOnYvVq1c3+qSeNa+99hr+8Y9/YPDgwVAoFLh8+TLKy8sxfPhw/P3vf8fq1asxaNAgAMC+ffvEelKpFEuXLsWiRYswaNAglJaWIiUlBT179sQ///lPTJ8+HUD9Jx9dXV3xxRdf4OOPP8bdd9+N2tpaXLp0CXq9HrGxsViyZAk++eQTuwYeS5cuxcKFC9G/f38YDAZcvnwZWq0Wo0ePxooVK/DNN9+gS5cuAGDRa6dUKvGPf/wDvXr1QlVVFTIzMy2GQO3pd7/7HTZv3owJEyZAqVQiMTERUqkUs2bNwrp168RyDd0fRB2JRGjNJAAiIhKtW7cO7777Lu6880588803jm5Oh1ZVVSUGxQcOHGhwjjOijoI9ZEREdlBTU4O77roLU6ZMQW5urtUypsXI+/Tp05ZN65DeeustPPjggw0+sGG6lv7+/gzGqFNgQEZEZAeurq4ICAjAyZMnsXjxYosJXGtqarB8+XLs///27hbHQSgKw/AXDFeCqSKoJu0augESgkGjWUmTyruHrqCmG6jAsQAsQbSiwbVJScWISWpmxAiSM515nxUc+Sb355xOcs796NHDf7dardR1nbz3X9ZetW2r7XYr6XOFEvAXcGQJADNp21Z1Xet+v8s5pzRNFQSBhmHQ7XZTGIba7XYqisJ61F/v8Xi8HklIUpIkiqJI1+v19YVJlmXy3n/7Bx3wbggyAJjR5XLRfr9X0zQ6n896Pp9aLBbabDaqqkrL5dJ6xLcxTZOOx6MOh4P6vtc4jorjWOv1WmVZKs9z6xGB2RBkAAAAxrhDBgAAYIwgAwAAMEaQAQAAGCPIAAAAjBFkAAAAxggyAAAAYwQZAACAMYIMAADA2AeQQSo0Sm48LgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Review books raiting\n",
    "ax = sns.histplot(df_books_sample[\"average_rating\"].astype(float),\n",
    "            bins= 30,\n",
    "            kde= True)\n",
    "ax.set(xlabel='Average rating', ylabel='Books count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_books_sample[\"text_reviews_count\"].value_counts().head(60).plot(kind='barh', figsize=(10,15))\n",
    "#sns.countplot(y=\"text_reviews_count\", data=df_books_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ok3rXl8Zc9/rA9qka26dqbJ+qsX2q1lTap0kGsqtXryIkJAT29vbYunWrNFG/uLgYn376Kb799lv8+9//xu7du6VeMVNDj+WJYunKp/o5atU5xhZk3ikblvVt6yF9dnJQSp81xbr6rBIREVGjYnYgmzRpErp164Z33333rmX/85//IDo6Gvv37zf3cjXywQcfICsrC0uXLjV6alKpVGLatGn4888/cebMGfzwww/SU5hFRUWVnQ5qtRoA4OTkBABwdnau9jGOjo61u5lyiot1yM4usNj59P+ySE/PRUGBBunpuTU6vrBQg7TbZfVp1cwBhYWa0ro6lP14aYp1NT53Q2DYPlQR26dqbJ+qsX2qxvapWmNpHw8PJyiV8ruWM3sO2cmTJ3HlypVqlb127Rpu3bpl7qVqRK1W4+zZsxAEAQ888ECF/YIgYPDgwQCAS5cuSfPAMjIypB6w8vRzxvRzyfTHpKenV1qHnJwcCIIAT0/P2t1QAyeKIrJy1dL3Hb3Luo65MCwREVH1VKuH7Pr169K6W4bi4+Mxbdq0So8TRREpKSmIi4urt2CSk5ODkpISyGQyaWixPP12rVYLDw8PeHt7IzU1FQkJCfDx8TEqq9PpcOPGDQBAt27djL7GxsaaPH9sbCxEUUSHDh2kXrWmqlCtQ5GmbDiyg7crbmWV9pg52nHZCyIiouqoViDz9fVFfHw8Lly4IG0TBAGZmZnYuXNntS40evRosypYUy1atICHhweys7Nx+PBhk2uLHTt2DADQo0cPAMDgwYOxZcsW7N+/X1oOw7Bsbm4uunfvjlatWgEAOnbsCB8fH8THx+P8+fPo27ev0TF79+4FAAwZMsTCd9fwZOWWDds62iukJyz13+vpSkRodSVQyJvkg71ERES1Uu05ZLNnz8aGDRuk73/88Ue0bNkSgwYNqvQYQRDg5OSEnj171lsgk8lkCAoKwldffYUPP/wQPj4+UKlUAICSkhIsW7YMkZGRcHNzw9NPPw0AmDhxIrZt24YVK1bAz89PClgpKSmIiIgAAISHhxtdZ9KkSXj//fcxe/ZsfPfdd9LLyo8fP441a9ZAqVTi+eefr5d7tqbbOWXDlc1c7Y32GQYyACjS6ODiyEBGRERUXrUDWe/evbFgwQLp+x9//BEdO3Y02tZQvPLKK4iKisKhQ4fw1FNPoX///nB3d0dUVBSSk5Ph5OSE//73v2jevDkAoHv37pg6dSoWL16MCRMmwM/PDw4ODjhx4gQKCgowbtw4jBo1yugaQUFBOHLkCA4fPozAwED4+/sjNzcXp0+fhiiKWLhwYaN6j6W5DOePlQ9k9nbGQ8aFaq30WiUiIiIqY/ZTlmvWrIGra8Nc+0OpVOKrr77C9u3bsX37dkRFRUGtVsPLywv//Oc/8eKLL6JDhw5Gx4SFhcHX1xerV6/GhQsXIAgCfH19ERQUhDFjxlS4hkwmw5IlS7Bu3Tps374dR48ehYuLCwICAhAeHg4/P7/6ul2rul1FIJMJApQKmTR/rFCtrde6ERERNRZmB7KGHjgEQcDTTz8tDUtWx7BhwzBs2LBql1cqlQgJCUFISIg5VWz0cgo0KCgqC1ke5QIZACjlDGRERER3U+uFYaOionD27Fnk5uZCq9VWunQEALz66qu1vRw1ILcyy9Yfs1fKjZ6q1FMqZMDfnWiFai4OS0REZIrZgUyr1WL69OnYvXt3tY9hIGta8ovKXijuYC83+eYCpaJsEn+hhj1kREREppgdyNatW4dffvkFAODu7o6OHTtK728k22A4XGmnMP30pGEgK+KQJRERkUlmB7IdO3ZAEASEhYXhtddek94JSbajwCBgKRWmF+E1DGQFDGREREQmmR3I4uLi0KJFC0ydOpUv2bZRhYY9ZMpq9JBpOIeMiIjIFLO7tZRKJby8vBjGbJhxD9ndAxl7yIiIiEwzO5B1794d8fHx0Gg0lqwPNSKGAavSOWRyziEjIiK6G7MD2aRJk1BQUIBly5ZZsj7UiBgNWVY2h8xgKJPLXhAREZlm9hyywYMH4/nnn8eKFSsQGxuLIUOGwNvbG0pl5a/GGThwoLmXowaoWkOWcsNAxh4yIiIiU8wOZH369JE+Hzx4EAcPHqyyvCAIuHLlirmXowbIaMiykkn9hj1nDGRERESmmR3IqlqR3xLlqeEzHLKszqR+LgxLRERkmtmBLCoqypL1oEbIeFL/3dch4xwyIiIi07iaK5lFFEWjlfor6yFTGK1Dxh4yIiIiUxjIyCzqYh1KDIahK10Y1mBSv1YnQqsrqfO6ERERNTZmD1nOmDGjRuUFQcD8+fPNvRw1MOWHH5UKGUxNE1QqjBcOLtLo4OLIfwcQEREZMjuQ/fjjjxAEweRk/fKr94uiyEDWxBQUFUuf5TIBcpkMWl3FnwW53Dh8Fam1cHGsfGkUIiIiW2R2IBs9enSlr00qKChAWloaLl26BK1Wi5CQEHTu3NnsSlLDU51V+gFAJgiQywToSkrDWlGxDgfOJGHYgHZ1XkciIqLGwuxAtnDhwruWuXXrFiZPnoyffvoJO3fuNPdS1AAZTehXmn7CUtqvkEH394vFizQ6FHMeGRERkZE6nczTqlUrfPrpp7hz5w6WLFlSl5eieladVfr1FHI+aUlERFSVOp9d7ePjg86dO+N///tfXV+K6lFhNYcsAePAVsS1yIiIiCqot8fdMjMz6+tSVA8KimoQyIx6yBjIiIiIyqvzQHb27Flcu3YNnp6edX0pqkdGQ5Z3mUOmMFj6gkOWREREFZk9qf+HH36odJ8oitBoNIiLi5OWxxg8eLC5l6IGqCY9ZIZzyNTFugpLYRAREdk6swPZrFmzKl32wpAoimjZsiVeeuklcy9FDVBNJvUbzSHT6ODMhWGJiIiMmB3I2rRpU/WJFQo0a9YM/fv3x3PPPQdvb29zL0UNUE0m9Rs9ZanWwZkLwxIRERkxO5AdPHjQkvWgRqam65DpcQ4ZERFRRRw7IrNUd6V+gE9ZEhER3Y3ZPWSGUlJScOjQIcTFxSE/Px/Ozs7w8fHBoEGD0LFjR0tcghqYQoN3Wd59YdiyuYYpmfnwbedeZ/UiIiJqjGoVyHQ6HRYtWoQNGzZApyvt+dC/SBwofcn4v/71L8yYMQN2dna1ry01GMY9ZHdb9qIssBVr+dokIiKi8moVyN566y3s3bsXoijC29sbPXv2hIuLC3JycnDlyhWkp6dj06ZNyM7OxmeffWapOpOVFWt10OpE6Xs7ZfWHLPkeSyIioorMDmS//vor9uzZA2dnZ0RERGDUqFFG+0VRxK5duzB37lzs3bsXTz75JIYOHVrrCpP1GU7oB2q27IWWPWREREQVmD2pf8uWLRAEAfPnz68QxoDS4connngCCxYsgCiKVS4kS42L4XClTADksqrXo1Owh4yIiKhKZgeyS5cuwcvLC4GBgVWWCwwMhJeXFy5dumTupaiBMVryQiG/6wLBCvaQERERVcnsQJabm1vtxV5btWqF27dvm3spamCMJvTfZf4YYDyHrEQEdCViFaWJiIhsj9mBzMPDA4mJiXctJ4oiEhMT4e7OpQ6aisIavDaptIxxD5qWw5ZERERGzA5k/fr1Q3Z2NjZt2lRluY0bNyIrKwv9+vUz91LUwNTkxeKA8RwygMOWRERE5ZkdyCZMmABRFPHBBx9g5cqVyM/PN9qfn5+Pr7/+GvPnz4cgCAgKCqp1ZalhMH6xeNVrkAGlD3gYLg7Lif1ERETGzF72YuDAgZg4cSLWr1+PTz/9FP/973/h4+MDFxcX5OXlIT4+HjqdDqIoYsKECQgICLBkvcmKajpkCZT2kmn/XjyYQ5ZERETGarUw7OzZs9G2bVssX74cOTk5uHbtmtF+d3d3hIWFITQ0tFaVpIbF8H2UNQlkwN+BjEOWRERERmr9Lsvnn38ezz77LE6fPo0bN24gLy8Pzs7O6Ny5MwYMGAAHBwdL1JMaELVBICs/P6wyxmuR8SlLIiIiQxZ5ubidnR0CAgI4LGkjiooNesjkVa9BJpUzeNKSQ5ZERETGzJrUn5CQUOX+zZs34+rVq2ZViBo+ox6yGg1ZluKQJRERkbEaBbLU1FRMnjwZI0eOrDSUaTQafPjhhxg7dixeeeUVZGRkWKSi1HCoNWWT+s0bsmQgIyIiMlTtQHbz5k2MGzcOhw4dgiiKOHPmjMlyCQkJUCgUEEURBw8exDPPPIOUlBSLVZisz3DIUlHNIUujHjIGMiIiIiPVCmQ6nQ7/+c9/kJaWhubNm+O9997DyJEjTZbt2rUr/vjjD0RERMDd3R23bt3ClClTIIqcyN1UmDOp32gOmZY/C0RERIaq9dt0165diI6ORseOHfHTTz9h/PjxcHJyqrS8nZ0dxo0bh61bt6Jly5a4fPky9uzZY7FKk3UZTeo3Yw4ZhyyJiIiMVeu36Z49eyAIAt5++214enpW++Tt27fHjBkzIIoidu3aZXYlqWExa9kLBYcsiYiIKlOt36aXL1+Gi4sLHnnkkRpfYOTIkXB2dsbly5drfGxt3b59G4sWLUJgYCB69+6N+++/H6GhoTh+/LjJ8pGRkQgJCcHAgQPRr18/PP3009i6dWulw61arRabN2/G2LFj0b9/f/j5+SE0NBQnTpyoy9uyKlEUywWyai57wacsiYiIKlWtQJaVlYX27dubdQG5XA4fHx/cvn3brOPNdePGDTz11FP49ttvUVxcjMGDB6Nt27Y4evQoQkJCsH//fqPyGzduREhICE6dOoWePXvC398f169fx6xZszBz5swK5xdFEdOnT8ecOXOQlJSEgIAAqFQqREZGIjg4GNu2bauvW61XmuISGMZTc56yZA8ZERGRsWotDKtUKiu8PLwmioqKIJff/SXUlqLVavHGG28gLS0NkyZNwjvvvCNd/6effsL06dPxzjvvYPDgwbC3t0dcXBwiIiLg4uKCtWvXomfPngCAlJQUBAcHY/v27Rg8eLDRgwzbtm3Drl270KNHD6xevRoeHh4AgOPHjyM8PBzz5s1DQEAAWrduXW/3XR8M548B5s4h46R+IiIiQ9X6bdquXTukpKSguLi4xhfQaDRISkpCixYtanysuX799VdcvXoVAwYMwMyZM43C4OjRo/HQQw+hefPm0uK1q1atgk6nQ2hoqBTGAKBNmzaYM2eOVMbQihUrAACzZs2SwhhQ+tL14OBgqNVqrF27tq5u0WoM1yATAMhlZqzUzyFLIiIiI9UKZP3794dWq8WPP/5Y4wv8/PPPUKvV6NWrV42PNZf+ic7Q0FAIQsXAsHLlSvz222/o27cvAODQoUMAgBEjRlQoGxAQAFdXV1y8eBGpqakAgGvXruHmzZto0aIFBgwYUOGYwMBAAMCBAwcscj8NieGLxe3t5Cbb1xQOWRIREVWuWoFszJgxEEURS5YsqdEirykpKfjss88gCAKGDx9udiVr6tKlSwCAfv36ITs7Gxs2bMCcOXPw/vvvY/fu3dDpykJFRkYGMjMzoVQq0blz5wrnksvl0vbo6GgAQExMDIDSNddMBRL99ps3b6KwsNDi92dN6mLjQFZdDGRERESVq1Yg69u3Lx599FFpTtaxY8fueszRo0fx3HPPISMjA926dcNjjz1W68pWh0ajQXJyMhQKBaKjozFy5EjMmzcPmzdvxvr16/H666/jmWeekXq70tLSAAAtW7aETGa6Oby8vIzK6r96e3ubLG9vbw83NzeUlJQ0uVdHGT5h6aCsfiAznGtWIgLFHLYkIiKSVGtSPwBERETg4sWLSEpKwgsvvIDu3bsjICAA3bt3R/PmzaHVapGVlYWYmBhERkYiOjoaoiiiVatWWLFiRaVhx9Ly8vIAlD4FOXnyZPTu3RvTpk1Dp06dEBMTgw8//BAXL17Eyy+/jK1bt6KgoAAA4ODgUOk57e3tAUAqq3/AoTrH1OZhCFOUSjk8PV0tek4A8PR0hZOT3V3PbfdXrvTZ2ckOjo520ve6EhGenq7SNsPPKPfn7+zqAHcXewvVvu7VRZs3JWyfqrF9qsb2qRrbp2pNpX2qHchcXFywZcsWTJ8+Hb///juuXr2KqKgok2X163YNGjQIc+fOrbQnqS5oNBoApa97ateuHb755hsolUoApUOY3333HR599FFcvnwZ+/fvl+pWnblQ+vvSPyRQ3flTTUmRumxSv6N9tX98KjyNWajWNqpARkREVJeq/xsVQPPmzbFy5Urs3r0bW7ZswenTp6HVao3KuLu7Y+jQoXjssccwaNAgi1a2Ogx7rYKCgqQwpufq6oonnngC3377LY4fP46JEycCKF2aozJqtRoApNdFOTs7V/sYR0dHM+6icsXFOmRnF1jsfPp/WaSn56KgQIP09Nwqy6dnlvX4yQAUFmqk77U6EenpudI2w89anQh7pVyag5ZyKwfykoY/bGnYPlQR26dqbJ+qsX2qxvapWmNpHw8PJyirMcWnRoFMb9SoURg1ahSKi4tx8+ZN3LlzB05OTvD09ETz5s2t2nPk6uoKOzs7aDQatGvXzmQZ/fasrCyphywjIwOiKJqsu37OmH4umf6Y9PR0k+dXq9XIycmBIAg1etVUY1BksOxFTSb1A4CDXVkgM5yLRkREZOvMCmR6SqUSvr6+lqqLRcjlcnTt2hWXL1+WJu6Xp59o37x5c3h4eMDb2xupqalISEiAj4+PUVmdTocbN24AALp162b0NTY21uT5Y2NjIYoiOnToUOVL2Bsjw6csazKpHwDsDcqrixnIiIiI9Opnpn09GzJkCABg586dFfaJoogjR44AAPz9/QEAgwcPBoAKr1MCgGPHjiE3Nxfdu3dHq1atAAAdO3aEj48P0tLScP78+QrH7N2716geTYnhOmQONewhs2MgIyIiMqlJBrLx48fDzc0NJ0+exJIlS6TJ+KIo4osvvsClS5fQvn176WXpEydOhFwux4oVK4wCVkpKCiIiIgAA4eHhRteYNGkSAGD27NlGS1scP34ca9asgVKpxPPPP1+Xt2kVao1565CVli/7cWMgIyIiKlOrIcuGysvLC4sXL8Z//vMffPnll9ixYwe6deuGmJgYJCQkwN3dHZ9++ins7EqXZOjevTumTp2KxYsXY8KECfDz84ODgwNOnDiBgoICjBs3DqNGjTK6RlBQEI4cOYLDhw8jMDAQ/v7+yM3NxenTpyGKIhYuXNjk3mMJlBuyrGkgM+wh4xwyIiIiSZMMZADw0EMPYefOnVi+fDkiIyNx+PBhtGzZEuPGjUN4eDjat29vVD4sLAy+vr5YvXo1Lly4AEEQ4Ovri6CgIIwZM6bC+WUyGZYsWYJ169Zh+/btOHr0KFxcXBAQEIDw8HD4+fnV163WK6NXJ3EOGRERkUU02UAGlM71WrBgQbXLDxs2DMOGDat2eaVSiZCQEISEhJhTvUap/JBlTYIVAxkREZFpTXIOGdWdIqMhy5rleU7qJyIiMo2BjGpEbaEhS42m4S8KS0REVF/qZMgyNTUV+/fvh06nw6BBgxrcWmVkPsOFYWs8qd+OPWRERESm1KqHLCoqCi+++CIWLVokbTtz5gxGjhyJ+fPnY9GiRXjiiSfwzTff1Lqi1DAYBqma95Bx2QsiIiJTzO4hS05OxrPPPov8/Hyj1w3NmzcPhYWFcHFxQevWrREbG4tPPvkEAwYMQN++fS1RZ7ISURRrtTAsJ/UTERGZZnYP2erVq5GXl4e+fftiypQpAEp7zGJiYqBUKrFt2zb8/PPPmD17NkRRxLp16yxWabKOYm0J/l5jF0DtApmGgYyIiEhidiCLjIyEvb09li5dinvuuQcApFcS/eMf/0DHjh0BABMmTECzZs1w5swZC1SXrKmoXIiq+Ur9ZeWLuDAsERGRxOxA9tdff8HHxwfNmzeXth07dgyCIGDgwIHSNkEQ0KZNG6PXC1HjVH51fbsaziHjshdERESmmR3IDOeNAYBarca5c+cAoMIq9bm5uVAomvQatDbBMJAp5AJk5X4G7oZDlkRERKaZHcjatWuHxMREFBYWAgCOHj0KjUYDDw8PaQgTAG7cuIGkpKQKryqixsdwyFIhr/mPjvGkfq5DRkREpGd2ILv//vtRUFCAmTNn4sCBA/jkk08gCAKGDx8ulbl8+TKmTp0KURTx0EMPWaTCZD3GPWRmBDKuQ0ZERGSS2eOIL774Inbv3o29e/di7969EEURzs7OCAsLA1A66T80NBSiKKJNmzZ4/vnnLVZpsg7DRWEV8poNVwLG65AVa0tQUiJCJqv5eYiIiJoas3vIvL29sXnzZowcORKdOnXC0KFDsX79erRr1w5A6Yu9ZTIZHn74YWzevNlo8j81ToZPRioVtRuyBNhLRkREpFermfbt27fHZ599ZnJf27ZtcezYMXh4eNTmEtSAqC04hwwondjvaM+HPYiIiMzuIbt9+/Zdy+jDWGFhId5//31zL0UNxKW4sj9zcwJZ+V419pARERGVMjuQhYSEICcn567lTp48iSeeeAIbN24091LUQBguVaFU1HzulyAIRnPPuDgsERFRKbMDWXR0NEJCQpCbm2tyf2FhISIiIvDcc88hKSkJDg4OZleSGgatrmypCnN6yMofp+HSF0RERABqEci6dOmCy5cv44UXXkB+fr7RvpMnT+LJJ5/Ehg0bUFJSAj8/P+zYsaPWlSXr0urKXmRpiUDGIUsiIqJSZgeyNWvWQKVS4c8//0RYWBgKCwuNesUSExPh4uKCiIgIrFmzBh06dLBkvckKLNNDVjZkyUBGRERUyuxH3Jo3b441a9YgJCQEZ8+eRUhICDIzM5GUlARRFPHII49g7ty58PT0tGR9yYqMApkZc8gA9pARERGZUqs1Bzw8PPD999/jueeew/nz5wEAnp6emDVrFgIDAy1RP2pAtNqyIUsle8iIiIgsxuwhSz03NzesWbMG9957LwCgc+fOGDp0aK0rRg2PRmvhSf18ypKIiAhANXvIfvjhh7uWGTVqFGJiYnDy5ElMnjwZI0eOrFDmmWeeqXkNqcHQaMsCVPlFXqtLziFLIiKiCqoVyGbNmgVBqN6cIVEUcezYMRw7dqzCPgayxs1wHTJHBzkOn0+utKxcLpjcb7QOGQMZERERgGoGsjZt2tR1PaiB0+pKjJa9sFfKjSb5V3ZMecZDllyHjIiICKhmIDt48GBd14MauLzCYqPv7e3MG7LkpH4iIqKKaj2pn2xDvkEgk8kEyGVc9oKIiMhSarXshV5KSgoOHTqEuLg45Ofnw9nZGT4+Phg0aBA6duxoiUuQlRn2kNkrZX/PKRQrP6ASDGREREQV1SqQ6XQ6LFq0CBs2bIBOV/rLVRRF6QEAQRDwr3/9CzNmzICdnV3ta0tWk1eolT6b+4QlYDxkqWEgIyIiAlDLQPbWW29h7969EEUR3t7e6NmzJ1xcXJCTk4MrV64gPT0dmzZtQnZ2Nj777DNL1ZmsIL+orIfMrlaBjD1kRERE5ZkdyH799Vfs2bMHzs7OiIiIwKhRo4z2i6KIXbt2Ye7cudi7dy+efPJJLhjbiBkPWVqmh0xdzKcsiYiIgFpM6t+yZQsEQcD8+fMrhDGgdLjyiSeewIIFCyCKYrUWl6WGy3BSv53S/GdBjHrIuFI/ERERgFoEskuXLsHLy+uu76wMDAyEl5cXLl26ZO6lqAGwVA8ZV+onIiKqyOxAlpubC29v72qVbdWqFW7fvm3upagBqJshSwYyIiIioBaBzMPDA4mJiXctJ4oiEhMT4e7ubu6lqAFIzsiXPltqyLJYW4KSkpovnUFERNTUmP2btV+/fsjOzsamTZuqLLdx40ZkZWWhX79+5l6KGgDD+V6W6iED2EtGREQE1CKQTZgwAaIo4oMPPsDKlSuRn59vtD8/Px9ff/015s+fD0EQEBQUVOvKkvUYBidLLXsBcC0yIiIioBbLXgwcOBATJ07E+vXr8emnn+K///0vfHx84OLigry8PMTHx0On00EURUyYMAEBAQGWrDfVI1EUjYJTrSb1y9hDRkREVF6tFoadPXs22rZti+XLlyMnJwfXrl0z2u/u7o6wsDCEhobWqpJkXUUaHQynetnbmT+HTBAEKOQCtLrSE3ItMiIiIgu8y/L555/Hs88+i9OnT+PGjRvIy8uDs7MzOnfujAEDBsDBwcES9SQrMlylHwDsFOb3kAGlw5bav1+1xR4yIiIiC71c3M7ODgEBARyWbKLyDd5j6WivgKzcsGNNlc4jYyAjIiLSs0ggO3v2LH777TfExcUhPz8fzs7O8PHxwUMPPYSBAwda4hJkRYZrkDk71P5HxmgtMq7WT0REVLtAlpeXh+nTp+PgwYMASid/6wmCgNWrV+Ohhx7CRx99xHXIGjHDQObiqKz1+bhaPxERkTGzA1lJSQlefvllnD59GqIool+/fujVqxdcXFyQk5ODS5cu4cKFCzhy5AimTJmC77//3pL1pnpk6UDG1fqJiIiMmR3IfvzxR5w6dQotW7bEkiVL0Ldv3wplzp49iylTpuDkyZPYuXMnnnzyydrUlazEcFJ/VYFMIRdw+HzyXc9nuBaZhkOWRERE5i8M+9NPP0EQBHz66acmwxgA9O/fH4sXL4Yoivjpp5/MvRRZmdEcsrv0kGl1d1/GwjCQFbGHjIiIyPxAFhMTg7Zt28LPz6/Kcv7+/mjbti1iYmLMvRRZWb6FJ/UrOamfiIjIiNmBLD8/H82aNatW2ebNmyMnJ8fcS1nMlClT0K1bN2zfvt3k/sjISISEhGDgwIHo168fnn76aWzdutXoYQVDWq0WmzdvxtixY9G/f3/4+fkhNDQUJ06cqMvbqHd5BsteWGQOmYI9ZERERIbMDmSenp6Ij49HcXFxleU0Gg3i4uLQokULcy9lEVu3bsW+ffsq3b9x40aEhITg1KlT6NmzJ/z9/XH9+nXMmjULM2fOrFBeFEVMnz4dc+bMQVJSEgICAqBSqRAZGYng4GBs27atLm+nXlV3Dll1KQ2HLNUMZERERGYHsvvuuw95eXlYsWJFleVWrFiBvLw83HfffeZeqtbi4uIwf/78KvdHRETAxcUFW7ZswTfffIPly5dj9+7d6NChA7Zv3469e/caHbNt2zbs2rULPXr0wP79+7FkyRKsW7cO3377Lezs7DBv3jz89ddfdX1r9cLyT1kaBDKNtoqSREREtsHsQBYcHAxBELB06VK89957uHHjhtH+GzduYO7cuVi2bBlkMhkmTZpU68qaQ6PR4M0334RMJkPPnj1Nllm1ahV0Oh1CQ0ONyrRp0wZz5syRyhjSB9FZs2bBw8ND2j5w4EAEBwdDrVZj7dq1Fr4b68ivwaT+6lAquA4ZERGRIbMD2T333IPXX38doihi8+bNeOyxx9CvXz8MGjQI/fr1w2OPPYYtW7ZAFEVMnToVvXv3tmS9q+2zzz7D5cuXMWfOHLRu3dpkmUOHDgEARowYUWFfQEAAXF1dcfHiRaSmpgIArl27hps3b6JFixYYMGBAhWMCAwMBAAcOHLDUbdQLhVzAgTNJRttKSkQUFJX1YlkikBn3kDGQERERmR3IAODFF1/Ef//7X3Ts2BGiKKKwsBDp6ekoLCyEKIrw8fHB559/jrCwMEvVt0YiIyPx3Xff4bHHHsNTTz1lskxGRgYyMzOhVCrRuXPnCvvlcrm0PTo6GgCkJ0a7du0KQaj4Xkf99ps3b6KwsNBSt1MvisstW5FfVAzDRxpcHCwxqb+szRjIiIiILPAuy8DAQAQGBiIhIQE3btxAXl4enJ2d0blzZ/j4+Figiua5ffs2pk2bhlatWuG9996rtFxaWhoAoGXLlpDJTOdTLy8vo7L6r97e3ibL29vbw83NDXfu3EFGRgbat29v7m1YXW5B2XClTAAc7eW1PqfhpH4155ARERFZ5uXiANCxY0d07NjRUqertXfffReZmZlYvXo13NzcKi1XUFAAAHBwcKi0jL29vVHZ/Pz8ah+jL2spSqUcnp6uFj0nAHh6usLR0Q66EtHo/Gm5Gumzg50CXl5ucHS0g1wmQFdiejkQABX268/r6GgHF+eyXjF1cUmd3I+lNYY6WhPbp2psn6qxfarG9qlaU2kfiwUyvdTUVFy5cgWiKKJXr16V9iLVpfXr1+PgwYN48cUX4e/vX2VZfa+YqaHH8vTrkcnl8mof09jdyVNLn+3tat87BhivQ1ao1kIURZtoSyIiosrUOJAdPXoUsbGxcHZ2xqBBg6SJ8iUlJfjggw+wZcsW6HSlPSCCIGDkyJGYO3cu3N3dLVvzSsTGxuKjjz5Cr1698Nprr921vLOzMwCgqKio0jJqdWkocXJyqvExjo6O1at4NRUX65CdXWCx8+n/ZZGenovCQg20OhHp6bnS/uTUsgV9lQqZVE4uF6DTVdFDVm6//ryFhRroDJ6s1JWI+OtWjtGTlw2JYftQRWyfqrF9qsb2qRrbp2qNpX08PJygVN69Q6PagSw2NhZTpkxBfHy8tE0mkyE8PBxTpkzB/PnzsXHjRqNV7UVRxJ49e5CYmIjNmzdXOkfLkj755BMUFRXBwcEBM2bMMNp3+fJlAMCWLVsQGRmJ+++/X3oiMiMjo9KeGv2cMf1cMn2vX3p6usk6qNVq5OTkQBAEeHp6WubGrCTPYA6ZfTV+oKqjfPgq0mihVNhZ5NxERESNUbUC2e3btzFp0iRkZWXBzs4OXbt2hVarRWxsLL766itotVps2rQJcrkcL730Eh555BEIgoDDhw9j+fLluHTpEnbu3InRo0fX8e2UzfM6c+YMzpw5Y7LMuXPncO7cOSgUCvzrX/+Ct7c3UlNTkZCQUOFBBJ1OJ62x1q1bN6OvsbGxJs8fGxsLURTRoUMHqVetoSsoKkZMYjbcnI2DkeGkfosNWcqNQ2+RRgfXxtFMREREdaJagez7779HVlYWhgwZgoULF0oLod68eRNhYWFYuXIlAOD999/HuHHjpOO6deuGTp06YcqUKdi3b1+9BLKqFmOdPHkyDhw4gAULFmDs2LHS9sGDB2PLli3Yv39/hSU6jh07htzcXHTv3h2tWrUCUPoAg4+PD+Lj43H+/Hn07dvX6Bj9qv5DhgyxzE3Vgw2/xeJ0VDpkAjDSrwNaupcOteYVlk3qt1QPmSAIsFfKpUVh+YJxIiKyddUaQzxy5AgUCgUWLVpktCp9hw4dMG3aNIiiCDs7O4wZM6bCsSNGjECLFi2kNbwaookTJ0Iul2PFihU4f/68tD0lJQUREREAgPDwcKNj9G8emD17NjIyMqTtx48fx5o1a6BUKvH888/XfeUtJPLSLQBAiQj8fCxe2m7YQ2antNyQs2FvG9ciIyIiW1etHrKUlBS0b9/e5MT8/v37AygNZwqF6dO1bdtWWky1IerevTumTp2KxYsXY8KECfDz84ODgwNOnDiBgoICjBs3DqNGjTI6JigoCEeOHMHhw4cRGBgIf39/5Obm4vTp0xBFEQsXLqz0zQANXVZu2ZOVuQavTXKwUA8ZADjYyZHz94ogfJ8lERHZumoFspycHHTq1MnkPv0aX/onD01eRKGQnjpsqMLCwuDr64vVq1fjwoULEAQBvr6+CAoKMtnzJ5PJpBeKb9++HUePHoWLiwsCAgIQHh4OPz8/K9yFZeTklw1T5hWUfbazcCDTYw8ZERHZumoFMlEUpbW3ytM/OVnZ/oZk2bJlVe4fNmwYhg0bVu3zKZVKhISEICQkpLZVa1ByDEKYYQ+ZpSb1A8a9bQxkRERk6xrm4k9Ur0rKrbifk18awtTFOmiKy95taalJ/QDgYF/2bwEOWRIRka1jICNotMY9VCWiiJIS0WgNMsDCgcygt01dzB4yIiKybdVeGDY3NxenTp0ya39ubsNeRdfWmVp2IjtPjVyDJS+UChlkMsu93sieQ5ZERESSGq3Ur1/qoTxBEKrcTw2b4bCkXsadImgMeq4s2TsGlL6oXK9IzUBGRES2rdqBzPCVSObgy6MbLnVxxTlcmXeKAIM/stpO6FfIBRw+nyx9b/SUpYnrExER2ZJqBbIDBw7UdT3Iikz3kBXC3qAXyxI9ZFpd2XW47AUREVGZagWytm3b1nU9yIpMzSHLuFMEd5ey91o6WHDJi/Ln46uTiIjI1lV7yJKaLlNDlhl3iowm8dfpHDIGMiIisnEMZGRyyDLzThGcDNYKs+SisOXPx3XIiIjI1nEdMjI5ZJiZU4Q7Biv221vwxeIA55AREREZYiAjkwuz6kpEJKXlSd9bfg4ZhyyJiIj0GMio0pXyDYOSpeeQ2bOHjIiISMJARkYLwFbG0nPIHA3Op9WVGC2JQUREZGsYyKha75K0/FOWxufj+yyJiMiWMZDRXXvIBKH0XZaWVL7Hja9PIiIiW8ZARkZPWfq0dkXHVq5G+0XR8q++kstkkBusc1bEHjIiIrJhDGRkNFzoYCfHW+P7oqW7g7SthcFnS1LIy378uBYZERHZMgYyMgpkcpkMzg5KzHnuftzXzRNeHo7o3bl5nVxXITfoIeOTlkREZMO4Uj8ZzSGT/x2SXByVmDymNwDgtzOJdXJdhcG8NL7PkoiIbBl7yMgoDBnO66prdgoOWRIREQEMZAQgJSNf+qyQ1d+PhPEcMvaQERGR7WIgIxRrKw5Z1gfDQMYhSyIismUMZASdTpQ+y+uxh8xwbbNCBjIiIrJhDGQErWEgq6KHTCEXcPh8ssWua/yUJeeQERGR7WIgI+hKyt4jqbjLpH5LvnNSyacsiYiIADCQEYxDlrXmkHFSPxER2TIGMrLaHDIGMiIiolIMZDZOVyJCV2IYyKyzDlmBurjerktERNTQMJDZOE25l3rL5fX3I2FvJ5c+5xYwkBERke1iILNxFQJZPfaQOTCQERERAWAgs3nln26sz0n99sqyQFao1lr0CU4iIqLGhIHMxqkNeshkMgEyoR4DmUEPGcBeMiIisl0MZDbOMJDV53AlUPqUpeHisLkFmnq9PhERUUPBQGbjDOeQKepxuFLPsJcsh4GMiIhsFAOZjTOcQ1afa5DpOdgppM8csiQiIlvFQGbjrN5DpuSTlkRERAxkNs6ac8iA8ktfcMiSiIhsEwOZjTPsISu/KOzh88l1fn0GMiIiIgYym2c8h6ysh+zw+eR6WRfMcMgyJ59DlkREZJsYyGycurgsdBm+7Lu+Fmk16iErZA8ZERHZJgYyG2c0h6zcpH65XKjzYUu+z5KIiIiBzOYZPWVpYlJ/XfeUcQ4ZERERA5nNU1cxqb8+GPaQFap1KNbyfZZERGR7GMhsnMbay14ojd9nmVfIYUsiIrI9DGQ2zugpSyssDCsv9z7LnHwOWxIRke1hILNxaqM5ZNb5cTBarZ9PWhIRkQ1iILNxVT1lWV+MnrTkWmRERGSDGMhsnPG7LBtADxmftCQiIhuksHYF6tKOHTvwww8/ICoqCoWFhWjRogX+8Y9/ICwsDL6+vhXKR0ZGYuXKlYiKikJRURE6d+6M8ePH45lnnoEgmFgSQqvFtm3bsHnzZsTHx0OhUKB3794ICwuDv79/fdxirVW2Un99Ml4clj1kRERke5pkD5koinjzzTcxbdo0nD17Fr6+vnjooYcgl8vx008/YezYsTh69KjRMRs3bkRISAhOnTqFnj17wt/fH9evX8esWbMwc+ZMk9eYPn065syZg6SkJAQEBEClUiEyMhLBwcHYtm1bfd1urTSIIUuj1yexh4yIiGxPk+wh27lzJ3bt2gVPT0+sWrUK3bt3BwDodDp88cUXWL58OaZNm4Zff/0Vzs7OiIuLQ0REBFxcXLB27Vr07NkTAJCSkoLg4GBs374dgwcPxsiRI6VrbNu2Dbt27UKPHj2wevVqeHh4AACOHz+O8PBwzJs3DwEBAWjdunW9339NaBrCpH6u1k9ERDauSfaQ/fDDDwCAN998UwpjACCXyzF16lR07doVmZmZOHbsGABg1apV0Ol0CA0NlcIYALRp0wZz5syRyhhasWIFAGDWrFlSGAOAgQMHIjg4GGq1GmvXrq2T+7Mkay97ARj3kCWl51mlDkRERNbUJAOZm5sbfH19cd9991XYJwgCOnXqBABITU0FABw6dAgAMGLEiArlAwIC4OrqiosXL0rlr127hps3b6JFixYYMGBAhWMCAwMBAAcOHLDMDdUha0zqL/+OTMMesiKDgEhERGQrmuSQ5dKlSyvdp9PpcPnyZQBA69atkZGRgczMTCiVSnTu3LlCeblcjs6dO+PPP/9EdHQ0vL29ERMTAwDo2rWrycn++u03b95EYWEhHB0dLXRnlqe20kr9hu/INJzUr2YgIyIiG9QkA1lVNmzYgOTkZHh4eGDgwIFISEgAALRs2RKySuZQeXl5AQDS0tKMvnp7e5ssb29vDzc3N9y5cwcZGRlo3769xeqvVMrh6elqkXPpdCXQ6kTpe2dnezjaK+Dp6QpHRzvIZQJ0JaLJY6vad7f95fd5GLy+slhXAlc3RzjYN6wfTUu1eVPF9qka26dqbJ+qsX2q1lTap0kOWVbm+PHj+OijjwAAb731FpydnVFQUAAAcHBwqPQ4e3t7AJDK5ufnV/sYfdmGyLB3DAAUVlr2wtFBYTRcmnq7wCr1ICIispaG1Q1Rhw4dOoSpU6dCo9EgKCgI48aNAwCpV8zU0GN5oljaqyOXy6t9jKUVF+uQnW2ZwJJTbhHW4mItiot1SE/PRWGhBnK5AJ2ukl6uKvbdbX/5fVqdiBbuDlIQi47LgJPCOuGwPP2/vNLTc61ck4aJ7VM1tk/V2D5VY/tUrbG0j4eHE5QGD69VxiZ6yNauXYtXXnkFRUVFmDhxIubOnSvtc3Z2BgAUFRVVerxarQYAODk51fiYhjx/zMVRieZupb187s6lQ5SKchPu64une1lvY3p25e1KRETUFDXpHjKtVov3338fmzdvhiAIeP311/HSSy8ZldHPA8vIyIAoiiZ7vfRzxvRzyfTHpKenm7yuWq1GTk4OBEGAp6enxe7H0mSCgEWvPojvfr4M72YO0r0bTrivL54eZcE1Pbuw3q9PRERkTU22h6yoqAjh4eHYvHkzHB0d8fnnn1cIYwDg4eEBb29vaDQaaYK/IZ1Ohxs3bgAAunXrZvQ1NjbW5LVjY2MhiiLat28v9ao1VK1aOKNHp+ZwtPIkegYyIiKyZU0ykOl0Orzyyis4evQoWrRogTVr1hitsl/e4MGDAQD79++vsO/YsWPIzc1F9+7d0apVKwBAx44d4ePjg7S0NJw/f77CMXv37gUADBkypPY3YyM8PQyHLBnIiIjItjTJQPbVV1/h6NGjcHJywvfff4977723yvITJ06EXC7HihUrjAJWSkoKIiIiAADh4eFGx0yaNAkAMHv2bGRkZEjbjx8/jjVr1kCpVOL555+30B01fYY9ZBl3iqQHKIiIiGxBk5tDdufOHXzzzTcASud86V9xZMoTTzyBwYMHo3v37pg6dSoWL16MCRMmwM/PDw4ODjhx4gQKCgowbtw4jBo1yujYoKAgHDlyBIcPH0ZgYCD8/f2Rm5uL06dPQxRFLFy4sMG/x7IhaeleFsiKtSW4k6+Bh4u9FWtERERUf5pcIDt58qS0Xlh8fDzi4+MrLdujRw9puDIsLAy+vr5YvXo1Lly4AEEQ4Ovri6CgIIwZM6bCsTKZDEuWLMG6deuwfft2HD16FC4uLggICEB4eDj8/Pzq5P6aKicHBeyVMqiLSx8oSM8uZCAjIiKb0eQC2fDhwxEdHW3WscOGDcOwYcOqXV6pVCIkJAQhISFmXY+MOTsqoS4uXS4kPbsQXdt5WLdCRERE9aRJziGjxsnFUSl95lpkRERkSxjIqMEwDmR80pKIiGwHAxk1GAxkRERkqxjIqMFwcWIgIyIi28RARg2GYQ9Zdp4GmmKdFWtDRERUfxjIqMFwslfA8FWiGXc4sZ+IiGwDAxk1GDKZYNRLlppVYMXaEBER1R8GMmpQ3J3tpM8pGflWrAkREVH9YSCjBsXDtWx1/uR0BjIiIrINDGTUoHi4lPWQJTGQERGRjWAgowbFsIcsJSMfWl2JFWtDRERUPxjIqEFxc1ZKT1qWiCLSsrgeGRERNX0MZNSgyGUyuBo8aZmUnmfF2hAREdUPBjJqcNxcOLGfiIhsCwMZNTiGS18kc+kLIiKyAQxk1OAYBTIOWRIRkQ1gIKMGx91g6Yu0rEK+05KIiJo8BjJqcJwdlVDIS380RQB/ZfIVSkRE1LQxkFGDIxMEtGnpJH3PJy2JiKipYyCjBqltSxfp881UBjIiImraGMioQercxk36HJ2YZcWaEBER1T0GMmqQunfwkD4npuYhv6jYepUhIiKqYwxkZHUKuYDD55ONtrVp6Qx7pRxA6cT+mJvZ9V8xIiKiesJARg1C+ZeIC4IA7+aO0vdRDGRERNSEMZBRg+XVrCyQRd/MwoEzSVasDRERUd1hIKMGy7tZ2dIXiWl5yOM8MiIiaqIYyKjBcnNWwsGubB5ZRnahdStERERURxjIqMESBMFo2DIti4GMiIiaJgYyatC8DQLZrdt8hRIRETVNDGTUoLVu6Sx9zi0oRnJGvhVrQ0REVDcYyKhBc3FUopmrvfT9meg0K9aGiIiobjCQUYPXzrOsl+x0VLoVa0JERFQ3GMiowWvnVfai8aT0PKRyLhkRETUxDGTU4Lk62Rn3knHYkoiImhgGMmoU7uvmJX0+Hc1hSyIialoYyKhRGNDNU/qccCsXMYnZfJUSERE1GQxk1OAo5AIOn0822tampTM8PRyk77ceugaNVlffVSMiIqoTDGTUIGl1JUbfC4KAfqqW0vfXU3KQnM41yYiIqGlgIKNGw7u5E9oYLBR74XomCtVaAODwJRERNWoMZNSo3OvbAoJQ+jmvsBgR359Gcnoeisv1qBERETUmDGTUqLg522HYgHbS97duFyBiTWkoIyIiaqwYyKjBKz/Jf/zDXdGrU3Ppe01xCSIv3sKhsxy2JCKixomBjBoFw0n+MpmAPl1aYHDfNlAqSn+ERQBr98dg57E4K9WQiIjIfAxk1Gi19XTGw/3bGr18/Kff4/Db6UQr1oqIiKjmGMioUXN3sce7/x4AN2c7aduG32Jx/PItK9aKiIioZhjIqNFr7uaAR+5vCyd7hbRt1c9XsP9UIkRRtGLNiIiIqoeBjJoEZwclBvdrA1cnJYDSOWWbDsTi291XEX8rh8GMiIgaNMXdi1BV4uLisHTpUpw5cwaZmZlo1aoVHn30UYSHh8PJycna1Wtyyj9xafi9q5MdpgX1w8L1Z5FfVLpg7LGLt3Ds4i24OCoxflgX/KNXK8j0C5kRERE1EOwhq4ULFy5g7Nix+Pnnn9GyZUsMGTIEBQUFWL58OcaPH4+8PK6NVRfKv1ZJ/71CLiA2+Q5G+LVHc4OJ/kDpIrKrdl3FgrVncCX+NnvMiIioQWEPmZm0Wi3eeOMNFBQU4MMPP8QzzzwDACgqKsLrr7+OgwcP4tNPP8WcOXOsXFPbotWVwNFegUfub4e4lFzE/ZWDzDtF0Mev6yk5+GTTebT1dIaPtysAwNXZDu29XNCmhTOaudpDqZAhOT0fKZn5aNfKDV3ae1jtfoiIyDYwkJnpl19+QWJiIgYOHCiFMQBwcHDA/Pnz8fDDD2PLli147bXX4O7ubsWa2iaFXIau7d3RqY0bBqg8sWT7RSSmlfVYJqfn1+jl5F4ejhjavy0G3dsGTg78z4aIiCyLv1nMdPDgQQDA8OHDK+xr1qwZ/P39cejQIfz+++94/PHH67t69DeFXEDUzSwM6tMaqbcLcDkuC7duF9T4PGnZhdh88Bq2HLwGuVyATBDgYK+Ak70CHi528GrmCDdneyjlpfPTCtU6FBXr4Ggvh7uTHVyd7eDmbAd3Jzt4uNrD2UEBoYq5bCWiCK22BHZKudn3TkREjQcDmZliYmIAAN26dTO5v0uXLjh06BCioqIYyKxMW1I6x8y7uRNauDuij28LbD18HVpdCWSCgNwCDW7nqFGo1kKjLZuP5u5ijyKNDvmFxdK5RABanQhAhEarQU6+BrduFyDqZnaN6qSQC/BwsYe7ix3sFHLIZQLUxTrkFRZL/xNFwN5ODg+X0gDnYCeHTCagpESEKAJKhazsf/LSrwp56bRQjbYExcW60q/aEggCIAgCBAGQ6b/KBAgQIJOV7pPp98kEyCBAkJV+X7pNf1xpuRIR0GpLoFDKodGWIDevCMV/X0uplMPZQQGlQgYBgvQyeEEABAj4+/9K/58I6EpE6EpElJT7CgAyfR1kQoW6yGSC9IciorRN9NfR75c+C2X1KK/K2YSV7KzsmPJzE52dS+cy5uerK7/E3/U3vHdRBOQyAXK5ALlMBoWs8vqbUf06OdCcazk72UFE1e1jSeZOHTXvMDOPMjjMyal0fcWCAo1Z56rJtap9jLk/VXVwmJPj3+1TWK59zLyWo4MCD9zTCs3dHMw7QS0xkJkpLS0NAODt7W1yv5eXl1E5S1Eq5fD0dLXoOYcMaG9y+9+/L2u8rzbH1sc+AcCbz95nvF8E3F3skJ2nhiiW/hLXBwddiYgitRaFaq3Zf6ETEVHD52CvgKeni1WuzUBmpoKC0mEvBwfTSVq/XV+uIfNwsb97IRvRzNU6/zIiIiLbxmUvzCSXl87tqWoeEFBxCIOIiIioPAYyMzk7OwMACgsLTe4vKioCADg6OtZbnYiIiKhxYiAzk36OWHp6usn9+rlj+nJERERElWEgM5P+6cpr166Z3K/fXtlTmERERER6DGRmGjx4MABg3759FfZlZWXhxIkTUCqVeOCBB+q7akRERNTIMJCZafjw4WjTpg2OHj2K9evXS9uLiorw7rvvoqCgAM888wxatmxpxVoSERFRYyCIfAzQbCdOnEBYWBiKiorQq1cvtGvXDufOnUNaWhp69uyJtWvXwsXFOuuZEBERUePBQFZLMTExWLJkCU6ePImCggK0a9cOgYGBCA0NZRgjIiKiamEgIyIiIrIyziEjIiIisjIGMiIiIiIrYyAjIiIisjIGMiIiIiIrYyAjIiIisjIGMiIiIiIrYyAjIiIisjIGMiIiIiIrU1i7AmQdcXFxWLp0Kc6cOYPMzEy0atUKjz76KMLDw+Hk5GTt6tVafHw8Ro8ejbFjx2LOnDkmy0RGRmLlypWIiopCUVEROnfujPHjx+OZZ56BIAgVymu1Wmzbtg2bN29GfHw8FAoFevfujbCwMPj7+5u8xp07d7Bq1Sr8+uuvSElJgZubGx588EG88soraN++vUXv+W527NiBH374AVFRUSgsLESLFi3wj3/8A2FhYfD19a1Q3tbaZ8+ePVi/fj0uX74MURTRvn17jBo1CiEhIXBwcKhQ3tbap7wpU6Zg3759WLBgAcaOHVthv620T1xcHEaOHFllmePHj6N58+bS97bSNoZu376NlStX4uDBg0hJSYGDgwPuvfdevPDCCxg4cGCF8rbYRlyp3wZduHABwcHBKCgowL333ovWrVvj7NmzSE9PR7du3bBhw4ZG/dqnjIwMTJo0CdevX8fEiRNNBrKNGzfivffeg1KphL+/P5RKJf744w8UFhZi7NixWLBggVF5URTx1ltvYdeuXXB3d4efnx+ys7Nx5swZiKKIDz/8EE8//bTRMdnZ2Zg4cSKuXbuGDh06oEePHoiLi0NMTAxcXFywfv16dO/evU7bonzd9X9JNW/eHFFRUUhOToaDgwOWLl2KBx980CbbBwC+/PJLLFmyBHK5HAMGDICLiwsuXLiAjIwMdO3aFevXr4e7u7vNtk95W7duxaxZswDAZCCzpfb55Zdf8MYbb6BLly7o0aOHyTLz5s2Ds7MzANtqG70bN24gODgYaWlpaNu2LXr27ImkpCRcvXoVgiDgiy++wIgRI6TytthG+hshG1JcXCwOGzZMVKlU4tatW6XthYWF4ksvvSSqVCpx3rx5Vqxh7Vy5ckUcPny4qFKpKr2XGzduiD169BD79+8vXr58WdqenJwsPvLII6JKpRL37NljdMzWrVtFlUolPvXUU2JWVpa0PTIyUuzdu7fYu3dvMSUlxeiYt99+W1SpVOI777wjarVaaftXX30lqlQq8fHHHxd1Op2F7rxyP/30k6hSqcQHHnhAvHr1qrRdq9WKn376qahSqcSBAweKeXl5oijaXvucOnVKVKlU4n333WfUPvn5+WJoaKioUqnE9957T9pua+1T3o0bN8S+fftK/41t27atwn5bap+FCxeKKpVK3LRp013L2lrbiGLp75ynnnpKVKlU4gcffGBUnx9//FFUqVRiv379xKKiIlEUbbON9BjIbIz+l3NwcHCFfbdv3xb79u0r9urVS8zOzq7/ytVCdna2+NFHH4n33HOPqFKpxIcffrjSQDZz5kxRpVKJS5curbDvyJEjokqlEp9++mmj7fq/CE6dOlXhmE8++URUqVTiokWLpG2JiYlijx49xPvuu0/Mzc2tcMw///lPUaVSiYcPHzbndmvk2WefFVUqlbh9+/YK+0pKSsTHHntMVKlU4r59+0RRtL320d/vihUrKuz7888/RZVKJQ4ZMqRCeVtpH0NqtVocM2aM2L9/f3H06NEmA5mttc+kSZNElUplFB4qY2ttI4qiuHv3blGlUolBQUFiSUlJhf0vvPCCOGzYMPHcuXOiKNpmG+lxUr+NOXjwIABg+PDhFfY1a9YM/v7+KC4uxu+//17fVauVNWvWYNWqVWjevDm++uorjB49utKyhw4dAgCjLnK9gIAAuLq64uLFi0hNTQUAXLt2DTdv3kSLFi0wYMCACscEBgYCAA4cOCBt+9///gedToeAgACTw7+mjqkrbm5u8PX1xX333VdhnyAI6NSpEwBI92tr7TNv3jzs3r0b48ePr7BPp9MBAORyubTN1trH0GeffYbLly9jzpw5aN26tckyttY+V65cgb29Pbp27XrXsrbWNkDp3EwACA0NNTn3a+XKlfjtt9/Qt29fALbZRnoMZDYmJiYGANCtWzeT+7t06QIAiIqKqrc6WUKrVq0wffp07Nu3Dw8//HCl5TIyMpCZmQmlUonOnTtX2C+Xy6Xt0dHRAMrarGvXrib/QtFvv3nzJgoLC42OUalUJutRn+28dOlS7N692+QkVZ1Oh8uXLwMAWrdubZPto1Ao4OvrCzc3N6Ptf/31FxYtWgQA0hwpW2wfvcjISHz33Xd47LHH8NRTT5ksY2vtk5iYiJycHPj4+GDjxo0YO3Ys+vXrB39/f0yePBkXLlyQytpa2+hdunQJANCvXz9kZ2djw4YNmDNnDt5//33s3r1b+kcPYLttpMdAZmPS0tIAAN7e3ib3e3l5GZVrLMaNG4fnn3/e5NNwhvT31bJlS8hkpn/8y7fB3drM3t4ebm5uKCkpQUZGRrWOaSjtvGHDBiQnJ8PDwwMDBw5k+wBYuHAhgoKCMGzYMFy8eBHPP/88XnrpJaP62Fr73L59G9OmTUOrVq3w3nvvVVrO1tpHHzaio6OxaNEiODs74x//+AecnJxw4MABBAUFYefOnUZ1sZW2AQCNRoPk5GQoFApER0dj5MiRmDdvHjZv3oz169fj9ddfxzPPPCP1dtliGxliILMxBQUFAFBpcNFv15drau52/0Dpf8CGZfPz86t9jL6s/lhHR0eT5RtCOx8/fhwfffQRAOCtt96Cs7Mz2wfAtm3bcPbsWeh0OshkMqSnp0t/kdtq+7z77rvIzMzEokWLKvQkGrK19rly5QqA0h6V3bt3Y+3atfjqq69w4MABvPHGG9BqtXj33XeRkJBgc20DAHl5eQBKn4KcPHkyVCqV9N/Xpk2b0Lt3b1y5cgUvv/wydDqdTbaRIQYyG6OfC2Oqa9eQ2ERXQ9H/q+tu9w+UtUF128xQQ2/nQ4cO4aWXXoJGo0FQUBDGjRsHgO0DADt37sSff/6JrVu3on///vj5558RFBSEgoICm2yf9evX4+DBgwgNDa10PSc9W2uf1157Db/++ivWrl2Ljh07SttlMhnCw8MxdOhQaDQabNy40ebaBijtIQNKp0a0a9cO33zzDe655x44OzujX79++O677+Dp6YnLly9j//79NtlGhhjIbIx+LRz9WHp5RUVFACr/l0Njp79//X2aolarAUBaILcmx+jbrSG389q1a/HKK6+gqKgIEydOxNy5c6V9bJ/SuXT6RStXrlwJlUqFpKQkbNmyxebaJzY2Fh999BF69eqF11577a7lba19FAoFOnToYLToq6Fhw4YBAC5evGhzbQMY91oFBQVBqVQa7Xd1dcUTTzwBoLTH3hbbyBBX6rcxXl5eyM7ORnp6uslJ3voxc/0YelOjnzOQkZEBURRN/gupfBvoj0lPTzd5TrVajZycHAiCAE9Pz2odY4121mq1eP/997F582YIgoDXX39dmhulZ8vtY4qdnR0effRRxMTE4MqVK9LTu7bSPp988gmKiorg4OCAGTNmGO3TPwyyZcsWREZG4v7775eeTrOV9rkb/ZOohYWFNvnflqurK+zs7KDRaNCuXTuTZfTbs7KybLKNDLGHzMbon668du2ayf367ZU9hdnYeXh4wNvbGxqNBgkJCRX263Q63LhxA0BZG+i/xsbGmjxnbGys9Kod/b/a7nZMfbdzUVERwsPDsXnzZjg6OuLzzz+vEMYA22yfL774AlOnTsWtW7dM7rezswNQGmhtrX30c2jOnDmDn3/+2eh/+vY6d+4cfv75Z5w7d87m2ufDDz/Eq6++Wmk9/vrrLwClT4HbWtsApUOD+uVA9BP3y9PPz2zevLlNtpEhBjIbM3jwYADAvn37KuzLysrCiRMnoFQq8cADD9R31eqNvg32799fYd+xY8eQm5uL7t27o1WrVgCAjh07wsfHB2lpaTh//nyFY/bu3QsAGDJkiLTtoYcegkwmw9GjR01ODNW3/9ChQ2t7O3el0+nwyiuv4OjRo2jRogXWrFlT5bv3bK19jh07hj179uCXX34xuf9///sfAKB3794AbKt91q5di+joaJP/0w/HLViwANHR0Vi4cCEA22qfy5cv49dff8Xu3btN7t+xYweAsrrbUtvo6eumf9rUkCiKOHLkCABI8xNtsY0k9bQALTUQhYWF4pAhQ0SVSiWuW7fOaPvLL78sqlQqce7cudaroIV88cUXla7Uf/XqVenVHPrVoUXR+NUcv/zyi9Ex69atk16nkZ6eLm3Xv5qjV69eFV7N8Z///EdUqVTiW2+9JarVamn78uXLRZVKJY4aNcrkytWW9uWXX4oqlUrs27evGBMTc9fyttY+O3bskNrnzz//lLZrNBrx448/ll4tdefOHVEUba99KqP/+6L8Sv221D67du0SVSqVeM8994jHjx+Xtmu1WnHRokWiSqUSR4wYIb0WyJbaRi81NVW87777RJVKJX755ZfSdUtKSsTPP/9cVKlU4rBhw6R62mIb6fHl4jboxIkTCAsLQ1FREXr16oV27drh3LlzSEtLQ8+ePbF27dpG/XJxoOxl0ZW9XPzrr7/G4sWLIZfL4efnBwcHB5w4cQIFBQUYN24cPvjgA6PyJSUlePnll3H48GG4uLjA398fubm5OH36NERRxMKFCyu8HSAtLQ0TJkxAYmIi2rRpg969e0svr3Vzc8O6devqvEv8zp07GDJkCAoKCuDj4yP18pjyxBNPSP86tZX20ZszZw42b94MmUyGfv36wc3NDVevXsWtW7fg4eGBr7/+Gn369JHK21r7mDJ58mQcOHDA5MvFbal95s6di02bNkEQBPTp0wfe3t64dOkSkpOT4enpie+//x6+vr422TZ6R44cwX/+8x8UFRWhQ4cO6NatG2JiYpCQkAB3d3esWrUK9957r1TeFtsIABjIbFRMTAyWLFmCkydPoqCgAO3atUNgYCBCQ0MbfRgD7h7IgNLXYqxevRqXL1+WXiEUFBSEMWPGmFyUsLi4GOvWrcP27duRkJAAFxcX9OjRA+Hh4fDz8zN5jdu3b2PZsmU4ePAg0tLS4OXlBT8/P7zyyismH6qwtF9//RWvvvpqtcpOmzYNoaGh0ve20D6Gdu/ejY0bN+Ly5cvQaDRo06YNhgwZgtDQUJMLSNpa+5RXVSADbKt99uzZgw0bNuDKlSvQaDRo3bo1Hn74YYSFhZl8AtOW2kYvISEBy5cvR2RkJDIzM9GyZUs8+OCDCA8PN1kfW2wjBjIiIiIiK+OkfiIiIiIrYyAjIiIisjIGMiIiIiIrYyAjIiIisjIGMiIiIiIrYyAjIiIisjIGMiIiIiIrYyAjIiIisjIGMiIiIiIrYyAjIklSUhK6deuGbt26Yfr06Xctf+LECal8Q/XOO++gW7dueOutt6xdlTqVl5eHefPmYdCgQbjnnnvw4IMPYuvWrdauFhFVEwMZEZn0008/4dChQ9auBlXTW2+9hQ0bNiA9PR2dOnVCs2bN0LZtW2tXi4iqSWHtChBRwzV79mz88ssvcHd3t3ZVqAoFBQU4fPgwAOC9997D+PHjrVshIqox9pARkUmCICA9PR0ffPCBtatCd3Hnzh2IoggA8PPzs3JtiMgcDGREZNLEiRMBADt37sSBAwesXBuqik6nkz7b2dlZsSZEZC4OWRKRSf/+978RFRWF06dPY+7cuRgwYAA8PDyqdez27dsxY8YMeHt748iRIxX2JyUlYdiwYQCAAwcOoF27dgCAL7/8EkuWLMFLL72EZ599FkuWLMGhQ4eQlZUFb29vPPbYY3jllVdgZ2eHEydO4Ouvv8aFCxegVqvh6+uL4OBgjB49utJ6xcXF4fPPP8cff/yBoqIi+Pj44PHHH8ekSZNgb29v8phTp05h7dq1OHv2LLKzs+Hm5oa+ffvi3//+NwYOHFih/MMPP4zk5GTs2LEDP/zwA3bs2AGtVotOnTrh22+/rVYbxsXFYfXq1YiMjMRff/0Fe3t7+Pr6YtSoURg/fjwcHByksuUfqNC365gxY7Bw4cIqr/POO+/gxx9/xIwZMzBo0CB89tlnOHXqFDQaDTp27IgxY8Zg/PjxJtumqKgI27Ztw2+//Ybo6Gjk5OTAzs4Obdq0wYMPPoiQkBB4e3tXOK6wsBCbNm3Cjh07kJCQAKVSif79+2Py5Mm4du0aZsyYYbLuGo0GGzduxO7du3Ht2jUUFxejdevWGDJkCEJDQ+Hl5VXhWnfu3MG3336L33//HUlJSVCr1fDy8oKfnx8mTZrUoB9GIdvDQEZEJgmCgAULFuDJJ59Eeno6IiIisHjx4nq5dkJCAp566ilkZWWhS5cukMvlSExMxPLly5GYmIj7778f8+bNg6OjI3x8fJCUlIQrV65g+vTpKCoqMjmH6tq1a3jmmWeQn5+Prl27QqfTISoqClFRUdizZw++++67CnPlPvnkE6xcuRIA4O7uDpVKhbS0NBw4cAAHDhzACy+8gLffftvkPcybNw9nz55Fly5dUFhYCDs7u2qFsZ07d+Ldd9+FRqOBg4MDVCoV8vPz8eeff+LPP//Etm3bsHLlSrRq1QoA0L9/f2g0Gly6dAkAcM8998DOzg4+Pj7Vbu/o6Gh88cUXKCgoQNeuXaHVanH16lVcvXoV+/btw4oVK+Dq6iqVv337NoKDgxETEwNBENChQwe0bt0aqampiI2NRWxsLHbu3Int27dL9QSArKwshIWF4cKFCxAEAb6+vpDJZDh8+DB+//13PPTQQybrl5aWhrCwMFy9ehWCIKBNmzbw8PDAtWvXsHr1avz0009YtmwZBgwYIB2TnZ2Nf/7zn0hISICdnR06dOgApVKJhIQEKSgvW7as0msS1TuRiOhviYmJokqlElUqlRgfHy+Koih+//330rZff/3VqPwff/wh7TO0bds2UaVSiYMGDbrrdRITE6XtX3zxhbT90UcfFePi4qR9//3vf6V93bt3FxcsWCCq1WpRFEWxoKBADA0NFVUqlTh06FCja02fPl06bvjw4WJ0dLS07/z58+IDDzwgqlQq8e233zY6buPGjaJKpRLvu+8+cceOHdL2kpIS8ZdffhH79u0rqlQqccuWLUbHDR06VLreL7/8Im3PzMw02RaGzp8/L/bs2VNUqVTirFmzxNzcXGnflStXxBEjRogqlUocM2aMWFxcfNf2vBvDthk6dKh45coVad/Zs2fFgIAAUaVSibNnzzZ53PDhw43+jERRFI8cOSL26dNHVKlU4sKFC432vfHGG6JKpRKHDRsmXr16Vdp+7do18bHHHpPqMn36dGlfSUmJ+K9//UtUqVRiUFCQeP36dWlfTk6OOGPGDFGlUon+/v5iWlqatO/jjz8WVSqVOH78eKO2z8nJEV999VVRpVKJgYGB1W4rorrGOWREVKV///vfuP/++wEAc+fORVZWVr1cd8GCBUa9PC+++CLkcjkAYMCAAXjnnXek+VKOjo4ICwsDACQnJ+POnTsVzieXy/HVV19BpVJJ2/r06YOPP/4YAPDzzz8jOTkZQOnw2JdffgkAmD9/Pp588knpGEEQMGrUKKln7Msvv4RWq61wvfvuuw+jRo2Svm/evPld7/mLL76AVqvFgw8+iIiICLi4uEj7evTogVWrVsHBwQGXL1/GL7/8ctfzVZdMJsOyZcvQo0cPaVu/fv2waNEiAMDWrVuRmpoKANBqtTh9+jQEQcCMGTMq9MQNGjRIuu+YmBhp+/Xr17Fr1y4IgoClS5eie/fu0j5fX1+sWLHCaChW78CBAzh37hy8vLywatUqdO7cWdrn6uqKDz/8EH369EFWVhZWr14t7YuKigIABAYGGrW9q6srZs2ahYCAANx///0oKiqqaXMR1QkGMiKqkiAImD9/PhwdHZGRkYGIiIg6v6aHhwf69OljtM3R0VH6xWpqmMlwDlFeXl6F/QMHDoSvr6/J7e3atUNJSQl+//13AMC5c+eQkZEBZ2dnaU5WeU8++SRkMhlSU1Nx5cqVCvsNh8+qo6CgACdOnAAATJo0yWSZ9u3b45FHHgEAiz5o8Y9//MMoIOk9+OCDUtvo16RTKBT47bff8Oeff2LIkCEVjhFFEU5OTgBgFHZ+/fVXAKXtYmruVtu2baV7M/Tbb78BAB555BHpvIYEQZACs+G6efqguGrVKuzcuRO5ubnSPm9vb3z33XeIiIgwGQKJrIFzyIjorjp06IA333wTH3zwAX755ReMHDkSI0aMqLPrtW7d2uR2fY+Yqd4mhaLsrzPx7yUgDPXs2bPS63Xr1g1JSUm4fv06ACA2NhYAUFxcLD1taopcLkdJSQlu3LiBe++912ifp6dnpceZkpiYiOLiYgCl88Aqc88992DXrl2Ii4ur0fmrUr7uhvRtEx8fb7Td3t4emZmZOH/+POLj45GUlIQbN27g6tWrUg9lSUmJVF7fpqaCn57+3gzpe9kOHTok9XqVl5OTAwCIj4+HKIoQBAGhoaHYu3cv0tPT8fbbb0OhUKB3794ICAjAQw89hD59+kAQhErrQlTfGMiIqFqeffZZ7Nu3D6dOncJ7772H++67r86u5ejoWOV+mazmnfvOzs533afv0dH3pmg0Gpw9e/au59YHAkM17Xkx7NUznEBfnn4YMz8/v0bnr0pVC//qe6UM7zE9PR2LFi3C3r17pRAJlP659e7dGzqdDmfOnDE6j36o21Qvl57hEK2evl3++usv/PXXX1Xeh06nQ35+PlxcXNC6dWvs2LEDK1aswN69e5Gamopz587h3LlzWLp0Kdq2bYuZM2ea7JUjsgYGMiKqFv3Q5ZNPPonMzEy8//77CAoKqvIYUz1VQOnSB/WtoKCg0n36AObm5gagLBD26tUL27dvr/vKwTgw5ubmokWLFibL6XufqgqYNVVV2+gDkb4+arUawcHBuH79Ojw8PBAUFIR77rkHvr6+6NChA+RyOT777LMKgUzfpqaGk/VMhUz9cbNnz8azzz5bo/tq0aIFZs6ciZkzZyI6OhonT57EH3/8gaNHjyI5ORlTpkzBpk2bquwhJKovnENGRNWmH7oEgD179mDfvn0my+kn32s0GpP709LS6qaCVbhx44bJ7aIo4urVqwAgTfjv1KkTgNIhMFMT9vXH/fHHH4iPj6/0PmtCvywDAGkJC1P0+zp27Fjra+rphxNN0Q8TdunSBUDpnK7r169DoVBg8+bNmDp1Kh555BF06tRJ+nO/detWhfPo2zY6Ovqu1zKk/7Ooqo5//fUXzp8/b/RzlZqaKq03B5QOvf773//G0qVLceDAAbRt2xY6na7CECmRtTCQEVGNPPvss9LreTZs2GCyTLNmzQCU9uZkZmZW2K+f4F2fjh49Kj0paOi3337DrVu3YGdnhwcffBAAcP/998PV1RX5+fmV9pD9/PPPCA4OxqOPPmoygNSUk5MT/P39AQBr1qwxWSYxMREHDx4EYPrBBnMdOXIE6enpFbYfOnQIf/31F+zs7PDwww8DKF3UFyjtoTO11llGRob0Xk3DNwjo5xyeO3dOmqtn6Pbt2yZ/LoYOHQoA2L17t8mfJQCYOXMm/vWvf+GNN94AUPok6OjRoxEcHCzVxVDLli2lgGg4z43ImhjIiKhG9EOXTk5OlQ5J9unTB0qlEqIoYv78+VIvRXFxMb7//nts2bKlPqsMoHSY9OWXX5aWtgCAyMhIvPvuuwBKn2zUD8s5OTlJy2h8+OGH2LZtm9Ev7t9++w1z584FADz66KPo0KGDRer46quvQqFQ4OjRo5g9e7bR8F5UVBRefPFFqNVqdO/evco3EtRUQUEBJk+ebDRH68SJE5gxYwYAICwsTJrXpl924s6dO/j++++NfgbOnz+PkJAQZGdnAzAemu7RowceeeQRlJSU4NVXX8W1a9ekfSkpKXj55ZdNDmeOGjUKKpUKOTk5CA0NNeopy8vLw3vvvYfIyEgIgiD9mSkUCjz22GMASv/8Lly4YHTO/fv34+jRowAsG2yJaoNzyIioxtq3b48333yz0iUw3N3dERoaiuXLl2PXrl34/fff0a5dOyQnJyM7OxtBQUE4ePCgyR6rujJ06FAcO3YMI0aMkFbPT0hIAAAMHz4cU6dONSr/4osvIjExEVu2bMHMmTPx8ccfo127dkhNTZWGxvr372/Rl6/369cPH374IWbNmoUtW7Zg586d8PX1RUFBgfRUpUqlwpIlSyz6zkofHx9cvXoVjzzyCFQqFQoKCqSnKh9//HGEh4dLZR9++GH069cP586dw/z587Fy5Up4e3sjPT0dqampEAQBAQEBiIyMRFpamvTUIwBEREQgISEBsbGxePzxx6W3MMTGxkKhUKBbt26Ijo6Whj4BQKlUYtmyZXjhhRdw9epVPP744+jUqRMcHR0RHx8vzX+bMWOGUbh6/fXXcebMGVy5cgXjxo1D27Zt0axZM6SlpUl/fkFBQQxk1GCwh4yIzDJx4kRp6NKU119/HZ988gkGDBiA4uJixMXFoVOnTvj444/x3nvv1V9F/9a/f39s2rQJAQEBSEpKQlpaGvr06YP58+fjiy++kOZv6QmCgIiICHzzzTcYPnw4FAoFrl69ivz8fPTt2xezZs3C999/X+VTg+YYPXo0duzYgX/+859o2bIlYmNjkZWVhf79+2POnDn44Ycf0L59e4tes3fv3ti4cSMCAgKQkJCArKws+Pn54bPPPsPixYuNwp9cLsfq1avx1ltvoUePHigsLERMTAwUCgVGjRqFdevWYdmyZbC3t0d2drbRU6rNmzfHli1b8Morr6Bz5864efMmUlJSMHToUGzZsgV9+/YFUPEp2/bt2+PHH3/EtGnT0KdPH6SnpyMmJgbOzs4IDAzEunXrEBwcbHSMs7Mz1q5diylTpqBXr17Izs5GVFQURFHEsGHDsGLFCqv8HBJVRhArG3MgIqImTf9y8SeeeAKffPKJtauDKVOmYN++fXj99dfx0ksvWbs6RPWKPWRERFTnjh07hmHDhuG1114zub+wsBAnT54EUPUivkRNFQMZERHVuR49eiA1NRV79+7FN998Y7ScSGZmJt58801kZWWhc+fOGDhwoBVrSmQdnNRPRER1rnnz5njrrbewYMECfPTRR/j666/Rtm1bFBYWSq+NatWqFT7//PMK8/mIbAEDGRER1YvnnnsOAwYMwPfff4+LFy/ixo0bUCqV6NKlCx555BFMnDhRWsOOyNZwUj8RERGRlXEOGREREZGVMZARERERWRkDGREREZGVMZARERERWRkDGREREZGVMZARERERWRkDGREREZGVMZARERERWdn/Abt7U5QV65R9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Review pages distribution\n",
    "ax = sns.histplot(df_books_sample[\"num_pages\"].astype(float),\n",
    "            #bins= 30,\n",
    "            kde= True)\n",
    "ax.set(xlabel='Number of pages', ylabel='Books Count')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text_reviews_count       0\n",
       "average_rating           0\n",
       "description              0\n",
       "num_pages               72\n",
       "publication_year         0\n",
       "image_url                0\n",
       "book_id                  0\n",
       "ratings_count            0\n",
       "title                    0\n",
       "title_without_series     0\n",
       "author                   0\n",
       "name                     0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Check if books have values with 0\n",
    "df_books_sample.isin([0]).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Replace pages with value 0 with the median\n",
    "pages_median = df_books_sample['num_pages'].replace(0,pd.NA).median()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books_sample[\"num_pages\"] = df_books_sample[\"num_pages\"].replace(0,pages_median)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "text_reviews_count      0\n",
       "average_rating          0\n",
       "description             0\n",
       "num_pages               0\n",
       "publication_year        0\n",
       "image_url               0\n",
       "book_id                 0\n",
       "ratings_count           0\n",
       "title                   0\n",
       "title_without_series    0\n",
       "author                  0\n",
       "name                    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books_sample.isin([0]).sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books_sample = data_cleaning_books(df_books_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>text_reviews_count</th>\n",
       "      <th>average_rating</th>\n",
       "      <th>description</th>\n",
       "      <th>num_pages</th>\n",
       "      <th>publication_year</th>\n",
       "      <th>image_url</th>\n",
       "      <th>book_id</th>\n",
       "      <th>ratings_count</th>\n",
       "      <th>title</th>\n",
       "      <th>title_without_series</th>\n",
       "      <th>author</th>\n",
       "      <th>name</th>\n",
       "      <th>description_text_clean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>27</td>\n",
       "      <td>3.33</td>\n",
       "      <td>A love story about a girl who comes back from ...</td>\n",
       "      <td>208</td>\n",
       "      <td>2015</td>\n",
       "      <td>https://images.gr-assets.com/books/1415581655m...</td>\n",
       "      <td>22965343</td>\n",
       "      <td>52</td>\n",
       "      <td>Who Is Mackie Spence?</td>\n",
       "      <td>Who Is Mackie Spence?</td>\n",
       "      <td>8456833</td>\n",
       "      <td>Lin Kaymer</td>\n",
       "      <td>[love, story, girl, come, back, neardeath, exp...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10</td>\n",
       "      <td>3.83</td>\n",
       "      <td>Military service was once taken for granted as...</td>\n",
       "      <td>256</td>\n",
       "      <td>2006</td>\n",
       "      <td>https://s.gr-assets.com/assets/nophoto/book/11...</td>\n",
       "      <td>80118</td>\n",
       "      <td>72</td>\n",
       "      <td>AWOL: The Unexcused Absence of America's Upper...</td>\n",
       "      <td>AWOL: The Unexcused Absence of America's Upper...</td>\n",
       "      <td>45600</td>\n",
       "      <td>Kathy Roth-Douquet</td>\n",
       "      <td>[military, service, taken, granted, natural, p...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>61</td>\n",
       "      <td>3.95</td>\n",
       "      <td>Raenef is the black sheep of the demon court, ...</td>\n",
       "      <td>192</td>\n",
       "      <td>2003</td>\n",
       "      <td>https://images.gr-assets.com/books/1288648957m...</td>\n",
       "      <td>29497</td>\n",
       "      <td>4399</td>\n",
       "      <td>Demon Diary, Volume 01</td>\n",
       "      <td>Demon Diary, Volume 01</td>\n",
       "      <td>16589</td>\n",
       "      <td>Kara</td>\n",
       "      <td>[raenef, black, sheep, demon, court, minimal, ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>207</td>\n",
       "      <td>4.02</td>\n",
       "      <td>Set in South Carolina in 1964, The Secret Life...</td>\n",
       "      <td>333</td>\n",
       "      <td>2003</td>\n",
       "      <td>https://images.gr-assets.com/books/1444392146m...</td>\n",
       "      <td>6081433</td>\n",
       "      <td>3607</td>\n",
       "      <td>The Secret Life of Bees</td>\n",
       "      <td>The Secret Life of Bees</td>\n",
       "      <td>4711</td>\n",
       "      <td>Sue Monk Kidd</td>\n",
       "      <td>[set, south, carolina, secret, life, beestells...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>4.09</td>\n",
       "      <td>Cathy Cramer is a former lawyer and investigat...</td>\n",
       "      <td>305</td>\n",
       "      <td>2013</td>\n",
       "      <td>https://images.gr-assets.com/books/1356054466m...</td>\n",
       "      <td>16182612</td>\n",
       "      <td>47</td>\n",
       "      <td>Truth Stained Lies</td>\n",
       "      <td>Truth Stained Lies</td>\n",
       "      <td>19811</td>\n",
       "      <td>Terri Blackstock</td>\n",
       "      <td>[cathy, cramer, former, lawyer, investigative,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   text_reviews_count  average_rating   \n",
       "0                  27            3.33  \\\n",
       "1                  10            3.83   \n",
       "2                  61            3.95   \n",
       "3                 207            4.02   \n",
       "4                   6            4.09   \n",
       "\n",
       "                                         description  num_pages   \n",
       "0  A love story about a girl who comes back from ...        208  \\\n",
       "1  Military service was once taken for granted as...        256   \n",
       "2  Raenef is the black sheep of the demon court, ...        192   \n",
       "3  Set in South Carolina in 1964, The Secret Life...        333   \n",
       "4  Cathy Cramer is a former lawyer and investigat...        305   \n",
       "\n",
       "   publication_year                                          image_url   \n",
       "0              2015  https://images.gr-assets.com/books/1415581655m...  \\\n",
       "1              2006  https://s.gr-assets.com/assets/nophoto/book/11...   \n",
       "2              2003  https://images.gr-assets.com/books/1288648957m...   \n",
       "3              2003  https://images.gr-assets.com/books/1444392146m...   \n",
       "4              2013  https://images.gr-assets.com/books/1356054466m...   \n",
       "\n",
       "    book_id  ratings_count                                              title   \n",
       "0  22965343             52                              Who Is Mackie Spence?  \\\n",
       "1     80118             72  AWOL: The Unexcused Absence of America's Upper...   \n",
       "2     29497           4399                             Demon Diary, Volume 01   \n",
       "3   6081433           3607                            The Secret Life of Bees   \n",
       "4  16182612             47                                 Truth Stained Lies   \n",
       "\n",
       "                                title_without_series   author   \n",
       "0                              Who Is Mackie Spence?  8456833  \\\n",
       "1  AWOL: The Unexcused Absence of America's Upper...    45600   \n",
       "2                             Demon Diary, Volume 01    16589   \n",
       "3                            The Secret Life of Bees     4711   \n",
       "4                                 Truth Stained Lies    19811   \n",
       "\n",
       "                 name                             description_text_clean  \n",
       "0          Lin Kaymer  [love, story, girl, come, back, neardeath, exp...  \n",
       "1  Kathy Roth-Douquet  [military, service, taken, granted, natural, p...  \n",
       "2                Kara  [raenef, black, sheep, demon, court, minimal, ...  \n",
       "3       Sue Monk Kidd  [set, south, carolina, secret, life, beestells...  \n",
       "4    Terri Blackstock  [cathy, cramer, former, lawyer, investigative,...  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_books_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "author_id    0\n",
       "name         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_authors_sample.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "authors_group = df_books_sample.groupby('name', as_index=False)['book_id'].count().sort_values(by='book_id', ascending=False).head(10)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>name</th>\n",
       "      <th>book_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>Agatha Christie</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6324</th>\n",
       "      <td>James Patterson</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14330</th>\n",
       "      <td>Stephen King</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4860</th>\n",
       "      <td>Francine Pascal</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15864</th>\n",
       "      <td>William Shakespeare</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11616</th>\n",
       "      <td>Nora Roberts</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14930</th>\n",
       "      <td>Terry Pratchett</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2043</th>\n",
       "      <td>C.S. Lewis</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>Anonymous</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11422</th>\n",
       "      <td>Neil Gaiman</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                      name  book_id\n",
       "196        Agatha Christie       54\n",
       "6324       James Patterson       49\n",
       "14330         Stephen King       42\n",
       "4860       Francine Pascal       37\n",
       "15864  William Shakespeare       36\n",
       "11616         Nora Roberts       31\n",
       "14930      Terry Pratchett       28\n",
       "2043            C.S. Lewis       26\n",
       "1113             Anonymous       23\n",
       "11422          Neil Gaiman       23"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "authors_group"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Top 10 authors in the dataset sample\n",
    "ax = sns.set(rc={'figure.figsize':(16,9)})\n",
    "\n",
    "\n",
    "sns.barplot(data= authors_group, x='name', y='book_id', hue='name', dodge=False).set(xticklabels=[],xlabel='Author', ylabel='Books Count');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 523874 entries, 0 to 523873\n",
      "Data columns (total 11 columns):\n",
      " #   Column        Non-Null Count   Dtype \n",
      "---  ------        --------------   ----- \n",
      " 0   user_id       523874 non-null  object\n",
      " 1   book_id       523874 non-null  int64 \n",
      " 2   review_id     523874 non-null  object\n",
      " 3   rating        523874 non-null  int64 \n",
      " 4   review_text   523669 non-null  object\n",
      " 5   date_added    523874 non-null  object\n",
      " 6   date_updated  523874 non-null  object\n",
      " 7   read_at       435137 non-null  object\n",
      " 8   started_at    307493 non-null  object\n",
      " 9   n_votes       523874 non-null  int64 \n",
      " 10  n_comments    523874 non-null  int64 \n",
      "dtypes: int64(4), object(7)\n",
      "memory usage: 44.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_reviews_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
       "      <td>On loan to Diana Sonn</td>\n",
       "      <td>Wed Dec 05 12:25:38 -0800 2007</td>\n",
       "      <td>Wed Mar 22 11:45:51 -0700 2017</td>\n",
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      "text/plain": [
       "                            user_id   book_id   \n",
       "0  8842281e1d1347389f2ab93d60773d4d  20873740  \\\n",
       "1  8842281e1d1347389f2ab93d60773d4d  23310161   \n",
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       "4  8842281e1d1347389f2ab93d60773d4d      1953   \n",
       "\n",
       "                          review_id  rating   \n",
       "0  ef77b0cd7d5db0a85aa64f291d63d450       5  \\\n",
       "1  f4b4b050f4be00e9283c92a814af2670       4   \n",
       "2  8666dfd554e310084c4bc72b2d357f3d       4   \n",
       "3  28007503b429dc6724da7076ae7306b4       5   \n",
       "4  70c420b9b26d869a1ef8548b20a9edeb       4   \n",
       "\n",
       "                                         review_text   \n",
       "0  I haven't read a ton of \"history of the world\"...  \\\n",
       "1                        Fun sequel to the original.   \n",
       "2  A hilarious romp through the known and unknown...   \n",
       "3  One of the most enjoyable stories I've read in...   \n",
       "4                              On loan to Diana Sonn   \n",
       "\n",
       "                       date_added                    date_updated   \n",
       "0  Tue May 17 20:59:38 -0700 2016  Tue May 17 20:59:38 -0700 2016  \\\n",
       "1  Tue Nov 17 11:37:35 -0800 2015  Tue Nov 17 11:38:05 -0800 2015   \n",
       "2  Tue Jul 10 20:26:21 -0700 2012  Wed Sep 12 23:32:04 -0700 2012   \n",
       "3  Tue Jan 13 11:12:38 -0800 2009  Wed Mar 22 11:46:19 -0700 2017   \n",
       "4  Wed Dec 05 12:25:38 -0800 2007  Wed Mar 22 11:45:51 -0700 2017   \n",
       "\n",
       "                          read_at                      started_at  n_votes   \n",
       "0  Sat Aug 13 00:00:00 -0700 2016  Fri Jul 01 00:00:00 -0700 2016       68  \\\n",
       "1                             NaN                             NaN        7   \n",
       "2  Mon Sep 10 00:00:00 -0700 2012  Tue Sep 04 00:00:00 -0700 2012        1   \n",
       "3  Sat May 23 00:00:00 -0700 2009                             NaN        5   \n",
       "4                             NaN                             NaN        2   \n",
       "\n",
       "   n_comments  \n",
       "0           5  \n",
       "1           0  \n",
       "2           0  \n",
       "3           0  \n",
       "4           0  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(523874, 11)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(523874, 7)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Remove unnecesary columns\n",
    "df_reviews_sample.drop(columns=['date_added','date_updated','read_at','started_at'], inplace= True)\n",
    "df_reviews_sample.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0</td>\n",
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       "      <th>3</th>\n",
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       "      <td>One of the most enjoyable stories I've read in...</td>\n",
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       "      <td>0</td>\n",
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       "      <th>4</th>\n",
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       "      <td>1953</td>\n",
       "      <td>70c420b9b26d869a1ef8548b20a9edeb</td>\n",
       "      <td>4</td>\n",
       "      <td>On loan to Diana Sonn</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                            user_id   book_id   \n",
       "0  8842281e1d1347389f2ab93d60773d4d  20873740  \\\n",
       "1  8842281e1d1347389f2ab93d60773d4d  23310161   \n",
       "2  8842281e1d1347389f2ab93d60773d4d  12953520   \n",
       "3  8842281e1d1347389f2ab93d60773d4d     77430   \n",
       "4  8842281e1d1347389f2ab93d60773d4d      1953   \n",
       "\n",
       "                          review_id  rating   \n",
       "0  ef77b0cd7d5db0a85aa64f291d63d450       5  \\\n",
       "1  f4b4b050f4be00e9283c92a814af2670       4   \n",
       "2  8666dfd554e310084c4bc72b2d357f3d       4   \n",
       "3  28007503b429dc6724da7076ae7306b4       5   \n",
       "4  70c420b9b26d869a1ef8548b20a9edeb       4   \n",
       "\n",
       "                                         review_text  n_votes  n_comments  \n",
       "0  I haven't read a ton of \"history of the world\"...       68           5  \n",
       "1                        Fun sequel to the original.        7           0  \n",
       "2  A hilarious romp through the known and unknown...        1           0  \n",
       "3  One of the most enjoyable stories I've read in...        5           0  \n",
       "4                              On loan to Diana Sonn        2           0  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews_sample.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 523874 entries, 0 to 523873\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count   Dtype \n",
      "---  ------       --------------   ----- \n",
      " 0   user_id      523874 non-null  object\n",
      " 1   book_id      523874 non-null  int64 \n",
      " 2   review_id    523874 non-null  object\n",
      " 3   rating       523874 non-null  int64 \n",
      " 4   review_text  523669 non-null  object\n",
      " 5   n_votes      523874 non-null  int64 \n",
      " 6   n_comments   523874 non-null  int64 \n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 28.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_reviews_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reviews_sample['review_text'] = df_reviews_sample['review_text'].astype(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 523874 entries, 0 to 523873\n",
      "Data columns (total 7 columns):\n",
      " #   Column       Non-Null Count   Dtype \n",
      "---  ------       --------------   ----- \n",
      " 0   user_id      523874 non-null  object\n",
      " 1   book_id      523874 non-null  int64 \n",
      " 2   review_id    523874 non-null  object\n",
      " 3   rating       523874 non-null  int64 \n",
      " 4   review_text  523874 non-null  object\n",
      " 5   n_votes      523874 non-null  int64 \n",
      " 6   n_comments   523874 non-null  int64 \n",
      "dtypes: int64(4), object(3)\n",
      "memory usage: 28.0+ MB\n"
     ]
    }
   ],
   "source": [
    "df_reviews_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reviews_sample_p = data_cleaning_reviews(df_reviews_sample)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>afcd096765a8c354be0706ab1324999b</td>\n",
       "      <td>555999</td>\n",
       "      <td>a4f8b16d5c0c5dc08d0f3fa118a262b4</td>\n",
       "      <td>5</td>\n",
       "      <td>My mother gave me this book as a gift... it's ...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[mother, gave, book, gift..., adorable.]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523871</th>\n",
       "      <td>bd2f9703203bda8e2defdfffc7eb5a10</td>\n",
       "      <td>17262138</td>\n",
       "      <td>9d9c9843ac68b2c5ec877641b3c29baa</td>\n",
       "      <td>4</td>\n",
       "      <td>This is an interesting snapshot of Paris right...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[interesting, snapshot, paris, right, time, ro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523872</th>\n",
       "      <td>1be08d25e7d8114b3c49a126ed57acdf</td>\n",
       "      <td>13596809</td>\n",
       "      <td>9517373abb3f5933a479cc5032f669d7</td>\n",
       "      <td>5</td>\n",
       "      <td>The author is brilliant. The storyline was wel...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[author, brilliant., storyline, well, made, st...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>523873</th>\n",
       "      <td>1be08d25e7d8114b3c49a126ed57acdf</td>\n",
       "      <td>164719</td>\n",
       "      <td>95aabb1cb49d31fe899ea2dd9dfc3632</td>\n",
       "      <td>5</td>\n",
       "      <td>I thought the end of this story would be a tra...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>[thought, end, story, would, tragedy..., ive, ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>523874 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 user_id   book_id   \n",
       "0       8842281e1d1347389f2ab93d60773d4d  20873740  \\\n",
       "1       8842281e1d1347389f2ab93d60773d4d  23310161   \n",
       "2       8842281e1d1347389f2ab93d60773d4d  12953520   \n",
       "3       8842281e1d1347389f2ab93d60773d4d     77430   \n",
       "4       8842281e1d1347389f2ab93d60773d4d      1953   \n",
       "...                                  ...       ...   \n",
       "523869  480ac8b4df39533a6c7fe6ae109aa56a     65112   \n",
       "523870  afcd096765a8c354be0706ab1324999b    555999   \n",
       "523871  bd2f9703203bda8e2defdfffc7eb5a10  17262138   \n",
       "523872  1be08d25e7d8114b3c49a126ed57acdf  13596809   \n",
       "523873  1be08d25e7d8114b3c49a126ed57acdf    164719   \n",
       "\n",
       "                               review_id  rating   \n",
       "0       ef77b0cd7d5db0a85aa64f291d63d450       5  \\\n",
       "1       f4b4b050f4be00e9283c92a814af2670       4   \n",
       "2       8666dfd554e310084c4bc72b2d357f3d       4   \n",
       "3       28007503b429dc6724da7076ae7306b4       5   \n",
       "4       70c420b9b26d869a1ef8548b20a9edeb       4   \n",
       "...                                  ...     ...   \n",
       "523869  6251b6332ee9f898fe0e975821eb747e       2   \n",
       "523870  a4f8b16d5c0c5dc08d0f3fa118a262b4       5   \n",
       "523871  9d9c9843ac68b2c5ec877641b3c29baa       4   \n",
       "523872  9517373abb3f5933a479cc5032f669d7       5   \n",
       "523873  95aabb1cb49d31fe899ea2dd9dfc3632       5   \n",
       "\n",
       "                                              review_text  n_votes   \n",
       "0       I haven't read a ton of \"history of the world\"...       68  \\\n",
       "1                             Fun sequel to the original.        7   \n",
       "2       A hilarious romp through the known and unknown...        1   \n",
       "3       One of the most enjoyable stories I've read in...        5   \n",
       "4                                   On loan to Diana Sonn        2   \n",
       "...                                                   ...      ...   \n",
       "523869  Does not give a satisfying ending to the serie...        0   \n",
       "523870  My mother gave me this book as a gift... it's ...        0   \n",
       "523871  This is an interesting snapshot of Paris right...        0   \n",
       "523872  The author is brilliant. The storyline was wel...        0   \n",
       "523873  I thought the end of this story would be a tra...        0   \n",
       "\n",
       "        n_comments                                  review_text_clean  \n",
       "0                5  [havent, read, ton, history, world, books,, fa...  \n",
       "1                0                           [fun, sequel, original.]  \n",
       "2                0  [hilarious, romp, known, unknown, universe, ex...  \n",
       "3                0  [one, enjoyable, story, ive, read, time., capt...  \n",
       "4                0                                [loan, diana, sonn]  \n",
       "...            ...                                                ...  \n",
       "523869           0  [give, satisfying, ending, series., disappoint...  \n",
       "523870           0           [mother, gave, book, gift..., adorable.]  \n",
       "523871           0  [interesting, snapshot, paris, right, time, ro...  \n",
       "523872           0  [author, brilliant., storyline, well, made, st...  \n",
       "523873           0  [thought, end, story, would, tragedy..., ive, ...  \n",
       "\n",
       "[523874 rows x 8 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_reviews_sample_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 111704 entries, 0 to 111703\n",
      "Data columns (total 2 columns):\n",
      " #   Column   Non-Null Count   Dtype \n",
      "---  ------   --------------   ----- \n",
      " 0   book_id  111704 non-null  int64 \n",
      " 1   genres   111704 non-null  object\n",
      "dtypes: int64(1), object(1)\n",
      "memory usage: 1.7+ MB\n"
     ]
    }
   ],
   "source": [
    "df_genres_sample.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_genres_group = df_genres_sample.groupby('genres')['genres'].count().rename_axis('genre_name').reset_index(name='count')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "book_genres_group = book_genres_group.sort_values(by='count', ascending= False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1600x900 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Book genre and count\n",
    "ax = sns.set(rc={'figure.figsize':(16,9)})\n",
    "sns.barplot(data= book_genres_group, x='genre_name', y='count', hue='genre_name', dodge=False).set(xticklabels=[],xlabel='Book genre', ylabel='Count');\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books_sample.to_csv('../data/sample_data/processed/books_sample.csv', index=False)\n",
    "df_authors_sample.to_csv('../data/sample_data/processed/authors_sample.csv', index=False)\n",
    "df_reviews_sample_p.to_csv('../data/sample_data/processed/reviews_sample.csv', index=False)\n",
    "df_genres_sample.to_csv('../data/sample_data/processed/genres_sample.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_books_sample.to_csv('../data/sample_data/processed/books_sample.csv', index=False)\n",
    "df_reviews_sample_p.to_csv('../data/sample_data/processed/reviews_sample.csv', index=False)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "dev1",
   "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": "3.9.16"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}