Twitter_bot_detection_capstone_draft-part2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Twitter Bot detection part 2\n",
    "\n",
    "### Summary from part 1\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "Bots, trolls and fake accounts are quite problematic in this era of social media-dependent consciouness. They take part in creating hypes and spams, spread fake news, ultimately displaying non-genuine behavior in micro-blogging platforms. Genuine posts are valuable not only because that help us understand the human sentiments, but also becuase that increase diversity in the sphere.   \n",
    "\n",
    "### Goal \n",
    "The goal of this project is to create a classifier that predicts if an account is a troll or a human. Using APIs, NLP and Machine Learning, I predict important features from which we detect a troll account and avoid spending time communicating with trolls. \n",
    "\n",
    "The outcome from this project could be used for marketers who target real users for selling their products. A curated list of real users will help them reach out to customers easily and attain business goals. \n",
    "\n",
    "\n",
    "### Data\n",
    "\n",
    "The data comprises existing data from Kaggle [], data collected via Tweepy API from twitter between dates x/x/18 - x/x/18. \n",
    "\n",
    "### Project Workflow\n",
    "\n",
    "#### Approach 1: \n",
    "We tried to build a classifier from existing user account data to predict bots. The model(s) achieved high accuracy, implicating model overfitting. Upon inspection of the datasets, we found we have small training and testing datasets with high sampling bias. Therefore, we needed to gather more data. \n",
    "\n",
    "#### Approach 2:\n",
    "We gathered more data from Twitter. However, we found difficilty in getting  data that could be distinctively labeled for bots and non-bots. Precisely, many Twitter verified accounts that are claimed to be authentic have many bots or they are representative of an organization. The bot data that we gathered [from Website](www.) are small in number (xxx) and are suspended Russian bots. This makes the sample biased.  \n",
    "\n",
    "#### Approach 3:\n",
    "Becuse of this we decided to do a sentiment analysis on #TOPIC and identify bots from the tweets. \n",
    "\n",
    "\n",
    "### Analysis\n",
    "\n",
    "Predicting bots from text is an NLP (Natural Languae Processing) problem.\n",
    "I leveraged NLTK as my tokenizer and Sklearn tfidfVectorizer to perform my Bag of words Analysis and tfidf transformation. Word2Vec\n",
    "\n",
    "For the final model I evaluated model complexity by accuarcy and speed. I eventually settled on _____\n",
    "- Why? and limiting the max features in the SVD step to explain a lot of  __________________\n",
    "\n",
    "-.\n",
    "\n",
    "-.\n",
    "\n",
    "-.\n",
    "\n",
    "\n",
    "### Conclusions\n",
    "- .\n",
    "\n",
    "-.\n",
    "\n",
    "-.\n",
    "\n",
    "-.\n",
    "\n",
    "\n",
    "\n",
    "## Approach 1: Bot detection from user account data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn import preprocessing\n",
    "from sklearn.utils import resample\n",
    "\n",
    "# Import sklearn models \n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.ensemble import GradientBoostingClassifier, ExtraTreesClassifier\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "\n",
    "from sklearn.model_selection import train_test_split, cross_val_score, StratifiedKFold\n",
    "from sklearn.metrics import roc_auc_score, precision_score, recall_score, confusion_matrix, accuracy_score\n",
    "\n",
    "#nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "\n",
    "# Time \n",
    "import time as t\n",
    "\n",
    "import warnings\n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### to do next - \n",
    "\n",
    " - try creating the histograms for each of the plots above to show how much overlap and the overall distribution of each class. \n",
    "- use the log features for the inputs, probably not required for RFC, \n",
    "- try all the classifiers with the log features and ses if that imporives the accuracy/metrics of the other classifiers. \n",
    "- bring more data - https://botometer.iuni.iu.edu/bot-repository/datasets.html\n",
    "- try more feature engineering - log, and get some more features may be from the paper\n",
    "- try gettin more tweets from the bots and the real users - try sentitment analysis - give each person a score overall on how positive and negative feature. \n",
    "\n",
    "- write up what you did so far - this is the baseline - \n",
    "- 100 % accuracy > more data > how the data is biased > use the cluster plots to show how they are biased - \n",
    "- now bring in some fresh data from the other reposi https://botometer.iuni.iu.edu/bot-repository/datasets.html\n",
    "- feature engineering and see how incorporate NLP and how sentiment analysis to score [positivity/negativity ] for the last 10 tweets. \n",
    "- "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Summary of baseline \n",
    "\n",
    "To train our system we initially used a publicly available dataset consisting of 7k bot and non-bot accounts in total. Later we collected data; employed Twitter scraping and incorporated data from various publicly available resources. This procedure yielded a dataset of 57.4k with 39k nonbot and 18k bot accounts. \n",
    "\n",
    "We benchmarked our system using several off-the-shelf algorithms provided in the scikit-learn library (Pedregosa et. al. 2011). We measured the models' accuracy by measuring the Area Under the Receiver Operating Curve (ROC-AUC) with 5-fold cross validation. We compared Bernoulli Naive Bayes, Logistic Regression, Gradient Boosting Classifier, K-nearest neighbors, Random Forest Classifier and Support Vector Classifier. The best classification performance of 98.76 was obtained by the Random Forest Algorithm. The Random Forest model was trained with 100 estimators and the GIni coefficient to measure the quality of splits.\n",
    "\n",
    "For the rest of the project I am going to focus on the latest verified dataset from [this resource](https://botometer.iuni.iu.edu/bot-repository/datasets.html) as the data we collected had sampling bias. Another advantage of choosing the following dataset is that we can access the tweets provided with these datasets. \n",
    "\n",
    "In the following I take a subset of certified bot and non-bot accounts, create features and try to improve the model accuracy. This dataset has almost 39k datapoints with 36% certified bots. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Getting more data \n",
    "all_hums = pd.read_csv('Data/all_hums.csv', encoding='utf-8')\n",
    "all_bots = pd.read_csv('Data/all_bots.csv', encoding='utf-8')\n",
    "new_fake = pd.read_csv('Data/fake_users_.csv')\n",
    "fake_users_2 = pd.read_csv('Data/fake_users_2.csv')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating certified humans and non-human dataset \n",
    "cert_hum = all_hums\n",
    "cert_fake = pd.concat([all_bots, fake_users_2, new_fake], axis=0, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((24700, 20), (14245, 20))"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cert_hum.shape, cert_fake.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(38945, 20)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# This dataset consists of all certified humans and bots accounts, and their meta-data\n",
    "\n",
    "df_cert = pd.concat([cert_hum, cert_fake], axis=0, ignore_index=True)\n",
    "\n",
    "df_cert.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(35588, 20)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Checking for duplicates \n",
    "df_cert = df_cert.drop_duplicates(subset='id')\n",
    "df_cert.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    24697\n",
       "1    10891\n",
       "Name: bot, dtype: int64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cert.bot.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Features \n",
    "\n",
    "__Existing features__: So far we have trained our models on the numerical features. And the most imporeant features are 'statuses_count' and 'favourites_count', followed by 'followers_count' and 'friends_count'. In the following I create new features, based on the meta-data provided with the user accounts. However, the main difficulty in creating these features is that a large percentage of the values are missing in these features. \n",
    "\n",
    "__New features from meta-data__:\n",
    "\n",
    "- Length of screen names \n",
    "- Length of description \n",
    "- calculate the ratio of (# of friends/2 * # of followers). It has been claimed that spambots have a high ratio value (i.e., lower ratio values mean legitimate users). [here](https://arxiv.org/pdf/1509.04098.pdf)\n",
    "\n",
    "__New features from tweets__:\n",
    "\n",
    "- Sentiment and subjectivity features \n",
    "\n",
    "- The content of spambots’ tweets exhibits the so-called message similarity. The score is higher for bots. (Not implemented)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "19273 24532\n"
     ]
    }
   ],
   "source": [
    "# There are several null values or blanks \n",
    "print(df_cert.screen_name.isna().sum(),\n",
    "df_cert.description.isna().sum())\n",
    "\n",
    "#Filling blanks with 0 \n",
    "\n",
    "df_cert.screen_name = df_cert.screen_name.fillna(0)\n",
    "df_cert.description = df_cert.description.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Features from meta-data\n",
    "\n",
    "# Length of screen name \n",
    "df_cert.screen_name = df_cert.screen_name.astype('str')\n",
    "df_cert['len_screen_name'] = df_cert.screen_name.apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     19273\n",
       "12     2627\n",
       "15     2303\n",
       "13     2250\n",
       "14     2098\n",
       "11     2098\n",
       "10     1673\n",
       "9      1364\n",
       "8       914\n",
       "7       611\n",
       "6       279\n",
       "5        88\n",
       "4         8\n",
       "3         2\n",
       "Name: len_screen_name, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# all null values are converted to 1 . # 19k 1 length \n",
    "\n",
    "df_cert.len_screen_name.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Length of description a\n",
    "df_cert.description = df_cert.description.astype('str')  # null values will be converted to 1 in the next step\n",
    "df_cert['len_desc'] = df_cert.description.apply(lambda x: len(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df_cert.len_desc.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_cert['ratio_fr_2xfl'] = df_cert.friends_count /(2* df_cert.followers_count)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bot</th>\n",
       "      <th>created_at</th>\n",
       "      <th>default_profile</th>\n",
       "      <th>default_profile_image</th>\n",
       "      <th>description</th>\n",
       "      <th>favourites_count</th>\n",
       "      <th>followers_count</th>\n",
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       "      <th>id</th>\n",
       "      <th>...</th>\n",
       "      <th>location</th>\n",
       "      <th>name</th>\n",
       "      <th>screen_name</th>\n",
       "      <th>status</th>\n",
       "      <th>statuses_count</th>\n",
       "      <th>url</th>\n",
       "      <th>verified</th>\n",
       "      <th>len_screen_name</th>\n",
       "      <th>len_desc</th>\n",
       "      <th>ratio_fr_2xfl</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <th>0</th>\n",
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       "      <td>1466</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24503</td>\n",
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       "      <td>Roberto Bonanzinga</td>\n",
       "      <td>Bonanzinga</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4340.0</td>\n",
       "      <td>http://t.co/32VHs0bxbY</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10</td>\n",
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       "      <td>0.145005</td>\n",
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       "      <th>1</th>\n",
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       "      <td>Sun Nov 26 15:19:32 +0000 2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>L'uomo ha creato dio a sua immagine e somiglia...</td>\n",
       "      <td>12.0</td>\n",
       "      <td>132</td>\n",
       "      <td>194</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22903</td>\n",
       "      <td>...</td>\n",
       "      <td>Napoli, Italy</td>\n",
       "      <td>effeffe</td>\n",
       "      <td>effeffe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>164.0</td>\n",
       "      <td>http://t.co/CX7EHdk9DJ</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7</td>\n",
       "      <td>159</td>\n",
       "      <td>0.734848</td>\n",
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       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>Sun Dec 31 02:03:17 +0000 2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Research Director at ISI Foundation. Data scie...</td>\n",
       "      <td>871.0</td>\n",
       "      <td>1154</td>\n",
       "      <td>832</td>\n",
       "      <td>NaN</td>\n",
       "      <td>382393</td>\n",
       "      <td>...</td>\n",
       "      <td>Torino, Piedmont</td>\n",
       "      <td>Ciro Cattuto</td>\n",
       "      <td>ciro</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1070.0</td>\n",
       "      <td>http://t.co/26dNjSYe5Q</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4</td>\n",
       "      <td>121</td>\n",
       "      <td>0.360485</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>Wed Dec 27 14:55:17 +0000 2006</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Web and social media developer from Italy</td>\n",
       "      <td>478.0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>286543</td>\n",
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       "      <td>Milan, Italy</td>\n",
       "      <td>Alessio Bragadini</td>\n",
       "      <td>abragad</td>\n",
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       "      <td>6892.0</td>\n",
       "      <td>http://t.co/xoOdZi9jic</td>\n",
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       "      <td>Tue Jan 02 09:01:50 +0000 2007</td>\n",
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       "      <td>NaN</td>\n",
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       "      <td>fullcaffeine</td>\n",
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       "      <td>2885.0</td>\n",
       "      <td>http://www.fullcaffeine.com</td>\n",
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      "text/plain": [
       "   bot                      created_at  default_profile  \\\n",
       "0    0  Mon Nov 27 06:55:12 +0000 2006              NaN   \n",
       "1    0  Sun Nov 26 15:19:32 +0000 2006              NaN   \n",
       "2    0  Sun Dec 31 02:03:17 +0000 2006              NaN   \n",
       "3    0  Wed Dec 27 14:55:17 +0000 2006              NaN   \n",
       "4    0  Tue Jan 02 09:01:50 +0000 2007              NaN   \n",
       "\n",
       "   default_profile_image                                        description  \\\n",
       "0                    NaN  Partner at Balderton Capital (formerly Benchma...   \n",
       "1                    NaN  L'uomo ha creato dio a sua immagine e somiglia...   \n",
       "2                    NaN  Research Director at ISI Foundation. Data scie...   \n",
       "3                    NaN          Web and social media developer from Italy   \n",
       "4                    NaN                                                  0   \n",
       "\n",
       "   favourites_count  followers_count  friends_count  has_extended_profile  \\\n",
       "0             267.0             5055           1466                   NaN   \n",
       "1              12.0              132            194                   NaN   \n",
       "2             871.0             1154            832                   NaN   \n",
       "3             478.0              930            535                   NaN   \n",
       "4              41.0              173            444                   NaN   \n",
       "\n",
       "       id      ...                         location                name  \\\n",
       "0   24503      ...        Between Europe and the US  Roberto Bonanzinga   \n",
       "1   22903      ...                   Napoli, Italy              effeffe   \n",
       "2  382393      ...                 Torino, Piedmont        Ciro Cattuto   \n",
       "3  286543      ...                     Milan, Italy   Alessio Bragadini   \n",
       "4  438023      ...                          Bologna        fullcaffeine   \n",
       "\n",
       "    screen_name status statuses_count                          url  verified  \\\n",
       "0    Bonanzinga    NaN         4340.0       http://t.co/32VHs0bxbY       NaN   \n",
       "1       effeffe    NaN          164.0       http://t.co/CX7EHdk9DJ       NaN   \n",
       "2          ciro    NaN         1070.0       http://t.co/26dNjSYe5Q       NaN   \n",
       "3       abragad    NaN         6892.0       http://t.co/xoOdZi9jic       NaN   \n",
       "4  fullcaffeine    NaN         2885.0  http://www.fullcaffeine.com       NaN   \n",
       "\n",
       "   len_screen_name len_desc  ratio_fr_2xfl  \n",
       "0               10       56       0.145005  \n",
       "1                7      159       0.734848  \n",
       "2                4      121       0.360485  \n",
       "3                7       41       0.287634  \n",
       "4               12        1       1.283237  \n",
       "\n",
       "[5 rows x 23 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_cert.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "#len(set(df_cert.screen_name))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bot', 'created_at', 'default_profile', 'default_profile_image',\n",
       "       'description', 'favourites_count', 'followers_count', 'friends_count',\n",
       "       'has_extended_profile', 'id', 'id_str', 'lang', 'listed_count',\n",
       "       'location', 'name', 'screen_name', 'status', 'statuses_count', 'url',\n",
       "       'verified', 'len_screen_name', 'len_desc', 'ratio_fr_2xfl'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df_cert[df_cert.id == 1124249136]\n",
    "df_cert.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_to_keep = ['followers_count', 'friends_count', 'favourites_count',\n",
    "       'statuses_count', 'len_screen_name', 'len_desc', 'ratio_fr_2xfl']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['followers_count', 'friends_count', 'favourites_count',\n",
       "       'statuses_count', 'len_screen_name', 'len_desc', 'ratio_fr_2xfl'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df_cert[col_to_keep]\n",
    "X.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.fillna(0)\n",
    "X = X.applymap(lambda x: x if x != np.inf else -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, df_cert.bot, test_size= .3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "================================================================================\n",
      "BNB Classifier\n",
      "________________________________________________________________________________\n",
      "Training: \n",
      "BernoulliNB(alpha=1.0, binarize=0.0, class_prior=None, fit_prior=True)\n",
      "train time: 0.021s\n",
      "Crossvalidating: \n",
      "Crossvalidated accuracy: 0.958 with std 0.003\n",
      "test time:  0.004s\n",
      "confusion matrix \n",
      " [[7290   86]\n",
      " [ 381 2920]]\n",
      "Test precision score 0.971 \n",
      "Test recall score 0.885 \n",
      "ROC AUC score 0.936\n",
      "\n",
      "================================================================================\n",
      "LogReg\n",
      "________________________________________________________________________________\n",
      "Training: \n",
      "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
      "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
      "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
      "          tol=0.0001, verbose=0, warm_start=False)\n",
      "train time: 0.508s\n",
      "Crossvalidating: \n",
      "Crossvalidated accuracy: 0.939 with std 0.004\n",
      "test time:  0.001s\n",
      "confusion matrix \n",
      " [[6939  437]\n",
      " [ 254 3047]]\n",
      "Test precision score 0.875 \n",
      "Test recall score 0.923 \n",
      "ROC AUC score 0.932\n",
      "\n",
      "================================================================================\n",
      "GBC\n",
      "________________________________________________________________________________\n",
      "Training: \n",
      "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
      "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
      "              max_features=None, max_leaf_nodes=None,\n",
      "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "              min_samples_leaf=1, min_samples_split=2,\n",
      "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
      "              n_iter_no_change=None, presort='auto', random_state=None,\n",
      "              subsample=1.0, tol=0.0001, validation_fraction=0.1,\n",
      "              verbose=0, warm_start=False)\n",
      "train time: 1.547s\n",
      "Crossvalidating: \n",
      "Crossvalidated accuracy: 0.983 with std 0.003\n",
      "test time:  0.016s\n",
      "confusion matrix \n",
      " [[7281   95]\n",
      " [  75 3226]]\n",
      "Test precision score 0.971 \n",
      "Test recall score 0.977 \n",
      "ROC AUC score 0.982\n",
      "\n",
      "================================================================================\n",
      "kNN\n",
      "________________________________________________________________________________\n",
      "Training: \n",
      "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
      "           metric_params=None, n_jobs=None, n_neighbors=10, p=2,\n",
      "           weights='uniform')\n",
      "train time: 0.105s\n",
      "Crossvalidating: \n",
      "Crossvalidated accuracy: 0.964 with std 0.001\n",
      "test time:  0.157s\n",
      "confusion matrix \n",
      " [[7198  178]\n",
      " [ 255 3046]]\n",
      "Test precision score 0.945 \n",
      "Test recall score 0.923 \n",
      "ROC AUC score 0.949\n",
      "\n",
      "================================================================================\n",
      "Random forest\n",
      "________________________________________________________________________________\n",
      "Training: \n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "train time: 1.686s\n",
      "Crossvalidating: \n",
      "Crossvalidated accuracy: 0.988 with std 0.001\n",
      "test time:  0.103s\n",
      "confusion matrix \n",
      " [[7327   49]\n",
      " [  68 3233]]\n",
      "Test precision score 0.985 \n",
      "Test recall score 0.979 \n",
      "ROC AUC score 0.986\n",
      "\n",
      "================================================================================\n",
      "SVC\n",
      "________________________________________________________________________________\n",
      "Training: \n",
      "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
      "  decision_function_shape='ovr', degree=3, gamma='auto_deprecated',\n",
      "  kernel='rbf', max_iter=-1, probability=False, random_state=None,\n",
      "  shrinking=True, tol=0.001, verbose=False)\n",
      "train time: 49.967s\n",
      "Crossvalidating: \n",
      "Crossvalidated accuracy: 0.798 with std 0.003\n",
      "test time:  3.465s\n",
      "confusion matrix \n",
      " [[7372    4]\n",
      " [2215 1086]]\n",
      "Test precision score 0.996 \n",
      "Test recall score 0.329 \n",
      "ROC AUC score 0.664\n",
      "\n"
     ]
    }
   ],
   "source": [
    "def baseline_models(clf, X_train, y_train, X_val, y_val):\n",
    "    print('_' * 80)\n",
    "    print(\"Training: \")\n",
    "    print(clf)\n",
    "    t0 = t.time()\n",
    "    clf.fit(X_train, y_train)\n",
    "    train_time = t.time() - t0\n",
    "    print(\"train time: %0.3fs\" % train_time)\n",
    "\n",
    "    print(\"Crossvalidating: \")\n",
    "    t0 = t.time()\n",
    "    cv_score = cross_val_score(clf, X_train, y_train, cv=5)\n",
    "    train_time = t.time() - t0\n",
    "    print(\"Crossvalidated accuracy: %0.3f with std %0.3f\" % (cv_score.mean(), cv_score.std()))\n",
    "    \n",
    "    #Metrics\n",
    "    t0 = t.time()\n",
    "    pred = clf.predict(X_val)\n",
    "    test_time = t.time() - t0\n",
    "    print(\"test time:  %0.3fs\" % test_time)\n",
    "    \n",
    "    #score = accuracy_score(y_test, pred)\n",
    "    #print(\"accuracy:   %0.3f\" % score)\n",
    "    \n",
    "\n",
    "    print('confusion matrix \\n', confusion_matrix(y_val, pred))  \n",
    "    precision = precision_score(y_val, pred)\n",
    "    print('Test precision score %.3f ' % precision)\n",
    "    recall = recall_score(y_val, pred)\n",
    "    print('Test recall score %.3f ' % recall)\n",
    "    rocauc = roc_auc_score(y_val, pred)\n",
    "    print('ROC AUC score %.3f' % rocauc)\n",
    "\n",
    "    print()\n",
    "    clf_descr = str(clf).split('(')[0]\n",
    "    return clf_descr, cv_score.mean(), train_time, test_time, precision, recall, rocauc\n",
    "\n",
    "\n",
    "results = []\n",
    "\n",
    "bnb = BernoulliNB()\n",
    "logReg = LogisticRegression()\n",
    "gbc = GradientBoostingClassifier()\n",
    "knn = KNeighborsClassifier(n_neighbors=10)\n",
    "rfc = RandomForestClassifier(n_estimators=100)\n",
    "svc = SVC()\n",
    "\n",
    "for clf, name in (\n",
    "        (bnb, \"BNB Classifier\"),\n",
    "        (logReg, \"LogReg\"),\n",
    "        (gbc, \"GBC\"),\n",
    "        (knn, \"kNN\"),\n",
    "        (rfc, \"Random forest\"),\n",
    "        (svc, \"SVC\")\n",
    "):\n",
    "    print('=' * 80)\n",
    "    print(name)\n",
    "    results.append(baseline_models(clf, X_train, y_train, X_test, y_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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>Classifier</th>\n",
       "      <th>Mean CV accuracy</th>\n",
       "      <th>Precision score</th>\n",
       "      <th>Recall score</th>\n",
       "      <th>ROC-AUC score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BernoulliNB</td>\n",
       "      <td>0.957649</td>\n",
       "      <td>0.971391</td>\n",
       "      <td>0.88458</td>\n",
       "      <td>0.93646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>LogisticRegression</td>\n",
       "      <td>0.938702</td>\n",
       "      <td>0.874569</td>\n",
       "      <td>0.923054</td>\n",
       "      <td>0.931904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GradientBoostingClassifier</td>\n",
       "      <td>0.983301</td>\n",
       "      <td>0.971394</td>\n",
       "      <td>0.97728</td>\n",
       "      <td>0.9822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>KNeighborsClassifier</td>\n",
       "      <td>0.963791</td>\n",
       "      <td>0.944789</td>\n",
       "      <td>0.922751</td>\n",
       "      <td>0.949309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>RandomForestClassifier</td>\n",
       "      <td>0.988399</td>\n",
       "      <td>0.98507</td>\n",
       "      <td>0.9794</td>\n",
       "      <td>0.986379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>SVC</td>\n",
       "      <td>0.79764</td>\n",
       "      <td>0.99633</td>\n",
       "      <td>0.328991</td>\n",
       "      <td>0.664224</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Classifier Mean CV accuracy Precision score Recall score  \\\n",
       "0                 BernoulliNB         0.957649        0.971391      0.88458   \n",
       "1          LogisticRegression         0.938702        0.874569     0.923054   \n",
       "2  GradientBoostingClassifier         0.983301        0.971394      0.97728   \n",
       "3        KNeighborsClassifier         0.963791        0.944789     0.922751   \n",
       "4      RandomForestClassifier         0.988399         0.98507       0.9794   \n",
       "5                         SVC          0.79764         0.99633     0.328991   \n",
       "\n",
       "  ROC-AUC score  \n",
       "0       0.93646  \n",
       "1      0.931904  \n",
       "2        0.9822  \n",
       "3      0.949309  \n",
       "4      0.986379  \n",
       "5      0.664224  "
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "display_scores = pd.DataFrame(columns=['Classifier', 'Mean CV accuracy', 'Precision score', 'Recall score', 'ROC-AUC score'])\n",
    "\n",
    "row_index = 0\n",
    "for clf in results:\n",
    "    display_scores.loc[row_index, 'Classifier'] = clf[0]\n",
    "    display_scores.loc[row_index, 'Mean CV accuracy'] = clf[1]\n",
    "    display_scores.loc[row_index, 'Precision score'] = clf[4]\n",
    "    display_scores.loc[row_index, 'Recall score'] = clf[5]\n",
    "    display_scores.loc[row_index, 'ROC-AUC score'] = clf[6]\n",
    "    row_index +=1\n",
    "display_scores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "    Classifier\tMean CV accuracy\tPrecision score\tRecall score\tROC-AUC score\n",
    "        1. BernoulliNB\t0.883124\t0.934934\t0.7821\t0.878373\n",
    "    2.\tLogisticRegression\t0.670871\t0.489813\t0.878174\t0.726035\n",
    "    3.\tGradientBoostingClassifier\t0.95639\t0.941787\t0.927854\t0.950569\n",
    "    4.\tKNeighborsClassifier\t0.933453\t0.916793\t0.88347\t0.923059\n",
    "    5.\tRandomForestClassifier\t0.980397\t0.982381\t0.967489\t0.979703\n",
    "    6.\tSVC\t0.887504\t0.99349\t0.696804\t0.847338"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "followers_count 0.06591565437557992\n",
      "friends_count 0.045825340868702845\n",
      "favourites_count 0.17824335146933515\n",
      "statuses_count 0.29826097290222214\n",
      "len_screen_name 0.23357381879344957\n",
      "len_desc 0.049842434933246596\n",
      "ratio_fr_2xfl 0.12833842665746384\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(l,f) for l,f in zip(X_train.columns, rfc.feature_importances_)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Interim findings before incorporating NLP features. \n",
    "\n",
    "Random forest performs very well (CV accuracy 98.8%, ROC-AUC score .986 with 98.82% and 97.9% Precision and Recall scores). This is comparatively great improvement given that our metrics for the baseline Random Forest model were: \n",
    " - Cross-validated accuracy 98.03%\n",
    " - ROC-AUC score 0.979\n",
    " - Precision: 98.23%, Recall: 96.74%\t\n",
    " \n",
    "The most important features are: 'statuses_count', length of the screen name, 'favourites_count', and the ratio of the friends count and 2 times followers count. \n",
    "\n",
    "In the following, I am want to see how the clusters of distinguishing features wrt the bot and real users. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "from sklearn.cluster import MeanShift, estimate_bandwidth\n",
    "from sklearn.datasets.samples_generator import make_blobs\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['followers_count', 'friends_count', 'favourites_count',\n",
       "       'statuses_count', 'len_screen_name', 'len_desc', 'ratio_fr_2xfl'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "cluster_cols = ['len_screen_name', 'len_desc', 'ratio_fr_2xfl', ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of estimated clusters : 20\n"
     ]
    }
   ],
   "source": [
    "# Compute clustering with MeanShift\n",
    "\n",
    "# The following bandwidth can be automatically detected using\n",
    "\n",
    "bandwidth = estimate_bandwidth(X_train[cluster_cols], quantile=0.2, n_samples=500)\n",
    "\n",
    "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
    "X_train[cluster_cols] \n",
    "\n",
    "ms.fit(X_train[cluster_cols])\n",
    "labels = ms.labels_\n",
    "cluster_centers = ms.cluster_centers_\n",
    "\n",
    "labels_unique = np.unique(labels)\n",
    "n_clusters_ = len(labels_unique)\n",
    "\n",
    "print(\"number of estimated clusters : %d\" % n_clusters_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 3 is out of bounds for axis 0 with size 3",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-86-83cf3b013005>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     15\u001b[0m     \u001b[0mcluster_center\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcluster_centers\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 17\u001b[0;31m     plt.plot(cluster_center[k], cluster_center[k+1], 'o', markerfacecolor=col,\n\u001b[0m\u001b[1;32m     18\u001b[0m              markeredgecolor='k', markersize=14)\n\u001b[1;32m     19\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcluster_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmy_members\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mX_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcluster_cols\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mmy_members\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfeature2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcol\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m'.'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 3 is out of bounds for axis 0 with size 3"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot result\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "#plt.clf()\n",
    "\n",
    "feature1 = 0\n",
    "feature2 = 1\n",
    "feature3 = 2\n",
    "\n",
    "colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\n",
    "for k, col in zip(range(n_clusters_), colors):\n",
    "    my_members = labels == k\n",
    "    cluster_center = cluster_centers[k]\n",
    "    \n",
    "    plt.plot(cluster_center[k], cluster_center[k+1], 'o', markerfacecolor=col,\n",
    "             markeredgecolor='k', markersize=14)\n",
    "    plt.plot(X_train[cluster_cols].values[my_members, feature1], X_train[cluster_cols].values[my_members, feature2], col + '.')\n",
    "    plt.xlabel(cluster_cols[feature1])\n",
    "    plt.ylabel(cluster_cols[feature2])\n",
    "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'len_screen_name'"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_cols[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Distribution of the length of the screen names of bots and real accounts')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.plot(X_train[X_train.bot==False][cluster_cols[2]].values, X_train[X_train.bot==False]['favourites_count'].values, 'bx')\n",
    "plt.plot(X_train[X_train.bot==True][cluster_cols[2]].values, X_train[X_train.bot==True]['favourites_count'].values,  'r.')\n",
    "plt.xlabel(cluster_cols[2])\n",
    "plt.ylabel('statuses_count')\n",
    "plt.title('Distribution of the length of the screen names of bots and real accounts')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    }
   ],
   "source": [
    "X_train['bot'] = y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['len_screen_name', 'len_desc', 'ratio_fr_2xfl']"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cluster_cols"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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l4XZgAyPr4j6Ud1ImjL2JcqX+G5R5GIaVmWsoV/o/Stn5rgHOZAS/kcy8kTLp6sWUhvdR4B7KiQGUOZ4A7ouIa7YtYfuqHfyVlDn1mieAfTrlf3wYuBn4T6rhr1FWNPvCMHV+P+W7spbyXjUPfx3u8x/yNSnzI20Cfk55D04f5b/6j5SG7FeUoX1/XtX355SDi19FWQWrZUhDZv6CMg/JX1G+y28A3lDNKbLDqquwr6TM1XEXpQv/X1Dm9NretvdShpYto5wgHkaZF6XxvfgPylXY30TEvWOpp6QdYpu6fbapI2hTh/APlM/gTsr795MBj18AHFa1af8yyPZ/TmkzbgB+Rhme9+eDPE/S1PPtqq1YQ7lgfh5lYYyGj1AWqPpJlNVmf0B/b7hDq/SjlP3c5zOzr2qn3kBZrOLXlOP+t42yXt+iDC+9jnIhvNHjbdhj2sz8AWW+80so+/VDGNl8tsPqtrYmMx+jHJf8V9V2HD2CauxQO97kDEqP+EcogemvDXj8Y8CFVX1OHlDfJyjfuddQjos+D7yzOq/TBBVlzlJJdYmI3YEHgUMz89ZO12eyiIjbgPdUBxFTTnUF8g7g7ZnZt73nS5JsUyVpIoiIpOyHV3e6Lu1gWyMV9iCUahARb4iIXaPMqXEu5Wr/bZ2tlTqtGvIwqxpO8VHK0IWBvUgkSU1sUyVJ7WZbI23LAKFUjxMpQ1DvonTtPyXtnis4hjJUujHs+U2Z+fjwm0hS17NNlSS1m22NNIBDjCVJkiRJkqQuZg9CSZIkSZIkqYv1dLoCY7XPPvvkgQce2OlqSJJqcPXVV9+bmbM7XY92sc2SpKljKrdZtleSNHWMtL2a9AHCAw88kFWrVnW6GpKkGkTE7Z2uQzvZZknS1DGV2yzbK0maOkbaXjnEWJIkSZIkSepiBgglSZIkSZKkLmaAUJIkSZIkSepiBgglSZIkSZKkLmaAUJIkSZIkSepiBgglSZIkSZKkLmaAUJIkSZIkSepiBgglSZIkSZKkLmaAUJIkSZIkSepibQ0QRsTOEfHTiLg+Im6MiI9X+QdFxFURsToivhYRM6v8nar06urxA9tZP0mSJEmSJKnbtbsH4UbgdzLzcOAI4NURcTTwF8BnMvOZwAPAu6vnvxt4oMr/TPU8SZIkSZIkSW3S1gBhFo9WyRnVXwK/A3yjyr8QeFN1/8QqTfX48RER7ayjJEmSJEmS1M3aPgdhREyPiOuAe4DvA7cAD2bm5uopdwBzq/tzgTUA1eMPAXu3u46SNKRly6CvrzWvr6/kS5IK95WSxsr9iCR1VNsDhJm5JTOPAOYBLwaeM9YyI+K0iFgVEavWrVs35jpK0pAWLICTT+4/YO3rK+kFCzpbL0maSNxXShor9yOS1FHjtopxZj4I9AHHALMioqd6aB5wZ3X/TmB/gOrxpwL3DVLW+Zk5PzPnz549u+11l9TFenthxYpygLpkSbldsaLkS5IK95WSxsr9iCR1VLtXMZ4dEbOq+7sArwBupgQK31I97V3At6r7l1Zpqsf/IzOznXWUpO3q7YWFC+Hss8utB6qStC33lZLGyv2IJHVMu3sQ7gf0RcQNwErg+5n5HeAjwIciYjVljsELqudfAOxd5X8I+JM210+Stq+vD5Yvh7POKrcD58eRJLmvlDR27kckqWN6tv+UHZeZNwBHDpL/K8p8hAPzNwBvbWedJGlUGvPfNIa49PY65EWSBnJfKWms3I9IUkeN2xyEkjQprVzZemDamB9n5crO1kuSJhL3lZLGyv2IJHVUTPYp/ubPn5+rVq3qdDUkSTWIiKszc36n69EutlmSNHVM5TbL9kqSpo6Rtlf2IJQkSZIkSZK6mAFCSZIkSZIkqYsZIJQkSdLEtmzZtquZ9vWVfElS/dzvSl3HAKEkSZImtgULymqmjZPVxmqnCxZ0tl6SNFW535W6Tk+nKyBJkiQNq7Ga6cknw8KFsHx562qnkqR6ud+Vuo49CCVJkjTx9faWk9Szzy63nqRKUnu535W6igFCSZIkTXx9faUHy1lnlduBc2NJkurlflfqKgYIJUldLyJ2joifRsT1EXFjRHy8yj8oIq6KiNUR8bWImFnl71SlV1ePH9jJ+ktTXmPuqxUrYOnS/mFvnqyqC9lmaVy435W6jgFCSZJgI/A7mXk4cATw6og4GvgL4DOZ+UzgAeDd1fPfDTxQ5X+mep6kdlm5snXuq8bcWCtXdrZeE4krjnaTqdlm+R2eWNzvSl3HAKEkqetl8WiVnFH9JfA7wDeq/AuBN1X3T6zSVI8fHxExTtWVus+iRdvOfdXbW/JVuOJo15iybZbf4YnF/a7UdQwQSpIERMT0iLgOuAf4PnAL8GBmbq6ecgcwt7o/F1gDUD3+ELD3+NZYkpo0rzi6ZEn/0EAXFZiSpmSb5XdYkjrKAKEkSUBmbsnMI4B5wIuB59RRbkScFhGrImLVunXr6ihSkgbniqNdox1t1oRor/wOS1LHGCCUJKlJZj4I9AHHALMioqd6aB5wZ3X/TmB/gOrxpwL3DVHe+Zk5PzPnz549u611l9TlXHG069TZZk2I9srvsCR1jAFCSVLXi4jZETGrur8L8ArgZspJ11uqp70L+FZ1/9IqTfX4f2Rmjl+NJWkAVxztGlO2zfI7LEkdZYBQkiTYD+iLiBuAlcD3M/M7wEeAD0XEasp8TRdUz78A2LvK/xDwJx2osyT1c8XRbjI12yy/w5LUUTERLx6Nxvz583PVqlWdroYkqQYRcXVmzu90PdrFNkuSpo6p3GbZXknS1DHS9soehJIkSZIkSVIXM0AoSZIkSZIkdTEDhJIkSZIkSVIXM0AoSZIkSZIkdTEDhJIkSRqbZcugr681r6+v5EvSSLgfkaSOMkAoSZKksVmwAE4+uf/kvq+vpBcs6Gy9JE0e7kckqaN6Ol0BSZIkTXK9vbBiRTmZX7gQli8v6d7eTtdM0mThfkSSOsoehJIkSRq73t5yUn/22eXWk3pJo+V+RJI6xgChJEmSxq6vr/T4OeuscjtwLjFJ2h73I5LUMQYIJUmSNDaNucJWrIClS/uHCXpyL2mk3I9IUkcZIJQkSdLYrFzZOldYYy6xlSs7Wy9Jk4f7EUnqKBcpkSRJ0tgsWrRtXm+v84dJGjn3I5LUUfYglCRJkiRJkrqYAUJJkiRJkiSpixkglCRJkiRJkrqYAUJJkiRJkiSpixkglCRJkiRJkrqYAUJJkiRJkiSpixkglCRJkiRJkrqYAUJJkiRJkiSpixkglCRJkiRJkrqYAUJJkiSNzbJl0NfXmtfXV/IlaSTcj0hSRxkglCRJ0tgsWAAnn9x/ct/XV9ILFnS2XpImD/cjktRRPZ2ugCRJkia53l5YsaKczC9cCMuXl3Rvb6drJmmycD8iSR1lD0JJkiSNXW9vOak/++xy60m9pNFyPyJJHWMPQkmSpKlu2bIyTK/5ZLuvD1auhEWL6nmNvr7S4+ess8ptb68n95JGbtky6Olp3Y/MmgWbN9e3n5IkDckehJIkSVNdu+f2apS3YgUsXdo/THDgggMTmQskSJ11xRVwxhmweHHZjyxeXNJXXNHpmklSVzBAKEmSNNU1z+21ZEl/MK+uHn4rV7aW13i9lSvrKX88uECC1Flz5sAuu5Tg4JIl5XaXXUq+JKntDBBKkiR1g3bO7bVo0bbl9fZOrmGB7Q6i2kNRGt4hh5T90xNPtN4eckinayZJXaGtAcKI2D8i+iLipoi4MSI+WOV/LCLujIjrqr/XNm2zOCJWR8QvIuJV7ayfJElS1xg4R+BkGv47XtoZRLWHojS8BQvg4x+HrVtLeuvWkvY3Iknjot09CDcDH87Mw4CjgfdHxGHVY5/JzCOqv+8CVI+dAjwPeDXw+YiY3uY6SpIkTW1TYY7A8dDOIGq7eyhKU8GWLbBxIxx7bLndsqXTNZKkrtHWAGFmrs3Ma6r7jwA3A3OH2eRE4OLM3JiZtwKrgRe3s46SJElT3lSYI7DdxiOI2s4eitJkd/HFMH06nHpqWZjk1FNL+uKLO10zSeoK4zYHYUQcCBwJXFVlfSAiboiIv42IPau8ucCaps3uYJCAYkScFhGrImLVunXr2lhrSZKkKWCyzxE4HvP3jUcQ1WHe0tDWrIG3vx0uu6z8Ri67rKTXrNn+tpKkMRuXAGFE7A5cApyemQ8Dy4FDgCOAtcBfjqa8zDw/M+dn5vzZs2fXXl9JkiRNIOMxf1+7g6gO85aGd8IJ8IUvwOLF5TeyeHFJn3BCp2smSV2h7QHCiJhBCQ5+NTP/GSAz787MLZm5FfgS/cOI7wT2b9p8XpUnSZKkbrVyZQkWNM/ft3jx5Boi7TBvaXg/+AG8731wzjnld37OOSX9gx90umaS1BV62ll4RARwAXBzZp7XlL9fZq6tkicB/1PdvxT4x4g4D5gDHAr8tJ11lCRJ0gTX6EH4mteU+ftOPbUED1as6HTNRm6wnoi9vc5DKDWceea2v/Ovf31y/c4laRJra4AQeClwKvCziLiuyvso8LsRcQSQwG3AewEy88aIWAHcRFkB+f2Z6dJVkiRJ3ay3t/QYPOOMsrrpRRfBuecaXJOmEn/nktRRbQ0QZuaPgRjkoe8Os80ngE+0rVKSJEmq17JlpZdf84l8X18ZPlvHHH59faXH4DveAV/5Sn8PwiOPNHggTRXvfW9Zsbj5d/7xj8MvfgFf/GKnaydJU964rWIsSZKkKardi4g05iBsXt10ss1BKGn7tmyBSy8tv/NLLy1pSdK4aPcQY0mSJE11jQU3Tj4ZFi6E5ctbF+QYq0YAslFmb29/WtLUcMop8LWvQWZJZ0JPT8mXJLWdPQglSZI0dr29JTh49tnlts6hv64ALE19K1fCN78JH/xg2Y988IMl7e9cksaFAUJJkiSNXV9f6Tl41lnltjHcuA6LFm0bcOztrWd+w/GybNm270lfX8mXVFx7bet+5NprO10jSeoaBgglSZI0No05B1esgKVL+4cb1xkknOzaPU+jNNn19JQVjBcvLvuRxorGPc6KJUnjwQChJKnrRcT+EdEXETdFxI0R8cEq/2MRcWdEXFf9vbZpm8URsToifhERr+pc7aUJwCHA29c8T+OSJa1zKkqjMGXbrM2b4dxzywrlS5aU23PPLfmSpLbzcowkSbAZ+HBmXhMRewBXR8T3q8c+k5nnNj85Ig4DTgGeB8wBfhARz8pMl1tUdxpsqG9jMRH1a56n8ayzfH+0o6Zmm9XYjzz4YP9v5EMf6mydJKmL2INQktT1MnNtZl5T3X8EuBmYO8wmJwIXZ+bGzLwVWA28uP01lTSptXOeRnWNKd1m+RuRpI4xQChJUpOIOBA4EriqyvpARNwQEX8bEXtWeXOBNU2b3cHwJ2eSup3zNKoNplSb5W9EkjrKAKEkSZWI2B24BDg9Mx8GlgOHAEcAa4G/3IEyT4uIVRGxat26dbXWV9Ik4jyNqlndbVbH2yt/I5LUUc5BKEkSEBEzKCdaX83MfwbIzLubHv8S8J0qeSewf9Pm86q8bWTm+cD5APPnz8/6ay5pUnCeRtWoHW1Wx9srfyOS1FH2IJQkdb2ICOAC4ObMPK8pf7+mp50E/E91/1LglIjYKSIOAg4Ffjpe9ZUkdS/bLElSO9iDUJIkeClwKvCziLiuyvso8LsRcQSQwG3AewEy88aIWAHcRFlN8v0TbjVISdJUZZslSaqdAUJJUtfLzB8DMchD3x1mm08An2hbpSRJGoRtliSpHRxiLEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEnSsmXQ19ea19dX8iW1n79BSeooA4SSJEnSggVw8sn9AYq+vpJesKCz9ZK6hb9BSeqMgadJAAAgAElEQVSonk5XQJIkSeq43l5YsaIEJBYuhOXLS7q3t9M1k7qDv0FJ6ih7EEqSJElQAhELF8LZZ5dbAxPS+PI3KEkdY4BQkiRJgjKkcflyOOuscjtwPjRJ7eVvUJI6xgChJEmS1JjvbMUKWLq0f6ijAQppfPgblKSOMkAoSZIkrVzZOt9ZYz60lSs7Wy+pW/gblKSOcpESSZIkadGibfN6e50DTRov/gYlqaPsQShJkiRJkiR1MQOEkiRJkiRJUhczQChJkiRJkiR1sbYGCCNi/4joi4ibIuLGiPhglb9XRHw/In5Z3e5Z5UdEfC4iVkfEDRHxonbWT5IkSZIkSep27e5BuBn4cGYeBhwNvD8iDgP+BPhhZh4K/LBKA7wGOLT6Ow1Y3ub6SZIkSe23bBn09bXm9fWVfEmazNy/SVNCWwOEmbk2M6+p7j8C3AzMBU4ELqyediHwpur+icA/ZPETYFZE7NfOOkpqIw8WJEkqFiyAk0/ubxf7+kp6wYLO1kuSxsr9mzQljNschBFxIHAkcBXwtMxcWz30G+Bp1f25wJqmze6o8iRNRh4sSJJU9PbCihWlHVyypNyuWFHyJWkyc/8mTQnjEiCMiN2BS4DTM/Ph5scyM4EcZXmnRcSqiFi1bt26GmsqqVYeLEiS1K+3FxYuhLPPLre2h5KmCvdv0qTX9gBhRMygBAe/mpn/XGXf3Rg6XN3eU+XfCezftPm8Kq9FZp6fmfMzc/7s2bPbV3lJY+fBgiRJRV8fLF8OZ51VbgdOwyFJk5X7N2nSa/cqxgFcANycmec1PXQp8K7q/ruAbzXlv7Nazfho4KGmociSJiMPFiRJ6p9mY8UKWLq0v4e97aKkyc79mzQltLsH4UuBU4HfiYjrqr/XAp8CXhERvwROqNIA3wV+BawGvgT8YZvrJ6mdPFiQJKlYubJ1mo3GNBwrV3a2XpI0Vu7fpCmhp52FZ+aPgRji4eMHeX4C729nnSSNo+EOFhxqLGmyWLasLK7UvN/q6yv7skWLOlcvTS6DfVd6e20PpQb3tZOX+zdpShi3VYwldaFFi7Y9MOjt9SBP0uTiiuyS1H7uayWpowwQSpIkDccV2Ttv2bJtp6fo6yv5kqaGlSth8eLWfe3ixQ5TlaRxYoBQkiRpe1yRvbPsWSRNfQsWwDnnwGteU/a1r3lNSfs7l6RxYYBQktT1ImL/iOiLiJsi4saI+GCVv1dEfD8iflnd7lnlR0R8LiJWR8QNEfGizv4HajtXZO8se3FKT5qybVZvb+kxeNFFcOyx5XbxYn/nkjRODBBKkgSbgQ9n5mHA0cD7I+Iw4E+AH2bmocAPqzTAa4BDq7/TgOXjX2WNG1dknxjsxSk1TM02q6+v9Bh8xzvgiivK7TnnuK+VpHFigFCS1PUyc21mXlPdfwS4GZgLnAhcWD3tQuBN1f0TgX/I4ifArIjYb5yrrfEy3IrsGj/24pSAKdxmNeYgvOyy8ju/7DLnIJSkcdTT6QpIkjSRRMSBwJHAVcDTMnNt9dBvgKdV9+cCa5o2u6PKW8sAEXEapccGz3jGM9pSZ7XZYCuv9/bag208NffibLz3DjOWam2zOt5eNeYaHex3LklqO3sQSpJUiYjdgUuA0zPz4ebHMjOBHG2ZmXl+Zs7PzPmzZ8+uqaZSl7EXp7SNutusjrdX/s4lqaPsQShJEhARMygnWl/NzH+usu+OiP0yc201HOueKv9OYP+mzedVeZLawV6cUosp2Wb5O5ekjrIHoSSp60VEABcAN2fmeU0PXQq8q7r/LuBbTfnvrFaGPBp4qGlYlyRJbWObJUlqB3sQSpIELwVOBX4WEddVeR8FPgWsiIh3A7cDJ1ePfRd4LbAaeAz4X+NbXUlSF7PNkiTVzgChJKnrZeaPgRji4eMHeX4C729rpSRJGoRtliSpHRxiLEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSFzNAKEmSJEmSJHUxA4SSJEmSJElSF+vpdAUkSapDRPwVkEM9npl/NI7VkbrLsmWwYAH09vbn9fXBypWwaFHn6iVNULZZmhDcd0tqYoBQkjRVrOp0BaSutWABnHwyrFhRTjT7+vrTkgZjm6XOc98tqYkBQkkajldWJ5MTMvPUiPhgZn6205WRukpvbzmhPPlkWLgQli/vP+GUNBjbLHWe+25JTZyDUJKG07iy2tdX0o0rqwsWdLZeGsxvRcQc4A8iYs+I2Kv5r9OVk6a83t5ygnn22eXWE0xpOLZZmhjcd0uq2INQkobjldXJ5AvAD4GDgauBaHosq3xJ7dLXV/aRZ51Vbnt73VdKQ7PN0sTgvltSxR6EkrQ9XlmdFDLzc5n5XOBvM/PgzDyo6c8TLamdmuetWrq0/8JKo/e1pBa2WZoQ3HdLamKAUJK2Z+CVVQ+aJrpLBmZExLs6URGpa6xc2dq7utH7euXKztZLmvhss9Q57rslNXGIsSQNp/nKamPIRXNaAFx5JVx+ORx3HBxzTKdrw5KIeDNwBrA78GVgI3BhR2slTWWDLdrkMDVpJGyz1DnuuyU1GXEPwoj4fkTMakrvGRH/3p5qSdIE4ZXV7brySjj++NLB8vjjS7rDXg7cAlwH/Bj4x8x8S2erJEnSoGyzJEkTwmh6EO6TmQ82Epn5QETs24Y6SdLE4ZXV7br8cnjiCdiypdxefnnHexHuCbyYcsI1DzggIiIzs6O1kiRpW7ZZkqQJYTRzEG6NiGc0EhFxAGWFLUmT3JVXwjnnTIieX5qEjjsOZs6E6dPL7XHHdbpG/AT4t8x8NbAAmAP8V2erJEnSoGyzJEkTwmh6EP4p8OOI+E8ggGOB09pSK0njpjE89IknSnDnhz/seO+vrjTB5vAblWOOKd+bCVT/EzLz1wCZ+TjwRxHxsg7XSZKkwdhmSZImhBH3IMzMfwNeBHwNuBj4rcx0DkJ1r2XLoK+vtfddX1/Jr8trXwvnndead955Jb8Oz3kOl//xt1qHh/7xt+A5z6mn/Ha/R1X5LSbbZ7BsGVf+9dWtc/j99dX1/g/tVH0GxxwDixdXwcG6P4PRWztI3k3jXgtJkrbPNkuSNCHESKe3iIiXAtdl5vqIeAclWPjZzLy9nRXcnvnz5+eqVas6WQV1q6OO4sobduPlm37Api3TmDF9K/854wSOeeF6uOqqel7juc+Fn/+cvXd9lPsf2429dl3PfY/tXgJ4N9889vIjuJKjeQlXUDoUb+a/OZZj+AnUMfXNLrtw5RO/xW/zI7Zunca0aVv5MS/jmJlXw+OPj738CAD23mMD9z+yE3vtsZH7Htm5PFbX1D3Vaxy45/38+sE9ecasB7jtgb3qe41p0zgnP8JH+SSlc3byST7K4vgL2Lp17OXPng0veQmH/fJb/OIX8Oxnw02Hngj//d+wbt3Yy582DSLYe7fH+j+D9buW92YH6h8RV2fm/B2pSkT0Al8BdgauAU7LzNuqx67JzBftSLl1ss2SpKljKrdZtleSNHWMtL0azRyEy4HHIuJw4EOUiXT/YQfrJ42b88+HV72q3NZq331ZtuEDbNpSAkibtgTLNnwA9q1x7Z5nPpO9uZv7H9sVSO5/bFf25m545jPrKT+C0/lLYDplStHpJV0FxcZswwb+cOvn2Lq1lLd1a/CHWz8HGzbUUz6U9+eRmUBy/yMzy/tTswP5Jbc/MIvM5PYHZnEgv6yv8Ez+L0saCYCSrivAed99HHbp2dx8c7J1K9x8c3LYpWfDfffVU34me29d2/oZbF1bX/1HZxnwqszcBzgf+H5EHF09VtOXWlJHjEePcWl82WYN5O9ckjpqNAHCzdVqWicCf5OZfwPs0Z5qSfU4/3x473vhe98rt7UGCT/0Ia7g2JasKzgWPvSh+l5jzhzuZ58qUY4V72cfmDOnnvLnzWMlR7WUv5KjYN68esqfPp2f8fyWrJ/x/LKaRR2mTRv8/Zk2ml3bduyyC7dzcMtr3M7BsMsu9ZQ/Ywab2Kml/E3sBDNm1FN+Tw8/53ktWT/nedAzmilohzfoZ9AZMzPzRoDM/AbwJuDCiHgTLqolTW4LFsDJJ/cHD/r6SnrBgs7WS9pxtlkD+TuXpI4azVn0IxGxGDgV+NeImAbUdAYrtccllwyfHpOVK3ko9mzJeij2hJUr63uN664bXf5ovfCF2xyBZpVfizlz2DpgN7OVafUFOIfqpVZn77UDDhhd/mhNb/TebJb1BVE3b2YmrT02Z7IBNm+up3wABg4lrmFo9I7ZFBFPbySqE6/jgY8Bh3aqUpJq0NsLK1aUYMGSJeV2xYqSL01OtlkD+TuXpI4aTYDwbcBG4A8y8zfAPODTbamVNBn09DAtN7VkTctNtfbM4ulPJwYEW4Kt8PSnD7HBKE2fTg9bWrJ62FJfcOr1r2cmG1uyZrIRXv/6espvd/AO4FnPYtAA3rOeVU/5L3rR4OW/qKaphw44gD14uCVrDx6u7z3afffR5bfXnwBPa87IzDuAlwOf6kSFJNWotxcWLoSzzy63Bg00udlmDcbfuSR1zIgXKQGIiAOAQzPzBxGxKzA9Mx9pW+1GwAl0NZx58+DOO/vTc+fCHXfUVPj06czZ+mvWMofG4hL7cRd3TXtGWRK4Dvvuy5x11277GrOPhHvuGXv5ETyFB3mEpzxZ/h48zMPMqm2Rkqds+M225e/89NoWKQk2UeZQLOXDFpIZtS5S0tbXiGAGG9nMjCfL72FTGWZcR/m778609Q+STfUPtrB1t1nw6KNjL7+nh54tj7Glqf7T2cTm6bvuUC/FsUz4PorXuCQz39zO1xiKbZY0Bo3hhgsXwvLlk69n0bJlZahkc537+srIg0WLOlcv7bCp3GZ1pL1atqxcaD/nnP7f+eLF5XjC34gk7bDaFymJiP8NfAP4YpU1F/iXHaueND4OOWT49JjssgtH0bpa8VFcVd/cdJWP8bHqXg5I1+NIrh02PSYbNrAX97Zk7cW9tS5SMn1A77uB6XoM3FXWOMchsBuPDJsek/XrB+8lun59PeVv2cK0Ae/5NLK+IHl7HLz9p0iaUBrBwRUrYOnS/mGIAxc0mMicX007pnvarJ4eOOOMEhRcurTcnnFGvaNzJElDGs1Z7vuBl0IZq5aZvwRqXK5VqtmyZbx9/s9bst4+/+f1rYS2ZAmP7dSYg7AESB7bac8yZ0pdjjqK0/gyL+ZKetjEi7mS0/gyHHXU9rcdobfz1epeDkjX47f5r5by+9P1mP3kqsU5IF2f/iBnDkjX49n8oqX8/nQ99mZdS/n96XocydUt5fenJ6zunPxdmsxWrmztMdiYq6zOeX/bzfnVtGO6p83avBnOPbf0IFyypNyee27N8yZLkoYymssxGzPziYiySmVE9NBNDZYmn1tu4b4Lv0awhCSISO77m6/Bu+6qp/wFC/jJxsY8cWVo5U82vgjq7AiwejXv4EJ+yjEA/JRjeAcXctHqc+opf8YMlm76sypR/oel/BmnzbiwtvIv2/TqlvIv49X1rdALPEgjSFvK70/X5+ncw/3MfvI1nk4Nw7ub3EVj1ehSfn+6HvfQmLOylN+frsfOPNFSfn9akmoy2PDC3t7JF1xrnl/trLMmX/2ldmr8zh98sP838qEPdbZOktRFRtOD8D8j4qPALhHxCuDrwLfbUy2pBqecwt5xXxXFTjJh77gPTjmlnvI//WkO7lnTknVwzxr4dI1r96xfzyW8pUqU4PwlvKW+4aGvelU1v2G/tcyBV72qnvKnTeMB9mrJeoC9YFp9Q3RzwG5sYHrMdt6Z9bQOG1/PLrDzzvWUP20a+wzokbgP99b8HsWw6bG6mecMm56A6n0DJGmk+vrKvGpnnVVuJ9MQaXVKd7VZ/kYkqWNGcwb6J8A64GfAe4HvAn823AYR8bcRcU9E/E9T3sci4s6IuK76e23TY4sjYnVE/CIiaopQqGv19nLtKz5SJcqx1bWv+Eh9V+t//Wteubk1Rv7Kzd+GX/+6nvIBDj+czQN+ppuZBocfXk/5c+awK63Bxl1ZD3PmDLHBKD3taYMHp572tCE2GKVZs9iT+1qy9uQ+mDWrnvIB9t2XWTzU+rI8BPvWNMPC1q08ZcAqw0/hYdi6dYgNRmnnnZlJ65yPM9lQX4Czp4e5tPbKnctdHZsvKCKmR8T2xsl/ZDuPS1L9psI8iqqVbdYA/kYkqaNGHCDMzK2Z+aXMfGtmvqW6v70hxn8PvHqQ/M9k5hHV33cBIuIw4BTgedU2n4+I6SOtnzSYm27bZdj0mNx6K5fv/LqWrMt3fh3cemt9r0Gbe3999asMugDHV2uah/Chh9j2wndU+fWU/yi7t2Q9yu71lQ/w619z94DpVu9m31oDwddx+LDpMdmwYfDvUF0LxQw1L1CH5gvKzC3AARExc5jnfG8cqyRJxVSYR1G1ss0awN+IJHXUdrt4RMTPGGauwcx84TCP/SgiDhxhXU4ELs7MjcCtEbEaeDFw5Qi3l1r19bHhxt2B/tW8N9x4C/RdX08vwvXreeJI4Lr+n8cTz3khXFvT8F+AY48lv9MaJ0+mw7HH1lP+xo1spPWYdCMzYePGesqfM4eZD23giaYhujPZUF8PxUwe5SktWY/yFNjutYvReZQ9hk2PyfTprN+yW0vWenaD6TVdH+npYevm1rK2Mr3WHn63c8Cw6Q74FfBfEXEp9HeRzczzOlclSV1vqsyj2E7LlpVVnZvfk76+EiAa7P2bGmyzJEkTwkh6EL4eeAPwb9Xf26u/yyjDjHfEByLihmoIcmNFgblA84Rud1R524iI0yJiVUSsWreu3tU4NYVcfDHvnvmVlqx3z/wKXHxxPeUvW8bGu1q/fxvvWlffKskAl1zC3gPmp9ube+GSS+opf8sWBu3ht2VLPeVn8la+0UgAlHStAbyBQ3FrGprb0NPD7jzakrU7j9YXYJs2jf1Y25K1H2vrm4PwxS9mJq0B35lshBe/uJ7ygS0DmpKB6Q64BfgOpY3bo+lPkjSR3XILvOlN/UNK+/pK+pZbOluv9rLNaliwoHVIcWPI8YI6VwAUy5ZtO2y7r6/ecxhJk9J2z+Iy8/bMvB14RWYuysyfVX8fAV65A6+5HDgEOAJYC/zlaAvIzPMzc35mzp89e/YOVEGjceWVcM455XZSlX/IIbzg0+9i+vQSAJs+PXjBp98FhxxST/k9Pcy+58aWrNn33Fjv3GsRHMRtLVkHcRtETcOM581j5wFzEO7MephX0yq6s2axx5Pllzrvwfp65wgcbIh0nTLZwE4tWRvYqb4g5267cTCtw9IP5lbYbbchNhil668nBnQCDxKuv76e8oHHByziMjA9XiKicUXgwcz8+MC/jlRKkjRyp5xSjnHe9CZYsqTcRtS3wNwEYps1iMaQ4pNPLp9/Yz5Ce9nWy0CspCGM5kw6IuKlTYmXjHJ7ADLz7szckplbgS9RhhED3Ans3/TUeVWeOujKK+H448tCYscfX38Qr63lL1rEP9z4W092htuyBf7hxt+qb4jKD34AT9+vNe/p+5X8uhx+OIdG46p5CfIcGrfUt0jJfvuxZcBMA1vogf32G2KDUbrqKn5D64Ikv+FpcNVV9ZQ/VBCtruAawKGH8jCtAc2HmQWHHlpP+Q8/zO0tuz5K+uGHh9hglNavZ9OAYeSbmFnfStiMw0rSI/dbETEH+IOI2DMi9mr+61SlpBGxR4dUAkHf/GaZx/bss8vtN785VQNEtlkDNfZ3CxeWz3/hwtZ81cNArKQhjOYs7t2UhUNui4jbgM8DfzDaF4yI5sjDSUBjheNLgVMiYqeIOAg4FPjpaMtXvS6/HJ54ogTXnniipCdN+cuWwZ0DYsx33lnfQcaZZ7Jh3SMtWRvWPQJnnllP+QBr1rDu0JdUidIDb92hL4E1a4beZjRuvJFNA3rHbWInuPHGITYYpUyezt0tWU/n7vp63z3+ODvxeEvWTjwOjz8+xAY74LHH2G3AKsO78TA89lg95W/dym4D/ofdeLy+VYyBzcwYNj1Wv0MjKJ4D0uPuC8APgecAVw/4W9WpSkkjYo+Oqc8g8Mg1jhNqnlN4gpnabdaOfN8XLICTToLPfrb0HvjsZ0va/WD9entbA7EGByUxulWMr87Mw4HDgcOrFYivaTweEe8auE1E/BNlkZFnR8QdEfFuYFlE/CwibgB6gT+uyr8RWAHcRJnr8P3Vyl7qoOOOg5kzy3oJM2eW9KQpf8EC3vmj97DTjK1EwE4ztvLOH72nvoOMlSs57gX3VYlyAHvcC+6rd6W1/fdn9i//u+U1Zv/yv2H//YfeZjT22ou9acyjWMrfm3WwV00XrnfemafwYEv5T+FB2HnnesqfOZMP8rmW8j/I58qXqS6PP865fKTlNc7lI7UGIfPJeRNzQLoe+z85TD0HpOtxBNcPmx4vmfm5zHwu8LeZeXBmHtT0d3DjeU1z30oThz06Oq/dATyDwNvXmHNw5swSIJo5s3VOwilkyrdZO/J9v/hi2LSpfyqdiJKua/5w9evrg+XLy+9s+fIp+RuTNHo7MkT4ocx8aJCHPjjIc383M/fLzBmZOS8zL8jMUzPzBZn5wsx8Y2aubXr+JzLzkMx8dmZeNtq6qX7HHAM//GG5uPTDH5b0pCm/t5djvrmIvl1fxyeO+z59u76OY765qL6TrSuu4OHrflUlyoHMw9f9Cq64op7yAW64gWuzMZy4vMa1eTjccENtL/EYu7eU35+uwTvfyQre1lL+Ct4G73xnPeXvsUfT8N/qM2AW7FHj3N6PPMJpfJm37/mv7MW9vH3Pf+U0vgyPPLL9bUfiGc9gNc+uEuV/WM2z4RnPqKf8pz6V3djQUv5ubICnPrWe8oF/5s0t5fenOyMzF27nKT8cLLNaOOueiPifpryPRcSdEXFd9ffapscWR8TqiPhFRLyqpuqrm9mjo7PaHcAzCLx9F19cgkLf/CYsXVpuI6Z0gGhH2qxJ0V7t6Pd9xgx44xvLfvCNbyxp1auxb1uxovzOGp+TQUKp69U5UVRNqyZoojnmGFi8uP7gYNvLf+1r4dprOeaPFrC475Uc80cL4NprS34drr9+28VCImpd/IGtW1lP63x669mtvuGnDz/MvgOGAO/L3fXNf3fIIayf1hqsWz9tj/oWijnoIG7isJasmzgMDjqonvIBpk/nfN7DVx94HfezD1994HWcz3tKt9c63HPP4AvF3HNPPeU/5SkMHKCVVX5djuInzSU3pSesodqrvwdePUj+Z6pe80dk5ncBIuIw4BTgedU2n4+Imr4U6lr26Ois8QjgGQQe3iGHtM452JiTsK7jhslpsDbr75kM7dVov+9f/GL57V10ERx7bLldsqTkqz4rV7bu2xr7vjpHQUmalOoMEE7pSUI0CZ1wApxxBud/6j5edfD/4/xP3QdnnFHy6zB3Lkfm1VWifP2PzKth7tx6ygd485s5YL9NLVkH7LcJ3lxTD63nPpc5rG3JmsNaeO5z6yl/6VJeubXRGbi8R6/celm5WlmHtWu5l71bsu5lb1i7dogNdkBPDxfw7ipRjtEv4N31rVZ90EEcMmDI7yHcVl+Q8667OJ3PVonyGZzOZ+Guu+opH3geNwNbKO/Plio9oQ3aXmXmj4D7R1jGicDFmbkxM28FVtO/6JY0evbomBjaHcAzCDy8RYOM9OjtrW+BuclpmzZr0rRXo/2+9/XBOefAO95RRuS84x0l7e+kXv7OJA3BHoSauo48kvN7FvLeTX/N9371TN676a85v2chHHlkPeW/8IUs5w+rRPn6L+cP4YUvrKd8gB/9iL3W/k9L1l5r/wd+9KN6yr/zTu5ln5ase9ln28VddtRjj7GGxlDZ8h6t4Rn1LfCxbh0zeaIlayZPwLp1Q2ywA577XO6idVXnu9ivviDqvffy4IBVkh9kFtx7bz3lbxliKteh8nfA3twLTKecw0yv0lPKByLihmpIV2MuqLlA82pBd1R524iI0yJiVUSsWlfnd1NTiz06JoZ2BvAmexDYRVYmg4nTXu3I9/3Tn4a3vhUuu6z8Bi+7rKQ//emx1UWSNCJ1Bgj/q8aypLH79Ke55KAzqkQJTl1y0Bn1HWSccgq/orWX1684CE45pZ7yAW65ZXT5o/X857PPXq0XpvfZK+H5z6+nfOBXHDxseky2bBk8uFZj8ItPfpIHaF205QH2gk9+sraXyAGdAwamx+r/8n+rezEgXY9reVFL+f3pCWs0F7SWA4cARwBrgb8c7Ytl5vmZOT8z58+ePXu0m6tbtLtHh8Gd7Wt3AK/dQWAXWZmqRtpmTaz2ake+7yecAF/4Qpl7aOnScvuFL9Q3+kdj0859jG2UNCGMOEAYETtFxO9FxEcjYknjr/F4Zn6gPVWUdtCZZ/LmW8+tEiXg8uZbz4Uzz6yn/Isv5ui4qqX8o+Oq2ifSvn9AcGpgekzOPJOb7289ALz5/tn1vUfz5vFyLq8S5T16OZfDvHn1lJ/JLrT2RtyFxyBrDLCddx678OiA13gUzjuvnvKfeIIDWi7sU9JPPDHEBqN3/4Bh2APTYzJwHs7t5Y+TiDg8Ij5Q/R0+4OHjR1pOZt6dmVsycyvwJfqHZd0JNC8nPq/K01Q0FU5cDO5sX7sDeO0OAt9yC5x0UutnfNJJ9V1UdJGVtqmjzZpw7dWOfN/PPx/e8IYyrHjJknL7hjeUfHVeO9sR2yhpQhhND8JvUeaw2Aysb/qTJqZrr+W0zcv54owP8MqDV/PFGR/gtM3Ly0IldbjzTo7LyymBrwCypOsangvwspdxb7QG8O6N2fCyl9VT/sUXDx48qivIefrp7PHkbqIEjPZgPZx+ej3lz5nDi7imSpSg4Iu4BubMqQrBW00AACAASURBVKd8gNWriQEX74OA1avrKX/OnMF7otb1P+y7Lzs/uYpxsTMbYN996ym/p4cjB3wGR3JNfXM07oCI+CDwVWDf6u+iiPg/jcczc6TzNhERzePLTwIaY/4vBU6pLp4dBBwK/HSsddcENRVOXAzubN9kn5frlFPKBbKTTiqf8UknlXSdIxvaPUfjVAjGj1JdbdaUaK+e+Uy49NIyHdDZZ5fbSy8t+eq8drYjtlHShDCaAOG8/5+9d4+Pqr7z/59nZnIhgSQSglzEgimXalGoG5Ct0qSoLY+ChXVt2Sr4a21xabtfXcT8ZPtAt/Jo0Sisdbcq+vVnS7VfresXWi20CiSiS1SqICoqiBcQUG4SICGXmfn8/vicIXMOyUgy75lkkvfz8cjjzPsN8z5nbufyOu+LMea7xpgqY8zS2F/KtkxRkmXtWrj7bubeUsxf3x/F3FuK4e67rV+CrVspDtbRWvnhWHvrVpn4LqOcHQntZClhf0I7KR56iG14e/Vt40vw0EMy8fft40W+6hr2c3iRr8oOKXnvPYqo87iKqJMTCI8fZ5+vx+E+BsPx4+08oYPs308I76CbEC1yU5LD4bZLjMNhmfid4zpgojHmVmPMrcBFwI8+70mO4/wfoBYY7TjOx47jXAdUOY7zhuM4W4EK4F8BjDFvAX8AtgF/AX5ijBGsbVe6FT3hwiUmsMSLO/H+7k4vFI46TEUFrFplM9AXL7bLVatkv6epHrLSE8T4jtPhY1aPPV4NHQpZWfDcc1BYaJdZWa0DAPU33/Wk8iaBTnlXlC6nIwLhRsdxxqZsSxRFmvJyAGrv3cSSimepvXeTx580U6dyKHQmjps15WA4FDoTpk6ViQ+wdy+V0TtxiAAGhwiV0TvlJtCWltJ3aBHxImffoUVQWioTf98+Gsn1uBrJlRPwWlo4Qb7HdYJ8aGlp5wmdwBjO4DOP6ww+kytjHjy4bQFy8OB2ntBxQkQT2knR3vsgWebdcew45VZiI5YTYoz5J2PMYGNMljHmLGPMw8aY2caYscaY840xVxhj9sX9/18YY0qNMaONMWsSxVZ6AJl+4bJzpy3V+9WvrLjzq19ZW6r8NNX0BOEoXSJnrMWDdKuHdAxZ6QlifMfp8DGrxx6vNmyw53CBANS550YtLdafib/5nkiqBznplHdF6VI6IhBeDLzqOM677nSs2B0qRemehELU3vTfTD72NP9WfRmTjz1N7U3/LVf6uHw5xT/5LsYtLzY4FP/ku7B8uUx8gCFD+LXzUwxBAAxBfu38VK78tLKSc87zCmznnJcvV0513nlcx8OuYQWj63gYzjtPJj7w//CIJ36rLcTIkZQTO0Gx6yinGkaOlIk/bhwX8jdP/Av5G4wbJxMfuIxnPfFbbQFyc9suMc7Nbf85qecR4GXHcf7dcZx/B16Ck19ERekcmX7hMnq0nSAfG+IUiVh79Oiu3a7TpScIR6kWOWM9B0Mh+z0Nhbw9CZMlXZO2M12M7zh6zIoRu0Ed9d3IjEQy8zff00jlTYJMn/KuKD2EjgiEU7E9Ky4HpgPT3KWiJEVtre1BXFsrHPjBB6kadDfhqBXXwtEgVYPuFm10vLlhjPvI8dlCbNnC02aaZx1Pm2mwZYvYKo4cSWwnxauvshNvNuJOSuHVVwVXkmK2b+foyUnJ9jM4ShFs3y4TPxjk/ZPvkY3/PqUQDMrEh7b7QErR0sIhBgBRN37U2pJZnB3EGLMM+D5w2P37vjHmni7bICXz6QkXLuGwbbNhjBVejLF217YD6BiZLhylWuR8/HH7ua5aZb+nq1ZZW6qvcLp6NGa6GN9B9JgVx9e/3rZ/x47M/M33NFJ5kyBdNyAURUnIaQuExpiPsBOwvu4+bujI8xWlLWprYcoUew44ZYqwSDh3Lns/8X5F934SgLlzZeJXVfHJi16R6JMXt8uWCo0bd0oxaNT1i1BVxeE9XrHo8J56udcwYgSP8T3XsOLUY3wPRoxo/zkd5Pdc7YnfagsRjbbdR9F/d7uzrF9P//zYEBGbgdc/vxHWr5eJD21vvxS5ubzFl7CHAwMErN0FGYSO4xS4y/7Ah8Cj7t9Hrk9ROkdPunCJlf93bRuAztEThKNUipylpd6eg7GehFJtQ9JBTxDjTxM9ZrXBPffYnoOxm6QB9zy+oCBzf/M9iVTeJMj0IVGK0kM4bYHPcZzbgP8XWOi6srAHMUXpNDU1tod2JGKXNTWCwefPp+isvq5hL4SKzuoL8+fLxA+FOPymtxfg4Tf3yk5vnTWLJl8PvyZy5SYS7tzJwT1NxN4fMNaW6klVWsoADnpcAzgod7HSty+t2x7DuH4h+vZlB95y4h2MlFvHyJFsrz/LNazIub3+LLkSZmAXZye0k+V5yt1Hjs9OO793l68Cf4v7i9mK0jl6woXLCy/ATTfZC+5Fi+zyppusPxPoKcJRKkXOnvA9TbUY372G3egxy09jo61AiERg7NjWm7E5OZn7m1cURckgOpIBOBO4AmxtmjFmL9AvFRul9B7KyyE7294ozM6Wmx8CQHU12/Z4S0O37SmSO7FYu5Zdfb299Hb1PU9uSjLAtGmcmXXY4zoz6zBMm9bOEzrImjUU4a0pLuIIrBHqX7116yldth3XL8Lx4xzHK9Qdp6/cBGB3HUco9LiOUCi3jpdfJoy3nDhMEF5+WSY+tkNmIjsp6utpU6StFyxjPk2MsfX4xpgRxphz4v5GGGPOSfsGKYoiR0/I4uwpImcqSbXI2Y2G3egxqw2Ki+0yKwsGDmzNJCwuzszfvKIoSobREYGw2RhjcK8EHcfJ/5z/ryify6RJsG6drbRZt87aYmzaxIAzvGWgA86Iyp1Y3HwzfRq82XF9Gg7CzTfLxAf4whf4uxZv3fXftdTCF74gE3/aNBayxDWsyLOQJXIC5NGjbGeUx7WdUXD0qEx8IOgrwvbbEmTTlNBOitxcvog3Y/OL7BQt0R3P6wntZDlyskdj23a6cRxn3en4FKVXccklsHSpzchZvNguly61/kxAs+MUCbrhsBs9ZsUxYgRccYXNIly3zmYSXnFFa2uaTPvNK4qiZBgdEQj/4DjOcqDIcZwfAWuBh1KzWYoiQCjERYf/7HFddPjPciXAFRV8ZVxMjLLi2lfGRWVPMtvrESXVO+q+++D8C7y+8y+wfgmGDCHqyyGM4shNYQb6cSyhLcFo3nUfGZ8twNSpFDjebS5wjsHUqXKrIJYRany2DCHCCe104ThOrtu3aYDjOGc4jtPf/RsODO2SjVKU7kJlJYwfD467T3Yca+vFdvroCSJnT6CbDLvRY1YbrF4NN97Yeq4eCll79equ3S5FUZReQkeGlNwN/DfwFDAauNUY85+p2jCld5DSISVr11JQ8RU4KVA51pYqAa6u5rUtsZ+QXcdrWwKypUKlpQziU49rEJ/K9fCrrubhNye6hn0ND785Ue41GMOVPBUzAKwt2Bz/bHYltCX4gFilj+OzBZg1i4+MtyfgR+ZsuT6TwGa+4j5yfLYAgQATiJVD2891Ai+3NhZPL9djezeNcZexvz8C/9UVG6Qo3Ybqapg5015wL1pklzNnanmr0vvoPsNu9Jjlp7oapk+309UvucQup0/X/ZSiKEqa6NAVnDHmOWPMzcaYBcaY51K1UUrvIaVDSlavZkvWRI9rS9ZEubuQmzbhlJR4XE5JiWyp0DPPUFAcy3i04ktBcQieeUYm/i23kBtt8Lhyow1wyy0y8SdPZnLoJa8r9BJMniwT33E4SoHHdZSC1gwZCXJyCBHxuEJEbMNsCa69lma8sZrJgWuvlYkPvMTEhHZSRKO0Hkpi73tAbspzBzDG/MoYMwJYENfHaYQx5gJjTO+82FKUGI8/bm/OrFpl+9+tWmXtxx/v6i1TlPTRjfpA6jGrDZYutT2Mly6FDRu8tqIoipJyPlcgdBznmOM4R9v4O+Y4jlwjMaVXktIhJcCVVya2k6KsjBuO/8LjuuH4L2QbXX/722w5NMw1rPiy5dAw+Pa3ZeIfO8a5bPO4zmUbHBMq092yhaciM1zDbv9TkRmwZYtM/JwcDjDA4zrAADnxDiASYSLegSETedmq2hLs2UMR3kE0RRyGPXtk4gMHfe+R306WNrNEuxBjzH86jvNlx3G+4zjOnNhfl26UonQ1paVWFIzvf7dqlVxGuqJkAt2wD6Qes+KI9UadP9/a8+e39k5VFEVRUs7nNmMzxuikYiVlxIaU1NRYcVB0SAkwd65dPvWUFQdjtgjz5jF3ztdhnOPGd5i7pcn2s3nnHZl1PP88JcRUTSu+lLAfnn9eJv477zA+711oaI0/Pu9due3ft48jJjZl2MY/YvrCvn0y8YNByqnhMWafjF9OTevUOwnCYaayhlXMxNPDLyzUZy8aZRi7eJvzTsYfxi65k2HH4WvG+x59jRrZLMtuhuM4twHlwLnAamAq8CKwogs3S1G6lrb63FVUdOlwBkVJO93wd6DHrDjaqvKZP79VMFQURVFSSpc0iVKUeCZNgoUL5cXBGHPnwl//KiwOAowcCQ88wNzjy2z848vggQesX4qLL2bHySnAVtDZwSi4+GKZ+IWFHGroA0QBB4eotQsLZeJ/8glbiQ1Bsdu/lQvgk09k4tfX0496T/x+1NtyFEHWEBsY4vhsGbbxZU/8VlsAY9hNrMehjb+bs0X7QD51UsR2fHaX8Y/AFOATY8z3gQsAoS+1omQoVVWnllFWV1u/oihdiR6zYuh+SlEUpUtRgVBROsv8+dCnDyxYYHvqLVhgbcm7nNEouecMJX7QSu45Q+Wyy7KyOEIBdldgMASsnZUlE3/QoLanGA8aJBMf+IQzE9oSvHtSpG3bTopAAINXrDMYuSEfgQBbGetxbWWs6BCR7lZiDDQaY6JA2HGcAmA/MOxznqMoPZuyMm+vtVgvNsm2GKkkHcKBihNK16DHLLC/s1DIu59atswOKcmU/ZTSPdB9uaJ0GhUIlR5PbS0sWSI8IRlsv5rFi2056wsv2OXixbJ9bFavhrN854hnDZMbtNLQwMP80DXcKcb8EBoa2n9OR6iroy/ebL6+1ENdnUz8QKDtKc+SE3SnTaMFr2DaQhZMmyYT3xgOU+xxHaZYLsNv1Ciivl19lACMEhI5HYexvEkWLYAhixbG8maXlTA7juMAWx3HKQIewk6EfA2Q3gMoSmYR67X2ne/Arbe2DmqQKq1M9QVZOgTOTBdRlYxDj1lxlJXZydJXXWV/d3Pm2Jvvc+Z0aY9IJQPRfbmidBoVCJUuJ2UCnhu7vBx+9jO7FF1H7EQm1osuHLa25MGnqoqDHx33uA5+dFzugisU4oiviuUIhfYOrgRFRWTT7HFl0wxFRTLxo1HG85pruD0UeU22mXV1NXW+96iOQrmJh8a0LUBKCYTvvtu2QPjuuzLxjaGGcncdAaI41FAuWsLcsc0xBphgjDlijHkAuAy41i3bUpTeTUWF7ZO7eLFdSvZdS/UFWaoFznStQ1Hi0GNWHBUVdt/0wAMweDD87ndw6aXw5JMq7CgdQ/flitJpVCBUupTaWruv/tnP7FJUwKuqYsWSj2lutlpFczOsWPKxnLi2eXNrpt0ll9hlQ4P1S1FWRvbunXCyBNVYW+pEyXHoi3cYeV+OymV/jR1LC9keVwvZMHZsO0/oIIEAm/mKa9ht3sxXZDMIT5ygCG/GYxF1cOKE3DpSiTH0xSsy9+W4qIBXTg0BIljpMWoHxXQtrzmOUwZgjPnQGLO1qzdIUboF1dVw//32Ztb998vd6ID0CXipEjjTuQ5F8aLHrBjz51tR8I03YNgwWLvWNirX36HSUXRfriidQgVC5XNJZYbfihXQ1GS1iqYma4tRVsYnf9lCvLj2yV+2yIlrTzwBeXkwe7YtMZ4929pPPCETH6Cigt35Yzyu3flj5A5ydXU4vt2AQ0CuBPjFF4n44kcIwIsvysSPRnmJiR7XS0yUzSCMRjlGX4/rGH1F1xHxDZT328nSzycQ+u1keYMvu0KwQwvZvCE5ZKVzTARqHcfZ6TjOVsdx3nAcp/decCkKtGb0/eEPcPvtrWKetEiYyguyVAqc6VyHonjRY1aMZcusKDh2LOzebcXCJUv0d6h0HN2XK0qnUIFQSUhtLUyZYvetU6akRiRMGRUVDPrmOI9r0DfHyV2wFBfDtdfy4P8t5hvnbOfB/2ttios//7kdoDGak9BOirw8CvnM4yrkMyt0SnD0KOfzhsd1Pm/A0aPtPKHj7GVIQluCNkuMBcmmMaGdLAcYkNBOliUsdB85PrvL+AZQCnwdmA5Mc5eK0nvZtMmb0RfL+JPs7ZXKC7J0CJzpWIeinIoes8D+zhYtgn/+Z9i3z954X7vW9iTUHoRKR9B9uaJ0GhUIlYTU1NjS3EjELmtqZOPPmQPZ2baiNTvb2pKMn3aW+8j4bAEuvZQH7w9zff0ynn1/JNfXL+PB+8P2bqcgX/3SQfeR8dkyXMz/eOK32gLk53PRyT7bNv5F1EJ+vtgqhrA3oZ00AweSQ5PHlUMTDBwotop8GhLayZLrExz9dnLBczmOV1A+Th7k5sqto4MYYz5q66/LNkhRugOVlafeIKuosH4JUn1Blg6BMx3rUBQfesxyiQ3/e/JJ+7tbsQLuvtsutQeh0hF0X64onUYFQiUh5eVWuAsG7bK8XDb+pElWdPzFL+xy0iTZ+Ic27XR7ozkEiHBo00654OEw95z5C4/rnjN/0Tq0RILqasq3PeAaNjurfNsDchdceXnsIDbN1sbfwSi5DMJwmC2M98TfwnjR96jA10PRbyfN/v1M4GXXsCLnBF6G/fvFVnGCPgntZBnFjoR2UjQ28g2edQ37/nyDZ6FRNgtSUZRuTqovyFItcKZrHYqitE1lpT0/jN+PzJ8PTz+two7SMXRfriidRgVCJSGTJsG6dfaG3rp18gJebB0LF6YgdnU15U/+lJwch2AQcnIcyp/8qZy4VlmJU1zicTnFJbIHn7vuovy6UoJBK64Fgw7l15XCXXfJxM/NZYizz+Ma4uyTy/4qKeFKZ6VrWPHoSmcllJS0/5wO0khuQluG2K7S8dky5PgmPfvtZDmXtxPayXIebwNR7PsTdW1FUXoVekH2+VRVnXoOUl0tNzytJ6DvkaK0jf42FEVJAyoQKp9LygS8VHPXXUy69TLuuTfIlClwz71BJt16mZy4BtzwzXfcR8ZnC3Hzzax66FMiERs/EjGseuhTuPlmmfgXXMAo430No8w7cMEFMvEPH2aD+arHtcF8FQ4flokPlBM7WTI+W45xxCZTG58twyh8nwGy36PxvOaJ32rLcIQC7OHEAAHXVhSlV6EXr59PWZm37DpWlq3lk63oe9S72bkTZs70fv4zZ1p/b0d/G4qipAEVCJWey803U3v7c9z4vyKsWwc3/q8Itbc/JyeuVVfD8uWu4WaWLV8uPhHyN7nzPK7f5ApOhhwyhD8wyzXsa/gDs2CI0KCP+nqe5Due+E/yHaivl4kPFHEUK0zZ7LUi6RLjk+uIuOuIiK9jD7HemI7PlmEzX/HEb7VleOZkL3XHZyuK0mvQi9fPJ1Z2/Z3vwK23tvZslJ72nMnoe9S7mTULjLGi4K232qUx1t/b0d+GoihpQAVCpedSUUHNVf9Fc5OxQ1aaotRc9V9yB9K77uLh4kpay04dawtmKAL0KcxJaCdFaSmfZg/zuD7NHgalpTLxAwGiJ98fSxQHAnK7nrf4EvYzsNlr1palhkuAoLuOoGvL0YcTCe1keYmJCe1kafD1TPTbiqL0AlJ98dpTMhQrKmDePNu7ZZ7gDb+ehL5HvZdNm+C22+xkxMWL7fK227QHYQz9bSiKkmJUIFS6nNpaWLLELqUp/0EpoSA4RAgFHcp/ICR8Adx8M7l7dhIr2wRjbakMRZeLR8Z6BBqfLUBNDdFw1OOKhqNy46onTKAIbzlxEYdhwgSZ+Lm5/JFvu4YVIv/It8Un6G7ga551tNoCOA4thDyuFkJ2tLcQ71Ga0E6Ws9md0FYU5TToCQJYKi9ee0qGYnU13H8/LFpkl5JVBz0FfY96Lzt32s896p6bRqPW1hJji/42FEVJMSoQKl1KbS1MmWKPc1OmpEAkfPVVTCQMOHb56qtysSsq6H/RaI+r/0WjZS+Irr+eA9XbXMMKRgeqt8H118vEHz6crKg3Wy0regKGD5eJ//bbOL4MQgcH3hYaYtHY2PYEYOEJum1mQUphDM2+wSrN5NqSGjH82yu4/UCdr+eg31YU5TToCQJYKi9ee0J5Xewz/cMf4PbbW1+PXuS3ou9R72b0aGhogKYmuOQSu2xosP7ejv42FEVJAyoQKl1KTY2tHohE7FIqcQ2A66+nZsEzhJ1sDAHCTjY1C56RE9eAw8GShHbSrFnDON9AiXG8BmvWyMQfPpzS4jqPq7S4Tk4gdByGstfjGspe0ey4bN/EX78tQS5NCe1kaSIroZ0sRXyW0E6Wnb6MRL+tKMppkOkluum4eM308rpNm7yfaewz1/LJVvQ96t28+y7k50MwCC+8ADk51n733czLqJZGfxuKoqQBFQiVLqW8HLKz7XlAdra1JSnmIFFjxaiocSjmoGj8xv11Ce2kyc+nKHIYxy0vdohSFDlsT5YkqKyk8LyzPa7C886GykqZ+EePcpBij+sgxXBUbsjHUPYktCVo8Ql2fjtZor4SY7+dLMfpl9BOlijBhLaiKKdJJpfopuPiNdPL6yorT/1MKyrkjrk9AX2PejelpfYGQyjuPOj22+0y0zKqpdHfhqIoaUAFQqVLmTQJ1q2z10Lr1llbjOXLOTT7RuJ7BB6afWPc5OEkqa7mut3/7nFdt/vfZS9YfvQjijmIcYdwGAJW5PzRj8RWcfDdAwntpMjO5hMGe1yfMNiqwRIEg/T3ZcP15zOrOAuS48tK9NtJkZ9PX455XH05JicCA4W+98hvJ0sBRxLaiqKcJplcopvqi1ctr1OUnk9ZGfz857aX9OzZtrzo3/4NHn8881oKKIqiZCAqECpdzqRJsHChsDjo8taJUlon3DquLcRddzH2BxPIyrIZillZDmN/MEF2inE4zGP9/8U17Hoe6/8vEA7LxJ8+nQGfvuVxDfj0LZg+XSb++ecTpMXjCtIC558vEx8YyXb3kfHZcoziHc86Wm0B8vJowjuZuokcyMsTW8XF/I/7yPhsGUbxXkJbUZTTQEt0E6PldYrS83n8cduGZuVKWLECrrnG9iEsLc2s/ZWiKEqGogKh0qN5uabefeT4bAGGDaPmwe1EwlZ0iUQMNQ9uh2HD5NYRCvH+4SKP6/3DRd7Si2R4/XXODXoFtXOD2+H112XiDxxIgS87roBjMHCgTPziYg4Qi+UOcWEgFBe3/5xOcJgBnnW02gIcOMCXfILjl3gHDshlcrb5HglyLm8ntBVFOQ20RDcx6Siv6wmTpBUlk9m922Y4V1TY396aNXDZZfDmm5m1v1IURclQVCBUei7V1fzDoYc8rn849JDcCcasWRQ7h4gaAEM0CsXOIZg1SyY+wEMP8TVqXMMKkV+jBh56qL1ndIypUxkf3OqJPz64FaZOlYm/YweXF7zsiX95wcuwY4dM/NxcxrHZE38cm21piiBZxKYiG58tw9U85onfastQwn5P/FZbhjmsIItmIEoWzcxhhWh8RekVaIlu19MTJkmnEhVQlVQzbJjdPy1bZn97CxfCK6/AN76h+ytFUZQ0oAKh0nPZtAlmzCCWNQWOtaWyMe66i83n/GNrbLC2ZIlxQwOvMNGzjleYCA0NMvFnzWJN9Bue+Gui35ATOT/7jH5HY1OMbfx+R/fCZ0I98Orq2M5oT/ztjIY62WExH5ycyuv4bBkOMQCHCODgEOGQZIYisINR7iPHZ8sRcQ8nET2sKEr3JNNLdNMhTmX6JOlU0xME1Ez/DHo6a9bYc9yf/czerL79dmu//npm7a8URVEyFL2SU7qc2lpYssQuRams5JF1wz2uR9YNl8vG+OADtm2LelzbtkXhgw9k4gP06cOHjPC4PmQE9OkjE7+igs39Lva4Nve7WO5iqL6el04KnJaXmAj1cqXe7/rELr8tQfSkyNy2nSx2EE0QO4gmKD5tu5nshHay3MISd/JygCghbmGJaHxFUQTI9AmY6RKnMnmSdKpJtYCaDjL9M+jpTJsGLS3273e/g+PH7eNp0zJrf6UoipKhqECodCm1tTBlim2HNGWKvEjoHwQrOBgWKipoxFvK2kiu7Iny3Lnk+3r45XMM5s6ViV9dzSefecWiTz7LlivhuPpq9jLU49rLULj6apn4x49T4hPTSjhoTygF6Ut9QjtZNvMV95HjswU4+2yyfVOXs2mGs88WW8VWxia0FUVRkiZd4lQmT5JOB5k86AZ6xmfQk7nvPrjiCohErB2JWPu++7p2uxRFUXoJKhAqXUpNjR1OFonYZU2NYPCqKhb+g3f4w8J/eEeujCQSYWRgp2u4E3QDO1tPaiQIh2kKelXNpmC+3BTja6+lxTdBt4UcuPZamfhbtjCEvR7XEPbCli0y8bOyqKPA46qjALKyZOK75PgENr+dLGv5ekI7Kfbvb3vS8365PoS5vp6MfltRFEWEVItTOkn680n1oJt0lZJn8mfQk6muhuef9/qef977ndAycUVRlJShAqHSpRQXQ9St0o1GhYfPlpUx9uF/JRS0KwgFo4x9+F/lykhKSzkwZLxruNNhh4yHUsH+dGVlp+iNkQhyr+HoUYo47HEVcRiOHpWJ7zgMx1tyPZwPwBEq0S0p4aCvX99BBkBJiUx8lxyf4OW3k+VDhie0k6Kxse0pxo1yr+EiXk5oK4qiiJBqcUonSScmHQJqqkuAq6rsAIz4z2DZMhWXugu33NJaBTJsmF0eP279MbRMXFEUJWWoQKh0KYcOtWpFjmNtMSoqWDH5fxOO2BWEIw4rJv9vuTvFoRBX/itGuwAAIABJREFUfvwfrmEzs678+D8gFJKJD7BpE3m53j6HeblRuYuVwYMJ+/rRhcmGwYNl4u/b1zF/RzlwgKF87Br2MxjKx3DggEx8lyLqEtrJEnC3vT07WY6czLI0PluGSu4iSAsQJUgLlQgO6lEUJTNIdVZPOsQpnSSdmHQIqKkuAQ6FYMECOx339tvtcsEC2XM3pfN8+GFrWfGuXa3lxh9+2Pp/tExcURQlZaRUIHQc5/9zHGe/4zhvxvn6O47znOM4O9zlGa7fcRznXsdx3nMcZ6vjOIJNuJTuSnExGFcLMUY4gxBg6NDEdjI89FCc4bTjT5KyslMSvRobkbtLet99nMmnHteZfCrX6yUvr+3suLw8mfglJezlLNewn8FezhLPIDxCUUI7WQb7RM5WW4BQiLdO9gS079FbjBW/GAr4loqi9DJSndWT6VOYIfNfQ7oG3aSyBDgchrvvttPxbr3VLu++W651i5IcOTmQnW17Dt16q11mZ1t/PFomriiKkhJSfS33G+CbPt8twDpjzEhgnWsDTAVGun9zgftTvG1KN+DQIQi438JAQDiDEBifF+tBaHy2ACNG8NSof6NVHHSsPWJEomd1jLvuoqhf2LOOon5huEsoQ2vTJgb39/bTG9y/We5iZfLktqcMT54sE3/PHowv285gYM8emfgujb4sS7+dLMcodB85PluA/Py23yPBiT0rmEMLWUCAFrJYwRyx2IqiZAipzurJ9CnM0DNeQzpIZRl2ZSXMn+8Vl+bP18+gu7BrF/zlL1awXbzYLv/yF+uPJ5NL9RVFUboxKRUIjTEbwNfgDL4N/NZ9/FtgRpx/hbG8BBQ5jiNU56h0V8rL7U3BYNAuy8sFg1dXc+iB/3YNBwesLXUSsXo1V97kFQOvvGkErF4tEx/g5pv5efQ2OCnwGGvffLNM/A8/pPGwdyJv4+F6bylHMmzYgInPrgRrb9ggEz872/1kW3Fw7N1mQfpzJKGdLFNZ4z4yPluAcJi+NHhcfWnQbAlFUeRJZVaPDiboHaSjDFvFpe5LVRVs3uwtL9q82fs7z/RSfUVRlG5MV1SDnWmMiTUg+wQ40308FNgd9/8+dn1KD2bSJFi3zl5LrFtnbTE2beLIFdcQy8oyONaWyo6rqoJ3fRmJ7wpOSQbYtIkNo3/oGvZkacPoH8q9hurquCElNn4Rh+VOsnbvZjKxaXQ2/mSeh927239OR+jXj34nxTobvx9HoF8/mfgu43nNs45WOwM4cYLLeNY17PZfxrNw4oTYKjL6/VEURY5UCi/pGEygImTXk+oybBWXujcbNsBNN9myokWL7PKmm7w3ljO9VF9RFKUb06XtoowxBjrejd9xnLmO4/zNcZy/HRAeRqCknzfesC1G3nhDOHBlJU+/Ptzjevr14XJlJGVlPPWfe4nP7nvqP/fKXqyUlfH0a94ee0+/dpbcOgoKeJFYua+N/yKToUBoiMXYsZTzAvY9coCotceO/ZwnniYNDexkpGvY7d/JSGhoaP85naDNKcCC/InpnvittgCBAP2IZYna+P2ob63tF2ANUz3xW21FUXoNqRZe0jGYYOdOmDnTK0LOnGn9SnpIdRm2ikvdm9hvLT6DMN4PWqqvKIqSQrpCIPw0VjrsLve7/j3AsLj/d5brOwVjzIPGmL8zxvxdifAwAiW9PPggXH89PPusXT74oGx8x0lsJ0VFBVxwvmu4JzAXnC97sbJpE+cUeyfmnlNcJ3ciW1xMI7keVyO5ctNizjiDI6UXYoUjAwSsfcYZMvH79CFMlscVJgv69JGJf5LYJGnjs2Uo5GhCOylMO/dg2vN3gu2+PpN+O1PQwVpKu2hm2edz1112Imy88LJwoVzP3FjMVA8maGmxouCtt9plS4v8OpSuo4eISz32eFVQYHsORSL2dx6JWLsjN65PZ3+t+3RFUZQ26QqB8E/Ate7ja4E/xvnnuAexi4C6uFJkpYfy1FOJ7WS54YbEdrJs/miA+8jx2UKEQpx3qMY1rKBz3qEauQm0wSDFeLNwizlgm0JKcPPN/OH9C13Dvkd/eP9CuR6KJSXk4B3znEOj+BTjLcTOpR2fLUAgQBBvP8AgYbkMv2i07RLgqJzIeZR+Ce0M4jfoYC2lLbS89fMZNsxmDsa/R7ffbv1SpKN3nOPYFgyLF9ul6J1FRRHjN/TE49W+fVYUbGqydlOTtfd14JLwdPbX6dinK4qiZCApFQgdx/k/QC0w2nGcjx3HuQ64A7jMcZwdwKWuDbAaeB94D3gI+HEqt03pHlx5ZWI7WebOheXL4fLL7XLuXNn4+cGGhHbShMM8nz/NNexFyvP50+QGTOzfzwjH2w9whLMb9u9v5wkd5JZbOGj6e1wHTX+45ZZ2ntBBjh+nGO/o62IOwfHjMvFdAj4Bz28nRTRKPt5+gPmckBPwQiE2+wTOzXxFTmQGDjIgoZ0p6GAtpV3SUd6a6Ress2bZzOT47DtjrF+CdPSOi72G5mZrNzfLvgZFEaLHHq/Gjz/1HDcctv7T5XT21+nYpyuKomQgqZ5i/E/GmMHGmCxjzFnGmIeNMYeMMVOMMSONMZcaYw67/9cYY35ijCk1xow1xvwtldumdA9SLeCBbXdXXi7X9u4kEyfynYPem7DfOXg/TJwot46dO8mtP+hx5dYfFO2HVMRn7iPjswU4epS+eMW6vhyHo0IltNOmcZy+Htdx+sK0ae08oXOM4r2EdlIEAm1n+EllELZXHidYNpdFc0I7w0l6sJb2ze0hpLq8NdMvWCsqYNUqK6otXmyXq1bJbf+8eXDVVd4S5quusn5JUtmbJNOzRJXuTuYfr2pq2v4N1tR0LM7p7K/T0bJAURQlw+jSISVKL8c9UZ47F/76V1ccFD5Rrq2FKVNsNdKUKdYWo6SEorBXvCsKHxQvb83yZav57aQYOJAXzVddwx1SYr4KA4WGcBw8yJlZXsHxzKzP4ODBdp7QQZ58kkDQuxsLBAPw5JMy8V3O5e2EdrKkdAiK4zCHFQSIAoYAUeawQvSiN5+GhHZPobODtbRvbg8hHeWtmXzBWlUFmze37lscx9pSx/SRI+GBB2DZMmsvW2btkSMTP68jzJwJjY225xnYZWOj9UuwcyfMmOHNEp0xQ4egKOJk7PHq2LFTeyQbY/0d4XT21+nYpys9A725o/QiVCBUPpfaWliyRFhcg7SUU9XUtLYvaWrq+A3IhDzzDKuG/dQ17MnMqmE/hWeekVvHrFmUBLwVJCWBw3LlThUVnPANKTlBrtxF6VVXcaTFOzDkSEsfm/UhwcKF9It4359+kcO2Mb4gx8h3HxmfLUB2NlcSa75p41/JU5CdLRN/4EB+zTyi7u4+SoBfM09OBAb6+7JO/XaGk/RgLaUHkI7y1th6MvWCdcMGuOkm+3jRIru86Sbrl2DIELtfXLAAJk+2y+xs65ciO9ueMESj9jVEo9aW2h/PmmWF0xkzbJbojBnW1hJmRYbMP17F37yMr6ToyE3N09lfp2ufrvQMMr0FiKJ0ABUIlYSkNAMvDeVUxcWtrdyiUbnhvDFe3x+r0HB8thCbNnHut0Z4XOd+a4TcFOMnnqCPL9urDw3wxBMy8YcP57OA9w70Z4ESGD5cJv7tt1Pkm/hbxFF7sidFIMCzXO4a9nN+lsszpwT4009Zw1TXsNu/hqnw6acy8fPzOYtdrmEFzrPYBfmCImrXooO1FLvPjT8+xY5fUvtiSP0Fa6ozIOIzB9taJsusWZCba+O98IJd5ubKimsTJthlSwv85jet++GYP1kqKmDlSttTbfFiu1y5Uu68R7NcejuZf7waENfDOL4Xc8x/Ot/n09lfp2Of3h3RfUTnyPQWIIrSAVQgVBJSU2PbCEUidimagQcpL6c6dKhVxwkErC3J2f0OJ7QlmDN8A1m0AIYsWpgzXCgbA+DoUYafFHcsw9kl1yNwwwYiUe/FYSTqyGWUFBSQQ5PHlUMTFBTIxAfo04d86j2ufOqhT592ntBB+vblKWLTeex79RRXQt++7T+ng0xljfvI+GwBmpt5ny+6ht3+9/lia5P/DEIHayntUll56vGposL6pUj1BWuqMyAuuQSWLrXlgIsX2+XSpdYvQUUFfO973rt+3/ue7HlDNGrPRRwHdu+2y3nz5IZGxcqwYyWUxsiWYaf6M1ZxoduQUcerjnxvjh+3wn88WVnWf7rf59PZX6djn94d0Uy4zpPJLUAUpSMYYzL678ILLzS9nY0bjfnlL+0yFbH79DEmGLRL8XWsX282Fn7T/LLiWbOx8JvGrF8vGj6l2790qdnIJANhY8/yw2Yjk4xZulRuHWPGmI1cZAK0GDAmQIvZyEXGjBkjEx/Mcn5oIHrybzk/NObk3KAkCQbN5fzZE/9y/mw/EAmg7fhS22+MMY7T9nvkOGLxK/mlJ34lv5SLD23Hl3qPwFzNbz3xr+a3nY4P/M10g2NLqv70mKV0KevXGzNggDGLFtml8DHXrF9vD7Zgl5Lxly61+8VQyMYPhawtecxdv96Y3FzjHtTtX26u3OtYutTGzM+3n0F+vrWlX0OqPuNY7FhMv50J3Hnnqdu7fr31d4KefMwSO1515HvzhS94f3+xv8LCzPuudVdSfRzoqej7pmQ4p3u80gzCDCelJcDApEmwbp29WbJunbXFqK6mdmYVUxqfYdGGy5jS+Ay1M9u4y5gEkybBPffY9+aee4S3f+1aVlX8BxB0HUFrr10rt47SUm5hCVF3HVGC3MISKC2Vie84jOVNAoSxAyzCjOVNuZKw3FwayYutDMDa/rvDnSUUajt7LRSSiQ9gDDs5x+PayTmnNtFOIv5RilzDvoajFMnFB+47mSzg+GwZ6k9OknZ8tqIo3YZYtk58BkS8P1liAzeys+1JSXa2dyBHsjzxhI2Zn2/j5+dbW6olBsC//ZsdSgKtmY+NjdYvwRNP2ONf0D1vCAatLfkaUpnl0hPK7DSDKv105HuzO27Ycnzvz7o6zdqSoqICLrjg1H1EOrKBMzULWXtWKr0IFQgznJSXAGNFtYULhcU1gE2bqLnqv2hqCdohIi1Baq76L9H+H7W1cOONVty88UZhAXX1an6/Y6LH9fsdE2H1arl1TJ7M+/nne1zv559vG7RLUFZGFTcTJQQEiBKiipvlTpSHDWMr3u3fyvkwbFg7T+gg+fmcw3uuYQW1c3hPvP/d77nafeT4bAHaE0ulRNQxY2gmx+NqJgfGjJGJD7zEhIS2oijdgLIyK9jde68V2O6919pS+/tbbrEnIytX2guolSutfcstMvHHjbP7xfj4ubnWL0VsmvBll9k+h5dd5vUny7hxVvT49rftxfm3v21tqddQVWWnO8cPulm2TPbiO9PL7HqCyJmJnO73JlbO7zj2wib+hnWmDW7qrlRXw9/+Zlvl3HuvtdMllGeqQN9be1YqvRIVCDOc8nJ7bhkM2mV5eVdvUQeorKS4rNQ7RKSsVLT/R0qnGHOqhiOl6Zzkqaf4XuPDrmEFsO81PgxPPdX+czrCW2+xF+8EyL0Mgbfekon//e+Tm+3t3ZSbHYXvf18m/tGjHKG/a9iTyCP0l+uhCDBmDH044XH14YScwNbURAFHXMN+xgUcsV9YCd55h4F4B5IM5FN45x2Z+EDdyQzItm1FUboJjuPtfyeVLR7Dn/ksmAlNaal3oEds4IdURj3Yycjz5tlKgEsusct586xfguXL4bbb4NFHbfxHH7X28uUy8UMhu60LF1oRdeFCa0tm1WfypO0YmS5yZiKn+72J3eCN30/F/Jq1lTwxQW7lSvjzn+37O22avVmUDqE8UwX63tqzUumVqECY4aS0BDgNbN6c2E6WVE8xzspKbCeN41Aa2e5xlUa2y13UFRdTTuxEy56ElVMt90bdfTcXNXsHklzUvAHuvlsmvjEMYa/HNYS9shelQDOhhHZSGMMzTHcN+7k+w3TR1xAinNBOlmayE9qKonQDNm2yF4U33GBPGm64wdpSGRB33GEPgjNm2Au/GTOsfccdn//c0yEdF2hlZfDkk3DNNTaD8JprrC2V3VJdDUuWeOMvWSIneITD9vi6ZIn9DJYssXZYaJ/fU8rseoLImUl05HszZ07bMebM0awtCeIz4Soq7HGgocHu49Il0qlAryjdGhUIewApKwHuAaRagMzJSWwnzd69/IobXMOKR7/iBti7t/3ndIRdu9jOaE/87YyGXbvaf05HOHaMBmJplVbwaiAXjh2TiR+L548vyc6dHMM7FfkYBXIlZ0A9fRLaydLke0/8dlI4DlG8gnUURz4zSVGU5IgJafHCSLw/WWIZfeGwvfALh70Zf5nApk32hGrNGvserVljbSlBItXxKyth/nzvxff8+XKfcU8os+spImcm0ZHvzauvensPgrVffbX1uZq11Xnib7TEC+Wvv56+34AK9IrSrRFMg1GUjjNnDjz0kC0BDgbbv3HYXWluTmwnTV0d9SeHfFjqybPNmoXYzAUJ7WSp4evuIwcwcbYM/8MlnvitthDRKCd8gt0J+rSmpgowntf5KG4QynheF4tNIIDxbapx/SIYQzbNNMUdTrJpFs/iVBQlSeKFkVj2SCrKu/ylgZnEzp3w+OOwalXrezRjBsyaJRM/1n+rrc9ACv/Fd2w9ErQlzEjGTweJxKpMeh2ZREe/N6lsVaBY0nU86C7rVRTltNEMQqVLeeMNKw6CXb7xhmz8OXNay36zsuQFyLZucopy9dUUnexPZyniCFwtNCTjW9/iACUe1wFK4FvfkolfUUHUt5uJEpA7CTj7bPrhFUv7UQdnny0THyArixARjytERK6e3HGYyhrXsCfBU1kjl4E3ahQ5NHpcOTTCqFEy8YE+NCW0FUXpBqQ6+yvVU4zThX/fK5kNnerPYOJE208sPjtu2jTrVyzaS6x7s307tLTYpt6LFtllS4v1K3J0VTZwT8hCVpQejgqESpdyzz2JbQkcp/VPmpRnEO7a1XZ/N6kS4J07afRlKDaSJ1c+O2wYX8Y78OTLvCU3xXj/fs5nq2tYce18tsL+/TLxAXJyKOV9j6uU9+XqyUMhHvNNSX6Mq+Wayu/ezRfY7XF9gd2we3c7T+g4w/kwoa0oSjcg1cLI44/bA238lGHHsf5MYflyu93xDfRXrpQbIpJqBg6EEyda+6ls3mztgQO7drs6QlXVqaJydbXsJGal++LPQE5FRrJ+x7pOKFeBXlG6PSoQKl1KKm/Ug51aHInY84pIRH6KsT8JSzApy3LgAKMDXrFudGAnHDggE3/XLnJ92V65NMkJkI88wn38GIhgBbyItR95RCZ+UxPv80XXsF+e9/mi3ARggIsv5iJe8rgu4iW4+GKZ+C0t7IwrLwas3dIiE7++nnN52+M6l7ehvl4mPnARLye0FUXpBaRjynA6SGUD/ViJcUyciJXbSQ1BefppO5RkwQKYPNku777b+jOFVL9HSvfmu9+1N2CbmuxvsKnJ2t/9rtw69DumKIrSLioQKl3KDTcktpOlvLy11VogYG1JKitt70SwS/EbYOPGMTXwV9dwy08Df4Vx42Tit7RwMc974l/M83LiVHY2q7gCcN8kgtaWqsUOhTiH91zDbv85vCeXfQewdSvHyPes4xj5sHVr+8/pIOfxpid+qy1Dm9ufQfEVRckAekpmSCob6MeGlMRnKEoOKQE7lOTii+2U5IsvtnYmESs5jH+PtD9Z76G0FH7wA6/vBz+QvdGg3zFFUZR2UYFQ6VLmzrWVO5dfbpdz5woGr6rijf9+56TW1dICb/z3O+IlBLFZFYIzK1rZs4dD4UICGMAhgOFQuBD27JGJH41yhP6uYTPwjtBf7sXceiu/z/2hJ/7vc39oT8gkGDSIN04OVbHx3+ACGDRIJj7Axx/zHJd71vEcl8PHH8vEz8+ndVccS6ENuH4BcnN5nnJP/Ocpt319hHiZizzxW21FUZQMItUTbsvKYMkSmDrVZkdNnWptycylZcvgxRfhkkvsctkyudjpIpVZnEr3JhRqnbAe4/77ZW/8gn7HFEVR2kEFQqXLGTvWZvaNHSscuKyMp/5zL7GsJjDWFjwRv/Zab3uUa68VC2157z3KZ/YnELI/1UAoQPnM/vDee5/zxNPkuus4zBke12HOgOuuk4kP9OkbTGgnxf79HGCAx3WAAbI9CKdPpw/ectw+1MP06TLxT5wg/jt6cnnihEz85ua4uDGMaMPMiSdLsI3PVhRFySBS3UC/osJmDD76qBXwHn3U2lLixLJlrWXFGza0lhtnmkiYyixOpXsT/1lfcknbfqn16HdMURTlFFQgVLqU2lp7Xvyzn9llba1g8IoKrvyXIa5hhYsr/2WI6F3CDz9MbCfN177GG6t3EQ7b7Q+HDW+s3gVf+5pM/NJS6nIHe1x1uYPlSjk2bKDloHcKc8vBI/bCRYIzzyQLbzl0Fi1w5pky8QGef57v8gfXsJ/Dd/kDPP98+8/pCAUFbGa8a9gMvM2Mh4ICmfgXXcRx+nlcx+kHF8ll+fU7KaA6PltRFCWDSHWZdHW1zRi85hpbAnzNNdaWEifWrrWiYKyseP58a69dKxM/HaQ6i1Pp3uzfD3l5MHu2/Y3Mnm1tyRu/+h1TFEVpFxUIlS5lxQrbf9gYu1yxQjb+2H8cQ9CxAzKCToSx/zhGLnhVFcNLvELI8JJ68RLmh1tmJ7STIhTiS42veVxfanxNrpTjlVc4RJHHdYgieOUVmfh1dRRzyOMq5hDU1cnEBwiHeZZvuIYVwJ7lGxAOy8Q/coQmvBORm8iBI0faeUIH2b2biM8Vcf1SvMTEhLaiKEpGkOrpprEehGvW2MylNWvkexBmOqnO4lS6N++/b/tsx/9Gxo2zfin0O6YoitIuKhAqXcq2bYntZFmx5GMiJggEiJggK5YI9Y0D2LmTBYcXEl8WuuDwQti5M9GzOsasWeyNejP89kYHw6xZMvHvuIOrecw17Ou4msfgjjtk4ufm0oK3110LuXL974YMobVvXwzH9QsRibCNcz2ubZxrx2JLEAwSwNvzMUC0dfpNsuzeTdBXYhzEiAqE71Ga0FYURckIUj3dNNaDMD5zSbIH4aWXekuKYyXHl14qEz/VAir0nGE3SucoKYGNG+Hv/97+Rv7+761dUiK3Dv2OKYqitIsKhEqXcuBAYjsprr8e/vJXr+8vf7V+CWbNYnPkAo9rc+QCOfEOYNMmDga95bIHg2fK3eU8coTNfMU1YuWtX5HLXtu1i2EDGj2uYQMaYdcumfj9+vE1alzDimBfowb69WvvGR2nuRnjEyENjlwPv5wc8jnuceVzHHJy2nlCxxnORwntZAn5chT9tqIoSkaQ6inDqc5cWrvW9sddsAAmT7bL6dPlSoxTLaCmg3SInErn2bMHAgH405/g7LPtMhCQG86XSeh3VVGULkAFQqVLGT06sZ0sc7IfJytohaOsoGFO9uNywSsq+GSCd1DFJxOmy05CC4XIjnjLmLMj9XIlwN//PtscX3accy58//sy8aurmVK30uOaUrdSrs/LsWO8yZddw4p4b/JlOHZMJj5Anz6EfH0OQ7RAnz4y8S+9lDpfGXYdRXIZH0A9+QntZBlMLDPX+GxFUU4bvRhMTDren3RMGU4lH3xgBZXhw23/tuHDrf3BBzLxY4JmvIAaL3hmAj1B5OzJNDdD1K2qiFU6RKPt35TtyftN/a4qitIFqECodCmVlZCVZR9nZQln9y9fDnfcQSDSjEOUQKTZls4uXy4Tv6rq1F53dXWyJyVPPEF/PvO4+vMZPPGETPwtW9huvuhxbTdfhC1bZOJfey3HwrFMOCseHQvnyI17fvttdjDS49rBSHj7bZn4APn5FOLNqCzkCOQLiWxr1rTdg3DNGpn4eXl8ijcL9VPOtE2/hdjD2e4jx2crinLa6MVgYtLx/mzaBFdd5Z0yfNVVchl+O3fCzJne1zBzplxrkh/9yC4/+MAOuooJgzG/BBUVMG+eFVDnzcsscRB6hsjZk2lvQFt7/p6839TvqqIoXYAKhEqXMmmSHQb7y1/a5aRJsvFrjl1Ii5ONIUCLk03NsQvlgodCDHrTW7Yz6M21ctl9AAMHMg6vWDeOLTBwoEz8t94igrfXXYQgvPWWTPw9e3jZTHANKx69bCbIlYoEAoxih8c1ih22HEWKAwe4CO9QlYt4Ra4ePhwm4CvJDRCRG4KSlcV0/uQaVqSdzp9alXkBzuH9hLaiKKdBpl8MpjqTJx3vzyOPwP332wzuF16wy/vvt34JZs2yU9lmzrSvYeZMa0u2Jolx9Kh8TLCf6f332wES99+fmZNfM13k7KlUVUF9fdv/1tTUtj/T95ufh35XFUVJMyoQKl3OpEm2xY+0OAhQfPAdosYBDFHjUHzwHbngTzzBnKzHyaEJhyg5NDEn63G57D6ASy4hf2Rs4IYVd/JHDrGZDRLk5zPipJhj44/gfbnsuOxssoj1IHRLvWmE7GyZ+NEolxPrM2njX85fW8tThKjkLrJoAaJk0UIld4nG/xrPu4+Mzxagro6heAXZoewRnfQ8kE/cR8ZnK4rSITL5YjAdmTypfn++6GbUP/ecPc4+95zXnywVFbBqlS2XXLzYLletknsdDz5ohzrE8/d/b/0SxD7T+CEr8Z95ptATRM7uTGdvFpSVQUNDx9eXyfvNz0O/q4qipBkVCJUup7bWtviprRUOXF3N5l/HgroDOH5dK3dw3bePScFXuHfi77mMZ7l34u+ZFHwF9u2TiQ9QVsaz753jGvY1PPveOXIXXOecw3bGeOJvZwycc077z+kIl17KR4zwxP+IEXL99UpKWMFsT/wVzJaddmcMk3iJ7/A4/TnEd3icSbxksz4kKCsjj1hvHfsa8mgWvai+nx974rfaMrxE7ILU8dmKonSITL4YTPWAj6oqO5U3/v1Ztky2rcf8+a3tF154wS7z8qxfEsfxLqUWCaIYAAAgAElEQVT4+tftxFdovZG4caP1S5DqISuQ+kzUniJydmc6e7OgoqL9Kpz2Mghj8TN1v5kI/a4qitIVGGMy+u/CCy80SuaycaMxffoYEwza5caNgsHvvNPMuPhTY5Uc+zfj4k+NufNOmfhLl5qNXGSyOWEcwiabE2YjFxmzdKlMfGOMmTrV9AsdNxB1X0PU9AsdN2bqVLH4edR74udRLxd/7lxTwGFP/AIOGzN3rkz8wkKTi/f9yeW4MYWFMvHddVTyS3cd9q+SX8qto6TEDOJjz2sYxMfGlJTIxAeTTYMnfjYN9gchgeOYcbzqiT+OV41xnE5uLn8z3eDYkqo/PWYp7TJ3rjEFBcasX2/t9eutLbW/vPPO1tgx1q+XOyauX2/MgAHGzJ5t9y+zZ1vbv87OMmaMjRs7xi5dau0xY2TiG2OPffPmGRMK2dihkLWljonr19tjR0GBMYsW2WVhofx7lJ9v4+fny79HqSb2PYr/HUh+j6ZOPfU8benSTn/GPfmYldTxKva5LVrUsc8v/qTd/+fnzjvtZxcff+lSY/Ly2l5fqveB0utL9/YqitKjOd3jlWYQKp9LyjL8gJoaW2ETidhlTY1g8MpKGODr1TdgoNwklPHjWRH8Ac3kYAjSTA4rgj+A8eNl4gNceinBsPeuaTDcJJeBN3w44O91F3b9AqxZQx7efjJ51MsN4GhoYJhvYu4wPu5ciUoC7vNl4N0nmYF34AB1FHpcdRTK9TjMz+dcvENbzuVtuTJyY9ouMTZCGZaK0pvwZ5RJZpilekBGRYXNGIwf8LFwoVy5X6zMd9Eim6G4aJHXL0Gs52A4bF9DONzak1CCW26BlhZbVnz77XbZ0mL9EowYAUuX2ozHxYvtculS688UUt1Tbtgw+97H/w5uv936FTnSUfZbVma/I7H9THW1vWBZvLjtrNZ0DzRJdn2Vlae+bxUVwhMdFUVRvKhAqCSkthamTLHn4VOmyIuE5eW2HV0waJfl5bLxU8qmTWz7wjc9rm1f+KZsqU04TMkAr6tkAHIDLB55hKhvSEmUoFxD9t27iYlqrTiuX4CWFgbjLekezD57wSVFXR3NeHsmNpMt2sMv6BtS4reT4sQJ7uMnQASIAhFrnzghtor1THEfOT5bUZTTprS0VRCJX5aWysRP9YCM2MX5NdfY8txrrrG2VDnc009bsauhwQoADQ3WfvppmfgADz1kl3l59oQkVm4c8yfLuHGnDojKyrJ+CVavtjcp48stx4+3/kwileJSOgfF9GY6U/ab6P/k5p7qq6iwv/8lS7xi8vz5bYto6R5o0tMHqLRHqtsEKIqSUlQgVBKS0gw/7GCSe+6x4uM998gPKhk0KLGdFGVlNH7gFacaP9gneyeyspJjof4e17FQf7m7h5FI2xl4ETmBKpemhHZywXM5gFdBPcCAtk8kO4vjkIc3IzGPBrnMHsdhsG+ox2A+kYsfjfLvLAKCWAEvaG3BQS5tisyKonSMsjJ7oTt1qhVGpk61ttQxJdUDMmI9CNessaLAmjWyPQjBil2xHmWhkGzGPtgJqjk5cOWV9j268kprtzdZtaMsXw4rV3oFg5UrrV+CntKzLJU95TZtgttu8/4ObrtN9nva2+ns93DTpvarG3Jy2vZ3VExO90CTnjxApT3SnampKIooKhAqCUl1hl9tLdx4I6xbZ5fSGYoFBYntpPjxj7nOeLMKrjMPwY8Fy0+rqvjsgDcb7rMDLXJ34fr0Id9XApxPPfTpIxO/sJCwTywKE4TCwnae0EEaGynhoMdVwkFobGznCZ3AGI7T1+M6Tl+5EtqcHA7jFYEP07/9k+FO8AKT3UeOz5ahmAMJbUVRToNNm+Cqq7wlulddJS9cpGpAxs6drWJA/FKqhPn6661oGonY9ycSsfb118vEBxvPceB3v7Pr+N3vrD11qtw6UikYpGOISKpJtci5c6cVZ2M3yaJRa0t9T5XOfw8rK9s/fzt2zGvHMtI6Kiane6BJTx2gkojemjmpKD0EFQiVhEyaZMW7xYvtUjrDL9UZiv54ovELCiDLW3pKVrasChkKEY14M72ikWj7U946ytVX8x7e8rX3KIWrr5aJf8klfOYTvz6jf+t0xWQpKWEfgz2ufQyWnWKcn08A72cQICrXwy8nh3ryPK568uQEQsehkCOuYUXNQo7IiQNZWQR970+Q6KlldIqiJCYUggcesP3uXnjBLh94QG5/f/31MH26jbdokV1Ony4nsG3Zcmr7i3DY+iVYs8ZOMv3nf4YNG+yyqUmupy3Anj1WoMjKsp9BVpa19+yRW0dvFAw6QqpFztGjbUZoU5M9F2lqsvbo0TLxleR657VXwRKNnpqRFgp1TExOd4ZtZ9bXU8pze2PmpKL0EFQgVD6XSZNslZC0OAgpzlCsqmJI1n6Pa0jWfrmDbHExv8r/mcf1q/yfQXGxTHyABx8kN+jNIMwNtsCDD8rEv/9+Wnz99VrIthctEvz5z4R8JcUhmuDPf5aJf+AAuznL49rNWXIDPgCamriKJ13DCmxX8aS9qJCgro5cvP0Aczkh1+MwEODYySEoVhQ8RiEEhHb/LS1EfFmiEYKyfSAVpTcQDlvRa+1aK1ysXWttqZ6ze/ZYIeS22+zF6m23WVtK/Bo3ru3eblL99QYPtu0jHnvMxn/sMWsPHvz5zz1dhg61MWP7r5YWaw8dKhP/+uvt+xIvGMycKSfSprq0Lx3iRaoHMzzxhL0Bl5NjReDY4yeekImvpA5/Rlo43DExOd0Ztp1ZX3crz+3sb15vhChK5nI6o46789+FF14oNvpZ6RqWLzfm8svtUpT1683Gwm+aUDBswJhQMGw2Fn7TmPXrZeIvXWq+xBsGosZeBUXNl3jDmKVLZeK76zjXt45zJdeRnW2KOOiJX8RBY7KzZeKDKeAzT/wCPjMGxOIP5mNP/MF8LBffXYcBM4jdBiJmELtP+qTip/o9stveGv/kaxCKX8kv3fj2r5Jfdjo+8DfTDY4tqfrTY5bSLuvXGzNggDGzZ9vfz+zZ1pY6Zk2dasy8eTbmokV2OW+e9Uuwfr0xhYX2+AF2WVgot/2xdeTl2fh5ebKx/fFjf5LrmTDBGy+2vgkTZOLHYsZ/xtLvf3xMvy3BnXeeGm/9euuXYO5cY/LzjcnJsZ9vTo61587tVLiefMzqkuNVIOD9/cX+AgH7nQa77Mmk8jfc2W3pyG8+HfuJVJLqfZCidBGne7zSDEKlS6mthX/5F3juObsU7UFYUcGklZX8OncBl5+zg1/nLmDSyjbuTHeW+fO5cfLrrmEzy26c/LqdniZFOMxZA2L9WOw6zhrQKJdRUljIBF72xJ/Ay3I9AoEzTvYIND5bhtms8MRvteX4Bn/mE4YCDp8wlG8glAHpMp0/uY+Mz5bhbHZ54rfaMpTyfkJbUZTTINVDPm6+GZ580jsE5cknrV+Cigqb3dPcbO3mZmtLl5YZ411K8vjjtv1CtptZn51t7ccfl4l/xx22bHnGDPvezJhh7TvukIkPqS3tS0dvsVRnUMWmFceXGMf7la6lvVLvoUN7T0ZadyrP7cxvPtN7oXa3LE5FSTenoyJ25z/Nxshg7rzT/PP03Z4bhP88fbfoHZqNG43JCbYYh7DJCbaYjRvFQp/MUHQIG4gaB+EMRWOM+da32s7w+9a3ZOIXFZkJ1HriT6DWmKIimfhZWSaPOk/8POqMycqSiQ9mHK964o/jVSOdQRikybOOIE1y6wBzNb/1ZOBdzW9F4/dnv2f7+7NfNP7lrPHEv5w1nY5PD87GMHrMUhKRjqyLpUuNcRxjLrnELiUz3mOZWfEZhElkZrUbv7DQZtYUFsrGj60jL8+7jrw82XWkIwsy1dlHqc7kSuVrmDvXmIICb6ZuQYFmEHaX41VWlmkzgxAyNyOto3SnDMIYvSV7M0Z3/AwUJUlO93ilGYRK11FWBs8+RyyrCYy1Be/QrFiyh6ZIEEOQpkiQFUsEG41v2sSPQ8uxPyMHQ4Afh5bL3iE7cIBmcj2uZnLleuwNGsRnnOFxfcYZMGiQTPzLLyeEt+F0iAhcfrlM/Px8tnGux7WNc+UGiKSDPn14kqtcw/YIfJKr5CZJA/2I9TM0PluGcWz2xG+1FUU5bVKddXH99fDzn8M119jea9dcY22p/nd799qehllZNssnK8vae/fKxN+wwca79Vbbv+/WW629YYNM/BhZWbBypV3HypWyA5eqqmDzZm8W5ObNcj38Uj2EoaoKli3zZnItWyY/QCGVGVSlpbb/Znym7m23Wb/S9STqX5ypGWkdId2DVE53m3pL9maM7pTFqShpRgVCpeuoqGD8T2KTT+zJ8vifTJLbCU+cCKtXe32rV1u/BI88wvZDXnFt+6Ez4JFHZOIDXHghQZo9riDNcOGFMvF37+YAAzyuAwyA3btl4rc3XVJq6mR9PUG85dZBwvaiUYpgsG2RMxhs5wkdJBxue8iHVBm547Dv5CAXK0Du4yy5KcbAUYo88VttRVFOm1QPZwA7IfSPf7QXen/8Y/sTQzvD669bMS02dTkUsvbrryd+3ukyebK9+fPzn1tx8Oc/t/bkyTLxwR774suiY2XTUsfEUAgWLLBDohYtsssFC+QmVadaZI5t/8KFVrxYuFB2+2OkUpAoK4MlS7wCzJIlWj7YHSkoaP/fpPeN3YXuVp7bHQXLdNAbRVFFcVGBUOlS1rw/xn3k+GwBSkoYH3nF4xofeQVKSsRWMYS9Ce2kWb8eE/BmL5hAFqxfLxO/vp4ijnhcRRyRE9iiUerxZvPVkw/RqEx8oD+HEtpJE4kQ9AmEQSJyF9YtLeTjfb/zqZebAmwMYZ8AGSYo2r/rE85MaCuK0o2I3RwQvEkAwPnn2/3WhAk262LCBGuff75M/OXL4emn7c2TxYvt8umnrV+KYcOs8Bjfe+rnP7d+Ce65x4pp8SJqKGT9EqRaZA6H4e67raB26612effdcje0IPWCRHcTYBTvpNz4/dLRo62PpfdX3ZV03CjqCHfdZW8ExP9eFi60/p5KbxVFFcVFBUKlS9ny0omEdlI88wybJ/3U49o86afwzDMy8UeM4NIv7PS4Lv3CThgxQiY+wNy5ZEcbPK7saAPMnSsTf8wYvsr/uIYVjL7K/8AYOaHW4CS0k6WZnIS2BLk0JLS7O3m+7fXbyXKY/gltRVFOg/iL5BjV1bLlm8EgXHGFFdiuuEIuExrssXXePDt1bNgwu5w3T+6YGyO+PFeaWbOsEBE/RMRx5AZYpFpETTWVlXYQW3zp3fz5suJFqgWJ7ibAKN6hEHl5bf+f9vxKarn5ZnsjIP6myZIlcsOtuiN6E0Hp5ahAqHQd1dX02f+hx9Vn/4eyd2guuCCxnQyXXkrBR1tdw16oFHy0FS69VG4d775Ljq+ENocwvPuuTPz6enYwyjWscLeDUaIlugGiCe1kKfT10/PbErSZBSlIo6/PpN9Olh9zn/vI+GwZtnBBQltRlNMg1ZMTly+3vdYefdROb330UWtLZeBVV9upyGPH2pLcsWOtLXVMr662gl12ti37ys62tuQ5Q0WF7TsYn6W4cqVc65PJk60wGy+iXnGFbJl0qkl16V1vFCR6O/GTcocObfv/TJmS3m3qStJxs+h0Scfk8u6G3kRQejkqECpdx6ZN3HijAyczyhxrC96hGX/wWfeR8dkCzJ/P033/yTXsa3i67z/Zu+mC+HMkRHMmBg+myZdx10QODB4sE3/MGII+QTBIVC5D8eyzOe4T647//+y9e3hU1bk//tlzyyQDuQBBQIMoUm6iCBICp2AoYE8UKOi3/doq+JzWRtJTtY8oVc8vWvUpNmj4avttFU5tH+nlS49FEI/SVggRehILAiogiiBUEJD7NZeZzKzfH++s7L12ZiZzeSfJhPV5ns3Ou5j5rLUvs9fe7/687wsfMHAgD38YEQvFMMKFQEw7VczGGhgIgvSbQczGGlb+dDtQNTQ6HR3xwJbuBzHpaLEWKbE6YlLFli3AN78J7NxJDsidO8nmmtPvvhvw+9UCIn4/tXMh3UVEXC4Ki77qKnKiXnUV2dw5/NKFjgi927KFFIPW38Gjj2r1TneHLAqxZ0/k/1/De9/SpZHul0WJQhfs0NC4pKAdhBqdh4ULUV49DEuXUlHbpUuB8uphfG9olizB9j/LEGBy4G3/8z6quMfEf+aCelN/5oKLjx8A3nkHyLKFzGZlUTsHtm2DH2qOQz/cwLZtPPwffwxhc2kKCODjj3n4P/8cF9FTabqInsDnn/Pwh+FBU0w7JRgG7AXlBRys+XYW42EIOAE4IODEYvAqMXrgXExbQyPj0REPbNIJZX0Qs7anCul4sVZv5XS8bNxIirLnnqO/n3uObK4qw6dOAU1N5LADaN3URO1c2LQJWLBALSKyYAG1c6ClBZg5E9i/nxSE+/eTzZnDL53oiNA7WUSkrIx+B2VluojIpQCpTE0nupIyLxa6mmpPF+zQ0LikoB2EGp2O8nLgr3/lS6vXimXLgBEjYVUoYsRIaufAunXI76He1Of3aAHWrePhB4CvfQ0FzUeUpoLmI8DXvsbDLwSGQn1bOxR7WHM75aAxpp0qeuFETJsDV2N/TDslCAEvmpUmL5pZj8FhDIhpp4qetpyGdltDI+PREQ9s48YBc+YAL7xAD2IvvEA2l2Mk3dVbjx+nqsI33ED2DTeQffw4D/+119J6wQIKyV2wQG3PBEgF4fTppCCcPj2zFIQdEXoncw5aQ+GtOQk1uh+sytRoL0c5Xpp2NWVeLHQV1Z4u2KGhcclBOwg1Oh319fSMUl/PTPzii5j3xTPwuIMwDMDjDmLeF8/wvaGcNg1jLqhKvjEX3uHNQThoEIqukAY5jIquoHYWDBiAMqxV+MuwFhjA5EByOHAr3lT4b8WbpMrggGFgKGQ+RuIfik/SUO1OhkkLm82DXJxW+E2bB0NancDCZvPgJtQq/KatodGN0BEPbEKoVYY5C3GkW/31j3+Qs8vqRH3jDWrnwPXXA+6w4l0q+txu3tzCkyYB1dVAKETHORQie9IkHv5164D580n9WFlJ6/nzeV8sphMdocBKdyi8RteD9dqUmxv5M9HaE0FXU+bFQldR7XXlgh2ZogjV0MgwaAehRqeivp7mmv/4D1qzOgmnTMGEVQtRmzMDPy19G7U5MzBhVYS338li3Tr8T8EsWBWK/1Mwi/1G/zMhqyIbNpsBTU1YizKFfy3KKGyLA6EQdkKqO4h/J66lhy4OCIHNGK/wb8Z49uqWezFU6cO0eXAUMim3YbMZYBjYhjEK/zaMYXWiRjzGGhrdDel+YNuyBVi9Grj/fnJO3X8/2V3hQSxedBXVS7JYuJCUj1Yn7Q038CnkHn6YCrdY1Tivvpo5BTg6QoGV7lB4ja6HSMrUdCETrlFdSbXXlQt2ZJIiVEMjg6AdhBqdiuXLgeZwNGVzM9msmDIFE+4fh0c33IwJ94/jvREoLcWZQI7SdCaQA5SW8vWxciWu/kLmbyKn19VfbARWruTh790b220VZ7fjeqB3bx5+nw8fYbjS9BGGU9gZB1wunLflIDyPnrzhWg4H3LYQYDea+VSQLhcKcUxpKsQxvm0QAo1Qz9NG5LA6UT/FNTFtDY2MR0c8sMkHLqsT0tqeKvbto5Bl68PUnDnUzoV0OlGdTiBgK+AUCFA7F8aPB265ha6/lZW0vuUWaufAs8+q4bIynPbZZ3n4042OCrVPZyi8RtfGuSg5jKO1J4quosyLha6s2utKyCRFqIZGBqHTHISGYRwwDGOHYRjvG4bxXritl2EYbxuG8Wl4XdBZ49PoJkjnjcC+fbiu8R8ww04F2ZwPW9u34xCKwgYpGg6hyEzSnio+/hgXoIZtXEAuXxGRhgbYdWpGuJ0FLS0I2S5jITh4E76HQpFzBHKpIFta0AcnlaY+OMm6DaPxfkw7VfSyhUTbbQ2NjEdHPLDdey857KyOkTlzqJ0Dd9xBLwbmzKGHqTlzyL7jDh7+dDtR33wzsfZk4HCQgv7OO2kb7ryTbK4XQkVFwJNPqk7aJ5+kdg50VLXtdCqwMt2JqpEaor285Hip2d41Kt2/n3j5u7Jqr6shExShGhoZhs5WEE4RQowWQtwYth8BsF4IMQTA+rCt0Y0xbx7g8VAUj8dDNhvCNwLL5m3C1//xFJbN28T7sHLHHShxbA4bdONS4tjM97AFAAUFOIV8pekU8oECPt95Mzwx7ZQgBHrigtLUExdY1WuGLR+g3eZAE7Jj2qniIwyLaaeKr9jyNJo2D3rifEy7O0C/1LrE0VEPbPZrI2e6hClTKGTZ76eHKb+fbK4HqnQ7UYWgnIOe8Bzl8ZDNuY8KCoBZs4CXXqJCKC+9RDbXnHvHHXTDM3s2OWlnzyab675h3z7itDogZ8/OHJUoQOHW1pyDMichVxj2JZC3LKPnq2jO+ESd9JGO84oVwG23Rb9GpTtkVYfE8sN6PaquBpYsafv/3ei3raHRIRBCdMoC4ACAPra2TwD0D//dH8An7fGMHTtWaGQ26uqEWLSI1qyoqhJLH9wt6OmBlqUP7haiqoqti5tvPCGAUJg/JG6+8QQbt0Rv5ymlj97OU3zkPp/woEHh96BBCJ+Ph9/hEP1xSOHvj0NCOBw8/IBwoUnhd6GJDjYXANEDZ5Q+euAMXx+AMBBQ+A0EWPlHYIfCPwI7WPlHY6vCPxpbk+YH8J7opHkp1hJlzloM4JHw348AqGqPR89ZGlFRVSVEdbUQffoIUVlJ6+pq1jlL1NQIkZNDv8+cHLIzBcXFQmRlCZGXR/snL4/s4mK+PmpqaL+PGkX7aNQosjn3UzqPQU0N7ZfcXNpHublkc/Uh94/ks9tckLzyd8DJX15O+8W6Dbm51J4EuuKcldHz1bBh5k27dRk2LDGeZM/VdJ57HcGfKaiqarvtNTWJzXf2Y1pdLYRh0DrS/3fk2DQ0uiDina86U0EoAPzNMIythmGUh9suE0IcCf99FMBlkb5oGEa5YRjvGYbx3vHjxztirBppxIQJFD0yYQIz8cKFWLlTVWKt3DmMVfFRmO8P/yVsNhPy89EcVPMrNQedQH5+lC8kiKIi+OFWmvxw84U7CQHRGoIdboJgVXw40RLT5kA/HI1pp4p0qyAP2oqe2O1UcQZ5Me1ujG8AeCX89ysAZnfiWDQyHfv2UbhpWRkp/MrKyOZSf8mcg9b8etachF0dffuS6vHxxyk88PHHye7bl6+PLVuAiROBHTtoHtyxg2zu3F9yDmScCwGQImrVKkpR8fTTtF61KnNUooCp9rGGDVrbORAMqirOYJCPu+siM+arTz9NrD0aks1Pl+6QVR0SS+BQU9qvRw8+CDz3HB3vVHISaqWnxqWOeLyI6VgAXB5e9wXwAYDJAM7YPnO6PR6txtCIhYUL1ReQCxcyktfUiJud61QFoXMd79tAt1v0hKog7IlTQrjdPPxFRcKHswq/D2eFKCri4QdErm38uTglONVrERWQnApCwxD9cFDpox8O0ltKDqRbBQkIt20fuTn3ESD62VSi/XAoaX50QTUGDQv7AWwDsBVAebjtjOX/DfscZvm/cgDvAXhv4MCBSe0XjU5GRygKampIvQ0IMWkSrX0+vjmFWTnVBmVlpnJDorqa2rn4KypU9U1FBR+/EMQHCDF2rLquqODhT7cKUqpQs7Np3NnZ/CrUdEOel9Z9ZD1vOfjlfgfM45Ekf1ecszJ6voqkHpRLMqispO9WVsb3+XQq/LhU4qnOR11FIZeufZ3oMe/IsWlodCLina86TUEohPgivD4GYBWAYgBfGobRHwDC62PRGTQ02kd+PqX3AWjNJbwDAKxYgdtdq5Wm212rKccJFzweBG0KvyAsOZhSxfHjGIttStNYbAO4lLl9++IC1IrFF+DjU3w4HPCiSWnygjGhPAAIgVNQqzqfQu+MyqMYMQ8kIwK2vJV2u5vgq0KIMQDKAPy7YRiTrf8ZnngjnhRCiGVCiBuFEDcWFhZ2wFA12NFRigKnE8jKAjZtojVnhd7Bg9WcgzIn4eDBPPz79wMLFpg5oJYsIXv/fh7+oiLgD39QFZZ/+AOf4h0ADhygnIPbtgGTJtF61ixq54DDATQ3A9/5Dqkgv/MdsrnmrI0baZ87HKQSdTjI3riRh7+jYBiqylLeyHFgyxZzvwPm8eheFWL1fAUkni9TXtdlnkJrEROOXHYuF/DQQxQ29dRTtH7oIWpPBKnOR11FIZcONSVXjlSt9NS4hNEpDkLDMHyGYfSUfwO4GcBOAGsA3B3+2N0AXu+M8Wl0E9xyC0qP/D94vfSM5fUCpUf+H3DLLTz8Bw9i38S7gNY6vQbZBw/y8AOAEHDYnEUOhPicU01N+AxXKU2f4Sqq2siBEydgfw8h4ABOnODhFwJ+mzPKDw972FYQzph2SnA4ELLxheBkdXJei10x7VQRtB1ju90doF9qXeJINlwtEWzZAjzxhPnbdzjI5nJcdFShlQULqMDHggW8vO+/T3PT735Hzrvf/Y7s9xmrsj/8MFBXB9x1Fzlp77qLbK4CGekughLNkcbpYEs3KirIYffAA/Rw/sADZMtQ41SxaRM5Dtzhl69uN9mbNvHwdwHo+QrJVVWXIat33EGfBchesSJ+B1qsIjgtLRQC+8wzNI888wzZLQmmxok1H8VThKcj5rN4wF3wKJlj3lFj09DIJMQjM+ReAFwNCiv+AMAuAP8Rbu8Nql78KYB1AHq1x6VDjDWiIpystu6+P1IRlPv+qCavTRXl5eIa41MltPIa41O+cC0hhKipEZNRq/QxGbV8UnevV2TZwk+z0CCE18vD7/MJDxptIcCNrEVQHPAr/A74+YqgCCFEBxQpuQYfq+cRPmbln49fKfzz8StW/snYYDtHNyTNj64ZruUD0NPydx2AfwXwLNSk74vb49JzVoaDI3QpGtIdWpluzImFJqIAACAASURBVJhhhgJalxkzePhLSojP5VLXJSU8/EKkP4w53UVQZAijtQgKZ4hxR4QmyvPI56NjIMPuuc4jaxEMGcqfTBGMMLranJXx85VhRL6OJJrWJdVzNZ4Q00h9zJhBYevl5fR/1lQOsn+ueSQSTyLFWdI5n7WHdBQ84ro+dVQxJg2NDka881WnT2SpLvphSyMmqqvFQjwjrvF+LhbiGT7noBBClJeL4a3VYWkZjh28DsJZs0QdSoQzXOXWiYCoQ4kQs2bx8OfliSvxqbINV+JTejDlwLBh4ma8qfDfjDeTvhFvA0D0wpcKfy98KdicX0II4XRG3gank4cfEEtxj8K/FPfwbQMg6lAiXGFHqgt+OocY+TnH39UetmhI+qXWJY+OqDBcXq7mQpO50jjnlHSiuJhezlgf6h0Ovvx6xcWmU1AuLhdvFeNbbyVeea9QXU32rbfy8FdV0fwNUK5fgGyu80g6OK05CDkdnOnOYymEEIWF5vG1OvAKC3n4pQPS7mhO0gHZ1easjJ+vuHMQpoL2HGiRrtk+H+UJz8kxl7w8c/6wzyPJOp1iOTDt/yedk1bIFwmdlWOvq+RBjIR059PV0OgkaAehhoaQRUpMxwVrkZLycpFrU5bl4gzvjbLbLepQIhxoCavjWsi5w1WkZMYMcQX+qWzDFfgn35t6t1tcjgMK/+U4wDf+dBfgEEKIgQNFbxxT+uiNY0JwJe8GxCI8IoBgmD8oFuERvm3oAAfkaGxV9s9obE2av6s9bHEves7KUMyYoSrQwwp1tmulEJn/UGJVZlkXrhdC0llnXzhf/BUXk4I+N5cenHNzyeZyQqa7CIrk93pp/F4vL3+6948QpuPUvnAVT6uqMveTXCoqknZMdOc565J2EMajIIym+raqeAEh5s5VnYPxKtOiOdHKy9vnsTo37f8v5y957dQKORVaQajRTaEdhBoZg7o6QSHAdfzcV+SpFXqvyDvLR15eLq7BJwr/NfiE10FYXCxGY5vN+bKNVZFRiMMKfyEO8/E7HMIIOzclv4EWvhBgt1u4bRWA3Wjic0AKIYTTKRxhBafsw4EAq4IwooqT0YHX31ZluH8KVYYj8ftwTuH34VzS/N35YUvoOStzUV5OyhCrY8TnY08pkdaHknQrNvLzRcSH+vx8Hv6iosgKRS7HkRDmA7+1wi1nmPfQoUJMnEgP55Mm0XriRGrnQHm56RSU6juvl+88lU5at1tdczppa2pofrUeZ6eTv4qxx0PcHk+3q2LMtWSsg5ArvDiea3FNjarYlWHFsk0qVCsrEx9XtHFEUgRaeSI5N61tPl/b32xXUe91NuQxsu4vbpW0hkYnId75qvtlktfIKNTXA1OnUg7YqVPJZsOSJcDZ02rb2dNmhcVUcfgwBuCw0jQAh4HDh6N8IQmMHo09GKI07cEQYPRoHv79+9GAHkpTA3rwVZ0cPhwGgkqTgSAwfDgPf0sLXPArTS74E0/4HAvBYJuawqFwOwuGD8fnrYViKJH857iKbx+53TgFtXz3KeSbCdoZEPEYa2h0JyxdCrzxBl1bnn6a1m+8Qe1c2LKFqlpaE8c/+ihfkZJ0V64UIrH2RNHcDIRCZmVnp5NsWY2WA1OmUGEYa4XbJ57gS95fXk43OtOmUVGMadPILi/n4R88GHjrLSrismkTrd96i69SdUsLFVUJBMgOBMjmnHO3b287vwaD1M6BFSto3NnZdPOZnU32ihU8/Bqdj1SvdbJYibXi+3/9V/RrsSwCZBh0ns6eDXg8wNy5dO56PMDPf079J1IoKloxkaVLo/NEK9QBmFV5H3yQlnjHcSlBnjuAub9aWqhwjYbGJQLtINToVNTWAn4/zZ9+P9lsWLcOyCuAtcow8gqonQNCYA+uUZr24Bq+hyEA+NOf4MMFpcmHC8Cf/sTD39iIfJxSmvJxCmhs5OH/8kuY+1/CCLczQAg0IkdpakQO7zEA0PZSyXjp/OQTuG1OTjf8wCef8PC3tCAb6vHMRiPrA10jfDFtDY1uA3ltYb/GgB5MnnkGKCujh5KyMrK5HHjprlzp8STWnijkPg8GgaIi04nEeSzuvZecRllZZGdlkX3vvTz8LS3A/Pl0HzJpEq3nz+e7Hi9cSA6Kv/+d+P/+d7K5HvxdLnKMWyttv/EGtXPh+edNfgmHg9q54HYDq1aRA2XVKtYXZhpphqzSa63My12lN96K7xs2AHPm0PlfWUnrRx+la9PjjwNr11KV4uxs+j0mU1F3yhTTUVVR0f42RHNurlihq/LGA7m/5syhY5eTw3t909DIAGgHoUanorSUnh2cTlqXljKSv/UWbpqRqzTdNCOX3qZz4PLLcQq9lKZT6AVcfjkPPwDk5qIR2UpTI7KB3NwoX0gQzc3IxXm1S5znU2SMH4+Q7TITggMYP56HH0BEByQnHA4A9gdQ0fYBJlmEQsiyOfCy0EjKGA7MnIlzyFOaziEPmDmThx+AE4GYtoZGxmPDBlMVUllJ69mzeR+ypkyhh8vf/54eJn//e7K5HHiyj0QeNhPB1Vcn1p4oevemtcMBHDxoXoNlOwc2bgQaGuimpLKS1g0N1M6BffuAP/4RuOsuUvjddRfZ+/bx8C9ZAjz0ED3YbtxI64ce4ouc+OQTelgOhchJGwqRzfVCCwB69CBet5uOgdtNdo8e7X83HgweTE5BqwNl1So+laVG8rA6+SJhwwZ6YTJ7Njlwxo2Lrg5M5lonnY/2PqONa8UKekGxejU5m1evputSaSk5/f/rv0ipt2oVXdNjqRBjbXMijr1Izk0AeO21tqpC7SSMDr+fxBILFtBx1ftL4xKCdhBqdB4WL8aEpg247z7gqquA++4DJjTFmIiTQM+ese2UsHYtQlDfKoXgojeGXJgxI7H2RJGTg0KcUJoKcYLemHHgww9h2JxrBgTw4Yc8/B0BjwdOW8isE0E+VQyAC8iNaaeEPXsiO2n37GHrYgQ+jmlraGQ8Vqyg8DGr6sgweMMSN2wgxaDVefTMM7wPJYk+bCaCw4fN8F8Jp5Mv7cbkyXTdlS9PQiGyJ0/m4QdMR501ZNDazoFAAFizho7BmjVmuC4Hli0jRaIMH3zwQbKXLePh37iRxjt9Ojlpp08nm8uBCgAXLpBTUN6H5OSQfeFC7O/Fi9/8Bnj1VbXt1VepXYMHiTraJNpTS0snjWGQY84aTmt3ikW71sUaW6KhyYMHk/PI6mx+6y26JlkddVJ9mGgYb7RwYfv429vfiYZMX+pYscJ8Gfjii9Sm95fGJQTtINToPIwbh2Uz12DxYoG9e4HFiwWWzVzDF04F4OjR2HZKuPxy5OKs0pSLs7wKwkGDAIdN2u5wUTsHhg/HRxiqNH2EoXz575qaIjsIm5p4+H1RQlmjtSeD7Gzk2Y5zHs5SyAgThE31aLdThdOWRdFup4oI+koNje6FjlAdyRyEa9fSg8natbw5CO+9l1Q31ofNOXP4wmebmii0ToZrut1kc13vhw4lVYcVfj+1c0Eez8ZGUh7JdBtcx/mOO2i/WEPV3W6+/FYvvkjOLquD49VXzYfcVJGbS2HXmzfTObp5M9lcUQ0A8MMfAn/9K3D//XQM7r+f7B/+kIe/oID2xw9+QPYPfkB2QQEPv0byOQDbU/lJReD99wMPPBBdHRjLsRZrbImGJscbipyswzRex157+zvecWrQvnvtNfNloDWHo95fGpcItINQo/MwZQpeLvqJ0vRy0U9YQ55OnYptp4TrrsNAHFKaBuIQcN11fH3s24cLIa/SdCHk5VMz7N6NE7hMaTqBy4Ddu3n4hYisXuPKGXXxIhCphMjFizz8AHDhAq7FLqXpWuziUzN4vYm1J4qLF+GEmt/KiRbWfbQPg2PaGhoZj454wNq3D3jySfWh9sknedVr9msvZ/4++dIkEKBwOqmM43qZ8vzz5Eyz5gd0u3lz0+3eDUycqKoUJ07kmxO3bKEHT+nceOABsrmcwOnOMzl6NIUUWx2cLhdf4TTA/E1Z1V/W9lRx3XV03rz4IjBwIK3dbt57t0sd6Sq49POfU0XDn/8ceOGF6EroWI619n4j6UjDkKzDNN55J92/+0sJWm2poaEdhBqdiwHD8mLaqcIuXOASMgAABg+GZ5iaW8kz7GpeRcmmTYm1J4qmJkR0sHHtqPHjEVFfxpiDsB++jGmnjJYWjID6cDgCu/mSyjc1RS4iwnUMjh5FDhqUphw0sMppXbYQbLutoaERB95/P3L11vff5+FfutTMpSQfIlev5qvEPGqU+bd1jrK2p4KmJnI6WgsCBAK8E/v48cC2bWrbtm18c1a6nV9AevNMAhQ2/o1vEP83vtE2rDxVxBtWmSzef98c88GDtHY6+X5nGvRS46mn1IJLTz2V+ssOIYBbbqG1YaiOMev50Z5jLdZvxB6afO+9yan/7H1HcuBt2ZI6t7WPaNuUrIKxqyMd26XVlhoa2kGo0bkou1rmKhM2mwdDhsS2U8LChcgPnQwbNP780EneSWTvXvTDEaWPfjgC7N3Lw3/ZZciCLEhC/FloBi67LPp3EoFh4EocUPivxAEzrxMDfwHUY1CAk3z8AOBwIBdnlD5ycYavSInDgVGQORmJfxQ+5OP3eNAfXyhN/fEFaw7FckgHg7DZGpyor6eUdPX1nT0SjbTgZz8jp9fs2fQQOXs22T/7GQ+/fGiyPkRa21PF5Mkmp0RFBV+OQPnyTTpR5ZrzpdyePabDUYbNNjXx5WxNt/NL9pGuPJNLlwJPPKEW0nniCT4nM5B+BU/fvkBTE+pRgmfwCOpRQse4b18efg0KmW9pQf3v9uKZQS+h/nd76aVqe6H07Z2rq1cTz+rVpvI2zvNDmT+j/UYi/T5XrKBUDImo/yI5rgDg+utVB16yysJIiPW75+ynKyEd29VdnakaGolACJHRy9ixY4VGhqKmRizKeVo4jJAAhHA4QmJRztNC1NSwdTF8uBD0qpGW4cPZqIWorhaX458CCIX5Q+Jy/FOI6mq+Pnw+cYWtjyvwTyF8Ph7+vDxhoEXhN9AiRF4eG38uTin8uTjFxw8IIKDwAwE62FxwOEQx6pU+ilEvhMPBww9EPo+4tmHYMJGDcwp/Ds4JMWwYDz8gluKeMD8tS3FP0uMH8J7oAnNLupZk56y6OiGys4VwOmldV5cUjUZXR02NEDk59PvJyWGdD0VRkRBuN11/Kytp7XZTOwfKy4XwetVJ1+uldg5UVQlRUaHyV1RQOxcKC03uSZPMvwsLefiHDqUxW1FRQe0cMAyam+R5U1NDtmHw8JeXC5GbK8TcubRf5s4lm+sYdwRmzRJ1KBHZuCicCIhsXBR1KBFi1qyk6LrznJX0M1ZNjajLmaru45yp7V/PqqrU37d9CXOLsrK2XDU1Ua8FyvyZ1SLq8v5V/Y306WPy2u/hq6uFKC6mz1RWmp9tZ/uVz9XU0O9EXnvt/9ced1VV7O2N1J+dK55+MhHc2xXPvtTQyFDEO19pBaFG52HLFpRWlSHLa8DpBLKyDJRWlbHmeWhsjG2nhHXr0GdAFtBaUMIge906vj6KiiKHnxYV8fCfOwc31PAsN5qAc+fY+M/ZKvKeQy4fv2GgrRDawasgDIVwGmry8tMoMHNUMSDiMebCNdfAZctB6EILcM01bF08gSfCfxk2W4MLtbVUjyEYpHVtbWePSCNtEEJdc8Hno5DchnDKgYYGsrmKOn3wASmxsrJIxZKVRfYHH/DwA3TdcoULd7lcrNcxALTPR46kv2WY9MiRfMdiyBDgpZeAJUvIXrKEbK7whqwsmpu++12yv/tdsmXexlTx/vt0AXr9dTrGr79ONmd47i23mPtHYskSaufA9u2oNb4GPzwIwgU/3Kg1vgZs387DrwFs2YLa0ifUfVz6RPT7e6naai8CZ8kSUohNm5aQcqzN/PnN/xtZofrww2rleFlZ/mc/axu+G0tpZg8pnj2b7kvthS82bIgvJUB7Srl4VLfpTj3QWdiyxQxll9uViuJP53PU0NAKQo3Ox9KlQtx8M625MXu2+vJx9uzM4hcVFaIfDinqr3441FaBkCy8XuFCk8LvQhOpPjgwcqSATaEItAgxciQPv9MZmd/p5OEXQoj8fJGPk0of+TgpRH4+D7/XK3ra+HviJN8xqKkRWbig8GfhAt/bUEB40KDwe9Bgvu1PmK77qjFECnOWVhBeAigvJ9WgVeGXk8OnziouphPIOmk5ndTOgbw84rOOX9ocGDbMHLdV3celhhaCFESzZqn7aNYsaueAVIgaBm2DYfAqRaVi0Dp+q6IwVVRXE6fPR8fY5yObM3Kiupr2i+S026miuFjUOb8aVrf5Sd3m/GrSv4PuPGelpCD0TaN9bLTQPvZNi34eWlVasRSE1vMgAeVYQvNnJN5YbbGUZpWVNO6pEdSTUgEY73akqpTjVNq1p2jsSMjrw9y5tF3V1TyKP3nsKit5xqmh0QUQ73zV6ZNPqot2EGY20v3QW1dnPg85ncz8ZWVi9rV7VAfhtXv4HiTCfXjQaHO+NPL1kW4HYXGxAII2B16Q74G0I0KMAZGDs0ofOTjL1wcgHLZtcHBug88XOYycK0wdEL1xTOHvjWNJj787P2yJFOesujohFi3SzsFuixkzIjtGZszg4a+pMR06cvH5+JxHcvzTpxP39Om843e7idftpoc2q80Fa14SqxOSKz9JVRUdV5eLeF0usjkfrO1OFs7QODn+7Gzizs7mH7900lqdqJxO2qIiIQBR554sFk35m6hzT6ZtSTLUvjvPWUnPV0VFQni9ou4X79Gc9Yv36L4yL699R1ksB+Hcueq5Jp04c+dG5rQgofnT6hyK5Aj0+ei8tzrd7KH28TjkEg1nTdZpxR0221XCcGW/0ik4dy7PywT7sSsv7zoOUQ2NFBDvfKVDjDU6FR0RNidrPXDVfGjFtGk4sPM80FqlV5A9bRpfHzt3Imj7mQbhAHbuZOuiAKdi2inhyJHE2hOFw4GIIcbMB7sFnph2qnDaQoDtdqrIxsWYdqoYiV0xbQ0eTJgAPPoorTW6IYJBYP58Cml7/HFaz5/ftrJxKvD7Y9up4I03aLxvv01pMN5+m+w33uDhl6HQgQCFkwUCajsH9u9PrD1R7NsHPPYYFVoAaP3YY6lXd5VYvBj49rfVtm9/mz/BvkzjwZnOQ8LhANasAQYNojDvQYPI5prXw+fLBM9WPPrVTZjg2aq0azDg+uuB5mZM8L9Dc5b/HaC5GRg2LHqorAyBjYW1aym1gAzvffFFYO5cKpYjw9KjhBvHPX/ai32sWNE2fPepp+j/ATNs11qEJd5iRIkU5IlWhCSeohrchX+6Shiu3K4HH6Tj8LvfAXfdZV5fkwFXoRoNjUxGPF7ErrxoBWFmI90Kwvnz1ZeP8+fz8ud71dDKfG8DbweFhcKDizYF4UW+hOl9+4osG38WLgrRty8P/8CBwrCp4wwEhBg4kIff7Y7Mz6koSbeC0OUS/W1h5P1xiJQlHHC7xWysVPhnYyXfPgLEZGxQ+CdjQ9L7B91YjSFSnLO0grCbI90FIMLKKUVWn4JyKiK/1yvEqFHEO2oU2Zz8hqFO6obBxy+EWaRE9iPXXHNuSYk5dqtCsaSEh1+qKg2D1C9y/FzXe6lqzckhfhkuzRliXF5unpu5uea5yvU7KCujNC1WFWRFRdIKxe48ZyU9X4ULCtVhglg06CVRhwlmQSEZZi9DQqUyq7raLNAUbZHnX0WF+l2pXrZyJqPwSkQZV1NDisjsbBp3bq75uXhCcBMJ0401rmj/1xGqt3SH4ca7jzoifLq8vHsWedG4pBDvfKUVhBqdigkTgPXr6eXb+vWZp4zJLsiOaaeM3r0xAKrabgCOAL178/AbBvw2NZwfHj5VwNGjcENVqLjhB44e5eEfNw5eW5EVL5p43+oZBoJwKU1BuPj2kRCRFYRCRPlCgigowFfwiewMAMguKIj+nQTxOQbGtDVSR309MHUqiQemTiVbo5vhjjtILfj73wOTJtE6GDRVKaniVFgd7vXSieT1qu2pwjCoKMmOHTT+HTvI5rpWNjW1vS4KQe1cmDMHcDrNfoQge84cHv4zZ2jtdpM6zu1W21OFVFV6POpatqeKP/2JOOW43W6y//QnHn6A9ncwSOfNuXO0DgapnQM7dwK//rVZaCwUIpsxMuOSx7hxqP/jfkw11qPywPcw1ViP+j/uN5WCt99Oaq+yMrKXLAEeeogeBmLhmWeoWM3WraYCThb6cDhMzhUrklN4JaK0W7GCKh82NgILFgCrV1MxknvvpWIrdjXdlClqEZb2Co/EO65oar477ojNH4/yMBaiKRq5sHgxqUWt27BkCfD1rwMbN6rbNWcOUFIC9OgRXa3ZXl/y8/LYbdhA55o8v5YuNdWi11+vi5ZodG/E40XsyotWEGrEwtKl6stH7kIok6/+XFVOXf05bwdDh4orvYfDfdBypfewEEOH8vCnO4efwxG5QIbDwcZ/PbYq/NdjKx9/uA8H/EofDvj5+kh3DsI+fcQIY6fCP8LYSW9AOQCIK7FX4b8Se5MeP7qxGkOkMGctWqQKvxYtSopGoytDKlKysuhAZ2VFztmVLGbMIF6r+gvgyxFYUWFOtlKtCPAV1bLmB7QuXPkBhRBi/PjIfYwfz8Of7iIo6d5H5eV0TlpVrnl5fOo+IWhuGjRIHf+gQXxzljUPp1XFmWRe3u48Z6WiIFw0/BXhRIDmLPjFouGvqLkGZb64uXPNnH60Q6Mv9pyANTWk3PP5aPF4zGtcItfNZIpu2FWL1dWJ/xY4lW+R1Hyx+FPJI9gROQij5ResqKBjLudGa3Ev63jksYvn2EbbHmvBE3mu2dWiGhoZhHjnq06ffFJdtINQIxYWLTIL+jkczA/Vw4aJ+fiV4hiZj1/xVlS89dbIRURuvZWHP91VgEtKhNs2fjea+MKpDCPyMTAMHn4hhAAiF4rhcuAZRuQiIlzb4HSK4dih8A/HDr5jDEQOYdYOQtY5q66Onn0Mg9Y6zLgbIt0FIGRopXQM5uSkFFoZld866XLye70iotOAq6iWEGaIrn3hDtG1FljhDNGtqhJi5Eh17CNH8p5D6SwgIgSlOIkUCs+V+sRe5VkuSb70685zVtLPWCUlog4lwoMmYaBFeNAk6lBCjmqrI8bqaDZ3aPRFhs7OmEEOoalTzevCkCHq7yqRENtEHV52B9KYMW1/x/GG87YXppuIgyuSIzAWv0xrYa/O3N64O6qKsdwu6ciX54nVWZedHftFWrzHNlphEmsouc9ntuswY40MRLzzlQ4x1uh01NdT1AB7yNzixSjtuVUpUlLacytfsu6TJzEPy+E0KIG80whiHpYDJ0/y8APAiROW8FNB/aAFOHGCh79nT/TEWYW/J84CPXvy8O/ejd44rvD3xnFg924efgCf4UqF37T50APnlD5MmwFCoADynCH+ApzkCzEOBjETskgAcc7EG6yFDxrgVfhNW4MT1icljW6IffsoRMzjobAtj4dsrgIWJ08Cv/2tWiDjt7/lm7NKS4EvvlBDN7/4gtozBf37J9aeKJ5/nm5GWlooDLulheznn+fh37gR2GUrErVrF7VzQBYMGTOGQqTHjDELinChuZnWwSAVu5FzlWxPFdFClblCmDXod+90QcCAgAMCBuB0Ufujj5qhwWvXUpGR116LLyRUFozYsIFSC6xfT+HzTifw6ac0OebkUOj74cPxh/BGCtO97ba2n5MhuPbiGNu2maH2GzaofcUK240nTNcairx4MYXZWrdjyRJg5szIBVHa47/jDroGPf20WSAmntDseEKoU4XcZ2VldK2ZNImuNffeS3098ADQ0EAh3vffHz3kN96CKrJIjtwXMkQbAG68kfoRgtpTLfKiodHVEY8XsSsvWkGY2UhrkZKaGrHU9yNF2bTU9yO+Nz7l5WKpq0JYw3+Xuip4Q22GDhVA0KbwC/KFGDudwmsLAfbiAp+6rKhIeKAWcvGggS+pvGFEHj+zgjCtYdiGEVllybUN+fmRi4jk5/PwAyIXpxX+XJxOev+gG6sxRApzVroLLml0AciQNalCkWqzdIQAW0MruUKAZeisx0MqDBnuN2sWD/+wYeaYZfEKgFe1X1TUVmHmcPDNWTJ8fOxYdZ2VxcNvDe2W+x/gG781/Ff2xRn+K4QZCm9fuH4HWkGY9vlK+HyRozu83rahm0KYoes1NZGPjVykck1eC1wu8/9kQZ7qalMZJ/uprDTDf2Op36TaburUtmOURVQihelKfp9PVbS1V+QkkYIo1jBbOUfIgi92BbK1qEYs/kSUeB0NuW0yjDsvzwwlrq5OfNztKTUjqTBjFaLR0MhAxDtfaQWhRqeitpZeCgeDtK6tZSSfMgUvF/1EaXq56CesiWVfCP4w/Jdhs5lw002JtSeKyy9HCGoC+RAM4PLLefgbGyMX+Ghs5OG/9Va0QH3r3wIncOutPPwdgZwcBGzbEICT3oJz4MwZfIhRStOHGMWXFN8wLCpUQk+c5StMoAGgbV0frjo/Gl0IkyYBzz1HkvrHH6f1c89ROweCQbMwyaZNtPZ6+dTE77xDSeVlH14v2e+8w8MvlZQOBxWvkOEBXApLgFRJoZB5/TIMsrkKoch9s3UrqeO2blXbU8WhQ7R2OAC/39xHsj1VOBzAgQNAr17AwYO0PnDA7IcDn34KjBypto0cSe0cEFEk2NHaNRLHxYs4isuUpqO4jH5Ht91GqjWriuuOO2j/r1gRm1f+1o8cAbKyTDU0QN+fPp2um04nnf+ffGKqwvx+6idacRCXy1TbbdlCarxHH6X/mzfPLKIixyy/J5V7q1bRGBobqS/r/0d67ohWeOTZZyMrCa+/noqw3HUXjXHePOL/7/8mJaNVqThlCjB4cOyCK3J8q1dTkRU5bqnQtCKR4iWcCIWA7GxSKAtBytDvfAd47DG6Nv/3fwNv7fWXEwAAIABJREFUvkn/N2dOdBVqe0pK+7GUisPt22mfWAvRJFoARUMjExGPF7ErL1pBmNlIaxGRqiox+uqzyhvM0Vef5cuRUVMjhmOXwj8cu3jfLt16q3CgWenDgWa+HISzZkUuIsKl+PB6Iyv8uHJGFRaKbJxX+LNxXojCQh5+IdJfpGT48MgKRa6k8g6H6IlTCn9PnGItsqKLlKR/ztIKwksI7SkdkoXMcShVNy4Xb47DoiK6tlvzinm9rIrxiKoiTsW4zHdnVylyqerz8iJvQ14eD38s9RU3v1WhyMVv7yPd28DA353nrKSfsYDICkIgepSNVHDFOj7V1RRBY82lKq8Lubn0/YoKs+iJXWVmzSvXpw8pBXNy6J7aqhqTCsS8vLb57yTsSkSpxpOcyV7D7Uq/8nLaHmuuQKk8tubkSzQnnhy/VTnn9VKO8PaUk/Fw23NAyv1qnW8ifU7mmCwro3arqrOmhtrj4bb+n9weOQfalZWyL/v3iovNOc1+fnDnW9TQ6ADEO191+uST6qIdhJmNtBYRqakRI4yPlBuUEcZHrBUhh+MDYQ0xHo4P+MJghBDC7RZOm4PQiWa+hOnpdn75fJH5k6wW2AaA6IUvFf5e+FKwPqw4ncKwOfAMBFiLfEQMI+faBsOI7ETleqgGRD8cUvj74VDS4+/OD1sihTlLFym5BGB9eJEPgpwOvOJisyiGLDDhdlM7B6whwNYQZq4QYKuD0BpKy+kglAVi7Et2Ng9/up1f6S7kku7x2/uwHmftIMyY+UoA4SIljeEiJY1UpEReD9oL8Y229OljpjJwu4WYPl39/+nTzRDc8nIzpF9WP3a76f62ulrtKy+P7t2tzqTqaiEGDKD/HzSobXiptSiH9Xuxqt3GW9zD6rTz+dSwYpkqYuxYtYpyMs82dseiTGtRURE5pDkRXulgLS83nbXW0Gu5L+ztMqxYHkO3m46j3JeJOuis+1xu74wZbYuNyDFJp6Wci2V16pIS0/GsC5RoZCi0g1AjI5DuHISTHRsVx8Vkx0a+i3phoXDYHEcOBHjVa8OHCw8uKn14cJFPXZafH1lByJWfrqgo8j7iUpQ4naIXjtkchMf4nHdCCGEYImKlZ66HUocjsgOSUeGXY3MQ5uC84HzY8kFV6vpwNmn+7vywJVKcs+rq6CWKdg52U8iHM3sOQq4Kt8OH0+9SKsTlgzbXfDJ+vIj4UD9+PA9/Rzin0q1STPc2ZDp/R/ShHYTpn6/C+7MOJWIRHjGdgwA5WbKy1Dx6UvknlXfRlupq8yWH00l/ezzEOWQIfWbuXHLyyJyu0oFWXU2fkwpq6XiS3G43OdLtjiqphPZ6TSeVVPVJNaR8uWPN+ycVaFYnocxpJ78XTflXVWUqsadOJYWjz0fVkuU1vLzctKdPT+xFknSa2Z1ncjt8vujKyXghnX9yH8v9FSm3Y1aWqvSUTjmp6JaOubw8+qw8TvGirEw93/Ly6B7b4VAVo1bHsstlOo1ldewhQ0w1ZSTHroZGF0e885XOQajRqZgwgYqQPf00rSdMYCR/9tm2lfUGDaJ2Dowfj5CtKRRuZ8OkSfAjS2nyI4svJ5XHQ9XlLBAwqBobE1zwx7RTwpAhuIgeStNF9ACGDOHro7AwsfZEYRjwQs1v5UUTaw6/bDTGtFNFk61qsd3W4MGOHZSndceOzh6JRlqwbBlVo7TmIJw5k9o5MGgQMGsW8MYbwOTJtJ41i68C7ebNibV3RUS77uqcqhoaCWMHrkUtbsIOXGs2vvEGcNVVlNNv7Fhg9mzgm98EXnoJ+NrXYhM+/DBVLXa5yK0XCAD/+39Tfr29eykH4Wuv0f+/+y5wyy3A669TJdyHHqL8cjfeSFyBAOUwdLsph2YgQDnvNmwAZsygfInz5wPLlwM//SklSm9sBL7/feAPfwAuXgSGDiWuffuAJ5+kqszf/KaZ92/0aMphd/fd5jYIQZwyh6DME2jN87dpE/D731OF5w8+AAYOpP62baNt/OtfiUNu89tvUy7Q9vIELl5M/cg8jLJa8b33mvaDD1KeSFk5eO1a+v9YeQklrx3jxpnVx0Mh2n8PPUT5EWWuw0ceAa691qxIPGsWfd7ppPy4bjftw8pK4Px54vvpT9VckO1t97Rp1O+SJfS94mIaTygE/Pa3NJZgkObdtWvpvGxpob9vvtnM0/vpp/Sgun07zc3tVXvW0MhUxONF7MqLVhBqREVNjeiHw0INfTzMWsU4B+dsyqxzvFWMZ8wQEfPTcYUx5+VFrjLMlQ9p1qzI6juuHIdlZcJlC8F2oZneFnLB54u8DVxh0kVFkVWQXCrLggLRDwdtv4ODQhQU8PADotD2OyvEYaHVGLxzVlrztXYHxBu61ZUhFYMybM4aLscJqQyZNImXN93KL58vMjfXtViI9G+D5u/8PrSCMO3zlQDEUtwjrCl4luIe2scytFOqw1wuUmj5fGql9ViLw0Hfk2pA+V2nUw0FrakxFWxz51Lf9tyZvXrROjfXVIrJyuUOhxADBxKXVFzLpajIDMP1eqnPkhI1RLeiwkzlYA0FlurAG25omxevpoa43G66F5efBWhbsrPNbbjmGvre+PFt5wpryKxVJSj3lVTNud2mWq6qyhyzVMtVVFC/1krBUvloz+loDQGWVYfl/pf7Vs5rktO6X61Vqa0h5NbcsE6nmXojlgJTtsm/5bk1apQ6HoBCyOU+leO1Vzu3q8s500lpaHQQ4p2vOn3ySXXRDkKNqCgvF4U4anNcHOVz4FVViX491dDKfj0Zi6AIIUR5uXDacvg54efbhqFDRT5OKvz5OElJoDngcomI+fVcLh7+8eNFji28NQdn+ULahBACac4RmJsbOY9ibi4Pv8MhXGgSqhO1iS+E2TAiFylJMiSvOz9siRTmrJtvNu9LAbI1LLA/KCSTtL0rQD7EyNxrFRW8/NIJKXMQcjofMz08tyP60Pyd34d2EKZ9vhKAuBlrlfuCm7GW9nFhoeq4k4t0HMXjIJSfl9cXp5P4ZPhyYSE5cG69VbQ6fTweWnJyVIcTYBYPkv3L/7d/zro4nWZY89ixZoiyHNeoUeY19oYbqG3MGBqXdADK71odTtZwV2tOVI/HdBY6HObYJk2ibXK7zdx8M2bQWKSTNDeX9kV5OTkxARq79Rj4fGp+x7w801koHYo+H40hJ4f6kGG/MnTY6aTF56PP5OVRvzK9hRyz3GZ5XOQYevQw/778cup70KC2+x2InXvR6sC05j8sKDB5rA7CSMe2vfOvpCSzXkBqaIj456tOn3xSXbSDUCMqysvFQvdzwvoGc6H7OT7nWnm5GGGoVYxHGLt4FYSFhZHVX1x5DvPyRC5OK/y5OM2nIHQ6RUTnGmOBj744ovD3xRHB/bASsZI0Vx8Ohyi0bUMhjvA58PLyIh8DxqqZI7BD/R1gR9L7pzs/bIkU5qw77xTKvemddyZF071hTeyeic5BOX6pcBg1inc7pALPmuNQPhhywK64kAtjPtWoCxcyzDnV7fg7og9m/u48Z6XiILwTryj333fiFdrHhYXkGJLKNev+txcdac+BIx1NcpGFkqwOnooK05EHkNNJ5heMxC2dcl5v7M/JRTqdpEoQoDzeADm3ysuJy359dDjU8WdlmYpAax5E+X/y5ZFUPBqG6VBzOslhl5dnVl+XTr7qarN4kdNJ/2/dz9Kxav2MPXejdITJ7RsyxHQCSmen9VhKR2hhobndclvl+IYMMZ10c+eqha3aq2Ytj8nUqdGjB4qLzbFZHartLfE6qWX+Qg2NDEK885XOQajRfXHwIAYPV/P3DR6eBRw8yMO/ahVmiNeVphnidWDVKh5+ADh/HvPw+7AhAIDs8+d5+JubIVozKYrwvyEzZ0iqGDAAPXFG4e+JM8CAATz8Lhd645jC3xvHKP8MF/LyMA3rlD6mYR2Ql8fDLwT62LahD47RLQgHzp5FDi4o/Dm4AJw9y8MP4AZsU/hNW4MLx4/HtjVAuYUqKiipbUWFmaMoU1BRAXzlK8DOnZT7aedOsisqePgbw7lH33lHXTcy5SQN2bPyttOuoaHRbXEcfcN/Gap98iTdYwaDlNszO9v80ttvxyaV+bGDQeDyyyk/nxUff0ycMmccALz4IuWOu+IKsoWgHIjR7rHk9bC5mXIT2j/ndqv3mKdP0zoQAF59FejTBzgTvu89cAD49a8pn+G996q5TEMhYOtW025uppyC8no/cyZtR2Ul5eN76SXKjXfqlLkdFy6Y++PUKcpTeO6cOZ5z54AFC6h/+bm+fdW8sKEQzTPWz1x5JfC739FYHnwQWLiQ8ve99hrlRdy7l/ICNjTQuFtaaHE6KSfitm2UJ/HECeJ3OqmtXz9zfL170/7NyqL9tmmTmQ/3xAkaZzQIQd+traV9fMstlDcSoHHOnEn7yu83c0vKcyInJzqv5I4HOi+tRjeGdhBqdF8MGoQXPiwNG3Qhf+HDUr6E7OPHYw+GKk17MJS3SMm0aXgXJWGDtuFdlFDCXQ707YvzkI4u4j+PvNgTcyJoakIRDiv8RThs3oikijFj8BkGK/yfYTAwZgwPPwA0NGA9pip9rMdUujHiQGEh/omrFP5/4iq+IigAWiCLzhg2mwf/g39R+E1bgwv204Hx9Og+2LCBHgYrK2kdKWl6V4bDAdTV0cPNxo20rqujdg4UF9N6zRp6wbFmjdquoaGhwYRC24vPVjsUIidNKETOm0ReUPgtRe6++IKcUnZEc/AcOgSMHEl/79oF5OZG/px0/FgdcFbI4iaRHERNTeTckv/ndtN2OhzAb34T+Vrudpt/9+4NLFpE1/6LF+nvL78kp6PDQcUxor1gr6ujcX3lK2abfV8YBjn3mpsBr9fcnu3b1c8cOEDO2GefJYdbSQk5Gp96Cvi3f6PiK4EAfb6lxewnGKSKk04n/X///jTuYJA+c/Qofc7ppCIyN9xA+0c+E1x3nTmOY8dU53EkjBtHc30gALz5Jm37ggVU1OU3v4lccJHr3t3hALZs4eHS0IiGSAWA4inMkyIMwaVS6STceOON4r333uvsYXRPLF5MF1+rCmPDBrogLlyYOr/TCQwciPo/7kdtLVBaCkz4zlXA55+rb/+ShWHgKuzFAVwNclwIDMJn2I9reNRZhoER2IHdGNnKPxy78BFG8am/DAM9cQYXkNvaRw+cw3nks22DE36E4Grld6AFQXjY+AdhL/5pOQZX4jMcYDwGXjSgGd5W/iw0oQk5rMfAQDDMT30AAgJOtm3IwymcQ34rfy7O4Cx6sfG70Iwg3K38TgTQgiw2fg8aEUBWK78bzfAjOyl+wzC2CiFuTH1gXRPJzln9+5v31gC9iD9yhHFgmY4NG9pWhLTaqSLd8yEA5OeTOlwI4KtfBf7+d3pY69nTVKSkgliKB6ZrQUbzd0Qfmr/z+2Dm785zVtLPWIaB/jiEoxgAeV/QD4dxBFdE/rzDkbzSOJXv2pGTw+dAMgw6n664Ajh8uP0xyqq9EhMnkvPtvfdMFZ7b3f4LdrfbdN7Zcdll5HAEyOl48qR6zufnk8IvFCIHYiBgjskwgOeeoxdwjY3t/1akM9Su6LT+v3QkArTvGxvJcRtPhIvDQTdCh8MihB49VIeuYdDLr3/8o32uZGAYWh2vkX4w39vGO19pBaFGdIwbRyeh9FzLk5KrrPvAgag/0A9T/6URlZXA1H9pRP2BfvTmhwktcMW0U0UhTsS0OeCHO6adKrxojGmnigKcjWmnDvsEnXkTtoCIaacKB4Ix7VQRgjOmrZE6TpyIbV/y2LJFvWGaMoVsrjf86Z4PAeDxx+mBwzAo3Eo+gDz+OF8fAOpRgmfwCOpb1emZhUwfv0bXgD6P0osT6BPTVhDD0dLucYrw3aSPLZdzECAHWp8+pFyMx5Fk/0xdHYUCy/ZgMLrjz4pYn/nyS6CggP4+caKtk8/qYGtqUhV42dmkzmtoiM+RHgrR56IJPuz/19BAfcSb/iYUMp2D9rFL/nQ5ByV/pkUpaGQe5L3st75F94KcL75jgNdbotG9YD0pKypIxs15Uu7fj9qCxfCfcSMYBPxwoTZ/DibsZ1JjVFXh9I97KU2n0QuoquLhLy/HiGW7sRE3tTaNwG6gvJyHHwCKi+HaHIIlqAIuhPhCwoqK0HhQzcfRiBygqIiH3+fDlxcvU5q+xGWAz8fD73AgEFLzTAaQxReSF+6DbtCsTq8QXx8OBxpCPZWmBvTk4y8qQuCg6lQOwM13jAH0xFmcQW/F1tDoUERS8U2ZwjdfpXs+BIB16ygEbtcuskMhstetozxQqcLhQH2oGFOxHn544IEf6zEVExyb2/9uF0E9StqOH+929rA0Mgz6PEo/7C6kiC4ll0sNE/Z6FYVcMsepSx3bRN7kxeN044iukjkTI8EeOm0N/07GeWrdJrvSU5b7sCKZPjgVpIliy5bMy3WskXmw5teurOyQc04rCDViI81J30vfWggXWmAgCBeCKH2LyTkIAC4X8nFKacrHKb4CFmvX4jyko4smufPwAWvX8vADwIcfwgt1wvSiAfjwQx7+/v0hoIbbCBgUz8iBxkach+r8Oo+erEnxDZsazkCQ92YhFMIQ7FOahmAfXx+CQpZtjXzhWgcPtrnQO8LtXAjaVK12WyN12E+HDM8OknmQ+V6s86G1nQNbt5JzUD6gGQbZ1kT2qSAUQi1K4YcHQbjghxu1KM2oMKmI408DtLqse6OjzqNLGSGbBsVuA2ibQ9AWPpvMcdLHNkWk6+YiXfNMZ85fXOlFNDRioRPya2sHoUZspPukvO22VteICNtsWLcOuVCr/ebiPKkxONC/P95EuGpW2Mn2JmbwOdcAwOmEB2q4gAcBytvBhHycjmmnBKcTPXBOaeqBc3zjLypCNtQbymw0sarj4HDgJFQl6kn04lP4FRbCZTvGLgRYq1B4bPvIbqcKJ1pi2hqpwy4c4BASaCSAceOAOXOAF16g+fCFF8jmDDGW1R2FoDxM8kHt3Lno30kQpaiFB344EYAHAZSilo27I9AR45cKpEo8jalYn5FOQu3gjI1S1MIZfjntRDDjfgeZgLgUhO0gmd97V73G6d+khoZGwrDmHHzqKTOSJc1OQu0g1IiOdJ+UV12F2qNDEYQLAk4E4ULt0aHAVVe1/914sHZt5ByBXAq/zZvRYsu11gIn5QzhwsWL8NqcOV40UXUzDmzeHJmfaxsCAThtCj8n4syjEg8OHsRgm7pvMPaxquMQCuECeihNF9CD763lsWMI2t6sB+GiCm5MGIo9Me1UUQyZ50XYbA2NbgQhVHUft9LCOvdZnYJccyKACXgX6zEVT+PxtIXe3YVX0BvHcBdeYeeegHfxPB7AVKzH83ggLeOvRSmawwqkZngyToHUHRycHQHDttbghn3PJr6no12vYjnb5He+j1/jbts1qLOcdNF+k9ppmD60t2/1vtfICKQ7v3YU6ByEGtER66TkCDX+/HOU9vsEnrMu+P2Ax+NCad4nVMWYCSOwGxtbb+4NyhHIiH44ir3IVWxu5NnyudntVHHBFgJst1PFeeTFtFPFYfSPaXPAsBU+sdupImKYNyNSv02PjT0YZmEWFltDo5tgyxZg9Wp6QWbNA8OZg2jQIFImyxyEAOUgZCzcBdADdLpyct2FV/AHzAWA1vXvcTcbfz1KcB9+gUA4dHAUdrJvS2+cCBdaEgjBid5pKD5WjxLUohSlqGUfvxpiKVCLUp1fz4ZalKIl/HK6BSG9j7oAov0m7NereHMMvoK74YcHr+BurMdUAOi03ISRfpOdOZ7ujvbOkS6Vp1JDIxbSnV87CrSCUCM6Fi5sewJOmcKXcyEYxIQjr2H9enreWr8emHDkNb7YOSEwr66iNZrV6QTm1VXwqT5qatDLoYZ+9XKcA2pqePgBYNgwZCklSkD2MCYHzLBh6GN7+OmDE3z8hYWYiTVhg/b7TKzhC5/Nz8cpW2W8U+gD5Ofz8ANAXh4VhrHAhRCQx+To9HrhtoUYuxGgZN0cyMuLXE2Qa/weDy5ALXRzATlq9TsNjUyHnPesKTes7RxwOoFdu1Rlw65drCklAGAZ7sHXsRbLcA8rLwCswczwX4bN5sFyzIMfWRBwwo8sLMc8Vn4AOIk+oPnKgAERtvmQboVfR4TPZrr6pquGoXYv2F+khqKeM8twDybjHfx/UX4T1vPNnmNwOea14Y2Uh7AjcxPafx+RzjfO8WT67zEakt2u9vZtPPu+u+5TDY14oBWEGp2OCRNoSQd27DD9jcEg2Wx9rViBfON2pSnfOA2sWMnn2T9yxFJohRxs+TgFHDnCwz95Mr71yZ+xWCxs5f+W8Wdg8mQe/hMnMBmbWpUkADAZmxKr7BYLZ8+iEF/iKC6HHH8hvgTOMqosL1xAT5zFRfRo7aMnzgIXLvDwX3UVbt+9MryPiP92rOQLK2xowE2oVfhvQm1y1eIiwe/Hd/FbLMYjrfzfxW8Bvz/29zQ0MgnWlBvy7a3V5sBf/oJ6lGAKNrQqGzZgCib85S88/KAH8XuxDADwN3wdAFCOX7PxX4ZjOI98xebEUVwW0+bALgyHVEMLGGGbDx2h8AvCCRF2EXLDPEfd8CBA52iGqW9kGGq6VJwaQFsNihOVeLqNYqseJfghfokWuAEYaAaU34Rd7fU8HoAHfvgh4EAI/4l7IGAgy8LbGydgIAQHWhQHsPxeOp3C0dRp9vNtB66NOEau/iJ9rrPP92hjiNSeispPOmSjHev2/j9W38pYFy/WhUo0uiW0g1CjW2PlyrZ2eTkT+eDB2FUwCThhZrLZVTAJGPw+UwcAPB58hmta+QFBNpc66/Bh7BFfV/j3iCHA4b/z8AuBRXhE4V+ER1AumB5Ic3MROisvY8QfgosS/HPBMHAcfZU+jqOvmYssVRw/jslXH8YfPjObJl99GDh+nIe/pQUjsRv0Nt8JIGzbqwcmC8PAbLEGz+FhhOCAAyHMxhq+/aOh0RWQ7pQbANCjB5afmYdmZIEelEkhN6HHxzz8AF7G98J/0bXsZXyP1UE4AEewF19RbE7swsiYNgfesaQlAYTF5gE9jIbCi2B3VCzGw+G8tgaCMLAYD2MVbm/3e/FiOaznqIPO0TQ4HH6MRXgNt+M2rEQVHmPn/wkqsQmTUYtJ+CtuZefXaAt7iG0tSvE5BiIIB+TvzYEQSlHb6oj5HAPRDA9CcKEJwErcjvvwAt7ATHyMEeGULORYXI55WI55+E98D0G44EBIyVVqddIBwDN4pPXv9pxn8TjY6lGCn+CJ1vE2A/gJnsBP8KQSKl2PEvwILyAEB5y2MSbab7TwZet3EnW2yT574wROok/ruhTvYIJzc3yRXrY8vZEcvZL7R3ihzdhSeZEic9WuxO24HSvbfK+9FwTR+m6zH3N2I036Fg2NToV2EGp0a4weDfztb6rNhoULYfxCbTK82bxvk268EeO/DGDvNkCqs8aPCQCX3cjDf+wY9mCo0rQHQ/kKZBQX4+xmNdz3LPKB4mIe/okT0fIXt1Ier8VwAxMn8vADwPTpcPwFCFr6cBjUzoKrr8bLm6WDIfzQ/tkUlBev4uH//vfR+9engZADtKOc6O04DdzzfR7+rCwsb5oXztllIASDHhizGB3lGhqdjY7IA3PZZTgaHAKcN5uO9hwCXMankhuAwzHtVPFPFMW0U8V+DIppc+C8LWWC3U4VqzELQbgBAEG4sRqzWB1s/8D4mHaqeNfGZ7c58GMsCqvS0brmdBJ+HW/ibygDAPwNZfg63tROwg6ADLHtjROtjhYnWuBGC/ygO6AHUY0duBb/jl+2OtBkTlABJ/6Gr7eqnwmm2vdlfBeBsBJR3o88ip9iO8ZgHpa3fmMHrm11SgEhhOCCAOBACLOwBl/BJ6jFFAzAYSzEswDazxconUfkHHSGeZ14G9OwCZOV70gHVAguGAhgO8a0Oivtyrqb8A4CcMGNFryDm9r0a1XDuRDEZozDk3gCLXC1jjWms83lUl4Y27fDQBACTjgQJJVmMIqaTvI5neRAtDkIa40p8AtZ/An4d/wSAg4YCCEIB0S4vTasSJapEkIwzFQJhkHpdxobo59kHg/q/WNaj+8mTI6YqzZSLl65PWeQCwdCEDZ1Zy1K0RROcdEMA7Xnx2oHoUa3hHYQanRrnDsX204V9nSG3EUt8dZbuDhKVpylm6CLfjfw1ls8/GPHIrBZvQwE4ALGjuXhTzfcbmQJtQpzlmgC3G6+Ph5+GLlrzyp5qHLFWeDhh3n4z5/HKahO1FPIB86fj/KFBHH4MJ4O/d+wQefQ06FHUX74hzz8Y8bgozo1BO8jDAfGjOHh19DoChg2DPja14Bf/cps+8EPKOfsx0wKv4YGHDjfW2k6cL43XzoAAIfRL/yXsNk8OI/cmHaqSHdBJwA4i4KYdqpYBhnGQNfjZShndX6dQO+Ydqr4yFaEym5z4Ff4Qfgv2ke/wg9Y99E7UF/KmbZG+iAwFlvxPbyM7RjT6mgJwYGB2I9DGIgQHKjGQxAwLC8dBRB238ncoObayu5AAB7L/xNOoRAvYT5eQjkMCAiFx2H5LDkUV2OOwvs6ZiMbF9GILABOxcG2DPfgZXwPfnhwHH3QBA8EXMr4TMeX6ZSTIdAGyDH3a3wXAs42zsdH8EyrwzMAN+7Bf+LX+H4bx9bdWI6j6Iu3cAtexzfC22igCcCP8H/gRVN4RLa8pG43EDBzYNsVkOR4pTUpOI1WxXB9zlRMbViDZnjgQAi/xL+j3HiZnIMTJwL19UB+PpZd+DZWGv8LhcEjgKAxkH7aBcABAy0QlqJQZ3peQS/JnC4YQTouBgDk5QMtOTQfFhWh/so7ULs9F6UX36T9kZMDNDcDQmC589/QFPRCwIEmkIpzNLbjfdyA27ESo7BTUUiWohY7cC0q8CJCrWpW0rXehxewGrMwD6+gF060nj8hOHDmqeeBR39Bc9tkAAAa7UlEQVTU9lRfvBgYN059gbhhA0Uc6JBkjUyAECKjl7FjxwqNzEZdnRCLFtGaG/PnC0FuO1rmz+flX7hQ5V+4kJdfVFeL/jgkgFC4j5Doj0NCVFfz8BcXi0IcVfgLcVSI4mIe/pIS4YBf4XfAL0RJCQ9/Xp64EnsV/iuxV4i8PB5+IYQYNkwMxw6lj+HYIcSwYWz8BTih8BfgBB9/UZFwIGA7BgEhiop4+LOyxAjb/hmBHUJkZSVFB+A90QXmlnQtyc5Z1uuMXDTaIm3zycCBQgBieP5B4XAIMTz/IB2EgQP5+gBENs4rv6VsnOc72IAAWhR+oIWZP2DjD2QOf2sfQVsfQdZtMGzHwGA/Bukdf1rPodY+Muc86s5zVtLPWBHPw1Dr+ehoPYfsi4jydzyfifR565wZ7+eErU0duxN+sRT3iDvxSpTxBSN8v0VMxgYxBB8LH84Jo/X8U8dgICBuxlpRhxKxEIsi/pYdCIg78YqYjZWiPw6FrydBS9/R9luo9XpzM94UxagXQ/Cx6IsjYjS2iiH4OPz9YMzvA0FxM94Ug7CvTX85OCdGYIdYinvEQvdzohBftsMVsvRJPE74RR1KxCLjUeFEwNLeLG7OeUcsMh4Td+a8KgyDvu9Ai5jtXCOW/q+/ivmFfxazjdXCieZ2+gxE2E77vjaPXbRzsDeOCVFW1vb8r6kRok8fWkeyOwpVVW37rKmhdo1LEvHOV50++aS6aAdhZqOuTojsbCGcTlpzP9TV1Qnh8QhhGLRm5a+qEnW/eE84nfRLcjqFqPvFe7wX3pwcUeg8qdy4FDpPCpGTw8OflyfycFyZ8PJwnM/B5vMJNxoVfjcahfD5ePidTnE9tir812MrHQwu+HyWG0Hq4068wrcNVVWiV9ZZhb9X1lm+86ikRORA5c/BWT4nLSBG247BaGwNTy/J0GXWwxaAfwXwCYC9AB5p7/PaQZg+pHU+qa4Ww/GB8pAwHB/wvawRQogOce5o/k7tQ/N3fh/M/Jk0Z3XUfBV5HwvL3/G0x+Pgs/+//e941tGcgdHGF48TMt7v2cdhd1ZFGmcsrljjb2888XLF+5329mX0fjy4YHmxnehYI+3/aMe8vfMg9t8GAmIRHhF1dRFeTkqnYGVl5zgHrWPobEelRpdBvPOVvcSUhkbHobAQtd9bDr+fFOl+P1D7veVAYSEPv2Fgwq29UFsL/PSnQG0tMOHWXnzFE378Yyy/bzOC4eR0waDA8vs2Az/+MQ8/ADQ04N+CS4HWEAhBNlfI2dmzuIA8pekC8viqAF+8iAH4QmkagC+Aixd5+IPBcA4ZE3544kugHC8uXsSnlqT7AMjm2oYf/xieZjWc2NN8nu88evdd3IhtStON2Aa8y5fzKuIxuARgGIYTwC8BlAEYAeDbhmGM6NxRdW3U1wPPPENrbtTWAk1N9PNvaiKbDQ8+iN24NmzQHLIb1wIPPsjXhyPKLVm0dg0NDY040TXmKwE1NNgaDmy/N1dDhWVewfg/b/2MfW0di308dtv+2Wjjj/R9+/ei8Rsx/rZyiAifs38mEr8cS7TvR/uufSzRxhzp8/b+rWOQf0fu24+ccIi5nbu9cVj/tp8nkc4B+/fs54/9HFHHKeDEf+CnmDQJuOkmoLISmDo1fH8zZQpQUQE8/TStOfMVxwtZTO1b3wIef5zW1mJrGhpR0OXuOg3D+FfDMD4xDGOvYRiPtP+NVPszF40OxsSJKN39Iowg5b8wggGU7n6Rr8BEQQFw+jRumtiIxx4DbprYCJw+Te1MOIrLYtocoLw7QQAhAME0VPMLtWOnhsfws/BfwmbzIAB3TJsD6U7s3wunY9qp85+KaacKD/wx7W6MYgB7hRCfCSH8AFYA+EYnj6nLor6ebp6Vm2hG7NpF7/UBWu/axcufdqxbl1h7ovB6E2vX0NDoTujE+Spy3kDz/2J9L9XPp+uzkRxOqfRh/3wsh1Yy/didd1xob0yRHJLxfq89h2OscSSzrbGcxZEh4EAwSKkcW8UutaCcgy++SDc8L75IdmegKzgqNTIOXcpB2NFvt+xOQe0k7GC8/jp2XHcnWsIJcFvgwo7r7gRef52H/9QpeNCAALwABALwwoMG4BSTc0QIrLbdW63GN8wnVKY+DASAcLJmwEk2Vx9VVa3VFCWCcANVVTz81dW4Fy8qTffiRaC6moe/rAx7MURp2oshQFkZDz8QPs6zpAGAqlCyHYPiYkuSd+L8CMP4Kj3X1EQ+T2tqePirqvA+rlea3sf1fOdQ18blAA5a7EPhNgWGYZQbhvGeYRjvHT9+vMMG19VQWwtVMV7Ly/+Pf8S2U8IPfpBYezJ4LMrLn2jtiaKpKbF2DQ2N7oROnq/idWJpaGQK1PPVMACPByjtudVU6z31lKni6wwnYVdxVGpkFLqUgxBajXHJYWU/WUnVsNk8CCBb4TdtLnC8sWoP8mdq2GwGRKumxVVla906RBw/lyJm2rTE2pPBhg2IuA1ck+ztt0fmv/12Hv4VKxDxPF2xgod/3LjI/OPG8fB3AwghlgkhbhRC3FjIlUIhA1FaSjfPTmf4JrqUl/+222LbKeHPf0ZEtfWf/8zXx5b/v717D5erKu84/v0lJ0CAFoxyE9BQRCwgCRCEI8gTLlIIltgHbIMXFKmJlaC21lS0tfp4KcSCyEVKuIUqioqoVKkJTwilfRAIlwQSIMhFFEwM1kRLsYQkb/9Y6+RMTuZc5sw+Z8+e+X2e5zxnZu09a79r1sx+96zZa8+Smm30fAGxKZcXpU79hao3RatIIx3/aGyj6s9RO/TBaLShmkYmX0Wfv3plo718oHVaIT4vb93lfddJxzSzZsGiRdD94qItp/L2TPUtNJcPweLFrTNQaZXSagOEQ/p2y9rHaasvy7eiz/1ijOP3W9Tfe78AixdT9yCzyB3vhz5UfxtFnbUy0vWfcEL9+osawLv44vr1X3xxMfVDPtuxzjaKOgtyzpz69Rc1SNtTX9/6izJtWv36p00rbhut6zlg75r7e+Uyq6O7Ox08f+5z+SC6u9j6L7ggvW1e97r0v9CTWKdMIS68hNrpT3HhJTBlSnHb2LiRYBzpkhIC8v0Cr6lat/4C3cXRm7e05f1i9MYfjET8o7GNqj9H7dAHo9GGFjTK+arvcUHt4MrGPvf7/o308v7iapX4yly+qen6t+XFQdZppP5NWy0fy/ohPH7rx2lz25p9/tI6J7KAOczli19MMyKuuCIf18yZs/VU3mOPLfi4fgiWLGmNgUqrHEWR0yGbJOl04KSI+Mt8/z3AERExu896M4GZAK95zWsOe+aZZ5rYZu/tFnoqOsP06XDLLcw7+FK+u/tsTlt9GTMfOhdOPbWYacYTJsDatXma8XjG8XvWs326BmER04zziydNAe4CNrD5ILOoF9NIb8P1l78N19+nOt0fEQWOuowcSV3A48DxpA9aS4B3RkS/V7+bMmVK3HfffcPcXu9t56s2NGYM7LorrF7dW7b77rBmDWwqYFC/5wVU++KpV9Zk/T859xvcsccZTF31TbovfWfh9XP77emDzuLFcNxxxdVfu40DD4Tly+Ggg3ovZlnkc/SKadzx8R8x9Uun0L321sLr31xf0TuNnvrGjEkD12PH9r42i+6DkdpGwc9RVXLWqOargY4L9t8/Lf/AB2DePFi5MpXvtFPa3/XcH0E/4UjuYCpTuYNuivvBtkqS2vuAoqsLNmxI/zdt2jKXjhmT9i8vv9x7v3b5dtvBSy9t+fyMG5fqqy1r5+fP2spQ81XXaATTgCF9uxUR84B5kJJXMxv0e7pEd90Fp57KzB/MTqO9zIbpt6XyIuQfJFn/m55pxeNhQvrhkiJF9LyNukbs8ikjvQ3XX/42XH/1RMQGSbOBBaQLhV470Iet5rc3UjVbS6g3CFg7WNisei+gIl9UJ58MJ5xA99+cQTo59AyYuKq4S0pccEG6dEHt2RC3317s2RDjxsHrX58GB6F3kPDxx4upX4Jdd6V79Y/Sc3Tej3oHgYtU+2s9RV9gu2fgDrYewKvSNkbyOWpBo52v0jbrHBc89ljvCvV+BX6bbdJ78Mwze9/vPe/Bs86CpUvh/PN79wOLF6dLpuy7b+8ZWnPnwpNPwowZddfrBrqffBJmfDEtnzs3DSB961sweTJceSUccUT6wmbZsvT6mD8fHnww7c8mTkyDm4cfnn5ta+7cdPs730n7pMceS9tbsmTLs8Z61qs9u6x2vb5x98S1cmVv+2bNSo+78sr6ddSq9zzMmgW//CW85S296/d9/LRp6T336lf3Pvaii9LsnEmTBn7sUNo5mEYeP0hfA1vX1ehz2ExbzCqo1c4gHNWzMczMrLVU5WyM4XLOMjNrH+2cs5yvzMzaRyXPICzj2y0zMzMzMzMzM7NO1lIDhAARcStwa9lxmJmZmZmZmZmZdYJW+xVjMzMzMzMzMzMzG0UeIDQzMzMzMzMzM+tgHiA0MzMzMzMzMzPrYB4gNDMzMzMzMzMz62AeIDQzMzMzMzMzM+tgHiA0MzMzMzMzMzPrYB4gNDMzMzMzMzMz62AeIDQzMzMzMzMzM+tgHiA0MzMzMzMzMzPrYB4gNDMzMzMzMzMz62CKiLJjaIqk54Fnyo5jEK8Cfl12EE2qehuqHj9Uvw1Vjx+q34YqxP/aiNil7CBGinPWqKh6/FD9NlQ9fqh+G6oeP1SjDW2bswrIV1Xov2a5je3BbWwPbuPAhpSvKj9AWAWS7ouIKWXH0Yyqt6Hq8UP121D1+KH6bah6/DY6qv46qXr8UP02VD1+qH4bqh4/tEcbOlkn9J/b2B7cxvbgNhbDU4zNzMzMzMzMzMw6mAcIzczMzMzMzMzMOpgHCEfHvLIDKEDV21D1+KH6bah6/FD9NlQ9fhsdVX+dVD1+qH4bqh4/VL8NVY8f2qMNnawT+s9tbA9uY3twGwvgaxCamZmZmZmZmZl1MJ9BaGZmZmZmZmZm1sE8QGhmZmZmZmZmZtbBPEA4giTtLWmxpEckrZD0kbJjGg5JYyU9KOmHZccyHJJ2lnSTpMckPSqpu+yYGiHpr/PrZ7mkb0raruyYBiPpWklrJC2vKZsg6TZJP83/X1FmjAPpJ/4v5dfQQ5K+J2nnMmMcTL021Cz7mKSQ9KoyYrPW43zVGqqer6B6Oavq+Qqqn7Ocr9qLpJMkrZT0hKRPlB1PUST9TNLDkpZKui+XVWpfUU8j+0All+S+fUjSoeVFPnT9tPEzkp7L/blU0rSaZeflNq6U9CflRN2Y/o7j2qkvB2hj2/SlpO0k3StpWW7jZ3P5PpLuyW35lqRtcvm2+f4TefnEZmPwAOHI2gB8LCIOAI4EzpF0QMkxDcdHgEfLDqIJXwF+HBFvACZRobZI2hP4MDAlIg4CxgIzyo1qSOYDJ/Up+wSwKCL2Axbl+61qPlvHfxtwUEQcDDwOnDfaQTVoPlu3AUl7AycCPx/tgKylOV+1hsrmK6hszppPtfMVVD9nzcf5qi1IGgtcDpwMHACcUdFc0p9jI2JyREzJ96u2r6hnPkPfB54M7Jf/ZgJXjFKMzZpPnX0M8OXcn5Mj4laA/HqdARyYH/PV/Lpudf0dx7VTXw50rNouffkScFxETAImAydJOhK4gNTG1wFrgbPz+mcDa3P5l/N6TfEA4QiKiFUR8UC+/T+kA/09y42qMZL2Ak4Bri47luGQtBNwDHANQESsj4h15UbVsC5gvKQuYHvglyXHM6iIuBP4TZ/i6cD1+fb1wNtHNagG1Is/IhZGxIZ8925gr1EPrAH99AGk5DEH8C9U2WbOV+Vrk3wFFctZVc9XUP2c5XzVVt4EPBERT0XEeuBG0vupXVVqX1FPg/vA6cC/RnI3sLOkPUYn0uEbYB9Tz3Tgxoh4KSKeBp4gva5b2gDHcW3Tl8M4Vq1cX+b+eCHfHZf/AjgOuCmX9+3Hnv69CThekpqJwQOEoySf7nkIcE+5kTTsYtLB2aayAxmmfYDngeuUpp1dLWmHsoMaqoh4Dvhn0rfnq4DfRsTCcqMatt0iYlW+vRrYrcxgmvR+4N/LDqJRkqYDz0XEsrJjsdblfFWaSucraKuc1U75CiqYs5yvKmtP4Bc195+lYl82DSCAhZLulzQzl7XbvqJHf+1qt/6dnafXXlszPbzybexzHNeWfVnnWLVt+lLpcjlLgTWk2QBPAutqvvSrbcfmNublvwVe2cz2PUA4CiTtCHwX+GhE/K7seIZK0tuANRFxf9mxNKELOBS4IiIOAf6XCp3+n3dw00kfHF8N7CDp3eVG1byICCp6RoCkT5FOcb+h7FgaIWl74JPAp8uOxVqX81WpKp2voD1zVpXzFVQzZzlfWYs6OiIOJU3NPEfSMbULq76v6E+7tos0pXZf0jTOVcCF5YZTjIGO49qlL+u0sa36MiI2RsRk0pn/bwLeMJrb9wDhCJM0jvQCviEibi47ngYdBZwq6WekKQLHSfp6uSE17Fng2Yjo+XbhJtIHsKo4AXg6Ip6PiJeBm4E3lxzTcP2q59T1/H9NyfE0TNL7gLcB78pJtkr2JX1oX5bf03sBD0javdSorGU4X5Wu6vkK2idnVT5fQaVzlvNVdT0H7F1zf69cVnn5DGkiYg3wPdIH97bYV9TRX7vapn8j4ld5IGYTcBW9U08r28Z+juPaqi/rtbEd+xIgX2ZmMdBNmgLelRfVtmNzG/PynYD/bma7HiAcQXn+9zXAoxFxUdnxNCoizouIvSJiIukCn7dHRKXOBIiI1cAvJO2fi44HHikxpEb9HDhS0vb59XQ8FbtofY1bgPfm2+8FflBiLA2TdBJp+uKpEfFi2fE0KiIejohdI2Jifk8/Cxya3yPW4ZyvytcG+QraJ2dVOl9BtXOW81WlLQH2y7+4uQ1pf3xLyTE1TdIOkv6g5zbpx3OW0wb7in70165bgDOVHEm6jMSqehW0uj7X2/szUn9CauOM/Ouw+5B+xOPe0Y6vUQMcx7VNX/bXxnbqS0m7SNo53x4PvJV0HLUYOD2v1rcfe/r3dNLxb1NfCHYNvoo14SjgPcDDeR45wCcj/7KOjZpzgRvygcpTwFklxzNkEXGPpJuAB0hThB4E5pUb1eAkfROYCrxK0rPAPwLnA9+WdDbwDPDn5UU4sH7iPw/YFrgtX/v17oj4YGlBDqJeGyLimnKjshbmfNUaKpuvoJo5q+r5Cqqfs5yv2kdEbJA0G1hA+hXzayNiRclhFWE34Hv5vdQFfCMifixpCRXaV9TT4D7wVmAa6cceXqQiOaqfNk6VNJk05fZnwCyAiFgh6dukL+g2AOdExMYy4m5Q3eM42qsv+2vjGW3Ul3sA1yv92vIY4NsR8UNJjwA3Svo86diqJ0deA3xN0hOkH+KZ0WwAqtaMAzMzMzMzMzMzMyuSpxibmZmZmZmZmZl1MA8QmpmZmZmZmZmZdTAPEJqZmZmZmZmZmXUwDxCamZmZmZmZmZl1MA8QmpmZmZmZmZmZdTAPEJqZmZmZmZmZmXUwDxBaR5P0QtkxmJmZDYVzlpmZdbKRzIOSJkpaPlL1m1WBBwjNWoCksWXHYGZmNhTOWWZmZmbtxwOEZpmkj0taIukhSZ/NZRMlPSrpKkkrJC2UNH6AOj4s6ZFcx425bEdJ10l6OJeflstfkHShpGVAt6TDJP2HpPslLZC0R15vX0k/zuX/KekNuXy+pEsk3SXpKUmnDxDXVEl3SLpJ0mOSbpCkvOzTud3LJc2rKb9D0pcl3Zefg8Ml3Szpp5I+X1P3uyXdK2mppCv9wdHMbOQ5ZzlnmZl1soLy4GGSluXcdk5N+VhJX6qpf1Yu30PSnTmHLJf0llx+kqQHcl2LRrjpZiPGA4RmgKQTgf2ANwGTgcMkHZMX7wdcHhEHAuuA0wao6hPAIRFxMPDBXPYPwG8j4o25/PZcvgNwT0RMAu4BLgVOj4jDgGuBL+T15gHn5vK/Bb5as709gKOBtwHnD9LMQ4CPAgcAfwQclcsvi4jDI+IgYHyuq8f6iJgC/AvwA1LiPAh4n6RXSvpj4C+AoyJiMrAReNcgcZiZWROcs5yzzMw6WYF58DpSzprUp/xsUi48HDgc+ICkfYB3AgtyDpkELJW0C3AVcFqu5x2FNNKsBF1lB2DWIk7Mfw/m+zuSksvPgacjYmkuvx+YOEA9DwE3SPo+8P1cdgIwo2eFiFibb24Evptv70/6EHNbPhliLLBK0o7Am4Hv5HKAbWu29/2I2AQ8Imm3Qdp4b0Q8CyBpaW7HfwHHSpoDbA9MAFYA/5Yfc0v+/zCwIiJW5cc/BexN+qB3GLAkxzceWDNIHGZm1hznLOcsM7NO1nQelLQzsHNE3JmLvgacXFP/wTVnu++U618CXCtpHCmnLZU0FbgzIp4GiIjfFNJCsxJ4gNAsEfBPEXHlFoXSROClmqKNpA8U/TkFOAb4U+BTkt44wLr/FxEba7a/IiK6+2z/D4F1+VuqempjUz/r1Ft3I9AlaTvS2R1TIuIXkj4DbFfnMZv6PH4Taf8h4PqIOG+QbZuZWXGcs5yzzMw6WVF5cKD6z42IBVstSGcqngLMl3QRsLbvOmZV5SnGZskC4P357Ack7Slp10YqkDQG2DsiFgN/R/qmaUfgNra8psUr6jx8JbCLpO68zjhJB0bE74CnJb0jl0tS31Pgm9HzwerXue39XhOqH4uA03ueK0kTJL22wPjMzGxrzlnOWWZmnazpPBgR64B1ko7ORbWXnFgA/FU+UxBJr5e0Q84Zv4qIq4CrgUOBu4Fj8hRkJE1opmFmZfIZhGZARCzM1yb6SZ529ALwbtK3TkM1Fvi6pJ1I3zpdEhHrlC6Ofrmk5bm+zwI399n++nwK+yX58V3AxaSpU+8CrpD098A44EZg2fBbu8V210m6ClgOrCadNt/I4x/JcS3MHzZfJn2wfKaI+MzMbGvOWc5ZZmadrKA8CHAWacpwAAtryq8mTU1+QGkDzwNvB6YCH5f0ct7mmRHxvKSZwM05t6wB3jrctpmVSRFRdgxmZmZmZmZmZmZWEk8xNjMzMzMzMzMz62CeYmw2DJIuB47qU/yViLiujHh65AvMf61P8UsRcUQZ8ZiZWfmcs8zMrJO1ah40azWeYmxmZmZmZmZmZtbBPMXYzMzMzMzMzMysg3mA0MzMzMzMzMzMrIN5gNDMzMzMzMzMzKyDeYDQzMzMzMzMzMysg/0/uN0xoCsQat4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.plot(X_train[X_train.bot==True][cluster_cols[0]].values, X_train[X_train.bot==True][cluster_cols[1]].values,  'rx')\n",
    "plt.plot(X_train[X_train.bot==False][cluster_cols[0]].values, X_train[X_train.bot==False][cluster_cols[1]].values, 'b.')\n",
    "plt.xlabel(cluster_cols[0])\n",
    "plt.ylabel(cluster_cols[1])\n",
    "plt.title('Screen name length vs. description length')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.plot(X_train[X_train.bot==True][cluster_cols[0]].values, X_train[X_train.bot==True][cluster_cols[2]].values,  'rx')\n",
    "plt.plot(X_train[X_train.bot==False][cluster_cols[0]].values, X_train[X_train.bot==False][cluster_cols[2]].values, 'b.')\n",
    "plt.xlabel(cluster_cols[0])\n",
    "plt.ylabel(cluster_cols[2])\n",
    "plt.title('Screen name length vs. ratio')\n",
    "\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.plot(X_train[X_train.bot==True][cluster_cols[1]].values, X_train[X_train.bot==True][cluster_cols[2]].values,  'rx')\n",
    "plt.plot(X_train[X_train.bot==False][cluster_cols[1]].values, X_train[X_train.bot==False][cluster_cols[2]].values, 'b.')\n",
    "plt.xlabel(cluster_cols[1])\n",
    "plt.ylabel(cluster_cols[2])\n",
    "plt.title('Description length vs. the ratio')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Converting the values to log scale \n",
    "X_train_log = pd.DataFrame(columns=cluster_cols)\n",
    "\n",
    "# 0 values are converted to inf in log scale, so changing them to -1\n",
    "X_train_log = X_train[cluster_cols].applymap(lambda x: np.log10(x) if x >= 1 else -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train_log['bot'] = y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "number of estimated clusters : 6\n"
     ]
    }
   ],
   "source": [
    "# Compute clustering with MeanShift\n",
    "\n",
    "# The following bandwidth can be automatically detected using\n",
    "\n",
    "\n",
    "bandwidth = estimate_bandwidth(X_train_log[cluster_cols], quantile=0.2, n_samples=500)\n",
    "\n",
    "ms = MeanShift(bandwidth=bandwidth, bin_seeding=True)\n",
    "X_train_log[cluster_cols] \n",
    "\n",
    "ms.fit(X_train_log[cluster_cols])\n",
    "labels = ms.labels_\n",
    "cluster_centers = ms.cluster_centers_\n",
    "\n",
    "labels_unique = np.unique(labels)\n",
    "n_clusters_ = len(labels_unique)\n",
    "\n",
    "print(\"number of estimated clusters : %d\" % n_clusters_)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x864 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "# Plot result\n",
    "import matplotlib.pyplot as plt\n",
    "from itertools import cycle\n",
    "\n",
    "plt.figure(figsize=(12,12))\n",
    "plt.clf()\n",
    "\n",
    "feature1 = 0\n",
    "feature2 = 1\n",
    "feature3 = 2\n",
    "feature4 = 3\n",
    "colors = cycle('bgrcmykbgrcmykbgrcmykbgrcmyk')\n",
    "for k, col in zip(range(n_clusters_), colors):\n",
    "    my_members = labels == k\n",
    "    cluster_center = cluster_centers[k]\n",
    "    \n",
    "    plt.plot(cluster_center[0], cluster_center[1], 'o', markerfacecolor=col,\n",
    "             markeredgecolor='k', markersize=14)\n",
    "    plt.plot(X_train_log[cluster_cols].values[my_members, feature1], X_train_log[cluster_cols].values[my_members, feature2], col + '.')\n",
    "    plt.xlabel(cluster_cols[feature1])\n",
    "    plt.ylabel(cluster_cols[feature2])\n",
    "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating index for passing on to the plots to show the following plots for each feature. \n",
    "\n",
    "members_bot_ind = y_train[y_train == True].index.values # index to array \n",
    "members_nonbot_ind = y_train[y_train == False].index.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1296x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18, 6))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.plot(X_train_log[X_train_log.bot==True][cluster_cols[0]].values, X_train_log[X_train_log.bot==True][cluster_cols[1]].values,  'rx')\n",
    "plt.plot(X_train_log[X_train_log.bot==False][cluster_cols[0]].values, X_train_log[X_train_log.bot==False][cluster_cols[1]].values, 'b.')\n",
    "plt.xlabel(cluster_cols[0])\n",
    "plt.ylabel(cluster_cols[1])\n",
    "plt.title('Screen name length vs. description length')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.plot(X_train_log[X_train_log.bot==True][cluster_cols[0]].values, X_train_log[X_train_log.bot==True][cluster_cols[2]].values,  'rx')\n",
    "plt.plot(X_train_log[X_train_log.bot==False][cluster_cols[0]].values, X_train_log[X_train_log.bot==False][cluster_cols[2]].values, 'b.')\n",
    "plt.xlabel(cluster_cols[0])\n",
    "plt.ylabel(cluster_cols[2])\n",
    "plt.title('Screen name length vs. ratio')\n",
    "\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "plt.plot(X_train_log[X_train_log.bot==True][cluster_cols[1]].values, X_train_log[X_train_log.bot==True][cluster_cols[2]].values,  'rx')\n",
    "plt.plot(X_train_log[X_train_log.bot==False][cluster_cols[1]].values, X_train_log[X_train_log.bot==False][cluster_cols[2]].values, 'b.')\n",
    "plt.xlabel(cluster_cols[1])\n",
    "plt.ylabel(cluster_cols[2])\n",
    "plt.title('Description length vs. the ratio')\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the plots above we see that among the new features, the ratio of the friend's count and the 2xfollower's count distinctively shows that the bots have a higher score for the ratio. \n",
    " \n",
    "Next we're are going to incorporate tweets and basic NLP (sentiment analysis and subjectivity analysis) on the given data. \n",
    " \n",
    "Problems encountered: \n",
    "\n",
    "__Gathering recent tweets from Twitter__\n",
    "\n",
    "- Most fake accounts used by the Italian research group that correspond to the current dataset have restricted access, hence we were not able to scroll recent tweets using Tweepy.\n",
    "\n",
    "- Using Tweepy I was able scroll sufficient amount of tweets from the real users. Most tweets were from Aug - Nov. 2018. While collecting tweets from the fake accounts, I realized that some fake accounts (spambots) do not exist or is not active on twitter in recent times. Their Twitter activity shows they were mostly active during 2013 and 2015. \n",
    "\n",
    "- Tweets are very time-sensitive and they strongly reflect recent topics. Therefore, collecting tweets from past (although I do not think Twitter allows gathering tweets from 5 years back) and comparing them to recent tweets does not make sense. Therefore, I decided to use the existing tweets corresponding to the users from these esisting datasets. \n",
    "\n",
    "__Gathering tweets from existing sources__\n",
    "\n",
    "- Since these existing datasets originated in Italy there are plenty of Italian tweets that are mixed with English tweets (along many other languages). The existing tweet datasets do not have a variable for language. Therefore, I used the LangDetect library to detect languages of the tweets from the existing datasets. (This is a long and slow process and took more than 12 hours to get all language information on the tweets). \n",
    "\n",
    "- Once I gathered the tweets I segregated the English tweets from other languages. I then perfomed sentiment and subjectivity analyses on the tweets with the TextBlob libray. Then I summarized the sentiment scores (count of tweets, sum and average of sentiment score, average of subjectivity score for each user). \n",
    "\n",
    "- After incorporating the sentiment scores I realized that the tweets dataset have less number of unique real users than unique bots. That is, although it contain 2 million tweets, the number of unique users is inversely proportionate to the number of tweets. The distribution shows that there are thousands of tweets from relatively few users. The fake accounts, on the other hand, have more accounts with relatively small numbers of tweets from each. This caused the final dataset to be imbalanced number of bots (e.g., 2405 bot account and 1000 real accounts.) The final dataset with tweet score is 'final_data.csv.'\n",
    "\n",
    "\n",
    "In the following I select a subset of tweet users from the training dataset that corresponds to the user_ids from the sentiment score assigned dataset. This will decrease the number of datapoints available for the model and I expect overfitting. In summary, due to restricted access to tweets and significant reduction in sample size we do not have a way to evaluate the effectiveness of these features. \n",
    "\n",
    "To compare, I'll first run the existing models on the reduced dataset, and then I'll incorporate the new sentiment features on the same dataset. \n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2461\n",
       "0    1083\n",
       "Name: bot, dtype: int64"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sentiment scored data \n",
    "\n",
    "sents = pd.read_csv('Data/Untitled Folder/final_data.csv')\n",
    "\n",
    "sents.bot.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Matching id numners:  3544\n"
     ]
    }
   ],
   "source": [
    "n = pd.DataFrame()\n",
    "counter = 0\n",
    "for uid in sents.id.unique():\n",
    "    if uid in df_cert.id.values:\n",
    "        #print('Yes', uid)\n",
    "        #df_cert_copy = df_cert_copy.append(df_cert_copy)\n",
    "        counter +=1\n",
    "print('Matching id numners: ', counter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to create a subset of where the index values match between the smaller subset with sentiment scores \n",
    "# and the main dataset\n",
    "\n",
    "def match_get(df_s, df_b):\n",
    "    start = t.time()\n",
    "    new_df = pd.DataFrame(columns=df_b.columns)\n",
    "    \n",
    "    '''matching senti_scored dataframe userid\n",
    "    to existing dataframe and saving a subset \n",
    "    of matching userids\n",
    "    '''\n",
    "    counter = 0\n",
    "    for uid in df_s.id.unique(): \n",
    "        if uid in df_b.id.values:\n",
    "            new_df = new_df.append(df_b[df_b.id == uid])\n",
    "            counter += 1\n",
    "    print('Total matches found: ', counter)\n",
    "    #print(new_df.shape, len(new_df.id.unique()), len(new_df.id))\n",
    "    #print(new_df.bot.value_counts())\n",
    "    #print('A peak into the new df: \\n', new_df.head(3))\n",
    "    print('match time:', t.time()-start)\n",
    "    \n",
    "    '''sorting ids so that they are matched by index'''\n",
    "    new_df1 = new_df.sort_values(by='id')\n",
    "    df_s1 = df_s.sort_values(by='id')\n",
    "    [print('no' for id1, id2 in zip(new_df1.id, df_s1.id) if id1 != id2)]\n",
    "    df_s1 = df_s1.loc[:, 'sent_count':'subj_mean']\n",
    "    \n",
    "    \n",
    "    '''reset index for concatenating'''\n",
    "    new_df1.reset_index(drop=True, inplace=True)\n",
    "    df_s1.reset_index(drop=True, inplace=True)\n",
    "    \n",
    "    '''creating the new_df for output'''\n",
    "    new_df = pd.concat([new_df1, df_s1], axis=1)\n",
    "    \n",
    "    print('total time ', t.time()-start)\n",
    "    return new_df\n",
    "    \n",
    "    \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total matches found:  3544\n",
      "(3544, 23) 3544 3544\n",
      "1    2461\n",
      "0    1083\n",
      "Name: bot, dtype: int64\n",
      "match time: 38.99750304222107\n",
      "<generator object match_get.<locals>.<genexpr> at 0x120c511a8>\n",
      "total time  39.017293214797974\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1    2461\n",
       "0    1083\n",
       "Name: bot, dtype: int64"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sent_matched = match_get(sents, df_cert)\n",
    "\n",
    "sent_matched.bot.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This shows that the dataset is reduced to 3544 from 35588 which will significantly affect the model performance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_to_keep = ['followers_count',\n",
    " 'friends_count',\n",
    " 'favourites_count',\n",
    " 'statuses_count',\n",
    " 'len_screen_name',\n",
    " 'len_desc',\n",
    " 'ratio_fr_2xfl' ]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = sent_matched[col_to_keep]\n",
    "X.columns\n",
    "\n",
    "X = X.fillna(0)\n",
    "X = X.applymap(lambda x: x if x != np.inf else -1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Training: \n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "train time: 0.193s\n",
      "Crossvalidating: \n",
      "Crossvalidated accuracy: 0.998 with std 0.002\n",
      "test time:  0.020s\n",
      "confusion matrix \n",
      " [[350   0]\n",
      " [  3 817]]\n",
      "Test precision score 1.000 \n",
      "Test recall score 0.996 \n",
      "ROC AUC score 0.998\n",
      "\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, sent_matched.bot.astype(int), test_size= .33)\n",
    "\n",
    "results.append(baseline_models(rfc, X_train, y_train, X_test, y_test))\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model seems to be overfitting as the cross-validated accuracy raises to 99.8% with ROC AUC score .998. \n",
    "\n",
    "Now in the following I incorporate the sentiment scores to see if the sentiment feature import the model. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Incorporating sentiment scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    2461\n",
       "0    1083\n",
       "Name: bot, dtype: int64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sents.bot.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# But is has about 4k duplicates \n",
    "\n",
    "#check duplicates \n",
    "#pd.concat(g for _, g in df_cert.groupby(\"id\") if len(g) > 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_to_keep = ['followers_count',\n",
    " 'friends_count',\n",
    " 'favourites_count',\n",
    " 'statuses_count',\n",
    " 'len_screen_name',\n",
    " 'len_desc',\n",
    " 'ratio_fr_2xfl',\n",
    "'sent_count', 'sent_sum', 'sent_mean', 'subj_mean' ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['followers_count', 'friends_count', 'favourites_count',\n",
       "       'statuses_count', 'len_screen_name', 'len_desc', 'ratio_fr_2xfl',\n",
       "       'sent_count', 'sent_sum', 'sent_mean', 'subj_mean'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = sent_matched[col_to_keep]\n",
    "X.columns\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.fillna(0)\n",
    "X = X.applymap(lambda x: x if x != np.inf else -1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, sent_matched.bot.astype(int), test_size= .33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "________________________________________________________________________________\n",
      "Training: \n",
      "RandomForestClassifier(bootstrap=True, class_weight=None, criterion='gini',\n",
      "            max_depth=None, max_features='auto', max_leaf_nodes=None,\n",
      "            min_impurity_decrease=0.0, min_impurity_split=None,\n",
      "            min_samples_leaf=1, min_samples_split=2,\n",
      "            min_weight_fraction_leaf=0.0, n_estimators=100, n_jobs=None,\n",
      "            oob_score=False, random_state=None, verbose=0,\n",
      "            warm_start=False)\n",
      "train time: 0.252s\n",
      "Crossvalidating: \n",
      "Crossvalidated accuracy: 0.996 with std 0.003\n",
      "test time:  0.020s\n",
      "confusion matrix \n",
      " [[332   1]\n",
      " [  0 837]]\n",
      "Test precision score 0.999 \n",
      "Test recall score 1.000 \n",
      "ROC AUC score 0.998\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('RandomForestClassifier',\n",
       " 0.9962078614257163,\n",
       " 1.0079569816589355,\n",
       " 0.019881248474121094,\n",
       " 0.9988066825775657,\n",
       " 1.0,\n",
       " 0.9984984984984985)"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# running the model on the new features from sentiment score \n",
    "\n",
    "baseline_models(rfc, X_train, y_train, X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "followers_count 0.17679913974044736\n",
      "friends_count 0.014709025212972128\n",
      "favourites_count 0.19778398824240384\n",
      "statuses_count 0.23844827454536255\n",
      "len_screen_name 0.0010354655931021101\n",
      "len_desc 0.0005277219078243113\n",
      "ratio_fr_2xfl 0.05896909466072141\n",
      "sent_count 0.2590837714546794\n",
      "sent_sum 0.04976126339026449\n",
      "sent_mean 0.0011118607058871483\n",
      "subj_mean 0.0017703945463351742\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None, None, None, None, None, None, None]"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[print(ftr, val) for ftr, val in zip(X.columns, rfc.feature_importances_)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Conclusions and future steps \n",
    "\n",
    "Bot detection should be a difficult process as bots are becoming more and more sophisticated each day. Our model accuracy has been high throughout the course of this project, and so did the existing papers associated with the dataset we used. We found that bots have unusually high number of posting ('statuses_count'), their friends count is significantly higher than their followers count. \n",
    "\n",
    "\n",
    "Despite the fact that our models achieved high accuracy, there are certain limitations of the project. \n",
    "\n",
    "(1) Getting reliably annotated data is a problem and this affected our models significantly. \n",
    "\n",
    "(2) Restriction on accessing tweets is also another problem that required us to resort to alternatives. Due to this were not able to perform more feature engineering and NLP analysese. \n",
    "\n",
    "For future,\n",
    "\n",
    "- Create more features: \n",
    "    - check content similarity: bots often post same content from different accounts. \n",
    "    - check frequency of tweets: bots often tweets at the same time of the day, or in exact intervals \n",
    "    - check number of websites posted and compare with real users posts. \n",
    "    - Incorporate more NLP analyses (Word2Vec and more fine-grained sentiment analysis)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "baseline: over the models, select the best model (RF) \n",
    "once I selected the RF - benchmark - \"scored the model\" - and feature engineering and compare against the benchmark\n",
    "- before ftr engineering - any time you create a score \n",
    "\n",
    "\"I selected the best model and I do Hype sear in that model space\"\n",
    "hyperparameter searching - \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- cleaning, collections, \n",
    "- \n",
    "find the best and do a grid sreach "
   ]
  }
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