{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Natural Language Processing Guide\n",
    "#### Cornell Data Science\n",
    "##### Author: Christopher Elliott"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Installation Guide\n",
    "Before starting the guide: the lastest release of Anaconda should install all of the modules used in this guide **except** spaCY. You **MUST** install spaCY to use many parts of this guide. If spaCy is not installed, some modules will **NOT** work. to install spaCY run \"pip install -U spacy\" in your terminal or command prompt. Additonally, run \"python -m spacy download en_core_web_sm\" to download the corpus used in this guide. For more information please go to https://spacy.io/usage/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import modules\n",
    "import nltk\n",
    "from nltk import word_tokenize as wtk\n",
    "import urllib\n",
    "from collections import Counter\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "import spacy\n",
    "from PIL import Image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Scrapping and Word Tokenization from online sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Scraping utf8 txt from the Guetenburg Project Website (The Adventures of Sherlock Holmes: Doyle)\n",
    "website = \"http://www.gutenberg.org/cache/epub/1661/pg1661.txt\"\n",
    "webresponse = urllib.request.urlopen(website)\n",
    "raw_txt = webresponse.read().decode('utf8')\n",
    "\n",
    "# Now that the raw text is a string, it is possible to tokenize the words.\n",
    "# Luckily, the NLTK module can tokenize large strings automatically.\n",
    "# Tokenzation is the process in which a string of sentences is broken up into words and puncuation. This is important\n",
    "# as it allows for further statistical analysis.\n",
    "tokenized = wtk(raw_txt)\n",
    "\n",
    "#Manually Find the Beginning of the Text\n",
    "beginning = raw_txt.find(\"*** START OF THIS PROJECT GUTENBERG EBOOK THE ADVENTURES OF SHERLOCK HOLMES ***\")\n",
    "\n",
    "#Manually Find the End of the Text\n",
    "end = raw_txt.find(\"End of the Project Gutenberg EBook \") \n",
    "raw_txt= raw_txt[beginning:end]\n",
    "\n",
    "#Parse the list of words into a NLTK 'text' object\n",
    "text = nltk.Text(tokenized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have created a NLTK text object, we can use methods in the NLTK module to tokenize specific words in the text. Doing this is an important step in text preprocessing as it allows for data scientists to get a better understanding of which words in the text are more important than others."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('the', 5793),\n",
       " ('and', 3061),\n",
       " ('i', 2990),\n",
       " ('of', 2777),\n",
       " ('to', 2761),\n",
       " ('a', 2693),\n",
       " ('in', 1818),\n",
       " ('that', 1757),\n",
       " ('it', 1736),\n",
       " ('you', 1536)]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The first step in tokenizing words is to remove puncuation from the text and convert the text to lowercase.\n",
    "# This is important as it make for only important key words to tokenized once.\n",
    "\n",
    "#filters tokenized words\n",
    "alpha = [wrd.lower() for wrd in text if wrd.isalpha()]\n",
    "text = nltk.Text(alpha)\n",
    "\n",
    "# Once the text is converted into lowercase and puncuation is removed, It is possible to create of collection of words\n",
    "# with thier total frequencies in the text This is known as a bag of words. This can be used to \n",
    "# identifity topics in a text. This is a good way of also identifying the overall significance of a word in a text. \n",
    "\n",
    "#Basic Bag of Words\n",
    "basic_bag_of_words = Counter(text)\n",
    "basic_bag_of_words.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at the most common words in the tokenized text above, you may notice that the words with the highest frequencies are  articles and do not really give any insight about the text. This is because many of the most common words in this bag of words are considered 'stop words'. Stop words are words which appear in a text that are exceptionally common in a language. It is best to remove them so that they do not distrupt the accuracy of any future analysis. The NLTK module has an already defined set of stop words for the english language.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('said', 486),\n",
       " ('upon', 467),\n",
       " ('holmes', 466),\n",
       " ('one', 374),\n",
       " ('would', 333),\n",
       " ('man', 303),\n",
       " ('could', 288),\n",
       " ('little', 269),\n",
       " ('see', 232),\n",
       " ('may', 210)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Import NLTK's Stop Words Corpus\n",
    "from nltk.corpus import stopwords\n",
    "stopwords = stopwords.words('english')\n",
    "\n",
    "#removing stop words\n",
    "bag_of_words_no_stop = Counter([word for word in text if word not in stopwords])\n",
    "\n",
    "bag_of_words_no_stop.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at some of the most common words in the bag of words above, we can now see that the words can give some insight about the text's meaning. In additon to this, the bag of words can now be used for more advanced statical analysis and modeling. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Another aspect of Natural Language Processing is classifying text based off of predetermined labels. This practice draws heavily from elements of supervised machine learning and word tokenization practices that were previously discussed. The data set used in this exercise is called the **fake_real_news_dataset** which was created by **George McIntire (geo.mcintire@gmail.com)** "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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>Unnamed: 0</th>\n",
       "      <th>title</th>\n",
       "      <th>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8476</td>\n",
       "      <td>You Can Smell Hillary’s Fear</td>\n",
       "      <td>Daniel Greenfield, a Shillman Journalism Fello...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10294</td>\n",
       "      <td>Watch The Exact Moment Paul Ryan Committed Pol...</td>\n",
       "      <td>Google Pinterest Digg Linkedin Reddit Stumbleu...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3608</td>\n",
       "      <td>Kerry to go to Paris in gesture of sympathy</td>\n",
       "      <td>U.S. Secretary of State John F. Kerry said Mon...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10142</td>\n",
       "      <td>Bernie supporters on Twitter erupt in anger ag...</td>\n",
       "      <td>— Kaydee King (@KaydeeKing) November 9, 2016 T...</td>\n",
       "      <td>FAKE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>875</td>\n",
       "      <td>The Battle of New York: Why This Primary Matters</td>\n",
       "      <td>It's primary day in New York and front-runners...</td>\n",
       "      <td>REAL</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                                              title  \\\n",
       "0        8476                       You Can Smell Hillary’s Fear   \n",
       "1       10294  Watch The Exact Moment Paul Ryan Committed Pol...   \n",
       "2        3608        Kerry to go to Paris in gesture of sympathy   \n",
       "3       10142  Bernie supporters on Twitter erupt in anger ag...   \n",
       "4         875   The Battle of New York: Why This Primary Matters   \n",
       "\n",
       "                                                text label  \n",
       "0  Daniel Greenfield, a Shillman Journalism Fello...  FAKE  \n",
       "1  Google Pinterest Digg Linkedin Reddit Stumbleu...  FAKE  \n",
       "2  U.S. Secretary of State John F. Kerry said Mon...  REAL  \n",
       "3  — Kaydee King (@KaydeeKing) November 9, 2016 T...  FAKE  \n",
       "4  It's primary day in New York and front-runners...  REAL  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Data Set = George McIntire fake_real_news_dataset\n",
    "df = pd.read_csv('fake_or_real_news.csv')\n",
    "\n",
    "#Using the Pandas Dataframe head() method, we are able to look at how the dataset is organized \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the heading above we can see that each cell in the csv file holds a different value for each text. For example, the fourth cell holds the label data which we will be using to classify the text. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "title         0\n",
       "text          0\n",
       "label         0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Next step in preprocessing data is to check if there are any missing values in the data set\n",
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the output above we can see that there are 0 missing values in the \"Unnamed\" feature along with the other features for each data point. as a result, we know that are no missing datapoints in this dataset  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now that the data has been preprocessed, it is time to split the data set into train and test sets\n",
    "# a typical convention is to use two-thirds of the dataset for training while leaving the rest of the dataset to evaluate\n",
    "# our model's accuracy via a \"test\" set.\n",
    "\n",
    "# Set the qualitative features into dummy variables before splitting the data into training and test sets\n",
    "\n",
    "# Also important note: 'y' is often used as the varaible name for the target classification label. In this dataset, y can be\n",
    "# set into binary notation with 0 indicating a Real news article while 1 indicates a fake news article. \n",
    "\n",
    "#Converting y into a binary label \n",
    "y = pd.get_dummies(df['label'])\n",
    "y = y['FAKE']\n",
    "\n",
    "#Setting x to all of the text in the article\n",
    "x = df['text']\n",
    "\n",
    "#Splitting data into training and test sets\n",
    "X_train, X_test, y_train, y_test = train_test_split(x,y,test_size = 0.10, random_state = 53)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# In order to processs text efficiently, it is nessecary to use a CountVectorizer. A Count Vectorzier takes text\n",
    "# and converts it to Bag of Words vectors while removing stop words as well as punctuation. The Count Vectorizer is\n",
    "# called on both the training and test set to create a bag of words vector for each of the sets. \n",
    "\n",
    "# Initialize a CountVectorizer\n",
    "cv = CountVectorizer()\n",
    "\n",
    "#Call the Count Vectorizer on each of the sets \n",
    "count_train = cv.fit_transform(X_train.values)\n",
    "count_test = cv.transform(X_test.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9053627760252366"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Initialize the Multinomial Naive Bayes Classifier\n",
    "classifier = MultinomialNB()\n",
    "\n",
    "#Fit the Classifier to the training set \n",
    "classifier.fit(count_train,y_train)\n",
    "\n",
    "#Run predictions for test set  \n",
    "predict = classifier.predict(count_test)\n",
    "\n",
    "#Evaluate the accuracy of the model\n",
    "metrics.accuracy_score(y_test,predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Named Entity Recognition and Parts of Speech Tagging"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Named Entity Recognition (NER) is a excerise done in natural language processing used to identify significant entities in text (people, places, events ... etc). It can be used to extract information from large sets of texts quickly."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NER using spaCY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(S\n",
      "  (ORGANIZATION Cornell/NNP University/NNP)\n",
      "  is/VBZ\n",
      "  a/DT\n",
      "  private/JJ\n",
      "  and/CC\n",
      "  statutory/JJ\n",
      "  (ORGANIZATION Ivy/NNP League/NNP)\n",
      "  research/NN\n",
      "  university/NN\n",
      "  located/VBN\n",
      "  in/IN\n",
      "  (GPE Ithaca/NNP)\n",
      "  ,/,\n",
      "  (GPE New/NNP York/NNP)\n",
      "  ./.\n",
      "  Founded/VBN\n",
      "  in/IN\n",
      "  1865/CD\n",
      "  by/IN\n",
      "  (PERSON Ezra/NNP Cornell/NNP)\n",
      "  and/CC\n",
      "  (PERSON Andrew/NNP Dickson/NNP White/NNP)\n",
      "  ,/,\n",
      "  the/DT\n",
      "  university/NN\n",
      "  was/VBD\n",
      "  intended/VBN\n",
      "  to/TO\n",
      "  teach/VB\n",
      "  and/CC\n",
      "  make/VB\n",
      "  contributions/NNS\n",
      "  in/IN\n",
      "  all/DT\n",
      "  fields/NNS\n",
      "  of/IN\n",
      "  knowledge—from/NN\n",
      "  the/DT\n",
      "  classics/NNS\n",
      "  to/TO\n",
      "  the/DT\n",
      "  sciences/NNS\n",
      "  ,/,\n",
      "  and/CC\n",
      "  from/IN\n",
      "  the/DT\n",
      "  theoretical/JJ\n",
      "  to/TO\n",
      "  the/DT\n",
      "  applied/VBN\n",
      "  ./.\n",
      "  These/DT\n",
      "  ideals/NNS\n",
      "  ,/,\n",
      "  unconventional/JJ\n",
      "  for/IN\n",
      "  the/DT\n",
      "  time/NN\n",
      "  ,/,\n",
      "  are/VBP\n",
      "  captured/VBN\n",
      "  in/IN\n",
      "  (GPE Cornell/NNP)\n",
      "  's/POS\n",
      "  motto/NN\n",
      "  ,/,\n",
      "  a/DT\n",
      "  popular/JJ\n",
      "  1865/CD\n",
      "  Ezra/NNP\n",
      "  Cornell/NNP\n",
      "  quotation/NN\n",
      "  :/:\n",
      "  ``/``\n",
      "  I/PRP\n",
      "  would/MD\n",
      "  found/VB\n",
      "  an/DT\n",
      "  institution/NN\n",
      "  where/WRB\n",
      "  any/DT\n",
      "  person/NN\n",
      "  can/MD\n",
      "  find/VB\n",
      "  instruction/NN\n",
      "  in/IN\n",
      "  any/DT\n",
      "  study/NN\n",
      "  ./.\n",
      "  ''/''\n",
      "  The/DT\n",
      "  university/NN\n",
      "  is/VBZ\n",
      "  broadly/RB\n",
      "  organized/VBN\n",
      "  into/IN\n",
      "  seven/CD\n",
      "  undergraduate/JJ\n",
      "  colleges/NNS\n",
      "  and/CC\n",
      "  seven/CD\n",
      "  graduate/NN\n",
      "  divisions/NNS\n",
      "  at/IN\n",
      "  its/PRP$\n",
      "  main/JJ\n",
      "  Ithaca/NNP\n",
      "  campus/NN\n",
      "  ,/,\n",
      "  with/IN\n",
      "  each/DT\n",
      "  college/NN\n",
      "  and/CC\n",
      "  division/NN\n",
      "  defining/VBG\n",
      "  its/PRP$\n",
      "  own/JJ\n",
      "  admission/NN\n",
      "  standards/NNS\n",
      "  and/CC\n",
      "  academic/JJ\n",
      "  programs/NNS\n",
      "  in/IN\n",
      "  near/JJ\n",
      "  autonomy/NN\n",
      "  ./.\n",
      "  The/DT\n",
      "  university/NN\n",
      "  also/RB\n",
      "  administers/VBZ\n",
      "  two/CD\n",
      "  satellite/NN\n",
      "  medical/JJ\n",
      "  campuses/NNS\n",
      "  ,/,\n",
      "  one/CD\n",
      "  in/IN\n",
      "  (GPE New/NNP York/NNP City/NNP)\n",
      "  and/CC\n",
      "  one/CD\n",
      "  in/IN\n",
      "  (GPE Education/NNP City/NNP)\n",
      "  ,/,\n",
      "  (PERSON Qatar/NNP)\n",
      "  ,/,\n",
      "  and/CC\n",
      "  (ORGANIZATION Cornell/NNP Tech/NNP)\n",
      "  ,/,\n",
      "  a/DT\n",
      "  graduate/NN\n",
      "  program/NN\n",
      "  that/WDT\n",
      "  incorporates/VBZ\n",
      "  technology/NN\n",
      "  ,/,\n",
      "  business/NN\n",
      "  ,/,\n",
      "  and/CC\n",
      "  creative/JJ\n",
      "  thinking/NN\n",
      "  ./.\n",
      "  The/DT\n",
      "  program/NN\n",
      "  moved/VBD\n",
      "  from/IN\n",
      "  (GPE Google/NNP)\n",
      "  's/POS\n",
      "  (PERSON Chelsea/NNP Building/NNP)\n",
      "  in/IN\n",
      "  (GPE New/NNP York/NNP City/NNP)\n",
      "  to/TO\n",
      "  its/PRP$\n",
      "  permanent/JJ\n",
      "  campus/NN\n",
      "  on/IN\n",
      "  (ORGANIZATION Roosevelt/NNP Island/NNP)\n",
      "  in/IN\n",
      "  September/NNP\n",
      "  2017/CD\n",
      "  ./.)\n"
     ]
    }
   ],
   "source": [
    "#Open Text File\n",
    "NER_text = open('NER Cornell Wikipedia.txt')\n",
    "NER_text = NER_text.read()\n",
    "\n",
    "\n",
    "# In order to do NER, text must be properly preprocessed via tokenization. \n",
    "\n",
    "#Tokenize the text\n",
    "tokenized_text = nltk.word_tokenize(NER_text)\n",
    "\n",
    "\n",
    "# Once the text is preprocessed, the text needs to be tagged for Parts of Speech (POS). POS tags different aspects\n",
    "# of speech such as pronouns verbs and adjectives that are found in the text. This is done by examining the rules of grammer\n",
    "# in the english language. \n",
    "\n",
    "#Parts Of Speech tagging\n",
    "tagged_text = nltk.pos_tag(tokenized_text)\n",
    "\n",
    "# Finally, the tagged text is passed into the ne_chunck() method to return the sentence as a tree. Using the chunck method\n",
    "# on the tagged text will return specifc words in the text tagged with thier own words. You can read what each tag represents\n",
    "# in the link below:\n",
    "\n",
    "# Link to Penn Parts of speech Guide: https://cs.nyu.edu/grishman/jet/guide/PennPOS.html\n",
    "\n",
    "print(nltk.ne_chunk(tagged_text))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### NER and POS using spaCY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cornell University ORG\n",
      "Ithaca PERSON\n",
      "New York GPE\n",
      "1865 DATE\n",
      "Ezra Cornell PERSON\n",
      "Andrew Dickson White PERSON\n",
      "Cornell ORG\n",
      "1865 DATE\n",
      "Ezra Cornell PERSON\n",
      "\n",
      " GPE\n"
     ]
    }
   ],
   "source": [
    "#Load spaCY english language core\n",
    "nlp = spacy.load('en_core_web_sm')\n",
    "\n",
    "#Parse the text into the nlp object to create a spaCY document object with the text\n",
    "document = nlp(NER_text)\n",
    "\n",
    "# Once the document is created, the NER entities are stored under the attribute ents while the label is stored \n",
    "# under the attribute ent  \n",
    "\n",
    "for i in range (0,10):\n",
    "    print(document.ents[i] , document.ents[i].label_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While NLTK and spaCY both are capable of doing NER, spaCY has a great interactive visualization tool called displaCy that can be used in demos and presentations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "    Serving on port 5000...\n",
      "    Using the 'ent' visualizer\n",
      "\n",
      "\n",
      "    Shutting down server on port 5000.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#Sample code for how displaCY can be used for NER visualization.\n",
    "spacy.displacy.serve(document, style='ent')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "###### Visualization Example:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vEUVt9jLNROep0IBIl3vY8pFpmo1vtf73V6SOxHmxayb99uCKCbopJ9bG5zjyv5tOiGgn3bfFjFMjg8H6J8mK7qBWtk6l3iBeOWYGwoVch+Vd7TwxHXJzXDTAoH8LU7Kt9jzL2d3KhriD1MF7PVEdHM0/uGQ9V+GwuclfOjwS559b59LOr9g/6nR46ifnGDIOaDxIf7Ho8LjlbHkx+VUCd7wgzieuXIu6O6M7G31eu95LQ086Q3tvTKo3ALnAgg+rjE294RwU1xl5Ttd+F68HIvWBDrbHppb0DcN1WpRp6ec66TZtac+1zqBpMI9EunVH29/GpeHKZoS5X6ZtKVTuApSxk6oNTLeNqrPVS72yI+ym2bS3u6v1qe7lt7Bdq5bbsZedg6x6ly47VXtYt/5TTbaliI/kqVtxuR6gCebridi2uvW4ZJ30YV4WGk69JLVCi28kfwv7V2J7aT+5SHksW4bKp294+7Cor0w5L2W/6+Rfn5jk3/A2+eb6JiXLTf19CuHT7ptgvlgwfk36a9EifmjhfCnbBlvH5fUP4/bvAueYOGi8UfQLK6kXD+Mgesrf9NiW1QV9vj4aOezUK3GGxYMzjm/AY8p/3+Z+pv+xSosHdLqcN/Az641Vh9Pps8pVRbQfibfaw3Xf2Hpv+3HTMA1vHoi/HE1QQ33q9z0v0cfvc8uH9awT5Vz4v/3yGOZLoJXXF1O/8XjKg/ldpLVIf8JPyfOVqDN1t9kBil2reMDKYNLoa0dr60sX9K9L18VqGaiHTxYHDtjAgEijfNmuMlbANw3j66PYmOfE+idvTan6n/Jxrecn7EJik3dcyIfOY2/fxre9XP0091tTn710uzgo6mwfDTzkfmVdwF/ifHqNugbT5xM+/1iUXr5smb5XvQKl9aaxAaueAWvg1WAMmzCITsMXcWws2lZ5KI+LoTCdkOTD0WlwG7O44RUdA+6NCP1lRJHru3ABhfyqNWDVQ3/wABcEeo1uuFJuz7/G1Z5nd0bns7+Yuv/tv6fL5DSD63bS0M+T+xeSOM+lgXupXcnAU+Ie3KBU4ssne5v9DELnc4NVnme3VgGr2EjxZT43ZoDDqVNxQ8c1vqIxjxwHtrEUDVr0htgQzSQqqbEfwmin3nRR8A2+r0EzcNuNPUo3xLUhGspo+iLnvJH9EQ3CM9PRAFvSKdRveCScLI8dE4QdwWoess6C6TAw52UxA4LXe95mPqk+p3Ydah+czY6fqTXo6KfO91ekjpi8SAVaLbRz4+aXz3HkfpeDJzKPBjwdq4X90rmw7187uoHPuZcOKIfMdXKMo8IP/JlPyWuvI8G2mvUXBOZZJt7SLH+vXjKfq3QmlfPr1jnO+Y2nfmDqjMoHp+xyDqj5LTV4w8A6sNn55HNiY7vO1kfjWFfSjrUHVcZE5/qw505MnXHsd/F6IKklYFXMNwzXaV+6zdt8ybavbB6FYMumxNwvZyOLlDsfZe2kpy2Z3yfqnthvJsqjpC0yz8a2X/UuvyE4u2bK7UZ7qg+bM8KfkvUuVXZC9rBk/4mzDbmfcREfia9bmeVaPg/91nGis1yyTvpQddLjMziE/asAWX5ygfJYvgwFyEhfOvivKGebGuVfG4wddexhqXLTAJ8igElDonwV8UOD8PlStn1RxxXzD+P2j/lqMC5rF4vrpE45T2NyW2XcKYe8bTGDjD7/IC9xmrj2MEK9bOja5ax6k1mHBfE9WHlVe/uRHuTPJqs+cQOd4npROnhbufSkyp/ETCrH1RdeefuLPHx5qN5/kf5EiHLnK1NnvJS02aVgrxXypyX+7SaNbDtac1+6mH9dti6Gy0AZnywQlDL+byCNRfH1UWzissROiWf8E6cfUddxIZPHju2J8W0vVz/N/dbSZ6+lXeRfAC7gL3E+vcZcI9XnE6j+MheU0mUyVBfXmMYGrFLRbYXJzLFj+gebt/wPIeLdRZo/OhNNaSI/k+u35iBNPhydBrcxy+qkmevX0FHnAgr5VWvAKv1FVLz9y3L75+nGBWb724s0/IOn6cav7aPLtn+ePmatZ5UIzrx0v/oyig2K+fQ2ffFHf09/fuBv6OovD9Mnr7TWs2pQEMcE7yKxU/HZASg7iOcLWPnPmZhysIFBqZCC6KBRsQZwlZZPztHsoUn1qf611jzHHqeJNdqmvt2UfCvFML9XntPjcKc6LDa6U8MFdwo2eNU3fKZpwTeoWwIuX9RvojO6qhszO/06r5KNu8eOCcz5+QEVfZy38dH553kuLmzn2sbjUEfktNnRgGTOwaR631+ROmLyIvyGnnE8kg6zz3FM/24cRL+TmXompu0KBfx0RzHZvmrnPjCnNh9oyqBoW531LO3tpe81gO5YhJ6rr86Z35P3ZRxW15H2OIasAyr2NW8EHlqgFfcNRBvueOOsh/JJoOpecrDU3JNv0MMMROQbFDEvJ0jbp39KwdnnEvUgItTB9tjUkr5huE777Zlq++ztZfMoDF82BeZ+OZtdpNx5KG8nxT3KATC3bRe/RefSnWC7XVXlN9nJq2/5tcjbnkXP1/5a2sVXRvUzrmf/qUbbkt9H4uqWGTwKlWuBboMTAzsl66QPOWAVXWPLOM2dDiWmWn68Pk9EcT+5SHksX4YMxdPH19fytqme/vXKG/M0d1hONTVKw+Jeqms8OPawTLlphE9hWF2mhaOzNP2onGJpkPqt9awS5auAH2qTL1/KPsNy/qGyU56ya/JxL3dGk073epxtMeU/8EJOTkz5T09RX8XU0XSfzldvjP8SqsMCU16tQdWa248seysoXJ/YumGlw/nKIEL71YlyHn+ZoL4Ay0imB648SPTvRfoTQcqcr5Y+Vb1sdh7yXiurnfFvN2nk2tHa+9JF/OvydTEuA3X0yVRgLB04UAEFT/ClJCp9vmenMM8pyz+x75Hve1mExoVSGLvlqzO+7eXqu7nf8n32WtpFz3ipILe/ZMop44uYa6Rti0AHgFMfGOh+FfeCSavQ4DWs+EIazEzvQ1ii2S/puaWlKuuF0zcYzZdp5khNns+ThsBDjjDbc1UwHi6gkF+1Bqz8X1CZgMsVL9m/v03DX/t8HJyK1qS68nK13tT2a6LfuDWpLn32bescfu350d/QJ01wSujDl1xOH4vWvBqmj8vfGhLg8QWjkuIDUPkCVtU8yXetRiuEMcw+A+myenKSBo3TItS7WTiy0dysI+pNF6du8IZfk1Gf+IY0bD8UXOPF/Wbj2X5mkabMujXCgVh/u3+hz0Loe6/OF6sbQ+30q3yr3rt6+8d1wPx5YfKddxay8jArr5KYa2Uqcb0iNts4pdmdLEVj7i9PHcm7ryrbSeeYL+/c7yY/shU/fz0IEHT+2QEXk1fZYut4ijq11THMsyx9r37yPFdfnTO/u/ljHPqEE+hzDD2+yeobU/EaJjIvh3wLA3PH58knAXfvvnsy+PKCRz9DT4dPwdXZEvUgwt+B9pY3T/7HeNqy/HU6idpu52/ZPArjfY6e+zHkLnceuDLFofLBtZNy4MzKu6jOmH1EHlTsdlV3Jj1TNdWn/Erq3Z75yqinfGrS5cbCV4Y9v+d+xrl9JC7tecq1wKTRHhT13Y8howynkdMFV5+hXKdh8shSotwYsspHXf1kQfp6tZSh8uljB91yPUO9j2ub6uFfvzVLo1eZwXRR9zeIe7lFrrFh1ip0rlmm3DTApxBPgRYeHYyDU9E6F9f2qzVjdqg3+e3yZZ5BHj80olC+lH2G5rds2WU72P4F89GUabeN0+koY1tykFU/Jek6mlVv8tRhiXsftdR9fa7Qy3p1rU9+GxShn7VrR1deHtNrqQnJNeYenc091bCCKw8S3+8GvT1VvnyUOZ/5LVt2eauvzQ5T7FoZzziwnaszBrOtnn1pQ9pPylOnJPrZJWxKuAzU5JPZ/o75wpb9yqk8WXklMc/CZ/+452h+y5S37thw9cjGtz38bHzH+e43f5/dnDdb9jXiZ8EF9yV5/aWATQ6WR7YerOoZDmp/8aORtEnASjh9D8moY4UGHpqlRSdD+YLnSUOw4RWY7bkqGA8XUMiv5gaszHpU5975Tdr2EycI9dL90f5cwOqyF6z9fPrJd+hTcvrAS4bphpcWaU9iu77PRgSsTD5FYvJRi/8yyhOw0nmR+j3ntRqtEKZ+5HJqTs/oNUFGaEoYyWS7ztcpn+GPyKhPfEMath8KvU/iKyz9W+EGT7Hy5ixNWgtMRnPI12NaDvNWhv58On6DRX/9oP7Wb/+k3tbw54XJd84RzM7DrLxKYq5lL+LIKl60v6jNNo3oWK70NOr+8tSRvPtyjglf3rnfTX6IjhyXz5biBWw9HcME7ECBzquLh6JFVrlrGMmFO8PUsa2OYZ6luVd3rm2b4KBImjzP1ezj5jF/XwLTAbE6P2ownnEMg77JCi0dmaSR66uOrFxnIWGfuePz5JOAu3fvPWl8ecGjn2Hwi0euzpaoBxHmuHRd85a3kr5h/jqdRG238zerHki4PArjfY4ZbbMiR7nzwJUpjjifLDtpOo2mrYym3ojfbtRtpflbt6O566SmWPltRHvmK6PhcpAuNxa+Mhws2/mfcbaPxKVd/5b1tbNJo/3VRTDdglxlmGF5nqb3DelF3YWEvzvrDCAEy0e9/WRB+no1lKG6py/PM9T7eGxTaf/arFEkv8Y4suh8CWju37lmmXLTCJ9Cr0fVt2uK5t0v+sz1rPJlnkEeP7R4vpR9hvq3gv5hsP0L5qPnmZp0lLEtOciqnxKzj/0VVvi4PHVY4t6HLw9szD7F2o/61ydfOjRMOY95f5nmD43TUPxlV59o8/M+Sd99Zty/2R7MW5sy59O/FakzDWhTvBS+VsYzDmw3aeSev9lWz760Ie0nmTQWrYsSPv8NpXwy4Xe5U8aZZRH8UyeXIyuvJFlliXuO5rf840IhdB5766Vve/jZ+I7z3m/uPrs+bz3bRYtMfylgk4PlUbByeDjaHo9FNihQWm/aJGAlGliZmc4nfQYzL3PyfJ40BBtegdnuLfzZcAGF/GpmwGqRbr5V/jZMf34qua/UvdOqUNsBK/Pbx77xWmJfTjIYJvfdNH0qvf3U07QpcC81yQ4uhc7P7mcHrDg5z8V3rUQgq6rk1231Uwgzb2/WW/YS2cGS+7KfBZv5ap26EWzoMuoTb7x13Q29NWfOy322XbjBc1hdptn71Ge5lV35pprxoT4JVm8GqUE4e9oi/WZ49IaN+n96HTC/LeUciCr+4xQ580JjylBouowkRW22WfMr+RaVn8bcX546km9f4xwnp6nyOSvp301+JKdBCHJCrxfHTrOi0dNzuO1rVA4Twd+y1LGtjmGeZel79WNsX2jAykyL4Na5kA1MrLGkHUN2vb8s30SzenqWxqKpvCo0csQqXNzx+s0wfiqXKsohT37dGbTrgrD9cdHlIthZ1FM3JOpsiXoQEepge8pbVv572rL8dTqJ2m7nb9k8CuN9jp778eEtdx5qsZOynkRTiUTlVr34YU+Vq86t2gr1//QUKg0pv7nt2jLNbJX3JZ6/701KcV9q6iu3jITtYbrcWPjKcFbZ1uR+xl4fiUu7bl+y3mw+rqdDs6dELlknc/P+Ci0cUEGFns2TtKh/loTKR939ZEH6euXLUP3TV0fbVNS/1m09vw6GKVvONcuUm7r7FOb5ObZNY+yjXb7y2UxN4Xwp+wzL+YfB9q9uAauctiUHpvyHvvZQfhI/dTJfb3L2a0x5jKeobFz7Uf/6FPK1BKGAlcXKySkVROnpo8nX9Y9BfPeZcf9me6p8+ShzvuJ1phFtio/i18p4xoHt6XatSi0+YnH/umxdlITLQCmfTGACVOo56PSx68TVRlZeSbLKEvcczfPLPy4UIqte+raXqZ/h+83XZ29Au8jh85cC5SpYHiUmQKXLuHqO3LpWrUV7BKzMb+z5RGc2WgjTPZ8nDYGHHBG8Vj64gEJ+NTNgFbjWL9+mob9eF+2fWMPKpGHDPhpO7J8W90WX0Z5nd0ZfdjU8YBX46inxhRU7JaArJm9zfXlV1VoErOIy7RlssQnVTRlwiTp0Tt0INnQZ9Yk33qaB8c/jaxY/Tn6i63PgNWbwNpejWnxgkEVfc/DQknor3HmDIeoAya+q3lBviac7S35bGnIEQ8cpTB7nvD89723uNzBK2GxzP/nm76/z/RWoI/HCnKF9zflSz1umKV0+ud/N4q+537YyHQ2vwyu/TNBvMibaP/M5uDsdZQlKPPdSz9IMaHsXJ/fdawCz3pNvAVkr/W6dC9pAbQPkAIEKWlfCtjJPermBM/Z4MwAR+NR/VeSv3Mcpz8F7EoTtj4v+Kkbcu29AKG5fnDpbuB5EhDrYnvKWlf+esl2kTtuo7Xb+mg61v+Pgy6MQ3ucYrKseigzY1mAnq513cZ9R2+jYJn1c3K4yL7fUtfwG84q3a2b+en5RcIFpU1NlJGwP0+XGwqTTfT6+3zlyP2POR+LTrgYAQnV4VdyXNUhgyEp38LnkxRP0CZSP0DMo5ScLuOuVLUP1T195+82T378O1tP4/p3zlCk3dfcpGL8lpnquxH2ZdOXwQ4vnS9lnKI4r4R8G27+6BazkdVQ+1vpVgsnPiu/lnjOifMhBPse/Dtcb0ZLpt9lD/RpzDvsezEsQqamjDSXbj/rXp4xgRs6AlcT3QhiP7z7D9x9vZ+slR5nzFa8zjWhTfJS5lloTytN3Mb4eUwaC5a0BfWkDd49l66I4W7AMhPIzXHfEeaVNkfem86Lo+oV5yMorSVZZYp+jsRd5x4WClB2/K1M/M+43V5+9Ae2iF8ZfCpSrYHnUVL8a01/65fA51pr2+sJKPuTE2yYr4lx9VKkohyV5Pk8agsZDYLZ7C382XEAhv0JBJL1t3b009La7rZYvrD5Nl72QnA5w5+M304fXMQEruebVf/5M9Pu5d36Hdr5TPSbS4tN0w9Nqf/OF1R98+cXkdIAL36ZP9a5rXMAqESzKzo/kPdoBK/0MQufLcS07MLYmASthWNV0NnJqihlachfXXp6lqSOqtJs3bzbunU867W9N09AFleJOU0Z94o23aWBE50F+Buu0hCvHxqlPbmc6lsbJ37h3LtmArohzRm8OS9kN1xLNc4u+ntGD//bgtX4zzduBYNEDxjtGokVmU05b9FZxP43dJ/OdexMzYEt1R4D9WiPDBlfz2G38fcgBRGlnKzQk6koqme8v09z+cZqJn2MJm22mqpDTQhxO5/HykSmajc9f7/vLX0cy910Rz3yLvL8+Gj+RfDI+Z4X9PV6YeIimXmfcweU5mthnf2FoBv34dC09OUS9Ot9T7Z9w0KI6tXmMZpkMXXl9mkYfz+PO1LGtjuGeZQ336sV0vNWUWMkcT6bfrcdhZ18PaAuHcEJ2+nyOIeebvDlPc+5UQoLVI+rNtoQt8vg20qZLu8XZUnp/iWZ2ybIs6rXzllypDkyAVWGvog7H5hGacd8attsXt84WrgeSdgxY1ZBHAfzPUecDF+AuUu68lLeTEjW4sJFGdom2UQau9O8K/Xx3j0VvCnJvi9e3/Jawa6a8VAZo4oRTbiN/RDxLc85EGQnbQ67cxPjKMPd77mdcwEfy1i09BRGXF4KlwyNR+1NJfDEvKFkneVZo4Ziw6245pEWajBbPdupBwL+qu58sYMtjyTLUiPSVs01Fyo4H8+WTSG9i38T9O/ahVLmpt09hvpLZSGPHkjlgnytpfwr4oSXypXT7UsI/DLZ/um7x+VgsYBW2LSu0eGCG97ccTPln+x/CT5reqp6XGzzOqjdZ/ZqVExM0INO/eZwW7GJSsu5ntR/1r0/FA1YrJ+dpgXmBavFxGaDP+6a/7z4z7t9sT5UvHyXPV7DONMJm+yhzLfM1Tc8twmbbdsmqG2wZCLSjmfauRF/awPpJZetiRhko5ZNp1Ms8IzR1UOav5+svke6Jq8Rz2TKV+Ao8L1l5JckqS2Z7sr0qOi4Upvj4naRc/Qzfb84+e73bRXHd3P5SoFyZewv203RQbuTgVHTuXF91rzFtErASj/GQnrahsp4GdoyJQjRCA3LeW2FYph/nCp4nDRnGI97uLfzZcAGF/Mr46ukeFUQ6a/3NdOkD99Kmv/5OHPApHrD6Je156X46N7reH9G5Qzvp6gd20kWX/BH1rLuZbnz83mj/ZMBK6jW6eUjsI49b9wn6WHTcPrp08BNRECre/+fiXi5R1/zwJX8h0iv2GfoMfbhnHX3qwLfpMnl8IwJWQnYwqvr1lC07MGXnGROwEuLXu5JapBtvNPsLMdda+4CVZIlmzEJ+crHwaHHNCRq9fX1Ur+JG6Ey1YejdPEyjcp/tfdQrG6RD0yrK79SNoOHPqE+88TZ1d5Km9wlnxtT5/WM0skWlt6fSL66Xdn/iwU2xT2XDUJT+id1yrYKKMN5T/jeCL+ijwegaEzS2Y0CvbSCcJOsa6u2vvFPWVTFvALKNlHmrU4p9SyVgS81nveLZrL9dPE9hE0cPm70Cx0Xo7bmddoFVNmR+RQtHR+WjX+dXctqu4jZb8NZMvAi8XAhYXWNUz3Fu518D7i9vHYlI7qvKp6kr8rq9NPxM2gHwOSu+3+UAQ+QQCZn6KPNj+CpdD9wBPvmMzGLK5hmZfL9gmGae9L89Hz8vWZ62yAWY1bGDm9V9Vh7K0+0v89xLPkvuXmXeyPRm3KuXRPkboJG91fyrXFVNf7IsZDv76ishNRjgdSI530R39no3D6q02DZQ5Od8rjmtV2n+oX79bHupT5frqp2rUP9DTsdVkHVPZrubF374dKg6I+uLbl+YOlu4Hoi/2jFgxeeReebhPPLhf47mDUHTVorr7Nb5WKTcBSlnJyNMfgtVOHulvz7xveFY7/Jbpj1bemZY32fVpqp6J+vcNE2yZTRsD/lyo/GVYe733M9YpyeHjxRKu6zDZpH9uA6bPBS/SfuaKlcl6ySPtgni+Zm2fWL3MPVF9l76t045DPlX9faTBb7yWKoMNSB9vG3Kst9Fyo6P6oCoaZPNOfr2mft37GHZclNnn6Labpn80ueqDNG0Plfa/uT1Q0vkSw1tcFH/MNj+advD52PBgJUgaVu0PZO2Jarb6XvhMOV/7MnpKAAW26jYRojfRB/Nba2y642A8ytNWRC/RWXLDSAKuLpv6nHZ9qP+9al4wErlmbinqyxfzpSjrU4gzYvvPrPuX2/PWS5qOV+hOtMQm+2hxLXkSzsTcf/f6huJct23e4omfGUgOE4hqW9f2uD1k0rVxXAZKOWTGeKvGoV8L3AYeynyYsKajjQvWXklySpLZnuqvbLKUrXNlM+PHxcKUnj8ThJ+NvF257is+83VZxfUtV00ac3jL4XKVVymelV9EnmYLjfVmSFCs1m1Em0TsJKO1uLhccuoiIqxb4YWRQeHL3ieNGQZD7PdW/iz4QIK+RUKWAn9fJ5u/Otr6MPRPn9E597zNN2rt5UJWEnd+8Lf0KbrVLBJBqA+vv1vaNvPxDY93V06YCV1inY+LY67UQag1Hk/fMnnadPXnqad9npYP3uRrtt+DZ2zTu6zjs65bpiue0GuaaXvs0EBK3dKvuQ92EGp0Db7GSQDU4lj3On/nKBVawSsJHpx7VuMI6Cd+0dnacmOlLw7T3KxP2UklRGePCZ34OtU0PBn1CfeeNvXWaHFQ6PxoIYy5JM0F6qcifQrh2z8iGxw9HkTDdcKLRwSduXaap6oQMmUcw09RVFoXS0PZp5i3hkx00BxHVdJ2JauvjFNo2axdJE3Y/G6E+Hj+LzIgXxj5tFqY6zyV+SX6OjMpy5U1GZrVpZoNnEN0bG+RZRB8QyrxbRB9yeukKuORKT35ctOFZ+zEnRiludEeR6MO81RflwryvSheVpOvS0u0M/IrjNRvsv0BwcKxB29PkPjsaMpJAdMUnmfRZ3a6pjAs6zhXr2sLNLMvuogRdSpeXQuymuTfreuBsuzJJ4yQ3SGfFPzcb7Juws0LdLSH9cF3ck/qNKTIMO3WT4+nXy2Iq8Gd0zQDPf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V69O4gJVet2j42j7qjYx0D1U2DNDIo3O0/L7ex8I7aPj+Ms09OkKDm3ujc/T0VGj9VcM0fpQfLFw+Pk3j2/tpfUVds6eynvq3j9P08RKDi+8u0qy89lXqrQJ5vt7N4tqHF2lF75JiZZFm9g1T/4aKuv4FfTS8b4YWz/gDVva9r74xI9Kv80ykfWi/lV8rCzS1Y0Dfm8iHLSM0dYJJiW/QLvH7Ci0eFs/H5GucTrWrTelnY67HiQlerbxu3buQP691R0ifY+nwKA1cII/ppYkTag/J8tFJGrnFLn/94h5FfurtReEcyzwygSpOUfBq/gHql39v+xYtpo5/lZ7cJvf9Y9r7irvtJ3R84rPstsX5b9H4tmtoU6+51rm0acttND79InMNCOKVB1VvLZsrbMngjilacJZhWXljliZ3DFZto6ivfcI2z7yerOEmUMUpFbzSNmjAnFPYzIEdkzTHVPKiASv3d3M8p1TwqkC6QtS9bTCUSF/SnqprTB4T1/AErHz5HbMs1wKz2kpZHm4R5zyeTHfechNROmC1QktHuPZilpYS5Xg19jH6ojZHKJB39X9+ybYv2WbqZ8L6RlmDh4Htrk+jn9PUSWadpYLlKq/tKMPKm6rcxM/JlK8jS6z/liiv+p7NscYX8SarAfW9SvKZR/Vwi/ZJ9XXm31V7loXrHBVRsrNXfUty8IE7U/tmDtwc30GvfHkjDXxcnaPn7LNp4Oar6ZUX7f2qAaOHn7J/13rqShU0u3mQfu1u+/GtNCq3bbkxvY3RqfvVV1YD91v3Evq6au5OekGkf9BaKLp3w4U08mV7IXJLZtAmGnTbRa99eb1axPxs0QnX9+wNWD13Aw1FHfaP5gqcmS/GhvcXX7DaPON4UXyTbk4yX16+kYbl/88ODNCZfMz5LCClTAr1XWu0L8tzCTur+vnz4jpZbU42nE9aRidf+Drtue2zdOE6lcaedefTZbftoskX+JcHTZ9tzwvi75/9gJ7YMxj3pc7bNEh7Dv6ATjHHRTp9lL4/fhv1//E58bX6t91P3/8nZl8IYpRFrr5MVC+TPmXkr9cAZ4sKqWSA6Rd/ezWNXP1R7eeeResv30iPHRBtmOd8WYPPWdt/PTNIj918YXW9HOGD9G/5HL0wY7ex99AvDt5AD26x1tVhfRXj73Cy2saEH2Afr5RKk1w76mqRD9+8k207E+313HZ69q5Px8f2bvg0Pbh/u39tTkbwHTpDjYa7Zl69ds+6qAwM7OX6DbxevUv1E4bctade3q78cGtdNVPu7fJiyianhH3Q660NX2rskPB1Pn6+8OtvpV8w62glfPbnbqTRP1H9gV5utoac6nYaFLAywRnlJI/un6CJ/aM0pDvzla0zqaAB21E/IxzezfJ30djePirOMUFjxjFODUgt0eyuvuj8yske0/tXG+z+h+b9Aw4pjLOtGvoxeQ97R3RgpIf6HlpIn+utGRq2Bjii+96tBj0qN43R6E1ymzsYUb33yUPj1CfSPrQ7ea3KjllaOa3O3Xu9Ssvo7aYj0kfj7gLdmQGrWXFNkVcXiI7FXudaN02JnExS+tmszNOUzIP7htTgwcVDKh+lDsoOjWGVFh4ZUPcjA2fy/q3y0rvdLS/VDtbC8yOxMZJSDtyquj/xd2XDkHoO4h5VYK35nafFFx6gvXtH6e4/++MoTRv+7M7ob6mHX3hV7PMiPXyFTP82+v6/Osf/6xTdpe/tsokXk9uWj9LkFrFt3S56Lv7tBL301cvpI9Ex59Km27apa+20Oly3PULHE+dplHT6dPptRR1Bd/+Fr9P19n57vpfeJ9L3aI+9nyvuOPfcKdl56Dluy9fpZLzNSQObVvv+PecXem6PdZ6ea2hygd/PDnw26yu9LJYODel6q22JqLfD0SCJYy9MUMOxzarhT9qwpeeVjRi5Xt3rwA71t9Tsm3onyVuzNGJskLbR8Tkr/TTh2MVaA1Yrx6dUurdujM6zcau6D6kpO8hSMF0h6t42SAqnT9jTh+Qiz3K7eX5jelCrl8buG4nSUiRgtSTsdvQmlZOGqK22z1Og3ESUCVidWaCJLTrQYLU/6mWOZLkw91Tdz8rvyhDNeMpQ/Z5fte2bf2ZY5IMM3pn21/g6wj8R95880vgzBQNWb03TUHTO3jj/R3WAKRWkLViuctuOwlj+hJU/xheT+SN9CtfPicvrEeXbmOdRLW8VGjzoHiVoQH1P3r/1zGWemXJk+Ug9F4zRHPOyUV64zlERpQaAzJuPZ6+nZ1NfBQUCVs+J46L569Wg1NiuK2ns5vNVXp59Hj383eqA0XuPXRJds98OJGmduv88lS89l9ArP05uk4Ncsmz4BqtSiu9lsz6X/+uq9566kgaiAJIoP6JzPCrS//CuS2jYBK/+8NP07HPV/SNZA1Wv3aPXh4hUDVCxAauXb6WxKK8+Sg/KwTvzu1c76NkB5jw5lRp0mhuiJ+T93bFe+fefWB89r4el9g7Rr+N8Oqt6jCM1CNb4NcBCA2SJchAaSBNyy0xosKNsPudRGGPLq/bIbnPSfdfy9mVVtM/9un/ttuO99wkfIUrHWgasXqQnhy9S9xAFjlTf6+5tl+vg1Tl02X3fSwWf4oDVtPDzN4k+U/9tdLc+7rzons6h67/h9seE/vFbdIfYX26/8E+dY9Z9lva+dCJ9TL3l7evk6ONItXK/yzkf3w+y0pnosyXVzv2uzL7MMem3iTqpx2aqPqXw14/m90dcOFtUSIUDVvfQqzvPU2mPgkGbRfuymUYul4PQH6KxO1RfrH4Bq3tEG3y+zqsPUd+WS6L2bHSLGqS2949t/x9+lIa3qnZv9Do9OJ7we+6kF6J2caMKsPScTyOmndw1WP0K3Ruw4tP08FYrALXlxtTX7HF7/U3hbwk/ofdPHH9KtLuDuQMD8B2iZ+2o83yH2uGumU930lNXyns/LzCdeFqmH9AzbNcb08eo9iMeHhb1T9eXvp1/GQdrf33wuqjcjQ2oYNnGAWljVFl84qDxq3fQU1erY22/fki/VFcZuDH14lrssz9lvgLTym370up2Ghawmtk3wbxdukKzu6UDvJEmTuqfNFxHfelAf/SAh59xT7RCi28kf1t4RAUo+nbN0JL7BdeKGZCq0Mjzed8wWaDJL03Torv7+4s0GQ2kjtBsYpu4RjRo0UdjLzsHvb9EMyaYxgzGqHuvUOWCIZp+S/8oiYNCG6lvcyU1CBUP+MhBL/1bRFbASjgyvVunk/kU31cPjb7sdGdqfDbxdfUbfC4rOuhUcdMkzqUCT+5z0x2sm4ZpePNA+mu7N6fUF0upQJc44+uLbBrywDmWRWQcX87pfemrMph1TiqQc+rZO+kjPefSeTLY5DrfPxOdI3mfO6fiDtfid2+LglUf+dNReuln1r5Sp1+kJ3TnbdNXn0luq7NsJ58TF7BKdiCkfEGejI5TJOfYzI6TlpvHvo4Tk4b0PeUIWDHp8nWKWq7jtDqn3lS/eJwWXJu7vEhLdpU9MUmjh5i3e9+YVE68a8MEyu7wAQ/b3o67bw2aQfbNE2KvKrUGrAzmPPyUgMXTFaLubUOJ9FXt81TqC9yVExM0EA1YCTntjS+/V0+Mq2DV5hGase9JsrJIU4etqxctN4UDVsJX2ap8g4FHFlLXWT4yTbPWY14+PE4TzBdMK0dGozzaKM5hU//np9u+i/uo7yom/96dozF93qSPpY8rFLBapbnd6lzj1pfLEe8v0+KbdsoKlqsitqMgcXndIq7nnsfyxfqcZ2XKa6XSS0NPOoEpU956Rmku4R41or67Nkc/G+G3VTaL55O4jPGpe6j/ABNMywnXOSoibgDo1/9lo3oOqa+cfAGrv6SHfQEY8yXRhivpNfPbnOgIyvOkvnK6k574E3HdP/yQuH56wEO9xVmsU2wCSdH9+b6uigNb59PD300HkE7tF3VdHmffg5QZ6NjyaRresJGe+m/pY1MBq3hh6o/S2GN5glVSOt/P/lzy+jmVGnQyMulnvjZ77283qzJwx63M29ziecv1zuJAYCNkylpA9gBCxqBTJOs+W3PQqWjftaR9eXeWRqR9qwzR1BvJPmO1zy2vt3YBq5e+qvs7w4/Q8dPO9p89Q3v/VH4FdQ7d8V354mB1m/G1P7LuXPrCY05g6tj9qn+ZesFQnC8KVl1Ee55Lnu/f/vHr9AUZINsk+mX273WV/wVBJb4P0lb9rtT5uEBTjoBVO/e7BOG+jKjHlbQ/YnzUnmvTLyXnhbNFhWQCVj45bUjsQzBrJv36u9WXQ+oVsPrF11Rgr3K5aKPdr6FfHKSnvlnd/xf7P0cP/236/L/+5iXROTbe435J4fN7tEzb49xLnAdcmo7fSc/eqnwTdx0fc4+Vsz9EQ+6X7s9crf1Z5oUeVvAdWHWc71A73DXzyeR33jKp9V31AlqyDP0lPTZ0A/00VV+202OibyD7tC8428I2Ywc9e9eVzpeTUrvohWg2ifRMD8ZnH97yURrYyX+FVVTdTtPXsFp9eVQVCmewj+uoq9/Swa0UK8JxlgX24sDAwJtTykBfX76xNphgje0syEES+dvG/Z4UnJmj0Whgzzcw2kMDzNu7xjGRAzvzTp9AdjTG5LbKePK+MwNWo/zbcvrZVB5K3kNNz0YSDFiJjs3F/jTRuzPq0+DEwJ0ZXPMEIPVb+e7gYa1wjmURhQJW//bKKG0Q2zY4gaTn9ogOVf8D9EQU0LqNnrSCUKee3had766nzVt7osP0KZkvg/TET6v7JfSvU3S37DituzP9NVed5HaAUoGcF3YxwR2+M8R3EOx97Q5S8hyJY30dIKFUcM1+U9B7HJdet6OXHbBKXTvnvi3RcTL1mgk25WeJpq6V95Qe1DB2kQtYGXs74BmkXdgv37xL2qdwJ89nl9O/m/NwAasy6Qph8qBebUPx9Gn73DNMM55pgZYODKh05ApYiecdDZoN0JT9tVxhPOWmYMBq9eiY6hTtnksEjApjAjBOG1fv5yd/V23fRpGvfIrje7rPvicuIGXDbTdfyrsDnGkKl6u62A6O7PIqn9VYNNA6lgg+mefBlwUTvKskgneNqe+uzTHPxlNnzAs6zMwFeeE6R0XEd/bkW7nqLdnRx+y3YPmBGzkwE+Ul88WU1Gu7ZKDJ7hiKzuLN8jxO5zMaODiLxr52Q9QvSA4c3UlPXCqOudS3vpZHZoqaT3yOHvRMRWimM/FPmVN9azjxVnA80PFRfnpDoUTA6kfb6YkoXz9KI/8l/9Qpcb4zg0N5VGbQKTiwpKdt3FjDFClhOQNOXPrkAKpv0Mne3xmMMuXcHnTyDY42QmXh+q5l7cvCI6q+Dh/2WHDT516rgJV5oe9TgSCRmY5d9LHsWSeMr/0R62XAqk7Q93fK+zqH9rxU/V2+LCiP6U/NhKH00l7Zh+Ondq9dbn8k3Yd4bg/Xr2izfhcTaEoHpaz0pLYptXW/S8D71sZ/8PkjxmceopmSjgJniwrJBKz+8KPUf+l5aQ0NJgado7YjsD6Nma63LgGrH99KY9ELJznXz/HJTDmcam94vyeWaWMS95KdB9V0J9tYc4+VrVzAR/giW+V5z6IHv+tu4wTfIZW+LvMd8sJdM58y6odPwTKUlpl9wS2LWTbDJ/OFl3uc8tlF/eOmJS+pbqexAavVZVo4OkvTj8ppAgap31rPKk/ASg4GRAMvW8Zp7jQ/OCMxgZbwG6Zm8KVgY/3uIs0fnYmmtpNT4fRbc4LbzsL8Xvlbf2AQLmsNK8+xZkqkvZzLv0TT0TSDOQftsgaJ3uK31/JsIsx1uYCV7gxVvAOM3MCk6WC5bztr4jf/1NdXGanLDedYFpFxfL2dARlIusLuOKnfoqkAdUCrGpzSwSw7iCU6X5fJfGE7WVWp45Kdrbop0aHwT7OQ0gu79DEywJXV4bA7SMlORiJYlivwpGVdP5Fu73F2GiwlpsXIClhZ28W5n7M6RtwXaK3Xcap+XTByaIFW3C8lGFbemKe5w3JqvVEavlbYUzPlDDOoYTpfbqesOnictqUG7sWIcCfPZ5fTv5vzpANW5dIVor5tQ4n0mTYhFNAx6cgTsDJtwfaZQoGK3OXG1/Z5mN8nz5M/oKBYpeWTczR7aFJNNSvSY6aa4wNWdWzbTduXCmTZ6GBX4k1aLiBlw2+vfrk+TQvedUzKlPvitiMXZnA1IwCpnnsy+GTKq+8r/KUn3e2Nqu/u+fSz8b6QpZ936IWtDLjOURF5O3vmy6jEQt1cx9QMoPjfLOU6hua6dkBM/Sbf0txFz8r21Q5O6c5t+u3nbKmAmUyjUGrdBB0Iy3o79KB6G7Sy07q+6XAHgmhxwOq5O+mpKFj1IRr+WpFglZTO96LBOi2T18UGnfxT96j8PL/Ql25FZAYMIjkDml7Zg0vO/XDnM3ki1XKDTjn7ruXsi2mfPH2wiKw2JxvOJ80r80Jfeip1W8YHH6AnrDWmjK/tfnlldPwxd7sJYvn7PCY9jfDdEwEYT5CGVbv1uxL9y6qSeZp1H+3e78rqy/j9WTVOVdTfrcLZokIyAas89lj4DpEtZgMuWp7zef2R0HbdNhcLgtxDv3jqVnrhwaujKccGL7XWs0q1hxkD8qbtse9l5jr9skAgD4RejdbxSQafzD2O/Bf+BZpTD4S3JwXfIVOd5DvUAHfNfDL1o+QXVsy0fHIdq1f/9sZo+snRLedRv7WeVamA1Y930Gt/O0hPfVlOrXkh9VvrWbnHmWeefFmvNnU7DQpYrdLCo4Pxg4zWEbi2X635sEO9je124PmO+grN7dVrZwjJNQW4BbNN4+1OZecSHDxKsUSzX6peW66h0X/tYLTWglnDpOosmGBUyHnPClh5Bj08g4EKc86cg3ZZg3mewFItzybCc94Ic3+Zsu9Rd4RumrYG5JKsvDym51YXkmtkPDpb0zRDEs6xLKJwwMoEkiynPQpSfZYenpd/Cyd8XQ99JO4M6K+pbnuEFs05tPMf7qBV02EHv+olu+NSxLmvHqc6QtW/uQ5gvo5T4vpZHadEcMk6Nk/ASvw+aV232unJCFhZ546uZ18rEfhSasWO0+obU/G6fdJGDu2bpnlubPatWRq9ygQZeqiyoZ/6b5FrHozRUPRFRHpQQ9mddKesaveyZbcz4U5efrtszpMehC6XrhD1bRtKpE9fI/gyiCcdbH7nOZ9N0XJTKGBl8iPUbidZPTlJg6a8C/VuFumJ1kgaUW+T52o7NYWfn0S3fVxbGsMNFGYNHnq2n1mkqe1yLS+5Ta6JME7Tx+tT7nPbjiLkLF+cz+izD4Z0vS933yH48pL1zLOebTZc56iIQp09MzCycXhID7pwAzfVOeKzlLiGHiSoxIMKOkilB3hUuqpBMPUVV/rrqFwyX1mxx+cc0DGDGvYAlPktMQd/Uqrzu44GLtdTE7pT/ORSzqCaR2UHndh7Nm+FN2rBdHPNSAXe2LWPa9tBpyJ9V0kZ+6J/C04vVrtd4nzSvMrbz1F9BtPPUjLHcgEMe3vVD0/2H0Kqv+9u94f8ATNObdfvSvSRRL8rPtZOb7JvlrpmB/S7fL5K0N8UqO1+HycLzhYVUpGAld6XW6MyVh0DVua3vAPM7z11NQ2a4JRQ74bzqD9aX0qvVVWPgFWePBDi0m5+S7XXzvZ87RZ8h0x1hO9QO9w188mUsXJrWCVeAhPnemFIr30ndfbZ1H/phTQyXF2rqljA6h567csXWjGND1HfpeeptetuVl95usepZy5fMrPPU5u6nYYErJafUesv9O2aonn36xs9oOB24IMN7fI8Te8bihcUl2tfzFprN5jGe+yY/sFDfA133YcUq7TwkFo7aeChWVp03ixOOwtm4GIs4JibfdL3GLz3MoNaTQlYaTKeTUSOgNXGrWpBd79mrY5RVgdL8/4yzR8ary4cLN/mfj7nQCkD51gWUbqTk5R5A8+8tRetaxVPZ6Hf4DN/66+pEufSAavr3TnXHZl03P0cv728kp0PX2cvLauDoTsM4U6Cp+OUeFvPCRBldpyS14w7Lt7j3DRwaQoHrKrXM50tz32l9m+djpNihZaOTNLI9dWBbbkeUGz5VxfUej3yi8cji87XFB4bJvB3rswxAzTC2oqqpo5Xo9RFO3m+39MD14Zy6QpR37ahRPpMe+2u6WPjSQeb33nOZyhTbkoFrELttsXpGb0m0YjIH/fLXb5Nqu/zk+Rp+/Q+ia+wsgYPw9tX3pylyR0DscMerQ8VT+FbolzFZNiOouQsX5zP6LMPhnS9r+W+efjykvXMs55tNlznqIjCnT3REY3mjf8oPfhdOaASCljZC5Lzqi6ErM8tO7mf0OtC6XWt4reG9RvK6m/9FdfZBQYhEjJp5I7X93TlDfkCVnZwyvwWGMQznd+xu6w1NgpPW+SZkjCnSg86mSCiNXWjWZ/CP31ijTIDmcF0MfINOtm/C5k8sAedUipy3YLyU7TvKiljX/RvgZcG62GXXH+0iPL2c1SwRfjg/1j9zRxbPGB1Od2xd5T2BvTwC/xXW6Vl91GYfoNfbdjvcn9n97PSyVyzer327Xf5fJWgvylQ2/0+ThacLSqkEgGrwdDLGQ0IWI0dSO+f0gs36jU1Nwp/ZEc1mBJJ+wIp+18+YBXMAyEu7ea3+gSs4Dtkqu19h/rAXTOvXo2+FOyhgb35X8gyx1S/FLyHXtspX+w6iwZ2DtJPnak0fWUxVB/M2nZ9t15Hr77glH9TR53j4oBVHdci63YaELCSi5jLB8Wve2Dm/C8UsDK8v0ILB1QwrGfzJC3qn805800JmL0eg5yuZlwOTHmmSDBzd1cbfXPPIv3eYJiZQiF9j3Uf1GpmwMrgeTYRoYDVyYloLlp33awwWR2sNCsnp9RgY08fTb6ufywI51gWUbqT4+hfp+gueV/RlH4v0sNXJNe0UnOkqzcB1f8vT7wVaKYNzDclYCPmUrcdf/uNt7DsDkHcQQx2dOzrcGI6bfXuOHGdHLvjFh0fCljxnSr7TUW3s9yKHSeX1dOzNBZN9VWhkSPa0J4Yj+wCv6acWXMmPajh71yt0OwOua3Y9K5FO3m+39MD14Zy6QpR37ahRPr0s+OnrdMcG2PTwea3tvdZU7ZFlCk3hQJWJj/yfXUtX8SR98NOGWfW0SzSdtYSsAq92W7yILHmSNbgYc7BxdVlmr1PTRNY2WWmDq5PuWdtR1Fyli9uKkiffTCk632z6nuWv5Pz2QXgOkdFlDn4MXOdeut4gwwscYEfsx7Veno2tbBxWGpOevVWpnrb0l7TSq+DEL2hq/7PL+KdR6GAlVlrIt+UgIkpCbmBKkfVzq/ojN9zfnQObiH6LJnBHjkgUnRqn/KDTtXrqkEF/awbuGB6YjCIGQBMyhpUcAaXWFnnS1zH1ZoMOhXtu0rK2Bd9neDLHnoqwRrsEueT5pVZUyrflIDJ9YGNr50/YPUqPblNnic5tWBTZPc7PH0bTm3Z72J+T/eL+L6VUmf0u4r2ZQxqu9/HyYKzRYVk7G+grYtlpvoKfHn8wYHN7Pmy/BFuu1lDM+trJik5gC33ZafT0y/NpO1/iYCVXqsp8YURIzVon/zy29teO9u9Ppsj+A6d7jvUB+6auWXK+ydEHcnzfF8WdS21fttf0oPRNOT6JTZHZq3Z/AEruRavfC7OWrlaxm64xyFgVX8aELAKdZ7lnP/qa5dSAasIJvDz7oyaqiM0h78ZxMmzdkYowCKurxaNTzb6ZjFb3+Lb1QVo0/dY90Et36Bd1mCe575rejaSUH6aBdBDzy5FVgeLx6xBkR5ozgfnWBZRupPjSnd61gnH/5j8gsoJKmln/frHX1RfWyXWu5LS0wSK/H/ip/bvlv5VOOxyn9BCxKWV7ND4OntJ+YI69u9u8CvUcfIEyurdcUqkoZru5JQagYCV1clMlIdU0Kt6TCt2nFicAIY/wCMILMwd6lwtHVRTy3oX/GYo2snz/R66nzLpClHvtqFw+oR9jhYQrozSXPxFjU21TXfTwee3sN3RQNcwzXjXRFKUKjdZbZyDucbG/dmtT6g8yrWKos5ckbazloBVj2eBfMHSAfWMBw7avoj/hZkIE+hh6mGa9EBk3cq9J/iZHxPIDJSvVZGHch/H5/DZBwNXHptT37P8HVMm8jw7Hq5zVER5Bj9eu0dNZzdw/61s4EetVyDysuibs7qTO/jgnRR9QeVMFRMN5sivqp5RX1uVn1M+FLCqrnHlT/89Ii1yH2dKQW6gylGy8yvPo/KyMnADnTqe3t8v2flWb6T2iesVCdzVMugUDdaZaXz01IqNWzDdGQyy01XToFP6udvXyTvwVw95CfW1PH3XcvbFrN9X8U7FH7eJNdglzifNrZ8+QrfL64f6Osa/t6dWFzK+dv6A1U/o+Dcuj367/WCdv6DKUugLJ6/atN/F/u7ei5VO95od0u/y+SpBf1MQ8mHzwNmiQioSsPrxrarvcfYl9Ar7Yob8Ylq1Zanz6UBWdapgW+arHcdmG9vvGei2pdpjPhgkX5oxgZ2aA1bxizCfpmedL0Vi/VicV+7jpNvbXjvb87db8B3i32N1kO9QJ7hr5pdVxu64NWPKR7Oeq8g/+wvEYJkysz0UCViF6m3VBrnHIWBVfxr4hdVGGjuWdGSXnhyi3kregNUKLRzjFuNepMlrxb6VMWvdCTMNgpyGcIaW3GNWFmhii7xuH42fyHyvW2DeHhPpSXwxtSLS2UcVfQ+JRt90FCoDNHHCGbxYEQ7/5oo4zr1HRd0HtXyDdlmDeZ7OTm3PRqI7PKnfFfHC7vfN0nLqnCu0eGiUJq3F0ePzeTpYKyfn2QXiFx+XQcUKjR3NUwbScI5lIWmn+CN3+7+AWjwoHdE/pjuGB1TgKrFdO+Y776S71yW/vjI6eWCAPiKv8aeik2a9MRjp9Iv0xPBF4vzn0BcONMLxtjsOOZ17uwMSUqITwQSLEudhOk+ZHSdP2r3H8QGrxO9bdtGeREfK7JN8o8+v5H20XMfpzXmac6d8FaweUV+8xgPm5isdUV8T4fyEXUwPaphOWXLgXfPuLI3I4ypDNPU6M1i8PEcT++wvTIp38rx2Wdvkyn3MVxwl0hWi7m1D4fStijSoN4i4ttVu0910+PLbTBkcTSvnJmFlkaae0WGEMuUmq41zMdMOyuliD6fL2fKRKZrVeW++sNq4dz753N+apqELRHp0WsNtp0VNASs+/1aOjVOf3M4EGM3b9Rv3ziVf2onyU50zeb0lmj/qTn0oOKO/Jrve+sqraLnKazsEKt/zt9vB8vX+Es3sUlNmDT2TrIW+8mow2xO+a1Pqe9jfqZaJtA3NC9c5KqJcgx8//kt6cINM5zraGA3COB3A+G3J9fTEf2OCPi/eSg/fxSysbAZ1bt4YHZ9Kw0H5pvZ5NHaHfCuaf0syn8IBq3iaoLPPp4e/m07/qf0bo7pZcReHZgeqkkp3fnfRK3dYQStn/6Ceu5GG9dobvVdfSa+4U5xI/XgX/fSbV9KD+6t56R10Mh36jLeeVUBvIz2xV9qhYusUFFZicIl/XmbQ0TvoFBxEU2q9QacSfdeS9mVVtF/RSw6bR2jGnVnEbhNrsEucT5pfJ+il+2R/p4c2DT9Cx08723/2DO39UznbxEW056XkOldlAlb/9tNv0R2iX9azboAe/iETtPqnKdq75xGm71Gjsvo+nNq135Xn9z2i38XtI9QR/S5B0b6MQW33+zhZcLaokIoErOIXPETf49YbUy9mnHpgPfWe7QlYma+chG1/wllDRh6n7JJrs81UYvz1PnhxkJ74ptrffGFVXZdT67kbaOgPz1LnT7Ufxn84n2/7PH6AmY4smgbY9V2O30nP3irTfBYNfS3Z/vjb6+T2Qu0WfIdInek7CHSfmx1zyQl3zUL6kSgTUT/BX8Z+/dQNNLpJ1f20/6u/sJLPJlH3d4nn9lGqaJuRKov6uadnYDBfWK2jsQPJtNg2yH2GCFjVn4asYSUd2WjQpKeX+qIFyUdpeHNv1KmfflK9QZsdsNIDNnLB2OgcEzSxe5j6okW6KzR0yK1QSzRjFgcXxwzsUGsijW7v0+su9NLwM/kr4dIhPb2dOdfeERqQayFtHqfpx3lnYemZYX2tCq3fIheGn6CxHQO0XnQS+h+apsloACrtTNR9UKvhAauiz2aV5u5THaXKhiEa3T9GI7vtQZwlmt6qBz2tZze2YzA+53iBgJVy5sQzuGpYXEuuIaHLn7z+1tCc62E4x7KQ/nUqCjTJgNGFN22jvV+6je466DjAluP9EedNL6njE5+Ntvmn9DtBz9332Sho1dNzLm26TVxn7yjdve1yulBf+7L7vhecMrAW2c69G6ThlNw/JE9QyPo9cS63c5TVcfK9peg9zhewEkqci9vHPjYsu4PUch0nbZt6N4vOwV5Zz0S93qI7A8JOzscD5tX6XdkwoBb/juyi6BjsM3aRGdSIv6LpVTZh9xBNWHag2s7INFh1/SqdhsSUaMU7eV67bL4KlTbmdmH/RNswerh6paLpClH3tkFQOH1nhL29StvnC/poeLdlUy8Yphndprvp8AcAVmn+IbMovPARbqm2lVH7GZ+nRLkpGrCSvDVDw1E7I6/TH9+fWvvQynuZDzqwY/JN+ReyzZtWXx01K2B1yyRN7+uz2kur7lX6RZ4zwR0TXBH7qHZY3KeoU9I/GTg4xUzfpK8lnvlg3Car/I9e/nGuUahc6XvPth0iFXvlOUfZl114nPKlfZRq2qU/5gQdBf7yqjDbXd+18fU97O/E2zkbmhOuc1REeQc/3vvu5+K84gYD3hOdxjgvN3yaRqN1qy6h4U1nq7x0gz1a0VdU0XFMB9G8qS1V00LdGQErIZn+/qizbKV/eCMNfFx3rjeJY923xUsFrKSqb5gWDlq9PEQPXm7yTOgPP0r9l56ntOFD8e/28/QPOpn1LUQ6Pr5e3PNmGtnKPCczLaTUn1xXeFqhYjJvxabvw6hTB52K913L2hfezqo2Ufa3dZtYg13ifNJiepGeuO3c6J571p1P/dvujPpDd912EZ0Xpe1cuv1AespA42sXClgJnRK+/6bovD103qZBuitat2ob3X7Z+apP9mcNCFg5QR/3CyFObdvvCpyPvSdvny2slu53CYr2ZQxqu9/HyYKzRYVkggF2e+Pqy9bXM3LwWg9My2OGt2p/QLZRf/hpevYBfkpAKfNFd0/Ph2jg5s3iONEu/Yk4bsMl9MQun79yJz27Rbd/Z59N/VsuidbNHL1O+R/x/vagum7nR7d8VNi9s2jowRuU3WPaD/MVec8fnk8jw/K81ldRXj/gHnp153nazn6I+nSaxm4+n9ZHvsZZ1L8z/cWTv71Obi/cbsF36FzfIZqFKt8U+T64axbW3F/Sw1YZq3y8ah/6dMA00nV8Pp96UPe9RB2O6r7xwTd8jp76f3nKougnjOn6tP46UcfEMaM66Frtl5j6p23Q2evpKW2D3GeIgFX9aUjASrJybNIaQJGO8yTNy69e9EBFdsBqlZZfFue4pV8PMghFAycTNMO9yRqhF/C+xQSpREGPBqCmaC7viEHMKi0eHo8DHdFA3b4ZWjwTHthYeX2GxiOHXV5fBa4mj8qLmwGotDNR90GthgesSjybM4s0vdss2i46N/eZ9S8MKyq/zSCPkHx2g6LczL7pnjPcwVo9PUeTOwZF46kHWfWg6MThReeaxeAcy6I6dezrdFe/7kD1XkR3T7tv4r1ID/fLNJ9Ddz+bfOsvklmnKvX1VVInX/g67bntszpIpa51/bZReoJ786+ucjpPzFt3sgOgOoJ2B4J7MzB5rmrn0Rcs8rytJ1Wgo5PopJYJWAml3+TzdPC4zqXdibOu2XIdp3cXaHrfMPUbG2mCxAfn0l9Kivo/I/ZVAWg1sDsu6qOwJN7AimTl2IQOHEh7MEBT7vpzy6qum/NGdf1ace5D86k0FO3khezy6hvTNHp9tW0Yc9fcKZCuEHVvGwxF0/f+Ms09qge+9D1H7aG8bU86MgMAx6dFW1ltQ6K2et80zdtGvWi5KROwkqws0ay4v8G4LKs2Y/LIUrLNeHde5Fs18BG178fkHnybVP/nZ19HfX1sPxPZXgZ9nUT6dX6Ke4zPm7jeCi0cEm3ytQX8qbzlKrft0FP85VnzzMEtX1k+SmZ51dtd3zWiofU97O/wz64YXOeoiPIPftxDrw6rt6a9gZ8Xb6XHbr7Q6piKTuKln6YHHxyiX3imv4vXV2AHM8z6WLUODGQHrCK9OERP3fVp6tdBqij9V2+kh/dv54NlpQNWQserQaveLTcWHMi5h35x8AZ6WOR1Na2ijn/8PBq8+Up6yllM3j/oJPSj7fTU1vO1nRD3e8cgc68m/0pM+1hGZnBUix9QkOqsQafifdfa7MvyUauvL+3e9nFtY2u3S5xPWlyv0vHp++mOLSZI1UMf+ePP0u07H6Dve9abMr520YBVpH+aovFt19CmXnWt6MXBKwZpz+Pfo5PuV171kt1PkXL6OKpvZPogbdzvCpzPTYe7T8f0uwRF+zIGtd3v42TB2aJCcmwyK7cdPL6DXvly9aWPKHB11430U/m1kTkf23beQz/d/zk1sCz3iQavr4u+Ugr7K7vo1DevppGrZQBKpal3w4U08uVBOmV/4fTykPBTqkGj9ZdvpMcOyHZNBzzY9mMXvbprvXXMdfRTsy3DD5Bt9YNbztPHCol8kO30s9zX6HL/UHttbS/XbsF3UNs6yXfQa/KG1kfOAXfNsjJlzA5S9W44j4avU+u4RsHaIeZrSLfuG5vxo3BZfO+ZG2hUBrX1MWPfrJa1Xx8QNuFy/eJcZEuuplflNJ26rPBlBAGretKwgBUAnQbnWEKc7I4Nr6hz4ukg2OI7GYFgUaLjZnXG3A6dR6kOqreDFA5YpfPA7OPrDNqyj63eg9vBc9WIzhQAoJXIGlzsMJZnaEjc78jzfJAJ1AeucwRB9ZUedGrggumu7EEhv+oz6MSpUQNR3QLnk0IeJb5W4qT7IO3c7/L2x5jtiX3Q76oHnC2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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Image\n",
    "Image('displaCY demo.png')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}