Minado de datos en Twitter.ipynb

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    "# Table of Contents\n",
    " <p><div class=\"lev1 toc-item\"><a href=\"#Minando-datos-de-Twitter-con-Pandas-+-Tweepy\" data-toc-modified-id=\"Minando-datos-de-Twitter-con-Pandas-+-Tweepy-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span><strong>Minando datos de Twitter con Pandas + Tweepy</strong></a></div><div class=\"lev2 toc-item\"><a href=\"#Importamos-nuestras-librerías\" data-toc-modified-id=\"Importamos-nuestras-librerías-11\"><span class=\"toc-item-num\">1.1&nbsp;&nbsp;</span>Importamos nuestras librerías</a></div><div class=\"lev2 toc-item\"><a href=\"#Creamos-una-App-de-Twitter\" data-toc-modified-id=\"Creamos-una-App-de-Twitter-12\"><span class=\"toc-item-num\">1.2&nbsp;&nbsp;</span>Creamos una App de Twitter</a></div><div class=\"lev2 toc-item\"><a href=\"#Extraemos-tweets\" data-toc-modified-id=\"Extraemos-tweets-13\"><span class=\"toc-item-num\">1.3&nbsp;&nbsp;</span>Extraemos tweets</a></div><div class=\"lev2 toc-item\"><a href=\"#Creamos-un-dataframe\" data-toc-modified-id=\"Creamos-un-dataframe-14\"><span class=\"toc-item-num\">1.4&nbsp;&nbsp;</span>Creamos un dataframe</a></div><div class=\"lev2 toc-item\"><a href=\"#Añadimos-información-relevante\" data-toc-modified-id=\"Añadimos-información-relevante-15\"><span class=\"toc-item-num\">1.5&nbsp;&nbsp;</span>Añadimos información relevante</a></div><div class=\"lev1 toc-item\"><a href=\"#Visualización-y-estadística-básica\" data-toc-modified-id=\"Visualización-y-estadística-básica-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span><strong>Visualización y estadística básica</strong></a></div><div class=\"lev2 toc-item\"><a href=\"#Promedios-y-popularidad\" data-toc-modified-id=\"Promedios-y-popularidad-21\"><span class=\"toc-item-num\">2.1&nbsp;&nbsp;</span>Promedios y popularidad</a></div><div class=\"lev2 toc-item\"><a href=\"#Series-de-tiempo\" data-toc-modified-id=\"Series-de-tiempo-22\"><span class=\"toc-item-num\">2.2&nbsp;&nbsp;</span>Series de tiempo</a></div><div class=\"lev2 toc-item\"><a href=\"#Pie-charts-de-dispositivos\" data-toc-modified-id=\"Pie-charts-de-dispositivos-23\"><span class=\"toc-item-num\">2.3&nbsp;&nbsp;</span>Pie charts de dispositivos</a></div><div class=\"lev1 toc-item\"><a href=\"#Análisis-de-sentimientos\" data-toc-modified-id=\"Análisis-de-sentimientos-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span><strong>Análisis de sentimientos</strong></a></div><div class=\"lev2 toc-item\"><a href=\"#Importando-textblob\" data-toc-modified-id=\"Importando-textblob-31\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>Importando textblob</a></div><div class=\"lev2 toc-item\"><a href=\"#Analizando-sentimientos\" data-toc-modified-id=\"Analizando-sentimientos-32\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>Analizando sentimientos</a></div><div class=\"lev2 toc-item\"><a href=\"#Analizando-resultados\" data-toc-modified-id=\"Analizando-resultados-33\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>Analizando resultados</a></div><div class=\"lev1 toc-item\"><a href=\"#Referencias\" data-toc-modified-id=\"Referencias-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>Referencias</a></div>"
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   "source": [
    "# **Minando datos de Twitter con Pandas + Tweepy**"
   ]
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   "source": [
    "## Importamos nuestras librerías"
   ]
  },
  {
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   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   },
   "source": [
    "**Pandas** es una biblioteca de código abierto, que proporciona estructuras de datos de alto rendimiento y fácil de usar y herramientas de análisis de datos para el lenguaje de programación Python.\n",
    "\n",
    "**Tweepy** es una biblioteca de Python fácil de usar para acceder a la API de Twitter."
   ]
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   "source": [
    "**NumPy** es el paquete fundamental para la computación científica con Python. Contiene entre otras cosas:\n",
    "* Un potente objeto de matriz N-dimensional\n",
    "* Sofisticadas funciones de radiodifusión\n",
    "* Herramientas para integrar código C / C ++ y Fortran\n",
    "* Álgebra lineal útil, transformada de Fourier y capacidades de números aleatorios\n",
    "\n",
    "Además de sus usos científicos obvios, NumPy también puede ser utilizado como un eficiente contenedor multidimensional de datos genéricos. Se pueden definir tipos de datos arbitrarios. Esto permite a NumPy integrarse de forma transparente y rápida con una amplia variedad de bases de datos."
   ]
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   "source": [
    "**Matplotlib** es una biblioteca de trazado de Python 2D que produce figuras de calidad de publicación en una variedad de formatos impresos y entornos interactivos entre plataformas. Matplotlib se puede utilizar en scripts Python, en el shell Python e IPython, en el Jupyter Notebook, en servidores de aplicaciones web y para kits de herramientas de interfaz gráfica de usuario.\n",
    "\n",
    "**Seaborn** es una biblioteca de visualización de Python basada en matplotlib. Proporciona una interfaz de alto nivel para dibujar gráficos estadísticos atractivos."
   ]
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   "source": [
    "# General:\n",
    "import tweepy           # Para consumir la API de Twitter\n",
    "import pandas as pd     # Para manejo de datos\n",
    "import numpy as np      # Para operaciones numéricas\n",
    "\n",
    "# Para ploteo y visualización:\n",
    "import seaborn as sns\n",
    "from IPython.display import display\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "## Creamos una App de Twitter"
   ]
  },
  {
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   "source": [
    "Para poder extraer twits para un posterior análisis, debemos acceder a nuestra cuenta de Twitter y crear una app. El sitio para poder hacer eso es [https://apps.twitter.com/](https://apps.twitter.com/).\n",
    "\n",
    "De esta app que creamos necesitaremos actualizar nuestro script `credentials.py` con la siguiente información:\n",
    "* Consumer Key (API Key)\n",
    "* Consumer Secret (API Secret)\n",
    "* Access Token\n",
    "* Access Token Secret\n",
    "\n",
    "Habiendo hehco esto, podemos proceder a consumir el API de Twitter. Para ello, creamos una función que nos permita autenticar nuestras llaves de acceso y consumo."
   ]
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   "source": [
    "# Importamos nuestras llaves de acceso:\n",
    "from credentials import *\n",
    "\n",
    "# Configuración del API:\n",
    "def twitter_config():\n",
    "    \"\"\"\n",
    "    Función de utilidad para configurar el consumo del \n",
    "    API de Twitter con las llaves proporcionadas.\n",
    "    \"\"\"\n",
    "    # Autenticar y acceder usando llaves:\n",
    "    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n",
    "    auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n",
    "\n",
    "    # Regresar acceso al API:\n",
    "    api = tweepy.API(auth)\n",
    "    return api"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Extraemos tweets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora podemos crear un extractor de datos de Twitter para un usuario en específico.\n",
    "\n",
    "En mi caso, utilizaré a Donald Trump ([@realDonaldTrump](https://twitter.com/realDonaldTrump)) como usuario a minar.\n",
    "\n",
    "\n",
    "<!-- <iframe src='https://twitter.com/realDonaldTrump' width=700 height=350></iframe> -->"
   ]
  },
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   "execution_count": 3,
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     "text": [
      "Tweets obtenidos: 200.\n",
      "\n",
      "Los primeros 5 tweets:\n",
      "\n",
      "On behalf of @FLOTUS Melania &amp; myself, THANK YOU for today's update &amp; GREAT WORK! #SouthernBaptist @SendRelief,… https://t.co/4yZCeXCt6n\n",
      "\n",
      "I will be going to Texas and Louisiana tomorrow with First Lady. Great progress being made! Spending weekend working at White House.\n",
      "\n",
      "Stock Market up 5 months in a row!\n",
      "\n",
      "'President Donald J. Trump Proclaims September 3, 2017, as a National Day of Prayer' #HurricaneHarvey #PrayForTexas… https://t.co/tOMfFWwEsN\n",
      "\n",
      "Texas is healing fast thanks to all of the great men &amp; women who have been working so hard. But still so much to do. Will be back tomorrow!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Creamos un objeto extractor:\n",
    "extractor = twitter_config()\n",
    "\n",
    "# Creamos una lista de tweets:\n",
    "tweets = extractor.user_timeline(screen_name=\"realDonaldTrump\", count=200)\n",
    "print(\"Tweets obtenidos: {}.\\n\".format(len(tweets)))\n",
    "\n",
    "# Imprimimos los primeros 5 tweets:\n",
    "print(\"Los primeros 5 tweets:\\n\")\n",
    "for tweet in tweets[:5]:\n",
    "    print(tweet.text)\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creamos un dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ahora tenemos información inicial para construir un `DataFrame` de pandas, para poder manipular la información de manera más sencilla.\n",
    "\n",
    "La función `display` de Jupyter Notebook nos permite tener un ploteo más amigable, y el método `head` de un dataframe nos permite visualizar sólo los primeros 5 elementos (o el número que elementos que se le pasen como argumento)."
   ]
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>On behalf of @FLOTUS Melania &amp;amp; myself, THA...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I will be going to Texas and Louisiana tomorro...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stock Market up 5 months in a row!</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'President Donald J. Trump Proclaims September...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Texas is healing fast thanks to all of the gre...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>...get things done at a record clip. Many big ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>General John Kelly is doing a great job as Chi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Wow, looks like James Comey exonerated Hillary...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>THANK YOU to all of the incredible HEROES in T...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @FoxNews: .@KellyannePolls on Harvey recove...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "                                              Tweets\n",
       "0  On behalf of @FLOTUS Melania &amp; myself, THA...\n",
       "1  I will be going to Texas and Louisiana tomorro...\n",
       "2                 Stock Market up 5 months in a row!\n",
       "3  'President Donald J. Trump Proclaims September...\n",
       "4  Texas is healing fast thanks to all of the gre...\n",
       "5  ...get things done at a record clip. Many big ...\n",
       "6  General John Kelly is doing a great job as Chi...\n",
       "7  Wow, looks like James Comey exonerated Hillary...\n",
       "8  THANK YOU to all of the incredible HEROES in T...\n",
       "9  RT @FoxNews: .@KellyannePolls on Harvey recove..."
      ]
     },
     "metadata": {},
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    }
   ],
   "source": [
    "# Podemos crear un dataframe como sigue:\n",
    "datos = pd.DataFrame(data=[tweet.text for tweet in tweets], columns=['Tweets'])\n",
    "\n",
    "# Hacemos un display del dataframe:\n",
    "display(datos.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Algo interesante es la cantidad de métodos internos que tiene la estructura de un tweet en Tweepy:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
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     "text": [
      "['__class__', '__delattr__', '__dict__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getstate__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__le__', '__lt__', '__module__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', '__weakref__', '_api', '_json', 'author', 'contributors', 'coordinates', 'created_at', 'destroy', 'entities', 'favorite', 'favorite_count', 'favorited', 'geo', 'id', 'id_str', 'in_reply_to_screen_name', 'in_reply_to_status_id', 'in_reply_to_status_id_str', 'in_reply_to_user_id', 'in_reply_to_user_id_str', 'is_quote_status', 'lang', 'parse', 'parse_list', 'place', 'possibly_sensitive', 'retweet', 'retweet_count', 'retweeted', 'retweets', 'source', 'source_url', 'text', 'truncated', 'user']\n"
     ]
    }
   ],
   "source": [
    "# Imprimimos métodos internos:\n",
    "print(dir(tweets[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De aquí resulta interesante que, si queremos obtener datos como la fecha de creación de un tweet o el dispositivo con el que fue creado, podemos acceder a esta información con métodos que aparecen en esta lista. Un ejemplo es el siguiente:"
   ]
  },
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     "text": [
      "903778130850131970\n",
      "2017-09-02 00:34:32\n",
      "Twitter for iPhone\n",
      "24572\n",
      "5585\n",
      "None\n",
      "None\n",
      "{'hashtags': [{'text': 'SouthernBaptist', 'indices': [90, 106]}], 'symbols': [], 'user_mentions': [{'screen_name': 'FLOTUS', 'name': 'Melania Trump', 'id': 818876014390603776, 'id_str': '818876014390603776', 'indices': [13, 20]}, {'screen_name': 'sendrelief', 'name': 'Send Relief', 'id': 3228928584, 'id_str': '3228928584', 'indices': [107, 118]}], 'urls': [{'url': 'https://t.co/4yZCeXCt6n', 'expanded_url': 'https://twitter.com/i/web/status/903778130850131970', 'display_url': 'twitter.com/i/web/status/9…', 'indices': [121, 144]}]}\n"
     ]
    }
   ],
   "source": [
    "# Imprimimos info del primer tweet:\n",
    "print(tweets[0].id)\n",
    "print(tweets[0].created_at)\n",
    "print(tweets[0].source)\n",
    "print(tweets[0].favorite_count)\n",
    "print(tweets[0].retweet_count)\n",
    "print(tweets[0].geo)\n",
    "print(tweets[0].coordinates)\n",
    "print(tweets[0].entities)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Añadimos información relevante"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Esta información de cada tweet puede ser agregada a nuestro dataframe:"
   ]
  },
  {
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   "outputs": [],
   "source": [
    "# Añadimos datos relevantes:\n",
    "datos['len'] = np.array([len(tweet.text) for tweet in tweets])\n",
    "datos['ID']  = np.array([tweet.id for tweet in tweets])\n",
    "datos['Creado'] = np.array([tweet.created_at for tweet in tweets])\n",
    "datos['Fuente'] = np.array([tweet.source for tweet in tweets])\n",
    "datos['Likes']  = np.array([tweet.favorite_count for tweet in tweets])\n",
    "datos['RTs']    = np.array([tweet.retweet_count for tweet in tweets])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Reimprimendo parte de nuestro dataframe, vemos que la actualización se realiza:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:22:43.176975Z",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "      <th>len</th>\n",
       "      <th>ID</th>\n",
       "      <th>Creado</th>\n",
       "      <th>Fuente</th>\n",
       "      <th>Likes</th>\n",
       "      <th>RTs</th>\n",
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       "      <th>0</th>\n",
       "      <td>On behalf of @FLOTUS Melania &amp;amp; myself, THA...</td>\n",
       "      <td>144</td>\n",
       "      <td>903778130850131970</td>\n",
       "      <td>2017-09-02 00:34:32</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>24572</td>\n",
       "      <td>5585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I will be going to Texas and Louisiana tomorro...</td>\n",
       "      <td>132</td>\n",
       "      <td>903770196388831233</td>\n",
       "      <td>2017-09-02 00:03:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stock Market up 5 months in a row!</td>\n",
       "      <td>34</td>\n",
       "      <td>903766326631698432</td>\n",
       "      <td>2017-09-01 23:47:38</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>44518</td>\n",
       "      <td>9134</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'President Donald J. Trump Proclaims September...</td>\n",
       "      <td>140</td>\n",
       "      <td>903705867891204096</td>\n",
       "      <td>2017-09-01 19:47:23</td>\n",
       "      <td>Media Studio</td>\n",
       "      <td>47009</td>\n",
       "      <td>15127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Texas is healing fast thanks to all of the gre...</td>\n",
       "      <td>143</td>\n",
       "      <td>903603043714957312</td>\n",
       "      <td>2017-09-01 12:58:48</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>77680</td>\n",
       "      <td>15398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>...get things done at a record clip. Many big ...</td>\n",
       "      <td>113</td>\n",
       "      <td>903600265420578819</td>\n",
       "      <td>2017-09-01 12:47:46</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>54664</td>\n",
       "      <td>11424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>General John Kelly is doing a great job as Chi...</td>\n",
       "      <td>140</td>\n",
       "      <td>903597166249246720</td>\n",
       "      <td>2017-09-01 12:35:27</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>59840</td>\n",
       "      <td>11678</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Wow, looks like James Comey exonerated Hillary...</td>\n",
       "      <td>130</td>\n",
       "      <td>903587428488839170</td>\n",
       "      <td>2017-09-01 11:56:45</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>110667</td>\n",
       "      <td>35936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>THANK YOU to all of the incredible HEROES in T...</td>\n",
       "      <td>110</td>\n",
       "      <td>903348312421670912</td>\n",
       "      <td>2017-08-31 20:06:35</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>112012</td>\n",
       "      <td>29064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @FoxNews: .@KellyannePolls on Harvey recove...</td>\n",
       "      <td>140</td>\n",
       "      <td>903234878124249090</td>\n",
       "      <td>2017-08-31 12:35:50</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>6638</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Tweets  len                  ID  \\\n",
       "0  On behalf of @FLOTUS Melania &amp; myself, THA...  144  903778130850131970   \n",
       "1  I will be going to Texas and Louisiana tomorro...  132  903770196388831233   \n",
       "2                 Stock Market up 5 months in a row!   34  903766326631698432   \n",
       "3  'President Donald J. Trump Proclaims September...  140  903705867891204096   \n",
       "4  Texas is healing fast thanks to all of the gre...  143  903603043714957312   \n",
       "5  ...get things done at a record clip. Many big ...  113  903600265420578819   \n",
       "6  General John Kelly is doing a great job as Chi...  140  903597166249246720   \n",
       "7  Wow, looks like James Comey exonerated Hillary...  130  903587428488839170   \n",
       "8  THANK YOU to all of the incredible HEROES in T...  110  903348312421670912   \n",
       "9  RT @FoxNews: .@KellyannePolls on Harvey recove...  140  903234878124249090   \n",
       "\n",
       "               Creado              Fuente   Likes    RTs  \n",
       "0 2017-09-02 00:34:32  Twitter for iPhone   24572   5585  \n",
       "1 2017-09-02 00:03:00  Twitter for iPhone   44748   8825  \n",
       "2 2017-09-01 23:47:38  Twitter for iPhone   44518   9134  \n",
       "3 2017-09-01 19:47:23        Media Studio   47009  15127  \n",
       "4 2017-09-01 12:58:48  Twitter for iPhone   77680  15398  \n",
       "5 2017-09-01 12:47:46  Twitter for iPhone   54664  11424  \n",
       "6 2017-09-01 12:35:27  Twitter for iPhone   59840  11678  \n",
       "7 2017-09-01 11:56:45  Twitter for iPhone  110667  35936  \n",
       "8 2017-08-31 20:06:35  Twitter for iPhone  112012  29064  \n",
       "9 2017-08-31 12:35:50  Twitter for iPhone       0   6638  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Hacemos un display del dataframe:\n",
    "display(datos.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Muy bien, estamos listos para realizar un posterior análisis.\n",
    "\n",
    "¡Sube el nivel!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Visualización y estadística básica**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Promedios y popularidad"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Primero nos interesa conocer estadísticos básicos sobre la información que extragimos, como la media de las longitudes de cada tweet, cuál fue el tweet con más favoritos, cuál fue el tweet más retweeteado, etc."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:22:50.136159Z",
     "start_time": "2017-09-02T03:22:50.127691Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El promedio de caracteres en tweets: 125.925\n"
     ]
    }
   ],
   "source": [
    "# Extraemos el promedio:\n",
    "media = np.mean(datos['len'])\n",
    "\n",
    "print(\"El promedio de caracteres en tweets: {}\".format(media))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:22:52.201735Z",
     "start_time": "2017-09-02T03:22:52.173380Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El tweet con más likes es: \n",
      "The United States condemns the terror attack in Barcelona, Spain, and will do whatever is necessary to help. Be tough &amp; strong, we love you!\n",
      "Número de likes: 222205\n",
      "144 caracteres.\n",
      "\n",
      "El tweet con más retweets es: \n",
      "The United States condemns the terror attack in Barcelona, Spain, and will do whatever is necessary to help. Be tough &amp; strong, we love you!\n",
      "Número de retweets: 66099\n",
      "144 caracteres.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Extraemos el tweet con más FAVs y con más RTs:\n",
    "\n",
    "fav_max = np.max(datos['Likes'])\n",
    "rt_max  = np.max(datos['RTs'])\n",
    "\n",
    "fav = datos[datos.Likes == fav_max].index[0]\n",
    "rt  = datos[datos.RTs == rt_max].index[0]\n",
    "\n",
    "# Max FAVs:\n",
    "print(\"El tweet con más likes es: \\n{}\".format(datos['Tweets'][fav]))\n",
    "print(\"Número de likes: {}\".format(fav_max))\n",
    "print(\"{} caracteres.\\n\".format(datos['len'][fav]))\n",
    "\n",
    "# Max RTs:\n",
    "print(\"El tweet con más retweets es: \\n{}\".format(datos['Tweets'][rt]))\n",
    "print(\"Número de retweets: {}\".format(rt_max))\n",
    "print(\"{} caracteres.\\n\".format(datos['len'][rt]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Series de tiempo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dado que tenemos todo un vector de fechas de creación, podemos construir una serie de tiempo con respecto a longitudes de tweets, favoritos y retweets."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:22:54.706966Z",
     "start_time": "2017-09-02T03:22:54.700391Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Creamos series de tiempo para datos:\n",
    "\n",
    "tlen = pd.Series(data=datos['len'].values, index=datos['Creado'])\n",
    "tfav = pd.Series(data=datos['Likes'].values, index=datos['Creado'])\n",
    "tret = pd.Series(data=datos['RTs'].values, index=datos['Creado'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Visualizamos las series de tiempo en plots:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:22:56.848418Z",
     "start_time": "2017-09-02T03:22:56.478088Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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StW6hWJgBlB0qhpATPjwGAHSHi5ENgOMEOKP13Ydl3iKYATj7DwapcCv9Kf9w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\noCi4nnsJOSOGK28RNaG5Xi9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ePJkPUs6NM8SRRA3NrW54j2hoHwJWQYW27UDMFvB9/GHN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\n6kR7rYi69WCZTGRT8or7H0jujDNJ/Pwe4vfdu/IbOqyIkkwyfctdzH3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0ltIvwpqa62wE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\n0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3064e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualización de longitudes:\n",
    "tlen.plot(figsize=(16,4), color='r');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:22:58.905107Z",
     "start_time": "2017-09-02T03:22:58.482217Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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LAiQzJjLDLxzfXr96j3NHuC1PjMyYglmCIAiCIIoMCmYJWVFzZjZs/lQfCWZZsc+seodN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\nO7WK4MFHTRJ1Wg0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mNhDksO69k2AZBreumRM1Xn0B9JiV0lBTCh5A7xBlZ6cqfmG+0Vfg\nwWzP4DjMJl1cs8WSosvMhq5Lh1qH0grQeZ7HH9cfg8sbwG1r5mDVgpCr/9GO4ZyOM1+oMXnQ2eeM\nShAFVDjGZAwI1wdLZe4yswzDQKNhFftt0nIEslqt5wH4ic1mW2O1WucCeBqh2d1BAHfbbDbOarU+\nAOBaAAEAX7TZbNvl2DbV2JSQGYsnUyYf1WgpRWvPGHqH3WiszazvaTLE9hGJJu1j4yGZcbkgs2NZ\nBjPrytBmd8Af4HKmj5eitMy4LYt6WSC0ohP7c8autOm0GsxprMDG/T042T2a897GhUTyvS3PseBw\n++ELcBPMCliWwWdvWICf/WUPNh+0o7q8BDevbpHlM+MRlqAK55E3D5lZ6bkfyswK/1BpNOvP8ar1\nmMuH9TtPo7xUj8uWTuw/zIQNoCa+9v293egdcuGSpY2YVhO6Tovuy4ZCy8wKdbP2IVfcWkOi+PEX\nSWbW7Q0k7Nup17JgGaaIamZD/3e6/fjDq0dw982Lkm6/09aP3cf6cebMSqxZ0giWYVBbUYJjp0fy\n1g4yl6gxM7vpYA8A4IJFDdh0wJ6X0rpsGBD8R2pyKDMGAK2GCatFck3KyMZqtd4L4PcAxG/9CwDf\nstlsFyE0fbrBarUuBXAxgPMA3A7gN3Jsm84XYBRIzfKTkIHMqI04GstJV6rMrFvMzEZWNJvqzQhy\nfDgQzgWxvS+VJBvzJyCyX6VBgjfmBBQzswDVzU4gye6W61AYTHLxNeg0+J9bz0ZdpRGvbm7De3u7\n5PlQCbFfQ5QZ5yMzG9XTOciFzz21TmFynZl9fUs7vL4grls5Cwb9xABUk8AAyuUJ4B8ftqJEr8EN\nF8wOPy4GBIVUMwuEZMYAORpPZUQlWL9C7TByhccXDPeTjYVhGBgNmqJzMwaA/acGk27rdPvxp/U2\n6LQs7rj6rHDgam2qLNq6WbUZQAU5DlsO9aK0RIulQmeLfHTwyAZxscuSQ5kxAGhVlpk9CeBmAM8J\n/14G4H3h7zcAXAHABmC9zWbjAXRYrVat1Wq1yLDtS6kGZ9BrYLHkdhWaFyauxhJd2p81f64FePcE\nhl1+WcfXKxyEDMPEfV9x3jhrRiUsghnSgrkWbNjThaHxAJYnGUs24zSXRYIMlo0/tlzRNegCywBL\n5jdM6EmXDnrhNTU1ZdAIvdqk1yaGAaY1lKO+vhxGoW+vkt8vFfkei7bEE/47dixSY6ZsxnmsR2i9\n1FgZ930sAL5/1yp87dcb8ce3bGhurMTy+Q2T/rxYjIJ832DQwmIxo7Ym5H5ryOCaEI/JvFYncW8u\nrzCivDTUgqYky7HkjATXKjkYHHVjw54uWKqMuPVya3iRQYrRGMrwVFaZYKmK9NN79vXDcLr9+MTV\n8zCnuSb8uF9YZa8oNyYctxp/5wWa0HcfdvpUOT41kOx38fgC0GoKu18nLyxpDTq8BXsMBIMcfAEO\n5WWGqO8g/bvUqIMvwBXsd5QiLV9LNK8TefbPuzDm8uOO6+ZjobU+/PjyBdOw6YAdXUNuLFs4Pafj\nzZRs91GpZG6phv2947AdY+M+XHvBbNQJc+wSo14VY0sXpyekPrK21MIS02NWRI7vo9dpwMv0XqlI\nOfO32WwvWq3WZslDjBCIAoADQAWAcgDSJSXx8Wy3TUkwyEU1980For7c5w2k/Vll+tAN8Xj7sGzj\n4zgeHUIWMpDge/cJLoZ+jz/8fHVpaDcfOtGPpXMm9m0DJjZJzpQxRySg8WTwO2ULx/E42TWCabWl\ncIy6MZlPDQqywr5+R3giM+6JNMTWaVgMCFnt5gYzjrQPo+30UEIZlJJku9827u/G8c5R3LZmTlQ2\nPxNGJRb1sWORBrPZjPOUUA9kYJmE76MD8IVbFuFnf96DHz+7A/f9+1LMnlY+6c+U4nSHjgevcGx7\nhOB2aMSNvr6xSZkvTXbfSX/vwcFx+D3RY1MDN100Gy9tbAUAjLt9ORvXs2/Z4A9wuO78WRgZjp+R\n9AvH4MCAE4xwrg+NefDy+ydRZTZg1fy6qPGJJQZBfzDuuLM953IFz/PQa1m0dY+qcnz5JtV++9SP\n30V5qR6//MKFCo5KXsTr7eCoB13dIwVnYgaEzNyAkGxQ3F+x+06vZTHs8BbFcR7tq8In/E77Tw7g\nvV2daG4wT7hmTRdqH3cetmPlvLpcDjcj5LhWjo1FVAZq2N+vbwrd15bOrYFTmPc6HB5VjC1dOvvG\noGEZ8P74cwa57nEsA/h88s1LkgXFk1mClJ55ZgAjAMaEv2Mfz3bblCjTmkeQ8WUgM64o1aO0RCur\nzLh/xB2WwCWSXjjcfmjYkAxHpLG2DBqWyXl7HhElZcb2IRd8fg7NWdSIiYGIVIYorZmV1hnPaQwF\nR61F0m924/4efLi/Bw8+tQO2yRpIJDWAkudYGBTa8qSykp8zvQKf/egC+AMcfrVun+xGKOKxohMk\nqM9vOIHfv3pY1s9ISWzNrDg2ZUeRlOsvmI0nvrYG5SZdztoX9Y24sXFfN+qrTVi1KHEWPtxnVvK7\nvfTBKfgDHG5e3TKhNla8fhWaARTLMGioNsE+5FJtOwu1I+0dmYoP9/fg2Gl1OcgGJDX8/aOeJFuq\nF7HNSYkhcSBeYtDC7Q2qToI6GaTfgUlwFXd7A3jmTRs0LINPXTMPGjb62lRbaYyqmy0m1LSPnW4/\n9h7vx/TaUjQ3mMP7oRBa5EkZGPGgutyQ824wGg0bVjrlmsncrfdYrdY1wt9XA9gIYBOAK61WK2u1\nWpsAsDabbUCGbVN/AUX7zKb/GoZh0GgpQ9+wWzbbbtHJOBkOlw/lpfqoTJFOy2J6bSlO9zkVcYZT\n8sTOtl4WkNbMRh6T7jOtZFLbMj0kGDhZJMGseDyMOn346V/24LUtbRm/R7K9rUTNbCxLzrTg3z9y\nJsZcfvzf3/aGHb7lRBrobDnUiza7csdDbGueiN26YkNIC62GhU6rmdDmSi5e+bAVQY7HjRfOnjC5\nk8LG1Mx29Dqw+aAdM+vKsHJB4iBYV2A1swDQUGOCL8BhqEADmULhdJ8TT75+BOs2nMj3UKKQThwL\n1QTKLQazSUqGjHotOJ5XxCk916QzJ1v33kkMO7y4duUszEhgPnlWU1VR1s2qKU7cfqQXgSCPCxY1\nCG69glN+ioBt04EebDvcm/D5jfu78aPndinSXtDnD2J03JfTtjwiWg2j6tY8XwHwXavVugWAHsAL\nNpttF0LB5xYALwK4W45t0/oCivaZzeyzZlhCrRLkMuSQGjglOsHHXH6YjRPlr031ZfAFOPTkqG1D\nlMOqgv0+RSfj5obJy0nFvSpd0ZS25tFHBbOCCVR3cZhAcRwPvY7Fff+xBFVmA158/xT2nhjI6D2S\nLZzKmZk16DQoLUmvJvqyZTNw9flN6B1249cv7pdtQUk8VvQxtZmvb2mX5f0zJchxksysyqJZhEyU\n/DlwfA4EOWw/0of6ahOWp5DVsRLlBc/z+Nu7J8ADWHvJ3KT3j9h9XAhMFxyZc3WdJ0K8vfM0APVl\nP6UqiEI1gRLb0yQygJI+VwwmUFFzuTiXo6Ptw3hvTxcaLaW4blVzwvexNlWGti+yfrNqysxuOmAH\nwyC8CCreW5K1o9xzvB9/eO0I/vLOsYTbPPX6UZzoGg0nZ3KJ6GScSuUmB1qWRUChzGxaM0ObzdYG\n4Hzh72MIuRHHbvMggAdjHst621Qo0WdWvNhkGsw2CsXhnf3OrDKHIqKTcZlRF3cFx+cPwusLhnvM\nSmmqN2PTATs6eh2ytgoSkR6uSh28ANBuHwPDIKtWObFuxkGOi/oOUlOZcpMedVVGtHaPFYUNPseF\nvsMZMyrxpdvOwYNP7cCf3z6GebOqZGlNItd9aGDUg9qKkozOwVsunoPhMS+2Hu7FE/88jM/fuFC2\nxa9Yp9tdtn70DI6H27vkkuiFIx6q1BkL6LRsTrInnf1OBIIc5s2qSnkOivs8yPE4cGoIR9qHsbCl\nGgtmx/cPEFGijZncNEgcjRe11KTYmpgMYy4fth4KZVnGxn3wB4JxjcfygT/AhdphBPmCzcyGZcZJ\nglmxjMrtC6ZnriIT9iEXtCyD2kr5slpclMw4Gq8/iKffOAqGAe64el5SczIxmLV1DOOK5TNlG1++\nUUss2z0wjtaeMSxqqUFlWch0MZyZTZBd6h9x4w+vHgEQSjSlao+pxHcdGA1dF+Q8hhOh1TKKZJuB\nyWVmVYWirXky/CgxaOwaSC0PToeugXGU6DWoKS+Je9A7XCHjBHOcRuOzhJrSnK38REkflTl4OZ5H\ne58T02pK47bkSJdwzazwHWJlkbqYG8ic6eUY9wTQWwTZD2lA3mgpwxXLZ2Jg1JOR3DjZyqkcq6ou\nTwBubyDjnmgsw+COa+bhrKZK7D7Wj7/86/ikxxP7OukNScMy4AG8sa1jUu+dDZxKa2ZF9FpNTmpm\nxZr12WksEorHd5DjsW7DCTAMsHbN3JSvK8RgVlxMsQ/Kc8/JFw6XD50yt7VzuHxhc6FseH9vNwJB\nLrzYNzTmTfEKZeB5HoEghwbBnVRuvwCl8HhTy4zF59wKZmYDQQ4/em4XfvDsTllLV5Ldk17eeAp9\nI25csXxmWBWWiNqK4qybVct3kfaWFUnU9g0ILSw99vJBuLwBVAhz8lFn8muFEt9VzMxaFMrMBgVF\nVK4pvLt1DBoFa2Yzz8yKvWazn1j4Axx6h1xotJSCZaP7uoqEe8waJwazM+vKwACKmEApVTPbO+SC\n1xcMB+qTRVykEC8ksZLU2No5sW72VBHUzXJ8tFT/oxfMRnW5AW9s7UCPDBNiOQ4F0fyppjzzi69O\ny+K/b16ExtpS/GtXJ97afjq7wQg/lVSC2jzNjIZqE7YctGNoLPeyQz5q4Shyo1CjSECnDd3M5F7g\nahVaNaXjVi1OOD7Y142ugXFcuGhawrozKYVmAAUA9VVGMCj8XrNPvHIIDzy5HYfbhmR7z288ugk/\n/9veuM+lO4kMBDls2N2JEr0GFy8OtUBR4pxPB1FNVFGqh8mgLeDMbGqZsdEQCmaVlBkfbR+G0+3H\nmMuPP79zXLb3jW0DKHKqewzrd5xGXZURN17UktZ7iXWzp3uLp25WDbEsx/HYctAOk0GLJWfUhh8X\nWznGq5n967vH0WZ34IJFDVi1MBQAjzjl9+/IlIERQWasRGZWyFwrodYsvLt1DErUzE52slhaokOV\n2RBV6zpZeodcCHI8GmvLADBJM7PlpRNlxkaDFnXVJnT0OnOySiJ9x1TF8HIRqZfNLpgVFyl4IfLy\nBlJkZhvFutnCD2aDHB91Dhn0Gvz7R85EkOPxx/XHsj5W5DjWMjF/ioepRIcvrT0HlWV6PL/hBLYf\nSWzEkIjYbxGVmWUYXHP+LAQ5Hm9uz312VrqQFUxVcJVnxIBQbhOoVvsY9DoW02rj98iTwgi76oN9\n3dDr2LQnhmqRjmaCXqdBTUVJQdfMDo56cKhtGDwPPPbyQVkyjIEgh3b7GFp7HHGDvHSNEXfa+jDi\n9OHCs6eFpYax94t8Icr5dFoNLFVG9I94VJPVygRRZiwGrPEw6iMyY6XYdawfAFBZpse2w73YLfw7\nW6LvkaFruD/A4anXj4DngTuuPivtkh+p1LhYUEPN7OG2IYw4fVgxvz7qvqAJq36irwHbDvdiw+4u\nzLCU4uNXWFFpDl0rhlNkZpX4rv2CzFiJzKwY7CshNS74YHYy/R0zhQvLjDP/rMbaUgyNeeHyZLeC\n2CkExI2WUrBM/INebCuQqF/orPoyuLyBsMxAVvhEE+zcIYeTMSBt3RH6ty/mBhkrN5xhKYNOy+Jk\nV+GbQPEcH85ciSw5oxZnz6nBkfZhbEsj8EtuAJXtCCU1HllcfKvLS/CltYtRotfg968envTNPmwA\nJc3WMwzOX1CP6nIDPtjXHe5JmzNiJP0R5UhuP3Yy6IRJmJxSY48vgO6BcTTXm5O6GIuI122eB65c\n3oQqYWKRikKUGQMhqfHYuC+qV3amDI568jaJ3HrYDgBYOLsa454AHn5xfzhbN1lGnb7webL/5OCE\n59P9rm/v6AQD4CPLZoSPj1y1nsoUcRxaDQNLpRGBIIdRFWSCMiUtAyiDsjJjjuOx51g/zCYdvvyx\nxdBqWDz7lk2Wa710IUW8hL+2pQ1dA+NYs6QR1qaqtN/rLGHbYjKBki7I5Oua9OEBQWK8MNr9Pl7N\nbM/gOJ5+4ygMeg3uunEhDDoNqoSFr2FHqmBWzlHHZ2DEA52WRXmcckS5EWu8lYgJCvNuLUEJA6hI\na55JBLOC1Lg7y7pZUao8o7YUDJM8MxvPzRgI9eAEQj0W5b4oRGVmFaqZbbc7wCDk1JwN4m4VfxNv\njPtqrNxQq2HR3GBGZ78z60lWvgly/IRziGEY/MflZ0KnZfG3f51IuRATT/Iefk6OzGwWMmMpM+vK\ncPfNi8DzwMMvHsislj3ma2hYNrwIwDKhY2LVwmnw+bmcOxJKz65gUN01s6KqQc4Jf7vdAZ4HmtOQ\nGAMRmXF5qR5XndeU9ucUoswYAKZJTKAmQ9fAOL722Ga8sqlNxlGlB8/z2HzQDq2GxeduWIBLlzai\nq38cf3jtSFbXEukk8sCpicFsOnOtk12jaO0Zwzlza1FXZZIEs8plB5MhnmM6LYs6QULYN6xshn5w\n1IM3trVnNQdIzwBKG7VtrjneOYIxlx9LzrBghqUMN140G2PjvqQOtekSe+id7nPitS3tqC434LY1\nczJ6r5qKElgqhbpZNfW0yQLp18hHLOvy+LHn+AAaqk0T6pbFe4uoRvT6gnj0pYPw+oO44+qzwh4G\nYmZ2JFUwK/fg4zAw6s7YTHOyRGTGlJlNiakkfuAmJ5HWPJm/NiQLjmRWJ4sYzDZaysAw8Wt8RFOC\neAZQALBmyXTMaSzH1sO9eHVzW1bjSYYSMmOO59He60BDjSmpUUQ6hFt3iDWzKTKzQGhhgOeBtp7c\n1yDnEo7n40r1LZVGXLeqGaPjPry08VTyN0myu+W4hGUrM5ayoLkad1xzFlzeAH75/N6UK6UTkFwE\ntDHHhTj5yvkqpOTcd7r9UHNqVqcVZHMy3swyqZcFAJMw8b3xwtlJpYuxFGpmVnR2f+TF/Xhja3vG\n2athR+h8e2t7R+5VBjG02R3oGXRhyRm1MJXocPtlZ8A6sxK7bP1Z3bOk8r4j7cMTfBGkE/9EQbPY\njufyc2cAiBwfaul1Kk4YtRoWdVWhYLZ/RNl63p/9dQ/WbTiJLQczL+UQSccAKiwzVigzK0qMz7Va\nAABXrpiJ2dPM2HKoF3uPZ9bKLhkcz+PJ148gyPH4zyvPyuh6JWJtqoLLG8DpIuk3Kz0f8yGb3360\nDzv1rXAAACAASURBVP4AF+4tK0VUBoneFc+tt6FrYByXLZ2BFfPqw9uJmdmRPMuMXZ4Axj0BRXrM\nApHMbECBa2Rh3q0l1FfnfqdM1gAKQNhZMNueb10DTphNOpSX6kPjSJaZjdOaBwjV0vz3zWejptyA\nlza2YufRvqzGJEV6DvJIvwZpsvQNu+HxBWVpeSTuVnHIE2pm40xqi6XfrNiaJx5XrWhCfbUJ7+7u\nTJptTLqnZcrMajWMbLKYVQun4ebVLRgc8+JX6/alNSGK9y3ErKN4XZD2M80l0ndv73WoPDMbmnTK\neTNrswtOxincPUUuOmc6vrT2nLBhT7oUajC7ckEDrlvVDH+Qw7r3TuLexzbjlQ9b05Ydi8evxxfE\nOzuzNEzLkC0HQxLjlYKcT6thcddNC8P3rD3HJ1enOCyoO+qqjPAHOByNKTOIancV5/wddnixy9aP\nRkspzpoVknLqVSoz1mnZcD2c0o7GfcI8Jxu334xkxgooo3iex+5j/TAatOF9r2FZfOqaedBqGDzz\n1tGsJP1SPL4g2u0OrFzQgLPnTK611llFVjcbNbfMQ2Z28wE7GER6y0rRSNq+bdzfg80H7Zg9zYy1\nl0a75VeU6cEgDZmxXINOQKQtT+7rZQFJZpZkxupAvLmnUZ41AbNgxiQGmpPB6wuif8QTbvXDIHTQ\nx67ihDOzcdyMRSpK9bjn1nNgEGoHW3tyY2KUa6mxOKFtztLJGJDW1CVwM45jBDOnsTgcjRNlZoHQ\npOgTV5wJngeefcs2qSBNjpvP4KgH1eUlsvb0vXblLFy8eDo6+px49OWDactgpCMQgx1xWJHa61xn\nZiN/ttkdqu4zq81BZvZU9xjKjLq0DSyMBi0WtdSkvRgp3oATeQ+oHZZlcPPqFvzsrlW46aLZYBgG\nL3/Yiq89uhkvvHcy7K2QCOml++2dnbK0s0mHQJDDtiO9MJt0WCjpAVxu0uMLt5wNvZbF7/55eFIl\nO0PCJFJc0Iitm5Ve2uJd597d3Ykgx+Pyc2eGjyPV1cwGJcFsODObJ0fjLK5FacmMhefELG4uabM7\nMDTmxeK5NVF9XhstZfjoBbMx6vThr/+Sz91Yr2Pxbx85Y9Kvt84srrrZfGZm7UMunOgaxfzmKlTH\nKXMSa2Z7Bsfxx/XHUFqixV03LpywEKrVsDCX6vNuABVpy6NMZpYMoFRG2M14ElfocmFClM1KZfdg\nRGIMSGo8Y7Ybc/mh1TDhhuKJmFFXhs9+dAH8AQ4Pv7g/c6llHGJPwlxbcctl/gRI+syKbsYTgtmJ\np0mV2YAqswEnu8dU4bY3WTgucTALAPObq3He/Hq09ozhg33dcbdJ9u2zvfl4/UGMufxZmT/Fg2EY\nfPyKM3H2nBocah3CM28eTb4f4zwXzswK/w63eMrxKqQ0c9Q37IZLyCyrMJaNyIxkupk5XD4MjHrQ\n3GDOWc3PTz63Cvd/fCnKEngPFAqmEh2uv2A2fnrXSqy9ZC70Og1e39qOex/bjL+8czzhdV88vmrK\nDXB7A3hnV6ci4z3YOgSHy4/z5tVHBQ0A0FRvxqeunQePL4iHX9yfcYAtftfl1joYDVrsPzkYPUmW\nnFOxmVmfP4j393ajzKjD+fMj0kFxkVM1wWwgIjOuNpdAwzIF2Z4nkplNIjNWMDO7yxZSAyyz1k14\n7qrzmjCr3oxNB+xxjcUmQ2mJLqtrj1g3ayuSutl8GkBtEo2fFk2L+7yYmR0Y9SAQ5PBf181PKOGt\nLNNjxOFN+h1y/fUGRrI308wEXZLWRXJDwWwaZFOSVqLXQKthMJZFZlZsHi+aSTExmUQRh8sHs0mf\n1iRv8dxarL10LkacPvz6hf2y92vLdd1gxPxJhswsG3E7BSa2EYltzSMyp7ECY+O+cE1nIcLxiWXG\nIh+7dC5K9Bq8+H6CrE4OL85DMpk/xUPDsrjrhoVobghNRv7xYWvK10h/qvAihygzViAzy/M8/rje\nFvWYuLAzmcW2XCN39kpsx5VuvexkqDIbcMaMypy9v9KU6LW46rwm/PRzK/Efl5+JMpMOb+88jfse\n34xn37KFJzgi4vG7Zkkjyow6vL3jtCK1iZtjJMaxrJhXj2vOn4XeYTcef+VQRhP1YacXLMugurwE\nC2ZXY2DUA7ukfZH0nI29d2093Aun24+LF0+HXtIiRQw4th7ujXqvfBGQZGZZlkFtRUlY9qs02VyL\nPL4gNCyTVOYvBrq5zszyPI9dtj7odSwWSNQCIloNi09dOw8alsEzbx7NumsFII+p6VlNVXAXSd1s\nvmTGHBcyoyvRa7DkTEvcbRiGCc+frjl/Fs6ZWxt3OyBUN+sLcEmvpbkO1vvFzKwCPWaBSOaaMrMq\nIZvWPAzDwGzSZ5WZjTgZlwnjCD0ee9w73P6E9bLxuGL5TKw+Zxraex34wz8PTXp8QCQ7J/5GuQxm\neZ5He68T9dWmSRkkxBKpmY0vM45qwyJhzvTC7zfLcal7NVeWGXDT6haMewJYt+HEhOeT7elsL85y\nmj/Fw6DX4J7bzkFtRQle2dSWUfZZNICKZGZzXzO7fsfpCfIxUXKvwlg2vBAk181MLIvIZTBbrOh1\nGly2bAZ+/NmVuOPqs1BtLsF7e7rw9d9uxR9ePYweQQEkHr+mEh2uWD4T454A3t2d2+ysy+PH3uMD\nmFZjSto3/ObVLVjUUoODp4bw4vsn037/4TEvqs0GsCyDs1tCtYjSTFpUZlZyrPI8j7d3noaGZXDp\n0hlR7znDUoqrVjShd8iF7z+zE/tPymcENBnCNbPCOWepNMLp9itmkiQXHl8wqcQYiEiQc91NoHtg\nHL3DbixqqUnY63VmXRmuX9WMYYcXf3s3e7mxHIqTSIuewq+bzVdm9kjHMIYdXqyYV5e0z++shjIs\naqnBTatnJ30/sSVcMiWkUpnZXM2nYtGyJDNOHwWO7XBmdpJLZmaTLquaWbGFyHShZhZMdCYRCAVg\nXl8wozqvkNTSiiqzAZv2dWU3CRdeKtabBXN48PaNuOH2BpJOejKBjfk9RZmxuLcTZmaFVkeFbAIV\nTGIAJeXSpY1oqi/DpoP2icYSmalzM2Igh5lZkYpSPb78scUoM+rw7Ju2tOViYuYgGK6pz21mdvex\nfjz/7sTFhLZwZlZ9aMOteeT5TVq7xWBWnnN/KqLVsLjonOn44Z3n4c7r56OhxoRNB+341u+24fF/\nHAwrgTQsg8uWzUBpiRZvbT+d08Bhp60fgSCHVQsnOoZKYVkGn/3ofNRXm/DGto5wT9pkcDyPEacX\nNYL8b1FLKMMWFcwmyMwebR9GV/84llktE/oTMwyDtZfOxWeum49AkMOv1u3Ha1va8lZ2EpYZC9el\nfNbNZhOPeXyBlB0KWJaBQa+BO4PM7L4TA3hzWwc6eh1p76OIxDh+Zk7kmpWz0FRXho37e3CwNX25\n8a/W7ZvwmBzVE9awCVTh181Kd5WSqunNKSTGIt/8z3PxxdvOTtnzXGzPk6xuNufB7KgHRoMGpSXZ\nJ4HSQYwHnlt/LOcLT4UfzCoAH87MTu715SY9vP7ghFrMdOnqd6K63ACTcADG9kUFUjsZJ0KrYbFw\ndjUcLj/aeyffZkYciUaBJsmirFIOiTEgycxyYmY2NCkQHRMTyZ2a6sugYRmc7CrczCzP8+G6j2Ro\nWBafuNIKBsAf1x+LWmnLZc2smJnNdY1HQ7UJ/3PL2dBoGDz28sFItlMg3teI7aEayczKP77WnjE8\n8coh6HUaXHR25OZqMmhhF/uJqjCaFSfW/mD2ckCe59Fqd6C63ICKMkPqFxBJ0bAszl/QgO99egXu\nvmkhmurN2H6kD69taQcQui4aDVpcvnwmnG4/NuzuytlYxInj+fPjS4ylmEp0+J9bFsFo0ODp14+m\n7OvscPkR5HjUCA6eFWUGzGow49jpkXDWUnq7kjpvvr0zlJG+/NyZCd9/5cIG3P/xpagqN+DF90/h\nsX8cgleh/qdSAlykZhaImLzkJZjN4rUeXxAlKXw/gJAJVCY1s8+8eRTPbziBB5/agS//ZhP+8Nph\nbBMk5InYdawfWg2Dc+Yklo8CMXLjN46mnQ3fF2fhVI7MbHV5CeoqjUVRNyud58rlGp0KtzeAXbZ+\n1FUZMVcw+0wEyzBp7bPKsjQysznMzvE8j4FRD2orjIr0mAUi16LugXF8sDe+6k0uKJhNA/FaMNkD\nQAwwJyM1drr9GHH6wv1qgYmZRAAYE967fBIOnGItyMHWoYxfG0s4M5vDC6iYiZI9MytcSLyB0ETE\nFA5m499Y9ToNmurL0NHrgD+g/ORFDkKZ2fS2nTO9AqsXT0fXwHi452Iqsl1pzLXMWMrcGRW48/oF\n8PmD+OW6/RNqCYHoa0C4HlQI7MWFWbkzswOjbvz6hf3wBzl89oYFUcf9rAazxMxYfdFsWGYsQ2Z2\n2OHF2LgPsxtIYiwnLMNgmbUO3/nkufjibedgTmPo951WHVICfWTZDBgNWry5vSOrIG3TgZ6wykhK\n/4gbxzpHcVZTZdrn+bSaUnzmesHE8O/7kzo0i31zpcYs58ypQZDjcbgtpDKJas0jnM99wy7sOzGA\n2dPKw+71iWhuKMd3/t9ynDmjAjuP9uGHz+3Mm/mSeBUQe80q3Z4nNIjJXYt4nk9LZgyEFloy8frw\n+oOoKNNj5YJ68ByPTQfs+O0rh3DPrzbiB8/uxMsbT+FE12i4E0PfsAun+5yY31ydVjlTU70Z166c\nhcExb9xynHSRK9CwNlUWRd2sdHE4lRO7XOw82gdfgMMFKZQimSAqO0acib9DLjOzDrcfXn9QMfMn\nAFFGfu/u7sqpnwgFs2kg7oDJHtPmsKNx5qtKYhsC0fwJiNysOBkys0DIsZZhgEPZBLPCWMJ9t3Io\nM5Y7MxubUfMJEzYxE57MiKJlegWCXKiGt9AI14JnIDm45eI5KDPq8I8PW8PmTEnd+bJcaRwY84Bl\nmAkSv1yxzGrBv33kDIyN+/CL5/clXbWfkJkVZcYyLuS4PAH86oX9GB334fbLzsDiubVR5Q5SN2+F\nFlszQmzNI0fNjFgv20wS45zAMAzOnlODb3x8GR778sWYOyMUwJlKdLj83BlwuPx4b+/ksrMDo278\n4bUj+Pbvt014bsuh5MZPiVg8txY3rm7B0Jg3aXut4bFQJqRGEswuEnp4HjgVqnON52b8zq5O8AAu\nXx5dK5uI8lI9vvpvS3Dp0kZ09o/je0/vwOG27BeIJ4to8tI/orxB4WQvRYEghyDHp5QZAyETKHcG\niyv+AI9qcwk+c/0C/OILF+KBTy7HzatbcMbMSrTbHXhlUxt+9Nwu3POrD/Hoywfx4vunAABLE5j/\nxOO6Vc2YYSnFe3u7J73v5TCAAiJ1s0faC7tuVjq/UCqYFV2MM70mJaNKyMyO5KlmdmBEWfMnIJLc\nAkKLagdkcvyOBwWzaRCRGSufme0SnYxrJcFsnHGEe8xOIjNbZtRh7oxKnOwanbRZhHgOanMsM+Z5\nHu12B+qrjOFgM1sYNvLeAOAVghNTCpkxEDGBOtVVeHWzHJd5MFtm1GHtJXPh83P48zupzS7kyMxW\nmfUp61Hk5CPnzsRVK5pgH3Lh1y/uj8q6x+sz+//Ze+/wOM77WvjM7Gyv6L2TWIC9i5JIWV22ZMlF\nTmxFjhzbsWPH10pPfJPrPPlyEyfxvYntFNvXTXG3JDuqliLLkiiRFCl2ggBRSPQOLIDtfWe+P2be\n2dnFltnd2QUg4TwPH4DYNjvlnfe85/zOjzwuLooodEeKxlh845leTC/6cMf+RtHqKB2HlHInFAsi\n4VeAzI5shj+VBBTF1yNKceeBJug0Krz41sSqgDw5SNeageM4nOqdg5qhcSBF65NseO+NLThgr8LQ\npBM/TdPrk/SYrbTFFYm2WgtMerXYoie5z2wgFMWJnlnYTJqctotR0fjo3Xb8znu6EAzH8M+PXxLJ\neqlRJXzfxZXSJy3nOwaSGlh5yqwKkSgra6GM4zhEYyzUwuSapii01Jrx3pta8YWH9+Frjx7F5z6w\nE7fuqYdey+DcwALODiyAooC9WzNbjKUgdmOaovCfLw7kVSeoVD/1eN3sRiez8d9LQWYXVvwYmnKh\nu6UsbZudfGCTEQBVzBAgh6u0bXmAeNkhwStFDBLcJLMyEG/Nk98gYylAmZ0SlNnGqrjNWMmaWYK9\n9mrEWC7vwACyKfnWzLIcJ4vsL7qC8IeiivSXJUgmIWSyRsiyJpMy20BCoDZe3axIZnM8r2/aWYut\njVZcGFrE5euOjIS1EGIXjbF8cEsRw5/S4UO3deBQdzWuT7nw7eeuplSfST0o6amsZJI3x3H4yctD\n6Btdxq6OCjx0x1bxMenhWvfKrGgzLpzMjs2S8oJNMltqmPRq3LG/EW5fGK+nSfzOhOS+sQQjs27M\nrwSwr7Mqr2R6iqLwifu60VhlwmsXpvF6CuXY6V2tzNI0hZ3t5XB6w5hc8IJLUmZPXJlFMBzDbfsa\n0257Jtyyux5/8fA+MCparEEuNXQaBhaDuqTKLJl/vHV1Pq/Xx3vMyqmZFdrzyFBnyRidbmHaoGOw\n316FR97dhS9/9kb8/aduwEN3bsVn3rcjZ4GgtdaC9xxuhsMVxM+PyU/cJlBKCCi36FBdpsfQ1Mau\nm2UlBM9VAjJLWoTdpKAqCwBGHQNGRa9ZABQpe6hcI2XWatSgd2S5aG3MNsmsDHAK2YzdeSmzPlAA\n6ioM4t8okXzFn1eIMgtA7KNVkNUY8ZM3V1vhaxem8cf/fnJV8E4yxueUn9DGFwf4n4TM6mUos1VW\nHcwGNUY2YKJxcgqvXNAUhd++xw6aovDjl4cy1loXMjgve0LguMRJaKlAUxQ+ed82dDbZcG5wEScE\n25EUyT1UicWeU2Di8NKZSRy7NIPmahM+877tCcdoV0clKAC/dedWVNv04nm6LmtmmfyU2Z5hB547\nOSpOwliOw9icG7XlBsUcGZvIDXcfbIJWrcKLp8cVywgQe8tuz3/iqNMw+PyDO2HUMfjpr6+t6mks\n9qpOUiSI1bhneClh0S0SZfHKuSkwKhrv2lOf93ZtabCiu6UMMw5fSXqRJ/dHB/hE4yV3UKwDLTY6\nm3g1cGzOg2tTuS+ME2Kql2MzFkKi5LjJyHxEzsIERVGoqzDirgNNONiVu1sAAB64uQ31lUa8emEa\nAznafJXM3+hqtiEQimFiIf9wz7WG9H7qLqAriBywHF9LrdWo8nKKZAJFUSgza8TFtVQo5pKDo0Rh\nmlJIr7fb9zUAAF49Xxx1dpPMygC5lvK2GRuJzTi3C5HjOEwvelFdpk9o1p5KmY0HQOWnzNpbyqHV\nqNBbYI0P6SuVzlaWDuPzHsRYLmtiJiG7LTWmjM/LBcn9QUORGBgVLU7EmQxklqIodNRbseQOZbGP\nrD+8dGYCQNzKngsaq0y4+2ATHK4gfpbG3gcU1heulOFPqaBmaLzvCN87jtwIpHwx2UJL0asXmfLB\n+cEFPPnaddhMGjz6oV2r6sesRg2++4XbceeBJlAUFb8W1h+XjSuzMsksy3F45sQovvpkD546PirW\naM4v+xEIxTZb8qwhzAYNbt/XAKc3jDcur17cyYRU40A0xuLM1XlYjBpsbysraNuqbHpsbytHOMrC\nn5R4OrHghVatQlWZIeHvO9oqQFFAz0gimb10zYEFZwA3bq/JK1BRip3tpDa3eLViBP/54gAAYGgy\nTiKrbXrEWA5L7tLcm6QK4PGe3M4RIE5m5aUZ8+OiHDIr9uDNcC9XEmqGxifv6wZFAY+92J8yOC3d\nvTGUYlEiX9hJv9nxjduiR3o/LbbNeGjCiSV3EAfsVatKLZRAmUkLty+cdnGpmG291prMHuiqhs2k\nwcne2aL0vn5HkNkYy2JgfCXvE6XQ1jxiAFSOF6LLF4YvGEVDVSJxI5sh/TZxm3F+N181Q6O7uQzz\ny/6UKa7ZQIJ+VHmmGRMieGZgIWOdCVFmlbQZE6WbHGeSpkhqZk26zAsEJP1zZINZjYldcDnPic4D\nR1qzBjMVMjaXqi1PJmRSW0XVMZLYmqcQFWR01o1vP3cVGrUKf/Ch3SiXYbEmLoV1yGVXqdeZEArH\n8M2ne/HMiVFUWnXQaxn84vURuHxhMfxps152bXHPoWZo1DReOD0u65gSpHpmz/ASfMEoDm+rUaQm\nniz6SEOBAqEoZhZ9aK01r2pBZtKr0dFgxfC0K2Gh+XgPPy7esV9e8FMmxIOmik9mCaYW44nR8RCo\n0iQaS8f74TxyJOI2YznKbC4248QevKVAW50F776hGYvOIH7x+mq7cTTNgn8u11U22Js2ft1sKQOg\nSPDTkSy9ZfOFzawFxwGuNInGxUz7dTgDMBvUsq4tpSC1GWsYFW7d24BAKFaUHIENT2blHPoTPbP4\n8k8v4uzAQl6fEU8zzrdmVlBmMySjpsK0cFOShj9Jt0N63nv8ETAqWlatSTqQFj19+aizwrYwdH4T\nepLwFgrHcLY/9XEi4U/VNj0MWQhmLiBzHMJXQhGezN5zQzMefXAXasoN6V8MPtEYwIayGk87fOKA\nmu/wqdMw+K07t2Z8TkHKLLEHrkHNLAGVdG4kBEAJq45kfFAVqMw6XAF8TdKCR+6CTXNtZmU2GA2h\n19Gf30YVCLmteZZcQfzDj87j3OAiOpts+F8fO4AH39WOQCiKJ169hlGhXnaTzK4tLEYNbtvbgBVP\nSJz4yUGqceCUAhZjKci9T7rqPz7nAQegrT71ebOrvQIch4SUTV8wCjVDo6m6cPdPtU2PmnIDro6v\nKJLonQ7pUtdFMrtSGjJLxsKOegtml/wZ0+BTQVRmZQZAATKV2Rxsxkri/UfaUFdhwCvnpxIUcyC9\nWyWsoM1YWjdbKqu50ihVAFQwHMW5wUVUWnXYKiwCKA2x12waq3GxuCzLcVhyBxUNtJIDaQAUTVN4\n154GqGgKr5yfUlyF3vBkVg6uTfEk4/J1R16v54QxIN+aWa1aBTVD53whiknGVclkVtiuhACoMMwG\ndUE9sXYQMptH3SzZEjEAKkeb8YonBJNeDQrp7UlLriB8QWXDn4DVymwoHINWo4LFoMEeGUmGrbVm\nUFR+K9FrhbP9+QV0JCNb24JCxqu1SN9LBp10bkiRbFkjiyL51Mz6g1F87Um+X+ZDQgseudjRVoHu\nljLsbKtI+fhL46/iGz2PYcJTvCTBdGBk1Mxem3Lib79/FhMLXty6px5/+pE9sBg0uHVPA1pqzTjV\nN48z/fNQ0RSaFSwv2ER+ePehZqgZGr88NSbfPi65JjiOgzcQweVhBxqqjIodU1I7Lu09ShKw29Ms\nguwSlNNLSXODcrNWsf6SO9vLEQrHcG1SOasnx3GYWvTil6dIO5njKZ9XamWWgLR1yvWemBOZFZX4\n9WczJlAzKnzi3m6AAr73Qj9CkiTwdGOi0oSmq7mMr5vdgO0DgfgCiUHLwJVH7oxcnB9cRCgSw007\nahVLlE6G2GvWk+Z7FInMOj0hRGOcmHBeKjASN4yKpmA1anCwuxqzS35cVbhl1DuCzI4J1tS+sZW8\nZHyxH2eeJzhFUbAY1DnXzE6JPWYTb/Z0GmU23yRjguoyPSqtOlwdW8k7/S6f1jyhSIxPKK4xYVtr\nGa5Pu8T+ulKMieFPypJZsT8ox8WbtqvlK9x6LYOGShPG5jxFXX1XChzH4Uwa9TtXUBSF37xtS8bP\nyhfEZlxuKU2P2VSIh62R7xEfA5ItazSdn8UeAB5/9RqmHT7cub8RdwoteOTCpFfjzx7ai66W1HWH\n11b4fone8OprqtggNqN01rnjl2fw5Z9chC8QxcN3deK377GLYwhNU3jkHjso8ONbY5UJakb5OqZN\n5AarSYt37anHkjskBjhlg3QYiMZYnB1YQDTG4abttYqRRr1AgKS201Gh9KM9jTLbVG2C1aSJ18QL\nkGPvl4tdQt1sT4FW40iURe/IEn78qyH8xTdP4a+/ewa/eH0EwzOuhLFIujury3gyu1Bim/GWBl7Z\nup4jmfUHeWIqJ9ma1NUGQ/JtxuoSK7MA0NFgxd0Hm7CwEsBTb4zEt0lBO3EmdIktejZm3SyZQ1hN\nGoTCsYQFASVBnCY3FcliDEjIbBpltlg243i9bGmVWSZJmQXi5RtKB0G97clsMBzF7BI/iXP7wpha\nyH11qtDWPABgMmhy7jM7veiDiqZQU5Z4AiYrs6EIf4EXGlZBURS2t5XDH4piNEuqcDLItohpxjlY\nWojF2GbW4uhuPj3yRAp1dnxe+XpZINFKGo1xQtP23CbNHQ0WhKOsaA1fz5hc8Coaj96Y5ByQopDb\n9ZI7CKtRs6YERjw3MtTMEkgXRXKBPxjFW1fnUWXT4SN3ZLZt54pILIJJQZENx0rTcF4KdZoAqBjL\n4qe/vobHXhyATqPCH394N+7Y37hqjG2rs+BWIQVxM/xp/eA9N7SAUdF4/k156qx0USsYjuFU7xwo\nADdsq1Fsm0gNpVSpG5l1w2rUpK3tpyhKJJtSKLmAZm+2QcPQuDKSu+PJ5QvjeM8M/v2/ruDRrx3H\nvzxxGa9cmIIvGMWh7mp86r3b8NXPHxFdVcmwGjXQMHTpamYFaamjwQIKvMsrVfhROvgFVd0gg8xu\nBGWW4ANH21FTbsDLZydxXXAKlmrhWwyB2qB1s2ToIBbdYliNHc4ABiac6GyyobqIrWtEm3GJw0LF\ntjwldrlJySwpw+qot6KtzoxL1x155fOkw9uezE7Me8Fx8ZtTXhbaAlvzAHz/tXCUlT2wsxyHGYcP\ndRWGFHUeicpsvC1P4XWk21vztxoD+dmMyYVdZtZi79YqGHUM3uydXTXYjxUh/AmQKN0sJ676aXMs\nkicr/8MboG6W1I7v7khtS80VpNY61YQxX2WWZTksu0NrlmRMEO9BzP9fOgYkr/In9yuWizP98whH\nWRzZVZ9zm6RsGPdMIcrx53SYLW5bg1RQM6vJrC8YwVef7MHL5yZRX2nEFz92ANtaU0/GAeDBR92r\nSwAAIABJREFUWzpwx/5GRQJ5NqEMysxa3LK7Dg5XUFZPUeklMbngxfVpF7pbyxRVQOM1s/z5vuLh\nE+bb6y0ZF6J3pRgHy83KbZeaUaFLZosejuMwMe/BcydH8Xc/OIc//rcTeOyFAVwYWoTNpMHdB5vw\nZw/txdcePYLPvG8HbtxRC7NBkzZ5laIoVNn0WHQGZI3FHMdhxRPK31EjvMygZdDZZMPYnAd//s03\n8eLp8YzBjgSk/lWOMkueE5CjzEbXpmaWQKNW4RP3dgHg7cbhSCxtAJTSKDNrUVOmx7UNWjfLSpRZ\noDhk9k0hkOjmncr2lk2GzZyZzBZLmRXDNEttM5YEQEmdrbfvawTHAa9ezNy9JBe87cksSb+9+2Az\nAGTsiZkOhdqMAYiqqdxes8uuIEKR2CqLMb8d/E9ywyk0yViK7tYyUFTuZJZlOahqxsEyvDKZi9WS\nFMOXmXVQMzRu3F4Ltz+Cy9fjtiwS/lRp1cGoYPhTjI3ByfLkjuU4sd4qZ2W2ntQIre9EY95iPA+t\nWoV99sz1rnJBURRUNJWSeOY7NnsCEcRYLmtacrGRlzKbo834eM8MKKo4CYqjrnHx97VQZpmk9kWz\nSz783ffPoW90Gbs7KvBXv70f1WWZA9YMOgYP39WZcizcxNrh3sMtUNEUnn9zLOskWTpJOyZMYJQK\nfiIQa2YF0iQ3AXtba/mqpGOlSxsytegJR2LoGXbgBy8N4k+//ib+5rGzeOr4KMZmPbA32/Dh27fg\nS58+jH/4vRvxkTu2orulbBUpyzQ3qbLpEQjF4AtmJpNjc278008u4k/+4yQuDC3m8S3jJX8UBXz+\nwZ144OZWRGMsnjw2jD//xim8kIXUjs97QCE+6c8Eco8OygqASnSOrQW2Ntpw54EmzC378fSJ0YQF\nPpNenXdPWzmwb+C6WVGZNRZHmeU4DievzEKjphXvLZuMMoGQp+01W6T1jUUhf6RqHdiMAeBQdw3M\nBjWOX55RzDb+tiezpC/pro4KNNeYcG3KmZPtBZDYjAvYW0Q1lVs3K9bLVq62cIp1fML/lVRmjTo1\n2ussGJ5259QLysMtQdPSjyUNn5qaC5klNuMywYJBrMakRQLAW069gYji9bJPXnsWb/ifAGV0wRuI\nICgqs7mR2doKA/RaZt0nGo/NebDoDGL3lgoc6qpBW50Fv//+Hcq8eYpDnu8KPxngCknnVgLJ1uFU\nacbic8V+xavfZ2TGnXKBaGrBi9FZD3a2VxSFuA+7xsTf10KZJbV80SiLKyNL+LsfnMf8SgDvOdyM\nzz+4S5YCs4n1iXKLDkd312N+JYAzVzPX4EuHgYvXHNCoaexXaDGNgNhOSc0saZWWLslYfJ2gIkqh\npGIMpG/RM7XoxZ/8x0l89ckeHLs4jXAkhsPba/CZ923Hv/7BEfz5b+3DPYeaUZslUV86LiUPuSQE\naiFNovGyO4jvPH8Vf/uf58TE3bxJjyRbwKBT4/1H2/Hlz96EB25uRYzl8PNjw/jid86kXPyYX/Hj\n+hSv2MspmUplK0+HuM14be8nH3xXO6pterx0ZkLc1xaDGl/69OGiqoIbuW6W3HttAhFUOgTq2pQL\ni84g9ndWF/1+pGZUMOnVaclssbR6hzMICsqPa9kgXfCXLhiqGRrv2lMPn1BipQTeAWTWA51Gheoy\nvql6NMZhMMdUQW4NlNl0ScbA6ppZJZVZgLeNshyHgRzSxmLgJxBRir9hxnKoB5HajAE+lKO11owr\nI0viY8XoLzvunsSJ6dMAAEodgssbFhc6cgmAAvhzo73egvmVQM7tCEoJ0vboUHcNtBoVvvixAzhQ\nxBXhfNvURAQyq8nxOCiN1QFQcWiSiHammtlvPtOLrzxxWTyPCd4QFmyO7lJeleU4LkmZLf15SQuq\n/cCEE1998jIiURafun8bfuPWLYpbqjdRetx7uBkqmsJzb45ldCRwkmlajOWwv7NK8X6HuqRWLaIy\nW5u9ndPOpLrZcoUXlsQWPWMrCWFoL56egC8YxW37GvCFh/fhq48ewafv345D3TW5tZ/LcCmRBNMF\nZ2JOQigcw9PHR/CX3zqNN3vn0FxtwiP32AEALl9+NX1SZZbAKJDa//PZm9Beb8GSO5iyN+zJK8Tq\nKW8sFAO/cgqAWtsxR6tW4eP3doHjgJ8f43vP3rKnASa9uqhEeyPXzZLbqbVINbMk+KnYFmMCm0mT\n1masdLsaAocrAJtZW/KacYMuPsYn06db9zSAppRr0/O2JrOBUBRzS3601ppBUxR2CK0rekdzSxWM\n18sVEgBFlFmZZDZNkjG/HcIvYs0sP0ktNACKgNRA9ubSb1boXxSj+Is0H5ux1Fp0dHc9OC4+0JDw\np1YZExM5YDkWTww9E59kUSycvnBOrQGS0SEoAOtVneU4DmcH5qHXqrCzPX2NotKfmQ/CwoRPs0aB\nHQSr2u1IhoCuZhv2d1bh0Qd3JTw3eVK/5ArC4QqC5Tg89kK/OLGKRGM43TcPs0GN3Tm04pGLhYAD\n3ogPZVp+VT6yBjZjIK7Ochzw6Id2Km4v3cTaodKqx807azG37M/Yxz15GLhxh/LngLRmlmU5jM66\nUVdhSJhQpcORXXUJqkwxFIyd7eUIRWK4NsUvpnv8YZwdmEdtuQEfvasTnU02qOj8xjsqA5slfdJ/\n8N+D+PZzfbh03YETPbP4wrdO4dmTY9DrGHz83i789e8cxKFuPpCL9CDPFZmGe4OOEXuGJyf5shyH\nN3tnodOosrZ7I9DlEABFxtzkBPq1gL25DHfsaxTvcYRga9TF27YysxY15QYMTW68ulkuSZlVksyG\nIjGcHVhAuUWbthuA0rCZtQiGYymdj8XgstEYi2VPCFVrkD+ilYgRyfyp3KLDvs5KTC54xfaphWDt\nr+wCkWmyPLngBYe4mrelwQqNms69HjSFxTBXEKIp12Y8veiDRk2nTB9LVovcCtqMAb7GSK9V5bSf\nWIEURogym6PNWEVTCdt/Q3cNNAyNEz2zYDlO8fCn07PnMOaegFbFHxeKZuHyhkQym2sAFMBH8APr\nt252eMaNJXcIe7ZUlcxule/gHCbK7BrbwpIDoKRQ0TQ+98GdYi/idMrskDB5NenVmFjw4qUzEwCA\nt/rm4A1EcNOO2qIEk4w4xwAAXeV8QvJa2IyBRDt2TZb62E1sPNx7YytoSlBn01zw0r9bTRpsa1F+\nMU1aMzu77EcwHEvbXzYZJr0an3rvNuF9VEWxG+5Kshof75lFNMbhtr0NBbcnyvTy7pYy3HdjC4w6\nNU71zeNff96D773Qj0AwigdubsU/fPowjgrhc3qtCmqGhqtAwpBuexgx3TzxPBkYX8GyO4RD3dUJ\nE+BMUDM0GBUtKwCK1OyvVQBUMh68tV2c2xGCrS3yva6r2YZgeOPVzRZTmb0wtIhguLi9ZZNByulS\nWY2Locwuu4PgOKCixPWyQHYBkIQ6vqJAm571cWUXCWOCzYioeWqGRldzGWaX/Fh2Z04VlIKcX4XY\n4swimc1+IcZYFrNLPjRUGlNeYOQEKUaaMcAP+F3NZVhYCcjuT0cUzijF79ecbMbeEGwmTcJ3NegY\n7LdXY8EZwNCEUwx/MukL/46+iB9PD78ArUqDu1tuAwDQKg5ObxihSH4BUEA8aGS9Jhqf6edrEw51\nFzfkQIqCldkirlbLQXIAVCYFJF0A1DWhrOHT92+D1ajBMyfGMLfsx8tv8aT26K56pTcbADAiWIwJ\nmQ2tlTK7xta+TRQX1TY9btxRgxmHDxcGUwcHSYeBG7fVFsVirpP0mSXumGz1slKohPNUySRjKexN\nNmjUfIseluVw7OI0NGpaEXujdJpww7bE8Z1R0XjwXR348mdvxP965ADuOdSEOw804kufPoz3H21P\nsHtTFAWrUZM3mRU7P6QZJ8XWfUnzg7jVM7dyC71WJSslmThrSkVYskGnYfDJ+7phMWrQXMMv0Bf7\nXmcX6mY3mtWYLIRZDBpQUJbMiufdjuL1lk2G2Gs2hdU437KsTFgUkoyrSpxkLAedTTY0VhlxfnCx\n4HZFb28yK1pT42qeaKHNQXVUojWPRSCabl92dWRhJYBojENDZer0zmLXzAIQ+9ZdlbufhG0JIwiA\ny6rMegMRXJ9y4fjlGbi84ZTphbfs5geYZ0+OwuOPoKVGGVX2uZGX4Iv4cW/bXajQ8d9Tr6Pg9Ibi\nNbN5kFmTXo3acgNGZtw5J9oWGyzH4dzAAow6RrwGSoG3jzLLf5EA5cTjg08hwq6eQMUDoJKVWRc0\nahpdLWX46N2diMZYfPPpXlwcWkBHgwX1KULelMCIawxalQatliYAa1MzC6xdb8dNlA7vvbEVFAU8\nezK1Oiu9Ju67qaUo26CiaWgYGoFQFKOz/L0/W5Jx4uv567dM4SRjAjWjQlcz36Ln2KVpOFxBHN5W\nm1ttbFrEJyfvOZx6/1JCrsOHb9+K37qzM62V2mrSwO0LF9YmJKsyGyezgVAU5wcXUV2mxxbB3SQX\neg0jK6gyVS3vWsPeXIav/I+bxXaI5RYdLAY13nNDc3E+r4m30W60EChyHjIqCiaDGi6Z7sZsWHYH\n0T+2gi0NVtGKXwqIvWZTKLPFaM0jtuVZA2UWAL74sQP44w/vTvkYRVG4Y38jWI7DawW26cnbS2O3\n2y8CIBLUKID/B+BrAKIAfjU4OPj/2e12GsDXAewGEALwu4ODg9ftdvthuc/Nd/sAYGzWA72WQVVZ\n/CASktY3uoxbdstTRESbcQEjoajMBrKvKk0vknrZ1JNcsWRWoswyKlrR5Nftkv10696GrM9nxdsF\nBzCRjGT23MACvvF0b0JyW2OK2uDOJhuqy/QYEAZfJSzGE+4pnJg+jVpDNW5rPIIex1UAgF5Hw+EL\nI0BsxnkGD3XUW3Cyd45X1tdRK5Frk044vWEc2VVXUqtVvoNzOLI+lNlktXWJuYar073YVbkd3RWd\nCc9VpbAZewMRzDh8YjuN/fZq7O+swnmh9UWxVFlfxI85/wK6yrZCp+InrRF2rZTZTTL7dkdNuQGH\nt9XgVN88Ll1zrKp7JAuv99/UqmhrtWTotIyozDIqGk3V8sdgcv0WS5kF+KCpnuElPPEqP7W5fV/2\ne6scSKcmhaqPVqMWMdYNbyCScw6H2PkhzeOpbMYnemYRjrK4eWddznMsnVYlK91W3K71xGaRuD2M\nisZXHz1atM9KrpvNtz671JAeO4tRgxV3YQoewZu9c+BQuuAnAiLcOFPUpXNFEEEWBXflWimz2RYU\nD2+vxc+PDeONS9O4/6bWvBe/83qV3W7XAcDg4OCtwr+PA/gmgN8CcATADXa7fR+A9wPQDQ4O3gjg\nCwD+WXiLXJ6bFwKhKOaW/WipMSUM7rXlBlRYtLg6tixbPRNtxgUMhFqNCho1DY8MZXYqQ5IxILEZ\nC//3+COwGNWKDtTVZQZU2XS4Or4iKzCA4+LPoZgwYhkagvcML4EDcOveBvzOe7rwhYf34eG7Olc9\nj6KohJTXQtvysByLx4eeBgcOv9n5fqhoFRiaJ606LY0Yy8EhrGLluzDQTupmZ9ZX3eyZAZJiXDqL\ncSEIR9eHMksGVmJ7joI/P1zh1cc3Xsse/xuxGEtbfzx8dycMWgZ6rapovQVJinG7tQUaoS58zZTZ\nTTL7jsB7b2oFBd5Jk1xeEJ+QFncb9BoVPP4wphZ8aKkx5bSQ0lBlQl2FAXuKEMZGQFr0hKMsOhos\nosW0UCi5W60kaCePEKi4AirPZhyNsXjp7AQ0ahq37sl9Yc9s0CAUjmXtIBC3P7+zQepmx+c2Tt2s\n1BlpMWjgD0UTEsHzfc+TvXNQMzQOdtUosZmyQWpmU9lqi2Hoc6yxMpsNWrUKR3fVw+2P4FyGEMFs\nyFeZ3Q3AYLfbfyW8x98A0A4ODg4DgN1ufwnAHQDqAPw3AAwODp622+0H7Ha7Re5z5WyI0ahFVdXq\nG8KVYQcAYFt75arH93fX4ldvjcMViqGzOXuCmUFYnSyzGVJ+llzYTFr4QtGs7+EQTvJd9pqURdtG\nYXtswvZ4AhE0VpsK2jYAq15/oLsWL54agzMQQ1drZvuPVscAvKAMSh2GWsuk3Z6ZZT80DI0/fGgf\nVFkmG/e/awueemMELAfs214nhgDkg1dHTmLMPYGbmvbjiH0vAKAyxq8aGY08aSLWj9oaS17788D2\nOvzwpUHMLAcKPh5yke1zYjEWF4ccsBg1uGV/c9Z9ng8oig+ySN4W02S8fjiX/aHR8upNZYWxZPsx\nFcxCPVZUuMtwDD/Bi6nDq7aLERQnRq0SH5s8xZPKgzvqxL9VVZnxT58/ikg0hubG4iQozs7yLX/2\ntnSjvob/DE7Frsm+1EuUuPJyI6oqimOrLiXW8pxcr6iqMuPo3ga8cXEaY4t+HJKkVs84+QmVyaQr\n6r4zGzWYF/qpbutYfe/P9NlVAL71l3cVbdvI5zdUmTC96MX737VFsX2h18cV1ELfs75aeD2jyvm9\n1IKjKd3rLIK12WTmz4NXzk5g2R3CA0fb0d5SkfI1BKnec9fWKvSNLmPRE0Zbc/ryGaMwb7BY9e/o\na/fQjjq8fmkGU0t+3LBbGVdANhS6vxlhQbu62oLqciP6x1eg1mkSHJe5YmBsGfPLftyytwEtTaVJ\nMSZQ6/hr1R+Ordo3BoNG8fPT6QtDRVPY2l6Z0Os1G0p5nTx4ZydeOjuB13tm8MBtW/N6j3zJrB/A\n/wXwHQBbAbwIQGrE9wBoB2BB3IoMADHhb245z7Xb7czg4GDGggifL4TFRc+qv18Swm6qrdpVj3fU\n8QfpxIVJlOmz7wKPQHDc7kDKz5ILo47B5IIPCwvujCrqyJQLRh2DWCiCxcXVXz8Y5Fchl5d90FAc\nQuEY9GpVQdtWVWVe9fp2QQk9cXEKFcbM1rBAULKKy4Th9aY+LtEYi/FZN5przFhe9snatjsPNMHp\nDSEcCGNRhk07FXwRP3506SloVBrc1/xucdu8bv79aIpf6ZsU6qwDac6rbDAwvDW2b8RR0PGQi1TH\nLRlXx5bh9IZw65562fs8V3Ac32omeVtc7niAmJz90TuyhFfOT4mW8mAgXJL9mA5kVTggXHMhNgjQ\nwPTy4qrtIupAMBARH7s8tAgVTaHCqE54vpGhUFVXVrTv1js7BAoUyrgqOBxeaGg1fMHCxq+8IVHp\nlpd9UG2w1hDJkHPNvVNx174GHL84jR++cBWtVQbxPrci9DgN+PMbV+WCkUzW6my6hM9aL8ft7oON\nuHTNgc56i2LbQ+YEgLxxNhPUwi4cn3aisTw3whAOR0Fl2IawsJ2OJR/mzRo8/vIgVDSFoztqM253\numNHtu9s7yzaqtMvks0I93WPO7guzoG1Qp1gNT3fP49bSmCvVeKaCwk10UsOD7QMf3KOTi4D0fzb\nNP7yON/j90BnZcnPB5bjoKIpzC/5Vn22x6v8+Tnr8KHcosXyknw1vtRjpQrA7o5KXLruwFuXp9Ge\nJrgvE8HOV6IZAvCjwcFBbnBwcAg8CZUui5nBk1u38Lv085L/lva52YhsJoynCH8i6G4pA0XJD4Ei\nPvZCLVJmgwbRGJuyYThBJBrD/IofDZXGrLZhluMkScbKhT8RdLfYQFOUrBY9UlsZpQ6ntSbPOHyI\nsRxaauTXMn3kjq34zPt2yH5+Kjw38hK8ER/ubb0TNm1cZWZofjFDLXD1ZaEeI9+aWRVNo63WgplF\nn6xgilLgTD9v3TjYXVo7DZB7mvELp8dxeXgJ40IrprXuM0tRFDRqGiGhhjcGYWErtNpmLPaZFb5z\nKBzDxLwHLbXmvM+nfBBjYxh3T6LeVAs9w09eNCrNmrXmWQ+9HTdRGjRUmbC/qxpjcx5cGYnfNzgF\ncifkQJrMm0uScSlxdFc9Pv/gLmWD0RTcrRZj/v08uSzbIg2AunzNgdklPw5vq0FFnj0w2xusUNEU\nBiczhxr9UnDI+IJrMwauF9hMWtSWGzA0tXH6zXIcBwr82GEWg1Tzz38IR2J4q38BZWZtUVqEZQNN\nUbCZNClb8yhtMw5FYnD7wuvWYiwFadPzlScu4Xu/7EfPsCMnO3m+o+knINS02u32egAGAD673d5h\nt9spAPcAOA7gJIB7hecdBnBlcHDQDSAs57l5bhsAvi2PQcugyrb6IJr0arTVWTA87ZZFOMjktNBg\nBXIhZmrPM7vkB8chY3iQdDviScbKh2oYdGq011swMuOGP8tNgIOUzIbS1sySRYYmhWqF5GDCw4c+\n1RiqcVvTkYTHCJllknZfIWFaHQ1WcABGZ9e+bjYaY3F+cAFWowZ2Sd1mqZALl/UHo2LzbL9wXWpK\nSALTQcOoxHRl0nrKFV69ainWzAp3pOEZF2Ish87G0u73Se80ImwU7dZW8W9qWo3IGrXmkdbMvtNr\n1t4JuP+mVgCJtbMlq5nV8uOFUcegOsW9/+0KWsEryyqQ2VQBNVnBZW5fJiWzvzzNE8x3p0lflgOt\nWoXWOjPG5zyyWvQE18kC81qiq9mG0Aaqm2URv7cWstBCcPGaA4FQFDduL06LMDmwmbVweVcnhivd\nZ3ZpHbflSca21jK870gbGIbGiSuz+OqTPfjDfzuObz3Xh1N9c5hcyHy+5ktmvwvAZrfbTwB4HDy5\n/V0APwZwBsDFwcHBtwA8BSBot9vfBPAVAH8kvP4zOTw3Z/iDUcyvBNBSa067EryjrRwsx2FgPHvP\nLaVuxPFes+mJYbYkY+l2JCqzxUmI3C7sp/7xzCufCWQ2Q5oxadjdnIMyWwhYjsUTgyT06X0ieSVQ\nCwFQDJO4vYUoaR31pN/s2pPZ/vEV+IJRHLBXF3XgTndt5JJm3De2LJ43hMyuh7YuWrVK2C4OUfDX\nmyuFMkvqUWLCdx6e5on51sbc2k0UihFJ+BOBRqVZuz6z6+AYbqJ0aKo2YV9nFUZm3Lg6xt9fOYUW\nhLNBp+XH97Z6y7pLri0qFPyqpHWIy5d7aiwHLuM8iQRA9Y2tYGTGjb1bK9FQYGsye1MZWI7D8HT2\n++076pxIA3szadGzMfrNclz8nCILLW4ZCdbpcLKX9DQubYqxFDaTFjGWgyeJlCvdmcfh4su8NoIy\nS1EU3nekDf/8uZvxlx/dj7sPNsGgZXC6bx7ffu4q/uZ7ZzK+Pq+a2cHBwTD4NOJkHE56HgueuCa/\n/rTc5+YD0WJcl179295WjmdPjqF3dBl7k9oIJIMQtUIHQhJzn+lCnHIIScYZBngxzZiL963NNUJf\nLra3leOZE6PoG1vGfnv6/cRBkmasDomBOcmYnPeAolK34ikGTs+ex6h7Avuqd6GrfHVhOSG3tCq+\nvRo1XRDxI37/kWlXlmcWH2eE2vGDa5RinMvg3HPdIf7uD64jZZa0B1JFAIr/Qu6wW7jJxs8Tcs6Q\nsgSSVlhdwh52ADDiHAOABGVWo1JjJbRWacabE8h3Gu6/qRUXhhbxzMlRYUGU/3vxbcb8eNGeQ3/Z\ntwOU3KtmgxoU8lS/soz3JHzw+GU+oO7eAlRZgs4mG144PY7BSWf2HuqbQxHszbxTaGDCmbYn8XoC\ny65WZl15KrMrnhD6RpfRXm9B3RoGEZZJes1KQ03ldliRi0UheK9yAyizBDRFYUujFVsarfjw7Vsw\nNufB//7+uWxDS97K7LoGqbdrrU1/Q2urs0CvVcmqByWlBcrZjOUos+nJHtkMjuPEvrXFqJkFgLY6\nM/RaBn2jSxmfl2CPYMKIxVZ73VmOw8SCF3UVxpLUEPojfjwz/AI0Kg0+uOW9KZ9DyCxFc+LxldZd\n5QOrSYtKqw7DM27FbSO5IBJlcWHIgTKzFltKrA4SyP3+LMfhykj8HCPKrHYdqHqEUFNM/LqNsFEE\nosGE55Hzh9yP3H6y0FS8vprJ4DgOI64xWDVmVOjiKY0aWo1ILLIm5+Nmn9l3HlpqzehuKcP1KRfc\n/rBEmS3u51YIabl2GV0K3lZQcL8yKhomgzovmzGHzA42UnIQYzl0NdvQ0VD4fWlLgxUUBQxlqZsF\niu8M2AiwmbSoq+DrZqMp5mnrDRzHgbTEFQWhPMns6b45cBxw8461U2UBvucvADg9ScpsVsqWG4gy\nW7UBlNlUoCgKbXUWfOjWjqzPfVvOMsbmeLtJpr6kjIpGd0s5FpwBLKz4M76fGF5R4N6K24zTX4jT\niz5YTRqY9OknwHEyK6mZzZI2nC9UNI1tLWVYdAYxn2E/JdbMhlPajBedAQTDsZJZjKWhT2W61HWL\nDMUT1xgXhUXYhzoFiHZ7vQXeQAQLzkD2JxcJfaPLCISiONhVXZqbeIpxWC53Gpv1wO2PiNsZEkLS\n1GvcZxaQWM6ZxEWo5F6zZBeTc9/jD4OiAGOGa1lpLAdX4Ap70G5tTVDBNCoNOHCIsqWvGdu0Gb8z\nUVvBOxLc3rC4IFxsZfborjr81W/vR3fLO4vMZqpTzQdWoyYv9YvLkgClkrg0lFBlAcCgY9BcbcbI\njBuRaPpwTaD4NdsbBfbmMr5udn79JztznDI1sxzH4cSVWTAqCoe2lT4MUwqbOa7MSqF0JpeDKLN5\nBqytF8jJsHlbzjLG5jww6pisB5BYUrKps2RCXmxlNhCKYskdRGOWGhJy4+K4uOe+WMosIG8/JdbM\npiazYr1sdfHDnyY8UzieJvRJCkaomY2yUdHuoS0g/Imgo55fcR6RUcdTLJwZ4C3Gh9YgxZhArhLY\nI/SFJhYoAtHiu4Yg20AJPWY1NH8dJ9fNUhQFmqLEOmG3PwKzXl1SNWDYNQYgsV4W4MksgDVJNFZv\nKrPvSFgNcUtgPM24uJ+pZlSKqH0bDUrvV6tRg0AoKgbfyUfmmlkyFjRXm7JbgnNAZ5MN0RiLkSw5\nFZs1szy6hPvs4ER2NXutwXGc6OhgVDSMOkZ0PeWCsTkPZpf82Lu1CkZd6RaYU4HUpZNSJAKlnVOL\nrgA0DC0uAmxUvCPJrD8YwcJKAK0Zwp8IyGCarUUPq9CNOFvN7Iwju8VYuh0cB3iE3pbpDTp4AAAg\nAElEQVTmIqo/uZJZMBFEY6sVoAlhFbDYymy20CcpyGNRNgabcMEXkmRM0N5AQqDWpm42HInh4jUH\nKq06tGWoHS825JaA9AwvQUVT2N1RIf5NRVPrwqKqZRJtxnVG3qLkTpFoTNPxmlmPLwxziW8iYviT\nrTXh74SAh0sUAhVjY3h2+L+xEnSuixCvTZQeFlNcRSHDwCaZ2BiwiiFQuY0XHJfZ8dxSa0ZDlRG/\nefsWRc+FTiGpP5vVePPs40E6GwxsgBAoluMSnAcWoyYvZfbElbUPfiKI24yTlNkipBlXWHUbftzV\nqrOX/r3tZhmkXrYlQ70sQbVNj+oyPfrHVzLWDohktsChMJsyO03IbBZllig9HMfB7QtDzdCKELB0\nqJKxnxKUWQqIYHUSYjzJuLjk6i0h9GlvmtAnKVQUv98ibFS0fiihzDZXm8GoqDVLNL4ysoRQOIaD\n3dVrOpDJWWl0eUMYm/Nga6MVJkl96XohQZokm3GdiVe6UyUa0zSFGMshGmPhD0WLFsyWDiOuMahp\nNZpMDQl/16gEMlsiZfbEzFt4afxVfOXCNxIXJDb2PXUTOSCVMrtGnTDe9iiGMgvkQWaBjNd4uUWH\n//3JG7CtVdn+nlubeDU+K5ndPP8A8IsVdRUGXJtyrfu6WY5DQiCnxaCBNxDJabsjURZnrs7DatQo\n6gjIFyQAKrnXrJJc1h+MwheMpmxPWgpE2SimvbMIxwqfc+i070BldkwMf5JHmLa3lSMYjmW0p4g2\n4wLvxBq1ClqNalUcN8HUopBknC3pl7TmAU+MzQZ10QlLtv1EJisamr8JRhBc9ZyJeQ8qLNqM9cCF\nwh/x42kh9OnBNKFPUlAUBYZmEOWi4g1ciZpZNUOjpcaMqQUvQjlbtQrHmf4FAMChrlJZjFOff3IG\n5x4h+GlXR2UC8VkPScYAoCU2YzV/3TYIymxyzSwA0WZczP7P6RCIBjHjnUOLpREqOnHfkeuydMos\n78xYCq6sm0WJTZQW0vq2uLtpk00UA0rvV1GZ9ebYnidLn9lCwHEcvn/1Zzg2dXLVYxaDBvWVRlyf\ndmckOZvnXxwbpW6W5RKt62RcyRSkmozL1x3wBfnesip67e9HWo0Kei2TomZWOTYbb8tTmnpZT9iL\ny4t9ePr6C/iX81/Hn7zx1/jSma/g2eEXC37vd6TNeDRHMrujVYaFVsF6H7NeLVqDk0GSjOsrM7fy\nEAdkDvAEwkWtlyXItp9Iax6DiifiUSqRzLq8Ibh84aKrss+N/AreiA/vab0jbehTMhiKQZSNinUM\nOg2DCBvFqxNvIBjNvdceQUeDFTGWE90CpUIoHMPlYQdqyvQlC9uiG6/Crx9f9Xc5Q3PPME9md2+p\nSCSz64QEJacZ15vqAADuUAqbMUWBZeMBFaVQZjmOw9+/9S/40zf+Ghy4hJY8BGqizCqwSioHBnV8\nDFsPVvFNlB7SNhpK9WrfRGqQ3arU/s1fmc3iMy4AgWgQZ+Yu4MmhZ1I+3tlkQygSEx1gqbB5/sVB\n6mYHxte31VgaAAXkFwJFLMY3rQOLMYHNpBFtxtLSQaUgtuUpQpIxy7GY8c7hxPRp/ODq4/ibU/+E\nL5z4W3zryvfx8sQxjLjGUa2vBAA4gtk7xmSDnA4jhfUgWQdIPvjjc26Y9GpUyFyN6Gopg4qm0Du6\njA/c0p7yOUr2yLMYNRif86zqUQnwNuNKqy7rgSMCcTAcRTjClkT96WopA01R6BtLv58AwMSY4Iws\nI0olpviOCzeYpurikatJzzSOT59CjaEKtzcdlf06hlYhysZgFWq8tBoVLsxfxi+uPw+tSoubG27I\na3vEfrMzbrGmpxS4POxAOMLiYHdNSVaiY2wMqpoxODEG4MGEx7LZjKMxFn2jy6iy6VBbbsCiJP15\nvSizcTLL3zxrjdWgQKVWZmmizArBbEWqmf3HM19FvakOj2z7MH9j8c2Jj3WkILOiMsuWRpmV1qlv\n1D6zjw8+jfMLl2BSm2BSG2HWGGHSmFAzVw46oub/rzbBRH6qDasU8XcyxElnQmuejXkurHsIu1Wp\n/SuS2Vzb8xSPy8IXiXdTcIXcsGoTS8k6m6w4dnEaQ5NO8d6bjM3zLw7Svmpwwon7blzjjcmAdMps\nuuyZZLi8IfSOLKOl1ozGbK7HEqLMrMXskh/hSAw0RSHGcYrWzIpteRToMRuMBjHmnsSIawwjrnGM\nuScSWhPqGR22ldvRbm1Bm7UFrZYmqGk1Hj32PxEqQBAikOOW3PBkVgpvIIJFZxA72splT+L1WgYd\n9RZcm3LBG4iktMBK6304jkMgGsBKyIWVoBPOkAvOkAsrQf5nKBbCQ10PokFQb5Jh1qsRYzkEQjEY\ndPHd7/aH4faFsWdLZdZtJt+NrEyZ9asnzG/OnEGNoRodSUEw+UKvZdDRYMH16dT7SVRmGX6wiFGJ\nJzAJf2opkjLLciweF0Of3p8x9CkZDM0rs03VJjAqGo1VRiwHJwAAvmjmtk2ZQBKNSx0CJVqMu6tL\n8nksl97WlW1svjbpRDAcw80763jL9zpUZonNmNTMmgUCk65mlmU58UZbrIWmSe8MJr0zIpmVoi0p\nyRiQ1MyWSJmNsXFr/UZVZgdWhuCP8BOCBf9iPBdgOv1rjIxBJLc82eUJsFkkvUaYNaZ3BPnVqlXQ\naVR8AJSCC8KbWA2l9y9Z2HX5cpuIZuszWwh8UZ/4+6hrHHuqdyY83tkYD4F69w3NxdmItxGsRk1C\n3ex6Haf5NOP4SWXNUZk91TcPluNwZGfqOflaIVXdrJJpxo48lVmO47DgdeDsXB9GXOMYcY1hxjuX\nkItTbajE7sodInmtNVaDTtG7VE0zCMYUILMyambfVmSWeP9bZFqMCba3V2BoyoWrY8sp25hwHAem\n/jq+MXAWnog7a4jKd3p/iL848Ch0zOoVEWmvWSmZnVkkScaZw5+A+MonsQBZknrMXlsZwY8Hfg6L\nxoy/ufEvoFUpow5tbyvHtSkXBsZXcKArkSiRE93E8NsfoxNtxhMLxQ1/emvuAkbd47JCn5LB0Awi\nsQgqrXr82x8chVpN42eDpwCgIJtxuUULq0mD4WlXSiW+GAiEougZXkJ9pbFkq5CZht9sgzOplyUp\nxuuRzEptxlyUgYpWwaqxYCHgWPVcmuJXkt0+fowohs04eZ+ykiNQqa+AUb26TIGQ2ZfGX8WMdw4d\ntha0WJoVGxuSEeOkZHaDEhgOsGhM+NKRLyLGxuCPBuAJe6EysJhaXIQ34oMn7IU34oNX+En+v+B3\nJCa8p4GB0QvklpBe/qdJbRR/Fx9XGzcc+bUI/UqV6giwidQgtXZKlQNajaRmNvc042Jps1Jldtg1\ntorMllt0qLLpMDTpBJtEgAg2ldlEdDWX4bWL0xif86CjwYp5/yKuOK6i19GPOd8CPr/3U2mFmVKB\n4xKD48SuIDLILMdxONk7CxVN4YY17i2bDLHXrCckjovFUGYrsyizETaKSc80RlxjGHWNY8Q1ntCp\nQU0z6LC1ot3ainZrC1otzTBr5M0ttSotQkqQWRk1s28rMjs2yyslrTKSjKXY0VaOp94YQd9oajLL\ncgBTM46VcAwNxlrYdFbYtDaUaa2waa0o0/E/bVornh/5FV6ZfAM/G3wKH9v2kVUExmyMJxrXSELV\n5CYZAxJlVgyZiU9IOY7D86Mv8Y+HPXht8gTe3Xp7DnsjPba3lePp46PoHV1eTWaFTH6joMyy9Gpl\n1qhjUG7RKrItUvgjfjx9/ZfQ0GpZoU/JYCgVAhx/4ZMk45UQr6YWsqpEURQ66q24MLSIFU8I5Zbi\nF+JfvLaIaIzFoa7SqLI80g/A2QbnnuElaNS02F92fQZAxW3GXJS/fi1aM6a8MwhGgwmLVipBmfUE\nilczK1ViOY5L+H+LuTHla7baOlBrqMa4exLj7kkAAE3RaDI1oN3WgnZrKzqsrause/kiysZbcyW7\nNNYrko+llIyqaBXMArGsqjKjhq7P+F4sx8IX8cfJLiG6hPQKBJj8zIX8xpVfnuTur94Ne/mW/L94\nEWExarDodIl9xzfJRHHAKmzj1mtV0DA0nDm3QOGKZjOe8cZLKUZdq/MZAL5u9uSVOUwv+lKWNG2e\nfonY2mTB69ev4Knh5+GbnMaCP3GBdsozsw7ILJeyZlZOPffEvBfTiz7s76wqavBoPhB7zXpDwvfj\nZLcylAOHKwi9lknbU3fB78BPBn6OUdc4opLFZ6vGgsNN+9Cga0CHtRUNprqcnI5S6FTaggQhAhVN\nZw2SfFuR2fEcw58IWmrMMOoY9I0tp1TQWJYFNFHUGxrwhUOPZnyvBzrejeuuUZydvwh72RbcWH8w\n4XFiCU72+0/LTTJGfEAWbcYSK+PgynVcd46is2wLpr0zeHn8GI403ACTOjtJzoa2WgsMWgZ9o6v3\nE5mIGYXPianiymwgFMXCSgDdLWVFUSefH+VDn97X/h7ZoU9SEJuxFM6gQGajq1OZc0FHvQUXhhYx\nPOMuCZklFuODJbIYA5kJK8cBlM4rhidJseAMYHbJjz1bKqEWerlKVbz1QmZ5hZjjbcZ+/vq0aXjS\n5wq5EwgQRdKMBWXWbFT+Biolr4FoYm16syU1ma01VuOLh/8UnrAXI64xDDvHMOIaw4RnGuOeSbw2\neQIAUKkrR7uwCtthbU1rH8oG6c0xnFQ/vx5xfPoUHh98GofrDuAh+weholUIxkLQ0vktRtAULZJf\nOSDk15tEcvmfPngicSLsDfuw6F8Sx9zTs+fw+7s/kbMjpRSwGjV8P3ThXrWeycSrk8dhVptwsHbv\nWm9KzuCHYBY0pcyUjqKovPp5FstmPOQYwfMjL8HA6KGiVZjwTCMSi4jBdgSEzA5NOlPnc6zj869U\n8EcCuLo8iCuOq+hzDELbHcBoFNBwGuyu3I4dldvgCXvw7Mh/r/WmAhDEJMktiDgR5dTMnixy8FMo\nFsbL48dwe9ORhNBDOYj3mg2Lp6VSNmOO47DoCqC2LPU2sRyLH1z9GUbdE2gyN4iqa7u1BWVaG6qr\nLVhcLDy4VMto4Q34sj9RzntlmQ9ucDKbeODH5jwwG9Q5q380TWF7WznO9C9gdsmP+iR1NMbFQNEc\ndKrsZIShGXxi+8P4x7NfxeNDT6PV2ow6Y1zttYjKbOKFOOXwgaYo1JZnvyAIIST1LESZ5TgOz4/w\nquwHttyL6ysj+MX15/Gr8dfwwTwUy2TQNIVtrWU4N7iI+ZVAwraSiZVBZQQ4gFPFV2MmRYux8rbX\nSc8M3pgSQp+a5Yc+ScGT2cT2OaIyGw3iG5cfwxZbG+5quTXn9yZBFMPTLhwsslrqC0bQN7qMpmoT\n6ioKX7yQC1IvnfIxjoO6rQ+0yYkR1wG0S+o5e67zq8C7tlSIf1OtQ5uxVq0CKBYUzYKNEWVWILNh\nD2qM8eNK0xSiEVa80RZDmY1JyKw34oOeidfEdNo6Mr7WrDFhd9UO7K7aAYCvoZ3wTGHYOSqGO5yZ\nu4AzcxcAAHpGL9zkWtFh5a3JGlV2gi6tmY0gTmaL1bajEJyZuyDW25+aPQt32IOH7B+EJ+xFc0VX\nSbYhgfwas9vhWI6FPxLAqHsc37nyQ3z7yg/wR/s+i0ZzZsW41CAqitNLyOz6O/4Ev7j2HABsSDK7\niBHoD70KavSwYu9pNWkwNutJa9lNCQWVJQJnyIV/Pv8txDgWn9jxMHod/Tg2dRITnulVmSB2IWhx\ncNKJO/avXthbr84Ad9iDb/X8AO/fci+22NoUf/8FvwO9jqu44ujHddeouCBaprWBXa5DYLECX/r4\nA9Cr+bnzmzNnAUCWW6TY4AOg4nMBuTWz0RiL01fnYTaosbO9IuNz88UTQ0/j9Ow5LAdX8Mi2D6d9\n3hXHVVCgsKOyW/ybSGZFZRZgFWr56/FHEI6wqEzTY/aN6VMYdU9gf/VufGLHw8p8aAroVFqEYmFF\nyuyyWY03NJnVH3oJc7F7ALTCG4jA4QpiR7v88CcptrfyZLZvdHkVmY0KNjk5ZBYAKvXleLjrN/Cd\n3h/ie70/xp8d+B/QCLVphHi6JT2yOI7D9KIPNeV6WT0ZHdEp0KYVuH064T35yWXf0gBG3RPYXbUD\nzeZG1Blq8OrkCbw+9SZuazySl2qZjO1t5Tg3uIi+0eVEMivYjGmKBsVqwaniAw2pZVa6XpblWDwx\n9BQ4cPiNzvflbYVgaBViXAwsx4KmaASjIVHxmvLOYim4jN6lftzaePOqleBsaK21gKaojH2MlcKF\nwUXEWK5kwU8EmVYTOQ6gVBFQFIf/7PsJ/uehPxTJF2nJs0tyo1EnKLOJ10IoFsaP+p/g7bHmBjSb\nG9Fkrk8gc8WARq0CKOE7cvw2WTX8uexOCoESbcb+MBgVLavWI1ewSWRWK4xLWpUmrTKbDhqVGlts\nbeIEiuVYzPsXMeIcw7CL/9e3NIC+pQEAgIpSCSu5LeiwtqLd1gqLZvV1LV0cCnH5h6gVAlfIg6tL\nA+hbHkSjqT5lucXlxT78sP8J6Bgdfn/3x/HC6K/RtzSAfzz7NQBIWIhcT6ApGiaNETsrt+GRbR/G\n9/p+gq9f/h7+9MDnUK4rW+vNE2E1EDKb2IJiE8pihroCAGBbzuDZYQPua7ur4Ppqm1GLGOuGNxCR\nvSjHK7PKHeRILIJvX/khVoIufHDLe9Fd3gl/xI9jUycx4hpbRWarbHrYTBoMTTpTTqDX62LKKxNv\nYNQ9jq9f/i7+5V1/V/D7xdgYRt0TuCIQ2Hn/gvhYi7kJOyu7saNyGxpNdfjRy0N4bXAa0wtBbGng\nCRbZT0oGEuULvmY2ftzUjAp6rSorme0ZXoI3EMHdB5uKFm415uKDQjOVo701ex4/6H8cNEXjrw79\nMWqFxW/RZuwJiY4BpRYPFjP0mF0OruDZ4RdhYPT4UOcDinxeOmgZLThwCMXC0DGFlRhm6/Kyocks\nAIxGLwG4A2Nz+dXLEmxvE/qoji3jroNNCY9FOZ54pgp0Soe91TtxS8NNeGP6Tfz82rP44Jb7Meub\nx2hoDOrmfpwK9GDsohGf2f1xeLxRBEJRcRsy4ezcRZwMPA11uw7ufj752GLQCLWyvwIFCve13QWA\n7y15X/vd+FH/E3hh9GU83P0bsrc/HcT9NLqcsPJJ1DkaNOiYFlEmPoGdnC9O+NNbcxcw4hrH3qqd\n6C7vzPt9GMGaFWNjoFU0nKF4+vCSpEfWlaV+7KveldN7azUqNFYbMTbnKXpi4JkBYjEu7QSczVYz\nKxDBpeAKfjb4FH5n20MIR1gMTDjRWGVKsF9L9w+xHhOcmj2LCws9AIBz85fEv1fqK9BsbkCT5J8S\ntnoCbQKZ5e86pLbUmdSeh6YosBzg9kVgMaqLMnliJUq4N+wTycuOiu50L5ENmqJRZ6xBnbFGbEnl\nCnkw6oqT2wnPFMbcE3h18jgAoEpfIdqSO2ytqDZUIcbFbftB1o9S3GpYjsWUZwZXlvrR5xjAuGdS\nfGxg+doqMjuwfA3f6/0RGJrB53Z/Am3WFnx218fx16f+URwD1iuZlWJ/zR64Qm784vrz+I9L38Wf\n7P/9nC1vxUJyfdt6VcZiSc6cjQYVBLJJs3hp/FVU6StWlTjlCouQaOz2huWTWQXJD8dx+NngUxhz\nT+BoyyGx3R7poz2Som6Woih0Ntlwpn9hlXsMWL8uY2lgXr4IRAO4ujSEK45+XF0aEDsxqGk1dlZu\n4wlsRfeqXISu5jK8dmEagxMr2NLAd2AQba8Fb1Xh4JJa8wD8nDcbmRUtxjuK11t2JeQEAJRprSkf\n73X040cDT0JNM4iwUfz82rP43O5PCjZ+NSiKr5klAVesQkWz8STjRM5CrqlQLIyPdv9myoVoJaFT\n8QQ2FAsVTGalgbmpsOHJbLkQxJFvvaz4PhYd6iuNGJhYQSTKJiikUfCrLnpVbgfjg1vuw4hrDCdn\nzuDNmbPiqgtTC7gAuFYAR2AJDgd/GBqzhD9dWOjB96/+DAAHShNCMBIFQMFsUOOyow+Tnmnsr96d\nULB/Q+0+/HridZyaPYc7mm9BbYGTs0qrHrXlBvRPrCSQM/LdKIqCitUhpnEjykbB0Awm5j3QMDTq\nZFio5cIfCcRDn7beX9B7EUU3ykWhhjqBzErxWN9P0L80hHtab0elPvvCA0FHvRUT815MLnjRVqdM\nwE4y3P4w+sdW0FprRnUaa0kmsByLCws9WPQ7cKh2Hypy+H7ZlFlQLLiwBu2VtTg3fwnbyu3QelsQ\njbHYvSXR/pNgM5YosyzH4tjkCTA0gz/a9xksBVYw6ZkW/11Y6BGJLgCU68p4YmtqQLOFJ7j5Dtwa\nNb2KzFqEmll3KLGuhKIoxARltlhWb6ky6wy50MjxY2A+ta1yYNWasad6p5geGo6FMe6eFMntqGsc\nb82dx1tz5wHw7WmkLa0CrA9A6pt9oQhGgxhYuY4+Rz96lwbEFEaaotFp68D2yi6cmj2HxaRgkxHX\nOP7fle8DAH5v58fEdkYqWsUvWm4gMgsAtzffgpWQC69OHsc3e76Pz+/53ZxdJMVAvF/p+lZmpTXe\npUqeVxIqLpFs/nL0ZRyo2VPQOWAjFnFfCI2QXyKk1K47NnUSp+fOodnciN878DBcK/w5VKazoUxr\nw4hrLOWxsgtkdmjSuZrMrsPDuuhfEjMLch3Dg9EgzsxdwKXFXlxzjoj3BpvWiiPVN2Bn5TZ0lm3J\nWBpCrNkDkn6zpBwkUwlRqcBxAFQRcT4J8ItkC84AWJYDTa8+qG5/GD3DS2iuNhWtgwbAu8UAPrU3\nGcPOMXyn90dQUSp8fs+n8OLYr9G/PIQex1XsrtoOFU3DatTA6RFUXYrN2spQLuJJxolzwfMLl9G3\nNAB72RYcrt2vzIdlACGzwVio4BnA+49ktt9veDLLUPzKw9hsYWQW4K3GL5+bxPUpJ7pb45P5GPgT\nNhdlFuCV0U/seBiP9f4YekaPelMtavQ1+P7TM6hsn4dXPypajIHMbXkuLfbisb6fQKvSgOH08MIJ\n0DGoaQ3UDIVfjiSqsgQ0ReOB9nfjW1e+j+dGXsKndj6S03dIhe1t5Xjl/BSGp11i422yhkdTFFQc\nfwJ7wj6Y1WZMO3xoqTWnHHTyRaGhT1KIZFZYnV8JOlc95wNb7sObM2fw5uwZnJ47hxtq9+OelttR\nZchei9HRYMFrF6cxPO0qGpm9MLgIluNSpnFnw7WVYfzX9ecx4eEbaL4w9mvsq96FO5tvRZOMGryM\nAVDglVmOVeF3tj+EfzjzVTw+9BS6Ary9ZVdH4v6T2oy1EmW219GPxcASbqw7iFZLM1otzdhfs5v/\nDI7DctCJSc8UJj3TmPBOY9I9jcuLvbi82Cu+h1VjEezJcQXXprVmnbjyQVSJ39Gq5ccZV3i1zTgU\njoHluKKEPwGJZLbHcRXbhLrOYpHZZGhUGmwt68DWsg5xe+Z8Cxh2jWLYyfel80X9aDI3YNIzDX8R\nyGyEjeKnA7/A+flLIhExqY24oXY/dlR2o7t8q2g/718awpxvXpwMTXlm8PXL30OUjeJ3d/z2quAk\nlWQ/Frr4V0p8YMt9cIZc4qLnR7t/I+d7FoFShC65Zna9KrMRSbu9MBspWsuqYkHFqgFhuDRrTFgJ\nOXF8+hRub74l7/e0mnJvzyNUGxWMgeVr+K/rz8OsMeHTOx+BhtEAiFs5260tOL9wGYuBJVQbKhNe\n20nqZiecuGV34v1rPS5SPNb3E/F3uWO4I7CM16dO4s2ZswjGeBWu2dyAHYIC22RqkP1dLUYN6iuN\nuDblFAUK8bXrQJqNUSE4W3+Fb1+5js/u/jgAfps5DvAEIuKCmRRv9c0jxnK4qUS9ZSNJ7TpnvHP4\nRs9jiHEx/N7Oj6HD1ooPbb0ff3/mK/jFteewrbwTapUaZWYtJhe8ULf2Ql8xiYj7NxXZHoeLPyeq\nJMqsN+LDk0PPQE2r8ZD9wZJcC1pBjQ0pkGjc1ZK5fGbDk1lytY3NeWAxqMWi6nywo50ns72jy0lk\nlj9R86nNqzFU4QuH/jDhbz+LvI5IZBEQ3i5bkvEVx1V8r/fHYGgGv7/7k/hF76vwhp2gmDAsOiMu\nLl7BjG8ON9TuTwijIdhVuQ1tlhZcWuzFmHsCrRa+oTix5Y24xtHNtmVtN0FAyGzf2LJIZkVlFhQY\njv9irpAHbieNGMuhOVWyYJ7gQ5/eRLWhMu/QJykYobaIJBonK7NalQa3Nx3F7U1HcX7+Ml4cewWn\nZs/irbnzOFizF+9uvR3Vhqq0799Rz0/ki1k3e6Z/HgByDpn61dhreGbkRQDAgZo9sJdtwbGpkzg3\nfwnn5i+hq2wr7mq5FfayLWkHv8wBUABFseBYBpX6CnzE/kH859Wfoif2axh0N4n7hkCqzKolyiyx\ntN7WdGTVZ1AUhQp9GSr0ZaJ6yHEcXGE3T2490wLRnUHvUj96l/rF15rURqH2Nk5wK3SJqdtSmzEn\nKrOkZtYjft4Vx1X4ynrALrQAoIsS/hSOhXFx4Yr4/8GV6/CEveJ+WAvQFI16Uy3qTbU42sAv7Qej\nIahoFf7w2F/CH/Uq+nlRNorv9v4IVxxXUWOoxt7qndhR0Y0WS2PKySC5oQZjIfiCK/j3S99BMBrE\nI9s+jN1V21c9X0Xx40GFrnxDkRqaovFI94fhDntwcfEKLjv60GxuxFZbO7aWdaDD2iKL3DoCy/jy\n2X/FzspteHDrewuyLBMyS1rzrCWZ+HnfC3B7fHh36x2rFMtILD4ZDUaDGY87y7EYc0+iZ7EP454p\n3Np4EzrLOopeu58JtESZvbXxCF4eP4b/Hn8VN9Yfgj7PBQ2LzKAdKfg2s4Uf4yeHngEFCp/a8UjK\nxeo2gcyOusZXkdm6SiP+//buOzyus0z4//dMkzQa9d67jm25t9hxEqd30iAJJZCEGtqyy7IL78Jv\nd4HfuwvssgtLWZYlQIAQSEiBkIT04iRO4i5bth9blmT1YvUuTXn/ODOjkSXLkkW7sDEAACAASURB\nVDWS5jj357p8SRqNxke655T7PPdzP64YO8cauqf8XATmspOqWCycOZn1+XxU99TycuPrVHZU4cNH\nvCOOK/O3szV7I4lnKHWdDT0/kea9g9S19gdLjSEyGkDhMP4+oeftwHuzf3Bs2mT2jYPG2rJbFmlt\n2fGQ1TA6h41zzLB7mI8svzPY9CkzNoNLc7fxUsMOXmx4jWsLryDRFUVtSz/2FGNKzLA2dSDlXJzq\nCcyZnTgmPXb8zwyMD3Jr6Q2zGoQJh9CR2YV2HiSzRmfgzr4RVpekzOtkWZ6XiM2qUVXbxe2XTTwe\nLDM+x5PC6eKdDgbdRgLgw0fjqUFsVsu05aFVnYqfHfw1Vs3CZ1Z/lJLEQqIs/ufZx4l12ni69nks\nmoXri66c9v/TNI2bS67je/t+wqPH/8zatJUc7zlBdU8tw4GlZ45jXLyUvuesb/Rl+YlYLcbf6bZL\nSoK/BxgXVDb/yGzf6AC9bcbBOVylHkbTJ6Pz6B1lt5xz06dQgdcIHJAC8yBc9lgGxgfJis0MXiRv\nylzHhow17Guv5Jm6F3m7dQ/vtO5lY8Y6riu8fNqbCelJMcRG26humr58eb56B0ZRDT2U5MSTMs2E\n/5ns7ziEVbPyxQ2fDt7k2Jq1iSNdx3j+5Csc7T7O0e7j5MXlcGX+dtalrZrSWGSmMmOP1ws2N4wZ\n79lNmevY1VxFFZWkL6ufMlpvn9TN2Ph/GvqbOd5Tw7KkslmveadpWnDt51WpK4KP943109DfHBzF\nbehv4nCX4nCXCj7HaYuZlNymR4XMufEnszaLDZc9lt6xPmp763m8+ilO9NZCPFgSnXi7M8KWzA6M\nD3Lo1BEOdFRxpOvYpLvAXp+XfR1GefVMF0KLLTA/xmmLYTCMyazH6+EXVQ9x8NRhliWV8anV95y1\nu3IgMWkdbOeXVQ/RPz7A+/Vb2Zy5ftrnB0Y6sqbZlyOd3WrnvtX38GL9a6juaur6Gqjrq+f5+lew\naJZZJbdtQ+0Muod4q3U3R7qO8YFlt03ah+Yi/rQLzaVKJo50HuPhQ0a34qrOo3x05V2TkqDQi9ER\n98iUeYVGAlvP3vZK9rUfnHTD81h3NRbNQmF8PsuSy1ieXE5BXO68GzDNRegRONbu5KqC7TxZ8ywv\n1r/GjcVXn9NrJromj6rPajt8XrDPbymOofEhWofaWZZUNqXBU0CgK35Nbx0XZE0ul7RoGmW5Cew7\nfopTvcOTLujnc33o8/loG2rHB6THpIYtvuOeib/vdJUL4143e9sO8HLDDhoGmgFjFPayvItZn746\nLNdAy0+bN2sJlhkvfTLrtYxPObMFGsv1Do1xesvD+rZ+6tsHWFuaOuX4E9btCqmQCpyT+8cG+OGB\n/6V3rI/bSm+c8t68vugqdrXt49m6l7ggcwOJpw2+BQbO5qujd4R4p50ofwPKI13HeLt1D3lxOVyW\nO3VAYKFEhcyZXWimT2Z9+OY9XzYgym6lLDeRIye76Q254+PRAiOz4Ulm45x2esa9WDEmfLecGiQ7\n1Tnlwv5I1zF+evABNE3jvtX3UpZUDEC0xbhTrtnG0JKaaRvqYFv2ZlJjzpyEliUVU5GyjKrOo9T0\n1gFG45x1aasoTMhnX2clBzsOc6RTcXn+JVxTcNkZ7+JHO2yU5iRwrKGHgeFxXDH2SXNmo3xGHJ6s\nfYaMXmPHCVcy+07rXmp661ibtorlKefe9CnURJlxIJk1LlQyY9Op7qkl+7RSQ4tmYUPGWtalr2Z/\nxyGeqX2BXW172d22jw0Za7iu8IpJ5YmaplGSk0Dlic5J76tw2a068Plg87K534Uc8YzgtMcEE1kw\ntndFis6KFJ2TfQ28UP8q+9oP8ouq3/Kn6CQuz7+ErVmbgknCTCc8j2/cWNLGPfE754xs5uDIcdqj\nD6G6qtGTS4PfC90HAkvzvDzDqOxcxTviqEjRqUjRg48NjA/S2N8cTG7r+xtR3dWo7urgc2xpgVJU\nbdJrNQ+28u97fggY8ytbBtuwRA/iZX5rzHaNdFPZcZgDHYcmLaWQ4UzHqlloHmwNPndP2wEALJbI\nSWYD4h1xwZHj+fJ4PTxw+Hfs7zhIWWIxn1p996yWCQqcUH9a+QCD7iFuLrkuOII8nY4ho8u2mUqM\nQ8XYYrix+Bpu5BpG3KPU9p7kWM8JjnfXcLJ/+uS2PKmE4oRCom1RwcSuIC6PxoFmflL5SzZnrud9\nZTcRO8dR2ii7lWiHlZExoxR8KUZm3V43jxz/I5qmsSZ1Jfs7DvLtXd/ng8veF5yqELrOeGAUIZjA\ntlWyr2MigY2xRXNB5gYK4/PpGe3F6/NS3VNLba9RYv907fPE2KIpTypleXIZ69PXzPnvNhujnjFq\ne09S3VNDh+PQpO9dmnsRrzS+wUsNr7E998JZr3ccKiHWX2Y8OPsL0eGMPbjjG2gaWD3rG4+nC0x3\nmakze64rG7vFPm0TKDDmge47fopjDT2Tk9k5bIfH66F5sJXqnlqO99RwoqeWgXEjUbdbbGTFZpLr\nyiY3LpscVxY5rqxzukYcDakKCP28f2yAHU07ea1pJ/1jA2horEtbxWV5F1OcUBDWfSlQmn30ZDc3\nbC0M3nWKgGbGYJ16M2WmqoE3Dxnnxm0LXGIcHAjCqOwYcY/w4wM/p33oFFflX8oV05T4x9iiubnk\nen5z5GEer36KdNe2Sd/34p7yM3Pl9fro7B2hwJ8PjXrGeOjoY1g0Cx9a9r5FvckWuKk9EoYy47Mx\nfTILUOtPZgvmmcyCUWp85GQ3h2u72OrvghaYMxu+ZNaBz78PdvUPM+b2kpM6+WRzrLua/6n8JQCf\nWnXPpAt+h3+esGYfpSf2GDbNyrWFV5z1/72z/FZeaXydXFc25Uklk8p3bl59Bc9WvcHj1U/x3MmX\nebtlNzeXXM+mzHXTlu5VFCWjGno4XNfF5uUZk0Zmk9yFNLbX05zeSIvlcWzJK8mdYT5wgMfrmXFH\nM5o+Pe1v+jT/dXMD7NpEAyiAruFuYmwxJPib/GS5pu+GZ9EsrE9fzdq0lVR2VPF03QvsbtvPnrYD\nrE9fzbWFV5Dt/9ni7HgqT3RS09zLurIzlySfi3eOtKEBG89hHdth9wgxMyw5VRCfx8dW3kXHUCcv\nNbzGzpZdPHLsjzxd+zzbcy5ke+62GUdmxzAO+D73RNJRVdPPeM8aYire4YHDv+MfNv8NLsfU94fD\nbqV3tI/dbfvJcKazIiQBDSeXPZZlyWWT5k4Ou4dp7G/mwKkqXm54HS3an5D5Ji4gEqLiaR5sJdoa\nxafXfBSXPZZvvv3vaNHGBc9cRmZ9Ph8tg21UnqriQMeh4AUdQGF8PmtSK1idVkFmbDpvNL3Nb9Wj\nABTF51PbZywPEEkjswHxjjhah9pB84LPcs4jc16fl18feZg97QcoSSjkvtX3Bpc7O5vATZdB9xBX\nF1zG1QWXzfj8wLEsY4apA2YRbYtieUp58Mbf2ZLbgrjc4E3MrdkbKUko4tdHHuad1r0c7TrO+/Vb\ng2sUz1Z8rIORMaPsbT5tE7w+L6OeUUY9Y4y4R/2fj/o/H2PEM0pqdPKUm5wvN7xO21AHV5dcws0F\nN/JO614eUo/x86oHOd5Tw3tLb5xU7dA62M6u1n2nJbAxbMncyLr0VSxLLpt2NGxofIhj3Sc40nWM\nI13HgnP2nz/5Cp9b+/EZp6LMxrB7mBM9dVT31FLdU8PJ/sZJo0MTfETboriu8EoePvYEz9a9dE5L\ncMQ57WjMbc6sO94ol2waaDnnZLYhkMzGnTmZtVqsFMbnUd1Ty9D4ME775Kq28nzj2uZYQw8Xrpx5\nO8Y847QPddA61E7rYDutQ+20DbbTPtQxqTFYUlQimzLWGfPuB5ppHmihvr8RWiZeKzUmhVxXVjDJ\nzXVln7Uvw5h34u874hmhsb+ZlxtfZ3frPtw+DzG2aK7Iv4TtOdtIiVmYZbfiYx3kpMZyvKkXt8cb\nsh740mezPutEIvT9fT+lJKEQnzUJLO4pyazb4+WtqlZcMfYpzSXDbXB8ogJh2DPCTw/+ivr+RrZm\nbeLmkuvO+HMXZK7n9aa32NN+gCsSJh+rPGFIZnsGRvF4fcFOxk/VPEfnSBdX5V9KXlzOvF9/LqKk\nzHgufNS1zG9ZnlAVhck8wgkOhSazgZFZe3jmxMQ57fjzY9q6jZN8aLJ3vLuG/z7wC3w+H59Y9ZEp\nJ+fAnC5begNjln6251w4q7UFU2KSztj5V9M0NmSsYVXqcp6vf5XnT77Mr478nh1NO7m9/GYK4icv\nV1RRlMxjr9VQVdvlbzo0MTJrs9oYr1vJ+y+4gEeqn8Beup8nap3cWnoj9jOUxLzc8DqPVf+ZbdkX\ncH3RldN2nv1z7XP0jw9wU/G1YV1LMXBh4vEaa82eGu4kx5UdvKjLjp25tbtFs7A2fRWr0yo4eOow\nz9S+wJ72A+xtr2Rt+iquK7xi0rzZcCazp3qGOd7YS3le4jnNFx9xj5AYdfb9Js2Zwp36rVxfdBWv\nNr7Ja41v8nTdCzxf/yplicXB5w2OD00ahRj1DYMGvnEjoRgYHudEUy8lOQVsLErijzXP8Jujj/Cp\nVXdPOeE77BZea3wTj8/DZXkXLVqDIzAuXsuSSrBabEYya/FfNPomj8yC0RiuNLHIGN3xaWjRxhyf\nuFkks62D7expP8CetgPBtQAtmoVlSWWsSTMS2NPnQoUuIL8+ffVEMhuBE8IS/NuuxfTjG5r7nC6j\ntK+Dv9S9xK62fRTFF/CZNR+dU5v/wKjUJTkXclPxtbP+ufkmH5FoNsltIEEKNC380obP8mL9azxV\n+xw/PfgrNmas5faym6e9ATWd+FgH7d1D2HKqaR5JYiUTF5ker4ee0V66RnroGumme9T42DXSQ99Y\nP6OeMUbdo4x4Rqc0WZmOhsa3LvrH4LbV9p7kyZpncdljuXPVexjp87E5cz35cbncf+g37GjaSW3v\nSS7NnRgh+a16FLfXPasENpTT7gx2/fb5fHQMd/JG89u8UP8q393zYz675mNzWgd6YGyQ6l4jca3u\nrqFxoGXSTeP8uFxKE4soSyzmvyt/Efy5wCjItuzNvFj/GjuadnJZ3kWkxCQzMDbI49VP0TrUzurU\nFWzIWHvGzvw2qwWX0x5cVmkuHjj8O6p7arm97KY5d1Q+2d8IzJzMgrFEz/GeGur66qfc6MxLdxHt\nsKIaQqb2aB7axpp4s7mW1qE22gaN5LVzpHtKdVGU1UG2K4tcVxalicWUJhZN6fDv8XpoHWqnaaCF\nxv5mGgeMf/s7DrE/pPGg0xYzKbnNcWWRGZsefD+dfkPiX3d9DzBKmS/Nu4gLMjfMe1mT2dDzE2na\nO0hdSz+awziXzLTs3qKxTbz/jnVXc8xfMRW9AV4a2E/9wSyirA4cVge9fR6GErsozU5mZ+vbOCwO\noqwOoqxROPzPifL/C3x+rtcVg+PDwc8PdxrTlFalruAD+m0z3rywaBbuKL+Z7+z+AfuHXwHWBb/n\nZe772ukCzZ9SE2I42dfASw07SI1J4frTmsMuhsCc2d+px3i7ZTdWixWr5v8X/NyCzWILflyXvppy\nf3PJuTB9Mjvo7eVUfw1xqRYG6GRs0IbdasduMf45rPY5vVlz013ExzqoqusKdnT0aMYbzBmuObOx\nDvDP827vNi58A52Ma3rr+HHlz3H7PHxy1UeCk8dDBZJZi6sXC1auKbh8ynPOlcPq4Iaiq9iatZHH\nq59ib3sl39n9A7ZkbuSmkuuCXVwLMuJwxdiDf6fA6JwFDav/9numVsZo1VYSVx7i1cY3qe2t52Mr\n75py8hwaH+ap2ufx+rzsaNrJO617uCr/Ui7PvyQ4qtIYaPoUkzqvDo3TCW0A1T3Sg9vnIc2ZwsaM\ntYy4R4Lr2p2NRbOwJm0lq1MrONR5hKdrn2dfeyX72itZmVyBJSaBE2GeN/v6AWMOzeblcx+V9Xg9\njHnHiZ5D45I4h4sbi6/mqoJL2dm8ixcbXps03/Tbu77Px1d9OHghMu5PZvGXGR+q7cTng9XFKVxZ\nsI4jXcc4eOowO5re4pLcyaWfFouXHc1vEWtzcsEZ5jcutEBnW2uy0WDLEjsRv0DSPjhu7MM2iw2b\nJxaff2Q2zjlxEXes+wSHOxUenwePz4Pb66FxbxMne4wLN7vFxtq0laxJW8nKlGUzNt1Z7h9BvrHo\natalr+bR6j8b27aIyf5sbchYza62vUQtfxuf28F3K9/CYbNh02xYLVZsmg2b/6Rms1ixWmzYNCs2\ni43B8UFqe+uDDVIK4vP47NqPzrlD78U5W4PVKLMpzduYsZbdbfvJPceRJTOZLrmt6a3j1HAXq1ON\n5lhWi5WrCy9jVdoKfnPkEXa37Ud1VXOnfivr/A3XZpLgdKA5RrDnnOCPrSdo8h2ha6SH7pEeekZ7\nzzhNIdoaRbQtGqc9hqToRKKtUUTZjAvTKGuU8bXVQZTN+Fp1V7OvvZKT/Q1UpCxjYHyQ+w89iNfn\n5d6KDxIX5WIEo4orMzadv9v4ef5w/I+80fwOvzn6SPD/dXvdlCUW87m1Hz/n+YiappHuTOXW0htI\njUnh9+pxvrfvJ3xy1d1TumcH9Iz2+kddjQS2ZbAt+D2bZqU4oZCyJCOxKoovOGOCEyiFtVls3Fh8\nNQ8c/h1P1T7PmrSVPHT0UfrHjSqTur56/lTzF4ri89mQsZb16WuC5/eAhFgHnX2zH1XRRuPwRfWT\n4Uzjjea3g8uAlCYWUZpYRFpM6ln3wfq+Rlz2WJLPskrBxLzZk1OSWavFQmluAodquoLLQkWteJs/\ntvbBxAwN4uwuShOLyHCmkRmbQaYznczY9Fl1ubdarMHy4sD8+0DjwYnktoWm/maO9ZzgWM+JiZ/V\nrGTFZpxxubhPr76XFSn6oh7Tl+Un8dLeJo7Wd5MdKASMgFw2kMx+dfMXSYyKp6b3JJVtx3mtuopB\nVxf7OyYvu2bPhZPASTXNa03DbrH5E1sj4Y2yTE52Jz6PmvRYx3DnpNcpSSjioxUfmlUZb0F8Hluz\nNrGzZRfW9In10N3a/JPZDn/zp+QEOw8e/QM+fHxo2XtnNSUn3EKPUWeuJJlsR9Nb3FB0FdcUXj6n\n97/pk9lmrwL/dL9v7Xp92udYNSvR1ijSnWnBrps5sZlku7KmzGWxaBoVhcnsrGqloX2A/Iw4vP6R\nWac9TGXGMfZgV9TAGy8n1UVdXz0/2n8/bq+bj1V86IxNN0KXjiiLWTOlWUU4JEcn8bGVd3FJ9wke\nOf4n3mrdzf6Og1xbeAWX5V2EzWJjRWES7xxpp7VrKOSOsRWrf+5eTUsfvhEXl7nupDt+D2+17uZb\nu77Ph5ffPqlU7aWGHQy7h3lP8TU4bTE8Vfs8f659jh1NO7mh6Gq2ZG3k9/6mT7eX33zG0d1zFbo0\nT/uwcWBMj0mlPKnknO4QaZrGqtQVrExZTlXnUZ6ufYFDXVVErYK6nhOc7E2nIGH2d+gDfD4fHp8H\nDS14wHx9fxOaBhv0uSWzHq+H4z01wMTds7mIsjq4NG8bF+ds4acHH+BQ51EAOke6+e6eH3Nn+S1c\nmL15oszYPzJbecI4AawuScGiWbi74v38yzv/yWPVT1KaWBQsywY4MXyYwfEhrim4fNYlpeFm0Saf\nmLSoibuxsXbjBlToiJHDG4/b0QzW8WCZ8Yh7lP89+CuG3MOTXstqsbIyZTkbMtawOnXFrJO0pOhE\nfnDZt4IH+pKEQk701kXkshMVKctIi0mhY7gTzTqCx2dnxO3B4x1i3OfG4zWS+zNJiU5mRYpOSWIh\nmzLWndNSM1FWxxkTiOncs+ID3LX8jrAfZ8wg2hZ1xnL+rNgM/nbDZ3ipYQdP1jzLzw79mvXpq7mj\n/JYZ52TGuxxgn7hI2922Hw2jQVtxQgHJ0UkkRSeSHJ1EcnQSKdGJJEYlznk0KjEq3khm+xpYnlzO\nA1W/o3u0hxuLrpk2/g6rnQ8uex9liSU8pB4NrhkJcFHOlrA01gG4OGcLcfZYflH1W3584OfcveL9\nbMhYg9fnRXVXs7etkuM9JyZdHDssdpYllfmTwGIK4/NmHOG0eO14LcZxaGBsovxxY8Zanj/5SnAd\naJvFxi0l17M1exOVHYfZ07Yf1V1NbV89jx5/kvKkEjZmrGVt2kqcdicJrigaOwYZG/f4lyibmeY1\nrm2+sumveeLE0+xu3TdpDeoERxzr0lfzvrKbgser6p5a/lL3IuvTjeNg50gXK5L1sx7PChOMC7/a\nGebNHqrp4lhjL1jHscT2kWhP5vqSy8h0ZpARm4bLHt61wEMbD4YORIy4R2gebKWxvyU4gts80Eqj\nv6HT6aYbxFhogdJsVd9NVonxt9/RtJO8uByyXZlhm2Y3ZzbjZoTLEYvT7mRl6nLK4st54UkXZcUJ\nfPIWnTHPGN1DQ3znd7tITrTxgauLGfeMMeodY9QzxpjH+Bj43Ph61P/1ePDz/rF+TnnGJs2hn408\nVzb3zaIZYaibSq5lX3slvtyJ3hzeMCSzgZHZJg7SNNDChVmbKE8qPctPLYzQ9Xe/eeH/IcERj9fn\nDd7QD9zc93i9eHxuukZ6+M2RR/hz7XPU9J3knhUfmHW/AVOfrcdOrCY1DTr7hijLd1GYHcu4d5wx\nj5tx77jxz2N8HHIPc7K/gdq+yQe+BEd8SIKbRbYrk+WF8eysaqWqrsufzAZGZsNUZhzSAKi9Z4ho\nh5MBrYMf7v8Zo54x7q34YHCJkekELrB9Hiubky8MyzadSVlSCV/Z9AXeaH6bJ2ue5YkTT/Nm8zu8\nt+w9VBQm886Rdg7Vdk1qAGX1rxVa61/7tyQzieWFd1CaWMTvjz3BTw/+ii2ZG/HipaG/idbBduLs\nLi7Lu5goq4NNmet5of5VXqx/jd+qR3mq9nl6x/pYm7ZyQeZNBpNZn5vuYWPIPO20dv/nQtM0VqYu\npyJlGYe7FA/se5LBxDa+s+e/WJZUhssRi9vr9v/zMO4dn/jom/yY2//Rh49oazRfu+CLeEaiUPXd\nLC9ImrGplM/no3u0h7q+Bmp7T1LX10BDf2Ow0cuJntpz/h2tFiulicXBZPbTq+/lgcO/48Gjf6C2\n9ySj/kTF53bg9fo4VNNFUlwUef6lmhKjEvjQstv56cEH+EXVb/m7jZ/3//G87O/Zi1WzThmxXUzW\n0+8MahO3qmOnmXYQ5UlkiGasKS3BkdmdLbsYcg9zae42Lsjc4C+vsVCSncNg77nNkbFMKjVew4ne\nuoicM2vRLGzP3cYfjv8JgE8u+wTFaZPL9gM3adxeN26fB4/XONE5rPZzalwzX5qmBefRi8ksmoUr\n87ezKmU5vzn6CHvbKznWfYI7ym9hffrqaROQBKcDzX9BqsdV8KGVN5EYFR/2RiT5ccZUmPr+Rp6t\ne5nDXYoVKTrXFM48R3pT5jry43L40YH76RzpJtbmZE3q1CWb5mNt+io+a/8Y/1NpHOcqT1VxvLsm\nuE51tDWaipRllPlLWvPicuaUTOf13MDJ5CcAGBifaLhm0SzcUno9/33gF+TF5fCRFXeS5W9sdmH2\nJi7M3kTvaD/72iuNEXd/47vfqcdZkaLjjU8Fi4PewTHSplltYSpjbrzDaueO8pt5X9l7aB1s53hP\nDdU9NRzpOsYrjW9wdcFlxNqdPFX7PM+ffAUfPo50HeNB/6vMphzbZY8lw5lOdW8tb7fsYXPm+knv\nPz3PmIp0rL4HS4zxNymIKWFb9gWz+D3CK9oWTXFC4aQqL6/Py7d3/dcZE9rFFu90kJNmzJu9xGsk\ntq1D7fzH3h8DxgBHdmwmOS7jOjk7NpMMZ9qCNhTy+XzBkdlY20RSE+Ww4rBb6B/0BM8R+w8P4e5P\n4NKNpaxJy5/29WYrULU2kfROfB6aGGuaxrr0VcRYo+f8d4h3xHFD0VXByiogWAU6H6d6htGiBtnV\n8yZxDhe3lt4w79c8V6EDJS57rJEfaFasWHFM8+dKd6bxlc1f4JdVD3G4U/GtXd/nEys/PKvjganP\n2J7ObLr7LLjHvVx14WrWls6cgIx73bQNttM82ErzQCtNgy00D7QGGzYEaGhErXLyUmcVvtpljDja\n8Xms2Kzh2WlDyw87+0bIzrb51z4c5SMr7gx2WDyTWJux87pbC0mvmLkUJxwsmoWLc7ayIX0NT9U+\nz2tNO/nvyl9QFl+GFp1BVW0XJE4tM671z2XO83cy3pq9iYL4PH526Ne81bobMMqaixIKuL7oymBJ\ncYwtmvcUX8PFOVt4quZ5drbs8jd9mn6+73wl+hs9neipCzaBSouZfzIboGkaFSnLuCE1jl+/+QbZ\nK5s42n186vPQsFms2Cx2bBYrdoudGFs0dovdX45po2e0l86RLjqGT3FCGe+j00uMR9yj1Pc3UtdX\nT11vPXV99fSO9U/6f7Jdmbjssaju6nnPpQgdMViZupwvb/oCPzv4K95s2RXs+OIbd1DT3MfA8Djb\n12ZPuuhYk1bBxTlb2dG0kydOPAXEYS86SOfYKbZkzW/9vPmacoKalMxOvaufMrqMLvsx7HnHGPYO\nYfU6eblhB3aLjesKr5w0z9DpiGGQ/imvMVebM9dT3VMz58Y8i2VL1sZgMjsdTdOC729hDhmx6fzN\n+k/zSsPr/KnmWX5e9SB72w9wp37rlNLJ+FgHmn9kNt9ZtGBNbBKi4kiMSkB1VXPo1FGSohK5e8X7\nZ1WqlhGbzn2r7+X/vvMfXJC1Yc7zPGejPKmUv15/Hz86cD+72/YTY4thW/YFbM5cT3FCwbxKSq3u\nGNyt+dgy60k6rZ9ERcoyvnnh/yHeETftBXdCVByX5m3j0rxtdA53saftALva9nHw1GGIguh1Vh46\n3sJlRZtYnlw+437q07yT+gqErkG9PfdCHj3+JC817OBo13FeaXyd+v4mUqKTua3sRt5oejs4ZaUo\nfnbJyM0l1/HA4Yf41ZHfs6/jIB/Q3xsslS7MisNus6AaetD8yWyyI3znIPCuKQAAIABJREFU9fmy\naBbKk0poHGhGQ+PLm77At3Z9b0mniyzLS6KpY5AEdwF3Lb8Dn89Ly2Bb8Hr59HXabZqVjNh0smOz\nyHEZ1Y45rkwSHPFhqRTy+UCzj2HxOqa8d+OdjkkNoN442IJF09haMf8u9FaLlRiLdcFHo7fnbuPx\nqlfxRhnXAeEYmW3vHcZRdAiPz8Md5bfMa53w+UqOSSY5Osm4hp3l+d1lj+Uzaz7KM3Uv8kztC3x3\nz4+4w1/pNxPTXz2MjRs12LNZlsdusRmT8OOyJz0+ND5E82AbzQMtNPkT3VpvE0OWBp6qNerZNasn\nbGV88U5H8IDvi+qnO20vHvcYdy2//YxrH4ZKtWcxUnkRvpHYSYnxQnPandxefjPbsi/g0eNPcrT7\nONGrqlEdTST4y0ktmhYsM+7uHyUlPhpXzMQ2Zrsy+fKmL3Cip5aU6CTSnKlnPHgnRiXwoeXv46qC\n7Xh9vrA2fQq1Jm0lLnssrze/HZwnd/pC7OFQkpOAty+Vgv6V/M0VxnvQZrFht9j8E+CtZ32P/aXu\nRZ6seRaPz8s7R9qxWiAnz8ebzbuo6zNGXZsHWifNQ0twxLM2bSWF8fkUxueTH58bvHHg9XnnffI8\nvWIhNSaZL274LA8fe4KdLbuMB90ODpwwSrhXl0ztMnhb6Y0c76nh1cY3iVrtxBI9RF5sHneU3zKv\nbZsvq3b6xd/E3zXPZXQGXJY0UcLo0GIZbyzHUXiYR4//idVpFXSOdHNxztZZN8yZK6c9ho+v+vCC\nvHY4xNiiGW8sxZrcisu++COtYmFYNAuX51/CytTl/ObIH9jfcYjj3TXcUX4zGzLWBo9loclsrHVh\n9oGAgvg8DvjXzv7YyrvmVEaa7crk7zd+/py78M5GXlwOX9n0BRr7m9GTy8JWyu71wXijTkp0Kjdf\nMrWTatJZ5p8GpMQkc3XhZVxdeBnNA608sv81jvZVofqrUJVVOG0xrE1bxcaMtZQlFU89d/i7lp9J\n4Bz+qyO/B+CCzA3cXn4zMbZo1qRWsKttH80DrSxPnt2ye2vSKvjq5i/y6yMPc/DUYWp66rhTv4UN\nGWuxWS2UZMej6nuwFXcD4LRG1vHnusIr6Brp4drCK8iLy+Zzaz4+4xKLC03PT+TFvY2ohl7ec+HG\nKd/vHxuYNBDUPNBK82ArTQMt7JqY4k2szUm2K5OS1HySrSlkuzLJis2c89QBr8+HZhvD6p1aFZAQ\n66CutR+vz1jesq61n9UlKSS4Fr5ZVrhYLVZSBzfQHvUKAB7L/NeZbUNhie9mdWoF69LO3tNgIdkt\nNv55y9/POJVoOhbNwg1FV1EYn88DVQ/xW/UoNX0n+WL6x874M6ZPZsFY3DtxHm9gp90ZbFAQ8PBL\nx3l2/zG2bR9nT++b4djMoNAup/bCKtyajw8tex9bsqYePKajaeAbMQ7Kc1n+I1yyXZl8bu3HqTxV\nxQOVjzOaXhscX+p392MNWeolP2PqySPK6phTufBCdxW1W+1cknshT9c+z/GeGmJtzgVZFzA7JZZo\nh5Wa5j6SoqefD302geTqhdo3aElsJzq/j/888Jfg9+0WO8UJBRQmGIlrUXz+jBcy4bgLvD59Nb/Y\nsYNU98QFiMNq567lt9PWEM2xUyfxjcZQeaITm9XCioKp3TMdVjsfrfgg39n9A9z+bsD3Lv9IMOle\nKnEOF3F2V7BpimaZSGbTnCl8fetXJpXCWjQNT3se9uxW9rQf4HhPDRoal+ddvOjbHknczaW4m0ux\nXXJenHJEiHRnGn+9/lO81riTP554ml8cfog97ZW8X7+VhKh4Rq3dWJOMK12nbWFHCcoSiznQcYjb\nym6kKGHupYand+1fCIE5leHk9frAayV+uDxso0nZrky2JG/nwOspXH95AiQ1s7ftAG+2vMObLe+Q\n4IhjffoaNmSspTA+z7h5ofnQZkhmA80fY2wxfEC/bVIVmqZps7qZf7qUmGT+at0nea1xJ0+ceJqf\nV/2WfR2HeH/5rZTnJXK0vgdbqlHK62VuF9ULzWl38omQG5Gnr1yx2ELnzb7nwsIp349zuNCTSyct\nFRlYAaJ5wEhqA5WPgfV5Q6VGJ5PtL1POcWWRHZtJWkzKGUt03V4Pmn0c69jUa5j4WAcer4+hETdv\nLNLasgshK6qApoZy7HnHGLd38t09P8ZpiybGFoPTHkOMLYYYWzROWwxOm/9rezROmxOnLZpoW3Tw\nOq5zqIextENYvHbu1G+JiD4aVotRVnwuKlJ0vrzpC/zvoV/xVstu4DxPZsOxJM/pVhWn8Jd3YrB0\n54Al3MnsxEilZvFxaeo1Zx1CDxVYgsNhsxA1XeH5ItA0jTVpK7mrIJWfvPEk9jyjbDbBkUCzZaJU\nIj9j/mv/LoZLcrby3MmXcXvdpDoX5s6oxaJRlBXPkZPdDI6MExs991H1wIT6o71HsCZAnD2ZZSlF\nxqhrQh45sVmLuig2GKPLvvo1OFKnKbv1lDJebzze0D7AyqLkM75nc1xZ3Fl+Cw8e/QOe3hQSo5f+\nLnqU1cG/XPQ17vv1z7CHNGoIOL0zt8WiARrZw1tpiHqGvrF+1qatXJCRfiEihUWzcGneNipSlvHg\n0UeoPFVFdU8NJYmFHDx1BIsLPL0pZMQs7MXmJTlbWZZcFpwX+m7hDawmEOZr10SXA9CwjCRzW9lG\nbiu9keqeGna37Wdf+0FebnydlxtfJzU6mQ0Za/FZxtF8Z76sXJ5czgf026hIWTbr0eLZCLz/VqSU\n86vDD7OvvZLq7houTr560vPslqW9ORrpAvNmqxuN9WZt1rPf7LZoFtKdaaQ70yb1ehn1jDFi76eq\n8YR/NLeV5oEWKk9VUXmqKvg8m8VGVmwG2bGZIUluFvEOV3AZHqZ5X8f7+4T09I+y81ArsdE21i7w\n2rILIckVhbuqGC1qCHtKO3V99bPq+hsq2mp0fR9zj6PZ3OSObVnS6VnhlBKTxHtL38P39v1kxued\nJ8ls+BOm0txEHDYL1XWjUHz258+FzWrB7jPuUI+dXM41F8xt1CZws2U261gutIr8NHyPlTJ8KgdL\n9CDJa1OwWiZ63083MhuJ4hwuNmes482WXaSHcb7s6UpyjGS2trmPlcVzP/BuyjTWJHv6tXa6WmP4\nz3+8ieGBkXBvZth4T2vtP12JcagLszdz/+/b8Hls2O2R0dDIollmLJ2b/Fxj50yPTkfP384L9a9y\ndcHMDWiEOF+kOVP4q3Wf5PWmt3j8xNMcPHWEPFcO1Xsy8famYt2ysPu01WJ91yWyYMwthMDNtPAJ\nJAx9g0YDL2OeZynlSaXcUX4LR7qOsbttP5WnDvPsyZeMK8qxM9+ktVlsXJSzJazbGCrdmcYXN3ya\nF+tf48+1z/F022M4iiemlUVblqgjr4kE5s3WtvRRlnvuNxyirA5yUwpJ8E6c830+H31jAzQPthij\nuP4y5ZbBNhr6myb9vMseG6zkiHZPreYKVCW+WdVK7+AYl63LwW5bmsGd+QhUlY7XrSR7JI5/vHsj\no55Rht0jDLmHGRofZtg9zJB7OPjY8Hjo10PGx/FhRj1jeLrT0NNm7rtjNsUJBWedMnJeJLMFC5DM\n2m0W9PwkDtZ0EhPmZBYgbqiM9n1pxNnjgieM2TNOWIs5X/ZMohxWynITOFrvwzsejQZYQ+7mFZhk\nZBbgivztHOiomtMyHnNVnG3cLTtxjslsjC2a0ujVtNe/zdrSVFwx9ohOZgPrDwesPkuTNgDfqHHQ\nskRAiUyApzcFex64WwtmfJ5/ujhxTgc3FV/LFXmXLNhcWSEikUWzcEnuhaxKXUHH8CnKEkv4zOuv\nMYonovbp80lgZDbcZYWBC+3egamNaWwWG6tSV7AqdQWjnjEOnTrML998Bfvo0lahWDQLVxVcysrU\n5fzq8O+ppzH4Pcc5LEP3bhOYN3u0vmdeyex0NE0jISqOhKi4SfOiPV4PHcOdwfm3xnzcFtqHOwBw\nuqcuPRi4bn5ln5EEm7HEGCApbuI96fP50DSNaH/5cBJz+/u/ur+JB95WpJcvXdOnhWC1WLmh6OoZ\nn3NeJLMLMTILsLIomYM1nYwe2YzdZoHLw/fa8c4o2rujycma+4WuJYJGZgEqipI5Wm8saYMGNv8G\numLsk3bUSJcZm863L/6nBZ1nUJzt75zc3HvOr/HOEWP+2eldjCNRaC6bmewkfVbLO0Qe31ACw3su\nB8/MN5ACF+vxTgeapkkiexq3Z27lU8K8kqITg6WkCU4H7WPDSC67MLzeQJlxeP/A0Q4rDpuFnsGZ\nu6xGWR1syFjLbxoHlmzq0+myYjP40obP8h+vPkqdz1g9Ick+dYRPTKb7580ePTn9vNmFYLVYyYxN\nJzM2nfXpq4OPdw0O8Hc/fZm4/Knz3wPLEY6MechKcVKUZZ6Bk1CJIdfI3nmeHgNrzKYmnH8VCGdb\nojGiklld1y3Aj4E1wCjwcaXU1IlqIZLiohase1lFkXHg8/YnY4kK7wE6MKqakzb3Mtxgl8gIGJkF\nWFmUwqOvGhP9NQguzZOX7oqICehzsdDbG+90kJ4YQ21zH16fb84XHz6fj11H27HbLKyZxSjnUgsd\nmT1biXHE85z95lHgvR8XGxn7ZqQZ9/jO/iRx3ol3OWjvGTbd+cAsFmrOrKZpxMdOXgJlJj7jh8K7\nEfNgtVi5Jv9yvv9kDFg8aKVn/5l3uzing9y0WE409TLu9hoDOUvEYYnCNxo7baPK0IrGbauyTHts\nMealG0JXoTgXHT3DALNcE/r8ElHJLHALEK2U2qrr+hbgu8DNM/3AQo3KAmSlOEmKi6K7fxRfmK/B\nAqOqOWlzH7mJpDmzAHmnzYsNlBmbqcR4MRXnxPNWVRtP7KghJmpuu+DwqIeWziE26Glz/tmF1jMw\nxjNvn5z0WNOpweDna8yezM6CZpkYmRVTud0yMvtulODfH0x6vRnxAiM64Z4zC0apcU1z35Rj+3RG\nxz04I+y8VJqTiG9EKmTmQs9PorFjkEdfPUGC69zPZa7YKAb8863PxeiY0X16uuNGIJnVNNhakXnO\n/8dSC12JpX9w6jXUXNS29GG1aPNa3cWsIuuoAxcBfwFQSr2l6/pZ16rZsCKTtLSFS5q2rMrimTfr\nSHBFhfX/KStIYkdlMxsrsub8urlDxlpUxXmJYd2m+bzWhmXp7DnaTk5WIn2jxpl106q5/27vBhtX\nZPFWVRt/fvPcD1rXbC0M/m0j4W8c73RwqneER14+Me33HTYLW9flzekubyT8XgEVxSlU1XQCM29X\nRopxY0cvTiUt5ewXUJH0Oy6k3HQXje0DFOUnkZJg/rvG75a4hUthTgJ7jnWQn5O4pPE/X+O2bW02\nv3/+GFtWZ4f9d8zPiqe6qfeMx/bTJcVHL8jfORyvWV6USlry+TWfcCFsW5vDi3saeW5Xw1JvCgCp\nSc4p8Y+LjyEmysqqkjTKiyO/Sm0mWSmxtHQO0jc0Puv97EwKs+LJyJj/Ci9mO1ZqpzdoWUq6rv8M\neFQp9Yz/63qgWCnlnu75+w63+LJTYhfkbmTAyJib4429ZKfEkhLGOvRxt5dTvcNkzeKC93Q+n4+G\n9gGyU2Nn1Tp9NtLS4ujo6D/7E89geNRNV98IOWkuvD4fzR2D5Kabo5PxYvN4vRyr72HsHEepoh1W\nyvMS0TRt3nELl46eYZpDRmFDRTuspCXGkBw/u/3nVO8wdps1OCcmEni8Xt6qamPz8vQZOyaOuz20\ndQ3P6r0fKbFbDCNjbrr6RsmeZvkms3k3xS1cRsc9nOoZPqdpNeFyPsfN6zWuCfIyXGGfNzs04qa6\nqWfW1WmFmXFhn/o139id6h2mrXuYikKZMzsbPp+P4429DI9Oe+k9awkJMfT2Ds/rNTRNoyw3YdpK\ntPbuIeKcjoirUpurrr4RegfHZl3OP5P8jLh596qJ1GNlWlrcGQ9ukZbM/gfwllLqYf/XjUqp3DM9\nv6OjP3I23uQi9c0rZiZxMy+JnTlJ3MxJ4mZeEjtzkriZU6TGbaZkNjIWcpzwBnA9gH/O7MGl3Rwh\nhBBCCCGEEJEo0sbmHweu0nX9TYzGuPcu8fYIIYQQQgghhIhAEZXMKqW8wH1LvR1CCCGEEEIIISJb\npJUZCyGEEEIIIYQQZyXJrBBCCCGEEEII05FkVgghhBBCCCGE6UTU0jxCCCGEEEIIIcRsyMisEEII\nIYQQQgjTkWRWCCGEEEIIIYTpSDIrhBBCCCGEEMJ0JJkVQgghhBBCCGE6kswKIYQQQgghhDAdSWaF\nEEIIIYQQQpiOJLNCCCGEEEIIIUxHklkhhBAihK7r2lJvg5g7iZsQi0v2OREJJJl9F9F1PdP/UQ4+\nJqLr+hr/R9tSb4uYG13Xv6jr+n1LvR1izjKXegPEOclY6g0Q50bX9Qt0XXct9XaIOQvuc3JtaR66\nrjuWehvCSfP5fEu9DWIB6bquKaV8uq5/DsgF/kEp5V3q7RKzo+v6HcD/AOlKqfFAPJd6u8TMdF2/\nHvgMsA24Vyn1xBJvkpgFf9w+B/QBzwF/VEp1yn4X2XRdvwH4LNAN7AB+q5TqW9qtErOh6/rlwD8D\nJ4DXMGI3uqQbJc5K1/UbgU8DbcBe4GGlVLscKyOXrusWwAb8O/C4UurlJd6ksJGR2fNU4A6ZP5FN\nBe4CdOCq0O+LyDJNXHKAMeA7/q+ti7tFYrZ0Xdd0Xbfouv5l4OPAF4EfA3FLu2ViNvwn+k8A38O4\nuC4B/hGM4+jSbZmYif+Y+WngB8B/AmuBlCXdKDEX7we+Cvx/wAByjot4uq47MW76/RdG7BKAfwA5\nVkYy/0BWKnAHcIWu61lLvElhI8nseUjX9RiMUdiAcuAt4Angcl3Xk+WAE3lC46brerT/Ii0VWAO8\nV9f1dUDiEm6iOAN/7PL8J4vHlFK3KaWOAT6gwf8cOd5GGF3XXbquX6HregGQhjGy9zqgMG5EVOi6\nfrH/uXIDMEKExC0fKAZqlVLPAK3ApUCerut5/ufKfhdBQmJXqOt6HMYxshB4ALgEeNA/WiuxiyD+\nuF3p36+ygSGl1LNKqRZgP7BN1/XN/ufKsTJC6Lru1HU9L2RfuhJ4FMgC1p8v09fkQHF+ug94MOTr\nSuAbwEFAA25aio0SZxWMm1JqxH/Dwa6UagX2AK9gnOxF5AmN3fGQx9OA7f7Hpbw/gui6/l7gDeCD\nwB+AUSAPuNS/7zUDDwM3g4w4RIpp4tYJ/JX/2x8FjgCX+b8n+10EOS12j2CMwqZjXGB/Sin1eeAx\n4GsgsYsUIXH7AEYidArI0HX9bv/cy1TgVeBGkGNlpNB1/QMYcfsH4H/9D7+plPossAu4AuNGkulJ\nMnue8ZcNXAms0nX9w/6HR5VSXRh3z/YBK3VdX+Z/vtxBiwDTxU3X9dXAdl3XH8O4YGsDevzfk303\nQoTEbmVI7KL8334MsPhHbkWE0HXdDnwI+LhS6mPASYwLse8BXwuZ99WEsd/JPhcBpolbLfDJwMWz\nUuobSqlbgW8BB3Rdl0qWCHGGfe4mjH3uYiBwzHwBqJJjZmSYJm71GMfKLwLLgD8BKzDmzTb6f0aO\nlUvMXwp+B0bPjk8Dybqu/6tSqtr/lF8DLmCLruuxS7Wd4SJvuPNPCnA/xsX1NwD8jYOs/rucuzHK\nei70f0/uoEWG6eJWCRwAfqaU+jjwf4C/939P7lhHjuliF9rAJA6ZNxtpXMBRYNj/dR1gU0o9hXFB\n9g1/qeNHACfIPhchTo9bPdAOoOt6qq7rN+u6vgX4CTCmlOpZms0U05hun7MopV7FaNr1SV3X78SY\n96wppYanfRWx2M50rHwbo7fADzBuSFwEOECOlRHCCbQAgZtCnwc+4J9Sg1JqEHgG44ZSwZJsYRhJ\nMnseOG10tQrYp5TaA1Tquv5t/+OBhlDHgf9WSv18kTdTnOYscQs0fPqUUuppAKXUo0qpaxd7O8VU\ns9znAHZilPE4F3HzxBmEjBj0A/+klDrk/7oUYzoGGCMOhzBKV19XSn19cbdSnG6GuJVhjAiBcdFd\nDnwdeEcp9bnF3UoxnVnuc1/BmEZzE7BDYrf0zhK30H0uFaP0+IRS6oeLu5XidCFxc2M0D83Rdd2l\nlGoEfg98M/BcpdTjwI+VUocXf0vDS5bmMSFd1z8FrAJ2KaUe8D9mOf1umK7ruRiNny5SStVN9xyx\neOYRN5tSyi0t75fOucbO/5hDKTW2yJssmF3cdF1fj1Gqep+u63cDrUqpZwP73dJs+bvbOcatWSn1\nvOxvS+scY9emlPqLv4LMszRb/u52jnFrAl7G6O0xshTb/W6n6/onMMq9K0PiFrhmvBPYgLEMz07/\n9MJ7mOg8fd7kA5LMmkTI3Za/A7YA3wX+BWNtrx/6n7MZuBb4NjCulPLquv5joFsp9dUl2Ox3PYmb\neUnszGkucVNKfUPX9a9jnPA7gGTg7/ydqMUimmfcUoAvSdyWhuxz5iT7nDn54+bDmHq2CWMJuf8L\nvKKU+g//c9ZgNAxNxlji8R2Muc9/VEp9bym2eyFJmbEJ+NvXa/67KJnAq0qp1zG6AQ7qxtqWn8NY\nCHmPf75eoKz4M3JRvTQkbuYlsTOnOcbtgP/HEjFK5x5RSt0sF2eLLwxxu0nitjRknzMn2efMKSRu\nPozldf6klDqIkdh+Sdf1Vbqu/x1GH4/dwL8BD2Es8fiD8zGRBRmZjXi6rn8N2IYxR+EJjLsx3wKO\nA/diLAfSCfxIKXVymp+X0tQlIHEzL4mdOZ1r3HRdX6OUOjD1FcVikLiZl8TOnCRu5hQStz3Ac8A6\njEHJHymlxnRdfxV4HGPN5t7zqYz4bGRkNoLpur4do6TjXozF4O8GPBh3ypYB6cpolX41RhlBoI16\nkFxULz6Jm3lJ7MzpHOMW6LwpF2dLROJmXhI7c5K4mdNpcWsHrscoIU4FHtB1/SmMddFvB6L8U56s\nS7W9i02S2QiiT13zdSNGZ79WjC5k+4D7gASgGbDpxhqXJ4BBMJbhWbwtFiBxMzOJnTmFKW7SJGiR\nSdzMS2JnThI3c5pF3A5jlBl/E6M0/OvA70Kew7upmZoksxFCNxZ3T/F/Hribshf4NIBSqh2jS2qg\nYcJ+4GcYb+o/yR2zpSFxMy+JnTlJ3MxJ4mZeEjtzkriZ0yzi1oYxH3YYowFUF/DXwJMYy8q960gy\nGwF0Xf8oxkHkppDHLEqpl4FDuq7///6HjwJpGHde/g1jjsPlgXbcYnFJ3MxLYmdOEjdzkriZl8TO\nnCRu5jTHuMUAPUqpVzDmyV6ilPrNIm9yRLAt9Qa8m+m6filGS/QGoBd4GyZKA3RdXwF8H7hf1/WX\nMeJVBkT5n/fO4m+1kLiZl8TOnCRu5iRxMy+JnTlJ3MzpHONWCgTmMz+7+FsdOSSZXVrrge8qpV7S\ndf0fMOrfq/wlBt8CKoCrgL/FWAPsMuCbSqk9S7XBApC4mZnEzpwkbuYkcTMviZ05SdzMSeI2D7I0\nzyIJLNeh6/p9wLBS6gF/6YDX3ynuz8BXlFJ7dV0vBQqUUi8u7VYLiZt5SezMSeJmThI385LYmZPE\nzZwkbuEnc2YXiZpYruMK4O9D3rhR/k5xb2C02kYpVR144+rvotbakUjiZl4SO3OSuJmTxM28JHbm\nJHEzJ4lb+Ekyu8B0Xc8M+fwS4BTQCHzP/7Db//Eo0KvrujP059W7qLV2JJG4mZfEzpwkbuYkcTMv\niZ05SdzMSeK2cKTMeIHoup4L/DOQjtEu+xlgDKPd9kmgGtimlKr1P/864FPAJ5RSHUuxzULiZmYS\nO3OSuJmTxM28JHbmJHEzJ4nbwpOR2YVzD8YC1F/AmMj9ZWBIKXVEKTUE3A/8Z+DJSqlngPvljbvk\n7kHiZlb3ILEzo3uQuJnRPUjczOoeJHZmdA8SNzO6B4nbgpKR2TDSdf1e4FLgBFCE0Wmsxj+B+5NA\nk1Lq+yHP7wI+rJR6aim2VxgkbuYlsTMniZs5SdzMS2JnThI3c5K4LS4ZmQ0TXde/BVyHsQ7UGuBu\njDIBMGriXwAKdF1PDvmx9wO1i7mdYjKJm3lJ7MxJ4mZOEjfzktiZk8TNnCRui0+S2fBJAH6qlNoL\n/BD4EfBBXdfXKqVGgHYgGhjQdV0DUEo9p5Q6vGRbLEDiZmYSO3OSuJmTxM28JHbmJHEzJ4nbIrMt\n9QacD3RdtwCPAW/7H7oT+BNwEPi+ruufAK7EmOxt9bfeFktM4mZeEjtzkriZk8TNvCR25iRxMyeJ\n29KQObNhput6PEYJwU1KqVZd178KJAMZwJeUUq1LuoFiWhI385LYmZPEzZwkbuYlsTMniZs5SdwW\nj4zMhl8Oxps3Qdf1/wIOAV9RSo0v7WaJs5C4mZfEzpwkbuYkcTMviZ05SdzMSeK2SCSZDb9LgK8A\n64FfK6UeXOLtEbMjcTMviZ05SdzMSeJmXhI7c5K4mZPEbZFIMht+Y8DXgH+XWnhTkbiZl8TOnCRu\n5iRxMy+JnTlJ3MxJ4rZIJJkNv18qpWQisvlI3MxLYmdOEjdzkriZl8TOnCRu5iRxWyTSAEoIIYQQ\nQgghhOnIOrNCCCGEEEIIIUxHklkhhBBCCCGEEKYjyawQQgghhBBCCNORZFYIIYQQQgghhOlIN2Mh\nhBBikei6Hg/8K7AdcAPdwN8qpfaG+f/5JfCKUuqX4XxdIYQQIpLIyKwQQgixCHRdtwBPA13AWqXU\nWuAbwDO6rqcs6cYJIYQQJiRL8wghhBCLQNf1K4BfAIVKKW/I49cDLuBLgBU4BHwW+BGw0v/Yt5VS\nD/lHdu8HcoFs4AXg4/6X+i5wI9Ds/5n7lVK/1HX9XuBvAR+wB/icUmpggX9dIYQQYsHJyKwQQgix\nONYB+0MTWQCl1NNAO1AOXK6Uuhv4GrBHKbUBuAT4qq7rxcAN/tfYCpRhlCuvB97rf/0K4HagFEDX\n9VXAV4HtSqlVwCDwTwv9iwohhBCLQebMCiGEEIvDC4zM8H2llOoe7L7DAAABkklEQVT1f34l4NR1\n/aP+r2OBCv/o7GZd1/8aWA6kYIzqXgo8ppQaBzp0XX/a/3PbgSeVUp3+r3+KMToshBBCmJ6MzAoh\nhBCLYzewXtd1LfRBXdf/BdCA4ZCHrcBdSqnA3NotwF90Xf888G9AB/AD4LD/Z33+jwFu/8fTz/Ma\nciNbCCHEeUKSWSGEEGJx7MAoJ/4nXdetALquXwPcC6Sf9tyXgE/7n5MFVAL5wFXA/yilHgSigbUY\nie8LwB26rkfpup4EXOt/nVeAm3RdT/Z//Qng5QX57YQQQohFJsmsEEIIsQiUUj7gJqAEOKTreiXw\nZeB6oO20p38diNF1/RBGYvv3SqkTwPcwkuGD/s/fBIqUUn/ESFwPAX/CGLFFKVWJsRTQq7quHwUS\nMebjCiGEEKYn3YyFEEIIIYQQQpiOjMwKIYQQQgghhDAdSWaFEEIIIYQQQpiOJLNCCCGEEEIIIUxH\nklkhhBBCCCGEEKYjyawQQgghhBBCCNORZFYIIYQQQgghhOlIMiuEEEIIIYQQwnT+H2XGJNSXty8f\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ccdfb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Visualización de likes vs retweets:\n",
    "tfav.plot(figsize=(16,4), label=\"Likes\", legend=True)\n",
    "tret.plot(figsize=(16,4), label=\"Retweets\", legend=True);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pie charts de dispositivos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Limpiamos las fuentes de creación de tweets para hacer un pie chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:23:04.730613Z",
     "start_time": "2017-09-02T03:23:04.718663Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fuentes de creación de contenido:\n",
      "* Twitter for iPhone\n",
      "* Media Studio\n"
     ]
    }
   ],
   "source": [
    "# Obtenemos posibles fuentes:\n",
    "fuentes = []\n",
    "for fuente in datos['Fuente']:\n",
    "    if fuente not in fuentes:\n",
    "        fuentes.append(fuente)\n",
    "\n",
    "# Imprimimos fuentes:\n",
    "print(\"Fuentes de creación de contenido:\")\n",
    "for fuente in fuentes:\n",
    "    print(\"* {}\".format(fuente))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Contamos dispositivos y creamos un pie chart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:23:09.325071Z",
     "start_time": "2017-09-02T03:23:09.108949Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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uvxD4AuFqVfOBs4ARhMcuX2ut3d/q+fsCA4F+ANbaRmPMPwL9jTFzgSuBy4wx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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11226f048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creamos un vector mapeado a etiquetas:\n",
    "percent = np.zeros(len(fuentes))\n",
    "\n",
    "for fuente in datos['Fuente']:\n",
    "    for index in range(len(fuentes)):\n",
    "        if fuente == fuentes[index]:\n",
    "            percent[index] += 1\n",
    "            pass\n",
    "\n",
    "percent /= 100\n",
    "\n",
    "# Pie chart:\n",
    "pie_chart = pd.Series(percent, index=fuentes, name='Fuentes')\n",
    "pie_chart.plot.pie(fontsize=11, autopct='%.2f', figsize=(6, 6));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ya tenemos estadístigas y gráficas muy básicas sobre los datos obtenidos.\n",
    "\n",
    "¿Y si hacemos análisis de sentimientos?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Análisis de sentimientos**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importando textblob"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TextBlob** es una biblioteca de Python (2 y 3) para procesar datos de texto. Proporciona una API sencilla para ahondar en las tareas comunes de procesamiento del lenguaje natural (PNL), como el etiquetado de parte del habla, la extracción de frase nominal, el análisis de sentimientos, la clasificación, la traducción y mucho más."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:23:16.245473Z",
     "start_time": "2017-09-02T03:23:15.503957Z"
    },
    "collapsed": true
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   "outputs": [],
   "source": [
    "from textblob import TextBlob\n",
    "import re\n",
    "\n",
    "def limpia_tweet(tweet):\n",
    "    '''\n",
    "    Función de utilidad para limpiar el texto de un tweet removiendo \n",
    "    enlaces y caracteres especiales usando regex.\n",
    "    '''\n",
    "    return ' '.join(re.sub(\"(@[A-Za-z0-9]+)|([^0-9A-Za-z \\t])|(\\w+:\\/\\/\\S+)\", \" \", tweet).split())\n",
    "\n",
    "def analiza_sentimiento(tweet):\n",
    "    '''\n",
    "    Función de utilidad para clasificar el sentimiento del tweet\n",
    "    proporcionado utilizando textblob\n",
    "    '''\n",
    "    analysis = TextBlob(limpia_tweet(tweet))\n",
    "    if analysis.sentiment.polarity > 0:\n",
    "        return 1\n",
    "    elif analysis.sentiment.polarity == 0:\n",
    "        return 0\n",
    "    else:\n",
    "        return -1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analizando sentimientos"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con estas funciones podemos realizar un análisis de sentimientos de tweets de nuestro dataframe."
   ]
  },
  {
   "cell_type": "code",
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     "end_time": "2017-09-02T03:23:40.349808Z",
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    {
     "data": {
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       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Tweets</th>\n",
       "      <th>len</th>\n",
       "      <th>ID</th>\n",
       "      <th>Creado</th>\n",
       "      <th>Fuente</th>\n",
       "      <th>Likes</th>\n",
       "      <th>RTs</th>\n",
       "      <th>AdS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>On behalf of @FLOTUS Melania &amp;amp; myself, THA...</td>\n",
       "      <td>144</td>\n",
       "      <td>903778130850131970</td>\n",
       "      <td>2017-09-02 00:34:32</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>24572</td>\n",
       "      <td>5585</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I will be going to Texas and Louisiana tomorro...</td>\n",
       "      <td>132</td>\n",
       "      <td>903770196388831233</td>\n",
       "      <td>2017-09-02 00:03:00</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>44748</td>\n",
       "      <td>8825</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Stock Market up 5 months in a row!</td>\n",
       "      <td>34</td>\n",
       "      <td>903766326631698432</td>\n",
       "      <td>2017-09-01 23:47:38</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>44518</td>\n",
       "      <td>9134</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>'President Donald J. Trump Proclaims September...</td>\n",
       "      <td>140</td>\n",
       "      <td>903705867891204096</td>\n",
       "      <td>2017-09-01 19:47:23</td>\n",
       "      <td>Media Studio</td>\n",
       "      <td>47009</td>\n",
       "      <td>15127</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Texas is healing fast thanks to all of the gre...</td>\n",
       "      <td>143</td>\n",
       "      <td>903603043714957312</td>\n",
       "      <td>2017-09-01 12:58:48</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>77680</td>\n",
       "      <td>15398</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>...get things done at a record clip. Many big ...</td>\n",
       "      <td>113</td>\n",
       "      <td>903600265420578819</td>\n",
       "      <td>2017-09-01 12:47:46</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>54664</td>\n",
       "      <td>11424</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>General John Kelly is doing a great job as Chi...</td>\n",
       "      <td>140</td>\n",
       "      <td>903597166249246720</td>\n",
       "      <td>2017-09-01 12:35:27</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>59840</td>\n",
       "      <td>11678</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Wow, looks like James Comey exonerated Hillary...</td>\n",
       "      <td>130</td>\n",
       "      <td>903587428488839170</td>\n",
       "      <td>2017-09-01 11:56:45</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>110667</td>\n",
       "      <td>35936</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>THANK YOU to all of the incredible HEROES in T...</td>\n",
       "      <td>110</td>\n",
       "      <td>903348312421670912</td>\n",
       "      <td>2017-08-31 20:06:35</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>112012</td>\n",
       "      <td>29064</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>RT @FoxNews: .@KellyannePolls on Harvey recove...</td>\n",
       "      <td>140</td>\n",
       "      <td>903234878124249090</td>\n",
       "      <td>2017-08-31 12:35:50</td>\n",
       "      <td>Twitter for iPhone</td>\n",
       "      <td>0</td>\n",
       "      <td>6638</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "                                              Tweets  len                  ID  \\\n",
       "0  On behalf of @FLOTUS Melania &amp; myself, THA...  144  903778130850131970   \n",
       "1  I will be going to Texas and Louisiana tomorro...  132  903770196388831233   \n",
       "2                 Stock Market up 5 months in a row!   34  903766326631698432   \n",
       "3  'President Donald J. Trump Proclaims September...  140  903705867891204096   \n",
       "4  Texas is healing fast thanks to all of the gre...  143  903603043714957312   \n",
       "5  ...get things done at a record clip. Many big ...  113  903600265420578819   \n",
       "6  General John Kelly is doing a great job as Chi...  140  903597166249246720   \n",
       "7  Wow, looks like James Comey exonerated Hillary...  130  903587428488839170   \n",
       "8  THANK YOU to all of the incredible HEROES in T...  110  903348312421670912   \n",
       "9  RT @FoxNews: .@KellyannePolls on Harvey recove...  140  903234878124249090   \n",
       "\n",
       "               Creado              Fuente   Likes    RTs  AdS  \n",
       "0 2017-09-02 00:34:32  Twitter for iPhone   24572   5585    1  \n",
       "1 2017-09-02 00:03:00  Twitter for iPhone   44748   8825    1  \n",
       "2 2017-09-01 23:47:38  Twitter for iPhone   44518   9134    0  \n",
       "3 2017-09-01 19:47:23        Media Studio   47009  15127    0  \n",
       "4 2017-09-01 12:58:48  Twitter for iPhone   77680  15398    1  \n",
       "5 2017-09-01 12:47:46  Twitter for iPhone   54664  11424    1  \n",
       "6 2017-09-01 12:35:27  Twitter for iPhone   59840  11678    1  \n",
       "7 2017-09-01 11:56:45  Twitter for iPhone  110667  35936    1  \n",
       "8 2017-08-31 20:06:35  Twitter for iPhone  112012  29064    1  \n",
       "9 2017-08-31 12:35:50  Twitter for iPhone       0   6638    0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creamos columna de resultados del análisis\n",
    "datos['AdS'] = np.array([ analiza_sentimiento(tweet) for tweet in datos['Tweets'] ])\n",
    "\n",
    "# Hacemos display de los sentimientos:\n",
    "display(datos.head(10))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analizando resultados"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Podemos construir una lista con todos los tweets clasificados, así como reportar porcentajes del análisis realizado."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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     "end_time": "2017-09-02T03:23:45.403975Z",
     "start_time": "2017-09-02T03:23:45.386585Z"
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    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Construimos listas con tweets clasificados:\n",
    "\n",
    "tweets_positivos = [ tweet for index, tweet in enumerate(datos['Tweets']) if datos['AdS'][index] > 0]\n",
    "tweets_neutros   = [ tweet for index, tweet in enumerate(datos['Tweets']) if datos['AdS'][index] == 0]\n",
    "tweets_negativos = [ tweet for index, tweet in enumerate(datos['Tweets']) if datos['AdS'][index] < 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-09-02T03:23:46.639845Z",
     "start_time": "2017-09-02T03:23:46.631584Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Porcentaje de tweets positivos: 51.0%\n",
      "Porcentaje de tweets neutros: 27.0%\n",
      "Porcentaje de tweets negativos: 22.0%\n"
     ]
    }
   ],
   "source": [
    "# Imprimimos porcentajes:\n",
    "\n",
    "print(\"Porcentaje de tweets positivos: {}%\".format(len(tweets_positivos)*100/len(datos['Tweets'])))\n",
    "print(\"Porcentaje de tweets neutros: {}%\".format(len(tweets_neutros)*100/len(datos['Tweets'])))\n",
    "print(\"Porcentaje de tweets negativos: {}%\".format(len(tweets_negativos)*100/len(datos['Tweets'])))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Referencias"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. [Documentación oficial de Tweepy](http://tweepy.readthedocs.io/en/v3.5.0/).\n",
    "2. [Documentación oficial de NumPy](https://docs.scipy.org/doc/numpy-dev/index.html).\n",
    "3. [Tutorial de NumPy](https://docs.scipy.org/doc/numpy-dev/user/quickstart.html).\n",
    "4. [Tutorial de Pandas](https://pandas.pydata.org/pandas-docs/stable/tutorials.html).\n",
    "5. [Documentación oficial de Pandas](https://pandas.pydata.org/pandas-docs/stable/index.html).\n",
    "6. [Documentación oficial de Matplotlib](http://matplotlib.org/index.html).\n",
    "7. [Tutorial de Pyplot](http://matplotlib.org/users/pyplot_tutorial.html).\n",
    "8. [Sitio oficial de Seaborn](https://seaborn.pydata.org/).\n",
    "9. [Documentación oficial de TextBlob](https://textblob.readthedocs.io/en/dev/).\n",
    "10. [Creación de clasificadores con TextBlob](https://textblob.readthedocs.io/en/dev/classifiers.html)."
   ]
  }
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