006_PyLadiesMadrid_NLP.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"pylogo.PNG\" alt=\"Logo\" style=\"width: 500px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 006 - PyLadiesMadrid: Procesamiento del Lenguaje Natural"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### **Claudia Guirao Fernández**\n",
    "_Data Scientist @ Kernel Analytics_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Objetivos de hoy:\n",
    "* Introducir las herramientas y librerías de text mining y NLP en Python\n",
    "* Ver cómo se pueden realizar estas operaciones en Python\n",
    "* Descubrir con un corpus en Español algunas curiosidades interesantes sobre los Premios Goya al cine español"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. _Basics_ sobre string en Python"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "¡Hola 'Pyladies Madrid'!\n"
     ]
    }
   ],
   "source": [
    "mystring = '¡Hola Pyladies Madrid!'\n",
    "mystring2 = \"¡Hola 'Pyladies Madrid'!\"\n",
    "print(mystring2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 longitud de la cadena\n",
      "2 posición\n",
      "3 veces\n"
     ]
    }
   ],
   "source": [
    "print(str(len(mystring)) + ' longitud de la cadena') \n",
    "print(str(mystring.index(\"o\")) + ' posición')  #primera ocurrencia\n",
    "print(str(mystring.count(\"a\")) + ' veces') #contar el número de ocurrencias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "¡HOLA PYLADIES MADRID!\n",
      "¡hola pyladies madrid!\n"
     ]
    }
   ],
   "source": [
    "print(mystring.upper())\n",
    "print(mystring.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a\n",
      "Hola\n",
      "!dirdaM seidalyP aloH¡\n"
     ]
    }
   ],
   "source": [
    "print(mystring[4])\n",
    "print(mystring[1:5])\n",
    "print(mystring[::-1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Más información: [Basic String Operations](https://www.learnpython.org/en/Basic_String_Operations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Conceptos básicos al trabajar con texto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* **corpus**: conjunto estructurado de textos / documentos \n",
    "* **stopwords**: es castellano \"palabras vacías\", se filtran de los análisis por su escaso aporte/significado \n",
    "\n",
    "\n",
    "**Normalizar**: el texto consiste en unificar la forma de los textos. Para ello hay distintas operaciones y algoritmos que pueden ayudarnos. \n",
    "1. **tokenizar**: operación consistente en identificar las distintas unidades o tokens, en una frase identificamos las distintas palabras\n",
    "2. **stemming**: consiste en reducir las palabras a su tema (Algoritmo de Porter, Snowball, entre otros)\n",
    "3. **lematizar**: es un proceso lingüístico que consiste en, dada una forma flexionada (es decir, en plural, en femenino, conjugada, etc), hallar el lema correspondiente. El lema es la forma que por convenio se acepta como representante de todas las formas flexionadas de una misma palabra\n",
    "\n",
    "_¿Qué es mejor lematizar o hacer stemming? ¿qué algoritmos funcionan mejor?_\n",
    "\n",
    "Operaciones con el texto normalizado:\n",
    "\n",
    "* **TF**: Calcular la frecuencia de aparición de los términos en uno o varios documentos\n",
    "* **n-gramas**: conjuntos de n palabras que aparecen en el mismo documento\n",
    "* **TF-IDF**:  frecuencia de término – frecuencia inversa de documento (o sea, la frecuencia de ocurrencia del término en la colección de documentos), es una medida numérica que expresa cuán relevante es una palabra para un documento en una colección"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Librerías más comunes para el tratamiento de texto"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [**NLTK**](https://www.nltk.org/): Librería principal para el procesamiento de texto en Python. Dispone de un [manual online](http://www.nltk.org/book/). Posee una interfaz sencilla y cubre la mayor parte de las operaciones. Fue la primera y es de las más extendidas. \n",
    "* [**Textblob**](http://textblob.readthedocs.io/en/dev/) : De nuevo cumple bastantes de las operaciones de procesamiento del lenguaje, está construida sobre NLTK y pretende ser más intuitiva que la ésta. Incluye un módulo de traducción y detección del idioma. [Ver tutorial](https://www.analyticsvidhya.com/blog/2018/02/natural-language-processing-for-beginners-using-textblob/) Es la segunda en velocidad por delante de NLTK.\n",
    "* [**SpaCy**](https://spacy.io): es la librería más reciente, su interfaz es muy fácil de utilizar. Posee bastantes modelos ya entrenados incluyendo _deep learning_\n",
    "\n",
    "_(las tres soportan multiples idiomas, quizá SpaCy es la más limitada porque lleva menos años disponible, aunque todas soportan el castellano)_\n",
    "\n",
    "* [**Gensim**](https://radimrehurek.com/gensim/): Complemento de las anteriores, especialmente centrada en el modelado de temática (LDA)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "<img src=\"comparativa.PNG\" alt=\"Comparativa\" style=\"width: 600px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ¿Cómo instalar las librerías?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```conda install -c conda-forge spacy```\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```python -m spacy download es```\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```conda install -c anaconda nltk```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "```conda install -c anaconda gensim ```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# nltk.download()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Let's play"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "Importamos las librerías necesarias"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from IPython.display import display, HTML\n",
    "import nltk\n",
    "import spacy\n",
    "import gensim\n",
    "import re\n",
    "import os\n",
    "import codecs\n",
    "from sklearn import feature_extraction\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos el modelo en español para SpaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import spacy\n",
    "import es_core_news_sm\n",
    "nlp = es_core_news_sm.load()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Importamos el fichero de texto que contiene la sinopsis para las películas nominadas desde la edición 14 de los premios.\n",
    "\n",
    "Para obtener esta información he _escrapeado_ la web oficial de los [Premios Goya](premiosgoya.com)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "info_pelis = pd.read_csv(\"sinopsis_nominados_goya.csv\",encoding='latin-1')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Al hacer el scrapping de la web el texto contiene algunas cosas que nos gustaría eliminar antes de comenzar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "def clean_Goya(tit):\n",
    "    return(' '.join(tit.split()))\n",
    "    \n",
    "info_pelis['titulo'] = info_pelis.titulo.apply(clean_Goya)\n",
    "info_pelis['descripcion'] = info_pelis.descripcion.apply(clean_Goya)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos el fichero que contiene si las películas han sido nominadas o ganadoras en la categoría mejor película o mejor dirección y añadimos esas columnas a nuestro _dataframe_ original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82 peliculas nominadas a mejor película o a mejor dirección\n"
     ]
    }
   ],
   "source": [
    "premios = pd.read_csv(\"titulo_edicion.csv\", encoding='latin-1', sep=';')\n",
    "info_pelis = pd.merge(info_pelis, premios[['titulo','edicion','nom_mejor_pelicula','gana_mejor_pelicula','nom_mejor_direc','gana_mejor_direc']], on = ['titulo', 'edicion'], how='inner')\n",
    "info_nominadas = info_pelis[(info_pelis.nom_mejor_pelicula == 1) | (info_pelis.nom_mejor_direc==1)]\n",
    "print(str(len(info_nominadas)) + ' peliculas nominadas a mejor película o a mejor dirección')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>descripcion</th>\n",
       "      <th>edicion</th>\n",
       "      <th>titulo</th>\n",
       "      <th>url_peli</th>\n",
       "      <th>nom_mejor_pelicula</th>\n",
       "      <th>gana_mejor_pelicula</th>\n",
       "      <th>nom_mejor_direc</th>\n",
       "      <th>gana_mejor_direc</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Álvaro quiere ser escritor, pero todo lo que e...</td>\n",
       "      <td>32</td>\n",
       "      <td>El autor</td>\n",
       "      <td>https://www.premiosgoya.com/pelicula/el-autor</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Frida, una niña de seis años, afronta el prime...</td>\n",
       "      <td>32</td>\n",
       "      <td>Estiu 1993</td>\n",
       "      <td>https://www.premiosgoya.com/pelicula/estiu-1993</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Tras haber luchado en la Primera Guerra Carlis...</td>\n",
       "      <td>32</td>\n",
       "      <td>Handia</td>\n",
       "      <td>https://www.premiosgoya.com/pelicula/handia</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A finales de los años 50 Florence Green decide...</td>\n",
       "      <td>32</td>\n",
       "      <td>La librería</td>\n",
       "      <td>https://www.premiosgoya.com/pelicula/la-libreria</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Madrid, años 90. En plena noche, la policía re...</td>\n",
       "      <td>32</td>\n",
       "      <td>Verónica</td>\n",
       "      <td>https://www.premiosgoya.com/pelicula/veronica</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         descripcion  edicion       titulo  \\\n",
       "0  Álvaro quiere ser escritor, pero todo lo que e...       32     El autor   \n",
       "1  Frida, una niña de seis años, afronta el prime...       32   Estiu 1993   \n",
       "2  Tras haber luchado en la Primera Guerra Carlis...       32       Handia   \n",
       "3  A finales de los años 50 Florence Green decide...       32  La librería   \n",
       "4  Madrid, años 90. En plena noche, la policía re...       32     Verónica   \n",
       "\n",
       "                                           url_peli  nom_mejor_pelicula  \\\n",
       "0     https://www.premiosgoya.com/pelicula/el-autor                 1.0   \n",
       "1   https://www.premiosgoya.com/pelicula/estiu-1993                 1.0   \n",
       "2       https://www.premiosgoya.com/pelicula/handia                 1.0   \n",
       "3  https://www.premiosgoya.com/pelicula/la-libreria                 1.0   \n",
       "4     https://www.premiosgoya.com/pelicula/veronica                 1.0   \n",
       "\n",
       "   gana_mejor_pelicula  nom_mejor_direc  gana_mejor_direc  \n",
       "0                  0.0              1.0               0.0  \n",
       "1                  0.0              0.0               0.0  \n",
       "2                  0.0              1.0               0.0  \n",
       "3                  1.0              1.0               1.0  \n",
       "4                  0.0              1.0               0.0  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(info_pelis.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Este es el tipo de información con la que vamos a trabajar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "El autor\n",
      "............\n",
      "Álvaro quiere ser escritor, pero todo lo que escribe es falso, pretencioso, insípido. Trabaja como escribiente en una notaría de Sevilla y su vida es gris, coloreada sólo por sus sueños. Su mujer, Amanda, es todo lo contrario. Siempre ha tenido los pies en la tierra y nunca ha soñado con ser escritora. Sin embargo, es ella la que se pone a escribir y le sale un best-seller. Ironías de la vida. La separación es inevitable. Y en ese momento, Álvaro decide afrontar su sueño: escribir una gran novela. Pero es incapaz; no tiene talento ni imaginación. Guiado por Juan, su profesor de escritura, indaga en los fundamentos de la novela, hasta que un día descubre que la ficción se escribe con la realidad. Álvaro comienza a manipular a sus vecinos y amistades para crear una historia, una historia real que supera a la ficción.\n"
     ]
    }
   ],
   "source": [
    "print(info_nominadas.iloc[0].titulo)\n",
    "print('............')\n",
    "print(info_nominadas.iloc[0].descripcion)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estiu 1993\n",
      "............\n",
      "Frida, una niña de seis años, afronta el primer verano de su vida con su nueva família adoptiva, tras la muerte de su madre.\n"
     ]
    }
   ],
   "source": [
    "print(info_nominadas.iloc[1].titulo)\n",
    "print('............')\n",
    "print(info_nominadas.iloc[1].descripcion)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Entidades"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a localizar entidades mediante SpaCy, de una forma muy sencilla."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('Frida', 'PROPN'), (',', 'PUNCT'), ('una', 'DET'), ('niña', 'NOUN'), ('de', 'ADP'), ('seis', 'NUM'), ('años', 'NOUN'), (',', 'PUNCT'), ('afronta', 'VERB'), ('el', 'DET'), ('primer', 'ADJ'), ('verano', 'NOUN'), ('de', 'ADP'), ('su', 'DET'), ('vida', 'NOUN'), ('con', 'ADP'), ('su', 'DET'), ('nueva', 'ADJ'), ('família', 'NOUN'), ('adoptiva', 'ADJ'), (',', 'PUNCT'), ('tras', 'ADP'), ('la', 'DET'), ('muerte', 'NOUN'), ('de', 'ADP'), ('su', 'DET'), ('madre', 'NOUN'), ('.', 'PUNCT')]\n"
     ]
    }
   ],
   "source": [
    "doc = nlp(info_nominadas.iloc[1].descripcion)\n",
    "print([(w.text, w.pos_) for w in doc])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "También es posible visualizar esta información y las relaciones entre términos mediante **displacy**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"Frida.PNG\" alt=\"Comparativa\" style=\"width: 1200px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# from spacy import displacy\n",
    "# displacy.serve(doc, style='dep')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para cada documento SpaCy nos ofrece mucha otra información:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dep</th>\n",
       "      <th>is_alpha</th>\n",
       "      <th>is_stop</th>\n",
       "      <th>lemma</th>\n",
       "      <th>pos</th>\n",
       "      <th>shape</th>\n",
       "      <th>tag</th>\n",
       "      <th>text</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>nsubj</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>Frida</td>\n",
       "      <td>PROPN</td>\n",
       "      <td>Xxxxx</td>\n",
       "      <td>PROPN___</td>\n",
       "      <td>Frida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>punct</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT</td>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT__PunctType=Comm</td>\n",
       "      <td>,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>det</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>uno</td>\n",
       "      <td>DET</td>\n",
       "      <td>xxx</td>\n",
       "      <td>DET__Definite=Ind|Gender=Fem|Number=Sing|PronT...</td>\n",
       "      <td>una</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>appos</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>niño</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>NOUN__Gender=Fem|Number=Sing</td>\n",
       "      <td>niña</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>case</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>de</td>\n",
       "      <td>ADP</td>\n",
       "      <td>xx</td>\n",
       "      <td>ADP__AdpType=Prep</td>\n",
       "      <td>de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>nummod</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>seis</td>\n",
       "      <td>NUM</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>NUM__Number=Plur|NumType=Card</td>\n",
       "      <td>seis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>nmod</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>año</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>NOUN__Gender=Masc|Number=Plur</td>\n",
       "      <td>años</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>punct</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT</td>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT__PunctType=Comm</td>\n",
       "      <td>,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ROOT</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>afrontar</td>\n",
       "      <td>VERB</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>VERB__Mood=Ind|Number=Sing|Person=3|Tense=Pres...</td>\n",
       "      <td>afronta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>det</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>el</td>\n",
       "      <td>DET</td>\n",
       "      <td>xx</td>\n",
       "      <td>DET__Definite=Def|Gender=Masc|Number=Sing|Pron...</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>amod</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>﻿1</td>\n",
       "      <td>ADJ</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>ADJ__Gender=Masc|Number=Sing|NumType=Ord</td>\n",
       "      <td>primer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>obj</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>verano</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>NOUN__Gender=Masc|Number=Sing</td>\n",
       "      <td>verano</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>case</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>de</td>\n",
       "      <td>ADP</td>\n",
       "      <td>xx</td>\n",
       "      <td>ADP__AdpType=Prep</td>\n",
       "      <td>de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>det</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>su</td>\n",
       "      <td>DET</td>\n",
       "      <td>xx</td>\n",
       "      <td>DET__Number=Sing|Person=3|Poss=Yes|PronType=Prs</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>nmod</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>vida</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>NOUN__Gender=Fem|Number=Sing</td>\n",
       "      <td>vida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>case</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>con</td>\n",
       "      <td>ADP</td>\n",
       "      <td>xxx</td>\n",
       "      <td>ADP__AdpType=Prep</td>\n",
       "      <td>con</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>det</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>su</td>\n",
       "      <td>DET</td>\n",
       "      <td>xx</td>\n",
       "      <td>DET__Number=Sing|Person=3|Poss=Yes|PronType=Prs</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>amod</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>nuevo</td>\n",
       "      <td>ADJ</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>ADJ__Gender=Fem|Number=Sing</td>\n",
       "      <td>nueva</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>obl</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>família</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>NOUN__Gender=Fem|Number=Sing</td>\n",
       "      <td>família</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>amod</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>adoptivo</td>\n",
       "      <td>ADJ</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>ADJ__Gender=Fem|Number=Sing</td>\n",
       "      <td>adoptiva</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>punct</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT</td>\n",
       "      <td>,</td>\n",
       "      <td>PUNCT__PunctType=Comm</td>\n",
       "      <td>,</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>case</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>tras</td>\n",
       "      <td>ADP</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>ADP__AdpType=Prep</td>\n",
       "      <td>tras</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>det</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>lo</td>\n",
       "      <td>DET</td>\n",
       "      <td>xx</td>\n",
       "      <td>DET__Definite=Def|Gender=Fem|Number=Sing|PronT...</td>\n",
       "      <td>la</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>obl</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>muerte</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>NOUN__Gender=Fem|Number=Sing</td>\n",
       "      <td>muerte</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>case</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>de</td>\n",
       "      <td>ADP</td>\n",
       "      <td>xx</td>\n",
       "      <td>ADP__AdpType=Prep</td>\n",
       "      <td>de</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>det</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>su</td>\n",
       "      <td>DET</td>\n",
       "      <td>xx</td>\n",
       "      <td>DET__Number=Sing|Person=3|Poss=Yes|PronType=Prs</td>\n",
       "      <td>su</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>nmod</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>madre</td>\n",
       "      <td>NOUN</td>\n",
       "      <td>xxxx</td>\n",
       "      <td>NOUN__Gender=Fem|Number=Sing</td>\n",
       "      <td>madre</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>punct</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>.</td>\n",
       "      <td>PUNCT</td>\n",
       "      <td>.</td>\n",
       "      <td>PUNCT__PunctType=Peri</td>\n",
       "      <td>.</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       dep is_alpha is_stop     lemma    pos  shape  \\\n",
       "0    nsubj     True   False     Frida  PROPN  Xxxxx   \n",
       "1    punct    False   False         ,  PUNCT      ,   \n",
       "2      det     True    True       uno    DET    xxx   \n",
       "3    appos     True   False      niño   NOUN   xxxx   \n",
       "4     case     True    True        de    ADP     xx   \n",
       "5   nummod     True    True      seis    NUM   xxxx   \n",
       "6     nmod     True   False       año   NOUN   xxxx   \n",
       "7    punct    False   False         ,  PUNCT      ,   \n",
       "8     ROOT     True   False  afrontar   VERB   xxxx   \n",
       "9      det     True    True        el    DET     xx   \n",
       "10    amod     True    True        ﻿1    ADJ   xxxx   \n",
       "11     obj     True   False    verano   NOUN   xxxx   \n",
       "12    case     True    True        de    ADP     xx   \n",
       "13     det     True    True        su    DET     xx   \n",
       "14    nmod     True   False      vida   NOUN   xxxx   \n",
       "15    case     True    True       con    ADP    xxx   \n",
       "16     det     True    True        su    DET     xx   \n",
       "17    amod     True    True     nuevo    ADJ   xxxx   \n",
       "18     obl     True   False   família   NOUN   xxxx   \n",
       "19    amod     True   False  adoptivo    ADJ   xxxx   \n",
       "20   punct    False   False         ,  PUNCT      ,   \n",
       "21    case     True    True      tras    ADP   xxxx   \n",
       "22     det     True    True        lo    DET     xx   \n",
       "23     obl     True   False    muerte   NOUN   xxxx   \n",
       "24    case     True    True        de    ADP     xx   \n",
       "25     det     True    True        su    DET     xx   \n",
       "26    nmod     True   False     madre   NOUN   xxxx   \n",
       "27   punct    False   False         .  PUNCT      .   \n",
       "\n",
       "                                                  tag      text  \n",
       "0                                            PROPN___     Frida  \n",
       "1                               PUNCT__PunctType=Comm         ,  \n",
       "2   DET__Definite=Ind|Gender=Fem|Number=Sing|PronT...       una  \n",
       "3                        NOUN__Gender=Fem|Number=Sing      niña  \n",
       "4                                   ADP__AdpType=Prep        de  \n",
       "5                       NUM__Number=Plur|NumType=Card      seis  \n",
       "6                       NOUN__Gender=Masc|Number=Plur      años  \n",
       "7                               PUNCT__PunctType=Comm         ,  \n",
       "8   VERB__Mood=Ind|Number=Sing|Person=3|Tense=Pres...   afronta  \n",
       "9   DET__Definite=Def|Gender=Masc|Number=Sing|Pron...        el  \n",
       "10           ADJ__Gender=Masc|Number=Sing|NumType=Ord    primer  \n",
       "11                      NOUN__Gender=Masc|Number=Sing    verano  \n",
       "12                                  ADP__AdpType=Prep        de  \n",
       "13    DET__Number=Sing|Person=3|Poss=Yes|PronType=Prs        su  \n",
       "14                       NOUN__Gender=Fem|Number=Sing      vida  \n",
       "15                                  ADP__AdpType=Prep       con  \n",
       "16    DET__Number=Sing|Person=3|Poss=Yes|PronType=Prs        su  \n",
       "17                        ADJ__Gender=Fem|Number=Sing     nueva  \n",
       "18                       NOUN__Gender=Fem|Number=Sing   família  \n",
       "19                        ADJ__Gender=Fem|Number=Sing  adoptiva  \n",
       "20                              PUNCT__PunctType=Comm         ,  \n",
       "21                                  ADP__AdpType=Prep      tras  \n",
       "22  DET__Definite=Def|Gender=Fem|Number=Sing|PronT...        la  \n",
       "23                       NOUN__Gender=Fem|Number=Sing    muerte  \n",
       "24                                  ADP__AdpType=Prep        de  \n",
       "25    DET__Number=Sing|Person=3|Poss=Yes|PronType=Prs        su  \n",
       "26                       NOUN__Gender=Fem|Number=Sing     madre  \n",
       "27                              PUNCT__PunctType=Peri         .  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mytokens = []\n",
    "for token in doc:\n",
    "    mytokens.append({'text': token.text, 'lemma': token.lemma_, 'pos': token.pos_,'tag':token.tag_,'dep':token.dep_,\n",
    "          'shape':token.shape_, 'is_alpha': token.is_alpha, 'is_stop': token.is_stop})\n",
    "pd.DataFrame(mytokens)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stopwords"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_¿Qué entendemos por palabras vacías?_"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cargamos las stopwords predefinidas en español en NLTK "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313\n",
      "['de', 'la', 'que', 'el', 'en', 'y', 'a', 'los', 'del', 'se', 'las', 'por', 'un', 'para', 'con', 'no', 'una', 'su', 'al', 'lo', 'como']\n"
     ]
    }
   ],
   "source": [
    "stopwords_spa = nltk.corpus.stopwords.words('spanish')\n",
    "print(len(stopwords_spa))\n",
    "print(stopwords_spa[0:21])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Idéntica operación en SpaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "551\n",
      "['trata', 'ya', 'mías', 'intentas', 'mencionó', 'propia', 'vaya', 'parte', 'casi', 'mucho', 'emplean', 'ésos', 'no', 'haces', 'peor', 'ello', 'aun', 'estado', 'alli', 'embargo', 'paìs']\n"
     ]
    }
   ],
   "source": [
    "from spacy.lang.es.stop_words import STOP_WORDS\n",
    "print(len(STOP_WORDS))\n",
    "print(list(STOP_WORDS)[0:21])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Como primera operación vamos a calcular la frecuencia en la que aparece cada término. Excluyendo aquellos que son stopwords, nombres propios o particulas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/claudiag/anaconda3/lib/python3.6/site-packages/ipykernel/__main__.py:15: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function, unicode_literals\n",
    "import spacy\n",
    "from collections import defaultdict, Counter\n",
    "\n",
    "pos_counts = defaultdict(Counter)\n",
    "words = []\n",
    "for sinop in info_pelis.descripcion:\n",
    "    doc = nlp(sinop)\n",
    "    for token in doc:\n",
    "        if token.is_stop != True and token.is_punct != True and token.pos_ not in ['PROPN', 'CONJ', 'ADP', 'DET']:\n",
    "            words.append(token.lemma_)\n",
    "word_TF = Counter(words)\n",
    "word_TF = pd.DataFrame.from_dict(word_TF, orient='index').reset_index()\n",
    "word_TF = word_TF.rename(index=str, columns={\"index\": \"token\", 0: \"TF\"})\n",
    "word_TF = word_TF.sort('TF', ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Las 10 palabras más frecuentes son:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token</th>\n",
       "      <th>TF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>vida</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>año</td>\n",
       "      <td>332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>vivir</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>joven</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>hijo</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>historia</td>\n",
       "      <td>171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mujer</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>casar</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>llegar</td>\n",
       "      <td>146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>mundo</td>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        token   TF\n",
       "8        vida  391\n",
       "48        año  332\n",
       "207     vivir  205\n",
       "130     joven  202\n",
       "152      hijo  176\n",
       "44   historia  171\n",
       "12      mujer  155\n",
       "124     casar  155\n",
       "131    llegar  146\n",
       "64      mundo  143"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(word_TF.head(10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>token</th>\n",
       "      <th>TF</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>vida</td>\n",
       "      <td>391</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>207</th>\n",
       "      <td>vivir</td>\n",
       "      <td>205</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>joven</td>\n",
       "      <td>202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>152</th>\n",
       "      <td>hijo</td>\n",
       "      <td>176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>mujer</td>\n",
       "      <td>155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>padre</td>\n",
       "      <td>130</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>hombre</td>\n",
       "      <td>109</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>madre</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>película</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3134</th>\n",
       "      <td>cine</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1016</th>\n",
       "      <td>español</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>654</th>\n",
       "      <td>director</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         token   TF\n",
       "8         vida  391\n",
       "207      vivir  205\n",
       "130      joven  202\n",
       "152       hijo  176\n",
       "12       mujer  155\n",
       "136      padre  130\n",
       "67      hombre  109\n",
       "53       madre   95\n",
       "271   película   81\n",
       "3134      cine   36\n",
       "1016   español   29\n",
       "654   director   28"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(word_TF[word_TF.token.isin(['cine', 'director', 'español', 'película', 'vida', \n",
    "                                  'vivir', 'mujer', 'hombre', 'joven', 'hijo', 'madre', 'padre'])])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En el contexto en el que estamos trabajando conviene añadir algunas otras palabras muy comunes en las sinopsis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "MY_STOP_WORDS = STOP_WORDS.copy()\n",
    "MY_STOP_WORDS = MY_STOP_WORDS.union(['cine','actor','director','español','película','documental','historia',\n",
    "                                     'vida','vivir','año','vivir', 'mujer', 'hombre', 'joven', 'hijo', 'madre', \n",
    "                                     'padre', 'niño','niña', 'hijo', 'hija' ])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stemmers y lematizadores"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NLTK ofrece distintos _stemmers_ y _lemmatizers_ con un rendimiento desigual en español:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Cargamos uno de los stemmer de NLTK: [SnowballStemmer](https://snowballstem.org/algorithms/spanish/stemmer.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.stem.snowball import SnowballStemmer\n",
    "stemmer_eng = SnowballStemmer(\"english\")\n",
    "stemmer = SnowballStemmer(\"spanish\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "girl\n",
      "chic\n"
     ]
    }
   ],
   "source": [
    "print(stemmer_eng.stem('girls'))\n",
    "print(stemmer.stem('chicas'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "girl\n",
      "chicas\n"
     ]
    }
   ],
   "source": [
    "from nltk.stem import WordNetLemmatizer\n",
    "wordnet_lemmatizer = WordNetLemmatizer()\n",
    "print(wordnet_lemmatizer.lemmatize('girls'))\n",
    "print(wordnet_lemmatizer.lemmatize('chicas'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En SpaCy sólo se han desarrollado lematizadores, aquí un interesante hilo en GitHub [Built-in stemmer?](https://github.com/explosion/spaCy/issues/327)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "chico\n"
     ]
    }
   ],
   "source": [
    "doc = nlp('chica')\n",
    "for token in doc:\n",
    "    print(token.lemma_.lower())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Clustering (NLTK, SpaCy, Sklearn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "En este apartado vamos a tratar de identificar segmentos de películas agrupando mediante las sinopsis. Esta operación puede apoyarse tanto en las funciones disponibles en NLTK como en SpaCy. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.1. Funciones para tokenizar y lematizar. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funciones con NLTK"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tokenize_and_stem(text):\n",
    "    # Primero se extraen los tokens de una frase, despues se extraen las palabras, \n",
    "    # para asegurar que la puntuación toma su propio tokent \n",
    "    \n",
    "    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n",
    "    filtered_tokens = []\n",
    "    # Solo tomamos los tokens que contienen letras, para evitar tokens numericos y puntuacion\n",
    "    for token in tokens:\n",
    "        if re.search('[a-zA-Z]', token):\n",
    "            filtered_tokens.append(token)\n",
    "    stems = [stemmer.stem(t) for t in filtered_tokens] # se extrae el tema para cada palabra\n",
    "    return stems\n",
    "\n",
    "def tokenize_only(text):\n",
    "    tokens = [word.lower() for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n",
    "    filtered_tokens = []\n",
    "    for token in tokens:\n",
    "        if re.search('[a-zA-Z]', token):\n",
    "            filtered_tokens.append(token)\n",
    "    return filtered_tokens"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Funciones son SpaCy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def tokenize_and_lemm_spacy(text):\n",
    "    doc = nlp(text)\n",
    "    lemmas = []\n",
    "    for token in doc:\n",
    "        if token.is_stop== False: # aprovechamos para eliminar ya las stopwords\n",
    "            if token.is_alpha== True: # Nos quedamos solo con los tokens que contienen letras \n",
    "                if token.pos_ not in ['PROPN', 'CONJ', 'ADP', 'DET']: # eliminamos nombres propios, conjunciones, determinantes\n",
    "                    lemmas.append(token.lemma_.lower())\n",
    "    return lemmas\n",
    "\n",
    "def tokenize_only_spacy(text):\n",
    "    doc = nlp(text)\n",
    "    tokens = []\n",
    "    for token in doc:\n",
    "        if token.is_stop== False :\n",
    "            if token.is_alpha== True:\n",
    "                if token.pos_ not in ['PROPN', 'CONJ', 'ADP', 'DET']:\n",
    "                    tokens.append(token.text.lower())\n",
    "    return tokens"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frida, una niña de seis años, afronta el primer verano de su vida con su nueva família adoptiva, tras la muerte de su madre.\n",
      "['frida', 'una', 'niña', 'de', 'seis', 'años', 'afronta', 'el', 'primer', 'verano', 'de', 'su', 'vida', 'con', 'su', 'nueva', 'família', 'adoptiva', 'tras', 'la', 'muerte', 'de', 'su', 'madre']\n",
      "['frid', 'una', 'niñ', 'de', 'seis', 'años', 'afront', 'el', 'prim', 'veran', 'de', 'su', 'vid', 'con', 'su', 'nuev', 'famili', 'adopt', 'tras', 'la', 'muert', 'de', 'su', 'madr']\n"
     ]
    }
   ],
   "source": [
    "print(info_nominadas.iloc[1].descripcion)\n",
    "print(tokenize_only(info_nominadas.iloc[1].descripcion))\n",
    "print(tokenize_and_stem(info_nominadas.iloc[1].descripcion))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Frida, una niña de seis años, afronta el primer verano de su vida con su nueva família adoptiva, tras la muerte de su madre.\n",
      "['niña', 'años', 'afronta', 'verano', 'vida', 'família', 'adoptiva', 'muerte', 'madre']\n",
      "['niño', 'año', 'afrontar', 'verano', 'vida', 'família', 'adoptivo', 'muerte', 'madre']\n"
     ]
    }
   ],
   "source": [
    "print(info_nominadas.iloc[1].descripcion)\n",
    "print(tokenize_only_spacy(info_nominadas.iloc[1].descripcion))\n",
    "print(tokenize_and_lemm_spacy(info_nominadas.iloc[1].descripcion))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.2. Análisis sobre nuestro corpus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Vamos a crear una lista con todas nuestras sinopsis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "synopses = list(info_pelis['descripcion'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Así como una lista de todos los términos encontrados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "totalvocab_stemmed = []\n",
    "totalvocab_tokenized = []\n",
    "for i in synopses:\n",
    "    allwords_stemmed = tokenize_and_lemm_spacy(i)\n",
    "    totalvocab_stemmed.extend(allwords_stemmed) \n",
    "    \n",
    "    allwords_tokenized = tokenize_only_spacy(i)\n",
    "    totalvocab_tokenized.extend(allwords_tokenized)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con estas dos listas, de tokens y lemas, vamos a crear un DataFrame, que contendrá el lema y en el índice el token. Así podemos rescatar en cualquier momento el token original."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "29140 en el vocab_frame\n"
     ]
    }
   ],
   "source": [
    "vocab_frame = pd.DataFrame({'words': totalvocab_tokenized}, index = totalvocab_stemmed)\n",
    "print( str(vocab_frame.shape[0]) + ' en el vocab_frame')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>words</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>escritor</th>\n",
       "      <td>escritor</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>escribir</th>\n",
       "      <td>escribe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>falso</th>\n",
       "      <td>falso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pretencioso</th>\n",
       "      <td>pretencioso</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>insípido</th>\n",
       "      <td>insípido</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trabaja</th>\n",
       "      <td>trabaja</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>escribiente</th>\n",
       "      <td>escribiente</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>notar</th>\n",
       "      <td>notaría</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vida</th>\n",
       "      <td>vida</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>gris</th>\n",
       "      <td>gris</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   words\n",
       "escritor        escritor\n",
       "escribir         escribe\n",
       "falso              falso\n",
       "pretencioso  pretencioso\n",
       "insípido        insípido\n",
       "trabaja          trabaja\n",
       "escribiente  escribiente\n",
       "notar            notaría\n",
       "vida                vida\n",
       "gris                gris"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vocab_frame.head(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación definimos los parametros para la función de frecuencia inversa (tf-idf), para poder convertir la lista de sinopsis en una matriz de términos y documentos. \n",
    "\n",
    "Para alcanzar esta matriz, en primer lugar contamos el número de veces que ocurre una palabra en un documento. Así obtenemos una matriz de términos documentos. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"tfidf.png\" alt=\"dtm\" style=\"width: 350px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A continuación esta matriz es ponderada por la frecuencia inversa: palabras que ocurren frecuentemente en un documento pero no en el corpus reciben un peso mayor, asumiendo que aportan un mayor significado en el documento.\n",
    "\n",
    "Sobre los parámetros:\n",
    "* **max_df**:  Es la máxima frecuencia dentro de los documentos que un _feature_ dado puede ser utilizado en la matriz tfi-idf. Si fijamos aquí un umbral mayor al 80% probablemente contenga poco significado, al menos en el contexto de este análisis. \n",
    "* **min_idf**: Puede tomar un porcentaje o un entero. Por ejemplo 3, en ese caso debe aparecer al menos 3 documentos para ser considerado. \n",
    "* **ngram_range**: Vamos a fijarnos solo en unigramas, no en bigramas o trigramas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 41 s, sys: 600 ms, total: 41.6 s\n",
      "Wall time: 20.9 s\n",
      "(921, 2432)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "#define vectorizer parameters\n",
    "tfidf_vectorizer = TfidfVectorizer(max_df=0.8, max_features=200000,\n",
    "                                 min_df=3, stop_words=MY_STOP_WORDS,\n",
    "                                 use_idf=True, tokenizer=tokenize_and_lemm_spacy, ngram_range=(1,3))\n",
    "\n",
    "%time tfidf_matrix = tfidf_vectorizer.fit_transform(synopses) #fit the vectorizer to synopses\n",
    "\n",
    "print(tfidf_matrix.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "_terms_ contiene la lista de features con la que vamos a realizar el cluster"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "terms = tfidf_vectorizer.get_feature_names()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculamos la distancia como 1- similaridad coseno, esto es la medida de similaridad entre nuestros documentos. La resta nos permitirá representarlo en el plano "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.metrics.pairwise import cosine_similarity\n",
    "dist = 1 - cosine_similarity(tfidf_matrix)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.3. Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 2.68 s, sys: 24 ms, total: 2.71 s\n",
      "Wall time: 1.37 s\n"
     ]
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "num_clusters = 3\n",
    "km = KMeans(n_clusters=num_clusters)\n",
    "%time km.fit(tfidf_matrix)\n",
    "clusters = km.labels_.tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.externals import joblib\n",
    "\n",
    "#Salvamos nuestro modelo, o bien lo cargamos si ya lo hemos ejecutado previamente\n",
    "joblib.dump(km,  'doc_cluster.pkl')\n",
    "#km = joblib.load('doc_cluster.pkl')\n",
    "clusters = km.labels_.tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Construimos un pandas dataframe para que sea más cómodo trabajar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "films = {'title':  list(info_pelis.titulo),\n",
    "         'synopsis': list(info_pelis.descripcion), \n",
    "         'edition': list(info_pelis.edicion), \n",
    "         'cluster': clusters, \n",
    "         'nom_mejor_pelicula': list(info_pelis.nom_mejor_pelicula), \n",
    "         'nom_mejor_dire': list(info_pelis.nom_mejor_direc), \n",
    "         'gana_mejor_pelicula': list(info_pelis.gana_mejor_pelicula)}\n",
    "\n",
    "frame = pd.DataFrame(films, index = [clusters] , columns = ['title', 'synopsis', 'edition', 'cluster', 'nom_mejor_pelicula','nom_mejor_dire', 'gana_mejor_pelicula'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "distribution_clusters = frame[['cluster', 'title']].groupby('cluster').count().reset_index() #numero de peliculas por cluster \n",
    "distribution_clusters_nom = frame[frame.nom_mejor_pelicula==1][['cluster', 'title']].groupby('cluster').count().reset_index() #numero de peliculas nominadas a mejor pelicula por cluster"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exploramos el resultado de la clusterización"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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vFX2+iIjUXo1XkGVmZjJmzBiGDRsGwNq1azl69GiNDd97770sWLAAgAYNGlBY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\nCgMREUFhICIiKAxERASFgYiIAP8PGG/MAqIrpHwAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcea6f396d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.barplot(x=\"cluster\", y=\"title\", data=distribution_clusters)\n",
    "ax.set(ylabel='# películas', xlabel='Cluster')\n",
    "ax.set_title('Clusters de películas nominadas a los Goya')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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r1iAtLQ16enqIj4/HhAkTMHfuXLRp0waGhoZYtWrV428BERGpTWXonzhxAvv3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcea2412a20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.barplot(x=\"cluster\", y=\"title\", data=distribution_clusters_nom)\n",
    "ax.set(ylabel='# películas', xlabel='Cluster')\n",
    "ax.set_title('Clusters de películas nominadas a mejor película en los Goya')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cluster 0: \n",
      "['Estiu 1993', 'Verónica', 'Tarde para la ira', 'Un monstruo viene a verme', 'A cambio de nada', 'Truman', 'Loreak', 'Caníbal', 'La gran familia española', 'La herida', 'Grupo 7', 'Lo imposible', 'La piel que habito', 'No habrá paz para los malvados', 'Balada triste de trompeta', 'Pa negre', 'El secreto de sus ojos', 'La soledad', 'El laberinto del fauno', 'Volver', 'La mala educación', 'Roma', 'Mi vida sin mí', 'El otro lado de la cama', 'Hable con ella', 'Sin noticias de Dios', 'El Bola', 'La comunidad', 'Leo', 'You\\x92re the One (Una historia de entonces)', 'La lengua de las mariposas', 'Solas']\n",
      "Cluster 1: \n",
      "['El autor', 'Handia', 'La librería', 'Julieta', 'La novia', 'Nadie quiere la noche', 'La isla mínima', 'Magical Girl', 'Relatos salvajes', '15 años y un día', 'Vivir es fácil con los ojos cerrados', 'Blancanieves', 'Agora', 'Celda 211', 'El baile de la Victoria', 'Camino', 'Los crímenes de Oxford', 'Los girasoles ciegos', 'Siete mesas de billar francés', '7 vírgenes', 'Obaba', 'Mar adentro', 'Planta 4', 'Te doy mis ojos', 'En la ciudad sin límites', 'Los lunes al sol', 'Juana la Loca', 'Lucía y el sexo', 'Cuando vuelvas a mi lado', 'Todo sobre mi madre']\n",
      "Cluster 2: \n",
      "['El hombre de las mil caras', 'Que Dios nos perdone', 'Un día perfecto', 'El Niño', 'El artista y la modelo', 'Blackthorn. Sin destino', 'La voz dormida', 'Buried \\x96 Enterrado', 'También la lluvia', 'Sólo quiero caminar', 'El orfanato', 'Las 13 rosas', 'Alatriste', 'Salvador (Puig Antich)', 'La vida secreta de las palabras', 'Princesas', 'Tiovivo c. 1950', 'Soldados de Salamina', 'Los otros']\n"
     ]
    }
   ],
   "source": [
    "print('Cluster 0: ')\n",
    "print(list(frame[(frame.cluster==0) & (frame.nom_mejor_pelicula ==1)].title))\n",
    "print('Cluster 1: ')\n",
    "print(list(frame[(frame.cluster==1) & (frame.nom_mejor_pelicula ==1)].title))\n",
    "print('Cluster 2: ')\n",
    "print(list(frame[(frame.cluster==2) & (frame.nom_mejor_pelicula ==1)].title))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# sólo las películas nominadas a mejor película\n",
    "frame_nom = frame[frame.nom_mejor_pelicula==1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "over_edition = frame[['edition', 'cluster', 'title']].groupby(['edition', 'cluster']).count().reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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yoxGJiKj6K7PoT5gwARKJpNQHmEskEvz66686TYyIiLSvzKJ/5MiRysyDiIgq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcea23f4240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.barplot(x=\"edition\", y=\"title\", hue=\"cluster\", data=over_edition)\n",
    "ax.set(ylabel='# películas', xlabel='Edición Premios Goya')\n",
    "ax.set_title('Cluster de películas nominadas a los Goya')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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5iIuLg5mZGTw8PODs7AwfHx/UrVsXbdq0UXk3RET0IsvPz8f8+fPRrVs3fadS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\nI0gp0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcea274bef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.set_style(\"whitegrid\")\n",
    "ax = sns.barplot(x=\"edition\", y=\"title\", hue=\"cluster\", data=frame[frame.nom_mejor_pelicula==1][['edition', 'cluster', 'title']].groupby(['edition', 'cluster']).count().reset_index())\n",
    "ax.set(ylabel='# películas', xlabel='Edición Premios Goya')\n",
    "ax.set_title('Cluster de películas nominadas a los Goya en la candidatura Mejor Película')\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>cluster</th>\n",
       "      <th colspan=\"3\" halign=\"left\">gana_mejor_pelicula</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>sum</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>32</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0.281250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>30</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>19</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.105263</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  cluster gana_mejor_pelicula                \n",
       "                        count   sum      mean\n",
       "0       0                  32   9.0  0.281250\n",
       "1       1                  30  11.0  0.366667\n",
       "2       2                  19   2.0  0.105263"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# nominadas en cada cluster y pct de ganadoras del premio Goya a mejor película\n",
    "frame_nom[['cluster', 'gana_mejor_pelicula']].groupby(['cluster']).agg(['count', 'sum', 'mean']).reset_index()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ¿Cuáles son los términos que hay detrás del clúster?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Palabras top por cluster:\n",
      "\n",
      "Palabras Cluster 0 : casa, descubre, noche, familia, amigas, amor, relación, cambiarán, llamada, situación, mundo, recibe, barrio, pueblo, muerte, profesor, nan, encontrando, sale, vuelve, música, perdido, inesperada, busca, vecinos, chica,\n",
      "\n",
      "Palabras Cluster 1 : viaje, decide, mundo, ciudad, casa, formará, aventuras, destino, pequeña, chica, encontrando, familia, busca, descubre, cambiarán, crucen, amor, abuela, acaba, amigas, países, novia, soñado, luchado, huida, camino,\n",
      "\n",
      "Palabras Cluster 2 : guerra, llegan, nan, unidos, desaparecido, grupo, dejando, extraños, española, familia, mundial, civiles, descubre, muere, mundo, pueblo, encontrando, casa, guerra, convierte, luchado, nan, político, nan, vieja, pequeña,\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "\n",
    "# frame.index = range(len(frame.index))\n",
    "\n",
    "print(\"Palabras top por cluster:\")\n",
    "print()\n",
    "order_centroids = km.cluster_centers_.argsort()[:, ::-1] \n",
    "for i in range(num_clusters):\n",
    "    print(\"Palabras Cluster %d :\" % i, end='')\n",
    "    \n",
    "    for ind in order_centroids[i, :26]: #15 palabras por cluster\n",
    "        print(' %s' % str(vocab_frame.ix[terms[ind].split(' ')].values.tolist()[0][0]), end=',')\n",
    "    print()\n",
    "    print()\n",
    "    \n",
    "#     print(\"Cluster %d titles:\" % i, end='')\n",
    "#     for title in frame.ix[i]['title'].values.tolist():\n",
    "#         print(' %s,' % title, end='')\n",
    "#     print() #add whitespace\n",
    "#     print() #add whitespace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import os  # for os.path.basename\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "from sklearn.manifold import MDS\n",
    "\n",
    "MDS()\n",
    "\n",
    "# Convertimos el resultado en 2 componentes para poder representarlo\n",
    "\n",
    "mds = MDS(n_components=2, dissimilarity=\"precomputed\", random_state=1)\n",
    "\n",
    "pos = mds.fit_transform(dist)  # shape (n_components, n_samples)\n",
    "\n",
    "xs, ys = pos[:, 0], pos[:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#set up colors per clusters using a dict\n",
    "cluster_colors = {0: '#1b9e77', 1: '#d95f02', 2: '#7570b3', 3: '#e7298a', 4: '#66a61e'}\n",
    "\n",
    "#set up cluster names using a dict\n",
    "cluster_names = {0: 'Familia y costumbrismo', \n",
    "                 1: 'Amor y aventura', \n",
    "                 2: 'Histórico, sucesos y acción '\n",
    "                }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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OHQzenzt3jvnz58ujDB8QSfaFEEIIIYQQD0129g3iYq/cU9m42CvkZN/A+gGN\n4U9JSSEwMJCoqCi+//57duzYgVKpxMfHh6ZNm7Jz507OnTtHaGgox44dIzw8HCMjIzw8PJg4cSKh\noaEkJyeTkpLC6NGjWbVqFfPmzSM6OpqIiAiUSiVDhgzh5ZdfZuvWraW2L89nn31GXFwcWq2WAQMG\n0Lt3b1q3bk1MTAwAgYGBDBo0CHd3dz788EPUajVWVlZ89dVX6HQ6PvnkE7Kzs9FqtUycOJHGjRuX\nOr7hw4cTExPDnDlzMDY2xsnJiRkzZpCWlsZHH32EUqlEq9Xy5ZdfUqdOHQBycnLo27cv27ZtQ6FQ\nsHHjRuLj4/n444/1sW/cuJHIyEiUSiVubm5MmzaNoqIixo8fz6VLl6hWrRqzZs2iRo0apZbt3buX\nc+fOERQUxLJly9i6dSsAnTp14u233+bbb7+lcePGnDx5ktTUVEJCQvDw8Hggn4X/Akn2hRBCCCGE\nEA9N/N/G6N9JSYmOuBNXeLada4X2cfHiRQICAvTvL126xNChQw3KLFmyhN9//x0jIyNWrVrFc889\nh7u7O8HBwdjY2DBnzhyio6OxsLBg+PDhHDhwAICioiJWrlypT8TVajULFixg48aNFBYWEhQUxPPP\nP1/m9m3atCkVa1ZWFv/73//YuXMnRUVFrF+/vtzjWrx4Me3atWPw4MGEh4ezf/9+zpw5Q/v27enb\nty/nz59n+vTpLF26tNTxAUyePJmlS5dSq1Ytpk6dyqZNm8jJyeHZZ59l5MiRxMfHc/36dX2yb21t\nTaNGjTh69CgtW7Zk165dvPnmmwYx5efns2jRIqytrRk0aBBnzpwhNjYWe3t7Zs+ezZYtW9i1axcm\nJiallpmZ3byIk5yczPr161m3bh0Affv2pUuXLvrzvXjxYlatWkV0dLQk+xUgyb4QQgghhBDiocnX\nFFWsfH7FygPUr1+fiIgI/fvQ0NBSZXx9fRkyZAjdu3enZ8+eBusSExNxcXHBwsICgGeeeYZTp04B\n4OXlZVD2woULNGjQADMzM8zMzAgLCyM+Pr7M7ctK9m1tbXF1dWXEiBF06dIFPz+/co/r5MmTjBkz\nBoA33ngDgNWrV5ORkcHGjRuBm8l3WceXlZWFQqGgVq1aALRu3ZpDhw7x2muvMWrUKHJzc/H19aVF\nixYG+3zllVfYunUrnp6epKSk0LRpU4P1NjY2vPvuuwAkJCSQlZVFfHw8bdvenLy3W7duAHz66ael\nlkVFRQGUWRX9AAAgAElEQVQ35z9o1qwZxsY309OWLVty+vRpAJ5++mkAnJ2diY2NLffciNIk2RdC\nCCGEEGWaOXOm/k5ffn4+TzzxBDY2NsyfP7+yQxOPsermJhUrX71i5e/VlClTSEhI4KeffiIgIIAf\nfvhBv06hUKDT/dX7oKioiGrVbj4ZwMTEMB6lUklJSYnBsjttX5ZFixYRHx/P5s2b2bBhA0uWLDFY\nX1R084KHkZFRqX2ZmJgQHBxcKkn/+/EtXry4VEwKhYKnnnqKDRs2sHfvXr766iv69OljcMGhQ4cO\nzJ07lwMHDuDj42Owj8LCQqZOncqGDRtwcHDgnXfeKTfOspbd6XwplUr9drfcXkbcnczGL4QQQggh\nDFzR5LDs1H6qd2nFi5+8S783Anj55ZeJiIiQRF/8Yx5NnVEqFfdUVqlU4NnU+YHHkJuby/z582nY\nsCGjRo3CxsYGtVqNQqFAq9Xi6upKUlISarUagIMHD+Lp6VlmXQ0aNODixYvk5eVRUFDAkCFDKrR9\nSkoKy5cvx8PDg6CgILKysoCbCXB+fj75+fn6XgWenp764QSrV69m/fr1NGvWjJ07dwJw/vx5li5d\nWubxKZVKFAoFqampBjFt2bKFc+fO8eKLLzJmzBji4uIM4jMxMaFVq1bMmzePHj16GKzLy8vDyMgI\nBwcHLl++TFxcHEVFRTRt2lQf5+7du/n222/LXHaLu7s7x44do7i4mOLiYo4fPy6THz4AcmdfCCGE\nEEIAkJavZuKBDWxLiqdY99cdONMTf9CgwJRh+WoSYuNZsmQJGo2GoKAghg0bVmoSsYMHD5KZmUlS\nUhIpKSmMGTOGH3/8kUuXLrFw4UJq1apFUFAQV69eRaPRMHr0aHx8fAgICKBt27bExMSQmZnJt99+\nS+3atSvrdIh/iY2NGZ5ezsQeu3zXsp5ezg9scr7bWVlZkZmZyauvvoq5uTktWrTA1taWZ555hsDA\nQBYsWMC4ceN48803USqVeHt78/TTT7N///5SdZmbmxMYGMiQIUOAm93rzc3Ny9z+1KlT7Nixg8DA\nQP32jo6OHD16lK1bt2JiYkKfPn0AGDBgAK+99hoNGzbUj1N//fXXGTduHAEBAVhYWBASEgLAxx9/\nzMCBAykpKWHChAnlHt+0adMYO3YsxsbG1KtXj27dunHmzBkmT56Mubk5RkZGZU4k2LVrV2JjY3Fx\ncTFYbmdnx3PPPUefPn1o3Lgxb775JjNmzGD9+vXs27cPf39/jI2N+eKLL7Czsyu1bO/evQDUrVuX\nfv364e/vj06no2/fvvp5A8T9U9ypK4RKpdJ5e9/9URcqlYp7KScefdKWVYu0Z9UhbVl1SFtWLVWp\nPdPy1fhtCSMxN73UOuPYJJRpOdR+5QUmObZixuQpbN++HVNT0zJnDD948CCJiYnMnj2bOXPmcOrU\nKb7//nu+/vprbG1t6dGjB3v27KFXr14kJyczZswYoqKiCAgIoHPnzgQEBBASEoK9vb1+XPK/rSq1\n5eNAnVvAN/P23vHxezXtzRkZ+ByWVuV3fy/Lo96Ws2bNYty4cZUdRoXMmzePOnXq6C9EPCwPoi3/\nrOPeupJUMXJnXwghhBBCEHxgY5mJ/u0Sc9NZkraPRo0aYWpqeseytybxcnBw0C+zt7cnKysLa2tr\nTpw4wZo1a1Aqlfpuy2A4Gdfty0XVYmlVjZGBzxEdFUdcrOHs/EqlAk8vZ/x6e1Y40X/UZWRk4Ovr\nW9lhVMjbb7+NmZkZI0eOrOxQRAVJsi+EEEII8R93RZPDT0lxdy8IxFy9yEvKsv+EvDWJGKCfVfvv\nr3U6HZs3byY7O5uVK1eSlZXFq6++ql8vk3H9d1haVcP/dW+ys28Qf+IK+flFVK9ugmfTf6fr/qOg\nRo0a1KhRo7LDqJDvv/++skMQ90mSfSGEEEKI/7jtfxujfydaXQlXNDn697cmEQP0k4jdTWZmJnXr\n1kWpVLJjxw4KCwsrHrSoMmxszHi2nWtlhyFElSOz8QshhBD3aPPmzXh4eJCRkVHZoQjxQGUVlD9u\nuizFJVr961uTiH388cf6ScTupnPnzvzyyy+8/vrrVK9eHWdnZ5nlXwghHjC5sy+EEEKUozgzFfWR\nDWjzMjCyqMHGqBjq1avH9u3bGTBgQGWHJ8QDY1vN/I7ri73+moG7xMWBfv3e1L8fM2YMY8aMMSjf\nunVr/Wt/f/8yX2/atEn/umfPngCMGjWqzLJCCCEqTpJ9IYQQ4m+Kc65xLWI06iPRoC0GILdIyfHj\nLozr6kbUhvUMGDCAffv28fnnn2Nvb0/9+vWpUaMGo0ePZtasWRw5cgStVsugQYPw8/MjICAANzc3\nrl27Rp06dfjoo49u7qu4mC+++IInnniiMg9Z/Mf5ungwOWbTPXXlN1Yo6eJyb3fwhRBCVB7pxi+E\nEELcpjjnGsnT26M+tE6f6AP8nmFJK9s8mlz7mQunjnPpfDwhISHMmjWLxYsX68cqHzp0iHPnzrF6\n9WqWLVvG/PnzUavVALi5uTFkyBCuXbvGyJEjiYiIoE+fPqxcubJSjlWIW5zNrenq4nlPZbu6eOJk\nbv0vRySEEOKfkmRfCCGEuM21yECKrp4vtfzX61Z0sM/FSAHP2mbxw8zRXLp0iSZNmmBkZESHDh0A\niIuLo1WrVgCYm5vz5JNPkpSUBICXlxdw81FkERERDBo0iGXLlsnjxcQjYVqbnrha1bxjGVermkxr\n0/MhRSSEEOKfkGRfCCGE+FNxZipq1fpSy9MKjDirrsbiJHsCj9fjSJY5O49egNu6PCsUCoP/31JU\nVIRSefPn1sTEBIB58+bRrl07VqxYIc8tFo8M++qWRHcbQQ9XL4wVhn8iGiuU9HD1IrrbCOyrW1ZS\nhEIIISpCxuwLIYQQf1If2WDQdf+WPWlWdHPOZphrOgA6Hbxz9AnyC/JISEjA1dWVvXv30rp1azw9\nPQkLC+Ptt98mLy+PP/74AxcXF4P6MjMzeeKJJ9DpdOzatYuSknt75JkQ/zb76paE+QzkiiaH7Unx\nZBfmY2NanS4uHtJ1XwghHjOS7AshhBB/0uaV/Ui9X9Mt+eDJa/r3CgV0dMzFwqM1o0ePpm7dujRo\n0AClUsnTTz+Np6cngwYNori4mLFjx2JubjjTeb9+/Zg2bRp16tQhICCA4OBgfv/9d9q1a/evHp8Q\n98rZ3JrX3dtWdhhCCCH+AUn2hRBCiD8ZWdQoc/lcr5RSy/rXzeTCk670+2gUdevWZdKkSfoZ9d9/\n//1S5SMiIgBQqVT4+Pjg4+OjX/fbb789iPArXUpKCj169MDT0xOdTkdhYSFvvfUWL730kr7Mnj17\nSElJYeDAgZUYqRBCCFH1SbIvhBBC/Mmy5StcW/lemV35SzEyxsytLaNGjcLCwoKaNWvi6+v77wf5\niKtfv77+wkZWVha9evWiffv2mJmZAegnMhRCCCHEv0uSfSGEEOJPxna1sfTuhfrgD3cta+ndixe6\nvMILXV55CJE9urKzbxB/4gr5miLyNOlotTr9OltbWxwcHJg8eTKmpqZkZWXh4+PDuXPnGDRoEOPH\nj6devXqcOXMGd3d3pk+fzqVLlxg/fjxarZbatWvzxRdfkJaWxoQJEygqKsLIyIjPPvuM2rVr89ln\nnxEXF4dWq2XAgAH07t2bJUuWsH37dkpKSnj++ecZNWoUJ0+eZMqUKZiammJqasqcOXOwtpbx50II\nIao2SfaFEEKI2zj6z6Mg6WiZj9+7xcTpSRz95z3EqB496twCon+MI+7EFUpKbib4Gk0GVy7nELlM\nhV9vT7Kyr5OVlYVWq8XGxoZp06YRFRWlryM+Pp45c+ZQs2ZNOnToQE5ODnPmzOGNN96gU6dOzJo1\ni7i4ONasWcPQoUN59tln+fXXX1mwYAEffvgh//vf/9i5cydFRUWsX//XUxRWrlyJUqmkU6dOvPHG\nG0RFRTFgwAD8/PzYv38/169fl2RfCCFElSfJvhBCCHEbY2tH6k34jWuRgTcfw3d7l34jYyy9e+Ho\nPw9ja8fKC7KSqXML+GbeXtLTNKXW5aqvs+DbYBYuVuJa34EvvviCNWvW4OXlVarsE088gYODAwCO\njo7k5uZy8uRJJkyYAMC4ceMAGD9+PBcvXiQsLAytVkuNGjWwtbXF1dWVESNG0KVLF/z8/AAwMzPD\n398fY2NjMjMzycrKolOnTnz66ackJiby8ssv07Bhw3/r1AghhBCPDEn2hfgPuH3SrNuFhoYyc+ZM\nfH19DSYLGz9+fKllt+vYsSObNm3CwsKiQnFERUVhZWVlMFnX7U6fPk21atWoX79+herdvXs327dv\nZ+bMmWWuDw0Nxc7ODn9/f/2yjIwMAgIC6NixI2PHjq3Q/kTVZ2ztSO13V1OcmYr6yAa0eZkYWdhh\n6e2HsW2tyg6v0kVHxZWZ6ANYWjjQrs27AHg1r0WLFi1Ys2YNJiYmpcoaGRkZvNfpdBgZGaHT6QyW\nm5iYMHfuXBwdDS+wLFq0iPj4eDZv3syGDRuYNm0a4eHhrF+/HgsLC7p37w5A27ZtWbduHbt372b8\n+PGMGzeONm3a3PfxCyGEEI8DSfaF+I+4fdKsytK7d+87rt+xYweenp4VTvbvR0JCAi4uLpLoizsy\ntquNbacRlR3GIyU7+wZxsVfuqWxc7BVysm9UqH5PT08OHDjAyy+/zNy5c2nVqhXNmjVj586dDBw4\nkP3795OWlkaLFi345ZdfGDx4MB4eHvTu3ZvMzExq1KiBhYUF8fHxXLp0iaKiIiIjI3n++efp2bMn\nOp2OU6dOSbIvhBCiypNkX4gqLL0on2Wn9vNHcjLpN/K4osnB2fzex6mq1WrGjh2LRqPhxo0bBAcH\n67vifvfddxw+fBgjIyO++eYbLCwsCA4OJjk5meLiYgIDA2nbti0BAQG4ubmxfft2OnbsSKNGjXBz\nc2PFihWcPXuW3Nxc6tSpw8iRI1m9ejU1atSgZs2a5OfnM2fOHLKysqhZsyZLly7F1NRUH9uZM2cI\nCgrCxsZG/7gzgBUrVrBp0yaUSiUvvvgiQ4cOLfPYgoKCuHz5Mh9++CFvvvkmwcHBJCYm0rBhQ779\n9lvUajWBgYH68cW9e/emW7du/Prrrzg4OLB//35sbGwICQnBw8ODZcuWsXXrVgA6derE22+/XeH2\nuqWsngj/1N16VTwKzp07R48ePWjVqpXB8vJ6oNyLQ4cO0aBBA2rWrMmIESMICwsrs9z99iopy/32\nfLkf99Or5Z+Kv22M/t2UlOiIO3FvFwZuCQwM5OOPP2blypXUqlWLUaNG0bBhQz755BO2bNmCQqFg\nxowZODo6cvToUbZu3YqJiQl9+vTB3d0dCwsL+vfvj7e3N/3792fKlCkMHTqUMWPGYGVlhampKTNm\nzLifQxdCCCEeK5LsC1EFpeWrmXhgAz8lxqFFhyIrj2p5WbRZO5OuLp5Ma9MT++qWd63n+vXr9O3b\nlxdffJH9+/ezcOFCQkNDAWjUqBEffPABX3zxBRs2bMDKygoHBwc+//xzMjIyeP3119m0aRO6ogLq\nai8T8Fx9dh3+jSfrOQMQGxuLiYkJkZGR9O/fn+eff5727dvj6+uLl5cXXbp0YenSpdSqVYupU6ey\nadMm+vTpo49twYIFjBo1ihdffJHJkycDkJyczLZt21i1ahUAAwYMoEuXLmUeW4MGDXBwcCAkJIS9\ne/fSt29f/T43bdpUZlLp6+vLuXPnyM/P5+mnn6Zt27ZER0djbW3N+vXrWbduHYC+rtsvQlS2u/Wq\nqEy3ZnNPSEhCqTRi/vyF2NiYPZC6f/zxR4YOHUrNmjXLTfTh4fYqedzla4rKXWduXoMX2r1vWD6/\nyOBixO2fxdsn67v9dXh4uEEdTk5OLF68uNT+5syZU2pZWeVAHvknhBDiv0eSfSGqmLR8NX5bwkjM\nTTdYrsxQYxz5Kzv4ld+Mv+Plls8y6/M7392yt7dnwYIFLF68mMLCQszNzfXrWrduDUDTpk05fPgw\nWq0WlUrFkSNHALiRryFpXl/yEw7iXHwd+2rFLEx6gvT1k0lPbE2dWk4ojU2pX78+arWa06dPs23b\nNjp27EhWVhYFBQVMnz6dRo0aUVBQwO7du/n555/RaDQEBQWRkJBAy5Ytbx5zWhpHjx7l6NGjZGZm\nMnjwYLKzs0lMTGTEiBEUFRXRr18/fex79+7l+PHjlJSUcPDgQfbu3cvatWuZOXMmxsbGBAQEoFar\nSUpKYuDAgVhZWVFSUsKyZctISEjAx8eH06dP4+zsTGxsLKdOnaJZs2bMnDmT2NhYrl+/zi+//MIb\nb7zBrFmzOHLkCFqtlkGDBuHn50dAQABt27YlJiaGzMxMvv32W2rXrl1mG9zaPicnh7fffhs/Pz+i\no6OJjIzExMSExo0b6y923LJ161bCw8MxMjLCw8ODiRMnGtzhLSumu9UZHR1NREQESqWSIUOG8PLL\nL/Pzzz+zZMkSjI2N8fT0ZPz48URFRbFnzx6uXbvG2LFjCQkJwdzcHH9/f3Jzc4mMjESpVOLm5kbQ\nuImsWXmQpctnodUWYW3pTEmJjhlTd2Fpk8HxE5uoVs0UJycnlEolGRkZDBo0CKVSiVarZfjw4YSF\nhWFsbIxSqWTu3Lmo1Wo++ugjzM3NGTRoEDt37uTcuXOEhobSq1cvYmJiSh1r//79DXqVJCYmGsQ5\nbdo0/XnIycmhb9++bNu2DYVCwcaNG4mPj+fjjz8u1XanT59mypQpBvHZ2trq10dFRXHo0CEyMzM5\nd+4c77//Pps3byYhIYGQkBCaNWtWZo+R++3VUla734/q5qXH3t+xfPWKlRdCCCHEg6Gs7ACEEA9W\n8IGNpRJ9gJIaltwY1J4bg9qT1a8tuS81vWtdy5Ytw8nJiVWrVvHpp58arFMoFAavTUxMGD58OBER\nESz9Zjbfe6dQcCQK0GGi1GFrosXSuIRr+UryT/3K5bPHePnF5/V1KJVKHB0dOX36NAqFArVaja+v\nLwBarRaFQsHZs2dZvHgxnp6e6HQ6FAoFhw8fJiMjgw4dOvDee++h0+lYuHAhtWvXZsmSJWzYsIGn\nnnqK/Px8/b6ee+45vLy8cHNz45lnnuHnn3/myy+/RKVSodPpKCwsZO3atVhaWrJy5Uratm1LTk6O\nQay33IrjypUrXLlyhbVr1+Lp6cnRo0c5dOgQ586dY/Xq1Sxbtoz58+ejVqsBsLKyYtmyZXTo0IGf\nf/65zPN/+/YTJkzQb7948WJCQ0NZtWoVnp6e3Ljx15jovLw85syZw9KlS1m1ahUpKSkcOHCgzDpv\nj+lOdarVahYsWMCKFStYvHgxmzZtIi8vj7CwMJYvX05kZCSXL19GpVIBcPnyZVasWIGTkxOnTp0i\nJCQEHx8f8vPzWbRoEatXr+bcufNMm7KWbdu3YmXpTPu2o7CydAJudv1eH/0djd1e49uwxdjY2JCS\nksKRI0d49tlniYiIYMKECSQmJhIcHExERAQtW7Zk06ZNAPp9duzYEXd3d2bMmGFwMeXvx+ri4kL7\n9u354IMP8PLyMojzwoULnDlzRr+ttbU1jRo14ujRowDs2rVLPwnc36Wnp5cZ3+0SExMJCwvjnXfe\n4bvvvuObb77h7bffZvPmzSQnJ7N+/XpWrFjBihUr+Omnn/jjjz/0vVqWLVum/yze3qtlxYoV/Pzz\nz6Smpt613e+HR1NnlErF3QsCSqUCz6bO97UfIYQQQvwzcmdfiCrkiiaHn5Li7qnsT0lxXNXk3LFM\nZmYmjRo1AtA/y/qWw4cP4+vry/Hjx2nQoAHW1tb6xOfM9yNYdSiDwX/rxe5iXkBCXjVa2WnI0JTQ\nMn0b8KZ+fZ06dTh27BiBgYHk5+fTuHFjEhMTSUpKolWrVhQVFenH7devX5+4uDhOnTrFmTNn+OOP\nP7hw4QL5+fkkJyfj6+vLu+++y+DBg7G3t8fSsvxhCwUFBXz//fesWbOGvLw8cnNzSUxMpKioiOTk\nZKKiosjMzCx3e3d3d+Lj43njjTcoLi7m8uXLLFiwgJ9//lk//tzc3Jwnn3ySpKQkAJ5++mkAnJ2d\nycrK0tcVEBBAw4YNsbOzIy4uTr+9mZmZfvvu3bszcuRIevbsSffu3TEz+6vLe2JiIi4uLlhYWLB7\n926uX7/OqVOn9Otvr/P2mO5U54ULF2jQoAFmZmaYmZkRFhbGr7/+yvnz5xk2bBhw8473jBkzaNWq\nFU2bNtVfDKpXrx52dnYA2NjY8O67N2dpP3P6HOam6eSqr1Kzxs3HoNnZuaDTafltXyia/Cx27V7C\n+YRdvDH05rPR3d3dWb58Obm5ufj6+vLMM88QEhLCjRs3uHbtGj169Ci1z7Lc6Vj/HmdCQoJB+wC8\n8sorbN26FU9PT1JSUmjatOwLZzVr1iwzvtt5enqiUChwcHCgUaNGGBkZYW9vz5EjR/Q9RoyNb/5U\nt2zZktOnTxv0amndujV79uzhxIkTJCUlMXjwYODmRZ9Lly7dtd09PDzKPU/lsbExw9PLmdhjl+9a\n1tPLGesHNCRDCCGEEBVz12T/1p2aB1VOPPqkLR9fW9POU6wrKXOdMkON2YrfDJZ9XlgTTXo658+f\nx9r6r4n70v9c1qhRI8LCwli7di2dO3cmJSWF2bNnU1BQwJ49e/juu+8AaNeuHdWqVUOj0dC9qy+K\na6cZULf0DNz1qhey45o1KfkmmBmVQNxGjuzZjk6n4+TJk7i7uxMeHs6XX35J/fr1+eCDD8jIyMDW\n1paaNWuSl5en/3y+8MILTJ06leLiYmrVqkXt2rUZPnw4O3bsYPTo0SiVSl544QU0Gg3R0dF06tTJ\n4LOdk5NDfn4+mzdvpqCggCtXbk4iZmtry9atW7F3dsLMuSa9/AdQmJtHnbp1uHr1Knl5eaSmppKZ\nmcn58+dJT0/n6tWruLi4sHTpUtavX89zzz3HlStXSElJobi4WL/ftLQ0Tp8+TW5uLmfOnEGj0fDH\nH3+Qm5urL5Obm8u1a9cwNTVFq9WWuf3TTz+Nq6srMTExvPbaa0yaNAkrKyvgZrKfnZ2NSqXi/Pnz\nqNVqLl26pL+IcT91XrhwgczMTIPzl5aWRqNGjXjvvfcASE1NpaSkhISEBK5evYpKpeL69esUFRWh\nUqkoLi4mODiYmTNnUs3Uik8++Rz4s2cEf94l1oFCYUTrVsP4bd882rV5F4UC4k6c5saNG+Tn5/Pp\np58SGxvLlClTyM7OJiAggGbNmrF582ZSU1OJi4vT7/PW+Tx58iS5ubn64y7rWG995s3NzfVx2tra\n8uWXX3L27Fl9wg1gaWnJr7/+Su3atWnUqFGp78yCggKOHTtGSEgIPXr0MIjvVlmVSkViYiLp6en6\ntrq93dLS0rhw4QLXr1/Xb5OamsqFCxfQaDTExsZiZWVFQkIC6enp/PHHH3h4ePDmm28axJKamnrH\ndr+9B0dF1H9Sy4UEI9S52nLLWFoZUf9J7X/iN+W/cIz/FdKWVYe0ZdUhbXn/7prse3t737USlUp1\nT+XEo0/a8vH2+7EsSC29XGdrgeaD0ncVG7ZsQeDbpR89t3DhQv3r1157Tf/61gzz5T2u7plnniFr\nVxjXIkbpl83w+Ovu4usuGVy+YcrRbHPeck1DUaKlQdEFvL29adKkCX5+fly5coXr168zbNgw/Pz8\n9GPN3dzcDD6f3t7evPrqqxw5coRZs2bx3XffUVRUxNatW9m2bRvffPMN/v7+2NjYYGlpyZNPPmnw\n2W7YsCG+vr7Y29vToEEDVq9ezYkTJwgYPBivoCHs3bmbEiMFxe1bYLbyN8642VDCDXr17UOLJp7M\nmzePnTt3YmJiwuLFixk1ahRLlixh4cKFnDx5kuDgYGrVqsWhQ4c4cOAAw4cP59y5c3z//ff6ruCL\nFi0iNjaW/Px8OnfujI+PD1ZWVjg6OmJra6vvin327Flee+01srOz6dq1K9999x2jRo3i5MmTFBQU\n8NVXX/H222/TvXt3jI2NOXXqFHPmzKF+/fpoNBq8vb2ZPn06w4cPp3Hjxrzzzju89dZb5Ofnc/bs\nWX744QfUajXr1q3DxcWFbdu2ERoaiqWlJXPnzsXd3Z2QkBCmT5+OUqmkpKSEWbNmMXnyZFxdXTl/\n/jwhISE4Od3sht+8eXOSkpLYs2cPaWlpTJgwgQEDBmBmZkanTp3YtPEgWVkplJRosbR0JCs7mdq1\nvMjISgTA1MQcUKDJz8S8uh1nzyZSq1YtMjMzsbS0ZPjw4Tz99NOMHDmSjh07Urt2bebOnUvz5s3x\n9PTE3Nxc39a3zrW7uzvGxsa0aNGCuXPnMmrUKHx9fSkoKMDBwQF7e3vq16/PU089pY/z8uXLJCcn\nU79+/VLfix06dGDr1q3Mnj0bFxcXg3XVqlWjefPmFBcXl4rP29tb/zlOSkqiqKgIb29vcnJySEhI\nMHjdvXt3tm7dSrNmzYCbifvEiROJi4vDyMgIb29voqOjqVmzJj179mT9+vU0adIEMzMzpk+fzocf\nfsi+ffuws7OjcePGhIWF4e3tTV5env6zdPs8HBXVzKuA6Kg44mINZ+dXKhV4ejnj19sTS6tq913/\n40J+N6sOacuqQ9qy6ngQbflfvlgg3fiFqEJsq1XsD3cb0+oPPAZtXsYd1z9vn8uc806MffLqn+UN\nu8e/9NJLjB8/nokTJ97T/lq2bEnr1q3p168fOp2OgQMHAlC7dm2GDBmCtbU11tbWDBkypMztmzRp\non9UV5NmXtCy4f/Zu++wKK7ugePfXapUFQVEQRMbCmLBaOwmYomJBiugYEks8dVgEt8oFjRiwYIa\nQfP6s0bBGkWssZtiQ4IaBCNGjQQkioXe2+8PwoYVUDAqiufzPD7P7sydmTs7Antm7j2HM+t3kNmv\nLTr7f0EnMhZFZjZZjcy5dv46a8J/xrt+fbKzs1myZAmWlpZMnjyZ9PR06tevrzq+ra0tKSkpODs7\nE/BR5tEAACAASURBVBQUxPz581m4cCFXr17lwIEDJCcn07FjR1q2bMmtW7fw8/NTqwCQkpKCh4cH\nZ8+e5dChQyxYsIDZs2ejp6eHvr4+H3zwASkpKbzzzjvMmDGDAQMG4ODgwPr16xkxYgS//PILJ06c\noHr16mrDzFu3bk2VKlX47LPPiImJwc3NjS+//FKVsE1HR4eWLVvi5+eHn58f+/bto1OnThgaGqKh\noUFWVhbGxsbo6upibm7O6NGjuX79Ou+99x4LFixgxIgR3LhxA2tra/744w8sLS3x8fHhiy++oEOH\nDgwYMAAjQwsavPkO4b/toUPb//DLxU2cDv4fRgbm5OfncurcNyiVmpz8eTGgwNrajvr162BmZoaX\nlxd6enpoaGjg5ubG+PHjsbS0xM3NDS8vL3r37q12fdu0aYO7uzvffPMNUJBvQV9fHycnJwwNDbG0\ntKRJkya0bt2auXPn4u3treqntbU1o0aNwtvbm6CgILS0/kk099577xEWFlYs0C/K1dX1if17nDp1\n6uDk5ISrqyv5+fkMGjSI2rVrM27cOKZOncqmTZuwtLQkOzsbCwsLhg0bxtChQ9HQ0MDBwUFtekLr\n1q2xtbVl6NCh5OTkMGnSpH8V6AMYGOrgOtxeVU0hPT2bKlW0sG0mQ/eFEEKIl4EiP7/0WrmhoaH5\n8mT/9SLX8tV2Jy2Jt3csKHUof1GaCiXBgz0w0zN6YtvyePTJ/pOYuq2gardxz7QPT2vcyS3suxWm\ntkyRkIpO0HkyRvwTiPepZ8cIvfp888035ObmEh0dzcSJE9VKivn5+WFoaMiIESMICAjg3r17fP75\n5wQEBJCQkMDYsWPx9vbmypUrKJVK7ty5w4kTJ3Bzc8PT0xNDQ0Pmzp1LQkICSUlJmJubq424WL16\nNd999x3m5gXJz+7fv8/q1asZN24cmzZtonr16hw8eJCffvqJCRMm4OTkxIQJE3BxcaF///74+voy\nZswYNm/erDa//erVq8XmmdevX5/Q0FA8PT1V7WJiYnB3d2f9+vW4uLjw/fffA3D48GFCQkJo2rQp\nv/32G9OnTyc1NZU+ffpw4sQJAM6cukXQrrLllgBwHGBL+471ytz+RfD19aV27dpq5SDLSn7PVi5y\nPSsPuZaVh1zLyuNZPdm3t7cvW2bZSkae7AtRiZjrGfFeXdtiAWtJ3qtr+8wDfQCDVh8St+UzyM15\ncmMNTQzsn67817NW3uSGv21aw7o1a6lfvz5eXl4ltis6z7vo6/z8fPbv309iYiJbtmwhISGBgQMH\nqm3r6+tLx44dcXFx4dChQ+zatUttvba2NgMHDmTs2LFqywurAwDk5RXc9Dl9+jSJiYmqpIA5OQXX\nRkNDg0dv+M6bN4/Ro0fTuXNn1q1bR1paGhoaGqp9PUqhUKjtIzs7W3X8oudclE0zc/bujlAb+l2a\nlzGb+5gxY9DV1WX8+PEV3RUhhBBCiFJJ6T0hKpk5b/elnqHJY9vUMzRhztt9n8vxNatZYGDfr0xt\nDez7oVm11nPpR3kdjooo04gIgJz8PBKSEqlVqxZJSUkEBwerVSooi/j4eOrUqYNSqeTo0aNkZWUV\nW29lZUV+fj7Hjx9XBeiF7OzsOHnyJHl5eWRmZqpqwRdWKQAIDg4GoFevXpiYmNCgQQPu3btHdHQ0\nUDDVoLAs3/Llyzlz5gwJCQlYWVmRlZXFjz/+SHZ2NjY2Nly4cIGcnBzu37+vFuQaGxujUChUZd7O\nnz+Pra3tY8+9MJt7WbyM2dxXr16Nr68vGhoaFd0VIYQQQohSSbAvRCVTo4oBQe+Po089OzRQH7Gk\nqVDSp54dQe+Po0aV0kvR/Vumrr5omTV4bBstswaYuvo+tz6UV0JmWqnrCisZFP5Txj7EpkcXXFxc\n8PT0ZNSoUfzf//0fcXFxZT5ejx49OHHiBMOHD6dKlSqYm5uzYsUK1XonJyfmzJnDqFGjeP/99/nt\nt984deqUan3RXAVDhw5VlVAbN24cixcvZvTo0ao55sbGxrRv354BAwawbNkymjRpAoC7uzs7duzA\n1dWVmJgY2rZtq5pn7u7ujpubG7t37yYlJYUPP/xQtc7NzU3tXObMmcOkSZNwc3MjJyeH999//4nn\n79jfFpMaj58zblJDD8f+j79x8CIsWLAANzc3evXqRZcuXXBzc2PChLJPVXnUjh072Lp1q9qy3Nzc\nYvv86quvCAoKKvN+O3fuTGZm5hPbfffdd/j4+BAVFaWWgLMsfvjhB7Zv316ubYQQQghRMWTOvlAj\n17JyOXLuFH8Za5CYlY6xdhV61bV5LkP3S5KTFEdcgDspobvVh/RraGJg3w9TV180jUxfSF/KYuNv\nZ5l+bk+Z2897+0OGN2n3HHukrjL+bKYkv9zZ3FWJ59KyqaKnxR9RwcTE3GLKlCn/ar9TpkyhRYsW\nuLi4lNrmwYMH3Lp1q1zXvHPnzhw9ehQdncd/Zt999x1RUVEMGjSIL7/8kh07dpT5GKK4yviz+bqS\na1l5yLWsPGTO/r8jc/aFqMRMtKrQo0nF/LHTNDLF4j/byImPJeXCHnJT49HQr4aBveNLM3S/qJ51\nbZgVvK/MyQ171bV5Ab2q3F7WbO4pyZkE7Qon/LL6TYjo21fQ1k0mJTkTA0MdsrOzmTJlCnFxcaSn\np+Pu7k6XLgUjPjp06MCpU6fQ0tKiT58+7N69G21tbTZs2ADAr7/+ypEjR7h37x5Tp06lbdu2dOnS\nhdOnT3Pt2jXmzJmDUqnE0NAQb29vHj58qCrneO3aNZo1a1ZqrogrV64wZ84cNDU10dDQwNfXFyOj\nx9/kK3qjYN68edja2rJ27VrWrl2LmZkZf/75J5MmTWLw4MFERUXx3//+l7lz5xIREUFmZiZDhw59\nqmSFQgghhHh+ZBi/EOK50qxmQdVu4zDpO42q3ca9lIE+/JPcsCyeV3LD15WxsS7tO9ajW/eGtO9Y\nr8ID/ZW+pwn79a9iCQTz8/O5F5fCSt/TpCRnkpCQQOfOnQkICMDHx0dtGoa5uTnbtm0jIyODtLQ0\ntm7dSkZGBtevXwcgISGBDRs2sHjxYr7++mu148yZM4f58+ezceNG2rRpoxryf/nyZSZPnsyuXbs4\nduwYqampJZ7DgwcPmDVrFv7+/tjZ2bF///6n+iy6d+/OyZMnATh+/Dg9evRQrUtPT6devXps3bqV\ngIAAli9f/lTHEEIIIcTzI0/2hRDib3Pe7svlB7e5lfyg1DbPM7mhqHhBgeE8uF96/gaAB/fTCAoM\nZ7BLMy5dusS2bdtQKBQkJCSo2tjZ2QFQs2ZNVY4EExMTkpOTAWjTpg0ATZo0ISYmRm3/YWFhTJs2\nDYDMzExatGgBFCRfNDExUe03JSUFfX39Yv2rUaMGixcvJjMzk7t379KvX9kSZj6qR48eLFu2DGdn\nZ44fP463t7cqoaOuri4PHjzA2dkZLS0t4uPjn+oYQgghhHh+JNgXQoi/FSY39Dy3l++jwtWG9Gsq\nlLxX15Y5b/d9rskNRcVJTMwgPOxOmdqGh90hXxGpemp///59hgwZolpfNFN/0deFeXIKyxM++hpA\nX18ff39/tWVRUVHFShmWlnNnzpw5TJgwgfbt27N69epilRxKUrQPhe2tra2JjY3lr7/+IiMjA0tL\nS1Wwf+7cOX755RcCAgLQ0NCgVatWTzyGEEIIIV4sCfaFEKKIGlUM+N87Q7iTlsThqIgKSW4oKkbE\nI3P0HycvL5+rV6OpU6cOCoWCI0eOlKv8YmhoKCNHjiQiIgIrKyu1dQ0aNOD06dN06NCBvXv3Ympq\nSq1aZZ/+Eh8fj6WlJZmZmfz000+89dZbT9zGwMCAuLg4atWqxa+//qoaTdCxY0eWLVuGg4NDsWNY\nWFigqanJ0aNHycvLIycnp9gNCSGEEEJUHJmzL4QQJTDXM2J4k3a4N3+X4U3aSaD/GkhPK3uwDtDM\nth1Hjx5l+PDhVK1alWrVqrFq1aoybVutWjU++eQTPDw8+Pzzz9XWzZgxgxUrVuDq6sq+ffto2rRp\nufrl6urKuHHjmDhxIm5ubuzatYtr1649dpuhQ4cyevRo3N3dadSokWp5jx492L9/Pz179lRr36FD\nB65fv46bmxuxsbF07Nix1ISBQgghhKgYUnpPqJFrWbnI9aw85Fo+f2dO3SJoV3iZ2zsOsKV9x3rl\nPo5cy8pFrmflIdey8pBrWXlI6b1/R57sCyGEEIBNM3OUyrJ9F1AqFdg2M3/OPRJCCCGEeHoS7Ash\nhBAUlAC0tStbAG9rZ16hJQKFEEIIIZ5Egn0hhBDib479bTGpoffYNiY19HDsb/uCeiSEEEII8XQk\n2BdCCCH+ZmCow3j3Dti1qFVsSL9SqcCuRS3Gu3fAwFCngnoohBBCCFE2UiNHCCGEKMLAUAfX4fYk\nJmYQcfkO6enZVKmihW0zGbovhBBCiFeHBPtCCCFECYyNdZ8q274QQgghxMtAhvELIYQQQgghhBCV\njAT7QgghhBBCCCFEJSPBvhBCCCGEEEIIUclIsC+EEEIIIYQQQlQyEuwLIYQQQgghhBCVjAT7Qggh\nhBBCCCFEJSPBvhBCCCGEEEIIUclIsC+EEEIIIYQQQlQyEuwLIYQQQgghhBCVjAT7QgghhBBCCCFE\nJSPBvhBCCCGEEEIIUclIsC+EEEIIIYQQQlQyEuwLIYQQQggh/pWYmBhatmyJm5sbbm5uODk54enp\nSW5ubqnb+Pn5ERAQUOr6q1ev8scffzyP7qqJiYmhf//+5drmp59+YsuWLc+pR0I8G5oV3QEhhBBC\nCCHEqyknPpaUC3tIiP4TqxoGbPBdiGY1CwA8PDzYt28fjo6OT7Xvo0ePYmtryxtvvPEsu/xMdO7c\nuaK7IMQTSbAvhBBCCCGEKJecpDji/D8l5UIQ5OYQn6FJzgNzbv73DQzs+2Hq6oudnR1RUVEAbN68\nmX379qFUKnFwcOCjjz76Z185OUyZMoW7d++SlpbGp59+ioWFBdu2baN69eqYmJiQnp7OsmXL0NTU\nxMzMDG9vb+7fv8+XX36JUqkkNzeXxYsXU7t2bdV+T506xddff42uri4mJib4+Phw48YNZs+ejaam\nJkqlkuXLl6vaHzt2jOPHj+Pt7Q3A1KlTcXBwIDU1lYCAAJRKJQ0bNmTOnDkEBgby+++/M2XKlBLP\n7cqVK8yePRttbW20tbVZtmwZRkZGL+jqCFFAgn0hhBBCCCFEmeUkxRE9rxPZd68XX5mbQ8r570j9\n4yLH4towxG040dHRHDp0iK1btwLg4uJCr169VJskJibSsWNH+vXrR3R0NBMnTiQwMJBOnTrRs2dP\n7Ozs6NWrFxs2bKBWrVp4eXmxb98+kpKSaN++PePHjyciIoJ79+6pBfsBAQF4eHjQunVrjhw5QkJC\nAg8ePMDT05OmTZuyfPly9u3bxzvvvANAp06dWLBgAXl5eeTn5xMSEsLs2bPZvXs3a9euxcjIiKFD\nhxIZGak6RmnnFhgYiIuLC46Ojpw9e5Z79+5JsC9eOAn2K4kFCxaofsmlp6djZWWFsbEx48eP5+jR\no7i7u5d5X4GBgRgaGtK9e/fn2OMC58+fZ+LEicyfP1/1i/bChQssWLAALS0t7O3t+eKLL4iJiaFP\nnz7Y2toCUK1aNXx9fUlOTmbSpEkkJyejp6fHkiVLqFq16nPvtxBCCCHE6youwL3EQP92ujZTIwqC\n7VupuQx5OwYHBwcOHjxIVFQUw4YNAyA1NZXbt2+rtjMyMuLy5cts374dpVJJQkKC2n4TEhJQKBTU\nqlULgLZt2xISEsLgwYOZMGECycnJ9OzZk5YtW6pt16tXL2bNmkWfPn14//33qVmzJg8ePMDHx4eM\njAzi4uLo06ePqr2Ojg5NmzYlLCyMnJwcmjdvjra2NsbGxvznP/8B4MaNG2r9u3z5conn1q1bN776\n6itu3bpF7969qV+//lN/3kI8LQn2X3GJiRlEXL7DW60G0LmjM39EBRMTc4spU6ao2jRp0qRc+yxv\ngpKn9eeff7JhwwZatWqltvyrr75i6dKlNGjQgGnTpnHhwgVMTU1544038Pf3V2u7ceNG2rRpw6hR\no9i+fTtr1qzhyy+/fCH9F6+OmJgY3N3dCQwMfCb7K+km1fHjx1m9ejVaWlpUr16dxYsXo6Oj80yO\nJ4QQQrwscuJjSQndXeK62lWy8LYpCOK9I82p8eASOQl/oaWlRdeuXfHy8lJrf+7cOQD2799PYmIi\nW7ZsISEhgYEDB6q1UygU5Ofnq95nZ2ejUCho1KgRe/bs4fTp0yxdupQBAwao5QdwdHSkU6dOHDt2\njHHjxrF8+XLmzZvH6NGj6dy5M+vWrSMtLU3tWD169ODkyZNkZWXRs2dPsrKy8PLyYs+ePdSsWZOx\nY8eqtS/t3AB27tzJyZMn8fDwYPLkybz99ttP+niFeKYkG/8rKiU5k4BvQ/H2Ok7QrnAOfx9J0K5w\n9u+5wpWIu6QkZwIQHByseqp/8OBBBg8ejIuLC3PnziU3N5d33nmHzMyCtufPn2fZsmWqzKjjx48n\nJCQEgIyMDN59911yc3NZtGgRzs7ODBo0iKCgoGJ9CwoKYsCAAQwaNIiDBw+Weg41a9ZkxYoVGBoa\nqi2/d+8eDRo0AKBjx46cPn261H2cPXtWNQLhnXfe4ezZs2X9CIV4KqXdpNq0aRNr164lICAAfX19\njhw5UkE9FEIIIZ6flAt7IDfnie1G1r3PxltVuX/2O2xsbAgODiY9PZ38/Hzmzp1LRkaGqm18fDx1\n6tRBqVRy9OhRsrKygIIgPzc3F2NjYxQKBbGxsUDBd1ZbW1sOHDjA77//joODAxMnTiQ8PFytDytX\nrkRTUxMnJyd69+6teipvZWVFVlYWP/74I9nZ2WrbdO3alZCQEM6fP0/nzp1JTU1FQ0ODmjVr8tdf\nfxEeHq62TWnnFhAQQEJCAn379mX48OH89ttvT/2ZC/G05Mn+KyglOZOVvqd5cD+t2Lr8/HzuxaWw\n0vc04907qJanpqaybNkygoKC0NfX55NPPiEkJIR27dpx9uxZunbtyvHjx2nTpo1qm+7du3PixAne\neustTp8+TYcOHbhw4QK///4727ZtIy0tjb59++Lg4ICBgUFB31JS+Oabb9i7dy9ZWVlMmTKF3r17\nl3geVapUKXF5nTp1CAkJoXXr1pw5cwYNDQ0A7t+/j7u7O3FxcQwZMoS+ffty//59qlevDoCJiQlx\ncXFP96GKSqlw5EtMdAypKVkkJmZgbKyrWn/mzBmWL1+OlpYWRkZGfP3112RmZvLZZ5+RlZVFVlYW\nM2fOxMbGRrVN4U2q6dOnqx1r48aNQEGSoXv37mFmZqa23s/Pj+joaGJiYvD392fJkiVcuHCB3Nxc\nhg4diqOjI0FBQQQEBKClpYW1tTWzZs1S9TEjI4PatWuXqY+ichg1ahS//fYbc+fOVY0geZ48PDzo\n2bNnmY717rvvsm/fPvT19Z97v4QQL5fc1Idlameum0N7kxTW7f2J6e+5M2zYMIYOHYqGhgYODg7o\n6v7z97hHjx6MGzeOS5cuMWDAAMzNzVmxYgWtW7dm7ty56OvrM2fOHCZNmoSmpiaWlpa8//77REZG\nMmvWLPT09NDQ0GDGjBlqfbCwsGDkyJEYGRlhZGTEyJEjiY+PZ/z48VhaWuLm5oaXl5fad1UDAwOM\njIzQ1dVV/evQoQMDBgzA2tqaUaNG4e3tzfDhw1XHKOncrKysmDhxIoaGhmhra6uS/gnxIkmw/woK\nCgwvMdAv6sH9NIICw2loXfD+zJkzJCcnM3z4cPLy8tDR0eHy5cv06NGDEydO0LVrV06dOsXUqVO5\nePEiUPBlbt26dUyZMoXjx49To0YNfHx8aNiwIe7u7vj6+tKgQQOioqJUgcbNmzd58803Vb8cR4wY\noWpbVvPmzWPevHloaGhgbW1NSkoKVatWZeLEifTt25fk5GQGDRpUbChU0eFd4vWWkpxJ0K5wwi/f\nIS8vn7S0h8THp+PtdRxbO3Mc+9tiYKhDYmIiPj4+WFpaMnnyZE6dOkVOTg5mZmbMnz+f6OjoYvV9\nS7tJBQX5Lnx9fXn33XfVbpwVys7OZsuWLYSEhJR402zdunWsXr2aWrVqsWvXLjIyMlR9jIuLY/v2\n7WXqo6gc1q5di4eHR0V3Qwgh1GjoVy9xuZluDsvsYtSWDbN6iOnAbgAMHTqUoUOHqq3/9NNPVa/3\n7dunet23b1/V6wEDBqheFybBK2RjY8POnTtL7Wu/fv3o16+f2jInJyecnJxU7wtHiBad6rdq1Sq1\nbRYsWKD2fuTIkWzfvl31QKqkc+vcubOU5xMVTobxv2ISEzMID7tTprbhYXc4cC2c3+PvMmOeF2YW\ntdi5cye7du0iLy+PM2fO0L59e3755RciIyOxtLRUC2SMjIwwNTXl5s2bXLx4EXt7e9577z21Y2Rn\nZ6NU/vPfSKlUkpeX96/OsVGjRmzcuJH169djZWVFnTp1MDAwYMCAAar50La2tty8eRNTU1Pu3bsH\nwN27dzE1Nf1XxxavvsKRL2G//kVenvoNoLy8fMIu/cVK39OkJGdSvXp1ZsyYgaurK8HBwSQkJNCi\nRQsuXbrEzJkziYqKKtcf6v79+3Ps2DESExPVvrQUsrOzAyA8PJy33noLAD09PdVNsw8++IDx48fz\n7bff0qVLF3R1dVV99PLyeiZ9FC+vnPhYEo7/jwd755Fw/H/kxMeq1gUGBrJw4UKgYKTWu+++C8De\nvXsZPHgwzs7OeHp6qtpOnz6dTz75hF69evHdd9+V2rYkKSkpjB07Fjc3NwYNGkRYWFiZ+p+cnMz4\n8eNxc3PDxcWFiIgIoOSpXevXr8fJyYlBgwaxYsWKcn5SQoiKZNDqQ9Ao4/NCDU0M7B2f3O4Vc/Hi\nRdasWUP79u0ruitCPJYE+6+YiL+fVJZFXl4+xy5c5ffYP7lvqMFvt6MY/f0GHmSkoqenx9ixY1my\nZAkPHz7Ezc1NNRy+sMboxx9/zNWrV1mwYAEtWrRg7dq1GBsbc/XqVRITExk7dizBwcEcPnwYKBg9\n8NVXX3Hu3DnGjBlDcnIyCxcuLPcT96lTp3L16lVyc3PZs2cPXbt25dy5c6rhT2lpaVy9epU33niD\nDh06cOjQIQCOHDlCp06dynWsV0VMTAwtW7bEzc0NV1dXBg8ezNGjR4GCobcnT54s9z4Lr1tZ5efn\n4+zsjJ+fX7mP9SKVZ+TLtGnTmDlzJgEBAXTrVvDkwdTUlD179tCjRw+2bt1apkAkMzOTn376CQBN\nTU26detGaGhosXZaWlpAwRzEogpvmo0dO5YVK1aQn5/P8OHDiY+PV/Vx5syZ/6qP4uWVkxRH7Eon\nbv73DeL8J/AgcCZx/hO4+d83SLv6I7lpCaVum56eztq1a9m2bRs3b95UlYO6du0aK1euZOXKlQQE\nBDy27aPu3bvHoEGD8Pf354svvmDNmjVlOo+NGzfSvHlz/P39mTZtGt7e3qqpXZs3b2bdunVqN8G2\nbNnCjh07CAwMJCUlpawflxCigmlWs8DAvt+TGwIG9v3QrFrrOffoxWvZsiXHjh2TYF+89CTYf8Wk\np2U/uVERmnkakJlDnnk1Mt+x4eTi1XT5sDdvWjeiZcuW1K5dm1mzZpGZmckPP/yg2i43N5d169Yx\nYsQIfv75Z1UtVBsbG+rVq8cvv/zCgwcPWLBgAXv37iUhIYHExESWLVvGvHnzCAsLY+DAgXTp0gWF\nQsG8efOIjo5W69sPP/yAm5sbP//8M0uXLuWjjz4CYODAgUydOpWBAwfi4OBAo0aNaN26NYmJiTg5\nOTFs2DDGjBmDmZkZbm5uhIeHM2TIEIKDgxk1atS/+4BfMoVP+hKOrcSqhgEbfBcSEBDA6tWrmT9/\nvlpym/KIiYnhwIED5drmu+++K5bE5mVT3pEvyUnJ1KpVi6SkJIKDg8nOzubMmTOcOXOGjh074unp\nWSzZT0k0NDTw9PTk7t27AISFhfHGG2+U2t7W1pbg4GCg4Cntn3/+Sd26dVm2bBk1a9Zk5MiRtGjR\ngtjYWFJSUqhVqxapqan/qo/i5VRYqzolZGfxhFe5OWTfv8X9HR7kpieVuH1hOShXV1e1clAtWrRA\nQ0MDc3NzkpOTH9v2UTVq1ODw4cO4uLjg4+NTartHhYeH07ZtWwCaNWtGVFSU2tQuIyMj/ve//wGg\nq6uLq6srw4YNIz4+vszHEEK8HExdfdEya/DYNlpmDTB1Lfs0TiHEsydz9l8xVfS0Hrveqo76PGGN\nOubkZNdFkZVDbuPa5DauTQYQW68uOjoFc5YPHjyInZ0dFy5cAApK9Tk4OADQoEEDHB0d6dKlC2vX\nrgVg0KBBJCQkqN7v2bOH6Oho1XDj3NxcdHR0GDt2LLVr1+bGjRvFkplBQbbTrl27Fltub2/P7t3q\nJV00NTWLzZcC0NfX55tvvnnsZ/IqykmKI87/U1IuBEFuDvEZmuQ8MOfmf9/AwL4fpq6+1KxZUzWF\nAQqG3k6aNIm0tDQyMjJUw3S7d+/O4MGD+eGHH8jKymLDhg14eXkRFhbGihUrGDhwoKpcYU5ODgsX\nLsTKykqtPw8fPmTfvn04Oztz507ZgumK8LiRLymp9zh17p//KzbWH9Cly/u4uLhQr149Ro0ahZ+f\nH0uWLMHPz4+1a9eiUChU1SwK/fDDD6xbt46bN28SERGBv78/69evx8vLi/Hjx6OtrU2NGjWYOHFi\nqf1s3bo1tra2DB06lJycHCZNmoSenh76+vo4OTlhaGiIpaUlTZo0YciQIbi4uGBsbFzmPopXR0m1\nqlNylOgo89BSQn4+5CfEknx2M1gW/F7OySm4KfC4clCamup/3p9UOqqojRs3YmZmxuLFi7l8+TKL\nFi0q07k8WhorLy+vxKldt2/f5ttvv2X37t3o6+vzwQcflGn/QoiXh6aRKZbTfyYuwL2gDF/RLUfT\nhgAAIABJREFUm5UamqrvKppGMr1SiIokwf4rxqaZOXt3R5RpKH8eeUQbPyDfxBCN0Jtq676/8SuH\nNWpx7tw5/P390dLSomXLlqr1hQlHoOTEd48OQ1YoFEybNo3Vq1dTv379EmuNirIpfNL3aAAAQG4O\nKee/I+rKReIf1qFWrX+GxhUOvXVwcODs2bOsWbOGESNGkJubS/369Rk9ejSff/45586d4+OPP2bz\n5s1MmDCBsLAwxo8fz9tvv83OnTvZsmVLsaRgixcv5vPPP+fWrVvP+ez/ndJGvujpVeeDnvOLLe/Z\nszuLfWap3hcm8Xk0AVBRpd2k6tKlC126dCl1u6JJiAA+//zzYm3GjBnDmDFj1JZNnDiRiRMnEhoa\nir29fZn6KF4NpdWqXvVHTdpWS6WjSQox6drUrpLFH9G/cFfZCEA1ReRJ5aCKKk/b+Ph4GjduDBRM\n6yrriJ5mzZoRHBysyinRsGFD3nzzTf744w9SU1PR1NTkk08+YdKkSVSvXh19fX0iIiK4ffv2Sz9q\nSIjKLCYmBnd3d7UEdWWhaWSKxX+2Ffwuu7CH3NR41p+8imnTtgwfPf459fbxAgMDVaX4noZUGhGV\njQT7rxhjY11s7cwJu/TXE9tGGz8gQysb3jBF+2Q4Gr//RW7DWpCfj/LkZeZs/4W2di3Q0tLi+PHj\n5Obmqp4YPcmVK1dIT09HqVRy48YNrKysVMONC4dEF35ZFOVT0pM+gNvp2kyNqE1+Pmgr05jcU0/t\n6V2NGjX45ptvWLduHVlZWejp6anWtW7dGkA1pNfQ0FC1rmbNmsydOxc/Pz+SkpKKlXALCQlBQ0OD\nVq1avfTB/pNGvhRrX6V87YV4lkqrVT2kzkOWXjdj71/GtK6WhrluDkaayQT+Foabm5tqelS1atUe\nWw6qqNLaBgUFqXJJFPrwww+ZMmUKhw4dYujQoezfv59du3apZcQGGD16tOrG8AcffMCwYcOYNm0a\nw4YNIz8/n5kzZ6Knp4e7uzsjR44EYMSIETRp0gR9fX2cnZ2xt7fH2dmZ2bNn8+233z6jT1YIURaF\nQXpC9J/kJt8jJz4WzWoW5d6PZjULqnYbB4BulB8aVYyfdVeFEE9Jgv1XkGN/W27HJD42CVmydjq/\n1P77ab5CQYZTB7S/v4jW6augoSS3nikfeo0neMkGXF1dcXBwoGvXrqxfv55q1ao9sQ9NmzZl2rRp\n3Lp1C2dnZ4yMjFTDjYsOif7iiy+e1Wm/Fkp70gdQu0oW3ja3/1kQG0dOwj83fR439PZxIzV8fX3p\n2LEjLi4uHDp0SC13A8Dx48cJDw9n8ODBPHz4kKysLCwtLXF0fPmy65Zn5ItSqcC2mfkL6JUQJSut\nVrVFlWx8mqmXr9LTzGf9p+9h0ncagCo/SUnloIrS19fnxIkTZWpbdP3333+vel2YGLKown0+qqQy\nq3369KFPnz5qy9atW1fi9kKI568sUwU1jUyJjIzEy8sLpVKJvr4+CxYsQENDg88++4ysrCyysrKY\nOXNmsYcEAMHBwWzevFn1O6Ft27YEBwfj5uZGu3btCA4OJj4+nlWrVmFhYcGyZcv45ZdfyM3NxdXV\nlQ8++IBTp07x9ddfo6uri4mJCT4+Pmo3J/fu3UtAQABKpZKGDRsyZ86cEs83NjaWL7/8EqVSSW5u\nLosXL8bU1JSZM2cSHR1NVlYW7u7udOzY8fl84EJUIAn2X0EGhjqMd+9AUGA44WHqc5QLh+7/Uvsm\nmZr/DIvMN9Alc1A7tf2YVq2uVpt0xIgRqqHChd555x3eeecdAPz9/VXLC5MwFVU43LhQ4XBjmY9Z\ndqU96StRbg4poUGqt+UZeqtUKlWjOOLj47GysiI/P5/jx48Xm19bdEh/YGAgt2/ffikDfSjfyBdb\nO3OMjHVfQK+EKFlptapLb//kG7FCCPE4ZZkqmBl1EcvpPzNv3jwmT55M8+bNWbduHZs2bcLa2hoz\nMzPmz59PdHQ0f/zxR7n7YGhoyMaNG/Hx8eHIkSPY2tpy+/ZtNm/eTFZWFv369cPBwYGAgAA8PDxo\n3bo1R44cISEhgZo1a6r2U1hhxMjIiKFDh5ZaYeTw4cO0b9+e8ePHExERwb179wgJCUFbW5uAgADu\n3r3LsGHDyl2lSIhXgWTjf0UZGOrgOtyeqTO74TjAlp69G/NunwbsbxrK6bqRaoF+STQVSnrVLX4n\nVlSs0p70ld4+XvX6ww8/ZMOGDXz00UfY2dlx7969Yk/pC9WvX58rV64wf/58nJycmDNnDqNGjeL9\n99/n/PnznDp16t+cRoVy7G+LSQ29x7YxqaGHY3/bF9QjIUomtaqFEC9aaVMFi8q+e524AHdu3LhB\n8+bNgYKHPFeuXFHl5Jg5cyZRUVF07ty53H0oOrUwJSWFCxcu8Ouvv+Lm5sbHH39MXl4e9+7do1ev\nXsyaNYtVq1bRpEkTtUAfyl5hpEOHDuzZs4cFCxaQlZVFixYt1KqHmJmZoa2tLVVBRKUkT/ZfccbG\nurTvWE/1/h1FY/bdCnvidu/VtcVMz+g59kw8jdKe9Jnp5rDMLqbYcg39aixYME31/tGht6GhoUya\nNEm1bMqUKarXRW8EFI7eAPj5559L7V///v0ffwIvgceNfFEqFdjamePY3xYDQ50K7KUQ/9SqTjn/\n3RPbVtZa1UKIF+dxUwUflRK6m/y8fxI3Z2dno1QqMTU1Zc+ePQQHB7N161YuXbrEhAkTim3/aCLn\nojmhHp1aqK2tzcCBA4tVCbG0tKRTp04cO3aMcePGsXz5curXrw+Ur8JIo0aN2LNnD6dPn2bp0qWq\n/CNFpzVmZWWhVMozUFH5yP/qSmbO232pZ2jy2Db1DE2Y83bfF9QjUR7ypO/ZKGnki+MAW6bN7Ibr\ncHsJ9MVLQ2pVCyFelPJOFXyzpgEXL14ECpL12tracubMGc6cOUPHjh3x9PQkPDy8xM0NDAyIi4sD\n4OrVq6SmppZ6KDs7O06ePEleXh6ZmZmqufcrV65EU1MTJycnevfuzY0bN1TblKfCyIEDB1QZ+idO\nnEh4eLiqegjAX3/9hVKpxMhIHoKJykee7FcyNaoYEPT+ODzP7eX7qHBy8v+Zf62pUPJeXVvmvN2X\nGlUMKrCXojTypO/ZenTky9OIiYmhT58+2NqqD/v38/NjwYIF9OzZU21kRHnExsZy//597Ozs/lUf\nxatLalULIV6Ux00VLKz4U2ik1X0+69OKpUuXolAoMDY2xtvbm4SEBL788kvWrl2LQqHA3d29xP1Z\nW1ujp6eHs7MzLVu2pHbt2iW2A2jVqhVt27bFycmJ/Px8hgwZAoCFhQUjR47EyMgIIyMjtaSi5alG\nUq9ePWbNmoWenh4aGhrMmDGDunXrcv78edzc3MjOzpaS0aLSUpRUQ71QaGhoftFkbY9pR1naiRfr\nTloSh6MiSMxKx1i7Cr3q2jxx6L5cy4r32OQ5f9Mya4Dl9J+fGADI9fz3Hld/2MPD418F+4GBgaSl\npeHq6vrEtnItK4/SrmXRWtUa+tUwsHeUG3qvAPnZrDwq+7VMOP4/4vyLD7kvjanbClVJvVdNZb+W\nr5NncS3/3ofiyS0rH3myX4mZ6xkxvEm7JzcULxV50vdyeNr6w1evXmX27NloamqiVCpZvnw5KSkp\najcM+vfvz1dffcWKFSvQ1NSkVq1a1KlTp1iJo8jISNavX09aWhp9+/aVLy6VXNFa1UII8awZtPqQ\nuC2flW0ov0wVFKJSkGBfiJeQppEpFv/ZJk/6KkBZ6w+X5sGDB3h6etK0aVOWL1/Ovn37SnzyX716\ndfr160e1atXo1q0bw4YNK1biqG3btly7do3Dhw9z+fLl53naQgghKjmZKijE60eCfSFeYvKk78Uq\nbQrF7XRtpoaZQdgZFAGdsO7cl7kLFpe4DxMTE3x8fMjIyCAuLo4+ffqU6diPljhasWIFbdu2pXHj\nxmhra/+7ExNCCCEoSAqaGXXxiVMFJSmoEJXDE4P90NDQMu2orO3Ey0+uZeUi17PsdL+fhlYJX4Bq\nV8nC2+a26n22QRihoaE8ePCA69evq2XwnTt3Ln369KF58+bs37+f2NhYwsPDSUtLU12L5ORkwsPD\niY2NJTk5mdDQULKzs1Xrr127RlJSEteuXSM1NVW1XK5l5SHXEu7evcumTZtITEwkLy+PRo0aMWTI\nENavX0+bNm1o1arVE/exatWqMrd9nuR6Vh6vw7VUfLACnR990Lx5EkVermp5vlKDnDffIaXLf3n4\nezQQXXGdfAZeh2v5upBr+fSeGOxLgr7Xi1zLykWuZ9nlxMdy848fytRW648faFzfAhMTExo0aKD2\nGefk5PDuu+9iYWHB8uXLadGiBe3atcPb25tWrVpx//597t+/j62tLdevX8fAwAB7e3uaNm2KUqmk\nZcuWhIaG0rFjRxo1akRISAj29vZyLSuR1/Va/jMt6SGKKtWY/c1+pk73pF27gtwy69evJzAwsMSf\nq9KUp+3z8rpez8rotbqWnbpX6qmCr9W1rOSeVYK+15UM4xdCCB5ff/jRkkQAX+xaVWJbV1dXxo8f\nj6WlJW5ubnh5edG7d2/at2+vKhHUpEkTAFq2bMmUKVOoXr06M2bMYPbs2WoljiIiIp7tSQpRAR7N\ngwEQGq9HjXhj6l5cTo5NfTSNTBk5ciS9evXCysqKkydPsnHjRh4+fIi3tzc2NjZ4e3sTFhZGZmYm\nLi4uDBo0CKBcbYUQ/5CpgkJUfhLsCyEEpdcfNtPNYUfbm8WWm9TUocPHs4std3JywsnJSfW+e/fu\nAHh7exdrW6dOHU6dOqV67+/vr7a+bdu2tG3btmwnIMRLqLQ8GDEZWtTXyyDl/HdkRl1UlRJt2LAh\nqampAHz77becPHmSVatW4ePjQ+3atZk6dSoZGRk4ODioBfDlaSuEEEK8LpQV3QEhhHgZaOhXL2f7\nas+pJ0JUHnEB7iUmAlMAefkFr7PvXicuwB2A/Px8lEolb7/9NgB2dnb88ccf6OjokJiYiLOzM6NH\njyY+Pl61r/K0FUIIIV4nEuwLIQQF9YfRKONgJ6k/LMQT5cTHkhK6u8R1dapk83uKjup9SuhusuNj\nuX79OlpaWmptFQoF58+f59y5c/j7++Pv719qhYrytBVCiFfZ5s2bGTx4MK6urgwcOJAzZ86orZ81\naxZBQUEABAcH4+7u/sz7MG/ePKKjX75EjqtXr+bixYsV3Y2XggzjF0IIpP6wEM/a4/JgtDBOY0OU\nCb/E69G6Whrk5rBm8Szs7e3Jy8sjNDSU3r17c+nSJd58803i4+MxNzdHS0uL48ePk5ubS1ZWFkCZ\n20rQL4R41SUmZhBx+Q4x0TFs2BDA9u07MDEx5NatW8yYMYP27dsDcP/+fT788MPnXqlk+vTpz3X/\nT2vMmDEV3YWXhgT7QgjxN6k/LMSzU1oeDAClAryaxLLyZk02R1cnD7DN+4vZq5bj5eUFwCeffMJf\nf/3FokWLsLCwYM2aNbi6uuLg4EDXrl356quvVPsrS9v58+c/5zMWQojnIyU5k6Bd4YRfvkNeXj6J\nSbHEP0zG2+soLVrVxbG/LQEBAQBERkbi5eWFUqlEX1+fBQsWqO3r4MGDfPvtt2hoaGBjY8OMGTPU\n1p85c4b58+dTs2ZNzM3NsbCwoE2bNmzevBlf34LvP23btiU4OBg3Nzc8PT2xsLBg2rRpJCYmkpub\ny4wZM7C2tqZHjx507twZExMT+vXrx7Rp08jOzkahUDBv3jwsLS1Vx83OzsbDw4Pbt2+jo6PDokWL\n0NfXZ/HixWhpaZGRkYGnpyd2dnY4ODgwePBgDh06RN26dbGxsVG9XrJkCR4eHvTs2ZP4+HhCQ0O5\nefMmFy5cuAosjoyMXNe4ceOhwKdALhARGRk5pnHjxiOA9wALwDkyMvI2lYAE+0II8TdNI1Msp/9M\nXIB7wfDjok8lNTQxsO+HqasvmkamFddJIV4RT8qDUU07lxnWd1TvTd080NPTK/bFtNDOnTtVr0eM\nGPHYfZenrRBCvMxSkjNZ6XuaB/fTVMuMjSyoWtWKwyfmcemyNT+fasHCRf+hajV95s2bx+TJk2ne\nvDnr1q1j06ZNqmS/qampLFu2jKCgIPT19fnkk084d+6cKvcJwJIlS1i6dCmNGjXi448/xsLC4ol9\n3LhxI506dWLQoEFcv36defPmsWHDBnJycujcuTOdO3dm6tSpDBw4kN69e3Po0CFWrFjBwoULVfsI\nCgqiRo0aLFmyhAMHDnD8+HHatWtH165d+eSTTzh79ixr1qzBz8+PvLw8mjZtyujRo+natSs9evRg\n586ddO3alaSkJLW+Xbt2jcmTJ+Pq6uoIbAPWAfpAr8jIyITGjRv/1Lhx42Z/N7cC2kdGRuY/3dV6\n+UiwL4QQRWgamWLxn22Vuv6wEC+CQasPidvyWalD+dVIHgwhhChRUGC4WqBfyL75EJJT7hJ3L5KL\nlw4xePAZDh8J5MaNGzRv3hwoeAK/YsUKVbB/69Yt6tati76+PgBt2rTht99+Uwv279y5Q6NGjVTb\nZ2ZmPrGPFy9e5OHDh+zduxeA9PR01To7OzsAwsPDmTRpkmq/K1euVNtHREQE7dq1A+D9998HIDk5\nmZCQEH788UeysrLQ09NT269CocDExISmTZsCUL16dZKTk9X226JFC5RKJUAMYPz34ofAnsaNGwM0\nAUz+Xh5SmQJ9kGBfCCFKJPWHhfh3JA+GEEL8O4mJGYSH3Sm2PD8/n7y8HAwNzDA0MOPNeh05/uNC\nrkX+odYuOzu7MNAFCpKY5ufnq63X0dGhNBoaGqrtisrJUb+Jq6WlhaenJy1btiy2j8Kkq0WP/Wi/\nCo+Vl5entmzjxo1Uq1aNdevWcfnyZRYtWlSsb4++Lnp+AJqaauGuonHjxtrASqB5ZGTkncaNG+8v\nsj6r2Am84iQbvxBCCCGeC1NXX7TMGjy2jeTBEEKIkkX8PUf/UX/GBHMp/LsiwXMG+eTzV2w2DRs2\nVGWiDwkJwdbWVrVdvXr1iIqKIiUlBYDz58+rrQcwMzPj+vWC3EVnz54FwMDAgLi4OACuXr1Kamqq\n2jbNmzfn2LFjAFy/fp0NGzYU63OzZs0IDg4usV+F68+dOwfAyZMnWbVqFfHx8ZiZmQFw7NgxsrOz\nH/+BlY0hkPN3oG8JtAYqbQZXCfaFEC+dJ5WTKaq0cjLu7u6qPypPUp62T1I4VK4synOeV65c+Vdl\nc3777TdVYh0hXpTCPBgGbQYVL22poYlBm0FYTv9Z8mAIIUQJ0tNKDm6t6rRBR9uAn84s53Tw/wgO\nXU+zpo7k5iqZMWMGS5cuZdiwYVy+fJlhw4apttPT02Py5MmMGjWKIUOG0LRpU1q3bq2274kTJ/LZ\nZ58xcuRITEwKRrdbW1ujp6eHs7Mze/bsoXbt2mrbuLq68ueffzJkyBBmzJhRbJ9Q8F0rKCiIYcOG\nERgYWOw7Te/evUlPT8fV1ZWNGzfSr18/PvzwQw4ePMhHH32EnZ0d9+7dY9euXU/1WRaKjIx8ABxt\n3LhxCDALWAQsA7Qeu+ErSobxCyFeCmUtJ1NZxMTEsGPHDnbu3ImWltZzP88mTZrQpEmT57JvIR5H\n8mAIIcTTqaJXcvypUCixbdK3ePsqWjRoUA9/f3+15W3btlU9jOjRowc9evQo9ZhdunShS5cuAAQE\nBBAfH49SqWT9+vWqNlOmTAFQO46fn1+xfZ04cUL12szMjLVr15Z6XG1tbbVh+oXb+Pj4YG9vD0C3\nbt0AGDBggKpNYGBgsdePJnoNDQ0lMjIyBagHEBkZOeKRwy8ttWOvOAn2hRAV6lmWk1mzZg0HDhzA\nwsJCNUTtzp07fPnll0DBHLOFCxdiZWVVYtvk5GQ8PDxISkoiJyeHGTNmYGNjw9y5cwkPDyc3NxcX\nFxf69++vOmZOTg6TJk3izp07NGvWTLX8+vXreHl5oVAoVH01MjL657xTUsjMzCQ7OxstLS3q1aun\nOs8zZ86wfPlytLS0MDIy4uuvv1Y7z/Xr13P48GHy8vLo0qULEyZMwM/Pj/j4eKKiooiJiWHixIns\n2rWL27dvs2bNGmJjY1Vlc7p37063bt24ePEihoaGrF69mri4uBI/JyGeFcmDIYQQ5WPTzJy9uyNK\nHMr/KKVSgW0z8xfQK/EqkWH8QogKU1hOJuzXv1R/yIqWk/l241ImfeFLQnzB3LDCcjL+/v689dZb\nbNq0SbWvpKQktm7dyvbt21m0aBG///47AHFxcYwfPx5/f38GDBjAli1bSm27ceNGmjdvjr+/P9Om\nTcPb25uEhAR++OEHtm3bxpYtW4olpTl9+jQ5OTls376dPn36kJCQAMCcOXPw8vJi48aNdOjQgc2b\nN6ttZ21tjZ2dHd26dcPDw4ODBw+q9p2YmIiPjw8BAQEYGBhw6tSpYp/dli1b2LFjB4GBgaqbFYmJ\niaxbt45evXoRFBSken38+HG1baOjo3F0dGT79u0kJSURGRlZ4uckhBBCiIpjbKyLrV3ZAnhbO3OM\njHWf6fFdXV359NNPn+k+xYslT/aFEBXmWZaTiYqKokGDBujo6KCjo4ONjQ0ANWvWZO7cufj5+ZGU\nlISNjU2pbcPDwxk3ruDJY7NmzYiKiqJq1arUq1ePcePG0atXLxwd1cuDXb9+XZV9tnnz5ujqFvyh\nDQsLw9PTE4CsrCy1p/6FFi1axI0bN/j5559Zu3YtW7duZdOmTVSvXp0ZM2aQm5tLdHS0WkkcAF1d\nXVxdXdHU1CQ+Pl51g6HwGDVr1lS1rVGjhmp9IQMDA6ytrQEwNzcnOTkZS0vLYp+TEEIIISqWY39b\nbscklvh9qZBJDT0c+9uWul68viTYF0JUiGddTiY/P7/YewBfX186duyIi4sLhw4d4ocffii17aMl\naQpLwKxdu5aIiAj279/Pnj171OatPbqvwm2qVKnCpk2bipWrKbpdVlYW9evXp379+ri5ufHee+8R\nGxvLtGnTWL16NfXr18fLy0ttu9u3b/Ptt9+ye/du9PX1+eCDD1TripaXKfr60TI0RUvUFK4v6XMS\nQgghRMUyMNRhvHsHggLDCQ9Tz86vVCqwtTPHsb8tBoall9ATry8J9oUQFeJx5WTuP7xJKzsXFApF\nieVkWrZsWaxsi5WVFTdu3CArK4usrCzCw8MBiI+Px8rKivz8fI4fP05eXl6pbQvLwrRo0YJLly7R\nsGFDYmJiOHHiBMOGDcPGxkZtvj7AG2+8wYEDBwC4cOECWVkFJVqtra356aef6NKlCwcOHKB69eq0\na9dOtd3OnTsJCQlh4cKFKBQKkpOTycvLw8TEhJSUFGrVqkVSUhLBwcE0btxYtV18fDzVq1dHX1+f\niIgIbt++/UxK0ZT0OQkhhBCi4hkY6uA63F6VzDg9PZsqVbSwbfbsh+6LykWCfSFEhXhcOZnklDh+\nOrMcTU0d8vJy1crJzJ49G4VCgbGxMd7e3kRERABQtWpVHB0dcXZ2pk6dOqoh7U5OTsyZM4fatWvj\n5uaGp6cn4eHhJbYdNmwY06ZNY9iwYeTn5zNz5kxMTU25ePEiBw8eREtLSy0DLEDnzp3ZtWsXrq6u\nWFtbq+rBTp8+HU9PT9asWYOOjg5LlixR265///7cvHmTQYMGoaenp0oIqKury5AhQ3BxcaFevXqM\nGjUKPz8/+vXrBxRk1dfX18fZ2Rl7e3ucnZ2ZPXu2KlPt0yrpczp16hQdO3b8V/sVQgghxLNhbKxL\n+471Krob4hWieHR4Z1GhoaH5ZfkCGRoa+q+/aIqXg1zLyuVlvp5nTt0iaFd4mds7DrB9rf/AvczX\nUpSPXMvKRa5n5SHXsvKQa1l5PItr+fc+Sp5XWclJNn4hRIWwaWaOUlm237tSTkYIIYQQQojykWBf\nCFEhKrqcjBBCCCGEEJWZBPtCiArj2N8Wkxp6j20j5WSEEEIIIYQoPwn2hRAVprCcjF2LWsWG9CuV\nCuxa1GK8ewcpJyOEEEIIIUQ5STZ+IUSFknIyQgghhBBCPHsS7AshXgpSTkYIIYQQQohnR4bxCyGE\nEEIIIYQQlYwE+0IIIYQQQgghRCUjwb4QQgghhBBCCFHJSLAvhBBCCCGEEEJUMhLsCyGEEEIIIYQQ\nlYwE+0IIIcokJiaGli1b4ubmpvYvISEBDw8PTp48+cL7dOjQoafazt3dneDg4Gfcm/L76aef2LJl\nS0V3QwghhBCVkJTeE0II8Vh30pI4HBXBn9HRGNUyZfH/rcRcz6iiuwXA6tWr6dWrV0V346l17ty5\norsghBBCiEpKgn0hhBAlup+ewoxzezgUFUFOfh6KhFR0UhN4e8cC3qtry5y3+1KjikGp2586dYqv\nv/4aXV1dTExM8PHx4eHDh0yfPp3s7Gw0NDSYO3cuFhYWBAUF4e/vj1KpZOTIkfTu3ZsePXrQtGlT\nOnToQMuWLfHy8kKhUKCvr8+CBQvYsWMHkZGRTJgwgRUrVrBo0SIuXLhAbm4uQ4cOxdHRUa0/a9as\n4cCBA1hYWJCSkgJASkoK06ZNIzExkdzcXGbMmIG1tbVqm+zsbL788kvu3btHVlYWn376KW+++Sbu\n7u4EBgYC0L9/f3x9fbl161ax89XS0lLtKzg4mGXLlpGenk6DBg3w9vZm//79/P7770yZMoWNGzdy\n8OBBALp168aYMWNK/AyL7lMIIYQQojQS7AshhCjmfnoKjgf+x63kB8XW5eTnse9WGJcf3Cbo/XGl\nBvwBAQF4eHjQunVrjhw5QkJCAsuXL+ejjz6iffv2/Pjjj3zzzTd4eHjwzTffsHfvXrKyspgyZQq9\ne/cmOjqalStX0rBhQ4YPH46Xlxf16tVj8+bNbN68mXHjxrFmzRpWrFhBSEgIv//+O9u2bSMtLY2+\nffvi4OCAgUFB35KSkti6dSvff/892dnZdO/eHYCNGzfSqVMnBg0axPXr15k3bx4bNmzwK0XIAAAg\nAElEQVRQncO1a9eIj49n8+bNJCUl8eOPP5b6mZV0vjVr1lStnzVrFhs2bCA2NpYDBw6wb98+FAoF\nANHR0ezevZudO3cCMGjQIHr16vXEfQohhBBClEaC/VLExMSoPbkRQojXiee5vSUG+sqHKehu/hn+\nn707j4uq6h84/hmYAVlkUxZRFLdGBFfMJa20NEsxzVRcADUrl5QnNc0NTZHU0sw0/aWZKbhUiimZ\nS5pZ5o4rLmO4EIgKsQjDPsP8/kAmR0CxNBW/79er1zNzz7nnnnsP4zNnzjnfA1wDem87yu4v15Za\nxssvv8y0adPo1q0bXbt2xdnZmWPHjnHp0iWWLFmCXq/HycmJixcvUqdOHSpVqkSlSpVYsmQJAFZW\nVtSvXx+AkydPEhISAkB+fj6NGjUyuVZMTAxPP/00ANbW1tSrV4+4uDi8vb0BiIuLo169elhaWmJp\naWk8fuzYMVJTU9m8eTMAOTk5JuXWqVOHrKwsxo0bR6dOnejatSuJiYnlvt9i6enpKBQKqlWrRmJi\nIq1ateLw4cM0bNgQgLNnz9KkSROUyqL/W27evDnnzp27Y5lCCCGEEHcinf1bFK9LTc/LhvQsdIWF\nD7tKQgjxn7uWncHWuJhS0wqdbMkd8Kzx/SWFGdezM0rN26NHD5599ll27tzJ8OHDWbBgASqVigUL\nFuDi4mLMFxMTQ2Ep/97eOl3dysqKVatWGUfCb3f78YKCAszM/o5BazAYSrwvvkZISAjNmjUrtVwr\nKyu+/fZbjh49ysaNG9m9ezcjR440yaPT6cq837p16xrrV3zN4vrdWufS0s3MzO5YphBCCCHEnUg0\nfoqmqw7bvZrW385m8oFNfHzsJ+Ye+4mzaVcZvnsNf+UUre2MjY0lKCiIgQMHMmLECDIyir7grly5\nEn9/f/z9/Vm6dOnDvBUhhPjXtt9co18eOkMh2+JOl5r2+eefo1Qq8ff3p0uXLly4cIEmTZqwc+dO\nAPbv309UVBR16tTh0qVLZGVlkZeXx+DBg006vgANGjTg119/BWDLli3s378f+LvT7uPjY4yun5WV\nxZ9//kmtWrWM59esWZMLFy6Qn5+PVqslJqbox4xb6xMbG2syhR/g9OnTREVF0aJFCz744AMuXLiA\nra0tKSkpGAwGkpOTiY+PL/N+i9nb26NQKIyzAg4dOoSPj48x3cvLi+PHj6PT6dDpdJw4cQIvL687\nlimEEEIIcSdP/Mj+ndalGsBkXWpoaGiJNaN+fn6lrrOsWbPmf3wnQghxf6TnZZeZdus0/mLn7Z4q\nNa+7uzuDBw/Gzs4OOzs7Bg8eTJMmTZg0aRJbtmxBoVAwa9YsrK2tCQ4OZvDgwQAMGjSoxEj95MmT\nCQkJYdmyZVhaWjJv3jygqJPcq1cv1q9fj4+PDwMGDECn0zF27Fisra2N5zs4ONCjRw/69u1LjRo1\njMsAAgICmDhxIv3796ewsJDJkyebXLdGjRp88sknfPPNN5ibmzNkyBDs7e155plneP3112nQoAFe\nXl5l3u+tQkNDGTt2LDk5OTRs2JCuXbsalw/UqFEDf39/AgICMBgM9O7dm+rVq9+1TCHEoy0uLo5Z\ns2aRklL0PdPd3Z1p06bh5OT0wK4ZExPDnDlzjO9jY2P5/PPPad68+QO7phDi0aS4ffTkVtHR0QZf\nX9+7FhIdHU158j2Khu9eQ9TlkyWOK9KzsPz+ELmDOgDQzbMxB8Z8bByJKV4z2rJlS37//XemT58O\nFH2Za9WqFS+99NJ/dxP30ePclqIkac+K479sy5Vn9zP5wKZy5w9r3Z2BXm0eYI0qllvb8ptvviE+\nPp733nvvIddK/FPy72zFcb/asnhZaGqOlsiJcwiZOpWX2hZts7l06VI0Go3xB8sHLSMjgxEjRrBq\n1SqTpUwVnXwuK4770ZY3yyh9HWAF90SP7N9pXerttsbF4FapUok1oz/99FOp6yyFEOJx1bmWN9MO\nRpVrKr9SYcbLtbz/g1pVPMeOHWPZsmXMmDHjYVdFCHEf3L5dqfnF6yitDbz9xzZeyU8gtPWrvPnm\nm8bvjRMmTEClUpGenk6HDh2M23BmZWXRrVs3fv75Z1544QV69OjBgQMHUKlULFy4ECsrK6ZOnUp8\nfDz5+fkEBwfTrl27Uuu0fPlyBg4cKN9NhXhCPdGf/Htdl+rgUa3EmtGy1lkKIcTjys3ajldq+dw9\nI/BKLR9cre0ecI0qpmbNmrFz506eeeaZh10VIcS/VLws9IfLp4zfLRUpmRQ62xm3K+2xZQmpedmY\nm5sbz7O3t2fhwoV3LLtu3bqsWbMGLy8vNm7cyJYtW7CwsCAiIoKFCxcSGhpa6nm5ubns3buXF198\n8f7dqBDisfJEj+zfaV0qlFyb2mRIIF988YXJmlEHB4dS11kKIcTjLLT1q5xKuVJqPJNinpWrENr6\n1f+wVkII8WgqdbtShQIK/579eXXFD3T9+ifs8gzGeB2NGze+a9lt2hQtk2ratCkHDhxAqVTSqlUr\nAFxdXbGwsCA9PR0HBweT83bu3En79u1lVF+IJ9gT3dl3sLQuM83gYEP2mG4mxxo2bcKcfsNK5B0w\nYAADBgy47/UTQoiHpaqVLd93HU7Igc1sjYsxmQWlVJjxSi0fQlu/SlUr24dYSyGEMJWQkEBwcDCR\nkZH/uIxly5aRl5dXYpvNspS1LLSwamVUR/7eQSOvVxuuK8zImhfF+fPngb+3GL11iWjxdp7Fiqf9\nGwwGY75bl5Dm5+eX2qHfvXs3/fr1K9c9CCEqpie6sy/rUoUQomxVrWxZ0qG/MdjUjfwc7C2seLmW\nt0zdF0I8UnRpiWiPbiI9/k/0mcno0hJROrrfczn5+fk0adKEli1blvucspaFFtZyRrE7BvM/rqKv\nX63o2NVU9Hpdic65ra0tSUlJQFEwsVsdOXKEzp07c/z4cerVq4etrS0HDx6ka9euXL16FTMzM+zs\nSv6bHBMTQ4MGDcp9H0KIiueJ7uwXr0stLRr/7WRdqhDiSeVmbSfR9oUQjyRdRhJJ4aPQHv0e9DrS\ncpXoUty4+F5tbH1fwyXgM5R2LgQGBhISEsJTTz1FREQEaWlpjBo1ipkzZ3Ly5EnMzc3x9/fH19eX\nCRMmEBUVhcFgYOzYsWRnZ5Obm0tISIjJtPuPP/4YT09P0usXbaNntWwnOQHPgpVlUQYDFFapjMXW\noyg26ym0twZbK5w83LG0tDS5j5SUFHbt2oWvry/16tVDoVAQGxtLcnIys2fPZtq0adSrV4+2bdsS\nERGBs7MzPXr0ICEhgSVLlrBy5Up+/PFHAF588UXefvttMjIysLWV2VdCPMme+EU8oa1fxbNylTvm\nkXWpQgghhBCPFl1GEvFhz6I9vB70plPf0evQHvqO+LBn0WUklXr+vn37uHbtGt9++y1jxoxh//79\nJunJycn07t2b8PBwxowZw7Jly0zSu3fvztatW3GwtEbxVwaFDtZ/d/QB5Zl4DA425AR3JXt4ZxQK\nBbl922JtYwPA7Nmz6dChaIvn1atXs3XrVqKjo+nTpw9btmwhNDQUR0dHfvjhB0aNGkXbtm157rnn\ncHJyokuXLtjY2BAZGYmbmxsbN25k9erVxnL+/PPPEvcjhHjyPNEj+yDrUoUQQgghHkdJEcEUXI+9\nY56C67EkRQQDqhJpp0+fpnnz5gA8/fTTJabWV61alcWLF7N8+XLy8/OxtjaN9fTUU0+RkZFBq8ru\nWPxxDZ23h0m62ZVUzOP/wjz+ZuA+nR5lIThVsilRFz8/P9555x1effVV/Pz8qFSpEidPnkSn0/HW\nW2+h1+tp1KgRAOPHj6dXr168/vrr1KxZkx07dtCkSROUyqKv9c2bN+fcuXPUrFnzjs9GCFHxPfGd\nfZB1qUIIIYQQjxNdWiLa6I3lyquN3ohB1+Pvc28GwDM3N6ewsOy4TStXrsTV1ZWPP/6YU6dO8dFH\nH5XI4+fnx/G9+3G+nk1CizqmiWZm5D+jRt/w7x8Bunk2RvvLlRLlDB06lG7durF9+3YGDhxIREQE\nVlZW/P777ybB+wC0Wi2WlpZcv34dKArud2vAvoKCAonAL4QAZBq/ieJ1qcFNXmCgVxvp6AshhBBC\nPIK0RzeVnLpfFr2OSroMkpOTATh69CgAjRo14uDBgwCcOXOGFStWmJyWlpZmHB3fuXMnBQUFJYr2\n8/MjMjKSlnW98HRyNb2suyPKP64WvcnKo+r+i6UuCy0sLGT+/Pk4OzszePBgmjZtSmJiIg0aNODX\nX38FYMuWLcZp+TNnzmT+/PkkJSVx/PhxvLy8OH78ODqdDp1Ox4kTJ/Dy8irfsxFCVGgysi+EEEII\nIR4r+qzUMtOu5Fgw8XR14/vBNf/i1Za1mTFjBrVq1TJ24J9++ml27dpF//79Aejdu7dJOd27d+f9\n999n27ZtDBgwgB9++IENGzbw+uuvG/NUrVoVa2trXu/eg6ktfU2Wheq9qmMel4xV+B4cVFZMfe/9\nUpeFmpmZYWNjg7+/P5UrV8bDwwMvLy8mT55MSEgIy5Ytw9LSknnz5rF161bc3Nxo0KAB48ePZ9y4\ncXzzzTf4+/sTEBCAwWCgd+/eVK9evcR1hBBPHsWt035uFx0dbfD19b1rIdHR0ZQnn3j0SVtWLNKe\nFYe0ZcUhbVmxSHs+HOm7lpAUPrLc+V0CF+Hw4vA75vknbZmamsqbb77J+vXrjVPnZVnowyefy4rj\nfrTlzTIUd89Z8cjIvhBCCCGEeKzYNu9O0pp3yzeV31yJrW+Pu+e7Rzt37uSzzz5j4sSJJmvkZbtS\nIcSjQtbsCyGEEEKIu0pISKBZs2YEBgaa/Jeenn7XcyMjI5kzZ84/vvbo0aPJzc01vlc6umPr+1rp\neU/W4Hru3+NZtr6voXSoVmrenj17kpCQcM/XB+jYsSObN2+mTZu/O/a33ueWLVuYOnUqer3+ruUP\nH1406+DcuXNcunTprvmFEKI8ZGRfCCGEEEKUSZeWiPboJtLj/6RmVVtWfDYHpaP7f1qH+fPnlzjm\nEvAZeXHH7rj9nsq1Hi4Bnz2Q69+Nvb09M2bMKFfeJUuWAPDTTz/h4+ND7dq17/l6QghxO+nsCyGE\nEEKIEnQZSSSFj0J79HvQ60jLVaJLcePie7Wx9X0Nl4DPUNq5ADBhwgScnZ05c+YMiYmJzJ07F29v\n71LLnTVrFidPniQvL49+/fqVCIz3+++/88knn2Bubk6XLl0YNGgQL7zwAlFRUYSGhtK5c2c6dOjA\nb9Gn2VrwMu+2vM68b/dwLsOCGlYF6AwKMFdyrXZnFp+2QPXOWMzMzFiwYAEODg7MnDmTY8eOUbt2\n7VIj7C9dupSffvoJMzMzOnTowLBhw0yuX977/OKLL3BxcSEtLY1Vq1Zhbm7OmTNnGDZsGL/99htn\nz55l/PjxdOzYkVatWrFq1SrWrVuHk5MTVapUIT8/n08++QSlUkm1atUIDQ0lLy+Pd999l/z8fPLz\n85k6dWqZ1xdCiLt29qOjo8tVUHnziUeftGXFIu1ZcUhbVhzSlhVLRWxPRXYq1uvfwuxGfMlEvQ7t\noe/I0Bwgu9cyDNZOpKSkkJeXx4gRI9i5cydLly4lKCjIeMrly5e5fv06+/fvp7CwkPfee4/8/Hze\nffdd6tT5e396g8HAxIkTmT59Ora2tsybNw+1Wk1eXh7Hjx8nJSWF2NhY7OzsiI2NJS0zm8PV+3Ha\n4i9mj2xBWkoyI1fsI/v1ZVzNyKe3T2U8PT357rvvWLx4Md7e3uzdu5fQ0FDS0tLYunUrMTExxj3r\no6OjWbp0KYsXL8bMzIydO3cSHR1tcv3y3Gd0dDSZmZmcOXOGzMxMTpw4wdy5czl37hxz5szh008/\nJTY2lq+++gpHR0d0Oh1arZaGDRvSsmVLCgoKmDRpEpMnT8bW1pY1a9awZMkSLCwsUCqVjBgxguvX\nr3Pw4MESywtEkYr4uXxSSVv+c3ft7Es0/ieLtGXFIu1ZcUhbVhzSlhVLRW3PxMV90ZbS0Tfd1q4Q\nz4RpzNuwjypVqtCxY0d8fX3JyMhgx44dJs8lLi6OgoIC2rRpw5EjR/j4449RqVRotVqTfCkpKdjb\n29OhQwcA1q1bB4ClpSVNmzalSpUq1KtXz3idCxcuYGlpSZu2z9L0jWkA1Pz5FbxbPkctrZa5c+eS\nm5tLUlIS3bp1K8rbpg1PP/10Ud6aNfHx8aFGjRrGtuzatSsLFy7Ez8+PkSNHYmtra3L98tynr68v\nlStXpmHDhqSlpdG4cWNat26Nk5MT27dvp23btjg7O7Nz5058fX1RKpX4+voa769WrVokJyfz5Zdf\nApCdnU2DBg3o3r07UVFRbNq0iZdeeokuXbrcv0avQCrq5/JJdL+i8T+pZBq/EEIIIcQj5rvvvmPz\n5s3G9zExMRw7duyu5x05coRVq1Yxe/ZsrK2t/9G1dWmJaKM3lppW3SqfWd5X/j5gfh1d+tWil+bm\nxsNlbe186NAhDhw4QHh4OCqVimbNmpmkm5mZUVhYWGbdFIq/d8/S6XTGa90aDb/4/LCwMN566y2e\ne+45li9fTnZ2dpl5bzV9+nQuXLjA1q1bCQwM5LvvvjNJL8993k6pVJb6uiwqlQoXFxfCw8NLpG3a\ntImDBw+ydu1ajh8/zsiR5d+CUAjxZJFo/EIIIYQQj5jevXsTHh5OeHg4o0aNokeP8m0dl5uby4IF\nC/5xRx9Ae3RT+ba0g6Ip/dHfl7vstLQ03NzcUKlU7Nq1C71eT35+vjHd0dERvV7P9evXMRgMDB06\nlIyMDGO6jY0NycnJwN+jdbVr1+b06dMYDAauXLnClStFP0akp6dTs2ZN8vPz2bNnDwUFBWXmLZaZ\nmcmiRYuoW7cuI0eOxN7eHq1WW+77+7cUCgV6vR57e3sAYmOLgg+Gh4dz7tw59u3bx759+2jXrh0h\nISHExMT8Z3UTQjx+ZGRfCCGEEOIRURz5Xp+VirmNE7bNu/P5558zd+5ck3wrV64kMzPTOKobGBjI\n5MmT+eGHHygoKKB+/fqMGzcOa2trAgICuHHjBsuXL8fNzQ1HR0dat25N9+7dCQkJIT4+Hp1OR3Bw\nMG3atGHY3LV4Fzhy8oYVGTpzQhpcxcVSR2SiAxezLPE/VBsnCz0OKj1anRlc+IpMvYq4uDjatWt3\nx/t75plnWLZsGQEBAXTs2JH27dvzwQcf8OGHHxrzTJs2jeDgYABeeeUV7OzsjGndu3fnvffeY/v2\n7Xh5eQHQoEEDnnrqKfz9/fH09KRBgwYABAQE8M477+Dh4UFgYCAzZsygS5cupeYtVrlyZdLS0ujV\nqxfW1tY0a9YMBweHf9ia965FixbMnDkTGxsbwsLCmDhxonGU39/fH1tbW8aNG8eXX36JQqEwPich\nhCiN4k7Tj6Kjow2yZv/JIm1ZsUh7VhzSlhWHtGXFcr/a8/bI98XOZ1uzI9+HT1ZtMka+B7h69Sqj\nRo1i/fr1pKen079/f3788UcmTJhA586dqV+/Pn5+fuzevRt7e3vat29PZGQk1tbWxrXoZmZmXLp0\nidGjR5OamsrAgQOJioqin98LtCg4TrdqN/g6rgqOFjrq2eSx7bo9Y+tfp6AQ/nfSg/mNEvjoDzfe\nGDyIDkNnsmPHDpo1a4azs/O/fh4Pg3w2Kw5py4rjfq3Z9/X1Vdw9Z8UjI/tCCCGEEA+RLiOJ+LBn\nS90vfsdVG56veor4sGfxmPybscNfrVo1FAoFSUlJ7Nu3j44dO5Y418PDA0dHR1JSUrC1taVq1aoA\ntGnTBoBjx44RHR3N0aNHAcjLyyM/Px9z26p4G4q2pKtqoSNTZ8bZzEpoMi2NwfkMKEgrMKetczYf\nbzyMxvB/dO3a9bHt6AshREUknX0hhBBCiIcoKSK41I4+QEyGFUNrJ1NwPZakiGDcR6wzpnXs2JFf\nfvmFvXv3MnTo0BLnqlQqoGQAu+IgdyqVimHDhuHn52dynkJlSeX6L8DFH4rOR4FSUUgn10x6V08z\nyVvvuZfo7f85O3fuZPjw4SxYsIC6dev+g6cghBDifpMAfUIIIYQQD8mdIt+n5JtTybwQ1c1va9ro\njcbI9wCdOnViz549xMXF4e3tXeY1HBwcSE9P58aNG+Tm5nLo0CEAmjRpwq5du4qulZLCJ598YjzH\nyW8iKtd6xvfqynkcSrWm0AD5hQq+uFQVlWs9NuQ2R6lU4u/vT5cuXbhw4cI/fhZCCCHuLxnZF0II\nIYR4SO4U+T4tX4m9Sv/3gZuR7x1eHA5AnTp1iI+Pv2tQPKVSyfDhwxkwYAC1atXCx8cHMzMzXnnl\nFQ4cOEDfvn3R6/UmW7iZ2zrhMfk3LMf1gwsxeFXOpZF9DuNiamBAQc829fGYvJoau35n8ODB2NnZ\nYWdnx+DBg//9QxFCCHFfSGdfCCGE+A8lJyfj5eXFxo0bjZHAIyMjAejZs+ddz9+9ezfbt29n7Nix\nLFy4kBkzZtzX+kVGRlK5cmU6dep0X8pr1aoVBw8evC9l3UlkZCR//PEH77///gO/1v2kz0otM62e\nbR7Tva6aHNNnmU6j37x5s8n72bNnG18X/10BVKlShYiICBwcHBgyZAg1a9ZEqVQSFhZW4rq37u3+\nzhe7jDsEjM5Kw9zGEVvfHigdqgHw2muv8dprr5XjToUQQvzXpLMvhBBC/AeKO0yqE4eo7ebI3Nkf\n8uXXq/5xec7Ozve9ow/l+8FB3D/mNk73mN/xH10nNzeXgQMHYmVlhZeXF82bNy/3uUpHd+NsAiGE\nEI8P6ewLIYQQD9DtW6pZ5CqpVeBE3h9/8sPErrw8cYVJ/lmzZnHy5Eny8vLo168fvXv3RqPR8P77\n72Nvb0/NmjUBSEhIIDg4mMjISI4cOcInn3yCUqmkWrVqhIaGYmFhYSzz2rVrBAcHY2lpiY+PDzEx\nMYSHh5uMugcHBzNgwAAOHTqEo6MjAQEBzJ8/nyNHjqDX6wkICMDPz48JEyagUqlIT0/n008/LXWf\n9tLs27ePBQsWoFKpsLOz49NPPzWp48KFC8nIyODSpUvEx8czadIknn/+eX788Ue+/vprzM3N8fb2\nZsqUKWRkZPDee++h1WqpXLmyca15UlISo0aNIjY2liFDhtCrV6+7PpuHzbZ5d5LWvFvmVH4T5kps\nfXv8o+v06NGDHj3+2blCCCEeTxKgTwghhHhAirdU0x5eX6IzF1TjL5buiOHPmc+iz8kAirY+q169\nOmvXrmXNmjUsWLAAgMWLFzNy5EhWrlxpElW92MyZM1m8eDGrVq2iSpUqbNu2zSR95cqVvPrqq4SH\nh+PoWL6R4SNHjnDlyhVWr17NqlWrWLJkCbm5uQDY29uzcOFCoqKicHZ2Jjw8nM8//5wPP/ywzPJu\n3LjB3LlziYiIwNbWlr1795bIc/36db788ksmT57MN998Q1ZWFvPnz2fFihWsXbuWhIQEDhw4wPLl\ny2nXrh1r1qyhTZs27N+/H4D4+Hg+/fRTPv/8c+NU9Ls9m4dN6eiOrW/5psHb+r5mnD4vhBBC3I2M\n7AshhBAPyJ22VHO3KqCuTS4/n0nHLHs1Di8Mx9LSkhs3btC3b19UKhVpaUXrsy9cuGCcdt2qVSt+\n/fVXYzl//fUXcXFxjBo1CoDs7OwSHfqLFy/SpUsXAFq2bMlvv/1217ofPXqUEydOEBgYCEBhYSHJ\nyckANG7cGCh7n/bSRs6dnJyYMmUKer2e+Ph4WrduXSJP8T26ubmRmZnJ5cuXqVWrFjY2Nsa6nz17\nljNnzvC///0PgEGDBgFF69ObNGmCubk5rq6uZGZmluvZPApcAj4jL+5YmX8rACrXergEfPYf1koI\nIcTjTjr7QgghxANwpy3VivWtkca0s+50NRylcnY6hw4d4sCBA4SHh6NSqWjWrBlQtE968d7ohYWF\nJmWoVCpcXFxMgqrd7tbzzc3NS81TUFBg8t7CwoJevXrdcf/2svZpL82kSZNYunQpdevWLTPWgFJp\n+rVEoVBgMBhM6mhpaYm5uXmJ51Da+eV5No8CpZ0LHpN/IykiuOhv5tZZIOZKbH1fwyXgM5R2Lg+v\nkkIIIR47Mo1fCCGEeADutKVaMUcLPa2dsth21Za8y0dJS0vDzc0NlUrFrl270Ov15OfnU7t2bWJi\nYgBKRLa3t7cHIDa2aFQ4PDycc+fOmeSpU6cOJ06cADBOeYeiznROTg45OTmcPXvW5JzGjRuze/du\nCgsLycvLIzQ0tET977RPe4nnodVSrVo1MjIyOHjwYIkfF0rj6elJXFwcWq0WgEOHDuHj44OPjw8H\nDhwAYN26dWzcWPqPKuV5No8KpZ0L7iPWUWfuJVwCF1GlZygugYuoM+8y7iPWSUdfCCHEPZORfSGE\nEOIBuNOWard6zT2NrdftKMzL5plnnmHZsmUEBATQsWNH2rdvzwcffMDw4cOZOHEiq1atwsPDo0RH\nOSwsjIkTJxpHsv39/U3SAwICePfdd9m2bZtxuz+Afv360adPH+rWrYu3t7fJOc2bN6dVq1b4+/tj\nMBjo379/ibrfaZ/22/Xv359+/frh6enJm2++ycKFC+nQoQMuLmV3Yq2trRk/fjxvvvkmZmZm+Pr6\n0qJFC9RqNePHjycwMBAbGxvmzp3Ljh07Si3jbs/mUSOR74UQQtwvilunx90uOjra4Ovre9dCoqOj\nKU8+8eiTtqxYpD0rDmnLx0/6riUkhZfd+b2dS+Ci/6STd/78eUJDQx/5qe2PC/lsVhzSlhWHtGXF\ncT/a8mYZivtUpceKTOMXQgghHgDb5t3BvJwT6P7FlmpCCCGEEKWRzr4QQgjxADyqW6o99dRTMqov\nhBBCPAGksy+EEEI8IC4Bn6FyrXfHPLKlmhBCCCEeBOnsCyGEEA9I8ZZqti17l5zSb67EtmVvPCb/\nJpHWhRBCCHHfSWdfCFGhJCQk0KxZMwIDA03+S09PZ8KECezevdskf2nHyrJ06bOpoc4AACAASURB\nVFKOHTtGZGQkc+bMuee6RUREsHDhwnLlfeGFF8jKyrprPn9/f+OWbMXmzZvHV199RVhYGPHx8aWe\np9Vq2bt3b7nqAjB69Ghyc3PLnV/87fYt1fJaD5Mt1YQQQgjxwMnWe0KICkGXloj26CbS4/+kZlVb\nVnw2B6Wj+329xttvvw3ApUuX7mu5/4afnx9bt27Fx8fHeGzHjh2sWrUKV1fXMs87ffo0v//+O+3a\ntSvXdebPn/+v6/qkK95S7YJDNA4SJVoIIYQQD5h09oUQjzVdRhJJ4aPQHv0e9DrScpXoUty4+F5t\nbH1fwyXgs7uOnO7evZuVK1eSmprKrFmz8Pb2ZtasWZw8eZK8vDz69etH7969mTBhAp07dzY5d/Xq\n1URFRWFmZkbHjh154403TNL379/Phx9+SNWqVXF2dsbDwwMo6jwfOXIEvV5PQEAAfn5+pdbt3Llz\nTJ8+ndzcXOzs7FiwYAEODg7G9C5dutCvXz/GjRsHQExMDC4uLri6uhIYGEhISAhubm689957aLVa\nKleuzCeffMKMGTPQarV4enry/PPPM2nSJAoKClAoFISFhaFQKBg3bhzW1tYEBAQQGhpKVFQU8fHx\nTJ8+HaVSiZmZWYn6CCGEEEKIR4NM4xdCPLZ0GUnEhz2L9vB60OtME/U6tIe+Iz7sWXQZSXct6+uv\nv2b06NH83//9H3l5eVSvXp21a9eyZs0aFixYUOo58fHxbNu2jbVr17J69Wp27NhBYmKiSZ558+bx\n8ccfs2LFCtLS0gA4cuQIV65cYfXq1axatYolS5aUOUU+JSWFkJAQpkyZQvPmzYmKijJJr1KlCh4e\nHpw8eRKArVu30q1bN5M8y5cvp127dqxZs4Y2bdqwf/9+hgwZQpcuXfD392fBggX06tWL8PBw+vfv\nz6JFiwA4e/Ysc+fOpUOHDiXqEx4eXmp9hBBCCCHEo0FG9oUQj62kiGAKrseWOH4lx4KJp6sXvTmd\ng2dsD+Zt2FdmOa1btwagcePGzJs3D0tLS27cuEHfvn1RqVTGTvrtTp06RVxcHEFBQQBkZWVx5coV\n3N3/Xj5w5coVGjRoAMDTTz9NXl4eR48e5cSJEwQGBgJQWFhIcnKycdT/VlWqVGHu3LkkJyeTk5NT\noiMPRVP5f/zxRxo3bszPP//MunXrTNLPnDnD//73PwAGDRoEQGRkpDE9JiaGsWPHAtCqVSs+//xz\nADw8PHB0dCy1Prm5uSQlJZVaHyGEEEII8fBJZ18I8VjSpSWijd5Yalp1q3xmeV/5+4D5dXTpV8tV\nrkKh4NChQxw4cIDw8HBUKhXNmjUrNa9KpaJ9+/bMmDGjzPLMzP6eQGUwGACwsLCgV69eDB069K71\nCQsL46233sLGxobjx4+TnZ1dIk+nTp34v//7P7p27Yqnpyf29vYm6ebm5hQWFpZ5DYVCYaxbQUGB\nsc4qlarM+jz33HMsX7681PoIIYR4vFy+fJkPP/yQ1NRUCgsLadasGe+//z4WFhb3XNavv/5KQkIC\n/fv3fwA1FULcC5nGL4R4LGmPbio5db8seh3a6O/LTI6Ojgbg+PHj1KlTh7S0NNzc3FCpVOzatQu9\nXk9+fn6J87y9vTl48CA5OTkYDAZmzpxZYjq+q6srFy9exGAwcOjQIaBoBsHu3bspLCwkLy+P0NDQ\nMuuWnp5OzZo1KSgoYM+ePRQUFJTIY2tri1qt5osvvih1pN3Hx4cDBw4AsG7dOjZu3IiZmRk6XdHz\na9SoEQcPHgTg8OHDJsH+yqpPfn5+mfURQgjxaNOlJZK+awkpm8NI+elzRo0Yzptvvsn69evZsGED\ngHGW17167rnnpKMvxCNCRvaFEI8lfVZqmWkm0/hvevfsecCyzHOGDRvG1atX+eijj3B3d2fZsmUE\nBATQsWNH2rdvzwcffFDiHHd3d4KCghgwYADm5uZ07NiRSpUqmV733Xf53//+h7u7O25ubgA0b96c\nVq1a4e/vj8FguOOXooCAAN555x3s7Ox44403mDFjBl26dDEuDSjWrVs3xo8fz9y5c0uUMXDgQMaP\nH09gYCA2NjbMnTuXxMRE5s6di5ubG8HBwUyePJlvv/0WlUrFhx9+WGYnvrg+Hh4eBAYGllkfIYQQ\nj57bg9oCRKdZ43zDnhpHPkHXoCio7bhx4zAzMyszWK2TkxOnT58mNTWVt956i8jISNLS0oiIiOCn\nn37ijz/+YMCAAUyYMAEPDw80Gg1eXl6EhYVx7dq1EkFhS1vGJoT49xTFUzdLEx0dbfAtx/ZA0dHR\nlCefePRJW1YsFbk903ctISl8ZLnzuwQuwuHF4Q+wRg9WRW7LJ420ZcUi7VlxVPS2LA5qe3usm01X\n7cnTm9GnRhoq13p4TP4NpZ0LeXl5fPPNNwQFBZGbm0vHjh3Zu3cvEyZMwMXFhTFjxjB27Fjs7OyY\nNm0a48aNo3PnzmRkZBg7+926dWPHjh1UqVKF5557jh9//JFZs2bx7LPP0qVLF7Zt28bu3buZM2fO\nfb3Xit6WT5L70ZY3y1Dcpyo9VmQavxDisWTbvDuYl3NykrkSW98eD7ZCQgghxCOsrKC2CqA4qkvB\n9ViSIoIBTILVvvXWWybBahs3bgyAi4sLDRs2BKBq1apkZmaalF2zZk2cnZ0xMzPDxcWFzMxMYmJi\naNmyJVAUFPbMmTP3+U6FEMWksy+EeCwpHd2x9X2tXHltfV9D6VDtAddICCGEeDTdKahtDasCzmv/\nXoKmjd5IdlKcSbDa8PBwk2B95ubmpb6+fcbwrWnF6WUFhRVC3H/y6RJCPLZcAj5D5VrvjnlUrvVw\nCfjsP6qREEII8ei5U1DbpvbZJOcpOZRqDUChTsfsqeMJDQ0tV7Dae3UvQWGFEP+OdPaFEI8tpZ0L\nHpN/w7Zl75JT+s2V2LbsbVx7KIQQQjyp7hTU1kwB070S2ZZkz+iTNXg/pjo25gbWrFlDXFwcAQEB\nxMfHlxms9l4FBwfz/fffExQURGRkJMHBwf+6TCFE6SQavxDisaa0c8F9xLqiKYpHN6HPSsPcxhFb\n3x4ydV8IIYQAzG2c7pjuZKFnaoOrxvcuPTtQuXJl1q9fbzw2aNCgEue9//77pb4uFhkZWerrL7/8\nslz1FkL8O9LZF0JUCEpH98c62r4QQgjxoNg2707SmnfLnMpvQoLaClFhyDR+IYQQQgghKjAJaivE\nk0k6+0IIIYQQQlRwEtRWiCePdPaFEEIIIYSo4CSorRBPHlmzL4QQQgghxBNAgtoK8WSRzr4QQggh\nhBBPEAlqK8STQabxCyGEEEIIIYQQFYyM7D/G4uPjCQsLIzk5GYPBQIsWLRg7diyWlpb/uMz169cz\nceJEXF1d0el0eHh4MGHCBJycnIiMjKRy5cp06tTpPt7FwzNnzhzq169Pz549H3ZVhBBCCCGEEOK+\nkpH9x8yNG7ns23uZn7ZpeGPwUF5/vR8bNmwgMjISNzc3pk6d+q+vERQURHh4OGvXrqV169aMGDEC\ngJ49e1aYjr4QQgghhBBCVGQysv+Y0Gbm8f2GGGJOXaOw0EBS8jl0Bbbs2qblemI0PXr6MHjwYF5+\n+WVSU1P56KOP6Ny5Mx06dGD37t1s376d2bNns3r1aqKiojAzM6Njx4688cYbd7xuz549iYqK4tix\nY+zduxdHR0cCAgL46KOPOHr0KHq9ngEDBtCjRw++//57IiIiUKlUNGjQgGnTppmU1alTJ1588UWO\nHTtG5cqVWbp0KVlZWUyYMIGMjAx0Oh1TpkzB29ubmTNnEhMTg16vp1+/fiaj7wcPHmTZsmVYWFiQ\nmJhI586dGT58OLGxscyYMQOFQoGNjQ2zZ88mIyODcePGYW1tTUBAABkZGXz55Ze4urpSqVIl6tev\nT0FBAVOnTiU+Pp78/HyCg4Np164dnTp1ok+fPvzyyy/k5+ezYsUKrKysCAkJIT4+Hp1OR3BwMG3a\ntHkgbS6EEEIIIYQQ/5R09h8D2sw8Pv/sd1L+yjYey9QmYW9XncJCAyePX+VKwg3eCW5L/fr1uXTp\nUqnlxMfHs23bNtauXQtAv379ePnll3F3d7/j9X18fIiNjTW+P3z4MH/88Qfr1q0jOzubV199lY4d\nO7J8+XKWLl1KtWrV2LBhA7m5uVSqVMnk+j169GDChAn06dMHjUbDrl27aNKkCW+//TanTp1i1qxZ\nLFq0iF9++YWdO3dSUFDAxo0bS9QpJiaGXbt2oVQqeeWVV+jbty+hoaHMmDEDT09PVq9ezerVq+nW\nrRtnz55l9+7dODg40KFDBzZs2ICdnZ3xB4QtW7ZgYWFBREQE169fJygoiO3bt6PX66lbty5vvfUW\no0eP5sCBA2i1Wpydnfnwww9JTU1l4MCBREVFlb8xhRBCCCGEEOI/IJ39++j8+fOMGDGCQYMGERAQ\nAMCECRM4ffo0Dg4OAAwZMoT27dvftazU1FTeeecd+vTpw66fTqFUNMDCwvqWHAYMFBrfpfyVzfeR\nMRgMBgwGQ6llnjp1iri4OIKCggDIysriypUrd+3sZ2VlYW5ubnwfExPD008/DYC1tTVubm4EBQXh\n5+fHoEGDqFSpEosWLTLp6APY2trSoEEDANzc3MjMzCQmJobhw4uiwTZq1Ii4uDgcHBzw9PRk+PDh\nvPzyy/To0aNEnZo0aYKNjQ0A9evXJz4+npMnTxISEgJAfn4+jRo1AsDDwwNHR0dSU1OxsbGhSpUq\nADRv3tx4P61atQLA1dUVCwsL0tPTAWjRooVJfY8fP050dDRHjx4FIC8vj/z8fCwsLO74DIUQQggh\nhKhIVq9ezaZNm7CwsCA3N5cxY8bwzDPPlJr34MGDrF69ms8+++wfXev8+fOEhoYSHh5+z+empqai\nVqt/AroC54F4QA9UAnZoNJoy10Gr1ep1wGCNRpNTnmup1ep+wCqgmkaj+esueXtpNJr1arX6ZaC2\nRqNZUka+vzQaTdXbjq0FPtFoNIfvdA3p7N8n2dnZhIaGljqle8yYMXTo0OGeytNoNMycOZOff/6V\n8+djadqouUm6rY0Ll+P3mxw7deIq58//QZ06dVAoFMbjOp0OAJVKRfv27ZkxY8Y91SUmJoY+ffpw\n5coVAJOyby1/6NChuLm5kZKSwrBhw4iIiMDR0dGY79YfDAAMBgMKhcLkx4nCwqIfML788ktOnz7N\nDz/8wKZNm/jqq69Mzi3OV1wOgJWVFatWrTKpX0JCAiqVyvjezMysxHm3v87Pzzfmu7XOBoMBlUrF\nsGHD8PPzK+VJCSGEEEIIUXHduJHL6VPXSIhPYMWKCL755luqVKnM5cuXmTJlSpmd/Ydp1apVANM0\nGk2+Wq0GeEWj0WjVarUZ8JNarW6n0Wj2lnauRqPpe4+X6w9cAHoB/1dWJrVabQGMAdZrNJpt93gN\nbp67Sa1Wt9JoNKWP9CKd/X9Nl5aI9ugm8jL+4uP+LfnmVOY9na/Vahk7dizZ2dnk5uYSEhJC48aN\nWbx4MW3atGHH9j2kpV8nOycNaytHTp+LIjXtMoWFerJz0rmedJa8/Cwuxe0lPz8LO3trnJycsLGx\nITk5mTVr1vD9999Tp04dvL29ee+993j99ddp3LgxYWFhPP/883z33XfGX9l++OEHvL29CQwMpE2b\nNmzevJmkpCTs7OyMdfbx8eHTTz9l165dmJub88cff6BWq5k/fz5Xr17llVdeoWnTpoSFhXHlyhXy\n8vLo169fqfffqFEjDh48SNOmTTl+/Dj169cnISGBn3/+maCgILy9vUuNln/mzBlycnIwMzMjNjYW\nT09PGjRowK+//srzzz/Pli1bcHJywsPDw3iOg4MDmZmZZGRkYGVlxdGjR2natKmxDl27duXq1auY\nmZmZ3O+tmjRpwq5du/Dz8yMlJYWVK1cyZsyYe2pzIYQQQgghHie3xw+7kZFIWmoms2b8RNPmtejR\n04eIiAgA9u3bx4IFC1CpVNjZ2fHpp58ay1m5ciWZmZmMHDkSgMDAQCZPnsy+ffvYvn07hYWFPP/8\n84wcOZJr164xdepUnJycuNlJB+DHH3/k66+/xtzcHG9vb6ZMmcLChQuJj48nISGB8PBw44BdYmIi\nSUlJaDSafbffk0ajKVSr1YeB+mq1uh7go9Fo3lOr1bZAjEaj8VSr1ZcBH6AOsBJIB44AzhqNZtCt\n5anVaiegJfAGMJ6bnX21Wv0LsBPoAFQFugHvA43UavVi4NAt1x5P0Q8FhcBEjUaz+2YZM4CXgBSg\nm0ajuapWq88DL94su1QSjf8f0mUkkfi5Pxffq01S+EhubPqAjG/eJW3HAm78tgJdRpIxb0REBEFB\nQYwePZrU1FSTcpKTk+nduzfh4eGMGTOGZcuWGdMqV67MqHdm4uLcgKvXTvFX6gUyMq/xbJtRtG01\nHHMzczSxP3Ei5jt0ujxcqqrp0L47ubm5dO/eneXLl7Nt2zb+/PNPY3kODg5Mnz6dPn364OzsXOr0\n81WrVnH27FnWrVuHWq2mZ8+e7Nixw5jeokUL8vPzSUlJQa/X06FDB8zNzbGxsWHPnj3Mnz+fwsJC\nGjVqxNq1a1mzZg0LFiwo9TkGBQVx+vRpgoKCmDdvHpMnT8bFxYVjx47Rt29fAgMDef3110ucV7du\nXSZNmkTfvn3p27cvdnZ2TJ48mS+++IKAgAAiIyPx8vIyOcfMzIyRI0cSEBBAcHAw9evXB6Br167o\n9XoCAwMZPXr0HWc+vPLKK1hbW9O3b1+GDRuGr69vmXmFEEIIIYR43BXHDzt54iqFhUWDyPZ27jg4\n1GT7z2F8vfITxo75jPS0LABu3LjB3LlziYiIwNbWlr17/x40f+mll/jll18ASE9PJyUlxbjMd82a\nNXz77bdERkai1WpZtWoVbdq0ITw8HBcXF6BoefH8+fNZsWIFa9euJSEhgQMHDgBQUFDAmjVrTGbm\nHjp0yOSHglup1Worijrgd5wKf9M0YIZGo+kA1CojT2/gB2AbRT8gVL8l7YZGo3kR2Ar0BD4GNBqN\nZsQt9alPUUe/NRAADLiZ5ETRDIDWN183vnn815v1L9MTM7Jf3jUl5VlPostIIj7sWQqux5ZMNBSS\nd/kY8WHP4jH5N7p3746DgwNeXl4sXbqURYsWmWyPV7VqVRYvXszy5cvJz8/H2vrvdfktWrQgLUWF\nVSUH8guySE9PoKpTXQCUSkvs7NxpUL8zcfH7ib9yFBsbZ1q1fJZKlSrRqFEjtm/fDsDIkSMJDAxk\nz5499O/fn6FDh5rc760sLCzYsWMHgYGBTJo0CS8vLyIiIkhPT2fUqFHGfI6OjkyZMoXGjRtz4sQJ\n5s6dy9tvv83FixeNuwAsXLiQvn37olKpSEtL4/Tp08bzb32+pT3r+fPnl/n8oWiU/vY8devWZc2a\nNSXyRUZGGt/36tWLXr16lSgvLCysxLGff/7Z+Pr999+/Y14hhBBCCCEqou8jY0wChRfzbdKfTO11\nkpI1HDu+jT599rF9RyROTk5MmTIFvV5PfHw8rVu3NsbaqlatGgqFgqSkJPbt20fHjh0BqFSpEgEB\nASiVStLS0khPT+fChQu8+OKLALRq1YrffvuNy5cvU6tWLWN5LVu25OzZswA0bty4RB2TkpJwcnK6\n/fBWtVqtv/l6qUajiVGr1S3u8hi8gN9vvt4MdCwlT38gVKPR6NVq9XrAH/jkZtpvN/83AahSxjWa\nAQc1Gk0hEAu8efN4hkajOXnz9RXA/pay2t2p0k9EZz8hIYFvv/2W9evXo1Kp/vWakqSI4NI7+rco\nuB5LUkQwbUasMx574YUX+OCDD0zyrVy5EldXVz7++GNOnTrFRx99ZEwzNzfHu5EbCgVgAIUCCk3W\nt+tQKBQ08elNYaGeC5d+4cPZB2n9zCaTtfI9evRg27ZtJCYmMnr0aJPr377+Xq/XG1/fvl79VgaD\nwbiu/db188UOHTrEgQMHCA8PR6VS0axZs7IelRBCCCGEEOIRdONGLjEnr5U4bjAYKCzUUdnWlcq2\nrtTxbMeuPXM4r7nEpEmTWLp0KXXr1i11xmzHjh355Zdf2Lt3L0OHDuXKlSt8/fXXbNy4ERsbG2Ns\nrOL4XvB3f+P2eF8FBQVYWloCmMTpuotXNBqN9vZbuuV1aQUpwBgdvcQaebVaXQNoBcxTq9UGwJqi\nKf/FnX3dbWWVRk/pM+91t70v6/wSKvQ0/mvZGaw8u58vjuwiKTOdhIyiKfSenp5ERESg0WgYMGAA\ngYGBDBs2zBiBvdhXX32Fv78/vXv3ZtGiRQCcOrCHYV/tZ+Lp6kw9445WV/Yj1EZv5J2hbxEfHw8U\njaIXTx0vlpaWRs2aNQGMW83dyt6+Eu7Vi9aPO9h7kJJyAQCdLo/s7BRsrKtyRvMjTXx6M27s//Hc\nc8+SmJhoUsZzzz3H4cOHycjIoEaNGiZptra2JCUVLTk4d+4cubm5d3mqRWrXrk1MTIzxvm6XlpaG\nm5sbKpWKXbt2odfryc/PL1fZd9OqVat/HMnzSeXv729sr2Lz5s0rEfiwLIWFhQQHB/P777/fMd/o\n0aPL/TckhBBCCCEebadvrtG/3Z8JBzke852x411QkIsBA1cTC9BqtVSrVo2MjAwOHjxYon/TqVMn\n9uzZQ1xcHN7e3qSlpRljjp0+fZorV65QUFBA7dq1jVuKF/c3PD09iYuLQ6st6qsfOnQIHx+fMuvv\n4uJSYhl1GTKAajdflzZafgEoHv1/pZT0fsDnGo2miUajaQqoASe1Wl23jOsVUnLgPRpoq1arlWq1\n2lWtVpfcf9xUdYpG98tUITv7f+VoGbZ7Na2/nc3kA5tYkXaW6w4qOnfsxPMD+/DN95HodDrCwsIY\nP3484eHhPP3008WRGk3cvnbk2xWL6eJ6g1neV3i9ehppBUWj37FaSyaers6u5MpsvmbPxNPVycwt\n5LUmzrz77rsEBASwZ88eYzCKYt27d2fFihW88cYbNG7cmOTkZDZs2GCSp3ETd6ysVVRxqoO9fQ32\n7v+cfYe+oKG6K0qlJUqlJfsOL2LbT5+iUChKrFW3sLCgbt26pe4I0KBBA+Ma9E2bNlG1atUSeUoT\nFBTEhg0bGDJkCDdu3CiR/swzzxAXF0dAQADx8fG0b9++xKwG8eClFOSw8ux+bJvW5+OI5VzLzjCm\n7dixg65du5arnMuXLxMcHEzbtm3vmG/+/PkltlwUQgghhBCPp5zsglKP16zREksLW37dt4DfDy7h\nYPRXNGrYA73ejP79+9OvXz9CQkJ48803+eKLL0hOTjaeW6dOHeLj443fK728vLCxsaFv3778+OOP\n9O3bl+nTpxMUFMQvv/xi0t+wtrZm/PjxvPnmm/Tv35+GDRsat8ouzdNPP41GoynPre4C1DeD6TUA\nbp+6PBOYq1artwNJFI3C36ofsKL4zc0I+SuBsqL5XwUs1Gr1d7eccxkIp2gt/vfA3UY3nwN23ymD\noqw92QGio6MN5QlAFh0d/cgEKvsrR0uPLUu4nJlSIk3xVwbml5KwOXuVRu61+PPSZfbtKwrMGBMT\nw6JFixg8eLBxzf6aNWuIiopCqVQSExNDVFQUp7+ZzexVW3i2ipZnq2qpZX3n0eoqPUOp8uqkf31f\n2sw8vo+MIeak6a9rZmYKfBq70aOnD7aVLUs9Ny8vj/79+/P1119TuXLlO17nUWpL8c/9laNlyoFN\nbL0cgx4DZOVhFbGHgmEv80otHwbY1eeLTz9j2rRpzJgxA4VCgY2NDbNnzyYjI4Nx48ZhbW1NQEAA\n1tbWzJ8/H6VSiaurK7NmzeKHH34gOjqalJQULl++zJAhQ+jduzcvvPACUVFRpKenM2HCBPR6Pe7u\n7syZM4c//viD6dOno1QqMTMzY8GCBTg4ODzsR/XYkM9mxSFtWbFIe1Yc0pYVh7Tl/bNv72W+3xBz\n94w39Xjdh2faed6369+PtgwICODw4cOtNRpNyenI5aRWq1sD2RqN5qRarZ4IKDQazYf/qmL/glqt\ndqUoGGDLJ2rrvZADm0t29A0G0BdiqGqHrqodN3zrcm7Fr+Sk/D2lo6CgwGQP9rLWjrRq6s0nx77g\ncJo182NdeKNWCo3tc8qsj7mNY5lp98K2siUBA32Ne1vm5BRgZaXCp5EbdvZlj6QeP36cqVOnMmTI\nkLt29EXFUOoPXjaWFDrYUHjlL6IMJ9n/+0aCO3cjNDSUGTNm4OnpyerVq1m9ejXdunXj7Nmz7N69\nG0dHR15++WVWrFhBtWrVmDFjBlFRUSgUCs6fP8+6deu4fPkyY8aMoXfv3sbLzZ8/n0GDBvHiiy/y\n0UcfERMTg1arJSQkhIYNG7JgwQKioqIIDAx8CE9I/BuzZ8/m9OnTJCcnk5OTQ82aNbG3tzcudSqv\ns2fPMmfOHL7++usHU9FyiIuLY9y4cXz77bd3zbtkyRLatm1bavAfIYQQoqLybuTG5o2nS53Kfzsz\nMwU+jdz+g1rdm6CgIA4fPhyqVqv9NBrNP11XnAcsV6vVOUA2RcH4Hqb5wMg7dfShHJ396Ojocl2t\nvPkepJSCHH68fKrEceXJOMz+/It8P9+iKHd5BWQV5NGgYQPWrl3LU089xebNm3FycuL8+fOkpaWx\nb98+LC0tOXfuHJcuXSI+Pp7jx49z6nA8zxSqaO+sxQBczLIss7NvMDPnoqoOhvv8bCytiv4D+CO2\n5AyG202bNg14vNpS/HMfxe0vdWaLvmENlGevkO/uRNaZi2zpkETs8eO8++67QNEPXnXr1qV27dpU\nrVqVixcvotVqycvLIzExkcTERJydndmzZw+enp64u7tz/PhxcnNzSUlJITo6mry8PI4fP87Ro0fx\n8/MjOjqaF198EZ1OR1JSEgsWLCAvL4+0tDTatm0rf2v36GE+r5SCHA7euIK2cXWealab+icvkX41\niQEDBvyjul2+fJmMjIyHek/Xrl0jKyurXHVo2bIlBQUF962+8rdfsUh774FYGQAAIABJREFUVhz/\nz96dh0VV/Q8cfw8MO7IKIoqIGyibioZLbrkvFGrmhlq55UalpbiQCRJmlqKWlUsqbj9TUsncK5cU\nNXCDUMME2RRF9n2G+f3Bl5GRQVARFc/reXpi7j333HPvBeTcc87nI55l7SGeZfVpYKNDfFzlMZka\n2Ojwb0xUpeUe19M+S3Nzc65du9bnaeq4du3aBaD9UzWkGl27dq1KLxsq7ey/TNP4N0WfKZmy/BCZ\nsy3aqVnobj4OWppQrKCwlwvdug3it+C9HDhwAGNjYwIDA4mKiuL8+fMMGTKEgwcP8tVXX+Hm5sao\nUaPYtWsX77//Pl/9/hu6+aloaSj4sGlKhe2p024I9l37PstLrnYvyrMUnszt3EzOXtmtdp+shTV6\nZ66j0aohxaaGhBfdx0pfn19++UUlK0NCQgJGRka4ubmRkZGBjo6O8nsiOTmZ+/fv07hxY4qKinBz\ncyMnJwdtbW3c3NzQ0dGhdevWGBgY4OLiopLqZMWKFUyfPp2uXbuyfv16cnNzxffaY3heP5ulS0IO\nxkUhUzxYvqadfIsmBdrMa2VPXT1DioqKmDNnDikpKeTl5eHt7U23bt0YOXIkrVq1QiKR8P777/PR\nRx+ho6ODvb298vvswIEDbNq0CalUiouLC7Nnz1ZpwyeffIKxsTExMTFkZGSwZMkSHBwcWLx4MVFR\nUaSkpJCUlMRff/3FF198gb6+PhkZGSxevJiZM2eSn59Pfn4+n332Gc7Ozsp64+LiMDAwwM3NjT17\n9rBt2zY0NDSwt7dn0aJF5dpgb2/PgQMHsLW15c6dOwQFBfHDDz9w8OBBTExMeO+99xg6dGil97T0\nWbq7u6sNcFpdcnJy8PDwUEkjKlQ/8e9m7SGeZe0hnmX1sm9RwLcr/1Kbfq+UeV193hvfucJlxU+q\nOp7lq/zip1ZN408vqOAbUENC4RvO5TZr1zMjODhYZZu7uzvu7u4ArF+/Xm11nfYfJj6gyyPT72nV\na4all4gYL9SsQw91yFToaFFsYYTWmWvIHG2QK4oxsanPiRMn6NatG/v378fMzAwbGxvlIcbGxkgk\nEpKSkrC2tubcuXO4ubmppGhUx8nJibCwMAYMGEBQUBDt27cnPT2dRo0aUVhYyPHjx2ndunV1Xrrw\nDDwqBkqxQsF/mXfx3L+GPQOnoMjOo2vXrnh6enLz5k1mz55Nt27dgJJAoMOGDSMwMJC33nqL0aNH\ns2bNGmJiYsjKymLt2rXs2LEDbW1tpk+fzqVLl3B1dVU5n0KhYNOmTRw5coRvVq3ktYnvECstoN9n\n3hz76keSk5M5dOgQAKampvj5+fHff/8xYsQIevbsyalTp1i/fj0rVqxQe615eXls2LABQ0NDRowY\nwY0bN2jaVH0A3du3b7N9+3by8vJo3Lgxp06dIjc3l379+lWpsy8IgiAILxPDOjpM8+78xPHDhOen\nVnX2TXT0H6u8sbbeE51HamSJzfyTpGzxJjv8F5CXSX2oKcXQbTCWXiuRGlk+Uf2C8KQqfOH1P7JW\nNujsD6fAo2QWUodxQ/nhhx9Yu3YtOjo6fP3118pUJqX8/f2ZNWsWUqkUGxsbBg4cyL59+x55Hm9v\nb+bOncu2bduoX78+06dPx8vLi2nTpqGtrc3Vq1e5ceMGAwYMICAgAF9fX1q0aKG2rmPHjtGlSxe0\ntbVVtn/88ccEBgaqjf6flJTEvXv3xPrqp6Q2BspDYrNS8Q3bR1Dnt7l48SI7duxAIpGopDItHU2P\niYnhrbfeAkqmxZ89e5br16+TlJTE+PHjAcjMzCQxMbFcZ79Tp07cy8tmW85/nLp0nt8i6qB1LZo/\npxxG404GGkjYE7oPG+sGyudet25dvv32W5YuXcrt27eRSqX89ttvDBgwgA0bNrBv3z7i4uJYvXo1\nTZo0YdCgQcjlcu7fv8/777/PnDlz2L17N4mJidjZ2QElLwWysrLw9PSkd+/eyGQy3NzcaNy4Mffv\n32fatGlkZmYik8lYsGABjo6OymuQyWTMmjWLmJgYOnbsqNweExNTLlCmkZGRcn9ISIjagJj79u1j\ny5YtaGho0Lx5c/z9/cnOzmbGjBkUFBSojISoKysIgiAIVfWk8cOE56tWdfb72jqy8GxoxSObZUgl\nGvSzday0XIXHG1liPXUHsrQksiP2Is9JQ9PAFEM3T6Qm9SuvQBCegcpeeMntrcm1t1Z+tmvShM+3\nbVOtw8SEkJAQ5ed27dqxfft2lTJDhgxRfm1gYKCcJlz6fwMDA2XgtYyMfM6eiaeuaVvmz3Vn/U8B\n2NraMm7cOBwcHCq9po0bN9KhQ4dynf3ly5dXeExYWBi5ubmis/8UbudmciCuatF3D8RF0i5JTm5u\nLtu3b+fevXuMGvVgKZmWlpby69JAqKWZYLS0tHBxceHHH3985DnS83Px3L+GuOREdACNuLtoJqQi\nc7ZFo24a0quJXL5+FSNjY+X5NmzYgKWlJQqFgrVr17Jy5UpCQ0MZMGAAAN98842yQ5+fn4+npycp\nKSmkp6djbm7Onj17lLMBTpw4UXJfbt/mnXfeYdasWXTv3h1bW1tatmyJr68vQ4cOxdXVlUmTJnHl\nyhUCAwPZsmWL8hr++usvZDIZfn5+SKVS5cwydYEyp0yZonL96gJi5uXlsW7dOoyMjBg9ejTXrl3j\n77//pnnz5sybN4/ffvuN/fv3A6gta29vX6XnKwiCIAiljI11qzXavvBs1arOvpW+Ef1tnQiNvVxp\n2f62TtTTN6q0XGWkptaY9JxSeUFBqAE1+cKrMtlZBezZHUnklQfTvQoLczl/LoLBnlPYty+UkSNH\nKsvfvn2bTz/9FCgZAf3yyy+JiIjg4sWLTJw4kYCAAObNm6dMCejv709oaCgXLlxgxYoV6OrqYm5u\nzsKFC1m9ejVSqZT69etja2v7yFFTQb1HLgl5iExRzPnYa7Rq2BCJRMLhw4cpKiqfl9fOzo4rV67g\n4OBAWFgYUJJr9/r169y/fx8zMzNWrFjB6NGjsbCwUDn2+/27iH3NGs3EVBR1jZDkFVJspIc0OgF5\nIwtQQEHzekT8G01pjoe0tDRMTU1p0qQJx48fR6FQsGbNGgB0dXX55JNPiI2Npbi4mDp16qCvr0+j\nRo04ffo0AwYMoH79khe3devWVV5PgwYN0NLSQltbG0tLS+rUqUNeXh5hYWHI5XJlrl9nZ2fi4uJU\nriEmJoY2bdoA4OrqqpyVcvnyZXx9fQEoLCxUiStQqnXr1mhqamJlZUVWVhZQssxm6tSpANy4cYP0\n9HRu3LhB+/YlM3dee+015fHqygqCIAiCULtpVF7k5eLf4U0a1zF/ZJnGdczx7/BmDbVIEGpO6Quv\nqqiuF17qZGeVBHK5fClZZV1X0u1L1LNsRW6WJdHRMfx345ZyX0pKCtOmTSM4OJihQ4eybds2PD09\nsbCwYO3atWhpaREdHc2yZcvo0aOH8rgtW7bg4+PDli1bGDhwIHK5nMGDBzN27Fh69uypHDXdtGkT\nnTt3ZuvWrc/kmmubypaEPKxBO2eOHDnCuHHjMDExwdTUlO+//16lzLhx49ixYwfjx48nJycHAEND\nQ+bMmcOECRMYMWIEOTk55Tr6+fIiYu/fQefn02j9dY3CTvbI7SxLpu/HpyK9FAsaEqRR8WTdu69s\n+5AhQ9i3bx8XL17Ezc2N5ORk9uzZo0yt+sUXX9CsWTMaNmyIi4sLO3fu5MyZM4wfP55Dhw6hqalZ\n7jrLBrM0NTUlISGB6OhoUlJSMDMzY926dcr9xcWqL0sUCoVKitfS/Xp6emzevJng4GD+7//+jwUL\nFpQ7r1Sq+m6+sLAQPz8/li9fzpYtW5TLHsqeo7T+iso+SwkJCbRp04YxY8ao/Jeeno6Pjw9//PGH\nSvmQkBC6detGQUGBcpuPjw8JCQmcOHGCbf+bgXT69GlGjRpFdHT0I2f3CIIgCIJQy0b2AerqGbJn\n4BR8w/ZxIC5SZWRKKtGgv60T/h3epK6e4XNspSA8O/4d3uRKauIj11o/6xdee0Ii1UZsTUiKoEWz\n3kgkGlhZOvPN1xuV+ywsLFi8eDGrVq0iMzNTZa1zKRsbG0xNTVW29evXj4ULF+Lh4cHAgQPLdRSr\nMmoqlFfZkhCZi63K50aNGuEbGqr8PHDgQAA++OAD5TYbGxt27y6fLaJ///7079+/wnMl5WRQ1MIa\neZN6KtvlTjYUZxdQ2PN/z1ShQO+HI/ynWdJhdHV15ddff+Wtt97C3d2dAwcO8MEHH9CsWTPMzMxw\ncHBg4cKFeHl5sWrVKn799VdMTU3x8vJCV1eXtLQ05bn69OmDi4sLGzduZNq0aRQUFBAXF8eePXuY\nNm0ab731FgYGBsoXBBcvXqR58+Yq7bWzs2P//v24uroSERFBYWFJql8HB4dygTLLrulXJycnB01N\nTSwsLEhOTiYyMpKioiLs7OyIjIykb9++ykj/FZV9FkrXcibEJ2Bp0YDVq9diXMW1nEZGRmzatIlJ\nkyapbO/atavy65s3b7Jp0yYWL15Ms2bNqrXtgiAIglDb1LrOPpR0+Nf0GMXt3EwOxUWRUZiHsbYe\n/Wwdn9lIpiC8KMq+8Pot9opKOsqaeOGVkZFP5OXb5bbn5aWTln6LqOh9RCFBLi8kPT0Ou6YlHbiV\nK1fy+uuvM3LkSA4ePMiff/5Zro6ya79LeXp60qVLF44ePcqUKVMICgpS2V86alp2RFao3Iu0JKRQ\nLgPKP3vNfxIoGNTuwQaJBJlzIyKOn4I3SgIB6uvr4+3tzXvvvQfAu+++S8uWLTEwMGDEiBG4ubkx\nYsQIFi1aVKXUPq1atWLevHnExsYyYsQIlSUhY8eOZd68eYwdOxaFQsFnn32mcmzXrl3ZvXs3fn5+\ntG/fnnr1Sr7358+fj6+vr0qgzMqYmprSuXNnhg4dioODAxMmTCAwMJDg4GA+/PBDxo0bp7yeisru\n2bNH7c/Uk3h42U5u7n3S0vII9DtW5SjNo0aNYtu2bbzzzjuYmJgot4eEhPDvv/8yZ84cNm7cSHx8\nPFevXiUxMZHRo0eTk5ODj49PhYERBUEQBOFVVSs7+6Ws9I0Y1/LRoyOCUBuVvvA6HHaKZGPNGn3h\nFXVFNSVLqYTkC9jZdsapZcmMAoVCwbHjgdy8GQuUrK9u1KhRyfZjx5RTkCUSySNT/X377bd4eXkx\nfPhwUlNTuXHjBhKJBJmsJEvGk4yaCs8nBkpFhn0yjYthe8ttz3/vjXLbijo70LPDWyrbPDw88PDw\nUNlWUWrVUl5eXmq/Lk3NWlbZFK4rV1acclVLS4vvvvtOmTO4dLp+06ZNldPU1akoIOaSJUtUypW+\n0CjbHm9v70eWrQ6ly3bUzeYpLlZw+WIyiQkZTPN+dP5lHR0d3nvvPb7//nt8fHzUlomPj8fT0xMf\nHx/eeecdrl27xrFjxx4ZGFEQBEEQXlW1bs2+ILxMEhISVP6Qr27mWnp89+5HeLu+wbiWHSvtkA0Z\nMoSEhIQq1V1R2bxc9dODE5Mu0Khhe+VniUSCTYP2ZGSUTJUePnw4/v7+TJgwgYEDB3Lu3DlOnTrF\na6+9xqhRo1SmVJdlbW3Ne++9x7vvvsvVq1fp0qULbdq0Yd26dezbt4/58+fzww8/4OXlRUhICC1b\ntqzS9QkvTgyUvraOSCVV++fqWc8yEMqraNlOds5dToV9x6mw79j76zImTvio0ro8PT05f/48iYmJ\navcbGhoqs3iUBiuMjIxUvoRRFxhREARBEF5VtXpkXxCEmqenr35acPfXZ5bbZt+8N3N8PqZFi8a0\naNFCJfDeyZMnAXj99deV28qmBCwd3Rw8eDCDBw9Wqbdz586cOnVK+flRo6ZCxV6UGCgv0iwDQVVF\ny3YADA0seL3DVOVnDQ0JmRn5j6xPQ0ODGTNmEBQUpBLMsNTDQRMVCgUSiUSZyhHKB0YUBEEQhFeV\n6OwLwnNQGk/iVnw8qfk53M7NxKpMB+X06dMEBQWhpaWFkZERK1asUMkz/88//7Bo0SK0tbXR1tZm\n+fLlSCSScutWy4qJiVGbgm7x4sVcuHABOzs7ZdCuq1evsmjRIqRSKRoaGgQFBWFiYqK27MMcna3Y\n90uU2qn8D9PQkODkbPUkt1CoIS9KDJQXIfCkUF5Fy3bUKS5WEHlF/YuBsrp3786GDRuUKQYr4+zs\nzNmzZ2ndurXawIiCIAiC8KoSnX1BqEH38rJZELaXg//LYS5Jz0EnJ50OO5eojJJmZGSwbNkybGxs\nmD17NqdOneKNNx6sTw4JCWHkyJF4enpy5swZ7t69y4EDB8qtW/3444+Vx5SmoGvcuDFbt25l69at\n9O7dm4iICHbt2sWdO3fo3bs3AKmpqfj6+tKqVSuCgoIIDQ2lY8eOass+zNhYFycXKy5fTK70fji5\nWGFUxUjdwvP1vGOgvCizDARVFS3bgQfT+MuybzW1gtKqPvnkE4YNG1alspUFRhQEQRCEV5Xo7AtC\nDbmXl43n/jVqRyZlimJCYy9zJTWRPQOnYGZmxoIFC5DL5cTHx9OhQweV8j179uTzzz8nNjaWAQMG\n0LRpUyIjI5kyZQqgft2quhR0MTExuLq6oqGhQf369bGxsQHA3NycZcuWkZ+fT0pKCh4eHhWWVcdz\niBOJCRlq1/GWMq+rj+cQp6rdPEHgxZllIDxQ0bIdfX0zBvX9otz2li2dGD9xULntD8cucXFx4dq1\nawA0bNhQub00nSCoBkN8VGBEQRAEQXhVic6+INQQ37B9j5yCDBCblYpv2D6uL17Pjz/+SNOmTfHz\n8ytXrmPHjuzatYs//vgDHx8fZs+eXem6VXUp6A4cOKCyLrb0mICAACZOnEjXrl1Zv349ubm5KBQK\ntWXVMayjwzTvzuwJiSTysuo0Xw0NSZVTcQmCOs97loHwgFi2IwiCIAgvLtHZF4QacDs3kwNxkVUq\neyAuEousLOrXr09mZiZnz57F3t5epcyWLVvo1q0bb775JgqFgujo6ErXrapLQWdnZ8emTZtQKBQk\nJSUpI2Cnp6fTqFEjCgsLOX78OK1bt66wbEUM6+jgNc6NjIx8oq7cJi+vCD09LZycxdR9QagtxLId\nQRAEQXhxic6+INSAQ/9bo6+Oxv1sdLeeVH4u7OGIc99ujBw5ksaNGzNhwgRWrVpFjx49sLS0BKBR\no0Z8+OGH1KlTB21tbQIDA9HV1S23bjUzM1NZ7/z58/H19WXt2rXo6Ojw9ddfY2JiQosWLRg+fDiN\nGzdWprTy8vJi2rRp2NjYMGbMGPz8/BgwYIDaspUxNtal0+uNn/DOCYLwohPLdgRBEAThxSQ6+4JQ\nA9IL1P8RrDAxIHemR7ntrdv2wdv/K+Xnh1PLde3ala5du5Y77uF1q+Hh4co1rk2bNlWbgk7dMoHh\nw4czfPhw5efSYHzqygqC8GoTy3YEQRAE4cUkOvuCUANMdPQfq7yxtt4zaokgCEL1E8t2BEEQBOHF\nIzr7glAD+to6svBsaIVT+cuSSjToZ+tYA60SBEGoXmLZjiAIgiC8ODQqLyIIwtOy0jeiv23V1qv2\nt3USacQEQRAEQRAEQXgqorMvCDXEv8ObNK5j/sgyjeuY49/hzRpqkSAIgiAIgiAItZWYxi8INaSu\nniF7Bk7BN2wfB+IiVab0SyUa9Ld1wr/Dm9TVM3yOrRQEQRAEQXhxJSQk4OHhgZOTEwqFgsLCQiZO\nnKgMJiwIwgOisy8INaiuniFreozidm4mh+KiyCjMw1hbj362jmLqviAIgiAIQgVK/3a6FR+PUX1L\nvvrhW6z0jUhPT2fw4MF06dIFXV0REFQQyhKdfUF4Dqz0jRjXsuPzboYgCIIgCMIL7V5eNgvC9nIw\nLgqZohhJeg46Oel02LlEOSvSwsKCu3fvkpOTw6JFi8jPz8fIyIigoCBMTEyUdYWEhBAeHk5qaiqx\nsbGMHz+eYcOG8ffff/PNN98glUqpX78+/v7+SCQSPv30U5KSkmjTpg0HDhzgxIkTjBkzhubNmwMw\nc+ZM5s2bR0ZGBnK5nAULFuDg4PC8bpUglCM6+4IgCIIgCIIgvHDu5WXjuX8NsVmp5fbJFMWExl7m\nYsxV9O/fp379+pw9exZfX1/y8vI4deoUoaGhjBkzRuW469evs2PHDmJjY5k5cybDhg1j8eLFbNy4\nERMTE5YuXcrBgwcxNDSkoKCAnTt38scff7Bp0yZlHc2bN2fkyJF8++23dOnShWHDhhETE0NAQAA/\n/fTTM78vglBVorMvCIIgCIIgCC+gCRMmEB0dzeLFi+nRo0e5/W+88QahoaFs3bqV9u3b06ZNG7X1\nXL16FR0dHezs7J51k6uVb9g+tR19jfvZ6G49CShIkWriPnIQUqkUc3Nzli1bxt27d8nLy8PDw6Pc\nsa1bt0ZTUxMrKyuysrK4d+8ecXFxzJgxA4Dc3FxMTU25c+cObdu2BaBbt25IpQ+6TS4uLgBcuHCB\n+/fvs2/fPgDy8vKq+xYIwlMRnX1BEARBEARBeAHI0pLIjtiLPOc+mgZmfP+VHwu+XFnpcZMmTXrk\n/iNHjuDk5PRSdfZv52ZyIC5S7b5iM0PyR3dRfj4tyeBObiYBAQFMnDgRAwMDLl68SG5ubrljy3ba\nAbS0tLC0tCQ4OFhl+48//oimpiYAEomk3DGl//f19a3wJYsgPG+isy8IgiAIgiAIz5EsM4WU4Blk\nR+wBuUy5PWXbR+SmtuVWM1u+PHeOOXPmkJOTg4eHB7///ruynI+PD3379qVr1674+voSHx+PTCbD\n29sbMzMzduzYgZmZGebm5sTGxrJlyxY0NDRo3rw5/v7+z+OSK3Xof2v0q0KmKOZgXBTp6ek0atSI\n27dvc/z4cVq3bl3pscbGxgDExMTQrFkzgoODad++PY0aNeLQoUMAnDp1CrlcXu5YV1dXjh49Sps2\nbYiJieHkyZO89957j3GVgvBsaTzvBgiCIAiCIAjCq0qWmUJ8QBeyz+9S6egDIJdRdC+W9EMrKC4o\nP0r9sNDQUCwsLAgODubbb7/liy++wN7eni5dujBz5kxcXFzIy8tj3bp17Nixg//++49r1649oyt7\nOulVuN6yMgrz8PLyYtq0aQQFBTFmzBh++eUXrl69WumxAQEBzJ07l1GjRhEeHk6TJk3o0aMH2dnZ\njBw5kr///lsl0F8pLy8vbt26xahRo1iwYAHt2rV7rDYLwrMmRvYFQRAEQRAE4TlJ2eJN0Z2YR5aR\nZ94lN/p3YNEjy124cIHw8HAiIiIAKCgooLCwUKWMsbExU6dOBeDGjRukp6c/eeOfIRMdfbXbFSYG\n5L9bPn6BsbYew4cPZ/jw4YSHh+Pm5kbv3r1VygwZMkT5tYGBgXJ2RLt27fj5559Vyqanp/P222/T\nt29f7ty5oxzlLzvd39DQkFWrVj3ZBQpCDRCdfUEQBEEQBEF4DmRpSWSH/1Jue7ZMAx2NYrQ0QKEA\nPc1i7tz5F1l6MjKJ+k4wlKwh/+CDDxg0aJDa/YWFhfj5+bF3714sLCyYPHlytV1Ldetr68jCs6FV\nmsovlWjQz9axWs9vYGDAgQMHWL9+PcXFxcydO7da6xeEmiCm8QuCIAiCIAjCc5Adsbf81H3g+5sW\nhN03RKGAhDxtsmQapBVokB2+h/Dw8Arrc3V15dixYwCkpqbyzTffACUB5uRyOTk5OWhqamJhYUFy\ncjKRkZEUFRU9m4t7Slb6RvS3dapS2f62TtTTN6rW82tpabFixQp27tzJrl276NatW7XWLwg1QXT2\nBUEQBKGWSUhIwN7enosXL6psHzp0KD4+Ps/knF9++SUhISFVLp+QkMCQIUM4ePDgM2nP43J3d6+2\nugICAoiPj1fZdv369XL5vgVBnnNf7fZRDe+zN9mE2ZENaGeaSx/LTBLztPhg2Xb++++/ctHhS/Xv\n3x99fX1GjBjBBx98gJubG1AyTX3x4sVcvXqVzp07M3ToUFavXs2ECRMIDAx8YTv8/h3epHEd80eW\naVzHHP8Ob9ZQiwTh5SKm8QuCIAhCLVGatis9/hYN6hoTuvv/lNGo4+LiyMzMfM4thIyMfKKu3CYh\nPoHsrALWrdtAv379nnezqtX8+fOfdxOEl4SmgZna7dZ6RSxzTlDZttwlAcsxPpj0nMCECRMAlGvO\nlyxZoiwXEBBQrr6hQ4cydOhQADp27Kiy70WOHl9Xz5A9A6fgG7aPA3GRKlP6pRIN+ts64d/hTerq\nGT7HVgrCi0t09gVBEAThJfdw2q60fCnNMOd46A7erfcf9ceuYv/+/XTu3Jn8/HwA9u3bVy79VkhI\nCCdOnCAlJYXly5dTr149ALKzs5k1axa5ubnk5+fj6+uLi4sLe/fuZd26ddSrVw9dXV2aN2+OXC4v\nl/qrY8eOjBo5mjqGdkRFXaSgMBsXx6EkJyeTmChj1MhpDPLoxrlzp4mJiUFLS4usrCwyMjJo3Lgx\nvXv3ZtKkScjlcnr16sXBgwfR0dHh3LlzbN68mcDAQHx8fMjMzEQmk7FgwQKMjIyYN28eADk5OeTk\n5CgDbAHIZDJmzZrF7du3cXZ2Vm6/du0afn5+aGhoYGBgwJIlS7h27RobNmwgNzeXOXPm8NFHH/HG\nG29w5swZWrRowdGjR/nrr7/o2rUrn3zyCWPGjMHX1xcjIyM+/PBDtLW1sbe3V55jw4YNHDp0iOLi\nYrp168b06dNr4ttEeAEZtn2LlG0fqZ3KX46mFEM3z2ffqBdMXT1D1vQYxe3cTA7FRZFRmIexth79\nbB2rfeq+INQ2Yhq/IAiCILzEKkrbpSlR0MIwnzNH9xMf0IWjhw+prDmtKP1WcnIyW7duVXb0Ae7e\nvcuwYcMIDg5m5syZrF27FoVCwfLly9m4cSNr1qwhLi4OUJ/6KzurgMTEDFLvFdHJ/QMsLRxIuXsN\nHZ06GBhY0NCqP78f+ZeEhESmTp3K3LlzMTQ0pFu3bkyaNIkDBw5a7TIsAAAgAElEQVRw69YtNDU1\n6dixI2fOnAHg2LFj9O3bl02bNuHq6kpwcDDz5s0jMDAQGxsbgoODCQ4OxsbGhpkzZ6rct7/++guZ\nTMb//d//4eHhoYxIHhAQwOzZs5W5tjdv3gyUTMNfv349Tk5OJCQkMHz4cHbu3MmhQ4fo168fO3fu\nZPfu3Srn2Lx5MwMGDCA4OBhLS0uVfdu2bWPnzp2EhISQnZ39VN8DwstLamqNodvgKpU1dBuM1KT+\nM27Ri8tK34hxLTvi7foG41p2FB19QagC0dkXBEEQhJfYo9J2vW6ezYl7dYi5eQuTgmT09R9E8S5N\nv+Xl5aWSfsvZ2bnceuC6dety6NAhRo4cybJly0hPTyctLQ0DAwPMzc3R0tKibdu2QEnqr2PHjjFm\nzBg+/PBDCgoK2P3zRYqKijE3awKAnq4JMnm+yjmyswvQ1amPubk5/v7+3Lt3j/Pnz5OVlUXbtm2V\nubL79OmjnLp86tQpevToQWRkpHLNvbOzs/LFA8DPP/+MkZERffv2VTlfTEwMbdq0AUqCmunq6gIl\nqchcXV2BknX8//zzDwD29vZoa2sDJem2mjZtip6eHjo6Ojg6OqKrq0txsWrU8Bs3bijPUTYmgK6u\nLl5eXowdO5a0tLQXNvWZUDMsvVaiVa/ZI8to1WuGpdfKGmqRIAi1hZjGLwiCIAgvqYrSdpVyNc7l\n+5sWmGrLcDeIQZ6dCjw6/ZaWlla5ejZt2kS9evX46quvuHLlCkuXLgVAQ+PBmIFCoVAeXzb1V0ZG\nPoF+JdHBJZIyYwyK8u1Nu1+Aj89c3n//Pf7991+k0pI/U4qKipTn6tSpE0uXLuXatWvY2NhgaGiI\nRCJRnh9Qdrpv3rzJ9u3b2bp1a7lzKRQKlfY/3FF/+LylHX0ATU1NlXKl7XzUOUrrT0xMZOPGjfzy\nyy8YGBhUmCJNeHVIjSyxmX+SlC3eJT/PZaf0a0oxdBuMpddKpEaWFVciCIKghhjZFwRBEISXVEVp\nu0ppaYCjUR5HUox4zTiLvOunAB47/VZaWhqNGjUC4OjRoxQVFWFiYkJWVhaZmZkUFRUREREBlE/9\ntejzLyguVtOz/x9FmYBbCoWCjIxMOnbsSEREBGFhYeTn53Pp0iVatmwJlHS6HRwcWL9+vTKwn7Oz\nM2fPngXg4sWLNG/enMLCQubOnUtAQAB6enrlzmtnZ0dkZCQAERERFBYWAtC8eXMuXLgAwPnz53Fy\nqlrqL3XKnqO0fWlpaZiZmWFgYEBUVBSJiYkvbCR0oeZIjSyxnrqDJstuYjlmNeZD/LEcs5omX8di\nPXWH6OgLgvBExMi+IAiCILykKkrbVdbr5tlkFmliIC3mXn42oI+pqaky/ZaDg4My/da4cePU1vHW\nW28xZ84cDh48yOjRo/n111/55ZdfmD59Ol5eXjRo0IDmzZsDJam/wsLCGDFiBHK5nC6dhpBwS33b\ncnLvI0HBgSOfoaWtj6mxDV27DGTWrFlIpVJyc3P54osvmDZtGg0aNFAe17t3b3x8fFiwYAEAY8eO\nZd68eYwdOxaFQsFnn33G4cOHuXnzJl988YXyuO+//x4DAwMAunbtyu7du/Hy8sLBwUEZo2DBggUs\nWrQIiUSCsbExgYGBREVFVXqf1Rk7diwfffQRR44coUWLFgC0bNkSAwMDRowYgZubGyNGjGDRokVs\n3Ljxic4h1C5SU2tMek553s0QBKGWkJSd9vaw8PBwRWl+zkcJDw+nKuWEF594lrWLeJ61h3iWtUd1\nPsv0Y2tICa56JHfLMatrvCNx+lQse3ZHVrm851AnOr3e+Nk1qJqJn83aQzzL2kM8y9qjOp7l/+qQ\nVF6y9hHT+AVBEAThJWXY9i3QrOIkveeUtsvUXM7xv5arbLt6/RD/xZ4qV1ZDQ4KTs1WldQ4ZMoSE\nhAR+/PFH5ZR7QRAEQRBUic6+IAiCILykXtS0XbK0JNKPrSF1XwCKKz+jp6tZ+UGAk4sVRsa6VT7P\npEmTlNHua9Lw4cOVa/FLff3112zYsKFc2bNnz+Lt7V1TTRMEQRAEJbFmXxAEQRBeYpZeKymIu1Bh\n+j2oubRdsswUUoJnkB2xRxk4MDVfil5OQ0y0MkkvUs2LHfnPXtIybiGXy3By7IbnkDn4+PhgYWHB\nP//8Q1JSEsuWLcPR0ZHFixdz4cIF7OzslAHtfHx86Nu3L+3bt2fWrFnk5uaSn5+Pr68vLi4u1X59\nt3MzORQXhWHr5ny1ZT1ffrZIue/w4cNs3ry52s8pCIIgCE9KdPYFQRAE4SX2oqTtkmWmEB/QRe1L\nh+RcDc7/MZ9MqQ35GJKTd59mdt3R0zfDxckT+5ZmrPvpUwzrfAaUpLxbv34927dvZ8+ePejo6BAR\nEcGuXbu4c+cOvXv3Vqn/7t27DBs2jF69enHmzBnWrl3LqlWrqu3a7uVlsyBsLwfjopApiqFOAXp7\nj9PBYQnuRtbMoBhLS0vS09OZOXMmGhoaGBgYsGTJEmUdmzZtIisri+nTS2IsjBkzhvnz53Pr1i02\nbNiAVCrFyckJHx8fQkJCOHHiBCkpKSxfvlwZPFAQBEEQHofo7AuCIAjCS640bZcsLYnsiL3Ic9LQ\nNDDF0M2zxqbup2zxrnB2QQO9Qr50TABuQduxrIvviI6OAQUFOcTc3ERiig7p6WnK8u3atQPAysqK\ny5cvExMTg6urKxoaGtSvXx8bGxuV+uvWrct3333H+vXrKSwsRF9fv9qu615eNp771xCblfpgo4EO\nxSYGFCfe4y9FMdfWHMe7rwcBAQHMnj0bV1dX1q9fz+bNm3F3dwegT58+zJgxg+nTp5Oenk5qaio2\nNjbMnTuX//u//0NbW5sPP/yQ8PBwAJKTk9mxYwcSySsZU0oQBEGoBqKzLwiCIAi1xPNK2yVLSyqZ\nVVAVl7bRwGEhmnranDkTzvbtW9HS0lJZe6+p+WCNv0KhQKFQoKHxIMxQcXGxSpWbNm2iXr16fPXV\nV1y5coWlS5c+3QWV4Ru2T7Wj/z/yVg2RRidSaG1Gzj//cdqziBs3buDq6gqAu7s7q1evVnb269ev\nj0QiISUlhdOnT9OrVy9iYmJISkpi/PjxAGRlZZGUlASAs7Oz6OgLgiAIT0V09gVBEARBeCrZEXtV\nlw88ilxGYdJV9Jq+hpWVFVpaWhw7dgy5XE5hYaHaQ+zs7Ni0aRMKhYKkpCQSExNV9qelpWFvbw/A\n0aNHlWv6n9bt3EwOxKlPGyhrYY3emetotGpIsakhR+/doL7iwUuIoqIilRcUAL169eLPP//k1KlT\nTJ48GYlEgpOTE+vXr1cpFxISgpaWVrVcgyAIgvDqEtH4BUEQBEF4KvKc+49Vvrgonzp16hAXF4eX\nlxfx8fF0796dzz//XG15BwcHWrRowfDhwwkKCsLBwUFl/1tvvcVPP/3E+++/j4uLC3fv3mX37t1P\nejlKh0rX6Kujo0WxhRFaZ64hc7RBpijGqIGVMhXg+fPncXJyUjmkd+/eHD9+nLi4OBwdHbGzs+PG\njRukppbMHFi5ciV37tx56nYLgiAIAoiRfUGoNc6ePcvWrVtZufJBxO1du3YRHR2Nl5fXY9X15Zdf\n0rx5c4YMGcLixYuxt7dn2LBh1d1kpdDQUFavXk1AQIByrW5VHDp0iL59+z6zdgmCUDWaBmYV7qun\nK2O5S4LKtsmDe2DScxzjxo1Tbnv33XfLHdujRw969OgBgJ+fX7n9ZQPgHThwQPl1z549q9z2R0kv\nyH3kflkrG3T2h1Pg0R6Aju++zTfffINEIsHY2JjAwECioqKU5Zs0aUJ8fDyvv/46AHp6esybN4+J\nEyeira1Nq1atsLR8toEUBUEQhFeH6OwLwksuIyOfqCu3uXQpgdR7OWRk5GP8GHmqHyUtLQ1PT89y\no1PV7fTp03z66aeP1dFPSEhg//79orMvCC8Aw7ZvkbLto6pN5deUYujm+ewbVQ1MdB4d6E9ub02u\nvbXys12TJnweHKxSxt3dXbluH2Dfvn0q+/v06UOfPn1Utg0ZMuRJmywIgiAISqKzL6i4e/cuQ4YM\nISQk5LGOW7VqFaampri5uXHkyBG8vb1xd3fn7NmzT92mmhhZfhllZxWwZ3ckkVduU1ys4F5qPImJ\nmQT6HcPJxQrPIQ866AkJCfj4+GBjY8O1a9do2bIlAQEBKvXt3buXdevWUa9ePXR1dWnevDmmpqb0\n69ePs2fPcvv2bT799FMAZDIZX375JY0aNVIef/bsWdauXYu2tjZJSUn07duXKVOmEBMTg5+fHxKJ\nRJmKKjMzk08//RR9fX1Gjx7NiRMniIyMxMjIiPT09HJpqIqKivDx8SExMREdHR2WLl2Kn58fly9f\nZvXq1YwbNw4fHx8yMzORyWQsWLAAR0fHmnkQgiAgNbXG0G0w2ed+rrSsodvgGssQ8LT62jqy8Gxo\nxVP5y5BKNOhnK37vCIIgCC8O0dkXAJTpmrQunUOedRdZWhJSU+vKD3xIy5YtadmyZbW1q6ZGll82\n2VkFfLvyL1LvlZ9iWlys4PLFZBITMpDJFMrtUVFRLF++HHNzc7p27UpmZiZGRkZASbTr5cuXs3v3\nboyMjNSOKqWkpDBt2jQ6dOjArl272LZtGz4+PiplIiMjOXbsGFKplP79+zNixAj8/f3x8/OjcePG\nbN26la1bt+Lh4UF0dDR//PEHpqamHD58mL59++Lo6IiXl1e5NFT//fcfdevW5euvv2b//v0cO3aM\n8ePHs3XrVqZPn87q1atxdXVl0qRJXLlyhcDAQLZs2VLNd10QhEex9FpJQdyFCtPvAWjVa4al18oK\n979orPSN6G/rRGjs5UrL9rd1op6+UQ20ShAEQRCqRnT2X3GyzBRSgmeQHbEH5DK086XIUq347xM7\nDN0GY+m1EqmRJadPnyYoKAgtLS2MjIxYsWIF2tra5ep7eN344sWLiYyMxNzcnBUrVvD9998THx9P\nQkICwcHBrFy5kr///hu5XI6XlxeDBg3Cx8cHLS0t0tPTWbVqVZVHll8le0Ii1Xb0y0q9l0tySgGt\nW5ekbmrUqBEWFhYAWFpakpWVpezsp6WlYWBggLm5OQBt27YtV5+FhQWLFy9m1apVZGZmqh05d3V1\nxcDAAIDmzZsTHx/P5cuX8fX1BaCwsBBnZ2cAbGxsMDU1VTm+ojRUUVFRdOzYEYCBAwcCqMwaiYyM\nZMqUknRjzs7OxMXFPfLeCEJ1SkhIwNvb+7FnRD2ptWvXUlBQwPTp02vkfFV1MjyKA0X9+Oi1OyVp\n+MpO6deUqvybAiWxOn766Se0tLQoKipi8uTJj1yWs3btWvbu3cvq1atp3LhxlduVk5ODh4cHv//+\n+xNdl3+HN7mSmqg2/V6pxnXM8e/w5hPVLwiCIAjPiujsv8JkmSnEB3RRPwojl5F97mcK4i5gM/8k\nGRkZLFu2DBsbG2bPns2pU6d44403Hll/eno6gwYNYsGCBXh7e3Py5EmgJB3Rtm3b+Pvvv0lMTGTr\n1q0UFhYyePBgevXqBYCxsTH+/v4q9VVlZPlVkJGRT+Tl2+W2a2sbUlSUp7Lt3t10DAyMAdW81VAy\nml9W2RRRD++DkijRr7/+OiNHjuTgwYP8+eef5cqUzX1dWoeenh6bN29WyRedkJCgNq2UlpaW2jRU\nFy9eLJdXuyyJRKLS5keVFYTqUjojKj3+1lPNiHochYWFuLq68tprrz3T8zwpDW09rKfuUN4beU4a\nmgamGLp5qkzdv3DhAhs3bmTDhg2YmJiQnZ3NxIkTMTIyUr7Ye9jJkyf56quvHqujXx3q6hmyZ+AU\nfMP2cSAuUmVKv1SigbuRNd/2f5e6eoY12i5BEARBqEylnf3w8PAqVVTVcsKLQ/fAPLQeMd0SoOhO\nDNdWjuGerRcffvghcrmclJQUrKysMDY2VpZLSkoiKysLhUJBWloa4eHhaGlpIZfLCQ8Px8zMjJMn\nT5Kfn4+pqSnh4eGEhoZy7tw5PD1LAjXl5uby+++/k5qaiq2trfJ7SiaTER4eTmpqKps2bSIwMJCc\nnBzs7Oxeye+7f6/lUFxcvjNuaGBBfn462Tn3MDSoS0FBNndTb3AvpWTkOzc3V3m/cnNziYyMVKZ4\nKi4uJjU1lRMnTqCjo8Nff/2FiYkJ4eHhyvt/8+ZN7O3t+fvvv/n5558pLi5Wuf/Xr1/n0qVLnD59\nGolEwj///EN6ejrW1tZs2LCB1q1bc/r0aYyMjKhXr55Ke1JTU4mJiUFHR4fo6Gh+//13jI2N2bVr\nF2+88QaGhoaEhoZiaWlJREQEt27dwt7ennv37hEeHo65uTkhISHI5XL+/fdf6tWrVyu/N2rjNb2M\nJLn30Tn+FdL//kRSLCftfzOibsxqjKxJDwq6fYJC34xbt26xceNGJBIJurq6TJkyBQ0NDYKCgpDJ\nZBQVFfHee+9hZ2enrDsnJ4dvv/2WvLw89PX1mTFjBr/++ispKSncvXsXX19fPv74Y5YuXcrZs2eJ\njo4mKyuLhIQEhg8fzunTp0lMTGTatGk0a9aMw4cPK38m27Vrx8CBA9m1axe5ubkkJyeTkpLCmDFj\naN26NefOneO3335DQ0ODJk2a4OXlxb179/juu+/Q0NBALpczdepU5QwhgFu3brFmzRoMDQ2xtLRU\n/s4vd16TJCBJedzKlSvp378/N27cUG4bOHAgQUFBpKam8t1337Fo0SLu3r3LypUr6devH1euXGHm\nzJlMnTqVS5cucebMGQDatWvHm2++yffff4+mpibZ2dlMnjyZFStWUFRUhL29PQUFBU/98zPByJ7B\nDo04m5FItrwIQ00tOhg3wExLj7h/riHmE9UO4vds7SGeZe0hnuWTq7Sz7+bmVmkl4eHhVSonvDhk\naUn8d/PPKpXVuvknG48V8OO69TRt2lS5/rrsMz99+jSmpqY0b96c8+fP4+bmhqamprJMREQEmpqa\n5OTkKAP5XblyBWtrayZPnqxyvhMnTuDg4KA8ViqV4ubmxty5cxk0aJDKyPKr+H2XnvovkFluu4aG\nJm6tR3Mp8uf/jXIrcG7liZ2dPfYt9dDX11feL319fZycnGjYsKHy+JkzZ7Js2TIaNGiAi4uL8hmX\n3v/Jkyfz5Zdf0qBBA8aMGYOvry95eXnKFFIymQwHBwd+/vlnYmNjGTt2LF27dqVBgwb4+vryxx9/\noKOjw9dff012drZKe8zNzWnWrBmdOnXi888/Z/Xq1co0VL169aJbt24sWLCA5cuXI5VK+fLLL9HS\n0mLdunUcOnQIHx8f5s2bR1BQEAqFQpk6sDYRv2dfDCUzokapnRElKZajFXMU/axYbOafJCgoCD8/\nP1xdXVm/fj2XLl3CwcEBc3NzfvjhB+Lj47l586bKc12+fDkDBw5k7NixbNy4kby8PKytrcnPz1fO\neNHR0aF169bcuXOH8+fPs337dn7++WeCg4PZs2cPISEhXL9+nU6dOvHPP/8oo7+PHDmSCRMmYG1t\nzb///svOnTs5ceIEO3bsYMSIESxevJjdu3cr42VAyYvcPn36MG3aNKKioigqKqJ169bK9m7evJnZ\ns2fTq1cvFi5cSEFBAZaWlhWet1R6ejpvvvmmSpo5e3t7Nm/ezKBBg7h58yaxsbGcPHmSgIAA3Nzc\nCA8Px9fXFz09PdavX8/evXsBGDZsGOPHj8fc3Jy6devyySefsHXrVtzc3Jg3bx6//fZbtf789Hno\ns/jZrD3Es6w9xLOsParjWb7KLwvENP5XVHbE3qqlSAKQy8jMSKN+/fpkZmZy9uxZ7O3tKz0sPz+f\nyMhInJycuHTpEsOGDePy5QdBjlxcXFi6dCkTJ06kqKiIpUuXKtd2q5OWlkajRo1QKBQcO3bslZ2q\nradffvp7KRNjGzq7T1Etr6dFw4YNVdYTq1tb/Pbbb/P222+X2166Nr5svmtAuSxD5fwmJixfvlxl\nW9OmTdm2bVu5cmXbUDZXtro0VNra2ixdurTc+couJSiNEyEIz1LKFu9HBqCDkhlRKVu8uXEjHldX\nV6Ak/drq1asZMWIES5Ys4bPPPqNPnz507dpV5dh//vlH2dEuzTsfHR2Ni4uL2nM5OTkhkUiwsLDA\n3t4eTU1N6tatS0REBFeuXCEuLo6xY8cCJbMGEhMTgQdxOaysrMjKyqowXkbnzp2ZPn06WVlZ9O3b\nlzZt2qic/8aNG8q63N3dOXHiRIXnLdvZl0gk5X6HKxQK5XKiyZMnM2LECJUXv6Wio6NxdXVFKpUq\nr+Xq1asAyvt048YN2rdvD/DCLnkQhJrycDwleJBFycvL6zm2rERSUhL37t2r8PdcdQgICGDs2LHY\n2Ng8s3MIwotIdPZfUfKc+xXuS8zTZm5UA+Xn9xrdY2jHFowcOZLGjRszYcIEVq1aRY8ePVRGZR5m\naWlJaGgogYGBmJub8/rrr6t09tu2bYu7uzvDhw9HoVAwatSoR7Z5+PDh+Pv7q4wsnzp1Sjmy/Kpw\ndLZi3y9RaqfyP0wiASdnqxpolSDUfrK0pJLAc1WQHf4LiuIHHeOioiI0NDSwtLQkMDCQoqIitm/f\nzsWLF1UC7Wlqaqp9kakuxgWg7PA+/LVCoUBLS4vu3bvj5+enckxYWJhK2dL61cXLgJK0nH/99Rff\nfPMNQ4cOVS69Kj1PaTyO0nZXdN6ymjRpQmRkJFZWD34/RUdH06xZMwDy8krij6Smlg+K93CMjtJ7\nW3ru0naVbntVXwwLwu3cTA7FRREVc5HYzFRu52Zi9QJmjAgLCyM3N/eZdvbnz5//zOoWhBdZrens\nJyQk4OHhUS5F26pVqzAxMSlXvuxbxMd525ecnMy0adNwd3dnzpw5QElueh8fH/Lz8zE3NycwMBAD\nAwOOHj3KmjVr0NbWZuDAgXh5eZGXl4ePjw+pqakUFBQwdepUldHSmqJpYKZ2ez1dGTvd/yu3/fUh\nb+DT88GI8eDBg1X2z5gxQ/m1u7s7oH7kt2w5gI8//piPP/5YZVvZUV54vJHlV4GxsS5OLlZcvphc\nadmGjXQxMtatgVaVPPfSZy8ItdHjzohqYmHIhQsXaNOmDefPn8fJyYnTp08THR3N+PHjadasGZ9/\n/rnKYU5OToSFheHi4sKOHTvQ0dF54vY6OjqybNky8vLy0NXVJSAggE8++URtWTs7O27cuEFqairm\n5uasXLmS4cOH8/fff2NjY0OvXr0wMTHh4MGDKp19Ozs7IiMj6dKli/J3dUXn1dV98Lto7NixLFy4\nkLZt22JmZkZ2djbLly9n1qxZAHz99dfMmDGDEydO8NtvvzFgwADlsS1btmTVqlXIZCXP4tKlS0ye\nPJmjR4+Wa1ffvn1VMncIwqvgXl42C8L2cjAuCpmiGI24u2jdT6LDziX0t3VSyRyRkJCAj48PNjY2\nXLt2jZYtWxIQEKBSX69evXjnnXc4ePAgtra2ODo6Kr/++uuvuX37NvPmzaOoqAiJREJAQAASiURt\nvadOnWLFihXo6upibm7OwoULWb16NVKplPr167Nx40blMjxTU1Pl7IPr16/j7+9PcHAwv/32Gxs3\nbkRTUxNHR0cWLFigbOvVq1f54osvlDOkVq9ejZGREUeOHMHX1xdra2vmzZtHRkYGcrmcBQsW4ODg\nQO/evXnnnXf4888/KSws5KeffkJPTw9fX1/i4+ORyWR4e3uXCyC6YcMGDh06RHFxMd26dVN5eVva\nls2bN6u0pVOnTvj5+SGRSDAwMGDJkiVkZmby6aefoq+vj5eXFwEBAY+853fu3GH+/PkUFRWhqanJ\n4sWLVWZPCUKpWtPZh5J/3IODg6tUtuxbxMd52zdv3jw6duyoMlLwww8/0LNnT0aNGsWePXsIDg5m\n0qRJ+Pv788svv2BiYsLEiRPp1asXERERODk5MXHiRBITE3n//fefS2ffsO1bpGz7qGp/uGpKMXTz\nrLycUGM8hziRmJDxyPR75nX1adteRIcWhOryuDOiPvJoyzfffINEIsHY2JjAwEDS09MJDAzkzz//\nRCKR4O3trVLPuHHjmD17NmPGjMHAwIBly5bx008/PVF7ra2tGTt2LKNHj0ZTU5NevXqpdLjL0tPT\nY968eUycOFEZL8PS0pLGjRuzcOFC9PX10dTUVPmjGmDKlCnMnTuXzZs3Y2NjQ1FRUZXO27p1az7+\n+GMmTJigTL03duxY2rVrx6VLl0hKSqJHjx60bt2aMWPGqCx3aNiwIcOHD8fLywuFQsGwYcNo0KCB\nSv2enp5MmzaNcePGiXW7wivlXl42nvvXqE0VKVMUExp7mSupiXgUaVCa/DYqKorly5djbm5O165d\nyczMVKbmhZLZMa1atWLixIl0796dPn36sGvXLrp3705mZiZBQUG8/fbbDBgwgIMHD7J69WpmzJih\ntt4tW7bg4+NDu3btOHz4MHK5nMGDB2NqakrPnj2Vnf2RI0eyatWqcteQk5PD8uXL2bNnDwYGBnzw\nwQeEhYXRoUMHABwcHEhJSSEnJweA33//nTVr1nDkyBEANm3aRJcuXRg2bBgxMTEEBATw008/IZfL\nadq0KRMnTuTjjz8mLCyM7OxsLCws+OKLL7h//z7jxo0jNDS0XJu2bduGhoYGPXv25N1338XQ0FCl\nLaX3s7Qts2fPVsa/2rp1K1u3bsXDw4Po6Gj++OMPTE1N8ff3r/Sev//++3Tq1Injx4/z3XffsXjx\n4qf63hFqp5e+s186RelWfDyp+TlqpyhV5S2ir68vhw4dUvsGsaxVq1Zx+PBh/v33X+W2uLg45UhH\nly5d+Oijjxg2bBhGRkaYmZWMoHfo0IHTp08zZMgQ5XHJycnUq1fvWd2aR5KaWmPoNpjscz9XWtbQ\nbbBKyiTh6T1tXm7DOjpM8+7MnpBI9u/fh6amLtZWJfnrNTQkOLlYsfLbqfTq9111NvuJHTt2jC5d\nuqCtrf1Yx5UG2iv7c1MRd3f3GhnBq2ydY+kSkxYtWjzztjR1My4AACAASURBVAg163FnRFk2sSN4\nomqsCUNDQz7//PMKO6B16tRhzZo1KtsenhFVmi++7M9F2ZlPZb8ePXo0o0ePrrC+Fi1aKP+dUxcv\nw9HRkV27dqlta+n+0kB8Zak778O6d+9O9+7dy213dXVl3bp1QMnI3q+//gqg8u+xuvrLzgozMjJS\nKf/wSxVBqK18w/ap7eiXFZuVyu8J9xlbr2QZTaNGjZRZNiwtLcnKylLp7ENJPAyJRIK5uTmtWrUC\nwMzMjKysLCIjI5Wzctzd3fn2228rrLdfv34sXLgQDw8PBg4cqJLdo+y5Kmx7bCy2trYYGBgAJTE5\noqOjlZ19KPkdeOnSJZo1a4a2trbK39oXLlzg/v37yt9bpUuGoCSzBzyIZXLx4kXCw8OJiIgAoKCg\ngMLCQpW/ZXR1dfHy8kIqlZKWlkZ6erqys1/alpMnT9KmTRtlWy5fvqyMUVVYWIizc8nfbzY2Npia\nmiqPfdQ9v3DhAjdv3mTNmjXI5XJlf0MQHvbSdvYfnqIkSc9BJyddZYpSac7bqrxFrKqyP8ClWrRo\nwZ9//omTkxMnT54kNTUVMzMzcnJyiI2NpUGDBpw9e1YlSNCIESO4/f/snXdYFNf3h99dehEQFBEV\ne0RB0WjUqFhREqMGKyLFWGKJiDEaC4hGjTWWKKjxZ4kKWIjBgjVGsUWxF1BjBwEVEUF6W/b3x353\nZNmlaIwt8z5Pnqwzd+7cmVl27jn3nM95/JhffvnlH9+LV8XSYzm5sZdKFZvSqVIPSw9R+OxdxLiC\nHh6Dm9PTxY5rUY/Jzs7HwEAH+8ZWmJjqE/Bu2PkAbNiwgdatW7+0sS8i8i4hRkSJiIi8yzzOSmN/\nbLTadrmhHpKcfJVtdx/FoWOiMJi1tLRU28vVNYGKtin6WanboTymqIaGpn5dXFxwdHTkzz//ZPTo\n0SxbtkztXErtDaUeCCCk7WjS7Cie7tStWzeWL19OxYoVcXZ2Vuvb399fTWxU03Xp6OgwatQoevTo\nodYWICEhgQ0bNrBjxw6MjIw0tuvWrRvBwcGkpKQIYzEwMGDTpk0q1xcfH6+mzVLaPdfR0WHZsmWl\nameJiABI3/YAXgVliNKemCgK5C/C6aXPMtAOPsahOSvo2PsLJvlOBRC8iL/88gsNGzbU6EX8J4wc\nOZJ79+7h4eFBUlKS8MM3f/58fH198fb2VilxBrB161ZWrVrF999/r/FH9U2gbWJJDb8TGLfsD1rF\n/D5a2hi37E8NvxNom4g/JK+L589zOHUyhlMnYsjMyOP58xyV/adOnRLCU7/55hvy8vKEfTKZjE6d\nOpGbmwvA2bNn8fb2ZtOmNdyLOUmHTrXZ9ttSvh4xWCWUq7Q+QaH+7erqiqenJ8OGDSMtLY2MjAx8\nfHwYPHgwHh4egtL1X3/9Rd++fRkwYIDgJOvWrRs//vgjq1atIjExkeHDhzN48GCGDh3Kw4cP2blz\nJ5cvX+brr78mLy+PefPm4ebmRp8+ffjtN/XIkl27dtGzZ0+GDx9OXFyccO2+vr54enri5uYm1NfW\nhKbrTU9PZ9iwYXh6euLq6sq1a9dUjgkICMDPz4+hQ4fSs2dPjh8/Dihy8VxdXenfvz+BgYEqxxQU\nFDBhwgQ8PDzo06cPERERwr7t27fj6elJv379SEhI4MyZM4wcORJPT0+io6M19qvpOYi8WygjosqD\nGBElIiLypjn4vwWw4sjNjZGkZyNJyVBsyMpFEptEUqXXo+nTuHFjIbJOqU9SEitWrEBbWxtXV1e6\nd+/O3bt3kUgkgjFfFGNjY5KSkoAXpdNq1apFbGwsGRmKazl79qza+Zo2bUpCQgJHjx5VM/YdHBwE\nfY87d+6Umibl4ODA4cOHAYVY6JIlS1T2p6SkYG5ujpGREdeuXSMhIYH8fFWnStOmTbl7967KWGxt\nbYV5xt69e0ud05Q2NuV1nD59WmN6gYgIvKcr+yWFKBWaG5Pj7ghADpBeSxEWUx4vImj2IJYHExMT\n4Qfg3r17REZGAorQImXJscWLF1OtWjWio6OxsLCgatWqNGzYEJlMxrNnz7CwsCj3+V4n2iaWWH+z\nVaEyfXEXcbejqVHfHuPmLuJE9TWSkZ7Lzt+jiY56TGGhnKysZ6SkZDNv1mHsm1jh0sce4wp6PH/+\nnEWLFlGjRg0mTZrEyZMn6dy5M6Dw6n766aecPn2ajh07cvjwYZydnYmJiQEUhnhBQQHbtm3jypUr\nQghraX2Cogyfm5sbLi4unD59mqSkJA4cOKCW07Z+/XpmzpzJ1q1bMTU15ZtvvmHgwIEUFBTQvn17\n2rdvj6+vr8YcsuXLl7NmzRrkcjnVqlVj6tSp5OTk4OTkRP/+/YWxyOVyli5dyu+//46JiYkQphwe\nHl6uvLmSrregoIAqVaowd+5cobZ5cRITE1m/fj03b95k8uTJQo5w8Vy8oudp164dvXv3Ji4ujnHj\nxgmh05UqVSIoKIjg4GCCgoLo1KkTt27d4uDBg+jq6nL27Fm1fjU9h+JhlCKaiYmJEb4bhYWFNGvW\njMmTJ5cYSVJSKkZ5UkEsPZaTdf8Si0+n8ShHh0K5hCE1n2JnkkOhHDY9sODPpxU5VaTElSZhVxER\nEZHXTWpuCTo+WlJye7VAb/8lkAPIye3aBJnh64m28/Hxwc/Pj9DQUHR0dJg7d66a0avE2tqaIUOG\nYGJigomJCUOGDMHIyIjJkyerhaJ37dqVkSNHcvXqVSHE3tDQkEmTJjF8+HCkUinNmzcX9imRSCR8\n9NFHPH36VE20zsPDg6lTpzJo0CAKCwtL1e36/PPPiYyMZODAgchkMhXxPVCIhRoZGTFw4ECaN2/O\nwIEDmTlzpkrEsEQioVmzZty4cUMYi5+fH/7+/qxZswY9PT0WL14sOC/Ki7e3N76+vuzduxeJRMK8\nefNe6niR/w7vnbFfUoiSJvbHRpOYlcb2X4Pw8PDA1dWV5OTkEr2ImjyI5SE0NBSZTIabmxthYWGC\nITV8+HAWLFiAgYEBERERDBkyhN27d5OQkICfnx9Pnz4lKytLJT/nbaFd0RqzLqO5a3YBM1HM6LWS\nkZ7LiuV/aRTTKyyUc/XyIxLinzPGpy3m5uZMmzYNmUxGXFycSg4aKFbRjxw5QseOHTl58iRjx44V\nvNJ37twRwtIcHBwEQayy+uzSpQs//PADMTExdO/enbp162rMaXv27Bl6enrCy3j16tVCH8r8urJy\nyPT0FA6NgQMHoqOjQ0pKisr+lJQUjIyMBOeXsn73pUuXysybU6Lpetu1a8fPP/9cYm1zQFDYbdCg\nAYmJiYDmXDwlJiYmREVFsW3bNqRSqco+ZVWCJk2acOLECTp16kSDBg2E8WrqV9NzECkbmUzG2LFj\n8ff3p2XLlsjlcn788UdWrFihVunjdaBtYsklhykY31/LQtPzxKZLWXbHkiVN4tn+yILqHzVGkpmu\nEhGlSdhVRERE5HVjpmdY4r5Cq4rkDHJU2Waqa0D16tVV9IM0aQkpdUKK7y/6WamzURRNbatXr65W\n0alt27acPHkSgF69XlQKqFatmqDZATBmzBhAs75IcYYMGaKijVJUw0OT8F/RayzqlC1emaAoWlpa\nGsuVFmfGjBkq/65bt66wGKjEzMxM5X6V556X59wiIu+dsV9SiBIowvj1Q1TLsa2vaIdtOb2ImjyI\nRUlMTGTixIkkJSWRnZ1NdHQ0M2bMoEuXLvj4+LBjxw5q1KghlPsYMGAAQ4cORSKRMGLECMzNzRk4\ncCB+fn4MGjSInJwcpk+fLuQ2iXyY7AyLLlU1HyD5aRY7w6L5dZMv//d//0fdunU11qhu06YNCxcu\n5ObNm9SoUUNFQ6JoXWl4UVva17f0Pj/99FO2b99OREQEU6ZMYdKkSRpz2lJSUko0VpR5ZmXlkJ09\ne5bIyEiCgoLQ0dHRmDNX9BqUKS5l5c0VRdP1WlpasmvXLs6cOaOxtjmo1+IuKxdvz549PH/+nM2b\nN5Oamkq/fv2EfUWjhJSflYZ+Sf1qeg7FHTMiL1BGI/11IYoaxnI+rq9IlZJIJHz//ffC9ygkJITw\n8HCkUilOTk4MHTr0RR//S8V4/PixIJAEilSQZcuWoaOjg4mJCT///LOKY8nF1YNe/QehlZWM9rEt\nZC7YjKXnFMbadsO0Wl02FitBqUnYVUREROR141zTjhlnwkucJxdFWyLls5p2b2BUIiIib5P3ztgv\nKURJbmZE1nc91bYb1apKb4fO5fYiavIgKqlSpUqJpf1CQkLUtmnyPOrr67N48WKNfYh8eDx/nkP0\n1cflaht99THpaelUrVqVtLQ0zpw5Q4MGDVTa6OrqYmtry7p16/jss89U9tWuXZu9e/cCcPHiRSE3\nPyMjo9Q+g4OD6dChA7169UIul3Pjxg0hF6xZs2bcuXOHEydOMGTIEGQyGYmJiVhaWjJq1Ch++ukn\nlb6Uxw0aNIjTp0/z9OlTevbsiUQiQSaTkZKSgpWVFTo6Ohw+fBiZTKayQm9mZkZ6ejppaWkYGBhw\n8eJFmjZtKuTN9ejRg+TkZDZu3Mh3332n8T5qut5Tp06Rn59Phw4dNNY2B0U0z9dff83ff/+NtbV1\nmbl4KSkpVK9eHalUyqFDh1S0EM6fP0+TJk24fPkyderUUTlPSf1qeg6isa9OQdoTngSNJePiTpAV\ncP2RKdYyKfcm1sa4eW8sPZaj/79V9bi4OA4cOMCWLVsAcHNzU/m7edXUF0FESc+anfckfOnqiVmX\n0SWOWZOwq4iIiMjrxsrQhM9r2hMec7XMtp/XtKeKoZgqJiLyofPeGfulhShpwlTX4F8aiYhI2Vz7\nX46+JjIykzgZ+UIy3862Bx06fIGbmxu1atVi+PDhBAQE0KlTJ5WV8q5duzJlyhS1etft27fn999/\nx8PDA1tbW6HUzKBBg0rt08bGhnHjxlGhQgV0dXWZN28e+vr6GnPaZsyYIZSw+vzzz9VyykvKIWvZ\nsiWDBg1i9erVrFmzBg8PD5ycnOjYsSM//PADc+fOBRSr+t7e3nh4eFCtWjXq168vnKu0vLmiaLre\nxYsXExAQwNq1azXWNgeFQTZq1CgSEhLw9fUtMRdPGRbYrVs3Ro8ezeXLl+nbty9WVlaC2F5ycjLD\nhw8nLS2N5cuXExsbK5ynpH6HDh2q9hxEVClIe0LcHEeVCiISoBBAVkDG2d/Ijb0kCItGRUURGxuL\nl5cXoKjPnJCQIBz7qqkvSkJCQrh27dpbraoiIiIiUpTZrXsRlZxQavm9WhUsmN26V4n7RUREPhwk\npSnBX7hwQV5SLeBi7UqsGfy6eZyVRuvQ+eUOUTozYIrouXwJ3uSz/C9w+I/bHNx/s9ztnbs3oEvX\n+q/t/OLzLB8lCba9S4jPEh6uHEjGWdUKDhdTDdnz2JTpto+Ebbof9yXvsx+JjY3lxIkTaukryued\nk5ODVCoVQvsbN25MVFQUXbp0UUkFsbe3V6lpD/Dbb79x4MABVq5cqVb2SZPQX1hYGLdv32by5Mni\ns/zAEJ/nh8OH8iyfZmfgH7mb/bHRKvNlbYlUrTz1h8qH8ixFXs+z/F8fkrJbfni8dyv7YoiSyPuE\ngaFO2Y2Ktjd4ufYiIv8VClIeknFhh9r2pqZZ/BprwdlnhrQ0z6JQDit2nKBqVigDvYayaNEisrOz\n0dfXZ86cOUycOFE49lVTX+Li4ti6dSvBwcFqhr6IiIjI26aSgTGrOg3icVYaB2Ov8TwvG1NdAz6r\naSfOi0VE/mO8d8Y+iCFKIu8Pdo2t2L3jWomh/EWRSiXYN7Z6A6MSKc7YsWPf9hBEyiDj4i6QqZdE\nlUpgZsOHBN6zZEu8OdoSOU3NsvmquTnm1tZ4eXnh7u6OlpYWTk5OQqg+vHrqy2+//UZqaiojRowQ\n+lq3bh0LFizg1q1bZGRk4OnpSefOnenevbuasGu/fv3EFScREZF/HStDEwY3/PRtD0NEROQt8l4a\n+5UMjNn5xej/fIiSyLuPqak+9k2suHr5UZlt7ZtYYWKqX2Y7EZH/IrLMZyXuM9eVqYTxA8izFaUQ\n3d3dcXd3V9lX1LmzcuUL3QylDsa4ceOEqiqAmsDrd999p1Eg0t/fX+P4igu7vkxpVxERERERERGR\nV+W9NPZBDFESeX9w6WNPQvzzUsvvWVQyxKWP/RsclYjI+4WWkXnZjVTaV/yXRiLyT5k/fz7Xrl0T\noh1sbGwwNTUVBC7Ly5w5c7C3t+fLL7/8V8bp4+PDokWLVMoulpejR4+SmJiIq6vrvzAyERERERGR\n8vHeF3hXhij5OHRmcMNPRUNf5J3DuIIeY3za0qRpVaRSVW0QqVRCk6ZVGePTFuMKYu6viEhJGH/8\nJWiV0z+tpY1xc5d/d0Air8yUKVMICgpixIgRdO/enaCgoJc29N8Ey5cvfyVDH6Bjx46ioV8K8fHx\naqKX5SEsLIxDhw79CyNSZ/RoRTlNT09Pbt26pbLvzJkzGqu6iIiIiLxrvLcr+yIi7xPGFfTwGNyc\n589zuBb1mOzsfAwMdLBvLIbuv05WrlxJTk6OEGJdWFhI7969WbBgAba2tm9kDBkZGVy+fJl27dr9\nI5X/v/76i4CAAORyObm5ubRu3fq15HkvWbIEW1tb7t69qzY2T09P/P39+eijj16p75Kud/To0axa\ntUrjMX///Td6enrUrl271L61K1qTZ9uD/9v9F951k0pta9y8N9pmVYV/R0REcPDgQebPn1/OKykf\nDx8+5OnTpzRp0uS19lsaERERnDp1SiiH+T5RkPKQjIu7kGU+Q8vIHFmmuoPzp59+4vLly8hkMry8\nvOjevbvK/h07drB+/XqsrKzQ0dHB3t6evLw8/P39SUhIIC8vj/Hjx5Odnc2hQ4eEEpZTp07ls88+\no0OHDgDExsYybdo0qlatypUrVxg0aBA3btzgypUreHl54ebmRvv27Tl06BCRkZEEBASgp6eHpaUl\nP/30E9raL6ZP165dY+HChejo6FC1alXmzJnDrl27iI2NZeLEiaxfv56DBw8CipKdw4YN49ixY6X2\n+aGijMZ8EBdHck4mj7PSsHqJRZpXcRC8KiX9ZomIiIi8T3z4bxYRkXcIU1N92rSr9baH8cGhnEBm\nt6jN9snz6dqnF41r1eP333/HwcHhpQz95ORk5s6di5GREXXr1mXw4MEvNZZr167x119/0a5du5e9\nDIGEhARmz57NunXrqFatGnl5eQwdOpTffvuN/v37v3K/f//9N9euXeO7774jICDglft5WUqbNB86\ndAh7e/syjX0A25GrGP/UkfzEko19nSr1sPRY/krjfFkiIyPJysp6o8Z+p06d+O2337h69eobPe8/\noSDtCU+CxpJxcaeKyGLSUzMyKjSiIG0I2iaWREZGkpycTEhICDk5OfTt2xcnJydhdb2wsJCff/6Z\nnTt3YmxsjIuLInpj9+7dGBsbExwczKNHjxgyZAjh4eEsWLCAvLw8tLS0uHLlCrNnz1YZ1/Xr1wkM\nDOTp06e4uLhw+PBhMjIymDhxIm5ubkK74OBg/Pz8aNasGQcOHOD58+dYWFgAIJfLWbduHaGhoVSp\nUoXp06ezb98+4djY2Fj27NlDaGgooDBWnZ2dS+3zQ+RpdgbTIndxIPYaBfJCJKmZ6GWm0jp0vorO\n0s2bN5k1axZSqRQjIyPmz5+PmZmZ0I/SoVi/fn02bdqElpYW169fZ9SoUZw4cYIbN24wadIknJyc\ncHR0xNnZmaioKKpUqcKiRYvIzc1lypQppKWlUVBQwLRp07Czs+PHH38kOjqatLQ0hg8fTp8+fVRK\naG7fvp0bN26QnZ3NsmXLVK7tjz/+YP369Whra2Nvb8+UKVPe6L0VERERKQ3R2BcREXlvKT6BBND6\nuBq9J35D56/diVm3mS0hmwE0TiJv3rxJSEgIy5crjMNWrVqxa9cuBgwYIHyOj4/n+++/x9DQEA8P\nDypUqMCSJUvQ1tamatWqzJ49WyXUd9asWWRkZFCrVi0Abt26xciRI4mJicHPz4/27duXOTncsmUL\nnp6eVKtWDQBdXV08PT0JDAykf//+dO3alQEDBnD06FHy8vL49ddfMTAwwN/fn7i4OAoKCvDx8eHT\nT1VVmIOCglSMmJIICwvjwoULJCcnExMTw7Bhw+jfvz+7d+8mODgYqVRK/fr11Yynkq5XOWneuXMn\nwcHB6OjoYGtry8CBA9m6dSvm5uZYWFiQnZ3N0qVL0dbWpkqVKsybN489e/Zw/Phxnjx5woQJE5hz\nswGB3ZqxZ/8f7HlYAalEjo1BHt4fpWDcvDeWHsvRNrHk5s2bTJ48GVNTU2xsbITxhYSEEB4ejlQq\nxcnJiaFDh6qM/86dO8yaNQuJRCJ8T9LS0pgyZQo1atTg5s2bNGzYkAkTJhAYGCh8D6pXr17i9wvg\n/v37ODs74+3tTUxMDAsXLkQikdCsWTMmT56s8bwGBgZ8//33JCUlkZeXx9ixY2nfvj0eHh5s2rSJ\nRYsWlfks3zYFaU+Im+NIfuId9Z2FMvIe3SRujiM1/E5w6dIlLl68iKenJwAymYykpCTh7yA5ORlT\nU1MqVlToMTRr1gyA6Oho4btetWpVJBIJubm5ODo6cuLECUxNTWnVqpXaynnNmjUxNTVFIpFQuXJl\nLC0t0dfXJyMjQ6XdZ599hr+/P7169eKLL75QMcqfPXuGjo6OUMmhdevWXLlyhXr16gEK51+zZs2E\nczdr1oybN2+W2ueHxtPsDFz2rtJYQalAXkh4zFWikhPY+cVo5syZw6RJk3BwcGDdunVs2rSpxHD5\nGzducODAAc6dO8fEiRM5fPgwV65cISgoCCcnJ548eUKPHj2YNm0aY8eO5fjx4/z99984ODgwYsQI\noqKimDdvHoGBgRw9epQ///yTM2fOEBsbq3auSpUqERQURHBwMEFBQXTq1AmAzMxMVq1axbZt29DV\n1WXcuHFifXcREZF3ivc+Z19EROS/iXICuScmSqUih8yuBiSncfiXTTz9qDJyQ0WosHISGRQUxCef\nfMKmTZvKfa4bN26waNEiOnXqxI8//sjKlSvZtGkTFhYWHDhwQKXtsGHD6N69u5Cvm5qayurVq5k2\nbRpbt24VJoebNm0SViKLq7Pfu3ePRo0aqWyrVKkSKSkpFBYWIpPJqFu3LiEhIVSvXp3IyEjCw8Op\nXLkyQUFBrFixgrlz56pdR2RkJC1atCjXNd+6dYsVK1awYsUKgoODAcjOzmbt2rVs3bqVe/fucfPm\nTbXjil9vUdatW0dAQABbtmzB3t6emjVr4ujoyHfffUeTJk2YMWMGS5cuJTg4GFNTU8LDwwF49OgR\nISEhCoNKqo31N1sx7jOPFT98y7rpo0g0aUTB6CNYf7MVbRNFebyVK1fi7e3Nxo0bkUoVr7q4uDgO\nHDjAli1bCAkJ4Y8//uDhw4cqY5w9ezazZs1i48aNtG3bVjDWlRER27dv59ixY2hra9O7d2+8vLzo\n0qVLid+vq1evsmDBArZu3Sqo8m/cuJGZM2eydetWkpOThUiO4ue9desWKSkphISEsG7dOp4/fw7A\nxx9/zPnz58v1HN82T4J9NBv6RchPvMOTYB90dHQYMGAAQUFBBAUFceDAAcHQB8UquvJZgmKlH0Ai\nkSCXvyhvWlBQgEQi4csvv+TAgQMcPnyYHj16qJ23qPGvpaWlcp6i9O3blw0bNmBiYiI4spQUP3de\nXp7KGIvvz8/PRyqVltrnh4Z/5O5SSyUDxKQn4x+5m7t37+Lg4AAonK/Xr18v8RhbW1t0dXWpXLky\ntWrVwtDQEAsLC9LT0wEwNDSkadOmADRt2pT79+8THR1Nq1atAGjcuDGxsbGYmZlRq1YtRo8eTWRk\npBAxUhTlMU2aNOH+/fvC9jt37vDw4UOGDRuGp6cnsbGxar8pIiIiIm8T0dgXERF5LylxAimRkNfe\nDumDpzxpXBX/yN0ALzWJLE6NGjWoWLEiT58+JTY2lrFjx+Lp6cmZM2dITEws9diPP/4YgCpVqpCe\nnl6uyaFEIkEmk2nsTyJRiDwqjXYrKyvS09O5dOkShw8fxtPTk3HjxpGbm0teXp7Ksenp6SohsaX1\n37RpU7S0tIT+AUxNTfnmm2/w8PDg7t27pKamlnm9RenRowdjxoxhw4YNdOjQQaXmfWpqKhKJhKpV\nFbn2rVq14saNG4BiUq4clxLzqjWZuu0C40KvE5ucRbpMddX27t27wliUE/WoqChiY2Px8vLCy8uL\nzMxMEhISVI67evUq/v7+eHp6snv3bpKTFd8xGxsbKleujFQqxdLSUu3aSvp+NWrUCAMDA4yMjIS2\njx49ElJLFi5cSLVq1TSet06dOmRmZvL9998TGRnJF198AYC+vj75+fklfkfeFQpSHpJxYUe52mZc\n2IF93epERERQWFhIdnY2P/74o0obCwsLUlJSyMjIIC8vj0uXLgGK74cy3Do+Ph49PT2MjIxo3Lgx\n8fHxXL9+XfguvAqBgYHo6ekxcOBAnJ2duXv3rrDP3NwcmUzG48ePATh37hz29i8qq9jZ2XHp0iUK\nCgrIz88nKiqKhg0bltrnh8TjrDT2x0aXq+3+2GgKizhulY6RkijqrNGkd6B0BoHCgSORSNScL8o2\na9euxdvbm9jYWEaNGqXWV9Hfn6KflboRSgfVzp076dmzZ1mXKiJCfHw8DRo04PLlyyrb+/bt+0qp\nIOPHjycnJ+elzq9JAyMrK4vp06fTp08fBg0ahLu7O9HRir/h48ePs3nzZrVjOnfuTGZm5kuPWeTN\nIIbxi4iIvHeUNYGUmxkiN9YHbS32x0aTmJWmsl85iUxMTOTChQv069ePwsJCMjMz1QxkUEzolP+3\ntLRUq5teGtra2kRERAg5u8rJ4bp160o8pk6dOkRHR6uswiclJVG5cmVholl8JVJHR4dRo0axbdu2\nEkX2ik5Szc3NhZViJc+ePaNy5crCuIuSl5fHrFmzONwG/AAAIABJREFU2LVrF5UrV2bkyJElXm9x\nsrKyiIuLY+TIkfTs2ZODBw/SpUsXlXugaQVUOd4TJ05w5swZYZW3PGNRTu7hxYReR0eHjh07MmvW\nLI1jBzAwMGDTpk0q9yo+Pl7lfiv7L4miRoqm+1HccVHSeQFCQ0O5ePEiO3bsICIiQhCcex/IuLhL\nJUe/VGQF2Bbep1mzZri6uiKXy9WEHrW0tBgzZgyDBg2ievXqNGjQAICePXty7tw5PD09KSgoYObM\nmcIxbdq0EVb6XxUrKysGDx6MiYkJZmZmfP311yr7hw8fzvjx49HW1qZmzZp89tlnhIWFAQonUe/e\nvfH09EQul+Pm5oaVlVWZfX4oHCySYlUWBfJCTKpZcenSJZo1a6bmOHlZcnJyiI6Oxt7ensuXL9Ov\nXz/y8vI4c+YMTZs25fLly9SvX5/4+HiOHDmCl5cX7u7uzJkzR62v8+fP06RJEy5fvkydOnWE7bVr\n1+bu3bskJydjYWHB8uXLcXV1FdI6RESKoxQqTY17QLVKpoT/vk2IQImNjSUtLa2MHjSzdOnS1zK+\nefPmYWNjI7wnL1y4wPjx49m3bx/t27d/LecQebOIxr6IiMh7x8tOIA/EXqN+/fpcunSJxrWqcHxL\nALW0Mli+cA5mFcwE8SUXFxc2btxIt27dNPZlamoKKEI369WrJ4RsFxUAlEqlFBSUbOCUZ3Lo5ubG\n4MGD6dy5MzY2NuTn5xMcHFyqWKCDgwOHDx8GFKvkS5YsEaoSKDE2Nub58+eYmpryySefMGXKFIYM\nGYKBgQHnz5+nQoUKJa78Z2ZmoqWlReXKlXn06BHR0dHk5+eXOJ6iGBoaUq1aNZYuXYq3tzdDhgxh\n7dq1PHnyRIhiUOZOP3z4EGtra86ePUvz5s01rl6XZyy1a9cmOjoaR0dHYdXXzs6ORYsWkZ2djb6+\nPnPmzGHixIkqEQa2trYcP36cDh06sHfvXszNzalRo4bG65JIJMKzVn6/ymOkVKtWjStXruDg4ICv\nry/Dhg3TeF4TExPu3LnDl19+iYODA+7u7oDCiNHW1lZzQLxryDKflbrfyVI1OkKWmcLEib6lHjNg\nwAAGDBigtl2TE6SwsJBz585pNN5q1qwpOOBMTEyEcm5FPx8/fhyAfv360a9fvxLH1LBhQzXHhFIY\nEBAiSYpSVp8fCqm5WSXukz7LQD/khPDvvE52fPpVP5YsWYJEIsHU1PQfObfMzMzYvXs3c+fOpXLl\nyrRr144WLVrg6+uLl5cXcrmc6dOnY2lpyaVLl9ixYwfXr19XcR6GhYWRlJREcnIyw4cPJy0tjeXL\nlwt5/QYGBvj6+vL1118TGxtLhw4dsLS0LHNs5a188uzZM8aPH8+cOXOoXr36K9+Lt8U/qUjzqpw5\nc4aQkJCXFtdVcuvWLWbPnv1STv3yUFyoNCVHm3pYcCx8K19VuUdVrwD27t1L27ZthRV6TTo56enp\n+Pj4kJOTQ4cOHQgNDeXIkSN07tyZ8PBwUlNTmTJlCjKZDGtraxYsWMDt27eZOXMm2traSKVSNZFJ\nJRkZGZw6dUrFYdq8eXP27duHjo4OYWFh3L59G3d3dxUtI5F3G9HYFxERee8obQKpied52Uz5djQz\nvhtOYUYSRloyOlZKw0ZWgcyUR/Tp+DEtu/SkWrVqDB48mB9++IFvv/0WNzc3mjdvzt27d4mPjycj\nIwNdXV0GDBiAVCqlZcuWtGvXDg8PD0G8zdramsuXL6OlpUV4eDjGxsa0bNlSGEtoaCh6enp06dKF\nihUr0qlTJ2Fy+Oeff3L48GHmzZvHokWLcHV1xcTEhISEBHr37o2LiwsLFiwgMzMTmUyGr68vf/31\nF3p6ekyfPh1DQ0OuX7/OrFmzSE5OZsyYMejp6XH27Fk2bdpE48aNcXd3p2LFihQUFNCzZ0+GDBnC\nw4cPyc7OxtraWliRVFJQUMCzZ88YO3YseXl5dO3alZYtWzJ8+HDmzZvHzp07+fzzz4WJRoMGDUhI\nSODIkSPk5uYCCOkLRkZGuLq6UqFCBSQSCXXq1OHx48eMHz+eOnXqoK2tjbe3N0ZGRmRlZXHlyhWk\nUqnQT0ZGBg8ePMDHx4eCggK6d+9Os2bNVMaijMIYPXo0U6dOZdOmTdSoUYP8/Hysra2F1TstLS2c\nnJxUDH0APz8//P39WbNmDXp6eixevFhNsE2JUlzP3NycadOmMXPmTBUj5dq1axqP8/LyEsoANm3a\nlLp162o8r0QiYcmSJWzbtg0tLS2GDRsGwKVLl8qtvfA20TIyf8n2FV/buR88eMC4cePo0aNHic6a\nf4vz58/z66+/anQy/Ncw0zPUuF1uZkTWd+rh7rXr1OGHUoyssWPHCp+V6TkfffSRYJgV/Qzg66vq\nPDI2NhYEWZU8zkqj6YgBmMfF8XBuAJejowCF0RgWFsaIESPUwp2trKyE83fr1o1u3brh6enJqFGj\n/lEUSXHu3LlDQEAAJiblL08o8u5RklCplkTOR8Y5nP5zL5/EXebPm7aMHfetUKpTqZNjYmKCu7s7\nN2/e5OzZs9StW5dp06YJmjJFWbp0KV999RVdunRh4cKFREdHk5GRgb+/P40aNWLZsmWEh4cLIpNF\niYuLo1atWmrpM8r3alFu3LhBREQEFStW1CjWK/LuIBr7IiIi7x0lTSCVyM2MyPnqxYvMIj8Hvd9G\nM9vmxYt21yNT6hjlMaB6IpCATkYmE3eeQNvEklmzZuHj4yOoQNetWxdQqIEvWLBAeGGam5ujo6PD\ntWvXWLp0KRYWFrRv3559+/bh7+/PzJkzcXJyYsaMGZiamuLr68vYsWPZu3cvAP379+err74SJoeO\njo7Mnz+fwsJC7O3tMTIyIjw8HCcnJ2F1GmDy5MkcOXKEypUrc+zYMZ49e8bgwYMJDw/nwYMH+Pv7\ns2HDBk6fPk3Hjh05fPgwzs7OyOVyEhISCAoKYvv27dy5c4dffvmFfv36cfz4cfLz89mxY4ewcqo0\n/GfMmEHv3r2Ji4tj3LhxghEzZMgQQBHm7urqyvjx42nZsiXjx49n3LhxwmRCqUw9YsQIRowYAShW\ntqRSKS1atGDNmjW0bt1aGNM333yDi4sL+/fvJz8/n65duwIKZ4iyj6ioKBYsWKA2FiV2dnbs3r1b\n7bvh7u4urJBrom7dumo5iWZmZipOEOXn6tWrc/LkSWF78ZWgVq1aCQYBIDxDGxsbtmzZUuZ5AY3p\nHiEhIcJ9fJcx/vhLnmz+tnyh/FraGDdXF0Z7VWxsbNixo3x6Aa+bFi1a8Oeff76Vc79rONe0Y8aZ\n8HJFYmlLpHxW0+4NjEqBpnKAOhb6JCfH4rpqDiu+GqfSfuPGjUJZxS5dumj8G9y/fz9z5swhNTWV\nVatWYWlpyeTJk0lMTCQrK4uxY8cKv4vFy/nFx8ezfv16srKymDx5MufPn2ffvn3I5XI6dOiAt7e3\nyrk8PT2xt7cnOjqa3Nxcli5dSpUqVTSeb/To0YLT8sKFC0RERKhEk2kqU/js2TO+//57QOH0XbBg\nATY2NnTr1o1GjRrRtm1bqlWrxrJly9DR0cHExISff/5ZpTpNURYuXMjFixeRyWS4u7vj4uKiVqFl\nxowZKsc4OTnRuXNnTp8+jaOjI3K5nL/++ov27dszceJETp06pXb+os/i9OnTwn3z9PTEz8+PU6dO\ncfDgQQoLC4X7+vjxY8aNG4eurq6QHgSwb98+NmzYgJaWFnZ2dkybNo3r168zc+ZMdHV10dXVZenS\npWU6Y0oTKm1nkcHxpxWoqPsAs1xTDA1fzG+UOjmAoJNz9+5dYQGheDocKEqK+vn5ATBp0iRAUXZ3\n0aJF5OTk8OTJkxJ1JYrrBS1fvpxz586RkpIi9KlEqWUk8u4jCvSJiIi8dzjXtENbUr6fL22JlDan\n1qu9aCVA0emnUhG8NCwsLFiyZAkeHh7s3btXEKjTJN6mSSDuxo0bODg4oK2tjba2Nh9//DF///23\n0L+enh6NGjXi6tWripSDhvXJOrGOwpx0JNmpFKS8EPIrS5CvW7duHDlyBICTJ0/SqVMnPvnkE9LS\n0ujevTsbN24kNTVVRYl63759KkrUffr0oWfPnkRFRTFw4EAmT56sUZTP2NiYunXrYmBggKGhIXZ2\ndujr66sIZJWEsoKAu7u7MKbY2Fjq1auHnp4exsbG2NkpDABNStr/NY4ePYqVlRVNmjR520MpE+2K\n1hg3712utsbNe6NtVvVfHpHIm8bK0ITPa5Yv7/7zmvZUMXx9K9hFHaTFKamaC0Bu+0ZcCA3nyz0r\nSc9ThFPHxcWxY8cOQkJCCAkJYf/+/Tx48ECtXwsLCzZu3CiUWH3+/Dnt2rUjODiYZcuWERAQILRV\nlvNzcXERHIW3bt1i3bp12Nvbk5+fz+bNmwkNDSUsLExjhFHFihUJCgqiZ8+ebNy4scTzrVq1iqCg\nID777DMGDRqkpimgLFO4bds25HK5UO50zJgxBAUF0bdvX8EZGRcXx5gxY+jfvz/Pnz9n0aJFBAcH\nY2xsrOL8LMq5c+e4ffs2W7duZePGjQQGBpKRkaFWoaW4wJzSkRwaGiqMPzQ0lN9//x2g1PO3bNmS\no0ePAorUtuTkZCHlrvh93bRpE927dycoKEiItMvMzGTp0qX8+uuvbNmyhfj4eCIjIwkLC8PNzY2g\noCCGDx9OUlKSxmtWUpZQqYNpFlfTDDj+1JhW2neQZShEYZXaNMoKNUoB2KJVSTRFkWhpaalpysyZ\nMwcvLy+Cg4OFSkGasLGxISYmRphL+Pj4EBQUpPHZaFrtF3k3EY19ERGR946XmUD2q1yN/Cvhatur\nG+RzK0M1hDvl3A5uXPhL7QWqzMsu6YWpSbxNk0BcSWW4itKtWzcOH9jDrkXf4vBgK0+CvJFnP0ea\nkci9ibVJvRiOLDsNHR0dOnfuTF5eHnK5XKjrraRNmzb88ccfTJw4kWfPntG3b198fX356quv8Pb2\nJjc3l2PHjvHjjz+yevVqrl69SnR0NKNGjeLs2bN4e3sTEBCAr68vKSkpWFtbI5PJePLkCRERESrn\nKn79mkTpSmL58uW0a9eOkJAQxowZI9y/ovdFec9KUtL+L9GxY0emTZv2todRbiw9lqNTpV6pbXSq\n1MPSY3mpbUTeX2a37kWtChaltqlVwYLZrXu9oRGVXg5Qbm5MoZUZ8WevsuOuQim9LEetEmUUU5Uq\nVcjIyMDExKREZ6mmcn4NGjQQVsZNTU0ZMWIEY8aMIT09XaOj9dNPPwVelBYs7Xy3b99m586dwmp9\nUTSVKdTkiAWFTkH9+vUBhBQmDw8Pzpw5o3GMoHDUfvLJJ8K56tWrR2xsbKkVWqBsR3Jp57ewsEAi\nkfDkyROOHj2Kk5MToKhm4uHhgZeXFykpKcJqebNmzVSeS0xMDDVr1hQqqbRs2ZIbN27QpUsXVq1a\nxc8//4yFhYUQ+VcSZQmV6kjBziSbQ09MaGmaTvYthcOiJG0aGxsbFXX84tjb2xMZGQnAsmXLOHXq\nFKmpqdjY2JCXl8exY8dK1NsxNDTEyclJJUIiOTmZmzdvoqenV+p1iry7iMa+iIjIe0l5J5DfauVp\nfNE2Nc0iKVebs88UIXOFcthwz4xdQb9gbGxMcnIycrmcpKQk4uLiAMr9woQXAnHwYoWpYcOGXL58\nmYKCAgoKCrhy5QoNGzZUOa7dx3b8tTuIi7fiaG6iEDDTl8opkEuQFRQQfSeOZ+HzsP+oNmfPnmXR\nokUsW7aMBw8eqKxq6OrqUqlSJS5cuMDkyZPx8/Pj1q1bWFpasmTJEuzs7GjTpg23b99mzpw5dOjQ\ngbZt25KamiqE/YNCSb9SpUo4OjrSp08fTExMVFan/ikpKSnY2Nggl8s5fPiwMJm5e/cueXl5ZGRk\nCPexaIk1pZK2yLuNtoklNfxOYNyyP2gVcwJpaWPcsj81/BTpMyIfJpUMjNn5xWh61mqiFpGlLZHS\ns1YTdn4xmkoGxm9kPOUpB5jX1hadyFtcTYojLS+7XI5aUK+SsmfPHp4/f87mzZsJDAxUaaupnJ/S\n0I+JiWH9+vWsXLmSlStXlqjurxyT0rlc0vlyc3Px9fVl7ty5Go02TWUKNTliQXVF19fXl+nTpxMc\nHEyXLl00jrH4tcKL+zdy5EgCAwORy+UMHjyYlJQUlXZlOZLLOr+TkxNHjx7lyJEjODs7k5CQwIYN\nG1i7di1BQUFChZeiDubSnPMSiYRPP/2U7du3U6dOHaZMmSIY1iVRllApKEL56xrlYqRdSGGOIoKj\nYsWKtG3blr59+xIYGCho0/Tu3Zvz58/j6enJ06dP1b6HPj4+hIaG4uHhQXx8PK1atcLDw4MxY8bg\n4+ODp6cnO3bsKFGLZsqUKWhpaeHi4oK7uzujR4/Gw8ODNm3alHkdIu8mYs6+iIjIe4lyAukfuZv9\nsdEqoZjaEimf17RnduteSA4tQ5Ocn1QCMxs+JPCeJVvizdGWyGlqls2wjraYmprSpk0b+vbti62t\nrWCQK1+YNWrUwNPTk1mzZtG9e3eN49MkEFe9enVcXV3x8PBALpfTv39/YbKhJGvnVIwKM6moX4ie\nlmKi0blyGlvizZl30wobgzxkac9p/mQ3+0xMcXFxQS6Xo6urq7aqUrduXf788086d+7M48ePqVix\nInPmzCErK4svv/wSf39/OnbsyMWLF8nNzeXEiROMGjWKzZs3M3bsWH799VcaNmzIgQMHOHjwIPr6\n+mRnZ2sMX31VXF1dmT17NtWqVRMUqqOjo3FxcWHgwIFUr16dxo0bAwphu+JK2iLvPtomllh/s1Uo\nOSXLTEHLqCLGzV3E0P3/CJUMjFnVaRCPs9I4GHuN53nZmOoa8FlNu9caul8eylXNxUgfWf2qaF26\nR1S9BJzadSAgIECI8rpy5UqJ5UeLkpKSQvXq1ZFKpRw6dEgl1aqkcn6gEDU1MzNDX1+fqKgoEhIS\nNDqXi/ZRt27dEs+3cOFCXFxcSnSQaipTeP78eRVHrKZIqoyMDKpWrUpaWhpnzpxRyXcvir29PatW\nrWLEiBFkZmby4MEDatasqVKh5c6dOzx8+PCl8sDLOn/Xrl1ZvHgx8fHx2NnZER0djbm5OUZGRly7\ndk24r0rnvL29veBQrlWrFrGxsWRkZGBsbMzZs2cZPXo0wcHBdOjQgV69eiGXy7lx4watW7cucYwl\nCZVW0S9gfL0nAHxSMYtPKipmKp80aUjXLqMBBCFXJUOGDCEhIYExY8bg6OjIpUuXOHfuHICQtmdk\nZMSGDRtUjnN1dVWJRlTq4BQX5AWFQ2XChAlMmDBBbV9RocqixyrPLfJuIhr7IiIi7y3lmUCmlqII\nbq4rY7rtI5VtOhUU7TWVfCrPC7OoeNvLCsQpc/v8bVUjEdpXyuDUM2Om2T4WtmVf2snde58SGhpK\n3bp1hZq4RUXi6tWrxyeffIKJiYlg7C9cuJDFixfTsWNHTpw4werVq6lbty6DBg3CxcWFFi1acOLE\nCYyNFatsZmZmDBs2jJMnT/LTTz+RmpqqVjKsaG6sps+aShgVVc8uqgp84oSiFFe7du1UVpOUFFfS\nFnl/0K5ojdn/JrEi/02sDE0Y3PDTtzqG8lZzyW9VH51L98mSlc9Rq4lu3boxevRoLl++TN++fbGy\nshJW3Esq5wfQqFEjjIyMGDhwIM2bN2fgwIHMnDlTzYh7+PAhw4YNIz09nYCAAPLz8zWeb8uWLTRv\n3pwDBw4AMG7cOJWKHprKFMrlcjVHbPGc/EGDBuHm5katWrUYPnw4AQEBKhVmlLRo0QJ7e3vc3d0p\nKChgwoQJGBoaqlRoqVGjhlqkW1loOn/RkrN16tQhLi6Odu3aAYroOk33dc6cOXz77bccOnRIKIdo\naGjIpEmTGD58OFKplObNm9OiRQuysrIYN24cFSpUQFdXt8zykK9bqLRChQps2LCBFStWAKgJ54mI\nFEdSXMShKBcuXJAr849K48KFC5Snnci7j/gsPyzE56kwoO9NrF3uF22dxTFvbbUx9fAqngR5q21P\nzNFm/i0rljaJV9nuftWeYydPU1BQgJubG15eXirOiKI1jpW1g1evXk2vXr3YuXMnxsbGDBs2jNGj\nR9OiRQu+++47tLW1adOmDS4uLsLxeXl5PH/+nPHjx7Nt2zYCAgJKFGISKRvx7/LDQnye7xcbb5zG\nL3JXudvPaf3lW3dQaEJpgCuN039Cq1atShU0fB95l/4uH64cSMbZ38psZ9yyP9bfbH0DI3q/eB3P\n8n99vL66mO8RYs6+iIjIB837pAheWm5fQrYuU69VE/67la5H308/ws3NDX9/f4YPH87q1at58uRJ\nqecouloxaNAgGjVqJKzwdO3alYMHD9K5c2eVY5TK/oMHD8bAwEBldUpERETkfeJlq7m8yXKAIu8W\nISEhDBgwAA8PD/r168epU6cAhVivUsunPJRXqPRSVcVc5fjx4xpLsQIkJSW9thS2iIgIpkyZUq62\nAQEBdOvWDU9PT9zc3Jg0aRLPninmLGFhYRw6dOi1jEnk9SOu7IuoID7LDwvxeSooSHtC3BzHEuvc\nguJF+7aFwkpa2S8JS8/A9yIsOj4+np49e2Jvr1pBISAggPnz5+Ps7KwSyh8WFsayZcuEfFFdXV0W\nLlxIpUqVNPYfFhbG7du3mTx58r96Ha8L8e/yw0J8nu8foyM2Ex5ztcx2PWs1YVWnQW9gRCKvm1f9\nu1SmBT6Ii+OPxf/HltBQqpuaExMTw7Rp0wgODn6l8RSkPeFJsI+iDF/RSEMtbYyb98bMdTFDx0xg\n69Y3t7IfERHBwYMH1bQBNFE0UhAU793Q0NA3Ml5xZf+fIebsi4iIfPAoFcFLe9Faeix/64rgrzu3\n721TdNJkUtWSn1avwKqcYlzdu3cXjPfAwEB+//33cgliiYiIiJTF7Na9iEpOKLH8Hrz5coAib5en\n2RlMi9zFgf8JOEoTn6OXlkK7bfP5vH5TZrfuJRj6yhSKgwcPkpaWxv3794mLi8PX15cOHToIfZ45\nc4aQkBAA7t+/j7OzM6MWLeFy+DoWbv4DLW0dKlSxYeGgn/lp2TJu3rzJDz/8QJMmTQQH9po1azh4\n8CBSqZTvvvuO6tWr4+PjQ1hYGLt37yY4OBipVEr9+vWZPXs2Dx8+5Pvvv0cqlSKTyfjpp59U9CVu\n3rzJ5MmTMTU1xcbGRtgeEhJCeHg4UqkUJycnhg4dWur96tOnD+Hh4Vy6dImTJ08KjoCFCxdy8eJF\nZDIZ7u7uuLi4sHPnToKDg9HR0cHW1pYZM2a8zkcnUgZiGL/Ie0N8fDwNGjTg8uXLKtv79u0rhCGN\nHz+enJycMvvS1O5lwpnK2+ersnfvXqZPn45MJnst/Ym8UASvs+g+lp6BWPSZjaVnIHUWx2D9zda3\nbujD+5VyUBpPszMYFRFC69D5+EXu4v+unSAhM5XWofMZHbGZp9maS/6URHJyslB66syZMwwcOBAP\nDw8mTJigom4NCmFFNzc3+vTpw2+/lZ0jKSIi8t/jXSsHKPJ2eZqdgcveVeyJiRIqNRRWMUVWtSK6\nq/ZzMPBXPpv/PY/TU9WOTUxMZO3atfj5+bFt2za1/VevXmXBggVs3bqVoKAgtCtas/zP2/gtCGTL\nniO0auPIpk2bGDZsGLVr1+aHH34Qjo2JieHgwYOEhoby008/ER4ertJ3dnY2a9euZevWrdy7d4+b\nN29y8OBB2rRpQ1BQEH5+fiQlJakcs3LlSry9vdm4caNQti8uLo4DBw6wZcsWQkJC+OOPP3j48GGZ\n983e3p47d15ETJ47d47bt2+zdetWNm7cSGBgIBkZGaxbt46AgAC2bNmCvb39a5sri5QPcWVf5J2n\n6OqgWZXKhO4Mo2nTpgDExsaSlpYmtF26dGm5+ixvu5fhdfZpamoqqKuLvF7edUVwS4/l5MZeKjPl\nwNLj3VSlV06aNK2YFcgLCY+5SlRyQpkT6X379hEdHU1KSgpGRkZMmjQJgBkzZvDrr79StWpVZs2a\nRXh4uFDDOTc3l2rVqjF16lRycnJwcnKif//+/86FioiIvNdoquby/HESIxw/f+PlAEXeLv6RuzW+\ns/J6tiD/aRpa95+QevQiX150JXL3AZU2H3/8MQBWVlakp6er9dGoUSMMDAxUtt29excHBwdAIY4Y\nGBioUtZOyfXr13FwcEAqlVKzZk3mzJlDfPwLoV5TU1O++eYboc/U1FTatm2Lt7c36enpODs706xZ\nM7VzK8fcqlUrjh8/TlRUFLGxsXh5eQGQmZlJQkIC1tbWpd63zMxMtLS0hH9HR0fzySefAAp9oHr1\n6hEbG0uPHj0YM2YMvXr1okePHujr65far8jrRVzZF3ln0bQ6+NRCj9/+2Meow8E8zc5g7969tG3b\nVjimc+fOZGZmCv/+888/mTp1qvDvqVOncvjwYaHdzZs3cXFxYfDgwSp1QstaHfy///s/+vfvj6ur\nK7/88ovKuadMmcLixYsZNmwYn3/+OdeuXVM5NiwsjKlTpzJq1Ci6dOnCnj17GDVqFF27duXKlSsA\nrF69mlu3br2eGynyXqFMOTBu2R+0ivljtbQxbtn/rWsLlEZJkybpswz0Q06gH3KCx7/spP/Yr0vt\np3v37gQFBbFnzx7c3NyYPn06qampSCQSqlZVRDS0atWKGzduCMfo6enx/PlzBg4cyNdff01KSsrr\nvTgREZEPDmU5QB+HznSvVE809P9jPM5KY39stPoOuRwKZMgrmVDwST1yvDryLCmJq3dV52ba2iWv\nmyYlJXHu3DmVbQEBAWRnZwv/zs/PF1bYAaZMmSK817S0tCgsLCQ+Pl7NGZCXl8esWbNYunQpwcHB\ngvPgo48+YteuXbRo0YIlS5awc+fOYpclFxzkhYWKKAYdHR06duxIUFAQQUFBhIeHC0Z7cYqOJTQ0\nlNq1awv7lP0Wv7aRI0cSGBiIXC5n8ODB4ruaP5huAAAgAElEQVT5DSMa+yLvJJpCqgDkUiky64rs\nO34El72rOHjokEp+VHEcHR05d+4chYWFyGQyzp07h6Ojo7BfUziTcnVwy5YtbN68mWXLlqn1u379\nerZs2cLWrVsxMVGfGOTn57Nu3Tq8vLzUfmhBEZq1atUqRo4cyerVq1mxYgUjRoxgz549L3WfRD5M\nNKUc5HSY9E6lHGiixEkTUGhuTI67o/Df/Xa1ScxK09i2OM7Ozpw/fx6JREJRUdn8/HyVycXZs2eJ\njIwUJiy6urr/7IJERERERD5oDv4vR7842ldj0d1/SWH0A+TmgxzOZTwus8/HWWlsvHGa/bHXyJUV\n8LjYu87S0pJLly4BitB3e3t7Ice+KHZ2dkL+e0FBAWPGjBH2KVfVK1euzKNHj4iOjiY/P5+9e/dy\n+/ZtnJycGDduHNHRqu/k2rVrC9uU5Rbt7Ow4c+YM2dnZyOVyfvzxxzJD7bdt24ZUKlUp/Whvby/0\nmZmZyYMHD6hZsyZLly6lcuXKDBkyhKZNm5YrRUDk9SGG8Yu8k5S0Ogggs62G9vV4Yo30kevIMDQ0\nLLEfPT09GjVqxNWrVykoKMDBwUHFANAUzlR0dVBHR0ejB9LZ2ZkhQ4bQo0cPevVSF/BRljKzsrLi\n6lV1xV97e3skEgmVK1emQYMGaGlpUalSJS5evFj6jRH5T1E05SDmwoV3NkdfSUmTJk0UyAs5EHut\n7IbAlStXqF27NqampkgkEh4+fIi1tTVnz56lefPmwgQpJSUFKysrdHR0OHz4MDKZjLy8PNHo/48z\nf/58rl27RlJSEtnZ2djY2GBqalqu8pEbNmwgKytLCJV9HSxduhQrKyvc3NxeW5/FSUxMZNWqVSr5\nv6+DtLQ0+vbtK5bZEvlgSM3N0ri9oHFNdJPT0d90DHS0oFBOnlMTsiQlaynly2SMiggRRP607seh\nV5BP69D5fF7THjkKx4GzszNLlizhwYMH5OTkYGVlhbm5Ofn5+Zw5c4bq1asTFxfHX3/9RevWrfn2\n22958OABvr6+REVFcf/+fcaNG4eenh59+vShYcOGDB8+nHnz5uHn5yeI62lra7NmzRrS09OZMmUK\naWlppKWlMXv2bGrWrMnFixexsrJi/PjxaGlp4e7ujpaWFk5OTujr63Pnzh1mzZqFRCIhMTGRgoIC\ndu/ezd27dzl16hQVK1YE4OnTp+zcuRNra2vi4+NxdXUFYMKECRgaGmJkZISrqysVKlSgRo0aNGzY\n8HU+QpEyEI19kXeO0lYHAWS1LNH94wpyY30e2ZiQUsIPtZJu3boRERFBXl4ezs7OKvs0hTMVXR3U\n0dFRy3cCmDlzJnfv3mX//v14enqqhfoXzWHSVN6yaNhX0c+llcIUef+Ij48XVHP/LQ4fPoyjo+M7\nYdCWNGmCF2H8Rbll8lEJrV/k7CtRGi2zZ89mwoQJaGtrU6NGDb744gt2794NQJs2bVizZg0eHh44\nOTnRsWNHfvjhB+bOnfsPrkrkfaUg5SEZF3fxdSMZWp+04dBDPe49THpvSjT+E6pUqfLaDX0RkQ8R\nM70SFoykEvI6N1bbbKprQFBQEIDKqrZ5DWvi+zTjZEyUsK2wmjnI5WgHH+MQx9CvaMD233/n6+HD\nWbt2Ldu2bcPLy0vQmDl58iRTpkxBV1eXoKAgIiIiCAsLIyAgAB8fH1q3bo2LiwsRERGYmZmxcOFC\nbG1thUWnIUOGsHTpUnx8fPDy8mLDhg0kJiZy7NgxHBwcGDFiBFFRUSxYsIA1a9Zga2vL4sWLsbW1\nZcCAAcycOVPFEJ89ezazZs2iVq1ahISEkJaWRs+ePfHx8WHZsmV07twZUMyzXV1dadSoEcuWLcPc\n3BxPT0+hnxEjRjBixIhXf0gi/wjR2Bd55yhzdVBLiqxGJbSvxJD9dVfOPr5fan8dO3Zk8+bN5Obm\nMm7cOJV9ynAmR0dHIfSorNXB9PR0Nm7ciLe3N97e3pw/f56MjJdTFxcReV1s2LCB1q1bvxPGfkmT\nJrmZEVnf9VTb/lGjhgzuq17ep0+fPhrFikARNbNlyxa19kq2b98ufP7qq6/KM2yRD4yCtCc8+X/2\n7jsqquNt4Ph3lyZdRRFRbNhB1GBEjT3EFhO7WABr8tOAqDE2BDvRGI1RjCQabDRj7MaoiYZobKjY\nAI0tQhALFtqCuLvA+wfvXllZioolOp9zPGe9d+7c2b1c2LnzzDMh41Cc3q61jOXde+VRmDdGnT5C\nmgqzcOFCzp07h1qtxtPTk48++ojDhw+zcOFCrK2tqVixInXq1CmybEFz5szh4sWLWktO/fLLL2zY\nsAE9PT2cnJykHDLnzp3jt99+4+7du0yfPh0XFxd69uxJ/fr16dixI46OjsybNw+5XI65uTkLFizA\n3NxcOtfPP//MmTNnuH//PlevXuXzzz9n165d/PPPPyxZsgQLCwsmT57Mpk2bpGMSEhLw8/OjatWq\nnDt3jiFDhnDx4kXOnTuHp6cngwcPZvv27YSHhyOXy2nQoAFz5swhIyMDb29vVCqVFAkH+V/w27Zt\ni42NDR9//DEzZsxArVajr69PQEAANjY2ZX9xBaGMda3pwKyoXaWKStOXyelW00HnvqIiUjVT2ACy\ngWoxDwCKjSJt1aoVAE5OTixZskTafu/ePRISEhg3bhwAWVlZ0ui6xoULF6Tvupq/gZs3b2bs2Pwo\nwSZNmpCQkACAmZkZDRs2BHQnGDx//jz+/v5Afo6AJk0KP/wAsLKyYvHixWRnZ5OcnFzod6PwaonO\nvvDaKW50UCOnYTVkDx9BOQMy1UpkxZQ1MzPDwsKCcuXKFcoAOnbsWKZPn86GDRuws7NDpVKVODpo\nbm5OSkoK/fv3x8TEhObNm1O+fPnnecvCGyYtLZu4mNvcSLxBpkJJWlo2lpaPf/YuXbrE3Llzkcvl\nmJqasnDhQvT09JgwYQJKpRKlUsnMmTNxcHj8pSIrKwsvLy/S09NRq9X4+flx5coVzp49yyeffEJA\nQAC+vr6YmJjg7u6OiYkJS5cuRV9fnypVqrBgwQLu3btX7Pq7z6usvjQJwrNSpyeTGNBO92oWuTko\nb10iMaAddjP+4mTcNeLj44mIiCAzM5OPP/6Y999/nyVLlrB06VLq1q3LqFGjqFOnDseOHdNZVjON\n7P79+xw9epR9+/ahVCrZsWMHCoWCZcuWsWPHDkxMTBg9ejSnTp0CIDU1lbVr13Lx4kVmzpxJREQE\nCQkJrFy5kjp16uDh4cGXX36JnZ0dGzZsICIiotDI2L///ktISAgRERGsXr2arVu3smnTJn799VcG\nDRqk8/O5cOECK1as4N69e/Tu3ZsDBw6gUCj44osvGDx4MA8fPmTNmjWYmZkxaNAgrl27xpEjR2jc\nuDFTp05l586d7Nu3D8jPb/P+++/z3nvvMXXqVD799FNcXFw4cOAAQUFBzJkzpwyvrCC8GDYmFnSv\n6ciu+MJTLp/UvaajzgSOJUWkFnQ1LZl0ZXapokhBO+mdgYEB1tbWUmSBLpqkfk/WUTByVLO/YBQq\nFI4uNTY2ZsOGDVptKLgagEZAQACffPIJ7du3Jzg4mKyskr/HCy+P6OwLr53iRgeVPZ0ByKlrQ07d\n/FGDxs2bMmzIGACtjPoFaTLma2jKOTg4SCHABZU0Oqh50qmrzoULF0rbOnXqRKdOnbTKFRyFLLi/\n4OvifpELry9FxiO2b4klNuY2ubl5ZGU9ICXlIQvmHsDRyYbefR0xMzciICCAKVOm0LRpU4KDg9mw\nYQMNGzakSpUqfPnllyQmJnL9unbEyt69e7XC8BYsWEBoaCjLly9n9erVpKSkcPHiRSIjI6lQoQLd\nunUrtERdeno6bdq0wcvLS5rDXJad/bL40iQIzyM51KfYZSsBVHeukhzqQ6zB+7Rs2RIAU1NTateu\nzb///sudO3eoV68eAO+++y55eXnExsbqLKsZFatYsSLVqlXDy8uLrl270qtXL/7++2/s7e2lBwIt\nW7bkwoUL0muARo0aSV+ezc3NpSiC8+fP4+vrC+R3qjXLzRbUpEkTZDIZ1tbWNGzYELlcTqVKlQol\n5CqoZs2aUu6LypUrY21tTbly5aToNEtLS/73v/8hk8m4fv06KSkpXL16VUps6+LiQmBgIJDfMdBk\nAD9z5gyJiYno6emhVqupXLlysddAEF4n81p9TMz9pCJzRQHUMrdiXqvCOZrg6fLV5OblcfHBLexT\n7HRGkQJER0fTo0cPzp49K/1OgPz7E+Dq1avUrVuXkJAQ3n33Xen3EOTnhDp+/DhOTk5s3LgRIyMj\nmjRpQlRUFM2aNePs2bPS77eSNGzYkEOHDtGhQwd2795NxYoVsbOzK1QuNTWVGjVqoFQqOXjwoM7f\nV8KrIzr7wmtHjA4K/0WKjEd8t/wI9+8VfqKdm5vH+bO3SLqRhpfPezrX2B00aBDffvstM2fOpEuX\nLrRv316rjn/++YeBAwcC2mF4BdnZ2VGhQgWdS9SdPHmSgQMHFrv+bll43i9NgvCs1Ck3UURvK1VZ\nRfQ28pq5kFfggZNmmaiCo1i5ubnIZDKdK0EUXC5LJpOxZs0aYmNj2bVrFzt37sTHx6fQMZqOf8Fz\naF4bGBhI20xNTUt86FtwVK6kPDEaBXPEPHnMo0ePCAgIYOfOnVhZWTFq1KhCbXxyxFDTZgMDA1as\nWEHFihWLbbMgvI4qGZux/cOx+B/fyZ6EWK3vn/oyOd1rOjKv1cdUMjbTeXxpIlILephTdBSpxpgx\nY7h16xaLFi3SOjYgIIDp06dLo/yaZHgaw4YNY8qUKXh4eGBqasrixYsB8PX1xdPTk7y8PGbOnFmq\nds6YMQN/f39Wr16NkZERS5Ys0Tlt1d3dHS8vL+zs7PDw8GDu3Ln06NFD6yGE8OqIzr7w2hGjg8J/\n0fatsTo7+gXdv5fF9q3ao26aToO1tTU7duwgKiqKiIgIzp49i7e3t1SuqDC8gjRfvItaok6z/u6R\nI0f45ptv6NevH717936m91uU5/3SJAjPSnF6h9Yc/WLlqLHXv8tPURcZNWoUCoWCpKQkatSogZWV\nFQkJCdSoUYOTJ0/SsmVLmjRpQnBwcKGyGomJiRw6dIihQ4fi4OBAv379qFOnDv/88w9ZWVkYGxtz\n6tQpxo8fT2RkJNHR0YwYMYK4uDitejTq1q3LkSNHeO+999i5cyfW1tbSPN4XJSMjA0NDQ6ysrEhK\nSuLChQuoVCopt42rq6uU2+ZJTk5O7N+/n4EDB3L06FFSU1Pp0aPHC22vIJSlSsZmBHUawu2sdPYl\nxJGmfIiloTHdajqU+D2zuIjU7OHa0Z2qdo1o16o75ubmT5VjRpPot0WLFoWSQhdkbm5OUFBQoe3L\nly8vtK3g/axrv729PeHh4VrbypcvL7VFE9Hq5uam9dDhgw8+KO6tCC+Z6OwLryUxOij8l6SlZRN7\nvuS1dwFiz9+mdm17zpw5Q/PmzaU1do8ePYpKpaJDhw7UrVu3UCbtOnXq6AzDk8lkhdbmLWqJut27\nd2NnZ4erqyvly5dn7969Zd7Zh+f70iQIzyon88FTlW9WzYzTigoMHToUtVrN1KlTKVeuHJ9//jle\nXl5Uq1YNW1tb4PHSrE+W1ahSpQpRUVHs2rULAwMDBgwYgJmZGZ9//jkjR45EJpPh4uJCs2bNpKk2\nY8aMISkpCT8/v0Jt8/PzY9asWQQFBWFsbKyVpOtFqVSpEi1atKB///40atSIUaNGERAQQGhoKOPG\njWP48OFFRgONHz+e6dOns2PHDuRyudZ0NkH4L7ExsWBYo9ZPdYyISBVeZ7Liwr2io6PznJ2dS6wk\nOjqa0pQTXn+v07W891AhRgef0+t0Pd9kRw/Hs31L4XmyWVkP+OOvxZS3rC5tc2jYky7dHdi560dk\nMhmWlpYsWLCA1NRUJk+ejL6+PjKZDB8fH1q0aCEdd/jwYTZt2kRqaqoUhlevXj2mT58uzeGfNWuW\n9MT91KlTLFmyRFqibu7cuVy6dIlZs2ZhYmKCnp4efn5+2Nvbv/gPSNAi7ssXI/VAEMkh3iUX/H/W\nHiso//7Y5z6vuJ5vDnEt3xwv+1qOjQwvVUTqR7WcCOo05CW06M1RFtfy/+soLp/3G0uM7AuvLTE6\nKPxXPMxS6dxuYlKRnl0Lr/FesYJtofm4ZmZmhZaUK8jY2FhnmN2CBQuk15qOPuheos7BwUErbFAQ\n3iRm7/QiOXxC6UL59fQxcy77qBZBEN5OIiJVeF2Jzr7w2nuWkCpBeJmMTQxKLlSwvPHTlRcEoWT6\nFWwxc+6D4kTR81k1zJz7oF++6ktolSAIbwORr0Z4XclLLiIIgiAUx6GJDXJ56aLD5HIZjk1sXnCL\nytaNGze0lox8EcaOff5w6icFBgYSGhpapnVOnDiR7OzsMq1TKDvW7ssxqFK32DIGVepi7V44SkYQ\nBOF5aCJSjw+cRkCrXkx5pwsBrXoRNXAaQZ2GiI6+8EqIzr5QSFhYGAMHDsTd3Z3+/ftz9OhRIH+5\nj8TExCKPO3nyJPfvFx2+9Lq6fPkyHh4epSo7bdo0IiMjtbbdvXtXWsakc+fOZGZm6ixXGrt27aJr\n166cOnXqqY8tjXnz5rFly5YXUvfbzNKyHI5OpevAOzrZYGFZruSCrwF1yk1SDwSRuv87cjLuok65\n+cLOpSt78Oto6dKlWonZhNeLvoU1djP+wqzlANB7InhRTx+zlgOwm/EX+hbWr6aBgiC88TQRqT5N\nOzOsUWsx9VR4pUQYv6Dl7t27bNq0ic2bN2NgYEB8fDx+fn60adOGGTNmFHvsli1bGDlyJFZWVi+p\nta+HypUrM3fu3DKp6+jRo0yePFkrMVtZefDgAd27d38hdQvQu68jSTfSil1+z6qSCb37Or7EVj0b\ndXoyySHjUJzeDjlqUrL1Ud+34Z8vamPm3Adr9+XoW1hz6dIl5s6di1wux9TUlIULF6Knp8eECRNQ\nKpUolUpmzpyJg8PjzMO3b99m/PjxGBgY0KJFC6KjowkJCcHFxYWoqCg8PDyklQY+/fRTJk+enN8m\ntZqvvvqKGjVqMH/+fGJjY8nJyWHw4MH07duXX3/9lXXr1qGnp4eDg0OhDOdLly7l1KlT5OTk4O7u\nTs+ePTl8+DDffvst5cqVw8rKisWLF2utda5ZolBPT48ePXowfPhwOnfuzK5du1AoFMyYMQOVSoWe\nnh7z58/H1taWVatWSaseqNVqRowYQePGjZk2bRrp6emo1Wr69euHs7OzzvchPD99C2tsP9uIOuUm\nitM7yMlMQc+0AmbOvUXoviAIgvBWEZ19AUD6UqQ6eZSHKXd4ePdfDGztqVWrlhQG6+Hhgb+/P7a2\ntvj6+pKWlkZOTg5+fn7cv3+f/fv3c+XKFQIDA4mNjWXNmjXo6+vj6OjItGnTtM43bdo0KlasSFxc\nHA8ePOCTTz5h69atpKSkEBoaiomJCf7+/iQmJqJWq/Hx8aF169Zs376d0NBQDAwMaNiwIbNmzeLo\n0aMsW7YMAwMDLCws+Pbbb3n06FGpOhyGhoY0aNBA2v7bb78V226AyMhI1q9fz4MHD1iwYAGWlpb4\n+PhoJUfTUCgUTJo0iaysLLKzs/H398fJyYkuXbrQvn17rKyspPDlI0eOcOjQIWJjY7GwsMDPz4/G\njRvz3nvvUa1atULv8cyZM4SFhQFw/fp1unbtire3NxcuXGDOnDnIZDJsbW1xdnbm6tWrzJ07F5lM\nJnXKjI2NmTx5Mnfv3kWpVDJu3Djat2///D9MbykzcyO8fN5j+9ZYYs/fJjf38UoncrkMRycbevd1\nxMzc6BW2smTq9GQSA9qhunO18M4cNYoTP/Mo4Qx2M/4iICCAKVOm0LRpU4KDg9mwYQMNGzakSpUq\nfPnllyQmJnL9+nWtKtatW0f37t0ZPnw4ixYt0tmGevXqMXjwYM6fP4+XlxetWrVi8+bNhIeHM2bM\nGP7880/279+PSqVi27ZtZGZmsnTpUrZv346pqSljxozh+PHjUn2nTp0iKSmJsLAwlEolffr0wdXV\nldDQUKZNm0aLFi347bffSE1NpXLlygDk5eUxZ84cNm7ciKWlJZ999hmDBg2S6ly2bBkjR46kTZs2\nHDx4kJUrV/LFF18QFhbGvn37UCgUdOnShREjRrB+/XqaNm3Kp59+SkxMDH5+fri6uhZ6H0LZ0q9g\nWybZ9gVBEAThv0p09t9yT47gNQDq5Fjj2qUrrepWpovHeLr16o++/uMflfXr19OuXTsGDBjA1atX\nCQgIYO3atTRq1Ah/f38sLS0JCgrip59+wtDQkPHjx+tcNkNfX5/169czadIkzpw5w7p165g8eTJR\nUVEoFAoqV67Ml19+yYMHDxg2bBi7du0iODiYVatWUbVqVbZs2UJ2djZpaWksXrwYOzs7pkyZwuHD\nh1Gr1cV2ODZs2ECPHj0YNmwYq1at4tKlS2RmZpaq3ZDfYYmMjOT7779n6tSpRX6+d+/eZcCAAbi6\nunLs2DFWr15NYGAgarWa9u3ba3Wu33vvPdq1a0fXrl1p2bIliYmJfPfdd9SrV489e/YUeo+mpqac\nP3+ePXv2kJubS+fOnfH29mb+/PnMmTOHhg0bMmrUKJKSkpg3bx5z586lVq1ahIWFERYWRvv27UlJ\nSSEsLIz09HQOHjz4jD9FgoaZuRHuw5xJS8smLuY2Dx+qMDY2wLHJfyd0PznUR3dHvwDVnaskh/pw\n7VoiTZs2BfLXIV+xYgWDBg3i22+/ZebMmdJDrYKuXbtGjx49gPxpLzExMYXqd3JyAvKjZubPn09g\nYCDp6ek4ODhQvnx5atWqxdixY+nWrRu9e/fmypUr1KxZE1NTUwBatmzJxYsXpfpOnz7NuXPnpOk6\nubm53L17l27dujFr1iw++ugjPvzwQ6mjD/mRMEZGRlSsWBGAH374QauNZ86c4fr16wQFBZGTk0PF\nihX5999/qV+/PuXKlaNcuXLS+4iNjZUe6jVp0oQ7d+7ofB+CIAiCIAhlqcTOfnR0dKkqKm054fUh\ny3qAyeZPkKdpz8P/vF4yiVkGnE57wMqF/gSvD2XGzNlkZGRw4cIFDh48SHp6ujSq/OjRI6Kjo6X9\nZ86c4d9//2XgwIEAZGVlceTIEa1z3L9/n1q1ahEdHU1eXh6mpqZER0eTk5NDTEwMV65c4dKlS1IH\nNC0tjaioKJo3b86IESNo27Ytbdq0IS4ujnv37jF+/HhycnJITk7GxsYGJycnjh8/zmeffca7776L\nk5OT1s/o6dOn6du3L9HR0VhaWpKRkcEvv/xSqnY3bdpUaveFCxeIjY0lKyuL6OhoHj16xNmzZ7l/\n/z5Xr15FX1+fiIgIli1bhlqtxsjISCqXl5dX6L7RHGdhYYGRkRHp6elER0frfI+VK1fG1taWCxcu\nAPlhztHR0Vy5coXMzEyio6P57LPPuH37NmfPnmXChAkAqFQq7O3tadq0KcnJyYwaNYoWLVpIIdVC\n2TAyzv8HcOVq2eSyeNHXR6a4i+mprZQm1WDGqa2olI/vq8uXL5Oenk5iYiKzZs3iwoULBAUFsWfP\nHq3w9LS0NC5duoRKpeLy5ctkZGQQHR0t/fxmZGRw5coVsrOz+f7776lbty4jR44kKiqKM2fOEB0d\nzdixY7l+/ToHDx4kJCSEwYMHk5aWJrUlPj4eQ0NDMjMzycjIICcnh9atW9OrVy+pHcnJydjZ2TFp\n0iROnTrF8OHDGT9+PNWqVct/fxkZ0n1UkOYeV6vVjBo1igoVKkj7Ll68KL0fTR2az+XixYuo1flL\nwuXm5up8H9OnT3/6iya8FsTvzjeHuJZvDnEt3xziWj67Ejv7ukY1n1TU6Kfweru5chCKJzr6eXmg\nypNhZ6LCziSNnjZpeF00pmrVqpibm9O4cWOsrKyYPn06zZs31zpWs1+tVtOsWTOCg4OLPLeVlRUN\nGjTA2dmZ/fv3U6dOHel1zZo1yczMpEePHvTs2VPrOBcXF27evMm+fftYsmQJoaGhTJkyhVWrVmFv\nby+NXru6utKhQweioqKIiIggMzMTb29vqR4LCwsaN26Mo6Mjcrkcc3NzmjRpUqp2a9p6//59TExM\ncHR0xMTEBGdnZ4yMjGjWrBlWVlbUrVuXc+fO0ahRI4KDg4mJiWHRokVSuRYtWkgjkQXrr1u3rlRG\nc1/peo/VqlXj5MmTUhl9fX2cnZ0xNDSUtmnuTVNTU7Zt24ZMpt2N2717N6dPn2bbtm38+++/Wmu2\nC6+Xl/F7NvVAEMm5OaUqK8vNoX7V8sjlcpo3b050dDRt27bl0aNHqFQqRowYQffu3Zk9e7ZWux0d\nHVGr1Tg7O3Po0CHMzc1xdnaWfn41v0fq16+Pnp4ebdu25Z133mHjxo1YWFhQpUoV/vjjDzw9Penf\nvz99+/ale/fuBAUF0aBBA8zMzFi5ciVjx47l2LFjVKhQgcaNG7No0SKaN2+OSqVi0aJF+Pv78913\n3+Hu7k7nzp0xMzOjXLlyWm1duHAh1atXx9ramjFjxvD1119L93irVq1ITk6Wonbu3bvH+++/zw8/\n/ICTkxMZGRnSSH9GRgZpaWk4Oztz9uxZqlevrvN9iL+j/03iO9CbQ1zLN4e4lm+OsriWb/PDApGN\n/y2lTrmJIrrwHNHfki1Yca0yef8/3ThLLSfnkQJLPZVUpmnTpuzfvx+Aq1evsnbtWgBkMhk5OTnU\nrl2ba9euSZn5ly9fzp07d56qfU2bNuXAgQNA/mj3N998Q25uLkuXLqVy5cqMGDGCZs2acfPmTRQK\nBVWrViU9PZ2oqChUKhVHjx7l6NGjtG3bFn9/f2JjY7Xqr127trQtKipK2laadmt+YZw9e5Y6deoU\n+z5SUlKoUaMGgDQ391noeo9Fsbe359y5cwCsWrWKa9eu0bBhQw4dOgTkd/CPHTtGXFwcu3btokWL\nFsyePZtr1649U9uEN0dO5oMi9yU9NJ2Hnp8AACAASURBVGR6XDXp3+UMIyZ89A7ffPMNnp6exMTE\n4OnpSY0aNfj+++/x8PBgypQpjB49WqseT09PfvrpJ4YPHw6AXF70nyE3NzfmzZvH6NGj+fDDDzlx\n4gTx8fGcOXOGQYMG4eHhQb9+/TAxMZHONWTIEBo3bqyViPKdd97BxcUFNzc3hg4dKuXvsLW1ZcSI\nEQwfPpy///6bdu3aaZ1/1qxZ+Pj4MGjQIFq3bo2FxeOMyt7e3hw4cIChQ4fy3Xff0axZMypVqkTP\nnj0ZMGAAAQEBODk5oaenh6enJ3FxcXh6erJkyRKGDRuGtbV1ofchCIIgCIJQlsSc/beU4vQOyFEX\n2u5qnc6NhwZMiqmOsV4u6jwZn9a6izpuj1TG3d2d6dOnM2TIEHJzc6Us/S1btsTHx4eVK1fi6+vL\nJ598gqGhIY0bN8ba+umWOerevTvHjx9n0KBB5OTk4O3tLWX8dnNzw9zcHDs7Oxo1asSQIUMYPHgw\ntWrVYvTo0QQGBrJkyRICAwP58ccfkclk+Pj4aNXv6enJhAkT+P3336lfvz4AxsbGpW73mDFjuHXr\nVpEJxjR69erF1KlT2bt3L0OHDuWXX355pqXvdL3Hzz//XGfZGTNmMHv2bACqVq2Kvb09M2bMwN/f\nn9WrV2NkZMSSJUuQyWR88803/PTTT+jp6TFq1KinbpfwZtEzrahze5Vyaja5/FNou3Wd2oR8on0P\nmJmZERERUeQ5VCoV/v7+ODs788svv/DgQf4DBs1Dt5CQEKlsp06d6NSpk/T/v/76C4C2bdsWqrdL\nly506dJFa9u4ceOk1xMnTmTixIla+/v06UOfPn2KbGvr1q1p3bq11rY//vgDAFNTU51RQLVq1cLb\n2xt9fX0++ugjqlevjpmZGcuXP17XPTo6GkNDQ5YuXVrkuQVBEARBEJ6XLC8vr8id0dHReSKM/810\nf2cA97fOLHV5q77zsPrY9wW2SHgRxL355ngZ11KdcpN/vqit80FgIXr61FkS/9RLmd28eZNJkyYh\nk8mQy+UsWLAAOzu7Z2zx62fVqlX8+uuvGBoa0rlzZ8aMGVOojLgv3yzier45xLV8c4hr+eYoqzB+\nZ2fn0qQkeuOIMP63VFEjeEWXr1ByIUEQ/tP0K9hi5lz0SHdBZs59nmnNcltbWyIiIggPDyc0NPSN\n6ugDfPrpp2zfvp1Nmzbp7OgLgiAIb44bN25oJaF9XpqVW17GuXSZNm0akZGRz1z27t27zJyZP5h4\n8uRJaWqs8OqIzv5byuydXqBXylkcevqYOYtloQThbWDtvhyDKnWLLWNQpS7W7suLLSMIgiAIb6rb\nWemsv3iMtReOcj87k9tZ6WVSb1BQUJnU86pUrlyZuXPnArBlyxbR2X8NiDn7bynNCJ7ixM8lln3W\nETxBEP579C2ssZvxF8mhPvlJPAuG9OvpY+bcB2v35ehbPF0eDkEQBEH4r7v3UIHf8R3sTYhDnZeL\nLDUTo8xUWm1aSPeajsxr9TGVjM04evQoy5Ytw8DAAAsLC7799lsMDQ2legIDA7l9+za3bt3i7t27\nTJ48mfbt2+Pi4kJUVBRXr15l7ty5yGQyTE1NWbhwYZFtmjdvHmfPnqV27dr8888/LF++nBUrVtC1\na1c6depEZGQk+/btY+HChYSFhbFr1y7kcjmurq6MHDmyUH1RUVGEhoZy69YtFi9eTOPGjVmwYAHn\nz5/n0aNHDB48mAEDBgAQGRnJ+vXrefDgAQsWLMDS0hIfHx8mTZrE/v37uXLlCoGBgZw9e5Z169ah\np6eHg4MDfn5+ZX9xBJ1EZ/8tZu2+nEcJZ1DduVpkGTGCJwhvH30La2w/25i/asfpHeRkpqBnWgEz\n597iwZ8gCILwVrr3UEHv3UHEZxQerVbn5bIr/jwx95PY/uFY0tLSWLx4MXZ2dkyZMoXDhw/TuXNn\nrWPu3LnDmjVruHTpElOnTqV9+/bSvnnz5klLLYeFhREWFsZHH31U6LxXrlzh3LlzbN68mVu3bhVK\nVFtQYmIie/fulZLoDh48mG7dumFra6tVTiaTERwczMaNG9m2bRv29vZUq1aN6dOnk52djaurq9TZ\nB1i3bh2RkZF8//33TJ06FYD33nuPRo0a4e/vj6WlJUuXLmX79u2YmpoyZswYjh8/TqtWrUrxqQvP\nS3T232JiBE8QhOLoV7Cl/PtFzx8UBEEQhLeF//GdOjv6BcVn3Mf/+E6GV7THz8+PnJwcEhMTdXZs\nNau9NGjQoNBSz+fPn8ff3x8ApVJJkyZNdJ7v2rVrODk5IZPJsLW1pXr16kW2LSYmhoSEBDw9PQHI\nzMwkKSmpUGdfkwyvSpUqnDt3DiMjI9LS0hg0aBAGBgakpKRIZTXvy8nJiSVLluj+TOLjqVmzJqam\npkD+6l0XL14Unf2XRHT233JPjuAlXonFrp6jGMETBEEQBEEQBPLn6O9JiC1V2T0JsVzcsJrg1T9i\nb28vzWF/Um5ubpF1GBsbs2HDBmSyxwnkb9y4UahcXl6eVhl9/fyuXcFtanX+YJ6BgQEdO3Yssj0a\nenp6WvWfOHGC48ePExISgoGBAc2bN9d5XMFzPrm94OpvKpUKIyOjYtsglB2RoE8AHo/gKd8dSfn3\nx76Ujv7LyCpalgICAkhMTNTadvnyZTw8PEp1/NOUFYRX6Vnvza1bt/L777/r3BcVFYWPj8/zNg3I\n/4I0adIk/vzzT537C2YDFgRBEITnte//5+iXhjovl9T0NKpWrUp6ejpRUVGoVKpC5aKjowH4+++/\nC42uN2zYkEOHDgGwe/dujh07pvNcderUISYmhry8PG7evEl8fDwApqam3L17V+s8Dg4OREVF8fDh\nQ/Ly8pg/fz7Z2dklvp+UlBRsbGwwMDDgwIED5OTkoFQqteo+e/YsderU0TpOJpORk5NDrVq1SEhI\nQKFQAHDixAkcHR1LPK9QNsTIviCU0owZM151EwThhdJE+KQm/ktOxl3UKTfRr2Bb8oH/72U9vEtK\nSmLcuHHUqlVL5/6C2YAFQRAE4XmlPsoqcp/8gYJyYX9J/1d2csChSwcGDx5MrVq1GD16NIGBgXTq\n1Alr68dTY83MzBgzZgxJSUn4+vpq1Tljxgz8/f1ZvXo1RkZGLFmyROosF9SgQQMaNmxI//79qV27\nNvb29gD06tWLL774gn379tGoUSMgf+lbT09Phg4dip6eHq6urpQrV67E996mTRtWr16Nu7s7rq6u\ndOzYkdmzZ0v7x4wZw61bt1i0aJHWcS1btsTHx4eVK1cyZcoURo8ejVwux9nZmRYtWpR4XqFsiM6+\n8NLdzkpnX0Ic/yYmSsuV2JhYSPtLymCanp7OF198gUKhwNzcnG+++Ybc3FymTZtGeno6arUaPz8/\nHBwccHV1pXPnzhw7dox27dqRl5fHkSNHaN++PV988QUeHh74+/tTv359QkNDSUlJoWXLloSFhQFw\n/fp1unbtire3t1TWwsKC8ePHY2hoSIMGDaR2rVmzhn379pGbm0uHDh3w9vbm9u3bOssKwutEnZ5M\ncsg4FKe3Q46alGx91Pdt+OeL2lq5O0qTXbhChQr06tWLCRMmoFQqUSqVhUbZdd0rBXl4eODi4sKR\nI0eQy+X07t2bbdu2oaenx7p165DJZHz++eds3bqVadOmUblyZS5cuMDNmzdZvHixlA1469atuLq6\nMnDgQPbu3UvNmjVxcHCQXi9ZsoS///6bOXPmoK+vj1wuZ9myZZQvX/6lfO6CIAjCf0N5IxOd2/PK\nm5L1eeHEeV1a9WJYo9bS//v06VOoTLNmzXB3d9faFhUVBYC9vT3h4eHabShfnq1btxaqp+DDbc1D\n9yZNmrBv375CZYcOHcrQoUN1vhdAK+t/p06d6NSpEwCbN2+Wtg8fPrzI4wGpjd7e3tLf93r16hWb\nPFB4cUQYv/DS3HuoYExkGK02LWTG8R2sivuLpP9frmRsZDj3HuY/sdRkMA0NDcXMzIzDhw9r1RMc\nHEzbtm0JDw+ndevWHDt2jPXr19O0aVNCQkLw9fVlwYIFQH44spubG5s2bSIkJIRu3bqxadMmtmzZ\nUmxbz58/z1dffcXGjRsJCQnR2rdhwwZ69OhBSEiI1hNagPDwcDZt2sTWrVtRKBTFlhWE14E6PZnE\ngHYoTm7WTtIJkKNGceJnEgPaoU5PLvHe1Dh27BhVqlQhJCSExYsX61xn98l75UmVK1cmIiKCnJwc\n0tLSCA8PJycnh8uXLxcqq1KpCA4OxtPTk+3bt2vty83NpXHjxmzZsoXTp09TrVo1Nm/eTHR0NOnp\n6dy/fx9/f39CQkJ455132LVr11N8em+GGzdu0Lx5czw8PPDw8MDNzY1Tp04xbdo0IiMjX2pbDh06\nVOgLriC8KE/+7M+cOZNTp06V+viJEyeWKgwawMfHR+rIPW27NP9SU1OLPGbv3r2lqvtZREZGMm3a\ntBdW/39B15oO6MtK123Sl8npVtPhBbdIEEpHjOwLL8XTLFdSsWLFYjOYXrhwgfHjxwOPny5u3ryZ\nsWPzs4Y3adKEhIQEID9EShPSZGJigoODA/r6+sUmRQFo3LgxxsbGOvddu3aNbt26AeDi4sJff+WH\nbpUrVw53d3f09fVJSUkhNTW1yLKC8LpIDvUpdvlNANWdqySH+lDReWKJ2YUhf7Ti22+/ZebMmXTp\n0oX27dtrfcnVda+YmZlp1eHk5ASAtbU1jRs3BqBSpUpkZGRgbm6uVVYTDmhjY8P58+cLtUeTqdjK\nykqqq2LFimRkZGBlZcXixYvJzs4mOTlZ59JGb6LHyyo+ID1bj9o1qksPNk+ePElQUBCVK1d+6e0q\nuPSUUNiNGzf46KOPpPmuSqWSyZMn06JFC6ZNmyatq11anTt3ZteuXVKWbMi//nXq1MHKykrn/rKm\nUqmYN28ely9fRk9PDz09PRYuXIitrS1jx44lKCjoqeq7fPky8+bNK/Sgvii1a9cmJCSEffv2cffu\nXWbPns348eP54IMPSjx26dKlT9W2gu7fv8+XX36Jqakp9vb2DBs2TGe7SmvVqlXS9w2h7NmYWNC9\npiO74gv/jXlS95qOVCkQsarLuHHjyqppWnSN/AtvN9HZF16Kp1mu5PL8YFatWlVkBlM9Pb1CnfUn\nM31q9hfMKAqPs5TqoslWWlK5vLw85HK51nmSkpJYt24d27Ztw9TUlJ49exZZVhBeF+qUm/nLbpaC\nInobvj/fZtWPwcVmF4b8DvqOHTuIiooiIiKCs2fP8u677wJF3ytPKnjvPpkZuLiyJe1/smxAQACf\nfPIJ7du3Jzg4mKysoudlvgmenLIBcD9bn0c3qnJz5SCs3Zdz7949rK2tpc9SoVAwadIksrKyyM7O\nxt/fHycnJz744AMGDhzIn3/+iVKpZO3atRgZGTFt2jSSkpIwMjJi0aJFmJqalvr43377jStXrjB1\n6lTCwsLYtWsXcrkcV1dXhg0bhqurK3v37sXIyIgTJ06wYcMGFi5ciK+vL2lpaeTk5ODn50fDhg1Z\ntWoVv//+O3K5nE6dOjFmzJhX+dE/l7S0bOJibnMj8QbWlauxYsVqLC3LSQ9mgoODy+xcW7ZsYeTI\nkVhZWZVZncX55ZdfkMvlbNy4EYBt27YRHh7OF1988dQd/dIq+HlmKpRcvHiN3bt306BBA5o0acIH\nH3yg9fAkMjKSffv24e3tzeTJkzExMcHd3Z158+axa9cuFAoFM2bMQKVSoaenx/z587G1tWX16tXs\n3r0bW1vbQhFMKpWKgQMH4uLiwo4dO0rddl1Tl44dO8alS5fw9vZmxYoVLF26lFOnTpGTk4O7uzs9\ne/Zk2rRpGBgYkJqaSqdOnTh06BDJycksXbqUNWvWcP78eR49esTgwYMZMGCAtPa7paUlNWrUkM6/\nfv16fv31VwDef/99Pv3007K5KP8B81p9TMz9pGK/z9Yyt2Jeq49fYqsEoXiisy+8cE+7XEnljAyt\nDKZPznV3dHTk+PHjODk5sXHjRoyMjGjSpAlRUVE0a9aMs2fPUq9evVKdz8zMjLt371K/fn1Onz5d\nquNq165NbGwsjo6O0mhlSkoKFStWxNTUlLi4OJKSklCpVDrLCsV7cuRKIzAwkIULFxYaudLME39y\n3tuziIqKIiwsjOXLlz93XRp9+/Zl+fLlxa59+6LOXdDWrVsxNzfHwsJCOo/i9I7CoftFyVGTnpZS\n7L2pcfToUVQqFR06dKBu3brMnj1b6uwXda+8KqmpqdSoUQOlUsnBgwdp1qzZK2vLi6aZsqErkiMp\ny4Bxa4+i3tCOVANrgteu5ccffwTyVzcYMGAArq6uHDt2jNWrVxMYGEhOTg729vZ88sknTJw4kePH\nj5OSkkKlSpVYsmQJu3fv5sCBA7Ru3brUx2skJiayd+9eIiIiABg8eDDdunWTpm517NiRAwcO0LVr\nV9avX0+7du0YMGAAV69eJSAggLVr17JmzRoOHz6Mnp6eVM9/jSLjEdu3xBIbc5vc3Dyysh6QkvKQ\nBXMP4Ohkg6nF7UJTxIp6OHPkyBG++eYb9PT06NGjh9a821u3buHl5YWnpyf79+/nypUrBAYGAhAW\nFsbBgwfJycnhxx9/xMjIiJkzZ5KYmIhSqcTHx4e2bdtKUTxWVlYkJCQU6pA6OOgOa05PTyczM1P6\nf8G5zS4uLkRFReHh4UHr1q2JiooiJSWF77//Xit7eVH5cX777TfWrFmDvr4+jo6OeHtNZP2aPwnb\n+C0yZKjUj1BkJjNgQH9y1NkcOnSIPn36EBoayt27d/nhhx/Ytm0bMTExWFjkj9TGxcXRqFEj1qxZ\nw/3790lLS2PFihWMHDmSNm3acPDgQSkhWUREBHv27EGlUpUqUqC0NFOXIiIi2L59OzNmzGD16tWs\nWLGCU6dOkZSURFhYGEqlkj59+uDq6gqApaUl8+bNY+vWrdy6dYuNGzeiVCqpVq0a06dPJzs7G1dX\nVwYMGMDKlSvx9vbG1dWVWbNmAfn35bZt26T52wMGDKBbt25aDwPeZJWMzdj+4Vj8j+9kT0KsVnZ+\nfZmc7jUdmdfqYyoZmxVTiyC8XKKzL7xwxS1XoiuDaZOuxWcwHTZsGFOmTMHDwwNTU1MWL14MgK+v\nL56enuTl5ZV62S03Nzfmzp1LzZo1S/3HytPTkwkTJvD7779Tv359ABo1aoSpqSmDBg3C2dmZQYMG\nMWfOHAICAgqVFXQrauRKKBuapD0FHzrlZD4osnzSQ0Omx1WT/j+ixj36ta5fYnZhgBo1ajB58mR+\n/PFHZDIZPj4+5OTkAEXfK+vWrSvDd1t67u7ueHl5YWdnh4eHB3PnzqVHjx40bNjwlbTnRSpuykY1\nYyULHJIAeFC3ERMnTpQ6TZUqVWLlypUEBwejVCoxMXmcqKrgFIqMjAzi4uJo3To/KdWHH34IQEZG\nRqmP14iJiSEhIQFPT08AMjMzSUpKokuXLvzxxx907NiRw4cPM27cOCZMmMCDBw/YuXMnAA8fPgSg\na9eujBgxgp49e/Lxx/+9kTZFxiO+W36E+/e0o00UmXc5dPQ7/jysQqnMYN26NVr7dT2cWb58OXPm\nzGHjxo1YWlry2WefMWjQIAAePXrElClTmD9/vpTfwt/fX+pM16tXj08//ZTPP/+c48ePo1AoMDQ0\nJDQ0lDt37uDp6cm+fftQq9W0b9+e9u3bM23atEId0qI6+x9//DHbtm2ja9eudOjQgS5duujM1G1u\nbs769etZvHgxv/32m9bDCk1+nGHDhrFq1SouXbpEZmYmQUFB/PTTTxgaGuLlNY6ZfhuIj79MZav6\nNKj3AbfvxBH39y80dezHPwlHcG7+IXv2/MBnn+VPC4yPj2ft2rX8+eefTJ48GQC5XI6vry9Nmzbl\n3XffJTw8nDNnznD9+nWCgoLIycmhYsWKJCQkULduXYyMjDAyMiry/Rfl+vXrWsv11q5dW4qoKm7q\n0unTpzl37px0bG5urrQEm2Z6FORPeZTJZBgZGZGWlsagQYMwMDAgJSUFyJ+y+M477wD5D10OHTrE\nxYsXadq0qRT9+M477/D333+/NZ19yO/wB3UaIiWbTlM+xNLQmG41HUoM3ReEV0F09oUXrqjlSorK\nYNrsnS74zPta+v+TGUzNzc11hvbpGhEt2LHR9bpjx4507Nix0HEuLi6FyhacO/fzzz8XOqaoMEpd\nZYXHShq56t3XETNzo2LruHz5Mv/73/+Ij49nxowZtG/fnl9//ZV169ahp6eHg4MDfn5+BAYGkpKS\nQkJCAjdu3GD8+PFs2bKFpKQkVq9eDeQniPTy8iIpKYkPPvgALy8vPDw8pKiPzz//XGfIcEHz58/n\nzJkz1K5dWxq1vnPnjs4wT110ZauPj49n0aJFGBoaYmhoyNKlS6WRJig5g/3KlSupUKGCVvSKnmlF\n6fWq65W4rCiHXJaHV527RLT8h6VXq3DvkT6PcmWkqvUY07czceuOYmVlRUxMDOHh4UyaNAnInwbz\n1VdfUaNGDebPn09eXh7Z2dkMHjxY+mKqua9KCjkueK8VvK8LvtbMSywqc7Bm/x9//FHomIKv3dzc\ncHNzk7aX5ejb6+RppmxUvL4HQ/1u0pSH9evXU6VKFb7++mtiYmK0lld6clqErmlWT3O8hoGBAR07\ndiw0XUSpVLJo0SIuXbqEnZ0dZmZmGBgY4O/vT/PmzbXKzpkzh2vXrrFnzx48PDz4+eefi52i9brZ\nvjW2UEcfwMy0Mm1bfQZAhuIOn3n58Oefj5Oz6Xo48+DBA4yMjKhYMf+e/+GHH6Tys2fPpnPnzlI+\niyc5OzsDUKVKFemBjuZerlKlCoaGhlLiuIKdyZJyaWhUqFCBbdu2ER0dzeHDh5k0aRL9+vXDx8dH\nq1zB+p5MVKcrP87Vq1e5efMmo0aNAiAh4Q7VbSpjXbk+J6LXoVI/pGL5WujpPV5RRE9WCbUqj7S0\nNGQyGTVq1MDY2Fjr50alUtG0aVMAjIyM+PvvvzEwMGDZsmVaDz7Pnz8vTeMD3VOMilPcnP3ipi4Z\nGhrSv39//ve//xU6zsDAoNDrEydOcPz4cUJCQjAwMJDuo7y8PGQyGfB4GuKTUyZVKpXWe3yb2JhY\naGXbF4TX1dt5hwovVVHLlRTF0lB3YjzhzaMZuTp/7ha5udpfWHJz8zh/9hbfLT+CIuNRsfWkpqby\nww8/4Ofnx8aNG8nMzGTp0qWsXbuWiIgIbty4IYUIp6WlERwcTLdu3di+fbv0+sCBAwBcunSJRYsW\nSas2aL5U1qtXj5kzZ0ohw+vXr2f27Nl89dVXWm25evUqp0+f5ueff2bSpElcv34dgGXLljFy5EjW\nr1/PsGHDWLlyZbHv6cls9QcPHmTw4MGEhIQwevRoaaSmoKfJYA9g9k4v0NPnbKox95T6LG5yA88a\n9/nrnhkZaj2aW2ax0DGJqfVvE55ohZlzb63PIjk5GS8vL0JCQujXrx/h4eGkpqby559/snHjRsLD\nw7VyYQivztNM2cjIziX55g3p2qWkpEgjd/v37y922kWTJk2key0yMpLvv//+qY7XcHBwICoqiocP\nH5KXl8f8+fPJzs7G0NCQhg0bSvctQNOmTdm/fz+Qf/+tXbuWjIwMVqxYgb29Pd7e3lhaWupc9eF1\nlZaWTez52yWWMzergloJVy7HS9s0D1ciIiKktbDlcnmReWOqVKnCjh07UCqVOvfr6lgW7PAplUqp\nw1ewM1lSLo2Cx+fl5dGiRQsmTJhAeHh4oVU1SqpPV34cAwMDHB0dCQkJYcWK1bR5dzzVq72DhXlV\nOrabhFWF2lz5JxKl6vEDFaUyi4fZWRgaGGNsbCz9rEZHR+tsu+a8BX8Gjx07xq5du6hRowbXrl1D\nqVSiUCiIjS3ddMZnpflMnJyciIyMJDc3l0ePHjFv3rxij0tJScHGxgYDAwMOHDhATk4OSqVSmoYI\njwc9GjVqxNmzZ1Gr1ajVas6dOyet4S4Iwuvpv/OIW/jP6lrTgVlRu4oM5S9ILFfydilq5EqReZfD\nxx93hs+e/5mIn4pO1KQJNdSMPMXHx1OzZk0pg3TLli25ePEikN8ZAbQyjVeqVEnq1Ds6OkrH2dvb\nk5iYCDwesTpz5ozOkGGNq1ev0rRpU+RyOVWrVsXOzk467skwz6Loylbv7OxMUFAQ8fHx9OjRQ1pl\noqDiMtjrol/BFjPnPlzb9juNzPOXj3K0yMbRIht1LlxRGLEv2QIZoJCbo1++qtZ5KleuzPz58wkM\nDCQ9PR0HBwfKly9PrVq1GDt2LN26daN3795Fvk/h5SluygZoT9tQ5cr4vN+7RKXnd9x69erF1KlT\n2bt3L0OHDuWXX34pcvnSHj16cPToUenn96uvvuLOnTulPl7D1tYWT09Phg4dip6eHq6urpQrlz+t\nR5M8zc/PD8ifijF9+nSGDBlCbm4uM2bMwNzcnJSUFPr374+JiQnNmzenfPnyT/WZvUpx/x/pVBKl\nMouHj9K5m/y4bEpKijQFQ/NwpUKFCuTk5HDnzh2sra0ZM2YMX3+dH0E3YcIEgoOD+e6775g4cSIy\nmUyadqOLJkfOhx9+yK1bt5DL5VpRRk/L19cXFxcXBgwYAOTPv9f83iwtXflxateuzbVr17h//z6X\nLmZw4e891KzRmvsP/sHUxIqqNk3Izc0h+lw4MRd28DA7leOnVmNdqRF37mTSpk0bFi1axKhRo7Q6\ntEZGRpw5c4bmzZujVCpp3LgxgwcPxtfXl927dyOTyViwYAHly5end+/eDBo0iOrVq0t/ewrasGED\n+/bt0/mz+WQYPyBNJdClUaNG9O/fn82bN+Pi4oKbmxt5eXkMGTKk2M+uTZs2rF69Gnd3d1xdXenY\nsSOzZ89m7NixTJ8+nQ0bNmBnZ4dKpaJ69eq4ubnh7u5OXl4eAwYMoFq1asXWLwjCqyU6+8ILV9bL\nlQhvhuJGrgqGqQLI5TLS04peR1K5ewAAIABJREFUy/jJ0FxdoYZGRkaFyhZ8rSmvCVssWBc8HrEq\nKmS4YD0FwxoLjjI9GeapS1HZ6h0dHdm8ebO03vGUKVMKLX33NBnsNazdl2Owvx3qjHta2w/eMydD\nrcdChySyK9Rl4tnHDyc0n8Xy5ctp27YtgwcPZu/evfz5558A/Pjjj8TFxfHLL7+wY8cO1qzRnlMs\nvHwFp2w8qUo5NZtc/tHaZu3SlD7vj5X+v2fPHun1+++/D0C/fv2kbVOnTpVeFwzTh/yHcE9zvMbQ\noUMZOnRooe3du3ene/fu0v/NzMykZHIF+fv7F9r2X/Ewq+joh4IPQ3NzVTg59KVgsERRD2dmzZol\nhcZ3795dq4M+ZswY3Nzc+OCDD2jZsiU+Pj5FRh99+OGHnDhxAg8PD1QqVbErczxp4sSJLFiwQHpw\nA/md/ZkzZ7J161YMDQ3R19eXIhJKS1cuHWNjY3x9ffnkk094mJVLXm5FyhlZYGZamXOxm9HXN0KG\njE7tJmFoYMrBI0upUL4WBvrlUKlyqFOnDu3atZOmD/38889Ur16dTZs2MWfOHGQyGa1atWL06NGY\nmZnpnJ7k5eWFl5eXzjbb2Njw3Xff6dxXvXp1zpw5o3NfwakSBacurV+/Xto+ceJEJk6cqHVcwSlP\nmhwukD81UpNwD9DKhaB5qF1QUfelIAivJ9HZF14KsVyJ8KTSjlxBfkh/bEzJIa0atWrVIiEhAYVC\ngZmZGSdOnGDs2LEcO3asxGMvXLjAw4cPkcvlXLt2rVDiIU24ZvPmzbl69Sp//fUXI0aMkPbXrl2b\n9evXk5eXx82bN0lKStI6bsiQIRw7dox79+7pXNO9qGz1+/btw9ramo8//pi8vDwuXrxY5Dr3T0Pf\nwpq2PoGs/HIGffXOcy1dzm93LLApp6aKcQ4WLgM4beKK6tQ6nW2tUaMGeXl5HDhwgNzcXG7cuMEf\nf/yBp6cnDg4OWl8qhVfH7J1eJIdPKF0ov56+NGVDeDWMTQx0bjcxqUjPrl8WLm9soNWZ0/VwBZCS\nJ2oUzGmxbVt+TgdHR0e8vb0L7S/4QCYgIKBQGwqWLSqXhq516StWrMiKFSsKbQfdOXN0rbxSrVo1\nnflxunTpQpcuXTh6OJ7tW/JD0stbVqfDexMKl+38+OFQ926OuLjU0pm/p27dukXOpRcEQXjdiM6+\n8FKI5UqEJ5V25EqjQePPiihdmImJCVOmTGH06NHI5XKcnZ1p0aJFqTr7jRs3xtfXl/j4eAYNGlQo\nPFVXyHBBDRs2pH79+ri5uVGrVi0peZ+3t3ehME9dispW3759e8aPH4+5uTmGhoZFHv8sWnXswp/H\nTzPrdE1yMu/zxdAWmFWwYur6g0yPMqJfP2tsbGwKfSF3c3Nj3rx5VKtWDQ8PD/z9/YmPj+fMmTP8\n+uuvGBgYaI3eCq+OZsqG4kTJCUPNnPtIUzaEV8OhiQ07t8WV6oGoXC7DsYnNS2jVf5f4PAVBeFvJ\nigvvjI6OztNkYS1OdHQ0pSknvP5exrUUy5W8PK/zvVlwpKU0evdzpE3bWi+uQa+51/laCk/nVV1L\ndXoyiQHtilx+D8CgSl3sZvyFvkXx002Ex17U9QxdH835s7dKLOfUrCruw8TvhpKIz/PtIv5mvjnK\n4lr+fx2ykku+eUQ2fuGl0yxX4tO0M8MatRYd/beUQxMb5PLS/d4VIy2C8Pz0Layxm/EXZi0HgN4T\ngX16+pi1HCA6+q+R3n0dsapU/Go2VpVM6N3X8SW16L+tNJ+nmbme+DwFQXijiDB+QRBeCUvLcjg6\n2ZRqpMXRyQYLy3IllhMEoXj6FtbYfrYRdcpNFKd3kJOZgp5pBcyce4vQ/deMmbkRXj7vsX1rLLHn\ntXOcyOUyHJ1s6N3XETNzo1fYyv+O0nyetevmiM9TEIQ3iujsC4LwyvTu60jSjTSdy+9piJErQSh7\n+hVsKV8g277wejIzN8J9mDNpadnExdzm4UMVxsYGODYRD0CfRUmfZ3R09KtuoiAIQpkSnX1BEF4Z\nMXIlCIJQMkvLcm91zpKyJj5PQRDeFqKzLwjCKyVGrgRBEARBEASh7InOviAIrwUx0iIIgiAIgiAI\nZUdk4xcEQRAEQRAEQRCEN4zo7AuCIAiCIAiCIAjCG0Z09gVBEARBEARBEAThDSM6+4IgCIIgCIIg\nCILwhhGdfUEQBEEQBEEQBEF4w4jOviAIgiAIgiAIgiC8YURnXxAE4S1z48YNmjdvjoeHh9a/1NRU\npk2bRmRk5FPXuWvXLrp27cqpU6eeu30KhYLDhw8/dz2CIAiCIAhvM9HZFwRBeAvVrl2bkJAQrX/l\ny5d/5vqOHj3K5MmTadGixXO3LS4ujiNHjjx3PYIgCK/Skw9W3dzcin0geuPGDfr27VuqujMzM+nc\nuXOpyt68eZPz588X2r5161Z+//33Io/bu3dvqep/ko+PD1FRUaUq+6znKAuv8tyC8LLov+oGCIIg\nCC9HWlo2cTG3uZF4g0yFkrS0bCwty5V43O3bt/H19UWlUiGTyQgICEAmkzF58mRMTEwYOnQohw4d\nIjY2FgsLC2JjY9m3bx+5ubl06NABb29vAgMDSU9P5/r16yQmJuLr60uHDh1Ys2ZNobJz585FoVBQ\nq1YtOnToUOjcdnZ2L+HTEgRBeDa3s9LZlxDHv4mJWFS15usfvsPGxIKTJ08SFBREcHDwS23P8ePH\nycrKwsnJSWt7cQ8WlEol69ato1u3bi+0batWrXrh53gdzy0IL4vo7AuCILzhFBmP2L4lltiY2+Tm\n5pGV9YCUlIcsmHsARycbevd1xMzcqMjjly1bRv/+/enRowd79+5lxYoVjBs3josXLxIZGUmFChX4\n7bff6Nq1Ky1btiQ2Npbw8HDkcjnvv/8+w4cPB+DOnTv8+OOPHDp0iI0bN9KhQweAQmVHjRrFlStX\ncHNzY/r06YXO/dVXX72Mj00QBOGp3HuowO/4DvYmxKHOy0WWmolRZiqtNi2ke01HOqUbY21tDcDf\nf//NnDlz0NfXRy6Xs2zZMq26du7cSWhoKHK5nHr16jFv3jwUCgXjxo3j0aNHODs7F1tW48GDB6z4\nv/buPK7qKv/j+OteVoFQEZTUFMckDRQTSy3HR2W/KUvNUh9i4DKTlo5FU07hqJhKplOOuzmGlHRZ\nskZFcWvMFmdSsVBT1MxlNNwNWUW4LPf3B8NNZNUxl6/v5+Ph43G53/M953zvkeXz/Z5zPgsW4Ojo\nyJ133snSpUtp06YNAA0bNqRhw4Y8/fTT/OlPf8JqtWK1Wpk0aRL/+Mc/OHDgAJMnTyYyMpLIyEjS\n09MpLi4mPDycbt26VehvdHQ0a9eupWnTpuTl5QFlS7LGjx9PdnY2JSUlTJw4kbZt29rPWbJkCQcO\nHOCll15iyJAhfPDBB+Tn5xMREcFPP/3E0qVLcXBwICAggIkTJzJ//nwyMzM5duwYx48f55VXXmH5\n8uWcOHGC6OhoTp48SXR0NM7Ozhw6dIhnnnmG0aNHc+jQIaZOnYrJZMLd3Z0ZM2bwySef2NueM2cO\nERERnDlzhvz8fF5++WUeeeSRa/ufQ+QG0TR+EREDy8stZOG8b9j9/SlKS22/vH/hHJu3LOS9v0fy\n1FP9+cu4CdXWkZaWxgMPPABAly5d2LdvHwB33XUXDRs2rFTe1dWVsLAwhg4dSmZmJllZWQB06tQJ\nAF9fX3Jzc2ssW1vbIiI3k58v5tFv7SLWHN1Dsa3U/r75fB6OcV+zceo83pwWxbOhgwHIyMggMjIS\ni8VCp06dSE5OrlDfxYsXWbJkCR9//DFHjhzhwIEDrFq1ijZt2pCQkEC7du1qLFvOy8uLZ555hqFD\nh9KzZ08A2rRpw6RJk+xltm7dSpMmTbBYLMycOZOMjAyef/55WrVqxeTJk0lOTsbHxweLxcLChQt5\n++23K/Q1JyeHxMREli1bxjvvvMPBgwcBiI2N5be//S2xsbFMnjy50o3aESNG4OHhwYIFCwD48ccf\niYmJoVWrVsyePZsPP/yQxMREjh8/zrZt2wDIzs4mJiaGJ554gqSkJPvrTZs2AWW/M959912mTJnC\np59+SmZmJlFRUUydOpXY2Fgeeugh4uPjK7SdnZ1N9+7diYuLY+7cucyfP/8q/geI3Jz0ZF9ExMCS\nVqSR8XN+pfc93H3o3vWP9q8D2t1ZbR0mkwmbrexGQVFREWZz2X1iJyenSmVPnDjB0qVLWblyJe7u\n7vTu3dt+zNHRsc5la2tbRORmErltNUdzMyq9X+rlQUHobwEozMjlxZfHsH3jlzRq1IiZM2dSUFDA\n2bNn6dOnT4Xz6tevzx//WPYz+vDhw2RlZXH48GHuv/9+APtN0OrK1uTy6fwdO3Zkzpw5TJo0id/9\n7nf06NGD48eP24/v3LmT1NRUduzYUXYdhYVYrVacnZ0BOHbsGHfffTcuLi64uLgQEBBgP+/8+fOs\nXr0aKLspUZN77rkHZ2dnDh48SMuWLXF3d7df6/79+wFo3749AD4+PvbzvL297dccFBSEu7s7zs7O\ntGnThvT0dHbv3k1kZCRQtjyhvI5ynp6e7Nmzh2XLlmE2m2v9/ERuJQr2RUQMKju7gLTdp+tUNm33\naXKyC6o81r59e1JSUujduzfffvstgYGB1daTmZmJl5cX7u7u7N27lxMnTlBUVHRFZc1mM8XFxVfc\ntojIjXA6P4f1x9JqLWdrdAe5tiL2/Ocgs6a9zciRI+nRowcxMTHk5/9yU9ZqtTJ16lRWrVqFj48P\nL774Ytn5Npv9hmdpaWmNZWty+Y3axo0bs2rVKlJSUkhMTGTXrl3069evQvlRo0ZVeUP28n6Vf11+\nXmRkJPfdd1+tfQLsNw8uvckLZTd6XVzKlppdetP40tfl5cs/l0vfq1evHh999BEmk6nKdtesWUN2\ndjYJCQlkZWUxYMCAOvVX5FagRyQiIga1979r9KuSd+Ec/972nv3f5i0LWb3qqyrLhoeHk5SUxNCh\nQ1mxYgXh4eHVttmuXTvc3d0JCQlh3bp1hISEMGXKlCsqe++997J+/XpiYmKuqG0RkRvhs/+u0a/V\nRSvkFbDz4s9kZWXRokULrFYrX3/9dYWbohcuXMDBwQEfHx9OnTpFWloaRUVFtGrVirS0spsK5bvd\nV1f2UiaTyX4DtSpbtmxhy5YtdO/encjISNLS0jCbzZSUlABlT8vLp8lnZGQwa9asCue3aNGCw4cP\nY7VaycvLs/cxKCiIzz//HIBDhw7x4YcfVmr70qC+nJ+fH8eOHbOv/d++fXudb/Tu27ePixcvYrVa\nOXToEH5+frRt25bNmzcDsHbtWrZu3Vqh7czMTJo3b47ZbGbjxo1YrdY6tSVyK9CTfRERg7qYX/UT\ndTc3L3o//nal9++8szVhQyvvTNykSROWLFlS6f0VK1bYX8+YMcP+uradpv39/bFYLPayx48fp0+f\nPvY/5qKiomjVqhX9+/dnxowZhIaG1mmzpA0bNlzVzspDhgwhMjISf3//WsuGh4cTGhpKly5drqiN\nLl261CkV1YoVKzh48CARERFXVL+I3DhZhZWXSpUzn8/DNf5fZV+UlGD9vw7k2YoICwtjzJgx3HXX\nXQwZMoSpU6fy5JNPAmUb5z300EP079+ftm3bMmLECKZPn47FYuGVV15h2LBh9g36qiublJRkf4J/\n3333ERERgZeXV5V9bNGiBa+//jpLlizBZDIRHh6Oj48PRUVFhIeHM2vWLLZt20ZISAglJSW89NJL\nFc5v0KAB/fr1IyQkhObNm9unyYeFhfGXv/yF5557jtLSUiZMqLw3TLt27RgwYACvv/66/T03Nzfe\neOMNRowYgdlsJjg4mM6dO9uD9Jq0bt2a8ePHs2/fPkJCQvD09GTChAlERkYSHR2Ni4sLf/vb3yq0\nPWfOHEaPHs2uXbvo378/vr6+LFiwoNJ1ityKTFXdUSuXmppqu3S3zxrKUZdycvPTWBqLxtM4rmYs\nt/z7KEnLa59aWq5f/0Ae7O53hT27epemp/p8TgzL/vEpvm6eFcqMGzeOxx9/vE7B/rPPPlvhBkRd\nXe9gv6axVLB/69HPWeO42rGM3b+VCdtW1bn8tK5PM6xdt9oLyhVJSUkhPj6eefPm6fvSQK7FWP63\njqrXcRicnuyLiBhUQHtfVq/cW+1U/kuZzSYC2/teh17Vnp4qqmtfvOt5VHv+6dOnGT9+PEVFRZhM\nJqZNm8Znn312RSmcqqu3/OlScXExf/3rX2nRokWVKaVyc3MZN24cOTk5FBcXM3HiRPumVOXnjx07\nltOnT1fYDOrQoUO89dZbeHp62lNAeXpWvMFRbvr06ezevZvCwkIGDx7MwIEDKxz/5ptvmDVrFg4O\nDjz55JMMHz68yhRcK1as4NtvvyUzM5ODBw/y6quvsmbNGg4fPszMmTMJCgqqtS0Rqd7jLQN4MyW5\nTlP5HU1mnmgZUGs5EZFrQWv2RUQMqn59VwI71C2AD+zgi2d911+5R9WnpwIotpWSfHQ3/dYu4ueL\nedXWMXfuXAYMGIDFYuG5555jwYIFV5XC6XJnz55lzJgxWCwW+vfvT0JCQo0ppYKCgrBYLIwfP57p\n06dXqOubb76huLiYZcuW0adPH/vuzlFRUTz//PMVUkBVpbCwkGbNmpGYmEhCQkKlHNw2m40pU6YQ\nHR1NYmIiW7dupaCgoNoUXEePHmXRokW8+OKLLF68mIULF/LCCy+wZs2aWtsSkZr5unnSq2Xd1pT3\nahlIE7eqb/DJ/6ZLly7MmzfvRndD5KaiJ/siIgbW79lAThzPrjL9XrlG3m70e/b67HJfXXqqS9e1\nngYGbtjBl0sSq6wjLS2NsWPHAmV/3C1cuLBSmdpSOHXt2rXSOT4+Prz11lvMnz+fnJwcAgICqk0p\nlZaWxujRo4GyjAHHjh2rUNehQ4fsO1AHBQXh6lp2I2X37t1kZ2ezbNmyKlNAlXNxcSE7O5uQkBCc\nnJzIzMyscPz8+fO4uLjY1+AuXrwYqD4FV2BgICaTCR8fH+655x4cHBzw9vZmx44dtbYlIrWL6tqX\nPRknqvz5Vs7vjkZEde17HXslIrc7BfsiIgbmcYcLY8IfImlFGmm7K+7ObzabCOzgS79nA/G4w+VX\n70tN6akuzUUN8B+TmTP5OVWWvTQtU3mqvsvVJYXT5ebNm0f37t0ZPHgwGzZs4Kuvvqo2pdTl9V6a\n7qm83KXnlR+vV68eEydOpHPnzlX2odz27dvZtm0bFosFJyenSqmrzGZzpTZrSsFVU7qq2toSkdp5\n1/Mg6anRRG5bzfpjaRVmLjmazHVaoiQicq0p2BcRMTiPO1wIGxZMdnYBe/ec5uLFIurVcyKw/fWZ\nul+uzumpKJvSv+HY3iqPtW/fnpSUFHr37s23335r38W/thROHh4ebN++3f5E/nKZmZm0aNECm83G\npk2bKC0trZBSymq12lNKlfehY8eO7Nq1izZt2lSoq1WrVqxduxaAHTt22FM5tW3blu+//57OnTuz\ndu1avLy86Nat8kZdmZmZ+Pr64uTkxKZNmygpKcFqtdpvYjRs2JCSkhLOnDlD48aNGTVqFDNmzKg1\nBVd1111TWyK3ksuze1itVvz9/Zk8eTIODg6/atve9TxY9Mhz9s1Hs60Xqe9cjydaBmjqvojcEAr2\nRURuE/Xru17X3fYvV+f0VP/1o2fVu+OHh4czYcIEPvnkE5ycnHj77bI0gleSwqkqgwYNIioqimbN\nmtl36E9LS6sypdTQoUMZP348Q4cOxWazMWnSpAp19ejRg+XLlxMWFkbbtm1p0qQJABMmTODVV1/l\nyy+/rJAC6nIPPvgg0dHRhIWF8dhjj/Hwww8zefJk+7UCvPnmm4SHhwPQq1evalNwDRs2rNrPva5t\nidzMyoPrrMJ8yLrAXS1b2tN7QllWj+TkZPr163dd+uPr5qnd9kXkpqDUe1KBxtJYNJ7GYYSxVHqq\nMkYYS/mFxvPGuTyzB4Ap6wKuSdt5bPIr9mnzCQkJnDt3jgYNGrBu3ToAevbsyQsvvMC4cePw8vJi\n7969nDhxgvDwcFasWEFmZiZxcXGYTCbGjh1Lfn4+BQUFREZG0qFDhxt52VIH+r40DqXe+99oN34R\nEbkuHm8ZgKOpbr92lJ5KRGpSU2YPG9gze5zOyWTTpk3Ur1+flStXEh8fT3x8POvXr+enn34Cyvax\niI2N5a677mLnzp0sXboUf39/UlJSOHfuHAMHDsRisfDaa68RHR19A65WROTqaBq/iIhcF+XpqZKP\n7q61rNJTiUhNqsvsAb8sCzoN9JwWxyujx9C0aVOCgoLsG1R26tSJH374AcD+pL5hw4bce++9AHh7\ne5Obm4u3tzfvvfceMTExWK1W3Nzcfv2LExG5RmoN9lNTU+tUUV3Lyc1PY2ksGk/jMMJYDnBryXfO\nRzhlzau2zJ3OHgxwa2mI662Oka/tdqTxvL4yii6y7uieao9XyO6xMoUcawFHjhzh3Llz9rE6efIk\nR44cISMjg//85z/299PT00lNTeXMmTM4OTkxffp0bDYbf/7znzly5Ajx8fEa71uExsk4NJZXr9Zg\nX2v2by8aS2PReBqHkcZyfYeg2zo9lZHGUjSeN0Ls/q2UUP2eU5cqfCSQ5cuXY4mOYd26dQQFBQFl\nwf7EiRM5evQod999N8HBwcTHx+Pn50dwcDCff/45fn5+XLhwgY4dOxIcHMzmzZtxdXXVeN8C9H1p\nHNdqzf7tStP4RUTkulJ6KhH5X9SU2eNytgbu+D0QxOrVqxk0aBBhYWHYbDYGDhxIs2bNaj3/6aef\nJiIigg0bNhAaGsqaNWtYvnw5/fv3/18uQUTkulCwLyIiN4TSU4nI1WjgUv26eVsDdwqGP1LhvSeG\nD7b/rAkNDa1wbMaMGfbXoaGh9ieIERER9vfXr19vf92zZ8+r77iIyHWm3fhFRERE5JahzB4iInWj\nYF9EREREbhnlmT3qQpk9ROR2pmBfRERERG4pUV374ndHoxrL+N3RiKiufa9Tj0REbj4K9kVERETk\nluJdz4Okp0bTx69DpSn9jiYzffw6kPTUaENn9hARqY026BMRERGRW44ye4iI1EzBvoiIiIjcspTZ\nQ0SkaprGLyIiIiIiImIwCvZFREREREREDEbBvoiIiIiIiIjBKNgXERERERERMRgF+yIiIiIiIiIG\no2BfRERERERExGAU7IuIiIiIiIgYjIJ9EREREREREYNRsC8iIiIiIiJiMAr2RURERERERAxGwb6I\niIiIiIiIwSjYFxERERERETEYBfsiIiIiIiIiBqNgX0RERERERMRgFOyLiIiIiIiIGIyCfRERERER\nERGDUbAvIiIiIiIiYjAK9kVEREREREQMRsG+iIiIiIiIiMEo2BcRERERERExGAX7IiIiIiIiIgaj\nYF9ERERERETEYBTsi4iIiIiIiBiMgn0RERERERERg1GwLyIiIiIiImIwCvZFREREREREDEbBvoiI\niIiIiIjBKNgXERERERERMRgF+yIiIiIiIiIGo2BfRERERERExGAU7IuIyDVx/Phxnn322V+1jUcf\nfZQLFy78qm2IiIiIGIHjje6AiIjcuk7n5/DZsb1kFeZD1gWKS0tvdJdEREREBAX7IiJyFX6+mMfE\nbavYcGwvxbayAN+UdQHXzFOM/jKBqK598a7nwYEDB5g6dSpmsxl3d3dmzJjBgQMH+OCDD8jPzyci\nIoKTJ0/ywQcf4OjoSGBgIOPGjSMvL4+xY8eSn59PQUEBkZGRdOjQwd7+qVOnGDNmDH//+99p3Ljx\njfoYRERERG5amsYvIiI1unx6/s8X8+i3dhFrju6xB/rlbEDy0d30W7uIny/mMW3aNN544w0sFgv3\n338/H330EQA//vgjMTExtGrVikWLFvHRRx8RFxfHqVOnSE1N5dy5cwwcOBCLxcJrr71GdHS0vY3C\nwkKGDBlCTk4O+fn5V3VNmzdvJiEhocK1vfrqqxQUFFxVfddCcXExw4cP5/333+ef//wne/furbbs\nuHHj+PLLL69j70RERORWoyf7IiJyRSK3reZobkaNZY7mZhC5bTWHDx8mKCgIgC5durBgwQK6dOnC\nPffcg7OzM/v37+fkyZM8//zzAOTm5nLy5En8/f157733iImJwWq14ubmZq978uTJmEwm5s2bh5+f\n31VdQ48ePYCyGxnlZs+efVV1XSvp6emMHDmS3NxckpKSmDdv3g3tj4iIiNzaFOyLiEiVytfj/5Se\nTkbBBU7n5wCw/lgaAOajZ3HevA8czNhcnbE+HFDh/PV7tuOWlUlYWBidO3fmq6++omnTprz++uuY\nTCY+/fRTiouLKSkpwWaz4enpSUxMDDt37mTQoEGUlpbi5OREx44dOXr0qL3evLw8jh8/zoQJE5g5\ncybLli1j9+7dFBYWMnjwYAYOHMi4cePw8vJi7969nD9/npEjR7JixQoyMzOJi4tj48aNHDx4kNDQ\nUHu9jz76KMnJyaSnpzNlyhQcHR0xm83MnTuXBg0a2MuVlJQQGRlJeno6xcXFhIeH061bN5KSkoiL\ni8PJyYm2bdvy5ptvMmTIEAIDA0lLS6OwsJDZs2dTUlLC2LFjOXPmDPn5+bz88ss88sgjLF68GB8f\nH/bt28fJkyc5cOAAAQEBxMbGsm7dOgB69uzJCy+8UOGzqGm5g4iIiNy+NI1fREQq+PliHqO+jKfr\nJzOYsG0V7+/9FycuZNH1kxn8fuPSX9boFxRR2Pd+CkJ7YHN2xCH95wr1mLcfxNPHm7Fjx2K1WsnN\nzSUwMJCzZ8/SunVrBg4ciJubG66ursydOxcPDw8iIiLIzMzk1KlThIWF8fHHH7N69WqKiors9c6f\nPx9fX18CAgJo1qwZzZo1IzExkYSEBObOnWsv5+joSGxsLP7+/uzcuZOlS5fi7+9PSkpKjdefkZFB\nZGQkFouFTp06kZycXOGC0f5BAAAG8UlEQVR4cnIyPj4+WCwWFi5cyNtvvw1ATEwM8+fPJzExkcDA\nQPuSgIYNG2KxWOjTpw+xsbHk5eXRvXt34uLimDt3LvPnz7fXXVRURExMDEOHDiUpKYn09HRWrlxJ\nfHw88fHxrF+/np9++slevqblDiIiInJ705N9ERGxK1+PX9U0/WJbKXvOn7R/bavnjMv6nVBaiikr\nn1IfT8zn83CN/xcA5nPZdAgbwKxZs8jLy+PChQsMHTqUxYsX4+HhAYCvry8NGzbkscceo6ioiODg\nYBo0aEBAQADx8fF88cUXODg4cO7cOZYvX25vu2nTpnz33XccPHiQ7OxsQkJCcHJyIjMz016m/Al3\n48aN+c1vfgOAt7c3ubm5NX4GjRo1YubMmRQUFHD27Fn69OlT4fjOnTtJTU1lx44dQNkeAlarld69\nezNmzBj69u1L7969cXV1BaBbt24AdOzYkc2bN9OzZ0/+/e9/s2zZMsxmM1lZWfa6O3fubP9cdu/e\nzf79+wkKCsLRsezXdadOnfjhhx/s5b29vatd7iAiIiK3NwX7IiJiV5f1+OVc1u2gYGA3bN6eOP/z\ne3BzIf+1XwJjl0+28Ju2/ox7fTKpqanMmTMHDw8PXF1d7evRx48fz/vvv0/r1q2ZOnUqgYGBmEwm\nvLy8iIuLA8rW+m/atAmA/v37A9in12dlZbFt2zYsFgtOTk7cd9999vYdHByqfG2z2Wq8rmnTpjFy\n5Eh69OhBTExMpU0AnZycGDVqFL17967w/osvvkifPn347LPPGDZsmL3/5e3ZbDZMJhNbtmwhOzub\nhIQEsrKyGDBgQLX9NJlMFfpbVFSE2fzLpLzY2FiaNGnCu+++y549e3jnnXdqvDYRERG5fWgav4iI\nAGVr9MvX49eFqbAIm6cbFFhx+OkclFbcmd/UwB2v84VA2e73VcnLy+POO+8kJyeHlJSUCtP16yIz\nMxNfX1+cnJzYtGkTJSUlWK3WK6rjcllZWbRo0QKr1crXX39dqU9BQUH2mw8ZGRnMmjWL0tJSZs+e\njY+PD7///e/p2LEjJ0+WzYL47rvvANi1axetW7cmNzeX5s2bYzab2bhxY439bdeuHbt27aK4uJji\n4mK+//572rVrV+H6W7RoAcDnn39+xZ+fiIiIGJeCfRERAeCzY3srpdIrVz49v/yf+eR5ijr9Bte4\nzbhs2EVRlzY4bf0RU94vqeseevpJ1q1cxfDhw8vqMFf+lfPcc88xePBgIiMjGTFiBIsXL+bcuXN1\n7vODDz7IsWPHCAsLIz09nYcffpjJkydf0XVfLiwsjDFjxhAeHs6QIUNYuXJlhanzvXr1ws3NjZCQ\nEEaNGkVwcDBmsxl3d3cGDRrEsGHDMJlM9qC8PNvAmjVrGD58OA888ABffPEFw4YNo169evj6+rJg\nwYIq+9K8eXMGDRpEWFgYoaGhDBw4kGbNmtmPP/3003z44Yf84Q9/oEOHDpWWO4iIiMjty1TTdMbU\n1FRbcHBwrZWkpqZSl3Jy89NYGovG0ziux1jO3bWJd3duvCZ1+d3RiFn3/A4HazHBwcGsWbOGlJQU\noqKirkn9t4ohQ4YQGRmJv7+//T19XxqLxtM4NJbGobE0jmsxlv+tw3SNunRL0Zp9EREBoIHLlW3u\n1t6rKfszT1eYDeBoMtOrZSBRXftizcxh7NixmEwmzGYz06dPv9ZdFhEREZFqKNgXEREAHm8ZwJsp\nydVO5b+Uo8nM0v8bjo2y6f/Z1ovUd67HEy0DaOLmWVaongeJiYm/bqdvchaL5UZ3QURERG5TCvZF\nRAQAXzdPerUMJPno7lrL9moZaA/qh7Xr9mt3TURERESukDboExERu6iuffG7o1GNZfzuaERU177X\nqUciIiIicjUU7IuIiJ13PQ+SnhpNH78OOJoq/opwNJnp49eBpKdG413P4wb1UERERETqQtP4RUSk\nAu96Hix65DlO5+dUvx5fRERERG5qtabeu459EREREREREbmmlHqvGnXJa6hclsahsTQWjadxaCyN\nQ2NpLBpP49BYGofG0jiuxVimpqZeo97cerRmX0RERERERMRgFOyLiIiIiIiIGIyCfRERERERERGD\nUbAvIiIiIiIiYjAK9kVEREREREQMRsG+iIiIiIiIiMEo2BcRERERERExGAX7IiIiIiIiIgajYF9E\nRERERETEYBTsi4iIiIiIiBiMgn0RERERERERg1GwLyIiIiIiImIwCvZFREREREREDMZks9mqPZia\nmlr9QREREREREZGbXHBwsOlG9+FGqDHYFxEREREREZFbj6bxi4iIiIiIiBiMgn0RERERERERg1Gw\nLyIiIiIiImIwCvZFREREREREDEbBvoiIiIiIiIjB/D8S1vl9bwgmVQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcea2451898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline \n",
    "\n",
    "#crear dataframe, con los resultados de la descomposicion y otra información\n",
    "df = pd.DataFrame(dict(x=xs, y=ys, label=clusters, title=list(frame.title),\n",
    "                       nom_mejor_pelicula = list(frame.nom_mejor_pelicula),\n",
    "                       gana_mejor_pelicula = list(frame.gana_mejor_pelicula),\n",
    "                      ))\n",
    "df = df[df.nom_mejor_pelicula==1]\n",
    "df.index = range(len(df.index))\n",
    "\n",
    "#agrupamos por cluster\n",
    "groups = df.groupby('label')\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(17, 9)) # set size\n",
    "ax.margins(0.05) # Optional, just adds 5% padding to the autoscaling\n",
    "\n",
    "for name, group in groups:\n",
    "    ax.plot(group.x, group.y, marker='o', linestyle='', ms=12, \n",
    "            label=cluster_names[name], color=cluster_colors[name], \n",
    "            mec='none')\n",
    "    ax.set_aspect('auto')\n",
    "    ax.tick_params(\\\n",
    "        axis= 'x',          \n",
    "        which='both',     \n",
    "        bottom='off',     \n",
    "        top='off',        \n",
    "        labelbottom='off')\n",
    "    ax.tick_params(\\\n",
    "        axis= 'y',        \n",
    "        which='both',     \n",
    "        left='off',      \n",
    "        top='off',       \n",
    "        labelleft='off')\n",
    "    \n",
    "ax.legend(numpoints=1)  \n",
    "\n",
    "for i in range(len(df)):\n",
    "    ax.text(df.ix[i]['x'], \n",
    "            df.ix[i]['y'],\n",
    "            df.ix[i]['title'], size=10)  \n",
    "  \n",
    "    \n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 5.4. Clustering jerárquico"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El [método Ward](https://es.wikipedia.org/wiki/Método_de_Ward) es un algoritmo de clustering jerárquico. Nos permite en cada etapa identificar el par de clusters con la mínima distancia. Para realizarlo emplearemos la distancia previamente calculada. \n",
    "\n",
    "Devuelve 2 clusters principales, reemplazando a los 5 anteriores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from scipy.cluster.hierarchy import ward, dendrogram\n",
    "\n",
    "linkage_matrix = ward(dist) #define the linkage_matrix using ward clustering pre-computed distances\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 20))\n",
    "ax = dendrogram(linkage_matrix, orientation=\"right\", labels=list(info_pelis.titulo));\n",
    "\n",
    "plt.tick_params(\\\n",
    "    axis= 'x',    \n",
    "    which='both', \n",
    "    bottom='off', \n",
    "    top='off',    \n",
    "    labelbottom='off')\n",
    "\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.savefig('ward_clusters.png', dpi=500) \n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"ward_clusters.png\" alt=\"Logo\" style=\"width: 500px;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Topic modelling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Latent Dirichlet Allocation (LDA) es un modelo generativo que permite que conjuntos de observaciones puedan ser explicados por grupos no observados que explican por qué algunas partes de los datos son similares. Por ejemplo, si las observaciones son palabras en documentos, presupone que cada documento es una mezcla de un pequeño número de categorías (también denominados como tópicos/temas) y la aparición de cada palabra en un documento se debe a una de las categorías a las que el documento pertenece.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"lda.jpg\" alt=\"LDA\" style=\"width: 500px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import string\n",
    "def strip_proppers(text):\n",
    "    # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n",
    "    tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent) if word.islower()]\n",
    "    return \"\".join([\" \"+i if not i.startswith(\"'\") and i not in string.punctuation else i for i in tokens]).strip()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def strip_proppers_spacy(text):\n",
    "    token_list = []\n",
    "    doc = nlp(text)\n",
    "    for token in doc:\n",
    "        if token.is_stop == False and token.is_alpha == True and token.pos_ not in ['PROPN', 'CONJ', 'ADP', 'DET']:\n",
    "            token_list.append(token.text.lower())\n",
    "    return ' '.join(token_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from nltk.tag import pos_tag\n",
    "def strip_proppers_POS(text):\n",
    "    tagged = pos_tag(text.split())\n",
    "    non_propernouns = [word for word,pos in tagged if pos != 'NNP' and pos != 'NNPS']\n",
    "    return non_propernouns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 37.4 s, sys: 400 ms, total: 37.8 s\n",
      "Wall time: 18.9 s\n",
      "CPU times: user 22.2 s, sys: 188 ms, total: 22.4 s\n",
      "Wall time: 11.2 s\n",
      "CPU times: user 12 ms, sys: 0 ns, total: 12 ms\n",
      "Wall time: 4.82 ms\n"
     ]
    }
   ],
   "source": [
    "from gensim import corpora, models, similarities \n",
    "\n",
    "#quitar los nombres propios\n",
    "%time preprocess = [strip_proppers_spacy(doc) for doc in synopses]\n",
    "\n",
    "#tokenizar\n",
    "%time tokenized_text = [tokenize_and_lemm_spacy(text) for text in preprocess]\n",
    "\n",
    "#quitar las stopwords\n",
    "%time texts = [[word for word in text if word not in MY_STOP_WORDS] for text in tokenized_text]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#crear un diccionario de Gemsim a partir de los documentos\n",
    "dictionary = corpora.Dictionary(texts)\n",
    "\n",
    "#quitar los extremos ( similar al min max fijado en el tf-idf)\n",
    "dictionary.filter_extremes(no_below=1, no_above=0.8)\n",
    "\n",
    "#convertir el diccionario en 'bag of words corpus' para usarlo como referencia\n",
    "corpus = [dictionary.doc2bow(text) for text in texts]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4min 21s, sys: 392 ms, total: 4min 21s\n",
      "Wall time: 4min 21s\n"
     ]
    }
   ],
   "source": [
    "%time lda = models.LdaModel(corpus, num_topics=3, id2word=dictionary, update_every=5, chunksize=10000, passes=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0,\n",
       "  '0.007*\"mundo\" + 0.005*\"viajar\" + 0.004*\"amigo\" + 0.004*\"llegar\" + 0.004*\"descubrir\" + 0.003*\"grupo\" + 0.003*\"casar\" + 0.003*\"amor\" + 0.003*\"convertir\" + 0.003*\"personaje\"'),\n",
       " (1,\n",
       "  '0.007*\"casar\" + 0.006*\"familia\" + 0.006*\"descubrir\" + 0.006*\"decidir\" + 0.005*\"llegar\" + 0.005*\"encontrar\" + 0.004*\"mundo\" + 0.004*\"amor\" + 0.004*\"viajar\" + 0.003*\"poblar\"'),\n",
       " (2,\n",
       "  '0.005*\"llegar\" + 0.005*\"casar\" + 0.005*\"noche\" + 0.004*\"muerte\" + 0.004*\"pequeño\" + 0.004*\"mundo\" + 0.003*\"ciudad\" + 0.003*\"familia\" + 0.003*\"poblar\" + 0.003*\"buscar\"')]"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lda.show_topics()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# REFERENCIAS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [NLTK book](http://www.nltk.org/book/)\n",
    "* [SpaCy 101](https://spacy.io/usage/spacy-101)\n",
    "* [SpaCy tutorial](https://www.analyticsvidhya.com/blog/2017/04/natural-language-processing-made-easy-using-spacy-%E2%80%8Bin-python/)\n",
    "* [Textblob](https://www.analyticsvidhya.com/blog/2018/02/natural-language-processing-for-beginners-using-textblob/)\n",
    "* [NLP essential](https://www.analyticsvidhya.com/blog/2017/10/essential-nlp-guide-data-scientists-top-10-nlp-tasks/)\n",
    "* [Document clustering](http://brandonrose.org/clustering)\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ¡¡¡GRACIAS POR VUESTRA ATENCIÓN!!!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[PANDAS DOCUMENTATION SPRINT](https://www.meetup.com/es/PyData-Madrid/events/247525766/?eventId=247525766)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"pandas_logo.png\" alt=\"pandas logo\" style=\"width: 500px;\"/>"
   ]
  }
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