002_wordcount.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ejemplos con Wordcount"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## RDD paralelizando datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ParallelCollectionRDD[0] at parallelize at PythonRDD.scala:392\n"
     ]
    }
   ],
   "source": [
    "data = [1, 2, 3, 4, 5]\n",
    "distData = sc.parallelize(data)\n",
    "print distData "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Uso el contenido de un archivo de texto como fuente de datos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "textFile = sc.textFile(\"/usr/local/bin/spark-1.3.1-bin-hadoop2.6/README.md\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cuento palabras (flatMap, map, reduceByKey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "wordCounts = textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).reduceByKey(lambda a, b: a+b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Muestro todas las tuplas (collect)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'all', 1),\n",
       " (u'help', 1),\n",
       " (u'when', 1),\n",
       " (u'automated', 1),\n",
       " (u'Hadoop', 4),\n",
       " (u'\"local\"', 1),\n",
       " (u'including', 3),\n",
       " (u'computation', 1),\n",
       " (u'[\"Third', 1),\n",
       " (u'file', 1),\n",
       " (u'high-level', 1),\n",
       " (u'find', 1),\n",
       " (u'web', 1),\n",
       " (u'Shell', 2),\n",
       " (u'cluster', 2),\n",
       " (u'how', 2),\n",
       " (u'using:', 1),\n",
       " (u'Big', 1),\n",
       " (u'guidance', 3),\n",
       " (u'run:', 1),\n",
       " (u'Scala,', 1),\n",
       " (u'Running', 1),\n",
       " (u'should', 2),\n",
       " (u'environment', 1),\n",
       " (u'to', 14),\n",
       " (u'only', 1),\n",
       " (u'given.', 1),\n",
       " (u'rich', 1),\n",
       " (u'directory.', 1),\n",
       " (u'Apache', 1),\n",
       " (u'Interactive', 2),\n",
       " (u'sc.parallelize(range(1000)).count()', 1),\n",
       " (u'Building', 1),\n",
       " (u'do', 2),\n",
       " (u'guide,', 1),\n",
       " (u'return', 2),\n",
       " (u'graphs', 1),\n",
       " (u'Programs', 1),\n",
       " (u'Many', 1),\n",
       " (u'Try', 1),\n",
       " (u'built,', 1),\n",
       " (u'\"yarn-client\"', 1),\n",
       " (u'YARN,', 1),\n",
       " (u'not', 1),\n",
       " (u'using', 2),\n",
       " (u'Example', 1),\n",
       " (u'scala>', 1),\n",
       " (u'Once', 1),\n",
       " (u'Spark\"](http://spark.apache.org/docs/latest/building-spark.html).', 1),\n",
       " (u'Because', 1),\n",
       " (u'cluster.', 1),\n",
       " (u'name', 1),\n",
       " (u'Testing', 1),\n",
       " (u'Spark', 14),\n",
       " (u'Streaming', 1),\n",
       " (u'./bin/pyspark', 1),\n",
       " (u'SQL', 2),\n",
       " (u'through', 1),\n",
       " (u'GraphX', 1),\n",
       " (u'them,', 1),\n",
       " (u'[run', 1),\n",
       " (u'\"yarn-cluster\"', 1),\n",
       " (u'the', 21),\n",
       " (u'abbreviated', 1),\n",
       " (u'set', 2),\n",
       " (u'[project', 2),\n",
       " (u'Scala', 2),\n",
       " (u'##', 8),\n",
       " (u'thread,', 1),\n",
       " (u'library', 1),\n",
       " (u'see', 1),\n",
       " (u'examples', 2),\n",
       " (u'MASTER', 1),\n",
       " (u'runs.', 1),\n",
       " (u'[Apache', 1),\n",
       " (u'Pi', 1),\n",
       " (u'instructions.', 1),\n",
       " (u'More', 1),\n",
       " (u'Python,', 2),\n",
       " (u'#', 1),\n",
       " (u'processing,', 2),\n",
       " (u'for', 11),\n",
       " (u'its', 1),\n",
       " (u'version', 1),\n",
       " (u'wiki](https://cwiki.apache.org/confluence/display/SPARK).', 1),\n",
       " (u'provides', 1),\n",
       " (u'print', 1),\n",
       " (u'Configuration', 1),\n",
       " (u'supports', 2),\n",
       " (u'command,', 2),\n",
       " (u'[params]`.', 1),\n",
       " (u'refer', 2),\n",
       " (u'available', 1),\n",
       " (u'be', 2),\n",
       " (u'run', 7),\n",
       " (u'./bin/run-example', 2),\n",
       " (u'Versions', 1),\n",
       " (u'This', 2),\n",
       " (u'Hadoop,', 2),\n",
       " (u'Documentation', 1),\n",
       " (u'use', 3),\n",
       " (u'downloaded', 1),\n",
       " (u'distributions.', 1),\n",
       " (u'Spark.', 1),\n",
       " (u'example:', 1),\n",
       " (u'`examples`', 2),\n",
       " (u'-DskipTests', 1),\n",
       " (u'Maven](http://maven.apache.org/).', 1),\n",
       " (u'[\"Building', 1),\n",
       " (u'works', 1),\n",
       " (u'package', 1),\n",
       " (u'of', 5),\n",
       " (u'changed', 1),\n",
       " (u'programming', 1),\n",
       " (u'optimized', 1),\n",
       " (u'against', 1),\n",
       " (u'site,', 1),\n",
       " (u'graph', 1),\n",
       " (u'For', 2),\n",
       " (u'or', 3),\n",
       " (u'mvn', 1),\n",
       " (u'learning,', 1),\n",
       " (u'and', 10),\n",
       " (u'contains', 1),\n",
       " (u'stream', 1),\n",
       " (u'can', 6),\n",
       " (u'overview', 1),\n",
       " (u'package.)', 1),\n",
       " (u'prefer', 1),\n",
       " (u'one', 2),\n",
       " (u'documentation,', 1),\n",
       " (u'Data.', 1),\n",
       " (u'(You', 1),\n",
       " (u'Online', 1),\n",
       " (u'tools', 1),\n",
       " (u'your', 1),\n",
       " (u'threads.', 1),\n",
       " (u'Tests', 1),\n",
       " (u'fast', 1),\n",
       " (u'from', 1),\n",
       " (u'package.', 1),\n",
       " (u'APIs', 1),\n",
       " (u'SparkPi', 2),\n",
       " (u'structured', 1),\n",
       " (u'system', 1),\n",
       " (u'submit', 1),\n",
       " (u'systems.', 1),\n",
       " (u'start', 1),\n",
       " (u'Version\"](http://spark.apache.org/docs/latest/building-with-maven.html#specifying-the-hadoop-version)',\n",
       "  1),\n",
       " (u'params', 1),\n",
       " (u'Hadoop-supported', 1),\n",
       " (u'way', 1),\n",
       " (u'basic', 1),\n",
       " (u'README', 1),\n",
       " (u'<http://spark.apache.org/>', 1),\n",
       " (u'It', 2),\n",
       " (u'Spark](#building-spark).', 1),\n",
       " (u'engine', 1),\n",
       " (u'building', 3),\n",
       " (u'configure', 1),\n",
       " (u'on', 6),\n",
       " (u'Note', 1),\n",
       " (u'N', 1),\n",
       " (u'usage', 1),\n",
       " (u'\"local[N]\"', 1),\n",
       " (u'>>>', 1),\n",
       " (u'particular', 3),\n",
       " (u'instance:', 1),\n",
       " (u'./bin/spark-shell', 1),\n",
       " (u'general', 2),\n",
       " (u'with', 4),\n",
       " (u'easiest', 1),\n",
       " (u'protocols', 1),\n",
       " (u'must', 1),\n",
       " (u'And', 1),\n",
       " (u'also', 5),\n",
       " (u'versions', 1),\n",
       " (u'this', 1),\n",
       " (u'setup', 1),\n",
       " (u'page](http://spark.apache.org/documentation.html)', 1),\n",
       " (u'shell:', 2),\n",
       " (u'will', 1),\n",
       " (u'See', 1),\n",
       " (u'`./bin/run-example', 1),\n",
       " (u'guide](http://spark.apache.org/docs/latest/configuration.html)', 1),\n",
       " (u'following', 2),\n",
       " (u'locally', 2),\n",
       " (u'distribution', 1),\n",
       " (u'example', 3),\n",
       " (u'are', 1),\n",
       " (u'detailed', 2),\n",
       " (u'tests](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-AutomatedTesting).',\n",
       "  1),\n",
       " (u'mesos://', 1),\n",
       " (u'project', 1),\n",
       " (u'computing', 1),\n",
       " (u'URL,', 1),\n",
       " (u'is', 6),\n",
       " (u'in', 5),\n",
       " (u'higher-level', 1),\n",
       " (u'tests', 1),\n",
       " (u'1000:', 2),\n",
       " (u'an', 3),\n",
       " (u'sample', 1),\n",
       " (u'To', 2),\n",
       " (u'at', 2),\n",
       " (u'have', 1),\n",
       " (u'1000).count()', 1),\n",
       " (u'[\"Specifying', 1),\n",
       " (u'Party', 1),\n",
       " (u'[building', 1),\n",
       " (u'You', 3),\n",
       " (u'if', 4),\n",
       " (u'different', 1),\n",
       " (u'MASTER=spark://host:7077', 1),\n",
       " (u'no', 1),\n",
       " (u'programs,', 1),\n",
       " (u'Java,', 1),\n",
       " (u'that', 3),\n",
       " (u'storage', 1),\n",
       " (u'MLlib', 1),\n",
       " (u'same', 1),\n",
       " (u'machine', 1),\n",
       " (u'application', 1),\n",
       " (u'need', 1),\n",
       " (u'other', 1),\n",
       " (u'analysis.', 1),\n",
       " (u'build', 3),\n",
       " (u'which', 2),\n",
       " (u'online', 1),\n",
       " (u'you', 4),\n",
       " (u'several', 1),\n",
       " (u'A', 1),\n",
       " (u'distribution.', 1),\n",
       " (u'About', 1),\n",
       " (u'HDFS', 1),\n",
       " (u'[Configuration', 1),\n",
       " (u'The', 1),\n",
       " (u'sc.parallelize(1', 1),\n",
       " (u'locally.', 1),\n",
       " (u'Hive', 2),\n",
       " (u'running', 1),\n",
       " (u'uses', 1),\n",
       " (u'a', 9),\n",
       " (u'variable', 1),\n",
       " (u'core', 1),\n",
       " (u'data', 2),\n",
       " (u'class', 2),\n",
       " (u'built', 1),\n",
       " (u'Distributions\"](http://spark.apache.org/docs/latest/hadoop-third-party-distributions.html)',\n",
       "  1),\n",
       " (u'Please', 3),\n",
       " (u'Thriftserver', 1),\n",
       " (u'processing.', 1),\n",
       " (u'programs', 2),\n",
       " (u'documentation', 3),\n",
       " (u'pre-built', 1),\n",
       " (u'Alternatively,', 1),\n",
       " (u'Python', 2),\n",
       " (u'./dev/run-tests', 1),\n",
       " (u'comes', 1),\n",
       " (u'clean', 1),\n",
       " (u'<class>', 1),\n",
       " (u'spark://', 1),\n",
       " (u'first', 1),\n",
       " (u'requires', 1),\n",
       " (u'talk', 1),\n",
       " (u'latest', 1)]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordCounts.collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Las 5 palabras que más aparecen (takeOrdered)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'the', 21), (u'to', 14), (u'Spark', 14), (u'for', 11), (u'and', 10)]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wordCounts.takeOrdered(5, key = lambda x: -x[1])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Las 5 palabras que más aparecen pero calculandolo afuera de Spark"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[(u'the', 21), (u'to', 14), (u'Spark', 14), (u'for', 11), (u'and', 10), (u'a', 9), (u'##', 8), (u'run', 7), (u'can', 6), (u'on', 6)]\n"
     ]
    }
   ],
   "source": [
    "words = wordCounts.collect()\n",
    "words.sort(key = lambda x: -x[1])\n",
    "print words[0:10]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Las 5 palabras que más aparecen usando grupos (groupByKey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(u'the', <pyspark.resultiterable.ResultIterable at 0xb0e9bf4c>),\n",
       " (u'to', <pyspark.resultiterable.ResultIterable at 0xb0e2004c>),\n",
       " (u'Spark', <pyspark.resultiterable.ResultIterable at 0xb0e2006c>),\n",
       " (u'for', <pyspark.resultiterable.ResultIterable at 0xb0e2008c>),\n",
       " (u'and', <pyspark.resultiterable.ResultIterable at 0xb0e200ac>)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textFile.flatMap(lambda line: line.split()).map(lambda word: (word, 1)).groupByKey().takeOrdered(5, lambda (key, value): -1 * len(value))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## La palabra más larga (reduce)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "u'tests](https://cwiki.apache.org/confluence/display/SPARK/Contributing+to+Spark#ContributingtoSpark-AutomatedTesting).'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textFile.flatMap(lambda line: line.split()).reduce(lambda a, b: a if (len(a) > len(b)) else b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Palabras que empiezan con a (filter)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'a',\n",
       " u'and',\n",
       " u'and',\n",
       " u'and',\n",
       " u'an',\n",
       " u'analysis.',\n",
       " u'also',\n",
       " u'a',\n",
       " u'and',\n",
       " u'and',\n",
       " u'a',\n",
       " u'and',\n",
       " u'and',\n",
       " u'a',\n",
       " u'available',\n",
       " u'at',\n",
       " u'also',\n",
       " u'also',\n",
       " u'a',\n",
       " u'a',\n",
       " u'and',\n",
       " u'also',\n",
       " u'an',\n",
       " u'abbreviated',\n",
       " u'are',\n",
       " u'all',\n",
       " u'automated',\n",
       " u'and',\n",
       " u'against',\n",
       " u'at',\n",
       " u'a',\n",
       " u'and',\n",
       " u'also',\n",
       " u'a',\n",
       " u'application',\n",
       " u'a',\n",
       " u'an']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textFile.flatMap(lambda line: line.split()).filter(lambda word: word.startswith('a')).collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Palabras únicas que empiezan con a (distinct)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'a',\n",
       " u'and',\n",
       " u'abbreviated',\n",
       " u'also',\n",
       " u'available',\n",
       " u'all',\n",
       " u'against',\n",
       " u'an',\n",
       " u'application',\n",
       " u'analysis.',\n",
       " u'automated',\n",
       " u'at',\n",
       " u'are']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textFile.flatMap(lambda line: line.split()).filter(lambda word: word.startswith('a')).distinct().collect()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cantidad de palabras por frecuencia de repetición ordenados (sortByKey)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(1, 194),\n",
       " (2, 41),\n",
       " (3, 13),\n",
       " (4, 4),\n",
       " (5, 3),\n",
       " (6, 3),\n",
       " (7, 1),\n",
       " (8, 1),\n",
       " (9, 1),\n",
       " (10, 1),\n",
       " (11, 1),\n",
       " (14, 2),\n",
       " (21, 1)]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rangeCounts = wordCounts.map(lambda set: (set[1], 1)).reduceByKey(lambda a, b: a+b).sortByKey().collect()\n",
    "rangeCounts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Graficando cantidad de palabras por frecuencia de repetición"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cDoccDodbW05Ojs/79WowPXjwoEpKSvTUU0+V27Zs2TItW7ZMb775pm677TaP\nx6ekpMhms7m1Wa1WRUdHe7NMAAAAXIHdbpfdbndrczqdiouL82m/Xg2m99xzj2JiYtzaSktL9fzz\nz6tv374aPny4oqKiKjzeZrMpNjbWmyUBAACghqhSMN20aZPOnj2rM2fOSLo4Q5qeni5J6tOnjyIj\nIxUZGenx2CZNmqh79+6/sFwAAADUVlUKptOnT9fx48clSRaLRenp6UpPT5fFYtHChQvVvHlznxQJ\nAACA2q9KwfTyRbCVtWbNmms6DgAAAHWHT26wf60yMy/eHooLngAAAOoeo4JpQkKC63FWVhbhFAAA\noA7x+ic//TJvSkqRJOXn5/u3FAAAAFQrw4JpO0m2q+4FAACA2sewYAoAAIC6imAKAAAAIxBMAQAA\nYASCKQAAAIxAMAUAAIARCKYAAAAwAsEUAAAARiCYAgAAwAgEUwAAABiBYAoAAAAjEEwBAABgBIIp\nAAAAjEAwBQAAgBEIpgAAADACwRQAAABGIJgCAADACARTAAAAGIFgCgAAACMEVmXnwsJCJScna9++\nfdqzZ4/y8vKUmJioxMREt/127Nih5cuXa8+ePTp48KCKi4vlcDjUvHlzrxYPAACA2qNKM6a5ubla\ntmyZiouL1bdv3wr3czqdcjqdioyMVJcuXWSxWH5xoQAAAKjdqjRjGhkZqdTUVEkXQ2paWprH/UaP\nHu2aRV20aJEyMjJ+YZkAAACo7XyyxpQZUgAAAFQVFz8BAADACARTAAAAGKFKa0x974C/CwAAAICf\nGBZMX3U9mjhxokJCQmS322W32/1YEwAAQN3icDjkcDjc2nJycnzer1HBNCUlRTabTVarVdHR0f4u\nBwAAoE7yNDHodDoVFxfn036NCqY2m02xsbH+LgMAAAB+UOVgumnTJp09e1ZnzpyRJB08eFDp6emS\npD59+igoKEi5ubmue5fu37/fdVx4eLgiIiLUvXt3b9UPAACAWqLKwXT69Ok6fvy4pIv3K01PT1d6\nerosFosWLlyo5s2b68CBA3r99dddx1gsFk2fPl2SFBMTo2nTpnmpfAAAANQWVQ6mly+E9SQmJkZr\n1qy5poIAAABQNxm1xjQzM1OSuPgJAACgDjIqmCYkJLgeZ2VlEU4BAADqEMM++elNSSmSpPz8fP+W\nAgAAgGplWDBtJ8nm7yIAAADgB4YFUwAAANRVBFMAAAAYgWAKAAAAIxBMAQAAYASCKQAAAIxAMAUA\nAIARCKZP3g3aAAAgAElEQVQAAAAwAsEUAAAARiCYAgAAwAgEUwAAABiBYAoAAAAjEEwBAABgBIIp\nAAAAjEAwBQAAgBEIpgAAADACwRQAAABGIJgCAADACIGV3bGwsFDJycnat2+f9uzZo7y8PCUmJiox\nMbHcvllZWZo9e7YyMzMVEBCgHj166IknnlCLFi28WjwAAABqj0rPmObm5mrZsmUqLi5W3759K9zv\n0KFDmjBhgkpKSvTaa6/pxRdf1JEjR/Tss88qNzfXK0UDAACg9qn0jGlkZKRSU1MlXQypaWlpHveb\nN2+egoKCNGXKFAUHB0uSOnbsqFGjRmnRokUaN26cF8oGAABAbePVNaYlJSX65ptv1K9fP1colaTm\nzZsrJiZGX3/9tTe7AwAAQC3i1WB69OhRFRUVqX379uW23XjjjTp69KjOnz/vzS4BAABQS3g1mObl\n5UmSrFZruW1Wq1WlpaXKz8/3ZpcAAACoJSq9xrR6HPB3AQAAAPATrwbTsLAwSfI4K5qfny+LxeJx\nNvX/vOp6NHHiRIWEhMhut8tut3uzTAAAAFyBw+GQw+Fwa8vJyfF5v14Npq1atVJQUJD27dtXbtv+\n/fvVqlUr1a9fv8LjU1JSZLPZZLVaFR0d7c3SAAAAUEmeJgadTqfi4uJ82q9X15gGBATolltu0bp1\n61RYWOhqP378uDIyMtSvX78rHm+z2RQbG0soBQAAqIOqNGO6adMmnT17VmfOnJEkHTx4UOnp6ZKk\nPn36KCgoSI8++qieeOIJvfTSS3r44Yd17tw5ffjhh4qIiNDIkSO9/wwAAABQK1QpmE6fPl3Hjx+X\nJFksFqWnpys9PV0Wi0ULFy5U8+bN1aZNGyUlJWnOnDn605/+pICAAMXGxuqJJ55QeHi4T54EAAAA\nar4qBdPLF8FWpGPHjvrzn/98TQUBAACgbjLqdlGZmZmSxMVPAAAAdZBRwTQhIcH1OCsri3AKAABQ\nh3j1qvxf7k1JKZI83wsVAAAAtZdhwbSdJJu/iwAAAIAfGBZMAQAAUFcRTAEAAGAEgikAAACMQDAF\nAACAEQimAAAAMALBFAAAAEYgmAIAAMAIBFMAAAAYgWAKAAAAIxBMAQAAYASCKQAAAIxAMAUAAIAR\nCKYAAAAwAsEUAAAARiCYAgAAwAgEUwAAABiBYAoAAAAjBPripJmZmZo3b5527typ0tJSde7cWb//\n/e/VpUsXX3QHAACAWsDrM6a7du3S+PHjdf78eU2aNEmTJk1SUVGRXnjhBe3cudPb3QEAAKCW8How\nnTdvnsLCwjR16lTddttt6tu3r9555x01bNhQs2bN8nZ3AAAAqCW8Hky/++47de/eXQ0aNHC1BQcH\nq2vXrvr+++91+vRpb3cJAACAWsDrwbS4uNgtlJapX7++JOnAgQPe7hIAAAC1gNeDaVRUlL7//nuV\nlpa62kpKSpSZmSlJys3N9XaXAAAAqAW8Hkx/+9vf6siRI5oxY4ZOnjypEydOaNq0aTpx4sTFDutd\nqcsDkjK9XRIAAABqAK/fLmrQoEHKyclRSkqKvvjiC0lSly5d9OCDD8rhcKhJkyZXOPpV16OJEycq\nJCREdrtddrvd22UCAACgAg6HQw6Hw60tJyfH5/365D6mdrtdDzzwgI4cOaKGDRuqWbNmevfddxUc\nHKyOHTtWeFxKSopsNpusVquio6N9URoAAACuwtPEoNPpVFxcnE/79UkwlaTAwEBFRUVJko4fP661\na9dqyJAhHi+MKmOz2RQbG+urkgAAAGAwrwfTAwcOKD09XZ06dVKDBg20b98+LVy4UK1bt9bvf/97\nb3cHAACAWsLrwbR+/fratm2bPvvsMxUWFioyMlLDhg3Tww8/rKCgIG93BwAAgFrC68H0hhtuUFJS\nkrdPCwAAgFrOZ2tMr0XZvU65+AkAAKDuMSqYJiQkuB5nZWURTgEAAOoQr99g/5d5U1KKJCk/P9+/\npQAAAKBaGRZM20my+bsIAAAA+IFhwRQAAAB1FcEUAAAARiCYAgAAwAgEUwAAABiBYAoAAAAjEEwB\nAABgBIIpAAAAjEAwBQAAgBEIpgAAADACwRQAAABGIJgCAADACARTAAAAGIFgCgAAACMQTAEAAGAE\ngikAAACMQDAFAACAEQimAAAAMEKgL066e/duLViwQLt379bPP/+sZs2aqX///nrwwQcVFBTkiy4B\nAABQw3k9mO7fv1/PPPOMoqKi9Mwzzyg8PFzbtm3TggULlJWVpcmTJ3u7SwAAANQCXg+ma9asUXFx\nsV577TW1bNlSkhQTE6NTp05p6dKlKigoUGhoqLe7BQAAQA3n9TWmDRo0kCSFhIS4tYeEhKhevXqq\nX7++t7sEAABALeD1YDpo0CBFREQoKSlJP/30k86cOaONGzdq6dKluu+++1hjCgAAAI+8/lZ+06ZN\nlZSUpJdfflmPPPKIq3348OF66qmnvN0dAAAAagmvB9NDhw7pP/7jP9SyZUv94Q9/UEREhHbu3KmP\nPvpIZ86c0cSJE73dJQAAAGoBrwfTuXPn6sKFC5o6darrbfuuXbsqPDxcU6dO1d13363u3btXcPQB\nb5cDAACAGsLrwfTgwYNq27ZtubWknTp1cm2vOJi+6no0ceJEhYSEyG63y263e7tMAAAAVMDhcMjh\ncLi15eTk+LxfrwfTZs2aaf/+/SosLFRwcLCr/fvvv5d0cQ1qRVJSUmSz2WS1WhUdHe3t0gAAAFAJ\nniYGnU6n4uLifNqv14PpiBEj9PLLL2vixIkaMWKEwsLCtHPnTjkcDkVFRal3794VHmuz2RQbG+vt\nkgAAAFADeD2Y9unTR0lJSfr44481c+ZMFRQUqFmzZvrNb36jRx55RAEBAd7uEgAAALWA14OpJHXr\n1k3dunXzxakBAABQS/kkmF6rzMxMSWKNKQAAQB1kVDBNSEhwPc7KyiKcAgAA1CFe/0jSX+ZNSSmS\npPz8fP+WAgAAgGplWDBtJ8nm7yIAAADgB4YFUwAAANRVBFMAAAAYgWAKAAAAIxBMAQAAYASCKQAA\nAIxAMAUAAIARCKYAAAAwAsEUAAAARiCYAgAAwAgEUwAAABiBYAoAAAAjEEwBAABgBIIpAAAAjEAw\nBQAAgBEIpgAAADACwRQAAABGIJgCAADACARTAAAAGCHQFyd96623tHLlygq3z5w5UzabzRddAwAA\noIbySTAdPXq0hg0b5tZWWlqql19+WUFBQercubMvugUAAEAN5pNg2rJlS7Vs2dKtLSMjQ7m5uRo1\napQsFosvugUAAEANVm1rTNPS0mSxWDR48ODq6hIAAAA1SLUE04KCAv3v//6vYmNj1bx58+roEgAA\nADVMtQTTNWvWqKioiNlSAAAAVMgna0wvl5aWpvDwcPXr1+8qex6ojnIAAABgIJ8H03379ikrK0vD\nhw9XYODVunvV9WjixIkKCQmR3W6X3W73bZEAAABwcTgccjgcbm05OTk+79fnwTQtLU2SNGTIkKvu\nm5KSIpvNJqvVqujoaF+XBgAAAA88TQw6nU7FxcX5tF+fBtOioiKtWrVKNptNUVFRV93fZrMpNjbW\nlyUBAADAUD69+Onrr79Wfn4+Fz0BAADgqnwaTL/88ksFBwcrPj7el90AAACgFvDpW/nvvPOOL08P\nAACAWqRabhdVWZmZmVfczkVRAAAAtZdRwTQhIeGq+2RlZRFOAQAAaiGjgqn0pqSKLpTKlJSg/Pz8\naqwHAAAA1cWwYNpOEreLAgAAqIt8elU+AAAAUFkEUwAAABiBYAoAAAAjEEwBAABgBIIpAAAAjEAw\nBQAAgBEIpgAAADACwRQAAABGIJgCAADACARTAAAAGIFgCgAAACMQTAEAAGAEgikAAACMQDAFAACA\nEQimAAAAMALBFAAAAEYgmAIAAMAIgb468Y4dO/Txxx9r586dKioqUtOmTXX33Xdr1KhRvuoSAAAA\nNZhPgumqVas0ZcoU3XnnnXrppZcUHByso0eP6vTp077oDgAAALWA14Npdna23n33XQ0dOlTjx493\ntcfExHi7KwAAANQiXl9jmpaWpnPnzslut3v71AAAAKjFvD5jum3bNoWFhengwYOaNGmSDh48qLCw\nMPXr109/+MMf1LBhQ293CQAAgFrA68H05MmTOnv2rN544w098sgjuummm7Rr1y7Nnz9fBw4c0Hvv\nveftLgEAAFALeD2YlpaWqqioSImJia6387t3767AwEDNnDlTTqdTsbGxFRx9QJKzgm2Z3i4VAAAA\nBvF6MA0LC9PRo0fVs2dPt/ZevXpp5syZ2rt37xWC6av//lcxq9XqnUIBAADgkcPhkMPhcGvLycnx\neb9eD6YdOnRQZua1zW6mpKTIZrNVuN1qtSo6OvpaSwMAAEAl2O32cheyO51OxcXF+bRfrwfT22+/\nXampqdq0aZM6dOjgav/mm28k6YrB02azXWE2FQAAALWZ14NpXFycbrnlFi1YsEClpaWy2WzavXu3\nFixYoFtuuUVdu3b1dpcAAACoBXzyyU9/+tOflJycrKVLlyo5OVlNmjTRAw88oMTERF90BwAAgFrA\nJ8G0QYMGGjt2rMaOHeuL0wMAAKAW8kkwvVZXu2iKi58AAABqL6OCaUJCwlX3ycrKIpwCAADUQkYF\nU+lNSYMr2JYpKUH5+fnVWA8AAACqi2HBtJ0kbhcFAABQF9XzdwEAAACARDAFAACAIQimAAAAMALB\nFAAAAEYgmAIAAMAIBFMAAAAYgWAKAAAAIxBMAQAAYASCKQAAAIxAMAUAAIARCKYAAAAwAsEUAAAA\nRiCYAgAAwAgEUwAAABiBYAoAAAAjEEwBAABgBIIpAAAAjBDoi5NmZGTo+eef97ht5syZstlsvugW\nAAAANZhPgmmZsWPHKiYmxq0tKirKl10CAACghvJpMG3VqhWzowAAAKgUn64xLS0t9eXpAQAAUIv4\nNJjOmDFDAwYM0L333qsXX3xRO3bs8GV3AAAAqMF88lZ+aGiohg8frpiYGIWFheno0aP65JNPNGHC\nBE2ZMkU9e/b0RbcAAACowXwSTDt06KAOHTq4vu7atav69u2rxx57THPmzLlCMD0gyVnBtkxvlwkA\nAACD+PTip0uFhoaqT58+Sk1NVVFRkRo0aOBhr1f//a9iVqvVJ/UBAADgIofDIYfD4daWk5Pj836r\nLZheymKxeGxPSUm54lX8VqtV0dHRvioLAAAAkux2u+x2u1ub0+lUXFycT/uttmCan5+vDRs2qEOH\nDqpfv77HfWw2m2JjY6urJAAAABjEJ8F08uTJatGihaKjoxUWFqYjR45o8eLFys3N1UsvveSLLgEA\nAFDD+SSYtm/fXmvXrtWSJUtUWFiosLAwde3aVS+//LI6derkiy4BAABQw/kkmHpalwAAAABciV8u\nfqpIZmblbwnFhVAAAAC1i1HBNCEhoUr7Z2VlEU4BAABqCaOCqfSmpMGV2C9TUoLy8/N9XA8AAACq\ni2HBtJ0kbhcFAABQF9XzdwEAAACARDAFAACAIQimAAAAMALBFAAAAEYgmAIAAMAIBFMAAAAYgWAK\nAAAAIxBMAQAAYASCKQAAAIxAMAUAAIARCKYAAAAwAsEUAAAARiCYAgAAwAgEUwAAABiBYAoAAAAj\nEEwBAABgBIIpAAAAjEAwBQAAgBGqJZguW7ZM8fHxGjx4cHV0BwAAgBrI58E0Oztbs2bNUuPGjWWx\nWHzdHQAAAGoonwfTpKQk9ejRQzfffLNKS0t93R0AAABqKJ8G03/84x/avn27xo8fTygFAADAFfks\nmJ4+fVp/+ctfNG7cODVp0sRX3QAAAKCW8FkwnTFjhqKiojR06FBfdQEAAIBaJNAXJ01PT9fGjRs1\nd+7cKh55QJKzEvtlXkNVAAAAMJnXg2lhYaFmzJih+++/X40aNVJBQYEk6fz585KkgoICBQQEKDg4\n2MPRr/77X+VYrVYvVAwAAIBLORwOORwOt7acnByf9+v1YJqbm6ucnBwtXrxYixcvLrd96NCh6tu3\nr954441y21JSUmSz2SrVj9VqVXR09C+uFwAAAO7sdrvsdrtbm9PpVFxcnE/79XowbdSokaZNm+Z2\nz9LS0lI5HA5t27ZNb7/9tsLDwz0ea7PZFBsb6+2SAAAAUAN4PZg2aNBAMTEx5dqXL1+uevXqqXv3\n7t7uEgAAALVAtXwkqSRZLBY++QkAAAAVqrZg+sc//lHLli2rru4AAABQw/jkdlHXKjPT3NtAcbEV\nAACAbxkVTBMSEvxdwhVlZWURTgEAAHzEqGAqvSlpsL+L8CBTUoLy8/P9XQgAAECtZVgwbSeJ20UB\nAADURdV28RMAAABwJQRTAAAAGIFgCgAAACMQTAEAAGAEgikAAACMQDAFAACAEQimAAAAMALBFAAA\nAEYgmAIAAMAIBFMAAAAYgWAKAAAAIxBMAQAAYASCKQAAAIxAMAUAAIARCKYAAAAwAsEUAAAARiCY\nAgAAwAiBvjjp3r17NXfuXB08eFA5OTkKCgpS69atNWzYMN11112+6BIAAAA1nE+CaUFBgZo3b64B\nAwaoSZMmKiws1KpVqzRlyhQdP35cCQkJvugWAAAANZhPgmlMTIxiYmLc2m655RYdO3ZMS5cuJZgC\nAACgnGpdYxoWFqaAgIDq7BIAAAA1hE+DaWlpqUpKSpSTk6MlS5bo22+/1ciRI33ZJQAAAGoon7yV\nXyYpKUlLly6VJNWrV0+PP/64hg0b5ssuAQAAUEP5NJgmJCTo3nvvVU5OjtavX69Zs2bp/Pnzstvt\nFRxxQJLTlyVdo0x/FwAAAFDr+TSYNmvWTM2aNZMk9erVS5I0b948DRo0SBERER6OePXf/8xktVr9\nXQIAAIDPORwOORwOt7acnByf9+vTYHq5zp07KzU1VT/99JPHYJqSkiKbzVadJVWa1WpVdHS0v8sA\nAADwObvdXu4dbqfTqbi4OJ/2W63BdOvWrapXr55atmzpcbvNZlNsbGx1lgQAAABD+CSY/vnPf1Zo\naKg6deqk66+/Xrm5uUpPT9dXX32lhx56SOHh4b7oFgAAADWYT4Jply5d9OWXX2rFihUqKChQcHCw\nOnTooEmTJmnAgAG+6BIAAAA1nE+C6cCBAzVw4EBfnBoAAAC1VLWuMb2azExuy1QXcWEZAACQDAum\nCQkJ/i4BfpKVlUU4BQCgjjMqmEpvShrs7yJQrTIlJSg/P9/fhQAAAD8zLJi2k8TtogAAAOqiev4u\nAAAAAJAIpgAAADAEwRQAAABGIJgCAADACARTAAAAGIFgCgAAACMQTAEAAGAEgikAAACMQDAFAACA\nEQimAAAAMALBFAAAAEYgmAIAAMAIBFMAAAAYgWAKAAAAIxBMAQAAYASCKQAAAIxAMAUAAIARAn1x\n0i1btmjlypXauXOnTp48qdDQUHXq1EmjR49Wx44dfdElAAAAajifzJimpqbqxIkTGjFihN566y09\n/fTTysnJ0VNPPaWtW7f6oksAAADUcD6ZMR0/fryuv/56t7ZevXopISFBH3/8sXr06OGLbgEAAFCD\n+WTG9PJQKknBwcFq27atsrOzfdElAAAAarhqu/ipoKBAWVlZioqKqq4uAQAAUINUWzCdMWOGioqK\nlJCQUF1dAgAAoAbxyRrTy82bN0+rV6/Ws88+q+jo6CvseUCSszpKgjEy/V0AAAAwhM+DaXJyslJS\nUjRmzBjdd999V9n71X//Q11jtVr9XQIAAPg3h8Mhh8Ph1paTk+Pzfn0aTJOTk5WcnKxHH31UDz/8\n8FX3T0lJkc1m82VJMJDVar3KTDoAAKhOdrtddrvdrc3pdCouLs6n/fosmC5YsEDJyckaNWqURo8e\nXaljbDabYmNjfVUSAAAADOaTYLp48WLNnz9fvXr1Uu/evbVz50637TfddJMvugUAAEAN5pNgunHj\nRlksFm3evFmbN29222axWLR69WpfdAsAAIAazCfBNCkpyRenBQAAQC1WLbeLqqzMTG4dBAAA4E01\n6SJjo4IpN98HAADwvqysrBoRTo0KptKbkgb7uwgAAIBaIlNSgvLz8/1dSKUYFkzbSeJ2UQAAAHVR\nPX8XAAAAAEgEUwAAABiCYAoAAAAjEEwBAABgBIIpAAAAjEAwBQAAgBEIpgAAADACwRQAAABGIJgC\nAADACARTAAAAGIFgCgAAACMQTAEAAGAEgikAAACMQDAFAACAEQimAAAAMALBFAAAAEYgmAIAAMAI\ngb44aWFhoZKTk7Vv3z7t2bNHeXl5SkxMVGJioi+6AwAAQC3gkxnT3NxcLVu2TMXFxerbt68vugAA\nAEAt45MZ08jISKWmpkq6GFLT0tJ80Q1wjRyS7P4uAnUCYw3VhbGG2oE1pqiDHP4uAHUGYw3VhbGG\n2oFgCgAAACMQTAEAAGAEgikAAACM4JOLn67den8XgDrhiKSP/V0E6gTGGqoLYw0VOSBJyszM/MVn\n8sY5rsaIYNq4cWM1btxYp07NkjTL3+WgTkjwdwGoMxhrqC6MNVQsIcE746Mss/mKMcF09uzZOnXq\nlL9LAQAAQAVqbDDdtGmTzp49qzNnzkiSDh48qPT0dElSnz59FBQU5La/r58oAAAAzGZZu3ZtqS9O\nbLfbdfz48YudWCwqLS11PV64cKGaN2/ui24BAABQQ/ksmAIAAABV4dc1poWFhfrggw+Unp6uvLw8\ntWnTRna7XfHx8f4sCzVYRkaGnn/+eY/bZs6cKZvN5vo6KytLs2fPVmZmpgICAtSjRw898cQTatGi\nRXWVixqisLBQycnJ2rdvn/bs2aO8vDwlJiYqMTGx3L5VGVd///vftWTJEh07dkxNmjTRPffco4SE\nBAUEBFTH04KBKjvW3nrrLa1cubLc8W3atNH8+fPLtTPWcKktW7Zo5cqV2rlzp06ePKnQ0FB16tRJ\no0ePVseOHd32re7XNL8G0//3//6fdu/erXHjxumGG27QqlWrNHnyZJWWlqp///7+LA013NixYxUT\nE+PWFhUV5Xp86NAhTZgwQdHR0Xrttdd07tw5ffjhh3r22Wc1d+5chYeHV3PFMFlubq6WLVumDh06\nqG/fvkpLS/O4X1XGVUpKij788EM9/PDDuvnmm7Vr1y598MEHOnnypF544YXqemowTGXHmiQFBQVp\n2rRp5doux1jD5VJTU5Wbm6sRI0YoKipKOTk5+vTTT/XUU09p6tSp6tGjhyT/vKb5LZh+88032rJl\ni1555RXXDGlMTIyOHz+uv/71r7rzzjtVrx73/8e1adWqldvs6OXmzZunoKAgTZkyRcHBwZKkjh07\natSoUVq0aJHGjRtXXaWiBoiMjFRqaqqki8GhorBQ2XGVm5urjz76SPfee68ee+wxSVL37t1VXFys\nefPmacSIEWrbtm01PDOYprJjTZLq1at3xde5snMw1nC58ePH6/rrr3dr69WrlxISEvTxxx+7gqk/\nXtP8lvy+/vprNWzYUHfccYdb+6BBg3Tq1KlquYkraq+yi+08KSkp0TfffKN+/fq5/qNJUvPmzRUT\nE6Ovv/66OkpELVOVcbV582adP39eAwcOdDvHoEGDVFpayhhEpVzpda4MYw2eXB5KJSk4OFht27ZV\ndna2JP+9pvktmB44cEBt2rQpNyvarl07SRdvLwVcqxkzZmjAgAG699579eKLL2rHjh2ubUePHlVR\nUZHat29f7rgbb7xRR48e1fnz56uzXNQCVRlXZa9vN954o9t+jRo1Unh4OK9/qJRz585p+PDh6t+/\nv0aOHKn33ntP+fn5bvsw1lBZBQUFysrKci1789drmt/eys/Ly1OrVq3KtYeFhbm2A1UVGhqq4cOH\nKyYmRmFhYTp69Kg++eQTTZgwQVOmTFHPnj1dY8tqtZY73mq1qrS0VPn5+WrUqFF1l48arCrjKjc3\nV/Xr1/e4HtBqtfL6h6vq0KGDOnTo4JrM2bZtmz799FM5nU7NmjXLNcPFWENlzZgxQ0VFRa5PiPLX\na5oRn/wEeEvZi3WZrl27qm/fvnrsscc0Z84c9ezZ04/VAcD/b+/+YpuqAjiOf+/auo6NVoaocwTd\n7MDE1f3TTcka5kwEwouJaHTLDMFpNCbIjBowISHqk/qw+OfBTXFFUbMY4kzQRh2zYjASYrspiTo6\nx9Tg0JW0sDRUZ30gvVKKDATWi/t9kj30nNN7z21O7n7tPefe82P16tUZr+vq6vB4PGzevJkdO3Zk\n1YuczpYtW+jv72fdunVUVFTktC85u5TvcrmIxWJZ5elUnf7lVORcFRUVcfPNNxOJREgmk+bYOvmS\nV7rMMIxTfkMUOZ2zGVdut5s//viDZDKZ1TYej+v8J/+Jz+fD6XRmrNHQWJPp+P1+3nrrLdrb27nj\njjvM8lyd03IWTMvLyxkbG+Ovv/7KKP/xxx+Bf+aaipxPhmFQWlpKfn4+kUgkq35kZITS0lIcDkcO\neicXs7MZV+l5WCe3jUajxONxnf/kP0mlUlkLojTW5HT8fj9+v581a9bQ0tKSUZerc1rOgqnP5yOR\nSBAMBjPKA4EAl1122bS3wBA5U0eOHGH37t14PB4cDgc2m41bbrmFXbt2kUgkzHbj4+OEw2F8Pl8O\neysXq7MZV/X19VxyySUEAoGMbQQCAQzDoLGxccb6Lf8fwWCQY8eOcf3115tlGmvyb7Zu3Yrf76et\nrY377rsvqz5X5zTbmjVrNp/10ZwHpaWlfPvtt+zYsQOXy8Xk5CTbtm0jGAzS0dGRtbJL5Ew8++yz\nDA8Pc/ToUQ4fPszevXt5/vnnmZiYYMOGDeaTKsrKyujr6yMUClFcXMzIyAgvvPACeXl5bNy4EafT\nmeMjEav56quviEQijIyMsHv3bi699FIMw+DAgQOUlJRgt9vPeFylFwj09vby559/kpeXRzAYpKen\nh5UrV2bdckVml+nG2u+//85TTz1FMpkkHo/zyy+/8OGHH9Ld3c2iRYvo6OjAbj++hERjTU6lt7eX\n145WgwUAAAZ+SURBVF57jfr6elatWsVvv/2W8bdgwQLgzP9Xns9xZgwMDEx/I7QLJP1I0s8++4x4\nPM7VV19NS0sLt956a666JBe5d955h4GBAQ4ePEgikcDlcuH1emlpaWHJkiUZbX/44Qe6urrYt28f\nNpuN2tpaPZJU/tW9997L+Pg4cHxKSPqSqWEYvP3221xxxRXA2Y2rEx/fN3/+fFasWKHHRMq0Y62w\nsJDnnnuO/fv3c/jwYaamprjyyivx+Xy0trYyZ86crG1qrMmJOjo6GBoaOuW9cA3DoL+/33w90+e0\nnAZTEREREZE0PfNTRERERCxBwVRERERELEHBVEREREQsQcFURERERCxBwVRERERELEHBVEREREQs\nQcFURERERCxBwVRERERELEHBVEREREQsQcFURGalYDBIc3MzO3fuzKprb2+nubmZPXv2ZNW1trby\n4IMPXrB+hcNhmpubGRwcvGD7EBGxKgVTEZmVqqurMQwjKwDG43FGRkYoKCjIqjt06BAHDx6kpqZm\nJrsqIjJrKJiKyKzkdrspKysjHA5nlA8ODmK321m5ciWhUCijLt22urr6nPadTCbP6f0iIv9X9lx3\nQEQkV6qrq9m+fTvRaJTi4mLgePi87rrraGho4P333yeRSFBQUGDW2Ww2qqqqSCaT+P1+du7cycTE\nBG63m8bGRu6//36KiorMfdxzzz2Ul5ezYsUK3nzzTcbGxli9ejUPPPAAY2NjvPzyy3zzzTfk5+ez\nbNkyGhoasvo5PDzMli1b+P777zl69ChutxuPx8Njjz3GggULZubDEhGZAQqmIjJr1dTUsH37dnNe\nJxwPn0uXLqWyshLDMBgaGjLDYjgcxuPxMGfOHJ588klCoRCtra14vV4ikQg9PT3s27ePV155BYfD\nAYBhGAwPD3PgwAHa2tooKSnB6XQSjUZZv349DoeD9evXM2/ePD799FNefPHFjD4mEgkef/xxrrrq\nKh599FGKi4uZmJggHA6TSCRm9gMTEbnAFExFZNa64YYbMAzDDKaxWIzR0VEefvhhCgoKqKioIBQK\n0dDQwPj4OL/++itNTU3s2bOHvXv38tBDD3H33XcDUFdXx+WXX87TTz/Nxx9/zKpVqwBIpVLEYjHe\neOMNSktLzX13dXURi8Xo7u6mvLwcgPr6ep544gkOHTpkthsbG+PIkSO0tbWxdOlSs7ypqWkGPiER\nkZmlOaYiMmvNnTsXj8djzh0dHBwkLy+PyspKAKqqqsy6E+eXpueeLl++PGN7y5Ytw+l08vXXX2eU\nl5WVZYRSgFAoxDXXXGOG0rTbbrst4/XChQuZO3cur776Kh988AGjo6PncMQiItamYCois1p1dTU/\n//yzeXl8yZIlOJ1O4Pgvqvv372dycpJwOIzdbsfr9RKPx7HZbLjd7oxtGYbBvHnziMfjGeXz58/P\n2m88HjfntZ7o5LLCwkI6OzvxeDy8/vrrrF27lrvuuouenh6mpqbO9fBFRCxFwVREZrX0rZ/C4TCD\ng4NUVVWZdV6vl1QqxdDQEOFwmMWLF1NQUIDL5WJqaopYLJaxrVQqRTQaPWVgPZnL5SIajWaVn6qs\nrKyMTZs20dfXR3d3N01NTWzdupXe3t7/dMwiIlalYCois5rX6yUvL49gMMjo6GhGMC0qKsLj8RAI\nBBgfHzdDbF1dHQCffPJJxrY+//xzjh07Rm1t7bT7ra2tZXR0lEgkklHe399/2vdde+21PPLIIxQW\nFjI8PHxGxygicrHQ4icRmdUKCwtZvHgxX3zxBTabDa/Xm1FfVVXFe++9B/xz/9Ibb7yRm266ia6u\nLiYnJ6msrDRX5VdUVHD77bdPu98777yTjz76iI0bN7J27VpzVf5PP/2U0e7LL7+kr6+PxsZGSkpK\nSKVS7Nq1i8nJSTMgi4j8XyiYisisV1NTw3fffYfH4zHvWZqWDqYOhyMjtD7zzDP4/X4CgQDbtm3D\n7XazfPly2tvbsdv/ObWe6jI+HJ9L2tnZyUsvvURnZyf5+fn4fD7WrVvHpk2bzHYLFy6kqKiId999\nl4mJCex2O4sWLWLDhg1nFIBFRC4mxsDAQCrXnRARERER0RxTEREREbEEBVMRERERsQQFUxERERGx\nBAVTEREREbEEBVMRERERsQQFUxERERGxBAVTEREREbEEBVMRERERsQQFUxERERGxBAVTEREREbEE\nBVMRERERsQQFUxERERGxhL8BSYlSj0VTjDsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb0e2b12c>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "ranges = [t[0] for t in rangeCounts]\n",
    "y_pos = np.arange(len(ranges))\n",
    "words = [t[1] for t in rangeCounts]\n",
    "\n",
    "plt.barh(y_pos, words)\n",
    "plt.yticks(y_pos, ranges)\n",
    "plt.xlabel('Words')\n",
    "plt.title('Word count by word frequency')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}