{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk.corpus.reader import CategorizedPlaintextCorpusReader\n",
    "reader = CategorizedPlaintextCorpusReader('/media/storage/dpla-data/new/words-6mill/colls/', r'.*\\.txt', cat_pattern=r'(\\w+)\\.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['gpo']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "reader.categories('gpo.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "gpowords = reader.words('gpo.txt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "36646650"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(gpowords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "438479"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(set([w.lower() for w in reader.words('gpo.txt')]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cfd = nltk.ConditionalFreqDist(\n",
    "(genre, word)\n",
    "for genre in ['gpo', 'artstor']\n",
    "for word in reader.words(categories=genre))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'filter' object is not subscriptable",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-fee8e157bba3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mgpofiltered\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: 'filter' object is not subscriptable"
     ]
    }
   ],
   "source": [
    "gpofiltered[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import re\n",
    "gpotokens = re.split(r'\\W+', reader.raw('gpo.txt'))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gpotokens)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from nltk.corpus import stopwords\n",
    "gpofiltered = [w for w in gpotokens if w.lower() not in nltk.corpus.stopwords.words('english')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['United',\n",
       " 'States',\n",
       " 'National',\n",
       " 'Archives',\n",
       " 'Records',\n",
       " 'Service',\n",
       " '1975',\n",
       " '1975',\n",
       " 'Includes',\n",
       " 'bibliographical']"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gpofiltered[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'gpofiltered' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-3-f64a9ad921dc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mfd\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnltk\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFreqDist\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtoken\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtoken\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mgpofiltered\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#fd = nltk.FreqDist((word) for word in reader.words(categories='gpo'))\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmost_common\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;31m#fd[1]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'gpofiltered' is not defined"
     ]
    }
   ],
   "source": [
    "fd = nltk.FreqDist((token) for token in gpofiltered)\n",
    "#fd = nltk.FreqDist((word) for word in reader.words(categories='gpo'))\n",
    "type(fd.most_common())\n",
    "#fd[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "/j8KyVPj1O8MAQAAAABJRU5ErkJggg==\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac2c3c0410>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#fd.most_common(10)\n",
    "%matplotlib inline\n",
    "fd.plot(50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "9oiIn0t6N9kqsOL+8veh2v1qu/oN1eP4ondc/9yIeGX3zrK4vD9wMPCmct+YxvCI30wUkpZKemJt\n",
       "127AjaSjvlvSRsBcZIO3LSJkkPLDF9WOBXAJsI+k7YsdG0p6YonzbxYR3wGOB3adw2sbMxQe8ZtJ\n",
       "YyPgZEmbkWJhPwaOAv6bDOXcAVza57mDMnRWA0dL+ixwHbke0HlixF2SDge+JKnKInon8GvgbEnr\n",
       "kzOF4+b4voyZNdbqMWYtkbQd8I2yMGzM2ONQjzGjwSMos2DwiN8YYyYMj/iNMWbCsOM3xpgJw47f\n",
       "GGMmDDt+Y4yZMOz4jTFmwrDjN8aYCeP/A/x7OPwUGqCMAAAAAElFTkSuQmCC\n"
      ],
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fac61ae5850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "fd.plot(50, cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "pickle.dump( gpofiltered, open( \"/media/storage/dpla-data/pickles/gpofiltered.p\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot serialize '_io.BufferedReader' object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-5-3f88e95676c4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mpickle\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mpickle\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdump\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mgpowords\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mopen\u001b[0m\u001b[1;33m(\u001b[0m \u001b[1;34m\"/media/storage/dpla-data/pickles/gpowords.p\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"wb\"\u001b[0m \u001b[1;33m)\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: cannot serialize '_io.BufferedReader' object"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "pickle.dump( gpowords, open( \"/media/storage/dpla-data/pickles/gpowords.p\", \"wb\" ) )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nltk.corpus.reader.util.StreamBackedCorpusView"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(gpowords)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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