{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Handling Texts\n",
    "\n",
    "This notebook shows how you can handle texts you already have."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Opening text files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "ls"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This string has 1344061 characters.\n"
     ]
    }
   ],
   "source": [
    "# Importing a text file\n",
    "with open(\"Hume Treatise.txt\", \"r\") as f:\n",
    "    Text1 = f.read()\n",
    "    \n",
    "print(\"This string has\", len(Text1), \"characters.\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This string has 1,344,061 characters\n"
     ]
    }
   ],
   "source": [
    "print(\"This string has\", \"{:,}\".format(len(Text1)), \"characters\") # This uses a mini format language"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple handling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Extracting the Text\n",
    "Now we want to get just the text out and remove the Gutenberg stuff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "*** START OF THIS PROJECT GUTENBERG EBOOK A TREATISE OF HUMAN NATURE ***\n",
      "\n",
      "\n",
      "\n",
      "\n",
      "Produced by Col Choat\n",
      "\n",
      " \n",
      "+++++++++++++++++++++\n",
      " e\n",
      "same object can only be different by their different feeling, I should\n",
      "have been nearer the truth.\n"
     ]
    }
   ],
   "source": [
    "startText = \"*** START OF THIS PROJECT GUTENBERG EBOOK\"\n",
    "endText = \"End of Project Gutenberg's A Treatise of Human Nature, by David Hume\"\n",
    "\n",
    "start = Text1.find(startText)\n",
    "end = Text1.find(endText)\n",
    "HumeString1 = Text1[start:end].strip()\n",
    "print(HumeString1[:100:],\"\\n+++++++++++++++++++++\\n\",HumeString1[-100:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Finding things\n",
    "\n",
    "Now we will use the built in tools to find things and show context."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "70\n",
      "72\n"
     ]
    }
   ],
   "source": [
    "## This will count the instances of the word we want.\n",
    "## Note how I handle the possibility of a capitalized version of the word\n",
    "print(HumeString1.count(\"Truth\"))\n",
    "print(HumeString1.count(\"truth\"))\n",
    "print((HumeString1.count(\"truth\") + HumeString1.count(\"Truth\")))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What word to find?thened\n",
      "e same difficulties, it is burthened with some additional\n",
      "on \n",
      "\n",
      "le contempt is likewise\n",
      "strengthened by the two relations of \n",
      "\n",
      " \n",
      "\n",
      "------------------------------------------------------\n",
      "Thened\n",
      " \n",
      "\n"
     ]
    }
   ],
   "source": [
    "word2find = input(\"What word to find?\")\n",
    "context = 30\n",
    "start = 0\n",
    "while start != -1:\n",
    "    start = HumeString1.find(word2find,start+1)\n",
    "    print(HumeString1[(start-context):(start+context)].strip(\"\\n\"), \"\\n\")\n",
    "print(\"------------------------------------------------------\")\n",
    "word2find2 = word2find.capitalize()\n",
    "print(word2find2)\n",
    "start = 0\n",
    "while start != -1:\n",
    "    start = HumeString1.find(word2find2,start+1)\n",
    "    print(HumeString1[(start-context):(start+context)].strip(\"\\n\"), \"\\n\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What root to look for?the\n",
      "Number of matches  21682\n",
      "Variant forms:  {'mathematician', 'burthened', 'strengthen', 'THERE', 'theory', 'Father', 'Mathematical', 'hypothesis', 'Otherwise', 'FARTHER', 'Theologians', 'otherwise', 'Either', 'others', 'strengthening', 'further', 'either', 'sympathetic', 'theirs', 'mathematics', 'farthest', 'breathe', 'anther', 'Others', 'then', 'mathematical', 'Neither', 'ether', 'hypotheses', 'there', 'strengthens', 'ANOTHER', 'farther', 'neither', 'authentic', 'father', 'atheist', 'Athenians', 'smoother', 'They', 'forefathers', 'other', 'Then', 'brothers', 'nevertheless', 'Other', 'Mathematics', 'Another', 'There', 'another', 'theft', 'their', 'hitherto', 'together', 'thereby', 'THEM', 'OTHERS', 'Whether', 'The', 'brother', 'thence', 'Themistocles', 'the', 'whether', 'them', 'THE', 'therefore', 'THESE', 'theologians', 'these', 'atheists', 'theatre', 'tothe', 'weather', 'mathematicians', 'atheism', 'These', 'THEY', 'themselves', 'hypothetical', 'THEREFORE', 'THEIR', 'WHETHER', 'Their', 'grandfather', 'Rather', 'northern', 'Mathematician', 'rather', 'altogether', 'they', 'OTHER', 'gathers', 'mothers', 'mother', 'strengthened'}\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "word2check = input(\"What root to look for?\")\n",
    "pattern2check = \"\\w*\" + word2check + \"\\w*\" \n",
    "# \\w matches a word character, \\w* means match zero, one or more word characters\n",
    "# By deleting the \"\\w*\" on either side you \n",
    "theMatches = re.compile(pattern2check, re.IGNORECASE).findall(HumeString1)\n",
    "print(\"Number of matches \", len(theMatches))\n",
    "print(\"Variant forms: \", set(theMatches)) # set removes duplicates"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simple exploration using NLTK\n",
    "Now we will show how to use NLTK to do more."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we have to import the NLTK library."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import nltk"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we have to tokenize the text. We also lowercase it and eliminate non alphabetic tokens."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['start', 'of', 'this', 'project', 'gutenberg', 'ebook', 'a', 'treatise', 'of', 'human']\n"
     ]
    }
   ],
   "source": [
    "Hume1TokensLower = nltk.word_tokenize(HumeString1.lower()) # This lowercases all words and then tokenizes \n",
    "Hume1TokensLower2 = [word for word in Hume1TokensLower if word[0].isalpha()] # This eliminates non words\n",
    "print(Hume1TokensLower2[:10])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get a distribution of words."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "  the    of   and    to    is     a  that    in    it    we \n",
      "13675 10241  8637  6804  4786  4623  4524  4335  3552  2856 \n"
     ]
    }
   ],
   "source": [
    "Hume1TokensLower2Dist = nltk.FreqDist(Hume1TokensLower2)\n",
    "Hume1TokensLower2Dist.tabulate(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the distribution of the top 25 words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAZAAAAEsCAYAAADtt+XCAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10cf83128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib\n",
    "%matplotlib inline\n",
    "Hume1TokensLower2Dist.plot(25, title=\"Top Frequency Word Tokens in Hume's Treatise\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we get a concordance the NLTK way."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "What word to concord?give\n",
      "Displaying 10 of 267 matches:\n",
      "he most momentous we are not able to give any certain decision disputes are mu\n",
      " so the only solid foundation we can give to this science itself must be laid \n",
      "r ignorance and perceive that we can give no reason for our most general and m\n",
      "ever appear in the contrary order to give a child an idea of scarlet or orange\n",
      " by any accident the faculties which give rise to any impressions are obstruct\n",
      "me ideas which perhaps in their turn give rise to other impressions and ideas \n",
      "ure and principles of the human mind give a particular account of ideas before\n",
      "mpossible it is sufficient if we can give any satisfactory account of them fro\n",
      "f this mutual complaisance i can not give a more evident instance than in the \n",
      "ly defect of our senses is that they give us disproportioned images of things \n"
     ]
    }
   ],
   "source": [
    "word2concord = input(\"What word to concord?\")\n",
    "Hume1Text = nltk.Text(Hume1TokensLower2)\n",
    "Hume1Text.concordance(word2concord, lines=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "it relation pride pleasure matter objects idea passion belief love\n",
      "nature causes property reasoning power resemblance reason society\n",
      "ideas mind\n"
     ]
    }
   ],
   "source": [
    "Hume1Text.similar(word2concord)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "human nature; continued existence; every thing; give rise; may\n",
      "observe; properly speaking; constant conjunction; difference betwixt;\n",
      "general rules; present impression; take place; external objects; every\n",
      "one; right line; shall find; first sight; double relation; like\n",
      "manner; great measure; infinite divisibility\n"
     ]
    }
   ],
   "source": [
    "Hume1Text.collocations(num=20, window_size=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYwAAAEZCAYAAACEkhK6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ed67ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Hume1Text.dispersion_plot([\"true\",\"false\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Getting high frequency content words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['i', 'me', 'my', 'myself', 'we', 'our', 'ours', 'ourselves', 'you', 'your', 'yours', 'yourself', 'yourselves', 'he', 'him', 'his', 'himself', 'she', 'her', 'hers', 'herself', 'it', 'its', 'itself', 'they', 'them', 'their', 'theirs', 'themselves', 'what', 'which', 'who', 'whom', 'this', 'that', 'these', 'those', 'am', 'is', 'are', 'was', 'were', 'be', 'been', 'being', 'have', 'has', 'had', 'having', 'do', 'does', 'did', 'doing', 'a', 'an', 'the', 'and', 'but', 'if', 'or', 'because', 'as', 'until', 'while', 'of', 'at', 'by', 'for', 'with', 'about', 'against', 'between', 'into', 'through', 'during', 'before', 'after', 'above', 'below', 'to', 'from', 'up', 'down', 'in', 'out', 'on', 'off', 'over', 'under', 'again', 'further', 'then', 'once', 'here', 'there', 'when', 'where', 'why', 'how', 'all', 'any', 'both', 'each', 'few', 'more', 'most', 'other', 'some', 'such', 'no', 'nor', 'not', 'only', 'own', 'same', 'so', 'than', 'too', 'very', 's', 't', 'can', 'will', 'just', 'don', 'should', 'now', 'd', 'll', 'm', 'o', 're', 've', 'y', 'ain', 'aren', 'couldn', 'didn', 'doesn', 'hadn', 'hasn', 'haven', 'isn', 'ma', 'mightn', 'mustn', 'needn', 'shan', 'shouldn', 'wasn', 'weren', 'won', 'wouldn', 'may', 'one', 'us', 'must', 'upon', 'every', 'without', 'though', 'therefore', 'first', 'two', 'would']\n"
     ]
    }
   ],
   "source": [
    "stopwords = nltk.corpus.stopwords.words(\"english\")\n",
    "wordsToAppend = [\"may\",\"one\",\"us\",\"must\",\"upon\",\"every\",\"without\",\"though\",\"therefore\",\"first\",\"two\",\"would\"]\n",
    "stopwords2 = stopwords + wordsToAppend\n",
    "print(stopwords2)\n",
    "# I need to find a way to append a bunch of words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    idea   object  objects    ideas     mind relation  present passions   reason   nature \n",
      "     822      692      664      633      599      464      449      432      431      415 \n"
     ]
    }
   ],
   "source": [
    "Hume1ContentWords = [word for word in Hume1TokensLower2 if word not in stopwords2]\n",
    "Hume1ContentWordsDist = nltk.FreqDist(Hume1ContentWords)\n",
    "Hume1ContentWordsDist.tabulate(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYMAAAFLCAYAAAA03+DLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nMzPDnYGZmeHOwMzMcGdgZma4MzAzM+D/uEpeDBkp+nEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1143d97b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Hume1ContentWordsDist.plot(25, title=\"Top Frequency Content Words in Hume's Treatise\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining fequency lists"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "---\n",
    "\n",
    "[CC BY-SA](https://creativecommons.org/licenses/by-sa/4.0/) From [The Art of Literary Text Analysis](ArtOfLiteraryTextAnalysis.ipynb) by [Stéfan Sinclair](http://stefansinclair.name) &amp; [Geoffrey Rockwell](http://geoffreyrockwell.com)<br /> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}