NLTK IPython Notebook

nltk-notebook.ipynb

{
 "metadata": {
  "name": "nltk-notebook"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "# Learning NLTK\n",
      "\n",
      "This notebook introduces the Natural Language Toolkit (NLTK)."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## NLTK Example Texts\n",
      "\n",
      "The first step is to import the ``nltk`` library and to load some example texts."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import nltk"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nltk.book import *"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "*** Introductory Examples for the NLTK Book ***\n",
        "Loading text1, ..., text9 and sent1, ..., sent9\n",
        "Type the name of the text or sentence to view it.\n",
        "Type: 'texts()' or 'sents()' to list the materials.\n",
        "text1:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Moby Dick by Herman Melville 1851\n",
        "text2:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Sense and Sensibility by Jane Austen 1811\n",
        "text3:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The Book of Genesis\n",
        "text4:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Inaugural Address Corpus\n",
        "text5:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Chat Corpus\n",
        "text6:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Monty Python and the Holy Grail\n",
        "text7:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " Wall Street Journal\n",
        "text8: Personals Corpus\n",
        "text9:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " The Man Who Was Thursday by G . K . Chesterton 1908\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Dispersion Plots"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text1.dispersion_plot(['Ahab','whale','Ishmael','Queequeg', 'Moby', 'dive'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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/+hNw/jywYYNcLy+Xl9xiY4Fu3YD0dPnGn50N/PCDPLOKiJCXzzIz5TaFhcDp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\nYII=\n"
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text4.dispersion_plot([\"citizens\", \"democracy\", \"freedom\", \"duties\", \"America\"])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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ISFkMKSIiUhZDioiIlMWQIiIiZTGkiIhIWQwpIiJSFkOKiIiUxZAiIiJlMaSI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\n"
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Concordances"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text1.concordance('Ishmael')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Building index...\n",
        "Displaying 20 of 20 matches:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "SONG . CHAPTER 1 Loomings . Call me Ishmael . Some years ago -- never mind how \n",
        "ED STATES . \" WHALING VOYAGE BY ONE ISHMAEL . \" BLOODY BATTLE IN AFFGHANISTAN .\n",
        "f silver ,-- So , wherever you go , Ishmael , said I to myself , as I stood in \n",
        "de to lodge for the night , my dear Ishmael , be sure to inquire the price , an\n",
        "nkling glasses within . But go on , Ishmael , said I at last ; don ' t you hear\n",
        "g and teeth - gnashing there . Ha , Ishmael , muttered I , backing out , Wretch\n",
        "emen who had gone before me . Yes , Ishmael , the same fate may be thine . But \n",
        " ? thought I . Do you suppose now , Ishmael , that the magnanimous God of heave\n",
        "l , which , if left to myself , I , Ishmael , should infallibly light upon , fo\n",
        " Bildad . Now then , my young man , Ishmael ' s thy name , didn ' t ye say ? We\n",
        "say ? Well then , down ye go here , Ishmael , for the three hundredth lay .\" \" \n",
        "why don ' t you speak ? It ' s I -- Ishmael .\" But all remained still as before\n",
        "l fear ! CHAPTER 41 Moby Dick . I , Ishmael , was one of that crew ; my shouts \n",
        "lain , would be to dive deeper than Ishmael can go . The subterranean miner tha\n",
        "oul ; thou surrenderest to a hypo , Ishmael . Tell me , why this strong young c\n",
        " snows of prairies ; all these , to Ishmael , are as the shaking of that buffal\n",
        "ubtle meanings , how may unlettered Ishmael hope to read the awful Chaldee of t\n",
        "onditional skeleton . But how now , Ishmael ? How is it , that you , a mere oar\n",
        " for exhibition ? Explain thyself , Ishmael . Can you land a full - grown whale\n",
        "le witness have you hitherto been , Ishmael ; but have a care how you seize the\n"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text1.similar('Ishmael')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Building word-context index...\n",
        "ahab did in it what about admit am and been bound bury could do except\n",
        "extreme for goes guess had"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text1.similar('whale')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "ship boat sea time captain deck man pequod world other whales air crew\n",
        "head water line thing side way body\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text1.collocations()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Building collocations list\n",
        "Sperm Whale; Moby Dick; White Whale; old man; Captain Ahab; sperm\n",
        "whale; Right Whale; Captain Peleg; New Bedford; Cape Horn; cried Ahab;\n",
        "years ago; lower jaw; never mind; Father Mapple; cried Stubb; chief\n",
        "mate; white whale; ivory leg; one hand"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text1.concordance(\"monstrous\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Displaying 11 of 11 matches:\n",
        "ong the former , one was of a most monstrous size . ... This came towards us , \n",
        "ON OF THE PSALMS . \" Touching that monstrous bulk of the whale or ork we have r\n",
        "ll over with a heathenish array of monstrous clubs and spears . Some were thick\n",
        "d as you gazed , and wondered what monstrous cannibal and savage could ever hav\n",
        "that has survived the flood ; most monstrous and most mountainous ! That Himmal\n",
        "they might scout at Moby Dick as a monstrous fable , or still worse and more de\n",
        "th of Radney .'\" CHAPTER 55 Of the Monstrous Pictures of Whales . I shall ere l\n",
        "ing Scenes . In connexion with the monstrous pictures of whales , I am strongly\n",
        "ere to enter upon those still more monstrous stories of them which are to be fo\n",
        "ght have been rummaged out of this monstrous cabinet there is no telling . But \n",
        "of Whale - Bones ; for Whales of a monstrous size are oftentimes cast up dead u\n"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text2.concordance(\"monstrous\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Building index...\n",
        "Displaying 11 of 11 matches:"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        ". \" Now , Palmer , you shall see a monstrous pretty girl .\" He immediately went\n",
        "your sister is to marry him . I am monstrous glad of it , for then I shall have\n",
        "ou may tell your sister . She is a monstrous lucky girl to get him , upon my ho\n",
        "k how you will like them . Lucy is monstrous pretty , and so good humoured and \n",
        " Jennings , \" I am sure I shall be monstrous glad of Miss Marianne ' s company \n",
        " usual noisy cheerfulness , \" I am monstrous glad to see you -- sorry I could n\n",
        "t however , as it turns out , I am monstrous glad there was never any thing in \n",
        "so scornfully ! for they say he is monstrous fond of her , as well he may . I s\n",
        "possible that she should .\" \" I am monstrous glad of it . Good gracious ! I hav\n",
        "thing of the kind . So then he was monstrous happy , and talked on some time ab\n",
        "e very genteel people . He makes a monstrous deal of money , and they keep thei\n"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text1.similar(\"monstrous\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "abundant candid careful christian contemptible curious delightfully\n",
        "determined doleful domineering exasperate fearless few gamesome\n",
        "horrible impalpable imperial lamentable lazy loving\n"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "text2.similar(\"monstrous\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Building word-context index...\n",
        "very exceedingly heartily so a amazingly as extremely good great\n",
        "remarkably sweet vast"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Lexical Diversity"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def lexical_diversity(text):\n",
      "    return len(text)/(1.0*len(set(text)))\n",
      "\n",
      "def percentage(count, total):\n",
      "    return 100*count/total"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lexical_diversity(text3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "16.050197203298673"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lexical_diversity(text4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "14.941049825712529"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "lexical_diversity(text5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "7.420046158918563"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fdist1 = FreqDist(text1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "vocabulary1 = fdist1.keys()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fdist1['whale']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 19,
       "text": [
        "906"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fdist1['monstrous']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "10"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fdist1.plot(50, cumulative=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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p7e3aAQMGCJM1eXkBFhZ14yejm3CNhGHqIXl5whtHceCIiQE0mifbW7YU3jSKg0f37twB\nkKkduEZSArnnKDlfqzt6tWHrwQM1rlwBvvwS8PYWxp3y8gIOH1bj4kVhrKr+/YFPPxU+183MBLZu\nVePDD4XOgU8HEV2+FrqqJ6UtrpEwDANASFf9+itw+LAwVezx46W3OzoCo0YBK1YIc3G0aFE3fjIN\nHKphJkyYQCYmJmRvby+2ZWZm0oABA8jBwYEGDhxIDx48ELctW7aMunfvTvb29vTrr7+K7dHR0eTk\n5ES2trY0bdo0sT03N5dGjRpF9vb29PLLL1NSUpK47ccffyRbW1uytbWlrVu3avWxFk6bYWoEjYbo\nyhWiFSuI+vYl0tMjEr65Epb27YnGjiUKDia6e7euvWUaEhU9N2v8iXr69GmKjY0tFUiCgoJozZo1\nRES0Zs0aMTBER0eTq6srFRYWkkqlIisrK8rPzyciIgcHB4qNjSUiouHDh9PevXuJiGjVqlU0ffp0\nIiLat28f+fr6EhHRnTt3yNramrKzsyk7O5usra3prpa/NA4kjJzIzSU6fJho6lQiS8vSgaNRI6JX\nXyVatYooIUEINAxTF1T03KzxGknfvn3R+qmZZw4dOoTAwEAAQEBAAMLDwwEA4eHhGD16NPT09GBm\nZgY7OztERUUhOTkZGo0Gzs7OZY4pqeXr64vIyEhoNBocPXoU3t7e0NfXh76+PgYPHoyjR49WyXe5\n5yg5X6s7epXZSk8HfvwReOMNoY+Gt7cwLlXr1mqYmADjxgG7dwMZGUI6a+ZMwMxMXW6xXNevRUPS\nk9JWvauRpKenw+ifHk3GxsZIS0sDAKSmpsLT01Pcz9zcHCqVCnp6erAo8W2imZkZVCoVAEClUonb\nlEoljIyMkJaWhtTUVJibm5fRYhg5QARcvw788oswiVNISOnZAHv0AIYNA3x8hJkAlfwZDKNDNNhi\n+7x589C0aVMAgKurKzw8PGD4Ty+s4oj89HoxT28vbqvseDnqGRoasl4t6enrG+LUKeDcOTUiI4Hw\ncGG7kxPg4qKGkZEhfH0BLy81TE2Lj9eup239ef2TSq+i69vQ9Eq2yV0vPj4eJ0+eRO7TE8uUR2V5\nsaysLCosLCQiouvXr9NPP/1Eubm5FR5z69atUjWSzp07U3p6OhERpaWlkbW1NRERLV26lL744gtx\nvyFDhtDZs2cpOTmZ7OzsxPawsDCaPHkyERF5enrSxYsXiYioqKiIjI2NqaioiLZt20YffPCBeMzU\nqVMpODi4XP+e4bQZ5rl4/Jjo55+JAgKIWrcuXe8wMiIaN45ozx6irKy69pRhqkZFz81KX6D79euH\nwsJCpKSkYODAgdi5cyfGjx9feYQqgY+PD4KDgwEAwcHB8PHxEdtDQ0NRWFgIlUqFhIQEuLm5wcLC\nAkqlEnFxcQCAHTt2wNvbu4zW/v374e7uDqVSCS8vL0RERCA7OxvZ2dmIiIjAgAEDquSn3HOUnK+V\np15enjAMydixwvSxr78uzDluaalG167A7NnAmTPAvXtCXcTLSw0DA3n4LoWelLbkrielLdnVSF54\n4QXs27cPQUFBmD17Nnr06KF13zFjxuDUqVPIyMiAhYUFli5diiVLlsDf3x+bN29Gu3btEBYWBgBw\ncXHBiBEj4OjoCKVSiY0bN6Jx48YAgC1btmDixInIz8+Hl5cX/Pz8AABBQUEIDAyEg4MDDAwMEBIS\nAgDo0KEDFixYgN69ewMAFi5cCFNT0ypfEIZ5FgoKhN7kp04B330H/P33k23OzsCbbwJDhwoj6DJM\nfafSsbacnZ3x/fffY9q0adi0aRPs7Ozg6OiIy5cvS+VjjcNjbTHPg0YjvFns3Ans2SP0HC+mRw+h\nY+CbbwJdutSdjwxTW1RrrK1Vq1Zh8eLFeP3112FnZ4ekpCT069evxp1kGDlCJMzbsXMnEBoq9C4v\nxtZWmEZ21CjAxqbufGSYuqbSGklmZibCw8Mxd+5cAICVlVW9DSRyz1FyvlY6vZs3hfnIu3UDJk9W\nY/VqIYhYWgLz5gGXLgEJCcCHH6q1BpH6ci10yZbc9aS0JWWNpNJAsnz58jJtn332WZUNMYzcuXMH\nWLNGmKPDxgZYvFgIKIaGQFCQMP3srVvA8uXCGFc8ki7DCGitkRw+fBiHDh1CaGgoRo8eLebGHj16\nhJiYGMTHx0vqaE3CNRKmmEePgJ9+Er6mOnHiSSdBAwOh1/lbbwkTPzVqsD2uGEbguWokHTp0gIuL\nC/bv3w8XFxdRoHnz5lixYkXteMowEkAEREcDP/wg1D6ysoT2Jk2AIUOE4DFkCNCsWd36yTA6Q2Wd\nUIoHUaxPaDvtkqMS18Q2uetJaUsOeunpRP/5zwNycCjdUbB3b6Jt2x6QNkk5+K7LelLakruelLZq\nWq+icFHpC/uZM2ewdOlSpKSkQPPP9GsKhQJ//vlnLYc4hqk+Go3Q32PTJuDnnwE7O2GsK2NjIDAQ\nmDRJaFOreZ5yhnleKu1H0qlTJ2zYsAE9e/aEnp6e2G5sbFzrztUWXCOp/yQnA1u2CMvt20KbUgkM\nGiQEj2HDhFQWwzDPRrX6kbRv314cnoRh5Ex+vjC67qZNwJEjTwrnVlZC8Bg/HigxQDTDMDXEM421\nNW/ePJw/fx6xsbHiUh+R+3fc/E17+e03bwJz5ghB4s03gXv31GjcWOgseOwY8McfwP/9n7Cdr630\nelLakruelLZkNdbWhQsXoFAoEBUVVar9xIkTVTbGMDVFfj6wYwfwn/8I410VY28v9Pl4/XVhwiiG\nYWqfSmsk9RGukeguiYlC8Ni2DXjwQGhr3lx4+3jnHWFSKO4oyDA1T7VqJEuWLBEFFCX+QhcuXFhz\nHjJMBTx8CISFCQHk/Pkn7S4uQvAYMwZo2bLu/GOYhk6lNZIWLVqgRYsW0NfXh1KpxOHDh5GUlCSB\na9Ij9xxlQ8vXxsUBixap0aEDMHGiEEQMDID33gOiotSIjgamTCkbRORwvlLakruelLbkrielLVnV\nSGbNmlVqfe7cuRg0aFCVDTHMs/DwIbBrF7BxI3DxojAtbVYW4O4uvH2MGgW0aCH0+2AYRh5UuUZy\n//59ODs743bxx/k6CNdI5MeVK0Lw2L79yZAlhoZCp8F33xWK6AzD1B3VqpE4lJjiTaPR4O7du5g3\nb17Necc0WHJzgd27hRkGIyOftLu7CymrN98UCukMw8ibSmskBw4cwIEDB3Dw4EFEREQgNTUVs2fP\nlsI3yZF7jrK+5Gv//BOYO1fo1zF2LPDokTCH+dSpwjwfkZHAuHFCEOFrq/t6UtqSu56UtmRVI7Gy\nskJUVBTOnDkDhUKBfv36oVevXlU2xDRsNBogPBzYsAE4fPhJr3NnZ+Djj4V+H/r6desjwzDPR6U1\nkpUrV2L79u3w8/MDEeHnn39GYGAg5syZI5WPNQ7XSKQjNxf4/nthwqjij/1eeAHw9xfeQNzcuN8H\nw+gCFT03Kw0k3bt3R1xcHJo2bQoAyM3NhbOzM65du1bznkoEB5LaJy9PGPNq2TJh5kEA6NQJeP99\nYMIEYfRdhmF0h4qem5XWSABAqVSW+3t9Q+45Sl3I16anq/Hdd8CLLwpDldy5I3zCe+CAGr//Dsye\nXTaIyOFa6MK11VU9KW3JXU9KW7KqkQQEBMDV1bVMaothSlJQIExXu2ePMPIuADg4AEuWAMOHC5/0\n1uP/gzBMg+aZ+pFERkbi7NmzUCgU8PDwgLu7+3MZW7RoEXbu3AmlUgl7e3ts27YNubm58Pf3x717\n99C+fXuEhobC8J8ZhpYvX47t27dDT08Pq1evxsCBAwEAMTExmDx5MvLz8zFgwACsXbsWAJCXl4ex\nY8fi6tWraNmyJUJCQmBpaVn2pDm1VWNoNEIHwoULhVF2AWGiqMWLAT8/Dh4MU1+o8LmpberEyMhI\nCg8PL9MeHh5OFy5c0DrlojZ+++036tSpE+Xl5RER0ahRo2jTpk0UFBREa9asISKiNWvW0LRp04iI\nKDo6mlxdXamwsJBUKhVZWVmJ0/46ODhQbGwsERENHz6c9u7dS0REq1atounTpxMR0b59+8jX17dc\nXyo4beYZ0WiIfvmFSk1b27Ur0c6dREVFde0dwzA1TUXPTa3/X5w1axZsbW3LtNvZ2ZUZNuVZaNOm\nDRo3boyHDx+isLAQjx49QseOHXHo0CExVRYQEIDw8HAAQHh4OEaPHg09PT2YmZnBzs4OUVFRSE5O\nhkajgbOzc5ljSmr5+voiMjKySm8ecs9RyiVfe/KkGn36AL6+Qo90CwuhsJ6YCAwerC73LUTu10Iu\n17Y+6klpS+56UtqSskaiNZDk5OTAysqqTLulpSWys7OrbKhNmzaYOXMmOnbsiA4dOsDQ0BCvvfYa\n0tPTYfTPxBHGxsZIS0sDAKSmpsK8xHR25ubmUKlUSE1NhYWFhdhuZmYGlUoFAFCpVOI2pVIJIyMj\nUY+pPteuAd7ewIwZwgCKxsbCZ703bwozEDaqtOLGMEx9ROuffmFhodaDCgoKqmzojz/+wFdffYWk\npCS0atUKb775JoKDg6usU1PMmzdP/KTZ1dUVHh4eYm2mOCI/vV7M09uL2yo7Xo56hoaGleqlpqrx\n44/A4sWGKCwEXn4ZWLtWjQkTDGFgIOyfm/vsejXtn1z1tK03NL2Krm9D0yvZJne9+Ph4nDx5Erm5\nuagUbTmvd999lz777LMy7cuXL6d33nmnyvm1kJAQmjRpkri+bds2eu+996hz586Unp5ORERpaWlk\nbW1NRERLly6lL774Qtx/yJAhdPbsWUpOTiY7OzuxPSwsjCZPnkxERJ6ennTx4kUiIioqKiJjY2Mq\nKidhX8FpMyUoKiL64QciExOhBqJQEE2ZQpSWVteeMQwjNRU9N7WmtlavXo1z587BxsYGY8aMwZgx\nY2BjY4PTp09j9erVlUeop3jxxRdx4cIFPH78GESEY8eOwdraGj4+PuKbSXBwMHx8fAAAPj4+CA0N\nRWFhIVQqFRISEuDm5gYLCwsolUrExcUBAHbs2AFvb2/xmGKt/fv3w93dvUr9XuSeo5QyXxsZqcZL\nLwkpq7Q0wMMDiIkRBlhs3LjufZe7npS25K4npS2560lpS8oaidbUlr6+PsLDw3Ht2jVcvnwZCoUC\nCxcuRPfu3atsBAB69eqFkSNHwtHREUqlEs7OzggKCsKjR4/g7++PzZs3o127dggLCwMAuLi4YMSI\nEeL+GzduROPGjQEAW7ZswcSJE5Gfnw8vLy/4+fkBAIKCghAYGAgHBwcYGBggJCTkuXxtyKSnA3Pm\nAPHxwmJmBnzxhTCVLQ9lwjBMefCc7QwAoKhImMr2X/8S5kJv0gSYNQuYP58HU2QYpprzkTD1n5gY\nYQysixeF9UGDgK+/Brp0qVu/GIbRDbjfcQnknqOsab0HD4DFi9Xo1UsIImZmwhAnhw8DbdvK23e5\n60lpS+56UtqSu56UtqSskTxTIDl27Bg2bdoEAMjIyMCtW7eqbIiRD0RASAjQrRuwfz+gpycMpnj9\nOvDGG1wLYRimalRaI5k/fz4SEhJw48YN3Lx5E/fu3YOvry+ioqKk8rHGacg1ktu3gffeAyIihPV+\n/YTJpuzs6tYvhmHkTbWGkf/555+xf/9+tGjRAgBgamqKvLy8mvWQqXWKioC1a4WAEREBGBoCP/wA\nnDzJQYRhmOpRaSBp3Lhxqb4Yubm5yM/Pr1Wn6gq55yifVy8mRo2XXwY++gh4+BAYNUoY7mTiRODv\nv+Xtu67qSWlL7npS2pK7npS2ZFUjGTlyJKZMmQK1Wo3Nmzfjtddew7hx46psiJGe/Hzg//4PmDIF\n+N//AHNz4JdfgNBQoF27uvaOYZj6wjP1I/nll19w5J/ZigYNGoRhw4bVumO1SUOokVy/Drz9NhAb\nKxTPp04Vpr1t2bKuPWMYRhep1pztq1evxujRo2FmZlYrztUF9TmQEAEbNwIffww8fgxYWQHbtwtD\nnDAMwzwv1Sq2Z2dnY+DAgfDw8MD69etx7969GndQLsg9R1nZMenpwrS2778vBJGxY4FLlwB7e/n7\nXt/0pLQldz0pbcldT0pbsqqRLF68GImJifjmm2/w119/oV+/fvDy8qqyIaZ2+d//hDnSDxwAWrUC\ndu4Etm66BuahAAAgAElEQVTlVBbDMLXPM4+19ddff2HPnj3YuXMncnJycPny5dr2rdaoT6ktjUYY\nD+vzz4X1fv2EVFbHjnXrF8Mw9YtqpbY2bNiA/v37w8vLCxkZGdi0aZNOB5H6RGGh8Anv558LsxMu\nWwYcP85BhGEYaak0kCQnJ+Orr77C1atXsWTJknLnca8vyD1HWbI9Nxd4800hfdW8OXDokBrz5wvD\nndS0LdaTty2560lpS+56UtqSskaidfTfrKwstGzZErNnz4ZCocD9+/dLbW/Tpk2VjTE1Q3Y28Prr\nwtuHoSFw6BDwnNPEMAzDVButNZIhQ4YgPDwcVlZWUJQzip8uD9yoyzWSzEzAx0corpuaAkeOAI6O\nde0VwzD1nWr1I6mP6GoguXMHGDgQSEwU+occPQq8+GJde8UwTEOgWsX28j71ra+f/8o5R/nnn8Ck\nSWokJgK2tsDZs6WDiJx9b4h6UtqSu56UtuSuJ6UtWdRIHj9+jEePHiE9Pb1UfeThw4e4fft2lQ0x\nz8+NG4CnJ2BiAvTqJUw8ZWRU114xDMMIaE1tffXVV1i7di3u3LmDDh06iO3NmjXDpEmTMHPmTMmc\nrGl0KbWVkAAMGADcuwf07QuEhwMGBnXtFcMwDY1q1UjWrVuHadOm1YpjdYWuBJK4OOC114QCu5eX\nMJvhP9PCMAzDSEq1aiTTpk1DbGwsQkJCsG3bNnGpj8gpR3nxopDOKv5K68ABoKCA87W6oielLbnr\nSWlL7npS2pJFjaSYefPmISoqComJiRgyZAgOHz4MDw8PjB07tsrGmGcjIUEIHsX9RXbtAl54AeCJ\nKRmGkSVUCV26dKHCwkLq0aMHERGlp6fTwIEDKzusXB48eEAjR44kR0dH6tatG50/f54yMzNpwIAB\n5ODgQAMHDqQHDx6I+y9btoy6d+9O9vb29Ouvv4rt0dHR5OTkRLa2tjRt2jSxPTc3l0aNGkX29vb0\n8ssvU1JSUrl+PMNp1xknThC1aEEEEI0aRZSfX9ceMQzDVPzcrDS11apVK+jp6YGIkJOTAyMjI/zx\nxx/PFbTeeecd+Pn54dKlS0hMTIStrS0WLVqEIUOG4PLly/D29saiRYsAADExMdi7dy+uXLmCiIgI\nTJkyBQUFBQCACRMmYPPmzUhMTMTt27exb98+AMD69evRvn17XLlyBbNnz9a52s7p08KbyMOHQGAg\nsGMH0LhxXXvFMAxTMZUGEhcXF2RlZWH8+PFwcnKCs7Mz3N3dq2woMzMT8fHxGDNmjGBYqUTLli1x\n6NAhBAYGAgACAgIQHh4OAAgPD8fo0aOhp6cHMzMz2NnZISoqCsnJydBoNHB2di5zTEktX19fREZG\nVqmoXpc5yrg4YNgwYR6R+fPV2LJFGIixNv2raBvr6Y4tuetJaUvuelLaklWN5LvvvgMAzJgxAz4+\nPsjLy4Pjc4zJ8dtvv6Ft27YYNWoUrl69ip49e+Kbb75Beno6jP7pFGFsbIy0tDQAQGpqKjw9PcXj\nzc3NoVKpoKenBwsLC7HdzMwMKpUKAKBSqcRtSqUSRkZGSEtLg6mpaRl/5s2bh6ZNmwIAXF1d4VFi\nCsHiC2loaCiu5+TklFovuT0nJ6fM/iWpTC8hQY2PPwaysgzx5pvAe+/lIDv7+fWe1b+K1lmvenol\naeh6lV3fhqZX3eeFVHrx8fE4efIkcnNzURlaP/+NiYkpd4ytYnr27FmpeEkiIyPxyiuvIDIyEr16\n9cJHH32EJk2a4LvvvkNWVpa4X8uWLZGVlYUpU6bA09MT/v7+AID33nsP/fv3h6WlJZYuXYrDhw8D\nAM6fP48lS5YgIiICNjY2OHPmDExMTAAA3bp1w6lTp8oEEjl9/puaCvTpA9y+LXzqe+CAUFhnGIaR\nExU9N7W+kcycObPCQHLixIkqOWFhYQEzMzP06tULADBy5EgsXboUJiYmyMjIgLGxMdLT08UgYG5u\njpSUFPH44rcNbe3FxyQnJ8PExAQajQaZmZlo27ZtlfyUksxMYeys27eBl14C9u7lIMIwjO6htUZy\n8uRJnDhxQutSVSwsLGBsbIybN28CAI4dO4bu3bvD29sbwcHBAIDg4GD4+PgAAHx8fBAaGorCwkKo\nVCokJCTAzc0NFhYWUCqViIuLAwDs2LED3t7e4jHFWvv374e7uzuUykrLQCJS5ij/+kuNIUOAq1cB\nOzuhx7q+/vPryT2/2tD0pLQldz0pbcldT0pbsqqRbN26tdw3k+fpR/LDDz/g7bffxqNHj2BpaYkd\nO3aAiODv74/NmzejXbt2CAsLAyAU+UeMGAFHR0colUps3LgRjf/5hGnLli2YOHEi8vPz4eXlBT8/\nPwBAUFAQAgMD4eDgAAMDA4SEhFTZRynIywM++QSIihJG8f31V4Cnd2EYRlepdIiUoKAgMZA8fvwY\nx48fR8+ePbFnzx5JHKwN6rJGUlQEjBkD7N4tDMJ49izQpUuduMIwDPPM1Oh8JNnZ2Rg1apRY7NZF\n6iqQEAHvvw9s3Ai0bAmcOgU4OUnuBsMwTJWp1lhbT/PCCy/gxo0b1XZKjtR2jvKTT4Qg0rQpcOCA\nWmsQ4Xyt7utJaUvuelLakruelLZkVSMZNmyY+LtGo0FiYiKGDx9eZUMNna++Aj77DNDTA8LCeHpc\nhmHqD5Wmtk6ePCn+rlQqYW5ujs6dO9e2X7WK1Kmt7duB4m8TfvwRGDdOMtMMwzA1Qo3USNRqNTQa\njbjeRoc/M5IykISHA8OHC0X21auBjz+WxCzDMEyNUq0ayddff422bduiR48ecHFxgYuLC1xdXWvc\nSTlQ0znFM2fUGDlSCCLz5pUOInLJeepqvlbuelLakruelLbkrielLVnVSFavXo1r167B2Ni4yuIN\nmStXgPnzgdxcYPJkYNmyuvaIYRimdqg0teXt7Y19+/aJAxzWB2o7tfXoEeDsDNy8Cfj5AaGhZUfy\nZRiG0SWqVSOJjY3F+PHj4e7ujiZNmoiC69atq3lPJaK2A8kHHwAbNghDn1y8CDRrVmumGIZhJKFa\nNZJ3330XAwYMwEsvvQRXV1exTlIfqYmc4uHDQhBp3Bj48Ue11iAil5ynruZr5a4npS2560lpS+56\nUtqSVY0EAL788ssqCzdEMjOBiROF3z/9FHjxxbr1h2EYRgoqTW0tWLAAVlZWGDp0KF4oMcY5f/5b\nGiLgzTeBn34C+vYFTpwQOh8yDMPUB6pVI7Gysip39N9bt27VjHd1QG0Ekm3bhI6GBgbA5cvCqL4M\nwzD1hWrVSJKSknDr1q0yS33kefOG16+rERQk/P7110+CiC7kPHU1Xyt3PSltyV1PSlty15PSlqxq\nJDU5H0l9pKhI6COSnS186suXhWGYhgbPR1JNPv8cmDsXaNdO6ITI/TYZhqmP8HwkT1FTgeTyZcDV\nFSgoAA4dAv6Z8ZdhGKbewfORPCNVyRsWFQGTJglBZOFCdblBRBdynrqar5W7npS25K4npS2560lp\nS1Y1Ep6PpHzWrQOiowFzc2DKlLr2hmEYpu6o8nwkFhYW6NSpU237VatUN7V16xZgby+MqXXwIDBk\nSA06xzAMI0Mqem5qfSO5ceMG0tPT0b9//1LtZ8+eRWFhIbp06VKjTuoKRMIbyKNHwJgxHEQYhmG0\n1kg++OADtGzZskx7q1atEFTcaaKe8Sx5w23bgKNHgTZthOlzKzpOF3Keuuq73PWktCV3PSltyV1P\nSltS1ki0BpK0tDQ4ljOxuIODA+7evVtlQ/WBe/eAGTOE37/6CjAxqVt/GIZhZAFpwc7OTtumCrdV\nRmFhITk5OdHQoUOJiCgzM5MGDBhADg4ONHDgQHrw4IG477Jly6h79+5kb29Pv/76q9geHR1NTk5O\nZGtrS9OmTRPbc3NzadSoUWRvb08vv/wyJSUlletDBaddIf7+RADRwIFEGs1zSTAMw+gkFT03tb6R\n2NvbIzg4uEz7jh07YGdn99yBa+3atbC1tRU7OS5atAhDhgzB5cuX4e3tjUWLFgEAYmJisHfvXly5\ncgURERGYMmUKCgoKAAATJkzA5s2bkZiYiNu3b2Pfvn0AgPXr16N9+/a4cuUKZs+ejWnTpj23n09z\n4IAwQVXz5sDGjUA5nf0ZhmEaJtoizF9//UU9evSg1157jebNm0fz5s2jgQMHkqOjI925c+e5IlpK\nSgp5eXnR8ePHxTeSzp07U0ZGBhERpaenk7W1NRERLVmyhFatWiUeO2TIEDpz5gzdvn271BvR7t27\nadKkSURE5OnpSdHR0UREVFRURMbGxqQp59VB22mXfBsqyd9/E7322gMCiNasefbjtLU/77aa1pPS\nVkPTk9KW3PWktCV3PSlt1bReBeGCtH611a5dO8TExODIkSO4fPkyFAoFPvroIwwcOBB6zzk++owZ\nM/DFF18gKytLbEtPT4eRkREAwNjYGGlpaQCA1NRUeHp6ivuZm5tDpVJBT08PFhYWYruZmRlUKhUA\nQKVSiduUSiWMjIyQlpYGU1PTMr7MmzdPnD7Y1dUVHh4e4rbiYpOhoSEA4Msv1WjVKgdubob48MOy\n23NyckqtP12senp/tVqNnJycMvtLrVfROutVT68kDV2vsuvb0PR05XkRHx+PkydPIjc3F5WiNcTU\nMAcOHKCpU6cSEdGJEyfENxIDA4NS+xWvv/vuu7Rr1y6xfcqUKbRz506KjIykwYMHi+2RkZE0aNAg\nIiLq2rUr3bt3T9xmY2NDd+/eLeNLVU77/HmhLtKoEdHly898GMMwTL2ioufmM82QWBNERkbil19+\nwaFDh5Cbm4usrCwEBgaibdu2yMjIgLGxMdLT02Hyz6dQ5ubmSElJEY8vftvQ1l58THJyMkxMTKDR\naJCZmYm2bds+t8+FhcD77wu/z54NODg8txTDMEy9pcpjbT0vy5YtQ0pKCm7duoVdu3bB09MT27dv\nh4+Pj1jUDw4Oho+PDwDAx8cHoaGhKCwshEqlQkJCAtzc3GBhYQGlUom4uDgAQvHf+5+Brkpq7d+/\nH+7u7lAqn/0Un34l/OYbID5emF9k2rSqf3etC9+F66rvcteT0pbc9aS0JXc9KW1J2Y9EsjeSpyn+\namvJkiXw9/fH5s2b0a5dO4SFhQEAXFxcMGLECDg6OkKpVGLjxo1o3LgxAGDLli2YOHEi8vPz4eXl\nBT8/PwDCkPeBgYFwcHCAgYEBQkJCntu/O3eATz4Rfv/6a+CfcgrDMAzzFFUeRr4+8Cxjbfn7A2Fh\nwPDhwM8/S+QYwzCMTKnR+UjqA5UFkiNHgEGDhD4jV68ClpYSOscwDCNDanQ+kvqMWq1Gbi7wwQfC\n+sKFT4KIHHKUnK/VHT0pbcldT0pbcteT0paUNRIOJE+xciXw+++Are2TcbUYhmEY7XBqqwS//y7M\nM5KXB5w6BfTrVwfOMQzDyBBObT0DREBQkBBExo7lIMIwDPOscCD5hz17gHv31GjdGvjii7Lb5ZCj\n5Hyt7uhJaUvuelLakruelLa4RlIHrFwp/PzsM55nhGEYpipwjQSASgVYWAif+2ZkAM2a1aFzDMMw\nMoRrJJVw4IDwc+BADiIMwzBVhQMJgF9+EX6OGiXvHCXna3VHT0pbcteT0pbc9aS0xTUSCcnOBo4f\nF2Y8dHeva28YhmF0jwZfI9mzB3jzTaBPH+Ds2Tp2jGEYRqZwjaQCitNavr516wfDMIyu0qADSWEh\nEB4u/D58uPxzlJyv1R09KW3JXU9KW3LXk9IW10gk4tw54P59oGtXwMamrr1hGIbRTRp0jWTmTODL\nL4FZs8rvzc4wDMMIcI2kHIiA/fuF34cPr1tfGIZhdJkGG0iuXQP++AMwNn7y2a/cc5Scr9UdPSlt\nyV1PSlty15PSFtdIJKD4a62hQwE9vbr1hWEYRpdpsDWSl14iXLgA7N0LjBhR1x4xDMPIG56z/SkU\nCgUUCkKTJsIgjfr6de0RwzCMvOFiezkQAQMGlA4ics9Rcr5Wd/SktCV3PSltyV1PSltcI5EI7s3O\nMAxTfSRLbaWkpODtt9/GgwcPkJ+fj0mTJmHOnDm4f/8+/P39ce/ePbRv3x6hoaEwNDQEACxfvhzb\nt2+Hnp4eVq9ejYEDBwIAYmJiMHnyZOTn52PAgAFYu3YtACAvLw9jx47F1atX0bJlS4SEhMDS0rLs\nSSsUAAipqUCHDlKcPcMwjG4ji9RWkyZNsGHDBly5cgUxMTHYtGkTLl26hEWLFmHIkCG4fPkyvL29\nsWjRIgBCsNi7dy+uXLmCiIgITJkyBQUFBQCACRMmYPPmzUhMTMTt27exb98+AMD69evRvn17XLly\nBbNnz8a0adO0+uPmxkGEYRimJpAskJiamsLe3h4AoK+vD0dHR6SmpuLQoUMIDAwEAAQEBCD8n8Gv\nwsPDMXr0aOjp6cHMzAx2dnaIiopCcnIyNBoNnJ2dyxxTUsvX1xeRkZFaI2h5aS255yg5X6s7elLa\nkruelLbkrielLSlrJI2qfEQNkJSUhIsXL2Lz5s1IT0+HkZERAMDY2BhpaWkAgNTUVHh6eorHmJub\nQ6VSQU9PDxYWFmK7mZkZVCoVAEClUonblEoljIyMkJaWBlNT0zI+pKTMw+LFTQEArq6u8PDwELcV\nX8jiFJtarUZOTk6p9ZLbc3JyyuxfErnqVbTOetXTK0lD16vs+jY0PV15XsTHx+PkyZPIzc1FpZDE\nZGdnk4uLC+3bt4+IiAwMDEptL15/9913adeuXWL7lClTaOfOnRQZGUmDBw8W2yMjI2nQoEFERNS1\na1e6d++euM3Gxobu3r1bxgcApNHU3DkxDMPUdyoKF5J+tVVQUIA33ngDb7/9Nl5//XUAQNu2bZGR\nkQEASE9Ph4mJCQDhDSQlJUU8tvhtQ1t78THJyckAAI1Gg8zMTLRt27ZcXxSKmj8/hmGYhohkgYSI\nMGnSJNja2mLGjBliu4+PD4KDgwEAwcHB8PHxEdtDQ0NRWFgIlUqFhIQEuLm5wcLCAkqlEnFxcQCA\nHTt2wNvbu4zW/v374e7uDqXy2U9R7jlKztfqjp6UtuSuJ6UtuetJaate1kjOnTuH4OBgODo6ioXy\n5cuXY8mSJfD398fmzZvRrl07hIWFAQBcXFwwYsQIODo6QqlUYuPGjWjcuDEAYMuWLZg4cSLy8/Ph\n5eUFPz8/AEBQUBACAwPh4OAAAwMDhISESHV6DMMwDZYGO0RKAzxthmGY50YW/UgYhmGY+gkHkhLI\nPUfJ+Vrd0ZPSltz1pLQldz0pbUlZI+FAwjAMw1QLrpEwDMMwlcI1EoZhGKbW4EBSArnnKDlfqzt6\nUtqSu56UtuSuJ6UtrpEwDMMwOgPXSBiGYZhK4RoJwzAMU2twICmB3HOUnK/VHT0pbcldT0pbcteT\n0hbXSBiGYRidgWskDMMwTKVwjYRhGIapNTiQlEDuOUrO1+qOnpS25K4npS2560lpi2skDMMwjM7A\nNRKGYRimUrhGwjAMw9QaHEhKIPccJedrdUdPSlty15PSltz1pLTFNRKGYRhGZ+AaCcMwDFMpXCNh\nGIZhag0OJCWQe46S87W6oyelLbnrSWlL7npS2uIaSTWJiIiAg4MDbG1tsXLlymc+7uzZszW6Te56\nUtpqaHpS2pK7npS25K4npa3a8F0b9S6Q5OXl4f3330dERAQuX76MPXv2IC4u7pmOjY6OrtFtcteT\n0lZD05PSltz1pLQldz0pbdWG79qod4EkKioKdnZ2MDMzQ6NGjeDv74/w8PC6dothGKbeUu8CiUql\ngoWFhbhubm4OlUr1TMfm5ubW6Da560lpq6HpSWlL7npS2pK7npS2asN3bdS7z3937tyJ06dP49tv\nvwUA7Nq1CydPnsR3330n7qNQKOrKPYZhGJ1FW7hoJLEftY65uTlSUlLE9ZSUlFJvKID2i8EwDMNU\nnXqX2urVqxcSEhKQmpqKgoIChIWFwdvbu67dYhiGqbfUuzeSpk2b4ttvv8WgQYOg0WgQGBiInj17\n1rVbDMMw9ZZ6VyPRxl9//YU2bdrghRdeqNJxd+/eRbt27WrEh7S0NPz+++8oLCwU2/r16/fMtp/3\nHKpKTZ4zo53Vq1eXWlcoFPj4448BANu3b0dgYGBduMXoIGlpafj222/RtGlTvP/++2jZsqWk9uvd\nG4k2AgIC8Mcff2DkyJFYtWrVMx83adKkGvl8eN26dfjuu+9w584dODs748KFC3B3d8fx48ef2fbT\n50BEOHXqFFJSUqDRaAAID6OxY8ciNzcXTZs2LaVXsq1Tp04AABMTE0RFRZXaz8fHB7Gxsc98bmvX\nrsX06dPFdX19fRQWFqJx48Zl9lUoFPjPf/6DIUOGQF9fH0uXLsXFixfxySefwM3NDQBw5swZ9OzZ\nEy1atMC2bdsQHR2N6dOnIzc3F1OmTEFGRgauX7+OxMRE7NmzB4sWLarwWlSHV199FQDQpk0b/PTT\nT9XSeprs7GytH348evRI63F+fn6YNGkSvL29oVSWzU6fPXsWHh4epdrOnTuHPn36VHpfVIRarRav\nLSBcE0Dod/D0dffz80Nqairmzp2Le/fu4ejRo7hx4waOHDkCAwODMvsvXLiwUvvFZGdnw8DAoMJ9\ntm7dWmq9+DqPHTsW+vr6Wq+7QqHA+fPntd5nFeHl5YX//ve/pdo8PT2xbds2mJubV3Zapajs3+Tp\n+/3f//43unXrhu7du+Oll17CgQMHYG1tXeqY7OxspKSkwNbWtoxednY2Vq5ciTt37mDz5s34448/\ncPXqVQwbNuzZHKYGRFFRESUkJJRqU6lU9Pbbb9OAAQOIiOj69eu0ceNGrRoBAQFERLRmzZpS7S1a\ntCB9ff1yFwMDA+rSpQs9evSIevToQUREN2/epBEjRpTxZffu3bRnzx5KTU2t9BxGjhxJ7u7u9P77\n71NQUJC4EBE5OzuXOba8tvJwcnIq0zZjxgzKycmhvLw8evXVV6lVq1a0efNmrfvb29tr1S/edurU\nKerfvz8dOHCAevXqVWZ7TEwMOTs70/r166lfv37k5uZGFy5cEO1pNBqytbWt8Fpo83vVqlWUnZ1N\nGo2GJkyYQPb29nTw4MEyviYlJVFSUhKlpKRU+m9cWFgo3h9P86z2SlKeVkBAAB05coTGjBlDnTp1\norlz59L169dL7VPev0fxfVfZffHOO++U2b5u3ToyNjamjh07kpWVFVlZWVGnTp2IiGjMmDHk4uJC\nY8eOpfHjx4sLEVH//v1p165d5ODgQEREBQUF1Lx5cxo1ahStXLmSVq1aJS5ERLm5ubRixQoaMmQI\nDRkyhD7//HPKy8sT/Th58iRZW1uTubk5ERFduXKFRo4cSX369CEbGxsiIkpISKDFixfTBx98IN4D\nkydPpk6dOtEbb7xR6rwWLFhAGzdupL///pv+/vtv+v777+mTTz6p8D4r73nx9ddfU0ZGBjk4OFBm\nZqa4JCcnU+fOncnR0bHMNSUiysrKogULFtCECROIiOj333+nX375hYiIOnfuTO7u7jRnzhw6ePAg\nqdXqUsc+fb+3adNG/NuPiIggCwsLsre3p4iICBo5ciSFhYWRtbU1WVpaitfOx8dH1PP19aUVK1aI\n5/n48WPx3+1ZaFCBpDzKu9nt7Oy07t+1a1dKTU0Vb5qMjIxSN4+2m9PFxYWIhIdk8R9Ht27dRN2t\nW7dShw4dKDAwkAICAsjMzIy2bdtWoe9dunQhjUZTqu3OnTsUHR1NNjY2FBMTQ9HR0RQTE0OnT58m\na2vrZ7om33zzTZm24gfRnj17aNKkSaRWq8nCwoKGDh1KrVq1oqFDh4qLp6cneXh4aNUv/sOaO3cu\nBQcHE1Hph1nxH/DixYtp06ZN4vbi40o+KIv/3cq7Ftr8dnBwEI8LDw+nESNG0JUrV8p9ABc/ON3c\n3LSeT0leeeUVKigoKNP+rPZK8vT2wsJC6tKli7j+4MED+vbbb8nMzIzc3d1p/vz5tHLlSjIzM6PV\nq1eLD+mlS5fSiy+++Ez3RXk+WVpaUnp6erk+2tjYlHvdS55zSc2mTZtqPd+33nqLJkyYQP/973/p\n2LFjNGnSJHrrrbfE7T169KDbt2+X0mvWrJnWh35JsrKyaPDgwaXaevbsWWa/nj17Vniflfe8aNeu\nHVlZWVGTJk3E+8XKyoq6d+9Oq1atonHjxlF0dHQZW5U9vJOSkig4OJjee+896tixo3gvE5W93196\n6SVKSUkR1/Py8uj27dv08OFDSk1NJVtbW3rw4EG550T05D9vJbeXtFcZDSa1pY3MzEz4+/tjxYoV\nAIBGjRqhUSPtl+W9996Dl5cX/vzzT7i4uJTZ3qZNG8TExIjr77zzDlxcXGBmZoasrCwMHToUXl5e\naN26danPklesWIErV66I6YL79+/Dw8Ojwjx5z549kZaWBlNTU7HtyJEj+PHHH5GamoqZM2eK7c2a\nNcOnn376DFcEmDp1apm2goICAMChQ4cwcuRItGrVCgYGBpg5cybS09Mxa9Ys8bPqZs2awdnZWau+\nmZkZpk6dil9//RXz5s1Dfn5+qbqRgYEBPv/8c+zYsQOnT5+GRqNBQUEBjI2N8fvvv4v7HTx4EEZG\nRlqvhTa/9fT0RHsREREICAiAvb19ub7eunVL/P3+/fvaLxqEf3sLCwu4u7vD19cXzZs3B1B6+O3K\n7AHAsmXLsHz5cjx+/LhUCkdPTw/jx48HAGRkZCA4OBjBwcHo2bMn3nrrLezevRsxMTEoKipCdna2\neFyzZs0wceJEzJo1q9L7om3btmX86d69O/T19cv11d3dHTdu3EC3bt3KbGvRogUyMjLE9bi4OBgY\nGCAhIaHc84+Pj0diYqK47uXlBTs7O3GdiNCxY8dSxxARevfuLa4rFAro6emV0X7hhRdw48aNUm0a\njQY7d+7EqFGjoFAoEBYWBiJCmzZttN5n5T0vTE1NER8fj6+//hoffvhhGds2NjbYvn07LC0t0aJF\nCx7BU8IAABoQSURBVNFPIsL+/fuxa9cuAMKHQsWpSpVKhXPnzuHMmTOIj4+HnZ0d+vbtK2o+fb9v\n3ry5VDq0SZMm4rVq3rw5GjVqBENDw1J+lfyba9KkCR4/fiyuJycnlzmPimjwgaRFixbIzMwU1+Pi\n4iosZs+YMQMzZszA+++/j/Hjx+P06dNQKBTo168f3Nzc4OzsXO7N+csvvwAAli9fjiNHjiA3NxeD\nBw8upV0cRACgdevWlfZ3uXv3LmxsbODm5ib6rFAocOLECezZswcjR46s8vXQho+PD+zt7dG4cWN8\n++23yMzMRPPmzdG/f39cuHChSlq7du1CREQEZsyYAUNDQ9y9e7dU3SokJAQhISH44Ycf0K5dO6hU\nKsycORPu7u4YP348rl+/jo4dO8LExAQtW7bEsGHDkJOTU+61KM/vRo0awd7eHj4+Pvj999+xYsUK\n5OTkVOp3z549K8yt//nnn7C2toa1tTU0Gg1ycnJARFAoFHBychLtLV++vEJ7//rXv/Cvf/0L8+fP\nx4wZM/Dbb7+V+qMfMWIErl+/jrFjx+LAgQNo3749AGD06NHo3bs3wsLCYGlpWUZ3/vz5ld4X6enp\nZdo+++wzuLm5wd3dHU2aNBHPd926dRg3bhzc3NzQrl27Utf98uXL+PLLLzF48GD8+eef6NevH5KT\nk9GiRQs4OzujU6dOZfZXKpVISkqClZUVACApKalUDcjCwgLnzp0DIDwEv/vuO7Ro0aLch37J3L5G\no0FiYiKGDx9e6rxCQ0Px4YcfYsqUKQCAl19+GaGhodBoNBg3bpx4n7Vt2xahoaEAKn5etGzZEtu2\nbStz/Y4cOYK7d+/izJkzAIQPbExNTeHn56f14d2xY0f06tUL8+fPx7fffived8Xnpe1+L37OPI2t\nrS127NiBwsJC3Lp1Cxs2bECvXr3E7YsWLYKXlxdUKhXGjh2LEydO4Pvvvy9XqzwazFdb2jh//jyC\ngoLw+++/o0ePHkhOTsbu3btLXeTyWLlyJbZv3w4/Pz8AwL59+xAYGIjXX38dH374Ic6fPw9AuDm/\n/vprdOnSpUK9Dz/8EL/99htGjx4NIsLu3bthbW2Nr7/+WusxJ0+eLLe9f//+0Gg02LdvH27cuFHq\nIVSVoubTpKWlwcjICHp6enj48CFeffVV/O9//yu3eKlQKJCVlfXctsrj4cOH2L17N1JSUnDv3j20\nbdsWSUlJGDduXLmT7igUCrzyyitl/M7KyoKJiQliYmKQn58PjUaDjIwMpKSklPpooCLS0tLKPOBf\neeUV8ffi/4kbGxsDAIqKihATE4OuXbvC0NAQ9+/fR0pKCnr06KHVRvEHGn/99RecnJzEDzRmzZqF\n+Ph4nDt3DgqFAh4eHpg+fTqaNWsG4MkHAk9fi+PHj+P+/ftYsGCB+FB75ZVX8O9//xutW7cGADg7\nO5cZ5NTV1RX9+vWDg4MDlEqlGBzHjRsHa2trrFmzBvb29qUe+lZWVnj06BFWrVqFCxcuQKlUws3N\nDf7+/lCr1WUeqpaWljh06BAmTpwIGxsbAMBvv/2GTZs2wcfHB4DwH6epU6fi2LFjUCgUGDBgAGbN\nmoVZs2YhNjYWbdu2FR/6xcMiKRQKKJVKmJmZoXPnzpX+uxb/W+np6SEjIwNEVOot7fz58+Lf6tPP\ni6CgIPHv4PHjxzh+/Dh69uyJXr16lfus6NatG1asWIHffvsN3t7e4sPb29sbly5dwpkzZ3DmzBkk\nJyejS5cu6NevH1588UXxvLTd7+WRk5ODRYsW4ciRIwCAQYMG4dNPPxXvmczMTBQWFor/Lh4eHnj8\n+LH4UU5lNPhAAgD5+fm4fPkyAMDR0VH8X1dFdO/eHXFxceKXFbm5uXB2dsa1a9eey4c5c+bAyckJ\nZ8+eFR8OcXFx+Pzzz59Lb/z48dBoNDh+/Djeeecd7N69G71798YPP/xQJZ3//ve/8PLywk8//ST+\nkRTfMsVf59QUffr0wblz57QGpp49e8LU1BQuLi6l0hczZ87EnDlzylyruXPnYuXKlYiJicGNGzdQ\nUFAg6qrV6nIf0hV9RVeMtgf88ePHERsbi4CAAPGNo0mTJggLCyujUfwwrqiPU9euXXHp0iW4u7sj\nPj4eN2/eFFOBHTp0wJgxY0BECA0NRWpqKg4ePAig9Oitubm52LdvH5RKJb744gt4e3vD3d0dAQEB\nICKEhIQgMjIShw8fBgBs2LChTGrT1dVV64iwHh4eWocdHzp0aBk/T58+DT09vTIP1Tlz5iA0NBSD\nBw/GjRs3EBERgdOnT2PZsmXi13wVkZmZCSISAzcApKam4vz581AoFHB3d0eHDh1KHZOTk4ONGzeW\n+c/WsWPHMHjwYPj7+8PT01O8ZzQaDdatW4epU6fi8uXLICI4OjpqzWBkZ2dj1KhRSEpKKvOscHJy\nwtKlS/HKK6+UeniX/Ow+Ozsb586dw+nTp/H/7Z15UFPn18e/YVFBRBSlYMtPrIpBCRBQNgkgjriw\n2NIqWoWquIC1FuqotVaRMmgpWMpYVHSoDspUtKVuIE4roFALyiKbiCyKji0MqMOOGLjvH8y9b0Ju\nAiQBtDyfGUfIcreQc55zzvece/bsWQD/H7VUV1fDwMCAcQTt7e2ora2Vavhv376NgwcPSqjlaLtn\nb2+Pa9euYfz48QCA+/fvY8WKFWKpRlmMeEdC9ZLRicoEZWFiYoLCwkLG6XR2dsLc3BxpaWmIjY2V\n+MB+/vlnmdtjWwnOnj0b9+/fl3htXwa3qakJXC4XDx48gLm5OQoLC9He3o4lS5bg5s2bsi9IL4KD\ngxESEoJ169axpnVOnTo1oO0pgqmpKUpKSlifk3b9PD09kZOTg9LSUri5ueHatWtwcHBAUVGRmJGu\nqKjA7t27kZSU1Odx9Dbwou+1srJCVFQU0x/k4eGBf/75B+PGjWO9funp6VL3QxtwHo+HvLw8jBo1\nCiYmJlBVVZW4DrKuDQDY2toiOzsbPB4PxcXFYs+ZmZkxBoWNvXv3wsjICO7u7mJGc+LEidi6dSua\nmprg5uYmlvby8vJiPabRo0ejsbGRdQFGH9utW7cQHByMHTt2IDQ0lNWR0CtyoVAIGxsbie+bkZER\n9uzZg4ULFwIA0tLScOjQIbGao4eHBywsLJCQkIDg4GAkJCSAy+Xi0KFDuHr1Ks6dO4f8/Hx4eHjA\n29sbAoEAdnZ2TLahLzo7O8HlcjF69GhWW6GtrS0hvaeZO3cuOjo6YG9vD0dHRwgEArF0JZ/PR25u\nLrOgev36NWxtbcXqs6JMmzYN0dHRrJEjACQnJyM8PBwpKSkoLy+Hr68vEhISYGFh0a9zHfE1kpUr\nV+LZs2ewsLAQW+X25UjWrl2LuXPnwsvLCxRF4eLFi/Dx8cGyZcvg6uqKxYsXMx+YrCGRx44dw9Gj\nR1FVVQUej8c83tbWJvVDpPPEsvLsdEOSmpoaamtroaOjg5qaGpnnxEZISAgAICYmhkkrifYSDCUO\nDg4Shdq+rl9SUhLKyspgZWWFU6dOoaGhAWvWrIG2tjY0NDTQ1dWFzs5OzJw5s9/RpKz3dnZ2ijWZ\nXrlyBWZmZlLTkLKYMmUKq0BDX18fd+7cYQzs3bt3xSIbUVFAd3c3cnNzUVdXB6Dn7+H27duwt7cH\n0JOqkSUuAYCEhARwOBwcPHhQ7PFHjx6hra0NampqTMqExsvLC5aWlhLHKVpQBsD6c0pKCjZu3Ah3\nd3fs378fVlZWEtEwTXh4OF68eCERpfZHvFJdXY0rV67g8uXL+PTTT7F27VoIBAKMHTsW3t7e8Pb2\nxsuXL7F9+3Y4Ozujq6sLtra2+OKLL/Dxxx9j7NixYpGltLqMnp4eq61oampCVFQUsy2aiRMnIiUl\nBXp6elI/k+7ubrHzVVdXZ4QlbBgaGsLT01Pq825ubujs7MSiRYvQ0tKCpKQkJsXYH0a8IyksLER5\nefmAJwLv3bsXCxYsYFJRx48fh52dHS5cuDCguzJ+8sknWLp0Kb766iuEh4eLKZ96K5AGgpubG5qa\nmrBjxw6YmZlBRUUF69evV2h7bGmloYB2EF1dXYiLixMr1HZ3dyM5OVnq9Zs3bx5UVVVBURRaWlqg\nq6uLqqoqzJ49W6aKThbSDDz93KFDh5h0zrlz52BgYACKonDr1i0JRyxrwdJboLF582ZoaWnh6dOn\nsLW1haGhITgcDp48eSL2pRcVBaioqOC9995jUpqxsbHw9fVliryampqsBWJRHj9+LPW506dPSzzG\n4/HA4/EgFAoljlNXV5fVqALS1Xy0Uo2NyMhIphAuSlxcXJ/iFdp4a2hooLS0FHp6ekxtJSMjA4mJ\niUhNTcW8efOY9GRBQYFYSogmPT1dTA1HX3e6LsNmK4yMjBjRgiiPHj2CmpoaAgICpNayxo0bh+Tk\nZLi5uQHoERnIatLct28fNm3aBBcXF7HIsXdE3NTUhOnTp+Onn35iPTZpjPjU1qpVqxAdHa2Q0Rbl\nm2++gYODg4QiazhpaWlBV1cXk/+Uh75SJ4OJLEPW3NwMHo+H58+fsy4Gvv76a3z//feIi4tDTEwM\ntLS0wOPxcObMGeY1oiq6/tTHRKHfm5iYiISEBERGRuLhw4dM1CgQCBAWFgZ/f3/WyFeWmKI3sq4D\nh8NhUh+tra2Ijo5GVlYWVFRUMH/+fAQGBkJDQ0NMsFBbWws9PT2pneVs9TFRvLy88PjxYwQEBIid\n7549e6R2cnM4HDx79kysFmhnZwegx4ilpqaCz+dj5syZqK2tRVFREVxdXQH0jAg6ePAgU+8CgIcP\nH+L69esScuL+iFdiY2OxevVq5OTkYN26dejs7MSBAwcQEREBCwsLeHt7w8PDQ6r0WRb19fXQ0dFh\nne7QH/qqZZWVlWHVqlWMgmzy5Mn45ZdfWGXYALBmzRqUl5djzpw5YlGgk5OTROGe/p0WVPSHEetI\nRGV0BQUF/ZbR9YWWlhba2towatQo5o9oMBRMfSFa+xH9iOUdGeLv749t27bJ7H8YDtzc3JCcnCy1\nyDh//nw4OTnBwcEBqqqq6OjogJmZmdKPY9asWUhPT8eSJUuQkZEh8cW0tbWVK/KVB7YiN12Md3Z2\nlipY6E1/6mMODg7YvHkzVq9eDaBH2h0bGyvXfb/7wtHREb6+voiMjERsbCzi4+Nx8eJFNDU1SciJ\nN27ciEmTJok5OG9vb7HzEHWqolFiYGCg1EUX29iXkydPIjs7G3p6eti3bx98fHzQ0NCAzs5OxMXF\n4cMPP2TdlqyxJP2tZdXX14PD4YiJDNjgcrkoKysbtL+/EetI6Jz1rl27EBERIfbF37VrF+7cuSP3\ntvuShg4FK1asUHgFDIinlSoqKlj1/28Ca9asgZOTEwQCAUxMTJjH09LSkJmZiaysLFRWVsLS0hIC\ngQCBgYFK3X9UVBROnDiB6upqCXUQh8OBtbW1UiNfWbBFj/Rj8kSW0gxucHAwI+YQpa/ivbzQxlXU\nyFpYWCA2NlZCTnzixAkkJiaCz+djw4YNWLJkiYQRleZUt2zZwqi56MiHFswsWLAA/v7+CAsLQ1FR\nEYRCIbS0tHD58mU0NjZi06ZNSE1Nha2tLR48eAAvLy9WwQwALF++HPb29oiPj0dpaSk6OjpgbW2N\noqIi8Pl8xMTEiNWyPvvsMwQFBcHHxweHDx8Wixzo/+mhn73x8/PDzp07JSKWFStW4MKFC2L1RZqB\nfL9HbI3E2dkZQI/aobeR709zmjRkSUOHEnlrP725cuWKko5ocPHz80NWVha2b9+Oqqoq8Pl8xmE4\nOjoiNzcXaWlpOH78OEpKSpTuSOhGVX9/f7G7cdKRb11d3YAayBSBrchNF+PZBAt9Ias+NnbsWKYB\nFwDOnz/f50BFeaEnBejq6iIlJQX6+vqoqamBn58fIyf28/ODj48PwsLCEBoaykx6+Pzzz7Fy5Ups\n2LCB6cVoaGhgFUHQaq4///xTTM0FsHe2czgcJv22f/9+2NraAuiJAmSltqqrq6V2tkurZdHDVHsP\n/KQdiTT++usvxMfHSywEU1NTASjhe97vYSr/MY4ePUqZmppSGhoalKmpKfPv/fffp7y8vOTebu/h\njOXl5RLDGYcCb29vqra2dsj3O5y8fv2a+vvvv6mwsDDK0NCQMjY2plxcXCgbGxsqMDCQ+vXXX6m6\nurohPab09HQqPT2dmjdvHpWRkcH8Tj+mTOi/YS6XS3E4HOp///sfNXXqVIrD4TBz3bhcLqWmpkbN\nnDmTeX1fw/lkzZ6rrKykFi1axAyuXLx4MVVZWanU86K5dOkS1djYSOXl5VF2dnYUn8+n3n33Xaq9\nvZ15TXt7u9gMu4KCAmr79u2UsbEx5e/vT/H5fCooKIiiKIrasmULVVxcLLEfevYVPXNLKBRSdnZ2\nFEX1zLRqaGhgZlLl5+dTmpqazHt7zyqTNU/N0tKSamtrY15TU1PD2I2Wlhbq1KlT1Lfffktt3bqV\nOnDgABUSEtLPKyXJo0ePWP8pixEbkQyWWqq3NNTY2FjuJkV5kHeEwtvOwoUL0draCjs7Ozg4OCA3\nNxd6enoICgpCbm4uSkpKoK2tjQkTJsDOzo5p5BpsBivyZUPWqpJerdLF2oEgLYrp6urCgQMHJKS/\ngwUtX7W0tMTt27cB9PRzsUmIo6OjER8fD11dXWzcuBGRkZFQV1cHRVEYM2YM/vjjD1YVIIfDkanm\n+uGHH+Dq6io29qWjo4OJwnrPRxMdgdIbWWNJRKNAto78f//9d0D9anS/SG/6Gqnf39ruiHUk48eP\nx/jx45mwUlnIkoYOBXTRdNeuXbh06ZJE7ee/ipmZGavDiIqKAtCTCjh9+jTWr1+P2tpavHr1akiO\nS54+IXmRZiwG+hoaWbJrOn/+9OlTCIXCPntRlAGb8dTU1GSVE7948QJJSUkSM8c4HA6uXr0qc2TR\n9evX0dTUhNDQULi6ujJqLgBMQ+JAJ2Gwcf78eXzwwQd49eoVTExMEBERwSxipaXdaAbaryYNZS1m\nRmyxfShQRFaqKAPplP8vQTuMyMhIZhhkZmYm8vLyMG3aNAgEAggEAri4uAzJ8TQ2NuLly5dKj3yH\nAllyY6DHKfn6+qKsrExi2rG0oq8i8Pl8uLq6wsrKSsx4GhgYsMqJ5UWWuICScxIGG7KEIH2pJNm+\n38MJcST/MURXwKJ3SKNXwMq+y9+bwpEjR1gdRl5eHhwdHWFpaSm3pp8giY+PD86cOQMdHR0EBQVJ\nPN/X3QTlYaiMpyyJtLLUkDRCoVBMCKKhoYHy8nKYmJigsrJSqkryTetXI47kP8bbvAJWhIiICOIw\nhhBZfTMAmPt3KJOhMp6yJNLGxsZK6wfqXdcTCATMWBRp0aCpqSmz79bW1mHvV6MhjoRAIAyYvvpm\nqqurlbYv0YLwUBhPWWklZU7CoIUgY8aMgb29PZycnPotBJHWNzVcEEdCIBDkpnffzGAy2MZTVvNt\nTU0NnJyclD4JA5Cs6/VHCJKWloasrCxkZmZK9E0NB8SREAiEt4LBNp6yxAXZ2dnQ19dX6iQMaXW9\n/gpBpNVXhgPiSAgEwlvDcBtPZaohFanryaqvDAcjto+EQCC8XUhrOh0KBqMfaOfOnXIfj7S+qaFq\ntO0NiUgIBMJbgSLFaUV5U9WQ8tRXBgPiSAgEwlvFm2I8hxNF6yvKhqS2CATCW0Fv47lhwwYIBILh\nPqxhoaOjAzt27Hhj+qZIREIgEN4KSNPpmwtxJAQCgUBQCJW+X0IgEAgEgnSIIyEQCASCQhBHQiAQ\nCASFII6EQFCAffv2YdasWTA3N4e5uTlycnIGbV/Ozs7Iy8sbtO0TCPJC5L8EgpxkZGTgxo0bKCkp\ngbq6OpqamtDW1jZo++NwOEoZX04gKBsSkRAIclJfX4/JkyczUlRtbW3o6+sjJCQE1tbW4HK5WLdu\nHXOXPWdnZ3z55ZewtbWFiYkJ7t69i48++gjTp0/H7t27AfQMDuRyufD19YWpqSnc3d1ZndPly5dh\nZWUFHo+H5cuXo7m5GUDP2I05c+bAwsJiUO5SSCCwQRwJgSAnixcvRnV1NUxMTBAQEIAbN24A6Bnl\ncefOHTx48ADd3d34/fffAfREFBoaGsjOzkZAQACWL1+O2NhYlJWV4ezZs6ivrwcAPHz4ENu2bUNJ\nSQmmTJmC6Ohosf3W1dXhu+++Q1ZWFoqLi2Fvb4/w8HDU19fj2rVrKC0txb179xAaGjq0F4QwYiGp\nLQJBTrS1tXHv3j3cvHkTt27dwtq1axEaGgpNTU0cPnwYQqEQz58/B5fLZd7j7u4OoOdOd6amppg0\naRIAYMaMGXj27Bl0dHRgaGgIa2trAMDq1asRGRnJvJ+iKGRmZqKiogL29vYAgM7OTtjY2GDixIlQ\nV1eHn58fli1bBg8Pj6G6FIQRDnEkBIICqKqqwsXFBS4uLuDxeDh27BiKiopQWFgIAwMDhISEQCgU\nMq+nb4akoqLC/Ez/TqfAROsgFEWx1kWWLl2K+Ph4icdzcnJw48YN/Pbbb4iJiUFaWprSzpVAkAZJ\nbREIclJRUSF2M6SCggJMmzYNqqqqmDBhAtrb23HhwoUBb/fJkye4e/cuACAxMREODg7McxwOBwKB\nAOnp6Xjy5AmAnrlLVVVVaG1tRXNzM5YuXYrDhw8jPz9fsRMkEPoJiUgIBDlpbm7G1q1b0draCqFQ\niBkzZuDkyZOYNGkSuFwupk6dChsbG9b3ylJgzZo1C0eOHEF+fj6MjIzw448/ij3/zjvv4MSJE/D0\n9AQAdHd3IywsDJqamvD09IRQKERXVxeioqKUe8IEghTIrC0C4Q3i8ePH8PDwQHFx8XAfCoHQb0hq\ni0B4wyC9IoS3DRKREAgEAkEhSERCIBAIBIUgjoRAIBAICkEcCYFAIBAUgjgSAoFAICgEcSQEAoFA\nUAjiSAgEAoGgEP8H9D/AU8r1L50AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hapaxes1 = fdist1.hapaxes()\n",
      "print len(hapaxes1)\n",
      "print hapaxes1[1000:1010]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "9002\n",
        "['Gull', 'Gurry', 'HACKLUYT', 'HAILS', 'HALF', 'HAMLET', 'HANDS', 'HANGING', 'HARD', 'HARDY']\n"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## n-grams"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "thursday_sents = nltk.corpus.gutenberg.sents('chesterton-thursday.txt')\n",
      "sent22 = thursday_sents[22]\n",
      "' '.join(sent22)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 23,
       "text": [
        "'THE suburb of Saffron Park lay on the sunset side of London , as red and ragged as a cloud of sunset .'"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nltk.bigrams(w for w in sent22 if w.isalpha())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "[('THE', 'suburb'),\n",
        " ('suburb', 'of'),\n",
        " ('of', 'Saffron'),\n",
        " ('Saffron', 'Park'),\n",
        " ('Park', 'lay'),\n",
        " ('lay', 'on'),\n",
        " ('on', 'the'),\n",
        " ('the', 'sunset'),\n",
        " ('sunset', 'side'),\n",
        " ('side', 'of'),\n",
        " ('of', 'London'),\n",
        " ('London', 'as'),\n",
        " ('as', 'red'),\n",
        " ('red', 'and'),\n",
        " ('and', 'ragged'),\n",
        " ('ragged', 'as'),\n",
        " ('as', 'a'),\n",
        " ('a', 'cloud'),\n",
        " ('cloud', 'of'),\n",
        " ('of', 'sunset')]"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import networkx as nx\n",
      "G = nx.Graph()\n",
      "begin_sent = 22\n",
      "end_sent = 24\n",
      "sents = thursday_sents[begin_sent:end_sent+1]\n",
      "for sent in sents:\n",
      "    G.add_edges_from(nltk.bigrams(w for w in sent if w.isalpha()))\n",
      "nx.draw(G)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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bGyubN2+Wd999V/r37y/Vq1cXi8UipUuXlrZubvKa83vkAVIeZGm2709jkOEg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\nwVcppZQqY/8PuEq8OIhor50AAAAASUVORK5CYII=\n"
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Lexical clusters in Dickens\n",
      "\n",
      "Below [BKL] refers to \"Natural Language Processing with Python\" by Bird, Klein and Loper and [MAR] refers to \"Mining the Social Web\" by Matthew A. Russell."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Getting the raw text\n",
      "\n",
      "See page 95 of [BKL]."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import codecs, nltk, pprint\n",
      "hard_times_path = \"/home/matthew/workspace/resources/C/Corpus Stylistics/Dickens, Charles/786-0.txt\"\n",
      "david_copperfield_path = \"/home/matthew/workspace/resources/C/Corpus Stylistics/Dickens, Charles/pg766.txt\"\n",
      "f = codecs.open(hard_times_path, encoding = 'utf-8')\n",
      "david_copperfield_file = codecs.open(david_copperfield_path, encoding = 'utf-8')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 129
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text = f.read()\n",
      "len(hard_times_raw_text)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 27,
       "text": [
        "610690"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "david_copperfield_raw_text = david_copperfield_file.read()\n",
      "len(david_copperfield_raw_text)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 130,
       "text": [
        "1992524"
       ]
      }
     ],
     "prompt_number": 130
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Sentence tokenizing\n",
      "\n",
      "See page 112 of [BKL]."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sent_tokenizer = nltk.data.load('tokenizers/punkt/english.pickle')\n",
      "sents = sent_tokenizer.tokenize(hard_times_raw_text)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sents[171]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "He had reached the neutral ground upon the outskirts of the town, which\r\n",
        "was neither town nor country, and yet was either spoiled, when his ears\r\n",
        "were invaded by the sound of music.\n"
       ]
      }
     ],
     "prompt_number": 29
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(sents)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 30,
       "text": [
        "4498"
       ]
      }
     ],
     "prompt_number": 30
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "An alternative approach based on [MAR]."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sents = nltk.tokenize.sent_tokenize(hard_times_raw_text)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 31
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print sents[171]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "He had reached the neutral ground upon the outskirts of the town, which\r\n",
        "was neither town nor country, and yet was either spoiled, when his ears\r\n",
        "were invaded by the sound of music.\n"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DC_sents = nltk.tokenize.sent_tokenize(david_copperfield_raw_text)\n",
      "print DC_sents[171]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Calls a house a rookery when there's not a rook near it,\r\n",
        "and takes the birds on trust, because he sees the nests!\n"
       ]
      }
     ],
     "prompt_number": 131
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Word tokenizing"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tokens = [nltk.tokenize.word_tokenize(s) for s in sents]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 33
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(tokens)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 34,
       "text": [
        "4498"
       ]
      }
     ],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print tokens[171]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'He', u'had', u'reached', u'the', u'neutral', u'ground', u'upon', u'the', u'outskirts', u'of', u'the', u'town', u',', u'which', u'was', u'neither', u'town', u'nor', u'country', u',', u'and', u'yet', u'was', u'either', u'spoiled', u',', u'when', u'his', u'ears', u'were', u'invaded', u'by', u'the', u'sound', u'of', u'music', u'.']\n"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DC_tokens = [nltk.tokenize.word_tokenize(s) for s in DC_sents]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 134
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print DC_tokens[171]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[u'Calls', u'a', u'house', u'a', u'rookery', u'when', u'there', u\"'s\", u'not', u'a', u'rook', u'near', u'it', u',', u'and', u'takes', u'the', u'birds', u'on', u'trust', u',', u'because', u'he', u'sees', u'the', u'nests', u'!']\n"
       ]
      }
     ],
     "prompt_number": 135
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### POS tagging\n",
      "\n",
      "Warning: Very slow"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# pos_tagged_tokens = [nltk.pos_tag(t) for t in tokens]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 36
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### n-grams"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "' ' .join(tokens[171])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 63,
       "text": [
        "u'He had reached the neutral ground upon the outskirts of the town , which was neither town nor country , and yet was either spoiled , when his ears were invaded by the sound of music .'"
       ]
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bigram = nltk.bigrams(w for w in tokens[171] if w.isalpha())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 61
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import networkx as nx\n",
      "G = nx.Graph()\n",
      "G.add_edges_from(bigram)\n",
      "nx.draw(G)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "display_data",
       "png": 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tj4PiZOf27RxJSsKTf9sCU9HWCd6FdjXUFK125Ppf+yjaOrVHgWaAr9XK0eho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\nrkJggg==\n"
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print nltk.ngrams((w for w in tokens[171] if w.isalpha()), 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[(u'He', u'had', u'reached', u'the', u'neutral'), (u'had', u'reached', u'the', u'neutral', u'ground'), (u'reached', u'the', u'neutral', u'ground', u'upon'), (u'the', u'neutral', u'ground', u'upon', u'the'), (u'neutral', u'ground', u'upon', u'the', u'outskirts'), (u'ground', u'upon', u'the', u'outskirts', u'of'), (u'upon', u'the', u'outskirts', u'of', u'the'), (u'the', u'outskirts', u'of', u'the', u'town'), (u'outskirts', u'of', u'the', u'town', u'which'), (u'of', u'the', u'town', u'which', u'was'), (u'the', u'town', u'which', u'was', u'neither'), (u'town', u'which', u'was', u'neither', u'town'), (u'which', u'was', u'neither', u'town', u'nor'), (u'was', u'neither', u'town', u'nor', u'country'), (u'neither', u'town', u'nor', u'country', u'and'), (u'town', u'nor', u'country', u'and', u'yet'), (u'nor', u'country', u'and', u'yet', u'was'), (u'country', u'and', u'yet', u'was', u'either'), (u'and', u'yet', u'was', u'either', u'spoiled'), (u'yet', u'was', u'either', u'spoiled', u'when'), (u'was', u'either', u'spoiled', u'when', u'his'), (u'either', u'spoiled', u'when', u'his', u'ears'), (u'spoiled', u'when', u'his', u'ears', u'were'), (u'when', u'his', u'ears', u'were', u'invaded'), (u'his', u'ears', u'were', u'invaded', u'by'), (u'ears', u'were', u'invaded', u'by', u'the'), (u'were', u'invaded', u'by', u'the', u'sound'), (u'invaded', u'by', u'the', u'sound', u'of'), (u'by', u'the', u'sound', u'of', u'music')]\n"
       ]
      }
     ],
     "prompt_number": 75
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now consider the full text of \"Hard Times\", here represented by the list ```tokens```."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fivegrams = []\n",
      "for t in tokens:\n",
      "    fivegrams += nltk.ngrams((w for w in t if w.isalpha()), 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 67
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print ' '.join(fivegrams[2000])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "board with a dry Ogre\n"
       ]
      }
     ],
     "prompt_number": 73
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Create a dictionary to count occurrences of specific 5-grams."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "D = {}\n",
      "for gram in fivegrams:\n",
      "    if not D.get(gram):\n",
      "        D[gram] = 1\n",
      "    else:\n",
      "        D[gram] += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Iterate through the keys of the dictionary and print out and 5-grams that have more than 3 occurrences in the text."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for gram in D.keys():\n",
      "    if D[gram] > 3:\n",
      "        print ' '.join(gram), D[gram]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "and why did he not 4\n",
        "did he not come back 4\n",
        "as if she would have 4\n",
        "and venison with a gold 4\n",
        "why did he not come 4\n",
        "sir returned Sparsit with a 5\n",
        "man and why did he 4\n",
        "The emphasis was helped by 4\n",
        "venison with a gold spoon 4\n",
        "I am Josiah Bounderby of 4\n",
        "to the Project Gutenberg Literary 6\n",
        "was the man and why 4\n",
        "his hands in his pockets 8\n",
        "No little Gradgrind had ever 4\n",
        "this town and I know 4\n",
        "the terms of this agreement 7\n",
        "am Josiah Bounderby of Coketown 4\n",
        "with his hands in his 6\n",
        "town and I know the"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 4\n",
        "soup and venison with a 4\n",
        "emphasis was helped by the 4\n",
        "turtle soup and venison with 4\n",
        "the man and why did 4\n",
        "Project Gutenberg Literary Archive Foundation 13\n",
        "of this town and I 4\n",
        "the Project Gutenberg Literary Archive 11\n",
        "a law to punish me 4\n"
       ]
      }
     ],
     "prompt_number": 86
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we can try to find the occurrences of one of the 5-grams, 'vension with a gold spoon', in the original text."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text.find('venison with a gold spoon')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 87,
       "text": [
        "250056"
       ]
      }
     ],
     "prompt_number": 87
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print hard_times_raw_text[250010:250082]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "That object is, to be fed on turtle soup and venison with a gold spoon.\n"
       ]
      }
     ],
     "prompt_number": 116
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text.find('venison with a gold spoon',250057)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 96,
       "text": [
        "250156"
       ]
      }
     ],
     "prompt_number": 96
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print hard_times_raw_text[250084:250182]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Now, they\u2019re not a-going\u2014none of \u2019em\u2014ever to be fed on turtle soup and\r\n",
        "venison with a gold spoon.\n"
       ]
      }
     ],
     "prompt_number": 113
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "We only find 2, instead of the 4 we counted, though. Probably an issue of punctuation."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text.find('venison with a gold spoon', 250157)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 115,
       "text": [
        "-1"
       ]
      }
     ],
     "prompt_number": 115
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "five_gram_2 = 'his hands in his pockets'\n",
      "hard_times_raw_text.find(five_gram_2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 122,
       "text": [
        "39377"
       ]
      }
     ],
     "prompt_number": 122
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text.find(five_gram_2, 39378)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 123,
       "text": [
        "41788"
       ]
      }
     ],
     "prompt_number": 123
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text.find(five_gram_2, 41789)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 124,
       "text": [
        "58759"
       ]
      }
     ],
     "prompt_number": 124
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text.find(five_gram_2, 58760)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 125,
       "text": [
        "476235"
       ]
      }
     ],
     "prompt_number": 125
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text.find(five_gram_2, 476236)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 126,
       "text": [
        "514354"
       ]
      }
     ],
     "prompt_number": 126
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_raw_text.find(five_gram_2, 514355)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 127,
       "text": [
        "-1"
       ]
      }
     ],
     "prompt_number": 127
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Now we will try to reproduce Table 3.3 on p.47 of [MM]."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DC_bigrams = []\n",
      "for t in DC_tokens:\n",
      "    DC_bigrams += nltk.ngrams((w for w in t if w.isalpha()), 2)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 136
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DC_D = {}\n",
      "for gram in DC_bigrams:\n",
      "    if not DC_D.get(gram):\n",
      "        DC_D[gram] = 1\n",
      "    else:\n",
      "        DC_D[gram] += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 138
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for gram in DC_D.keys():\n",
      "    if DC_D[gram] > 500:\n",
      "        print ' '.join(gram), DC_D[gram]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "to be 848\n",
        "that I 885\n",
        "at the 529\n",
        "with a 613\n",
        "it was 578\n",
        "my aunt 631\n",
        "I am 653\n",
        "I was 950\n",
        "to me 616\n",
        "on the 660\n",
        "of the"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 1392\n",
        "to the 762\n",
        "in a 694\n",
        "and I 668\n",
        "I have 639\n",
        "in the 1521\n",
        "of my 546\n",
        "I had 904\n"
       ]
      }
     ],
     "prompt_number": 139
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DC_trigrams = []\n",
      "for t in DC_tokens:\n",
      "    DC_trigrams += nltk.ngrams((w for w in t if w.isalpha()), 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 140
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DC_D_3 = {}\n",
      "for gram in DC_trigrams:\n",
      "    if not DC_D_3.get(gram):\n",
      "        DC_D_3[gram] = 1\n",
      "    else:\n",
      "        DC_D_3[gram] += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 141
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for gram in DC_D_3.keys():\n",
      "    if DC_D_3[gram] > 60:\n",
      "        print ' '.join(gram), DC_D_3[gram]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I had been 88\n",
        "said my aunt 219\n",
        "that it was 83\n",
        "I can not 62\n",
        "I should have 71\n",
        "out of the 124\n",
        "that she was"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 61\n",
        "that I was 124\n",
        "I am sure 88\n",
        "a good deal 71\n",
        "one of the"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 65\n",
        "if I had 82\n",
        "as if he 81\n",
        "would have been 62\n",
        "that I had"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 90\n",
        "I am not 65\n",
        "I could not 125\n",
        "I do know 102\n",
        "there was a 73\n"
       ]
      }
     ],
     "prompt_number": 142
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DC_fourgrams = []\n",
      "for t in DC_tokens:\n",
      "    DC_fourgrams += nltk.ngrams((w for w in t if w.isalpha()), 4)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 144
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "DC_D_4 = {}\n",
      "for gram in DC_fourgrams:\n",
      "    if not DC_D_4.get(gram):\n",
      "        DC_D_4[gram] = 1\n",
      "    else:\n",
      "        DC_D_4[gram] += 1"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 145
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "for gram in DC_D_4.keys():\n",
      "    if DC_D_4[gram] > 18:\n",
      "        print ' '.join(gram), DC_D_4[gram]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "as well as I 22\n",
        "for a long time 22\n",
        "as if he were 30\n",
        "as if he had 19\n",
        "I have no doubt"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 27\n",
        "in the course of 28\n",
        "it would have been"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 22\n",
        "a good deal of 28\n",
        "in a state of 23\n",
        "I could not help"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 32\n",
        "if I had been"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        " 19\n",
        "I am sure I 26\n",
        "as if it were 28\n",
        "as if I had 25\n",
        "for a little while 23\n",
        "I do know what 31\n"
       ]
      }
     ],
     "prompt_number": 149
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "By comparison with [MM] there is a problem in how I am handling punctuation. For example, I record the most frequent four gram as \"I do know what\" but in [MM] it is \"I don't know what\"."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "david_copperfield_raw_text.find(\"I do know what\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 163,
       "text": [
        "-1"
       ]
      }
     ],
     "prompt_number": 163
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "david_copperfield_raw_text.find(\"I don't know what\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 151,
       "text": [
        "19264"
       ]
      }
     ],
     "prompt_number": 151
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print david_copperfield_raw_text[19264:19296]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "I don't know what's the\r\n",
        "matter.\n"
       ]
      }
     ],
     "prompt_number": 162
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "At the very least I ought to reduce all text to lowercase, as done in [MM]."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Finding suspended quotations\n",
      "\n",
      "We begin with an extract."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "test_extract = sents[1024: 1037]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 40
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print ' '.join(test_extract)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Then, withdrawing\r\n",
        "his hand and swallowing his mouthful of chop, he said to Stephen:\r\n",
        "\r\n",
        "\u2018Now you know, this good lady is a born lady, a high lady. You are not\r\n",
        "to suppose because she keeps my house for me, that she hasn\u2019t been very\r\n",
        "high up the tree\u2014ah, up at the top of the tree! Now, if you have got\r\n",
        "anything to say that can\u2019t be said before a born lady, this lady will\r\n",
        "leave the room. If what you have got to say _can_ be said before a born\r\n",
        "lady, this lady will stay where she is.\u2019\r\n",
        "\r\n",
        "\u2018Sir, I hope I never had nowt to say, not fitten for a born lady to year,\r\n",
        "sin\u2019 I were born mysen\u2019,\u2019 was the reply, accompanied with a slight flush. \u2018Very well,\u2019 said Mr. Bounderby, pushing away his plate, and leaning\r\n",
        "back. \u2018Fire away!\u2019\r\n",
        "\r\n",
        "\u2018I ha\u2019 coom,\u2019 Stephen began, raising his eyes from the floor, after a\r\n",
        "moment\u2019s consideration, \u2018to ask yo yor advice. I need \u2019t overmuch. I\r\n",
        "were married on Eas\u2019r Monday nineteen year sin, long and dree. She were\r\n",
        "a young lass\u2014pretty enow\u2014wi\u2019 good accounts of herseln. Well! She went\r\n",
        "bad\u2014soon. Not along of me. Gonnows I were not a unkind husband to her.\u2019\r\n",
        "\r\n",
        "\u2018I have heard all this before,\u2019 said Mr. Bounderby.\n"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Interview Task"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import codecs, nltk\n",
      "little_dorrit_path = \"/home/matthew/workspace/resources/C/Corpus Stylistics/Dickens, Charles/pg963.txt\"\n",
      "f = codecs.open(little_dorrit_path, encoding = 'utf-8')\n",
      "little_dorrit_file = codecs.open(little_dorrit_path, encoding = 'utf-8')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "little_dorrit_raw = little_dorrit_file.read()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(little_dorrit_raw)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "1936177"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "little_dorrit_raw.find(u'At the close of this recital')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "1725461"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "end_phrase = 'producing the money.'\n",
      "little_dorrit_raw.find(end_phrase)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "1727185"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "task_string = little_dorrit_raw[1725461:1727185 + len(end_phrase)]\n",
      "print task_string"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "At the close of this recital, Arthur turned his eyes upon the impudent\r\n",
        "and wicked face. As it met his, the nose came down over the moustache\r\n",
        "and the moustache went up under the nose. When nose and moustache had\r\n",
        "settled into their places again, Monsieur Rigaud loudly snapped his\r\n",
        "fingers half-a-dozen times; bending forward to jerk the snaps at Arthur,\r\n",
        "as if they were palpable missiles which he jerked into his face.\r\n",
        "\r\n",
        "'Now, Philosopher!' said Rigaud.'What do you want with me?'\r\n",
        "\r\n",
        "'I want to know,' returned Arthur, without disguising his abhorrence,\r\n",
        "'how you dare direct a suspicion of murder against my mother's house?'\r\n",
        "\r\n",
        "'Dare!' cried Rigaud. 'Ho, ho! Hear him! Dare? Is it dare? By Heaven, my\r\n",
        "small boy, but you are a little imprudent!'\r\n",
        "\r\n",
        "'I want that suspicion to be cleared away,' said Arthur. 'You shall\r\n",
        "be taken there, and be publicly seen. I want to know, moreover,\r\n",
        "what business you had there when I had a burning desire to fling you\r\n",
        "down-stairs. Don't frown at me, man! I have seen enough of you to know\r\n",
        "that you are a bully and coward. I need no revival of my spirits from\r\n",
        "the effects of this wretched place to tell you so plain a fact, and one\r\n",
        "that you know so well.'\r\n",
        "\r\n",
        "White to the lips, Rigaud stroked his moustache, muttering, 'By Heaven,\r\n",
        "my small boy, but you are a little compromising of my lady, your\r\n",
        "respectable mother'--and seemed for a minute undecided how to act.\r\n",
        "His indecision was soon gone. He sat himself down with a threatening\r\n",
        "swagger, and said:\r\n",
        "\r\n",
        "'Give me a bottle of wine. You can buy wine here. Send one of your\r\n",
        "madmen to get me a bottle of wine. I won't talk to you without wine.\r\n",
        "Come! Yes or no?'\r\n",
        "\r\n",
        "'Fetch him what he wants, Cavalletto,' said Arthur, scornfully,\r\n",
        "producing the money.\n"
       ]
      }
     ],
     "prompt_number": 106
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re\n",
      "\n",
      "re_1 = r\"'[^']+'\"\n",
      "re_2 = r\"'[a-zA-Z0-9_,!? ]+(?:[-'][a-zA-Z0-9_,!? ]+)*'\"\n",
      "re_3 = r\"'[^']+[\\.,!?]'\"\n",
      "\n",
      "nltk.re_show(re_1, task_string[423:])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\r\n",
        "{'Now, Philosopher!'} said Rigaud.{'What do you want with me?'}\r\n",
        "\r\n",
        "{'I want to know,'} returned Arthur, without disguising his abhorrence,\r\n",
        "{'how you dare direct a suspicion of murder against my mother'}s house?{'\r\n",
        "\r\n",
        "'}Dare!{' cried Rigaud. '}Ho, ho! Hear him! Dare? Is it dare? By Heaven, my\r\n",
        "small boy, but you are a little imprudent!{'\r\n",
        "\r\n",
        "'}I want that suspicion to be cleared away,{' said Arthur. '}You shall\r\n",
        "be taken there, and be publicly seen. I want to know, moreover,\r\n",
        "what business you had there when I had a burning desire to fling you\r\n",
        "down-stairs. Don{'t frown at me, man! I have seen enough of you to know\r\n",
        "that you are a bully and coward. I need no revival of my spirits from\r\n",
        "the effects of this wretched place to tell you so plain a fact, and one\r\n",
        "that you know so well.'}\r\n",
        "\r\n",
        "White to the lips, Rigaud stroked his moustache, muttering, {'By Heaven,\r\n",
        "my small boy, but you are a little compromising of my lady, your\r\n",
        "respectable mother'}--and seemed for a minute undecided how to act.\r\n",
        "His indecision was soon gone. He sat himself down with a threatening\r\n",
        "swagger, and said:\r\n",
        "\r\n",
        "{'Give me a bottle of wine. You can buy wine here. Send one of your\r\n",
        "madmen to get me a bottle of wine. I won'}t talk to you without wine.\r\n",
        "Come! Yes or no?{'\r\n",
        "\r\n",
        "'}Fetch him what he wants, Cavalletto,' said Arthur, scornfully,\r\n",
        "producing the money.\n"
       ]
      }
     ],
     "prompt_number": 397
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "re.findall(re_1, task_string)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 396,
       "text": [
        "[u\"'Now, Philosopher!'\",\n",
        " u\"'What do you want with me?'\",\n",
        " u\"'I want to know,'\",\n",
        " u\"'how you dare direct a suspicion of murder against my mother'\",\n",
        " u\"'\\r\\n\\r\\n'\",\n",
        " u\"' cried Rigaud. '\",\n",
        " u\"'\\r\\n\\r\\n'\",\n",
        " u\"' said Arthur. '\",\n",
        " u\"'t frown at me, man! I have seen enough of you to know\\r\\nthat you are a bully and coward. I need no revival of my spirits from\\r\\nthe effects of this wretched place to tell you so plain a fact, and one\\r\\nthat you know so well.'\",\n",
        " u\"'By Heaven,\\r\\nmy small boy, but you are a little compromising of my lady, your\\r\\nrespectable mother'\",\n",
        " u\"'Give me a bottle of wine. You can buy wine here. Send one of your\\r\\nmadmen to get me a bottle of wine. I won'\",\n",
        " u\"'\\r\\n\\r\\n'\"]"
       ]
      }
     ],
     "prompt_number": 396
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Using the NLTK built-in corpus reader (optional)"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nltk.corpus import PlaintextCorpusReader\n",
      "corpus_root = \"/home/matthew/workspace/resources/C/Corpus Stylistics/Dickens, Charles\"\n",
      "wordlists = PlaintextCorpusReader(corpus_root, '.*')\n",
      "wordlists.fileids()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "pyout",
       "prompt_number": 290,
       "text": [
        "['786-0.txt',\n",
        " 'pg1023.txt',\n",
        " 'pg580.txt',\n",
        " 'pg730.txt',\n",
        " 'pg766.txt',\n",
        " 'pg963.txt',\n",
        " 'pg967.txt']"
       ]
      }
     ],
     "prompt_number": 290
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(hard_times_sents_raw)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "ename": "NameError",
       "evalue": "name 'hard_times_sents_raw' is not defined",
       "output_type": "pyerr",
       "traceback": [
        "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m\n\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
        "\u001b[1;32m<ipython-input-43-f401c44dc2d0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhard_times_sents_raw\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
        "\u001b[1;31mNameError\u001b[0m: name 'hard_times_sents_raw' is not defined"
       ]
      }
     ],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a = 0\n",
      "for sentence in hard_times_sents_raw:\n",
      "    a += len(sentence)\n",
      "print a"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import re\n",
      "s = hard_times_sents_raw[1000]\n",
      "# re.findall(r'\\W+', sentence)\n",
      "print s"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Removing extraneous text (optional)\n",
      "\n",
      "In this section I show how to remove extraneous text from the raw string for \"Hard Times\". This is done by hand by first identifying the first and last sentences of the text. There are some issue about Unicode that I haven't yet resolved."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_first_sentence = '\\xe2 \\x80\\x98 NOW , what I want is , Facts .'\n",
      "hard_times_first_sentence.split() in hard_times_sents_raw"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "first_sentence_index = hard_times_sents_raw.index(hard_times_first_sentence.split())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "' '.join(hard_times_sents_raw[first_sentence_index])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_last_sentence = 'We shall sit with lighter bosoms on the hearth , to see the ashes of our fires turn gray and cold .'\n",
      "hard_times_last_sentence.split() in hard_times_sents_raw"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "last_sentence_index = hard_times_sents_raw.index(hard_times_last_sentence.split())"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "' '.join(hard_times_sents_raw[last_sentence_index])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hard_times_sents = hard_times_sents_raw[first_sentence_index:last_sentence_index + 1]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}