Chiaroscuro Detector.ipynb

{
 "worksheets": [
  {
   "cells": [
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "#Chiaroscuro Detector\n\nThis program counts the number of words in a text that pertain to lightness or darkness, as determined by the Princeton Wordnet hyponym tree for those concepts. See the appendices at the bottom of this page for a list of words used. "
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "# Display plots in this window.  \n%matplotlib inline\n\nimport nltk #using the Python Natural Language Toolkit\nfrom nltk.tokenize import RegexpTokenizer\nfrom nltk.corpus import wordnet\n\n#Logging\nimport logging\nlogger = logging.getLogger()\nlogger.setLevel(logging.WARN)\n\ndef readText(filename): \n    \"\"\" \n    read custom text file (text of novel from Project Gutenberg) \n    and return NLTK Text object\n    \"\"\"\n    logging.debug('Opening text file...')\n    textfile=open(filename,'rU').read() \n    #tokens = nltk.word_tokenize(file)\n    #text = nltk.Text(tokens)\n    logging.debug('Text file read successfully.')\n    \n    logging.debug('Tokenizing text.')\n    tokenizer = RegexpTokenizer(r'\\w+')\n    tokens = tokenizer.tokenize(textfile)\n    logging.debug('Tokenizing completed.')\n    return tokens\n\ndef cleanText(tokens): \n    \"\"\"\n    clean the text by removing puncuation, making all tokens lowercase, and lemmatizing. \n    \"\"\" \n    logging.debug('Cleaning text...')\n    # normalize words by making them all lowercase\n    tokensLower = [word.lower() for word in tokens] \n    from nltk.corpus import stopwords\n    englishStopwords = stopwords.words('english')\n    tokensNostops = [word for word in tokensLower if word not in englishStopwords]\n    return tokensNostops\n\ndef flattenList(biglist): \n    return [item for sublist in biglist for item in sublist]\n\ndef replaceUnderscores(wordList): \n    newList = [] \n    for word in wordList: \n        newWord = word.replace('_', ' ')\n        newList.append(newWord)\n    return newList\n\ndef getDarkAndLightWords(): \n    \"\"\" \n    Uses wordnet to generate words pertaining to lightness and darkness, \n    specifically, hyponyms of \"dark,\" \"darkness,\" \"light,\" and \"lightness.\" \n    \"\"\"\n    darks = wordnet.synsets('dark') + wordnet.synsets('darkness')\n    lights = wordnet.synsets('light') + wordnet.synsets('lightness')\n    darksSyns = [syn.hyponyms() for syn in darks]\n    lightsSyns = [syn.hyponyms() for syn in lights]\n    \n    # Flatten the lists, so that it returns a one-dimensional array. \n    flatDarks = flattenList(darksSyns)\n    flatLights = flattenList(lightsSyns)\n    \n    # Get all word forms for all of the words in our synsets. \n    darksLemmas = [[str(lemma.name()) for lemma in flatDark.lemmas()] for flatDark in flatDarks] \n    lightsLemmas = [[str(lemma.name()) for lemma in flatLight.lemmas()] for flatLight in flatLights]\n    darksList = flattenList(darksLemmas)\n    lightsList = flattenList(lightsLemmas)\n    \n    # Turn underscores into spaces. \n    finalDarkWords = replaceUnderscores(darksList)\n    finalLightWords = replaceUnderscores(lightsList)\n    \n    # Let's take all bigrams and break them up, since a search for \"star\" will match on \n    # \"shooting star,\" anyway, and \"beam of light\" will be matched by searching for the words \n    # \"beam\" and \"light.\" We'll filter out non-unique words, too, since breaking up \"beam of light\" \n    # will give us an extra \"beam\" and an extra \"light.\" \n    curatedDarkWords = list(set(flattenList([word.split(' ') for word in finalDarkWords])))\n    curatedLightWords = list(set(flattenList([word.split(' ') for word in finalLightWords])))\n    \n    wordsToRemove = ['fairy', 'fatuus', 'priming', 'room', 'pocket', 'buoyancy', 'euphoria', 'fuse', 'signal', 'sconce', 'friars', 'of', 'theater', 'ignuus']\n    wordsToAdd = ['brightness', 'bright', 'light', 'sun', 'sunshine', 'sunlight', 'sunlit', 'sunstruck', 'ablaze']\n    for word in wordsToRemove: \n        if word in curatedLightWords: \n            curatedLightWords.remove(word)\n    for word in wordsToAdd: \n        if word not in curatedLightWords: \n            curatedLightWords.append(word)\n\n    darkWordsToRemove = ['wedding', 'weeknight']\n    darkWordsToAdd = ['dim', 'fog', 'dark', 'shadow', 'shade', 'fog', 'dingy', 'dismal', 'gloomy', 'gloom', 'black']\n\n    for word in darkWordsToRemove: \n        if word in curatedDarkWords: \n            curatedDarkWords.remove(word)\n    for word in darkWordsToAdd: \n        if word not in curatedDarkWords: \n            curatedDarkWords.append(word)\n    \n    return curatedDarkWords, curatedLightWords",
     "prompt_number": 410,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "def countChiaroscuro(filename): \n    tokens = readText(filename)\n    logging.debug('Creating text object...')\n    text = nltk.Text(tokens)\n    newtokens = cleanText(tokens)\n    \n    # get lists of dark and light words \n    finalDarkWords, finalLightWords = getDarkAndLightWords()\n    \n    # initialize variables\n    wordCountBright=0\n    wordCountDark=0\n\n    #count words cumulatively\n    for word in finalLightWords: \n        wordCountBright=wordCountBright+text.count('word')\n    for word in finalDarkWords:\n        wordCountDark=wordCountDark+text.count('word')\n\n    totalWords=len(text)\n    print(\"\\nTotals for text \" + filename + \":\")\n    print(\"Total words in text: \"+str(totalWords))\n    print(\"Bright words: \"+str(wordCountBright))\n    print(\"Dark words: \"+str(wordCountDark))\n    wordCountBright=float(wordCountBright) #python 2 needs these to be floating point in order to divide them properly\n    wordCountDark=float(wordCountDark) \n    totalWords=float(totalWords) \n    proportionBright=(wordCountBright/totalWords)\n    proportionDark=(wordCountDark/totalWords)\n    combinedProportion=(proportionBright+proportionDark)*100\n    print(\"Proportion of bright words: \" + str(proportionBright)) \n    print(\"Proportion of dark words: \" + str(proportionDark))\n    print(\"Combined proportion, as percentage (x100): \" + str(combinedProportion))\n    return proportionBright, proportionDark\n    \ndef plotChiaroscuro(textsToAnalyze, textLabels, setLabel): \n    import numpy as np\n    import matplotlib.pyplot as plt\n    plt.figure(num=None, figsize=(10, 6), dpi=80, facecolor='w', edgecolor='k')\n\n    texts = [countChiaroscuro(text) for text in textsToAnalyze] \n    brights = [x[0] for x in texts]\n    darks = [x[1] for x in texts]\n    maxSum = max([sum(x) for x in texts])\n    \n    N = len(texts)  # number of texts\n\n    ind = np.arange(N)    # the x locations for the groups\n    width = 0.5       # the width of the bars: can also be len(x) sequence\n    opacity = 0.4\n\n    p1 = plt.bar(ind, brights, width, color='r', alpha=opacity)\n    p2 = plt.bar(ind, darks, width, color='b', alpha=opacity, bottom=brights)\n\n    plt.ylabel('Scores')\n    plt.title('Proportions of Light and Dark Words in ' + setLabel)\n    plt.xticks(ind+width/2., textLabels)\n    plt.yticks(np.arange(0,maxSum,maxSum/10))\n    plt.legend( (p1[0], p2[0]), ('Bright Words', 'Dark Words') )\n\n    plt.show()",
     "prompt_number": 413,
     "outputs": [],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "setLabel = \"Dickens Novels\" \ntextsToAnalyze = ['pickwick.txt', 'ot-text.txt', 'nn-text.txt', 'bh-text.txt', 'ht-text.txt']\ntextLabels = ('Pickwick', 'Oliver', 'Nickelby', 'Bleak', 'Hard', 'Cities')\nplotChiaroscuro(textsToAnalyze, textLabels, setLabel)",
     "prompt_number": 405,
     "outputs": [
      {
       "text": "\nTotals for text pickwick.txt:\nTotal words in text: 313359\nBright words: 11110\nDark words: 2310\nProportion of bright words: 0.035454542553429134\nProportion of dark words: 0.007371736570514969\nCombined proportion, as percentage (x100): 4.28262791239441\n\nTotals for text ot-text.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 162312\nBright words: 8686\nDark words: 1806\nProportion of bright words: 0.05351421952782296\nProportion of dark words: 0.011126718911725566\nCombined proportion, as percentage (x100): 6.464093843954853\n\nTotals for text nn-text.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 331041\nBright words: 20301\nDark words: 4221\nProportion of bright words: 0.06132473016937479\nProportion of dark words: 0.012750686470860105\nCombined proportion, as percentage (x100): 7.40754166402349\n\nTotals for text bh-text.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 362309\nBright words: 12928\nDark words: 2688\nProportion of bright words: 0.035682249129886365\nProportion of dark words: 0.007419081502253601\nCombined proportion, as percentage (x100): 4.310133063213996\n\nTotals for text ht-text.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 109113\nBright words: 6767\nDark words: 1407\nProportion of bright words: 0.06201827463272021\nProportion of dark words: 0.012894888785021033\nCombined proportion, as percentage (x100): 7.491316341774123\n",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
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ZW7p0KY0bN+ann34qtc+OHTuSmppqTm/cuJEJEybYzNuwYQMdO3YEYPny5URE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\niAdRuBMRERHxIAp3IiIiIh7kfwEY5q/u44OY8QAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x7f73a6c00d30>",
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "setLabel = \"Misc Victorian Novels\" \ntextsToAnalyze = ['bh-text.txt', 'mm-text.txt', 'ud-text.txt', 'ts-text.txt', 'pp-text.txt']\ntextLabels = ('Bleak House', 'Middlemarch', 'Mysteries of Udolpho', 'Turn of the Screw', 'Pride and Prejudice')\nplotChiaroscuro(textsToAnalyze, textLabels, setLabel)",
     "prompt_number": 406,
     "outputs": [
      {
       "text": "\nTotals for text bh-text.txt:\nTotal words in text: 362309\nBright words: 12928\nDark words: 2688\nProportion of bright words: 0.035682249129886365\nProportion of dark words: 0.007419081502253601\nCombined proportion, as percentage (x100): 4.310133063213996\n\nTotals for text mm-text.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 326961\nBright words: 8888\nDark words: 1848\nProportion of bright words: 0.02718367022366582\nProportion of dark words: 0.005652050244524576\nCombined proportion, as percentage (x100): 3.2835720468190397\n\nTotals for text ud-text.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 297288\nBright words: 3131\nDark words: 651\nProportion of bright words: 0.010531874814994215\nProportion of dark words: 0.0021897957536126584\nCombined proportion, as percentage (x100): 1.2721670568606873\n\nTotals for text ts-text.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 46624\nBright words: 1616\nDark words: 336\nProportion of bright words: 0.034660260809883325\nProportion of dark words: 0.007206588881262869\nCombined proportion, as percentage (x100): 4.186684969114619\n\nTotals for text pp-text.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 126070\nBright words: 4949\nDark words: 1029\nProportion of bright words: 0.039255968906163244\nProportion of dark words: 0.008162132148806218\nCombined proportion, as percentage (x100): 4.741810105496946\n",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "png": 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GYebMmbh8+TKKioowZMgQ2T7WtE2mpqaIi4vDxIkTcfDgwUeOd/9ziA8GxH79+iE5ORn7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\nEImIiIhIhgEiEREREckwQCQiIiIimf8HozXeTEFs5V4AAAAASUVORK5CYII=\n",
       "text": "<matplotlib.figure.Figure at 0x7f73a6cab668>",
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "setLabel = \"Chapters of A Portrait of the Artist as a Young Man\"  \ntextsToAnalyze = ['portrait/portrait-ch1.txt', 'portrait/portrait-ch2.txt', 'portrait/portrait-ch3.txt', 'portrait/portrait-ch4.txt', 'portrait/portrait-ch5.txt']\ntextLabels = ('Chapter 1', 'Chapter 2', 'Chapter 3', 'Chapter 4', 'Chapter 5')\nplotChiaroscuro(textsToAnalyze, textLabels, setLabel)",
     "prompt_number": 407,
     "outputs": [
      {
       "text": "\nTotals for text portrait/portrait-ch1.txt:\nTotal words in text: 17919\nBright words: 606\nDark words: 126\nProportion of bright words: 0.03381885149840951\nProportion of dark words: 0.007031642390758413\nCombined proportion, as percentage (x100): 4.085049388916792\n\nTotals for text portrait/portrait-ch2.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 14905\nBright words: 808\nDark words: 168\nProportion of bright words: 0.054209996645421\nProportion of dark words: 0.011271385441127138\nCombined proportion, as percentage (x100): 6.548138208654813\n\nTotals for text portrait/portrait-ch3.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 16658\nBright words: 808\nDark words: 168\nProportion of bright words: 0.04850522271581222\nProportion of dark words: 0.010085244327050066\nCombined proportion, as percentage (x100): 5.859046704286229\n\nTotals for text portrait/portrait-ch4.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 9651\nBright words: 404\nDark words: 84\nProportion of bright words: 0.04186094705211895\nProportion of dark words: 0.008703761268262356\nCombined proportion, as percentage (x100): 5.056470832038131\n\nTotals for text portrait/portrait-ch5.txt:",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "text": "\nTotal words in text: 26739\nBright words: 2525\nDark words: 525\nProportion of bright words: 0.09443135494969894\nProportion of dark words: 0.019634242118254236\nCombined proportion, as percentage (x100): 11.406559706795317\n",
       "output_type": "stream",
       "stream": "stdout"
      },
      {
       "png": 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4cAAzZ85UGLd//365r//85z/w9PSEtbU1Xn75ZVy8eFFup6Ojg5UrV8LT0xNe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\n+j+PjbZZYfL39gAAAABJRU5ErkJggg==\n",
       "text": "<matplotlib.figure.Figure at 0x7f73a6ba1898>",
       "metadata": {},
       "output_type": "display_data"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "# Appendix: Words Counted in this Analysis\n\n## Dark Synsets"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "for sense in darks:\n    print(sense.name() + ': ' + sense.definition())   ",
     "prompt_number": 271,
     "outputs": [
      {
       "text": "dark.n.01: absence of light or illumination\niniquity.n.01: absence of moral or spiritual values\ndarkness.n.02: an unilluminated area\nnight.n.01: the time after sunset and before sunrise while it is dark outside\ndark.n.05: an unenlightened state\ndark.a.01: devoid of or deficient in light or brightness; shadowed or black\ndark.a.02: (used of color) having a dark hue\ndark.s.03: brunet (used of hair or skin or eyes)\nblack.s.05: stemming from evil characteristics or forces; wicked or dishonorable\ndark.s.05: secret\ndark.s.06: showing a brooding ill humor\nbenighted.s.02: lacking enlightenment or knowledge or culture\ndark.s.08: marked by difficulty of style or expression\nblue.s.08: causing dejection\ncolored.s.02: having skin rich in melanin pigments\ndark.s.11: not giving performances; closed\ndark.n.01: absence of light or illumination\ndarkness.n.02: an unilluminated area\niniquity.n.01: absence of moral or spiritual values\ndark.n.05: an unenlightened state\ndarkness.n.05: having a dark or somber color\ndarkness.n.06: a swarthy complexion\n",
       "output_type": "stream",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "##Light Sensets"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "for sense in lights: \n    print(sense.definition())",
     "prompt_number": 273,
     "outputs": [
      {
       "text": "(physics) electromagnetic radiation that can produce a visual sensation\nany device serving as a source of illumination\na particular perspective or aspect of a situation\nthe quality of being luminous; emitting or reflecting light\nan illuminated area\na condition of spiritual awareness; divine illumination\nthe visual effect of illumination on objects or scenes as created in pictures\na person regarded very fondly\nhaving abundant light or illumination\nmental understanding as an enlightening experience\nmerriment expressed by a brightness or gleam or animation of countenance\npublic awareness\na divine presence believed by Quakers to enlighten and guide the soul\na visual warning signal\na device for lighting or igniting fuel or charges or fires\nmake lighter or brighter\nbegin to smoke\nto come to rest, settle\ncause to start burning; subject to fire or great heat\nfall to somebody by assignment or lot\nalight from (a horse)\nof comparatively little physical weight or density\n(used of color) having a relatively small amount of coloring agent\nof the military or industry; using (or being) relatively small or light arms or equipment\nnot great in degree or quantity or number\npsychologically light; especially free from sadness or troubles\ncharacterized by or emitting light\n(used of vowels or syllables) pronounced with little or no stress\neasily assimilated in the alimentary canal; not rich or heavily seasoned\n(used of soil) loose and large-grained in consistency\n(of sound or color) free from anything that dulls or dims\nmoving easily and quickly; nimble\ndemanding little effort; not burdensome\nof little intensity or power or force\n(physics, chemistry) not having atomic weight greater than average\nweak and likely to lose consciousness\nvery thin and insubstantial\nmarked by temperance in indulgence\nless than the correct or legal or full amount often deliberately so\nhaving little importance\nintended primarily as entertainment; not serious or profound\nsilly or trivial\ndesigned for ease of movement or to carry little weight\nhaving relatively few calories\n(of sleep) easily disturbed\ncasual and unrestrained in sexual behavior\nwith few burdens\na feeling of joy and pride\nthe property of being comparatively small in weight\nthe gracefulness of a person or animal that is quick and nimble\nhaving a light color\nthe visual effect of illumination on objects or scenes as created in pictures\nthe trait of being lighthearted and frivolous\n",
       "output_type": "stream",
       "stream": "stdout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "markdown",
     "source": "# Final Words Counted"
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "curatedDarkWords",
     "prompt_number": 408,
     "outputs": [
      {
       "prompt_number": 408,
       "text": "['darkness',\n 'night',\n 'dimout',\n 'blackness',\n 'semidarkness',\n 'blackout',\n 'brownout',\n 'lightlessness',\n 'black',\n 'pitch',\n 'total',\n 'foulness',\n 'dim',\n 'fog',\n 'dark',\n 'shadow',\n 'shade',\n 'dingy',\n 'dismal',\n 'gloomy',\n 'gloom']",
       "metadata": {},
       "output_type": "pyout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    },
    {
     "metadata": {},
     "cell_type": "code",
     "input": "curatedLightWords",
     "prompt_number": 409,
     "outputs": [
      {
       "prompt_number": 409,
       "text": "['lighting',\n 'irradiation',\n 'aureole',\n 'moonlight',\n 'incandescence',\n 'blinker',\n 'running',\n 'flood',\n 'headlight',\n 'sunlight',\n 'gegenschein',\n 'gloriole',\n 'luminescence',\n 'stoplight',\n 'flame',\n 'lamp',\n 'photoflood',\n 'lantern',\n 'illumination',\n 'brightness',\n 'highlighting',\n 'aura',\n 'primer',\n 'starlight',\n 'Moon',\n 'fuze',\n 'counterglow',\n 'glow',\n 'gaslight',\n 'headlamp',\n 'sunshine',\n 'corona',\n 'half-light',\n 'kindle',\n 'strip',\n 'fuzee',\n 'torch',\n 'cigarette',\n 'candle',\n 'fire',\n 'spotlight',\n 'up',\n 'daylight',\n 'friction',\n 'fluorescence',\n 'flasher',\n 'houselights',\n 'beam',\n 'sidelight',\n 'reignite',\n \"friar's\",\n 'sun',\n 'meteor',\n 'twilight',\n 'torchlight',\n 'shaft',\n 'searchlight',\n 'conflagrate',\n 'ray',\n 'radiance',\n 'traffic',\n 'lighter',\n 'jacklight',\n 'light',\n 'shooting',\n 'glory',\n 'anchor',\n 'streamer',\n 'nimbus',\n 'candlelight',\n 'sunniness',\n 'lamplight',\n 'halo',\n 'flare',\n 'highlight',\n \"will-o'-the-wisp\",\n 'airiness',\n 'navigation',\n 'inflame',\n 'ignis',\n 'enkindle',\n 'euphory',\n 'glowing',\n 'moonshine',\n 'illuminance',\n 'firelight',\n 'floodlight',\n 'night-light',\n 'star',\n 'lucifer',\n 'match',\n 'scintillation',\n 'cigar',\n \"jack-o'-lantern\",\n 'fusee',\n 'panel',\n 'riding',\n 'bright',\n 'sunlit',\n 'sunstruck',\n 'ablaze']",
       "metadata": {},
       "output_type": "pyout"
      }
     ],
     "language": "python",
     "trusted": true,
     "collapsed": false
    }
   ],
   "metadata": {}
  }
 ],
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  "signature": "sha256:ad8feb5b3d16a07f05335ab3a0b8fa6e016dc07e827555c844d3917f60f7a829",
  "name": ""
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 "nbformat": 3
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