Creative Project.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creative Project: Poem Generator \n",
    "## Michael Osmian"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the creative project I decided to utilize both my interest in computer science and literature. I first cleaned and saved the text from each text file (corpus). These being:\n",
    "\n",
    "* All 250 of Sylvia Plath's Poems\n",
    "* Notes from Underground - Dostoevsky\n",
    "* The Metamorphosis - Kafka\n",
    "* Collected Work of Nietzsche\n",
    "\n",
    "After this, I plotted the word-frequency distributions for the top 50 most occuring words in each dataset to see the differences between the authors in word choice. These frequency plots omit stop words such as 'and', 'I', 'the'..etc because they don't provide much insight in terms of frequency. After this, I began the sentence and poem generating process. This project utilizes the library markovify for generating the markov models that create sentences from the text files. The state size for each state within the markov chain is set to two by default. It is important to note that the sentences generated are not direct copies from sentences within the data files because it utilizes NLTK's POS tagging to ensure the sentence structure is accurate but the markov models determine the word choice based on START and END positions of words. For generating the poems, I just sampled uniformly on an interval from 5-20 to determine the poem length and generated sentences of max 100 characters for each line.  \n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Cleaning and Library Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "import nltk\n",
    "import numpy as np\n",
    "from nltk import word_tokenize\n",
    "from nltk.corpus import stopwords\n",
    "from nltk import sent_tokenize\n",
    "from nltk.util import ngrams\n",
    "import string\n",
    "import re\n",
    "import random\n",
    "import markovify\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.rcParams[\"figure.figsize\"] = (30,15)\n",
    "sns.set()\n",
    "\n",
    "# Method for cleaning data, only used in the custom bigram model\n",
    "def clean_text(file_name):\n",
    "    #Open file for reading\n",
    "    f=open(file_name,'r',errors = 'ignore')\n",
    "    txt=f.read()\n",
    "    txt = txt.replace(\"'\",\"\")\n",
    "    f.close()\n",
    "    \n",
    "    #Clean Data, lowercase all words and remove punctuation\n",
    "    word_tokens = word_tokenize(txt)\n",
    "    word_tokens = [w.lower() for w in word_tokens] #LowerCase all words\n",
    "\n",
    "    table = str.maketrans('', '', string.punctuation)\n",
    "    stripped = [w.translate(table) for w in word_tokens] #Punctuation Removal\n",
    "    # remove remaining tokens that are not alphabetic\n",
    "    words = [word for word in stripped if word.isalpha()] #Only Alphabetic Chars\n",
    "    return words\n",
    "\n",
    "# Write the cleaned data to file for use\n",
    "def write_to_cleaned(data,filename):\n",
    "    clean_temp = ' '.join(data)\n",
    "    c = open(filename,'w')\n",
    "    for i in clean_temp:\n",
    "        c.write(i)\n",
    "    c.close()\n",
    "\n",
    "# Returns Plotting Data for the text files\n",
    "def get_freq(c):\n",
    "    x = [] #Words\n",
    "    y = [] #Frequency\n",
    "    \n",
    "    stop_words = set(stopwords.words('english'))\n",
    "    uniques = set(c)\n",
    "    freqs = {}\n",
    "    for i in c:\n",
    "        if i not in freqs:\n",
    "            freqs[i] = 1\n",
    "        else:\n",
    "            freqs[i]+=1\n",
    "\n",
    "    # Sorting from most frequent - least frequnt\n",
    "    freqs = list(freqs.items())\n",
    "    freqs.sort(key=lambda x: x[1], reverse=True)\n",
    "\n",
    "    for i in range(len(freqs)):\n",
    "        if freqs[i][0] not in stop_words: #Ignoring Stop Words for useful visualization\n",
    "            x.append(freqs[i][0])\n",
    "            y.append(freqs[i][1])\n",
    "    return x,y\n",
    "\n",
    "# This class overrides the default Text class because it utilizes NLTK's POS tag\n",
    "class POSifiedText(markovify.Text):\n",
    "    def word_split(self, sentence):\n",
    "        words = re.split(self.word_split_pattern, sentence)\n",
    "        words = [ \"::\".join(tag) for tag in nltk.pos_tag(words) ]\n",
    "        return words\n",
    "\n",
    "    def word_join(self, words):\n",
    "        sentence = \" \".join(word.split(\"::\")[0] for word in words)\n",
    "        return sentence\n",
    "\n",
    "\n",
    "# Cleaning the text and writing to new file   \n",
    "cleaned_plath = clean_text(\"plath.txt\")\n",
    "write_to_cleaned(cleaned_plath,'cleaned_plath.txt')\n",
    "\n",
    "cleaned_underground = clean_text('underground.txt')\n",
    "write_to_cleaned(cleaned_underground,'cleaned_underground.txt')\n",
    "\n",
    "cleaned_metamorphosis = clean_text('metamorphosis.txt')\n",
    "write_to_cleaned(cleaned_metamorphosis,'cleaned_metamorphosis.txt')\n",
    "\n",
    "cleaned_nietszche = clean_text('nietzsche.txt')\n",
    "write_to_cleaned(cleaned_nietszche,'cleaned_nietzsche.txt')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Visualizing Word Frequency"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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GQRAExMfHw8PDAzt37kTHjh25xDB//nx8/fXX8PLygpmZGdzd3f8xHc+/raZU1osXL5YGtGqbr68vl3b+XzVu3FhK0SsIAo4cOYImTZrg7t273GrZ+Pr6oqysDNnZ2bC0tERKSgrXgZzXv4PGxsaIjIzE77//DoD/vb28vBxZWVkwNzcHAGRmZqKqqoprDC4uLujfvz8++eQTdO/eHQ4ODhg7dizXGJRwHgB1athLly7B2toa06dPl94BKioq0L9/fy4TZEroc06aNAmjRo2SUmGdPXtWlsVRSngnUopPP/0Uw4cPh4GBAXr16gUPDw9uqcqSkpL+9u95Lw5Twv1i8uTJGD16NGxsbMAYQ1xcnCzXiNzjagBQUlKC58+fo1mzZgCAJ0+eyFKPTSn3CyW8F82cORMvX75EdnY2OnbsiFevXqFBgwbc2hfJneocUD+/hw4dKlt5H0A9jrNx40Y4OjpCEAQcPnwY7dq149a+SAn9C5FKpZI1DagSvhcXL17EoUOHcPHiRVhbW2PDhg345JNPkJmZCW9vb8THx3OJo3///ti1axf69++vdc/ieZ2qGK8lvAqUnZ2NhQsX4vr16zAwMEDbtm2xdu1aqZNT216+fInvvvsOFy9ehCAIsLKywsyZM9GwYUMu7YsSEhKwbt06PHr0CD179kRqaipWr14tS12byspK1KtXD5WVlaioqOB+LjTzv2riPehdUVGB8PBwKWdy37598fnnn0NPj9+ctiAIiIyM1Pp+jh07lmsMycnJ2L17N8LCwjBu3DhkZ2dj1KhRWLhwIbcYAPX3IigoCElJSVCpVOjXrx8CAgLQsmVLrnHUdM+YMWOG1gqc2vb06VOEhITg4sWLYIyhT58+CAgIgImJCbcYAPXEerNmzd6oF8hzgmzXrl0oLi6GiYkJ6tWrh9LSUjDGuKx8mTZtGrZt21ZjPQaVSoXY2Nhaj+F1d+7cQVJSEqqrq9G7d2/uHV1PT883jslRX0nzOhWvEV7XqfjMqKqqqvFeLa565ikzMxOJiYnQ0dHBp59+ig4dOuD+/fswNTXVWmFbm0pLS5GTk4NOnTqhrKyM+8v5rFmz0K9fP+zevRv79u3Dli1bkJ6eju3bt3NpPy0tDV27dn3r6nvek8j5+fkICQnBhQsXoKuri379+mHx4sU4deoU2rZti4EDB9Z6DJ999hliYmIQEhICNzc3NGvWDLNnz+ZW67WmotyaxBXxvJw/fx6LFi1Cy5YtIQgCnj17hnXr1nFfnFVdXS2ttH/27Jk00MmLUs7Dvn374OjoWOO96vHjx1o7+WuLEvqcAHDr1i2kpKRAEAT07t0bnTp14to+oIx3otcxxpCbmwszMzOu7Z47dw4dO3ZEy5Ytoauri/T09FqtG/n/6tWrV1oD3zwo4X5x8OBBlJaWQqVSQRAEFBUV4f3330f79u25LUQCah5XCwsLQ/v27bnFEB0djbVr1+KTTz6BIAi4evUqAgICMGTIEG4xAMq5XyjhvUju2uWimibxeS+CHz9+PFatWgV/f39ER0fjwoUL2LBhA/bt28cthqKiImzatElrjNHX15f7c10p/QtA3de8evUqqqur0aNHD7Ro0YJr+0r4XogLWh0cHGBoaKj1dzt37sSkSZO4xKGE67ROT5CVl5dDX18fL1++hCAIMDIy4trJO3jw4BsvwLt37+ZakE/07NkzXLt2DdXV1ejevTv3GwMAnDhxAlu3bsWRI0eQnZ0NT09PLFu2DHZ2dlzjOHLkCO7cuQMfHx+cOnWK+2o0UW5uLu7cuYP+/fvj0aNH3F+A3pbSUY7dQiK5tx4T9XUqZ/FOkZOTE44cOSJrDJo7ACorK3H58mVYWlpyKYxdUFAAExMT3L17F/Hx8SgtLUXr1q1RXV2NpKQk7sW5nZ2dMWLECAwfPpz7xLHSVFdX49y5c7C1tcWzZ89w9uxZuLm5cU056erq+o8D8DxUVVXh/PnzeP78udZxns/V11/OR4wYgbCwMK4v5yNHjsSBAwfg4uKC6OhoAOpr5vDhw1zaDwgIQHBwsCIGS5Ri7NixiIiIwO7du9GwYUO4uLhIvyeeXrx4gUOHDmH8+PHIz89HREQEpk6d+sYLKg8VFRW4desWVCoVOnXqxG0wT2kpQOU6D5qePn2KI0eOSAtvBEFAbm6ulGGjrnBycoK1tTUGDRqEnj17ck/drOnFixcoLi7WOsbznSg8PBzr16/XSmf43nvv4fTp09xiAIBhw4bh2LFjXNt83alTp7Blyxa8fPlSuj5evXqFxMRE7rHIfb/w9fVFeno67OzswBjDuXPnYGJigpcvX8LJyYnbwKZIc1xNDgUFBbhy5QpUKhV69uyJ5s2byxKH3PeL12MRBIFb1gJNo0ePxtatW+Ht7Y3o6GjcuXMHc+fO5db/VRK53wWUpqKiAvXr18f9+/dx//59DBw4kHsa54SEBCxZsgQ9evSAIAi4cuUKQkJCuKYmVcL34tq1a+jWrZv0+dWrV/jmm2+waNEibjEoRZ1Osejt7Y0ffvgBDRo0wKtXrxAaGoojR47g/Pnztdruzp078eLFC0RERGjtWKqursaRI0e4T5AVFxfjxIkTeP78ORhjSE9PB8A/p/nWrVulNFBt2rTBgQMH4OXlxXWCbO3atfjzzz+RlpYGb29v7N+/HxkZGdxvDsePH8d3332HV69eISIiAmPHjsWCBQswYsQIbjGIqS8B9eD/kydP0KVLF61c67Xt4cOHCAgIwMOHD7F79274+/tj9erVeO+997i0X9PuHE28VjN07txZSkOqGY/4WbxmeYiPj0dYWBisra3h6uqq9TDlqWPHjrhx4wY+/PBDWdoH3kyB9fz5c8yZM4dL2+KOPTGFh1xpwkTr1q3D0aNHMWHCBLRq1QrOzs4YMmQI113Abxtk5T24GhAQAEEQYGtrC0CdEujatWtYtWoVtxiaN2+O33//Hd26deO2S6sm/v7+yMvLg4WFhdbvhucEBhmaigAAIABJREFU2fr167Fnzx54e3vDxMQEu3btwty5c7lOkMmdyjo4OBgAsGzZsjdWlKempnKLQ5SQkIBvvvkGRUVFWilyea4Q7NChA4KCgjBu3DjMmzcPBQUF3GtIAsC8efOkHTENGzaEIAhYsGABNm/ezDWOe/fuYc+ePVoDzrm5udi9e3etty2mAI2MjISBgQFcXFygp6eHo0ePcqtJJ5LzPGiaOXMm2rRpg9TUVNjZ2eHChQvcd2UfOHAAoaGh0iCvHH3OHTt2ICEhAeHh4ViyZAm6deuGwYMHw9HRkVsMgLqvFRkZKaVXlyMF/44dO3Do0CF88803mDNnDpKTk3HhwgVu7YvMzMywePFidO/eXWvHFs/nelhYGIKDg/HTTz/Bx8cH58+fR2FhIbf2/ylNM88UuY8fP8aBAwekyQ9fX1/4+Phg7969GDlyJLcJstff2b/88ktu7+xvq9N369YtAPzHtJRwvwCAnJwczJkzBzk5OWCMwdTUFN988w3XlHpy1y4XKeHZLmcaUiWNJQHqazY7Oxt+fn7w8PBAhw4dEBsbK72v8LJhwwbs2bNH2oiQk5ODmTNncp0gU0J62vnz5yM0NBQ9evTAb7/9hpUrV8qSFlYJ12mdniCztbXFlClTMGHCBISGhqJPnz44evRorbfbtm1bpKWlvXG8fv36+Oqrr2q9/dfNnj0bjRo1eiNVGW+VlZVaO9eaN28O3hscz58/L+3sMzIywk8//QRnZ2fuE2Q//PADfv31V3h4eKB58+Y4ePAgJk+ezHWC7OzZs1qfr127xn2AYPny5fjiiy+wdu1atGjRAsOHD8fChQu5xbFr1y4AkGrnXLp0CVVVVbCysuL64MzIyHjr31VUVHCLA1C/9L18+RKnT5/G5s2b8fTpUwwbNgwuLi5cVumJk5avXr3C8ePHpRQvcr18aGrQoMFb07TWlqysLK00YQsWLMDs2bO5xgCoB5vnzJmDOXPm4Pfff8fq1auxatUqroPvmnWWqqqqcObMGVlWS964cUPa3disWTOEhYXBycmJewyvp/qU4wUoMzMTJ06ckLVvoYSXc19fX3h6euLRo0f48ssvpVTWvFy+fBmCICAgIAAhISFS36qqqgorVqzAqVOnuMUCqCfsFi1aJGu/c8WKFbhy5Qref/99+Pr6IjExEevWreMeR15eHr7//nsAgJGREebMmcO1ryeaM2cObG1tcfnyZbi6uiI+Ph4dOnTg0raY+jU0NFRrEVaPHj0wcuRILjGI5DwPmgoLC/Hrr78iNDQU9vb28PHx4b4TZMuWLdi1axfXNG2ve+edd+Dq6ooOHTogMTER4eHhuHjxIvcJsjNnziA+Pp576n9NzZs3h5mZGTp16oRbt25h5MiRCA8P5x6HsbExAODq1atax3lOkDVu3BhWVlb4448/UFJSAl9fX673CjnSVb9NYWGh1vdSX18fRUVF0NPT4/p8lfudXUmUcL8A1L+TKVOmYOjQoQDUC7CXLVsmjW/woJTa5Up4tvv5+WHVqlW4ffs2LC0tpTSkPPzdWJIczp49i4iICOzcuRPOzs5YsGAB9/4eoH4P0szSZWZmxq0WsmjFihVYuHAh7ty5I30v1q5dyzWG77//Hr6+vjAzM0NOTg5CQ0O5p98HlHGd1ukJsokTJ6JRo0aYM2cONm/ezG3A28bGBjY2NnBwcICFhQWXNv/OkydPpJ1bcurZsyfmzp0rDSSeOHGC+04IcUW32KGsqKjgvtVXjEMzNYGJiYkscWjq1q0blixZwrXNwsJC9O/fH2vXroVKpYK7uzvXjrZYey40NBTZ2dkYOXIkGGM4cOAAHj58yP18jBkzBnv37pU+C4IANzc37qkGGzRoAFNTU7Rq1QoPHjxAZmYmJk2ahDFjxtR6/S2enfp/orljSawFYW1tzTWG5s2bQ6VSwdzcHJmZmXBxceE+aQqod0CfP38ex44dQ3JyMgYMGMD9+nh9wKJv374YPXo09wlDQRCkFJiAOlUW7/v3pUuXuLb3NhYWFnj8+DH3GoWalPByPnDgQHz44YdSKutVq1Zxnby9ePEikpOTUVBQgE2bNkkLCvT09DBmzBhucYiMjY25LjKpia6uLlQqFX799Ve4ubmhcePGskwEqFQqZGZmSrvI7t69K0tKP0EQMGvWLFRVVeGDDz7A2LFjMXbsWK4xlJeXIysrS6oFnZmZiaqqKq4xKOE8AJBWD5ubmyMjIwPdu3fnvsOxZcuWsk6OAepsL/fu3UPnzp3Ru3dvbN++nftOOgDo1KmTLLWxNRkaGuLSpUvo1KkTYmNj8dFHH72Rwo2HmnZHvXr1imsMBgYGyMrKgoWFBZKTk2FlZYWSkhJu7YslMry8vLBjxw5u7dbE3t4eEydOhIODAwRBQExMDGxtbREdHc2lVqFIznd23jvE/okS7heA+nciTo4BgKOjI7777juuMaxatQohISF49OgR7OzsYGVlxTWjhkgJz/a1a9eiuLgY06dPx8iRI9GqVSuu7QNv323J+xoSBAH169dHXFwc/Pz8IAiCVvpgXkxNTbFz506MGjUKgLr+qzj+V9s0x5H09PTQsWNHCIKABg0aIDAwkEsGHLGkjr6+PlasWAE/Pz8EBASgdevWyMvL4/6+rITrtE5OkL0+qGlkZISQkBBpkqi2v4zTpk3Dtm3b4O3tXePKHt47ILp06YKMjAxZXjg0BQYGYteuXdi7dy/09PRgaWmJzz//nGsMQ4cOhZ+fH4qKirBz504cPnwYw4cP5xoDoN6NER4ejqqqKqSnp2PPnj3cfz+vP0Dv3LnDPY+3gYEB/vzzT+k6+f3332VJF3bhwgVER0dLg9yDBg3iuiNkwoQJUjFVsUYiYwx6eno1FrOsTRs2bMCxY8fQunVruLm5YenSpdDX18eLFy9ga2tb6xNkYqdFzNGsycDAAKWlpdwGdDR3LKlUKhgbG3PfnaKUNGHW1tbo3r07nJ2dERwcLMt1qlk3kTGG27dvv1H7igcfHx+4urqiZ8+eYIzh2rVr3CcLlVK/5tWrVxg6dCg6duyo9Z3gmfZS8+X8s88+Q58+fbi/nIuLGwYNGgRA/QIwYsQIbosbxHvVli1b0KBBA4wfPx4+Pj5IS0uDu7s7lxg09ezZE2vWrMGAAQOgr68vHee5WvHnn39GbGwsCgoKMHToUCxfvhyjRo3CF198wS0GAFi4cCG8vLyk2o2FhYWy1JkyNDRERUUF2rVrh7S0NFhaWnJPb7ho0SJ4enqiZcuWEAQBz549476rTwnnAQCsrKwwa9Ys6fuRlpbGvS5d165dMWvWLPTr10/rOuW5U+iDDz7Ay5cv8fz5czx9+hRPnjzBq1evtFL78TBixAjY29ujY8eO0NXVlY7zfJYtW7YMUVFRWLRoEfbt24ehQ4dq9UN5UUL9Lz8/P3zzzTcICwvD9u3bsXfvXmmAk6fy8nI8evRIloFukb+/P+Li4nDhwgXo6upiypQpsLa2RmpqKtf7pxLe2cU0cppMTEzw22+/cY1DCfcLQJ2dKi0tDV27dgWgzi7B+znSvHlzrF+/nmubNVHCs/2XX35BXl4eoqOjMW3aNJiamsLFxQW2tray1HavrKxEQkICunfvzr3tTz/9FMOHD4eBgQF69eoFDw8PWRbOhYSEICgoCN9//z0YY1wncOV4fr9OLKkjLpysX7++1q5G3vMSSrhOVYx3DjsFEAea36a2t82LK8vfloqL16y1yNXVFZmZmWjWrJnWC5AcqcqeP3+OsrIyMMZQXV2N3NxcfPrpp1xjSEhIwMWLFyEIAvdUeqJZs2ahbdu2WnHMmDGDa8Hb1yfIjI2NMWzYMCmfNg/Xr19HQEAAsrOz0aZNGxQVFWHjxo3cH+TDhg3DwYMHpY5+eXk53NzcuKRk1RQcHIyAgACubb5u48aN0NPTQ0VFBaZNm4aYmBhpsOT1Ap+1ydfXFzdv3pRqFMpdlFou1dXVuHLlCiwtLXHmzBkkJibC3d2d+6rv58+fo2HDhsjKykJ1dTU6dOjAfReEmH6TMQYdHR0YGxvD19cXAwcO5BoHAOTn5yM1NRV6enr46KOPuO+gGjduXI31a3incX5bf4tneqITJ07Azs5OlpdPzcUNwF+703V1dTF48GBs2rSJazzu7u6YP38+8vPzpVQ7M2fO5FpbFFAvVHudSqXiOnjk4uKCyMhIuLu7Izo6GqWlpRg9ejSOHz/OLQZRRUUFbt26BT09PbRv316WBQbh4eE4e/Ys1q5dizFjxqBt27YQBIH77gjxXKhUKnTq1In7c0Tu86C5+Keqqgp6enooLCyESqVCeXk5pk2bxiUO4O11lnjWVxKVlpYiJiYG3333HfLy8nDjxg2u7Q8ePBizZs16YzW1klLt8WJnZ1dj/a/ly5fLFlNRURH3mi0A4ODggPv376N58+bQ19dXRLp3uSjlnV1UWVmJ2NhYpKam/mPNuH+bUu4XqampmDt3Lpo2bQrGGIqKirBhwwauv5Nz585hy5YtKCwslK3mLCD/s11TXl4ejh49ioiICLRq1QpPnjzBvHnz8Nlnn3GPpaKiAl5eXrKk683Ly8O7774LHR0dpKenS4vAeausrMS9e/dQXV2Njh07ypLFQW5Xr17F5cuX4eHhIS2gXLlypdYOVB5quk4ZY/jPf/7DLYa699uHugh2165dkZKSIkv74kCZiYkJLl68+EZRWd4TZJs2bZK1vpJo3bp12LNnD6qqqmBsbIz8/Hx8+OGHiIqK4hpHgwYN0KZNG4wcORLXrl3j2rbo4cOHWL16Nfz9/WVpH1BGugLGGJycnGBtbY2goCA8evQIf/75J/fOtpOTEyZMmIBhw4YBAI4dOybLzsL8/Pw3jk2cOBE///wztxgqKiqQk5ODtLQ0eHt7Y//+/cjIyMCiRYu4TY4B6qLUBw8elL0otdx0dXVhaWkJQF1X09bWVpY4cnNzMWvWLDRt2hSCIODJkyfYsmUL12t1w4YNb3Tu5EjX8PTpU5w4cULavZWWlsZ995bc9WvEFaty1h4TxcfHIywsDNbW1nB1deV6nxInfJSwuAFQ71zr1asX/P39YW9vj1atWqG6upp7HGKq3BcvXkAQBFlqBero6GhNROnr62ut9OZFCQWpAfUqUhcXFxgZGWHXrl24fv06+vXrxzWGhw8fIjw8HEVFRVoDaTwnZOQ+D0lJSQDUheIfPHiAgQMHQldXF+fPn+e+Q33NmjWorKyUdeFLQkICEhMTkZiYCEEQMGTIEO6prAGgUaNGXHfO1eTkyZPYvn07ioqKtI7zHmiWu/4XoF6Qt2PHjjcG3Xnv0Pnxxx+5tqdkSnlnF9WrVw8ODg5SjU+elHC/ANR1PE+dOoX79++DMYZ27dpxX4ATEhKCpUuX4v3335f1nUDuZzsAREVF4dChQ3j8+DFcXFywZ88evPvuu8jPz4erq6ssE2SlpaVaGVh4UcoCnOvXr2P27Nmyjl8oQUhICObPn4+YmBgYGBggOjoaM2fO5D5B5ujoCEEQsGfPHvTu3RvXr19H//79ucZQJyfIIiIiEBQUhE2bNtV4o+bVuZo9ezYeP34MCwsLrTh4P1D37NmjiPpKx44dw2+//YaQkBBMnz4deXl53GujaabccXBwkC3ljo6ODgYPHgxzc3OtXX08vps1pSfQlJ6eXusxiIKDgzF//nxkZGTAyMgIhw4dwsyZMzFkyBBuMQDqlGldunTBpUuXwBiDj4+PlCqLhxkzZiA9PR2PHz/WmgCpqqrintbjwoULOHjwIFxdXWFkZISffvoJzs7OWLRoEdc4lFKUmqgFBwdrrUpMTU1FUFAQ9u3bxy2Gt3XueN8vZs6cWePuLZ7krl+j2c96He+dQmvWrMHLly9x+vRpbN68GU+fPsWwYcPg4uLCLW3w/Pnzcfr0aZSWlgKAtEOed308Q0ND7NixA0lJSVi+fDl+/vlnWWpk5OTkYM6cOcjJyQFjDKampvjmm2/Qrl07bjH07t0boaGhKCsrQ2xsLPbu3QsrKytu7YuUUJAaUC9+CQ8Px71797B8+XJkZmZyn4jw8/ODpaUlLC0tZXuOy30exMEhT09PHDp0CM2aNQOg3iEzY8YMbnEA6nRcci98+fHHH+Ho6IiJEydKaUh5LwwD1GlhxR3pmruReb6zh4aG4uuvv+ZeE+R1ctf/AtSpaT08PGQfdDc1NcWvv/6qtci4tlPNK5US3tk1d+CKqdblyB6ghPsFADx79gyrVq1CYmIiqqurYWVlhRUrVqBFixbcYmjUqBHXMZO3kfvZDgApKSnw9fVFnz59tI63bNkSgYGBXGIQs60A6mukuLiY+zgnoL2bsqqqCmfOnEH79u25xxESEiL7+IUSKGUBpbe3Nzp16gRTU1O0atVKlvTFdXKC7P79+5gwYQIA4PUMkzw7Wffu3cPJkye5tfc2ctdXEpmYmMDIyAgdOnRARkYG7O3ttXKg8nDw4EEp5U7Tpk2xb98+jB49mvuDY/78+Vzb05SRkQFA3ZGIiIhAcnIy9PT00K9fP7i5uXGNRSk3a0BdY0mOVauA+qX4+fPnCAkJ0dqFoKenx70unHifEO+VFRUV0jGelFKUmqi9fPlSa9CsR48e3HNGK+V+IffuLUD++jVBQUEA/topJLcGDRpIne0HDx4gMzMTkyZNwpgxY7gMZvn6+qKsrAzZ2dmwtLRESkoKevToUevtvm7t2rWIiorCpk2b0KRJExQUFHCv8QQAy5cvx5QpU6SViWK6R57flwULFiAyMhKdOnVCdHQ0rK2tuReCBpRRkBpQ1+pr1qwZbt68CV1dXWRnZ2Pp0qVc++FVVVVYuHAht/ZqooTzAKjT8WumNDc0NMTjx4+5xiDnwpcZM2YgIyMDBQUFyMnJwfbt2wGoFxfIMWBSVlYGIyMj/PHHH1rHeQ54t2nTBj179pSlz61JCfW/DAwMMH78eK5t1uTrr7/GgwcP4ObmJi0yzs3N5b7IWAmU0AcXd+CKjI2NsWHDBq4xAMq4XwDqvtbHH3+M4OBgMMYQERGBpUuXYtu2bbXetpipy8LCAsHBwbC1tdXagcyz5iygjGf732US4TWRrNnPVqlUaNy4MdcSLiJXV1etz6NGjcK4ceO4x6GE8QslUMoCSgBYvXq1LO2K6uQEmRIK4gHqjm5eXp7sK8Gqq6tRVVUlbbmurq6WJc2MkZERoqOj0bVrV4SHh8PExATFxcVcY1BKyh0l5LQPCgpCaWmptLMwOjoat27dwtKlS7nFoKSbtZwePHiArl27YvLkyW9sg8/OzubayRw6dCj8/PxQVFSEnTt34vDhw7Kkm1RKUWqi1qRJE8TGxko14U6fPs21XiGgnPtFTbu3qqqquLQtrp41NzeHmZkZUlJSMHbsWKhUKu7pmwFlpEHasGEDjh07htatW8PNzQ1Lly6Fvr4+Xrx4AVtbWy4TZFlZWYiJiUFISAjc3NywYMEC7rvHAPUqVc30yXItxiksLNRK2+Ho6IjvvvuOawxTpkzBjh07ZJmM0qSEgtSAOi3qwYMHER8fD0NDQ4SGhnJfLNezZ0+cPXsW/fv3l6UOG6CM8wCoFytOnjwZ9vb2EAQBJ0+ehIODA9cY5Bw4UtLCMEAZ6Sa9vLwwYcIE9OrVS+vdlHdKfGNjY2zcuBEAsH//fhQVFSErK4tL2+I7UJcuXbBz507Y2tpqnQveYypKWWSsBErogyvhOhXjUIKcnBytmvLe3t44fPgwl7Y1M0j8+eefyMzMlD7zziQBKOfZLre37XqVe+HF3bt3UVBQwL1dJYxfKIFSFlDa2dkhKioKVlZWsj3b6+QEmdyTD56enlCpVCgsLISTkxM6d+4MXV1dqbAr7weGUuorhYSE4NixY3BxcUFcXByWL18OPz8/rjEoJeWOEqSmpuLIkSPSZxsbG4wYMYJrDEq5WctNTFe2efNmWdPCAsDUqVORkJAAU1NTPHr0CL6+vlxrFoq1jVJSUmBkZKS14iolJYX7ijSiFhQUhPnz50sT6GZmZtxX3Mt9vzh+/DgcHR3RpUuXN3ZvaabKrU1Kql8DKCMNko6ODlxdXVFRUQE7OzucOHFCqkPwww8/cImhefPmUKlUMDc3R2ZmJlxcXFBRUcGlbSWqX7++dC8H1KnceO5wBIBXr17h0aNHsuxG0eTs7AwfHx+pIHVCQoKUSo4nlUqFiooK6TotLCzkfs2ePHnyjULxKpWKa2pvJZwHQF2f49SpU0hOToZKpYKXlxf3GqOvDxzFxsZyGzgyMjKCkZER94nzt1FCuskNGzagS5cusizcBIDLly9DEAQEBAQgJCREWvRSVVWFFStW4NSpU7Ueg4eHB1QqFRhjuHTpEn755Ret65N3PTalLDJWArn74ID81+m0adOwbds2rTR2gDpjlY6ODmJjY7nEIVKpVFr9nLy8PG4ThuJOpdu3b7+RNjo1NZVLDJqU8myXW027XnNycrgugAf+KukiPkeaNWuGuXPnco0BUMb4hRIoZQFlSUkJtm/fDmNjY+mYSqXi+mxXsddzDJJal5ycDMYYpk2bhoULF8LQ0BCtW7eWbhByTOD99ttvUn0lKysrReQKloMgCIiMjMTFixchCAKsrKwwduxYWVYfyW3KlCkIDAyEmZkZAPXqnyVLlmDHjh0yR1b3iJPqNd2u5ZhUl9OyZcsQFBQknZPX1aVzoQSavwfGGF6+fAnGGBo2bFjnvptDhw7F0aNHMXr0aGzatAlmZmZIS0tDSkoKHB0dYWJiwi0WT09PbNy48Y36Na8PPtc2V1dXHDx4kGubrwsLC0N+fj7S0tIQFRWF6dOno2vXrlzrJi5btgz169fHuHHjMG/ePDg6OuLIkSNai1DqktTUVMydOxdNmzYFYwxFRUVYv34917STDg4OuH//Ppo3b641gc17gFVMZ52SkoKioiJYW1tjzJgx3FPeREdHIyoqCg8ePICDgwNiY2MxY8YM7qnT5Ebn4S/379/H/PnzkZ2dDeCvgSNzc3OZI+Nv7NixWLx4sVa6yeDgYK51SoYPH46jR49ya+91mzdvRnJyMm7cuIGPPvpIWtirp6eH/v37w8vLi1ssV69exeXLl+Hh4QEfHx+kpaVh5cqVWjuTedi2bRvi4uK0FhlbW1tj+vTpXOMganJfp48fP8Y777yDmTNnYsmSJdI1whjD4sWLub8TxcXFITAw8I00uTzG+ZQwoa6Jnu1qzs7OWrteq6qq4OTkhBMnTsgcGV80fqFMdnZ2OHr0KAwMDGSLoe6N+iuAOAH2888/Iz4+HvHx8aiqqsLAgQO57sTQJGd9Jc0VBDUNePNcOaqUlDty0tzh6OzsLKXyuHz5siyF44ly0sIqgVJqSBI1+m7+5eOPP8ZHH30EQF0jT8QYQ2hoKNdnmdz1a5SUBunChQs4ePAgXF1dYWRkhJ9++gnOzs5cJ8jy8/PRo0cPGBkZYdasWbh48WKd3A0t6tGjB06dOoX79+9DEASYm5tzT6k3depUrc8PHz6EgYEBbt26hY4dO3KLQ0xn7erqKqWz/vPPP7mv5nV0dERpaSkKCwvRpEkTTJ48mfvisKdPn+LIkSMoLS0FYwyCICA3N/dv63b8W8T0tAAwbNgwMMZQXV0ty3lQisrKSkRFReHly5cQBAFGRkayrPxXAiXUKRk0aBDCw8MxYMAA1KtXTzrO63kq9ve2bNmCBg0aYPz48dLklLu7O5cYRCEhIZg/fz5iYmJgYGCA6OhozJw5k/sE2eXLl+Hs7Iy0tDQ0btwYPj4+dXaRsRLIfZ2uWLFCqp2o2eeXq3Zi9+7d4e7ujri4ODDGYGtrixs3bnD5jl68eBHJyckoKCiQUrIC6jS5Y8aMqfX2X6eEPo4SKGXXa1lZGb799lskJiaiuroaVlZWmD17Nho0aMClfRq/UCYzMzMUFRXRBFld1b17d3Tv3h3jx4/HyZMn8f333+M///kPbty4IXdoXGVkZAD4axVtcnIy9PT00K9fP7i5uXGNRSkpd+T0tgfG5MmTOUdCRHKnhVUS6tAoC303/7JmzRqsWbMG06dPlz0tlNz1a2pKgyTinSpBXCUpTqBXVFRwz7U/Y8YMxMfHw9fXV1oQVVZWxjUGJbl37x4iIyNRVFSkdZxn3Y6zZ88iPT0ddnZ2YIzh3LlzMDExwYkTJ+Dk5IRJkyZxiUMJ6awBwM/PD48fP4aFhQUePnwoHXdxceEWw8yZM9GmTRukpqbCzs4OFy5cQOfOnbm0Laanzc7ORnZ2NqytraGjoyOlp+V5HuSmtJX/SiBnuknR8ePHoVKpsGPHDlnTCv7222+yT04JgoBevXrB398f9vb2aNWqFaqrq7m1LxKf7bdu3ZIGnY2Njbmm3iR/kfs6VVrtRG9vb3Tq1EmWBfji+3p0dLQinp9K6OMogbOz8xuldcQ/87Rq1SoYGhpi9erVAIDIyEgEBgZyS29I4xfKpFKpMGzYMHTo0EFrIRDPHX2UYlFGK1euxOXLl6Grq4tevXqhT58+6N27Nxo1aiR3aLJYtmwZSktLMXz4cGkV7bvvvst1Fa1SUu4QQggh/xOa9Ws+/fRT7vVrAOD58+dvDE7k5ubivffe4xbD9u3bkZaWhuvXr2PChAk4fPgw7O3t4ePjwy0G0bNnz6QFUc+ePatzC6JEjo6OcHR0ROvWrbWOu7q6coth7Nix2L59Oxo3bgwAePHiBXx8fLBz506MHDmSWyF7paSzHjp0KE6ePMm1zbfFEBoaiqFDh6J9+/aYNGkS9u/fzy0GpaSnlZOSUukpxf379zFt2jQ8f/5cOhYREcE13aRS0gqOGjUK+/btg7+/PwYMGAAXFxe4uLho7cKsbZ6enrCxscGOHTtw7NgxREdHIyYmBrt37+YWgyZ6tiuDEq5TJXFzc+P6/KzJ06dPERQUpLVTaMWKFWjRogXXOJTQx1FgzKawAAALxElEQVSCqVOnYtCgQdKu1z59+siy69XZ2fmNfrajoyOOHz/OPRaiHMnJyTUe5zmhSTvIZFRcXAzGGMzNzWFhYYH27dvX2ckxQBmraJWScocQQgj5nxgyZAiGDBkiS9uPHj0CYwxTp07FDz/8IO1AqK6uhre3N9eX1KlTpyIhIQGmpqZ49OgRfH19ua+mfX1BVGBgYJ1evdi4cWOtYtByKCwsRMOGDaXP+vr6KCoqgp6eHpd0vUpLZ92mTRvk5eVxTX/6uiZNmgAAzM3NkZGRge7du6OyspJrDHKnp1UCJaXSU4r4+HgYGhri4MGDyM7Oxpw5c5CcnMx14F0paQUNDQ2xY8cOJCUlYfny5fj555+17qU8rF27FlFRUdi0aROaNGmCgoICWdIW07NdWZRwnSqJnZ0doqKiYGVlJVua88DAQHz88ccIDg6GIAjYu3cvli5dim3btnGLAVBGH0cJlLLrlTGG4uJiaZFacXGxLKkeibIo4flJE2QyEjtyd+/eRWJiInx8fPDy5UskJCTIHJk8WrZsiZycHGkVbUFBAd555x2uMSgl5Q4hhBDy32rTpk1ISkpCQUEBxo8fLx3X09OTZaXigAEDMGDAAO7timhBlDZXV1ds2LABVlZWWjUgevXqxS0Ge3t7TJw4EQ4ODhAEATExMbC1tUV0dDSXvqdS0lmLE3XPnj2Dk5MTOnfurDVIwTOtiZWVFWbNmoWFCxfCy8sLaWlpMDQ05NY+IH96WiVRQio9pYiMjERUVBQMDQ3RuXNnHDhwAO7u7lxr6SglraASJqdatmyptchi/vz5XNsX0bNdWZRwnSpJSUkJtm/fDmNjY+kY7zTnOTk5+Pbbb6XP3t7e3HboA8rq4yiBUkr8TJo0CaNHj4aNjQ0YY4iLi3tjowIhcqAJMhndu3cPiYmJSExMRHp6Orp37w5ra2u5w+JOSatoHz9+jAMHDkirGXx9feHj44O9e/di5MiRNEFGCCGE/AOxltT27dvphQe0IOp1ycnJuH79Ov744w/pmEql4jpQ4e/vj7i4OFy4cAG6urqYMmUKrK2tkZqaymWwVwmrJAFl1fWcMWMGIiIikJKSgrFjx0KlUr2RhrO2LV68WCs9rZeXlyzpaZVAKRMySlBZWalVD0Pzz7woYecWoJzJKSWgZ7uyKOE6VZKYmBgkJibCwMBAthhUKhUePXqEVq1aAQDy8vK0FkbVNiX1cZRAKbtenZycUFpaipKSEjRp0gSenp5cvxeEvA19C2U0e/Zs2NjYYNKkSfjkk0+4F41XCqWsogXkT7lDCCGE/G/h4eGBsLAwrdoDs2fPRoMGDeQOjStaEKXtxo0biImJkTsM2NjYvJFus0ePHjJFIw+lTNQBgJ+fHx4/fgwLCwtZ+9xypqdVEqVMyCiBnZ2dtOMUgLTjlCcl7Nwi2ujZrixKuE6VxMzMDEVFRbJOkM2ePRtjxoyRUvilpqYiKCiIW/tK6uMogVJ2vWr293Jzc6XjLi4u3GMhRJOKiYUhCCFYt24drly5opVyp2fPnmjXrh2OHj2KH3/8Ue4QCSGEkP8KixcvhqGhoVS3JjIyEiUlJQgLC5M5Mr6cnJxgY2ODgQMH1ukFUSJfX1/MmDEDnTt3ljsUoiBDhw7lWp+Q/L38/HxERUWhb9+++OSTTxAWFgZPT0+8++67cocmi5MnTyIlJQV6enro1asX7Ozs5A6JyIye7cpD1+lfvLy8cO3aNXTo0EFrNx3P3frPnj3Dnj17EBcXB8YYunXrhhYtWsheh7auE3e97tq1S5Zdr9TfI0pFE2SEvEYz5U7fvn2llDvm5uZSAXFCCCGE/D1nZ+c3ag04Ojri+PHjMkVElMDFxQW3bt1CixYttAZteNbFIMozdepUrFixAqampnKHQgghhPxXS05OrvE4z11Vbm5u6NSp0xvPdZogk8fbdr2OGDGCaxzU3yNKRRNkhBBCCCHkX+fk5ITdu3dLdT2Li4sxfvx4HDlyRObIiJzu3r2L+Ph4lJaWonXr1qiurkZSUlKd21lI1MRaxM+ePcOjR4/QuXNn6OrqSn/Pc7U7IYQQQv4dbm5u2L9/v9xhkP9L7l2v1N8jSkc1yAghhBBCyL9u0qRJGD16NGxsbMAYQ1xcHKZOnSp3WERmoaGhKCsrQ3Z2NiwtLZGSklLnan+Rv7ytFjEhhBBC/nvZ2dkhKioKVlZWWhMhtHNIHnIvUKT+HlE6miAjhBBCCCH/OmdnZ5SVlaGoqAhNmjSBp6cn9PSo61nXZWVlISYmBiEhIXBzc8OCBQswe/ZsucMiMuGZ7okQQgghfJSUlGD79u0wNjaWjqlUKkqpXUdRf48oHY1SEEIIIYSQf928efOQl5cHCwsL5ObmSsddXFxkjIrIrXnz5lCpVDA3N0dmZiZcXFxQUVEhd1iEEEIIIeRfEhMTg8TERBgYGMgdCiGE/COaICOEEEIIIf+6zMxMnDx5Uu4wiMJ06NABQUFBGDduHObNm4eCggJUVlbKHRYhhBBCCPmXmJmZoaioiCbICCH/FWiCjBBCCCGE/OssLCxQUFAAExMTuUMhCrJixQpcuXIF77//Pnx9fZGYmIh169bJHRYhhBBCCPmXqFQqDBs2DB06dEC9evWk47/88ouMURFCSM1UjDEmdxCEEEIIIeR/ly+++AJXrlxBx44dUb9+fek4vRgTQgghhBDyv1dycnKNx6kWFSFEiWiCjBBCCCGE/OvoxZgQQgghhBBCCCFKRhNkhBBCCCGEEEIIIYQQQgghpE7RkTsAQgghhBBCCCGEEEIIIYQQQniiCTJCCCGEEEIIIYQQQgghhBBSp9AEGSGEEEIIIYQoxJQpU7Bz507pc1ZWFjp16oR169ZJx54+fYoPP/wQJSUl/19tnDx5Ep6env/TUAkhhBBCCCHkvxpNkBFCCCGEEEKIQgwcOBDJycnS57i4ONjY2ODs2bPSsUuXLuHjjz9Go0aN5AiREEIIIYQQQv5XoAkyQgghhBBCCFGIgQMH4vfff4cgCADUE2RTp05FaWkpcnJyAACJiYkYNGgQbt++DU9PTzg5OcHZ2RnR0dEAgKSkJDg7O2Ps2LFwdnZGRUUFNm7cCDs7O4waNQqnT5+W7f9HCCGEEEIIIUqhJ3cAhBBCCCGEEELU2rVrhyZNmiAzMxOmpqbIyspCjx49MHDgQJw5cwaTJk1CYmIiPD09MX36dCxYsAD29vbIz8/H6NGj0bZtWwDA7du3ERsbi9atWyM2NhYxMTGIjo6GgYEBZsyYIfP/khBCCCGEEELkRzvICCGEEEIIIURBBg4ciKSkJMTHx6Nv377Q0dGBjY0Nzp8/j9zcXACArq4uysvLYW9vDwBo2bIl7O3tkZCQAABo1aoVWrduDUC94+yzzz6DkZER9PT04ObmJs9/jBBCCCGEEEIUhCbICCGEEEIIIURBxDSL586dw6BBgwAAVlZWyMjIkNIriikYNTHGUFVVBQBo0KCBdFylUoExJn3W1dWt3f8AIYQQQgghhPwXoAkyQgghhBBCCFGQPn36ID09HcnJyRgwYAAAwNDQEB988AHCw8NhbW0Nc3Nz1KtXDzExMQCA/Px8nDp1Cn379n3j3xswYABOnjyJ4uJiCIKAQ4cOcf3/EEIIIYQQQogS0QQZIYQQQgghhCiIgYEB2rVrB3NzczRq1Eg6bm1tjQcPHqBPnz6oV68etm7dil9++QVOTk6YPHkyZsyYASsrqzf+PWtra7i5ucHNzQ2jR4/W+jcJIYQQQgghpK5SMc1cG4QQQgghhBBCCCGEEEIIIYT8L0c7yAghhBBCCCGEEEIIIYQQQkidQhNkhBBCCCGEEEIIIYQQQgghpE6hCTJCCCGEEEIIIYQQQgghhBBSp9AEGSGEEEIIIYQQQgghhBBCCKlTaIKMEEIIIYQQQgghhBBCCCGE1Ck0QUYIIYQQQgghhBBCCCGEEELqFJogI4QQQgghhBBCCCGEEEIIIXUKTZARQgghhBBCCCGEEEIIIYSQOuX/AMg3DcuRv1WAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x1080 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x,y = get_freq(cleaned_plath)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title(\"Plath Frequency For Top 50 Most Occuring Words\")\n",
    "plt.ylabel('Number of Occurences')\n",
    "plt.xlabel(\"Word\")\n",
    "sns.barplot(x[:50],y[:50])\n",
    "plt.show()\n",
    "\n",
    "x,y = get_freq(cleaned_underground)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title(\"Notes from Underground Frequency For Top 50 Most Occuring Words\")\n",
    "plt.ylabel('Number of Occurences')\n",
    "plt.xlabel(\"Word\")\n",
    "sns.barplot(x[:50],y[:50])\n",
    "plt.show()\n",
    "\n",
    "x,y = get_freq(cleaned_metamorphosis)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title(\"The Metamorphosis Frequency For Top 50 Most Occuring Words\")\n",
    "plt.ylabel('Number of Occurences')\n",
    "plt.xlabel(\"Word\")\n",
    "sns.barplot(x[:50],y[:50])\n",
    "plt.show()\n",
    "\n",
    "x,y = get_freq(cleaned_nietszche)\n",
    "plt.xticks(rotation = 90)\n",
    "plt.title(\"Nietszche Frequency For Top 50 Most Occuring Words\")\n",
    "plt.ylabel('Number of Occurences')\n",
    "plt.xlabel(\"Word\")\n",
    "sns.barplot(x[:50],y[:50])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating the Markov Models from Text Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Opening the text files and creating models for use\n",
    "\n",
    "# All 250 Poems by Sylvia Plath\n",
    "with open(\"plath.txt\") as f:\n",
    "    plath_txt = f.read()\n",
    "    \n",
    "plath_model = markovify.NewlineText(plath_txt,state_size = 2)\n",
    "    \n",
    "# Notes From Underground - Dostoevsky\n",
    "with open('underground.txt') as f:\n",
    "    underground_txt = f.read()\n",
    "\n",
    "undegrnd_model = POSifiedText(underground_txt,state_size = 3)\n",
    "    \n",
    "# Metamorphosis - Kafka\n",
    "with open('metamorphosis.txt') as f:\n",
    "    metamorphosis_txt = f.read()\n",
    "    \n",
    "metamorph_model = POSifiedText(metamorphosis_txt)\n",
    "\n",
    "# Brothers Karamazov - Dostoevsky\n",
    "with open('karamazov.txt') as f: \n",
    "    karamazov_txt = f.read()\n",
    "    \n",
    "karamazov_model = POSifiedText(karamazov_txt)\n",
    "    \n",
    "# All of Nietzsche \n",
    "with open('nietzsche.txt') as f:\n",
    "    nietzsche_txt = f.read()\n",
    "    \n",
    "niet_model = POSifiedText(nietzsche_txt,state_size = 3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generating Poems from Sylvia Plath Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computer Generate Sylvia Plath Sentences & Poems\n",
      "\n",
      "100 Randomly Generated Sentences  \n",
      "\n",
      "My head on the nursery wall,\n",
      "Here he comes now, among the flat leer of the earth and the lovers\n",
      "As the light of the noon sun striking\n",
      "Of his mind. And now you try\n",
      "Backtalks at the margins of a clock,\n",
      "Makeshift of a white saint rave\n",
      "In spite of herself,\n",
      "Ticking your fingers on the stern seat, from the wall.\n",
      "The deathly guests had not asked for such a ruin.\n",
      "With voices of sorrow\n",
      "They are eaten immediately by the faces of my neighbor's rose bush\n",
      "Like a dead foot,\n",
      "I bent over the lead sea\n",
      "I had nothing to do violence on,\n",
      "I'll go out and sit in a cloud.\n",
      "Where once a day on her back as the gray people\n",
      "And the soul is a poor coat. I inhabit\n",
      "The jut of that ambiguous sea\n",
      "Sliding shut on the ceiling.\n",
      "The day-moon lights up like a castle, from the crest of the sea.\n",
      "Ponderous and extend despair on earth's bedrock. Almost thrown, not\n",
      "Moon full, moon dark, the pitying eye.\n",
      "Whiteness being what I did.\n",
      "With its prim bric-a-brac, without a moon or a cloud.\n",
      "Or even like black apples.\n",
      "We are full of dark slime at her foot distractedly, and at his bones.\n",
      "In a still virulence,\n",
      "The eye of the berries of dark.\n",
      "Come clear of the eye of the stars, and moon-skulled,\n",
      "1 talk to myself, I wake to listen :\n",
      "Yadwigha, pose on, that he put you on your kingdom's border\n",
      "Laugh-shaken on his neck. Resoluteness\n",
      "Lit like a spinnaker. Hat-brims\n",
      "It is so beautiful to have him near again —\n",
      "The body of whiteness\n",
      "That at such a good shape, so vivid.\n",
      "Or the hands of an idyll, the blue's worn out:\n",
      "Saws down a reed and swims from his crabbed midden where all's wrought so awry\n",
      "As out of the peripheral.\n",
      "Toward that great wound\n",
      "My eyes are squeezed by this iceribbed ship,\n",
      "In the marketplace they are no nearer.\n",
      "Burdened in such mild air\n",
      "The featurelessness of that color ?\n",
      "Where straight talk is the hearse of a body.\n",
      "Clam-diggers rose up out of the eye\n",
      "fantastic flesh down to the dock, outmoded, gaudy,\n",
      "Buried in the lavatory mirror\n",
      "But from the labeled elms, the tame cygnets steer.\n",
      "One more hook, and the berries has stunned them; they believe in miracles.\n",
      "I am reassured. I am dumb and brown. I am nobody; I have had my chances. I have been there.\n",
      "A machine to breathe and beat them off,\n",
      "For eyes to lumps\n",
      "I walk in a shadeless socket.\n",
      "Long gone to wheeze and seed, Ella Mason keeps cats, eleven at last count,\n",
      "Rave on the hill;\n",
      "Obscene and bald as a lady in a glass hat.\n",
      "plunges headlong into that state of mind ?\n",
      "They will wonder if I were a green-tipped leaf\n",
      "The stony actors poise and grieve as in my eye; dry frost\n",
      "The great bronze gate began to warm up, and grant\n",
      "Of grassheads and is easy.\n",
      "And no bodies, singing like arrows up to the island. It was a place meant for climbing. A peculiar logic\n",
      "The moon is no hope, it is I know.\n",
      "Everything in one gigantic step.\n",
      "And from the rhododendron,\n",
      "intrudes even when the window, those unborn, those undone\n",
      "Whitened by the faces of the ancient paraphernalia —\n",
      "The couch glared out at the end of her head,\n",
      "And paints the river's greening patinas;\n",
      "How your bad dreams and the people idle\n",
      "Gulls stiffen to their friendly\n",
      "Tireless, tied, as a finger, solitary bees\n",
      "To rock on all fours like a dark funnel, my father!\n",
      "The heart is a beautiful woman,\n",
      "I cannot help smiling at what it is given up. \n",
      "Startled at it, though the sound of poisons ?\n",
      "Picking among the mackerel gatherers\n",
      "Leaves and petals attend me. I am bled white as porkfat.\n",
      "Already veiling the gray paper,\n",
      "I let her go. I let her go. I let her go\n",
      "Claw swollen to a friend, in private, Rousseau confessed his eye with red: such red! under the sea,\n",
      "Under white wall and a distant loudspeaker\n",
      "Chair and bureau are the clothes of a doll in my white coat,\n",
      "One courting doctors of all that green altar\n",
      "Than flesh of any sort.\n",
      "A red lotus opens in its gape of perpetual grieving.\n",
      "Blind to what will happen without attention.\n",
      "In its easy peace, could only be perfect\n",
      "Come, so unlike us, to a difficult borning.\n",
      "All of it, that O-mouth\n",
      "She wore her broom straws to the black arrow-backs of swifts on their delicate feet over the lead sea\n",
      "Stiff as a diamond.\n",
      "But stay, sitting far out, in their bright needles, they bring me numbness in their wings.\n",
      "Enough for fingernails to make of these man-shaming clouds.\n",
      "On a green sea gardens stall,\n",
      "Lay over the plants in my stockings, my high heels,\n",
      "Yet desolation, stalled in his book wormed off the pages.\n",
      "Round, green, disconsolate as a Mexican bean.\n",
      "The eye of my eyes\n",
      "\n",
      "POEM: 1\n",
      "I think they would mend properly.\n",
      "Once, in its own string\n",
      "And what if they were alive, and that's\n",
      "Put up with silk, as if lit from within.\n",
      "Of nature: what better way to the bourbon.\n",
      "The gray sky lowers, the hills roll the notes of the mole.\n",
      "In the same impression\n",
      "In the parlor of the stuck record\n",
      "\n",
      "POEM: 2\n",
      "Blunt and flat enough to prove flesh real,\n",
      "From his orbit under the crowd's truculence,\n",
      "Now works his hell of Mondays in the meadow mute as mannequins in a sea\n",
      "It strives after them with conches. One is told:\n",
      "Peaks and craters of those who pierced time's core.\n",
      "Curded over with clouds and animals pay no notice;\n",
      "The moles look neutral as the charred knuckle-bones\n",
      "Faces blank as the great run\n",
      "\n",
      "POEM: 3\n",
      "An old beast ended in this bleak place ?\n",
      "Damned, howling in their belling dresses.\n",
      "Of grassheads and is easy.\n",
      "The hunt is on, and must go on looking in on.\n",
      "That glide ahead of the protracted evening!\n",
      "I am dying as I listened its cold gizzard ground those rounds.\n",
      "Lived under the ringed toes of the very thirsty.\n",
      "I should have junked them before they held him ?\n",
      "\n",
      "POEM: 4\n",
      "Cool as the Roman Forum.\n",
      "I felt her criticizing me in a blown sky.\n",
      "Up from the house still hugs in each lover's eye; the ardent look\n",
      "Floured his whirling beard, and at his hand's staunch hest, birds build.\n",
      "Six months in the dark, and she\n",
      "Rave on the edge\n",
      "And we shall do together,\n",
      "For the rock's dwarfed lump, for the coming down if the sky creates itself—\n",
      "\n",
      "POEM: 5\n",
      "Of golgotha at the couch which you,\n",
      "He does not wish to be natural.\n",
      "Ponderous and extend despair on earth's bedrock. Almost thrown, not\n",
      "disguising the constant horror in a fossil. Tell me I'm here.\n",
      "Mother Medea in a blown sky.\n",
      "And crimp their wrinkles in a Chinese screen.\n",
      "Tends to the wall.\n",
      "Fists my fingers up, ten white fingers.\n",
      "\n",
      "POEM: 1\n",
      "Up here among the low stars.\n",
      "Plumped the bellies of the eye, scalded by these hooks she climbs to my wound, it corresponds.\n",
      "They move through the candle's mouth like a well-done sum.\n",
      "This witch sidles, fingers crooked, as if a celestial burning took\n",
      "From Water-Tower Hill to the sea at the gate,\n",
      "I bed in the lift and drift of\n",
      "Nodding by night around my bed, with my root in the greenest light.\n",
      "Letting in the wall\n",
      "Whitened by the low brow\n",
      "The incalculable malice of the Grosse Fuge.\n",
      "Picked his trip-date by the words you know.\n",
      "Tonight, in the circus.\n",
      "I am overlooking them by looking too hard. I have never been so pure.\n",
      "Go to your attic ?\n",
      "For fever in a ring,\n",
      "\n",
      "POEM: 2\n",
      "Doing things with their sudden tongues and their bodies.\n",
      "Memories jostle each other up!\n",
      "Spins itself from that dual battle.\n",
      "Toward extinction in those terrible children\n",
      "Moves on its own right,\n",
      "To turn you luminous, without the eye or a cloud.\n",
      "Ordering its black shadow:\n",
      "The moon to a hole-mouth and meet an eye between two worlds and two entirely\n",
      "The spider on its flowery cart like a dark\n",
      "Guards the hard-hearted emerald of its own string\n",
      "But he is featureless.\n",
      "My heart is a formality—\n",
      "And how a god of shades,\n",
      "It were a material.\n",
      "Any day I may be a saint, and I filled\n",
      "\n",
      "POEM: 3\n",
      "Possession of the eye\n",
      "Warmed by much sun and the snakecharmer begins a snaky sphere\n",
      "Eclipsing the spitted ox I see myself as a pig, the white hiatus of winter—\n",
      "My body is a death. Is it any wonder he affects a black rock\n",
      "Blinds lowered on that dark guilt,\n",
      "Whose owl-eyes in the doom-noise of the dark's ragbag, these two\n",
      "It had opened its orange in my well-boiled hospital shift,\n",
      "In that rubble of skull plates and forks are for.\n",
      "Thin as the day forgets itself.\n",
      "If I pay the roots of my fears,\n",
      "Lit things, that lamp of the Public Gardens: one wants a vacatior\n",
      "Entering the tower of my crib ?\n",
      "The armadillo dozed in his one whiteness.\n",
      "Obscene bikinis hide in the leaf-filtered gloom\n",
      "With one foot in the dark\n",
      "\n",
      "POEM: 4\n",
      "Back to her slouch.\n",
      "Doze behind the glass with your own face on.\n",
      "We are a marriage with the other of us.\n",
      "You could tell almost at once of the sky and I couldn't sleep for a trinket,\n",
      "Here on the far\n",
      "The narcissi open white faces in the stroke of an atrocity.\n",
      "And leaves steadfast in shape and number and every earthquaking continent.\n",
      "Needling spumily down from groves where the small fish\n",
      "Glinting and clinking in a wooden girdle, root to top\n",
      "ending in a sea\n",
      "Its bowl of blood;\n",
      "Sunday's ghost of us,\n",
      "To this infant still in his weeping wife.\n",
      "And the white lines on a playing card.\n",
      "On a red star\n",
      "\n",
      "POEM: 5\n",
      "Meet the thin people simply stand in the moon's vat—\n",
      "It does not hear what the trouble was: she thought she was grateful.\n",
      "So might rigor mortis come to one bush of flies,\n",
      "Dusk hoods me in the sea drafts\n",
      "The first thing I saw slow flocked cows move\n",
      "We are shelves, we are hauled, though we would stop\n",
      "To start the spirit out of existence, then resurrect them.\n",
      "They are not mine. They do not trust the spirit. It escapes like steam\n",
      "On the green sea\n",
      "How long can I be mothering ?\n",
      "Cats play in the wall\n",
      "Steadily rooted though they weigh me down,\n",
      "As if I was quite limp —\n",
      "She, addled in her yard\n",
      "Attach her to keep all glory in the moist\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generating Sample Poems From Plath\n",
    "print('Computer Generate Sylvia Plath Sentences & Poems')\n",
    "print()\n",
    "print(\"100 Randomly Generated Sentences  \\n\")\n",
    "for i in range(100): \n",
    "    if plath_model.make_sentence()!='None':\n",
    "        print(plath_model.make_sentence(tries = 100))\n",
    "print()\n",
    "\n",
    "# Print three randomly-generated sentences of no more than 100 characters, also do it 4 times (similar to a poem?)\n",
    "for s in range(5):\n",
    "    print('POEM: ' + str(s+1))\n",
    "    for i in range(8):\n",
    "        print(plath_model.make_short_sentence(100)) # Cap the sentence length\n",
    "    print()\n",
    "    \n",
    "for s in range(5):\n",
    "    print('POEM: ' + str(s+1))\n",
    "    for i in range(15):\n",
    "        print(plath_model.make_short_sentence(100))\n",
    "    print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Sentences from Nietszche Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "But such a god does not know the full extent of his own susceptibility.\n",
      "\n",
      "Was it not necessary in the end even hallowing and vindicating it.\n",
      "\n",
      "He acknowledges under certain circumstances, which made him hesitate at first, that there are higher and deeper conceptions of good and evil.\n",
      "\n",
      "And to ask once more the path to the fundamental problems.\n",
      "\n",
      "There are books which have an inverse value for the soul and he feels himself once more free and courageous.\n",
      "\n",
      "On the other hand, progress in the sense and along the lines of the old maid about him; for, like her, he is not willy-nilly the absolute servant of nature.\n",
      "\n",
      "Pride is their support if the passion and the comprehension of men that they have something which they hide--namely, intellect.\n",
      "\n",
      "It is among them that the most powerful incentives to certain acts and away from other acts.\n",
      "\n",
      "And the disenchantment of woman is slowly evolving?\n",
      "\n",
      "A valiant army is evidence of the value of life, because in that case the rest of the cosmos on a pivot that he might master it.\n",
      "\n",
      "It is to be understood!\n",
      "\n",
      "This seems to him full of love, the former as if it were one of its playthings.\n",
      "\n",
      "This can be thought out and it is manifest that it was within his discretion not to have done these evil things.\n",
      "\n",
      "But if it be a living and not a means along with other tools.\n",
      "\n",
      "Eventually one must do everything oneself in order to make it possible to continue one's existence within them.\n",
      "\n",
      "They are the most important.\n",
      "\n",
      "The difference among men does not manifest itself only in the suffering of another, as in the other cases where the points of view are repudiated.\n",
      "\n",
      "Signs of nobility never to think of mortal power having placed it there.\n",
      "\n",
      "Eventually one must do everything oneself in order to be able to keep the promises one makes.\n",
      "\n",
      "And in that period the instinct of the populace.\n",
      "\n",
      "Neither is he a model man; he does not read aloud, he does not feel pain where he expected it.\n",
      "\n",
      "In all truth, a certain blind faith in the efficacy of her results?\n",
      "\n",
      "As it is, it perpetually confuses things as a result of the most dangerous and attractive qualities.\n",
      "\n",
      "The primordial belief of all organisms is, perhaps, that all the rest of mankind--and perhaps also a new mission!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Generating Sentences by Nietszche\n",
    "for i in range(25):\n",
    "    s = niet_model.make_sentence(tries = 100)\n",
    "    s = s[0]+ s[1:].lower()\n",
    "    if not re.search('\\d+', s) and '=' not in s:\n",
    "        print(s + '\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Sentences from Notes from Underground"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There was no one there.\n",
      "\n",
      "There was no answer, but continue staring at me just before.\n",
      "\n",
      "I am forty years old now, and you know that is the chief thing.\n",
      "\n",
      "He loves the process of the game, not the end of it; no historian would be equal to the job.\n",
      "\n",
      "Ech! gentlemen, but of course he knows himself that he is doing a just and honest thing.\n",
      "\n",
      "In short, if this could be arranged there would be a ball for the whole of the nineteenth century must and morally ought to be kept on a curb.\n",
      "\n",
      "For some reason I dislike those laws and the fact that the melting snow was still falling in heavy flakes.\n",
      "\n",
      "As the children grow up you feel that you are more sinned against than sinning.\n",
      "\n",
      "I returned home completely upset, it was just because I was more highly developed.\n",
      "\n",
      "I should certainly never have made up my mind for some reason I believe that the best definition of man is the ungrateful biped.\n",
      "\n",
      "The point was that all this was, as it were, especially, as it were, with tears in my eyes--but still I did go out again.\n",
      "\n",
      "I say that the world may go to pot for me so long as I was left TETE-A-TETE, was in a fever; my hair, soaked with perspiration, stuck to my forehead and temples.\n",
      "\n",
      "Is the world to myself just as I had imagined the scene not long before in a fit of depression.\n",
      "\n",
      "Now, are not you fancying, gentlemen, that I am her saviour, and that she loves me, loves me passionately.\n",
      "\n",
      "I take this example because it is easier for me to do so, for I knew no one.\n",
      "\n",
      "I had only to protest and I certainly would have been quite enough to have the consciousness by which all so-called direct persons and men of action live.\n",
      "\n",
      "But I had not been treated like a fly.\n",
      "\n",
      "I, for instance, was impossible.\n",
      "\n",
      "I tell you solemnly, that I have just rejected the palace of crystal that can never be destroyed and that one cannot keep it.\n",
      "\n",
      "I had got to the point of tears, though I knew perfectly well at that moment that all this was, as it were, afraid of them.\n",
      "\n",
      "I, of course, was a great vexation to me.\n",
      "\n",
      "There was not a question of being the first to speak of his wages.\n",
      "\n",
      "Should I not begin to hate her, perhaps, even tomorrow, just because I was fully conscious of the hopelessness of one's position.\n",
      "\n",
      "I know that I have just rejected the palace of crystal that can never be destroyed and that one cannot keep it.\n",
      "\n",
      "My schoolfellows met me with spiteful and merciless jibes because I was ashamed to look at any of them.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for i in range(25):\n",
    "    print(undegrnd_model.make_sentence(tries = 100))\n",
    "    print()\n",
    "#     print()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Generating Poems of varying length using the Slyvia Plath data (Saved to File)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Generating 25 poem lines from plath and saving them to a file\n",
    "final = []\n",
    "tf = open(\"plath_generated.txt\",'w')\n",
    "for i in range(100):\n",
    "    tf.write('\\n')\n",
    "    temp = []\n",
    "    length_of_poem = random.randint(5,20)\n",
    "    for i in range(length_of_poem):\n",
    "        s = plath_model.make_sentence(tries = 100)\n",
    "        temp.append(s)\n",
    "    tf.write('\\n'.join(temp))\n",
    "    tf.write('\\n')\n",
    "tf.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Combining Different Authors (Nietszche and Underground Man)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Whoever, like myself, prompted by some enigmatical desire, has long endeavoured to go to the length of contemplating making friends with them.\n",
      "\n",
      "Why, in the first place in order to feel pleasure oneself?\n",
      "\n",
      "And it isn't the beastliness of it that some say that it is well to be so.\n",
      "\n",
      "The light of the furthest stars is longest in reaching man; and before it all ancient education, controlled the whole domain of ethical notions is one of the sort who would consent to fight a duel?\n",
      "\n",
      "I don't consult a doctor from spite.\n",
      "\n",
      "But, you know, this is what is happening nowadays let us not be afraid!\n",
      "\n",
      "But as it always happened that such reflections impelled me, as though purposely, occurred to me on the head and they may belabour me from behind.\n",
      "\n",
      "So also an opinion gives happiness, therefore it is itself good and true.\n",
      "\n",
      "And this being so, can one help being tempted to rejoice that it has fallen, that all dogma lies on the ground--nay more, that it is inconsistent?\n",
      "\n",
      "Justice is therefore reprisal and exchange upon the basis of which a soul should grow up to such a building!\n",
      "\n",
      "But at this point i was not acting in the least ceasing to be a hypocrite; as in the case of madame de guyon.\n",
      "\n",
      "And all that for the most refined, the most upright, and also the present one.\n",
      "\n",
      "At last i made up my mind for some reason gratified me.\n",
      "\n",
      "But at first, in the way in which the ancient public world took delight.\n",
      "\n",
      "May it be forgiven me that i, too, might think well of her.\n",
      "\n",
      "Whence comes the conviction that one should feel the breaking of the too-rigid symmetry as intentional and as a great deal of it was that on the knee as soon as they prove to you that in reality they are not worth my little finger.\n",
      "\n",
      "Everything is so arranged that the worst of it is, gentlemen, that it is a question, always, of self preservation.\n",
      "\n",
      "I agree that man is grateful is this.\n",
      "\n",
      "Equally, he who is punished does not deserve to be as unalterable as his very being.\n",
      "\n",
      "The blockheads will be forced to fight.\n",
      "\n",
      "Inadequacy of distinction and error of comparison are the basis of an approximate equality of power.\n",
      "\n",
      "There is happiness in the midst of my sobs; but still i could not get rid of you as soon as ever a philosophy begins to believe in god and gods.\n",
      "\n",
      "The office will have vanished off the face of the earth--as though you had ruined her, beggared her, robbed her.\n",
      "\n",
      "And yet this suffering because of the lies i had told you.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "model_combo = markovify.combine([ niet_model, undegrnd_model ], [ 1, 1 ])\n",
    "for i in range(25):\n",
    "    s = model_combo.make_sentence(tries = 100)\n",
    "    s = s[0]+ s[1:].lower()\n",
    "    if not re.search('\\d+', s) and '=' not in s:\n",
    "        print(s + '\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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