sense_pauses.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from collections import defaultdict\n",
    "\n",
    "# Define charts settings\n",
    "plt.rc('xtick',labelsize=14)\n",
    "plt.rcParams[\"figure.figsize\"] = (20, 8)\n",
    "plt.rcParams[\"legend.framealpha\"] = 1\n",
    "\n",
    "# List of text titles in the same order as in FIG. 2a in the article\n",
    "text_names_sorted = ('genesisA', 'genesisB', 'ChristI', 'ChristII', 'ChristIII', 'Elene', 'Juliana',\n",
    "                     'KD_1', 'KD_2', 'Klaeber_part1', 'Klaeber_part2', 'ILIAD', 'ODYSSEY')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1><i>Functions</i></h1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_file(dir_, file):\n",
    "    '''\n",
    "    Read file and split the text to lines\n",
    "    '''\n",
    "\n",
    "    # Read file\n",
    "    with open(os.path.join(dir_, file)) as f:\n",
    "        x = f.read()    \n",
    "\n",
    "    # Remove blankspaces\n",
    "    x = re.sub(' +', '', x)\n",
    "\n",
    "    # Remove initial and final hashtag\n",
    "    x = re.sub('^#|#$', '', x)\n",
    "            \n",
    "    # Return list of lines and text_name\n",
    "    return x.split('#'), re.sub('\\.txt$', '', file)\n",
    "\n",
    "\n",
    "def pauses_ratio(text, punctuation):\n",
    "    '''\n",
    "    Calculate the ratio of intra-line to all sense-pauses\n",
    "    '''\n",
    "        \n",
    "    all_pauses = 0\n",
    "    intra_pauses = 0\n",
    "    \n",
    "    # Iterate over lines of text\n",
    "    for line in text:\n",
    "\n",
    "        # Replace subsequent punctuation with a single dot\n",
    "        # (in order to count consecutive punctuation marks as a single character)\n",
    "        line = re.sub('[' + ''.join(punctuation) + ']+', '.', line)\n",
    "        \n",
    "        # Iterate over characters\n",
    "        for i, char in enumerate(line):\n",
    "            # ...if it is a punctuation mark from a defined set\n",
    "            if char == '.':\n",
    "                all_pauses += 1\n",
    "                        \n",
    "                # ... if it is in intra-line position\n",
    "                if i < len(line) - 1:\n",
    "                    intra_pauses += 1\n",
    "        \n",
    "    # Return the intra-line to all sense-pauses ratio\n",
    "    return intra_pauses / all_pauses\n",
    "\n",
    "\n",
    "def plot_chart(data, text_names_sorted):\n",
    "    '''\n",
    "    Plot ratios into a bar chart\n",
    "    '''\n",
    "\n",
    "    x = np.arange(len(text_names_sorted))\n",
    "    plt.xticks(x, text_names_sorted, rotation='vertical')\n",
    "    \n",
    "    i = 0\n",
    "    for label in sorted(data):\n",
    "        if len(data) > 1:\n",
    "            position = i*0.5 / len(data) - 0.125\n",
    "        else:\n",
    "            position = 0\n",
    "        plt.bar(x+position, data[label], width=0.15, label=label)\n",
    "        i += 1\n",
    "    plt.legend(loc='lower right')\n",
    "    plt.show()\n",
    "    plt.clf()\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Replicate original chart</h1>\n",
    "<p>First we replicate the FIG. 2a using the same procedure as in authors' <a href='https://github.com/qcrit/NHB-2018-OEstylometry/tree/master/sense_pauses' target='_blank'>original script</a>."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc2af4e6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc2aefabe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "texts = dict()\n",
    "ratios = dict()\n",
    "\n",
    "ratios['(A) original'] = list()\n",
    "\n",
    "# Define relevant punctuation marks as in script by Neidorf et al.\n",
    "punctuation = ['!', ';', '?', '.', ':', '(', ')']\n",
    "\n",
    "# Directory \"src/selected\" - the one where files actually used for the experiment are stored in\n",
    "dir_ = os.path.join('src', 'selected')\n",
    "\n",
    "# Read the texts and split them to lines\n",
    "for file in os.listdir(dir_):\n",
    "    text, text_name = process_file(dir_, file)\n",
    "    texts[text_name] = text\n",
    "\n",
    "# Calculate the ratios\n",
    "ratios_original = list()\n",
    "for text_name in text_names_sorted:    \n",
    "    ratio = pauses_ratio(texts[text_name], punctuation)\n",
    "    ratios['(A) original'].append(ratio)\n",
    "\n",
    "# Plot the chart\n",
    "plot_chart(ratios, text_names_sorted)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Fix Genesis A/B issue</h1>\n",
    "<p>Unfortunately the original data for Genesis A/B are corrupted. File genesisA.txt actually contains lines 1-234 of Genesis A + first line of Genesis B, while file genesisB.txt contains all the remaining lines of Genesis A (852-2936). The original chart thus shows a 'marked difference' between two pieces of the same text.\n",
    "<p>To fix this we read the entire Genesis stored in 'src/corpus/genesis.txt' and split it on our own: Genesis A ~ lines 1-234 + lines 852-2936, Genesis B ~ lines 235-851."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc2aef5630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc2aefae80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "texts = dict()\n",
    "dir_ = os.path.join('src', 'selected')\n",
    "ratios['(B) genesis fixed'] = list()\n",
    "\n",
    "# Read the texts and split them to lines\n",
    "for file in os.listdir(dir_):\n",
    "    # ... but skip Genesis A and Genesis B this time\n",
    "    if file.startswith('genesis'):\n",
    "        continue\n",
    "    text, text_name = process_file(dir_, file)\n",
    "    texts[text_name] = text\n",
    "\n",
    "# Read Genesis from a single file and split it to A and B according to line numbers\n",
    "text, text_name = process_file(os.path.join('src', 'corpus'), 'genesis.txt')\n",
    "texts['genesisA'] = text[0:234] + text[851:]\n",
    "texts['genesisB'] = text[234:851]\n",
    "\n",
    "# Calculate the ratios\n",
    "ratios_genesis_fixed = list()\n",
    "for text_name in text_names_sorted:    \n",
    "    ratio = pauses_ratio(texts[text_name], punctuation)\n",
    "    ratios['(B) genesis fixed'].append(ratio)\n",
    "\n",
    "# Plot the chart\n",
    "plot_chart(ratios, text_names_sorted)    \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Add missing punctuation and remove commas</h1>\n",
    "<p>In the 'Methods' section of the article (p. 7) sense-pauses marks are enumerated as follows: <span style='color: green; font-weight: bold'> . ? ! ; : ( ) - ‘ ’ “ ”</span>, but in the original script the last five characters (dashes, quotes and double quotes) are not taken into account. This also causes errors: for instance in line <span style='color: green; font-weight: bold'>mid soðfæder symle wunian . \"</span> the period is incorrectly classified as intra-line because it is followed by other character. We fix this simply by extending the list of relevant punctuation.\n",
    "<p>For similar reasons we also delete all commas - as they are not treated as relevant punctuation, they cause e.g. right bracket in <span style='color: green; font-weight: bold'>fæmnan lufian , ( hine fyrwet bræc ) ,</span> to be incorrectly classified as intra-line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc1b9284a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc1b8d6898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratios['(C) punctuation fixed'] = list()\n",
    "\n",
    "# Extend the list of relevant punctuation\n",
    "punctuation.extend(['\\\"', '\\-', '\\—' ])\n",
    "\n",
    "# Calculate the ratios\n",
    "for text_name in text_names_sorted:\n",
    "    text_wo_trash = list()\n",
    "    # ... but first delete commas\n",
    "    for line in texts[text_name]:\n",
    "        text_wo_trash.append(re.sub(',', '', line))\n",
    "    ratio = pauses_ratio(text_wo_trash, punctuation)\n",
    "    ratios['(C) punctuation fixed'].append(ratio)\n",
    "\n",
    "# Plot the chart\n",
    "plot_chart(ratios, text_names_sorted)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Remove sequences of dots</h1>\n",
    "\n",
    "<p>Finally there are places in OE texts where sequences of dots are used to indicate missing pieces of texts (<span style='color:green; font-weight:bold'>þæt geond eorðb . . . . . .g eall eagna gesihþe</span>), not the punctuation. To disregard these cases we delete all the sequences of more dots in a row."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc1b897eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc1b617e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratios['(D) dots removed'] = list()\n",
    "\n",
    "# Calculate the ratios\n",
    "for text_name in text_names_sorted:\n",
    "    text_wo_dots = list()\n",
    "    # ... but first:\n",
    "    for line in texts[text_name]:\n",
    "        # ... delete commas\n",
    "        line_modified = re.sub(',', '', line)\n",
    "        # ... delete all sequences of dots\n",
    "        line_modified = re.sub('\\.\\.+', '', line_modified)\n",
    "        text_wo_dots.append(line_modified)\n",
    "    ratio = pauses_ratio(text_wo_dots, punctuation)\n",
    "    ratios['(D) dots removed'].append(ratio)\n",
    "\n",
    "# Plot the chart\n",
    "plot_chart(ratios, text_names_sorted)   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Final chart</h1>\n",
    "<p>Now simply plot only the first series (original data presented in the article) and the last one (all issues fixed) to see that the differences are sometimes really striking."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc2aef54e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc1b64d630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ratios_final = {\n",
    "    '(A) original': ratios['(A) original'],\n",
    "    '(B) fixed': ratios['(D) dots removed'],\n",
    "}\n",
    "\n",
    "# Plot the chart\n",
    "plot_chart(ratios_final, text_names_sorted)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,'')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc1b62d588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7efc1c0f70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "from scipy import stats\n",
    "import itertools\n",
    "import random\n",
    "\n",
    "text_names_sorted = text_names_sorted + ('andreas',)\n",
    "ratios_final['(A) original'].append(0)\n",
    "texts['andreas'], text_name = process_file(os.path.join('src', 'corpus'), 'andreas.txt')\n",
    "\n",
    "text_wo_dots = list()\n",
    "for line in texts['andreas']:\n",
    "    # ... delete commas\n",
    "    line_modified = re.sub(',', '', line)\n",
    "    # ... delete all sequences of dots\n",
    "    line_modified = re.sub('\\.\\.+', '', line_modified)\n",
    "    text_wo_dots.append(line_modified)\n",
    "ratio = pauses_ratio(text_wo_dots, punctuation)\n",
    "ratios_final['(B) fixed'].append(ratio)\n",
    "\n",
    "def sampling(texts, n=300, step=300):\n",
    "    '''\n",
    "    texts:  {dict} of texts \n",
    "    n:      number of lines in one sample\n",
    "    step:   after how many lines new sample should start (set step = n to get non-overlapping samples)\n",
    "    '''\n",
    "    \n",
    "    samples = defaultdict(lambda: defaultdict(list))\n",
    "    \n",
    "    # Iterate over texts\n",
    "    for text_name in texts:\n",
    "\n",
    "        # Restart samples indexing for each new text\n",
    "        sample_indices = [1]\n",
    "\n",
    "        # Iterate over lines of text\n",
    "        for i, line in enumerate(texts[text_name]):\n",
    "\n",
    "            # Iterate over current sample indices\n",
    "            for si in sample_indices:\n",
    "                samples[text_name][si].append(line)\n",
    "                    \n",
    "            # Add new sample index if step was reached  \n",
    "            if (i+1) % step == 0 and i > 1 and len(texts[text_name]) > i + n:\n",
    "                sample_indices.append( sample_indices[-1] + 1 )\n",
    "\n",
    "            # Remove first sample index if it reached the size\n",
    "            if len(sample_indices) > 0 and len(samples[text_name][sample_indices[0]]) == n:\n",
    "                sample_indices = sample_indices[1:]\n",
    "                \n",
    "    return samples\n",
    "\n",
    "samples = sampling(texts, n=100, step=100)\n",
    "\n",
    "# Calculate the ratios\n",
    "scatter_data = defaultdict(list)\n",
    "ratios_samples = defaultdict(list)\n",
    "\n",
    "for text_name in text_names_sorted:\n",
    "    for si in samples[text_name]:\n",
    "        text_wo_dots = list()\n",
    "        # ... but first:\n",
    "        for line in samples[text_name][si]:\n",
    "            # ... delete commas\n",
    "            line_modified = re.sub(',', '', line)\n",
    "            # ... delete all sequences of dots\n",
    "            line_modified = re.sub('\\.\\.+', '', line_modified)\n",
    "            text_wo_dots.append(line_modified)\n",
    "        ratio = pauses_ratio(text_wo_dots, punctuation)\n",
    "        scatter_data['names'].append(text_name)\n",
    "        scatter_data['values'].append(ratio)\n",
    "        ratios_samples[text_name].append(ratio)\n",
    "\n",
    "scatter_data = pd.DataFrame.from_dict(scatter_data)\n",
    "\n",
    "a4_dims = (15, 8.27)\n",
    "fig, ax = plt.subplots(figsize=a4_dims)\n",
    "\n",
    "x = np.arange(len(text_names_sorted))\n",
    "plt.xticks(x, text_names_sorted, rotation='vertical')\n",
    "    \n",
    "i = 0\n",
    "for label in sorted(ratios_final):\n",
    "    if len(ratios_final) > 1:\n",
    "        position = i*0.5 / len(ratios_final) - 0.25\n",
    "    else:\n",
    "        position = 0\n",
    "    ax.bar(x+position, ratios_final[label], width=0.25, label=label)\n",
    "    i += 1\n",
    "sns.catplot(x=\"names\", y=\"values\", data=scatter_data, jitter=0.05, ax=ax, color='black')\n",
    "ax.legend(loc='upper right')\n",
    "ax.set_ylabel('')    \n",
    "ax.set_xlabel('')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "genesisA genesisB\n",
      "\t Ttest_indResult(statistic=-2.0679739609529015, pvalue=0.048346344485947788)\n",
      "\t n1 = 23, n2 = 6\n",
      "genesisA ChristI\n",
      "\t Ttest_indResult(statistic=1.1794507158191299, pvalue=0.24932152286265144)\n",
      "\t n1 = 23, n2 = 4\n",
      "genesisA ChristII\n",
      "\t Ttest_indResult(statistic=-0.42543267870853901, pvalue=0.67416121695874565)\n",
      "\t n1 = 23, n2 = 4\n",
      "genesisA ChristIII\n",
      "\t Ttest_indResult(statistic=3.659652452240798, pvalue=0.0010382318992895241)\n",
      "\t n1 = 23, n2 = 7\n",
      "genesisA Elene\n",
      "\t Ttest_indResult(statistic=-1.7659115695565577, pvalue=0.086389941700252104)\n",
      "\t n1 = 23, n2 = 13\n",
      "genesisA Juliana\n",
      "\t Ttest_indResult(statistic=-1.0997197572180915, pvalue=0.28081987052362267)\n",
      "\t n1 = 23, n2 = 7\n",
      "genesisA KD_1\n",
      "\t Ttest_indResult(statistic=1.4491935320058629, pvalue=0.15453777289874368)\n",
      "\t n1 = 23, n2 = 22\n",
      "genesisA KD_2\n",
      "\t Ttest_indResult(statistic=1.2493716654942724, pvalue=0.22151942441340161)\n",
      "\t n1 = 23, n2 = 8\n",
      "genesisA Klaeber_part1\n",
      "\t Ttest_indResult(statistic=-1.5807573378072379, pvalue=0.12109632013869043)\n",
      "\t n1 = 23, n2 = 23\n",
      "genesisA Klaeber_part2\n",
      "\t Ttest_indResult(statistic=-0.45460919555437052, pvalue=0.65277520245547838)\n",
      "\t n1 = 23, n2 = 8\n",
      "genesisA ILIAD\n",
      "\t Ttest_indResult(statistic=18.74178077671916, pvalue=5.3386727807515546e-42)\n",
      "\t n1 = 23, n2 = 137\n",
      "genesisA ODYSSEY\n",
      "\t Ttest_indResult(statistic=17.179746962651212, pvalue=1.5617331426685714e-37)\n",
      "\t n1 = 23, n2 = 132\n",
      "genesisA andreas\n",
      "\t Ttest_indResult(statistic=2.6317448638037617, pvalue=0.012207795185647786)\n",
      "\t n1 = 23, n2 = 17\n",
      "genesisB ChristI\n",
      "\t Ttest_indResult(statistic=1.9370012303149011, pvalue=0.088756302105934867)\n",
      "\t n1 = 6, n2 = 4\n",
      "genesisB ChristII\n",
      "\t Ttest_indResult(statistic=1.3042039883741203, pvalue=0.22843460436256516)\n",
      "\t n1 = 6, n2 = 4\n",
      "genesisB ChristIII\n",
      "\t Ttest_indResult(statistic=4.0134948992295518, pvalue=0.0020386350503082979)\n",
      "\t n1 = 6, n2 = 7\n",
      "genesisB Elene\n",
      "\t Ttest_indResult(statistic=0.30289770112123965, pvalue=0.76564293151227947)\n",
      "\t n1 = 6, n2 = 13\n",
      "genesisB Juliana\n",
      "\t Ttest_indResult(statistic=0.52220143314019263, pvalue=0.61188440120105969)\n",
      "\t n1 = 6, n2 = 7\n",
      "genesisB KD_1\n",
      "\t Ttest_indResult(statistic=3.750666605631499, pvalue=0.00089270552334957129)\n",
      "\t n1 = 6, n2 = 22\n",
      "genesisB KD_2\n",
      "\t Ttest_indResult(statistic=3.0797543128752785, pvalue=0.0095422695774388579)\n",
      "\t n1 = 6, n2 = 8\n",
      "genesisB Klaeber_part1\n",
      "\t Ttest_indResult(statistic=1.1780677520906666, pvalue=0.24904895880297778)\n",
      "\t n1 = 6, n2 = 23\n",
      "genesisB Klaeber_part2\n",
      "\t Ttest_indResult(statistic=1.3139056448847051, pvalue=0.21344189547971354)\n",
      "\t n1 = 6, n2 = 8\n",
      "genesisB ILIAD\n",
      "\t Ttest_indResult(statistic=12.725503266998787, pvalue=3.5028443125827936e-25)\n",
      "\t n1 = 6, n2 = 137\n",
      "genesisB ODYSSEY\n",
      "\t Ttest_indResult(statistic=12.875267465672932, pvalue=2.6566743505603864e-25)\n",
      "\t n1 = 6, n2 = 132\n",
      "genesisB andreas\n",
      "\t Ttest_indResult(statistic=3.8940410880947747, pvalue=0.00083674090265105784)\n",
      "\t n1 = 6, n2 = 17\n",
      "ChristI ChristII\n",
      "\t Ttest_indResult(statistic=-0.93928524982746309, pvalue=0.38384058424436479)\n",
      "\t n1 = 4, n2 = 4\n",
      "ChristI ChristIII\n",
      "\t Ttest_indResult(statistic=1.0253979926790646, pvalue=0.33194501202167354)\n",
      "\t n1 = 4, n2 = 7\n",
      "ChristI Elene\n",
      "\t Ttest_indResult(statistic=-1.6385552838688657, pvalue=0.12210612087388967)\n",
      "\t n1 = 4, n2 = 13\n",
      "ChristI Juliana\n",
      "\t Ttest_indResult(statistic=-1.2100887708631267, pvalue=0.25706700165584578)\n",
      "\t n1 = 4, n2 = 7\n",
      "ChristI KD_1\n",
      "\t Ttest_indResult(statistic=-0.67295007397363193, pvalue=0.50740765667438392)\n",
      "\t n1 = 4, n2 = 22\n",
      "ChristI KD_2\n",
      "\t Ttest_indResult(statistic=-0.30143937223507594, pvalue=0.76925457637700434)\n",
      "\t n1 = 4, n2 = 8\n",
      "ChristI Klaeber_part1\n",
      "\t Ttest_indResult(statistic=-2.037316522619725, pvalue=0.052335672023204188)\n",
      "\t n1 = 4, n2 = 23\n",
      "ChristI Klaeber_part2\n",
      "\t Ttest_indResult(statistic=-1.0760184090231297, pvalue=0.30719350689527858)\n",
      "\t n1 = 4, n2 = 8\n",
      "ChristI ILIAD\n",
      "\t Ttest_indResult(statistic=6.6462920974841841, pvalue=6.3083775548430338e-10)\n",
      "\t n1 = 4, n2 = 137\n",
      "ChristI ODYSSEY\n",
      "\t Ttest_indResult(statistic=5.79622992147186, pvalue=4.6232259677330253e-08)\n",
      "\t n1 = 4, n2 = 132\n",
      "ChristI andreas\n",
      "\t Ttest_indResult(statistic=0.16699293988682326, pvalue=0.86914005156970353)\n",
      "\t n1 = 4, n2 = 17\n",
      "ChristII ChristIII\n",
      "\t Ttest_indResult(statistic=2.5095855562041902, pvalue=0.033333415438722848)\n",
      "\t n1 = 4, n2 = 7\n",
      "ChristII Elene\n",
      "\t Ttest_indResult(statistic=-0.64432733713281787, pvalue=0.52909387972332877)\n",
      "\t n1 = 4, n2 = 13\n",
      "ChristII Juliana\n",
      "\t Ttest_indResult(statistic=-0.37225618505852931, pvalue=0.71831655272723904)\n",
      "\t n1 = 4, n2 = 7\n",
      "ChristII KD_1\n",
      "\t Ttest_indResult(statistic=1.5023725125083252, pvalue=0.14604552775469598)\n",
      "\t n1 = 4, n2 = 22\n",
      "ChristII KD_2\n",
      "\t Ttest_indResult(statistic=1.3936095836365363, pvalue=0.19363127825685492)\n",
      "\t n1 = 4, n2 = 8\n",
      "ChristII Klaeber_part1\n",
      "\t Ttest_indResult(statistic=-0.45216139157807206, pvalue=0.65505169294405663)\n",
      "\t n1 = 4, n2 = 23\n",
      "ChristII Klaeber_part2\n",
      "\t Ttest_indResult(statistic=0.050086034203430052, pvalue=0.96104012593189647)\n",
      "\t n1 = 4, n2 = 8\n",
      "ChristII ILIAD\n",
      "\t Ttest_indResult(statistic=8.951309150585459, pvalue=2.0148008283529316e-15)\n",
      "\t n1 = 4, n2 = 137\n",
      "ChristII ODYSSEY\n",
      "\t Ttest_indResult(statistic=8.7318670065222328, pvalue=8.8251326649752962e-15)\n",
      "\t n1 = 4, n2 = 132\n",
      "ChristII andreas\n",
      "\t Ttest_indResult(statistic=1.9953840338647812, pvalue=0.060542158613455355)\n",
      "\t n1 = 4, n2 = 17\n",
      "ChristIII Elene\n",
      "\t Ttest_indResult(statistic=-3.634384002137296, pvalue=0.0018964175642593878)\n",
      "\t n1 = 7, n2 = 13\n",
      "ChristIII Juliana\n",
      "\t Ttest_indResult(statistic=-2.8507258028747584, pvalue=0.014603088092254999)\n",
      "\t n1 = 7, n2 = 7\n",
      "ChristIII KD_1\n",
      "\t Ttest_indResult(statistic=-3.3779571089850662, pvalue=0.0022323114041003862)\n",
      "\t n1 = 7, n2 = 22\n",
      "ChristIII KD_2\n",
      "\t Ttest_indResult(statistic=-2.0859457646843493, pvalue=0.057253304988473752)\n",
      "\t n1 = 7, n2 = 8\n",
      "ChristIII Klaeber_part1\n",
      "\t Ttest_indResult(statistic=-4.9068932175672417, pvalue=3.5774228485155675e-05)\n",
      "\t n1 = 7, n2 = 23\n",
      "ChristIII Klaeber_part2\n",
      "\t Ttest_indResult(statistic=-2.9262562249355852, pvalue=0.011797900494494659)\n",
      "\t n1 = 7, n2 = 8\n",
      "ChristIII ILIAD\n",
      "\t Ttest_indResult(statistic=5.9338200567846888, pvalue=2.1591913249712405e-08)\n",
      "\t n1 = 7, n2 = 137\n",
      "ChristIII ODYSSEY\n",
      "\t Ttest_indResult(statistic=4.1558392212672279, pvalue=5.6774224208545503e-05)\n",
      "\t n1 = 7, n2 = 132\n",
      "ChristIII andreas\n",
      "\t Ttest_indResult(statistic=-1.8076301523575116, pvalue=0.084358141172792972)\n",
      "\t n1 = 7, n2 = 17\n",
      "Elene Juliana\n",
      "\t Ttest_indResult(statistic=0.25613612365185817, pvalue=0.80075010968688565)\n",
      "\t n1 = 13, n2 = 7\n",
      "Elene KD_1\n",
      "\t Ttest_indResult(statistic=2.9877913111872672, pvalue=0.0052697676052882404)\n",
      "\t n1 = 13, n2 = 22\n",
      "Elene KD_2\n",
      "\t Ttest_indResult(statistic=2.1333848267147699, pvalue=0.046149819587580569)\n",
      "\t n1 = 13, n2 = 8\n",
      "Elene Klaeber_part1\n",
      "\t Ttest_indResult(statistic=0.72158252598793526, pvalue=0.47548453106559629)\n",
      "\t n1 = 13, n2 = 23\n",
      "Elene Klaeber_part2\n",
      "\t Ttest_indResult(statistic=0.87525432096564937, pvalue=0.39236601540384886)\n",
      "\t n1 = 13, n2 = 8\n",
      "Elene ILIAD\n",
      "\t Ttest_indResult(statistic=16.478607903051213, pvalue=2.647104619414014e-35)\n",
      "\t n1 = 13, n2 = 137\n",
      "Elene ODYSSEY\n",
      "\t Ttest_indResult(statistic=15.736189198200558, pvalue=5.2030166352420771e-33)\n",
      "\t n1 = 13, n2 = 132\n",
      "Elene andreas\n",
      "\t Ttest_indResult(statistic=3.4963307853186456, pvalue=0.0015915845258577706)\n",
      "\t n1 = 13, n2 = 17\n",
      "Juliana KD_1\n",
      "\t Ttest_indResult(statistic=2.1651019807818637, pvalue=0.039384065102968178)\n",
      "\t n1 = 7, n2 = 22\n",
      "Juliana KD_2\n",
      "\t Ttest_indResult(statistic=1.6171333295634978, pvalue=0.12984556530345659)\n",
      "\t n1 = 7, n2 = 8\n",
      "Juliana Klaeber_part1\n",
      "\t Ttest_indResult(statistic=0.21980824679907457, pvalue=0.82761516677242164)\n",
      "\t n1 = 7, n2 = 23\n",
      "Juliana Klaeber_part2\n",
      "\t Ttest_indResult(statistic=0.49980724674191129, pvalue=0.62556338322884442)\n",
      "\t n1 = 7, n2 = 8\n",
      "Juliana ILIAD\n",
      "\t Ttest_indResult(statistic=12.095275993859225, pvalue=1.359617798216067e-23)\n",
      "\t n1 = 7, n2 = 137\n",
      "Juliana ODYSSEY\n",
      "\t Ttest_indResult(statistic=11.67008302591948, pvalue=2.8072843584336682e-22)\n",
      "\t n1 = 7, n2 = 132\n",
      "Juliana andreas\n",
      "\t Ttest_indResult(statistic=2.616264722141457, pvalue=0.015766494902925348)\n",
      "\t n1 = 7, n2 = 17\n",
      "KD_1 KD_2\n",
      "\t Ttest_indResult(statistic=0.36883241912766895, pvalue=0.71502696927625564)\n",
      "\t n1 = 22, n2 = 8\n",
      "KD_1 Klaeber_part1\n",
      "\t Ttest_indResult(statistic=-3.3626223204152255, pvalue=0.0016305882736247894)\n",
      "\t n1 = 22, n2 = 23\n",
      "KD_1 Klaeber_part2\n",
      "\t Ttest_indResult(statistic=-1.6280695382904373, pvalue=0.11471207932652139)\n",
      "\t n1 = 22, n2 = 8\n",
      "KD_1 ILIAD\n",
      "\t Ttest_indResult(statistic=17.246118649851685, pvalue=4.5811868829557085e-38)\n",
      "\t n1 = 22, n2 = 137\n",
      "KD_1 ODYSSEY\n",
      "\t Ttest_indResult(statistic=15.619879299969927, pvalue=2.0446390064615956e-33)\n",
      "\t n1 = 22, n2 = 132\n",
      "KD_1 andreas\n",
      "\t Ttest_indResult(statistic=1.6610929835840686, pvalue=0.10514670110015793)\n",
      "\t n1 = 22, n2 = 17\n",
      "KD_2 Klaeber_part1\n",
      "\t Ttest_indResult(statistic=-2.575049784133165, pvalue=0.015389698659260048)\n",
      "\t n1 = 8, n2 = 23\n",
      "KD_2 Klaeber_part2\n",
      "\t Ttest_indResult(statistic=-1.3638718875036844, pvalue=0.19413547484061233)\n",
      "\t n1 = 8, n2 = 8\n",
      "KD_2 ILIAD\n",
      "\t Ttest_indResult(statistic=10.302325466062273, pvalue=5.8335134092837245e-19)\n",
      "\t n1 = 8, n2 = 137\n",
      "KD_2 ODYSSEY\n",
      "\t Ttest_indResult(statistic=9.3527147251718894, pvalue=2.0900449577143649e-16)\n",
      "\t n1 = 8, n2 = 132\n",
      "KD_2 andreas\n",
      "\t Ttest_indResult(statistic=0.83394486936622947, pvalue=0.41288978454845826)\n",
      "\t n1 = 8, n2 = 17\n",
      "Klaeber_part1 Klaeber_part2\n",
      "\t Ttest_indResult(statistic=0.63194015753759825, pvalue=0.53237662797566054)\n",
      "\t n1 = 23, n2 = 8\n",
      "Klaeber_part1 ILIAD\n",
      "\t Ttest_indResult(statistic=21.207082026165693, pvalue=4.4313640148222981e-48)\n",
      "\t n1 = 23, n2 = 137\n",
      "Klaeber_part1 ODYSSEY\n",
      "\t Ttest_indResult(statistic=20.220717544576658, pvalue=4.5517707954525592e-45)\n",
      "\t n1 = 23, n2 = 132\n",
      "Klaeber_part1 andreas\n",
      "\t Ttest_indResult(statistic=4.2792271041564369, pvalue=0.00012228262483908074)\n",
      "\t n1 = 23, n2 = 17\n",
      "Klaeber_part2 ILIAD\n",
      "\t Ttest_indResult(statistic=12.188399185646428, pvalue=7.0126290787572244e-24)\n",
      "\t n1 = 8, n2 = 137\n",
      "Klaeber_part2 ODYSSEY\n",
      "\t Ttest_indResult(statistic=11.645686090569631, pvalue=2.9427866846266167e-22)\n",
      "\t n1 = 8, n2 = 132\n",
      "Klaeber_part2 andreas\n",
      "\t Ttest_indResult(statistic=2.3217767139982031, pvalue=0.029456441428521704)\n",
      "\t n1 = 8, n2 = 17\n",
      "ILIAD ODYSSEY\n",
      "\t Ttest_indResult(statistic=-9.2093053105539795, pvalue=9.9806595343940459e-18)\n",
      "\t n1 = 137, n2 = 132\n",
      "ILIAD andreas\n",
      "\t Ttest_indResult(statistic=-12.980170218748423, pvalue=2.1191631733326384e-26)\n",
      "\t n1 = 137, n2 = 17\n",
      "ODYSSEY andreas\n",
      "\t Ttest_indResult(statistic=-11.033139220486529, pvalue=5.3594164986053246e-21)\n",
      "\t n1 = 132, n2 = 17\n"
     ]
    }
   ],
   "source": [
    "for text in list(itertools.combinations(text_names_sorted, 2)):\n",
    "    t = stats.ttest_ind(ratios_samples[text[0]],ratios_samples[text[1]])\n",
    "    print(text[0], text[1])\n",
    "    print('\\t', t)\n",
    "    print('\\t n1 = {0}, n2 = {1}'.format(len(ratios_samples[text[0]]), len(ratios_samples[text[1]]) ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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