From 5db9b7fde1ea761e6d9bc61560fd7f8e72d38d64 Mon Sep 17 00:00:00 2001 From: Seth Johnson Date: Thu, 8 Sep 2022 02:21:07 +0000 Subject: [PATCH 1/2] Adding sjohn248.ipynb file --- sjohn248.ipynb | 382 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 382 insertions(+) create mode 100644 sjohn248.ipynb diff --git a/sjohn248.ipynb b/sjohn248.ipynb new file mode 100644 index 0000000..59b489b --- /dev/null +++ b/sjohn248.ipynb @@ -0,0 +1,382 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Written text as operational data\n", + "\n", + "Written text is one type of data\n", + "\n", + "### Why people write?\n", + "\n", + " - To communicate: their thoughts, feelings, urgency, needs, information\n", + "\n", + "### Why people communicate?\n", + "\n", + "1. To express emotions\n", + "1. To share information\n", + "1. To enable or elicit an action\n", + "1. ...\n", + "\n", + "### We will use written text for the purpose other than \n", + "1. To experience emotion\n", + "1. To learn something the author intended us to learn\n", + "1. To do what the author intended us to do\n", + "\n", + "### Instead, we will use written text to recognize who wrote it\n", + " - By calculating and comparing word frequencies in written documents\n", + " \n", + "See, for example, likely fictional story https://medium.com/@amuse/how-the-nsa-caught-satoshi-nakamoto-868affcef595" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 1. Dictionaries in python (associative arrays)\n", + "\n", + "Plot the frequency distribution of words on a web page." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "class=\"menu-item\t54\n", + "\t38\n", + "\t35\n", + "
  • \t28\n", + "\t21\n", + "\t21\n" + ] + } + ], + "source": [ + "import requests, re\n", + "# re is a module for regular expressions: to detect various combinations of characters\n", + "import operator\n", + "\n", + "# Start from a simple document\n", + "r = requests .get('http://eecs.utk.edu')\n", + "\n", + "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", + "t = r.text\n", + "\n", + "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", + "wds = re.split('\\s+',t)\n", + "\n", + "# now populate a dictionary (wf)\n", + "wf = {}\n", + "for w in wds:\n", + " if w in wf: wf [w] = wf [w] + 1\n", + " else: wf[w] = 1\n", + "\n", + "# dictionaries can not be sorted, so lets get a sorted *list* \n", + "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "\n", + "# lets just have no more than 15 words \n", + "ml = min(len(wfs),15)\n", + "for i in range(1,ml,1):\n", + " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Example 2\n", + "\n", + "Lots of markup in the output, lets remove it --- \n", + "\n", + "use BeautifulSoup and nltk modules and practice some regular expressions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests, re, nltk\n", + "from bs4 import BeautifulSoup\n", + "from nltk import clean_html\n", + "from collections import Counter\n", + "import operator\n", + "\n", + "# we may not care about the usage of stop words\n", + "stop_words = nltk.corpus.stopwords.words('english') + [\n", + " 'ut', '\\'re','.', ',', '--', '\\'s', '?', ')', '(', ':', '\\'',\n", + " '\\\"', '-', '}', '{', '&', '|', u'\\u2014' ]\n", + "\n", + "# We most likely would like to remove html markup\n", + "def cleanHtml (html):\n", + " from bs4 import BeautifulSoup\n", + " soup = BeautifulSoup(html, 'html.parser')\n", + " return soup .get_text()\n", + "\n", + "# We also want to remove special characters, quotes, etc. from each word\n", + "def cleanWord (w):\n", + " # r in r'[.,\"\\']' tells to treat \\ as a regular character \n", + " # but we need to escape ' with \\'\n", + " # any character between the brackets [] is to be removed \n", + " wn = re.sub('[,\"\\.\\'&\\|:@>*;/=]', \"\", w)\n", + " # get rid of numbers\n", + " return re.sub('^[0-9\\.]*$', \"\", wn)\n", + " \n", + "# define a function to get text/clean/calculate frequency\n", + "def get_wf (URL):\n", + " # first get the web page\n", + " r = requests .get(URL)\n", + " \n", + " # Now clean\n", + " # remove html markup\n", + " t = cleanHtml (r .text) .lower()\n", + " \n", + " # split string into an array of words using any sequence of spaces \"\\s+\" \n", + " wds = re .split('\\s+',t)\n", + " \n", + " # remove periods, commas, etc stuck to the edges of words\n", + " for i in range(len(wds)):\n", + " wds [i] = cleanWord (wds [i])\n", + " \n", + " # If satisfied with results, lets go to the next step: calculate frequencies\n", + " # We can write a loop to create a dictionary, but \n", + " # there is a special function for everything in python\n", + " # in particular for counting frequencies (like function table() in R)\n", + " wf = Counter (wds)\n", + " \n", + " # Remove stop words from the dictionary wf\n", + " for k in stop_words:\n", + " wf. pop(k, None)\n", + " \n", + " #how many regular words in the document?\n", + " tw = 0\n", + " for w in wf:\n", + " tw += wf[w] \n", + " \n", + " \n", + " # Get ordered list\n", + " wfs = sorted (wf .items(), key = operator.itemgetter(1), reverse=True)\n", + " ml = min(len(wfs),15)\n", + "\n", + " #Reverse the list because barh plots items from the bottom\n", + " return (wfs [ 0:ml ] [::-1], tw)\n", + " \n", + "# Now populate two lists \n", + "(wf_ee, tw_ee) = get_wf('http://www.gutenberg.org/ebooks/1342.txt.utf-8')\n", + "(wf_bu, tw_bu) = get_wf('http://www.gutenberg.org/ebooks/76.txt.utf-8')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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AOHwz8mL809Q/Bz6PY9nT8X0hFwDrwl6XOOdKgEuAjvgnsR8N1rUjSj5j8A8C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\nCF5HRCRWvP322zRq1IiDBw8CsGHDBsyMm2++uSTNPffcw0UXXQTAmjV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#Plot the results: are there striking differences in language?\n", + "import numpy as np\n", + "import pylab\n", + "import matplotlib.pyplot as plt\n", + "\n", + "%matplotlib inline\n", + "def plotTwoLists (wf_ee, wf_bu, title):\n", + " f = plt.figure (figsize=(10, 6))\n", + " # this is painfully tedious....\n", + " f .suptitle (title, fontsize=20)\n", + " ax = f.add_subplot(111)\n", + " ax .spines ['top'] .set_color ('none')\n", + " ax .spines ['bottom'] .set_color ('none')\n", + " ax .spines ['left'] .set_color ('none')\n", + " ax .spines ['right'] .set_color ('none')\n", + " ax .tick_params (labelcolor='w', top='off', bottom='off', left='off', right='off', labelsize=20)\n", + "\n", + " # Create two subplots, this is the first one\n", + " ax1 = f .add_subplot (121)\n", + " plt .subplots_adjust (wspace=.5)\n", + "\n", + " pos = np .arange (len(wf_ee)+1) \n", + " ax1 .tick_params (axis='both', which='major', labelsize=14)\n", + " pylab .yticks (pos, [ x [0] for x in wf_ee ])\n", + " ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n", + "\n", + " ax2 = f .add_subplot (122)\n", + " ax2 .tick_params (axis='both', which='major', labelsize=14)\n", + " pos = np .arange (len(wf_bu)+1) \n", + " pylab .yticks (pos, [ x [0] for x in wf_bu ])\n", + " ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')\n", + "\n", + "plotTwoLists (wf_ee, wf_bu, 'Difference between Pride and Prejudice and Huck Finn')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "and\t2836\n", + "of\t2676\n", + "to\t2646\n", + "a\t2217\n", + "in\t1422\n", + "his\t1205\n", + "he\t928\n", + "that\t920\n", + "was\t823\n", + "for\t798\n", + "with\t797\n", + "as\t672\n", + "I\t505\n", + "you\t497\n" + ] + } + ], + "source": [ + "#In case Project gutenberg is blocked you can download text to your laptop and copy to the docker container via scp\n", + "#Assuming the file name you copy is pg4680.txt here is how you change the script\n", + "# Please note the option errors='replace'\n", + "# without it python invariably runs into unicode errors\n", + "f = open ('pg4680.txt', 'r', encoding=\"ascii\", errors='replace')\n", + " \n", + "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", + "t = f.read()\n", + "\n", + "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", + "wds = re.split('\\s+',t)\n", + "\n", + "# now populate a dictionary (wf)\n", + "wf = {}\n", + "for w in wds:\n", + " if w in wf: wf [w] = wf [w] + 1\n", + " else: wf [w] = 1\n", + "\n", + "# dictionaries can not be sorted, so lets get a sorted *list* \n", + "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "\n", + "# lets just have no more than 15 words \n", + "ml = min(len(wfs),15)\n", + "for i in range(1,ml,1):\n", + " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Assignment 1\n", + "\n", + "1. Compare word frequencies between two works of a single author.\n", + "1. Compare word frequencies between works of two authors.\n", + "1. Are there some words preferred by one author but used less frequently by another author?\n", + "\n", + "Extra credit\n", + "\n", + "1. The frequency of a specific word, e.g., \"would\" should follow a binomial distribution (each regular word in a document is a trial and with probability p that word is \"would\". The estimate for p is N(\"would\")/N(regular word)). Do these binomial distributions for your chosen word differ significantly between books of the same author or between authors? \n", + "\n", + "Project Gutenberg is a good source of for fiction and non-fiction.\n", + "\n", + "E.g below are two most popular books from Project Gutenberg:\n", + "- Pride and Prejudice at http://www.gutenberg.org/ebooks/1342.txt.utf-8\n", + "- Adventures of Huckleberry Finn at http://www.gutenberg.org/ebooks/76.txt.utf-8" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import requests, re, nltk\n", + "#In case your text is not on Project Gutenberg but at some other URL\n", + "#http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter2.html\n", + "# that contains 12 parts\n", + "t = \"\"\n", + "for i in range(2,13):\n", + " r = requests .get('http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter' + str(i) + '.html')\n", + " t = t + r.text" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1323653" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(t)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.5.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} From 442c2b6f705c012fe61dac495c81ef1feca766ed Mon Sep 17 00:00:00 2001 From: Seth Johnson Date: Thu, 15 Sep 2022 19:54:37 +0000 Subject: [PATCH 2/2] Project Finished --- sjohn248.ipynb | 389 ++++++++++++++----------------------------------- 1 file changed, 106 insertions(+), 283 deletions(-) diff --git a/sjohn248.ipynb b/sjohn248.ipynb index 59b489b..708eed3 100644 --- a/sjohn248.ipynb +++ b/sjohn248.ipynb @@ -4,363 +4,186 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Written text as operational data\n", - "\n", - "Written text is one type of data\n", - "\n", - "### Why people write?\n", - "\n", - " - To communicate: their thoughts, feelings, urgency, needs, information\n", + "# Assignment 1\n", "\n", - "### Why people communicate?\n", + "1. Compare word frequencies between two works of a single author.\n", + "1. Compare word frequencies between works of two authors.\n", + "1. Are there some words preferred by one author but used less frequently by another author?\n", "\n", - "1. To express emotions\n", - "1. To share information\n", - "1. To enable or elicit an action\n", - "1. ...\n", + "Extra credit\n", "\n", - "### We will use written text for the purpose other than \n", - "1. To experience emotion\n", - "1. To learn something the author intended us to learn\n", - "1. To do what the author intended us to do\n", + "1. The frequency of a specific word, e.g., \"would\" should follow a binomial distribution (each regular word in a document is a trial and with probability p that word is \"would\". The estimate for p is N(\"would\")/N(regular word)). Do these binomial distributions for your chosen word differ significantly between books of the same author or between authors? \n", "\n", - "### Instead, we will use written text to recognize who wrote it\n", - " - By calculating and comparing word frequencies in written documents\n", - " \n", - "See, for example, likely fictional story https://medium.com/@amuse/how-the-nsa-caught-satoshi-nakamoto-868affcef595" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example 1. Dictionaries in python (associative arrays)\n", + "Project Gutenberg is a good source of for fiction and non-fiction.\n", "\n", - "Plot the frequency distribution of words on a web page." + "E.g below are two most popular books from Project Gutenberg:\n", + "- Pride and Prejudice at http://www.gutenberg.org/ebooks/1342.txt.utf-8\n", + "- Adventures of Huckleberry Finn at http://www.gutenberg.org/ebooks/76.txt.utf-8" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "class=\"menu-item\t54\n", - "\t38\n", - "\t35\n", - "
  • \t28\n", - "\t21\n", - "\t21\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], - "source": [ - "import requests, re\n", - "# re is a module for regular expressions: to detect various combinations of characters\n", - "import operator\n", - "\n", - "# Start from a simple document\n", - "r = requests .get('http://eecs.utk.edu')\n", - "\n", - "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", - "t = r.text\n", - "\n", - "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", - "wds = re.split('\\s+',t)\n", - "\n", - "# now populate a dictionary (wf)\n", - "wf = {}\n", - "for w in wds:\n", - " if w in wf: wf [w] = wf [w] + 1\n", - " else: wf[w] = 1\n", - "\n", - "# dictionaries can not be sorted, so lets get a sorted *list* \n", - "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", - "\n", - "# lets just have no more than 15 words \n", - "ml = min(len(wfs),15)\n", - "for i in range(1,ml,1):\n", - " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Example 2\n", - "\n", - "Lots of markup in the output, lets remove it --- \n", - "\n", - "use BeautifulSoup and nltk modules and practice some regular expressions." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [], "source": [ "import requests, re, nltk\n", "from bs4 import BeautifulSoup\n", "from nltk import clean_html\n", "from collections import Counter\n", "import operator\n", + "import numpy as np\n", + "import pylab\n", + "import matplotlib.pyplot as plt\n", "\n", - "# we may not care about the usage of stop words\n", "stop_words = nltk.corpus.stopwords.words('english') + [\n", - " 'ut', '\\'re','.', ',', '--', '\\'s', '?', ')', '(', ':', '\\'',\n", - " '\\\"', '-', '}', '{', '&', '|', u'\\u2014' ]\n", + " '\\'re','.', ',', '--', '\\'s', '?', ')', '(', ':', '\\'',\n", + " '\\\"', '-', '}', '{', '&', '|', u'\\u2014', 'We', 'I', 'the', 'The' , 'would','could','said', 'chapter']\n", "\n", - "# We most likely would like to remove html markup\n", - "def cleanHtml (html):\n", - " from bs4 import BeautifulSoup\n", - " soup = BeautifulSoup(html, 'html.parser')\n", - " return soup .get_text()\n", "\n", - "# We also want to remove special characters, quotes, etc. from each word\n", "def cleanWord (w):\n", - " # r in r'[.,\"\\']' tells to treat \\ as a regular character \n", - " # but we need to escape ' with \\'\n", - " # any character between the brackets [] is to be removed \n", " wn = re.sub('[,\"\\.\\'&\\|:@>*;/=]', \"\", w)\n", - " # get rid of numbers\n", " return re.sub('^[0-9\\.]*$', \"\", wn)\n", + "\n", + "def cleanHTML(html):\n", + " from bs4 import BeautifulSoup\n", + " soup = BeautifulSoup(html, 'html.parser')\n", + " return soup .get_text()\n", + "\n", + "def getFreq(URL):\n", + " r = requests.get(URL)\n", + " t = cleanHTML(r.text).lower()\n", " \n", - "# define a function to get text/clean/calculate frequency\n", - "def get_wf (URL):\n", - " # first get the web page\n", - " r = requests .get(URL)\n", - " \n", - " # Now clean\n", - " # remove html markup\n", - " t = cleanHtml (r .text) .lower()\n", - " \n", - " # split string into an array of words using any sequence of spaces \"\\s+\" \n", - " wds = re .split('\\s+',t)\n", + " words = re .split('\\s+',t)\n", + " for i in range(len(words)):\n", + " words[i] = cleanWord(words[i])\n", " \n", - " # remove periods, commas, etc stuck to the edges of words\n", - " for i in range(len(wds)):\n", - " wds [i] = cleanWord (wds [i])\n", " \n", - " # If satisfied with results, lets go to the next step: calculate frequencies\n", - " # We can write a loop to create a dictionary, but \n", - " # there is a special function for everything in python\n", - " # in particular for counting frequencies (like function table() in R)\n", - " wf = Counter (wds)\n", + " wf = Counter (words)\n", " \n", - " # Remove stop words from the dictionary wf\n", " for k in stop_words:\n", - " wf. pop(k, None)\n", - " \n", - " #how many regular words in the document?\n", + " wf.pop(k, None)\n", + " \n", " tw = 0\n", " for w in wf:\n", " tw += wf[w] \n", - " \n", " \n", - " # Get ordered list\n", " wfs = sorted (wf .items(), key = operator.itemgetter(1), reverse=True)\n", " ml = min(len(wfs),15)\n", "\n", - " #Reverse the list because barh plots items from the bottom\n", " return (wfs [ 0:ml ] [::-1], tw)\n", - " \n", - "# Now populate two lists \n", - "(wf_ee, tw_ee) = get_wf('http://www.gutenberg.org/ebooks/1342.txt.utf-8')\n", - "(wf_bu, tw_bu) = get_wf('http://www.gutenberg.org/ebooks/76.txt.utf-8')" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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AOHwz8mL809Q/Bz6PY9nT8X0hFwDrwl6XOOdKgEuAjvgnsR8N1rUjSj5j8A8C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\nCF5HRCRWvP322zRq1IiDBw8CsGHDBsyMm2++uSTNPffcw0UXXQTAmjV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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#Plot the results: are there striking differences in language?\n", - "import numpy as np\n", - "import pylab\n", - "import matplotlib.pyplot as plt\n", - "\n", - "%matplotlib inline\n", - "def plotTwoLists (wf_ee, wf_bu, title):\n", - " f = plt.figure (figsize=(10, 6))\n", - " # this is painfully tedious....\n", - " f .suptitle (title, fontsize=20)\n", - " ax = f.add_subplot(111)\n", - " ax .spines ['top'] .set_color ('none')\n", - " ax .spines ['bottom'] .set_color ('none')\n", - " ax .spines ['left'] .set_color ('none')\n", - " ax .spines ['right'] .set_color ('none')\n", - " ax .tick_params (labelcolor='w', top='off', bottom='off', left='off', right='off', labelsize=20)\n", - "\n", - " # Create two subplots, this is the first one\n", - " ax1 = f .add_subplot (121)\n", - " plt .subplots_adjust (wspace=.5)\n", - "\n", - " pos = np .arange (len(wf_ee)+1) \n", - " ax1 .tick_params (axis='both', which='major', labelsize=14)\n", - " pylab .yticks (pos, [ x [0] for x in wf_ee ])\n", - " ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n", - "\n", - " ax2 = f .add_subplot (122)\n", - " ax2 .tick_params (axis='both', which='major', labelsize=14)\n", - " pos = np .arange (len(wf_bu)+1) \n", - " pylab .yticks (pos, [ x [0] for x in wf_bu ])\n", - " ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')\n", "\n", - "plotTwoLists (wf_ee, wf_bu, 'Difference between Pride and Prejudice and Huck Finn')" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "and\t2836\n", - "of\t2676\n", - "to\t2646\n", - "a\t2217\n", - "in\t1422\n", - "his\t1205\n", - "he\t928\n", - "that\t920\n", - "was\t823\n", - "for\t798\n", - "with\t797\n", - "as\t672\n", - "I\t505\n", - "you\t497\n" - ] - } - ], - "source": [ - "#In case Project gutenberg is blocked you can download text to your laptop and copy to the docker container via scp\n", - "#Assuming the file name you copy is pg4680.txt here is how you change the script\n", - "# Please note the option errors='replace'\n", - "# without it python invariably runs into unicode errors\n", - "f = open ('pg4680.txt', 'r', encoding=\"ascii\", errors='replace')\n", - " \n", - "# What comes back includes headers and other HTTP stuff, get just the body of the response\n", - "t = f.read()\n", - "\n", - "# obtain words by splitting a string using as separator one or more (+) space/like characters (\\s) \n", - "wds = re.split('\\s+',t)\n", + "(wf_ee, tw_ee) = getFreq('http://www.gutenberg.org/ebooks/2600.txt.utf-8')\n", + "(wf_bu, tw_bu) = getFreq('http://www.gutenberg.org/ebooks/3567.txt.utf-8')\n", "\n", - "# now populate a dictionary (wf)\n", - "wf = {}\n", - "for w in wds:\n", - " if w in wf: wf [w] = wf [w] + 1\n", - " else: wf [w] = 1\n", "\n", - "# dictionaries can not be sorted, so lets get a sorted *list* \n", - "wfs = sorted (wf .items(), key = operator .itemgetter (1), reverse=True) \n", - "\n", - "# lets just have no more than 15 words \n", - "ml = min(len(wfs),15)\n", - "for i in range(1,ml,1):\n", - " print (wfs[i][0]+\"\\t\"+str(wfs[i][1])) " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Assignment 1\n", + "f = plt.figure (figsize=(10, 6))\n", + "f .suptitle (\"Difference between War and Peace and Memoirs of Napoleon Bonaparte\", fontsize=20)\n", "\n", - "1. Compare word frequencies between two works of a single author.\n", - "1. Compare word frequencies between works of two authors.\n", - "1. Are there some words preferred by one author but used less frequently by another author?\n", "\n", - "Extra credit\n", "\n", - "1. The frequency of a specific word, e.g., \"would\" should follow a binomial distribution (each regular word in a document is a trial and with probability p that word is \"would\". The estimate for p is N(\"would\")/N(regular word)). Do these binomial distributions for your chosen word differ significantly between books of the same author or between authors? \n", + "ax1 = f.add_subplot (121)\n", + "plt .subplots_adjust (wspace=.5)\n", "\n", - "Project Gutenberg is a good source of for fiction and non-fiction.\n", + "pos = np .arange (len(wf_ee)) \n", + "ax1 .tick_params (axis='both', which='major', labelsize=14)\n", + "pylab .yticks (pos, [ x [0] for x in wf_ee ])\n", + "ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n", "\n", - "E.g below are two most popular books from Project Gutenberg:\n", - "- Pride and Prejudice at http://www.gutenberg.org/ebooks/1342.txt.utf-8\n", - "- Adventures of Huckleberry Finn at http://www.gutenberg.org/ebooks/76.txt.utf-8" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import requests, re, nltk\n", - "#In case your text is not on Project Gutenberg but at some other URL\n", - "#http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter2.html\n", - "# that contains 12 parts\n", - "t = \"\"\n", - "for i in range(2,13):\n", - " r = requests .get('http://www.fullbooks.com/Our-World-or-The-Slaveholders-Daughter' + str(i) + '.html')\n", - " t = t + r.text" + "ax2 = f .add_subplot (122)\n", + "ax2 .tick_params (axis='both', which='major', labelsize=14)\n", + "pos = np .arange (len(wf_bu)) \n", + "pylab .yticks (pos, [ x [0] for x in wf_bu ])\n", + "ax2 .barh (range (len(wf_bu)), [ x [1] for x in wf_bu ], align='center')\n" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "1323653" + "" ] }, - "execution_count": 23, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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GtFr7rHKczWhlOXW1ueDQKFwtX0Pdx1An9v0q+bmez3hnYs4iesP7pnT/8nVJvTb7kXrZv1qo15n9tR/px8j+LLxbwluSrgZOiHQh85Jqb7trlb+X1OmzOm0fi0Wr5OeOHFudUXl/n2+lvDJ9lRplr1VPaOU7sCPaO77e/Z6KiHckXUIal7kf6cKkrUhJ5F+j6++O1JnvoY7s17VI+3VJE9oleQ/fFRFPSdqGdNZkL9LZKIBnJJ0dET/v5vZcSLrN6+HAnfkCsINJZy7/0pEVd2UP7U55eTMiYloXLreo8sZvFhGT6py31i+MyvKOqePNqtdr+bm1npKiSnvOiYjjumj9q0nqWyOpXbNqncW/+0fE6120/sov1N1Jp2X6sDBpvRn4EqnXonJR4S2FeSvt+UtE7EPHdMc21OtmckJLujBldRbdLkg9D5V/SFK540ExmT+OFEgXS9iU7nZxcGsrr/PXNKQEYh3gB1F1KxhJ25MSoyVVeV/e30r5mq1M76r11nMM1bvvX8vP9XzGuyLmlP59y3HpUUn7k34sfEXS3yLib1Xr7/D+iog3ycMZJH0I2Jl0ivTAvI6P5qqVHsHWvgdXaWs17TWjxrTKttwXEVvWKK/ltfw8kBRLulqlTa29jwOq6nWX9o6v6vVfRvqxcjjp4tfD8/RLur5pS/Q9tCbpepVqXblfu+w9jIipwH6SliF9L+9OGopwrqQ3IuKXbS5gCdoTEXdLug/4gtJ/hv0EKQ6f0crZg8V0yb++zbes+G5++buuWGYrKv8t7KNt1mrc8mq5hxQ4d1bVrXvaqNuV7VmGNLi/2rD8fF9hWmf2xwIAtf6f4v5NOt2+GylxexMYn8sqCdwngR2BSRHxcmHeh0kBfTtJy9IxPfGetqd4P9qPVU2ruIU03u/TpFNVT0bEk4Xy9fLzn2osvzOneNvSE+u6t7XlKd0qavMuWk+1zhxD9e6PyjG3Z2u376lRtyuOz17zvuXTzZUE/IxCPFmi/RURz0TEFaSLUR8Ddqrcfog0Zh/S7SYXIWllFg5h6xKRbin3ELCxpPe1Vz+rbH/N/z5YwzvU1ztaif875SSm2q75+d4aZV1pp1Y+O8Pyc/F7ioh4iXSXhW0l7UjqGHmSdDalq3UmhlTaO6y6QNJ6pLHLT7ZyVrBe/yHdBWOzfNamWt3vYUS8HRETI+IM0r6FdIeqikoHWa1jbUnacyHp2puDWHiB9eiOtnuJE1pJawBXkt64p1l4Ork7XE46sL6fu8ar29JH6cbDHRIRE0inFPeRdFitOpI2zdvYKfmDdyXpV8nZ1R9aSSvlLwYi/UOKK4Chkk6plSRKGixpnTqbcXrx/m05mH4vv7y8UO980gUL50haLJgr3d+x+oul8h991qq14twDcxvpy/fzwLiImJvLniSNeTyGNObm5qp53yZdVToA+Lmk6nGMlfsnbrSE29Cl8imvh0m9QYeRTgM/UFWt0mP7w/x8U1X5tPw8rDgxj236Kl2rtXVtAXy7i9ZxDSmB2F9V95Yk9ab1X2yOLtDJY2hafh5WVa/mvo+IiaRx6puTTnVXL3/VPCSrq2NOa+0s5fsWEXeTrmD/MOkLre79JWl1SZvWqLYi6ZTw26Q7BhARs0if0x2L73+Ouz9j8XHTXeFnpItmLqv1ZS/pvZKKvbe/InUIHClp5xr1P1g16b/USNBbExHPksYuDyLdOrO47G1JwzZepYOnfJfAENLFssX1/y/ph9RjpGOg2kX5+SrSe/uLDoxhr1snY8hl+fl7Kvw753xsnU3KvTrS29mR9s0j5Q3vIQ3dKbZrMPAN0nfib9pajtI9fGt9niu958Ux6K1+7y9he35H6rk9kfTe/ysinmir3UV1DTnQwkH5fVj4r293In1A7wEOqOph61J54PC+pA/XXXlMZuVeZx8iXUCwKinD76j9SYnULyV9g/Rfhl4j/YL6COl2YNuTxnF01tF5OUcAwyTdQAqq65B6Dv4fCy/SOpr04f4hadD7ONL4og+QevK2ZuGv0Y54njSO7kFJfyNdWLYv6cN5YUTcVqkYEQ/nL43LgIckXU+6F+uypAP3o6Tx0cUbqt9ESlT/LOnvpB7YpyLiN1V1PkW6IKs6cbuJ9K+TqVEG6QOxGWnffVrSzaTxZGuQ9tOOpLMDU5ZgG7rDTXkdmwJ/rh4GkC8MeC6XV+oXXUgatvDHPPbvOdIxtBfpHo37dWFbf00aczxK0q6kix+GkN6zP3fFuiJitqThpC+f2yVdRTo2dyJt122kU8Pdoa5jiM7t+wNJn+HTJH0u/628/I+TjoVpuW5XxZze+L6dSrqK/fuSrshfjvXsr4HAfZImky64fIZ0JuRTpFOgP8+JbMVZpMTiDkl/JN3PfFdSvHiAdNx0mYi4TGm851HA4/m74GnS9SfrkPbl5aRjlYh4OQ/HuBq4RdI/8natnLf9Q3m+ipuALypdGHcvKXG4rRjnaziCdPHPWUr/qGJCXu7nST2+h1bts+5wPfBTSZ8g7ff1SGM43wIOq5WoRsQdkirv0XwWJpH1Wk2t//OZORFxFPV/D90p6UxSYvZgjiNvkHraNyHdSvOsTra3lpNJ321HK10MfQvplm9fICWWR1edAazly8DhOed4nPRDZjDpLOJc0vUeFeNJCe6x+YxHZazzefkit061JyLmSPoVKemFeoeQRMdvBVJ8zCVdqTmRdHuKvWjldjV04W27CuWDSD1xj5IO+NdJv7R/A3ymqu4Y2rmtTN7B38nbM5uUlD1JusnzcAq326L9+68tckuNwvQVSQf8JNKBMIt08I8C1qiquxwpsb2Thfdse5oUrI6lcC/Fdt63afnRn/TPGabnZU0lHTA1b2tDSrLGkG7lMpfUw/gg6eD6WFXdvqRe+SdYeB/ilhrLqxw7W1eVfSlPn0/hVlpVdUT6sN2U2zIvb8u4/L59aAm3odXjrb33u419/9nCNi92b71c57e5/J3qYyCX70D6In81Hy/jSKd9hlF1y5WOfG7aae9GpKtcXyQF3omk3shB1Li1EW18rlprXy7bI2/HnLxd15CSvVaX10p7K/U79L7UewzVu+/zPKuSbkb+H1Jceo10n9SfULgVVa7b4ZiztL1vpCEUAfxfvfuL1NFyan7vKvHuedJn40vUiHmkH9QP5bovkGLEqrR9267F9lFHygv1PkXqjX4xH4svkDqFfgx8uEb9jUk/YKbn+jNIV/0Pr6q3BqmXawbptPC7bWntmMhlA0k9nk/l5b9MurXa1jXqHtLWe0gbtw5ra3+RfpTcSPo+n0UaPrDY+qvmP4Y2buvWzryV/dHW47XOxpA8zxdz+SxSTHiIlAcsX297q/bXYvuXdOyfQcqL5pLiz7+Aj3fkOAW2zcfAA3n73iT1jl9O1T1/c/29SInt7ML+GtSZ9lQtd7O8rOeocZu0th7KCzAzMzMrjdyzejDpHuK1zvBZyUg6hJRE/zgiTqlrXie0ZmZmViZKd7B4lHR2cONwMlN6+cLEe0nDK9eJNMa7w7r6HyuYmZmZdYs8pnh90un8fqR/ce1ktsQk7US6CGwYabjg+fUms+CE1szMzMpjOOniuWeAb0ZErdvWWbnsTvqnFa+Qrss6sTML8ZADMzMzMyu1LvnHCmZmZmZmjeKE1szMzMxKzQmtmZmZmZWaE1ozMzMzKzUntGZmZmZWak5ozczMzKzUnNCamZmZWak5oTUzMzOzUnNCa2ZmZmal5oTWzMzMzErNCa2ZmZmZlZoTWjMzMzMrNSe0ZmZmZlZqTmjNzMzMrNSc0JqZmZlZqTmhNTMzM7NSc0JrZmZmZqXmhNbMzMzMSs0JrZmZmZmVmhNaMzMzMys1J7RmZmZmVmpOaM3MzMys1JzQmpmZmVmpLdPoBpTNaqutFoMGDWp0M8yWOhMnTnw5IlZvdDuscxw7zRpjaYmdTmjrNGjQICZMmNDoZpgtdSQ91eg2WOc5dpo1xtISOz3kwMzMzMxKzQmtmZmZmZWaE1ozMzMzKzUntGZmZmZWak5ozczMzKzUnNCamZmZWak5oTUzMzOzUnNCa2ZmZmal5oTWzMzMzErNCa2ZmZmZlZoTWjMzMzMrNSe0ZmZmZlZqyzS6AWUzefpMBp08ttHNMOs1po3cu9FNsB7g2GnWdRw3F+ceWjMzMzMrNSe0ZmZmZlZqTmjNzMzMrNSc0JqZdTNJwySFpNVaKR+Uy4e2sYx263SwLUPzcgbVMc8Kkq6WNLMyb61pS9IuM7Ml4YvCzMysPYcBOwM7AS/lx5E1ppmZNYQTWjMza896wNSImFyZIGmxaWZmjVLKIQeS+kkaJWmGpLck3SVpp1xWObW3m6S7Jc2RNEHSllXL2EHSrbl8uqSLJK3cmC0ys2YmqUXShZJOk/SypBclnS2pTy5fTtIZkp7NMeXfkvZsY3n9JP1F0r2S1igUrS3pX3kZUyTt0cYyFhvGUGtYgqS9JD2cY+XtwPo1lrWPpMmS5kp6RtJ3Jamy7cAxwM552S21pnVwV5qZdYtSJrTAmcB+pNNgWwCTgeslDSjUOR04GdgS+C9wRSFAbwr8E/gbsBmwD7A5cFmtlUkanpPiCQvmzOyWDTKzpncA8DawA3A0cCwpDgFcDuwC7A9sAvwKuFbSZtULyT+crwfeBwyLiBcLxT8Bfk6KS/8GrpS0UmcbLOlDwF+Bf5Fi3Hmk+FmssxXwR+DPwKakuPntvI2Q4uPlwHhgQH5da1qt9Tt2mlmPKN2QA0krksZufTUixuZpRwAfA74O3JirnhIRt+TyHwLjgIHAs8C3gKsi4qeF5R4J3CdpjaovGCJiNDAaoN+AIdGNm2dmzWtKRJya/35E0teA3STdA3wJGBQRT+fy8yXtDhwOHFVYxhrAb4DpwBci4q2qdZwTEdcCSPoOcBApER3XyTYfCTwNfCMiAnhY0vrAjwp1jgNujYjvF7ZtCHAScF5EvCJpDjAvIl6ozFRrWjXHTjPrKWXsoR0MLAvcUZkQEQtIPQUbFepNKvz9XH6unNrbCjhQ0uzKo7C8wd3SajMru0lVr58jxZQtAQFTqmLK3iweT24g/ajep0YyW72O6rjVGRsCd+VktmJ8jTp3VE0bBwz0MCwzK4vS9dC2oxi059eY3qfwfClwTo1lTO+GdplZ+c2veh2kWNIn/711jTpvVr2+DvgCaVjC/W2tIyIij5JqrePhnfyswrRlW6nbWe5VNbNSKGNC+zgwD9gx/42kvsD2wO86uIx7gY0j4rFuaaGZLU3uIyWVa1aGObXhFOAV4CZJu0XE/Uuw3sptsgYU/t68qs5U4HOSVOil3a5GnR2rpu0EPBsRs5agfWZmPaZ0Qw4i4g3gIuAMSZ+UtGF+/X7gwg4u5gxgG0kXS9pC0nqSPiXpkm5qtpn1UhHxCHAFMEbSvpLWVfrnBSdIWuxiqYj4LnAJcGOti8bq8BjwDDBC0vqSPg58r6rOxcAgYJSkDSTtCxxRVeenwC6SKss5ADieqovHzMyaWekS2uwk4CrSVbb3Ax8B9oqI5zsyc0RMIt0QfBBwK/AA6a4IM7qhrWbW+x1KikdnAg+ThhbsDDxVq3JEfAf4BamntlNJbUTMB74IrEuKYT8AvlNV52nSHQj2ynW+SbqLQbHOvcDngc8BDwIj8+P8zrTLzKwRtOi1AtaefgOGxICDRzW6GWa9xrSRe3eonqSJEbFE//bVGsex06zrdDRuwtITO8s4hrahNh3Ynwl1HEhmZubYaWbdq6xDDszMzMzMACe0ZmZmZlZyTmjNzMzMrNQ8hrZOk6fPZNDJYxvdDLOmUc/FCbb0cuxsbv4cW9m5h9bMzMzMSs0JrZmZmZmVmhNaMzMzMyu1UiS0kiL/y0YzMzMzs0WU5aKwAcCrjW6EmZmZmTWfpk5oJS0XEfMi4oVGt8XMzMzMmlOPDjmQ1CLpYknnSno1P86S1CeXT5M0QtJlkl4DrsjT3x1yIGlQfv05Sf+SNEfSFEl7VK3rw5L+JmmmpNmSxkvatFB+aJ7vLUmPSPpmpR1mZr2RpH6SRkmakWPfXZJ2ymXDcmzdTdLdObZOkLRl1TJ2kHRrLp8u6SJJKzdmi8zMkkYkcAfk9W4PHA4MB44tlB8HPAwMBb7TxnJ+Avwc2Az4N3ClpJUAJH0AGAcEsAewJXAB0DeXfw04DTgV2BA4HjgJOKoLts/MrFmdCewHHAZsAUwGrpc0oFDndOBkUtz8L3CFJAHkToF/An8jxd59gM2By3qo/WZmNTViyMHzwDciIoCHJa1PSmJ/lstvjYgzO7CccyLiWgBJ3wEOIgXWccDXgTeAz0fEvFz/kcK8pwAnRsTV+fWTkkaSEtrzq1ckaTgp8abvyqt3dDvNzJqGpBWBI4GvRsTYPO0I4GOkmHljrnpKRNySy39IiqkDgWeBbwFXRcRPC8s9ErhP0hoR8WLVOh07zaxHNKKH9q6czFaMBwYWTllN6OByJhX+fi4/r5GftwDGFZLZd0laHfgQcEkeijBb0mxgJDC41ooiYnREDI2IoX1X6N/B5pmZNZXBwLLAHZUJEbGAFIM3KtRrK7ZuBRxYFTsry1ssfjp2mllPacaLwt7oYL35lT8iIvIZsY4k6JU6RwB31tc0M7NeqdjJML/G9D6F50uBc2osY3o3tMvMrEMakdBuK0mFXtrtgOci4vWclHaF+0i9CMtV99JGxAxJzwGDI+LXXbVCM7Mm9zgwD9gx/42kvqTrGX7XwWXcC2wcEY91SwvNzDqpEUMOPgCMkrRBvnPBt6j9a39JXAisBPxB0taS1pP0JUmb5/LvAyfmOxtsIGkTSQdJ+nYXt8PMrClExBvARcAZkj4pacP8+v2kmNkRZwDb5LvVbJFj66ckXdJNzTYz65BG9NBeQbrbwN2k01m/pIsT2oiYLmln4CzglryeyeSLEyLiUklvkJLp04E3gYeocUGYmVkvclJ+vhxYhXQ2a6+IeF7SBu3NHBGTcmz9MXArKZY/Afyle5prZtYxjUho346Io4GjqwsiYlCtGSJChb+nAYuNTSjWya8fAj7ZWiMi4vfA7zvaaDOzsouIuaTbJB5bo6yFqthaK95GxARgr25qoplZp/gfCZiZmZlZqTXjXQ6a2qYD+zNh5N6NboaZWak4dppZd+rRhDYihvXk+szMzMys9/OQAzMzMzMrNSe0ZmZmZlZqHkNbp8nTZzLo5LGNboZZj5rmsY+2hJbW2OnPjlnPcA+tmZmZmZWaE1ozMzMzK7WmTGgltUjyf+0yMzMzs3Y16xjafYD5jW6EmZmZmTW/puyhjYhXImLWkixD0rI1pi23JMs0MzMzs+bTkIQ2Dym4WNK5kl7Nj7Mk9SmUn1+ov5ykMyQ9K2mOpH9L2rNQPkxSSPqkpHskzQP2zMu5SNLZkl4C7sj1N5I0VtIsSS9K+r2kNXt6P5iZdYdC7PuppFckvSTpGEn9JF0g6TVJT0v6cmGekZL+I+lNSdMknSlp+UL5CEkPSvqipMdz/PyrpNUas5VmZgs1sof2gLz+7YHDgeHAsa3UvRzYBdgf2AT4FXCtpM2q6p0BfA/4MHB3nnYgIOCjwEGSBgC3AQ8C2wC7AysB11QSajOzXuAAYBawLTASGAX8FXgEGEqKo5fmmAjwBnAYsCFwFPBF4LtVyxwE7Ad8Fvg4sAXwk+7bBDOzjmnkGNrngW9ERAAPS1ofOA74WbGSpMHAl4BBEfF0nny+pN1JifBRheojIuKfhXkBnoyI4wvTfgg8EBEnFaYdBLxCCvL3VDdU0nBSwk3flVfv9AabmfWghyJiBICknwEnA/Mj4tw87YfAScCOwNUR8aPCvNMknQacAJxSmL4McEhEzMzLGA0c2loDHDvNrKc0skfyrpzMVowHBkpauarelqQe1imSZlcewN7A4Kq6E2qsZ2LV662AnauW9Uwuq14eABExOiKGRsTQviv078CmmZk13KTKHznWvghMLkybD7wKrAEgaV9J4yS9kOPiOcBaVct8qpLMZs9V5q/FsdPMekqz3uWgqA8QwNYsfueDN6tev1Fj/uppfYCxpJ6HajM600AzsyZUHS+jlWl9JG0HXAn8APgm8Brw/4CzO7BMD9Uys4ZrZEK7rSQVemm3A56LiNfzUIGK+0g9tGtGxC1dsN57gS+Qehp8azAzszTsYHpx2IGktRvYHjOzujTyl/UHgFGSNpC0L/At0imuRUTEI8AVwJh8SmxdSUMlnSBpn06s9wKgP3CVpG3z8naXNFrSe5Zkg8zMSuoR0pCvA3JMPJJ07YKZWSk0MqG9AuhLuhvBL4BfUiOhzQ4l3engTOBh4DpgZ+CpelcaEc+ReiPeAa4HHiIluXPzw8xsqRIR1wJnke6EMAnYAzi1kW0yM6uHFr0uq4dWKrUAD0bE0T2+8iXUb8CQGHDwqEY3w6xHTRu5d6ObgKSJETG00e2wzllaY2czfHZs6ba0xE4P5jczMzOzUivDXQ6ayqYD+zPBv7jNzOri2Glm3akhCW1EDGvEes3MzMys9/GQAzMzMzMrNSe0ZmZmZlZqHkNbp8nTZzLo5LGNboZ1kq84NmuM3ho7HVPMmoN7aM3MzMys1Jo6oZW0mqTvS1qj0W0xMzMzs+bUtAmtJAG/AvpGxIt1zNci6fzua5mZmZmZNZOGJbSShkkKSau1UuUE4KWI8L9fNDNrMu48MLNm0rQXhUXEWY1ug5mZmZk1v0730OZf5xdKOk3Sy5JelHS2pD65/EBJ/5Y0K5f9UdLAXDYIuCUv6qXcUzsml+0l6XZJr0p6RdINkjasWvepkp6SNFfSC5J+Xb1drbWrvbaZmZVNjpuzJC2TX6+X4+rFhTo/lnRj/nsjSWMLMfD3ktYs1B0j6TpJx0ianuPx5ZJWqJQDuwBfz+uJHNfNzBpiSYccHAC8DewAHA0cC+yXy5YDvg9sBnwKWA34fS57Bvhc/ntjYABwTH69IjAK2IYUMF8BrpW0HICkz5GGIxwFDMnLvqeOdrXXNjOzshkHLA8Mza+HAS/nZwrTWiQNAG4DHiTF2d2BlYBrij/8gY8Cm+Ty/YDPsjBOHwOMBy4nxe8BpLhuZtYQSzrkYEphjOsjkr4G7Ab8PiIuK9R7QtKRwFRJH4yIZyW9kstejIiXKxUj4k/FFUj6CvA6KfCOA9YGngf+GRHzgaeBCR1tV15Hm22r3khJw4HhAH1XXr0Du8XMrOdExGxJE4FdgbtIyev5wMk5gZ0JbA2cDBwJPBARJ1Xml3QQqfNgKAs7CF4HjoiIBaT4+EdSHD09ImZKmgfMiYgXWmuXY6eZ9ZQl7aGdVPX6OWANAElbSromDw2YxcKkc622FihpA0l/lTRD0jvAG0Dfwnx/JPVEPCnpl5I+L6lfR9vVmbZFxOiIGBoRQ/uu0L+t5puZNUoLC3tkdwH+Adydp+1AOmt1D7AVsLOk2ZUHC3tXBxeWNyUnsxWLxNGOcOw0s56ypAnt/KrXQRq/uiJwAzAH+DKpZ2CvXGe5dpZ5bZ5vO1Liukxez3IAEfEMsAFwOKkH4afAxLzONtsFsIRtMzNrVi3Ajvmag5WBiXnarqSkdnxEzCPFwrHA5lWPIcB1heW1GkfNzJpNd93l4MOkcanfiYgnASTtU1VnXn7uW5mQb+E1BPhaYb7tgGWLM0bEW6SAPFbSSOAFYEfgn13UNjOzshkH9ANOBMZFxAJJLcAvgBnA9bnevcAXgKfysK3OmkchfpuZNVJ3/dp+GpgLHC1pXUl7Az+qqvMU6Rf/3pJWl7QSaQzXS6QrZ4dI2hW4gHSqDABJh0j6qqRNJa0DHErqSXi0C9tmZlYqETGb1Ct7IAvvInMX8EHSGa+WPO0CoD9wlaRtcxzcXdJoSe+pY5XTgG0kDVL6r47uvTWzhumWABQRLwEHA58BppDuKHBcVZ3pefpPSL0H50fEO8DngQ2BycB5wLdJCWjFa8BXgNtJV+l+Dtin0tvaFW0zMyupFtKZtxZ492zW3aQYek+e9hzpjNY7pF7bh0hJ7lwWjbXtOZvUSzuF1BHR5vURZmbdSRHR6DaUSr8BQ2LAwaMa3QzrpGkj9250E6yTJE2MiKHt17Rm1Ftjp2OKNbulJXb6FJGZmZmZlVrT/uvbZrXpwP5M8C9yM7O6OHaaWXdyD62ZmZmZlZoTWjMzMzMrNSe0ZmZmZlZqHkNbp8nTZzLo5LGNbobVwVchmzVeb42dji9mzcE9tGZmZmZWak5ozczMzKzUnNCamZmZWak5oTUzMzOzUnNCa2ZmZmal1tQJraQWSRdJ+qmkVyS9JOkYSf0kXSDpNUlPS/pyYZ6Rkv4j6U1J0ySdKWn5QvkISQ9K+qKkxyXNkvRXSas1ZivNzMzMbEk0dUKbHQDMArYFRgKjgL8CjwBDgV8Bl0oakOu/ARwGbAgcBXwR+G7VMgcB+wGfBT4ObAH8pPs2wczMzMy6SxkS2ociYkREPAr8DHgZmB8R50bEY8APAQE7AkTEjyLijoiYFhF/B04DvlS1zGWAQyJiUkSMB0YDu7XWAEnDJU2QNGHBnJldv4VmZr2QY6eZ9ZQy/GOFSZU/IiIkvQhMLkybL+lVYA0ASfsCxwLrASsBffOj6KmIKEbX5yrz1xIRo0lJL/0GDIkl2Rgzs6WFY6eZ9ZQy9NDOr3odrUzrI2k74ErgBuDTpKEE3wOW7cAyy7AvzMzMzKxKGXpo67EjMD0iflSZIGntBrbHzMzMzLpZb0toHwEGSjoAGA/syeLjZ83MzMysF+lVp9kj4lrgLNKdECYBewCnNrJNZmZmZta9mrqHNiKG1Zi2SY1paxb+/jbw7aoqFxXKRwAjquYfA4xZgqaamZmZWYP0qh5aMzMzM1v6NHUPbTPadGB/Jozcu9HNMDMrFcdOM+tO7qE1MzMzs1JzQmtmZmZmpeaE1szMzMxKzWNo6zR5+kwGnTy20c2wOkzzuD2zhusNsdOxxKx5uYfWzMzMzErNCa2ZmZmZlVrTJ7SShkoKSYMa3RYzs6WFpEE59g5to47js5k1haZPaM3MzMzM2lL6hFZSH0l9G90OMzMzM2uMLk1oJe0l6XZJr0p6RdINkjbMZZXTV5+T9C9JcyRNkbRHjWU8LOktSbcD61eVHyJptqRPSnoQmAdsKGk5SWdIejYv+9+S9izMd5ekkwuvf5vbs2Z+vYKkuZJ26sp9YmbWrCT1kzRK0owcc+9qKwa2F5/NzBqlq3toVwRGAdsAw4CZwLWSlivU+Qnwc2Az4N/AlZJWApD0IeCvwL+AzYHzgDNrrGd54BTgcGAj4CngcmAXYH9gE+BXed2b5XlacpsqdgFeLkzbAXgbuKfObTYzK6szgf2Aw4AtgMnA9ZIGVFesIz6bmfW4Lk1oI+JP+fFoREwCDgXWISW4FedExLUR8SjwHeB9pOAIcCTwNPCNiHg4Iv4AXFxjVX2BoyPijoh4BFgD+BLwhYi4LSKeiIjzgb+Tkl5ICe1OkpaRtB7QH7gE2DWXDwPGR8S86pVJGi5pgqQJC+bM7MyuMTNrKpJWJMXckyJibERMBY4AZgBfrzFLR+NzcR2OnWbWI7p6yMFgSb+T9Lik10mBsQ+wVqHapMLfz+XnNfLzhsBdERGFOuNrrOpt4P7C6y0BAVPycITZkmYDewODc51xQD9ga1LyOg64kYU9tMNISe9iImJ0RAyNiKF9V+hfq4qZWdkMBpYF7qhMiIgFpJi7UY36HY3P73LsNLOe0tX/Kew64FlSr+h0UuI5BSgOOZhf+SMiQhLUn1jPzYG3og8QpGR1flXdN/O6ZkuaSOqR3Qi4BbgLWCv32G4NnIyZmUX7VczMmkeX9dBKWhX4MHBaRNyYT1+9h/qS5qnAtspZbrZdB+a7j9RDu2ZEPFb1mF6o10JKaHcBWiLiLeBu4Lt4/KyZLV0eJ11Uu2NlQr5jzPakjohqnY3PZmbdriuHHLxKusjqa5LWk7QLaXzV23Us42JgEDBK0gaS9iWN6WpTHkd7BTBG0r6S1s03/D5B0j6Fqi2koQUrA/cWph1IK+Nnzcx6o4h4A7gIOCPfNWbD/Pr9wIU1ZulUfDYz6wldltBGxDukq2U/AjwIXEC6E8HcOpbxNLAPsBfwAPBNOj4M4FDSnQ7OBB4mDX/YmXQHhIpx+fn2wpCFFlIvcktH22lm1kucBFxFip33k+L3XhHxfHXFJYzPZmbdSouO77f29BswJAYcPKrRzbA6TBu5d6ObYF1A0sSIaPXfsFpz6w2x07HEymhpiZ2l/09hZmZmZrZ06+q7HPR6mw7szwT/Sjczq4tjp5l1J/fQmpmZmVmpOaE1MzMzs1JzQmtmZmZmpeYxtHWaPH0mg04e2+hmLLV8lbFZOZU5djrumDU/99CamZmZWak5oTUzMzOzUitFQiupj6RLJP1XUkga1ug2mZmZmVlzKMsY2k+S/rXtMOAJ4JWGtsbMzMzMmkZZEtr1gOcj4s5GN8TMzMzMmkvTDzmQNAY4B1grDzeYJmkvSbdLelXSK5JukLRh1XwfkHRFHqYwR9L9knYtlH9a0kRJb0l6UtJPJC3Xw5tnZtYpSo6X9KikuZKelXR6Lhsp6T+S3swx80xJyxfmHSHpQUkH5/I3JF0uaTlJR0l6JsfOn0nqU5hvOUln5HXNkfRvSXs2YvvNzIrK0EN7DPAUcBiwNbAA2BkYBUwC/gf4HnCtpI0iYp6kFYFbgReBzwDPAZtVFpgD8BV52bcBawEXA/2AE3pio8zMltBpwJHAcaQ4tjqwRS57gxQzpwMbkeLbXOCUwvyDgP8FPgUMBP4EDACeBz4OfBj4A3BHLgO4HBgM7A88SxoOdq2krSPigW7YRjOzDmn6hDYiZkqaBSyIiBfy5D8V60g6FHgd2AYYRwq2awLbR8TLudrjhVm+C5wVEZdXyiSdBPxW0rciIqqWPxwYDtB35dW7buPMzDpB0krAN4FjI+KyPPkxYDxARPyoUH2apNNIP9aLCW1f4NCImAk8KOl6YBdgYETMA6ZKugPYFfiTpMHAl4BBEfF0Xsb5knYHDgeOqtFOx04z6xFNn9DWkgPrj4BtSb0SffJjrVxlC2BSIZmtthWwTU5iK/qQenvXJPVQvCsiRgOjAfoNGLJIsmtm1gAbkc4o3VSrUNK+wLGk6w9WIiWvfauqPZ2T2YoZwCM5mS1OWyP/vSUgYIqk4nL6ATfXaodjp5n1lFImtMB1pNNdh5NOqb0NTAE6Oga2D/AD4I81yl7qigaamTWCpO2AK0kx7pvAa8D/A86uqjq/6nW0Mq2SCPfJr7euUe/NJWq0mdkSKl1CK2lV0tiuoyLiljxtSxbdlvuAL0tarZVe2nuBD0fEY93eYDOzrjeVNCZ2N+DRqrIdgenFYQeS1u6Cdd5H6qFdsxJ7zcyaRekSWuBV4GXga5KeIV3McBapl7bid8DJwDWSTib14m4CzMqB+IfAdZKeIl308HYu3yYiTuyxLTEz64SImCXpXOB0SXNJF4WtShpO9QgwUNIBpDG1e5LGvi7pOh+RdAUwRtLxpI6B95HvDx4Rf17SdZiZdVbT37arWkS8A+wHfAR4ELiAdKHD3EKdN0gXNzwLXJvr/YB0uoyIuAHYm3Sxwz35cTJQudDBzKzZfRs4gxT/ppIulv1gRFxL+pE/inQnmD2AU7tonYeS7nRwJvAwafjXzqQ70ZiZNYyqLui3dvQbMCQGHDyq0c1Yak0buXejm2ANImliRAxtdDusc8ocOx13rMyWlthZuh5aMzMzM7OiMo6hbahNB/Zngn+tm5nVxbHTzLqTe2jNzMzMrNSc0JqZmZlZqTmhNTMzM7NS8xjaOk2ePpNBJ49tdDOWSr7S2Ky8yho7HXfMysE9tGZmZmZWak5ozczMzKzUSpXQShokKST1+hsEm5k1G0kjJD3Y2mszs0Zp6oRWUouk8wuTngEGAPc3pkVmZmZm1mxKdVFYRCwAXmh0O8zMzMyseTRtD62kMcAuwNfzMIOoHnIgaVh+/QlJEyW9Kel2SR+UtIukByTNlnSdpFWrln+opCmS3pL0iKRvSmra/WFmVi9Je0maJWmZ/Hq9HDMvLtT5saQb898bSRqb53lR0u8lrdmo9puZdVQzJ3DHAOOBy0nDDAYAfVup+wPgWGBb4L3AVcCpwHBgGLAxMKJSWdLXgNNynQ2B44GTgKO6eiPMzBpoHLA8ULnuYBjwcn6mMK1F0gDgNuBBYBtgd2Al4Br/2DezZte0QSoiZgLzgDkR8UJEvAAsaKX6KRFxe0RMAi4GdgC+FRF3R8QE4FfArsX6wIkRcXVEPBkR1wIjaSWhlTRc0gRJExbMmdlFW2hm1r0iYjYwkYXxbxhwPrC2pAGSVgC2BlqAI4EHIuKkiJia4+lBpOS2UxfiOnaaWU9p2oS2TpMKf8/Iz5Orpq0BIGl14EPAJXk4wmxJs0kJ7eBaC4+I0RExNCKG9l2hf9e33sys+7SwsEd2F+AfwN152g7A28A9wFbAzlVx8Zk8X83Y2B7HTjPrKaW6KKwN8wt/B0BEVE+rJO+V5yOAO7u/aWZmDdUCHC1pQ2BlUo9tC6nX9kVgfETMy8MKxgIn1FjGjBrTzMyaRrMntPNofdxsp0TEDEnPAYMj4tdduWwzsyY0DugHnAiMi4gFklqAX5AS1etzvXuBLwBPVXUImJk1vWYfcjAN2Cbf3WA1uq693wdOzHc22EDSJpIOkvTtLlq+mVlTKIyjPRC4JU++C/ggsB2ptxbgAqA/cJWkbSWtK2l3SaMlvaeHm21mVpdmT2jPJvXSTgFeAt7pioVGxKXAYcCXgQeA20l3RHiyK5ZvZtZkWkhn5FoAIuIt0jjauaTxs0TEc8COpDh7PfAQKcmdmx9mZk1LEdHoNpRKvwFDYsDBoxrdjKXStJF7N7oJ1kCSJkaE/+11SZU1djruWNktLbGz2XtozczMzMza1OwXhTWdTQf2Z4J/sZuZ1cWx08y6k3tozczMzKzUnNCamZmZWak5oTUzMzOzUvMY2jpNnj6TQSePbXQzliq+ytis/MoYOx17zMrDPbRmZmZmVmpOaM3MzMys1JzQmpn1QpJC0r6NboeZWU9Y6hNaSS2Szm90O8zMzMysc5b6hNbMzMzMyq2pElpJe0maJWmZ/Hq9fNrs4kKdH0u6Mf+9kaSxeZ4XJf1e0pqFumMkXSfpGEnTJb0q6XJJK1TKgV2Ar+f1hKRBPbnNZtb75TNBF0n6qaRXJL2U41I/SRdIek3S05K+XJhnpKT/SHpT0jRJZ0pavlD+IUnX5OXNkfSwpC+20YZNJd2Yl/dKjo/9c9nHJc2TtGrVPKdJmpT/XjXH2GfzMh6SdGjX7y0zs/o1VUILjAOWB4bm18OAl/MzhWktkgYAtwEPAtsAuwMrAddIKm7XR4FNcvl+wGeBY3LZMcB44HJgQH4807WbZGYGwAHALGBbYCQwCvgr8Agp5v0KuDTHNoA3gMOADYGjgC8C3y0s70JgBWBXYGPgWOC1WiuWtCJwAzCbFC8/C+wAXJar3ESKtZ8vzCNgf+C3edLywL3Ap/L6zgUukbRbPTvBzKw7NFVCGxGzgYmkAA0peT0fWFvSgNyzujXQAhwJPBARJ0XE1IiYBBxECtZDC4t9HTgi1/kn8Edgt7y+mcA8YE5EvJAfC6rbJWm4pAmSJiyYM7PrN9zMlgYPRcSIiHgU+BkpgZwfEedGxGPADwEBOwJExI8i4o6ImBYRfwdOA75UWN7awLiIeCAinoyI6yPi+lbWvT+wIvDliJgcEbcCw4F9JK2X496VpKS7YkfgQ8DvcnumR8RZEXF/RDwREaOBP1e1aRGOnWbWU5oqoc1aWNgjuwvwD+DuPG0H4G3gHmArYGdJsysPFvauDi4sb0pVkvocsEY9DYqI0RExNCKG9l2hf31bY2aWTKr8EREBvAhMLkybD7xKjk+S9pU0TtILOb6dA6xVWN65wPckjc9DsbZqY90bApMiYlZh2p3AO8BG+fVvgR0lrZ1fHwDcGhHP5vb0lfRdSZMk/Te3aZ+qNi3CsdPMekqzJrQ7StoQWJnUY9tC6rUdBoyPiHmkto8FNq96DAGuKyxvftXyg+bcbjPr3WrFoprxSdJ2pB7TG4BPA1sA3wOWfbdixC+BdUhDptYH7pQ0ohPtiry8e4GHgf0lLUsafvDbQr0TgOOBs0hnuTYnDZlYrhPrNDPrUs34r2/HAf2AE0mn0xZIagF+AcwAKqfU7gW+ADyVezY6ax7QdwnmNzPrajsC0yPiR5UJhZ7Td+Xe09HAaEknka4LGFFjeVOBwyS9p9BLuwPpx/3UQr3fknpmHyQNUbi6ULYTcG1E/Ca3R6RE+rVObJ+ZWZdqup7KwjjaA4Fb8uS7gA8C25F6awEuAPoDV0naVtK6knaXNFrSe+pY5TRgG0mDJK1WdUGZmVkjPAIMlHRAjm1HUjVWVdK5+c4w60raHNgLmNLK8q4A5gC/znc72Bm4BPhzHr9brLcR8CNS8vp6VZt2k7STpA+Trm9YZ8k31cxsyTVr8tZC6j1uAYiIt0jjaOeSxs8SEc+RejHeIfXaPkRKcufmR0edTeqlnQK8RBvjwczMekJEXEs6tT+KNPZ2D+DUqmp9gPNIsetfpDNYB7eyvDnAnqRhXPcA15Du8HJYVb2nSGfJNmPR4QYAP87z/oN0h5k3SAmwmVnDKV2bYB3Vb8CQGHDwqEY3Y6kybeTejW6CNQFJEyNiaPs1rRmVMXY69lhvsLTEzmbtoTUzMzMz65BmvCisqW06sD8T/KvdzKwujp1m1p3cQ2tmZmZmpeaE1szMzMxKzQmtmZmZmZWax9DWafL0mQw6eWyjm7HU8FXGZr1DGWKn441ZebmH1szMzMxKzQmtmZmZmZVajyW0kvpIukTSfyWFpGE9te6qdgzK6+/1Nxk2MyuS1CLp/C5e5gmSpnXlMs3M6tWTY2g/CRwKDAOeAF7pwXWbmZmZWS/VkwntesDzEXFnrUJJy0XEvB5sj5mZmZn1Aj0y5EDSGOAcYK18un9aPvV1kaSzJb0E3JHrbiRprKRZkl6U9HtJaxaXJek6ScdImi7pVUmXS1qhUEeSjpf0qKS5kp6VdHpVs9aW9C9JcyRNkbRHD+wKM7MlImkvSbfn2PeKpBskbVgoP1XSUzn2vSDp120sazdJr0k6Ir8eKOnKvOxXcyweUjXPiXm5s/OyV+q2jTUz66CeGkN7DPBD4FlgALB1nn4gIOCjwEGSBgC3AQ8C2wC7k4LlNZKKbf0osEku3w/4bF5HxWnAKcDpwMbA54Fnqtr0E+DnwGbAv4ErJTkwm1mzWxEYRYqRw4CZwLWSlpP0OeAE4ChgCPAp4J5aC5G0L/AXYHhEXJw7BW4B3gJ2AbYHngdurHQYSPoC8GPg+8CWwH+A47plK83M6tAjQw4iYqakWcCCiHgBQBLAkxFxfKWepB8CD0TESYVpB5HG2w5lYWB+HTgiIhYAUyX9EdgNOD0npd8Ejo2Iy3L9x4DxVc06JyKuzev4DnAQsDkwrrr9koYDwwH6rrx6Z3eDmdkSi4g/FV9LOpQUE7cB1iYlof+MiPnA08CE6mXkmHYWsG9E/DNP/iKpg+HQiIhc73DgRVJi/AfgWOBXEXFJnucnknYlDSlbjGOnmfWURt+2a2LV662AnfOprNmSZrOwZ3Vwod6UnMxWPAeskf/eCOgH3NTOuidVzU9hGYuIiNERMTQihvZdoX87izUz6z6SBkv6naTHJb0OzCDF8rWAPwLLA09K+qWkz0vqV7WIzwAXAHsVkllI8XcdYFYh/s4E3svC+Lshi3cOVL9+l2OnmfWURv+nsDeqXvcBxpJOmVWbUfh7flVZUH9y/u4yIiJyj3GjE3wzs/ZcRxq+dTgwHXgbmAIsFxHPSNqAdMZqd+CnwPclbRsRlXj7ALAp8BVJd1V6Y0nx735ST20135XGzJpasyVw95LGvD4VEY9VPWZ1cBlTgbmkgG5m1mtIWhX4MHBaRNwYEVOB91DonIiItyJibER8k3S9wsbAjoXFPEkae/txYLTyr3lS/F0PeLlG/K0ktFOB7aqaVf3azKzHNVtCewHQH7hK0raS1pW0u6TRkt7TkQXkxPdc0njaQ/PpuW0kHdmdDTcz6wGvAi8DX5O0nqRdgItJvbRIOkTSVyVtKmkd0r2/5wOPFhcSEU8AuwJ7AZfkpPYK0pmwayTtImkdSTtL+mnhTgfnAgdL+pqkIZK+DWzb/ZttZta2pkpoI+I5Uk/CO8D1wEOkJHdufnTUt4EzSHc6mAr8CfhglzbWzKyHRcQ7pDu7fIR0N5gLSHGuEh9fA74C3J7LPwfsExFP1ljW46Se2k8AlwBvAjuT/vHNH4GHgV+RxtC+mue5ChhBukvMfaShCz/r4s00M6ubFg6fso7oN2BIDDh4VKObsdSYNnLvRjfBmoSkiRHhf1ldUmWInY431hstLbGzqXpozczMzMzq1ei7HJTOpgP7M8G/4s3M6uLYaWbdyT20ZmZmZlZqTmjNzMzMrNSc0JqZmZlZqXkMbZ0mT5/JoJPHNroZSwVfcWzWezR77HS8MSs399CamZmZWak5oTUzMzOzUuuxhFZSSNq3p9ZnZmZmZkuHnhxDO4D87xPNzMzMzLpKtye0kpaLiHkR8UJ3r8vMzMzMlj51DzmQ1CLpYknnSno1P86S1CeXT5M0QtJlkl4DrsjT3x1yIGlQfv05Sf+SNEfSFEl7VK3rw5L+JmmmpNmSxkvatFB+aJ7vLUmPSPpmpR25/PA8/S1JL0u6QdIyuWxTSTdJej0v+wFJu3ZmJ5qZlYWknSXdlePeTEn3SNokl+0g6dYck6dLukjSyoV5JelESY9LelPSZEkHNm5rzMySzo6hPSDPuz1wODAcOLZQfhzwMDAU+E4by/kJ8HNgM+DfwJWSVgKQ9AFgHBDAHsCWwAVA31z+NeA04FRgQ+B44CTgqFw+NNf/AbABsBtwfWHdvwOeB7YBNgdGAG/VsxPMzMok/6C/hhRbNwO2BUYBC3JnwT+Bv+WyfUix8bLCIn4MfAX4OrARcDpwiSTf88rMGqqzQw6eB74REQE8LGl9UhL7s1x+a0Sc2YHlnBMR1wJI+g5wECmAjiMFzDeAz0fEvFz/kcK8pwAnRsTV+fWTkkaSEtrzgbXy/H+LiFnAU8ADhfnXBs6OiIfz68daa6Sk4aSknb4rr96BzTIza0orA6sA10bE43nawwCSfg1cFRE/rVSWdCRwn6Q1SPH0OODjEXF7rvKkpG1I8Xqxm8w6dppZT+lsQntXTmYrxgM/KpyamtDB5Uwq/P1cfl4jP28BjCsks++StDrwIVLPwEWFomUA5b//RUpin5R0A6nn4c85uYWUfF8q6WDgJuBPheR2ERExGhgN0G/AkKhVx8ys2UXEK5LGADdIuokU+66OiKeBrYD1JO1XmKUSTwcDbwPLA9dLKsbBZYFprazPsdPMekR33bbrjQ7Wm1/5o5Agd6RNlTpHkHp0K49NgI3z8maRhil8AXga+DapN/kDuXwE6ZTZX4EdgEmSDutgu83MSikiDiUNNbgN+H/AfyTtSYqrl7JoTN0MGALcz8K4++mqOhsDH++JtpuZtaazPbTbSlIhCd0OeC4iXpfU1nz1uA84sHKXhGJBRMyQ9BwwOCJ+3doCIuJt4GbgZknfB14EPkXuMYiIR4FHgZ/nnt6vsuh4MTOzXiciHiANwTpD0j+Ag4F7gY0joubwK0lTgLnA2hFxc4811sysAzqb0H4AGCXpQmBT4FukiwW60oWkHtg/SPoJ6R62WwNTI+J+4PvAeflOCn8nnfbaEhgYEadL+hTpNNltwCvArsB7gKmS/gc4G/gj6VTZ+4GdgLu7eBvMzJqGpHVIF/L+DZgOrAt8BLgoT7tL0sXAJcAs4MPApyPi8IiYJels4GylnovbgJVIHRrv5OEFZmYN0dmE9grS3QbuJt2F4JfAOV3VKICImC5pZ+As4Ja8nsnkCwwi4lJJb5CS6dOBN4GHSBeEAbwGfIZ0F4QVgMeBr0bE7ZKWA94LjCH9w4f/AtcBJ3TlNpiZNZk5wPqkH/OrATNI8fyMiJifY+6PgVtJMf4J4C+F+U/J85xASoJfJw1H6MhFwGZm3aazCe3bEXE0cHR1QUQMqjVDRKjw9zQWXmxQs05+/RDwydYaERG/B37fStk4Uq9srbJ5wP6tLdfMrDeKiBmk23G1Vj4B2KuN8gDOyw8zs6bRXReFmZmZmZn1iG7/17e9zaYD+zNhpO8hbmZWD8dOM+tOdSe0ETGsG9phZmZmZtYpHnJgZmZmZqXmhNbMzMzMSs1jaOs0efpMBp282L8sty4yzWPszHqlZo6djjtm5eceWjMzMzMrNSe0ZmZmZlZqTmjNzMzMrNSc0JqZmZlZqTmhNTMzM7NSa9qEVsmJkh6X9KakyZIOzGU3Szq/qv7KkuZI2ie/Xk7SGZKezdP/LWnPQv1lJf1c0nOS5kp6RtLInt1KM7POyTHyeEmP5hj2rKTTc9mmkm7MsfMVSWMk9S/MO0bSdZJOkvSCpJmSRkrqI2mEpBfz9JOq1tlf0uhcPkvSrZKG9vS2m5lVa+bbdv0Y2Bf4OvAfYHvgF5JeBX4BXCDp+IiYm+t/CZgNXJtfXw4MBvYHngU+CVwraeuIeAD4BvBZ4IvANOCDwAY9sF1mZl3hNOBI4DjgNmB1YAtJKwI3APcA2wDvI8XMy4DPFebfmRQbhwFbAFcAmwP3ATsBHwMuknRjREyUJGAsMBP4FPAKcDBws6QNIuL57txYM7O2NGVCmwPyccDHI+L2PPlJSduQEtzPAufl5ytz+WHAryNivqTBpAR3UEQ8ncvPl7Q7cDhwFLA28Ahwe0QE8DRwZyvtGQ4MB+i78upduq1mZvWStBLwTeDYiLgsT34MGC/pa8CKwJcjYlauPxy4RdJ6EfFYrj8T+HpELAAelnQ8MCAi9srlj0g6GdgVmJifNwdWj4g3c51TJH0a+DJwZo12OnaaWY9oyoQW2AhYHrheUhSmLwtMi4i5kn5DSmKvlLQxqSfikFxvS0DAlNSp8K5+wM357zHAv0hB+5/A34F/RMQ71Y2JiNHAaIB+A4ZEdbmZWQ/biBTPbqpRtiEwqZLMZncC7+T5KgntlJzMVswAXqta1gxgjfz3VsAKwEtVcXV50tmwxTh2mllPadaEtjK299OkntOi+fn5UmCSpLVIie34iJhamD+ArQv1K94EiIh7JQ0C9gR2A34FPCBpj1pJrZlZL1BMKqtjY7QyrRKP+5AS3I/WWO7rXdI6M7NOataEdgowF1g7Im6uVSEiHpJ0N/A14EDgu4Xi+0g9tGtGxC2trST3YFwNXC1pDHAXsB5pKIKZWbOaSoqRuwGP1ig7TNJ7Cr20O5AS0ql03r3A+4F3IuKJJViOmVmXa8qENiJmSTobODtfiHAbsBKwHSmYjs5VfwFcTOpVuKow/yOSrgDG5HFh95IujBgGPBERf5Z0HPA8cH+ef39SL8Oz3b+FZmadl2PkucDpkuaSYuSqpGEBvwJ+APxa0qnAe4FLgD8Xxs92xo3AHcA1kk4EHgbWBPYCbixc72Bm1uOaMqHNTiGd3joBuIiUbN7PohceXAX8HPhj1XgxgENJvbZnku5g8Arpqt9Kj+0s4FvAENJptfuAT0TEnG7YFjOzrvZt4FVSrPwgKV7+OiLm5FsUjiLFvLeAa4BjlmRlERGSPkm6A80vSGNrZ5CS3F8vybLNzJaU0gX+5STpA6QxtrtExB09sc5+A4bEgINH9cSqlkrTRu7d6CZYk5I0MSJ8z9OSaubY6bhjvdnSEjubuYe2VZKWJZ1eOw24r6eSWTMzMzNrPqVMaIEdSUMHHgW+0JMr3nRgfyb417yZWV0cO82sO5UyoY2IFtJdDMzMzMxsKden/SpmZmZmZs3LCa2ZmZmZlVophxw00uTpMxl08thGN6PX8VXGZr1bs8ZOxx6z3sE9tGZmZmZWak5ozczMzKzUen1CK2mQpJDU6k2FJQ3NdQb1YNPMzMzMrAv0+oTWzMzMzHo3J7RmZmZmVmq9IqGV1E/SKEkzJL0l6S5JO7VRfy9JD+e6twPr92Bzzcy6jZITJT0u6U1JkyUdmMtulnR+Vf2VJc2RtE9+vZykMyQ9m6f/W9KehfrLSvq5pOckzZX0jKSRPbuVZmaL6hUJLXAmsB9wGLAFMBm4XtKA6oqSPgT8FfgXsDlwXp7fzKw3+DHwFeDrwEbA6cAlkvYGfgHsL6lfof6XgNnAtfn15cAuwP7AJsCvgGslbZbLvwF8FvgiMIQUe//TnRtkZtae0t+HVtKKwJHAVyNibJ52BPAxUkC/tGqWI4GngW9ERAAPS1of+FEb6xgODAfou/LqXb4NZmZdIcfD44CPR8TtefKTkrYhxcPPkn7Efxa4MpcfBvw6IuZLGkxKcAdFxNO5/HxJuwOHA0cBawOPALfnGPo0cGcr7XHsNLMeUfqEFhgMLAvcUZkQEQskjSf1TlTbELgrB+KK8W2tICJGA6MB+g0YEm3VNTNroI2A5UlnqIqxallgWkTMlfQbUhJ7paSNgW2AQ3K9LQEBUyQVl9sPuDn/PYZ0husRSf8E/g78IyLeqW6MY6eZ9ZTekNC2xQHUzJYmlWFknyb1nBbNz8+XApMkrUVKbMdHxNTC/AFsXahf8SZARNybb3G4J7AbaUjCA5L2qJXUmpn1hN6Q0D4OzAN2zH8jqS+wPfC7GvWnAp+TpEIv7XY90VAzs242BZgLrB0RN9eqEBEPSbob+BpwIPDdQvF9pB7aNSPiltZWEhGzgKuBqyWNAe4C1iMNRTAz63GlT2gj4g1JFwFnSHoZeBL4JvB+4ELSqbKii4HjgVGSLgQ2BY7owSabmXWLiJgl6WzgbKUxA7cBK5F+tL+ThwBAujjsYlIv7FWF+R+RdAUwRtLxwL3A+4BhwBMR8WdJxwHPA/fn+fcHXgee7f4tNDOrrbfc5eAkUlC+nBRkPwLsFRHPV1fMFzrsA+wFPEBKfk/usZaamXWvU4ARwAnAQ6Txrp8j/divuIp0ZusPube16FBSLD0TeBi4DtgZeCqXzwK+BdxDSng3Bz4REXO6flPMzDqm9D20ABExFzg2P6rLppFOoRWnjQXGVlW9ontaZ2bWc/JQqvPyozWrAP8D/LLG/PNJCfGIVpb/C1IPr5lZ0+gVCa2ZmbVP0rLAqsBpwH0RcUc7s5iZlYIT2jptOrA/E0bu3ehmmJl1xo7ALcCjwBd6csWOnWbWnZzQmpktJSKihaohWGZmvUFvuSjMzMzMzJZSTmjNzMzMrNQ85KBOk6fPZNDJ1TdIsM6a5jF1ZkuFZo2djkFmvYN7aM3MzMys1JzQmpmZmVmpNUVCK6lF0vmNboeZWW8jaZCkkDS00W0xM+suTZHQmplZ16jRQfAMMID0b8HNzHolXxRmZtaLRcQC4IVGt8PMrDs1ZQ+tpN0kvSbpCEljJF0n6RhJ0yW9KulySSsU6veTNErSDElvSbpL0k6F8rsknVx4/dt8Cm7N/HoFSXOL85iZlY2kMcAuwNdzjIvqIQeShuXXn5A0UdKbkm6X9EFJu0h6QNLsHHdXrVr+oZKm5Dj7iKRvSmrK7xEzW7o0XSCStC/wF2B4RFycJ38U2ATYHdgP+CxwTGG2M/P0w4AtgMnA9ZIG5PIWYFih/i7Ay4VpOwBvA/d06caYmfWsY4DxwOWkYQYDgL6t1P0BcCywLfBe4CrgVGA4KTZuDIyoVJb0NeC0XGdD4HjgJOCort4IM7N6NVVCK2k48Etg34j4Q6HodeCIiJgaEf8E/gjsludZETgSOCkixkbEVOAIYAbw9Tx/C7CTpGUkrQf0By4Bds3lw4DxETGvtXZJmiBpwoI5M7tug83MulBEzATmAXMi4oWIeAFY0Er1UyLi9oiYBFxM+mH/rYi4OyImAL9iYYwEOAU4MSKujognI+JaYCRtJLSOnWbWU5opof0McAGwV05ai6bkcWAVzwFr5L8HA8sCd1QKc93xwEZ50jigH7A1KXkdB9zIwh7aYaSkt6aIGB0RQyNiaN8V+te3VWZmzWlS4e8Z+Xly1bQ1ACStDnwIuCQPR5gtaTYpoR3c2gocO82spzTTRWEPAJsCX5F0V0REoWx+Vd2gY8l4AETEbEkTSb0NGwG3AHcBa+Ue262Bk1tdiplZ71OMq5VYWT2tEmcrz0cAd3Z/08zM6tNMPbRPknpKPw6MlqQOzvc46RTbjpUJkvoC2wNTCvVaSAntLkBLRLwF3A18F4+fNbPeYx6tj5vtlIiYQTozNjgiHqt+dOW6zMw6o5l6aImIJyTtSko+L5F0eAfmeUPSRcAZkl4mJcbfBN4PXFio2kK6iGEOcG9h2neBW1sbP2tmVjLTgG0kDQJm03UdF98HzpP0GvB30lCvLYGBEXF6F63DzKxTmqmHFoCIeJzUU/sJ0oVbHempPYl0he7lpJuHf4Q0Fvf5Qp1x+fn2wnjcFlJS37KEzTYzaxZnk3pppwAvAe90xUIj4lLSnWS+TBoidjvpjghPdsXyzcyWhBYdqmrt6TdgSAw4eFSjm9FrTBu5d6ObYCUhaWJE+N+3llSzxk7HIOvtlpbY2XQ9tGZmZmZm9WiqMbRlsOnA/kzwL3ozs7o4dppZd3IPrZmZmZmVmhNaMzMzMys1J7RmZmZmVmoeQ1unydNnMujksY1uRun5ymKzpUszxk7HIbPewz20ZmZmZlZqTmjNzMzMrNSc0AKShkkKSas1ui1mZs1CUouk8xvdDjOz9ixVCa2kfpIekbSOpA0kPS1JwJ3AAOC/DW6imZmZmdVpabso7FtA34h4UtKNwM2R/vfvPOCFxjbNzMzMzDpjqeqhBXYHnsl/7wZMAw85MLPmkk/1XyzpXEmv5sdZkvrk8uUknSHpWUlzJP1b0p5Vy9hZ0t2S3pI0Q9I5kpbr6DpaaVe76zUza4Slqoc2IoYV/lYDm2Jm1p4DgDHA9sBHgF8AzwM/Ay4HBgP7A88CnwSulbR1RDwgaSDwD+A3wCG57qXAO8DxHVxHLW2utwu22cysU5aqhLazJA0HhgP0XXn1BrfGzJYSzwPfyMOiHpa0PnCcpGuALwGDIuLpXPd8SbsDhwNH5cdzwFER8Q4wVdLJwCWSTomIOW2tgxoJraTBHVhv9TyOnWbWI5a2IQedEhGjI2JoRAztu0L/RjfHzJYOd+VEs2I8MBDYCRAwRdLsygPYm9R7CrBhnv+dwvzjgOWA9dpbh6SVa7Rnyw6sdxGOnWbWU9xDa2ZWPgFsDcyvmv5mB+ftjD5LuF4zs27jhNbMrDltK0mFHtTtSMMIxpN6SteMiFtamXcq8AVJfQq9tDuR7ujyeHvriIjXayzzvg6s18ysITzkwMysOX0AGJXvmb0v6baD50TEI8AVwBhJ+0paV9JQSSdI2ifPe2Ge/0JJG0raGxgJnF8YP9vqOmo1poPrNTNrCPfQmpk1pyuAvsDdpFP9v2Rhsnko8F3gTOCDwCvAPcAtABExXdIngLOA+4HXgN8B36ljHbW0uV4zs0ZxQgtERAvpVJqZWbN4OyKOBo6uLoiI+cCI/KgpIm4Dtu3sOvIyhtW7XjOzRvCQAzMzMzMrNffQ1mnTgf2ZMHLvRjfDzKxUHDvNrDs5oTUzazLVp/rLug4zs57iIQdmZmZmVmpOaM3MzMys1JzQmpmZmVmpOaE1MzMzs1JzQmtmZmZmpeaE1szMzMxKzQmtmZmZmZWaE1ozMzMzKzUntGZmZmZWak5ozczMzKzUnNCamZmZWak5oTUzMzOzUnNCa2ZmZmalpohodBtKRdIs4D+Nbkc3WA14udGN6AbernJpa7vWjojVe7Ix1nV6cezsCb31894TvO+Wkti5TKMbUEL/iYihjW5EV5M0wdtVHt4uK6FeGTt7gj8Xned9t/TwkAMzMzMzKzUntGZmZmZWak5o6ze60Q3oJt6ucvF2Wdn4ve0877vO875bSviiMDMzMzMrNffQmpmZmVmpOaE1MzMzs1JzQmtmZmZmpeaEtg6SjpL0pKS3JE2U9NFGt6k1kkZIiqrHC4Vy5TrPSXpTUoukjauW8V5Jv5E0Mz9+I2mVHt6OnSX9TdL0vA2HVJV3yXZI2lTSrXkZ0yWdKkkN3K4xNd6/u6rq9JN0nqSXJb2Rl/fBqjprSbo2l78s6eeSluvG7fq2pH9Lel3SS3ndm1TVKeV7Zp1TprjZU3pLfO4JvfU7wLqeE9oOkrQfcC5wGrAFcCfwD0lrNbRhbfsPMKDw2LRQdiJwPPB/wNbAi8C/JL2nUOd3wJbAXvmxJfCb7m/2IlYCHgSOAd6sUb7E2yFpZeBfwIy8jGOAbwHHdfG2FLW3XQA3suj798mq8lHA54AvAR8FVgauk9QXID+PBd6Ty78E7Av8tAu3o9ow4EJgB+BjwNvAjZLeV6hT1vfM6lTSuNlTekN87gm99TvAulpE+NGBB3A38IuqaY8Cpze6ba20dwTwYCtlAp4HvluY9j/ALODw/HpDIIAdC3V2ytM2aNA2zQYO6ertAI4EXgf+p1Dne8B08p1AenK78rQxwHVtzNMfmAccUJj2IeAdYM/8+hP59YcKdQ4E3gJW7qH3bCVgAfDp3vSe+dHh979UcbMH90uvi889tN965XeAH13zcA9tB+RTtFsB/6wq+iepJ6pZrZtPwzwp6UpJ6+bp6wBrUtieiHgTuI2F27M9KXjcWVjeHcAbNM82d9V2bA/cnuetuAH4ADCoOxreQTtJelHSI5J+IWmNQtlWwLIsuu3PAFNZdLum5ukVNwD98vw94T2kM0Gv5te9/T2zrMRxs6f09vjcExxP7F1OaDtmNaAv6XRE0QzSh6kZ3Q0cQjq98jVSO++UtCoL29zW9qwJvBT5pypA/vtFmmebu2o71mxlGcV19LTrgYOA3Uin07YBbpbUr9CuBcDLVfNVb3v1dr2c5+up7ToXuB8YX2gT9M73zBZVxrjZU5aG+NwTHE/sXcs0ugHWPSLiH8XXShcUPQEcDNxVcyZrGhFxZeHlZEkTgaeAvYE/N6ZV9ZH0M9KpvZ0iYkGj22PWLByfzbqee2g7ptKr9f6q6e8HXli8evOJiNnAQ8AQFra5re15AVi9eJVn/nsNmmebu2o7XmhlGcV1NFREPAc8S3r/ILWrL6kXrKh626u3q9Jr1q3bJekc0kVoH4uIJwpFS817ZuWPmz2ll8bnnuB4Yu9yQtsBETEPmAjsUVW0B4uOy2lakpYHPkwaQP8k6UO6R1X5R1m4PeNJF/RsX1jM9sCKNM82d9V2jAc+muet2AN4DpjWHQ2vl6TVgIGk9w/S8TifRbf9g6QLIIrbtaEWvZXXHsDcPH93tfVcFiazD1cVLzXv2dKuN8TNntJL43NPcDyxhRp9VVpZHsB+pKvKv0pKGs4lDTRfu9Fta6W9ZwO7kAbNbwtcR7qKc+1cfhIwE9gH2AS4kvThfU9hGf8AJpM+/Nvnv6/t4e1YCdg8P+YAp+a/1+qq7SDdMeCFPO8meVmvA8c3Yrty2dm5rYNIt8IaT+qhLW7XRXna7qRbIt1CGq/aN5f3zdt6cy7fnXTV7nnduF0X5H33MdLYs8pjpUKdUr5nfnTqeChV3OzB/dIr4nMP7ate+R3gRzccK41uQJkewFGkX2uVHq6dG92mNtpa+VDPy0nMn4CNCuUi3TrmedJtnG4FNqlaxnuB3+YP9uv571V6eDuGkW6vUv0Y05XbQboH5G15Gc8D36cbb9fS1naRbjtzA+mihXmksbNjKNx+Ky+jH3Ae8N8c6K+tUWct0pflnFzv50C/btyuWtsUwIiuPvZ6+j3zo9PHRGniZg/uk14Rn3toX/XK7wA/uv6h/EaamZmZmZWSx9CamZmZWak5oTUzMzOzUnNCa2ZmZmal5oTWzMzMzErNCa2ZmZmZlZoTWjMzMzMrNSe0ZmZmZlZqTmjNzMzMrNT+P0PNBJvl+t8EAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
    " + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], "source": [ - "len(t)" + "(wfBookThree, reg) = getFreq('http://www.gutenberg.org/ebooks/1938.txt.utf-8')\n", + "\n", + "f = plt.figure (figsize=(10, 6))\n", + "f .suptitle (\"Difference between War and Peace and Resurrection by Leo Tolstoy\", fontsize=20)\n", + "\n", + "\n", + "ax1 = f.add_subplot (121)\n", + "plt .subplots_adjust (wspace=.5)\n", + "\n", + "\n", + "pos = np .arange (len(wf_ee)) \n", + "ax1 .tick_params (axis='both', which='major', labelsize=14)\n", + "pylab .yticks (pos, [ x [0] for x in wf_ee ])\n", + "ax1 .barh (range(len(wf_ee)), [ x [1] for x in wf_ee ], align='center')\n", + "\n", + "\n", + "\n", + "ax4 = f .add_subplot (122)\n", + "ax4 .tick_params (axis='both', which='major', labelsize=14)\n", + "pos = np .arange (len(wfBookThree)) \n", + "pylab .yticks (pos, [ x [0] for x in wfBookThree ])\n", + "ax4 .barh (range (len(wfBookThree)), [ x [1] for x in wfBookThree ], align='center')" ] }, { "cell_type": "code", "execution_count": null, - "metadata": { - "collapsed": true - }, + "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -374,7 +197,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.8.10" } }, "nbformat": 4,