From 7667f1769db6cf4c723baca98b076c6b46862085 Mon Sep 17 00:00:00 2001 From: robgray2000 Date: Thu, 8 Sep 2022 01:40:59 +0000 Subject: [PATCH 1/4] Added rwill166.ipynb --- rwill166.ipynb | 382 +++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 382 insertions(+) create mode 100644 rwill166.ipynb diff --git a/rwill166.ipynb b/rwill166.ipynb new file mode 100644 index 0000000..59b489b --- /dev/null +++ b/rwill166.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\nLQD6A+c759YAOOe+Ak4FSoA3gjI+GuQTyutv+DuZhcA3QXoRkUPa+PHjOe200xg8eDC9e/emY8eO\nZGVlUadOHQAmTZpEdnY2gwcPJjs7m+LiYt544w3q1q0LwCmnnMK1117LkCFDaNy4Mffff391bo6I\nVANzTs+QJCq9WRvXbNhD1V0MkYPCqnvjGWnMy8rKorCwcP8Jpdx27NjBsccey69+9StuueWWSs8/\nvVkbVG+KVJ7y1J1mNtc5V+k/IX1AfgFHRERSw7x581i6dCnZ2dls3bqV++67j61bt3LJJZdUd9FE\nJEUomBQROcQ9+OCDfPLJJ9SsWZPOnTszffr00rEmRUT2R8FkBXRo0ZDCctxeFhFJNl26dKnSLgOq\nN0UOPhpLUUREREQSpmBSRERERBKmYFJEREREEqY+kxWwcO1mWo15tbqLIVIlyjP8hEgsqjclGal+\nqxjdmRQRERGRhCmYFBEREZGEKZgUERERkYQdUsGkmY0zs0X7SfOImRVUUZFERJJebm4ugwYNKjPN\noEGDyM3NrZoCiUhS0QM4IiJSpocffhjnXHUXQ0SSlIJJEREpU8OGDau7CCKSxJKqmdu8W8zsUzPb\nYWZrzOyeYF4HM3vHzH4ws01mNtnMGoYtO9nMXonIr8xmbTNLM7PxZvZd8HoISDtgGygiUk2mT59O\n9+7dycjIoGHDhmRnZ7No0SK+/fZbhgwZQsuWLalbty4nnXQSkyZN2mvZyGbu4uJicnNzycjIoGnT\nptx9991VvTkikkSSKpgE7gZ+A9wDnARcBHxpZvWBN4FtQDZwPnAKMLGC67sFuBq4BuiBDySHVjBP\nEZGksnv3bs4991x69uzJggUL+OCDD7jxxhtJS0tj+/btdO3alVdeeYXFixdzww03cM011zBlypSY\n+Y0aNYq3336b5557jilTpjBv3jymT59ehVskIskkaZq5zSwDuAm40TkXChJXALPM7GqgPnCZc25r\nkD4PmGZmrZ1zKxJc7Y3A/c65fwd53gD0208584A8gLTDGie4WhGRqrNlyxa+//57zjnnHDIzMwE4\n4YQTSuf/6le/Kv07Ly+PqVOn8swzz9CnT5998tq2bRtPPPEEEydOpF8/X11OmjSJli1bxlx/fn4+\n+fn5AOwp3lwp2yQiySOZ7kyeCKQD0b4OtwM+DgWSgZlASbBcuQVN5M2AWaFpzrkS4IOylnPO5Tvn\nspxzWWk9mV3ZAAAgAElEQVT11I9IRJLfEUccQW5uLv369WPgwIE8+OCDfPHFFwDs2bOHu+66i44d\nO3LkkUeSkZHB888/Xzo/0sqVK9m5cyc9evQonZaRkUGHDh1irj8vL4/CwkIKCwtRvSly8EmmYDJR\noUcMSwCLmFerissiIpKUJk2axAcffECvXr146aWXOP7443nzzTcZP348DzzwAL/61a+YMmUK8+fP\n57zzzmPnzp3VXWQRSRHJFEwuBXYA+7ar+HkdzKxB2LRT8OVfGvz/Df5OY7jOsVbmnNsMrAO6h6aZ\nmeH7ZIqIHHQ6derE6NGjKSgoICcnhyeffJIZM2ZwzjnncNlll9G5c2cyMzNZvnx5zDwyMzOpVasW\ns2fPLp1WVFTEokVlDuErIgexpAkmgybsh4F7zOwKM8s0s2wzGwE8DRQD/wie6u4FPA48H9ZfcirQ\nxcyGm1lrM7sVOHU/q30YuNXMLjSz44GH2DcgFRFJaZ9//jljxoxh5syZrF69mmnTpvHxxx9z4okn\n0rZtW6ZMmcKMGTNYtmwZI0eO5PPPP4+ZV0ZGBldeeSWjR4/m7bffZvHixQwfPpw9e/ZU4RaJSDJJ\nmgdwAmOB7/BPdLcE1gP/cM4Vm1k/fLA3B9gOvAjcEFrQOfemmf0OuAuohw9AHwMGl7G+B4CjgL8H\n//8zWK5dJW6TiEi1qlevHsuXL+eiiy5i48aNNG3alKFDhzJ69Gi2bdvG559/zoABA6hbty65ubkM\nHTqUJUuWxMxv/PjxFBUVcf7551OvXj3+3//7fxQVFVXhFolIMjH9qkHi0pu1cc2GPVTdxRCpEqvu\nHVjdRSiVlZVFYWFhdRdDEpDerA2qNyXZJFP9diCZ2VznXFZl55s0zdwiIiIiknqSrZk7pXRo0ZDC\nQ+TbjIhIZVC9KXLw0Z1JEREREUmYgkkRERERSZiCSRERERFJmPpMVsDCtZtpNebV6i6GyD4OlScT\nJfWo3pRkorqycujOpIiIiIgkLNWCyZbAROAr/E8vrsIPZP6TcubTEz/o+Sr8AOhfAK8B/SupnCIi\nIiKHhFQKJjOBucAV+F/B+RPwGf5XcGYBR8aZzwjgPfxvgL8X5PMucDrwOvDrSi21iIiIyEEslYLJ\nx4AmwP8A5wFjgDPwweDx+J9RLFNaWtqTAwYM+DP+buTJwGX4n3C8DMgCdpx99tm/r1Wr1j8OyBaI\niIiIHGRSJZjMBM7CN0s/GjHvDqAIHxDWLyuT+vXrp6elpdUElgOfRMxeCiyvUaNGjdq1a9eqjEKL\niIiIHOxSJZjsHby/BZREzNsKvA/UA7qXlcnWrVu37969eyfQFmgTMbst0KaoqGhLcXHxjooXWURE\nROTgV23BpJn1N7OtZlYz+L+1mTkz+2tYmj+Y2TvA8dOnT6dly5b9zWy7ma03sz+ZWe0g6ac5OTmc\neuqpv4lYx2QzeyV82rJlyxbht3vu999//1SvXr0+rlOnzq4mTZosGzNmzLcfffTR3AO75SIiyWH6\n9Ol0796djIwMGjZsSHZ2NosWLQJg5syZnH766dSrV48WLVowYsQItmzZUrqsc47777+fzMxM6tat\nS4cOHXjqqaeqa1NEpBpV553JGUAdfF9FgBxgY/BO2LSCJUuWNBswYADNmzf/HOgCXAkMAe4J0m0G\nSE9PT9/fSlevXr0O39fy+9tuu23oypUrO7z44os133rrrU2vvPLKhi1btmTtLw8RkVS3e/duzj33\nXHr27MmCBQv44IMPuPHGG0lLS2PhwoWcddZZDB48mAULFvD8888zf/58hg8fXrr87bffzhNPPMGj\njz7KkiVLGDt2LNdccw2vvqoxJEUONdU2aLlzbpuZzcU3Yc/GB46PAGPMrBk+QPwZMObuu+++oHnz\n5syYMePp2rVrLwWWmtkY4HEz+41zLu71HnfccS2Ad7799tuX/vrXvx512GGH5fXr12828JvZs2df\n2rRp013FxcUxlzezPCAPIO2wxolsuohItduyZQvff/8955xzDpmZmQCccMIJAFx++eVccskl3HLL\nLaXpJ0yYQJcuXdiwYQP169fnwQcf5K233uK0004D4LjjjmPOnDk8+uijDBy490DQ+fn55OfnA7Cn\neHNVbJ6IVKHq/gWcAnwQeQ9+aJ4/44PLHOAbYDcwZ9myZRndu3endu3ah4UtOwOoDbQGGgLs2LGj\nzL6OjRo1Oqxdu3adgI9atmx5j3Pu4s2bN0/HDzF0WUZGxvFdu3Y9ee3atUfFysM5lw/kA6Q3axN/\nFCsikkSOOOIIcnNz6devH3369KFPnz5ceOGFHHPMMcydO5cVK1bw7LPPlqYPfWlfuXIlNWvWZPv2\n7fTv3x8zK02za9cuWrVqtc+68vLyyMvLAyC9WWR3dRFJdckQTI40s3bAYfhxJAvwAeUGYJZzbucJ\nJ5ywLUjfNkoeDmhTo0YNNm3aFPmVd6+nso899tjm5mu+d7dv3x4ZCJYA04GTGzduHO+YlSIiKWvS\npEnceOONvPHGG7z00kv8+te/5r///S8lJSVcddVV3HTTTfss06JFCz7++GMAXn75ZY455pi95teq\npcEwRA411R1MzgDSgVuBGc65PWZWAPwNWA+8AVBUVDRr9uzZXfbs2XNWWlpaDXzg1xPY+cADD6wH\nTj3yyCP3zJgxIzL/TvjhhABIS0tLC/5sDKwEduGfAP8M4Lvvvjtq0aJFZGZm7jkQGysikmw6depE\np06dGD16NAMGDODJJ5+ka9euLF68mNatW0dd5sQTTyQ9PZ3Vq1dzxhlnVHGJRSTZVGswGdZv8pf4\nwcPB959sCRyHH5icNWvW3F2nTp1rrr/++lYDBgz4/XnnnTcLuBd45Oabbx4D1M/MzJy+a9eus8xs\nMPBJy5Ytx9SoUePYkpKSVaH1ffXVV+sbN24McKFzbryZPQHcZ2bf3HbbbQ2WL19+8Z49e1izZs3X\nVbUPRESqw+eff87jjz/O4MGDadGiBZ999hkff/wxI0aMYPDgwXTv3p1rr72Wa665hgYNGrBs2TJe\nfvllHn/8cRo0aMCoUaMYNWoUzjl69erFtm3bmD17NjVq1Cht0haRQ0MyjDNZgA9qCwCcc9uBD/C/\nvT0nmLa2b9++v/zwww93X3zxxb8+/PDDnxs4cOAXRUVFXYGbgOUXXHDBxfjf7Z4IvD98+PDcyy67\nLCN8RWvWrNm4fv36L4G6wIebNm1q1LNnz8116tR57W9/+9tzHTt2TMvMzPx8/fr131fNpouIVI96\n9eqxfPlyLrroItq2bcuwYcMYOnQoo0ePpmPHjkyfPp1Vq1Zx+umn06lTJ8aOHUvTpk1Ll7/zzjsZ\nN24c48eP56STTuLMM8/kueee47jjjqvGrRKR6mDleRI6CRwN/B7oj/8t7nXAC8DvgO8i0oY2zCKm\nGzAMyMU3gzcAtgDz8M3r/xtvYdKbtXHNhj1Urg0QqQqr7h24/0QpLCsri8LCwuouhiQgvVkbVG9K\nsjjY68pIZjbXOVfpQyBWd5/J8voSuCLOtJFBZIgDJgcvEREREamAZGjmFhEREZEUlWp3JpNKhxYN\nKTzEbpGLiFSE6k2Rg4/uTIqIiIhIwhRMioiIiEjCFEyKiIiISMLUZ7ICFq7dTKsxr1Z3MSQFHWrD\nUYiEqN6UA0l1a/XQnUkRERERSZiCSRERERFJmIJJEREREUnYIRdMmtlkM3tlP2leMbPJVVQkEZGU\nM27cONq3bx/zfxE5dByKD+DcQOyfWhQRERGRcjjkgknn3ObqLoOIiIjIwSIlm7nNrJeZzTazbWa2\n2czmmFl7MzvSzJ4xszVm9oOZLTazKyKW3auZ28zqBdO2mdl6M7ut6rdIROTAeuONN2jQoAG7d+8G\nYMWKFZgZ1157bWma22+/nb59+wKwZMkSBg4cSIMGDWjSpAlDhgzh66+/rpayi0hyS7lg0sxqAi8C\nM4BOQDfgIWAPUAf4CBgEnAQ8DDxuZn3KyHI8cCbwc6AP0AXodaDKLyJSHXr27Mn27dspLCwEoKCg\ngEaNGlFQUFCapqCggJycHNatW0evXr1o3749c+bM4Z133mHbtm2ce+65lJSUVNMWiEiySrlgEjgM\nOBx42Tm30jm3zDn3L+fcUufcWufcH51z851znznn8oHngSHRMjKzDOBK4Fbn3JvOuUXAFUDM2tLM\n8sys0MwK9xSrxVxEUkNGRgYnn3wy06ZNA3zgOHLkSFavXs26desoLi7mww8/JCcnhwkTJtCpUyfu\nu+8+2rVrR8eOHfnHP/7BnDlzSoPR8sjPzycrK4usrCxUb4ocfFIumHTObQImA2+a2atmdrOZHQNg\nZmlm9msz+9jMvjWzbcAFwDExsssEagOzwvLfBiwsY/35zrks51xWWr2GlbRVIiIHXk5OTumdyHff\nfZcBAwbQrVs3CgoKmDlzJjVr1iQ7O5u5c+cyffp0MjIySl9HH300ACtXriz3evPy8igsLKSwsBDV\nmyIHn5R8AMc5d4WZPQT0BwYDd5nZeUBn4Bb8E9sLgW3A3UCT6iqriEiyyMnJ4ZFHHmHp0qVs2bKF\nk08+mZycHKZNm0aTJk3o0aMHtWvXpqSkhIEDBzJ+/Ph98mjatGk1lFxEkllKBpMAzrkFwALgPjN7\nHRgGNMA3f/8TwMwMaAt8HyOblcAuoDvwWbBMfaB9ME9E5KDRs2dPduzYwf3330/Pnj1JS0sjJyeH\nq6++mqZNm9K/f38Aunbtyr///W+OPfZYatWqVc2lFpFkl3LN3GZ2nJnda2anmNmxZtYb6AgsAZYD\nfcysp5mdADwCHBcrr6BJ+wl8QHqmmZ0ETATSDvyWiIhUrVC/yaeeeorevXsD0L17d9asWcPs2bPJ\nyckB4Prrr2fz5s1ccsklfPDBB3z22We888475OXlsXXr1mrcAhFJRikXTALF+LuN/8EHj08CTwP3\nAX8A5gCvA9OBomBeWUYB04AXgvdFwbIiIgednJwcdu/eXRo41qlTh27dupGenk52djYAzZs35/33\n36dGjRr079+fk046ieuvv5709HTS09OrsfQikozMOVfdZUhZ6c3auGbDHqruYkgKWnXvwOouQkrL\nyspK6KliqX7pzdqgelMOFNWtZTOzuc65rMrONxXvTIqIiIhIkkjZB3CSQYcWDSnUtyARkbip3hQ5\n+OjOpIiIiIgkTMGkiIiIiCRMwaSIiIiIJEx9Jitg4drNtBrzanUXQ6qYnhYUSZzqTYmX6trUoTuT\nIiIiIpIwBZMiIiIikjAFkyIiIiKSsKQOJs3sFTObXN3lEBE5lOXm5jJo0KAy0wwaNIjc3NyqKZCI\nJJWkDiZFREREJLkd1MGkmdWq7jKIiIiIHMySJpg0s3pmNtnMtpnZejO7LWL+L83sQzPbamYbzOw/\nZtYibH6OmTkzO9vM5pjZTqBfMO9sM/vAzH4ws2/N7GUzq2NmvzWzRVHK8r6Z/fmAb7SISDm98cYb\nNGjQgN27dwOwYsUKzIxrr722NM3tt99O3759AZg+fTrdunWjTp06NG3alJtuuomdO3eWps3JyWHk\nyJF7rWN/zdrFxcXk5uaSkZFB06ZNufvuuytzE0UkxSRNMAmMB84Efg70AboAvcLm1wbuADoBg4BG\nwDNR8rkPuB04AfjAzPoDLwFvAycDvYF38ds+ETjBzLJDC5vZ8cApwBOVuG0iIpWiZ8+ebN++ncLC\nQgAKCgpo1KgRBQUFpWkKCgrIyclh7dq1DBgwgC5dujBv3jyeeOIJnnnmGcaOHVuhMowaNYq3336b\n5557jilTpjBv3jymT59eoTxFJHUlRTBpZhnAlcCtzrk3nXOLgCuAklAa59xE59xrzrnPnHNzgBHA\naWbWMiK7cc65t4J03wC/Af7POXe7c26Jc+5j59x451yxc24N8AYwPGz54cBc59yCGGXNM7NCMyvc\nU7y50vaBiEg8MjIyOPnkk5k2bRrgA8eRI0eyevVq1q1bR3FxMR9++CE5OTk89thjNG/enMcee4x2\n7doxaNAg7r33Xh555BGKi4sTWv+2bdt44oknuP/+++nXrx/t27dn0qRJ1KgR++MkPz+frKwssrKy\nUL0pcvBJimASyMTfeZwVmuCc2wYsDP1vZl3N7EUzW21mW4HCYNYxEXkVRvzfBZhSxrr/BvzCzOqa\nWRpwGWXclXTO5TvnspxzWWn1Gu5vu0REKl1OTk7pnch3332XAQMG0K1bNwoKCpg5cyY1a9YkOzub\npUuX0r17970CvZ49e7Jz505WrFiR0LpXrlzJzp076dGjR+m0jIwMOnToEHOZvLw8CgsLKSwsRPWm\nyMEnJX5O0czqA28C7+CDvQ34Zu738EFouKJyZv8qUIxvXt8MHA78qyLlFRE5kHJycnjkkUdYunQp\nW7Zs4eSTTyYnJ4dp06bRpEkTevToQe3akVXj3swMgBo1auCc22verl27DljZReTgkyx3JlcCu4Du\noQlBANk++PcEfPB4m3NuunNuGdAkzrzn4ftgRuWc2w1MxjdvDweed86pHUZEklbPnj3ZsWMH999/\nPz179iQtLa00mAz1lwRo164ds2fPpqSktMcQM2bMoHbt2mRmZgLQuHFj1q1bt1f+CxZE7eUDQGZm\nJrVq1WL27Nml04qKili0aJ9nGUXkEJEUwWTQpP0EcJ+ZnWlmJ+EfjkkLknwB7ABGmtlPzWwgcGec\n2d8FXGRmfzCzE83sJDO7yczqhaX5O3A6/sEePXgjIkkt1G/yqaeeonfv3gB0796dNWvWMHv27NJg\n8rrrruOrr77iuuuuY+nSpbz66quMGTOGkSNHUq+erwLPOOMMXn/9dV566SU++eQTbr75Zr788ssy\n133llVcyevRo3n77bRYvXszw4cPZs2fPAd9uEUlOSRFMBkYB04AXgvdFwHSA4EGaYcB5wBL8U903\nx5Opc+414HxgAP4u5bv4J7rDH+75LJj+BVBQGRsjInIg5eTksHv37tLAsU6dOnTr1o309HSys/0A\nFS1atOD1119n3rx5dO7cmeHDhzNkyJC9hvIZPnx46evUU0+lQYMGnH/++WWue/z48fTu3Zvzzz+f\n3r170759e3r16lXmMiJy8LLIvjKHKjNbAjztnLsr3mXSm7VxzYY9dABLJclo1b0Dq7sIh7ysrKzS\noXEktaQ3a4PqTYmH6trKZ2ZznXNZlZ1vSjyAcyCZWWPgQqAV8Hj1lkZEREQktRzywST+yfCNwDXO\nuY3VXRgRERGRVHLIB5POOUt02Q4tGlKo2/AiInFTvSly8EmmB3BEREREJMUomBQRERGRhCmYFBER\nEZGEHfJ9Jiti4drNtBrzanUXQ6qAhqgQqRyqNyUa1bGpTXcmRURERCRhqRZMtsT/zOJX+J9XXAU8\nBPwkgby6Av8C1gR5rcf/Cs7llVFQERERkUNBKjVzZwIzgSbAi8AyIBu4AegPnAp8G2deI4GHge+A\nV4G1wBFAe+Bs4B+VWXARERGRg1UqBZOP4QPJ/wH+Ejb9QeAm4C7g2jjyOQv4M/A2/pdvtkbMr1Xh\nkoqIiIgcIlKlmTsTHwSuAh6NmHcHUARcBtSPI68/Aj8Al7JvIAmwK+FSioiIiBxiUiWY7B28vwWU\nRMzbCrwP1AO67yef9kDHIJ9N3377bV9gFHAL0IfU2R8iIiIiSSFVgqfjg/floQlmVmBmE8zsgfr1\n65/euHFjhgwZco2ZpZvZo2b2vZl9YWaXBelbmdnCZ555hvbt22enp6fvfuaZZ97evHnzHy+77LLx\nTZo0eSc9PX137dq1vzCzG6tlK0VEEuCc44EHHqBNmzakp6fTsmVLxo4dC8DChQvp27cvdevW5Ygj\njiA3N5fNmzeXLpubm8ugQYO47777OOqoo2jYsCFjxoyhpKSEcePG0aRJE4466ijuu+++vda5efNm\n8vLyaNKkCQ0aNOD000+nsLCwSrdbRJJDqvSZbBi8b46YPhR48LXXXptYWFg4YtSoURcBDYA3gCxg\nGPB3M3sntMDYsWP54x//eFTnzp3XLVu2bGyzZs1Odc71+s9//rOqQ4cOA5YuXcqFF164PlZBzCwP\nyANIO6xxJW6iiEhibrvtNiZMmMCDDz5Ir169+Oabb5g3bx5FRUX069eP7Oxs5syZw6ZNm7j66qsZ\nPnw4zz33XOny06dPp2XLlhQUFDBv3jyGDh3K/Pnz6dKlCzNmzGDq1KmMGDGCvn37cvLJJ+OcY+DA\ngTRs2JBXXnmFI444gieffJIzzjiDTz75hGbNmu1Vvvz8fPLz8wHYUxxZjYtIqjPnXHWXIR75wNXB\n6+/g70wC6c65HsBdzrnbMjIyioqLi6c65wYHaWrh+1NeChQCn48fP55bbrkF4BRglpm9BGx0zl0J\nzMEHoZcCz+yvUOnN2rhmwx6q3C2VpKQBdZNLVlaW7oIFtm3bRqNGjXjooYe49tq9n0H829/+xqhR\no1izZg0NGjQAoKCggN69e/Ppp5/SunVrcnNzmTJlCqtWrSItLQ3w+3fXrl0sWLCgNK9WrVoxcuRI\nRo0axdSpUxk8eDDffPMNdevWLU3TuXNnLr30Um699daY5U1v1gbVmxJJdWzVMLO5zrmsys43VZq5\nQ19lG0ZM/zg03cyoW7fuFmBhaKZzbhd++J8moWlZWVkAXwOzgkkTgEvMbP7ZZ5+949133wU/5JCI\nSNJbsmQJO3bsoE+fPvvMW7p0KR07diwNJAFOOeUUatSowZIlS0qnnXjiiaWBJEDTpk1p3779Xnk1\nbdqUDRs2ADB37lyKi4tp3LgxGRkZpa9FixaxcuXKyt5EEUlyqdLM/Unw3jZieujJ6zYAO3fu3MG+\nT2M7woLm+vXrA3xfOtO5183sWGDAhg0brh84cCDdu3cf8M4779xUieUXEUkqZlb6d61atfaZF21a\nSYl//rGkpISmTZvy3nvv7ZPvYYcddgBKKyLJLFWCyWnB+1n4wDD8ie4G+AHLi4uKior3l1FJSckP\nQCv8MEJFAM65jcA/gVOeffbZbr/4xS/amlm6c25H5W2CiEjla9euHenp6UyZMoU2bdrsM2/ixIls\n3bq19O7kzJkzKSkpoV27dgmvs2vXrqxfv54aNWrw05/+tELlF5HUlyrN3Cvxw/m0Aq6PmPc7fGD4\nz5KSkvAOoCcEr7189dVXLwJ1gD8AZma/N7Pz7rzzzoGLFy++4rnnnnO1atX6QoGkiKSCBg0acMMN\nNzB27FgmTZrEypUrmTNnDhMmTGDo0KHUq1ePyy+/nIULFzJ9+nSuueYaLrjgAlq3bp3wOvv27cup\np57Kueeey+uvv87nn3/OrFmzuOOOO6LerRSRg1uqBJMA1wEb8L9e89+2bdseN2TIkPPwv36zHPh1\nRPqlwWsvv/3tb/8KzAduBGYNGzbsjKOPPnryPffc88ppp52WvmDBgmW7du0acEC3RESkEt1zzz2M\nHj2aO++8k3bt2vHzn/+cNWvWUK9ePd588022bNlCdnY25557Lj169GDixIkVWp+Z8dprr3HGGWdw\n9dVXc/zxx3PxxRfzySef0Lx580raKhFJFanyNHfI0cDv8b/FfSSwDngBf3fyu4i0oQ0z9pUBjAUu\nAo7F/yLOHGA8/g5oXPQ096FDTxomFz3Nnbr0NLdEozq2ahyop7lTpc9kyJfAFXGmjRZEhmzD38mM\nvJspIiIiIuWQasFkUunQoiGF+jYlIhI31ZsiB59U6jMpIiIiIklGwaSIiIiIJEzBpIiIiIgkTH0m\nK2Dh2s20GvNqdRdDKomeJhQ58FRvSjjVuwcH3ZkUERERkYQpmBQRERGRhCmYFBEREZGEHdLBpJmN\nM7NF1V0OERERkVR1SAeTIiIiIlIxCiZFREREJGFJE0yaWYGZTTCzB8xsk5l9Y2Y3mFm6mT1qZt+b\n2RdmdlmQvpWZOTPLisjHmdmFYf83N7OnzexbMys2s/lm1jtimV+Y2Uoz22pm/zWzRlWz1SIiVW/H\njh3ceOONNG3alDp16tC9e3dmzJgBQEFBAWbGlClT6NatG/Xq1SMrK4uPPvporzxmzpzJ6aefTr16\n9WjRogUjRoxgy5Yt1bE5IlLNkiaYDAwFtgLdgHuBh4D/AsuBLOBJ4O9m1iyezMysPvAu0Ao4D+gA\n/D4iWSvgEuB84CygC3BXxTZDRCR53XrrrTz77LNMnDiRefPm0aFDB/r378+6detK04wdO5Z7772X\njz76iCOPPJKhQ4finANg4cKFnHXWWQwePJgFCxbw/PPPM3/+fIYPH15dmyQi1SjZBi1f7JwbB2Bm\nDwJjgF3OuYeDab8HRgOnAoVx5HcpcBTQwzm3MZi2MiJNTSDXObc5WEc+cEWsDM0sD8gDSDuscXxb\nJSKSJIqKipgwYQJ///vfGTjQDxj917/+lalTp/Loo4/St29fAO6880569/aNOL/97W/p2bMna9eu\npWXLlvzxj3/kkksu4ZZbbinNd8KECXTp0oUNGzbQpEmTvdaZn59Pfn4+AHuKN1fFZopIFUq2O5Mf\nh/5w/ivwBmBh2LRdwHdAk30XjaoL8HFYIBnN6lAgGfiqrPydc/nOuSznXFZavYZxFkNEJDmsXLmS\nXbt2ceqpp5ZOS0tLo0ePHixZsqR0WseOHUv/bt68OQAbNmwAYO7cuTz11FNkZGSUvkL5rVwZ+X0d\n8vLyKCwspLCwENWbIgefZLszuSvifxdjWg2gJPjfQjPMrFYlrTPZgmwRkQPOrLQ6pVatWvtMLykp\nKX2/6qqruOmmm/bJo0WLFge4lCKSbJItmCyPb4L38P6TnSPSzAMuM7NG+7k7KSJySMjMzKR27dq8\n//77ZGZmArBnzx5mzZrFpZdeGlceXbt2ZfHixbRu3fpAFlVEUkTK3oFzzv0AzAZGm9lJZnYKMD4i\n2b/wTeUvmtlpZvZTMxsc+TS3iMihon79+owYMYLRo0fz2muvsXTpUkaMGMH69eu57rrr4spj9OjR\nzC6tTtIAACAASURBVJkzh2uvvZZ58+axYsUK/n97dx4eRZX2//99B5KwBGNQlgAKiKyyKS2IigY3\neIQR14dxHCWgE1QYN5gBB78O4jMqjCjMgEsYwHUWd8fxpyhowEEWg8uwCYJGFILIyBYiAZLz+6Mq\nsckCSdNJd4fP67rq6u6qU6dOVXVO7j51TtW//vUvRo4cWc2lF5FoFMstkwAjgL8AH+ENrLkVWFS8\n0Dm318zOB6YCbwAJwDqg7LUZEZFjxOTJkwEYPnw4O3fu5PTTT+ftt98mNTWVdevWHXH97t27s2jR\nIu655x7OP/98CgsLOeWUU7jiiiuqu+giEoWs+FYPUnWJqe1d6rBpkS6GhEnOQ4MiXQSppEAgQHZ2\nZW7oINEmMbU9qjelmOrdmmVmK5xzgSOnrJqYvcwtIiIiIpGnYFJEREREQhbrfSYjqlvLZLLVRC8i\nUmmqN0VqH7VMioiIiEjIFEyKiIiISMgUTIqIiIhIyNRn8iis3LyLNuPfjHQx5CjothQiNUv1Zu2k\nuvTYppZJEREREQmZgkkRERERCZmCSREREREJmYJJEZFabPDgwaSnpwOQlpbG6NGjD5u+a9euTJw4\nsfoLJiK1hgbg+MwsC1jlnDt8TSsiEqNeeeUV4uPjw5pnTk4Obdu25aOPPiIQCPsjf0UkBiiYFBE5\nRjRu3DjSRRCRWigqL3ObWZaZPW5mU83sBzP73sxuN7NEM5tpZjvNbJOZXe+nb2NmzswCpfJxZnZ1\n0Od7zexrMysws61m9ow//yngfGCUv44zszY1tsMiImGQn59Peno6SUlJNGvWjAceeOCQ5aUvc2/b\nto0hQ4ZQv359WrduzZw5c8rkaWZkZmZyzTXX0LBhQ0455RSee+65kuVt27YF4Mwzz8TMSEtLq56d\nE5GoFZXBpO86YA/QB3gImAa8BqwHAsDTwF/MLLUymZnZVcBY4FagPTAYWO4vvh1YAswFUv3pmwry\nyTCzbDPLLszfFdqeiYhUg7Fjx/Luu+/y8ssvs2DBAj755BMWLVpUYfr09HQ2bNjA/Pnzee2113jm\nmWfIyckpk27SpEkMGTKEzz77jKFDhzJixAg2bdoEwPLlXjX69ttvk5ubyyuvvFJm/czMTAKBAIFA\nANWbIrVPNAeTq51zE51zXwCPANuBA8656c65DcAkwIBzKplfayAXeMc5t8k5l+2cmwHgnNsF7Afy\nnXNb/amwvEycc5nOuYBzLlCnQfJR7qKISHjk5eUxe/ZspkyZwoABA+jatStz584lLq78an79+vW8\n9dZbZGZmcs4553D66afz9NNP8+OPP5ZJe/311/PLX/6SU089lfvvv5+6deuWBKlNmjQB4IQTTqB5\n8+blXkrPyMggOzub7OxsVG+K1D7RHEz+p/iNc84B24CVQfMOADuAppXM70WgHvCVmc02s2vMLDGM\n5RURiZiNGzeyf/9++vbtWzIvKSmJbt26lZt+7dq1xMXF0bt375J5rVu3pkWLFmXSdu/eveR93bp1\nadKkCdu2bQtj6UUklkVzMHmg1GdXwbw4oMj/bMULzOyQIYvOuW+AjsBIYDcwFVhhZg3DWGYRkZhi\nZkdMU3oEuJlRVFRUQWoROdZEczBZFd/7r8H9J3uWTuSc2+ece9M5dydwJnAaP10m3w/UqdZSiohU\nk3bt2hEfH8/SpUtL5u3du5dVq1aVm75Tp04UFRWV9HkE2LRpE1u2bKnSdhMSEgAoLCy3Z5CIHANq\nxa2BnHM/mtlSYJyZbQSSgQeD05hZOt7+LgPygKF4LZ1f+ElygN7+KO484AfnnH56i0hMSEpK4sYb\nb2TcuHE0adKEFi1aMGnSpAqDvI4dOzJw4EBGjhxJZmYm9evX56677qJ+/fpV2m7Tpk2pX78+8+bN\no02bNtSrV4/kZPWLFDmW1JaWSYAR/utHwJPAPaWW7wRuBD4AVgFXAVc6577ylz+M1zq5Bq+l8+Tq\nLrCISDg9/PDD9O/fnyuuuIL+/fvTtWtXzjvvvArTP/XUU7Rt25YLLriAn/3sZ/ziF7+gTZs2Vdpm\n3bp1+dOf/sRf/vIXWrRowZAhQ45yL0Qk1pg3tkVCkZja3qUOmxbpYshRyHloUKSLICEIBAJkZ2dH\nuhgSgsTU9qjerH1Ul8YGM1vhnAv7o6pqU8ukiIiIiNSwWtFnMlK6tUwmW7/GREQqTfWmSO2jlkkR\nERERCZmCSREREREJmYJJEREREQmZ+kwehZWbd9Fm/JuRLoaUQyMLRaKT6s3aRXWtgFomRUREROQo\nKJgUERERkZApmBQRERGRkFV7MGlmWWY2I0zZtQLmAFuAArznaU8DUo4iz/OAQsAB/3eU5RMRERE5\npsRSy2Q7YAUwHFgOPAp8CdwOLAFOCCHPRsDTQD5A48aNR5vZ2LCUVkTkGJCVlYWZsX379kgXRUQi\nJJaCyceApsBtwOXAeOACvKCyI/CHEPKcDiQDD4apjCIiIiLHlJoKJuua2XQz2+FPfzSzOAAzSzCz\nyWb2rZnlm9lHZjageEUzSzMzt2DBgkvOOOOMAj9ttpmd4Sf5/ezZswuSkpJGtmrVapCZrTKzvWb2\nvpm1DS6Emf3MzFaY2b6kpKTvJkyYMHznzp13AlvS0tLYsWNHMvBHM3Nm5mro2IiIRMzevXu54YYb\nSEpKolmzZjz44IMMHjyY9PR0APbv38+4ceNo1aoVDRo04Mwzz2TevHkA5OTk0L9/fwCaNGmCmZWs\nJyLHjpoKJq/zt9UXGAlkAHf4y+YC5wO/ALriXXZ+w8x6BGdw9913c+edd74LnAH8F3jezAzYs2PH\nji8KCgo4cODAJGCEv53jgSeK1/cD1OeBGddee22/V155JeHpp5/OS0lJ6QbwyiuvkJycvBuYBKT6\nk4hIrTZmzBgWLlzIq6++ynvvvcdnn33GBx98ULJ8+PDhLFy4kL/+9a+sWrWKYcOG8bOf/YzPPvuM\nk046iZdffhmA1atXk5uby/Tp0yO1KyISITV10/Jc4DbnnAM+N7MOwF1m9jpwLdDGObfJTzvDzC7C\nCzpvLc7g/vvvZ8CAAVnXX3/952Y2Cfg30BL4dvfu3d8dPHiw66xZs9647LLLlgOY2cPAHDMzf7sT\ngD865+YCrwOFycnJd2zevHlmYWHh6MaNGxMXF+eAPc65rRXtiJll4AXD1DmuSTiPkYhIjcrLy2PO\nnDk888wzXHzxxQDMnj2bVq1aAbBx40b+9re/kZOTw8knnwzA6NGjmT9/Pk8++SSPPfYYjRs3BqBp\n06aceOKJ5W4nMzOTzMxMAArzd1X3bolIDaupYHKpH9AVWwLcD5wLGLDGa2QskQi8Fzyje/fuAMW1\n0Bb/tSnwbUFBwY+JiYlcdtllBUGrbAES8EZ6/wD0AnrHx8dPSExMTCwoKCg4ePDg40D9ZcuWJZ99\n9tmV2hHnXCaQCZCY2l6XwkUkZm3cuJEDBw7Qu3fvknkNGzaka9euAHz88cc45+jSpcsh6xUUFHDB\nBRdUejsZGRlkZGQAkJjaPgwlF5FoEg2PU3TAmcCBUvN/DP4QHx9feh0Iukxft26ZXSmdJq5169bT\n33nnnVv37NnzXiAQuK04YY8ePc4LregiIrVXUVERZsZHH31Uug6mfv36ESqViESbmgom+wRdbgY4\nC6/lcAley2Rz59z7lcgnubyZiYmJxbXazsOs+3H37t1v6NChQz5wg3Mu+D4W5wLUrVu3EKhTiXKI\niMS8du3aER8fz0cffcQpp5wCQH5+PqtWraJdu3acfvrpOOfYunVryUCb0hISEgAoLCyssXKLSHSp\nqQE4LYBpZtbRzK4GfgM86pxbjzco5ikzu9rMTjGzgJmNNbMry8mnQ3mZH3fccc38t+sPU4ZJb731\nVvN777236apVq77//PPP3UsvveR++9vfOrxBQPTs2bPxoEGDHlq/fv3bZlZ+5x8RkVoiKSmJESNG\nMG7cOBYsWMCaNWu46aabSlokO3TowHXXXUd6ejovvfQSX375JdnZ2Tz88MO88sorALRu3Roz4803\n3+T7778nLy8vwnslIjWtpoLJ5/Fa/JYBs4DZePeHBO8m5HOBKcDnwL/wnkrzdTn5XELZMjdKSUkp\n7oSztKICOOfmzZw58/UXX3zxu169ehWefvrpB8aNG7fdObcEWAQwfvz49StXrvyhS5cuFwLfh7Kj\nIiKx5OGHH6Zfv35cdtll9O/fn+7duxMIBKhXrx4Ac+fOZfjw4fz2t7+lU6dODB48mEWLFtG6dWsA\nWrZsyX333ceECRNo1qwZo0ePjuTuiEgE2KHjYqLaPLxg8jbgz0HzHwHuBJ4Ebg6a38l//bwSeafj\nBbR/AO6pbIESU9u71GHTKptcalDOQ4MiXQSpRoFAgOzs7EgXo1YqKCigdevW/OY3v2HMmDFhzz8x\ntT2qN2sP1bWxxcxWOOcC4c43GgbgVNatwIfAn4ALgbVAH6A/3uXtCaXSr/VfDRERKdcnn3zC2rVr\n6d27N3v27GHy5Mns2bOHoUOHRrpoIhIjYimY3AgE8G4qPhC4FO/+ldOB+4AdkSuaiEjseuSRR1i3\nbh1169alZ8+eLFq0qORekyIiRxJLl7mjTiAQcLrUJlLzdJk7dunciUROdV3mrqkBOCIiIiJSCymY\nFBEREZGQKZgUERERkZDF0gCcqLNy8y7ajH8z0sWQILpNhUh0U71ZO6iulWBqmRQRERGRkCmYFBER\nEZGQKZgUERERkZDV2mDSzJyZXR3pcoiIRJPBgweTnp4e6WKISC1Sa4NJIBV4I9KFEBGpzSZOnEjX\nrl0jXQwRiaBaO5rbObc10mUQERERqe1iomXSzLLM7HEzm2pmP5jZ92Z2u5klmtlMM9tpZpvM7Pqg\ndQ65zG1m95rZ12ZWYGZbzeyZoGXnmdlSM8szs11mttzM9FNbRGJafn4+6enpJCUl0axZMx544IFD\nlu/YsYNhw4aRkpJC/fr1ueiii1i9enXJ8qeeeoqkpCQWLFhA165dadiwIf379+err74qWX7fffex\nevVqzAwz46mnnqrJXRSRKBATwaTvOmAP0Ad4CJgGvAasBwLA08BfzCy19IpmdhUwFrgVaA8MBpb7\ny+oCrwP/Bnr4+U8DCssrhJllmFm2mWUX5u8K5/6JiITV2LFjeffdd3n55ZdZsGABn3zyCYsWLSpZ\nnp6ezrJly3j99ddZvnw5DRo0YODAgfz4448laQoKCnjwwQeZM2cOS5YsYefOndx8880ADB06lDFj\nxtCxY0dyc3PJzc1l6NChZcqRmZlJIBAgEAigelOk9omly9yrnXMTAczsEWA8cMA5N92fNwkYB5wD\nvFRq3dZALvCOc+4AsAnI9pcdBxwPvOGc2+jP+7yiQjjnMoFMgMTU9u7od0tEJPzy8vKYPXs2c+bM\nYcCAAQDMnTuXVq1aAfDFF1/wz3/+k4ULF3LeeecB8Oyzz3LyySfz/PPPc9NNNwFw8OBBZs6cSceO\nHQEvQB0xYgTOOerXr09SUhJ169alefPmFZYlIyODjIwMABJT21fbPotIZMRSy+R/it845xywDVgZ\nNO8AsANoWs66LwL1gK/MbLaZXWNmif56PwBPAfPM7E0zu8vMTq6+3RARqX4bN25k//799O3bt2Re\nUlIS3bp1A2Dt2rXExcUdsjw5OZlu3bqxZs2aknmJiYklgSRAixYt2L9/Pzt27KiBvRCRWBBLweSB\nUp9dBfPK7JNz7hugIzAS2A1MBVaYWUN/+XC8y9uLgMuAdWY2IKylFxGJEWZW8r5u3brlLisqKqrR\nMolI9IqlYPKoOOf2OefedM7dCZwJnIZ3Sbx4+WfOucnOuTQgCxgWkYKKiIRBu3btiI+PZ+nSpSXz\n9u7dy6pVqwDo3LkzRUVFLFmypGT57t27WblyJV26dKn0dhISEigsLLeLuYgcI2Kpz2TIzCwdb1+X\nAXnAULxWzS/MrC1ei+U/gc3AKUB34PGIFFZEJAySkpK48cYbGTduHE2aNKFFixZMmjSpJPBr3749\nQ4YMYeTIkWRmZnL88cczYcIEjjvuOH7xi19Uejtt2rTh66+/5uOPP+bkk0+mUaNGJCYmVtduiUgU\nOlZaJncCNwIfAKuAq4ArnXNfAflAB7x+levxRoU/D0yOTFFFRMLj4Ycfpn///lxxxRX079+frl27\nlgy2AW9ATu/evbnsssvo3bs3+fn5vP3229SvX7/S27jqqqu49NJLufDCC2nSpAl/+9vfqmNXRCSK\nmTeWRUKRmNrepQ6bFuliSJCchwZFughSAwKBANnZ2UdOKFEnMbU9qjdjn+ra2GRmK5xzgXDne6y0\nTIqIiIhINTgm+kxWl24tk8nWrzMRkUpTvSlS+6hlUkRERERCpmBSREREREKmYFJEREREQqY+k0dh\n5eZdtBn/ZqSLcUzRCEKR2KZ6M3ap/pWKqGVSREREREKmYFJEREREQqZgUkRERERCpmBSRERIT09n\n8ODBZd6LiByJBuCIiAjTp0+n+PG6we9FRI5EwaSIiJCcnFzuexGRI4npy9xmlmhm08zsOzPbZ2ZL\nzexcf1mamTkzu9DMlplZvpllm9kZpfI428wW+ss3m9njZnZcZPZIRCQyDneZOy0tjVtuuYUxY8bQ\nuHFjmjRpwvTp0ykoKGDUqFEcf/zxnHzyyTz77LORKr6IRFBMB5PAFGAoMAI4HVgJvG1mqUFpHgTG\nA2cA/wWeNzMDMLNuwDvAP4EewJVAT2BOTe2AiEgseP7552nUqBHLli1j/Pjx3HHHHVx++eV06NCB\n7Oxshg0bxk033URubm6kiyoiNSxmg0kzawjcAoxzzr3pnFsL3Ax8B4wKSvr/nHPvO+c+ByYBnYCW\n/rLfAP9wzk11zn3hnFvm53mVmTWtYLsZfgtndmH+rmraOxGR6HLaaacxceJE2rdvz1133cWJJ55I\nfHw8t99+O6eeeir33nsvzjkWL15cZt3MzEwCgQCBQADVmyK1T8wGk0A7IB4oqbmcc4XAEqBLULr/\nBL3f4r8WB4q9gF+aWV7xFJRfu/I26pzLdM4FnHOBOg3Ur0hEjg3du3cveW9mNG3alG7dupXMi4+P\nJyUlhW3btpVZNyMjg+zsbLKzs1G9KVL71NYBOMHDEA+UMz8u6PUvwKPl5LG5GsolIhKT4uPjD/ls\nZuXOKyoqqsliiUgUiOVgciOwHzjHf4+Z1QH6An+tZB4fA6c55zZUSwlFREREarmYvcztnNsLPA5M\nNrNLzayz/7kZ8Fgls5kM9DazJ8zsdDM71cwGm9mT1VRsERERkVolllsmAcb5r3OB44FPgIHOuVwz\n63iklZ1z/zGz84D/AxYCdYAvgVerqbwiIiIitUpMB5POuQLgDn8qvSwLsFLzcsqZlw0MrLZCiojE\ngIKCApKSkgB46qmnDlmWlZVVJv2qVavKzNu6dWt1FE1EolzMXuYWEZGjd/DgQdasWcOSJUvo2rVr\npIsjIjFIwaSIyDFs1apVBAIBTjvtNEaNGnXkFURESonpy9yR1q1lMtkPDYp0MUREQtazZ0/y8/Nr\nbHuqN0VqH7VMioiIiEjIFEyKiIiISMgUTIqIiIhIyNRn8iis3LyLNuPfjHQxjhk56mclEvNUb8YO\n1blSWWqZFBEREZGQKZgUERERkZApmBQRERGRkMVUMHn88ce/cOaZZ24CtgAFQA4wDUipZBYNgeuA\nvwKfA3uBPUA2MAZICHORRUSiRk5ODmZGdnZ2pIsiIrVILA3AaffVV1+lmVkT4HW8YLA3cDves7XP\nAf57hDz6Ac8BPwDvA6/hBaKXAQ8DVwIXAvuqYwdERGpSWloaXbt2ZcaMGQCcdNJJ5ObmcuKJJ0a4\nZCJSm8RSMPlYSkpKE+A24M9B8x8B7gT+ANx8hDy2Ar8EXgT2B80fC2QBZwOjgKnhKbKISPSoU6cO\nzZs3j3QxRKSWiZXL3O2AS37+85/nmdkAADMbaGYfmNnwxo0bc/HFF9/Uo0ePM4pXMLM2ZubM7Coz\ne9fM8s3sr2a2jaBA0sy6mNnf4+PjuzRt2pRLLrnkLjNTbSsiMS09PZ2FCxcyc+ZMzAwzK3OZOysr\nCzPjrbfeolevXtSvX59+/frx7bffsnDhQnr06EFSUhKDBw/mv/899MLP3Llz6dKlC/Xq1aNDhw48\n+uijFBUVRWJXRSTCYiWY7A+wffv2LUHzGuL1l+z9z3/+88OUlJQ6GzZseMPMSvd7/APwJ6AH8BHw\ndzNLAjCzVGARsGr27Nl3z58/n7y8vDjgdTMr99iYWYaZZZtZdmH+rrDupIhIuEyfPp2+ffsyfPhw\ncnNzyc3NpbCwsNy0v//975k2bRrLli1jx44dDB06lEmTJpGZmUlWVharV69m4sSJJelnzZrF7373\nOyZNmsTatWuZOnUqkydP5rHHHis3/8zMTAKBAIFAANWbIrVPrFzm7giwZ8+e3cUznHMvBy3/5PTT\nTz+7UaNGqXj9KP8dtOxR59wbAGb2O+AGoKef5hbgM+fcOOAtgLlz507p1KnTI0AAWF66IM65TCAT\nIDG1vQvbHoqIhFFycjIJCQk0aNCg5NJ2Tk5OuWnvv/9++vXrB8DNN9/Mr3/9a1asWMEZZ3gXe4YN\nG8ZLL710SPopU6Zw9dVXA9C2bVvGjx/PY489xujRo8vkn5GRQUZGBgCJqe3Dto8iEh1iJZhMBti/\nf3/w5el2wP1An8TExFZ169bFOWfAyaXW/U/Q++KWzab+ay/gvISEhIKEhISEoqKioh9//PF+f1k7\nygkmRURqm+7du5e8b9asGQDdunU7ZN62bdsA+P777/nmm28YOXIkt9xyS0magwcP4px+X4sci2Il\nmCzPv4BvgZFvvPHG1W3atBnZqVOnoqKiotKXuQ8Uv3HOOTODny7vx7Vp0+bjd955p1dhYeH3s2bN\nGvrII4984y/7rtr3QEQkCsTHx5e89+vIMvOK+0MWvz7xxBOcffbZNVhKEYlWsRJM7gJISEhIADCz\nE4BOwK3OufeByz/++GOKioqq1Ae0b9++ed9///3A1q1b5yYkJPSfOnXquqlTNZBbRGqHhISECvtJ\nhqpZs2a0aNGCjRs3csMNN4Q1bxGJTbESTK4DaNSo0XF4LYY7gO3Ar8zsm1dffbX3Aw88gJkVVuEy\nyzUvvvji5T169Chq1qzZyp07dx4PnOJP/wuMcc7tCf+uiIjUjDZt2rB8+XJycnJISkoK22jr++67\nj1//+tccf/zxXHrppRw4cICPP/6YzZs3c/fdd4dlGyISO2JlNPf7ACeeeGILAOdcETAU6A6smjBh\nQq/77ruvwDlXqZuNjxo1qh/wt5YtW265/vrrL965c+ce4G1gNTAT7+k6BdWwHyIiNWbs2LEkJCTQ\npUsXmjRpQlxceKr8m266iTlz5vDss8/So0cP+vXrR2ZmJm3btg1L/iISWyyGOkzPu/baay9Zs2bN\nx5999lmvoPnFNy1/kkNvWt7Jf/28VD7DgDnA13i3HPo61AIlprZ3qcOmhbq6VFHOQ4MiXQSJEoFA\nQI8EjFGJqe1RvRkbVOfWPma2wjkXCHe+MXGZ28zq3nzzzY8sXrz4ooyMjDPwHoO4FuiDFxCuByaU\nWm1t8epB8/rjBZJxeK2dw8vZ3E68+1eKiIiIyBHERMukmfUEPmzYsOGSdevWbW3ZsuUFwAlALvAq\ncB9eP8pgxTsWHEymA3OPsLmvgTaVKVcgEHBqHRGpeWqZjF06dyKRc0y3TDrnPgUaVHE1K2feU/4k\nIiIiImEQKwNwRERERCQKKZgUERERkZDFxGXuaLVy8y7ajH8z0sWo9TSiUKT2UL0Z/VTnSlWpZVJE\nREREQqZgUkRERERCpmBSREREREJWLcGkmWWZ2YxQlx/Fdp2ZXR3ufEVEjmUTJ06ka9euh00zevRo\n0tLSaqZAIhJVIjUA50rgQIS2LSIiIiJhEpFg0jn3QyS2KyIiIiLhVZ19Juua2XQz2+FPfzSzOCh7\nmdvMcszsHjN70sx2m9m3Zvab4MzMrIOZLTSzfWa2zswuNbM8M0uvqABm1tLM/h5UhjfNrL2/rI2Z\nFZlZoNQ6vzKz7WaWENajISJSTZxzTJ06lfbt25OYmEirVq24++67AVi5ciUXXXQR9evXp3HjxqSn\np7Nr166SddPT0xk8ePAh+R3psnZhYSFjx44lJSWFlJQU7rjjDgoLC6tn50Qk6lVnMHmdn39fYCSQ\nAdxxmPR3AiuBM4DJwBQz6wvgB6GvAgeBs/Cesf17ILGizMysAfA+sA843y9HLjDfzBo453KAd4ER\npVYdATzrnNtf+V0VEYmc3/3ud9x///3cfffdrF69mhdffJGTTjqJvXv3MmDAAJKSkli+fDmvvvoq\nH374ISNGlK72qmbq1KnMmjWLJ598kiVLllBYWMjzzz8fpr0RkVhTnZe5c4HbnHMO+NzMOgB3AY9U\nkP4d51xxa+Wfzew24EJgCXAx0BG4xDm3GcDM7gQWH2b7P8d7PvdwvwyY2UhgGzAYeAGYBcwys7uc\nc/vMrDNesPqrijI1swy8wJg6xzU5wiEQEaleeXl5PProo0ybNq0kSDz11FPp27cvs2bNYu/evTz7\n7LM0atQIgMzMTPr378+GDRs49dRTQ9rmtGnT+O1vf8v//u//AjB9+nTmzZtXYfrMzEwyMzMBKMzf\nVWE6EYlN1dkyubQ4iPMtAVqa2XEVpP9Pqc9bgKb++07AluJA0vcRUHSY7fcC2gJ7/MvhecAuIAVo\n56d5HdiPNyAIvFbJ5c65VRVl6pzLdM4FnHOBOg2SD7N5EZHqt2bNGgoKCrjwwgvLLFu7di3du3cv\nCSQBzj77bOLi4lizZk1I29u1axe5ubn07du3ZF5cXBx9+vSpcJ2MjAyys7PJzs5G9aZI7RNNj1Ms\nPbrbcXTBbhzwKV4LZWk/ADjnDpjZM8AIM3sBuB649yi2KSISE8wM8ALBQ3/3w4EDutmGiFRenvY7\nXQAAFv1JREFUdbZM9rHi2spzFl7r4u4Q8vocaGFmLYLmBTh8+T8GTgW2O+c2lJqCR5P/BegP3Ao0\nAv4eQvlERCKic+fOJCYmsmDBgnKXrVy5kj179pTM+/DDDykqKqJz584ANGnShNzc3EPW+/TTTyvc\nXnJyMqmpqSxdurRknnOO5cuXH+2uiEiMqs5gsgUwzcw6+jcS/w3waIh5vQusA542sx5mdhZe38uD\neC2Y5Xke+A543czON7O2ZnaemU0tHtEN4JxbB/wb+CPwUojBrohIRDRq1Ijbb7+du+++m7lz57Jx\n40aWL1/O448/znXXXUeDBg244YYbWLlyJYsWLWLkyJFceeWVJf0lL7jgAj755BPmzJnDhg0bmDJl\nCosXH647Otx+++1MmTKFl156iXXr1nHHHXeUCUhF5NhRncHk80AdYBneQJfZhBhMOueKgCvwRm8v\nB54G/oAXSO6rYJ184DzgS+BFvNbNp/H6TO4olXw2kOC/iojElAcffJBx48Zx//3307lzZ6666iq+\n/fZbGjRowLx589i9eze9e/dmyJAh9O3blzlz5pSsO2DAAH7/+98zYcIEevXqRU5ODrfeeuthtzdm\nzBiGDx/OTTfdRJ8+fSgqKuK6666r7t0UkShlpfvKxAoz64HXJzLgnFtxlHmNA250znWoynqJqe1d\n6rBpR7NpqYSchwZFuggSZQKBANnZ2ZEuhoQgMbU9qjejm+rc2svMVjjnAkdOWTXRNADnsMzsCmAv\n8AXQBu8y92d4fSNDzTMJaA3cjtfSKSIiIiJVEDPBJN7gmMnASXiXqbOAO93RNa3OAK4F/gk8WdWV\nu7VMJlu/4EREKk31pkjtEzPBpHPuGeCZMOeZjvc0HREREREJQXUOwBERERGRWk7BpIiIiIiETMGk\niIiIiIQsZvpMRqOVm3fRZvybkS5GraJbUojUbqo3o5PqXjkaapkUERERkZApmBQRERGRkCmYFBER\nEZGQRV0waWZZZjYj0uUQERERkSOLumBSRERiS1paGqNHj450MUQkQhRMioiIiEjIoj6YNLMLzWyn\nmd1sZk+Z2b/M7HYz22xmO8xsrpk1CEqfaGbTzOw7M9tnZkvN7Nyg5UvNbHzQ5+fMzJlZc/9zAzMr\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 5c241a2b07b94e5be1442a00a2c754c03d38118b Mon Sep 17 00:00:00 2001 From: robgray2000 Date: Thu, 8 Sep 2022 03:21:54 +0000 Subject: [PATCH 2/4] Completed Assignment 1 --- rwill166.ipynb | 221 ++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 172 insertions(+), 49 deletions(-) diff --git a/rwill166.ipynb b/rwill166.ipynb index 59b489b..6ffcd28 100644 --- a/rwill166.ipynb +++ b/rwill166.ipynb @@ -41,27 +41,27 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "class=\"menu-item\t54\n", - "\t38\n", - "\t35\n", - "
  • \t28\n", - "\t21\n", - "\t21\n" + "\t1\n", + "\t1\n", + "403\t1\n", + "Forbidden\t1\n", + "\t1\n", + "

    Forbidden

    \t1\n", + "

    You\t1\n", + "don't\t1\n", + "have\t1\n", + "permission\t1\n", + "to\t1\n" ] } ], @@ -107,10 +107,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 31, + "metadata": {}, "outputs": [], "source": [ "import requests, re, nltk\n", @@ -185,14 +183,30 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 32, "metadata": {}, "outputs": [ + { + "ename": "ValueError", + "evalue": "The number of FixedLocator locations (16), usually from a call to set_ticks, does not match the number of ticklabels (15).", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m pylab \u001b[38;5;241m.\u001b[39myticks (pos, [ x [\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m wf_bu ])\n\u001b[1;32m 31\u001b[0m ax2 \u001b[38;5;241m.\u001b[39mbarh (\u001b[38;5;28mrange\u001b[39m (\u001b[38;5;28mlen\u001b[39m(wf_bu)), [ x [\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m wf_bu ], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 33\u001b[0m \u001b[43mplotTwoLists\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mwf_ee\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwf_bu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDifference between Pride and Prejudice and Huck Finn\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36mplotTwoLists\u001b[0;34m(wf_ee, wf_bu, title)\u001b[0m\n\u001b[1;32m 22\u001b[0m pos \u001b[38;5;241m=\u001b[39m np \u001b[38;5;241m.\u001b[39marange (\u001b[38;5;28mlen\u001b[39m(wf_ee)\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m) \n\u001b[1;32m 23\u001b[0m ax1 \u001b[38;5;241m.\u001b[39mtick_params (axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m, which\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmajor\u001b[39m\u001b[38;5;124m'\u001b[39m, labelsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m)\n\u001b[0;32m---> 24\u001b[0m \u001b[43mpylab\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myticks\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mwf_ee\u001b[49m\u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m ax1 \u001b[38;5;241m.\u001b[39mbarh (\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(wf_ee)), [ x [\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m wf_ee ], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 27\u001b[0m ax2 \u001b[38;5;241m=\u001b[39m f \u001b[38;5;241m.\u001b[39madd_subplot (\u001b[38;5;241m122\u001b[39m)\n", + "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/pyplot.py:1876\u001b[0m, in \u001b[0;36myticks\u001b[0;34m(ticks, labels, **kwargs)\u001b[0m\n\u001b[1;32m 1874\u001b[0m l\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m 1875\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1876\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_yticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m locs, labels\n", + "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py:75\u001b[0m, in \u001b[0;36m_axis_method_wrapper.__set_name__..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axis.py:1798\u001b[0m, in \u001b[0;36mAxis._set_ticklabels\u001b[0;34m(self, labels, fontdict, minor, **kwargs)\u001b[0m\n\u001b[1;32m 1796\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fontdict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1797\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate(fontdict)\n\u001b[0;32m-> 1798\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_ticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mminor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mminor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axis.py:1720\u001b[0m, in \u001b[0;36mAxis.set_ticklabels\u001b[0;34m(self, ticklabels, minor, **kwargs)\u001b[0m\n\u001b[1;32m 1716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(locator, mticker\u001b[38;5;241m.\u001b[39mFixedLocator):\n\u001b[1;32m 1717\u001b[0m \u001b[38;5;66;03m# Passing [] as a list of ticklabels is often used as a way to\u001b[39;00m\n\u001b[1;32m 1718\u001b[0m \u001b[38;5;66;03m# remove all tick labels, so only error for > 0 ticklabels\u001b[39;00m\n\u001b[1;32m 1719\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 1720\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1721\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe number of FixedLocator locations\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1722\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m), usually from a call to\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1723\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m set_ticks, does not match\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1724\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m the number of ticklabels (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(ticklabels)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1725\u001b[0m tickd \u001b[38;5;241m=\u001b[39m {loc: lab \u001b[38;5;28;01mfor\u001b[39;00m loc, lab \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs, ticklabels)}\n\u001b[1;32m 1726\u001b[0m func \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_with_dict, tickd)\n", + "\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (16), usually from a call to set_ticks, does not match the number of ticklabels (15)." + ] + }, { "data": { - "image/png": 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AOHwz8mL809Q/Bz6PY9nT8X0hFwDrwl6XOOdKgEuAjvgnsR8N1rUjSj5j8A8C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\nCF5HRCRWvP322zRq1IiDBw8CsGHDBsyMm2++uSTNPffcw0UXXQTAmjV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\n", "text/plain": [ - "" + "

    " ] }, "metadata": {}, @@ -237,7 +251,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 33, "metadata": {}, "outputs": [ { @@ -312,55 +326,164 @@ }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, + "execution_count": 34, + "metadata": {}, "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" + "\n", + "yellow_wallpaper = \"\"\n", + "yellow_wallpaper_in = requests .get('https://www.gutenberg.org/files/1952/1952-h/1952-h.htm')\n", + "yellow_wallpaper = yellow_wallpaper + yellow_wallpaper_in.text\n", + " \n", + "herland = \"\"\n", + "herland_in = requests.get('https://www.gutenberg.org/files/32/32-h/32-h.htm')\n", + "herland = herland + herland_in.text\n", + "\n", + "sherlock = \"\"\n", + "sherlock_in = requests.get('https://www.gutenberg.org/files/1661/1661-h/1661-h.htm')\n", + "sherlock = sherlock + sherlock_in.text" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 35, "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "1323653" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "The Yellow Wallpaper: \n", + "\n", + "and\t330\n", + "I\t289\n", + "

    \t267\n", + "

    \t257\n", + "of\t213\n", + "to\t207\n", + "a\t192\n", + "is\t150\n", + "in\t146\n", + "that\t114\n", + "it\t108\n", + "you\t85\n", + "or\t83\n", + "Project\t80\n", + "\n", + "\n", + "Herland: \n", + "\n", + "of\t1764\n", + "and\t1678\n", + "to\t1459\n", + "

    \t1383\n", + "

    \t1371\n", + "a\t1167\n", + "in\t765\n", + "we\t710\n", + "was\t694\n", + "I\t669\n", + "that\t630\n", + "as\t554\n", + "had\t508\n", + "they\t443\n", + "\n", + "\n", + "The Adventures of Sherlock Holmes: \n", + "\n", + "and\t2794\n", + "of\t2726\n", + "to\t2702\n", + "a\t2575\n", + "I\t2531\n", + "

    \t2523\n", + "

    \t2483\n", + "in\t1706\n", + "that\t1557\n", + "was\t1361\n", + "his\t1096\n", + "is\t1073\n", + "you\t1034\n", + "he\t1014\n" + ] } ], "source": [ - "len(t)" + "yellow_wallpaper_wds = re.split('\\s+',yellow_wallpaper)\n", + "herland_wds = re.split('\\s+', herland)\n", + "sherlock_wds = re.split('\\s+', sherlock)\n", + "\n", + "# now populate a dictionary (wf)\n", + "yellow_wallpaper_wf = {}\n", + "for w in yellow_wallpaper_wds:\n", + " if w in yellow_wallpaper_wf: yellow_wallpaper_wf [w] = yellow_wallpaper_wf [w] + 1\n", + " else: yellow_wallpaper_wf [w] = 1\n", + "herland_wf = {}\n", + "for w in herland_wds:\n", + " if w in herland_wf: herland_wf [w] = herland_wf [w] + 1\n", + " else: herland_wf [w] = 1\n", + "sherlock_wf = {}\n", + "for w in sherlock_wds:\n", + " if w in sherlock_wf: sherlock_wf [w] = sherlock_wf [w] + 1\n", + " else: sherlock_wf [w] = 1 \n", + " \n", + "# dictionaries can not be sorted, so lets get a sorted *list* \n", + "yellow_wallpaper_wfs = sorted (yellow_wallpaper_wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "herland_wfs = sorted (herland_wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "sherlock_wfs = sorted (sherlock_wf .items(), key = operator .itemgetter (1), reverse=True) \n", + "\n", + "# lets just have no more than 15 words \n", + "yellow_wallpaper_ml = min(len(yellow_wallpaper_wfs),15)\n", + "herland_ml = min(len(herland_wfs),15)\n", + "sherlock_ml = min(len(sherlock_wfs),15)\n", + "\n", + "# Print\n", + "print('The Yellow Wallpaper: \\n')\n", + "for i in range(1,yellow_wallpaper_ml,1):\n", + " print (yellow_wallpaper_wfs[i][0]+\"\\t\"+str(yellow_wallpaper_wfs[i][1])) \n", + "\n", + "print('\\n')\n", + " \n", + "print('Herland: \\n')\n", + "for i in range(1,herland_ml,1):\n", + " print (herland_wfs[i][0]+\"\\t\"+str(herland_wfs[i][1])) \n", + " \n", + "print('\\n')\n", + "\n", + "print('The Adventures of Sherlock Holmes: \\n')\n", + "for i in range(1,sherlock_ml,1):\n", + " print (sherlock_wfs[i][0]+\"\\t\"+str(sherlock_wfs[i][1])) " + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "All three texts use the basic words such as and, to, of, I, and a at pretty similar rates. The two texts of The Yellow Wallpaper and Herland by Charlotte Perkins Gilman both use pretty much the same top 15 words, except that Herland uses more plural words, while The Yellow Wallpaper uses more singular ones. The Adventures of Sherlock Holmes by Arthur Conan Doyle uses male pronouns very frequently compared to the two books by Charlotte Perkins Gilman. A even further analysis could show more differences between the the two works of the same author as well as the works of the two different authors.\n" + ] + } + ], + "source": [ + "print('All three texts use the basic words such as and, to, of, I, and a at pretty similar rates. The two texts of The Yellow Wallpaper and Herland by Charlotte Perkins Gilman both use pretty much the same top 15 words, except that Herland uses more plural words, while The Yellow Wallpaper uses more singular ones. The Adventures of Sherlock Holmes by Arthur Conan Doyle uses male pronouns very frequently compared to the two books by Charlotte Perkins Gilman. A even further analysis could show more differences between the the two works of the same author as well as the works of the two different authors.')" ] }, { "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 +497,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.8.10" } }, "nbformat": 4, From 237161ce871d33025ec6ed10b72aaa45461da1c7 Mon Sep 17 00:00:00 2001 From: robgray2000 Date: Thu, 8 Sep 2022 19:43:02 +0000 Subject: [PATCH 3/4] Updated a block --- rwill166.ipynb | 38 +++++++++++--------------------------- 1 file changed, 11 insertions(+), 27 deletions(-) diff --git a/rwill166.ipynb b/rwill166.ipynb index 6ffcd28..9ed3a11 100644 --- a/rwill166.ipynb +++ b/rwill166.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -183,30 +183,14 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 3, "metadata": {}, "outputs": [ - { - "ename": "ValueError", - "evalue": "The number of FixedLocator locations (16), usually from a call to set_ticks, does not match the number of ticklabels (15).", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 30\u001b[0m pylab \u001b[38;5;241m.\u001b[39myticks (pos, [ x [\u001b[38;5;241m0\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m wf_bu ])\n\u001b[1;32m 31\u001b[0m ax2 \u001b[38;5;241m.\u001b[39mbarh (\u001b[38;5;28mrange\u001b[39m (\u001b[38;5;28mlen\u001b[39m(wf_bu)), [ x [\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m wf_bu ], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 33\u001b[0m \u001b[43mplotTwoLists\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mwf_ee\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mwf_bu\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDifference between Pride and Prejudice and Huck Finn\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n", - "Input \u001b[0;32mIn [32]\u001b[0m, in \u001b[0;36mplotTwoLists\u001b[0;34m(wf_ee, wf_bu, title)\u001b[0m\n\u001b[1;32m 22\u001b[0m pos \u001b[38;5;241m=\u001b[39m np \u001b[38;5;241m.\u001b[39marange (\u001b[38;5;28mlen\u001b[39m(wf_ee)\u001b[38;5;241m+\u001b[39m\u001b[38;5;241m1\u001b[39m) \n\u001b[1;32m 23\u001b[0m ax1 \u001b[38;5;241m.\u001b[39mtick_params (axis\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mboth\u001b[39m\u001b[38;5;124m'\u001b[39m, which\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmajor\u001b[39m\u001b[38;5;124m'\u001b[39m, labelsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m14\u001b[39m)\n\u001b[0;32m---> 24\u001b[0m \u001b[43mpylab\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myticks\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43mpos\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mfor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01min\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mwf_ee\u001b[49m\u001b[43m \u001b[49m\u001b[43m]\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 25\u001b[0m ax1 \u001b[38;5;241m.\u001b[39mbarh (\u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(wf_ee)), [ x [\u001b[38;5;241m1\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m x \u001b[38;5;129;01min\u001b[39;00m wf_ee ], align\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcenter\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 27\u001b[0m ax2 \u001b[38;5;241m=\u001b[39m f \u001b[38;5;241m.\u001b[39madd_subplot (\u001b[38;5;241m122\u001b[39m)\n", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/pyplot.py:1876\u001b[0m, in \u001b[0;36myticks\u001b[0;34m(ticks, labels, **kwargs)\u001b[0m\n\u001b[1;32m 1874\u001b[0m l\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m 1875\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1876\u001b[0m labels \u001b[38;5;241m=\u001b[39m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_yticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1878\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m locs, labels\n", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axes/_base.py:75\u001b[0m, in \u001b[0;36m_axis_method_wrapper.__set_name__..wrapper\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 74\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mwrapper\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[0;32m---> 75\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mget_method\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axis.py:1798\u001b[0m, in \u001b[0;36mAxis._set_ticklabels\u001b[0;34m(self, labels, fontdict, minor, **kwargs)\u001b[0m\n\u001b[1;32m 1796\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m fontdict \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 1797\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate(fontdict)\n\u001b[0;32m-> 1798\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mset_ticklabels\u001b[49m\u001b[43m(\u001b[49m\u001b[43mlabels\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mminor\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mminor\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m/usr/local/lib/python3.8/dist-packages/matplotlib/axis.py:1720\u001b[0m, in \u001b[0;36mAxis.set_ticklabels\u001b[0;34m(self, ticklabels, minor, **kwargs)\u001b[0m\n\u001b[1;32m 1716\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(locator, mticker\u001b[38;5;241m.\u001b[39mFixedLocator):\n\u001b[1;32m 1717\u001b[0m \u001b[38;5;66;03m# Passing [] as a list of ticklabels is often used as a way to\u001b[39;00m\n\u001b[1;32m 1718\u001b[0m \u001b[38;5;66;03m# remove all tick labels, so only error for > 0 ticklabels\u001b[39;00m\n\u001b[1;32m 1719\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(ticklabels) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 1720\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 1721\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe number of FixedLocator locations\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1722\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m), usually from a call to\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1723\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m set_ticks, does not match\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m 1724\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m the number of ticklabels (\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(ticklabels)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m).\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 1725\u001b[0m tickd \u001b[38;5;241m=\u001b[39m {loc: lab \u001b[38;5;28;01mfor\u001b[39;00m loc, lab \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(locator\u001b[38;5;241m.\u001b[39mlocs, ticklabels)}\n\u001b[1;32m 1726\u001b[0m func \u001b[38;5;241m=\u001b[39m functools\u001b[38;5;241m.\u001b[39mpartial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_format_with_dict, tickd)\n", - "\u001b[0;31mValueError\u001b[0m: The number of FixedLocator locations (16), usually from a call to set_ticks, does not match the number of ticklabels (15)." - ] - }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "

    " + "
    " ] }, "metadata": {}, @@ -235,14 +219,14 @@ " ax1 = f .add_subplot (121)\n", " plt .subplots_adjust (wspace=.5)\n", "\n", - " pos = np .arange (len(wf_ee)+1) \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", " ax2 = f .add_subplot (122)\n", " ax2 .tick_params (axis='both', which='major', labelsize=14)\n", - " pos = np .arange (len(wf_bu)+1) \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", "\n", @@ -251,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -326,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -347,7 +331,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -458,7 +442,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 7, "metadata": {}, "outputs": [ { From a466141d46351a99dfb61a687f50c00bf5af98e7 Mon Sep 17 00:00:00 2001 From: robgray2000 Date: Tue, 13 Sep 2022 13:15:05 +0000 Subject: [PATCH 4/4] Updated text analysis --- rwill166.ipynb | 211 +++++++++++++++++++++++++++++++++++-------------- 1 file changed, 152 insertions(+), 59 deletions(-) diff --git a/rwill166.ipynb b/rwill166.ipynb index 9ed3a11..b36f0fc 100644 --- a/rwill166.ipynb +++ b/rwill166.ipynb @@ -41,7 +41,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -107,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -183,7 +183,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -235,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -310,7 +310,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -318,20 +318,23 @@ "\n", "yellow_wallpaper = \"\"\n", "yellow_wallpaper_in = requests .get('https://www.gutenberg.org/files/1952/1952-h/1952-h.htm')\n", - "yellow_wallpaper = yellow_wallpaper + yellow_wallpaper_in.text\n", + "ywt = cleanHtml(yellow_wallpaper_in.text).lower()\n", + "yellow_wallpaper = yellow_wallpaper + ywt\n", " \n", "herland = \"\"\n", "herland_in = requests.get('https://www.gutenberg.org/files/32/32-h/32-h.htm')\n", - "herland = herland + herland_in.text\n", + "ht = cleanHtml(herland_in.text).lower()\n", + "herland = herland + ht\n", "\n", "sherlock = \"\"\n", "sherlock_in = requests.get('https://www.gutenberg.org/files/1661/1661-h/1661-h.htm')\n", - "sherlock = sherlock + sherlock_in.text" + "st = cleanHtml(sherlock_in.text).lower()\n", + "sherlock = sherlock + st" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 28, "metadata": {}, "outputs": [ { @@ -340,60 +343,46 @@ "text": [ "The Yellow Wallpaper: \n", "\n", - "and\t330\n", - "I\t289\n", - "

    \t267\n", - "

    \t257\n", - "of\t213\n", - "to\t207\n", - "a\t192\n", - "is\t150\n", - "in\t146\n", - "that\t114\n", - "it\t108\n", - "you\t85\n", - "or\t83\n", - "Project\t80\n", + "and\t349\n", + "i\t293\n", + "of\t231\n", + "to\t222\n", + "a\t197\n", + "it\t177\n", + "is\t154\n", + "in\t152\n", + "that\t123\n", + "you\t110\n", + "or\t89\n", + "project\t84\n", + "for\t78\n", + "this\t76\n", "\n", "\n", "Herland: \n", "\n", - "of\t1764\n", - "and\t1678\n", - "to\t1459\n", - "

    \t1383\n", - "

    \t1371\n", - "a\t1167\n", - "in\t765\n", - "we\t710\n", - "was\t694\n", - "I\t669\n", - "that\t630\n", - "as\t554\n", - "had\t508\n", - "they\t443\n", - "\n", - "\n", - "The Adventures of Sherlock Holmes: \n", - "\n", - "and\t2794\n", - "of\t2726\n", - "to\t2702\n", - "a\t2575\n", - "I\t2531\n", - "

    \t2523\n", - "

    \t2483\n", - "in\t1706\n", - "that\t1557\n", - "was\t1361\n", - "his\t1096\n", - "is\t1073\n", - "you\t1034\n", - "he\t1014\n" + "of\t1822\n", + "and\t1780\n", + "to\t1512\n", + "a\t1190\n", + "we\t941\n", + "in\t803\n", + "that\t714\n", + "was\t708\n", + "i\t680\n", + "it\t640\n", + "they\t598\n", + "as\t597\n", + "had\t526\n", + "with\t459\n" ] } ], "source": [ + "yellow_wallpaper = cleanWord(yellow_wallpaper)\n", + "herland = cleanWord(herland)\n", + "sherlock = cleanWord(sherlock)\n", + "\n", "yellow_wallpaper_wds = re.split('\\s+',yellow_wallpaper)\n", "herland_wds = re.split('\\s+', herland)\n", "sherlock_wds = re.split('\\s+', sherlock)\n", @@ -433,7 +422,77 @@ "for i in range(1,herland_ml,1):\n", " print (herland_wfs[i][0]+\"\\t\"+str(herland_wfs[i][1])) \n", " \n", - "print('\\n')\n", + "#print('\\n')\n", + "\n", + "#print('The Adventures of Sherlock Holmes: \\n')\n", + "#for i in range(1,sherlock_ml,1):\n", + "# print (sherlock_wfs[i][0]+\"\\t\"+str(sherlock_wfs[i][1])) " + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Charlotte Perkins Gilman's texts: \n", + "\n", + "and\t2129\n", + "of\t2053\n", + "to\t1734\n", + "a\t1387\n", + "i\t973\n", + "we\t968\n", + "in\t955\n", + "that\t837\n", + "it\t817\n", + "was\t747\n", + "as\t656\n", + "they\t613\n", + "had\t548\n", + "with\t534\n", + "\n", + "\n", + "The Adventures of Sherlock Holmes: \n", + "\n", + "and\t2935\n", + "of\t2767\n", + "to\t2735\n", + "a\t2649\n", + "i\t2598\n", + "in\t1789\n", + "that\t1653\n", + "it\t1470\n", + "he\t1416\n", + "was\t1396\n", + "you\t1312\n", + "his\t1150\n", + "is\t1116\n", + "my\t955\n" + ] + } + ], + "source": [ + "cpgt_wds = yellow_wallpaper_wds + herland_wds\n", + "\n", + "cpgt_wf = {}\n", + "for w in cpgt_wds:\n", + " if w in cpgt_wf: cpgt_wf [w] = cpgt_wf [w] + 1\n", + " else: cpgt_wf [w] = 1\n", + "\n", + "cpgt_wfs = sorted(cpgt_wf.items(), key = operator .itemgetter (1), reverse=True) \n", + "\n", + "cpgt_ml = min(len(cpgt),15)\n", + "\n", + "print('Charlotte Perkins Gilman\\'s texts: \\n')\n", + "\n", + "for i in range(1, cpgt_ml, 1):\n", + " print(cpgt_wfs[i][0]+\"\\t\"+str(cpgt_wfs[i][1]))\n", + "\n", + "print(\"\\n\")\n", "\n", "print('The Adventures of Sherlock Holmes: \\n')\n", "for i in range(1,sherlock_ml,1):\n", @@ -442,7 +501,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 19, "metadata": {}, "outputs": [ { @@ -454,7 +513,39 @@ } ], "source": [ - "print('All three texts use the basic words such as and, to, of, I, and a at pretty similar rates. The two texts of The Yellow Wallpaper and Herland by Charlotte Perkins Gilman both use pretty much the same top 15 words, except that Herland uses more plural words, while The Yellow Wallpaper uses more singular ones. The Adventures of Sherlock Holmes by Arthur Conan Doyle uses male pronouns very frequently compared to the two books by Charlotte Perkins Gilman. A even further analysis could show more differences between the the two works of the same author as well as the works of the two different authors.')" + "print('All three texts use the basic words such as and, to, of, I, and a at pretty similar rates. The two texts of The Yellow Wallpaper and Herland by Charlotte Perkins Gilman both use pretty much the same top 15 words, except that Herland uses more plural words, while The Yellow Wallpaper uses more singular ones. The Adventures of Sherlock Holmes by Arthur Conan Doyle uses male pronouns very frequently compared to the two books by Charlotte Perkins Gilman. An even further analysis could show more differences between the the two works of the same author as well as the works of the two different authors.')" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Further Analysis Update: \n", + "\n", + "Charlotte Perkins Gilman and Arthur Conan Doyle have the same top 5 words in the texts that I have analyzed, \n", + "and among the top 15 words, 9 words match between them. Doyle also uses more male pronouns, while Gilman \n", + "uses plural pronouns more often. Within Gilman's writing, the top 5 words do not match, likely due to there \n", + "being a much smaller amount of words in The Yellow Wallpaper. Of the top 15, though, only 8 words match \n", + "between the two texts. This is very interesting as more words matched between the different authors than for \n", + "the same author. I again believe this is due to the analysis between authors having a larger sample size in \n", + "terms of word count compared to the analysis of Gilman's two texts.\n" + ] + } + ], + "source": [ + "print(\"Improved Analysis Update: \\n\") #Removed bad characters and did a combined analysis between the authors\n", + "print(\"Charlotte Perkins Gilman and Arthur Conan Doyle have the same top 5 words in the texts that I have analyzed, \")\n", + "print(\"and among the top 15 words, 9 words match between them. Doyle also uses more male pronouns, while Gilman \")\n", + "print(\"uses plural pronouns more often. Within Gilman's writing, the top 5 words do not match, likely due to there \")\n", + "print(\"being a much smaller amount of words in The Yellow Wallpaper. Of the top 15, though, only 8 words match \")\n", + "print(\"between the two texts. This is very interesting as more words matched between the different authors than for \")\n", + "print(\"the same author. I again believe this is due to the analysis between authors having a larger sample size in \")\n", + "print(\"terms of word count compared to the analysis of Gilman's two texts.\")" ] }, { @@ -462,7 +553,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "#Add automatic comparison of top 30 word vectors to enhance quality of analysis" + ] } ], "metadata": {