{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import time\n",
    "import glob\n",
    "import codecs\n",
    "import matplotlib.pyplot as plt\n",
    "import requests\n",
    "%pylab inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Reading Corpus"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available Books: \n",
      "\n",
      "Book 1 - The Philosopher's Stone\n",
      "Book 2 - The Chamber of Secrets\n",
      "Book 3 - The Prisoner of Azkaban\n",
      "Book 4 - The Goblet of Fire\n",
      "Book 5 - The Order of the Phoenix\n",
      "Book 6 - The Half Blood Prince\n",
      "Book 7 - The Deathly Hallows\n"
     ]
    }
   ],
   "source": [
    "# Books present\n",
    "books = sorted(glob.glob(\"data/harrypotter/*.txt\"))\n",
    "\n",
    "print \"Available Books: \\n\"\n",
    "for i in books:\n",
    "    print i.split(\"/\")[2].split(\"_\")[0]\n",
    "\n",
    "# Read data from all books to single corpus variable\n",
    "temp = \"\"\n",
    "t = \"\"\n",
    "chars = []\n",
    "book_raw = []\n",
    "for book in books:\n",
    "    with codecs.open(book, \"rb\", \"utf-8\") as infile:\n",
    "        temp = infile.read()\n",
    "        book_raw.append(temp)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Calling the API"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def get_readability_score(data):\n",
    "    \"\"\"\n",
    "    Sends a POST request to FORMCEPT's Text Redability API\n",
    "    \"\"\"\n",
    "    endpoint = 'http://<fchost>:<fcport>/fc/readability'\n",
    "    text = data\n",
    "    payload = {'txt_readability': text}\n",
    "    headers = {'Content-type': 'application/json', 'Accept': 'application/json'}\n",
    "    r = requests.post(endpoint, json = payload, headers = headers)\n",
    "    return r.json()['response']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# get readability score for each of the 7 parts of the Harry Potter Book\n",
    "readability_scores = []\n",
    "for book in range(len(book_raw)):\n",
    "    readability_scores.append(get_readability_score(book_raw[book].strip().lower().replace(\"\\n\",\" \").replace(\"\\t\",\" \").replace(\"\\r\", \" \")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Bookwise Readability Score\n",
    "\n",
    "1. <b>Less</b> the score the <b>more readabile</b> the text. \n",
    "2. <b>More</b> the score the <b>difficult</b> the text."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Container object of 7 artists>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAXkAAAEZCAYAAABy91VnAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nDxIWgRxueYuaAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7ffb8d3df290>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = readability_scores\n",
    "N = len(y)\n",
    "x = [i+1 for i in range(N)]\n",
    "width = 1/1.5\n",
    "\n",
    "mini = min(readability_scores)\n",
    "maxi = max(readability_scores)\n",
    "\n",
    "pylab.title(\"Readability Comparison of text\")\n",
    "pylab.xlabel(\"Book\")\n",
    "pylab.ylabel(\"Readability Score\")\n",
    "\n",
    "colors = [\"yellow\"]*7\n",
    "colors[readability_scores.index(mini)] = \"green\"\n",
    "colors[readability_scores.index(maxi)] = \"red\"\n",
    "plt.bar(x, y, width, align='center', color=colors)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}