p2.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/agha/Sir Naeem/next/project/November\n",
      "/home/agha/Sir Naeem/next/project/grand_data.png\n",
      "/home/agha/Sir Naeem/next/project/data_processing3.py\n",
      "/home/agha/Sir Naeem/next/project/December\n",
      "/home/agha/Sir Naeem/next/project/October\n",
      "/home/agha/Sir Naeem/next/project/September\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'/home/agha/Sir Naeem'"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os \n",
    "import matplotlib.pyplot as plt\n",
    "parent_dir = \"/home/agha/Sir Naeem/next/project\"\n",
    "\n",
    "############################### Grand OutPut File ###################################\n",
    "#global grand_out\n",
    "grand_out = 'grand_data.out'\n",
    "\n",
    "############################## IF GRANDE OUTPUT EXSIST ###############################\n",
    "\n",
    "#if grand_out in os.listdir(directory):\n",
    "#    os.remove(grand_out)\n",
    "\n",
    "################################## Data Refine #######################################\n",
    "\n",
    "for filename in os.listdir(parent_dir):\n",
    "    #if '.' not in filename:       \n",
    "    child_dir = parent_dir + '/' + filename\n",
    "    print(child_dir)\n",
    "    #if grand_out in os.listdir(child_dir):\n",
    "    #    os.remove(grand_out)\n",
    "os.path.realpath(os.getcwd())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/home/agha/Sir Naeem/next/project/Nov 1/grand_data.out'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-2-0b7d8a1e6be8>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mfile\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'/home/agha/Sir Naeem/next/project/Nov 1/grand_data.out'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfile\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mtemp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtemp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msplit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'\\n'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0melement\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/home/agha/Sir Naeem/next/project/Nov 1/grand_data.out'"
     ]
    }
   ],
   "source": [
    "file = '/home/agha/Sir Naeem/next/project/Nov 1/grand_data.out'\n",
    "f = open(file, 'r')\n",
    "temp = f.read()\n",
    "temp = temp.split('\\n')\n",
    "element = []\n",
    "key = ''\n",
    "value = 0\n",
    "day_dict = {}\n",
    "#print(temp)\n",
    "for x in temp:\n",
    "    #print(x.split())\n",
    "    element = x.split()\n",
    "    for y in element:\n",
    "        if y != element[-1]:\n",
    "            if y is element[-2]:\n",
    "                key = key + str(y)\n",
    "            else:\n",
    "                key = key + str(y) + ' '\n",
    "            \n",
    "        if y == element[-1]:\n",
    "            value = y.strip('()')\n",
    "            day_dict[key] = value\n",
    "            key = ''\n",
    "            value = 0\n",
    "            \n",
    "def removekey(d, key):\n",
    "    r = dict(d)\n",
    "    del r[key]\n",
    "    return r\n",
    "\n",
    "for key, value in day_dict.items():\n",
    "    if '\"' in key:\n",
    "        removekey(day_dict, key)\n",
    "        #del day_dict[key]\n",
    "    #print(key)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "import operator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'day_dict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-4-45acbdf3a589>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m#day_dict = sorted(day_dict)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m#sorted(day_dict, key=day_dict.get, reverse=True)[:5]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCounter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mday_dict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmost_common\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m7\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'day_dict' is not defined"
     ]
    }
   ],
   "source": [
    "#day_dict = sorted(day_dict)\n",
    "#sorted(day_dict, key=day_dict.get, reverse=True)[:5]\n",
    "dict(Counter(day_dict).most_common(7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'day_dict' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-92a564eb8d43>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0;32mfor\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mday_dict\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'day_dict' is not defined"
     ]
    }
   ],
   "source": [
    "for key, value in day_dict.items():\n",
    "    print(key,value)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'Panama'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-44cdd5e58bdd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mday_dict\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Panama'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mKeyError\u001b[0m: 'Panama'"
     ]
    }
   ],
   "source": [
    "day_dict['Panama']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "monthly ={'Nov 4': {'Sharif': '77.7778', 'Asad': '43.75', 'best': '40', 'team': '40', 'Karachi': '40', 'Tando': '37.5', 'Bago': '37.5'}, 'Nov 1': {'policy': '46.1538', 'Sharif': '45.4545', 'fresh': '44', 'seats': '40', 'delimitation': '36', 'party': '27.2727', 'The': '27.2727'}, 'Nov 3': {'says': '40', 'court': '40', 'Panama': '40', 'Papers': '40', 'petitions': '40', 'Nawaz': '40', 'Sharif': '40'}, 'Nov 2': {'Bank': '56.25', 'Pakistan': '50', 'border': '50', 'Iran': '50', 'Bajwa': '50', 'tax': '47.8261', 'would': '37.5'}}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import collections"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "monthly = dict(collections.OrderedDict(sorted(monthly.items())))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Nov 1': {'Sharif': '45.4545',\n",
       "  'The': '27.2727',\n",
       "  'delimitation': '36',\n",
       "  'fresh': '44',\n",
       "  'party': '27.2727',\n",
       "  'policy': '46.1538',\n",
       "  'seats': '40'},\n",
       " 'Nov 2': {'Bajwa': '50',\n",
       "  'Bank': '56.25',\n",
       "  'Iran': '50',\n",
       "  'Pakistan': '50',\n",
       "  'border': '50',\n",
       "  'tax': '47.8261',\n",
       "  'would': '37.5'},\n",
       " 'Nov 3': {'Nawaz': '40',\n",
       "  'Panama': '40',\n",
       "  'Papers': '40',\n",
       "  'Sharif': '40',\n",
       "  'court': '40',\n",
       "  'petitions': '40',\n",
       "  'says': '40'},\n",
       " 'Nov 4': {'Asad': '43.75',\n",
       "  'Bago': '37.5',\n",
       "  'Karachi': '40',\n",
       "  'Sharif': '77.7778',\n",
       "  'Tando': '37.5',\n",
       "  'best': '40',\n",
       "  'team': '40'}}"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import math \n",
    "dates = []\n",
    "words_in_dates = []\n",
    "magnitude_per_words = []\n",
    "temp_w = []\n",
    "temp_m = []\n",
    "temp_area = []\n",
    "area = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for day, value in monthly.items():\n",
    "    dates.append(day)\n",
    "    for words, magnitude in value.items():\n",
    "        temp_w.append(words)\n",
    "        temp_m.append(float(magnitude))\n",
    "        for x in magnitude.split():\n",
    "            temp_area.append(round(2 * math.pi * float(x))*4)\n",
    "    \n",
    "    \n",
    "    area.append(temp_area)        \n",
    "    words_in_dates.append(temp_w)\n",
    "    magnitude_per_words.append(temp_m)\n",
    "    temp_w = []\n",
    "    temp_m = []\n",
    "    temp_area = []\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Nov 1', 'Nov 2', 'Nov 3', 'Nov 4']"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['policy', 'Sharif', 'fresh', 'seats', 'delimitation', 'party', 'The'],\n",
       " ['Bank', 'Pakistan', 'border', 'Iran', 'Bajwa', 'tax', 'would'],\n",
       " ['says', 'court', 'Panama', 'Papers', 'petitions', 'Nawaz', 'Sharif'],\n",
       " ['Sharif', 'Asad', 'best', 'team', 'Karachi', 'Tando', 'Bago']]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "words_in_dates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1160, 1144, 1104, 1004, 904, 684, 684],\n",
       " [1412, 1256, 1256, 1256, 1256, 1204, 944],\n",
       " [1004, 1004, 1004, 1004, 1004, 1004, 1004],\n",
       " [1956, 1100, 1004, 1004, 1004, 944, 944]]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "area"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[46.1538, 45.4545, 44.0, 40.0, 36.0, 27.2727, 27.2727],\n",
       " [56.25, 50.0, 50.0, 50.0, 50.0, 47.8261, 37.5],\n",
       " [40.0, 40.0, 40.0, 40.0, 40.0, 40.0, 40.0],\n",
       " [77.7778, 43.75, 40.0, 40.0, 40.0, 37.5, 37.5]]"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "magnitude_per_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1, 1, 1, 1, 1, 1, 1], [2, 2, 2, 2, 2, 2, 2], [3, 3, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4]]\n",
      "[[1, 2, 3, 4, 5, 6, 7], [1, 2, 3, 4, 5, 6, 7], [1, 2, 3, 4, 5, 6, 7], [1, 2, 3, 4, 5, 6, 7]]\n"
     ]
    }
   ],
   "source": [
    "x_coordinates = []\n",
    "y_coordinates = []\n",
    "temp_x = []\n",
    "temp_y = []\n",
    "for i in range(len(magnitude_per_words)):\n",
    "    for j in range(len(magnitude_per_words[i])):\n",
    "        temp_x.append(i+1)\n",
    "        temp_y.append(j+1)\n",
    "    x_coordinates.append(temp_x)\n",
    "    y_coordinates.append(temp_y)\n",
    "    temp_x = []\n",
    "    temp_y = []\n",
    "print(x_coordinates)\n",
    "print(y_coordinates)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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fVJzczZkFzyn/1ToMT31TK2tKO4ZOro9LLXwhRJgQYokQ4oAQYr8QYqwr5dWn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\nCS1RAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f68be902c88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "for i in range(len(dates)):\n",
    "    for j in range(len(magnitude_per_words)):\n",
    "        for k in range(len(magnitude_per_words[j])):\n",
    "            #print(magnitude_per_words[j][k])\n",
    "            plt.text(x=x_coordinates[j][k], y=y_coordinates[j][k], s=words_in_dates[j][k],\n",
    "                     fontsize=8, horizontalalignment='center')\n",
    "            \n",
    "\n",
    "plt.title(\"WORDS USED IN TODAY'S NEWS\")\n",
    "plt.xlabel('Month Of November')\n",
    "plt.ylabel('Number Of Trends')\n",
    "# xticks( arange(5), ('Tom', 'Dick', 'Harry', 'Sally', 'Sue') )\n",
    "#plt.yticks(list(range(1, 10+ 1)))\n",
    "plt.xticks(list(range(1, len(dates) + 1)), dates)\n",
    "            \n",
    "plt.scatter(x_coordinates, y_coordinates,s=area, linewidths=2, edgecolor='w')\n",
    "plt.show()\n",
    "\n",
    "#plt.savefig('/home/agha/Sir Naeem/grand_data.png', format='png', dpi=1000)\n",
    "plt.clf()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.2,  0.4,  0.6,  0.8,  1. ])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "locs, labels = plt.yticks()\n",
    "#import numpy as np\n",
    "locs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Nov 4\n",
      "Nov 1\n",
      "Nov 5\n",
      "Nov 3\n",
      "Nov 2\n",
      "Dec 5\n",
      "Dec 2\n",
      "Dec 1\n",
      "Dec 4\n",
      "Dec 3\n"
     ]
    }
   ],
   "source": [
    "month_dir = ''\n",
    "day_dir = ''\n",
    "root = '/home/agha/Sir Naeem/next/project/'\n",
    "for month_folder in os.listdir(root):\n",
    "    if '.' not in month_folder:        \n",
    "        month_dir = parent_dir + '/' + month_folder + '/'\n",
    "        os.chdir(month_dir)\n",
    "        for day_folder in os.listdir(month_dir):\n",
    "            if '.' not in day_folder: \n",
    "                print(day_folder)\n",
    "        \n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "year_dict = {'November': {'Nov 1': {'political': '66.6667', 'Trump': '48.5714', 'policy': '46.1538', 'Sharif': '45.4545', 'fresh': '44'}, 'Nov 2': {'Iran': '50', 'Pakistan': '50', 'border': '50', 'Bajwa': '50', 'trust': '40.625'}, 'Nov 3': {'biometric': '58.3333', 'verification': '50', 'Saudi': '50', 'first': '44.4444', 'list': '44.4444'}, 'Nov 4': {'Sharif': '77.7778', 'bombs': '66.6667', 'Myanmar': '53.3333', 'Asad': '43.75', 'best': '40'}, 'Nov 5': {'counsel': '55.5556', 'Supreme': '50', 'JIT': '41.6667', 'members': '41.6667', 'provided': '41.6667'}}, 'December': {'Dec 1': {'political': '66.6667', 'Trump': '48.5714', 'policy': '46.1538', 'Sharif': '45.4545', 'fresh': '44'}, 'Dec 2': {'Iran': '50', 'Pakistan': '50', 'border': '50', 'Bajwa': '50', 'trust': '40.625'}, 'Dec 3': {'biometric': '58.3333', 'verification': '50', 'Saudi': '50', 'first': '44.4444', 'list': '44.4444'}, 'Dec 4': {'Sharif': '77.7778', 'bombs': '66.6667', 'Myanmar': '53.3333', 'Asad': '43.75', 'best': '40'}, 'Dec 5': {'counsel': '55.5556', 'Supreme': '50', 'JIT': '41.6667', 'members': '41.6667', 'provided': '41.6667'}}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'December': {'Sharif': '77.7778',\n",
       "  'biometric': '58.3333',\n",
       "  'bombs': '66.6667',\n",
       "  'counsel': '55.5556',\n",
       "  'political': '66.6667'},\n",
       " 'November': {'Sharif': '77.7778',\n",
       "  'biometric': '58.3333',\n",
       "  'bombs': '66.6667',\n",
       "  'counsel': '55.5556',\n",
       "  'political': '66.6667'}}"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_dt = {}\n",
    "updated = {}\n",
    "for month, words_per_day in year_dict.items():\n",
    "    for key, value in words_per_day.items():\n",
    "        #print(value)\n",
    "        temp_dt.update(value)\n",
    "    \n",
    "    #print(temp_dt)\n",
    "    updated[month] = dict(Counter(temp_dt).most_common(5))\n",
    "    temp_dt = {}\n",
    "        #for key, value in value.items():\n",
    "        #    print(key)\n",
    "        #    temp_dt[month] = {key:value}\n",
    "year_dict = updated\n",
    "year_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {
    "collapsed": true,
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "def get_coordinates(year_dict):\n",
    "\n",
    "    dates = []\n",
    "    words_in_dates = []\n",
    "    magnitude_per_words = []\n",
    "    temp_w = []\n",
    "    temp_m = []\n",
    "    temp_area = []\n",
    "    area = []\n",
    "    x_coordinates = []\n",
    "    y_coordinates = []\n",
    "    temp_x = []\n",
    "    temp_y = []\n",
    "\n",
    "    for day, value in month_dict.items():\n",
    "        dates.append(day)\n",
    "        for words, magnitude in value.items():\n",
    "            temp_w.append(words)\n",
    "            temp_m.append(float(magnitude))\n",
    "            for x in magnitude.split():\n",
    "                temp_area.append(round(2 * math.pi * float(x))*4)\n",
    "                #temp_area.append(float(x))\n",
    "\n",
    "        area.append(temp_area)\n",
    "        words_in_dates.append(temp_w)\n",
    "        magnitude_per_words.append(temp_m)\n",
    "        temp_w = []\n",
    "        temp_m = []\n",
    "        temp_area = []\n",
    "\n",
    "\n",
    "    for i in range(len(magnitude_per_words)):\n",
    "        for j in range(len(magnitude_per_words[i])):\n",
    "            temp_x.append(i + 1)\n",
    "            temp_y.append(j + 1)\n",
    "        x_coordinates.append(temp_x)\n",
    "        y_coordinates.append(temp_y)\n",
    "        temp_x = []\n",
    "        temp_y = []\n",
    "\n",
    "\n",
    "\n",
    "    return dates ,x_coordinates, y_coordinates, words_in_dates, magnitude_per_words, area\n",
    "\n",
    "\n",
    "months ,x_coordinates, y_coordinates, words_in_dates, magnitude_per_words, area = get_coordinates(year_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[66.6667,\n",
       "  48.5714,\n",
       "  46.1538,\n",
       "  77.7778,\n",
       "  44.0,\n",
       "  50.0,\n",
       "  50.0,\n",
       "  50.0,\n",
       "  50.0,\n",
       "  40.625,\n",
       "  58.3333,\n",
       "  50.0,\n",
       "  50.0,\n",
       "  44.4444,\n",
       "  44.4444,\n",
       "  66.6667,\n",
       "  53.3333,\n",
       "  43.75,\n",
       "  40.0,\n",
       "  55.5556,\n",
       "  50.0,\n",
       "  41.6667,\n",
       "  41.6667,\n",
       "  41.6667],\n",
       " [66.6667,\n",
       "  48.5714,\n",
       "  46.1538,\n",
       "  77.7778,\n",
       "  44.0,\n",
       "  50.0,\n",
       "  50.0,\n",
       "  50.0,\n",
       "  50.0,\n",
       "  40.625,\n",
       "  58.3333,\n",
       "  50.0,\n",
       "  50.0,\n",
       "  44.4444,\n",
       "  44.4444,\n",
       "  66.6667,\n",
       "  53.3333,\n",
       "  43.75,\n",
       "  40.0,\n",
       "  55.5556,\n",
       "  50.0,\n",
       "  41.6667,\n",
       "  41.6667,\n",
       "  41.6667]]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "magnitude_per_words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'December': {'Dec 1': {'Sharif': '45.4545',\n",
       "   'Trump': '48.5714',\n",
       "   'fresh': '44',\n",
       "   'policy': '46.1538',\n",
       "   'political': '66.6667'},\n",
       "  'Dec 2': {'Bajwa': '50',\n",
       "   'Iran': '50',\n",
       "   'Pakistan': '50',\n",
       "   'border': '50',\n",
       "   'trust': '40.625'},\n",
       "  'Dec 3': {'Saudi': '50',\n",
       "   'biometric': '58.3333',\n",
       "   'first': '44.4444',\n",
       "   'list': '44.4444',\n",
       "   'verification': '50'},\n",
       "  'Dec 4': {'Asad': '43.75',\n",
       "   'Myanmar': '53.3333',\n",
       "   'Sharif': '77.7778',\n",
       "   'best': '40',\n",
       "   'bombs': '66.6667'},\n",
       "  'Dec 5': {'JIT': '41.6667',\n",
       "   'Supreme': '50',\n",
       "   'counsel': '55.5556',\n",
       "   'members': '41.6667',\n",
       "   'provided': '41.6667'}},\n",
       " 'November': {'Nov 1': {'Sharif': '45.4545',\n",
       "   'Trump': '48.5714',\n",
       "   'fresh': '44',\n",
       "   'policy': '46.1538',\n",
       "   'political': '66.6667'},\n",
       "  'Nov 2': {'Bajwa': '50',\n",
       "   'Iran': '50',\n",
       "   'Pakistan': '50',\n",
       "   'border': '50',\n",
       "   'trust': '40.625'},\n",
       "  'Nov 3': {'Saudi': '50',\n",
       "   'biometric': '58.3333',\n",
       "   'first': '44.4444',\n",
       "   'list': '44.4444',\n",
       "   'verification': '50'},\n",
       "  'Nov 4': {'Asad': '43.75',\n",
       "   'Myanmar': '53.3333',\n",
       "   'Sharif': '77.7778',\n",
       "   'best': '40',\n",
       "   'bombs': '66.6667'},\n",
       "  'Nov 5': {'JIT': '41.6667',\n",
       "   'Supreme': '50',\n",
       "   'counsel': '55.5556',\n",
       "   'members': '41.6667',\n",
       "   'provided': '41.6667'}}}"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "year_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'December': {'Asad': '43.75',\n",
       "  'Bajwa': '50',\n",
       "  'Iran': '50',\n",
       "  'JIT': '41.6667',\n",
       "  'Myanmar': '53.3333',\n",
       "  'Pakistan': '50',\n",
       "  'Saudi': '50',\n",
       "  'Sharif': '77.7778',\n",
       "  'Supreme': '50',\n",
       "  'Trump': '48.5714',\n",
       "  'best': '40',\n",
       "  'biometric': '58.3333',\n",
       "  'bombs': '66.6667',\n",
       "  'border': '50',\n",
       "  'counsel': '55.5556',\n",
       "  'first': '44.4444',\n",
       "  'fresh': '44',\n",
       "  'list': '44.4444',\n",
       "  'members': '41.6667',\n",
       "  'policy': '46.1538',\n",
       "  'political': '66.6667',\n",
       "  'provided': '41.6667',\n",
       "  'trust': '40.625',\n",
       "  'verification': '50'},\n",
       " 'November': {'Asad': '43.75',\n",
       "  'Bajwa': '50',\n",
       "  'Iran': '50',\n",
       "  'JIT': '41.6667',\n",
       "  'Myanmar': '53.3333',\n",
       "  'Pakistan': '50',\n",
       "  'Saudi': '50',\n",
       "  'Sharif': '77.7778',\n",
       "  'Supreme': '50',\n",
       "  'Trump': '48.5714',\n",
       "  'best': '40',\n",
       "  'biometric': '58.3333',\n",
       "  'bombs': '66.6667',\n",
       "  'border': '50',\n",
       "  'counsel': '55.5556',\n",
       "  'first': '44.4444',\n",
       "  'fresh': '44',\n",
       "  'list': '44.4444',\n",
       "  'members': '41.6667',\n",
       "  'policy': '46.1538',\n",
       "  'political': '66.6667',\n",
       "  'provided': '41.6667',\n",
       "  'trust': '40.625',\n",
       "  'verification': '50'}}"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "updated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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