trajectory_time_distribution.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:afa48032348f534a69501969f56810ee09a74502dfc7e5ce25214df1b0752b3d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import trj_builder\n",
      "import scipy.stats as stats\n",
      "import matplotlib\n",
      "import matplotlib.pyplot as plt\n",
      "import matplotlib.gridspec as gridspec\n",
      "from pylab import plot,show,hist\n",
      "import json\n",
      "import datetime, time\n",
      "from scipy.stats.kde import gaussian_kde\n",
      "from scipy.stats import norm\n",
      "from numpy import linspace,hstack\n",
      "import numpy\n",
      "import scipy\n",
      "fmt=\"%Y%m%dT%H%M%S\"\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 38
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "json_file = \"raw_data.json\"\n",
      "with open(json_file, \"r\")  as jsonf:\n",
      "    data_dict = json.load(jsonf)\n",
      "###============================================\n",
      "\n",
      "t_list=[]\n",
      "for user,v in data_dict.iteritems():\n",
      "    user_trj=0\n",
      "    #print v[\"info\"][\"name\"]\n",
      "    for day in v[\"data\"]:\n",
      "        if (day!={}):\n",
      "            dist_list,total_trj,diff=trj_builder.trajectory_builder(v[\"data\"][day])\n",
      "            for i in range(0,len(total_trj)):\n",
      "                s_t=total_trj[i][0]['time'] \n",
      "                e_t=total_trj[i][-1]['time'] \n",
      "                s_t= datetime.datetime.strptime(s_t.split('+')[0],fmt)\n",
      "                e_t= datetime.datetime.strptime(e_t.split('+')[0],fmt)  \n",
      "                diff= e_t-s_t\n",
      "                t_list.append(diff.seconds/60)\n",
      "                user_trj+=1\n",
      "print 'total number of trajectories:',len(t_list)\n",
      "fig = plt.figure(figsize=(8,6))\n",
      "plt.hist(t_list, bins=300, normed=True)\n",
      "plt.xlim(0,400)\n",
      "plt.xlabel('Trajectory Duration (Minutes)')\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "total number of trajectories: 1094\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAfUAAAF/CAYAAABQY1CvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4510ecd7d0>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,6))\n",
      "plt.hist(t_list,normed=True,bins=500,alpha=.4, color='g',label= '')\n",
      "plt.xlim(0,160)\n",
      "pdf = gaussian_kde(t_list)\n",
      "cdf = stats.binom.cdf\n",
      "x = linspace(0,160,300)\n",
      "plot(x,pdf(x), 'b')\n",
      "plt.xlabel(\"Trajectory Duration (Minutes)\")\n",
      "plt.title(\"Time distribution of trajectories\")\n",
      "plt.ylabel(\"Probability\")\n",
      "plt.legend()\n",
      "plt.savefig(\"time_dist\",format='pdf')\n",
      "plt.show()\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAgMAAAGJCAYAAAAXGDB0AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4511351e90>"
       ]
      }
     ],
     "prompt_number": 32
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "fig = plt.figure(figsize=(8,6))\n",
      "print 'Avrage trajectory time:', numpy.mean(t_list)\n",
      "plt.xlabel(\"Time (Minutes)\")\n",
      "plt.title(\"Cumulative distribution function\")\n",
      "plt.ylabel(\"Probability\")\n",
      "plt.legend()\n",
      "plt.savefig(\"time_cdf\", format='pdf')\n",
      "plt.plot(x,cdf(x, 200, 0.2))\n",
      "#plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Avrage trajectory time: 36.5374771481\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 42,
       "text": [
        "[<matplotlib.lines.Line2D at 0x7f45127c18d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAfcAAAGJCAYAAACXXXqWAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nt2dIsB0AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f450f605c50>"
       ]
      }
     ],
     "prompt_number": 42
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}