basic-stats.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:c5f873fe0852ff812d9b1080345f8dc12ee12830d191ea27c345041461003609"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline\n",
      "import seaborn as sb\n",
      "import numpy as np\n",
      "import pandas as pd"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "with open('results-latest.json', 'r') as r:\n",
      "    results = json.load(r)['results']\n",
      "with open('profiles-latest.json', 'r') as p:\n",
      "    profiles = json.load(p)['profiles']\n",
      "print \"{} results loaded\".format(len(results))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "23460 results loaded\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "minAppVersionCode = 50\n",
      "parametersVersion = '3.2-production-4'\n",
      "min_n_results = 10\n",
      "good_profiles = [p for p in profiles\n",
      "                 if p['profile_data'].get('appVersionCode') >= minAppVersionCode\n",
      "                 and '+' not in p['profile_data'].get('appVersionName')\n",
      "                 and p['n_results'] >= min_n_results\n",
      "                 and p['profile_data'].get('parametersVersion') == parametersVersion]\n",
      "good_profile_ids = [p['id'] for p in good_profiles]\n",
      "print (\"{n_profiles} profiles worth keeping (>= {min_n_results} results, \"\n",
      "       \"appVersionCode >= {minAppVersionCode}, parametersVersion == {parametersVersion})\").format(\n",
      "            n_profiles=len(good_profiles), min_n_results=min_n_results, \n",
      "            minAppVersionCode=minAppVersionCode, parametersVersion=parametersVersion)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "6 profiles worth keeping (>= 10 results, appVersionCode >= 50, parametersVersion == 3.2-production-4)\n"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "good_results = [r for r in results if r['profile_id'] in good_profile_ids]\n",
      "print \"{} results uploaded by those good profiles\".format(len(good_results))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "2211 results uploaded by those good profiles\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "all_n_results = [p['n_results'] for p in profiles]\n",
      "plt.hist(all_n_results, 100);\n",
      "plt.title(\"Histogram of #results uploaded per profile\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "<matplotlib.text.Text at 0x7fb7b8fd47d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jwXw9cHpEzAG2A/YGlkxkvQNzt2dwsH/8GXuUtWnGOjVnrZqxTs1Yp+nTNLSHI2IGcC5w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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7fb7b904dad0>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def get_by_field(l, name, value):\n",
      "    values = [item.get(name) for item in l]\n",
      "    c = values.count(value)\n",
      "    if c == 0:\n",
      "        raise ValueError(\"'{}' == {} not found\".format(name, value))\n",
      "    if c > 1:\n",
      "        raise ValueError(\"'{}' == {} found {} times\".format(name, value, c))\n",
      "    return l[values.index(value)]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "focus_data = {'profile_id': [],\n",
      "              'focus': [],\n",
      "              'surroundings': [],\n",
      "              'location': []}\n",
      "\n",
      "for r in good_results:\n",
      "    if r['result_data'].get('type') == 'probe' and r['result_data'].get('status') == 'completed':\n",
      "        thought_page = get_by_field(r['result_data']['pageGroups'], 'name', 'thought')['pages'][0]\n",
      "        thought_focus_sliders = get_by_field(thought_page['questions'], 'questionName',\n",
      "                                             'probe.thought.focus')['answer']['sliders']\n",
      "        thought_surroundings_sliders = get_by_field(thought_page['questions'], 'questionName', \n",
      "                                                    'probe.thought.surround')['answer']['sliders']\n",
      "        locations = get_by_field(get_by_field(r['result_data']['pageGroups'], 'name', 'context')['pages'],\n",
      "                                'name', 'location')['questions'][0]['answer']['choices']\n",
      "        for loc in locations:\n",
      "            focus_data['profile_id'].append(r['profile_id'])\n",
      "            focus_data['focus'].append(thought_focus_sliders['How focused were you on what you were doing?'])\n",
      "            focus_data['surroundings'].append(thought_surroundings_sliders['How aware were you of your surroundings?'])\n",
      "            focus_data['location'].append(loc)\n",
      "focus = pd.DataFrame(focus_data)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sb.pairplot(focus, hue='location')\n",
      "plt.title(\"Focus and awareness of surroundings depending on location\")"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.text.Text at 0x7fb7b841d510>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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+4QGfV+t2kR6m1fTZzM5w97uB3wHPmNmD0UsnA9d0sdzrCL0UXhE9n+ruS6LH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\nsijHMVyM7v5Obnzk+3t6Nm589AdMazy0ID0DpTy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       "text": [
        "<matplotlib.figure.Figure at 0x7fb7b8f618d0>"
       ]
      }
     ],
     "prompt_number": 12
    }
   ],
   "metadata": {}
  }
 ]
}