twiliocon2.ipynb

{
 "metadata": {
  "name": "twiliocon"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "Check our post explaining this plots at [http://googlecloudplatform.blogspot.com/2013/09/introducing-corr-to-google-bigquery.html](http://googlecloudplatform.blogspot.com/2013/09/introducing-corr-to-google-bigquery.html)\n\n"
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import bq\nimport datetime\nimport pandas as pd\nimport tabulate\nclient = bq.Client.Get()",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "import matplotlib.pylab as pylab\npylab.rcParams['figure.figsize'] = 8, 6  # that's default image size for this interactive session",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 28
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "query = \"\"\"SELECT room, COUNT(*) c\nFROM [data-sensing-lab:twiliocon.twiliocon]\nGROUP BY 1\"\"\"\nfields, rows = client.ReadSchemaAndRows(client.Query(query)['configuration']['query']['destinationTable'], max_rows = 1000)\nrows = [[x[0], float(x[1])] for x in rows]\nrows",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\rWaiting on bqjob_reee3f41cfc200d6_00000141376a4b5f_29 ... (0s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_reee3f41cfc200d6_00000141376a4b5f_29 ... (1s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_reee3f41cfc200d6_00000141376a4b5f_29 ... (2s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_reee3f41cfc200d6_00000141376a4b5f_29 ... (3s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_reee3f41cfc200d6_00000141376a4b5f_29 ... (3s) Current status: DONE   "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n"
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 62,
       "text": "[[u'booths', 103726.0],\n [u'lounge', 144160.0],\n [u'cafe', 51464.0],\n [u'create', 87645.0],\n [u'registration', 159101.0],\n [u'east_hallway', 51082.0],\n [u'sun_room', 51469.0],\n [None, 122130.0],\n [u'west_hallway', 56510.0],\n [u'google_booth', 17791.0]]"
      }
     ],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "df = pd.DataFrame(rows)\nfig = figure()\nax = subplot(111)\nwidth=0.8\nax.bar(range(len(df[0])), df[1], width=width)\nax.set_xticks(np.arange(len(df[0])) + width/2)\n_ = ax.set_xticklabels(df[0], rotation=90)",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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RiI+PB1A3QmjkyJGqPDExEVVVVcjOzkZWVhYCAwPh7OwMOzs7pKenQ0SQkJCAESNGXPJc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\n2VRZW+Dn54cdO3aoI5TS0lL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Bgwfjueee0yyGb7/9FpMmTUJ5eTmA/40+0uJogUmBSEf1\n0yE3XAzez89Pl5Eu9D/V1dXYt28ftm/fjg8++AC33norvv/+e83qX7hwIYD/XWnfoUMHODg4aLJc\nLE8fEenopptuQk1Njbp/8uTJRtNvkPZCQkJw5swZ3HPPPRg0aBD27duHbt26aRpDw+Vigbrp3LVa\nLpafPiIdTZ8+HY8++ihOnDiBP/7xjxg4cCD+8Ic/6B1Wm9a7d2/Y2triu+++w6FDh/Ddd9/h3Llz\nmsbQcLnYhQsXarpcLI8UiHT0xBNPoF+/fti2bRsAIDk5GXfffbfOUbVtb731FgCgsrISK1asQHR0\nNIqKinDhwgXNYtBzuVgmBSKd3X333UwEBvLuu+9i165dyMjIQI8ePTBlyhQ1Ckgrei4Xy45mIqIG\nFixYgPvuuw/+/v6wtbXVLY5vvvkGX375JUwmEwYOHKiWi21tTApERKSwo5mIiBQmBSIiUpgUiIhI\nYVIgIiKFSYGIiJT/DxmTNeqGm4X2AAAAAElFTkSuQmCC\n",
       "text": "<matplotlib.figure.Figure at 0x55a3c10>"
      }
     ],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "query = '''\nSELECT a.room, a.sensortype, b.room, b.sensortype, CORR(a.data, b.data) corr, COUNT(*) c\nFROM (\n  SELECT TIME(USEC_TO_TIMESTAMP(INTEGER(Timestamp / 60000000) * 60000000 - 8*60*60000000)) time, AVG(DATA) data, room, sensortype\n  FROM [data-sensing-lab:twiliocon.twiliocon]\n  WHERE DATE(USEC_TO_TIMESTAMP(Timestamp- 8*60*60000000)) = '2013-09-18'\n  AND sensortype != 'pressure' AND sensortype != 'altitude' AND sensortype != 'totalmotion' AND sensortype != 'humidity'\n  GROUP each BY time, room, sensortype) a\nJOIN EACH (\n  SELECT TIME(USEC_TO_TIMESTAMP(INTEGER(Timestamp / 60000000) * 60000000 - 8*60*60000000)) time, AVG(data) data, room, sensortype\n  FROM [data-sensing-lab:twiliocon.twiliocon]\n  WHERE DATE(USEC_TO_TIMESTAMP(Timestamp- 8*60*60000000)) = '2013-09-18'\n  AND sensortype != 'pressure' AND sensortype != 'altitude' AND sensortype != 'totalmotion' AND sensortype != 'humidity'\n  GROUP each BY time, room, sensortype) b\nON a.time=b.time\nWHERE a.room < b.room\nGROUP BY 1, 2, 3, 4\nHAVING corr is not null\nORDER BY corr DESC\n'''\nfields, rows = client.ReadSchemaAndRows(client.Query(query)['configuration']['query']['destinationTable'], max_rows = 1000)\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\rWaiting on bqjob_r5575378022cea6fa_00000141376a8fdc_30 ... (0s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r5575378022cea6fa_00000141376a8fdc_30 ... (1s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r5575378022cea6fa_00000141376a8fdc_30 ... (2s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r5575378022cea6fa_00000141376a8fdc_30 ... (3s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r5575378022cea6fa_00000141376a8fdc_30 ... (3s) Current status: DONE   "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n"
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print tabulate.tabulate(rows[0:20])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "------------  -----------  ------------  -----------  --------  ----\ncafe          temperature  registration  temperature  0.967113  1432\nregistration  temperature  west_hallway  temperature  0.965861  1396\nsun_room      temperature  west_hallway  temperature  0.957649  1392\ncafe          temperature  east_hallway  temperature  0.955361  1425\ncafe          temperature  west_hallway  temperature  0.954679  1395\nregistration  temperature  sun_room      temperature  0.948838  1428\ncafe          temperature  sun_room      temperature  0.938821  1427\neast_hallway  temperature  west_hallway  temperature  0.934553  1392\neast_hallway  temperature  sun_room      temperature  0.920812  1421\neast_hallway  temperature  registration  temperature  0.920482  1426\nbooths        temperature  cafe          temperature  0.912682  1429\nbooths        temperature  east_hallway  temperature  0.881605  1422\nbooths        temperature  registration  temperature  0.87587   1429\nbooths        temperature  west_hallway  temperature  0.857088  1392\ncafe          light        east_hallway  light        0.841369  1410\nbooths        temperature  sun_room      temperature  0.841148  1424\nbooths        gas          lounge        gas          0.833786  1414\nbooths        gas          google_booth  gas          0.810003   424\nregistration  light        west_hallway  temperature  0.788401  1396\nlounge        light        registration  light        0.784041  1433\n------------  -----------  ------------  -----------  --------  ----\n"
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print tabulate.tabulate(rows[-20:])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "------------  -----------  ------------  -----------  ---------  ----\nlounge        gas          sun_room      light        -0.592363  1414\nbooths        gas          sun_room      light        -0.618244  1425\neast_hallway  light        lounge        gas          -0.628131  1412\nbooths        temperature  lounge        gas          -0.628935  1414\nbooths        gas          registration  mic          -0.659795  1429\nlounge        gas          registration  mic          -0.667738  1418\nbooths        gas          registration  light        -0.670266  1429\nbooths        gas          east_hallway  light        -0.69318   1419\nlounge        gas          registration  light        -0.704142  1418\nbooths        gas          lounge        light        -0.736001  1429\neast_hallway  temperature  lounge        gas          -0.789501  1414\ncafe          temperature  lounge        gas          -0.790408  1417\nlounge        gas          registration  temperature  -0.821811  1418\nlounge        gas          west_hallway  temperature  -0.824311  1385\nlounge        gas          sun_room      temperature  -0.845697  1413\nbooths        gas          east_hallway  temperature  -0.853517  1422\nbooths        gas          cafe          temperature  -0.871581  1428\nbooths        gas          west_hallway  temperature  -0.88931   1393\nbooths        gas          registration  temperature  -0.895455  1429\nbooths        gas          sun_room      temperature  -0.912105  1424\n------------  -----------  ------------  -----------  ---------  ----\n"
      }
     ],
     "prompt_number": 53
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print tabulate.tabulate([x for x in rows if x[1]=='mic'][0:20])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "------------  ---  ------------  -----------  --------  ----\nbooths        mic  lounge        mic          0.727837  1419\nregistration  mic  west_hallway  temperature  0.725982  1396\nregistration  mic  sun_room      temperature  0.717888  1428\nbooths        mic  west_hallway  temperature  0.668492  1385\nlounge        mic  west_hallway  temperature  0.658121  1396\nbooths        mic  east_hallway  temperature  0.643513  1412\nbooths        mic  registration  temperature  0.636738  1419\nbooths        mic  west_hallway  mic          0.631887  1408\nbooths        mic  sun_room      temperature  0.630367  1414\nbooths        mic  cafe          temperature  0.624933  1418\nlounge        mic  sun_room      temperature  0.62175   1428\nlounge        mic  registration  mic          0.617497  1433\nbooths        mic  registration  mic          0.613749  1419\nlounge        mic  registration  temperature  0.608534  1433\nbooths        mic  registration  light        0.607512  1419\nbooths        mic  lounge        light        0.602171  1419\nlounge        mic  west_hallway  mic          0.597601  1422\ncreate        mic  sun_room      temperature  0.590251  1428\nregistration  mic  sun_room      light        0.589262  1429\ncreate        mic  west_hallway  temperature  0.584864  1396\n------------  ---  ------------  -----------  --------  ----\n"
      }
     ],
     "prompt_number": 54
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "print tabulate.tabulate([x for x in rows if x[0]=='create'][0:20])\nprint tabulate.tabulate([x for x in rows if x[0]=='create'][-20:])",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "------  -----  ------------  -----------  --------  ----\ncreate  light  lounge        light        0.764741  1431\ncreate  mic    sun_room      temperature  0.590251  1428\ncreate  mic    west_hallway  temperature  0.584864  1396\ncreate  mic    east_hallway  temperature  0.582547  1426\ncreate  light  registration  light        0.549755  1431\ncreate  mic    registration  mic          0.546955  1433\ncreate  mic    registration  temperature  0.545615  1433\ncreate  mic    lounge        light        0.532813  1433\ncreate  light  east_hallway  light        0.520559  1421\ncreate  mic    sun_room      light        0.517144  1429\ncreate  mic    lounge        mic          0.502934  1433\ncreate  light  west_hallway  temperature  0.487674  1395\ncreate  light  registration  rf           0.482773  1430\ncreate  mic    registration  light        0.481488  1433\ncreate  mic    east_hallway  light        0.455389  1423\ncreate  light  sun_room      temperature  0.45516   1427\ncreate  light  sun_room      light        0.450955  1427\ncreate  light  registration  temperature  0.450146  1431\ncreate  light  registration  mic          0.412962  1431\ncreate  light  lounge        mic          0.407997  1431\n------  -----  ------------  -----------  --------  ----\n------  -----------  ------------  -----------  -----------  ----\ncreate  mic          lounge        temperature   0.0657353   1433\ncreate  temperature  west_hallway  mic           0.0634842   1418\ncreate  temperature  lounge        motion        0.0612123   1353\ncreate  temperature  sun_room      rf            0.0537885   1428\ncreate  temperature  google_booth  mic           0.0488256    403\ncreate  light        lounge        temperature   0.0410105   1431\ncreate  mic          google_booth  mic           0.0144591    403\ncreate  temperature  lounge        temperature   0.012081    1429\ncreate  temperature  west_hallway  light         0.0101916   1387\ncreate  temperature  google_booth  gas          -0.00189062   424\ncreate  temperature  east_hallway  rf           -0.0165526   1426\ncreate  temperature  google_booth  temperature  -0.0482564    421\ncreate  temperature  google_booth  light        -0.0522026    408\ncreate  mic          google_booth  gas          -0.10033      424\ncreate  temperature  lounge        gas          -0.115112    1415\ncreate  mic          google_booth  temperature  -0.199188     421\ncreate  light        google_booth  temperature  -0.251924     421\ncreate  light        lounge        gas          -0.359512    1417\ncreate  light        google_booth  gas          -0.465565     424\ncreate  mic          lounge        gas          -0.527529    1418\n------  -----------  ------------  -----------  -----------  ----\n"
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "base_query = '''select t, a_data, b_data\nfrom(\nSELECT a.time t, a.data a_data, sum(a_data) over() a_sum, b.data b_data, sum(b_data) over() b_sum, count(a_data) over() c\nFROM (\n  SELECT TIME(USEC_TO_TIMESTAMP(INTEGER(Timestamp / 60000000) * 60000000 - 8*60*60000000)) time, AVG(DATA) data, room\n  FROM [data-sensing-lab:twiliocon.twiliocon]\n  WHERE DATE(USEC_TO_TIMESTAMP(Timestamp- 8*60*60000000)) = '2013-09-18'\n  and sensortype='%s'\n  and room = '%s'\n  and INTEGER(Timestamp / 60000000) %% 9 = 0\n  GROUP each BY time, room) a\nJOIN EACH (\n  SELECT TIME(USEC_TO_TIMESTAMP(INTEGER(Timestamp / 60000000) * 60000000 - 8*60*60000000)) time, AVG(data) data, room\n  FROM [data-sensing-lab:twiliocon.twiliocon]\n  WHERE DATE(USEC_TO_TIMESTAMP(Timestamp- 8*60*60000000)) = '2013-09-18'\n  and sensortype='%s'\n  AND room = '%s'\n  GROUP each BY time, room) b\nON a.time=b.time\n)\norder by t;'''",
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 76
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": "r1 = 'create'\nr2 = 'lounge'\ns1 = 'mic'\ns2 = 'gas'\n\nquery = base_query % (s1,r1,s2,r2)\nfields, rows = client.ReadSchemaAndRows(client.Query(query)['configuration']['query']['destinationTable'], max_rows = 1000)\nrows = [[x[0], float(x[1]), float(x[2])] for x in rows]\ndf = pd.DataFrame(rows, columns=[['time'], s1, s2]).plot(title='2nd day in %s' % r1)\n_ = df.set_xticklabels([rows[min(int(x), len(rows)-1)][0][0:5] for x in df.get_xticks()])\n\n",
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\rWaiting on bqjob_r72b819a9511a1647_0000014137707fae_42 ... (0s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r72b819a9511a1647_0000014137707fae_42 ... (1s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r72b819a9511a1647_0000014137707fae_42 ... (2s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r72b819a9511a1647_0000014137707fae_42 ... (3s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r72b819a9511a1647_0000014137707fae_42 ... (5s) Current status: RUNNING"
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": " \rWaiting on bqjob_r72b819a9511a1647_0000014137707fae_42 ... (5s) Current status: DONE   "
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": "\n"
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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4+wIWxS1Q1qKslBKZXDDoYnAFwUxmprcKSDksy9gpDJ0WxS00AnNtqrbBhKYT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\nhZC/vzrPs2fV55w+nfvotrQ0UQ43b2ruP3ZM/Bb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       "text": "<matplotlib.figure.Figure at 0x59ad6d0>"
      }
     ],
     "prompt_number": 80
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": "- Follow the most interesting BigQuery news at [http://www.reddit.com/r/bigquery](http://www.reddit.com/r/bigquery). \n\n- Ask your technical questions using the [google-bigquery] tag on [Stack Overflow](http://stackoverflow.com/questions/tagged/google-bigquery)."
    }
   ],
   "metadata": {}
  }
 ]
}