G.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:64bbf3ca4725bb376029120240df8f656d2562a5c1a5aafb5608bf78ff71bf7f"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "%matplotlib inline"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.array([1,2,3,4])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "array([1, 2, 3, 4])"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def pLength(m):\n",
      "    return 1 + 2**(0.5*np.log2(m))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "pLength(100)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "9.0"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "m = np.arange(0,2000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "x = np.arange(0,1000,6.0/20)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 34
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "y = np.asarray(map(pLength,m))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(m,y)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "[<matplotlib.lines.Line2D at 0xadc8aaec>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xade39e8c>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.sin(m)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 24,
       "text": [
        "array([ 0.        ,  0.84147098,  0.90929743, ..., -0.86887624,\n",
        "       -0.05290298,  0.81170904])"
       ]
      }
     ],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(np.sin(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 36,
       "text": [
        "[<matplotlib.lines.Line2D at 0xad82368c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BY+oyNX1kdTDXGryt9SxlZ27S0Fu7a/lf7mTAEsyp1NpJegsCve2lbHdptR+s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\nQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad91120c>"
       ]
      }
     ],
     "prompt_number": 36
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 35,
       "text": [
        "3334"
       ]
      }
     ],
     "prompt_number": 35
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "np.fft.fft(np.sin(x))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 37,
       "text": [
        "array([ 1.56727595+0.j        ,  1.56735660-0.0194553j ,\n",
        "        1.56759859-0.03891518j, ...,  1.56800203+0.0583842j ,\n",
        "        1.56759859+0.03891518j,  1.56735660+0.0194553j ])"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(np.abs(_37))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 41,
       "text": [
        "[<matplotlib.lines.Line2D at 0xad8c0e2c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VfpvfACwHriYMHl8a29tjezvhrGIR796dJEkaZs322zpTl9GsWbBpk11GkoZfrcvo0Ufh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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xada5f60c>"
       ]
      }
     ],
     "prompt_number": 41
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s=np.sin(x)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 43
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "l = []\n",
      "for bit in xrange(0,len(s)-40,10):\n",
      "    l.append(np.fft.fft(s[bit:bit+40]))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 62
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sp=np.asarray(l)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 63
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sp.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 64,
       "text": [
        "(330, 40)"
       ]
      }
     ],
     "prompt_number": 64
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(np.abs(sp))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 65,
       "text": [
        "[<matplotlib.lines.Line2D at 0xad5a17ac>,\n",
        " <matplotlib.lines.Line2D at 0xad5a1aac>,\n",
        " <matplotlib.lines.Line2D at 0xad5a1ccc>,\n",
        " <matplotlib.lines.Line2D at 0xad5a1e6c>,\n",
        " <matplotlib.lines.Line2D at 0xad5a1fec>,\n",
        " <matplotlib.lines.Line2D at 0xad5a61cc>,\n",
        " <matplotlib.lines.Line2D at 0xad5a636c>,\n",
        " <matplotlib.lines.Line2D at 0xad570f8c>,\n",
        " <matplotlib.lines.Line2D at 0xad5a66ac>,\n",
        " <matplotlib.lines.Line2D at 0xad5a684c>,\n",
        " <matplotlib.lines.Line2D at 0xad5a69ec>,\n",
        " <matplotlib.lines.Line2D at 0xad5a6b8c>,\n",
        " <matplotlib.lines.Line2D at 0xad5a6d2c>,\n",
        " <matplotlib.lines.Line2D at 0xad5a6ecc>,\n",
        " <matplotlib.lines.Line2D at 0xad5a64ec>,\n",
        " <matplotlib.lines.Line2D at 0xad5ab22c>,\n",
        " <matplotlib.lines.Line2D at 0xad5ab3cc>,\n",
        " <matplotlib.lines.Line2D at 0xad5ab56c>,\n",
        " <matplotlib.lines.Line2D at 0xad5ab70c>,\n",
        " <matplotlib.lines.Line2D at 0xad5ab8ac>,\n",
        " <matplotlib.lines.Line2D at 0xad5aba4c>,\n",
        " <matplotlib.lines.Line2D at 0xad5abbec>,\n",
        " <matplotlib.lines.Line2D at 0xad5abd8c>,\n",
        " <matplotlib.lines.Line2D at 0xad5abf2c>,\n",
        " <matplotlib.lines.Line2D at 0xad5300ec>,\n",
        " <matplotlib.lines.Line2D at 0xad53028c>,\n",
        " <matplotlib.lines.Line2D at 0xad53042c>,\n",
        " <matplotlib.lines.Line2D at 0xad5305cc>,\n",
        " <matplotlib.lines.Line2D at 0xad5abbcc>,\n",
        " <matplotlib.lines.Line2D at 0xad53090c>,\n",
        " <matplotlib.lines.Line2D at 0xad530aac>,\n",
        " <matplotlib.lines.Line2D at 0xad530c4c>,\n",
        " <matplotlib.lines.Line2D at 0xad530dec>,\n",
        " <matplotlib.lines.Line2D at 0xad530f8c>,\n",
        " <matplotlib.lines.Line2D at 0xad53414c>,\n",
        " <matplotlib.lines.Line2D at 0xad53074c>,\n",
        " <matplotlib.lines.Line2D at 0xad53448c>,\n",
        " <matplotlib.lines.Line2D at 0xad53462c>,\n",
        " <matplotlib.lines.Line2D at 0xad5347cc>,\n",
        " <matplotlib.lines.Line2D at 0xad53496c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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zPfjrYAZbBPPs4bNNrhkbHssQ8xDu/HVnne3R+6MZ1C6I277f1uSaOek59Ovg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\nkVqcS705lF5YlElYO5MY3HYKZcYG1v5Qwb79fVGr1+Hjs58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rPgBoA3YCfbm27A8XtPfj\nKrf9tTgxSQv8sjaYG/AzzrvMr3Bh9M4TXD03SsFp34tvlF5O6y83WpQ4zwROcXUslRXJb/X/etnj\nR4FfJrJfjfolwGbgzYv2Xys+uJL+a8UHflzontAAe4GRXBv2v5L2XxcuuCptf61NTIrCabgcnEO8\nftHsg7Of/Wob0vgBUANYcN6/mMK/1vokTl8U4yzE9mdzsf77cSaZPJx9ip/z2/sXV5v+wTgvoXO4\nMARtLNeODy6nfxzXjg96A8dw6s8D5p/bfy3Y/0rarxXbu3DhwoULFy5cuHDhwoULFy5cuHDhwoUL\nFy5cuHDhwoULFy5cuHDhwoULFy5cuHDhwoULFy5cuHDh4lrl/wEB6vmB6URt5wAAAABJRU5ErkJg\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad5af2cc>"
       ]
      }
     ],
     "prompt_number": 65
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "print np.abs(sp)[20]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "[  0.2302298    1.36707951  20.1824085    1.99595831   1.16000802\n",
        "   0.8462316    0.67776874   0.57175442   0.4988555    0.44587619\n",
        "   0.40594174   0.37509788   0.35090388   0.33177329   0.31663524\n",
        "   0.30474763   0.29558849   0.28879007   0.28409797   0.28134552\n",
        "   0.28043823   0.28134552   0.28409797   0.28879007   0.29558849\n",
        "   0.30474763   0.31663524   0.33177329   0.35090388   0.37509788\n",
        "   0.40594174   0.44587619   0.4988555    0.57175442   0.67776874\n",
        "   0.8462316    1.16000802   1.99595831  20.1824085    1.36707951]\n"
       ]
      }
     ],
     "prompt_number": 66
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(s[0:40])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 68,
       "text": [
        "[<matplotlib.lines.Line2D at 0xad49d70c>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KO+F13HHQtSu8+67rSNxS0pe41K1rwzffe891JPFbuxZWr7aVFyWcVOJR0pcE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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad4b380c>"
       ]
      }
     ],
     "prompt_number": 68
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.load('periscope1hr.npy')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 71
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plt.plot(data[:10])\n",
      "f=plt.gcf()\n",
      "f.set_size_inches([20,5])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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dd+DII6FJExg7NuqKJElSqvnsM2jUCL7/PnQdGR4p0yUyQLoKmAVMB+7Md74P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\n",
       "text": [
        "<matplotlib.figure.Figure at 0xad39a86c>"
       ]
      }
     ],
     "prompt_number": 78
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from nupic.frameworks.opf.modelfactory import ModelFactory"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 85
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%cd ~/nupic/examples/opf/clients/hotgym/anomaly/one_gym"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "/home/ruaridh/nupic/examples/opf/clients/hotgym/anomaly/one_gym\n"
       ]
      }
     ],
     "prompt_number": 88
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%ls"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\u001b[0m\u001b[01;34mmodel_params\u001b[0m/            README.md              \u001b[01;32mremove_tuesdays.py\u001b[0m*\r\n",
        "nupic_anomaly_output.py  rec-center-hourly.csv  \u001b[01;32mrun.py\u001b[0m*\r\n"
       ]
      }
     ],
     "prompt_number": 89
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%load model_params/rec_center_hourly_model_params.py"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 92
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "MODEL_PARAMS = \\\n",
      "{ 'aggregationInfo': { 'days': 0,\n",
      "                       'fields': [],\n",
      "                       'hours': 0,\n",
      "                       'microseconds': 0,\n",
      "                       'milliseconds': 0,\n",
      "                       'minutes': 0,\n",
      "                       'months': 0,\n",
      "                       'seconds': 0,\n",
      "                       'weeks': 0,\n",
      "                       'years': 0},\n",
      "  'model': 'CLA',\n",
      "  'modelParams': { 'anomalyParams': { u'anomalyCacheRecords': None,\n",
      "                                      u'autoDetectThreshold': None,\n",
      "                                      u'autoDetectWaitRecords': None},\n",
      "                   'clParams': { 'alpha': 0.01962508905154251,\n",
      "                                 'clVerbosity': 0,\n",
      "                                 'regionName': 'CLAClassifierRegion',\n",
      "                                 'steps': '1'},\n",
      "                   'inferenceType': 'TemporalAnomaly',\n",
      "                   'sensorParams': { 'encoders': { '_classifierInput': { 'classifierOnly': True,\n",
      "                                                                         'clipInput': True,\n",
      "                                                                         'fieldname': 'kw_energy_consumption',\n",
      "                                                                         'maxval': 53.0,\n",
      "                                                                         'minval': 0.0,\n",
      "                                                                         'n': 115,\n",
      "                                                                         'name': '_classifierInput',\n",
      "                                                                         'type': 'ScalarEncoder',\n",
      "                                                                         'w': 21},\n",
      "                                                   u'kw_energy_consumption': { 'clipInput': True,\n",
      "                                                                               'fieldname': 'kw_energy_consumption',\n",
      "                                                                               'maxval': 53.0,\n",
      "                                                                               'minval': 0.0,\n",
      "                                                                               'n': 29,\n",
      "                                                                               'name': 'kw_energy_consumption',\n",
      "                                                                               'type': 'ScalarEncoder',\n",
      "                                                                               'w': 21},\n",
      "                                                   u'timestamp_dayOfWeek': None,\n",
      "                                                   u'timestamp_timeOfDay': { 'fieldname': 'timestamp',\n",
      "                                                                             'name': 'timestamp',\n",
      "                                                                             'timeOfDay': ( 21,\n",
      "                                                                                            6.090344152692538),\n",
      "                                                                             'type': 'DateEncoder'},\n",
      "                                                   u'timestamp_weekend': { 'fieldname': 'timestamp',\n",
      "                                                                           'name': 'timestamp',\n",
      "                                                                           'type': 'DateEncoder',\n",
      "                                                                           'weekend': ( 21,\n",
      "                                                                                        1)}},\n",
      "                                     'sensorAutoReset': None,\n",
      "                                     'verbosity': 0},\n",
      "                   'spEnable': True,\n",
      "                   'spParams': { 'columnCount': 2048,\n",
      "                                 'globalInhibition': 1,\n",
      "                                 'inputWidth': 0,\n",
      "                                 'maxBoost': 2.0,\n",
      "                                 'numActiveColumnsPerInhArea': 40,\n",
      "                                 'potentialPct': 0.8,\n",
      "                                 'seed': 1956,\n",
      "                                 'spVerbosity': 0,\n",
      "                                 'spatialImp': 'cpp',\n",
      "                                 'synPermActiveInc': 0.05,\n",
      "                                 'synPermConnected': 0.1,\n",
      "                                 'synPermInactiveDec': 0.08568228006654939},\n",
      "                   'tpEnable': True,\n",
      "                   'tpParams': { 'activationThreshold': 12,\n",
      "                                 'cellsPerColumn': 32,\n",
      "                                 'columnCount': 2048,\n",
      "                                 'globalDecay': 0.0,\n",
      "                                 'initialPerm': 0.21,\n",
      "                                 'inputWidth': 2048,\n",
      "                                 'maxAge': 0,\n",
      "                                 'maxSegmentsPerCell': 128,\n",
      "                                 'maxSynapsesPerSegment': 32,\n",
      "                                 'minThreshold': 10,\n",
      "                                 'newSynapseCount': 20,\n",
      "                                 'outputType': 'normal',\n",
      "                                 'pamLength': 1,\n",
      "                                 'permanenceDec': 0.1,\n",
      "                                 'permanenceInc': 0.1,\n",
      "                                 'seed': 1960,\n",
      "                                 'temporalImp': 'cpp',\n",
      "                                 'verbosity': 0},\n",
      "                   'trainSPNetOnlyIfRequested': False},\n",
      "  'predictAheadTime': None,\n",
      "  'version': 1}"
     ],
     "language": "python",
     "metadata": {},
     "outputs": []
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}