sc3x3_tc.ipynb

{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import kid_readout.utils.parse_srs\n",
      "reload(kid_readout.utils.parse_srs)\n",
      "import time"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "times,temps = kid_readout.utils.parse_srs.get_all_temperature_data()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "failed to parse '\\r' skipping\n",
        "failed to parse '\\r' skipping\n",
        "failed to parse '\\r' skipping\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from kid_readout.utils.easync import EasyNetCDF4"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = EasyNetCDF4('/home/data/2013-12-19_230736.nc')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts = nc.timestreams.timestream_20131219230739.data[:].view('complex')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tt = nc.timestreams.timestream_20131219230739.epoch[:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tstemp = np.interp(tt,times,temps)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "(420, 8192)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(tstemp,abs(ts.mean(1)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "[<matplotlib.lines.Line2D at 0x43c5950>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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yTjDkiUjNuOPVCZ4MRURqdl928hcuAG+9pewXDTt5IlKz+zLkX3wR+PWvgdRU4Pp1x/My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\nvAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x354d190>"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nc = EasyNetCDF4('/home/data/2013-12-20_085953.nc')\n",
      "nc.group.sync()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts = nc.timestreams.timestream_20131220085956.data[:].view('complex')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "ts.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "(301, 8192)"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tt = nc.timestreams.timestream_20131220085956.epoch[:]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tstemp = np.interp(tt,times,temps)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(tstemp,abs(ts.mean(1)))\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "[<matplotlib.lines.Line2D at 0x50ce0d0>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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27XNuuummy1negAaqv7q62vne9753mauK3rFjx5y6ujrHcRzn1KlTzg033ODs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\njxnrbVsxyAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x4946cd0>"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "plot(abs(ts.mean(1)))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 20,
       "text": [
        "[<matplotlib.lines.Line2D at 0x4cd3590>]"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x43cd110>"
       ]
      }
     ],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}