wonkoderverstaendige/intan_digital_load.ipynb

## intan_digital_load.ipynb
{
 "metadata": {
  "name": ""
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%pylab inline\n",
      "from numba import autojit"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Reading Intan \"File per Type\" format\n",
      "Ditial In: Data is stored as unsigned 16-bit integer. Each bit per word represents the state of one of the digital input channels.\n",
      "Timestamps: 32-bit signed integers.\n",
      "\n",
      "\n"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data = np.fromfile('digitalin.dat', np.uint16)\n",
      "times = np.fromfile('time.dat', np.int32)\n",
      "print times.shape, data.shape"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1051920,) (1051920,)\n"
       ]
      }
     ],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "First three seconds of recording."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n = 60000\n",
      "fs = 20000.\n",
      "p = plot(times[0:n]/(fs), data[0:n])\n",
      "plt.ylim(0, 3.1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "(0, 3.1)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x77e02d0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "To extract the value per channel, each channel is extracted with element-wise logical AND with the value of the bit."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "channels = np.zeros([data.shape[0], 2], np.double)\n",
      "print channels.shape\n",
      "for n in xrange(2):\n",
      "    channels[:, n] = np.bitwise_and(data, 2**n)/2.**n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "(1051920, 2)\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "p = plot(times[0:100000]/(fs),channels[0:100000, 0:2])\n",
      "plt.ylim(0, 1.1)\n",
      "plt.legend(['trigger', 'response'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "<matplotlib.legend.Legend at 0x7af06b0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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5OTlX3efxxx9HVFQU4uLisG/fPrcHSURE7XOZyK1WKxYsWACTyYSDBw8iPz8f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\n0gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x35e63f0>"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "### Cross correlogram\n",
      "Or how I have little idea what I am doing. I assume I should bin these beforehand. Otherwise it takes forever."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "start = 0\n",
      "end = 100000\n",
      "c = np.correlate(channels[start:end, 0], channels[start:end, 1], 'same', )\n",
      "plot(c)\n",
      "print ((end-start)/2-argmax(c))/fs*1000, \"ms\""
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "42.65 ms\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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AgNbWVhQUFCA1NRXLly9H+5DtAouKiuBwOJCWloZDhw55jp84cQJZWVlwOBzY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\nQRAEERJkSAiCIIiQ+P8KQGY+S2LRtQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0xcf118b0>"
       ]
      }
     ],
     "prompt_number": 37
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "## Ignore the following..."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "@autojit\n",
      "def cross_corr_numba(data, limits, maxlags):\n",
      "    return plt.xcorr(data[limits[0]:limits[1], 0], data[limits[0]:limits[1], 1], maxlags=maxlags)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def cross_corr(data, limits, maxlags):\n",
      "    return plt.xcorr(data[limits[0]:limits[1], 0], data[limits[0]:limits[1], 1], maxlags=maxlags)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit cross_corr(channels, , 10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 2.45 s per loop\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7d78fb0>"
       ]
      }
     ],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "%timeit cross_corr_numba(channels, (0, 100000), 10000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "1 loops, best of 3: 2.57 s per loop\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0x7c1f350>"
       ]
      }
     ],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "cross_corr_numba(channels, (0, 100000), 5000)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 43,
       "text": [
        "(array([-5000, -4999, -4998, ...,  4998,  4999,  5000]),\n",
        " array([ 0.58371192,  0.58381137,  0.58391082, ...,  0.42205149,\n",
        "        0.42195204,  0.42185259]),\n",
        " <matplotlib.collections.LineCollection at 0xd7a5e10>,\n",
        " <matplotlib.lines.Line2D at 0xd0479f0>)"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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       "text": [
        "<matplotlib.figure.Figure at 0xe6b7d90>"
       ]
      }
     ],
     "prompt_number": 43
    }
   ],
   "metadata": {}
  }
 ]
}