read_ibw_with_python.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#set plots to show inline\n",
    "%matplotlib inline\n",
    "#import necessary libraries\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import os\n",
    "#import igor library. \n",
    "#if you are running Anaconda this can be installed via command line: \"pip install igor\".\n",
    "# the package can be found here: https://pypi.python.org/pypi/igor\n",
    "import igor\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#get the current directory and add the filename\n",
    "filename = os.getcwd()+\"\\\\Q0_Roi.ibw\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#use the 'igor' library to read in the data. this creates a dictionary containing several keys and 'subkeys'\n",
    "data = igor.binarywave.load(filename)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys(['wave', 'version'])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# here are the keys\n",
    "data.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'version': 5,\n",
       " 'wave': {'bin_header': {'checksum': 21615,\n",
       "   'dataEUnitsSize': 0,\n",
       "   'dimEUnitsSize': array([0, 0, 0, 0]),\n",
       "   'dimLabelsSize': array([0, 0, 0, 0]),\n",
       "   'formulaSize': 0,\n",
       "   'noteSize': 0,\n",
       "   'optionsSize1': 0,\n",
       "   'optionsSize2': 0,\n",
       "   'sIndicesSize': 0,\n",
       "   'wfmSize': 16704},\n",
       "  'data_units': b'',\n",
       "  'dimension_units': b'',\n",
       "  'formula': b'',\n",
       "  'labels': [[], [], [], []],\n",
       "  'note': b'',\n",
       "  'sIndices': array([], dtype=float64),\n",
       "  'wData': array([[  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "         [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "         [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "         ..., \n",
       "         [ -4.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "         [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "         [  1.,   1.,   1., ...,   1.,   1., -87.]], dtype=float32),\n",
       "  'wave_header': {'aModified': 3,\n",
       "   'bname': b'Roi',\n",
       "   'botFullScale': 0.0,\n",
       "   'creationDate': 3460467370,\n",
       "   'dFolder': 102891752,\n",
       "   'dLock': 0,\n",
       "   'dataEUnits': 0,\n",
       "   'dataUnits': array([b'', b'', b'', b''], \n",
       "         dtype='|S1'),\n",
       "   'depID': 936,\n",
       "   'dimEUnits': array([0, 0, 0, 0]),\n",
       "   'dimLabels': array([0, 0, 0, 0]),\n",
       "   'dimUnits': array([[b'', b'', b'', b''],\n",
       "          [b'', b'', b'', b''],\n",
       "          [b'', b'', b'', b''],\n",
       "          [b'', b'', b'', b'']], \n",
       "         dtype='|S1'),\n",
       "   'fileName': 0,\n",
       "   'formula': 0,\n",
       "   'fsValid': 0,\n",
       "   'kindBits': b'\\x00',\n",
       "   'modDate': 3460467370,\n",
       "   'nDim': array([64, 64,  0,  0]),\n",
       "   'next': 102898856,\n",
       "   'npnts': 4096,\n",
       "   'sIndices': 0,\n",
       "   'sfA': array([ 1.,  1.,  1.,  1.]),\n",
       "   'sfB': array([ 0.,  0.,  0.,  0.]),\n",
       "   'srcFldr': 0,\n",
       "   'swModified': 1,\n",
       "   'topFullScale': 0.0,\n",
       "   'type': 2,\n",
       "   'useBits': b'\\x00',\n",
       "   'wModified': 1,\n",
       "   'waveNoteH': 0,\n",
       "   'whUnused': array([196610,      2,      0,      0,      0,      0,      0,      0,\n",
       "               0,      0,      0,      0,      0,      0,      0,      0]),\n",
       "   'whVersion': 1,\n",
       "   'whpad1': array([b'', b'', b'', b'\\x03', b'\\x03', b'\\x03'], \n",
       "         dtype='|S1'),\n",
       "   'whpad2': 0,\n",
       "   'whpad3': 1253,\n",
       "   'whpad4': 6}}}"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#calling 'data' will display the whole dictionary structure and its contents. \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "       [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "       [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "       ..., \n",
       "       [ -4.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "       [  1.,   1.,   1., ...,   1.,   1.,   1.],\n",
       "       [  1.,   1.,   1., ...,   1.,   1., -87.]], dtype=float32)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#I'm guessing most people will be interested in 'wData', which is loaded by the library as numpy array\n",
    "data['wave']['wData']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0xb8dd2b0>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bjz8FPLQStDkGsHnz5glTcV1C4wFFp/D2A/X8QkSKnF+ISBm6sN9N/Q1T2q+qMN9N/VWV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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xb87ae10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#we can also display it..\n",
    "plt.matshow(data['wave']['wData'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
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