|
| 1 | +{ |
| 2 | + "cells": [ |
| 3 | + { |
| 4 | + "cell_type": "code", |
| 5 | + "execution_count": 35, |
| 6 | + "metadata": { |
| 7 | + "collapsed": true |
| 8 | + }, |
| 9 | + "outputs": [], |
| 10 | + "source": [ |
| 11 | + "#set plots to show inline\n", |
| 12 | + "%matplotlib inline\n", |
| 13 | + "#import necessary libraries\n", |
| 14 | + "import matplotlib.pyplot as plt\n", |
| 15 | + "import seaborn as sns\n", |
| 16 | + "import numpy as np\n", |
| 17 | + "import os\n", |
| 18 | + "#import igor library. \n", |
| 19 | + "#if you are running Anaconda this can be installed via command line: \"pip install igor\".\n", |
| 20 | + "# the package can be found here: https://pypi.python.org/pypi/igor\n", |
| 21 | + "import igor\n" |
| 22 | + ] |
| 23 | + }, |
| 24 | + { |
| 25 | + "cell_type": "code", |
| 26 | + "execution_count": 10, |
| 27 | + "metadata": { |
| 28 | + "collapsed": false |
| 29 | + }, |
| 30 | + "outputs": [], |
| 31 | + "source": [ |
| 32 | + "#get the current directory and add the filename\n", |
| 33 | + "filename = os.getcwd()+\"\\\\Q0_Roi.ibw\"\n" |
| 34 | + ] |
| 35 | + }, |
| 36 | + { |
| 37 | + "cell_type": "code", |
| 38 | + "execution_count": 39, |
| 39 | + "metadata": { |
| 40 | + "collapsed": false |
| 41 | + }, |
| 42 | + "outputs": [], |
| 43 | + "source": [ |
| 44 | + "#use the 'igor' library to read in the data. this creates a dictionary containing several keys and 'subkeys'\n", |
| 45 | + "data = igor.binarywave.load(filename)\n" |
| 46 | + ] |
| 47 | + }, |
| 48 | + { |
| 49 | + "cell_type": "code", |
| 50 | + "execution_count": 42, |
| 51 | + "metadata": { |
| 52 | + "collapsed": false |
| 53 | + }, |
| 54 | + "outputs": [ |
| 55 | + { |
| 56 | + "data": { |
| 57 | + "text/plain": [ |
| 58 | + "dict_keys(['wave', 'version'])" |
| 59 | + ] |
| 60 | + }, |
| 61 | + "execution_count": 42, |
| 62 | + "metadata": {}, |
| 63 | + "output_type": "execute_result" |
| 64 | + } |
| 65 | + ], |
| 66 | + "source": [ |
| 67 | + "# here are the keys\n", |
| 68 | + "data.keys()" |
| 69 | + ] |
| 70 | + }, |
| 71 | + { |
| 72 | + "cell_type": "code", |
| 73 | + "execution_count": 43, |
| 74 | + "metadata": { |
| 75 | + "collapsed": false |
| 76 | + }, |
| 77 | + "outputs": [ |
| 78 | + { |
| 79 | + "data": { |
| 80 | + "text/plain": [ |
| 81 | + "{'version': 5,\n", |
| 82 | + " 'wave': {'bin_header': {'checksum': 21615,\n", |
| 83 | + " 'dataEUnitsSize': 0,\n", |
| 84 | + " 'dimEUnitsSize': array([0, 0, 0, 0]),\n", |
| 85 | + " 'dimLabelsSize': array([0, 0, 0, 0]),\n", |
| 86 | + " 'formulaSize': 0,\n", |
| 87 | + " 'noteSize': 0,\n", |
| 88 | + " 'optionsSize1': 0,\n", |
| 89 | + " 'optionsSize2': 0,\n", |
| 90 | + " 'sIndicesSize': 0,\n", |
| 91 | + " 'wfmSize': 16704},\n", |
| 92 | + " 'data_units': b'',\n", |
| 93 | + " 'dimension_units': b'',\n", |
| 94 | + " 'formula': b'',\n", |
| 95 | + " 'labels': [[], [], [], []],\n", |
| 96 | + " 'note': b'',\n", |
| 97 | + " 'sIndices': array([], dtype=float64),\n", |
| 98 | + " 'wData': array([[ 1., 1., 1., ..., 1., 1., 1.],\n", |
| 99 | + " [ 1., 1., 1., ..., 1., 1., 1.],\n", |
| 100 | + " [ 1., 1., 1., ..., 1., 1., 1.],\n", |
| 101 | + " ..., \n", |
| 102 | + " [ -4., 1., 1., ..., 1., 1., 1.],\n", |
| 103 | + " [ 1., 1., 1., ..., 1., 1., 1.],\n", |
| 104 | + " [ 1., 1., 1., ..., 1., 1., -87.]], dtype=float32),\n", |
| 105 | + " 'wave_header': {'aModified': 3,\n", |
| 106 | + " 'bname': b'Roi',\n", |
| 107 | + " 'botFullScale': 0.0,\n", |
| 108 | + " 'creationDate': 3460467370,\n", |
| 109 | + " 'dFolder': 102891752,\n", |
| 110 | + " 'dLock': 0,\n", |
| 111 | + " 'dataEUnits': 0,\n", |
| 112 | + " 'dataUnits': array([b'', b'', b'', b''], \n", |
| 113 | + " dtype='|S1'),\n", |
| 114 | + " 'depID': 936,\n", |
| 115 | + " 'dimEUnits': array([0, 0, 0, 0]),\n", |
| 116 | + " 'dimLabels': array([0, 0, 0, 0]),\n", |
| 117 | + " 'dimUnits': array([[b'', b'', b'', b''],\n", |
| 118 | + " [b'', b'', b'', b''],\n", |
| 119 | + " [b'', b'', b'', b''],\n", |
| 120 | + " [b'', b'', b'', b'']], \n", |
| 121 | + " dtype='|S1'),\n", |
| 122 | + " 'fileName': 0,\n", |
| 123 | + " 'formula': 0,\n", |
| 124 | + " 'fsValid': 0,\n", |
| 125 | + " 'kindBits': b'\\x00',\n", |
| 126 | + " 'modDate': 3460467370,\n", |
| 127 | + " 'nDim': array([64, 64, 0, 0]),\n", |
| 128 | + " 'next': 102898856,\n", |
| 129 | + " 'npnts': 4096,\n", |
| 130 | + " 'sIndices': 0,\n", |
| 131 | + " 'sfA': array([ 1., 1., 1., 1.]),\n", |
| 132 | + " 'sfB': array([ 0., 0., 0., 0.]),\n", |
| 133 | + " 'srcFldr': 0,\n", |
| 134 | + " 'swModified': 1,\n", |
| 135 | + " 'topFullScale': 0.0,\n", |
| 136 | + " 'type': 2,\n", |
| 137 | + " 'useBits': b'\\x00',\n", |
| 138 | + " 'wModified': 1,\n", |
| 139 | + " 'waveNoteH': 0,\n", |
| 140 | + " 'whUnused': array([196610, 2, 0, 0, 0, 0, 0, 0,\n", |
| 141 | + " 0, 0, 0, 0, 0, 0, 0, 0]),\n", |
| 142 | + " 'whVersion': 1,\n", |
| 143 | + " 'whpad1': array([b'', b'', b'', b'\\x03', b'\\x03', b'\\x03'], \n", |
| 144 | + " dtype='|S1'),\n", |
| 145 | + " 'whpad2': 0,\n", |
| 146 | + " 'whpad3': 1253,\n", |
| 147 | + " 'whpad4': 6}}}" |
| 148 | + ] |
| 149 | + }, |
| 150 | + "execution_count": 43, |
| 151 | + "metadata": {}, |
| 152 | + "output_type": "execute_result" |
| 153 | + } |
| 154 | + ], |
| 155 | + "source": [ |
| 156 | + "#calling 'data' will display the whole dictionary structure and its contents. \n", |
| 157 | + "data" |
| 158 | + ] |
| 159 | + }, |
| 160 | + { |
| 161 | + "cell_type": "code", |
| 162 | + "execution_count": 44, |
| 163 | + "metadata": { |
| 164 | + "collapsed": false |
| 165 | + }, |
| 166 | + "outputs": [ |
| 167 | + { |
| 168 | + "data": { |
| 169 | + "text/plain": [ |
| 170 | + "array([[ 1., 1., 1., ..., 1., 1., 1.],\n", |
| 171 | + " [ 1., 1., 1., ..., 1., 1., 1.],\n", |
| 172 | + " [ 1., 1., 1., ..., 1., 1., 1.],\n", |
| 173 | + " ..., \n", |
| 174 | + " [ -4., 1., 1., ..., 1., 1., 1.],\n", |
| 175 | + " [ 1., 1., 1., ..., 1., 1., 1.],\n", |
| 176 | + " [ 1., 1., 1., ..., 1., 1., -87.]], dtype=float32)" |
| 177 | + ] |
| 178 | + }, |
| 179 | + "execution_count": 44, |
| 180 | + "metadata": {}, |
| 181 | + "output_type": "execute_result" |
| 182 | + } |
| 183 | + ], |
| 184 | + "source": [ |
| 185 | + "#I'm guessing most people will be interested in 'wData', which is loaded by the library as numpy array\n", |
| 186 | + "data['wave']['wData']" |
| 187 | + ] |
| 188 | + }, |
| 189 | + { |
| 190 | + "cell_type": "code", |
| 191 | + "execution_count": 45, |
| 192 | + "metadata": { |
| 193 | + "collapsed": false |
| 194 | + }, |
| 195 | + "outputs": [ |
| 196 | + { |
| 197 | + "data": { |
| 198 | + "text/plain": [ |
| 199 | + "<matplotlib.image.AxesImage at 0xb8dd2b0>" |
| 200 | + ] |
| 201 | + }, |
| 202 | + "execution_count": 45, |
| 203 | + "metadata": {}, |
| 204 | + "output_type": "execute_result" |
| 205 | + }, |
| 206 | + { |
| 207 | + "data": { |
| 208 | + "image/png": 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bjz8FPLQStDkGsHnz5glTcV1C4wFFp/D2A/X8QkSKnF+ISBm6sN9N/Q1T2q+qMN9N/VWV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\nAABJRU5ErkJggg==\n", |
| 209 | + "text/plain": [ |
| 210 | + "<matplotlib.figure.Figure at 0xb87ae10>" |
| 211 | + ] |
| 212 | + }, |
| 213 | + "metadata": {}, |
| 214 | + "output_type": "display_data" |
| 215 | + } |
| 216 | + ], |
| 217 | + "source": [ |
| 218 | + "#we can also display it..\n", |
| 219 | + "plt.matshow(data['wave']['wData'])" |
| 220 | + ] |
| 221 | + }, |
| 222 | + { |
| 223 | + "cell_type": "code", |
| 224 | + "execution_count": null, |
| 225 | + "metadata": { |
| 226 | + "collapsed": true |
| 227 | + }, |
| 228 | + "outputs": [], |
| 229 | + "source": [] |
| 230 | + } |
| 231 | + ], |
| 232 | + "metadata": { |
| 233 | + "kernelspec": { |
| 234 | + "display_name": "Python 3", |
| 235 | + "language": "python", |
| 236 | + "name": "python3" |
| 237 | + }, |
| 238 | + "language_info": { |
| 239 | + "codemirror_mode": { |
| 240 | + "name": "ipython", |
| 241 | + "version": 3 |
| 242 | + }, |
| 243 | + "file_extension": ".py", |
| 244 | + "mimetype": "text/x-python", |
| 245 | + "name": "python", |
| 246 | + "nbconvert_exporter": "python", |
| 247 | + "pygments_lexer": "ipython3", |
| 248 | + "version": "3.5.1" |
| 249 | + } |
| 250 | + }, |
| 251 | + "nbformat": 4, |
| 252 | + "nbformat_minor": 0 |
| 253 | +} |
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