print str(len(spikeTimestamps)) + " Spikes loaded"
#Load spikewaveform and averagespikewaveform all 4 channels
spikeWFCh0 = list(processedData['spikeWFCh0'])
spikeWFCh1 = list(processedData['spikeWFCh1'])
spikeWFCh2 = list(processedData['spikeWFCh2'])
spikeWFCh3 = list(processedData['spikeWFCh3'])
averageSpikeWFCh0 = list(processedData['averageSpikeWFCh0'])
averageSpikeWFCh1 = list(processedData['averageSpikeWFCh1'])
averageSpikeWFCh2 = list(processedData['averageSpikeWFCh2'])
averageSpikeWFCh3 = list(processedData['averageSpikeWFCh3'])
print "SpikeWaveform Data Loaded"
print 'Generating Rate Map...'
#get the raw rateMap, spikeMap and occupancy map
rateMap, spikeMap, occMap, rawSpikeMap, posSpikeX, posSpikeY, angleSpike = rateMapUtils.generateRateMap(piCameraData,spikeTimestamps,k=bin_width)
print 'Rate Map generated for ' + filename
#function to generate gaussian smoothed rate map, calculate spatial info, mean firing rate, peak firing rate
occMapPlot, spikeMapPlot, gaussRateMap, spatialInfoScore, meanFiringRate, peakFiringRate = rateMapUtils.generateGaussRateMap(occMap, spikeMap, samplingRate, occMapThreshold)
print 'Spatial Information Score: ' + str(spatialInfoScore)
print 'Mean Firing Rate: ' + str(meanFiringRate)
print 'Peak Firing Rate: ' + str(peakFiringRate)
print 'Gaussian Smoothed Rate Map Generated'
#save the rate map for the processed data, remember this will over write the rate map from Run#1
filename = filename.split('_ProcessedData.mat')[0]
rateMapFileName = os.path.join(filename + '_rawMap_k' + str(bin_width) + '.mat')
sio.savemat(rateMapFileName, mdict={'rateMap':rateMap, 'spikeMap': spikeMap, 'occMap': occMap,'gaussRateMap':gaussRateMap})
print 'Rate Map ' + rateMapFileName + ' saved! \n'
nlxData['pos_y'] = list(processedData['nlx_posY'][0])
nlxData['pos_t'] = list(processedData['nlx_time'][0])
nlxData['angle'] = []
nlxData['width'] = int(processedData['nlx_width'][0])
nlxData['height'] = int(processedData['nlx_height'][0])
print "Position Data loaded"
#load the spike times
spikeTimestamps = list(processedData['spikeTimestamps'][0])
spikeMaxHeight = list(processedData['spikeMaxHeight'][0])
spikeMaxWidth = list(processedData['spikeMaxWidth'][0])
print str(len(spikeTimestamps)) + " Spikes loaded"
print 'Generating Rate Map...'
#get the raw rateMap, spikeMap and occupancy map
rateMap, spikeMap, occMap, rawSpikeMap, posSpikeX, posSpikeY, angleSpike = rateMapUtils.generateRateMap(nlxData,spikeTimestamps,k=bin_width)
print 'Rate Map generated for ' + filename
#function to generate gaussian smoothed rate map, calculate spatial info, mean firing rate, peak firing rate
occMapPlot, spikeMapPlot, gaussRateMap, spatialInfoScore, meanFiringRate, peakFiringRate = rateMapUtils.generateGaussRateMap(occMap, spikeMap, samplingRate, occMapThreshold)
print 'Spatial Information Score: ' + str(spatialInfoScore)
print 'Mean Firing Rate: ' + str(meanFiringRate)
print 'Peak Firing Rate: ' + str(peakFiringRate)
print 'Gaussian Smoothed Rate Map Generated'
#save the rate map for the processed data, remember this will over write the rate map from Run#1
filename = filename.split('_nlx_ProcessedData.mat')[0]
rateMapFileName = os.path.join(filename + '_nlx_rawMap_k' + str(bin_width) + '.mat')
sio.savemat(rateMapFileName, mdict={'rateMap':rateMap, 'spikeMap': spikeMap, 'occMap': occMap, 'gaussRateMap':gaussRateMap})
print 'Rate Map ' + rateMapFileName + ' saved! \n\n'
else:
Pos['width'] = piCameraData['width'][0]
Pos['height'] = piCameraData['height'][0]
distance = sio.loadmat(PICAMERA_DISTANCE_FILE_NAME)
distance = distance['distance'][0][piCameraStartMazeIndex:piCameraEndMazeIndex]
Pos['red_x'] = Pos['red_x'][distance > 0.35]
Pos['red_y'] = Pos['red_y'][distance > 0.35]
Pos['pos_t'] = Pos['pos_t'][distance > 0.35]
print "\nPosition Data loaded"
# load the spikedata
SpikeData = pickle.load(open(SPIKE_FILE_NAME))
print "\nSpike Data loaded"
#get the raw rateMap, spikeMap and occupancy map
rateMap, spikeMap, occMap = rateMapUtils.generateRateMap(Pos, SpikeData, k=1)
rateMap_k5, spikeMap_k5, occMap_k5 = rateMapUtils.generateRateMap(Pos,
SpikeData,
k=5)
#save the raw maps in .npy format
sio.savemat(RAW_MAP_FILE_NAME,
mdict={
'rateMap': rateMap,
'spikeMap': spikeMap,
'occMap': occMap
})
sio.savemat(RAW_MAP_K5_FILE_NAME,
mdict={
'rateMap': rateMap_k5,
'spikeMap': spikeMap_k5,