def runKKFeaturesFromFiles(name, files, nProbes, featuresPerProbe, featuresTim, featuresMask ):
"""run (as a simple script) all the steps necessary to prepare for running KK"""
fetFeatures = []
maskFeatures = {}
fM = featuresMask.keys()
for c in range(1, nProbes+1):
for f in featuresPerProbe:
fetFeatures.append(f + str(c))
for f in fM:
maskFeatures[f + str(c)] = featuresMask[f]
fetFeatures.append('Time in File (1/10000 s)')
fetFeatures = tuple(fetFeatures)
print fetFeatures
featuresToCompute = []
featuresToCompute.extend(fetFeatures)
featuresToCompute.extend(featuresTim)
#TODO add mean waveforms
# create data file handler objects
spikeFiles = []
if files[-3:] == 'smr':
fileType = 'son'
else:
fileType = 'nlynx'
if fileType == 'son':
for fname in files:
spikeFiles.append(sonIO.SONfile(fname))
elif fileType == 'nlynx':
for fname in files:
spikeFiles.append(neuralynx_io.TTFile(fname))
if fileType == 'son':
adcMarkChans = spikeFiles[0].channelsOfKind('ADCMarkerChannel')
elif fileType == 'nlynx':
adcMarkChans = (0,)
for chan in adcMarkChans:
fname = name + ".peak.1" #principal components file name needed
# by klusters
spkname = name + ".spk.1" # spike waveform file, needed by
# klusters
parname = name + ".xml"
timname = name + ".tim.1"
if fileType == 'son':
ttFiles = []
for sp in spikeFiles:
ttFiles.append(sonIO.TTFile(sp))
else:
ttFiles = spikeFiles
computer = computeSpikeFeatures.FeatureComputer(ttFiles, spkname, params=None, interleave=120)
computer.setMaskFromFeatures(maskFeatures)
computer.computeFeatures(featuresToCompute)
features = computer.computedFeatures
computeSpikeFeatures.writeFetFile(fname, features, fetFeatures)
computeSpikeFeatures.writeTimFile(timname, features)
computeSpikeFeatures.makeXMLParFile(spikeFiles, parname, len(featuresPerProbe))
def getAvWaveform(files, clufile, clusterIdx, nProbes, features):
"""run (as a simple script) all the steps necessary to prepare for running KK"""
featuresToCompute = []
for c in range(1, nProbes + 1):
for f in features:
featuresToCompute.append(f + str(c))
# create data file handler objects
spikeFiles = []
if files[0][-3:] == 'smr':
fileType = 'son'
elif files[0][-5:-2] == 'spk':
fileType = 'klusters'
else:
fileType = 'nlynx'
if fileType == 'son':
for fname in files:
spikeFiles.append(sonIO.SONfile(fname))
elif fileType == 'nlynx':
for fname in files:
spikeFiles.append(neuralynx_io.TTFile(fname))
elif fileType == 'klusters':
print('klusters_io')
for fname in files:
spikeFiles.append(klusters_io.TTFile(fname))
if fileType == 'son':
adcMarkChans = spikeFiles[0].channelsOfKind('ADCMarkerChannel')
elif fileType == 'nlynx':
adcMarkChans = (0, )
name = files[0][:-4]
name = name + "_c_" + clusterIdx + 'wv.mat'
print name
outfh = pymatfile.pymatfile(name, 'w')
if fileType == 'son':
ttFiles = []
for sp in spikeFiles:
ttFiles.append(sonIO.TTFile(sp))
else:
ttFiles = spikeFiles
print(ttFiles)
computer = computeSpikeFeatures.FeatureComputer(ttFiles,
spkname=None,
params=None,
interleave=120)
computer.setMaskFromCluFile(clufile, clusterIdx)
computer.computeFeatures(featuresToCompute)
features = computer.computedFeatures
a = array([])
for f in featuresToCompute:
fg = reshape(features[f], (1, len(features[f])))
if len(a):
a = concatenate([a, fg], 0)
else:
a = fg
outfh.writeItem(a, 'waveformstd')
outfh.close()
def runKKFeaturesFromFiles(name,
files,
nProbes,
featuresPerProbe,
featuresTim,
featuresMask,
featuresMat=None,
output='KK',
extFeatures=None,
realignPeaks=False,
shiftTimes=None,
startTime=None,
stopTime=None):
"""run (as a simple script) all the steps necessary to prepare for running KK"""
fetFeatures = []
maskFeatures = {}
if featuresMask is not None:
fM = featuresMask.keys()
else:
fM = []
for c in range(1, nProbes + 1):
for f in featuresPerProbe:
fetFeatures.append(f + str(c))
for f in fM:
if f[-2:] == ': ':
for c in range(1, nProbes + 1):
maskFeatures[f + str(c)] = featuresMask[f]
else:
maskFeatures[f] = featuresMask[f]
if output == 'KK':
fetFeatures.append('Time (Klusters Units)')
fetFeatures = tuple(fetFeatures)
# print fetFeatures
featuresToCompute = []
featuresToCompute.extend(fetFeatures)
if output == 'KK':
featuresToCompute.extend(featuresTim)
#TODO add mean waveforms
# create data file handler objects
spikeFiles = []
fileType = None
if files[0][-3:] == 'smr':
fileType = 'son'
elif files[0][-3:] == 'ntt':
fileType = 'nlynx'
elif files[0][-3:] == 'spk':
fileType = 'KK'
if not fileType:
raise ValueError('unrecognized file name ' + files)
if fileType == 'son':
for fname in files:
spikeFiles.append(sonIO.SONfile(fname))
elif fileType == 'nlynx':
for fname in files:
spikeFiles.append(neuralynx_io.TTFile(fname))
elif fileType == 'KK':
for fname in files:
spikeFiles.append(klusters_io.TTfile(fname))
if fileType == 'son':
adcMarkChans = spikeFiles[0].channelsOfKind('ADCMarkerChannel')
elif fileType == 'nlynx' or fileType == 'KK':
adcMarkChans = (0, )
for chan in adcMarkChans:
spkname = None
if output == 'KK':
fname = name + ".fet.1" #principal components file name needed
# by klusters
spkname = name + ".spk.1" # spike waveform file, needed by
# klusters
parname = name + ".xml"
timname = name + ".tim.1"
elif output == 'mat':
import pymatfile
matFname = name + '_features.mat'
matf = pymatfile.pymatfile(matFname, 'w')
if fileType == 'son':
ttFiles = []
for sp in spikeFiles:
ttFiles.append(sonIO.TTFile(sp))
else:
ttFiles = spikeFiles
computer = computeSpikeFeatures.FeatureComputer(ttFiles,
spkname,
params=None,
interleave=120,
shiftTimes=shiftTimes,
startTime=startTime,
stopTime=stopTime)
computer.setMaskFromFeatures(maskFeatures)
computer.computeFeatures(featuresToCompute, realignPeaks)
features = computer.computedFeatures
if output == 'KK':
computeSpikeFeatures.writeFetFile(fname, features, fetFeatures)
computeSpikeFeatures.writeTimFile(timname, features)
computeSpikeFeatures.makeXMLParFile(spikeFiles, parname,
len(featuresPerProbe),
extFeatures)
elif output == 'mat':
for key in fetFeatures:
value = features[key]
# print shape(value)
matVarname = re.sub(': ', '_', key)
matVarname = re.sub(' ', '', matVarname)
# print matVarname
matf.writeItem(value.astype(float64), matVarname)
matf.close()