def klusters_files(table,basename):
CluFileName,FetFileName,ResFileName,SpkFileName,XMLFileName = (basename + ext for ext in [".clu.1",".fet.1",".res.1",".spk.1",".xml"])
output.write_clu(table.cols.clu[:],CluFileName)
output.write_fet(to2d(table.cols.fet[:]),FetFileName,samples=table.cols.time[:])
output.write_res(table.cols.time[:],ResFileName)
output.write_spk(table.cols.wave[:],SpkFileName)
output.write_xml(n_ch=N_CH,n_samp=S_TOTAL,n_feat=N_CH*FPC,sample_rate=SAMPLE_RATE,filepath=XMLFileName)
def klusters_files(table, basename):
CluFileName, FetFileName, ResFileName, SpkFileName, XMLFileName = (
basename + ext
for ext in [".clu.1", ".fet.1", ".res.1", ".spk.1", ".xml"])
output.write_clu(table.cols.clu[:], CluFileName)
output.write_fet(to2d(table.cols.fet[:]),
FetFileName,
samples=table.cols.time[:])
output.write_res(table.cols.time[:], ResFileName)
output.write_spk(table.cols.wave[:], SpkFileName)
output.write_xml(n_ch=N_CH,
n_samp=S_TOTAL,
n_feat=N_CH * FPC,
sample_rate=SAMPLE_RATE,
filepath=XMLFileName)
def extract_intra_spikes(DatFileName,IntraChannel,output_dir=None,ExtraChannels=None):
"""extracts spikes times from intracellular data"""
THRESH_FRAC = .5
DatFileName = os.path.abspath(DatFileName)
DatDir = os.path.dirname(DatFileName)
basename = intra_basename(DatFileName)
OutDir = join(output_dir,basename) if output_dir else join(DatDir,basename)
with indir(OutDir):
SpkFileName = basename+'.spk.1'
n_ch_dat,sample_rate = get_dat_pars(DatFileName)
global N_CH
N_CH = 1 if ExtraChannels is None else 1 + len(ExtraChannels)
set_globals_samples(sample_rate)
print("extracting intracellular spikes from %s"%DatFileName)
n_samples = num_samples(DatFileName,n_ch_dat,n_bytes=np.nbytes[DTYPE])
AllDataArr = np.memmap(DatFileName,dtype=np.int16,shape=(n_samples,n_ch_dat),mode='r')
b,a = signal.butter(3,100./(SAMPLE_RATE/2),'high') #filter at 100 Hz
IntraArr = AllDataArr[:,IntraChannel].copy()
IntraArr = signal.filtfilt(b,a,IntraArr)
Thresh = IntraArr.max()*THRESH_FRAC
Segs = contig_segs(np.flatnonzero(IntraArr > Thresh),padding=2)
TmList = map(lambda Seg: Seg[IntraArr[Seg].argmax()],Segs)
CluList = np.ones(len(TmList),dtype=np.int)
FetList = np.zeros((len(TmList),1),dtype=np.int)
SpkList = [get_padded(IntraArr,PeakSample-S_BEFORE,PeakSample+S_AFTER) for PeakSample in TmList]
SpkArr = np.array(SpkList)[:,:,np.newaxis]
if ExtraChannels is not None:
ExtraArr = AllDataArr[:,ExtraChannels].copy()
#b,a = signal.butter(BUTTER_ORDER,(F_LOW/(SAMPLE_RATE/2),.95),'pass')
ExtraArr = filtfilt2d(b,a,ExtraArr)
ExtraSpkList = [get_padded(ExtraArr,PeakSample-S_BEFORE,PeakSample+S_AFTER) for PeakSample in TmList]
ExtraSpkArr = np.array(ExtraSpkList)
SpkArr *= ExtraSpkArr[0].max()/SpkArr[0].max()
SpkArr = np.concatenate((np.array(ExtraSpkList),SpkArr),axis=2)
output.write_spk(np.array(SpkArr),SpkFileName)
write_files(basename,CluList,TmList,FetList,[])
def extract_intra_spikes(DatFileName,
IntraChannel,
output_dir=None,
ExtraChannels=None):
"""extracts spikes times from intracellular data"""
THRESH_FRAC = .5
DatFileName = os.path.abspath(DatFileName)
DatDir = os.path.dirname(DatFileName)
basename = intra_basename(DatFileName)
OutDir = join(output_dir, basename) if output_dir else join(
DatDir, basename)
with indir(OutDir):
SpkFileName = basename + '.spk.1'
n_ch_dat, sample_rate = get_dat_pars(DatFileName)
global N_CH
N_CH = 1 if ExtraChannels is None else 1 + len(ExtraChannels)
set_globals_samples(sample_rate)
print("extracting intracellular spikes from %s" % DatFileName)
n_samples = num_samples(DatFileName,
n_ch_dat,
n_bytes=np.nbytes[DTYPE])
AllDataArr = np.memmap(DatFileName,
dtype=np.int16,
shape=(n_samples, n_ch_dat),
mode='r')
b, a = signal.butter(3, 100. / (SAMPLE_RATE / 2),
'high') #filter at 100 Hz
IntraArr = AllDataArr[:, IntraChannel].copy()
IntraArr = signal.filtfilt(b, a, IntraArr)
Thresh = IntraArr.max() * THRESH_FRAC
Segs = contig_segs(np.flatnonzero(IntraArr > Thresh), padding=2)
TmList = map(lambda Seg: Seg[IntraArr[Seg].argmax()], Segs)
CluList = np.ones(len(TmList), dtype=np.int)
FetList = np.zeros((len(TmList), 1), dtype=np.int)
SpkList = [
get_padded(IntraArr, PeakSample - S_BEFORE, PeakSample + S_AFTER)
for PeakSample in TmList
]
SpkArr = np.array(SpkList)[:, :, np.newaxis]
if ExtraChannels is not None:
ExtraArr = AllDataArr[:, ExtraChannels].copy()
#b,a = signal.butter(BUTTER_ORDER,(F_LOW/(SAMPLE_RATE/2),.95),'pass')
ExtraArr = filtfilt2d(b, a, ExtraArr)
ExtraSpkList = [
get_padded(ExtraArr, PeakSample - S_BEFORE,
PeakSample + S_AFTER) for PeakSample in TmList
]
ExtraSpkArr = np.array(ExtraSpkList)
SpkArr *= ExtraSpkArr[0].max() / SpkArr[0].max()
SpkArr = np.concatenate((np.array(ExtraSpkList), SpkArr), axis=2)
output.write_spk(np.array(SpkArr), SpkFileName)
write_files(basename, CluList, TmList, FetList, [])
