def unit(u_, Data0, cc, blockID):
# number of time bins to include in the LFP array
nlfpsamp = 0
for tt, trial in enumerate(Data0['trial'][0][0][0]):
thislfpsamp = trial['LFP'].shape[1]
if thislfpsamp>nlfpsamp:
nlfpsamp = thislfpsamp
ntrials = Data0['trial'][0][0][0].size # find number of trials
nstimID = Data0['trial'][0][0][0][0]['Epoch_Value'][0].size
# initialze LFP, spike times, spike trials, spike waveform
lfp = np.ndarray((0, nlfpsamp), dtype = 'float32')
spktimes = np.ndarray(0)
spktrials = np.ndarray(0)
spkwaveform = np.ndarray((0, 22))
# initialize frequency and attenuation IDs
stimID = np.ndarray((0, nstimID), dtype = 'float32')
ttt = 0 # valid trial counter
for tt in range(ntrials):
trial = Data0['trial'][0][0][0][tt]
thisstimID = np.float32(trial['Epoch_Value'][0])
# if not ((blockID.startswith('b')) and (thisstimID[0] < 2)):
# get the LFP for this trial and pad it with nans so it can fit in a matrix (since
# some of the trials have +/-1 data point for LFP)
lfpchannel = trial['LFP'][cc]
lfpchannel = np.concatenate((lfpchannel, np.zeros(nlfpsamp - len(lfpchannel)) * np.nan))
lfp = np.vstack((lfp, lfpchannel))
spktime = trial['CH'][0][cc]['latency']
if np.prod(spktime.shape) > 0:
spktimes = np.append(spktimes, spktime)
spktrials = np.append(spktrials, np.ones(spktime.size) * ttt)
spkwaveform = np.concatenate((spkwaveform, trial['CH'][0][cc]['spkwaveform'].T), 0)
# add to Epoch_Value
stimID = np.vstack((stimID, thisstimID))
ttt += 1 # increment valid trial counter
# end if valid ID
# end trial loop
if spktimes.size == 0: # if no spikes
print 'No spikes detected for this unit.'
spktimes = np.array([np.nan])
spktrials = np.array([np.nan])
spkwaveform = np.array([np.nan])
rast = np.array([np.nan])
else:
# filter out unwanted trials
remID = np.array([np.nan, np.nan])
if blockID.startswith('b'):
remID = np.array([1., 70.])
elif blockID.startswith('r'):
remID = np.array([0., 0.])
spktimes, spktrials, spkwaveform, lfp, stimID = \
remove_trials(spktimes, spktrials, spkwaveform, lfp, stimID, remID)
# create raster
ntrials = stimID.shape[0]
nbins = np.ceil(1000 * spktimes.max())+1
rast = Spikes.calc_rast(spktimes, spktrials, ntrials, nbins)
# save out to file
u_.create_dataset('chan', data = cc)
u_.create_dataset('blockID', data = blockID)
# add stimulus ID datasets to this stimset on this unit
u_.create_dataset('stimID', data = stimID)
u_.create_dataset('lfp', data = lfp, compression = 'gzip')
u_.create_dataset('spktimes', data = spktimes, compression = 'gzip')
u_.create_dataset('spktrials', data = spktrials, compression = 'gzip')
u_.create_dataset('spkwaveform', data = spkwaveform, compression = 'gzip')
u_.create_dataset('rast', data = rast, compression = 'gzip')
if blockID.startswith('b'):
rf = RF.calc_rf(rast, stimID)
u_.create_dataset('rf', data = rf, compression = 'gzip')
