def _recent_stream_for_dir(dir, maxdate=None):
dir = os.path.abspath(dir)
files = FileReader.ListDatFiles(d=dir)
#The returned list is in ascending order, assume the last is most recent
best_stream = None
for fn in files:
temp_stream = FileReader.bcistream(fn)
temp_date = datetime.datetime.fromtimestamp(temp_stream.datestamp)
if not best_stream\
or (maxdate and temp_date<=maxdate)\
or (not maxdate and temp_date > datetime.datetime.fromtimestamp(best_stream.datestamp)):
best_stream = temp_stream
return best_stream
def add_trials_from_file(self, filename):
if self.span_type=='period' and filename:
bci_stream=FileReader.bcistream(filename)
sig,states=bci_stream.decode(nsamp='all')
sig,chan_labels=bci_stream.spatialfilteredsig(sig)
erpwin = [int(bci_stream.msec2samples(ww)) for ww in bci_stream.params['ERPWindow']]
x_vec = np.arange(bci_stream.params['ERPWindow'][0],bci_stream.params['ERPWindow'][1],1000/bci_stream.samplingfreq_hz,dtype=float)
trigchan = bci_stream.params['TriggerInputChan']
trigchan_ix = find(trigchan[0] in chan_labels)
trigthresh = bci_stream.params['TriggerThreshold']
trigdetect = find(np.diff(np.asmatrix(sig[trigchan_ix,:]>trigthresh,dtype='int16'))>0)+1
intensity_detail_name = 'dat_TMS_powerA' if self.detail_values.has_key('dat_TMS_powerA') else 'dat_Nerve_stim_output'
#Get approximate data segments for each trial
trig_ix = find(np.diff(states['Trigger'])>0)+1
for i in np.arange(len(trigdetect)):
ix = trigdetect[i]
dat = sig[:,ix+erpwin[0]:ix+erpwin[1]]
self.trials.append(Datum(subject_id=self.subject_id\
, datum_type_id=self.datum_type_id\
, span_type='trial'\
, parent_datum_id=self.datum_id\
, IsGood=1, Number=0))
my_trial=self.trials[-1]
my_trial.detail_values[intensity_detail_name]=str(states['StimulatorIntensity'][0,trig_ix[i]])
if int(bci_stream.params['ExperimentType']) == 1:#SICI intensity
my_trial.detail_values['dat_TMS_powerB']=str(bci_stream.params['StimIntensityB'])#TODO: Use the state.
my_trial.detail_values['dat_TMS_ISI']=str(bci_stream.params['PulseInterval'])
my_trial.store={'x_vec':x_vec, 'data':dat, 'channel_labels': chan_labels}
Session.commit()
def _recent_stream_for_dir(dir, maxdate=None): dir=os.path.abspath(dir) files=FileReader.ListDatFiles(d=dir) #The returned list is in ascending order, assume the last is most recent best_stream = None for fn in files: temp_stream = FileReader.bcistream(fn) temp_date = datetime.datetime.fromtimestamp(temp_stream.datestamp) if not best_stream\ or (maxdate and temp_date<=maxdate)\ or (not maxdate and temp_date > datetime.datetime.fromtimestamp(best_stream.datestamp)): best_stream=temp_stream return best_stream
def add_trials_from_file(self, filename):
if self.span_type == 'period' and filename:
bci_stream = FileReader.bcistream(filename)
sig, states = bci_stream.decode(nsamp='all')
sig, chan_labels = bci_stream.spatialfilteredsig(sig)
erpwin = [
int(bci_stream.msec2samples(ww))
for ww in bci_stream.params['ERPWindow']
]
x_vec = np.arange(bci_stream.params['ERPWindow'][0],
bci_stream.params['ERPWindow'][1],
1000 / bci_stream.samplingfreq_hz,
dtype=float)
trigchan = bci_stream.params['TriggerInputChan']
trigchan_ix = find(trigchan[0] in chan_labels)
trigthresh = bci_stream.params['TriggerThreshold']
trigdetect = find(
np.diff(
np.asmatrix(sig[trigchan_ix, :] > trigthresh,
dtype='int16')) > 0) + 1
intensity_detail_name = 'dat_TMS_powerA' if self.detail_values.has_key(
'dat_TMS_powerA') else 'dat_Nerve_stim_output'
#Get approximate data segments for each trial
trig_ix = find(np.diff(states['Trigger']) > 0) + 1
for i in np.arange(len(trigdetect)):
ix = trigdetect[i]
dat = sig[:, ix + erpwin[0]:ix + erpwin[1]]
self.trials.append(Datum(subject_id=self.subject_id\
, datum_type_id=self.datum_type_id\
, span_type='trial'\
, parent_datum_id=self.datum_id\
, IsGood=1, Number=0))
my_trial = self.trials[-1]
my_trial.detail_values[intensity_detail_name] = str(
states['StimulatorIntensity'][0, trig_ix[i]])
if int(bci_stream.params['ExperimentType']
) == 1: #SICI intensity
my_trial.detail_values['dat_TMS_powerB'] = str(
bci_stream.params['StimIntensityB']
) #TODO: Use the state.
my_trial.detail_values['dat_TMS_ISI'] = str(
bci_stream.params['PulseInterval'])
my_trial.store = {
'x_vec': x_vec,
'data': dat,
'channel_labels': chan_labels
}
Session.commit()