def import_spiketrains(epoch, protocol, segment):
for (i, spike_train) in enumerate(segment.spiketrains):
params = {'t_start_ms': spike_train.t_start.rescale(pq.ms).item(),
't_stop_ms': spike_train.t_stop.rescale(pq.ms).item(),
'sampling_rate_hz': spike_train.sampling_rate.rescale(pq.Hz).item(),
'description': spike_train.description,
'file_origin': spike_train.file_origin}
if spike_train.name:
name = spike_train.name
else:
name = "spike train {}".format(i + 1)
inputs = Maps.newHashMap()
for m in iterable(epoch.getMeasurements()):
inputs.put(m.getName(), m)
ar = epoch.addAnalysisRecord(name,
inputs,
protocol,
to_map(params))
#
spike_train.labels = ['spike time' for i in spike_train.shape]
spike_train.sampling_rates = [spike_train.sampling_rate for i in spike_train.shape]
spike_train.waveforms.labels = ['channel index', 'time', 'spike']
spike_train.waveforms.sampling_rates = [0, spike_train.sampling_rate, 0] * pq.Hz
insert_numeric_analysis_artifact(ar,
name,
{'spike times': spike_train,
'spike waveforms': spike_train.waveforms})
def should_round_trip_multi_element_data_frame(self):
arr1 = np.random.randn(10,10) * pq.s
arr1.labels = [u'volts', u'other']
arr1.name = u'name'
arr1.sampling_rates = [1.0 * pq.Hz, 10.0 * pq.Hz]
arr2 = np.random.randn(10,10) * pq.V
arr2.labels = [u'volts', u'other']
arr2.name = u'name'
arr2.sampling_rates = [1.0 * pq.Hz, 10.0 * pq.Hz]
epoch = self.expt.insertEpoch(DateTime(), DateTime(), self.protocol, None, None)
ar = epoch.addAnalysisRecord("record", to_map({}), self.protocol, to_map({}))
result_name1 = 'result'
result_name2 = 'other-result'
expected = {result_name1: arr1,
result_name2: arr2}
record_name = "record-name"
artifact = insert_numeric_analysis_artifact(ar, record_name, expected)
assert artifact is not None
sleep(0.5)
actual = as_data_frame(ar.getOutputs().get(record_name))
assert_data_frame_equals(expected, actual)
