def test_read_nse_data(self):
t_start, t_stop = None, None # in samples
nio = NeuralynxIO(self.sn, use_cache='never')
seg = Segment('testsegment')
for el_id, el_dict in nio.parameters_nse.iteritems():
filepath = nio.parameters_nse[el_id]['recording_file_name']
filename = filepath.split('/')[-1].split('\\')[-1].split('.')[0]
nio.read_nse(filename, seg, t_start=t_start, t_stop=t_stop,
waveforms=True)
spiketrain = seg.filter({'electrode_id': el_id},
objects=SpikeTrain)[0]
# target_data = np.zeros((500, 32))
# timestamps = np.zeros(500)
entries = []
with open(self.pd + '/%s.txt' % filename) as datafile:
for i, line in enumerate(datafile):
line = line.strip('\xef\xbb\xbf')
entries.append(line.split())
entries = np.asarray(entries, dtype=float)
target_data = entries[:-1, 11:]
timestamps = entries[:-1, 0]
timestamps = (timestamps * pq.microsecond -
nio.parameters_global['t_start'])
np.testing.assert_array_equal(timestamps.magnitude,
spiketrain.magnitude)
np.testing.assert_array_equal(target_data,
spiketrain.waveforms)
def test_read_nse_data(self):
t_start, t_stop = None, None # in samples
nio = NeuralynxIO(self.sn, use_cache='never')
seg = Segment('testsegment')
for el_id, el_dict in nio.parameters_nse.items():
filepath = nio.parameters_nse[el_id]['recording_file_name']
filename = filepath.split('/')[-1].split('\\')[-1].split('.')[0]
nio.read_nse(filename, seg, t_start=t_start, t_stop=t_stop,
waveforms=True)
spiketrain = seg.filter({'electrode_id': el_id},
objects=SpikeTrain)[0]
# target_data = np.zeros((500, 32))
# timestamps = np.zeros(500)
entries = []
with open(self.pd + '/%s.txt' % filename) as datafile:
for i, line in enumerate(datafile):
line = line.strip('\xef\xbb\xbf')
entries.append(line.split())
entries = np.asarray(entries, dtype=float)
target_data = entries[:-1, 11:]
timestamps = entries[:-1, 0]
timestamps = (timestamps * pq.microsecond -
nio.parameters_global['t_start'])
np.testing.assert_array_equal(timestamps.magnitude,
spiketrain.magnitude)
np.testing.assert_array_equal(target_data,
spiketrain.waveforms)
def test_read_block(self):
"""Read data in a certain time range into one block"""
t_start, t_stop = 3 * pq.s, 4 * pq.s
nio = NeuralynxIO(self.sn, use_cache='never')
block = nio.read_block(t_starts=[t_start], t_stops=[t_stop])
self.assertEqual(len(nio.parameters_ncs), 2)
self.assertTrue({
'event_id': 11,
'name': 'Starting Recording',
'nttl': 0
} in nio.parameters_nev['Events.nev']['event_types'])
# Everything put in one segment
self.assertEqual(len(block.segments), 1)
seg = block.segments[0]
self.assertEqual(len(seg.analogsignals), 1)
self.assertEqual(seg.analogsignals[0].shape[-1], 2)
self.assertEqual(seg.analogsignals[0].sampling_rate.units,
pq.CompoundUnit('32*kHz'))
self.assertEqual(seg.analogsignals[0].t_start, t_start)
self.assertEqual(seg.analogsignals[0].t_stop, t_stop)
self.assertEqual(len(seg.spiketrains), 2)
# Testing different parameter combinations
block = nio.read_block(lazy=True)
self.assertEqual(len(block.segments[0].analogsignals[0]), 0)
self.assertEqual(len(block.segments[0].spiketrains[0]), 0)
block = nio.read_block(cascade=False)
self.assertEqual(len(block.segments), 0)
block = nio.read_block(electrode_list=[0])
self.assertEqual(len(block.segments[0].analogsignals), 1)
self.assertEqual(len(block.channel_indexes[-1].units), 1)
block = nio.read_block(t_starts=None,
t_stops=None,
events=True,
waveforms=True)
self.assertEqual(len(block.segments[0].analogsignals), 1)
self.assertEqual(len(block.segments[0].spiketrains), 2)
self.assertEqual(len(block.segments[0].spiketrains[0].waveforms),
len(block.segments[0].spiketrains[0]))
self.assertGreater(len(block.segments[0].events), 0)
self.assertEqual(len(block.channel_indexes[-1].units), 2)
block = nio.read_block(t_starts=[t_start],
t_stops=[t_stop],
unit_list=[0],
electrode_list=[0])
self.assertEqual(len(block.channel_indexes[-1].units), 1)
block = nio.read_block(t_starts=[t_start],
t_stops=[t_stop],
unit_list=False)
self.assertEqual(len(block.channel_indexes[-1].units), 0)
def test_analogsignal_shortening_warning(self):
nio = NeuralynxIO(self.sn, use_cache='never')
with reset_warning_registry():
with warnings.catch_warnings(record=True) as w:
seg = Segment('testsegment')
nio.read_ncs(os.path.join(self.sn, 'Tet3a.ncs'), seg)
self.assertGreater(len(w), 0)
self.assertTrue(issubclass(w[0].category, UserWarning))
self.assertTrue('Analogsignalarray was shortened due to gap in'
' recorded data of file' in str(w[0].message))
def test_gap_warning(self):
nio = NeuralynxIO(self.sn, use_cache='never')
with reset_warning_registry():
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
nio.read_block(t_starts=None, t_stops=None)
self.assertGreater(len(w), 0)
self.assertTrue(issubclass(w[0].category, UserWarning))
self.assertEqual('Substituted t_starts and t_stops in order to'
' skip gap in recording session.',
str(w[0].message))
def test_analogsignal_shortening_warning(self):
nio = NeuralynxIO(self.sn, use_cache='never')
with reset_warning_registry():
with warnings.catch_warnings(record=True) as w:
seg = Segment('testsegment')
nio.read_ncs(os.path.join(self.sn, 'Tet3a.ncs'), seg)
self.assertGreater(len(w), 0)
self.assertTrue(issubclass(w[0].category, UserWarning))
self.assertTrue('Analogsignalarray was shortened due to gap in'
' recorded data of file'
in str(w[0].message))
def test_gap_warning(self):
nio = NeuralynxIO(self.sn, use_cache='never')
with reset_warning_registry():
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter('always')
nio.read_block(t_starts=None, t_stops=None)
self.assertGreater(len(w), 0)
self.assertTrue(issubclass(w[0].category, UserWarning))
self.assertEqual('Substituted t_starts and t_stops in order to'
' skip gap in recording session.',
str(w[0].message))
def test_read_segment(self):
"""Read data in a certain time range into one block"""
nio = NeuralynxIO(self.sn, use_cache='never')
seg = nio.read_segment(t_start=None, t_stop=None)
self.assertEqual(len(seg.analogsignals), 1)
self.assertEqual(seg.analogsignals[0].shape[-1], 5)
self.assertEqual(seg.analogsignals[0].sampling_rate.units,
pq.CompoundUnit('32*kHz'))
self.assertEqual(len(seg.spiketrains), 0)
# Testing different parameter combinations
seg = nio.read_segment(lazy=True)
self.assertEqual(len(seg.analogsignals[0]), 0)
self.assertEqual(len(seg.spiketrains), 0)
seg = nio.read_segment(cascade=False)
self.assertEqual(len(seg.analogsignals), 0)
self.assertEqual(len(seg.spiketrains), 0)
seg = nio.read_segment(electrode_list=[0])
self.assertEqual(len(seg.analogsignals), 1)
seg = nio.read_segment(t_start=None, t_stop=None, events=True,
waveforms=True)
self.assertEqual(len(seg.analogsignals), 1)
self.assertEqual(len(seg.spiketrains), 0)
self.assertTrue(len(seg.events) > 0)
def test_read_nev_data(self):
t_start, t_stop = 0 * pq.s, 1000 * pq.s
nio = NeuralynxIO(self.sn, use_cache='never')
seg = Segment('testsegment')
filename = 'Events'
nio.read_nev(filename + '.nev', seg, t_start=t_start, t_stop=t_stop)
timestamps = []
nttls = []
names = []
event_ids = []
with open(self.pd + '/%s.txt' % filename) as datafile:
for i, line in enumerate(datafile):
line = line.strip('\xef\xbb\xbf')
entries = line.split('\t')
nttls.append(int(entries[5]))
timestamps.append(int(entries[3]))
names.append(entries[10].rstrip('\r\n'))
event_ids.append(int(entries[4]))
timestamps = (np.array(timestamps) * pq.microsecond -
nio.parameters_global['t_start'])
# masking only requested spikes
mask = np.where(timestamps < t_stop)[0]
# return if no event fits criteria
if len(mask) == 0:
return
timestamps = timestamps[mask]
nttls = np.asarray(nttls)[mask]
names = np.asarray(names)[mask]
event_ids = np.asarray(event_ids)[mask]
for i in range(len(timestamps)):
events = seg.filter({'nttl': nttls[i]}, objects=Event)
events = [
e for e in events
if (e.annotations['marker_id'] == event_ids[i]
and e.labels == names[i])
]
self.assertTrue(len(events) == 1)
self.assertTrue(timestamps[i] in events[0].times)
def test_gap_handling(self):
nio = NeuralynxIO(self.sn, use_cache='never')
block = nio.read_block(t_starts=None, t_stops=None)
# known gap values
n_gaps = 1
self.assertEqual(len(block.segments), n_gaps + 1)
# one channel index for analogsignals for each of the 3 segments and
# one for spiketrains
self.assertEqual(len(block.channel_indexes), len(block.segments) + 1)
self.assertEqual(len(block.channel_indexes[-1].units), 2)
for unit in block.channel_indexes[-1].units:
self.assertEqual(len(unit.spiketrains), n_gaps + 1)
anasig_channels = [i for i in block.channel_indexes
if 'analogsignal' in i.name]
self.assertEqual(len(anasig_channels), n_gaps + 1)
def test_gap_handling(self):
nio = NeuralynxIO(self.sn, use_cache='never')
block = nio.read_block(t_starts=None, t_stops=None)
# known gap values
n_gaps = 1
self.assertEqual(len(block.segments), n_gaps + 1)
# one channel index for analogsignals for each of the 3 segments and
# one for spiketrains
self.assertEqual(len(block.channel_indexes), len(block.segments) + 1)
self.assertEqual(len(block.channel_indexes[-1].units), 2)
for unit in block.channel_indexes[-1].units:
self.assertEqual(len(unit.spiketrains), n_gaps + 1)
anasig_channels = [i for i in block.channel_indexes
if 'analogsignal' in i.name]
self.assertEqual(len(anasig_channels), n_gaps + 1)
def test_read_nev_data(self):
t_start, t_stop = 0 * pq.s, 1000 * pq.s
nio = NeuralynxIO(self.sn, use_cache='never')
seg = Segment('testsegment')
filename = 'Events'
nio.read_nev(filename + '.nev', seg, t_start=t_start, t_stop=t_stop)
timestamps = []
nttls = []
names = []
event_ids = []
with open(self.pd + '/%s.txt' % filename) as datafile:
for i, line in enumerate(datafile):
line = line.strip('\xef\xbb\xbf')
entries = line.split('\t')
nttls.append(int(entries[5]))
timestamps.append(int(entries[3]))
names.append(entries[10].rstrip('\r\n'))
event_ids.append(int(entries[4]))
timestamps = (np.array(timestamps) * pq.microsecond -
nio.parameters_global['t_start'])
# masking only requested spikes
mask = np.where(timestamps < t_stop)[0]
# return if no event fits criteria
if len(mask) == 0:
return
timestamps = timestamps[mask]
nttls = np.asarray(nttls)[mask]
names = np.asarray(names)[mask]
event_ids = np.asarray(event_ids)[mask]
for i in range(len(timestamps)):
events = seg.filter({'nttl': nttls[i]}, objects=Event)
events = [e for e in events
if (e.annotations['marker_id'] == event_ids[i] and
e.labels == names[i])]
self.assertTrue(len(events) == 1)
self.assertTrue(timestamps[i] in events[0].times)
def test_read_block(self):
"""Read data in a certain time range into one block"""
t_start, t_stop = 3 * pq.s, 4 * pq.s
nio = NeuralynxIO(self.sn, use_cache='never')
block = nio.read_block(t_starts=[t_start], t_stops=[t_stop])
self.assertEqual(len(nio.parameters_ncs), 2)
self.assertTrue(
{'event_id': 11, 'name': 'Starting Recording', 'nttl': 0} in
nio.parameters_nev['Events.nev']['event_types'])
# Everything put in one segment
self.assertEqual(len(block.segments), 1)
seg = block.segments[0]
self.assertEqual(len(seg.analogsignals), 1)
self.assertEqual(seg.analogsignals[0].shape[-1], 2)
self.assertEqual(seg.analogsignals[0].sampling_rate.units,
pq.CompoundUnit('32*kHz'))
self.assertEqual(seg.analogsignals[0].t_start, t_start)
self.assertEqual(seg.analogsignals[0].t_stop, t_stop)
self.assertEqual(len(seg.spiketrains), 2)
# Testing different parameter combinations
block = nio.read_block(lazy=True)
self.assertEqual(len(block.segments[0].analogsignals[0]), 0)
self.assertEqual(len(block.segments[0].spiketrains[0]), 0)
block = nio.read_block(cascade=False)
self.assertEqual(len(block.segments), 0)
block = nio.read_block(electrode_list=[0])
self.assertEqual(len(block.segments[0].analogsignals), 1)
self.assertEqual(len(block.channel_indexes[-1].units), 1)
block = nio.read_block(t_starts=None, t_stops=None, events=True,
waveforms=True)
self.assertEqual(len(block.segments[0].analogsignals), 1)
self.assertEqual(len(block.segments[0].spiketrains), 2)
self.assertEqual(len(block.segments[0].spiketrains[0].waveforms),
len(block.segments[0].spiketrains[0]))
self.assertGreater(len(block.segments[0].events), 0)
self.assertEqual(len(block.channel_indexes[-1].units), 2)
block = nio.read_block(t_starts=[t_start], t_stops=[t_stop],
unit_list=[0], electrode_list=[0])
self.assertEqual(len(block.channel_indexes[-1].units), 1)
block = nio.read_block(t_starts=[t_start], t_stops=[t_stop],
unit_list=False)
self.assertEqual(len(block.channel_indexes[-1].units), 0)
def test_read_segment(self):
"""Read data in a certain time range into one block"""
nio = NeuralynxIO(self.sn, use_cache='never')
seg = nio.read_segment(t_start=None, t_stop=None)
self.assertEqual(len(seg.analogsignals), 1)
self.assertEqual(seg.analogsignals[0].shape[-1], 5)
self.assertEqual(seg.analogsignals[0].sampling_rate.units,
pq.CompoundUnit('32*kHz'))
self.assertEqual(len(seg.spiketrains), 0)
# Testing different parameter combinations
seg = nio.read_segment(lazy=True)
self.assertEqual(len(seg.analogsignals[0]), 0)
self.assertEqual(len(seg.spiketrains), 0)
seg = nio.read_segment(cascade=False)
self.assertEqual(len(seg.analogsignals), 0)
self.assertEqual(len(seg.spiketrains), 0)
seg = nio.read_segment(electrode_list=[0])
self.assertEqual(len(seg.analogsignals), 1)
seg = nio.read_segment(t_start=None, t_stop=None, events=True,
waveforms=True)
self.assertEqual(len(seg.analogsignals), 1)
self.assertEqual(len(seg.spiketrains), 0)
self.assertTrue(len(seg.events) > 0)
def test_read_ncs_data(self):
t_start, t_stop = 0, 500 * 512 # in samples
nio = NeuralynxIO(self.sn, use_cache='never')
seg = Segment('testsegment')
for el_id, el_dict in nio.parameters_ncs.items():
filepath = nio.parameters_ncs[el_id]['recording_file_name']
filename = filepath.split('/')[-1].split('\\')[-1].split('.')[0]
nio.read_ncs(filename, seg, t_start=t_start, t_stop=t_stop)
anasig = seg.filter({'electrode_id': el_id},
objects=AnalogSignal)[0]
target_data = np.zeros((16679, 512))
with open(self.pd + '/%s.txt' % filename) as datafile:
for i, line in enumerate(datafile):
line = line.strip('\xef\xbb\xbf')
entries = line.split()
target_data[i, :] = np.asarray(entries[4:])
target_data = target_data.reshape((-1, 1)) * el_dict['ADBitVolts']
np.testing.assert_array_equal(target_data[:len(anasig)],
anasig.magnitude)
def test_read_ncs_data(self):
t_start, t_stop = 0, 500 * 512 # in samples
nio = NeuralynxIO(self.sn, use_cache='never')
seg = Segment('testsegment')
for el_id, el_dict in nio.parameters_ncs.iteritems():
filepath = nio.parameters_ncs[el_id]['recording_file_name']
filename = filepath.split('/')[-1].split('\\')[-1].split('.')[0]
nio.read_ncs(filename, seg, t_start=t_start, t_stop=t_stop)
anasig = seg.filter({'electrode_id': el_id},
objects=AnalogSignal)[0]
target_data = np.zeros((16679, 512))
with open(self.pd + '/%s.txt' % filename) as datafile:
for i, line in enumerate(datafile):
line = line.strip('\xef\xbb\xbf')
entries = line.split()
target_data[i, :] = np.asarray(entries[4:])
target_data = target_data.reshape((-1, 1)) * el_dict['ADBitVolts']
np.testing.assert_array_equal(target_data[:len(anasig)],
anasig.magnitude)