def start(self, current, selections):
current.progress.begin('Creating spike waveform plot')
current.progress.set_status('Loading spikes')
spikes = {}
strong = {}
if self.spike_mode == 1:
spikes = current.spikes_by_unit()
elif self.spike_mode == 2:
strong = current.spikes_by_unit()
spike_trains = None
if self.inc_spikes:
current.progress.set_status('Loading spike trains')
spike_trains = current.spike_trains_by_unit_and_segment()
for u, trains in spike_trains.iteritems():
s = []
for st in trains.values():
if st.waveforms is not None:
s.extend(convert.spike_train_to_spikes(st))
if not s:
continue
spikes.setdefault(u, []).extend(s)
if self.inc_extracted:
current.progress.set_status('Extracting spikes from signals')
signals = current.analog_signals_by_segment_and_channel(
conversion_mode=3)
if spike_trains is None:
spike_trains = current.spike_trains_by_unit_and_segment()
for u, trains in spike_trains.iteritems():
s = []
rcg = u.recordingchannelgroup
for seg, train in trains.iteritems():
if seg not in signals:
continue
train_sigs = []
for rc in signals[seg]:
if rcg in rc.recordingchannelgroups:
train_sigs.append(signals[seg][rc])
if not train_sigs:
continue
s.extend(
extract_spikes(train, train_sigs, self.length * pq.ms,
self.align * pq.ms))
if not s:
continue
spikes.setdefault(u, []).extend(s)
fade = 0.2 if self.fade else 1.0
plot.spikes(spikes,
self.plot_type * 2 + self.split_type + 1,
strong,
anti_alias=self.anti_aliased,
fade=fade,
subplot_layout=self.layout,
progress=current.progress)
def start(self, current, selections):
current.progress.begin("Creating spike waveform plot")
current.progress.set_status("Loading spikes")
spikes = {}
strong = {}
if self.spike_mode == 1:
spikes = current.spikes_by_unit()
elif self.spike_mode == 2:
strong = current.spikes_by_unit()
spike_trains = None
if self.inc_spikes:
current.progress.set_status("Loading spike trains")
spike_trains = current.spike_trains_by_unit_and_segment()
for u, trains in spike_trains.iteritems():
s = []
for st in trains.values():
if st.waveforms is not None:
s.extend(convert.spike_train_to_spikes(st))
if not s:
continue
spikes.setdefault(u, []).extend(s)
if self.inc_extracted:
current.progress.set_status("Extracting spikes from signals")
signals = current.analog_signals_by_segment_and_channel(conversion_mode=3)
if spike_trains is None:
spike_trains = current.spike_trains_by_unit_and_segment()
for u, trains in spike_trains.iteritems():
s = []
rcg = u.recordingchannelgroup
for seg, train in trains.iteritems():
if seg not in signals:
continue
train_sigs = []
for rc in signals[seg]:
if rcg in rc.recordingchannelgroups:
train_sigs.append(signals[seg][rc])
if not train_sigs:
continue
s.extend(extract_spikes(train, train_sigs, self.length * pq.ms, self.align * pq.ms))
if not s:
continue
spikes.setdefault(u, []).extend(s)
fade = 0.2 if self.fade else 1.0
plot.spikes(
spikes,
self.plot_type * 2 + self.split_type + 1,
strong,
anti_alias=self.anti_aliased,
fade=fade,
subplot_layout=self.layout,
progress=current.progress,
)
def test_extract_spikes(self):
s1 = sp.zeros(10000)
s2 = sp.ones(10000)
t = sp.arange(0.0, 10.1, 1.0)
sig1 = neo.AnalogSignal(s1 * pq.uV, sampling_rate=pq.kHz)
sig2 = neo.AnalogSignal(s2 * pq.uV, sampling_rate=pq.kHz)
train = neo.SpikeTrain(t * pq.s, 10 * pq.s)
spikes = tools.extract_spikes(train, [sig1, sig2], 100 * pq.ms,
10 * pq.ms)
self.assertEqual(len(spikes), 9)
for s in spikes:
self.assertAlmostEqual(s.waveform[:, 0].mean(), 0.0)
self.assertAlmostEqual(s.waveform[:, 1].mean(), 1.0)
def test_extract_spikes(self):
s1 = sp.zeros(10000)
s2 = sp.ones(10000)
t = sp.arange(0.0, 10.1, 1.0)
sig1 = neo.AnalogSignal(s1 * pq.uV, sampling_rate=pq.kHz)
sig2 = neo.AnalogSignal(s2 * pq.uV, sampling_rate=pq.kHz)
train = neo.SpikeTrain(t * pq.s, 10 * pq.s)
spikes = tools.extract_spikes(
train, [sig1, sig2], 100 * pq.ms, 10 * pq.ms)
self.assertEqual(len(spikes), 9)
for s in spikes:
self.assertAlmostEqual(s.waveform[:, 0].mean(), 0.0)
self.assertAlmostEqual(s.waveform[:, 1].mean(), 1.0)
def test_extract_different_spikes(self):
s1 = sp.ones(10500)
s2 = -sp.ones(10500)
for i in xrange(10):
s1[i * 1000 + 500:i * 1000 + 1500] *= i
s2[i * 1000 + 500:i * 1000 + 1500] *= i
t = sp.arange(0.0, 10.1, 1.0)
sig1 = neo.AnalogSignal(s1 * pq.uV, sampling_rate=pq.kHz)
sig2 = neo.AnalogSignal(s2 * pq.uV, sampling_rate=pq.kHz)
train = neo.SpikeTrain(t * pq.s, 10 * pq.s)
spikes = tools.extract_spikes(train, [sig1, sig2], 100 * pq.ms,
10 * pq.ms)
self.assertEqual(len(spikes), 10)
for i, s in enumerate(spikes):
self.assertAlmostEqual(s.waveform[:, 0].mean(), i)
self.assertAlmostEqual(s.waveform[:, 1].mean(), -i)
def test_extract_different_spikes(self):
s1 = sp.ones(10500)
s2 = -sp.ones(10500)
for i in xrange(10):
s1[i * 1000 + 500:i * 1000 + 1500] *= i
s2[i * 1000 + 500:i * 1000 + 1500] *= i
t = sp.arange(0.0, 10.1, 1.0)
sig1 = neo.AnalogSignal(s1 * pq.uV, sampling_rate=pq.kHz)
sig2 = neo.AnalogSignal(s2 * pq.uV, sampling_rate=pq.kHz)
train = neo.SpikeTrain(t * pq.s, 10 * pq.s)
spikes = tools.extract_spikes(
train, [sig1, sig2], 100 * pq.ms, 10 * pq.ms)
self.assertEqual(len(spikes), 10)
for i, s in enumerate(spikes):
self.assertAlmostEqual(s.waveform[:, 0].mean(), i)
self.assertAlmostEqual(s.waveform[:, 1].mean(), -i)
