def test__issue_285(self):
##Spiketrain
train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0)
unit = Unit()
train.unit = unit
unit.spiketrains.append(train)
epoch = Epoch([0, 10, 20], [2, 2, 2], ["a", "b", "c"], units="ms")
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
seg.epochs.append(epoch)
epoch.segment = seg
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].unit, Unit)
self.assertIsInstance(r_seg.epochs[0], Epoch)
os.remove('blk.pkl')
##Epoch
train = Epoch(times=np.arange(0, 30, 10)*pq.s,durations=[10, 5, 7]*pq.ms,labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S'))
train.segment = Segment()
unit = Unit()
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
os.remove('blk.pkl')
##Event
train = Event(np.arange(0, 30, 10)*pq.s,labels=np.array(['trig0', 'trig1', 'trig2'],dtype='S'))
train.segment = Segment()
unit = Unit()
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
os.remove('blk.pkl')
##IrregularlySampledSignal
train = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3],units='mV', time_units='ms')
train.segment = Segment()
unit = Unit()
train.channel_index = ChannelIndex(1)
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
blk.segments[0].block = blk
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
self.assertIsInstance(r_seg.spiketrains[0].channel_index, ChannelIndex)
os.remove('blk.pkl')
def test__issue_285(self):
##Spiketrain
train = SpikeTrain([3, 4, 5] * pq.s, t_stop=10.0)
unit = Unit()
train.unit = unit
unit.spiketrains.append(train)
epoch = Epoch([0, 10, 20], [2, 2, 2], ["a", "b", "c"], units="ms")
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
seg.epochs.append(epoch)
epoch.segment = seg
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].unit, Unit)
self.assertIsInstance(r_seg.epochs[0], Epoch)
os.remove('blk.pkl')
##Epoch
train = Epoch(times=np.arange(0, 30, 10)*pq.s,durations=[10, 5, 7]*pq.ms,labels=np.array(['btn0', 'btn1', 'btn2'], dtype='S'))
train.segment = Segment()
unit = Unit()
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
os.remove('blk.pkl')
##Event
train = Event(np.arange(0, 30, 10)*pq.s,labels=np.array(['trig0', 'trig1', 'trig2'],dtype='S'))
train.segment = Segment()
unit = Unit()
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
os.remove('blk.pkl')
##IrregularlySampledSignal
train = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3],units='mV', time_units='ms')
train.segment = Segment()
unit = Unit()
train.channel_index = ChannelIndex(1)
unit.spiketrains.append(train)
blk = Block()
seg = Segment()
seg.spiketrains.append(train)
blk.segments.append(seg)
blk.segments[0].block = blk
reader = PickleIO(filename="blk.pkl")
reader.write(blk)
reader = PickleIO(filename="blk.pkl")
r_blk = reader.read_block()
r_seg = r_blk.segments[0]
self.assertIsInstance(r_seg.spiketrains[0].segment, Segment)
self.assertIsInstance(r_seg.spiketrains[0].channel_index, ChannelIndex)
os.remove('blk.pkl')
def read_spiketrain(self):
# TODO add parameter to allow user to read raw data or not?
assert (SpikeTrain in self.readable_objects)
spike_trains = []
channel_group_files = glob.glob(
os.path.join(self._path, self._base_filename) + ".[0-9]*")
for raw_filename in sorted(channel_group_files):
with open(raw_filename, "rb") as f:
params = parse_header_and_leave_cursor(f)
channel_group_index = int(raw_filename.split(".")[-1])
bytes_per_timestamp = params.get("bytes_per_timestamp", 4)
bytes_per_sample = params.get("bytes_per_sample", 1)
num_spikes = params.get("num_spikes", 0)
num_chans = params.get("num_chans", 1)
samples_per_spike = params.get("samples_per_spike", 50)
timebase = int(
params.get("timebase", "96000 hz").split(" ")[0]) * pq.Hz
sampling_rate = params.get("rawrate", 48000) * pq.Hz
bytes_per_spike_without_timestamp = samples_per_spike * bytes_per_sample
bytes_per_spike = bytes_per_spike_without_timestamp + bytes_per_timestamp
timestamp_dtype = ">u" + str(bytes_per_timestamp)
waveform_dtype = "<i" + str(bytes_per_sample)
dtype = np.dtype([("times", (timestamp_dtype, 1), 1),
("waveforms", (waveform_dtype, 1),
samples_per_spike)])
data = np.fromfile(f,
dtype=dtype,
count=num_spikes * num_chans)
assert_end_of_data(f)
# times are saved for each channel
times = data["times"][::num_chans] / timebase
assert len(times) == num_spikes
waveforms = data["waveforms"]
waveforms = np.reshape(waveforms,
(num_spikes, num_chans, samples_per_spike))
# TODO HACK !!!! findout if recording is sig - ref or the other
# way around, this determines the way of the peak which should be
# possible to set in a parameter e.g. peak='negative'/'positive'
waveforms = -waveforms.astype(float)
channel_gain_matrix = np.ones(waveforms.shape)
for i in range(num_chans):
channel_gain_matrix[:, i, :] *= self._channel_gain(
channel_group_index, i)
waveforms = scale_analog_signal(waveforms, channel_gain_matrix,
self._adc_fullscale,
bytes_per_sample)
# TODO get left_sweep form setfile?
spike_train = SpikeTrain(times,
t_stop=self._duration,
waveforms=waveforms * pq.uV,
sampling_rate=sampling_rate,
left_sweep=0.2 * pq.ms,
**params)
spike_trains.append(spike_train)
channel_index = self._channel_group_to_channel_index[
channel_group_index]
spike_train.channel_index = channel_index
unit = Unit(
) # TODO unit can have several spiketrains from different segments, not necessarily relevant here though
unit.spiketrains.append(spike_train)
channel_index.units.append(unit)
return spike_trains
def setUp(self):
self.fname = '/tmp/test.exdir'
if os.path.exists(self.fname):
shutil.rmtree(self.fname)
self.n_channels = 5
self.n_samples = 20
self.n_spikes = 50
blk = Block()
seg = Segment()
blk.segments.append(seg)
chx1 = ChannelIndex(index=np.arange(self.n_channels),
channel_ids=np.arange(self.n_channels))
chx2 = ChannelIndex(index=np.arange(self.n_channels),
channel_ids=np.arange(self.n_channels) * 2)
blk.channel_indexes.extend([chx1, chx2])
wf1 = np.random.random(
(self.n_spikes, self.n_channels, self.n_samples))
ts1 = np.sort(np.random.random(self.n_spikes))
t_stop1 = np.ceil(ts1[-1])
sptr1 = SpikeTrain(
times=ts1,
units='s',
waveforms=np.random.random(
(self.n_spikes, self.n_channels, self.n_samples)) * pq.V,
name='spikes 1',
description='sptr1',
t_stop=t_stop1,
**{'id': 1})
sptr1.channel_index = chx1
unit1 = Unit(name='unit 1')
unit1.spiketrains.append(sptr1)
chx1.units.append(unit1)
seg.spiketrains.append(sptr1)
ts2 = np.sort(np.random.random(self.n_spikes))
t_stop2 = np.ceil(ts2[-1])
sptr2 = SpikeTrain(
times=ts2,
units='s',
waveforms=np.random.random(
(self.n_spikes, self.n_channels, self.n_samples)) * pq.V,
description='sptr2',
name='spikes 2',
t_stop=t_stop2,
**{'id': 2})
sptr2.channel_index = chx2
unit2 = Unit(name='unit 2')
unit2.spiketrains.append(sptr2)
chx2.units.append(unit2)
seg.spiketrains.append(sptr2)
wf3 = np.random.random(
(self.n_spikes, self.n_channels, self.n_samples))
ts3 = np.sort(np.random.random(self.n_spikes))
t_stop3 = np.ceil(ts3[-1])
sptr3 = SpikeTrain(
times=ts3,
units='s',
waveforms=np.random.random(
(self.n_spikes, self.n_channels, self.n_samples)) * pq.V,
description='sptr3',
name='spikes 3',
t_stop=t_stop3,
**{'id': 3})
sptr3.channel_index = chx2
unit3 = Unit(name='unit 3')
unit3.spiketrains.append(sptr3)
chx2.units.append(unit3)
seg.spiketrains.append(sptr3)
t_stop = max([t_stop1, t_stop2, t_stop3]) * pq.s
ana = AnalogSignal(np.random.random(self.n_samples),
sampling_rate=self.n_samples / t_stop,
units='V',
name='ana1',
description='LFP')
assert t_stop == ana.t_stop
seg.analogsignals.append(ana)
epo = Epoch(np.random.random(self.n_samples),
durations=[1] * self.n_samples * pq.s,
units='s',
name='epo1')
seg.epochs.append(epo)
self.blk = blk
