def GroupTrialsByEpoch(seg=None, trials=None, startoftrial=None,
endoftrial=None, endeventmissing='next'):
"""Given a segment object and a trials dataframe, will go through
each epoch type and collect when the trial started and stopped.
Started is by the start events in startoftrial, stopped is by
event in endoftrial. If endoftrial is missing, stopped can be determined
by two modes:
endeventmissing = 'next': end of trial is the start of the next one
(last trial ends on its last event)
endeventmissing = 'last': end of trial is the last event of that trial"""
# Makes sure seg is a segment object
if not isinstance(seg, neo.core.Segment):
raise TypeError('%s must be a segment object' % seg)
# Assign trials for segment object if trials is not given
if trials is None:
trials = seg.dataframes['trials']
# Calculate epochs for results column
epoch_list = CalculateStartsAndDurations(trials=trials, epoch_column='results', startoftrial=startoftrial, endoftrial=endoftrial,
endeventmissing=endeventmissing)
# Adds durations and start times to create an Epoch
for epoch in epoch_list:
seg.epochs.append(Epoch(times=np.array(epoch[0]) * pq.s,
durations=np.array(epoch[1]) * pq.s, name=epoch[2]))
# Now calculates epochs with previous trial results
# Gets a set of the epochs
prev_epoch_list = CalculateStartsAndDurations(trials=trials,
epoch_column='with_previous_results', startoftrial=startoftrial,
endoftrial=endoftrial, endeventmissing=endeventmissing)
# Adds durations and start times to create an Epoch
for epoch in prev_epoch_list:
seg.epochs.append(Epoch(times=np.array(epoch[0]) * pq.s,
durations=np.array(epoch[1]) * pq.s, name=epoch[2]))
def setup_epochs(self):
epochname11 = 'epoch 1 1'
epochname12 = 'epoch 1 2'
epochname21 = 'epoch 2 1'
epochname22 = 'epoch 2 2'
epochtime11 = 10 * pq.ms
epochtime12 = 20 * pq.ms
epochtime21 = 30 * pq.s
epochtime22 = 40 * pq.s
epochdur11 = 11 * pq.s
epochdur12 = 21 * pq.s
epochdur21 = 31 * pq.ms
epochdur22 = 41 * pq.ms
self.epochnames1 = [epochname11, epochname12]
self.epochnames2 = [epochname21, epochname22]
self.epochnames = [epochname11, epochname12, epochname21, epochname22]
epoch11 = Epoch(epochtime11, epochdur11,
label=epochname11, name=epochname11, channel_index=1,
testattr=True)
epoch12 = Epoch(epochtime12, epochdur12,
label=epochname12, name=epochname12, channel_index=2,
testattr=False)
epoch21 = Epoch(epochtime21, epochdur21,
label=epochname21, name=epochname21, channel_index=1)
epoch22 = Epoch(epochtime22, epochdur22,
label=epochname22, name=epochname22, channel_index=2)
self.epoch1 = [epoch11, epoch12]
self.epoch2 = [epoch21, epoch22]
self.epoch = [epoch11, epoch12, epoch21, epoch22]
def create_epoch(self, parent=None, name='Epoch'):
epoch = Epoch(times=[1.0, 2.5, 10.0] * pq.s, durations=[1.0, 0.5, 1.0] * pq.ms,
labels=np.array([chr(0) + 'btn1', chr(0) + 'btn2', chr(0) + 'btn3']))
epoch.segment = parent
self._assign_basic_attributes(epoch, name=name)
return epoch
def _read_epocharray(self, node, parent):
attributes = self._get_standard_attributes(node)
times = self._get_quantity(node["times"])
durations = self._get_quantity(node["durations"])
labels = node["labels"].value
epoch = Epoch(times=times, durations=durations, labels=labels, **attributes)
epoch.segment = parent
return epoch
def _read_epocharray(self, node, parent):
attributes = self._get_standard_attributes(node)
times = self._get_quantity(node["times"])
durations = self._get_quantity(node["durations"])
labels = node["labels"].value
epoch = Epoch(times=times, durations=durations, labels=labels, **attributes)
epoch.segment = parent
return epoch
def read_epocharray(self,
lazy=False,
cascade=True,
channel_index=0,
t_start=0.,
segment_duration=0.):
"""function to read digital timestamps. this function reads the event
onset and offset and outputs onset and duration. to get only onsets use
the event array function"""
if lazy:
epa = Epoch(file_origin=self.filename,
times=None,
durations=None,
labels=None)
else:
#create temporary empty lists to store data
tempNames = list()
tempTimeStamp = list()
durations = list()
#get entity from file
digEntity = self.fd.get_entity(channel_index)
#transform t_start into index (reading will start from this index)
startat = digEntity.get_index_by_time(
t_start, 0) #zero means closest index to value
#get the last index to read, using segment duration and t_start
endat = digEntity.get_index_by_time(
float(segment_duration + t_start),
-1) #-1 means last index before time
#run through entity using only odd "i"s
for i in range(startat, endat + 1, 1):
if i % 2 == 1:
#get in which digital bit was the trigger detected
tempNames.append(digEntity.label[-8:])
#get the time stamps of even events
tempData, onOrOff = digEntity.get_data(i - 1)
#if this was an onset event, save it to the list
#on triggered recordings it seems that only onset events are
#recorded. On continuous recordings both onset(==1)
#and offset(==255) seem to be recorded
#if onOrOff == 1:
#append the time stamp to them empty list
tempTimeStamp.append(tempData)
#get time stamps of odd events
tempData1, onOrOff = digEntity.get_data(i)
#if onOrOff == 255:
#pass
durations.append(tempData1 - tempData)
epa = Epoch(file_origin=self.filename,
times=np.array(tempTimeStamp) * pq.s,
durations=np.array(durations) * pq.s,
labels=np.array(tempNames, dtype="S"),
description="digital events with duration")
return epa
def _read_epocharray(self, node, parent):
attributes = self._get_standard_attributes(node)
times = self._get_quantity(node["times"])
durations = self._get_quantity(node["durations"])
if self._lazy:
labels = np.array((), dtype=node["labels"].dtype)
else:
labels = node["labels"].value
epoch = Epoch(times=times, durations=durations, labels=labels, **attributes)
epoch.segment = parent
if self._lazy:
epoch.lazy_shape = node["times"].shape
return epoch
def _get_stim(self, channel):
epo = Epoch()
ttls = self._kwe['event_types']['TTL']['events']['time_samples'].value
event_channels = self._kwe['event_types']['TTL']['events'][
'user_data']['event_channels'].value
event_id = self._kwe['event_types']['TTL']['events']['user_data'][
'eventID'].value
epo.times = (ttls[(event_channels == channel) & (event_id == 1)] /
self._attrs['kwe']['sample_rate']) * pq.s
off_times = (ttls[(event_channels == channel) & (event_id == 0)] /
self._attrs['kwe']['sample_rate']) * pq.s
epo.durations = off_times - epo.times # TODO check length match
epo.name = 'StimulusTTL'
return epo
def fake_epoch(seed=None, n=1):
"""
Create a fake Epoch.
We use this separate function because the attributes of
Epoch are not independent (must all have the same size)
"""
kwargs = get_annotations()
if seed is not None:
np.random.seed(seed)
size = np.random.randint(5, 15)
for i, attr in enumerate(Epoch._necessary_attrs +
Epoch._recommended_attrs):
if seed is not None:
iseed = seed + i
else:
iseed = None
if attr[0] in ('times', 'durations', 'labels'):
kwargs[attr[0]] = get_fake_value(*attr,
seed=iseed,
obj=Epoch,
shape=size)
else:
kwargs[attr[0]] = get_fake_value(*attr, seed=iseed, obj=Epoch, n=n)
kwargs['seed'] = seed
obj = Epoch(**kwargs)
return obj
def make_stimulus_off_epoch(epo, include_boundary=False):
'''
Creates a neo.Epoch of off periods.
Parameters
----------
epo : neo.Epoch
stimulus epoch
include_boundary :
add 0 to be first off period
Returns
------
out : neo.Epoch
'''
from neo.core import Epoch
times = epo.times[:-1] + epo.durations[:-1]
durations = epo.times[1:] - times
if (include_boundary):
times = np.append([0], times) * pq.s
durations = np.append(epo.times[0], durations) * pq.s
off_epoch = Epoch(labels=[None] * len(times),
durations=durations,
times=times)
return off_epoch
def epoch_overview(epo, period, expected_num_epochs=None):
'''
Makes a new Epoch with start and stop time as first and last event in
a burst of epochs, bursts are separated by > period + stim duration*2
Parameters
----------
epo : neo.Epoch
Returns
-------
out : neo.Epoch
'''
is_quantities(period, dtype='scalar')
if len(epo.times) == 1:
return epo
from neo import Epoch
pause = np.diff(epo.times)
pause = pause > period + np.median(epo.durations) * 2
start_ind = np.concatenate((np.array([1]), pause))
stop_ind = np.concatenate((pause, np.array([1])))
stop_times = epo.times[stop_ind == 1]
start_times = epo.times[start_ind == 1]
if expected_num_epochs is not None:
assert len(start_times) == expected_num_epochs
return Epoch(times=start_times,
durations=stop_times - start_times,
description=epo.description)
def _read_epocharray(self, node, parent):
attributes = self._get_standard_attributes(node)
times = self._get_quantity(node["times"])
durations = self._get_quantity(node["durations"])
if self._lazy:
labels = np.array((), dtype=node["labels"].dtype)
else:
labels = node["labels"].value
epoch = Epoch(times=times,
durations=durations,
labels=labels,
**attributes)
epoch.segment = parent
if self._lazy:
epoch.lazy_shape = node["times"].shape
return epoch
def create_all_annotated(cls):
times = cls.rquant(1, pq.s)
signal = cls.rquant(1, pq.V)
blk = Block()
blk.annotate(**cls.rdict(3))
cls.populate_dates(blk)
seg = Segment()
seg.annotate(**cls.rdict(4))
cls.populate_dates(seg)
blk.segments.append(seg)
asig = AnalogSignal(signal=signal, sampling_rate=pq.Hz)
asig.annotate(**cls.rdict(2))
seg.analogsignals.append(asig)
isig = IrregularlySampledSignal(times=times,
signal=signal,
time_units=pq.s)
isig.annotate(**cls.rdict(2))
seg.irregularlysampledsignals.append(isig)
epoch = Epoch(times=times, durations=times)
epoch.annotate(**cls.rdict(4))
seg.epochs.append(epoch)
event = Event(times=times)
event.annotate(**cls.rdict(4))
seg.events.append(event)
spiketrain = SpikeTrain(times=times, t_stop=pq.s, units=pq.s)
d = cls.rdict(6)
d["quantity"] = pq.Quantity(10, "mV")
d["qarray"] = pq.Quantity(range(10), "mA")
spiketrain.annotate(**d)
seg.spiketrains.append(spiketrain)
chx = ChannelIndex(name="achx", index=[1, 2], channel_ids=[0, 10])
chx.annotate(**cls.rdict(5))
blk.channel_indexes.append(chx)
unit = Unit()
unit.annotate(**cls.rdict(2))
chx.units.append(unit)
return blk
def create_all_annotated(cls):
times = cls.rquant(1, pq.s)
signal = cls.rquant(1, pq.V)
blk = Block()
blk.annotate(**cls.rdict(3))
seg = Segment()
seg.annotate(**cls.rdict(4))
blk.segments.append(seg)
asig = AnalogSignal(signal=signal, sampling_rate=pq.Hz)
asig.annotate(**cls.rdict(2))
seg.analogsignals.append(asig)
isig = IrregularlySampledSignal(times=times, signal=signal,
time_units=pq.s)
isig.annotate(**cls.rdict(2))
seg.irregularlysampledsignals.append(isig)
epoch = Epoch(times=times, durations=times)
epoch.annotate(**cls.rdict(4))
seg.epochs.append(epoch)
event = Event(times=times)
event.annotate(**cls.rdict(4))
seg.events.append(event)
spiketrain = SpikeTrain(times=times, t_stop=pq.s, units=pq.s)
d = cls.rdict(6)
d["quantity"] = pq.Quantity(10, "mV")
d["qarray"] = pq.Quantity(range(10), "mA")
spiketrain.annotate(**d)
seg.spiketrains.append(spiketrain)
chx = ChannelIndex(name="achx", index=[1, 2], channel_ids=[0, 10])
chx.annotate(**cls.rdict(5))
blk.channel_indexes.append(chx)
unit = Unit()
unit.annotate(**cls.rdict(2))
chx.units.append(unit)
return blk
def load(self, time_slice=None, strict_slicing=True):
'''
*Args*:
:time_slice: None or tuple of the time slice expressed with quantities.
None is the entire signal.
:strict_slicing: True by default.
Control if an error is raise or not when one of time_slice member (t_start or t_stop)
is outside the real time range of the segment.
'''
t_start, t_stop = consolidate_time_slice(time_slice, self.t_start,
self.t_stop, strict_slicing)
_t_start, _t_stop = prepare_time_slice(time_slice)
timestamp, durations, labels = self._rawio.get_event_timestamps(
block_index=self._block_index,
seg_index=self._seg_index,
event_channel_index=self._event_channel_index,
t_start=_t_start,
t_stop=_t_stop)
dtype = 'float64'
times = self._rawio.rescale_event_timestamp(timestamp, dtype=dtype)
units = 's'
if durations is not None:
durations = self._rawio.rescale_epoch_duration(durations,
dtype=dtype) * pq.s
h = self._rawio.header['event_channels'][self._event_channel_index]
if h['type'] == b'event':
ret = Event(times=times,
labels=labels,
units='s',
name=self.name,
file_origin=self.file_origin,
description=self.description,
**self.annotations)
elif h['type'] == b'epoch':
ret = Epoch(times=times,
durations=durations,
labels=labels,
units='s',
name=self.name,
file_origin=self.file_origin,
description=self.description,
**self.annotations)
if time_slice is None:
ret.array_annotate(**self.array_annotations)
else:
# TODO handle array_annotations with time_slice
pass
return ret
def _create_extra_stimuli_channel(from_channel: Event, time_threshold: Quantity) -> Epoch:
stimuli: List[Tuple[int, int]] = _group_stimuli(from_channel, time_threshold)
#data_points: List[Quantity] = [from_channel.times[start:stop] for start, stop in stimuli]
# the label of each timespan is the label of the first event
labels = np.array([from_channel.labels[start] for start, _ in stimuli])
# the timestamp of each timespan is the timestamp of the first event
times: Quantity = np.array([from_channel.times[start] for start, _ in stimuli]) * from_channel.units
# the duration is the time difference between the first and last event
durations: Quantity = np.array([from_channel.times[stop-1] - from_channel.times[start] for start, stop in stimuli]) * from_channel.units
frequencies: Quantity = None
# with this deactivate the divide by 0 warning from NP
# it just returns infinity
with np.errstate(divide="ignore"):
# the frequency is the number of pulses by the duration.
# FIXME: shouldn't it be the number -1 as the last one marks exactly the end, so we only count the ones in the middle?
frequencies = np.array([(stop - start) / durations[i] for i, (start, stop) in enumerate(stimuli)]) / from_channel.units
frequencies.units = Hz
epoch = Epoch(times=times, durations=durations, labels=labels, units=from_channel.units)
epoch.array_annotate(frequencies=frequencies)
return epoch
def test_epoch_write(self):
block = Block()
seg = Segment()
block.segments.append(seg)
epoch = Epoch(times=[1, 1, 10, 3]*pq.ms, durations=[3, 3, 3, 1]*pq.ms,
labels=np.array(["one", "two", "three", "four"]),
name="test epoch", description="an epoch for testing")
seg.epochs.append(epoch)
self.write_and_compare([block])
def process_epoch(seg, trials, event, duration, epochname, mask=None):
if not isinstance(trials, np.ndarray):
warnings.warn("not a valid trials!")
return
if mask is None:
mask = [True] * len(trials)
subtrial = trials[mask]
subtrial = subtrial[subtrial['eventname'] == event]
seg.epochs.append(
Epoch(times=subtrial['time'] * duration[0].units + duration[0],
durations=[duration[1] - duration[0]] * len(subtrial),
name=epochname))
def read_epoch(fh, block_id, array_id):
nix_block = fh.handle.blocks[block_id]
nix_da = nix_block.data_arrays[array_id]
params = {
'times': nix_da[0], # TODO think about lazy data loading
'durations': nix_da[1], # TODO think about lazy data loading
'labels': [x.encode('UTF-8') for x in nix_da.dimensions[0].labels]
}
name = Reader.Help.get_obj_neo_name(nix_da)
if name:
params['name'] = name
epoch = Epoch(**params)
for key, value in Reader.Help.read_attributes(nix_da.metadata, 'epoch').items():
setattr(epoch, key, value)
epoch.annotations = Reader.Help.read_annotations(nix_da.metadata, 'epoch')
return epoch
def test__issue_285(self):
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)
def test_make_stimulus_off_epoch():
from neo.core import Epoch
from exana.stimulus.tools import (make_stimulus_off_epoch)
times = np.linspace(0, 10, 11) * pq.s
durations = np.ones(len(times)) * pq.s
labels = np.ones(len(times))
stim_epoch = Epoch(labels=labels, durations=durations, times=times)
stim_off_epoch = make_stimulus_off_epoch(stim_epoch)
assert (stim_off_epoch.times == np.linspace(1, 10, 10)).all()
assert (stim_off_epoch.durations == np.zeros(10)).all()
assert (stim_off_epoch.labels == [None] * 10)
stim_off_epoch = make_stimulus_off_epoch(stim_epoch, include_boundary=True)
assert (stim_off_epoch.times == np.linspace(0, 10, 11)).all()
assert (stim_off_epoch.durations == np.zeros(11)).all()
assert (stim_off_epoch.labels == [None] * 11)
times = np.arange(0.5, 11, 0.5)[::2] * pq.s
durations = np.ones(len(times)) * 0.5 * pq.s
labels = np.ones(len(times))
stim_epoch = Epoch(labels=labels, durations=durations, times=times)
stim_off_epoch = make_stimulus_off_epoch(stim_epoch)
assert (stim_off_epoch.times == np.arange(1, 11, 1)).all()
assert (stim_off_epoch.durations == np.ones(10) * 0.5).all()
assert (stim_off_epoch.labels == [None] * 10)
stim_off_epoch = make_stimulus_off_epoch(stim_epoch, include_boundary=True)
assert (stim_off_epoch.times == np.arange(0, 11, 1)).all()
assert (stim_off_epoch.durations == np.ones(11) * 0.5).all()
assert (stim_off_epoch.labels == [None] * 11)
def random_epoch():
size = random.randint(1, 7)
times = np.cumsum(np.random.uniform(5, 10, size=size))
durations = np.random.uniform(1, 3, size=size)
labels = [random_string() for i in range(size)]
obj = Epoch(
times=times,
durations=durations,
labels=labels,
units="ms",
name=random_string(),
array_annotations=None, # todo
**random_annotations(3)
)
return obj
def setUp(self):
self.segment = Segment()
self.epoch = Epoch(name='my epoch')
self.segment.epochs.append(self.epoch)
self.signal = AnalogSignal(np.random.randn(1000, 1),
units='V',
sampling_rate=1 * pq.Hz,
name='my signal')
self.segment.analogsignals.append(self.signal)
self.trials = pd.DataFrame()
self.trials.name = 'trials'
self.segment2 = Segment()
self.segment2.epochs.append(self.epoch)
self.segment2.analogsignals.append(self.signal)
self.segment2.dataframes = [self.trials]
def test_anonymous_objects_write(self):
nblocks = 2
nsegs = 2
nanasig = 4
nirrseg = 2
nepochs = 3
nevents = 4
nspiketrains = 3
nchx = 5
nunits = 10
times = self.rquant(1, pq.s)
signal = self.rquant(1, pq.V)
blocks = []
for blkidx in range(nblocks):
blk = Block()
blocks.append(blk)
for segidx in range(nsegs):
seg = Segment()
blk.segments.append(seg)
for anaidx in range(nanasig):
seg.analogsignals.append(AnalogSignal(signal=signal,
sampling_rate=pq.Hz))
for irridx in range(nirrseg):
seg.irregularlysampledsignals.append(
IrregularlySampledSignal(times=times,
signal=signal,
time_units=pq.s)
)
for epidx in range(nepochs):
seg.epochs.append(Epoch(times=times, durations=times))
for evidx in range(nevents):
seg.events.append(Event(times=times))
for stidx in range(nspiketrains):
seg.spiketrains.append(SpikeTrain(times=times,
t_stop=times[-1]+pq.s,
units=pq.s))
for chidx in range(nchx):
chx = ChannelIndex(name="chx{}".format(chidx),
index=[1, 2],
channel_ids=[11, 22])
blk.channel_indexes.append(chx)
for unidx in range(nunits):
unit = Unit()
chx.units.append(unit)
self.writer.write_all_blocks(blocks)
self.compare_blocks(blocks, self.reader.blocks)
def test_multiref_write(self):
blk = Block("blk1")
signal = AnalogSignal(name="sig1",
signal=[0, 1, 2],
units="mV",
sampling_period=pq.Quantity(1, "ms"))
othersignal = IrregularlySampledSignal(name="i1",
signal=[0, 0, 0],
units="mV",
times=[1, 2, 3],
time_units="ms")
event = Event(name="Evee", times=[0.3, 0.42], units="year")
epoch = Epoch(name="epoche",
times=[0.1, 0.2] * pq.min,
durations=[0.5, 0.5] * pq.min)
st = SpikeTrain(name="the train of spikes",
times=[0.1, 0.2, 10.3],
t_stop=11,
units="us")
for idx in range(3):
segname = "seg" + str(idx)
seg = Segment(segname)
blk.segments.append(seg)
seg.analogsignals.append(signal)
seg.irregularlysampledsignals.append(othersignal)
seg.events.append(event)
seg.epochs.append(epoch)
seg.spiketrains.append(st)
chidx = ChannelIndex([10, 20, 29])
seg = blk.segments[0]
st = SpikeTrain(name="choochoo",
times=[10, 11, 80],
t_stop=1000,
units="s")
seg.spiketrains.append(st)
blk.channel_indexes.append(chidx)
for idx in range(6):
unit = Unit("unit" + str(idx))
chidx.units.append(unit)
unit.spiketrains.append(st)
self.writer.write_block(blk)
self.compare_blocks([blk], self.reader.blocks)
def to_epoch(self, durations=None):
from neo.core import Epoch, Event, Segment
if durations is None:
# times = self.times[:]
# durations = np.diff(self.times)[:]
# labels = self.labels[:]
times = self.times[:-1]
durations = np.diff(self.times)
# labels = self.labels[:-1]
labels = np.array([
"{}-{}".format(a, b)
for a, b in zip(self.labels[:-1], self.labels[1:])
])
else:
times = self.times
labels = self.labels
return Epoch(time=times, durations=durations, labels=labels)
def load(self, time_slice=None, strict_slicing=True):
"""
Load EpochProxy args:
:param time_slice: None or tuple of the time slice expressed with quantities.
None is all of the intervals.
:param strict_slicing: True by default.
Control if an error is raised or not when one of the time_slice members
(t_start or t_stop) is outside the real time range of the segment.
"""
if time_slice:
raise NotImplementedError("todo")
else:
start_times = self._time_intervals.start_time[self._index]
stop_times = self._time_intervals.stop_time[self._index]
durations = stop_times - start_times
labels = self._time_intervals.tags[self._index]
return Epoch(times=start_times * pq.s,
durations=durations * pq.s,
labels=labels,
name=self.name)
def read_epoch(self, path, cascade=True, lazy=False):
group = self._exdir_directory[path]
if lazy:
times = []
else:
times = pq.Quantity(group['timestamps'].data,
group['timestamps'].attrs['unit'])
if "durations" in group and not lazy:
durations = pq.Quantity(group['durations'].data,
group['durations'].attrs['unit'])
elif "durations" in group and lazy:
durations = []
else:
durations = None
if 'data' in group and not lazy:
if 'unit' not in group['data'].attrs:
labels = group['data'].data
else:
labels = pq.Quantity(group['data'].data,
group['data'].attrs['unit'])
elif 'data' in group and lazy:
labels = []
else:
labels = None
annotations = {'exdir_path': path}
annotations.update(group.attrs.to_dict())
if lazy:
lazy_shape = (group.attrs['num_samples'], )
else:
lazy_shape = None
epo = Epoch(times=times,
durations=durations,
labels=labels,
lazy_shape=lazy_shape,
**annotations)
return epo
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(np.array([0, 10, 20]),
np.array([2, 2, 2]),
np.array(["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
epoch = Epoch(times=np.arange(0, 30, 10) * pq.s,
durations=[10, 5, 7] * pq.ms,
labels=np.array(['btn0', 'btn1', 'btn2'], dtype='U'))
epoch.segment = Segment()
blk = Block()
seg = Segment()
seg.epochs.append(epoch)
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.epochs[0].segment, Segment)
os.remove('blk.pkl')
# Event
event = Event(np.arange(0, 30, 10) * pq.s,
labels=np.array(['trig0', 'trig1', 'trig2'], dtype='U'))
event.segment = Segment()
blk = Block()
seg = Segment()
seg.events.append(event)
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.events[0].segment, Segment)
os.remove('blk.pkl')
# IrregularlySampledSignal
signal = IrregularlySampledSignal([0.0, 1.23, 6.78], [1, 2, 3],
units='mV',
time_units='ms')
signal.segment = Segment()
blk = Block()
seg = Segment()
seg.irregularlysampledsignals.append(signal)
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.irregularlysampledsignals[0].segment,
Segment)
os.remove('blk.pkl')
def generate_one_simple_segment(seg_name='segment 0', supported_objects=[], nb_analogsignal=4,
t_start=0. * pq.s, sampling_rate=10 * pq.kHz, duration=6. * pq.s,
nb_spiketrain=6, spikerate_range=[.5 * pq.Hz, 12 * pq.Hz],
event_types={'stim': ['a', 'b', 'c', 'd'],
'enter_zone': ['one', 'two'],
'color': ['black', 'yellow', 'green'], },
event_size_range=[5, 20],
epoch_types={'animal state': ['Sleep', 'Freeze', 'Escape'],
'light': ['dark', 'lighted']},
epoch_duration_range=[.5, 3.],
# this should be multiplied by pq.s, no?
array_annotations={'valid': np.array([True, False]),
'number': np.array(range(5))}
):
if supported_objects and Segment not in supported_objects:
raise ValueError('Segment must be in supported_objects')
seg = Segment(name=seg_name)
if AnalogSignal in supported_objects:
for a in range(nb_analogsignal):
anasig = AnalogSignal(rand(int((sampling_rate * duration).simplified)),
sampling_rate=sampling_rate,
t_start=t_start, units=pq.mV, channel_index=a,
name='sig %d for segment %s' % (a, seg.name))
seg.analogsignals.append(anasig)
if SpikeTrain in supported_objects:
for s in range(nb_spiketrain):
spikerate = rand() * np.diff(spikerate_range)
spikerate += spikerate_range[0].magnitude
# spikedata = rand(int((spikerate*duration).simplified))*duration
# sptr = SpikeTrain(spikedata,
# t_start=t_start, t_stop=t_start+duration)
# #, name = 'spiketrain %d'%s)
spikes = rand(int((spikerate * duration).simplified))
spikes.sort() # spikes are supposed to be an ascending sequence
sptr = SpikeTrain(spikes * duration, t_start=t_start, t_stop=t_start + duration)
sptr.annotations['channel_index'] = s
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(len(spikes)) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
sptr.array_annotate(**arr_ann)
seg.spiketrains.append(sptr)
if Event in supported_objects:
for name, labels in event_types.items():
evt_size = rand() * np.diff(event_size_range)
evt_size += event_size_range[0]
evt_size = int(evt_size)
labels = np.array(labels, dtype='S')
labels = labels[(rand(evt_size) * len(labels)).astype('i')]
evt = Event(times=rand(evt_size) * duration, labels=labels)
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(evt_size) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
evt.array_annotate(**arr_ann)
seg.events.append(evt)
if Epoch in supported_objects:
for name, labels in epoch_types.items():
t = 0
times = []
durations = []
while t < duration:
times.append(t)
dur = rand() * (epoch_duration_range[1] - epoch_duration_range[0])
dur += epoch_duration_range[0]
durations.append(dur)
t = t + dur
labels = np.array(labels, dtype='S')
labels = labels[(rand(len(times)) * len(labels)).astype('i')]
assert len(times) == len(durations)
assert len(times) == len(labels)
epc = Epoch(times=pq.Quantity(times, units=pq.s),
durations=pq.Quantity(durations, units=pq.s),
labels=labels,)
assert epc.times.dtype == 'float'
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(len(times)) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
epc.array_annotate(**arr_ann)
seg.epochs.append(epc)
# TODO : Spike, Event
seg.create_many_to_one_relationship()
return seg
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_roundtrip(self):
annotations = {"session_start_time": datetime.now()}
# Define Neo blocks
bl0 = Block(name='First block', **annotations)
bl1 = Block(name='Second block', **annotations)
bl2 = Block(name='Third block', **annotations)
original_blocks = [bl0, bl1, bl2]
num_seg = 4 # number of segments
num_chan = 3 # number of channels
for blk in original_blocks:
for ind in range(num_seg): # number of Segments
seg = Segment(index=ind)
seg.block = blk
blk.segments.append(seg)
for seg in blk.segments: # AnalogSignal objects
# 3 Neo AnalogSignals
a = AnalogSignal(np.random.randn(44, num_chan) * pq.nA,
sampling_rate=10 * pq.kHz,
t_start=50 * pq.ms)
b = AnalogSignal(np.random.randn(64, num_chan) * pq.mV,
sampling_rate=8 * pq.kHz,
t_start=40 * pq.ms)
c = AnalogSignal(np.random.randn(33, num_chan) * pq.uA,
sampling_rate=10 * pq.kHz,
t_start=120 * pq.ms)
# 2 Neo IrregularlySampledSignals
d = IrregularlySampledSignal(
np.arange(7.0) * pq.ms,
np.random.randn(7, num_chan) * pq.mV)
# 2 Neo SpikeTrains
train = SpikeTrain(times=[1, 2, 3] * pq.s,
t_start=1.0,
t_stop=10.0)
train2 = SpikeTrain(times=[4, 5, 6] * pq.s, t_stop=10.0)
# todo: add waveforms
# 1 Neo Event
evt = Event(times=np.arange(0, 30, 10) * pq.ms,
labels=np.array(['ev0', 'ev1', 'ev2']))
# 2 Neo Epochs
epc = Epoch(times=np.arange(0, 30, 10) * pq.s,
durations=[10, 5, 7] * pq.ms,
labels=np.array(['btn0', 'btn1', 'btn2']))
epc2 = Epoch(times=np.arange(10, 40, 10) * pq.s,
durations=[9, 3, 8] * pq.ms,
labels=np.array(['btn3', 'btn4', 'btn5']))
seg.spiketrains.append(train)
seg.spiketrains.append(train2)
seg.epochs.append(epc)
seg.epochs.append(epc2)
seg.analogsignals.append(a)
seg.analogsignals.append(b)
seg.analogsignals.append(c)
seg.irregularlysampledsignals.append(d)
seg.events.append(evt)
a.segment = seg
b.segment = seg
c.segment = seg
d.segment = seg
evt.segment = seg
train.segment = seg
train2.segment = seg
epc.segment = seg
epc2.segment = seg
# write to file
test_file_name = "test_round_trip.nwb"
iow = NWBIO(filename=test_file_name, mode='w')
iow.write_all_blocks(original_blocks)
ior = NWBIO(filename=test_file_name, mode='r')
retrieved_blocks = ior.read_all_blocks()
self.assertEqual(len(retrieved_blocks), 3)
self.assertEqual(len(retrieved_blocks[2].segments), num_seg)
original_signal_22b = original_blocks[2].segments[2].analogsignals[1]
retrieved_signal_22b = retrieved_blocks[2].segments[2].analogsignals[1]
for attr_name in ("name", "units", "sampling_rate", "t_start"):
retrieved_attribute = getattr(retrieved_signal_22b, attr_name)
original_attribute = getattr(original_signal_22b, attr_name)
self.assertEqual(retrieved_attribute, original_attribute)
assert_array_equal(retrieved_signal_22b.magnitude,
original_signal_22b.magnitude)
original_issignal_22d = original_blocks[2].segments[
2].irregularlysampledsignals[0]
retrieved_issignal_22d = retrieved_blocks[2].segments[
2].irregularlysampledsignals[0]
for attr_name in ("name", "units", "t_start"):
retrieved_attribute = getattr(retrieved_issignal_22d, attr_name)
original_attribute = getattr(original_issignal_22d, attr_name)
self.assertEqual(retrieved_attribute, original_attribute)
assert_array_equal(
retrieved_issignal_22d.times.rescale('ms').magnitude,
original_issignal_22d.times.rescale('ms').magnitude)
assert_array_equal(retrieved_issignal_22d.magnitude,
original_issignal_22d.magnitude)
original_event_11 = original_blocks[1].segments[1].events[0]
retrieved_event_11 = retrieved_blocks[1].segments[1].events[0]
for attr_name in ("name", ):
retrieved_attribute = getattr(retrieved_event_11, attr_name)
original_attribute = getattr(original_event_11, attr_name)
self.assertEqual(retrieved_attribute, original_attribute)
assert_array_equal(
retrieved_event_11.rescale('ms').magnitude,
original_event_11.rescale('ms').magnitude)
assert_array_equal(retrieved_event_11.labels, original_event_11.labels)
original_spiketrain_131 = original_blocks[1].segments[1].spiketrains[1]
retrieved_spiketrain_131 = retrieved_blocks[1].segments[1].spiketrains[
1]
for attr_name in ("name", "t_start", "t_stop"):
retrieved_attribute = getattr(retrieved_spiketrain_131, attr_name)
original_attribute = getattr(original_spiketrain_131, attr_name)
self.assertEqual(retrieved_attribute, original_attribute)
assert_array_equal(
retrieved_spiketrain_131.times.rescale('ms').magnitude,
original_spiketrain_131.times.rescale('ms').magnitude)
original_epoch_11 = original_blocks[1].segments[1].epochs[0]
retrieved_epoch_11 = retrieved_blocks[1].segments[1].epochs[0]
for attr_name in ("name", ):
retrieved_attribute = getattr(retrieved_epoch_11, attr_name)
original_attribute = getattr(original_epoch_11, attr_name)
self.assertEqual(retrieved_attribute, original_attribute)
assert_array_equal(
retrieved_epoch_11.rescale('ms').magnitude,
original_epoch_11.rescale('ms').magnitude)
assert_allclose(
retrieved_epoch_11.durations.rescale('ms').magnitude,
original_epoch_11.durations.rescale('ms').magnitude)
assert_array_equal(retrieved_epoch_11.labels, original_epoch_11.labels)
os.remove(test_file_name)
def _mtag_eest_to_neo(self, nix_mtag, lazy):
neo_attrs = self._nix_attr_to_neo(nix_mtag)
neo_type = nix_mtag.type
time_unit = nix_mtag.positions.unit
if lazy:
times = pq.Quantity(np.empty(0), time_unit)
lazy_shape = np.shape(nix_mtag.positions)
else:
times = pq.Quantity(nix_mtag.positions, time_unit)
lazy_shape = None
if neo_type == "neo.epoch":
if lazy:
durations = pq.Quantity(np.empty(0), nix_mtag.extents.unit)
labels = np.empty(0, dtype='S')
else:
durations = pq.Quantity(nix_mtag.extents,
nix_mtag.extents.unit)
labels = np.array(nix_mtag.positions.dimensions[0].labels,
dtype="S")
eest = Epoch(times=times, durations=durations, labels=labels,
**neo_attrs)
elif neo_type == "neo.event":
if lazy:
labels = np.empty(0, dtype='S')
else:
labels = np.array(nix_mtag.positions.dimensions[0].labels,
dtype="S")
eest = Event(times=times, labels=labels, **neo_attrs)
elif neo_type == "neo.spiketrain":
if "t_start" in neo_attrs:
if "t_start.units" in neo_attrs:
t_start_units = neo_attrs["t_start.units"]
del neo_attrs["t_start.units"]
else:
t_start_units = time_unit
t_start = pq.Quantity(neo_attrs["t_start"], t_start_units)
del neo_attrs["t_start"]
else:
t_start = None
if "t_stop" in neo_attrs:
if "t_stop.units" in neo_attrs:
t_stop_units = neo_attrs["t_stop.units"]
del neo_attrs["t_stop.units"]
else:
t_stop_units = time_unit
t_stop = pq.Quantity(neo_attrs["t_stop"], t_stop_units)
del neo_attrs["t_stop"]
else:
t_stop = None
if "sampling_interval.units" in neo_attrs:
interval_units = neo_attrs["sampling_interval.units"]
del neo_attrs["sampling_interval.units"]
else:
interval_units = None
if "left_sweep.units" in neo_attrs:
left_sweep_units = neo_attrs["left_sweep.units"]
del neo_attrs["left_sweep.units"]
else:
left_sweep_units = None
eest = SpikeTrain(times=times, t_start=t_start,
t_stop=t_stop, **neo_attrs)
if len(nix_mtag.features):
wfda = nix_mtag.features[0].data
wftime = self._get_time_dimension(wfda)
if lazy:
eest.waveforms = pq.Quantity(np.empty((0, 0, 0)),
wfda.unit)
eest.sampling_period = pq.Quantity(1, wftime.unit)
eest.left_sweep = pq.Quantity(0, wftime.unit)
else:
eest.waveforms = pq.Quantity(wfda, wfda.unit)
if interval_units is None:
interval_units = wftime.unit
eest.sampling_period = pq.Quantity(
wftime.sampling_interval, interval_units
)
if left_sweep_units is None:
left_sweep_units = wftime.unit
if "left_sweep" in wfda.metadata:
eest.left_sweep = pq.Quantity(
wfda.metadata["left_sweep"], left_sweep_units
)
else:
return None
self._neo_map[nix_mtag.name] = eest
if lazy_shape:
eest.lazy_shape = lazy_shape
return eest
def plot_segment(seg,
analist=(),
spklist=(),
epchlist=(),
showevent=None,
embedpeaks=True,
recenter=None):
anas = [x for x in seg.analogsignals if x.name in analist]
spks = [x for x in seg.spiketrains if x.name in spklist]
evts = next((e for e in seg.events if e.name == 'Events'), None)
if not evts:
warnings.warn("No Events presented in data. Turning showevent off.")
showevent = False
if (not anas) and (not spks):
print("Empty data lists. Nothing plotted")
return
if not epchlist:
if spks:
tunit = spks[0].t_start.units
tstart = np.min([s.t_start for s in spks])
tstop = np.max([s.t_stop for s in spks])
elif anas:
tunit = anas[0].t_start.units
tstart = np.min([a.t_start for a in anas])
tstop = np.max([np.max(a.times) for a in anas])
else:
tunit = pq.s
tstart = 0
tstop = 0
epchlist = [
Epoch(name='all',
times=[tstart] * tunit,
durations=[tstop - tstart] * tunit)
]
else:
epchlist = [ep for ep in seg.epochs if ep.name in epchlist]
nrow = len(analist) + len(spklist)
ncol = len(epchlist)
fig = plt.figure(figsize=(ncol * 18, nrow * 6))
fig.suptitle(seg.name)
gs = grdspc.GridSpec(nrow, ncol)
for epid, epch in enumerate(epchlist):
row = 0
cur_anas = [slice_signals(a, epch, recenter) for a in anas]
cur_spks = [slice_signals(s, epch, recenter) for s in spks]
if showevent:
cur_evts = slice_signals(evts, epch, recenter)
for ana in cur_anas:
ax = fig.add_subplot(gs[row, epid])
for aid, a in enumerate(ana):
ax.plot(a.times, a, linewidth=0.3, alpha=0.5, color='darkgrey')
if embedpeaks:
arrlen = (np.max(a) - np.min(a)) / 10
peaks = next(
(spk[aid]
for spk in cur_spks if spk[aid].name == a.name), [])
for p in peaks:
pidx = np.argmin(np.abs(a.times - p))
pidx = int(pidx)
ax.annotate('',
xy=(p, a[pidx]),
xytext=(p, a[pidx] + arrlen),
arrowprops={'arrowstyle': "->"})
if showevent:
for et in cur_evts[aid].times:
ax.add_patch(
pch.Rectangle((et - showevent, np.min(a)),
showevent * 2,
np.max(a) - np.min(a),
alpha=0.2,
edgecolor='none'))
ana_mean = np.mean(trim_signals(ana), axis=0)
ax.plot(ana[0].times, ana_mean)
ax.set_xlabel("time (s)")
ax.set_ylabel("signal")
# ax.set_ylim(0, 1)
ax.set_title("Signal: " + ana[0].name + "\n" + epch.name)
row += 1
for spk in cur_spks:
ax = fig.add_subplot(gs[row, epid])
for sid, s in enumerate(spk):
if s.times.size:
ax.vlines(s.times, sid, sid + 1)
if showevent:
for et in cur_evts[sid].times:
ax.add_patch(
pch.Rectangle((et - showevent, sid),
showevent * 2,
1,
alpha=0.2,
edgecolor='none'))
ax.set_title("Signal: " + spk[0].name + "\n" + epch.name)
ax.set_xlabel("time (s)")
ax.set_ylabel("spike ID")
row += 1
return fig
def read_segment(self, lazy=False, cascade=True):
fid = open(self.filename, 'rb')
global_header = HeaderReader(fid, GlobalHeader).read_f(offset=0)
# ~ print globalHeader
#~ print 'version' , globalHeader['version']
seg = Segment()
seg.file_origin = os.path.basename(self.filename)
seg.annotate(neuroexplorer_version=global_header['version'])
seg.annotate(comment=global_header['comment'])
if not cascade:
return seg
offset = 544
for i in range(global_header['nvar']):
entity_header = HeaderReader(fid, EntityHeader).read_f(
offset=offset + i * 208)
entity_header['name'] = entity_header['name'].replace('\x00', '')
#print 'i',i, entityHeader['type']
if entity_header['type'] == 0:
# neuron
if lazy:
spike_times = [] * pq.s
else:
spike_times = np.memmap(self.filename, np.dtype('i4'), 'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
spike_times = spike_times.astype('f8') / global_header[
'freq'] * pq.s
sptr = SpikeTrain(
times=spike_times,
t_start=global_header['tbeg'] /
global_header['freq'] * pq.s,
t_stop=global_header['tend'] /
global_header['freq'] * pq.s,
name=entity_header['name'])
if lazy:
sptr.lazy_shape = entity_header['n']
sptr.annotate(channel_index=entity_header['WireNumber'])
seg.spiketrains.append(sptr)
if entity_header['type'] == 1:
# event
if lazy:
event_times = [] * pq.s
else:
event_times = np.memmap(self.filename, np.dtype('i4'), 'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
event_times = event_times.astype('f8') / global_header[
'freq'] * pq.s
labels = np.array([''] * event_times.size, dtype='S')
evar = Event(times=event_times, labels=labels,
channel_name=entity_header['name'])
if lazy:
evar.lazy_shape = entity_header['n']
seg.events.append(evar)
if entity_header['type'] == 2:
# interval
if lazy:
start_times = [] * pq.s
stop_times = [] * pq.s
else:
start_times = np.memmap(self.filename, np.dtype('i4'), 'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
start_times = start_times.astype('f8') / global_header[
'freq'] * pq.s
stop_times = np.memmap(self.filename, np.dtype('i4'), 'r',
shape=(entity_header['n']),
offset=entity_header['offset'] +
entity_header['n'] * 4)
stop_times = stop_times.astype('f') / global_header[
'freq'] * pq.s
epar = Epoch(times=start_times,
durations=stop_times - start_times,
labels=np.array([''] * start_times.size,
dtype='S'),
channel_name=entity_header['name'])
if lazy:
epar.lazy_shape = entity_header['n']
seg.epochs.append(epar)
if entity_header['type'] == 3:
# spiketrain and wavefoms
if lazy:
spike_times = [] * pq.s
waveforms = None
else:
spike_times = np.memmap(self.filename, np.dtype('i4'), 'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
spike_times = spike_times.astype('f8') / global_header[
'freq'] * pq.s
waveforms = np.memmap(self.filename, np.dtype('i2'), 'r',
shape=(entity_header['n'], 1,
entity_header['NPointsWave']),
offset=entity_header['offset'] +
entity_header['n'] * 4)
waveforms = (waveforms.astype('f') *
entity_header['ADtoMV'] +
entity_header['MVOffset']) * pq.mV
t_stop = global_header['tend'] / global_header['freq'] * pq.s
if spike_times.size > 0:
t_stop = max(t_stop, max(spike_times))
sptr = SpikeTrain(
times=spike_times,
t_start=global_header['tbeg'] /
global_header['freq'] * pq.s,
#~ t_stop = max(globalHeader['tend']/
#~ globalHeader['freq']*pq.s,max(spike_times)),
t_stop=t_stop, name=entity_header['name'],
waveforms=waveforms,
sampling_rate=entity_header['WFrequency'] * pq.Hz,
left_sweep=0 * pq.ms)
if lazy:
sptr.lazy_shape = entity_header['n']
sptr.annotate(channel_index=entity_header['WireNumber'])
seg.spiketrains.append(sptr)
if entity_header['type'] == 4:
# popvectors
pass
if entity_header['type'] == 5:
# analog
timestamps = np.memmap(self.filename, np.dtype('i4'), 'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
timestamps = timestamps.astype('f8') / global_header['freq']
fragment_starts = np.memmap(self.filename, np.dtype('i4'), 'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
fragment_starts = fragment_starts.astype('f8') / global_header[
'freq']
t_start = timestamps[0] - fragment_starts[0] / float(
entity_header['WFrequency'])
del timestamps, fragment_starts
if lazy:
signal = [] * pq.mV
else:
signal = np.memmap(self.filename, np.dtype('i2'), 'r',
shape=(entity_header['NPointsWave']),
offset=entity_header['offset'])
signal = signal.astype('f')
signal *= entity_header['ADtoMV']
signal += entity_header['MVOffset']
signal = signal * pq.mV
ana_sig = AnalogSignal(
signal=signal, t_start=t_start * pq.s,
sampling_rate=entity_header['WFrequency'] * pq.Hz,
name=entity_header['name'],
channel_index=entity_header['WireNumber'])
if lazy:
ana_sig.lazy_shape = entity_header['NPointsWave']
seg.analogsignals.append(ana_sig)
if entity_header['type'] == 6:
# markers : TO TEST
if lazy:
times = [] * pq.s
labels = np.array([], dtype='S')
markertype = None
else:
times = np.memmap(self.filename, np.dtype('i4'), 'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
times = times.astype('f8') / global_header['freq'] * pq.s
fid.seek(entity_header['offset'] + entity_header['n'] * 4)
markertype = fid.read(64).replace('\x00', '')
labels = np.memmap(
self.filename, np.dtype(
'S' + str(entity_header['MarkerLength'])),
'r', shape=(entity_header['n']),
offset=entity_header['offset'] +
entity_header['n'] * 4 + 64)
ea = Event(times=times,
labels=labels.view(np.ndarray),
name=entity_header['name'],
channel_index=entity_header['WireNumber'],
marker_type=markertype)
if lazy:
ea.lazy_shape = entity_header['n']
seg.events.append(ea)
seg.create_many_to_one_relationship()
return seg
def read_segment(self, cascade=True, lazy=False, ):
"""
Arguments:
"""
f = StructFile(open(self.filename, 'rb'))
# Name
f.seek(64, 0)
surname = f.read(22).decode('ascii')
while surname[-1] == ' ':
if len(surname) == 0:
break
surname = surname[:-1]
firstname = f.read(20).decode('ascii')
while firstname[-1] == ' ':
if len(firstname) == 0:
break
firstname = firstname[:-1]
#Date
f.seek(128, 0)
day, month, year, hour, minute, sec = f.read_f('bbbbbb')
rec_datetime = datetime.datetime(year + 1900, month, day, hour, minute,
sec)
f.seek(138, 0)
Data_Start_Offset, Num_Chan, Multiplexer, Rate_Min, Bytes = f.read_f(
'IHHHH')
#~ print Num_Chan, Bytes
#header version
f.seek(175, 0)
header_version, = f.read_f('b')
assert header_version == 4
seg = Segment(name=str(firstname + ' ' + surname),
file_origin=os.path.basename(self.filename))
seg.annotate(surname=surname)
seg.annotate(firstname=firstname)
seg.annotate(rec_datetime=rec_datetime)
if not cascade:
f.close()
return seg
# area
f.seek(176, 0)
zone_names = ['ORDER', 'LABCOD', 'NOTE', 'FLAGS', 'TRONCA', 'IMPED_B',
'IMPED_E', 'MONTAGE',
'COMPRESS', 'AVERAGE', 'HISTORY', 'DVIDEO', 'EVENT A',
'EVENT B', 'TRIGGER']
zones = {}
for zname in zone_names:
zname2, pos, length = f.read_f('8sII')
zones[zname] = zname2, pos, length
#~ print zname2, pos, length
# reading raw data
if not lazy:
f.seek(Data_Start_Offset, 0)
rawdata = np.fromstring(f.read(), dtype='u' + str(Bytes))
rawdata = rawdata.reshape((-1, Num_Chan))
# Reading Code Info
zname2, pos, length = zones['ORDER']
f.seek(pos, 0)
code = np.fromstring(f.read(Num_Chan*2), dtype='u2', count=Num_Chan)
units = {-1: pq.nano * pq.V, 0: pq.uV, 1: pq.mV, 2: 1, 100: pq.percent,
101: pq.dimensionless, 102: pq.dimensionless}
for c in range(Num_Chan):
zname2, pos, length = zones['LABCOD']
f.seek(pos + code[c] * 128 + 2, 0)
label = f.read(6).strip(b"\x00").decode('ascii')
ground = f.read(6).strip(b"\x00").decode('ascii')
(logical_min, logical_max, logical_ground, physical_min,
physical_max) = f.read_f('iiiii')
k, = f.read_f('h')
if k in units.keys():
unit = units[k]
else:
unit = pq.uV
f.seek(8, 1)
sampling_rate, = f.read_f('H') * pq.Hz
sampling_rate *= Rate_Min
if lazy:
signal = [] * unit
else:
factor = float(physical_max - physical_min) / float(
logical_max - logical_min + 1)
signal = (rawdata[:, c].astype(
'f') - logical_ground) * factor * unit
ana_sig = AnalogSignal(signal, sampling_rate=sampling_rate,
name=str(label), channel_index=c)
if lazy:
ana_sig.lazy_shape = None
ana_sig.annotate(ground=ground)
seg.analogsignals.append(ana_sig)
sampling_rate = np.mean(
[ana_sig.sampling_rate for ana_sig in seg.analogsignals]) * pq.Hz
# Read trigger and notes
for zname, label_dtype in [('TRIGGER', 'u2'), ('NOTE', 'S40')]:
zname2, pos, length = zones[zname]
f.seek(pos, 0)
triggers = np.fromstring(f.read(length), dtype=[('pos', 'u4'), (
'label', label_dtype)])
if not lazy:
keep = (triggers['pos'] >= triggers['pos'][0]) & (
triggers['pos'] < rawdata.shape[0]) & (
triggers['pos'] != 0)
triggers = triggers[keep]
ea = Event(name=zname[0] + zname[1:].lower(),
labels=triggers['label'].astype('S'),
times=(triggers['pos'] / sampling_rate).rescale('s'))
else:
ea = Event(name=zname[0] + zname[1:].lower())
ea.lazy_shape = triggers.size
seg.events.append(ea)
# Read Event A and B
# Not so well tested
for zname in ['EVENT A', 'EVENT B']:
zname2, pos, length = zones[zname]
f.seek(pos, 0)
epochs = np.fromstring(f.read(length),
dtype=[('label', 'u4'), ('start', 'u4'),
('stop', 'u4'), ])
ep = Epoch(name=zname[0] + zname[1:].lower())
if not lazy:
keep = (epochs['start'] > 0) & (
epochs['start'] < rawdata.shape[0]) & (
epochs['stop'] < rawdata.shape[0])
epochs = epochs[keep]
ep = Epoch(name=zname[0] + zname[1:].lower(),
labels=epochs['label'].astype('S'),
times=(epochs['start'] / sampling_rate).rescale('s'),
durations=((epochs['stop'] - epochs['start']) / sampling_rate).rescale('s'))
else:
ep = Epoch(name=zname[0] + zname[1:].lower())
ep.lazy_shape = triggers.size
seg.epochs.append(ep)
seg.create_many_to_one_relationship()
f.close()
return seg
def read_segment(self, lazy=False, cascade=True):
fid = open(self.filename, 'rb')
global_header = HeaderReader(fid, GlobalHeader).read_f(offset=0)
# ~ print globalHeader
#~ print 'version' , globalHeader['version']
seg = Segment()
seg.file_origin = os.path.basename(self.filename)
seg.annotate(neuroexplorer_version=global_header['version'])
seg.annotate(comment=global_header['comment'])
if not cascade:
return seg
offset = 544
for i in range(global_header['nvar']):
entity_header = HeaderReader(
fid, EntityHeader).read_f(offset=offset + i * 208)
entity_header['name'] = entity_header['name'].replace('\x00', '')
#print 'i',i, entityHeader['type']
if entity_header['type'] == 0:
# neuron
if lazy:
spike_times = [] * pq.s
else:
spike_times = np.memmap(self.filename,
np.dtype('i4'),
'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
spike_times = spike_times.astype(
'f8') / global_header['freq'] * pq.s
sptr = SpikeTrain(times=spike_times,
t_start=global_header['tbeg'] /
global_header['freq'] * pq.s,
t_stop=global_header['tend'] /
global_header['freq'] * pq.s,
name=entity_header['name'])
if lazy:
sptr.lazy_shape = entity_header['n']
sptr.annotate(channel_index=entity_header['WireNumber'])
seg.spiketrains.append(sptr)
if entity_header['type'] == 1:
# event
if lazy:
event_times = [] * pq.s
else:
event_times = np.memmap(self.filename,
np.dtype('i4'),
'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
event_times = event_times.astype(
'f8') / global_header['freq'] * pq.s
labels = np.array([''] * event_times.size, dtype='S')
evar = Event(times=event_times,
labels=labels,
channel_name=entity_header['name'])
if lazy:
evar.lazy_shape = entity_header['n']
seg.events.append(evar)
if entity_header['type'] == 2:
# interval
if lazy:
start_times = [] * pq.s
stop_times = [] * pq.s
else:
start_times = np.memmap(self.filename,
np.dtype('i4'),
'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
start_times = start_times.astype(
'f8') / global_header['freq'] * pq.s
stop_times = np.memmap(self.filename,
np.dtype('i4'),
'r',
shape=(entity_header['n']),
offset=entity_header['offset'] +
entity_header['n'] * 4)
stop_times = stop_times.astype(
'f') / global_header['freq'] * pq.s
epar = Epoch(times=start_times,
durations=stop_times - start_times,
labels=np.array([''] * start_times.size,
dtype='S'),
channel_name=entity_header['name'])
if lazy:
epar.lazy_shape = entity_header['n']
seg.epochs.append(epar)
if entity_header['type'] == 3:
# spiketrain and wavefoms
if lazy:
spike_times = [] * pq.s
waveforms = None
else:
spike_times = np.memmap(self.filename,
np.dtype('i4'),
'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
spike_times = spike_times.astype(
'f8') / global_header['freq'] * pq.s
waveforms = np.memmap(self.filename,
np.dtype('i2'),
'r',
shape=(entity_header['n'], 1,
entity_header['NPointsWave']),
offset=entity_header['offset'] +
entity_header['n'] * 4)
waveforms = (
waveforms.astype('f') * entity_header['ADtoMV'] +
entity_header['MVOffset']) * pq.mV
t_stop = global_header['tend'] / global_header['freq'] * pq.s
if spike_times.size > 0:
t_stop = max(t_stop, max(spike_times))
sptr = SpikeTrain(
times=spike_times,
t_start=global_header['tbeg'] / global_header['freq'] *
pq.s,
#~ t_stop = max(globalHeader['tend']/
#~ globalHeader['freq']*pq.s,max(spike_times)),
t_stop=t_stop,
name=entity_header['name'],
waveforms=waveforms,
sampling_rate=entity_header['WFrequency'] * pq.Hz,
left_sweep=0 * pq.ms)
if lazy:
sptr.lazy_shape = entity_header['n']
sptr.annotate(channel_index=entity_header['WireNumber'])
seg.spiketrains.append(sptr)
if entity_header['type'] == 4:
# popvectors
pass
if entity_header['type'] == 5:
# analog
timestamps = np.memmap(self.filename,
np.dtype('i4'),
'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
timestamps = timestamps.astype('f8') / global_header['freq']
fragment_starts_offset = entity_header[
'offset'] + entity_header['n'] * 4
fragment_starts = np.memmap(self.filename,
np.dtype('i4'),
'r',
shape=(entity_header['n']),
offset=fragment_starts_offset)
fragment_starts = fragment_starts.astype(
'f8') / global_header['freq']
t_start = timestamps[0] - fragment_starts[0] / float(
entity_header['WFrequency'])
del timestamps, fragment_starts
if lazy:
signal = [] * pq.mV
else:
signal_offset = fragment_starts_offset + entity_header[
'n'] * 4
signal = np.memmap(self.filename,
np.dtype('i2'),
'r',
shape=(entity_header['NPointsWave']),
offset=signal_offset)
signal = signal.astype('f')
signal *= entity_header['ADtoMV']
signal += entity_header['MVOffset']
signal = signal * pq.mV
ana_sig = AnalogSignal(
signal=signal,
t_start=t_start * pq.s,
sampling_rate=entity_header['WFrequency'] * pq.Hz,
name=entity_header['name'],
channel_index=entity_header['WireNumber'])
if lazy:
ana_sig.lazy_shape = entity_header['NPointsWave']
seg.analogsignals.append(ana_sig)
if entity_header['type'] == 6:
# markers : TO TEST
if lazy:
times = [] * pq.s
labels = np.array([], dtype='S')
markertype = None
else:
times = np.memmap(self.filename,
np.dtype('i4'),
'r',
shape=(entity_header['n']),
offset=entity_header['offset'])
times = times.astype('f8') / global_header['freq'] * pq.s
fid.seek(entity_header['offset'] + entity_header['n'] * 4)
markertype = fid.read(64).replace('\x00', '')
labels = np.memmap(
self.filename,
np.dtype('S' + str(entity_header['MarkerLength'])),
'r',
shape=(entity_header['n']),
offset=entity_header['offset'] +
entity_header['n'] * 4 + 64)
ea = Event(times=times,
labels=labels.view(np.ndarray),
name=entity_header['name'],
channel_index=entity_header['WireNumber'],
marker_type=markertype)
if lazy:
ea.lazy_shape = entity_header['n']
seg.events.append(ea)
seg.create_many_to_one_relationship()
return seg
def generate_one_simple_segment(seg_name='segment 0', supported_objects=[], nb_analogsignal=4,
t_start=0. * pq.s, sampling_rate=10 * pq.kHz, duration=6. * pq.s,
nb_spiketrain=6, spikerate_range=[.5 * pq.Hz, 12 * pq.Hz],
event_types={'stim': ['a', 'b', 'c', 'd'],
'enter_zone': ['one', 'two'],
'color': ['black', 'yellow', 'green'], },
event_size_range=[5, 20],
epoch_types={'animal state': ['Sleep', 'Freeze', 'Escape'],
'light': ['dark', 'lighted']},
epoch_duration_range=[.5, 3.],
# this should be multiplied by pq.s, no?
array_annotations={'valid': np.array([True, False]),
'number': np.array(range(5))}
):
if supported_objects and Segment not in supported_objects:
raise ValueError('Segment must be in supported_objects')
seg = Segment(name=seg_name)
if AnalogSignal in supported_objects:
for a in range(nb_analogsignal):
anasig = AnalogSignal(rand(int(sampling_rate * duration)), sampling_rate=sampling_rate,
t_start=t_start, units=pq.mV, channel_index=a,
name='sig %d for segment %s' % (a, seg.name))
seg.analogsignals.append(anasig)
if SpikeTrain in supported_objects:
for s in range(nb_spiketrain):
spikerate = rand() * np.diff(spikerate_range)
spikerate += spikerate_range[0].magnitude
# spikedata = rand(int((spikerate*duration).simplified))*duration
# sptr = SpikeTrain(spikedata,
# t_start=t_start, t_stop=t_start+duration)
# #, name = 'spiketrain %d'%s)
spikes = rand(int((spikerate * duration).simplified))
spikes.sort() # spikes are supposed to be an ascending sequence
sptr = SpikeTrain(spikes * duration, t_start=t_start, t_stop=t_start + duration)
sptr.annotations['channel_index'] = s
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(len(spikes)) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
sptr.array_annotate(**arr_ann)
seg.spiketrains.append(sptr)
if Event in supported_objects:
for name, labels in event_types.items():
evt_size = rand() * np.diff(event_size_range)
evt_size += event_size_range[0]
evt_size = int(evt_size)
labels = np.array(labels, dtype='S')
labels = labels[(rand(evt_size) * len(labels)).astype('i')]
evt = Event(times=rand(evt_size) * duration, labels=labels)
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(evt_size) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
evt.array_annotate(**arr_ann)
seg.events.append(evt)
if Epoch in supported_objects:
for name, labels in epoch_types.items():
t = 0
times = []
durations = []
while t < duration:
times.append(t)
dur = rand() * (epoch_duration_range[1] - epoch_duration_range[0])
dur += epoch_duration_range[0]
durations.append(dur)
t = t + dur
labels = np.array(labels, dtype='S')
labels = labels[(rand(len(times)) * len(labels)).astype('i')]
assert len(times) == len(durations)
assert len(times) == len(labels)
epc = Epoch(times=pq.Quantity(times, units=pq.s),
durations=pq.Quantity(durations, units=pq.s),
labels=labels,)
assert epc.times.dtype == 'float'
# Randomly generate array_annotations from given options
arr_ann = {key: value[(rand(len(times)) * len(value)).astype('i')] for (key, value) in
array_annotations.items()}
epc.array_annotate(**arr_ann)
seg.epochs.append(epc)
# TODO : Spike, Event
seg.create_many_to_one_relationship()
return seg
def _handle_timeseries(self, name, timeseries):
# todo: check timeseries.attrs.get('schema_id')
# todo: handle timeseries.attrs.get('source')
subtype = timeseries.attrs['ancestry'][-1]
data_group = timeseries.get('data')
dtype = data_group.dtype
if self._lazy:
data = np.array((), dtype=dtype)
lazy_shape = data_group.value.shape # inefficient to load the data to get the shape
else:
data = data_group.value
if dtype.type is np.string_:
if self._lazy:
times = np.array(())
else:
times = timeseries.get('timestamps')
durations = timeseries.get('durations')
if durations:
# Epoch
if self._lazy:
durations = np.array(())
obj = Epoch(times=times,
durations=durations,
labels=data,
units='second')
else:
# Event
obj = Event(times=times,
labels=data,
units='second')
else:
units = get_units(data_group)
if 'starting_time' in timeseries:
# AnalogSignal
sampling_metadata = timeseries.get('starting_time')
t_start = sampling_metadata.value * pq.s
sampling_rate = sampling_metadata.attrs.get('rate') * pq.Hz
assert sampling_metadata.attrs.get('unit') == 'Seconds'
# todo: handle data.attrs['resolution']
obj = AnalogSignal(data,
units=units,
sampling_rate=sampling_rate,
t_start=t_start,
name=name)
elif 'timestamps' in timeseries:
# IrregularlySampledSignal
if self._lazy:
time_data = np.array(())
else:
time_data = timeseries.get('timestamps')
assert time_data.attrs.get('unit') == 'Seconds'
obj = IrregularlySampledSignal(time_data.value,
data,
units=units,
time_units=pq.second)
else:
raise Exception("Timeseries group does not contain sufficient time information")
if self._lazy:
obj.lazy_shape = lazy_shape
return obj