def _handle_epochs_group(self, block):
# Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch.
epochs = self._file.get('epochs')
# todo: handle epochs.attrs.get('tags')
for name, epoch in epochs.items():
# todo: handle epoch.attrs.get('links')
timeseries = []
for key, value in epoch.items():
if key == 'start_time':
t_start = value * pq.second
elif key == 'stop_time':
t_stop = value * pq.second
else:
# todo: handle value['count']
# todo: handle value['idx_start']
timeseries.append(self._handle_timeseries(key, value.get('timeseries')))
segment = Segment(name=name)
for obj in timeseries:
obj.segment = segment
if isinstance(obj, AnalogSignal):
segment.analogsignals.append(obj)
elif isinstance(obj, IrregularlySampledSignal):
segment.irregularlysampledsignals.append(obj)
elif isinstance(obj, Event):
segment.events.append(obj)
elif isinstance(obj, Epoch):
segment.epochs.append(obj)
segment.block = block
block.segments.append(segment)
def test__children(self):
params = {'test2': 'y1', 'test3': True}
epc = Epoch([1.1, 1.5, 1.7]*pq.ms, durations=[20, 40, 60]*pq.ns,
labels=np.array(['test epoch 1',
'test epoch 2',
'test epoch 3'], dtype='S'),
name='test', description='tester',
file_origin='test.file',
test1=1, **params)
epc.annotate(test1=1.1, test0=[1, 2])
assert_neo_object_is_compliant(epc)
segment = Segment(name='seg1')
segment.epochs = [epc]
segment.create_many_to_one_relationship()
self.assertEqual(epc._single_parent_objects, ('Segment',))
self.assertEqual(epc._multi_parent_objects, ())
self.assertEqual(epc._single_parent_containers, ('segment',))
self.assertEqual(epc._multi_parent_containers, ())
self.assertEqual(epc._parent_objects, ('Segment',))
self.assertEqual(epc._parent_containers, ('segment',))
self.assertEqual(len(epc.parents), 1)
self.assertEqual(epc.parents[0].name, 'seg1')
assert_neo_object_is_compliant(epc)
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__children(self):
params = {'test2': 'y1', 'test3': True}
epc = Epoch([1.1, 1.5, 1.7] * pq.ms, durations=[20, 40, 60] * pq.ns,
labels=np.array(['test epoch 1', 'test epoch 2', 'test epoch 3'], dtype='S'),
name='test', description='tester', file_origin='test.file', test1=1, **params)
epc.annotate(test1=1.1, test0=[1, 2])
assert_neo_object_is_compliant(epc)
segment = Segment(name='seg1')
segment.epochs = [epc]
segment.create_many_to_one_relationship()
self.assertEqual(epc._single_parent_objects, ('Segment',))
self.assertEqual(epc._multi_parent_objects, ())
self.assertEqual(epc._single_parent_containers, ('segment',))
self.assertEqual(epc._multi_parent_containers, ())
self.assertEqual(epc._parent_objects, ('Segment',))
self.assertEqual(epc._parent_containers, ('segment',))
self.assertEqual(len(epc.parents), 1)
self.assertEqual(epc.parents[0].name, 'seg1')
assert_neo_object_is_compliant(epc)
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__add_epoch(self):
proxy_event = EventProxy(rawio=self.reader,
event_channel_index=0,
block_index=0,
seg_index=0)
loaded_event = proxy_event.load()
regular_event = Event(times=loaded_event.times -
1 * loaded_event.units)
loaded_event.annotate(nix_name='neo.event.0')
regular_event.annotate(nix_name='neo.event.1')
seg = Segment()
seg.events = [regular_event, proxy_event]
# test cutting with two events one of which is a proxy
epoch = add_epoch(seg, regular_event, proxy_event)
assert_neo_object_is_compliant(epoch)
exp_annos = {
k: v
for k, v in regular_event.annotations.items() if k != 'nix_name'
}
self.assertDictEqual(epoch.annotations, exp_annos)
assert_arrays_almost_equal(epoch.times, regular_event.times, 1e-12)
assert_arrays_almost_equal(
epoch.durations,
np.ones(regular_event.shape) * loaded_event.units, 1e-12)
def test_roundtrip_with_json_metadata(self):
sample_data = np.random.uniform(size=(200, 3))
filename = "test_roundtrip_with_json_metadata.txt"
metadata_filename = "test_roundtrip_with_json_metadata_about.json"
signal1 = AnalogSignal(sample_data,
units="pA",
sampling_rate=2 * pq.kHz)
seg1 = Segment()
block1 = Block()
seg1.analogsignals.append(signal1)
seg1.block = block1
block1.segments.append(seg1)
iow = AsciiSignalIO(filename, metadata_filename=metadata_filename)
iow.write_block(block1)
self.assert_(os.path.exists(metadata_filename))
ior = AsciiSignalIO(filename)
block2 = ior.read_block()
assert len(block2.segments[0].analogsignals) == 3
signal2 = block2.segments[0].analogsignals[1]
assert_array_almost_equal(signal1.magnitude[:, 1],
signal2.magnitude.reshape(-1),
decimal=7)
self.assertEqual(signal1.units, signal2.units)
self.assertEqual(signal1.sampling_rate, signal2.sampling_rate)
assert_array_equal(signal1.times, signal2.times)
os.remove(filename)
os.remove(metadata_filename)
def test__children(self):
params = {'test2': 'y1', 'test3': True}
evt = Event(1.5 * pq.ms,
label='test epoch',
name='test',
description='tester',
file_origin='test.file',
test1=1,
**params)
evt.annotate(test1=1.1, test0=[1, 2])
assert_neo_object_is_compliant(evt)
segment = Segment(name='seg1')
segment.events = [evt]
segment.create_many_to_one_relationship()
self.assertEqual(evt._single_parent_objects, ('Segment', ))
self.assertEqual(evt._multi_parent_objects, ())
self.assertEqual(evt._single_parent_containers, ('segment', ))
self.assertEqual(evt._multi_parent_containers, ())
self.assertEqual(evt._parent_objects, ('Segment', ))
self.assertEqual(evt._parent_containers, ('segment', ))
self.assertEqual(len(evt.parents), 1)
self.assertEqual(evt.parents[0].name, 'seg1')
assert_neo_object_is_compliant(evt)
def read_segment(self,
lazy = False,
cascade = True,
group = 0,
series = 0):
seg = Segment( name = 'test')
if cascade:
tree = getbyroute(self.pul.tree,[0,group,series])
for sw,sweep in enumerate(tree['children']):
if sw == 0:
starttime = pq.Quantity(float(sweep['contents'].swTimer),'s')
for ch,channel in enumerate(sweep['children']):
sig = self.read_analogsignal(group=group,
series=series,
sweep=sw,
channel = ch)
annotations = sweep['contents'].__dict__.keys()
annotations.remove('readlist')
for a in annotations:
d = {a:str(sweep['contents'].__dict__[a])}
sig.annotate(**d)
sig.t_start = pq.Quantity(float(sig.annotations['swTimer']),'s') - starttime
seg.analogsignals.append(sig)
annotations = tree['contents'].__dict__.keys()
annotations.remove('readlist')
for a in annotations:
d = {a:str(tree['contents'].__dict__[a])}
seg.annotate(**d)
create_many_to_one_relationship(seg)
return seg
def setUp(self):
self.segment = Segment()
self.df = pd.DataFrame()
self.df2 = pd.DataFrame()
self.segment2 = Segment()
self.segment2.dataframes = {}
self.segment2.dataframes.update({'test': pd.DataFrame()})
def read_block(self, lazy=False, cascade=True, channel_index=None):
"""
Arguments:
Channel_index: can be int, iterable or None to select one, many or all channel(s)
"""
blk = Block()
if cascade:
seg = Segment(file_origin=self._filename)
blk.segments += [seg]
if channel_index:
if type(channel_index) is int: channel_index = [channel_index]
if type(channel_index) is list:
channel_index = np.array(channel_index)
else:
channel_index = np.arange(0, self._attrs['shape'][1])
chx = ChannelIndex(name='all channels', index=channel_index)
blk.channel_indexes.append(chx)
ana = self.read_analogsignal(channel_index=channel_index,
lazy=lazy,
cascade=cascade)
ana.channel_index = chx
seg.duration = (self._attrs['shape'][0] /
self._attrs['kwik']['sample_rate']) * pq.s
# neo.tools.populate_RecordingChannel(blk)
blk.create_many_to_one_relationship()
return blk
def _handle_epochs_group(self, block):
# Note that an NWB Epoch corresponds to a Neo Segment, not to a Neo Epoch.
epochs = self._file.get('epochs')
# todo: handle epochs.attrs.get('tags')
for name, epoch in epochs.items():
# todo: handle epoch.attrs.get('links')
timeseries = []
for key, value in epoch.items():
if key == 'start_time':
t_start = value * pq.second
elif key == 'stop_time':
t_stop = value * pq.second
else:
# todo: handle value['count']
# todo: handle value['idx_start']
timeseries.append(self._handle_timeseries(key, value.get('timeseries')))
segment = Segment(name=name)
for obj in timeseries:
obj.segment = segment
if isinstance(obj, AnalogSignal):
segment.analogsignals.append(obj)
elif isinstance(obj, IrregularlySampledSignal):
segment.irregularlysampledsignals.append(obj)
elif isinstance(obj, Event):
segment.events.append(obj)
elif isinstance(obj, Epoch):
segment.epochs.append(obj)
segment.block = block
block.segments.append(segment)
def test__children(self):
signal = self.signals[0]
segment = Segment(name='seg1')
segment.analogsignals = [signal]
segment.create_many_to_one_relationship()
rchan = RecordingChannel(name='rchan1')
rchan.analogsignals = [signal]
rchan.create_many_to_one_relationship()
self.assertEqual(signal._single_parent_objects,
('Segment', 'RecordingChannel'))
self.assertEqual(signal._multi_parent_objects, ())
self.assertEqual(signal._single_parent_containers,
('segment', 'recordingchannel'))
self.assertEqual(signal._multi_parent_containers, ())
self.assertEqual(signal._parent_objects,
('Segment', 'RecordingChannel'))
self.assertEqual(signal._parent_containers,
('segment', 'recordingchannel'))
self.assertEqual(len(signal.parents), 2)
self.assertEqual(signal.parents[0].name, 'seg1')
self.assertEqual(signal.parents[1].name, 'rchan1')
assert_neo_object_is_compliant(signal)
def test__children(self):
signal = self.signals[0]
segment = Segment(name='seg1')
segment.analogsignals = [signal]
segment.create_many_to_one_relationship()
chx = ChannelIndex(name='chx1', index=np.arange(signal.shape[1]))
chx.analogsignals = [signal]
chx.create_many_to_one_relationship()
self.assertEqual(signal._single_parent_objects,
('Segment', 'ChannelIndex'))
self.assertEqual(signal._multi_parent_objects, ())
self.assertEqual(signal._single_parent_containers,
('segment', 'channel_index'))
self.assertEqual(signal._multi_parent_containers, ())
self.assertEqual(signal._parent_objects, ('Segment', 'ChannelIndex'))
self.assertEqual(signal._parent_containers,
('segment', 'channel_index'))
self.assertEqual(len(signal.parents), 2)
self.assertEqual(signal.parents[0].name, 'seg1')
self.assertEqual(signal.parents[1].name, 'chx1')
assert_neo_object_is_compliant(signal)
def read_block(self, lazy=False, cascade=True, channel_index=None):
"""
Arguments:
Channel_index: can be int, iterable or None to select one, many or all channel(s)
"""
blk = Block()
if cascade:
seg = Segment(file_origin=self._filename)
blk.segments += [seg]
if channel_index:
if type(channel_index) is int:
channel_index = [channel_index]
if type(channel_index) is list:
channel_index = np.array(channel_index)
else:
channel_index = np.arange(0, self._attrs["shape"][1])
chx = ChannelIndex(name="all channels", index=channel_index)
blk.channel_indexes.append(chx)
ana = self.read_analogsignal(channel_index=channel_index, lazy=lazy, cascade=cascade)
ana.channel_index = chx
seg.duration = (self._attrs["shape"][0] / self._attrs["kwik"]["sample_rate"]) * pq.s
# neo.tools.populate_RecordingChannel(blk)
blk.create_many_to_one_relationship()
return blk
def setUp(self):
self.signal = AnalogSignal(np.random.randn(1000, 1),
units='V',
sampling_rate=1 * pq.Hz)
self.signal2 = AnalogSignal(np.random.randn(1000, 1),
units='V',
sampling_rate=1 * pq.Hz)
self.signal_start = 10
self.signal_end = 10
self.evt = Event(np.arange(0, 100, 1) * pq.s,
labels=np.repeat(np.array(['t0', 't1'], dtype='S'),
50))
self.evt2 = Event(np.arange(0, 100, 1) * pq.s,
labels=np.repeat(np.array(['t2', 't3'], dtype='S'),
50))
self.evt_start = 15
self.evt_pre_start = self.evt_start - 5
self.evt_end = 85
self.evt_post_end = self.evt_end + 5
self.not_segment = [100]
self.segment = Segment()
self.segment.analogsignals.append(self.signal)
self.segment.events.append(self.evt)
self.segment2 = Segment()
self.segment2.analogsignals.append(self.signal2)
self.segment2.events.append(self.evt2)
self.segments = [self.segment, self.segment2]
def test__get_events(self):
starts_1 = Event(times=[0.5, 10.0, 25.2] * pq.s)
starts_1.annotate(event_type='trial start', pick='me')
starts_1.array_annotate(trial_id=[1, 2, 3])
stops_1 = Event(times=[5.5, 14.9, 30.1] * pq.s)
stops_1.annotate(event_type='trial stop')
stops_1.array_annotate(trial_id=[1, 2, 3])
proxy_event = EventProxy(rawio=self.reader,
event_channel_index=0,
block_index=0,
seg_index=0)
proxy_event.annotate(event_type='trial start')
seg = Segment()
seg.events = [starts_1, stops_1, proxy_event]
# test getting multiple events including a proxy
extracted_starts = get_events(seg, event_type='trial start')
self.assertEqual(len(extracted_starts), 2)
# make sure the event is loaded and a neo.Event object is returned
self.assertTrue(isinstance(extracted_starts[0], Event))
self.assertTrue(isinstance(extracted_starts[1], Event))
def test__add_epoch(self):
starts = Event(times=[0.5, 10.0, 25.2] * pq.s)
starts.annotate(event_type='trial start')
starts.array_annotate(trial_id=[1, 2, 3])
stops = Event(times=[5.5, 14.9, 30.1] * pq.s)
stops.annotate(event_type='trial stop')
stops.array_annotate(trial_id=[1, 2, 3])
seg = Segment()
seg.events = [starts, stops]
# test cutting with one event only
ep_starts = add_epoch(seg, starts, pre=-300 * pq.ms, post=250 * pq.ms)
assert_neo_object_is_compliant(ep_starts)
assert_same_annotations(ep_starts, starts)
assert_arrays_almost_equal(ep_starts.times, starts.times - 300 * pq.ms,
1e-12)
assert_arrays_almost_equal(
ep_starts.durations,
(550 * pq.ms).rescale(ep_starts.durations.units) * np.ones(
(len(starts))), 1e-12)
# test cutting with two events
ep_trials = add_epoch(seg, starts, stops)
assert_neo_object_is_compliant(ep_trials)
assert_same_annotations(ep_trials, starts)
assert_arrays_almost_equal(ep_trials.times, starts.times, 1e-12)
assert_arrays_almost_equal(ep_trials.durations, stops - starts, 1e-12)
def test__children(self):
segment = Segment(name='seg1')
segment.spikes = [self.spike1]
segment.create_many_to_one_relationship()
unit = Unit(name='unit1')
unit.spikes = [self.spike1]
unit.create_many_to_one_relationship()
self.assertEqual(self.spike1._single_parent_objects,
('Segment', 'Unit'))
self.assertEqual(self.spike1._multi_parent_objects, ())
self.assertEqual(self.spike1._single_parent_containers,
('segment', 'unit'))
self.assertEqual(self.spike1._multi_parent_containers, ())
self.assertEqual(self.spike1._parent_objects,
('Segment', 'Unit'))
self.assertEqual(self.spike1._parent_containers,
('segment', 'unit'))
self.assertEqual(len(self.spike1.parents), 2)
self.assertEqual(self.spike1.parents[0].name, 'seg1')
self.assertEqual(self.spike1.parents[1].name, 'unit1')
assert_neo_object_is_compliant(self.spike1)
def read_segment(self, lazy=False, cascade=True):
data, metadata = self._read_file_contents()
annotations = dict(
(k, metadata.get(k, 'unknown'))
for k in ("label", "variable", "first_id", "last_id"))
seg = Segment(**annotations)
if cascade:
if metadata['variable'] == 'spikes':
for i in range(metadata['first_index'],
metadata['last_index']):
spiketrain = self._extract_spikes(data, metadata, i, lazy)
if spiketrain is not None:
seg.spiketrains.append(spiketrain)
seg.annotate(
dt=metadata['dt']
) # store dt for SpikeTrains only, as can be retrieved from sampling_period for AnalogSignal
else:
for i in range(metadata['first_index'],
metadata['last_index']):
# probably slow. Replace with numpy-based version from 0.1
signal = self._extract_signal(data, metadata, i, lazy)
if signal is not None:
seg.analogsignals.append(signal)
create_many_to_one_relationship(seg)
return seg
def test__get_epochs(self):
a = Epoch([0.5, 10.0, 25.2] * pq.s, durations=[5.1, 4.8, 5.0] * pq.s)
a.annotate(epoch_type='a', pick='me')
a.array_annotate(trial_id=[1, 2, 3])
b = Epoch([5.5, 14.9, 30.1] * pq.s, durations=[4.7, 4.9, 5.2] * pq.s)
b.annotate(epoch_type='b')
b.array_annotate(trial_id=[1, 2, 3])
proxy_epoch = EpochProxy(rawio=self.reader,
event_channel_index=1,
block_index=0,
seg_index=0)
proxy_epoch.annotate(epoch_type='a')
seg = Segment()
seg.epochs = [a, b, proxy_epoch]
# test getting multiple epochs including a proxy
extracted_epochs = get_epochs(seg, epoch_type='a')
self.assertEqual(len(extracted_epochs), 2)
# make sure the epoch is loaded and a neo.Epoch object is returned
self.assertTrue(isinstance(extracted_epochs[0], Epoch))
self.assertTrue(isinstance(extracted_epochs[1], Epoch))
def test__children(self):
params = {"test2": "y1", "test3": True}
evt = Event(
1.5 * pq.ms,
label="test epoch",
name="test",
description="tester",
file_origin="test.file",
test1=1,
**params
)
evt.annotate(test1=1.1, test0=[1, 2])
assert_neo_object_is_compliant(evt)
segment = Segment(name="seg1")
segment.events = [evt]
segment.create_many_to_one_relationship()
self.assertEqual(evt._single_parent_objects, ("Segment",))
self.assertEqual(evt._multi_parent_objects, ())
self.assertEqual(evt._single_parent_containers, ("segment",))
self.assertEqual(evt._multi_parent_containers, ())
self.assertEqual(evt._parent_objects, ("Segment",))
self.assertEqual(evt._parent_containers, ("segment",))
self.assertEqual(len(evt.parents), 1)
self.assertEqual(evt.parents[0].name, "seg1")
assert_neo_object_is_compliant(evt)
def test__children(self):
signal = self.signals[0]
segment = Segment(name='seg1')
segment.analogsignals = [signal]
segment.create_many_to_one_relationship()
rchan = RecordingChannel(name='rchan1')
rchan.analogsignals = [signal]
rchan.create_many_to_one_relationship()
self.assertEqual(signal._single_parent_objects,
('Segment', 'RecordingChannel'))
self.assertEqual(signal._multi_parent_objects, ())
self.assertEqual(signal._single_parent_containers,
('segment', 'recordingchannel'))
self.assertEqual(signal._multi_parent_containers, ())
self.assertEqual(signal._parent_objects,
('Segment', 'RecordingChannel'))
self.assertEqual(signal._parent_containers,
('segment', 'recordingchannel'))
self.assertEqual(len(signal.parents), 2)
self.assertEqual(signal.parents[0].name, 'seg1')
self.assertEqual(signal.parents[1].name, 'rchan1')
assert_neo_object_is_compliant(signal)
def test__children(self):
signal = self.signals[0]
segment = Segment(name='seg1')
segment.analogsignals = [signal]
segment.create_many_to_one_relationship()
chx = ChannelIndex(name='chx1', index=np.arange(signal.shape[1]))
chx.analogsignals = [signal]
chx.create_many_to_one_relationship()
self.assertEqual(signal._single_parent_objects, ('Segment', 'ChannelIndex'))
self.assertEqual(signal._multi_parent_objects, ())
self.assertEqual(signal._single_parent_containers, ('segment', 'channel_index'))
self.assertEqual(signal._multi_parent_containers, ())
self.assertEqual(signal._parent_objects, ('Segment', 'ChannelIndex'))
self.assertEqual(signal._parent_containers, ('segment', 'channel_index'))
self.assertEqual(len(signal.parents), 2)
self.assertEqual(signal.parents[0].name, 'seg1')
self.assertEqual(signal.parents[1].name, 'chx1')
assert_neo_object_is_compliant(signal)
def proc_dam(filename):
'''Load an dam file that has already been processed by the official matlab
file converter. That matlab data is saved to an m-file, which is then
converted to a numpy '.npz' file. This numpy file is the file actually
loaded. This function converts it to a neo block and returns the block.
This block can be compared to the block produced by BrainwareDamIO to
make sure BrainwareDamIO is working properly
block = proc_dam(filename)
filename: The file name of the numpy file to load. It should end with
'*_dam_py?.npz'. This will be converted to a neo 'file_origin' property
with the value '*.dam', so the filename to compare should fit that pattern.
'py?' should be 'py2' for the python 2 version of the numpy file or 'py3'
for the python 3 version of the numpy file.
example: filename = 'file1_dam_py2.npz'
dam file name = 'file1.dam'
'''
with np.load(filename) as damobj:
damfile = damobj.items()[0][1].flatten()
filename = os.path.basename(filename[:-12]+'.dam')
signals = [res.flatten() for res in damfile['signal']]
stimIndexes = [int(res[0, 0].tolist()) for res in damfile['stimIndex']]
timestamps = [res[0, 0] for res in damfile['timestamp']]
block = Block(file_origin=filename)
rcg = RecordingChannelGroup(file_origin=filename)
chan = RecordingChannel(file_origin=filename, index=0, name='Chan1')
rcg.channel_indexes = np.array([1])
rcg.channel_names = np.array(['Chan1'], dtype='S')
block.recordingchannelgroups.append(rcg)
rcg.recordingchannels.append(chan)
params = [res['params'][0, 0].flatten() for res in damfile['stim']]
values = [res['values'][0, 0].flatten() for res in damfile['stim']]
params = [[res1[0] for res1 in res] for res in params]
values = [[res1 for res1 in res] for res in values]
stims = [dict(zip(param, value)) for param, value in zip(params, values)]
fulldam = zip(stimIndexes, timestamps, signals, stims)
for stimIndex, timestamp, signal, stim in fulldam:
sig = AnalogSignal(signal=signal*pq.mV,
t_start=timestamp*pq.d,
file_origin=filename,
sampling_period=1.*pq.s)
segment = Segment(file_origin=filename,
index=stimIndex,
**stim)
segment.analogsignals = [sig]
block.segments.append(segment)
create_many_to_one_relationship(block)
return block
def _read_segment(self, fobject):
'''
Read a single segment with a single analogsignal
Returns the segment or None if there are no more segments
'''
try:
# float64 -- start time of the AnalogSignal
t_start = np.fromfile(fobject, dtype=np.float64, count=1)[0]
except IndexError:
# if there are no more Segments, return
return False
# int16 -- index of the stimulus parameters
seg_index = np.fromfile(fobject, dtype=np.int16, count=1)[0].tolist()
# int16 -- number of stimulus parameters
numelements = np.fromfile(fobject, dtype=np.int16, count=1)[0]
# read the name strings for the stimulus parameters
paramnames = []
for _ in range(numelements):
# unit8 -- the number of characters in the string
numchars = np.fromfile(fobject, dtype=np.uint8, count=1)[0]
# char * numchars -- a single name string
name = np.fromfile(fobject, dtype=np.uint8, count=numchars)
# exclude invalid characters
name = str(name[name >= 32].view('c').tostring())
# add the name to the list of names
paramnames.append(name)
# float32 * numelements -- the values for the stimulus parameters
paramvalues = np.fromfile(fobject, dtype=np.float32, count=numelements)
# combine parameter names and the parameters as a dict
params = dict(zip(paramnames, paramvalues))
# int32 -- the number elements in the AnalogSignal
numpts = np.fromfile(fobject, dtype=np.int32, count=1)[0]
# int16 * numpts -- the AnalogSignal itself
signal = np.fromfile(fobject, dtype=np.int16, count=numpts)
sig = AnalogSignal(signal.astype(np.float) * pq.mV,
t_start=t_start * pq.d,
file_origin=self._filename,
sampling_period=1. * pq.s,
copy=False)
# Note: setting the sampling_period to 1 s is arbitrary
# load the AnalogSignal and parameters into a new Segment
seg = Segment(file_origin=self._filename, index=seg_index, **params)
seg.analogsignals = [sig]
return seg
def _group_to_neo(self, nix_group):
neo_attrs = self._nix_attr_to_neo(nix_group)
neo_segment = Segment(**neo_attrs)
neo_segment.rec_datetime = datetime.fromtimestamp(
nix_group.created_at
)
self._neo_map[nix_group.name] = neo_segment
return neo_segment
def test_segment_write(self):
block = Block(name=self.rword())
segment = Segment(name=self.rword(), description=self.rword())
block.segments.append(segment)
self.write_and_compare([block])
segment.annotate(**self.rdict(2))
self.write_and_compare([block])
def test_segment_write(self):
block = Block(name=self.rword())
segment = Segment(name=self.rword(), description=self.rword())
block.segments.append(segment)
self.write_and_compare([block])
segment.annotate(**self.rdict(2))
self.write_and_compare([block])
def proc_dam(filename):
'''Load an dam file that has already been processed by the official matlab
file converter. That matlab data is saved to an m-file, which is then
converted to a numpy '.npz' file. This numpy file is the file actually
loaded. This function converts it to a neo block and returns the block.
This block can be compared to the block produced by BrainwareDamIO to
make sure BrainwareDamIO is working properly
block = proc_dam(filename)
filename: The file name of the numpy file to load. It should end with
'*_dam_py?.npz'. This will be converted to a neo 'file_origin' property
with the value '*.dam', so the filename to compare should fit that pattern.
'py?' should be 'py2' for the python 2 version of the numpy file or 'py3'
for the python 3 version of the numpy file.
example: filename = 'file1_dam_py2.npz'
dam file name = 'file1.dam'
'''
with np.load(filename) as damobj:
damfile = damobj.items()[0][1].flatten()
filename = os.path.basename(filename[:-12] + '.dam')
signals = [res.flatten() for res in damfile['signal']]
stimIndexes = [int(res[0, 0].tolist()) for res in damfile['stimIndex']]
timestamps = [res[0, 0] for res in damfile['timestamp']]
block = Block(file_origin=filename)
chx = ChannelIndex(file_origin=filename,
index=np.array([0]),
channel_ids=np.array([1]),
channel_names=np.array(['Chan1'], dtype='S'))
block.channel_indexes.append(chx)
params = [res['params'][0, 0].flatten() for res in damfile['stim']]
values = [res['values'][0, 0].flatten() for res in damfile['stim']]
params = [[res1[0] for res1 in res] for res in params]
values = [[res1 for res1 in res] for res in values]
stims = [dict(zip(param, value)) for param, value in zip(params, values)]
fulldam = zip(stimIndexes, timestamps, signals, stims)
for stimIndex, timestamp, signal, stim in fulldam:
sig = AnalogSignal(signal=signal * pq.mV,
t_start=timestamp * pq.d,
file_origin=filename,
sampling_period=1. * pq.s)
segment = Segment(file_origin=filename,
index=stimIndex,
**stim)
segment.analogsignals = [sig]
block.segments.append(segment)
block.create_many_to_one_relationship()
return block
def read_segment(self,
lazy=False,
cascade=True,
gdf_id_list=None,
time_unit=pq.ms,
t_start=None,
t_stop=None,
id_column=0,
time_column=1,
**args):
"""
Read a Segment which contains SpikeTrain(s) with specified neuron IDs
from the GDF data.
Parameters
----------
lazy : bool, optional, default: False
cascade : bool, optional, default: True
gdf_id_list : list or tuple, default: None
Can be either list of GDF IDs of which to return SpikeTrain(s) or
a tuple specifying the range (includes boundaries [start, stop])
of GDF IDs. Must be specified if the GDF file contains neuron
IDs, the default None then raises an error. Specify an empty
list [] to retrieve the spike trains of all neurons with at least
one spike.
time_unit : Quantity (time), optional, default: quantities.ms
The time unit of recorded time stamps.
t_start : Quantity (time), default: None
Start time of SpikeTrain. t_start must be specified, the default None
raises an error.
t_stop : Quantity (time), default: None
Stop time of SpikeTrain. t_stop must be specified, the default None
raises an error.
id_column : int, optional, default: 0
Column index of neuron IDs.
time_column : int, optional, default: 1
Column index of time stamps.
Returns
-------
seg : Segment
The Segment contains one SpikeTrain for each ID in gdf_id_list.
"""
if isinstance(gdf_id_list, tuple):
gdf_id_list = range(gdf_id_list[0], gdf_id_list[1] + 1)
# __read_spiketrains() needs a list of IDs
if gdf_id_list is None:
gdf_id_list = [None]
# create an empty Segment and fill in the spike trains
seg = Segment()
seg.spiketrains = self.__read_spiketrains(gdf_id_list, time_unit,
t_start, t_stop, id_column,
time_column, **args)
return seg
def _read_segment(self, node, parent):
attributes = self._get_standard_attributes(node)
segment = Segment(**attributes)
signals = []
for name, child_node in node['analogsignals'].items():
if "AnalogSignal" in name:
signals.append(self._read_analogsignal(child_node, parent=segment))
if signals and self.merge_singles:
segment.unmerged_analogsignals = signals # signals will be merged later
signals = []
for name, child_node in node['analogsignalarrays'].items():
if "AnalogSignalArray" in name:
signals.append(self._read_analogsignalarray(child_node, parent=segment))
segment.analogsignals = signals
irr_signals = []
for name, child_node in node['irregularlysampledsignals'].items():
if "IrregularlySampledSignal" in name:
irr_signals.append(self._read_irregularlysampledsignal(child_node, parent=segment))
if irr_signals and self.merge_singles:
segment.unmerged_irregularlysampledsignals = irr_signals
irr_signals = []
segment.irregularlysampledsignals = irr_signals
epochs = []
for name, child_node in node['epochs'].items():
if "Epoch" in name:
epochs.append(self._read_epoch(child_node, parent=segment))
if self.merge_singles:
epochs = self._merge_data_objects(epochs)
for name, child_node in node['epocharrays'].items():
if "EpochArray" in name:
epochs.append(self._read_epocharray(child_node, parent=segment))
segment.epochs = epochs
events = []
for name, child_node in node['events'].items():
if "Event" in name:
events.append(self._read_event(child_node, parent=segment))
if self.merge_singles:
events = self._merge_data_objects(events)
for name, child_node in node['eventarrays'].items():
if "EventArray" in name:
events.append(self._read_eventarray(child_node, parent=segment))
segment.events = events
spiketrains = []
for name, child_node in node['spikes'].items():
raise NotImplementedError('Spike objects not yet handled.')
for name, child_node in node['spiketrains'].items():
if "SpikeTrain" in name:
spiketrains.append(self._read_spiketrain(child_node, parent=segment))
segment.spiketrains = spiketrains
segment.block = parent
return segment
def read_segment(self, n_start, n_stop, chlist=None, lazy=False, cascade=True):
"""Reads a Segment from the file and stores in database.
The Segment will contain one AnalogSignal for each channel
and will go from n_start to n_stop (in samples).
Arguments:
n_start : time in samples that the Segment begins
n_stop : time in samples that the Segment ends
Python indexing is used, so n_stop is not inclusive.
Returns a Segment object containing the data.
"""
# If no channel numbers provided, get all of them
if chlist is None:
chlist = self.loader.get_neural_channel_numbers()
# Conversion from bits to full_range units
conversion = self.full_range / 2**(8*self.header.sample_width)
# Create the Segment
seg = Segment(file_origin=self.filename)
t_start = float(n_start) / self.header.f_samp
t_stop = float(n_stop) / self.header.f_samp
seg.annotate(t_start=t_start)
seg.annotate(t_stop=t_stop)
# Load data from each channel and store
for ch in chlist:
if lazy:
sig = np.array([]) * conversion
else:
# Get the data from the loader
sig = np.array(\
self.loader._get_channel(ch)[n_start:n_stop]) * conversion
# Create an AnalogSignal with the data in it
anasig = AnalogSignal(signal=sig,
sampling_rate=self.header.f_samp*pq.Hz,
t_start=t_start*pq.s, file_origin=self.filename,
description='Channel %d from %f to %f' % (ch, t_start, t_stop),
channel_index=int(ch))
if lazy:
anasig.lazy_shape = n_stop-n_start
# Link the signal to the segment
seg.analogsignals.append(anasig)
# Link the signal to the recording channel from which it came
#rc = self.channel_number_to_recording_channel[ch]
#rc.analogsignals.append(anasig)
return seg
def read_segment(self, lazy=False, cascade=True,
gdf_id_list=None, time_unit=pq.ms, t_start=None,
t_stop=None, id_column=0, time_column=1, **args):
"""
Read a Segment which contains SpikeTrain(s) with specified neuron IDs
from the GDF data.
Parameters
----------
lazy : bool, optional, default: False
cascade : bool, optional, default: True
gdf_id_list : list or tuple, default: None
Can be either list of GDF IDs of which to return SpikeTrain(s) or
a tuple specifying the range (includes boundaries [start, stop])
of GDF IDs. Must be specified if the GDF file contains neuron
IDs, the default None then raises an error. Specify an empty
list [] to retrieve the spike trains of all neurons with at least
one spike.
time_unit : Quantity (time), optional, default: quantities.ms
The time unit of recorded time stamps.
t_start : Quantity (time), default: None
Start time of SpikeTrain. t_start must be specified, the default None
raises an error.
t_stop : Quantity (time), default: None
Stop time of SpikeTrain. t_stop must be specified, the default None
raises an error.
id_column : int, optional, default: 0
Column index of neuron IDs.
time_column : int, optional, default: 1
Column index of time stamps.
Returns
-------
seg : Segment
The Segment contains one SpikeTrain for each ID in gdf_id_list.
"""
if isinstance(gdf_id_list, tuple):
gdf_id_list = range(gdf_id_list[0], gdf_id_list[1] + 1)
# __read_spiketrains() needs a list of IDs
if gdf_id_list is None:
gdf_id_list = [None]
# create an empty Segment and fill in the spike trains
seg = Segment()
seg.spiketrains = self.__read_spiketrains(gdf_id_list,
time_unit, t_start,
t_stop,
id_column, time_column,
**args)
return seg
def _read_segment(self, node, parent):
attributes = self._get_standard_attributes(node)
segment = Segment(**attributes)
signals = []
for name, child_node in node['analogsignals'].items():
if "AnalogSignal" in name:
signals.append(self._read_analogsignal(child_node, parent=segment))
if signals and self.merge_singles:
segment.unmerged_analogsignals = signals # signals will be merged later
signals = []
for name, child_node in node['analogsignalarrays'].items():
if "AnalogSignalArray" in name:
signals.append(self._read_analogsignalarray(child_node, parent=segment))
segment.analogsignals = signals
irr_signals = []
for name, child_node in node['irregularlysampledsignals'].items():
if "IrregularlySampledSignal" in name:
irr_signals.append(self._read_irregularlysampledsignal(child_node, parent=segment))
if irr_signals and self.merge_singles:
segment.unmerged_irregularlysampledsignals = irr_signals
irr_signals = []
segment.irregularlysampledsignals = irr_signals
epochs = []
for name, child_node in node['epochs'].items():
if "Epoch" in name:
epochs.append(self._read_epoch(child_node, parent=segment))
if self.merge_singles:
epochs = self._merge_data_objects(epochs)
for name, child_node in node['epocharrays'].items():
if "EpochArray" in name:
epochs.append(self._read_epocharray(child_node, parent=segment))
segment.epochs = epochs
events = []
for name, child_node in node['events'].items():
if "Event" in name:
events.append(self._read_event(child_node, parent=segment))
if self.merge_singles:
events = self._merge_data_objects(events)
for name, child_node in node['eventarrays'].items():
if "EventArray" in name:
events.append(self._read_eventarray(child_node, parent=segment))
segment.events = events
spiketrains = []
for name, child_node in node['spikes'].items():
raise NotImplementedError('Spike objects not yet handled.')
for name, child_node in node['spiketrains'].items():
if "SpikeTrain" in name:
spiketrains.append(self._read_spiketrain(child_node, parent=segment))
segment.spiketrains = spiketrains
segment.block = parent
return segment
def create_segment(self, parent=None, name='Segment'):
segment = Segment()
segment.block = parent
self._assign_basic_attributes(segment, name=name)
self._assign_datetime_attributes(segment)
self._assign_index_attribute(segment)
self._create_segment_children(segment)
self._assign_annotations(segment)
return segment
def create_segment(self, parent=None, name='Segment'):
segment = Segment()
segment.block = parent
self._assign_basic_attributes(segment, name=name)
self._assign_datetime_attributes(segment)
self._assign_index_attribute(segment)
self._create_segment_children(segment)
self._assign_annotations(segment)
return segment
def read_segment(self,
lazy = False,
cascade = True,
group = 0,
series = 0):
seg = Segment( name = 'test')
if cascade:
tree = getbyroute(self.pul.tree,[0,group,series])
for sw,sweep in enumerate(tree['children']):
if sw == 0:
starttime = pq.Quantity(float(sweep['contents'].swTimer),'s')
for ch,channel in enumerate(sweep['children']):
sig = self.read_analogsignal(group=group,
series=series,
sweep=sw,
channel = ch)
annotations = sweep['contents'].__dict__.keys()
annotations.remove('readlist')
for a in annotations:
d = {a:str(sweep['contents'].__dict__[a])}
sig.annotate(**d)
sig.t_start = pq.Quantity(float(sig.annotations['swTimer']),'s') - starttime
seg.analogsignals.append(sig)
annotations = tree['contents'].__dict__.keys()
annotations.remove('readlist')
for a in annotations:
d = {a:str(tree['contents'].__dict__[a])}
seg.annotate(**d)
create_many_to_one_relationship(seg)
### add protocols to signals
for sig_index,sig in enumerate(seg.analogsignals):
pgf_index = sig.annotations['pgf_index']
st_rec = self.pgf.tree['children'][pgf_index]['contents']
chnls = [ch for ch in self.pgf.tree['children'][pgf_index]['children']]
for ch_index, chnl in enumerate(chnls):
ep_start = sig.t_start
for se_epoch_index, se_epoch in enumerate(chnl['children']):
se_rec = se_epoch['contents']
se_duration = pq.Quantity(float(se_rec.seDuration),'s')
if not(int(se_rec.seVoltageSource)):
se_voltage = pq.Quantity(float(se_rec.seVoltage),'V')
else:
se_voltage = pq.Quantity(float(chnl['contents'].chHolding),'V')
epoch = neo.Epoch(ep_start,se_duration,'protocol_epoch',value=se_voltage,channel_index=ch_index)
fully_annototate(chnl,epoch)
epoch.annotations['sig_index'] = sig_index
ep_start = ep_start + se_duration
seg.epochs.append(epoch)
return seg
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 read_block(self, lazy=False, cascade=True):
# TODO read block
blk = Block()
if cascade:
seg = Segment(file_origin=self._absolute_folder_path)
for name in self._processing:
if (name == "Position"):
seg.irregularlysampledsignals += self.read_tracking(
path="")
if (name == "LFP"):
seg.analogsignals += self.read_analogsignal(path="")
if (name == "EventWaveform"):
seg.spiketrains += self.read_spiketrain(path="")
for key in self._processing[name]:
if (key == "Position"):
seg.irregularlysampledsignals += self.read_tracking(
path=name)
if (key == "LFP"):
seg.analogsignals += self.read_analogsignal(path=name)
if (key == "EventWaveform"):
seg.spiketrains += self.read_spiketrain(path=name)
#blk.channel_indexes = self._channel_indexes
blk.segments += [seg]
# TODO add duration
#seg.duration = self._duration
# TODO May need to "populate_RecordingChannel"
#blk.create_many_to_one_relationship()
return blk
def setUp(self):
self.evt = Event(times=np.arange(0, 100, 1) * pq.s,
name='Ch1',
labels=np.repeat(np.array(['t0', 't1'], dtype='S'),
50))
self.evt2 = Event(times=np.arange(0, 100, 3) * pq.s,
name='Ch2',
labels=np.repeat(np.array(['t2', 't3'], dtype='S'),
17))
self.segment = Segment()
self.segment.events.append(self.evt)
self.segment.events.append(self.evt2)
self.df = pd.DataFrame(data=[[1, 0], [1, 1]],
index=['start', 'stop'],
columns=['Ch1', 'Ch2'])
self.startoftrial = ['start']
self.epochs = ['results']
self.name = 'MyEvents'
self.typeframe = pd.DataFrame(data=['start', 'results'],
columns=['type'],
index=['start', 'stop'])
ProcessEvents(seg=self.segment,
tolerance=1,
evtframe=self.df,
name=self.name)
self.columns = ['time', 'event', 'trial_idx', 'results', \
'with_previous_results', 'event_type']
def test_segment_with_proxy(self):
seg = Segment()
proxy_anasig = AnalogSignalProxy(
rawio=self.reader,
global_channel_indexes=None,
block_index=0,
seg_index=0,
)
seg.analogsignals.append(proxy_anasig)
proxy_sptr = SpikeTrainProxy(rawio=self.reader,
unit_index=0,
block_index=0,
seg_index=0)
seg.spiketrains.append(proxy_sptr)
proxy_event = EventProxy(rawio=self.reader,
event_channel_index=0,
block_index=0,
seg_index=0)
seg.events.append(proxy_event)
proxy_epoch = EpochProxy(rawio=self.reader,
event_channel_index=1,
block_index=0,
seg_index=0)
seg.epochs.append(proxy_epoch)
def read_segment(self, lazy=False, cascade=True):
segment = Segment(file_origin=self.filename)
if cascade:
segment.analogsignals.append(
self.read_analogsignal(lazy=lazy, cascade=cascade))
segment.analogsignals[-1].segment = segment
return segment
def setUp(self):
self.params = {'test0': 'y1', 'test1': ['deeptest'], 'test2': True}
self.seg = Segment()
self.epc = Epoch(times=[10, 20, 30, 40, 50] * pq.s, durations=[10, 5, 7, 14, 9] * pq.ms,
labels=np.array(['btn0', 'btn1', 'btn2', 'btn0', 'btn3'], dtype='S'),
**self.params)
self.epc.segment = self.seg
def __save_segment(self):
'''
Write the segment to the Block if it exists
'''
# if this is the beginning of the first condition, then we don't want
# to save, so exit
# but set __seg from None to False so we know next time to create a
# segment even if there are no spike in the condition
if self.__seg is None:
self.__seg = False
return
if not self.__seg:
# create dummy values if there are no SpikeTrains in this condition
self.__seg = Segment(file_origin=self._filename, **self.__params)
self.__spiketimes = []
times = pq.Quantity(self.__spiketimes, dtype=np.float32, units=pq.ms)
train = SpikeTrain(times,
t_start=0 * pq.ms,
t_stop=self.__t_stop * pq.ms,
file_origin=self._filename)
self.__seg.spiketrains = [train]
self.__unit.spiketrains.append(train)
self._blk.segments.append(self.__seg)
# set an empty segment
# from now on, we need to set __seg to False rather than None so
# that if there is a condition with no SpikeTrains we know
# to create an empty Segment
self.__seg = False
def read_segment(self, lazy=False, cascade=True):
data, metadata = self._read_file_contents()
annotations = dict((k, metadata.get(k, 'unknown')) for k in ("label", "variable", "first_id", "last_id"))
seg = Segment(**annotations)
if cascade:
if metadata['variable'] == 'spikes':
for i in range(metadata['first_index'], metadata['last_index'] + 1):
spiketrain = self._extract_spikes(data, metadata, i, lazy)
if spiketrain is not None:
seg.spiketrains.append(spiketrain)
seg.annotate(dt=metadata['dt']) # store dt for SpikeTrains only, as can be retrieved from sampling_period for AnalogSignal
else:
signal = self._extract_signals(data, metadata, lazy)
if signal is not None:
seg.analogsignals.append(signal)
seg.create_many_to_one_relationship()
return seg
def test__children(self):
signal = self.signals[0]
segment = Segment(name='seg1')
segment.analogsignalarrays = [signal]
segment.create_many_to_one_relationship()
rcg = RecordingChannelGroup(name='rcg1')
rcg.analogsignalarrays = [signal]
rcg.create_many_to_one_relationship()
self.assertEqual(signal._container_child_objects, ())
self.assertEqual(signal._data_child_objects, ())
self.assertEqual(signal._single_parent_objects,
('Segment', 'RecordingChannelGroup'))
self.assertEqual(signal._multi_child_objects, ())
self.assertEqual(signal._multi_parent_objects, ())
self.assertEqual(signal._child_properties, ())
self.assertEqual(signal._single_child_objects, ())
self.assertEqual(signal._container_child_containers, ())
self.assertEqual(signal._data_child_containers, ())
self.assertEqual(signal._single_child_containers, ())
self.assertEqual(signal._single_parent_containers,
('segment', 'recordingchannelgroup'))
self.assertEqual(signal._multi_child_containers, ())
self.assertEqual(signal._multi_parent_containers, ())
self.assertEqual(signal._child_objects, ())
self.assertEqual(signal._child_containers, ())
self.assertEqual(signal._parent_objects,
('Segment', 'RecordingChannelGroup'))
self.assertEqual(signal._parent_containers,
('segment', 'recordingchannelgroup'))
self.assertEqual(signal.children, ())
self.assertEqual(len(signal.parents), 2)
self.assertEqual(signal.parents[0].name, 'seg1')
self.assertEqual(signal.parents[1].name, 'rcg1')
signal.create_many_to_one_relationship()
signal.create_many_to_many_relationship()
signal.create_relationship()
assert_neo_object_is_compliant(signal)
def test__children(self):
params = {'testarg2': 'yes', 'testarg3': True}
evta = EventArray([1.1, 1.5, 1.7]*pq.ms,
labels=np.array(['test event 1',
'test event 2',
'test event 3'], dtype='S'),
name='test', description='tester',
file_origin='test.file',
testarg1=1, **params)
evta.annotate(testarg1=1.1, testarg0=[1, 2, 3])
assert_neo_object_is_compliant(evta)
segment = Segment(name='seg1')
segment.eventarrays = [evta]
segment.create_many_to_one_relationship()
self.assertEqual(evta._container_child_objects, ())
self.assertEqual(evta._data_child_objects, ())
self.assertEqual(evta._single_parent_objects, ('Segment',))
self.assertEqual(evta._multi_child_objects, ())
self.assertEqual(evta._multi_parent_objects, ())
self.assertEqual(evta._child_properties, ())
self.assertEqual(evta._single_child_objects, ())
self.assertEqual(evta._container_child_containers, ())
self.assertEqual(evta._data_child_containers, ())
self.assertEqual(evta._single_child_containers, ())
self.assertEqual(evta._single_parent_containers, ('segment',))
self.assertEqual(evta._multi_child_containers, ())
self.assertEqual(evta._multi_parent_containers, ())
self.assertEqual(evta._child_objects, ())
self.assertEqual(evta._child_containers, ())
self.assertEqual(evta._parent_objects, ('Segment',))
self.assertEqual(evta._parent_containers, ('segment',))
self.assertEqual(evta.children, ())
self.assertEqual(len(evta.parents), 1)
self.assertEqual(evta.parents[0].name, 'seg1')
evta.create_many_to_one_relationship()
evta.create_many_to_many_relationship()
evta.create_relationship()
assert_neo_object_is_compliant(evta)
def read_block(self,
lazy=False,
cascade=True,
channel_index=None
):
"""
Arguments:
Channel_index: can be int, iterable or None to select one, many or all channel(s)
"""
blk = Block()
if cascade:
seg = Segment( file_origin=self._filename )
blk.segments += [ seg ]
if channel_index:
if type(channel_index) is int: channel_index = [ channel_index ]
if type(channel_index) is list: channel_index = np.array( channel_index )
else:
channel_index = np.arange(0,self._attrs['shape'][1])
rcg = RecordingChannelGroup(name='all channels',
channel_indexes=channel_index)
blk.recordingchannelgroups.append(rcg)
for idx in channel_index:
# read nested analosignal
ana = self.read_analogsignal(channel_index=idx,
lazy=lazy,
cascade=cascade,
)
chan = RecordingChannel(index=int(idx))
seg.analogsignals += [ ana ]
chan.analogsignals += [ ana ]
rcg.recordingchannels.append(chan)
seg.duration = (self._attrs['shape'][0]
/ self._attrs['kwik']['sample_rate']) * pq.s
# neo.tools.populate_RecordingChannel(blk)
blk.create_many_to_one_relationship()
return blk
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 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 test__children(self):
params = {'testarg2': 'yes', 'testarg3': True}
epc = Epoch(1.5*pq.ms, duration=20*pq.ns,
label='test epoch', name='test', description='tester',
file_origin='test.file',
testarg1=1, **params)
epc.annotate(testarg1=1.1, testarg0=[1, 2, 3])
assert_neo_object_is_compliant(epc)
segment = Segment(name='seg1')
segment.epochs = [epc]
segment.create_many_to_one_relationship()
self.assertEqual(epc._container_child_objects, ())
self.assertEqual(epc._data_child_objects, ())
self.assertEqual(epc._single_parent_objects, ('Segment',))
self.assertEqual(epc._multi_child_objects, ())
self.assertEqual(epc._multi_parent_objects, ())
self.assertEqual(epc._child_properties, ())
self.assertEqual(epc._single_child_objects, ())
self.assertEqual(epc._container_child_containers, ())
self.assertEqual(epc._data_child_containers, ())
self.assertEqual(epc._single_child_containers, ())
self.assertEqual(epc._single_parent_containers, ('segment',))
self.assertEqual(epc._multi_child_containers, ())
self.assertEqual(epc._multi_parent_containers, ())
self.assertEqual(epc._child_objects, ())
self.assertEqual(epc._child_containers, ())
self.assertEqual(epc._parent_objects, ('Segment',))
self.assertEqual(epc._parent_containers, ('segment',))
self.assertEqual(epc.children, ())
self.assertEqual(len(epc.parents), 1)
self.assertEqual(epc.parents[0].name, 'seg1')
epc.create_many_to_one_relationship()
epc.create_many_to_many_relationship()
epc.create_relationship()
assert_neo_object_is_compliant(epc)
def test__children(self):
signal = self.signals[0]
segment = Segment(name="seg1")
segment.analogsignals = [signal]
segment.create_many_to_one_relationship()
rchan = RecordingChannel(name="rchan1")
rchan.analogsignals = [signal]
rchan.create_many_to_one_relationship()
self.assertEqual(signal._container_child_objects, ())
self.assertEqual(signal._data_child_objects, ())
self.assertEqual(signal._single_parent_objects, ("Segment", "RecordingChannel"))
self.assertEqual(signal._multi_child_objects, ())
self.assertEqual(signal._multi_parent_objects, ())
self.assertEqual(signal._child_properties, ())
self.assertEqual(signal._single_child_objects, ())
self.assertEqual(signal._container_child_containers, ())
self.assertEqual(signal._data_child_containers, ())
self.assertEqual(signal._single_child_containers, ())
self.assertEqual(signal._single_parent_containers, ("segment", "recordingchannel"))
self.assertEqual(signal._multi_child_containers, ())
self.assertEqual(signal._multi_parent_containers, ())
self.assertEqual(signal._child_objects, ())
self.assertEqual(signal._child_containers, ())
self.assertEqual(signal._parent_objects, ("Segment", "RecordingChannel"))
self.assertEqual(signal._parent_containers, ("segment", "recordingchannel"))
self.assertEqual(signal.children, ())
self.assertEqual(len(signal.parents), 2)
self.assertEqual(signal.parents[0].name, "seg1")
self.assertEqual(signal.parents[1].name, "rchan1")
signal.create_many_to_one_relationship()
signal.create_many_to_many_relationship()
signal.create_relationship()
assert_neo_object_is_compliant(signal)
def read_segment(self,
lazy = False,
cascade = True,
delimiter = '\t',
t_start = 0.*pq.s,
unit = pq.s,
):
"""
Arguments:
delimiter : columns delimiter in file '\t' or one space or two space or ',' or ';'
t_start : time start of all spiketrain 0 by default
unit : unit of spike times, can be a str or directly a Quantities
"""
unit = pq.Quantity(1, unit)
seg = Segment(file_origin = os.path.basename(self.filename))
if not cascade:
return seg
f = open(self.filename, 'Ur')
for i,line in enumerate(f) :
alldata = line[:-1].split(delimiter)
if alldata[-1] == '': alldata = alldata[:-1]
if alldata[0] == '': alldata = alldata[1:]
if lazy:
spike_times = [ ]
t_stop = t_start
else:
spike_times = np.array(alldata).astype('f')
t_stop = spike_times.max()*unit
sptr = SpikeTrain(spike_times*unit, t_start=t_start, t_stop=t_stop)
if lazy:
sptr.lazy_shape = len(alldata)
sptr.annotate(channel_index = i)
seg.spiketrains.append(sptr)
f.close()
seg.create_many_to_one_relationship()
return seg
def test__children(self):
segment = Segment(name="seg1")
segment.spikes = [self.spike1]
segment.create_many_to_one_relationship()
unit = Unit(name="unit1")
unit.spikes = [self.spike1]
unit.create_many_to_one_relationship()
self.assertEqual(self.spike1._container_child_objects, ())
self.assertEqual(self.spike1._data_child_objects, ())
self.assertEqual(self.spike1._single_parent_objects, ("Segment", "Unit"))
self.assertEqual(self.spike1._multi_child_objects, ())
self.assertEqual(self.spike1._multi_parent_objects, ())
self.assertEqual(self.spike1._child_properties, ())
self.assertEqual(self.spike1._single_child_objects, ())
self.assertEqual(self.spike1._container_child_containers, ())
self.assertEqual(self.spike1._data_child_containers, ())
self.assertEqual(self.spike1._single_child_containers, ())
self.assertEqual(self.spike1._single_parent_containers, ("segment", "unit"))
self.assertEqual(self.spike1._multi_child_containers, ())
self.assertEqual(self.spike1._multi_parent_containers, ())
self.assertEqual(self.spike1._child_objects, ())
self.assertEqual(self.spike1._child_containers, ())
self.assertEqual(self.spike1._parent_objects, ("Segment", "Unit"))
self.assertEqual(self.spike1._parent_containers, ("segment", "unit"))
self.assertEqual(self.spike1.children, ())
self.assertEqual(len(self.spike1.parents), 2)
self.assertEqual(self.spike1.parents[0].name, "seg1")
self.assertEqual(self.spike1.parents[1].name, "unit1")
self.spike1.create_many_to_one_relationship()
self.spike1.create_many_to_many_relationship()
self.spike1.create_relationship()
assert_neo_object_is_compliant(self.spike1)
def proc_src_comments(srcfile, filename):
'''Get the comments in an src file that has been#!N
processed by the official
matlab function. See proc_src for details'''
comm_seg = Segment(name='Comments', file_origin=filename)
commentarray = srcfile['comments'].flatten()[0]
senders = [res[0] for res in commentarray['sender'].flatten()]
texts = [res[0] for res in commentarray['text'].flatten()]
timeStamps = [res[0, 0] for res in commentarray['timeStamp'].flatten()]
timeStamps = np.array(timeStamps, dtype=np.float32)
t_start = timeStamps.min()
timeStamps = pq.Quantity(timeStamps - t_start, units=pq.d).rescale(pq.s)
texts = np.array(texts, dtype='S')
senders = np.array(senders, dtype='S')
t_start = brainwaresrcio.convert_brainwaresrc_timestamp(t_start.tolist())
comments = Event(times=timeStamps, labels=texts, senders=senders)
comm_seg.events = [comments]
comm_seg.rec_datetime = t_start
return comm_seg
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)
def read_segment(self, import_neuroshare_segment = True,
lazy=False, cascade=True):
"""
Arguments:
import_neuroshare_segment: import neuroshare segment as SpikeTrain with associated waveforms or not imported at all.
"""
seg = Segment( file_origin = os.path.basename(self.filename), )
if sys.platform.startswith('win'):
neuroshare = ctypes.windll.LoadLibrary(self.dllname)
elif sys.platform.startswith('linux'):
neuroshare = ctypes.cdll.LoadLibrary(self.dllname)
neuroshare = DllWithError(neuroshare)
#elif sys.platform.startswith('darwin'):
# API version
info = ns_LIBRARYINFO()
neuroshare.ns_GetLibraryInfo(ctypes.byref(info) , ctypes.sizeof(info))
seg.annotate(neuroshare_version = str(info.dwAPIVersionMaj)+'.'+str(info.dwAPIVersionMin))
if not cascade:
return seg
# open file
hFile = ctypes.c_uint32(0)
neuroshare.ns_OpenFile(ctypes.c_char_p(self.filename) ,ctypes.byref(hFile))
fileinfo = ns_FILEINFO()
neuroshare.ns_GetFileInfo(hFile, ctypes.byref(fileinfo) , ctypes.sizeof(fileinfo))
# read all entities
for dwEntityID in range(fileinfo.dwEntityCount):
entityInfo = ns_ENTITYINFO()
neuroshare.ns_GetEntityInfo( hFile, dwEntityID, ctypes.byref(entityInfo), ctypes.sizeof(entityInfo))
# EVENT
if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_EVENT':
pEventInfo = ns_EVENTINFO()
neuroshare.ns_GetEventInfo ( hFile, dwEntityID, ctypes.byref(pEventInfo), ctypes.sizeof(pEventInfo))
if pEventInfo.dwEventType == 0: #TEXT
pData = ctypes.create_string_buffer(pEventInfo.dwMaxDataLength)
elif pEventInfo.dwEventType == 1:#CVS
pData = ctypes.create_string_buffer(pEventInfo.dwMaxDataLength)
elif pEventInfo.dwEventType == 2:# 8bit
pData = ctypes.c_byte(0)
elif pEventInfo.dwEventType == 3:# 16bit
pData = ctypes.c_int16(0)
elif pEventInfo.dwEventType == 4:# 32bit
pData = ctypes.c_int32(0)
pdTimeStamp = ctypes.c_double(0.)
pdwDataRetSize = ctypes.c_uint32(0)
ea = Event(name = str(entityInfo.szEntityLabel),)
if not lazy:
times = [ ]
labels = [ ]
for dwIndex in range(entityInfo.dwItemCount ):
neuroshare.ns_GetEventData ( hFile, dwEntityID, dwIndex,
ctypes.byref(pdTimeStamp), ctypes.byref(pData),
ctypes.sizeof(pData), ctypes.byref(pdwDataRetSize) )
times.append(pdTimeStamp.value)
labels.append(str(pData.value))
ea.times = times*pq.s
ea.labels = np.array(labels, dtype ='S')
else :
ea.lazy_shape = entityInfo.dwItemCount
seg.eventarrays.append(ea)
# analog
if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_ANALOG':
pAnalogInfo = ns_ANALOGINFO()
neuroshare.ns_GetAnalogInfo( hFile, dwEntityID,ctypes.byref(pAnalogInfo),ctypes.sizeof(pAnalogInfo) )
dwIndexCount = entityInfo.dwItemCount
if lazy:
signal = [ ]*pq.Quantity(1, pAnalogInfo.szUnits)
else:
pdwContCount = ctypes.c_uint32(0)
pData = np.zeros( (entityInfo.dwItemCount,), dtype = 'float64')
total_read = 0
while total_read< entityInfo.dwItemCount:
dwStartIndex = ctypes.c_uint32(total_read)
dwStopIndex = ctypes.c_uint32(entityInfo.dwItemCount - total_read)
neuroshare.ns_GetAnalogData( hFile, dwEntityID, dwStartIndex,
dwStopIndex, ctypes.byref( pdwContCount) , pData[total_read:].ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
total_read += pdwContCount.value
signal = pq.Quantity(pData, units=pAnalogInfo.szUnits, copy = False)
#t_start
dwIndex = 0
pdTime = ctypes.c_double(0)
neuroshare.ns_GetTimeByIndex( hFile, dwEntityID, dwIndex, ctypes.byref(pdTime))
anaSig = AnalogSignal(signal,
sampling_rate = pAnalogInfo.dSampleRate*pq.Hz,
t_start = pdTime.value * pq.s,
name = str(entityInfo.szEntityLabel),
)
anaSig.annotate( probe_info = str(pAnalogInfo.szProbeInfo))
if lazy:
anaSig.lazy_shape = entityInfo.dwItemCount
seg.analogsignals.append( anaSig )
#segment
if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_SEGMENT' and import_neuroshare_segment:
pdwSegmentInfo = ns_SEGMENTINFO()
if not str(entityInfo.szEntityLabel).startswith('spks'):
continue
neuroshare.ns_GetSegmentInfo( hFile, dwEntityID,
ctypes.byref(pdwSegmentInfo), ctypes.sizeof(pdwSegmentInfo) )
nsource = pdwSegmentInfo.dwSourceCount
pszMsgBuffer = ctypes.create_string_buffer(" "*256)
neuroshare.ns_GetLastErrorMsg(ctypes.byref(pszMsgBuffer), 256)
for dwSourceID in range(pdwSegmentInfo.dwSourceCount) :
pSourceInfo = ns_SEGSOURCEINFO()
neuroshare.ns_GetSegmentSourceInfo( hFile, dwEntityID, dwSourceID,
ctypes.byref(pSourceInfo), ctypes.sizeof(pSourceInfo) )
if lazy:
sptr = SpikeTrain(times, name = str(entityInfo.szEntityLabel), t_stop = 0.*pq.s)
sptr.lazy_shape = entityInfo.dwItemCount
else:
pdTimeStamp = ctypes.c_double(0.)
dwDataBufferSize = pdwSegmentInfo.dwMaxSampleCount*pdwSegmentInfo.dwSourceCount
pData = np.zeros( (dwDataBufferSize), dtype = 'float64')
pdwSampleCount = ctypes.c_uint32(0)
pdwUnitID= ctypes.c_uint32(0)
nsample = int(dwDataBufferSize)
times = np.empty( (entityInfo.dwItemCount), dtype = 'f')
waveforms = np.empty( (entityInfo.dwItemCount, nsource, nsample), dtype = 'f')
for dwIndex in range(entityInfo.dwItemCount ):
neuroshare.ns_GetSegmentData ( hFile, dwEntityID, dwIndex,
ctypes.byref(pdTimeStamp), pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double)),
dwDataBufferSize * 8, ctypes.byref(pdwSampleCount),
ctypes.byref(pdwUnitID ) )
times[dwIndex] = pdTimeStamp.value
waveforms[dwIndex, :,:] = pData[:nsample*nsource].reshape(nsample ,nsource).transpose()
sptr = SpikeTrain(times = pq.Quantity(times, units = 's', copy = False),
t_stop = times.max(),
waveforms = pq.Quantity(waveforms, units = str(pdwSegmentInfo.szUnits), copy = False ),
left_sweep = nsample/2./float(pdwSegmentInfo.dSampleRate)*pq.s,
sampling_rate = float(pdwSegmentInfo.dSampleRate)*pq.Hz,
name = str(entityInfo.szEntityLabel),
)
seg.spiketrains.append(sptr)
# neuralevent
if entity_types[entityInfo.dwEntityType] == 'ns_ENTITY_NEURALEVENT':
pNeuralInfo = ns_NEURALINFO()
neuroshare.ns_GetNeuralInfo ( hFile, dwEntityID,
ctypes.byref(pNeuralInfo), ctypes.sizeof(pNeuralInfo))
if lazy:
times = [ ]*pq.s
t_stop = 0*pq.s
else:
pData = np.zeros( (entityInfo.dwItemCount,), dtype = 'float64')
dwStartIndex = 0
dwIndexCount = entityInfo.dwItemCount
neuroshare.ns_GetNeuralData( hFile, dwEntityID, dwStartIndex,
dwIndexCount, pData.ctypes.data_as(ctypes.POINTER(ctypes.c_double)))
times = pData*pq.s
t_stop = times.max()
sptr = SpikeTrain(times, t_stop =t_stop,
name = str(entityInfo.szEntityLabel),)
if lazy:
sptr.lazy_shape = entityInfo.dwItemCount
seg.spiketrains.append(sptr)
# close
neuroshare.ns_CloseFile(hFile)
seg.create_many_to_one_relationship()
return seg
