def make(self, nwb_content: NWBFile):
position = Position(name='position')
cameras_ids = get_cameras_ids(self.dataset_names, self.metadata)
meters_per_pixels = get_meters_per_pixels(cameras_ids, self.metadata)
pos_online_paths = []
for dataset in self.datasets:
for pos_file in dataset.get_all_data_from_dataset('pos'):
if pos_file.endswith('.pos_online.dat'):
pos_online_paths.append(
os.path.join(dataset.get_data_path_from_dataset('pos'),
pos_file))
first_timestamps = []
for series_id, (conversion, pos_online_path) in enumerate(
zip(meters_per_pixels, pos_online_paths)):
position_df = self.get_position_with_corrected_timestamps(
pos_online_path)
position.create_spatial_series(
name=f'series_{series_id}',
description=', '.join(position_df.columns.tolist()),
data=np.asarray(position_df),
conversion=conversion,
reference_frame='Upper left corner of video frame',
timestamps=np.asarray(position_df.index),
)
first_timestamps.append(position_df.index[0])
# check if timestamps are in order
first_timestamps = np.asarray(first_timestamps)
assert np.all(first_timestamps[:-1] < first_timestamps[1:])
logger.info('Position: Injecting into Processing Module')
nwb_content.processing['behavior'].add(position)
def generate_position_data(filename):
'''
Read position data from .bin or .pos file and convert to
pynwb.behavior.SpatialSeries objects.
Parameters:
-------
filename (Path or Str): Full filename of Axona file with any
extension.
Returns:
-------
position (pynwb.behavior.Position)
'''
position = Position()
position_channel_names = 't,x1,y1,x2,y2,numpix1,numpix2,unused'.split(',')
position_data = read_bin_file_position_data(filename)
position_timestamps = position_data[:, 0]
for ichan in range(0, position_data.shape[1]):
spatial_series = SpatialSeries(
name=position_channel_names[ichan],
timestamps=position_timestamps,
data=position_data[:, ichan],
reference_frame='start of raw aquisition (.bin file)')
position.add_spatial_series(spatial_series)
return position
def create(position: Position, series_id: int, fl_position: FlPosition):
validate_parameters_not_none(__name__, fl_position.column_labels, fl_position.position_data,
fl_position.conversion)
position.create_spatial_series(
name='series_' + str(series_id),
description=fl_position.column_labels,
data=fl_position.position_data,
conversion=fl_position.conversion,
reference_frame='Upper left corner of video frame',
timestamps=fl_position.timestamps,
)
def add_behavior(nwbfile, expt):
bd = expt.behaviorData(imageSync=True)
fs = 1 / expt.frame_period()
behavior_module = nwbfile.create_processing_module(
name='Behavior', description='Data relevant to behavior')
# Add Normalized Position
pos = Position(name='Normalized Position')
pos.create_spatial_series(name='Normalized Position',
rate=fs,
data=bd['treadmillPosition'][:, np.newaxis],
reference_frame='0 is belt start',
conversion=0.001 * bd['trackLength'])
behavior_module.add_container(pos)
# Add Licking
licking = BehavioralTimeSeries(name='Licking')
licking.create_timeseries(
name='Licking',
data=bd['licking'],
rate=fs,
unit='na',
description='1 if mouse licked during this imaging frame')
behavior_module.add_container(licking)
# Add Water Reward Delivery
water = BehavioralTimeSeries(name='Water')
water.create_timeseries(
name='Water',
data=bd['water'],
rate=fs,
unit='na',
description='1 if water was delivered during this imaging frame')
behavior_module.add_container(water)
# Add Lap Times
laps = BehavioralEvents(name='Lap Starts')
# TODO probably not best to have laps as data and timestamps here
laps.create_timeseries(name='Lap Starts',
data=bd['lap'],
timestamps=bd['lap'],
description='Frames at which laps began',
unit='na')
behavior_module.add_container(laps)
def write_position(nwbfile, f, name='Trajectory 100'):
"""
Parameters
----------
nwbfile: pynwb.NWBFile
f: h5py.File
name: str (optional)
Returns
-------
pynwb.core.ProcessingModule
"""
obj = f[name]
behavior_mod = check_module(nwbfile, 'behavior')
spatial_series = SpatialSeries('Position',
data=np.array([obj['x'], obj['y']]).T,
reference_frame='NA',
conversion=1 / 100.,
resolution=np.nan,
rate=float(1 / np.diff(obj['t'][:2]) *
1000))
behavior_mod.add_data_interface(Position(spatial_series))
return behavior_mod
def run_conversion(self, nwbfile: NWBFile, metadata: dict):
file_path = self.source_data['file_path']
file = h5py.File(file_path, 'r')
cell_info = file['cell_info']
cell_ids = [
''.join([chr(x[0]) for x in file[x[0]]])
for x in cell_info['cell_id']
]
times = get_data(file, 'time')
body_pos = get_data(file, 'body_position')
body_speed = get_data(file, 'body_speed')
horizontal_eye_pos = get_data(file, 'horizontal_eye_position')
vertial_eye_pos = get_data(file, 'vertical_eye_position')
horizontal_eye_vel = get_data(file, 'horiztonal_eye_velocity')
vertial_eye_vel = get_data(file, 'vertical_eye_velocity')
all_spike_times = [
file[x[0]][:].ravel() for x in cell_info['spike_times']
]
behavior = nwbfile.create_processing_module(
name='behavior', description='contains processed behavioral data')
spatial_series = SpatialSeries(
name='position',
data=body_pos,
timestamps=times,
conversion=.01,
reference_frame='on track. Position is in VR.')
behavior.add(Position(spatial_series=spatial_series))
behavior.add(
TimeSeries(name='body_speed',
data=body_speed,
timestamps=spatial_series,
unit='cm/s'))
behavior.add(
EyeTracking(
spatial_series=SpatialSeries(name='eye_position',
data=np.c_[horizontal_eye_pos,
vertial_eye_pos],
timestamps=spatial_series,
reference_frame='unknown')))
behavior.add(
TimeSeries(name='eye_velocity',
data=np.c_[horizontal_eye_vel, vertial_eye_vel],
timestamps=spatial_series,
unit='unknown'))
for spike_times, cell_id in zip(all_spike_times, cell_ids):
id_ = int(cell_id.split('_')[-1])
nwbfile.add_unit(spike_times=spike_times, id=id_)
return nwbfile
def test_init(self):
sS = SpatialSeries('test_sS',
np.ones((2, 2)),
'reference_frame',
timestamps=[1., 2., 3.])
pc = Position(sS)
self.assertEqual(pc.spatial_series.get('test_sS'), sS)
def setUp(self):
spatial_series = SpatialSeries(
name="position",
data=np.linspace(0, 1, 20),
rate=50.0,
reference_frame="starting gate",
)
self.position = Position(spatial_series=spatial_series)
def test_init(self):
sS = SpatialSeries('test_sS',
'a hypothetical source',
list(),
'reference_frame',
timestamps=list())
pc = Position('test_pc', sS)
self.assertEqual(pc.source, 'test_pc')
self.assertEqual(pc.spatial_series, [sS])
def test_show_position(self):
position = Position(spatial_series=self.spatial_series)
show_position(position, default_neurodata_vis_spec)
def run_conversion(self,
nwbfile: NWBFile,
metadata_dict: dict,
stub_test: bool = False):
session_path = Path(self.source_data["folder_path"])
session_id = session_path.stem
# Stimuli
[nwbfile.add_stimulus(x) for x in get_events(session_path)]
# States
sleep_state_fpath = session_path / f"{session_id}.SleepState.states.mat"
# label renaming specific to Peyrache
state_label_names = dict(WAKEstate="Awake",
NREMstate="Non-REM",
REMstate="REM")
if sleep_state_fpath.is_file():
matin = loadmat(sleep_state_fpath)["SleepState"]["ints"][0][0]
table = TimeIntervals(name="states",
description="Sleep states of animal.")
table.add_column(name="label", description="Sleep state.")
data = []
for name in matin.dtype.names:
for row in matin[name][0][0]:
data.append(
dict(start_time=row[0],
stop_time=row[1],
label=state_label_names[name]))
[
table.add_row(**row)
for row in sorted(data, key=lambda x: x["start_time"])
]
check_module(nwbfile, "behavior",
"Contains behavioral data.").add(table)
# Position
pos_names = ["RedLED", "BlueLED"]
pos_idx_from = [0, 2]
pos_idx_to = [2, 4]
# Raw position
whlfile_path = session_path / f"{session_id}.whl"
whl_data = np.loadtxt(whlfile_path)
for name, idx_from, idx_to in zip(pos_names, pos_idx_from, pos_idx_to):
nwbfile.add_acquisition(
peyrache_spatial_series(
name=name,
description=
"Raw sensor data. Values of -1 indicate that LED detection failed.",
data=whl_data[:, idx_from:idx_to],
conversion=np.nan, # whl file is in arbitrary grid units
))
# Processed position
posfile_path = session_path / f"{session_id}.pos"
if posfile_path.is_file(
): # at least Mouse32-140820 was missing a .pos file
try:
pos_data = np.loadtxt(posfile_path)
pos_obj = Position(name="SubjectPosition")
for name, idx_from, idx_to in zip(pos_names, pos_idx_from,
pos_idx_to):
pos_obj.add_spatial_series(
peyrache_spatial_series(
name=name,
description=
("(x,y) coordinates tracking subject movement through the maze."
"Values of -1 indicate that LED detection failed."
),
data=pos_data[:, idx_from:idx_to],
conversion=1e-2, # from cm to m
))
check_module(nwbfile, "behavior",
"Contains behavioral data.").add(pos_obj)
except ValueError: # data issue present in at least Mouse17-170201
warn(f"Skipping .pos file for session {session_id}!")
# Epochs - only available for sessions with raw data
epoch_file = session_path / "raw" / f"{session_id}-raw-info" / f"{session_id}-behaviors.txt"
if epoch_file.is_file():
epoch_data = pd.read_csv(epoch_file, header=1)[f"{session_id}:"]
epoch_dat_inds = []
epoch_names = []
for epochs in epoch_data:
inds, name = epochs.split(": ")
epoch_dat_inds.append(inds.split(" "))
epoch_names.append(name)
epoch_windows = [0]
for epoch in epoch_dat_inds:
exp_end_times = []
for dat_ind in epoch:
recording_file = session_path / "raw" / f"{session_id}{dat_ind}.dat"
info_extractor = NeuroscopeRecordingExtractor(
recording_file)
dat_end_time = info_extractor.get_num_frames(
) / info_extractor.get_sampling_frequency() # seconds
exp_end_times.extend([dat_end_time])
epoch_windows.extend([epoch_windows[-1] + sum(exp_end_times)] *
2)
epoch_windows = np.array(epoch_windows[:-1]).reshape(-1, 2)
for j, epoch_name in enumerate(epoch_names):
nwbfile.add_epoch(start_time=epoch_windows[j][0],
stop_time=epoch_windows[j][1],
tags=[epoch_name])
# Data interfaces
# ---------------
#
# NWB provides the concept of a *data interface*--an object for a standard
# storage location of specific types of data--through the :py:class:`~pynwb.core.NWBDataInterface` class.
# For example, :py:class:`~pynwb.behavior.Position` provides a container that holds one or more
# :py:class:`~pynwb.behavior.SpatialSeries` objects. :py:class:`~pynwb.behavior.SpatialSeries` is a subtype of
# :py:class:`~pynwb.base.TimeSeries` that represents the spatial position of an animal over time. By putting
# your position data into a :py:class:`~pynwb.behavior.Position` container, downstream users and
# tools know where to look to retrieve position data. For a comprehensive list of available data interfaces, see the
# :ref:`overview page <modules_overview>`. Here is how to create a :py:class:`~pynwb.behavior.Position` object
# named '`Position'` [#]_.
from pynwb.behavior import Position
position = Position()
####################
# You can add objects to a data interface as a method of the data interface:
position.create_spatial_series(name='position1',
data=np.linspace(0, 1, 20),
rate=50.,
reference_frame='starting gate')
####################
# or you can add pre-existing objects:
from pynwb.behavior import SpatialSeries
spatial_series = SpatialSeries(name='position2',
def run_conversion(self, nwbfile: NWBFile, metadata: dict):
mat_file_path = self.source_data["mat_file_path"]
mat_file = loadmat(mat_file_path)
trial_info = mat_file["SessionNP"]
nwbfile.add_trial_column(
name="reward_time",
description="Time when subject began consuming reward.")
nwbfile.add_trial_column(
name="left_or_right",
description="Time when subject began consuming reward.")
l_r_dict = {1: "Right", 2: "Left"}
for trial in trial_info:
nwbfile.add_trial(start_time=trial[0],
stop_time=trial[1],
reward_time=trial[2],
left_or_right=l_r_dict[int(trial[3])])
# Position
pos_info = mat_file["whlrl"]
pos_data = [pos_info[:, 0:1], pos_info[:, 2:3]]
starting_time = 0.0
rate = 20000 / 512 # from CRCNS info
conversion = np.nan # whl are arbitrary units
pos_obj = Position(name="Position")
for j in range(2):
spatial_series_object = SpatialSeries(
name=f"SpatialSeries{j+1}",
description=
"(x,y) coordinates tracking subject movement through the maze.",
data=H5DataIO(pos_data[j], compression="gzip"),
reference_frame="unknown",
conversion=conversion,
starting_time=starting_time,
rate=rate,
resolution=np.nan,
)
pos_obj.add_spatial_series(spatial_series_object)
get_module(nwbfile=nwbfile,
name="behavior",
description="Contains processed behavioral data."
).add_data_interface(pos_obj)
linearized_pos = mat_file["whlrld"][:, 6]
lin_pos_obj = Position(name="LinearizedPosition")
lin_spatial_series_object = SpatialSeries(
name="LinearizedTimeSeries",
description=
("Linearized position, with '1' defined as start position (the position at the time of last nose-poking "
"in the trial), and d=2 being the end position (position at the tiome just before reward consumption). "
"d=0 means subject is not performing working memory trials."),
data=H5DataIO(linearized_pos, compression="gzip"),
reference_frame="unknown",
conversion=conversion,
starting_time=starting_time,
rate=rate,
resolution=np.nan,
)
lin_pos_obj.add_spatial_series(lin_spatial_series_object)
get_module(nwbfile=nwbfile,
name="behavior").add_data_interface(lin_pos_obj)
for j in np.arange(96,192):
nwb.add_unit(electrodes=[j],spike_times=np.ravel(f_info['spikes'][j,1]),electrode_group=electrode_group_S1)
nwb.add_unit(electrodes=[j],spike_times=np.ravel(f_info['spikes'][j,2]),electrode_group=electrode_group_S1)
# Add behavioral information
# SpatialSeries and Position data interfaces to store cursor_pos
cursor_pos = SpatialSeries(name='cursor_position', data=f_info['cursor_pos'],
reference_frame='0,0', conversion=1e-3, resolution=1e-17,
timestamps=np.ravel(f_info['t']),
description='The position of the cursor in Cartesian coordinates (x, y) in mm')
cursor_position = Position(name='CursorPosition',spatial_series=cursor_pos)
# SpatialSeries and Position data interfaces to store finger_pos
finger_pos = SpatialSeries(name='finger_position', data=f_info['finger_pos'],
reference_frame='0,0', conversion=1e-2, resolution=1e-17,
timestamps=np.ravel(f_info['t']),
description='The position of the working fingertip in Cartesian coordinates (z, -x, -y), as reported by the hand tracker in cm')
finger_position = Position(name='FingerPosition',spatial_series=finger_pos)
# SpatialSeries and Position data interfaces to store target_pos
target_pos = SpatialSeries(name='target_position', data=f_info['target_pos'],
reference_frame='0,0', conversion=1e-3, resolution=1e-17,
timestamps=np.ravel(f_info['t']),
def create_all(self, fl_positions: list) -> Position:
position = Position(name='position')
for series_id, fl_position in enumerate(fl_positions):
self.create(position, series_id, fl_position)
return position
def yuta2nwb(session_path='/Users/bendichter/Desktop/Buzsaki/SenzaiBuzsaki2017/YutaMouse41/YutaMouse41-150903',
subject_xls=None, include_spike_waveforms=True, stub=True):
subject_path, session_id = os.path.split(session_path)
fpath_base = os.path.split(subject_path)[0]
identifier = session_id
mouse_number = session_id[9:11]
if '-' in session_id:
subject_id, date_text = session_id.split('-')
b = False
else:
subject_id, date_text = session_id.split('b')
b = True
if subject_xls is None:
subject_xls = os.path.join(subject_path, 'YM' + mouse_number + ' exp_sheet.xlsx')
else:
if not subject_xls[-4:] == 'xlsx':
subject_xls = os.path.join(subject_xls, 'YM' + mouse_number + ' exp_sheet.xlsx')
session_start_time = dateparse(date_text, yearfirst=True)
df = pd.read_excel(subject_xls)
subject_data = {}
for key in ['genotype', 'DOB', 'implantation', 'Probe', 'Surgery', 'virus injection', 'mouseID']:
names = df.iloc[:, 0]
if key in names.values:
subject_data[key] = df.iloc[np.argmax(names == key), 1]
if isinstance(subject_data['DOB'], datetime):
age = session_start_time - subject_data['DOB']
else:
age = None
subject = Subject(subject_id=subject_id, age=str(age),
genotype=subject_data['genotype'],
species='mouse')
nwbfile = NWBFile(session_description='mouse in open exploration and theta maze',
identifier=identifier,
session_start_time=session_start_time.astimezone(),
file_create_date=datetime.now().astimezone(),
experimenter='Yuta Senzai',
session_id=session_id,
institution='NYU',
lab='Buzsaki',
subject=subject,
related_publications='DOI:10.1016/j.neuron.2016.12.011')
print('reading and writing raw position data...', end='', flush=True)
ns.add_position_data(nwbfile, session_path)
shank_channels = ns.get_shank_channels(session_path)[:8]
all_shank_channels = np.concatenate(shank_channels)
print('setting up electrodes...', end='', flush=True)
hilus_csv_path = os.path.join(fpath_base, 'early_session_hilus_chans.csv')
lfp_channel = get_reference_elec(subject_xls, hilus_csv_path, session_start_time, session_id, b=b)
print(lfp_channel)
custom_column = [{'name': 'theta_reference',
'description': 'this electrode was used to calculate LFP canonical bands',
'data': all_shank_channels == lfp_channel}]
ns.write_electrode_table(nwbfile, session_path, custom_columns=custom_column, max_shanks=max_shanks)
print('reading LFPs...', end='', flush=True)
lfp_fs, all_channels_data = ns.read_lfp(session_path, stub=stub)
lfp_data = all_channels_data[:, all_shank_channels]
print('writing LFPs...', flush=True)
# lfp_data[:int(len(lfp_data)/4)]
lfp_ts = ns.write_lfp(nwbfile, lfp_data, lfp_fs, name='lfp',
description='lfp signal for all shank electrodes')
for name, channel in special_electrode_dict.items():
ts = TimeSeries(name=name, description='environmental electrode recorded inline with neural data',
data=all_channels_data[channel], rate=lfp_fs, unit='V', conversion=np.nan, resolution=np.nan)
nwbfile.add_acquisition(ts)
# compute filtered LFP
print('filtering LFP...', end='', flush=True)
all_lfp_phases = []
for passband in ('theta', 'gamma'):
lfp_fft = filter_lfp(lfp_data[:, all_shank_channels == lfp_channel].ravel(), lfp_fs, passband=passband)
lfp_phase, _ = hilbert_lfp(lfp_fft)
all_lfp_phases.append(lfp_phase[:, np.newaxis])
data = np.dstack(all_lfp_phases)
print('done.', flush=True)
if include_spike_waveforms:
print('writing waveforms...', end='', flush=True)
for shankn in np.arange(1, 9, dtype=int):
ns.write_spike_waveforms(nwbfile, session_path, shankn, stub=stub)
print('done.', flush=True)
decomp_series = DecompositionSeries(name='LFPDecompositionSeries',
description='Theta and Gamma phase for reference LFP',
data=data, rate=lfp_fs,
source_timeseries=lfp_ts,
metric='phase', unit='radians')
decomp_series.add_band(band_name='theta', band_limits=(4, 10))
decomp_series.add_band(band_name='gamma', band_limits=(30, 80))
check_module(nwbfile, 'ecephys', 'contains processed extracellular electrophysiology data').add_data_interface(decomp_series)
[nwbfile.add_stimulus(x) for x in ns.get_events(session_path)]
# create epochs corresponding to experiments/environments for the mouse
sleep_state_fpath = os.path.join(session_path, '{}--StatePeriod.mat'.format(session_id))
exist_pos_data = any(os.path.isfile(os.path.join(session_path, '{}__{}.mat'.format(session_id, task_type['name'])))
for task_type in task_types)
if exist_pos_data:
nwbfile.add_epoch_column('label', 'name of epoch')
for task_type in task_types:
label = task_type['name']
file = os.path.join(session_path, session_id + '__' + label + '.mat')
if os.path.isfile(file):
print('loading position for ' + label + '...', end='', flush=True)
pos_obj = Position(name=label + '_position')
matin = loadmat(file)
tt = matin['twhl_norm'][:, 0]
exp_times = find_discontinuities(tt)
if 'conversion' in task_type:
conversion = task_type['conversion']
else:
conversion = np.nan
for pos_type in ('twhl_norm', 'twhl_linearized'):
if pos_type in matin:
pos_data_norm = matin[pos_type][:, 1:]
spatial_series_object = SpatialSeries(
name=label + '_{}_spatial_series'.format(pos_type),
data=H5DataIO(pos_data_norm, compression='gzip'),
reference_frame='unknown', conversion=conversion,
resolution=np.nan,
timestamps=H5DataIO(tt, compression='gzip'))
pos_obj.add_spatial_series(spatial_series_object)
check_module(nwbfile, 'behavior', 'contains processed behavioral data').add_data_interface(pos_obj)
for i, window in enumerate(exp_times):
nwbfile.add_epoch(start_time=window[0], stop_time=window[1],
label=label + '_' + str(i))
print('done.')
# there are occasional mismatches between the matlab struct and the neuroscope files
# regions: 3: 'CA3', 4: 'DG'
df_unit_features = get_UnitFeatureCell_features(fpath_base, session_id, session_path)
celltype_names = []
for celltype_id, region_id in zip(df_unit_features['fineCellType'].values,
df_unit_features['region'].values):
if celltype_id == 1:
if region_id == 3:
celltype_names.append('pyramidal cell')
elif region_id == 4:
celltype_names.append('granule cell')
else:
raise Exception('unknown type')
elif not np.isfinite(celltype_id):
celltype_names.append('missing')
else:
celltype_names.append(celltype_dict[celltype_id])
custom_unit_columns = [
{
'name': 'cell_type',
'description': 'name of cell type',
'data': celltype_names},
{
'name': 'global_id',
'description': 'global id for cell for entire experiment',
'data': df_unit_features['unitID'].values},
{
'name': 'max_electrode',
'description': 'electrode that has the maximum amplitude of the waveform',
'data': get_max_electrodes(nwbfile, session_path),
'table': nwbfile.electrodes
}]
ns.add_units(nwbfile, session_path, custom_unit_columns, max_shanks=max_shanks)
trialdata_path = os.path.join(session_path, session_id + '__EightMazeRun.mat')
if os.path.isfile(trialdata_path):
trials_data = loadmat(trialdata_path)['EightMazeRun']
trialdatainfo_path = os.path.join(fpath_base, 'EightMazeRunInfo.mat')
trialdatainfo = [x[0] for x in loadmat(trialdatainfo_path)['EightMazeRunInfo'][0]]
features = trialdatainfo[:7]
features[:2] = 'start_time', 'stop_time',
[nwbfile.add_trial_column(x, 'description') for x in features[4:] + ['condition']]
for trial_data in trials_data:
if trial_data[3]:
cond = 'run_left'
else:
cond = 'run_right'
nwbfile.add_trial(start_time=trial_data[0], stop_time=trial_data[1], condition=cond,
error_run=trial_data[4], stim_run=trial_data[5], both_visit=trial_data[6])
"""
mono_syn_fpath = os.path.join(session_path, session_id+'-MonoSynConvClick.mat')
matin = loadmat(mono_syn_fpath)
exc = matin['FinalExcMonoSynID']
inh = matin['FinalInhMonoSynID']
#exc_obj = CatCellInfo(name='excitatory_connections',
# indices_values=[], cell_index=exc[:, 0] - 1, indices=exc[:, 1] - 1)
#module_cellular.add_container(exc_obj)
#inh_obj = CatCellInfo(name='inhibitory_connections',
# indices_values=[], cell_index=inh[:, 0] - 1, indices=inh[:, 1] - 1)
#module_cellular.add_container(inh_obj)
"""
if os.path.isfile(sleep_state_fpath):
matin = loadmat(sleep_state_fpath)['StatePeriod']
table = TimeIntervals(name='states', description='sleep states of animal')
table.add_column(name='label', description='sleep state')
data = []
for name in matin.dtype.names:
for row in matin[name][0][0]:
data.append({'start_time': row[0], 'stop_time': row[1], 'label': name})
[table.add_row(**row) for row in sorted(data, key=lambda x: x['start_time'])]
check_module(nwbfile, 'behavior', 'contains behavioral data').add_data_interface(table)
if stub:
out_fname = session_path + '_stub.nwb'
else:
out_fname = session_path + '.nwb'
print('writing NWB file...', end='', flush=True)
with NWBHDF5IO(out_fname, mode='w') as io:
io.write(nwbfile)
print('done.')
print('testing read...', end='', flush=True)
# test read
with NWBHDF5IO(out_fname, mode='r') as io:
io.read()
print('done.')
def test_init(self):
sS = SpatialSeries('test_sS', list(), 'reference_frame', timestamps=list())
pc = Position(sS)
self.assertEqual(pc.spatial_series.get('test_sS'), sS)
###
from pynwb.behavior import SpatialSeries, Position
position_data = np.array([np.linspace(0, 10, 100), np.linspace(1, 8, 100)]).T
tt_position = np.linspace(0, 100) / 200
spatial_series_object = SpatialSeries(name='name',
source='source',
data=position_data,
reference_frame='unknown',
conversion=np.nan,
resolution=np.nan,
timestamps=tt_position)
pos_obj = Position(source='source',
spatial_series=spatial_series_object,
name='name')
behavior_module = nwbfile.create_processing_module(
name='behavior', source='source', description='data relevant to behavior')
behavior_module.add_container(pos_obj)
###
from pynwb.file import Subject
nwbfile.subject = Subject(age='9 months',
description='description',
species='rat',
genotype='genotype',
sex='M',
def add_behavior(nwbfile, behavior_file, metadata_behavior, t0):
# The timestamps for behavior data, coming from .mat files, are in milliseconds
# and should be transformed to seconds with / 1000
print("adding behavior...")
# process raw behavior
behavior_data = loadmat(behavior_file)
behavior_module = nwbfile.create_processing_module(
name='behavior', description='preprocessed behavioral data')
# Player Position
meta_pos = metadata_behavior['Position']
pos = Position(name=meta_pos['name'])
all_pos = np.atleast_1d([[], [], []]).T
all_tme = []
for iepoch in range(len(behavior_data["behavior"])):
if 'posdat' in behavior_data['behavior'][iepoch]:
epoch_data = behavior_data["behavior"][iepoch]
all_pos = np.r_[all_pos, epoch_data['posdat']]
all_tme = np.r_[all_tme, epoch_data['tme']]
# Metadata for SpatialSeries stored in Position
pos.create_spatial_series(
name=meta_pos['spatial_series'][0]['name'],
data=all_pos,
reference_frame=meta_pos['spatial_series'][0]['reference_frame'],
timestamps=all_tme / 1000. - t0,
conversion=np.nan)
behavior_module.add(pos)
# nlx eye movements
nlxeye = EyeTracking(name='EyeTracking')
# metadata for SpatialSeries stored in EyeTracking
meta_et = metadata_behavior['EyeTracking']
tt = np.array(behavior_data["nlxtme"]) / 1000. - t0
nlxeye.create_spatial_series(
name=meta_et['spatial_series'][0]['name'],
data=np.array(behavior_data["nlxeye"]).T,
reference_frame=meta_et['spatial_series'][0]['reference_frame'],
rate=len(tt) / (tt[-1] - tt[0]),
starting_time=tt[0],
#timestamps=np.array(behavior_data["nlxtme"]) / 1000. - t0,
conversion=np.nan)
behavior_module.add(nlxeye)
# trial columns
nwbfile.add_trial_column(
name='environment',
description='trial environment (calibration if calibration trial)')
# nwbfile.add_trial_column(name='trial_vars', description='trial variables - different between calibration and task')
# event key dictionary
event_dict = {
"new_trial": 1000,
"right_trial": 1001,
"left_trial": 1002,
"reward_on": 1,
"reward_off": 0,
"end_trial": 100,
"end_presentation": 101,
"successful_trial": 200
}
# process behavior here
for iepoch in range(len(behavior_data["behavior"])):
epoch_data = behavior_data["behavior"][iepoch]
if iepoch < 2:
process_behavior_calibration(nwbfile, iepoch + 1, epoch_data, t0)
else:
banana_flag = 1
process_behavior(nwbfile, iepoch + 1, epoch_data, banana_flag,
event_dict, t0)
def convert_data(
self, nwbfile: NWBFile, metadata_dict: dict, stub_test: bool = False, include_spike_waveforms: bool = False
):
session_path = self.input_args["folder_path"]
# TODO: check/enforce format?
task_types = metadata_dict.get("task_types", [])
subject_path, session_id = os.path.split(session_path)
fpath_base = os.path.split(subject_path)[0]
[nwbfile.add_stimulus(x) for x in get_events(session_path)]
exist_pos_data = any(
os.path.isfile(os.path.join(session_path, "{}__{}.mat".format(session_id, task_type["name"])))
for task_type in task_types
)
if exist_pos_data:
nwbfile.add_epoch_column("label", "name of epoch")
for task_type in task_types:
label = task_type["name"]
file = os.path.join(session_path, session_id + "__" + label + ".mat")
if os.path.isfile(file):
pos_obj = Position(name=label + "_position")
matin = loadmat(file)
tt = matin["twhl_norm"][:, 0]
exp_times = find_discontinuities(tt)
if "conversion" in task_type:
conversion = task_type["conversion"]
else:
conversion = np.nan
for pos_type in ("twhl_norm", "twhl_linearized"):
if pos_type in matin:
pos_data_norm = matin[pos_type][:, 1:]
spatial_series_object = SpatialSeries(
name=label + "_{}_spatial_series".format(pos_type),
data=H5DataIO(pos_data_norm, compression="gzip"),
reference_frame="unknown",
conversion=conversion,
resolution=np.nan,
timestamps=H5DataIO(tt, compression="gzip"),
)
pos_obj.add_spatial_series(spatial_series_object)
check_module(nwbfile, "behavior", "contains processed behavioral data").add_data_interface(pos_obj)
for i, window in enumerate(exp_times):
nwbfile.add_epoch(start_time=window[0], stop_time=window[1], label=label + "_" + str(i))
trialdata_path = os.path.join(session_path, session_id + "__EightMazeRun.mat")
if os.path.isfile(trialdata_path):
trials_data = loadmat(trialdata_path)["EightMazeRun"]
trialdatainfo_path = os.path.join(fpath_base, "EightMazeRunInfo.mat")
trialdatainfo = [x[0] for x in loadmat(trialdatainfo_path)["EightMazeRunInfo"][0]]
features = trialdatainfo[:7]
features[:2] = (
"start_time",
"stop_time",
)
[nwbfile.add_trial_column(x, "description") for x in features[4:] + ["condition"]]
for trial_data in trials_data:
if trial_data[3]:
cond = "run_left"
else:
cond = "run_right"
nwbfile.add_trial(
start_time=trial_data[0],
stop_time=trial_data[1],
condition=cond,
error_run=trial_data[4],
stim_run=trial_data[5],
both_visit=trial_data[6],
)
sleep_state_fpath = os.path.join(session_path, "{}.SleepState.states.mat".format(session_id))
# label renaming specific to Watson
state_label_names = {"WAKEstate": "Awake", "NREMstate": "Non-REM", "REMstate": "REM"}
if os.path.isfile(sleep_state_fpath):
matin = loadmat(sleep_state_fpath)["SleepState"]["ints"][0][0]
table = TimeIntervals(name="states", description="Sleep states of animal.")
table.add_column(name="label", description="Sleep state.")
data = []
for name in matin.dtype.names:
for row in matin[name][0][0]:
data.append({"start_time": row[0], "stop_time": row[1], "label": state_label_names[name]})
[table.add_row(**row) for row in sorted(data, key=lambda x: x["start_time"])]
check_module(nwbfile, "behavior", "contains behavioral data").add_data_interface(table)
def run_conversion(self, nwbfile: NWBFile, metadata: dict):
session_path = Path(self.source_data["folder_path"])
session_id = session_path.stem
# Load the file with behavioral data
behavior_file_path = Path(session_path) / f"{session_id}.behavior.mat"
behavior_mat = read_matlab_file(str(behavior_file_path))["behavior"]
# Add trials
events = behavior_mat["events"]
trial_interval_list = events["trialIntervals"]
data = []
for start_time, stop_time in trial_interval_list:
data.append(
dict(
start_time=float(start_time),
stop_time=float(stop_time),
))
[
nwbfile.add_trial(**row)
for row in sorted(data, key=lambda x: x["start_time"])
]
trial_list = events["trials"]
direction_list = [trial.get("direction", "") for trial in trial_list]
trial_type_list = [trial.get("type", "") for trial in trial_list]
if not all([direction == "" for direction in direction_list]):
nwbfile.add_trial_column(name="direction",
description="direction of the trial",
data=direction_list)
if not all([trial_type == "" for trial_type in trial_type_list]):
nwbfile.add_trial_column(name="trial_type",
description="type of trial",
data=trial_type_list)
# Position
module_name = "behavior"
module_description = "Contains behavioral data concerning position."
processing_module = get_module(nwbfile=nwbfile,
name=module_name,
description=module_description)
timestamps = np.array(behavior_mat["timestamps"])[..., 0]
position = behavior_mat["position"]
pos_data = [[x, y, z]
for (x, y,
z) in zip(position["x"], position["y"], position["y"])
]
pos_data = np.array(pos_data)[..., 0]
unit = behavior_mat.get("units", "")
if unit == ["m", "meter", "meters"]:
conversion = 1.0
else:
warnings.warn(f"Spatial units {unit} not listed in meters; "
"setting conversion to nan.")
conversion = np.nan
description = behavior_mat.get("description",
"generic_position_tracking").replace(
"/", "-")
rotation_type = behavior_mat.get("rotationType", "non_specified")
pos_obj = Position(name=f"{description}_task".replace(" ", "_"))
spatial_series_object = SpatialSeries(
name="position",
description="(x,y,z) coordinates tracking subject movement.",
data=H5DataIO(pos_data, compression="gzip"),
reference_frame="unknown",
unit=unit,
conversion=conversion,
timestamps=timestamps,
resolution=np.nan,
)
pos_obj.add_spatial_series(spatial_series_object)
# Add error if available
errorPerMarker = behavior_mat.get("errorPerMarker", None)
if errorPerMarker:
error_data = np.array([error for error in errorPerMarker])[..., 0]
spatial_series_object = SpatialSeries(
name="error_per_marker",
description=
"Estimated error for marker tracking from optitrack system.",
data=H5DataIO(error_data, compression="gzip"),
reference_frame="unknown",
conversion=conversion,
timestamps=timestamps,
resolution=np.nan,
)
pos_obj.add_spatial_series(spatial_series_object)
processing_module.add_data_interface(pos_obj)
# Compass
try:
orientation = behavior_mat["orientation"]
orientation_data = [[
x, y, z, w
] for (x, y, z, w) in zip(orientation["x"], orientation["y"],
orientation["z"], orientation["w"])]
orientation_data = np.array(orientation_data)[..., 0]
compass_obj = CompassDirection(name=f"allocentric_frame_tracking")
spatial_series_object = SpatialSeries(
name="orientation",
description=
f"(x, y, z, w) orientation coordinates, orientation type: {rotation_type}",
data=H5DataIO(orientation_data, compression="gzip"),
reference_frame="unknown",
conversion=conversion,
timestamps=timestamps,
resolution=np.nan,
)
compass_obj.add_spatial_series(spatial_series_object)
processing_module.add_data_interface(compass_obj)
except KeyError:
warnings.warn(f"Orientation data not found")
# States
module_name = "ecephys"
module_description = "Contains behavioral data concerning classified states."
processing_module = get_module(nwbfile=nwbfile,
name=module_name,
description=module_description)
# Sleep states
sleep_file_path = session_path / f"{session_id}.SleepState.states.mat"
if Path(sleep_file_path).exists():
mat_file = read_matlab_file(sleep_file_path)
state_label_names = dict(WAKEstate="Awake",
NREMstate="Non-REM",
REMstate="REM",
MAstate="MA")
sleep_state_dic = mat_file["SleepState"]["ints"]
table = TimeIntervals(name="sleep_states",
description="Sleep state of the animal.")
table.add_column(name="label", description="Sleep state.")
data = []
for sleep_state in state_label_names:
values = sleep_state_dic[sleep_state]
if len(values) != 0 and isinstance(values[0], int):
values = [values]
for start_time, stop_time in values:
data.append(
dict(
start_time=float(start_time),
stop_time=float(stop_time),
label=state_label_names[sleep_state],
))
[
table.add_row(**row)
for row in sorted(data, key=lambda x: x["start_time"])
]
processing_module.add(table)
# Add epochs
lfp_file_path = session_path / f"{session_path.name}.lfp"
raw_file_path = session_path / f"{session_id}.dat"
xml_file_path = session_path / f"{session_id}.xml"
if raw_file_path.is_file():
recorder = NeuroscopeRecordingExtractor(
file_path=raw_file_path, xml_file_path=xml_file_path)
else:
recorder = NeuroscopeRecordingExtractor(
file_path=lfp_file_path, xml_file_path=xml_file_path)
num_frames = recorder.get_num_frames()
sampling_frequency = recorder.get_sampling_frequency()
end_of_the_session = num_frames / sampling_frequency
session_start = 0.0
start_trials_time = min(
[interval[0] for interval in trial_interval_list])
end_trials_time = max(
[interval[1] for interval in trial_interval_list])
end_of_the_session = end_of_the_session
nwbfile.add_epoch(start_time=session_start,
stop_time=start_trials_time,
tags="before trials")
nwbfile.add_epoch(start_time=start_trials_time,
stop_time=end_trials_time,
tags="during trials")
nwbfile.add_epoch(start_time=end_trials_time,
stop_time=end_of_the_session,
tags="after trials")
def run_conversion(self,
nwbfile: NWBFile,
metadata: dict,
stub_test: bool = False):
session_path = Path(self.source_data["folder_path"])
task_types = [
dict(name="OpenFieldPosition_ExtraLarge"),
dict(name="OpenFieldPosition_New_Curtain", conversion=0.46),
dict(name="OpenFieldPosition_New", conversion=0.46),
dict(name="OpenFieldPosition_Old_Curtain", conversion=0.46),
dict(name="OpenFieldPosition_Old", conversion=0.46),
dict(name="OpenFieldPosition_Oldlast", conversion=0.46),
dict(name="EightMazePosition", conversion=0.65 / 2),
]
subject_path = session_path.parent
session_id = session_path.stem
[nwbfile.add_stimulus(x) for x in get_events(session_path)]
sleep_state_fpath = session_path / f"{session_id}--StatePeriod.mat"
exist_pos_data = any([
(session_path / "{session_id}__{task_type['name']}.mat").is_file()
for task_type in task_types
])
if exist_pos_data:
nwbfile.add_epoch_column("label", "Name of epoch.")
# Epoch intervals
for task_type in task_types:
label = task_type["name"]
file = session_path / f"{session_id}__{label}.mat"
if file.is_file():
pos_obj = Position(name=f"{label}_position")
matin = loadmat(file)
tt = matin["twhl_norm"][:, 0]
exp_times = find_discontinuities(tt)
if "conversion" in task_type:
conversion = task_type["conversion"]
else:
conversion = np.nan
for pos_type in ("twhl_norm", "twhl_linearized"):
if pos_type in matin:
pos_data_norm = matin[pos_type][:, 1:]
spatial_series_object = SpatialSeries(
name=f"{label}_{pos_type}_spatial_series",
data=H5DataIO(pos_data_norm, compression="gzip"),
reference_frame="unknown",
conversion=conversion,
resolution=np.nan,
timestamps=H5DataIO(tt, compression="gzip"),
)
pos_obj.add_spatial_series(spatial_series_object)
check_module(
nwbfile, "behavior",
"Contains processed behavioral data.").add_data_interface(
pos_obj)
for i, window in enumerate(exp_times):
nwbfile.add_epoch(
start_time=window[0],
stop_time=window[1],
tags=f"{label}_{str(i)}",
)
# Trial intervals
trialdata_path = session_path / f"{session_id}__EightMazeRun.mat"
if trialdata_path.is_file():
trials_data = loadmat(trialdata_path)["EightMazeRun"]
trialdatainfo_path = subject_path / "EightMazeRunInfo.mat"
trialdatainfo = [
x[0]
for x in loadmat(trialdatainfo_path)["EightMazeRunInfo"][0]
]
features = trialdatainfo[:7]
features[:2] = (
"start_time",
"stop_time",
)
[
nwbfile.add_trial_column(x, "description")
for x in features[4:] + ["condition"]
]
for trial_data in trials_data:
if trial_data[3]:
cond = "run_left"
else:
cond = "run_right"
nwbfile.add_trial(
start_time=trial_data[0],
stop_time=trial_data[1],
condition=cond,
error_run=trial_data[4],
stim_run=trial_data[5],
both_visit=trial_data[6],
)
# SLeep states
if sleep_state_fpath.is_file():
matin = loadmat(sleep_state_fpath)["StatePeriod"]
table = TimeIntervals(name="states",
description="sleep states of animal")
table.add_column(name="label", description="sleep state")
data = []
for name in matin.dtype.names:
for row in matin[name][0][0]:
data.append(
dict(start_time=row[0], stop_time=row[1], label=name))
[
table.add_row(**row)
for row in sorted(data, key=lambda x: x["start_time"])
]
check_module(nwbfile, "behavior",
"Contains behavioral data.").add_data_interface(table)
def convert_data(
self,
nwbfile: NWBFile,
metadata_dict: dict,
stub_test: bool = False,
include_spike_waveforms: bool = False,
):
"""Convert the behavioral portion of a particular session of the GrosmarkAD dataset."""
session_path = self.input_args["folder_path"]
subject_path, session_id = os.path.split(session_path)
# Stimuli
[nwbfile.add_stimulus(x) for x in get_events(session_path)]
# States
sleep_state_fpath = os.path.join(session_path,
"{session_id}.SleepState.states.mat")
# label renaming specific to Watson
state_label_names = dict(WAKEstate="Awake",
NREMstate="Non-REM",
REMstate="REM")
if os.path.isfile(sleep_state_fpath):
matin = loadmat(sleep_state_fpath)["SleepState"]["ints"][0][0]
table = TimeIntervals(name="states",
description="Sleep states of animal.")
table.add_column(name="label", description="Sleep state.")
data = []
for name in matin.dtype.names:
for row in matin[name][0][0]:
data.append(
dict(
start_time=row[0],
stop_time=row[1],
label=state_label_names[name],
))
[
table.add_row(**row)
for row in sorted(data, key=lambda x: x["start_time"])
]
check_module(nwbfile, "behavior",
"contains behavioral data").add_data_interface(table)
# Position
pos_filepath = Path(
session_path) / f"{session_id}.position.behavior.mat"
pos_mat = loadmat(str(pos_filepath.absolute()))
starting_time = float(
pos_mat["position"]["timestamps"][0][0]
[0]) # confirmed to be a regularly sampled series
rate = float(
pos_mat["position"]["timestamps"][0][0][1]) - starting_time
if pos_mat["position"]["units"][0][0][0] == "m":
conversion = 1.0
else:
warnings.warn(
f"Spatial units ({pos_mat['position']['units'][0][0][0]}) not listed in meters; "
"setting conversion to nan.")
conversion = np.nan
pos_data = [[x[0], y[0]] for x, y in zip(
pos_mat["position"]["position"][0][0]["x"][0][0],
pos_mat["position"]["position"][0][0]["y"][0][0],
)]
linearized_data = [[
lin[0]
] for lin in pos_mat["position"]["position"][0][0]["lin"][0][0]]
label = pos_mat["position"]["behaviorinfo"][0][0]["MazeType"][0][0][
0].replace(" ", "")
pos_obj = Position(name=f"{label}Position")
spatial_series_object = SpatialSeries(
name=f"{label}SpatialSeries",
description=
"(x,y) coordinates tracking subject movement through the maze.",
data=H5DataIO(pos_data, compression="gzip"),
reference_frame="unknown",
conversion=conversion,
starting_time=starting_time,
rate=rate,
resolution=np.nan,
)
pos_obj.add_spatial_series(spatial_series_object)
check_module(
nwbfile, "behavior",
"contains processed behavioral data").add_data_interface(pos_obj)
lin_pos_obj = Position(name=f"{label}LinearizedPosition")
lin_spatial_series_object = SpatialSeries(
name=f"{label}LinearizedTimeSeries",
description=
"Linearized position, defined as starting at the edge of reward area, "
"and increasing clockwise, terminating at the opposing edge of the reward area.",
data=H5DataIO(linearized_data, compression="gzip"),
reference_frame="unknown",
conversion=conversion,
starting_time=starting_time,
rate=rate,
resolution=np.nan,
)
lin_pos_obj.add_spatial_series(lin_spatial_series_object)
check_module(nwbfile, "behavior",
"contains processed behavioral data").add_data_interface(
lin_pos_obj)
# Epochs
epoch_names = list(pos_mat["position"]["Epochs"][0][0].dtype.names)
epoch_windows = [[float(start), float(stop)]
for x in pos_mat["position"]["Epochs"][0][0][0][0]
for start, stop in x]
nwbfile.add_epoch_column("label", "name of epoch")
for j, epoch_name in enumerate(epoch_names):
nwbfile.add_epoch(
start_time=epoch_windows[j][0],
stop_time=epoch_windows[j][1],
label=epoch_name,
)
def test_show_position(self):
position = Position(spatial_series=self.spatial_series)
show_multi_container_interface(position, default_neurodata_vis_spec)
matin = loadmat(file)
tt = matin['twhl_norm'][:, 0]
pos_data = matin['twhl_norm'][:, 1:3]
exp_times = find_discontinuities(tt)
spatial_series_object = SpatialSeries(name=label + ' spatial_series',
source='position sensor0',
data=gzip(pos_data),
reference_frame='unknown',
conversion=np.nan,
resolution=np.nan,
timestamps=gzip(tt))
pos_obj = Position(source=source,
spatial_series=spatial_series_object,
name=label + '_position')
module_behavior.add_container(pos_obj)
for i, window in enumerate(exp_times):
nwbfile.create_epoch(start_time=window[0],
stop_time=window[1],
tags=tuple(),
description=label + '_' + str(i),
timeseries=all_ts + [spatial_series_object])
print('done.')
## load celltypes
matin = loadmat(os.path.join(fpath_base,
'DG_all_6__UnitFeatureSummary_add.mat'),
def convert(
input_file,
session_start_time,
subject_date_of_birth,
subject_id='I5',
subject_description='naive',
subject_genotype='wild-type',
subject_sex='M',
subject_weight='11.6g',
subject_species='Mus musculus',
subject_brain_region='Medial Entorhinal Cortex',
surgery='Probe: +/-3.3mm ML, 0.2mm A of sinus, then as deep as possible',
session_id='npI5_0417_baseline_1',
experimenter='Kei Masuda',
experiment_description='Virtual Hallway Task',
institution='Stanford University School of Medicine',
lab_name='Giocomo Lab'):
"""
Read in the .mat file specified by input_file and convert to .nwb format.
Parameters
----------
input_file : np.ndarray (..., n_channels, n_time)
the .mat file to be converted
subject_id : string
the unique subject ID number for the subject of the experiment
subject_date_of_birth : datetime ISO 8601
the date and time the subject was born
subject_description : string
important information specific to this subject that differentiates it from other members of it's species
subject_genotype : string
the genetic strain of this species.
subject_sex : string
Male or Female
subject_weight :
the weight of the subject around the time of the experiment
subject_species : string
the name of the species of the subject
subject_brain_region : basestring
the name of the brain region where the electrode probe is recording from
surgery : str
information about the subject's surgery to implant electrodes
session_id: string
human-readable ID# for the experiment session that has a one-to-one relationship with a recording session
session_start_time : datetime
date and time that the experiment started
experimenter : string
who ran the experiment, first and last name
experiment_description : string
what task was being run during the session
institution : string
what institution was the experiment performed in
lab_name : string
the lab where the experiment was performed
Returns
-------
nwbfile : NWBFile
The contents of the .mat file converted into the NWB format. The nwbfile is saved to disk using NDWHDF5
"""
# input matlab data
matfile = hdf5storage.loadmat(input_file)
# output path for nwb data
def replace_last(source_string, replace_what, replace_with):
head, _sep, tail = source_string.rpartition(replace_what)
return head + replace_with + tail
outpath = replace_last(input_file, '.mat', '.nwb')
create_date = datetime.today()
timezone_cali = pytz.timezone('US/Pacific')
create_date_tz = timezone_cali.localize(create_date)
# if loading data from config.yaml, convert string dates into datetime
if isinstance(session_start_time, str):
session_start_time = datetime.strptime(session_start_time,
'%B %d, %Y %I:%M%p')
session_start_time = timezone_cali.localize(session_start_time)
if isinstance(subject_date_of_birth, str):
subject_date_of_birth = datetime.strptime(subject_date_of_birth,
'%B %d, %Y %I:%M%p')
subject_date_of_birth = timezone_cali.localize(subject_date_of_birth)
# create unique identifier for this experimental session
uuid_identifier = uuid.uuid1()
# Create NWB file
nwbfile = NWBFile(
session_description=experiment_description, # required
identifier=uuid_identifier.hex, # required
session_id=session_id,
experiment_description=experiment_description,
experimenter=experimenter,
surgery=surgery,
institution=institution,
lab=lab_name,
session_start_time=session_start_time, # required
file_create_date=create_date_tz) # optional
# add information about the subject of the experiment
experiment_subject = Subject(subject_id=subject_id,
species=subject_species,
description=subject_description,
genotype=subject_genotype,
date_of_birth=subject_date_of_birth,
weight=subject_weight,
sex=subject_sex)
nwbfile.subject = experiment_subject
# adding constants via LabMetaData container
# constants
sample_rate = float(matfile['sp'][0]['sample_rate'][0][0][0])
n_channels_dat = int(matfile['sp'][0]['n_channels_dat'][0][0][0])
dat_path = matfile['sp'][0]['dat_path'][0][0][0]
offset = int(matfile['sp'][0]['offset'][0][0][0])
data_dtype = matfile['sp'][0]['dtype'][0][0][0]
hp_filtered = bool(matfile['sp'][0]['hp_filtered'][0][0][0])
vr_session_offset = matfile['sp'][0]['vr_session_offset'][0][0][0]
# container
lab_metadata = LabMetaData_ext(name='LabMetaData',
acquisition_sampling_rate=sample_rate,
number_of_electrodes=n_channels_dat,
file_path=dat_path,
bytes_to_skip=offset,
raw_data_dtype=data_dtype,
high_pass_filtered=hp_filtered,
movie_start_time=vr_session_offset)
nwbfile.add_lab_meta_data(lab_metadata)
# Adding trial information
nwbfile.add_trial_column(
'trial_contrast',
'visual contrast of the maze through which the mouse is running')
trial = np.ravel(matfile['trial'])
trial_nums = np.unique(trial)
position_time = np.ravel(matfile['post'])
# matlab trial numbers start at 1. To correctly index trial_contract vector,
# subtracting 1 from 'num' so index starts at 0
for num in trial_nums:
trial_times = position_time[trial == num]
nwbfile.add_trial(start_time=trial_times[0],
stop_time=trial_times[-1],
trial_contrast=matfile['trial_contrast'][num - 1][0])
# Add mouse position inside:
position = Position()
position_virtual = np.ravel(matfile['posx'])
# position inside the virtual environment
sampling_rate = 1 / (position_time[1] - position_time[0])
position.create_spatial_series(
name='Position',
data=position_virtual,
starting_time=position_time[0],
rate=sampling_rate,
reference_frame='The start of the trial, which begins at the start '
'of the virtual hallway.',
conversion=0.01,
description='Subject position in the virtual hallway.',
comments='The values should be >0 and <400cm. Values greater than '
'400cm mean that the mouse briefly exited the maze.',
)
# physical position on the mouse wheel
physical_posx = position_virtual
trial_gain = np.ravel(matfile['trial_gain'])
for num in trial_nums:
physical_posx[trial ==
num] = physical_posx[trial == num] / trial_gain[num - 1]
position.create_spatial_series(
name='PhysicalPosition',
data=physical_posx,
starting_time=position_time[0],
rate=sampling_rate,
reference_frame='Location on wheel re-referenced to zero '
'at the start of each trial.',
conversion=0.01,
description='Physical location on the wheel measured '
'since the beginning of the trial.',
comments='Physical location found by dividing the '
'virtual position by the "trial_gain"')
nwbfile.add_acquisition(position)
# Add timing of lick events, as well as mouse's virtual position during lick event
lick_events = BehavioralEvents()
lick_events.create_timeseries(
'LickEvents',
data=np.ravel(matfile['lickx']),
timestamps=np.ravel(matfile['lickt']),
unit='centimeter',
description='Subject position in virtual hallway during the lick.')
nwbfile.add_acquisition(lick_events)
# Add information on the visual stimulus that was shown to the subject
# Assumed rate=60 [Hz]. Update if necessary
# Update external_file to link to Unity environment file
visualization = ImageSeries(
name='ImageSeries',
unit='seconds',
format='external',
external_file=list(['https://unity.com/VR-and-AR-corner']),
starting_time=vr_session_offset,
starting_frame=[[0]],
rate=float(60),
description='virtual Unity environment that the mouse navigates through'
)
nwbfile.add_stimulus(visualization)
# Add the recording device, a neuropixel probe
recording_device = nwbfile.create_device(name='neuropixel_probes')
electrode_group_description = 'single neuropixels probe http://www.open-ephys.org/neuropixelscorded'
electrode_group_name = 'probe1'
electrode_group = nwbfile.create_electrode_group(
electrode_group_name,
description=electrode_group_description,
location=subject_brain_region,
device=recording_device)
# Add information about each electrode
xcoords = np.ravel(matfile['sp'][0]['xcoords'][0])
ycoords = np.ravel(matfile['sp'][0]['ycoords'][0])
data_filtered_flag = matfile['sp'][0]['hp_filtered'][0][0]
if data_filtered_flag:
filter_desc = 'The raw voltage signals from the electrodes were high-pass filtered'
else:
filter_desc = 'The raw voltage signals from the electrodes were not high-pass filtered'
num_recording_electrodes = xcoords.shape[0]
recording_electrodes = range(0, num_recording_electrodes)
# create electrode columns for the x,y location on the neuropixel probe
# the standard x,y,z locations are reserved for Allen Brain Atlas location
nwbfile.add_electrode_column('rel_x', 'electrode x-location on the probe')
nwbfile.add_electrode_column('rel_y', 'electrode y-location on the probe')
for idx in recording_electrodes:
nwbfile.add_electrode(id=idx,
x=np.nan,
y=np.nan,
z=np.nan,
rel_x=float(xcoords[idx]),
rel_y=float(ycoords[idx]),
imp=np.nan,
location='medial entorhinal cortex',
filtering=filter_desc,
group=electrode_group)
# Add information about each unit, termed 'cluster' in giocomo data
# create new columns in unit table
nwbfile.add_unit_column(
'quality',
'labels given to clusters during manual sorting in phy (1=MUA, '
'2=Good, 3=Unsorted)')
# cluster information
cluster_ids = matfile['sp'][0]['cids'][0][0]
cluster_quality = matfile['sp'][0]['cgs'][0][0]
# spikes in time
spike_times = np.ravel(matfile['sp'][0]['st'][0]) # the time of each spike
spike_cluster = np.ravel(
matfile['sp'][0]['clu'][0]) # the cluster_id that spiked at that time
for i, cluster_id in enumerate(cluster_ids):
unit_spike_times = spike_times[spike_cluster == cluster_id]
waveforms = matfile['sp'][0]['temps'][0][cluster_id]
nwbfile.add_unit(id=int(cluster_id),
spike_times=unit_spike_times,
quality=cluster_quality[i],
waveform_mean=waveforms,
electrode_group=electrode_group)
# Trying to add another Units table to hold the results of the automatic spike sorting
# create TemplateUnits units table
template_units = Units(
name='TemplateUnits',
description='units assigned during automatic spike sorting')
template_units.add_column(
'tempScalingAmps',
'scaling amplitude applied to the template when extracting spike',
index=True)
# information on extracted spike templates
spike_templates = np.ravel(matfile['sp'][0]['spikeTemplates'][0])
spike_template_ids = np.unique(spike_templates)
# template scaling amplitudes
temp_scaling_amps = np.ravel(matfile['sp'][0]['tempScalingAmps'][0])
for i, spike_template_id in enumerate(spike_template_ids):
template_spike_times = spike_times[spike_templates ==
spike_template_id]
temp_scaling_amps_per_template = temp_scaling_amps[spike_templates ==
spike_template_id]
template_units.add_unit(id=int(spike_template_id),
spike_times=template_spike_times,
electrode_group=electrode_group,
tempScalingAmps=temp_scaling_amps_per_template)
# create ecephys processing module
spike_template_module = nwbfile.create_processing_module(
name='ecephys',
description='units assigned during automatic spike sorting')
# add template_units table to processing module
spike_template_module.add(template_units)
print(nwbfile)
print('converted to NWB:N')
print('saving ...')
with NWBHDF5IO(outpath, 'w') as io:
io.write(nwbfile)
print('saved', outpath)
def main():
import os.path
# prerequisites: start
import numpy as np
rate = 10.0
np.random.seed(1234)
data_len = 1000
ephys_data = np.random.rand(data_len)
ephys_timestamps = np.arange(data_len) / rate
spatial_timestamps = ephys_timestamps[::10]
spatial_data = np.cumsum(np.random.normal(size=(2,
len(spatial_timestamps))),
axis=-1).T
# prerequisites: end
# create-nwbfile: start
from datetime import datetime
from dateutil.tz import tzlocal
from pynwb import NWBFile
f = NWBFile(
'the PyNWB tutorial',
'my first synthetic recording',
'EXAMPLE_ID',
datetime.now(tzlocal()),
experimenter='Dr. Bilbo Baggins',
lab='Bag End Laboratory',
institution='University of Middle Earth at the Shire',
experiment_description=
'I went on an adventure with thirteen dwarves to reclaim vast treasures.',
session_id='LONELYMTN')
# create-nwbfile: end
# save-nwbfile: start
from pynwb import NWBHDF5IO
filename = "example.h5"
io = NWBHDF5IO(filename, mode='w')
io.write(f)
io.close()
# save-nwbfile: end
os.remove(filename)
# create-device: start
device = f.create_device(name='trodes_rig123', source="a source")
# create-device: end
# create-electrode-groups: start
electrode_name = 'tetrode1'
source = "an hypothetical source"
description = "an example tetrode"
location = "somewhere in the hippocampus"
electrode_group = f.create_electrode_group(electrode_name,
source=source,
description=description,
location=location,
device=device)
# create-electrode-groups: end
# create-electrode-table-region: start
for idx in [1, 2, 3, 4]:
f.add_electrode(idx,
x=1.0,
y=2.0,
z=3.0,
imp=float(-idx),
location='CA1',
filtering='none',
description='channel %s' % idx,
group=electrode_group)
electrode_table_region = f.create_electrode_table_region(
[0, 2], 'the first and third electrodes')
# create-electrode-table-region: end
# create-timeseries: start
from pynwb.ecephys import ElectricalSeries
from pynwb.behavior import SpatialSeries
ephys_ts = ElectricalSeries(
'test_ephys_data',
'an hypothetical source',
ephys_data,
electrode_table_region,
timestamps=ephys_timestamps,
# Alternatively, could specify starting_time and rate as follows
# starting_time=ephys_timestamps[0],
# rate=rate,
resolution=0.001,
comments=
"This data was randomly generated with numpy, using 1234 as the seed",
description="Random numbers generated with numpy.random.rand")
f.add_acquisition(ephys_ts)
spatial_ts = SpatialSeries(
'test_spatial_timeseries',
'a stumbling rat',
spatial_data,
'origin on x,y-plane',
timestamps=spatial_timestamps,
resolution=0.1,
comments="This data was generated with numpy, using 1234 as the seed",
description="This 2D Brownian process generated with "
"np.cumsum(np.random.normal(size=(2, len(spatial_timestamps))), axis=-1).T"
)
f.add_acquisition(spatial_ts)
# create-timeseries: end
# create-data-interface: start
from pynwb.ecephys import LFP
from pynwb.behavior import Position
lfp = f.add_acquisition(LFP('a hypothetical source'))
ephys_ts = lfp.create_electrical_series(
'test_ephys_data',
'an hypothetical source',
ephys_data,
electrode_table_region,
timestamps=ephys_timestamps,
resolution=0.001,
comments=
"This data was randomly generated with numpy, using 1234 as the seed", # noqa: E501
description="Random numbers generated with numpy.random.rand")
pos = f.add_acquisition(Position('a hypothetical source'))
spatial_ts = pos.create_spatial_series(
'test_spatial_timeseries',
'a stumbling rat',
spatial_data,
'origin on x,y-plane',
timestamps=spatial_timestamps,
resolution=0.1,
comments="This data was generated with numpy, using 1234 as the seed",
description="This 2D Brownian process generated with "
"np.cumsum(np.random.normal(size=(2, len(spatial_timestamps))), axis=-1).T"
) # noqa: E501
# create-data-interface: end
# create-epochs: start
epoch_tags = ('example_epoch', )
f.add_epoch(name='epoch1',
start_time=0.0,
stop_time=1.0,
tags=epoch_tags,
description="the first test epoch",
timeseries=[ephys_ts, spatial_ts])
f.add_epoch(name='epoch2',
start_time=0.0,
stop_time=1.0,
tags=epoch_tags,
description="the second test epoch",
timeseries=[ephys_ts, spatial_ts])
# create-epochs: end
# create-compressed-timeseries: start
from pynwb.ecephys import ElectricalSeries
from pynwb.behavior import SpatialSeries
from pynwb.form.backends.hdf5 import H5DataIO
ephys_ts = ElectricalSeries(
'test_compressed_ephys_data',
'an hypothetical source',
H5DataIO(ephys_data, compress=True),
electrode_table_region,
timestamps=H5DataIO(ephys_timestamps, compress=True),
resolution=0.001,
comments=
"This data was randomly generated with numpy, using 1234 as the seed",
description="Random numbers generated with numpy.random.rand")
f.add_acquisition(ephys_ts)
spatial_ts = SpatialSeries(
'test_compressed_spatial_timeseries',
'a stumbling rat',
H5DataIO(spatial_data, compress=True),
'origin on x,y-plane',
timestamps=H5DataIO(spatial_timestamps, compress=True),
resolution=0.1,
comments="This data was generated with numpy, using 1234 as the seed",
description="This 2D Brownian process generated with "
"np.cumsum(np.random.normal(size=(2, len(spatial_timestamps))), axis=-1).T"
)
f.add_acquisition(spatial_ts)
def conversion_function(source_paths,
f_nwb,
metadata,
add_raw=False,
add_processed=True,
add_behavior=True,
plot_rois=False):
"""
Copy data stored in a set of .npz files to a single NWB file.
Parameters
----------
source_paths : dict
Dictionary with paths to source files/directories. e.g.:
{'raw_data': {'type': 'file', 'path': ''},
'raw_info': {'type': 'file', 'path': ''}
'processed_data': {'type': 'file', 'path': ''},
'sparse_matrix': {'type': 'file', 'path': ''},
'ref_image',: {'type': 'file', 'path': ''}}
f_nwb : str
Path to output NWB file, e.g. 'my_file.nwb'.
metadata : dict
Metadata dictionary
add_raw : bool
Whether to convert raw data or not.
add_processed : bool
Whether to convert processed data or not.
add_behavior : bool
Whether to convert behavior data or not.
plot_rois : bool
Plot ROIs
"""
# Source files
file_raw = None
file_info = None
file_processed = None
file_sparse_matrix = None
file_reference_image = None
for k, v in source_paths.items():
if source_paths[k]['path'] != '':
fname = source_paths[k]['path']
if k == 'raw_data':
file_raw = h5py.File(fname, 'r')
if k == 'raw_info':
file_info = scipy.io.loadmat(fname,
struct_as_record=False,
squeeze_me=True)
if k == 'processed_data':
file_processed = np.load(fname)
if k == 'sparse_matrix':
file_sparse_matrix = np.load(fname)
if k == 'ref_image':
file_reference_image = np.load(fname)
# Initialize a NWB object
nwb = NWBFile(**metadata['NWBFile'])
# Create and add device
device = Device(name=metadata['Ophys']['Device'][0]['name'])
nwb.add_device(device)
# Creates one Imaging Plane for each channel
fs = 1. / (file_processed['time'][0][1] - file_processed['time'][0][0])
for meta_ip in metadata['Ophys']['ImagingPlane']:
# Optical channel
opt_ch = OpticalChannel(
name=meta_ip['optical_channel'][0]['name'],
description=meta_ip['optical_channel'][0]['description'],
emission_lambda=meta_ip['optical_channel'][0]['emission_lambda'])
nwb.create_imaging_plane(
name=meta_ip['name'],
optical_channel=opt_ch,
description=meta_ip['description'],
device=device,
excitation_lambda=meta_ip['excitation_lambda'],
imaging_rate=fs,
indicator=meta_ip['indicator'],
location=meta_ip['location'],
)
# Raw optical data
if add_raw:
print('Adding raw data...')
for meta_tps in metadata['Ophys']['TwoPhotonSeries']:
if meta_tps['name'][-1] == 'R':
raw_data = file_raw['R']
else:
raw_data = file_raw['Y']
def data_gen(data):
xl, yl, zl, tl = data.shape
chunk = 0
while chunk < tl:
val = data[:, :, :, chunk]
chunk += 1
print('adding data chunk: ', chunk)
yield val
xl, yl, zl, tl = raw_data.shape
tps_data = DataChunkIterator(data=data_gen(data=raw_data),
iter_axis=0,
maxshape=(tl, xl, yl, zl))
# Change dimensions from (X,Y,Z,T) in mat file to (T,X,Y,Z) nwb standard
#raw_data = np.moveaxis(raw_data, -1, 0)
tps = TwoPhotonSeries(
name=meta_tps['name'],
imaging_plane=nwb.imaging_planes[meta_tps['imaging_plane']],
data=tps_data,
rate=file_info['info'].daq.scanRate)
nwb.add_acquisition(tps)
# Processed data
if add_processed:
print('Adding processed data...')
ophys_module = ProcessingModule(
name='Ophys',
description='contains optical physiology processed data.',
)
nwb.add_processing_module(ophys_module)
# Create Image Segmentation compartment
img_seg = ImageSegmentation(
name=metadata['Ophys']['ImageSegmentation']['name'])
ophys_module.add(img_seg)
# Create plane segmentation and add ROIs
meta_ps = metadata['Ophys']['ImageSegmentation'][
'plane_segmentations'][0]
ps = img_seg.create_plane_segmentation(
name=meta_ps['name'],
description=meta_ps['description'],
imaging_plane=nwb.imaging_planes[meta_ps['imaging_plane']],
)
# Add ROIs
indices = file_sparse_matrix['indices']
indptr = file_sparse_matrix['indptr']
dims = np.squeeze(file_processed['dims'])
for start, stop in zip(indptr, indptr[1:]):
voxel_mask = make_voxel_mask(indices[start:stop], dims)
ps.add_roi(voxel_mask=voxel_mask)
# Visualize 3D voxel masks
if plot_rois:
plot_rois_function(plane_segmentation=ps, indptr=indptr)
# DFF measures
dff = DfOverF(name=metadata['Ophys']['DfOverF']['name'])
ophys_module.add(dff)
# create ROI regions
n_cells = file_processed['dFF'].shape[0]
roi_region = ps.create_roi_table_region(description='RoiTableRegion',
region=list(range(n_cells)))
# create ROI response series
dff_data = file_processed['dFF']
tt = file_processed['time'].ravel()
meta_rrs = metadata['Ophys']['DfOverF']['roi_response_series'][0]
meta_rrs['data'] = dff_data.T
meta_rrs['rois'] = roi_region
meta_rrs['timestamps'] = tt
dff.create_roi_response_series(**meta_rrs)
# Creates GrayscaleVolume containers and add a reference image
grayscale_volume = GrayscaleVolume(
name=metadata['Ophys']['GrayscaleVolume']['name'],
data=file_reference_image['im'])
ophys_module.add(grayscale_volume)
# Behavior data
if add_behavior:
print('Adding behavior data...')
# Ball motion
behavior_mod = nwb.create_processing_module(
name='Behavior',
description='holds processed behavior data',
)
meta_ts = metadata['Behavior']['TimeSeries'][0]
meta_ts['data'] = file_processed['ball'].ravel()
tt = file_processed['time'].ravel()
meta_ts['timestamps'] = tt
behavior_ts = TimeSeries(**meta_ts)
behavior_mod.add(behavior_ts)
# Re-arranges spatial data of body-points positions tracking
pos = file_processed['dlc']
n_points = 8
pos_reshaped = pos.reshape(
(-1, n_points, 3)) # dims=(nSamples,n_points,3)
# Creates a Position object and add one SpatialSeries for each body-point position
position = Position()
for i in range(n_points):
position.create_spatial_series(
name='SpatialSeries_' + str(i),
data=pos_reshaped[:, i, :],
timestamps=tt,
reference_frame=
'Description defining what the zero-position is.',
conversion=np.nan)
behavior_mod.add(position)
# Trial times
trialFlag = file_processed['trialFlag'].ravel()
trial_inds = np.hstack(
(0, np.where(np.diff(trialFlag))[0], trialFlag.shape[0] - 1))
trial_times = tt[trial_inds]
for start, stop in zip(trial_times, trial_times[1:]):
nwb.add_trial(start_time=start, stop_time=stop)
# Saves to NWB file
with NWBHDF5IO(f_nwb, mode='w') as io:
io.write(nwb)
print('NWB file saved with size: ', os.stat(f_nwb).st_size / 1e6, ' mb')
dimension=[250, 250]))
centroid_fname = 'm655_D11_S1_centroids.mat'
pos_path = os.path.join(base_dir, centroid_fname)
with File(pos_path, 'r') as file:
pos_data = np.array(file['c']).T
spatial_series = SpatialSeries(name='position',
data=pos_data,
starting_time=0.0,
rate=5.0,
units='unknown',
reference_frame='unknown')
pos = Position(name='position',
spatial_series=spatial_series,
starting_time=0.0,
rate=np.nan)
module_pos = nwbfile.create_processing_module(name='position',
description='position')
module_pos.add_container(pos)
data_names = ['cellImages', 'cellTraces', 'centroids', 'eventTimes']
mat_data = {}
mat_output_fpath = os.path.join(base_dir, 'm655_D11_S1_output.mat')
with File(mat_output_fpath, 'r') as file:
for name in data_names:
mat_data[name] = file['output'][name][:]
optical_channel = OpticalChannel('my channel', 'description', np.nan)
def convert_data(self,
nwbfile: NWBFile,
metadata_dict: dict,
stub_test: bool = False,
include_spike_waveforms: bool = False):
session_path = self.input_args['folder_path']
# TODO: check/enforce format?
task_types = metadata_dict['task_types']
subject_path, session_id = os.path.split(session_path)
fpath_base = os.path.split(subject_path)[0]
[nwbfile.add_stimulus(x) for x in get_events(session_path)]
sleep_state_fpath = os.path.join(
session_path, '{}--StatePeriod.mat'.format(session_id))
exist_pos_data = any(
os.path.isfile(
os.path.join(
session_path, '{}__{}.mat'.format(session_id,
task_type['name'])))
for task_type in task_types)
if exist_pos_data:
nwbfile.add_epoch_column('label', 'name of epoch')
for task_type in task_types:
label = task_type['name']
file = os.path.join(session_path,
session_id + '__' + label + '.mat')
if os.path.isfile(file):
pos_obj = Position(name=label + '_position')
matin = loadmat(file)
tt = matin['twhl_norm'][:, 0]
exp_times = find_discontinuities(tt)
if 'conversion' in task_type:
conversion = task_type['conversion']
else:
conversion = np.nan
for pos_type in ('twhl_norm', 'twhl_linearized'):
if pos_type in matin:
pos_data_norm = matin[pos_type][:, 1:]
spatial_series_object = SpatialSeries(
name=label + '_{}_spatial_series'.format(pos_type),
data=H5DataIO(pos_data_norm, compression='gzip'),
reference_frame='unknown',
conversion=conversion,
resolution=np.nan,
timestamps=H5DataIO(tt, compression='gzip'))
pos_obj.add_spatial_series(spatial_series_object)
check_module(
nwbfile, 'behavior',
'contains processed behavioral data').add_data_interface(
pos_obj)
for i, window in enumerate(exp_times):
nwbfile.add_epoch(start_time=window[0],
stop_time=window[1],
label=label + '_' + str(i))
trialdata_path = os.path.join(session_path,
session_id + '__EightMazeRun.mat')
if os.path.isfile(trialdata_path):
trials_data = loadmat(trialdata_path)['EightMazeRun']
trialdatainfo_path = os.path.join(fpath_base,
'EightMazeRunInfo.mat')
trialdatainfo = [
x[0]
for x in loadmat(trialdatainfo_path)['EightMazeRunInfo'][0]
]
features = trialdatainfo[:7]
features[:2] = 'start_time', 'stop_time',
[
nwbfile.add_trial_column(x, 'description')
for x in features[4:] + ['condition']
]
for trial_data in trials_data:
if trial_data[3]:
cond = 'run_left'
else:
cond = 'run_right'
nwbfile.add_trial(start_time=trial_data[0],
stop_time=trial_data[1],
condition=cond,
error_run=trial_data[4],
stim_run=trial_data[5],
both_visit=trial_data[6])
if os.path.isfile(sleep_state_fpath):
matin = loadmat(sleep_state_fpath)['StatePeriod']
table = TimeIntervals(name='states',
description='sleep states of animal')
table.add_column(name='label', description='sleep state')
data = []
for name in matin.dtype.names:
for row in matin[name][0][0]:
data.append({
'start_time': row[0],
'stop_time': row[1],
'label': name
})
[
table.add_row(**row)
for row in sorted(data, key=lambda x: x['start_time'])
]
check_module(nwbfile, 'behavior',
'contains behavioral data').add_data_interface(table)