def test_init(self):
sS = SpatialSeries('test_sS',
np.ones((2, 2)),
'reference_frame',
timestamps=[1., 2., 3.])
cd = CompassDirection(sS)
self.assertEqual(cd.spatial_series['test_sS'], sS)
def test_init(self):
sS = SpatialSeries('test_sS',
'a hypothetical source',
list(),
'reference_frame',
timestamps=list())
cd = CompassDirection('test_cd', sS)
self.assertEqual(cd.source, 'test_cd')
self.assertEqual(cd.spatial_series, [sS])
def test_init(self):
sS = SpatialSeries('test_sS', list(), 'reference_frame', timestamps=list())
cd = CompassDirection(sS)
self.assertEqual(cd.spatial_series['test_sS'], sS)
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")