def from_data_file(cls,
data_file: EyeTrackingFile,
sync_file: SyncFile,
z_threshold: float = 3.0,
dilation_frames: int = 2) -> "EyeTrackingTable":
"""
Parameters
----------
data_file
sync_file
z_threshold : float, optional
See EyeTracking.from_lims
dilation_frames : int, optional
See EyeTracking.from_lims
"""
cls._logger.info(f"Getting eye_tracking_data with "
f"'z_threshold={z_threshold}', "
f"'dilation_frames={dilation_frames}'")
sync_path = Path(sync_file.filepath)
frame_times = sync_utilities.get_synchronized_frame_times(
session_sync_file=sync_path,
sync_line_label_keys=Dataset.EYE_TRACKING_KEYS,
trim_after_spike=False)
eye_tracking_data = process_eye_tracking_data(data_file.data,
frame_times, z_threshold,
dilation_frames)
return EyeTrackingTable(eye_tracking=eye_tracking_data)
def test_get_synchronized_frame_times(monkeypatch, mock_dataset_fixture,
sync_line_label_keys, expected):
monkeypatch.setattr(su, "Dataset", mock_dataset_fixture)
obtained = su.get_synchronized_frame_times("dummy_path",
sync_line_label_keys)
assert np.allclose(obtained, expected)
def load_sync_file_timings(sync_file: Path,
pupil_params_rows: int) -> pd.Series:
"""Load sync file timings from .h5 file.
Parameters
----------
sync_file : Path
Path to .h5 sync file.
pupil_params_rows : int
Number of rows in pupil params.
Returns
-------
pd.Series
A series of frame times. (New frame times according to synchronized
timings from DAQ)
Raises
------
RuntimeError
If the number of eye tracking frames (pupil_params_rows) does not match
up with number of new frame times from the sync file.
"""
# Add synchronized frame times
frame_times = su.get_synchronized_frame_times(
session_sync_file=sync_file,
sync_line_label_keys=Dataset.EYE_TRACKING_KEYS)
if (pupil_params_rows != len(frame_times)):
raise RuntimeError("The number of camera sync pulses in the "
f"sync file ({len(frame_times)}) do not match "
"with the number of eye tracking frames "
f"({pupil_params_rows})!!!")
return frame_times
def get_eye_tracking(self,
z_threshold: float = 3.0,
dilation_frames: int = 2) -> Optional[pd.DataFrame]:
"""Gets corneal, eye, and pupil ellipse fit data
Parameters
----------
z_threshold : float, optional
The z-threshold when determining which frames likely contain
outliers for eye or pupil areas. Influences which frames
are considered 'likely blinks'. By default 3.0
dilation_frames : int, optional
Determines the number of additional adjacent frames to mark as
'likely_blink', by default 2.
Returns
-------
Optional[pd.DataFrame]
*_area
*_center_x
*_center_y
*_height
*_phi
*_width
likely_blink
where "*" can be "corneal", "pupil" or "eye"
Will return None if class attr _skip_eye_tracking is True.
"""
if self._skip_eye_tracking:
return None
self.logger.info(f"Getting eye_tracking_data with "
f"'z_threshold={z_threshold}', "
f"'dilation_frames={dilation_frames}'")
filepath = Path(self.extractor.get_eye_tracking_filepath())
sync_path = Path(self.extractor.get_sync_file())
eye_tracking_data = load_eye_tracking_hdf(filepath)
frame_times = sync_utilities.get_synchronized_frame_times(
session_sync_file=sync_path,
sync_line_label_keys=Dataset.EYE_TRACKING_KEYS,
trim_after_spike=False)
eye_tracking_data = process_eye_tracking_data(eye_tracking_data,
frame_times,
z_threshold,
dilation_frames)
return eye_tracking_data
def get_eye_tracking(self,
z_threshold: float = 3.0,
dilation_frames: int = 2):
logger = logging.getLogger("BehaviorOphysLimsApi")
logger.info(f"Getting eye_tracking_data with "
f"'z_threshold={z_threshold}', "
f"'dilation_frames={dilation_frames}'")
filepath = Path(self.get_eye_tracking_filepath())
sync_path = Path(self.get_sync_file())
eye_tracking_data = load_eye_tracking_hdf(filepath)
frame_times = sync_utilities.get_synchronized_frame_times(
session_sync_file=sync_path,
sync_line_label_keys=Dataset.EYE_TRACKING_KEYS)
eye_tracking_data = process_eye_tracking_data(eye_tracking_data,
frame_times, z_threshold,
dilation_frames)
return eye_tracking_data
def write_ecephys_nwb(output_path,
session_id,
session_start_time,
stimulus_table_path,
invalid_epochs,
probes,
running_speed_path,
session_sync_path,
eye_tracking_rig_geometry,
eye_dlc_ellipses_path,
eye_gaze_mapping_path,
pool_size,
optotagging_table_path=None,
session_metadata=None,
**kwargs):
nwbfile = pynwb.NWBFile(
session_description='Data and metadata for an Ecephys session',
identifier=f"{session_id}",
session_id=f"{session_id}",
session_start_time=session_start_time,
institution="Allen Institute for Brain Science")
if session_metadata is not None:
nwbfile = add_metadata_to_nwbfile(nwbfile, session_metadata)
stimulus_columns_to_drop = [
"colorSpace", "depth", "interpolate", "pos", "rgbPedestal", "tex",
"texRes", "flipHoriz", "flipVert", "rgb", "signalDots"
]
stimulus_table = read_stimulus_table(
stimulus_table_path, columns_to_drop=stimulus_columns_to_drop)
nwbfile = add_stimulus_timestamps(
nwbfile, stimulus_table['start_time'].values
) # TODO: patch until full timestamps are output by stim table module
nwbfile = add_stimulus_presentations(nwbfile, stimulus_table)
nwbfile = add_invalid_times(nwbfile, invalid_epochs)
if optotagging_table_path is not None:
optotagging_table = pd.read_csv(optotagging_table_path)
nwbfile = add_optotagging_table_to_nwbfile(nwbfile, optotagging_table)
nwbfile = add_probewise_data_to_nwbfile(nwbfile, probes)
running_speed, raw_running_data = read_running_speed(running_speed_path)
add_running_speed_to_nwbfile(nwbfile, running_speed)
add_raw_running_data_to_nwbfile(nwbfile, raw_running_data)
add_eye_tracking_rig_geometry_data_to_nwbfile(nwbfile,
eye_tracking_rig_geometry)
# Collect eye tracking/gaze mapping data from files
eye_tracking_frame_times = su.get_synchronized_frame_times(
session_sync_file=session_sync_path,
sync_line_label_keys=Dataset.EYE_TRACKING_KEYS)
eye_dlc_tracking_data = read_eye_dlc_tracking_ellipses(
Path(eye_dlc_ellipses_path))
if eye_gaze_mapping_path:
eye_gaze_data = read_eye_gaze_mappings(Path(eye_gaze_mapping_path))
else:
eye_gaze_data = None
add_eye_tracking_data_to_nwbfile(nwbfile, eye_tracking_frame_times,
eye_dlc_tracking_data, eye_gaze_data)
Manifest.safe_make_parent_dirs(output_path)
with pynwb.NWBHDF5IO(output_path, mode='w') as io:
logging.info(f"writing session nwb file to {output_path}")
io.write(nwbfile, cache_spec=True)
probes_with_lfp = [p for p in probes if p["lfp"] is not None]
probe_outputs = write_probewise_lfp_files(probes_with_lfp,
session_id,
session_metadata,
session_start_time,
pool_size=pool_size)
return {'nwb_path': output_path, "probe_outputs": probe_outputs}
def write_ecephys_nwb(output_path,
session_id,
session_start_time,
stimulus_table_path,
invalid_epochs,
probes,
running_speed_path,
session_sync_path,
eye_tracking_rig_geometry,
eye_dlc_ellipses_path,
eye_gaze_mapping_path,
pool_size,
optotagging_table_path=None,
session_metadata=None,
**kwargs):
nwbfile = pynwb.NWBFile(session_description='EcephysSession',
identifier='{}'.format(session_id),
session_start_time=session_start_time)
if session_metadata is not None:
nwbfile = add_metadata_to_nwbfile(nwbfile, session_metadata)
stimulus_table = read_stimulus_table(stimulus_table_path)
nwbfile = add_stimulus_timestamps(
nwbfile, stimulus_table['start_time'].values
) # TODO: patch until full timestamps are output by stim table module
nwbfile = add_stimulus_presentations(nwbfile, stimulus_table)
nwbfile = add_invalid_times(nwbfile, invalid_epochs)
if optotagging_table_path is not None:
optotagging_table = pd.read_csv(optotagging_table_path)
nwbfile = add_optotagging_table_to_nwbfile(nwbfile, optotagging_table)
nwbfile = add_probewise_data_to_nwbfile(nwbfile, probes)
running_speed, raw_running_data = read_running_speed(running_speed_path)
add_running_speed_to_nwbfile(nwbfile, running_speed)
add_raw_running_data_to_nwbfile(nwbfile, raw_running_data)
# --- Add eye tracking ellipse fits to nwb file ---
eye_tracking_frame_times = su.get_synchronized_frame_times(
session_sync_file=session_sync_path,
sync_line_label_keys=Dataset.EYE_TRACKING_KEYS)
eye_dlc_tracking_data = read_eye_dlc_tracking_ellipses(
Path(eye_dlc_ellipses_path))
if eye_tracking_data_is_valid(eye_dlc_tracking_data=eye_dlc_tracking_data,
synced_timestamps=eye_tracking_frame_times):
add_eye_tracking_ellipse_fit_data_to_nwbfile(
nwbfile,
eye_dlc_tracking_data=eye_dlc_tracking_data,
synced_timestamps=eye_tracking_frame_times)
# --- Append eye tracking rig geometry info to nwb file (with eye tracking) ---
append_eye_tracking_rig_geometry_data_to_nwbfile(
nwbfile, eye_tracking_rig_geometry=eye_tracking_rig_geometry)
# --- Add gaze mapped positions to nwb file ---
if eye_gaze_mapping_path:
eye_gaze_data = read_eye_gaze_mappings(Path(eye_gaze_mapping_path))
add_eye_gaze_mapping_data_to_nwbfile(nwbfile,
eye_gaze_data=eye_gaze_data)
Manifest.safe_make_parent_dirs(output_path)
io = pynwb.NWBHDF5IO(output_path, mode='w')
logging.info(f"writing session nwb file to {output_path}")
io.write(nwbfile)
io.close()
probes_with_lfp = [p for p in probes if p["lfp"] is not None]
probe_outputs = write_probewise_lfp_files(probes_with_lfp,
session_start_time,
pool_size=pool_size)
return {'nwb_path': output_path, "probe_outputs": probe_outputs}
