def test_process_eye_tracking_data_raises_on_sync_error(eye_tracking_df,
frame_times):
"""
Test that an error is raised when the number of sync timestamps exceeds
the number of eye tracking frames by more than 15
"""
with pytest.raises(RuntimeError, match='Error! The number of sync file'):
process_eye_tracking_data(eye_tracking_df, frame_times)
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_process_eye_tracking_data_truncation(eye_tracking_df,
frame_times):
"""
Test that the array of sync times is truncated when the number
of raw sync timestamps exceeds the numer of eye tracking frames
by <= 15
"""
df = process_eye_tracking_data(eye_tracking_df, frame_times)
np.testing.assert_array_almost_equal(df.timestamps.to_numpy(),
np.array([0.0, 0.1]),
decimal=10)
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
len(e)
s.line_labels()
s.line_labels
len(e.index)
fts.shape
len(e.index) - fts.shape
len(e.index) - fts.shape[0]
e = sdk_eye.load_eye_tracking_hdf(r"\\allen\programs\braintv\production\visualbehavior\prod0\specimen_1035469407\ecephys_session_1055240613\eye_tracking\1055240613_ellipse.h5")
s = sync_dataset(r"\\allen\programs\braintv\production\visualbehavior\prod0\specimen_1035469407\ecephys_session_1055240613\1055324726\1055240613_533539_20201007.sync")
fts = s.get_rising_edges('eye_frame_received')
fts.shape
len(e)
e = e.iloc[1:]
len(e)
fts = pd.Series(fts)
e_p = sdk_eye.process_eye_tracking_data(e, fts)
e_p.keys()
plt.plot(e_p['time'], e_p['pupil_area'])
plt.plot(e_p['time'], e_p['pupil_center_x'])
pa = e_p['pupil_area'].loc[~e_p['likely_blink']]
e_p['likely_blink'].head()
np.sum(e_p['likely_blink'])
len(e_p)
pa = e_p['pupil_area'].loc[!e_p['likely_blink']]
pa = e_p['pupil_area'].loc[e_p['likely_blink']]
plt.plot(e_p['time'], e_p['pupil_area'])
plt.plot(e_p['time'], e_p['likely_blink']*100)
plt.plot(e_p['time'], e_p['likely_blink']*100000)
pa = e_p['pupil_area'].values
pa[e_p['likely_blink']] = np.nan
pa[e_p['likely_blink'].values] = np.nan
def test_process_eye_tracking_data(eye_tracking_df, frame_times, expected):
obtained = process_eye_tracking_data(eye_tracking_df, frame_times)
pd.testing.assert_frame_equal(obtained, expected)
def test_process_eye_tracking_data(eye_tracking_df, frame_times, expected):
obtained = process_eye_tracking_data(eye_tracking_df, frame_times)
assert expected.equals(obtained)
def test_process_eye_tracking_data_raises_on_sync_error(
eye_tracking_df, frame_times):
with pytest.raises(RuntimeError, match='Error! The number of sync file'):
_ = process_eye_tracking_data(eye_tracking_df, frame_times)