def test_get_licks_failure(self, tmpdir):
stimulus_filepath = self._create_test_stimulus_file(
lick_events=[12, 15, 90, 136, 201], # len(timestamps) == 200,
tmpdir=tmpdir)
stimulus_file = StimulusFile.from_json(
dict_repr={'behavior_stimulus_file': str(stimulus_filepath)})
timestamps = StimulusTimestamps(timestamps=np.arange(0, 2.0, 0.01))
with pytest.raises(IndexError):
Licks.from_stimulus_file(stimulus_file=stimulus_file,
stimulus_timestamps=timestamps)
def test_get_licks_excess(self, tmpdir):
"""
Test that Licks.from_stimulus_file
in the case where
there is an extra frame at the end of the trial log and the mouse
licked on that frame
https://github.com/AllenInstitute/visual_behavior_analysis/blob
/master/visual_behavior/translator/foraging2/extract.py#L640-L647
"""
stimulus_filepath = self._create_test_stimulus_file(
lick_events=[12, 15, 90, 136, 200], # len(timestamps) == 200,
tmpdir=tmpdir)
stimulus_file = StimulusFile.from_json(
dict_repr={'behavior_stimulus_file': str(stimulus_filepath)})
timestamps = StimulusTimestamps(timestamps=np.arange(0, 2.0, 0.01))
licks = Licks.from_stimulus_file(stimulus_file=stimulus_file,
stimulus_timestamps=timestamps)
expected_dict = {
'timestamps': [0.12, 0.15, 0.90, 1.36],
'frame': [12, 15, 90, 136]
}
expected_df = pd.DataFrame(expected_dict)
assert expected_df.columns.equals(licks.value.columns)
np.testing.assert_array_almost_equal(
expected_df.timestamps.to_numpy(),
licks.value['timestamps'].to_numpy(),
decimal=10)
np.testing.assert_array_almost_equal(expected_df.frame.to_numpy(),
licks.value['frame'].to_numpy(),
decimal=10)
def test_from_stimulus_file2(self, tmpdir):
"""
Test that Licks.from_stimulus_file returns a dataframe
of licks whose timestamps are based on their frame number
with respect to the stimulus_timestamps
"""
stimulus_filepath = self._create_test_stimulus_file(
lick_events=[12, 15, 90, 136], tmpdir=tmpdir)
stimulus_file = StimulusFile.from_json(
dict_repr={'behavior_stimulus_file': str(stimulus_filepath)})
timestamps = StimulusTimestamps(timestamps=np.arange(0, 2.0, 0.01))
licks = Licks.from_stimulus_file(stimulus_file=stimulus_file,
stimulus_timestamps=timestamps)
expected_dict = {
'timestamps': [0.12, 0.15, 0.90, 1.36],
'frame': [12, 15, 90, 136]
}
expected_df = pd.DataFrame(expected_dict)
assert expected_df.columns.equals(licks.value.columns)
np.testing.assert_array_almost_equal(
expected_df.timestamps.to_numpy(),
licks.value['timestamps'].to_numpy(),
decimal=10)
np.testing.assert_array_almost_equal(expected_df.frame.to_numpy(),
licks.value['frame'].to_numpy(),
decimal=10)
def from_nwb(cls, nwbfile: NWBFile, **kwargs) -> "BehaviorSession":
behavior_session_id = BehaviorSessionId.from_nwb(nwbfile)
stimulus_timestamps = StimulusTimestamps.from_nwb(nwbfile)
running_acquisition = RunningAcquisition.from_nwb(nwbfile)
raw_running_speed = RunningSpeed.from_nwb(nwbfile, filtered=False)
running_speed = RunningSpeed.from_nwb(nwbfile)
metadata = BehaviorMetadata.from_nwb(nwbfile)
licks = Licks.from_nwb(nwbfile=nwbfile)
rewards = Rewards.from_nwb(nwbfile=nwbfile)
stimuli = Stimuli.from_nwb(nwbfile=nwbfile)
task_parameters = TaskParameters.from_nwb(nwbfile=nwbfile)
trials = TrialTable.from_nwb(nwbfile=nwbfile)
date_of_acquisition = DateOfAcquisition.from_nwb(nwbfile=nwbfile)
return BehaviorSession(behavior_session_id=behavior_session_id,
stimulus_timestamps=stimulus_timestamps,
running_acquisition=running_acquisition,
raw_running_speed=raw_running_speed,
running_speed=running_speed,
metadata=metadata,
licks=licks,
rewards=rewards,
stimuli=stimuli,
task_parameters=task_parameters,
trials=trials,
date_of_acquisition=date_of_acquisition)
def setup_class(cls):
dir = Path(__file__).parent.resolve()
test_data_dir = dir / 'test_data'
cls.stimulus_file = StimulusFile(filepath=test_data_dir /
'behavior_stimulus_file.pkl')
expected = pd.read_pickle(str(test_data_dir / 'licks.pkl'))
cls.expected = Licks(licks=expected)
def setup_class(cls):
dir = Path(__file__).parent.resolve()
test_data_dir = dir / 'test_data'
stimulus_file = StimulusFile(filepath=test_data_dir /
'behavior_stimulus_file.pkl')
ts = StimulusTimestamps.from_stimulus_file(stimulus_file=stimulus_file)
cls.licks = Licks.from_stimulus_file(stimulus_file=stimulus_file,
stimulus_timestamps=ts)
def test_empty_licks(self, tmpdir):
"""
Test that Licks.from_stimulus_file in the case where
there are no licks
"""
stimulus_filepath = self._create_test_stimulus_file(lick_events=[],
tmpdir=tmpdir)
stimulus_file = StimulusFile.from_json(
dict_repr={'behavior_stimulus_file': str(stimulus_filepath)})
timestamps = StimulusTimestamps(timestamps=np.arange(0, 2.0, 0.01))
licks = Licks.from_stimulus_file(stimulus_file=stimulus_file,
stimulus_timestamps=timestamps)
expected_dict = {'timestamps': [], 'frame': []}
expected_df = pd.DataFrame(expected_dict)
assert expected_df.columns.equals(licks.value.columns)
np.testing.assert_array_equal(expected_df.timestamps.to_numpy(),
licks.value['timestamps'].to_numpy())
np.testing.assert_array_equal(expected_df.frame.to_numpy(),
licks.value['frame'].to_numpy())
def _read_data_from_stimulus_file(cls, stimulus_file: StimulusFile,
stimulus_timestamps: StimulusTimestamps,
trial_monitor_delay: float):
"""Helper method to read data from stimulus file"""
licks = Licks.from_stimulus_file(
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps)
rewards = Rewards.from_stimulus_file(
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps)
stimuli = Stimuli.from_stimulus_file(
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps)
task_parameters = TaskParameters.from_stimulus_file(
stimulus_file=stimulus_file)
trials = TrialTable.from_stimulus_file(
stimulus_file=stimulus_file,
stimulus_timestamps=stimulus_timestamps,
licks=licks,
rewards=rewards,
monitor_delay=trial_monitor_delay)
return licks, rewards, stimuli, task_parameters, trials
def test_from_stimulus_file(self):
st = StimulusTimestamps.from_stimulus_file(
stimulus_file=self.stimulus_file)
licks = Licks.from_stimulus_file(stimulus_file=self.stimulus_file,
stimulus_timestamps=st)
assert licks == self.expected