def test_add_stimulus_presentations(nwbfile, stimulus_presentations_behavior,
stimulus_timestamps, roundtrip,
roundtripper,
stimulus_templates: StimulusTemplate):
nwb.add_stimulus_timestamps(nwbfile, stimulus_timestamps)
nwb.add_stimulus_presentations(nwbfile, stimulus_presentations_behavior)
nwb.add_stimulus_template(nwbfile=nwbfile,
stimulus_template=stimulus_templates)
# Add index for this template to NWB in-memory object:
nwb_template = nwbfile.stimulus_template[stimulus_templates.image_set_name]
compare = (stimulus_presentations_behavior['image_set'] ==
nwb_template.name)
curr_stimulus_index = stimulus_presentations_behavior[compare]
nwb.add_stimulus_index(nwbfile, curr_stimulus_index, nwb_template)
if roundtrip:
obt = roundtripper(nwbfile, BehaviorNwbApi)
else:
obt = BehaviorNwbApi.from_nwbfile(nwbfile)
expected = stimulus_presentations_behavior.copy()
expected['is_change'] = [False, True, True, True, True]
obtained = obt.get_stimulus_presentations()
pd.testing.assert_frame_equal(expected[sorted(expected.columns)],
obtained[sorted(obtained.columns)],
check_dtype=False)
def _add_stimulus_templates(nwbfile: NWBFile,
stimulus_templates: StimulusTemplate,
stimulus_presentations: pd.DataFrame):
nwb.add_stimulus_template(nwbfile=nwbfile,
stimulus_template=stimulus_templates)
# Add index for this template to NWB in-memory object:
nwb_template = nwbfile.stimulus_template[
stimulus_templates.image_set_name]
stimulus_index = stimulus_presentations[
stimulus_presentations['image_set'] == nwb_template.name]
nwb.add_stimulus_index(nwbfile, stimulus_index, nwb_template)
return nwbfile
def test_add_stimulus_presentations(nwbfile, stimulus_presentations_behavior, stimulus_timestamps, roundtrip, roundtripper, stimulus_templates):
nwb.add_stimulus_timestamps(nwbfile, stimulus_timestamps)
nwb.add_stimulus_presentations(nwbfile, stimulus_presentations_behavior)
for key, val in stimulus_templates.items():
nwb.add_stimulus_template(nwbfile, val, key)
# Add index for this template to NWB in-memory object:
nwb_template = nwbfile.stimulus_template[key]
curr_stimulus_index = stimulus_presentations_behavior[stimulus_presentations_behavior['image_set'] == nwb_template.name]
nwb.add_stimulus_index(nwbfile, curr_stimulus_index, nwb_template)
if roundtrip:
obt = roundtripper(nwbfile, BehaviorOphysNwbApi)
else:
obt = BehaviorOphysNwbApi.from_nwbfile(nwbfile)
pd.testing.assert_frame_equal(stimulus_presentations_behavior, obt.get_stimulus_presentations(), check_dtype=False)
def to_nwb(self, nwbfile: NWBFile,
stimulus_presentations: Presentations) -> NWBFile:
stimulus_templates = self.value
unwarped_images = []
warped_images = []
image_names = []
for image_name, image_data in stimulus_templates.items():
image_names.append(image_name)
unwarped_images.append(image_data.unwarped)
warped_images.append(image_data.warped)
image_index = np.zeros(len(image_names))
image_index[:] = np.nan
visual_stimulus_image_series = \
StimulusTemplateExtension(
name=stimulus_templates.image_set_name,
data=warped_images,
unwarped=unwarped_images,
control=list(range(len(image_names))),
control_description=image_names,
unit='NA',
format='raw',
timestamps=image_index)
nwbfile.add_stimulus_template(visual_stimulus_image_series)
# Add index for this template to NWB in-memory object:
nwb_template = nwbfile.stimulus_template[
stimulus_templates.image_set_name]
stimulus_index = stimulus_presentations.value[
stimulus_presentations.value[
'image_set'] == nwb_template.name]
nwb.add_stimulus_index(nwbfile, stimulus_index, nwb_template)
return nwbfile
def save(self, session_object):
nwbfile = NWBFile(
session_description=str(session_object.metadata['session_type']),
identifier=str(session_object.ophys_experiment_id),
session_start_time=session_object.metadata['experiment_datetime'],
file_create_date=pytz.utc.localize(datetime.datetime.now()))
# Add stimulus_timestamps to NWB in-memory object:
nwb.add_stimulus_timestamps(nwbfile,
session_object.stimulus_timestamps)
# Add running data to NWB in-memory object:
unit_dict = {
'v_sig': 'V',
'v_in': 'V',
'speed': 'cm/s',
'timestamps': 's',
'dx': 'cm'
}
nwb.add_running_data_df_to_nwbfile(nwbfile,
session_object.running_data_df,
unit_dict)
# Add stimulus template data to NWB in-memory object:
for name, image_data in session_object.stimulus_templates.items():
nwb.add_stimulus_template(nwbfile, image_data, name)
# Add index for this template to NWB in-memory object:
nwb_template = nwbfile.stimulus_template[name]
stimulus_index = session_object.stimulus_presentations[
session_object.stimulus_presentations['image_set'] ==
nwb_template.name]
nwb.add_stimulus_index(nwbfile, stimulus_index, nwb_template)
# Add stimulus presentations data to NWB in-memory object:
nwb.add_stimulus_presentations(nwbfile,
session_object.stimulus_presentations)
# Add trials data to NWB in-memory object:
nwb.add_trials(nwbfile, session_object.trials,
TRIAL_COLUMN_DESCRIPTION_DICT)
# Add licks data to NWB in-memory object:
if len(session_object.licks) > 0:
nwb.add_licks(nwbfile, session_object.licks)
# Add rewards data to NWB in-memory object:
if len(session_object.rewards) > 0:
nwb.add_rewards(nwbfile, session_object.rewards)
# Add max_projection image data to NWB in-memory object:
nwb.add_max_projection(nwbfile, session_object.max_projection)
# Add average_image image data to NWB in-memory object:
nwb.add_average_image(nwbfile, session_object.average_projection)
# Add segmentation_mask_image image data to NWB in-memory object:
nwb.add_segmentation_mask_image(nwbfile,
session_object.segmentation_mask_image)
# Add metadata to NWB in-memory object:
nwb.add_metadata(nwbfile, session_object.metadata)
# Add task parameters to NWB in-memory object:
nwb.add_task_parameters(nwbfile, session_object.task_parameters)
# Add roi metrics to NWB in-memory object:
nwb.add_cell_specimen_table(nwbfile,
session_object.cell_specimen_table)
# Add dff to NWB in-memory object:
nwb.add_dff_traces(nwbfile, session_object.dff_traces,
session_object.ophys_timestamps)
# Add corrected_fluorescence to NWB in-memory object:
nwb.add_corrected_fluorescence_traces(
nwbfile, session_object.corrected_fluorescence_traces)
# Add motion correction to NWB in-memory object:
nwb.add_motion_correction(nwbfile, session_object.motion_correction)
# Write the file:
with NWBHDF5IO(self.path, 'w') as nwb_file_writer:
nwb_file_writer.write(nwbfile)
return nwbfile