def test_add_rewards(nwbfile, roundtrip, roundtripper, rewards):
nwb.add_rewards(nwbfile, rewards)
if roundtrip:
obt = roundtripper(nwbfile, BehaviorOphysNwbApi)
else:
obt = BehaviorOphysNwbApi.from_nwbfile(nwbfile)
pd.testing.assert_frame_equal(rewards, obt.get_rewards(),
check_dtype=False)
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
def save(self, session_object):
# Cannot type session_object due to a circular dependency
# TODO fix circular dependency and add type
session_metadata: BehaviorOphysMetadata = \
session_object.api.get_metadata()
session_type = session_metadata.session_type
nwbfile = NWBFile(
session_description=session_type,
identifier=str(session_object.ophys_experiment_id),
session_start_time=session_metadata.date_of_acquisition,
file_create_date=pytz.utc.localize(datetime.datetime.now()),
institution="Allen Institute for Brain Science",
keywords=[
"2-photon", "calcium imaging", "visual cortex", "behavior",
"task"
],
experiment_description=get_expt_description(session_type))
# Add stimulus_timestamps to NWB in-memory object:
nwb.add_stimulus_timestamps(nwbfile,
session_object.stimulus_timestamps)
# Add running acquisition ('dx', 'v_sig', 'v_in') data to NWB
# This data should be saved to NWB but not accessible directly from
# Sessions
nwb.add_running_acquisition_to_nwbfile(
nwbfile, session_object.api.get_running_acquisition_df())
# Add running data to NWB in-memory object:
nwb.add_running_speed_to_nwbfile(nwbfile,
session_object.running_speed,
name="speed",
from_dataframe=True)
nwb.add_running_speed_to_nwbfile(nwbfile,
session_object.raw_running_speed,
name="speed_unfiltered",
from_dataframe=True)
# Add stimulus template data to NWB in-memory object:
# Use the semi-private _stimulus_templates attribute because it is
# a StimulusTemplate object. The public stimulus_templates property
# of the session_object returns a DataFrame.
session_stimulus_templates = session_object._stimulus_templates
self._add_stimulus_templates(
nwbfile=nwbfile,
stimulus_templates=session_stimulus_templates,
stimulus_presentations=session_object.stimulus_presentations)
# search for omitted rows and add stop_time before writing to NWB file
set_omitted_stop_time(
stimulus_table=session_object.stimulus_presentations)
# 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, behavior_only=False)
# 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,
session_object.metadata)
# 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)
# Add eye tracking and rig geometry to NWB in-memory object
# if eye_tracking data exists.
if session_object.eye_tracking is not None:
self.add_eye_tracking_data_to_nwb(
nwbfile, session_object.eye_tracking,
session_object.eye_tracking_rig_geometry)
# Add events
self.add_events(nwbfile=nwbfile, events=session_object.events)
# Write the file:
with NWBHDF5IO(self.path, 'w') as nwb_file_writer:
nwb_file_writer.write(nwbfile)
return nwbfile
def save(self, session_object):
session_metadata: BehaviorMetadata = \
session_object.api.get_metadata()
session_type = str(session_metadata.session_type)
nwbfile = NWBFile(
session_description=session_type,
identifier=str(session_object.behavior_session_id),
session_start_time=session_metadata.date_of_acquisition,
file_create_date=pytz.utc.localize(datetime.datetime.now()),
institution="Allen Institute for Brain Science",
keywords=["visual", "behavior", "task"],
experiment_description=get_expt_description(session_type))
# Add stimulus_timestamps to NWB in-memory object:
nwb.add_stimulus_timestamps(nwbfile,
session_object.stimulus_timestamps)
# Add running acquisition ('dx', 'v_sig', 'v_in') data to NWB
# This data should be saved to NWB but not accessible directly from
# Sessions
nwb.add_running_acquisition_to_nwbfile(
nwbfile, session_object.api.get_running_acquisition_df())
# Add running data to NWB in-memory object:
nwb.add_running_speed_to_nwbfile(nwbfile,
session_object.running_speed,
name="speed",
from_dataframe=True)
nwb.add_running_speed_to_nwbfile(nwbfile,
session_object.raw_running_speed,
name="speed_unfiltered",
from_dataframe=True)
# Add stimulus template data to NWB in-memory object:
# Use the semi-private _stimulus_templates attribute because it is
# a StimulusTemplate object. The public stimulus_templates property
# of the session_object returns a DataFrame.
session_stimulus_templates = session_object._stimulus_templates
self._add_stimulus_templates(
nwbfile=nwbfile,
stimulus_templates=session_stimulus_templates,
stimulus_presentations=session_object.stimulus_presentations)
# search for omitted rows and add stop_time before writing to NWB file
set_omitted_stop_time(
stimulus_table=session_object.stimulus_presentations)
# 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 metadata to NWB in-memory object:
nwb.add_metadata(nwbfile, session_object.metadata, behavior_only=True)
# Add task parameters to NWB in-memory object:
nwb.add_task_parameters(nwbfile, session_object.task_parameters)
# Write the file:
with NWBHDF5IO(self.path, 'w') as nwb_file_writer:
nwb_file_writer.write(nwbfile)
return nwbfile