def _find_stimulus_presentation_group(
nwb_file,
stimulus_name,
base_path=_STIMULUS_PRESENTATION_PATH,
group_patterns=_STIMULUS_PRESENTATION_PATTERNS):
''' Searches an NWB file for a stimulus presentation group.
Parameters
----------
nwb_file : h5py.File
File to search
stimulus_name : str
Identifier for this stimulus. Corresponds to the relative name of its h5
group.
base_path : str, optional
Begin the search from here. Defaults to 'stimulus/presentation'
group_patterns : array-like of str, optional
Patterns for the relative name of the stimulus' h5 group. Defaults to
the name, and the name suffixed by '_stimulus'
Returns
-------
h5py.Group, h5py.Dataset :
h5 object found
'''
group_candidates = [
pattern.format(stimulus_name) for pattern in group_patterns
]
matcher = functools.partial(h5_utilities.h5_object_matcher_relname_in,
group_candidates)
matches = h5_utilities.locate_h5_objects(matcher, nwb_file, base_path)
if len(matches) == 0:
raise MissingStimulusException(
'Unable to locate stimulus: {}. '
'Looked for this stimulus under the names: {} '.format(
stimulus_name, group_candidates))
if len(matches) > 1:
raise MissingStimulusException(
'Unable to locate stimulus: {}. '
'Found multiple matching stimuli: {}'.format(
stimulus_name, [match.name for match in matches]))
return matches[0]
def _get_abstract_feature_series_stimulus_table(nwb_file, stimulus_name):
''' Return the a stimulus table for an abstract feature series.
Returns
-------
stimulus table: pd.DataFrame
'''
k = "stimulus/presentation/%s" % stimulus_name
with h5py.File(nwb_file, 'r') as f:
if k not in f:
raise MissingStimulusException("Stimulus not found: %s" %
stimulus_name)
stim_data = f[k + '/data'].value
features = [v.decode('UTF-8') for v in f[k + '/features'].value]
frame_dur = f[k + '/frame_duration'].value
stimulus_table = pd.DataFrame(stim_data, columns=features)
stimulus_table.loc[:, 'start'] = frame_dur[:, 0].astype(int)
stimulus_table.loc[:, 'end'] = frame_dur[:, 1].astype(int)
return stimulus_table
def _get_indexed_time_series_stimulus_table(nwb_file, stimulus_name):
''' Return the a stimulus table for an indexed time series.
Returns
-------
stimulus table: pd.DataFrame
'''
k = "stimulus/presentation/%s" % stimulus_name
with h5py.File(nwb_file, 'r') as f:
if k not in f:
k = "stimulus/presentation/%s" % (stimulus_name + "_stimulus")
if k not in f:
raise MissingStimulusException("Stimulus not found: %s" %
stimulus_name)
inds = f[k + '/data'].value
frame_dur = f[k + '/frame_duration'].value
stimulus_table = pd.DataFrame(inds, columns=['frame'])
stimulus_table.loc[:, 'start'] = frame_dur[:, 0].astype(int)
stimulus_table.loc[:, 'end'] = frame_dur[:, 1].astype(int)
return stimulus_table