def test_cache_extractor(self):
cache_rec = se.CacheRecordingExtractor(self.RX)
check_recording_return_types(cache_rec)
check_recordings_equal(self.RX, cache_rec)
cache_rec.move_to('cache_rec')
assert cache_rec.filename == 'cache_rec.dat'
check_dumping(cache_rec)
cache_rec = se.CacheRecordingExtractor(self.RX, save_path='cache_rec2')
check_recording_return_types(cache_rec)
check_recordings_equal(self.RX, cache_rec)
assert cache_rec.filename == 'cache_rec2.dat'
check_dumping(cache_rec)
# test saving to file
del cache_rec
assert Path('cache_rec2.dat').is_file()
# test tmp
cache_rec = se.CacheRecordingExtractor(self.RX)
tmp_file = cache_rec.filename
del cache_rec
assert not Path(tmp_file).is_file()
cache_sort = se.CacheSortingExtractor(self.SX)
check_sorting_return_types(cache_sort)
check_sortings_equal(self.SX, cache_sort)
cache_sort.move_to('cache_sort')
assert cache_sort.filename == 'cache_sort.npz'
check_dumping(cache_sort)
# test saving to file
del cache_sort
assert Path('cache_sort.npz').is_file()
cache_sort = se.CacheSortingExtractor(self.SX, save_path='cache_sort2')
check_sorting_return_types(cache_sort)
check_sortings_equal(self.SX, cache_sort)
assert cache_sort.filename == 'cache_sort2.npz'
check_dumping(cache_sort)
# test saving to file
del cache_sort
assert Path('cache_sort2.npz').is_file()
# test tmp
cache_sort = se.CacheSortingExtractor(self.SX)
tmp_file = cache_sort.filename
del cache_sort
assert not Path(tmp_file).is_file()
# cleanup
os.remove('cache_rec.dat')
os.remove('cache_rec2.dat')
os.remove('cache_sort.npz')
os.remove('cache_sort2.npz')
def save_si_object(object_name: str, si_object, output_folder,
cache_raw=False, include_properties=True, include_features=False):
"""
Save an arbitrary SI object to a temprary location for NWB conversion.
Parameters
----------
object_name: str
The unique name of the SpikeInterface object.
si_object: RecordingExtractor or SortingExtractor
The extractor to be saved.
output_folder: str or Path
The folder where the object is saved.
cache_raw: bool
If True, the Extractor is cached to a binary file (not recommended for RecordingExtractor objects)
(default False).
include_properties: bool
If True, properties (channel or unit) are saved (default True).
include_features: bool
If True, spike features are saved (default False)
"""
Path(output_folder).mkdir(parents=True, exist_ok=True)
if isinstance(si_object, se.RecordingExtractor):
if not si_object.is_dumpable:
cache = se.CacheRecordingExtractor(si_object, save_path=output_folder / "raw.dat")
elif cache_raw:
# save to json before caching to keep history (in case it's needed)
json_file = output_folder / f"{object_name}.json"
si_object.dump_to_json(output_folder / json_file)
cache = se.CacheRecordingExtractor(si_object, save_path=output_folder / "raw.dat")
else:
cache = si_object
elif isinstance(si_object, se.SortingExtractor):
if not si_object.is_dumpable:
cache = se.CacheSortingExtractor(si_object, save_path=output_folder / "sorting.npz")
elif cache_raw:
# save to json before caching to keep history (in case it's needed)
json_file = output_folder / f"{object_name}.json"
si_object.dump_to_json(output_folder / json_file)
cache = se.CacheSortingExtractor(si_object, save_path=output_folder / "sorting.npz")
else:
cache = si_object
else:
raise ValueError("The 'si_object' argument shoulde be a SpikeInterface Extractor!")
pkl_file = output_folder / f"{object_name}.pkl"
cache.dump_to_pickle(
output_folder / pkl_file,
include_properties=include_properties,
include_features=include_features
)
def test_convert_sorting_extractor_to_nwb(self, se_class, dataset_path, se_kwargs):
print(f"\n\n\n TESTING {se_class.extractor_name}...")
dataset_stem = Path(dataset_path).stem
self.dataset.get(dataset_path)
sorting = se_class(**se_kwargs)
sf = sorting.get_sampling_frequency()
if sf is None: # need to set dummy sampling frequency since no associated acquisition in file
sf = 30000
sorting.set_sampling_frequency(sf)
if test_nwb:
nwb_save_path = self.savedir / f"{se_class.__name__}_test_{dataset_stem}.nwb"
se.NwbSortingExtractor.write_sorting(sorting, nwb_save_path)
nwb_sorting = se.NwbSortingExtractor(nwb_save_path, sampling_frequency=sf)
check_sortings_equal(sorting, nwb_sorting)
if test_caching:
sort_cache = se.CacheSortingExtractor(sorting)
check_sortings_equal(sorting, sort_cache)
