def copy_sortings(self):
sorter_folders = self.study_folder / 'sorter_folders'
sorting_folders = self.study_folder / 'sortings'
log_olders = self.study_folder / 'sortings' / 'run_log'
log_olders.mkdir(parents=True, exist_ok=True)
for rec_name, sorter_name, output_folder in iter_output_folders(
sorter_folders):
SorterClass = sorter_dict[sorter_name]
fname = rec_name + '[#]' + sorter_name
npz_filename = sorting_folders / (fname + '.npz')
sorting = SorterClass.get_result_from_folder(output_folder)
try:
sorting = SorterClass.get_result_from_folder(output_folder)
NpzSortingExtractor.write_sorting(sorting, npz_filename)
except:
if npz_filename.is_file():
npz_filename.unlink()
if (output_folder / 'spikeinterface_log.json').is_file():
shutil.copyfile(
output_folder / 'spikeinterface_log.json',
sorting_folders / 'run_log' / (fname + '.json'))
self.scan_folder()
def _run_from_folder(cls, output_folder, params, verbose):
import mountainsort4
recording = load_extractor(output_folder /
'spikeinterface_recording.json')
# alias to params
p = params
samplerate = recording.get_sampling_frequency()
# Bandpass filter
if p['filter'] and p['freq_min'] is not None and p[
'freq_max'] is not None:
if verbose:
print('filtering')
recording = bandpass_filter(recording=recording,
freq_min=p['freq_min'],
freq_max=p['freq_max'])
# Whiten
if p['whiten']:
if verbose:
print('whitenning')
recording = whiten(recording=recording)
print(
'Mountainsort4 use the OLD spikeextractors mapped with RecordingExtractorOldAPI'
)
old_api_recording = RecordingExtractorOldAPI(recording)
# Check location no more needed done in basesorter
old_api_sorting = mountainsort4.mountainsort4(
recording=old_api_recording,
detect_sign=p['detect_sign'],
adjacency_radius=p['adjacency_radius'],
clip_size=p['clip_size'],
detect_threshold=p['detect_threshold'],
detect_interval=p['detect_interval'],
num_workers=p['num_workers'],
verbose=verbose)
# Curate
if p['noise_overlap_threshold'] is not None and p['curation'] is True:
if verbose:
print('Curating')
old_api_sorting = mountainsort4.mountainsort4_curation(
recording=old_api_recording,
sorting=old_api_sorting,
noise_overlap_threshold=p['noise_overlap_threshold'])
# convert sorting to new API and save it
unit_ids = old_api_sorting.get_unit_ids()
units_dict_list = [{
u: old_api_sorting.get_unit_spike_train(u)
for u in unit_ids
}]
new_api_sorting = NumpySorting.from_dict(units_dict_list, samplerate)
NpzSortingExtractor.write_sorting(new_api_sorting,
str(output_folder / 'firings.npz'))
def iter_computed_sorting(study_folder):
"""
Iter over sorting files.
"""
sorting_folder = Path(study_folder) / 'sortings'
for filename in os.listdir(sorting_folder):
if filename.endswith('.npz') and '[#]' in filename:
rec_name, sorter_name = filename.replace('.npz', '').split('[#]')
sorting = NpzSortingExtractor(sorting_folder / filename)
yield rec_name, sorter_name, sorting
def _get_result_from_folder(cls, output_folder):
output_folder = Path(output_folder)
result_fname = output_folder / 'firings.npz'
sorting = NpzSortingExtractor(result_fname)
return sorting