def run(self):
from spikeforest import SFMdaRecordingExtractor, SFMdaSortingExtractor
print('test1', self.firings_path, self.samplerate)
sorting = SFMdaSortingExtractor(firings_file=self.firings_path)
samplerate = self.samplerate
max_samples = self.max_samples
max_dt_msec = self.max_dt_msec
bin_size_msec = self.bin_size_msec
max_dt_tp = max_dt_msec * samplerate / 1000
bin_size_tp = bin_size_msec * samplerate / 1000
autocorrelograms = []
for unit_id in sorting.get_unit_ids():
print('Unit::g {}'.format(unit_id))
(bin_counts, bin_edges) = compute_autocorrelogram(sorting.get_unit_spike_train(unit_id), max_dt_tp=max_dt_tp, bin_size_tp=bin_size_tp, max_samples=max_samples)
autocorrelograms.append(dict(
unit_id=unit_id,
bin_counts=bin_counts,
bin_edges=bin_edges
))
ret = dict(
autocorrelograms=autocorrelograms
)
with open(self.json_out, 'w') as f:
json.dump(serialize_np(ret), f)
def load_sorting_results_info(firings_path, *, recording_path, epoch_name, ntrode_name, curated=False):
if not mt.findFile(firings_path):
return None
sorting = SFMdaSortingExtractor(firings_file=firings_path)
total_num_events = 0
for unit_id in sorting.get_unit_ids():
spike_times = sorting.get_unit_spike_train(unit_id=unit_id)
total_num_events = total_num_events + len(spike_times)
return dict(
type='sorting_results',
epoch_name=epoch_name,
ntrode_name=ntrode_name,
curated=curated,
firings_path=firings_path,
recording_path=recording_path,
unit_ids=sorting.get_unit_ids(),
num_events=total_num_events
)
