def test_whiten():
rec, sort = se.example_datasets.toy_example(duration=10, num_channels=4)
rec_w = whiten(rec)
cov_w = np.cov(rec_w.get_traces())
assert np.allclose(cov_w, np.eye(4), atol=0.3)
# should size should not affect
rec_w2 = whiten(rec, chunk_size=30000)
assert np.array_equal(rec_w.get_traces(), rec_w2.get_traces())
def test_whiten():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=10, num_channels=4, seed=0)
rec_w = whiten(rec)
cov_w = np.cov(rec_w.get_traces())
assert np.allclose(cov_w, np.eye(4), atol=0.3)
# should size should not affect
rec_w2 = whiten(rec, chunk_size=30000)
assert np.array_equal(rec_w.get_traces(), rec_w2.get_traces())
check_dumping(rec_w)
shutil.rmtree('test')
def _run(self, recording, output_folder):
recording = recover_recording(recording)
# Sort
# alias to params
p = self.params
if recording.is_filtered and p['filter']:
print(
"Warning! The recording is already filtered, but Mountainsort4 filter is enabled. You can disable "
"filters by setting 'filter' parameter to False")
samplerate = recording.get_sampling_frequency()
# Bandpass filter
if p['filter'] and p['freq_min'] is not None and p[
'freq_max'] is not None:
recording = bandpass_filter(recording=recording,
freq_min=p['freq_min'],
freq_max=p['freq_max'])
# Whiten
if p['whiten']:
recording = whiten(recording=recording)
# Check location no more needed done in basesorter
sorting = ml_ms4alg.mountainsort4(
recording=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=self.verbose)
# Curate
if p['noise_overlap_threshold'] is not None and p['curation'] is True:
if self.verbose:
print('Curating')
sorting = ml_ms4alg.mountainsort4_curation(
recording=recording,
sorting=sorting,
noise_overlap_threshold=p['noise_overlap_threshold'])
se.MdaSortingExtractor.write_sorting(
sorting, str(output_folder / 'firings.mda'))
samplerate_fname = str(output_folder / 'samplerate.txt')
with open(samplerate_fname, 'w') as f:
f.write('{}'.format(samplerate))
def _run(self, recording, output_folder):
# Sort
# alias to params
p = self.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:
recording = bandpass_filter(recording=recording,
freq_min=p['freq_min'],
freq_max=p['freq_max'])
# Whiten
if p['whiten']:
recording = whiten(recording=recording)
# Check location
if 'location' not in recording.get_shared_channel_property_names():
for i, chan in enumerate(recording.get_channel_ids()):
recording.set_channel_property(chan, 'location', [0, i])
sorting = ml_ms4alg.mountainsort4(
recording=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=self.verbose)
# Curate
if p['noise_overlap_threshold'] is not None and p['curation'] is True:
if self.verbose:
print('Curating')
sorting = ml_ms4alg.mountainsort4_curation(
recording=recording,
sorting=sorting,
noise_overlap_threshold=p['noise_overlap_threshold'])
se.MdaSortingExtractor.write_sorting(
sorting, str(output_folder / 'firings.mda'))
samplerate_fname = str(output_folder / 'samplerate.txt')
with open(samplerate_fname, 'w') as f:
f.write('{}'.format(samplerate))
def _run(self, recording, output_folder):
# Sort
# alias to params
p = self.params
print(p)
ind = self.recording_list.index(recording)
# Bandpass filter
if p['filter'] and p['freq_min'] is not None and p[
'freq_max'] is not None:
recording = bandpass_filter(recording=recording,
freq_min=p['freq_min'],
freq_max=p['freq_max'])
# Whiten
if p['whiten']:
recording = whiten(recording=recording)
# Check location
if 'location' not in recording.get_channel_property_names():
for i, chan in enumerate(recording.get_channel_ids()):
recording.set_channel_property(chan, 'location', [0, i])
sorting = ml_ms4alg.mountainsort4(
recording=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'])
# Curate
if p['noise_overlap_threshold'] is not None and p['curation'] is True:
print('Curating')
sorting = ml_ms4alg.mountainsort4_curation(
recording=recording,
sorting=sorting,
noise_overlap_threshold=p['noise_overlap_threshold'])
se.MdaSortingExtractor.write_sorting(
sorting, str(output_folder / 'firings.mda'))