def test_bandpass_filter_with_cache():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
seed=0)
rec_filtered = bandpass_filter(rec,
freq_min=5000,
freq_max=10000,
cache_to_file=True,
chunk_size=10000)
rec_filtered2 = bandpass_filter(rec,
freq_min=5000,
freq_max=10000,
cache_to_file=True,
chunk_size=None)
rec_filtered3 = bandpass_filter(rec,
freq_min=5000,
freq_max=10000,
cache_chunks=True,
chunk_size=10000)
rec_filtered3.get_traces()
assert rec_filtered3._filtered_cache_chunks.get('0') is not None
rec_filtered4 = bandpass_filter(rec,
freq_min=5000,
freq_max=10000,
cache_chunks=True,
chunk_size=None)
assert np.allclose(rec_filtered.get_traces(),
rec_filtered2.get_traces(),
rtol=1e-02,
atol=1e-02)
assert np.allclose(rec_filtered.get_traces(),
rec_filtered3.get_traces(),
rtol=1e-02,
atol=1e-02)
assert np.allclose(rec_filtered.get_traces(),
rec_filtered4.get_traces(),
rtol=1e-02,
atol=1e-02)
check_dumping(rec_filtered)
check_dumping(rec_filtered2)
check_dumping(rec_filtered3)
check_dumping(rec_filtered4)
shutil.rmtree('test')
def test_bandpass_filter():
rec, sort = se.example_datasets.toy_example(duration=10, num_channels=4)
rec_fft = bandpass_filter(rec, freq_min=5000, freq_max=10000, type='fft')
assert check_signal_power_signal1_below_signal2(
rec_fft.get_traces(),
rec.get_traces(),
freq_range=[1000, 5000],
fs=rec.get_sampling_frequency())
assert check_signal_power_signal1_below_signal2(
rec_fft.get_traces(),
rec.get_traces(),
freq_range=[10000, 15000],
fs=rec.get_sampling_frequency())
rec_sci = bandpass_filter(rec,
freq_min=3000,
freq_max=6000,
type='butter',
order=3)
assert check_signal_power_signal1_below_signal2(
rec_sci.get_traces(),
rec.get_traces(),
freq_range=[1000, 3000],
fs=rec.get_sampling_frequency())
assert check_signal_power_signal1_below_signal2(
rec_sci.get_traces(),
rec.get_traces(),
freq_range=[6000, 10000],
fs=rec.get_sampling_frequency())
rec_cache = bandpass_filter(rec,
freq_min=3000,
freq_max=6000,
type='butter',
order=3,
cache_to_file=True)
assert check_signal_power_signal1_below_signal2(
rec_cache.get_traces(),
rec.get_traces(),
freq_range=[1000, 3000],
fs=rec.get_sampling_frequency())
assert check_signal_power_signal1_below_signal2(
rec_cache.get_traces(),
rec.get_traces(),
freq_range=[6000, 10000],
fs=rec.get_sampling_frequency())
def test_highpass_filter():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=2,
num_channels=4,
seed=0)
rec_fft = highpass_filter(rec, freq_min=5000, filter_type='fft')
assert check_signal_power_signal1_below_signal2(
rec_fft.get_traces(),
rec.get_traces(),
freq_range=[1000, 5000],
fs=rec.get_sampling_frequency())
rec_sci = bandpass_filter(rec,
freq_min=3000,
freq_max=6000,
filter_type='butter',
order=3)
assert check_signal_power_signal1_below_signal2(
rec_sci.get_traces(),
rec.get_traces(),
freq_range=[1000, 3000],
fs=rec.get_sampling_frequency())
traces = rec.get_traces().astype('uint16')
rec_u = se.NumpyRecordingExtractor(
traces, sampling_frequency=rec.get_sampling_frequency())
rec_fu = bandpass_filter(rec_u,
freq_min=5000,
freq_max=10000,
filter_type='fft')
assert check_signal_power_signal1_below_signal2(
rec_fu.get_traces(),
rec_u.get_traces(),
freq_range=[1000, 5000],
fs=rec.get_sampling_frequency())
assert check_signal_power_signal1_below_signal2(
rec_fu.get_traces(),
rec_u.get_traces(),
freq_range=[10000, 15000],
fs=rec.get_sampling_frequency())
assert not str(rec_fu.get_dtype()).startswith('u')
check_dumping(rec_fft)
shutil.rmtree('test')
def test_bandpass_filter_with_cache():
rec, sort = se.example_datasets.toy_example(duration=10, num_channels=4)
rec_filtered = bandpass_filter(rec,
freq_min=5000,
freq_max=10000,
cache_to_file=True,
chunk_size=10000)
rec_filtered2 = bandpass_filter(rec,
freq_min=5000,
freq_max=10000,
cache_to_file=True,
chunk_size=None)
rec_filtered3 = bandpass_filter(rec,
freq_min=5000,
freq_max=10000,
cache_chunks=True,
chunk_size=10000)
rec_filtered3.get_traces()
assert rec_filtered3._filtered_cache_chunks.get('0') is not None
rec_filtered4 = bandpass_filter(rec,
freq_min=5000,
freq_max=10000,
cache_chunks=True,
chunk_size=None)
assert np.allclose(rec_filtered.get_traces(),
rec_filtered2.get_traces(),
rtol=1e-02,
atol=1e-02)
assert np.allclose(rec_filtered.get_traces(),
rec_filtered3.get_traces(),
rtol=1e-02,
atol=1e-02)
assert np.allclose(rec_filtered.get_traces(),
rec_filtered4.get_traces(),
rtol=1e-02,
atol=1e-02)
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'))