def test_biocam_extractor(self):
path1 = self.test_dir + '/raw.brw'
se.BiocamRecordingExtractor.write_recording(self.RX, path1)
RX_biocam = se.BiocamRecordingExtractor(path1)
check_recording_return_types(RX_biocam)
check_recordings_equal(self.RX, RX_biocam)
check_dumping(RX_biocam)
def test_remove_artifacts():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
triggers = [15000, 30000]
ms = 10
ms_frames = int(ms * rec.get_sampling_frequency() / 1000)
traces_all_0_clean = rec.get_traces(start_frame=triggers[0] - ms_frames, end_frame=triggers[0] + ms_frames)
traces_all_1_clean = rec.get_traces(start_frame=triggers[1] - ms_frames, end_frame=triggers[1] + ms_frames)
rec_rmart = remove_artifacts(rec, triggers, ms_before=10, ms_after=10)
traces_all_0 = rec_rmart.get_traces(start_frame=triggers[0] - ms_frames, end_frame=triggers[0] + ms_frames)
traces_short_0 = rec_rmart.get_traces(start_frame=triggers[0] - 10, end_frame=triggers[0] + 10)
traces_all_1 = rec_rmart.get_traces(start_frame=triggers[1] - ms_frames, end_frame=triggers[1] + ms_frames)
traces_short_1 = rec_rmart.get_traces(start_frame=triggers[1] - 10, end_frame=triggers[1] + 10)
assert not np.any(traces_all_0)
assert not np.any(traces_all_1)
assert not np.any(traces_short_0)
assert not np.any(traces_short_1)
rec_rmart_lin = remove_artifacts(rec, triggers, ms_before=10, ms_after=10, mode="linear")
traces_all_0 = rec_rmart_lin.get_traces(start_frame=triggers[0] - ms_frames, end_frame=triggers[0] + ms_frames)
traces_all_1 = rec_rmart_lin.get_traces(start_frame=triggers[1] - ms_frames, end_frame=triggers[1] + ms_frames)
assert not np.allclose(traces_all_0, traces_all_0_clean)
assert not np.allclose(traces_all_1, traces_all_1_clean)
rec_rmart_cub = remove_artifacts(rec, triggers, ms_before=10, ms_after=10, mode="cubic")
traces_all_0 = rec_rmart_cub.get_traces(start_frame=triggers[0] - ms_frames, end_frame=triggers[0] + ms_frames)
traces_all_1 = rec_rmart_cub.get_traces(start_frame=triggers[1] - ms_frames, end_frame=triggers[1] + ms_frames)
assert not np.allclose(traces_all_0, traces_all_0_clean)
assert not np.allclose(traces_all_1, traces_all_1_clean)
check_dumping(rec_rmart)
shutil.rmtree('test')
def test_transform():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=2,
num_channels=4,
seed=0)
scalar = 3
offset = 50
rec_t = transform(rec, scalar=scalar, offset=offset)
assert np.allclose(rec_t.get_traces(),
scalar * rec.get_traces() + offset,
atol=0.001)
scalars = np.random.randn(4)
offsets = np.random.randn(4)
rec_t_arr = transform(rec, scalar=scalars, offset=offsets)
for (tt, to, s, o) in zip(rec_t_arr.get_traces(), rec.get_traces(),
scalars, offsets):
assert np.allclose(tt, s * to + o, atol=0.001)
check_dumping(rec_t)
check_dumping(rec_t_arr)
shutil.rmtree('test')
def test_thresh_l_ratios():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
K=10,
seed=0)
l_ratios_thresh = 0
l_ratios = compute_l_ratios(sort, rec, apply_filter=False, seed=0)
sort_l_ratios = threshold_l_ratios(sort,
rec,
l_ratios_thresh,
"less",
apply_filter=False,
seed=0)
original_ids = sort.get_unit_ids()
new_l_ratios = []
for unit in sort_l_ratios.get_unit_ids():
new_l_ratios.append(l_ratios[original_ids.index(unit)])
new_l_ratios = np.array(new_l_ratios)
assert np.all(new_l_ratios >= l_ratios_thresh)
check_dumping(sort_l_ratios)
shutil.rmtree('test')
def test_resample():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
resample_rate_low = 0.1 * rec.get_sampling_frequency()
resample_rate_high = 2 * rec.get_sampling_frequency()
rec_rsl = resample(rec, resample_rate_low)
rec_rsh = resample(rec, resample_rate_high)
assert rec_rsl.get_num_frames() == int(rec.get_num_frames() * 0.1)
assert rec_rsh.get_num_frames() == int(rec.get_num_frames() * 2)
# with times
times = rec.frame_to_time(np.arange(rec.get_num_frames())) - 10
times[1000:] += 0.5
rec.set_times(times)
rec_rsl = resample(rec, resample_rate_low)
rec_rsh = resample(rec, resample_rate_high)
assert rec_rsl.get_num_frames() == int(rec.get_num_frames() * 0.1)
assert rec_rsh.get_num_frames() == int(rec.get_num_frames() * 2)
check_dumping(rec_rsl)
check_dumping(rec_rsh)
shutil.rmtree('test')
def test_thresh_silhouettes():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
K=10,
seed=0)
silhouette_thresh = .5
silhouette = compute_silhouette_scores(sort,
rec,
apply_filter=False,
seed=0)
sort_silhouette = threshold_silhouette_scores(sort,
rec,
silhouette_thresh,
"less",
apply_filter=False,
seed=0)
original_ids = sort.get_unit_ids()
new_silhouette = []
for unit in sort_silhouette.get_unit_ids():
new_silhouette.append(silhouette[original_ids.index(unit)])
new_silhouette = np.array(new_silhouette)
assert np.all(new_silhouette >= silhouette_thresh)
check_dumping(sort_silhouette)
shutil.rmtree('test')
def test_thresh_isolation_distances():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
K=10,
seed=0)
s_threshold = 200
iso = compute_isolation_distances(sort, rec, apply_filter=False, seed=0)
sort_iso = threshold_isolation_distances(sort,
rec,
s_threshold,
'less',
apply_filter=False,
seed=0)
original_ids = sort.get_unit_ids()
new_iso = []
for unit in sort_iso.get_unit_ids():
new_iso.append(iso[original_ids.index(unit)])
new_iso = np.array(new_iso)
assert np.all(new_iso >= s_threshold)
check_dumping(sort_iso)
shutil.rmtree('test')
def test_thresh_noise_overlaps():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
K=10,
seed=0)
noise_thresh = 0.3
noise_overlaps = compute_noise_overlaps(sort,
rec,
apply_filter=False,
seed=0)
sort_noise = threshold_noise_overlaps(sort,
rec,
noise_thresh,
'less',
apply_filter=False,
seed=0)
original_ids = sort.get_unit_ids()
new_noise = []
for unit in sort_noise.get_unit_ids():
new_noise.append(noise_overlaps[original_ids.index(unit)])
new_noise = np.array(new_noise)
assert np.all(new_noise >= noise_thresh)
check_dumping(sort_noise)
shutil.rmtree('test')
def test_spykingcircus_extractor(self):
path1 = self.test_dir + '/sc'
se.SpykingCircusSortingExtractor.write_sorting(self.SX, path1)
SX_spy = se.SpykingCircusSortingExtractor(path1)
check_sorting_return_types(SX_spy)
check_sortings_equal(self.SX, SX_spy)
check_dumping(SX_spy)
def test_hs2_extractor(self):
path1 = self.test_dir + '/firings_true.hdf5'
se.HS2SortingExtractor.write_sorting(self.SX, path1)
SX_hs2 = se.HS2SortingExtractor(path1)
check_sorting_return_types(SX_hs2)
check_sortings_equal(self.SX, SX_hs2)
self.assertEqual(SX_hs2.get_sampling_frequency(),
self.SX.get_sampling_frequency())
check_dumping(SX_hs2)
def test_rectify():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
rec_rect = rectify(rec)
assert np.allclose(rec_rect.get_traces(), np.abs(rec.get_traces()))
check_dumping(rec_rect)
shutil.rmtree('test')
def test_cell_explorer_extractor(self):
sorter_id = "cell_explorer_sorter"
cell_explorer_dir = Path(self.test_dir) / sorter_id
spikes_matfile_path = cell_explorer_dir / f"{sorter_id}.spikes.cellinfo.mat"
se.CellExplorerSortingExtractor.write_sorting(
sorting=self.SX, save_path=spikes_matfile_path)
SX_cell_explorer = se.CellExplorerSortingExtractor(
spikes_matfile_path=spikes_matfile_path)
check_sorting_return_types(SX_cell_explorer)
check_sortings_equal(self.SX, SX_cell_explorer)
check_dumping(SX_cell_explorer)
def test_hdsort_extractor(self):
path = self.test_dir + '/results_test_hdsort_extractor.mat'
locations = np.ones((10, 2))
se.HDSortSortingExtractor.write_sorting(self.SX,
path,
locations=locations,
noise_std_by_channel=None)
SX_hd = se.HDSortSortingExtractor(path)
check_sorting_return_types(SX_hd)
check_sortings_equal(self.SX, SX_hd)
check_dumping(SX_hd)
def test_blank_saturation():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
threshold = 2
rec_bs = blank_saturation(rec, threshold=threshold)
index_below_threshold = np.where(rec.get_traces() < threshold)
assert np.all(rec_bs.get_traces()[index_below_threshold] < threshold)
check_dumping(rec_bs)
shutil.rmtree('test')
def test_curation_sorting_extractor():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
K=3,
seed=0)
# Dummy features for testing merging and splitting of features
sort.set_unit_spike_features(
1, 'f_int', range(0 + 1,
len(sort.get_unit_spike_train(1)) + 1))
sort.set_unit_spike_features(2, 'f_int',
range(0, len(sort.get_unit_spike_train(2))))
sort.set_unit_spike_features(
2, 'bad_features', np.repeat(1, len(sort.get_unit_spike_train(2))))
sort.set_unit_spike_features(3, 'f_int',
range(0, len(sort.get_unit_spike_train(3))))
CSX = st.curation.CurationSortingExtractor(parent_sorting=sort)
merged_unit_id = CSX.merge_units(unit_ids=[1, 2])
assert np.allclose(merged_unit_id, 4)
original_spike_train = np.concatenate(
(sort.get_unit_spike_train(1), sort.get_unit_spike_train(2)))
indices_sort = np.argsort(original_spike_train)
original_spike_train = original_spike_train[indices_sort]
original_features = np.concatenate(
(sort.get_unit_spike_features(1, 'f_int'),
sort.get_unit_spike_features(2, 'f_int')))
original_features = original_features[indices_sort]
assert np.allclose(CSX.get_unit_spike_train(4), original_spike_train)
assert np.allclose(CSX.get_unit_spike_features(4, 'f_int'),
original_features)
assert CSX.get_unit_spike_feature_names(4) == ['f_int']
assert np.allclose(CSX.get_sampling_frequency(),
sort.get_sampling_frequency())
unit_ids_split = CSX.split_unit(unit_id=3,
indices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
assert np.allclose(unit_ids_split[0], 5)
assert np.allclose(unit_ids_split[1], 6)
original_spike_train = sort.get_unit_spike_train(3)
original_features = sort.get_unit_spike_features(3, 'f_int')
split_spike_train_1 = CSX.get_unit_spike_train(5)
split_spike_train_2 = CSX.get_unit_spike_train(6)
split_features_1 = CSX.get_unit_spike_features(5, 'f_int')
split_features_2 = CSX.get_unit_spike_features(6, 'f_int')
assert np.allclose(original_spike_train[:10], split_spike_train_1)
assert np.allclose(original_spike_train[10:], split_spike_train_2)
assert np.allclose(original_features[:10], split_features_1)
assert np.allclose(original_features[10:], split_features_2)
check_dumping(CSX)
def test_center():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
rec_c = center(rec, mode='mean')
assert np.allclose(np.mean(rec_c.get_traces(), axis=1), 0, atol=0.001)
check_dumping(rec_c)
rec_c = center(rec, mode='median')
assert np.allclose(np.median(rec_c.get_traces(), axis=1), 0, atol=0.001)
check_dumping(rec_c)
shutil.rmtree('test')
def test_mda_extractor(self):
path1 = self.test_dir + '/mda'
path2 = path1 + '/firings_true.mda'
se.MdaRecordingExtractor.write_recording(self.RX, path1)
se.MdaSortingExtractor.write_sorting(self.SX, path2)
RX_mda = se.MdaRecordingExtractor(path1)
SX_mda = se.MdaSortingExtractor(path2)
check_recording_return_types(RX_mda)
check_recordings_equal(self.RX, RX_mda)
check_sorting_return_types(SX_mda)
check_sortings_equal(self.SX, SX_mda)
check_dumping(RX_mda)
check_dumping(SX_mda)
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())
assert check_signal_power_signal1_below_signal2(rec_fft.get_traces(end_frame=30000),
rec.get_traces(end_frame=30000), freq_range=[1000, 5000],
fs=rec.get_sampling_frequency())
check_dumping(rec_fft)
shutil.rmtree('test')
def test_notch_filter():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
rec_n = notch_filter(rec, 3000, q=10)
assert check_signal_power_signal1_below_signal2(rec_n.get_traces(), rec.get_traces(), freq_range=[2900, 3100],
fs=rec.get_sampling_frequency())
assert check_signal_power_signal1_below_signal2(rec_n.get_traces(end_frame=30000),
rec.get_traces(end_frame=30000), freq_range=[2900, 3100],
fs=rec.get_sampling_frequency())
check_dumping(rec_n)
shutil.rmtree('test')
def test_clip():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
threshold = 5
rec_clip = clip(rec, a_min=-threshold, a_max=threshold)
index_below_threshold = np.where(rec.get_traces() < -threshold)
index_above_threshold = np.where(rec.get_traces() > threshold)
assert np.all(rec_clip.get_traces()[index_below_threshold] == -threshold)
assert np.all(rec_clip.get_traces()[index_above_threshold] == threshold)
check_dumping(rec_clip)
shutil.rmtree('test')
def test_npz_extractor(self):
path = self.test_dir + '/sorting.npz'
se.NpzSortingExtractor.write_sorting(self.SX, path)
SX_npz = se.NpzSortingExtractor(path)
# empty write
sorting_empty = se.NumpySortingExtractor()
path_empty = self.test_dir + '/sorting_empty.npz'
se.NpzSortingExtractor.write_sorting(sorting_empty, path_empty)
check_sorting_return_types(SX_npz)
check_sortings_equal(self.SX, SX_npz)
check_dumping(SX_npz)
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 test_remove_bad_channels():
rec, sort = se.example_datasets.toy_example(dump_folder='test', dumpable=True, duration=2, num_channels=4, seed=0)
rec_rm = remove_bad_channels(rec, bad_channel_ids=[0])
assert 0 not in rec_rm.get_channel_ids()
rec_rm = remove_bad_channels(rec, bad_channel_ids=[1, 2])
assert 1 not in rec_rm.get_channel_ids() and 2 not in rec_rm.get_channel_ids()
check_dumping(rec_rm)
shutil.rmtree('test')
timeseries = np.random.randn(4, 60000)
timeseries[1] = 10 * timeseries[1]
rec_np = se.NumpyRecordingExtractor(timeseries=timeseries, sampling_frequency=30000)
rec_np.set_channel_locations(np.ones((rec_np.get_num_channels(), 2)))
se.MdaRecordingExtractor.write_recording(rec_np, 'test')
rec = se.MdaRecordingExtractor('test')
rec_rm = remove_bad_channels(rec, bad_channel_ids=None, bad_threshold=2)
assert 1 not in rec_rm.get_channel_ids()
check_dumping(rec_rm)
rec_rm = remove_bad_channels(rec, bad_channel_ids=None, bad_threshold=2, seconds=0.1)
assert 1 not in rec_rm.get_channel_ids()
check_dumping(rec_rm)
rec_rm = remove_bad_channels(rec, bad_channel_ids=None, bad_threshold=2, seconds=10)
assert 1 not in rec_rm.get_channel_ids()
check_dumping(rec_rm)
shutil.rmtree('test')
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, test_relative=True)
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_relative=True)
# 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_relative=True)
# 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_relative=True)
# 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 test_exdir_extractors(self):
path1 = self.test_dir + '/raw.exdir'
se.ExdirRecordingExtractor.write_recording(self.RX, path1)
RX_exdir = se.ExdirRecordingExtractor(path1)
check_recording_return_types(RX_exdir)
check_recordings_equal(self.RX, RX_exdir)
check_dumping(RX_exdir)
path2 = self.test_dir + '/firings.exdir'
se.ExdirSortingExtractor.write_sorting(self.SX, path2, self.RX)
SX_exdir = se.ExdirSortingExtractor(path2)
check_sorting_return_types(SX_exdir)
check_sortings_equal(self.SX, SX_exdir)
check_dumping(SX_exdir)
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_thresh_num_spikes():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
K=10,
seed=0)
s_threshold = 25
sort_ns = threshold_num_spikes(sort, s_threshold, 'less')
new_ns = compute_num_spikes(sort_ns, sort.get_sampling_frequency())
assert np.all(new_ns >= s_threshold)
check_dumping(sort_ns)
shutil.rmtree('test')
def test_thresh_frs():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
K=10,
seed=0)
fr_thresh = 2
sort_fr = threshold_firing_rates(sort, fr_thresh, 'less',
rec.get_num_frames())
new_fr = compute_firing_rates(sort_fr, rec.get_num_frames())
assert np.all(new_fr >= fr_thresh)
check_dumping(sort_fr)
shutil.rmtree('test')
def test_mearec_extractors(self):
path1 = self.test_dir + '/raw.h5'
se.MEArecRecordingExtractor.write_recording(self.RX, path1)
RX_mearec = se.MEArecRecordingExtractor(path1)
tr = RX_mearec.get_traces(channel_ids=[0, 1], end_frame=1000)
check_recording_return_types(RX_mearec)
check_recordings_equal(self.RX, RX_mearec)
check_dumping(RX_mearec)
path2 = self.test_dir + '/firings_true.h5'
se.MEArecSortingExtractor.write_sorting(
self.SX, path2, self.RX.get_sampling_frequency())
SX_mearec = se.MEArecSortingExtractor(path2)
check_sorting_return_types(SX_mearec)
check_sortings_equal(self.SX, SX_mearec)
check_dumping(SX_mearec)
def test_thresh_presence_ratios():
rec, sort = se.example_datasets.toy_example(dump_folder='test',
dumpable=True,
duration=10,
num_channels=4,
K=10,
seed=0)
s_threshold = 0.18
sort_pr = threshold_presence_ratios(sort, s_threshold, 'less',
rec.get_num_frames())
new_pr = compute_presence_ratios(sort_pr, rec.get_num_frames(),
sort.get_sampling_frequency())
assert np.all(new_pr >= s_threshold)
check_dumping(sort_pr)
shutil.rmtree('test')
