def test_BaseSorting():
num_seg = 2
file_path = 'test_BaseSorting.npz'
create_sorting_npz(num_seg, file_path)
sorting = NpzSortingExtractor(file_path)
print(sorting)
assert sorting.get_num_segments() == 2
assert sorting.get_num_units() == 3
# annotations / properties
sorting.annotate(yep='yop')
assert sorting.get_annotation('yep') == 'yop'
sorting.set_property('amplitude', [-20, -40., -55.5])
values = sorting.get_property('amplitude')
assert np.all(values == [-20, -40., -55.5])
# dump/load dict
d = sorting.to_dict()
sorting2 = BaseExtractor.from_dict(d)
sorting3 = load_extractor(d)
# dump/load json
sorting.dump_to_json('test_BaseSorting.json')
sorting2 = BaseExtractor.load('test_BaseSorting.json')
sorting3 = load_extractor('test_BaseSorting.json')
# dump/load pickle
sorting.dump_to_pickle('test_BaseSorting.pkl')
sorting2 = BaseExtractor.load('test_BaseSorting.pkl')
sorting3 = load_extractor('test_BaseSorting.pkl')
# cache
folder = Path('./my_cache_folder') / 'simple_sorting'
sorting.save(folder=folder)
sorting2 = BaseExtractor.load_from_folder(folder)
# but also possible
sorting3 = BaseExtractor.load(folder)
spikes = sorting.get_all_spike_trains()
# print(spikes)
spikes = sorting.to_spike_vector()
def test_BaseRecording():
num_seg = 2
num_chan = 3
num_samples = 30
sampling_frequency = 10000
dtype = 'int16'
files_path = [f'test_base_recording_{i}.raw' for i in range(num_seg)]
for i in range(num_seg):
a = np.memmap(files_path[i],
dtype=dtype,
mode='w+',
shape=(num_samples, num_chan))
a[:] = np.random.randn(*a.shape).astype(dtype)
rec = BinaryRecordingExtractor(files_path, sampling_frequency, num_chan,
dtype)
print(rec)
assert rec.get_num_segments() == 2
assert rec.get_num_channels() == 3
assert np.all(rec.ids_to_indices([0, 1, 2]) == [0, 1, 2])
assert np.all(
rec.ids_to_indices([0, 1, 2], prefer_slice=True) == slice(0, 3, None))
# annotations / properties
rec.annotate(yep='yop')
assert rec.get_annotation('yep') == 'yop'
rec.set_property('quality', [1., 3.3, np.nan])
values = rec.get_property('quality')
assert np.all(values[:2] == [
1.,
3.3,
])
# dump/load dict
d = rec.to_dict()
rec2 = BaseExtractor.from_dict(d)
rec3 = load_extractor(d)
# dump/load json
rec.dump_to_json('test_BaseRecording.json')
rec2 = BaseExtractor.load('test_BaseRecording.json')
rec3 = load_extractor('test_BaseRecording.json')
# dump/load pickle
rec.dump_to_pickle('test_BaseRecording.pkl')
rec2 = BaseExtractor.load('test_BaseRecording.pkl')
rec3 = load_extractor('test_BaseRecording.pkl')
# cache to binary
cache_folder = Path('./my_cache_folder')
folder = cache_folder / 'simple_recording'
rec.save(format='binary', folder=folder)
rec2 = BaseExtractor.load_from_folder(folder)
assert 'quality' in rec2.get_property_keys()
# but also possible
rec3 = BaseExtractor.load('./my_cache_folder/simple_recording')
# cache to memory
rec4 = rec3.save(format='memory')
traces4 = rec4.get_traces(segment_index=0)
traces = rec.get_traces(segment_index=0)
assert np.array_equal(traces4, traces)
# cache joblib several jobs
rec.save(name='simple_recording_2', chunk_size=10, n_jobs=4)
# set/get Probe only 2 channels
probe = Probe(ndim=2)
positions = [[0., 0.], [0., 15.], [0, 30.]]
probe.set_contacts(positions=positions,
shapes='circle',
shape_params={'radius': 5})
probe.set_device_channel_indices([2, -1, 0])
probe.create_auto_shape()
rec2 = rec.set_probe(probe, group_mode='by_shank')
rec2 = rec.set_probe(probe, group_mode='by_probe')
positions2 = rec2.get_channel_locations()
assert np.array_equal(positions2, [[0, 30.], [0., 0.]])
probe2 = rec2.get_probe()
positions3 = probe2.contact_positions
assert np.array_equal(positions2, positions3)
# from probeinterface.plotting import plot_probe_group, plot_probe
# import matplotlib.pyplot as plt
# plot_probe(probe)
# plot_probe(probe2)
# plt.show()
# test return_scale
sampling_frequency = 30000
traces = np.zeros((1000, 5), dtype='int16')
rec_int16 = NumpyRecording([traces], sampling_frequency)
assert rec_int16.get_dtype() == 'int16'
print(rec_int16)
traces_int16 = rec_int16.get_traces()
assert traces_int16.dtype == 'int16'
# return_scaled raise error when no gain_to_uV/offset_to_uV properties
with pytest.raises(ValueError):
traces_float32 = rec_int16.get_traces(return_scaled=True)
rec_int16.set_property('gain_to_uV', [.195] * 5)
rec_int16.set_property('offset_to_uV', [0.] * 5)
traces_float32 = rec_int16.get_traces(return_scaled=True)
assert traces_float32.dtype == 'float32'
def test_BaseRecording():
num_seg = 2
num_chan = 3
num_samples = 30
sampling_frequency = 10000
dtype = 'int16'
file_paths = [f'test_base_recording_{i}.raw' for i in range(num_seg)]
for i in range(num_seg):
a = np.memmap(file_paths[i],
dtype=dtype,
mode='w+',
shape=(num_samples, num_chan))
a[:] = np.random.randn(*a.shape).astype(dtype)
rec = BinaryRecordingExtractor(file_paths, sampling_frequency, num_chan,
dtype)
assert rec.get_num_segments() == 2
assert rec.get_num_channels() == 3
assert np.all(rec.ids_to_indices([0, 1, 2]) == [0, 1, 2])
assert np.all(
rec.ids_to_indices([0, 1, 2], prefer_slice=True) == slice(0, 3, None))
# annotations / properties
rec.annotate(yep='yop')
assert rec.get_annotation('yep') == 'yop'
rec.set_channel_groups([0, 0, 1])
rec.set_property('quality', [1., 3.3, np.nan])
values = rec.get_property('quality')
assert np.all(values[:2] == [
1.,
3.3,
])
# missing property
rec.set_property('string_property', ["ciao", "bello"], ids=[0, 1])
values = rec.get_property('string_property')
assert values[2] == ""
# setting an different type raises an error
assert_raises(Exception,
rec.set_property,
key='string_property_nan',
values=["ciao", "bello"],
ids=[0, 1],
missing_value=np.nan)
# int properties without missing values raise an error
assert_raises(Exception,
rec.set_property,
key='int_property',
values=[5, 6],
ids=[1, 2])
rec.set_property('int_property', [5, 6], ids=[1, 2], missing_value=200)
values = rec.get_property('int_property')
assert values.dtype.kind == "i"
times0 = rec.get_times(segment_index=0)
# dump/load dict
d = rec.to_dict()
rec2 = BaseExtractor.from_dict(d)
rec3 = load_extractor(d)
# dump/load json
rec.dump_to_json('test_BaseRecording.json')
rec2 = BaseExtractor.load('test_BaseRecording.json')
rec3 = load_extractor('test_BaseRecording.json')
# dump/load pickle
rec.dump_to_pickle('test_BaseRecording.pkl')
rec2 = BaseExtractor.load('test_BaseRecording.pkl')
rec3 = load_extractor('test_BaseRecording.pkl')
# dump/load dict - relative
d = rec.to_dict(relative_to=".")
rec2 = BaseExtractor.from_dict(d, base_folder=".")
rec3 = load_extractor(d, base_folder=".")
# dump/load json
rec.dump_to_json('test_BaseRecording_rel.json', relative_to=".")
rec2 = BaseExtractor.load('test_BaseRecording_rel.json', base_folder=".")
rec3 = load_extractor('test_BaseRecording_rel.json', base_folder=".")
# cache to binary
cache_folder = Path('./my_cache_folder')
folder = cache_folder / 'simple_recording'
rec.save(format='binary', folder=folder)
rec2 = BaseExtractor.load_from_folder(folder)
assert 'quality' in rec2.get_property_keys()
values = rec2.get_property('quality')
assert values[0] == 1.
assert values[1] == 3.3
assert np.isnan(values[2])
groups = rec2.get_channel_groups()
assert np.array_equal(groups, [0, 0, 1])
# but also possible
rec3 = BaseExtractor.load('./my_cache_folder/simple_recording')
# cache to memory
rec4 = rec3.save(format='memory')
traces4 = rec4.get_traces(segment_index=0)
traces = rec.get_traces(segment_index=0)
assert np.array_equal(traces4, traces)
# cache joblib several jobs
folder = cache_folder / 'simple_recording2'
rec2 = rec.save(folder=folder, chunk_size=10, n_jobs=4)
traces2 = rec2.get_traces(segment_index=0)
# set/get Probe only 2 channels
probe = Probe(ndim=2)
positions = [[0., 0.], [0., 15.], [0, 30.]]
probe.set_contacts(positions=positions,
shapes='circle',
shape_params={'radius': 5})
probe.set_device_channel_indices([2, -1, 0])
probe.create_auto_shape()
rec_p = rec.set_probe(probe, group_mode='by_shank')
rec_p = rec.set_probe(probe, group_mode='by_probe')
positions2 = rec_p.get_channel_locations()
assert np.array_equal(positions2, [[0, 30.], [0., 0.]])
probe2 = rec_p.get_probe()
positions3 = probe2.contact_positions
assert np.array_equal(positions2, positions3)
assert np.array_equal(probe2.device_channel_indices, [0, 1])
# test save with probe
folder = cache_folder / 'simple_recording3'
rec2 = rec_p.save(folder=folder, chunk_size=10, n_jobs=2)
rec2 = load_extractor(folder)
probe2 = rec2.get_probe()
assert np.array_equal(probe2.contact_positions, [[0, 30.], [0., 0.]])
positions2 = rec_p.get_channel_locations()
assert np.array_equal(positions2, [[0, 30.], [0., 0.]])
traces2 = rec2.get_traces(segment_index=0)
assert np.array_equal(traces2, rec_p.get_traces(segment_index=0))
# from probeinterface.plotting import plot_probe_group, plot_probe
# import matplotlib.pyplot as plt
# plot_probe(probe)
# plot_probe(probe2)
# plt.show()
# test return_scale
sampling_frequency = 30000
traces = np.zeros((1000, 5), dtype='int16')
rec_int16 = NumpyRecording([traces], sampling_frequency)
assert rec_int16.get_dtype() == 'int16'
traces_int16 = rec_int16.get_traces()
assert traces_int16.dtype == 'int16'
# return_scaled raise error when no gain_to_uV/offset_to_uV properties
with pytest.raises(ValueError):
traces_float32 = rec_int16.get_traces(return_scaled=True)
rec_int16.set_property('gain_to_uV', [.195] * 5)
rec_int16.set_property('offset_to_uV', [0.] * 5)
traces_float32 = rec_int16.get_traces(return_scaled=True)
assert traces_float32.dtype == 'float32'
# test with t_start
rec = BinaryRecordingExtractor(file_paths,
sampling_frequency,
num_chan,
dtype,
t_starts=np.arange(num_seg) * 10.)
times1 = rec.get_times(1)
folder = cache_folder / 'recording_with_t_start'
rec2 = rec.save(folder=folder)
assert np.allclose(times1, rec2.get_times(1))
# test with time_vector
rec = BinaryRecordingExtractor(file_paths, sampling_frequency, num_chan,
dtype)
rec.set_times(np.arange(num_samples) / sampling_frequency + 30.,
segment_index=0)
rec.set_times(np.arange(num_samples) / sampling_frequency + 40.,
segment_index=1)
times1 = rec.get_times(1)
folder = cache_folder / 'recording_with_times'
rec2 = rec.save(folder=folder)
assert np.allclose(times1, rec2.get_times(1))
rec3 = load_extractor(folder)
assert np.allclose(times1, rec3.get_times(1))