def test_DataIO_probes():
# initialze dataio
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
print(dataio)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
probe_filename = 'A4x8-5mm-100-400-413-A32.prb'
dataio.download_probe(probe_filename)
dataio.download_probe('A4x8-5mm-100-400-413-A32')
#~ print(dataio.channel_groups)
#~ print(dataio.channels)
#~ print(dataio.info['probe_filename'])
assert dataio.nb_channel(0) == 8
assert probe_filename == dataio.info['probe_filename']
dataio = DataIO(dirname='test_DataIO')
print(dataio)
def test_DataIO_probes():
# initialze dataio
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
print(dataio)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
probe_filename = 'neuronexus/A4x8-5mm-100-400-413-A32.prb'
dataio.download_probe(probe_filename)
dataio.download_probe('neuronexus/A4x8-5mm-100-400-413-A32')
#~ print(dataio.channel_groups)
#~ print(dataio.channels)
#~ print(dataio.info['probe_filename'])
assert dataio.nb_channel(0) == 8
assert probe_filename.split('/')[-1] == dataio.info['probe_filename']
dataio = DataIO(dirname='test_DataIO')
print(dataio)
def test_DataIO():
# initialze dataio
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
print(dataio)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
#~ dataio.set_channels(range(4))
dataio.set_manual_channel_group(range(14))
for seg_num in range(dataio.nb_segment):
for i_stop, sigs_chunk in dataio.iter_over_chunk(seg_num=seg_num, chunksize=1024):
assert sigs_chunk.shape[0] == 1024
assert sigs_chunk.shape[1] == 14
#~ print(seg_num, i_stop, sigs_chunk.shape)
#reopen existing
dataio = DataIO(dirname='test_DataIO')
print(dataio)
#~ exit()
for seg_num in range(dataio.nb_segment):
#~ print('seg_num', seg_num)
for i_stop, sigs_chunk in dataio.iter_over_chunk(seg_num=seg_num, chunksize=1024):
assert sigs_chunk.shape[0] == 1024
assert sigs_chunk.shape[1] == 14
def test_make_catalogue():
if os.path.exists('test_catalogueconstructor'):
shutil.rmtree('test_catalogueconstructor')
dataio = DataIO(dirname='test_catalogueconstructor')
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
#~ dataio.set_manual_channel_group(range(14))
dataio.set_manual_channel_group([5, 6, 7, 8, 9])
catalogueconstructor = CatalogueConstructor(dataio=dataio)
catalogueconstructor.set_preprocessor_params(chunksize=1024,
#signal preprocessor
highpass_freq=300,
backward_chunksize=1280,
#peak detector
peak_sign='-', relative_threshold=7, peak_span=0.0005,
)
t1 = time.perf_counter()
catalogueconstructor.estimate_signals_noise(seg_num=0, duration=10.)
t2 = time.perf_counter()
print('estimate_signals_noise', t2-t1)
t1 = time.perf_counter()
catalogueconstructor.run_signalprocessor()
t2 = time.perf_counter()
print('run_signalprocessor', t2-t1)
print(catalogueconstructor)
t1 = time.perf_counter()
catalogueconstructor.extract_some_waveforms(n_left=-12, n_right=15, nb_max=10000)
t2 = time.perf_counter()
print('extract_some_waveforms', t2-t1)
# PCA
t1 = time.perf_counter()
catalogueconstructor.project(method='pca', n_components=12, batch_size=16384)
t2 = time.perf_counter()
print('project', t2-t1)
# cluster
t1 = time.perf_counter()
catalogueconstructor.find_clusters(method='kmeans', n_clusters=13)
t2 = time.perf_counter()
print('find_clusters', t2-t1)
# trash_small_cluster
catalogueconstructor.trash_small_cluster()
catalogueconstructor.make_catalogue()
def test_RawDataSource():
localdir, filenames, params = download_dataset(name='olfactory_bulb')
datasource = RawDataSource(filenames=filenames, **params)
assert datasource.total_channel == 16
assert datasource.sample_rate == 10000.
assert datasource.nb_segment==3
assert datasource.get_segment_shape(0) == (150000, 16)
data = datasource.get_signals_chunk(seg_num=0)
assert data.shape==datasource.get_segment_shape(0)
def test_get_auto_params_for_catalogue():
if os.path.exists('test_cataloguetools'):
shutil.rmtree('test_cataloguetools')
dataio = DataIO(dirname='test_cataloguetools')
#~ localdir, filenames, params = download_dataset(name='olfactory_bulb')
localdir, filenames, params = download_dataset(name='locust')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
params = get_auto_params_for_catalogue(dataio)
pprint(params)
def test_RawDataSource():
from tridesclous.datasource import RawDataSource
localdir, filenames, params = download_dataset(name='olfactory_bulb')
datasource = RawDataSource(filenames=filenames, **params)
assert datasource.total_channel == 16
assert datasource.sample_rate == 10000.
assert datasource.nb_segment == 3
assert datasource.get_segment_shape(0) == (150000, 16)
data = datasource.get_signals_chunk(seg_num=0)
assert data.shape == datasource.get_segment_shape(0)
def test_DataIO():
# initialze dataio
if os.path.exists('test_DataIO'):
shutil.rmtree('test_DataIO')
dataio = DataIO(dirname='test_DataIO')
print(dataio)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
#with geometry
channels = list(range(14))
channel_groups = {
0: {
'channels': range(14),
'geometry': {c: [0, i]
for i, c in enumerate(channels)}
}
}
dataio.set_channel_groups(channel_groups)
#with no geometry
channel_groups = {0: {'channels': range(4)}}
dataio.set_channel_groups(channel_groups)
# add one group
dataio.add_one_channel_group(channels=range(4, 8), chan_grp=5)
channel_groups = {0: {'channels': range(14)}}
dataio.set_channel_groups(channel_groups)
for seg_num in range(dataio.nb_segment):
for i_stop, sigs_chunk in dataio.iter_over_chunk(seg_num=seg_num,
chunksize=1024):
assert sigs_chunk.shape[0] == 1024
assert sigs_chunk.shape[1] == 14
#~ print(seg_num, i_stop, sigs_chunk.shape)
#reopen existing
dataio = DataIO(dirname='test_DataIO')
print(dataio)
#~ exit()
for seg_num in range(dataio.nb_segment):
#~ print('seg_num', seg_num)
for i_stop, sigs_chunk in dataio.iter_over_chunk(seg_num=seg_num,
chunksize=1024):
assert sigs_chunk.shape[0] == 1024
assert sigs_chunk.shape[1] == 14
def compare_nb_waveforms():
if os.path.exists('test_catalogueconstructor'):
shutil.rmtree('test_catalogueconstructor')
dataio = DataIO(dirname='test_catalogueconstructor')
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
dataio.set_manual_channel_group(range(14))
catalogueconstructor = CatalogueConstructor(dataio=dataio)
catalogueconstructor.set_preprocessor_params(chunksize=1024,
#signal preprocessor
highpass_freq=300,
backward_chunksize=1280,
#peak detector
peak_sign='-', relative_threshold=7, peak_span=0.0005,
)
t1 = time.perf_counter()
catalogueconstructor.estimate_signals_noise(seg_num=0, duration=10.)
t2 = time.perf_counter()
print('estimate_signals_noise', t2-t1)
t1 = time.perf_counter()
catalogueconstructor.run_signalprocessor()
t2 = time.perf_counter()
print('run_signalprocessor', t2-t1)
print(catalogueconstructor)
fig, axs = pyplot.subplots(nrows=2)
colors = ['r', 'g', 'b']
for i, nb_max in enumerate([100, 1000, 10000]):
t1 = time.perf_counter()
catalogueconstructor.extract_some_waveforms(n_left=-20, n_right=30, nb_max=nb_max)
t2 = time.perf_counter()
print('extract_some_waveforms', nb_max, t2-t1)
print(catalogueconstructor.some_waveforms.shape)
wf = catalogueconstructor.some_waveforms
wf = wf.swapaxes(1,2).reshape(wf.shape[0], -1)
axs[0].plot(np.median(wf, axis=0), color=colors[i], label='nb_max {}'.format(nb_max))
axs[1].plot(np.mean(wf, axis=0), color=colors[i], label='nb_max {}'.format(nb_max))
axs[0].legend()
axs[0].set_title('median')
axs[1].set_title('mean')
pyplot.show()
def make_catalogue():
if os.path.exists(dirname):
shutil.rmtree(dirname)
dataio = DataIO(dirname=dirname)
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
dataio.add_one_channel_group(channels=channels)
cc = CatalogueConstructor(dataio=dataio)
params = {
'duration': 300.,
'preprocessor': {
'highpass_freq': 300.,
'chunksize': 1024,
'lostfront_chunksize': 100,
},
'peak_detector': {
'peak_sign': '-',
'relative_threshold': 7.,
'peak_span': 0.0005,
#~ 'peak_span' : 0.000,
},
'extract_waveforms': {
'n_left': -25,
'n_right': 40,
'nb_max': 10000,
},
'clean_waveforms': {
'alien_value_threshold': 60.,
},
'noise_snippet': {
'nb_snippet': 300,
},
'feature_method': 'global_pca',
'feature_kargs': {
'n_components': 20
},
'cluster_method': 'kmeans',
'cluster_kargs': {
'n_clusters': 5
},
'clean_cluster': False,
'clean_cluster_kargs': {},
}
apply_all_catalogue_steps(cc, params, verbose=True)
cc.order_clusters(by='waveforms_rms')
cc.move_cluster_to_trash(4)
cc.make_catalogue_for_peeler()
def test_apply_all_catalogue_steps():
if os.path.exists('test_cataloguetools'):
shutil.rmtree('test_cataloguetools')
dataio = DataIO(dirname='test_cataloguetools')
#~ localdir, filenames, params = download_dataset(name='olfactory_bulb')
localdir, filenames, params = download_dataset(name='locust')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
params = get_auto_params_for_catalogue(dataio)
cc = CatalogueConstructor(dataio, chan_grp=0)
apply_all_catalogue_steps(cc, params, verbose=True)
def compare_nb_waveforms():
if os.path.exists('test_catalogueconstructor'):
shutil.rmtree('test_catalogueconstructor')
dataio = DataIO(dirname='test_catalogueconstructor')
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
dataio.add_one_channel_group(channels=range(14), chan_grp=0)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
catalogueconstructor.set_preprocessor_params(
chunksize=1024,
#signal preprocessor
highpass_freq=300.,
lowpass_freq=5000.,
lostfront_chunksize=128,
#peak detector
peak_sign='-',
relative_threshold=7,
peak_span=0.0005,
)
t1 = time.perf_counter()
catalogueconstructor.estimate_signals_noise(seg_num=0, duration=10.)
t2 = time.perf_counter()
print('estimate_signals_noise', t2 - t1)
t1 = time.perf_counter()
catalogueconstructor.run_signalprocessor()
t2 = time.perf_counter()
print('run_signalprocessor', t2 - t1)
print(catalogueconstructor)
fig, axs = plt.subplots(nrows=2)
colors = ['r', 'g', 'b']
for i, nb_max in enumerate([100, 1000, 10000]):
t1 = time.perf_counter()
catalogueconstructor.extract_some_waveforms(n_left=-20,
n_right=30,
nb_max=nb_max)
t2 = time.perf_counter()
print('extract_some_waveforms', nb_max, t2 - t1)
print(catalogueconstructor.some_waveforms.shape)
wf = catalogueconstructor.some_waveforms
wf = wf.swapaxes(1, 2).reshape(wf.shape[0], -1)
axs[0].plot(np.median(wf, axis=0),
color=colors[i],
label='nb_max {}'.format(nb_max))
axs[1].plot(np.mean(wf, axis=0),
color=colors[i],
label='nb_max {}'.format(nb_max))
axs[0].legend()
axs[0].set_title('median')
axs[1].set_title('mean')
plt.show()
def compare_nb_waveforms():
if os.path.exists('test_catalogueconstructor'):
shutil.rmtree('test_catalogueconstructor')
dataio = DataIO(dirname='test_catalogueconstructor')
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
dataio.add_one_channel_group(channels=range(14), chan_grp=0)
cc = CatalogueConstructor(dataio=dataio)
cc.set_global_params(
chunksize=1024,
memory_mode='ram',
mode='dense',
n_jobs=1,
#~ adjacency_radius_um=None,
)
cc.set_preprocessor_params(
#signal preprocessor
highpass_freq=300,
lowpass_freq=5000.,
common_ref_removal=False,
smooth_size=0,
lostfront_chunksize=None)
cc.set_peak_detector_params(
#peak detector
method='global',
engine='numpy',
peak_sign='-',
relative_threshold=7,
peak_span_ms=0.5,
#~ adjacency_radius_um=None,
)
t1 = time.perf_counter()
cc.estimate_signals_noise(seg_num=0, duration=10.)
t2 = time.perf_counter()
print('estimate_signals_noise', t2 - t1)
t1 = time.perf_counter()
cc.run_signalprocessor()
t2 = time.perf_counter()
print('run_signalprocessor', t2 - t1)
print(cc)
fig, axs = plt.subplots(nrows=2)
cc.set_waveform_extractor_params(wf_left_ms=-2.0, wf_right_ms=3.0)
t1 = time.perf_counter()
cc.sample_some_peaks(mode='rand', nb_max=5000)
t2 = time.perf_counter()
print('sample_some_peaks', t2 - t1)
colors = ['r', 'g', 'b', 'y']
for i, nb_max in enumerate([100, 500, 1000, 2000]):
cc.sample_some_peaks(mode='rand', nb_max=nb_max)
#~ catalogueconstructor.extract_some_waveforms(wf_left_ms=-2.0, wf_right_ms=3.0, nb_max=nb_max)
#~ print(catalogueconstructor.some_waveforms.shape)
t1 = time.perf_counter()
wf = cc.get_some_waveforms()
t2 = time.perf_counter()
print('get_some_waveforms', nb_max, t2 - t1)
#~ wf = catalogueconstructor.some_waveforms
wf = wf.swapaxes(1, 2).reshape(wf.shape[0], -1)
axs[0].plot(np.median(wf, axis=0),
color=colors[i],
label='nb_max {}'.format(nb_max))
axs[1].plot(np.mean(wf, axis=0),
color=colors[i],
label='nb_max {}'.format(nb_max))
axs[0].legend()
axs[0].set_title('median')
axs[1].set_title('mean')
plt.show()
def test_catalogue_constructor():
if os.path.exists('test_catalogueconstructor'):
shutil.rmtree('test_catalogueconstructor')
dataio = DataIO(dirname='test_catalogueconstructor')
localdir, filenames, params = download_dataset(name='olfactory_bulb')
#~ localdir, filenames, params = download_dataset(name='locust')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
channels = range(14)
#~ channels=list(range(4))
dataio.add_one_channel_group(channels=channels, chan_grp=0)
cc = CatalogueConstructor(dataio=dataio)
for memory_mode in ['ram', 'memmap']:
for mode in ['dense', 'sparse']:
print('*' * 5)
print('memory_mode', memory_mode, 'mode', mode)
if mode == 'dense':
peak_engine = 'numpy'
peak_method = 'global'
adjacency_radius_um = None
elif mode == 'sparse':
peak_engine = 'numpy'
peak_method = 'geometrical'
adjacency_radius_um = 450.
cc.set_global_params(
chunksize=1024,
memory_mode=memory_mode,
mode=mode,
n_jobs=1,
#~ adjacency_radius_um=adjacency_radius_um,
)
cc.set_preprocessor_params(
#signal preprocessor
highpass_freq=300,
lowpass_freq=5000.,
common_ref_removal=False,
smooth_size=0,
lostfront_chunksize=None)
cc.set_peak_detector_params(
#peak detector
method=peak_method,
engine=peak_engine,
peak_sign='-',
relative_threshold=7,
peak_span_ms=0.5,
adjacency_radius_um=adjacency_radius_um,
)
t1 = time.perf_counter()
cc.estimate_signals_noise(seg_num=0, duration=10.)
t2 = time.perf_counter()
print('estimate_signals_noise', t2 - t1)
t1 = time.perf_counter()
cc.run_signalprocessor(duration=10., detect_peak=True)
t2 = time.perf_counter()
print('run_signalprocessor_loop', t2 - t1)
for seg_num in range(dataio.nb_segment):
mask = cc.all_peaks['segment'] == seg_num
print('seg_num', seg_num, 'nb peak', np.sum(mask))
# redetect peak
cc.re_detect_peak(method=peak_method,
engine=peak_engine,
peak_sign='-',
relative_threshold=5,
peak_span_ms=0.7,
adjacency_radius_um=adjacency_radius_um)
for seg_num in range(dataio.nb_segment):
mask = cc.all_peaks['segment'] == seg_num
print('seg_num', seg_num, 'nb peak', np.sum(mask))
cc.set_waveform_extractor_params(n_left=-25, n_right=40)
t1 = time.perf_counter()
cc.clean_peaks(alien_value_threshold=100,
mode='extremum_amplitude')
t2 = time.perf_counter()
print('clean_peaks extremum_amplitude', t2 - t1)
t1 = time.perf_counter()
cc.clean_peaks(alien_value_threshold=100, mode='full_waveform')
t2 = time.perf_counter()
print('clean_peaks full_waveforms', t2 - t1)
t1 = time.perf_counter()
cc.sample_some_peaks(mode='rand', nb_max=5000)
t2 = time.perf_counter()
print('sample_some_peaks', t2 - t1)
print(cc)
#extract_some_noise
t1 = time.perf_counter()
cc.extract_some_noise(nb_snippet=400)
t2 = time.perf_counter()
print('extract_some_noise', t2 - t1)
if mode == 'dense':
# PCA
t1 = time.perf_counter()
cc.extract_some_features(method='global_pca', n_components=12)
t2 = time.perf_counter()
print('project pca', t2 - t1)
# cluster
t1 = time.perf_counter()
cc.find_clusters(method='kmeans', n_clusters=11)
t2 = time.perf_counter()
print('find_clusters', t2 - t1)
elif mode == 'sparse':
# PCA
t1 = time.perf_counter()
cc.extract_some_features(method='pca_by_channel',
n_components_by_channel=3)
t2 = time.perf_counter()
print('project pca', t2 - t1)
# cluster
t1 = time.perf_counter()
cc.find_clusters(method='pruningshears')
t2 = time.perf_counter()
print('find_clusters', t2 - t1)
print(cc)
t1 = time.perf_counter()
cc.auto_split_cluster()
t2 = time.perf_counter()
print('auto_split_cluster', t2 - t1)
t1 = time.perf_counter()
cc.trash_not_aligned()
t2 = time.perf_counter()
print('trash_not_aligned', t2 - t1)
t1 = time.perf_counter()
cc.auto_merge_cluster()
t2 = time.perf_counter()
print('auto_merge_cluster', t2 - t1)
t1 = time.perf_counter()
cc.trash_low_extremum()
t2 = time.perf_counter()
print('trash_low_extremum', t2 - t1)
t1 = time.perf_counter()
cc.trash_small_cluster()
t2 = time.perf_counter()
print('trash_small_cluster', t2 - t1)
def test_catalogue_constructor():
if os.path.exists('test_catalogueconstructor'):
shutil.rmtree('test_catalogueconstructor')
dataio = DataIO(dirname='test_catalogueconstructor')
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
channels=range(14)
dataio.set_manual_channel_group(channels, chan_grp=0)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
for memory_mode in ['ram', 'memmap']:
#~ for memory_mode in ['memmap']:
print()
print(memory_mode)
catalogueconstructor.set_preprocessor_params(chunksize=1024,
memory_mode=memory_mode,
#signal preprocessor
highpass_freq=300,
backward_chunksize=1280,
#~ backward_chunksize=1024*2,
#peak detector
peakdetector_engine='numpy',
peak_sign='-', relative_threshold=7, peak_span=0.0005,
#waveformextractor
#~ n_left=-20, n_right=30,
)
t1 = time.perf_counter()
catalogueconstructor.estimate_signals_noise(seg_num=0, duration=10.)
t2 = time.perf_counter()
print('estimate_signals_noise', t2-t1)
t1 = time.perf_counter()
for seg_num in range(dataio.nb_segment):
#~ print('seg_num', seg_num)
catalogueconstructor.run_signalprocessor_loop_one_segment(seg_num=seg_num, duration=10.)
t2 = time.perf_counter()
print('run_signalprocessor_loop', t2-t1)
t1 = time.perf_counter()
catalogueconstructor.finalize_signalprocessor_loop()
t2 = time.perf_counter()
print('finalize_signalprocessor_loop', t2-t1)
for seg_num in range(dataio.nb_segment):
mask = catalogueconstructor.all_peaks['segment']==seg_num
print('seg_num', seg_num, np.sum(mask))
t1 = time.perf_counter()
catalogueconstructor.extract_some_waveforms(n_left=-25, n_right=40, mode='rand', nb_max=5000)
t2 = time.perf_counter()
print('extract_some_waveforms rand', t2-t1)
print(catalogueconstructor.some_waveforms.shape)
t1 = time.perf_counter()
catalogueconstructor.find_good_limits()
t2 = time.perf_counter()
print('find_good_limits', t2-t1)
print(catalogueconstructor.some_waveforms.shape)
t1 = time.perf_counter()
catalogueconstructor.extract_some_waveforms(n_left=None, n_right=None, mode='rand', nb_max=2000)
t2 = time.perf_counter()
print('extract_some_waveforms rand', t2-t1)
print(catalogueconstructor.some_waveforms.shape)
#~ break
# PCA
t1 = time.perf_counter()
catalogueconstructor.project(method='pca', n_components=7, batch_size=16384)
t2 = time.perf_counter()
print('project pca', t2-t1)
# peak_max
#~ t1 = time.perf_counter()
#~ catalogueconstructor.project(method='peak_max')
#~ t2 = time.perf_counter()
#~ print('project peak_max', t2-t1)
#~ print(catalogueconstructor.some_features.shape)
t1 = time.perf_counter()
catalogueconstructor.extract_some_waveforms(index=np.arange(1000))
t2 = time.perf_counter()
print('extract_some_waveforms others', t2-t1)
print(catalogueconstructor.some_waveforms.shape)
# cluster
t1 = time.perf_counter()
catalogueconstructor.find_clusters(method='kmeans', n_clusters=11)
t2 = time.perf_counter()
print('find_clusters', t2-t1)
def test_catalogue_constructor():
if os.path.exists('test_catalogueconstructor'):
shutil.rmtree('test_catalogueconstructor')
dataio = DataIO(dirname='test_catalogueconstructor')
localdir, filenames, params = download_dataset(name='olfactory_bulb')
#~ localdir, filenames, params = download_dataset(name='locust')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
channels = range(14)
#~ channels=list(range(4))
dataio.add_one_channel_group(channels=channels, chan_grp=0)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
for memory_mode in ['ram', 'memmap']:
#~ for memory_mode in ['memmap']:
print()
print(memory_mode)
catalogueconstructor.set_preprocessor_params(
chunksize=1024,
memory_mode=memory_mode,
#signal preprocessor
highpass_freq=300,
lowpass_freq=5000.,
common_ref_removal=False,
smooth_size=0,
lostfront_chunksize=128,
#peak detector
peakdetector_engine='numpy',
peak_sign='-',
relative_threshold=7,
peak_span=0.0005,
#waveformextractor
#~ n_left=-20, n_right=30,
)
t1 = time.perf_counter()
catalogueconstructor.estimate_signals_noise(seg_num=0, duration=10.)
t2 = time.perf_counter()
print('estimate_signals_noise', t2 - t1)
#~ t1 = time.perf_counter()
#~ for seg_num in range(dataio.nb_segment):
#~ print('seg_num', seg_num)
#~ catalogueconstructor.run_signalprocessor_loop_one_segment(seg_num=seg_num, duration=10.)
catalogueconstructor.run_signalprocessor(duration=10.,
detect_peak=True)
t2 = time.perf_counter()
print('run_signalprocessor_loop', t2 - t1)
for seg_num in range(dataio.nb_segment):
mask = catalogueconstructor.all_peaks['segment'] == seg_num
print('seg_num', seg_num, 'nb peak', np.sum(mask))
#redetect peak
catalogueconstructor.re_detect_peak(peakdetector_engine='numpy',
peak_sign='-',
relative_threshold=5,
peak_span=0.0002)
for seg_num in range(dataio.nb_segment):
mask = catalogueconstructor.all_peaks['segment'] == seg_num
print('seg_num', seg_num, 'nb peak', np.sum(mask))
t1 = time.perf_counter()
catalogueconstructor.extract_some_waveforms(n_left=-25,
n_right=40,
mode='rand',
nb_max=5000)
t2 = time.perf_counter()
print('extract_some_waveforms rand', t2 - t1)
print(catalogueconstructor.some_waveforms.shape)
t1 = time.perf_counter()
catalogueconstructor.find_good_limits()
t2 = time.perf_counter()
print('find_good_limits', t2 - t1)
print(catalogueconstructor.some_waveforms.shape)
t1 = time.perf_counter()
catalogueconstructor.extract_some_waveforms(n_left=None,
n_right=None,
mode='rand',
nb_max=5000)
t2 = time.perf_counter()
print('extract_some_waveforms rand', t2 - t1)
print(catalogueconstructor.some_waveforms.shape)
t1 = time.perf_counter()
catalogueconstructor.clean_waveforms(alien_value_threshold=60.)
t2 = time.perf_counter()
print('clean_waveforms', t2 - t1)
print(catalogueconstructor)
#extract_some_noise
t1 = time.perf_counter()
catalogueconstructor.extract_some_noise(nb_snippet=400)
t2 = time.perf_counter()
print('extract_some_noise', t2 - t1)
# PCA
t1 = time.perf_counter()
catalogueconstructor.project(method='global_pca',
n_components=7,
batch_size=16384)
t2 = time.perf_counter()
print('project pca', t2 - t1)
# peak_max
#~ t1 = time.perf_counter()
#~ catalogueconstructor.project(method='peak_max')
#~ t2 = time.perf_counter()
#~ print('project peak_max', t2-t1)
#~ print(catalogueconstructor.some_features.shape)
#~ t1 = time.perf_counter()
#~ catalogueconstructor.extract_some_waveforms(index=np.arange(1000))
#~ t2 = time.perf_counter()
#~ print('extract_some_waveforms others', t2-t1)
#~ print(catalogueconstructor.some_waveforms.shape)
# cluster
t1 = time.perf_counter()
catalogueconstructor.find_clusters(method='kmeans', n_clusters=11)
t2 = time.perf_counter()
print('find_clusters', t2 - t1)
print(catalogueconstructor)
def test_download_dataset():
download_dataset(name='locust')
download_dataset(name='olfactory_bulb')
Here a demo.
"""
import numpy as np
import tridesclous as tdc
import tridesclous.online
import pyqtgraph as pg
import pyacq
# get sigs
localdir, filenames, params = tdc.download_dataset(name='olfactory_bulb')
filename = filenames[0] #only first file
sigs = np.fromfile(filename,
dtype=params['dtype']).reshape(-1, params['total_channel'])
sigs = sigs.astype('float32')
sample_rate = params['sample_rate']
# This will impact the latency
chunksize = 1024
# Here a convinient fonction to create a fake device in background
# by playing signal more or less at the good speed
man = pyacq.create_manager(auto_close_at_exit=True)
ng0 = man.create_nodegroup() # process in background
dev = tridesclous.online.make_pyacq_device_from_buffer(sigs,
sample_rate,
def test_download_dataset():
download_dataset(name='locust')
download_dataset(name='olfactory_bulb')
