def debug_compare_peeler():
dataio = DataIO(dirname='test_peeler')
print(dataio)
all_spikes = []
#~ for peeler_class in [Peeler,]:
#~ for peeler_class in [Peeler_OpenCl,]:
for peeler_class in [Peeler, Peeler_OpenCl]:
print()
print(peeler_class)
initial_catalogue = dataio.load_catalogue(chan_grp=0)
peeler = peeler_class(dataio)
peeler.change_params(catalogue=initial_catalogue, chunksize=1024)
t1 = time.perf_counter()
#~ peeler.run_offline_loop_one_segment(duration=None, progressbar=False)
peeler.run_offline_loop_one_segment(duration=4., progressbar=False)
t2 = time.perf_counter()
print('peeler.run_loop', t2-t1)
all_spikes.append(dataio.get_spikes(chan_grp=0).copy())
def test_peeler_geometry():
dataio = DataIO(dirname='test_peeler')
catalogue0 = dataio.load_catalogue(chan_grp=0)
catalogue1 = dataio.load_catalogue(chan_grp=0, name='with_oversampling')
for catalogue in (catalogue0, catalogue1):
print()
print('engine=geometrical')
print('inter_sample_oversampling',
catalogue['inter_sample_oversampling'])
peeler = Peeler(dataio)
peeler.change_params(engine='geometrical',
catalogue=catalogue,
chunksize=1024,
argmin_method='numba')
#~ argmin_method='opencl')
t1 = time.perf_counter()
peeler.run(progressbar=False)
t2 = time.perf_counter()
print('peeler.run_loop', t2 - t1)
spikes = dataio.get_spikes(chan_grp=0).copy()
labels = catalogue['clusters']['cluster_label']
count_by_label = [
np.sum(spikes['cluster_label'] == label) for label in labels
]
print(labels)
print(count_by_label)
def test_peeler_several_chunksize():
dataio = DataIO(dirname='test_peeler')
print(dataio)
catalogue = dataio.load_catalogue(chan_grp=0)
all_spikes = []
sig_length = dataio.get_segment_length(0)
chunksizes = [174, 512, 1024, 1023, 10000, 150000]
#~ chunksizes = [512, 1024,]
for chunksize in chunksizes:
print('**', chunksize, '**')
peeler = Peeler(dataio)
peeler.change_params(engine='geometrical',
catalogue=catalogue,
chunksize=chunksize,
argmin_method='numba')
t1 = time.perf_counter()
peeler.run(progressbar=False)
t2 = time.perf_counter()
print('extra_size', peeler.peeler_engine.extra_size, 'n_span',
peeler.peeler_engine.n_span, 'peak_width',
peeler.peeler_engine.peak_width)
print('peeler.run_loop', t2 - t1)
# copy is need because the memmap is reset at each loop
spikes = dataio.get_spikes(seg_num=0, chan_grp=0).copy()
all_spikes.append(spikes)
print(spikes.size)
# clip to last spike
last = min([spikes[-1]['index'] for spikes in all_spikes])
for i, chunksize in enumerate(chunksizes):
spikes = all_spikes[i]
all_spikes[i] = spikes[spikes['index'] <= last]
previsous_spikes = None
for i, chunksize in enumerate(chunksizes):
print('**', chunksize, '**')
spikes = all_spikes[i]
is_sorted = np.all(np.diff(spikes['index']) >= 0)
assert is_sorted
labeled_spike = spikes[spikes['cluster_label'] >= 0]
unlabeled_spike = spikes[spikes['cluster_label'] < 0]
print('labeled_spike.size', labeled_spike.size, 'unlabeled_spike.size',
unlabeled_spike.size)
print(spikes)
# TODO: Peeler chunksize influence the number of spikes
if previsous_spikes is not None:
assert previsous_spikes.size == spikes.size
np.testing.assert_array_equal(previsous_spikes['index'],
spikes['index'])
np.testing.assert_array_equal(previsous_spikes['cluster_label'],
spikes['cluster_label'])
previsous_spikes = spikes
def open_PeelerWindow():
dataio = DataIO(dirname='test_peeler')
initial_catalogue = dataio.load_catalogue(chan_grp=0)
app = pg.mkQApp()
win = PeelerWindow(dataio=dataio, catalogue=initial_catalogue)
win.show()
app.exec_()
def setup_module():
setup_catalogue('test_report', dataset_name='striatum_rat')
dataio = DataIO(dirname='test_report')
initial_catalogue = dataio.load_catalogue(chan_grp=0)
peeler = Peeler(dataio)
peeler.change_params(catalogue=initial_catalogue, chunksize=1024)
peeler.run(progressbar=False)
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_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_peeler():
dataio = DataIO(dirname='test_peeler')
initial_catalogue = dataio.load_catalogue(chan_grp=0)
peeler = Peeler(dataio)
peeler.change_params(engine='classic', catalogue=initial_catalogue,chunksize=1024)
t1 = time.perf_counter()
peeler.run(progressbar=False)
t2 = time.perf_counter()
print('peeler.run_loop', t2-t1)
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 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 test_peeler_several_chunksize():
dataio = DataIO(dirname='test_peeler')
print(dataio)
catalogue = dataio.load_catalogue(chan_grp=0)
all_spikes = []
sig_length = dataio.get_segment_length(0)
chunksizes = [174, 512, 1024, 1023, 10000, 150000]
#~ chunksizes = [512, 1024,]
for chunksize in chunksizes:
print('**', chunksize, '**')
peeler = Peeler(dataio)
peeler.change_params(catalogue=catalogue, chunksize=chunksize)
t1 = time.perf_counter()
peeler.run_offline_loop_one_segment(seg_num=0, progressbar=False)
t2 = time.perf_counter()
print('n_side', peeler.n_side, 'n_span', peeler.n_span, 'peak_width',
peeler.peak_width)
print('peeler.run_loop', t2 - t1)
spikes = dataio.get_spikes(seg_num=0, chan_grp=0)
all_spikes.append(spikes)
# clip to last spike
last = min([spikes[-1]['index'] for spikes in all_spikes])
for i, chunksize in enumerate(chunksizes):
spikes = all_spikes[i]
all_spikes[i] = spikes[spikes['index'] <= last]
previsous_spikes = None
for i, chunksize in enumerate(chunksizes):
print('**', chunksize, '**')
spikes = all_spikes[i]
is_sorted = np.all(np.diff(spikes['index']) >= 0)
assert is_sorted
labeled_spike = spikes[spikes['cluster_label'] >= 0]
unlabeled_spike = spikes[spikes['cluster_label'] < 0]
print('labeled_spike.size', labeled_spike.size, 'unlabeled_spike.size',
unlabeled_spike.size)
if previsous_spikes is not None:
assert previsous_spikes.size == spikes.size
np.testing.assert_array_equal(previsous_spikes['index'],
spikes['index'])
np.testing.assert_array_equal(previsous_spikes['cluster_label'],
spikes['cluster_label'])
previsous_spikes = spikes
def debug_interp_centers0():
dataio = DataIO(dirname='test_catalogueconstructor')
catalogueconstructor = CatalogueConstructor(dataio=dataio)
catalogue = catalogueconstructor.make_catalogue()
centers = catalogue['centers0']
interp_centers = catalogue['interp_centers0']
subsample_ratio = catalogue['subsample_ratio']
def test_pruningshears():
dirname = 'test_cluster'
dataio = DataIO(dirname=dirname)
print(dataio)
cc = CatalogueConstructor(dataio=dataio)
#~ print(cc.mode)
#~ exit()
#~ cc.extract_some_features(method='pca_by_channel')
#~ print(dataio)
#~ print(cc)
if dataset_name == 'olfactory_bulb':
kargs = dict(adjacency_radius_um = 420)
else:
kargs = {}
t0 = time.perf_counter()
#~ cc.find_clusters(method='pruningshears', print_debug=True)
#~ cc.find_clusters(method='pruningshears', print_debug=True, debug_plot=True, **kargs)
cc.find_clusters(method='pruningshears', print_debug=False, debug_plot=False, **kargs)
t1 = time.perf_counter()
print('cluster', t1-t0)
if __name__ == '__main__':
app = mkQApp()
win = CatalogueWindow(cc)
win.show()
app.exec_()
def test_make_catalogue():
dataio = DataIO(dirname='test_catalogueconstructor')
cc = CatalogueConstructor(dataio=dataio)
#~ cc.make_catalogue()
cc.make_catalogue_for_peeler()
def open_catalogue_window():
dataio = DataIO(dirname='test_peeler')
catalogueconstructor = CatalogueConstructor(dataio=dataio)
app = pg.mkQApp()
win = CatalogueWindow(catalogueconstructor)
win.show()
app.exec_()
def test_sawchaincut():
#~ dirname = 'test_catalogueconstructor'
#~ dirname = '/home/samuel/Documents/projet/tridesclous/example/tridesclous_locust/'
#~ dirname = '/home/samuel/Documents/projet/DataSpikeSorting/GT 252/tdc_20170623_patch1/'
#~ dirname = '/home/samuel/Documents/projet/tridesclous/example/tridesclous_locust/'
#~ dirname = '/home/samuel/Documents/projet/tridesclous/example/tridesclous_olfactory_bulb/'
#~ dirname = '/home/samuel/Documents/projet/tridesclous/example/tridesclous_olfactory_bulb/'
#~ dirname = '/home/samuel/Documents/projet/DataSpikeSorting/kampff/tdc_2015_09_03_Cell9.0/'
#~ dirname = '/home/samuel/Documents/projet/DataSpikeSorting/spikesortingtest/tdc_silico_0/'
dirname = '/home/samuel/Documents/projet/tridesclous/example/tridesclous_purkinje/'
dataio = DataIO(dirname=dirname)
cc = catalogueconstructor = CatalogueConstructor(dataio=dataio)
print(dataio)
print(cc)
t0 = time.perf_counter()
cc.find_clusters(method='sawchaincut')
t1 = time.perf_counter()
print('cluster', t1 - t0)
#~ exit()
print(cc)
app = mkQApp()
win = CatalogueWindow(catalogueconstructor)
win.show()
app.exec_()
def test_summary_after_peeler_clusters():
dataio = DataIO(dirname='test_report')
summary_after_peeler_clusters(dataio, chan_grp=0, labels=[0])
summary_after_peeler_clusters(dataio,
chan_grp=0,
labels=[0],
neighborhood_radius=200)
def test_peeler_sparse_opencl():
dataio = DataIO(dirname='test_peeler')
initial_catalogue = dataio.load_catalogue(chan_grp=0)
peeler = Peeler(dataio)
peeler.change_params(
catalogue=initial_catalogue,
chunksize=1024,
use_sparse_template=True,
sparse_threshold_mad=1.5,
use_opencl_with_sparse=True,
)
t1 = time.perf_counter()
peeler.run(progressbar=False)
t2 = time.perf_counter()
print('peeler.run_loop', t2 - t1)
def test_feature_with_lda_selection():
dataio = DataIO(dirname='test_catalogueconstructor')
cc = CatalogueConstructor(dataio=dataio)
print(cc)
selection = np.in1d(cc.all_peaks['cluster_label'], [1, 2, 3, 4])
print(np.sum(selection), '/', cc.all_peaks.size)
cc.extract_some_features(method='global_lda', selection=selection)
print(cc.some_features.shape)
print(cc.some_features)
def test_trash_small_cluster():
dirname = 'test_cleancluster'
restore_savepoint(dirname, savepoint='after_trash_low_extremum')
dataio = DataIO(dirname=dirname)
cc = CatalogueConstructor(dataio=dataio)
t1 = time.perf_counter()
cc.trash_small_cluster()
t2 = time.perf_counter()
print('trash_small_cluster', t2 - t1)
def test_all_decomposition():
dirname = 'test_catalogueconstructor'
dataio = DataIO(dirname=dirname)
cc = catalogueconstructor = CatalogueConstructor(dataio=dataio)
print(dataio)
print(cc)
methods = ['global_pca', 'pca_by_channel', 'peak_max','neighborhood_pca' ] #'neighborhood_pca', 'tsne', 'pca_by_channel_then_tsne'
for method in methods:
t0 = time.perf_counter()
cc.extract_some_features(method=method)
t1 = time.perf_counter()
print('extract_some_features', method, t1-t0)
def test_plot_signals():
dataio = DataIO('test_matplotlibplot')
catalogueconstructor = CatalogueConstructor(dataio=dataio, chan_grp=0)
plot_signals(dataio, signal_type='initial')
plot_signals(dataio, signal_type='processed')
plot_signals(catalogueconstructor,
signal_type='processed',
with_peaks=True,
time_slice=(2., 3))
plot_signals(catalogueconstructor,
signal_type='processed',
with_span=True,
time_slice=(2., 3))
def test_auto_merge():
dirname = 'test_cleancluster'
restore_savepoint(dirname, savepoint='after_trash_not_aligned')
dataio = DataIO(dirname=dirname)
cc = CatalogueConstructor(dataio=dataio)
t1 = time.perf_counter()
cc.auto_merge_cluster()
t2 = time.perf_counter()
print('auto_merge_cluster', t2 - t1)
cc.create_savepoint(name='after_auto_merge_cluster')
def setup_catalogue(dirname, dataset_name='olfactory_bulb'):
if os.path.exists(dirname):
shutil.rmtree(dirname)
dataio = DataIO(dirname=dirname)
localdir, filenames, params = download_dataset(name=dataset_name)
dataio.set_data_source(type='RawData', filenames=filenames, **params)
if dataset_name=='olfactory_bulb':
channels = [5, 6, 7, 8, 9]
else:
channels = [0,1,2,3]
dataio.add_one_channel_group(channels=channels)
catalogueconstructor = CatalogueConstructor(dataio=dataio)
params = {
'duration' : 60.,
'preprocessor' : {
'highpass_freq' : 300.,
'chunksize' : 1024,
'lostfront_chunksize' : 100,
},
'peak_detector' : {
'peak_sign' : '-',
'relative_threshold' : 7.,
'peak_span_ms' : 0.5,
},
'extract_waveforms' : {
'wf_left_ms' : -2.5,
'wf_right_ms' : 4.0,
'nb_max' : 10000,
},
'clean_waveforms' : {
'alien_value_threshold' : 60.,
},
'noise_snippet' : {
'nb_snippet' : 300,
},
'feature_method': 'global_pca',
'feature_kargs':{'n_components': 5},
'cluster_method' : 'kmeans',
'cluster_kargs' : {'n_clusters': 12},
'clean_cluster' : False,
'clean_cluster_kargs' : {},
}
apply_all_catalogue_steps(catalogueconstructor, params, verbose=True)
catalogueconstructor.trash_small_cluster()
catalogueconstructor.order_clusters(by='waveforms_rms')
catalogueconstructor.make_catalogue_for_peeler()
def test_peeler_argmin_methods():
dataio = DataIO(dirname='test_peeler')
catalogue = dataio.load_catalogue(chan_grp=0, name='with_oversampling')
argmin_methods = ['opencl', 'numba', 'pythran']
#~ argmin_methods = ['opencl', 'pythran']
for argmin_method in argmin_methods:
peeler = Peeler(dataio)
peeler.change_params(
engine='classic',
catalogue=catalogue,
chunksize=1024,
argmin_method=argmin_method,
cl_platform_index=0,
cl_device_index=0,
)
t1 = time.perf_counter()
peeler.run(progressbar=False)
t2 = time.perf_counter()
print(argmin_method, 'peeler.run_loop', t2 - t1)
def test_pruningshears():
dirname = 'test_cluster'
dataio = DataIO(dirname=dirname)
print(dataio)
cc = CatalogueConstructor(dataio=dataio)
cc.extract_some_features(method='pca_by_channel')
#~ print(dataio)
#~ print(cc)
t0 = time.perf_counter()
cc.find_clusters(method='pruningshears', print_debug=True)
t1 = time.perf_counter()
print('cluster', t1 - t0)
def test_auto_split():
dirname = 'test_cleancluster'
restore_savepoint(dirname, savepoint='after_find_clusters')
dataio = DataIO(dirname=dirname)
cc = CatalogueConstructor(dataio=dataio)
cc.find_clusters(method='pruningshears')
print(cc)
print(cc.n_jobs)
t1 = time.perf_counter()
cc.auto_split_cluster()
t2 = time.perf_counter()
print('auto_split_cluster', t2 - t1)
print(cc)
cc.create_savepoint(name='after_auto_split')
def debug_one_decomposition():
dirname = 'test_catalogueconstructor'
dataio = DataIO(dirname=dirname)
cc = catalogueconstructor = CatalogueConstructor(dataio=dataio)
print(dataio)
print(cc)
t0 = time.perf_counter()
#~ cc.extract_some_features(method='global_pca', n_components=7)
#~ cc.extract_some_features(method='peak_max')
#~ cc.extract_some_features(method='pca_by_channel', n_components_by_channel=3)
cc.extract_some_features(method='neighborhood_pca',
n_components_by_neighborhood=3,
radius_um=500)
print(cc.channel_to_features)
print(cc.channel_to_features.shape)
t1 = time.perf_counter()
print('extract_some_features', t1 - t0)
def test_sawchaincut():
dirname = 'test_cluster'
dataio = DataIO(dirname=dirname)
cc = CatalogueConstructor(dataio=dataio)
#~ print(dataio)
#~ print(cc)
t0 = time.perf_counter()
cc.find_clusters(method='sawchaincut', print_debug=True)
t1 = time.perf_counter()
print('cluster', t1 - t0)
#~ exit()
#~ print(cc)
if __name__ == '__main__':
app = mkQApp()
win = CatalogueWindow(cc)
win.show()
app.exec_()
def test_pruningshears():
dirname = 'test_cluster'
dataio = DataIO(dirname=dirname)
print(dataio)
cc = CatalogueConstructor(dataio=dataio)
#~ cc.extract_some_features(method='pca_by_channel')
#~ print(dataio)
#~ print(cc)
t0 = time.perf_counter()
cc.find_clusters(method='pruningshears', print_debug=True)
t1 = time.perf_counter()
print('cluster', t1 - t0)
if __name__ == '__main__':
app = mkQApp()
win = CatalogueWindow(cc)
win.show()
app.exec_()
def setup_catalogue():
if os.path.exists('test_peeler'):
shutil.rmtree('test_peeler')
dataio = DataIO(dirname='test_peeler')
localdir, filenames, params = download_dataset(name='olfactory_bulb')
dataio.set_data_source(type='RawData', filenames=filenames, **params)
dataio.add_one_channel_group(channels=[5, 6, 7, 8, 9])
catalogueconstructor = CatalogueConstructor(dataio=dataio)
fullchain_kargs = {
'duration': 60.,
'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,
},
}
apply_all_catalogue_steps(catalogueconstructor,
fullchain_kargs,
'global_pca', {'n_components': 12},
'kmeans', {'n_clusters': 12},
verbose=True)
catalogueconstructor.trash_small_cluster()
catalogueconstructor.make_catalogue_for_peeler()
def test_peeler_with_and_without_preprocessor():
if ON_CI_CLOUD:
engines = ['geometrical']
else:
engines = ['geometrical', 'geometrical_opencl']
#~ engines = ['geometrical_opencl']
for engine in engines:
for i in range(2):
#~ for i in [1]:
print()
if i == 0:
print(engine, 'without processing')
dataio = DataIO(dirname='test_peeler')
else:
print(engine, 'with processing')
dataio = DataIO(dirname='test_peeler2')
catalogue = dataio.load_catalogue(chan_grp=0)
peeler = Peeler(dataio)
peeler.change_params(engine=engine,
catalogue=catalogue,
chunksize=1024)
t1 = time.perf_counter()
peeler.run(progressbar=False)
t2 = time.perf_counter()
print('peeler run_time', t2 - t1)
spikes = dataio.get_spikes(chan_grp=0).copy()
labels = catalogue['clusters']['cluster_label']
count_by_label = [
np.sum(spikes['cluster_label'] == label) for label in labels
]
print(labels)
print(count_by_label)
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)