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_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_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 test_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)
app = mkQApp()
win = CatalogueWindow(catalogueconstructor)
win.show()
app.exec_()