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_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_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 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_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)
