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 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_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)
