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)