N=5, remove_baseline=True, dview=dview, robust_std=False, Athresh=0.1, thresh_C=0.3, num_traces_per_group=20) #%% from caiman.components_evaluation import evaluate_components_CNN predictions, final_crops = evaluate_components_CNN( A, dims, gSig, model_name='use_cases/CaImAnpaper/cnn_model') #%% threshold = .95 from caiman.utils.visualization import matrixMontage pl.figure() matrixMontage( np.squeeze(final_crops[np.where(predictions[:, 1] >= threshold)[0]])) pl.figure() matrixMontage( np.squeeze(final_crops[np.where(predictions[:, 0] >= threshold)[0]])) #%% thresh = .95 idx_components_cnn = np.where(predictions[:, 1] >= thresh)[0] print(' ***** ') print((len(final_crops))) print((len(idx_components_cnn))) #print((len(idx_blobs))) #% idx_components_r = np.where((r_values >= .99))[0] idx_components_raw = np.where(fitness_raw < -60)[0] idx_components_delta = np.where(fitness_delta < -60)[0]
Npeaks = 10 traces = C + YrA idx_components, idx_components_bad, fitness_raw, fitness_delta, r_values = cm.components_evaluation.estimate_components_quality(traces, Y, A, C, b, f, final_frate=final_frate, Npeaks=10, r_values_min=.85, fitness_min=-30, fitness_delta_min=-30, return_all=True, N=5, remove_baseline=True, dview=dview, robust_std=False, Athresh=0.1, thresh_C=0.3, num_traces_per_group=20) #%% from caiman.components_evaluation import evaluate_components_CNN predictions, final_crops = evaluate_components_CNN( A, dims, gSig, model_name='model/cnn_model') #%% threshold = .95 from caiman.utils.visualization import matrixMontage pl.figure() matrixMontage(np.squeeze( final_crops[np.where(predictions[:, 1] >= threshold)[0]])) pl.figure() matrixMontage(np.squeeze( final_crops[np.where(predictions[:, 0] >= threshold)[0]])) #%% thresh = .95 idx_components_cnn = np.where(predictions[:, 1] >= thresh)[0] print(' ***** ') print((len(final_crops))) print((len(idx_components_cnn))) # print((len(idx_blobs))) #% idx_components_r = np.where((r_values >= .99))[0] idx_components_raw = np.where(fitness_raw < -60)[0] idx_components_delta = np.where(fitness_delta < -60)[0]