'nb_patch': 0, 'ssub_B': 2, 'init_iter': 2, 'ring_size_factor': 1.4, 'method_init': 'corr_pnr', 'method_deconvolution': 'oasis', 'update_background_components': True, 'center_psf': True, 'border_pix': 0, 'normalize_init': False, 'del_duplicates': True, 'only_init': True } mouse_row_new = main_source_extraction( mouse_row, parameters_source_extraction, dview, session_wise=True) states_df = db.append_to_or_merge_with_states_df( states_df, mouse_row_new) db.save_analysis_states_database( states_df, path=analysis_states_database_path, backup_path=backup_path) selected_rows = db.select(states_df, 'source_extraction', mouse=mouse_number, session=session, is_rest=is_rest, decoding_v=decoding_version,
selected_rows = db.select( states_df, 'source_extraction', mouse=mouse_number, session=session, is_rest=is_rest, decoding_v=decoding_version, cropping_v=cropping_version, motion_correction_v=motion_correction_version, alignment_v=alignment_version, source_extraction_v=0) ### all aligned videos are save in one file aligned_row = selected_rows.iloc[0] mouse_row_new = main_source_extraction(aligned_row, parameters_source_extraction, dview) states_df = db.append_to_or_merge_with_states_df( states_df, mouse_row_new) db.save_analysis_states_database(states_df, path=analysis_states_database_path, backup_path=backup_path) source_extraction_version = mouse_row_new.name[8] #%% run separately component evaluation # evaluation dview.terminate() for session in [1, 2, 4]: print(session) # Run decoding for group of data tha have the same cropping parameters (same mouse)
'nb': 0, 'nb_patch': 0, 'ssub_B': 2, 'init_iter': 2, 'ring_size_factor': 1.4, 'method_init': 'corr_pnr', 'method_deconvolution': 'oasis', 'update_background_components': True, 'center_psf': True, 'border_pix': 0, 'normalize_init': False, 'del_duplicates': True, 'only_init': True } main_source_extraction(index, row, parameters_source_extraction, dview) #%% ## plot countours source_extraction_output = eval(row.loc['source_extraction_output']) corr_path, pnr_path = source_extraction_output['meta']['corr'][ 'main'], source_extraction_output['meta']['pnr']['main'] source_extraction_parameters = db.get_parameters('source_extraction', index[0], index[1], index[2], index[3], download_=False) cn_filter = np.load(db.get_file(corr_path)) pnr = np.load(db.get_file(pnr_path))