# --------------------------------------------------------------------------------- if CALCULATE_U_STATS: # -- skip problems with already calculated U-stats if CALCULATE_ONLY_NEW_U_STATS: task_names = tstat.filter_calculated(mat_file_root,HCTSA_name_search_pattern = 'HCTSA_(.*)_N_70_100_reduced.mat') file_paths = [mat_file_root+"HCTSA_{0:s}_N_70_100_reduced.mat".format(s) for s in task_names] # -- calculate U-stats for all problems else: file_paths = mat_file_paths _,task_names = tstat.get_calculated_names(mat_file_root,HCTSA_name_search_pattern = 'HCTSA_(.*)_N_70_100_reduced.mat') u_stat_file_paths = tstat.calculate_ustat_mult_tasks(mat_file_paths,task_names,ustat_data_out_folder,is_from_old_matlab = IS_FROM_OLD_MATLAB) if CALCULATE_U_STATS_ALL_CLASSES_AVG: all_classes_avg = tstat.calculate_ustat_avg_mult_task(mat_file_paths,u_stat_file_paths,all_classes_avg_out_path ,is_from_old_matlab = IS_FROM_OLD_MATLAB) # --------------------------------------------------------------------------------- # -- Calculate the best features # --------------------------------------------------------------------------------- # all_classes_avg_non_corr_path = intermediate_data_root + 'all_classes_avg_non_corr_no_time_picking.npy' # ind_dict_non_corr_path = intermediate_data_root + 'ind_dict_non_corr_no_time_picking.pckl' # corr_feat_mask_path = intermediate_data_root +'/mask_no_time_picking.npy' # if CALCULATE_BEST_FEATURES: # if not CALCULATE_U_STATS_ALL_CLASSES_AVG: # all_classes_avg = np.load(all_classes_avg_path) # # -- get the filenames for all problems under investigation
# -- calculate U-stats for all problems else: file_paths = mat_file_paths _, task_names = tstat.get_calculated_names( mat_file_root, HCTSA_name_search_pattern='HCTSA_(.*)_N_70_100_reduced.mat') u_stat_file_paths = tstat.calculate_ustat_mult_tasks( mat_file_paths, task_names, ustat_data_out_folder, is_from_old_matlab=IS_FROM_OLD_MATLAB) if CALCULATE_U_STATS_ALL_CLASSES_AVG: all_classes_avg = tstat.calculate_ustat_avg_mult_task( mat_file_paths, u_stat_file_paths, all_classes_avg_out_path, is_from_old_matlab=IS_FROM_OLD_MATLAB) # --------------------------------------------------------------------------------- # -- Calculate the best features # --------------------------------------------------------------------------------- # all_classes_avg_non_corr_path = intermediate_data_root + 'all_classes_avg_non_corr_no_time_picking.npy' # ind_dict_non_corr_path = intermediate_data_root + 'ind_dict_non_corr_no_time_picking.pckl' # corr_feat_mask_path = intermediate_data_root +'/mask_no_time_picking.npy' # if CALCULATE_BEST_FEATURES: # if not CALCULATE_U_STATS_ALL_CLASSES_AVG: # all_classes_avg = np.load(all_classes_avg_path) # # -- get the filenames for all problems under investigation # calced_names = tstat.get_calculated_names(mat_file_root)[0]