try:
        for i in range(1, 3):
            k = i
            model = pca_train_k.pca(train_data, k)
            if al_type == "SPE":
                limit = model["QCUL_95"]
            elif al_type == "FAI":
                limit = model["Kesi_95"]
            else:
                limit = model["T2CUL_95"]
            _, test_data, f_m = get_test_data_1(train_data, samples, amplitudes, fault_index, 1)
            data = (test_data - model["Train_X_mean"]) / model["Train_X_std"]
            t_r = get_rb_pca(data, model, limit, al_type, f_m)
            result.append(t_r)
    except Exception as e:
        with open('log.log', "a") as f:
            f.write(f"{str(datetime.datetime.now())}{traceback.format_exc()}")



# for index in range(data.shape[0]):
            #     line = data[index] @ m @ data[index].T
            #     lines.append(line)
            # x = list(range(data.shape[0]))
            # limits_line = list(repeat(limit, data.shape[0]))
            # plt.plot(x, lines)
            # plt.plot(x, limits_line)
            # plt.title(f'k={k},limit={limit}')