How to use the pyxrf.model.fileio.output_data function in pyxrf

output_path = os.path.join(working_directory, output_folder)
            output_data(output_dir=output_path,
                        interpolate_to_uniform_grid=interpolate_to_uniform_grid,
                        dataset_name="dataset_fit",  # Sum of all detectors: should end with '_fit'
                        quant_norm=quant_norm,
                        param_quant_analysis=param_quant_analysis,
                        distance_to_sample=quant_distance_to_sample,
                        dataset_dict=dataset, positions_dict=positions_dict,
                        file_format="txt", scaler_name=scaler_name,
                        scaler_name_list=scaler_name_list,
                        use_average=use_average)

        if save_tiff is True:
            output_folder = 'output_tiff_'+prefix_fname
            output_path = os.path.join(working_directory, output_folder)
            output_data(output_dir=output_path,
                        interpolate_to_uniform_grid=interpolate_to_uniform_grid,
                        dataset_name="dataset_fit",  # Sum of all detectors: should end with '_fit'
                        quant_norm=quant_norm,
                        param_quant_analysis=param_quant_analysis,
                        dataset_dict=dataset, positions_dict=positions_dict,
                        file_format="tiff", scaler_name=scaler_name,
                        scaler_name_list=scaler_name_list,
                        use_average=use_average)

    if fit_channel_each:
        img_dict, data_sets, mdata = read_hdf_APS(working_directory, file_name,
                                                  spectrum_cut=spectrum_cut,
                                                  load_each_channel=True)

        # Find the detector channels and the names of the channels
        det_channels = [_ for _ in data_sets.keys() if re.search(r"_det\d+$", _)]

scaler_dict = get_scaler_set(img_dict)
        scaler_name_list = list(scaler_dict.keys())
        positions_dict = get_positions_set(img_dict)
        # Generate dataset
        dataset = copy.deepcopy(scaler_dict)
        dataset.update(result_map_sum)

        # Set parameters for quantitative normalization
        param_quant_analysis.experiment_incident_energy = incident_energy_used
        param_quant_analysis.experiment_distance_to_sample = quant_distance_to_sample
        param_quant_analysis.experiment_detector_channel = "sum"

        if save_txt is True:
            output_folder = 'output_txt_'+prefix_fname
            output_path = os.path.join(working_directory, output_folder)
            output_data(output_dir=output_path,
                        interpolate_to_uniform_grid=interpolate_to_uniform_grid,
                        dataset_name="dataset_fit",  # Sum of all detectors: should end with '_fit'
                        quant_norm=quant_norm,
                        param_quant_analysis=param_quant_analysis,
                        distance_to_sample=quant_distance_to_sample,
                        dataset_dict=dataset, positions_dict=positions_dict,
                        file_format="txt", scaler_name=scaler_name,
                        scaler_name_list=scaler_name_list,
                        use_average=use_average)

        if save_tiff is True:
            output_folder = 'output_tiff_'+prefix_fname
            output_path = os.path.join(working_directory, output_folder)
            output_data(output_dir=output_path,
                        interpolate_to_uniform_grid=interpolate_to_uniform_grid,
                        dataset_name="dataset_fit",  # Sum of all detectors: should end with '_fit'

scaler_dict = get_scaler_set(img_dict)
            scaler_name_list = list(scaler_dict.keys())
            positions_dict = get_positions_set(img_dict)
            # Generate dataset
            dataset = copy.deepcopy(scaler_dict)
            dataset.update(result_map_det)

            # Set parameters for quantitative normalization
            param_quant_analysis.experiment_incident_energy = incident_energy_used
            param_quant_analysis.experiment_distance_to_sample = quant_distance_to_sample
            param_quant_analysis.experiment_detector_channel = det_channel_names[i]

            if save_txt is True:
                output_folder = 'output_txt_'+prefix_fname
                output_path = os.path.join(working_directory, output_folder)
                output_data(output_dir=output_path,
                            interpolate_to_uniform_grid=interpolate_to_uniform_grid,
                            dataset_name=f"dataset_{det_channel_names[i]}_fit",  # ..._det1_fit, etc.
                            quant_norm=quant_norm,
                            param_quant_analysis=param_quant_analysis,
                            dataset_dict=dataset, positions_dict=positions_dict,
                            file_format="txt", scaler_name=scaler_name,
                            scaler_name_list=scaler_name_list,
                            use_average=use_average)

            if save_tiff is True:
                output_folder = 'output_tiff_'+prefix_fname
                output_path = os.path.join(working_directory, output_folder)
                output_data(output_dir=output_path,
                            interpolate_to_uniform_grid=interpolate_to_uniform_grid,
                            dataset_name=f"dataset_{det_channel_names[i]}_fit",  # ..._det1_fit, etc.
                            quant_norm=quant_norm,

positions_dict = self.img_dict["positions"]
        else:
            positions_dict = {}

        # Scalers are located in a separate dataset in 'img_dict'. They are also referenced
        #   in each '_fit' dataset (and in the selected dataset 'self.dict_to_plot')
        #   The list of scaler names is used to avoid attaching the detector channel name
        #   to file names that contain scaler data (scalers typically do not depend on
        #   the selection of detector channels.
        scaler_dsets = [_ for _ in self.img_dict.keys() if re.search(r"_scaler$", _)]
        if scaler_dsets:
            scaler_name_list = list(self.img_dict[scaler_dsets[0]].keys())
        else:
            scaler_name_list = None

        output_data(output_dir=output_dir,
                    interpolate_to_uniform_grid=self.map_interpolation,
                    dataset_name=self.img_title, quant_norm=self.quantitative_normalization,
                    param_quant_analysis=self.param_quant_analysis,
                    dataset_dict=self.dict_to_plot, positions_dict=positions_dict,
                    file_format=file_format, scaler_name=scaler_v,
                    scaler_name_list=scaler_name_list)

output_folder = 'output_txt_'+prefix_fname
                output_path = os.path.join(working_directory, output_folder)
                output_data(output_dir=output_path,
                            interpolate_to_uniform_grid=interpolate_to_uniform_grid,
                            dataset_name=f"dataset_{det_channel_names[i]}_fit",  # ..._det1_fit, etc.
                            quant_norm=quant_norm,
                            param_quant_analysis=param_quant_analysis,
                            dataset_dict=dataset, positions_dict=positions_dict,
                            file_format="txt", scaler_name=scaler_name,
                            scaler_name_list=scaler_name_list,
                            use_average=use_average)

            if save_tiff is True:
                output_folder = 'output_tiff_'+prefix_fname
                output_path = os.path.join(working_directory, output_folder)
                output_data(output_dir=output_path,
                            interpolate_to_uniform_grid=interpolate_to_uniform_grid,
                            dataset_name=f"dataset_{det_channel_names[i]}_fit",  # ..._det1_fit, etc.
                            quant_norm=quant_norm,
                            param_quant_analysis=param_quant_analysis,
                            dataset_dict=dataset, positions_dict=positions_dict,
                            file_format="tiff", scaler_name=scaler_name,
                            scaler_name_list=scaler_name_list,
                            use_average=use_average)

    t1 = time.time()
    print(f"Processing time: {t1 - t0}")