How to use the pyxrf.core.quant_analysis.ParamQuantitativeAnalysis function in pyxrf

def __init__(self):
        self.fig = plt.figure(figsize=(3, 2))
        matplotlib.rcParams['axes.formatter.useoffset'] = True

        # Do not apply scaler norm on following data
        self.name_not_scalable = ['r2_adjust', 'r_factor', 'alive', 'dead', 'elapsed_time',
                                  'scaler_alive', 'i0_time', 'time', 'time_diff', 'dwell_time']

        self.param_quant_analysis = ParamQuantitativeAnalysis()
        self.param_quant_analysis.set_experiment_distance_to_sample(distance_to_sample=0.0)
        self.param_quant_analysis.set_experiment_incident_energy(incident_energy=self.incident_energy)

interpolate the result to uniform grid before saving to tiff and txt files
        The grid dimensions match the dimensions of positional data for X and Y axes.
        The range of axes is chosen to fit the values of X and Y.
    data_from : str, optional
        where do data come from? Data format includes data from NSLS-II, or 2IDE-APS
    dask_client: dask.distributed.Client
        Dask client object. If None, then Dask client is created automatically.
        If a batch of files is processed, then creating Dask client and
        passing the reference to it to the processing functions will save
        execution time: `client = Client(processes=True, silence_logs=logging.ERROR)`
    """
    fpath = os.path.join(working_directory, file_name)

    # Load quantitative calibration files (if necessary)
    quant_norm = False  # Indicates if at least one calibration file is loaded
    param_quant_analysis = ParamQuantitativeAnalysis()
    if fln_quant_calib_data:
        if isinstance(fln_quant_calib_data, str):
            fln_quant_calib_data = [fln_quant_calib_data]
        for fln in fln_quant_calib_data:
            if os.path.isabs(fln):
                f = fln
            else:
                f = os.path.join(working_directory, fln)
            try:
                param_quant_analysis.load_entry(f)
                quant_norm = True
                logger.info(f"Quantitative calibration is loaded successfully from file '{f}'")
            except Exception as ex:
                logger.error(f"Error occurred while loading quantitative calibration from file '{f}': {ex}")

    t0 = time.time()

def _update_qn(self, change):

        # Propagate current value of 'self.param_quant_analysis' (activate 'observer' functions)
        tmp = self.param_quant_analysis
        self.param_quant_analysis = ParamQuantitativeAnalysis()
        self.param_quant_analysis = tmp

        self.set_low_high_value()  # reset low high values based on normalization
        self.show_image()
        self.update_img_wizard_items()