How to use the pyxrf.model.scan_metadata.ScanMetadataXRF function in pyxrf

def read_MAPS(working_directory,
              file_name, channel_num=1):
    # data_dict = OrderedDict()
    data_sets = OrderedDict()
    img_dict = OrderedDict()

    # Empty container for metadata
    mdata = ScanMetadataXRF()

    #  cut off bad point on the last position of the spectrum
    # bad_point_cut = 0

    fit_val = None
    fit_v_pyxrf = None

    file_path = os.path.join(working_directory, file_name)
    print('file path is {}'.format(file_path))

    with h5py.File(file_path, 'r+') as f:

        data = f['MAPS']
        fname = file_name.split('.')[0]

        #  for 2D MAP

def _extract_metadata_from_header(hdr):
    """
    Extract metadata from start and stop document. Metadata extracted from other document
    in the scan are beamline specific and added to dictionary at later time.
    """

    start_document = hdr.start

    mdata = ScanMetadataXRF()

    data_locations = {
        "scan_id": ["scan_id"],
        "scan_uid": ["uid"],
        "scan_instrument_id": ["beamline_id"],
        "scan_instrument_name": [],
        "scan_time_start": ["time"],
        "scan_time_start_utc": ["time"],

        "instrument_mono_incident_energy": ["beamline_status/energy"],
        "instrument_beam_current": [],
        "instrument_detectors": ["detectors"],

        "sample_name": ["sample/name", "sample"],

        "experiment_plan_name": ["plan_name"],

load fitting results
    load_roi_results : bool
        load results of roi computation

    Returns
    -------
    data_dict : dict
        with fitting data
    data_sets : dict
        data from each channel and channel summed, a dict of DataSelection objects
    """
    data_sets = OrderedDict()
    img_dict = OrderedDict()

    # Empty container for metadata
    mdata = ScanMetadataXRF()

    file_path = os.path.join(working_directory, file_name)

    # defined in other_list in config file
    try:
        dict_sc = retrieve_data_from_hdf_suitcase(file_path)
    except Exception:
        dict_sc = {}

    with h5py.File(file_path, 'r+') as f:

        # Retrieve metadata if it exists
        if "xrfmap/scan_metadata" in f:  # Metadata is always loaded
            metadata = f["xrfmap/scan_metadata"]
            for key, value in metadata.attrs.items():
                # Convert ndarrays to lists (they were lists before they were saved)

load_each_channel = Bool(False)

    # Used while loading data from database
    # True: overwrite existing data file if it exists
    # False: create new file with unique name (original name + version number)
    file_overwrite_existing = Bool(False)

    p1_row = Int(-1)
    p1_col = Int(-1)
    p2_row = Int(-1)
    p2_col = Int(-1)

    data_ready = Bool(False)

    # Scan metadata
    scan_metadata = Typed(ScanMetadataXRF)
    # Indicates if metadata is available for recently loaded scan
    scan_metadata_available = Bool(False)
    # Indicates if the incident energy is available in metadata for recently loaded scan
    incident_energy_available = Bool(False)

    # Changing this variable sets incident energy in the ``plot_model``
    #   Must be linked with the function ``plot_model.set_incident_energy``
    # This value is not updated if incident energy parameter is changed somewhere else, therefore
    #   its value should not be used for computations!!!
    incident_energy_set = Float(0.0)

    def __init__(self, **kwargs):
        self.working_directory = kwargs['working_directory']
        self.mask_data = None

        # Display PyXRF version in the window title