Function read_MAPS

pyxrf/model/fileio.py:1799–1892

(working_directory, file_name, channel_num=1)

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
        # data_dict[fname] = data

        # raw data
        exp_data = data["mca_arr"][:]

        # data from channel summed
        roi_channel = data["channel_names"][()]
        roi_val = data["XRF_roi"][:]

        scaler_names = data["scaler_names"][()]
        scaler_val = data["scalers"][:]

        try:
            # data from fit
            fit_val = data["XRF_fits"][:]
        except KeyError:
            logger.info("No fitting from MAPS can be loaded.")

        try:
            fit_data = f["xrfmap/detsum"]
            fit_v_pyxrf = fit_data["xrf_fit"][:]
            fit_n_pyxrf = fit_data["xrf_fit_name"][()]
            print(fit_n_pyxrf)
        except KeyError:
            logger.info("No fitting from pyxrf can be loaded.")

    # exp_shape = exp_data.shape
    exp_data = exp_data.T
    exp_data = np.rot90(exp_data, 1)
    logger.info("File : {} with total counts {}".format(fname, np.sum(exp_data)))
    DS = DataSelection(filename=fname, raw_data=exp_data)
    data_sets.update({fname: DS})

    # save roi and fit into dict

    temp_roi = {}