Class ParamQuantitativeAnalysis

pyxrf/core/quant_analysis.py:1126–1858

r""" The class is used to load and manipulate quantitative calibration data and apply quantitative normalization to XRF maps. Calibration data is loaded as entries, one calibration entry is produced by processing one calibration scan based on a reference standards. Typically calibration

class ParamQuantitativeAnalysis:
    r"""
    The class is used to load and manipulate quantitative calibration data and apply quantitative
    normalization to XRF maps. Calibration data is loaded as entries, one calibration entry is
    produced by processing one calibration scan based on a reference standards. Typically calibration
    entry is stored in JSON file, but it can be added to the class directly. Multiple entries
    based on different reference standards may be loaded simultaneously. The same emission line
    may be used in different reference standards and therefore be present in multiple calibration
    entries. The class allows to select the source of calibration data (entry) for each emission
    line.

    The functions should be applied in the following sequence:

    pqa = ParamQuantitativeAnalysis()
    # Load calibration entries
    pqa.load_entry(<file_path1>)
    pqa.load_entry(<file_path2>)
    ...
    # or add calibration entries (the 1st parameter doesn't need to be a file path)
    pqa.add_entry("calibration1", calibration_data=<calib1>)
    pqa.add_entry("calibration2", calibration_data=<calib2>)
    ...
    # Entry may be removed as
    pqa.remove_entry(<file_path1>)
    # Get the list of file paths (or IDs)
    file_paths = pqa.get_file_path_list()
    # Get the number of entries
    n_entries = pqa.get_n_entries()
    # Entry index (index of loaded entry in the internal dictionaries)
    entry_index = pqa.find_entry_index(<file_path>)
    # Get text preview of loaded calibration entry
    text_preview = pqa.get_entry_text_preview(<file_path>)
    # Update the internal state of the object (including the list of available emission lines)
    #   Should be run each time the fields of the object are manually modified.
    pqa.update_emission_line_list()
    # Get the complete set of loaded calibration entries for the given emission line
    #   Note, then the index of the entry in 'eline_info' may be different from the
    #   index of the entry, since 'eline_info' contains only the entries which contain calibration
    #   for the emission line. The list 'eline_info' may be useful for GUI presentation of data.
    eline_info = pqa.get_eline_info_complete(<emission line, e.g. "Cu_K">)
    # Select emission line from the standard (useful when emission line is present in mutliple standards)
    pqa.select_eline(<emission_line>, <file_path>)
    # Check if emission line for particular standard is selected
    pqa.is_eline_selected(<emission_line>, <file_path>)
    # Get calibration data for the emission line (only from the selected entry)
    #   The information is useful at the stage of applying quantitative normalization
    eline_calib = pqa.get_calibrations_selected(<emission_line>)
    # Get calibration data on all emission lines as a list of dictionaries (the function
    #   calls 'get_calibrations_selected' for each emission line in the set)
    all_elines_calib = pqa.get_calibrations_selected
    # Set parameters of the experiment (before processing XRF map)
    pqa.set_experiment_incident_energy(<incident_energy>)
    pqa.set_experiment_detector_channel(<detector_channel>)
    pqa.set_experiment_distance_to_sample(<distance_to_sample>)
    # Process XRF map using selected calibrations and set experiment parameters
    data_normalized = apply_quantitative_normalization(<data_in>, scaler_dict=<scaler_dict>,
                                                       scaler_name_default = <scaler_name_default>,
                                                       data_name = <emission_line>,