ewoksfluo.xrffit.batch.fit_xrf_urls#

ewoksfluo.xrffit.batch.fit_xrf_urls(fit_io_uris, configuration, individual_weights=None, positive_peak_areas=None, mlines=None, quantification=None, energy_multiplier=None, fast_fitting=None, native_fitting=None, native_legacy_fitting=None, diagnostics=None, max_workers=None, block_size=None)[source]#
Parameters:

  • fit_io_uris (List[FitUris]) – List of URI’s to XRF spectra with shape (num_spectra, num_channels) to fit and the associated URI to save the fit results.

  • configuration (Union[PyMcaXrfConfiguration, str]) – PyMca configuration.

  • individual_weights (Optional[bool]) – When fitting with weights, use the weight of each spectrum (slow) instead of the average weight.

  • positive_peak_areas (Optional[bool])

  • mlines (Optional[dict]) – elements (keys) which M line group must be replaced by some M subgroups (values). Defaults to None.

  • quantification (Union[Dict[str, float], bool, None]) – Calculate mass fractions from peak area’s.

  • energy_multiplier (Optional[float]) – adds a higher energy bound equal to energy*energy_multiplier to include high-energy peaks. Default: no bound is added.

  • fast_fitting (Optional[bool]) – Fast fitting means fit all spectra by solving a single linear system of equations.

  • native_fitting (Optional[bool]) – Use native PyMca batch processing.

  • native_legacy_fitting (Optional[bool]) – Use legacy native PyMca batch processing.

  • diagnostics (Optional[bool]) – fit model and residuals.

  • max_workers (Optional[int]) – Number of parallel fitting.

  • block_size (Optional[int]) – Number of spectra to read and fit at once.

Return type:

None