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[None,bool,dict]) – 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