THORONDOR: a software for fast treatment and analysis of low‐energy XAS data. (28th September 2020)

• Record Type: Journal Article
• Authors: Simonne, David Horst; Martini, Andrea; Signorile, Matteo; Piovano, Alessandro; Braglia, Luca; Torelli, Piero; Borfecchia, Elisa; Ricchiardi, Gabriele
• Is Part Of: Journal of synchrotron radiation. Volume 27:Part 6(2020)
• Page Start: 1741
• Page End: 1752
• Publication Date: 2020-09-28
• Subjects: NEXAFS -- in situ measurements -- data treatment -- peak fitting -- graphical user interface -- Python; Synchrotron radiation -- Periodicals; Free electron lasers -- Periodicals; 539.73505
• DOI: 10.1107/S1600577520011388
• Languages: English
• ISSNs: 0909-0495

Abstract

Abstract : THORONDOR is a Python software realized for the analysis of multiple NEXAFS spectra collected in situ . It allows the correction and normalization of the acquired spectra through a fast and intuitive interface. Moreover, it offers a sophisticated toolbox for the fitting of the NEXAFS rising‐edge peaks. Abstract : THORONDOR is a data treatment software with a graphical user interface (GUI) accessible via the browser‐based Jupyter notebook framework. It aims to provide an interactive and user‐friendly tool for the analysis of NEXAFS spectra collected during in situ experiments. The program allows on‐the‐fly representation and quick correction of large datasets from single or multiple experiments. In particular, it provides the possibility to align in energy several spectral profiles on the basis of user‐defined references. Various techniques to calculate background subtraction and signal normalization have been made available. In this context, an innovation of this GUI involves the usage of a slider‐based approach that provides the ability to instantly manipulate and visualize processed data for the user. Finally, the program is characterized by an advanced fitting toolbox based on the lmfit package. It offers a large selection of fitting routines as well as different peak distributions and empirical ionization potential step edges, which can be used for the fit of the NEXAFS rising‐edge peaks. Statistical parameters describing the goodness of a fit such as χ 2 or the R ‐factor together with the parameter uncertainty distributions and the related correlations can be extracted for each chosen model.