Simonne David Horst, Martini Andrea, Signorile Matteo, Piovano Alessandro, Braglia Luca, Torelli Piero, Borfecchia Elisa, Ricchiardi Gabriele
Department of Chemistry, INSTM Reference Center and NIS and CrisDi Interdepartmental Centers, University of Torino, Via P. Giuria 7, Torino 10125, Italy.
CNR-IOM, TASC Laboratory, SS 14 km 163.5, Trieste 34149, Italy.
J Synchrotron Radiat. 2020 Nov 1;27(Pt 6):1741-1752. doi: 10.1107/S1600577520011388. Epub 2020 Sep 28.
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 or the R-factor together with the parameter uncertainty distributions and the related correlations can be extracted for each chosen model.
THORONDOR是一款数据处理软件,具有通过基于浏览器的Jupyter笔记本框架访问的图形用户界面(GUI)。它旨在为原位实验期间收集的NEXAFS光谱分析提供一个交互式且用户友好的工具。该程序允许对来自单个或多个实验的大型数据集进行实时表示和快速校正。特别是,它提供了根据用户定义的参考在能量上对齐多个光谱轮廓的可能性。已经提供了各种计算背景扣除和信号归一化的技术。在此背景下,此GUI的一项创新涉及使用基于滑块的方法,该方法为用户提供了即时操作和可视化处理后数据的能力。最后,该程序的特点是基于lmfit包的高级拟合工具箱。它提供了大量的拟合例程以及不同的峰分布和经验电离势阶跃边缘,可用于拟合NEXAFS上升沿峰。对于每个选定的模型,可以提取描述拟合优度的统计参数,如χ或R因子,以及参数不确定性分布和相关的相关性。