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一个用于基于模型分析镜面反射中子和X射线反射测量数据集的紧凑软件包。

: a compact software package for model-based analysis of specular neutron and X-ray reflectometry data sets.

作者信息

Koutsioubas Alexandros

机构信息

Jülich Centre for Neutron Science (JCNS) at Heinz Maier-Leibnitz Zentrum (MLZ), Forschungszentrum Jülich GmbH, Lichtenbergstrasse 1, 85748 Garching, Germany.

出版信息

J Appl Crystallogr. 2021 Oct 20;54(Pt 6):1857-1866. doi: 10.1107/S1600576721009262. eCollection 2021 Dec 1.

DOI:10.1107/S1600576721009262
PMID:34963772
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8662969/
Abstract

constitutes a set of open-source Python scripts that facilitate a range of specular neutron and X-ray reflectivity calculations, involving the generation of theoretical curves and the comparison/fitting of interfacial model reflectivity against experimental data sets. The primary focus of the software is twofold: on one hand to offer a more natural framework for model definition, requiring minimum coding literacy, and on the other hand to include advanced analysis methods that have been proposed in recent work. Particular attention is given to the ability to co-refine reflectivity data and to the estimation of model-parameter uncertainty and covariance using bootstrap analysis and Markov chain Monte Carlo sampling. The compactness and simplicity of model definition together with the streamlined analysis do not present a steep learning curve for the user, an aspect that may accelerate the generation of reproducible, easily readable and statistically accurate reports in future neutron and X-ray reflectivity related literature.

摘要

由一组开源Python脚本组成，这些脚本有助于进行一系列镜面中子和X射线反射率计算，包括生成理论曲线以及将界面模型反射率与实验数据集进行比较/拟合。该软件的主要重点有两个方面：一方面为模型定义提供一个更自然的框架，所需的编码知识最少；另一方面纳入近期工作中提出的先进分析方法。特别关注共精炼反射率数据的能力以及使用自助分析和马尔可夫链蒙特卡罗采样估计模型参数的不确定性和协方差。模型定义的紧凑性和简单性以及简化的分析对用户来说不会构成陡峭的学习曲线，这一方面可能会加速未来中子和X射线反射率相关文献中可重复、易于阅读且统计准确的报告的生成。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/ba6c1969228c/j-54-01857-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/bb7e7e7b555a/j-54-01857-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/e150aee62575/j-54-01857-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/9f41ee73e3dd/j-54-01857-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/6675c19b1ba9/j-54-01857-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/b1cec7f89e72/j-54-01857-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/4272fdf1c827/j-54-01857-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/e8cc4d67e437/j-54-01857-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/a03dedc0a9ce/j-54-01857-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/63f066ef5870/j-54-01857-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/ba6c1969228c/j-54-01857-fig10.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/bb7e7e7b555a/j-54-01857-fig1.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/e150aee62575/j-54-01857-fig2.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/9f41ee73e3dd/j-54-01857-fig3.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/6675c19b1ba9/j-54-01857-fig4.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/b1cec7f89e72/j-54-01857-fig5.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/4272fdf1c827/j-54-01857-fig6.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/e8cc4d67e437/j-54-01857-fig7.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/a03dedc0a9ce/j-54-01857-fig8.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/63f066ef5870/j-54-01857-fig9.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a551/8662969/ba6c1969228c/j-54-01857-fig10.jpg

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