SASfit, McSAS and beyond: Prospect of complementary tools for SAS data analysis
- Data analysis of SAS measurements has been dominated by the classical curve fitting approach. This method finds optimal parameters of a scattering model composed of analytical expressions. SASfit represents such a classical curve fitting toolbox: it is one of the mature programs for small-angle scattering data analysis and has been available and used for many years. The latest developments [1] will be extended by improving the interoperability of the extensive data base of models with third-party analysis software. An updated format of model definitions is also presented, which allows model function plug-ins to be used with the Python language.
To complement the classical curve fitting method, the user-friendly opensource Monte Carlo regression package McSAS was developed. Most importantly, the form-free Monte Carlo approach of McSAS means that it is not necessary to provide any further mathematical restrictions to the Parameter distribution. Future developments include separating theData analysis of SAS measurements has been dominated by the classical curve fitting approach. This method finds optimal parameters of a scattering model composed of analytical expressions. SASfit represents such a classical curve fitting toolbox: it is one of the mature programs for small-angle scattering data analysis and has been available and used for many years. The latest developments [1] will be extended by improving the interoperability of the extensive data base of models with third-party analysis software. An updated format of model definitions is also presented, which allows model function plug-ins to be used with the Python language.
To complement the classical curve fitting method, the user-friendly opensource Monte Carlo regression package McSAS was developed. Most importantly, the form-free Monte Carlo approach of McSAS means that it is not necessary to provide any further mathematical restrictions to the Parameter distribution. Future developments include separating the core optimization from the GUI (allowing 'headless' integration), as well as parallel computing which reduces the computing time proportional to the number of available computing cores. The headless mode is presented by an example of Operation within interactive programming environments such as a Jupyter notebook.
The promising results of Monte Carlo based data analysis for determining form-free Parameter distributions motivated the evaluation of the method with dynamic light scattering (DLS) data. For this purpose, the method was adapted for analyzing correlation curves such as those from multi-angle dynamic light scattering (DLS) data. The development of McDLS intends to overcome limitations of existing methods at reliably determining the modality of size distributions. An example of Monte Carlo based data analysis of multimodal DLS measurements will be presented.…