MANTiS: Multivariate Analysis Tool for Spectromicroscopy

X-ray spectromicroscopy involves taking a series of images at energies around an x-ray absorption edge, yielding spectrum-per-pixel data.In studies of complex mixtures or reactive phases, the resulting data are too rich and complex to understand in a direct visualization.As a result, the APS has worked with a commercial scientific software development firm (2ndlook.co) to develop MANTiS, a Python-based open source analysis code.This code uses a number of multivariate statistical analysis and classification techniques developed in collaboration with the APS, including principal component analysis and clustering to find the dominant spectroscopic themes within a dataset, and (in collaboration with Northwestern University and the Mathematics and Computer Science division at Argonne) optimization-based approaches with non-negative constrains to improve the analysis.

 

Distribution & Impact

This software is written in Python and is distributed as source code and binary images for Windows, Mac OS X and Linux from the MANTiS web site.

 

Funding Source

This project has been produced using operational funding from the APS, contract DE-AC02-06CH11357.

 

Please cite

Lerotic M, Mak R, Wirick S, Meirer F, Jacobsen C. (2014) MANTiS: a program for the analysis of X-ray spectromicroscopy data. J. Synchrotron Rad 21(5); 1206–1212 DOI: 10.1107/S1600577514013964
Related Publications

Mak R, Lerotic M, Fleckenstein H, Vogt S, Wild SM, Leyffer S, Sheynkin Y, Jacobsen C. (2014) Non-Negative Matrix Analysis for Effective Feature Extraction in X-Ray Spectromicroscopy. Faraday Discussions; 171, 357-371. DOI: 10.1039/C4FD00023D