BioXTAS RAW, a software program for high-throughput automated small-angle X-ray scattering data reduction and preliminary analysis

A fully open source software program for automated two‐dimensional and one‐dimensional data reduction and preliminary analysis of isotropic small‐angle X‐ray scattering (SAXS) data is presented. The program is freely distributed, following the open‐source philosophy, and does not rely on any commerc...

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Veröffentlicht in:Journal of applied crystallography 2009-10, Vol.42 (5), p.959-964
Hauptverfasser: Nielsen, S. S., Toft, K. Noergaard, Snakenborg, D., Jeppesen, M. G., Jacobsen, J. K., Vestergaard, B., Kutter, J. P., Arleth, L.
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Sprache:eng
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Zusammenfassung:A fully open source software program for automated two‐dimensional and one‐dimensional data reduction and preliminary analysis of isotropic small‐angle X‐ray scattering (SAXS) data is presented. The program is freely distributed, following the open‐source philosophy, and does not rely on any commercial software packages. BioXTAS RAW is a fully automated program that, via an online feature, reads raw two‐dimensional SAXS detector output files and processes and plots data as the data files are created during measurement sessions. The software handles all steps in the data reduction. This includes mask creation, radial averaging, error bar calculation, artifact removal, normalization and q calibration. Further data reduction such as background subtraction and absolute intensity scaling is fast and easy via the graphical user interface. BioXTAS RAW also provides preliminary analysis of one‐dimensional data in terms of the indirect Fourier transform using the objective Bayesian approach to obtain the pair‐distance distribution function, PDDF, and is thereby a free and open‐source alternative to existing PDDF estimation software. Apart from the TIFF input format, the program also accepts ASCII‐format input files and is currently compatible with one‐dimensional data files from SAXS beamlines at a number of synchrotron facilities. BioXTAS RAW is written in Python with C++ extensions.
ISSN:1600-5767
0021-8898
1600-5767
DOI:10.1107/S0021889809023863