SrRietveld is a highly automated software toolkit designed to simplify Rietveld refinement using neutron and x-ray powder diffraction data. It is designed to simplify and automate the refinement of large numbers of datasets, such as are becoming available from modern high-throughput powder diffractometers. SrRietveld takes the existing Rietveld programs, GSAS and FullProf, and wraps them in a Python layer allowing the Rietveld refinement process to be automated in creative ways. The Python wrapping allows tasks to be automated, such as the strategy (which parameters are switched on in which order) for a single dataset, and the automation and visualization of refinements of large numbers of datasets.
SrRietveld is the Rietveld refinement application of the NSF DMR funded DANSE (Distributed Data Analysis for Neutron Scattering Experiments) software project.
SrRietveld is developed in the Python language using object oriented programming (OOP) concepts. Python is extensively used in scientific software development. It is cross-platform and suitable for fast development. Also there are many well designed packages for scientific programming and modeling, such as Matplotlib, Numpy and Scipy. The OOP design enables SrRietveld to be easily maintained and extended.
The philosophy of the SrRietveld design is to improve the efficiency of a typical work flow by controlling existing Rietveld refinement engines. A uniform data structure is implemented in SrRietveld so that the differences in input and output in different refinement engines will not be noticeable to the users; i.e., the same data can be refined using either GSAS or FullProf. Pre-written scripts are provided for automating common tasks, such as refining from coarse guesses of initial parameters, sequential refinement, etc.. It is also possible for advanced users to develop their own macro scripts and contribute to the development of SrRietveld.
The functions of SrRietveld include:
Automatic and user-guided Le-Bail and Rietveld refinements
Perform multi-step refinement on single experiment data sets
Sequential Rietveld refinement
Sequentially fit related data sets from a series of experiments
Examine the refinement results
Plot refinement parameters or calculated patterns at each refinement step
Work with different diffraction types
Analyze data from time-of-flight neutron, constant wavelength neutron, and constant wavelength X-ray diffraction experiments
SrRietveld-dev Google groups – Please share your comments, desires and suggestions with the developers by posting to the DiffPy developers’ forum.
Professor Simon BillingeApplied Physics and Applied Mathematics DepartmentThe Fu Foundation School of Engineering and Applied Science200 S. W. Mudd BuildingMC 4701, 500 W. 120th StreetNew York, NY 10027email: sb2896@columbia.edu