Abstract
<h2>Abstract</h2> Significant interest has been placed on Scanning X-ray Diffraction Microscopy (SXDM) techniques for its ability to spatially resolve material/chemical strain at nanometer length scales. As instrumentation that employs this technique pushes the bleeding edge of research, the ability to process the influx of data produced is becoming difficult on traditional hardware. Traditionally, the analysis protocols were left for the individual to develop, leading to inefficiencies and long computational times for analysis. The python module <i>sxdm</i> was developed to reduce efficiencies by standardizing a data structure, displaying experimental analysis metrics in a graphical user interface, and providing resource-efficient analysis protocols.
Highlights
The ever-growing expansion of scientific technologies has created a world that allows single users to create more raw data than could be processed by that individual alone
The second technique improvement is through the incremental upgrade of silicon chips used in central processing units (CPUs) or random access memory (RAM) modules by leading manufacturers
Even though this software package was developed for Sector 26-ID-C at the Advanced Photon Source, the goal of the proposed software package is to give researchers a standard platform for Scanning X-ray Diffraction Microscopy (SXDM) data analysis that is open-sourced, multi-threaded, resource-efficient, and which can be tailored for the specific needs of the scientist
Summary
The ever-growing expansion of scientific technologies has created a world that allows single users to create more raw data than could be processed by that individual alone This immense flux of data generation required innovative techniques to perform the analysis in a reasonable time frame, which can be carried out in one of three ways. The third avenue for improvement is to optimize open-source software used in data analysis for low-cost computational systems. This brings a deeper level of democratization of large data throughput to the average, computer-owning, individual. Some examples can be found through PyNx [1], AtomSK [2], and RMCProfile [3] With their ease of use and low computational cost, these tools have paved the way for accessibility in materials science research. The code presented in this paper aims to contribute toward such outcome
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