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Figure 1
Multi-step data assembly workflow. (a) Progressive processing of single-crystal data sets as accumulative wedges. (b) Classification based on unit-cell variations. (c) Data assembly for each cluster that qualified (completeness > 90%). The data assembly procedure optimizes data quality by iterative crystal and frame rejections. PyMDA produces N optimized data sets, each corresponding to a different set of unit-cell parameters.

Journal logoJOURNAL OF
APPLIED
CRYSTALLOGRAPHY
ISSN: 1600-5767
Volume 53| Part 1| February 2020| Pages 277-281
https://doi.org/10.1107/S160057671901673X
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