This project aims to implement a comprehensive set of tools for the standardization and automation of GID data processing. The packages can be used separately or combined together in the pipeline.
As a convention, packages that rely on machine learning start with
ml, while other packages (such as those for conversion to reciprocal space and conventional peak fitting) start withpy.
mlgidBASE - simple pipeline user interface
pygid - conversion of raw GID data to reciprocal space
mlgidDETECT - ML-based Bragg peak detection
pygidFIT - fitting of Bragg peaks
mlgidMATCH - ML-based matching of crystal structures with Bragg peaks
mlgidGUI - graphical user interface for annotating GID data
pygidSIM - simulating synthetic GID data from crystal structures
The following is the list of papers related to the mlgid project.
List of papers
pygid: a Python package for fast data reduction in grazing-incidence diffraction
A. Abukaev, C. Völter, M. Romodin, S. Schwartzkopff, F. Bertram, O. Konovalov, A. Hinderhofer, D. Lapkin and F. Schreiber. J. Appl. Crystallogr. 59, 263 (2026) [https://doi.org/10.1107/S1600576725010593]
mlgidGUI - an annotation program for 2D scattering data
C. Völter, V. Starostin, M. Romodin, E. Kneschaurek, D. Lapkin, A. Hinderhofer, and F. Schreiber. J. Open Source Softw. 10, 8499 (2025) [https://joss.theoj.org/papers/10.21105/joss.08499]
Tracking perovskite crystallization via deep learning-based feature detection on 2D X-ray scattering data
V. Starostin, V. Munteanu, A. Greco, E. Kneschaurek, A. Pleli, F. Bertram, A. Gerlach, A. Hinderhofer, and F. Schreiber. npj Comput. Mater. 8, 101 (2022) https://doi.org/10.1038/s41524-022-00778-8
End-to-end deep learning pipeline for real-time processing of surface scattering data at synchrotron facilities
V. Starostin, L. Pithan, A. Greco, V. Munteanu, A. Gerlach, A. Hinderhofer, and F. Schreiber. Synchrotron Radiat. News 35, 21 (2022) https://doi.org/10.1080/08940886.2022.2112499
Benchmarking deep learning for automated peak detection on GIWAXS data
C. Völter, V. Starostin, D. Lapkin, V. Munteanu, M. Romodin, M. Hylinski, A. Gerlach, A. Hinderhofer, F. Schreiber. J. Appl. Crystallogr. 58, 513 (2025) https://doi.org/10.1107/S1600576725000974
