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@ARTICLE{Abukaev:645945,
author = {Abukaev, Ainur and Voelter, Constantin and Romodin, Mikhail
and Schwartzkopff, Sebastian and Bertram, Florian and
Konovalov, Oleg and Hinderhofer, Alexander and Lapkin,
Dmitrii and Schreiber, Frank},
title = {pygid: a {P}ython package for fast data reduction in
grazing-incidence diffraction},
journal = {Journal of applied crystallography},
volume = {59},
number = {1},
issn = {0021-8898},
address = {Copenhagen},
publisher = {Munksgaard},
reportid = {PUBDB-2026-00701},
pages = {263 - 275},
year = {2026},
abstract = {Advances in X-ray and neutron sources, as well as in
area-detector technologies, enable the recording of several
terabytes of raw two-dimensional detector data in a single
experiment. While several efficient integration and
conversion tools are available for data collected in
transmission geometry, analogous solutions for
grazing-incidence diffraction (including grazing-incidence
X-ray diffraction and grazing-incidence wide-angle X-ray
scattering) experiments have not yet achieved the same level
of efficiency. The development of new data analysis tools,
including machine-learning-based software for X-ray data,
necessitates the establishment of a standardized format for
the converted data. To address these challenges, we have
developed a new Python library, pygid, which is designed to
facilitate fast data processing while providing
compatibility with various raw data formats, a standardized
data storage format and an intuitive interface for
straightforward use. pygid supports three types of
coordinate systems and both transmission and
grazing-incidence geometries. It is capable of handling
large datasets, performing one-dimensional line cuts and
simulating expected Bragg peak positions for given
structures. The package facilitates sample and experimental
metadata curation in accordance with the FAIR principles. As
an integral part of the broader mlgid pipeline, pygid serves
as the initial step linking raw scattering patterns with
machine learning tools for data analysis. The pygid package
is accessible at https://github.com/mlgid-project.},
cin = {DOOR ; HAS-User / FS-PETRA-D},
ddc = {540},
cid = {I:(DE-H253)HAS-User-20120731 /
I:(DE-H253)FS-PETRA-D-20210408},
pnm = {623 - Data Management and Analysis (POF4-623) / 6G3 - PETRA
III (DESY) (POF4-6G3) / DFG project G:(GEPRIS)460248799 -
DAPHNE4NFDI - DAten aus PHoton- und Neutronen Experimenten
für NFDI (460248799) / DFG project G:(GEPRIS)390727645 -
EXC 2064: Maschinelles Lernen: Neue Perspektiven für die
Wissenschaft (390727645)},
pid = {G:(DE-HGF)POF4-623 / G:(DE-HGF)POF4-6G3 /
G:(GEPRIS)460248799 / G:(GEPRIS)390727645},
experiment = {EXP:(DE-H253)P-P08-20150101},
typ = {PUB:(DE-HGF)16},
doi = {10.1107/S1600576725010593},
url = {https://bib-pubdb1.desy.de/record/645945},
}