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pygid: a Python package for fast data reduction in grazing-incidence diffraction.

Ainur Abukaev1, Constantin Völter1, Mikhail Romodin1

  • 1Institut für Angewandte Physik, Universität Tübingen, Auf der Morgenstelle 10, 72076 Tübingen, Germany.

Journal of Applied Crystallography
|February 6, 2026
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Summary
This summary is machine-generated.

A new Python library, pygid, offers efficient data processing for grazing-incidence diffraction experiments. It standardizes data formats, enabling faster analysis and compatibility with machine learning tools for X-ray data.

Keywords:
Python packagesdata analysisdata reductiongrazing-incidence X-ray diffraction GIXDgrazing-incidence small-angle X-ray scattering GISAXSgrazing-incidence wide-angle X-ray scattering GIWAXSsmall-angle X-ray scattering SAXSwide-angle X-ray scattering WAXS

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Area of Science:

  • Materials Science
  • Condensed Matter Physics
  • Data Science

Background:

  • Modern X-ray and neutron sources generate terabytes of data per experiment.
  • Existing tools for data processing are efficient for transmission geometry but not for grazing-incidence diffraction.
  • Need for standardized data formats for advanced analysis, including machine learning.

Purpose of the Study:

  • To develop a Python library, pygid, for efficient processing of grazing-incidence diffraction data.
  • To provide compatibility with various raw data formats and establish a standardized storage format.
  • To facilitate data analysis and metadata curation in line with FAIR principles.

Main Methods:

  • Development of the pygid Python library.
  • Implementation of support for multiple coordinate systems and geometries (transmission and grazing-incidence).
  • Capability for handling large datasets, performing 1D line cuts, and simulating Bragg peak positions.

Main Results:

  • pygid enables fast data processing for grazing-incidence X-ray diffraction and grazing-incidence wide-angle X-ray scattering.
  • The library ensures compatibility with diverse raw data formats and offers a standardized output.
  • pygid integrates seamlessly with machine learning pipelines for advanced data analysis.

Conclusions:

  • pygid addresses the need for efficient and standardized data processing in grazing-incidence diffraction.
  • The library enhances data accessibility and usability for scientific research.
  • pygid serves as a crucial first step in the mlgid pipeline for machine learning-driven analysis.