# PyNX: high performance computing toolkit for coherent X-ray imaging based on operators

arXiv: https://arxiv.org/abs/2008.11511 · HTML: https://mlsanity.com/paper/2008.11511/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Deep learning at the edge enables real-time streaming ptychographic imaging](https://mlsanity.com/paper/2209.09408/) (2022)
- [Predicting ptychography probe positions using single-shot phase retrieval neural network](https://mlsanity.com/paper/2405.20910/) (2024)
- [A modular software framework for the design and implementation of ptychography algorithms](https://mlsanity.com/paper/2205.04295/) (2022)
- [A unified self-supervised framework for single-frame Fresnel CDI and overlapped ptychography](https://mlsanity.com/paper/2602.21361/) (2026)

## Abstract (arXiv)

The open-source PyNX toolkit [Favre-Nicolin et al (2011) arXiv:1010.2641, Mandula et al (2016)] has been extended to provide tools for coherent X-ray imaging data analysis and simulation. All calculations can be executed on graphical processing units (GPU) to achieve high performance computing speeds. This can be used for Coherent Diffraction Imaging (CDI), Ptychography and wavefront propagation, in the far or near field regime. Moreover, all imaging operations (propagation, projections, algorithm cycles..) can be used in Python as simple mathematical operators, an approach which can be used to easily combine basic algorithms in a tailored chain. Calculations can also be distributed to multiple GPUs, e.g. for large Ptychography datasets. Command-line scripts are also available for on-line CDI and Ptychography analysis, either from raw beamline datasets or using the Coherent X-ray Imaging data format [Maia (2012)].