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Cdiutils: A python package for Bragg Coherent Diffraction Imaging processing, analysis and visualisation workflows

Authors: Atlan, Clement;

Cdiutils: A python package for Bragg Coherent Diffraction Imaging processing, analysis and visualisation workflows

Abstract

A python package to help Bragg Coherent Diffraction Imaging (BCDI) practitioners in their analysis and visualisation workflows. The package is designed to handle the three primary stages of a BCDI data processing workflow: * **Proprocessing** (data centering and cropping) * **Phase retrieval**: utilises PyNX for phasing (refer to [PyNX documentation](http://ftp.esrf.fr/pub/scisoft/PyNX/doc/)). * **Post processing** (orthogonalisation, phase manipulation, strain computation etc.) It is assumed that the phase retrieval is conducted using the PyNX package. The `BcdiPipeline` class runs all three stages and can manage connections to different machines, especially for GPU-based phase retrieval.

If you use this package for scientific work, please consider including a citation as below.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average