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darfix – data analysis for dark-field X-ray microscopy
A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and vi...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161887/ https://www.ncbi.nlm.nih.gov/pubmed/37000183 http://dx.doi.org/10.1107/S1600577523001674 |
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author | Garriga Ferrer, Júlia Rodríguez-Lamas, Raquel Payno, Henri De Nolf, Wout Cook, Phil Solé Jover, Vicente Armando Yildirim, Can Detlefs, Carsten |
author_facet | Garriga Ferrer, Júlia Rodríguez-Lamas, Raquel Payno, Henri De Nolf, Wout Cook, Phil Solé Jover, Vicente Armando Yildirim, Can Detlefs, Carsten |
author_sort | Garriga Ferrer, Júlia |
collection | PubMed |
description | A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and visualization of these data, providing the user with the essential tools to extract information from the acquired images in a fast and intuitive manner. These data processing and visualization tools can be either imported as library components or accessed through a graphical user interface as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited. The software can automatically extract the relevant instrument angle settings from the input files’ metadata. The currently available input file format is EDF and in future releases HDF5 will be incorporated. |
format | Online Article Text |
id | pubmed-10161887 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-101618872023-05-06 darfix – data analysis for dark-field X-ray microscopy Garriga Ferrer, Júlia Rodríguez-Lamas, Raquel Payno, Henri De Nolf, Wout Cook, Phil Solé Jover, Vicente Armando Yildirim, Can Detlefs, Carsten J Synchrotron Radiat Research Papers A Python package for the analysis of dark-field X-ray microscopy (DFXM) and rocking curve imaging (RCI) data is presented. DFXM is a non-destructive diffraction imaging technique that provides three-dimensional maps of lattice strain and orientation. The darfix package enables fast processing and visualization of these data, providing the user with the essential tools to extract information from the acquired images in a fast and intuitive manner. These data processing and visualization tools can be either imported as library components or accessed through a graphical user interface as an Orange add-on. In the latter case, the different analysis modules can be easily chained to define computational workflows. Operations on larger-than-memory image sets are supported through the implementation of online versions of the data processing algorithms, effectively trading performance for feasibility when the computing resources are limited. The software can automatically extract the relevant instrument angle settings from the input files’ metadata. The currently available input file format is EDF and in future releases HDF5 will be incorporated. International Union of Crystallography 2023-03-31 /pmc/articles/PMC10161887/ /pubmed/37000183 http://dx.doi.org/10.1107/S1600577523001674 Text en © Júlia Garriga Ferrer et al. 2023 https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited. |
spellingShingle | Research Papers Garriga Ferrer, Júlia Rodríguez-Lamas, Raquel Payno, Henri De Nolf, Wout Cook, Phil Solé Jover, Vicente Armando Yildirim, Can Detlefs, Carsten darfix – data analysis for dark-field X-ray microscopy |
title |
darfix – data analysis for dark-field X-ray microscopy |
title_full |
darfix – data analysis for dark-field X-ray microscopy |
title_fullStr |
darfix – data analysis for dark-field X-ray microscopy |
title_full_unstemmed |
darfix – data analysis for dark-field X-ray microscopy |
title_short |
darfix – data analysis for dark-field X-ray microscopy |
title_sort | darfix – data analysis for dark-field x-ray microscopy |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10161887/ https://www.ncbi.nlm.nih.gov/pubmed/37000183 http://dx.doi.org/10.1107/S1600577523001674 |
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