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BCDI stands for Bragg coherent X-ray diffraction imaging. It can be used for:
pre-processing BCDI and forward CDI data (masking aliens, detector gaps ...) before phase retrieval
post-processing after phase retrieval (phase offset and phase ramp removal, averaging, apodization, ...)
data analysis on diffraction data (stereographic projection, angular cross-correlation analysis, domain orientation fitting ...)
data analysis on phased data (resolution calculation, statistics on the retrieved strain ...)
simulation of diffraction intensity (including noise, detector gaps, displacement field ...)
creating figures for publication using templates
Considering that most parts of the analysis pipeline are actually beamline-independent, we tried to reuse as much as possible code, and leverage inheritance when it comes to facility or beamline-dependent details.
BCDI can be used as a python library with the following main modules:
1) :mod:bcdi.algorithms: PSF and image deconvolution using Richardson-Lucy algorithm
2) :mod:bcdi.experiment: definition of the experimental geometry
(beamline, setup, detector, diffractometer...).
3) :mod:bcdi.graph : generation of plots using predefined templates.
4) :mod:bcdi.postprocessing: methods for post-processing the complex output
of a phasing algorithm. Stereographic projection of a diffraction peak or a
reconstructed crystal. Automatic detection of reconstructed facets and statistics on
facet strain.
5) :mod:bcdi.preprocessing: methods for pre-processing the diffraction
intensity in Bragg CDI or forward CDI geometry.
6) :mod:bcdi.simulation: in BCDI geometry, calculation of the diffraction intensity
based on FFT or kinematical sum. It can include a displacement field, noise,
detector gaps etc... In forward CDI geometry, calculation of the Bragg peak
positions in 3D for a mesocrystal, knowing the unit cell and unit cell parameter.
7) :mod:bcdi.utils: generic functions about data loading, fitting functions, cropping/
padding, image registration, validation functions ...
8) :mod:bcdi.xcca: X-ray cross-correlation analysis related methods
The central module is :mod:bcdi.experiment, which contains all setup-related
implementation. This is the place where to look at if you want to add support for a new
beamline or detector.
We would like to acknowledge the following packages:
xrayutilities: (c) Dominik Kriegner, Eugen Wintersberger. See: J. Appl. Cryst. 46, 1162-1170 (2013).
nxsReady: (c) Andrea Resta @ SOLEIL SIXS
image_registration.py: original code from Xianhui Xiao @ APS Sector 2. See: Opt. Lett. 33, 156-158 (2008).
Some functions were adapted from PyNX: (c) Vincent Favre-Nicolin. See: http://ftp.esrf.fr/pub/scisoft/PyNX/ and J. Appl. Cryst. 49, 1842-1848 (2016).
Anton Mikhailov for the turbo colormap. See https://ai.googleblog.com/2019/08/turbo-improved-rainbow-colormap-for.html
The following third-party packages are required:
numpy, scipy, scikit-image, matplotlib, pyqt5, vtk, h5py, hdf5plugin, fabio, silx, xrayutilities
lmfit: for scripts performing fits
pytest: to run the tests
pytables: to load the devices dictionnary for SIXS data
moviepy, imagemagick <https://imagemagick.org> or
ffmpeg <https://ffmpeg.org/download.html> for creating movies
BCDI is available from:
python -m pip install bcdiGitHub <https://github.com/carnisj/bcdi>_, where you will find the latest
version:| - to install the main branch, type:
| python -m pip install git+https://github.com/carnisj/bcdi.git
| - to install a specific branch, type:
| python -m pip install git+https://github.com/carnisj/bcdi.git@branch_name
Add the flag --upgrade to the commands above in order to update an existing
installation.
Note that there are issues with installing scikit-image within an Anaconda environment. In such situation, the workaround is to create instead a virtual environment using pip.
If you want to contribute to bcdi development, install also extra dependencies:
python -m pip install bcdi[dev]
Please send feedback in GitHub issues <https://github.com/carnisj/bcdi/issues>_.
The documentation is available at: https://bcdi.readthedocs.io/en/latest/
All talks from the bcdiHackweek 2021 are available at the following links:
Carnis, J. - BCDI package overview: https://youtu.be/g4jkzmz8JGk
Li, N. - data preprocessing: https://youtu.be/D-fl19Mi7Ao
Carnis, J. - data preprocessing interactive: https://youtu.be/ddipN43HR1w
Dupraz, M. - data postprocessing + viz: https://youtu.be/WyDzOkJJu8c
Carnis, J. - all that is left in the BCDI package: https://youtu.be/egh8X6iI4Nw
Richard, M.-I. - paraview Facet analyser: https://youtu.be/RarHeUIOu08
Carnis, J. - bcdi Facet analyser: https://youtu.be/gucQk8p3vyk
Simonne, D. - jupyter GUI for bcdi package: https://youtu.be/9SDcGfJqiVw
Related package Cohere <https://github.com/AdvancedPhotonSource/cohere>_:
The BCDI library is distributed with a CeCILL-B license (an open-source license similar to the FreeBSD one). See http://cecill.info/licences/Licence_CeCILL-B_V1-en.html
If you use this package for scientific work, please consider including a citation using the following DOI: 10.5281/zenodo.3257616
This package contributed to the following peer-reviewed publications:
Y. Y. Kim, et al. Single Alloy Nanoparticle X-Ray Imaging during a Catalytic Reaction. Science Advances 7 (2021). DOI: 10.1126/sciadv.abh0757
J. Carnis, et al. Facet-dependent strain determination in electrochemically synthetized platinum model catalytic nanoparticles. Small 2007702 (2021). DOI: 10.1002/smll.202007702 Data available at CXIDB ID182: https://www.cxidb.org/id-182.html
J. Carnis, et al. Twinning/detwinning in an individual platinum nanocrystal during catalytic CO oxidation. Nature Communications 12 (1), 1-10 (2021). DOI: 10.1038/s41467-021-25625-0
J. Carnis, et al. Structural Study of a Self-Assembled Gold Mesocrystal Grain by Coherent X-ray Diffraction Imaging. Nanoscale 13, 10425-10435 (2021). DOI: 10.1039/D1NR01806J Data available at CXIDB ID183: https://www.cxidb.org/id-183.html
N. Li, et al. Mapping Inversion Domain Boundaries Along Single GaN Wires with Bragg Coherent X-ray Imaging. ACS Nano 14, 10305–10312 (2020). DOI: 10.1021/acsnano.0c03775
N. Li, et al. Continu-ous scanning for Bragg coherent X ray imaging. Sci. Rep. 10, 12760 (2020). DOI: 10.1038/s41598-020-69678-5
J. Carnis, et al. Towards a quantitative determination of strain in Bragg Coherent X-ray Diffraction Imaging: artefacts and sign convention in reconstructions. Scientific reports 9, 17357 (2019). DOI: 10.1038/s41598-019-53774-2
W. Hua, et al. Structural insights into the formation and voltage degradation of lithium- and manganese-rich layered oxides. Nat Commun 10, 5365 (2019). DOI: 10.1038/s41467-019-13240-z
G. Niu, et al. Advanced coherent X-ray diffraction and electron microscopy of individual InP nanocrystals on Si nanotips for III-V on Si electronics and optoelectronics. Phys. Rev. Applied 11, 064046 (2019). DOI: 10.1103/PhysRevApplied.11.064046
S. Fernández, et al. In situ structural evolution of single particle model catalysts under ambient pressure reaction conditions. Nanoscale 11, 331-338 (2019). DOI: 10.1039/c8nr08414a