laue-dials 0.5.1

A package for analyzing Laue x-ray crystallography data using the DIALS framework.

laue-dials

Data analysis package for Laue crystallography.

laue-dials is an extension to the DIALS code for analyzing polychromatic crystallographic data. Building off the DIALS framework, and including modern tools like numpy, scipy, and reciprocalspaceship, this package allows for analysis of X-ray crystallographic data using wide-bandwidth light sources. This package is intended to be used in conjunction with DIALS, careless, and Phenix in order to generate molecular models from raw data. For documentation, see https://rs-station.github.io/laue-dials/index.html. For source code, please visit the Github repository at https://github.com/rs-station/laue-dials.

Installation

To install laue-dials, the DIALS code must first be installed using

conda install -c conda-forge dials

Then, using the same python environment, run the following to install laue-dials from pip:

pip install laue-dials

or alternatively, install the development version of laue-dials from GitHub:

pip install git+https://github.com/rs-station/laue-dials.git

laue-dials consists of several command-line scripts for the processing of Laue diffraction data, which are

laue.find_spots
laue.index
laue.sequence_to_stills
laue.optimize_indexing
laue.refine
laue.predict
laue.integrate
laue.plot_wavelengths
laue.compute_rmsds

Note that you need to import the image data using dials.import. For information on how to use this command, visit https://dials.github.io/documentation/programs/dials_import.html. An example of how to analyze a full dataset lives at https://github.com/rs-station/laue-dials/blob/main/examples/pipeline.sh.

If any issues occur either with installation or use of the software, please file an issue at issue tracker. Any and all questions, concerns, or feature requests are welcome.

Publications

Laue-DIALS: Open-source software for polychromatic x-ray diffraction data. Hewitt, Rick A. and Kevin M. Dalton, Derek Mendez, Harrison K. Wang, Margaret A. Klureza, Dennis E. Brookner, Jack B. Greisman, David McDonagh, Vukica Šrajer, Nicholas K. Sauter, Aaron S. Brewster, Doeke R. Hekstra. Structural Dynamics, 2024