A demo implementation of fast, factorized, on the fly, tomographic far field X-ray diffraction microscopy imaging.
In the setting of scanning-3DXRD/HEDM we show how a special block-partioned system matrix factorization can be exploited to achieve a memory efficient and fast GPU-accelerated diffraction model implementation. This repository serves as a supplementary material for the publication:
Henningsson, A. & Hall, S. A. (2023).
An efficient system matrix factorization method for scanning diffraction based strain tensor tomography.
Acta Crystallographica Section A: Foundations and Advances, 79(6), 542–549.
After installing the lib, please head to the demo folder and checkout the simulate_diffraction.py script which generates diffraction data into the demo/data folder. The data represents a single grain slice of alpha-quartz featuring both intragranular misorientations as well as strain, as can be seen below:
The scripts reconstruct_strain.py and reconstruct_ub.py illustrate how intragranular strain and orientation reconstruction can take place using the simulated diffraction data.
Requirements: conda python environment, with 64 bit Python 3.7, 3.8 or 3.9.
Install anaconda and create a new conda environment in your terminal as
conda create -n flyxdm python=3.9
conda activate flyxdm
The ray-tracing primitives utilise the astra-toolbox for gpu acceleration. You may install these as:
conda install -c astra-toolbox astra-toolbox
Next we install some more dependencies
conda install -c conda-forge matplotlib pip xfab
You may now install flyxdm using pip as
git clone https://github.com/AxelHenningsson/flyxdm.git
cd flyxdm
pip install -e .
This library makes good use of the open source astra-toolbox ray-tracing primitives.