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 a publication currently under per-review that describes the mathematics and background for this demo library. DOI links will be made available upon publication.
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.