This is a repository containing code/algorithms for image-plane correction, intended for use with the OVRO-LWA radio telescope. This research is being performed by Zachary Huang as part of Caltech's 2024 SURF program (under the mentorship of Casey Law, Gregg Hallinan, and others).
We use JAX to get easy GPU acceleration for matrix operations. Numpy is still used in various places since some libraries don't support JAX arrays as a drop-in replacement. We use Astropy for astronomical operations and PyBDSF for source detection. Matplotlib is used for plotting.
OpenCV (with extra modules) is used for optical flow.
However, note that for CUDA support, OpenCV must be built from scratch.
This can be a bit of a pain, so I wrote down the installation steps that worked for me in docs/opencv.md.
All of the project dependencies (except for OpenCV) are specified in pyproject.toml.
To install these dependencies along with the source code, enter the project directory and run pip install ..
If you wish to run the notebooks in notebooks/, I would recommend using Jupyter Lab (which you can install along with the project dependencies with pip install .[dev].
For the sake of development, I would also recommend adding the -e flag to the pip commands above so that any changes made to the source code in this repository are immediately reflected by the scripts/notebooks (instead of requiring a "reinstall" of the package).
For GPU acceleration, ensure that JAX is installed with CUDA support.
As mentioned previously, OpenCV must be built from source for CUDA support and should be accessible as cv2 for the optical flow code to work properly.