[!WARNING] The official WaveDiff code and repository can be found at https://github.com/CosmoStat/wf-psf.
This repository includes:
- A differentiable PSF model entirely built in Tensorflow.
- A numpy-based PSF simulator here.
- All the scripts, jobs and notebooks required to reproduce the results in arXiv:2203.04908 and arXiv:2111.12541.
For more information on how to use the WaveDiff model through configurable scripts see the long-runs directory's README.
A schematic of the proposed framework can be seen below. The PSF model is estimated (trained) using star observations in the field-of-view.
wf-psf is pure python and can be easily installed with pip. After cloning the repository, run the following commands:
$ cd wf-psf
$ pip install .The package can then be imported in Python as import wf_psf as wf. We recommend using the release 1.2.0 for stability as the current main branch is under development.
- numpy [>=1.19.2]
- scipy [>=1.5.2]
- TensorFlow [==2.4.1]
- TensorFlow Addons [==0.12.1]
- Astropy [==4.2]
- zernike [==0.0.31]
- opencv-python [>=4.5.1.48]
- pillow [>=8.1.0]
- galsim [>=2.3.1]
Optional packages:
- matplotlib [=3.3.2]
- seaborn [>=0.11]
arXiv:2203.04908 Rethinking data-driven point spread function modeling with a differentiable optical model (2022)
Submitted.
- Use the release 1.2.0.
- All the scripts, jobs and notebooks to reproduce the figures from the article can be found here.
- The trained PSF models are found here.
- The input PSF field can be found here.
- The script used to generate the input PSF field is this one.
- The code required to run the comparison against pixel-based PSF models is in this directory.
- The training of the models was done using this script. In order to match the script's option for the different models with the article you should follow:
poly->WaveDiff-originalgraph->WaveDiff-graphmccd->WaveDiff-Polygraph
Note: To run the comparison to other PSF models you need to install them first. See RCA, PSFEx and MCCD.
arXiv:2111.12541 Rethinking the modeling of the instrumental response of telescopes with a differentiable optical model (2021)
NeurIPS 2021 Workshop on Machine Learning and the Physical Sciences.
- Use the release 1.2.0.
- All the scripts, jobs and notebooks to reproduce the figures from the article can be found here.
If you use wf-psf in a scientific publication, we would appreciate citations to the following paper:
Rethinking data-driven point spread function modeling with a differentiable optical model, T. Liaudat, J.-L. Starck, M. Kilbinger, P.-A. Frugier, arXiv:2203.04908, 2022.
The BibTeX citation is the following:
@misc{https://doi.org/10.48550/arxiv.2203.04908,
doi = {10.48550/ARXIV.2203.04908},
url = {https://arxiv.org/abs/2203.04908},
author = {Liaudat, Tobias and Starck, Jean-Luc and Kilbinger, Martin and Frugier, Pierre-Antoine},
keywords = {Instrumentation and Methods for Astrophysics (astro-ph.IM), Computer Vision and Pattern Recognition (cs.CV), FOS: Physical sciences, FOS: Physical sciences, FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Rethinking data-driven point spread function modeling with a differentiable optical model},
publisher = {arXiv},
year = {2022},
copyright = {arXiv.org perpetual, non-exclusive license}
}