s2scat 0.0.3

Differentiable and GPU accelerated scattering covariance statistics on the sphere

Differentiable scattering covariances on the sphere

S2SCAT is a Python package for computing third generation scattering covariances on the sphere (Mousset et al 2024) using JAX or PyTorch. It leverages autodiff to provide differentiable transforms, which are also deployable on hardware accelerators (e.g. GPUs and TPUs).

Documentation

Read the full documentation here.

Attribution

Should this code be used in any way, we kindly request that the following article is referenced. A BibTeX entry for this reference may look like:

@article{mousset:s2scat,
    author      = "Louise Mousset et al",
    title       = "TBD",
    journal     = "Astronomy & Astrophysics, submitted",
    year        = "2024",
    eprint      = "TBD"
}

You might also like to consider citing our related papers on which this code builds:

@article{price:s2fft,
    author       = "Matthew A. Price and Jason D. McEwen",
    title        = "Differentiable and accelerated spherical harmonic and {W}igner transforms",
    journal      = "Journal of Computational Physics",
    volume       = "510",
    pages        = "113109",
    year         = "2024",
    doi          = {10.1016/j.jcp.2024.113109},
    eprint       = "arXiv:2311.14670"
}

@article{price:s2wav,
    author      = {Matthew A. Price and Alicja Polanska and Jessica Whitney and Jason D. McEwen},
    title       = {"Differentiable and accelerated directional wavelet transform on the sphere and ball"},
    year        = {2024},
    eprint      = {arXiv:2402.01282}
}

License

We provide this code under an MIT open-source licence with the hope that it will be of use to a wider community.

Copyright 2024 Matthew Price, Louise Mousset, Erwan Allys and Jason McEwen

S2SCAT is free software made available under the MIT License. For details see the LICENSE file.