Metadata-Version: 2.1
Name: s2scat
Version: 0.0.2
Summary: Differentiable and GPU accelerated scattering covariance statistics on the sphere
Home-page: https://github.com/astro-informatics/s2scat
Author: Matthew A. Price, Louise Mousset, Erwan Allys, Jason D. McEwen
License: MIT
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Operating System :: OS Independent
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Requires-Python: >=3.8
Description-Content-Type: text/x-rst
License-File: LICENSE.txt
Requires-Dist: numpy ==1.26.4
Requires-Dist: optax
Requires-Dist: s2wav >=1.0.4
Requires-Dist: s2fft >=1.1.1

.. image:: https://github.com/astro-informatics/s2scat/actions/workflows/tests.yml/badge.svg?branch=main
    :target: https://github.com/astro-informatics/s2scat/actions/workflows/tests.yml
.. image:: https://codecov.io/gh/astro-informatics/s2scat/branch/main/graph/badge.svg?token=7QYAFAAWLE
    :target: https://codecov.io/gh/astro-informatics/s2scat
.. image:: https://img.shields.io/badge/License-MIT-yellow.svg
    :target: https://opensource.org/licenses/MIT
.. image:: http://img.shields.io/badge/arXiv-xxxx.xxxxx-orange.svg?style=flat
    :target: https://arxiv.org/abs/xxxx.xxxxx
.. image:: https://img.shields.io/badge/code%20style-black-000000.svg
    :target: https://github.com/psf/black
.. image:: https://colab.research.google.com/assets/colab-badge.svg
    :target: https://colab.research.google.com/github/astro-informatics/s2scat/blob/main/notebooks/auto_generation.ipynb


Differentiable scattering covariances on the sphere
=================================================================================================================

``S2SCAT`` is a Python package for computing third generation scattering covariances on the 
sphere `(Mousset et al 2024) <https://arxiv.org/abs/2311.14670>`_ 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 <https://astro-informatics.github.io/s2scat/>`_.

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:

.. code-block:: 

    @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:

.. code-block:: 

    @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"         
    }

.. code-block:: 

    @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.
