Notebooks

A series of tutorial notebooks which go through the absolute base level application of S2SCAT apis. Post alpha release we will add examples for more involved applications, in the time being feel free to contact contributors for advice! At a high-level the S2SCAT package is structured such that the 2 primary methods, emulation and compression, can easily be accessed.

Core usage 🚀

To import and use S2SCAT is as simple follows:

import s2scat, jax
# For statistical compression
encoder = s2scat.build_encoder(L, N)          # Returns a callable compression model.
covariance_statistics = encoder(alm)          # Generate statistics (can be batched).

# For generative modelling
key = jax.random.PRNGKey(seed)
generator = s2scat.build_generator(alm, L, N) # Returns a callable generative model.
new_samples = generator(key, 10)              # Generate 10 new spherical textures.

S2SCAT also provides JAX support for existing C backend libraries which are memory efficient but CPU bound; at launch we support SSHT, however this could be extended straightforwardly. This works by wrapping python bindings with custom JAX frontends.

For further details on usage see the documentation and associated notebooks.