S2fft

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What's Changed
✨ New features
* Performance improvements by CosmoMatt in https://github.com/astro-informatics/s2fft/pull/200
* Custom HEALPix FFT primitive when running on GPU by ASKabalan in https://github.com/astro-informatics/s2fft/pull/204
* Add stable high-spin transforms (precompute, standard) by CosmoMatt in https://github.com/astro-informatics/s2fft/pull/230
* Add stable forward/inverse memory efficient Wigner transforms by CosmoMatt in https://github.com/astro-informatics/s2fft/pull/238
* Add custom collective transforms by CosmoMatt in https://github.com/astro-informatics/s2fft/pull/239
* Avoid loops in `s2fft.sampling.reindex` functions to reduce compile and run times by matt-graham in https://github.com/astro-informatics/s2fft/pull/245
* Correct `healpix_forward` derivatives and add support for forward and higher order autodiff by matt-graham in https://github.com/astro-informatics/s2fft/pull/244
* Improvements to benchmarking system by matt-graham in https://github.com/astro-informatics/s2fft/pull/248
* Iterative refinement support for JAX and NumPy forward (spherical) transform implementations by matt-graham in https://github.com/astro-informatics/s2fft/pull/241
* Vectorize signal generator functions by matt-graham in https://github.com/astro-informatics/s2fft/pull/252
📖 Documentation improvements
* Feature/notebook plots by CosmoMatt in https://github.com/astro-informatics/s2fft/pull/202
* Execute main spherical transform notebook by jasonmcewen in https://github.com/astro-informatics/s2fft/pull/203
* Add low precision warning to docstring of inverse Wigner function by ElisR in https://github.com/astro-informatics/s2fft/pull/220
* Update citation details in docs and add CITATION.cff file by matt-graham in https://github.com/astro-informatics/s2fft/pull/236
* Indicate cubic memory overhead in `generate_precomputes` docstring by matt-graham in https://github.com/astro-informatics/s2fft/pull/257
🐛 Bug fixes
* Fix pass through of arguments to `generate_precomputes` in NumPy forward spherical transform by matt-graham in https://github.com/astro-informatics/s2fft/pull/256
* Fix failing `test_transform_forward_healpix_iter` test by matt-graham in https://github.com/astro-informatics/s2fft/pull/258
🛠 Other changes
* Switching to `pyproject.toml` for specifying package metadata by matt-graham in https://github.com/astro-informatics/s2fft/pull/173
* Updates to custom CUDA HEALPix FFT primitive by matt-graham in https://github.com/astro-informatics/s2fft/pull/231

New Contributors
* ElisR made their first contribution in https://github.com/astro-informatics/s2fft/pull/220
* ASKabalan made their first contribution in https://github.com/astro-informatics/s2fft/pull/204

**Full Changelog**: https://github.com/astro-informatics/s2fft/compare/v1.1.0...v1.2.0

This minor release of `S2FFT` aims to increase the accessibility of differentiable harmonic transforms to users without easy access to GPU compute resources. We provide custom JAX frontends for existing CPU bound C/C++ spherical harmonic libraries, at this point we capture the functionality of [`SSHT`](https://astro-informatics.github.io/ssht/) and [`HEALPix`](https://healpy.readthedocs.io/en/latest/) though in principle any spherical harmonics could easily be integrated.

> [!tip]
> For details on this approach see the original derivation in section 5.3.1 and 5.3.2 of [Price & McEwen 2023](https://arxiv.org/pdf/2311.14670.pdf).

Main changelog:

- JAX frontend support for HEALPix C++ library
- JAX frontend support for SSHT C library
- Reverse mode gradients for above validated against finite difference

Incremental version of `S2FFT` which adds support for:

- `PyTorch` precompute transforms
- Gauss-Legendre sampling schemes
- JAX harmonic space rotation functions
- JAX Risbo recursions

This version also implements some changes to reduce the compile time of HEALPix Fast Fourier transforms, though this issues is yet to be fully solved.

This is a relatively small patch which attempts to **mitigate the compile time of HEALPix transforms**. Though this is unlikely to have solved the overall scaling of the compile time, it has reduced the time by a factor of ~4 which should help.

This is the initial beta release to accompany the associated paper (see badges). In addition this version is deployed on PyPi with universal wheels which should be straightforward to install for various python versions and machine architectures.

This is a pre-alpha release version for use by collaborators. Most functionality is in place, but development of minor additions will be ongoing. Please submit an issue if you have one and we will respond asap!