diff --git a/.pip_readme.rst b/.pip_readme.rst index 0e796e03..d973c4dd 100644 --- a/.pip_readme.rst +++ b/.pip_readme.rst @@ -17,7 +17,7 @@ Differentiable and accelerated spherical transforms ================================================================================================================= `S2FFT` is a Python package for computing Fourier transforms on the sphere -and rotation group in JAX and PyTorch. It leverages autodiff to provide differentiable +and rotation group using JAX and PyTorch. It leverages autodiff to provide differentiable transforms, which are also deployable on hardware accelerators (e.g. GPUs and TPUs). diff --git a/README.md b/README.md index 5e86d4c6..c52bf3a5 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ # Differentiable and accelerated spherical transforms `S2FFT` is a Python package for computing Fourier transforms on the sphere -and rotation group [(Price & McEwen 2023)](https://arxiv.org/abs/2311.14670) Using +and rotation group [(Price & McEwen 2023)](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). @@ -87,7 +87,7 @@ into the active python environment by [pip](https://pypi.org) when running pip install s2fft ``` This will install all core functionality which includes JAX support. To install `S2FFT` -with PyTorch support run the following +with PyTorch support run ``` bash pip install s2fft[torch] diff --git a/docs/index.rst b/docs/index.rst index 9277de02..78c61339 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -2,7 +2,7 @@ Differentiable and accelerated spherical transforms =================================================== ``S2FFT`` is a Python package for computing Fourier transforms on the sphere and rotation -group `(Price & McEwen 2023) `_ in JAX and PyTorch. +group `(Price & McEwen 2023) `_ using JAX and PyTorch. It leverages autodiff to provide differentiable transforms, which are also deployable on modern hardware accelerators (e.g. GPUs and TPUs).