Update tutorials index page to be consistent with README

Author: matt-graham

docs/tutorials/index.rst

@@ -1,57 +1,42 @@
 :html_theme.sidebar_secondary.remove:
 
-**************************
-Notebooks
-**************************
-A series of tutorial notebooks which go through the absolute base level application of 
-``S2FFT`` 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 
-``S2FFT`` package is structured such that the 2 primary transforms, the Wigner and 
-spherical harmonic transforms, can easily be accessed.
+*****************************
+Tutorials
+*****************************
+
+This section contains a series of tutorial notebooks which go through some of the
+key features of the ``S2FFT`` package.
+
+At a high-level the ``S2FFT`` package is structured such that the two primary transforms,
+the Wigner and spherical harmonic transforms, can easily be accessed.
 
 Core usage |:rocket:|
------------------
-To import and use ``S2FFT``  is as simple follows: 
+-----------------------------
+
+To import and use ``S2FFT`` is as simple follows: 
 
 +-------------------------------------------------------+------------------------------------------------------------+
 |For a signal on the sphere                             |For a signal on the rotation group                          |
 |                                                       |                                                            |
 |.. code-block:: Python                                 |.. code-block:: Python                                      |
 |                                                       |                                                            |
+|   import s2fft                                        |   import s2fft                                             |
+|                                                       |                                                            |
+|   # Specify sampled signal and harmonic bandlimit     |   # Define sampled signal, harmonic & azimuthal bandlimits |
+|   f = ...                                             |   f = ...                                                  |
+|   L = ...                                             |   L, N = ...                                               |
+|                                                       |                                                            |
 |   # Compute harmonic coefficients                     |   # Compute Wigner coefficients                            |
-|   flm = s2fft.forward_jax(f, L)                       |   flmn = s2fft.wigner.forward_jax(f, L, N)                 |
+|   flm = s2fft.forward(f, L, method="jax")             |   flmn = s2fft.wigner.forward(f, L, N, method="jax")       |
 |                                                       |                                                            |
 |   # Map back to pixel-space signal                    |   # Map back to pixel-space signal                         |
-|   f = s2fft.inverse_jax(flm, L)                       |   f = s2fft.wigner.inverse_jax(flmn, L, N)                 |
+|   f = s2fft.inverse(flm, L, method="jax")             |   f = s2fft.wigner.inverse(flmn, L, N, method="jax")       |
 +-------------------------------------------------------+------------------------------------------------------------+
 
-C/C++ backend usage |:bulb:|
------------------
-``S2FFT`` also provides JAX support for existing C/C++ packages, specifically `HEALPix <https://healpix.jpl.nasa.gov>`_
-and `SSHT <https://github.com/astro-informatics/ssht>`_.  This works 
-by wrapping python bindings with custom JAX frontends. Note that currently this C/C++ to JAX interoperability is currently 
-limited to CPU, however for many applications this is desirable due to memory constraints.
-
-For example, one may call these alternate backends for the spherical harmonic transform by:
-
-.. code-block:: python
-
-   # Forward SSHT spherical harmonic transform
-   flm = s2fft.forward(f, L, sampling=["mw"], method="jax_ssht")  
-
-   # Forward HEALPix spherical harmonic transform
-   flm = s2fft.forward(f, L, nside=nside, sampling="healpix", method="jax_healpy")  
-
-All of these JAX frontends supports out of the box reverse mode automatic differentiation, 
-and under the hood is simply linking to the C/C++ packages you are familiar with. In this 
-way ``S2FFT`` enhances existing packages with gradient functionality for modern signal processing 
-applications!
-
-
 .. toctree::
    :hidden:
    :maxdepth: 3
-   :caption: Jupyter Notebooks
+   :caption: Tutorials
 
    spherical_harmonic/spherical_harmonic_transform.nblink
    wigner/wigner_transform.nblink