Update docs regarding spherical interpolators (#62)

Author: ebrahimebrahim

src/openlifu/seg/skinseg.py

@@ -308,6 +308,8 @@ def spherical_interpolator_from_mesh(
     Returns:
         A spherical interpolator, which is a callable that maps (theta,phi) pairs of spherical coordinates (phi being azimuthal)
         to r values (radial spherical coordinate values). The angles are in radians.
+        A spherical interpolator can also run in batch mode, operating on a numpy array of shape (...,2) consisting
+        of theta,phi pairs in the last axis.
 
     Summary of the algorithm:
         - Transform the input mesh based on the desired origin and orientation of the spherical coordinate system.

src/openlifu/virtual_fit.py

@@ -350,7 +350,7 @@ def progress_callback(progress_percent : int, step_description : str): # noqa: A
         planefit_points_unprojected_spherical = cartesian_to_spherical_vectorized(
             planefit_points_unprojected_cartesian
         ) # shape (num dthetas, num dphis, 3)
-        skin_projected_r_values = skin_interpolator(planefit_points_unprojected_spherical[...,1:]) # shape (num dthetas, num dphis) # TODO adjust docstrings to demand a *vectorizable* spherical interpolator
+        skin_projected_r_values = skin_interpolator(planefit_points_unprojected_spherical[...,1:]) # shape (num dthetas, num dphis)
         planefit_points_cartesian = spherical_to_cartesian_vectorized( # Could instead renormalize planefit_points_unprojected_cartesian, not sure if it would give a speedup versus this
             np.stack([
                 skin_projected_r_values, # New r values after projection to skin