super minor fixes for Sphinx rendering issues (#1585)

Author: peterdsharpe

physicsnemo/mesh/mesh.py

@@ -620,8 +620,8 @@ def cell_centroids(self) -> torch.Tensor:
         """Compute the centroids (geometric centers) of all cells.
 
         The centroid of a cell is computed as the arithmetic mean of its vertex positions.
-        For an n-simplex with vertices (v0, v1, ..., vn), the centroid is:
-            centroid = (v0 + v1 + ... + vn) / (n + 1)
+        For an n-simplex with vertices (v0, v1, ..., vn), the centroid is
+        ``centroid = (v0 + v1 + ... + vn) / (n + 1)``.
 
         The result is cached in ``_cache["cell", "centroids"]`` for efficiency.
 
@@ -1541,24 +1541,28 @@ def get_facet_mesh(
         ----------
         manifold_codimension : int, optional
             Codimension of extracted mesh relative to parent.
+
             - 1: Extract (n-1)-facets (default, immediate boundaries of all cells)
             - 2: Extract (n-2)-facets (e.g., edges from tets, vertices from triangles)
             - k: Extract (n-k)-facets
         data_source : {"points", "cells"}, optional
             Source of data inheritance:
+
             - "cells": Facets inherit from parent cells they bound. When multiple
               cells share a facet, data is aggregated according to data_aggregation.
             - "points": Facets inherit from their boundary vertices. Data from
               multiple boundary points is averaged.
         data_aggregation : {"mean", "area_weighted", "inverse_distance"}, optional
             Strategy for aggregating data from multiple sources
             (only applies when data_source="cells"):
+
             - "mean": Simple arithmetic mean
             - "area_weighted": Weighted by parent cell areas
             - "inverse_distance": Weighted by inverse distance from facet centroid
               to parent cell centroids
         target_counts : list[int] | {"boundary", "shared", "interior", "all"}, optional
             Which facets to keep based on how many parent cells share them:
+
             - "all": Keep all unique facets (default)
             - "boundary": Keep only boundary facets (appearing in exactly 1 cell)
             - "shared": Keep only shared facets (appearing in 2+ cells)
@@ -1969,6 +1973,7 @@ def get_cell_to_cells_adjacency(self, adjacency_codimension: int = 1):
         ----------
         adjacency_codimension : int, optional
             Codimension of shared facets defining adjacency.
+
             - 1 (default): Cells must share a codimension-1 facet (e.g., triangles
               sharing an edge, tetrahedra sharing a triangular face)
             - 2: Cells must share a codimension-2 facet (e.g., tetrahedra sharing
@@ -2201,6 +2206,7 @@ def draw(
         ----------
         backend : {"auto", "matplotlib", "pyvista"}
             Visualization backend to use:
+
             - "auto": Automatically select based on n_spatial_dims
               (matplotlib for 0D/1D/2D, PyVista for 3D)
             - "matplotlib": Force matplotlib backend (supports 3D via mplot3d)
@@ -2211,12 +2217,14 @@ def draw(
             customization before display.
         point_scalars : torch.Tensor or str or tuple[str, ...], optional
             Scalar data to color points. Mutually exclusive with cell_scalars. Can be:
+
             - None: Points use neutral color (black)
             - torch.Tensor: Direct scalar values, shape (n_points,) or
               (n_points, ...) where trailing dimensions are L2-normed
             - str or tuple[str, ...]: Key to lookup in mesh.point_data
         cell_scalars : torch.Tensor or str or tuple[str, ...], optional
             Scalar data to color cells. Mutually exclusive with point_scalars. Can be:
+
             - None: Cells use neutral color (lightblue if no scalars,
               lightgray if point_scalars active)
             - torch.Tensor: Direct scalar values, shape (n_cells,) or
@@ -2787,12 +2795,14 @@ def subdivide(
         levels : int, optional
             Number of subdivision iterations to perform. Each level
             increases mesh resolution exponentially:
+
             - 0: No subdivision (returns original mesh)
             - 1: Each cell splits into 2^n children
             - 2: Each cell splits into 4^n children
             - k: Each cell splits into (2^k)^n children
         filter : {"linear", "butterfly", "loop"}, optional
             Subdivision scheme to use:
+
             - "linear": Simple midpoint subdivision (interpolating).
               New vertices at exact edge midpoints. Works for any dimension.
               Preserves original vertices.

physicsnemo/mesh/transformations/geometric.py

@@ -454,8 +454,8 @@ def transform(
     -----
     Cache Handling:
         - areas: For square invertible matrices:
-            - Full-dimensional meshes: scaled by |det|
-            - Codimension-1 manifolds: per-element scaling using |det| × ||M^{-T} n||
+            - Full-dimensional meshes: scaled by ``|det|``
+            - Codimension-1 manifolds: per-element scaling using ``|det| * ||M^{-T} n||``
             - Higher codimension: invalidated
         - centroids: Always transformed
         - normals: For square invertible matrices, transformed by inverse-transpose

physicsnemo/mesh/visualization/draw_mesh.py

@@ -59,6 +59,7 @@ def draw_mesh(
         Mesh object to visualize.
     backend : {"auto", "matplotlib", "pyvista"}
         Visualization backend to use:
+
         - "auto": Automatically select based on n_spatial_dims
           (matplotlib for 0D/1D/2D, PyVista for 3D)
         - "matplotlib": Force matplotlib backend (supports 3D via mplot3d)
@@ -70,13 +71,15 @@ def draw_mesh(
     point_scalars : torch.Tensor or str or tuple[str, ...] or None, optional
         Scalar data to color points. Mutually exclusive with
         cell_scalars. Can be:
+
         - None: Points use neutral color (black)
         - torch.Tensor: Direct scalar values, shape (n_points,) or
           (n_points, ...) where trailing dimensions are L2-normed
         - str or tuple[str, ...]: Key to lookup in mesh.point_data
     cell_scalars : torch.Tensor or str or tuple[str, ...] or None, optional
         Scalar data to color cells. Mutually exclusive with
         point_scalars. Can be:
+
         - None: Cells use neutral color (lightblue if no scalars,
           lightgray if point_scalars active)
         - torch.Tensor: Direct scalar values, shape (n_cells,) or