Prepare the package for more difficult curvilinear grids (#487)

pde/grids/base.py

@@ -143,7 +143,7 @@ class GridBase(metaclass=ABCMeta):
 
     boundary_names: Dict[str, Tuple[int, bool]] = {}
     """dict: Names of boundaries to select them conveniently"""
-    cell_volume_data: Sequence[FloatNumerical]
+    cell_volume_data: Optional[Sequence[FloatNumerical]]
     """list: Information about the size of discretization cells"""
     coordinate_constraints: List[int] = []
     """list: axes that not described explicitly"""
@@ -445,13 +445,20 @@ def cell_coords(self) -> np.ndarray:
     @cached_property()
     def cell_volumes(self) -> np.ndarray:
         """:class:`~numpy.ndarray`: volume of each cell"""
+        if self.cell_volume_data is None:
+            raise RuntimeError(
+                "`cell_volumes` needs to be implemented if `cell_volume_data` is `None`"
+            )
         vols = functools.reduce(np.outer, self.cell_volume_data)
         return np.broadcast_to(vols, self.shape)
 
     @cached_property()
     def uniform_cell_volumes(self) -> bool:
         """bool: returns True if all cell volumes are the same"""
-        return all(np.asarray(vols).ndim == 0 for vols in self.cell_volume_data)
+        if self.cell_volume_data is None:
+            return False
+        else:
+            return all(np.asarray(vols).ndim == 0 for vols in self.cell_volume_data)
 
     def difference_vector_real(self, p1: np.ndarray, p2: np.ndarray) -> np.ndarray:
         """return vector(s) pointing from p1 to p2
@@ -1254,19 +1261,25 @@ def integrate(
             :class:`~numpy.ndarray`: The values integrated over the entire grid
         """
         # determine the volumes of the individual cells
-        if axes is None:
-            volume_list = self.cell_volume_data
+        if self.cell_volume_data is None:
+            if axes is None:
+                cell_volumes = self.cell_volumes
+            else:
+                raise NotImplementedError
         else:
-            # use stored value for the default case of integrating over all axes
-            if isinstance(axes, int):
-                axes = (axes,)
+            if axes is None:
+                volume_list = self.cell_volume_data
             else:
-                axes = tuple(axes)  # required for numpy.sum
-            volume_list = [
-                cell_vol if ax in axes else 1
-                for ax, cell_vol in enumerate(self.cell_volume_data)
-            ]
-        cell_volumes = functools.reduce(np.outer, volume_list)
+                # use stored value for the default case of integrating over all axes
+                if isinstance(axes, int):
+                    axes = (axes,)
+                else:
+                    axes = tuple(axes)  # required for numpy.sum
+                volume_list = [
+                    cell_vol if ax in axes else 1
+                    for ax, cell_vol in enumerate(self.cell_volume_data)
+                ]
+            cell_volumes = functools.reduce(np.outer, volume_list)
 
         # determine the axes over which we will integrate
         if not isinstance(data, np.ndarray) or data.ndim < self.num_axes:
@@ -1355,7 +1368,9 @@ def make_cell_volume_compiled(self, flat_index: bool = False) -> CellVolume:
         Returns:
             function: returning the volume of the chosen cell
         """
-        if all(np.isscalar(d) for d in self.cell_volume_data):
+        if self.cell_volume_data is not None and all(
+            np.isscalar(d) for d in self.cell_volume_data
+        ):
             # all cells have the same volume
             cell_volume = np.prod(self.cell_volume_data)  # type: ignore