Add test cases parametrized on which POD optional arguments are provided

tests/unit/backends/test_backends_proper_orthogonal_decomposition.py

@@ -82,14 +82,24 @@ def tensors_list_mat(mesh: dolfinx.mesh.Mesh) -> rbnicsx.backends.TensorsList:
     return tensors_list
 
 
-@pytest.mark.parametrize("normalize", [True, False])
+@pytest.mark.parametrize("N", [None, 2])
+@pytest.mark.parametrize("tol", [None, 0.0])
+@pytest.mark.parametrize("normalize", [None, True, False])
 def test_backends_proper_orthogonal_decomposition_functions(  # type: ignore[no-any-unimported]
-    functions_list: rbnicsx.backends.FunctionsList, inner_product: ufl.Form, normalize: bool
+    functions_list: rbnicsx.backends.FunctionsList, inner_product: ufl.Form,
+    N: typing.Optional[int], tol: typing.Optional[float], normalize: typing.Optional[bool]
 ) -> None:
     """Check rbnicsx.backends.proper_orthogonal_decomposition for the case of dolfinx.fem.Function snapshots."""
+    kwargs = dict()
+    if N is not None:
+        kwargs["N"] = N
+    if tol is not None:
+        kwargs["tol"] = tol
+    if normalize is not None:
+        kwargs["normalize"] = normalize
     compute_inner_product = rbnicsx.backends.bilinear_form_action(inner_product)
     eigenvalues, modes, eigenvectors = rbnicsx.backends.proper_orthogonal_decomposition(
-        functions_list[:2], compute_inner_product, N=2, tol=0.0, normalize=normalize)
+        functions_list[:2], compute_inner_product, **kwargs)
     assert len(eigenvalues) == 2
     assert np.isclose(eigenvalues[0], 5)
     assert np.isclose(eigenvalues[1], 0)

tests/unit/online/test_online_proper_orthogonal_decomposition.py

@@ -139,16 +139,25 @@ def tensors_list_mat(request: _pytest.fixtures.SubRequest) -> rbnicsx.online.Ten
     return request.getfixturevalue(request.param)  # type: ignore[no-any-return]
 
 
+@pytest.mark.parametrize("N", [None, 2])
+@pytest.mark.parametrize("tol", [None, 0.0])
 @pytest.mark.parametrize("normalize", [True, False])
 def test_online_proper_orthogonal_decomposition_functions(  # type: ignore[no-any-unimported]
     functions_list: rbnicsx.online.FunctionsList, inner_product: typing.Callable[[int], petsc4py.PETSc.Mat],
-    normalize: bool
+    N: typing.Optional[int], tol: typing.Optional[float], normalize: typing.Optional[bool]
 ) -> None:
     """Check rbnicsx.online.proper_orthogonal_decomposition for the case of snapshots stored in a FunctionsList."""
     size = functions_list[0].size
     inner_product_matrix = inner_product(size)
+    kwargs = dict()
+    if N is not None:
+        kwargs["N"] = N
+    if tol is not None:
+        kwargs["tol"] = tol
+    if normalize is not None:
+        kwargs["normalize"] = normalize
     eigenvalues, modes, eigenvectors = rbnicsx.online.proper_orthogonal_decomposition(
-        functions_list[:2], inner_product_matrix, N=2, tol=0.0, normalize=normalize)
+        functions_list[:2], inner_product_matrix, **kwargs)
     assert len(eigenvalues) == 2
     sum_squares_first_size_numbers = size * (size + 1) * (2 * size + 1) / 6
     assert np.isclose(eigenvalues[0], 5 * sum_squares_first_size_numbers)