test -> trial

test -> trial

fix hypre-ads

Author: leo-collins

firedrake/preconditioners/hypre_ads.py

@@ -1,7 +1,7 @@
 from firedrake.preconditioners.base import PCBase
 from firedrake.petsc import PETSc
 from firedrake.function import Function
-from firedrake.ufl_expr import TestFunction
+from firedrake.ufl_expr import TrialFunction
 from firedrake.dmhooks import get_function_space
 from firedrake.preconditioners.hypre_ams import chop
 from firedrake.interpolation import interpolate
@@ -31,12 +31,12 @@ def initialize(self, obj):
         NC1 = V.reconstruct(family="N1curl" if mesh.ufl_cell().is_simplex() else "NCE", degree=1)
         G_callback = appctx.get("get_gradient", None)
         if G_callback is None:
-            G = chop(assemble(interpolate(grad(TestFunction(P1)), NC1)).petscmat)
+            G = chop(assemble(interpolate(grad(TrialFunction(P1)), NC1)).petscmat)
         else:
             G = G_callback(P1, NC1)
         C_callback = appctx.get("get_curl", None)
         if C_callback is None:
-            C = chop(assemble(interpolate(curl(TestFunction(NC1)), V)).petscmat)
+            C = chop(assemble(interpolate(curl(TrialFunction(NC1)), V)).petscmat)
         else:
             C = C_callback(NC1, V)
 

firedrake/preconditioners/hypre_ams.py

@@ -1,7 +1,7 @@
 from firedrake.preconditioners.base import PCBase
 from firedrake.petsc import PETSc
 from firedrake.function import Function
-from firedrake.ufl_expr import TestFunction
+from firedrake.ufl_expr import TrialFunction
 from firedrake.dmhooks import get_function_space
 from firedrake.utils import complex_mode
 from firedrake.interpolation import interpolate
@@ -51,7 +51,7 @@ def initialize(self, obj):
         P1 = V.reconstruct(family="Lagrange", degree=1)
         G_callback = appctx.get("get_gradient", None)
         if G_callback is None:
-            G = chop(assemble(interpolate(grad(TestFunction(P1)), V)).petscmat)
+            G = chop(assemble(interpolate(grad(TrialFunction(P1)), V)).petscmat)
         else:
             G = G_callback(P1, V)
 

tests/firedrake/regression/test_interpolate_cross_mesh.py

@@ -661,14 +661,14 @@ def test_interpolate_matrix_cross_mesh():
     vom = VertexOnlyMesh(source_mesh, X.dat.data_ro, redundant=False)
     P0DG = FunctionSpace(vom, "DG", 0)
     # We get the interpolation matrix U -> P0DG which performs point evaluation
-    A = assemble(interpolate(TestFunction(U), P0DG))
+    A = assemble(interpolate(TrialFunction(U), P0DG))
     f_at_points = assemble(A @ f)
     f_at_points2 = assemble(interpolate(f, P0DG))
     assert np.allclose(f_at_points.dat.data_ro, f_at_points2.dat.data_ro)
     # To get the points in the correct order in V we interpolate into vom.input_ordering
     # We pass matfree=False which constructs the permutation matrix instead of using SFs
     P0DG_io = FunctionSpace(vom.input_ordering, "DG", 0)
-    B = assemble(interpolate(TestFunction(P0DG), P0DG_io, matfree=False))
+    B = assemble(interpolate(TrialFunction(P0DG), P0DG_io, matfree=False))
     f_at_points_correct_order = assemble(B @ f_at_points)
     f_at_points_correct_order2 = assemble(interpolate(f_at_points, P0DG_io))
     assert np.allclose(f_at_points_correct_order.dat.data_ro, f_at_points_correct_order2.dat.data_ro)

tests/firedrake/vertexonly/test_vertex_only_fs.py

@@ -121,7 +121,7 @@ def functionspace_tests(vm, petsc_raises):
     assert np.allclose(h.dat.data_ro_with_halos[idxs_to_include], np.prod(vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include].reshape(-1, vm.input_ordering.geometric_dimension()), axis=1))
     assert np.all(h.dat.data_ro_with_halos[~idxs_to_include] == -1)
     # Using permutation matrix
-    perm_mat = assemble(interpolate(TestFunction(V), W, matfree=False))
+    perm_mat = assemble(interpolate(TrialFunction(V), W, matfree=False))
     h2 = assemble(perm_mat @ g)
     assert np.allclose(h2.dat.data_ro_with_halos[idxs_to_include], h.dat.data_ro_with_halos[idxs_to_include])
     h2 = assemble(interpolate(g, W))
@@ -216,7 +216,7 @@ def vectorfunctionspace_tests(vm, petsc_raises):
     assert np.allclose(h.dat.data_ro[idxs_to_include], 2*vm.input_ordering.coordinates.dat.data_ro_with_halos[idxs_to_include])
     assert np.all(h.dat.data_ro_with_halos[~idxs_to_include] == -1)
     # Using permutation matrix
-    perm_mat = assemble(interpolate(TestFunction(V), W, matfree=False))
+    perm_mat = assemble(interpolate(TrialFunction(V), W, matfree=False))
     h2 = assemble(perm_mat @ g)
     assert np.allclose(h2.dat.data_ro_with_halos[idxs_to_include], h.dat.data_ro_with_halos[idxs_to_include])
     # check other interpolation APIs work identically
@@ -373,9 +373,9 @@ def test_tensorfs_permutation(tensorfs_and_expr):
     f = Function(V)
     f.interpolate(expr)
     f_in_W = assemble(interpolate(f, W))
-    python_mat = assemble(interpolate(TestFunction(V), W, matfree=False))
+    python_mat = assemble(interpolate(TrialFunction(V), W, matfree=False))
     f_in_W_2 = assemble(python_mat @ f)
     assert np.allclose(f_in_W.dat.data_ro, f_in_W_2.dat.data_ro)
-    petsc_mat = assemble(interpolate(TestFunction(V), W, matfree=True))
+    petsc_mat = assemble(interpolate(TrialFunction(V), W, matfree=True))
     f_in_W_petsc = assemble(petsc_mat @ f)
     assert np.allclose(f_in_W.dat.data_ro, f_in_W_petsc.dat.data_ro)