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Use dof ordering when generating tensor factors #695

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Sep 12, 2023
Merged

Use dof ordering when generating tensor factors #695

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77e2ebf
Add test of tensor product perm with dof_ordering input
mscroggs Sep 12, 2023
3f0d8e1
apply dof ordering to tensor permutation
mscroggs Sep 12, 2023
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95 changes: 54 additions & 41 deletions cpp/basix/e-lagrange.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -861,8 +861,20 @@ create_d_iso(cell::type celltype, int degree, element::lagrange_variant variant,
template <std::floating_point T>
std::vector<std::tuple<std::vector<FiniteElement<T>>, std::vector<int>>>
create_tensor_product_factors(cell::type celltype, int degree,
element::lagrange_variant variant)
element::lagrange_variant variant,
std::vector<int> dof_ordering)
{

if (dof_ordering.size() == 0)
{
std::size_t ndofs = celltype == cell::type::quadrilateral
? (degree + 1) * (degree + 1)
: (degree + 1) * (degree + 1) * (degree + 1);
std::vector<int> d(ndofs);
std::iota(d.begin(), d.end(), 0);
return create_tensor_product_factors<T>(celltype, degree, variant, d);
}

switch (celltype)
{
case cell::type::quadrilateral:
Expand All @@ -871,25 +883,25 @@ create_tensor_product_factors(cell::type celltype, int degree,
cell::type::interval, degree, variant, true);
std::vector<int> perm((degree + 1) * (degree + 1));
if (degree == 0)
perm[0] = 0;
perm[dof_ordering[0]] = 0;
else
{
int p = 0;
int n = degree - 1;
perm[p++] = 0;
perm[p++] = 2;
perm[dof_ordering[p++]] = 0;
perm[dof_ordering[p++]] = 2;
for (int i = 0; i < n; ++i)
perm[p++] = 4 + n + i;
perm[p++] = 1;
perm[p++] = 3;
perm[dof_ordering[p++]] = 4 + n + i;
perm[dof_ordering[p++]] = 1;
perm[dof_ordering[p++]] = 3;
for (int i = 0; i < n; ++i)
perm[p++] = 4 + 2 * n + i;
perm[dof_ordering[p++]] = 4 + 2 * n + i;
for (int i = 0; i < n; ++i)
{
perm[p++] = 4 + i;
perm[p++] = 4 + 3 * n + i;
perm[dof_ordering[p++]] = 4 + i;
perm[dof_ordering[p++]] = 4 + 3 * n + i;
for (int j = 0; j < n; ++j)
perm[p++] = 4 + i + (4 + j) * n;
perm[dof_ordering[p++]] = 4 + i + (4 + j) * n;
}
}
return {{{sub_element, sub_element}, std::move(perm)}};
Expand All @@ -900,57 +912,58 @@ create_tensor_product_factors(cell::type celltype, int degree,
cell::type::interval, degree, variant, true);
std::vector<int> perm((degree + 1) * (degree + 1) * (degree + 1));
if (degree == 0)
perm[0] = 0;
perm[dof_ordering[0]] = 0;
else
{
int p = 0;
int n = degree - 1;
perm[p++] = 0;
perm[p++] = 4;
perm[dof_ordering[p++]] = 0;
perm[dof_ordering[p++]] = 4;
for (int i = 0; i < n; ++i)
perm[p++] = 8 + 2 * n + i;
perm[p++] = 2;
perm[p++] = 6;
perm[dof_ordering[p++]] = 8 + 2 * n + i;
perm[dof_ordering[p++]] = 2;
perm[dof_ordering[p++]] = 6;
for (int i = 0; i < n; ++i)
perm[p++] = 8 + 6 * n + i;
perm[dof_ordering[p++]] = 8 + 6 * n + i;
for (int i = 0; i < n; ++i)
{
perm[p++] = 8 + n + i;
perm[p++] = 8 + 9 * n + i;
perm[dof_ordering[p++]] = 8 + n + i;
perm[dof_ordering[p++]] = 8 + 9 * n + i;
for (int j = 0; j < n; ++j)
perm[p++] = 8 + 12 * n + 2 * n * n + i + n * j;
perm[dof_ordering[p++]] = 8 + 12 * n + 2 * n * n + i + n * j;
}
perm[p++] = 1;
perm[p++] = 5;
perm[dof_ordering[p++]] = 1;
perm[dof_ordering[p++]] = 5;
for (int i = 0; i < n; ++i)
perm[p++] = 8 + 4 * n + i;
perm[p++] = 3;
perm[p++] = 7;
perm[dof_ordering[p++]] = 8 + 4 * n + i;
perm[dof_ordering[p++]] = 3;
perm[dof_ordering[p++]] = 7;
for (int i = 0; i < n; ++i)
perm[p++] = 8 + 7 * n + i;
perm[dof_ordering[p++]] = 8 + 7 * n + i;
for (int i = 0; i < n; ++i)
{
perm[p++] = 8 + 3 * n + i;
perm[p++] = 8 + 10 * n + i;
perm[dof_ordering[p++]] = 8 + 3 * n + i;
perm[dof_ordering[p++]] = 8 + 10 * n + i;
for (int j = 0; j < n; ++j)
perm[p++] = 8 + 12 * n + 3 * n * n + i + n * j;
perm[dof_ordering[p++]] = 8 + 12 * n + 3 * n * n + i + n * j;
}
for (int i = 0; i < n; ++i)
{
perm[p++] = 8 + i;
perm[p++] = 8 + 8 * n + i;
perm[dof_ordering[p++]] = 8 + i;
perm[dof_ordering[p++]] = 8 + 8 * n + i;
for (int j = 0; j < n; ++j)
perm[p++] = 8 + 12 * n + n * n + i + n * j;
perm[p++] = 8 + 5 * n + i;
perm[p++] = 8 + 11 * n + i;
perm[dof_ordering[p++]] = 8 + 12 * n + n * n + i + n * j;
perm[dof_ordering[p++]] = 8 + 5 * n + i;
perm[dof_ordering[p++]] = 8 + 11 * n + i;
for (int j = 0; j < n; ++j)
perm[p++] = 8 + 12 * n + 4 * n * n + i + n * j;
perm[dof_ordering[p++]] = 8 + 12 * n + 4 * n * n + i + n * j;
for (int j = 0; j < n; ++j)
{
perm[p++] = 8 + 12 * n + i + n * j;
perm[p++] = 8 + 12 * n + 5 * n * n + i + n * j;
perm[dof_ordering[p++]] = 8 + 12 * n + i + n * j;
perm[dof_ordering[p++]] = 8 + 12 * n + 5 * n * n + i + n * j;
for (int k = 0; k < n; ++k)
perm[p++] = 8 + 12 * n + 6 * n * n + i + n * j + n * n * k;
perm[dof_ordering[p++]]
= 8 + 12 * n + 6 * n * n + i + n * j + n * n * k;
}
}
}
Expand Down Expand Up @@ -1420,8 +1433,8 @@ basix::element::create_lagrange(cell::type celltype, int degree,

sobolev::space space
= discontinuous ? sobolev::space::L2 : sobolev::space::H1;
auto tensor_factors
= create_tensor_product_factors<T>(celltype, degree, variant);
auto tensor_factors = create_tensor_product_factors<T>(celltype, degree,
variant, dof_ordering);
return FiniteElement<T>(
family::P, celltype, polyset::type::standard, degree, {},
impl::mdspan_t<T, 2>(math::eye<T>(ndofs).data(), ndofs, ndofs), xview,
Expand Down
17 changes: 17 additions & 0 deletions test/test_tensor_products.py
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Original file line number Diff line number Diff line change
Expand Up @@ -200,3 +200,20 @@ def test_tensor_product_factorisation_hexahedron(degree):

dphi_tensor = dphi_tensor.reshape([Nq*Nq*Nq, Nd*Nd*Nd])
assert numpy.allclose(dphi_z, dphi_tensor)


@pytest.mark.parametrize("cell_type", [
basix.CellType.quadrilateral,
basix.CellType.hexahedron,
])
@pytest.mark.parametrize("family, args", [
(basix.ElementFamily.P, (basix.LagrangeVariant.equispaced, )),
(basix.ElementFamily.P, (basix.LagrangeVariant.gll_warped, )),
])
@pytest.mark.parametrize("degree", range(1, 5))
def test_dof_ordering(cell_type, family, args, degree):
e = basix.create_element(family, cell_type, degree, *args)
perm = e.get_tensor_product_representation()[0][1]
e2 = basix.create_element(family, cell_type, degree, *args, dof_ordering=perm)
for i, j in enumerate(e2.get_tensor_product_representation()[0][1]):
assert i == j
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