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[fdfd.functional] fix handling of mu in e_full and m2j sign

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Jan Petykiewicz 2026-04-17 20:30:28 -07:00
commit 593098bf8f
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import numpy
from numpy.testing import assert_allclose
from ..fdmath import vec, unvec
from ..fdfd import functional, operators
OMEGA = 1 / 1500
SHAPE = (2, 3, 2)
ATOL = 1e-9
RTOL = 1e-9
DXES = [
[numpy.array([1.0, 1.5]), numpy.array([0.75, 1.25, 1.5]), numpy.array([1.2, 0.8])],
[numpy.array([0.9, 1.4]), numpy.array([0.8, 1.1, 1.4]), numpy.array([1.0, 0.7])],
]
EPSILON = numpy.stack([
numpy.linspace(1.0, 2.2, numpy.prod(SHAPE)).reshape(SHAPE),
numpy.linspace(1.1, 2.3, numpy.prod(SHAPE)).reshape(SHAPE),
numpy.linspace(1.2, 2.4, numpy.prod(SHAPE)).reshape(SHAPE),
])
MU = numpy.stack([
numpy.linspace(2.0, 3.2, numpy.prod(SHAPE)).reshape(SHAPE),
numpy.linspace(2.1, 3.3, numpy.prod(SHAPE)).reshape(SHAPE),
numpy.linspace(2.2, 3.4, numpy.prod(SHAPE)).reshape(SHAPE),
])
E_FIELD = (numpy.arange(3 * numpy.prod(SHAPE)).reshape((3, *SHAPE)) + 0.5j).astype(complex)
H_FIELD = (numpy.arange(3 * numpy.prod(SHAPE)).reshape((3, *SHAPE)) * 0.25 - 0.75j).astype(complex)
TF_REGION = numpy.zeros((3, *SHAPE), dtype=float)
TF_REGION[:, 0, 1, 0] = 1.0
def apply_matrix(op: operators.sparse.spmatrix, field: numpy.ndarray) -> numpy.ndarray:
return unvec(op @ vec(field), SHAPE)
def assert_fields_match(actual: numpy.ndarray, expected: numpy.ndarray) -> None:
assert_allclose(actual, expected, atol=ATOL, rtol=RTOL)
def test_e_full_matches_sparse_operator_without_mu() -> None:
matrix_result = apply_matrix(
operators.e_full(OMEGA, DXES, vec(EPSILON)),
E_FIELD,
)
functional_result = functional.e_full(OMEGA, DXES, EPSILON)(E_FIELD)
assert_fields_match(functional_result, matrix_result)
def test_e_full_matches_sparse_operator_with_mu() -> None:
matrix_result = apply_matrix(
operators.e_full(OMEGA, DXES, vec(EPSILON), vec(MU)),
E_FIELD,
)
functional_result = functional.e_full(OMEGA, DXES, EPSILON, MU)(E_FIELD)
assert_fields_match(functional_result, matrix_result)
def test_eh_full_matches_sparse_operator_with_mu() -> None:
matrix_result = operators.eh_full(OMEGA, DXES, vec(EPSILON), vec(MU)) @ numpy.concatenate([vec(E_FIELD), vec(H_FIELD)])
matrix_e, matrix_h = (unvec(part, SHAPE) for part in numpy.split(matrix_result, 2))
functional_e, functional_h = functional.eh_full(OMEGA, DXES, EPSILON, MU)(E_FIELD, H_FIELD)
assert_fields_match(functional_e, matrix_e)
assert_fields_match(functional_h, matrix_h)
def test_e2h_matches_sparse_operator_with_mu() -> None:
matrix_result = apply_matrix(
operators.e2h(OMEGA, DXES, vec(MU)),
E_FIELD,
)
functional_result = functional.e2h(OMEGA, DXES, MU)(E_FIELD)
assert_fields_match(functional_result, matrix_result)
def test_m2j_matches_sparse_operator_without_mu() -> None:
matrix_result = apply_matrix(
operators.m2j(OMEGA, DXES),
H_FIELD,
)
functional_result = functional.m2j(OMEGA, DXES)(H_FIELD)
assert_fields_match(functional_result, matrix_result)
def test_m2j_matches_sparse_operator_with_mu() -> None:
matrix_result = apply_matrix(
operators.m2j(OMEGA, DXES, vec(MU)),
H_FIELD,
)
functional_result = functional.m2j(OMEGA, DXES, MU)(H_FIELD)
assert_fields_match(functional_result, matrix_result)
def test_e_tfsf_source_matches_sparse_operator_without_mu() -> None:
matrix_result = apply_matrix(
operators.e_tfsf_source(vec(TF_REGION), OMEGA, DXES, vec(EPSILON)),
E_FIELD,
)
functional_result = functional.e_tfsf_source(TF_REGION, OMEGA, DXES, EPSILON)(E_FIELD)
assert_fields_match(functional_result, matrix_result)
def test_e_tfsf_source_matches_sparse_operator_with_mu() -> None:
matrix_result = apply_matrix(
operators.e_tfsf_source(vec(TF_REGION), OMEGA, DXES, vec(EPSILON), vec(MU)),
E_FIELD,
)
functional_result = functional.e_tfsf_source(TF_REGION, OMEGA, DXES, EPSILON, MU)(E_FIELD)
assert_fields_match(functional_result, matrix_result)
def test_poynting_e_cross_h_matches_sparse_operator() -> None:
matrix_result = apply_matrix(
operators.poynting_e_cross(vec(E_FIELD), DXES),
H_FIELD,
)
functional_result = functional.poynting_e_cross_h(DXES)(E_FIELD, H_FIELD)
assert_fields_match(functional_result, matrix_result)