jan/meanas
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

[waveguide_3d] clean up docstrings

Browse Source
This commit is contained in:
jan 2025-12-09 22:56:16 -08:00
parent 4a80ca8b12
commit 684b891e0f
1 changed files with 2 additions and 5 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

7
meanas/fdfd/waveguide_3d.py
View File

@ -161,25 +161,22 @@ def compute_overlap_e(
axis: int, axis: int,
polarity: int, polarity: int,
slices: Sequence[slice], slices: Sequence[slice],
) -> cfdfield_t: # TODO DOCS ) -> cfdfield_t:
""" """
Given an eigenmode obtained by `solve_mode`, calculates an overlap_e for the Given an eigenmode obtained by `solve_mode`, calculates an overlap_e for the
mode orthogonality relation Integrate(((E x H_mode) + (E_mode x H)) dot dn) mode orthogonality relation Integrate(((E x H_mode) + (E_mode x H)) dot dn)
[assumes reflection symmetry]. [assumes reflection symmetry].
TODO: add reference TODO: add reference or derivation for compute_overlap_e
Args: Args:
E: E-field of the mode E: E-field of the mode
H: H-field of the mode (advanced by half of a Yee cell from E)
wavenumber: Wavenumber of the mode wavenumber: Wavenumber of the mode
omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
axis: Propagation axis (0=x, 1=y, 2=z) axis: Propagation axis (0=x, 1=y, 2=z)
polarity: Propagation direction (+1 for +ve, -1 for -ve) polarity: Propagation direction (+1 for +ve, -1 for -ve)
slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use
as the waveguide cross-section. slices[axis] should select only one item. as the waveguide cross-section. slices[axis] should select only one item.
mu: Magnetic permeability (default 1 everywhere)
Returns: Returns:
overlap_e such that `numpy.sum(overlap_e * other_e.conj())` computes the overlap integral overlap_e such that `numpy.sum(overlap_e * other_e.conj())` computes the overlap integral