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dx_prop -> prop_phase

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propagation direction wavenumber might be different from
operator-derived (2D) wavenumber due to numerical dispersion, so lump it
in with dx_prop
This commit is contained in:
jan 2019-09-05 22:35:23 +02:00
parent 8eac9df76e
commit b5ad284966
1 changed files with 10 additions and 10 deletions
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20
meanas/fdfd/waveguide.py
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@ -109,7 +109,7 @@ def normalized_fields_e(e_xy: numpy.ndarray,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfield_t,
mu: vfield_t = None, mu: vfield_t = None,
dx_prop: float = 0, prop_phase: float = 0,
) -> Tuple[vfield_t, vfield_t]: ) -> Tuple[vfield_t, vfield_t]:
""" """
Given a vector e_xy containing the vectorized E_x and E_y fields, Given a vector e_xy containing the vectorized E_x and E_y fields,
@ -121,13 +121,14 @@ def normalized_fields_e(e_xy: numpy.ndarray,
:param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types (2D) :param dxes: Grid parameters [dx_e, dx_h] as described in meanas.types (2D)
:param epsilon: Vectorized dielectric constant grid :param epsilon: Vectorized dielectric constant grid
:param mu: Vectorized magnetic permeability grid (default 1 everywhere) :param mu: Vectorized magnetic permeability grid (default 1 everywhere)
:param dxes_prop: Grid cell width in the propagation direction. Default 0 (continuous). :param prop_phase: Phase shift (dz * corrected_wavenumber) over 1 cell in propagation direction.
Default 0 (continuous propagation direction, i.e. dz->0).
:return: Normalized, vectorized (e, h) containing all vector components. :return: Normalized, vectorized (e, h) containing all vector components.
""" """
e = exy2e(wavenumber=wavenumber, dxes=dxes, epsilon=epsilon) @ e_xy e = exy2e(wavenumber=wavenumber, dxes=dxes, epsilon=epsilon) @ e_xy
h = exy2h(wavenumber=wavenumber, omega=omega, dxes=dxes, epsilon=epsilon, mu=mu) @ e_xy h = exy2h(wavenumber=wavenumber, omega=omega, dxes=dxes, epsilon=epsilon, mu=mu) @ e_xy
e_norm, h_norm = _normalized_fields(e=e, h=h, wavenumber=wavenumber, omega=omega, e_norm, h_norm = _normalized_fields(e=e, h=h, omega=omega, dxes=dxes, epsilon=epsilon,
dxes=dxes, epsilon=epsilon, mu=mu, dx_prop=dx_prop) mu=mu, prop_phase=prop_phase)
return e_norm, h_norm return e_norm, h_norm
@ -137,7 +138,7 @@ def normalized_fields_h(h_xy: numpy.ndarray,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfield_t,
mu: vfield_t = None, mu: vfield_t = None,
dx_prop: float = 0, prop_phase: float = 0,
) -> Tuple[vfield_t, vfield_t]: ) -> Tuple[vfield_t, vfield_t]:
""" """
Given a vector e_xy containing the vectorized E_x and E_y fields, Given a vector e_xy containing the vectorized E_x and E_y fields,
@ -154,19 +155,18 @@ def normalized_fields_h(h_xy: numpy.ndarray,
""" """
e = hxy2e(wavenumber=wavenumber, omega=omega, dxes=dxes, epsilon=epsilon, mu=mu) @ h_xy e = hxy2e(wavenumber=wavenumber, omega=omega, dxes=dxes, epsilon=epsilon, mu=mu) @ h_xy
h = hxy2h(wavenumber=wavenumber, dxes=dxes, mu=mu) @ h_xy h = hxy2h(wavenumber=wavenumber, dxes=dxes, mu=mu) @ h_xy
e_norm, h_norm = _normalized_fields(e=e, h=h, wavenumber=wavenumber, omega=omega, e_norm, h_norm = _normalized_fields(e=e, h=h, omega=omega, dxes=dxes, epsilon=epsilon,
dxes=dxes, epsilon=epsilon, mu=mu, dx_prop=dx_prop) mu=mu, prop_phase=prop_phase)
return e_norm, h_norm return e_norm, h_norm
def _normalized_fields(e: numpy.ndarray, def _normalized_fields(e: numpy.ndarray,
h: numpy.ndarray, h: numpy.ndarray,
wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfield_t,
mu: vfield_t = None, mu: vfield_t = None,
dx_prop: float = 0, prop_phase: float = 0,
) -> Tuple[vfield_t, vfield_t]: ) -> Tuple[vfield_t, vfield_t]:
# TODO documentation # TODO documentation
shape = [s.size for s in dxes[0]] shape = [s.size for s in dxes[0]]
@ -175,7 +175,7 @@ def _normalized_fields(e: numpy.ndarray,
E = unvec(e, shape) E = unvec(e, shape)
H = unvec(h, shape) H = unvec(h, shape)
phase = numpy.exp(-1j * wavenumber * dx_prop / 2) phase = numpy.exp(-1j * prop_phase / 2)
S1 = E[0] * numpy.conj(H[1] * phase) * dxes_real[0][1] * dxes_real[1][0] S1 = E[0] * numpy.conj(H[1] * phase) * dxes_real[0][1] * dxes_real[1][0]
S2 = E[1] * numpy.conj(H[0] * phase) * dxes_real[0][0] * dxes_real[1][1] S2 = E[1] * numpy.conj(H[0] * phase) * dxes_real[0][0] * dxes_real[1][1]
P = numpy.real(S1.sum() - S2.sum()) P = numpy.real(S1.sum() - S2.sum())