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612 lines
22 KiB
Python
612 lines
22 KiB
Python
"""
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Operators and helper functions for waveguides with unchanging cross-section.
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The propagation direction is chosen to be along the z axis, and all fields
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are given an implicit z-dependence of the form `exp(-1 * wavenumber * z)`.
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As the z-dependence is known, all the functions in this file assume a 2D grid
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(i.e. `dxes = [[[dx_e_0, dx_e_1, ...], [dy_e_0, ...]], [[dx_h_0, ...], [dy_h_0, ...]]]`).
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"""
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# TODO update module docs
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from typing import List, Tuple
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import numpy
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from numpy.linalg import norm
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import scipy.sparse as sparse
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from ..fdmath.operators import deriv_forward, deriv_back, curl_forward, curl_back, cross
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from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t
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from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
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from . import operators
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__author__ = 'Jan Petykiewicz'
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def operator_e(omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: vfdfield_t = None,
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) -> sparse.spmatrix:
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"""
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Waveguide operator of the form
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omega**2 * mu * epsilon +
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mu * [[-Dy], [Dx]] / mu * [-Dy, Dx] +
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[[Dx], [Dy]] / epsilon * [Dx, Dy] * epsilon
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for use with a field vector of the form `cat([E_x, E_y])`.
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More precisely, the operator is
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$$ \\omega^2 \\mu_{yx} \\epsilon_{xy} +
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\\mu_{yx} \\begin{bmatrix} -D_{by} \\\\
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D_{bx} \\end{bmatrix} \\mu_z^{-1}
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\\begin{bmatrix} -D_{fy} & D_{fx} \\end{bmatrix} +
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\\begin{bmatrix} D_{fx} \\\\
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D_{fy} \\end{bmatrix} \\epsilon_z^{-1}
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\\begin{bmatrix} D_{bx} & D_{by} \\end{bmatrix} \\epsilon_{xy} $$
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where
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\\( \\epsilon_{xy} = \\begin{bmatrix}
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\\epsilon_x & 0 \\\\
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0 & \\epsilon_y
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\\end{bmatrix} \\),
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\\( \\mu_{yx} = \\begin{bmatrix}
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\\mu_y & 0 \\\\
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0 & \\mu_x
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\\end{bmatrix} \\),
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\\( D_{fx} \\) and \\( D_{bx} \\) are the forward and backward derivatives along x,
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and each \\( \\epsilon_x, \\mu_y, \\) etc. is a diagonal matrix representing
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This operator can be used to form an eigenvalue problem of the form
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`operator_e(...) @ [E_x, E_y] = wavenumber**2 * [E_x, E_y]`
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which can then be solved for the eigenmodes of the system (an `exp(-i * wavenumber * z)`
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z-dependence is assumed for the fields).
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Args:
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omega: The angular frequency of the system.
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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epsilon: Vectorized dielectric constant grid
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mu: Vectorized magnetic permeability grid (default 1 everywhere)
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Returns:
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Sparse matrix representation of the operator.
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"""
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if numpy.any(numpy.equal(mu, None)):
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mu = numpy.ones_like(epsilon)
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Dfx, Dfy = deriv_forward(dxes[0])
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Dbx, Dby = deriv_back(dxes[1])
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eps_parts = numpy.split(epsilon, 3)
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eps_xy = sparse.diags(numpy.hstack((eps_parts[0], eps_parts[1])))
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eps_z_inv = sparse.diags(1 / eps_parts[2])
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mu_parts = numpy.split(mu, 3)
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mu_yx = sparse.diags(numpy.hstack((mu_parts[1], mu_parts[0])))
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mu_z_inv = sparse.diags(1 / mu_parts[2])
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op = omega * omega * mu_yx @ eps_xy + \
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mu_yx @ sparse.vstack((-Dby, Dbx)) @ mu_z_inv @ sparse.hstack((-Dfy, Dfx)) + \
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sparse.vstack((Dfx, Dfy)) @ eps_z_inv @ sparse.hstack((Dbx, Dby)) @ eps_xy
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return op
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def operator_h(omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: vfdfield_t = None,
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) -> sparse.spmatrix:
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"""
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Waveguide operator of the form
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omega**2 * epsilon * mu +
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epsilon * [[-Dy], [Dx]] / epsilon * [-Dy, Dx] +
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[[Dx], [Dy]] / mu * [Dx, Dy] * mu
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for use with a field vector of the form `cat([H_x, H_y])`.
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More precisely, the operator is
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$$ \\omega^2 \\epsilon_{yx} \\mu_{xy} +
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\\epsilon_{yx} \\begin{bmatrix} -D_{fy} \\\\
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D_{fx} \\end{bmatrix} \\epsilon_z^{-1}
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\\begin{bmatrix} -D_{by} & D_{bx} \\end{bmatrix} +
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\\begin{bmatrix} D_{bx} \\\\
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D_{by} \\end{bmatrix} \\mu_z^{-1}
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\\begin{bmatrix} D_{fx} & D_{fy} \\end{bmatrix} \\mu_{xy} $$
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where
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\\( \\epsilon_{yx} = \\begin{bmatrix}
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\\epsilon_y & 0 \\\\
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0 & \\epsilon_x
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\\end{bmatrix} \\),
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\\( \\mu_{xy} = \\begin{bmatrix}
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\\mu_x & 0 \\\\
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0 & \\mu_y
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\\end{bmatrix} \\),
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\\( D_{fx} \\) and \\( D_{bx} \\) are the forward and backward derivatives along x,
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and each \\( \\epsilon_x, \\mu_y, \\) etc. is a diagonal matrix.
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This operator can be used to form an eigenvalue problem of the form
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`operator_h(...) @ [H_x, H_y] = wavenumber**2 * [H_x, H_y]`
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which can then be solved for the eigenmodes of the system (an `exp(-i * wavenumber * z)`
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z-dependence is assumed for the fields).
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Args:
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omega: The angular frequency of the system.
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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epsilon: Vectorized dielectric constant grid
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mu: Vectorized magnetic permeability grid (default 1 everywhere)
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Returns:
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Sparse matrix representation of the operator.
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"""
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if numpy.any(numpy.equal(mu, None)):
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mu = numpy.ones_like(epsilon)
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Dfx, Dfy = operators.deriv_forward(dxes[0])
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Dbx, Dby = operators.deriv_back(dxes[1])
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eps_parts = numpy.split(epsilon, 3)
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eps_yx = sparse.diags(numpy.hstack((eps_parts[1], eps_parts[0])))
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eps_z_inv = sparse.diags(1 / eps_parts[2])
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mu_parts = numpy.split(mu, 3)
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mu_xy = sparse.diags(numpy.hstack((mu_parts[0], mu_parts[1])))
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mu_z_inv = sparse.diags(1 / mu_parts[2])
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op = omega * omega * eps_yx @ mu_xy + \
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eps_yx @ sparse.vstack((-Dfy, Dfx)) @ eps_z_inv @ sparse.hstack((-Dby, Dbx)) + \
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sparse.vstack((Dbx, Dby)) @ mu_z_inv @ sparse.hstack((Dfx, Dfy)) @ mu_xy
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return op
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def normalized_fields_e(e_xy: numpy.ndarray,
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wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: vfdfield_t = None,
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prop_phase: float = 0,
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) -> Tuple[vfdfield_t, vfdfield_t]:
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"""
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Given a vector `e_xy` containing the vectorized E_x and E_y fields,
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returns normalized, vectorized E and H fields for the system.
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Args:
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e_xy: Vector containing E_x and E_y fields
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wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
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It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy`
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omega: The angular frequency of the system
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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epsilon: Vectorized dielectric constant grid
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mu: Vectorized magnetic permeability grid (default 1 everywhere)
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prop_phase: Phase shift `(dz * corrected_wavenumber)` over 1 cell in propagation direction.
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Default 0 (continuous propagation direction, i.e. dz->0).
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Returns:
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`(e, h)`, where each field is vectorized, normalized,
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and contains all three vector components.
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"""
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e = exy2e(wavenumber=wavenumber, dxes=dxes, epsilon=epsilon) @ e_xy
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h = exy2h(wavenumber=wavenumber, omega=omega, dxes=dxes, epsilon=epsilon, mu=mu) @ e_xy
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e_norm, h_norm = _normalized_fields(e=e, h=h, omega=omega, dxes=dxes, epsilon=epsilon,
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mu=mu, prop_phase=prop_phase)
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return e_norm, h_norm
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def normalized_fields_h(h_xy: numpy.ndarray,
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wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: vfdfield_t = None,
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prop_phase: float = 0,
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) -> Tuple[vfdfield_t, vfdfield_t]:
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"""
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Given a vector `h_xy` containing the vectorized H_x and H_y fields,
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returns normalized, vectorized E and H fields for the system.
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Args:
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h_xy: Vector containing H_x and H_y fields
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wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
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It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy`
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omega: The angular frequency of the system
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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epsilon: Vectorized dielectric constant grid
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mu: Vectorized magnetic permeability grid (default 1 everywhere)
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prop_phase: Phase shift `(dz * corrected_wavenumber)` over 1 cell in propagation direction.
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Default 0 (continuous propagation direction, i.e. dz->0).
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Returns:
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`(e, h)`, where each field is vectorized, normalized,
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and contains all three vector components.
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"""
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e = hxy2e(wavenumber=wavenumber, omega=omega, dxes=dxes, epsilon=epsilon, mu=mu) @ h_xy
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h = hxy2h(wavenumber=wavenumber, dxes=dxes, mu=mu) @ h_xy
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e_norm, h_norm = _normalized_fields(e=e, h=h, omega=omega, dxes=dxes, epsilon=epsilon,
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mu=mu, prop_phase=prop_phase)
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return e_norm, h_norm
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def _normalized_fields(e: numpy.ndarray,
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h: numpy.ndarray,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: vfdfield_t = None,
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prop_phase: float = 0,
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) -> Tuple[vfdfield_t, vfdfield_t]:
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# TODO documentation
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shape = [s.size for s in dxes[0]]
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dxes_real = [[numpy.real(d) for d in numpy.meshgrid(*dxes[v], indexing='ij')] for v in (0, 1)]
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E = unvec(e, shape)
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H = unvec(h, shape)
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# Find time-averaged Sz and normalize to it
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# H phase is adjusted by a half-cell forward shift for Yee cell, and 1-cell reverse shift for Poynting
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phase = numpy.exp(-1j * -prop_phase / 2)
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Sz_a = E[0] * numpy.conj(H[1] * phase) * dxes_real[0][1] * dxes_real[1][0]
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Sz_b = E[1] * numpy.conj(H[0] * phase) * dxes_real[0][0] * dxes_real[1][1]
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Sz_tavg = numpy.real(Sz_a.sum() - Sz_b.sum()) * 0.5 # 0.5 since E, H are assumed to be peak (not RMS) amplitudes
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assert Sz_tavg > 0, 'Found a mode propagating in the wrong direction! Sz_tavg={}'.format(Sz_tavg)
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energy = epsilon * e.conj() * e
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norm_amplitude = 1 / numpy.sqrt(Sz_tavg)
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norm_angle = -numpy.angle(e[energy.argmax()]) # Will randomly add a negative sign when mode is symmetric
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# Try to break symmetry to assign a consistent sign [experimental TODO]
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E_weighted = unvec(e * energy * numpy.exp(1j * norm_angle), shape)
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sign = numpy.sign(E_weighted[:, :max(shape[0]//2, 1), :max(shape[1]//2, 1)].real.sum())
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norm_factor = sign * norm_amplitude * numpy.exp(1j * norm_angle)
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e *= norm_factor
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h *= norm_factor
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return e, h
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def exy2h(wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: vfdfield_t = None
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) -> sparse.spmatrix:
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"""
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Operator which transforms the vector `e_xy` containing the vectorized E_x and E_y fields,
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into a vectorized H containing all three H components
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Args:
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wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
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It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy`
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omega: The angular frequency of the system
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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epsilon: Vectorized dielectric constant grid
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mu: Vectorized magnetic permeability grid (default 1 everywhere)
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Returns:
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Sparse matrix representing the operator.
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"""
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e2hop = e2h(wavenumber=wavenumber, omega=omega, dxes=dxes, mu=mu)
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return e2hop @ exy2e(wavenumber=wavenumber, dxes=dxes, epsilon=epsilon)
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def hxy2e(wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: vfdfield_t = None
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) -> sparse.spmatrix:
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"""
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Operator which transforms the vector `h_xy` containing the vectorized H_x and H_y fields,
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into a vectorized E containing all three E components
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Args:
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wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
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It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy`
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omega: The angular frequency of the system
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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epsilon: Vectorized dielectric constant grid
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mu: Vectorized magnetic permeability grid (default 1 everywhere)
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Returns:
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Sparse matrix representing the operator.
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"""
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h2eop = h2e(wavenumber=wavenumber, omega=omega, dxes=dxes, epsilon=epsilon)
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return h2eop @ hxy2h(wavenumber=wavenumber, dxes=dxes, mu=mu)
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def hxy2h(wavenumber: complex,
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dxes: dx_lists_t,
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mu: vfdfield_t = None
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) -> sparse.spmatrix:
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"""
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Operator which transforms the vector `h_xy` containing the vectorized H_x and H_y fields,
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into a vectorized H containing all three H components
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Args:
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wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
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It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy`
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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mu: Vectorized magnetic permeability grid (default 1 everywhere)
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Returns:
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Sparse matrix representing the operator.
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"""
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Dfx, Dfy = deriv_forward(dxes[0])
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hxy2hz = sparse.hstack((Dfx, Dfy)) / (1j * wavenumber)
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if not numpy.any(numpy.equal(mu, None)):
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mu_parts = numpy.split(mu, 3)
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mu_xy = sparse.diags(numpy.hstack((mu_parts[0], mu_parts[1])))
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mu_z_inv = sparse.diags(1 / mu_parts[2])
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hxy2hz = mu_z_inv @ hxy2hz @ mu_xy
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n_pts = dxes[1][0].size * dxes[1][1].size
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op = sparse.vstack((sparse.eye(2 * n_pts),
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hxy2hz))
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return op
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def exy2e(wavenumber: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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) -> sparse.spmatrix:
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"""
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Operator which transforms the vector `e_xy` containing the vectorized E_x and E_y fields,
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into a vectorized E containing all three E components
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Args:
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wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
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It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy`
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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epsilon: Vectorized dielectric constant grid
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Returns:
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Sparse matrix representing the operator.
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"""
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Dbx, Dby = deriv_back(dxes[1])
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exy2ez = sparse.hstack((Dbx, Dby)) / (1j * wavenumber)
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if not numpy.any(numpy.equal(epsilon, None)):
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epsilon_parts = numpy.split(epsilon, 3)
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epsilon_xy = sparse.diags(numpy.hstack((epsilon_parts[0], epsilon_parts[1])))
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epsilon_z_inv = sparse.diags(1 / epsilon_parts[2])
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exy2ez = epsilon_z_inv @ exy2ez @ epsilon_xy
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n_pts = dxes[0][0].size * dxes[0][1].size
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op = sparse.vstack((sparse.eye(2 * n_pts),
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exy2ez))
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return op
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def e2h(wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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mu: vfdfield_t = None
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) -> sparse.spmatrix:
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"""
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Returns an operator which, when applied to a vectorized E eigenfield, produces
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the vectorized H eigenfield.
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Args:
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wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
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omega: The angular frequency of the system
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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mu: Vectorized magnetic permeability grid (default 1 everywhere)
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Returns:
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Sparse matrix representation of the operator.
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"""
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op = curl_e(wavenumber, dxes) / (-1j * omega)
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if not numpy.any(numpy.equal(mu, None)):
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op = sparse.diags(1 / mu) @ op
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return op
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def h2e(wavenumber: complex,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t
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) -> sparse.spmatrix:
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"""
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Returns an operator which, when applied to a vectorized H eigenfield, produces
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the vectorized E eigenfield.
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Args:
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wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
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omega: The angular frequency of the system
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dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
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epsilon: Vectorized dielectric constant grid
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Returns:
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Sparse matrix representation of the operator.
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"""
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op = sparse.diags(1 / (1j * omega * epsilon)) @ curl_h(wavenumber, dxes)
|
|
return op
|
|
|
|
|
|
def curl_e(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix:
|
|
"""
|
|
Discretized curl operator for use with the waveguide E field.
|
|
|
|
Args:
|
|
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
|
|
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
|
|
|
|
Return:
|
|
Sparse matrix representation of the operator.
|
|
"""
|
|
n = 1
|
|
for d in dxes[0]:
|
|
n *= len(d)
|
|
|
|
print(wavenumber, n)
|
|
Bz = -1j * wavenumber * sparse.eye(n)
|
|
Dfx, Dfy = deriv_forward(dxes[0])
|
|
return cross([Dfx, Dfy, Bz])
|
|
|
|
|
|
def curl_h(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix:
|
|
"""
|
|
Discretized curl operator for use with the waveguide H field.
|
|
|
|
Args:
|
|
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
|
|
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
|
|
|
|
Return:
|
|
Sparse matrix representation of the operator.
|
|
"""
|
|
n = 1
|
|
for d in dxes[1]:
|
|
n *= len(d)
|
|
|
|
Bz = -1j * wavenumber * sparse.eye(n)
|
|
Dbx, Dby = deriv_back(dxes[1])
|
|
return cross([Dbx, Dby, Bz])
|
|
|
|
|
|
def h_err(h: vfdfield_t,
|
|
wavenumber: complex,
|
|
omega: complex,
|
|
dxes: dx_lists_t,
|
|
epsilon: vfdfield_t,
|
|
mu: vfdfield_t = None
|
|
) -> float:
|
|
"""
|
|
Calculates the relative error in the H field
|
|
|
|
Args:
|
|
h: Vectorized H field
|
|
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
|
|
omega: The angular frequency of the system
|
|
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
|
|
epsilon: Vectorized dielectric constant grid
|
|
mu: Vectorized magnetic permeability grid (default 1 everywhere)
|
|
|
|
Returns:
|
|
Relative error `norm(A_h @ h) / norm(h)`.
|
|
"""
|
|
ce = curl_e(wavenumber, dxes)
|
|
ch = curl_h(wavenumber, dxes)
|
|
|
|
eps_inv = sparse.diags(1 / epsilon)
|
|
|
|
if numpy.any(numpy.equal(mu, None)):
|
|
op = ce @ eps_inv @ ch @ h - omega ** 2 * h
|
|
else:
|
|
op = ce @ eps_inv @ ch @ h - omega ** 2 * (mu * h)
|
|
|
|
return norm(op) / norm(h)
|
|
|
|
|
|
def e_err(e: vfdfield_t,
|
|
wavenumber: complex,
|
|
omega: complex,
|
|
dxes: dx_lists_t,
|
|
epsilon: vfdfield_t,
|
|
mu: vfdfield_t = None
|
|
) -> float:
|
|
"""
|
|
Calculates the relative error in the E field
|
|
|
|
Args:
|
|
e: Vectorized E field
|
|
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
|
|
omega: The angular frequency of the system
|
|
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
|
|
epsilon: Vectorized dielectric constant grid
|
|
mu: Vectorized magnetic permeability grid (default 1 everywhere)
|
|
|
|
Returns:
|
|
Relative error `norm(A_e @ e) / norm(e)`.
|
|
"""
|
|
ce = curl_e(wavenumber, dxes)
|
|
ch = curl_h(wavenumber, dxes)
|
|
|
|
if numpy.any(numpy.equal(mu, None)):
|
|
op = ch @ ce @ e - omega ** 2 * (epsilon * e)
|
|
else:
|
|
mu_inv = sparse.diags(1 / mu)
|
|
op = ch @ mu_inv @ ce @ e - omega ** 2 * (epsilon * e)
|
|
|
|
return norm(op) / norm(e)
|
|
|
|
|
|
def solve_modes(mode_numbers: List[int],
|
|
omega: complex,
|
|
dxes: dx_lists_t,
|
|
epsilon: vfdfield_t,
|
|
mu: vfdfield_t = None,
|
|
mode_margin: int = 2,
|
|
) -> Tuple[List[vfdfield_t], List[complex]]:
|
|
"""
|
|
Given a 2D region, attempts to solve for the eigenmode with the specified mode numbers.
|
|
|
|
Args:
|
|
mode_numbers: List of 0-indexed mode numbers to solve for
|
|
omega: Angular frequency of the simulation
|
|
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
|
|
epsilon: Dielectric constant
|
|
mu: Magnetic permeability (default 1 everywhere)
|
|
mode_margin: The eigensolver will actually solve for `(max(mode_number) + mode_margin)`
|
|
modes, but only return the target mode. Increasing this value can improve the solver's
|
|
ability to find the correct mode. Default 2.
|
|
|
|
Returns:
|
|
(e_xys, wavenumbers)
|
|
"""
|
|
|
|
'''
|
|
Solve for the largest-magnitude eigenvalue of the real operator
|
|
'''
|
|
dxes_real = [[numpy.real(dx) for dx in dxi] for dxi in dxes]
|
|
A_r = operator_e(numpy.real(omega), dxes_real, numpy.real(epsilon), numpy.real(mu))
|
|
|
|
eigvals, eigvecs = signed_eigensolve(A_r, max(mode_numbers) + mode_margin)
|
|
e_xys = eigvecs[:, -(numpy.array(mode_numbers) + 1)]
|
|
|
|
'''
|
|
Now solve for the eigenvector of the full operator, using the real operator's
|
|
eigenvector as an initial guess for Rayleigh quotient iteration.
|
|
'''
|
|
A = operator_e(omega, dxes, epsilon, mu)
|
|
for nn in range(len(mode_numbers)):
|
|
eigvals[nn], e_xys[:, nn] = rayleigh_quotient_iteration(A, e_xys[:, nn])
|
|
|
|
# Calculate the wave-vector (force the real part to be positive)
|
|
wavenumbers = numpy.sqrt(eigvals)
|
|
wavenumbers *= numpy.sign(numpy.real(wavenumbers))
|
|
|
|
return e_xys, wavenumbers
|
|
|
|
|
|
def solve_mode(mode_number: int,
|
|
*args,
|
|
**kwargs
|
|
) -> Tuple[vfdfield_t, complex]:
|
|
"""
|
|
Wrapper around `solve_modes()` that solves for a single mode.
|
|
|
|
Args:
|
|
mode_number: 0-indexed mode number to solve for
|
|
*args: passed to `solve_modes()`
|
|
**kwargs: passed to `solve_modes()`
|
|
|
|
Returns:
|
|
(e_xy, wavenumber)
|
|
"""
|
|
e_xys, wavenumbers = solve_modes(mode_numbers=[mode_number], *args, **kwargs)
|
|
return e_xys[:, 0], wavenumbers[0]
|