#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Author: Benjamin Vial
# This file is part of nannos
# License: GPLv3
# See the documentation at nannos.gitlab.io
__all__ = ["Simulation"]
from . import backend as bk
from . import get_types, jit, logger
from .formulations import fft
from .layers import _get_layer
from .plot import plot_structure, pyvista
from .utils import block, block2list, get_block, inv2by2block, norm, set_index
from .utils import time as timer
from .utils import unique
from .utils.helpers import _reseter
FLOAT, COMPLEX = get_types()
# from .parallel import parloop
_inv = bk.linalg.inv
[docs]
class Simulation:
"""Main simulation object.
Parameters
----------
layers : list
A list of :class:`~nannos.Layer` objects.
excitation : :class:`~nannos.PlaneWave`
A plane wave excitation .
nh : int
Number of Fourier harmonics (the default is ``100``).
formulation : str
Formulation type. (the default is ``'original'``).
Available formulations are ``'original'``, ``'tangent'``, ``'jones'`` and ``'pol'``.
"""
def __init__(
self,
layers,
excitation,
nh=100,
formulation="original",
):
# Layers
self.layers = layers
self.layer_names = [layer.name for layer in self.layers]
if not unique(self.layer_names):
raise ValueError("Layers must have different names")
# check if all layers share the same lattice
lattice0 = self.layers[0].lattice
for layer in self.layers:
assert layer.lattice == lattice0, ValueError(
"lattice must be the same for all layers"
)
self.lattice = lattice0
# nh0 is the number of harmonics required as input that might be different
# from the one used after truncation
nh0 = int(nh)
self.nh0 = nh0
# this is to avoid error when truncating to a single harmonic for example
# when all layers are uniform
if bk.all(bk.array([s.is_uniform for s in self.layers])) and nh0 != 1:
nh0 = 1
logger.info("All layers are uniform, setting nh=1")
if nh0 == 1:
nh0 = 2
# Check formulation
if formulation not in ["original", "tangent", "jones", "pol"]:
raise ValueError(
f"Unknown formulation {formulation}. Please choose between 'original', 'tangent', 'jones' or 'pol'"
)
if self.lattice.is_1D and formulation not in ["original", "tangent"]:
raise ValueError(
f"Formulation {formulation} not available for 1D gratings. Please choose between 'original' and 'tangent'."
)
self.formulation = formulation
# Get the harmonics
self.harmonics, self.nh = self.lattice.get_harmonics(nh0, sort=True)
# Build a dictionary of indices
idict = {}
for j, (i1, i2) in enumerate(self.harmonics.T):
i1 = int(i1)
i2 = int(i2)
if i1 in idict:
idict[i1][i2] = j
else:
idict[i1] = {i2: j}
self.harmidx_dict = idict
# Check if nh and resolution satisfy Nyquist criteria
maxN = bk.max(self.harmonics)
if self.lattice.discretization[0] <= 2 * maxN or (
self.lattice.discretization[1] <= 2 * maxN and not self.lattice.is_1D
):
raise ValueError(f"lattice discretization must be > {2*maxN}")
# Set the excitation (plane wave)
self.excitation = excitation
self.omega = 2 * bk.pi * self.excitation.frequency_scaled
self.incident_flux = bk.real(
bk.cos(self.excitation.theta)
/ bk.sqrt(self.layers[0].epsilon * self.layers[0].mu)
)
# Buid lattice vectors
self.k0para = (
bk.array(self.excitation.wavevector[:2])
* (self.layers[0].epsilon * self.layers[0].mu) ** 0.5
)
r = self.lattice.reciprocal
self.kx = (
self.k0para[0]
+ r[0, 0] * self.harmonics[0, :]
+ r[0, 1] * self.harmonics[1, :]
)
self.ky = (
self.k0para[1]
+ r[1, 0] * self.harmonics[0, :]
+ r[1, 1] * self.harmonics[1, :]
)
self.Kx = bk.diag(self.kx)
self.Ky = bk.diag(self.ky)
# Some useful matrices
self.IdG = bk.array(bk.eye(self.nh, dtype=COMPLEX))
self.ZeroG = bk.array(bk.zeros_like(self.IdG, dtype=COMPLEX))
# Initialize amplitudes
for index_zeroth_order, h in enumerate(self.harmonics.T):
if h[0] == 0 and h[1] == 0:
break
self.a0 = []
for i in range(self.nh * 2):
if i == index_zeroth_order:
a0 = self.excitation.a0[0]
elif i == self.nh + index_zeroth_order:
a0 = self.excitation.a0[1]
else:
a0 = 0
self.a0.append(a0)
self.a0 = bk.array(self.a0, dtype=COMPLEX)
self.bN = bk.array(bk.zeros(2 * self.nh, dtype=COMPLEX))
self.index_zeroth_order = index_zeroth_order
# This is a boolean checking that the eigenproblems are solved for all layers
# TODO: This is to avoid solving again, but could be confusing
self.is_solved = False
# dictionary to store intermediate S-matrices
# TODO: check memory consumption of doing that, maybe make it optional.
self._intermediate_S = {}
[docs]
def get_layer(self, id):
"""Helper to get layer index and name.
Parameters
----------
id : int or str
The index of the layer or its name.
Returns
-------
layer : nannos.Layer
The layer object.
index : nannos.Layer
The layer index in the stack.
"""
return _get_layer(id, self.layers, self.layer_names)
def get_layer_by_name(self, id):
return self.get_layer(id)[0]
[docs]
def solve(self):
"""
Solve the eigenproblem for all layers in the stack.
This method loops over all layers in the stack and calls the
:meth:`build_matrix` and :meth:`solve_eigenproblem` methods for each
layer. The solved layers are stored in the :attr:`layers` attribute of
the simulation object.
Notes
-----
This method is called automatically by the :meth:`run` method of the
simulation object.
Returns
-------
None
"""
_t0 = timer.tic()
logger.info("Solving")
layers_solved = []
for layer in self.layers:
_t0lay = timer.tic()
logger.info(f"Computing eigenpairs for layer {layer}")
layer = self.build_matrix(layer)
if layer.is_uniform:
layer.solve_uniform(self.omega, self.kx, self.ky, self.nh)
else:
layer.solve_eigenproblem(layer.matrix)
# ## normalize
# phi = layer.eigenvectors
# out = bk.conj(layer.eigenvectors).T @ layer.Qeps @ layer.eigenvectors
# layer.eigenvectors /= bk.diag(out)**0.5
layers_solved.append(layer)
_t1lay = timer.toc(_t0lay, verbose=False)
logger.info(
f"Done computing eigenpairs for layer {layer} in {_t1lay:0.3e}s"
)
self.layers = layers_solved
self.is_solved = True
_t1 = timer.toc(_t0, verbose=False)
logger.info(f"Done solving in {_t1:0.3e}s")
[docs]
def reset(self, param="all"):
"""
Reset some or all of the simulation.
Parameters
----------
param : str, optional
The parameter to reset. If ``"S"``, the S matrix is reset.
If ``"solve"``, the simulation is reset to unsolved state.
If ``"all"``, both are reset. The default is ``"all"``.
Returns
-------
None
"""
if param == "S":
_reseter(self, "S")
self._intermediate_S = {}
if param == "solve":
self.is_solved = False
if param == "all":
self.reset("S")
self.reset("solve")
[docs]
def get_S_matrix(self, indices=None):
"""Compute the scattering matrix.
Parameters
----------
indices : list
Indices ``[i_first,i_last]`` of the first and last layer (the default is ``None``).
By default get the S matrix of the full stack.
Returns
-------
list
The scattering matrix ``[[S11, S12], [S21,S22]]``.
"""
if not self.is_solved:
self.solve()
_t0 = timer.tic()
S11 = bk.array(bk.eye(2 * self.nh, dtype=COMPLEX))
S12 = bk.zeros_like(S11)
S21 = bk.zeros_like(S11)
S22 = bk.array(bk.eye(2 * self.nh, dtype=COMPLEX))
if indices is None:
n_interfaces = len(self.layers) - 1
stack = range(n_interfaces)
logger.info("Computing total S-matrix")
else:
stack = range(indices[0], indices[1])
logger.info(f"Computing S-matrix for indices {indices}")
try:
S = self._intermediate_S[f"{stack[0]},{stack[-1]}"]
except Exception:
for i in stack:
layer, layer_next = self.layers[i], self.layers[i + 1]
z = layer.thickness or 0
z_next = layer_next.thickness or 0
f, f_next = bk.diag(phasor(layer.eigenvalues, z)), bk.diag(
phasor(layer_next.eigenvalues, z_next)
)
I_ = _build_Imatrix(layer, layer_next)
Imat = [
[get_block(I_, i, j, 2 * self.nh) for j in range(2)]
for i in range(2)
]
A = Imat[0][0] - f @ S12 @ Imat[1][0]
S11 = bk.linalg.solve(A, f @ S11)
S12 = bk.linalg.solve(A, (f @ S12 @ Imat[1][1] - Imat[0][1]) @ f_next)
# B = _inv(A)
# S11 = B @ f @ S11
# S12 = B @ ((f @ S12 @ Imat[1][1] - Imat[0][1]) @ f_next)
S21 = S22 @ Imat[1][0] @ S11 + S21
S22 = S22 @ Imat[1][0] @ S12 + S22 @ Imat[1][1] @ f_next
S = [[S11, S12], [S21, S22]]
self._intermediate_S[f"{stack[0]},{i}"] = S
if indices is None:
self.S = S
_t1 = timer.toc(_t0, verbose=False)
logger.info(f"Done computing S-matrix {_t1:0.3e}s")
return S
[docs]
def get_z_poynting_flux(self, layer, an, bn):
"""
Compute the z-component of the Poynting vector.
Parameters
----------
layer : Layer
The layer
an : array
The amplitudes of the forward fields
bn : array
The amplitudes of the backward fields
Returns
-------
forward : array
The z-component of the Poynting vector of the forward fields
backward : array
The z-component of the Poynting vector of the backward fields
"""
if not self.is_solved:
self.solve()
q, phi = layer.eigenvalues, bk.array(layer.eigenvectors)
A = layer.Qeps @ phi @ bk.diag(1 / (self.omega * q))
phia, phib = phi @ an, phi @ bn
Aa, Ab = A @ an, A @ bn
cross_term = 0.5 * (bk.conj(phib) * Aa - bk.conj(Ab) * phia)
forward_xy = bk.real(bk.conj(Aa) * phia) + cross_term
backward_xy = -bk.real(bk.conj(Ab) * phib) + bk.conj(cross_term)
forward = forward_xy[: self.nh] + forward_xy[self.nh :]
backward = backward_xy[: self.nh] + backward_xy[self.nh :]
return bk.real(forward), bk.real(backward)
[docs]
def get_field_fourier(self, layer_index, z=0):
"""
Compute the fields in k-space for a given layer and z position.
Parameters
----------
layer_index : int
Index of the layer
z : float or array
The z position(s) at which to compute the fields
Returns
-------
fields_fourier : array
The fields in k-space for the given layer and z position(s)
"""
layer, layer_index = self.get_layer(layer_index)
_t0 = timer.tic()
logger.info(f"Retrieving fields in k-space for layer {layer}")
if not self.is_solved:
self.solve()
ai0, bi0 = self._get_amplitudes(layer_index, translate=False)
Z = z if hasattr(z, "__len__") else [z]
fields = bk.zeros((len(Z), 2, 3, self.nh), dtype=COMPLEX)
for iz, z_ in enumerate(Z):
ai, bi = _translate_amplitudes(layer, z_, ai0, bi0)
ht_fourier = layer.eigenvectors @ (ai + bi)
hx, hy = ht_fourier[: self.nh], ht_fourier[self.nh :]
A = (ai - bi) / (self.omega * layer.eigenvalues)
B = layer.eigenvectors @ A
et_fourier = layer.Qeps @ B
ey, ex = -et_fourier[: self.nh], et_fourier[self.nh :]
hz = (self.kx * ey - self.ky * ex) / self.omega
ez = (self.ky * hx - self.kx * hy) / self.omega
if layer.is_uniform:
ez = ez / layer.epsilon
else:
# ez = layer.eps_hat_inv @ ez
ez = bk.linalg.solve(layer.eps_hat, ez)
for i, comp in enumerate([ex, ey, ez]):
fields = set_index(fields, [iz, 0, i], comp)
for i, comp in enumerate([hx, hy, hz]):
fields = set_index(fields, [iz, 1, i], comp)
fields_fourier = bk.array(fields)
_t1 = timer.toc(_t0, verbose=False)
logger.info(
f"Done retrieving fields in k-space for layer {layer} in {_t1:0.3e}s"
)
return fields_fourier
[docs]
def get_ifft(self, u, shape, axes=(0, 1)):
"""
Compute the inverse Fourier transform of an array `u`.
Parameters
----------
u : array
The array to transform
shape : tuple
The shape of the output array
axes : tuple
The axes to transform over
Returns
-------
array
The inverse Fourier transformed array
"""
u = bk.array(u)
s = 0
for i in range(self.nh):
f = bk.zeros(shape, dtype=COMPLEX)
f = set_index(f, [self.harmonics[0, i], self.harmonics[1, i]], 1.0)
a = u[i]
s += a * f
return fft.inverse_fourier_transform(s, axes=axes)
[docs]
def get_ifft_amplitudes(self, amplitudes, shape, axes=(0, 1)):
"""
Compute the inverse Fourier transform of the amplitudes in k-space.
Parameters
----------
amplitudes : array
The amplitudes to transform
shape : tuple
The shape of the output array
axes : tuple
The axes to transform over
Returns
-------
array
The inverse Fourier transformed amplitudes
"""
_t0 = timer.tic()
logger.info("Inverse Fourier transforming amplitudes")
amplitudes = bk.array(amplitudes)
if len(amplitudes.shape) == 1:
amplitudes = bk.reshape(amplitudes, amplitudes.shape + (1,))
s = 0
for i in range(self.nh):
f = bk.zeros(shape + (amplitudes.shape[0],), dtype=COMPLEX)
f = bk.array(f)
f = set_index(f, [self.harmonics[0, i], self.harmonics[1, i]], 1.0)
# f[self.harmonics[0, i], self.harmonics[1, i], :] = 1.0
a = amplitudes[:, i]
s += a * f
ft = fft.inverse_fourier_transform(s, axes=axes)
_t1 = timer.toc(_t0, verbose=False)
logger.info(f"Done inverse Fourier transforming amplitudes in {_t1:0.3e}s")
return ft
[docs]
def get_field_grid(
self, layer_index, z=0, shape=None, field="all", component="all"
):
"""
Retrieve the fields in real-space for a given layer.
Parameters
----------
layer_index : int
The layer index
z : float, optional
The z coordinate of the field, by default 0
shape : tuple, optional
The shape of the output array, by default None
field : str, optional
The field type, by default "all". Must be one of "all", "E" or "H"
component : str, optional
The component of the field, by default "all". Must be one of "all", "x", "y" or "z"
Returns
-------
array
The fields in real space. The shape of the array depends on the arguments.
"""
if field not in ["all", "E", "H"]:
raise ValueError("Wrong field argument, must be `all`, `E` or `H`")
if component not in ["all", "x", "y", "z"]:
raise ValueError("Wrong component argument, must be `all`, `x`, `y` or `z`")
layer, layer_index = self.get_layer(layer_index)
_t0 = timer.tic()
logger.info("Retrieving fields in real-space for layer {layer}")
shape = shape or layer.epsilon.shape
fields_fourier = self.get_field_fourier(layer_index, z)
fe = fields_fourier[:, 0]
fh = fields_fourier[:, 1]
def _get_field(f, i):
return self.get_ifft_amplitudes(f[:, i, :], shape)
if component == "all":
if field == "all":
E = bk.stack([_get_field(fe, i) for i in range(3)])
H = bk.stack([_get_field(fh, i) for i in range(3)])
out = E, H
elif field == "H":
out = bk.stack([_get_field(fh, i) for i in range(3)])
elif field == "E":
out = bk.stack([_get_field(fe, i) for i in range(3)])
elif component == "x":
if field == "all":
E = _get_field(fe, 0)
H = _get_field(fh, 0)
out = E, H
elif field == "H":
out = _get_field(fh, 0)
elif field == "E":
out = _get_field(fe, 0)
elif component == "y":
if field == "all":
E = _get_field(fe, 1)
H = _get_field(fh, 1)
out = E, H
elif field == "H":
out = _get_field(fh, 1)
elif field == "E":
out = _get_field(fe, 1)
elif component == "z":
if field == "all":
E = _get_field(fe, 2)
H = _get_field(fh, 2)
out = E, H
elif field == "H":
out = _get_field(fh, 2)
elif field == "E":
out = _get_field(fe, 2)
_t1 = timer.toc(_t0, verbose=False)
logger.info(
f"Done retrieving fields in real space for layer {layer} in {_t1:0.3e}s"
)
return out
[docs]
def get_Efield_grid(self, layer_index, z=0, shape=None, component="all"):
"""
Retrieve the electric field in real space for a given layer.
Parameters
----------
layer_index : int
The layer index
z : float, optional
The z coordinate of the field, by default 0
shape : tuple, optional
The shape of the output array, by default None
component : str, optional
The component of the field, by default "all". Must be one of "all", "x", "y", "z"
Returns
-------
array
The electric field in real space. The shape of the array depends on the arguments.
"""
return self.get_field_grid(
layer_index, z=z, shape=shape, field="E", component=component
)
[docs]
def get_Hfield_grid(self, layer_index, z=0, shape=None, component="all"):
"""
Retrieve the magnetic field in real space for a given layer.
Parameters
----------
layer_index : int
The layer index
z : float, optional
The z coordinate of the field, by default 0
shape : tuple, optional
The shape of the output array, by default None
component : str, optional
The component of the field, by default "all". Must be one of "all", "x", "y", "z"
Returns
-------
array
The magnetic field in real space. The shape of the array depends on the arguments.
"""
return self.get_field_grid(
layer_index, z=z, shape=shape, field="H", component=component
)
[docs]
def diffraction_efficiencies(self, orders=False, complex=False, return_dict=False):
"""Compute the diffraction efficiencies.
Parameters
----------
orders : bool
If ``True``, returns diffracted orders, else returns the sum of
reflection and transmission for all propagating orders (the default is ``False``).
complex : bool
If ``True``, return complex valued quantities corresponding to ampitude related coefficients.
(the default is ``False``).
return_dict : bool
If ``True``, return quantities as nested dictionaries with keys corresponding to diffraction orders.
This works only if ``orders=True`` (the default is ``False``).
Returns
-------
tuple
The reflection and transmission ``R`` and ``T``.
"""
if not hasattr(self, "S"):
self.get_S_matrix()
if complex:
R, T = self._get_complex_orders()
if not orders:
R = bk.sum(R, axis=1)
T = bk.sum(T, axis=1)
else:
aN, b0 = self._solve_ext()
fwd_in, bwd_in = self.get_z_poynting_flux(self.layers[0], self.a0, b0)
fwd_out, bwd_out = self.get_z_poynting_flux(self.layers[-1], aN, self.bN)
R = -bwd_in / self.incident_flux
T = fwd_out / self.incident_flux
if not orders:
R = bk.sum(R)
T = bk.sum(T)
if return_dict and orders:
Rdict = self.harmidx_dict.copy()
Tdict = self.harmidx_dict.copy()
for i1, i2 in self.harmonics.T:
i1 = int(i1)
i2 = int(i2)
idx = self.harmidx_dict[i1][i2]
Rdict[i1][i2] = R[idx]
Tdict[i1][i2] = T[idx]
return Rdict, Tdict
return R, T
def _get_complex_orders(self):
"""
Compute the complex reflection and transmission coefficients for all diffraction orders.
Returns
-------
tuple
The reflection and transmission coefficients ``R`` and ``T``.
"""
nin = (self.layers[0].epsilon * self.layers[0].mu) ** 0.5
nout = (self.layers[-1].epsilon * self.layers[-1].mu) ** 0.5
gamma_in0 = (
self.omega**2 * nin**2 - self.k0para[0] ** 2 - self.k0para[1] ** 2
) ** 0.5
# gamma_out0 = (
# self.omega**2 * nout**2 - self.k0para[0] ** 2 - self.k0para[1] ** 2
# ) ** 0.5
gamma_in = (self.omega**2 * nin**2 - self.kx**2 - self.ky**2) ** 0.5
gamma_out = (self.omega**2 * nout**2 - self.kx**2 - self.ky**2) ** 0.5
norma_t2 = nin**2 * (gamma_out / gamma_in0)
norma_r2 = nin**2 * (gamma_in / gamma_in0)
norma_t = (norma_t2.real) ** 0.5
norma_r = (norma_r2.real) ** 0.5
t = self.get_field_fourier("Substrate")[0, 0] * norma_t
bx0 = self.get_field_fourier("Superstrate")[0, 0] * norma_r
o0 = bk.array(bk.zeros(self.nh))
o0 = set_index(o0, [self.index_zeroth_order], 1)
r = bk.stack([b - o0 * c for b, c in zip(bx0, self.excitation.amplitude)])
return r, t
[docs]
def get_z_stress_tensor_integral(self, layer_index, z=0):
"""
Compute the z-component of the stress tensor integral for the given layer
at a given z position.
Parameters
----------
layer_index : int
The index of the layer
z : float
The z position
Returns
-------
tuple
The x, y and z components of the stress tensor integral
"""
layer, layer_index = self.get_layer(layer_index)
fields_fourier = self.get_field_fourier(layer_index, z=z)
e = fields_fourier[0, 0]
h = fields_fourier[0, 1]
ex = e[0]
ey = e[1]
ez = e[2]
hx = h[0]
hy = h[1]
hz = h[2]
dz = (self.ky * hx - self.kx * hy) / self.omega
if layer.is_uniform:
dx = ex * layer.epsilon
dy = ey * layer.epsilon
else:
exy = bk.hstack((-ey, ex))
# FIXME: anisotropy here?
# dxy = layer.eps_hat @ exy
_eps_hat = block([[layer.eps_hat, self.ZeroG], [self.ZeroG, layer.eps_hat]])
dxy = _eps_hat @ exy
dx = dxy[self.nh :]
dy = -dxy[: self.nh]
Tx = bk.sum(ex * bk.conj(dz) + hx * bk.conj(hz), axis=-1).real
Ty = bk.sum(ey * bk.conj(dz) + hy * bk.conj(hz), axis=-1).real
Tz = (
0.5
* bk.sum(
ez * bk.conj(dz)
+ hz * bk.conj(hz)
- ex * bk.conj(dx)
- ey * bk.conj(dy)
- hx * bk.conj(hx)
- hy * bk.conj(hy),
axis=-1,
).real
)
return Tx, Ty, Tz
[docs]
def get_order_index(self, order):
"""
Return the index of the order (m,n) in the harmonics array.
Parameters
----------
order : tuple or int
The order to find the index of. If int, the second element of the tuple is assumed to be 0.
Returns
-------
int
The index of the order in the harmonics array.
Raises
------
ValueError
If the order is not a tuple of two integers for bi-periodic gratings.
"""
try:
len(order) == 2
except Exception as e:
if self.lattice.is_1D and isinstance(order, int):
order = (order, 0)
else:
raise ValueError(
"order must be a tuple of integers length 2 for bi-periodic gratings"
) from e
# return [
# k for k, i in enumerate(self.harmonics.T) if bk.allclose(i, bk.array(order))
# ][0]
return self.harmidx_dict[order[0]][order[1]]
[docs]
def get_order(self, A, order):
"""
Return the element of A at the index corresponding to the order.
Parameters
----------
A : array like
The array to index into
order : tuple or int
The order to find the index of. If int, the second element of the tuple is assumed to be 0.
Returns
-------
element
The element at the index corresponding to the order in A
"""
return A[self.get_order_index(order)]
def _get_toeplitz_matrix(self, u, transverse=False):
"""
Compute the Toeplitz matrix of the input array u.
Parameters
----------
u : array like
The array to compute the Toeplitz matrix of
transverse : bool, optional
If True, compute the Toeplitz matrix for transverse diffraction orders.
Returns
-------
array like
The Toeplitz matrix of the input array u
"""
if transverse:
return [
[self._get_toeplitz_matrix(u[i, j]) for j in range(2)] for i in range(2)
]
uft = fft.fourier_transform(u)
ix = bk.array(bk.arange(self.nh))
jx, jy = bk.meshgrid(ix, ix, indexing="ij")
delta = self.harmonics[:, jx] - self.harmonics[:, jy]
return uft[delta[0], delta[1]]
[docs]
def build_matrix(self, layer):
"""
Build the matrix for a given layer.
Parameters
----------
layer : int or Layer
The layer to build the matrix for. If int, the layer is identified by its index.
Returns
-------
Layer
The layer with the computed matrix
"""
_t0 = timer.tic()
layer, layer_index = self.get_layer(layer)
logger.info(f"Building matrix for layer {layer}")
if layer.iscopy:
layer.matrix = layer.original.matrix
layer.Kmu = layer.original.Kmu
layer.eps_hat = layer.original.eps_hat
layer.eps_hat_inv = layer.original.eps_hat_inv
layer.mu_hat = layer.original.mu_hat
layer.mu_hat_inv = layer.original.mu_hat_inv
layer.Keps = layer.original.Keps
layer.Peps = layer.original.Peps
layer.Qeps = layer.original.Qeps
return layer
Kx, Ky = self.Kx, self.Ky
mu = layer.mu
epsilon = layer.epsilon
if layer.is_uniform:
# if layer.is_epsilon_anisotropic:
# _epsilon = block(
# [
# [epsilon[0, 0] * self.IdG, epsilon[0, 1] * self.IdG],
# [epsilon[1, 0] * self.IdG, epsilon[1, 1] * self.IdG],
# ]
# )
# else:
# _epsilon = epsilon
#
# if layer.is_mu_anisotropic:
# _mu = block(
# [
# [mu[0, 0] * self.IdG, mu[0, 1] * self.IdG],
# [mu[1, 0] * self.IdG, mu[1, 1] * self.IdG],
# ]
# )
# else:
# _mu = mu
Keps = _build_Kmatrix(1 / epsilon * self.IdG, Ky, -Kx)
# Pmu = bk.eye(self.nh * 2)
Pmu = block([[mu * self.IdG, self.ZeroG], [self.ZeroG, mu * self.IdG]])
else:
epsilon_zz = epsilon[2, 2] if layer.is_epsilon_anisotropic else epsilon
if layer.is_epsilon_uniform:
eps_hat = self.IdG * epsilon_zz
eps_hat_inv = self.IdG / epsilon_zz
else:
eps_hat = self._get_toeplitz_matrix(epsilon_zz)
eps_hat_inv = _inv(eps_hat)
mu_zz = mu[2, 2] if layer.is_mu_anisotropic else mu
if layer.is_mu_uniform:
mu_hat = self.IdG * mu_zz
mu_hat_inv = self.IdG / mu_zz
else:
mu_hat = self._get_toeplitz_matrix(mu_zz)
mu_hat_inv = _inv(mu_hat)
if layer.is_epsilon_anisotropic:
eps_para_hat = self._get_toeplitz_matrix(epsilon, transverse=True)
else:
eps_para_hat = [[eps_hat, self.ZeroG], [self.ZeroG, eps_hat]]
Keps = _build_Kmatrix(eps_hat_inv, Ky, -Kx)
Kmu = _build_Kmatrix(mu_hat_inv, Kx, Ky)
# nh_tan = max(1000, self.nh)
nh_tan = self.nh
harmonics_tan, _ = self.lattice.get_harmonics(nh_tan, sort=True)
if self.formulation == "original":
Peps = block(eps_para_hat)
elif self.formulation == "tangent":
if self.lattice.is_1D:
N, nuhat_inv = self._get_nu_hat_inv(layer)
eps_para_hat = [[eps_hat, self.ZeroG], [self.ZeroG, nuhat_inv]]
Peps = block(eps_para_hat)
else:
t = layer.get_tangent_field(harmonics_tan, normalize=True)
Peps = self._get_Peps(layer, eps_para_hat, t, direct=False)
elif self.formulation == "jones":
t = layer.get_tangent_field(harmonics_tan, normalize=False)
J = layer.get_jones_field(t)
Peps = self._get_Peps(layer, eps_para_hat, J, direct=False)
else:
t = layer.get_tangent_field(harmonics_tan, normalize=False)
Peps = self._get_Peps(layer, eps_para_hat, t, direct=False)
if layer.is_mu_anisotropic:
mu_para_hat = [
[self._get_toeplitz_matrix(mu[i, j]) for j in range(2)]
for i in range(2)
]
Pmu = block(mu_para_hat)
else:
Pmu = block([[mu_hat, self.ZeroG], [self.ZeroG, mu_hat]])
# Qeps = self.omega ** 2 * bk.eye(self.nh * 2) - Keps
# matrix = Peps @ Qeps - Kmu
matrix = self.omega**2 * Peps @ Pmu - (Peps @ Keps + Kmu @ Pmu)
layer.matrix = matrix
layer.Kmu = Kmu
layer.Pmu = Pmu
layer.eps_hat = eps_hat
layer.eps_hat_inv = eps_hat_inv
layer.mu_hat = mu_hat
layer.mu_hat_inv = mu_hat_inv
layer.Keps = Keps
layer.Peps = Peps
Qeps = self.omega**2 * Pmu - Keps
layer.Qeps = Qeps
_t1 = timer.toc(_t0, verbose=False)
logger.info(f"Done building matrix for layer {layer} in {_t1:0.3e}s")
return layer
[docs]
def get_epsilon(self, layer_id, axes=(0, 1)):
# TODO: check formulation and anisotropy
"""
Return the epsilon_zz component of the epsilon tensor.
Parameters
----------
layer_id : int or Layer
The layer to get the epsilon from.
axes : tuple of int, optional
The axes to return the epsilon for. Default is (0, 1).
Returns
-------
array
The epsilon_zz component of the epsilon tensor.
"""
layer, layer_index = self.get_layer(layer_id)
if layer.is_uniform:
return layer.epsilon
epsilon_zz = (
layer.epsilon[2, 2] if layer.is_epsilon_anisotropic else layer.epsilon
)
eps_hat = self._get_toeplitz_matrix(epsilon_zz)
return self.get_ifft(
eps_hat[:, self.index_zeroth_order],
shape=self.lattice.discretization,
axes=axes,
)
# if inv:
# u = layer.eps_hat_inv ## nu_hat
# out = self.get_ifft(u[0,:], shape=self.lattice.discretization, axes=axes)
# return _inv(out)
def _get_amplitudes(self, layer_index, z=0, translate=True):
"""
Retrieve the amplitudes of the forward and backward fields.
Parameters
----------
layer_index : int
The index of the layer
z : float, optional
The z coordinate, by default 0
translate : bool, optional
Whether to translate the amplitudes to the given z coordinate, by default True
Returns
-------
ai : array
The amplitudes of the forward fields
bi : array
The amplitudes of the backward fields
"""
_t0 = timer.tic()
logger.info("Retrieving amplitudes")
layer, layer_index = self.get_layer(layer_index)
n_interfaces = len(self.layers) - 1
if layer_index == 0:
aN, b0 = self._solve_ext()
ai, bi = self.a0, b0
elif layer_index in [n_interfaces, -1]:
aN, b0 = self._solve_ext()
ai, bi = aN, self.bN
else:
ai, bi = self._solve_int(layer_index)
if translate:
ai, bi = _translate_amplitudes(self.layers[layer_index], z, ai, bi)
_t1 = timer.toc(_t0, verbose=False)
logger.info(f"Done retrieving amplitudes in {_t1:0.3e}s")
return ai, bi
def _solve_int(self, layer_index):
"""
Solve for the amplitudes of the forward and backward fields in the interior of a layer.
Parameters
----------
layer_index : int
The index of the layer
Returns
-------
ai : array
The amplitudes of the forward fields
bi : array
The amplitudes of the backward fields
"""
layer, layer_index = self.get_layer(layer_index)
n_interfaces = len(self.layers) - 1
S = self.get_S_matrix(indices=(0, layer_index))
P = self.get_S_matrix(indices=(layer_index, n_interfaces))
# q = _inv(bk.array(bk.eye(self.nh * 2)) - bk.matmul(S[0][1], P[1][0]))
# ai = bk.matmul(q, bk.matmul(S[0][0], self.a0))
Q = bk.array(bk.eye(self.nh * 2)) - bk.matmul(S[0][1], P[1][0])
ai = bk.linalg.solve(Q, bk.matmul(S[0][0], self.a0))
bi = bk.matmul(P[1][0], ai)
return ai, bi
def _solve_ext(self):
"""
Solve for the amplitudes of the forward and backward fields at the exterior boundaries.
Returns
-------
aN : array
The amplitudes of the forward fields at the top boundary
b0 : array
The amplitudes of the backward fields at the bottom boundary
"""
if not hasattr(self, "S"):
self.get_S_matrix()
aN = self.S[0][0] @ self.a0 + self.S[0][1] @ self.bN
b0 = self.S[1][0] @ self.a0 + self.S[1][1] @ self.bN
return aN, b0
def _get_nu_hat_inv(self, layer):
"""
Compute the inverse of the Toeplitz matrix of the effective permittivity
of a layer.
Parameters
----------
layer : Layer
The layer
Returns
-------
N : int
The number of harmonics
nuhat_inv : array
The inverse of the Toeplitz matrix of the effective permittivity
"""
epsilon = layer.epsilon
if layer.is_epsilon_anisotropic:
N = epsilon.shape[2]
eb = block(epsilon[:2, :2])
nu = inv2by2block(eb, N)
# nu = _inv(epsilon[2,2])
nu1 = bk.array(block2list(nu, N))
nuhat = self._get_toeplitz_matrix(nu1, transverse=True)
nuhat_inv = block2list(_inv(block(nuhat)), self.nh)
else:
N = epsilon.shape[0]
nuhat = self._get_toeplitz_matrix(1 / epsilon)
nuhat_inv = _inv((nuhat))
return N, nuhat_inv
def _get_Peps(self, layer, eps_hat, t, direct=False):
N, nuhat_inv = self._get_nu_hat_inv(layer)
if direct:
T = block([[t[1], bk.conj(t[0])], [-t[0], bk.conj(t[1])]])
invT = inv2by2block(T, N)
# invT = _inv(T)
Q = (
block(
[
[eps_hat[0][0], nuhat_inv[0][1]],
[eps_hat[1][0], nuhat_inv[1][1]],
]
)
if layer.is_epsilon_anisotropic
else block([[eps_hat[0][0], self.ZeroG], [self.ZeroG, nuhat_inv]])
)
That = block(
[
[self._get_toeplitz_matrix(get_block(T, i, j, N)) for j in range(2)]
for i in range(2)
]
)
invThat = block(
[
[
self._get_toeplitz_matrix(get_block(invT, i, j, N))
for j in range(2)
]
for i in range(2)
]
)
return That @ Q @ invThat
else:
norm_t = norm(t)
nt2 = bk.abs(norm_t) ** 2
Pxx = t[0] * bk.conj(t[0]) / nt2
Pyy = t[1] * bk.conj(t[1]) / nt2
Pxy = t[0] * bk.conj(t[1]) / nt2
Pyx = t[1] * bk.conj(t[0]) / nt2
Pxx_hat = self._get_toeplitz_matrix(Pxx)
Pyy_hat = self._get_toeplitz_matrix(Pyy)
Pxy_hat = self._get_toeplitz_matrix(Pxy)
Pyx_hat = self._get_toeplitz_matrix(Pyx)
Pi = block([[Pyy_hat, Pyx_hat], [Pxy_hat, Pxx_hat]])
# Delta = epsilon - 1 / epsilon
# Delta_hat = self._get_toeplitz_matrix(Delta)
eps_para_hat = block(eps_hat)
if layer.is_epsilon_anisotropic:
D = eps_para_hat - block(nuhat_inv)
else:
D = eps_para_hat - block(
[[nuhat_inv, self.ZeroG], [self.ZeroG, nuhat_inv]]
)
return eps_para_hat - D @ Pi
[docs]
def plot_structure(
self, plotter=None, nper=(1, 1), dz=0.0, null_thickness=None, **kwargs
):
"""
Plot the structure.
Parameters
----------
plotter : function, optional
Custom function to plot the structure. Signature should be
``plotter(simulation, nper, dz, null_thickness, **kwargs)``.
nper : tuple of int, optional
Number of periods in the x and y direction.
dz : float, optional
The distance of the structure from the z=0 plane.
null_thickness : float, optional
The thickness of the null layers at the top and bottom of the structure.
Returns
-------
None
"""
return plot_structure(self, plotter, nper, dz, null_thickness, **kwargs)
def phasor(q, z):
"""
Compute the phasor exp(i q z).
Parameters
----------
q : float or array
The wavevector
z : float or array
The position
Returns
-------
array
The phasor
"""
return bk.exp(1j * q * z)
# def _build_Kmatrix(u, Kx, Ky):
# return block(
# [
# [Kx @ u @ Kx, Kx @ u @ Ky],
# [Ky @ u @ Kx, Ky @ u @ Ky],
# ]
# )
def _build_Kmatrix(u, Kx, Ky):
def matmuldiag(A, B):
return bk.einsum("i,ik->ik", bk.array(bk.diag(A)), bk.array(B))
kxu = matmuldiag(Kx, u)
kyu = matmuldiag(Ky, u)
return block(
[
[matmuldiag(Kx.T, kxu.T).T, matmuldiag(Ky.T, kxu.T).T],
[matmuldiag(Kx.T, kyu.T).T, matmuldiag(Ky.T, kyu.T).T],
]
)
def _build_Mmatrix(layer):
phi = layer.eigenvectors
def matmuldiag(A, B):
return bk.einsum("ik,k->ik", bk.array(A), B)
# a = layer.Qeps @ phi @ (bk.diag(1 / layer.eigenvalues))
a = layer.Qeps @ matmuldiag(phi, 1 / layer.eigenvalues)
return block([[a, -a], [phi, phi]])
# TODO: check orthogonality of eigenvectors to compute M^-1 without inverting it for potential speedup
# cf: D. M. Whittaker and Imat. S. Culshaw, Scattering-matrix treatment of
# patterned multilayer photonic structures
# PHYSICAL REVIEW B, VOLUME 60, NUMBER 4, 1999
#
def _build_Mmatrix_inverse(layer):
phi = layer.eigenvectors
def matmuldiag(A, B):
return bk.einsum("ik,k->ik", bk.array(A), B)
# b = matmuldiag(phiT,layer.eigenvalues)
b = bk.diag(layer.eigenvalues) @ bk.conj(phi.T)
c = bk.conj(phi.T) @ layer.Qeps
return 0.5 * block([[b, c], [-b, c]])
def _build_Imatrix(layer1, layer2):
# if layer1.is_uniform:
# a1 = _build_Mmatrix(layer1)
# inv_a1 = _inv(a1)
# else:
# inv_a1 = _build_Mmatrix_inverse(layer1)
a1 = _build_Mmatrix(layer1)
a2 = _build_Mmatrix(layer2)
return bk.linalg.solve(a1, a2)
# inv_a1 = _inv(a1)
# return inv_a1 @ a2
def _translate_amplitudes(layer, z, ai, bi):
"""
Translate the amplitudes of the forward and backward fields
to the given z coordinate.
Parameters
----------
layer : Layer
The layer
z : float
The z coordinate
ai : array
The amplitudes of the forward fields
bi : array
The amplitudes of the backward fields
Returns
-------
aim : array
The translated amplitudes of the forward fields
bim : array
The translated amplitudes of the backward fields
"""
q = layer.eigenvalues
aim = ai * phasor(q, z)
bim = bi * phasor(q, layer.thickness - z)
return aim, bim
