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28 changed files with 205 additions and 414 deletions

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@ -157,8 +157,7 @@ def main():
e[1][tuple(grid.shape//2)] += field_source(t)
update_H(e, h)
avg_rate = (t + 1)/(time.perf_counter() - start))
print(f'iteration {t}: average {avg_rate} iterations per sec')
print('iteration {}: average {} iterations per sec'.format(t, (t+1)/(time.perf_counter()-start)))
sys.stdout.flush()
if t % 20 == 0:

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@ -3,7 +3,7 @@ import numpy
from numpy.linalg import norm
from meanas.fdmath import vec, unvec
from meanas.fdfd import waveguide_cyl, functional, scpml
from meanas.fdfd import waveguide_mode, functional, scpml
from meanas.fdfd.solvers import generic as generic_solver
import gridlock
@ -37,34 +37,29 @@ def test1(solver=generic_solver):
xyz_max = numpy.array([800, y_max, z_max]) + (pml_thickness + 2) * dx
# Coordinates of the edges of the cells.
half_edge_coords = [numpy.arange(dx / 2, m + dx / 2, step=dx) for m in xyz_max]
half_edge_coords = [numpy.arange(dx/2, m + dx/2, step=dx) for m in xyz_max]
edge_coords = [numpy.hstack((-h[::-1], h)) for h in half_edge_coords]
edge_coords[0] = numpy.array([-dx, dx])
# #### Create the grid and draw the device ####
grid = gridlock.Grid(edge_coords)
epsilon = grid.allocate(n_air**2, dtype=numpy.float32)
grid.draw_cuboid(epsilon, center=center, dimensions=[8e3, w, th], foreground=n_wg**2)
grid.draw_cuboid(epsilon, center=center, dimensions=[8e3, w, th], eps=n_wg**2)
dxes = [grid.dxyz, grid.autoshifted_dxyz()]
for a in (1, 2):
for p in (-1, 1):
dxes = scpml.stretch_with_scpml(
dxes,
omega=omega,
axis=a,
polarity=p,
thickness=pml_thickness,
)
dxes = scmpl.stretch_with_scpml(dxes, omega=omega, axis=a, polarity=p,
thickness=pml_thickness)
wg_args = {
'omega': omega,
'dxes': [(d[1], d[2]) for d in dxes],
'epsilon': vec(epsilon.transpose([0, 2, 3, 1])),
'epsilon': vec(g.transpose([1, 2, 0]) for g in epsilon),
'r0': r0,
}
wg_results = waveguide_cyl.solve_mode(mode_number=0, **wg_args)
wg_results = waveguide_mode.solve_waveguide_mode_cylindrical(mode_number=0, **wg_args)
E = wg_results['E']
@ -75,17 +70,20 @@ def test1(solver=generic_solver):
'''
Plot results
'''
def pcolor(fig, ax, v, title):
def pcolor(v):
vmax = numpy.max(numpy.abs(v))
mappable = ax.pcolormesh(v.T, cmap='seismic', vmin=-vmax, vmax=vmax)
ax.set_aspect('equal', adjustable='box')
ax.set_title(title)
ax.figure.colorbar(mappable)
pyplot.pcolor(v.T, cmap='seismic', vmin=-vmax, vmax=vmax)
pyplot.axis('equal')
pyplot.colorbar()
fig, axes = pyplot.subplots(2, 2)
pcolor(fig, axes[0][0], numpy.real(E[0]), 'Ex')
pcolor(fig, axes[0][1], numpy.real(E[1]), 'Ey')
pcolor(fig, axes[1][0], numpy.real(E[2]), 'Ez')
pyplot.figure()
pyplot.subplot(2, 2, 1)
pcolor(numpy.real(E[0][:, :]))
pyplot.subplot(2, 2, 2)
pcolor(numpy.real(E[1][:, :]))
pyplot.subplot(2, 2, 3)
pcolor(numpy.real(E[2][:, :]))
pyplot.subplot(2, 2, 4)
pyplot.show()

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@ -11,8 +11,7 @@ __author__ = 'Jan Petykiewicz'
try:
readme_path = pathlib.Path(__file__).parent / 'README.md'
with readme_path.open('r') as f:
with open(pathlib.Path(__file__).parent / 'README.md', 'r') as f:
__doc__ = f.read()
except Exception:
pass

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@ -1,12 +1,12 @@
"""
Solvers for eigenvalue / eigenvector problems
"""
from collections.abc import Callable
from typing import Callable
import numpy
from numpy.typing import NDArray, ArrayLike
from numpy.linalg import norm
from scipy import sparse
import scipy.sparse.linalg as spalg
from scipy import sparse # type: ignore
import scipy.sparse.linalg as spalg # type: ignore
def power_iteration(
@ -25,9 +25,8 @@ def power_iteration(
Returns:
(Largest-magnitude eigenvalue, Corresponding eigenvector estimate)
"""
rng = numpy.random.default_rng()
if guess_vector is None:
v = rng.random(operator.shape[0]) + 1j * rng.random(operator.shape[0])
v = numpy.random.rand(operator.shape[0]) + 1j * numpy.random.rand(operator.shape[0])
else:
v = guess_vector

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@ -91,12 +91,5 @@ $$
"""
from . import (
solvers as solvers,
operators as operators,
functional as functional,
scpml as scpml,
waveguide_2d as waveguide_2d,
waveguide_3d as waveguide_3d,
)
from . import solvers, operators, functional, scpml, waveguide_2d, waveguide_3d
# from . import farfield, bloch TODO

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@ -94,17 +94,16 @@ This module contains functions for generating and solving the
"""
from typing import Any, cast
from collections.abc import Callable, Sequence
from typing import Callable, Any, cast, Sequence
import logging
import numpy
from numpy import pi, real, trace
from numpy.fft import fftfreq
from numpy.typing import NDArray, ArrayLike
import scipy
import scipy.optimize
from scipy.linalg import norm
import scipy.sparse.linalg as spalg
import scipy # type: ignore
import scipy.optimize # type: ignore
from scipy.linalg import norm # type: ignore
import scipy.sparse.linalg as spalg # type: ignore
from ..fdmath import fdfield_t, cfdfield_t
@ -115,6 +114,7 @@ logger = logging.getLogger(__name__)
try:
import pyfftw.interfaces.numpy_fft # type: ignore
import pyfftw.interfaces # type: ignore
import multiprocessing
logger.info('Using pyfftw')
pyfftw.interfaces.cache.enable()
@ -232,7 +232,7 @@ def maxwell_operator(
Raveled conv(1/mu_k, ik x conv(1/eps_k, ik x h_mn)), returned
and overwritten in-place of `h`.
"""
hin_m, hin_n = (hi.reshape(shape) for hi in numpy.split(h, 2))
hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
#{d,e,h}_xyz fields are complex 3-fields in (1/x, 1/y, 1/z) basis
@ -303,12 +303,12 @@ def hmn_2_exyz(
k_mag, m, n = generate_kmn(k0, G_matrix, shape)
def operator(h: NDArray[numpy.complex128]) -> cfdfield_t:
hin_m, hin_n = (hi.reshape(shape) for hi in numpy.split(h, 2))
hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
d_xyz = (n * hin_m
- m * hin_n) * k_mag # noqa: E128
# divide by epsilon
return numpy.moveaxis(ifftn(d_xyz, axes=range(3)) / epsilon, 3, 0)
return numpy.array([ei for ei in numpy.moveaxis(ifftn(d_xyz, axes=range(3)) / epsilon, 3, 0)]) # TODO avoid copy
return operator
@ -341,7 +341,7 @@ def hmn_2_hxyz(
_k_mag, m, n = generate_kmn(k0, G_matrix, shape)
def operator(h: NDArray[numpy.complex128]) -> cfdfield_t:
hin_m, hin_n = (hi.reshape(shape) for hi in numpy.split(h, 2))
hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
h_xyz = (m * hin_m
+ n * hin_n) # noqa: E128
return numpy.array([ifftn(hi) for hi in numpy.moveaxis(h_xyz, 3, 0)])
@ -394,7 +394,7 @@ def inverse_maxwell_operator_approx(
Returns:
Raveled ik x conv(eps_k, ik x conv(mu_k, h_mn))
"""
hin_m, hin_n = (hi.reshape(shape) for hi in numpy.split(h, 2))
hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
#{d,e,h}_xyz fields are complex 3-fields in (1/x, 1/y, 1/z) basis
@ -561,10 +561,9 @@ def eigsolve(
prev_theta = 0.5
D = numpy.zeros(shape=y_shape, dtype=complex)
rng = numpy.random.default_rng()
Z: NDArray[numpy.complex128]
if y0 is None:
Z = rng.random(y_shape) + 1j * rng.random(y_shape)
Z = numpy.random.rand(*y_shape) + 1j * numpy.random.rand(*y_shape)
else:
Z = numpy.array(y0, copy=False).T
@ -574,7 +573,7 @@ def eigsolve(
try:
U = numpy.linalg.inv(ZtZ)
except numpy.linalg.LinAlgError:
Z = rng.random(y_shape) + 1j * rng.random(y_shape)
Z = numpy.random.rand(*y_shape) + 1j * numpy.random.rand(*y_shape)
continue
trace_U = real(trace(U))
@ -647,7 +646,8 @@ def eigsolve(
Qi_memo: list[float | None] = [None, None]
def Qi_func(theta: float, Qi_memo=Qi_memo, ZtZ=ZtZ, DtD=DtD, symZtD=symZtD) -> float: # noqa: ANN001
def Qi_func(theta: float) -> float:
nonlocal Qi_memo
if Qi_memo[0] == theta:
return cast(float, Qi_memo[1])
@ -656,7 +656,7 @@ def eigsolve(
Q = c * c * ZtZ + s * s * DtD + 2 * s * c * symZtD
try:
Qi = numpy.linalg.inv(Q)
except numpy.linalg.LinAlgError as err:
except numpy.linalg.LinAlgError:
logger.info('taylor Qi')
# if c or s small, taylor expand
if c < 1e-4 * s and c != 0:
@ -666,12 +666,12 @@ def eigsolve(
ZtZi = numpy.linalg.inv(ZtZ)
Qi = ZtZi / (c * c) - 2 * s / (c * c * c) * (ZtZi @ (ZtZi @ symZtD).conj().T)
else:
raise Exception('Inexplicable singularity in trace_func') from err
raise Exception('Inexplicable singularity in trace_func')
Qi_memo[0] = theta
Qi_memo[1] = cast(float, Qi)
return cast(float, Qi)
def trace_func(theta: float, ZtAZ=ZtAZ, DtAD=DtAD, symZtAD=symZtAD) -> float: # noqa: ANN001
def trace_func(theta: float) -> float:
c = numpy.cos(theta)
s = numpy.sin(theta)
Qi = Qi_func(theta)
@ -680,15 +680,15 @@ def eigsolve(
return numpy.abs(trace)
if False:
def trace_deriv(theta, sgn: int = sgn, ZtAZ=ZtAZ, DtAD=DtAD, symZtD=symZtD, symZtAD=symZtAD, ZtZ=ZtZ, DtD=DtD): # noqa: ANN001
def trace_deriv(theta):
Qi = Qi_func(theta)
c2 = numpy.cos(2 * theta)
s2 = numpy.sin(2 * theta)
F = -0.5 * s2 * (ZtAZ - DtAD) + c2 * symZtAD
F = -0.5*s2 * (ZtAZ - DtAD) + c2 * symZtAD
trace_deriv = _rtrace_AtB(Qi, F)
G = Qi @ F.conj().T @ Qi.conj().T
H = -0.5 * s2 * (ZtZ - DtD) + c2 * symZtD
H = -0.5*s2 * (ZtZ - DtD) + c2 * symZtD
trace_deriv -= _rtrace_AtB(G, H)
trace_deriv *= 2
@ -696,12 +696,12 @@ def eigsolve(
U_sZtD = U @ symZtD
dE = 2.0 * (_rtrace_AtB(U, symZtAD)
- _rtrace_AtB(ZtAZU, U_sZtD))
dE = 2.0 * (_rtrace_AtB(U, symZtAD) -
_rtrace_AtB(ZtAZU, U_sZtD))
d2E = 2 * (_rtrace_AtB(U, DtAD)
- _rtrace_AtB(ZtAZU, U @ (DtD - 4 * symZtD @ U_sZtD))
- 4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
d2E = 2 * (_rtrace_AtB(U, DtAD) -
_rtrace_AtB(ZtAZU, U @ (DtD - 4 * symZtD @ U_sZtD)) -
4 * _rtrace_AtB(U, symZtAD @ U_sZtD))
# Newton-Raphson to find a root of the first derivative:
theta = -dE / d2E
@ -781,7 +781,7 @@ def linmin(x_guess, f0, df0, x_max, f_tol=0.1, df_tol=min(tolerance, 1e-6), x_to
x_min, x_max, isave, dsave)
for i in range(int(1e6)):
if task != 'F':
logging.info(f'search converged in {i} iterations')
logging.info('search converged in {} iterations'.format(i))
break
fx = f(x, dfx)
x, fx, dfx, task = minpack2.dsrch(x, fx, dfx, f_tol, df_tol, x_tol, task,

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@ -1,8 +1,7 @@
"""
Functions for performing near-to-farfield transformation (and the reverse).
"""
from typing import Any, cast
from collections.abc import Sequence
from typing import Any, Sequence, cast
import numpy
from numpy.fft import fft2, fftshift, fftfreq, ifft2, ifftshift
from numpy import pi

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@ -5,7 +5,7 @@ Functional versions of many FDFD operators. These can be useful for performing
The functions generated here expect `cfdfield_t` inputs with shape (3, X, Y, Z),
e.g. E = [E_x, E_y, E_z] where each (complex) component has shape (X, Y, Z)
"""
from collections.abc import Callable
from typing import Callable
import numpy
from ..fdmath import dx_lists_t, fdfield_t, cfdfield_t, cfdfield_updater_t
@ -47,6 +47,7 @@ def e_full(
if mu is None:
return op_1
else:
return op_mu
@ -83,6 +84,7 @@ def eh_full(
if mu is None:
return op_1
else:
return op_mu
@ -114,6 +116,7 @@ def e2h(
if mu is None:
return e2h_1_1
else:
return e2h_mu
@ -148,6 +151,7 @@ def m2j(
if mu is None:
return m2j_1
else:
return m2j_mu

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@ -28,7 +28,7 @@ The following operators are included:
"""
import numpy
from scipy import sparse
import scipy.sparse as sparse # type: ignore
from ..fdmath import vec, dx_lists_t, vfdfield_t, vcfdfield_t
from ..fdmath.operators import shift_with_mirror, shift_circ, curl_forward, curl_back
@ -321,11 +321,11 @@ def poynting_e_cross(e: vcfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
"""
shape = [len(dx) for dx in dxes[0]]
fx, fy, fz = (shift_circ(i, shape, 1) for i in range(3))
fx, fy, fz = [shift_circ(i, shape, 1) for i in range(3)]
dxag = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[0], indexing='ij')]
dxbg = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[1], indexing='ij')]
Ex, Ey, Ez = (ei * da for ei, da in zip(numpy.split(e, 3), dxag, strict=True))
Ex, Ey, Ez = [ei * da for ei, da in zip(numpy.split(e, 3), dxag)]
block_diags = [[ None, fx @ -Ez, fx @ Ey],
[ fy @ Ez, None, fy @ -Ex],
@ -349,11 +349,11 @@ def poynting_h_cross(h: vcfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
"""
shape = [len(dx) for dx in dxes[0]]
fx, fy, fz = (shift_circ(i, shape, 1) for i in range(3))
fx, fy, fz = [shift_circ(i, shape, 1) for i in range(3)]
dxag = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[0], indexing='ij')]
dxbg = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[1], indexing='ij')]
Hx, Hy, Hz = (sparse.diags(hi * db) for hi, db in zip(numpy.split(h, 3), dxbg, strict=True))
Hx, Hy, Hz = [sparse.diags(hi * db) for hi, db in zip(numpy.split(h, 3), dxbg)]
P = (sparse.bmat(
[[ None, -Hz @ fx, Hy @ fx],

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@ -2,7 +2,7 @@
Functions for creating stretched coordinate perfectly matched layer (PML) absorbers.
"""
from collections.abc import Sequence, Callable
from typing import Sequence, Callable
import numpy
from numpy.typing import NDArray

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@ -2,14 +2,13 @@
Solvers and solver interface for FDFD problems.
"""
from typing import Any
from collections.abc import Callable
from typing import Callable, Dict, Any, Optional
import logging
import numpy
from numpy.typing import ArrayLike, NDArray
from numpy.linalg import norm
import scipy.sparse.linalg
import scipy.sparse.linalg # type: ignore
from ..fdmath import dx_lists_t, vfdfield_t, vcfdfield_t
from . import operators
@ -44,8 +43,7 @@ def _scipy_qmr(
nonlocal ii
ii += 1
if ii % 100 == 0:
cur_norm = norm(A @ xk - b)
logger.info(f'Solver residual at iteration {ii} : {cur_norm}')
logger.info('Solver residual at iteration {} : {}'.format(ii, norm(A @ xk - b)))
if 'callback' in kwargs:
def augmented_callback(xk: ArrayLike) -> None:
@ -69,12 +67,12 @@ def generic(
dxes: dx_lists_t,
J: vcfdfield_t,
epsilon: vfdfield_t,
mu: vfdfield_t | None = None,
pec: vfdfield_t | None = None,
pmc: vfdfield_t | None = None,
mu: Optional[vfdfield_t] = None,
pec: Optional[vfdfield_t] = None,
pmc: Optional[vfdfield_t] = None,
adjoint: bool = False,
matrix_solver: Callable[..., ArrayLike] = _scipy_qmr,
matrix_solver_opts: dict[str, Any] | None = None,
matrix_solver_opts: Optional[Dict[str, Any]] = None,
) -> vcfdfield_t:
"""
Conjugate gradient FDFD solver using CSR sparse matrices.

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@ -182,10 +182,10 @@ from typing import Any
import numpy
from numpy.typing import NDArray, ArrayLike
from numpy.linalg import norm
from scipy import sparse
import scipy.sparse as sparse # type: ignore
from ..fdmath.operators import deriv_forward, deriv_back, cross
from ..fdmath import vec, unvec, dx_lists_t, vfdfield_t, vcfdfield_t
from ..fdmath import unvec, dx_lists_t, vfdfield_t, vcfdfield_t
from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
@ -253,8 +253,7 @@ def operator_e(
mu_yx = sparse.diags(numpy.hstack((mu_parts[1], mu_parts[0])))
mu_z_inv = sparse.diags(1 / mu_parts[2])
op = (
omega * omega * mu_yx @ eps_xy
op = (omega * omega * mu_yx @ eps_xy
+ mu_yx @ sparse.vstack((-Dby, Dbx)) @ mu_z_inv @ sparse.hstack((-Dfy, Dfx))
+ sparse.vstack((Dfx, Dfy)) @ eps_z_inv @ sparse.hstack((Dbx, Dby)) @ eps_xy
)
@ -322,8 +321,7 @@ def operator_h(
mu_xy = sparse.diags(numpy.hstack((mu_parts[0], mu_parts[1])))
mu_z_inv = sparse.diags(1 / mu_parts[2])
op = (
omega * omega * eps_yx @ mu_xy
op = (omega * omega * eps_yx @ mu_xy
+ eps_yx @ sparse.vstack((-Dfy, Dfx)) @ eps_z_inv @ sparse.hstack((-Dby, Dbx))
+ sparse.vstack((Dbx, Dby)) @ mu_z_inv @ sparse.hstack((Dfx, Dfy)) @ mu_xy
)
@ -422,7 +420,7 @@ def _normalized_fields(
Sz_a = E[0] * numpy.conj(H[1] * phase) * dxes_real[0][1] * dxes_real[1][0]
Sz_b = E[1] * numpy.conj(H[0] * phase) * dxes_real[0][0] * dxes_real[1][1]
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
assert Sz_tavg > 0, f'Found a mode propagating in the wrong direction! {Sz_tavg=}'
assert Sz_tavg > 0, 'Found a mode propagating in the wrong direction! Sz_tavg={}'.format(Sz_tavg)
energy = epsilon * e.conj() * e
@ -720,109 +718,6 @@ def e_err(
return float(norm(op) / norm(e))
def sensitivity(
e_norm: vcfdfield_t,
h_norm: vcfdfield_t,
wavenumber: complex,
omega: complex,
dxes: dx_lists_t,
epsilon: vfdfield_t,
mu: vfdfield_t | None = None,
) -> vcfdfield_t:
r"""
Given a waveguide structure (`dxes`, `epsilon`, `mu`) and mode fields
(`e_norm`, `h_norm`, `wavenumber`, `omega`), calculates the sensitivity of the wavenumber
$\beta$ to changes in the dielectric structure $\epsilon$.
The output is a vector of the same size as `vec(epsilon)`, with each element specifying the
sensitivity of `wavenumber` to changes in the corresponding element in `vec(epsilon)`, i.e.
$$sens_{i} = \frac{\partial\beta}{\partial\epsilon_i}$$
An adjoint approach is used to calculate the sensitivity; the derivation is provided here:
Starting with the eigenvalue equation
$$\beta^2 E_{xy} = A_E E_{xy}$$
where $A_E$ is the waveguide operator from `operator_e()`, and $E_{xy} = \begin{bmatrix} E_x \\
E_y \end{bmatrix}$,
we can differentiate with respect to one of the $\epsilon$ elements (i.e. at one Yee grid point), $\epsilon_i$:
$$
(2 \beta) \partial_{\epsilon_i}(\beta) E_{xy} + \beta^2 \partial_{\epsilon_i} E_{xy}
= \partial_{\epsilon_i}(A_E) E_{xy} + A_E \partial_{\epsilon_i} E_{xy}
$$
We then multiply by $H_{yx}^\star = \begin{bmatrix}H_y^\star \\ -H_x^\star \end{bmatrix}$ from the left:
$$
(2 \beta) \partial_{\epsilon_i}(\beta) H_{yx}^\star E_{xy} + \beta^2 H_{yx}^\star \partial_{\epsilon_i} E_{xy}
= H_{yx}^\star \partial_{\epsilon_i}(A_E) E_{xy} + H_{yx}^\star A_E \partial_{\epsilon_i} E_{xy}
$$
However, $H_{yx}^\star$ is actually a left-eigenvector of $A_E$. This can be verified by inspecting
the form of `operator_h` ($A_H$) and comparing its conjugate transpose to `operator_e` ($A_E$). Also, note
$H_{yx}^\star \cdot E_{xy} = H^\star \times E$ recalls the mode orthogonality relation. See doi:10.5194/ars-9-85-201
for a similar approach. Therefore,
$$
H_{yx}^\star A_E \partial_{\epsilon_i} E_{xy} = \beta^2 H_{yx}^\star \partial_{\epsilon_i} E_{xy}
$$
and we can simplify to
$$
\partial_{\epsilon_i}(\beta)
= \frac{1}{2 \beta} \frac{H_{yx}^\star \partial_{\epsilon_i}(A_E) E_{xy} }{H_{yx}^\star E_{xy}}
$$
This expression can be quickly calculated for all $i$ by writing out the various terms of
$\partial_{\epsilon_i} A_E$ and recognizing that the vector-matrix-vector products (i.e. scalars)
$sens_i = \vec{v}_{left} \partial_{\epsilon_i} (\epsilon_{xyz}) \vec{v}_{right}$, indexed by $i$, can be expressed as
elementwise multiplications $\vec{sens} = \vec{v}_{left} \star \vec{v}_{right}$
Args:
e_norm: Normalized, vectorized E_xyz field for the mode. E.g. as returned by `normalized_fields_e`.
h_norm: Normalized, vectorized H_xyz field for the mode. E.g. as returned by `normalized_fields_e`.
wavenumber: Propagation constant for the mode. The z-axis is assumed to be continuous (i.e. without numerical dispersion).
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:
Sparse matrix representation of the operator.
"""
if mu is None:
mu = numpy.ones_like(epsilon)
Dfx, Dfy = deriv_forward(dxes[0])
Dbx, Dby = deriv_back(dxes[1])
eps_x, eps_y, eps_z = numpy.split(epsilon, 3)
eps_xy = sparse.diags(numpy.hstack((eps_x, eps_y)))
eps_z_inv = sparse.diags(1 / eps_z)
mu_x, mu_y, _mu_z = numpy.split(mu, 3)
mu_yx = sparse.diags(numpy.hstack((mu_y, mu_x)))
da_exxhyy = vec(dxes[1][0][:, None] * dxes[0][1][None, :])
da_eyyhxx = vec(dxes[1][1][None, :] * dxes[0][0][:, None])
ev_xy = numpy.concatenate(numpy.split(e_norm, 3)[:2]) * numpy.concatenate([da_exxhyy, da_eyyhxx])
hx, hy, hz = numpy.split(h_norm, 3)
hv_yx_conj = numpy.conj(numpy.concatenate([hy, -hx]))
sens_xy1 = (hv_yx_conj @ (omega * omega * mu_yx)) * ev_xy
sens_xy2 = (hv_yx_conj @ sparse.vstack((Dfx, Dfy)) @ eps_z_inv @ sparse.hstack((Dbx, Dby))) * ev_xy
sens_z = (hv_yx_conj @ sparse.vstack((Dfx, Dfy)) @ (-eps_z_inv * eps_z_inv)) * (sparse.hstack((Dbx, Dby)) @ eps_xy @ ev_xy)
norm = hv_yx_conj @ ev_xy
sens_tot = numpy.concatenate([sens_xy1 + sens_xy2, sens_z]) / (2 * wavenumber * norm)
return sens_tot
def solve_modes(
mode_numbers: list[int],
omega: complex,

View File

@ -4,11 +4,9 @@ Tools for working with waveguide modes in 3D domains.
This module relies heavily on `waveguide_2d` and mostly just transforms
its parameters into 2D equivalents and expands the results back into 3D.
"""
from typing import Any
from collections.abc import Sequence
from typing import Sequence, Any
import numpy
from numpy.typing import NDArray
from numpy import complexfloating
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, cfdfield_t
from . import operators, waveguide_2d
@ -23,7 +21,7 @@ def solve_mode(
slices: Sequence[slice],
epsilon: fdfield_t,
mu: fdfield_t | None = None,
) -> dict[str, complex | NDArray[complexfloating]]:
) -> dict[str, complex | NDArray[numpy.float_]]:
"""
Given a 3D grid, selects a slice from the grid and attempts to
solve for an eigenmode propagating through that slice.
@ -42,8 +40,8 @@ def solve_mode(
Returns:
```
{
'E': NDArray[complexfloating],
'H': NDArray[complexfloating],
'E': list[NDArray[numpy.float_]],
'H': list[NDArray[numpy.float_]],
'wavenumber': complex,
}
```

View File

@ -9,9 +9,9 @@ As the z-dependence is known, all the functions in this file assume a 2D grid
# TODO update module docs
import numpy
from scipy import sparse
import scipy.sparse as sparse # type: ignore
from ..fdmath import vec, unvec, dx_lists_t, vfdfield_t, cfdfield_t
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t, cfdfield_t
from ..fdmath.operators import deriv_forward, deriv_back
from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
@ -25,9 +25,6 @@ def cylindrical_operator(
"""
Cylindrical coordinate waveguide operator of the form
(NOTE: See 10.1364/OL.33.001848)
TODO: consider 10.1364/OE.20.021583
TODO
for use with a field vector of the form `[E_r, E_y]`.

View File

@ -741,24 +741,8 @@ the true values can be multiplied back in after the simulation is complete if no
normalized results are needed.
"""
from .types import (
fdfield_t as fdfield_t,
vfdfield_t as vfdfield_t,
cfdfield_t as cfdfield_t,
vcfdfield_t as vcfdfield_t,
dx_lists_t as dx_lists_t,
dx_lists_mut as dx_lists_mut,
fdfield_updater_t as fdfield_updater_t,
cfdfield_updater_t as cfdfield_updater_t,
)
from .vectorization import (
vec as vec,
unvec as unvec,
)
from . import (
operators as operators,
functional as functional,
types as types,
vectorization as vectorization,
)
from .types import fdfield_t, vfdfield_t, cfdfield_t, vcfdfield_t, dx_lists_t, dx_lists_mut
from .types import fdfield_updater_t, cfdfield_updater_t
from .vectorization import vec, unvec
from . import operators, functional, types, vectorization

View File

@ -3,18 +3,16 @@ Math functions for finite difference simulations
Basic discrete calculus etc.
"""
from typing import TypeVar
from collections.abc import Sequence, Callable
from typing import Sequence, Callable
import numpy
from numpy.typing import NDArray
from numpy import floating, complexfloating
from .types import fdfield_t, fdfield_updater_t
def deriv_forward(
dx_e: Sequence[NDArray[floating]] | None = None,
dx_e: Sequence[NDArray[numpy.float_]] | None = None,
) -> tuple[fdfield_updater_t, fdfield_updater_t, fdfield_updater_t]:
"""
Utility operators for taking discretized derivatives (backward variant).
@ -38,7 +36,7 @@ def deriv_forward(
def deriv_back(
dx_h: Sequence[NDArray[floating]] | None = None,
dx_h: Sequence[NDArray[numpy.float_]] | None = None,
) -> tuple[fdfield_updater_t, fdfield_updater_t, fdfield_updater_t]:
"""
Utility operators for taking discretized derivatives (forward variant).
@ -61,12 +59,9 @@ def deriv_back(
return derivs
TT = TypeVar('TT', bound='NDArray[floating | complexfloating]')
def curl_forward(
dx_e: Sequence[NDArray[floating]] | None = None,
) -> Callable[[TT], TT]:
dx_e: Sequence[NDArray[numpy.float_]] | None = None,
) -> fdfield_updater_t:
r"""
Curl operator for use with the E field.
@ -80,7 +75,7 @@ def curl_forward(
"""
Dx, Dy, Dz = deriv_forward(dx_e)
def ce_fun(e: TT) -> TT:
def ce_fun(e: fdfield_t) -> fdfield_t:
output = numpy.empty_like(e)
output[0] = Dy(e[2])
output[1] = Dz(e[0])
@ -94,8 +89,8 @@ def curl_forward(
def curl_back(
dx_h: Sequence[NDArray[floating]] | None = None,
) -> Callable[[TT], TT]:
dx_h: Sequence[NDArray[numpy.float_]] | None = None,
) -> fdfield_updater_t:
r"""
Create a function which takes the backward curl of a field.
@ -109,7 +104,7 @@ def curl_back(
"""
Dx, Dy, Dz = deriv_back(dx_h)
def ch_fun(h: TT) -> TT:
def ch_fun(h: fdfield_t) -> fdfield_t:
output = numpy.empty_like(h)
output[0] = Dy(h[2])
output[1] = Dz(h[0])
@ -123,7 +118,7 @@ def curl_back(
def curl_forward_parts(
dx_e: Sequence[NDArray[floating]] | None = None,
dx_e: Sequence[NDArray[numpy.float_]] | None = None,
) -> Callable:
Dx, Dy, Dz = deriv_forward(dx_e)
@ -136,7 +131,7 @@ def curl_forward_parts(
def curl_back_parts(
dx_h: Sequence[NDArray[floating]] | None = None,
dx_h: Sequence[NDArray[numpy.float_]] | None = None,
) -> Callable:
Dx, Dy, Dz = deriv_back(dx_h)

View File

@ -3,11 +3,10 @@ Matrix operators for finite difference simulations
Basic discrete calculus etc.
"""
from collections.abc import Sequence
from typing import Sequence
import numpy
from numpy.typing import NDArray
from numpy import floating
from scipy import sparse
import scipy.sparse as sparse # type: ignore
from .types import vfdfield_t
@ -30,12 +29,12 @@ def shift_circ(
Sparse matrix for performing the circular shift.
"""
if len(shape) not in (2, 3):
raise Exception(f'Invalid shape: {shape}')
raise Exception('Invalid shape: {}'.format(shape))
if axis not in range(len(shape)):
raise Exception(f'Invalid direction: {axis}, shape is {shape}')
raise Exception('Invalid direction: {}, shape is {}'.format(axis, shape))
shifts = [abs(shift_distance) if a == axis else 0 for a in range(3)]
shifted_diags = [(numpy.arange(n) + s) % n for n, s in zip(shape, shifts, strict=True)]
shifted_diags = [(numpy.arange(n) + s) % n for n, s in zip(shape, shifts)]
ijk = numpy.meshgrid(*shifted_diags, indexing='ij')
n = numpy.prod(shape)
@ -70,11 +69,12 @@ def shift_with_mirror(
Sparse matrix for performing the shift-with-mirror.
"""
if len(shape) not in (2, 3):
raise Exception(f'Invalid shape: {shape}')
raise Exception('Invalid shape: {}'.format(shape))
if axis not in range(len(shape)):
raise Exception(f'Invalid direction: {axis}, shape is {shape}')
raise Exception('Invalid direction: {}, shape is {}'.format(axis, shape))
if shift_distance >= shape[axis]:
raise Exception(f'Shift ({shift_distance}) is too large for axis {axis} of size {shape[axis]}')
raise Exception('Shift ({}) is too large for axis {} of size {}'.format(
shift_distance, axis, shape[axis]))
def mirrored_range(n: int, s: int) -> NDArray[numpy.int_]:
v = numpy.arange(n) + s
@ -83,7 +83,7 @@ def shift_with_mirror(
return v
shifts = [shift_distance if a == axis else 0 for a in range(3)]
shifted_diags = [mirrored_range(n, s) for n, s in zip(shape, shifts, strict=True)]
shifted_diags = [mirrored_range(n, s) for n, s in zip(shape, shifts)]
ijk = numpy.meshgrid(*shifted_diags, indexing='ij')
n = numpy.prod(shape)
@ -97,7 +97,7 @@ def shift_with_mirror(
def deriv_forward(
dx_e: Sequence[NDArray[floating]],
dx_e: Sequence[NDArray[numpy.float_]],
) -> list[sparse.spmatrix]:
"""
Utility operators for taking discretized derivatives (forward variant).
@ -124,7 +124,7 @@ def deriv_forward(
def deriv_back(
dx_h: Sequence[NDArray[floating]],
dx_h: Sequence[NDArray[numpy.float_]],
) -> list[sparse.spmatrix]:
"""
Utility operators for taking discretized derivatives (backward variant).
@ -198,7 +198,7 @@ def avg_forward(axis: int, shape: Sequence[int]) -> sparse.spmatrix:
Sparse matrix for forward average operation.
"""
if len(shape) not in (2, 3):
raise Exception(f'Invalid shape: {shape}')
raise Exception('Invalid shape: {}'.format(shape))
n = numpy.prod(shape)
return 0.5 * (sparse.eye(n) + shift_circ(axis, shape))
@ -219,7 +219,7 @@ def avg_back(axis: int, shape: Sequence[int]) -> sparse.spmatrix:
def curl_forward(
dx_e: Sequence[NDArray[floating]],
dx_e: Sequence[NDArray[numpy.float_]],
) -> sparse.spmatrix:
"""
Curl operator for use with the E field.
@ -235,7 +235,7 @@ def curl_forward(
def curl_back(
dx_h: Sequence[NDArray[floating]],
dx_h: Sequence[NDArray[numpy.float_]],
) -> sparse.spmatrix:
"""
Curl operator for use with the H field.

View File

@ -1,26 +1,26 @@
"""
Types shared across multiple submodules
"""
from collections.abc import Sequence, Callable, MutableSequence
from typing import Sequence, Callable, MutableSequence
import numpy
from numpy.typing import NDArray
from numpy import floating, complexfloating
# Field types
fdfield_t = NDArray[floating]
fdfield_t = NDArray[numpy.float_]
"""Vector field with shape (3, X, Y, Z) (e.g. `[E_x, E_y, E_z]`)"""
vfdfield_t = NDArray[floating]
vfdfield_t = NDArray[numpy.float_]
"""Linearized vector field (single vector of length 3*X*Y*Z)"""
cfdfield_t = NDArray[complexfloating]
cfdfield_t = NDArray[numpy.complex_]
"""Complex vector field with shape (3, X, Y, Z) (e.g. `[E_x, E_y, E_z]`)"""
vcfdfield_t = NDArray[complexfloating]
vcfdfield_t = NDArray[numpy.complex_]
"""Linearized complex vector field (single vector of length 3*X*Y*Z)"""
dx_lists_t = Sequence[Sequence[NDArray[floating]]]
dx_lists_t = Sequence[Sequence[NDArray[numpy.float_]]]
"""
'dxes' datastructure which contains grid cell width information in the following format:
@ -31,7 +31,7 @@ dx_lists_t = Sequence[Sequence[NDArray[floating]]]
and `dy_h[0]` is the y-width of the `y=0` cells, as used when calculating dH/dy, etc.
"""
dx_lists_mut = MutableSequence[MutableSequence[NDArray[floating]]]
dx_lists_mut = MutableSequence[MutableSequence[NDArray[numpy.float_]]]
"""Mutable version of `dx_lists_t`"""

View File

@ -4,8 +4,7 @@ and a 1D array representation of that field `[f_x0, f_x1, f_x2,... f_y0,... f_z0
Vectorized versions of the field use row-major (ie., C-style) ordering.
"""
from typing import overload
from collections.abc import Sequence
from typing import overload, Sequence
import numpy
from numpy.typing import ArrayLike

View File

@ -159,22 +159,8 @@ Boundary conditions
# TODO notes about boundaries / PMLs
"""
from .base import (
maxwell_e as maxwell_e,
maxwell_h as maxwell_h,
)
from .pml import (
cpml_params as cpml_params,
updates_with_cpml as updates_with_cpml,
)
from .energy import (
poynting as poynting,
poynting_divergence as poynting_divergence,
energy_hstep as energy_hstep,
energy_estep as energy_estep,
delta_energy_h2e as delta_energy_h2e,
delta_energy_j as delta_energy_j,
)
from .boundaries import (
conducting_boundary as conducting_boundary,
)
from .base import maxwell_e, maxwell_h
from .pml import cpml_params, updates_with_cpml
from .energy import (poynting, poynting_divergence, energy_hstep, energy_estep,
delta_energy_h2e, delta_energy_j)
from .boundaries import conducting_boundary

View File

@ -15,17 +15,13 @@ def conducting_boundary(
) -> tuple[fdfield_updater_t, fdfield_updater_t]:
dirs = [0, 1, 2]
if direction not in dirs:
raise Exception(f'Invalid direction: {direction}')
raise Exception('Invalid direction: {}'.format(direction))
dirs.remove(direction)
u, v = dirs
boundary_slice: list[Any]
shifted1_slice: list[Any]
shifted2_slice: list[Any]
if polarity < 0:
boundary_slice = [slice(None)] * 3
shifted1_slice = [slice(None)] * 3
boundary_slice = [slice(None)] * 3 # type: list[Any]
shifted1_slice = [slice(None)] * 3 # type: list[Any]
boundary_slice[direction] = 0
shifted1_slice[direction] = 1
@ -46,7 +42,7 @@ def conducting_boundary(
if polarity > 0:
boundary_slice = [slice(None)] * 3
shifted1_slice = [slice(None)] * 3
shifted2_slice = [slice(None)] * 3
shifted2_slice = [slice(None)] * 3 # type: list[Any]
boundary_slice[direction] = -1
shifted1_slice[direction] = -2
shifted2_slice[direction] = -3
@ -68,4 +64,4 @@ def conducting_boundary(
return ep, hp
raise Exception(f'Bad polarity: {polarity}')
raise Exception('Bad polarity: {}'.format(polarity))

View File

@ -7,8 +7,7 @@ PML implementations
"""
# TODO retest pmls!
from typing import Any
from collections.abc import Callable, Sequence
from typing import Callable, Sequence, Any
from copy import deepcopy
import numpy
from numpy.typing import NDArray, DTypeLike
@ -34,10 +33,10 @@ def cpml_params(
) -> dict[str, Any]:
if axis not in range(3):
raise Exception(f'Invalid axis: {axis}')
raise Exception('Invalid axis: {}'.format(axis))
if polarity not in (-1, 1):
raise Exception(f'Invalid polarity: {polarity}')
raise Exception('Invalid polarity: {}'.format(polarity))
if thickness <= 2:
raise Exception('It would be wise to have a pml with 4+ cells of thickness')
@ -112,7 +111,7 @@ def updates_with_cpml(
params_H: list[list[tuple[Any, Any, Any, Any]]] = deepcopy(params_E)
for axis in range(3):
for pp, _polarity in enumerate((-1, 1)):
for pp, polarity in enumerate((-1, 1)):
cpml_param = cpml_params[axis][pp]
if cpml_param is None:
psi_E[axis][pp] = (None, None)
@ -185,7 +184,7 @@ def updates_with_cpml(
def update_H(
e: fdfield_t,
h: fdfield_t,
mu: fdfield_t | tuple[int, int, int] = (1, 1, 1),
mu: fdfield_t = numpy.ones(3),
) -> None:
dyEx = Dfy(e[0])
dzEx = Dfz(e[0])

View File

@ -3,8 +3,7 @@
Test fixtures
"""
# ruff: noqa: ARG001
from typing import Any
from typing import Iterable, Any
import numpy
from numpy.typing import NDArray
import pytest # type: ignore
@ -21,18 +20,18 @@ FixtureRequest = Any
(5, 5, 5),
# (7, 7, 7),
])
def shape(request: FixtureRequest) -> tuple[int, ...]:
return (3, *request.param)
def shape(request: FixtureRequest) -> Iterable[tuple[int, ...]]:
yield (3, *request.param)
@pytest.fixture(scope='module', params=[1.0, 1.5])
def epsilon_bg(request: FixtureRequest) -> float:
return request.param
def epsilon_bg(request: FixtureRequest) -> Iterable[float]:
yield request.param
@pytest.fixture(scope='module', params=[1.0, 2.5])
def epsilon_fg(request: FixtureRequest) -> float:
return request.param
def epsilon_fg(request: FixtureRequest) -> Iterable[float]:
yield request.param
@pytest.fixture(scope='module', params=['center', '000', 'random'])
@ -41,7 +40,7 @@ def epsilon(
shape: tuple[int, ...],
epsilon_bg: float,
epsilon_fg: float,
) -> NDArray[numpy.float64]:
) -> Iterable[NDArray[numpy.float64]]:
is3d = (numpy.array(shape) == 1).sum() == 0
if is3d:
if request.param == '000':
@ -61,17 +60,17 @@ def epsilon(
high=max(epsilon_bg, epsilon_fg),
size=shape)
return epsilon
yield epsilon
@pytest.fixture(scope='module', params=[1.0]) # 1.5
def j_mag(request: FixtureRequest) -> float:
return request.param
def j_mag(request: FixtureRequest) -> Iterable[float]:
yield request.param
@pytest.fixture(scope='module', params=[1.0, 1.5])
def dx(request: FixtureRequest) -> float:
return request.param
def dx(request: FixtureRequest) -> Iterable[float]:
yield request.param
@pytest.fixture(scope='module', params=['uniform', 'centerbig'])
@ -79,7 +78,7 @@ def dxes(
request: FixtureRequest,
shape: tuple[int, ...],
dx: float,
) -> list[list[NDArray[numpy.float64]]]:
) -> Iterable[list[list[NDArray[numpy.float64]]]]:
if request.param == 'uniform':
dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
elif request.param == 'centerbig':
@ -91,5 +90,5 @@ def dxes(
dxe = [PRNG.uniform(low=1.0 * dx, high=1.1 * dx, size=s) for s in shape[1:]]
dxh = [(d + numpy.roll(d, -1)) / 2 for d in dxe]
dxes = [dxe, dxh]
return dxes
yield dxes

View File

@ -1,4 +1,4 @@
# ruff: noqa: ARG001
from typing import Iterable
import dataclasses
import pytest # type: ignore
import numpy
@ -61,24 +61,24 @@ def test_poynting_planes(sim: 'FDResult') -> None:
# Also see conftest.py
@pytest.fixture(params=[1 / 1500])
def omega(request: FixtureRequest) -> float:
return request.param
def omega(request: FixtureRequest) -> Iterable[float]:
yield request.param
@pytest.fixture(params=[None])
def pec(request: FixtureRequest) -> NDArray[numpy.float64] | None:
return request.param
def pec(request: FixtureRequest) -> Iterable[NDArray[numpy.float64] | None]:
yield request.param
@pytest.fixture(params=[None])
def pmc(request: FixtureRequest) -> NDArray[numpy.float64] | None:
return request.param
def pmc(request: FixtureRequest) -> Iterable[NDArray[numpy.float64] | None]:
yield request.param
#@pytest.fixture(scope='module',
# params=[(25, 5, 5)])
#def shape(request: FixtureRequest):
# return (3, *request.param)
#def shape(request):
# yield (3, *request.param)
@pytest.fixture(params=['diag']) # 'center'
@ -86,7 +86,7 @@ def j_distribution(
request: FixtureRequest,
shape: tuple[int, ...],
j_mag: float,
) -> NDArray[numpy.float64]:
) -> Iterable[NDArray[numpy.float64]]:
j = numpy.zeros(shape, dtype=complex)
center_mask = numpy.zeros(shape, dtype=bool)
center_mask[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = True
@ -96,7 +96,7 @@ def j_distribution(
elif request.param == 'diag':
j[numpy.roll(center_mask, [1, 1, 1], axis=(1, 2, 3))] = (1 + 1j) * j_mag
j[numpy.roll(center_mask, [-1, -1, -1], axis=(1, 2, 3))] = (1 - 1j) * j_mag
return j
yield j
@dataclasses.dataclass()

View File

@ -1,4 +1,4 @@
# ruff: noqa: ARG001
from typing import Iterable
import pytest # type: ignore
import numpy
from numpy.typing import NDArray
@ -44,30 +44,30 @@ def test_pml(sim: FDResult, src_polarity: int) -> None:
# Also see conftest.py
@pytest.fixture(params=[1 / 1500])
def omega(request: FixtureRequest) -> float:
return request.param
def omega(request: FixtureRequest) -> Iterable[float]:
yield request.param
@pytest.fixture(params=[None])
def pec(request: FixtureRequest) -> NDArray[numpy.float64] | None:
return request.param
def pec(request: FixtureRequest) -> Iterable[NDArray[numpy.float64] | None]:
yield request.param
@pytest.fixture(params=[None])
def pmc(request: FixtureRequest) -> NDArray[numpy.float64] | None:
return request.param
def pmc(request: FixtureRequest) -> Iterable[NDArray[numpy.float64] | None]:
yield request.param
@pytest.fixture(params=[(30, 1, 1),
(1, 30, 1),
(1, 1, 30)])
def shape(request: FixtureRequest) -> tuple[int, int, int]:
return (3, *request.param)
def shape(request: FixtureRequest) -> Iterable[tuple[int, ...]]:
yield (3, *request.param)
@pytest.fixture(params=[+1, -1])
def src_polarity(request: FixtureRequest) -> int:
return request.param
def src_polarity(request: FixtureRequest) -> Iterable[int]:
yield request.param
@pytest.fixture()
@ -78,7 +78,7 @@ def j_distribution(
dxes: dx_lists_mut,
omega: float,
src_polarity: int,
) -> NDArray[numpy.complex128]:
) -> Iterable[NDArray[numpy.complex128]]:
j = numpy.zeros(shape, dtype=complex)
dim = numpy.where(numpy.array(shape[1:]) > 1)[0][0] # Propagation axis
@ -106,7 +106,7 @@ def j_distribution(
j = fdfd.waveguide_3d.compute_source(E=e, wavenumber=wavenumber_corrected, omega=omega, dxes=dxes,
axis=dim, polarity=src_polarity, slices=slices, epsilon=epsilon)
return j
yield j
@pytest.fixture()
@ -115,9 +115,9 @@ def epsilon(
shape: tuple[int, ...],
epsilon_bg: float,
epsilon_fg: float,
) -> NDArray[numpy.float64]:
) -> Iterable[NDArray[numpy.float64]]:
epsilon = numpy.full(shape, epsilon_fg, dtype=float)
return epsilon
yield epsilon
@pytest.fixture(params=['uniform'])
@ -127,7 +127,7 @@ def dxes(
dx: float,
omega: float,
epsilon_fg: float,
) -> list[list[NDArray[numpy.float64]]]:
) -> Iterable[list[list[NDArray[numpy.float64]]]]:
if request.param == 'uniform':
dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
dim = numpy.where(numpy.array(shape[1:]) > 1)[0][0] # Propagation axis
@ -141,7 +141,7 @@ def dxes(
epsilon_effective=epsilon_fg,
thickness=10,
)
return dxes
yield dxes
@pytest.fixture()

View File

@ -1,5 +1,4 @@
# ruff: noqa: ARG001
from typing import Any
from typing import Iterable, Any
import dataclasses
import pytest # type: ignore
import numpy
@ -102,7 +101,7 @@ def test_poynting_divergence(sim: 'TDResult') -> None:
def test_poynting_planes(sim: 'TDResult') -> None:
mask = (sim.js[0] != 0).any(axis=0)
if mask.sum() > 1:
pytest.skip(f'test_poynting_planes can only test single point sources, got {mask.sum()}')
pytest.skip('test_poynting_planes can only test single point sources, got {}'.format(mask.sum()))
args: dict[str, Any] = {
'dxes': sim.dxes,
@ -151,8 +150,8 @@ def test_poynting_planes(sim: 'TDResult') -> None:
@pytest.fixture(params=[0.3])
def dt(request: FixtureRequest) -> float:
return request.param
def dt(request: FixtureRequest) -> Iterable[float]:
yield request.param
@dataclasses.dataclass()
@ -169,8 +168,8 @@ class TDResult:
@pytest.fixture(params=[(0, 4, 8)]) # (0,)
def j_steps(request: FixtureRequest) -> tuple[int, ...]:
return request.param
def j_steps(request: FixtureRequest) -> Iterable[tuple[int, ...]]:
yield request.param
@pytest.fixture(params=['center', 'random'])
@ -178,7 +177,7 @@ def j_distribution(
request: FixtureRequest,
shape: tuple[int, ...],
j_mag: float,
) -> NDArray[numpy.float64]:
) -> Iterable[NDArray[numpy.float64]]:
j = numpy.zeros(shape)
if request.param == 'center':
j[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = j_mag
@ -186,7 +185,7 @@ def j_distribution(
j[:, 0, 0, 0] = j_mag
elif request.param == 'random':
j[:] = PRNG.uniform(low=-j_mag, high=j_mag, size=shape)
return j
yield j
@pytest.fixture()
@ -200,7 +199,8 @@ def sim(
j_steps: tuple[int, ...],
) -> TDResult:
is3d = (numpy.array(shape) == 1).sum() == 0
if is3d and dt != 0.3:
if is3d:
if dt != 0.3:
pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)')
sim = TDResult(

View File

@ -1,3 +1,5 @@
from typing import Any
import numpy
from numpy.typing import NDArray
@ -8,25 +10,22 @@ PRNG = numpy.random.RandomState(12345)
def assert_fields_close(
x: NDArray,
y: NDArray,
*args,
**kwargs,
*args: Any,
**kwargs: Any,
) -> None:
x_disp = numpy.moveaxis(x, -1, 0)
y_disp = numpy.moveaxis(y, -1, 0)
numpy.testing.assert_allclose(
x, # type: ignore
y, # type: ignore
x, y, verbose=False, # type: ignore
err_msg='Fields did not match:\n{}\n{}'.format(numpy.moveaxis(x, -1, 0),
numpy.moveaxis(y, -1, 0)),
*args,
verbose=False,
err_msg=f'Fields did not match:\n{x_disp}\n{y_disp}',
**kwargs,
)
def assert_close(
x: NDArray,
y: NDArray,
*args,
**kwargs,
*args: Any,
**kwargs: Any,
) -> None:
numpy.testing.assert_allclose(x, y, *args, **kwargs)

View File

@ -39,7 +39,7 @@ include = [
]
dynamic = ["version"]
dependencies = [
"numpy~=1.26",
"numpy~=1.21",
"scipy",
]
@ -51,48 +51,3 @@ path = "meanas/__init__.py"
dev = ["pytest", "pdoc", "gridlock"]
examples = ["gridlock"]
test = ["pytest"]
[tool.ruff]
exclude = [
".git",
"dist",
]
line-length = 245
indent-width = 4
lint.dummy-variable-rgx = "^(_+|(_+[a-zA-Z0-9_]*[a-zA-Z0-9]+?))$"
lint.select = [
"NPY", "E", "F", "W", "B", "ANN", "UP", "SLOT", "SIM", "LOG",
"C4", "ISC", "PIE", "PT", "RET", "TCH", "PTH", "INT",
"ARG", "PL", "R", "TRY",
"G010", "G101", "G201", "G202",
"Q002", "Q003", "Q004",
]
lint.ignore = [
#"ANN001", # No annotation
"ANN002", # *args
"ANN003", # **kwargs
"ANN401", # Any
"ANN101", # self: Self
"SIM108", # single-line if / else assignment
"RET504", # x=y+z; return x
"PIE790", # unnecessary pass
"ISC003", # non-implicit string concatenation
"C408", # dict(x=y) instead of {'x': y}
"PLR09", # Too many xxx
"PLR2004", # magic number
"PLC0414", # import x as x
"TRY003", # Long exception message
"TRY002", # Exception()
]
[[tool.mypy.overrides]]
module = [
"scipy",
"scipy.optimize",
"scipy.linalg",
"scipy.sparse",
"scipy.sparse.linalg",
]
ignore_missing_imports = true