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style and type fixes (per mypy and flake8)

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This commit is contained in:
Jan Petykiewicz 2020-10-16 19:16:13 -07:00
parent 0e04f5ca77
commit d13a3796a9
25 changed files with 242 additions and 217 deletions
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26
.flake8 Normal file
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@ -0,0 +1,26 @@
[flake8]
ignore =
# E501 line too long
E501,
# W391 newlines at EOF
W391,
# E241 multiple spaces after comma
E241,
# E302 expected 2 newlines
E302,
# W503 line break before binary operator (to be deprecated)
W503,
# E265 block comment should start with '# '
E265,
# E123 closing bracket does not match indentation of opening bracket's line
E123,
# E124 closing bracket does not match visual indentation
E124,
# E221 multiple spaces before operator
E221,
# E201 whitespace after '['
E201,
per-file-ignores =
# F401 import without use
*/__init__.py: F401,

28
meanas/eigensolvers.py
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@ -2,10 +2,10 @@
Solvers for eigenvalue / eigenvector problems Solvers for eigenvalue / eigenvector problems
""" """
from typing import Tuple, Callable, Optional, Union from typing import Tuple, Callable, Optional, Union
import numpy import numpy # type: ignore
from numpy.linalg import norm from numpy.linalg import norm # type: ignore
from scipy import sparse from scipy import sparse # type: ignore
import scipy.sparse.linalg as spalg import scipy.sparse.linalg as spalg # type: ignore
def power_iteration(operator: sparse.spmatrix, def power_iteration(operator: sparse.spmatrix,
@ -30,7 +30,8 @@ def power_iteration(operator: sparse.spmatrix,
for _ in range(iterations): for _ in range(iterations):
v = operator @ v v = operator @ v
v /= norm(v) v /= numpy.abs(v).sum() # faster than true norm
v /= norm(v)
lm_eigval = v.conj() @ (operator @ v) lm_eigval = v.conj() @ (operator @ v)
return lm_eigval, v return lm_eigval, v
@ -59,16 +60,21 @@ def rayleigh_quotient_iteration(operator: Union[sparse.spmatrix, spalg.LinearOpe
(eigenvalues, eigenvectors) (eigenvalues, eigenvectors)
""" """
try: try:
_test = operator - sparse.eye(operator.shape[0]) (operator - sparse.eye(operator.shape[0]))
shift = lambda eigval: eigval * sparse.eye(operator.shape[0])
def shift(eigval: float) -> sparse:
return eigval * sparse.eye(operator.shape[0])
if solver is None: if solver is None:
solver = spalg.spsolve solver = spalg.spsolve
except TypeError: except TypeError:
shift = lambda eigval: spalg.LinearOperator(shape=operator.shape, def shift(eigval: float) -> spalg.LinearOperator:
dtype=operator.dtype, return spalg.LinearOperator(shape=operator.shape,
matvec=lambda v: eigval * v) dtype=operator.dtype,
matvec=lambda v: eigval * v)
if solver is None: if solver is None:
solver = lambda A, b: spalg.bicgstab(A, b)[0] def solver(A, b):
return spalg.bicgstab(A, b)[0]
v = numpy.squeeze(guess_vector) v = numpy.squeeze(guess_vector)
v /= norm(v) v /= norm(v)

62
meanas/fdfd/bloch.py
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@ -82,13 +82,13 @@ This module contains functions for generating and solving the
from typing import Tuple, Callable from typing import Tuple, Callable
import logging import logging
import numpy import numpy # type: ignore
from numpy import pi, real, trace from numpy import pi, real, trace # type: ignore
from numpy.fft import fftfreq from numpy.fft import fftfreq # type: ignore
import scipy import scipy # type: ignore
import scipy.optimize import scipy.optimize # type: ignore
from scipy.linalg import norm from scipy.linalg import norm # type: ignore
import scipy.sparse.linalg as spalg import scipy.sparse.linalg as spalg # type: ignore
from ..fdmath import fdfield_t from ..fdmath import fdfield_t
@ -96,8 +96,8 @@ logger = logging.getLogger(__name__)
try: try:
import pyfftw.interfaces.numpy_fft import pyfftw.interfaces.numpy_fft # type: ignore
import pyfftw.interfaces import pyfftw.interfaces # type: ignore
import multiprocessing import multiprocessing
logger.info('Using pyfftw') logger.info('Using pyfftw')
@ -116,7 +116,7 @@ try:
return pyfftw.interfaces.numpy_fft.ifftn(*args, **kwargs, **fftw_args) return pyfftw.interfaces.numpy_fft.ifftn(*args, **kwargs, **fftw_args)
except ImportError: except ImportError:
from numpy.fft import fftn, ifftn from numpy.fft import fftn, ifftn # type: ignore
logger.info('Using numpy fft') logger.info('Using numpy fft')
@ -139,7 +139,7 @@ def generate_kmn(k0: numpy.ndarray,
""" """
k0 = numpy.array(k0) k0 = numpy.array(k0)
Gi_grids = numpy.meshgrid(*(fftfreq(n, 1/n) for n in shape[:3]), indexing='ij') Gi_grids = numpy.meshgrid(*(fftfreq(n, 1 / n) for n in shape[:3]), indexing='ij')
Gi = numpy.stack(Gi_grids, axis=3) Gi = numpy.stack(Gi_grids, axis=3)
k_G = k0[None, None, None, :] - Gi k_G = k0[None, None, None, :] - Gi
@ -216,8 +216,8 @@ def maxwell_operator(k0: numpy.ndarray,
#{d,e,h}_xyz fields are complex 3-fields in (1/x, 1/y, 1/z) basis #{d,e,h}_xyz fields are complex 3-fields in (1/x, 1/y, 1/z) basis
# cross product and transform into xyz basis # cross product and transform into xyz basis
d_xyz = (n * hin_m - d_xyz = (n * hin_m
m * hin_n) * k_mag - m * hin_n) * k_mag
# divide by epsilon # divide by epsilon
e_xyz = fftn(ifftn(d_xyz, axes=range(3)) / epsilon, axes=range(3)) e_xyz = fftn(ifftn(d_xyz, axes=range(3)) / epsilon, axes=range(3))
@ -230,8 +230,8 @@ def maxwell_operator(k0: numpy.ndarray,
h_m, h_n = b_m, b_n h_m, h_n = b_m, b_n
else: else:
# transform from mn to xyz # transform from mn to xyz
b_xyz = (m * b_m[:, :, :, None] + b_xyz = (m * b_m[:, :, :, None]
n * b_n[:, :, :, None]) + n * b_n[:, :, :, None])
# divide by mu # divide by mu
h_xyz = fftn(ifftn(b_xyz, axes=range(3)) / mu, axes=range(3)) h_xyz = fftn(ifftn(b_xyz, axes=range(3)) / mu, axes=range(3))
@ -274,11 +274,11 @@ def hmn_2_exyz(k0: numpy.ndarray,
def operator(h: numpy.ndarray) -> fdfield_t: def operator(h: numpy.ndarray) -> fdfield_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 - d_xyz = (n * hin_m
m * hin_n) * k_mag - m * hin_n) * k_mag
# divide by epsilon # divide by epsilon
return numpy.array([ei for ei in numpy.rollaxis(ifftn(d_xyz, axes=range(3)) / epsilon, 3)]) #TODO avoid copy return numpy.array([ei for ei in numpy.rollaxis(ifftn(d_xyz, axes=range(3)) / epsilon, 3)]) # TODO avoid copy
return operator return operator
@ -311,8 +311,8 @@ def hmn_2_hxyz(k0: numpy.ndarray,
def operator(h: numpy.ndarray): def operator(h: numpy.ndarray):
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 + h_xyz = (m * hin_m
n * hin_n) + n * hin_n)
return [ifftn(hi) for hi in numpy.rollaxis(h_xyz, 3)] return [ifftn(hi) for hi in numpy.rollaxis(h_xyz, 3)]
return operator return operator
@ -371,8 +371,8 @@ def inverse_maxwell_operator_approx(k0: numpy.ndarray,
b_m, b_n = hin_m, hin_n b_m, b_n = hin_m, hin_n
else: else:
# transform from mn to xyz # transform from mn to xyz
h_xyz = (m * hin_m[:, :, :, None] + h_xyz = (m * hin_m[:, :, :, None]
n * hin_n[:, :, :, None]) + n * hin_n[:, :, :, None])
# multiply by mu # multiply by mu
b_xyz = fftn(ifftn(h_xyz, axes=range(3)) * mu, axes=range(3)) b_xyz = fftn(ifftn(h_xyz, axes=range(3)) * mu, axes=range(3))
@ -382,8 +382,8 @@ def inverse_maxwell_operator_approx(k0: numpy.ndarray,
b_n = numpy.sum(b_xyz * n, axis=3) b_n = numpy.sum(b_xyz * n, axis=3)
# cross product and transform into xyz basis # cross product and transform into xyz basis
e_xyz = (n * b_m - e_xyz = (n * b_m
m * b_n) / k_mag - m * b_n) / k_mag
# multiply by epsilon # multiply by epsilon
d_xyz = fftn(ifftn(e_xyz, axes=range(3)) * epsilon, axes=range(3)) d_xyz = fftn(ifftn(e_xyz, axes=range(3)) * epsilon, axes=range(3))
@ -553,6 +553,7 @@ def eigsolve(num_modes: int,
symZtAD = _symmetrize(Z.conj().T @ AD) symZtAD = _symmetrize(Z.conj().T @ AD)
Qi_memo = [None, None] Qi_memo = [None, None]
def Qi_func(theta): def Qi_func(theta):
nonlocal Qi_memo nonlocal Qi_memo
if Qi_memo[0] == theta: if Qi_memo[0] == theta:
@ -560,7 +561,7 @@ def eigsolve(num_modes: int,
c = numpy.cos(theta) c = numpy.cos(theta)
s = numpy.sin(theta) s = numpy.sin(theta)
Q = c*c * ZtZ + s*s * DtD + 2*s*c * symZtD Q = c * c * ZtZ + s * s * DtD + 2 * s * c * symZtD
try: try:
Qi = numpy.linalg.inv(Q) Qi = numpy.linalg.inv(Q)
except numpy.linalg.LinAlgError: except numpy.linalg.LinAlgError:
@ -568,10 +569,10 @@ def eigsolve(num_modes: int,
# if c or s small, taylor expand # if c or s small, taylor expand
if c < 1e-4 * s and c != 0: if c < 1e-4 * s and c != 0:
DtDi = numpy.linalg.inv(DtD) DtDi = numpy.linalg.inv(DtD)
Qi = DtDi / (s*s) - 2*c/(s*s*s) * (DtDi @ (DtDi @ symZtD).conj().T) Qi = DtDi / (s * s) - 2 * c / (s * s * s) * (DtDi @ (DtDi @ symZtD).conj().T)
elif s < 1e-4 * c and s != 0: elif s < 1e-4 * c and s != 0:
ZtZi = numpy.linalg.inv(ZtZ) ZtZi = numpy.linalg.inv(ZtZ)
Qi = ZtZi / (c*c) - 2*s/(c*c*c) * (ZtZi @ (ZtZi @ symZtD).conj().T) Qi = ZtZi / (c * c) - 2 * s / (c * c * c) * (ZtZi @ (ZtZi @ symZtD).conj().T)
else: else:
raise Exception('Inexplicable singularity in trace_func') raise Exception('Inexplicable singularity in trace_func')
Qi_memo[0] = theta Qi_memo[0] = theta
@ -582,7 +583,7 @@ def eigsolve(num_modes: int,
c = numpy.cos(theta) c = numpy.cos(theta)
s = numpy.sin(theta) s = numpy.sin(theta)
Qi = Qi_func(theta) Qi = Qi_func(theta)
R = c*c * ZtAZ + s*s * DtAD + 2*s*c * symZtAD R = c * c * ZtAZ + s * s * DtAD + 2 * s * c * symZtAD
trace = _rtrace_AtB(R, Qi) trace = _rtrace_AtB(R, Qi)
return numpy.abs(trace) return numpy.abs(trace)
@ -646,15 +647,16 @@ def eigsolve(num_modes: int,
v = eigvecs[:, i] v = eigvecs[:, i]
n = eigvals[i] n = eigvals[i]
v /= norm(v) v /= norm(v)
eigness = norm(scipy_op @ v - (v.conj() @ (scipy_op @ v)) * v ) eigness = norm(scipy_op @ v - (v.conj() @ (scipy_op @ v)) * v)
f = numpy.sqrt(-numpy.real(n)) f = numpy.sqrt(-numpy.real(n))
df = numpy.sqrt(-numpy.real(n + eigness)) df = numpy.sqrt(-numpy.real(n + eigness))
neff_err = kmag * (1/df - 1/f) neff_err = kmag * (1 / df - 1 / f)
logger.info('eigness {}: {}\n neff_err: {}'.format(i, eigness, neff_err)) logger.info('eigness {}: {}\n neff_err: {}'.format(i, eigness, neff_err))
order = numpy.argsort(numpy.abs(eigvals)) order = numpy.argsort(numpy.abs(eigvals))
return eigvals[order], eigvecs.T[order] return eigvals[order], eigvecs.T[order]
''' '''
def linmin(x_guess, f0, df0, x_max, f_tol=0.1, df_tol=min(tolerance, 1e-6), x_tol=1e-14, x_min=0, linmin_func): def linmin(x_guess, f0, df0, x_max, f_tol=0.1, df_tol=min(tolerance, 1e-6), x_tol=1e-14, x_min=0, linmin_func):
if df0 > 0: if df0 > 0:

31
meanas/fdfd/farfield.py
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@ -2,9 +2,9 @@
Functions for performing near-to-farfield transformation (and the reverse). Functions for performing near-to-farfield transformation (and the reverse).
""" """
from typing import Dict, List, Any from typing import Dict, List, Any
import numpy import numpy # type: ignore
from numpy.fft import fft2, fftshift, fftfreq, ifft2, ifftshift from numpy.fft import fft2, fftshift, fftfreq, ifft2, ifftshift # type: ignore
from numpy import pi from numpy import pi # type: ignore
from ..fdmath import fdfield_t from ..fdmath import fdfield_t
@ -60,7 +60,7 @@ def near_to_farfield(E_near: fdfield_t,
if padded_size is None: if padded_size is None:
padded_size = (2**numpy.ceil(numpy.log2(s))).astype(int) padded_size = (2**numpy.ceil(numpy.log2(s))).astype(int)
if not hasattr(padded_size, '__len__'): if not hasattr(padded_size, '__len__'):
padded_size = (padded_size, padded_size) padded_size = (padded_size, padded_size) # type: ignore # checked if sequence
En_fft = [fftshift(fft2(fftshift(Eni), s=padded_size)) for Eni in E_near] En_fft = [fftshift(fft2(fftshift(Eni), s=padded_size)) for Eni in E_near]
Hn_fft = [fftshift(fft2(fftshift(Hni), s=padded_size)) for Hni in H_near] Hn_fft = [fftshift(fft2(fftshift(Hni), s=padded_size)) for Hni in H_near]
@ -109,8 +109,8 @@ def near_to_farfield(E_near: fdfield_t,
outputs = { outputs = {
'E': E_far, 'E': E_far,
'H': H_far, 'H': H_far,
'dkx': kx[1]-kx[0], 'dkx': kx[1] - kx[0],
'dky': ky[1]-ky[0], 'dky': ky[1] - ky[0],
'kx': kx, 'kx': kx,
'ky': ky, 'ky': ky,
'theta': theta, 'theta': theta,
@ -120,7 +120,6 @@ def near_to_farfield(E_near: fdfield_t,
return outputs return outputs
def far_to_nearfield(E_far: fdfield_t, def far_to_nearfield(E_far: fdfield_t,
H_far: fdfield_t, H_far: fdfield_t,
dkx: float, dkx: float,
@ -166,14 +165,13 @@ def far_to_nearfield(E_far: fdfield_t,
raise Exception('All fields must be the same shape!') raise Exception('All fields must be the same shape!')
if padded_size is None: if padded_size is None:
padded_size = (2**numpy.ceil(numpy.log2(s))).astype(int) padded_size = (2 ** numpy.ceil(numpy.log2(s))).astype(int)
if not hasattr(padded_size, '__len__'): if not hasattr(padded_size, '__len__'):
padded_size = (padded_size, padded_size) padded_size = (padded_size, padded_size) # type: ignore # checked if sequence
k = 2 * pi k = 2 * pi
kxs = fftshift(fftfreq(s[0], 1/(s[0] * dkx))) kxs = fftshift(fftfreq(s[0], 1 / (s[0] * dkx)))
kys = fftshift(fftfreq(s[0], 1/(s[1] * dky))) kys = fftshift(fftfreq(s[0], 1 / (s[1] * dky)))
kx, ky = numpy.meshgrid(kxs, kys, indexing='ij') kx, ky = numpy.meshgrid(kxs, kys, indexing='ij')
kxy2 = kx * kx + ky * ky kxy2 = kx * kx + ky * ky
@ -201,18 +199,17 @@ def far_to_nearfield(E_far: fdfield_t,
E_far[i][invalid_ind] = 0 E_far[i][invalid_ind] = 0
H_far[i][invalid_ind] = 0 H_far[i][invalid_ind] = 0
# Normalized vector potentials N, L # Normalized vector potentials N, L
L = [0.5 * E_far[1], L = [0.5 * E_far[1],
-0.5 * E_far[0]] -0.5 * E_far[0]]
N = [L[1], N = [L[1],
-L[0]] -L[0]]
En_fft = [-( L[0] * sin_th + L[1] * cos_phi * cos_th)/cos_phi, En_fft = [-( L[0] * sin_th + L[1] * cos_phi * cos_th) / cos_phi,
-(-L[0] * cos_th + L[1] * cos_phi * sin_th)/cos_phi] -(-L[0] * cos_th + L[1] * cos_phi * sin_th) / cos_phi]
Hn_fft = [( N[0] * sin_th + N[1] * cos_phi * cos_th)/cos_phi, Hn_fft = [( N[0] * sin_th + N[1] * cos_phi * cos_th) / cos_phi,
(-N[0] * cos_th + N[1] * cos_phi * sin_th)/cos_phi] (-N[0] * cos_th + N[1] * cos_phi * sin_th) / cos_phi]
for i in range(2): for i in range(2):
En_fft[i][cos_phi == 0] = 0 En_fft[i][cos_phi == 0] = 0

4
meanas/fdfd/functional.py
View File

@ -5,8 +5,8 @@ Functional versions of many FDFD operators. These can be useful for performing
The functions generated here expect `fdfield_t` inputs with shape (3, X, Y, Z), The functions generated here expect `fdfield_t` inputs with shape (3, X, Y, Z),
e.g. E = [E_x, E_y, E_z] where each component has shape (X, Y, Z) e.g. E = [E_x, E_y, E_z] where each component has shape (X, Y, Z)
""" """
from typing import List, Callable, Tuple from typing import Callable, Tuple
import numpy import numpy # type: ignore
from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
from ..fdmath.functional import curl_forward, curl_back from ..fdmath.functional import curl_forward, curl_back

29
meanas/fdfd/operators.py
View File

@ -28,8 +28,8 @@ The following operators are included:
""" """
from typing import Tuple, Optional from typing import Tuple, Optional
import numpy import numpy # type: ignore
import scipy.sparse as sparse import scipy.sparse as sparse # type: ignore
from ..fdmath import vec, dx_lists_t, vfdfield_t from ..fdmath import vec, dx_lists_t, vfdfield_t
from ..fdmath.operators import shift_with_mirror, rotation, curl_forward, curl_back from ..fdmath.operators import shift_with_mirror, rotation, curl_forward, curl_back
@ -90,7 +90,7 @@ def e_full(omega: complex,
if numpy.any(numpy.equal(mu, None)): if numpy.any(numpy.equal(mu, None)):
m_div = sparse.eye(epsilon.size) m_div = sparse.eye(epsilon.size)
else: else:
m_div = sparse.diags(1 / mu) m_div = sparse.diags(1 / mu) # type: ignore # checked mu is not None
op = pe @ (ch @ pm @ m_div @ ce - omega**2 * e) @ pe op = pe @ (ch @ pm @ m_div @ ce - omega**2 * e) @ pe
return op return op
@ -270,7 +270,7 @@ def e2h(omega: complex,
op = curl_forward(dxes[0]) / (-1j * omega) op = curl_forward(dxes[0]) / (-1j * omega)
if not numpy.any(numpy.equal(mu, None)): if not numpy.any(numpy.equal(mu, None)):
op = sparse.diags(1 / mu) @ op op = sparse.diags(1 / mu) @ op # type: ignore # checked mu is not None
if not numpy.any(numpy.equal(pmc, None)): if not numpy.any(numpy.equal(pmc, None)):
op = sparse.diags(numpy.where(pmc, 0, 1)) @ op op = sparse.diags(numpy.where(pmc, 0, 1)) @ op
@ -297,7 +297,7 @@ def m2j(omega: complex,
op = curl_back(dxes[1]) / (1j * omega) op = curl_back(dxes[1]) / (1j * omega)
if not numpy.any(numpy.equal(mu, None)): if not numpy.any(numpy.equal(mu, None)):
op = op @ sparse.diags(1 / mu) op = op @ sparse.diags(1 / mu) # type: ignore # checked mu is not None
return op return op
@ -319,14 +319,13 @@ def poynting_e_cross(e: vfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
fx, fy, fz = [rotation(i, shape, 1) for i in range(3)] fx, fy, fz = [rotation(i, shape, 1) for i in range(3)]
dxag = [dx.ravel(order='C') for dx in numpy.meshgrid(*dxes[0], indexing='ij')] 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)] Ex, Ey, Ez = [ei * da for ei, da in zip(numpy.split(e, 3), dxag)]
block_diags = [[ None, fx @ -Ez, fx @ Ey], block_diags = [[ None, fx @ -Ez, fx @ Ey],
[ fy @ Ez, None, fy @ -Ex], [ fy @ Ez, None, fy @ -Ex],
[ fz @ -Ey, fz @ Ex, None]] [ fz @ -Ey, fz @ Ex, None]]
block_matrix = sparse.bmat([[sparse.diags(x) if x is not None else None for x in row] block_matrix = sparse.bmat([[sparse.diags(x) if x is not None else None for x in row]
for row in block_diags]) for row in block_diags])
P = block_matrix @ sparse.diags(numpy.concatenate(dxag)) P = block_matrix @ sparse.diags(numpy.concatenate(dxag))
return P return P
@ -351,10 +350,10 @@ def poynting_h_cross(h: vfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
Hx, Hy, Hz = [sparse.diags(hi * db) for hi, db in zip(numpy.split(h, 3), dxbg)] Hx, Hy, Hz = [sparse.diags(hi * db) for hi, db in zip(numpy.split(h, 3), dxbg)]
P = (sparse.bmat( P = (sparse.bmat(
[[ None, -Hz @ fx, Hy @ fx], [[ None, -Hz @ fx, Hy @ fx],
[ Hz @ fy, None, -Hx @ fy], [ Hz @ fy, None, -Hx @ fy],
[-Hy @ fz, Hx @ fz, None]]) [-Hy @ fz, Hx @ fz, None]])
@ sparse.diags(numpy.concatenate(dxag))) @ sparse.diags(numpy.concatenate(dxag)))
return P return P
@ -418,15 +417,17 @@ def e_boundary_source(mask: vfdfield_t,
jmask = numpy.zeros_like(mask, dtype=bool) jmask = numpy.zeros_like(mask, dtype=bool)
if periodic_mask_edges: if periodic_mask_edges:
shift = lambda axis, polarity: rotation(axis=axis, shape=shape, shift_distance=polarity) def shift(axis, polarity):
return rotation(axis=axis, shape=shape, shift_distance=polarity)
else: else:
shift = lambda axis, polarity: shift_with_mirror(axis=axis, shape=shape, shift_distance=polarity) def shift(axis, polarity):
return shift_with_mirror(axis=axis, shape=shape, shift_distance=polarity)
for axis in (0, 1, 2): for axis in (0, 1, 2):
if shape[axis] == 1: if shape[axis] == 1:
continue continue
for polarity in (-1, +1): for polarity in (-1, +1):
r = shift(axis, polarity) - sparse.eye(numpy.prod(shape)) # shifted minus original r = shift(axis, polarity) - sparse.eye(numpy.prod(shape)) # shifted minus original
r3 = sparse.block_diag((r, r, r)) r3 = sparse.block_diag((r, r, r))
jmask = numpy.logical_or(jmask, numpy.abs(r3 @ mask)) jmask = numpy.logical_or(jmask, numpy.abs(r3 @ mask))

8
meanas/fdfd/scpml.py
View File

@ -3,7 +3,7 @@ Functions for creating stretched coordinate perfectly matched layer (PML) absorb
""" """
from typing import Sequence, Union, Callable, Optional from typing import Sequence, Union, Callable, Optional
import numpy import numpy # type: ignore
from ..fdmath import dx_lists_t, dx_lists_mut from ..fdmath import dx_lists_t, dx_lists_mut
@ -69,7 +69,7 @@ def uniform_grid_scpml(shape: Union[numpy.ndarray, Sequence[int]],
s_function = prepare_s_function() s_function = prepare_s_function()
# Normalized distance to nearest boundary # Normalized distance to nearest boundary
def l(u, n, t): def ll(u, n, t):
return ((t - u).clip(0) + (u - (n - t)).clip(0)) / t return ((t - u).clip(0) + (u - (n - t)).clip(0)) / t
dx_a = [numpy.array(numpy.inf)] * 3 dx_a = [numpy.array(numpy.inf)] * 3
@ -82,8 +82,8 @@ def uniform_grid_scpml(shape: Union[numpy.ndarray, Sequence[int]],
s = shape[k] s = shape[k]
if th > 0: if th > 0:
sr = numpy.arange(s) sr = numpy.arange(s)
dx_a[k] = 1 + 1j * s_function(l(sr, s, th)) / s_correction dx_a[k] = 1 + 1j * s_function(ll(sr, s, th)) / s_correction
dx_b[k] = 1 + 1j * s_function(l(sr+0.5, s, th)) / s_correction dx_b[k] = 1 + 1j * s_function(ll(sr + 0.5, s, th)) / s_correction
else: else:
dx_a[k] = numpy.ones((s,)) dx_a[k] = numpy.ones((s,))
dx_b[k] = numpy.ones((s,)) dx_b[k] = numpy.ones((s,))

18
meanas/fdfd/solvers.py
View File

@ -2,12 +2,12 @@
Solvers and solver interface for FDFD problems. Solvers and solver interface for FDFD problems.
""" """
from typing import List, Callable, Dict, Any from typing import Callable, Dict, Any
import logging import logging
import numpy import numpy # type: ignore
from numpy.linalg import norm from numpy.linalg import norm # type: ignore
import scipy.sparse.linalg import scipy.sparse.linalg # type: ignore
from ..fdmath import dx_lists_t, vfdfield_t from ..fdmath import dx_lists_t, vfdfield_t
from . import operators from . import operators
@ -35,13 +35,13 @@ def _scipy_qmr(A: scipy.sparse.csr_matrix,
''' '''
Report on our progress Report on our progress
''' '''
iter = 0 ii = 0
def log_residual(xk): def log_residual(xk):
nonlocal iter nonlocal ii
iter += 1 ii += 1
if iter % 100 == 0: if ii % 100 == 0:
logger.info('Solver residual at iteration {} : {}'.format(iter, norm(A @ xk - b))) logger.info('Solver residual at iteration {} : {}'.format(ii, norm(A @ xk - b)))
if 'callback' in kwargs: if 'callback' in kwargs:
def augmented_callback(xk): def augmented_callback(xk):

18
meanas/fdfd/waveguide_2d.py
View File

@ -147,12 +147,12 @@ to account for numerical dispersion if the result is introduced into a space wit
# TODO update module docs # TODO update module docs
from typing import List, Tuple, Optional from typing import List, Tuple, Optional
import numpy import numpy # type: ignore
from numpy.linalg import norm from numpy.linalg import norm # type: ignore
import scipy.sparse as sparse import scipy.sparse as sparse # type: ignore
from ..fdmath.operators import deriv_forward, deriv_back, curl_forward, curl_back, cross from ..fdmath.operators import deriv_forward, deriv_back, cross
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t from ..fdmath import unvec, dx_lists_t, vfdfield_t
from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
@ -390,7 +390,9 @@ def _normalized_fields(e: numpy.ndarray,
# Try to break symmetry to assign a consistent sign [experimental TODO] # Try to break symmetry to assign a consistent sign [experimental TODO]
E_weighted = unvec(e * energy * numpy.exp(1j * norm_angle), shape) E_weighted = unvec(e * energy * numpy.exp(1j * norm_angle), shape)
sign = numpy.sign(E_weighted[:, :max(shape[0]//2, 1), :max(shape[1]//2, 1)].real.sum()) sign = numpy.sign(E_weighted[:,
:max(shape[0] // 2, 1),
:max(shape[1] // 2, 1)].real.sum())
norm_factor = sign * norm_amplitude * numpy.exp(1j * norm_angle) norm_factor = sign * norm_amplitude * numpy.exp(1j * norm_angle)
@ -536,7 +538,7 @@ def e2h(wavenumber: complex,
""" """
op = curl_e(wavenumber, dxes) / (-1j * omega) op = curl_e(wavenumber, dxes) / (-1j * omega)
if not numpy.any(numpy.equal(mu, None)): if not numpy.any(numpy.equal(mu, None)):
op = sparse.diags(1 / mu) @ op op = sparse.diags(1 / mu) @ op # type: ignore # checked that mu is not None
return op return op
@ -663,7 +665,7 @@ def e_err(e: vfdfield_t,
if numpy.any(numpy.equal(mu, None)): if numpy.any(numpy.equal(mu, None)):
op = ch @ ce @ e - omega ** 2 * (epsilon * e) op = ch @ ce @ e - omega ** 2 * (epsilon * e)
else: else:
mu_inv = sparse.diags(1 / mu) mu_inv = sparse.diags(1 / mu) # type: ignore # checked that mu is not None
op = ch @ mu_inv @ ce @ e - omega ** 2 * (epsilon * e) op = ch @ mu_inv @ ce @ e - omega ** 2 * (epsilon * e)
return norm(op) / norm(e) return norm(op) / norm(e)

19
meanas/fdfd/waveguide_3d.py
View File

@ -4,12 +4,11 @@ Tools for working with waveguide modes in 3D domains.
This module relies heavily on `waveguide_2d` and mostly just transforms This module relies heavily on `waveguide_2d` and mostly just transforms
its parameters into 2D equivalents and expands the results back into 3D. its parameters into 2D equivalents and expands the results back into 3D.
""" """
from typing import Dict, List, Tuple, Optional, Sequence, Union from typing import Dict, Optional, Sequence, Union, Any
import numpy import numpy # type: ignore
import scipy.sparse as sparse
from ..fdmath import vec, unvec, dx_lists_t, vfdfield_t, fdfield_t from ..fdmath import vec, unvec, dx_lists_t, fdfield_t
from . import operators, waveguide_2d, functional from . import operators, waveguide_2d
def solve_mode(mode_number: int, def solve_mode(mode_number: int,
@ -53,10 +52,10 @@ def solve_mode(mode_number: int,
# Find dx in propagation direction # Find dx in propagation direction
dxab_forward = numpy.array([dx[order[2]][slices[order[2]]] for dx in dxes]) dxab_forward = numpy.array([dx[order[2]][slices[order[2]]] for dx in dxes])
dx_prop = 0.5 * sum(dxab_forward)[0] dx_prop = 0.5 * dxab_forward.sum()
# Reduce to 2D and solve the 2D problem # Reduce to 2D and solve the 2D problem
args_2d = { args_2d: Dict[str, Any] = {
'omega': omega, 'omega': omega,
'dxes': [[dx[i][slices[i]] for i in order[:2]] for dx in dxes], 'dxes': [[dx[i][slices[i]] for i in order[:2]] for dx in dxes],
'epsilon': vec([epsilon[i][slices].transpose(order) for i in order]), 'epsilon': vec([epsilon[i][slices].transpose(order) for i in order]),
@ -68,15 +67,15 @@ def solve_mode(mode_number: int,
Apply corrections and expand to 3D Apply corrections and expand to 3D
''' '''
# Correct wavenumber to account for numerical dispersion. # Correct wavenumber to account for numerical dispersion.
wavenumber = 2/dx_prop * numpy.arcsin(wavenumber_2d * dx_prop/2) wavenumber = 2 / dx_prop * numpy.arcsin(wavenumber_2d * dx_prop / 2)
shape = [d.size for d in args_2d['dxes'][0]] shape = [d.size for d in args_2d['dxes'][0]]
ve, vh = waveguide_2d.normalized_fields_e(e_xy, wavenumber=wavenumber_2d, **args_2d, prop_phase=dx_prop * wavenumber) ve, vh = waveguide_2d.normalized_fields_e(e_xy, wavenumber=wavenumber_2d, prop_phase=dx_prop * wavenumber, **args_2d)
e = unvec(ve, shape) e = unvec(ve, shape)
h = unvec(vh, shape) h = unvec(vh, shape)
# Adjust for propagation direction # Adjust for propagation direction
h *= polarity h *= polarity # type: ignore # mypy issue with numpy
# Apply phase shift to H-field # Apply phase shift to H-field
h[:2] *= numpy.exp(-1j * polarity * 0.5 * wavenumber * dx_prop) h[:2] *= numpy.exp(-1j * polarity * 0.5 * wavenumber * dx_prop)

19
meanas/fdfd/waveguide_cyl.py
View File

@ -8,10 +8,9 @@ As the z-dependence is known, all the functions in this file assume a 2D grid
""" """
# TODO update module docs # TODO update module docs
from typing import List, Tuple, Dict, Union from typing import Dict, Union
import numpy import numpy # type: ignore
from numpy.linalg import norm import scipy.sparse as sparse # type: ignore
import scipy.sparse as sparse
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t
from ..fdmath.operators import deriv_forward, deriv_back from ..fdmath.operators import deriv_forward, deriv_back
@ -51,9 +50,9 @@ def cylindrical_operator(omega: complex,
Dbx, Dby = deriv_back(dxes[1]) Dbx, Dby = deriv_back(dxes[1])
rx = r0 + numpy.cumsum(dxes[0][0]) rx = r0 + numpy.cumsum(dxes[0][0])
ry = r0 + dxes[0][0]/2.0 + numpy.cumsum(dxes[1][0]) ry = r0 + dxes[0][0] / 2.0 + numpy.cumsum(dxes[1][0])
tx = rx/r0 tx = rx / r0
ty = ry/r0 ty = ry / r0
Tx = sparse.diags(vec(tx[:, None].repeat(dxes[0][1].size, axis=1))) Tx = sparse.diags(vec(tx[:, None].repeat(dxes[0][1].size, axis=1)))
Ty = sparse.diags(vec(ty[:, None].repeat(dxes[1][1].size, axis=1))) Ty = sparse.diags(vec(ty[:, None].repeat(dxes[1][1].size, axis=1)))
@ -108,7 +107,7 @@ def solve_mode(mode_number: int,
A_r = cylindrical_operator(numpy.real(omega), dxes_real, numpy.real(epsilon), r0) A_r = cylindrical_operator(numpy.real(omega), dxes_real, numpy.real(epsilon), r0)
eigvals, eigvecs = signed_eigensolve(A_r, mode_number + 3) eigvals, eigvecs = signed_eigensolve(A_r, mode_number + 3)
e_xy = eigvecs[:, -(mode_number+1)] e_xy = eigvecs[:, -(mode_number + 1)]
''' '''
Now solve for the eigenvector of the full operator, using the real operator's Now solve for the eigenvector of the full operator, using the real operator's
@ -128,8 +127,8 @@ def solve_mode(mode_number: int,
fields = { fields = {
'wavenumber': wavenumber, 'wavenumber': wavenumber,
'E': unvec(e_xy, shape), 'E': unvec(e_xy, shape),
# 'E': unvec(e, shape), # 'E': unvec(e, shape),
# 'H': unvec(h, shape), # 'H': unvec(h, shape),
} }
return fields return fields

4
meanas/fdmath/functional.py
View File

@ -3,8 +3,8 @@ Math functions for finite difference simulations
Basic discrete calculus etc. Basic discrete calculus etc.
""" """
from typing import Sequence, Tuple, Dict, Optional from typing import Sequence, Tuple, Optional
import numpy import numpy # type: ignore
from .types import fdfield_t, fdfield_updater_t from .types import fdfield_t, fdfield_updater_t

12
meanas/fdmath/operators.py
View File

@ -3,14 +3,14 @@ Matrix operators for finite difference simulations
Basic discrete calculus etc. Basic discrete calculus etc.
""" """
from typing import Sequence, List, Callable, Tuple, Dict from typing import Sequence, List
import numpy import numpy # type: ignore
import scipy.sparse as sparse import scipy.sparse as sparse # type: ignore
from .types import fdfield_t, vfdfield_t from .types import vfdfield_t
def rotation(axis: int, shape: Sequence[int], shift_distance: int=1) -> sparse.spmatrix: def rotation(axis: int, shape: Sequence[int], shift_distance: int = 1) -> sparse.spmatrix:
""" """
Utility operator for performing a circular shift along a specified axis by a Utility operator for performing a circular shift along a specified axis by a
specified number of elements. specified number of elements.
@ -46,7 +46,7 @@ def rotation(axis: int, shape: Sequence[int], shift_distance: int=1) -> sparse.s
return d return d
def shift_with_mirror(axis: int, shape: Sequence[int], shift_distance: int=1) -> sparse.spmatrix: def shift_with_mirror(axis: int, shape: Sequence[int], shift_distance: int = 1) -> sparse.spmatrix:
""" """
Utility operator for performing an n-element shift along a specified axis, with mirror Utility operator for performing an n-element shift along a specified axis, with mirror
boundary conditions applied to the cells beyond the receding edge. boundary conditions applied to the cells beyond the receding edge.

2
meanas/fdmath/types.py
View File

@ -1,8 +1,8 @@
""" """
Types shared across multiple submodules Types shared across multiple submodules
""" """
import numpy
from typing import Sequence, Callable, MutableSequence from typing import Sequence, Callable, MutableSequence
import numpy # type: ignore
# Field types # Field types

7
meanas/fdmath/vectorization.py
View File

@ -4,11 +4,12 @@ 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. Vectorized versions of the field use row-major (ie., C-style) ordering.
""" """
from typing import Optional, TypeVar, overload, Union, List from typing import Optional, overload, Union, List
import numpy import numpy # type: ignore
from .types import fdfield_t, vfdfield_t from .types import fdfield_t, vfdfield_t
@overload @overload
def vec(f: None) -> None: def vec(f: None) -> None:
pass pass
@ -60,5 +61,5 @@ def unvec(v: Optional[vfdfield_t], shape: numpy.ndarray) -> Optional[fdfield_t]:
""" """
if numpy.any(numpy.equal(v, None)): if numpy.any(numpy.equal(v, None)):
return None return None
return v.reshape((3, *shape), order='C') return v.reshape((3, *shape), order='C') # type: ignore # already check v is not None

2
meanas/fdtd/__init__.py
View File

@ -162,5 +162,5 @@ Boundary conditions
from .base import maxwell_e, maxwell_h from .base import maxwell_e, maxwell_h
from .pml import cpml from .pml import cpml
from .energy import (poynting, poynting_divergence, energy_hstep, energy_estep, from .energy import (poynting, poynting_divergence, energy_hstep, energy_estep,
delta_energy_h2e, delta_energy_h2e, delta_energy_j) delta_energy_h2e, delta_energy_j)
from .boundaries import conducting_boundary from .boundaries import conducting_boundary

9
meanas/fdtd/base.py
View File

@ -3,8 +3,7 @@ Basic FDTD field updates
""" """
from typing import List, Callable, Dict, Union from typing import Union
import numpy
from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
from ..fdmath.functional import curl_forward, curl_back from ..fdmath.functional import curl_forward, curl_back
@ -59,7 +58,7 @@ def maxwell_e(dt: float, dxes: dx_lists_t = None) -> fdfield_updater_t:
Returns: Returns:
E-field at time t=1 E-field at time t=1
""" """
e += dt * curl_h_fun(h) / epsilon e += dt * curl_h_fun(h) / epsilon # type: ignore # mypy gets confused around ndarray ops
return e return e
return me_fun return me_fun
@ -113,9 +112,9 @@ def maxwell_h(dt: float, dxes: dx_lists_t = None) -> fdfield_updater_t:
H-field at time t=1.5 H-field at time t=1.5
""" """
if mu is not None: if mu is not None:
h -= dt * curl_e_fun(e) / mu h -= dt * curl_e_fun(e) / mu # type: ignore # mypy gets confused around ndarray ops
else: else:
h -= dt * curl_e_fun(e) h -= dt * curl_e_fun(e) # type: ignore # mypy gets confused around ndarray ops
return h return h

5
meanas/fdtd/boundaries.py
View File

@ -4,10 +4,9 @@ Boundary conditions
#TODO conducting boundary documentation #TODO conducting boundary documentation
""" """
from typing import Callable, Tuple, Dict, Any, List from typing import Tuple, Any, List
import numpy
from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t from ..fdmath import fdfield_t, fdfield_updater_t
def conducting_boundary(direction: int, def conducting_boundary(direction: int,

33
meanas/fdtd/energy.py
View File

@ -1,9 +1,8 @@
# pylint: disable=unsupported-assignment-operation from typing import Optional, Union
from typing import Callable, Tuple, Dict, Optional, Union import numpy # type: ignore
import numpy
from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t from ..fdmath import dx_lists_t, fdfield_t
from ..fdmath.functional import deriv_back, deriv_forward from ..fdmath.functional import deriv_back
def poynting(e: fdfield_t, def poynting(e: fdfield_t,
@ -115,10 +114,10 @@ def delta_energy_j(j0: fdfield_t,
if dxes is None: if dxes is None:
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2)) dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
du = ((j0 * e1).sum(axis=0) * du = ((j0 * e1).sum(axis=0)
dxes[0][0][:, None, None] * * dxes[0][0][:, None, None]
dxes[0][1][None, :, None] * * dxes[0][1][None, :, None]
dxes[0][2][None, None, :]) * dxes[0][2][None, None, :])
return du return du
@ -135,12 +134,12 @@ def dxmul(ee: fdfield_t,
if dxes is None: if dxes is None:
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2)) dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
result = ((ee * epsilon).sum(axis=0) * result = ((ee * epsilon).sum(axis=0)
dxes[0][0][:, None, None] * * dxes[0][0][:, None, None]
dxes[0][1][None, :, None] * * dxes[0][1][None, :, None]
dxes[0][2][None, None, :] + * dxes[0][2][None, None, :]
(hh * mu).sum(axis=0) * + (hh * mu).sum(axis=0)
dxes[1][0][:, None, None] * * dxes[1][0][:, None, None]
dxes[1][1][None, :, None] * * dxes[1][1][None, :, None]
dxes[1][2][None, None, :]) * dxes[1][2][None, None, :])
return result return result

16
meanas/fdtd/pml.py
View File

@ -8,9 +8,9 @@ PML implementations
# TODO retest pmls! # TODO retest pmls!
from typing import List, Callable, Tuple, Dict, Any from typing import List, Callable, Tuple, Dict, Any
import numpy import numpy # type: ignore
from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t from ..fdmath import fdfield_t
__author__ = 'Jan Petykiewicz' __author__ = 'Jan Petykiewicz'
@ -48,8 +48,8 @@ def cpml(direction: int,
transverse = numpy.delete(range(3), direction) transverse = numpy.delete(range(3), direction)
u, v = transverse u, v = transverse
xe = numpy.arange(1, thickness+1, dtype=float) xe = numpy.arange(1, thickness + 1, dtype=float)
xh = numpy.arange(1, thickness+1, dtype=float) xh = numpy.arange(1, thickness + 1, dtype=float)
if polarity > 0: if polarity > 0:
xe -= 0.5 xe -= 0.5
elif polarity < 0: elif polarity < 0:
@ -76,14 +76,14 @@ def cpml(direction: int,
p0e, p1e, p2e = par(xe) p0e, p1e, p2e = par(xe)
p0h, p1h, p2h = par(xh) p0h, p1h, p2h = par(xh)
region = [slice(None)] * 3 region_list = [slice(None)] * 3
if polarity < 0: if polarity < 0:
region[direction] = slice(None, thickness) region_list[direction] = slice(None, thickness)
elif polarity > 0: elif polarity > 0:
region[direction] = slice(-thickness, None) region_list[direction] = slice(-thickness, None)
else: else:
raise Exception('Bad polarity!') raise Exception('Bad polarity!')
region = tuple(region) region = tuple(region_list)
se = 1 if direction == 1 else -1 se = 1 if direction == 1 else -1

22
meanas/test/conftest.py
View File

@ -1,18 +1,18 @@
from typing import List, Tuple """
import numpy
import pytest Test fixtures
"""
import numpy # type: ignore
import pytest # type: ignore
from .utils import PRNG from .utils import PRNG
#####################################
# Test fixtures
#####################################
@pytest.fixture(scope='module', @pytest.fixture(scope='module',
params=[(5, 5, 1), params=[(5, 5, 1),
(5, 1, 5), (5, 1, 5),
(5, 5, 5), (5, 5, 5),
#(7, 7, 7), # (7, 7, 7),
]) ])
def shape(request): def shape(request):
yield (3, *request.param) yield (3, *request.param)
@ -41,7 +41,7 @@ def epsilon(request, shape, epsilon_bg, epsilon_fg):
epsilon = numpy.full(shape, epsilon_bg, dtype=float) epsilon = numpy.full(shape, epsilon_bg, dtype=float)
if request.param == 'center': if request.param == 'center':
epsilon[:, shape[1]//2, shape[2]//2, shape[3]//2] = epsilon_fg epsilon[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = epsilon_fg
elif request.param == '000': elif request.param == '000':
epsilon[:, 0, 0, 0] = epsilon_fg epsilon[:, 0, 0, 0] = epsilon_fg
elif request.param == 'random': elif request.param == 'random':
@ -52,7 +52,7 @@ def epsilon(request, shape, epsilon_bg, epsilon_fg):
yield epsilon yield epsilon
@pytest.fixture(scope='module', params=[1.0])#, 1.5]) @pytest.fixture(scope='module', params=[1.0]) # 1.5
def j_mag(request): def j_mag(request):
yield request.param yield request.param
@ -70,7 +70,7 @@ def dxes(request, shape, dx):
dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)] dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
for eh in (0, 1): for eh in (0, 1):
for ax in (0, 1, 2): for ax in (0, 1, 2):
dxes[eh][ax][dxes[eh][ax].size // 2] *= 1.1 dxes[eh][ax][dxes[eh][ax].size // 2] *= 1.1
elif request.param == 'random': elif request.param == 'random':
dxe = [PRNG.uniform(low=1.0 * dx, high=1.1 * dx, size=s) for s in shape[1:]] 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] dxh = [(d + numpy.roll(d, -1)) / 2 for d in dxe]

16
meanas/test/test_fdfd.py
View File

@ -1,13 +1,12 @@
# pylint: disable=redefined-outer-name
from typing import List, Tuple from typing import List, Tuple
import dataclasses import dataclasses
import pytest import pytest # type: ignore
import numpy import numpy # type: ignore
#from numpy.testing import assert_allclose, assert_array_equal #from numpy.testing import assert_allclose, assert_array_equal
from .. import fdfd from .. import fdfd
from ..fdmath import vec, unvec from ..fdmath import vec, unvec
from .utils import assert_close, assert_fields_close from .utils import assert_close # , assert_fields_close
def test_residual(sim): def test_residual(sim):
@ -53,7 +52,7 @@ def test_poynting_planes(sim):
##################################### #####################################
# Also see conftest.py # Also see conftest.py
@pytest.fixture(params=[1/1500]) @pytest.fixture(params=[1 / 1500])
def omega(request): def omega(request):
yield request.param yield request.param
@ -74,11 +73,11 @@ def pmc(request):
# yield (3, *request.param) # yield (3, *request.param)
@pytest.fixture(params=['diag']) #'center' @pytest.fixture(params=['diag']) # 'center'
def j_distribution(request, shape, j_mag): def j_distribution(request, shape, j_mag):
j = numpy.zeros(shape, dtype=complex) j = numpy.zeros(shape, dtype=complex)
center_mask = numpy.zeros(shape, dtype=bool) center_mask = numpy.zeros(shape, dtype=bool)
center_mask[:, shape[1]//2, shape[2]//2, shape[3]//2] = True center_mask[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = True
if request.param == 'center': if request.param == 'center':
j[center_mask] = j_mag j[center_mask] = j_mag
@ -102,6 +101,9 @@ class FDResult:
@pytest.fixture() @pytest.fixture()
def sim(request, shape, epsilon, dxes, j_distribution, omega, pec, pmc): def sim(request, shape, epsilon, dxes, j_distribution, omega, pec, pmc):
"""
Build simulation from parts
"""
# is3d = (numpy.array(shape) == 1).sum() == 0 # is3d = (numpy.array(shape) == 1).sum() == 0
# if is3d: # if is3d:
# pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)') # pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)')

25
meanas/test/test_fdfd_pml.py
View File

@ -1,20 +1,15 @@
##################################### #####################################
# pylint: disable=redefined-outer-name import pytest # type: ignore
from typing import List, Tuple import numpy # type: ignore
import dataclasses from numpy.testing import assert_allclose # type: ignore
import pytest
import numpy
from numpy.testing import assert_allclose, assert_array_equal
from .. import fdfd from .. import fdfd
from ..fdmath import vec, unvec from ..fdmath import vec, unvec
from .utils import assert_close, assert_fields_close #from .utils import assert_close, assert_fields_close
from .test_fdfd import FDResult from .test_fdfd import FDResult
def test_pml(sim, src_polarity): def test_pml(sim, src_polarity):
dim = numpy.where(numpy.array(sim.shape[1:]) > 1)[0][0] # Propagation axis
e_sqr = numpy.squeeze((sim.e.conj() * sim.e).sum(axis=0)) e_sqr = numpy.squeeze((sim.e.conj() * sim.e).sum(axis=0))
# from matplotlib import pyplot # from matplotlib import pyplot
@ -43,10 +38,10 @@ def test_pml(sim, src_polarity):
# Test fixtures # Test fixtures
##################################### # ####################################
# Also see conftest.py # Also see conftest.py
@pytest.fixture(params=[1/1500]) @pytest.fixture(params=[1 / 1500])
def omega(request): def omega(request):
yield request.param yield request.param
@ -61,7 +56,6 @@ def pmc(request):
yield request.param yield request.param
@pytest.fixture(params=[(30, 1, 1), @pytest.fixture(params=[(30, 1, 1),
(1, 30, 1), (1, 30, 1),
(1, 1, 30)]) (1, 1, 30)])
@ -82,16 +76,15 @@ def j_distribution(request, shape, epsilon, dxes, omega, src_polarity):
other_dims = [0, 1, 2] other_dims = [0, 1, 2]
other_dims.remove(dim) other_dims.remove(dim)
dx_prop = (dxes[0][dim][shape[dim + 1] // 2] + dx_prop = (dxes[0][dim][shape[dim + 1] // 2]
dxes[1][dim][shape[dim + 1] // 2]) / 2 #TODO is this right for nonuniform dxes? + dxes[1][dim][shape[dim + 1] // 2]) / 2 # TODO is this right for nonuniform dxes?
# Mask only contains components orthogonal to propagation direction # Mask only contains components orthogonal to propagation direction
center_mask = numpy.zeros(shape, dtype=bool) center_mask = numpy.zeros(shape, dtype=bool)
center_mask[other_dims, shape[1]//2, shape[2]//2, shape[3]//2] = True center_mask[other_dims, shape[1] // 2, shape[2] // 2, shape[3] // 2] = True
if (epsilon[center_mask] != epsilon[center_mask][0]).any(): if (epsilon[center_mask] != epsilon[center_mask][0]).any():
center_mask[other_dims[1]] = False # If epsilon is not isotropic, pick only one dimension center_mask[other_dims[1]] = False # If epsilon is not isotropic, pick only one dimension
wavenumber = omega * numpy.sqrt(epsilon[center_mask].mean()) wavenumber = omega * numpy.sqrt(epsilon[center_mask].mean())
wavenumber_corrected = 2 / dx_prop * numpy.arcsin(wavenumber * dx_prop / 2) wavenumber_corrected = 2 / dx_prop * numpy.arcsin(wavenumber * dx_prop / 2)

35
meanas/test/test_fdtd.py
View File

@ -1,9 +1,8 @@
# pylint: disable=redefined-outer-name, no-member
from typing import List, Tuple from typing import List, Tuple
import dataclasses import dataclasses
import pytest import pytest # type: ignore
import numpy import numpy # type: ignore
from numpy.testing import assert_allclose, assert_array_equal #from numpy.testing import assert_allclose, assert_array_equal # type: ignore
from .. import fdtd from .. import fdtd
from .utils import assert_close, assert_fields_close, PRNG from .utils import assert_close, assert_fields_close, PRNG
@ -29,7 +28,7 @@ def test_initial_energy(sim):
e0 = sim.es[0] e0 = sim.es[0]
h0 = sim.hs[0] h0 = sim.hs[0]
h1 = sim.hs[1] h1 = sim.hs[1]
mask = (j0 != 0)
dV = numpy.prod(numpy.meshgrid(*sim.dxes[0], indexing='ij'), axis=0) dV = numpy.prod(numpy.meshgrid(*sim.dxes[0], indexing='ij'), axis=0)
u0 = (j0 * j0.conj() / sim.epsilon * dV).sum(axis=0) u0 = (j0 * j0.conj() / sim.epsilon * dV).sum(axis=0)
args = {'dxes': sim.dxes, args = {'dxes': sim.dxes,
@ -53,10 +52,10 @@ def test_energy_conservation(sim):
'epsilon': sim.epsilon} 'epsilon': sim.epsilon}
for ii in range(1, 8): for ii in range(1, 8):
u_hstep = fdtd.energy_hstep(e0=sim.es[ii-1], h1=sim.hs[ii], e2=sim.es[ii], **args) # pylint: disable=bad-whitespace u_hstep = fdtd.energy_hstep(e0=sim.es[ii - 1], h1=sim.hs[ii], e2=sim.es[ii], **args)
u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args) # pylint: disable=bad-whitespace u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args)
delta_j_A = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii-1], dxes=sim.dxes) delta_j_A = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii - 1], dxes=sim.dxes)
delta_j_B = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii], dxes=sim.dxes) # pylint: disable=bad-whitespace delta_j_B = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii], dxes=sim.dxes)
u += delta_j_A.sum() u += delta_j_A.sum()
assert_close(u_hstep.sum(), u) assert_close(u_hstep.sum(), u)
@ -70,8 +69,8 @@ def test_poynting_divergence(sim):
u_eprev = None u_eprev = None
for ii in range(1, 8): for ii in range(1, 8):
u_hstep = fdtd.energy_hstep(e0=sim.es[ii-1], h1=sim.hs[ii], e2=sim.es[ii], **args) # pylint: disable=bad-whitespace u_hstep = fdtd.energy_hstep(e0=sim.es[ii - 1], h1=sim.hs[ii], e2=sim.es[ii], **args)
u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args) # pylint: disable=bad-whitespace u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args)
delta_j_B = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii], dxes=sim.dxes) delta_j_B = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii], dxes=sim.dxes)
du_half_h2e = u_estep - u_hstep - delta_j_B du_half_h2e = u_estep - u_hstep - delta_j_B
@ -83,10 +82,10 @@ def test_poynting_divergence(sim):
continue continue
# previous half-step # previous half-step
delta_j_A = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii-1], dxes=sim.dxes) delta_j_A = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii - 1], dxes=sim.dxes)
du_half_e2h = u_hstep - u_eprev - delta_j_A du_half_e2h = u_hstep - u_eprev - delta_j_A
div_s_e2h = sim.dt * fdtd.poynting_divergence(e=sim.es[ii-1], h=sim.hs[ii], dxes=sim.dxes) div_s_e2h = sim.dt * fdtd.poynting_divergence(e=sim.es[ii - 1], h=sim.hs[ii], dxes=sim.dxes)
assert_fields_close(du_half_e2h, -div_s_e2h) assert_fields_close(du_half_e2h, -div_s_e2h)
u_eprev = u_estep u_eprev = u_estep
@ -105,8 +104,8 @@ def test_poynting_planes(sim):
u_eprev = None u_eprev = None
for ii in range(1, 8): for ii in range(1, 8):
u_hstep = fdtd.energy_hstep(e0=sim.es[ii-1], h1=sim.hs[ii], e2=sim.es[ii], **args) # pylint: disable=bad-whitespace u_hstep = fdtd.energy_hstep(e0=sim.es[ii - 1], h1=sim.hs[ii], e2=sim.es[ii], **args)
u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args) # pylint: disable=bad-whitespace u_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args)
delta_j_B = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii], dxes=sim.dxes) delta_j_B = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii], dxes=sim.dxes)
du_half_h2e = u_estep - u_hstep - delta_j_B du_half_h2e = u_estep - u_hstep - delta_j_B
@ -121,7 +120,7 @@ def test_poynting_planes(sim):
u_eprev = u_estep u_eprev = u_estep
continue continue
delta_j_A = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii-1], dxes=sim.dxes) delta_j_A = fdtd.delta_energy_j(j0=sim.js[ii], e1=sim.es[ii - 1], dxes=sim.dxes)
du_half_e2h = u_hstep - u_eprev - delta_j_A du_half_e2h = u_hstep - u_eprev - delta_j_A
s_e2h = -fdtd.poynting(e=sim.es[ii - 1], h=sim.hs[ii], dxes=sim.dxes) * sim.dt s_e2h = -fdtd.poynting(e=sim.es[ii - 1], h=sim.hs[ii], dxes=sim.dxes) * sim.dt
@ -158,7 +157,7 @@ class TDResult:
js: List[numpy.ndarray] = dataclasses.field(default_factory=list) js: List[numpy.ndarray] = dataclasses.field(default_factory=list)
@pytest.fixture(params=[(0, 4, 8),]) #(0,)]) @pytest.fixture(params=[(0, 4, 8)]) # (0,)
def j_steps(request): def j_steps(request):
yield request.param yield request.param
@ -167,7 +166,7 @@ def j_steps(request):
def j_distribution(request, shape, j_mag): def j_distribution(request, shape, j_mag):
j = numpy.zeros(shape) j = numpy.zeros(shape)
if request.param == 'center': if request.param == 'center':
j[:, shape[1]//2, shape[2]//2, shape[3]//2] = j_mag j[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = j_mag
elif request.param == '000': elif request.param == '000':
j[:, 0, 0, 0] = j_mag j[:, 0, 0, 0] = j_mag
elif request.param == 'random': elif request.param == 'random':

9
meanas/test/utils.py
View File

@ -1,11 +1,12 @@
import numpy import numpy # type: ignore
PRNG = numpy.random.RandomState(12345) PRNG = numpy.random.RandomState(12345)
def assert_fields_close(x, y, *args, **kwargs): def assert_fields_close(x, y, *args, **kwargs):
numpy.testing.assert_allclose(x, y, verbose=False, numpy.testing.assert_allclose(
err_msg='Fields did not match:\n{}\n{}'.format(numpy.rollaxis(x, -1), x, y, verbose=False,
numpy.rollaxis(y, -1)), *args, **kwargs) err_msg='Fields did not match:\n{}\n{}'.format(numpy.rollaxis(x, -1),
numpy.rollaxis(y, -1)), *args, **kwargs)
def assert_close(x, y, *args, **kwargs): def assert_close(x, y, *args, **kwargs):
numpy.testing.assert_allclose(x, y, *args, **kwargs) numpy.testing.assert_allclose(x, y, *args, **kwargs)