style and type fixes (per mypy and flake8)
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parent
0e04f5ca77
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d13a3796a9
26
.flake8
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26
.flake8
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@ -0,0 +1,26 @@
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[flake8]
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ignore =
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# E501 line too long
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E501,
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# W391 newlines at EOF
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W391,
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# E241 multiple spaces after comma
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E241,
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# E302 expected 2 newlines
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E302,
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# W503 line break before binary operator (to be deprecated)
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W503,
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# E265 block comment should start with '# '
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E265,
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# E123 closing bracket does not match indentation of opening bracket's line
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E123,
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# E124 closing bracket does not match visual indentation
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E124,
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# E221 multiple spaces before operator
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E221,
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# E201 whitespace after '['
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E201,
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per-file-ignores =
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# F401 import without use
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*/__init__.py: F401,
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@ -2,10 +2,10 @@
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Solvers for eigenvalue / eigenvector problems
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"""
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from typing import Tuple, Callable, Optional, Union
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import numpy
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from numpy.linalg import norm
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from scipy import sparse
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import scipy.sparse.linalg as spalg
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import numpy # type: ignore
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from numpy.linalg import norm # type: ignore
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from scipy import sparse # type: ignore
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import scipy.sparse.linalg as spalg # type: ignore
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def power_iteration(operator: sparse.spmatrix,
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@ -30,6 +30,7 @@ def power_iteration(operator: sparse.spmatrix,
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for _ in range(iterations):
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v = operator @ v
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v /= numpy.abs(v).sum() # faster than true norm
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v /= norm(v)
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lm_eigval = v.conj() @ (operator @ v)
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@ -59,16 +60,21 @@ def rayleigh_quotient_iteration(operator: Union[sparse.spmatrix, spalg.LinearOpe
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(eigenvalues, eigenvectors)
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"""
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try:
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_test = operator - sparse.eye(operator.shape[0])
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shift = lambda eigval: eigval * sparse.eye(operator.shape[0])
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(operator - sparse.eye(operator.shape[0]))
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def shift(eigval: float) -> sparse:
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return eigval * sparse.eye(operator.shape[0])
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if solver is None:
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solver = spalg.spsolve
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except TypeError:
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shift = lambda eigval: spalg.LinearOperator(shape=operator.shape,
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def shift(eigval: float) -> spalg.LinearOperator:
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return spalg.LinearOperator(shape=operator.shape,
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dtype=operator.dtype,
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matvec=lambda v: eigval * v)
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if solver is None:
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solver = lambda A, b: spalg.bicgstab(A, b)[0]
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def solver(A, b):
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return spalg.bicgstab(A, b)[0]
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v = numpy.squeeze(guess_vector)
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v /= norm(v)
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@ -82,13 +82,13 @@ This module contains functions for generating and solving the
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from typing import Tuple, Callable
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import logging
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import numpy
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from numpy import pi, real, trace
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from numpy.fft import fftfreq
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import scipy
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import scipy.optimize
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from scipy.linalg import norm
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import scipy.sparse.linalg as spalg
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import numpy # type: ignore
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from numpy import pi, real, trace # type: ignore
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from numpy.fft import fftfreq # type: ignore
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import scipy # type: ignore
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import scipy.optimize # type: ignore
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from scipy.linalg import norm # type: ignore
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import scipy.sparse.linalg as spalg # type: ignore
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from ..fdmath import fdfield_t
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@ -96,8 +96,8 @@ logger = logging.getLogger(__name__)
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try:
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import pyfftw.interfaces.numpy_fft
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import pyfftw.interfaces
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import pyfftw.interfaces.numpy_fft # type: ignore
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import pyfftw.interfaces # type: ignore
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import multiprocessing
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logger.info('Using pyfftw')
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@ -116,7 +116,7 @@ try:
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return pyfftw.interfaces.numpy_fft.ifftn(*args, **kwargs, **fftw_args)
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except ImportError:
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from numpy.fft import fftn, ifftn
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from numpy.fft import fftn, ifftn # type: ignore
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logger.info('Using numpy fft')
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@ -139,7 +139,7 @@ def generate_kmn(k0: numpy.ndarray,
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"""
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k0 = numpy.array(k0)
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Gi_grids = numpy.meshgrid(*(fftfreq(n, 1/n) for n in shape[:3]), indexing='ij')
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Gi_grids = numpy.meshgrid(*(fftfreq(n, 1 / n) for n in shape[:3]), indexing='ij')
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Gi = numpy.stack(Gi_grids, axis=3)
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k_G = k0[None, None, None, :] - Gi
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@ -216,8 +216,8 @@ def maxwell_operator(k0: numpy.ndarray,
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#{d,e,h}_xyz fields are complex 3-fields in (1/x, 1/y, 1/z) basis
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# cross product and transform into xyz basis
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d_xyz = (n * hin_m -
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m * hin_n) * k_mag
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d_xyz = (n * hin_m
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- m * hin_n) * k_mag
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# divide by epsilon
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e_xyz = fftn(ifftn(d_xyz, axes=range(3)) / epsilon, axes=range(3))
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@ -230,8 +230,8 @@ def maxwell_operator(k0: numpy.ndarray,
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h_m, h_n = b_m, b_n
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else:
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# transform from mn to xyz
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b_xyz = (m * b_m[:, :, :, None] +
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n * b_n[:, :, :, None])
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b_xyz = (m * b_m[:, :, :, None]
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+ n * b_n[:, :, :, None])
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# divide by mu
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h_xyz = fftn(ifftn(b_xyz, axes=range(3)) / mu, axes=range(3))
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@ -274,11 +274,11 @@ def hmn_2_exyz(k0: numpy.ndarray,
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def operator(h: numpy.ndarray) -> fdfield_t:
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hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
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d_xyz = (n * hin_m -
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m * hin_n) * k_mag
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d_xyz = (n * hin_m
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- m * hin_n) * k_mag
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# divide by epsilon
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return numpy.array([ei for ei in numpy.rollaxis(ifftn(d_xyz, axes=range(3)) / epsilon, 3)]) #TODO avoid copy
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return numpy.array([ei for ei in numpy.rollaxis(ifftn(d_xyz, axes=range(3)) / epsilon, 3)]) # TODO avoid copy
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return operator
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@ -311,8 +311,8 @@ def hmn_2_hxyz(k0: numpy.ndarray,
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def operator(h: numpy.ndarray):
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hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
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h_xyz = (m * hin_m +
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n * hin_n)
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h_xyz = (m * hin_m
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+ n * hin_n)
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return [ifftn(hi) for hi in numpy.rollaxis(h_xyz, 3)]
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return operator
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@ -371,8 +371,8 @@ def inverse_maxwell_operator_approx(k0: numpy.ndarray,
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b_m, b_n = hin_m, hin_n
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else:
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# transform from mn to xyz
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h_xyz = (m * hin_m[:, :, :, None] +
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n * hin_n[:, :, :, None])
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h_xyz = (m * hin_m[:, :, :, None]
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+ n * hin_n[:, :, :, None])
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# multiply by mu
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b_xyz = fftn(ifftn(h_xyz, axes=range(3)) * mu, axes=range(3))
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@ -382,8 +382,8 @@ def inverse_maxwell_operator_approx(k0: numpy.ndarray,
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b_n = numpy.sum(b_xyz * n, axis=3)
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# cross product and transform into xyz basis
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e_xyz = (n * b_m -
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m * b_n) / k_mag
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e_xyz = (n * b_m
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- m * b_n) / k_mag
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# multiply by epsilon
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d_xyz = fftn(ifftn(e_xyz, axes=range(3)) * epsilon, axes=range(3))
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@ -553,6 +553,7 @@ def eigsolve(num_modes: int,
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symZtAD = _symmetrize(Z.conj().T @ AD)
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Qi_memo = [None, None]
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def Qi_func(theta):
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nonlocal Qi_memo
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if Qi_memo[0] == theta:
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@ -560,7 +561,7 @@ def eigsolve(num_modes: int,
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c = numpy.cos(theta)
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s = numpy.sin(theta)
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Q = c*c * ZtZ + s*s * DtD + 2*s*c * symZtD
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Q = c * c * ZtZ + s * s * DtD + 2 * s * c * symZtD
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try:
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Qi = numpy.linalg.inv(Q)
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except numpy.linalg.LinAlgError:
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@ -568,10 +569,10 @@ def eigsolve(num_modes: int,
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# if c or s small, taylor expand
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if c < 1e-4 * s and c != 0:
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DtDi = numpy.linalg.inv(DtD)
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Qi = DtDi / (s*s) - 2*c/(s*s*s) * (DtDi @ (DtDi @ symZtD).conj().T)
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Qi = DtDi / (s * s) - 2 * c / (s * s * s) * (DtDi @ (DtDi @ symZtD).conj().T)
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elif s < 1e-4 * c and s != 0:
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ZtZi = numpy.linalg.inv(ZtZ)
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Qi = ZtZi / (c*c) - 2*s/(c*c*c) * (ZtZi @ (ZtZi @ symZtD).conj().T)
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Qi = ZtZi / (c * c) - 2 * s / (c * c * c) * (ZtZi @ (ZtZi @ symZtD).conj().T)
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else:
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raise Exception('Inexplicable singularity in trace_func')
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Qi_memo[0] = theta
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@ -582,7 +583,7 @@ def eigsolve(num_modes: int,
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c = numpy.cos(theta)
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s = numpy.sin(theta)
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Qi = Qi_func(theta)
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R = c*c * ZtAZ + s*s * DtAD + 2*s*c * symZtAD
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R = c * c * ZtAZ + s * s * DtAD + 2 * s * c * symZtAD
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trace = _rtrace_AtB(R, Qi)
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return numpy.abs(trace)
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@ -646,15 +647,16 @@ def eigsolve(num_modes: int,
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v = eigvecs[:, i]
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n = eigvals[i]
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v /= norm(v)
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eigness = norm(scipy_op @ v - (v.conj() @ (scipy_op @ v)) * v )
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eigness = norm(scipy_op @ v - (v.conj() @ (scipy_op @ v)) * v)
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f = numpy.sqrt(-numpy.real(n))
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df = numpy.sqrt(-numpy.real(n + eigness))
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neff_err = kmag * (1/df - 1/f)
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neff_err = kmag * (1 / df - 1 / f)
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logger.info('eigness {}: {}\n neff_err: {}'.format(i, eigness, neff_err))
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order = numpy.argsort(numpy.abs(eigvals))
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return eigvals[order], eigvecs.T[order]
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'''
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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):
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if df0 > 0:
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@ -2,9 +2,9 @@
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Functions for performing near-to-farfield transformation (and the reverse).
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"""
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from typing import Dict, List, Any
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import numpy
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from numpy.fft import fft2, fftshift, fftfreq, ifft2, ifftshift
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from numpy import pi
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import numpy # type: ignore
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from numpy.fft import fft2, fftshift, fftfreq, ifft2, ifftshift # type: ignore
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from numpy import pi # type: ignore
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from ..fdmath import fdfield_t
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@ -60,7 +60,7 @@ def near_to_farfield(E_near: fdfield_t,
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if padded_size is None:
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padded_size = (2**numpy.ceil(numpy.log2(s))).astype(int)
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if not hasattr(padded_size, '__len__'):
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padded_size = (padded_size, padded_size)
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padded_size = (padded_size, padded_size) # type: ignore # checked if sequence
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En_fft = [fftshift(fft2(fftshift(Eni), s=padded_size)) for Eni in E_near]
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Hn_fft = [fftshift(fft2(fftshift(Hni), s=padded_size)) for Hni in H_near]
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@ -109,8 +109,8 @@ def near_to_farfield(E_near: fdfield_t,
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outputs = {
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'E': E_far,
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'H': H_far,
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'dkx': kx[1]-kx[0],
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'dky': ky[1]-ky[0],
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'dkx': kx[1] - kx[0],
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'dky': ky[1] - ky[0],
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'kx': kx,
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'ky': ky,
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'theta': theta,
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@ -120,7 +120,6 @@ def near_to_farfield(E_near: fdfield_t,
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return outputs
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def far_to_nearfield(E_far: fdfield_t,
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H_far: fdfield_t,
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dkx: float,
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@ -166,14 +165,13 @@ def far_to_nearfield(E_far: fdfield_t,
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raise Exception('All fields must be the same shape!')
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if padded_size is None:
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padded_size = (2**numpy.ceil(numpy.log2(s))).astype(int)
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padded_size = (2 ** numpy.ceil(numpy.log2(s))).astype(int)
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if not hasattr(padded_size, '__len__'):
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padded_size = (padded_size, padded_size)
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padded_size = (padded_size, padded_size) # type: ignore # checked if sequence
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k = 2 * pi
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kxs = fftshift(fftfreq(s[0], 1/(s[0] * dkx)))
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kys = fftshift(fftfreq(s[0], 1/(s[1] * dky)))
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kxs = fftshift(fftfreq(s[0], 1 / (s[0] * dkx)))
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kys = fftshift(fftfreq(s[0], 1 / (s[1] * dky)))
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kx, ky = numpy.meshgrid(kxs, kys, indexing='ij')
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kxy2 = kx * kx + ky * ky
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@ -201,18 +199,17 @@ def far_to_nearfield(E_far: fdfield_t,
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E_far[i][invalid_ind] = 0
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H_far[i][invalid_ind] = 0
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# Normalized vector potentials N, L
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L = [0.5 * E_far[1],
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-0.5 * E_far[0]]
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N = [L[1],
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-L[0]]
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En_fft = [-( L[0] * sin_th + L[1] * cos_phi * cos_th)/cos_phi,
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-(-L[0] * cos_th + L[1] * cos_phi * sin_th)/cos_phi]
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En_fft = [-( L[0] * sin_th + L[1] * cos_phi * cos_th) / cos_phi,
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-(-L[0] * cos_th + L[1] * cos_phi * sin_th) / cos_phi]
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Hn_fft = [( N[0] * sin_th + N[1] * cos_phi * cos_th)/cos_phi,
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(-N[0] * cos_th + N[1] * cos_phi * sin_th)/cos_phi]
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Hn_fft = [( N[0] * sin_th + N[1] * cos_phi * cos_th) / cos_phi,
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(-N[0] * cos_th + N[1] * cos_phi * sin_th) / cos_phi]
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for i in range(2):
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En_fft[i][cos_phi == 0] = 0
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@ -5,8 +5,8 @@ Functional versions of many FDFD operators. These can be useful for performing
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The functions generated here expect `fdfield_t` inputs with shape (3, X, Y, Z),
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e.g. E = [E_x, E_y, E_z] where each component has shape (X, Y, Z)
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"""
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from typing import List, Callable, Tuple
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import numpy
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from typing import Callable, Tuple
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import numpy # type: ignore
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from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
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from ..fdmath.functional import curl_forward, curl_back
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@ -28,8 +28,8 @@ The following operators are included:
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"""
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from typing import Tuple, Optional
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import numpy
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import scipy.sparse as sparse
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import numpy # type: ignore
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import scipy.sparse as sparse # type: ignore
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from ..fdmath import vec, dx_lists_t, vfdfield_t
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from ..fdmath.operators import shift_with_mirror, rotation, curl_forward, curl_back
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@ -90,7 +90,7 @@ def e_full(omega: complex,
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if numpy.any(numpy.equal(mu, None)):
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m_div = sparse.eye(epsilon.size)
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else:
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m_div = sparse.diags(1 / mu)
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m_div = sparse.diags(1 / mu) # type: ignore # checked mu is not None
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op = pe @ (ch @ pm @ m_div @ ce - omega**2 * e) @ pe
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return op
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@ -270,7 +270,7 @@ def e2h(omega: complex,
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op = curl_forward(dxes[0]) / (-1j * omega)
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if not numpy.any(numpy.equal(mu, None)):
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op = sparse.diags(1 / mu) @ op
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op = sparse.diags(1 / mu) @ op # type: ignore # checked mu is not None
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if not numpy.any(numpy.equal(pmc, None)):
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op = sparse.diags(numpy.where(pmc, 0, 1)) @ op
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@ -297,7 +297,7 @@ def m2j(omega: complex,
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op = curl_back(dxes[1]) / (1j * omega)
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if not numpy.any(numpy.equal(mu, None)):
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op = op @ sparse.diags(1 / mu)
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op = op @ sparse.diags(1 / mu) # type: ignore # checked mu is not None
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return op
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@ -319,7 +319,6 @@ def poynting_e_cross(e: vfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
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fx, fy, fz = [rotation(i, shape, 1) for i in range(3)]
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|
||||
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)]
|
||||
|
||||
block_diags = [[ None, fx @ -Ez, fx @ Ey],
|
||||
@ -418,9 +417,11 @@ def e_boundary_source(mask: vfdfield_t,
|
||||
jmask = numpy.zeros_like(mask, dtype=bool)
|
||||
|
||||
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:
|
||||
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):
|
||||
if shape[axis] == 1:
|
||||
|
||||
@ -3,7 +3,7 @@ Functions for creating stretched coordinate perfectly matched layer (PML) absorb
|
||||
"""
|
||||
|
||||
from typing import Sequence, Union, Callable, Optional
|
||||
import numpy
|
||||
import numpy # type: ignore
|
||||
|
||||
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()
|
||||
|
||||
# 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
|
||||
|
||||
dx_a = [numpy.array(numpy.inf)] * 3
|
||||
@ -82,8 +82,8 @@ def uniform_grid_scpml(shape: Union[numpy.ndarray, Sequence[int]],
|
||||
s = shape[k]
|
||||
if th > 0:
|
||||
sr = numpy.arange(s)
|
||||
dx_a[k] = 1 + 1j * s_function(l(sr, s, th)) / s_correction
|
||||
dx_b[k] = 1 + 1j * s_function(l(sr+0.5, s, th)) / s_correction
|
||||
dx_a[k] = 1 + 1j * s_function(ll(sr, s, th)) / s_correction
|
||||
dx_b[k] = 1 + 1j * s_function(ll(sr + 0.5, s, th)) / s_correction
|
||||
else:
|
||||
dx_a[k] = numpy.ones((s,))
|
||||
dx_b[k] = numpy.ones((s,))
|
||||
|
||||
@ -2,12 +2,12 @@
|
||||
Solvers and solver interface for FDFD problems.
|
||||
"""
|
||||
|
||||
from typing import List, Callable, Dict, Any
|
||||
from typing import Callable, Dict, Any
|
||||
import logging
|
||||
|
||||
import numpy
|
||||
from numpy.linalg import norm
|
||||
import scipy.sparse.linalg
|
||||
import numpy # type: ignore
|
||||
from numpy.linalg import norm # type: ignore
|
||||
import scipy.sparse.linalg # type: ignore
|
||||
|
||||
from ..fdmath import dx_lists_t, vfdfield_t
|
||||
from . import operators
|
||||
@ -35,13 +35,13 @@ def _scipy_qmr(A: scipy.sparse.csr_matrix,
|
||||
'''
|
||||
Report on our progress
|
||||
'''
|
||||
iter = 0
|
||||
ii = 0
|
||||
|
||||
def log_residual(xk):
|
||||
nonlocal iter
|
||||
iter += 1
|
||||
if iter % 100 == 0:
|
||||
logger.info('Solver residual at iteration {} : {}'.format(iter, norm(A @ xk - b)))
|
||||
nonlocal ii
|
||||
ii += 1
|
||||
if ii % 100 == 0:
|
||||
logger.info('Solver residual at iteration {} : {}'.format(ii, norm(A @ xk - b)))
|
||||
|
||||
if 'callback' in kwargs:
|
||||
def augmented_callback(xk):
|
||||
|
||||
@ -147,12 +147,12 @@ to account for numerical dispersion if the result is introduced into a space wit
|
||||
# TODO update module docs
|
||||
|
||||
from typing import List, Tuple, Optional
|
||||
import numpy
|
||||
from numpy.linalg import norm
|
||||
import scipy.sparse as sparse
|
||||
import numpy # type: ignore
|
||||
from numpy.linalg import norm # type: ignore
|
||||
import scipy.sparse as sparse # type: ignore
|
||||
|
||||
from ..fdmath.operators import deriv_forward, deriv_back, curl_forward, curl_back, cross
|
||||
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t
|
||||
from ..fdmath.operators import deriv_forward, deriv_back, cross
|
||||
from ..fdmath import unvec, dx_lists_t, vfdfield_t
|
||||
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]
|
||||
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)
|
||||
|
||||
@ -536,7 +538,7 @@ def e2h(wavenumber: complex,
|
||||
"""
|
||||
op = curl_e(wavenumber, dxes) / (-1j * omega)
|
||||
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
|
||||
|
||||
|
||||
@ -663,7 +665,7 @@ def e_err(e: vfdfield_t,
|
||||
if numpy.any(numpy.equal(mu, None)):
|
||||
op = ch @ ce @ e - omega ** 2 * (epsilon * e)
|
||||
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)
|
||||
|
||||
return norm(op) / norm(e)
|
||||
|
||||
@ -4,12 +4,11 @@ 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 Dict, List, Tuple, Optional, Sequence, Union
|
||||
import numpy
|
||||
import scipy.sparse as sparse
|
||||
from typing import Dict, Optional, Sequence, Union, Any
|
||||
import numpy # type: ignore
|
||||
|
||||
from ..fdmath import vec, unvec, dx_lists_t, vfdfield_t, fdfield_t
|
||||
from . import operators, waveguide_2d, functional
|
||||
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t
|
||||
from . import operators, waveguide_2d
|
||||
|
||||
|
||||
def solve_mode(mode_number: int,
|
||||
@ -53,10 +52,10 @@ def solve_mode(mode_number: int,
|
||||
|
||||
# Find dx in propagation direction
|
||||
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
|
||||
args_2d = {
|
||||
args_2d: Dict[str, Any] = {
|
||||
'omega': omega,
|
||||
'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]),
|
||||
@ -68,15 +67,15 @@ def solve_mode(mode_number: int,
|
||||
Apply corrections and expand to 3D
|
||||
'''
|
||||
# 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]]
|
||||
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)
|
||||
h = unvec(vh, shape)
|
||||
|
||||
# Adjust for propagation direction
|
||||
h *= polarity
|
||||
h *= polarity # type: ignore # mypy issue with numpy
|
||||
|
||||
# Apply phase shift to H-field
|
||||
h[:2] *= numpy.exp(-1j * polarity * 0.5 * wavenumber * dx_prop)
|
||||
|
||||
@ -8,10 +8,9 @@ As the z-dependence is known, all the functions in this file assume a 2D grid
|
||||
"""
|
||||
# TODO update module docs
|
||||
|
||||
from typing import List, Tuple, Dict, Union
|
||||
import numpy
|
||||
from numpy.linalg import norm
|
||||
import scipy.sparse as sparse
|
||||
from typing import Dict, Union
|
||||
import numpy # type: ignore
|
||||
import scipy.sparse as sparse # type: ignore
|
||||
|
||||
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t
|
||||
from ..fdmath.operators import deriv_forward, deriv_back
|
||||
@ -51,9 +50,9 @@ def cylindrical_operator(omega: complex,
|
||||
Dbx, Dby = deriv_back(dxes[1])
|
||||
|
||||
rx = r0 + numpy.cumsum(dxes[0][0])
|
||||
ry = r0 + dxes[0][0]/2.0 + numpy.cumsum(dxes[1][0])
|
||||
tx = rx/r0
|
||||
ty = ry/r0
|
||||
ry = r0 + dxes[0][0] / 2.0 + numpy.cumsum(dxes[1][0])
|
||||
tx = rx / r0
|
||||
ty = ry / r0
|
||||
|
||||
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)))
|
||||
@ -108,7 +107,7 @@ def solve_mode(mode_number: int,
|
||||
|
||||
A_r = cylindrical_operator(numpy.real(omega), dxes_real, numpy.real(epsilon), r0)
|
||||
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
|
||||
@ -128,8 +127,8 @@ def solve_mode(mode_number: int,
|
||||
fields = {
|
||||
'wavenumber': wavenumber,
|
||||
'E': unvec(e_xy, shape),
|
||||
# 'E': unvec(e, shape),
|
||||
# 'H': unvec(h, shape),
|
||||
# 'E': unvec(e, shape),
|
||||
# 'H': unvec(h, shape),
|
||||
}
|
||||
|
||||
return fields
|
||||
|
||||
@ -3,8 +3,8 @@ Math functions for finite difference simulations
|
||||
|
||||
Basic discrete calculus etc.
|
||||
"""
|
||||
from typing import Sequence, Tuple, Dict, Optional
|
||||
import numpy
|
||||
from typing import Sequence, Tuple, Optional
|
||||
import numpy # type: ignore
|
||||
|
||||
from .types import fdfield_t, fdfield_updater_t
|
||||
|
||||
|
||||
@ -3,14 +3,14 @@ Matrix operators for finite difference simulations
|
||||
|
||||
Basic discrete calculus etc.
|
||||
"""
|
||||
from typing import Sequence, List, Callable, Tuple, Dict
|
||||
import numpy
|
||||
import scipy.sparse as sparse
|
||||
from typing import Sequence, List
|
||||
import numpy # type: ignore
|
||||
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
|
||||
specified number of elements.
|
||||
@ -46,7 +46,7 @@ def rotation(axis: int, shape: Sequence[int], shift_distance: int=1) -> sparse.s
|
||||
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
|
||||
boundary conditions applied to the cells beyond the receding edge.
|
||||
|
||||
@ -1,8 +1,8 @@
|
||||
"""
|
||||
Types shared across multiple submodules
|
||||
"""
|
||||
import numpy
|
||||
from typing import Sequence, Callable, MutableSequence
|
||||
import numpy # type: ignore
|
||||
|
||||
|
||||
# Field types
|
||||
|
||||
@ -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.
|
||||
"""
|
||||
|
||||
from typing import Optional, TypeVar, overload, Union, List
|
||||
import numpy
|
||||
from typing import Optional, overload, Union, List
|
||||
import numpy # type: ignore
|
||||
|
||||
from .types import fdfield_t, vfdfield_t
|
||||
|
||||
|
||||
@overload
|
||||
def vec(f: None) -> None:
|
||||
pass
|
||||
@ -60,5 +61,5 @@ def unvec(v: Optional[vfdfield_t], shape: numpy.ndarray) -> Optional[fdfield_t]:
|
||||
"""
|
||||
if numpy.any(numpy.equal(v, 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
|
||||
|
||||
|
||||
@ -162,5 +162,5 @@ Boundary conditions
|
||||
from .base import maxwell_e, maxwell_h
|
||||
from .pml import cpml
|
||||
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
|
||||
|
||||
@ -3,8 +3,7 @@ Basic FDTD field updates
|
||||
|
||||
|
||||
"""
|
||||
from typing import List, Callable, Dict, Union
|
||||
import numpy
|
||||
from typing import Union
|
||||
|
||||
from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
|
||||
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:
|
||||
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 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
|
||||
"""
|
||||
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:
|
||||
h -= dt * curl_e_fun(e)
|
||||
h -= dt * curl_e_fun(e) # type: ignore # mypy gets confused around ndarray ops
|
||||
|
||||
return h
|
||||
|
||||
|
||||
@ -4,10 +4,9 @@ Boundary conditions
|
||||
#TODO conducting boundary documentation
|
||||
"""
|
||||
|
||||
from typing import Callable, Tuple, Dict, Any, List
|
||||
import numpy
|
||||
from typing import Tuple, Any, List
|
||||
|
||||
from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
|
||||
from ..fdmath import fdfield_t, fdfield_updater_t
|
||||
|
||||
|
||||
def conducting_boundary(direction: int,
|
||||
|
||||
@ -1,9 +1,8 @@
|
||||
# pylint: disable=unsupported-assignment-operation
|
||||
from typing import Callable, Tuple, Dict, Optional, Union
|
||||
import numpy
|
||||
from typing import Optional, Union
|
||||
import numpy # type: ignore
|
||||
|
||||
from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
|
||||
from ..fdmath.functional import deriv_back, deriv_forward
|
||||
from ..fdmath import dx_lists_t, fdfield_t
|
||||
from ..fdmath.functional import deriv_back
|
||||
|
||||
|
||||
def poynting(e: fdfield_t,
|
||||
@ -115,10 +114,10 @@ def delta_energy_j(j0: fdfield_t,
|
||||
if dxes is None:
|
||||
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
|
||||
|
||||
du = ((j0 * e1).sum(axis=0) *
|
||||
dxes[0][0][:, None, None] *
|
||||
dxes[0][1][None, :, None] *
|
||||
dxes[0][2][None, None, :])
|
||||
du = ((j0 * e1).sum(axis=0)
|
||||
* dxes[0][0][:, None, None]
|
||||
* dxes[0][1][None, :, None]
|
||||
* dxes[0][2][None, None, :])
|
||||
return du
|
||||
|
||||
|
||||
@ -135,12 +134,12 @@ def dxmul(ee: fdfield_t,
|
||||
if dxes is None:
|
||||
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
|
||||
|
||||
result = ((ee * epsilon).sum(axis=0) *
|
||||
dxes[0][0][:, None, None] *
|
||||
dxes[0][1][None, :, None] *
|
||||
dxes[0][2][None, None, :] +
|
||||
(hh * mu).sum(axis=0) *
|
||||
dxes[1][0][:, None, None] *
|
||||
dxes[1][1][None, :, None] *
|
||||
dxes[1][2][None, None, :])
|
||||
result = ((ee * epsilon).sum(axis=0)
|
||||
* dxes[0][0][:, None, None]
|
||||
* dxes[0][1][None, :, None]
|
||||
* dxes[0][2][None, None, :]
|
||||
+ (hh * mu).sum(axis=0)
|
||||
* dxes[1][0][:, None, None]
|
||||
* dxes[1][1][None, :, None]
|
||||
* dxes[1][2][None, None, :])
|
||||
return result
|
||||
|
||||
@ -8,9 +8,9 @@ PML implementations
|
||||
# TODO retest pmls!
|
||||
|
||||
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'
|
||||
@ -48,8 +48,8 @@ def cpml(direction: int,
|
||||
transverse = numpy.delete(range(3), direction)
|
||||
u, v = transverse
|
||||
|
||||
xe = numpy.arange(1, thickness+1, dtype=float)
|
||||
xh = numpy.arange(1, thickness+1, dtype=float)
|
||||
xe = numpy.arange(1, thickness + 1, dtype=float)
|
||||
xh = numpy.arange(1, thickness + 1, dtype=float)
|
||||
if polarity > 0:
|
||||
xe -= 0.5
|
||||
elif polarity < 0:
|
||||
@ -76,14 +76,14 @@ def cpml(direction: int,
|
||||
p0e, p1e, p2e = par(xe)
|
||||
p0h, p1h, p2h = par(xh)
|
||||
|
||||
region = [slice(None)] * 3
|
||||
region_list = [slice(None)] * 3
|
||||
if polarity < 0:
|
||||
region[direction] = slice(None, thickness)
|
||||
region_list[direction] = slice(None, thickness)
|
||||
elif polarity > 0:
|
||||
region[direction] = slice(-thickness, None)
|
||||
region_list[direction] = slice(-thickness, None)
|
||||
else:
|
||||
raise Exception('Bad polarity!')
|
||||
region = tuple(region)
|
||||
region = tuple(region_list)
|
||||
|
||||
se = 1 if direction == 1 else -1
|
||||
|
||||
|
||||
@ -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
|
||||
|
||||
#####################################
|
||||
# Test fixtures
|
||||
#####################################
|
||||
|
||||
@pytest.fixture(scope='module',
|
||||
params=[(5, 5, 1),
|
||||
(5, 1, 5),
|
||||
(5, 5, 5),
|
||||
#(7, 7, 7),
|
||||
# (7, 7, 7),
|
||||
])
|
||||
def shape(request):
|
||||
yield (3, *request.param)
|
||||
@ -41,7 +41,7 @@ def epsilon(request, shape, epsilon_bg, epsilon_fg):
|
||||
|
||||
epsilon = numpy.full(shape, epsilon_bg, dtype=float)
|
||||
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':
|
||||
epsilon[:, 0, 0, 0] = epsilon_fg
|
||||
elif request.param == 'random':
|
||||
@ -52,7 +52,7 @@ def epsilon(request, shape, epsilon_bg, epsilon_fg):
|
||||
yield epsilon
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=[1.0])#, 1.5])
|
||||
@pytest.fixture(scope='module', params=[1.0]) # 1.5
|
||||
def j_mag(request):
|
||||
yield request.param
|
||||
|
||||
|
||||
@ -1,13 +1,12 @@
|
||||
# pylint: disable=redefined-outer-name
|
||||
from typing import List, Tuple
|
||||
import dataclasses
|
||||
import pytest
|
||||
import numpy
|
||||
import pytest # type: ignore
|
||||
import numpy # type: ignore
|
||||
#from numpy.testing import assert_allclose, assert_array_equal
|
||||
|
||||
from .. import fdfd
|
||||
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):
|
||||
@ -53,7 +52,7 @@ def test_poynting_planes(sim):
|
||||
#####################################
|
||||
# Also see conftest.py
|
||||
|
||||
@pytest.fixture(params=[1/1500])
|
||||
@pytest.fixture(params=[1 / 1500])
|
||||
def omega(request):
|
||||
yield request.param
|
||||
|
||||
@ -74,11 +73,11 @@ def pmc(request):
|
||||
# yield (3, *request.param)
|
||||
|
||||
|
||||
@pytest.fixture(params=['diag']) #'center'
|
||||
@pytest.fixture(params=['diag']) # 'center'
|
||||
def j_distribution(request, shape, j_mag):
|
||||
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
|
||||
center_mask[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = True
|
||||
|
||||
if request.param == 'center':
|
||||
j[center_mask] = j_mag
|
||||
@ -102,6 +101,9 @@ class FDResult:
|
||||
|
||||
@pytest.fixture()
|
||||
def sim(request, shape, epsilon, dxes, j_distribution, omega, pec, pmc):
|
||||
"""
|
||||
Build simulation from parts
|
||||
"""
|
||||
# is3d = (numpy.array(shape) == 1).sum() == 0
|
||||
# if is3d:
|
||||
# pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)')
|
||||
|
||||
@ -1,20 +1,15 @@
|
||||
#####################################
|
||||
# pylint: disable=redefined-outer-name
|
||||
from typing import List, Tuple
|
||||
import dataclasses
|
||||
import pytest
|
||||
import numpy
|
||||
from numpy.testing import assert_allclose, assert_array_equal
|
||||
import pytest # type: ignore
|
||||
import numpy # type: ignore
|
||||
from numpy.testing import assert_allclose # type: ignore
|
||||
|
||||
from .. import fdfd
|
||||
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
|
||||
|
||||
|
||||
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))
|
||||
|
||||
# from matplotlib import pyplot
|
||||
@ -43,10 +38,10 @@ def test_pml(sim, src_polarity):
|
||||
|
||||
|
||||
# Test fixtures
|
||||
#####################################
|
||||
# ####################################
|
||||
# Also see conftest.py
|
||||
|
||||
@pytest.fixture(params=[1/1500])
|
||||
@pytest.fixture(params=[1 / 1500])
|
||||
def omega(request):
|
||||
yield request.param
|
||||
|
||||
@ -61,7 +56,6 @@ def pmc(request):
|
||||
yield request.param
|
||||
|
||||
|
||||
|
||||
@pytest.fixture(params=[(30, 1, 1),
|
||||
(1, 30, 1),
|
||||
(1, 1, 30)])
|
||||
@ -82,16 +76,15 @@ def j_distribution(request, shape, epsilon, dxes, omega, src_polarity):
|
||||
other_dims = [0, 1, 2]
|
||||
other_dims.remove(dim)
|
||||
|
||||
dx_prop = (dxes[0][dim][shape[dim + 1] // 2] +
|
||||
dxes[1][dim][shape[dim + 1] // 2]) / 2 #TODO is this right for nonuniform dxes?
|
||||
dx_prop = (dxes[0][dim][shape[dim + 1] // 2]
|
||||
+ dxes[1][dim][shape[dim + 1] // 2]) / 2 # TODO is this right for nonuniform dxes?
|
||||
|
||||
# Mask only contains components orthogonal to propagation direction
|
||||
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():
|
||||
center_mask[other_dims[1]] = False # If epsilon is not isotropic, pick only one dimension
|
||||
|
||||
|
||||
wavenumber = omega * numpy.sqrt(epsilon[center_mask].mean())
|
||||
wavenumber_corrected = 2 / dx_prop * numpy.arcsin(wavenumber * dx_prop / 2)
|
||||
|
||||
|
||||
@ -1,9 +1,8 @@
|
||||
# pylint: disable=redefined-outer-name, no-member
|
||||
from typing import List, Tuple
|
||||
import dataclasses
|
||||
import pytest
|
||||
import numpy
|
||||
from numpy.testing import assert_allclose, assert_array_equal
|
||||
import pytest # type: ignore
|
||||
import numpy # type: ignore
|
||||
#from numpy.testing import assert_allclose, assert_array_equal # type: ignore
|
||||
|
||||
from .. import fdtd
|
||||
from .utils import assert_close, assert_fields_close, PRNG
|
||||
@ -29,7 +28,7 @@ def test_initial_energy(sim):
|
||||
e0 = sim.es[0]
|
||||
h0 = sim.hs[0]
|
||||
h1 = sim.hs[1]
|
||||
mask = (j0 != 0)
|
||||
|
||||
dV = numpy.prod(numpy.meshgrid(*sim.dxes[0], indexing='ij'), axis=0)
|
||||
u0 = (j0 * j0.conj() / sim.epsilon * dV).sum(axis=0)
|
||||
args = {'dxes': sim.dxes,
|
||||
@ -53,10 +52,10 @@ def test_energy_conservation(sim):
|
||||
'epsilon': sim.epsilon}
|
||||
|
||||
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_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **args) # pylint: disable=bad-whitespace
|
||||
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
|
||||
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)
|
||||
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)
|
||||
|
||||
u += delta_j_A.sum()
|
||||
assert_close(u_hstep.sum(), u)
|
||||
@ -70,8 +69,8 @@ def test_poynting_divergence(sim):
|
||||
|
||||
u_eprev = None
|
||||
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_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **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)
|
||||
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
|
||||
@ -83,10 +82,10 @@ def test_poynting_divergence(sim):
|
||||
continue
|
||||
|
||||
# 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
|
||||
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)
|
||||
u_eprev = u_estep
|
||||
|
||||
@ -105,8 +104,8 @@ def test_poynting_planes(sim):
|
||||
|
||||
u_eprev = None
|
||||
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_estep = fdtd.energy_estep(h0=sim.hs[ii], e1=sim.es[ii], h2=sim.hs[ii + 1], **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)
|
||||
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
|
||||
|
||||
@ -121,7 +120,7 @@ def test_poynting_planes(sim):
|
||||
u_eprev = u_estep
|
||||
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
|
||||
|
||||
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)
|
||||
|
||||
|
||||
@pytest.fixture(params=[(0, 4, 8),]) #(0,)])
|
||||
@pytest.fixture(params=[(0, 4, 8)]) # (0,)
|
||||
def j_steps(request):
|
||||
yield request.param
|
||||
|
||||
@ -167,7 +166,7 @@ def j_steps(request):
|
||||
def j_distribution(request, shape, j_mag):
|
||||
j = numpy.zeros(shape)
|
||||
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':
|
||||
j[:, 0, 0, 0] = j_mag
|
||||
elif request.param == 'random':
|
||||
|
||||
@ -1,9 +1,10 @@
|
||||
import numpy
|
||||
import numpy # type: ignore
|
||||
|
||||
PRNG = numpy.random.RandomState(12345)
|
||||
|
||||
def assert_fields_close(x, y, *args, **kwargs):
|
||||
numpy.testing.assert_allclose(x, y, verbose=False,
|
||||
numpy.testing.assert_allclose(
|
||||
x, y, verbose=False,
|
||||
err_msg='Fields did not match:\n{}\n{}'.format(numpy.rollaxis(x, -1),
|
||||
numpy.rollaxis(y, -1)), *args, **kwargs)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user