typing and formatting updates
This commit is contained in:
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d42a625e5f
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faecc79179
@ -2,16 +2,18 @@
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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 # type: ignore
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from numpy.linalg import norm # type: ignore
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import numpy
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from numpy.typing import NDArray, ArrayLike
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from numpy.linalg import norm
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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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guess_vector: Optional[numpy.ndarray] = None,
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iterations: int = 20,
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) -> Tuple[complex, numpy.ndarray]:
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def power_iteration(
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operator: sparse.spmatrix,
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guess_vector: Optional[NDArray[numpy.float64]] = None,
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iterations: int = 20,
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) -> Tuple[complex, NDArray[numpy.float64]]:
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"""
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Use power iteration to estimate the dominant eigenvector of a matrix.
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@ -37,12 +39,13 @@ def power_iteration(operator: sparse.spmatrix,
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return lm_eigval, v
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def rayleigh_quotient_iteration(operator: Union[sparse.spmatrix, spalg.LinearOperator],
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guess_vector: numpy.ndarray,
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iterations: int = 40,
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tolerance: float = 1e-13,
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solver: Optional[Callable[..., numpy.ndarray]] = None,
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) -> Tuple[complex, numpy.ndarray]:
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def rayleigh_quotient_iteration(
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operator: Union[sparse.spmatrix, spalg.LinearOperator],
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guess_vector: NDArray[numpy.float64],
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iterations: int = 40,
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tolerance: float = 1e-13,
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solver: Optional[Callable[..., NDArray[numpy.float64]]] = None,
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) -> Tuple[complex, NDArray[numpy.float64]]:
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"""
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Use Rayleigh quotient iteration to refine an eigenvector guess.
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@ -69,11 +72,13 @@ def rayleigh_quotient_iteration(operator: Union[sparse.spmatrix, spalg.LinearOpe
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solver = spalg.spsolve
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except TypeError:
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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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return spalg.LinearOperator(
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shape=operator.shape,
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dtype=operator.dtype,
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matvec=lambda v: eigval * v,
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)
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if solver is None:
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def solver(A: spalg.LinearOperator, b: numpy.ndarray) -> numpy.ndarray:
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def solver(A: spalg.LinearOperator, b: ArrayLike) -> NDArray[numpy.float64]:
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return spalg.bicgstab(A, b)[0]
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assert(solver is not None)
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@ -90,10 +95,11 @@ def rayleigh_quotient_iteration(operator: Union[sparse.spmatrix, spalg.LinearOpe
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return eigval, v
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def signed_eigensolve(operator: Union[sparse.spmatrix, spalg.LinearOperator],
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how_many: int,
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negative: bool = False,
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) -> Tuple[numpy.ndarray, numpy.ndarray]:
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def signed_eigensolve(
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operator: Union[sparse.spmatrix, spalg.LinearOperator],
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how_many: int,
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negative: bool = False,
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) -> Tuple[NDArray[numpy.float64], NDArray[numpy.float64]]:
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"""
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Find the largest-magnitude positive-only (or negative-only) eigenvalues and
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eigenvectors of the provided matrix.
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@ -80,7 +80,7 @@ This module contains functions for generating and solving the
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'''
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from typing import Tuple, Callable, Any, List, Optional, cast
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from typing import Tuple, Callable, Any, List, Optional, cast, Union
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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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@ -433,11 +433,10 @@ def find_k(
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`(k, actual_frequency)`
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The found k-vector and its frequency.
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"""
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direction = numpy.array(direction) / norm(direction)
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def get_f(k0_mag: float, band: int = 0) -> float:
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k0 = direction * k0_mag
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k0 = direction * k0_mag # type: ignore
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n, v = eigsolve(band + 1, k0, G_matrix=G_matrix, epsilon=epsilon, mu=mu)
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f = numpy.sqrt(numpy.abs(numpy.real(n[band])))
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if solve_callback:
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@ -482,6 +481,8 @@ def eigsolve(
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`(eigenvalues, eigenvectors)` where `eigenvalues[i]` corresponds to the
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vector `eigenvectors[i, :]`
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"""
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k0 = numpy.array(k0, copy=False)
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h_size = 2 * epsilon[0].size
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kmag = norm(G_matrix @ k0)
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@ -497,9 +498,9 @@ def eigsolve(
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y_shape = (h_size, num_modes)
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prev_E = 0
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d_scale = 1
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prev_traceGtKG = 0
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prev_E = 0.0
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d_scale = 1.0
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prev_traceGtKG = 0.0
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#prev_theta = 0.5
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D = numpy.zeros(shape=y_shape, dtype=complex)
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@ -545,7 +546,7 @@ def eigsolve(
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if prev_traceGtKG == 0 or i % reset_iters == 0:
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logger.info('CG reset')
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gamma = 0
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gamma = 0.0
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else:
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gamma = traceGtKG / prev_traceGtKG
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@ -695,7 +696,10 @@ def linmin(x_guess, f0, df0, x_max, f_tol=0.1, df_tol=min(tolerance, 1e-6), x_to
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return x, fx, dfx
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'''
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def _rtrace_AtB(A: NDArray[numpy.float64], B: NDArray[numpy.float64]) -> NDArray[numpy.float64]:
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def _rtrace_AtB(
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A: NDArray[numpy.float64],
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B: Union[NDArray[numpy.float64], float],
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) -> float:
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return real(numpy.sum(A.conj() * B))
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def _symmetrize(A: NDArray[numpy.float64]) -> NDArray[numpy.float64]:
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@ -2,19 +2,20 @@
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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 # 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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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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from ..fdmath import fdfield_t
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def near_to_farfield(E_near: fdfield_t,
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H_near: fdfield_t,
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dx: float,
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dy: float,
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padded_size: List[int] = None
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) -> Dict[str, Any]:
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def near_to_farfield(
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E_near: fdfield_t,
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H_near: fdfield_t,
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dx: float,
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dy: float,
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padded_size: List[int] = None
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) -> Dict[str, Any]:
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"""
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Compute the farfield, i.e. the distribution of the fields after propagation
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through several wavelengths of uniform medium.
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@ -120,12 +121,13 @@ 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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dky: float,
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padded_size: List[int] = None
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) -> Dict[str, Any]:
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def far_to_nearfield(
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E_far: fdfield_t,
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H_far: fdfield_t,
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dkx: float,
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dky: float,
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padded_size: List[int] = None
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) -> Dict[str, Any]:
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"""
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Compute the farfield, i.e. the distribution of the fields after propagation
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through several wavelengths of uniform medium.
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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 Callable, Tuple
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import numpy # type: ignore
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from typing import Callable, Tuple, Optional
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import numpy
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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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@ -15,11 +15,12 @@ from ..fdmath.functional import curl_forward, curl_back
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__author__ = 'Jan Petykiewicz'
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def e_full(omega: complex,
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dxes: dx_lists_t,
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epsilon: fdfield_t,
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mu: fdfield_t = None
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) -> fdfield_updater_t:
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def e_full(
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omega: complex,
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dxes: dx_lists_t,
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epsilon: fdfield_t,
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mu: fdfield_t = None
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) -> fdfield_updater_t:
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"""
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Wave operator for use with E-field. See `operators.e_full` for details.
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@ -50,11 +51,12 @@ def e_full(omega: complex,
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return op_mu
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def eh_full(omega: complex,
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dxes: dx_lists_t,
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epsilon: fdfield_t,
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mu: fdfield_t = None
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) -> Callable[[fdfield_t, fdfield_t], Tuple[fdfield_t, fdfield_t]]:
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def eh_full(
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omega: complex,
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dxes: dx_lists_t,
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epsilon: fdfield_t,
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mu: fdfield_t = None
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) -> Callable[[fdfield_t, fdfield_t], Tuple[fdfield_t, fdfield_t]]:
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"""
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Wave operator for full (both E and H) field representation.
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See `operators.eh_full`.
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@ -86,9 +88,10 @@ def eh_full(omega: complex,
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return op_mu
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def e2h(omega: complex,
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def e2h(
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omega: complex,
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dxes: dx_lists_t,
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mu: fdfield_t = None,
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mu: Optional[fdfield_t] = None,
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) -> fdfield_updater_t:
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"""
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Utility operator for converting the `E` field into the `H` field.
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@ -117,9 +120,10 @@ def e2h(omega: complex,
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return e2h_mu
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def m2j(omega: complex,
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def m2j(
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omega: complex,
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dxes: dx_lists_t,
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mu: fdfield_t = None,
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mu: Optional[fdfield_t] = None,
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) -> fdfield_updater_t:
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"""
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Utility operator for converting magnetic current `M` distribution
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@ -151,12 +155,13 @@ def m2j(omega: complex,
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return m2j_mu
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def e_tfsf_source(TF_region: fdfield_t,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: fdfield_t,
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mu: fdfield_t = None,
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) -> fdfield_updater_t:
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def e_tfsf_source(
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TF_region: fdfield_t,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: fdfield_t,
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mu: Optional[fdfield_t] = None,
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) -> fdfield_updater_t:
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"""
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Operator that turns an E-field distribution into a total-field/scattered-field
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(TFSF) source.
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@ -28,7 +28,7 @@ The following operators are included:
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"""
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from typing import Tuple, Optional
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import numpy # type: ignore
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import numpy
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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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@ -38,13 +38,14 @@ from ..fdmath.operators import shift_with_mirror, shift_circ, curl_forward, curl
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__author__ = 'Jan Petykiewicz'
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def e_full(omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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pec: Optional[vfdfield_t] = None,
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pmc: Optional[vfdfield_t] = None,
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) -> sparse.spmatrix:
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def e_full(
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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pec: Optional[vfdfield_t] = None,
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pmc: Optional[vfdfield_t] = None,
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) -> sparse.spmatrix:
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"""
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Wave operator
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$$ \\nabla \\times (\\frac{1}{\\mu} \\nabla \\times) - \\Omega^2 \\epsilon $$
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@ -96,8 +97,9 @@ def e_full(omega: complex,
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return op
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def e_full_preconditioners(dxes: dx_lists_t
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) -> Tuple[sparse.spmatrix, sparse.spmatrix]:
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def e_full_preconditioners(
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dxes: dx_lists_t,
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) -> Tuple[sparse.spmatrix, sparse.spmatrix]:
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"""
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Left and right preconditioners `(Pl, Pr)` for symmetrizing the `e_full` wave operator.
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@ -122,13 +124,14 @@ def e_full_preconditioners(dxes: dx_lists_t
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return P_left, P_right
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def h_full(omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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pec: Optional[vfdfield_t] = None,
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pmc: Optional[vfdfield_t] = None,
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) -> sparse.spmatrix:
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def h_full(
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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pec: Optional[vfdfield_t] = None,
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pmc: Optional[vfdfield_t] = None,
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) -> sparse.spmatrix:
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"""
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Wave operator
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$$ \\nabla \\times (\\frac{1}{\\epsilon} \\nabla \\times) - \\omega^2 \\mu $$
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@ -178,13 +181,14 @@ def h_full(omega: complex,
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return A
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def eh_full(omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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pec: Optional[vfdfield_t] = None,
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pmc: Optional[vfdfield_t] = None
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) -> sparse.spmatrix:
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def eh_full(
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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pec: Optional[vfdfield_t] = None,
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pmc: Optional[vfdfield_t] = None,
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) -> sparse.spmatrix:
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"""
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Wave operator for `[E, H]` field representation. This operator implements Maxwell's
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equations without cancelling out either E or H. The operator is
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@ -247,7 +251,8 @@ def eh_full(omega: complex,
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return A
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def e2h(omega: complex,
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def e2h(
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omega: complex,
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dxes: dx_lists_t,
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mu: Optional[vfdfield_t] = None,
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pmc: Optional[vfdfield_t] = None,
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@ -278,9 +283,10 @@ def e2h(omega: complex,
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return op
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def m2j(omega: complex,
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def m2j(
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omega: complex,
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dxes: dx_lists_t,
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mu: Optional[vfdfield_t] = None
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mu: Optional[vfdfield_t] = None,
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) -> sparse.spmatrix:
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"""
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Operator for converting a magnetic current M into an electric current J.
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@ -357,12 +363,13 @@ def poynting_h_cross(h: vfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
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return P
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def e_tfsf_source(TF_region: vfdfield_t,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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) -> sparse.spmatrix:
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def e_tfsf_source(
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TF_region: vfdfield_t,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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) -> sparse.spmatrix:
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"""
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Operator that turns a desired E-field distribution into a
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total-field/scattered-field (TFSF) source.
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@ -387,13 +394,14 @@ def e_tfsf_source(TF_region: vfdfield_t,
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return (A @ Q - Q @ A) / (-1j * omega)
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def e_boundary_source(mask: vfdfield_t,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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periodic_mask_edges: bool = False,
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) -> sparse.spmatrix:
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def e_boundary_source(
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mask: vfdfield_t,
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omega: complex,
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dxes: dx_lists_t,
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epsilon: vfdfield_t,
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mu: Optional[vfdfield_t] = None,
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periodic_mask_edges: bool = False,
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) -> sparse.spmatrix:
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"""
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Operator that turns an E-field distrubtion into a current (J) distribution
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along the edges (external and internal) of the provided mask. This is just an
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@ -3,7 +3,9 @@ Functions for creating stretched coordinate perfectly matched layer (PML) absorb
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"""
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from typing import Sequence, Union, Callable, Optional, List
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import numpy # type: ignore
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import numpy
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from numpy.typing import ArrayLike, NDArray
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__author__ = 'Jan Petykiewicz'
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@ -13,9 +15,10 @@ s_function_t = Callable[[float], float]
|
||||
"""Typedef for s-functions, see `prepare_s_function()`"""
|
||||
|
||||
|
||||
def prepare_s_function(ln_R: float = -16,
|
||||
m: float = 4
|
||||
) -> s_function_t:
|
||||
def prepare_s_function(
|
||||
ln_R: float = -16,
|
||||
m: float = 4
|
||||
) -> s_function_t:
|
||||
"""
|
||||
Create an s_function to pass to the SCPML functions. This is used when you would like to
|
||||
customize the PML parameters.
|
||||
@ -29,18 +32,19 @@ def prepare_s_function(ln_R: float = -16,
|
||||
of the cell width; needs to be divided by `sqrt(epilon_effective) * real(omega))`
|
||||
before use.
|
||||
"""
|
||||
def s_factor(distance: numpy.ndarray) -> numpy.ndarray:
|
||||
def s_factor(distance: NDArray[numpy.float64]) -> NDArray[numpy.float64]:
|
||||
s_max = (m + 1) * ln_R / 2 # / 2 because we assume periodic boundaries
|
||||
return s_max * (distance ** m)
|
||||
return s_factor
|
||||
|
||||
|
||||
def uniform_grid_scpml(shape: Union[numpy.ndarray, Sequence[int]],
|
||||
thicknesses: Union[numpy.ndarray, Sequence[int]],
|
||||
omega: float,
|
||||
epsilon_effective: float = 1.0,
|
||||
s_function: Optional[s_function_t] = None,
|
||||
) -> List[List[numpy.ndarray]]:
|
||||
def uniform_grid_scpml(
|
||||
shape: ArrayLike, # ints
|
||||
thicknesses: ArrayLike, # ints
|
||||
omega: float,
|
||||
epsilon_effective: float = 1.0,
|
||||
s_function: Optional[s_function_t] = None,
|
||||
) -> List[List[NDArray[numpy.float64]]]:
|
||||
"""
|
||||
Create dx arrays for a uniform grid with a cell width of 1 and a pml.
|
||||
|
||||
@ -67,7 +71,11 @@ def uniform_grid_scpml(shape: Union[numpy.ndarray, Sequence[int]],
|
||||
s_function = prepare_s_function()
|
||||
|
||||
# Normalized distance to nearest boundary
|
||||
def ll(u: numpy.ndarray, n: numpy.ndarray, t: numpy.ndarray) -> numpy.ndarray:
|
||||
def ll(
|
||||
u: NDArray[numpy.float64],
|
||||
n: NDArray[numpy.float64],
|
||||
t: NDArray[numpy.float64],
|
||||
) -> NDArray[numpy.float64]:
|
||||
return ((t - u).clip(0) + (u - (n - t)).clip(0)) / t
|
||||
|
||||
dx_a = [numpy.array(numpy.inf)] * 3
|
||||
@ -88,14 +96,15 @@ def uniform_grid_scpml(shape: Union[numpy.ndarray, Sequence[int]],
|
||||
return [dx_a, dx_b]
|
||||
|
||||
|
||||
def stretch_with_scpml(dxes: List[List[numpy.ndarray]],
|
||||
axis: int,
|
||||
polarity: int,
|
||||
omega: float,
|
||||
epsilon_effective: float = 1.0,
|
||||
thickness: int = 10,
|
||||
s_function: Optional[s_function_t] = None,
|
||||
) -> List[List[numpy.ndarray]]:
|
||||
def stretch_with_scpml(
|
||||
dxes: List[List[NDArray[numpy.float64]]],
|
||||
axis: int,
|
||||
polarity: int,
|
||||
omega: float,
|
||||
epsilon_effective: float = 1.0,
|
||||
thickness: int = 10,
|
||||
s_function: Optional[s_function_t] = None,
|
||||
) -> List[List[NDArray[numpy.float64]]]:
|
||||
"""
|
||||
Stretch dxes to contain a stretched-coordinate PML (SCPML) in one direction along one axis.
|
||||
|
||||
@ -132,7 +141,7 @@ def stretch_with_scpml(dxes: List[List[numpy.ndarray]],
|
||||
bound = pos[thickness]
|
||||
d = bound - pos[0]
|
||||
|
||||
def l_d(x: numpy.ndarray) -> numpy.ndarray:
|
||||
def l_d(x: NDArray[numpy.float64]) -> NDArray[numpy.float64]:
|
||||
return (bound - x) / (bound - pos[0])
|
||||
|
||||
slc = slice(thickness)
|
||||
@ -142,7 +151,7 @@ def stretch_with_scpml(dxes: List[List[numpy.ndarray]],
|
||||
bound = pos[-thickness - 1]
|
||||
d = pos[-1] - bound
|
||||
|
||||
def l_d(x: numpy.ndarray) -> numpy.ndarray:
|
||||
def l_d(x: NDArray[numpy.float64]) -> NDArray[numpy.float64]:
|
||||
return (x - bound) / (pos[-1] - bound)
|
||||
|
||||
if thickness == 0:
|
||||
|
||||
@ -2,11 +2,12 @@
|
||||
Solvers and solver interface for FDFD problems.
|
||||
"""
|
||||
|
||||
from typing import Callable, Dict, Any
|
||||
from typing import Callable, Dict, Any, Optional
|
||||
import logging
|
||||
|
||||
import numpy # type: ignore
|
||||
from numpy.linalg import norm # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import ArrayLike, NDArray
|
||||
from numpy.linalg import norm
|
||||
import scipy.sparse.linalg # type: ignore
|
||||
|
||||
from ..fdmath import dx_lists_t, vfdfield_t
|
||||
@ -16,10 +17,11 @@ from . import operators
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
def _scipy_qmr(A: scipy.sparse.csr_matrix,
|
||||
b: numpy.ndarray,
|
||||
**kwargs: Any,
|
||||
) -> numpy.ndarray:
|
||||
def _scipy_qmr(
|
||||
A: scipy.sparse.csr_matrix,
|
||||
b: ArrayLike,
|
||||
**kwargs: Any,
|
||||
) -> NDArray[numpy.float64]:
|
||||
"""
|
||||
Wrapper for scipy.sparse.linalg.qmr
|
||||
|
||||
@ -37,14 +39,14 @@ def _scipy_qmr(A: scipy.sparse.csr_matrix,
|
||||
'''
|
||||
ii = 0
|
||||
|
||||
def log_residual(xk: numpy.ndarray) -> None:
|
||||
def log_residual(xk: ArrayLike) -> None:
|
||||
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: numpy.ndarray) -> None:
|
||||
def augmented_callback(xk: ArrayLike) -> None:
|
||||
log_residual(xk)
|
||||
kwargs['callback'](xk)
|
||||
|
||||
@ -60,17 +62,18 @@ def _scipy_qmr(A: scipy.sparse.csr_matrix,
|
||||
return x
|
||||
|
||||
|
||||
def generic(omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
J: vfdfield_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: vfdfield_t = None,
|
||||
pec: vfdfield_t = None,
|
||||
pmc: vfdfield_t = None,
|
||||
adjoint: bool = False,
|
||||
matrix_solver: Callable[..., numpy.ndarray] = _scipy_qmr,
|
||||
matrix_solver_opts: Dict[str, Any] = None,
|
||||
) -> vfdfield_t:
|
||||
def generic(
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
J: vfdfield_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: vfdfield_t = None,
|
||||
pec: vfdfield_t = None,
|
||||
pmc: vfdfield_t = None,
|
||||
adjoint: bool = False,
|
||||
matrix_solver: Callable[..., ArrayLike] = _scipy_qmr,
|
||||
matrix_solver_opts: Optional[Dict[str, Any]] = None,
|
||||
) -> vfdfield_t:
|
||||
"""
|
||||
Conjugate gradient FDFD solver using CSR sparse matrices.
|
||||
|
||||
@ -90,8 +93,8 @@ def generic(omega: complex,
|
||||
adjoint: If true, solves the adjoint problem.
|
||||
matrix_solver: Called as `matrix_solver(A, b, **matrix_solver_opts) -> x`,
|
||||
where `A`: `scipy.sparse.csr_matrix`;
|
||||
`b`: `numpy.ndarray`;
|
||||
`x`: `numpy.ndarray`;
|
||||
`b`: `ArrayLike`;
|
||||
`x`: `ArrayLike`;
|
||||
Default is a wrapped version of `scipy.sparse.linalg.qmr()`
|
||||
which doesn't return convergence info and logs the residual
|
||||
every 100 iterations.
|
||||
|
||||
@ -179,8 +179,9 @@ to account for numerical dispersion if the result is introduced into a space wit
|
||||
# TODO update module docs
|
||||
|
||||
from typing import List, Tuple, Optional, Any
|
||||
import numpy # type: ignore
|
||||
from numpy.linalg import norm # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray, ArrayLike
|
||||
from numpy.linalg import norm
|
||||
import scipy.sparse as sparse # type: ignore
|
||||
|
||||
from ..fdmath.operators import deriv_forward, deriv_back, cross
|
||||
@ -191,11 +192,12 @@ from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
|
||||
__author__ = 'Jan Petykiewicz'
|
||||
|
||||
|
||||
def operator_e(omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
) -> sparse.spmatrix:
|
||||
def operator_e(
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Waveguide operator of the form
|
||||
|
||||
@ -257,11 +259,12 @@ def operator_e(omega: complex,
|
||||
return op
|
||||
|
||||
|
||||
def operator_h(omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
) -> sparse.spmatrix:
|
||||
def operator_h(
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Waveguide operator of the form
|
||||
|
||||
@ -324,14 +327,15 @@ def operator_h(omega: complex,
|
||||
return op
|
||||
|
||||
|
||||
def normalized_fields_e(e_xy: numpy.ndarray,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
prop_phase: float = 0,
|
||||
) -> Tuple[vfdfield_t, vfdfield_t]:
|
||||
def normalized_fields_e(
|
||||
e_xy: ArrayLike,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
prop_phase: float = 0,
|
||||
) -> Tuple[vfdfield_t, vfdfield_t]:
|
||||
"""
|
||||
Given a vector `e_xy` containing the vectorized E_x and E_y fields,
|
||||
returns normalized, vectorized E and H fields for the system.
|
||||
@ -358,14 +362,15 @@ def normalized_fields_e(e_xy: numpy.ndarray,
|
||||
return e_norm, h_norm
|
||||
|
||||
|
||||
def normalized_fields_h(h_xy: numpy.ndarray,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
prop_phase: float = 0,
|
||||
) -> Tuple[vfdfield_t, vfdfield_t]:
|
||||
def normalized_fields_h(
|
||||
h_xy: ArrayLike,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
prop_phase: float = 0,
|
||||
) -> Tuple[vfdfield_t, vfdfield_t]:
|
||||
"""
|
||||
Given a vector `h_xy` containing the vectorized H_x and H_y fields,
|
||||
returns normalized, vectorized E and H fields for the system.
|
||||
@ -392,14 +397,15 @@ def normalized_fields_h(h_xy: numpy.ndarray,
|
||||
return e_norm, h_norm
|
||||
|
||||
|
||||
def _normalized_fields(e: numpy.ndarray,
|
||||
h: numpy.ndarray,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
prop_phase: float = 0,
|
||||
) -> Tuple[vfdfield_t, vfdfield_t]:
|
||||
def _normalized_fields(
|
||||
e: ArrayLike,
|
||||
h: ArrayLike,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None,
|
||||
prop_phase: float = 0,
|
||||
) -> Tuple[vfdfield_t, vfdfield_t]:
|
||||
# TODO documentation
|
||||
shape = [s.size for s in dxes[0]]
|
||||
dxes_real = [[numpy.real(d) for d in numpy.meshgrid(*dxes[v], indexing='ij')] for v in (0, 1)]
|
||||
@ -434,12 +440,13 @@ def _normalized_fields(e: numpy.ndarray,
|
||||
return e, h
|
||||
|
||||
|
||||
def exy2h(wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None
|
||||
) -> sparse.spmatrix:
|
||||
def exy2h(
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Operator which transforms the vector `e_xy` containing the vectorized E_x and E_y fields,
|
||||
into a vectorized H containing all three H components
|
||||
@ -459,12 +466,13 @@ def exy2h(wavenumber: complex,
|
||||
return e2hop @ exy2e(wavenumber=wavenumber, dxes=dxes, epsilon=epsilon)
|
||||
|
||||
|
||||
def hxy2e(wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None
|
||||
) -> sparse.spmatrix:
|
||||
def hxy2e(
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Operator which transforms the vector `h_xy` containing the vectorized H_x and H_y fields,
|
||||
into a vectorized E containing all three E components
|
||||
@ -484,10 +492,11 @@ def hxy2e(wavenumber: complex,
|
||||
return h2eop @ hxy2h(wavenumber=wavenumber, dxes=dxes, mu=mu)
|
||||
|
||||
|
||||
def hxy2h(wavenumber: complex,
|
||||
dxes: dx_lists_t,
|
||||
mu: Optional[vfdfield_t] = None
|
||||
) -> sparse.spmatrix:
|
||||
def hxy2h(
|
||||
wavenumber: complex,
|
||||
dxes: dx_lists_t,
|
||||
mu: Optional[vfdfield_t] = None
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Operator which transforms the vector `h_xy` containing the vectorized H_x and H_y fields,
|
||||
into a vectorized H containing all three H components
|
||||
@ -517,10 +526,11 @@ def hxy2h(wavenumber: complex,
|
||||
return op
|
||||
|
||||
|
||||
def exy2e(wavenumber: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
) -> sparse.spmatrix:
|
||||
def exy2e(
|
||||
wavenumber: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Operator which transforms the vector `e_xy` containing the vectorized E_x and E_y fields,
|
||||
into a vectorized E containing all three E components
|
||||
@ -550,7 +560,8 @@ def exy2e(wavenumber: complex,
|
||||
return op
|
||||
|
||||
|
||||
def e2h(wavenumber: complex,
|
||||
def e2h(
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
mu: Optional[vfdfield_t] = None
|
||||
@ -574,7 +585,8 @@ def e2h(wavenumber: complex,
|
||||
return op
|
||||
|
||||
|
||||
def h2e(wavenumber: complex,
|
||||
def h2e(
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t
|
||||
@ -636,13 +648,14 @@ def curl_h(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix:
|
||||
return cross([Dbx, Dby, Bz])
|
||||
|
||||
|
||||
def h_err(h: vfdfield_t,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: vfdfield_t = None
|
||||
) -> float:
|
||||
def h_err(
|
||||
h: vfdfield_t,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: Optional[vfdfield_t] = None
|
||||
) -> float:
|
||||
"""
|
||||
Calculates the relative error in the H field
|
||||
|
||||
@ -670,13 +683,14 @@ def h_err(h: vfdfield_t,
|
||||
return norm(op) / norm(h)
|
||||
|
||||
|
||||
def e_err(e: vfdfield_t,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: vfdfield_t = None
|
||||
) -> float:
|
||||
def e_err(
|
||||
e: vfdfield_t,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: vfdfield_t = Optional[None]
|
||||
) -> float:
|
||||
"""
|
||||
Calculates the relative error in the E field
|
||||
|
||||
@ -703,13 +717,14 @@ def e_err(e: vfdfield_t,
|
||||
return norm(op) / norm(e)
|
||||
|
||||
|
||||
def solve_modes(mode_numbers: List[int],
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: vfdfield_t = None,
|
||||
mode_margin: int = 2,
|
||||
) -> Tuple[numpy.ndarray, List[complex]]:
|
||||
def solve_modes(
|
||||
mode_numbers: List[int],
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
mu: vfdfield_t = None,
|
||||
mode_margin: int = 2,
|
||||
) -> Tuple[NDArray[numpy.float64], List[complex]]:
|
||||
"""
|
||||
Given a 2D region, attempts to solve for the eigenmode with the specified mode numbers.
|
||||
|
||||
@ -752,10 +767,11 @@ def solve_modes(mode_numbers: List[int],
|
||||
return e_xys, wavenumbers
|
||||
|
||||
|
||||
def solve_mode(mode_number: int,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Tuple[vfdfield_t, complex]:
|
||||
def solve_mode(
|
||||
mode_number: int,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> Tuple[vfdfield_t, complex]:
|
||||
"""
|
||||
Wrapper around `solve_modes()` that solves for a single mode.
|
||||
|
||||
|
||||
@ -5,21 +5,23 @@ 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, Optional, Sequence, Union, Any
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t
|
||||
from . import operators, waveguide_2d
|
||||
|
||||
|
||||
def solve_mode(mode_number: int,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
axis: int,
|
||||
polarity: int,
|
||||
slices: Sequence[slice],
|
||||
epsilon: fdfield_t,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
) -> Dict[str, Union[complex, numpy.ndarray]]:
|
||||
def solve_mode(
|
||||
mode_number: int,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
axis: int,
|
||||
polarity: int,
|
||||
slices: Sequence[slice],
|
||||
epsilon: fdfield_t,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
) -> Dict[str, Union[complex, NDArray[numpy.float_]]]:
|
||||
"""
|
||||
Given a 3D grid, selects a slice from the grid and attempts to
|
||||
solve for an eigenmode propagating through that slice.
|
||||
@ -36,7 +38,13 @@ def solve_mode(mode_number: int,
|
||||
mu: Magnetic permeability (default 1 everywhere)
|
||||
|
||||
Returns:
|
||||
`{'E': List[numpy.ndarray], 'H': List[numpy.ndarray], 'wavenumber': complex}`
|
||||
```
|
||||
{
|
||||
'E': List[NDArray[numpy.float_]],
|
||||
'H': List[NDArray[numpy.float_]],
|
||||
'wavenumber': complex,
|
||||
}
|
||||
```
|
||||
"""
|
||||
if mu is None:
|
||||
mu = numpy.ones_like(epsilon)
|
||||
@ -97,16 +105,17 @@ def solve_mode(mode_number: int,
|
||||
return results
|
||||
|
||||
|
||||
def compute_source(E: fdfield_t,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
axis: int,
|
||||
polarity: int,
|
||||
slices: Sequence[slice],
|
||||
epsilon: fdfield_t,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
) -> fdfield_t:
|
||||
def compute_source(
|
||||
E: fdfield_t,
|
||||
wavenumber: complex,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
axis: int,
|
||||
polarity: int,
|
||||
slices: Sequence[slice],
|
||||
epsilon: fdfield_t,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Given an eigenmode obtained by `solve_mode`, returns the current source distribution
|
||||
necessary to position a unidirectional source at the slice location.
|
||||
@ -142,18 +151,21 @@ def compute_source(E: fdfield_t,
|
||||
return J
|
||||
|
||||
|
||||
def compute_overlap_e(E: fdfield_t,
|
||||
wavenumber: complex,
|
||||
dxes: dx_lists_t,
|
||||
axis: int,
|
||||
polarity: int,
|
||||
slices: Sequence[slice],
|
||||
) -> fdfield_t: # TODO DOCS
|
||||
def compute_overlap_e(
|
||||
E: fdfield_t,
|
||||
wavenumber: complex,
|
||||
dxes: dx_lists_t,
|
||||
axis: int,
|
||||
polarity: int,
|
||||
slices: Sequence[slice],
|
||||
) -> fdfield_t: # TODO DOCS
|
||||
"""
|
||||
Given an eigenmode obtained by `solve_mode`, calculates an overlap_e for the
|
||||
mode orthogonality relation Integrate(((E x H_mode) + (E_mode x H)) dot dn)
|
||||
[assumes reflection symmetry].
|
||||
|
||||
TODO: add reference
|
||||
|
||||
Args:
|
||||
E: E-field of the mode
|
||||
H: H-field of the mode (advanced by half of a Yee cell from E)
|
||||
@ -187,13 +199,14 @@ def compute_overlap_e(E: fdfield_t,
|
||||
return Etgt
|
||||
|
||||
|
||||
def expand_e(E: fdfield_t,
|
||||
wavenumber: complex,
|
||||
dxes: dx_lists_t,
|
||||
axis: int,
|
||||
polarity: int,
|
||||
slices: Sequence[slice],
|
||||
) -> fdfield_t:
|
||||
def expand_e(
|
||||
E: fdfield_t,
|
||||
wavenumber: complex,
|
||||
dxes: dx_lists_t,
|
||||
axis: int,
|
||||
polarity: int,
|
||||
slices: Sequence[slice],
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Given an eigenmode obtained by `solve_mode`, expands the E-field from the 2D
|
||||
slice where the mode was calculated to the entire domain (along the propagation
|
||||
|
||||
@ -9,7 +9,7 @@ As the z-dependence is known, all the functions in this file assume a 2D grid
|
||||
# TODO update module docs
|
||||
|
||||
from typing import Dict, Union
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
import scipy.sparse as sparse # type: ignore
|
||||
|
||||
from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t
|
||||
@ -17,11 +17,12 @@ from ..fdmath.operators import deriv_forward, deriv_back
|
||||
from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
|
||||
|
||||
|
||||
def cylindrical_operator(omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
r0: float,
|
||||
) -> sparse.spmatrix:
|
||||
def cylindrical_operator(
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
r0: float,
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Cylindrical coordinate waveguide operator of the form
|
||||
|
||||
@ -78,12 +79,13 @@ def cylindrical_operator(omega: complex,
|
||||
return op
|
||||
|
||||
|
||||
def solve_mode(mode_number: int,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
r0: float,
|
||||
) -> Dict[str, Union[complex, fdfield_t]]:
|
||||
def solve_mode(
|
||||
mode_number: int,
|
||||
omega: complex,
|
||||
dxes: dx_lists_t,
|
||||
epsilon: vfdfield_t,
|
||||
r0: float,
|
||||
) -> Dict[str, Union[complex, fdfield_t]]:
|
||||
"""
|
||||
TODO: fixup
|
||||
Given a 2d (r, y) slice of epsilon, attempts to solve for the eigenmode
|
||||
@ -99,7 +101,13 @@ def solve_mode(mode_number: int,
|
||||
r within the simulation domain.
|
||||
|
||||
Returns:
|
||||
`{'E': List[numpy.ndarray], 'H': List[numpy.ndarray], 'wavenumber': complex}`
|
||||
```
|
||||
{
|
||||
'E': List[NDArray[numpy.float_]],
|
||||
'H': List[NDArray[numpy.float_]],
|
||||
'wavenumber': complex,
|
||||
}
|
||||
```
|
||||
"""
|
||||
|
||||
'''
|
||||
|
||||
@ -5,13 +5,15 @@ Basic discrete calculus etc.
|
||||
"""
|
||||
from typing import Sequence, Tuple, Optional, Callable
|
||||
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray
|
||||
|
||||
from .types import fdfield_t, fdfield_updater_t
|
||||
|
||||
|
||||
def deriv_forward(dx_e: Optional[Sequence[numpy.ndarray]] = None
|
||||
) -> Tuple[fdfield_updater_t, fdfield_updater_t, fdfield_updater_t]:
|
||||
def deriv_forward(
|
||||
dx_e: Optional[Sequence[NDArray[numpy.float_]]] = None,
|
||||
) -> Tuple[fdfield_updater_t, fdfield_updater_t, fdfield_updater_t]:
|
||||
"""
|
||||
Utility operators for taking discretized derivatives (backward variant).
|
||||
|
||||
@ -33,8 +35,9 @@ def deriv_forward(dx_e: Optional[Sequence[numpy.ndarray]] = None
|
||||
return derivs
|
||||
|
||||
|
||||
def deriv_back(dx_h: Optional[Sequence[numpy.ndarray]] = None
|
||||
) -> Tuple[fdfield_updater_t, fdfield_updater_t, fdfield_updater_t]:
|
||||
def deriv_back(
|
||||
dx_h: Optional[Sequence[NDArray[numpy.float_]]] = None,
|
||||
) -> Tuple[fdfield_updater_t, fdfield_updater_t, fdfield_updater_t]:
|
||||
"""
|
||||
Utility operators for taking discretized derivatives (forward variant).
|
||||
|
||||
@ -56,7 +59,9 @@ def deriv_back(dx_h: Optional[Sequence[numpy.ndarray]] = None
|
||||
return derivs
|
||||
|
||||
|
||||
def curl_forward(dx_e: Optional[Sequence[numpy.ndarray]] = None) -> fdfield_updater_t:
|
||||
def curl_forward(
|
||||
dx_e: Optional[Sequence[NDArray[numpy.float_]]] = None,
|
||||
) -> fdfield_updater_t:
|
||||
"""
|
||||
Curl operator for use with the E field.
|
||||
|
||||
@ -83,7 +88,9 @@ def curl_forward(dx_e: Optional[Sequence[numpy.ndarray]] = None) -> fdfield_upda
|
||||
return ce_fun
|
||||
|
||||
|
||||
def curl_back(dx_h: Optional[Sequence[numpy.ndarray]] = None) -> fdfield_updater_t:
|
||||
def curl_back(
|
||||
dx_h: Optional[Sequence[NDArray[numpy.float_]]] = None,
|
||||
) -> fdfield_updater_t:
|
||||
"""
|
||||
Create a function which takes the backward curl of a field.
|
||||
|
||||
@ -110,7 +117,9 @@ def curl_back(dx_h: Optional[Sequence[numpy.ndarray]] = None) -> fdfield_updater
|
||||
return ch_fun
|
||||
|
||||
|
||||
def curl_forward_parts(dx_e: Optional[Sequence[numpy.ndarray]] = None) -> Callable:
|
||||
def curl_forward_parts(
|
||||
dx_e: Optional[Sequence[NDArray[numpy.float_]]] = None,
|
||||
) -> Callable:
|
||||
Dx, Dy, Dz = deriv_forward(dx_e)
|
||||
|
||||
def mkparts_fwd(e: fdfield_t) -> Tuple[Tuple[fdfield_t, ...]]:
|
||||
@ -121,7 +130,9 @@ def curl_forward_parts(dx_e: Optional[Sequence[numpy.ndarray]] = None) -> Callab
|
||||
return mkparts_fwd
|
||||
|
||||
|
||||
def curl_back_parts(dx_h: Optional[Sequence[numpy.ndarray]] = None) -> Callable:
|
||||
def curl_back_parts(
|
||||
dx_h: Optional[Sequence[NDArray[numpy.float_]]] = None,
|
||||
) -> Callable:
|
||||
Dx, Dy, Dz = deriv_back(dx_e)
|
||||
|
||||
def mkparts_back(h: fdfield_t) -> Tuple[Tuple[fdfield_t, ...]]:
|
||||
|
||||
@ -4,13 +4,18 @@ Matrix operators for finite difference simulations
|
||||
Basic discrete calculus etc.
|
||||
"""
|
||||
from typing import Sequence, List
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray
|
||||
import scipy.sparse as sparse # type: ignore
|
||||
|
||||
from .types import vfdfield_t
|
||||
|
||||
|
||||
def shift_circ(axis: int, shape: Sequence[int], shift_distance: int = 1) -> sparse.spmatrix:
|
||||
def shift_circ(
|
||||
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 +51,11 @@ def shift_circ(axis: int, shape: Sequence[int], shift_distance: int = 1) -> spar
|
||||
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.
|
||||
@ -67,7 +76,7 @@ def shift_with_mirror(axis: int, shape: Sequence[int], shift_distance: int = 1)
|
||||
raise Exception('Shift ({}) is too large for axis {} of size {}'.format(
|
||||
shift_distance, axis, shape[axis]))
|
||||
|
||||
def mirrored_range(n: int, s: int) -> numpy.ndarray:
|
||||
def mirrored_range(n: int, s: int) -> NDArray[numpy.int_]:
|
||||
v = numpy.arange(n) + s
|
||||
v = numpy.where(v >= n, 2 * n - v - 1, v)
|
||||
v = numpy.where(v < 0, - 1 - v, v)
|
||||
@ -87,7 +96,9 @@ def shift_with_mirror(axis: int, shape: Sequence[int], shift_distance: int = 1)
|
||||
return d
|
||||
|
||||
|
||||
def deriv_forward(dx_e: Sequence[numpy.ndarray]) -> List[sparse.spmatrix]:
|
||||
def deriv_forward(
|
||||
dx_e: Sequence[NDArray[numpy.float_]],
|
||||
) -> List[sparse.spmatrix]:
|
||||
"""
|
||||
Utility operators for taking discretized derivatives (forward variant).
|
||||
|
||||
@ -112,7 +123,9 @@ def deriv_forward(dx_e: Sequence[numpy.ndarray]) -> List[sparse.spmatrix]:
|
||||
return Ds
|
||||
|
||||
|
||||
def deriv_back(dx_h: Sequence[numpy.ndarray]) -> List[sparse.spmatrix]:
|
||||
def deriv_back(
|
||||
dx_h: Sequence[NDArray[numpy.float_]],
|
||||
) -> List[sparse.spmatrix]:
|
||||
"""
|
||||
Utility operators for taking discretized derivatives (backward variant).
|
||||
|
||||
@ -137,7 +150,9 @@ def deriv_back(dx_h: Sequence[numpy.ndarray]) -> List[sparse.spmatrix]:
|
||||
return Ds
|
||||
|
||||
|
||||
def cross(B: Sequence[sparse.spmatrix]) -> sparse.spmatrix:
|
||||
def cross(
|
||||
B: Sequence[sparse.spmatrix],
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Cross product operator
|
||||
|
||||
@ -203,7 +218,9 @@ def avg_back(axis: int, shape: Sequence[int]) -> sparse.spmatrix:
|
||||
return avg_forward(axis, shape).T
|
||||
|
||||
|
||||
def curl_forward(dx_e: Sequence[numpy.ndarray]) -> sparse.spmatrix:
|
||||
def curl_forward(
|
||||
dx_e: Sequence[NDArray[numpy.float_]],
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Curl operator for use with the E field.
|
||||
|
||||
@ -217,7 +234,9 @@ def curl_forward(dx_e: Sequence[numpy.ndarray]) -> sparse.spmatrix:
|
||||
return cross(deriv_forward(dx_e))
|
||||
|
||||
|
||||
def curl_back(dx_h: Sequence[numpy.ndarray]) -> sparse.spmatrix:
|
||||
def curl_back(
|
||||
dx_h: Sequence[NDArray[numpy.float_]],
|
||||
) -> sparse.spmatrix:
|
||||
"""
|
||||
Curl operator for use with the H field.
|
||||
|
||||
|
||||
@ -2,31 +2,20 @@
|
||||
Types shared across multiple submodules
|
||||
"""
|
||||
from typing import Sequence, Callable, MutableSequence
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray
|
||||
|
||||
|
||||
# Field types
|
||||
# TODO: figure out a better way to set the docstrings without creating actual subclasses?
|
||||
# Probably not a big issue since they're only used for type hinting
|
||||
class fdfield_t(numpy.ndarray):
|
||||
"""
|
||||
Vector field with shape (3, X, Y, Z) (e.g. `[E_x, E_y, E_z]`)
|
||||
fdfield_t = NDArray[numpy.float_]
|
||||
"""Vector field with shape (3, X, Y, Z) (e.g. `[E_x, E_y, E_z]`)"""
|
||||
|
||||
This is actually is just an unaltered `numpy.ndarray`
|
||||
"""
|
||||
pass
|
||||
|
||||
class vfdfield_t(numpy.ndarray):
|
||||
"""
|
||||
Linearized vector field (single vector of length 3*X*Y*Z)
|
||||
|
||||
This is actually just an unaltered `numpy.ndarray`
|
||||
"""
|
||||
pass
|
||||
vfdfield_t = NDArray[numpy.float_]
|
||||
"""Linearized vector field (single vector of length 3*X*Y*Z)"""
|
||||
|
||||
|
||||
dx_lists_t = Sequence[Sequence[numpy.ndarray]]
|
||||
'''
|
||||
dx_lists_t = Sequence[Sequence[NDArray[numpy.float_]]]
|
||||
"""
|
||||
'dxes' datastructure which contains grid cell width information in the following format:
|
||||
|
||||
[[[dx_e[0], dx_e[1], ...], [dy_e[0], ...], [dz_e[0], ...]],
|
||||
@ -34,15 +23,11 @@ dx_lists_t = Sequence[Sequence[numpy.ndarray]]
|
||||
|
||||
where `dx_e[0]` is the x-width of the `x=0` cells, as used when calculating dE/dx,
|
||||
and `dy_h[0]` is the y-width of the `y=0` cells, as used when calculating dH/dy, etc.
|
||||
'''
|
||||
"""
|
||||
|
||||
dx_lists_mut = MutableSequence[MutableSequence[numpy.ndarray]]
|
||||
'''
|
||||
Mutable version of `dx_lists_t`
|
||||
'''
|
||||
dx_lists_mut = MutableSequence[MutableSequence[NDArray[numpy.float_]]]
|
||||
"""Mutable version of `dx_lists_t`"""
|
||||
|
||||
|
||||
fdfield_updater_t = Callable[..., fdfield_t]
|
||||
'''
|
||||
Convenience type for functions which take and return an fdfield_t
|
||||
'''
|
||||
"""Convenience type for functions which take and return an fdfield_t"""
|
||||
|
||||
@ -5,7 +5,8 @@ Vectorized versions of the field use row-major (ie., C-style) ordering.
|
||||
"""
|
||||
|
||||
from typing import Optional, overload, Union, List
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import ArrayLike
|
||||
|
||||
from .types import fdfield_t, vfdfield_t
|
||||
|
||||
@ -15,10 +16,10 @@ def vec(f: None) -> None:
|
||||
pass
|
||||
|
||||
@overload
|
||||
def vec(f: Union[fdfield_t, List[numpy.ndarray]]) -> vfdfield_t:
|
||||
def vec(f: Union[fdfield_t, List[ArrayLike]]) -> vfdfield_t:
|
||||
pass
|
||||
|
||||
def vec(f: Optional[Union[fdfield_t, List[numpy.ndarray]]]) -> Optional[vfdfield_t]:
|
||||
def vec(f: Optional[Union[fdfield_t, List[ArrayLike]]]) -> Optional[vfdfield_t]:
|
||||
"""
|
||||
Create a 1D ndarray from a 3D vector field which spans a 1-3D region.
|
||||
|
||||
@ -37,14 +38,14 @@ def vec(f: Optional[Union[fdfield_t, List[numpy.ndarray]]]) -> Optional[vfdfield
|
||||
|
||||
|
||||
@overload
|
||||
def unvec(v: None, shape: numpy.ndarray) -> None:
|
||||
def unvec(v: None, shape: ArrayLike) -> None:
|
||||
pass
|
||||
|
||||
@overload
|
||||
def unvec(v: vfdfield_t, shape: numpy.ndarray) -> fdfield_t:
|
||||
def unvec(v: vfdfield_t, shape: ArrayLike) -> fdfield_t:
|
||||
pass
|
||||
|
||||
def unvec(v: Optional[vfdfield_t], shape: numpy.ndarray) -> Optional[fdfield_t]:
|
||||
def unvec(v: Optional[vfdfield_t], shape: ArrayLike) -> Optional[fdfield_t]:
|
||||
"""
|
||||
Perform the inverse of vec(): take a 1D ndarray and output a 3D field
|
||||
of form `[f_x, f_y, f_z]` where each of `f_*` is a len(shape)-dimensional
|
||||
|
||||
@ -9,9 +9,10 @@ from typing import Tuple, Any, List
|
||||
from ..fdmath import fdfield_t, fdfield_updater_t
|
||||
|
||||
|
||||
def conducting_boundary(direction: int,
|
||||
polarity: int
|
||||
) -> Tuple[fdfield_updater_t, fdfield_updater_t]:
|
||||
def conducting_boundary(
|
||||
direction: int,
|
||||
polarity: int
|
||||
) -> Tuple[fdfield_updater_t, fdfield_updater_t]:
|
||||
dirs = [0, 1, 2]
|
||||
if direction not in dirs:
|
||||
raise Exception('Invalid direction: {}'.format(direction))
|
||||
|
||||
@ -1,5 +1,5 @@
|
||||
from typing import Optional, Union
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
|
||||
from ..fdmath import dx_lists_t, fdfield_t
|
||||
from ..fdmath.functional import deriv_back
|
||||
@ -8,10 +8,11 @@ from ..fdmath.functional import deriv_back
|
||||
# TODO documentation
|
||||
|
||||
|
||||
def poynting(e: fdfield_t,
|
||||
h: fdfield_t,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
def poynting(
|
||||
e: fdfield_t,
|
||||
h: fdfield_t,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Calculate the poynting vector `S` ($S$).
|
||||
|
||||
@ -87,12 +88,13 @@ def poynting(e: fdfield_t,
|
||||
return s
|
||||
|
||||
|
||||
def poynting_divergence(s: Optional[fdfield_t] = None,
|
||||
*,
|
||||
e: Optional[fdfield_t] = None,
|
||||
h: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
def poynting_divergence(
|
||||
s: Optional[fdfield_t] = None,
|
||||
*,
|
||||
e: Optional[fdfield_t] = None,
|
||||
h: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Calculate the divergence of the poynting vector.
|
||||
|
||||
@ -124,13 +126,14 @@ def poynting_divergence(s: Optional[fdfield_t] = None,
|
||||
return ds
|
||||
|
||||
|
||||
def energy_hstep(e0: fdfield_t,
|
||||
h1: fdfield_t,
|
||||
e2: fdfield_t,
|
||||
epsilon: Optional[fdfield_t] = None,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
def energy_hstep(
|
||||
e0: fdfield_t,
|
||||
h1: fdfield_t,
|
||||
e2: fdfield_t,
|
||||
epsilon: Optional[fdfield_t] = None,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Calculate energy `U` at the time of the provided H-field `h1`.
|
||||
|
||||
@ -151,13 +154,14 @@ def energy_hstep(e0: fdfield_t,
|
||||
return u
|
||||
|
||||
|
||||
def energy_estep(h0: fdfield_t,
|
||||
e1: fdfield_t,
|
||||
h2: fdfield_t,
|
||||
epsilon: Optional[fdfield_t] = None,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
def energy_estep(
|
||||
h0: fdfield_t,
|
||||
e1: fdfield_t,
|
||||
h2: fdfield_t,
|
||||
epsilon: Optional[fdfield_t] = None,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Calculate energy `U` at the time of the provided E-field `e1`.
|
||||
|
||||
@ -178,15 +182,16 @@ def energy_estep(h0: fdfield_t,
|
||||
return u
|
||||
|
||||
|
||||
def delta_energy_h2e(dt: float,
|
||||
e0: fdfield_t,
|
||||
h1: fdfield_t,
|
||||
e2: fdfield_t,
|
||||
h3: fdfield_t,
|
||||
epsilon: Optional[fdfield_t] = None,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
def delta_energy_h2e(
|
||||
dt: float,
|
||||
e0: fdfield_t,
|
||||
h1: fdfield_t,
|
||||
e2: fdfield_t,
|
||||
h3: fdfield_t,
|
||||
epsilon: Optional[fdfield_t] = None,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Change in energy during the half-step from `h1` to `e2`.
|
||||
|
||||
@ -210,15 +215,16 @@ def delta_energy_h2e(dt: float,
|
||||
return du
|
||||
|
||||
|
||||
def delta_energy_e2h(dt: float,
|
||||
h0: fdfield_t,
|
||||
e1: fdfield_t,
|
||||
h2: fdfield_t,
|
||||
e3: fdfield_t,
|
||||
epsilon: Optional[fdfield_t] = None,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
def delta_energy_e2h(
|
||||
dt: float,
|
||||
h0: fdfield_t,
|
||||
e1: fdfield_t,
|
||||
h2: fdfield_t,
|
||||
e3: fdfield_t,
|
||||
epsilon: Optional[fdfield_t] = None,
|
||||
mu: Optional[fdfield_t] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Change in energy during the half-step from `e1` to `h2`.
|
||||
|
||||
@ -242,10 +248,11 @@ def delta_energy_e2h(dt: float,
|
||||
return du
|
||||
|
||||
|
||||
def delta_energy_j(j0: fdfield_t,
|
||||
e1: fdfield_t,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
def delta_energy_j(
|
||||
j0: fdfield_t,
|
||||
e1: fdfield_t,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
"""
|
||||
Calculate
|
||||
|
||||
@ -264,12 +271,13 @@ def delta_energy_j(j0: fdfield_t,
|
||||
return du
|
||||
|
||||
|
||||
def dxmul(ee: fdfield_t,
|
||||
hh: fdfield_t,
|
||||
epsilon: Optional[Union[fdfield_t, float]] = None,
|
||||
mu: Optional[Union[fdfield_t, float]] = None,
|
||||
dxes: Optional[dx_lists_t] = None
|
||||
) -> fdfield_t:
|
||||
def dxmul(
|
||||
ee: fdfield_t,
|
||||
hh: fdfield_t,
|
||||
epsilon: Optional[Union[fdfield_t, float]] = None,
|
||||
mu: Optional[Union[fdfield_t, float]] = None,
|
||||
dxes: Optional[dx_lists_t] = None,
|
||||
) -> fdfield_t:
|
||||
if epsilon is None:
|
||||
epsilon = 1
|
||||
if mu is None:
|
||||
|
||||
@ -8,7 +8,8 @@ PML implementations
|
||||
# TODO retest pmls!
|
||||
|
||||
from typing import List, Callable, Tuple, Dict, Sequence, Any, Optional
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from typing import NDArray
|
||||
|
||||
from ..fdmath import fdfield_t, dx_lists_t
|
||||
from ..fdmath.functional import deriv_forward, deriv_back
|
||||
@ -61,7 +62,7 @@ def cpml_params(
|
||||
expand_slice_l[axis] = slice(None)
|
||||
expand_slice = tuple(expand_slice_l)
|
||||
|
||||
def par(x: numpy.ndarray) -> Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]:
|
||||
def par(x: NDArray[numpy.float64]) -> Tuple[NDArray[numpy.float64], NDArray[numpy.float64], NDArray[numpy.float64]]:
|
||||
scaling = (x / thickness) ** m
|
||||
sigma = scaling * sigma_max
|
||||
kappa = 1 + scaling * (kappa_max - 1)
|
||||
|
||||
@ -4,7 +4,8 @@ Test fixtures
|
||||
|
||||
"""
|
||||
from typing import Tuple, Iterable, List, Any
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray, ArrayLike
|
||||
import pytest # type: ignore
|
||||
|
||||
from .utils import PRNG
|
||||
@ -34,11 +35,12 @@ def epsilon_fg(request: FixtureRequest) -> Iterable[float]:
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=['center', '000', 'random'])
|
||||
def epsilon(request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon_bg: float,
|
||||
epsilon_fg: float,
|
||||
) -> Iterable[numpy.ndarray]:
|
||||
def epsilon(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon_bg: float,
|
||||
epsilon_fg: float,
|
||||
) -> Iterable[NDArray[numpy.float64]]:
|
||||
is3d = (numpy.array(shape) == 1).sum() == 0
|
||||
if is3d:
|
||||
if request.param == '000':
|
||||
@ -72,10 +74,11 @@ def dx(request: FixtureRequest) -> Iterable[float]:
|
||||
|
||||
|
||||
@pytest.fixture(scope='module', params=['uniform', 'centerbig'])
|
||||
def dxes(request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
dx: float,
|
||||
) -> Iterable[List[List[numpy.ndarray]]]:
|
||||
def dxes(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
dx: float,
|
||||
) -> Iterable[List[List[NDArray[numpy.float64]]]]:
|
||||
if request.param == 'uniform':
|
||||
dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
|
||||
elif request.param == 'centerbig':
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
from typing import List, Tuple, Iterable, Optional
|
||||
import dataclasses
|
||||
import pytest # type: ignore
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray, ArrayLike
|
||||
#from numpy.testing import assert_allclose, assert_array_equal
|
||||
|
||||
from .. import fdfd
|
||||
@ -59,12 +60,12 @@ def omega(request: FixtureRequest) -> Iterable[float]:
|
||||
|
||||
|
||||
@pytest.fixture(params=[None])
|
||||
def pec(request: FixtureRequest) -> Iterable[Optional[numpy.ndarray]]:
|
||||
def pec(request: FixtureRequest) -> Iterable[Optional[NDArray[numpy.float64]]]:
|
||||
yield request.param
|
||||
|
||||
|
||||
@pytest.fixture(params=[None])
|
||||
def pmc(request: FixtureRequest) -> Iterable[Optional[numpy.ndarray]]:
|
||||
def pmc(request: FixtureRequest) -> Iterable[Optional[NDArray[numpy.float64]]]:
|
||||
yield request.param
|
||||
|
||||
|
||||
@ -75,10 +76,11 @@ def pmc(request: FixtureRequest) -> Iterable[Optional[numpy.ndarray]]:
|
||||
|
||||
|
||||
@pytest.fixture(params=['diag']) # 'center'
|
||||
def j_distribution(request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
j_mag: float,
|
||||
) -> Iterable[numpy.ndarray]:
|
||||
def j_distribution(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
j_mag: float,
|
||||
) -> Iterable[NDArray[numpy.float64]]:
|
||||
j = numpy.zeros(shape, dtype=complex)
|
||||
center_mask = numpy.zeros(shape, dtype=bool)
|
||||
center_mask[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = True
|
||||
@ -94,24 +96,25 @@ def j_distribution(request: FixtureRequest,
|
||||
@dataclasses.dataclass()
|
||||
class FDResult:
|
||||
shape: Tuple[int, ...]
|
||||
dxes: List[List[numpy.ndarray]]
|
||||
epsilon: numpy.ndarray
|
||||
dxes: List[List[NDArray[numpy.float64]]]
|
||||
epsilon: NDArray[numpy.float64]
|
||||
omega: complex
|
||||
j: numpy.ndarray
|
||||
e: numpy.ndarray
|
||||
pmc: numpy.ndarray
|
||||
pec: numpy.ndarray
|
||||
j: NDArray[numpy.float64]
|
||||
e: NDArray[numpy.float64]
|
||||
pmc: Optional[NDArray[numpy.float64]]
|
||||
pec: Optional[NDArray[numpy.float64]]
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def sim(request: FixtureRequest,
|
||||
def sim(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon: numpy.ndarray,
|
||||
dxes: List[List[numpy.ndarray]],
|
||||
j_distribution: numpy.ndarray,
|
||||
epsilon: NDArray[numpy.float64],
|
||||
dxes: List[List[NDArray[numpy.float64]]],
|
||||
j_distribution: NDArray[numpy.float64],
|
||||
omega: float,
|
||||
pec: Optional[numpy.ndarray],
|
||||
pmc: Optional[numpy.ndarray],
|
||||
pec: Optional[NDArray[numpy.float64]],
|
||||
pmc: Optional[NDArray[numpy.float64]],
|
||||
) -> FDResult:
|
||||
"""
|
||||
Build simulation from parts
|
||||
|
||||
@ -1,7 +1,8 @@
|
||||
from typing import Optional, Tuple, Iterable, List
|
||||
import pytest # type: ignore
|
||||
import numpy # type: ignore
|
||||
from numpy.testing import assert_allclose # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray, ArrayLike
|
||||
from numpy.testing import assert_allclose
|
||||
|
||||
from .. import fdfd
|
||||
from ..fdmath import vec, unvec, dx_lists_mut
|
||||
@ -48,12 +49,12 @@ def omega(request: FixtureRequest) -> Iterable[float]:
|
||||
|
||||
|
||||
@pytest.fixture(params=[None])
|
||||
def pec(request: FixtureRequest) -> Iterable[Optional[numpy.ndarray]]:
|
||||
def pec(request: FixtureRequest) -> Iterable[Optional[NDArray[numpy.float64]]]:
|
||||
yield request.param
|
||||
|
||||
|
||||
@pytest.fixture(params=[None])
|
||||
def pmc(request: FixtureRequest) -> Iterable[Optional[numpy.ndarray]]:
|
||||
def pmc(request: FixtureRequest) -> Iterable[Optional[NDArray[numpy.float64]]]:
|
||||
yield request.param
|
||||
|
||||
|
||||
@ -70,13 +71,14 @@ def src_polarity(request: FixtureRequest) -> Iterable[int]:
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def j_distribution(request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon: numpy.ndarray,
|
||||
dxes: dx_lists_mut,
|
||||
omega: float,
|
||||
src_polarity: int,
|
||||
) -> Iterable[numpy.ndarray]:
|
||||
def j_distribution(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon: NDArray[numpy.float64],
|
||||
dxes: dx_lists_mut,
|
||||
omega: float,
|
||||
src_polarity: int,
|
||||
) -> Iterable[NDArray[numpy.float64]]:
|
||||
j = numpy.zeros(shape, dtype=complex)
|
||||
|
||||
dim = numpy.where(numpy.array(shape[1:]) > 1)[0][0] # Propagation axis
|
||||
@ -108,47 +110,60 @@ def j_distribution(request: FixtureRequest,
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def epsilon(request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon_bg: float,
|
||||
epsilon_fg: float,
|
||||
) -> Iterable[numpy.ndarray]:
|
||||
def epsilon(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon_bg: float,
|
||||
epsilon_fg: float,
|
||||
) -> Iterable[NDArray[numpy.float64]]:
|
||||
epsilon = numpy.full(shape, epsilon_fg, dtype=float)
|
||||
yield epsilon
|
||||
|
||||
|
||||
@pytest.fixture(params=['uniform'])
|
||||
def dxes(request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
dx: float,
|
||||
omega: float,
|
||||
epsilon_fg: float,
|
||||
) -> Iterable[List[List[numpy.ndarray]]]:
|
||||
def dxes(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
dx: float,
|
||||
omega: float,
|
||||
epsilon_fg: float,
|
||||
) -> Iterable[List[List[NDArray[numpy.float64]]]]:
|
||||
if request.param == 'uniform':
|
||||
dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
|
||||
dim = numpy.where(numpy.array(shape[1:]) > 1)[0][0] # Propagation axis
|
||||
for axis in (dim,):
|
||||
for polarity in (-1, 1):
|
||||
dxes = fdfd.scpml.stretch_with_scpml(dxes, axis=axis, polarity=polarity,
|
||||
omega=omega, epsilon_effective=epsilon_fg,
|
||||
thickness=10)
|
||||
dxes = fdfd.scpml.stretch_with_scpml(
|
||||
dxes,
|
||||
axis=axis,
|
||||
polarity=polarity,
|
||||
omega=omega,
|
||||
epsilon_effective=epsilon_fg,
|
||||
thickness=10,
|
||||
)
|
||||
yield dxes
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def sim(request: FixtureRequest,
|
||||
def sim(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon: numpy.ndarray,
|
||||
epsilon: NDArray[numpy.float64],
|
||||
dxes: dx_lists_mut,
|
||||
j_distribution: numpy.ndarray,
|
||||
j_distribution: NDArray[numpy.float64],
|
||||
omega: float,
|
||||
pec: Optional[numpy.ndarray],
|
||||
pmc: Optional[numpy.ndarray],
|
||||
pec: Optional[NDArray[numpy.float64]],
|
||||
pmc: Optional[NDArray[numpy.float64]],
|
||||
) -> FDResult:
|
||||
j_vec = vec(j_distribution)
|
||||
eps_vec = vec(epsilon)
|
||||
e_vec = fdfd.solvers.generic(J=j_vec, omega=omega, dxes=dxes, epsilon=eps_vec,
|
||||
matrix_solver_opts={'atol': 1e-15, 'tol': 1e-11})
|
||||
e_vec = fdfd.solvers.generic(
|
||||
J=j_vec,
|
||||
omega=omega,
|
||||
dxes=dxes,
|
||||
epsilon=eps_vec,
|
||||
matrix_solver_opts={'atol': 1e-15, 'tol': 1e-11},
|
||||
)
|
||||
e = unvec(e_vec, shape[1:])
|
||||
|
||||
sim = FDResult(
|
||||
|
||||
@ -1,8 +1,9 @@
|
||||
from typing import List, Tuple, Iterable
|
||||
from typing import List, Tuple, Iterable, Any, Dict
|
||||
import dataclasses
|
||||
import pytest # type: ignore
|
||||
import numpy # type: ignore
|
||||
#from numpy.testing import assert_allclose, assert_array_equal # type: ignore
|
||||
import numpy
|
||||
from numpy.typing import NDArray, ArrayLike
|
||||
#from numpy.testing import assert_allclose, assert_array_equal
|
||||
|
||||
from .. import fdtd
|
||||
from .utils import assert_close, assert_fields_close, PRNG
|
||||
@ -32,8 +33,10 @@ def test_initial_energy(sim: 'TDResult') -> None:
|
||||
|
||||
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,
|
||||
'epsilon': sim.epsilon}
|
||||
args: Dict[str, Any] = {
|
||||
'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon,
|
||||
}
|
||||
|
||||
# Make sure initial energy and E dot J are correct
|
||||
energy0 = fdtd.energy_estep(h0=h0, e1=e0, h2=h1, **args)
|
||||
@ -49,8 +52,10 @@ def test_energy_conservation(sim: 'TDResult') -> None:
|
||||
e0 = sim.es[0]
|
||||
j0 = sim.js[0]
|
||||
u = fdtd.delta_energy_j(j0=j0, e1=e0, dxes=sim.dxes).sum()
|
||||
args = {'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon}
|
||||
args: Dict[str, Any] = {
|
||||
'dxes': sim.dxes,
|
||||
'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)
|
||||
@ -65,8 +70,10 @@ def test_energy_conservation(sim: 'TDResult') -> None:
|
||||
|
||||
|
||||
def test_poynting_divergence(sim: 'TDResult') -> None:
|
||||
args = {'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon}
|
||||
args: Dict[str, Any] = {
|
||||
'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon,
|
||||
}
|
||||
|
||||
u_eprev = None
|
||||
for ii in range(1, 8):
|
||||
@ -96,8 +103,10 @@ def test_poynting_planes(sim: 'TDResult') -> None:
|
||||
if mask.sum() > 1:
|
||||
pytest.skip('test_poynting_planes can only test single point sources, got {}'.format(mask.sum()))
|
||||
|
||||
args = {'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon}
|
||||
args: Dict[str, Any] = {
|
||||
'dxes': sim.dxes,
|
||||
'epsilon': sim.epsilon,
|
||||
}
|
||||
|
||||
mx = numpy.roll(mask, -1, axis=0)
|
||||
my = numpy.roll(mask, -1, axis=1)
|
||||
@ -149,13 +158,13 @@ def dt(request: FixtureRequest) -> Iterable[float]:
|
||||
class TDResult:
|
||||
shape: Tuple[int, ...]
|
||||
dt: float
|
||||
dxes: List[List[numpy.ndarray]]
|
||||
epsilon: numpy.ndarray
|
||||
j_distribution: numpy.ndarray
|
||||
dxes: List[List[NDArray[numpy.float64]]]
|
||||
epsilon: NDArray[numpy.float64]
|
||||
j_distribution: NDArray[numpy.float64]
|
||||
j_steps: Tuple[int, ...]
|
||||
es: List[numpy.ndarray] = dataclasses.field(default_factory=list)
|
||||
hs: List[numpy.ndarray] = dataclasses.field(default_factory=list)
|
||||
js: List[numpy.ndarray] = dataclasses.field(default_factory=list)
|
||||
es: List[NDArray[numpy.float64]] = dataclasses.field(default_factory=list)
|
||||
hs: List[NDArray[numpy.float64]] = dataclasses.field(default_factory=list)
|
||||
js: List[NDArray[numpy.float64]] = dataclasses.field(default_factory=list)
|
||||
|
||||
|
||||
@pytest.fixture(params=[(0, 4, 8)]) # (0,)
|
||||
@ -164,10 +173,11 @@ def j_steps(request: FixtureRequest) -> Iterable[Tuple[int, ...]]:
|
||||
|
||||
|
||||
@pytest.fixture(params=['center', 'random'])
|
||||
def j_distribution(request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
j_mag: float,
|
||||
) -> Iterable[numpy.ndarray]:
|
||||
def j_distribution(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
j_mag: float,
|
||||
) -> Iterable[NDArray[numpy.float64]]:
|
||||
j = numpy.zeros(shape)
|
||||
if request.param == 'center':
|
||||
j[:, shape[1] // 2, shape[2] // 2, shape[3] // 2] = j_mag
|
||||
@ -179,12 +189,13 @@ def j_distribution(request: FixtureRequest,
|
||||
|
||||
|
||||
@pytest.fixture()
|
||||
def sim(request: FixtureRequest,
|
||||
def sim(
|
||||
request: FixtureRequest,
|
||||
shape: Tuple[int, ...],
|
||||
epsilon: numpy.ndarray,
|
||||
dxes: List[List[numpy.ndarray]],
|
||||
epsilon: NDArray[numpy.float64],
|
||||
dxes: List[List[NDArray[numpy.float64]]],
|
||||
dt: float,
|
||||
j_distribution: numpy.ndarray,
|
||||
j_distribution: NDArray[numpy.float64],
|
||||
j_steps: Tuple[int, ...],
|
||||
) -> TDResult:
|
||||
is3d = (numpy.array(shape) == 1).sum() == 0
|
||||
|
||||
@ -1,24 +1,27 @@
|
||||
from typing import Any
|
||||
import numpy # type: ignore
|
||||
import numpy
|
||||
from typing import ArrayLike
|
||||
|
||||
|
||||
PRNG = numpy.random.RandomState(12345)
|
||||
|
||||
|
||||
def assert_fields_close(x: numpy.ndarray,
|
||||
y: numpy.ndarray,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
def assert_fields_close(
|
||||
x: ArrayLike,
|
||||
y: ArrayLike,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
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)
|
||||
|
||||
def assert_close(x: numpy.ndarray,
|
||||
y: numpy.ndarray,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
def assert_close(
|
||||
x: ArrayLike,
|
||||
y: ArrayLike,
|
||||
*args: Any,
|
||||
**kwargs: Any,
|
||||
) -> None:
|
||||
numpy.testing.assert_allclose(x, y, *args, **kwargs)
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user