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move stuff under fdmath

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Jan Petykiewicz 4 years ago
parent 1242e8794b
commit a956323b94
23 changed files with 304 additions and 272 deletions
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16
examples/fdfd.py
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@ -3,14 +3,18 @@ import numpy
from numpy.linalg import norm from numpy.linalg import norm
import meanas import meanas
from meanas import vec, unvec, fdtd from meanas import fdtd
from meanas.fdfd import waveguide_mode, functional, scpml, operators from meanas.fdmath import vec, unvec
from meanas.fdfd import waveguide_3d, functional, scpml, operators
from meanas.fdfd.solvers import generic as generic_solver from meanas.fdfd.solvers import generic as generic_solver
import gridlock import gridlock
from matplotlib import pyplot from matplotlib import pyplot
import logging
logging.basicConfig(level=logging.DEBUG)
__author__ = 'Jan Petykiewicz' __author__ = 'Jan Petykiewicz'
@ -134,10 +138,10 @@ def test1(solver=generic_solver):
'polarity': +1, 'polarity': +1,
} }
wg_results = waveguide_mode.solve_waveguide_mode(mode_number=0, omega=omega, epsilon=grid.grids, **wg_args) wg_results = waveguide_3d.solve_mode(mode_number=0, omega=omega, epsilon=grid.grids, **wg_args)
J = waveguide_mode.compute_source(E=wg_results['E'], wavenumber=wg_results['wavenumber'], J = waveguide_3d.compute_source(E=wg_results['E'], wavenumber=wg_results['wavenumber'],
omega=omega, epsilon=grid.grids, **wg_args) omega=omega, epsilon=grid.grids, **wg_args)
e_overlap = waveguide_mode.compute_overlap_e(E=wg_results['E'], wavenumber=wg_results['wavenumber'], **wg_args) e_overlap = waveguide_3d.compute_overlap_e(E=wg_results['E'], wavenumber=wg_results['wavenumber'], **wg_args)
pecg = gridlock.Grid(edge_coords, initial=0.0, num_grids=3) pecg = gridlock.Grid(edge_coords, initial=0.0, num_grids=3)
# pecg.draw_cuboid(center=[700, 0, 0], dimensions=[80, 1e8, 1e8], eps=1) # pecg.draw_cuboid(center=[700, 0, 0], dimensions=[80, 1e8, 1e8], eps=1)

3
meanas/__init__.py
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@ -6,9 +6,6 @@ See the readme or `import meanas; help(meanas)` for more info.
import pathlib import pathlib
from .types import dx_lists_t, field_t, vfield_t, field_updater
from .vectorization import vec, unvec
__author__ = 'Jan Petykiewicz' __author__ = 'Jan Petykiewicz'
with open(pathlib.Path(__file__).parent / 'VERSION', 'r') as f: with open(pathlib.Path(__file__).parent / 'VERSION', 'r') as f:

15
meanas/eigensolvers.py
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@ -37,20 +37,17 @@ def power_iteration(operator: sparse.spmatrix,
def rayleigh_quotient_iteration(operator: sparse.spmatrix or spalg.LinearOperator, def rayleigh_quotient_iteration(operator: sparse.spmatrix or spalg.LinearOperator,
guess_vectors: numpy.ndarray, guess_vector: numpy.ndarray,
iterations: int = 40, iterations: int = 40,
tolerance: float = 1e-13, tolerance: float = 1e-13,
solver=None, solver = None,
) -> Tuple[complex, numpy.ndarray]: ) -> Tuple[complex, numpy.ndarray]:
""" """
Use Rayleigh quotient iteration to refine an eigenvector guess. Use Rayleigh quotient iteration to refine an eigenvector guess.
TODO:
Need to test this for more than one guess_vectors.
Args: Args:
operator: Matrix to analyze. operator: Matrix to analyze.
guess_vectors: Eigenvectors to refine. guess_vector: Eigenvector to refine.
iterations: Maximum number of iterations to perform. Default 40. iterations: Maximum number of iterations to perform. Default 40.
tolerance: Stop iteration if `(A - I*eigenvalue) @ v < num_vectors * tolerance`, tolerance: Stop iteration if `(A - I*eigenvalue) @ v < num_vectors * tolerance`,
Default 1e-13. Default 1e-13.
@ -73,16 +70,16 @@ def rayleigh_quotient_iteration(operator: sparse.spmatrix or spalg.LinearOperato
if solver is None: if solver is None:
solver = lambda A, b: spalg.bicgstab(A, b)[0] solver = lambda A, b: spalg.bicgstab(A, b)[0]
v = numpy.atleast_2d(guess_vectors) v = numpy.squeeze(guess_vector)
v /= norm(v) v /= norm(v)
for _ in range(iterations): for _ in range(iterations):
eigval = v.conj() @ (operator @ v) eigval = v.conj() @ (operator @ v)
if norm(operator @ v - eigval * v) < v.shape[1] * tolerance: if norm(operator @ v - eigval * v) < tolerance:
break break
shifted_operator = operator - shift(eigval) shifted_operator = operator - shift(eigval)
v = solver(shifted_operator, v) v = solver(shifted_operator, v)
v /= norm(v, axis=0) v /= norm(v)
return eigval, v return eigval, v

28
meanas/fdfd/bloch.py
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@ -83,7 +83,7 @@ import scipy.optimize
from scipy.linalg import norm from scipy.linalg import norm
import scipy.sparse.linalg as spalg import scipy.sparse.linalg as spalg
from .. import field_t from ..fdmath import fdfield_t
logger = logging.getLogger(__name__) logger = logging.getLogger(__name__)
@ -154,8 +154,8 @@ def generate_kmn(k0: numpy.ndarray,
def maxwell_operator(k0: numpy.ndarray, def maxwell_operator(k0: numpy.ndarray,
G_matrix: numpy.ndarray, G_matrix: numpy.ndarray,
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None mu: fdfield_t = None
) -> Callable[[numpy.ndarray], numpy.ndarray]: ) -> Callable[[numpy.ndarray], numpy.ndarray]:
""" """
Generate the Maxwell operator Generate the Maxwell operator
@ -227,8 +227,8 @@ def maxwell_operator(k0: numpy.ndarray,
def hmn_2_exyz(k0: numpy.ndarray, def hmn_2_exyz(k0: numpy.ndarray,
G_matrix: numpy.ndarray, G_matrix: numpy.ndarray,
epsilon: field_t, epsilon: fdfield_t,
) -> Callable[[numpy.ndarray], field_t]: ) -> Callable[[numpy.ndarray], fdfield_t]:
""" """
Generate an operator which converts a vectorized spatial-frequency-space Generate an operator which converts a vectorized spatial-frequency-space
h_mn into an E-field distribution, i.e. h_mn into an E-field distribution, i.e.
@ -249,7 +249,7 @@ def hmn_2_exyz(k0: numpy.ndarray,
k_mag, m, n = generate_kmn(k0, G_matrix, shape) k_mag, m, n = generate_kmn(k0, G_matrix, shape)
def operator(h: numpy.ndarray) -> field_t: def operator(h: numpy.ndarray) -> fdfield_t:
hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)] hin_m, hin_n = [hi.reshape(shape) for hi in numpy.split(h, 2)]
d_xyz = (n * hin_m - d_xyz = (n * hin_m -
m * hin_n) * k_mag m * hin_n) * k_mag
@ -262,8 +262,8 @@ def hmn_2_exyz(k0: numpy.ndarray,
def hmn_2_hxyz(k0: numpy.ndarray, def hmn_2_hxyz(k0: numpy.ndarray,
G_matrix: numpy.ndarray, G_matrix: numpy.ndarray,
epsilon: field_t epsilon: fdfield_t
) -> Callable[[numpy.ndarray], field_t]: ) -> Callable[[numpy.ndarray], fdfield_t]:
""" """
Generate an operator which converts a vectorized spatial-frequency-space Generate an operator which converts a vectorized spatial-frequency-space
h_mn into an H-field distribution, i.e. h_mn into an H-field distribution, i.e.
@ -293,8 +293,8 @@ def hmn_2_hxyz(k0: numpy.ndarray,
def inverse_maxwell_operator_approx(k0: numpy.ndarray, def inverse_maxwell_operator_approx(k0: numpy.ndarray,
G_matrix: numpy.ndarray, G_matrix: numpy.ndarray,
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None mu: fdfield_t = None
) -> Callable[[numpy.ndarray], numpy.ndarray]: ) -> Callable[[numpy.ndarray], numpy.ndarray]:
""" """
Generate an approximate inverse of the Maxwell operator, Generate an approximate inverse of the Maxwell operator,
@ -366,8 +366,8 @@ def find_k(frequency: float,
tolerance: float, tolerance: float,
direction: numpy.ndarray, direction: numpy.ndarray,
G_matrix: numpy.ndarray, G_matrix: numpy.ndarray,
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None, mu: fdfield_t = None,
band: int = 0, band: int = 0,
k_min: float = 0, k_min: float = 0,
k_max: float = 0.5, k_max: float = 0.5,
@ -409,8 +409,8 @@ def find_k(frequency: float,
def eigsolve(num_modes: int, def eigsolve(num_modes: int,
k0: numpy.ndarray, k0: numpy.ndarray,
G_matrix: numpy.ndarray, G_matrix: numpy.ndarray,
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None, mu: fdfield_t = None,
tolerance: float = 1e-20, tolerance: float = 1e-20,
max_iters: int = 10000, max_iters: int = 10000,
reset_iters: int = 100, reset_iters: int = 100,

10
meanas/fdfd/farfield.py
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@ -6,11 +6,11 @@ import numpy
from numpy.fft import fft2, fftshift, fftfreq, ifft2, ifftshift from numpy.fft import fft2, fftshift, fftfreq, ifft2, ifftshift
from numpy import pi from numpy import pi
from .. import field_t from .. import fdfield_t
def near_to_farfield(E_near: field_t, def near_to_farfield(E_near: fdfield_t,
H_near: field_t, H_near: fdfield_t,
dx: float, dx: float,
dy: float, dy: float,
padded_size: List[int] = None padded_size: List[int] = None
@ -117,8 +117,8 @@ def near_to_farfield(E_near: field_t,
def far_to_nearfield(E_far: field_t, def far_to_nearfield(E_far: fdfield_t,
H_far: field_t, H_far: fdfield_t,
dkx: float, dkx: float,
dky: float, dky: float,
padded_size: List[int] = None padded_size: List[int] = None

52
meanas/fdfd/functional.py
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@ -2,32 +2,30 @@
Functional versions of many FDFD operators. These can be useful for performing Functional versions of many FDFD operators. These can be useful for performing
FDFD calculations without needing to construct large matrices in memory. FDFD calculations without needing to construct large matrices in memory.
The functions generated here expect `field_t` inputs with shape (3, X, Y, Z), The functions generated here expect `fdfield_t` inputs with shape (3, X, Y, Z),
e.g. E = [E_x, E_y, E_z] where each component has shape (X, Y, Z) e.g. E = [E_x, E_y, E_z] where each component has shape (X, Y, Z)
""" """
from typing import List, Callable, Tuple from typing import List, Callable, Tuple
import numpy import numpy
from .. import dx_lists_t, field_t from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
from ..fdmath.functional import curl_forward, curl_back from ..fdmath.functional import curl_forward, curl_back
__author__ = 'Jan Petykiewicz' __author__ = 'Jan Petykiewicz'
field_transform_t = Callable[[field_t], field_t]
def e_full(omega: complex, def e_full(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None mu: fdfield_t = None
) -> field_transform_t: ) -> fdfield_updater_t:
""" """
Wave operator for use with E-field. See `operators.e_full` for details. Wave operator for use with E-field. See `operators.e_full` for details.
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters [dx_e, dx_h] as described in meanas.types dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Dielectric constant epsilon: Dielectric constant
mu: Magnetic permeability (default 1 everywhere) mu: Magnetic permeability (default 1 everywhere)
@ -54,16 +52,16 @@ def e_full(omega: complex,
def eh_full(omega: complex, def eh_full(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None mu: fdfield_t = None
) -> Callable[[field_t, field_t], Tuple[field_t, field_t]]: ) -> Callable[[fdfield_t, fdfield_t], Tuple[fdfield_t, fdfield_t]]:
""" """
Wave operator for full (both E and H) field representation. Wave operator for full (both E and H) field representation.
See `operators.eh_full`. See `operators.eh_full`.
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters [dx_e, dx_h] as described in meanas.types dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Dielectric constant epsilon: Dielectric constant
mu: Magnetic permeability (default 1 everywhere) mu: Magnetic permeability (default 1 everywhere)
@ -90,15 +88,15 @@ def eh_full(omega: complex,
def e2h(omega: complex, def e2h(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
mu: field_t = None, mu: fdfield_t = None,
) -> field_transform_t: ) -> fdfield_updater_t:
""" """
Utility operator for converting the `E` field into the `H` field. Utility operator for converting the `E` field into the `H` field.
For use with `e_full` -- assumes that there is no magnetic current `M`. For use with `e_full` -- assumes that there is no magnetic current `M`.
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
mu: Magnetic permeability (default 1 everywhere) mu: Magnetic permeability (default 1 everywhere)
Return: Return:
@ -121,8 +119,8 @@ def e2h(omega: complex,
def m2j(omega: complex, def m2j(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
mu: field_t = None, mu: fdfield_t = None,
) -> field_transform_t: ) -> fdfield_updater_t:
""" """
Utility operator for converting magnetic current `M` distribution Utility operator for converting magnetic current `M` distribution
into equivalent electric current distribution `J`. into equivalent electric current distribution `J`.
@ -130,7 +128,7 @@ def m2j(omega: complex,
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
mu: Magnetic permeability (default 1 everywhere) mu: Magnetic permeability (default 1 everywhere)
Returns: Returns:
@ -153,12 +151,12 @@ def m2j(omega: complex,
return m2j_mu return m2j_mu
def e_tfsf_source(TF_region: field_t, def e_tfsf_source(TF_region: fdfield_t,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None, mu: fdfield_t = None,
) -> field_transform_t: ) -> fdfield_updater_t:
""" """
Operator that turns an E-field distribution into a total-field/scattered-field Operator that turns an E-field distribution into a total-field/scattered-field
(TFSF) source. (TFSF) source.
@ -168,7 +166,7 @@ def e_tfsf_source(TF_region: field_t,
(i.e. in the scattered-field region). (i.e. in the scattered-field region).
Should have the same shape as the simulation grid, e.g. `epsilon[0].shape`. Should have the same shape as the simulation grid, e.g. `epsilon[0].shape`.
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Dielectric constant distribution epsilon: Dielectric constant distribution
mu: Magnetic permeability (default 1 everywhere) mu: Magnetic permeability (default 1 everywhere)
@ -184,7 +182,7 @@ def e_tfsf_source(TF_region: field_t,
return neg_iwj / (-1j * omega) return neg_iwj / (-1j * omega)
def poynting_e_cross_h(dxes: dx_lists_t) -> Callable[[field_t, field_t], field_t]: def poynting_e_cross_h(dxes: dx_lists_t) -> Callable[[fdfield_t, fdfield_t], fdfield_t]:
""" """
Generates a function that takes the single-frequency `E` and `H` fields Generates a function that takes the single-frequency `E` and `H` fields
and calculates the cross product `E` x `H` = \\( E \\times H \\) as required and calculates the cross product `E` x `H` = \\( E \\times H \\) as required
@ -201,12 +199,12 @@ def poynting_e_cross_h(dxes: dx_lists_t) -> Callable[[field_t, field_t], field_t
instead. instead.
Args: Args:
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
Returns: Returns:
Function `f` that returns E x H as required for the poynting vector. Function `f` that returns E x H as required for the poynting vector.
""" """
def exh(e: field_t, h: field_t): def exh(e: fdfield_t, h: fdfield_t):
s = numpy.empty_like(e) s = numpy.empty_like(e)
ex = e[0] * dxes[0][0][:, None, None] ex = e[0] * dxes[0][0][:, None, None]
ey = e[1] * dxes[0][1][None, :, None] ey = e[1] * dxes[0][1][None, :, None]

70
meanas/fdfd/operators.py
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@ -9,7 +9,7 @@ E- and H-field values are defined on a Yee cell; `epsilon` values should be calc
cells centered at each E component (`mu` at each H component). cells centered at each E component (`mu` at each H component).
Many of these functions require a `dxes` parameter, of type `dx_lists_t`; see Many of these functions require a `dxes` parameter, of type `dx_lists_t`; see
the `meanas.types` submodule for details. the `meanas.fdmath.types` submodule for details.
The following operators are included: The following operators are included:
@ -31,7 +31,7 @@ from typing import List, Tuple
import numpy import numpy
import scipy.sparse as sparse import scipy.sparse as sparse
from .. import vec, dx_lists_t, vfield_t from ..fdmath import vec, dx_lists_t, vfdfield_t
from ..fdmath.operators import shift_with_mirror, rotation, curl_forward, curl_back from ..fdmath.operators import shift_with_mirror, rotation, curl_forward, curl_back
@ -40,10 +40,10 @@ __author__ = 'Jan Petykiewicz'
def e_full(omega: complex, def e_full(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
pec: vfield_t = None, pec: vfdfield_t = None,
pmc: vfield_t = None, pmc: vfdfield_t = None,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Wave operator Wave operator
@ -60,7 +60,7 @@ def e_full(omega: complex,
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Vectorized dielectric constant epsilon: Vectorized dielectric constant
mu: Vectorized magnetic permeability (default 1 everywhere). mu: Vectorized magnetic permeability (default 1 everywhere).
pec: Vectorized mask specifying PEC cells. Any cells where `pec != 0` are interpreted pec: Vectorized mask specifying PEC cells. Any cells where `pec != 0` are interpreted
@ -107,7 +107,7 @@ def e_full_preconditioners(dxes: dx_lists_t
The preconditioner matrices are diagonal and complex, with `Pr = 1 / Pl` The preconditioner matrices are diagonal and complex, with `Pr = 1 / Pl`
Args: Args:
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
Returns: Returns:
Preconditioner matrices `(Pl, Pr)`. Preconditioner matrices `(Pl, Pr)`.
@ -124,10 +124,10 @@ def e_full_preconditioners(dxes: dx_lists_t
def h_full(omega: complex, def h_full(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
pec: vfield_t = None, pec: vfdfield_t = None,
pmc: vfield_t = None, pmc: vfdfield_t = None,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Wave operator Wave operator
@ -142,7 +142,7 @@ def h_full(omega: complex,
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Vectorized dielectric constant epsilon: Vectorized dielectric constant
mu: Vectorized magnetic permeability (default 1 everywhere) mu: Vectorized magnetic permeability (default 1 everywhere)
pec: Vectorized mask specifying PEC cells. Any cells where `pec != 0` are interpreted pec: Vectorized mask specifying PEC cells. Any cells where `pec != 0` are interpreted
@ -180,10 +180,10 @@ def h_full(omega: complex,
def eh_full(omega: complex, def eh_full(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
pec: vfield_t = None, pec: vfdfield_t = None,
pmc: vfield_t = None pmc: vfdfield_t = None
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Wave operator for `[E, H]` field representation. This operator implements Maxwell's Wave operator for `[E, H]` field representation. This operator implements Maxwell's
@ -210,7 +210,7 @@ def eh_full(omega: complex,
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Vectorized dielectric constant epsilon: Vectorized dielectric constant
mu: Vectorized magnetic permeability (default 1 everywhere) mu: Vectorized magnetic permeability (default 1 everywhere)
pec: Vectorized mask specifying PEC cells. Any cells where `pec != 0` are interpreted pec: Vectorized mask specifying PEC cells. Any cells where `pec != 0` are interpreted
@ -249,8 +249,8 @@ def eh_full(omega: complex,
def e2h(omega: complex, def e2h(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
mu: vfield_t = None, mu: vfdfield_t = None,
pmc: vfield_t = None, pmc: vfdfield_t = None,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Utility operator for converting the E field into the H field. Utility operator for converting the E field into the H field.
@ -258,7 +258,7 @@ def e2h(omega: complex,
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
mu: Vectorized magnetic permeability (default 1 everywhere) mu: Vectorized magnetic permeability (default 1 everywhere)
pmc: Vectorized mask specifying PMC cells. Any cells where `pmc != 0` are interpreted pmc: Vectorized mask specifying PMC cells. Any cells where `pmc != 0` are interpreted
as containing a perfect magnetic conductor (PMC). as containing a perfect magnetic conductor (PMC).
@ -280,7 +280,7 @@ def e2h(omega: complex,
def m2j(omega: complex, def m2j(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
mu: vfield_t = None mu: vfdfield_t = None
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Operator for converting a magnetic current M into an electric current J. Operator for converting a magnetic current M into an electric current J.
@ -288,7 +288,7 @@ def m2j(omega: complex,
Args: Args:
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
mu: Vectorized magnetic permeability (default 1 everywhere) mu: Vectorized magnetic permeability (default 1 everywhere)
Returns: Returns:
@ -302,14 +302,14 @@ def m2j(omega: complex,
return op return op
def poynting_e_cross(e: vfield_t, dxes: dx_lists_t) -> sparse.spmatrix: def poynting_e_cross(e: vfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
""" """
Operator for computing the Poynting vector, containing the Operator for computing the Poynting vector, containing the
(E x) portion of the Poynting vector. (E x) portion of the Poynting vector.
Args: Args:
e: Vectorized E-field for the ExH cross product e: Vectorized E-field for the ExH cross product
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
Returns: Returns:
Sparse matrix containing (E x) portion of Poynting cross product. Sparse matrix containing (E x) portion of Poynting cross product.
@ -331,13 +331,13 @@ def poynting_e_cross(e: vfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
return P return P
def poynting_h_cross(h: vfield_t, dxes: dx_lists_t) -> sparse.spmatrix: def poynting_h_cross(h: vfdfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
""" """
Operator for computing the Poynting vector, containing the (H x) portion of the Poynting vector. Operator for computing the Poynting vector, containing the (H x) portion of the Poynting vector.
Args: Args:
h: Vectorized H-field for the HxE cross product h: Vectorized H-field for the HxE cross product
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
Returns: Returns:
Sparse matrix containing (H x) portion of Poynting cross product. Sparse matrix containing (H x) portion of Poynting cross product.
@ -358,11 +358,11 @@ def poynting_h_cross(h: vfield_t, dxes: dx_lists_t) -> sparse.spmatrix:
return P return P
def e_tfsf_source(TF_region: vfield_t, def e_tfsf_source(TF_region: vfdfield_t,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Operator that turns a desired E-field distribution into a Operator that turns a desired E-field distribution into a
@ -374,7 +374,7 @@ def e_tfsf_source(TF_region: vfield_t,
TF_region: Mask, which is set to 1 inside the total-field region and 0 in the TF_region: Mask, which is set to 1 inside the total-field region and 0 in the
scattered-field region scattered-field region
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Vectorized dielectric constant epsilon: Vectorized dielectric constant
mu: Vectorized magnetic permeability (default 1 everywhere). mu: Vectorized magnetic permeability (default 1 everywhere).
@ -388,11 +388,11 @@ def e_tfsf_source(TF_region: vfield_t,
return (A @ Q - Q @ A) / (-1j * omega) return (A @ Q - Q @ A) / (-1j * omega)
def e_boundary_source(mask: vfield_t, def e_boundary_source(mask: vfdfield_t,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
periodic_mask_edges: bool = False, periodic_mask_edges: bool = False,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
@ -405,7 +405,7 @@ def e_boundary_source(mask: vfield_t,
i.e. any points where shifting the mask by one cell in any direction i.e. any points where shifting the mask by one cell in any direction
would change its value. would change its value.
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Vectorized dielectric constant epsilon: Vectorized dielectric constant
mu: Vectorized magnetic permeability (default 1 everywhere). mu: Vectorized magnetic permeability (default 1 everywhere).

10
meanas/fdfd/scpml.py
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@ -5,14 +5,14 @@ Functions for creating stretched coordinate perfectly matched layer (PML) absorb
from typing import List, Callable from typing import List, Callable
import numpy import numpy
from .. import dx_lists_t from ..fdmath import dx_lists_t
__author__ = 'Jan Petykiewicz' __author__ = 'Jan Petykiewicz'
s_function_t = Callable[[float], float] s_function_t = Callable[[float], float]
"""Typedef for s-functions""" """Typedef for s-functions, see `prepare_s_function()`"""
def prepare_s_function(ln_R: float = -16, def prepare_s_function(ln_R: float = -16,
@ -63,7 +63,7 @@ def uniform_grid_scpml(shape: numpy.ndarray or List[int],
Default uses `prepare_s_function()` with no parameters. Default uses `prepare_s_function()` with no parameters.
Returns: Returns:
Complex cell widths (dx_lists_t) as discussed in `meanas.types`. Complex cell widths (dx_lists_t) as discussed in `meanas.fdmath.types`.
""" """
if s_function is None: if s_function is None:
s_function = prepare_s_function() s_function = prepare_s_function()
@ -102,7 +102,7 @@ def stretch_with_scpml(dxes: dx_lists_t,
Stretch dxes to contain a stretched-coordinate PML (SCPML) in one direction along one axis. Stretch dxes to contain a stretched-coordinate PML (SCPML) in one direction along one axis.
Args: Args:
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
axis: axis to stretch (0=x, 1=y, 2=z) axis: axis to stretch (0=x, 1=y, 2=z)
polarity: direction to stretch (-1 for -ve, +1 for +ve) polarity: direction to stretch (-1 for -ve, +1 for +ve)
omega: Angular frequency for the simulation omega: Angular frequency for the simulation
@ -113,7 +113,7 @@ def stretch_with_scpml(dxes: dx_lists_t,
of pml parameters. Default uses `prepare_s_function()` with no parameters. of pml parameters. Default uses `prepare_s_function()` with no parameters.
Returns: Returns:
Complex cell widths (dx_lists_t) as discussed in `meanas.types`. Complex cell widths (dx_lists_t) as discussed in `meanas.fdmath.types`.
Multiple calls to this function may be necessary if multiple absorpbing boundaries are needed. Multiple calls to this function may be necessary if multiple absorpbing boundaries are needed.
""" """
if s_function is None: if s_function is None:

17
meanas/fdfd/solvers.py
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@ -9,6 +9,7 @@ import numpy
from numpy.linalg import norm from numpy.linalg import norm
import scipy.sparse.linalg import scipy.sparse.linalg
from ..fdmath import dx_lists_t, vfdfield_t
from . import operators from . import operators
@ -60,16 +61,16 @@ def _scipy_qmr(A: scipy.sparse.csr_matrix,
def generic(omega: complex, def generic(omega: complex,
dxes: List[List[numpy.ndarray]], dxes: dx_lists_t,
J: numpy.ndarray, J: vfdfield_t,
epsilon: numpy.ndarray, epsilon: vfdfield_t,
mu: numpy.ndarray = None, mu: vfdfield_t = None,
pec: numpy.ndarray = None, pec: vfdfield_t = None,
pmc: numpy.ndarray = None, pmc: vfdfield_t = None,
adjoint: bool = False, adjoint: bool = False,
matrix_solver: Callable[..., numpy.ndarray] = _scipy_qmr, matrix_solver: Callable[..., numpy.ndarray] = _scipy_qmr,
matrix_solver_opts: Dict[str, Any] = None, matrix_solver_opts: Dict[str, Any] = None,
) -> numpy.ndarray: ) -> vfdfield_t:
""" """
Conjugate gradient FDFD solver using CSR sparse matrices. Conjugate gradient FDFD solver using CSR sparse matrices.
@ -78,7 +79,7 @@ def generic(omega: complex,
Args: Args:
omega: Complex frequency to solve at. omega: Complex frequency to solve at.
dxes: `[[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]]` (complex cell sizes) as dxes: `[[dx_e, dy_e, dz_e], [dx_h, dy_h, dz_h]]` (complex cell sizes) as
discussed in `meanas.types` discussed in `meanas.fdmath.types`
J: Electric current distribution (at E-field locations) J: Electric current distribution (at E-field locations)
epsilon: Dielectric constant distribution (at E-field locations) epsilon: Dielectric constant distribution (at E-field locations)
mu: Magnetic permeability distribution (at H-field locations) mu: Magnetic permeability distribution (at H-field locations)

126
meanas/fdfd/waveguide_2d.py
Unescape Escape View File

@ -14,7 +14,8 @@ import numpy
from numpy.linalg import norm from numpy.linalg import norm
import scipy.sparse as sparse import scipy.sparse as sparse
from .. import vec, unvec, dx_lists_t, field_t, vfield_t 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 ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
from . import operators from . import operators
@ -24,8 +25,8 @@ __author__ = 'Jan Petykiewicz'
def operator_e(omega: complex, def operator_e(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Waveguide operator of the form Waveguide operator of the form
@ -66,7 +67,7 @@ def operator_e(omega: complex,
Args: Args:
omega: The angular frequency of the system. omega: The angular frequency of the system.
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
@ -76,8 +77,8 @@ def operator_e(omega: complex,
if numpy.any(numpy.equal(mu, None)): if numpy.any(numpy.equal(mu, None)):
mu = numpy.ones_like(epsilon) mu = numpy.ones_like(epsilon)
Dfx, Dfy = operators.deriv_forward(dxes[0]) Dfx, Dfy = deriv_forward(dxes[0])
Dbx, Dby = operators.deriv_back(dxes[1]) Dbx, Dby = deriv_back(dxes[1])
eps_parts = numpy.split(epsilon, 3) eps_parts = numpy.split(epsilon, 3)
eps_xy = sparse.diags(numpy.hstack((eps_parts[0], eps_parts[1]))) eps_xy = sparse.diags(numpy.hstack((eps_parts[0], eps_parts[1])))
@ -95,8 +96,8 @@ def operator_e(omega: complex,
def operator_h(omega: complex, def operator_h(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Waveguide operator of the form Waveguide operator of the form
@ -137,7 +138,7 @@ def operator_h(omega: complex,
Args: Args:
omega: The angular frequency of the system. omega: The angular frequency of the system.
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
@ -169,10 +170,10 @@ def normalized_fields_e(e_xy: numpy.ndarray,
wavenumber: complex, wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
prop_phase: float = 0, prop_phase: float = 0,
) -> Tuple[vfield_t, vfield_t]: ) -> Tuple[vfdfield_t, vfdfield_t]:
""" """
Given a vector `e_xy` containing the vectorized E_x and E_y fields, Given a vector `e_xy` containing the vectorized E_x and E_y fields,
returns normalized, vectorized E and H fields for the system. returns normalized, vectorized E and H fields for the system.
@ -182,7 +183,7 @@ def normalized_fields_e(e_xy: numpy.ndarray,
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`. wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy` It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy`
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
prop_phase: Phase shift `(dz * corrected_wavenumber)` over 1 cell in propagation direction. prop_phase: Phase shift `(dz * corrected_wavenumber)` over 1 cell in propagation direction.
@ -203,10 +204,10 @@ def normalized_fields_h(h_xy: numpy.ndarray,
wavenumber: complex, wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
prop_phase: float = 0, prop_phase: float = 0,
) -> Tuple[vfield_t, vfield_t]: ) -> Tuple[vfdfield_t, vfdfield_t]:
""" """
Given a vector `h_xy` containing the vectorized H_x and H_y fields, Given a vector `h_xy` containing the vectorized H_x and H_y fields,
returns normalized, vectorized E and H fields for the system. returns normalized, vectorized E and H fields for the system.
@ -216,7 +217,7 @@ def normalized_fields_h(h_xy: numpy.ndarray,
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`. wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy` It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy`
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
prop_phase: Phase shift `(dz * corrected_wavenumber)` over 1 cell in propagation direction. prop_phase: Phase shift `(dz * corrected_wavenumber)` over 1 cell in propagation direction.
@ -237,10 +238,10 @@ def _normalized_fields(e: numpy.ndarray,
h: numpy.ndarray, h: numpy.ndarray,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
prop_phase: float = 0, prop_phase: float = 0,
) -> Tuple[vfield_t, vfield_t]: ) -> Tuple[vfdfield_t, vfdfield_t]:
# TODO documentation # TODO documentation
shape = [s.size for s in dxes[0]] 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)] dxes_real = [[numpy.real(d) for d in numpy.meshgrid(*dxes[v], indexing='ij')] for v in (0, 1)]
@ -276,8 +277,8 @@ def _normalized_fields(e: numpy.ndarray,
def exy2h(wavenumber: complex, def exy2h(wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None mu: vfdfield_t = None
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Operator which transforms the vector `e_xy` containing the vectorized E_x and E_y fields, Operator which transforms the vector `e_xy` containing the vectorized E_x and E_y fields,
@ -287,7 +288,7 @@ def exy2h(wavenumber: complex,
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`. wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy` It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy`
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
@ -301,8 +302,8 @@ def exy2h(wavenumber: complex,
def hxy2e(wavenumber: complex, def hxy2e(wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None mu: vfdfield_t = None
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Operator which transforms the vector `h_xy` containing the vectorized H_x and H_y fields, Operator which transforms the vector `h_xy` containing the vectorized H_x and H_y fields,
@ -312,7 +313,7 @@ def hxy2e(wavenumber: complex,
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`. wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy` It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy`
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
@ -325,7 +326,7 @@ def hxy2e(wavenumber: complex,
def hxy2h(wavenumber: complex, def hxy2h(wavenumber: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
mu: vfield_t = None mu: vfdfield_t = None
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Operator which transforms the vector `h_xy` containing the vectorized H_x and H_y fields, Operator which transforms the vector `h_xy` containing the vectorized H_x and H_y fields,
@ -334,13 +335,13 @@ def hxy2h(wavenumber: complex,
Args: Args:
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`. wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`.
It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy` It should satisfy `operator_h() @ h_xy == wavenumber**2 * h_xy`
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
Returns: Returns:
Sparse matrix representing the operator. Sparse matrix representing the operator.
""" """
Dfx, Dfy = operators.deriv_forward(dxes[0]) Dfx, Dfy = deriv_forward(dxes[0])
hxy2hz = sparse.hstack((Dfx, Dfy)) / (1j * wavenumber) hxy2hz = sparse.hstack((Dfx, Dfy)) / (1j * wavenumber)
if not numpy.any(numpy.equal(mu, None)): if not numpy.any(numpy.equal(mu, None)):
@ -358,7 +359,7 @@ def hxy2h(wavenumber: complex,
def exy2e(wavenumber: complex, def exy2e(wavenumber: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Operator which transforms the vector `e_xy` containing the vectorized E_x and E_y fields, Operator which transforms the vector `e_xy` containing the vectorized E_x and E_y fields,
@ -367,13 +368,13 @@ def exy2e(wavenumber: complex,
Args: Args:
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)` wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy` It should satisfy `operator_e() @ e_xy == wavenumber**2 * e_xy`
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
Returns: Returns:
Sparse matrix representing the operator. Sparse matrix representing the operator.
""" """
Dbx, Dby = operators.deriv_back(dxes[1]) Dbx, Dby = deriv_back(dxes[1])
exy2ez = sparse.hstack((Dbx, Dby)) / (1j * wavenumber) exy2ez = sparse.hstack((Dbx, Dby)) / (1j * wavenumber)
if not numpy.any(numpy.equal(epsilon, None)): if not numpy.any(numpy.equal(epsilon, None)):
@ -392,7 +393,7 @@ def exy2e(wavenumber: complex,
def e2h(wavenumber: complex, def e2h(wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
mu: vfield_t = None mu: vfdfield_t = None
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Returns an operator which, when applied to a vectorized E eigenfield, produces Returns an operator which, when applied to a vectorized E eigenfield, produces
@ -401,7 +402,7 @@ def e2h(wavenumber: complex,
Args: Args:
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)` wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
Returns: Returns:
@ -416,7 +417,7 @@ def e2h(wavenumber: complex,
def h2e(wavenumber: complex, def h2e(wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t epsilon: vfdfield_t
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
Returns an operator which, when applied to a vectorized H eigenfield, produces Returns an operator which, when applied to a vectorized H eigenfield, produces
@ -425,7 +426,7 @@ def h2e(wavenumber: complex,
Args: Args:
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)` wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
Returns: Returns:
@ -441,7 +442,7 @@ def curl_e(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix:
Args: Args:
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)` wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
Return: Return:
Sparse matrix representation of the operator. Sparse matrix representation of the operator.
@ -450,9 +451,10 @@ def curl_e(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix:
for d in dxes[0]: for d in dxes[0]:
n *= len(d) n *= len(d)
print(wavenumber, n)
Bz = -1j * wavenumber * sparse.eye(n) Bz = -1j * wavenumber * sparse.eye(n)
Dfx, Dfy = operators.deriv_forward(dxes[0]) Dfx, Dfy = deriv_forward(dxes[0])
return operators.cross([Dfx, Dfy, Bz]) return cross([Dfx, Dfy, Bz])
def curl_h(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix: def curl_h(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix:
@ -461,7 +463,7 @@ def curl_h(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix:
Args: Args:
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)` wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
Return: Return:
Sparse matrix representation of the operator. Sparse matrix representation of the operator.
@ -471,16 +473,16 @@ def curl_h(wavenumber: complex, dxes: dx_lists_t) -> sparse.spmatrix:
n *= len(d) n *= len(d)
Bz = -1j * wavenumber * sparse.eye(n) Bz = -1j * wavenumber * sparse.eye(n)
Dbx, Dby = operators.deriv_back(dxes[1]) Dbx, Dby = deriv_back(dxes[1])
return operators.cross([Dbx, Dby, Bz]) return cross([Dbx, Dby, Bz])
def h_err(h: vfield_t, def h_err(h: vfdfield_t,
wavenumber: complex, wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None mu: vfdfield_t = None
) -> float: ) -> float:
""" """
Calculates the relative error in the H field Calculates the relative error in the H field
@ -489,7 +491,7 @@ def h_err(h: vfield_t,
h: Vectorized H field h: Vectorized H field
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)` wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
@ -509,12 +511,12 @@ def h_err(h: vfield_t,
return norm(op) / norm(h) return norm(op) / norm(h)
def e_err(e: vfield_t, def e_err(e: vfdfield_t,
wavenumber: complex, wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None mu: vfdfield_t = None
) -> float: ) -> float:
""" """
Calculates the relative error in the E field Calculates the relative error in the E field
@ -523,7 +525,7 @@ def e_err(e: vfield_t,
e: Vectorized E field e: Vectorized E field
wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)` wavenumber: Wavenumber assuming fields have z-dependence of `exp(-i * wavenumber * z)`
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
mu: Vectorized magnetic permeability grid (default 1 everywhere) mu: Vectorized magnetic permeability grid (default 1 everywhere)
@ -545,17 +547,17 @@ def e_err(e: vfield_t,
def solve_modes(mode_numbers: List[int], def solve_modes(mode_numbers: List[int],
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
mu: vfield_t = None, mu: vfdfield_t = None,
mode_margin: int = 2, mode_margin: int = 2,
) -> Tuple[List[vfield_t], List[complex]]: ) -> Tuple[List[vfdfield_t], List[complex]]:
""" """
Given a 2D region, attempts to solve for the eigenmode with the specified mode numbers. Given a 2D region, attempts to solve for the eigenmode with the specified mode numbers.
Args: Args:
mode_numbers: List of 0-indexed mode numbers to solve for mode_numbers: List of 0-indexed mode numbers to solve for
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
epsilon: Dielectric constant epsilon: Dielectric constant
mu: Magnetic permeability (default 1 everywhere) mu: Magnetic permeability (default 1 everywhere)
mode_margin: The eigensolver will actually solve for `(max(mode_number) + mode_margin)` mode_margin: The eigensolver will actually solve for `(max(mode_number) + mode_margin)`
@ -570,17 +572,18 @@ def solve_modes(mode_numbers: List[int],
Solve for the largest-magnitude eigenvalue of the real operator Solve for the largest-magnitude eigenvalue of the real operator
''' '''
dxes_real = [[numpy.real(dx) for dx in dxi] for dxi in dxes] dxes_real = [[numpy.real(dx) for dx in dxi] for dxi in dxes]
A_r = waveguide.operator_e(numpy.real(omega), dxes_real, numpy.real(epsilon), numpy.real(mu)) A_r = operator_e(numpy.real(omega), dxes_real, numpy.real(epsilon), numpy.real(mu))
eigvals, eigvecs = signed_eigensolve(A_r, max(mode_number) + mode_margin) eigvals, eigvecs = signed_eigensolve(A_r, max(mode_numbers) + mode_margin)
e_xys = eigvecs[:, -(numpy.array(mode_number) + 1)] e_xys = eigvecs[:, -(numpy.array(mode_numbers) + 1)]
''' '''
Now solve for the eigenvector of the full operator, using the real operator's Now solve for the eigenvector of the full operator, using the real operator's
eigenvector as an initial guess for Rayleigh quotient iteration. eigenvector as an initial guess for Rayleigh quotient iteration.
''' '''
A = waveguide.operator_e(omega, dxes, epsilon, mu) A = operator_e(omega, dxes, epsilon, mu)
eigvals, e_xys = rayleigh_quotient_iteration(A, e_xys) for nn in range(len(mode_numbers)):
eigvals[nn], e_xys[:, nn] = rayleigh_quotient_iteration(A, e_xys[:, nn])
# Calculate the wave-vector (force the real part to be positive) # Calculate the wave-vector (force the real part to be positive)
wavenumbers = numpy.sqrt(eigvals) wavenumbers = numpy.sqrt(eigvals)
@ -592,7 +595,7 @@ def solve_modes(mode_numbers: List[int],
def solve_mode(mode_number: int, def solve_mode(mode_number: int,
*args, *args,
**kwargs **kwargs
) -> Tuple[vfield_t, complex]: ) -> Tuple[vfdfield_t, complex]:
""" """
Wrapper around `solve_modes()` that solves for a single mode. Wrapper around `solve_modes()` that solves for a single mode.
@ -604,4 +607,5 @@ def solve_mode(mode_number: int,
Returns: Returns:
(e_xy, wavenumber) (e_xy, wavenumber)
""" """
return solve_modes(mode_numbers=[mode_number], *args, **kwargs) e_xys, wavenumbers = solve_modes(mode_numbers=[mode_number], *args, **kwargs)
return e_xys[:, 0], wavenumbers[0]

32
meanas/fdfd/waveguide_3d.py
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@ -8,7 +8,7 @@ from typing import Dict, List, Tuple
import numpy import numpy
import scipy.sparse as sparse import scipy.sparse as sparse
from .. import vec, unvec, dx_lists_t, vfield_t, field_t from ..fdmath import vec, unvec, dx_lists_t, vfdfield_t, fdfield_t
from . import operators, waveguide_2d, functional from . import operators, waveguide_2d, functional
@ -18,8 +18,8 @@ def solve_mode(mode_number: int,
axis: int, axis: int,
polarity: int, polarity: int,
slices: List[slice], slices: List[slice],
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None, mu: fdfield_t = None,
) -> Dict[str, complex or numpy.ndarray]: ) -> Dict[str, complex or numpy.ndarray]:
""" """
Given a 3D grid, selects a slice from the grid and attempts to Given a 3D grid, selects a slice from the grid and attempts to
@ -28,7 +28,7 @@ def solve_mode(mode_number: int,
Args: Args:
mode_number: Number of the mode, 0-indexed mode_number: Number of the mode, 0-indexed
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
axis: Propagation axis (0=x, 1=y, 2=z) axis: Propagation axis (0=x, 1=y, 2=z)
polarity: Propagation direction (+1 for +ve, -1 for -ve) polarity: Propagation direction (+1 for +ve, -1 for -ve)
slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use
@ -71,7 +71,7 @@ def solve_mode(mode_number: int,
wavenumber = 2/dx_prop * numpy.arcsin(wavenumber_2d * dx_prop/2) wavenumber = 2/dx_prop * numpy.arcsin(wavenumber_2d * dx_prop/2)
shape = [d.size for d in args_2d['dxes'][0]] shape = [d.size for d in args_2d['dxes'][0]]
ve, vh = waveguide.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, **args_2d, prop_phase=dx_prop * wavenumber)
e = unvec(ve, shape) e = unvec(ve, shape)
h = unvec(vh, shape) h = unvec(vh, shape)
@ -98,16 +98,16 @@ def solve_mode(mode_number: int,
return results return results
def compute_source(E: field_t, def compute_source(E: fdfield_t,
wavenumber: complex, wavenumber: complex,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
axis: int, axis: int,
polarity: int, polarity: int,
slices: List[slice], slices: List[slice],
epsilon: field_t, epsilon: fdfield_t,
mu: field_t = None, mu: fdfield_t = None,
) -> field_t: ) -> fdfield_t:
""" """
Given an eigenmode obtained by `solve_mode`, returns the current source distribution Given an eigenmode obtained by `solve_mode`, returns the current source distribution
necessary to position a unidirectional source at the slice location. necessary to position a unidirectional source at the slice location.
@ -116,7 +116,7 @@ def compute_source(E: field_t,
E: E-field of the mode E: E-field of the mode
wavenumber: Wavenumber of the mode wavenumber: Wavenumber of the mode
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
axis: Propagation axis (0=x, 1=y, 2=z) axis: Propagation axis (0=x, 1=y, 2=z)
polarity: Propagation direction (+1 for +ve, -1 for -ve) polarity: Propagation direction (+1 for +ve, -1 for -ve)
slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use
@ -143,13 +143,13 @@ def compute_source(E: field_t,
return J return J
def compute_overlap_e(E: field_t, def compute_overlap_e(E: fdfield_t,
wavenumber: complex, wavenumber: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
axis: int, axis: int,
polarity: int, polarity: int,
slices: List[slice], slices: List[slice],
) -> field_t: # TODO DOCS ) -> fdfield_t: # TODO DOCS
""" """
Given an eigenmode obtained by `solve_mode`, calculates an overlap_e for the 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) mode orthogonality relation Integrate(((E x H_mode) + (E_mode x H)) dot dn)
@ -160,7 +160,7 @@ def compute_overlap_e(E: field_t,
H: H-field of the mode (advanced by half of a Yee cell from E) H: H-field of the mode (advanced by half of a Yee cell from E)
wavenumber: Wavenumber of the mode wavenumber: Wavenumber of the mode
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
axis: Propagation axis (0=x, 1=y, 2=z) axis: Propagation axis (0=x, 1=y, 2=z)
polarity: Propagation direction (+1 for +ve, -1 for -ve) polarity: Propagation direction (+1 for +ve, -1 for -ve)
slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use
@ -188,13 +188,13 @@ def compute_overlap_e(E: field_t,
return Etgt return Etgt
def expand_e(E: field_t, def expand_e(E: fdfield_t,
wavenumber: complex, wavenumber: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
axis: int, axis: int,
polarity: int, polarity: int,
slices: List[slice], slices: List[slice],
) -> field_t: ) -> fdfield_t:
""" """
Given an eigenmode obtained by `solve_mode`, expands the E-field from the 2D 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 slice where the mode was calculated to the entire domain (along the propagation
@ -205,7 +205,7 @@ def expand_e(E: field_t,
Args: Args:
E: E-field of the mode E: E-field of the mode
wavenumber: Wavenumber of the mode wavenumber: Wavenumber of the mode
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types`
axis: Propagation axis (0=x, 1=y, 2=z) axis: Propagation axis (0=x, 1=y, 2=z)
polarity: Propagation direction (+1 for +ve, -1 for -ve) polarity: Propagation direction (+1 for +ve, -1 for -ve)
slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use slices: `epsilon[tuple(slices)]` is used to select the portion of the grid to use

12
meanas/fdfd/waveguide_cyl.py
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@ -13,7 +13,7 @@ import numpy
from numpy.linalg import norm from numpy.linalg import norm
import scipy.sparse as sparse import scipy.sparse as sparse
from .. import vec, unvec, dx_lists_t, field_t, vfield_t from ..fdmath import vec, unvec, dx_lists_t, fdfield_t, vfdfield_t
from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration from ..eigensolvers import signed_eigensolve, rayleigh_quotient_iteration
from . import operators from . import operators
@ -23,7 +23,7 @@ __author__ = 'Jan Petykiewicz'
def cylindrical_operator(omega: complex, def cylindrical_operator(omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
r0: float, r0: float,
) -> sparse.spmatrix: ) -> sparse.spmatrix:
""" """
@ -41,7 +41,7 @@ def cylindrical_operator(omega: complex,
Args: Args:
omega: The angular frequency of the system omega: The angular frequency of the system
dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.types` (2D) dxes: Grid parameters `[dx_e, dx_h]` as described in `meanas.fdmath.types` (2D)
epsilon: Vectorized dielectric constant grid epsilon: Vectorized dielectric constant grid
r0: Radius of curvature for the simulation. This should be the minimum value of r0: Radius of curvature for the simulation. This should be the minimum value of
r within the simulation domain. r within the simulation domain.
@ -83,9 +83,9 @@ def cylindrical_operator(omega: complex,
def solve_mode(mode_number: int, def solve_mode(mode_number: int,
omega: complex, omega: complex,
dxes: dx_lists_t, dxes: dx_lists_t,
epsilon: vfield_t, epsilon: vfdfield_t,
r0: float, r0: float,
) -> Dict[str, complex or field_t]: ) -> Dict[str, complex or fdfield_t]:
""" """
TODO: fixup TODO: fixup
Given a 2d (r, y) slice of epsilon, attempts to solve for the eigenmode Given a 2d (r, y) slice of epsilon, attempts to solve for the eigenmode
@ -94,7 +94,7 @@ def solve_mode(mode_number: int,
Args: Args:
mode_number: Number of the mode, 0-indexed mode_number: Number of the mode, 0-indexed
omega: Angular frequency of the simulation omega: Angular frequency of the simulation
dxes: Grid parameters [dx_e, dx_h] as described in meanas.types. dxes: Grid parameters [dx_e, dx_h] as described in meanas.fdmath.types.
The first coordinate is assumed to be r, the second is y. The first coordinate is assumed to be r, the second is y.
epsilon: Dielectric constant epsilon: Dielectric constant
r0: Radius of curvature for the simulation. This should be the minimum value of r0: Radius of curvature for the simulation. This should be the minimum value of

8
meanas/fdmath/__init__.py
Unescape Escape View File

@ -91,4 +91,12 @@ and
while \\( \\hat{h} \\) and \\( \\tilde{h} \\) are located at \\((m \pm \\frac{1}{2}, n \\pm \\frac{1}{2}, p \\pm \\frac{1}{2})\\) while \\( \\hat{h} \\) and \\( \\tilde{h} \\) are located at \\((m \pm \\frac{1}{2}, n \\pm \\frac{1}{2}, p \\pm \\frac{1}{2})\\)
with components at \\((m, n \\pm \\frac{1}{2}, p \\pm \\frac{1}{2})\\) etc. with components at \\((m, n \\pm \\frac{1}{2}, p \\pm \\frac{1}{2})\\) etc.
TODO: Explain fdfield_t vs vfdfield_t / operators vs functional
TODO: explain dxes
""" """
from .types import fdfield_t, vfdfield_t, dx_lists_t, fdfield_updater_t
from .vectorization import vec, unvec
from . import operators, functional, types, vectorization

14
meanas/fdmath/functional.py
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@ -6,10 +6,10 @@ Basic discrete calculus etc.
from typing import List, Callable, Tuple, Dict from typing import List, Callable, Tuple, Dict
import numpy import numpy
from .. import field_t, field_updater from .types import fdfield_t, fdfield_updater_t
def deriv_forward(dx_e: List[numpy.ndarray] = None) -> field_updater: def deriv_forward(dx_e: List[numpy.ndarray] = None) -> fdfield_updater_t:
""" """
Utility operators for taking discretized derivatives (backward variant). Utility operators for taking discretized derivatives (backward variant).
@ -31,7 +31,7 @@ def deriv_forward(dx_e: List[numpy.ndarray] = None) -> field_updater:
return derivs return derivs
def deriv_back(dx_h: List[numpy.ndarray] = None) -> field_updater: def deriv_back(dx_h: List[numpy.ndarray] = None) -> fdfield_updater_t:
""" """
Utility operators for taking discretized derivatives (forward variant). Utility operators for taking discretized derivatives (forward variant).
@ -53,7 +53,7 @@ def deriv_back(dx_h: List[numpy.ndarray] = None) -> field_updater:
return derivs return derivs
def curl_forward(dx_e: List[numpy.ndarray] = None) -> field_updater: def curl_forward(dx_e: List[numpy.ndarray] = None) -> fdfield_updater_t:
""" """
Curl operator for use with the E field. Curl operator for use with the E field.
@ -67,7 +67,7 @@ def curl_forward(dx_e: List[numpy.ndarray] = None) -> field_updater:
""" """
Dx, Dy, Dz = deriv_forward(dx_e) Dx, Dy, Dz = deriv_forward(dx_e)
def ce_fun(e: field_t) -> field_t: def ce_fun(e: fdfield_t) -> fdfield_t:
output = numpy.empty_like(e) output = numpy.empty_like(e)
output[0] = Dy(e[2]) output[0] = Dy(e[2])
output[1] = Dz(e[0]) output[1] = Dz(e[0])
@ -80,7 +80,7 @@ def curl_forward(dx_e: List[numpy.ndarray] = None) -> field_updater:
return ce_fun return ce_fun
def curl_back(dx_h: List[numpy.ndarray] = None) -> field_updater: def curl_back(dx_h: List[numpy.ndarray] = None) -> fdfield_updater_t:
""" """
Create a function which takes the backward curl of a field. Create a function which takes the backward curl of a field.
@ -94,7 +94,7 @@ def curl_back(dx_h: List[numpy.ndarray] = None) -> field_updater:
""" """
Dx, Dy, Dz = deriv_back(dx_h) Dx, Dy, Dz = deriv_back(dx_h)
def ch_fun(h: field_t) -> field_t: def ch_fun(h: fdfield_t) -> fdfield_t:
output = numpy.empty_like(h) output = numpy.empty_like(h)
output[0] = Dy(h[2]) output[0] = Dy(h[2])
output[1] = Dz(h[0]) output[1] = Dz(h[0])

4
meanas/fdmath/operators.py
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@ -7,7 +7,7 @@ from typing import List, Callable, Tuple, Dict
import numpy import numpy
import scipy.sparse as sparse import scipy.sparse as sparse
from .. import field_t, vfield_t from .types import fdfield_t, vfdfield_t
def rotation(axis: int, shape: List[int], shift_distance: int=1) -> sparse.spmatrix: def rotation(axis: int, shape: List[int], shift_distance: int=1) -> sparse.spmatrix:
@ -155,7 +155,7 @@ def cross(B: List[sparse.spmatrix]) -> sparse.spmatrix:
[-B[1], B[0], zero]]) [-B[1], B[0], zero]])
def vec_cross(b: vfield_t) -> sparse.spmatrix: def vec_cross(b: vfdfield_t) -> sparse.spmatrix:
""" """
Vector cross product operator Vector cross product operator

6
meanas/types.py → meanas/fdmath/types.py
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@ -8,7 +8,7 @@ from typing import List, Callable
# Field types # Field types
# TODO: figure out a better way to set the docstrings without creating actual subclasses? # 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 # Probably not a big issue since they're only used for type hinting
class field_t(numpy.ndarray): class fdfield_t(numpy.ndarray):
""" """
Vector field with shape (3, X, Y, Z) (e.g. `[E_x, E_y, E_z]`) Vector field with shape (3, X, Y, Z) (e.g. `[E_x, E_y, E_z]`)
@ -16,7 +16,7 @@ class field_t(numpy.ndarray):
""" """
pass pass
class vfield_t(numpy.ndarray): class vfdfield_t(numpy.ndarray):
""" """
Linearized vector field (single vector of length 3*X*Y*Z) Linearized vector field (single vector of length 3*X*Y*Z)
@ -37,4 +37,4 @@ dx_lists_t = List[List[numpy.ndarray]]
''' '''
field_updater = Callable[[field_t], field_t] fdfield_updater_t = Callable[[fdfield_t], fdfield_t]

6
meanas/vectorization.py → meanas/fdmath/vectorization.py
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@ -7,13 +7,13 @@ Vectorized versions of the field use row-major (ie., C-style) ordering.
from typing import List from typing import List
import numpy import numpy
from .types import field_t, vfield_t from .types import fdfield_t, vfdfield_t
__author__ = 'Jan Petykiewicz' __author__ = 'Jan Petykiewicz'
def vec(f: field_t) -> vfield_t: def vec(f: fdfield_t) -> vfdfield_t:
""" """
Create a 1D ndarray from a 3D vector field which spans a 1-3D region. Create a 1D ndarray from a 3D vector field which spans a 1-3D region.
@ -28,7 +28,7 @@ def vec(f: field_t) -> vfield_t:
return numpy.ravel(f, order='C') return numpy.ravel(f, order='C')
def unvec(v: vfield_t, shape: numpy.ndarray) -> field_t: def unvec(v: vfdfield_t, shape: numpy.ndarray) -> fdfield_t:
""" """
Perform the inverse of vec(): take a 1D ndarray and output a 3D field 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 of form [f_x, f_y, f_z] where each of f_* is a len(shape)-dimensional

20
meanas/fdtd/base.py
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@ -4,27 +4,33 @@ Basic FDTD field updates
from typing import List, Callable, Tuple, Dict from typing import List, Callable, Tuple, Dict
import numpy import numpy
from .. import dx_lists_t, field_t, field_updater from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
from ..fdmath.functional import curl_forward, curl_back from ..fdmath.functional import curl_forward, curl_back
__author__ = 'Jan Petykiewicz' __author__ = 'Jan Petykiewicz'
def maxwell_e(dt: float, dxes: dx_lists_t = None) -> field_updater: def maxwell_e(dt: float, dxes: dx_lists_t = None) -> fdfield_updater_t:
curl_h_fun = curl_back(dxes[1]) if dxes is not None:
curl_h_fun = curl_back(dxes[1])
else:
curl_h_fun = curl_back()
def me_fun(e: field_t, h: field_t, epsilon: field_t): def me_fun(e: fdfield_t, h: fdfield_t, epsilon: fdfield_t):
e += dt * curl_h_fun(h) / epsilon e += dt * curl_h_fun(h) / epsilon
return e return e
return me_fun return me_fun
def maxwell_h(dt: float, dxes: dx_lists_t = None) -> field_updater: def maxwell_h(dt: float, dxes: dx_lists_t = None) -> fdfield_updater_t:
curl_e_fun = curl_forward(dxes[0]) if dxes is not None:
curl_e_fun = curl_forward(dxes[0])
else:
curl_e_fun = curl_forward()
def mh_fun(e: field_t, h: field_t): def mh_fun(e: fdfield_t, h: fdfield_t):
h -= dt * curl_e_fun(e) h -= dt * curl_e_fun(e)
return h return h

12
meanas/fdtd/boundaries.py
Unescape Escape View File

@ -5,12 +5,12 @@ Boundary conditions
from typing import List, Callable, Tuple, Dict from typing import List, Callable, Tuple, Dict
import numpy import numpy
from .. import dx_lists_t, field_t, field_updater from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
def conducting_boundary(direction: int, def conducting_boundary(direction: int,
polarity: int polarity: int
) -> Tuple[field_updater, field_updater]: ) -> Tuple[fdfield_updater_t, fdfield_updater_t]:
dirs = [0, 1, 2] dirs = [0, 1, 2]
if direction not in dirs: if direction not in dirs:
raise Exception('Invalid direction: {}'.format(direction)) raise Exception('Invalid direction: {}'.format(direction))
@ -23,13 +23,13 @@ def conducting_boundary(direction: int,
boundary_slice[direction] = 0 boundary_slice[direction] = 0
shifted1_slice[direction] = 1 shifted1_slice[direction] = 1
def en(e: field_t): def en(e: fdfield_t):
e[direction][boundary_slice] = 0 e[direction][boundary_slice] = 0
e[u][boundary_slice] = e[u][shifted1_slice] e[u][boundary_slice] = e[u][shifted1_slice]
e[v][boundary_slice] = e[v][shifted1_slice] e[v][boundary_slice] = e[v][shifted1_slice]
return e return e
def hn(h: field_t): def hn(h: fdfield_t):
h[direction][boundary_slice] = h[direction][shifted1_slice] h[direction][boundary_slice] = h[direction][shifted1_slice]
h[u][boundary_slice] = 0 h[u][boundary_slice] = 0
h[v][boundary_slice] = 0 h[v][boundary_slice] = 0
@ -45,14 +45,14 @@ def conducting_boundary(direction: int,
shifted1_slice[direction] = -2 shifted1_slice[direction] = -2
shifted2_slice[direction] = -3 shifted2_slice[direction] = -3
def ep(e: field_t): def ep(e: fdfield_t):
e[direction][boundary_slice] = -e[direction][shifted2_slice] e[direction][boundary_slice] = -e[direction][shifted2_slice]
e[direction][shifted1_slice] = 0 e[direction][shifted1_slice] = 0
e[u][boundary_slice] = e[u][shifted1_slice] e[u][boundary_slice] = e[u][shifted1_slice]
e[v][boundary_slice] = e[v][shifted1_slice] e[v][boundary_slice] = e[v][shifted1_slice]
return e return e
def hp(h: field_t): def hp(h: fdfield_t):
h[direction][boundary_slice] = h[direction][shifted1_slice] h[direction][boundary_slice] = h[direction][shifted1_slice]
h[u][boundary_slice] = -h[u][shifted2_slice] h[u][boundary_slice] = -h[u][shifted2_slice]
h[u][shifted1_slice] = 0 h[u][shifted1_slice] = 0

83
meanas/fdtd/energy.py
Unescape Escape View File

@ -2,12 +2,13 @@
from typing import List, Callable, Tuple, Dict from typing import List, Callable, Tuple, Dict
import numpy import numpy
from .. import dx_lists_t, field_t, field_updater, fdmath from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
from ..fdmath.functional import deriv_back, deriv_forward
def poynting(e: field_t, def poynting(e: fdfield_t,
h: field_t, h: fdfield_t,
dxes: dx_lists_t = None, dxes: dx_lists_t = None,
) -> field_t: ) -> fdfield_t:
if dxes is None: if dxes is None:
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2)) dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
@ -25,51 +26,51 @@ def poynting(e: field_t,
return s return s
def poynting_divergence(s: field_t = None, def poynting_divergence(s: fdfield_t = None,
*, *,
e: field_t = None, e: fdfield_t = None,
h: field_t = None, h: fdfield_t = None,
dxes: dx_lists_t = None, dxes: dx_lists_t = None,
) -> field_t: ) -> fdfield_t:
if s is None: if s is None:
s = poynting(e, h, dxes=dxes) s = poynting(e, h, dxes=dxes)
Dx, Dy, Dz = fdmath.functional.deriv_back() Dx, Dy, Dz = deriv_back()
ds = Dx(s[0]) + Dy(s[1]) + Dz(s[2]) ds = Dx(s[0]) + Dy(s[1]) + Dz(s[2])
return ds return ds
def energy_hstep(e0: field_t, def energy_hstep(e0: fdfield_t,
h1: field_t, h1: fdfield_t,
e2: field_t, e2: fdfield_t,
epsilon: field_t = None, epsilon: fdfield_t = None,
mu: field_t = None, mu: fdfield_t = None,
dxes: dx_lists_t = None, dxes: dx_lists_t = None,
) -> field_t: ) -> fdfield_t:
u = dxmul(e0 * e2, h1 * h1, epsilon, mu, dxes) u = dxmul(e0 * e2, h1 * h1, epsilon, mu, dxes)
return u return u
def energy_estep(h0: field_t, def energy_estep(h0: fdfield_t,
e1: field_t, e1: fdfield_t,
h2: field_t, h2: fdfield_t,
epsilon: field_t = None, epsilon: fdfield_t = None,
mu: field_t = None, mu: fdfield_t = None,
dxes: dx_lists_t = None, dxes: dx_lists_t = None,
) -> field_t: ) -> fdfield_t:
u = dxmul(e1 * e1, h0 * h2, epsilon, mu, dxes) u = dxmul(e1 * e1, h0 * h2, epsilon, mu, dxes)
return u return u
def delta_energy_h2e(dt: float, def delta_energy_h2e(dt: float,
e0: field_t, e0: fdfield_t,
h1: field_t, h1: fdfield_t,
e2: field_t, e2: fdfield_t,
h3: field_t, h3: fdfield_t,
epsilon: field_t = None, epsilon: fdfield_t = None,
mu: field_t = None, mu: fdfield_t = None,
dxes: dx_lists_t = None, dxes: dx_lists_t = None,
) -> field_t: ) -> fdfield_t:
""" """
This is just from (e2 * e2 + h3 * h1) - (h1 * h1 + e0 * e2) This is just from (e2 * e2 + h3 * h1) - (h1 * h1 + e0 * e2)
""" """
@ -80,14 +81,14 @@ def delta_energy_h2e(dt: float,
def delta_energy_e2h(dt: float, def delta_energy_e2h(dt: float,
h0: field_t, h0: fdfield_t,
e1: field_t, e1: fdfield_t,
h2: field_t, h2: fdfield_t,
e3: field_t, e3: fdfield_t,
epsilon: field_t = None, epsilon: fdfield_t = None,
mu: field_t = None, mu: fdfield_t = None,
dxes: dx_lists_t = None, dxes: dx_lists_t = None,
) -> field_t: ) -> fdfield_t:
""" """
This is just from (h2 * h2 + e3 * e1) - (e1 * e1 + h0 * h2) This is just from (h2 * h2 + e3 * e1) - (e1 * e1 + h0 * h2)
""" """
@ -97,7 +98,7 @@ def delta_energy_e2h(dt: float,
return du return du
def delta_energy_j(j0: field_t, e1: field_t, dxes: dx_lists_t = None) -> field_t: def delta_energy_j(j0: fdfield_t, e1: fdfield_t, dxes: dx_lists_t = None) -> fdfield_t:
if dxes is None: if dxes is None:
dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2)) dxes = tuple(tuple(numpy.ones(1) for _ in range(3)) for _ in range(2))
@ -108,12 +109,12 @@ def delta_energy_j(j0: field_t, e1: field_t, dxes: dx_lists_t = None) -> field_t
return du return du
def dxmul(ee: field_t, def dxmul(ee: fdfield_t,
hh: field_t, hh: fdfield_t,
epsilon: field_t = None, epsilon: fdfield_t = None,
mu: field_t = None, mu: fdfield_t = None,
dxes: dx_lists_t = None dxes: dx_lists_t = None
) -> field_t: ) -> fdfield_t:
if epsilon is None: if epsilon is None:
epsilon = 1 epsilon = 1
if mu is None: if mu is None:

12
meanas/fdtd/pml.py
Unescape Escape View File

@ -7,7 +7,7 @@ PML implementations
from typing import List, Callable, Tuple, Dict from typing import List, Callable, Tuple, Dict
import numpy import numpy
from .. import dx_lists_t, field_t, field_updater from ..fdmath import dx_lists_t, fdfield_t, fdfield_updater_t
__author__ = 'Jan Petykiewicz' __author__ = 'Jan Petykiewicz'
@ -16,7 +16,7 @@ __author__ = 'Jan Petykiewicz'
def cpml(direction: int, def cpml(direction: int,
polarity: int, polarity: int,
dt: float, dt: float,
epsilon: field_t, epsilon: fdfield_t,
thickness: int = 8, thickness: int = 8,
ln_R_per_layer: float = -1.6, ln_R_per_layer: float = -1.6,
epsilon_eff: float = 1, epsilon_eff: float = 1,
@ -25,7 +25,7 @@ def cpml(direction: int,
ma: float = 1, ma: float = 1,
cfs_alpha: float = 0, cfs_alpha: float = 0,
dtype: numpy.dtype = numpy.float32, dtype: numpy.dtype = numpy.float32,
) -> Tuple[Callable, Callable, Dict[str, field_t]]: ) -> Tuple[Callable, Callable, Dict[str, fdfield_t]]:
if direction not in range(3): if direction not in range(3):
raise Exception('Invalid direction: {}'.format(direction)) raise Exception('Invalid direction: {}'.format(direction))
@ -58,6 +58,7 @@ def cpml(direction: int,
expand_slice = [None] * 3 expand_slice = [None] * 3
expand_slice[direction] = slice(None) expand_slice[direction] = slice(None)
expand_slice = tuple(expand_slice)
def par(x): def par(x):
scaling = (x / thickness) ** m scaling = (x / thickness) ** m
@ -79,6 +80,7 @@ def cpml(direction: int,
region[direction] = slice(-thickness, None) region[direction] = slice(-thickness, None)
else: else:
raise Exception('Bad polarity!') raise Exception('Bad polarity!')
region = tuple(region)
se = 1 if direction == 1 else -1 se = 1 if direction == 1 else -1
@ -97,7 +99,7 @@ def cpml(direction: int,
# Note that this is kinda slow -- would be faster to reuse dHv*p2h for the original # Note that this is kinda slow -- would be faster to reuse dHv*p2h for the original
# H update, but then you have multiple arrays and a monolithic (field + pml) update operation # H update, but then you have multiple arrays and a monolithic (field + pml) update operation
def pml_e(e: field_t, h: field_t, epsilon: field_t) -> Tuple[field_t, field_t]: def pml_e(e: fdfield_t, h: fdfield_t, epsilon: fdfield_t) -> Tuple[fdfield_t, fdfield_t]:
dHv = h[v][region] - numpy.roll(h[v], 1, axis=direction)[region] dHv = h[v][region] - numpy.roll(h[v], 1, axis=direction)[region]
dHu = h[u][region] - numpy.roll(h[u], 1, axis=direction)[region] dHu = h[u][region] - numpy.roll(h[u], 1, axis=direction)[region]
psi_e[0] *= p0e psi_e[0] *= p0e
@ -108,7 +110,7 @@ def cpml(direction: int,
e[v][region] -= se * dt / epsilon[v][region] * (psi_e[1] + (p2e - 1) * dHu) e[v][region] -= se * dt / epsilon[v][region] * (psi_e[1] + (p2e - 1) * dHu)
return e, h return e, h
def pml_h(e: field_t, h: field_t) -> Tuple[field_t, field_t]: def pml_h(e: fdfield_t, h: fdfield_t) -> Tuple[fdfield_t, fdfield_t]:
dEv = (numpy.roll(e[v], -1, axis=direction)[region] - e[v][region]) dEv = (numpy.roll(e[v], -1, axis=direction)[region] - e[v][region])
dEu = (numpy.roll(e[u], -1, axis=direction)[region] - e[u][region]) dEu = (numpy.roll(e[u], -1, axis=direction)[region] - e[u][region])
psi_h[0] *= p0h psi_h[0] *= p0h

17
meanas/test/test_fdfd.py
Unescape Escape View File

@ -5,7 +5,8 @@ import pytest
import numpy import numpy
#from numpy.testing import assert_allclose, assert_array_equal #from numpy.testing import assert_allclose, assert_array_equal
from .. import fdfd, vec, unvec from .. import fdfd
from ..fdmath import vec, unvec
from .utils import assert_close, assert_fields_close from .utils import assert_close, assert_fields_close
@ -20,6 +21,10 @@ def test_poynting_planes(sim):
mask = (sim.j != 0).any(axis=0) mask = (sim.j != 0).any(axis=0)
if mask.sum() != 2: if mask.sum() != 2:
pytest.skip(f'test_poynting_planes will only test 2-point sources, got {mask.sum()}') pytest.skip(f'test_poynting_planes will only test 2-point sources, got {mask.sum()}')
# for dxg in sim.dxes:
# for dxa in dxg:
# if not (dxa == sim.dxes[0][0][0]).all():
# pytest.skip('test_poynting_planes skips nonuniform dxes')
points = numpy.where(mask) points = numpy.where(mask)
mask[points[0][0], points[1][0], points[2][0]] = 0 mask[points[0][0], points[1][0], points[2][0]] = 0
@ -43,7 +48,6 @@ def test_poynting_planes(sim):
assert_close(sum(planes), src_energy.sum()) assert_close(sum(planes), src_energy.sum())
##################################### #####################################
# Test fixtures # Test fixtures
##################################### #####################################
@ -102,6 +106,15 @@ def sim(request, shape, epsilon, dxes, j_distribution, omega, pec, pmc):
# if is3d: # if is3d:
# pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)') # pytest.skip('Skipping dt != 0.3 because test is 3D (for speed)')
# # If no edge currents, add pmls
# src_mask = j_distribution.any(axis=0)
# th = 10
# #if src_mask.sum() - src_mask[th:-th, th:-th, th:-th].sum() == 0:
# if src_mask.sum() - src_mask[th:-th, :, :].sum() == 0:
# for axis in (0,):
# for polarity in (-1, 1):
# dxes = fdfd.scpml.stretch_with_scpml(dxes, axis=axis, polarity=polarity,
j_vec = vec(j_distribution) j_vec = vec(j_distribution)
eps_vec = vec(epsilon) eps_vec = vec(epsilon)
e_vec = fdfd.solvers.generic(J=j_vec, omega=omega, dxes=dxes, epsilon=eps_vec, e_vec = fdfd.solvers.generic(J=j_vec, omega=omega, dxes=dxes, epsilon=eps_vec,

3
meanas/test/test_fdfd_pml.py
Unescape Escape View File

@ -6,7 +6,8 @@ import pytest
import numpy import numpy
from numpy.testing import assert_allclose, assert_array_equal from numpy.testing import assert_allclose, assert_array_equal
from .. import fdfd, vec, unvec 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 from .test_fdfd import FDResult

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