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142 lines
4.4 KiB
Python
142 lines
4.4 KiB
Python
#####################################
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import pytest # type: ignore
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import numpy # type: ignore
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from numpy.testing import assert_allclose # type: ignore
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from .. import fdfd
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from ..fdmath import vec, unvec
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#from .utils import assert_close, assert_fields_close
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from .test_fdfd import FDResult
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def test_pml(sim, src_polarity):
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e_sqr = numpy.squeeze((sim.e.conj() * sim.e).sum(axis=0))
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# from matplotlib import pyplot
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# pyplot.figure()
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# pyplot.plot(numpy.squeeze(e_sqr))
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# pyplot.show(block=True)
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e_sqr_tgt = e_sqr[16:19]
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e_sqr_rev = e_sqr[10:13]
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if src_polarity < 0:
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e_sqr_tgt, e_sqr_rev = e_sqr_rev, e_sqr_tgt
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assert_allclose(e_sqr_rev, 0, atol=1e-12)
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assert_allclose(e_sqr_tgt, 1, rtol=3e-6)
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# pyplot.figure()
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# pyplot.plot(numpy.squeeze(sim.e[0].real), label='Ex_real')
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# pyplot.plot(numpy.squeeze(sim.e[0].imag), label='Ex_imag')
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# pyplot.plot(numpy.squeeze(sim.e[1].real), label='Ey_real')
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# pyplot.plot(numpy.squeeze(sim.e[1].imag), label='Ey_imag')
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# pyplot.plot(numpy.squeeze(sim.e[2].real), label='Ez_real')
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# pyplot.plot(numpy.squeeze(sim.e[2].imag), label='Ez_imag')
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# pyplot.legend()
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# pyplot.show(block=True)
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# Test fixtures
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# ####################################
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# Also see conftest.py
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@pytest.fixture(params=[1 / 1500])
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def omega(request):
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yield request.param
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@pytest.fixture(params=[None])
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def pec(request):
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yield request.param
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@pytest.fixture(params=[None])
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def pmc(request):
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yield request.param
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@pytest.fixture(params=[(30, 1, 1),
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(1, 30, 1),
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(1, 1, 30)])
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def shape(request):
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yield (3, *request.param)
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@pytest.fixture(params=[+1, -1])
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def src_polarity(request):
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yield request.param
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@pytest.fixture()
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def j_distribution(request, shape, epsilon, dxes, omega, src_polarity):
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j = numpy.zeros(shape, dtype=complex)
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dim = numpy.where(numpy.array(shape[1:]) > 1)[0][0] # Propagation axis
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other_dims = [0, 1, 2]
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other_dims.remove(dim)
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dx_prop = (dxes[0][dim][shape[dim + 1] // 2]
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+ dxes[1][dim][shape[dim + 1] // 2]) / 2 # TODO is this right for nonuniform dxes?
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# Mask only contains components orthogonal to propagation direction
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center_mask = numpy.zeros(shape, dtype=bool)
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center_mask[other_dims, shape[1] // 2, shape[2] // 2, shape[3] // 2] = True
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if (epsilon[center_mask] != epsilon[center_mask][0]).any():
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center_mask[other_dims[1]] = False # If epsilon is not isotropic, pick only one dimension
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wavenumber = omega * numpy.sqrt(epsilon[center_mask].mean())
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wavenumber_corrected = 2 / dx_prop * numpy.arcsin(wavenumber * dx_prop / 2)
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e = numpy.zeros_like(epsilon, dtype=complex)
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e[center_mask] = 1 / numpy.linalg.norm(center_mask[:])
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slices = [slice(None), slice(None), slice(None)]
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slices[dim] = slice(shape[dim + 1] // 2,
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shape[dim + 1] // 2 + 1)
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j = fdfd.waveguide_3d.compute_source(E=e, wavenumber=wavenumber_corrected, omega=omega, dxes=dxes,
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axis=dim, polarity=src_polarity, slices=slices, epsilon=epsilon)
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yield j
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@pytest.fixture()
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def epsilon(request, shape, epsilon_bg, epsilon_fg):
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epsilon = numpy.full(shape, epsilon_fg, dtype=float)
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yield epsilon
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@pytest.fixture(params=['uniform'])
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def dxes(request, shape, dx, omega, epsilon_fg):
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if request.param == 'uniform':
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dxes = [[numpy.full(s, dx) for s in shape[1:]] for _ in range(2)]
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dim = numpy.where(numpy.array(shape[1:]) > 1)[0][0] # Propagation axis
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for axis in (dim,):
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for polarity in (-1, 1):
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dxes = fdfd.scpml.stretch_with_scpml(dxes, axis=axis, polarity=polarity,
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omega=omega, epsilon_effective=epsilon_fg,
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thickness=10)
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yield dxes
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@pytest.fixture()
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def sim(request, shape, epsilon, dxes, j_distribution, omega, pec, pmc):
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j_vec = vec(j_distribution)
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eps_vec = vec(epsilon)
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e_vec = fdfd.solvers.generic(J=j_vec, omega=omega, dxes=dxes, epsilon=eps_vec,
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matrix_solver_opts={'atol': 1e-15, 'tol': 1e-11})
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e = unvec(e_vec, shape[1:])
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sim = FDResult(
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shape=shape,
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dxes=dxes,
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epsilon=epsilon,
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j=j_distribution,
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e=e,
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pec=pec,
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pmc=pmc,
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omega=omega,
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)
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return sim
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