106 lines
3.8 KiB
Python
106 lines
3.8 KiB
Python
import numpy
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from numpy import pi
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import gridlock
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from gridlock import XYZExtent
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from meanas.fdfd import waveguide_2d, waveguide_cyl
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from meanas.fdmath import vec, unvec
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from matplotlib import pyplot, colors
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from scipy import sparse
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import skrf
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from skrf import Network
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wl = 1310
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dx = 10
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radius = 25e3
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width = 400
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thf = 161
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thp = 77
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eps_si = 3.51 ** 2
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eps_ox = 1.453 ** 2
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x0 = (width / 2) % dx
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omega = 2 * pi / wl
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grid = gridlock.Grid([
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numpy.arange(-3000, 3000 + dx, dx),
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numpy.arange(-1500, 1500 + dx, dx),
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numpy.arange(-5 * dx, 5 * dx + dx, dx)],
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periodic=True,
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)
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epsilon = grid.allocate(eps_ox)
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grid.draw_cuboid(epsilon, extent=XYZExtent(xctr=x0, lx=width + 5e3, ymin=0, ymax=thf, zmin=-1e6, zmax=0), foreground=eps_si)
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grid.draw_cuboid(epsilon, extent=XYZExtent(xmax=-width / 2, lx=1.5e3, ymin=thp, ymax=1e6, zmin=-1e6, zctr=0), foreground=eps_ox)
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grid.draw_cuboid(epsilon, extent=XYZExtent(xmin= width / 2, lx=1.5e3, ymin=thp, ymax=1e6, zmin=-1e6, zctr=0), foreground=eps_ox)
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dxes = [grid.dxyz, grid.autoshifted_dxyz()]
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dxes_2d = [[d[0], d[1]] for d in dxes]
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mode_numbers = numpy.arange(20)
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args = dict(dxes=dxes_2d, omega=omega, mode_numbers=mode_numbers)
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eps = epsilon[:, :, :, 2].ravel()
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rmin = radius + grid.xyz[0].min()
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eL_xys, wavenumbers_L = waveguide_2d.solve_modes(epsilon=eps, **args)
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eR_xys, ang_wavenumbers_R = waveguide_cyl.solve_modes(epsilon=eps, **args, rmin=rmin)
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linear_wavenumbers_R = waveguide_cyl.linear_wavenumbers(e_xys=eR_xys, angular_wavenumbers=ang_wavenumbers_R, rmin=rmin, epsilon=eps, dxes=dxes_2d)
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eh_L = [
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waveguide_2d.normalized_fields_e(e_xy, wavenumber=wavenumber, dxes=dxes_2d, omega=omega, epsilon=eps)
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for e_xy, wavenumber in zip(eL_xys, wavenumbers_L)]
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eh_R = [
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waveguide_cyl.normalized_fields_e(e_xy, angular_wavenumber=ang_wavenumber, dxes=dxes_2d, omega=omega, epsilon=eps, rmin=rmin)
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for e_xy, ang_wavenumber in zip(eR_xys, ang_wavenumbers_R)]
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ss = waveguide_2d.get_s(eh_L, wavenumbers_L, eh_R, linear_wavenumbers_R, dxes=dxes_2d)
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ss11 = waveguide_2d.get_s(eh_L, wavenumbers_L, eh_L, wavenumbers_L, dxes=dxes_2d)
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ss22 = waveguide_2d.get_s(eh_R, linear_wavenumbers_R, eh_R, linear_wavenumbers_R, dxes=dxes_2d)
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fig, axes = pyplot.subplots(2, 2)
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mb0 = axes[0, 0].pcolormesh(numpy.abs(ss[::-1])**2, cmap='hot', vmin=0)
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fig.colorbar(mb0)
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axes[1, 0].set_title('S Abs^2')
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mb2 = axes[1, 0].pcolormesh(ss[::-1].real, cmap='bwr', norm=colors.CenteredNorm())
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fig.colorbar(mb2)
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axes[1, 0].set_title('S Real')
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mb3 = axes[1, 1].pcolormesh(ss[::-1].imag, cmap='bwr', norm=colors.CenteredNorm())
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fig.colorbar(mb3)
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axes[1, 1].set_title('S Imag')
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pyplot.show(block=False)
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e1, h1 = eh_L[2]
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e2, h2 = eh_R[2]
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figE, axesE = pyplot.subplots(3, 2)
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figH, axesH = pyplot.subplots(3, 2)
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esqmax = max(numpy.abs(e1).max(), numpy.abs(e2).max()) ** 2
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hsqmax = max(numpy.abs(h1).max(), numpy.abs(h2).max()) ** 2
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for mm, (ee, hh) in enumerate(zip((e1, e2), (h1, h2))):
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E = unvec(ee, grid.shape[:2])
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H = unvec(hh, grid.shape[:2])
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for aa in range(3):
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axesE[aa, mm].pcolormesh((numpy.abs(E[aa]) ** 2).T, cmap='bwr', norm=colors.CenteredNorm(halfrange=esqmax))
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axesH[aa, mm].pcolormesh((numpy.abs(H[aa]) ** 2).T, cmap='bwr', norm=colors.CenteredNorm(halfrange=hsqmax))
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pyplot.show(block=False)
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net_wb = Network(f=[1 / wl], s = ss)
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net_bw = net_wb.copy()
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net_bw.renumber(numpy.arange(40), numpy.roll(numpy.arange(40), 20))
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wg_phase = sparse.diags_array(numpy.exp(-1j * wavenumbers_L * 100e3))
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bend_phase = sparse.diags_array(numpy.exp(-1j * ang_wavenumbers_R * pi / 2))
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net_propwg = Network(f=[1 / wl], s = sparse.block_array(([None, wg_phase], [wg_phase, None])).toarray()[None, ...])
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net_propbend = Network(f=[1 / wl], s = sparse.block_array(([None, bend_phase], [bend_phase, None])).toarray()[None, ...])
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cir = skrf.network.cascade_list([net_propwg, net_wb, net_propbend, net_bw, net_propwg])
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