#!/usr/bin/env python # -*- coding: UTF-8 -*- # # Copyright (C) 2009-2015 Ovidio Peña Rodríguez # # This file is part of python-scattnlay # # This program is free software: you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation, either version 3 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # The only additional remark is that we expect that all publications # describing work using this software, or all commercial products # using it, cite the following reference: # [1] O. Pena and U. Pal, "Scattering of electromagnetic radiation by # a multilayered sphere," Computer Physics Communications, # vol. 180, Nov. 2009, pp. 2348-2354. # # You should have received a copy of the GNU General Public License # along with this program. If not, see . # This test case calculates the differential scattering # cross section from a Luneburg lens, as described in: # B. R. Johnson, Applied Optics 35 (1996) 3286-3296. # The Luneburg lens is a sphere of radius a, with a # radially-varying index of refraction, given by: # m(r) = [2 - (r/a)**1]**(1/2) # For the calculations, the Luneburg lens was approximated # as a multilayered sphere with 500 equally spaced layers. # The refractive index of each layer is defined to be equal to # m(r) at the midpoint of the layer: ml = [2 - (xm/xL)**1]**(1/2), # with xm = (xl-1 + xl)/2, for l = 1,2,...,L. The size # parameter in the lth layer is xl = l*xL/500. According to # geometrical optics theory, the differential cross section # can be expressed as: # d(Csca)/d(a**2*Omega) = cos(Theta) # The differential cross section from wave optics is: # d(Csca)/d(a**2*Omega) = S11(Theta)/x**2 from scattnlay import scattnlay import numpy as np nL = 500.0 Xmax = 60.0 x = np.array([np.arange(1.0, nL + 1.0)*Xmax/nL], dtype = np.float64) m = np.array([np.sqrt((2.0 - ((x[0] - 0.5*Xmax/nL)/60.0)**2.0)) + 0.0j], dtype = np.complex128) theta = np.arange(0.0, 180.25, 0.25, dtype = np.float64)*np.pi/180.0 terms, Qext, Qsca, Qabs, Qbk, Qpr, g, Albedo, S1, S2 = scattnlay(x, m, theta) S11 = S1[0].real*S1[0].real + S1[0].imag*S1[0].imag + S2[0].real*S2[0].real + S2[0].imag*S2[0].imag result = np.vstack((theta*180.0/np.pi, S11/(2.0*Xmax*Xmax), np.cos(theta))).transpose() try: import matplotlib.pyplot as plt plt.plot(result[ : , 0], result[ : , 1], 'k', result[ : , 0], result[ : , 2], 'r') ax = plt.gca() ax.set_yscale('log') ax.set_ylim(1e-4, 1e3) plt.xlabel('Theta') plt.draw() plt.show() finally: np.savetxt("test04.txt", result, fmt = "%.5f") print result