Updated scripts for calculating field distributions.

tests/python/field.py

@@ -110,8 +110,8 @@ try:
     pos = list(cbar.ax.get_position().bounds)
     fig.text(pos[0] - 0.02, 0.925, '|E|/|E$_0$|', fontsize = 14)
 
-    plt.xlabel('X')
-    plt.ylabel('Y')
+    plt.xlabel('X ( $\mu$m )')
+    plt.ylabel('Y ( $\mu$m )')
 
     # This part draws the nanoshell
 #    from matplotlib import patches

tests/python/lfield.py

@@ -91,7 +91,7 @@ try:
     ax.legend()
 
     plt.xlabel('X|Y|Z')
-    plt.ylabel('|E|/|Eo|')
+    plt.ylabel('|E|/|E$_0$|')
 
     plt.draw()
     plt.show()

tests/python/test04_field.py

@@ -0,0 +1,136 @@
+#!/usr/bin/env python
+# -*- coding: UTF-8 -*-
+#
+#    Copyright (C) 2009-2015 Ovidio Peña Rodríguez <ovidio@bytesfall.com>
+#
+#    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 <http://www.gnu.org/licenses/>.
+
+# This test case calculates the the electric field in the
+# XY plane, for 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.
+
+from scattnlay import fieldnlay
+import numpy as np
+
+nL = 500.0
+Xmax = 60.0
+
+x = np.ones((1, nL), dtype = np.float64)
+x[0] = np.arange(1.0, nL + 1.0)*Xmax/nL
+
+m = np.ones((1, nL), dtype = np.complex128)
+m[0] = np.sqrt((2.0 - ((x[0] - 0.5*Xmax/nL)/60.0)**2.0)) + 0.0j
+
+print "x =", x
+print "m =", m
+
+npts = 501
+
+scan = np.linspace(-10.0*x[0, -1], 10.0*x[0, -1], npts)
+
+coordX, coordY = np.meshgrid(scan, scan)
+coordX.resize(npts*npts)
+coordY.resize(npts*npts)
+coordZ = np.zeros(npts*npts, dtype = np.float64)
+
+coord = np.vstack((coordX, coordY, coordZ)).transpose()
+
+terms, E, H = fieldnlay(x, m, coord)
+
+Er = np.absolute(E)
+
+# |E|/|Eo|
+Eh = np.sqrt(Er[0, :, 0]**2 + Er[0, :, 1]**2 + Er[0, :, 2]**2)
+
+result = np.vstack((coordX, coordY, coordZ, Eh)).transpose()
+
+try:
+    import matplotlib.pyplot as plt
+    from matplotlib import cm
+    from matplotlib.colors import LogNorm
+
+    min_tick = 0.1
+    max_tick = 1.0
+
+    edata = np.resize(Eh, (npts, npts))
+
+    fig = plt.figure()
+    ax = fig.add_subplot(111)
+    # Rescale to better show the axes
+    scale_x = np.linspace(min(coordX), max(coordX), npts)
+    scale_y = np.linspace(min(coordY), max(coordY), npts)
+
+    # Define scale ticks
+    min_tick = min(min_tick, np.amin(edata))
+    max_tick = max(max_tick, np.amax(edata))
+    scale_ticks = np.power(10.0, np.linspace(np.log10(min_tick), np.log10(max_tick), 6))
+
+    # Interpolation can be 'nearest', 'bilinear' or 'bicubic'
+    cax = ax.imshow(edata, interpolation = 'nearest', cmap = cm.afmhot,
+                    origin = 'lower', vmin = min_tick, vmax = max_tick,
+                    extent = (min(scale_x), max(scale_x), min(scale_y), max(scale_y)),
+                    norm = LogNorm())
+
+    # Add colorbar
+    cbar = fig.colorbar(cax, ticks = [a for a in scale_ticks])
+    cbar.ax.set_yticklabels(['%3.1e' % (a) for a in scale_ticks]) # vertically oriented colorbar
+    pos = list(cbar.ax.get_position().bounds)
+    fig.text(pos[0] - 0.02, 0.925, '|E|/|E$_0$|', fontsize = 14)
+
+    plt.xlabel('X')
+    plt.ylabel('Y')
+
+    # This part draws the nanoshell
+#    from matplotlib import patches
+
+#    s1 = patches.Arc((0, 0), 2.0*x[0, 0], 2.0*x[0, 0], angle=0.0, zorder=2,
+#                      theta1=0.0, theta2=360.0, linewidth=1, color='#00fa9a')
+#    ax.add_patch(s1)
+
+#    s2 = patches.Arc((0, 0), 2.0*x[0, 1], 2.0*x[0, 1], angle=0.0, zorder=2,
+#                      theta1=0.0, theta2=360.0, linewidth=1, color='#00fa9a')
+#    ax.add_patch(s2)
+    # End of drawing
+
+    plt.draw()
+
+    plt.show()
+
+    plt.clf()
+    plt.close()
+finally:
+    np.savetxt("test04_field.txt", result, fmt = "%.5f")
+    print result
+
+