#!/usr/bin/env python
# -*- coding: UTF-8 -*-
#
# Copyright (C) 2009-2015 Ovidio Peña Rodríguez <ovidio@bytesfall.com>
# Copyright (C) 2013-2015 Konstantin Ladutenko <kostyfisik@gmail.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 electric field in the
# E-k plane, for an spherical Si-Ag-Si nanoparticle. Core radius is 17.74 nm,
# inner layer 23.31nm, outer layer 22.95nm. Working wavelength is 800nm, we use
# silicon epsilon=13.64+i0.047, silver epsilon= -28.05+i1.525
import scattnlay
from scattnlay import fieldnlay
from scattnlay import scattnlay
import numpy as np
import cmath
epsilon_Si = 13.64 + 0.047j
epsilon_Ag = -28.05 + 1.525j
# epsilon_Si = 2.0 + 0.047j
# epsilon_Ag = -2.0 + 1.525j
# air = 1
# epsilon_Si = air*2
# epsilon_Ag = air*2
index_Si = np.sqrt(epsilon_Si)
index_Ag = np.sqrt(epsilon_Ag)
# # Values for 800 nm, taken from http://refractiveindex.info/
# index_Si = 3.69410 + 0.0065435j
# index_Ag = 0.18599 + 4.9886j
WL=800 #nm
core_width = 17.74 #nm Si
inner_width = 23.31 #nm Ag
outer_width = 22.95 #nm Si
core_r = core_width
inner_r = core_r+inner_width
outer_r = inner_r+outer_width
# n1 = 1.53413
# n2 = 0.565838 + 7.23262j
nm = 1.0
x = 2.0*np.pi*np.array([core_r, inner_r, outer_r], dtype = np.float64)/WL
m = np.array((index_Si, index_Ag, index_Si), dtype = np.complex128)/nm
print "x =", x
print "m =", m
npts = 281
factor=2.5
scan = np.linspace(-factor*x[2], factor*x[2], npts)
coordX, coordZ = np.meshgrid(scan, scan)
coordX.resize(npts*npts)
coordZ.resize(npts*npts)
coordY = np.zeros(npts*npts, dtype = np.float64)
terms, Qext, Qsca, Qabs, Qbk, Qpr, g, Albedo, S1, S2 = scattnlay(x, m)
terms, E, H = fieldnlay(x, m, coordX, coordY, coordZ)
print("Qabs = "+str(Qabs));
Er = np.absolute(E)
Hr = np.absolute(H)
# |E|/|Eo|
Eabs = np.sqrt(Er[:, 0]**2 + Er[:, 1]**2 + Er[:, 2]**2)
Eangle = np.angle(E[:, 0])/np.pi*180
Habs= np.sqrt(Hr[:, 0]**2 + Hr[:, 1]**2 + Hr[:, 2]**2)
Hangle = np.angle(H[:, 1])/np.pi*180
result = np.vstack((coordX, coordY, coordZ, Eabs)).transpose()
result2 = np.vstack((coordX, coordY, coordZ, Eangle)).transpose()
try:
import matplotlib.pyplot as plt
from matplotlib import cm
from matplotlib.colors import LogNorm
# min_tick = 0.0
# max_tick = 1.0
Eabs_data = np.resize(Eabs, (npts, npts)).T
Eangle_data = np.resize(Eangle, (npts, npts)).T
Habs_data = np.resize(Habs, (npts, npts)).T
Hangle_data = np.resize(Hangle, (npts, npts)).T
fig, axs = plt.subplots(2,2)#, sharey=True, sharex=True)
fig.tight_layout()
# Rescale to better show the axes
scale_x = np.linspace(min(coordX)*WL/2.0/np.pi/nm, max(coordX)*WL/2.0/np.pi/nm, npts)
scale_z = np.linspace(min(coordZ)*WL/2.0/np.pi/nm, max(coordZ)*WL/2.0/np.pi/nm, npts)
# Define scale ticks
# min_tick = min(min_tick, np.amin(Eabs_data))
# max_tick = max(max_tick, np.amax(Eabs_data))
# scale_ticks = np.power(10.0, np.linspace(np.log10(min_tick), np.log10(max_tick), 6))
# scale_ticks = np.linspace(min_tick, max_tick, 10)
# Interpolation can be 'nearest', 'bilinear' or 'bicubic'
axs[0,0].set_title('Eabs')
cax = axs[0,0].imshow(Eabs_data, interpolation = 'nearest', cmap = cm.jet,
origin = 'lower'
#, vmin = min_tick, vmax = max_tick
, extent = (min(scale_x), max(scale_x), min(scale_z), max(scale_z))
#,norm = LogNorm()
)
axs[0,0].axis("image")
axs[0,1].set_title('Eangle')
cax = axs[0,1].imshow(Eangle_data, interpolation = 'nearest', cmap = cm.jet,
origin = 'lower'
#, vmin = min_tick, vmax = max_tick
, extent = (min(scale_x), max(scale_x), min(scale_z), max(scale_z))
#,norm = LogNorm()
)
axs[1,0].set_title('Habs')
cax = axs[1,0].imshow(Habs_data, interpolation = 'nearest', cmap = cm.jet,
origin = 'lower'
#, vmin = min_tick, vmax = max_tick
, extent = (min(scale_x), max(scale_x), min(scale_z), max(scale_z))
#,norm = LogNorm()
)
axs[1,1].set_title('Hangle')
cax = axs[1,1].imshow(Hangle_data, interpolation = 'nearest', cmap = cm.jet,
origin = 'lower'
#, vmin = min_tick, vmax = max_tick
, extent = (min(scale_x), max(scale_x), min(scale_z), max(scale_z))
#,norm = LogNorm()
)
# Add colorbar
# cbar = fig.colorbar(cax, ticks = [a for a in scale_ticks])
# cbar.ax.set_yticklabels(['%5.3g' % (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('Z, nm')
# plt.ylabel('X, nm')
# This part draws the nanoshell
from matplotlib import patches
for m in (0,1):
for n in (0,1):
s1 = patches.Arc((0, 0), 2.0*core_r, 2.0*core_r, angle=0.0, zorder=2,
theta1=0.0, theta2=360.0, linewidth=1, color='black')
s2 = patches.Arc((0, 0), 2.0*inner_r, 2.0*inner_r, angle=0.0, zorder=2,
theta1=0.0, theta2=360.0, linewidth=1, color='black')
s3 = patches.Arc((0, 0), 2.0*outer_r, 2.0*outer_r, angle=0.0, zorder=2,
theta1=0.0, theta2=360.0, linewidth=1, color='black')
axs[m,n].add_patch(s1)
axs[m,n].add_patch(s2)
axs[m,n].add_patch(s3)
# axs[0,0].add_patch(s1)
# axs[0,0].add_patch(s2)
# axs[0,0].add_patch(s3)
# axs[1,0].add_patch(s1)
# axs[1,0].add_patch(s2)
# axs[1,0].add_patch(s3)
# axs[0,1].add_patch(s1)
# axs[0,1].add_patch(s2)
# axs[0,1].add_patch(s3)
# axs[1,1].add_patch(s1)
# axs[1,1].add_patch(s2)
# axs[1,1].add_patch(s3)
# for m in (0,1):
# for n in (0,1):
# print(m)
# print(n)
# axs[m,n].add_patch(s1)
# axs[m,n].add_patch(s2)
# axs[m,n].add_patch(s3)
# End of drawing
plt.savefig("SiAgSi.png")
plt.draw()
plt.show()
plt.clf()
plt.close()
finally:
np.savetxt("field.txt", result, fmt = "%.5f")
print result