scatt/tests/python/lfield-Ag-flow.py

#!/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 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


def get_index(array,value):
    idx = (np.abs(array-value)).argmin()
    return idx

#Ec = np.resize(Ec, (npts, npts)).T


def GetFlow(scale_x, scale_z, Ec, Hc, a, b, nmax):
    # Initial position
    flow_x = [a]
    flow_z = [b]
    x_pos = flow_x[-1]
    z_pos = flow_z[-1]
    x_idx = get_index(scale_x, x_pos)
    z_idx = get_index(scale_z, z_pos)
    S=np.cross(Ec[npts*z_idx+x_idx], Hc[npts*z_idx+x_idx]).real
    #if (np.linalg.norm(S)> 1e-4):
    Snorm_prev=S/np.linalg.norm(S)
    for n in range(0, nmax):
        #Get the next position
        #1. Find Poynting vector and normalize it
        x_pos = flow_x[-1]
        z_pos = flow_z[-1]
        x_idx = get_index(scale_x, x_pos)
        z_idx = get_index(scale_z, z_pos)
        #print x_idx, z_idx
        S=np.cross(Ec[npts*z_idx+x_idx], Hc[npts*z_idx+x_idx]).real
        #if (np.linalg.norm(S)> 1e-4):
        Snorm=S/np.linalg.norm(S)
        #2. Evaluate displacement = half of the discrete and new position
        dpos = abs(scale_z[0]-scale_z[1])/2.0
        dx = dpos*Snorm[0]
        dz = dpos*Snorm[2]
        x_pos = x_pos+dx
        z_pos = z_pos+dz
        #3. Save result
        flow_x.append(x_pos)
        flow_z.append(z_pos)
    return flow_x, flow_z

# # a)
#WL=400 #nm
#core_r = WL/20.0
#epsilon_Ag = -2.0 + 10.0j

# # b)
#WL=400 #nm
#core_r = WL/20.0
#epsilon_Ag = -2.0 + 1.0j

# c)
WL=354 #nm
core_r = WL/20.0
epsilon_Ag = -2.0 + 0.28j

# # d)
# WL=367 #nm
# core_r = WL/20.0
# epsilon_Ag = -2.71 + 0.25j


index_Ag = np.sqrt(epsilon_Ag)
print(index_Ag)


# n1 = 1.53413
# n2 = 0.565838 + 7.23262j
nm = 1.0

x = np.ones((1, 1), dtype = np.float64)
x[0, 0] = 2.0*np.pi*core_r/WL

m = np.ones((1, 1), dtype = np.complex128)
m[0, 0] = index_Ag/nm

print "x =", x
print "m =", m

npts = 281

factor=2
scan = np.linspace(-factor*x[0, 0], factor*x[0, 0], npts)

coord = np.zeros((npts, 3), dtype = np.float64)
coord[:, 2] = scan

terms, Qext, Qsca, Qabs, Qbk, Qpr, g, Albedo, S1, S2 = scattnlay(x, m)
terms, E, H = fieldnlay(x, m, coord)

#P = np.array(map(lambda n:  np.cross(E[0][n], H[0][n])[2].real, range(0, len(E[0]))))
P = np.array(map(lambda n:  abs(np.cross(E[0][n], H[0][n])[2]), range(0, len(E[0]))))

Ec = E[0, :, :]
Hc = H[0, :, :]

result = np.vstack((scan, P)).transpose()

try:
    import matplotlib.pyplot as plt

    fig = plt.figure()
    ax = fig.add_subplot(111)

    ax.errorbar(result[:, 0], result[:, 1], fmt = 'r', label = 'X axis')

    ax.legend()

    plt.xlabel('X')
#    plt.ylabel('|P|/|Eo|')

    plt.savefig("Ag-abs(Px)-nmie.png")
    plt.draw()
    plt.show()
finally:
    np.savetxt("lfield.txt", result, fmt = "%.5f")
    print result


# 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(P, (npts, npts)).T

#     #Eabs_data = np.resize(Eabs, (npts, npts)).T
#     #Eabs_data = np.resize(Eangle, (npts, npts)).T
#     #Eabs_data = np.resize(Habs, (npts, npts)).T
#     #Eabs_data = np.resize(Hangle, (npts, npts)).T

#     fig, ax = plt.subplots(1,1)#, 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 = np.amin(Eabs_data)
#     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, 11)

#     # Interpolation can be 'nearest', 'bilinear' or 'bicubic'
#     #ax.set_title('Eabs')
#     cax = ax.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()
#                     )
#     ax.axis("image")

#     # # 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
#     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')
#     ax.add_patch(s1)

#     from matplotlib.path import Path
#     #import matplotlib.patches as patches
#     flow_total = 41
#     for flow in range(0,flow_total):
#         flow_x, flow_z = GetFlow(scale_x, scale_z, Ec, Hc,
#                                  min(scale_x)+flow*(scale_x[-1]-scale_x[0])/(flow_total-1),
#                                  min(scale_z),
#                                  npts*6)
#         verts = np.vstack((flow_z, flow_x)).transpose().tolist()
#         #codes = [Path.CURVE4]*len(verts)
#         codes = [Path.LINETO]*len(verts)
#         codes[0] = Path.MOVETO
#         path = Path(verts, codes)
#         patch = patches.PathPatch(path, facecolor='none', lw=1, edgecolor='white')
#         ax.add_patch(patch)
#     # # Start powerflow lines in the middle of the image
#     # flow_total = 131
#     # for flow in range(0,flow_total):
#     #     flow_x, flow_z = GetFlow(scale_x, scale_z, Ec, Hc,
#     #                              min(scale_x)+flow*(scale_x[-1]-scale_x[0])/(flow_total-1),
#     #                              15.0, #min(scale_z),
#     #                              npts*6)
#     #     verts = np.vstack((flow_z, flow_x)).transpose().tolist()
#     #     #codes = [Path.CURVE4]*len(verts)
#     #     codes = [Path.LINETO]*len(verts)
#     #     codes[0] = Path.MOVETO
#     #     path = Path(verts, codes)
#     #     patch = patches.PathPatch(path, facecolor='none', lw=1, edgecolor='white')
#     #     ax.add_patch(patch)
 
#     plt.savefig("Ag-flow.png")
#     plt.draw()

#     plt.show()

#     plt.clf()
#     plt.close()
# finally:
#     print("Qabs = "+str(Qabs));
# #