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@@ -27,60 +27,16 @@
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# along with this program. If not, see <http://www.gnu.org/licenses/>.
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# This test case calculates the electric field in the
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-# E-k plane, for an spherical Si-Ag-Si nanoparticle. Core radius is 17.74 nm,
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-# inner layer 23.31nm, outer layer 22.95nm. Working wavelength is 800nm, we use
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-# silicon epsilon=13.64+i0.047, silver epsilon= -28.05+i1.525
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+# E-k plane, for an spherical Ag nanoparticle.
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import scattnlay
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from scattnlay import fieldnlay
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from scattnlay import scattnlay
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+from fieldplot import fieldplot
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+
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import numpy as np
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import cmath
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-
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-def get_index(array,value):
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- idx = (np.abs(array-value)).argmin()
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- return idx
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-
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-#Ec = np.resize(Ec, (npts, npts)).T
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-
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-
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-def GetFlow(scale_x, scale_z, Ec, Hc, a, b, nmax):
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- # Initial position
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- flow_x = [a]
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- flow_z = [b]
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- x_pos = flow_x[-1]
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- z_pos = flow_z[-1]
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- x_idx = get_index(scale_x, x_pos)
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- z_idx = get_index(scale_z, z_pos)
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- S=np.cross(Ec[npts*z_idx+x_idx], Hc[npts*z_idx+x_idx]).real
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- #if (np.linalg.norm(S)> 1e-4):
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- Snorm_prev=S/np.linalg.norm(S)
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- Snorm_prev=Snorm_prev.real
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- for n in range(0, nmax):
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- #Get the next position
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- #1. Find Poynting vector and normalize it
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- x_pos = flow_x[-1]
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- z_pos = flow_z[-1]
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- x_idx = get_index(scale_x, x_pos)
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- z_idx = get_index(scale_z, z_pos)
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- Epoint = Ec[npts*z_idx+x_idx]
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- Hpoint = Hc[npts*z_idx+x_idx]
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- S=np.cross(Epoint, Hpoint.conjugate())
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- #if (np.linalg.norm(S)> 1e-4):
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- Snorm=S.real/np.linalg.norm(S)
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- #Snorm=Snorm.real
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- #2. Evaluate displacement = half of the discrete and new position
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- dpos = abs(scale_z[0]-scale_z[1])/2.0
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- dx = dpos*Snorm[0];
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- dz = dpos*Snorm[2];
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- x_pos = x_pos+dx
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- z_pos = z_pos+dz
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- #3. Save result
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- flow_x.append(x_pos)
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- flow_z.append(z_pos)
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- return flow_x, flow_z
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-
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# # a)
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#WL=400 #nm
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#core_r = WL/20.0
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@@ -104,153 +60,38 @@ epsilon_Ag = -2.0 + 0.28j #original
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# core_r = WL/20.0
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# epsilon_Ag = -2.71 + 0.25j
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-
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index_Ag = np.sqrt(epsilon_Ag)
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-
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-
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# n1 = 1.53413
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# n2 = 0.565838 + 7.23262j
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nm = 1.0
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-x = np.ones((1, 2), dtype = np.float64)
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-x[0, 0] = 2.0*np.pi*core_r/WL/4.0*3.0
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-x[0, 1] = 2.0*np.pi*core_r/WL
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+x = np.ones((2), dtype = np.float64)
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+x[0] = 2.0*np.pi*core_r/WL/4.0*3.0
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+x[1] = 2.0*np.pi*core_r/WL
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-m = np.ones((1, 2), dtype = np.complex128)
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-m[0, 0] = index_Ag/nm
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-m[0, 1] = index_Ag/nm
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+m = np.ones((2), dtype = np.complex128)
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+m[0] = index_Ag/nm
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+m[1] = index_Ag/nm
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print "x =", x
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print "m =", m
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-npts = 281
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-
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-factor=3
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-scan = np.linspace(-factor*x[0, 0], factor*x[0, 0], npts)
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-
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-coordX, coordZ = np.meshgrid(scan, scan)
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-coordX.resize(npts*npts)
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-coordZ.resize(npts*npts)
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-coordY = np.zeros(npts*npts, dtype = np.float64)
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-
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-coord = np.vstack((coordX, coordY, coordZ)).transpose()
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-#coord = np.vstack((coordY, coordX, coordZ)).transpose()
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-
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-terms, Qext, Qsca, Qabs, Qbk, Qpr, g, Albedo, S1, S2 = scattnlay(x, m)
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-terms, E, H = fieldnlay(x, m, coord)
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-print(E)
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-P = np.array(map(lambda n: np.linalg.norm(np.cross(E[0][n], H[0][n])).real, range(0, len(E[0]))))
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-
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-Ec = E[0, :, :]
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-Hc = H[0, :, :]
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-
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+comment='bulk-Ag-flow'
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+WL_units='nm'
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+npts = 501
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+factor=2.1
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+flow_total = 39
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+#flow_total = 21
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+#flow_total = 0
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+crossplane='XZ'
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+#crossplane='YZ'
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+#crossplane='XY'
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-try:
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- import matplotlib.pyplot as plt
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- from matplotlib import cm
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- from matplotlib.colors import LogNorm
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+# Options to plot: Eabs, Habs, Pabs, angleEx, angleHy
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+field_to_plot='Pabs'
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+#field_to_plot='angleEx'
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- # min_tick = 0.0
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- # max_tick = 1.0
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+fieldplot(x,m, WL, comment, WL_units, crossplane, field_to_plot, npts, factor, flow_total, is_flow_extend=False)
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- Eabs_data = np.resize(P, (npts, npts)).T
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-
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- #Eabs_data = np.resize(Eabs, (npts, npts)).T
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- #Eabs_data = np.resize(Eangle, (npts, npts)).T
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- #Eabs_data = np.resize(Habs, (npts, npts)).T
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- #Eabs_data = np.resize(Hangle, (npts, npts)).T
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-
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- fig, ax = plt.subplots(1, 1)#, sharey=True, sharex=True)
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-
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- #idxs = np.where(np.abs(coordX) < 1e-10)
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- #print H[0, idxs][0, :, 1]
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- #axs[0].errorbar(coordZ[idxs]*WL/2.0/np.pi/nm, P[idxs], fmt = 'r', label = 'Poynting vector')
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- #axs[0].errorbar(coordZ[idxs]*WL/2.0/np.pi/nm, np.linalg.norm(E[0, idxs][0], axis = 1), fmt = 'g', label = 'E')
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- # axs[0].errorbar(coordZ[idxs]*WL/2.0/np.pi/nm, np.linalg.norm(H[0, idxs][0], axis = 1), fmt = 'b', label = 'H')
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- # axs[0].errorbar(coordZ[idxs]*WL/2.0/np.pi/nm, H[0, idxs][0, :, 1].real, fmt = 'k', label = 'H.r')
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- # axs[0].errorbar(coordZ[idxs]*WL/2.0/np.pi/nm, H[0, idxs][0, :, 1].imag, fmt = 'b', label = 'H.i')
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- #axs[0].errorbar(coordZ[idxs]*WL/2.0/np.pi/nm, H[0, idxs][0, :, 0].real, fmt = 'b', label = 'Px')
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- #axs[0].errorbar(coordZ[idxs]*WL/2.0/np.pi/nm, H[0, idxs][0, :, 1].real, fmt = 'k', label = 'Py')
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- #axs[0].errorbar(coordZ[idxs]*WL/2.0/np.pi/nm, H[0, idxs][0, :, 2].real, fmt = 'b', label = 'Pz')
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-
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- #axs[0].legend()
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-
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- #fig.tight_layout()
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- # Rescale to better show the axes
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- scale_x = np.linspace(min(coordX)*WL/2.0/np.pi/nm, max(coordX)*WL/2.0/np.pi/nm, npts)
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- scale_z = np.linspace(min(coordZ)*WL/2.0/np.pi/nm, max(coordZ)*WL/2.0/np.pi/nm, npts)
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-
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- # Define scale ticks
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- min_tick = np.amin(Eabs_data)
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- max_tick = np.amax(Eabs_data)
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- # scale_ticks = np.power(10.0, np.linspace(np.log10(min_tick), np.log10(max_tick), 6))
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- scale_ticks = np.linspace(min_tick, max_tick, 11)
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-
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- # Interpolation can be 'nearest', 'bilinear' or 'bicubic'
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- ax.set_title(r'$Re(E \times H)$')
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- cax = ax.imshow(Eabs_data, interpolation = 'nearest', cmap = cm.jet,
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- origin = 'lower'
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- #, vmin = min_tick, vmax = max_tick
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- , extent = (min(scale_x), max(scale_x), min(scale_z), max(scale_z))
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- #,norm = LogNorm()
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- )
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- ax.axis("image")
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-
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- # # Add colorbar
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- cbar = fig.colorbar(cax, ticks = [a for a in scale_ticks])
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- cbar.ax.set_yticklabels(['%5.3g' % (a) for a in scale_ticks]) # vertically oriented colorbar
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- # pos = list(cbar.ax.get_position().bounds)
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- # fig.text(pos[0] - 0.02, 0.925, '|E|/|E$_0$|', fontsize = 14)
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-
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- plt.xlabel('Z, nm')
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- plt.ylabel('X, nm')
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-
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- # This part draws the nanoshell
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- from matplotlib import patches
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- s1 = patches.Arc((0, 0), 2.0*core_r, 2.0*core_r, angle=0.0, zorder=2,
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- theta1=0.0, theta2=360.0, linewidth=1, color='black')
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- ax.add_patch(s1)
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-
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- from matplotlib.path import Path
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- #import matplotlib.patches as patches
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- flow_total = 39
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- for flow in range(0,flow_total):
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- flow_x, flow_z = GetFlow(scale_x, scale_z, Ec, Hc,
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- min(scale_x)+flow*(scale_x[-1]-scale_x[0])/(flow_total-1),
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- min(scale_z),
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- #0.0,
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- npts*16)
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- verts = np.vstack((flow_z, flow_x)).transpose().tolist()
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- #codes = [Path.CURVE4]*len(verts)
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- codes = [Path.LINETO]*len(verts)
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- codes[0] = Path.MOVETO
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- path = Path(verts, codes)
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- patch = patches.PathPatch(path, facecolor='none', lw=1, edgecolor='white')
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- ax.add_patch(patch)
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- # # Start powerflow lines in the middle of the image
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- # flow_total = 131
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- # for flow in range(0,flow_total):
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- # flow_x, flow_z = GetFlow(scale_x, scale_z, Ec, Hc,
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- # min(scale_x)+flow*(scale_x[-1]-scale_x[0])/(flow_total-1),
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- # 15.0, #min(scale_z),
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- # npts*6)
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- # verts = np.vstack((flow_z, flow_x)).transpose().tolist()
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- # #codes = [Path.CURVE4]*len(verts)
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- # codes = [Path.LINETO]*len(verts)
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- # codes[0] = Path.MOVETO
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- # path = Path(verts, codes)
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- # patch = patches.PathPatch(path, facecolor='none', lw=1, edgecolor='white')
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- # ax.add_patch(patch)
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-
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- plt.savefig("Ag-flow.png")
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- plt.draw()
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-
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- plt.show()
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-
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- plt.clf()
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- plt.close()
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-finally:
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- print("Qabs = "+str(Qabs));
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-#
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