def plt_mode_fields(sim_wguide,
ivals=None,
n_points=500,
quiver_steps=50,
xlim_min=None,
xlim_max=None,
ylim_min=None,
ylim_max=None,
EM_AC='EM_E',
num_ticks=None,
contours=False,
contour_lst=None,
stress_fields=False,
pdf_png='png',
prefix_str='',
suffix_str=''):
""" Plot E or H fields of EM mode, or the AC modes displacement fields.
Args:
sim_wguide : A ``Struct`` instance that has had calc_modes calculated
Keyword Args:
ivals (list): mode numbers of modes you wish to plot
n_points (int): The number of points across unitcell to
interpolate the field onto
xlim_min (float): Limit plotted xrange to xlim_min:(1-xlim_max) of unitcell
xlim_max (float): Limit plotted xrange to xlim_min:(1-xlim_max) of unitcell
ylim_min (float): Limit plotted yrange to ylim_min:(1-ylim_max) of unitcell
ylim_max (float): Limit plotted yrange to ylim_min:(1-ylim_max) of unitcell
EM_AC (str): Either 'EM' or 'AC' modes
num_ticks (int): Number of tick marks
contours (bool): Controls contours being overlaid on fields
contour_lst (list): Specify contour values
stress_fields (bool): Calculate acoustic stress fields
pdf_png (str): File type to save, either 'png' or 'pdf'
prefix_str (str): Add a string to start of file name
suffix_str (str): Add a string to end of file name.
"""
if EM_AC is not 'EM_E' and EM_AC is not 'EM_H' and EM_AC is not 'AC':
raise ValueError("EM_AC must be either 'AC', 'EM_E' or 'EM_H'.")
# Calculate the magnetic field from the electric field
if EM_AC == 'EM_H':
nnodes = 6
sim_wguide.sol1_H = NumBAT.h_mode_field_ez(
sim_wguide.k_0, sim_wguide.num_modes, sim_wguide.n_msh_el,
sim_wguide.n_msh_pts, nnodes, sim_wguide.table_nod,
sim_wguide.x_arr, sim_wguide.Eig_values, sim_wguide.sol1)
plt.clf()
# field mapping
x_tmp = []
y_tmp = []
for i in np.arange(sim_wguide.n_msh_pts):
x_tmp.append(sim_wguide.x_arr[0, i])
y_tmp.append(sim_wguide.x_arr[1, i])
x_min = np.min(x_tmp)
x_max = np.max(x_tmp)
y_min = np.min(y_tmp)
y_max = np.max(y_tmp)
area = abs((x_max - x_min) * (y_max - y_min))
n_pts_x = int(n_points * abs(x_max - x_min) / np.sqrt(area))
n_pts_y = int(n_points * abs(y_max - y_min) / np.sqrt(area))
v_x = np.zeros(n_pts_x * n_pts_y)
v_y = np.zeros(n_pts_x * n_pts_y)
if contours:
v_XX, v_YY = np.meshgrid(range(n_pts_x), range(n_pts_y))
i = 0
for x in np.linspace(x_min, x_max, n_pts_x):
for y in np.linspace(y_min, y_max, n_pts_y):
v_x[i] = x
v_y[i] = y
i += 1
v_x = np.array(v_x)
v_y = np.array(v_y)
# unrolling data for the interpolators
table_nod = sim_wguide.table_nod.T
x_arr = sim_wguide.x_arr.T
if ivals:
ival_range = ivals
else:
ival_range = range(len(sim_wguide.Eig_values))
for ival in ival_range:
# dense triangulation with multiple points
v_x6p = np.zeros(6 * sim_wguide.n_msh_el)
v_y6p = np.zeros(6 * sim_wguide.n_msh_el)
v_Ex6p = np.zeros(6 * sim_wguide.n_msh_el, dtype=np.complex128)
v_Ey6p = np.zeros(6 * sim_wguide.n_msh_el, dtype=np.complex128)
v_Ez6p = np.zeros(6 * sim_wguide.n_msh_el, dtype=np.complex128)
v_triang6p = []
i = 0
for i_el in np.arange(sim_wguide.n_msh_el):
# triangles
idx = np.arange(6 * i_el, 6 * (i_el + 1))
triangles = [[idx[0], idx[3], idx[5]], [idx[1], idx[4], idx[3]],
[idx[2], idx[5], idx[4]], [idx[3], idx[4], idx[5]]]
v_triang6p.extend(triangles)
for i_node in np.arange(6):
# index for the coordinates
i_ex = table_nod[i_el, i_node] - 1
# values
v_x6p[i] = x_arr[i_ex, 0]
v_y6p[i] = x_arr[i_ex, 1]
if EM_AC == 'EM_E' or EM_AC == 'AC':
v_Ex6p[i] = sim_wguide.sol1[0, i_node, ival, i_el]
v_Ey6p[i] = sim_wguide.sol1[1, i_node, ival, i_el]
v_Ez6p[i] = sim_wguide.sol1[2, i_node, ival, i_el]
if EM_AC == 'EM_H':
v_Ex6p[i] = sim_wguide.sol1_H[0, i_node, ival, i_el]
v_Ey6p[i] = sim_wguide.sol1_H[1, i_node, ival, i_el]
v_Ez6p[i] = sim_wguide.sol1_H[2, i_node, ival, i_el]
i += 1
v_E6p = np.sqrt(
np.abs(v_Ex6p)**2 + np.abs(v_Ey6p)**2 + np.abs(v_Ez6p)**2)
### Interpolate onto triangular grid - honest to FEM elements
# dense triangulation with unique points
v_triang1p = []
for i_el in np.arange(sim_wguide.n_msh_el):
# triangles
triangles = [[
table_nod[i_el, 0] - 1, table_nod[i_el, 3] - 1,
table_nod[i_el, 5] - 1
],
[
table_nod[i_el, 1] - 1, table_nod[i_el, 4] - 1,
table_nod[i_el, 3] - 1
],
[
table_nod[i_el, 2] - 1, table_nod[i_el, 5] - 1,
table_nod[i_el, 4] - 1
],
[
table_nod[i_el, 3] - 1, table_nod[i_el, 4] - 1,
table_nod[i_el, 5] - 1
]]
v_triang1p.extend(triangles)
# triangulations
triang6p = matplotlib.tri.Triangulation(v_x6p, v_y6p, v_triang6p)
triang1p = matplotlib.tri.Triangulation(x_arr[:, 0], x_arr[:, 1],
v_triang1p)
# building interpolators: triang1p for the finder, triang6p for the values
finder = matplotlib.tri.TrapezoidMapTriFinder(triang1p)
ReEx = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ex6p.real,
trifinder=finder)
ImEx = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ex6p.imag,
trifinder=finder)
ReEy = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ey6p.real,
trifinder=finder)
ImEy = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ey6p.imag,
trifinder=finder)
ReEz = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ez6p.real,
trifinder=finder)
ImEz = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ez6p.imag,
trifinder=finder)
AbsE = matplotlib.tri.LinearTriInterpolator(triang6p,
v_E6p,
trifinder=finder)
# interpolated fields
m_ReEx = ReEx(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ReEy = ReEy(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ReEz = ReEz(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ImEx = ImEx(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ImEy = ImEy(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ImEz = ImEz(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_AbsE = AbsE(v_x, v_y).reshape(n_pts_x, n_pts_y)
# Flip y order as imshow has origin at top left
v_plots = [
m_ReEx[:, ::-1], m_ReEy[:, ::-1], m_ReEz[:, ::-1], m_ImEx[:, ::-1],
m_ImEy[:, ::-1], m_ImEz[:, ::-1], m_AbsE[:, ::-1]
]
if EM_AC == 'EM_E':
v_labels = [
r"Re($E_x$)", r"Re($E_y$)", r"Re($E_z$)", r"Im($E_x$)",
r"Im($E_y$)", r"Im($E_z$)", r"$|E|$"
]
elif EM_AC == 'EM_H':
v_labels = [
r"Re($H_x$)", r"Re($H_y$)", r"Re($H_z$)", r"Im($H_x$)",
r"Im($H_y$)", r"Im($H_z$)", r"$|H|$"
]
else:
v_labels = [
r"Re($u_x$)", r"Re($u_y$)", r"Re($u_z$)", r"Im($u_x$)",
r"Im($u_y$)", r"Im($u_z$)", r"$|u|$"
]
# field plots
plot_threshold = 1e-4 # set negligible components to explicitly zero
plt.clf()
fig = plt.figure(figsize=(15, 15))
for i_p, plot in enumerate(v_plots):
ax = plt.subplot(3, 3, i_p + 1)
if np.max(np.abs(plot[~np.isnan(plot)])) < plot_threshold:
# im = plt.imshow(plot.T,cmap='viridis');
im = plt.imshow(np.zeros(np.shape(plot.T)))
else:
im = plt.imshow(plot.T)
# ax.set_aspect('equal')
# no ticks
plt.xticks([])
plt.yticks([])
# limits
axes = plt.gca()
xmin, xmax = axes.get_xlim()
ymin, ymax = axes.get_ylim()
width_x = xmax - xmin
width_y = ymax - ymin
if xlim_min != None:
ax.set_xlim(xmin + xlim_min * width_x,
xmax - xlim_max * width_x)
if ylim_min != None:
ax.set_ylim(ymin + ylim_min * width_y,
ymax - ylim_max * width_y)
# titles
plt.title(v_labels[i_p], fontsize=title_font - 4)
# colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
cbar = plt.colorbar(im, cax=cax)
if num_ticks:
cbarticks = np.linspace(np.min(plot),
np.max(plot),
num=num_ticks)
elif ylim_min != None:
if xlim_min / ylim_min > 3:
cbarticks = np.linspace(np.min(plot), np.max(plot), num=3)
if xlim_min / ylim_min > 1.5:
cbarticks = np.linspace(np.min(plot), np.max(plot), num=5)
else:
cbarticks = np.linspace(np.min(plot), np.max(plot), num=7)
else:
cbarticks = np.linspace(np.min(plot), np.max(plot), num=7)
cbar.set_ticks(cbarticks)
cbarlabels = ['%.2f' % t for t in cbarticks]
cbar.set_ticklabels(cbarlabels)
if contours:
if contour_lst:
cbarticks = contour_lst
if np.max(np.abs(plot[~np.isnan(plot)])) > plot_threshold:
CS2 = ax.contour(v_XX,
v_YY,
plot.T,
levels=cbarticks,
colors=colors[::-1],
linewidths=(1.5, ))
cbar.add_lines(CS2)
cbar.ax.tick_params(labelsize=title_font - 10)
if EM_AC == 'AC':
v_x_q = v_x.reshape(n_pts_x, n_pts_y)
v_y_q = v_y.reshape(n_pts_x, n_pts_y)
v_x_q = v_x_q[0::quiver_steps, 0::quiver_steps]
v_y_q = v_y_q[0::quiver_steps, 0::quiver_steps]
m_ReEx_q = m_ReEx[0::quiver_steps, 0::quiver_steps]
m_ReEy_q = m_ReEy[0::quiver_steps, 0::quiver_steps]
m_ImEx_q = m_ImEx[0::quiver_steps, 0::quiver_steps]
m_ImEy_q = m_ImEy[0::quiver_steps, 0::quiver_steps]
ax = plt.subplot(3, 3, i_p + 2)
plt.quiver(
v_x_q,
v_y_q,
(m_ReEx_q + m_ImEx_q),
(m_ReEy_q + m_ImEy_q), # data
np.sqrt(
np.real((m_ReEx_q + 1j * m_ImEx_q) *
(m_ReEx_q - 1j * m_ImEx_q) +
(m_ReEy_q + 1j * m_ImEy_q) *
(m_ReEy_q - 1j * m_ImEy_q))
), #colour the arrows based on this array
linewidths=(0.2, ),
edgecolors=('k'),
pivot='mid',
headlength=5) # length of the arrows
ax.set_aspect('equal')
plt.xticks([])
plt.yticks([])
axes = plt.gca()
xmin, xmax = axes.get_xlim()
ymin, ymax = axes.get_ylim()
if xlim_min != None:
ax.set_xlim(xlim_min * xmax, (1 - xlim_max) * xmax)
if ylim_min != None:
ax.set_ylim((1 - ylim_min) * ymin, ylim_max * ymin)
plt.title('Transverse', fontsize=title_font - 4)
if EM_AC == 'EM_E' or EM_AC == 'EM_H':
n_eff = sim_wguide.Eig_values[ival] * sim_wguide.wl_m / (2 * np.pi)
if np.imag(sim_wguide.Eig_values[ival]) < 0:
k_str = r'$k_z = %(re_k)f %(im_k)f i$'% \
{'re_k' : np.real(sim_wguide.Eig_values[ival]),
'im_k' : np.imag(sim_wguide.Eig_values[ival])}
n_str = r'$n_{eff} = %(re_k)f %(im_k)f i$'% \
{'re_k' : np.real(n_eff), 'im_k' : np.imag(n_eff)}
else:
k_str = r'$k_z = %(re_k)f + %(im_k)f i$'% \
{'re_k' : np.real(sim_wguide.Eig_values[ival]),
'im_k' : np.imag(sim_wguide.Eig_values[ival])}
n_str = r'$n_{eff} = %(re_k)f + %(im_k)f i$'% \
{'re_k' : np.real(n_eff), 'im_k' : np.imag(n_eff)}
# plt.text(10, 0.3, n_str, fontsize=title_font)
else:
n_str = ''
if np.imag(sim_wguide.Eig_values[ival]) < 0:
k_str = r'$\Omega/2\pi = %(re_k)f %(im_k)f i$ GHz'% \
{'re_k' : np.real(sim_wguide.Eig_values[ival]*1e-9),
'im_k' : np.imag(sim_wguide.Eig_values[ival]*1e-9)}
else:
k_str = r'$\Omega/2\pi = %(re_k)f + %(im_k)f i$ GHz'% \
{'re_k' : np.real(sim_wguide.Eig_values[ival]*1e-9),
'im_k' : np.imag(sim_wguide.Eig_values[ival]*1e-9)}
# plt.text(10, 0.5, k_str, fontsize=title_font)
plt.suptitle('Mode #' + str(ival) + ' ' + k_str + ' ' + n_str +
"\n",
fontsize=title_font)
# plt.tight_layout(pad=2.5, w_pad=0.5, h_pad=1.0)
fig.set_tight_layout(True)
if not os.path.exists("%sfields" % prefix_str):
os.mkdir("%sfields" % prefix_str)
if pdf_png == 'png':
plt.savefig('%(pre)sfields/%(s)s_field_%(i)i%(add)s.png' % {
'pre': prefix_str,
's': EM_AC,
'i': ival,
'add': suffix_str
})
#, bbox_inches='tight') - this caused error in Q calc... ?
elif pdf_png == 'pdf':
plt.savefig('%(pre)sfields/%(s)s_field_%(i)i%(add)s.pdf' % {
'pre': prefix_str,
's': EM_AC,
'i': ival,
'add': suffix_str
},
bbox_inches='tight')
else:
raise ValueError("pdf_png must be either 'png' or 'pdf'.")
plt.close()
if EM_AC == 'AC' and stress_fields is True:
### Interpolate onto rectangular Cartesian grid
xy = list(zip(v_x6p, v_y6p))
grid_x, grid_y = np.mgrid[x_min:x_max:n_pts_x * 1j,
y_min:y_max:n_pts_y * 1j]
m_ReEx = interpolate.griddata(xy,
v_Ex6p.real, (grid_x, grid_y),
method='linear')
m_ReEy = interpolate.griddata(xy,
v_Ey6p.real, (grid_x, grid_y),
method='linear')
m_ReEz = interpolate.griddata(xy,
v_Ez6p.real, (grid_x, grid_y),
method='linear')
m_ImEx = interpolate.griddata(xy,
v_Ex6p.imag, (grid_x, grid_y),
method='linear')
m_ImEy = interpolate.griddata(xy,
v_Ey6p.imag, (grid_x, grid_y),
method='linear')
m_ImEz = interpolate.griddata(xy,
v_Ez6p.imag, (grid_x, grid_y),
method='linear')
m_AbsE = interpolate.griddata(xy,
v_E6p.real, (grid_x, grid_y),
method='linear')
dx = grid_x[-1, 0] - grid_x[-2, 0]
dy = grid_y[0, -1] - grid_y[0, -2]
m_Ex = m_ReEx + 1j * m_ImEx
m_Ey = m_ReEy + 1j * m_ImEy
m_Ez = m_ReEz + 1j * m_ImEz
m_Ex = m_Ex.reshape(n_pts_x, n_pts_y)
m_Ey = m_Ey.reshape(n_pts_x, n_pts_y)
m_Ez = m_Ez.reshape(n_pts_x, n_pts_y)
m_AbsE = m_AbsE.reshape(n_pts_x, n_pts_y)
m_ReEx = np.real(m_Ex)
m_ReEy = np.real(m_Ey)
m_ReEz = np.real(m_Ez)
m_ImEx = np.imag(m_Ex)
m_ImEy = np.imag(m_Ey)
m_ImEz = np.imag(m_Ez)
del_x_Ex = np.gradient(m_Ex, dx, axis=0)
del_y_Ex = np.gradient(m_Ex, dy, axis=1)
del_x_Ey = np.gradient(m_Ey, dx, axis=0)
del_y_Ey = np.gradient(m_Ey, dy, axis=1)
del_x_Ez = np.gradient(m_Ez, dx, axis=0)
del_y_Ez = np.gradient(m_Ez, dy, axis=1)
del_z_Ex = 1j * sim_wguide.k_AC * m_Ex
del_z_Ey = 1j * sim_wguide.k_AC * m_Ey
del_z_Ez = 1j * sim_wguide.k_AC * m_Ez
# Flip y order as imshow has origin at top left
del_mat = np.array([
del_x_Ex[:, ::-1].real, del_x_Ey[:, ::-1].real,
del_x_Ez[:, ::-1].real, del_x_Ex[:, ::-1].imag,
del_x_Ey[:, ::-1].imag, del_x_Ez[:, ::-1].imag,
del_y_Ex[:, ::-1].real, del_y_Ey[:, ::-1].real,
del_y_Ez[:, ::-1].real, del_y_Ex[:, ::-1].imag,
del_y_Ey[:, ::-1].imag, del_y_Ez[:, ::-1].imag,
del_z_Ex[:, ::-1].real, del_z_Ey[:, ::-1].real,
del_z_Ez[:, ::-1].real, del_z_Ex[:, ::-1].imag,
del_z_Ey[:, ::-1].imag, del_z_Ez[:, ::-1].imag
])
v_labels = [
"Re($S_{xx}$)", "Re($S_{xy}$)", "Re($S_{xz}$)", "Im($S_{xx}$)",
"Im($S_{xy}$)", "Im($S_{xz}$)", "Re($S_{yx}$)", "Re($S_{yy}$)",
"Re($S_{yz}$)", "Im($S_{yx}$)", "Im($S_{yy}$)", "Im($S_{yz}$)",
"Re($S_{zx}$)", "Re($S_{zy}$)", "Re($S_{zz}$)", "Im($S_{zx}$)",
"Im($S_{zy}$)", "Im($S_{zz}$)"
]
# field plots
plt.clf()
fig = plt.figure(figsize=(15, 30))
for i_p, plot in enumerate(del_mat):
ax = plt.subplot(6, 3, i_p + 1)
im = plt.imshow(plot.T)
# no ticks
plt.xticks([])
plt.yticks([])
# limits
if xlim_min != None:
ax.set_xlim(xlim_min * n_points, (1 - xlim_max) * n_points)
if ylim_min != None:
ax.set_ylim((1 - ylim_min) * n_points, ylim_max * n_points)
# titles
plt.title(v_labels[i_p], fontsize=title_font - 4)
# colorbar
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=0.1)
cbar = plt.colorbar(im, cax=cax, format='%.2e')
if num_ticks:
cbarticks = np.linspace(np.min(plot),
np.max(plot),
num=num_ticks)
elif ylim_min != None:
if xlim_min / ylim_min > 3:
cbarlabels = np.linspace(np.min(plot),
np.max(plot),
num=3)
if xlim_min / ylim_min > 1.5:
cbarlabels = np.linspace(np.min(plot),
np.max(plot),
num=5)
else:
cbarlabels = np.linspace(np.min(plot),
np.max(plot),
num=7)
else:
cbarlabels = np.linspace(np.min(plot), np.max(plot), num=7)
cbar.set_ticks(cbarlabels)
cbarlabels = ['%.2f' % t for t in cbarlabels]
cbar.set_ticklabels(cbarlabels)
if contours:
if contour_lst:
cbarticks = contour_lst
print(contour_lst)
if np.max(np.abs(plot[~np.isnan(plot)])) > plot_threshold:
CS2 = ax.contour(v_XX,
v_YY,
plot.T,
levels=cbarticks,
colors=colors[::-1],
linewidths=(1.5, ))
cbar.add_lines(CS2)
cbar.ax.tick_params(labelsize=title_font - 10)
fig.set_tight_layout(True)
n_str = ''
if np.imag(sim_wguide.Eig_values[ival]) < 0:
k_str = r'$\Omega/2\pi = %(re_k)f %(im_k)f i$ GHz'% \
{'re_k' : np.real(sim_wguide.Eig_values[ival]*1e-9),
'im_k' : np.imag(sim_wguide.Eig_values[ival]*1e-9)}
else:
k_str = r'$\Omega/2\pi = %(re_k)f + %(im_k)f i$ GHz'% \
{'re_k' : np.real(sim_wguide.Eig_values[ival]*1e-9),
'im_k' : np.imag(sim_wguide.Eig_values[ival]*1e-9)}
plt.suptitle('Mode #' + str(ival) + ' ' + k_str + ' ' + n_str,
fontsize=title_font)
if pdf_png == 'png':
plt.savefig('%(pre)sfields/%(s)s_S_field_%(i)i%(add)s.png' % {
'pre': prefix_str,
's': EM_AC,
'i': ival,
'add': suffix_str
})
elif pdf_png == 'pdf':
plt.savefig('%(pre)sfields/%(s)s_S_field_%(i)i%(add)s.pdf' % {
'pre': prefix_str,
's': EM_AC,
'i': ival,
'add': suffix_str
},
bbox_inches='tight')
plt.close()
def plt_mode_fields(sim_wguide,
ivals=None,
n_points=501,
quiver_steps=50,
xlim_min=None,
xlim_max=None,
ylim_min=None,
ylim_max=None,
EM_AC='EM_E',
num_ticks=None,
colorbar=True,
contours=False,
contour_lst=None,
stress_fields=False,
pdf_png='png',
prefix_str='',
suffix_str='',
ticks=False,
comps=None,
decorator=None):
""" Plot E or H fields of EM mode, or the AC modes displacement fields.
Args:
sim_wguide : A ``Struct`` instance that has had calc_modes calculated
Keyword Args:
ivals (list): mode numbers of modes you wish to plot
n_points (int): The number of points across unitcell to
interpolate the field onto
xlim_min (float): Limit plotted xrange to xlim_min:(1-xlim_max) of unitcell
xlim_max (float): Limit plotted xrange to xlim_min:(1-xlim_max) of unitcell
ylim_min (float): Limit plotted yrange to ylim_min:(1-ylim_max) of unitcell
ylim_max (float): Limit plotted yrange to ylim_min:(1-ylim_max) of unitcell
EM_AC (str): Either 'EM' or 'AC' modes
num_ticks (int): Number of tick marks
contours (bool): Controls contours being overlaid on fields
contour_lst (list): Specify contour values
stress_fields (bool): Calculate acoustic stress fields
pdf_png (str): File type to save, either 'png' or 'pdf'
prefix_str (str): Add a string to start of file name
suffix_str (str): Add a string to end of file name.
"""
if EM_AC is not 'EM_E' and EM_AC is not 'EM_H' and EM_AC is not 'AC':
raise ValueError("EM_AC must be either 'AC', 'EM_E' or 'EM_H'.")
# Calculate the magnetic field from the electric field
if EM_AC == 'EM_H':
nnodes = 6
sim_wguide.sol1_H = NumBAT.h_mode_field_ez(
sim_wguide.k_0, sim_wguide.num_modes, sim_wguide.n_msh_el,
sim_wguide.n_msh_pts, nnodes, sim_wguide.table_nod,
sim_wguide.x_arr, sim_wguide.Eig_values, sim_wguide.sol1)
plt.clf()
# field mapping
x_tmp = []
y_tmp = []
for i in np.arange(sim_wguide.n_msh_pts):
x_tmp.append(sim_wguide.x_arr[0, i])
y_tmp.append(sim_wguide.x_arr[1, i])
x_min = np.min(x_tmp)
x_max = np.max(x_tmp)
y_min = np.min(y_tmp)
y_max = np.max(y_tmp)
area = abs((x_max - x_min) * (y_max - y_min))
n_pts_x = int(n_points * abs(x_max - x_min) / np.sqrt(area))
n_pts_y = int(n_points * abs(y_max - y_min) / np.sqrt(area))
v_x = np.zeros(n_pts_x * n_pts_y)
v_y = np.zeros(n_pts_x * n_pts_y)
v_XX = None
v_YY = None
if contours:
v_XX, v_YY = np.meshgrid(range(n_pts_x), range(n_pts_y))
i = 0
for x in np.linspace(x_min, x_max, n_pts_x):
for y in np.linspace(y_min, y_max, n_pts_y):
v_x[i] = x
v_y[i] = y
i += 1
v_x = np.array(v_x)
v_y = np.array(v_y)
# unrolling data for the interpolators
table_nod = sim_wguide.table_nod.T
x_arr = sim_wguide.x_arr.T
if ivals:
ival_range = ivals
else:
ival_range = range(len(sim_wguide.Eig_values))
for ival in ival_range:
# dense triangulation with multiple points
v_x6p = np.zeros(6 * sim_wguide.n_msh_el)
v_y6p = np.zeros(6 * sim_wguide.n_msh_el)
v_Ex6p = np.zeros(6 * sim_wguide.n_msh_el, dtype=np.complex128)
v_Ey6p = np.zeros(6 * sim_wguide.n_msh_el, dtype=np.complex128)
v_Ez6p = np.zeros(6 * sim_wguide.n_msh_el, dtype=np.complex128)
v_triang6p = []
i = 0
for i_el in np.arange(sim_wguide.n_msh_el):
# triangles
idx = np.arange(6 * i_el, 6 * (i_el + 1))
triangles = [[idx[0], idx[3], idx[5]], [idx[1], idx[4], idx[3]],
[idx[2], idx[5], idx[4]], [idx[3], idx[4], idx[5]]]
v_triang6p.extend(triangles)
for i_node in np.arange(6):
# index for the coordinates
i_ex = table_nod[i_el, i_node] - 1
# values
v_x6p[i] = x_arr[i_ex, 0]
v_y6p[i] = x_arr[i_ex, 1]
if EM_AC == 'EM_E' or EM_AC == 'AC':
v_Ex6p[i] = sim_wguide.sol1[0, i_node, ival, i_el]
v_Ey6p[i] = sim_wguide.sol1[1, i_node, ival, i_el]
v_Ez6p[i] = sim_wguide.sol1[2, i_node, ival, i_el]
if EM_AC == 'EM_H':
v_Ex6p[i] = sim_wguide.sol1_H[0, i_node, ival, i_el]
v_Ey6p[i] = sim_wguide.sol1_H[1, i_node, ival, i_el]
v_Ez6p[i] = sim_wguide.sol1_H[2, i_node, ival, i_el]
i += 1
v_E6p = np.sqrt(
np.abs(v_Ex6p)**2 + np.abs(v_Ey6p)**2 + np.abs(v_Ez6p)**2)
### Interpolate onto triangular grid - honest to FEM elements
# dense triangulation with unique points
v_triang1p = []
for i_el in np.arange(sim_wguide.n_msh_el):
# triangles
triangles = [[
table_nod[i_el, 0] - 1, table_nod[i_el, 3] - 1,
table_nod[i_el, 5] - 1
],
[
table_nod[i_el, 1] - 1, table_nod[i_el, 4] - 1,
table_nod[i_el, 3] - 1
],
[
table_nod[i_el, 2] - 1, table_nod[i_el, 5] - 1,
table_nod[i_el, 4] - 1
],
[
table_nod[i_el, 3] - 1, table_nod[i_el, 4] - 1,
table_nod[i_el, 5] - 1
]]
v_triang1p.extend(triangles)
# triangulations
triang6p = matplotlib.tri.Triangulation(v_x6p, v_y6p, v_triang6p)
triang1p = matplotlib.tri.Triangulation(x_arr[:, 0], x_arr[:, 1],
v_triang1p)
#fields are called Ex, Ey, etc regardless of whether we are plotting E, H, or u/S
# building interpolators: triang1p for the finder, triang6p for the values
#TODO: could be more efficient only interpolating the fields which are ultimately to be used?
finder = matplotlib.tri.TrapezoidMapTriFinder(triang1p)
ReEx = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ex6p.real,
trifinder=finder)
ImEx = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ex6p.imag,
trifinder=finder)
ReEy = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ey6p.real,
trifinder=finder)
ImEy = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ey6p.imag,
trifinder=finder)
ReEz = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ez6p.real,
trifinder=finder)
ImEz = matplotlib.tri.LinearTriInterpolator(triang6p,
v_Ez6p.imag,
trifinder=finder)
AbsE = matplotlib.tri.LinearTriInterpolator(triang6p,
v_E6p,
trifinder=finder)
# interpolated fields
m_ReEx = ReEx(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ReEy = ReEy(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ReEz = ReEz(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ImEx = ImEx(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ImEy = ImEy(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_ImEz = ImEz(v_x, v_y).reshape(n_pts_x, n_pts_y)
m_AbsE = AbsE(v_x, v_y).reshape(n_pts_x, n_pts_y)
v_x_q = v_x.reshape(n_pts_x, n_pts_y)
v_y_q = v_y.reshape(n_pts_x, n_pts_y)
v_x_q = v_x_q[0::quiver_steps, 0::quiver_steps]
v_y_q = v_y_q[0::quiver_steps, 0::quiver_steps]
m_ReEx_q = m_ReEx[0::quiver_steps, 0::quiver_steps]
m_ReEy_q = m_ReEy[0::quiver_steps, 0::quiver_steps]
m_ImEx_q = m_ImEx[0::quiver_steps, 0::quiver_steps]
m_ImEy_q = m_ImEy[0::quiver_steps, 0::quiver_steps]
### No longer needed as imshow is using origin=lower
# Flip y order as imshow has origin at top left
#v_plots = [m_ReEx[:,::-1],m_ReEy[:,::-1],m_ReEz[:,::-1],m_ImEx[:,::-1],m_ImEy[:,::-1],m_ImEz[:,::-1],m_AbsE[:,::-1]]
v_plots = [m_ReEx, m_ReEy, m_ReEz, m_ImEx, m_ImEy, m_ImEz, m_AbsE]
vq_plots = [m_ReEx_q, m_ReEy_q, m_ImEx_q, m_ImEy_q]
if EM_AC == 'EM_E':
v_labels = [
r"Re($E_x$)", r"Re($E_y$)", r"Re($E_z$)", r"Im($E_x$)",
r"Im($E_y$)", r"Im($E_z$)", r"$|E|$"
]
elif EM_AC == 'EM_H':
v_labels = [
r"Re($H_x$)", r"Re($H_y$)", r"Re($H_z$)", r"Im($H_x$)",
r"Im($H_y$)", r"Im($H_z$)", r"$|H|$"
]
else:
v_labels = [
r"Re($u_x$)", r"Re($u_y$)", r"Re($u_z$)", r"Im($u_x$)",
r"Im($u_y$)", r"Im($u_z$)", r"$|u|$"
]
if decorator == None: decorator = FieldDecorator()
plot_params = {
'xlim_min': xlim_min,
'xlim_max': xlim_max,
'ylim_min': ylim_min,
'ylim_max': ylim_max,
'ticks': ticks,
'num_ticks': num_ticks,
'colorbar': colorbar,
'contours': contours,
'contour_lst': contour_lst,
'EM_AC': EM_AC,
'prefix_str': prefix_str,
'suffix_str': suffix_str,
'pdf_png': pdf_png,
'stress_fields': stress_fields,
'decorator': decorator
}
if not os.path.exists("%sfields" % prefix_str):
os.mkdir("%sfields" % prefix_str)
decorator._set_for_multi()
plot_all_components(v_x, v_y, v_x_q, v_y_q, v_XX, v_YY, v_plots,
vq_plots, v_labels, plot_params, sim_wguide, ival)
decorator._set_for_single()
if comps != None:
for comp in comps:
if comp == 'Ex' and EM_AC == 'EM_E':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[0],
v_labels[0], plot_params, sim_wguide, ival,
comp)
elif comp == 'Ey' and EM_AC == 'EM_E':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[1],
v_labels[1], plot_params, sim_wguide, ival,
comp)
elif comp == 'Ez' and EM_AC == 'EM_E':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[5],
v_labels[5], plot_params, sim_wguide, ival,
comp)
elif comp == 'Eabs' and EM_AC == 'EM_E':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[6],
v_labels[6], plot_params, sim_wguide, ival,
comp)
elif comp == 'Et' and EM_AC == 'EM_E':
plot_component(v_x_q, v_y_q, v_XX, v_YY, vq_plots,
'$(E_x,E_y)$', plot_params, sim_wguide,
ival, comp)
elif comp == 'Hx' and EM_AC == 'EM_H':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[0],
v_labels[0], plot_params, sim_wguide, ival,
comp)
elif comp == 'Hy' and EM_AC == 'EM_H':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[1],
v_labels[1], plot_params, sim_wguide, ival,
comp)
elif comp == 'Hz' and EM_AC == 'EM_H':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[5],
v_labels[5], plot_params, sim_wguide, ival,
comp)
elif comp == 'Habs' and EM_AC == 'EM_H':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[6],
v_labels[6], plot_params, sim_wguide, ival,
comp)
elif comp == 'Ht' and EM_AC == 'EM_H':
plot_component(v_x_q, v_y_q, v_XX, v_YY, vq_plots,
'$(H_x,H_y)$', plot_params, sim_wguide,
ival, comp)
elif comp == 'ux' and EM_AC == 'AC':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[0],
v_labels[0], plot_params, sim_wguide, ival,
comp)
elif comp == 'uy' and EM_AC == 'AC':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[1],
v_labels[1], plot_params, sim_wguide, ival,
comp)
elif comp == 'uz' and EM_AC == 'AC':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[5],
v_labels[5], plot_params, sim_wguide, ival,
comp)
elif comp == 'uabs' and EM_AC == 'AC':
plot_component(v_x, v_y, v_XX, v_YY, v_plots[6],
v_labels[6], plot_params, sim_wguide, ival,
comp)
elif comp == 'ut' and EM_AC == 'AC':
plot_component(v_x_q, v_y_q, v_XX, v_YY, vq_plots,
'$(u_x, u_y)$', plot_params, sim_wguide,
ival, comp)