def output_component(self, c, h5file=None):
if self.fields is None:
raise RuntimeError("Fields must be initialized before calling output_component")
vol = self.fields.total_volume() if self.output_volume is None else self.output_volume
h5 = self.output_append_h5 if h5file is None else h5file
append = h5file is None and self.output_append_h5 is not None
self.fields.output_hdf5(c, vol, h5, append, self.output_single_precision, self.get_filename_prefix())
if h5file is None:
nm = self.fields.h5file_name(mp.component_name(c), self.get_filename_prefix(), True)
if c == mp.Dielectric:
self.last_eps_filename = nm
self.output_h5_hook(nm)
def plot_dft_fields(sim, field_cells=[], field_funcs=None,
ff_arrays=None, options=None, nf=0):
options = options if options else def_field_options
cmap = options['cmap']
edgecolors = options['line_color']
linewidth = options['line_width']
alpha = options['alpha']
fontsize = options['fontsize']
plot_method = options['plot_method']
num_contours = options['num_contours']
shading = 'gouraud' if linewidth==0.0 else 'none'
interpolation = 'gaussian' if linewidth==0.0 else 'none'
for ncell, cell in enumerate(field_cells):
(x,y,z,w,cEH,EH)=unpack_dft_cell(sim,cell,nf=nf)
X, Y = np.meshgrid(x, y)
if ff_arrays is None:
plt.figure()
plt.suptitle('DFT cell {}'.format(ncell+1))
if field_funcs==None:
ops=['Re', 'Im', 'abs']
field_funcs=[texify(op+'('+mp.component_name(c)+')') for c in cEH for op in ops]
rows,cols=len(cEH),len(ops)
else:
rows,cols=1,len(field_funcs)
def op(F,index):
return np.real(F) if op=='Re' else np.imag(F) if op=='Im' else np.abs(F)
for row in range(rows):
for col in range(cols):
nplot = row*cols + col
data = ff_arrays[nplot] if ff_arrays else op(EH[row],ops[col])
plt.subplot(rows, cols, nplot+1)
ax=plt.gca()
ax.set_title(field_funcs[nplot])
ax.set_xlabel(r'$x$', fontsize=fontsize, labelpad=0.5*fontsize)
ax.set_ylabel(r'$y$', fontsize=fontsize, labelpad=fontsize, rotation=0)
ax.tick_params(axis='both', labelsize=0.75*fontsize)
ax.set_aspect('equal')
plt.tight_layout()
if plot_method=='imshow':
img = plt.imshow(np.transpose(data), extent=(min(x), max(x), min(y), max(y)),
cmap=cmap, interpolation=interpolation, alpha=alpha)
elif plot_method=='pcolormesh':
img = plt.pcolormesh(x,y,np.transpose(data), cmap=cmap, shading=shading,
edgecolors=edgecolors, linewidth=linewidth, alpha=alpha)
else:
img = ax.contourf(X,Y,np.transpose(data),num_contours, cmap=cmap,alpha=alpha)
#cb=plt.colorbar(img,shrink=options['colorbar_shrink'], pad=options['colorbar_pad'])
cb=happy_cb(img,ax)
#cb.ax.set_xlabel(ff,fontsize=1.5*fontsize,rotation=0,labelpad=0.5*fontsize)
cb.locator = ticker.MaxNLocator(nbins=5)
cb.update_ticks()
plt.tight_layout()
plt.show(False)
plt.draw()
return 0
def fc_name(c,which):
name=mp.component_name(c)
return name if which=='scattered' else str(name[0].upper())+str(name[1])
def plot_dft_fields(sim,
field_cells=[],
field_funcs=None,
ff_arrays=None,
options=None,
nf=0):
options = options if options else def_field_options
cmap = options['cmap']
edgecolors = options['line_color']
linewidth = options['line_width']
alpha = options['alpha']
fontsize = options['fontsize']
plot_method = options['plot_method']
num_contours = options['num_contours']
shading = 'gouraud' if linewidth == 0.0 else 'none'
interpolation = 'gaussian' if linewidth == 0.0 else 'none'
for ncell, cell in enumerate(field_cells):
(x, y, z, w, cEH, EH) = unpack_dft_cell(sim, cell, nf=nf)
X, Y = np.meshgrid(x, y)
if ff_arrays is None:
plt.figure()
plt.suptitle('DFT cell {}'.format(ncell + 1))
if field_funcs == None:
ops = ['Re', 'Im', 'abs']
field_funcs = [
texify(op + '(' + mp.component_name(c) + ')') for c in cEH
for op in ops
]
rows, cols = len(cEH), len(ops)
else:
rows, cols = 1, len(field_funcs)
def op(F, index):
return np.real(F) if op == 'Re' else np.imag(
F) if op == 'Im' else np.abs(F)
for row in range(rows):
for col in range(cols):
nplot = row * cols + col
data = ff_arrays[nplot] if ff_arrays else op(EH[row], ops[col])
plt.subplot(rows, cols, nplot + 1)
ax = plt.gca()
ax.set_title(field_funcs[nplot])
ax.set_xlabel(r'$x$',
fontsize=fontsize,
labelpad=0.5 * fontsize)
ax.set_ylabel(r'$y$',
fontsize=fontsize,
labelpad=fontsize,
rotation=0)
ax.tick_params(axis='both', labelsize=0.75 * fontsize)
ax.set_aspect('equal')
plt.tight_layout()
if plot_method == 'imshow':
img = plt.imshow(np.transpose(data),
extent=(min(x), max(x), min(y), max(y)),
cmap=cmap,
interpolation=interpolation,
alpha=alpha)
elif plot_method == 'pcolormesh':
img = plt.pcolormesh(x,
y,
np.transpose(data),
cmap=cmap,
shading=shading,
edgecolors=edgecolors,
linewidth=linewidth,
alpha=alpha)
else:
img = ax.contourf(X,
Y,
np.transpose(data),
num_contours,
cmap=cmap,
alpha=alpha)
#cb=plt.colorbar(img,shrink=options['colorbar_shrink'], pad=options['colorbar_pad'])
cb = happy_cb(img, ax)
#cb.ax.set_xlabel(ff,fontsize=1.5*fontsize,rotation=0,labelpad=0.5*fontsize)
cb.locator = ticker.MaxNLocator(nbins=5)
cb.update_ticks()
plt.tight_layout()
plt.show(False)
plt.draw()
return 0
def fc_name(c, which):
name = mp.component_name(c)
return name if which == 'scattered' else str(name[0].upper()) + str(
name[1])