# to the boundary of the resonator. We add half of the box length and move it to the right side of the trap area box.
res_right_x = np.max(x_eval)
res_electrons_x += resonator_box_length / 2. + res_right_x - 1.5E-6
print("Switching to greater trap area...")
t = trap_analysis.TrapSolver()
c = trap_analysis.get_constants()
x_eval, y_eval, cropped_potentials = t.crop_potentials(output, ydomain=None, xdomain=None)
coefficients = np.array([Vres, Vtrap, Vrg, Vcg, Vtg])
combined_potential = t.get_combined_potential(cropped_potentials, coefficients)
# Note: x_eval and y_eval are 1D arrays that contain the x and y coordinates at which the potentials are evaluated
# Units of x_eval and y_eval are um
CMS = anneal.CombinedModelSolver(x_eval * 1E-6, y_eval * 1E-6, -combined_potential.T,
anneal.xy2r(res_electrons_x, res_electrons_y),
spline_order_x=3, spline_order_y=3, smoothing=0.01)
X_eval, Y_eval = np.meshgrid(x_eval * 1E-6, y_eval * 1E-6)
if 0:
# Plot the resonator electron configuration
fig1 = plt.figure(figsize=(9.5, 2.5))
common.configure_axes(12)
plt.pcolormesh(x_eval, y_eval, CMS.V(X_eval, Y_eval), cmap=plt.cm.Spectral_r, vmax=0.0, vmin=-0.75 * Vres)
plt.pcolormesh((y_box + res_right_x + resonator_box_length / 2.) * 1E6, x_box * 1E6, RS.V(X_box, Y_box).T,
cmap=plt.cm.Spectral_r, vmax=0.0, vmin=-0.75 * Vres)
plt.plot((y_init + res_right_x + resonator_box_length / 2.) * 1E6, x_init * 1E6, 'o', color='palegreen', alpha=0.5)
plt.plot(res_electrons_x * 1E6, res_electrons_y * 1E6, 'o', color='deepskyblue', alpha=1.0)
plt.xlabel("$x$ ($\mu$m)")
plt.ylabel("$y$ ($\mu$m)")
# to the boundary of the resonator. We add half of the box length and move it to the right side of the trap area box.
res_right_x = np.max(x_eval)
res_electrons_x += resonator_box_length / 2. + res_right_x - 1.5E-6
print("Switching to greater trap area...")
t = trap_analysis.TrapSolver()
c = trap_analysis.get_constants()
x_eval, y_eval, cropped_potentials = t.crop_potentials(output, ydomain=None, xdomain=None)
coefficients = np.array([Vres, Vtrap, Vrg, Vcg, Vtg])
combined_potential = t.get_combined_potential(cropped_potentials, coefficients)
# Note: x_eval and y_eval are 1D arrays that contain the x and y coordinates at which the potentials are evaluated
# Units of x_eval and y_eval are um
CMS = anneal.CombinedModelSolver(x_eval * 1E-6, y_eval * 1E-6, -combined_potential.T,
anneal.xy2r(res_electrons_x, res_electrons_y),
spline_order_x=3, spline_order_y=3, smoothing=smoothing)
X_eval, Y_eval = np.meshgrid(x_eval * 1E-6, y_eval * 1E-6)
if 1:
# Plot the resonator electron configuration
fig1 = plt.figure(figsize=(12, 3))
common.configure_axes(12)
plt.pcolormesh(x_eval, y_eval, CMS.V(X_eval, Y_eval), cmap=plt.cm.Spectral_r, vmax=0.0, vmin=-0.75 * Vres)
plt.pcolormesh((y_box + res_right_x + resonator_box_length / 2.) * 1E6, x_box * 1E6, RS.V(X_box, Y_box).T,
cmap=plt.cm.Spectral_r, vmax=0.0, vmin=-0.75 * Vres)
plt.plot((y_init + res_right_x + resonator_box_length / 2.) * 1E6, x_init * 1E6, 'o', color='palegreen', alpha=0.5)
plt.plot(res_electrons_x * 1E6, res_electrons_y * 1E6, 'o', color='deepskyblue', alpha=1.0)
plt.xlabel("$x$ ($\mu$m)")
plt.ylabel("$y$ ($\mu$m)")
def coordinate_transformation(r):
x, y = anneal.r2xy(r)
y_new = EP.map_y_into_domain(y)
r_new = anneal.xy2r(x, y_new)
return r_new
if res['status'] > 0:
cprint(
"WARNING: Step %d (Vres = %.2f V) did not converge!" % (k, Vres),
"red")
# This maps the electron positions within the simulation domain
res['x'] = coordinate_transformation(res['x'])
if len(annealing_steps) > 0:
res = EP.perturb_and_solve(EP.Vtotal, len(annealing_steps),
annealing_steps[0], res,
**minimizer_options)
res['x'] = coordinate_transformation(res['x'])
resonator_ns_area = anneal.get_electron_density_by_area(
anneal.xy2r(res['x'][::2], res['x'][1::2]))
resonator_ns_pos = anneal.get_electron_density_by_position(
anneal.xy2r(res['x'][::2], res['x'][1::2]))
print(
"Electron density on resonator = %.2e (by area) or %.2e (by position)"
% (resonator_ns_area, resonator_ns_pos))
PP = anneal.PostProcess(save_path=conv_mon_save_path,
res_pinw=res_pinw * 1E6,
edge_gapw=edge_gapw * 1E6,
center_gapw=center_gapw * 1E6,
box_length=box_length * 1E6)
PP.save_snapshot(res['x'],
xext=x_box,
yext=y_box,
Uext=EP.V,
'callback': None}
# We save the initial Jacobian of the system for purposes.
initial_jacobian = CMS.grad_total(electron_initial_positions)
res = minimize(CMS.Vtotal, electron_initial_positions, **trap_minimizer_options)
while res['status'] > 0:
# Try removing unbounded electrons and restart the minimization
if remove_unbound_electrons:
# Remove any electrons that are to the left of the trap
best_x, best_y = anneal.r2xy(res['x'])
idxs = np.where(np.logical_and(best_x > remove_bounds[0], best_x < remove_bounds[1]))[0]
best_x = np.delete(best_x, idxs)
best_y = np.delete(best_y, idxs)
# Use the solution from the current time step as the initial condition for the next timestep!
electron_initial_positions = anneal.xy2r(best_x, best_y)
if len(best_x) < len(res['x'][::2]):
print("%d/%d unbounded electrons removed. %d electrons remain." % (np.int(len(res['x'][::2]) - len(best_x)), len(res['x'][::2]), len(best_x)))
res = minimize(CMS.Vtotal, electron_initial_positions, **trap_minimizer_options)
else:
best_x, best_y = anneal.r2xy(res['x'])
idxs = np.union1d(np.where(best_x < -2E-6)[0], np.where(np.abs(best_y) > 2E-6)[0])
if len(idxs) > 0:
print("Following electrons are outside the simulation domain")
for i in idxs:
print("(x,y) = (%.3f, %.3f) um"%(best_x[i]*1E6, best_y[i]*1E6))
# To skip the infinite while loop.
break
if res['status'] > 0:
cprint("WARNING: Initial minimization for Trap did not converge!", "red")
# Note: x_eval and y_eval are 2D arrays that contain the x and y coordinates at which the potentials are evaluated
conv_mon_save_path = os.path.join(save_path, sub_dir, "Figures")
print("Switching to greater trap area...")
t = trap_analysis.TrapSolver()
c = trap_analysis.get_constants()
x_eval, y_eval, cropped_potentials = t.crop_potentials(output, ydomain=None, xdomain=None)
coefficients = np.array([Vres, Vtrap, Vrg, Vcg, Vtg])
combined_potential = t.get_combined_potential(cropped_potentials, coefficients)
# Note: x_eval and y_eval are 1D arrays that contain the x and y coordinates at which the potentials are evaluated
# Units of x_eval and y_eval are um
CMS = anneal.CombinedModelSolver(x_eval * 1E-6, y_eval * 1E-6, -combined_potential.T,
anneal.xy2r([], []),
spline_order_x=3, spline_order_y=3, smoothing=0.01)
X_eval, Y_eval = np.meshgrid(x_eval * 1E-6, y_eval * 1E-6)
VBG_mat = np.zeros((len(y_eval), len(x_eval)))
for j, X in enumerate(x_eval * 1E-6):
VBG_mat[:, j] = CMS.Vbg(X * np.ones(len(y_eval)), y_eval * 1E-6)
# Solve for the electron positions in the trap area!
ConvMon = anneal.ConvergenceMonitor(Uopt=CMS.Vtotal, grad_Uopt=CMS.grad_total, N=10,
Uext=CMS.V,
xext=xeval * 1E-6, yext=yeval * 1E-6, verbose=False, eps=epsilon,
save_path=conv_mon_save_path)
t.get_electron_frequency([fr[0], -fr[1] / 1E6], [ferr[0], -ferr[1] / 1E6])
# Rectangle
if N_cols * N_rows != N_electrons:
raise ValueError("N_cols and N_rows are not compatible with N_electrons")
else:
y_separation = 1.25E-6
x_separation = 1.0E-6
y0 = -15E-6
ys = np.linspace(y0, y0 + N_cols * y_separation, N_cols)
yinit = np.tile(np.array(ys), N_rows)
xs = np.linspace(-(N_rows - 1) / 2. * x_separation,
+(N_rows - 1) / 2. * x_separation, N_rows)
xinit = np.repeat(xs, N_cols)
electron_initial_positions = anneal.xy2r(xinit, yinit)
x_box = np.linspace(-3E-6, 3E-6, 501)
y_box = np.linspace(-box_length / 2, box_length / 2, 11)
X_box, Y_box = np.meshgrid(x_box, y_box)
def coordinate_transformation(r):
x, y = anneal.r2xy(r)
y_new = EP.map_y_into_domain(y)
r_new = anneal.xy2r(x, y_new)
return r_new
# This is where the actual solving starts...
os.mkdir(os.path.join(save_path, sub_dir))