def test_two_parameter_maximization(self):
optimizer = ScipyOptimizers(max_iter=200,
method='L-BFGS-B',
scaling_factor=1.0,
pgtol=1.0e-11)
fom = lambda params: np.sin(np.pi * params[0]) * np.sin(np.pi * params[
1])
jac = lambda params: np.pi * np.array([
np.cos(np.pi * params[0]) * np.sin(np.pi * params[1]),
np.sin(np.pi * params[0]) * np.cos(np.pi * params[1])
])
start = np.array([1.0e-5, 1.0 - 1e-5])
bounds = np.array([(self.machine_eps, 1.0 - self.machine_eps),
(self.machine_eps, 1.0 - self.machine_eps)])
def plot_fun():
pass
optimizer.initialize(start_params=start,
callable_fom=fom,
callable_jac=jac,
bounds=bounds,
plotting_function=plot_fun)
results = optimizer.run()
self.assertTrue(results.success)
self.assertAlmostEqual(results.x.size, 2)
self.assertAlmostEqual(results.x[0], 0.5, 11)
self.assertAlmostEqual(results.x[1], 0.5, 11)
self.assertAlmostEqual(results.fun, 1.0, 11)
self.assertLessEqual(results.nit, 3)
self.assertLessEqual(results.nfev, 15)
def test_single_parameter_maximization_with_scaling(self):
span = 1.0e-9
optimizer = ScipyOptimizers(max_iter=200,
method='L-BFGS-B',
scaling_factor=1.0 / span,
pgtol=1.0e-11)
fom = lambda param: -1.0 / np.sin(np.pi / span * param[0])
jac = lambda param: np.pi * np.cos(np.pi / span * param[0]) / np.power(
np.sin(np.pi / span * param[0]), 2) / span
start = np.array(1.0e-6) * span
bounds = np.array([(self.machine_eps, 1.0 - self.machine_eps)]) * span
def plot_fun():
pass
optimizer.initialize(start_params=start,
callable_fom=fom,
callable_jac=jac,
bounds=bounds,
plotting_function=plot_fun)
results = optimizer.run()
self.assertTrue(results.success)
self.assertAlmostEqual(results.x.size, 1)
self.assertAlmostEqual(results.x[0], 0.5 * span, 11)
self.assertAlmostEqual(results.fun, -1.0, 11)
self.assertLessEqual(results.nit, 31)
self.assertLessEqual(results.nfev, 46)
def setUp(self):
# Base simulation project files
self.base_TE_sim = os.path.join(
self.file_dir,
'co_optimization_parallel_plate_waveguide_TE_base.fsp')
self.base_TM_sim = os.path.join(
self.file_dir,
'co_optimization_parallel_plate_waveguide_TM_base.fsp')
# Simulation bandwidth
self.wavelengths = Wavelengths(start=1500e-9, stop=1600e-9, points=12)
# Polygon defining a rectangle that can grow or shrink along the y-axis to fill the gap
self.mesh_del = 10.0e-9
# must be kept in sych with self.base_script
initial_points_y = np.array(
[1.75 * self.mesh_del, 0.01 * self.mesh_del])
def wall(param=initial_points_y):
assert param.size == 2, "walls defined by two points."
self.wg_gap = 10.0 * self.mesh_del # must be kept in sych
points_x = 0.5 * np.array(
[-self.wg_gap, self.wg_gap, self.wg_gap, -self.wg_gap])
points_y = np.array([-param[0], -param[1], param[1], param[0]])
polygon_points = [(x, y) for x, y in zip(points_x, points_y)]
return np.array(polygon_points)
self.wg_width = 50.0 * self.mesh_del # must be kept in synch
bounds = [(0.0, self.wg_width / 2.0)] * initial_points_y.size
self.geometry = FunctionDefinedPolygon(
func=wall,
initial_params=initial_points_y,
bounds=bounds,
z=0.0, # must be kept in sych
depth=self.wg_width,
eps_out=Material(base_epsilon=1.0**2,
name='<Object defined dielectric>',
mesh_order=2), # must be kept in synch
eps_in=Material(base_epsilon=4.0**2,
name='<Object defined dielectric>',
mesh_order=1), # must be kept in sych
edge_precision=50,
dx=1.0e-10)
# Figure of merit
self.fom = ModeMatch(
monitor_name='fom', # must be kept in sych
mode_number=1, # must be kept in sych
direction='Forward',
multi_freq_src=True,
target_T_fwd=lambda wl: np.ones(wl.size),
norm_p=1)
# Scipy optimizer
self.optimizer = ScipyOptimizers(max_iter=5,
method='L-BFGS-B',
scaling_factor=1.0e7,
pgtol=1.0e-5,
ftol=1.0e-12,
target_fom=0.0,
scale_initial_gradient_to=None)
def runSim(params,
eps_bg,
eps_wg,
x_pos,
y_pos,
size_x,
filter_R,
beta_start=1):
######## DEFINE A 2D TOPOLOGY OPTIMIZATION REGION ########
geometry = TopologyOptimization2D(params=params,
eps_min=eps_bg,
eps_max=eps_wg,
x=x_pos,
y=y_pos,
z=0,
filter_R=filter_R,
beta=beta_start)
######## DEFINE FIGURE OF MERIT ########
# The base simulation script defines a field monitor named 'fom' at the point where we want to modematch to the fundamental TE mode
fom = ModeMatch(monitor_name='fom',
mode_number='Fundamental TE mode',
direction='Forward',
norm_p=2)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter=50,
method='L-BFGS-B',
scaling_factor=1,
pgtol=1e-6,
ftol=1e-4,
target_fom=0.5,
scale_initial_gradient_to=0.25)
######## LOAD TEMPLATE SCRIPT AND SUBSTITUTE PARAMETERS ########
script = load_from_lsf(
os.path.join(CONFIG['root'],
'examples/Ysplitter/splitter_base_2D_TE_topology.lsf'))
script = script.replace('opt_size_x=3.5e-6',
'opt_size_x={:1.6g}'.format(size_x))
wavelengths = Wavelengths(start=1450e-9, stop=1650e-9, points=11)
opt = Optimization(base_script=script,
wavelengths=wavelengths,
fom=fom,
geometry=geometry,
optimizer=optimizer,
use_deps=False,
hide_fdtd_cad=True,
plot_history=False,
store_all_simulations=False)
######## RUN THE OPTIMIZER ########
opt.run()
def setUp(self):
# Base simulation script
self.base_script = load_from_lsf(os.path.join(self.file_dir, 'optimization_waveguide_filter_TM_2D_base.lsf'))
# Simulation bandwidth
self.wavelengths = Wavelengths(start = 1300e-9,
stop = 1800e-9,
points = 41)
# Polygons to form the two gaps
self.mesh_del = 20.0e-9; # must be kept in sych with self.base_script
initial_param = 10.0 * np.array([self.mesh_del])
def rectangle(param = initial_param, offset = 0.0):
assert param.size == 1, "rectangle grows along a single dimension."
wg_width = 35.0 * self.mesh_del # must be kept in synch
points_x = 0.5 * np.array([-wg_width, wg_width, wg_width, -wg_width])
points_y = 0.5 * np.array([-param, -param, param, param]) + offset
polygon_points = [(x, y) for x, y in zip(points_x, points_y)]
return np.array(polygon_points)
bounds = [(self.mesh_del, 20.0 * self.mesh_del)]
z = 0.0 # must be kept in sych
depth = 200.0 * self.mesh_del # must be kept in sych
eps_in = Material(base_epsilon = 1.44 ** 2, mesh_order = 1) # must be kept in sych with
eps_out = Material(base_epsilon = 2.8 ** 2, mesh_order = 1) # must be kept in sych with
edge_precision = 25
dx = 1.0e-10
self.geometry = (FunctionDefinedPolygon(func = lambda param: rectangle(param[0], 2.0 * param[0]), initial_params = initial_param, bounds = bounds, z = z, depth = depth, eps_out = eps_out, eps_in = eps_in, edge_precision = edge_precision, dx = dx) *
FunctionDefinedPolygon(func = lambda param: rectangle(param[0],-2.0 * param[0]), initial_params = initial_param, bounds = bounds, z = z, depth = depth, eps_out = eps_out, eps_in = eps_in, edge_precision = edge_precision, dx = dx))
# Broadband figure of merit
target_T_fwd = lambda wl: 0.3 + 0.65*np.power(np.sin(np.pi * (wl - wl.min()) / (wl.max() - wl.min())), 6)
self.fom = ModeMatch(monitor_name = 'FOM', # must be kept in sych
mode_number = 1, # must be kept in sych
direction = 'Backward',
multi_freq_src = True,
target_T_fwd = target_T_fwd,
norm_p = 1)
# Scipy optimzier
self.optimizer = ScipyOptimizers(max_iter = 10,
method = 'L-BFGS-B',
scaling_factor = 1.0e7,
pgtol = 5.6e-3)
def __init__(self, max_iter, method, scaling_factor, pgtol, ftol,
wavelength_start, wavelength_stop, wavelength_points,
build_simulation, fom, geometry, hide_fdtd_cad):
# The optimizer must be generated anew at each iteration
self._new_local_optimizer = ScipyOptimizers(
max_iter=max_iter,
method=method,
scaling_factor=scaling_factor,
ftol=ftol,
pgtol=pgtol)
self._wl = Wavelengths(start=wavelength_start,
stop=wavelength_stop,
points=wavelength_points)
self._optimization = Optimization(base_script=build_simulation,
wavelengths=self._wl,
fom=fom,
geometry=geometry,
optimizer=self._new_local_optimizer,
hide_fdtd_cad=hide_fdtd_cad,
use_deps=True)
depth=wg_height,
eps_out=1.0**2,
eps_in=3.47668**2,
edge_precision=5,
dx=1.0e-5)
######## DEFINE FIGURE OF MERIT ########
fom = ModeMatch(monitor_name='fom',
mode_number=3,
direction='Backward',
target_T_fwd=lambda wl: 0.5 * np.ones(wl.size),
norm_p=1)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter=200,
method='L-BFGS-B',
scaling_factor=1.0,
pgtol=1.0e-4)
######## PUT EVERYTHING TOGETHER ########
opt = Optimization(base_script=base_sim,
wavelengths=wavelengths,
fom=fom,
geometry=geometry,
optimizer=optimizer,
hide_fdtd_cad=False,
use_deps=True)
######## RUN THE OPTIMIZER ########
opt.run()
eps_out=1.44**2,
eps_in='Si (Silicon) - Palik',
bounds=bounds,
depth=220e-9,
edge_precision=5)
# We must define the permittivities of the material making the optimizable
# geometry and of that surrounding it. Since this is a 2D simulation, the depth has no importance.
# edge_precision defines the discretization of the edges forming the optimizable polygon. It should be set such
# that there are at least a few points per mesh cell. An effective index of 2.8 is user to simulate a 2D slab of
# 220 nm thick
######## DEFINE FIGURE OF MERIT ########
fom = ModeMatch(modeorder=1, monitor_name='fom', wavelength=1550e-9)
# The base simulation script defines a field monitor named 'fom' at the point where we want to
# modematch to the 3rd order mode (fundamental TE mode)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter=20, method='L-BFGS-B', scaling_factor=1e6)
# This will run Scipy's implementation of the L-BFGS-B algoithm for at least 40 iterations. Since the variables are on the
# order of 1e-6, we scale them up to be on the order of 1
######## PUT EVERYTHING TOGETHER ########
opt = Optimization(base_script=script,
fom=fom,
geometry=geometry,
optimizer=optimizer)
######## RUN THE OPTIMIZER ########
opt.run()
def runGratingOptimization(bandwidth_in_nm, etch_depth_shallow, etch_depth_deep, n_grates, initial_params = None):
### Yet another parametrization which allows to enforce minimum feature size when the optimizer only supports box constraints
### params = [x0, a1, b1, ..., aN]
if initial_params is None:
params = np.zeros(4*n_grates)
for i in range(n_grates):
params[i*4] = 0.2 #< Width up
params[i*4+1] = 0.4*(i/n_grates) #< Width of the shallow etch
params[i*4+2] = 0.1 #< Width up
params[i*4+3] = 0.4*(i/n_grates) #< Width of the deep etch
params[0] = 0 #< Overwrite the first since it has a special meaning: Start of the grating at 0um
else:
params = initial_params
bounds = [(0, 1)]*(4*n_grates)
bounds[0] = (-3,3)
def grating_params_pos(params, output_waveguide_length = 0.5e-6, height = 220e-9, y0 = 0):
x_begin = -3e-6
y3 = y0+height
y2 = y3-etch_depth_deep
y1 = y3-etch_depth_shallow
x0 = params[0]*1e-6 #< First parameter is the starting position
verts = np.array( [ [x_begin,y0],[x_begin,y3],[x0,y3],[x0,y1] ] )
## Iterate over all but the last
for i in range(n_grates-1):
x1 = x0 + params[i*4+1]*1e-6 #< Width of the deep etch
x2 = x1 + params[i*4+2]*1e-6 #< Width up
x3 = x2 + params[i*4+3]*1e-6 #< Width of the shallow etch
x4 = x3 + params[i*4+4]*1e-6 #< Width up
verts = np.concatenate((verts,[[x1,y1],[x1,y3],[x2,y3],[x2,y2],[x3,y2],[x3,y3],[x4,y3],[x4,y1]]),axis=0)
x0 = x4
x1 = x0 + params[(n_grates-1)*4+1]*1e-6 #< Width of the deep etch
x2 = x1 + params[(n_grates-1)*4+2]*1e-6 #< Width up
x3 = x2 + params[(n_grates-1)*4+3]*1e-6 #< Width of the shallow etch
x_end = x3+output_waveguide_length
verts = np.concatenate((verts,[[x1,y1],[x1,y3],[x2,y3],[x2,y2],[x3,y2],[x3,y3],[x_end,y3],[x_end,y0]]),axis=0)
return verts
geometry = FunctionDefinedPolygon(func = grating_params_pos, initial_params = params, bounds = bounds, z = 0.0, depth = 220e-9, eps_out = 1.44 ** 2, eps_in = 3.47668 ** 2, edge_precision = 5, dx = 1e-3)
######## DEFINE FIGURE OF MERIT ########
fom = ModeMatch(monitor_name = 'fom', mode_number = 1, direction = 'Backward', target_T_fwd = lambda wl: np.ones(wl.size), norm_p = 1)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter = 250, method = 'L-BFGS-B', scaling_factor = 1, pgtol = 1e-6) #SLSQP
######## DEFINE BASE SIMULATION ########
base_script = load_from_lsf(os.path.join(os.path.dirname(__file__), 'grating_coupler_2D_2etch.lsf'))
######## PUT EVERYTHING TOGETHER ########
lambda_start = 1550 - bandwidth_in_nm/2
lambda_end = 1550 + bandwidth_in_nm/2
lambda_pts = int(bandwidth_in_nm/10)+1
wavelengths = Wavelengths(start = lambda_start*1e-9, stop = lambda_end*1e-9, points = lambda_pts)
opt = Optimization(base_script = base_script, wavelengths = wavelengths, fom = fom, geometry = geometry, optimizer = optimizer, hide_fdtd_cad = True, use_deps = True)
######## RUN THE OPTIMIZER ########
opt.run()
dx=0.01e-9)
######## DEFINE FIGURE OF MERIT ########
# The base simulation script defines a field monitor named 'fom' at the point where we want to modematch to the 3rd mode (fundamental TE mode).
fom = ModeMatch(monitor_name='fom',
mode_number=2,
direction='Forward',
multi_freq_src=False,
target_T_fwd=lambda wl: np.ones(wl.size),
norm_p=1)
######## DEFINE OPTIMIZATION ALGORITHM ########
# This will run Scipy's implementation of the L-BFGS-B algoithm for at least 40 iterations. Since the variables are on the
# order of 1e-6, thery are scale up to be on the order of 1.
optimizer = ScipyOptimizers(max_iter=500,
method='L-BFGS-B',
scaling_factor=1e6,
pgtol=1e-9)
######## PUT EVERYTHING TOGETHER ########
opt = Optimization(base_script=base_script,
wavelengths=wavelengths,
fom=fom,
geometry=geometry,
optimizer=optimizer,
hide_fdtd_cad=False,
use_deps=True)
######## RUN THE OPTIMIZER ########
opt.run()
def runGratingOptimization(bandwidth_in_nm, etch_depth, n_grates, params):
bounds = [(0.1, 1)] * 4
bounds[0] = (-3, 3) #< Starting position
bounds[1] = (0, 0.1) #< Scaling parameter R
bounds[2] = (1.5, 3) #< Parameter a
bounds[3] = (0, 2) #< Parameter b
def grating_params_pos(params,
output_waveguide_length=0.5e-6,
height=220e-9,
y0=0):
x_begin = -3e-6
y3 = y0 + height
y1 = y3 - etch_depth
x_start = params[0] * 1e-6 #< First parameter is the starting position
x0 = x_start
R = params[1] * 1e6 #< second parameter (unit is 1/um)
a = params[2] #< Third parameter (dim-less)
b = params[3] #< Fourth parameter (dim-less)
verts = np.array([[x_begin, y0], [x_begin, y3], [x0, y3], [x0, y1]])
lambda_c = 1.55e-6
F0 = 0.95
## Iterate over all but the last
for i in range(n_grates - 1):
F = F0 - R * (x0 - x_start)
Lambda = lambda_c / (a + F * b)
x1 = x0 + (1 - F) * Lambda #< Width of the etched region
x2 = x0 + Lambda #< Rest of cell
verts = np.concatenate(
(verts, [[x1, y1], [x1, y3], [x2, y3], [x2, y1]]), axis=0)
x0 = x2
F = F0 - R * (x0 - x_start)
Lambda = lambda_c / (a + F * b)
x1 = x0 + (1 - F) * Lambda #< Width of the etched region
x_end = x1 + output_waveguide_length
verts = np.concatenate(
(verts, [[x1, y1], [x1, y3], [x_end, y3], [x_end, y0]]), axis=0)
return verts
geometry = FunctionDefinedPolygon(func=grating_params_pos,
initial_params=params,
bounds=bounds,
z=0.0,
depth=110e-9,
eps_out=1.44**2,
eps_in=3.47668**2,
edge_precision=5,
dx=1e-3)
######## DEFINE FIGURE OF MERIT ########
fom = ModeMatch(monitor_name='fom',
mode_number=1,
direction='Backward',
target_T_fwd=lambda wl: np.ones(wl.size),
norm_p=1)
######## DEFINE OPTIMIZATION ALGORITHM ########
optimizer = ScipyOptimizers(max_iter=25,
method='L-BFGS-B',
scaling_factor=1,
pgtol=1e-6)
######## DEFINE BASE SIMULATION ########
base_script = load_from_lsf(
os.path.join(os.path.dirname(__file__),
'grating_coupler_2D_2etch.lsf'))
######## PUT EVERYTHING TOGETHER ########
lambda_start = 1550 - bandwidth_in_nm / 2
lambda_end = 1550 + bandwidth_in_nm / 2
lambda_pts = int(bandwidth_in_nm / 10) + 1
wavelengths = Wavelengths(start=lambda_start * 1e-9,
stop=lambda_end * 1e-9,
points=lambda_pts)
opt = Optimization(base_script=base_script,
wavelengths=wavelengths,
fom=fom,
geometry=geometry,
optimizer=optimizer,
hide_fdtd_cad=True,
use_deps=True)
######## RUN THE OPTIMIZER ########
opt.run()
if __name__ == '__main__':
import numpy as np
from lumopt.geometries.polygon import function_defined_Polygon, cross
from lumopt.optimizers.generic_optimizers import ScipyOptimizers
from lumopt.figures_of_merit.modematch import ModeMatch
from lumopt.utilities.load_lumerical_scripts import load_from_lsf
import os
import matplotlib.pyplot as plt
from lumopt import CONFIG
base_script = load_from_lsf(
os.path.join(CONFIG['root'],
'examples/crossing/crossing_base_TE_modematch_2D.lsf'))
fom = ModeMatch(modeorder=2)
optimizer = ScipyOptimizers(max_iter=20)
# optimizer=FixedStepGradientDescent(max_dx=20e-9,max_iter=100)
bounds = [(0.2e-6, 1e-6)] * 10
geometry = function_defined_Polygon(func=cross,
initial_params=np.linspace(
0.25e-6, 0.6e-6, 10),
eps_out='SiO2 (Glass) - Palik',
eps_in=2.8**2,
bounds=bounds,
depth=220e-9,
edge_precision=5)
opt = Optimization(base_script=base_script,
fom=fom,
geometry=geometry,
optimizer=optimizer)
interpolator = sp.interpolate.interp1d(points_x, points_y, kind='cubic')
polygon_points_y = [max(min(point,1e-6),-1e-6) for point in interpolator(polygon_points_x)]
polygon_points_up = [(x, y) for x, y in zip(polygon_points_x, polygon_points_y)]
polygon_points_down = [(x, -y) for x, y in zip(polygon_points_x, polygon_points_y)]
polygon_points = np.array(polygon_points_up[::-1] + polygon_points_down)
return polygon_points
bounds = [(0.2e-6, 1e-6)]*10
# final value from splitter_opt_2D.py optimization
initial_params = np.array([2.44788514e-07, 2.65915795e-07, 2.68748023e-07, 4.42233947e-07, 6.61232152e-07, 6.47561406e-07, 6.91473099e-07, 6.17511522e-07, 6.70669074e-07, 5.86141086e-07])
geometry_1 = FunctionDefinedPolygon(func = taper_splitter_1, initial_params = initial_params, bounds = bounds, z = 0.0, depth = 220e-9, eps_out = 1.44 ** 2, eps_in = 2.8 ** 2, edge_precision = 5, dx = 0.1e-9)
geometry_2 = FunctionDefinedPolygon(func = taper_splitter_2, initial_params = initial_params, bounds = bounds, z = 0.0, depth = 220e-9, eps_out = 1.44 ** 2, eps_in = 2.8 ** 2, edge_precision = 5, dx = 0.1e-9)
######## DEFINE FIGURE OF MERIT ########
# Although we are optimizing for the same thing, two separate fom objects must be create
fom_1 = ModeMatch(monitor_name = 'fom', mode_number = 3, direction = 'Forward')
fom_2 = ModeMatch(monitor_name = 'fom', mode_number = 3, direction = 'Forward')
######## DEFINE OPTIMIZATION ALGORITHM ########
#For the optimizer, they should all be set the same, but different objects. Eventually this will be improved
optimizer_1 = ScipyOptimizers(max_iter = 40)
optimizer_2 = ScipyOptimizers(max_iter = 40)
######## PUT EVERYTHING TOGETHER ########
opt_1 = Optimization(base_script = script_1, wavelengths = wavelengths, fom = fom_1, geometry = geometry_1, optimizer = optimizer_1)
opt_2 = Optimization(base_script = script_2, wavelengths = wavelengths, fom = fom_2, geometry = geometry_2, optimizer = optimizer_2)
opt = opt_1 + opt_2
######## RUN THE OPTIMIZER ########
opt.run()
#final value from splitter_opt_2D.py optimization initial_params=np.linspace(0,0.24e-6,10) #initial_params=np.linspace(-0.25e-6,0.25e-6,10) geometry_1550_lower = function_defined_Polygon(func=lower_coupler_arm,initial_params=initial_params,eps_out=1.44 ** 2, eps_in=2.8 ** 2,bounds=bounds,depth=220e-9,edge_precision=5) geometry_1550_upper = function_defined_Polygon(func=upper_coupler_arm,initial_params=initial_params,eps_out=1.44 ** 2, eps_in=2.8 ** 2,bounds=bounds,depth=220e-9,edge_precision=5) geometry_1550=geometry_1550_lower*geometry_1550_upper geometry_1310_lower = function_defined_Polygon(func=lower_coupler_arm,initial_params=initial_params,eps_out=1.44 ** 2, eps_in=2.8 ** 2,bounds=bounds,depth=220e-9,edge_precision=5) geometry_1310_upper = function_defined_Polygon(func=upper_coupler_arm,initial_params=initial_params,eps_out=1.44 ** 2, eps_in=2.8 ** 2,bounds=bounds,depth=220e-9,edge_precision=5) geometry_1310=geometry_1310_lower*geometry_1310_upper ######## DEFINE FIGURE OF MERIT ######## # Although we are optimizing for the same thing, two separate fom objects must be create fom_1550=ModeMatch(modeorder=2,wavelength=1550e-9,monitor_name='fom_1550') fom_1310=ModeMatch(modeorder=2,wavelength=1310e-9,monitor_name='fom_1310') ######## DEFINE OPTIMIZATION ALGORITHM ######## #For the optimizer, they should all be set the same, but different objects. Eventually this will be improved optimizer_1550=ScipyOptimizers(max_iter=40) optimizer_1310=ScipyOptimizers(max_iter=40) ######## PUT EVERYTHING TOGETHER ######## opt_1550=Optimization(base_script=script_1550,fom=fom_1550,geometry=geometry_1550,optimizer=optimizer_1550) opt_1310=Optimization(base_script=script_1310,fom=fom_1310,geometry=geometry_1310,optimizer=optimizer_1310) opt=opt_1550+opt_1310 ######## RUN THE OPTIMIZER ######## opt.run()
fom_1550 = ModeMatch(monitor_name='fom_1550',
mode_number=2,
direction='Forward',
target_T_fwd=lambda wl: np.ones(wl.size),
norm_p=1)
fom_1310 = ModeMatch(monitor_name='fom_1310',
mode_number=2,
direction='Forward',
target_T_fwd=lambda wl: np.ones(wl.size),
norm_p=1)
######## DEFINE OPTIMIZATION ALGORITHM ########
#For the optimizer, they should all be set the same, but different objects. Eventually this will be improved
optimizer_1550 = ScipyOptimizers(max_iter=40,
method='L-BFGS-B',
scaling_factor=1e6,
pgtol=1e-9)
optimizer_1310 = ScipyOptimizers(max_iter=40,
method='L-BFGS-B',
scaling_factor=1e6,
pgtol=1e-9)
######## PUT EVERYTHING TOGETHER ########
opt_1550 = Optimization(base_script=script_1550,
wavelengths=wavelengths_1551,
fom=fom_1550,
geometry=geometry_1550,
optimizer=optimizer_1550,
hide_fdtd_cad=False,
use_deps=True)
opt_1310 = Optimization(base_script=script_1310,