def run_opt(save_folder: str) -> None:
"""Main optimization script.
This function setups the optimization and executes it.
Args:
save_folder: Location to save the optimization data.
"""
os.makedirs(save_folder)
sim_space = create_sim_space("sim_fg.gds",
"sim_bg.gds",
box_thickness=2000,
wg_thickness=220,
etch_frac=0.5)
obj, monitors = create_objective(sim_space)
trans_list = create_transformations(obj,
monitors,
50,
200,
sim_space,
min_feature=100)
plan = optplan.OptimizationPlan(transformations=trans_list)
# Save the optimization plan so we have an exact record of all the
# parameters.
with open(os.path.join(save_folder, "optplan.json"), "w") as fp:
fp.write(optplan.dumps(plan))
# Execute the optimization and indicate that the current folder (".") is
# the project folder. The project folder is the root folder for any
# auxiliary files (e.g. GDS files).
problem_graph.run_plan(plan, ".", save_folder=save_folder)
def loads(serialized_plan: str) -> optplan.OptimizationPlan:
"""Loads a serialized optimization plan.
Args:
serialized_plan: Serialized plan.
Returns:
Stored `optplan.OptimizationPlan`.
"""
plan = optplan.OptimizationPlan(json.loads(serialized_plan))
validate(plan)
_validate_optplan_version(plan.version)
# Unpack the optimization plan.
# First create a dictionary mapping strings to the node.
node_map = {}
for node in plan.nodes:
node_map[node.name] = node
# Now, recursively fill any node names with the actual node.
def process_field(model: models.Model, child_model: str) -> models.Model:
return node_map[child_model]
visited = set()
if plan.transformations:
for transformation in plan.transformations:
_iter_optplan_fields(transformation, visited, process_field)
for node in plan.nodes:
_iter_optplan_fields(node, visited, process_field)
validate_references(plan)
return plan
def test_kerr(tmpdir):
folder = os.path.join(tmpdir, 'kerr_test_results')
_copyfiles(CUR_DIR, folder, ["sim_fg_kerr.gds", "sim_bg_kerr.gds"])
sim_space = kerr.create_sim_space("sim_fg_kerr.gds", "sim_bg_kerr.gds")
obj, monitors = kerr.create_objective(sim_space)
trans_list = kerr.create_transformations(
obj, monitors, sim_space, cont_iters=20, min_feature=100)
plan = optplan.OptimizationPlan(transformations=trans_list)
problem_graph.run_plan(plan, folder)
def test_dumps():
plan = optplan.OptimizationPlan(
nodes=[
optplan.Sum(
functions=[
optplan.Constant(value=optplan.ComplexNumber(real=2)),
optplan.Constant(value=optplan.ComplexNumber(real=3)),
],)
],)
plan_dict = json.loads(optplan.dumps(plan))
def test_wdm2(tmpdir):
folder = str(tmpdir.mkdir("wdm2"))
_copyfiles(CUR_DIR, folder, ["sim_fg.gds", "sim_bg.gds"])
sim_space = wdm2.create_sim_space("sim_fg.gds", "sim_bg.gds")
obj, monitors = wdm2.create_objective(sim_space)
trans_list = wdm2.create_transformations(obj,
monitors,
sim_space,
cont_iters=1,
min_feature=100)
plan = optplan.OptimizationPlan(transformations=trans_list)
problem_graph.run_plan(plan, folder)
def test_dumps_duplicate_name_raises_value_error():
plan = optplan.OptimizationPlan(
nodes=[
optplan.Sum(
functions=[
optplan.Constant(
name="const", value=optplan.ComplexNumber(real=2)),
optplan.Constant(
name="const", value=optplan.ComplexNumber(real=3)),
],)
],)
with pytest.raises(ValueError, match="Nonunique name found"):
optplan.dumps(plan)
def test_phc(tmpdir):
folder = os.path.join(tmpdir, 'phc_gvd_test')
fg = "sim_fg_rect.gds"
bg = "sim_bg_rect.gds"
_copyfiles(CUR_DIR, folder, [fg, bg])
sim_space = phc_rect.create_sim_space(fg, bg)
obj, monitors = phc_rect.create_objective(sim_space)
trans_list = phc_rect.create_transformations(obj,
monitors,
sim_space,
cont_iters=42,
min_feature=60,
num_stages=6)
plan = optplan.OptimizationPlan(transformations=trans_list)
problem_graph.run_plan(plan, folder)
def test_custom_function_node():
@optplan.register_node_type(optplan.NodeMetaType.OPTPLAN_NODE)
class CustomOp(optplan.Function):
type = schema_utils.polymorphic_model_type("custom_op")
int_val = types.IntType()
plan = optplan.OptimizationPlan(
nodes=[
optplan.Sum(
functions=[
optplan.Constant(value=optplan.ComplexNumber(real=2)),
CustomOp(int_val=3),
],)
],)
optplan.loads(optplan.dumps(plan))
def test_phase(tmpdir):
folder = os.path.join(tmpdir, 'GVD_test_wg')
_copyfiles(CUR_DIR, folder, ["sim_fg_wg.gds", "sim_bg_wg.gds"])
sim_space = phase.create_sim_space("sim_fg_wg.gds", "sim_bg_wg.gds")
obj, power_obj, monitors = phase.create_objective(sim_space)
trans_list = phase.create_transformations(obj,
monitors,
sim_space,
cont_iters=80,
min_feature=40,
num_stages=4)
# power_obj=power_obj, power_iters=6, power_stages=2)
plan = optplan.OptimizationPlan(transformations=trans_list)
# with open("plan_dump.json", "w") as plan_dump_file:
# plan_dump_file.write(optplan.dumps(plan))
problem_graph.run_plan(plan, folder)
def run_opt(save_folder: str) -> None:
"""Main optimization script.
This function setups the optimization and executes it.
Args:
save_folder: Location to save the optimization data.
"""
# os.makedirs(save_folder)
os.makedirs(save_folder, exist_ok=True)
sim_space = create_sim_space("sim_fg.gds",
"sim_bg.gds",
wg_thickness=wg_thickness,
wg_length=wg_length,
wg_width=wg_width,
buffer_len=buffer_len,
dx=dx,
num_pmls=num_pmls)
obj, monitors = create_objective(sim_space,
wg_thickness=wg_thickness,
wg_length=wg_length,
wg_width=wg_width,
buffer_len=buffer_len,
dx=dx,
num_pmls=num_pmls)
trans_list = create_transformations(obj,
monitors,
sim_space,
200,
num_stages=5,
min_feature=100)
plan = optplan.OptimizationPlan(transformations=trans_list)
# Save the optimization plan so we have an exact record of all the
# parameters.
with open(os.path.join(save_folder, "optplan.json"), "w") as fp:
fp.write(optplan.dumps(plan))
# Copy over the GDS files.
shutil.copyfile("sim_fg.gds", os.path.join(save_folder, "sim_fg.gds"))
shutil.copyfile("sim_bg.gds", os.path.join(save_folder, "sim_bg.gds"))
# Execute the optimization and indicate that the current folder (".") is
# the project folder. The project folder is the root folder for any
# auxiliary files (e.g. GDS files).
problem_graph.run_plan(plan, ".", save_folder=save_folder)
def test_grating(tmpdir):
folder = str(tmpdir.mkdir("grating"))
_copyfiles(CUR_DIR, folder, ["sim_fg.gds", "sim_bg.gds"])
sim_space = grating.create_sim_space(os.path.join(folder, "sim_fg.gds"),
os.path.join(folder, "sim_bg.gds"),
box_thickness=2000,
wg_thickness=220,
etch_frac=0.5)
obj, monitors = grating.create_objective(sim_space)
trans_list = grating.create_transformations(obj,
monitors,
1,
2,
sim_space,
min_feature=100)
plan = optplan.OptimizationPlan(transformations=trans_list)
problem_graph.run_plan(plan, folder)
def main() -> None:
"""Runs the optimization."""
# Create the simulation space using the GDS files.
sim_space = create_sim_space("sim_fg.gds", "sim_bg.gds")
# Setup the objectives and all values that should be recorded (monitors).
obj, monitors = create_objective(sim_space)
# Create the list of operations that should be performed during
# optimization. In this case, we use a series of continuous parametrizations
# that approximate a discrete structure.
trans_list = create_transformations(
obj, monitors, sim_space, cont_iters=100, min_feature=100)
# Execute the optimization and indicate that the current folder (".") is
# the project folder. The project folder is the root folder for any
# auxiliary files (e.g. GDS files). By default, all log files produced
# during the optimization are also saved here. This can be changed by
# passing in a third optional argument.
plan = optplan.OptimizationPlan(transformations=trans_list)
problem_graph.run_plan(plan, ".")
def run_opt(save_folder: str, sim_width: float, wg_width: float) -> None:
"""Main optimization script.
This function setups the optimization and executes it.
Args:
save_folder: Location to save the optimization data.
"""
os.makedirs(save_folder)
sim_space = create_sim_space("sim_fg.gds",
"sim_bg.gds",
sim_width=sim_width,
wg_width=wg_width)
obj, monitors = create_objective(sim_space,
sim_width=sim_width,
wg_width=wg_width) # or a grating length
trans_list = create_transformations(obj,
monitors,
60,
200,
sim_space,
min_feature=100)
plan = optplan.OptimizationPlan(transformations=trans_list)
# Save the optimization plan so we have an exact record of all the
# parameters.
with open(os.path.join(save_folder, "optplan.json"), "w") as fp:
fp.write(optplan.dumps(plan))
# Copy over the GDS files.
shutil.copyfile("sim_fg.gds", os.path.join(save_folder, "sim_fg.gds"))
shutil.copyfile("sim_bg.gds", os.path.join(save_folder, "sim_bg.gds"))
# Execute the optimization and indicate that the current folder (".") is
# the project folder. The project folder is the root folder for any
# auxiliary files (e.g. GDS files).
problem_graph.run_plan(plan, ".", save_folder=save_folder)
# Generate the GDS file.
gen_gds(save_folder, sim_width, wg_width)
