def discretize_to_pixelated_grating(cont_var: goos.Variable,
height_fracs: np.ndarray,
pixel_size: float,
start_height_ind: int,
end_height_ind: int,
min_features: np.ndarray,
max_features: np.ndarray = None,
divisions: int = 5,
use_edge_locs: bool = False,
discr_min_features: np.ndarray = None,
discr_max_features: np.ndarray = None,
**kwargs):
widths_or_edge_locs = goos.Variable(0)
height_index = goos.Variable(0)
grating = PixelatedGrating(widths_or_edge_locs,
height_index,
height_fracs,
use_edge_locs=use_edge_locs,
**kwargs)
action = GratingL2D(cont_var,
widths_or_edge_locs,
height_index,
pixel_size,
height_fracs,
start_height_ind,
end_height_ind,
min_features,
max_features,
divisions,
use_edge_locs=use_edge_locs,
discr_min_features=discr_min_features,
discr_max_features=discr_max_features)
goos.get_default_plan().add_action(action)
height_index.freeze()
return widths_or_edge_locs, height_index, grating
def scipy_minimize(objective: goos.Function, *args,
**kwargs) -> ScipyOptimizer:
optimizer = ScipyOptimizer(objective, *args, **kwargs)
goos.get_default_plan().add_action(optimizer)
return optimizer
def thaw(self):
if self._is_param:
raise ValueError("Cannot thaw a parameter.")
goos.get_default_plan().add_action(ThawVariable(self))
def freeze(self):
goos.get_default_plan().add_action(FreezeVariable(self))
def set(self, value):
if isinstance(value, numbers.Number) or isinstance(value, np.ndarray):
value = Constant(value)
goos.get_default_plan().add_action(SetVariable(self, value))
def eval(self, inputs: List) -> flows.NumericFlow:
return flows.NumericFlow(goos.get_default_plan().get_var_value(self))
def log_print(*args, **kwargs) -> LogPrint:
action = LogPrint(*args, **kwargs)
goos.get_default_plan().add_action(action)
return action
