tidy3d.plugins.dispersion.AdvancedFitterParam

class AdvancedFitterParam

Bases: Tidy3dBaseModel

Advanced fitter parameters

Parameters:

• attrs (dict = {}) – Dictionary storing arbitrary metadata for a Tidy3D object. This dictionary can be freely used by the user for storing data without affecting the operation of Tidy3D as it is not used internally. Note that, unlike regular Tidy3D fields, attrs are mutable. For example, the following is allowed for setting an attr obj.attrs['foo'] = bar. Also note that Tidy3D` will raise a TypeError if attrs contain objects that can not be serialized. One can check if attrs are serializable by calling obj.json().

• bound_amp (Optional[NonNegativeFloat] = None) – [units = Hz]. Upper bound of real and imagniary part of oscillator strength c in the model PoleResidue (The default ‘None’ will trigger automatic setup based on the frequency range of interest).

• bound_f (Optional[NonNegativeFloat] = None) – [units = Hz]. Upper bound of real and imaginary part of a that corresponds to pole damping rate and frequency in the model PoleResidue (The default ‘None’ will trigger automatic setup based on the frequency range of interest).

• bound_f_lower (NonNegativeFloat = 0.0) – [units = Hz]. Lower bound of imaginary part of a that corresponds to pole frequency in the model PoleResidue.

• bound_eps_inf (ConstrainedFloatValue = 10.0) – Upper bound of epsilon at infinity frequency. It must be no less than 1.

• constraint (Literal['hard', 'soft'] = hard) – Stability constraint: ‘hard’ constraints are generally recommended since they are faster to compute per iteration, and they often require fewer iterations to converge since the search space is smaller. But sometimes the search space is so restrictive that all good solutions are missed, then please try the ‘soft’ constraints for larger search space. However, both constraints improve stability equally well.

• nlopt_maxeval (PositiveInt = 5000) – Number of iterations in each inner optimization.

• random_seed (Optional[ConstrainedIntValue] = 0) – The fitting tool performs global optimizations with random starting coefficients. With the same random seed, one obtains identical results when re-running the fitter; on the other hand, if one wants to re-run the fitter several times to obtain the best results, the value of the seed should be changed, or set to None so that the starting coefficients are different each time.

Attributes

attrs

Methods

Inherited Common Usage

bound_amp

bound_f

bound_f_lower

bound_eps_inf

constraint

nlopt_maxeval

random_seed

__hash__()

Hash method.