tidy3d.plugins.dispersion.AdvancedFitterParam#

class AdvancedFitterParam[source]#

Bases: Tidy3dBaseModel

Advanced fitter parameters

Parameters:
  • bound_amp (Attribute: bound_amp) –

    Type

    Optional[NonNegativeFloat]

    Default

    = None

    Units

    Hz

    Description

    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 (Attribute: bound_f) –

    Type

    Optional[NonNegativeFloat]

    Default

    = None

    Units

    Hz

    Description

    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 (Attribute: bound_f_lower) –

    Type

    NonNegativeFloat

    Default

    = 0.0

    Units

    Hz

    Description

    Lower bound of imaginary part of a that corresponds to pole frequency in the model PoleResidue.

  • bound_eps_inf (Attribute: bound_eps_inf) –

    Type

    ConstrainedFloatValue

    Default

    = 10.0

    Description

    Upper bound of epsilon at infinity frequency. It must be no less than 1.

  • constraint (Attribute: constraint) –

    Type

    Literal[‘hard’, ‘soft’]

    Default

    = hard

    Description

    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 (Attribute: nlopt_maxeval) –

    Type

    PositiveInt

    Default

    = 5000

    Description

    Number of iterations in each inner optimization.

  • random_seed (Attribute: random_seed) –

    Type

    Optional[ConstrainedIntValue]

    Default

    = 0

    Description

    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

Methods

bound_amp#
bound_f#
bound_f_lower#
bound_eps_inf#
constraint#
nlopt_maxeval#
random_seed#
__hash__()#

Hash method.