tidy3d.plugins.AdvancedFitterParam#

class tidy3d.plugins.AdvancedFitterParam#

Bases: tidy3d.components.base.Tidy3dBaseModel

Advanced fitter parameters

Parameters

  • 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.

Show JSON schema
{
   "title": "AdvancedFitterParam",
   "description": "Advanced fitter parameters\n\nParameters\n----------\nbound_amp : Optional[NonNegativeFloat] = None\n    [units = Hz].  Upper bound of real and imagniary part of oscillator strength ``c`` in the model :class:`.PoleResidue` (The default 'None' will trigger automatic setup based on the frequency range of interest).\nbound_f : Optional[NonNegativeFloat] = None\n    [units = Hz].  Upper bound of real and imaginary part of ``a`` that corresponds to pole damping rate and frequency in the model :class:`.PoleResidue` (The default 'None' will trigger automatic setup based on the frequency range of interest).\nbound_f_lower : NonNegativeFloat = 0.0\n    [units = Hz].  Lower bound of imaginary part of ``a`` that corresponds to pole frequency in the model :class:`.PoleResidue`.\nbound_eps_inf : ConstrainedFloatValue = 10.0\n    Upper bound of epsilon at infinity frequency. It must be no less than 1.\nconstraint : Literal['hard', 'soft'] = hard\n    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.\nnlopt_maxeval : PositiveInt = 5000\n    Number of iterations in each inner optimization.\nrandom_seed : Optional[ConstrainedIntValue] = 0\n    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. ",
   "type": "object",
   "properties": {
      "bound_amp": {
         "title": "Upper bound of oscillator strength",
         "description": "Upper bound of real and imagniary part of oscillator strength ``c`` in the model :class:`.PoleResidue` (The default 'None' will trigger automatic setup based on the frequency range of interest).",
         "units": "Hz",
         "minimum": 0,
         "type": "number"
      },
      "bound_f": {
         "title": "Upper bound of pole frequency",
         "description": "Upper bound of real and imaginary part of ``a`` that corresponds to pole damping rate and frequency in the model :class:`.PoleResidue` (The default 'None' will trigger automatic setup based on the frequency range of interest).",
         "units": "Hz",
         "minimum": 0,
         "type": "number"
      },
      "bound_f_lower": {
         "title": "Lower bound of pole frequency",
         "description": "Lower bound of imaginary part of ``a`` that corresponds to pole frequency in the model :class:`.PoleResidue`.",
         "default": 0.0,
         "units": "Hz",
         "minimum": 0,
         "type": "number"
      },
      "bound_eps_inf": {
         "title": "Upper bound of epsilon at infinity frequency",
         "description": "Upper bound of epsilon at infinity frequency. It must be no less than 1.",
         "default": 10.0,
         "minimum": 1,
         "type": "number"
      },
      "constraint": {
         "title": "Type of constraint for stability",
         "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.",
         "default": "hard",
         "enum": [
            "hard",
            "soft"
         ],
         "type": "string"
      },
      "nlopt_maxeval": {
         "title": "Number of inner iterations",
         "description": "Number of iterations in each inner optimization.",
         "default": 5000,
         "exclusiveMinimum": 0,
         "type": "integer"
      },
      "random_seed": {
         "title": "Random seed for starting coefficients",
         "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. ",
         "default": 0,
         "exclusiveMaximum": 4294967296,
         "minimum": 0,
         "type": "integer"
      },
      "type": {
         "title": "Type",
         "default": "AdvancedFitterParam",
         "enum": [
            "AdvancedFitterParam"
         ],
         "type": "string"
      }
   },
   "additionalProperties": false
}

attribute bound_amp: pydantic.types.NonNegativeFloat = None#

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).

Constraints

  • minimum = 0

attribute bound_eps_inf: float = 10.0#

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

Constraints

  • minimum = 1

attribute bound_f: pydantic.types.NonNegativeFloat = None#

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).

Constraints

  • minimum = 0

attribute bound_f_lower: pydantic.types.NonNegativeFloat = 0.0#

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

Constraints

  • minimum = 0

Validated by

  • _validate_lower_frequency_bound

attribute 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.

attribute nlopt_maxeval: pydantic.types.PositiveInt = 5000#

Number of iterations in each inner optimization.

Constraints

  • exclusiveMinimum = 0

attribute random_seed: Optional[int] = 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.

Constraints

  • exclusiveMaximum = 4294967296

  • minimum = 0

classmethod add_type_field() → None#

Automatically place “type” field with model name in the model field dictionary.

classmethod construct(_fields_set: Optional[SetStr] = None, **values: Any) → Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(**kwargs) → tidy3d.components.base.Tidy3dBaseModel#

Copy a Tidy3dBaseModel. With deep=True as default.

dict(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False) → DictStrAny#

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

classmethod dict_from_file(fname: str, group_path: Optional[str] = None) → dict#

Loads a dictionary containing the model from a .yaml, .json, or .hdf5 file.

Parameters

  • fname (str) – Full path to the .yaml or .json file to load the Tidy3dBaseModel from.

  • group_path (str, optional) – Path to a group inside the file to use as the base level.

Returns

A dictionary containing the model.

Return type

dict

Example

>>> simulation = Simulation.from_file(fname='folder/sim.json')
classmethod dict_from_hdf5(fname: str, group_path: str = '') → dict#

Loads a dictionary containing the model contents from a .hdf5 file.

Parameters

  • fname (str) – Full path to the .hdf5 file to load the Tidy3dBaseModel from.

  • group_path (str, optional) – Path to a group inside the file to selectively load a sub-element of the model only.

Returns

Dictionary containing the model.

Return type

dict

Example

>>> sim_dict = Simulation.dict_from_hdf5(fname='folder/sim.hdf5')
classmethod dict_from_json(fname: str) → dict#

Load dictionary of the model from a .json file.

Parameters

fname (str) – Full path to the .json file to load the Tidy3dBaseModel from.

Returns

A dictionary containing the model.

Return type

dict

Example

>>> sim_dict = Simulation.dict_from_json(fname='folder/sim.json')
classmethod dict_from_yaml(fname: str) → dict#

Load dictionary of the model from a .yaml file.

Parameters

fname (str) – Full path to the .yaml file to load the Tidy3dBaseModel from.

Returns

A dictionary containing the model.

Return type

dict

Example

>>> sim_dict = Simulation.dict_from_yaml(fname='folder/sim.yaml')
classmethod from_file(fname: str, group_path: Optional[str] = None, **parse_obj_kwargs) → tidy3d.components.base.Tidy3dBaseModel#

Loads a Tidy3dBaseModel from .yaml, .json, or .hdf5 file.

Parameters

  • fname (str) – Full path to the .yaml or .json file to load the Tidy3dBaseModel from.

  • group_path (str, optional) – Path to a group inside the file to use as the base level. Only for .hdf5 files. Starting / is optional.

  • **parse_obj_kwargs – Keyword arguments passed to either pydantic’s parse_obj function when loading model.

Returns

An instance of the component class calling load.

Return type

Tidy3dBaseModel

Example

>>> simulation = Simulation.from_file(fname='folder/sim.json')
classmethod from_hdf5(fname: str, group_path: str = '', **parse_obj_kwargs) → tidy3d.components.base.Tidy3dBaseModel#

Loads Tidy3dBaseModel instance to .hdf5 file.

Parameters

  • fname (str) – Full path to the .hdf5 file to load the Tidy3dBaseModel from.

  • group_path (str, optional) – Path to a group inside the file to selectively load a sub-element of the model only. Starting / is optional.

  • **parse_obj_kwargs – Keyword arguments passed to pydantic’s parse_obj method.

Example

>>> simulation.to_hdf5(fname='folder/sim.hdf5')
classmethod from_json(fname: str, **parse_obj_kwargs) → tidy3d.components.base.Tidy3dBaseModel#

Load a Tidy3dBaseModel from .json file.

Parameters

fname (str) – Full path to the .json file to load the Tidy3dBaseModel from.

Returns

  • Tidy3dBaseModel – An instance of the component class calling load.

  • **parse_obj_kwargs – Keyword arguments passed to pydantic’s parse_obj method.

Example

>>> simulation = Simulation.from_json(fname='folder/sim.json')
classmethod from_orm(obj: Any) → Model#
classmethod from_yaml(fname: str, **parse_obj_kwargs) → tidy3d.components.base.Tidy3dBaseModel#

Loads Tidy3dBaseModel from .yaml file.

Parameters

  • fname (str) – Full path to the .yaml file to load the Tidy3dBaseModel from.

  • **parse_obj_kwargs – Keyword arguments passed to pydantic’s parse_obj method.

Returns

An instance of the component class calling from_yaml.

Return type

Tidy3dBaseModel

Example

>>> simulation = Simulation.from_yaml(fname='folder/sim.yaml')
classmethod generate_docstring() → str#

Generates a docstring for a Tidy3D mode and saves it to the __doc__ of the class.

classmethod get_sub_model(group_path: str, model_dict: dict | list) → dict#

Get the sub model for a given group path.

static get_tuple_group_name(index: int) → str#

Get the group name of a tuple element.

static get_tuple_index(key_name: str) → int#

Get the index into the tuple based on its group name.

help(methods: bool = False) → None#

Prints message describing the fields and methods of a Tidy3dBaseModel.

Parameters

methods (bool = False) – Whether to also print out information about object’s methods.

Example

>>> simulation.help(methods=True)
json(*, include: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, exclude: Optional[Union[AbstractSetIntStr, MappingIntStrAny]] = None, by_alias: bool = False, skip_defaults: Optional[bool] = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Optional[Callable[[Any], Any]] = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod parse_obj(obj: Any) → Model#
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#
to_file(fname: str) → None#

Exports Tidy3dBaseModel instance to .yaml, .json, or .hdf5 file

Parameters

fname (str) – Full path to the .yaml or .json file to save the Tidy3dBaseModel to.

Example

>>> simulation.to_file(fname='folder/sim.json')
to_hdf5(fname: str) → None#

Exports Tidy3dBaseModel instance to .hdf5 file.

Parameters

fname (str) – Full path to the .hdf5 file to save the Tidy3dBaseModel to.

Example

>>> simulation.to_hdf5(fname='folder/sim.hdf5')
to_json(fname: str) → None#

Exports Tidy3dBaseModel instance to .json file

Parameters

fname (str) – Full path to the .json file to save the Tidy3dBaseModel to.

Example

>>> simulation.to_json(fname='folder/sim.json')
to_yaml(fname: str) → None#

Exports Tidy3dBaseModel instance to .yaml file.

Parameters

fname (str) – Full path to the .yaml file to save the Tidy3dBaseModel to.

Example

>>> simulation.to_yaml(fname='folder/sim.yaml')
classmethod tuple_to_dict(tuple_values: tuple) → dict#

How we generate a dictionary mapping new keys to tuple values for hdf5.

classmethod update_forward_refs(**localns: Any) → None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.

updated_copy(**kwargs) → tidy3d.components.base.Tidy3dBaseModel#

Make copy of a component instance with **kwargs indicating updated field values.

classmethod validate(value: Any) → Model#