tidy3d.plugins.dispersion.AdvancedFitterParam#

class tidy3d.plugins.dispersion.AdvancedFitterParam(*, bound_amp: pydantic.v1.types.NonNegativeFloat = None, bound_f: pydantic.v1.types.NonNegativeFloat = None, bound_f_lower: pydantic.v1.types.NonNegativeFloat = 0.0, bound_eps_inf: pydantic.v1.types.ConstrainedFloatValue = 10.0, constraint: Literal['hard', 'soft'] = 'hard', nlopt_maxeval: pydantic.v1.types.PositiveInt = 5000, random_seed: Optional[pydantic.v1.types.ConstrainedIntValue] = 0, type: Literal['AdvancedFitterParam'] = '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.

__init__(**kwargs)#

Init method, includes post-init validators.

Methods

__init__(**kwargs)

Init method, includes post-init validators.

add_type_field()

Automatically place "type" field with model name in the model field dictionary.

construct([_fields_set])

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.

copy(**kwargs)

Copy a Tidy3dBaseModel.

dict(*[, include, exclude, by_alias, ...])

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

dict_from_file(fname[, group_path])

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

dict_from_hdf5(fname[, group_path, ...])

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

dict_from_hdf5_gz(fname[, group_path, ...])

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

dict_from_json(fname)

Load dictionary of the model from a .json file.

dict_from_yaml(fname)

Load dictionary of the model from a .yaml file.

from_file(fname[, group_path])

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

from_hdf5(fname[, group_path, custom_decoders])

Loads Tidy3dBaseModel instance to .hdf5 file.

from_hdf5_gz(fname[, group_path, ...])

Loads Tidy3dBaseModel instance to .hdf5.gz file.

from_json(fname, **parse_obj_kwargs)

Load a Tidy3dBaseModel from .json file.

from_orm(obj)

from_yaml(fname, **parse_obj_kwargs)

Loads Tidy3dBaseModel from .yaml file.

generate_docstring()

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

get_sub_model(group_path, model_dict)

Get the sub model for a given group path.

get_submodels_by_hash()

Return a dictionary of this object's sub-models indexed by their hash values.

get_tuple_group_name(index)

Get the group name of a tuple element.

get_tuple_index(key_name)

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

help([methods])

Prints message describing the fields and methods of a Tidy3dBaseModel.

json(*[, include, exclude, by_alias, ...])

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

parse_file(path, *[, content_type, ...])

parse_obj(obj)

parse_raw(b, *[, content_type, encoding, ...])

schema([by_alias, ref_template])

schema_json(*[, by_alias, ref_template])

to_file(fname)

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

to_hdf5(fname[, custom_encoders])

Exports Tidy3dBaseModel instance to .hdf5 file.

to_hdf5_gz(fname[, custom_encoders])

Exports Tidy3dBaseModel instance to .hdf5.gz file.

to_json(fname)

Exports Tidy3dBaseModel instance to .json file

to_yaml(fname)

Exports Tidy3dBaseModel instance to .yaml file.

tuple_to_dict(tuple_values)

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

update_forward_refs(**localns)

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

updated_copy(**kwargs)

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

validate(value)

Attributes

bound_amp

bound_f

bound_f_lower

bound_eps_inf

constraint

nlopt_maxeval

random_seed

class Config#

Bases: object

Sets config for all Tidy3dBaseModel objects.

allow_population_by_field_namebool = True

Allow properties to stand in for fields(?).

arbitrary_types_allowedbool = True

Allow types like numpy arrays.

extrastr = ‘forbid’

Forbid extra kwargs not specified in model.

json_encodersDict[type, Callable]

Defines how to encode type in json file.

validate_allbool = True

Validate default values just to be safe.

validate_assignmentbool

Re-validate after re-assignment of field in model.

__eq__(other)#

Define == for two Tidy3DBaseModels.

__ge__(other)#

define >= for getting unique indices based on hash.

__gt__(other)#

define > for getting unique indices based on hash.

__hash__() → int#

Hash method.

classmethod __init_subclass__() → None#

Things that are done to each of the models.

__iter__() → TupleGenerator#

so dict(model) works

__le__(other)#

define <= for getting unique indices based on hash.

__lt__(other)#

define < for getting unique indices based on hash.

__pretty__(fmt: Callable[[Any], Any], **kwargs: Any) → Generator[Any, None, None]#

Used by devtools (https://python-devtools.helpmanual.io/) to provide a human readable representations of objects

__repr_name__() → str#

Name of the instance’s class, used in __repr__.

__rich_repr__() → RichReprResult#

Get fields for Rich library

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

Same as update_forward_refs but will not raise exception when forward references are not defined.

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, .hdf5, or .hdf5.gz file.

Parameters

  • fname (str) – Full path to the 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 = '', custom_decoders: Optional[List[Callable]] = None) → 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.

  • custom_decoders (List[Callable]) – List of functions accepting (fname: str, group_path: str, model_dict: dict, key: str, value: Any) that store the value in the model dict after a custom decoding.

Returns

Dictionary containing the model.

Return type

dict

Example

>>> sim_dict = Simulation.dict_from_hdf5(fname='folder/sim.hdf5')
classmethod dict_from_hdf5_gz(fname: str, group_path: str = '', custom_decoders: Optional[List[Callable]] = None) → dict#

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

Parameters

  • fname (str) – Full path to the .hdf5.gz 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.

  • custom_decoders (List[Callable]) – List of functions accepting (fname: str, group_path: str, model_dict: dict, key: str, value: Any) that store the value in the model dict after a custom decoding.

Returns

Dictionary containing the model.

Return type

dict

Example

>>> sim_dict = Simulation.dict_from_hdf5(fname='folder/sim.hdf5.gz')
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, .hdf5, or .hdf5.gz file.

Parameters

  • fname (str) – Full path to the 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 = '', custom_decoders: Optional[List[Callable]] = None, **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.

  • custom_decoders (List[Callable]) – List of functions accepting (fname: str, group_path: str, model_dict: dict, key: str, value: Any) that store the value in the model dict after a custom decoding.

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

Example

>>> simulation = Simulation.from_hdf5(fname='folder/sim.hdf5')
classmethod from_hdf5_gz(fname: str, group_path: str = '', custom_decoders: Optional[List[Callable]] = None, **parse_obj_kwargs) → tidy3d.components.base.Tidy3dBaseModel#

Loads Tidy3dBaseModel instance to .hdf5.gz file.

Parameters

  • fname (str) – Full path to the .hdf5.gz 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.

  • custom_decoders (List[Callable]) – List of functions accepting (fname: str, group_path: str, model_dict: dict, key: str, value: Any) that store the value in the model dict after a custom decoding.

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

Example

>>> simulation = Simulation.from_hdf5_gz(fname='folder/sim.hdf5.gz')
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_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.

get_submodels_by_hash() → Dict[int, List[Union[str, Tuple[str, int]]]]#

Return a dictionary of this object’s sub-models indexed by their hash values.

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) → str#

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().

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, custom_encoders: Optional[List[Callable]] = None) → None#

Exports Tidy3dBaseModel instance to .hdf5 file.

Parameters

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

  • custom_encoders (List[Callable]) – List of functions accepting (fname: str, group_path: str, value: Any) that take the value supplied and write it to the hdf5 fname at group_path.

Example

>>> simulation.to_hdf5(fname='folder/sim.hdf5')
to_hdf5_gz(fname: str, custom_encoders: Optional[List[Callable]] = None) → None#

Exports Tidy3dBaseModel instance to .hdf5.gz file.

Parameters

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

  • custom_encoders (List[Callable]) – List of functions accepting (fname: str, group_path: str, value: Any) that take the value supplied and write it to the hdf5 fname at group_path.

Example

>>> simulation.to_hdf5_gz(fname='folder/sim.hdf5.gz')
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.