tidy3d.NonlinearSpec#

class tidy3d.NonlinearSpec(*, models: Tuple[Union[tidy3d.components.medium.NonlinearSusceptibility, tidy3d.components.medium.TwoPhotonAbsorption, tidy3d.components.medium.KerrNonlinearity], ...] = (), num_iters: pydantic.v1.types.PositiveInt = 5, type: Literal['NonlinearSpec'] = 'NonlinearSpec')#

Bases: abc.ABC, tidy3d.components.base.Tidy3dBaseModel

Abstract specification for adding nonlinearities to a medium.

Parameters

models (Tuple[Union[NonlinearSusceptibility, TwoPhotonAbsorption, KerrNonlinearity], ...] = ()) – The nonlinear models present in this nonlinear spec. Nonlinear models of different types are additive. Multiple nonlinear models of the same type are not allowed.
num_iters (PositiveInt = 5) – Number of iterations for solving nonlinear constitutive relation.

Note

The nonlinear constitutive relation is solved iteratively; it may not converge for strong nonlinearities. Increasing num_iters can help with convergence.

Example

>>> nonlinear_susceptibility = NonlinearSusceptibility(chi3=1)
>>> nonlinear_spec = NonlinearSpec(models=[nonlinear_susceptibility])
>>> medium = Medium(permittivity=2, nonlinear_spec=nonlinear_spec)

__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

`models`
`num_iters`

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.