tidy3d.plugins.dispersion.AdvancedFastFitterParam#

class tidy3d.plugins.dispersion.AdvancedFastFitterParam(*, loss_bounds: Tuple[float, float] = (0, inf), weights: Tuple[pydantic.v1.types.NonNegativeFloat, pydantic.v1.types.NonNegativeFloat] = None, show_progress: bool = True, show_unweighted_rms: bool = False, relaxed: Optional[bool] = None, smooth: Optional[bool] = None, logspacing: Optional[bool] = None, num_iters: pydantic.v1.types.PositiveInt = 20, passivity_num_iters: pydantic.v1.types.PositiveInt = 50, slsqp_constraint_scale: pydantic.v1.types.PositiveFloat = 1e+35, type: Literal['AdvancedFastFitterParam'] = 'AdvancedFastFitterParam')#

Bases: tidy3d.components.base.Tidy3dBaseModel

Advanced fast fitter parameters.

Parameters

loss_bounds (Tuple[float, float] = (0, inf)) – Bounds (lower, upper) on Im[eps]. Default corresponds to only passivity. A lower bound of 0 or greater ensures passivity. To fit a gain medium without additional constraints, use loss_bounds=(-np.inf, np.inf). Increasing the lower bound could help with simulation stability. A finite upper bound may be helpful when fitting lossless materials. In this case, consider also increasing the weight for fitting the imaginary part.
weights (Optional[Tuple[NonNegativeFloat, NonNegativeFloat]] = None) – Weights (real, imag) in objective function for fitting. The weights are applied to the real and imaginary parts of the permittivity epsilon. The weights will be rescaled together so they average to 1. If None, the weights are calculated according to the typical value of the real and imaginary part, so that the relative error in the real and imaginary part of the fit should be comparable. More precisely, the RMS value rms of the real and imaginary parts are calculated, and the default weights are 1 / max(rms, RMS_MIN). Changing this can be helpful if fitting either the real or imaginary part is more important than the other.
show_progress (bool = True) – Whether to show progress bar during fitter run.
show_unweighted_rms (bool = False) – Whether to show unweighted RMS error in addition to the default weighted RMS error. Requires td.config.logging_level = "INFO".
relaxed (Optional[bool] = None) – Whether to use relaxed fitting algorithm, which has better pole relocation properties. If None, will try both original and relaxed algorithms.
smooth (Optional[bool] = None) – Whether to use real starting poles, which can help when fitting smooth data. If None, will try both real and complex starting poles.
logspacing (Optional[bool] = None) – Whether to space the poles logarithmically. If None, will try both log and linear spacing.
num_iters (PositiveInt = 20) – Number of iterations of the fitting algorithm. Make this smaller to speed up fitter, or make it larger to try to improve fit.
passivity_num_iters (PositiveInt = 50) – Number of loss bounds enforcement iterations of the fitting algorithm. Make this smaller to speed up fitter. There will be a warning if this value is too small. To fit a gain medium, use the loss_bounds parameter instead.
slsqp_constraint_scale (PositiveFloat = 1e+35) – Passivity constraint is weighted relative to fit quality by this factor, before running passivity optimization using the SLSQP algorithm. There will be a warning if this value is too small.

__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

`loss_bounds`
`weights`
`show_progress`
`show_unweighted_rms`
`relaxed`
`smooth`
`logspacing`
`num_iters`
`passivity_num_iters`
`slsqp_constraint_scale`

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.