tidy3d.plugins.dispersion.DispersionFitter#

class tidy3d.plugins.dispersion.DispersionFitter(*, wvl_um: tidy3d.components.types.ArrayLike[dtype=float, ndim=1], n_data: tidy3d.components.types.ArrayLike[dtype=float, ndim=1], k_data: tidy3d.components.types.ArrayLike[dtype=float, ndim=1] = None, wvl_range: typing.Tuple[typing.Optional[float], typing.Optional[float]] = (None, None), type: typing.Literal['DispersionFitter'] = 'DispersionFitter')#

Bases: tidy3d.components.base.Tidy3dBaseModel

Tool for fitting refractive index data to get a dispersive medium described by PoleResidue model.

Parameters

wvl_um (ArrayLike[dtype=float, ndim=1]) – [units = um]. Wavelength data in micrometers.
n_data (ArrayLike[dtype=float, ndim=1]) – Real part of the complex index of refraction.
k_data (Optional[ArrayLike[dtype=float, ndim=1]] = None) – Imaginary part of the complex index of refraction.
wvl_range (Tuple[float, float] = (None, None)) – [units = um]. Truncate the wavelength, n and k data to the wavelength range ‘[wvl_min, wvl_max]’ for fitting.

__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.
`fit`([num_poles, num_tries, tolerance_rms, guess])	Fit data a number of times and returns best results.
`from_file`(fname, **loadtxt_kwargs)	Loads `DispersionFitter` from file containing wavelength, n, k data.
`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_url`(url_file[, delimiter, ignore_k])	loads `DispersionFitter` from url linked to a csv/txt file that contains wavelength (micron), n, and optionally k data.
`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, ...])
`plot`([medium, wvl_um, ax])	Make plot of model vs data, at a set of wavelengths (if supplied).
`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

`data_in_range`	Filter the wavelength-nk data to wavelength range for fitting.
`eps_data`	Convert filtered input n(k) data into complex permittivity.
`freqs`	Convert filtered input wavelength data to frequency.
`frequency_range`	Frequency range of filtered input data
`lossy`	Find out if the medium is lossy or lossless based on the filtered input data.
`wvl_um`
`n_data`
`k_data`
`wvl_range`

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.

property data_in_range: Tuple[tidy3d.components.types.ArrayLike[dtype=float, ndim=1], tidy3d.components.types.ArrayLike[dtype=float, ndim=1], tidy3d.components.types.ArrayLike[dtype=float, ndim=1]]#

Filter the wavelength-nk data to wavelength range for fitting.

Returns: Filtered wvl_um, n_data, k_data
Return type: Tuple[ArrayFloat1D, ArrayFloat1D, ArrayFloat1D]

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') 

property eps_data: complex#

Convert filtered input n(k) data into complex permittivity.

Returns: Complex-valued relative permittivty.
Return type: complex

fit(num_poles: int = 1, num_tries: int = 50, tolerance_rms: float = 0.01, guess: Optional[tidy3d.components.medium.PoleResidue] = None) → Tuple[tidy3d.components.medium.PoleResidue, float]#

Fit data a number of times and returns best results.

Parameters

num_poles (int, optional) – Number of poles in the model.
num_tries (int, optional) – Number of optimizations to run with different initial guesses.
tolerance_rms (float, optional) – RMS error below which the fit is successful and the result is returned.
guess (PoleResidue = None) – A PoleResidue medium to use as the initial guess in the first optimization run.

Returns

Best results of multiple fits: (dispersive medium, RMS error).

Return type

Tuple[PoleResidue, float]

property freqs: Tuple[float, ...]#

Convert filtered input wavelength data to frequency.

Returns: Frequency array converted from filtered input wavelength data
Return type: Tuple[float, …]

property frequency_range: Tuple[float, float]#

Frequency range of filtered input data

Returns: The minimal frequency and the maximal frequency
Return type: Tuple[float, float]

classmethod from_file(fname: str, **loadtxt_kwargs)#

Loads DispersionFitter from file containing wavelength, n, k data.

Parameters

fname (str) – Path to file containing wavelength (um), n, k (optional) data in columns.
**loadtxt_kwargs – Kwargs passed to np.loadtxt, such as skiprows, delimiter.

Hint

The data file should be in this format (delimiter and skiprows can be customized in **loadtxt_kwargs):

For lossless media:

wl       n
[float] [float]
.       .
.       .
.       .

For lossy media:

wl       n       k
[float] [float] [float]
.       .       .
.       .       .
.       .       .

Returns: A DispersionFitter instance.
Return type: DispersionFitter

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_url(url_file: str, delimiter: str = ',', ignore_k: bool = False, **kwargs)#

loads DispersionFitter from url linked to a csv/txt file that contains wavelength (micron), n, and optionally k data. Preferred from refractiveindex.info.

Hint

The data file from url should be in this format (delimiter not displayed here, and note that the strings such as “wl”, “n” need to be included in the file):

For lossless media:

wl       n
[float] [float]
.       .
.       .
.       .

For lossy media:

wl       n
[float] [float]
.       .
.       .
.       .
wl       k
[float] [float]
.       .
.       .
.       .

Parameters

url_file (str) – Url link to the data file. e.g. “https://refractiveindex.info/data_csv.php?datafile=data/main/Ag/Johnson.yml”
delimiter (str = ",") – E.g. in refractiveindex.info, it’ll be “,” for csv file, and “\t” for txt file.
ignore_k (bool = False) – Ignore the k data if they are present, so the fitted material is lossless.

Returns

A DispersionFitter instance.

Return type

DispersionFitter

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().

property lossy: bool#

Find out if the medium is lossy or lossless based on the filtered input data.

Returns: True for lossy medium; False for lossless medium
Return type: bool

plot(medium: tidy3d.components.medium.PoleResidue = None, wvl_um: tidy3d.components.types.ArrayLike[dtype=float, ndim=1] = None, ax: matplotlib.axes._axes.Axes = None) → matplotlib.axes._axes.Axes#

Make plot of model vs data, at a set of wavelengths (if supplied).

Parameters

medium (PoleResidue = None) – medium containing model to plot against data
wvl_um (ArrayFloat1D = None) – Wavelengths to evaluate model at for plot in micrometers.
ax (matplotlib.axes._subplots.Axes = None) – Axes to plot the data on, if None, a new one is created.

Returns

Matplotlib axis corresponding to plot.

Return type

matplotlib.axis.Axes

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.