tidy3d.NonlinearSusceptibility
tidy3d.NonlinearSusceptibility#
- class tidy3d.NonlinearSusceptibility(*, type: Literal['NonlinearSusceptibility'] = 'NonlinearSusceptibility', chi3: float = 0, numiters: pydantic.v1.types.PositiveInt = None)#
Bases:
tidy3d.components.medium.NonlinearModel
Model for an instantaneous nonlinear chi3 susceptibility. The expression for the instantaneous nonlinear polarization is given below.
- Parameters
chi3 (float = 0) – [units = um^2 / V^2]. Chi3 nonlinear susceptibility.
numiters (Optional[PositiveInt] = None) – Deprecated. The old usage ‘nonlinear_spec=model’ with ‘model.numiters’ is deprecated and will be removed in a future release. The new usage is ‘nonlinear_spec=NonlinearSpec(models=[model], num_iters=num_iters)’. Under the new usage, this parameter is ignored, and ‘NonlinearSpec.num_iters’ is used instead.
Note
Note
This model uses real time-domain fields, so
must be real. For complex fields (e.g. when using Bloch boundary conditions), the nonlinearity is applied separately to the real and imaginary parts, so that the above equation holds when both E and are replaced by their real or imaginary parts. The nonlinearity is applied to the real and imaginary components separately since each of those represents a physical field.Note
Different field components do not interact nonlinearly. For example, when calculating
, we approximate . This approximation is valid when the E field is predominantly polarized along one of the x, y, or z axes.Example
>>> nonlinear_susceptibility = NonlinearSusceptibility(chi3=1)
- __init__(**kwargs)#
Init method, includes post-init validators.
Methods
__init__
(**kwargs)Init method, includes post-init validators.
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.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.
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 fileto_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 fileto_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
Whether the model uses complex fields.
chi3
numiters
- 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.
- property complex_fields: bool#
Whether the model uses complex fields.
- 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 hdf5fname
atgroup_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 hdf5fname
atgroup_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.