tidy3d.ModeSpec#

class tidy3d.ModeSpec(*, num_modes: pydantic.v1.types.PositiveInt = 1, target_neff: pydantic.v1.types.PositiveFloat = None, num_pml: Tuple[pydantic.v1.types.NonNegativeInt, pydantic.v1.types.NonNegativeInt] = (0, 0), filter_pol: Literal['te', 'tm'] = None, angle_theta: float = 0.0, angle_phi: float = 0.0, precision: Literal['single', 'double'] = 'single', bend_radius: float = None, bend_axis: Literal[0, 1] = None, track_freq: Optional[Literal['central', 'lowest', 'highest']] = 'central', group_index_step: Union[pydantic.v1.types.PositiveFloat, bool] = False, type: Literal['ModeSpec'] = 'ModeSpec')#

Bases: tidy3d.components.base.Tidy3dBaseModel

Stores specifications for the mode solver to find an electromagntic mode. Note, the planar axes are found by popping the injection axis from {x,y,z}. For example, if injection axis is y, the planar axes are ordered {x,z}.

Parameters

  • num_modes (PositiveInt = 1) – Number of modes returned by mode solver.

  • target_neff (Optional[PositiveFloat] = None) – Guess for effective index of the mode.

  • num_pml (Tuple[NonNegativeInt, NonNegativeInt] = (0, 0)) – Number of standard pml layers to add in the two tangential axes.

  • filter_pol (Optional[Literal['te', 'tm']] = None) – The solver always computes the num_modes modes closest to the given target_neff. If filter_pol==None, they are simply sorted in order of decresing effective index. If a polarization filter is selected, the modes are rearranged such that the first n_pol modes in the list are the ones with the selected polarization fraction larger than or equal to 0.5, while the next num_modes - n_pol modes are the ones where it is smaller than 0.5 (i.e. the opposite polarization fraction is larger than 0.5). Within each polarization subset, the modes are still ordered by decreasing effective index. te-fraction is defined as the integrated intensity of the E-field component parallel to the first plane axis, normalized to the total in-plane E-field intensity. Conversely, tm-fraction uses the E field component parallel to the second plane axis.

  • angle_theta (float = 0.0) – [units = rad]. Polar angle of the propagation axis from the injection axis.

  • angle_phi (float = 0.0) – [units = rad]. Azimuth angle of the propagation axis in the plane orthogonal to the injection axis.

  • precision (Literal['single', 'double'] = single) – The solver will be faster and using less memory under single precision, but more accurate under double precision.

  • bend_radius (Optional[float] = None) – [units = um]. A curvature radius for simulation of waveguide bends. Can be negative, in which case the mode plane center has a smaller value than the curvature center along the tangential axis perpendicular to the bend axis.

  • bend_axis (Optional[Literal[0, 1]] = None) – Index into the two tangential axes defining the normal to the plane in which the bend lies. This must be provided if bend_radius is not None. For example, for a ring in the global xy-plane, and a mode plane in either the xz or the yz plane, the bend_axis is always 1 (the global z axis).

  • track_freq (Optional[Literal['central', 'lowest', 'highest']] = central) – Parameter that turns on/off mode tracking based on their similarity. Can take values 'lowest', 'central', or 'highest', which correspond to mode tracking based on the lowest, central, or highest frequency. If None no mode tracking is performed.

  • group_index_step (Union[PositiveFloat, bool] = False) – Control the computation of the group index alongside the effective index. If set to a positive value, it sets the fractional frequency step used in the numerical differentiation of the effective index to compute the group index. If set to True, the default of 0.005 is used.

Example

>>> mode_spec = ModeSpec(num_modes=3, target_neff=1.5)
__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.

assign_default_on_true(val)

Assign the default fractional frequency step value if not provided.

bend_axis_given(val, values)

Check that bend_axis is provided if bend_radius is not None

bend_radius_not_zero(val, values)

Check that bend_raidus magnitude is not close to zero.`

check_group_step_size(val)

Ensure a reasonable group index step is used.

check_precision(values)

Verify critical ModeSpec settings for group index calculation.

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.

glancing_incidence(val)

Warn if close to glancing incidence.

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

num_modes

target_neff

num_pml

filter_pol

angle_theta

angle_phi

precision

bend_radius

bend_axis

track_freq

group_index_step

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 assign_default_on_true(val)#

Assign the default fractional frequency step value if not provided.

classmethod bend_axis_given(val, values)#

Check that bend_axis is provided if bend_radius is not None

classmethod bend_radius_not_zero(val, values)#

Check that bend_raidus magnitude is not close to zero.`

classmethod check_group_step_size(val)#

Ensure a reasonable group index step is used.

classmethod check_precision(values)#

Verify critical ModeSpec settings for group index calculation.

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.

classmethod glancing_incidence(val)#

Warn if close to glancing incidence.

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.