tidy3d.GridSpec#

class tidy3d.GridSpec(*, grid_x: Union[tidy3d.components.grid.grid_spec.UniformGrid, tidy3d.components.grid.grid_spec.CustomGrid, tidy3d.components.grid.grid_spec.AutoGrid] = AutoGrid(type='AutoGrid', min_steps_per_wvl=10.0, max_scale=1.4, dl_min=0.0, mesher=GradedMesher(type='GradedMesher')), grid_y: Union[tidy3d.components.grid.grid_spec.UniformGrid, tidy3d.components.grid.grid_spec.CustomGrid, tidy3d.components.grid.grid_spec.AutoGrid] = AutoGrid(type='AutoGrid', min_steps_per_wvl=10.0, max_scale=1.4, dl_min=0.0, mesher=GradedMesher(type='GradedMesher')), grid_z: Union[tidy3d.components.grid.grid_spec.UniformGrid, tidy3d.components.grid.grid_spec.CustomGrid, tidy3d.components.grid.grid_spec.AutoGrid] = AutoGrid(type='AutoGrid', min_steps_per_wvl=10.0, max_scale=1.4, dl_min=0.0, mesher=GradedMesher(type='GradedMesher')), wavelength: float = None, override_structures: Tuple[Union[tidy3d.components.structure.Structure, tidy3d.components.structure.MeshOverrideStructure], ...] = (), type: Literal['GridSpec'] = 'GridSpec')#

Bases: tidy3d.components.base.Tidy3dBaseModel

Collective grid specification for all three dimensions.

Parameters

grid_x (Union[UniformGrid, CustomGrid, AutoGrid] = AutoGrid(type='AutoGrid', min_steps_per_wvl=10.0, max_scale=1.4, dl_min=0.0, mesher=GradedMesher(type='GradedMesher'))) – Grid specification along x-axis
grid_y (Union[UniformGrid, CustomGrid, AutoGrid] = AutoGrid(type='AutoGrid', min_steps_per_wvl=10.0, max_scale=1.4, dl_min=0.0, mesher=GradedMesher(type='GradedMesher'))) – Grid specification along y-axis
grid_z (Union[UniformGrid, CustomGrid, AutoGrid] = AutoGrid(type='AutoGrid', min_steps_per_wvl=10.0, max_scale=1.4, dl_min=0.0, mesher=GradedMesher(type='GradedMesher'))) – Grid specification along z-axis
wavelength (Optional[float] = None) – [units = um]. Free-space wavelength for automatic nonuniform grid. It can be ‘None’ if there is at least one source in the simulation, in which case it is defined by the source central frequency. Note: it only takes effect when at least one of the three dimensions uses AutoGrid.
override_structures (Tuple[Annotated[Union[tidy3d.components.structure.Structure, tidy3d.components.structure.MeshOverrideStructure], FieldInfo(default=PydanticUndefined, discriminator='type', extra={})], ...] = ()) – A set of structures that is added on top of the simulation structures in the process of generating the grid. This can be used to refine the grid or make it coarser depending than the expected need for higher/lower resolution regions. Note: it only takes effect when at least one of the three dimensions uses AutoGrid.

Example

>>> uniform = UniformGrid(dl=0.1)
>>> custom = CustomGrid(dl=[0.2, 0.2, 0.1, 0.1, 0.1, 0.2, 0.2])
>>> auto = AutoGrid(min_steps_per_wvl=12)
>>> grid_spec = GridSpec(grid_x=uniform, grid_y=custom, grid_z=auto, wavelength=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.
`auto`([wavelength, min_steps_per_wvl, ...])	Use the same `AutoGrid` along each of the three directions.
`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().
`make_grid`(structures, symmetry, periodic, ...)	Make the entire simulation grid based on some simulation parameters.
`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.
`uniform`(dl)	Use the same `UniformGrid` along each of the three directions.
`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)
`wavelength_from_sources`(sources)	Define a wavelength based on supplied sources.

Attributes

`auto_grid_used`	True if any of the three dimensions uses `AutoGrid`.
`custom_grid_used`	True if any of the three dimensions uses `CustomGrid`.
`override_structures_used`	Along each axis, `True` if any override structure is used.
`grid_x`
`grid_y`
`grid_z`
`wavelength`
`override_structures`

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 auto(wavelength: Optional[pydantic.v1.types.PositiveFloat] = None, min_steps_per_wvl: pydantic.v1.types.PositiveFloat = 10.0, max_scale: pydantic.v1.types.PositiveFloat = 1.4, override_structures: List[Union[tidy3d.components.structure.Structure, tidy3d.components.structure.MeshOverrideStructure]] = (), dl_min: pydantic.v1.types.NonNegativeFloat = 0.0, mesher: tidy3d.components.grid.mesher.GradedMesher = GradedMesher(type='GradedMesher')) → tidy3d.components.grid.grid_spec.GridSpec#

Use the same AutoGrid along each of the three directions.

Parameters

wavelength (pd.PositiveFloat, optional) – Free-space wavelength for automatic nonuniform grid. It can be ‘None’ if there is at least one source in the simulation, in which case it is defined by the source central frequency.
min_steps_per_wvl (pd.PositiveFloat, optional) – Minimal number of steps per wavelength in each medium.
max_scale (pd.PositiveFloat, optional) – Sets the maximum ratio between any two consecutive grid steps.
override_structures (List[StructureType]) – A list of structures that is added on top of the simulation structures in the process of generating the grid. This can be used to refine the grid or make it coarser depending than the expected need for higher/lower resolution regions.
dl_min (pd.NonNegativeFloat) – Lower bound of grid size.
mesher (MesherType = GradedMesher()) – The type of mesher to use to generate the grid automatically.

Returns

GridSpec with the same automatic nonuniform grid settings in each direction.

Return type

GridSpec

property auto_grid_used: bool#: True if any of the three dimensions uses AutoGrid.

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 custom_grid_used: bool#: True if any of the three dimensions uses CustomGrid.

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().

Make the entire simulation grid based on some simulation parameters.

Parameters

structures (List[Structure]) – List of structures present in the simulation. The first structure must be the simulation geometry with the simulation background medium.
symmetry (Tuple[Symmetry, Symmetry, Symmetry]) – Reflection symmetry across a plane bisecting the simulation domain normal to each of the three axes.
sources (List[SourceType]) – List of sources.
num_pml_layers (List[Tuple[float, float]]) – List containing the number of absorber layers in - and + boundaries.

Returns

Entire simulation grid.

Return type

Grid

property override_structures_used: List[bool, bool, bool]#: Along each axis, True if any override structure is used. However, it is still False if only MeshOverrideStructure is supplied, and their dl[axis] all take the None value.

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 uniform(dl: float) → tidy3d.components.grid.grid_spec.GridSpec#

Use the same UniformGrid along each of the three directions.

Parameters: dl (float) – Grid size for uniform grid generation.
Returns: GridSpec with the same uniform grid size in each direction.
Return type: GridSpec

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.

static wavelength_from_sources(sources: List[Union[tidy3d.components.source.UniformCurrentSource, tidy3d.components.source.PointDipole, tidy3d.components.source.GaussianBeam, tidy3d.components.source.AstigmaticGaussianBeam, tidy3d.components.source.ModeSource, tidy3d.components.source.PlaneWave, tidy3d.components.source.CustomFieldSource, tidy3d.components.source.CustomCurrentSource, tidy3d.components.source.TFSF]]) → pydantic.v1.types.PositiveFloat#: Define a wavelength based on supplied sources. Called if auto mesh is used and self.wavelength is None.