tidy3d.AutoGrid#

class tidy3d.AutoGrid#

Bases: tidy3d.components.grid.grid_spec.GridSpec1d

Specification for non-uniform grid along a given dimension.

Parameters

  • min_steps_per_wvl (ConstrainedFloatValue = 10.0) – Minimal number of steps per wavelength in each medium.

  • max_scale (ConstrainedFloatValue = 1.4) – Sets the maximum ratio between any two consecutive grid steps.

  • dl_min (NonNegativeFloat = 0) – Lower bound of the grid size along this dimension regardless of structures present in the simulation, including override structures with enforced=True. It is a soft bound, meaning that the actual minimal grid size might be slightly smaller.

  • mesher (GradedMesher = GradedMesher(type='GradedMesher')) – The type of mesher to use to generate the grid automatically.

Example

>>> grid_1d = AutoGrid(min_steps_per_wvl=16, max_scale=1.4)

Show JSON schema
{
   "title": "AutoGrid",
   "description": "Specification for non-uniform grid along a given dimension.\n\nParameters\n----------\nmin_steps_per_wvl : ConstrainedFloatValue = 10.0\n    Minimal number of steps per wavelength in each medium.\nmax_scale : ConstrainedFloatValue = 1.4\n    Sets the maximum ratio between any two consecutive grid steps.\ndl_min : NonNegativeFloat = 0\n    Lower bound of the grid size along this dimension regardless of structures present in the simulation, including override structures with ``enforced=True``. It is a soft bound, meaning that the actual minimal grid size might be slightly smaller.\nmesher : GradedMesher = GradedMesher(type='GradedMesher')\n    The type of mesher to use to generate the grid automatically.\n\nExample\n-------\n>>> grid_1d = AutoGrid(min_steps_per_wvl=16, max_scale=1.4)",
   "type": "object",
   "properties": {
      "type": {
         "title": "Type",
         "default": "AutoGrid",
         "enum": [
            "AutoGrid"
         ],
         "type": "string"
      },
      "min_steps_per_wvl": {
         "title": "Minimal number of steps per wavelength",
         "description": "Minimal number of steps per wavelength in each medium.",
         "default": 10.0,
         "minimum": 6.0,
         "type": "number"
      },
      "max_scale": {
         "title": "Maximum Grid Size Scaling",
         "description": "Sets the maximum ratio between any two consecutive grid steps.",
         "default": 1.4,
         "exclusiveMaximum": 2.0,
         "minimum": 1.2,
         "type": "number"
      },
      "dl_min": {
         "title": "Lower bound of grid size",
         "description": "Lower bound of the grid size along this dimension regardless of structures present in the simulation, including override structures with ``enforced=True``. It is a soft bound, meaning that the actual minimal grid size might be slightly smaller.",
         "default": 0,
         "minimum": 0,
         "type": "number"
      },
      "mesher": {
         "title": "Grid Construction Tool",
         "description": "The type of mesher to use to generate the grid automatically.",
         "default": {
            "type": "GradedMesher"
         },
         "allOf": [
            {
               "$ref": "#/definitions/GradedMesher"
            }
         ]
      }
   },
   "additionalProperties": false,
   "definitions": {
      "GradedMesher": {
         "title": "GradedMesher",
         "description": "Implements automatic nonuniform meshing with a set minimum steps per wavelength and\na graded mesh expanding from higher- to lower-resolution regions.\n\nParameters\n----------",
         "type": "object",
         "properties": {
            "type": {
               "title": "Type",
               "default": "GradedMesher",
               "enum": [
                  "GradedMesher"
               ],
               "type": "string"
            }
         },
         "additionalProperties": false
      }
   }
}

attribute dl_min: pydantic.types.NonNegativeFloat = 0#

Lower bound of the grid size along this dimension regardless of structures present in the simulation, including override structures with enforced=True. It is a soft bound, meaning that the actual minimal grid size might be slightly smaller.

Constraints

  • minimum = 0

attribute max_scale: float = 1.4#

Sets the maximum ratio between any two consecutive grid steps.

Constraints

  • exclusiveMaximum = 2.0

  • minimum = 1.2

attribute mesher: tidy3d.components.grid.mesher.GradedMesher = GradedMesher(type='GradedMesher')#

The type of mesher to use to generate the grid automatically.

attribute min_steps_per_wvl: float = 10.0#

Minimal number of steps per wavelength in each medium.

Constraints

  • minimum = 6.0

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, or .hdf5 file.

Parameters

  • fname (str) – Full path to the .yaml or .json 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 = '') → 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.

Returns

Dictionary containing the model.

Return type

dict

Example

>>> sim_dict = Simulation.dict_from_hdf5(fname='folder/sim.hdf5')
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, or .hdf5 file.

Parameters

  • fname (str) – Full path to the .yaml or .json 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 = '', **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.

  • **parse_obj_kwargs – Keyword arguments passed to pydantic’s parse_obj method.

Example

>>> simulation.to_hdf5(fname='folder/sim.hdf5')
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_orm(obj: Any) → Model#
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.

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) → unicode#

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_coords(axis: Literal[0, 1, 2], structures: List[Union[tidy3d.components.structure.Structure, tidy3d.components.structure.MeshOverrideStructure]], symmetry: Tuple[typing.Literal[0, - 1, 1], typing.Literal[0, - 1, 1], typing.Literal[0, - 1, 1]], periodic: bool, wavelength: pydantic.types.PositiveFloat, num_pml_layers: Tuple[pydantic.types.NonNegativeInt, pydantic.types.NonNegativeInt]) → tidy3d.components.types.ArrayLike_dtype=<class 'float'>_ndim=1#

Generate 1D coords to be used as grid boundaries, based on simulation parameters. Symmetry, and PML layers will be treated here.

Parameters

  • axis (Axis) – Axis of this direction.

  • structures (List[StructureType]) – List of structures present in simulation, the first one being the simulation domain.

  • symmetry (Tuple[Symmetry, Symmetry, Symmetry]) – Reflection symmetry across a plane bisecting the simulation domain normal to each of the three axes.

  • wavelength (float) – Free-space wavelength.

  • num_pml_layers (Tuple[int, int]) – number of layers in the absorber + and - direction along one dimension.

Returns

1D coords to be used as grid boundaries.

Return type

Coords1D

classmethod parse_file(path: Union[str, pathlib.Path], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod parse_obj(obj: Any) → Model#
classmethod parse_raw(b: Union[str, bytes], *, content_type: unicode = None, encoding: unicode = 'utf8', proto: pydantic.parse.Protocol = None, allow_pickle: bool = False) → Model#
classmethod schema(by_alias: bool = True, ref_template: unicode = '#/definitions/{model}') → DictStrAny#
classmethod schema_json(*, by_alias: bool = True, ref_template: unicode = '#/definitions/{model}', **dumps_kwargs: Any) → unicode#
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) → None#

Exports Tidy3dBaseModel instance to .hdf5 file.

Parameters

fname (str) – Full path to the .hdf5 file to save the Tidy3dBaseModel to.

Example

>>> simulation.to_hdf5(fname='folder/sim.hdf5')
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.

classmethod validate(value: Any) → Model#