tidy3d.Coords#

class tidy3d.Coords#

Bases: tidy3d.components.base.Tidy3dBaseModel

Holds data about a set of x,y,z positions on a grid.

Parameters

  • x (ArrayLike_dtype=<class 'float'>_ndim=1) – 1-dimensional array of x coordinates.

  • y (ArrayLike_dtype=<class 'float'>_ndim=1) – 1-dimensional array of y coordinates.

  • z (ArrayLike_dtype=<class 'float'>_ndim=1) – 1-dimensional array of z coordinates.

Example

>>> x = np.linspace(-1, 1, 10)
>>> y = np.linspace(-1, 1, 11)
>>> z = np.linspace(-1, 1, 12)
>>> coords = Coords(x=x, y=y, z=z)

Show JSON schema
{
   "title": "Coords",
   "description": "Holds data about a set of x,y,z positions on a grid.\n\nParameters\n----------\nx : ArrayLike_dtype=<class 'float'>_ndim=1\n    1-dimensional array of x coordinates.\ny : ArrayLike_dtype=<class 'float'>_ndim=1\n    1-dimensional array of y coordinates.\nz : ArrayLike_dtype=<class 'float'>_ndim=1\n    1-dimensional array of z coordinates.\n\nExample\n-------\n>>> x = np.linspace(-1, 1, 10)\n>>> y = np.linspace(-1, 1, 11)\n>>> z = np.linspace(-1, 1, 12)\n>>> coords = Coords(x=x, y=y, z=z)",
   "type": "object",
   "properties": {
      "x": {
         "title": "ArrayLike",
         "description": "1-dimensional array of x coordinates.",
         "type": "ArrayLike"
      },
      "y": {
         "title": "ArrayLike",
         "description": "1-dimensional array of y coordinates.",
         "type": "ArrayLike"
      },
      "z": {
         "title": "ArrayLike",
         "description": "1-dimensional array of z coordinates.",
         "type": "ArrayLike"
      },
      "type": {
         "title": "Type",
         "default": "Coords",
         "enum": [
            "Coords"
         ],
         "type": "string"
      }
   },
   "required": [
      "x",
      "y",
      "z"
   ],
   "additionalProperties": false
}

attribute x: tidy3d.components.types.ArrayLike_dtype=<class 'float'>_ndim=1 [Required]#

1-dimensional array of x coordinates.

Constraints

  • title = ArrayLike

  • type = ArrayLike

attribute y: tidy3d.components.types.ArrayLike_dtype=<class 'float'>_ndim=1 [Required]#

1-dimensional array of y coordinates.

Constraints

  • title = ArrayLike

  • type = ArrayLike

attribute z: tidy3d.components.types.ArrayLike_dtype=<class 'float'>_ndim=1 [Required]#

1-dimensional array of z coordinates.

Constraints

  • title = ArrayLike

  • type = ArrayLike

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 = '', 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_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 = '', 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_file(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().

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, 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_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#
property to_dict#

Return a dict of the three Coord1D objects as numpy arrays.

property to_list#

Return a list of the three Coord1D objects as numpy arrays.