tidy3d.CustomMedium#

class tidy3d.CustomMedium#

Bases: tidy3d.components.medium.AbstractMedium

Medium with user-supplied permittivity distribution.

Parameters

name (Optional[str] = None) – Optional unique name for medium.
frequency_range (Optional[Tuple[float, float]] = None) – [units = (Hz, Hz)]. Optional range of validity for the medium.
eps_dataset (PermittivityDataset) – User-supplied dataset containing complex-valued permittivity as a function of space. Permittivity distribution over the Yee-grid will be interpolated based on interp_method.
interp_method (Literal['nearest', 'linear'] = nearest) – Interpolation method to obtain permittivity values that are not supplied at the Yee grids; For grids outside the range of the supplied data, extrapolation will be applied. When the extrapolated value is smaller (greater) than the minimal (maximal) of the supplied data, the extrapolated value will take the minimal (maximal) of the supplied data.

Example

>>> Nx, Ny, Nz = 10, 9, 8
>>> X = np.linspace(-1, 1, Nx)
>>> Y = np.linspace(-1, 1, Ny)
>>> Z = np.linspace(-1, 1, Nz)
>>> freqs = [2e14]
>>> data = np.ones((Nx, Ny, Nz, 1))
>>> eps_diagonal_data = ScalarFieldDataArray(data, coords=dict(x=X, y=Y, z=Z, f=freqs))
>>> eps_components = {f"eps_{d}{d}": eps_diagonal_data for d in "xyz"}
>>> eps_dataset = PermittivityDataset(**eps_components)
>>> dielectric = CustomMedium(eps_dataset=eps_dataset, name='my_medium')
>>> eps = dielectric.eps_model(200e12)

Show JSON schema

{
   "title": "CustomMedium",
   "description": ":class:`.Medium` with user-supplied permittivity distribution.\n\nParameters\n----------\nname : Optional[str] = None\n    Optional unique name for medium.\nfrequency_range : Optional[Tuple[float, float]] = None\n    [units = (Hz, Hz)].  Optional range of validity for the medium.\neps_dataset : PermittivityDataset\n    User-supplied dataset containing complex-valued permittivity as a function of space. Permittivity distribution over the Yee-grid will be interpolated based on ``interp_method``.\ninterp_method : Literal['nearest', 'linear'] = nearest\n    Interpolation method to obtain permittivity values that are not supplied at the Yee grids; For grids outside the range of the supplied data, extrapolation will be applied. When the extrapolated value is smaller (greater) than the minimal (maximal) of the supplied data, the extrapolated value will take the minimal (maximal) of the supplied data.\n\nExample\n-------\n>>> Nx, Ny, Nz = 10, 9, 8\n>>> X = np.linspace(-1, 1, Nx)\n>>> Y = np.linspace(-1, 1, Ny)\n>>> Z = np.linspace(-1, 1, Nz)\n>>> freqs = [2e14]\n>>> data = np.ones((Nx, Ny, Nz, 1))\n>>> eps_diagonal_data = ScalarFieldDataArray(data, coords=dict(x=X, y=Y, z=Z, f=freqs))\n>>> eps_components = {f\"eps_{d}{d}\": eps_diagonal_data for d in \"xyz\"}\n>>> eps_dataset = PermittivityDataset(**eps_components)\n>>> dielectric = CustomMedium(eps_dataset=eps_dataset, name='my_medium')\n>>> eps = dielectric.eps_model(200e12)",
   "type": "object",
   "properties": {
      "name": {
         "title": "Name",
         "description": "Optional unique name for medium.",
         "type": "string"
      },
      "frequency_range": {
         "title": "Frequency Range",
         "description": "Optional range of validity for the medium.",
         "units": [
            "Hz",
            "Hz"
         ],
         "type": "array",
         "minItems": 2,
         "maxItems": 2,
         "items": [
            {
               "type": "number"
            },
            {
               "type": "number"
            }
         ]
      },
      "type": {
         "title": "Type",
         "default": "CustomMedium",
         "enum": [
            "CustomMedium"
         ],
         "type": "string"
      },
      "eps_dataset": {
         "title": "Permittivity Dataset",
         "description": "User-supplied dataset containing complex-valued permittivity as a function of space. Permittivity distribution over the Yee-grid will be interpolated based on ``interp_method``.",
         "allOf": [
            {
               "$ref": "#/definitions/PermittivityDataset"
            }
         ]
      },
      "interp_method": {
         "title": "Interpolation method",
         "description": "Interpolation method to obtain permittivity values that are not supplied at the Yee grids; For grids outside the range of the supplied data, extrapolation will be applied. When the extrapolated value is smaller (greater) than the minimal (maximal) of the supplied data, the extrapolated value will take the minimal (maximal) of the supplied data.",
         "default": "nearest",
         "enum": [
            "nearest",
            "linear"
         ],
         "type": "string"
      }
   },
   "required": [
      "eps_dataset"
   ],
   "additionalProperties": false,
   "definitions": {
      "PermittivityDataset": {
         "title": "PermittivityDataset",
         "description": "Dataset storing the diagonal components of the permittivity tensor.\n\nParameters\n----------\neps_xx : ScalarFieldDataArray\n    Spatial distribution of the xx-component of the relative permittivity.\neps_yy : ScalarFieldDataArray\n    Spatial distribution of the yy-component of the relative permittivity.\neps_zz : ScalarFieldDataArray\n    Spatial distribution of the zz-component of the relative permittivity.\n\nExample\n-------\n>>> x = [-1,1]\n>>> y = [-2,0,2]\n>>> z = [-3,-1,1,3]\n>>> f = [2e14, 3e14]\n>>> coords = dict(x=x, y=y, z=z, f=f)\n>>> sclr_fld = ScalarFieldDataArray((1+1j) * np.random.random((2,3,4,2)), coords=coords)\n>>> data = PermittivityDataset(eps_xx=sclr_fld, eps_yy=sclr_fld, eps_zz=sclr_fld)",
         "type": "object",
         "properties": {
            "type": {
               "title": "Type",
               "default": "PermittivityDataset",
               "enum": [
                  "PermittivityDataset"
               ],
               "type": "string"
            },
            "eps_xx": {
               "title": "DataArray",
               "description": "Spatial distribution of the xx-component of the relative permittivity.",
               "type": "xr.DataArray",
               "properties": {
                  "_dims": {
                     "title": "_dims",
                     "type": "Tuple[str, ...]"
                  }
               },
               "required": [
                  "_dims"
               ]
            },
            "eps_yy": {
               "title": "DataArray",
               "description": "Spatial distribution of the yy-component of the relative permittivity.",
               "type": "xr.DataArray",
               "properties": {
                  "_dims": {
                     "title": "_dims",
                     "type": "Tuple[str, ...]"
                  }
               },
               "required": [
                  "_dims"
               ]
            },
            "eps_zz": {
               "title": "DataArray",
               "description": "Spatial distribution of the zz-component of the relative permittivity.",
               "type": "xr.DataArray",
               "properties": {
                  "_dims": {
                     "title": "_dims",
                     "type": "Tuple[str, ...]"
                  }
               },
               "required": [
                  "_dims"
               ]
            }
         },
         "required": [
            "eps_xx",
            "eps_yy",
            "eps_zz"
         ],
         "additionalProperties": false
      }
   }
}

attribute eps_dataset: tidy3d.components.data.dataset.PermittivityDataset [Required]#

User-supplied dataset containing complex-valued permittivity as a function of space. Permittivity distribution over the Yee-grid will be interpolated based on interp_method.

Validated by

_eps_inf_greater_no_less_than_one_sigma_positive
_single_frequency

attribute frequency_range: Tuple[float, float] = None#: Optional range of validity for the medium.

attribute interp_method: Literal['nearest', 'linear'] = 'nearest'#: Interpolation method to obtain permittivity values that are not supplied at the Yee grids; For grids outside the range of the supplied data, extrapolation will be applied. When the extrapolated value is smaller (greater) than the minimal (maximal) of the supplied data, the extrapolated value will take the minimal (maximal) of the supplied data.

attribute name: str = None#

Optional unique name for medium.

Validated by

field_has_unique_names

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') 

static eps_complex_to_eps_sigma(eps_complex: complex, freq: float) → Tuple[float, float]#

Convert complex permittivity at frequency freq to permittivity and conductivity values.

Parameters

eps_complex (complex) – Complex-valued relative permittivity.
freq (float) – Frequency to evaluate permittivity at (Hz).

Returns

Real part of relative permittivity & electric conductivity.

Return type

Tuple[float, float]

static eps_complex_to_nk(eps_c: complex) → Tuple[float, float]#

Convert complex permittivity to n, k values.

Parameters: eps_c (complex) – Complex-valued relative permittivity.
Returns: Real and imaginary parts of refractive index (n & k).
Return type: Tuple[float, float]

eps_dataset_freq(frequency: float) → tidy3d.components.data.dataset.PermittivityDataset#

Permittivity dataset at frequency. The dispersion comes from DC conductivity that results in nonzero Im[eps].

Parameters: frequency (float) – Frequency to evaluate permittivity at (Hz).
Returns: The permittivity evaluated at frequency.
Return type: PermittivityDataset

eps_diagonal(frequency: float) → Tuple[complex, complex, complex]#: Main diagonal of the complex-valued permittivity tensor at frequency. Spatially, we take max{||eps||}, so that autoMesh generation works appropriately.

eps_diagonal_on_grid(frequency: float, coords: tidy3d.components.grid.grid.Coords) → Tuple[numpy.ndarray, numpy.ndarray, numpy.ndarray]#

Spatial profile of main diagonal of the complex-valued permittivity at frequency interpolated at the supplied coordinates.

Parameters

frequency (float) – Frequency to evaluate permittivity at (Hz).
coords (Coords) – The grid point coordinates over which interpolation is performed.

Returns

The complex-valued permittivity tensor at frequency interpolated at the supplied coordinate.

Return type

Tuple[Numpy, Numpy, Numpy]

eps_model(frequency: float) → complex#: Spatial and poloarizaiton average of complex-valued permittivity as a function of frequency.

static eps_sigma_to_eps_complex(eps_real: float, sigma: float, freq: float) → complex#

convert permittivity and conductivity to complex permittivity at freq

Parameters

eps_real (float) – Real-valued relative permittivity.
sigma (float) – Conductivity.
freq (float) – Frequency to evaluate permittivity at (Hz). If not supplied, returns real part of permittivity (limit as frequency -> infinity.)

Returns

Complex-valued relative permittivity.

Return type

complex

classmethod from_eps_raw(eps: tidy3d.components.data.data_array.ScalarFieldDataArray, interp_method: Literal['nearest', 'linear'] = 'nearest') → tidy3d.components.medium.CustomMedium#

Construct a CustomMedium from datasets containing raw permittivity values.

Parameters

eps (ScalarFieldDataArray) – Dataset containing complex-valued permittivity as a function of space.
interp_method (InterpMethod, optional) – Interpolation method to obtain permittivity values that are not supplied at the Yee grids.

Returns

Medium containing the spatially varying permittivity data.

Return type

CustomMedium

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_nk(n: tidy3d.components.data.data_array.ScalarFieldDataArray, k: Optional[tidy3d.components.data.data_array.ScalarFieldDataArray] = None, interp_method: Literal['nearest', 'linear'] = 'nearest') → tidy3d.components.medium.CustomMedium#

Construct a CustomMedium from datasets containing n and k values.

Parameters

n (ScalarFieldDataArray) – Real part of refractive index.
k (ScalarFieldDataArray = None) – Imaginary part of refrative index.
interp_method (InterpMethod, optional) – Interpolation method to obtain permittivity values that are not supplied at the Yee grids.

Returns

Medium containing the spatially varying permittivity data.

Return type

CustomMedium

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.

grids(bounds: Tuple[Tuple[float, float, float], Tuple[float, float, float]]) → Dict[str, tidy3d.components.grid.grid.Grid]#: Make a Grid corresponding to the data in each eps_ii component. The min and max coordinates along each dimension are bounded by bounds.

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().

nk_model(frequency: float) → Tuple[float, float]#

Real and imaginary parts of the refactive index as a function of frequency.

Parameters: frequency (float) – Frequency to evaluate permittivity at (Hz).
Returns: Real part (n) and imaginary part (k) of refractive index of medium.
Return type: Tuple[float, float]

static nk_to_eps_complex(n: float, k: float = 0.0) → complex#

Convert n, k to complex permittivity.

Parameters

n (float) – Real part of refractive index.
k (float = 0.0) – Imaginary part of refrative index.

Returns

Complex-valued relative permittivty.

Return type

complex

static nk_to_eps_sigma(n: float, k: float, freq: float) → Tuple[float, float]#

Convert n, k at frequency freq to permittivity and conductivity values.

Parameters

n (float) – Real part of refractive index.
k (float = 0.0) – Imaginary part of refrative index.
frequency (float) – Frequency to evaluate permittivity at (Hz).

Returns

Real part of relative permittivity & electric conductivity.

Return type

Tuple[float, float]

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#

plot(freqs: float, ax: matplotlib.axes._axes.Axes = None) → matplotlib.axes._axes.Axes#

Plot n, k of a Medium as a function of frequency.

Parameters

freqs (float) – Frequencies (Hz) to evaluate the medium properties at.
ax (matplotlib.axes._subplots.Axes = None) – Matplotlib axes to plot on, if not specified, one is created.

Returns

The supplied or created matplotlib axes.

Return type

matplotlib.axes._subplots.Axes

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#