tidy3d.ModeSpec#

class tidy3d.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.

Example

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

Show JSON schema

{
   "title": "ModeSpec",
   "description": "Stores specifications for the mode solver to find an electromagntic mode.\nNote, the planar axes are found by popping the injection axis from {x,y,z}.\nFor example, if injection axis is y, the planar axes are ordered {x,z}.\n\nParameters\n----------\nnum_modes : PositiveInt = 1\n    Number of modes returned by mode solver.\ntarget_neff : Optional[PositiveFloat] = None\n    Guess for effective index of the mode.\nnum_pml : Tuple[NonNegativeInt, NonNegativeInt] = (0, 0)\n    Number of standard pml layers to add in the two tangential axes.\nfilter_pol : Optional[Literal['te', 'tm']] = None\n    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.\nangle_theta : float = 0.0\n    [units = rad].  Polar angle of the propagation axis from the injection axis.\nangle_phi : float = 0.0\n    [units = rad].  Azimuth angle of the propagation axis in the plane orthogonal to the injection axis.\nprecision : Literal['single', 'double'] = single\n    The solver will be faster and using less memory under single precision, but more accurate under double precision.\nbend_radius : Optional[float] = None\n    [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.\nbend_axis : Optional[Literal[0, 1]] = None\n    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).\ntrack_freq : Optional[Literal['central', 'lowest', 'highest']] = central\n    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.\n\nExample\n-------\n>>> mode_spec = ModeSpec(num_modes=3, target_neff=1.5)",
   "type": "object",
   "properties": {
      "num_modes": {
         "title": "Number of modes",
         "description": "Number of modes returned by mode solver.",
         "default": 1,
         "exclusiveMinimum": 0,
         "type": "integer"
      },
      "target_neff": {
         "title": "Target effective index",
         "description": "Guess for effective index of the mode.",
         "exclusiveMinimum": 0,
         "type": "number"
      },
      "num_pml": {
         "title": "Number of PML layers",
         "description": "Number of standard pml layers to add in the two tangential axes.",
         "default": [
            0,
            0
         ],
         "type": "array",
         "minItems": 2,
         "maxItems": 2,
         "items": [
            {
               "type": "integer",
               "minimum": 0
            },
            {
               "type": "integer",
               "minimum": 0
            }
         ]
      },
      "filter_pol": {
         "title": "Polarization filtering",
         "description": "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.",
         "enum": [
            "te",
            "tm"
         ],
         "type": "string"
      },
      "angle_theta": {
         "title": "Polar Angle",
         "description": "Polar angle of the propagation axis from the injection axis.",
         "default": 0.0,
         "units": "rad",
         "type": "number"
      },
      "angle_phi": {
         "title": "Azimuth Angle",
         "description": "Azimuth angle of the propagation axis in the plane orthogonal to the injection axis.",
         "default": 0.0,
         "units": "rad",
         "type": "number"
      },
      "precision": {
         "title": "single or double precision in mode solver",
         "description": "The solver will be faster and using less memory under single precision, but more accurate under double precision.",
         "default": "single",
         "enum": [
            "single",
            "double"
         ],
         "type": "string"
      },
      "bend_radius": {
         "title": "Bend radius",
         "description": "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.",
         "units": "um",
         "type": "number"
      },
      "bend_axis": {
         "title": "Bend axis",
         "description": "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).",
         "enum": [
            0,
            1
         ],
         "type": "integer"
      },
      "track_freq": {
         "title": "Mode Tracking Frequency",
         "description": "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.",
         "default": "central",
         "enum": [
            "central",
            "lowest",
            "highest"
         ],
         "type": "string"
      },
      "type": {
         "title": "Type",
         "default": "ModeSpec",
         "enum": [
            "ModeSpec"
         ],
         "type": "string"
      }
   },
   "additionalProperties": false
}

attribute angle_phi: float = 0.0#: Azimuth angle of the propagation axis in the plane orthogonal to the injection axis.

attribute angle_theta: float = 0.0#

Polar angle of the propagation axis from the injection axis.

Validated by

glancing_incidence

attribute bend_axis: 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).

Validated by

bend_axis_given

attribute bend_radius: float = None#: 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.

attribute filter_pol: 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.

attribute num_modes: pydantic.types.PositiveInt = 1#

Number of modes returned by mode solver.

Constraints

exclusiveMinimum = 0

attribute num_pml: Tuple[pydantic.types.NonNegativeInt, pydantic.types.NonNegativeInt] = (0, 0)#: Number of standard pml layers to add in the two tangential axes.

attribute precision: Literal['single', 'double'] = 'single'#: The solver will be faster and using less memory under single precision, but more accurate under double precision.

attribute target_neff: pydantic.types.PositiveFloat = None#

Guess for effective index of the mode.

Constraints

exclusiveMinimum = 0

attribute 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.

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().

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#