tidy3d.FieldProjectionCartesianData#

Bases: tidy3d.components.data.monitor_data.AbstractFieldProjectionData

Data associated with a FieldProjectionCartesianMonitor: components of projected fields.

Parameters

monitor (FieldProjectionCartesianMonitor) – Field projection monitor with a Cartesian projection grid.
Er (FieldProjectionCartesianDataArray) – Spatial distribution of r-component of the electric field.
Etheta (FieldProjectionCartesianDataArray) – Spatial distribution of the theta-component of the electric field.
Ephi (FieldProjectionCartesianDataArray) – Spatial distribution of phi-component of the electric field.
Hr (FieldProjectionCartesianDataArray) – Spatial distribution of r-component of the magnetic field.
Htheta (FieldProjectionCartesianDataArray) – Spatial distribution of theta-component of the magnetic field.
Hphi (FieldProjectionCartesianDataArray) – Spatial distribution of phi-component of the magnetic field.
medium (Union[Medium, AnisotropicMedium, PECMedium, PoleResidue, Sellmeier, Lorentz, Debye, Drude, FullyAnisotropicMedium, CustomMedium, CustomPoleResidue, CustomSellmeier, CustomLorentz, CustomDebye, CustomDrude, CustomAnisotropicMedium, PerturbationMedium, PerturbationPoleResidue, Medium2D] = Medium(name=None, frequency_range=None, allow_gain=False, nonlinear_spec=None, modulation_spec=None, heat_spec=None, type='Medium', permittivity=1.0, conductivity=0.0)) – Background medium through which to project fields.
projection_surfaces (Tuple[FieldProjectionSurface, ...]) – Surfaces of the monitor where near fields were recorded for projection

Example

>>> from tidy3d import FieldProjectionCartesianDataArray
>>> f = np.linspace(1e14, 2e14, 10)
>>> x = np.linspace(0, 5, 10)
>>> y = np.linspace(0, 10, 20)
>>> z = np.atleast_1d(5)
>>> coords = dict(x=x, y=y, z=z, f=f)
>>> values = (1+1j) * np.random.random((len(x), len(y), len(z), len(f)))
>>> scalar_field = FieldProjectionCartesianDataArray(values, coords=coords)
>>> monitor = FieldProjectionCartesianMonitor(
...     center=(1,2,3), size=(2,2,2), freqs=f, name='n2f_monitor', x=x, y=y,
...     proj_axis=2, proj_distance=50
...     )
>>> data = FieldProjectionCartesianData(
...     monitor=monitor, Er=scalar_field, Etheta=scalar_field, Ephi=scalar_field,
...     Hr=scalar_field, Htheta=scalar_field, Hphi=scalar_field,
...     projection_surfaces=monitor.projection_surfaces,
...     )

__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.
`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_data_array`(data)	Make an xr.DataArray with data and same coords and dims as fields of self.
`make_dataset`(keys, vals)	Make an xr.Dataset with keys and data with same coords and dims as fields.
`make_renormalized_data`(phase, proj_distance)	Helper to apply the re-projection phase to a copied dataset.
`normalize`(source_spectrum_fn)	Return copy of self after normalization is applied using source spectrum function.
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`propagation_phase`(dist, k)	Phase associated with propagation of a distance with a given wavenumber.
`renormalize_fields`(proj_distance)	Return a `FieldProjectionCartesianData` with fields re-normalized to a new projection distance, by applying a phase factor based on `proj_distance`.
`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.
`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)
`wavenumber`(medium, frequency)	Complex valued wavenumber associated with a frequency.

Attributes

`coords`	Coordinates of the fields contained.
`coords_spherical`	Coordinates grid for the fields in the spherical system.
`dims`	Dimensions of the radiation vectors contained.
`eta`	Returns the complex wave impedance associated with the background medium.
`f`	Frequencies.
`field_components`	Maps the field components to their associated data.
`fields_cartesian`	Get all field components in Cartesian coordinates relative to the monitor's local origin for all projection grid points and frequencies specified in the `AbstractFieldProjectionMonitor`.
`fields_spherical`	Get all field components in spherical coordinates relative to the monitor's local origin for all projection grid points and frequencies specified in the `AbstractFieldProjectionMonitor`.
`k`	Returns the complex wave number associated with the background medium.
`nk`	Returns the real and imaginary parts of the background medium's refractive index.
`power`	Get power measured on the projection grid relative to the monitor's local origin.
`radar_cross_section`	Radar cross section in units of incident power.
`symmetry_expanded`	Return self with symmetry applied.
`symmetry_expanded_copy`	Return copy of self with symmetry applied.
`x`	X positions.
`y`	Y positions.
`z`	Z positions.
`monitor`
`projection_surfaces`
`Er`
`Etheta`
`Ephi`
`Hr`
`Htheta`
`Hphi`

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 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

property coords: Dict[str, numpy.ndarray]#: Coordinates of the fields contained.

property coords_spherical: Dict[str, numpy.ndarray]#: Coordinates grid for the fields in the spherical system.

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, .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') 

property dims: Tuple[str, ...]#: Dimensions of the radiation vectors contained.

property eta: complex#: Returns the complex wave impedance associated with the background medium.

property f: numpy.ndarray#: Frequencies.

property field_components: Dict[str, tidy3d.components.data.data_array.DataArray]#: Maps the field components to their associated data.

property fields_cartesian: xarray.core.dataset.Dataset#

Get all field components in Cartesian coordinates relative to the monitor’s local origin for all projection grid points and frequencies specified in the AbstractFieldProjectionMonitor.

Returns: xarray dataset containing (Ex, Ey, Ez, Hx, Hy, Hz) in Cartesian coordinates.
Return type: xarray.Dataset

property fields_spherical: xarray.core.dataset.Dataset#

Get all field components in spherical coordinates relative to the monitor’s local origin for all projection grid points and frequencies specified in the AbstractFieldProjectionMonitor.

Returns: xarray dataset containing (Er, Etheta, Ephi, Hr, Htheta, Hphi) in spherical coordinates.
Return type: xarray.Dataset

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().

property k: complex#: Returns the complex wave number associated with the background medium.

make_data_array(data: numpy.ndarray) → xarray.core.dataarray.DataArray#: Make an xr.DataArray with data and same coords and dims as fields of self.

make_dataset(keys: Tuple[str, ...], vals: Tuple[numpy.ndarray, ...]) → xarray.core.dataset.Dataset#: Make an xr.Dataset with keys and data with same coords and dims as fields.

make_renormalized_data(phase: numpy.ndarray, proj_distance: float) → tidy3d.components.data.monitor_data.AbstractFieldProjectionData#: Helper to apply the re-projection phase to a copied dataset.

property nk: Tuple[float, float]#: Returns the real and imaginary parts of the background medium’s refractive index.

normalize(source_spectrum_fn: Callable[[float], complex]) → tidy3d.components.data.monitor_data.AbstractFieldProjectionData#: Return copy of self after normalization is applied using source spectrum function.

property power: xarray.core.dataarray.DataArray#

Get power measured on the projection grid relative to the monitor’s local origin.

Returns: Power at points relative to the local origin.
Return type: xarray.DataArray

static propagation_phase(dist: Optional[float], k: complex) → complex#: Phase associated with propagation of a distance with a given wavenumber.

property radar_cross_section: xarray.core.dataarray.DataArray#: Radar cross section in units of incident power.

renormalize_fields(proj_distance: float) → tidy3d.components.data.monitor_data.FieldProjectionCartesianData#

Return a FieldProjectionCartesianData with fields re-normalized to a new projection distance, by applying a phase factor based on proj_distance.

Parameters: proj_distance (float = None) – (micron) new plane distance relative to the monitor’s local origin.
Returns: Copy of this FieldProjectionCartesianData with fields re-projected to proj_distance.
Return type: FieldProjectionCartesianData

property symmetry_expanded: tidy3d.components.data.monitor_data.MonitorData#: Return self with symmetry applied.

property symmetry_expanded_copy: tidy3d.components.base_sim.data.monitor_data.AbstractMonitorData#: Return copy of self with symmetry applied.

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 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 wavenumber(medium: Union[tidy3d.components.medium.Medium, tidy3d.components.medium.AnisotropicMedium, tidy3d.components.medium.PECMedium, tidy3d.components.medium.PoleResidue, tidy3d.components.medium.Sellmeier, tidy3d.components.medium.Lorentz, tidy3d.components.medium.Debye, tidy3d.components.medium.Drude, tidy3d.components.medium.FullyAnisotropicMedium, tidy3d.components.medium.CustomMedium, tidy3d.components.medium.CustomPoleResidue, tidy3d.components.medium.CustomSellmeier, tidy3d.components.medium.CustomLorentz, tidy3d.components.medium.CustomDebye, tidy3d.components.medium.CustomDrude, tidy3d.components.medium.CustomAnisotropicMedium, tidy3d.components.medium.PerturbationMedium, tidy3d.components.medium.PerturbationPoleResidue, tidy3d.components.medium.Medium2D], frequency: float) → complex#: Complex valued wavenumber associated with a frequency.

property x: numpy.ndarray#: X positions.

property y: numpy.ndarray#: Y positions.

property z: numpy.ndarray#: Z positions.