tidy3d.CustomSourceTime#

class tidy3d.CustomSourceTime(*, amplitude: pydantic.v1.types.NonNegativeFloat = 1.0, phase: float = 0.0, type: Literal['CustomSourceTime'] = 'CustomSourceTime', freq0: pydantic.v1.types.PositiveFloat, fwidth: pydantic.v1.types.PositiveFloat, offset: float = 0.0, source_time_dataset: Optional[tidy3d.components.data.dataset.TimeDataset])#

Bases: tidy3d.components.source.Pulse

Custom source time dependence consisting of a real or complex envelope modulated at a central frequency, as shown below.

Parameters

amplitude (NonNegativeFloat = 1.0) – Real-valued maximum amplitude of the time dependence.
phase (float = 0.0) – [units = rad]. Phase shift of the time dependence.
freq0 (PositiveFloat) – [units = Hz]. Central frequency of the pulse.
fwidth (PositiveFloat) – [units = Hz]. Standard deviation of the frequency content of the pulse.
offset (float = 0.0) – Time delay of the envelope in units of 1 / (2pi * fwidth).
source_time_dataset (Optional[TimeDataset]) – Dataset for storing the envelope of the custom source time. This envelope will be modulated by a complex exponential at frequency freq0.

Note

\[amp\_time(t) = amplitude \cdot \ e^{i \cdot phase - 2 \pi i \cdot freq0 \cdot t} \cdot \ envelope(t - offset / (2 \pi \cdot fwidth))\]

Note

Depending on the envelope, field decay may not occur. If field decay does not occur, then the simulation will run for the full run_time. Also, if field decay does not occur, then source normalization of frequency-domain monitors is not meaningful.

Note

The source time dependence is linearly interpolated to the simulation time steps. The sampling rate should be sufficiently fast that this interpolation does not introduce artifacts. The source time dependence should also start at zero and ramp up smoothly. The first and last values of the envelope will be used for times that are out of range of the provided data.

Example

>>> cst = CustomSourceTime.from_values(freq0=1, fwidth=0.1,
...     values=np.linspace(0, 9, 10), dt=0.1)

__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.
`amp_time`(time)	Complex-valued source amplitude as a function of time.
`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.
`frequency_range`([num_fwidth])	Frequency range within 5 standard deviations of the central frequency.
`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_values`(freq0, fwidth, values, dt)	Create a `CustomSourceTime` from a numpy array.
`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().
`parse_file`(path, *[, content_type, ...])
`parse_obj`(obj)
`parse_raw`(b, *[, content_type, encoding, ...])
`plot`(times[, val, ax])	Plot the complex-valued amplitude of the time-dependence.
`plot_spectrum`(times[, num_freqs, val, ax])	Plot the complex-valued amplitude of the source time-dependence.
`plot_spectrum_in_frequency_range`(times, ...)	Plot the complex-valued amplitude of the time-dependence.
`schema`([by_alias, ref_template])
`schema_json`(*[, by_alias, ref_template])
`spectrum`(times, freqs, dt)	Complex-valued spectrum as a function of frequency.
`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)

Attributes

`offset`
`source_time_dataset`

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.

amp_time(time: float) → complex#

Complex-valued source amplitude as a function of time.

Parameters: time (float) – Time in seconds.
Returns: Complex-valued source amplitude at that time.
Return type: complex

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, .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') 

frequency_range(num_fwidth: float = 4.0) → Tuple[float, float]#

Frequency range within 5 standard deviations of the central frequency.

Parameters: num_fwidth (float = 4.) – Frequency range defined as plus/minus num_fwidth * self.fwdith.
Returns: Minimum and maximum frequencies of the GaussianPulse or ContinuousWave power.
Return type: Tuple[float, float]

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_values(freq0: float, fwidth: float, values: tidy3d.components.types.ArrayLike[dtype=complex, ndim=1], dt: float) → tidy3d.components.source.CustomSourceTime#

Create a CustomSourceTime from a numpy array.

Parameters

freq0 (float) – Central frequency of the source. The envelope provided will be modulated by a complex exponential at this frequency.
fwidth (float) – Estimated frequency width of the source.
values (ArrayComplex1D) – Complex values of the source envelope.
dt (float) – Time step for the values array. This value should be sufficiently small that the interpolation to simulation time steps does not introduce artifacts.

Returns

CustomSourceTime with envelope given by values, modulated by a complex exponential at frequency freq0. The time coordinates are evenly spaced between 0 and dt * (N-1) with a step size of dt, where N is the length of the values array.

Return type

CustomSourceTime

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().

plot(times: tidy3d.components.types.ArrayLike[dtype=float, ndim=1], val: typing.Literal['real', 'imag', 'abs'] = 'real', ax: matplotlib.axes._axes.Axes = None) → matplotlib.axes._axes.Axes#

Plot the complex-valued amplitude of the time-dependence.

Parameters

times (np.ndarray) – Array of times (seconds) to plot source at. To see source time amplitude for a specific Simulation, pass simulation.tmesh.
val (Literal['real', 'imag', 'abs'] = 'real') – Which part of the spectrum to plot.
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

plot_spectrum(times: tidy3d.components.types.ArrayLike[dtype=float, ndim=1], num_freqs: int = 101, val: typing.Literal['real', 'imag', 'abs'] = 'real', ax: matplotlib.axes._axes.Axes = None) → matplotlib.axes._axes.Axes#

Plot the complex-valued amplitude of the source time-dependence. Note: Only the real part of the time signal is used.

Parameters

times (np.ndarray) – Array of evenly-spaced times (seconds) to evaluate source time-dependence at. The spectrum is computed from this value and the source time frequency content. To see source spectrum for a specific Simulation, pass simulation.tmesh.
num_freqs (int = 101) – Number of frequencies to plot within the SourceTime.frequency_range.
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

plot_spectrum_in_frequency_range(times: tidy3d.components.types.ArrayLike[dtype=float, ndim=1], fmin: float, fmax: float, num_freqs: int = 101, val: typing.Literal['real', 'imag', 'abs'] = 'real', ax: matplotlib.axes._axes.Axes = None) → matplotlib.axes._axes.Axes#

Plot the complex-valued amplitude of the time-dependence. Note: Only the real part of the time signal is used.

Parameters

times (np.ndarray) – Array of evenly-spaced times (seconds) to evaluate time-dependence at. The spectrum is computed from this value and the time frequency content. To see spectrum for a specific Simulation, pass simulation.tmesh.
fmin (float) – Lower bound of frequency for the spectrum plot.
fmax (float) – Upper bound of frequency for the spectrum plot.
num_freqs (int = 101) – Number of frequencies to plot within the [fmin, fmax].
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

spectrum(times: tidy3d.components.types.ArrayLike[dtype=float, ndim=1], freqs: tidy3d.components.types.ArrayLike[dtype=float, ndim=1], dt: float) → complex#

Complex-valued spectrum as a function of frequency. Note: Only the real part of the time signal is used.

Parameters

times (np.ndarray) – Times to use to evaluate spectrum Fourier transform. (Typically the simulation time mesh).
freqs (np.ndarray) – Frequencies in Hz to evaluate spectrum at.
dt (float or np.ndarray) – Time step to weight FT integral with. If array, use to weigh each of the time intervals in times.

Returns

Complex-valued array (of len(freqs)) containing spectrum at those frequencies.

Return type

np.ndarray

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.