TimeDomainModel

TimeDomainModel(pole_residue_matrix, time_step)

Time-domain model based on a pole-residue representation of an S matrix.

Args

• pole_residue_matrix (PoleResidueMatrix): Underlying pole-residue matrix.
• time_step (float): Time step to be used for this model.

Example

>>> pole_res = pf.PoleResidueMatrix(
...     poles=[-pf.C_0 / 1.0],
...     residues={("P0", "P0"): [2.0 * pf.C_0, 0.0]},
... )
>>> time_domain = pf.TimeDomainModel(pole_res, time_step=0.001)

Note

Time-domain models cannot be added to components at the moment. Component models are only those used to compute frequency-domain S parameters.

Methods

Method	Description
copy	Create a copy of this time-domain model.
reset	Reset the internal model state.
step	Compute the outputs for multiple time steps.
step_single	Compute the output for a single time step using array-based API.

copy

copy(self)

Create a copy of this time-domain model.

Returns

TimeDomainModel: New copy.

reset

reset(self)

Reset the internal model state.

Returns

TimeDomainModel: This object.

step

step(self, inputs=None, steps=0, time_step=0, show_progress=True)

Compute the outputs for multiple time steps.

Note

This method is not thread-safe. Do not call step concurrently from multiple threads on the same TimeDomainModel instance. For parallel simulations, create separate model instances (e.g., via copy()) for each thread.

Args

• inputs (TimeSeries | None): Inputs to the time domain model. If None, uses zero inputs for steps time steps.
• steps (int): The number of time steps to execute. Only used if inputs is None.
• time_step (float): The interval between time steps (in seconds). Only used if inputs is None. Must match the model’s time_step attribute.
• show_progress (bool): If True, show computation progress.

Returns

TimeSeries: Computed outputs.

step_single

step_single(
    self, inputs, outputs, time_index=0, update_state=True, shutdown=False
)

Compute the output for a single time step using array-based API.

Note

This method is not thread-safe. Do not call it concurrently from multiple threads on the same TimeDomainModel instance, even with update_state=False. For parallel simulations, create a separate model instance (e.g., via copy()) for each thread.

Args

• inputs (ndarray): Input values at the current time step. Must be a contiguous 1D array of complex values ordered according to keys.
• outputs (ndarray): Pre-allocated output array where results will be stored. Same size and type as inputs.
• time_index (int): Time series index for the current input. Not used.
• update_state (bool): Whether to update the internal model state.
• shutdown (bool): Whether this is the last call. Not used.

Returns

None. Results are written in-place to the outputs array.

Attributes

Attribute	Description
keys	tuple[str]: input/output array keys (read only).
path_delays	dict[tuple[str, str], int]: Minimum delay in time steps of each fitted (input key, output key) path (read only).
pole_residue_matrix	PoleResidueMatrix: Underlying pole-residue matrix (read only).
properties	Properties: Object properties.
time_step	float: Time step.

keys

tuple[str]: input/output array keys (read only).

path_delays

dict[tuple[str, str], int]: Minimum delay in time steps of each fitted (input key, output key) path (read only).

pole_residue_matrix

PoleResidueMatrix: Underlying pole-residue matrix (read only).

properties

Properties: Object properties.

time_step

float: Time step.