SMatrixTimeStepper

SMatrixTimeStepper(
    pole_residue_matrix: photonforge.PoleResidueMatrix | None = None,
    pole_residue_reference: Literal['absolute', 'baseband'] = 'baseband',
    s_matrix_kwargs: dict[str, typing.Any] = {},
    carrier_frequency: Annotated[float, minimum=0, units='Hz']
    | None = None,
    initial_poles: collections.abc.Sequence[complex] = (),
    min_poles: Annotated[int, minimum=0] = 0,
    max_poles: Annotated[int, minimum=0] = 6,
    loss_factor: Annotated[float, exclusiveMinimum=0] = 0.001,
    rms_error_tolerance: Annotated[float, exclusiveMinimum=0] = 0.0001,
    max_iterations: Annotated[int, exclusiveMinimum=0] = 100,
    max_stale_iterations: Annotated[int, exclusiveMinimum=0] = 3,
    passive: bool = True,
    feedthrough: bool | None = None,
    delays: Literal['auto']
    | Annotated[float, minimum=0, units='s']
    | dict[
        tuple[str, str], Annotated[float, minimum=0, units='s']
    ] = 'auto',
) -> None

Time stepper based on a time-domain model.

The TimeDomainModel can be calculated automatically using pole_residue_fit and the component’s S matrix or from the provided PoleResidueMatrix.

Args

• pole_residue_matrix: Pole-residue matrix for the underlying time-domain model. If provided, fitting arguments are not used.
• pole_residue_reference: Frequency reference for an explicitly provided matrix. A "baseband" matrix is used unchanged, while an "absolute" matrix is shifted to the resolved carrier during setup.
• s_matrix_kwargs: Keyword arguments for Component.s_matrix.
• carrier_frequency: Carrier frequency for this stepper. If None, it is resolved from the circuit: optical references inherit the circuit carrier and electrical references default to 0 (baseband).
• initial_poles: Sequence of poles used as initial guess.
• min_poles: Minimal number of poles to try. It has no effect when initial pole guesses are provided.
• max_poles: Maximal number of poles to try. It has no effect when initial pole guesses are provided.
• loss_factor: For complex initial pole guesses, ratio between their real and imaginary parts.
• rms_error_tolerance: RMS error level to break the fitting loop.
• max_iterations: Maximal number of fitting iterations.
• max_stale_iterations: Maximal number of iterations without error progress.
• passive: Whether to attempt to enforce passivity.
• feedthrough: Whether to include a feedthrough (constant) term in the pole-residue model.
• delays: Time delays (in seconds), one per matrix element. Missing elements have no time delay applied. If a single number is given, this is used as a global delay for all matrix elements. If "auto", the delays are estimated from the provided data.

Attributes

• pole_residue_matrix: After setup, the stepper’s pole-residue matrix, delays included: the one given, prepared for the carrier, or the fit.
• time_domain_model: After setup, the model stepped: pole_residue_matrix less the delays that output_delay_steps carries on the output lines.

Important

Poles, in pole_residue_matrix and initial_poles, follow the s = -i h f convention of PoleResidueMatrix and describe the carrier-shifted (envelope) response, except for an "absolute"-referenced matrix, which is shifted to the carrier during setup.

Methods

Method	Description
bind_native	Bind a compiled step function.
reset	Reset the state of the internal time domain model and the declared lines.
setup	Initialize the time stepper.
setup_state	Initialize internal state, building a time domain model if necessary.
step	Compute the outputs of this time stepper, given inputs.
step_single	Take a single time step on the given inputs.
update	Update this time stepper.
write_verilog_a	Write this configured time stepper as one Verilog-A module.

bind_native

bind_native(self, step, keys, state=None, cstate=None, context=None)

Bind a compiled step function.

Instead of implementing step_single, a time stepper can hand the simulator the address of a C function with the signature:

int64 step(inputs, outputs, time_index, update_state, shutdown,
           context, state, cstate)

inputs and outputs are complex128 arrays with one entry per key, state and cstate are the float64 and complex128 arrays, and a nonzero return signals failure. The function must overwrite every element of outputs and only modify the state arrays when update_state is true. The arrays must not be reallocated after binding; reset restores them to the values given here, so a subclass overriding reset must call super().reset().

Calls with update_state false must give the same outputs: circuits may call the function several times in a time step, and setup checks this for steppers that declare path_delays. Random numbers must therefore come from a generator whose state lives in the arrays (e.g., a seed and a counter): output a draw kept there and draw the next one only when update_state is true, which also makes reset replay the stream. Do not use numpy.random in the function: numba draws it from a global stream that differs on every call and survives a reset.

The function can be compiled with numba (when available).

Args

• step (int | object): Address of the step function, or an object with an address attribute such as a numba cfunc.
• keys (Sequence[str]): Input/output keys.
• state (ndarray | None): Contiguous float64 array the function may use.
• cstate (ndarray | None): Contiguous complex128 array the function may use.
• context (int | None): Opaque pointer passed to the function.

Example

A two-port element that leaks the field entering its first port out of its second one:

>>> from numba import cfunc
>>> from photonforge.time_steppers import STEP_SIGNATURE
>>>
>>> @cfunc(STEP_SIGNATURE, cache=True)  # doctest: +SKIP
... def leaky_step(inputs, outputs, time_index, update_state,
...                shutdown, context, state, cstate):
...     # state[0]: decay per step
...     # cstate[0]: field leaving the second port
...     outputs[0] = 0j
...     outputs[1] = cstate[0] * state[0] + inputs[0]
...     if update_state:
...         cstate[0] = outputs[1]
...     return 0
...
>>> class LeakyTimeStepper(pf.TimeStepper):
...     def __init__(self, *, k=0.5):
...         super().__init__(k=k)
...
...     def setup_state(self, *, component, time_step, **kwargs):
...         keys = tuple(f"{p}@0" for p in sorted(component.ports))
...         state = numpy.array([
...             self.parametric_kwargs["k"] ** time_step
...         ])
...         self.bind_native(
...             leaky_step, keys, state, numpy.zeros(1, complex)
...         )
...
>>> pf.register_time_stepper_class(LeakyTimeStepper)

reset

reset(self) -> None

Reset the state of the internal time domain model and the declared lines.

setup

setup(
    self,
    component,
    time_step,
    *,
    carrier_frequency=0,
    show_progress=True,
    **kwargs,
)

Initialize the time stepper.

Args

• component (Component): Component for the time stepper.
• time_step (float): The interval between time steps (in seconds).
• carrier_frequency (float): The carrier frequency used to construct the time stepper. The carrier should be omitted from the input signals, as it is handled automatically by the time stepper.
• show_progress (bool): If True, show setup progress.
• **kwargs (object): Keyword args forwarded to setup_state.

Returns

TimeStepper: This time stepper.

setup_state

setup_state(
    self,
    *,
    component: photonforge.Component,
    time_step: float,
    carrier_frequency: Annotated[float, minimum=0, units='Hz']
    | None = None,
    frequencies: collections.abc.Sequence[
        Annotated[float, minimum=0, units='Hz']
    ]
    | None = None,
    **kwargs: object,
) -> object

Initialize internal state, building a time domain model if necessary.

Args

• component: Component for the time stepper.
• time_step: The interval between time steps (in seconds).
• carrier_frequency: The carrier frequency used to construct the time stepper. The carrier should be omitted from the input signals, as it is handled automatically by the time stepper.
• frequencies: Frequencies used to build the S matrix for component, if a pole_residue_matrix is not provided. If None, one Nyquist band around the carrier, with more points than the default 101 when a model computed in place has a delay the default spacing cannot resolve. Two frequencies near the carrier, within the range of these, are added to the request to measure the delay.
• kwargs: Unused.

step

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

Compute the outputs of this time stepper, given inputs.

Args

• inputs (TimeSeries): Inputs to the time stepper.
• steps (int): The number of time steps to execute. If inputs is defined, this value is not used.
• time_step (float): The interval between time steps (in seconds). If inputs is a defined, this value is not used.
• show_progress (bool): If True, show computation progress.

Returns

TimeSeries: Computed outputs.

step_single

step_single(
    self,
    inputs: numpy.ndarray,
    outputs: numpy.ndarray,
    time_index: int,
    update_state: bool,
    shutdown: bool,
) -> None

Take a single time step on the given inputs.

Args

• inputs: Input values at the current time step. Must be a 1D array of complex values ordered according to keys.
• outputs: Pre-allocated output array where results will be stored. Same size and type as inputs.
• time_index: Time series index for the current input.
• update_state: Whether to update the internal stepper state.
• shutdown: Whether this is the last call to the single stepping function for the provided TimeSeries.

update

update(self, *args, **kwargs)

Update this time stepper.

All contents of the time stepper are updated with the contents from the updated version. Object contents or modifications introduced after the time stepper creation (outside of the original parametric function) are not propagated.

Args

• *args (object): Positional arguments to the parametric time stepper function.
• **kwargs (object): Keyword arguments to the parametric time stepper function.

Returns

TimeStepper: This time stepper.

Note

The original keyword arguments used to generate the time stepper are stored and updated with the passed kwargs. This updated version is used in the function call and will be stored for future updates. Positional arguments are not stored.

write_verilog_a

write_verilog_a(
    self,
    path,
    *,
    backend='openvaf',
    port_convention='travelling_wave',
    module_name=None,
    z0=50.0,
    field_impedance=None,
)

Write this configured time stepper as one Verilog-A module.

The time stepper must first be configured with setup or Component.setup_time_stepper. The writer creates source only; it does not compile the model or create a simulator deck. Requires a PhotonForge Photonic Canvas license.

Args

• path (str | os.PathLike): Destination .va file.
• backend (Literal[“openvaf”, “spectre”]): Compiler capability target recorded in the source.
• port_convention (Literal[“travelling_wave”, “gf_45spclo_npe”]): External optical port convention. GF packed ports require the Spectre backend.
• module_name (str | None): Module identifier derived from path when omitted.
• z0 (float): Travelling-wave optical reference impedance.
• field_impedance (float | None): GF electric-field scaling.

Returns

pathlib.Path: Absolute destination path.

Properties

Property	Description
keys	Tuple of input/output keys.
path_delays	Minimum delay in time steps of each fitted (input, output) path, lines included.
status

keys

Tuple of input/output keys.

path_delays

Minimum delay in time steps of each fitted (input, output) path, lines included.

status

No description available.

Attributes

Attribute	Description
parametric_function	str: Function used to update the time stepper.
parametric_kwargs	dict[str, object]: Keyword arguments used to update the time stepper.
properties	Properties: Object properties.
random_variables	list[RandomVariable]: Random variables associated to the time stepper’s parameters.

parametric_function

str: Function used to update the time stepper.

parametric_kwargs

dict[str, object]: Keyword arguments used to update the time stepper.

properties

Properties: Object properties.

random_variables

list[RandomVariable]: Random variables associated to the time stepper’s parameters.