DataModel

class photonforge.DataModel(s_matrix=None, s_array=None, frequencies=None, ports=None, interpolation_method='linear', interpolation_coords='mag_phase', poleresidue_kwargs={})

Model based on existing S matrix data.

Parameters:

• s_matrix (SMatrix | None) – Model data as an SMatrix instance.

• s_array (Sequence[Sequence[Sequence[complex]]] | None) – Complex array with dimensions (F, N, N), in which N is the number of ports.

• frequencies (Sequence[Annotated[float, minimum=0, units='Hz']] | None) – Frequency array with length F.

• ports (Sequence[str] | None) – List of port names. If not set, the sorted list of port components is used.

• interpolation_method (Literal['linear', 'barycentric', 'cubicspline', 'pchip', 'akima', 'makima', 'poleresidue']) – Interpolation method used for sampling frequencies. See table below for options.

• interpolation_coords (Literal['real_imag', 'mag_phase']) – Coordinate system used for interpolation. One of "mag_phase" or "real_imag". Not used for "poleresidue" interpolation.

• poleresidue_kwargs (Annotated[dict[str, Any], _]) – Keyword arguments to pole_residue_fit() used when using "poleresidue" interpolation.

When s_matrix is provided, s_array, frequencies, and ports should be None, otherwise only ports is optional.

Interpolation method	Description
"linear"	Linear interpolation between neighboring points
"poleresidue"	Pole-residue fitting
"barycentric"	Barycentric Lagrange interpolation
"cubicspline"	Cubic spline interpolation
"pchip"	Piecewise cubic Hermite interpolating polynomial
"akima"	Akima interpolation
"makima"	Modified Akima interpolation

Important

Use of any interpolation method other than "linear" or "poleresidue" requires scipy >= 1.7, and "makima" requires scipy >= 1.13.

Note

The conversion from array to dictionary for s_data is equivalent to s_dict[(ports[i], ports[j])] = s_array[:, j, i].

See also

Data Model guide

Methods

black_box_component([port_spec, technology, ...])	Create a black-box component using this model for testing.
estimate_cost(*args, **kwargs)	Estimate the cost for S matrix computation.
from_bytes(byte_repr)	De-serialize this model.
s_matrix(component, frequencies[, ...])	Compute the S matrix for a component using this model.
setup_time_stepper(component, time_step[, ...])	Obtain a time stepper for a component using this model.
start(component, frequencies, **kwargs)	Start computing the S matrix response from a component.
update(*args, **kwargs)	Update this model.

Attributes

parametric_function	Function used to update a parametric component.
parametric_kwargs	Keyword arguments used to update a parametric component.
properties	Object properties.
random_variables	Random variables associated to this modles's parameters.
time_stepper	Time stepper associated with this model.

black_box_component(port_spec=None, technology=None, name=None)

Create a black-box component using this model for testing.

Parameters:

• port_spec (str | PortSpec | Sequence[str | PortSpec] | None) – Port specification used in the component. If None, look for "port_spec" in config.default_kwargs.

• technology (Technology | None) – Component technology. If None, the default technology is used.

• name (str | None) – Component name. If None a default is used.

Returns:

Component with ports and model.

Return type:

Component

classmethod from_bytes(byte_repr)

De-serialize this model.

Parameters:

byte_repr (bytes)

Return type:

DataModel

start(component, frequencies, **kwargs)

Start computing the S matrix response from a component.

Parameters:

• component (Component) – Component from which to compute the S matrix.

• frequencies (Sequence[float]) – Sequence of frequencies at which to perform the computation.

• **kwargs (Any) – Unused.

Returns:

Model result with attributes status and s_matrix.

Return type:

SMatrix