pmrf.fitting.FitContext

class pmrf.fitting.FitContext(model, measured, frequency, features, measured_features, output_path=None, output_root=None, sparam_kind=None, logger=None)

Bases: object

Context object holding the state and data required for a fit execution.

Parameters:

• model (Model)

• measured (Network | NetworkCollection)

• frequency (Frequency)

• features (list[tuple[str, str, tuple[int, int]]])

• measured_features (ndarray)

• output_path (str | None)

• output_root (str | None)

• sparam_kind (str | None)

• logger (Logger | None)

model

The parametric model being fitted.

Type:

Model

measured

The target measured data.

Type:

skrf.Network or NetworkCollection

frequency

The frequency object defining the domain.

Type:

Frequency

features

The specific features to match against.

Type:

list of FeatureT

measured_features

The values of the features extracted from the measured data.

Type:

np.ndarray

output_path

Directory path for output files.

Type:

str or None

output_root

Root filename for outputs.

Type:

str or None

sparam_kind

The S-parameter representation kind (e.g., ‘all’, ‘transmission’).

Type:

str or None

logger

Logger instance for tracking progress.

Type:

logging.Logger or None

__init__(model, measured, frequency, features, measured_features, output_path=None, output_root=None, sparam_kind=None, logger=None)

Parameters:

• model (Model)

• measured (Network | NetworkCollection)

• frequency (Frequency)

• features (list[tuple[str, str, tuple[int, int]]])

• measured_features (ndarray)

• output_path (str | None)

• output_root (str | None)

• sparam_kind (str | None)

• logger (Logger | None)

Return type:

None

Methods

__delattr__(name, /)	Implement delattr(self, name).
__dir__()	Default dir() implementation.
__eq__(other)	Return self==value.
__format__(format_spec, /)	Default object formatter.
__ge__(value, /)	Return self>=value.
__getattribute__(name, /)	Return getattr(self, name).
__gt__(value, /)	Return self>value.
__init__(model, measured, frequency, ...[, ...])
__init_subclass__	This method is called when a class is subclassed.
__le__(value, /)	Return self<=value.
__lt__(value, /)	Return self<value.
__ne__(value, /)	Return self!=value.
__new__(**kwargs)
__reduce__()	Helper for pickle.
__reduce_ex__(protocol, /)	Helper for pickle.
__repr__()	Return repr(self).
__setattr__(name, value, /)	Implement setattr(self, name, value).
__sizeof__()	Size of object in memory, in bytes.
__str__()	Return str(self).
__subclasshook__	Abstract classes can override this to customize issubclass().
make_feature_function([as_numpy])	Create a JIT-compiled function to extract features from model parameters.
model_param_names()	Get the names of the flat parameters of the model.
settings([solver_kwargs, fitter_kwargs])	Create a FitSettings object from the current context.

Attributes

__annotations__
__dataclass_fields__
__dataclass_params__
__dict__
__doc__
__hash__
__match_args__
__module__
__weakref__	list of weak references to the object (if defined)
logger
output_path
output_root
sparam_kind
model
measured
frequency
features
measured_features

model: Model

measured: Network | NetworkCollection

frequency: Frequency

features: list[tuple[str, str, tuple[int, int]]]

measured_features: ndarray

output_path: str | None = None

output_root: str | None = None

sparam_kind: str | None = None

logger: Logger | None = None

model_param_names()

Get the names of the flat parameters of the model.

Returns:

The list of parameter names.

Return type:

list of str

make_feature_function(as_numpy=False)

Create a JIT-compiled function to extract features from model parameters.

Parameters:

as_numpy (bool, default=False) – If True, the returned function handles NumPy arrays; otherwise JAX arrays.

Returns:

A function taking theta and returning feature values.

Return type:

callable

settings(solver_kwargs=None, fitter_kwargs=None)

Create a FitSettings object from the current context.

Parameters:

• solver_kwargs (dict, optional) – Solver specific arguments.

• fitter_kwargs (dict, optional) – Fitter specific arguments.

Returns:

The populated settings object.

Return type:

FitSettings

__init__(model, measured, frequency, features, measured_features, output_path=None, output_root=None, sparam_kind=None, logger=None)

Parameters:

• model (Model)

• measured (Network | NetworkCollection)

• frequency (Frequency)

• features (list[tuple[str, str, tuple[int, int]]])

• measured_features (ndarray)

• output_path (str | None)

• output_root (str | None)

• sparam_kind (str | None)

• logger (Logger | None)

Return type:

None