torchsig.transforms.dataset_transforms.NonlinearAmplifierDatasetTransform¶

class torchsig.transforms.dataset_transforms.NonlinearAmplifierDatasetTransform(gain_range: Tuple[float, float] = (1.0, 4.0), psat_backoff_range: Tuple[float, float] = (5.0, 20.0), phi_range: Tuple[float, float] = (0.0, 0.0), auto_scale: bool = True, **kwargs)[source]¶

Bases: DatasetTransform

Applies a memoryless nonlinear amplifier model to DatasetSignal.

gain_range¶

Small-signal gain range (linear). Defaults to (1.0, 4.0).

Type:: Tuple[float, float]

gain_distribution¶

Random draw from gain distribution.

Type:: Callable[[], float]

psat_backoff_range¶

Psat backoff factor (linear) reflecting saturated power level (Psat) relative to input signal mean power. Defaults to (5.0, 20.0).

Type:: Tuple[float, float]

past_backoff_distribution¶

Random draw from psat_backoff distribution.

Type:: Callable[[], float]

phi_range¶

Maximum signal relative phase shift at saturation power level (radians). Defaults to (0.0, 0.0).

Type:: Tuple[float, float]

phi_distribution¶

Random draw from phi distribution.

Type:: Callable[[], float]

auto_scale¶

Automatically rescale output power to match full-scale peak input power prior to transform, based on peak estimates. Default True.

Type:: bool

Methods

`add_parent`	Add parent Seedable object and set up RNGs accordingly
`get_distribution`
`get_second_seed`	Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds
`seed`	Seed number generators with given seed.
`setup_rngs`	Initialize torch and numpy number generators, and update its children.
`update`	Updates bookkeeping to transforms in DatasetSignal's SignalMetadata and checks signal valididty.
`update_from_parent`	Update numpy and torch number generators with parent seed

__init__(gain_range: Tuple[float, float] = (1.0, 4.0), psat_backoff_range: Tuple[float, float] = (5.0, 20.0), phi_range: Tuple[float, float] = (0.0, 0.0), auto_scale: bool = True, **kwargs)[source]¶: Transform initialization as Seedable.

__call__(signal: DatasetSignal) → DatasetSignal[source]¶

Performs transforms.

Parameters:: signal (DatasetSignal) – DatasetSignal to be transformed.
Raises:: NotImplementedError – Inherited classes must override this method.
Returns:: Transformed DatasetSignal.
Return type:: DatasetSignal

__repr__() → str¶

Transform string representation. Should be able to recreate class from this string.

Returns:: Transform representation.
Return type:: str

__str__() → str¶: Return str(self).

add_parent(parent) → None¶: Add parent Seedable object and set up RNGs accordingly

get_second_seed(seed: int) → int¶

Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds

Parameters:: seed (int) – Seed to use.
Returns:: New seed.
Return type:: int

seed(seed: int) → None¶

Seed number generators with given seed.

Parameters:: seed (int) – Seed to use.

setup_rngs() → None¶: Initialize torch and numpy number generators, and update its children.

update(signal: DatasetSignal) → None¶

Updates bookkeeping to transforms in DatasetSignal’s SignalMetadata and checks signal valididty. Inherited classes should always call self.update() after performing transform operation (inside __call__).

Parameters:: signal (DatasetSignal) – transformed DatasetSignal.

update_from_parent() → None¶: Update numpy and torch number generators with parent seed