torchsig.transforms.impairments.Impairments

class torchsig.transforms.impairments.Impairments(level: int, **kwargs)

Bases: Transform

Applies signal and dataset transformations at specific impairment levels.

This class applies a set of signal and dataset transforms based on a given impairment level. The impairment level must be between 0 and 2, where each level corresponds to different sets of transformations for signals and datasets.

• Level 0: Perfect (no impairments)

• Level 1: Cabled environment (transmit impairments only)

• Level 2: Wireless environment (transmit impairments + channel models)

Parameters:

• level – The impairment level (must be between 0 and 2).

• **kwargs – Additional keyword arguments passed to the parent class Transform.

Raises:

ValueError – If the provided impairment level is outside the valid range (0, 1, 2).

level

The specified impairment level.

signal_transforms

The composed signal transformations corresponding to the given impairment level.

dataset_transforms

The composed dataset transformations corresponding to the given impairment level.

Methods

add_parent	Add parent Seedable object and set up RNGs accordingly.
get_dataset_transforms	Get the dataset transforms for this impairment level.
get_distribution	Create distribution function with proper seeding.
get_second_seed	Gets second seed, usually used to seed both torch and numpy generators with slightly different seeds.
get_signal_transforms	Get the signal transforms for this impairment level.
seed	Seed number generators with given seed.
setup_rngs	Initialize torch and numpy number generators, and update its children.
update_from_parent	Update numpy and torch number generators with parent seed.

__init__(level: int, **kwargs)

Initialize the Impairments class.

Parameters:

• level – The impairment level (must be between 0 and 2).

• **kwargs – Additional keyword arguments passed to the parent class Transform.

get_signal_transforms() → list[Transform]

Get the signal transforms for this impairment level.

Returns:

List of signal transforms configured for the current impairment level.

get_dataset_transforms() → list[Transform]

Get the dataset transforms for this impairment level.

Returns:

List of dataset transforms configured for the current impairment level.

__call__(signal)

Validate signal, performs transform, update bookeeping.

Parameters:

signal – Signal to be transformed.

Raises:

NotImplementedError – Inherited classes must override this method.

Returns:

Transformed Signal.

__repr__() → str

Transform string representation.

Should be able to recreate class from this string.

Returns:

Transform representation.

__str__() → str

String representation of the transform.

Returns:

String representation of the transform.

add_parent(parent: Seedable, register: bool = True) → None

Add parent Seedable object and set up RNGs accordingly.

Parameters:

• parent – Parent Seedable object to add.

• register – If True (default), add self to parent.children so that future seed propagation reaches this object. Pass False for transient objects (e.g. per-sample Signal instances) that only need the parent link for metadata/RNG access during their lifetime but must not accumulate in the parent’s child list, which would otherwise cause unbounded memory growth.

get_distribution(params: list | tuple | float, scaling: str = 'linear') → Distribution

Create distribution function with proper seeding.

Parameters:

• params – Parameters for distribution.

• scaling – Scaling param for distribution. Defaults to ‘linear’.

Returns:

Distribution function, seeded.

Return type:

Distribution

get_second_seed(seed: int) → int

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

Parameters:

seed – Seed to use.

Returns:

New seed.

seed(seed: int) → None

Seed number generators with given seed.

Parameters:

seed – Seed to use.

setup_rngs() → None

Initialize torch and numpy number generators, and update its children.

update_from_parent() → None

Update numpy and torch number generators with parent seed.