torchsig.transforms.transforms.AdjacentChannelInterference

class torchsig.transforms.transforms.AdjacentChannelInterference(sample_rate: float = 1.0, power_range: tuple = (0.01, 10.0), center_frequency_range: tuple = (0.2, 0.3), phase_sigma_range: tuple = (0.0, 1.0), time_sigma_range: tuple = (0.0, 10.0), filter_weights: ndarray | None = None, **kwargs)

Bases: SignalTransform

Apply adjacent channel interference to signal.

This transform adds interference from an adjacent channel with configurable parameters.

sample_rate

Sample rate (normalized). Defaults to 1.0.

power_range

Range bounds for interference power level (W). Defaults to (0.01, 10.0).

power_distribution

Random draw of interference power.

center_frequency_range

Range bounds for interference center frequency (normalized). Defaults to (0.2, 0.3).

center_frequency_distribution

Random draw of interference power.

phase_sigma_range

Range bounds for interference phase sigma. Defaults to (0.0, 1.0).

phase_sigma_distribution

Random draw of phase sigma.

time_sigma_range

Range bounds for interference time sigma. Defaults to (0.0, 10.0).

time_sigma_distribution

Random draw of time sigma.

filter_weights

Predefined baseband lowpass filter, fixed for all calls. Defaults to low_pass(0.125, 0.125, 1.0).

Methods

add_parent	Add parent Seedable object and set up RNGs accordingly.
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.
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__(sample_rate: float = 1.0, power_range: tuple = (0.01, 10.0), center_frequency_range: tuple = (0.2, 0.3), phase_sigma_range: tuple = (0.0, 1.0), time_sigma_range: tuple = (0.0, 10.0), filter_weights: ndarray | None = None, **kwargs)

Initialize the AdjacentChannelInterference transform.

Parameters:

• sample_rate – Sample rate (normalized). Defaults to 1.0.

• power_range – Range bounds for interference power level (W). Defaults to (0.01, 10.0).

• center_frequency_range – Range bounds for interference center frequency (normalized). Defaults to (0.2, 0.3).

• phase_sigma_range – Range bounds for interference phase sigma. Defaults to (0.0, 1.0).

• time_sigma_range – Range bounds for interference time sigma. Defaults to (0.0, 10.0).

• filter_weights – Predefined baseband lowpass filter, fixed for all calls. Defaults to low_pass(0.125, 0.125, 1.0).

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

__call__(signal: Signal) → Signal

Validates signal, performs transform, updates bookeeping, (optionally) enforces data type.

Parameters:

signal – Signal to be transformed.

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.