torchsig.transforms.transforms.TimeReversal

class torchsig.transforms.transforms.TimeReversal(allow_spectral_inversion: bool | float = True, **kwargs)

Bases: SignalTransform

Apply a time reversal to the input.

Note that applying a time reversal inherently also applies a spectral inversion. If a time-reversal without spectral inversion is desired, the undo_spectral_inversion argument can be set to True. By setting this value to True, an additional, manual spectral inversion is applied to revert the time-reversal’s inversion effect.

allow_spectral_inversion

Whether to allow spectral inversion as a time reversal side effect (True) or not (False). Defaults to True. * If bool, applied to all signals. * If float, applied as probability to add signals.

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__(allow_spectral_inversion: bool | float = True, **kwargs) → None

Initialize the TimeReversal transform.

Parameters:

• allow_spectral_inversion – Whether to allow spectral inversion as a time reversal side effect. Defaults to True.

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

Raises:

TypeError – If allow_spectral_inversion is not bool or float.

__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.