torchsig.transforms.base_transforms.Normalize¶

class torchsig.transforms.base_transforms.Normalize(norm: float | Literal['fro', 'nuc'] | None = 2, flatten: bool = False, **kwargs)[source]¶

Bases: Transform

Normalize an IQ data vector.

This transform normalizes the IQ data according to the specified norm.

norm¶: Order of the norm (refer to numpy.linalg.norm).

flatten¶: Specifies if the norm should be calculated on the flattened representation of the input tensor.

Example

>>> import torchsig.transforms as ST
>>> transform = ST.Normalize(norm=2) # normalize by l2 norm
>>> transform = ST.Normalize(norm=1) # normalize by l1 norm
>>> transform = ST.Normalize(norm=2, flatten=True) # normalize by l1 norm of the 1D representation

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__(norm: float | Literal['fro', 'nuc'] | None = 2, flatten: bool = False, **kwargs) → None[source]¶

Initialize the Normalize transform.

Parameters:

norm – Order of the norm (refer to numpy.linalg.norm). Defaults to 2.
flatten – Specifies if the norm should be calculated on the flattened representation of the input tensor. Defaults to False.
**kwargs – Additional keyword arguments passed to the parent class.

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

Normalize the signal data.

Parameters:: signal – Signal to be transformed.
Returns:: Transformed signal with normalized data.

__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.