torchsig.transforms.transforms.Quantize

class torchsig.transforms.transforms.Quantize(num_bits: tuple[int, int] = (6, 18), ref_level_adjustment_db: tuple[float, float] = (-10, 3), rounding_mode: list[str] = ['floor', 'ceiling'], **kwargs)

Bases: SignalTransform

Quantize signal I/Q samples into specified levels with a rounding method.

num_levels

Number of quantization levels.

num_levels_distribution

Random draw from num_levels distribution.

rounding_mode

Quantization rounding method. Must be ‘floor’ or ‘ceiling’.

rounding_mode_distribution

Random draw from rounding_mode distribution.

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__(num_bits: tuple[int, int] = (6, 18), ref_level_adjustment_db: tuple[float, float] = (-10, 3), rounding_mode: list[str] = ['floor', 'ceiling'], **kwargs)

Initialize the Quantize transform.

Parameters:

• num_bits – Number of quantization bits. Defaults to (6, 18).

• ref_level_adjustment_db – Reference level adjustment in dB. Defaults to (-10, 3).

• rounding_mode – Quantization rounding method. Must be ‘floor’ or ‘ceiling’. Defaults to [“floor”, “ceiling”].

• **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.