torchsig.transforms.dataset_transforms.QuantizeDatasetTransform¶

class torchsig.transforms.dataset_transforms.QuantizeDatasetTransform(num_bits: Tuple[int, int] = (6, 18), ref_level_adjustment_db: Tuple[float, float] = (-10, 3), **kwargs)[source]¶

Bases: DatasetTransform

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.

Type:: Callable[[], int]

round_type¶

Quantization rounding method. Must be ‘floor’, ‘nearest’ or ‘ceiling’. Defaults to ‘ceiling’.

Type:: str, List[str]

round_type_distribution¶

Random draw from round_type distribution.

Type:: Callable[[], str]

Methods

`add_parent`	Add parent Seedable object and set up RNGs accordingly
`get_distribution`
`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`	Updates bookkeeping to transforms in DatasetSignal's SignalMetadata and checks signal valididty.
`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), **kwargs)[source]¶: Transform initialization as Seedable.

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

Performs transforms.

Parameters:: signal (DatasetSignal) – DatasetSignal to be transformed.
Raises:: NotImplementedError – Inherited classes must override this method.
Returns:: Transformed DatasetSignal.
Return type:: DatasetSignal

__repr__() → str¶

Transform string representation. Should be able to recreate class from this string.

Returns:: Transform representation.
Return type:: str

__str__() → str¶: Return str(self).

add_parent(parent) → None¶: Add parent Seedable object and set up RNGs accordingly

get_second_seed(seed: int) → int¶

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

Parameters:: seed (int) – Seed to use.
Returns:: New seed.
Return type:: int

seed(seed: int) → None¶

Seed number generators with given seed.

Parameters:: seed (int) – Seed to use.

setup_rngs() → None¶: Initialize torch and numpy number generators, and update its children.

update(signal: DatasetSignal) → None¶

Updates bookkeeping to transforms in DatasetSignal’s SignalMetadata and checks signal valididty. Inherited classes should always call self.update() after performing transform operation (inside __call__).

Parameters:: signal (DatasetSignal) – transformed DatasetSignal.

update_from_parent() → None¶: Update numpy and torch number generators with parent seed