torchsig.transforms.dataset_transforms.TimeVaryingNoise¶

class torchsig.transforms.dataset_transforms.TimeVaryingNoise(noise_power_low=(-80.0, -60.0), noise_power_high=(-40.0, -20.0), inflections=[0, 10], random_regions: List | bool = True, **kwargs)[source]¶

Bases: DatasetTransform

Add time-varying noise to DatasetSignal regions.

noise_power_low¶: Range bounds for minimum noise power in dB.

noise_power_low_distribution¶

Random draw from noise_power_low distribution.

Type:: Callable[[], float]

noise_power_high¶: Range bounds for maximum noise power in dB.

noise_power_high_distribution¶

Random draw from noise_power_high distribution.

Type:: Callable[[], float]

inflections¶: Number of inflection points over IQ data.

inflections_distribution¶

Random draw from inflections distribution.

Type:: Callable[[], float]

random_regions¶

Inflections points spread randomly (True) or not (False).

Type:: List | bool

random_regions_distribution¶

Random draw from random_regions distribution.

Type:: Callable[[], bool]

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__(noise_power_low=(-80.0, -60.0), noise_power_high=(-40.0, -20.0), inflections=[0, 10], random_regions: List | bool = True, **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