torchsig.transforms.transforms.IQImbalance¶

class torchsig.transforms.transforms.IQImbalance(amplitude_imbalance=(-1.0, 1.0), phase_imbalance=(-0.03490658503988659, 0.03490658503988659), dc_offset_db=(0, 3), dc_offset_rads=(0, 6.283185307179586), **kwargs)[source]¶

Bases: SignalTransform

Apply a set of I/Q imbalance effects to a signal: amplitude, phase, and DC offset.

amplitude_imbalance¶: Range bounds of IQ amplitude imbalance (dB).

amplitude_imbalance_distribution¶: Random draw from amplitude imbalance distribution.

phase_imbalance¶: Range bounds of IQ phase imbalance (radians).

phase_imbalance¶: Random draw from phase imbalance distribution.

dc_offset_db¶: Range bounds for DC offset in relative power.

dc_offset_db_distribution¶: Random draw from dc_offset_db distribution.

dc_offset_phase_rads¶: Range bounds for phase of DC offset

dc_offset_phase_rads_distribution¶: Random draw from dc_offset_phase_rads 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__(amplitude_imbalance=(-1.0, 1.0), phase_imbalance=(-0.03490658503988659, 0.03490658503988659), dc_offset_db=(0, 3), dc_offset_rads=(0, 6.283185307179586), **kwargs)[source]¶

Initialize the IQImbalance transform.

Parameters:

amplitude_imbalance – Range bounds of IQ amplitude imbalance (dB). Defaults to (-1.0, 1.0).
phase_imbalance – Range bounds of IQ phase imbalance (radians). Defaults to (-2.0 * np.pi / 180.0, 2.0 * np.pi / 180.0).
dc_offset_db – Range bounds for DC offset in relative power. Defaults to (0, 3).
dc_offset_rads – Range bounds for phase of DC offset. Defaults to (0, 2 * np.pi).
**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.