Conj

class deeptrack.elementwise.Conj(feature: Feature | None = None, **kwargs: Any)

Bases: ElementwiseFeature

Apply the complex conjugate function elementwise.

This feature applies xp.conj to each element in a NumPy array or a PyTorch tensor. It supports both direct input and pipeline composition.

For real-valued inputs, the result is unchanged. For complex-valued inputs, it returns the complex conjugate (i.e., a + bj → a - bj).

Parameters

feature: Feature | None, optional

The input feature to which the conjugate function will be applied. If None, the function is applied directly to the input array or tensor.

Examples

>>> import deeptrack as dt
>>> from deeptrack.elementwise import Conj

Use with NumPy directly:

>>> import numpy as np
>>> result = Conj()(np.array([1+2j, 3+0j, -4.5]))
>>> result
array([ 1.-2.j,  3.-0.j, -4.5+0.j])

Use with PyTorch directly:

>>> import torch
>>> result = Conj()(torch.tensor([1+2j, 3+0j, -4.5+0j]))
>>> result
tensor([ 1.-2.j,  3.-0.j, -4.5+0.j])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([1+2j, 3+0j, -4.5]))
>>> pipeline = value >> Conj()
>>> result = pipeline()
>>> result
array([ 1.-2.j,  3.-0.j, -4.5+0.j])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor([1+2j, 3+0j, -4.5+0j]))
>>> pipeline = value >> Conj()
>>> result = pipeline()
>>> result
tensor([ 1.-2.j,  3.-0.j, -4.5+0.j])

These are equivalent to:

>>> pipeline = Conj(value)