Sin

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

Bases: ElementwiseFeature

Apply the sine function elementwise.

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

Parameters

feature: Feature | None, optional

The input feature to which the sine 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 Sin

Use with NumPy directly:

>>> import numpy as np
>>>
>>> result = Sin()(np.array([0, np.pi / 2, np.pi]))
>>> result
array([0.0000000e+00, 1.0000000e+00, 1.2246468e-16])

Use with PyTorch directly:

>>> import torch
>>>
>>> result = Sin()(torch.tensor([0, torch.pi / 2, torch.pi]))
>>> result
tensor([ 0.0000e+00,  1.0000e+00, -8.7423e-08])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([0, np.pi / 2, np.pi]))
>>> pipeline = value >> Sin()
>>> result = pipeline()
>>> result
array([0.0000000e+00, 1.0000000e+00, 1.2246468e-16])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor([0, torch.pi / 2, torch.pi]))
>>> pipeline = value >> Sin()
>>> result = pipeline()
>>> result
tensor([ 0.0000e+00,  1.0000e+00, -8.7423e-08])

These are equivalent to:

>>> pipeline = Sin(value)