Sinh

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

Bases: ElementwiseFeature

Apply the hyperbolic sine function elementwise.

This feature applies xp.sinh 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 hyperbolic 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 Sinh

Use with NumPy directly:

>>> import numpy as np
>>>
>>> result = Sinh()(np.array([-1.0, 0.0, 1.0]))
>>> result
array([-1.17520119,  0.        ,  1.17520119])

Use with PyTorch directly:

>>> import torch
>>>
>>> result = Sinh()(torch.tensor([-1.0, 0.0, 1.0]))
>>> result
tensor([-1.1752,  0.0000,  1.1752])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([-1.0, 0.0, 1.0]))
>>> pipeline = value >> Sinh()
>>> result = pipeline()
>>> result
array([-1.17520119,  0.        ,  1.17520119])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor([-1.0, 0.0, 1.0]))
>>> pipeline = value >> Sinh()
>>> result = pipeline()
>>> result
tensor([-1.1752,  0.0000,  1.1752])

These are equivalent to:

>>> pipeline = Sinh(value)