Log

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

Bases: ElementwiseFeature

Apply the natural logarithm function elementwise.

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

The input must be strictly positive. Passing zero or negative values will return -inf or NaN, and may raise warnings or errors depending on the backend.

Parameters

feature: Feature | None, optional

The input feature to which the natural logarithm 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 Log

Use with NumPy directly:

>>> import numpy as np
>>> result = Log()(np.array([1.0, np.e, 10.0]))
>>> result
array([0.        , 1.        , 2.30258509])

Use with PyTorch directly:

>>> import torch
>>> result = Log()(torch.tensor([1.0, torch.exp(torch.tensor(1.0)), 10.0]))
>>> result
tensor([0.0000, 1.0000, 2.3026])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([1.0, np.e, 10.0]))
>>> pipeline = value >> Log()
>>> result = pipeline()
>>> result
array([0.        , 1.        , 2.30258509])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor(
...     [1.0, torch.exp(torch.tensor(1.0)), 10.0])
... )
>>> pipeline = value >> Log()
>>> result = pipeline()
>>> result
tensor([0.0000, 1.0000, 2.3026])

These are equivalent to:

>>> pipeline = Log(value)