Ceil

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

Bases: ElementwiseFeature

Apply the ceiling function elementwise.

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

The ceiling function returns the smallest integer greater than or equal to each element of the input.

Parameters

feature: Feature | None, optional

The input feature to which the ceil 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 Ceil

Use with NumPy directly:

>>> import numpy as np
>>>
>>> result = Ceil()(np.array([-1.7, -0.5, 0.0, 0.5, 1.7]))
>>> result
array([-1., -0.,  0.,  1.,  2.])

Use with PyTorch directly:

>>> import torch
>>>
>>> result = Ceil()(torch.tensor([-1.7, -0.5, 0.0, 0.5, 1.7]))
>>> result
tensor([-1., -0.,  0.,  1.,  2.])

Use in a pipeline with a NumPy value:

>>> value = dt.Value(value=np.array([-1.7, -0.5, 0.0, 0.5, 1.7]))
>>> pipeline = value >> Ceil()
>>> result = pipeline()
>>> result
array([-1., -0.,  0.,  1.,  2.])

Use in a pipeline with a PyTorch value:

>>> value = dt.Value(value=torch.tensor([-1.7, -0.5, 0.0, 0.5, 1.7]))
>>> pipeline = value >> Ceil()
>>> result = pipeline()
>>> result
tensor([-1., -0.,  0.,  1.,  2.])

These are equivalent to:

>>> pipeline = Ceil(value)