stack

Documentation

treetensor.numpy.stack(arrays, *args, **kwargs)[source]

Numpy Version Related

This documentation is based on numpy.stack in numpy v1.24.4. Its arguments’ arrangements depend on the version of numpy you installed.

If some arguments listed here are not working properly, please check your numpy’s version with the following command and find its documentation.

1
python -c 'import numpy as np;print(np.__version__)'

The arguments and keyword arguments supported in numpy v1.24.4 is listed below.

Description From Numpy v1.24

Join a sequence of arrays along a new axis.

The axis parameter specifies the index of the new axis in the dimensions of the result. For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.

New in version 1.10.0.

Parameters

arrays : sequence of array_like

Each array must have the same shape.

axis : int, optional

The axis in the result array along which the input arrays are stacked.

out : ndarray, optional

If provided, the destination to place the result. The shape must be correct, matching that of what stack would have returned if no out argument were specified.

dtype : str or dtype

If provided, the destination array will have this dtype. Cannot be provided together with out.

New in version 1.24.

casting : {‘no’, ‘equiv’, ‘safe’, ‘same_kind’, ‘unsafe’}, optional

Controls what kind of data casting may occur. Defaults to ‘same_kind’.

New in version 1.24.

Returns

stacked : ndarray

The stacked array has one more dimension than the input arrays.

See Also

concatenate : Join a sequence of arrays along an existing axis. block : Assemble an nd-array from nested lists of blocks. split : Split array into a list of multiple sub-arrays of equal size.

Examples

>>> arrays = [np.random.randn(3, 4) for _ in range(10)]
>>> np.stack(arrays, axis=0).shape
(10, 3, 4)

>>> np.stack(arrays, axis=1).shape
(3, 10, 4)

>>> np.stack(arrays, axis=2).shape
(3, 4, 10)

>>> a = np.array([1, 2, 3])
>>> b = np.array([4, 5, 6])
>>> np.stack((a, b))
array([[1, 2, 3],
       [4, 5, 6]])

>>> np.stack((a, b), axis=-1)
array([[1, 4],
       [2, 5],
       [3, 6]])