numpy.dstack

numpy.dstack(tup)

Stack arrays in sequence depth wise (along third axis).

Takes a sequence of arrays and stack them along the third axis to make a single array. Rebuilds arrays divided by dsplit. This is a simple way to stack 2D arrays (images) into a single 3D array for processing.

This function continues to be supported for backward compatibility, but you should prefer np.concatenate or np.stack. The np.stack function was added in NumPy 1.10.

Parameters:

tup : sequence of arrays

Arrays to stack. All of them must have the same shape along all but the third axis.

Returns:

stacked : ndarray

The array formed by stacking the given arrays.

See also

stack

Join a sequence of arrays along a new axis.

vstack

Stack along first axis.

hstack

Stack along second axis.

concatenate

Join a sequence of arrays along an existing axis.

dsplit

Split array along third axis.

Notes

Equivalent to np.concatenate(tup, axis=2) if tup contains arrays that are at least 3-dimensional.

Examples

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

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