numpy.ma.dstack#

ma.dstack = <numpy.ma.extras._fromnxfunction_seq object>#

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

This is equivalent to concatenation along the third axis after 2-D arrays of shape (M,N) have been reshaped to (M,N,1) and 1-D arrays of shape (N,) have been reshaped to (1,N,1). Rebuilds arrays divided by dsplit.

This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis). The functions concatenate, stack and block provide more general stacking and concatenation operations.

Parameters:

tupsequence of arrays: The arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape.

Returns:

stackedndarray: The array formed by stacking the given arrays, will be at least 3-D.

See also

concatenate: Join a sequence of arrays along an existing axis.
stack: Join a sequence of arrays along a new axis.
block: Assemble an nd-array from nested lists of blocks.
vstack: Stack arrays in sequence vertically (row wise).
hstack: Stack arrays in sequence horizontally (column wise).
column_stack: Stack 1-D arrays as columns into a 2-D array.
dsplit: Split array along third axis.

Notes

The function is applied to both the _data and the _mask, if any.

Examples

>>> import numpy as np
>>> 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]]])