numpy.ma.dstack

numpy.ma.dstack(*args, **kwargs) = <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

>>> 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]]])