numpy.ma.dstack¶

numpy.ma.dstack(tup) = <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:

Parameters:	tup : sequence 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:	stacked : ndarray The array formed by stacking the given arrays, will be at least 3-D.

tup : sequence 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:

stacked : ndarray

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

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

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]]])