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 arraysof 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 bydsplit.

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

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.

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]]])