numpy.dstack(tup)
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
.
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
,
and
provide more general stacking and concatenation operations.
Parameters:tupsequence of arraysThe arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape.
Returns:stackedndarrayThe array formed by stacking the given arrays, will be at least 3-D.
See also
Join a sequence of arrays along an existing axis.
Join a sequence of arrays along a new axis.
Assemble an nd-array from nested lists of blocks.
Stack arrays in sequence vertically (row wise).
Stack arrays in sequence horizontally (column wise).
Stack 1-D arrays as columns into a 2-D array.
Split array along third axis.
Examples
>>> importnumpyasnp>>> a=np.array((1,2,3))>>> b=np.array((4,5,6))>>> np.dstack((a,b))array([[[1, 4], [2, 5], [3, 6]]])>>> a=np.array([[1],[2],[3]])>>> b=np.array([[4],[5],[6]])>>> np.dstack((a,b))array([[[1, 4]], [[2, 5]], [[3, 6]]])