Stack arrays in sequence depth wise (along third axis).
LAX-backend implementation of
dstack(). Original docstring below.
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.
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.
stacked – The array formed by stacking the given arrays, will be at least 3-D.
- Return type
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.
>>> 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([,,]) >>> b = np.array([,,]) >>> np.dstack((a,b)) array([[[1, 2]], [[2, 3]], [[3, 4]]])