dstack(*args, **kwargs)
This is equivalent to concatenation along the third axis after 2-D arrays of shape :None:None:`(M,N)` have been reshaped to :None:None:`(M,N,1)` and 1-D arrays of shape :None:None:`(N,)` have been reshaped to :None:None:`(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.
The function is applied to both the _data and the _mask, if any.
The arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape.
The array formed by stacking the given arrays, will be at least 3-D.
Stack arrays in sequence depth wise (along third axis).
block
Assemble an nd-array from nested lists of blocks.
column_stack
Stack 1-D arrays as columns into a 2-D array.
concatenate
Join a sequence of arrays along an existing axis.
dsplit
Split array along third axis.
hstack
Stack arrays in sequence horizontally (column wise).
stack
Join a sequence of arrays along a new axis.
vstack
Stack arrays in sequence vertically (row wise).
>>> 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]])See :
... b = np.array([[2],[3],[4]])
... np.dstack((a,b)) array([[[1, 2]], [[2, 3]], [[3, 4]]])
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