numpy.dstack
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numpy.dstack(tup)
[source] -
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 bydsplit
.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
andblock
provide more general stacking and concatenation operations.Parameters: -
tup : sequence of arrays
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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
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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.
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vstack
- Stack along first axis.
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hstack
- Stack along second axis.
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concatenate
- Join a sequence of arrays along an existing axis.
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dsplit
- Split array along third axis.
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]]])
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Licensed under the 3-clause BSD License.
https://docs.scipy.org/doc/numpy-1.17.0/reference/generated/numpy.dstack.html