numpy.dstack
- 2019 年 10 月 7 日
- 筆記
原文链接：https://numpy.org/doc/1.17/reference/generated/numpy.dstack.html?highlight=dstack#numpy.dstack
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 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.
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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. |
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Returns: |
stacked : ndarray The array formed by stacking the given arrays, will be at least 3-D. |
See also
Join a sequence of arrays along a new axis.
Stack along first axis.
Stack along second axis.
Join a sequence of arrays along an existing axis.
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]]])
