dstack(*args, **kwargs) = <numpy.ma.extras._fromnxfunction_seq object>¶
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
stackand 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 : ndarray
The array formed by stacking the given arrays, will be at least 3-D.
- 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.
The function is applied to both the _data and the _mask, if any.
>>> 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]]])