nums.numpy.dstack

nums.numpy.dstack(tup)[source]

Stack arrays in sequence depth wise (along third axis).

This docstring was copied from numpy.dstack.

Some inconsistencies with the NumS version may exist.

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.

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.
Returns: stacked – The array formed by stacking the given arrays, will be at least 3-D.
Return type: BlockArray

See also

concatenate: Join a sequence of arrays along an existing axis.
stack: Join a sequence of arrays along a new axis.
block: Assemble an nd-array from nested lists of blocks.
vstack: Stack arrays in sequence vertically (row wise).
hstack: Stack arrays in sequence horizontally (column wise).
column_stack: Stack 1-D arrays as columns into a 2-D array.
dsplit: Split array along third axis.

Examples

The doctests shown below are copied from NumPy. They won’t show the correct result until you operate get().

>>> a = nps.array((1,2,3))  
>>> b = nps.array((2,3,4))  
>>> nps.dstack((a,b)).get()  
array([[[1, 2],
        [2, 3],
        [3, 4]]])

>>> a = nps.array([[1],[2],[3]])  
>>> b = nps.array([[2],[3],[4]])  
>>> nps.dstack((a,b)).get()  
array([[[1, 2]],
       [[2, 3]],
       [[3, 4]]])

nums.numpy.vstack

nums.numpy.row_stack