numpoly.dstack

numpoly.dstack(tup: Sequence[numpoly.typing.PolyLike]) → numpoly.baseclass.ndpoly

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.

Args:

tup:

The arrays must have the same shape along all but the third axis. 1-D or 2-D arrays must have the same shape.

Return:

The array formed by stacking the given arrays, will be at least 3-D.

Example:

>>> poly1 = numpoly.variable(3)
>>> const1 = numpoly.polynomial([1, 2, 3])
>>> numpoly.dstack([poly1, const1])
polynomial([[[q0, 1],
             [q1, 2],
             [q2, 3]]])
>>> const2 = numpoly.polynomial([[1], [2], [3]])
>>> poly2 = poly1.reshape(3, 1)
>>> numpoly.dstack([const2, poly2])
polynomial([[[1, q0]],

            [[2, q1]],

            [[3, q2]]])