"""Stack arrays in sequence horizontally (column wise)."""
from __future__ import annotations
from typing import Sequence
import numpy
import numpoly
from ..baseclass import ndpoly, PolyLike
from ..dispatch import implements
[docs]@implements(numpy.hstack)
def hstack(tup: Sequence[PolyLike]) -> ndpoly:
"""
Stack arrays in sequence horizontally (column wise).
This is equivalent to concatenation along the second axis, except for 1-D
arrays where it concatenates along the first axis. Rebuilds arrays divided
by `hsplit`.
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 second axis,
except 1-D arrays which can be any length.
Returns:
The array formed by stacking the given arrays.
Examples:
>>> poly1 = numpoly.variable(3)
>>> const1 = numpoly.polynomial([1, 2, 3])
>>> numpoly.hstack([poly1, const1])
polynomial([q0, q1, q2, 1, 2, 3])
>>> const2 = numpoly.polynomial([[1], [2], [3]])
>>> poly2 = poly1.reshape(3, 1)
>>> numpoly.hstack([const2, poly2])
polynomial([[1, q0],
[2, q1],
[3, q2]])
"""
arrays = numpoly.align_exponents(*tup)
arrays = numpoly.align_dtype(*arrays)
result = numpy.hstack([array.values for array in arrays])
return numpoly.aspolynomial(
result, names=arrays[0].indeterminants)