"""Subtract arguments, element-wise."""
from __future__ import annotations
from typing import Any, Optional
import numpy
import numpy.typing
from ..baseclass import ndpoly, PolyLike
from ..dispatch import implements, simple_dispatch
[docs]@implements(numpy.subtract)
def subtract(
x1: PolyLike,
x2: PolyLike,
out: Optional[ndpoly] = None,
where: numpy.typing.ArrayLike = True,
**kwargs: Any,
) -> ndpoly:
"""
Subtract arguments, element-wise.
Args:
x1, x2:
The arrays to be subtracted from each other. If
``x1.shape != x2.shape``, they must be broadcastable to a common
shape (which becomes the shape of the output).
out:
A location into which the result is stored. If provided, it must
have a shape that the inputs broadcast to. If not provided or
`None`, a freshly-allocated array is returned. A tuple (possible
only as a keyword argument) must have length equal to the number of
outputs.
where:
This condition is broadcast over the input. At locations where the
condition is True, the `out` array will be set to the ufunc result.
Elsewhere, the `out` array will retain its original value. Note
that if an uninitialized `out` array is created via the default
``out=None``, locations within it where the condition is False will
remain uninitialized.
kwargs:
Keyword args passed to numpy.ufunc.
Return:
The difference of `x1` and `x2`, element-wise.
This is a scalar if both `x1` and `x2` are scalars.
Example:
>>> q0, q1 = numpoly.variable(2)
>>> numpoly.subtract(q0, 4)
polynomial(q0-4)
>>> poly1 = q0**numpy.arange(9).reshape((3, 3))
>>> poly2 = q1**numpy.arange(3)
>>> numpoly.subtract(poly1, poly2)
polynomial([[0, -q1+q0, -q1**2+q0**2],
[q0**3-1, q0**4-q1, q0**5-q1**2],
[q0**6-1, q0**7-q1, q0**8-q1**2]])
"""
return simple_dispatch(
numpy_func=numpy.subtract,
inputs=(x1, x2),
out=None if out is None else (out,),
where=where,
**kwargs,
)