"""Return (x1 == x2) element-wise."""
from __future__ import annotations
from typing import Any, Optional
import numpy
import numpy.typing
import numpoly
from ..baseclass import PolyLike
from ..dispatch import implements
[docs]@implements(numpy.equal)
def equal(
x1: PolyLike,
x2: PolyLike,
out: Optional[numpy.ndarray] = None,
where: numpy.typing.ArrayLike = True,
**kwargs: Any,
) -> numpy.ndarray:
"""
Return (x1 == x2) element-wise.
Args:
x1, x2:
Input arrays. 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:
Output array, element-wise comparison of `x1` and `x2`. Typically of
type bool, unless ``dtype=object`` is passed. This is a scalar if both
`x1` and `x2` are scalars.
Example:
>>> q0, q1, q2 = q0q1q2 = numpoly.variable(3)
>>> numpoly.equal(q0q1q2, q0)
array([ True, False, False])
>>> numpoly.equal(q0q1q2, [q1, q1, q2])
array([False, True, True])
>>> numpoly.equal(q0, q1)
False
"""
x1, x2 = numpoly.align_polynomials(x1, x2)
if out is None:
out = numpy.ones(x1.shape, dtype=bool)
if not out.shape:
return equal(x1.ravel(), x2.ravel(), out=out.ravel()).item()
for coeff1, coeff2 in zip(x1.coefficients, x2.coefficients):
out &= numpy.equal(coeff1, coeff2, where=numpy.asarray(where), **kwargs)
return out