Mixin defining all operator special methods using __array_ufunc__.
This class implements the special methods for almost all of Python’s builtin operators defined in the operator module, including comparisons (==, >, etc.) and arithmetic (+, *, -, etc.), by deferring to the __array_ufunc__ method, which subclasses must implement.
It is useful for writing classes that do not inherit from numpy.ndarray, but that should support arithmetic and numpy universal functions like arrays as described in NEP 13 — A mechanism for overriding Ufuncs.
As an trivial example, consider this implementation of an ArrayLike class that simply wraps a NumPy array and ensures that the result of any arithmetic operation is also an ArrayLike object:
>>> import numbers
>>> class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin):
... def __init__(self, value):
... self.value = np.asarray(value)
...
... # One might also consider adding the built-in list type to this
... # list, to support operations like np.add(array_like, list)
... _HANDLED_TYPES = (np.ndarray, numbers.Number)
...
... def __array_ufunc__(self, ufunc, method, *inputs, **kwargs):
... out = kwargs.get('out', ())
... for x in inputs + out:
... # Only support operations with instances of
... # _HANDLED_TYPES. Use ArrayLike instead of type(self)
... # for isinstance to allow subclasses that don't
... # override __array_ufunc__ to handle ArrayLike objects.
... if not isinstance(
... x, self._HANDLED_TYPES + (ArrayLike,)
... ):
... return NotImplemented
...
... # Defer to the implementation of the ufunc
... # on unwrapped values.
... inputs = tuple(x.value if isinstance(x, ArrayLike) else x
... for x in inputs)
... if out:
... kwargs['out'] = tuple(
... x.value if isinstance(x, ArrayLike) else x
... for x in out)
... result = getattr(ufunc, method)(*inputs, **kwargs)
...
... if type(result) is tuple:
... # multiple return values
... return tuple(type(self)(x) for x in result)
... elif method == 'at':
... # no return value
... return None
... else:
... # one return value
... return type(self)(result)
...
... def __repr__(self):
... return '%s(%r)' % (type(self).__name__, self.value)
In interactions between ArrayLike objects and numbers or numpy arrays, the result is always another ArrayLike:
>>> x = ArrayLike([1, 2, 3]) >>> x - 1 ArrayLike(array([0, 1, 2])) >>> 1 - x ArrayLike(array([ 0, -1, -2])) >>> np.arange(3) - x ArrayLike(array([-1, -1, -1])) >>> x - np.arange(3) ArrayLike(array([1, 1, 1]))
Note that unlike numpy.ndarray, ArrayLike does not allow operations with arbitrary, unrecognized types. This ensures that interactions with ArrayLike preserve a well-defined casting hierarchy.
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