numpy.lib.mixins.NDArrayOperatorsMixin

class numpy.lib.mixins.NDArrayOperatorsMixin

混合定义所有运算符的特殊方法 __array_ufunc__.

这个类实现了在 operator module, including comparisons (==, >, etc.) and arithmetic (+, *, -, etc.), by deferring to the ``_ _数组“ufunc”方法，子类必须实现该方法。

它对于编写不从继承的类很有用 numpy.ndarray, but that should support arithmetic and numpy universal functions like arrays as described in A Mechanism for Overriding Ufuncs .

作为一个简单的例子，考虑 ArrayLike 类，它只包装一个numpy数组并确保任何算术运算的结果也是 ArrayLike 对象：

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)

在相互作用中 ArrayLike 对象和数字或numpy数组，结果总是另一个 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]))

注意，与 numpy.ndarray ， ArrayLike 不允许对任意的、无法识别的类型执行操作。这样可以确保与arraylike的交互保持定义良好的转换层次结构。

1.13 新版功能.

numpy.byte_bounds numpy.lib.NumpyVersion