numpy.lib.mixins.NDArrayOperatorsMixin#
- class numpy.lib.mixins.NDArrayOperatorsMixin[source]#
混合类,使用 __array_ufunc__ 定义所有运算符特殊方法。
此类实现了
operator
模块中定义的几乎所有 Python 内置运算符的特殊方法,包括比较 (==
,>
等) 和算术运算 (+
,*
,-
等),通过委托给__array_ufunc__
方法,子类必须实现此方法。它对于编写不继承自
numpy.ndarray
但应该像数组一样支持算术运算和 NumPy 通用函数的类很有用,如 覆盖通用函数的机制 中所述。作为一个简单的例子,考虑这个
ArrayLike
类的实现,它只是包装了一个 NumPy 数组,并确保任何算术运算的结果也是一个ArrayLike
对象>>> 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)
在
ArrayLike
对象与数字或 NumPy 数组之间的交互中,结果始终是另一个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]))
请注意,与
numpy.ndarray
不同,ArrayLike
不允许与任意、无法识别的类型进行操作。这确保了与 ArrayLike 的交互保留了定义明确的转换层次结构。版本 1.13 中的新内容。