numpy.lib.mixins.NDArrayOperatorsMixin#
- class numpy.lib.mixins.NDArrayOperatorsMixin[source]#
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 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 anArrayLike
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 anotherArrayLike
:>>> 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.New in version 1.13.