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:class ArrayLike(np.lib.mixins.NDArrayOperatorsMixin): def __init__(self, value): self.value = np.asarray(value) # One might also consider adding the builtin 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 welldefined casting hierarchy.New in version 1.13.