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r c s � fdd�}dj |�|_|S )z>Implement a forward binary method with a ufunc, e.g., __add__.c s t |�r tS � | |�S )N)r �NotImplemented)�self�other)�ufuncr r �func s z _binary_method.<locals>.funcz__{}__)�format�__name__)r �namer
r )r r �_binary_method s r c s � fdd�}dj |�|_|S )zAImplement a reflected binary method with a ufunc, e.g., __radd__.c s t |�r tS � || �S )N)r r )r
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r )r r �_reflected_binary_method s r c s � fdd�}dj |�|_|S )zAImplement an in-place binary method with a ufunc, e.g., __iadd__.c s � | || fd�S )N)�outr )r
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r )r r �_inplace_binary_method$ s r c C s t | |�t| |�t| |�fS )zEImplement forward, reflected and inplace binary methods with a ufunc.)r r r )r r r r r �_numeric_methods, s r c s � fdd�}dj |�|_|S )z.Implement a unary special method with a ufunc.c s � | �S )Nr )r
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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
<../../neps/nep-0013-ufunc-overrides.html>`_.
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::
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.
.. versionadded:: 1.13
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