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� \ Z' Z( Z) e e j* d � \ Z+ Z, Z- e e j. d � \ Z/ Z0 Z1 e e j2 d � Z3 e4 e j2 d � Z5 e e j6 d � \ Z7 Z8 Z9 e e j: d � \ Z; Z< Z= e e j> d � \ Z? Z@ ZA e e jB d � \ ZC ZD ZE e e jF d � \ ZG ZH ZI e e jJ d � \ ZK ZL ZM eN e jO d � ZP eN e jQ d � ZR eN e jS d � ZT eN e jU d � ZV d S) �NDArrayOperatorsMixinaw
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.
This class does not yet implement the special operators corresponding
to ``matmul`` (``@``), because ``np.matmul`` is not yet a NumPy ufunc.
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 :ref:`A Mechanism for Overriding Ufuncs
<neps.ufunc-overrides>`.
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.
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