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G dd� d�Z dS )zEMixin classes for custom array types that don't inherit from ndarray.� )�umath�NDArrayOperatorsMixinc C s" z| j du W S ty Y dS w )z)True when __array_ufunc__ is set to None.NF)�__array_ufunc__�AttributeError)�obj� r �2/usr/lib/python3/dist-packages/numpy/lib/mixins.py�_disables_array_ufunc s
�r c � � fdd�}d� |�|_|S )z>Implement a forward binary method with a ufunc, e.g., __add__.c s t |�rtS � | |�S �N�r �NotImplemented��self�other��ufuncr r �func �
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)zAImplement a reflected binary method with a ufunc, e.g., __radd__.c s t |�rtS � || �S r r r r r r r r z&_reflected_binary_method.<locals>.funcz__r{}__r r r r r �_reflected_binary_method r r c r
)zAImplement an in-place binary method with a ufunc, e.g., __iadd__.c s � | || fd�S )N)�outr r r r r r & s z$_inplace_binary_method.<locals>.funcz__i{}__r r r r r �_inplace_binary_method$ � 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 r
)z.Implement a unary special method with a ufunc.c s � | �S r r )r r r r r 5 s z_unary_method.<locals>.funcr r r r r 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
<https://numpy.org/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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