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"""Mixin classes for custom array types that don't inherit from ndarray."""
from numpy.core import umath as um
__all__ = ['NDArrayOperatorsMixin']
def _disables_array_ufunc(obj):
"""True when __array_ufunc__ is set to None."""
try:
return obj.__array_ufunc__ is None
except AttributeError:
return False
def _binary_method(ufunc, name):
"""Implement a forward binary method with a ufunc, e.g., __add__."""
def func(self, other):
if _disables_array_ufunc(other):
return NotImplemented
return ufunc(self, other)
func.__name__ = '__{}__'.format(name)
return func
def _reflected_binary_method(ufunc, name):
"""Implement a reflected binary method with a ufunc, e.g., __radd__."""
def func(self, other):
if _disables_array_ufunc(other):
return NotImplemented
return ufunc(other, self)
func.__name__ = '__r{}__'.format(name)
return func
def _inplace_binary_method(ufunc, name):
"""Implement an in-place binary method with a ufunc, e.g., __iadd__."""
def func(self, other):
return ufunc(self, other, out=(self,))
func.__name__ = '__i{}__'.format(name)
return func
def _numeric_methods(ufunc, name):
"""Implement forward, reflected and inplace binary methods with a ufunc."""
return (_binary_method(ufunc, name),
_reflected_binary_method(ufunc, name),
_inplace_binary_method(ufunc, name))
def _unary_method(ufunc, name):
"""Implement a unary special method with a ufunc."""
def func(self):
return ufunc(self)
func.__name__ = '__{}__'.format(name)
return func
class NDArrayOperatorsMixin:
"""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
"""
__slots__ = ()
# Like np.ndarray, this mixin class implements "Option 1" from the ufunc
# overrides NEP.
# comparisons don't have reflected and in-place versions
__lt__ = _binary_method(um.less, 'lt')
__le__ = _binary_method(um.less_equal, 'le')
__eq__ = _binary_method(um.equal, 'eq')
__ne__ = _binary_method(um.not_equal, 'ne')
__gt__ = _binary_method(um.greater, 'gt')
__ge__ = _binary_method(um.greater_equal, 'ge')
# numeric methods
__add__, __radd__, __iadd__ = _numeric_methods(um.add, 'add')
__sub__, __rsub__, __isub__ = _numeric_methods(um.subtract, 'sub')
__mul__, __rmul__, __imul__ = _numeric_methods(um.multiply, 'mul')
__matmul__, __rmatmul__, __imatmul__ = _numeric_methods(
um.matmul, 'matmul')
# Python 3 does not use __div__, __rdiv__, or __idiv__
__truediv__, __rtruediv__, __itruediv__ = _numeric_methods(
um.true_divide, 'truediv')
__floordiv__, __rfloordiv__, __ifloordiv__ = _numeric_methods(
um.floor_divide, 'floordiv')
__mod__, __rmod__, __imod__ = _numeric_methods(um.remainder, 'mod')
__divmod__ = _binary_method(um.divmod, 'divmod')
__rdivmod__ = _reflected_binary_method(um.divmod, 'divmod')
# __idivmod__ does not exist
# TODO: handle the optional third argument for __pow__?
__pow__, __rpow__, __ipow__ = _numeric_methods(um.power, 'pow')
__lshift__, __rlshift__, __ilshift__ = _numeric_methods(
um.left_shift, 'lshift')
__rshift__, __rrshift__, __irshift__ = _numeric_methods(
um.right_shift, 'rshift')
__and__, __rand__, __iand__ = _numeric_methods(um.bitwise_and, 'and')
__xor__, __rxor__, __ixor__ = _numeric_methods(um.bitwise_xor, 'xor')
__or__, __ror__, __ior__ = _numeric_methods(um.bitwise_or, 'or')
# unary methods
__neg__ = _unary_method(um.negative, 'neg')
__pos__ = _unary_method(um.positive, 'pos')
__abs__ = _unary_method(um.absolute, 'abs')
__invert__ = _unary_method(um.invert, 'invert')
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