ó °żv]c@`s”dZddlmZmZmZddlZddlmZgZ d„Z d„Z d„Z d„Z d „Zd „Zd efd „ƒYZdS( sEMixin classes for custom array types that don't inherit from ndarray.i(tdivisiontabsolute_importtprint_functionN(tumathcC`s*y|jdkSWntk r%tSXdS(s)True when __array_ufunc__ is set to None.N(t__array_ufunc__tNonetAttributeErrortFalse(tobj((s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyt_disables_array_ufunc s c`s%‡fd†}dj|ƒ|_|S(s>Implement a forward binary method with a ufunc, e.g., __add__.c`st|ƒrtSˆ||ƒS(N(R tNotImplemented(tselftother(tufunc(s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pytfuncs s__{}__(tformatt__name__(R tnameR((R s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyt_binary_methodsc`s%‡fd†}dj|ƒ|_|S(sAImplement a reflected binary method with a ufunc, e.g., __radd__.c`st|ƒrtSˆ||ƒS(N(R R (R R (R (s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyR s s__r{}__(RR(R RR((R s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyt_reflected_binary_methodsc`s%‡fd†}dj|ƒ|_|S(sAImplement an in-place binary method with a ufunc, e.g., __iadd__.c`sˆ||d|fƒS(Ntout((R R (R (s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyR*ss__i{}__(RR(R RR((R s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyt_inplace_binary_method(scC`s(t||ƒt||ƒt||ƒfS(sEImplement forward, reflected and inplace binary methods with a ufunc.(RRR(R R((s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyt_numeric_methods0s  c`s%‡fd†}dj|ƒ|_|S(s.Implement a unary special method with a ufunc.c`s ˆ|ƒS(N((R (R (s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyR9ss__{}__(RR(R RR((R s`/home/ec2-user/environment/lambda-staging/venv/lib64/python2.7/dist-packages/numpy/lib/mixins.pyt _unary_method7stNDArrayOperatorsMixincB`sueZdZeejdƒZeejdƒZeej dƒZ eej dƒZ eej dƒZeejdƒZeejdƒ\ZZZeejdƒ\ZZZeejd ƒ\ZZZeejd ƒ\ZZ Z!e"j#j$d kreej%d ƒ\Z&Z'Z(neej)d ƒ\Z*Z+Z,eej-dƒ\Z.Z/Z0eej1dƒ\Z2Z3Z4eej5dƒZ6e7ej5dƒZ8eej9dƒ\Z:Z;Z<eej=dƒ\Z>Z?Z@eejAdƒ\ZBZCZDeejEdƒ\ZFZGZHeejIdƒ\ZJZKZLeejMdƒ\ZNZOZPeQejRdƒZSeQejTdƒZUeQejVdƒZWeQejXdƒZYRS(s 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. 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