DŽgRdZddlmZdgZdZdZdZdZdZ d Z Gd dZ y ) zG Mixin classes for custom array types that don't inherit from ndarray. )umathNDArrayOperatorsMixinc> |jduS#t$rYywxYw)z)True when __array_ufunc__ is set to None.NF)__array_ufunc__AttributeError)objs X/home/mcse/projects/flask_80/flask-venv/lib/python3.12/site-packages/numpy/lib/mixins.py_disables_array_ufuncr s*""d** s  c>fd}dj||_|S)z>Implement a forward binary method with a ufunc, e.g., __add__.c8t|rtS||SNr NotImplementedselfotherufuncs r funcz_binary_method..funcs  '! !T5!!__{}__format__name__rnamers` r _binary_methodrs"OOD)DM Krc>fd}dj||_|S)zAImplement a reflected binary method with a ufunc, e.g., __radd__.c8t|rtS||Sr rrs r rz&_reflected_binary_method..funcs  '! !UD!!rz__r{}__rrs` r _reflected_binary_methodrs!"$$T*DM Krc>fd}dj||_|S)zAImplement an in-place binary method with a ufunc, e.g., __iadd__.c|||fS)N)outrs r rz$_inplace_binary_method..func(sT5tg..rz__i{}__rrs` r _inplace_binary_methodr$&s/$$T*DM KrcHt||t||t||fS)zEImplement forward, reflected and inplace binary methods with a ufunc.)rrr$)rrs r _numeric_methodsr&.s* 5$ ' $UD 1 "5$ / 11rc>fd}dj||_|S)z.Implement a unary special method with a ufunc.c|Sr r#)rrs r rz_unary_method..func7s T{rrrrs` r _unary_methodr)5sOOD)DM Krc2eZdZdZdZeejdZeejdZ eejdZ eejdZeejdZeej"dZeej(d \ZZZeej0d \ZZZeej8d \ZZZeej@d \Z!Z"Z#eejHd \Z%Z&Z'eejPd\Z)Z*Z+eejXd\Z-Z.Z/eej`dZ1e2ej`dZ3eejhd\Z5Z6Z7eejpd\Z9Z:Z;eejxd\Z=Z>Z?eejd\ZAZBZCeejd\ZEZFZGeejd\ZIZJZKeLejdZNeLejdZPeLejdZReLejdZTy)ra<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 `_. 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: >>> import numbers >>> 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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