bgZdZddlmZdgZdZdZdZdZdZ d Z Gd dZ d S) zEMixin classes for custom array types that don't inherit from ndarray.)umathNDArrayOperatorsMixinc8 |jduS#t$rYdSwxYw)z)True when __array_ufunc__ is set to None.NF)__array_ufunc__AttributeError)objs G/opt/cloudlinux/venv/lib64/python3.11/site-packages/numpy/lib/mixins.py_disables_array_ufuncr s7"d** uus  cFfd}d||_|S)z>Implement a forward binary method with a ufunc, e.g., __add__.cHt|rtS||SNr NotImplementedselfotherufuncs r funcz_binary_method..funcs+  ' ' "! !uT5!!!__{}__format__name__rnamers` r _binary_methodrs6"""""OOD))DM KrcFfd}d||_|S)zAImplement a reflected binary method with a ufunc, e.g., __radd__.cHt|rtS||Sr rrs r rz&_reflected_binary_method..funcs+  ' ' "! !uUD!!!rz__r{}__rrs` r _reflected_binary_methodrs8"""""$$T**DM KrcFfd}d||_|S)zAImplement an in-place binary method with a ufunc, e.g., __iadd__.c"|||fS)N)outrs r rz$_inplace_binary_method..func&suT5tg....rz__i{}__rrs` r _inplace_binary_methodr$$s6/////$$T**DM Krc`t||t||t||fS)zEImplement forward, reflected and inplace binary methods with a ufunc.)rrr$)rrs r _numeric_methodsr&,s3 5$ ' ' $UD 1 1 "5$ / / 11rcFfd}d||_|S)z.Implement a unary special method with a ufunc.c|Sr r#)rrs r rz_unary_method..func5suT{{rrrrs` r _unary_methodr)3s4OOD))DM KrceZdZdZdZeejdZeej dZ eej dZ eej dZeejdZeejdZeejd \ZZZeejd \ZZZeejd \ZZZeej d \Z!Z"Z#eej$d \Z%Z&Z'eej(d\Z)Z*Z+eej,d\Z-Z.Z/eej0dZ1e2ej0dZ3eej4d\Z5Z6Z7eej8d\Z9Z:Z;eej<d\Z=Z>Z?eej@d\ZAZBZCeejDd\ZEZFZGeejHd\ZIZJZKeLejMdZNeLejOdZPeLejQdZReLejSdZTdS)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:: 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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