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편집 파일: mixins.cpython-37.pyc
B ��Fdt � @ st d Z ddlmZmZmZ ddlZddlmZ g Z dd� Z dd� Z d d � Z d d � Z d d� Zdd� ZG dd� de�ZdS )zEMixin classes for custom array types that don't inherit from ndarray.� )�division�absolute_import�print_functionN)�umathc C s$ y | j dkS tk r dS X dS )z)True when __array_ufunc__ is set to None.NF)Z__array_ufunc__�AttributeError)�obj� r �C/opt/alt/python37/lib64/python3.7/site-packages/numpy/lib/mixins.py�_disables_array_ufunc s r c s � fdd�}d� |�|_|S )z>Implement a forward binary method with a ufunc, e.g., __add__.c s t |�r tS � | |�S )N)r �NotImplemented)�self�other)�ufuncr r �func s z _binary_method.<locals>.funcz__{}__)�format�__name__)r �namer r )r r �_binary_method s r c s � fdd�}d� |�|_|S )zAImplement a reflected binary method with a ufunc, e.g., __radd__.c s t |�r tS � || �S )N)r r )r r )r r r r s z&_reflected_binary_method.<locals>.funcz__r{}__)r r )r r r r )r r �_reflected_binary_method s r c s � fdd�}d� |�|_|S )zAImplement an in-place binary method with a ufunc, e.g., __iadd__.c s � | || fd�S )N)�outr )r r )r r r r * s z$_inplace_binary_method.<locals>.funcz__i{}__)r r )r r r r )r r �_inplace_binary_method( s r c C s t | |�t| |�t| |�fS )zEImplement forward, reflected and inplace binary methods with a ufunc.)r r r )r r r r r �_numeric_methods0 s r c s � fdd�}d� |�|_|S )z.Implement a unary special method with a ufunc.c s � | �S )Nr )r )r r r r 9 s z_unary_method.<locals>.funcz__{}__)r r )r r r r )r r � _unary_method7 s r c @ s� e Zd ZdZeejd�Zeejd�Z eej d�Z eej d�Z eejd�Zeejd�Zeejd�\ZZZeejd �\ZZZeejd �\Z Z Z e j j!d k r�eej"d �\Z#Z$Z%eej&d �\Z'Z(Z)eej*d�\Z+Z,Z-eej.d�\Z/Z0Z1eej2d�Z3e4ej2d�Z5eej6d�\Z7Z8Z9eej:d�\Z;Z<Z=eej>d�\Z?Z@ZAeejBd�\ZCZDZEeejFd�\ZGZHZIeejJd�\ZKZLZMeNejOd�ZPeNejQd�ZReNejSd�ZTeNejUd�ZVdS ) �NDArrayOperatorsMixinaw 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. This class does not yet implement the special operators corresponding to ``matmul`` (``@``), because ``np.matmul`` is not yet a NumPy ufunc. 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 :ref:`A Mechanism for Overriding Ufuncs <neps.ufunc-overrides>`. 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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