为什么从numpy数组继承的类的方法返回不同的东西? [英] Why do methods of classes that inherit from numpy arrays return different things?

问题描述

让我们看一下numpy数组的一些非常简单的行为:

Let's look at some very simple behaviour of numpy arrays:

import numpy as np
arr = np.array([1,2,3,4,5])
max_value = arr.max() # returns 5

现在，我创建一个继承自np.ndarray的类:

Now I create a class which inherits from np.ndarray:

class CustomArray(np.ndarray):
    def __new__(cls, *args, **kwargs):
        obj = np.array(*args, **kwargs).view(cls)
        return obj
new_arr = CustomArray([1,2,3,4,5])

我实际上并没有改变任何行为-我只是在名义上改变了对象是什么类.

I haven't actually changed any behaviour - I just made a nominal change in what class the object is.

但是:

new_max_value = new_arr.max() # returns CustomArray(5)

返回值是一个CustomArray实例?为什么? arr.max()没有返回np.ndarray实例，只是一个简单的numpy整数.

The return value is an CustomArray instance? Why? arr.max() didn't return a np.ndarray instance, just a plain numpy integer.

同样，为什么arr == new_arr和new_arr == arr都返回CustomArray实例?前一个调用arr.__eq__(new_arr)不应返回一个np.ndarray实例吗?

Similarly, why do both arr == new_arr and new_arr == arr return CustomArray instances? Shouldn't the former call arr.__eq__(new_arr), which should return a np.ndarray instance?

请注意，为了方便构造函数，我重写了__new__方法.例如. np.array([1,2,3,4,5])的等价物可以是CustomArray([1,2,3,4,5])，而如果我明确地继承自np.ndarray，则必须执行类似new_arr = CustomArray((5,))的操作； new_arr[:] = np.array([1,2,3,4,5]).

Note that I override the __new__ method for the sake of easy constructors. E.g. the equivalent of np.array([1,2,3,4,5]) can just be CustomArray([1,2,3,4,5]), whereas if I plainly inherit from np.ndarray I'd have to do something like new_arr = CustomArray((5,)); new_arr[:] = np.array([1,2,3,4,5]).

推荐答案

在numpy文档之后: array_wrap 在numpy ufuncs和其他numpy函数的末尾被调用，以允许子类设置返回值的类型以及更新属性和元数据.

Following the numpy docs: array_wrap gets called at the end of numpy ufuncs and other numpy functions, to allow a subclass to set the type of the return value and update attributes and metadata.

class CustomArray(np.ndarray):
    def __new__(cls, a, dtype=None, order=None):
        obj = np.asarray(a, dtype, order).view(cls)
        return obj

    def __array_wrap__(self, out_arr, context=None):
        return np.ndarray.__array_wrap__(self, out_arr, context)

c = CustomArray([1,2,3,4])
c.max() # returns 4

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