在ndarray子类中跳过numpy __new__（或者可能覆盖/定义C或cython中的类）

本文介绍了在ndarray子类中跳过numpy __new__（或者可能覆盖/定义C或cython中的类）的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！ 问题描述 终极目标： MyClass 调用 np.ndarray .__ new __（）。 我已经实现了所有的 numpy.ndarray 并且我想设置它，以便它将通过 isinstance 检查 ndarray ，但我不要实际从 ndarray 调用 __ new __ $ c>。 最初，我在想这样的： import numpy as np class BlockingClass（np.ndarray）： def __new __（cls，* args，** kwargs）： return object .__ new __（cls）$ b $不幸的是，试图实例化 Dummy（）会产生这个错误关于它不安全： TypeError：object .__ new __（Dummy）不安全，使用numpy.ndarray .__ new __ 如果它是一个子类对象的类，它会工作： class BlockingClass2（object）： def __new __（cls，* args，** kwargs）： return object .__ new __（cls） BlockingClass2（）＃没有错误 我确定这是因为 ndarray 是一个C类，所以我想在C类（或者，最好是一个Cython类）重写它，并使用多继承来获得typecheck工作，而不调用 __ new __ 。所以我的类将是： class MyClass（BlockingClass，np.ndarray）： pass 其中 BlockingClass 将是c定义的函数。我真的更喜欢在Cython这样做，但我不知道如何让它工作。我试过： cdef class BlockingClass： def __new __（cls，* args，** kwargs） ： return object .__ new __（cls） 但这会产生相同的不安全错误以及 __ cinit __ 。 cdef class BlockingClass： def __cinit __（self，* args，** kwargs）：＃do stuff return self b $ b 但是，当 BlockingClass 是使用定义 __ new __ 仍然调用 __ new __ 方法。如果我不能在Cython中做到这一点，那么我需要定义一个通过多重继承跳过 ndarray ' __ new __ ？也许我可以导入一个函数来实例化类，而不需要上升mro？解决方案我不知道是否可能假冒 isinstance 和 issubclass ，但是在下面的方法中你可以定义你的类传递给 np.ndarray .__ new __ 只有您想要的参数： import numpy as np class BlockingClass（np.ndarray）： def __new __（cls，* args，** kwargs）： ndarray_kw = ['shape'，'dtype'，'buffer''offset'，'strides '，'order'] to_ndarray = {} to_myclass = {} for k，v in kwargs.items（）：如果k不在ndarray_kw： to_myclass [k] = v else： to_ndarray [k] = v new = np.ndarray .__ new __（cls，* args，** to_ndarray） for k ，v in to_myclass.items（）： setattr（new，k，v） return new def __init __（self，* args，** kwargs）： self.test = 1 self.args = args self.kwargs = kwargs Ultimate goal: have isinstance(MyClass(), np.ndarray) and issubclass(MyClass, np.ndarray) both return True without MyClass calling np.ndarray.__new__().Let's say I've implemented all the methods of numpy.ndarray and I want to set it up so that it will pass isinstance checks for ndarray, but I don't want it to actually call __new__ from ndarray.Initially, I was thinking of something like this:import numpy as npclass BlockingClass(np.ndarray): def __new__(cls, *args, **kwargs): return object.__new__(cls)Unfortunately, trying to instantiate Dummy() yields this error about it not being safe:TypeError: object.__new__(Dummy) is not safe, use numpy.ndarray.__new__()This works if it's a class that subclasses object:class BlockingClass2(object): def __new__(cls, *args, **kwargs): return object.__new__(cls)BlockingClass2() # No errorI'm pretty sure that it's because ndarray is a C class, so I was thinking of overriding it in a c-class (or, preferably, a Cython class) and using multiple inheritance to get the typecheck to work without calling __new__. So my class would be:class MyClass(BlockingClass, np.ndarray): passwhere BlockingClass would be the c-defined function. I'd really prefer to do this in Cython instead, but I can't figure out how to get it to work. I've tried doing:cdef class BlockingClass: def __new__(cls, *args, **kwargs): return object.__new__(cls)but this generates the same 'unsafe' error as well as with __cinit__.cdef class BlockingClass: def __cinit__(self, *args, **kwargs): # do stuff return selfBut, when BlockingClass is subclassed with multiple inheritance like above with an object that defines __new__, that __new__ method is still called. If I can't do this in Cython, what's the minimal amount of C code I would need to define a baseclass that, through multiple inheritance, would skip ndarray's __new__? Maybe I can cimport a function to instantiate the class without going up the mro? 解决方案 I don't know if it is possible to fake isinstance and issubclass, but in the following approach you can define your class passing to np.ndarray.__new__ only the arguments that you want:import numpy as npclass BlockingClass(np.ndarray): def __new__(cls, *args, **kwargs): ndarray_kw = ['shape', 'dtype', 'buffer' 'offset', 'strides', 'order'] to_ndarray = {} to_myclass = {} for k,v in kwargs.items(): if k not in ndarray_kw: to_myclass[k] = v else: to_ndarray[k] = v new = np.ndarray.__new__(cls, *args, **to_ndarray) for k,v in to_myclass.items(): setattr(new, k, v) return new def __init__(self, *args, **kwargs): self.test = 1 self.args = args self.kwargs = kwargs 这篇关于在ndarray子类中跳过numpy __new__（或者可能覆盖/定义C或cython中的类）的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！