Python - 如何包装np.ndarray类？

Question

I'm trying to add a functionality to the np.ndarray class.

I was hoping it would be as simple as the following:

class myArray(np.ndarray):

    def __init__(self, *args, **kwargs):
        self = np.array(*args, **kwargs)
        # super(ImArray, self).__init__(*args, **kwargs) <-- my first attempt didn't work
        if self.ndim == 4:
            self.myShape = (self.shape[0]*self.shape[1], self.shape[2]*self.shape[3])
        else:
            self.myShape = self.shape

    def myStuff(self):
        self = self.reshape(self.myShape)

a = np.zeros([2, 2, 2, 2])
myArray(a)
# TypeError: only length-1 arrays can be converted to Python scalars
a = range(10)
myArray(a)
# AttributeError: 'numpy.ndarray' object has no attribute 'myShape'

Please let me know if you want more information or something similar.

EDIT:

To give a bit more information abuot why I want to do this, as someone suggested simply making a function might be more appropriate.

I want to add the following:

A.newFun(B)

which would be the same as:

def newFun(A,B):
    oldShapeA = A.shape
    A = np.matrix( A.reshape([A.shape[0]*A.shape[1], A.shape[2]*A.shape[3]]) )
    oldShapeB = B.shape
    B = np.matrix( B.reshape([1, -1]) )
    out = A*B
    out = np.reshape(out.A, oldShapeA[:2]+oldShapeB)
    return out

I have left out a lot of checks such as that the dimenions are correct etc but hopefully you get the point

Answer 1

Subclassing ndarray can be done, but has some subtleties. These are explained at length in the NumPy manual .

I don't really follow what you're trying to do in the subclass, but it's worthing considering the question whether subclassing is the right approach to the problem.

Answer 2

Sub-classing np.ndarray requires a bit of finesse. The gory details are here: http://docs.scipy.org/doc/numpy/user/basics.subclassing.html

Specifically, I think this does what you wanted:

class myArray(np.ndarray):
    def __new__(cls, *args, **kwargs):
        this = np.array(*args, **kwargs)
        this = np.asarray(this).view(cls)
        return this

    def __array_finalize__(self, obj):
        if obj.ndim == 4:
            self.myShape = (self.shape[0]*self.shape[1], self.shape[2]*self.shape[3])
        else:
            self.myShape = self.shape

    def myStuff(self):
        self = self.reshape(self.myShape)

To see this in a (more elaborate) real-life use-case, take a look here: https://github.com/arokem/nitime/blob/master/nitime/timeseries.py#L101