Make class convertable to ndarray

Question

Other than by subclassing (from list for example) how do I make a python object implicitly convertable to ndarray?

Example:

import numpy
arg=[0,1,2]
numpy.dot(arg,arg) # OK, arg is converted to ndarray

#Looks somewhat array like, albeit without support for slices
class A(object):
    def __init__(self, x=0,y=1,z=2):
        (self.x,self.y,self.z)=(x,y,z)
    def __getitem__(self, idx):
        if idx==0:
            return self.x
        elif idx==1:
            return self.y
        elif idx==2:
            return self.z
        else:
            raise IndexError()
    def __setitem__(self, idx, val):
        if idx==0:
            self.x=val
        elif idx==1:
            self.y=val
        elif idx==2:
            self.z=val
        else:
            raise IndexError()
    def __iter__(self):
        for v in (self.x,self.y,self.z):
            yield v
     # Is there a set of functions I can add here to allow
     # numpy to convert instances of A into ndarrays?
arg=A()
numpy.dot(arg,arg) # does not work

Error:

>>> scipy.dot(a,a) # I use scipy by default
---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
/home/dave/tmp/<ipython-input-9-f73d996ba2b6> in <module>()
----> 1 scipy.dot(a,a)

TypeError: unsupported operand type(s) for *: 'A' and 'A'

It's calling array(arg) but that yields an array like [arg,], it's shape==() so dot tries to multiply the A instances together.

It's ok (in fact, expected) that the conversion to ndarray will require copying the data.

Answer 1

__len__ seems to be the key feature: just adding a

def __len__(self):
    return 3

made the class work in numpy.dot.