How to link instance attributes to a numpy array?

Question

I have a list of Python objects, representing several classes. The classes obviously differ, but nevertheless have a couple of common attributes (with different values for each object). For example:

class Super1:
    def __init__(self, value1, value2):
        self.value1 = value1
        self.value2 = value2
        #Lot's of other stuff

class Sub1(Super1):
    def __init__(self, value1, value2, value3):
        Super1.__init__(self, value1, value2)
        self.value3 = value3
        #Lot's of stuff

class Sub2(Super1):
    def __init__(self, value1, value2, value4):
        Super1.__init__(self, value1, value2)
        self.value4 = value4
        #Lot's of stuff

objects = [Sub1(1.,2.,3.), Sub1(213.,2.,23.), Sub2(23.,10.,0.2), Sub1(3.,2.,12.)]

Now, for both convenience and performance, I would need to have to a NumPy array of all these values. I know I can read them like this:

np.array([objects[ii].value1 for ii in range(4)])

But I also need to change the values, in both the array form as well as individually within instance methods. Is it possible to somehow dynamically link the object attributes and the corresponding values in the arrays, by using pointers or something?

And no, the objects here does not have to be a list. Suggestions welcome.

Answer 1

Numpy arrays are "a contiguous one-dimensional segment of computer memory", so there really is no way you can create a numpy array that is made up of chunks of memory here and there.

The only possibility is to go the other way around, first create the array, then asign single element slices of that array to your objects, e.g.

class Super1(object):
    def __init__(self, value1):
        self._value1 = value1

    @property
    def value1(self):
        return self._value1[0]

    @value1.setter
    def value1(self, value):
        self._value1[0] = value

And now:

>>> a = np.arange(4)
>>> obj = [Super1(a[j:j+1]) for j in xrange(len(a))]
>>> obj[0].value1
0
>>> obj[0].value1 = 5
>>> a
array([5, 1, 2, 3])
>>> obj[2].value1
2
>>> a[2] = 8
>>> obj[2].value1
8