Mixing numpy and OO in numerical work

I would advise against using an array of objects because you end up loosing nearly all of the performance benefits of using numpy. We structure or code more like this:

class Points:    def __init__(self, x, y):        self.x = np.asarray(x)        self.y = np.asarray(y)    def shift_left(self, distance):        self.x -= distancex = np.zeros(10000)y = np.zeros(10000)points_obj = Points(x, y)

Now you can create functions, methods, and so on that operate on points_obj knowing that points_obj.x and point_obj.y are numpy arrays (maybe with size 1, or possibly bigger). If you need to be able to index in to points_obj, you can always define a __getitem__ method on your class.

You can use the same numerical algorithm with or without object orientation. I do not really understand your question. OO is more about program structure and connetion between data. The numerics inside methods can be the same as in normal procedural program.--edit--

You can make array of your parabolas quite quickly when you vectorize its methods. You may of course vectorize even much more complicated ones.

import numpy as npclass parabola:    a = 0.0    b = 0.0    c = 0.0    def __init__(self,a,b,c):        self.a = a        self.b = b        self.c = c    def set_a(self, new_a):        self.a = new_a    def set_b(self, new_b):        self.b = new_b    def set_c(self, new_c):        self.c = new_c    def get_a(self):        return self.a    def get_b(self):        return self.b    def get_c(self):        return self.cvpara = np.vectorize(parabola)vgeta = np.vectorize(parabola.get_a)vgetb = np.vectorize(parabola.get_b)vgetc = np.vectorize(parabola.get_c)a = np.zeros(10000)b = np.zeros(10000)c = np.zeros(10000)a[:]  = [i for i in xrange(10000)]b[:]  = [2*i for i in xrange(10000)]c[:]  = [i*i for i in xrange(10000)]objs = np.empty((10000), dtype=object)objs[:] = vpara(a,b,c)print vgeta(objs[1:10:2]),vgetc(objs[9900:9820:-3])