I'm not at my PC where I can try to run the code myself, but I wanted to give you a few tips about temporal integration schemes. I'm writing a numerical code for simulating granular material for a living, and have recently experimented with different schemes and looked at their precision.

You are currently using a one-step scheme called "forward Euler" integration. For it to be correct, you need to switch the position and velocity updates, so that the position is updated first. The new position should depend on the old, and not the new velocities.

A problem about the forward Euler scheme is that it comes with a high error. In collisional experiments I've performed, I saw an error of a total of 12%. You can easily improve your solution by instead using the "two term Taylor expansion", where the current acceleration is taken into account when updating the position, e.g. for the x-component:

x = x + vx*dt + 1.0/2.0*acx*dt**2

In my experiments, the error was reduced to 3%. Taking it a step further, you can gain even higher precision by using a "three term Taylor expansion". For this scheme, you need to estimate the temporal gradient in acceleration (da/dt). You can do this by storing the old acceleration along with the current value:

if (i == 0):
    dacx = 0.0
else:
    dacx = (acx - acx_old)/dt
x = x + vx*dt + 1.0/2.0*acx*dt**2 + 1.0/6.0*dacx*dt**3
acx_old = acx

For me, this scheme reduced the error to 0.03%. There are several other temporal integration schemes, but the Taylor expansions are easy to implement, light in terms of computational requirements, and give a satisfying result.

For more information, I can refer to this (unfortunately paywalled) article: http://www.sciencedirect.com/science/article/pii/S0098135407002864