Wow. Just wow.

I'm working on a script that evaluates probably millions of trig functions in a given run (it's a geometric ray trace of an arbitrary surface, so the math gets nasty fast...). I've been wrapping the numpy sin, cos, etc to work with the functions I get from mathematica, and it takes about 12 hours for me to process a few thousand data points in this way.

I just rewrote my wrapper script to use the math.py modules, and wow. That 12 hours just dropped to 3. I've always felt numpy to be superior, but I just got a good lesson in considering applications. The traces are running 4 times faster with the basic imports. While I lose the ability to handle arrays, I don't really care :D The code uses a differential approximation that isn't well suited for arrays anyway (as for as I know...)

So yeah...for applications where speed counts, (*cough* don't use python! :D *cough*), but if you must, math > numpy!