Charles R Harris wrote: > On 10/5/07, Neal Becker <[EMAIL PROTECTED]> wrote: >> >> I'm thinking (again) about using numpy for signal processing >> applications. One issue is that there are more data types that are >> commonly used in signal processing that are not available in numpy (or >> python). Specifically, it is frequently required to convert floating >> point >> algorithms into integer algorithms. numpy is fine for arrays of integers >> (of various sizes), but it is also very useful to have arrays of >> complex<integers>. While numpy has complex<double,float>, it doesn't >> have >> complex<int,int_64...> Has anyone thought about this? > > > A bit. Multiplication begins to be a problem, though. Would you also want > fixed point multiplication with scaling, a la PPC with altivec? What about > division? So on and so forth. I think something like this would best be > implemented in a specialized signal processing package but I am not sure > of the best way to do it. >
I'd keep things as simple as possible. No fixed point/scaling. It's simple enough to explictly rescale things as you wish. That is (using c++ syntax): complex<int> a, b; complex<int> c = a * b; complex<int> d = d >> 4; Complicating life is interoperability (conversion) of types. I've used this concept for some years with c++/python - but not with numpy. It's pretty trivial to make a complex<int> type as a C extension to python. Adding this to numpy would be really useful. _______________________________________________ Numpy-discussion mailing list [email protected] http://projects.scipy.org/mailman/listinfo/numpy-discussion
