Charles R Harris wrote:
On 10/5/07, Neal Becker <ndbecker2@gmail.com> 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.