[Numpy-discussion] More complex data types

Neal Becker ndbecker2 at gmail.com
Fri Oct 5 12:50:21 EDT 2007


Charles R Harris wrote:

> On 10/5/07, Neal Becker <ndbecker2 at gmail.com> wrote:
>>
>> Charles R Harris wrote:
>>
>> > On 10/5/07, Neal Becker <ndbecker2 at 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.
> 
> 
> How about fiddling with floating point to emulate integers by subclassing
> ndarray? That wouldn't buy you the speed and size advantage of true fixed
> point but would make a flexible emulator. Which raises the question, what
> are your main goals in using such a data type? Not that I don't see the
> natural utility of having complex integer numbers (Gaussian integers), but
> if you are trying to emulate hardware something more flexible might be
> appropriate.
> 
> Chuck

Yes, this is intended for modelling hardware.  I don't know what you mean
by "more flexible".  I design my hardware algorithms to use integer
arithmetic.  What did you have in mind?




More information about the NumPy-Discussion mailing list