Re: [Numpy-discussion] More complex data types

5 Oct 2007


      On 10/5/07, Neal Becker  wrote:
...
Charles R Harris wrote:
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On 10/5/07, Neal Becker  wrote:
...
Charles R Harris wrote:
...
On 10/5/07, Neal Becker  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, it
doesn't
have
complex  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?
Well, you could do bounds and overflow checking, use mixed integer
precisions, and also deal with oddball sizes, such as 12 bits. You might
also support 16 bit floats, something that is not available as a native type
on most platforms. I'm just tossing some ideas out there. As I say, I don't
see any reason not to have integer complex arrays, if only for the data
type. Then again, maybe you could define your own data type and just
overload the operators, something like

In [5]: complex32 = dtype('int16,int16')

In [6]: zeros(2, dtype=complex32)
Out[6]:
array([(0, 0), (0, 0)],
      dtype=[('f0', '