On Fri, Dec 19, 2008 at 6:53 AM, John Hunter <jdh2358@gmail.com> wrote:
On Thu, Dec 18, 2008 at 8:27 PM, Bradford Cross
<bradford.n.cross@gmail.com> wrote:
> This is a new project I just released.
> I know it is C#, but some of the design and idioms would be nice in
> numpy/scipy for working with discrete event simulators, time series, and
> event stream processing.
> http://code.google.com/p/incremental-statistics/

I think an incremental stats module would be a boon to numpy or scipy.
 Eric Firing has a nice module wrtten in C with a pyrex wrapper
(ringbuf) that does trailing incremental mean, median, std, min, max,
and percentile.  It maintains a sorted queue to do the last three
efficiently, and handles NaN inputs.  I would like to see this
extended to include exponential or other weightings to do things like
incremental trailing exponential moving averages and variances.  I
don't know what the licensing terms are of this module, but it might
be a good starting point for an incremental numpy stats module, at
least if you were thinking about supporting a finite lookback window.
We have a copy of this in the py4science examples dir if you want to
take a look:

   svn co https://matplotlib.svn.sourceforge.net/svnroot/matplotlib/trunk/py4science/examples/pyrex/trailstats
   cd trailstats/
  python movavg_ringbuf.py

Other things that would be very useful are incremental covariance and

Some sort of Kalman filter?