[Numpy-discussion] diff of find of diff trick

[Robin]

Hello,

As a converting MATLAB user I am bringing over some of my code. A core
function I need is to sample the probability distribution of a given set of
data. Sometimes these sets are large so I would like to make it as efficient
as possible. (the data are integers representing members of a discrete
space)

In MATLAB the best way I found was the "diff of find of diff" trick which
resulted in the completely vectorised solution (below). Does it make sense
to translate this into numpy? I don't have a feel yet for what is fast/slow
- are the functions below built in and so quick (I thought diff might not
be).

Is there a better/more pythonic way to do it?

--------
function Pr=prob(data, nR)

Pr    = zeros(nR,1);
% diff of find of diff trick for counting number of elements
temp     = sort(data(data>0));    % want vector excluding P(0)
dtemp    = diff([temp;max(temp)+1]);
count     = diff(find([1;dtemp]));
indx     = temp(dtemp>0);
Pr(indx)= count ./ numel(data);    % probability
--------

Thanks

Robin
-------------- next part --------------
An HTML attachment was scrubbed...
URL: http://projects.scipy.org/pipermail/numpy-discussion/attachments/20071009/10daddfb/attachment.html