[Numpy-discussion] diff of find of diff trick

Robin robince@gmail....
Tue Oct 9 12:55:12 CDT 2007


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

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

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


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