tuse histogram and normalize results - cosmo - front and backend for Markov-Chain Monte Carlo inversion of cosmogenic nuclide concentrations
  HTML git clone git://src.adamsgaard.dk/cosmo
   DIR Log
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   DIR LICENSE
       ---
   DIR commit 6728f85140437a9c1e2020adbc0d5899e748655d
   DIR parent 07d26edb640d2c3fd9a20b97c8f60bb2f05f01be
  HTML Author: Anders Damsgaard <anders.damsgaard@geo.au.dk>
       Date:   Mon,  9 Nov 2015 13:24:10 +0100
       
       use histogram and normalize results
       
       Diffstat:
         M matlab/generate_plots.m             |       5 ++---
       
       1 file changed, 2 insertions(+), 3 deletions(-)
       ---
   DIR diff --git a/matlab/generate_plots.m b/matlab/generate_plots.m
       t@@ -209,9 +209,8 @@ for i1 = 1:M % for each model parameter
            %subplot(5,2,isub)
            subplot(M,Nwalkers,isub)
            %Nhistc=histc(Ss{iwalk}.ms(i1,:),xbins{i1});
       -    Nhistc = histcounts(Ss{iwalk}.ms(i1,:),xbins{i1});
       -    bar(xbins{i1},Nhistc,'histc')
       -
       +    %bar(xbins{i1},Nhistc,'histc')
       +    histogram(Ss{iwalk}.ms(i1,:), xbins{i1}, 'Normalization', 'probability');
            
            if i1 == 1
                title(['MCMC walker ' num2str(iwalk)])