91 lines
3.8 KiB
Matlab
91 lines
3.8 KiB
Matlab
function pick
|
|
%
|
|
% Copyright © 2001-2017 European Commission
|
|
% Copyright © 2017-2023 DynareTeam
|
|
|
|
% This file is part of GLUEWIN
|
|
% GLUEWIN is a MATLAB code designed for analysing the output
|
|
% of Monte Carlo runs when empirical observations of the model output are available
|
|
% and implements the GSA-GLUE methodology by Ratto et al. [1], based on a combination
|
|
% of GLUE (Generalised Likelihood Uncertainty Estimation) by K. Beven [2] and GSA
|
|
% Global Sensitivity Analysis) [3].']
|
|
% The program has been developed by M. Ratto, European Commission, Joint Research Centre,
|
|
% Institute for the Protection and Security of The Citizen, Technological and Economic Risk Management,
|
|
% Applied Statistics, as a deliverable of the IMPACT project
|
|
% (EC Fifth Framework Programme, SCA Project, IST-1999-11313, DG-INFSO).
|
|
%
|
|
% The graphical layout of the code is inspired by the freeware GLUE package by K. Beven,
|
|
% vailable at the Lancaster University web site on the page [4]:
|
|
% http://www.es.lancs.ac.uk/hfdg/glue.html
|
|
% to which the GLUEWIN code introduces several extensions and additional options.
|
|
% Thanks are due to R. Girardi, A. Rossi, A. Saltelli, S. Tarantola and U. Callies for numerous
|
|
% comments and suggestions.
|
|
% For more information, please contact marco.ratto@ec.europa.eu
|
|
%
|
|
% Disclaimer: This software has been developed at the Joint Research Centre of European Commission
|
|
% by officers in the course of their official duties. This software is not subject to copyright
|
|
% protection and is in the public domain. It is an experimental system. The Joint Research Centre
|
|
% of European Commission assumes no responsibility whatsoever for its use by other parties
|
|
% and makes no guarantees, expressed or implied, about its quality, reliability, or any other
|
|
% characteristic. We would appreciate acknowledgement if the software is used.
|
|
%
|
|
% [1] Ratto, M., Tarantola, S., A. Saltelli, Sensitivity analysis in model calibration: GSA-GLUE approach.
|
|
% 'Computer Physics Communications, 136, 2001, 212-224
|
|
% [2] Beven K.J., Binley A., The Future of Distributed Models: Model Calibration and Uncertainty
|
|
% 'Prediction, Hydrological Processes, 6, 279-298, 1992
|
|
% [3] Saltelli, A., K. Chan, M. Scott, Editors, (2000), Sensitivity analysis, John Wiley & Sons
|
|
% 'publishers, Probability and Statistics series.
|
|
% [4] Beven K., GLUE for Windows User manual, 1998.
|
|
|
|
|
|
|
|
pmenu=findobj(gcf,'type','uicontextmenu','Tag','Run viewer');
|
|
button1=findobj(gcf,'type','uimenu','Tag','save params');
|
|
button2=findobj(gcf,'type','uimenu','Tag','eval params');
|
|
%button=get(pmenu,'children');
|
|
gg=gco;
|
|
ax0=gca;
|
|
set(gg,'buttondownfcn',[]);
|
|
c=get(gca,'currentpoint');
|
|
x=c(1,1);
|
|
y=c(1,2);
|
|
X=get(gco,'xdata');
|
|
Y=get(gco,'ydata');
|
|
dx=get(gca,'xlim');
|
|
dy=get(gca,'ylim');
|
|
pos=get(gca,'position');
|
|
scalex=dx(2)-dx(1);
|
|
scaley=dy(2)-dy(1);
|
|
if length(X)>1
|
|
K = dsearchn([(Y./scaley)' (X./scalex)'],[y/scaley x/scalex]);
|
|
else
|
|
az=get(gca,'children');
|
|
T =get(az(end),'ydata');
|
|
[dum K]=max(T);
|
|
end
|
|
|
|
KK=K;
|
|
|
|
set(button1,'Label',['Save ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''save'')']);
|
|
set(button2,'Label',['Eval ',num2str(K)],'Callback',['scatter_callback(',num2str(KK),',''eval'')']);
|
|
hh_obj=findobj(gcf,'type','axes','Tag','scatter');
|
|
for k=1:length(hh_obj)
|
|
axes(hh_obj(k));
|
|
dum=get(gca,'children');
|
|
dumx=get(dum(end),'xdata');
|
|
dumy=get(dum(end),'ydata');
|
|
xmid=min(dumx) + 0.5*(max(dumx)-min(dumx));
|
|
hold on
|
|
plot(dumx(KK),dumy(KK),'or');
|
|
if dumx(KK) < xmid
|
|
text(dumx(KK),dumy(KK),[' ',num2str(K)], ...
|
|
'FontWeight','Bold',...
|
|
'Color','r');
|
|
else
|
|
text(dumx(KK),dumy(KK),[num2str(K),' '], ...
|
|
'HorizontalAlignment','right', ...
|
|
'FontWeight','Bold',...
|
|
'Color','r');
|
|
end
|
|
hold off
|
|
end |