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https://github.com/aportelli/LatAnalyze.git
synced 2024-09-19 21:25:36 +01:00
2-pt fitter moved here from UKAnalyze
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parent
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commit
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@ -1,3 +1,3 @@
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SUBDIRS = lib utils examples
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SUBDIRS = lib utils physics examples
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ACLOCAL_AMFLAGS = -I .buildutils/m4
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@ -131,7 +131,8 @@ AC_SUBST([LIBS])
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AC_SUBST([AM_CFLAGS])
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AC_SUBST([AM_LDFLAGS])
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AC_CONFIG_FILES([Makefile lib/Makefile utils/Makefile examples/Makefile])
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AC_CONFIG_FILES([Makefile lib/Makefile utils/Makefile physics/Makefile
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examples/Makefile])
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AC_OUTPUT
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echo "*********************************************"
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332
physics/2pt-fit.cpp
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332
physics/2pt-fit.cpp
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#include <LatCore/OptParser.hpp>
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#include <LatAnalyze/CompiledModel.hpp>
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#include <LatAnalyze/Io.hpp>
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#include <LatAnalyze/MatSample.hpp>
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#include <LatAnalyze/Math.hpp>
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#include <LatAnalyze/MinuitMinimizer.hpp>
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#include <LatAnalyze/NloptMinimizer.hpp>
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#include <LatAnalyze/Plot.hpp>
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#include <LatAnalyze/XYSampleData.hpp>
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using namespace std;
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using namespace Latan;
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int main(int argc, char *argv[])
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{
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// parse arguments /////////////////////////////////////////////////////////
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OptParser opt;
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bool parsed, doPlot, doHeatmap, doCorr, fold;
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string corrFileName, model, outFileName, outFmt;
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Index ti, tf, shift, nPar, thinning;
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double svdTol;
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Minimizer::Verbosity verbosity;
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opt.addOption("" , "ti" , OptParser::OptType::value , false,
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"initial fit time");
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opt.addOption("" , "tf" , OptParser::OptType::value , false,
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"final fit time");
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opt.addOption("t" , "thinning", OptParser::OptType::value , true,
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"thinning of the time interval", "1");
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opt.addOption("s", "shift" , OptParser::OptType::value , true,
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"time variable shift", "0");
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opt.addOption("m", "model" , OptParser::OptType::value , true,
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"fit model (exp|exp2|exp3|cosh|cosh2|cosh3|<interpreter code>)", "cosh");
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opt.addOption("" , "nPar" , OptParser::OptType::value , true,
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"number of model parameters for custom models "
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"(-1 if irrelevant)", "-1");
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opt.addOption("" , "svd" , OptParser::OptType::value , true,
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"singular value elimination threshold", "0.");
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opt.addOption("v", "verbosity", OptParser::OptType::value , true,
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"minimizer verbosity level (0|1|2)", "0");
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opt.addOption("o", "output", OptParser::OptType::value , true,
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"output file", "");
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opt.addOption("" , "uncorr" , OptParser::OptType::trigger, true,
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"only do the uncorrelated fit");
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opt.addOption("" , "fold" , OptParser::OptType::trigger, true,
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"fold the correlator");
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opt.addOption("p", "plot" , OptParser::OptType::trigger, true,
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"show the fit plot");
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opt.addOption("h", "heatmap" , OptParser::OptType::trigger, true,
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"show the fit correlation heatmap");
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opt.addOption("", "help" , OptParser::OptType::trigger, true,
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"show this help message and exit");
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parsed = opt.parse(argc, argv);
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if (!parsed or (opt.getArgs().size() != 1) or opt.gotOption("help"))
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{
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cerr << "usage: " << argv[0] << " <options> <correlator file>" << endl;
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cerr << endl << "Possible options:" << endl << opt << endl;
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return EXIT_FAILURE;
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}
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corrFileName = opt.getArgs().front();
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ti = opt.optionValue<Index>("ti");
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tf = opt.optionValue<Index>("tf");
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thinning = opt.optionValue<Index>("t");
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shift = opt.optionValue<Index>("s");
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model = opt.optionValue("m");
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nPar = opt.optionValue<Index>("nPar");
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svdTol = opt.optionValue<double>("svd");
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outFileName = opt.optionValue<string>("o");
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doCorr = !opt.gotOption("uncorr");
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fold = opt.gotOption("fold");
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doPlot = opt.gotOption("p");
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doHeatmap = opt.gotOption("h");
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switch (opt.optionValue<unsigned int>("v"))
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{
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case 0:
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verbosity = Minimizer::Verbosity::Silent;
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break;
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case 1:
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verbosity = Minimizer::Verbosity::Normal;
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break;
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case 2:
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verbosity = Minimizer::Verbosity::Debug;
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break;
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default:
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cerr << "error: wrong verbosity level" << endl;
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return EXIT_FAILURE;
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}
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// load correlator /////////////////////////////////////////////////////////
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DMatSample tmp, corr;
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Index nSample, nt;
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tmp = Io::load<DMatSample>(corrFileName);
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nSample = tmp.size();
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nt = tmp[central].rows();
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tmp = tmp.block(0, 0, nt, 1);
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corr = tmp;
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FOR_STAT_ARRAY(corr, s)
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{
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for (Index t = 0; t < nt; ++t)
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{
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corr[s]((t - shift + nt)%nt) = tmp[s](t);
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}
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}
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if (fold)
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{
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tmp = corr;
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FOR_STAT_ARRAY(corr, s)
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{
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for (Index t = 0; t < nt; ++t)
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{
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corr[s](t) = 0.5*(tmp[s](t) + tmp[s]((nt - t) % nt));
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}
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}
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}
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// make model //////////////////////////////////////////////////////////////
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DoubleModel mod;
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bool coshModel = false;
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if ((model == "exp") or (model == "exp1"))
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{
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nPar = 2;
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mod.setFunction([](const double *x, const double *p)
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{
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return p[1]*exp(-p[0]*x[0]);
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}, 1, nPar);
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}
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else if (model == "exp2")
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{
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nPar = 4;
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mod.setFunction([](const double *x, const double *p)
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{
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return p[1]*exp(-p[0]*x[0]) + p[3]*exp(-p[2]*x[0]);
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}, 1, nPar);
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}
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else if (model == "exp3")
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{
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nPar = 6;
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mod.setFunction([](const double *x, const double *p)
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{
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return p[1]*exp(-p[0]*x[0]) + p[3]*exp(-p[2]*x[0])
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+ p[5]*exp(-p[4]*x[0]);
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}, 1, nPar);
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}
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else if ((model == "cosh") or (model == "cosh1"))
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{
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coshModel = true;
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nPar = 2;
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mod.setFunction([nt](const double *x, const double *p)
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{
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return p[1]*(exp(-p[0]*x[0])+exp(-p[0]*(nt-x[0])));
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}, 1, nPar);
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}
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else if (model == "cosh2")
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{
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coshModel = true;
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nPar = 4;
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mod.setFunction([nt](const double *x, const double *p)
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{
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return p[1]*(exp(-p[0]*x[0])+exp(-p[0]*(nt-x[0])))
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+ p[3]*(exp(-p[2]*x[0])+exp(-p[2]*(nt-x[0])));
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}, 1, nPar);
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}
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else if (model == "cosh3")
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{
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coshModel = true;
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nPar = 6;
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mod.setFunction([nt](const double *x, const double *p)
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{
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return p[1]*(exp(-p[0]*x[0])+exp(-p[0]*(nt-x[0])))
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+ p[3]*(exp(-p[2]*x[0])+exp(-p[2]*(nt-x[0])))
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+ p[5]*(exp(-p[2]*x[0])+exp(-p[4]*(nt-x[0])));
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}, 1, nPar);
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}
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else
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{
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if (nPar > 0)
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{
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mod = compile(model, 1, nPar);
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}
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else
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{
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cerr << "error: please specify the number of model parameter"
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" using the --nPar function" << endl;
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return EXIT_FAILURE;
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}
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}
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// fit /////////////////////////////////////////////////////////////////////
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DMatSample tvec(nSample);
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XYSampleData data(nSample);
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SampleFitResult fit;
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DVec init(nPar);
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NloptMinimizer globMin(NloptMinimizer::Algorithm::GN_CRS2_LM);
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MinuitMinimizer locMin;
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vector<Minimizer *> unCorrMin{&globMin, &locMin};
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FOR_STAT_ARRAY(tvec, s)
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{
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tvec[s] = DVec::LinSpaced(nt, 0, nt - 1);
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}
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data.addXDim(nt, "t/a", true);
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data.addYDim("C(t)");
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data.setUnidimData(tvec, corr);
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for (Index p = 0; p < nPar; p += 2)
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{
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mod.parName().setName(p, "E_" + strFrom(p/2));
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mod.parName().setName(p + 1, "Z_" + strFrom(p/2));
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}
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init(0) = log(data.y(nt/4, 0)[central]/data.y(nt/4 + 1, 0)[central]);
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init(1) = data.y(nt/4, 0)[central]/(exp(-init(0)*nt/4));
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for (Index p = 2; p < nPar; p += 2)
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{
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init(p) = 2*init(p - 2);
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init(p + 1) = init(p - 1)/2.;
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}
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for (Index p = 0; p < nPar; p += 2)
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{
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globMin.setLowLimit(p, 0.);
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globMin.setHighLimit(p, 10.*init(p));
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globMin.setLowLimit(p + 1, -10.*init(p + 1));
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globMin.setHighLimit(p + 1, 10.*init(p + 1));
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locMin.setLowLimit(p, 0.);
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}
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globMin.setPrecision(0.001);
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globMin.setMaxIteration(100000);
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globMin.setVerbosity(verbosity);
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locMin.setMaxIteration(1000000);
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locMin.setVerbosity(verbosity);
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for (Index t = 0; t < nt; ++t)
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{
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data.fitPoint((t >= ti) and (t <= tf)
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and ((t - ti) % thinning == 0), t);
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}
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if (doCorr)
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{
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cout << "-- uncorrelated fit..." << endl;
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}
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cout << "using model '" << model << "'" << endl;
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data.setSvdTolerance(svdTol);
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data.assumeYYCorrelated(false, 0, 0);
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fit = data.fit(unCorrMin, init, mod);
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fit.print();
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if (doCorr)
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{
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cout << "-- correlated fit..." << endl;
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cout << "using model '" << model << "'" << endl;
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init = fit[central];
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data.assumeYYCorrelated(true, 0, 0);
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fit = data.fit(locMin, init, mod);
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fit.print();
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}
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// plots ///////////////////////////////////////////////////////////////////
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if (doPlot)
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{
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Plot p;
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DMatSample effMass(nSample);
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DVec effMassT, fitErr;
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Index maxT = (coshModel) ? (nt - 2) : (nt - 1);
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double e0, e0Err;
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p << PlotRange(Axis::x, 0, nt - 1);
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p << LogScale(Axis::y);
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p << Color("rgb 'blue'") << PlotPredBand(fit.getModel(_), 0, nt - 1);
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p << Color("rgb 'blue'") << PlotFunction(fit.getModel(), 0, nt - 1);
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p << Color("rgb 'red'") << PlotData(data.getData());
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p.display();
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effMass.resizeMat(maxT, 1);
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effMassT.setLinSpaced(maxT, 1, maxT);
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fitErr = fit.variance().cwiseSqrt();
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e0 = fit[central](0);
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e0Err = fitErr(0);
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if (coshModel)
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{
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FOR_STAT_ARRAY(effMass, s)
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{
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for (Index t = 1; t < nt - 1; ++t)
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{
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effMass[s](t - 1) = acosh((corr[s](t-1) + corr[s](t+1))
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/(2.*corr[s](t)));
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}
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}
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}
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else
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{
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FOR_STAT_ARRAY(effMass, s)
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{
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for (Index t = 1; t < nt; ++t)
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{
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effMass[s](t - 1) = log(corr[s](t-1)/corr[s](t));
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}
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}
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}
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p.reset();
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p << PlotRange(Axis::x, 1, maxT);
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p << PlotRange(Axis::y, e0 - 20.*e0Err, e0 + 20.*e0Err);
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p << Color("rgb 'blue'") << PlotBand(0, maxT, e0 - e0Err, e0 + e0Err);
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p << Color("rgb 'blue'") << PlotHLine(e0);
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p << Color("rgb 'red'") << PlotData(effMassT, effMass);
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p.display();
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p.save("test");
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}
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if (doHeatmap)
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{
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Plot p;
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Index n = data.getFitVarMat().rows();
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DMat id = DMat::Identity(n, n);
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p << PlotMatrix(Math::varToCorr(data.getFitVarMat()));
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p << Caption("correlation matrix");
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p.display();
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if (svdTol > 0.)
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{
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p.reset();
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p << PlotMatrix(id - data.getFitVarMat()*data.getFitVarMatPInv());
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p << Caption("singular space projector");
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p.display();
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}
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}
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// output //////////////////////////////////////////////////////////////////
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if (!outFileName.empty())
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{
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Io::save(fit, outFileName);
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}
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return EXIT_SUCCESS;
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}
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1
physics/LatAnalyze
Symbolic link
1
physics/LatAnalyze
Symbolic link
@ -0,0 +1 @@
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../lib
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15
physics/Makefile.am
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15
physics/Makefile.am
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@ -0,0 +1,15 @@
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if CXX_GNU
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COM_CXXFLAGS = -Wall -W -pedantic -Wno-deprecated-declarations
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else
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if CXX_INTEL
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COM_CXXFLAGS = -wd1682 -Wall
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endif
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endif
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bin_PROGRAMS = latan-2pt-fit
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latan_2pt_fit_SOURCES = 2pt-fit.cpp
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latan_2pt_fit_CXXFLAGS = $(COM_CXXFLAGS)
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latan_2pt_fit_LDFLAGS = -L../lib/.libs -lLatAnalyze
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ACLOCAL_AMFLAGS = -I .buildutils/m4
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