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576 lines
20 KiB
C++
576 lines
20 KiB
C++
#include <LatAnalyze/Core/OptParser.hpp>
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#include <LatAnalyze/Functional/CompiledModel.hpp>
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#include <LatAnalyze/Io/Io.hpp>
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#include <LatAnalyze/Statistics/MatSample.hpp>
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#include <LatAnalyze/Core/Math.hpp>
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#include <LatAnalyze/Numerical/MinuitMinimizer.hpp>
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#include <LatAnalyze/Numerical/NloptMinimizer.hpp>
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#include <LatAnalyze/Core/Plot.hpp>
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#include <LatAnalyze/Statistics/XYSampleData.hpp>
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using namespace std;
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using namespace Latan;
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struct TwoPtFit
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{
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SampleFitResult result;
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Index tMin, tMax;
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};
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void setFitRange(XYSampleData &data, const Index ti, const Index tf,
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const Index thinning, const Index nt)
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{
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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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}
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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, doScan;
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string corrFileName, model, outFileName, outFmt, savePlot;
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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|sinh|cosh|cosh2|cosh3|explin|const|<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("", "save-plot", OptParser::OptType::value, true,
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"saves the source and .pdf", "");
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opt.addOption("", "scan", OptParser::OptType::trigger, true,
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"scan all possible fit ranges within [ti,tf]");
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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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savePlot = opt.optionValue("save-plot");
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doScan = opt.gotOption("scan");
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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 models /////////////////////////////////////////////////////////////
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DoubleModel mod;
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bool sinhModel = false, coshModel = false, linearModel = false, constModel = 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 == "sinh")
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{
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sinhModel = 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 == "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 if (model == "explin")
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{
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linearModel = true;
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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] - p[0]*x[0];
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}, 1, nPar);
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}
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else if (model == "const")
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{
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constModel = true;
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nPar = 1;
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mod.setFunction([](const double *x __dumb, const double *p)
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{
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return p[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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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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// set parameter name ******************************************************
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if(constModel)
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{
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mod.parName().setName(0, "const");
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}
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else
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{
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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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}
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// set initial values ******************************************************
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if (linearModel)
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{
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init(0) = 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] + nt/4*init(0);
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}
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else if(constModel)
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{
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init(0) = data.y(nt/4, 0)[central];
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}
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else
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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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}
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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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// set limits for minimisers ***********************************************
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for (Index p = 0; p < nPar; p += 2)
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{
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if (linearModel)
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{
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globMin.setLowLimit(p, -10.*fabs(init(p)));
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globMin.setHighLimit(p, 10.*fabs(init(p)));
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}
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else if(constModel)
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{
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globMin.setLowLimit(p, -10*fabs(init(0)));
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locMin.setLowLimit(p, -10*fabs(init(0)));
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globMin.setHighLimit(p, 10*fabs(init(0)));
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locMin.setHighLimit(p, 10*fabs(init(0)));
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}
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else
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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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}
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if(!constModel)
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{
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globMin.setLowLimit(p + 1, -10.*fabs(init(p + 1)));
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globMin.setHighLimit(p + 1, 10.*fabs(init(p + 1)));
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}
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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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// fit /////////////////////////////////////////////////////////////////////
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if (!doScan)
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{
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SampleFitResult fit;
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setFitRange(data, ti, tf, thinning, nt);
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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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if (!outFileName.empty())
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{
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Io::save(fit, outFileName);
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}
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// plots ***************************************************************
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if (doPlot)
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{
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if (!constModel)
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{
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Plot p;
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p << PlotRange(Axis::x, 0, nt - 1);
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if (!linearModel and !constModel)
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{
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p << LogScale(Axis::y);
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}
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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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if(savePlot != "")
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{
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p.save(savePlot + "_corr");
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}
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}
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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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effMass.resizeMat(maxT, 1);
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effMassT.setLinSpaced(maxT, 0, maxT-1);
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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 or sinhModel)
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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 if (linearModel)
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{
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FOR_STAT_ARRAY(effMass, s)
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{
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for (Index t = 0; t < nt - 1; ++t)
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{
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effMass[s](t) = corr[s](t) - corr[s](t+1);
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}
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}
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}
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else if (constModel)
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{
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FOR_STAT_ARRAY(effMass, s)
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{
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for (Index t = 0; t < nt - 1; ++t)
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{
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effMass[s](t) = 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, 0, 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 << Caption("Effective Mass");
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p.display();
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if(savePlot != "")
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{
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p.save(savePlot + "_effMass");
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}
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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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}
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}
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// scan fits ///////////////////////////////////////////////////////////////
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else
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{
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Index nFit = 0, f = 0, ti0 = ti + (tf - ti)/4, tf0 = tf - (tf - ti)/4,
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matSize = tf - ti - nPar + 1;
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DMat err, pVal(matSize, matSize), relErr(matSize, matSize),
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ccdf(matSize, matSize), val(matSize, matSize);
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map<double, TwoPtFit> fit;
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SampleFitResult tmpFit;
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cout << "-- initial uncorrelated fit on [" << ti0 << ", " << tf0 << "]..." << endl;
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if (thinning != 1)
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{
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cerr << "warning: thinning different from 1 ignored in scan mode"
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<< endl;
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thinning = 1;
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}
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setFitRange(data, ti0, tf0, thinning, nt);
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data.setSvdTolerance(svdTol);
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data.assumeYYCorrelated(false, 0, 0);
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tmpFit = data.fit(unCorrMin, init, mod);
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tmpFit.print();
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cout << "-- scanning all possible fit ranges..." << endl;
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init = tmpFit[central];
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data.assumeYYCorrelated(doCorr, 0, 0);
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pVal.fill(Math::nan);
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relErr.fill(Math::nan);
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val.fill(Math::nan);
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ccdf.fill(Math::nan);
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for (Index ta = ti; ta < tf; ++ta)
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for (Index tb = ta + nPar; tb < tf; ++tb)
|
|
{
|
|
nFit++;
|
|
}
|
|
for (Index ta = ti; ta < tf; ++ta)
|
|
for (Index tb = ta + nPar; tb < tf; ++tb)
|
|
{
|
|
Index i = ta - ti, j = tb - ti;
|
|
|
|
setFitRange(data, ta, tb, thinning, nt);
|
|
tmpFit = data.fit(locMin, init, mod);
|
|
err = tmpFit.variance().cwiseSqrt();
|
|
pVal(i, j) = tmpFit.getPValue();
|
|
ccdf(i, j) = tmpFit.getCcdf();
|
|
val(i, j) = tmpFit[central](0);
|
|
relErr(i, j) = err(0)/fabs(val(i, j));
|
|
fit[pVal(i, j)].result = tmpFit;
|
|
fit[pVal(i, j)].tMin = ta;
|
|
fit[pVal(i, j)].tMax = tb;
|
|
f++;
|
|
cout << "\r[" << ta << ", " << tb << "] "<< ProgressBar(f, nFit);
|
|
}
|
|
cout << endl << endl;
|
|
cout << "TOP 10 fits" << endl;
|
|
cout << "-----------" << endl;
|
|
auto it = fit.rbegin();
|
|
unsigned int k = 0;
|
|
while (k < 10)
|
|
{
|
|
auto &f = it->second;
|
|
|
|
cout << "#" << k + 1 << " -- [" << f.tMin << ", " << f.tMax << "] -- ";
|
|
f.result.print();
|
|
cout << endl;
|
|
k++;
|
|
it++;
|
|
}
|
|
// plots ***************************************************************
|
|
if (doPlot)
|
|
{
|
|
Plot p;
|
|
|
|
p << PlotMatrix(pVal);
|
|
p << Caption("p-value matrix");
|
|
p << Label("tMin - " + strFrom(ti), Axis::x);
|
|
p << Label("tMax - " + strFrom(ti), Axis::y);
|
|
p.display();
|
|
if(savePlot != "")
|
|
{
|
|
p.save(savePlot + "_pValMatrix");
|
|
}
|
|
p.reset();
|
|
p << PlotMatrix(relErr);
|
|
p << Caption("Relative error matrix");
|
|
p << Label("tMin - " + strFrom(ti), Axis::x);
|
|
p << Label("tMax - " + strFrom(ti), Axis::y);
|
|
p.display();
|
|
if(savePlot != "")
|
|
{
|
|
p.save(savePlot + "_relErrMatrix");
|
|
}
|
|
p.reset();
|
|
p << PlotMatrix(val);
|
|
p << Caption("Fit result matrix");
|
|
p << Label("tMin - " + strFrom(ti), Axis::x);
|
|
p << Label("tMax - " + strFrom(ti), Axis::y);
|
|
p.display();
|
|
if(savePlot != "")
|
|
{
|
|
p.save(savePlot + "_valMatrix");
|
|
}
|
|
p.reset();
|
|
p << PlotMatrix(ccdf);
|
|
p << Caption("chi^2 CCDF matrix");
|
|
p << Label("tMin - " + strFrom(ti), Axis::x);
|
|
p << Label("tMax - " + strFrom(ti), Axis::y);
|
|
p.display();
|
|
if(savePlot != "")
|
|
{
|
|
p.save(savePlot + "_ccdfMatrix");
|
|
}
|
|
}
|
|
}
|
|
|
|
return EXIT_SUCCESS;
|
|
}
|