mirror of
https://github.com/aportelli/LatAnalyze.git
synced 2025-11-04 08:04:32 +00:00
rewrite of the 2pt fitter using the new physics classes
This commit is contained in:
@@ -1,11 +1,13 @@
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#include <LatAnalyze/Core/Math.hpp>
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#include <LatAnalyze/Core/OptParser.hpp>
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#include <LatAnalyze/Core/Plot.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/Physics/CorrelatorFitter.hpp>
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#include <LatAnalyze/Physics/EffectiveMass.hpp>
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#include <LatAnalyze/Statistics/MatSample.hpp>
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#include <LatAnalyze/Statistics/XYSampleData.hpp>
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using namespace std;
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@@ -17,17 +19,6 @@ struct TwoPtFit
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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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@@ -47,7 +38,7 @@ int main(int argc, char *argv[])
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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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"fit model (exp<n>|sinh<n>|cosh<n>|linear|cst|<interpreter code>)", "exp1");
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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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@@ -138,91 +129,15 @@ int main(int argc, char *argv[])
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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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// make model //////////////////////////////////////////////////////////////
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CorrelatorFitter fitter(corr);
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DoubleModel mod;
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auto modelPar = CorrelatorModels::parseModel(model);
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if ((model == "exp") or (model == "exp1"))
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if (modelPar.type != CorrelatorType::undefined)
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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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mod = CorrelatorModels::makeModel(modelPar, nt);
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nPar = mod.getNPar();
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}
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else
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{
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@@ -240,81 +155,44 @@ int main(int argc, char *argv[])
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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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// set fitter **************************************************************
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fitter.setModel(mod);
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fitter.data().setSvdTolerance(svdTol);
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fitter.setThinning(thinning);
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// set initial values ******************************************************
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if (modelPar.type != CorrelatorType::undefined)
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{
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init = CorrelatorModels::parameterGuess(corr, modelPar);
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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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init.fill(0.1);
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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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if ((modelPar.type == CorrelatorType::exp) or
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(modelPar.type == CorrelatorType::cosh) or
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(modelPar.type == CorrelatorType::sinh))
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{
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globMin.setLowLimit(p, 0.);
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locMin.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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else
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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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}
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globMin.setPrecision(0.001);
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globMin.setMaxIteration(100000);
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@@ -322,28 +200,28 @@ int main(int argc, char *argv[])
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locMin.setMaxIteration(1000000);
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locMin.setVerbosity(verbosity);
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// fit /////////////////////////////////////////////////////////////////////
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// standard fit ////////////////////////////////////////////////////////////
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if (!doScan)
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{
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// fit *****************************************************************
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SampleFitResult fit;
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setFitRange(data, ti, tf, thinning, nt);
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fitter.setFitRange(ti, tf);
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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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fitter.setCorrelation(false);
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fit = fitter.fit(unCorrMin, init);
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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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fitter.setCorrelation(true);
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fit = fitter.fit(locMin, init);
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fit.print();
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}
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if (!outFileName.empty())
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@@ -353,84 +231,50 @@ int main(int argc, char *argv[])
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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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DMatSample tvec(nSample);
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tvec.fill(DVec::LinSpaced(nt, 0, nt - 1));
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if (modelPar.type != CorrelatorType::cst)
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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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if ((modelPar.type == CorrelatorType::exp) or
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(modelPar.type == CorrelatorType::cosh) or
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(modelPar.type == CorrelatorType::sinh))
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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 << Color("rgb 'red'") << PlotData(fitter.data().getData());
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p << Label("t/a", Axis::x) << Caption("Correlator");
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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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if (modelPar.type != CorrelatorType::undefined)
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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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Plot p;
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EffectiveMass effMass(modelPar.type);
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DMatSample em;
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DVec fitErr, emtvec;
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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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emtvec = effMass.getTime(nt);
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em = effMass(corr);
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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 << PlotRange(Axis::x, 0, nt - 1);
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p << PlotRange(Axis::y, e0 - 30.*e0Err, e0 + 30.*e0Err);
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p << Color("rgb 'blue'") << PlotBand(0, nt - 1, 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 << Color("rgb 'red'") << PlotData(emtvec, em);
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p << Label("t/a", Axis::x) << Caption("Effective Mass");
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p.display();
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if(savePlot != "")
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{
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@@ -440,16 +284,19 @@ int main(int argc, char *argv[])
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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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Index n = fitter.data().getFitVarMat().rows();
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DMat id = DMat::Identity(n, n),
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var = fitter.data().getFitVarMat();
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p << PlotMatrix(Math::varToCorr(data.getFitVarMat()));
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p << PlotMatrix(Math::varToCorr(var));
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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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DMat proj = id - var*fitter.data().getFitVarMatPInv();
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p.reset();
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p << PlotMatrix(id - data.getFitVarMat()*data.getFitVarMatPInv());
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p << PlotMatrix(proj);
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p << Caption("singular space projector");
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p.display();
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}
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@@ -460,8 +307,9 @@ int main(int argc, char *argv[])
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// scan fits ///////////////////////////////////////////////////////////////
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else
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{
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// fits ****************************************************************
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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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matSize = tf - ti + 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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@@ -474,14 +322,13 @@ int main(int argc, char *argv[])
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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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fitter.setFitRange(ti0, tf0);
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fitter.setCorrelation(false);
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tmpFit = fitter.fit(unCorrMin, init);
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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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fitter.setCorrelation(doCorr);
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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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@@ -496,8 +343,8 @@ int main(int argc, char *argv[])
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||||
{
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||||
Index i = ta - ti, j = tb - ti;
|
||||
|
||||
setFitRange(data, ta, tb, thinning, nt);
|
||||
tmpFit = data.fit(locMin, init, mod);
|
||||
fitter.setFitRange(ta, tb);
|
||||
tmpFit = fitter.fit(locMin, init);
|
||||
err = tmpFit.variance().cwiseSqrt();
|
||||
pVal(i, j) = tmpFit.getPValue();
|
||||
ccdf(i, j) = tmpFit.getCcdf();
|
||||
@@ -531,8 +378,8 @@ int main(int argc, char *argv[])
|
||||
|
||||
p << PlotMatrix(pVal);
|
||||
p << Caption("p-value matrix");
|
||||
p << Label("tMin - " + strFrom(ti), Axis::x);
|
||||
p << Label("tMax - " + strFrom(ti), Axis::y);
|
||||
p << Label("tMax - " + strFrom(ti), Axis::x);
|
||||
p << Label("tMin - " + strFrom(ti), Axis::y);
|
||||
p.display();
|
||||
if(savePlot != "")
|
||||
{
|
||||
@@ -541,8 +388,8 @@ int main(int argc, char *argv[])
|
||||
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 << Label("tMax - " + strFrom(ti), Axis::x);
|
||||
p << Label("tMin - " + strFrom(ti), Axis::y);
|
||||
p.display();
|
||||
if(savePlot != "")
|
||||
{
|
||||
@@ -551,8 +398,8 @@ int main(int argc, char *argv[])
|
||||
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 << Label("tMax - " + strFrom(ti), Axis::x);
|
||||
p << Label("tMin - " + strFrom(ti), Axis::y);
|
||||
p.display();
|
||||
if(savePlot != "")
|
||||
{
|
||||
@@ -561,8 +408,8 @@ int main(int argc, char *argv[])
|
||||
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 << Label("tMax - " + strFrom(ti), Axis::x);
|
||||
p << Label("tMin - " + strFrom(ti), Axis::y);
|
||||
p.display();
|
||||
if(savePlot != "")
|
||||
{
|
||||
|
||||
Reference in New Issue
Block a user