#include <LatAnalyze/Core/OptParser.hpp>
#include <LatAnalyze/Functional/CompiledModel.hpp>
#include <LatAnalyze/Io/Io.hpp>
#include <LatAnalyze/Statistics/MatSample.hpp>
#include <LatAnalyze/Core/Math.hpp>
#include <LatAnalyze/Numerical/MinuitMinimizer.hpp>
#include <LatAnalyze/Numerical/NloptMinimizer.hpp>
#include <LatAnalyze/Core/Plot.hpp>
#include <LatAnalyze/Statistics/XYSampleData.hpp>
using namespace std;
using namespace Latan;
struct TwoPtFit
{
SampleFitResult result;
Index tMin, tMax;
};
void setFitRange(XYSampleData &data, const Index ti, const Index tf,
const Index thinning, const Index nt)
{
for (Index t = 0; t < nt; ++t)
{
data.fitPoint((t >= ti) and (t <= tf)
and ((t - ti) % thinning == 0), t);
}
}
int main(int argc, char *argv[])
{
// parse arguments /////////////////////////////////////////////////////////
OptParser opt;
bool parsed, doPlot, doHeatmap, doCorr, fold, doScan;
string corrFileName, model, outFileName, outFmt, savePlot;
Index ti, tf, shift, nPar, thinning;
double svdTol;
Minimizer::Verbosity verbosity;
opt.addOption("" , "ti" , OptParser::OptType::value , false,
"initial fit time");
opt.addOption("" , "tf" , OptParser::OptType::value , false,
"final fit time");
opt.addOption("t" , "thinning", OptParser::OptType::value , true,
"thinning of the time interval", "1");
opt.addOption("s", "shift" , OptParser::OptType::value , true,
"time variable shift", "0");
opt.addOption("m", "model" , OptParser::OptType::value , true,
"fit model (exp|exp2|exp3|sinh|cosh|cosh2|cosh3|explin|const|<interpreter code>)", "cosh");
opt.addOption("" , "nPar" , OptParser::OptType::value , true,
"number of model parameters for custom models "
"(-1 if irrelevant)", "-1");
opt.addOption("" , "svd" , OptParser::OptType::value , true,
"singular value elimination threshold", "0.");
opt.addOption("v", "verbosity", OptParser::OptType::value , true,
"minimizer verbosity level (0|1|2)", "0");
opt.addOption("o", "output", OptParser::OptType::value , true,
"output file", "");
opt.addOption("" , "uncorr" , OptParser::OptType::trigger, true,
"only do the uncorrelated fit");
opt.addOption("" , "fold" , OptParser::OptType::trigger, true,
"fold the correlator");
opt.addOption("p", "plot" , OptParser::OptType::trigger, true,
"show the fit plot");
opt.addOption("h", "heatmap" , OptParser::OptType::trigger, true,
"show the fit correlation heatmap");
opt.addOption("", "save-plot", OptParser::OptType::value, true,
"saves the source and .pdf", "");
opt.addOption("", "scan", OptParser::OptType::trigger, true,
"scan all possible fit ranges within [ti,tf]");
opt.addOption("", "help" , OptParser::OptType::trigger, true,
"show this help message and exit");
parsed = opt.parse(argc, argv);
if (!parsed or (opt.getArgs().size() != 1) or opt.gotOption("help"))
{
cerr << "usage: " << argv[0] << " <options> <correlator file>" << endl;
cerr << endl << "Possible options:" << endl << opt << endl;
return EXIT_FAILURE;
}
corrFileName = opt.getArgs().front();
ti = opt.optionValue<Index>("ti");
tf = opt.optionValue<Index>("tf");
thinning = opt.optionValue<Index>("t");
shift = opt.optionValue<Index>("s");
model = opt.optionValue("m");
nPar = opt.optionValue<Index>("nPar");
svdTol = opt.optionValue<double>("svd");
outFileName = opt.optionValue<string>("o");
doCorr = !opt.gotOption("uncorr");
fold = opt.gotOption("fold");
doPlot = opt.gotOption("p");
doHeatmap = opt.gotOption("h");
savePlot = opt.optionValue("save-plot");
doScan = opt.gotOption("scan");
switch (opt.optionValue<unsigned int>("v"))
{
case 0:
verbosity = Minimizer::Verbosity::Silent;
break;
case 1:
verbosity = Minimizer::Verbosity::Normal;
break;
case 2:
verbosity = Minimizer::Verbosity::Debug;
break;
default:
cerr << "error: wrong verbosity level" << endl;
return EXIT_FAILURE;
}
// load correlator /////////////////////////////////////////////////////////
DMatSample tmp, corr;
Index nSample, nt;
tmp = Io::load<DMatSample>(corrFileName);
nSample = tmp.size();
nt = tmp[central].rows();
tmp = tmp.block(0, 0, nt, 1);
corr = tmp;
FOR_STAT_ARRAY(corr, s)
{
for (Index t = 0; t < nt; ++t)
{
corr[s]((t - shift + nt)%nt) = tmp[s](t);
}
}
if (fold)
{
tmp = corr;
FOR_STAT_ARRAY(corr, s)
{
for (Index t = 0; t < nt; ++t)
{
corr[s](t) = 0.5*(tmp[s](t) + tmp[s]((nt - t) % nt));
}
}
}
// make models /////////////////////////////////////////////////////////////
DoubleModel mod;
bool sinhModel = false, coshModel = false, linearModel = false, constModel = false;
if ((model == "exp") or (model == "exp1"))
{
nPar = 2;
mod.setFunction([](const double *x, const double *p)
{
return p[1]*exp(-p[0]*x[0]);
}, 1, nPar);
}
else if (model == "exp2")
{
nPar = 4;
mod.setFunction([](const double *x, const double *p)
{
return p[1]*exp(-p[0]*x[0]) + p[3]*exp(-p[2]*x[0]);
}, 1, nPar);
}
else if (model == "exp3")
{
nPar = 6;
mod.setFunction([](const double *x, const double *p)
{
return p[1]*exp(-p[0]*x[0]) + p[3]*exp(-p[2]*x[0])
+ p[5]*exp(-p[4]*x[0]);
}, 1, nPar);
}
else if (model == "sinh")
{
sinhModel = true;
nPar = 2;
mod.setFunction([nt](const double *x, const double *p)
{
return p[1]*(exp(-p[0]*x[0])-exp(-p[0]*(nt-x[0])));
}, 1, nPar);
}
else if ((model == "cosh") or (model == "cosh1"))
{
coshModel = true;
nPar = 2;
mod.setFunction([nt](const double *x, const double *p)
{
return p[1]*(exp(-p[0]*x[0])+exp(-p[0]*(nt-x[0])));
}, 1, nPar);
}
else if (model == "cosh2")
{
coshModel = true;
nPar = 4;
mod.setFunction([nt](const double *x, const double *p)
{
return p[1]*(exp(-p[0]*x[0])+exp(-p[0]*(nt-x[0])))
+ p[3]*(exp(-p[2]*x[0])+exp(-p[2]*(nt-x[0])));
}, 1, nPar);
}
else if (model == "cosh3")
{
coshModel = true;
nPar = 6;
mod.setFunction([nt](const double *x, const double *p)
{
return p[1]*(exp(-p[0]*x[0])+exp(-p[0]*(nt-x[0])))
+ p[3]*(exp(-p[2]*x[0])+exp(-p[2]*(nt-x[0])))
+ p[5]*(exp(-p[2]*x[0])+exp(-p[4]*(nt-x[0])));
}, 1, nPar);
}
else if (model == "explin")
{
linearModel = true;
nPar = 2;
mod.setFunction([](const double *x, const double *p)
{
return p[1] - p[0]*x[0];
}, 1, nPar);
}
else if (model == "const")
{
constModel = true;
nPar = 1;
mod.setFunction([](const double *x __dumb, const double *p)
{
return p[0];
}, 1, nPar);
}
else
{
if (nPar > 0)
{
mod = compile(model, 1, nPar);
}
else
{
cerr << "error: please specify the number of model parameter"
" using the --nPar function" << endl;
return EXIT_FAILURE;
}
}
// fit /////////////////////////////////////////////////////////////////////
DMatSample tvec(nSample);
XYSampleData data(nSample);
DVec init(nPar);
NloptMinimizer globMin(NloptMinimizer::Algorithm::GN_CRS2_LM);
MinuitMinimizer locMin;
vector<Minimizer *> unCorrMin{&globMin, &locMin};
FOR_STAT_ARRAY(tvec, s)
{
tvec[s] = DVec::LinSpaced(nt, 0, nt - 1);
}
data.addXDim(nt, "t/a", true);
data.addYDim("C(t)");
data.setUnidimData(tvec, corr);
// set parameter name ******************************************************
if(constModel)
{
mod.parName().setName(0, "const");
}
else
{
for (Index p = 0; p < nPar; p += 2)
{
mod.parName().setName(p, "E_" + strFrom(p/2));
mod.parName().setName(p + 1, "Z_" + strFrom(p/2));
}
}
// set initial values ******************************************************
if (linearModel)
{
init(0) = data.y(nt/4, 0)[central] - data.y(nt/4 + 1, 0)[central];
init(1) = data.y(nt/4, 0)[central] + nt/4*init(0);
}
else if(constModel)
{
init(0) = data.y(nt/4, 0)[central];
}
else
{
init(0) = log(data.y(nt/4, 0)[central]/data.y(nt/4 + 1, 0)[central]);
init(1) = data.y(nt/4, 0)[central]/(exp(-init(0)*nt/4));
}
for (Index p = 2; p < nPar; p += 2)
{
init(p) = 2*init(p - 2);
init(p + 1) = init(p - 1)/2.;
}
// set limits for minimisers ***********************************************
for (Index p = 0; p < nPar; p += 2)
{
if (linearModel)
{
globMin.setLowLimit(p, -10.*fabs(init(p)));
globMin.setHighLimit(p, 10.*fabs(init(p)));
}
else if(constModel)
{
globMin.setLowLimit(p, -10*fabs(init(0)));
locMin.setLowLimit(p, -10*fabs(init(0)));
globMin.setHighLimit(p, 10*fabs(init(0)));
locMin.setHighLimit(p, 10*fabs(init(0)));
}
else
{
globMin.setLowLimit(p, 0.);
globMin.setHighLimit(p, 10.*init(p));
}
if(!constModel)
{
globMin.setLowLimit(p + 1, -10.*fabs(init(p + 1)));
globMin.setHighLimit(p + 1, 10.*fabs(init(p + 1)));
}
}
globMin.setPrecision(0.001);
globMin.setMaxIteration(100000);
globMin.setVerbosity(verbosity);
locMin.setMaxIteration(1000000);
locMin.setVerbosity(verbosity);
// fit /////////////////////////////////////////////////////////////////////
if (!doScan)
{
SampleFitResult fit;
setFitRange(data, ti, tf, thinning, nt);
if (doCorr)
{
cout << "-- uncorrelated fit..." << endl;
}
cout << "using model '" << model << "'" << endl;
data.setSvdTolerance(svdTol);
data.assumeYYCorrelated(false, 0, 0);
fit = data.fit(unCorrMin, init, mod);
fit.print();
if (doCorr)
{
cout << "-- correlated fit..." << endl;
cout << "using model '" << model << "'" << endl;
init = fit[central];
data.assumeYYCorrelated(true, 0, 0);
fit = data.fit(locMin, init, mod);
fit.print();
}
if (!outFileName.empty())
{
Io::save(fit, outFileName);
}
// plots ***************************************************************
if (doPlot)
{
if (!constModel)
{
Plot p;
p << PlotRange(Axis::x, 0, nt - 1);
if (!linearModel and !constModel)
{
p << LogScale(Axis::y);
}
p << Color("rgb 'blue'") << PlotPredBand(fit.getModel(_), 0, nt - 1);
p << Color("rgb 'blue'") << PlotFunction(fit.getModel(), 0, nt - 1);
p << Color("rgb 'red'") << PlotData(data.getData());
p.display();
if(savePlot != "")
{
p.save(savePlot + "_corr");
}
}
{
Plot p;
DMatSample effMass(nSample);
DVec effMassT, fitErr;
Index maxT = (coshModel) ? (nt - 2) : (nt - 1);
double e0, e0Err;
effMass.resizeMat(maxT, 1);
effMassT.setLinSpaced(maxT, 0, maxT-1);
fitErr = fit.variance().cwiseSqrt();
e0 = fit[central](0);
e0Err = fitErr(0);
if (coshModel or sinhModel)
{
FOR_STAT_ARRAY(effMass, s)
{
for (Index t = 1; t < nt - 1; ++t)
{
effMass[s](t - 1) = acosh((corr[s](t-1) + corr[s](t+1))
/(2.*corr[s](t)));
}
}
}
else if (linearModel)
{
FOR_STAT_ARRAY(effMass, s)
{
for (Index t = 0; t < nt - 1; ++t)
{
effMass[s](t) = corr[s](t) - corr[s](t+1);
}
}
}
else if (constModel)
{
FOR_STAT_ARRAY(effMass, s)
{
for (Index t = 0; t < nt - 1; ++t)
{
effMass[s](t) = corr[s](t);
}
}
}
else
{
FOR_STAT_ARRAY(effMass, s)
{
for (Index t = 1; t < nt; ++t)
{
effMass[s](t - 1) = log(corr[s](t-1)/corr[s](t));
}
}
}
p.reset();
p << PlotRange(Axis::x, 0, maxT);
p << PlotRange(Axis::y, e0 - 20.*e0Err, e0 + 20.*e0Err);
p << Color("rgb 'blue'") << PlotBand(0, maxT, e0 - e0Err, e0 + e0Err);
p << Color("rgb 'blue'") << PlotHLine(e0);
p << Color("rgb 'red'") << PlotData(effMassT, effMass);
p << Caption("Effective Mass");
p.display();
if(savePlot != "")
{
p.save(savePlot + "_effMass");
}
}
if (doHeatmap)
{
Plot p;
Index n = data.getFitVarMat().rows();
DMat id = DMat::Identity(n, n);
p << PlotMatrix(Math::varToCorr(data.getFitVarMat()));
p << Caption("correlation matrix");
p.display();
if (svdTol > 0.)
{
p.reset();
p << PlotMatrix(id - data.getFitVarMat()*data.getFitVarMatPInv());
p << Caption("singular space projector");
p.display();
}
}
}
}
// scan fits ///////////////////////////////////////////////////////////////
else
{
Index nFit = 0, f = 0, ti0 = ti + (tf - ti)/4, tf0 = tf - (tf - ti)/4,
matSize = tf - ti - nPar + 1;
DMat err, pVal(matSize, matSize), relErr(matSize, matSize),
ccdf(matSize, matSize), val(matSize, matSize);
map<double, TwoPtFit> fit;
SampleFitResult tmpFit;
cout << "-- initial uncorrelated fit on [" << ti0 << ", " << tf0 << "]..." << endl;
if (thinning != 1)
{
cerr << "warning: thinning different from 1 ignored in scan mode"
<< endl;
thinning = 1;
}
setFitRange(data, ti0, tf0, thinning, nt);
data.setSvdTolerance(svdTol);
data.assumeYYCorrelated(false, 0, 0);
tmpFit = data.fit(unCorrMin, init, mod);
tmpFit.print();
cout << "-- scanning all possible fit ranges..." << endl;
init = tmpFit[central];
data.assumeYYCorrelated(doCorr, 0, 0);
pVal.fill(Math::nan);
relErr.fill(Math::nan);
val.fill(Math::nan);
ccdf.fill(Math::nan);
for (Index ta = ti; ta < tf; ++ta)
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;
}