#include <LatAnalyze/Core/Math.hpp>
#include <LatAnalyze/Core/OptParser.hpp>
#include <LatAnalyze/Core/Plot.hpp>
#include <LatAnalyze/Functional/CompiledModel.hpp>
#include <LatAnalyze/Io/Io.hpp>
#include <LatAnalyze/Numerical/DWT.hpp>
#include <LatAnalyze/Numerical/MinuitMinimizer.hpp>
#include <LatAnalyze/Numerical/NloptMinimizer.hpp>
#include <LatAnalyze/Physics/CorrelatorFitter.hpp>
#include <LatAnalyze/Physics/EffectiveMass.hpp>
#include <LatAnalyze/Statistics/MatSample.hpp>
#include <LatAnalyze/Statistics/XYSampleData.hpp>
using namespace std;
using namespace Latan;
struct TwoPtFit
{
SampleFitResult result;
Index tMin, tMax;
};
int main(int argc, char *argv[])
{
// parse arguments /////////////////////////////////////////////////////////
OptParser opt;
bool parsed, doLaplace, doPlot, doHeatmap, doCorr, fold, doScan, noGuess;
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<n>|sinh<n>|cosh<n>|linear|cst|<interpreter code>)", "exp1");
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("l" , "laplace" , OptParser::OptType::trigger, true,
"apply Laplace filter to 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("", "no-guess" , OptParser::OptType::trigger, true,
"do not try to guess fit parameters");
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");
doLaplace = opt.gotOption("l");
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");
noGuess = opt.gotOption("no-guess");
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;
corr = Io::load<DMatSample>(corrFileName);
nSample = corr.size();
nt = corr[central].rows();
corr = corr.block(0, 0, nt, 1);
corr = CorrelatorUtils::shift(corr, shift);
if (doLaplace)
{
vector<double> filter = {1., -2., 1.};
DVec buf;
FOR_STAT_ARRAY(corr, s)
{
DWT::filterConvolution(buf, corr[s], filter, 1);
corr[s] = buf;
}
}
if (fold)
{
corr = CorrelatorUtils::fold(corr);
}
// make model //////////////////////////////////////////////////////////////
CorrelatorFitter fitter(corr);
DoubleModel mod;
auto modelPar = CorrelatorModels::parseModel(model);
if (modelPar.type != CorrelatorType::undefined)
{
mod = CorrelatorModels::makeModel(modelPar, nt);
nPar = mod.getNPar();
}
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 /////////////////////////////////////////////////////////////////////
DVec init(nPar);
NloptMinimizer globMin(NloptMinimizer::Algorithm::GN_CRS2_LM);
MinuitMinimizer locMin;
vector<Minimizer *> unCorrMin{&globMin, &locMin};
// set fitter **************************************************************
fitter.setModel(mod);
fitter.data().setSvdTolerance(svdTol);
fitter.setThinning(thinning);
// set initial values ******************************************************
if ((modelPar.type != CorrelatorType::undefined) and !noGuess)
{
init = CorrelatorModels::parameterGuess(corr, modelPar);
}
else
{
init.fill(1.);
init(0) = 0.2;
}
// set limits for minimisers ***********************************************
for (Index p = 0; p < nPar; p += 2)
{
if ((modelPar.type == CorrelatorType::exp) or
(modelPar.type == CorrelatorType::cosh) or
(modelPar.type == CorrelatorType::sinh))
{
globMin.setLowLimit(p, 0.);
locMin.setLowLimit(p, 0.);
globMin.setHighLimit(p, 10.*init(p));
globMin.setLowLimit(p + 1, -10.*fabs(init(p + 1)));
globMin.setHighLimit(p + 1, 10.*fabs(init(p + 1)));
}
else if(modelPar.type == CorrelatorType::linear)
{
globMin.setLowLimit(p, -10.*fabs(init(p)));
locMin.setLowLimit(p, -10.*fabs(init(p)));
globMin.setHighLimit(p, 10.*init(p));
globMin.setLowLimit(p + 1, -10.*fabs(init(p + 1)));
globMin.setHighLimit(p + 1, 10.*fabs(init(p + 1)));
}
else
{
globMin.setLowLimit(p, -10*fabs(init(p)));
globMin.setHighLimit(p, 10*fabs(init(p)));
}
}
globMin.setPrecision(0.001);
globMin.setMaxIteration(100000);
globMin.setVerbosity(verbosity);
locMin.setMaxIteration(1000000);
locMin.setVerbosity(verbosity);
// standard fit ////////////////////////////////////////////////////////////
if (!doScan)
{
// fit *****************************************************************
SampleFitResult fit;
fitter.setFitRange(ti, tf);
if (doCorr)
{
cout << "-- uncorrelated fit..." << endl;
}
cout << "using model '" << model << "'" << endl;
fitter.setCorrelation(false);
fit = fitter.fit(unCorrMin, init);
fit.print();
if (doCorr)
{
cout << "-- correlated fit..." << endl;
cout << "using model '" << model << "'" << endl;
init = fit[central];
fitter.setCorrelation(true);
fit = fitter.fit(locMin, init);
fit.print();
}
if (!outFileName.empty())
{
Io::save(fit, outFileName);
}
// plots ***************************************************************
if (doPlot)
{
DMatSample tvec(nSample);
tvec.fill(DVec::LinSpaced(nt, 0, nt - 1));
if (modelPar.type != CorrelatorType::cst)
{
Plot p;
p << PlotRange(Axis::x, 0, nt - 1);
if ((modelPar.type == CorrelatorType::exp) or
(modelPar.type == CorrelatorType::cosh) or
(modelPar.type == CorrelatorType::sinh))
{
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(fitter.data().getData());
p << Label("t/a", Axis::x) << Caption("Correlator");
p.display();
if(savePlot != "")
{
p.save(savePlot + "_corr");
}
}
if (modelPar.type != CorrelatorType::undefined)
{
Plot p;
EffectiveMass effMass(modelPar.type);
DMatSample em;
DVec fitErr, emtvec;
double e0, e0Err;
emtvec = effMass.getTime(nt);
em = effMass(corr);
fitErr = fit.variance().cwiseSqrt();
e0 = fit[central](0);
e0Err = fitErr(0);
p.reset();
p << PlotRange(Axis::x, 0, nt - 1);
p << PlotRange(Axis::y, e0 - 30.*e0Err, e0 + 30.*e0Err);
p << Color("rgb 'blue'") << PlotBand(0, nt - 1, e0 - e0Err, e0 + e0Err);
p << Color("rgb 'blue'") << PlotHLine(e0);
p << Color("rgb 'red'") << PlotData(emtvec, em);
p << Label("t/a", Axis::x) << Caption("Effective Mass");
p.display();
if(savePlot != "")
{
p.save(savePlot + "_effMass");
}
}
if (doHeatmap)
{
Plot p;
Index n = fitter.data().getFitVarMat().rows();
DMat id = DMat::Identity(n, n),
var = fitter.data().getFitVarMat();
p << PlotCorrMatrix(Math::varToCorr(var));
p << Caption("correlation matrix");
p.display();
if (svdTol > 0.)
{
DMat proj = id - var*fitter.data().getFitVarMatPInv();
p.reset();
p << PlotMatrix(proj);
p << Caption("singular space projector");
p.display();
}
}
}
}
// scan fits ///////////////////////////////////////////////////////////////
else
{
// fits ****************************************************************
Index nFit = 0, f = 0, ti0 = ti + (tf - ti)/4, tf0 = tf - (tf - ti)/4,
matSize = tf - ti + 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;
}
fitter.setFitRange(ti0, tf0);
fitter.setCorrelation(false);
tmpFit = fitter.fit(unCorrMin, init);
tmpFit.print();
cout << "-- scanning all possible fit ranges..." << endl;
init = tmpFit[central];
fitter.setCorrelation(doCorr);
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;
fitter.setFitRange(ta, tb);
tmpFit = fitter.fit(locMin, init);
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("tMax - " + strFrom(ti), Axis::x);
p << Label("tMin - " + strFrom(ti), Axis::y);
p.display();
if(savePlot != "")
{
p.save(savePlot + "_pValMatrix");
}
p.reset();
p << PlotMatrix(relErr);
p << Caption("Relative error matrix");
p << Label("tMax - " + strFrom(ti), Axis::x);
p << Label("tMin - " + strFrom(ti), Axis::y);
p.display();
if(savePlot != "")
{
p.save(savePlot + "_relErrMatrix");
}
p.reset();
p << PlotMatrix(val);
p << Caption("Fit result matrix");
p << Label("tMax - " + strFrom(ti), Axis::x);
p << Label("tMin - " + 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("tMax - " + strFrom(ti), Axis::x);
p << Label("tMin - " + strFrom(ti), Axis::y);
p.display();
if(savePlot != "")
{
p.save(savePlot + "_ccdfMatrix");
}
}
}
return EXIT_SUCCESS;
}