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Merge remote-tracking branch 'fork/develop' into develop

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This commit is contained in:
AndrewYongZhenNing 2021-05-20 11:14:53 +01:00
commit 113b433b5e
6 changed files with 92 additions and 28 deletions
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33
lib/Physics/CorrelatorFitter.cpp
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@ -253,16 +253,39 @@ DMatSample CorrelatorUtils::shift(const DMatSample &c, const Index ts)
}
}
DMatSample CorrelatorUtils::fold(const DMatSample &c)
DMatSample CorrelatorUtils::fold(const DMatSample &c, const CorrelatorModels::ModelPar &par)
{
const Index nt = c[central].rows();
DMatSample buf = c;
FOR_STAT_ARRAY(buf, s)
int sign;
bool fold = false;
switch (par.type)
{
for (Index t = 0; t < nt; ++t)
case CorrelatorType::cosh:
case CorrelatorType::cst:
sign = 1;
fold = true;
break;
case CorrelatorType::sinh:
sign = -1;
fold = true;
break;
case CorrelatorType::linear:
cout << "Linear model is asymmetric: will not fold." << endl;
break;
default:
break;
}
if (fold)
{
FOR_STAT_ARRAY(buf, s)
{
buf[s](t) = 0.5*(c[s](t) + c[s]((nt - t) % nt));
for (Index t = 0; t < nt; ++t)
{
buf[s](t) = 0.5*(c[s](t) + sign*c[s]((nt - t) % nt));
}
}
}

2
lib/Physics/CorrelatorFitter.hpp
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@ -56,7 +56,7 @@ namespace CorrelatorModels
namespace CorrelatorUtils
{
DMatSample shift(const DMatSample &c, const Index ts);
DMatSample fold(const DMatSample &c);
DMatSample fold(const DMatSample &c, const CorrelatorModels::ModelPar &par);
DMatSample fourierTransform(const DMatSample &c, FFT &fft,
const unsigned int dir = FFT::Forward);
};

55
lib/Statistics/XYSampleData.cpp
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@ -234,6 +234,21 @@ DVec XYSampleData::getYError(const Index j)
return data_.getYError(j);
}
bool XYSampleData::checkFit()
{
return goodFit_;
}
void XYSampleData::checkChi2PerDof(double Chi2PerDof)
{
if(Chi2PerDof >= 2 or Chi2PerDof < 0 or isnan(Chi2PerDof))
{
goodFit_ = false;
cerr << "chi2PerDof = " << Chi2PerDof << ". Aborting fit now." << endl;
}
}
// get total fit variance matrix and its pseudo-inverse ////////////////////////
const DMat & XYSampleData::getFitVarMat(void)
{
@ -292,24 +307,34 @@ SampleFitResult XYSampleData::fit(std::vector<Minimizer *> &minimizer,
result.resize(nSample_);
result.chi2_.resize(nSample_);
result.model_.resize(v.size());
double chi2PerDof;
goodFit_ = true;
FOR_STAT_ARRAY(result, s)
{
setDataToSample(s);
if (s == central)
if(goodFit_)
{
sampleResult = data_.fit(minimizer, initCopy, v);
initCopy = sampleResult.segment(0, initCopy.size());
}
else
{
sampleResult = data_.fit(*(minimizer.back()), initCopy, v);
}
result[s] = sampleResult;
result.chi2_[s] = sampleResult.getChi2();
for (unsigned int j = 0; j < v.size(); ++j)
{
result.model_[j].resize(nSample_);
result.model_[j][s] = sampleResult.getModel(j);
setDataToSample(s);
if (s == central)
{
sampleResult = data_.fit(minimizer, initCopy, v);
initCopy = sampleResult.segment(0, initCopy.size());
chi2PerDof = sampleResult.getChi2PerDof();
checkChi2PerDof(chi2PerDof);
}
else
{
sampleResult = data_.fit(*(minimizer.back()), initCopy, v);
chi2PerDof = sampleResult.getChi2PerDof();
checkChi2PerDof(chi2PerDof);
}
result[s] = sampleResult;
result.chi2_[s] = sampleResult.getChi2();
for (unsigned int j = 0; j < v.size(); ++j)
{
result.model_[j].resize(nSample_);
result.model_[j][s] = sampleResult.getModel(j);
}
}
}
result.nPar_ = sampleResult.getNPar();

3
lib/Statistics/XYSampleData.hpp
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@ -91,6 +91,8 @@ public:
const DMat & getXYVar(const Index i, const Index j);
DVec getXError(const Index i);
DVec getYError(const Index j);
bool checkFit(); // check fit candidate based on chi2PerDof
void checkChi2PerDof(double Chi2PerDof);
// get total fit variance matrix and its pseudo-inverse
const DMat & getFitVarMat(void);
const DMat & getFitVarMatPInv(void);
@ -133,6 +135,7 @@ private:
Index nSample_, dataSample_{central};
bool initData_{true}, computeVarMat_{true};
bool initXMap_{true};
bool goodFit_{true}; // used to break minimisation if central sample chi2PerDof is bad
};
/******************************************************************************

9
physics/2pt-fit.cpp
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@ -110,10 +110,6 @@ int main(int argc, char *argv[])
nt = corr[central].rows();
corr = corr.block(0, 0, nt, 1);
corr = CorrelatorUtils::shift(corr, shift);
if (fold)
{
corr = CorrelatorUtils::fold(corr);
}
// make model //////////////////////////////////////////////////////////////
CorrelatorFitter fitter(corr);
@ -140,6 +136,11 @@ int main(int argc, char *argv[])
}
}
if (fold)
{
corr = CorrelatorUtils::fold(corr,modelPar);
}
// fit /////////////////////////////////////////////////////////////////////
DVec init(nPar);
NloptMinimizer globMin(NloptMinimizer::Algorithm::GN_CRS2_LM);

18
utils/sample-fake.cpp
View File

@ -18,30 +18,42 @@
*/
#include <LatAnalyze/Io/Io.hpp>
#include <LatAnalyze/Core/OptParser.hpp>
using namespace std;
using namespace Latan;
int main(int argc, char *argv[])
{
OptParser opt;
Index nSample;
double val, err;
string outFileName;
if (argc != 5)
opt.addOption("r", "seed" , OptParser::OptType::value, true,
"random generator seed (default: random)");
opt.addOption("", "help" , OptParser::OptType::trigger, true,
"show this help message and exit");
bool parsed = opt.parse(argc, argv);
if (!parsed or (opt.getArgs().size() != 4) or opt.gotOption("help"))
{
cerr << "usage: " << argv[0];
cerr << " <central value> <error> <nSample> <output file>" << endl;
cerr << endl << "Possible options:" << endl << opt << endl;
return EXIT_FAILURE;
}
val = strTo<double>(argv[1]);
err = strTo<double>(argv[2]);
nSample = strTo<Index>(argv[3]);
outFileName = argv[4];
random_device rd;
mt19937 gen(rd());
SeedType seed = (opt.gotOption("r")) ? opt.optionValue<SeedType>("r") : rd();
mt19937 gen(seed);
normal_distribution<> dis(val, err);
DSample res(nSample);
@ -59,4 +71,4 @@ int main(int argc, char *argv[])
Io::save<DSample>(res, outFileName);
return EXIT_SUCCESS;
}
}