2pt fit: option to bypass parameter guess

.github/workflows/build-macos.yml

@@ -1,6 +1,6 @@
 name: Build macOS
 
-on: [push]
+on: [push, workflow_dispatch]
 
 jobs:
   build:

examples/exRand.cpp

@@ -7,7 +7,7 @@
 using namespace std;
 using namespace Latan;
 
-constexpr Index  size          = 8;
+constexpr Index  n             = 8;
 constexpr Index  nDraw         = 20000;
 constexpr Index  nSample       = 2000;
 const     string stateFileName = "exRand.seed";
@@ -40,14 +40,14 @@ int main(void)
     p << PlotFunction(compile("return exp(-x_0^2/2)/sqrt(2*pi);", 1), -5., 5.);
     p.display();
 
-    DMat       var(size, size);
+    DMat       var(n, n);
-    DVec       mean(size);
+    DVec       mean(n);
-    DMatSample sample(nSample, size, 1);
+    DMatSample sample(nSample, n, 1);
 
     cout << "-- generating " << nSample << " Gaussian random vectors..." << endl;
-    var   = DMat::Random(size, size);
+    var   = DMat::Random(n, n);
     var  *= var.adjoint();
-    mean  = DVec::Random(size); 
+    mean  = DVec::Random(n); 
     RandomNormal mgauss(mean, var, rd());
     sample[central] = mgauss();
     FOR_STAT_ARRAY(sample, s)

lib/Core/Utilities.hpp

@@ -108,23 +108,6 @@ inline std::string strFrom(const T x)
 }
 
 // specialization for vectors
-template<>
-inline std::vector<Index> strTo<std::vector<Index>>(const std::string &str)
-{
-    std::vector<Index>  res;
-    std::vector<double> vbuf;
-    double              buf;
-    std::istringstream  stream(str);
-    
-    while (!stream.eof())
-    {
-        stream >> buf;
-        res.push_back(buf);
-    }
-    
-    return res;
-}
-
 template<>
 inline DVec strTo<DVec>(const std::string &str)
 {

lib/Physics/CorrelatorFitter.cpp

@@ -253,39 +253,16 @@ DMatSample CorrelatorUtils::shift(const DMatSample &c, const Index ts)
     }
 }
 
-DMatSample CorrelatorUtils::fold(const DMatSample &c, const CorrelatorModels::ModelPar &par)
+DMatSample CorrelatorUtils::fold(const DMatSample &c)
 {
     const Index nt  = c[central].rows();
     DMatSample  buf = c;
-    int         sign;
-    bool        fold = false;
 
-    switch (par.type)
+    FOR_STAT_ARRAY(buf, s)
     {
-        case CorrelatorType::cosh:
+        for (Index t = 0; t < nt; ++t)
-        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)
         {
-            for (Index t = 0; t < nt; ++t)
+            buf[s](t) = 0.5*(c[s](t) + c[s]((nt - t) % nt));
-            {
-                buf[s](t) = 0.5*(c[s](t) + sign*c[s]((nt - t) % nt));
-            }
         }
     }
 

lib/Physics/CorrelatorFitter.hpp

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

lib/Statistics/Histogram.cpp

@@ -146,16 +146,6 @@ double Histogram::getX(const Index i) const
     return x_(i);
 }
 
-double Histogram::getXMin(void) const
-{
-    return xMin_;
-}
-
-double Histogram::getXMax(void) const
-{
-    return xMax_;
-}
-
 double Histogram::operator[](const Index i) const
 {
     return bin_(i)*(isNormalized() ? norm_ : 1.);

lib/Statistics/Histogram.hpp

@@ -52,8 +52,6 @@ public:
     const StatArray<double> & getData(void) const;
     const StatArray<double> & getWeight(void) const;
     double                    getX(const Index i) const;
-    double                    getXMin(void) const;
-    double                    getXMax(void) const;
     double                    operator[](const Index i) const;
     double                    operator()(const double x) const;
     // percentiles & confidence interval

lib/Statistics/XYSampleData.cpp

@@ -300,67 +300,6 @@ const XYStatData & XYSampleData::getData(void)
 }
 
 // fit /////////////////////////////////////////////////////////////////////////
-
-void XYSampleData::fitSample(std::vector<Minimizer *> &minimizer,
-                             const std::vector<const DoubleModel *> &v,
-                             SampleFitResult &result,
-                             DVec &init,
-                             Index s)
-{
-    result.resize(nSample_);
-    result.chi2_.resize(nSample_);
-    result.model_.resize(v.size());
-    FitResult sampleResult;
-    setDataToSample(s);
-    if (s == central)
-    {
-        sampleResult        = data_.fit(minimizer, init, v);
-        init                = sampleResult.segment(0, init.size());
-        result.nPar_        = sampleResult.getNPar();
-        result.nDof_        = sampleResult.nDof_;
-        result.parName_     = sampleResult.parName_;
-        result.corrRangeDb_ = Math::svdDynamicRangeDb(getFitCorrMat());
-    }
-    else
-    {
-        sampleResult = data_.fit(*(minimizer.back()), init, 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);
-    }
-
-    
-}
-
-SampleFitResult XYSampleData::fit(std::vector<Minimizer *> &minimizer,
-                                  const DVec &init,
-                                  const std::vector<const DoubleModel *> &v,
-                                  Index s)
-{
-    computeVarMat();
-    
-    SampleFitResult result;
-    DVec      initCopy = init;
-
-    fitSample(minimizer, v, result, initCopy, s);
-    
-    return result;
-}
-
-SampleFitResult XYSampleData::fit(Minimizer &minimizer,
-                                  const DVec &init,
-                                  const std::vector<const DoubleModel *> &v,
-                                  Index s)
-{
-    vector<Minimizer *> mv{&minimizer};
-    
-    return fit(mv, init, v, s);
-}
-
 SampleFitResult XYSampleData::fit(std::vector<Minimizer *> &minimizer,
                                   const DVec &init,
                                   const std::vector<const DoubleModel *> &v)
@@ -368,14 +307,43 @@ SampleFitResult XYSampleData::fit(std::vector<Minimizer *> &minimizer,
     computeVarMat();
     
     SampleFitResult      result;
+    FitResult            sampleResult;
     DVec                 initCopy = init;
     Minimizer::Verbosity verbCopy = minimizer.back()->getVerbosity();
     
+    result.resize(nSample_);
+    result.chi2_.resize(nSample_);
+    result.model_.resize(v.size());
     FOR_STAT_ARRAY(result, s)
     {
-        fitSample(minimizer, v, result, initCopy, s);
+        setDataToSample(s);
+        if (s == central)
+        {
+            sampleResult = data_.fit(minimizer, initCopy, v);
+            initCopy     = sampleResult.segment(0, initCopy.size());
+            if (verbCopy != Minimizer::Verbosity::Debug)
+            {
+                minimizer.back()->setVerbosity(Minimizer::Verbosity::Silent);
+            }
+        }
+        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);
+        }
     }
     minimizer.back()->setVerbosity(verbCopy);
+    result.nPar_       = sampleResult.getNPar();
+    result.nDof_       = sampleResult.nDof_;
+    result.parName_    = sampleResult.parName_;
+    result.corrRangeDb_ = Math::svdDynamicRangeDb(getFitCorrMat());
     
     return result;
 }

lib/Statistics/XYSampleData.hpp

@@ -103,13 +103,6 @@ public:
     // get internal XYStatData
     const XYStatData & getData(void);
     // fit
-    void            fitSample(std::vector<Minimizer *> &minimizer, 
-                              const std::vector<const DoubleModel *> &v, 
-                              SampleFitResult &sampleResult, DVec &init, Index s);
-    SampleFitResult fit(std::vector<Minimizer *> &minimizer, const DVec &init, 
-                        const std::vector<const DoubleModel *> &v, Index s);
-    SampleFitResult fit(Minimizer &minimizer, const DVec &init, 
-                        const std::vector<const DoubleModel *> &v, Index s);
     SampleFitResult fit(std::vector<Minimizer *> &minimizer, const DVec &init,
                         const std::vector<const DoubleModel *> &v);
     SampleFitResult fit(Minimizer &minimizer, const DVec &init,

physics/2pt-fit.cpp

@@ -24,7 +24,7 @@ int main(int argc, char *argv[])
 {
     // parse arguments /////////////////////////////////////////////////////////
     OptParser            opt;
-    bool                 parsed, doLaplace, doPlot, doHeatmap, doCorr, fold, doScan;
+    bool                 parsed, doLaplace, doPlot, doHeatmap, doCorr, fold, doScan, noGuess;
     string               corrFileName, model, outFileName, outFmt, savePlot;
     Index                ti, tf, shift, nPar, thinning;
     double               svdTol;
@@ -59,6 +59,8 @@ int main(int argc, char *argv[])
                   "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,
@@ -87,6 +89,7 @@ int main(int argc, char *argv[])
     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"))
@@ -114,7 +117,6 @@ int main(int argc, char *argv[])
     nt      = corr[central].rows();
     corr    = corr.block(0, 0, nt, 1);
     corr    = CorrelatorUtils::shift(corr, shift);
-
     if (doLaplace)
     {
         vector<double> filter = {1., -2., 1.};
@@ -156,11 +158,6 @@ int main(int argc, char *argv[])
         }
     }
     
-    if (fold)
-    {
-        corr = CorrelatorUtils::fold(corr,modelPar);
-    }
-
     // fit /////////////////////////////////////////////////////////////////////
     DVec                init(nPar);
     NloptMinimizer      globMin(NloptMinimizer::Algorithm::GN_CRS2_LM);
@@ -173,13 +170,14 @@ int main(int argc, char *argv[])
     fitter.setThinning(thinning);
 
     // set initial values ******************************************************
-    if (modelPar.type != CorrelatorType::undefined)
+    if ((modelPar.type != CorrelatorType::undefined) and !noGuess)
     {
         init = CorrelatorModels::parameterGuess(corr, modelPar);
     }
     else
     {
-        init.fill(0.1);
+        init.fill(1.);
+        init(0) = 0.2;
     }
 
     // set limits for minimisers ***********************************************

utils/sample-fake.cpp

@@ -18,42 +18,30 @@
  */
 
 #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;
 
-    opt.addOption("r", "seed"      , OptParser::OptType::value,   true,
+    if (argc != 5)
-                    "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;
-    SeedType              seed = (opt.gotOption("r")) ? opt.optionValue<SeedType>("r") : rd();
+    mt19937               gen(rd());
-    mt19937               gen(seed);
     normal_distribution<> dis(val, err);
     DSample               res(nSample);
 

utils/sample-read.cpp

@@ -38,23 +38,9 @@ int main(int argc, char *argv[])
     {
         DMatSample s    = Io::load<DMatSample>(fileName);
         string     name = Io::getFirstName(fileName);
-        Index nRows     = s[central].rows();
-        Index nCols     = s[central].cols();
         cout << scientific;
-        cout << "central value +/-  standard deviation\n" << endl;
+        cout << "central value:\n"      << s[central]               << endl;
-        cout << "Re:" << endl;
+        cout << "standard deviation:\n" << s.variance().cwiseSqrt() << endl;
-        for(Index i = 0; i < nRows; i++)
-        {
-            cout << s[central](i,0) << " +/- " << s.variance().cwiseSqrt()(i,0) << endl;
-        }
-        if(nCols == 2)
-        {
-            cout << "\nIm:" << endl;
-            for(Index i = 0; i < nRows; i++)
-            {
-                cout << s[central](i,1) << " +/- " << s.variance().cwiseSqrt()(i,1) << endl;
-            }   
-        }
         if (!copy.empty())
         {
             Io::save(s, copy, File::Mode::write, name);
@@ -65,8 +51,8 @@ int main(int argc, char *argv[])
         DSample s    = Io::load<DSample>(fileName);
         string  name = Io::getFirstName(fileName);
         cout << scientific;
-        cout << "central value +/-  standard deviation\n" << endl;
+        cout << "central value:\n"      << s[central]         << endl;
-        cout << s[central] << " +/- " << sqrt(s.variance()) << endl;
+        cout << "standard deviation:\n" << sqrt(s.variance()) << endl;
         if (!copy.empty())
         {
             Io::save(s, copy, File::Mode::write, name);