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// Original Author: David Nisson
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// Fits a two-dimensional model in eta and phi consisting of summed
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// Gaussians to a histogram, supplied in the function fit_histo.
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// This histogram can be of mass, energy, or momentum.
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#ifndef UserCode_JetFitAnalyzer_HistoFitter_h
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#define UserCode_JetFitAnalyzer_HistoFitter_h
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#include <TH2.h>
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#include <vector>
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#include <string>
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#include <TString.h>
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#include <TFormula.h>
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#include <TMinuit.h>
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class HistoFitter {
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public:
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// the objective function that gets minimized by TMinuit
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static void fcn(int&, double *, double&, double *, int);
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// the correction to Gaussians drifting outside of histogram
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static double outsiderCorrection(double *, double XbinSize,
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double YbinSize,
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double cutoffRad = 0.2);
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// place where fitting trouble happened
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enum TroubleStage {
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T_NULL = 0,
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T_SIMPLEX,
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T_MIGRAD,
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T_MINOS
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};
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// the histogram class used internally by HistoFitter
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struct VectorHisto {
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std::vector< std::vector<double> > bins;
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double Xlo, Xhi, Ylo, Yhi;
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};
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// information about fitting trouble
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struct Trouble {
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TroubleStage occ;
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int istat;
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};
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// structure that stores the fit results
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struct FitResults {
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TH2 *rebinnedHisto;
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std::vector< double > chisquare;
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std::vector< std::vector<TString> > pars;
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std::vector< std::vector<double> > pval;
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std::vector< std::vector<double> > perr;
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std::vector< std::vector<double> > plo;
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std::vector< std::vector<double> > phi;
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};
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class ModelDefinition {
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public:
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int numberOfGaussians();
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void setNumberOfGaussians(int);
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int maxNumberOfGaussians() {
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return theMaxNumberOfGaussians;
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}
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void setMaxNumberOfGaussians(int newNumberOfGaussians) {
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theMaxNumberOfGaussians = newNumberOfGaussians;
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}
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double fitFunction(double, double, double *);
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TFormula *indivFormula();
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void setFormula(TFormula *);
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void indivParameter(int, std::string&, double&, double&, double&, double&);
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void setIndivParameter(int, std::string, double, double, double, double);
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unsigned indivEnergy();
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void setIndivEnergy(unsigned);
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unsigned indivMeanX();
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void setIndivMeanX(unsigned);
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unsigned indivMeanY();
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void setIndivMeanY(unsigned);
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virtual double chisquareSigma(double, double) = 0;
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double formulaIntegral(double, double, double, double,
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double*, double, double);
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std::vector< std::vector< double > > subtractCurrentFit(VectorHisto,
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FitResults);
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void initParsFromModelDef(std::vector<TString> &,
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double *, double *, double *, double *,
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int, FitResults);
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void findInitialValues(std::vector<TString> &,
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double *, double *, double *, double *,
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int, FitResults, int);
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double initNewN(FitResults, int);
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double initNewMeanX(FitResults, int);
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double initNewMeanY(FitResults, int);
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void initParameters(TH2 *, TMinuit *, std::vector<TString> &,
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double *, double *, double *, double *,
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int, FitResults);
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private:
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TFormula *theIndivFormula; // formula for individual Gaussian
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std::vector<std::string> indivParNames;
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std::vector<double> indivParStarts;
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std::vector<double> indivParStartingStepSizes;
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std::vector<double> indivParLowerLimit;
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std::vector<double> indivParUpperLimit;
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unsigned theIndivEnergy;
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unsigned theIndivMeanX;
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unsigned theIndivMeanY;
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int theNumberOfGaussians;
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int theMaxNumberOfGaussians;
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};
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int getNumberOfDegreesOfFreedom(VectorHisto, int);
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FitResults putParsInFitResults(std::vector<TString>,
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double *, double *, double *, double *,
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FitResults&);
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static VectorHisto loadTH2IntoVectorHisto(TH2*);
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Trouble minimize(TMinuit*, VectorHisto, int);
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int findNnewGauss(TH2 *, VectorHisto, FitResults);
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FitResults fit_histo(TH2 *hist,
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std::vector<Trouble> &t,
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int start_theNumberOfGaussians = 1,
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double P_cutoff_val = 0.5);
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ModelDefinition& curr_ModelDefinition();
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void set_ModelDefinition(ModelDefinition *_mdef);
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double fitFunction_TF2(double *, double *);
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protected:
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static ModelDefinition *static_mdef;
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static VectorHisto energy;
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};
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#endif
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