ToyFitterExclusion.h

#ifndef TOY_FITTER
#define TOY_FITTER

#include "XeLikelihoods.h"
#include "TGraphAsymmErrors.h"
#include "TGraph.h"
#include "XeUtils.h"
#include "dataHandler.h"
#include "TFile.h"
#include "TGraph.h"
#include "TRandom.h"
#include "TRint.h"
#include "TString.h"
#include "TFile.h"
#include "TTree.h"
#include "TList.h"
#include "TParameter.h"
#include "XeStat.h"
#include "TTree.h"
#include "TH2F.h"
#include <map>
#include <vector>
#include <stdio.h>
#include "plotHelpers.h"

#include "Math/Minimizer.h"
#include "Math/Factory.h"
#include "Math/Functor.h"
#include "Math/BrentMinimizer1D.h"

using namespace std;

class ToyFitterExclusion: public errorHandler {

  public:
    
    ToyFitterExclusion(TString CollectionName);
    
    double computeTS(double mu) ;

    void fit(double mu, int stopAt=-999);

    void setTheLikelihood(ProfileLikelihood *like) { likeHood = like; };


    TGraphAsymmErrors computeTSDistros(TString fileName, double *mu_list, int mu_size);


    TGraphAsymmErrors computeTSDistros(TTree *tree, double *mu_list, int mu_size);
    
    void spitTheLimit(TGraphAsymmErrors *ninety_quantiles,  int sopAt = -999);

    void setInputDir(TString path) { dirPath = path; };
    
    void setOutputDir(TString path){ OutDir = path; };

    // \brief set or unset the parameter randomization. Default true.
    void setRandomizeMeasure(bool doOrNot) { randomizeMeasure = doOrNot ;};

    // \brief in case you want to change the tree name on the fly
    void setTreeName(TString newName) { treeName = newName; };

    // \brief setting random seed for measured param generation
    // TRandom likes a integer seed.
    void setSeed(ULong_t seed) { rambo.SetSeed(seed); }; 

    // \brief you must set this if you want to use calibration in fit
    void setCalibrationTreeName(TString newName) { calTreeName = newName; };

    // \brief set output file suffix, useful for conditional fit of null 
    void setOutputSuffix(TString name) { Suffix = name; } ;

    void setGeneration(int generation) { Gen = generation; };
    
  private:

    void for_each_tree( double (ToyFitterExclusion::*p2method)(double), TTree *outTree, double mu, int stopAt = -999);

    double limitLoop(double initial_mu);

    void saveParameters(double *params);

    void fillTrueParams( );

    void measureParameters();

    double eval_testStatMinuit( double mu );

    double getBestAsympoticGuessForMu(double one_over_sigma_squared, double mu_hat );

    void saveNames(string *names);

    ProfileLikelihood *likeHood;
    TString       dirPath;
    TString       treeName;
    TString       OutDir;               
    TGraphAsymmErrors *graph_of_quantiles;
    TString       calTreeName;
    TString       Suffix;
    int           IndexHolder;
    int           CurrentTreeIndex;
    int           Gen;
    
    double        likelihood_uncond;    
    double        likelihood_cond;      
    double        true_params[50] = {0.} ;    
    double        measured_params[50] = {0.};  
    double        uncond_params[50] = {0.};   
    double        cond_params[50] = {0.};     
    bool          limit_converged;      
    double        testStat_limit;       
    double        testStat_at0;         
    vector<string> name_params;         
    vector<string> name_true_params;    
    TRandom3      rambo;                
    double        mu_fit;               
    double        mu_limit;             
    double        lower_mu_limit;       
    double        limit;                
    double        lower_limit;          
    double        testStat;             
    double        q_tilde;
    int           numberOfParams;       
    bool          randomizeMeasure;     
};

#endif