given a histogram, get a generator for the distribution in that histogram, then run some pseudo-experiments and collect the mean and rms
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#include <generate.h>

Collaboration diagram for generate::experiment:

Public Member Functions
	experiment (TH1D *h, int Nexperiments=10, int fevents=0)
	given a distribution, gnerate some number of pseudo experiments to estimate the uncertainties in the mean and rms more appropriately (root errors assume a gaussian approximation) More...

	experiment (const experiment &)=delete

experiment	operator= (const experiment &)=delete

double	hmean () const

double	hrms () const

double	mean () const

double	mean_error () const

double	rms () const

double	rms_error () const

generator_base *	gen ()

TH1D *	rmshisto ()

Private Attributes
int	m_N

std::vector< double >	m_mean
	number of experiments More...

std::vector< double >	m_rms
	means from each experiment More...

double	m_hmean
	rms from each experiment More...

double	m_hrms
	actual histogram mean95 More...

double	m_global_mean
	actual histogram rms95 More...

double	m_global_mean_error

double	m_global_rms

double	m_global_rms_error

generator_base *	m_gen

TH1D *	m_THrms

Detailed Description

given a histogram, get a generator for the distribution in that histogram, then run some pseudo-experiments and collect the mean and rms

Definition at line 132 of file generate.h.

Constructor & Destructor Documentation

◆ experiment() [1/2]

generate::experiment::experiment	(	TH1D *	h,
		int	Nexperiments = `10`,
		int	fevents = `0`
	)

given a distribution, gnerate some number of pseudo experiments to estimate the uncertainties in the mean and rms more appropriately (root errors assume a gaussian approximation)

NB: Here we need to keep track of the number of events in the histogram. To correctly estimate the uncertainty on the rms, we should use the same number of entries N, for each pseudo-experiment but this can be computaionally very expensive and time consuming, so we generate in this case with fewer events, n, and estimate the uncertainty by scaling by sqrt( n/N )

std::cout << "generate() " << h->GetName() << " requested " << Nevents << " using default max of " << Nevent_max << std::endl;

set the correct error scaling to the correct number of events

loop over the pseudo experiments

Definition at line 302 of file generate.cxx.

   : m_N(Nexperiments) { 
     
     std::vector<double>& mean = m_mean;
     std::vector<double>& rms  = m_rms;
     
     mean.clear();
     rms.clear();
  
     double fnscale = 1;
  
 #if 0
     std::cout << "mean "  << h->GetMean() << " +- " << h->GetMeanError() << " (" << findMean(h,0.95) << ")"
           << "\trms " << h->GetRMS()  << " +- " << h->GetRMSError()  << " (" << rms95(h) << ")"
           << "\thistogram" << std::endl;
 #endif    
  
     //    std::cout << "experiment() actual histogram " << std::endl;
     
     //    m_hmean = h->GetMean();
     //    m_hrms  = rms95(h, findMean(h,0.95) );
     m_hmean = findMean(h, frac);
     m_hrms  = rmsFrac(h, frac, m_hmean );
     //   m_hrms  = rms95(h, m_hmean );
  
     //    std::cout << "central mean " << m_hmean << "\trms95 " << m_hrms << std::endl; 
  
     h->GetXaxis()->SetRange(1,h->GetNbinsX());
  
     m_gen = new hist_generator( h );
     generator_base& g = *m_gen;
     
     
     
     //    int Nevents = Entries(h);
     int Nevents = h->GetEffectiveEntries()+0.5; // Entries(h);
  
     //    std::cout << "Nevents " << Nevents << "\tfevents " << fevents << std::endl;
  
     if ( fevents!=0 ) Nevents = fevents;
  
  
     //    std::cout << "Nevents: " << Entries(h) << "\tweights: " << Nevents << " ( max " << Nevent_max << "  min " << Nevent_min << ")"; // << std::endl; 
  
      
     double _Nevents = Nevents; 
  
     if ( Nevents>Nevent_max ) { 
       Nevents = Nevent_max;
     }
  
     if ( Nevents<Nevent_min ) Nevents = Nevent_min;
  
     fnscale = std::sqrt( Nevents/_Nevents );
  
     //    std::cout << "\tusing: " << Nevents << std::endl;
  
     TH1D* hmean = 0;
     TH1D* hrms  = 0;
     
     if ( printPad ) { 
       gStyle->SetOptStat(1111);    
       hmean = new TH1D("mean", "", 500, -0.001, 0.001);
       hrms  = new TH1D("rms",  "", 500, 0.001, 0.01);
       hmean->SetDirectory(0);
       hrms->SetDirectory(0);
     }
  
     //    std::cout << "experiment() pseudo data " << std::endl;
  
  
     m_THrms = new TH1D("rms95", "", 50, 0.0006, 0.0009 );
     m_THrms->SetDirectory(0);
  
     for ( int j=0 ; j<m_N ; j++ ) { 
       
       TH1D* h3 = (TH1D*)h->Clone();
       Zero( h3 );
       
       for ( int i=Nevents ; i-- ; )  h3->Fill( g.generate() );
       
       double _mean95  = findMean( h3, frac );
       //      double _rms95 = 1.1479538518*rmsFrac( h3, 0.95, _mean95 ); 
       double _rms95 = rmsFrac( h3, frac, _mean95 );
  
  
 #if 0
       std::cout << j << "\texpt mean " << _mean95 << "\trms95 " << _rms95 << std::endl; 
  
       if ( std::string(h->GetTitle()).find("[ 34.4747 - 42.1697 ]")!=std::string::npos ) { 
  
       std::cout << "mean " << h3->GetMean() << " +- " << h3->GetMeanError() 
         << " (" << _mean95 << ") " 
         << "\trms " << h3->GetRMS() << " +- " << h3->GetRMSError()  
         << " (" << _rms95 << ") " 
         << "\t pseudo data (" << Nevents << " events)"  
         << std::endl;
     } 
  
 #endif    
       
  
       //  mean.push_back( h3->GetMean() );
       mean.push_back( _mean95 );
       rms.push_back( _rms95 );
       
       m_THrms->Fill( _rms95 );
  
       delete h3;
     }
     
     //   std::cout << "averages" << std::endl;
     
 #if 0
     if ( std::string(h->GetTitle()).find("[ 34.4747 - 42.1697 ]")!=std::string::npos ) { 
     std::cout << "mean "  << h->GetMean() << " +- " << h->GetMeanError() << " (" << findMean(h,0.95) << ")"
           << "\trms " << h->GetRMS()  << " +- " << h->GetRMSError()  << " (" << rms95(h) << ")"
           << "\thistogram" << std::endl;
  
     std::cout << "mean " << get_mean(mean) << " +- " << get_rms(mean);
     std::cout << "\trms " << get_mean(rms) << " +- " << get_rms(rms) 
           << " after " << Nexperiments << " experiment" << ( Nexperiments==1 ? "" : "s" ) 
           << std::endl;
     }
 #endif
     
     m_global_mean       = get_mean(mean);
     m_global_mean_error = get_rms(mean)*fnscale;
     
     m_global_rms       = get_mean(rms);
     m_global_rms_error = get_rms(rms)*fnscale;
     delete m_gen;
     
  
     //    std::cout << "\t\t mean " << m_global_mean << " +- " <<  m_global_mean_error 
     //        << "\t mean " << m_global_rms  << " +- " <<  m_global_rms_error
     //        << std::endl;
  
 }

◆ experiment() [2/2]

generate::experiment::experiment ( const experiment & )

delete

Member Function Documentation

◆ gen()

generator_base* generate::experiment::gen ( )

inline

Definition at line 149 of file generate.h.

149 { return m_gen; }

◆ hmean()

double generate::experiment::hmean ( ) const

inline

Definition at line 140 of file generate.h.

140 { return m_hmean; }

◆ hrms()

double generate::experiment::hrms ( ) const

inline

Definition at line 141 of file generate.h.

141 { return m_hrms; }

◆ mean()

double generate::experiment::mean ( ) const

inline

Definition at line 143 of file generate.h.

143 { return m_global_mean; }

◆ mean_error()

double generate::experiment::mean_error ( ) const

inline

Definition at line 144 of file generate.h.

144 { return m_global_mean_error; }

◆ operator=()

experiment generate::experiment::operator= ( const experiment & )

delete

◆ rms()

double generate::experiment::rms ( ) const

inline

Definition at line 146 of file generate.h.

146 { return m_global_rms; }

◆ rms_error()

double generate::experiment::rms_error ( ) const

inline

Definition at line 147 of file generate.h.

147 { return m_global_rms_error; }

◆ rmshisto()

TH1D* generate::experiment::rmshisto ( )

inline

Definition at line 151 of file generate.h.

151 { return m_THrms; }

Member Data Documentation

◆ m_gen

generator_base* generate::experiment::m_gen

private

Definition at line 170 of file generate.h.

◆ m_global_mean

double generate::experiment::m_global_mean

private

actual histogram rms95

overall mean and rms from the pseudo experiments

Definition at line 164 of file generate.h.

◆ m_global_mean_error

double generate::experiment::m_global_mean_error

private

Definition at line 165 of file generate.h.

◆ m_global_rms

double generate::experiment::m_global_rms

private

Definition at line 167 of file generate.h.

◆ m_global_rms_error

double generate::experiment::m_global_rms_error

private

Definition at line 168 of file generate.h.

◆ m_hmean

double generate::experiment::m_hmean

private

rms from each experiment

Definition at line 160 of file generate.h.

◆ m_hrms

double generate::experiment::m_hrms

private

actual histogram mean95

Definition at line 161 of file generate.h.

◆ m_mean

std::vector<double> generate::experiment::m_mean

private

number of experiments

Definition at line 157 of file generate.h.

◆ m_N

int generate::experiment::m_N

private

Definition at line 155 of file generate.h.

◆ m_rms

std::vector<double> generate::experiment::m_rms

private

means from each experiment

Definition at line 158 of file generate.h.

◆ m_THrms

TH1D* generate::experiment::m_THrms

private

Definition at line 172 of file generate.h.

The documentation for this class was generated from the following files:

Public Member Functions

Private Attributes

Detailed Description

Constructor & Destructor Documentation

◆ experiment() [1/2]

◆ experiment() [2/2]

Member Function Documentation

◆ gen()

◆ hmean()

◆ hrms()

◆ mean()

◆ mean_error()

◆ operator=()

◆ rms()

◆ rms_error()

◆ rmshisto()

Member Data Documentation

◆ m_gen

◆ m_global_mean

◆ m_global_mean_error

◆ m_global_rms

◆ m_global_rms_error

◆ m_hmean

◆ m_hrms

◆ m_mean

◆ m_N

◆ m_rms

◆ m_THrms