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build_hbb_workspace2.c
1159 lines (786 loc) · 46.4 KB
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build_hbb_workspace2.c
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#include "TMath.h"
#include "TSystem.h"
#include "RooArgSet.h"
#include "RooConstVar.h"
#include "RooRealVar.h"
#include "RooFormulaVar.h"
#include "RooWorkspace.h"
#include "RooPoisson.h"
#include "RooGaussian.h"
#include "RooProdPdf.h"
#include "RooDataSet.h"
#include "RooFitResult.h"
#include "RooArgSet.h"
#include "RooUniform.h"
#include "RooStats/ModelConfig.h"
#include "RooPosDefCorrGauss.h"
#include "getFileValue.c"
#include <fstream>
using namespace RooFit ;
using namespace RooStats ;
const int bins_of_nb(3) ;
const int max_bins_of_met(50) ;
int bins_of_met ;
int first_met_bin_array_index(0) ;
RooArgSet* globalObservables ;
RooArgSet* allNuisances ;
RooArgSet* allNuisancePdfs ;
RooRealVar* rv_smc_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_smc_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_smc_msig_mcstat_syst[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_smc_msb_mcstat_syst[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
int n_shape_systs(0) ;
int syst_type(0) ;
//--- prototypes here.
RooAbsReal* makeLognormalConstraint( const char* NP_name, double NP_val, double NP_err ) ;
RooAbsReal* makeGaussianConstraint( const char* NP_name, double NP_val, double NP_err, bool allowNegative = false ) ;
RooAbsReal* makeCorrelatedLognormalConstraint( const char* NP_name, double NP_val, double NP_err, const char* NP_base_name, bool changeSign=false ) ;
RooAbsReal* makeCorrelatedGaussianConstraint( const char* NP_name, double NP_val, double NP_err, const char* NP_base_name, bool changeSign=false, bool allowNegative = false ) ;
bool setupShapeSyst( const char* infile, const char* systName,
int constraintType, // 1=gaussian, 2=...
double target_mgl, double target_mlsp,
RooWorkspace& workspace
) ;
bool readSignalCounts( const char* susy_counts_file, float sig_mass ) ;
//===========================================================================================
void build_hbb_workspace2( const char* infile = "outputfiles/input-file.txt",
const char* outfile = "outputfiles/ws.root",
float sig_mass = 250.,
bool use3b = true,
bool combine_top_metbins = false,
int arg_syst_type = 2, // 1 = Gaussian, 2 = log-normal
bool drop_first_met_bin = false
) {
//-------------------------------------------------------------------------
syst_type = arg_syst_type ;
//-- Create workspace and other RooStats things.
printf("\n\n Creating workspace.\n\n") ;
RooWorkspace workspace("ws") ;
workspace.autoImportClassCode(true) ;
globalObservables = new RooArgSet("globalObservables");
allNuisances = new RooArgSet("allNuisances");
allNuisancePdfs = new RooArgSet("allNuisancePdfs");
RooArgSet* observedParametersList = new RooArgSet("observables") ;
//-------------------------------------------------------------------------
printf("\n\n Reading input file: %s\n\n", infile ) ;
float fileVal ;
char pname[1000] ;
char formula[1000] ;
sprintf( pname, "bins_of_met" ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
bins_of_met = TMath::Nint( fileVal ) ;
//-- save bins_of_met in the workspace for convenience.
RooRealVar bom( "bins_of_met", "bins_of_met", bins_of_met, 0., 1000. ) ;
bom.setConstant(kTRUE) ;
workspace.import(bom) ;
if ( !drop_first_met_bin ) {
first_met_bin_array_index = 0 ;
} else {
first_met_bin_array_index = 1 ;
}
RooRealVar fmbai( "first_met_bin_array_index", "first_met_bin_array_index", first_met_bin_array_index, -1, 2 ) ;
fmbai.setConstant(kTRUE) ;
workspace.import(fmbai) ;
//-- get signal input file and look for requested signal mass.
char susy_counts_filename[10000] ;
if ( !getFileStringValue( infile, "signal_counts_file", susy_counts_filename ) ) {
printf("\n\n *** Can't find input susy counts file: signal_counts_file line of %s.\n\n", infile ) ;
return ;
}
if ( !readSignalCounts( susy_counts_filename, sig_mass ) ) {
printf("\n\n *** Can't find signal mass of %.0f in %s\n\n", sig_mass, susy_counts_filename ) ;
return ;
}
//-- save bins_of_nb in the workspace for convenience.
int save_bins_of_nb = 3 ;
if ( !use3b ) save_bins_of_nb = 2 ;
RooRealVar bonb( "bins_of_nb", "bins_of_nb", save_bins_of_nb, 0., 1000. ) ;
bonb.setConstant(kTRUE) ;
workspace.import(bonb) ;
RooRealVar* rv_N_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooRealVar* rv_N_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_Rsigsb_corr[bins_of_nb][max_bins_of_met] ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
if ( (!use3b) && nbi==1 ) continue ;
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "N_%db_msig_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_N_msig[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_N_msig[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_N_msig[nbi][mbi] -> setConstant( kTRUE ) ;
observedParametersList -> add( *rv_N_msig[nbi][mbi] ) ;
sprintf( pname, "N_%db_msb_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, fileVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
rv_N_msb[nbi][mbi] = new RooRealVar( pname, pname, 0., 1.e6 ) ;
rv_N_msb[nbi][mbi] -> setVal( TMath::Nint(fileVal) ) ;
rv_N_msb[nbi][mbi] -> setConstant( kTRUE ) ;
observedParametersList -> add( *rv_N_msb[nbi][mbi] ) ;
if ( (!combine_top_metbins) || mbi==0 ) {
float corrVal, corrSyst ;
sprintf( pname, "Rsigsb_syst_%db_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, corrSyst ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
sprintf( pname, "Rsigsb_corr_%db_met%d", nbi+2, mbi+1 ) ;
if ( !getFileValue( infile, pname, corrVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
if ( syst_type == 1 ) {
rv_Rsigsb_corr[nbi][mbi] = makeGaussianConstraint( pname, corrVal, corrSyst ) ;
} else if ( syst_type == 2 ) {
rv_Rsigsb_corr[nbi][mbi] = makeLognormalConstraint( pname, corrVal, corrSyst ) ;
} else {
printf("\n\n *** Illegal syst_type %d\n\n", syst_type ) ; return ;
}
} else {
if ( mbi==1 ) {
float corrVal, corrSyst ;
sprintf( pname, "Rsigsb_syst_%db_metbins234", nbi+2 ) ;
if ( !getFileValue( infile, pname, corrSyst ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
sprintf( pname, "Rsigsb_corr_%db_metbins234", nbi+2 ) ;
if ( !getFileValue( infile, pname, corrVal ) ) { printf("\n\n *** Error. Can't find %s\n\n", pname ) ; return ; }
if ( syst_type == 1 ) {
rv_Rsigsb_corr[nbi][mbi] = makeGaussianConstraint( pname, corrVal, corrSyst ) ;
} else if ( syst_type == 2 ) {
rv_Rsigsb_corr[nbi][mbi] = makeLognormalConstraint( pname, corrVal, corrSyst ) ;
} else {
printf("\n\n *** Illegal syst_type %d\n\n", syst_type ) ; return ;
}
} else {
rv_Rsigsb_corr[nbi][mbi] = rv_Rsigsb_corr[nbi][1] ;
}
}
} // mbi.
} // nbi.
//-- Get list of shape systs.
char shape_syst_names[50][1000] ;
if ( !getFileMultiStringValue( infile, "list_of_shape_systs", n_shape_systs, shape_syst_names ) ) {
printf("\n\n *** Could not find line starting with list_of_shape_systs in %s\n\n", infile ) ;
return ;
}
for ( int ssi=0; ssi<n_shape_systs; ssi++ ) {
char shape_syst_file[10000] ;
char systname[1000] ;
sprintf( systname, "shape_syst_%s", shape_syst_names[ssi] ) ;
if ( !getFileStringValue( infile, systname, shape_syst_file ) ) {
printf("\n\n *** Can't find file for shape syst %s in %s\n\n", shape_syst_names[ssi], infile ) ;
return ;
}
printf("\n\n ======= Reading in shape syst %s from %s\n\n", shape_syst_names[ssi], shape_syst_file ) ;
setupShapeSyst( shape_syst_file, systname, syst_type, 175., 0., workspace ) ;
} // ssi.
//-- Finished reading input from file.
//-------------------------------------------------------------------------
printf("\n\n Creating and importing dataset into workspace.\n\n") ;
RooDataSet* dsObserved = new RooDataSet("hbb_observed_rds", "hbb observed data values", *observedParametersList ) ;
dsObserved -> add( *observedParametersList ) ;
workspace.import( *dsObserved ) ;
//-------------------------------------------------------------------------
//-- Define all floats.
printf("\n\n Defining all unconstrained floats (Ratios, signal strength).\n\n") ;
double R_msigmsb_initialval(0.15) ;
RooRealVar* rv_R_msigmsb[50] ;
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
if ( (!combine_top_metbins) || mbi==0 ) {
sprintf( pname, "R_msigmsb_met%d", mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_R_msigmsb[mbi] = new RooRealVar( pname, pname, R_msigmsb_initialval, 0., 3. ) ;
rv_R_msigmsb[mbi] -> setConstant( kFALSE ) ;
rv_R_msigmsb[mbi] -> Print() ;
} else {
if ( mbi==1 ) {
sprintf( pname, "R_msigmsb_metbins234" ) ;
printf( " %s\n", pname ) ;
rv_R_msigmsb[mbi] = new RooRealVar( pname, pname, R_msigmsb_initialval, 0., 3. ) ;
rv_R_msigmsb[mbi] -> setConstant( kFALSE ) ;
rv_R_msigmsb[mbi] -> Print() ;
} else {
rv_R_msigmsb[mbi] = rv_R_msigmsb[1] ;
}
}
} // mbi.
printf("\n") ;
sprintf( pname, "sig_strength" ) ;
RooRealVar* rv_sig_strength = new RooRealVar( pname, pname, 1.0, 0., 10. ) ;
rv_sig_strength -> setConstant(kFALSE) ;
rv_sig_strength -> Print() ;
printf(" %s\n\n", pname ) ;
//-------------------------------------------------------------------------
//-- Define all mu parameters.
printf("\n\n Defining mu parameters.\n\n") ;
RooAbsReal* rv_mu_bg_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_bg_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_sig_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_mu_sig_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
if ( (!use3b) && nbi==1 ) continue ;
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "mu_bg_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_bg_msb[nbi][mbi] = new RooRealVar( pname, pname, rv_N_msb[nbi][mbi] -> getVal(), 0., 1.e6 ) ;
rv_mu_bg_msb[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1 * @2" ) ;
sprintf( pname, "mu_bg_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_bg_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_Rsigsb_corr[nbi][mbi], *rv_R_msigmsb[mbi], *rv_mu_bg_msb[nbi][mbi] ) ) ;
rv_mu_bg_msig[nbi][mbi] -> Print() ;
//-- set up combination of all signal shape systematics.
char syst_prod_eqn[1000] ;
sprintf( syst_prod_eqn, "@0" ) ;
for ( int ssi=1; ssi<n_shape_systs; ssi++ ) {
char tmpstr[1000] ;
sprintf( tmpstr, "%s * @%d", syst_prod_eqn, ssi ) ;
sprintf( syst_prod_eqn, "%s", tmpstr ) ;
} // ssi.
RooArgSet shapeSystProdSet_msig ;
for ( int ssi=0; ssi<n_shape_systs; ssi++ ) {
sprintf( pname, "shape_syst_%s_msig_met%d_%db", shape_syst_names[ssi], mbi+1, nbi+2 ) ;
RooAbsReal* rar_sf = (RooAbsReal*) workspace.obj( pname ) ;
if ( rar_sf == 0x0 ) { printf("\n\n *** Missing %s shape syst for met %d, nb %d, (%s)\n\n", shape_syst_names[ssi], mbi+1, nbi+2, pname ) ; return ; }
shapeSystProdSet_msig.add( *rar_sf ) ;
} // ssi.
sprintf( pname, "shape_syst_prod_msig_met%d_%db", mbi+1, nbi+2 ) ;
RooFormulaVar* rfv_shape_syst_prod_msig = new RooFormulaVar( pname, syst_prod_eqn, shapeSystProdSet_msig ) ;
RooArgSet shapeSystProdSet_msb ;
for ( int ssi=0; ssi<n_shape_systs; ssi++ ) {
sprintf( pname, "shape_syst_%s_msb_met%d_%db", shape_syst_names[ssi], mbi+1, nbi+2 ) ;
RooAbsReal* rar_sf = (RooAbsReal*) workspace.obj( pname ) ;
if ( rar_sf == 0x0 ) { printf("\n\n *** Missing %s shape syst for met %d, nb %d, (%s)\n\n", shape_syst_names[ssi], mbi+1, nbi+2, pname ) ; return ; }
shapeSystProdSet_msb.add( *rar_sf ) ;
} // ssi.
sprintf( pname, "shape_syst_prod_msb_met%d_%db", mbi+1, nbi+2 ) ;
RooFormulaVar* rfv_shape_syst_prod_msb = new RooFormulaVar( pname, syst_prod_eqn, shapeSystProdSet_msb ) ;
sprintf( formula, "@0 * @1 * @2 * @3" ) ;
sprintf( pname, "mu_sig_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_sig_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_sig_strength, *rfv_shape_syst_prod_msig, *rv_smc_msig_mcstat_syst[nbi][mbi], *rv_smc_msig[nbi][mbi] ) ) ;
rv_mu_sig_msig[nbi][mbi] -> Print() ;
sprintf( formula, "@0 * @1 * @2 * @3" ) ;
sprintf( pname, "mu_sig_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_mu_sig_msb[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_sig_strength, *rfv_shape_syst_prod_msb, *rv_smc_msb_mcstat_syst[nbi][mbi], *rv_smc_msb[nbi][mbi] ) ) ;
rv_mu_sig_msb[nbi][mbi] -> Print() ;
} // mbi.
} // nbi.
//-- Finished defining mu parameters.
//-------------------------------------------------------------------------
//-- Defining small n's
printf("\n\n Defining small n's.\n\n") ;
RooAbsReal* rv_n_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_n_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
if ( (!use3b) && nbi==1 ) continue ;
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
sprintf( formula, "@0 + @1" ) ;
sprintf( pname, "n_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_n_msig[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_mu_sig_msig[nbi][mbi], *rv_mu_bg_msig[nbi][mbi] ) ) ;
rv_n_msig[nbi][mbi] -> Print() ;
workspace.import( *rv_n_msig[nbi][mbi] ) ;
sprintf( pname, "n_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_n_msb[nbi][mbi] = new RooFormulaVar( pname, formula, RooArgSet( *rv_mu_sig_msb[nbi][mbi], *rv_mu_bg_msb[nbi][mbi] ) ) ;
rv_n_msb[nbi][mbi] -> Print() ;
workspace.import( *rv_n_msb[nbi][mbi] ) ;
} // mbi.
} // nbi.
//-------------------------------------------------------------------------
//-- Define the Poisson pdfs for the observables.
printf("\n\n Defining Poisson pdfs for the observables.\n\n") ;
RooAbsReal* rv_pdf_msig[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooAbsReal* rv_pdf_msb[bins_of_nb][max_bins_of_met] ; // first index is number of btags, second is met bin.
RooArgSet pdflist ;
for ( int nbi=0; nbi<bins_of_nb; nbi++ ) {
if ( (!use3b) && nbi==1 ) continue ;
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
sprintf( pname, "pdf_%db_msig_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_pdf_msig[nbi][mbi] = new RooPoisson( pname, pname, *rv_N_msig[nbi][mbi], *rv_n_msig[nbi][mbi] ) ;
rv_pdf_msig[nbi][mbi] -> Print() ;
pdflist.add( *rv_pdf_msig[nbi][mbi] ) ;
sprintf( pname, "pdf_%db_msb_met%d", nbi+2, mbi+1 ) ;
printf( " %s\n", pname ) ;
rv_pdf_msb[nbi][mbi] = new RooPoisson( pname, pname, *rv_N_msb[nbi][mbi], *rv_n_msb[nbi][mbi] ) ;
rv_pdf_msb[nbi][mbi] -> Print() ;
pdflist.add( *rv_pdf_msb[nbi][mbi] ) ;
} // mbi.
} // nbi.
//-------------------------------------------------------------------------
//-- Build the likelihood.
printf("\n\n Building the likelihood.\n\n") ;
pdflist.add( *allNuisancePdfs ) ;
pdflist.Print() ;
printf("\n") ;
RooProdPdf* likelihood = new RooProdPdf( "likelihood", "hbb likelihood", pdflist ) ;
likelihood->Print() ;
//-------------------------------------------------------------------------
// printf("\n\n Running a test fit.\n\n") ;
// dsObserved -> Print() ;
// dsObserved -> printMultiline(cout, 1, kTRUE, "") ;
// printf("\n\n =============================================\n\n") ;
// likelihood -> fitTo( *dsObserved, PrintLevel(3), Hesse(0), Minos(0) ) ;
// printf("\n\n =============================================\n\n") ;
//-- Set up RooStats models.
printf("\n\n Setting up S+B model.\n\n") ;
RooArgSet poi( *rv_sig_strength, "poi" ) ;
RooUniform signal_prior( "signal_prior", "signal_prior", *rv_sig_strength ) ;
ModelConfig sbModel ("SbModel");
sbModel.SetWorkspace( workspace ) ;
sbModel.SetPdf( *likelihood ) ;
sbModel.SetParametersOfInterest( poi );
sbModel.SetPriorPdf(signal_prior);
sbModel.SetObservables( *observedParametersList );
sbModel.SetNuisanceParameters( *allNuisances );
sbModel.SetGlobalObservables( *globalObservables );
workspace.Print() ;
printf("\n\n Doing fit for S+B model.\n" ) ; fflush(stdout) ;
RooAbsReal* pNll = sbModel.GetPdf()->createNLL(*dsObserved);
RooAbsReal* pProfile = pNll->createProfile(RooArgSet());
pProfile->getVal();
RooArgSet* pPoiAndNuisance = new RooArgSet();
pPoiAndNuisance->add(*sbModel.GetParametersOfInterest());
if(sbModel.GetNuisanceParameters()) pPoiAndNuisance->add(*sbModel.GetNuisanceParameters());
printf("\n\n Will save these parameter points that correspond to the fit to data.\n\n") ; fflush(stdout) ;
pPoiAndNuisance->Print("v");
sbModel.SetSnapshot(*pPoiAndNuisance);
workspace.import (sbModel);
delete pProfile ;
delete pNll ;
delete pPoiAndNuisance ;
printf("\n\n Setting up BG-only model.\n\n") ;
ModelConfig bModel (*(RooStats::ModelConfig *)workspace.obj("SbModel"));
bModel.SetName("BModel");
bModel.SetWorkspace(workspace);
printf("\n\n Doing fit for BG-only model.\n" ) ; fflush(stdout) ;
pNll = bModel.GetPdf()->createNLL(*dsObserved);
pProfile = pNll->createProfile(*bModel.GetParametersOfInterest());
((RooRealVar *)(bModel.GetParametersOfInterest()->first()))->setVal(0.);
pProfile->getVal();
pPoiAndNuisance = new RooArgSet();
pPoiAndNuisance->add(*bModel.GetParametersOfInterest());
if(bModel.GetNuisanceParameters()) pPoiAndNuisance->add(*bModel.GetNuisanceParameters());
printf("\n\n Should use these parameter points to generate pseudo data for bkg only.\n\n") ; fflush(stdout) ;
pPoiAndNuisance->Print("v");
bModel.SetSnapshot(*pPoiAndNuisance);
workspace.import (bModel);
delete pProfile ;
delete pNll ;
delete pPoiAndNuisance ;
workspace.Print() ;
printf("\n\n Saving workspace in : %s\n\n", outfile ) ;
gSystem->Exec(" mkdir -p outputfiles " ) ;
workspace.writeToFile( outfile ) ;
} // build_hbb_workspace2.
//==============================================================================================
RooAbsReal* makeGaussianConstraint( const char* NP_name, double NP_val, double NP_err, bool allowNegative ) {
if ( NP_err <= 0. ) {
printf(" makeGaussianConstraint: Uncertainty is zero. Will return constant scale factor of %g for %s. Input val = %g, err = %g.\n", NP_val, NP_name, NP_val, NP_err ) ;
return new RooConstVar( NP_name, NP_name, NP_val ) ;
}
double max = NP_val + 6.*NP_err ;
double min = NP_val - 6.*NP_err ;
if ( min < 0. && !allowNegative ) { min = 1e-5 ; }
RooRealVar* np_rrv = new RooRealVar( NP_name, NP_name, min, max ) ;
np_rrv -> setVal( NP_val ) ;
np_rrv -> setConstant( kFALSE ) ;
//-- create const variables for mean and sigma so that they can be saved and accessed from workspace later.
char vname[1000] ;
sprintf( vname, "mean_%s", NP_name ) ;
RooRealVar* g_mean = new RooRealVar( vname, vname, NP_val, -1000., 1000. ) ;
g_mean->setConstant(kTRUE);
sprintf( vname, "sigma_%s", NP_name ) ;
RooConstVar* g_sigma = new RooConstVar( vname, vname, NP_err ) ;
char pdfname[1000] ;
sprintf( pdfname, "pdf_%s", NP_name ) ;
RooGaussian* np_pdf = new RooGaussian( pdfname, pdfname, *np_rrv, *g_mean, *g_sigma ) ;
allNuisances -> add( *np_rrv ) ;
allNuisancePdfs -> add( *np_pdf ) ;
globalObservables -> add( *g_mean ) ;
printf(" makeGaussianConstraint : created nuisance parameter %s : val = %g\n", NP_name, np_rrv -> getVal() ) ;
return np_rrv ;
} // makeGaussianConstraint.
//==============================================================================================================
RooAbsReal* makeLognormalConstraint( const char* NP_name, double NP_val, double NP_err ) {
if ( NP_err <= 0. ) {
printf(" makeLognormalConstraint: Uncertainty is zero. Will return constant scale factor of %g for %s. Input val = %g, err = %g.\n", NP_val, NP_name, NP_val, NP_err ) ;
return new RooConstVar( NP_name, NP_name, NP_val ) ;
}
char pname[1000] ;
sprintf( pname, "prim_%s", NP_name ) ;
printf(" makeLognormalConstraint : creating primary log-normal variable %s\n", pname ) ;
RooRealVar* np_prim_rrv = new RooRealVar( pname, pname, 0., -6., 6. ) ;
np_prim_rrv -> setVal( 0. ) ;
np_prim_rrv -> setConstant( kFALSE ) ;
sprintf( pname, "prim_mean_%s", NP_name ) ;
RooRealVar* np_prim_mean = new RooRealVar( pname, pname, 0., -6., 6. ) ;
np_prim_mean->setConstant(kTRUE) ;
sprintf( pname, "prim_sigma_%s", NP_name ) ;
RooConstVar* np_prim_sigma = new RooConstVar( pname, pname, 1. ) ;
char pdfname[1000] ;
sprintf( pdfname, "pdf_prim_%s", NP_name ) ;
RooGaussian* np_prim_pdf = new RooGaussian( pdfname, pdfname, *np_prim_rrv, *np_prim_mean, *np_prim_sigma ) ;
allNuisances -> add( *np_prim_rrv ) ;
allNuisancePdfs -> add( *np_prim_pdf ) ;
globalObservables -> add( *np_prim_mean ) ;
//-- create const variables for mean and sigma so that they can be saved and accessed from workspace later.
char vname[1000] ;
sprintf( vname, "mean_%s", NP_name ) ;
RooConstVar* g_mean = new RooConstVar( vname, vname, NP_val ) ;
sprintf( vname, "sigma_%s", NP_name ) ;
RooConstVar* g_sigma = new RooConstVar( vname, vname, NP_err ) ;
//-- compute the log-normal-distributed parameter from the primary parameter.
//--- This is the new way. RMS of lognormal is much closer to sigma when sigma is
// large, doing it this way. When sigma/mean is small, they are about the same.
// That is, exp(sigma/mean) is close to (sigma/mean + 1). This one is better when
// sigma/mean is not small. The high-side tail is not as strong.
//
RooFormulaVar* np_rfv = new RooFormulaVar( NP_name, "@0 * pow( ( @1/@0 + 1. ), @2)",
RooArgSet( *g_mean, *g_sigma, *np_prim_rrv ) ) ;
//------------------------------------------------------------------------------------------
printf(" makeLognormalConstraint : created log-normal nuisance parameter %s : val = %g\n", NP_name, np_rfv -> getVal() ) ;
return np_rfv ;
} // makeLognormalConstraint.
//==============================================================================================
RooAbsReal* makeCorrelatedGaussianConstraint(
const char* NP_name, double NP_val, double NP_err, const char* NP_base_name, bool changeSign, bool allowNegative ) {
if ( NP_err <= 0. ) {
printf(" makeCorrelatedGaussianConstraint: Uncertainty is zero. Will return constant scale factor of %g for %s. Input val = %g, err = %g.\n", NP_val, NP_name, NP_val, NP_err ) ;
return new RooConstVar( NP_name, NP_name, NP_val ) ;
}
RooRealVar* rrv_np_base_par = (RooRealVar*) allNuisances -> find( NP_base_name ) ;
if ( rrv_np_base_par == 0x0 ) {
printf("\n\n makeCorrelatedGaussianConstraint : creating base nuisance parameter - %s\n\n", NP_base_name ) ;
rrv_np_base_par = new RooRealVar( NP_base_name, NP_base_name, -6.0, 6.0 ) ;
rrv_np_base_par -> setVal( 0. ) ;
rrv_np_base_par -> setConstant( kFALSE ) ;
allNuisances -> add( *rrv_np_base_par ) ;
char vname[1000] ;
sprintf( vname, "mean_%s", NP_base_name ) ;
RooRealVar* g_mean = new RooRealVar( vname, vname, 0.0,-1000.,1000. ) ;
g_mean->setConstant(kTRUE);
sprintf( vname, "sigma_%s", NP_base_name ) ;
RooConstVar* g_sigma = new RooConstVar( vname, vname, 1.0 ) ;
char pdfname[100] ;
sprintf( pdfname, "pdf_%s", NP_base_name ) ;
printf("\n\n makeCorrelatedGaussianConstraint : creating base nuisance parameter pdf - %s\n\n", pdfname ) ;
RooGaussian* base_np_pdf = new RooGaussian( pdfname, pdfname, *rrv_np_base_par, *g_mean, *g_sigma ) ;
allNuisancePdfs -> add( *base_np_pdf ) ;
globalObservables -> add( *g_mean ) ;
}
//-- create const variables for mean and sigma so that they can be saved and accessed from workspace later.
char vname[1000] ;
sprintf( vname, "mean_%s", NP_name ) ;
RooConstVar* g_mean = new RooConstVar( vname, vname, NP_val ) ;
sprintf( vname, "sigma_%s", NP_name ) ;
RooConstVar* g_sigma = new RooConstVar( vname, vname, NP_err ) ;
RooAbsReal* rar(0x0) ;
if ( allowNegative ) {
char formula[1000] ;
if ( !changeSign ) {
sprintf( formula, "@0+@1*@2" ) ;
} else {
sprintf( formula, "@0-@1*@2" ) ;
}
rar = new RooFormulaVar( NP_name, formula, RooArgSet( *g_mean, *g_sigma, *rrv_np_base_par ) ) ;
printf(" makeCorrelatedGaussianConstraint : creating correlated gaussian NP with formula : %s, %s, val = %g\n", formula, NP_name, rar->getVal() ) ;
} else {
rar = new RooPosDefCorrGauss( NP_name, NP_name, *g_mean, *g_sigma, *rrv_np_base_par, changeSign ) ;
printf(" makeCorrelatedGaussianConstraint : creating pos-def correlated gaussian NP : %s, val = %g, err = %g\n", NP_name, rar->getVal(), NP_err ) ;
}
return rar ;
} // makeCorrelatedGaussianConstraint.
//==============================================================================================================
RooAbsReal* makeCorrelatedLognormalConstraint(
const char* NP_name, double NP_val, double NP_err, const char* NP_base_name, bool changeSign ) {
if ( NP_err <= 0. ) {
printf(" makeCorrelatedLognormalConstraint: Uncertainty is zero. Will return constant scale factor of %g for %s. Input val = %g, err = %g.\n", NP_val, NP_name, NP_val, NP_err ) ;
return new RooConstVar( NP_name, NP_name, NP_val ) ;
}
char prim_name[1000] ;
sprintf( prim_name, "prim_%s", NP_base_name ) ;
RooRealVar* rrv_np_base_par = (RooRealVar*) allNuisances -> find( prim_name ) ;
if ( rrv_np_base_par == 0x0 ) {
printf("\n\n makeCorrelatedLognormalConstraint : creating base nuisance parameter - %s\n\n", prim_name ) ;
rrv_np_base_par = new RooRealVar( prim_name, prim_name, -6.0, 6.0 ) ;
rrv_np_base_par -> setVal( 0. ) ;
rrv_np_base_par -> setConstant( kFALSE ) ;
allNuisances -> add( *rrv_np_base_par ) ;
char vname[1000] ;
sprintf( vname, "prim_mean_%s", NP_base_name ) ;
RooRealVar* g_mean = new RooRealVar( vname, vname, 0.0,-10.,10. ) ;
g_mean->setConstant(kTRUE);
sprintf( vname, "prim_sigma_%s", NP_base_name ) ;
RooConstVar* g_sigma = new RooConstVar( vname, vname, 1.0 ) ;
char pdfname[100] ;
sprintf( pdfname, "pdf_prim_%s", NP_base_name ) ;
printf("\n\n makeCorrelatedLognormalConstraint : creating base nuisance parameter pdf - %s\n\n", pdfname ) ;
RooGaussian* base_np_pdf = new RooGaussian( pdfname, pdfname, *rrv_np_base_par, *g_mean, *g_sigma ) ;
allNuisancePdfs -> add( *base_np_pdf ) ;
globalObservables -> add( *g_mean ) ;
}
//-- create const variables for mean and sigma so that they can be saved and accessed from workspace later.
char vname[1000] ;
sprintf( vname, "mean_%s", NP_name ) ;
RooConstVar* ln_mean = new RooConstVar( vname, vname, NP_val ) ;
sprintf( vname, "sigma_%s", NP_name ) ;
RooConstVar* ln_sigma = new RooConstVar( vname, vname, NP_err ) ;
RooAbsReal* rar(0x0) ;
char formula[1000] ;
if ( !changeSign ) {
sprintf( formula, "@0 * pow( ( @1/@0 + 1.), @2 )" ) ;
} else {
sprintf( formula, "@0 * pow( ( @1/@0 + 1.), -1.0 * @2 )" ) ;
}
rar = new RooFormulaVar( NP_name, formula, RooArgSet( *ln_mean, *ln_sigma, *rrv_np_base_par ) ) ;
printf(" makeCorrelatedLognormalConstraint : creating correlated log-normal NP with formula : %s, %s, val = %g, mean=%g, sigma=%g\n", formula, NP_name, rar->getVal(), NP_val, NP_err ) ;
return rar ;
} // makeCorrelatedLognormalConstraint.
//==============================================================================================================
//-- convention for each line in systematics file is
//
// Mparent Mlsp sys_msig_met1_nb2 sys_msig_met2_nb2 sys_msig_met3_nb2 sys_msig_met4_nb2 sys_msig_met1_nb3 sys_msig_met2_nb3 sys_msig_met3_nb3 sys_msig_met4_nb3 sys_msig_met1_nb4 sys_msig_met2_nb4 sys_msig_met3_nb4 sys_msig_met4_nb4 sys_msb_met1_nb2 sys_msb_met2_nb2 sys_msb_met3_nb2 sys_msb_met4_nb2 sys_msb_met1_nb3 sys_msb_met2_nb3 sys_msb_met3_nb3 sys_msb_met4_nb3 sys_msb_met1_nb4 sys_msb_met2_nb4 sys_msb_met3_nb4 sys_msb_met4_nb4
//
// where sys is the fractional uncertainty on the signal efficiency for that bin.
//
// The elements in a more compact notation are
//
// m1 m2 sig_m1_2b sig_m2_2b sig_m3_2b sig_m4_2b sig_m1_3b sig_m2_3b sig_m3_3b sig_m4_3b sig_m1_4b sig_m2_4b sig_m3_4b sig_m4_4b sb_m1_2b sb_m2_2b sb_m3_2b sb_m4_2b sb_m1_3b sb_m2_3b sb_m3_3b sb_m4_3b sb_m1_4b sb_m2_4b sb_m3_4b sb_m4_4b
//
// where the array index, counting from zero, is 2 + sig_or_sb * (Nmet*Nb) + nb_index * (Mmet) + met_index
//
// where sig_or_sb is : =0 for higgs mass signal box, =1 for higgs mass sideband
// nb_index is : =0 for 2b, =1 for 3b, =2 for 4b
// met_index is : =0 for bin1, =1 for bin2, =2 for bin3, =3 for bin4
//
//
bool setupShapeSyst( const char* infile,
const char* systname,
int constraintType,
double target_mgl, double target_mlsp,
RooWorkspace& workspace
) {
printf("\n\n\n setupShapeSyst : setting up %s systematic. Input file %s\n\n", systname, infile ) ;
if ( constraintType == 1 ) {
printf(" setupShapeSyst : Constraint type for %s : Gaussian (1).\n\n", systname ) ;
} else if ( constraintType == 2 ) {
printf(" setupShapeSyst : Constraint type for %s : log-normal (2).\n\n", systname ) ;
} else {
printf(" *** setupShapeSyst : Constraint type %d not implemented.\n\n", constraintType ) ;
return false ;
}
char command[1000] ;
sprintf( command, "head -1 %s | awk '{print NF}'", infile ) ;
const char* nfields_str = gSystem->GetFromPipe( command ) ;
int nfields ;
sscanf( nfields_str, "%d", &nfields ) ;
printf(" setupShapeSyst: Nfields in %s is %d\n", infile, nfields ) ;
int ArraySize = nfields ;
ifstream infq ;
infq.open(infile) ;
if ( !infq.good() ) {
printf("\n\n *** setupShapeSyst: Problem opening input file: %s.\n\n", infile ) ;
return false ;
}
double matchArrayContent[ArraySize] ;
double nearbyMatchArrayContent[ArraySize] ;
bool foundMatch = false ;
bool foundNearbyMatch = false ;
while ( infq.good() ) {
double readArrayContent[ArraySize] ;
for ( int i=0; infq && i<ArraySize; ++ i) {
infq >> readArrayContent[i] ;
}
double mgl = readArrayContent[0] ;
double mlsp = readArrayContent[1] ;
if ( fabs( mgl-target_mgl ) < 1. && fabs( mlsp - target_mlsp ) < 1. ) {
for ( int i=0; i<ArraySize; i++ ) { matchArrayContent[i] = readArrayContent[i] ; }
foundMatch = true ;
printf("\n\n setupShapeSyst : %s :Found mgl=%.0f, mlsp=%.0f\n\n", systname, mgl, mlsp ) ;
break ;
}
if ( fabs( mgl-target_mgl ) < 26. && fabs( mlsp - target_mlsp ) < 26. && !foundNearbyMatch ) {
for ( int i=0; i<ArraySize; i++ ) { nearbyMatchArrayContent[i] = readArrayContent[i] ; }
foundNearbyMatch = true ;
printf("\n\n setupShapeSyst : %s : Found nearby match mgl=%.0f, mlsp=%.0f (requested mgl=%.0f, mlsp=%.0f)\n\n", systname, mgl, mlsp, target_mgl, target_mlsp ) ;
}
} // reading file?
double ArrayContent[ArraySize] ;
if ( foundMatch ) {
for ( int i=0; i<ArraySize; i++ ) { ArrayContent[i] = matchArrayContent[i] ; }
} else if ( foundNearbyMatch ) {
for ( int i=0; i<ArraySize; i++ ) { ArrayContent[i] = nearbyMatchArrayContent[i] ; }
} else {
printf("\n\n *** setupShapeSyst : %s : Did not find match or nearby match for mgl=%.0f, mlsp=%.0f\n\n", systname, target_mgl, target_mlsp ) ;
return false ;
}
//-- convention for each line in systematics file is
//
// Mparent Mlsp sys_msig_met1_nb2 sys_msig_met2_nb2 sys_msig_met3_nb2 sys_msig_met4_nb2 sys_msig_met1_nb3 sys_msig_met2_nb3 sys_msig_met3_nb3 sys_msig_met4_nb3 sys_msig_met1_nb4 sys_msig_met2_nb4 sys_msig_met3_nb4 sys_msig_met4_nb4 sys_msb_met1_nb2 sys_msb_met2_nb2 sys_msb_met3_nb2 sys_msb_met4_nb2 sys_msb_met1_nb3 sys_msb_met2_nb3 sys_msb_met3_nb3 sys_msb_met4_nb3 sys_msb_met1_nb4 sys_msb_met2_nb4 sys_msb_met3_nb4 sys_msb_met4_nb4
//
// where sys is the fractional uncertainty on the signal efficiency for that bin.
//
// The elements in a more compact notation are
//
// m1 m2 sig_m1_2b sig_m2_2b sig_m3_2b sig_m4_2b sig_m1_3b sig_m2_3b sig_m3_3b sig_m4_3b sig_m1_4b sig_m2_4b sig_m3_4b sig_m4_4b sb_m1_2b sb_m2_2b sb_m3_2b sb_m4_2b sb_m1_3b sb_m2_3b sb_m3_3b sb_m4_3b sb_m1_4b sb_m2_4b sb_m3_4b sb_m4_4b
//
// where the array index, counting from zero, is 2 + sig_or_sb * (Nmet*Nb) + nb_index * (Mmet) + met_index
//
// where sig_or_sb is : =0 for higgs mass signal box, =1 for higgs mass sideband
// nb_index is : =0 for 2b, =1 for 3b, =2 for 4b
// met_index is : =0 for bin1, =1 for bin2, =2 for bin3, =3 for bin4
//
// Note: Always have 2b, 3b, and 4b in the syst file, even if creating workspace that doesn't use 3b.
double syst_msig[3][max_bins_of_met] ;
double syst_msb[3][max_bins_of_met] ;
double minSyst_msig(999.) ;
double maxSyst_msig(0.) ;
double minSyst_msb(999.) ;
double maxSyst_msb(0.) ;
for ( int nbi=0; nbi<3; nbi++ ) {
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
syst_msig[nbi][mbi] = ArrayContent[ 2 + nbi * (bins_of_met) + mbi ] ;
syst_msb[nbi][mbi] = ArrayContent[ 2 + nbi * (bins_of_met) + mbi + (bins_of_met*3) ] ;
if ( syst_msig[nbi][mbi] > maxSyst_msig ) maxSyst_msig = syst_msig[nbi][mbi] ;
if ( syst_msig[nbi][mbi] < minSyst_msig ) minSyst_msig = syst_msig[nbi][mbi] ;
if ( syst_msb[nbi][mbi] > maxSyst_msb ) maxSyst_msb = syst_msb[nbi][mbi] ;
if ( syst_msb[nbi][mbi] < minSyst_msb ) minSyst_msb = syst_msb[nbi][mbi] ;
} // mbi.
} // nbi.
printf("\n\n") ;
printf(" ====== Shape systematics for %s, sig observables\n", systname ) ;
for ( int nbi=0; nbi<3; nbi++ ) {
printf(" %s, sig, %db : ", systname, nbi+2 ) ;
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
printf(" %6.3f ", syst_msig[nbi][mbi] ) ;
} // mbi.
printf("\n") ;
} // nbi.
printf("\n\n") ;
printf(" ====== Shape systematics for %s, sb observables\n", systname ) ;
for ( int nbi=0; nbi<3; nbi++ ) {
printf(" %s, sb, %db : ", systname, nbi+2 ) ;
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
printf(" %6.3f ", syst_msb[nbi][mbi] ) ;
} // mbi.
printf("\n") ;
} // nbi.
printf("\n\n setupShapeSyst: %s : higgs mass sig bins: Min syst = %6.3f, Max syst = %6.3f\n\n", systname, minSyst_msig, maxSyst_msig ) ;
printf("\n\n setupShapeSyst: %s : higgs mass sb bins: Min syst = %6.3f, Max syst = %6.3f\n\n", systname, minSyst_msb , maxSyst_msb ) ;
for ( int nbi=0; nbi<3; nbi++ ) {
for ( int mbi=first_met_bin_array_index; mbi<bins_of_met; mbi++ ) {
char pname[100] ;
bool changeSign ;
RooAbsReal* rar_par ;
sprintf( pname, "%s_msig_met%d_%db", systname, mbi+1, nbi+2 ) ;
if ( syst_msig[mbi][nbi] < 0 ) { changeSign = true ; } else { changeSign = false ; }
if ( constraintType == 1 ) {