Double_t GetRand(TF1 *func, Double_t X_MIN, Double_t X_MAX, int seed )
{
Double_t Y_MAX;
Y_MAX = func->GetMaximum();
//std::cout << "Y_MAX=" << Y_MAX << std::endl;
TRandom2* r = new TRandom3(seed);
Double_t x =0;
Double_t y = r->Uniform();
Double_t f_y = 3e+6;
while(1)
{
x = r->Uniform(X_MIN, X_MAX);
//std::cout << "x=" << x << std::endl;
y = r->Uniform(0, Y_MAX);
//std::cout << "y=" << y << std::endl;
f_y = func->Eval(x);
//std::cout << "f_y=" << f_y << std::endl;
if(f_y > y)
break;
}
//std::cout << "x budur=" << x << std::endl;
return x;
}
void ThreeDFit() {
const int n = 1000;
double x[n], y[n], z[n], v[n];
double ev = 0.1;
// generate the data
TRandom2 r;
for (int i = 0; i < n; ++i) {
x[i] = r.Uniform(0,10);
y[i] = r.Uniform(0,10);
z[i] = r.Uniform(0,10);
v[i] = sin(x[i] ) + cos(y[i]) + z[i] + r.Gaus(0,ev);
}
// create a 3d binned data structure
ROOT::Fit::BinData data(n,3);
double xx[3];
for(int i = 0; i < n; ++i) {
xx[0] = x[i];
xx[1] = y[i];
xx[2] = z[i];
// add the 3d-data coordinate, the predictor value (v[i]) and its errors
data.Add(xx, v[i], ev);
}
TF3 * f3 = new TF3("f3","[0] * sin(x) + [1] * cos(y) + [2] * z",0,10,0,10,0,10);
f3->SetParameters(2,2,2);
ROOT::Fit::Fitter fitter;
// wrapped the TF1 in a IParamMultiFunction interface for teh Fitter class
ROOT::Math::WrappedMultiTF1 wf(*f3,3);
fitter.SetFunction(wf);
//
bool ret = fitter.Fit(data);
if (ret) {
const ROOT::Fit::FitResult & res = fitter.Result();
// print result (should be around 1)
res.Print(std::cout);
// copy all fit result info (values, chi2, etc..) in TF3
f3->SetFitResult(res);
// test fit p-value (chi2 probability)
double prob = res.Prob();
if (prob < 1.E-2)
Error("exampleFit3D","Bad data fit - fit p-value is %f",prob);
else
std::cout << "Good fit : p-value = " << prob << std::endl;
}
else
Error("exampleFit3D","3D fit failed");
}
TH1* GenGeResponse(double keV0,TH1* hist,int N,double reskeV,double frakpeak,double fraccomp,double contshap,double cont){
TF1* GeResponse=GenGeResponseA(keV0,frakpeak,fraccomp,contshap,cont);
TH1* response=hist;
if(!response)response=new TH1D("GeResponse","GeResponse",8000,0,2000);
double RES=reskeV*sqrt(keV0)/sqrt(500);//define res as res at 500keV
TRandom2 rand;rand.SetSeed();
for(int i=0;i<N;i++){
double E=GeResponse->GetRandom();
//Res at peak is normal, everything below photopeak = very messy
double res=pow(E/keV0,4);if(res>1)res=1;
res=(3-(res*2))*reskeV;
E+=rand.Gaus(0,res);
response->Fill(E);
}
delete GeResponse;
return response;
}
int main( int argc, char * argv[])
{
if( argc < 3) {
std::cout << "usage : tupleMaker input-filenames output-filename <Unweight>" << std::endl;
return 1;
}
std::string lastarg = std::string( argv[argc-1] );
int lastargisfilename = 0;
if (lastarg.length() > 1 ){
lastargisfilename =1;
}
if (!lastargisfilename){
if( argc < 5) {
std::cout << "usage : tupleMaker input-filenames output-filename <Unweight> <PDF reweight>" << std::endl;
return 1;
}
}
//
// read in some control parameters
//
bool Unweight = 0;
bool PDF_reweight = 0;
if (!lastargisfilename){
Unweight = atoi( argv[argc-2] );
PDF_reweight = atoi( argv[argc-1] );
std::cout<<"Unweight = "<<Unweight<<" PDF reweight = "<<PDF_reweight<<std::endl;
if(PDF_reweight==1) {
std::cout<<"========================================================================================================"<<std::endl;
std::cout<<"Need to make sure you have right PDF text files resbos_P/resbos_weights_PDF_*.dat"<<std::endl;
std::cout<<"========================================================================================================"<<std::endl;
}
}
// get input and output file names
std::vector<std::string> in_files;
int nInputs = argc-(2-lastargisfilename);
if(PDF_reweight) nInputs = argc-(3-lastargisfilename);
for( int i = 1; i < nInputs;++i) {
in_files.push_back( std::string( argv[i] ) );
std::cout<<"Input file: "<<std::string(argv[i])<<std::endl;
}
int nOutput = argc-(2-lastargisfilename);
if(PDF_reweight) nOutput = argc-(3-lastargisfilename);
std::string out_filename = std::string( argv[nOutput] );
std::cout<<"Output file: "<<out_filename<<std::endl;
// setup outputfile
TFile * of = new TFile(out_filename.c_str(),"RECREATE");
if( of == 0) {
std::cout << "Couldn't open output file : " << out_filename << std::endl;
return 1;
}
Output * output = new Output();
output->Reset();
// initialise random number generator
TRandom2 random;
random.SetSeed(19742005);
// loop over files
Double_t StandardWeight =0;
int nfiles = in_files.size();
if (Unweight){
std::cout << "All events will have weight one" << std::endl;
std::cout << "Multiple files are merged using pass/fail based on the weight of events in the first file -- Standard Weight as shown below" << std::endl;
}
if (!Unweight)
std::cout << "Unweighted events, Weights from generator are maintained"<< std::endl;
//
// read event weights for different PDF files
//
std::ifstream f_weights[45];
vector<float> pdfwgts;
pdfwgts.resize(45);
if(PDF_reweight) {
char name[50];
for(int i=0; i<44; i++) {
sprintf(name, "%s%d%s", "resbos_P/resbos_weights_PDF_", i+1, ".dat");
f_weights[i].open(name, std::ios::in);
if( !f_weights[i] ) std::cout<<"Could not find the weight file "<<name<<std::endl;
}
}
#ifdef __USE_PDFS_RESBOS__
Unweight = true;
///
{
std::ifstream tmp_file;
tmp_file.open( "weights_00.hep" , std::ios::in );
if( tmp_file )
{
int num ; double wgt ;
while( tmp_file >> num >> wgt )
{
if( wgt > StandardWeight )
StandardWeight = wgt;
}
tmp_file.close();
}
}
///
for( int i = 0 ; i < 45 ; i++ )
{
TString name;
name.Form( "weights_%02i.hep" , i );
f_weights[i].open( name.Data() , std::ios::in );
if( !f_weights[i] ) std::cout<<"Could not find the weight file "<<name<<std::endl;
}
#endif
for(int i =0; i < nfiles; ++i) {
// open file
std::cout << "Processing file: " << in_files[i].c_str() << std::endl;
std::ifstream f((in_files[i]).c_str());
if( !f ) {
std::cout << "couldn't open file : " << in_files[i] << std::endl;
return 1;
}
bool finished_file = false;
// this loops over events
while( ! finished_file ) {
int evn;
double evt_wt;
double Q2,that,uhat,x1,x2,flav1,flav2 ;
#ifdef __USE_PDFS__
f >> evn >> evt_wt >> Q2 >> that >> uhat >> x1 >> x2 >> flav1 >> flav2;
#else
f >> evn >> evt_wt;
#endif
if(evn % 100000==0) std::cout<<"Processing event: "<<evn<<std::endl;
if( evn == 0 ) {
finished_file = true;
continue;
}
#ifdef __USE_PDFS_RESBOS__
for( int j = 0 ; j<45 ; j++ )
{
double evn_tmp , wgt_tmp;
f_weights[j] >> evn_tmp >> wgt_tmp;
if( evn_tmp != evn )
{
cout << " WRONG EVENT NUMBER!!!! " << j << " " << evn_tmp << " " << evn << endl;
return 1;
}
if( j == 0 ) evt_wt = wgt_tmp;
if( evt_wt > 0 && pdfwgts[j] > 0 )
pdfwgts[j] = wgt_tmp / evt_wt;
else
pdfwgts[j] = 1.0;
if( pdfwgts[j] < 0 )
pdfwgts[j] = 0.0;
}
#endif
float vx,vy,vz;
f>> vx >> vy >>vz;
bool finished_particles = false;
if( !f.eof())
{
if (StandardWeight == 0. && Unweight){
StandardWeight = evt_wt;
std::cout << "Standard Weight = " << StandardWeight << std::endl;
}
}
else
{
finished_file = true;
}
Bool_t keeper = kTRUE;
if (Unweight){
Double_t weight_ratio= TMath::Abs(evt_wt/StandardWeight);
if (weight_ratio > 1.){
std::cout << "This event has a weight = " << weight_ratio <<" times the value of Standard Weight" << std::endl;
}
if (random.Rndm() > weight_ratio)
keeper = kFALSE;
}
if (Unweight)
evt_wt = TMath::Abs(evt_wt) / evt_wt ;
//
// read event weight for each PDF set
//
double weight_PDF[44];
if(PDF_reweight) {
for(int j=0; j<44; j++) (f_weights[j]) >> weight_PDF[i];
}
// save PDF information into the root file
// output->setPDFWeights(weight_PDF);
if (keeper) {
if(PDF_reweight) output->NewEvent( evn, evt_wt, 0 , vx,vy,vz, weight_PDF, 44);
else
{
output->NewEvent( evn, evt_wt, 0 , vx,vy,vz , Q2 , x1 , x2 , flav1 , flav2 , pdfwgts );
}
}
//bool doFlip = false;
//doFlip = ( random.Rndm() > 0.5);
// this loops over particles in an event
while( ! finished_particles && !f.eof()) {
int id;
f >> id;
if( id == 0 )
finished_particles = true;
else {
float px,py,pz,E;
f>> px >> py >> pz >> E;
int origin, udk;
f >> origin >> udk;
//std::cout << id << " " << px << " " << py << " " << pz << " " << E << std::endl;
// do the occaisional CP inversion
// flip W+ -> W-, e+->e-, nu -> nu-bar and invert all momenta
//if( doFlip ) {
// id is isajet ids
// if( id != 10 ) id = -id; // photon == anti-photon
// px = -px;
// py = -py;
// pz = -pz;
//}
if(keeper)
output->AddParticle( id, px,py,pz,E,origin);
}
}
if (keeper){
output->Fill();
}
if( f.eof()) finished_file = true;
}
f.close();
}
output->Write();
of = output->Tree()->GetCurrentFile();
of->Close();
return 0;
};
void plotFeedDown(int ntest=1, int centL=0,int centH=100)
{
// B cross-section
TFile *inf = new TFile("output_pp_Bmeson_5TeV_y1.root");
// TFile *inf = new TFile("outputBplus_D_pp_rap24.root");
// TFile *inf = new TFile("outputBplus_pp.root");
TH1D *hBPtMax = (TH1D*)inf->Get("hmaxall");
TH1D *hBPtMin = (TH1D*)inf->Get("hminall");
TH1D *hBPt = (TH1D*)inf->Get("hpt");
hBPt->SetName("hBPt");
hBPtMax->SetName("hBPtMax");
hBPtMin->SetName("hBPtMin");
TH1D *hBMaxRatio = (TH1D*)hBPt->Clone("hBMaxRatio");
hBMaxRatio->Divide(hBPtMax);
TH1D *hBMinRatio = (TH1D*)hBPt->Clone("hBMinRatio");
hBMinRatio->Divide(hBPtMin);
hBPt->Rebin(1);
// D cross-section
// TFile *infD = new TFile("outputD0_D_pp.root");
TFile *infD = new TFile("output_pp_d0meson_5TeV_y1.root");
TH1D *hDPtMax = (TH1D*)infD->Get("hmaxall");
TH1D *hDPtMin = (TH1D*)infD->Get("hminall");
TH1D *hDPt = (TH1D*)infD->Get("hpt");
hDPt->SetName("hDPt");
hDPtMax->SetName("hDPtMax");
hDPtMin->SetName("hDPtMin");
hDPt->Rebin(1);
// ratio of B->D0: not correct85% from PYTHIA
//hBPt->Scale(0.85);
hBPt->Scale(0.598);
// c->D (55.7%)
hDPt->Scale(0.557);
TFile *inf2 = new TFile("/data/HeavyFlavourRun2/BtoDPythia/treefile_merged.root");
// TFile *inf2 = new TFile("test.root");
TTree *hi = (TTree*) inf2->Get("ana/hi");
hi->SetAlias("yD","log((sqrt(1.86484*1.86484+pt*pt*cosh(eta)*cosh(eta))+pt*sinh(eta))/sqrt(1.86484*1.86484+pt*pt))");
hi->SetAlias("yB","log((sqrt(5.3*5.3+pt*pt*cosh(eta)*cosh(eta))+pt*sinh(eta))/sqrt(5.3*5.3+pt*pt))");
hi->SetAlias("yJ","log((sqrt(3.09692*3.09692+pt*pt*cosh(eta)*cosh(eta))+pt*sinh(eta))/sqrt(3.09692*3.09692+pt*pt))");
// 6.5, 8, 10, 13, 30
/*
TH1D *hBNoCut = (TH1D*)hBPt->Clone("hBNoCut");
TH1D *hBHasD = (TH1D*)hBPt->Clone("hBHasD");
hi->Draw("pt>>hBHasD","(abs(pdg)>500&&abs(pdg)<600&&abs(yB)<2.4)&&Sum$(abs(pdg)==421&&abs(yD)<2)>0");
hi->Draw("pt>>hBNoCut","(abs(pdg)>500&&abs(pdg)<600&&abs(yB)<2.4)");
;
hBNoCut->Divide(hBHasD);
hBPt->Divide(hBNoCut);
*/
// 0-100%
int npoint = 7;
double ptBins_npjpsi[8] = {1,3,6.5,8,10,13,30,300};
double raa_npjpsi[7];// = {1,0.6, 0.52,0.43,0.43,0.34,0.5};
double raaStat_npjpsi[7];// = {1,0.4,0.12,0.08,0.09,0.07,0.5};
double raaSyst_npjpsi[7];// = {0,0,0.06,0.05,0.05,0.04,0};
/*
0-10, 10-20, 20-30, 30-40, 40-50, 50-100
double nonPromptJpsiRAA_2012[] = {0.,0.38,0.43,0.48,0.52,0.65,0.69};
double nonPromptJpsiRAAError_2012[] = {0.,0.02,0.03,0.03,0.04,0.06,0.07};
double nonPromptJpsiRAAErrorSyst_2012[] = {0.,0.04,0.05,0.05,0.06,0.07,0.07};
*/
if (centL==0&¢H==100) {
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=0.6; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=0.4; // prelim
raa_npjpsi[2]=0.52; raaStat_npjpsi[2]=0.12; raaSyst_npjpsi[2]=0.06; // np jpsi pas
raa_npjpsi[3]=0.43; raaStat_npjpsi[3]=0.08; raaSyst_npjpsi[3]=0.05; // np jpsi pas
raa_npjpsi[4]=0.43; raaStat_npjpsi[4]=0.09; raaSyst_npjpsi[4]=0.05; // np jpsi pas
raa_npjpsi[5]=0.34; raaStat_npjpsi[5]=0.07; raaSyst_npjpsi[5]=0.04; // np jpsi pas
raa_npjpsi[6]=0.5; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.25; // b-jet
}
if (centL==0&¢H==10) {
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.38; raaStat_npjpsi[2]=0.02; raaSyst_npjpsi[2]=0.04; // np jpsi pas
raa_npjpsi[3]=0.38; raaStat_npjpsi[3]=0.02; raaSyst_npjpsi[3]=0.04; // np jpsi pas
raa_npjpsi[4]=0.38; raaStat_npjpsi[4]=0.02; raaSyst_npjpsi[4]=0.04; // np jpsi pas
raa_npjpsi[5]=0.38; raaStat_npjpsi[5]=0.02; raaSyst_npjpsi[5]=0.04; // np jpsi pas
raa_npjpsi[6]=0.39; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.20; // b-jet
}
if (centL==10&¢H==20) {
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.43; raaStat_npjpsi[2]=0.03; raaSyst_npjpsi[2]=0.05; // np jpsi pas
raa_npjpsi[3]=0.43; raaStat_npjpsi[3]=0.03; raaSyst_npjpsi[3]=0.05; // np jpsi pas
raa_npjpsi[4]=0.43; raaStat_npjpsi[4]=0.03; raaSyst_npjpsi[4]=0.05; // np jpsi pas
raa_npjpsi[5]=0.43; raaStat_npjpsi[5]=0.03; raaSyst_npjpsi[5]=0.05; // np jpsi pas
raa_npjpsi[6]=0.47; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.24; // b-jet
}
if (centL==20&¢H==30) {
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.48; raaStat_npjpsi[2]=0.03; raaSyst_npjpsi[2]=0.05; // np jpsi pas
raa_npjpsi[3]=0.48; raaStat_npjpsi[3]=0.03; raaSyst_npjpsi[3]=0.05; // np jpsi pas
raa_npjpsi[4]=0.48; raaStat_npjpsi[4]=0.03; raaSyst_npjpsi[4]=0.05; // np jpsi pas
raa_npjpsi[5]=0.48; raaStat_npjpsi[5]=0.03; raaSyst_npjpsi[5]=0.05; // np jpsi pas
raa_npjpsi[6]=0.47; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.24; // b-jet
}
if (centL==30&¢H==40) {
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.52; raaStat_npjpsi[2]=0.04; raaSyst_npjpsi[2]=0.06; // np jpsi pas
raa_npjpsi[3]=0.52; raaStat_npjpsi[3]=0.04; raaSyst_npjpsi[3]=0.06; // np jpsi pas
raa_npjpsi[4]=0.52; raaStat_npjpsi[4]=0.04; raaSyst_npjpsi[4]=0.06; // np jpsi pas
raa_npjpsi[5]=0.52; raaStat_npjpsi[5]=0.04; raaSyst_npjpsi[5]=0.06; // np jpsi pas
raa_npjpsi[6]=0.61; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.30; // b-jet
}
if (centL==40&¢H==50) {
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.65; raaStat_npjpsi[2]=0.06; raaSyst_npjpsi[2]=0.07; // np jpsi pas
raa_npjpsi[3]=0.65; raaStat_npjpsi[3]=0.06; raaSyst_npjpsi[3]=0.07; // np jpsi pas
raa_npjpsi[4]=0.65; raaStat_npjpsi[4]=0.06; raaSyst_npjpsi[4]=0.07; // np jpsi pas
raa_npjpsi[5]=0.65; raaStat_npjpsi[5]=0.06; raaSyst_npjpsi[5]=0.07; // np jpsi pas
raa_npjpsi[6]=0.61; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.30; // b-jet
}
if (centL==50&¢H==100) {
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.69; raaStat_npjpsi[2]=0.07; raaSyst_npjpsi[2]=0.07; // np jpsi pas
raa_npjpsi[3]=0.69; raaStat_npjpsi[3]=0.07; raaSyst_npjpsi[3]=0.07; // np jpsi pas
raa_npjpsi[4]=0.69; raaStat_npjpsi[4]=0.07; raaSyst_npjpsi[4]=0.07; // np jpsi pas
raa_npjpsi[5]=0.69; raaStat_npjpsi[5]=0.07; raaSyst_npjpsi[5]=0.07; // np jpsi pas
raa_npjpsi[6]=0.70; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.35; // b-jet
}
if (centL==0&¢H==20) { //averaged by ncoll
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.4; raaStat_npjpsi[2]=0.03; raaSyst_npjpsi[2]=0.05; // np jpsi pas
raa_npjpsi[3]=0.4; raaStat_npjpsi[3]=0.03; raaSyst_npjpsi[3]=0.05; // np jpsi pas
raa_npjpsi[4]=0.4; raaStat_npjpsi[4]=0.03; raaSyst_npjpsi[4]=0.05; // np jpsi pas
raa_npjpsi[5]=0.4; raaStat_npjpsi[5]=0.03; raaSyst_npjpsi[5]=0.05; // np jpsi pas
raa_npjpsi[6]=0.42; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.21; // b-jet
}
if (centL==10&¢H==30) { //averaged by ncoll
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.45; raaStat_npjpsi[2]=0.03; raaSyst_npjpsi[2]=0.05; // np jpsi pas
raa_npjpsi[3]=0.45; raaStat_npjpsi[3]=0.03; raaSyst_npjpsi[3]=0.05; // np jpsi pas
raa_npjpsi[4]=0.45; raaStat_npjpsi[4]=0.03; raaSyst_npjpsi[4]=0.05; // np jpsi pas
raa_npjpsi[5]=0.45; raaStat_npjpsi[5]=0.03; raaSyst_npjpsi[5]=0.05; // np jpsi pas
raa_npjpsi[6]=0.47; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.24; // b-jet
}
if (centL==30&¢H==50) { //averaged by ncoll
raa_npjpsi[0]=1.0; raaStat_npjpsi[0]=0.0; raaSyst_npjpsi[0]=1.0; // no measurement
raa_npjpsi[1]=1.0; raaStat_npjpsi[1]=0.0; raaSyst_npjpsi[1]=1.0; // no measurement
raa_npjpsi[2]=0.57; raaStat_npjpsi[2]=0.06; raaSyst_npjpsi[2]=0.07; // np jpsi pas
raa_npjpsi[3]=0.57; raaStat_npjpsi[3]=0.06; raaSyst_npjpsi[3]=0.07; // np jpsi pas
raa_npjpsi[4]=0.57; raaStat_npjpsi[4]=0.06; raaSyst_npjpsi[4]=0.07; // np jpsi pas
raa_npjpsi[5]=0.57; raaStat_npjpsi[5]=0.06; raaSyst_npjpsi[5]=0.07; // np jpsi pas
raa_npjpsi[6]=0.61; raaStat_npjpsi[6]=0.0; raaSyst_npjpsi[6]=0.30; // b-jet
}
TH1D *hNPJpsiRAA = new TH1D("hNPJpsiRAA","",npoint,ptBins_npjpsi);
for (int i=1;i<=npoint;i++)
{
hNPJpsiRAA->SetBinContent(i,raa_npjpsi[i-1]);
hNPJpsiRAA->SetBinError(i,sqrt(raaSyst_npjpsi[i-1]*raaSyst_npjpsi[i-1]+raaStat_npjpsi[i-1]*raaStat_npjpsi[i-1])); }
TCanvas *cJpsiRAA = new TCanvas("cJpsiRAA","",600,600);
cJpsiRAA->SetLogx();
TExec *setex2 = new TExec("setex2","gStyle->SetErrorX(0.5)");
setex2->Draw();
hNPJpsiRAA->SetXTitle("Non-prompt J/psi R_{AA} (GeV/c)");
hNPJpsiRAA->SetXTitle("Non-prompt J/psi p_{T} (GeV/c)");
hNPJpsiRAA->SetYTitle("R_{AA}");
hNPJpsiRAA->Draw("e1");
TCanvas *c = new TCanvas("c","",600,600);
TH2D *hJpsi= new TH2D("hJpsi","",hBPt->GetNbinsX(),hBPt->GetBinLowEdge(1),hBPt->GetBinLowEdge(hBPt->GetNbinsX()+1),
299*4,1,300);
TH2D *hD= new TH2D("hD","",hBPt->GetNbinsX(),hBPt->GetBinLowEdge(1),hBPt->GetBinLowEdge(hBPt->GetNbinsX()+1),
299*4,1,300);
hi->Draw("pt:BPt>>hJpsi","pdg==443&&BPt>0&&abs(yJ)<1");
hi->Draw("pt:BPt>>hD","abs(pdg)==421&&BPt>0&&abs(yD)<1");
hJpsi->Sumw2();
hD->Sumw2();
reweighthisto(hBPt,hD);
reweighthisto(hBPt,hJpsi);
hJpsi->ProjectionY()->Draw("hist");
hD->SetLineColor(4);
hD->SetMarkerColor(4);
hD->ProjectionY()->Draw("hist same");
hBPt->Draw("hist same");
hJpsi->SetXTitle("B p_{T} (GeV/c)");
hJpsi->SetYTitle("J/#psi p_{T} (GeV/c)");
hD->SetXTitle("B p_{T} (GeV/c)");
hD->SetYTitle("D^{0} p_{T} (GeV/c)");
TCanvas *c2= new TCanvas("c2","B RAA band",600,600);
TRandom2 rnd;
// hJpsi ->ProjectionX()->Draw("hist");
TH2D *hRAATmp = new TH2D("hRAATmp","",97,3,100,100,0,2);
hRAATmp->SetXTitle("B p_{T} (GeV/c)");
hRAATmp->SetYTitle("R_{AA}");
hRAATmp->Draw();
TCanvas *c3= new TCanvas("c3","D RAA band",600,600);
TH2D *hDRAATmp = new TH2D("hDRAATmp","",47,3,50,100,0,2);
hDRAATmp->SetXTitle("D^{0} p_{T} (GeV/c)");
hDRAATmp->SetYTitle("R_{AA}");
hDRAATmp->Draw();
TCanvas *c4= new TCanvas("c4","B->D fraction band",600,600);
TH2D *hBtoDTmp = new TH2D("hBtoDTmp","",47,3,50,100,0,2);
hBtoDTmp->SetXTitle("D^{0} p_{T} (GeV/c)");
hBtoDTmp->SetYTitle("Non-prompt D fraction");
hBtoDTmp->Draw();
TH1D *hDFromBPt= (TH1D*)hD->ProjectionY()->Clone("hDFromBPt");
TH1D *hDFromBPtFraction= (TH1D*)hD->ProjectionY()->Clone("hDFromBPtFraction");
hDFromBPtFraction->Divide(hDPt);
TH1D *hDFromBPtMax= (TH1D*)hD->ProjectionY()->Clone("hDFromBPtMax");
TH1D *hDFromBPtMin= (TH1D*)hD->ProjectionY()->Clone("hDFromBPtMin");
setHist(hDFromBPtMax,-1e10);
setHist(hDFromBPtMin,1e10);
for (int i=0;i<ntest;i++)
{
if (i%10==0) cout <<i<<endl;
TH1D *hRAASample = (TH1D*)hNPJpsiRAA->Clone(Form( "hRAASample_%d",i));
for (int j=1;j<=hRAASample->GetNbinsX();j++) {
double RAA = (rnd.Rndm()*2-1)*hNPJpsiRAA->GetBinError(j)+hNPJpsiRAA->GetBinContent(j);
hRAASample->SetBinContent(j,RAA);
}
TH2D *hJpsiClone = (TH2D*)hJpsi->Clone(Form("hJpsiClone_%d",i));
reweighthisto(hBPt,hJpsiClone,hRAASample,1);
TH1D *hBRAA = hJpsiClone->ProjectionX(Form("hBRAA_%d",i));
c2->cd();
hBRAA->Divide(hBPt);
hBRAA->SetLineWidth(3);
hBRAA->SetLineColor(kGray);
hBRAA->Rebin(4);
hBRAA->Scale(1./4.);
hBRAA->Draw("hist c same");
delete hJpsiClone;
TH2D *hDClone = (TH2D*)hD->Clone(Form("hDClone_%d",i));
reweighthisto(hBPt,hDClone,hBRAA,0,1);
TH1D *hDRAA = hDClone->ProjectionY(Form("hDRAA_%d",i));
getMaximum(hDFromBPtMax,hDRAA);
getMinimum(hDFromBPtMin,hDRAA);
c3->cd();
hDRAA->Divide(hDFromBPt);
hDRAA->SetLineWidth(3);
hDRAA->SetLineColor(kGray);
hDRAA->Draw("hist c same");
c4->cd();
TH1D *hBtoDFrac = hDClone->ProjectionY(Form("hBtoDFrac_%d",i));
hBtoDFrac->Divide(hDPt);
hBtoDFrac->SetLineWidth(3);
hBtoDFrac->SetLineColor(kGray);
hBtoDFrac->Draw("hist same");
delete hDClone;
// delete hBRAA;
// delete hDRAA;
}
TFile *outf = new TFile(Form("BtoD-%d-%d.root",centL,centH),"recreate");
TH1D *hDFromBPtCentral=(TH1D*)hDFromBPtMax->Clone("hDFromBPtCentral");
hDFromBPtCentral->Add(hDFromBPtMin);
hDFromBPtCentral->Scale(1./2);
hNPJpsiRAA->Write();
hDFromBPtMax->Write();
hDFromBPtMin->Write();
hDFromBPtCentral->Write();
hDFromBPt->Write();
hJpsi->Write();
hD->Write();
hDFromBPtFraction->Write();
outf->Write();
}
void write(int n) {
TRandom2 R;
TStopwatch timer;
R.SetSeed(1);
timer.Start();
double s = 0;
for (int i = 0; i < n; ++i) {
s += R.Gaus(0,10);
s += R.Gaus(0,10);
s += R.Gaus(0,10);
s += R.Gaus(100,10);
}
timer.Stop();
std::cout << s/double(n) << std::endl;
std::cout << " Time for Random gen " << timer.RealTime() << " " << timer.CpuTime() << std::endl;
TFile f1("mathcoreVectorIO_1.root","RECREATE");
// create tree
TTree t1("t1","Tree with new LorentzVector");
XYZTVector *v1 = new XYZTVector();
t1.Branch("LV branch","ROOT::Math::XYZTVector",&v1);
R.SetSeed(1);
timer.Start();
for (int i = 0; i < n; ++i) {
double Px = R.Gaus(0,10);
double Py = R.Gaus(0,10);
double Pz = R.Gaus(0,10);
double E = R.Gaus(100,10);
//CylindricalEta4D<double> & c = v1->Coordinates();
//c.SetValues(Px,pY,pZ,E);
v1->SetCoordinates(Px,Py,Pz,E);
t1.Fill();
}
f1.Write();
timer.Stop();
std::cout << " Time for new Vector " << timer.RealTime() << " " << timer.CpuTime() << std::endl;
t1.Print();
// create tree with old LV
TFile f2("mathcoreVectorIO_2.root","RECREATE");
TTree t2("t2","Tree with TLorentzVector");
TLorentzVector * v2 = new TLorentzVector();
TLorentzVector::Class()->IgnoreTObjectStreamer();
TVector3::Class()->IgnoreTObjectStreamer();
t2.Branch("TLV branch","TLorentzVector",&v2,16000,2);
R.SetSeed(1);
timer.Start();
for (int i = 0; i < n; ++i) {
double Px = R.Gaus(0,10);
double Py = R.Gaus(0,10);
double Pz = R.Gaus(0,10);
double E = R.Gaus(100,10);
v2->SetPxPyPzE(Px,Py,Pz,E);
t2.Fill();
}
f2.Write();
timer.Stop();
std::cout << " Time for old Vector " << timer.RealTime() << " " << timer.CpuTime() << endl;
t2.Print();
}
void readinhist(){
ifstream infile("sn124p_250.out");
ifstream infile("gampi_sn120_n_fsu30_187.out");
Double_t t1[1800],t2[1800],t3[1800],t4[1800],t5[1800],t6[1800];
char dummy;
for(int j = 0; j< 1800; j++){
t1[j] = .0;
t2[j] = .0;
t3[j] = .0;
t4[j] = .0;
t6[j] = 0.0;
}
if(!infile){
cout << "Cannot open file!!!" << endl;
}
else {
for(int i=0; i< 1800; i++){
//while(!infile.eof()){
infile >> dummy >> t1[i] >> t2[i] >> t3[i] >> t4[i];
t5[i] = t1[i] - 0.05;
}
}
for(i=0; i<1800; i++) cout << "t1[" << i << "]: " << t1[i] << ", t2[" << i << "]: " << t2[i] <<endl;
TGraph *gr1 = new TGraph(1800,t5,t2);
TRandom2 *fRandom = new TRandom2(0);
Double_t sigma_res = 2.0;
Double_t mean_res = 90.05; // just to have everything inside bin center is bin 901
TH1F *testH1 = new TH1F("testH1","testH1",1800,0.0,180.0);
Int_t n_trials = 100000;
Double_t val_random;
//Creating random distribution with the right sigma
for (int i=0 ; i<n_trials; i++) {
val_random = fRandom->Gaus(mean_res,sigma_res);
testH1->Fill(val_random);
}
Int_t n_counts = int(3*sigma_res/testH1->GetBinWidth(1)); // I will count from -3sigma to 3sigma
for (int i=0; i<1800; i++) {
for (int j=-n_counts; j<n_counts ; j++) {
if ((i+j)>=0 && (i+j)<1800) {
t6[i] = t6[i] + t2[i+j] * double(testH1->GetBinContent(901+j)) / double(n_trials);
}
}
}
TGraph *gr2 = new TGraph(1800,t5,t6);
TFile *fout = NULL;
if( fout )delete fout;
fout = new TFile("sn124p_250.root","RECREATE");
gr2->Write();
TCanvas *c1 = NULL;
if( c1 )delete c1;
c1 = new TCanvas("c1");
gr2->Draw("");
}
// this is the main program, no need to do anything here
int main(int argc, char* argv[]) {
// sum of squares error info for histograms
TH1D::SetDefaultSumw2();
// create analysis
// NoiseSystematics analyser(argc, argv);
// analyser.run();
TFile* ofile = new TFile("NoiseSystematics.root", "RECREATE");
std::cout << "Output file : " << ofile->GetName() << std::endl;
if (ofile->IsZombie()) {
std::exit(2);
}
TH1F hcosmic("hcosmic","",10,0,10);
TH1F hobs("hobs",";# expected events;",10,0,10);
TH1F hnoise("hnoise","",10,0,10);
TRandom2 rndm;
for (int i = 0; i < 10000; i++) {
// DT by RPC background: 0.96061 +/- 0.559146
double ncos = rndm.Poisson(rndm.Gaus (0.96, 0.56));
double nobs = rndm.PoissonD(2.);
hcosmic.Fill(ncos);
hobs.Fill(nobs);
hnoise.Fill(nobs-ncos);
//std::cout << ncos << "\t" << nobs << "\t" << nobs-ncos << std::endl;
}
double nq = 1;
Double_t xq[1] = {0.68}; // position where to compute the quantiles in [0,1]
Double_t yq[1] = {0.}; // array to contain the quantiles
hnoise.GetQuantiles(nq,yq,xq);
std::cout << xq[0] << "\t" << yq[0] << std::endl;
ofile->cd();
ofile->Write("",TObject::kOverwrite);
TCanvas c("c");
hnoise.SetStats(0);
hobs.SetStats(0);
hcosmic.SetStats(0);
hcosmic.SetLineColor(kRed);
//hcosmic.Scale(1.4);
hobs.SetLineColor(kBlue);
hnoise.SetLineColor(kBlack);
hcosmic.SetMaximum(hcosmic.GetMaximum()*1.1);
hcosmic.Draw("hist");
hobs.Draw("hist same");
hnoise.Draw("hist same");
c.Update();
TLine line(yq[0],0,yq[0],hcosmic.GetMaximum());
line.SetLineColor(kBlack);
line.SetLineStyle(5);
line.SetLineWidth(2);
line.Draw();
TLegend leg(0.6, 0.70, 0.88, 0.85);
leg.SetFillColor(kWhite);
leg.AddEntry("hcosmic", "N_{cosmic}", "l");
leg.AddEntry("hobs", "N_{obs} [#lambda = 2]","l");
leg.AddEntry("hnoise", "n_{noise} = N_{obs} - N_{cosmic}","l");
leg.SetBorderSize(0);
leg.Draw();
c.SaveAs("NoiseSystematics.pdf");
return 0;
}
static inline double GausAdd2(double val, double sigma)
{
return rnd.Gaus(val, val*sigma);
};
static inline double GausAdd(double val, double sigma)
{
return rnd.Gaus(val, sqrt(val)*sigma);
};
static inline double Gaus(double mean = 0, double sigma = 1)
{
return rnd.Gaus(mean, sigma);
};
void limit() {
//This program demonstrates the computation of 95 % C.L. limits.
//It uses a set of randomly created histograms.
//
//Author: [email protected] on 21.08.02
// Create a new canvas.
TCanvas *c1 = new TCanvas("c1","Dynamic Filling Example",200,10,700,500);
c1->SetFillColor(42);
// Create some histograms
TH1D* background = new TH1D("background","The expected background",30,-4,4);
TH1D* signal = new TH1D("signal","the expected signal",30,-4,4);
TH1D* data = new TH1D("data","some fake data points",30,-4,4);
background->SetFillColor(48);
signal->SetFillColor(41);
data->SetMarkerStyle(21);
data->SetMarkerColor(kBlue);
background->Sumw2(); // needed for stat uncertainty
signal->Sumw2(); // needed for stat uncertainty
// Fill histograms randomly
TRandom2 r;
Float_t bg,sig,dt;
for (Int_t i = 0; i < 25000; i++) {
bg = r.Gaus(0,1);
sig = r.Gaus(1,.2);
background->Fill(bg,0.02);
signal->Fill(sig,0.001);
}
for (Int_t i = 0; i < 500; i++) {
dt = r.Gaus(0,1);
data->Fill(dt);
}
THStack *hs = new THStack("hs","Signal and background compared to data...");
hs->Add(background);
hs->Add(signal);
hs->Draw("hist");
data->Draw("PE1,Same");
c1->Modified();
c1->Update();
c1->GetFrame()->SetFillColor(21);
c1->GetFrame()->SetBorderSize(6);
c1->GetFrame()->SetBorderMode(-1);
c1->Modified();
c1->Update();
gSystem->ProcessEvents();
// Compute the limits
cout << "Computing limits... " << endl;
TLimitDataSource* mydatasource = new TLimitDataSource(signal,background,data);
TConfidenceLevel *myconfidence = TLimit::ComputeLimit(mydatasource,50000);
cout << "CLs : " << myconfidence->CLs() << endl;
cout << "CLsb : " << myconfidence->CLsb() << endl;
cout << "CLb : " << myconfidence->CLb() << endl;
cout << "< CLs > : " << myconfidence->GetExpectedCLs_b() << endl;
cout << "< CLsb > : " << myconfidence->GetExpectedCLsb_b() << endl;
cout << "< CLb > : " << myconfidence->GetExpectedCLb_b() << endl;
// Add stat uncertainty
cout << endl << "Computing limits with stat systematics... " << endl;
TConfidenceLevel *mystatconfidence = TLimit::ComputeLimit(mydatasource,50000,true);
cout << "CLs : " << mystatconfidence->CLs() << endl;
cout << "CLsb : " << mystatconfidence->CLsb() << endl;
cout << "CLb : " << mystatconfidence->CLb() << endl;
cout << "< CLs > : " << mystatconfidence->GetExpectedCLs_b() << endl;
cout << "< CLsb > : " << mystatconfidence->GetExpectedCLsb_b() << endl;
cout << "< CLb > : " << mystatconfidence->GetExpectedCLb_b() << endl;
// Add some systematics
cout << endl << "Computing limits with systematics... " << endl;
TVectorD errorb(2);
TVectorD errors(2);
TObjArray* names = new TObjArray();
TObjString name1("bg uncertainty");
TObjString name2("sig uncertainty");
names->AddLast(&name1);
names->AddLast(&name2);
errorb[0]=0.05; // error source 1: 5%
errorb[1]=0; // error source 2: 0%
errors[0]=0; // error source 1: 0%
errors[1]=0.01; // error source 2: 1%
TLimitDataSource* mynewdatasource = new TLimitDataSource();
mynewdatasource->AddChannel(signal,background,data,&errors,&errorb,names);
TConfidenceLevel *mynewconfidence = TLimit::ComputeLimit(mynewdatasource,50000,true);
cout << "CLs : " << mynewconfidence->CLs() << endl;
cout << "CLsb : " << mynewconfidence->CLsb() << endl;
cout << "CLb : " << mynewconfidence->CLb() << endl;
cout << "< CLs > : " << mynewconfidence->GetExpectedCLs_b() << endl;
cout << "< CLsb > : " << mynewconfidence->GetExpectedCLsb_b() << endl;
cout << "< CLb > : " << mynewconfidence->GetExpectedCLb_b() << endl;
// show canonical -2lnQ plots in a new canvas
// - The histogram of -2lnQ for background hypothesis (full)
// - The histogram of -2lnQ for signal and background hypothesis (dashed)
TCanvas *c2 = new TCanvas("c2");
myconfidence->Draw();
// clean up (except histograms and canvas)
delete myconfidence;
delete mydatasource;
delete mystatconfidence;
delete mynewconfidence;
delete mynewdatasource;
}
