| 45 |
|
bool is_data; |
| 46 |
|
bool is_signal; |
| 47 |
|
bool is_active; |
| 48 |
+ |
float weightrenormalization; |
| 49 |
|
int groupindex; |
| 50 |
|
Color_t samplecolor; |
| 51 |
|
|
| 73 |
|
this->groupindex=m_groupindex; |
| 74 |
|
this->is_active=true; |
| 75 |
|
this->samplecolor=mycolor; |
| 76 |
+ |
this->weightrenormalization=1.0; |
| 77 |
|
if(!doesROOTFileExist(this->filename)) { |
| 78 |
|
stringstream message; |
| 79 |
|
message << "The " << write_mc_or_data(is_data) << " sample " << this->samplename << " is invalid because the associated file path, " << this->filename << " is incorrect."; |
| 89 |
|
gErrorIgnoreLevel=currlevel; |
| 90 |
|
this->events=(TTree*)(this->tfile)->Get(treename.c_str()); |
| 91 |
|
if(Verbosity>0) dout << "The " << write_mc_or_data(is_data) << " file " << this->filename << " has been added successfully to the list of samples. " << endl; |
| 92 |
< |
long long measured_nevents=(this->events)->GetEntries(); |
| 93 |
< |
if(((this->Nentries>1)||(this->Nentries==0))&measured_nevents!=this->Nentries) { |
| 92 |
> |
long long measured_nevents=(long)1; |
| 93 |
> |
TH1F *weight_histo = (TH1F*)(this->tfile)->Get("weight_histo"); |
| 94 |
> |
float average_weight = 1.0; |
| 95 |
> |
if(weight_histo) { |
| 96 |
> |
average_weight = weight_histo->Integral()/weight_histo->GetEntries(); |
| 97 |
> |
measured_nevents = (long)weight_histo->GetEntries(); |
| 98 |
> |
} else { |
| 99 |
> |
measured_nevents=(this->events)->GetEntries(); |
| 100 |
> |
} |
| 101 |
> |
|
| 102 |
> |
if(((this->Nentries>1)||(this->Nentries==0))&&measured_nevents!=this->Nentries) { |
| 103 |
|
//special cases: m_Nentries=1 : we want to give each event the full weight (->scans!) |
| 104 |
|
// m_Nentries=0: detect the number of events and set the nevents automatically |
| 105 |
+ |
// m_Nentries<0: set manually (see below) |
| 106 |
|
|
| 107 |
|
stringstream warning; |
| 108 |
|
warning << "Detected incorrect number of events in sample initialization of sample " << m_filename << " (detected Nevents: " << measured_nevents << " , definition claims: " << this->Nentries << "; will use measured number of events. If you want to use this algorithm to set the number of events anyway, set the number of events to 0."; |
| 109 |
|
if(m_Nentries>1) write_warning(__FUNCTION__,warning.str()); |
| 110 |
|
this->Nentries=measured_nevents; |
| 111 |
+ |
} else if ( this->Nentries<0 ) { |
| 112 |
+ |
this->Nentries = -this->Nentries; |
| 113 |
|
} |
| 114 |
< |
this->weight=(xs/(float)Nentries); |
| 115 |
< |
TH1F *weight_histo = (TH1F*)(this->tfile)->Get("weight_histo"); |
| 116 |
< |
float average_weight = 1.0; |
| 117 |
< |
if(weight_histo) { |
| 118 |
< |
average_weight = weight_histo->Integral()/weight_histo->GetEntries(); |
| 119 |
< |
this->Nentries=weight_histo->GetEntries(); |
| 114 |
> |
if ( average_weight>0 ) { |
| 115 |
> |
this->weight=(xs/(float)Nentries)/average_weight; |
| 116 |
> |
this->weightrenormalization=(1.0/average_weight); |
| 117 |
> |
} else { |
| 118 |
> |
if(average_weight>0) { |
| 119 |
> |
this->weight=(xs/(float)Nentries)/average_weight; |
| 120 |
> |
this->weightrenormalization=(1.0/average_weight); |
| 121 |
> |
} else { |
| 122 |
> |
this->weight = (xs/(float)Nentries); |
| 123 |
> |
this->weightrenormalization=1; |
| 124 |
> |
} |
| 125 |
|
} |
| 107 |
– |
this->weight/=average_weight; |
| 126 |
|
} |
| 127 |
|
else { |
| 128 |
|
this->is_active=false; |
| 160 |
|
|
| 161 |
|
TCut essentialcut("mll>0"); |
| 162 |
|
// The following cut (cutWeight) will reweight all the events: use "weight" to correct MC for pileUP, "1.0" otherwise |
| 163 |
< |
TCut cutWeight("weight3D"); |
| 163 |
> |
TCut cutWeight("weight"); |
| 164 |
> |
//TCut cutWeight("(weight*(((id1==id2&&id1==0)*1.2)+((id1==id2&&id1==1)/1.2)+(id1!=id2)))"); |
| 165 |
|
|
| 166 |
|
void setessentialcut(TCut ess) { |
| 167 |
|
essentialcut=ess; |
| 232 |
|
dout << "MC sample(s): " << endl; |
| 233 |
|
for (int isamp=0;isamp<(int)this->collection.size();isamp++) |
| 234 |
|
{ |
| 235 |
< |
if(!(this->collection)[isamp].is_data) dout << " - " << (this->collection)[isamp].samplename << " (" << (this->collection)[isamp].filename << ") xs=" << (this->collection)[isamp].xs << " pb, N(events)=" << (this->collection)[isamp].Nentries << endl; |
| 235 |
> |
if(!(this->collection)[isamp].is_data) dout << " - " << (this->collection)[isamp].samplename << " (" << (this->collection)[isamp].filename << ") xs=" << (this->collection)[isamp].xs << " pb, N(events)=" << (this->collection)[isamp].Nentries << " , RN = " << (this->collection)[isamp].weightrenormalization << endl; |
| 236 |
|
} |
| 237 |
|
} |
| 238 |
|
dout << "---------------------------------------------------------------------------------------------------" << endl; |
| 285 |
|
if(Verbosity>0) dout << endl << endl; |
| 286 |
|
if(Verbosity>0) dout << "-------------------------------------------------------------------------------------" << endl; |
| 287 |
|
if(Verbosity>0) dout << "histoname : " << m_histoname << " , m_var = " << m_var << ", m_xlabel=" << m_xlabel << " m_ylabel=" << m_ylabel << " m_is_data = " << m_is_data << " lumi = " << luminosity << " onlyindex size: " << onlyindex.size() << endl; |
| 288 |
+ |
|
| 289 |
|
if(HUSH==0) dout << "Drawing histo called " << m_histoname << "... " << endl; |
| 290 |
|
bool do_only_selected_samples=false; |
| 291 |
|
if(onlyindex.size()>0&&onlyindex[0]!=-1) { |
| 322 |
|
TH1F *tempdrawhisto = new TH1F("tempdrawhisto","tempdrawhisto",binning.size()-1,binningarray); |
| 323 |
|
tempdrawhisto->Sumw2(); |
| 324 |
|
if(use_this_sample) { |
| 305 |
– |
if(Verbosity>0) dout << "[samplecollection::Draw] : Added contribution from sample " << (this->collection)[isample].samplename << endl; |
| 325 |
|
(this->collection)[isample].events->Draw(drawthis.str().c_str(),(essentialcut&&Cut)*cutWeight);//this weight is based on PU etc. not XS |
| 326 |
+ |
//cout << "Drawing " << drawthis.str() << " with cut " << (const char*) (essentialcut&&Cut)*cutWeight << endl; |
| 327 |
|
if(addoverunderflowbins) { |
| 328 |
|
//now also adding the overflow & underflow bins: |
| 329 |
|
tempdrawhisto->SetBinContent(tempdrawhisto->GetNbinsX(),tempdrawhisto->GetBinContent(tempdrawhisto->GetNbinsX()+1)+tempdrawhisto->GetBinContent(tempdrawhisto->GetNbinsX())); |
| 333 |
|
} |
| 334 |
|
|
| 335 |
|
if(!(this->collection)[isample].is_data) tempdrawhisto->Scale(luminosity*((this->collection)[isample].weight));//weight applied here is XS & N(entries) |
| 336 |
+ |
if(Verbosity>0) dout << "[samplecollection::Draw] : Added contribution from sample " << (this->collection)[isample].samplename << ": " <<tempdrawhisto->Integral() << endl; |
| 337 |
|
histo->Add(tempdrawhisto); |
| 338 |
|
} |
| 339 |
|
tempdrawhisto->Delete(); |
| 377 |
|
float binningarray[binningx.size()+1]; |
| 378 |
|
float binningyarray[binningy.size()+1]; |
| 379 |
|
for(int i=0;i<(int)binningx.size();i++) {binningarray[i]=binningx[i];binningyarray[i]=binningy[i];} |
| 380 |
< |
TH2F *histo = new TH2F(m_histoname.c_str(),"",binningx.size()-1,binningarray,binningy.size(),binningyarray); |
| 380 |
> |
TH2F *histo = new TH2F(m_histoname.c_str(),"",binningx.size()-1,binningarray,binningy.size()-1,binningyarray); |
| 381 |
|
histo->Sumw2(); |
| 382 |
|
|
| 383 |
|
stringstream drawthis; |
| 398 |
|
if(use_this_sample) { |
| 399 |
|
if(Verbosity>0) dout << "[samplecollection::Draw] : Added contribution from sample " << (this->collection)[isample].samplename << endl; |
| 400 |
|
(this->collection)[isample].events->Draw(drawthis.str().c_str(),(essentialcut&&Cut)*cutWeight);//this weight is based on PU etc. not XS |
| 401 |
+ |
cout << "About to draw : " << drawthis.str() << " with cut " << (const char*) (essentialcut&&Cut)*cutWeight << endl; |
| 402 |
|
if(addoverunderflowbins) { |
| 403 |
|
//now also adding the overflow & underflow bins: |
| 404 |
|
tempdrawhisto->SetBinContent(tempdrawhisto->GetNbinsX(),tempdrawhisto->GetBinContent(tempdrawhisto->GetNbinsX()+1)+tempdrawhisto->GetBinContent(tempdrawhisto->GetNbinsX())); |
