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buchmann |
1.1 |
#include <iostream>
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buchmann |
1.2 |
#include <RooRealVar.h>
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#include <RooArgSet.h>
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#include <RooDataSet.h>
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buchmann |
1.4 |
#include <RooMCStudy.h>
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#include <RooCategory.h>
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buchmann |
1.3 |
#include <RooStats/ModelConfig.h>
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#include "RooStats/ProfileLikelihoodCalculator.h"
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#include "RooStats/LikelihoodInterval.h"
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#include "RooStats/HypoTestResult.h"
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#include "RooStats/SimpleLikelihoodRatioTestStat.h"
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#include "RooStats/ProfileLikelihoodTestStat.h"
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#include "RooStats/HybridCalculatorOriginal.h"
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#include "RooStats/HypoTestInverterOriginal.h"
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buchmann |
1.2 |
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buchmann |
1.1 |
using namespace std;
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using namespace PlottingSetup;
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buchmann |
1.2 |
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buchmann |
1.1 |
ShapeDroplet LimitsFromEdge(float low_fullCLs, float high_CLs, TTree *events, string addcut, string name, string mcjzb, string datajzb, vector<float> jzbbins, float jzbpeakerrormc, float jzbpeakerrordata) {
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write_error(__FUNCTION__,"Not implemented edge limits yet (returning crap)");
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ShapeDroplet beta;beta.observed=-12345;beta.SignalIntegral=1;return beta;
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}
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void PrepareEdgeShapes(string mcjzb, string datajzb, vector<float> jzbbins, float jzbpeakerrordata) {
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write_error(__FUNCTION__,"Not implemented edge shape storage yet");
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}
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buchmann |
1.2 |
///------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
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namespace EdgeFitter {
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void DoEdgeFit(string mcjzb, string datajzb, float DataPeakError, float MCPeakError, float jzb_cut, int icut, int is_data, TCut cut, TTree*);
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void DoEdgeFit(string mcjzb, string datajzb, float DataPeakError, float MCPeakError, vector<float> jzb_cut, int is_data, TCut cut, TTree*);
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buchmann |
1.4 |
void getMedianLimit(RooWorkspace *ws,vector<RooDataSet*> theToys,float &median,float &sigmaDown, float &sigmaUp, float &twoSigmaDown, float &twoSigmaUp);
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buchmann |
1.2 |
void InitializeVariables(float _mllmin, float _mllmax, float _jzbmax, TCut _cut);
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void PrepareDatasets(int);
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void DoFit();
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string RandomStorageFile();
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Yield Get_Z_estimate(float,int);
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Yield Get_T_estimate(float,int);
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buchmann |
1.4 |
float calcExclusion(RooWorkspace *ws, RooDataSet *data = NULL);
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void prepareLimits(RooWorkspace *ws);
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vector<RooDataSet*> generateToys(RooWorkspace *ws, int nToys);
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void prepareLimits(RooWorkspace *ws, bool ComputeBands);
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TGraph* prepareLM(float mass, float nEv);
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buchmann |
1.2 |
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float jzbmax;
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float mllmin;
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float mllmax;
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TCut cut;
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RooDataSet* AllData;
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RooDataSet* eeSample;
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RooDataSet* mmSample;
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RooDataSet* emSample;
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bool MarcoDebug;
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}
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buchmann |
1.4 |
TGraph* EdgeFitter::prepareLM(float mass, float nEv) {
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float massLM[1];
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massLM[0]=mass;
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float accEffLM[1];
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accEffLM[0]=nEv/PlottingSetup::luminosity;
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TGraph *lm = new TGraph(1, massLM, accEffLM);
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lm->GetXaxis()->SetNoExponent(true);
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lm->GetXaxis()->SetTitle("m_{cut} [GeV]");
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lm->GetYaxis()->SetTitle("#sigma #times A [pb] 95% CL UL");
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lm->GetXaxis()->SetLimits(0.,300.);
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lm->GetYaxis()->SetRangeUser(0.,0.08);
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lm->SetMarkerStyle(34);
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lm->SetMarkerColor(kRed);
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return lm;
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}
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vector<RooDataSet*> EdgeFitter::generateToys(RooWorkspace *ws, int nToys) {
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ws->var("nSig")->setVal(0.);
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ws->var("nSig")->setConstant(true);
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RooFitResult* fit = ws->pdf("combModel")->fitTo(*ws->data("data_obs"),RooFit::Save());
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vector<RooDataSet*> theToys;
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RooMCStudy mcEE(*ws->pdf("combModel"),RooArgSet(*ws->var("inv")),RooFit::Slice(*ws->cat("cat"),"EE"));
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mcEE.generate(nToys,44,true);
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RooMCStudy mcMM(*ws->pdf("combModel"),RooArgSet(*ws->var("inv")),RooFit::Slice(*ws->cat("cat"),"MM"));
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mcMM.generate(nToys,44,true);
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RooMCStudy mcOSOF(*ws->pdf("combModel"),RooArgSet(*ws->var("inv")),RooFit::Slice(*ws->cat("cat"),"OSOF"));
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mcOSOF.generate(nToys,44,true);
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RooRealVar mll("mll","mll",mllmin,mllmax,"GeV/c^{2}");
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RooRealVar id1("id1","id1",0,1,"GeV/c^{2}");
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RooRealVar id2("id2","id2",0,1,"GeV/c^{2}");
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RooRealVar jzb("jzb","jzb",-jzbmax,jzbmax,"GeV/c");
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RooRealVar weight("weight","weight",0,1000,"");
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RooArgSet observables(mll,jzb,id1,id2,weight);
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for(int i=0;i<nToys;i++) {
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RooDataSet* toyEE = (RooDataSet*)mcEE.genData(i);
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RooDataSet* toyMM = (RooDataSet*)mcMM.genData(i);
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RooDataSet* toyOSOF = (RooDataSet*)mcOSOF.genData(i);
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stringstream toyname;
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toyname << "theToy_" << i;
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write_warning(__FUNCTION__,"Problem while adding toys");
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// RooDataSet *toyData = RooDataSet(toyname.str(),toyname.str(),observables,RooFit::Index(ws->cat("cat")),RooFit::Import("OSOF",*toyOSOF),RooFit::Import("EE",*toyEE),RooFit::Import("MM",*toyMM));
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// theToys.push_back(toyData);
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}
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ws->var("nSig")->setVal(17.0);
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ws->var("nSig")->setConstant(false);
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return theToys;
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}
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void EdgeFitter::getMedianLimit(RooWorkspace *ws,vector<RooDataSet*> theToys,float &median,float &sigmaDown, float &sigmaUp, float &twoSigmaDown, float &twoSigmaUp) {
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TH1F *gauLimit = new TH1F("gausLimit","gausLimit",60,0.,80./PlottingSetup::luminosity);
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vector<float> theLimits;
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for(int itoy=0;itoy<theToys.size();itoy++) {
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float theLimit = calcExclusion(ws,theToys[itoy]);
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if(theLimit > 0 ) gauLimit->Fill(theLimit);
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}
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const Int_t nQ = 4;
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Double_t yQ[nQ] = {0.,0.,0.,0.};
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Double_t xQ[nQ] = {0.022750132,0.1586552539,0.84134474609999998,0.977249868};
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gauLimit->GetQuantiles(nQ,yQ,xQ);
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median = gauLimit->GetMean();
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// median = median1(gauLimit);
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twoSigmaDown = abs(yQ[0]-median);
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sigmaDown = abs(yQ[1]-median);
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sigmaUp = abs(yQ[2]-median);
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twoSigmaUp = abs(yQ[3]-median);
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cout << median * PlottingSetup::luminosity << " " << sigmaUp * PlottingSetup::luminosity << endl;
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}
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void EdgeFitter::prepareLimits(RooWorkspace *ws, bool ComputeBands) {
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if(ComputeBands) {
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vector<RooDataSet*> theToys = EdgeFitter::generateToys(ws,50);
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vector<float> medVals;
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vector<float> medLimits;
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vector<float> sigmaLimitsDown;
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vector<float> sigmaLimitsUp;
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vector<float> twoSigmaLimitsDown;
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vector<float> twoSigmaLimitsUp;
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for(int i=20;i<=320;i+=40) {
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ws->var("nSig")->setVal(10.0);
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medVals.push_back((float)i);
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ws->var("m0")->setVal((float)i);
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ws->var("m0")->setConstant(true);
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| 154 |
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float Smedian,SsigmaDown,SsigmaUp,StwoSigmaDown,StwoSigmaUp;
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EdgeFitter::getMedianLimit(ws,theToys,Smedian,SsigmaDown,SsigmaUp,StwoSigmaDown,StwoSigmaUp);
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medLimits.push_back(Smedian);
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sigmaLimitsDown.push_back(SsigmaDown);
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sigmaLimitsUp.push_back(SsigmaUp);
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| 159 |
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twoSigmaLimitsDown.push_back(StwoSigmaDown);
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twoSigmaLimitsUp.push_back(StwoSigmaUp);
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}
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write_warning(__FUNCTION__,"Still need to store limits");
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} else {
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| 164 |
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vector<float> theVals;
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| 165 |
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vector<float> theLimits;
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| 166 |
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for(int i=20;i<300;i+=5) {
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| 167 |
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ws->var("nSig")->setVal(0.0);
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| 168 |
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theVals.push_back((float)i);
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| 169 |
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ws->var("m0")->setVal((float)i);
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| 170 |
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ws->var("m0")->setConstant(true);
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| 171 |
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theLimits.push_back(calcExclusion(ws));
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| 172 |
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}
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| 173 |
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| 174 |
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for(int i=0;i<theLimits.size();i++) {
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if((theLimits[i]<2.0/PlottingSetup::luminosity)||(theLimits[i]>40.0/PlottingSetup::luminosity)) {
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| 176 |
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cout << i << " : " << theVals[i] << endl;
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| 177 |
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theLimits[i] = (theLimits[i+2]+theLimits[i-2])/2.0;
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| 178 |
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write_warning(__FUNCTION__,"Need to store limits");
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| 179 |
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}
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| 180 |
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write_warning(__FUNCTION__,"Need to store limits");
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| 181 |
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}
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| 182 |
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}
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| 183 |
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}
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| 184 |
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| 185 |
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| 186 |
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float EdgeFitter::calcExclusion(RooWorkspace *ws, RooDataSet *data) {
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| 187 |
buchmann |
1.3 |
RooRealVar mu("mu","nSig",0,10000,"");
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| 188 |
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RooArgSet poi = RooArgSet(mu);
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| 189 |
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RooArgSet *nullParams = (RooArgSet*)poi.snapshot();
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| 190 |
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nullParams->setRealValue("nSig",0);
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| 191 |
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RooStats::ModelConfig *model = new RooStats::ModelConfig();
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| 192 |
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model->SetWorkspace(*ws);
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| 193 |
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model->SetPdf("combModel");
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| 194 |
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model->SetParametersOfInterest(poi);
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| 195 |
buchmann |
1.4 |
if(!data) data = (RooDataSet*)ws->data("data_obs");
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| 196 |
buchmann |
1.3 |
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| 197 |
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RooStats::ProfileLikelihoodCalculator plc(*data, *model);
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| 198 |
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plc.SetNullParameters(*nullParams);
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| 199 |
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plc.SetTestSize(0.05);
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| 200 |
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RooStats::LikelihoodInterval* interval = plc.GetInterval();
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| 201 |
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RooStats::HypoTestResult *htr = plc.GetHypoTest();
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| 202 |
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double theLimit = interval->UpperLimit( mu );
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| 203 |
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cout << "Significance " << htr->Significance() << endl;
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| 204 |
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| 205 |
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ws->defineSet("obs","nB");
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| 206 |
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ws->defineSet("poi","nSig");
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| 207 |
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| 208 |
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RooStats::ModelConfig b_model = RooStats::ModelConfig("B_model", ws);
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| 209 |
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b_model.SetPdf(*ws->pdf("combModel"));
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| 210 |
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b_model.SetObservables(*ws->set("obs"));
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| 211 |
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b_model.SetParametersOfInterest(*ws->set("poi"));
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| 212 |
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ws->var("nSig")->setVal(0.0); //# important!
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| 213 |
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b_model.SetSnapshot(*ws->set("poi"));
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| 214 |
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| 215 |
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RooStats::ModelConfig sb_model = RooStats::ModelConfig("S+B_model", ws);
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| 216 |
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sb_model.SetPdf(*ws->pdf("combModel"));
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| 217 |
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sb_model.SetObservables(*ws->set("obs"));
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| 218 |
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sb_model.SetParametersOfInterest(*ws->set("poi"));
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| 219 |
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ws->var("nSig")->setVal(64.0); //# important!
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| 220 |
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sb_model.SetSnapshot(*ws->set("poi"));
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| 221 |
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| 222 |
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RooStats::SimpleLikelihoodRatioTestStat slrts = RooStats::SimpleLikelihoodRatioTestStat(*b_model.GetPdf(),*sb_model.GetPdf());
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| 223 |
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slrts.SetNullParameters(*b_model.GetSnapshot());
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| 224 |
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slrts.SetAltParameters(*sb_model.GetSnapshot());
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| 225 |
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RooStats::ProfileLikelihoodTestStat profll = RooStats::ProfileLikelihoodTestStat(*b_model.GetPdf());
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| 226 |
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| 227 |
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RooStats::HybridCalculatorOriginal hc = RooStats::HybridCalculatorOriginal(*data, sb_model, b_model,0,0);
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| 228 |
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hc.SetTestStatistic(2);
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| 229 |
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hc.SetNumberOfToys(50);
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| 230 |
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| 231 |
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RooStats::HypoTestInverterOriginal hcInv = RooStats::HypoTestInverterOriginal(hc,*ws->var("nSig"));
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| 232 |
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hcInv.SetTestSize(0.05);
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| 233 |
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hcInv.UseCLs(true);
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| 234 |
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hcInv.RunFixedScan(5,theLimit-0.5,theLimit+0.5);;
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| 235 |
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RooStats::HypoTestInverterResult* hcInt = hcInv.GetInterval();
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| 236 |
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float ulError = hcInt->UpperLimitEstimatedError();
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| 237 |
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theLimit = hcInt->UpperLimit();
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| 238 |
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cout << "Found upper limit : " << theLimit << "+/-" << ulError << endl;
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| 239 |
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| 240 |
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return theLimit/PlottingSetup::luminosity;
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| 241 |
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| 242 |
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}
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| 243 |
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| 244 |
buchmann |
1.2 |
TTree* SkimTree(int isample) {
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| 245 |
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TTree* newTree = allsamples.collection[isample].events->CloneTree(0);
|
| 246 |
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float xsweight=1.0;
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| 247 |
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if(allsamples.collection[isample].is_data==false) xsweight=luminosity*allsamples.collection[isample].weight;
|
| 248 |
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if(EdgeFitter::MarcoDebug) {
|
| 249 |
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cout << " Original tree contains " << allsamples.collection[isample].events->GetEntries() << endl;
|
| 250 |
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cout << " Going to reduce it with cut " << EdgeFitter::cut << endl;
|
| 251 |
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}
|
| 252 |
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TTreeFormula *select = new TTreeFormula("select", EdgeFitter::cut, allsamples.collection[isample].events);
|
| 253 |
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float wgt=1.0;
|
| 254 |
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allsamples.collection[isample].events->SetBranchAddress(cutWeight,&wgt);
|
| 255 |
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for (Int_t entry = 0 ; entry < allsamples.collection[isample].events->GetEntries() ; entry++) {
|
| 256 |
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allsamples.collection[isample].events->LoadTree(entry);
|
| 257 |
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if (select->EvalInstance()) {
|
| 258 |
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allsamples.collection[isample].events->GetEntry(entry);
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| 259 |
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wgt=wgt*xsweight;
|
| 260 |
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newTree->Fill();
|
| 261 |
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}
|
| 262 |
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}
|
| 263 |
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| 264 |
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if(EdgeFitter::MarcoDebug) cout << " Reduced tree contains " << newTree->GetEntries() << " entries " << endl;
|
| 265 |
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return newTree;
|
| 266 |
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}
|
| 267 |
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| 268 |
|
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void EdgeFitter::InitializeVariables(float _mllmin, float _mllmax, float _jzbmax, TCut _cut) {
|
| 269 |
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mllmin=_mllmin;
|
| 270 |
|
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mllmax=_mllmax;
|
| 271 |
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jzbmax=_jzbmax;
|
| 272 |
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cut=_cut;
|
| 273 |
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}
|
| 274 |
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| 275 |
|
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void EdgeFitter::PrepareDatasets(int is_data) {
|
| 276 |
|
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TTree *completetree;
|
| 277 |
|
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bool hashit=0;
|
| 278 |
|
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for(int isample=0;isample<allsamples.collection.size();isample++) {
|
| 279 |
|
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if(!allsamples.collection[isample].is_active) continue;
|
| 280 |
|
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if(is_data==1&&allsamples.collection[isample].is_data==false) continue;//kick all samples that aren't data if we're looking for data.
|
| 281 |
|
|
if(is_data==1&&allsamples.collection[isample].is_data==false) continue;//kick all samples that aren't data if we're looking for data.
|
| 282 |
|
|
if(is_data!=1&&allsamples.collection[isample].is_data==true) continue;//kick all data samples when looking for MC
|
| 283 |
|
|
if(is_data!=2&&allsamples.collection[isample].is_signal==true) continue;//remove signal sample if we don't want it.
|
| 284 |
|
|
if(EdgeFitter::MarcoDebug) cout << "Considering : " << allsamples.collection[isample].samplename << endl;
|
| 285 |
|
|
if(!hashit) {
|
| 286 |
|
|
hashit=true;
|
| 287 |
|
|
completetree = SkimTree(isample)->CloneTree();
|
| 288 |
|
|
} else {
|
| 289 |
|
|
completetree->CopyEntries(SkimTree(isample));
|
| 290 |
|
|
}
|
| 291 |
|
|
if(EdgeFitter::MarcoDebug) cout << "Complete tree now contains " << completetree->GetEntries() << " entries " << endl;
|
| 292 |
|
|
}
|
| 293 |
|
|
|
| 294 |
|
|
RooRealVar mll("mll","mll",mllmin,mllmax,"GeV/c^{2}");
|
| 295 |
|
|
RooRealVar id1("id1","id1",0,1,"GeV/c^{2}");
|
| 296 |
|
|
RooRealVar id2("id2","id2",0,1,"GeV/c^{2}");
|
| 297 |
|
|
RooRealVar jzb("jzb","jzb",-jzbmax,jzbmax,"GeV/c");
|
| 298 |
|
|
RooRealVar weight("weight","weight",0,1000,"");
|
| 299 |
|
|
RooArgSet observables(mll,jzb,id1,id2,weight);
|
| 300 |
|
|
|
| 301 |
|
|
string title="CMS Data";
|
| 302 |
|
|
if(is_data!=1) title="CMS SIMULATION";
|
| 303 |
|
|
RooDataSet LAllData("LAllData",title.c_str(),completetree,observables,"","weight");
|
| 304 |
|
|
completetree->Write();
|
| 305 |
|
|
// delete completetree;
|
| 306 |
|
|
|
| 307 |
|
|
EdgeFitter::eeSample = (RooDataSet*)LAllData.reduce("id1==id2&&id1==0");
|
| 308 |
|
|
EdgeFitter::mmSample = (RooDataSet*)LAllData.reduce("id1==id2&&id1==1");
|
| 309 |
|
|
EdgeFitter::emSample = (RooDataSet*)LAllData.reduce("id1!=id2");
|
| 310 |
|
|
EdgeFitter::AllData = (RooDataSet*)LAllData.reduce("id1!=id2||id1==id2");
|
| 311 |
|
|
|
| 312 |
|
|
eeSample->SetName("eeSample");
|
| 313 |
|
|
mmSample->SetName("mmSample");
|
| 314 |
|
|
emSample->SetName("emSample");
|
| 315 |
|
|
AllData->SetName("AllData");
|
| 316 |
|
|
|
| 317 |
|
|
if(EdgeFitter::MarcoDebug) {
|
| 318 |
|
|
cout << "Number of events in data sample = " << AllData->numEntries() << endl;
|
| 319 |
|
|
cout << "Number of events in ee sample = " << eeSample->numEntries() << endl;
|
| 320 |
|
|
cout << "Number of events in mm sample = " << mmSample->numEntries() << endl;
|
| 321 |
|
|
cout << "Number of events in em sample = " << emSample->numEntries() << endl;
|
| 322 |
|
|
}
|
| 323 |
|
|
}
|
| 324 |
|
|
|
| 325 |
|
|
string EdgeFitter::RandomStorageFile() {
|
| 326 |
|
|
TRandom3 *r = new TRandom3(0);
|
| 327 |
|
|
int rho = (int)r->Uniform(1,10000000);
|
| 328 |
|
|
stringstream RandomFile;
|
| 329 |
|
|
RandomFile << PlottingSetup::cbafbasedir << "/exchange/TempEdgeFile_" << rho << ".root";
|
| 330 |
|
|
delete r;
|
| 331 |
|
|
return RandomFile.str();
|
| 332 |
|
|
}
|
| 333 |
|
|
|
| 334 |
|
|
Yield EdgeFitter::Get_Z_estimate(float jzb_cut, int icut) {
|
| 335 |
|
|
if(MarcoDebug) write_error(__FUNCTION__,"Returning random Z yield");
|
| 336 |
|
|
Yield a(123,45,67); return a;
|
| 337 |
|
|
|
| 338 |
|
|
return PlottingSetup::allresults.predictions[icut].Zbkg;
|
| 339 |
|
|
}
|
| 340 |
|
|
|
| 341 |
|
|
Yield EdgeFitter::Get_T_estimate(float jzb_cut, int icut) {
|
| 342 |
|
|
if(MarcoDebug) write_error(__FUNCTION__,"Returning random TTbar yield");
|
| 343 |
|
|
Yield a(1234,56,78); return a;
|
| 344 |
|
|
return PlottingSetup::allresults.predictions[icut].Flavorsym;
|
| 345 |
|
|
}
|
| 346 |
|
|
|
| 347 |
|
|
void EdgeFitter::DoEdgeFit(string mcjzb, string datajzb, float DataPeakError, float MCPeakError, float jzb_cut, int icut, int is_data, TCut cut, TTree *signalevents=0) {
|
| 348 |
|
|
|
| 349 |
|
|
string storagefile=EdgeFitter::RandomStorageFile();
|
| 350 |
|
|
TFile *f = new TFile(storagefile.c_str(),"RECREATE");
|
| 351 |
|
|
EdgeFitter::InitializeVariables(iMllLow,iMllHigh,jzbHigh,cut);
|
| 352 |
|
|
|
| 353 |
|
|
Yield Zestimate=EdgeFitter::Get_Z_estimate(jzb_cut,icut);
|
| 354 |
|
|
Yield Testimate=EdgeFitter::Get_T_estimate(jzb_cut,icut);
|
| 355 |
|
|
cout << "Cut at JZB>" << jzb_cut << "; using estimates: " << endl;
|
| 356 |
|
|
cout << " Z : " << Zestimate << endl;
|
| 357 |
|
|
cout << " T : " << Testimate << endl;
|
| 358 |
|
|
|
| 359 |
|
|
EdgeFitter::PrepareDatasets(is_data);
|
| 360 |
|
|
|
| 361 |
|
|
EdgeFitter::eeSample->Write();
|
| 362 |
|
|
EdgeFitter::emSample->Write();
|
| 363 |
|
|
EdgeFitter::mmSample->Write();
|
| 364 |
|
|
EdgeFitter::AllData->Write();
|
| 365 |
|
|
f->Close();
|
| 366 |
|
|
|
| 367 |
|
|
write_warning(__FUNCTION__,"Work continues here");
|
| 368 |
|
|
|
| 369 |
|
|
if(EdgeFitter::MarcoDebug) write_warning(__FUNCTION__,"Need to uncomment the next line (remove the output file)");
|
| 370 |
|
|
// gSystem->Exec(("rm "+storagefile).c_str());
|
| 371 |
|
|
}
|
| 372 |
|
|
|
| 373 |
|
|
void DoEdgeFit(string mcjzb, string datajzb, float DataPeakError, float MCPeakError, vector<float> jzb_cut, int is_data, TCut cut, TTree *signalevents=0) {
|
| 374 |
|
|
for(int icut=0;icut<jzb_cut.size();icut++) {
|
| 375 |
|
|
stringstream addcut;
|
| 376 |
|
|
if(is_data==1) addcut << "(" << datajzb << ">" << jzb_cut[icut] << ")";
|
| 377 |
|
|
if(is_data!=1) addcut << "(" << mcjzb << ">" << jzb_cut[icut] << ")";
|
| 378 |
|
|
TCut jcut(addcut.str().c_str());
|
| 379 |
|
|
|
| 380 |
|
|
EdgeFitter::DoEdgeFit(mcjzb, datajzb, DataPeakError, MCPeakError, jzb_cut[icut], icut, is_data, jcut&&cut, signalevents);
|
| 381 |
|
|
|
| 382 |
|
|
}
|
| 383 |
|
|
}
|
