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Comparing UserCode/cbrown/AnalysisFramework/Plotting/Modules/LimitCalculation.C (file contents):
Revision 1.1 by buchmann, Tue Jul 19 08:15:45 2011 UTC vs.
Revision 1.8 by buchmann, Wed Jul 27 12:16:15 2011 UTC

#	Line 1 | Line 1
1		#include <iostream>
2		#include <vector>
3		#include <sys/stat.h>
4	+	#include <fstream>
5
6		#include <TCut.h>
7		#include <TROOT.h>
#	Line 22 | Line 23 | using namespace PlottingSetup;
23
24
25		void rediscover_the_top(string mcjzb, string datajzb) {
26	<	cout << "Hi! today we are going to (try to) rediscover the top!" << endl;
26	>	dout << "Hi! today we are going to (try to) rediscover the top!" << endl;
27		TCanvas *c3 = new TCanvas("c3","c3");
28		c3->SetLogy(1);
29		vector<float> binning;
#	Line 62 | Line 63 | void rediscover_the_top(string mcjzb, st
63		TH1F *datapredictiont = allsamples.Draw("datapredictiont",    "-"+datajzb, nbins,low,hi, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,  luminosity);
64		TH1F *datapredictiono = allsamples.Draw("datapredictiono",    "-"+datajzb, nbins,low,hi, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,data,  luminosity);
65		datapredictiont->Add(datapredictiono,-1);
66	<	cout << "Second way of doing this !!!! Analytical shape to the left :-D" << endl;
66	>	dout << "Second way of doing this !!!! Analytical shape to the left :-D" << endl;
67		vector<TF1*> functions = do_cb_fit_to_plot(datapredictiont,10);
68		datapredictiont->SetMarkerColor(kRed);
69		datapredictiont->SetLineColor(kRed);
#	Line 141 | Line 142 | ratio_binning.push_back(80);
142		ratio->Divide(datapredictiontb);
143
144		for (int i=0;i<=ratio_binning.size();i++) {
145	<	cout << observedtb->GetBinLowEdge(i+1) << ";"<<observedtb->GetBinContent(i+1) << ";" << datapredictiontb->GetBinContent(i+1) << " --> " << ratio->GetBinContent(i+1) << "+/-" << ratio->GetBinError(i+1) << endl;
145	>	dout << observedtb->GetBinLowEdge(i+1) << ";"<<observedtb->GetBinContent(i+1) << ";" << datapredictiontb->GetBinContent(i+1) << " --> " << ratio->GetBinContent(i+1) << "+/-" << ratio->GetBinError(i+1) << endl;
146		}
147
148		//  ratio->Divide(datapredictiontb);
#	Line 167 | Line 168 | ratio_binning.push_back(80);
168
169		}
170
171	<	void calculate_upper_limits(string mcjzb, string datajzb) {
172	<	write_warning("calculate_upper_limits","Upper limit calculation temporarily deactivated");
173	<	//  write_warning("calculate_upper_limits","Calculation of SUSY upper limits has been temporarily suspended in favor of top discovery");
174	<	//  rediscover_the_top(mcjzb,datajzb);
175	<	/*
176	<	TCanvas *c3 = new TCanvas("c3","c3");
177	<	c3->SetLogy(1);
178	<	vector<float> binning;
179	<	//binning=allsamples.get_optimal_binsize(mcjzb,cutmass&&cutOSSF&&cutnJets,20,50,800);
180	<	binning.push_back(50);
181	<	binning.push_back(100);
182	<	binning.push_back(150);
183	<	binning.push_back(200);
183	<	binning.push_back(500);
184	<	TH1F *datapredictiona = allsamples.Draw("datapredictiona",    "-"+datajzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,mc,  luminosity);
185	<	TH1F *datapredictionb = allsamples.Draw("datapredictionb",    "-"+datajzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,mc,  luminosity);
186	<	TH1F *datapredictionc = allsamples.Draw("datapredictionc",    datajzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,mc,  luminosity);
187	<	TH1F *dataprediction = (TH1F*)datapredictiona->Clone();
188	<	dataprediction->Add(datapredictionb,-1);
189	<	dataprediction->Add(datapredictionc);
190	<	TH1F *puresignal     = allsamples.Draw("puresignal",        mcjzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,mc,  luminosity,allsamples.FindSample("LM4"));
191	<	TH1F *signalpred     = allsamples.Draw("signalpred",    "-"+mcjzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,mc,  luminosity,allsamples.FindSample("LM4"));
192	<	TH1F *signalpredlo   = allsamples.Draw("signalpredlo",  "-"+mcjzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,mc,  luminosity,allsamples.FindSample("LM4"));
193	<	TH1F *signalpredro   = allsamples.Draw("signalpredro",      mcjzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,mc,  luminosity,allsamples.FindSample("LM4"));
194	<	TH1F *puredata       = allsamples.Draw("puredata",          datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,luminosity);
195	<	signalpred->Add(signalpredlo,-1);
196	<	signalpred->Add(signalpredro);
197	<	puresignal->Add(signalpred,-1);//subtracting signal contamination
198	<	ofstream myfile;
199	<	myfile.open ("ShapeFit_log.txt");
200	<	establish_upper_limits(puredata,dataprediction,puresignal,"LM4",myfile);
201	<	myfile.close();
202	<	*/
171	>	vector<float> compute_one_upper_limit(float mceff,float mcefferr, int ibin, string mcjzb, bool doobserved=false) {
172	>	float sigma95=0.0,sigma95A=0.0;
173	>	int nuisancemodel=1;
174	>	dout << "Now calling : CL95(" << luminosity << "," <<  lumiuncert*luminosity << "," << mceff << "," << mcefferr << "," << Npred[ibin] << "," << Nprederr[ibin] << "," << Nobs[ibin] << "," << false << "," << nuisancemodel<< ") " << endl;
175	>	sigma95 = CL95(luminosity, lumiuncert*luminosity, mceff, mcefferr, Npred[ibin], Nprederr[ibin], Nobs[ibin], false, nuisancemodel);
176	>	if(doobserved) {
177	>	dout << "Now calling : CLA(" << luminosity << "," <<  lumiuncert*luminosity << "," << mceff << "," << mcefferr << "," << Npred[ibin] << "," << Nprederr[ibin] << "," << nuisancemodel<< ") " << endl;
178	>	sigma95A = CLA(luminosity, lumiuncert*luminosity, mceff, mcefferr, Npred[ibin], Nprederr[ibin], nuisancemodel);
179	>	}
180	>	vector<float> sigmas;
181	>	sigmas.push_back(sigma95);
182	>	sigmas.push_back(sigma95A);
183	>	return sigmas;
184		}
185
186	<	void compute_upper_limits_from_counting_experiment(vector<vector<float> > uncertainties,vector<float> jzbcuts) {
187	<	cout << "Doing counting experiment ... " << endl;
186	>	void compute_upper_limits_from_counting_experiment(vector<vector<float> > uncertainties,vector<float> jzbcuts, string mcjzb, bool doobserved) {
187	>	dout << "Doing counting experiment ... " << endl;
188	>	vector<vector<string> > limits;
189	>	vector<vector<float> > vlimits;
190	>
191
192		for(int isample=0;isample<signalsamples.collection.size();isample++) {
193	<	cout << "Considering sample " << signalsamples.collection[isample].samplename << endl;
193	>	vector<string> rows;
194	>	vector<float> vrows;
195	>	dout << "Considering sample " << signalsamples.collection[isample].samplename << endl;
196	>	rows.push_back(signalsamples.collection[isample].samplename);
197		for(int ibin=0;ibin<jzbcuts.size();ibin++) {
198	<	cout << "   Considering bin " << jzbcuts[ibin] << endl;
199	<	for(int isyst=0;isyst<uncertainties[isample*jzbcuts.size()+ibin].size();isyst++) {
200	<	if(isyst==0) {
201	<	if(uncertainties[isample*jzbcuts.size()+ibin][isyst]!=jzbcuts[ibin]) cout << "WATCH OUT THERE SEEMS TO BE A PROBLEM - THE TEST BIN DOESN'T CORRESPOND TO YOUR BIN!" << endl;
202	<	continue;
203	<	}
204	<	cout << "     Considering syst " << isyst << " : " << uncertainties[isample*jzbcuts.size()+ibin][isyst] << endl;
205	<	}//end of syst loop
198	>	dout << "_________________________________________________________________________________" << endl;
199	>	float JZBcutat=uncertainties[isample*jzbcuts.size()+ibin][0];
200	>	float mceff=uncertainties[isample*jzbcuts.size()+ibin][1];
201	>	float staterr=uncertainties[isample*jzbcuts.size()+ibin][2];
202	>	float systerr=uncertainties[isample*jzbcuts.size()+ibin][3];
203	>	float toterr =uncertainties[isample*jzbcuts.size()+ibin][4];
204	>	float observed,null,result;
205	>	fill_result_histos(observed, null,null,null,null,null,null,null,mcjzb,JZBcutat,(int)5,result,(signalsamples.FindSample(signalsamples.collection[isample].filename)),signalsamples);
206	>	observed-=result;//this is the actual excess we see!
207	>	float expected=observed/luminosity;
208	>
209	>	dout << "Sample: " << signalsamples.collection[isample].samplename << ", JZB>"<<JZBcutat<< " : " << mceff << " +/- " << staterr << " (stat) +/- " << systerr << " (syst) --> toterr = " << toterr << endl;
210	>	vector<float> sigmas = compute_one_upper_limit(mceff,toterr,ibin,mcjzb,doobserved);
211	>
212	>	if(doobserved) {
213	>	rows.push_back(any2string(sigmas[0])+";"+any2string(sigmas[1])+";"+"("+any2string(expected)+")");
214	>	vrows.push_back(sigmas[0]);
215	>	vrows.push_back(sigmas[1]);
216	>	vrows.push_back(expected);
217	>	}
218	>	else {
219	>	rows.push_back(any2string(sigmas[0])+"("+any2string(expected)+")");
220	>	vrows.push_back(sigmas[0]);
221	>	vrows.push_back(expected);
222	>	}
223		}//end of bin loop
224	+	limits.push_back(rows);
225	+	vlimits.push_back(vrows);
226		}//end of sample loop
227	<	}
228	<
229	<	void susy_scan_axis_labeling(TH2F *histo) {
230	<	histo->GetXaxis()->SetTitle("#Chi_{2}^{0}-LSP");
231	<	histo->GetXaxis()->CenterTitle();
232	<	histo->GetYaxis()->SetTitle("m_{#tilde{q}}");
233	<	histo->GetYaxis()->CenterTitle();
234	<	}
235	<
236	<	void scan_susy_space(string mcjzb, string datajzb) {
237	<	TCanvas *c3 = new TCanvas("c3","c3");
238	<	vector<float> binning;
239	<	binning=allsamples.get_optimal_binsize(mcjzb,cutmass&&cutOSSF&&cutnJets,20,50,800);
240	<	/*
241	<	binning.push_back(50);
242	<	binning.push_back(75);
243	<	binning.push_back(100);
244	<	binning.push_back(150);
245	<	binning.push_back(200);
246	<	binning.push_back(500);
247	<	*/
248	<	float arrbinning[binning.size()];
249	<	for(int i=0;i<binning.size();i++) arrbinning[i]=binning[i];
250	<	TH1F *puredata   = allsamples.Draw("puredata",  datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,luminosity);
245	<	puredata->SetMarkerSize(DataMarkerSize);
246	<	TH1F *allbgs   = allsamples.Draw("allbgs",  "-"+datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,luminosity);
247	<	TH1F *allbgsb   = allsamples.Draw("allbgsb",  "-"+datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,data,luminosity);
248	<	TH1F *allbgsc   = allsamples.Draw("allbgsc",  datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,data,luminosity);
249	<	allbgs->Add(allbgsb,-1);
250	<	allbgs->Add(allbgsc);
251	<	int ndata=puredata->Integral();
252	<	ofstream myfile;
253	<	myfile.open ("susyscan_log.txt");
254	<	TFile *susyscanfile = new TFile("/scratch/fronga/SMS/T5z_SqSqToQZQZ_38xFall10.root");
255	<	TTree *suevents = (TTree*)susyscanfile->Get("events");
256	<	TH2F *exclusionmap = new TH2F("exclusionmap","",20,0,500,20,0,1000);
257	<	TH2F *exclusionmap1s = new TH2F("exclusionmap1s","",20,0,500,20,0,1000);
258	<	TH2F *exclusionmap2s = new TH2F("exclusionmap2s","",20,0,500,20,0,1000);
259	<	TH2F *exclusionmap3s = new TH2F("exclusionmap3s","",20,0,500,20,0,1000);
260	<
261	<	susy_scan_axis_labeling(exclusionmap);
262	<	susy_scan_axis_labeling(exclusionmap1s);
263	<	susy_scan_axis_labeling(exclusionmap2s);
264	<	susy_scan_axis_labeling(exclusionmap3s);
265	<
266	<	Int_t MyPalette[100];
267	<	Double_t r[]    = {0., 0.0, 1.0, 1.0, 1.0};
268	<	Double_t g[]    = {0., 0.0, 0.0, 1.0, 1.0};
269	<	Double_t b[]    = {0., 1.0, 0.0, 0.0, 1.0};
270	<	Double_t stop[] = {0., .25, .50, .75, 1.0};
271	<	Int_t FI = TColor::CreateGradientColorTable(5, stop, r, g, b, 100);
272	<	for (int i=0;i<100;i++) MyPalette[i] = FI+i;
273	<
274	<	gStyle->SetPalette(100, MyPalette);
275	<
276	<	for(int m23=50;m23<500;m23+=25) {
277	<	for (int m0=(2*(m23-50)+150);m0<=1000;m0+=50)
278	<	{
279	<	c3->cd();
280	<	stringstream drawcondition;
281	<	drawcondition << "pfJetGoodNum>=3&&(TMath::Abs(masses[0]-"<<m0<<")<10&&TMath::Abs(masses[2]-masses[3]-"<<m23<<")<10)&&mll>5&&id1==id2";
282	<	TH1F *puresignal = new TH1F("puresignal","puresignal",binning.size()-1,arrbinning);
283	<	TH1F *puresignall= new TH1F("puresignall","puresignal",binning.size()-1,arrbinning);
284	<	stringstream drawvar,drawvar2;
285	<	drawvar<<mcjzb<<">>puresignal";
286	<	drawvar2<<"-"<<mcjzb<<">>puresignall";
287	<	suevents->Draw(drawvar.str().c_str(),drawcondition.str().c_str());
288	<	suevents->Draw(drawvar2.str().c_str(),drawcondition.str().c_str());
289	<	if(puresignal->Integral()<60) {
290	<	delete puresignal;
291	<	continue;
292	<	}
293	<	puresignal->Add(puresignall,-1);//we need to correct for the signal contamination - we effectively only see (JZB>0)-(JZB<0) !!
294	<	puresignal->Scale(ndata/(20*puresignal->Integral()));//normalizing it to 5% of the data
295	<	stringstream saveas;
296	<	saveas<<"Model_Scan/m0_"<<m0<<"__m23_"<<m23;
297	<	cout << "PLEASE KEEP IN MIND THAT SIGNAL CONTAMINATION IS NOT REALLY TAKEN CARE OF YET DUE TO LOW STATISTICS! SHOULD BE SOMETHING LIKE THIS : "<< endl;
298	<	//        TH1F *signalpredlo   = allsamples.Draw("signalpredlo",  "-"+mcjzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,mc,  luminosity,allsamples.FindSample("LM4"));
299	<	//        TH1F *signalpredro   = allsamples.Draw("signalpredro",      mcjzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSOF&&cutnJets,mc,  luminosity,allsamples.FindSample("LM4"));
300	<	//        TH1F *puredata       = allsamples.Draw("puredata",          datajzb,binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,data,luminosity);
301	<	//        signalpred->Add(signalpredlo,-1);
302	<	//        signalpred->Add(signalpredro);
303	<	//        puresignal->Add(signalpred,-1);//subtracting signal contamination
304	<	//---------------------
305	<	//      cout << "(m0,m23)=("<<m0<<","<<m23<<") contains " << puresignal->Integral() << endl;
306	<	//    TH1F *puresignal = allsamples.Draw("puresignal",mcjzb,  binning, "JZB [GeV]", "events", cutmass&&cutOSSF&&cutnJets,mc,  luminosity,allsamples.FindSample("LM4"));
307	<	vector<float> results=establish_upper_limits(puredata,allbgs,puresignal,saveas.str(),myfile);
308	<	if(results.size()==0) {
309	<	delete puresignal;
310	<	continue;
227	>	dout << endl << endl << "PAS table 3: " << endl << endl;
228	>	dout << "\t";
229	>	for (int irow=0;irow<jzbcuts.size();irow++) {
230	>	dout << jzbcuts[irow] << "\t";
231	>	}
232	>	dout << endl;
233	>	for(int irow=0;irow<limits.size();irow++) {
234	>	for(int ientry=0;ientry<limits[irow].size();ientry++) {
235	>	dout << limits[irow][ientry] << "\t";
236	>	}
237	>	dout << endl;
238	>	}
239	>
240	>	if(!doobserved) {
241	>	dout << endl << endl << "LIMITS: " << endl;
242	>	dout << "\t";
243	>	for (int irow=0;irow<jzbcuts.size();irow++) {
244	>	dout << jzbcuts[irow] << "\t";
245	>	}
246	>	dout << endl;
247	>	for(int irow=0;irow<limits.size();irow++) {
248	>	dout << limits[irow][0] << "\t";
249	>	for(int ientry=0;ientry<jzbcuts.size();ientry++) {
250	>	dout << Round(vlimits[irow][2*ientry] / vlimits[irow][2*ientry+1],3)<< "\t";
251		}
252	<	exclusionmap->Fill(m23,m0,results[0]);
313	<	exclusionmap1s->Fill(m23,m0,results[1]);
314	<	exclusionmap2s->Fill(m23,m0,results[2]);
315	<	exclusionmap3s->Fill(m23,m0,results[3]);
316	<	delete puresignal;
317	<	cout << "(m0,m23)=("<<m0<<","<<m23<<") : 3 sigma at " << results[3] << endl;
252	>	dout << endl;
253		}
254	<	}//end of model scan for loop
254	>	}//do observed
255
256	<	cout << "Exclusion Map contains" << exclusionmap->Integral() << " (integral) and entries: " << exclusionmap->GetEntries() << endl;
257	<	c3->cd();
258	<	exclusionmap->Draw("CONTZ");
259	<	CompleteSave(c3,"Model_Scan/CONT/Model_Scan_Mean_values");
260	<	exclusionmap->Draw("COLZ");
261	<	CompleteSave(c3,"Model_Scan/COL/Model_Scan_Mean_values");
262	<
263	<	exclusionmap1s->Draw("CONTZ");
264	<	CompleteSave(c3,"Model_Scan/CONT/Model_Scan_1sigma_values");
330	<	exclusionmap1s->Draw("COLZ");
331	<	CompleteSave(c3,"Model_Scan/COL/Model_Scan_1sigma_values");
332	<
333	<	exclusionmap2s->Draw("CONTZ");
334	<	CompleteSave(c3,"Model_Scan/CONT/Model_Scan_2sigma_values");
335	<	exclusionmap2s->Draw("COLZ");
336	<	CompleteSave(c3,"Model_Scan/COL/Model_Scan_2sigma_values");
337	<
338	<	exclusionmap3s->Draw("CONTZ");
339	<	CompleteSave(c3,"Model_Scan/CONT/Model_Scan_3sigma_values");
340	<	exclusionmap3s->Draw("COLZ");
341	<	CompleteSave(c3,"Model_Scan/COL/Model_Scan_3sigma_values");
342	<
343	<	TFile *exclusion_limits = new TFile("exclusion_limits.root","RECREATE");
344	<	exclusionmap->Write();
345	<	exclusionmap1s->Write();
346	<	exclusionmap2s->Write();
347	<	exclusionmap3s->Write();
348	<	exclusion_limits->Close();
349	<	susyscanfile->Close();
256	>	dout << endl << endl << "Final selection efficiencies with total statistical and systematic errors, and corresponding observed and expected upper limits (UL) on ($\\sigma\\times$  BR $\\times$ acceptance) for the LM4 and LM8 scenarios, in the different regions. The last column contains the predicted ($\\sigma \\times $BR$\\times$ acceptance) at NLO obtained from Monte Carlo simulation." << endl;
257	>	dout << "Scenario \t Efficiency [%] \t Upper limits [pb] \t Prediction [pb]" << endl;
258	>	for(int icut=0;icut<jzbcuts.size();icut++) {
259	>	dout << "Region with JZB>" << jzbcuts[icut] << endl;
260	>	for(int isample=0;isample<signalsamples.collection.size();isample++) {
261	>	dout << limits[isample][0] << "\t" << Round(100*uncertainties[isample*jzbcuts.size()+icut][1],1) << "+/-" << Round(100*uncertainties[isample*jzbcuts.size()+icut][2],1) << " (stat) +/- " << Round(100*uncertainties[isample*jzbcuts.size()+icut][3],1) << " (syst) \t" << Round((vlimits[isample][2*icut]),3) << "\t" << Round(vlimits[isample][2*icut+1],3) << endl;
262	>	}
263	>	dout << endl;
264	>	}
265
266	<	myfile.close();
266	>	write_warning("compute_upper_limits_from_counting_experiment","Still need to update the script");
267		}
268
269
270	+
271	+	/********************************************************************** new : Limits using SHAPES ***********************************
272	+
273	+
274	+	SSSSSSSSSSSSSSS hhhhhhh
275	+	SS:::::::::::::::Sh:::::h
276	+	S:::::SSSSSS::::::Sh:::::h
277	+	S:::::S     SSSSSSSh:::::h
278	+	S:::::S             h::::h hhhhh         aaaaaaaaaaaaa  ppppp   ppppppppp       eeeeeeeeeeee        ssssssssss
279	+	S:::::S             h::::hh:::::hhh      a::::::::::::a p::::ppp:::::::::p    ee::::::::::::ee    ss::::::::::s
280	+	S::::SSSS          h::::::::::::::hh    aaaaaaaaa:::::ap:::::::::::::::::p  e::::::eeeee:::::eess:::::::::::::s
281	+	SS::::::SSSSS     h:::::::hhh::::::h            a::::app::::::ppppp::::::pe::::::e     e:::::es::::::ssss:::::s
282	+	SSS::::::::SS   h::::::h   h::::::h    aaaaaaa:::::a p:::::p     p:::::pe:::::::eeeee::::::e s:::::s  ssssss
283	+	SSSSSS::::S  h:::::h     h:::::h  aa::::::::::::a p:::::p     p:::::pe:::::::::::::::::e    s::::::s
284	+	S:::::S h:::::h     h:::::h a::::aaaa::::::a p:::::p     p:::::pe::::::eeeeeeeeeee        s::::::s
285	+	S:::::S h:::::h     h:::::ha::::a    a:::::a p:::::p    p::::::pe:::::::e           ssssss   s:::::s
286	+	SSSSSSS     S:::::S h:::::h     h:::::ha::::a    a:::::a p:::::ppppp:::::::pe::::::::e          s:::::ssss::::::s
287	+	S::::::SSSSSS:::::S h:::::h     h:::::ha:::::aaaa::::::a p::::::::::::::::p  e::::::::eeeeeeee  s::::::::::::::s
288	+	S:::::::::::::::SS  h:::::h     h:::::h a::::::::::aa:::ap::::::::::::::pp    ee:::::::::::::e   s:::::::::::ss
289	+	SSSSSSSSSSSSSSS    hhhhhhh     hhhhhhh  aaaaaaaaaa  aaaap::::::pppppppp        eeeeeeeeeeeeee    sssssssssss
290	+	p:::::p
291	+	p:::::p
292	+	p:::::::p
293	+	p:::::::p
294	+	p:::::::p
295	+	ppppppppp
296	+
297	+
298	+	*********************************************************************** new : Limits using SHAPES ***********************************/
299	+
300	+
301	+	void limit_shapes_for_systematic_effect(TFile *limfile, string identifier, string mcjzb, string datajzb, int JES,vector<float> binning, TCanvas *limcan) {
302	+	dout << "Creatig shape templates ... ";
303	+	if(identifier!="") dout << "for systematic called "<<identifier;
304	+	dout << endl;
305	+	int dataormc=mcwithsignal;//this is only for tests - for real life you want dataormc=data !!!
306	+	if(dataormc!=data) write_warning(__FUNCTION__,"WATCH OUT! Not using data for limits!!!! this is ok for tests, but not ok for anything official!");
307	+
308	+	TCut limitnJetcut;
309	+	if(JES==noJES) limitnJetcut=cutnJets;
310	+	else {
311	+	if(JES==JESdown) limitnJetcut=cutnJetsJESdown;
312	+	if(JES==JESup) limitnJetcut=cutnJetsJESup;
313	+	}
314	+	TH1F *ZOSSFP = allsamples.Draw("ZOSSFP",datajzb,binning, "JZB4limits", "events",cutmass&&cutOSSF&&limitnJetcut&&basiccut,dataormc,luminosity);
315	+	TH1F *ZOSOFP = allsamples.Draw("ZOSOFP",datajzb,binning, "JZB4limits", "events",cutmass&&cutOSOF&&limitnJetcut&&basiccut,dataormc,luminosity);
316	+	TH1F *ZOSSFN = allsamples.Draw("ZOSSFN","-"+datajzb,binning, "JZB4limits", "events",cutmass&&cutOSSF&&limitnJetcut&&basiccut,dataormc,luminosity);
317	+	TH1F *ZOSOFN = allsamples.Draw("ZOSOFN","-"+datajzb,binning, "JZB4limits", "events",cutmass&&cutOSOF&&limitnJetcut&&basiccut,dataormc,luminosity);
318	+
319	+	TH1F *SBOSSFP = allsamples.Draw("SBOSSFP",datajzb,binning, "JZB4limits", "events",cutOSSF&&limitnJetcut&&basiccut&&sidebandcut,dataormc,luminosity);
320	+	TH1F *SBOSOFP = allsamples.Draw("SBOSOFP",datajzb,binning, "JZB4limits", "events",cutOSOF&&limitnJetcut&&basiccut&&sidebandcut,dataormc,luminosity);
321	+	TH1F *SBOSSFN = allsamples.Draw("SBOSSFN","-"+datajzb,binning, "JZB4limits", "events",cutOSSF&&limitnJetcut&&basiccut&&sidebandcut,dataormc,luminosity);
322	+	TH1F *SBOSOFN = allsamples.Draw("SBOSOFN","-"+datajzb,binning, "JZB4limits", "events",cutOSOF&&limitnJetcut&&basiccut&&sidebandcut,dataormc,luminosity);
323	+
324	+	TH1F *LZOSSFP = allsamples.Draw("LZOSSFP",mcjzb,binning, "JZB4limits", "events",cutmass&&cutOSSF&&limitnJetcut&&basiccut,mc,luminosity,allsamples.FindSample("LM4"));
325	+	TH1F *LZOSOFP = allsamples.Draw("LZOSOFP",mcjzb,binning, "JZB4limits", "events",cutmass&&cutOSOF&&limitnJetcut&&basiccut,mc,luminosity,allsamples.FindSample("LM4"));
326	+	TH1F *LZOSSFN = allsamples.Draw("LZOSSFN","-"+mcjzb,binning, "JZB4limits", "events",cutmass&&cutOSSF&&limitnJetcut&&basiccut,mc,luminosity,allsamples.FindSample("LM4"));
327	+	TH1F *LZOSOFN = allsamples.Draw("LZOSOFN","-"+mcjzb,binning, "JZB4limits", "events",cutmass&&cutOSOF&&limitnJetcut&&basiccut,mc,luminosity,allsamples.FindSample("LM4"));
328	+
329	+	TH1F *LSBOSSFP = allsamples.Draw("LSBOSSFP",mcjzb,binning, "JZB4limits", "events",cutOSSF&&limitnJetcut&&basiccut&&sidebandcut,mc,luminosity,allsamples.FindSample("LM4"));
330	+	TH1F *LSBOSOFP = allsamples.Draw("LSBOSOFP",mcjzb,binning, "JZB4limits", "events",cutOSOF&&limitnJetcut&&basiccut&&sidebandcut,mc,luminosity,allsamples.FindSample("LM4"));
331	+	TH1F *LSBOSSFN = allsamples.Draw("LSBOSSFN","-"+mcjzb,binning, "JZB4limits", "events",cutOSSF&&limitnJetcut&&basiccut&&sidebandcut,mc,luminosity,allsamples.FindSample("LM4"));
332	+	TH1F *LSBOSOFN = allsamples.Draw("LSBOSOFN","-"+mcjzb,binning, "JZB4limits", "events",cutOSOF&&limitnJetcut&&basiccut&&sidebandcut,mc,luminosity,allsamples.FindSample("LM4"));
333	+
334	+	string obsname="data_obs";
335	+	string predname="background";
336	+	string signalname="signal";
337	+	if(identifier!="") {
338	+	obsname=("data_"+identifier);
339	+	predname=("background_"+identifier);
340	+	signalname="signal_"+identifier;
341	+	}
342	+
343	+	TH1F *obs = (TH1F*)ZOSSFP->Clone();
344	+	obs->SetName(obsname.c_str());
345	+	obs->Write();
346	+	TH1F *pred = (TH1F*)ZOSSFN->Clone();
347	+	pred->Add(ZOSOFP,1.0/3);
348	+	pred->Add(ZOSOFN,-1.0/3);
349	+	pred->Add(SBOSSFP,1.0/3);
350	+	pred->Add(SBOSSFN,-1.0/3);
351	+	pred->Add(SBOSOFP,1.0/3);
352	+	pred->Add(SBOSOFN,-1.0/3);
353	+	pred->SetName(predname.c_str());
354	+	pred->Write();
355	+
356	+	//  TH1F *Lobs = (TH1F*)LZOSSFP->Clone();
357	+	//  TH1F *Lpred = (TH1F*)LZOSSFN->Clone();
358	+
359	+	TH1F *Lobs = new TH1F("Lobs","Lobs",binning.size()-1,&binning[0]);
360	+	TH1F *Lpred = new TH1F("Lpred","Lpred",binning.size()-1,&binning[0]);
361	+	Lobs->Add(LZOSSFP);
362	+	Lpred->Add(LZOSSFN);
363	+	Lpred->Add(LZOSOFP,1.0/3);
364	+	Lpred->Add(LZOSOFN,-1.0/3);
365	+	Lpred->Add(LSBOSSFP,1.0/3);
366	+	Lpred->Add(LSBOSSFN,-1.0/3);
367	+	Lpred->Add(LSBOSOFP,1.0/3);
368	+	Lpred->Add(LSBOSOFN,-1.0/3);
369	+	TH1F *signal = (TH1F*)Lobs->Clone();
370	+	signal->Add(Lpred,-1);
371	+	signal->SetName(signalname.c_str());
372	+	signal->Write();
373	+
374	+	delete Lobs;
375	+	delete Lpred;
376	+
377	+	delete ZOSSFP;
378	+	delete ZOSOFP;
379	+	delete ZOSSFN;
380	+	delete ZOSOFN;
381	+
382	+	delete SBOSSFP;
383	+	delete SBOSOFP;
384	+	delete SBOSSFN;
385	+	delete SBOSOFN;
386	+
387	+	delete LZOSSFP;
388	+	delete LZOSOFP;
389	+	delete LZOSSFN;
390	+	delete LZOSOFN;
391	+
392	+	delete LSBOSSFP;
393	+	delete LSBOSOFP;
394	+	delete LSBOSSFN;
395	+	delete LSBOSOFN;
396
397	+	}
398	+
399	+	void prepare_datacard(TFile *f) {
400	+	TH1F *dataob = (TH1F*)f->Get("data_obs");
401	+	TH1F *signal = (TH1F*)f->Get("signal");
402	+	TH1F *background = (TH1F*)f->Get("background");
403
404	+	ofstream datacard;
405	+	ensure_directory_exists(get_directory()+"/limits");
406	+	datacard.open ((get_directory()+"/limits/susydatacard.txt").c_str());
407	+	datacard << "Writing this to a file.\n";
408	+	datacard << "imax 1\n";
409	+	datacard << "jmax 1\n";
410	+	datacard << "kmax *\n";
411	+	datacard << "---------------\n";
412	+	datacard << "shapes * * limitfile.root $PROCESS $PROCESS_$SYSTEMATIC\n";
413	+	datacard << "---------------\n";
414	+	datacard << "bin 1\n";
415	+	datacard << "observation "<<dataob->Integral()<<"\n";
416	+	datacard << "------------------------------\n";
417	+	datacard << "bin             1          1\n";
418	+	datacard << "process         signal     background\n";
419	+	datacard << "process         0          1\n";
420	+	datacard << "rate            "<<signal->Integral()<<"         "<<background->Integral()<<"\n";
421	+	datacard << "--------------------------------\n";
422	+	datacard << "lumi     lnN    1.10       1.0\n";
423	+	datacard << "bgnorm   lnN    1.00       1.4  uncertainty on our prediction (40%)\n";
424	+	datacard << "JES    shape    1          1    uncertainty on background shape and normalization\n";
425	+	datacard << "peak   shape    1          1    uncertainty on signal resolution. Assume the histogram is a 2 sigma shift, \n";
426	+	datacard << "#                                so divide the unit gaussian by 2 before doing the interpolation\n";
427	+	datacard.close();
428	+	}
429	+
430	+
431	+	void prepare_limits(string mcjzb, string datajzb, float jzbpeakerrordata, float jzbpeakerrormc, vector<float> jzbbins) {
432	+	ensure_directory_exists(get_directory()+"/limits");
433	+	TFile *limfile = new TFile((get_directory()+"/limits/limitfile.root").c_str(),"RECREATE");
434	+	TCanvas *limcan = new TCanvas("limcan","Canvas for calculating limits");
435	+	limit_shapes_for_systematic_effect(limfile,"",mcjzb,datajzb,noJES,jzbbins,limcan);
436	+	limit_shapes_for_systematic_effect(limfile,"peakUp",newjzbexpression(mcjzb,jzbpeakerrormc),newjzbexpression(datajzb,jzbpeakerrordata),noJES,jzbbins,limcan);
437	+	limit_shapes_for_systematic_effect(limfile,"peakDown",newjzbexpression(mcjzb,-jzbpeakerrormc),newjzbexpression(datajzb,-jzbpeakerrordata),noJES,jzbbins,limcan);
438	+	limit_shapes_for_systematic_effect(limfile,"JESUp",mcjzb,datajzb,JESup,jzbbins,limcan);
439	+	limit_shapes_for_systematic_effect(limfile,"JESDown",mcjzb,datajzb,JESdown,jzbbins,limcan);
440	+
441	+	prepare_datacard(limfile);
442	+	limfile->Close();
443	+	write_info("prepare_limits","limitfile.root and datacard.txt have been generated. You can now use them to calculate limits!");
444	+
445	+	}

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