benhoob/HWW/fitMVA.C

#include <cstdlib>
#include <vector>
#include <iostream>
#include <map>
#include <string>

#include "TFile.h"
#include "TH1.h"
#include "TTree.h"
#include "TString.h"
#include "TSystem.h"
#include "TROOT.h"
#include "TStopwatch.h"
#include "TChain.h"
#include "TMath.h"
#include "TChainElement.h"
#include "TCanvas.h"
#include "TCut.h"
#include "TStyle.h"
#include "TLegend.h"
#include "RooGaussian.h"
#include "RooNumConvPdf.h"
#include "RooGenericPdf.h"
#include "RooRealVar.h"
#include "RooPlot.h"
#include "RooDataHist.h"
#include "RooFitResult.h"
#include "RooHistPdf.h"
#include "RooAddPdf.h"
#include "RooDataSet.h"

using namespace std;
using namespace RooFit;


void fitMVA(){

  //-------------------------------
  // user parameters
  //-------------------------------
  
  const int  nTrials  = 1000;  // number of trials in fit validation
  const bool display  = false; // display fit? Set to false for large nTrials
  const bool printgif = true; // print canvases to gif files?
  
  //---------------------------------------
  // name of MVA branch in ntuple
  //---------------------------------------
  
  //const char*   mvaname = "nn_hww160_ww";
  const char*   mvaname = "bdt_hww160_ww";

  //-----------------------------------------
  // selection to apply to sig, bkg samples
  //-----------------------------------------
  
  TCut jet("njets == 0");  
  TCut mll100("dilep->mass() < 100");
  TCut OS("lq1*lq2 < 0");
  TCut pt2010("lep1->pt() >= 20 && lep2->pt() >= 10");
  TCut pmet1("pmet >= 20 && (type == 1 || type == 2)");  
  TCut pmet2("pmet >= 35 && (type == 0 || type == 3)");
  TCut pmet = pmet1 || pmet2;
  TCut lid("lid3 == 0");
  TCut lowptb("jetLowBtag <= 2.1");
  TCut softmu("nSoftMuons == 0");  
  TCut sel160("lep1.pt()>30 && lep2.pt()>25 && dilep.mass()<50 && dPhi<1.05");
 
  TCut sel       = jet + mll100 + OS + pt2010 + pmet;
  TCut weight    = "0.5 * scale1fb";
  TCut selweight = sel*weight;

  //--------------------------------------------------
  // get sig and bkg samples
  //--------------------------------------------------

  char* babyPath = "/smurf/benhoob/MVA/SmurfTraining/hww160_ww/output";

  TChain* sig = new TChain("tree");
  sig->Add(Form("%s/hww160.root",babyPath));

  TChain* bkg = new TChain("tree");
  bkg->Add(Form("%s/ww.root",babyPath));

  //------------------------------------
  // set binning
  //------------------------------------

  int   nbins = 0;
  float xmin  = 0.;
  float xmax  = 0.;

  if( strcmp( mvaname , "nn_hww160_ww" ) == 0 ){
    nbins   =   20;
    xmin    = -0.5;
    xmax    =  1.5;
  }
  else if( strcmp( mvaname , "bdt_hww160_ww" ) == 0 ){
    nbins   =   30;
    xmin    = -1.0;
    xmax    =  0.5;
  }else{
    cout << "Unrecognized MVA " << mvaname << ", quitting" << endl;
    exit(0);
  }
  
  //------------------------
  // declare variables
  //------------------------

  RooRealVar nsig ("nsig"   ,  "Signal Yield"       ,   50 ,    0 , 1000 );
  RooRealVar nbkg ("nbkg"   ,  "Background Yield"   ,  300 ,    0 , 1000 );
  RooRealVar mva  ("mva"    ,  "MVA Output"                , xmin , xmax );

  nsig.setVal(50);
  nbkg.setVal(300);
  mva.setBins(nbins);

  //---------------------------------------------------
  // get MVA output distributions from sig, bkg samp
  //---------------------------------------------------
  
  TH1F* mva_sig = new TH1F("mva_sig","MVA Output for Signal"     , nbins , xmin , xmax );
  TH1F* mva_bkg = new TH1F("mva_bkg","MVA Output for Background" , nbins , xmin , xmax );

  mva_sig->Sumw2();
  mva_bkg->Sumw2();

  TCanvas *ctemp = new TCanvas();
  ctemp->cd();
  sig->Draw(Form("%s >> mva_sig",mvaname),selweight);
  bkg->Draw(Form("%s >> mva_bkg",mvaname),selweight);
  delete ctemp;

  float nsigtrue = mva_sig->Integral();
  float nbkgtrue = mva_bkg->Integral();

  //---------------------------------------------------
  // convert MVA distributions to RooHistPdf objects
  //---------------------------------------------------

  RooDataHist sigpdfhist("sigpdfhist","Signal PDF Hist"     , RooArgSet(mva),mva_sig);
  RooDataHist bkgpdfhist("bkgpdfhist","Background PDF Hist" , RooArgSet(mva),mva_bkg);

  RooHistPdf sigpdf("sigpdf", "Signal PDF"     , RooArgSet(mva), sigpdfhist);
  RooHistPdf bkgpdf("bkgpdf", "Background PDF" , RooArgSet(mva), bkgpdfhist);

  nsig.setVal( nsigtrue );
  nbkg.setVal( nbkgtrue );

  RooAddPdf datapdf("datapdf", "Data PDF", RooArgList(sigpdf,bkgpdf), RooArgList(nsig,nbkg));

  //-------------------------------------------------
  // perform fit (nTrials iterations)
  //-------------------------------------------------

  float nsigfit[nTrials];
  float nbkgfit[nTrials];
  float nsigerrfit[nTrials];
  float nbkgerrfit[nTrials];
  float significance[nTrials];
  TCanvas *can[nTrials];

  TH1F* hsig            = new TH1F("hsig","Sig Yield",100,0,100);
  TH1F* hbkg            = new TH1F("hbkg","Bkg Yield",100,0,200);
  TH1F* hsigpull        = new TH1F("hsigpull","Sig Pull",100,-5,5);
  TH1F* hbkgpull        = new TH1F("hbkgpull","Bkg Pull",100,-5,5);
  TH1F* hsignificance   = new TH1F("hsignificance","Significance",100,0,10);

  for( int i = 0 ; i < nTrials ; ++i ){

    nsig.setVal( nsigtrue );
    nbkg.setVal( nbkgtrue );

    //-------------------------------------
    // generate pseudo-dataset from PDF
    //-------------------------------------

    RooDataSet *gendata = datapdf.generate(RooArgList(mva),nsigtrue+nbkgtrue,Extended(kTRUE));

    //-------------------------------------
    // perform fit with nsig fixed to 0
    //-------------------------------------

    nsig.setVal(0);
    nbkg.setVal(gendata->sumEntries());
    nsig.setConstant();

    RooAbsReal* mynll_bkg = bkgpdf.createNLL( *gendata );
    RooAbsReal* mynll_tot = datapdf.createNLL( *gendata );
    
    cout << "nll_bkg  " << mynll_bkg->getVal() << endl;
    cout << "nll_tot  " << mynll_tot->getVal() << endl;

    cout << endl;
    cout << "-------------------------------------------" << endl;
    cout << "Performing fit with signal yield fixed to 0" << endl;
    cout << "-------------------------------------------" << endl;
    cout << endl;

    RooFitResult *bkg_result = datapdf.fitTo( *gendata , Save() , Extended(kTRUE) );

    /*
    TCanvas *bkgcan = new TCanvas("bkgcan","bkgcan",600,600);
    bkgcan->cd();

    RooPlot* bkgframe = mva.frame();
    bkgframe->SetXTitle(mvaname);
    gendata->plotOn(bkgframe);
    datapdf.plotOn(bkgframe,Components(sigpdf));
    datapdf.plotOn(bkgframe,Components(bkgpdf),LineColor(kRed));
    datapdf.plotOn(bkgframe,LineColor(kOrange));
    bkgframe->Draw();
    */

    //-------------------------------------
    // perform fit with nsig floated in fit
    //-------------------------------------
    
    //nsig.setVal(50);
    nsig.setConstant(kFALSE);

    cout << endl;
    cout << "----------------------------------------" << endl;
    cout << "Performing fit with signal yield floated" << endl;
    cout << "----------------------------------------" << endl;
    cout << endl;

    RooFitResult *result = datapdf.fitTo( *gendata , Save() , Extended(kTRUE) );
    
    if( display ){

      can[i] = new TCanvas(Form("can_%i",i),Form("can_%i",i),600,600);
      can[i]->cd();
    
      RooPlot* frame = mva.frame();
      frame->SetXTitle(mvaname);
      gendata->plotOn(frame);
      datapdf.plotOn(frame,Components(sigpdf));
      datapdf.plotOn(frame,Components(bkgpdf),LineColor(kRed));
      datapdf.plotOn(frame,LineColor(kOrange));
      frame->Draw();

      if( printgif ) can[i]->Print(Form("plots/%s_%i.gif",mvaname,i));
    }

    //---------------------------------------------
    // print output to screen
    //---------------------------------------------

    float nll     = result->minNll();
    float bkg_nll = bkg_result->minNll();
    float signif  = sqrt( -2 * ( nll - bkg_nll ) );

    cout << endl << endl;
    cout << "Fit Results-------------------------------------------"       << endl;
    cout << "gen events = " << gendata->sumEntries()                       << endl;
    cout << "nll (BKG)  = " << bkg_nll                                     << endl;
    cout << "nll        = " << nll                                         << endl;
    cout << "sig        = " << signif                                      << endl;
    cout << "nsig       = " << nsig.getVal() << " +/- " << nsig.getError() << endl;
    cout << "nsig(true) = " << nsigtrue                                    << endl;
    cout << "nbkg       = " << nbkg.getVal() << " +/- " << nbkg.getError() << endl;
    cout << "nbkg(true) = " << nbkgtrue                                    << endl;
    cout << "------------------------------------------------------"       << endl;
    cout << endl << endl;
    
    
    nsigfit[i]       = nsig.getVal();
    nsigerrfit[i]    = nsig.getError();
    nbkgfit[i]       = nbkg.getVal();
    nbkgerrfit[i]    = nbkg.getError();
    significance[i]  = signif;

    hsig->Fill( nsigfit[i] );
    hsigpull->Fill( ( nsigfit[i] - nsigtrue ) / nsigerrfit[i] );
    hbkg->Fill( nbkgfit[i] );
    hbkgpull->Fill( ( nbkgfit[i] - nbkgtrue ) / nbkgerrfit[i] );
    hsignificance->Fill( significance[i] );
  }

  gStyle->SetOptFit(0111);

  TCanvas *c1 = new TCanvas("c1","",1200,800);
  c1->Divide(3,2);

  c1->cd(1);
  hsig->GetXaxis()->SetTitle("Sig Yield");
  hsig->Draw();
  hsig->Fit("gaus");

  c1->cd(2);
  hsigpull->GetXaxis()->SetTitle("Sig Pull");
  hsigpull->Draw();
  hsigpull->Fit("gaus");

  c1->cd(4);
  hbkg->GetXaxis()->SetTitle("Bkg Yield");
  hbkg->Draw();
  hbkg->Fit("gaus");

  c1->cd(5);
  hbkgpull->GetXaxis()->SetTitle("Bkg Pull");
  hbkgpull->Draw();
  hbkgpull->Fit("gaus");

  c1->cd(3);
  hsignificance->GetXaxis()->SetTitle("Significance");
  hsignificance->Draw();
  hsignificance->Fit("gaus");

  if( printgif ) c1->Print(Form("plots/%s_fitval.gif",mvaname));


}
Revision:	1.2
Committed:	Thu Apr 7 16:13:54 2011 UTC (14 years, 1 month ago) by benhoob
Content type:	text/plain
Branch:	MAIN
CVS Tags:	HEAD
Changes since 1.1:	+122 -180 lines
Log Message:	Random updates
#	User	Rev	Content
1	benhoob	1.1	#include <cstdlib>
2			#include <vector>
3			#include <iostream>
4			#include <map>
5			#include <string>
6
7			#include "TFile.h"
8			#include "TH1.h"
9			#include "TTree.h"
10			#include "TString.h"
11			#include "TSystem.h"
12			#include "TROOT.h"
13			#include "TStopwatch.h"
14			#include "TChain.h"
15			#include "TMath.h"
16			#include "TChainElement.h"
17			#include "TCanvas.h"
18			#include "TCut.h"
19			#include "TStyle.h"
20			#include "TLegend.h"
21			#include "RooGaussian.h"
22			#include "RooNumConvPdf.h"
23			#include "RooGenericPdf.h"
24			#include "RooRealVar.h"
25			#include "RooPlot.h"
26			#include "RooDataHist.h"
27			#include "RooFitResult.h"
28			#include "RooHistPdf.h"
29			#include "RooAddPdf.h"
30			#include "RooDataSet.h"
31
32			using namespace std;
33			using namespace RooFit;
34
35	benhoob	1.2
36	benhoob	1.1	void fitMVA(){
37
38	benhoob	1.2	//-------------------------------
39			// user parameters
40			//-------------------------------
41
42			const int nTrials = 1000; // number of trials in fit validation
43			const bool display = false; // display fit? Set to false for large nTrials
44			const bool printgif = true; // print canvases to gif files?
45	benhoob	1.1
46			//---------------------------------------
47			// name of MVA branch in ntuple
48			//---------------------------------------
49
50	benhoob	1.2	//const char* mvaname = "nn_hww160_ww";
51			const char* mvaname = "bdt_hww160_ww";
52	benhoob	1.1
53			//-----------------------------------------
54			// selection to apply to sig, bkg samples
55			//-----------------------------------------
56
57			TCut jet("njets == 0");
58			TCut mll100("dilep->mass() < 100");
59			TCut OS("lq1*lq2 < 0");
60			TCut pt2010("lep1->pt() >= 20 && lep2->pt() >= 10");
61			TCut pmet1("pmet >= 20 && (type == 1 \|\| type == 2)");
62			TCut pmet2("pmet >= 35 && (type == 0 \|\| type == 3)");
63			TCut pmet = pmet1 \|\| pmet2;
64	benhoob	1.2	TCut lid("lid3 == 0");
65			TCut lowptb("jetLowBtag <= 2.1");
66			TCut softmu("nSoftMuons == 0");
67			TCut sel160("lep1.pt()>30 && lep2.pt()>25 && dilep.mass()<50 && dPhi<1.05");
68	benhoob	1.1
69	benhoob	1.2	TCut sel = jet + mll100 + OS + pt2010 + pmet;
70			TCut weight = "0.5 * scale1fb";
71	benhoob	1.1	TCut selweight = sel*weight;
72
73			//--------------------------------------------------
74	benhoob	1.2	// get sig and bkg samples
75	benhoob	1.1	//--------------------------------------------------
76
77			char* babyPath = "/smurf/benhoob/MVA/SmurfTraining/hww160_ww/output";
78
79			TChain* sig = new TChain("tree");
80			sig->Add(Form("%s/hww160.root",babyPath));
81
82			TChain* bkg = new TChain("tree");
83			bkg->Add(Form("%s/ww.root",babyPath));
84
85	benhoob	1.2	//------------------------------------
86			// set binning
87			//------------------------------------
88
89			int nbins = 0;
90			float xmin = 0.;
91			float xmax = 0.;
92
93			if( strcmp( mvaname , "nn_hww160_ww" ) == 0 ){
94			nbins = 20;
95			xmin = -0.5;
96			xmax = 1.5;
97			}
98			else if( strcmp( mvaname , "bdt_hww160_ww" ) == 0 ){
99			nbins = 30;
100			xmin = -1.0;
101			xmax = 0.5;
102			}else{
103			cout << "Unrecognized MVA " << mvaname << ", quitting" << endl;
104			exit(0);
105			}
106
107	benhoob	1.1	//------------------------
108			// declare variables
109			//------------------------
110
111			RooRealVar nsig ("nsig" , "Signal Yield" , 50 , 0 , 1000 );
112			RooRealVar nbkg ("nbkg" , "Background Yield" , 300 , 0 , 1000 );
113			RooRealVar mva ("mva" , "MVA Output" , xmin , xmax );
114
115			nsig.setVal(50);
116			nbkg.setVal(300);
117			mva.setBins(nbins);
118
119			//---------------------------------------------------
120			// get MVA output distributions from sig, bkg samp
121			//---------------------------------------------------
122
123			TH1F* mva_sig = new TH1F("mva_sig","MVA Output for Signal" , nbins , xmin , xmax );
124			TH1F* mva_bkg = new TH1F("mva_bkg","MVA Output for Background" , nbins , xmin , xmax );
125
126			mva_sig->Sumw2();
127			mva_bkg->Sumw2();
128
129			TCanvas *ctemp = new TCanvas();
130	benhoob	1.2	ctemp->cd();
131	benhoob	1.1	sig->Draw(Form("%s >> mva_sig",mvaname),selweight);
132			bkg->Draw(Form("%s >> mva_bkg",mvaname),selweight);
133	benhoob	1.2	delete ctemp;
134	benhoob	1.1
135			float nsigtrue = mva_sig->Integral();
136			float nbkgtrue = mva_bkg->Integral();
137
138			//---------------------------------------------------
139			// convert MVA distributions to RooHistPdf objects
140			//---------------------------------------------------
141
142			RooDataHist sigpdfhist("sigpdfhist","Signal PDF Hist" , RooArgSet(mva),mva_sig);
143			RooDataHist bkgpdfhist("bkgpdfhist","Background PDF Hist" , RooArgSet(mva),mva_bkg);
144
145			RooHistPdf sigpdf("sigpdf", "Signal PDF" , RooArgSet(mva), sigpdfhist);
146			RooHistPdf bkgpdf("bkgpdf", "Background PDF" , RooArgSet(mva), bkgpdfhist);
147
148			nsig.setVal( nsigtrue );
149			nbkg.setVal( nbkgtrue );
150
151			RooAddPdf datapdf("datapdf", "Data PDF", RooArgList(sigpdf,bkgpdf), RooArgList(nsig,nbkg));
152
153	benhoob	1.2	//-------------------------------------------------
154			// perform fit (nTrials iterations)
155			//-------------------------------------------------
156
157	benhoob	1.1	float nsigfit[nTrials];
158			float nbkgfit[nTrials];
159			float nsigerrfit[nTrials];
160			float nbkgerrfit[nTrials];
161			float significance[nTrials];
162	benhoob	1.2	TCanvas *can[nTrials];
163	benhoob	1.1
164			TH1F* hsig = new TH1F("hsig","Sig Yield",100,0,100);
165	benhoob	1.2	TH1F* hbkg = new TH1F("hbkg","Bkg Yield",100,0,200);
166	benhoob	1.1	TH1F* hsigpull = new TH1F("hsigpull","Sig Pull",100,-5,5);
167			TH1F* hbkgpull = new TH1F("hbkgpull","Bkg Pull",100,-5,5);
168			TH1F* hsignificance = new TH1F("hsignificance","Significance",100,0,10);
169
170	benhoob	1.2	for( int i = 0 ; i < nTrials ; ++i ){
171	benhoob	1.1
172			nsig.setVal( nsigtrue );
173			nbkg.setVal( nbkgtrue );
174
175	benhoob	1.2	//-------------------------------------
176			// generate pseudo-dataset from PDF
177			//-------------------------------------
178
179	benhoob	1.1	RooDataSet *gendata = datapdf.generate(RooArgList(mva),nsigtrue+nbkgtrue,Extended(kTRUE));
180
181	benhoob	1.2	//-------------------------------------
182			// perform fit with nsig fixed to 0
183			//-------------------------------------
184
185	benhoob	1.1	nsig.setVal(0);
186	benhoob	1.2	nbkg.setVal(gendata->sumEntries());
187	benhoob	1.1	nsig.setConstant();
188	benhoob	1.2
189			RooAbsReal* mynll_bkg = bkgpdf.createNLL( *gendata );
190			RooAbsReal* mynll_tot = datapdf.createNLL( *gendata );
191	benhoob	1.1
192	benhoob	1.2	cout << "nll_bkg " << mynll_bkg->getVal() << endl;
193			cout << "nll_tot " << mynll_tot->getVal() << endl;
194
195			cout << endl;
196			cout << "-------------------------------------------" << endl;
197			cout << "Performing fit with signal yield fixed to 0" << endl;
198			cout << "-------------------------------------------" << endl;
199			cout << endl;
200
201	benhoob	1.1	RooFitResult bkg_result = datapdf.fitTo( gendata , Save() , Extended(kTRUE) );
202	benhoob	1.2
203			/*
204			TCanvas *bkgcan = new TCanvas("bkgcan","bkgcan",600,600);
205			bkgcan->cd();
206
207			RooPlot* bkgframe = mva.frame();
208			bkgframe->SetXTitle(mvaname);
209			gendata->plotOn(bkgframe);
210			datapdf.plotOn(bkgframe,Components(sigpdf));
211			datapdf.plotOn(bkgframe,Components(bkgpdf),LineColor(kRed));
212			datapdf.plotOn(bkgframe,LineColor(kOrange));
213			bkgframe->Draw();
214			*/
215
216			//-------------------------------------
217			// perform fit with nsig floated in fit
218			//-------------------------------------
219	benhoob	1.1
220	benhoob	1.2	//nsig.setVal(50);
221	benhoob	1.1	nsig.setConstant(kFALSE);
222	benhoob	1.2
223			cout << endl;
224			cout << "----------------------------------------" << endl;
225			cout << "Performing fit with signal yield floated" << endl;
226			cout << "----------------------------------------" << endl;
227			cout << endl;
228
229	benhoob	1.1	RooFitResult result = datapdf.fitTo( gendata , Save() , Extended(kTRUE) );
230
231	benhoob	1.2	if( display ){
232
233			can[i] = new TCanvas(Form("can_%i",i),Form("can_%i",i),600,600);
234			can[i]->cd();
235	benhoob	1.1
236	benhoob	1.2	RooPlot* frame = mva.frame();
237			frame->SetXTitle(mvaname);
238			gendata->plotOn(frame);
239			datapdf.plotOn(frame,Components(sigpdf));
240			datapdf.plotOn(frame,Components(bkgpdf),LineColor(kRed));
241			datapdf.plotOn(frame,LineColor(kOrange));
242			frame->Draw();
243
244			if( printgif ) can[i]->Print(Form("plots/%s_%i.gif",mvaname,i));
245			}
246
247			//---------------------------------------------
248			// print output to screen
249			//---------------------------------------------
250
251	benhoob	1.1	float nll = result->minNll();
252			float bkg_nll = bkg_result->minNll();
253			float signif = sqrt( -2 * ( nll - bkg_nll ) );
254
255			cout << endl << endl;
256			cout << "Fit Results-------------------------------------------" << endl;
257			cout << "gen events = " << gendata->sumEntries() << endl;
258			cout << "nll (BKG) = " << bkg_nll << endl;
259			cout << "nll = " << nll << endl;
260			cout << "sig = " << signif << endl;
261			cout << "nsig = " << nsig.getVal() << " +/- " << nsig.getError() << endl;
262			cout << "nsig(true) = " << nsigtrue << endl;
263			cout << "nbkg = " << nbkg.getVal() << " +/- " << nbkg.getError() << endl;
264			cout << "nbkg(true) = " << nbkgtrue << endl;
265			cout << "------------------------------------------------------" << endl;
266			cout << endl << endl;
267
268
269			nsigfit[i] = nsig.getVal();
270			nsigerrfit[i] = nsig.getError();
271			nbkgfit[i] = nbkg.getVal();
272			nbkgerrfit[i] = nbkg.getError();
273			significance[i] = signif;
274
275			hsig->Fill( nsigfit[i] );
276			hsigpull->Fill( ( nsigfit[i] - nsigtrue ) / nsigerrfit[i] );
277			hbkg->Fill( nbkgfit[i] );
278			hbkgpull->Fill( ( nbkgfit[i] - nbkgtrue ) / nbkgerrfit[i] );
279			hsignificance->Fill( significance[i] );
280			}
281
282	benhoob	1.2	gStyle->SetOptFit(0111);
283	benhoob	1.1
284	benhoob	1.2	TCanvas *c1 = new TCanvas("c1","",1200,800);
285			c1->Divide(3,2);
286	benhoob	1.1
287	benhoob	1.2	c1->cd(1);
288	benhoob	1.1	hsig->GetXaxis()->SetTitle("Sig Yield");
289			hsig->Draw();
290			hsig->Fit("gaus");
291
292	benhoob	1.2	c1->cd(2);
293	benhoob	1.1	hsigpull->GetXaxis()->SetTitle("Sig Pull");
294			hsigpull->Draw();
295			hsigpull->Fit("gaus");
296
297	benhoob	1.2	c1->cd(4);
298	benhoob	1.1	hbkg->GetXaxis()->SetTitle("Bkg Yield");
299			hbkg->Draw();
300			hbkg->Fit("gaus");
301
302	benhoob	1.2	c1->cd(5);
303	benhoob	1.1	hbkgpull->GetXaxis()->SetTitle("Bkg Pull");
304			hbkgpull->Draw();
305			hbkgpull->Fit("gaus");
306
307	benhoob	1.2	c1->cd(3);
308	benhoob	1.1	hsignificance->GetXaxis()->SetTitle("Significance");
309			hsignificance->Draw();
310			hsignificance->Fit("gaus");
311
312	benhoob	1.2	if( printgif ) c1->Print(Form("plots/%s_fitval.gif",mvaname));
313	benhoob	1.1
314
315			}