benhoob/HWW/likelihoodrefs.C

#include <vector>
#include <string>
#include "tmvaglob.C"


// this macro plots the reference distribuions for the Likelihood
// methods for the various input variables used in TMVA (e.g. running
// TMVAnalysis.C).  Signal and Background are plotted separately

// input: - Input file (result from TMVA),
//        - use of TMVA plotting TStyle
void likelihoodrefs( TString fin = "TMVA.root", Bool_t useTMVAStyle = kTRUE )
{
   // set style and remove existing canvas'
   TMVAGlob::Initialize( useTMVAStyle );
  
   // checks if file with name "fin" is already open, and if not opens one
   TFile* file = TMVAGlob::OpenFile( fin );  

   // get all titles of the method likelihood
   TList titles;
   UInt_t ninst = TMVAGlob::GetListOfTitles("Method_Likelihood",titles);
   if (ninst==0) {
      cout << "Could not locate directory 'Method_Likelihood' in file " << fin << endl;
      return;
   }
   // loop over all titles
   TIter keyIter(&titles);
   TDirectory *lhdir;
   TKey *key;
   while ((key = TMVAGlob::NextKey(keyIter,"TDirectory"))) {
      lhdir = (TDirectory *)key->ReadObj();
      likelihoodrefs( lhdir );
   }
}

void likelihoodrefs( TDirectory *lhdir ) {
   Bool_t newCanvas = kTRUE;
   
   const UInt_t maxCanvas = 200;
   TCanvas** c = new TCanvas*[maxCanvas];
   Int_t width  = 670;
   Int_t height = 380;

   // avoid duplicated printing
   std::vector<std::string> hasBeenUsed;
   const TString titName = lhdir->GetName();
   UInt_t ic = -1;   

   TIter next(lhdir->GetListOfKeys());
   TKey *key;
   while ((key = TMVAGlob::NextKey(next,"TH1"))) { // loop over all TH1
      TH1 *h = (TH1*)key->ReadObj();
      TH1F *b( 0 );
      TString hname( h->GetName() );

      // avoid duplicated plotting
      Bool_t found = kFALSE;
      for (UInt_t j = 0; j < hasBeenUsed.size(); j++) {
         if (hasBeenUsed[j] == hname.Data()) found = kTRUE;
      }
      if (!found) {

         // draw original plots
         if (hname.EndsWith("_sig_nice")) {

            if (newCanvas) {
               char cn[20];
               sprintf( cn, "cv%d_%s", ic+1, titName.Data() );
               ++ic;
               TString n = hname;         
               c[ic] = new TCanvas( cn, Form( "%s reference for variable: %s", 
                                              titName.Data(),(n.ReplaceAll("_sig","")).Data() ), 
                                    ic*50+50, ic*20, width, height ); 
               c[ic]->Divide(2,1);
               newCanvas = kFALSE;
            }      

            // signal
            Int_t color = 4; 
            TPad * cPad = (TPad*)c[ic]->cd(1);
            TString plotname = hname;

            h->SetMaximum(h->GetMaximum()*1.3);
            h->SetMinimum( 0 );
            h->SetMarkerColor(color);
            h->SetMarkerSize( 0.7 );
            h->SetMarkerStyle( 24 );
            h->SetLineWidth(1);
            h->SetLineColor(color);
            color++;
            h->Draw("e1");
            Double_t hSscale = 1.0/(h->GetSumOfWeights()*h->GetBinWidth(1));

            TLegend *legS= new TLegend( cPad->GetLeftMargin(), 
                                        1-cPad->GetTopMargin()-.14, 
                                        cPad->GetLeftMargin()+.77, 
                                        1-cPad->GetTopMargin() );
            legS->SetBorderSize(1);
            legS->AddEntry(h,"Input data (signal)","p");

            // background
            TString bname( hname );     
            b = (TH1F*)lhdir->Get( bname.ReplaceAll("_sig","_bgd") );
            cPad = (TPad*)c[ic]->cd(2);
            color = 2;
            b->SetMaximum(b->GetMaximum()*1.3);
            b->SetMinimum( 0 );
            b->SetLineWidth(1);
            b->SetLineColor(color);
            b->SetMarkerColor(color);
            b->SetMarkerSize( 0.7 );
            b->SetMarkerStyle( 24 );
            b->Draw("e1");       
            Double_t hBscale = 1.0/(b->GetSumOfWeights()*b->GetBinWidth(1));
            TLegend *legB= new TLegend( cPad->GetLeftMargin(), 
                                        1-cPad->GetTopMargin()-.14, 
                                        cPad->GetLeftMargin()+.77, 
                                        1-cPad->GetTopMargin() );
            legB->SetBorderSize(1);
            legB->AddEntry(b,"Input data (backgr.)","p");

            // register
            hasBeenUsed.push_back( bname.Data() );

            // the PDFs --------------

            // check for splines
            h = 0;
            b = 0;
            TString pname = hname; pname.ReplaceAll("_nice","");            
            for (int i=0; i<= 5; i++) {
               TString hspline = pname + Form( "_smoothed_hist_from_spline%i", i );
               h = (TH1F*)lhdir->Get( hspline );
               if (h) {
                  b = (TH1F*)lhdir->Get( hspline.ReplaceAll("_sig","_bgd") );
                  break;
               }
            }

            // check for KDE
            if (h == 0 && b == 0) {
               TString hspline = pname + Form( "_smoothed_hist_from_KDE", i );
               h = (TH1F*)lhdir->Get( hspline );
               if (h) {
                  b = (TH1F*)lhdir->Get( hspline.ReplaceAll("_sig","_bgd") );
               }
            }
               
            // found something ?
            if (h == 0 || b == 0) {
               cout << "--- likelihoodrefs.C: did not find spline for histogram: " << pname.Data() << endl;
            }
            else {
               
               Double_t pSscale = 1.0/(h->GetSumOfWeights()*h->GetBinWidth(1));
               h->Scale( pSscale/hSscale );
               color = 4;
               c[ic]->cd(1);
               h->SetLineWidth(2);
               h->SetLineColor(color);
               legS->AddEntry(h,"Estimated PDF (norm. signal)","l");
               h->Draw("histsame");
               legS->Draw();
          
               Double_t pBscale = 1.0/(b->GetSumOfWeights()*b->GetBinWidth(1));
               b->Scale( pBscale/hBscale );
               color = 2;
               c[ic]->cd(2);
               b->SetLineColor(color);
               b->SetLineWidth(2);
               legB->AddEntry(b,"Estimated PDF (norm. backgr.)","l");
               b->Draw("histsame");

               // draw the legends
               legB->Draw();
          
               hasBeenUsed.push_back( pname.Data() );
            }     

            c[ic]->Update();

            // write to file
            TString fname = Form( "plots/%s_refs_c%i", titName.Data(), ic+1 );
            TMVAGlob::imgconv( c[ic], fname );
            //  c[ic]->Update();

            newCanvas = kTRUE;
            hasBeenUsed.push_back( hname.Data() );
         }
      }
   }
}

Revision:	1.1
Committed:	Mon Feb 14 12:39:14 2011 UTC (14 years, 3 months ago) by benhoob
Content type:	text/plain
Branch:	MAIN
CVS Tags:	HEAD
Log Message:	Initial commit
#	User	Rev	Content
1	benhoob	1.1	#include <vector>
2			#include <string>
3			#include "tmvaglob.C"
4
5
6			// this macro plots the reference distribuions for the Likelihood
7			// methods for the various input variables used in TMVA (e.g. running
8			// TMVAnalysis.C). Signal and Background are plotted separately
9
10			// input: - Input file (result from TMVA),
11			// - use of TMVA plotting TStyle
12			void likelihoodrefs( TString fin = "TMVA.root", Bool_t useTMVAStyle = kTRUE )
13			{
14			// set style and remove existing canvas'
15			TMVAGlob::Initialize( useTMVAStyle );
16
17			// checks if file with name "fin" is already open, and if not opens one
18			TFile* file = TMVAGlob::OpenFile( fin );
19
20			// get all titles of the method likelihood
21			TList titles;
22			UInt_t ninst = TMVAGlob::GetListOfTitles("Method_Likelihood",titles);
23			if (ninst==0) {
24			cout << "Could not locate directory 'Method_Likelihood' in file " << fin << endl;
25			return;
26			}
27			// loop over all titles
28			TIter keyIter(&titles);
29			TDirectory *lhdir;
30			TKey *key;
31			while ((key = TMVAGlob::NextKey(keyIter,"TDirectory"))) {
32			lhdir = (TDirectory *)key->ReadObj();
33			likelihoodrefs( lhdir );
34			}
35			}
36
37			void likelihoodrefs( TDirectory *lhdir ) {
38			Bool_t newCanvas = kTRUE;
39
40			const UInt_t maxCanvas = 200;
41			TCanvas** c = new TCanvas*[maxCanvas];
42			Int_t width = 670;
43			Int_t height = 380;
44
45			// avoid duplicated printing
46			std::vector<std::string> hasBeenUsed;
47			const TString titName = lhdir->GetName();
48			UInt_t ic = -1;
49
50			TIter next(lhdir->GetListOfKeys());
51			TKey *key;
52			while ((key = TMVAGlob::NextKey(next,"TH1"))) { // loop over all TH1
53			TH1 h = (TH1)key->ReadObj();
54			TH1F *b( 0 );
55			TString hname( h->GetName() );
56
57			// avoid duplicated plotting
58			Bool_t found = kFALSE;
59			for (UInt_t j = 0; j < hasBeenUsed.size(); j++) {
60			if (hasBeenUsed[j] == hname.Data()) found = kTRUE;
61			}
62			if (!found) {
63
64			// draw original plots
65			if (hname.EndsWith("_sig_nice")) {
66
67			if (newCanvas) {
68			char cn[20];
69			sprintf( cn, "cv%d_%s", ic+1, titName.Data() );
70			++ic;
71			TString n = hname;
72			c[ic] = new TCanvas( cn, Form( "%s reference for variable: %s",
73			titName.Data(),(n.ReplaceAll("_sig","")).Data() ),
74			ic50+50, ic20, width, height );
75			c[ic]->Divide(2,1);
76			newCanvas = kFALSE;
77			}
78
79			// signal
80			Int_t color = 4;
81			TPad * cPad = (TPad*)c[ic]->cd(1);
82			TString plotname = hname;
83
84			h->SetMaximum(h->GetMaximum()*1.3);
85			h->SetMinimum( 0 );
86			h->SetMarkerColor(color);
87			h->SetMarkerSize( 0.7 );
88			h->SetMarkerStyle( 24 );
89			h->SetLineWidth(1);
90			h->SetLineColor(color);
91			color++;
92			h->Draw("e1");
93			Double_t hSscale = 1.0/(h->GetSumOfWeights()*h->GetBinWidth(1));
94
95			TLegend *legS= new TLegend( cPad->GetLeftMargin(),
96			1-cPad->GetTopMargin()-.14,
97			cPad->GetLeftMargin()+.77,
98			1-cPad->GetTopMargin() );
99			legS->SetBorderSize(1);
100			legS->AddEntry(h,"Input data (signal)","p");
101
102			// background
103			TString bname( hname );
104			b = (TH1F*)lhdir->Get( bname.ReplaceAll("_sig","_bgd") );
105			cPad = (TPad*)c[ic]->cd(2);
106			color = 2;
107			b->SetMaximum(b->GetMaximum()*1.3);
108			b->SetMinimum( 0 );
109			b->SetLineWidth(1);
110			b->SetLineColor(color);
111			b->SetMarkerColor(color);
112			b->SetMarkerSize( 0.7 );
113			b->SetMarkerStyle( 24 );
114			b->Draw("e1");
115			Double_t hBscale = 1.0/(b->GetSumOfWeights()*b->GetBinWidth(1));
116			TLegend *legB= new TLegend( cPad->GetLeftMargin(),
117			1-cPad->GetTopMargin()-.14,
118			cPad->GetLeftMargin()+.77,
119			1-cPad->GetTopMargin() );
120			legB->SetBorderSize(1);
121			legB->AddEntry(b,"Input data (backgr.)","p");
122
123			// register
124			hasBeenUsed.push_back( bname.Data() );
125
126			// the PDFs --------------
127
128			// check for splines
129			h = 0;
130			b = 0;
131			TString pname = hname; pname.ReplaceAll("_nice","");
132			for (int i=0; i<= 5; i++) {
133			TString hspline = pname + Form( "_smoothed_hist_from_spline%i", i );
134			h = (TH1F*)lhdir->Get( hspline );
135			if (h) {
136			b = (TH1F*)lhdir->Get( hspline.ReplaceAll("_sig","_bgd") );
137			break;
138			}
139			}
140
141			// check for KDE
142			if (h == 0 && b == 0) {
143			TString hspline = pname + Form( "_smoothed_hist_from_KDE", i );
144			h = (TH1F*)lhdir->Get( hspline );
145			if (h) {
146			b = (TH1F*)lhdir->Get( hspline.ReplaceAll("_sig","_bgd") );
147			}
148			}
149
150			// found something ?
151			if (h == 0 \|\| b == 0) {
152			cout << "--- likelihoodrefs.C: did not find spline for histogram: " << pname.Data() << endl;
153			}
154			else {
155
156			Double_t pSscale = 1.0/(h->GetSumOfWeights()*h->GetBinWidth(1));
157			h->Scale( pSscale/hSscale );
158			color = 4;
159			c[ic]->cd(1);
160			h->SetLineWidth(2);
161			h->SetLineColor(color);
162			legS->AddEntry(h,"Estimated PDF (norm. signal)","l");
163			h->Draw("histsame");
164			legS->Draw();
165
166			Double_t pBscale = 1.0/(b->GetSumOfWeights()*b->GetBinWidth(1));
167			b->Scale( pBscale/hBscale );
168			color = 2;
169			c[ic]->cd(2);
170			b->SetLineColor(color);
171			b->SetLineWidth(2);
172			legB->AddEntry(b,"Estimated PDF (norm. backgr.)","l");
173			b->Draw("histsame");
174
175			// draw the legends
176			legB->Draw();
177
178			hasBeenUsed.push_back( pname.Data() );
179			}
180
181			c[ic]->Update();
182
183			// write to file
184			TString fname = Form( "plots/%s_refs_c%i", titName.Data(), ic+1 );
185			TMVAGlob::imgconv( c[ic], fname );
186			// c[ic]->Update();
187
188			newCanvas = kTRUE;
189			hasBeenUsed.push_back( hname.Data() );
190			}
191			}
192			}
193			}
194