CITCommon/FitModels/ZSignals.hh

#include "TROOT.h"
#include "TH1D.h"
#include "RooDataSet.h"
#include "RooRealVar.h"
#include "RooAbsPdf.h"
#include "RooBreitWigner.h"
#include "RooCBShape.h"
#include "RooGaussian.h"
#include "RooFFTConvPdf.h"
#include "RooDataHist.h"
#include "RooHistPdf.h"
#include "RooVoigtianShape.h"
#include "RooKeysPdf.h"

class CSignalModel
{
public:
  CSignalModel():model(0){}
  virtual ~CSignalModel(){ delete model; }
  RooAbsPdf *model;
};

class CBreitWignerConvCrystalBall : public CSignalModel
{
public:
  CBreitWignerConvCrystalBall(RooRealVar &m, const Bool_t pass);
  ~CBreitWignerConvCrystalBall();
  RooRealVar     *mass, *width;
  RooBreitWigner *bw;
  RooRealVar     *mean, *sigma, *alpha, *n;
  RooCBShape     *cb;
};

class CMCTemplateConvGaussian : public CSignalModel
{
public:
  CMCTemplateConvGaussian(RooRealVar &m, TH1D* hist, const Bool_t pass, RooRealVar *sigma0=0);
  ~CMCTemplateConvGaussian();
  RooRealVar  *mean, *sigma;
  RooGaussian *gaus;
  TH1D        *inHist;
  RooDataHist *dataHist;
  RooHistPdf  *histPdf;
};

class CVoigtianCBShape : public CSignalModel
{
public:
  CVoigtianCBShape(RooRealVar &m, const Bool_t pass);
  ~CVoigtianCBShape();
  RooRealVar *mass, *width;
  RooRealVar *sigma, *alpha, *n;
};

class CMCDatasetConvGaussian : public CSignalModel
{
public:
  CMCDatasetConvGaussian(RooRealVar &m, TTree* tree, const Bool_t pass, RooRealVar *sigma0=0);
  ~CMCDatasetConvGaussian();
  RooRealVar  *mean, *sigma;
  RooGaussian *gaus;
  TTree       *inTree;
  RooDataSet  *dataSet;
  RooKeysPdf  *keysPdf;
};

//--------------------------------------------------------------------------------------------------
CBreitWignerConvCrystalBall::CBreitWignerConvCrystalBall(RooRealVar &m, const Bool_t pass)
{
  char name[10];
  if(pass) sprintf(name,"%s","Pass");
  else     sprintf(name,"%s","Fail");
  
  char vname[50];
  
  sprintf(vname,"mass%s",name);
  mass = new RooRealVar(vname,vname,91,80,100);    
  mass->setVal(91.1876);
  mass->setConstant(kTRUE);
  
  sprintf(vname,"width%s",name);
  width = new RooRealVar(vname,vname,2.5,0.1,10);    
  width->setVal(2.4952);
  width->setConstant(kTRUE);
  
  sprintf(vname,"bw%s",name);
  bw = new RooBreitWigner(vname,vname,m,*mass,*width);

  if(pass) {
    sprintf(vname,"mean%s",name);  mean  = new RooRealVar(vname,vname,0,-10,10);
    sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,1,0.1,5);
    sprintf(vname,"alpha%s",name); alpha = new RooRealVar(vname,vname,5,0,20);
    sprintf(vname,"n%s",name);     n     = new RooRealVar(vname,vname,1,0,10);
  } else {
    sprintf(vname,"mean%s",name);  mean  = new RooRealVar(vname,vname,0,-10,10);
    sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,1,0.1,5);
    sprintf(vname,"alpha%s",name); alpha = new RooRealVar(vname,vname,5,0,20);
    sprintf(vname,"n%s",name);     n     = new RooRealVar(vname,vname,1,0,10);
  }  
//  n->setVal(1.0);
//  n->setConstant(kTRUE);
  
  sprintf(vname,"cb%s",name);
  cb = new RooCBShape(vname,vname,m,*mean,*sigma,*alpha,*n);
        
  sprintf(vname,"signal%s",name);
  model = new RooFFTConvPdf(vname,vname,m,*bw,*cb);
}

CBreitWignerConvCrystalBall::~CBreitWignerConvCrystalBall()
{
  delete mass;
  delete width;
  delete bw;
  delete mean;
  delete sigma;
  delete alpha;
  delete n;
  delete cb;
}

//--------------------------------------------------------------------------------------------------
CMCTemplateConvGaussian::CMCTemplateConvGaussian(RooRealVar &m, TH1D* hist, const Bool_t pass, RooRealVar *sigma0)
{  
  char name[10];
  if(pass) sprintf(name,"%s","Pass");
  else     sprintf(name,"%s","Fail");
  
  char vname[50];  
  
  if(pass) {
    sprintf(vname,"mean%s",name);  mean  = new RooRealVar(vname,vname,0,-10,10);
    if(sigma0) { sigma = sigma0; }
    else       { sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,2,0,5); }
    sprintf(vname,"gaus%s",name);  gaus  = new RooGaussian(vname,vname,m,*mean,*sigma);
  } else {
    sprintf(vname,"mean%s",name);  mean  = new RooRealVar(vname,vname,0,-10,10);
    if(sigma0) { sigma = sigma0; }
    else       { sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,2,0,5); }
    sprintf(vname,"gaus%s",name);  gaus  = new RooGaussian(vname,vname,m,*mean,*sigma);
  }


  sprintf(vname,"inHist_%s",hist->GetName());
  inHist = (TH1D*)hist->Clone(vname);
  
  sprintf(vname,"dataHist%s",name); dataHist = new RooDataHist(vname,vname,RooArgSet(m),inHist);
  sprintf(vname,"histPdf%s",name);  histPdf  = new RooHistPdf(vname,vname,m,*dataHist,8);
//     sprintf(vname,"histPdf%s",name);  histPdf  = new RooHistPdf(vname,vname,m,*dataHist,10); //use this if fit doesn't converge well...
//      sprintf(vname,"histPdf%s",name);  histPdf  = new RooHistPdf(vname,vname,m,*dataHist,4); //use this if fit doesn't converge well...
//    sprintf(vname,"histPdf%s",name);  histPdf  = new RooHistPdf(vname,vname,m,*dataHist,1); //use this if fit doesn't converge well...
//      sprintf(vname,"histPdf%s",name);  histPdf  = new RooHistPdf(vname,vname,m,*dataHist,0); //use this if fit doesn't converge well...
  sprintf(vname,"signal%s",name);   model    = new RooFFTConvPdf(vname,vname,m,*histPdf,*gaus);
}

CMCTemplateConvGaussian::~CMCTemplateConvGaussian()
{
  delete mean;
  //delete sigma;
  delete gaus;
  delete inHist;
  delete dataHist;
  delete histPdf;
}

//--------------------------------------------------------------------------------------------------
CVoigtianCBShape::CVoigtianCBShape(RooRealVar &m, const Bool_t pass)
{
  char name[10];
  if(pass) sprintf(name,"%s","Pass");
  else     sprintf(name,"%s","Fail");
  
  char vname[50];
  
  sprintf(vname,"mass%s",name);
  mass = new RooRealVar(vname,vname,91,80,100);
    
  sprintf(vname,"width%s",name);
  width = new RooRealVar(vname,vname,2.5,0.1,10);    
  width->setVal(2.4952);
  width->setConstant(kTRUE);
  
  if(pass) {
    sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,1,0.1,10);
    sprintf(vname,"alpha%s",name); alpha = new RooRealVar(vname,vname,5,0,20);
  } else {
    sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,1,0.1,10);
    sprintf(vname,"alpha%s",name); alpha = new RooRealVar(vname,vname,5,0,20);
  }
  
  sprintf(vname,"n%s",name);     
  n = new RooRealVar(vname,vname,1,0,10);
  n->setVal(1.0);
  
  sprintf(vname,"signal%s",name);
  model = new RooVoigtianShape(vname,vname,m,*mass,*sigma,*alpha,*n,*width,0);  
}

CVoigtianCBShape::~CVoigtianCBShape()
{
  delete mass;
  delete width;
  delete sigma;
  delete alpha;
  delete n;
}

//--------------------------------------------------------------------------------------------------
CMCDatasetConvGaussian::CMCDatasetConvGaussian(RooRealVar &m, TTree* tree, const Bool_t pass, RooRealVar *sigma0)
{  
  char name[10];
  if(pass) sprintf(name,"%s","Pass");
  else     sprintf(name,"%s","Fail");
  
  char vname[50];  

  if(pass) {
    sprintf(vname,"mean%s",name);  mean  = new RooRealVar(vname,vname,0,-10,10);
    if(sigma0) { sigma = sigma0; }
    else       { sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,2,0,5); }
    sprintf(vname,"gaus%s",name);  gaus  = new RooGaussian(vname,vname,m,*mean,*sigma);
  } else {
    sprintf(vname,"mean%s",name);  mean  = new RooRealVar(vname,vname,0,-10,10);
    if(sigma0) { sigma = sigma0; }
    else       { sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,2,0,5); }
    sprintf(vname,"gaus%s",name);  gaus  = new RooGaussian(vname,vname,m,*mean,*sigma);
  }
  
  sprintf(vname,"inTree_%s",tree->GetName());
  inTree = (TTree*)tree->Clone(vname);
  
  sprintf(vname,"dataSet%s",name); dataSet = new RooDataSet(vname,vname,inTree,RooArgSet(m));
  sprintf(vname,"keysPdf%s",name); keysPdf = new RooKeysPdf(vname,vname,m,*dataSet,RooKeysPdf::NoMirror,1);
  sprintf(vname,"signal%s",name);  model   = new RooFFTConvPdf(vname,vname,m,*keysPdf,*gaus);
}

CMCDatasetConvGaussian::~CMCDatasetConvGaussian()
{
  delete mean;
  delete sigma;
  delete gaus;
  delete inTree;
  delete dataSet;
  delete keysPdf;
}
Revision:	1.1
Committed:	Sat Jul 21 13:39:48 2012 UTC (12 years, 9 months ago) by sixie
Content type:	text/plain
Branch:	MAIN
Log Message:	add various functional models
#	User	Rev	Content
1	sixie	1.1	#include "TROOT.h"
2			#include "TH1D.h"
3			#include "RooDataSet.h"
4			#include "RooRealVar.h"
5			#include "RooAbsPdf.h"
6			#include "RooBreitWigner.h"
7			#include "RooCBShape.h"
8			#include "RooGaussian.h"
9			#include "RooFFTConvPdf.h"
10			#include "RooDataHist.h"
11			#include "RooHistPdf.h"
12			#include "RooVoigtianShape.h"
13			#include "RooKeysPdf.h"
14
15			class CSignalModel
16			{
17			public:
18			CSignalModel():model(0){}
19			virtual ~CSignalModel(){ delete model; }
20			RooAbsPdf *model;
21			};
22
23			class CBreitWignerConvCrystalBall : public CSignalModel
24			{
25			public:
26			CBreitWignerConvCrystalBall(RooRealVar &m, const Bool_t pass);
27			~CBreitWignerConvCrystalBall();
28			RooRealVar mass, width;
29			RooBreitWigner *bw;
30			RooRealVar mean, sigma, alpha, n;
31			RooCBShape *cb;
32			};
33
34			class CMCTemplateConvGaussian : public CSignalModel
35			{
36			public:
37			CMCTemplateConvGaussian(RooRealVar &m, TH1D* hist, const Bool_t pass, RooRealVar *sigma0=0);
38			~CMCTemplateConvGaussian();
39			RooRealVar mean, sigma;
40			RooGaussian *gaus;
41			TH1D *inHist;
42			RooDataHist *dataHist;
43			RooHistPdf *histPdf;
44			};
45
46			class CVoigtianCBShape : public CSignalModel
47			{
48			public:
49			CVoigtianCBShape(RooRealVar &m, const Bool_t pass);
50			~CVoigtianCBShape();
51			RooRealVar mass, width;
52			RooRealVar sigma, alpha, *n;
53			};
54
55			class CMCDatasetConvGaussian : public CSignalModel
56			{
57			public:
58			CMCDatasetConvGaussian(RooRealVar &m, TTree* tree, const Bool_t pass, RooRealVar *sigma0=0);
59			~CMCDatasetConvGaussian();
60			RooRealVar mean, sigma;
61			RooGaussian *gaus;
62			TTree *inTree;
63			RooDataSet *dataSet;
64			RooKeysPdf *keysPdf;
65			};
66
67			//--------------------------------------------------------------------------------------------------
68			CBreitWignerConvCrystalBall::CBreitWignerConvCrystalBall(RooRealVar &m, const Bool_t pass)
69			{
70			char name[10];
71			if(pass) sprintf(name,"%s","Pass");
72			else sprintf(name,"%s","Fail");
73
74			char vname[50];
75
76			sprintf(vname,"mass%s",name);
77			mass = new RooRealVar(vname,vname,91,80,100);
78			mass->setVal(91.1876);
79			mass->setConstant(kTRUE);
80
81			sprintf(vname,"width%s",name);
82			width = new RooRealVar(vname,vname,2.5,0.1,10);
83			width->setVal(2.4952);
84			width->setConstant(kTRUE);
85
86			sprintf(vname,"bw%s",name);
87			bw = new RooBreitWigner(vname,vname,m,mass,width);
88
89			if(pass) {
90			sprintf(vname,"mean%s",name); mean = new RooRealVar(vname,vname,0,-10,10);
91			sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,1,0.1,5);
92			sprintf(vname,"alpha%s",name); alpha = new RooRealVar(vname,vname,5,0,20);
93			sprintf(vname,"n%s",name); n = new RooRealVar(vname,vname,1,0,10);
94			} else {
95			sprintf(vname,"mean%s",name); mean = new RooRealVar(vname,vname,0,-10,10);
96			sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,1,0.1,5);
97			sprintf(vname,"alpha%s",name); alpha = new RooRealVar(vname,vname,5,0,20);
98			sprintf(vname,"n%s",name); n = new RooRealVar(vname,vname,1,0,10);
99			}
100			// n->setVal(1.0);
101			// n->setConstant(kTRUE);
102
103			sprintf(vname,"cb%s",name);
104			cb = new RooCBShape(vname,vname,m,mean,sigma,alpha,n);
105
106			sprintf(vname,"signal%s",name);
107			model = new RooFFTConvPdf(vname,vname,m,bw,cb);
108			}
109
110			CBreitWignerConvCrystalBall::~CBreitWignerConvCrystalBall()
111			{
112			delete mass;
113			delete width;
114			delete bw;
115			delete mean;
116			delete sigma;
117			delete alpha;
118			delete n;
119			delete cb;
120			}
121
122			//--------------------------------------------------------------------------------------------------
123			CMCTemplateConvGaussian::CMCTemplateConvGaussian(RooRealVar &m, TH1D* hist, const Bool_t pass, RooRealVar *sigma0)
124			{
125			char name[10];
126			if(pass) sprintf(name,"%s","Pass");
127			else sprintf(name,"%s","Fail");
128
129			char vname[50];
130
131			if(pass) {
132			sprintf(vname,"mean%s",name); mean = new RooRealVar(vname,vname,0,-10,10);
133			if(sigma0) { sigma = sigma0; }
134			else { sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,2,0,5); }
135			sprintf(vname,"gaus%s",name); gaus = new RooGaussian(vname,vname,m,mean,sigma);
136			} else {
137			sprintf(vname,"mean%s",name); mean = new RooRealVar(vname,vname,0,-10,10);
138			if(sigma0) { sigma = sigma0; }
139			else { sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,2,0,5); }
140			sprintf(vname,"gaus%s",name); gaus = new RooGaussian(vname,vname,m,mean,sigma);
141			}
142
143
144			sprintf(vname,"inHist_%s",hist->GetName());
145			inHist = (TH1D*)hist->Clone(vname);
146
147			sprintf(vname,"dataHist%s",name); dataHist = new RooDataHist(vname,vname,RooArgSet(m),inHist);
148			sprintf(vname,"histPdf%s",name); histPdf = new RooHistPdf(vname,vname,m,*dataHist,8);
149			// sprintf(vname,"histPdf%s",name); histPdf = new RooHistPdf(vname,vname,m,*dataHist,10); //use this if fit doesn't converge well...
150			// sprintf(vname,"histPdf%s",name); histPdf = new RooHistPdf(vname,vname,m,*dataHist,4); //use this if fit doesn't converge well...
151			// sprintf(vname,"histPdf%s",name); histPdf = new RooHistPdf(vname,vname,m,*dataHist,1); //use this if fit doesn't converge well...
152			// sprintf(vname,"histPdf%s",name); histPdf = new RooHistPdf(vname,vname,m,*dataHist,0); //use this if fit doesn't converge well...
153			sprintf(vname,"signal%s",name); model = new RooFFTConvPdf(vname,vname,m,histPdf,gaus);
154			}
155
156			CMCTemplateConvGaussian::~CMCTemplateConvGaussian()
157			{
158			delete mean;
159			//delete sigma;
160			delete gaus;
161			delete inHist;
162			delete dataHist;
163			delete histPdf;
164			}
165
166			//--------------------------------------------------------------------------------------------------
167			CVoigtianCBShape::CVoigtianCBShape(RooRealVar &m, const Bool_t pass)
168			{
169			char name[10];
170			if(pass) sprintf(name,"%s","Pass");
171			else sprintf(name,"%s","Fail");
172
173			char vname[50];
174
175			sprintf(vname,"mass%s",name);
176			mass = new RooRealVar(vname,vname,91,80,100);
177
178			sprintf(vname,"width%s",name);
179			width = new RooRealVar(vname,vname,2.5,0.1,10);
180			width->setVal(2.4952);
181			width->setConstant(kTRUE);
182
183			if(pass) {
184			sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,1,0.1,10);
185			sprintf(vname,"alpha%s",name); alpha = new RooRealVar(vname,vname,5,0,20);
186			} else {
187			sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,1,0.1,10);
188			sprintf(vname,"alpha%s",name); alpha = new RooRealVar(vname,vname,5,0,20);
189			}
190
191			sprintf(vname,"n%s",name);
192			n = new RooRealVar(vname,vname,1,0,10);
193			n->setVal(1.0);
194
195			sprintf(vname,"signal%s",name);
196			model = new RooVoigtianShape(vname,vname,m,mass,sigma,alpha,n,*width,0);
197			}
198
199			CVoigtianCBShape::~CVoigtianCBShape()
200			{
201			delete mass;
202			delete width;
203			delete sigma;
204			delete alpha;
205			delete n;
206			}
207
208			//--------------------------------------------------------------------------------------------------
209			CMCDatasetConvGaussian::CMCDatasetConvGaussian(RooRealVar &m, TTree* tree, const Bool_t pass, RooRealVar *sigma0)
210			{
211			char name[10];
212			if(pass) sprintf(name,"%s","Pass");
213			else sprintf(name,"%s","Fail");
214
215			char vname[50];
216
217			if(pass) {
218			sprintf(vname,"mean%s",name); mean = new RooRealVar(vname,vname,0,-10,10);
219			if(sigma0) { sigma = sigma0; }
220			else { sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,2,0,5); }
221			sprintf(vname,"gaus%s",name); gaus = new RooGaussian(vname,vname,m,mean,sigma);
222			} else {
223			sprintf(vname,"mean%s",name); mean = new RooRealVar(vname,vname,0,-10,10);
224			if(sigma0) { sigma = sigma0; }
225			else { sprintf(vname,"sigma%s",name); sigma = new RooRealVar(vname,vname,2,0,5); }
226			sprintf(vname,"gaus%s",name); gaus = new RooGaussian(vname,vname,m,mean,sigma);
227			}
228
229			sprintf(vname,"inTree_%s",tree->GetName());
230			inTree = (TTree*)tree->Clone(vname);
231
232			sprintf(vname,"dataSet%s",name); dataSet = new RooDataSet(vname,vname,inTree,RooArgSet(m));
233			sprintf(vname,"keysPdf%s",name); keysPdf = new RooKeysPdf(vname,vname,m,*dataSet,RooKeysPdf::NoMirror,1);
234			sprintf(vname,"signal%s",name); model = new RooFFTConvPdf(vname,vname,m,keysPdf,gaus);
235			}
236
237			CMCDatasetConvGaussian::~CMCDatasetConvGaussian()
238			{
239			delete mean;
240			delete sigma;
241			delete gaus;
242			delete inTree;
243			delete dataSet;
244			delete keysPdf;
245			}