void FitSignals(TTree * treeB, TCut cut, Int_t max){
AliSignalProcesor proc;
TF1 * f1 = proc.GetAsymGauss();
TTreeSRedirector cstream("FitSignal.root");
TFile *f = cstream.GetFile();
char lname[100];
sprintf(lname,"Fit%s", cut.GetTitle());
TEventList *list = new TEventList(lname,lname);
sprintf(lname,">>Fit%s", cut.GetTitle());
treeB->Draw(lname,cut);
treeB->SetEventList(list);
Int_t nFits=0;
for (Int_t ievent=0; ievent<list->GetN(); ievent++){
if (nFits>max) break;
if (nFits%50==0) printf("%d\n",nFits);
char ename[100];
sprintf(ename,"Fit%d", ievent);
Double_t nsample = treeB->Draw("Graph.fY-Mean09:Graph.fX","","",1,ievent);
Double_t * signal = treeB->GetV1();
Double_t * time = treeB->GetV2();
Double_t maxpos =0;
Double_t max = 0;
for (Int_t ipos = 0; ipos<nsample; ipos++){
if (signal[ipos]>max){
max = signal[ipos];
maxpos = ipos;
}
}
Int_t first = TMath::Max(maxpos-10,0.);
Int_t last = TMath::Min(maxpos+60, nsample);
//
f->cd();
TH1F his(ename,ename,last-first,first,last);
for (Int_t ipos=0; ipos<last-first; ipos++){
his.SetBinContent(ipos+1,signal[ipos+first]);
}
treeB->Draw("Sector:Row:Pad","","",1,ievent);
Double_t sector = treeB->GetV1()[0];
Double_t row = treeB->GetV2()[0];
Double_t pad = treeB->GetV3()[0];
// TGraph graph(last-first,&time[first],&signal[first]);
f1->SetParameters(0.75*max,maxpos,1.1,0.8,0.25,0.2);
// TH1F * his = (TH1F*)graph.GetHistogram();
his.Fit(f1,"q");
his.Write(ename);
gPad->Clear();
his.Draw();
gPad->Update();
Double_t params[6];
for (Int_t ipar=0; ipar<6; ipar++) params[ipar] = f1->GetParameters()[ipar];
Double_t chi2 = TFitter::GetFitter()->Chisquare(6,params);
TMatrixD cov(6,6);
cov.SetMatrixArray(TFitter::GetFitter()->GetCovarianceMatrix());
//
// tail cancellation
//
Double_t x0[1000];
Double_t x1[1000];
Double_t x2[1000];
for (Int_t ipos=0; ipos<last-first; ipos++){
x0[ipos] = signal[ipos+first];
}
proc.TailCancelationALTRO1(x0,x1,0.85*0.339,0.09,last-first);
proc.TailCancelationALTRO1(x1,x2,0.85,0.789,last-first);
//
sprintf(ename,"Cancel1_%d", ievent);
TH1F his1(ename,ename,last-first,first,last);
for (Int_t ipos=0; ipos<last-first; ipos++){
his1.SetBinContent(ipos+1,x1[ipos]);
}
his1.Write(ename);
sprintf(ename,"Cancel2_%d", ievent);
TH1F his2(ename,ename,last-first,first,last);
for (Int_t ipos=0; ipos<last-first; ipos++){
his2.SetBinContent(ipos+1,x1[ipos]);
}
f1->SetParameters(0.75*max,maxpos,1.1,0.8,0.25,0.2);
his2.Fit(f1,"q");
his2.Write(ename);
Double_t params2[6];
for (Int_t ipar=0; ipar<6; ipar++) params2[ipar] = f1->GetParameters()[ipar];
Double_t chi22 = TFitter::GetFitter()->Chisquare(6,params2);
TMatrixD cov2(6,6);
cov2.SetMatrixArray(TFitter::GetFitter()->GetCovarianceMatrix());
TGraph gr0(last-first, &time[first],x0);
TGraph gr1(last-first, &time[first],x1);
TGraph gr2(last-first, &time[first],x2);
//
cstream<<"Fit"<<
"Sector="<<sector<<
"Row="<<row<<
"Pad="<<pad<<
"First="<<first<<
"Max="<<max<<
"MaxPos="<<maxpos<<
"chi2="<<chi2<<
"chi22="<<chi22<<
"Cov="<<&cov<<
"Cov2="<<&cov2<<
"gr0.="<<&gr0<<
"gr1.="<<&gr1<<
"gr2.="<<&gr2<<
"p0="<<params[0]<<
"p1="<<params[1]<<
"p2="<<params[2]<<
"p3="<<params[3]<<
"p4="<<params[4]<<
"p5="<<params[5]<<
"p02="<<params2[0]<<
"p12="<<params2[1]<<
"p22="<<params2[2]<<
"p32="<<params2[3]<<
"p42="<<params2[4]<<
"p52="<<params2[5]<<
"\n";
// delete his;
nFits++;
}
}
/*
* Creates the nE by nV (num edges by num vertices/tasks) fusion penalty
* matrix
*
* pairs is an nE * 2 matrix of which tasks are in which pair
*/
int gennetwork(double *y, int n, int K,
double corthresh, int cortype, double *C, int *pairs, int *edges)
{
int i, j, k, e,
nE = K * (K - 1) / 2;
double *S = NULL,
*R = NULL,
*W = NULL;
int *eFrom = NULL,
*eTo = NULL;
CALLOCTEST(S, K * K, sizeof(double));
CALLOCTEST(R, K * K, sizeof(double));
CALLOCTEST(W, K * K, sizeof(double));
CALLOCTEST(eFrom, nE, sizeof(int));
CALLOCTEST(eTo, nE, sizeof(int));
cov2(y, S, n, K);
cov2cor(S, R, K);
#ifdef DEBUG
if(!writematrixf(S, K, K, "S.txt"))
return FAILURE;
if(!writematrixf(R, K, K, "R.txt"))
return FAILURE;
#endif
for(i = 0 ; i < K ; i++)
{
for(j = 0 ; j < K ; j++)
{
k = i * K + j;
R[k] = fabs(R[k]) < corthresh ? 0 : R[k];
if(cortype == CORTYPE_IND)
W[k] = fabs(R[k]) > corthresh;
else if(cortype == CORTYPE_ABS)
W[k] = fabs(R[k]);
else if(cortype == CORTYPE_SQR)
W[k] = R[k] * R[k];
}
}
e = 0;
for(j = 0 ; j < K ; j++)
{
for(i = 0 ; i < K ; i++)
{
if(i < j && R[i * K + j] != 0)
{
eFrom[e] = i;
eTo[e] = j;
pairs[e] = i;
pairs[e + nE] = j;
e++;
}
}
}
for(e = 0 ; e < nE ; e++)
{
i = eFrom[e];
j = eTo[e];
C[e + nE * i] = W[i * K + j];
C[e + nE * j] = -W[i * K + j] * sign(R[i * K + j]);
}
/* make the (K-1) by K edges matrix */
for(j = 0 ; j < K ; j++)
{
e = 0;
for(i = 0 ; i < nE ; i++)
{
if(C[i + nE * j] != 0)
{
edges[e + (K - 1) * j] = i;
e++;
}
}
}
FREENULL(S);
FREENULL(R);
FREENULL(W);
FREENULL(eFrom);
FREENULL(eTo);
return SUCCESS;
}
