Hi, (EXCUSE MY ENGLISH)
It is clear, I not must delete the histograms (like gen); but I want that
at second, third, ..., value of ALFA the histos are new (not filled with
previous entries) how if it was executed to the first time.
Practly if don't put gen->Delete (now I know that is very wrong) at the
second valueof ALFA, the hist gen ( for example) is filled with the old
(the I don't want) and the new entries.
How can I resolve this ?
Thanks
Francesco.
On Wed, 7 Jul 2004, Philippe Canal wrote:
> Hi,
>
> After all the calls like:
> gen->Delete();
> You never re-initialize the value of gen. (and since TH1D *gen = ... is
> at the global scope level it is executed exactly once).
> Hence the next time you use 'gen' is point to a delete object resulting
> in (possible) crash.
>
> Cheers,
> Philippe
>
> -----Original Message-----
> From: owner-roottalk@pcroot.cern.ch
> [mailto:owner-roottalk@pcroot.cern.ch]On Behalf Of Perfetto Francesco
> Sent: Wednesday, July 07, 2004 5:12 AM
> To: roottalk@pcroot.cern.ch
> Cc: rene.brun@cern.ch
> Subject: [ROOT] Problem with global variable
>
>
>
> Hi All,
>
> I have the following problem:
> I have a program (compiled) in which there is a main (Likelihood.cpp)
> that call the likelihood.cc in which there are any function.
>
> Likelihood.cpp:
>
> #include <iostream.h>
> #include "TROOT.h"
> #include "TObject.h"
>
> void llhood(Double_t ALFA);
>
> int main(int argc, char* argv[])
> {
> Double_t ALFA[] = {0,-.45,-.07};//,.04,-.07,.07,-.12,.12,-.22,.22,-.45,.45,-.78,.97,1,.8};
>
> for(Int_t i=0; i<3; i++){
> llhood(ALFA[i]);
> }
>
> return 0;
>
> }
>
> likelihood.cc:
>
> #define res_class_cxx
> #include "res_class.h"
> #include <iostream.h>
> #include "TMath.h"
> #include "TFormula.h"
> #include "TH1.h"
> #include "TF1.h"
> #include "TROOT.h"
> #include "TLorentzVector.h"
> #include "TVectorD.h"
> #include "TObject.h"
> #include "TFile.h"
> #include "TTree.h"
> #include "TMinuit.h"
> #include "TGraph.h"
> #include "TCut.h"
> #include "TChain.h"
>
>
> Double_t func(Double_t *xv,Double_t *par)
> {
> Double_t z = xv[0];
> return (1. + par[0]*z);
> }
>
> const Int_t nparams = 1;
> const Int_t TreGaus = 10; // <<<<<-----------RICORDA DI CAMBIARE IL
> NUMERO DI 3G
> const Int_t TreGaus = 10; // <<<<<-----------RICORDA DI CAMBIARE IL
> NUMERO DI 3G
> const Int_t TreGaus = 10; // <<<<<-----------RICORDA DI CAMBIARE IL
> NUMERO DI 3G
> const Double_t PI = 3.1415926;
>
> //apro il file in cui salvo gli istogrammi per controllare la bonta' del
> fit
> TFile *f2 = new
> TFile("/home/perfetto/AUTO/tmpLLHOOD/llhood_camp2_10x60slices.root","RECREATE");
>
> // istogrammi calcolati con il tree t che utilizzo per il folding
> (riempire hist.ris)
> TH1D *gen = new TH1D("gen" ,"gen" ,nslices,0,1);
> TH1D *rgen = new TH1D("rgen","rgen",nslices,0,1);
>
> // istogrammi che calcolo con il tree t_eff che utilizzo per calcolare
> l'efficienza (nel folding)
> TH1D *gen_eff = new TH1D("gen_eff" ,"gen_eff" ,nslices,0,1);
> TH1D *rgen_eff = new TH1D("rgen_eff","rgen_eff",nslices,0,1);
> TH1D *eff = new TH1D("eff" ,"eff" ,nslices,0,1);
>
> // istogramma che calcolo con il tree t_eff che non utilizzo
> TH1D *rec_eff = new TH1D("rec_eff" ,"rec_eff" ,2*nslices,0,2);
> // istogramma che calcolo con il tree t che non utilizzo
> TH1D *rec = new TH1D("rec" ,"rec" ,2*nslices,0,2);
>
> // istogramma della variabile z_rec pesata per la funzione teorica
> TH1D *rec_alfa = new TH1D("rec_alfa" ,"rec_alfa" ,2*nslices,0,2);
>
> // istogramma che contiene gli eventi dopo il folding
> TH1D *ris = new TH1D("ris" ,"ris" ,2*nslices,0,2);
>
> //definizione della f teorica che utilizzo nel folding
> TF1 *f_th = new TF1("f_th",func,0,2,nparams);
>
>
>
> //per sapere quante Fermi Dirac ci sono tra le slices e quante ne uso per
> il fit
>
> Int_t last_FD = FermiDirac;
> Int_t usa_FD = 1;
>
>
> // definizione della Fermi Dirac e della tripla gaussiana
> Double_t norm_FD[] = {225.69};
> Double_t parFD[4][FermiDirac];
> Double_t par3G[9][TreGaus];
>
> void riempi()
> {
> ...
> }
>
> void fcnllhood(Int_t &npar, Double_t *gin, Double_t &f, Double_t *par,
> Int_t iflag)
> {
> // Calcolo -ln(llhood)da minimizzare.
> // calcolo gli eventi convoluti (dopo il folding)
>
> // il numero di bin e' pari al numero di slices
> Int_t nbins = nslices;
> // max e min bin nel fit
> Int_t maxbinfitted = 16; // <<<<<<--------RICORDA DI CAMBIARE I VALORI
> Int_t minbinfitted = 4; //
> ...
> //eventi puro spazio delle fasi
> Double_t c_gen = gen->GetBinContent(i)/n_ministeps;
> // efficienza
> Double_t c_eff = eff->GetBinContent(i);
> //funzione teorica
> Double_t fvalue = f_th->Eval(zp);
> //eventi convoluti
> c_prod += (c_gen * c_eff * c_gz)*fvalue;
> }
>
> }
> ris->SetBinContent(j,c_prod);
>
> }
> ...
> //Double_t n_misj = rec->GetBinContent(j);
> Double_t n_misj = rec_alfa->GetBinContent(j);
>
> llh += nj*n_misj;
> }
> // funzione da minimizzare
> f = -2.*llh;
> }
>
> void llhood(Double_t ALFA)
> {
>
> gen ->Sumw2();
> rgen->Sumw2();
> eff ->Sumw2();
> rec ->Sumw2();
> rec_alfa->Sumw2();
> ris ->Sumw2();
>
> // funzione che riempie le variabili con i parametri dai fit alle slices
> riempi();
>
>
> //creo una Chain di files MC con cui faccio le efficienze
> TChain *t_eff = new TChain("/data2/perfetto/bunch_1_minuit.root/res");
> t_eff->Add("/data2/perfetto/bunch_1_minuit.root/res");
> //creo una Chain di files MC con cui faccio il fit
> TChain *t = new TChain("/data2/perfetto/bunch_4_minuit.root/res");
> t->Add("/data2/perfetto/bunch_4_minuit.root/res");
> ...
> res_class *resc = new res_class(t);
> res_class *resc_eff = new res_class(t_eff);
>
> // per conoscere il numero di entries del tree t
> Int_t nentries = Int_t(t->GetEntries());
> Int_t nbytes = 0, nb = 0;
> cout<<nentries<<endl;
> // Loop per riempire tutti gli istogrammi tranne le efficienze
> for (Int_t jentry=0; jentry<nentries; jentry++) {
> Int_t ientry = resc->LoadTree(jentry);
> nb = resc->GetEntry(jentry);
> nbytes += nb;
>
> Bool_t bc1 = resc->eta_fit_pchi2 > 0.01;
> Bool_t bc2 = resc->chi_2_ord[0] < 5;
> Bool_t bc3 = (resc->chi_2_ord[1]-resc->chi_2_ord[0]) > 3;
> Bool_t bc4 = resc->eta_filter_flag==-1;
> Bool_t bc5 = resc->p4_ph_recA__fE[0]>320 &&
> resc->p4_ph_recA__fE[0]<400;
> Bool_t bc6 = resc->isecl==1;
> Bool_t bcene = resc->eta_ini_par[5]>10 && resc->eta_ini_par[10]>10 &&
> resc->eta_ini_par[15]>10 && resc->eta_ini_par[20]>10 &&
> resc->eta_ini_par[25]>10 && resc->eta_ini_par[30]>10;
> Bool_t bcang = resc->angolo_ini_ord[0] > 18;
>
> Bool_t bct = bc1&&bc2&&bc3&&bc4&&bc5&&bc6&&bcene&&bcang;
>
> gen->Fill(resc->daliz_gen);
> Double_t weight = (1.+ ALFA*(resc->daliz_gen));
> if(bct){
> Double_t zalfa = resc->daliz_rec_fit;
> rec_alfa->Fill(zalfa,weight);
>
> rec->Fill(resc->daliz_rec_fit);
> rgen->Fill(resc->daliz_gen);
>
> }
>
> }
>
> Int_t nentries_eff = Int_t(t_eff->GetEntries());
> Int_t nbytes_eff = 0, nb_eff = 0;
> cout<<nentries_eff<<endl;
> // Loop per riempire tutti gli istogrammi per le efficienze
> for (Int_t jentry=0; jentry<nentries_eff; jentry++) {
> Int_t ientry = resc_eff->LoadTree(jentry);
> nb_eff = resc_eff->GetEntry(jentry);
> nbytes_eff += nb_eff;
>
> Bool_t bc1 = resc_eff->eta_fit_pchi2 > 0.01;
> Bool_t bc2 = resc_eff->chi_2_ord[0] < 5;
> Bool_t bc3 = (resc_eff->chi_2_ord[1]-resc_eff->chi_2_ord[0]) > 3;
> Bool_t bc4 = resc_eff->eta_filter_flag==-1;
> Bool_t bc5 = resc_eff->p4_ph_recA__fE[0]>320 &&
> resc_eff->p4_ph_recA__fE[0]<400;
> Bool_t bc6 = resc_eff->isecl==1;
> Bool_t bcene = resc_eff->eta_ini_par[5]>10 &&
> resc_eff->eta_ini_par[10]>10 && resc_eff->eta_ini_par[15]>10 &&
> resc_eff->eta_ini_par[20]>10 && resc_eff->eta_ini_par[25]>10 &&
> resc_eff->eta_ini_par[30]>10;
> Bool_t bcang = resc_eff->angolo_ini_ord[0] > 18;
>
> Bool_t bct = bc1&&bc2&&bc3&&bc4&&bc5&&bc6&&bcene&&bcang;
>
> gen_eff->Fill(resc_eff->daliz_gen);
> if(bct){
> rec_eff->Fill(resc_eff->daliz_rec_fit);
> rgen_eff->Fill(resc_eff->daliz_gen);
> }
>
> }
> // calcolo l'efficienza
> eff->Divide(rgen_eff,gen_eff,1,1,"B");
>
>
> // USO DI MINUIT.
>
> // Inizializza minuit con nparams parametri.
> TMinuit *minuit = new TMinuit(nparams);
>
> // Fissa la fcn da minimizzare.
> minuit->SetFCN(fcnllhood);
>
> Double_t arglist[100];
> Int_t ierflag = 0;
>
> // Settiamo i parametri e lo step
>
> Double_t start[nparams] = {0.35};
> Double_t step[nparams] = {0.01};
>
>
> minuit->mnparm(0,"alpha",start[0],step[0],0,0,ierflag);
>
> // arglist = lista degli argomenti del comando che chiamo con mnexcm.
>
> arglist[0] = 1;
> minuit->mnexcm("CALL FCN",arglist,1,ierflag);
>
> arglist[0] = 0;
> minuit->mnexcm("MIGRAD",arglist,0,ierflag);
> minuit->mnexcm("MINOS" ,arglist,0,ierflag);
> minuit->mnexcm("SET PRINT",arglist,1,ierflag);
>
> minuit->mnscan();
>
> // Stampa dei risultati.
> Double_t amin,edm,errdef;
> Int_t nvpar,nparx,icstat;
> minuit->mnstat(amin,edm,errdef,nvpar,nparx,icstat);
> minuit->mnprin(4,amin);
> cout<<"icstat = "<<icstat<<endl;
> Double_t alfa,err_alfa;
> minuit->GetParameter(0,alfa,err_alfa);
> cout<<"alpha = "<<alfa<<endl;
> cout<<"err_alpha = "<<err_alfa<<endl;
>
> f2->Write();
>
> gen -> Delete();
> rgen -> Delete();
> gen_eff -> Delete();
> rgen_eff -> Delete();
> eff -> Delete();
> rec_eff -> Delete();
> rec -> Delete();
> rec_alfa -> Delete();
> ris -> Delete();
> f_th -> Delete();
> }
>
> The problem is that when run the program (compiling with gmake OK) the
> first value of ALFA (0) don't give any problem, but when keep the
> following value if I don't delate the histos (how do now at the end of
> program) the program refill the histos again on the sames (I give a bad
> value of fit) But if I delate the histos when run the program I have at
> second value the following error:
>
> lxkloe2:~/AUTO/tmpLLHOOD> Linux/Likelihood.exe > pippo2.txt
>
> *** Break *** segmentation violation
> Generating stack trace...
> 0x0804943a in main + 0x2e from Linux/Likelihood.exe
> 0x42015704 in __libc_start_main + 0xe4 from /lib/tls/libc.so.6
> 0x0804933d in TFile::TFile[in-charge](char const*, char const*, char
> const*, int) + 0x31 from Linux/Likelihood.exe
> Segnale di annullamento (creato file core)
>
> Can someone help me?
> Thanks
> Francesco.
>
> I use root 4.00/04 on redhat 9.0
>
>
>
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