[quintessenceanx]

My lab group and I are running a program called ROOT, since our professor wants us to use it, and the program run by utilizing a series of macros programmed in C/C++. Right now we need to plot xy error bars and in order to do that we need to impliment a print function into our macro, and even though the Bayes Divide function does include a print line, ROOT does not print it. I am unsure if this is because the function is defined in a C file called by the macro, or if there is something else going wrong. The project is due Tuesday and although we've been attempting to get answers from our professor, he is more quick to leave than give advice. Please help if possible.

Thanks.

Code attached.

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Since the code doesn't seem to attach, included below:

EffecFit.C:


{
gROOT->Reset();
gSystem->Load("libPhysics");
gROOT->LoadMacro("FitEffec2.C");
c1 = new TCanvas("c1","Fitted Effeciency",200,10,700,500);
c1->SetFillColor(42);
c1->SetGrid();

// draw a frame to define the range
TH1F *hr = c1->DrawFrame(1600,0,1850,1);
hr->SetYTitle("Effeciency");
hr->SetXTitle("Voltage");
c1->GetFrame()->SetFillColor(21);
c1->GetFrame()->SetBorderSize(12);
//histogram= new TH1F("histogram","",100,1600,1850,1);

// create first graph
Int_t n1 = 11;
Double_t x1[] = {1600, 1620,1640,1660,1680,1700,1720,1740,1760,1780,1800};
Double_t y1[] = {.313,.380,.488,.592,.682,.780,.825,.865,.900,.884,.892};
Double_t ex1[] = {0,0,0,0,0,0,0,0,0,0,0};
Double_t ey1[] = {.013,.025,.04,.056,0.07,0.08,0.017,0.086,0.078,0.071,0.079};
TF1 *fiteffec1 = new TF1("fiteffec",fiteffec,0.,1850,4);

gr1 = new TGraphErrors(n1,x1,y1,ex1,ey1);
gr1->SetMarkerColor(kBlue);
gr1->SetMarkerStyle(21);
gr1->Draw("P");
gr1->Fit(fiteffec1,"RE");



}





FitEffec2.C


Double_t fiteffec(Double_t *x, Double_t *par)
{
if (x[0]<1600) return 0.;
Double_t my_erf = par[0]+par[1]*TMath::Erf(x[0]*par[2]-par[3]);
return my_erf;
// Double_t fit = par[0]*TMath::Exp(-0.5*arg*arg)+par[3]+par[4]*x[0];

void TGraphAsymmErrors::BayesDivide(const TH1 *pass, const TH1 *total, Option_t *option)
{
TString opt = option; opt.ToLower();

Int_t nbins = pass->GetNbinsX();
if (nbins != total->GetNbinsX()){
Error("BayesDivide","Histograms must have the same number of X bins");
return;
}

if (opt.Contains("w")) {
//compare sum of weights with sum of squares of weights
Double_t stats[10];
pass->GetStats(stats);
if (TMath::Abs(stats[0] -stats[1]) > 1e-6) {
Error("BayesDivide","Pass histogram has not been filled with weights = 1");
return;
}
total->GetStats(stats);
if (TMath::Abs(stats[0] -stats[1]) > 1e-6) {
Error("BayesDivide","Total histogram has not been filled with weights = 1");
return;
}
}

//Set the graph to have a number of points equal to the number of histogram bins
Set(nbins);

// Ok, now set the points for each bin
// (Note: the TH1 bin content is shifted to the right by one:
// bin=0 is underflow, bin=nbins+1 is overflow.)

double mode, low, high; //these will hold the result of the Bayes calculation
int npoint=0;//this keeps track of the number of points added to the graph
for (int b=1; b<=nbins; ++b) { // loop through the bins

int t = (int)total->GetBinContent(b);
if (!t) continue; //don't add points for bins with no information

int p = (int)pass->GetBinContent(b);
if (p>t) {
Warning("BayesDivide","Histogram bin %d in pass has more entries than corresponding bin in total! (%d>%d)",b,p,t);
continue; //we may as well go on...
}

//This is the Bayes calculation...
Efficiency(p,t,0.683,mode,low,high);

//These are the low and high error bars
low = mode-low;
high = high-mode;

//If either of the errors are 0, set them to 1/10 of the other error
//so that the fitters don't get confused.
if (low==0.0) low=high/10.;
if (high==0.0) high=low/10.;

//Set the point center and its errors
SetPoint(npoint,pass->GetBinCenter(b),mode);
SetPointError(npoint,
pass->GetBinCenter(b)-pass->GetBinLowEdge(b),
pass->GetBinLowEdge(b)-pass->GetBinCenter(b)+pass->GetBinWidth(b),
low,high);
npoint++;//we have added a point to the graph

}

Set(npoint);//tell the graph how many points we've really added

if (opt.Contains("debug")) {
printf("BayesDivide: made a graph with %d points from %d bins\n",npoint,nbins);
Print();//The debug prints out what we get for each point
}

}
}

[quintessenceanx]

Since the code doesn't seem to attach, included below:

EffecFit.C:


{
gROOT->Reset();
gSystem->Load("libPhysics");
gROOT->LoadMacro("FitEffec2.C");
c1 = new TCanvas("c1","Fitted Effeciency",200,10,700,500);
c1->SetFillColor(42);
c1->SetGrid();

// draw a frame to define the range
TH1F *hr = c1->DrawFrame(1600,0,1850,1);
hr->SetYTitle("Effeciency");
hr->SetXTitle("Voltage");
c1->GetFrame()->SetFillColor(21);
c1->GetFrame()->SetBorderSize(12);
//histogram= new TH1F("histogram","",100,1600,1850,1);

// create first graph
Int_t n1 = 11;
Double_t x1[] = {1600, 1620,1640,1660,1680,1700,1720,1740,1760,1780,1800};
Double_t y1[] = {.313,.380,.488,.592,.682,.780,.825,.865,.900,.884,.892};
Double_t ex1[] = {0,0,0,0,0,0,0,0,0,0,0};
Double_t ey1[] = {.013,.025,.04,.056,0.07,0.08,0.017,0.086,0.078,0.071,0.079};
TF1 *fiteffec1 = new TF1("fiteffec",fiteffec,0.,1850,4);

gr1 = new TGraphErrors(n1,x1,y1,ex1,ey1);
gr1->SetMarkerColor(kBlue);
gr1->SetMarkerStyle(21);
gr1->Draw("P");
gr1->Fit(fiteffec1,"RE");



}





FitEffec2.C


Double_t fiteffec(Double_t *x, Double_t *par)
{
if (x[0]<1600) return 0.;
Double_t my_erf = par[0]+par[1]*TMath::Erf(x[0]*par[2]-par[3]);
return my_erf;
// Double_t fit = par[0]*TMath::Exp(-0.5*arg*arg)+par[3]+par[4]*x[0];

void TGraphAsymmErrors::BayesDivide(const TH1 *pass, const TH1 *total, Option_t *option)
{
TString opt = option; opt.ToLower();

Int_t nbins = pass->GetNbinsX();
if (nbins != total->GetNbinsX()){
Error("BayesDivide","Histograms must have the same number of X bins");
return;
}

if (opt.Contains("w")) {
//compare sum of weights with sum of squares of weights
Double_t stats[10];
pass->GetStats(stats);
if (TMath::Abs(stats[0] -stats[1]) > 1e-6) {
Error("BayesDivide","Pass histogram has not been filled with weights = 1");
return;
}
total->GetStats(stats);
if (TMath::Abs(stats[0] -stats[1]) > 1e-6) {
Error("BayesDivide","Total histogram has not been filled with weights = 1");
return;
}
}

//Set the graph to have a number of points equal to the number of histogram bins
Set(nbins);

// Ok, now set the points for each bin
// (Note: the TH1 bin content is shifted to the right by one:
// bin=0 is underflow, bin=nbins+1 is overflow.)

double mode, low, high; //these will hold the result of the Bayes calculation
int npoint=0;//this keeps track of the number of points added to the graph
for (int b=1; b<=nbins; ++b) { // loop through the bins

int t = (int)total->GetBinContent(b);
if (!t) continue; //don't add points for bins with no information

int p = (int)pass->GetBinContent(b);
if (p>t) {
Warning("BayesDivide","Histogram bin %d in pass has more entries than corresponding bin in total! (%d>%d)",b,p,t);
continue; //we may as well go on...
}

//This is the Bayes calculation...
Efficiency(p,t,0.683,mode,low,high);

//These are the low and high error bars
low = mode-low;
high = high-mode;

//If either of the errors are 0, set them to 1/10 of the other error
//so that the fitters don't get confused.
if (low==0.0) low=high/10.;
if (high==0.0) high=low/10.;

//Set the point center and its errors
SetPoint(npoint,pass->GetBinCenter(b),mode);
SetPointError(npoint,
pass->GetBinCenter(b)-pass->GetBinLowEdge(b),
pass->GetBinLowEdge(b)-pass->GetBinCenter(b)+pass->GetBinWidth(b),
low,high);
npoint++;//we have added a point to the graph

}

Set(npoint);//tell the graph how many points we've really added

if (opt.Contains("debug")) {
printf("BayesDivide: made a graph with %d points from %d bins\n",npoint,nbins);
Print();//The debug prints out what we get for each point
}

}
}