//
#include "AliTPCCalibViewer.h"
+using std::ifstream;
ClassImp(AliTPCCalibViewer)
// assignment operator - dummy
// not yet working!!!
//
+ if (this == ¶m) return (*this);
+
fTree = param.fTree;
fTreeMustBeDeleted = param.fTreeMustBeDeleted;
//fFile = new TFile(*(param.fFile));
}
//_____________________________________________________________________________
-void AliTPCCalibViewer::Delete(Option_t* option) {
+void AliTPCCalibViewer::Delete(Option_t* /*option*/) {
//
// Should be called from AliTPCCalibViewerGUI class only.
// If you use Delete() do not call the destructor.
// All objects (except those contained in fListOfObjectsToBeDeleted) will be deleted, the file will be closed.
//
- option = option; // to avoid warnings on compiling
if (fTree && fTreeMustBeDeleted) {
fTree->SetCacheSize(0);
fTree->Delete();
}
- if (fFile)
- delete fFile;
+ delete fFile;
delete fListOfObjectsToBeDeleted;
}
Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
nsigma[i] = sig;
}
-
+ delete sigmasTokens;
+
TString drawStr(drawCommand);
Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
if (dangerousToDraw) {
// sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
// sigmaStep: the binsize of the generated histogram
// Begin_Latex
- // f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #frac{#int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx + #int_{#mu}^{#mu - t #sigma} f(x, #mu, #sigma) dx }{ #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx }
+ // f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = (#int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx + #int_{#mu}^{#mu - t #sigma} f(x, #mu, #sigma) dx) / (#int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx)
// End_Latex
//
//
Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
nsigma[i] = sig;
}
-
+ delete sigmasTokens;
+
if (plotMean) {
cutHistoMean = AliTPCCalibViewer::SigmaCut(htemp, mean, sigma, sigmaMax, sigmaStep, pm);
if (cutHistoMean) {
}
-Int_t AliTPCCalibViewer::SigmaCutNew(const char* drawCommand, const char* sector, const char* cuts, Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, Bool_t pm, const char *sigmas, Float_t sigmaStep) const {
+Int_t AliTPCCalibViewer::SigmaCutNew(const char* drawCommand, const char* sector, const char* cuts, Float_t /*sigmaMax*/, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, Bool_t /*pm*/, const char *sigmas, Float_t /*sigmaStep*/) const {
//
// Creates a histogram, where you can see, how much of the data are inside sigma-intervals
// around the mean/median/LTM
//
// Double_t ltmFraction = 0.8; //unused
- // avoid compiler warnings:
- sigmaMax = sigmaMax;
- pm = pm;
- sigmaStep = sigmaStep;
TString drawStr(drawCommand);
drawStr += " >> tempHist";
Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
nsigma[i] = sig;
}
+ delete sigmasTokens;
if (plotMean) {
for (Int_t i = 0; i < entries; i++) {
// "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
// The actual work is done on the array.
/* Begin_Latex
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #frac{#int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx}{ #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx }
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
End_Latex
*/
if (sector >= 0 && sector < 72) {
// "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
// The actual work is done on the array.
/* Begin_Latex
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #frac{#int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx}{ #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx }
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
End_Latex
*/
Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
nsigma[i] = sig;
}
-
+ delete sigmasTokens;
+
TLegend * legend = new TLegend(.7,.7, .99, .99, "Integrated histogram");
//fListOfObjectsToBeDeleted->Add(legend);
TH1F *integralHistoMean = 0;
}
-Int_t AliTPCCalibViewer::Integrate(const char* drawCommand, const char* sector, const char* cuts, Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, const char *sigmas, Float_t sigmaStep) const {
+Int_t AliTPCCalibViewer::Integrate(const char* drawCommand, const char* sector, const char* cuts, Float_t /*sigmaMax*/, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, const char *sigmas, Float_t /*sigmaStep*/) const {
//
// Creates an integrated histogram Begin_Latex S(t, #mu, #sigma) End_Latex, out of the input distribution distribution Begin_Latex f(x, #mu, #sigma) End_Latex, given in "histogram"
// "mean" and "sigma" are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in "histogram", to be specified by the user
// "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
// The actual work is done on the array.
/* Begin_Latex
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #frac{#int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx}{ #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx }
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
End_Latex
*/
Double_t ltmFraction = 0.8;
- // avoid compiler warnings:
- sigmaMax = sigmaMax;
- sigmaStep = sigmaStep;
TString drawStr(drawCommand);
Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
nsigma[i] = sig;
}
-
+ delete sigmasTokens;
+
TLegend * legend = new TLegend(.7,.7, .99, .99, "Integrated histogram");
//fListOfObjectsToBeDeleted->Add(legend);
// pm: Decide weather Begin_Latex t > 0 End_Latex (first case) or Begin_Latex t End_Latex arbitrary (secound case)
// The actual work is done on the array.
/* Begin_Latex
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #frac{#int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx + #int_{#mu}^{#mu - t #sigma} f(x, #mu, #sigma) dx }{ #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx } , for t > 0
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = (#int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx + #int_{#mu}^{#mu - t #sigma} f(x, #mu, #sigma) dx) / (#int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx), for t > 0
or
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #frac{#int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx}{ #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx }
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{#mu}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
End_Latex
- begin_macro(source)
+ Begin_Macro(source)
{
Float_t mean = 0;
Float_t sigma = 1.5;
Float_t sigmaMax = 4;
gROOT->SetStyle("Plain");
- TH1F *distribution = new TH1F("Distribution1", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
+ TH1F *distribution = new TH1F("Distrib1", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
TRandom rand(23);
for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
Float_t *ar = distribution->GetArray();
- TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_SigmaCut", "", 350, 350);
+ TCanvas* macro_example_canvas = new TCanvas("cAliTPCCalibViewer1", "", 350, 350);
macro_example_canvas->Divide(0,3);
TVirtualPad *pad1 = macro_example_canvas->cd(1);
pad1->SetGridy();
shistPM->Draw();
return macro_example_canvas;
}
- end_macro
+ End_Macro
*/
Float_t *array = histogram->GetArray();
}
-TH1F* AliTPCCalibViewer::SigmaCut(Int_t n, const Double_t *array, Double_t mean, Double_t sigma, Int_t nbins, const Double_t *xbins, Double_t sigmaMax){
+TH1F* AliTPCCalibViewer::SigmaCut(Int_t /*n*/, const Double_t */*array*/, Double_t /*mean*/, Double_t /*sigma*/, Int_t /*nbins*/, const Double_t */*xbins*/, Double_t /*sigmaMax*/){
//
// SigmaCut for variable binsize
// NOT YET IMPLEMENTED !!!
//
printf("SigmaCut with variable binsize, Not yet implemented\n");
- // avoid compiler warnings:
- n=n;
- mean=mean;
- sigma=sigma;
- nbins=nbins;
- sigmaMax=sigmaMax;
- array=array;
- xbins=xbins;
return 0;
}
// "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
// The actual work is done on the array.
/* Begin_Latex
- f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #frac{#int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx}{ #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx }
+ f(x, #mu, #sigma) #Rightarrow S(t, #mu, #sigma) = #int_{-#infty}^{#mu + t #sigma} f(x, #mu, #sigma) dx / #int_{-#infty}^{+#infty} f(x, #mu, #sigma) dx
End_Latex
- begin_macro(source)
+ Begin_Macro(source)
{
Float_t mean = 0;
Float_t sigma = 1.5;
Float_t sigmaMax = 4;
gROOT->SetStyle("Plain");
- TH1F *distribution = new TH1F("Distribution2", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
+ TH1F *distribution = new TH1F("Distrib2", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
TRandom rand(23);
for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
Float_t *ar = distribution->GetArray();
- TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_Integrate", "", 350, 350);
+ TCanvas* macro_example_canvas = new TCanvas("cAliTPCCalibViewer2", "", 350, 350);
macro_example_canvas->Divide(0,2);
TVirtualPad *pad1 = macro_example_canvas->cd(1);
pad1->SetGridy();
return macro_example_canvas_Integrate;
}
- end_macro
+ End_Macro
*/
fitter->ClearPoints();
Int_t entries = Draw(drawStr.Data(), cutStr.Data(), "goff");
- if (entries == -1) return new TString("An ERROR has occured during fitting!");
+ if (entries == -1) {
+ delete formulaTokens;
+ return new TString("An ERROR has occured during fitting!");
+ }
Double_t **values = new Double_t*[dim+1] ;
for (Int_t i = 0; i < dim + 1; i++){
if (i < dim) centries = fTree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff");
else centries = fTree->Draw(drawStr.Data(), cutStr.Data(), "goff");
- if (entries != centries) return new TString("An ERROR has occured during fitting!");
+ if (entries != centries) {
+ delete [] values;
+ delete formulaTokens;
+ return new TString("An ERROR has occured during fitting!");
+ }
values[i] = new Double_t[entries];
memcpy(values[i], fTree->GetV1(), entries*sizeof(Double_t));
}
returnFormula.Append(" )");
delete formulaTokens;
delete fitter;
- for (Int_t i = 0; i < dim + 1; i++){
- delete [] values[i];
- }
- delete [] values;
+ for (Int_t i = 0; i < dim + 1; i++){
+ delete [] values[i];
+ }
+ delete [] values;
return preturnFormula;
}
}
delete fIn;
}
+ delete arrFileLine;
}
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff