]> git.uio.no Git - u/mrichter/AliRoot.git/blame - STEER/AliBaseCalibViewer.cxx
git://git.uio.no / u / mrichter / AliRoot.git / blame
? search:
summary | shortlog | log | commit | commitdiff | tree
blob | blame (incremental) | history | HEAD
Corrected raw->sdigits method. Using default altro raw-data reader class. Avoid alloc...
[u/mrichter/AliRoot.git] / STEER / AliBaseCalibViewer.cxx
CommitLineData
11a2ac51 1///////////////////////////////////////////////////////////////////////////////
2// //
3// Base class for the AliTPCCalibViewer and AliTRDCalibViewer //
4// used for the calibration monitor //
5// //
6// Authors: Marian Ivanov (Marian.Ivanov@cern.ch) //
7// Jens Wiechula (Jens.Wiechula@cern.ch) //
8// Ionut Arsene (iarsene@cern.ch) //
9// //
10///////////////////////////////////////////////////////////////////////////////
11
12
13#include <iostream>
14#include <fstream>
15#include <TString.h>
16#include <TRandom.h>
17#include <TLegend.h>
18#include <TLine.h>
19//#include <TCanvas.h>
20#include <TROOT.h>
21#include <TStyle.h>
22#include <TH1.h>
23#include <TH1F.h>
24#include <TMath.h>
25#include <THashTable.h>
26#include <TObjString.h>
27#include <TLinearFitter.h>
28#include <TTreeStream.h>
29#include <TFile.h>
30#include <TKey.h>
31#include <TGraph.h>
32#include <TDirectory.h>
33#include <TFriendElement.h>
34
35#include "AliBaseCalibViewer.h"
36
37ClassImp(AliBaseCalibViewer)
38
39AliBaseCalibViewer::AliBaseCalibViewer()
40 :TObject(),
41 fTree(0),
42 fFile(0),
43 fListOfObjectsToBeDeleted(0),
44 fTreeMustBeDeleted(0),
45 fAbbreviation(0),
46 fAppendString(0)
47{
48 //
49 // Default constructor
50 //
51}
52
53//_____________________________________________________________________________
54AliBaseCalibViewer::AliBaseCalibViewer(const AliBaseCalibViewer &c)
55 :TObject(c),
56 fTree(0),
57 fFile(0),
58 fListOfObjectsToBeDeleted(0),
59 fTreeMustBeDeleted(0),
60 fAbbreviation(0),
61 fAppendString(0)
62{
63 //
64 // dummy AliBaseCalibViewer copy constructor
65 // not yet working!!!
66 //
67 fTree = c.fTree;
68 fTreeMustBeDeleted = c.fTreeMustBeDeleted;
69 fListOfObjectsToBeDeleted = c.fListOfObjectsToBeDeleted;
70 fAbbreviation = c.fAbbreviation;
71 fAppendString = c.fAppendString;
72}
73
74//_____________________________________________________________________________
75AliBaseCalibViewer::AliBaseCalibViewer(TTree* tree)
76 :TObject(),
77 fTree(0),
78 fFile(0),
79 fListOfObjectsToBeDeleted(0),
80 fTreeMustBeDeleted(0),
81 fAbbreviation(0),
82 fAppendString(0)
83{
84 //
85 // Constructor that initializes the calibration viewer
86 //
87 fTree = tree;
88 fTreeMustBeDeleted = kFALSE;
89 fListOfObjectsToBeDeleted = new TObjArray();
90 fAbbreviation = "~";
91 fAppendString = ".fElements";
92}
93
94//_____________________________________________________________________________
95AliBaseCalibViewer::AliBaseCalibViewer(const Char_t* fileName, const Char_t* treeName)
96 :TObject(),
97 fTree(0),
98 fFile(0),
99 fListOfObjectsToBeDeleted(0),
100 fTreeMustBeDeleted(0),
101 fAbbreviation(0),
102 fAppendString(0)
103
104{
105 //
106 // Constructor to initialize the calibration viewer
107 // the file 'fileName' contains the tree 'treeName'
108 //
109 fFile = new TFile(fileName, "read");
110 fTree = (TTree*) fFile->Get(treeName);
111 fTreeMustBeDeleted = kTRUE;
112 fListOfObjectsToBeDeleted = new TObjArray();
113 fAbbreviation = "~";
114 fAppendString = ".fElements";
115}
116
117//____________________________________________________________________________
118AliBaseCalibViewer & AliBaseCalibViewer::operator =(const AliBaseCalibViewer & param)
119{
120 //
121 // assignment operator - dummy
122 // not yet working!!!
123 //
124 fTree = param.fTree;
125 fTreeMustBeDeleted = param.fTreeMustBeDeleted;
126 fListOfObjectsToBeDeleted = param.fListOfObjectsToBeDeleted;
127 fAbbreviation = param.fAbbreviation;
128 fAppendString = param.fAppendString;
129 return (*this);
130}
131
132//_____________________________________________________________________________
133AliBaseCalibViewer::~AliBaseCalibViewer()
134{
135 //
136 // AliBaseCalibViewer destructor
137 // all objects will be deleted, the file will be closed, the pictures will disappear
138 //
139 if (fTree && fTreeMustBeDeleted) {
140 fTree->SetCacheSize(0);
141 fTree->Delete();
142 }
143 if (fFile) {
144 fFile->Close();
145 fFile = 0;
146 }
147
148 for (Int_t i = fListOfObjectsToBeDeleted->GetEntriesFast()-1; i >= 0; i--) {
149 delete fListOfObjectsToBeDeleted->At(i);
150 }
151 delete fListOfObjectsToBeDeleted;
152}
153
154//_____________________________________________________________________________
155void AliBaseCalibViewer::Delete(Option_t* option) {
156 //
157 // Should be called from AliBaseCalibViewerGUI class only.
158 // If you use Delete() do not call the destructor.
159 // All objects (except those contained in fListOfObjectsToBeDeleted) will be deleted, the file will be closed.
160 //
161
162 option = option; // to avoid warnings on compiling
163 if (fTree && fTreeMustBeDeleted) {
164 fTree->SetCacheSize(0);
165 fTree->Delete();
166 }
167 if (fFile)
168 delete fFile;
169 delete fListOfObjectsToBeDeleted;
170}
171
172//_____________________________________________________________________________
173void AliBaseCalibViewer::FormatHistoLabels(TH1 *histo) const {
174 //
175 // formats title and axis labels of histo
176 // removes '.fElements'
177 //
178 if (!histo) return;
179 TString replaceString(fAppendString.Data());
180 TString *str = new TString(histo->GetTitle());
181 str->ReplaceAll(replaceString, "");
182 histo->SetTitle(str->Data());
183 delete str;
184 if (histo->GetXaxis()) {
185 str = new TString(histo->GetXaxis()->GetTitle());
186 str->ReplaceAll(replaceString, "");
187 histo->GetXaxis()->SetTitle(str->Data());
188 delete str;
189 }
190 if (histo->GetYaxis()) {
191 str = new TString(histo->GetYaxis()->GetTitle());
192 str->ReplaceAll(replaceString, "");
193 histo->GetYaxis()->SetTitle(str->Data());
194 delete str;
195 }
196 if (histo->GetZaxis()) {
197 str = new TString(histo->GetZaxis()->GetTitle());
198 str->ReplaceAll(replaceString, "");
199 histo->GetZaxis()->SetTitle(str->Data());
200 delete str;
201 }
202}
203
204//_____________________________________________________________________________
205TFriendElement* AliBaseCalibViewer::AddReferenceTree(const Char_t* filename, const Char_t* treename, const Char_t* refname){
206 //
207 // add a reference tree to the current tree
208 // by default the treename is 'tree' and the reference treename is 'R'
209 //
210 TFile *file = new TFile(filename);
211 fListOfObjectsToBeDeleted->Add(file);
212 TTree * tree = (TTree*)file->Get(treename);
213 return AddFriend(tree, refname);
214}
215
216//_____________________________________________________________________________
217TString* AliBaseCalibViewer::Fit(const Char_t* drawCommand, const Char_t* formula, const Char_t* cuts,
218 Double_t & chi2, TVectorD &fitParam, TMatrixD &covMatrix){
219 //
220 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
221 // returns chi2, fitParam and covMatrix
222 // returns TString with fitted formula
223 //
224
225 TString formulaStr(formula);
226 TString drawStr(drawCommand);
227 TString cutStr(cuts);
228
229 // abbreviations:
230 drawStr.ReplaceAll(fAbbreviation, fAppendString);
231 cutStr.ReplaceAll(fAbbreviation, fAppendString);
232 formulaStr.ReplaceAll(fAbbreviation, fAppendString);
233
234 formulaStr.ReplaceAll("++", fAbbreviation);
235 TObjArray* formulaTokens = formulaStr.Tokenize(fAbbreviation.Data());
236 Int_t dim = formulaTokens->GetEntriesFast();
237
238 fitParam.ResizeTo(dim);
239 covMatrix.ResizeTo(dim,dim);
240
241 TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim));
242 fitter->StoreData(kTRUE);
243 fitter->ClearPoints();
244
245 Int_t entries = Draw(drawStr.Data(), cutStr.Data(), "goff");
246 if (entries == -1) return new TString("An ERROR has occured during fitting!");
247 Double_t **values = new Double_t*[dim+1] ;
248
249 for (Int_t i = 0; i < dim + 1; i++){
250 Int_t centries = 0;
251 if (i < dim) centries = fTree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff");
252 else centries = fTree->Draw(drawStr.Data(), cutStr.Data(), "goff");
253
254 if (entries != centries) return new TString("An ERROR has occured during fitting!");
255 values[i] = new Double_t[entries];
256 memcpy(values[i], fTree->GetV1(), entries*sizeof(Double_t));
257 }
258
259 // add points to the fitter
260 for (Int_t i = 0; i < entries; i++){
261 Double_t x[1000];
262 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
263 fitter->AddPoint(x, values[dim][i], 1);
264 }
265
266 fitter->Eval();
267 fitter->GetParameters(fitParam);
268 fitter->GetCovarianceMatrix(covMatrix);
269 chi2 = fitter->GetChisquare();
270 chi2 = chi2;
271
272 TString *preturnFormula = new TString(Form("( %e+",fitParam[0])), &returnFormula = *preturnFormula;
273
274 for (Int_t iparam = 0; iparam < dim; iparam++) {
275 returnFormula.Append(Form("%s*(%e)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
276 if (iparam < dim-1) returnFormula.Append("+");
277 }
278 returnFormula.Append(" )");
279 delete formulaTokens;
280 delete fitter;
281 delete[] values;
282 return preturnFormula;
283}
284
285//_____________________________________________________________________________
286Double_t AliBaseCalibViewer::GetLTM(Int_t n, Double_t *array, Double_t *sigma, Double_t fraction){
287 //
288 // returns the LTM and sigma
289 //
290 Double_t *ddata = new Double_t[n];
291 Double_t mean = 0, lsigma = 0;
292 UInt_t nPoints = 0;
293 for (UInt_t i = 0; i < (UInt_t)n; i++) {
294 ddata[nPoints]= array[nPoints];
295 nPoints++;
296 }
297 Int_t hh = TMath::Min(TMath::Nint(fraction * nPoints), Int_t(n));
298 AliMathBase::EvaluateUni(nPoints, ddata, mean, lsigma, hh);
299 if (sigma) *sigma = lsigma;
300 delete [] ddata;
301 return mean;
302}
303
304//_____________________________________________________________________________
305Int_t AliBaseCalibViewer::GetBin(Float_t value, Int_t nbins, Double_t binLow, Double_t binUp){
306 // Returns the 'bin' for 'value'
307 // The interval between 'binLow' and 'binUp' is divided into 'nbins' equidistant bins
308 // avoid index out of bounds error: 'if (bin < binLow) bin = binLow' and vice versa
309 /* Begin_Latex
310 GetBin(value) = #frac{nbins - 1}{binUp - binLow} #upoint (value - binLow) +1
311 End_Latex
312 */
313
314 Int_t bin = TMath::Nint( (Float_t)(value - binLow) / (Float_t)(binUp - binLow) * (nbins-1) ) + 1;
315 // avoid index out of bounds:
316 if (value < binLow) bin = 0;
317 if (value > binUp) bin = nbins + 1;
318 return bin;
319
320}
321
322//_____________________________________________________________________________
323TH1F* AliBaseCalibViewer::SigmaCut(TH1F *histogram, Float_t mean, Float_t sigma, Float_t sigmaMax,
324 Float_t sigmaStep, Bool_t pm) {
325 //
326 // Creates a cumulative histogram Begin_Latex S(t, #mu, #sigma) End_Latex, where you can see, how much of the data are inside sigma-intervals around the mean value
327 // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'histogram'
328 // '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
329 // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
330 // sigmaStep: the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
331 // pm: Decide weather Begin_Latex t > 0 End_Latex (first case) or Begin_Latex t End_Latex arbitrary (secound case)
332 // The actual work is done on the array.
333 /* Begin_Latex
334 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
335 or
336 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 }
337 End_Latex
338 begin_macro(source)
339 {
340 Float_t mean = 0;
341 Float_t sigma = 1.5;
342 Float_t sigmaMax = 4;
343 gROOT->SetStyle("Plain");
344 TH1F *distribution = new TH1F("Distribution1", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
345 TRandom rand(23);
346 for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
347 Float_t *ar = distribution->GetArray();
348
349 TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_SigmaCut", "", 350, 350);
350 macro_example_canvas->Divide(0,3);
351 TVirtualPad *pad1 = macro_example_canvas->cd(1);
352 pad1->SetGridy();
353 pad1->SetGridx();
354 distribution->Draw();
355 TVirtualPad *pad2 = macro_example_canvas->cd(2);
356 pad2->SetGridy();
357 pad2->SetGridx();
358
359 TH1F *shist = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax);
360 shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)");
361 shist->Draw();
362 TVirtualPad *pad3 = macro_example_canvas->cd(3);
363 pad3->SetGridy();
364 pad3->SetGridx();
365 TH1F *shistPM = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax, -1, kTRUE);
366 shistPM->Draw();
367 return macro_example_canvas;
368 }
369 end_macro
370 */
371
372 Float_t *array = histogram->GetArray();
373 Int_t nbins = histogram->GetXaxis()->GetNbins();
374 Float_t binLow = histogram->GetXaxis()->GetXmin();
375 Float_t binUp = histogram->GetXaxis()->GetXmax();
376 return AliBaseCalibViewer::SigmaCut(nbins, array, mean, sigma, nbins, binLow, binUp, sigmaMax, sigmaStep, pm);
377}
378
379//_____________________________________________________________________________
380TH1F* AliBaseCalibViewer::SigmaCut(Int_t n, Float_t *array, Float_t mean, Float_t sigma, Int_t nbins, Float_t binLow, Float_t binUp, Float_t sigmaMax, Float_t sigmaStep, Bool_t pm){
381 //
382 // Creates a histogram Begin_Latex S(t, #mu, #sigma) End_Latex, where you can see, how much of the data are inside sigma-intervals around the mean value
383 // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'array', 'n' specifies the length of the array
384 // 'mean' and 'sigma' are Begin_Latex #mu End_Latex and Begin_Latex #sigma End_Latex of the distribution in 'array', to be specified by the user
385 // 'nbins': number of bins, 'binLow': first bin, 'binUp': last bin
386 // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
387 // sigmaStep: the binsize of the generated histogram
388 // Here the actual work is done.
389
390 if (TMath::Abs(sigma) < 1.e-10) return 0;
391 Float_t binWidth = (binUp-binLow)/(nbins - 1);
392 if (sigmaStep <= 0) sigmaStep = binWidth;
393 Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms
394 if (pm) kbins = 2 * (Int_t)(sigmaMax * sigma / sigmaStep) + 1;
395 Float_t kbinLow = !pm ? 0 : -sigmaMax;
396 Float_t kbinUp = sigmaMax;
397 TH1F *hist = new TH1F("sigmaCutHisto","Cumulative; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp);
398 hist->SetDirectory(0);
399 hist->Reset();
400
401 // calculate normalization
402 Double_t normalization = 0;
403 for (Int_t i = 0; i <= n; i++) {
404 normalization += array[i];
405 }
406
407 // given units: units from given histogram
408 // sigma units: in units of sigma
409 // iDelta: integrate in interval (mean +- iDelta), given units
410 // x: ofset from mean for integration, given units
411 // hist: needs
412
413 // fill histogram
414 for (Float_t iDelta = 0; iDelta <= sigmaMax * sigma; iDelta += sigmaStep) {
415 // integrate array
416 Double_t valueP = array[GetBin(mean, nbins, binLow, binUp)];
417 Double_t valueM = array[GetBin(mean-binWidth, nbins, binLow, binUp)];
418 // add bin of mean value only once to the histogram
419 for (Float_t x = binWidth; x <= iDelta; x += binWidth) {
420 valueP += (mean + x <= binUp) ? array[GetBin(mean + x, nbins, binLow, binUp)] : 0;
421 valueM += (mean-binWidth - x >= binLow) ? array[GetBin(mean-binWidth - x, nbins, binLow, binUp)] : 0;
422 }
423
424 if (valueP / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueP, normalization);
425 if (valueP / normalization > 100) return hist;
426 if (valueM / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueM, normalization);
427 if (valueM / normalization > 100) return hist;
428 valueP = (valueP / normalization);
429 valueM = (valueM / normalization);
430 if (pm) {
431 Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
432 hist->SetBinContent(bin, valueP);
433 bin = GetBin(-iDelta/sigma, kbins, kbinLow, kbinUp);
434 hist->SetBinContent(bin, valueM);
435 }
436 else { // if (!pm)
437 Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
438 hist->SetBinContent(bin, valueP + valueM);
439 }
440 }
441 if (!pm) hist->SetMaximum(1.2);
442 return hist;
443}
444
445//_____________________________________________________________________________
446TH1F* AliBaseCalibViewer::SigmaCut(Int_t n, Double_t *array, Double_t mean, Double_t sigma, Int_t nbins, Double_t *xbins, Double_t sigmaMax){
447 //
448 // SigmaCut for variable binsize
449 // NOT YET IMPLEMENTED !!!
450 //
451 printf("SigmaCut with variable binsize, Not yet implemented\n");
452 // avoid compiler warnings:
453 n=n;
454 mean=mean;
455 sigma=sigma;
456 nbins=nbins;
457 sigmaMax=sigmaMax;
458 array=array;
459 xbins=xbins;
460
461 return 0;
462}
463
464//_____________________________________________________________________________
465Int_t AliBaseCalibViewer::DrawHisto1D(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
466 const Char_t *sigmas, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM) const {
467 //
468 // Easy drawing of data, in principle the same as EasyDraw1D
469 // Difference: A line for the mean / median / LTM is drawn
470 // in 'sigmas' you can specify in which distance to the mean/median/LTM you want to see a line in sigma-units, separated by ';'
471 // example: sigmas = "2; 4; 6;" at Begin_Latex 2 #sigma End_Latex, Begin_Latex 4 #sigma End_Latex and Begin_Latex 6 #sigma End_Latex a line is drawn.
472 // "plotMean", "plotMedian" and "plotLTM": what kind of lines do you want to see?
473 //
474 Int_t oldOptStat = gStyle->GetOptStat();
475 gStyle->SetOptStat(0000000);
476 Double_t ltmFraction = 0.8;
477
478 TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
479 TVectorF nsigma(sigmasTokens->GetEntriesFast());
480 for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
481 TString str(((TObjString*)sigmasTokens->At(i))->GetString());
482 Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
483 nsigma[i] = sig;
484 }
485
486 TString drawStr(drawCommand);
487 Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
488 if (dangerousToDraw) {
489 Warning("DrawHisto1D", "The draw string must not contain ':' or '>>'.");
490 return -1;
491 }
492 drawStr += " >> tempHist";
493 Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts);
494 TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
495 // FIXME is this histogram deleted automatically?
496 Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
497
498 Double_t mean = TMath::Mean(entries, values);
499 Double_t median = TMath::Median(entries, values);
500 Double_t sigma = TMath::RMS(entries, values);
501 Double_t maxY = htemp->GetMaximum();
502
503 Char_t c[500];
504 TLegend * legend = new TLegend(.7,.7, .99, .99, "Statistical information");
505
506 if (plotMean) {
507 // draw Mean
508 TLine* line = new TLine(mean, 0, mean, maxY);
509 line->SetLineColor(kRed);
510 line->SetLineWidth(2);
511 line->SetLineStyle(1);
512 line->Draw();
513 sprintf(c, "Mean: %f", mean);
514 legend->AddEntry(line, c, "l");
515 // draw sigma lines
516 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
517 TLine* linePlusSigma = new TLine(mean + nsigma[i] * sigma, 0, mean + nsigma[i] * sigma, maxY);
518 linePlusSigma->SetLineColor(kRed);
519 linePlusSigma->SetLineStyle(2 + i);
520 linePlusSigma->Draw();
521 TLine* lineMinusSigma = new TLine(mean - nsigma[i] * sigma, 0, mean - nsigma[i] * sigma, maxY);
522 lineMinusSigma->SetLineColor(kRed);
523 lineMinusSigma->SetLineStyle(2 + i);
524 lineMinusSigma->Draw();
525 sprintf(c, "%i #sigma = %f",(Int_t)(nsigma[i]), (Float_t)(nsigma[i] * sigma));
526 legend->AddEntry(lineMinusSigma, c, "l");
527 }
528 }
529 if (plotMedian) {
530 // draw median
531 TLine* line = new TLine(median, 0, median, maxY);
532 line->SetLineColor(kBlue);
533 line->SetLineWidth(2);
534 line->SetLineStyle(1);
535 line->Draw();
536 sprintf(c, "Median: %f", median);
537 legend->AddEntry(line, c, "l");
538 // draw sigma lines
539 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
540 TLine* linePlusSigma = new TLine(median + nsigma[i] * sigma, 0, median + nsigma[i]*sigma, maxY);
541 linePlusSigma->SetLineColor(kBlue);
542 linePlusSigma->SetLineStyle(2 + i);
543 linePlusSigma->Draw();
544 TLine* lineMinusSigma = new TLine(median - nsigma[i] * sigma, 0, median - nsigma[i]*sigma, maxY);
545 lineMinusSigma->SetLineColor(kBlue);
546 lineMinusSigma->SetLineStyle(2 + i);
547 lineMinusSigma->Draw();
548 sprintf(c, "%i #sigma = %f",(Int_t)(nsigma[i]), (Float_t)(nsigma[i] * sigma));
549 legend->AddEntry(lineMinusSigma, c, "l");
550 }
551 }
552 if (plotLTM) {
553 // draw LTM
554 Double_t ltmRms = 0;
555 Double_t ltm = GetLTM(entries, values, &ltmRms, ltmFraction);
556 TLine* line = new TLine(ltm, 0, ltm, maxY);
557 //fListOfObjectsToBeDeleted->Add(line);
558 line->SetLineColor(kGreen+2);
559 line->SetLineWidth(2);
560 line->SetLineStyle(1);
561 line->Draw();
562 sprintf(c, "LTM: %f", ltm);
563 legend->AddEntry(line, c, "l");
564 // draw sigma lines
565 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
566 TLine* linePlusSigma = new TLine(ltm + nsigma[i] * ltmRms, 0, ltm + nsigma[i] * ltmRms, maxY);
567 //fListOfObjectsToBeDeleted->Add(linePlusSigma);
568 linePlusSigma->SetLineColor(kGreen+2);
569 linePlusSigma->SetLineStyle(2+i);
570 linePlusSigma->Draw();
571
572 TLine* lineMinusSigma = new TLine(ltm - nsigma[i] * ltmRms, 0, ltm - nsigma[i] * ltmRms, maxY);
573 //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
574 lineMinusSigma->SetLineColor(kGreen+2);
575 lineMinusSigma->SetLineStyle(2+i);
576 lineMinusSigma->Draw();
577 sprintf(c, "%i #sigma = %f", (Int_t)(nsigma[i]), (Float_t)(nsigma[i] * ltmRms));
578 legend->AddEntry(lineMinusSigma, c, "l");
579 }
580 }
581 if (!plotMean && !plotMedian && !plotLTM) return -1;
582 legend->Draw();
583 gStyle->SetOptStat(oldOptStat);
584 return 1;
585}
586
587//_____________________________________________________________________________
588Int_t AliBaseCalibViewer::SigmaCut(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
589 Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, Bool_t pm,
590 const Char_t *sigmas, Float_t sigmaStep) const {
591 //
592 // Creates a histogram, where you can see, how much of the data are inside sigma-intervals
593 // around the mean/median/LTM
594 // with drawCommand, sector and cuts you specify your input data, see EasyDraw
595 // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma)
596 // sigmaStep: the binsize of the generated histogram
597 // plotMean/plotMedian/plotLTM: specifies where to put the center
598 //
599
600 Double_t ltmFraction = 0.8;
601
602 TString drawStr(drawCommand);
603 Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
604 if (dangerousToDraw) {
605 Warning("SigmaCut", "The draw string must not contain ':' or '>>'.");
606 return -1;
607 }
608 drawStr += " >> tempHist";
609
610 Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff");
611 TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
612 // FIXME is this histogram deleted automatically?
613 Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
614
615 Double_t mean = TMath::Mean(entries, values);
616 Double_t median = TMath::Median(entries, values);
617 Double_t sigma = TMath::RMS(entries, values);
618
619 TLegend * legend = new TLegend(.7,.7, .99, .99, "Cumulative");
620 //fListOfObjectsToBeDeleted->Add(legend);
621 TH1F *cutHistoMean = 0;
622 TH1F *cutHistoMedian = 0;
623 TH1F *cutHistoLTM = 0;
624
625 TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
626 TVectorF nsigma(sigmasTokens->GetEntriesFast());
627 for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
628 TString str(((TObjString*)sigmasTokens->At(i))->GetString());
629 Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
630 nsigma[i] = sig;
631 }
632
633 if (plotMean) {
634 cutHistoMean = SigmaCut(htemp, mean, sigma, sigmaMax, sigmaStep, pm);
635 if (cutHistoMean) {
636 //fListOfObjectsToBeDeleted->Add(cutHistoMean);
637 cutHistoMean->SetLineColor(kRed);
638 legend->AddEntry(cutHistoMean, "Mean", "l");
639 cutHistoMean->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
640 cutHistoMean->Draw();
641 DrawLines(cutHistoMean, nsigma, legend, kRed, pm);
642 } // if (cutHistoMean)
643
644 }
645 if (plotMedian) {
646 cutHistoMedian = SigmaCut(htemp, median, sigma, sigmaMax, sigmaStep, pm);
647 if (cutHistoMedian) {
648 //fListOfObjectsToBeDeleted->Add(cutHistoMedian);
649 cutHistoMedian->SetLineColor(kBlue);
650 legend->AddEntry(cutHistoMedian, "Median", "l");
651 cutHistoMedian->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
652 if (plotMean && cutHistoMean) cutHistoMedian->Draw("same");
653 else cutHistoMedian->Draw();
654 DrawLines(cutHistoMedian, nsigma, legend, kBlue, pm);
655 } // if (cutHistoMedian)
656 }
657 if (plotLTM) {
658 Double_t ltmRms = 0;
659 Double_t ltm = GetLTM(entries, values, &ltmRms, ltmFraction);
660 cutHistoLTM = SigmaCut(htemp, ltm, ltmRms, sigmaMax, sigmaStep, pm);
661 if (cutHistoLTM) {
662 //fListOfObjectsToBeDeleted->Add(cutHistoLTM);
663 cutHistoLTM->SetLineColor(kGreen+2);
664 legend->AddEntry(cutHistoLTM, "LTM", "l");
665 cutHistoLTM->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
666 if ((plotMean && cutHistoMean) || (plotMedian && cutHistoMedian)) cutHistoLTM->Draw("same");
667 else cutHistoLTM->Draw();
668 DrawLines(cutHistoLTM, nsigma, legend, kGreen+2, pm);
669 }
670 }
671 if (!plotMean && !plotMedian && !plotLTM) return -1;
672 legend->Draw();
673 return 1;
674}
675
676//_____________________________________________________________________________
677Int_t AliBaseCalibViewer::Integrate(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
678 Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM,
679 const Char_t *sigmas, Float_t sigmaStep) const {
680 //
681 // 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"
682 // "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
683 // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
684 // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
685 // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
686 // The actual work is done on the array.
687 /* Begin_Latex
688 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 }
689 End_Latex
690 */
691
692 Double_t ltmFraction = 0.8;
693 // avoid compiler warnings:
694 sigmaMax = sigmaMax;
695 sigmaStep = sigmaStep;
696
697 TString drawStr(drawCommand);
698 Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
699 if (dangerousToDraw) {
700 Warning("Integrate", "The draw string must not contain ':' or '>>'.");
701 return -1;
702 }
703 drawStr += " >> tempHist";
704
705 Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff");
706 TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
707 TGraph *integralGraphMean = 0;
708 TGraph *integralGraphMedian = 0;
709 TGraph *integralGraphLTM = 0;
710 Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
711 Int_t *index = new Int_t[entries];
712 Float_t *xarray = new Float_t[entries];
713 Float_t *yarray = new Float_t[entries];
714 TMath::Sort(entries, values, index, kFALSE);
715
716 Double_t mean = TMath::Mean(entries, values);
717 Double_t median = TMath::Median(entries, values);
718 Double_t sigma = TMath::RMS(entries, values);
719
720 // parse sigmas string
721 TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
722 TVectorF nsigma(sigmasTokens->GetEntriesFast());
723 for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
724 TString str(((TObjString*)sigmasTokens->At(i))->GetString());
725 Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
726 nsigma[i] = sig;
727 }
728
729 TLegend * legend = new TLegend(.7,.7, .99, .99, "Integrated histogram");
730 //fListOfObjectsToBeDeleted->Add(legend);
731
732 if (plotMean) {
733 for (Int_t i = 0; i < entries; i++) {
734 xarray[i] = (values[index[i]] - mean) / sigma;
735 yarray[i] = float(i) / float(entries);
736 }
737 integralGraphMean = new TGraph(entries, xarray, yarray);
738 if (integralGraphMean) {
739 //fListOfObjectsToBeDeleted->Add(integralGraphMean);
740 integralGraphMean->SetLineColor(kRed);
741 legend->AddEntry(integralGraphMean, "Mean", "l");
742 integralGraphMean->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
743 integralGraphMean->Draw("alu");
744 DrawLines(integralGraphMean, nsigma, legend, kRed, kTRUE);
745 }
746 }
747 if (plotMedian) {
748 for (Int_t i = 0; i < entries; i++) {
749 xarray[i] = (values[index[i]] - median) / sigma;
750 yarray[i] = float(i) / float(entries);
751 }
752 integralGraphMedian = new TGraph(entries, xarray, yarray);
753 if (integralGraphMedian) {
754 //fListOfObjectsToBeDeleted->Add(integralGraphMedian);
755 integralGraphMedian->SetLineColor(kBlue);
756 legend->AddEntry(integralGraphMedian, "Median", "l");
757 integralGraphMedian->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
758 if (plotMean && integralGraphMean) integralGraphMedian->Draw("samelu");
759 else integralGraphMedian->Draw("alu");
760 DrawLines(integralGraphMedian, nsigma, legend, kBlue, kTRUE);
761 }
762 }
763 if (plotLTM) {
764 Double_t ltmRms = 0;
765 Double_t ltm = GetLTM(entries, values, &ltmRms, ltmFraction);
766 for (Int_t i = 0; i < entries; i++) {
767 xarray[i] = (values[index[i]] - ltm) / ltmRms;
768 yarray[i] = float(i) / float(entries);
769 }
770 integralGraphLTM = new TGraph(entries, xarray, yarray);
771 if (integralGraphLTM) {
772 //fListOfObjectsToBeDeleted->Add(integralGraphLTM);
773 integralGraphLTM->SetLineColor(kGreen+2);
774 legend->AddEntry(integralGraphLTM, "LTM", "l");
775 integralGraphLTM->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
776 if ((plotMean && integralGraphMean) || (plotMedian && integralGraphMedian)) integralGraphLTM->Draw("samelu");
777 else integralGraphLTM->Draw("alu");
778 DrawLines(integralGraphLTM, nsigma, legend, kGreen+2, kTRUE);
779 }
780 }
781 if (!plotMean && !plotMedian && !plotLTM) return -1;
782 legend->Draw();
783 return entries;
784}
785
786//_____________________________________________________________________________
787TH1F* AliBaseCalibViewer::Integrate(TH1F *histogram, Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){
788 //
789 // 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"
790 // "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
791 // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
792 // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
793 // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
794 // The actual work is done on the array.
795 /* Begin_Latex
796 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 }
797 End_Latex
798 begin_macro(source)
799 {
800 Float_t mean = 0;
801 Float_t sigma = 1.5;
802 Float_t sigmaMax = 4;
803 gROOT->SetStyle("Plain");
804 TH1F *distribution = new TH1F("Distribution2", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
805 TRandom rand(23);
806 for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
807 Float_t *ar = distribution->GetArray();
808
809 TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_Integrate", "", 350, 350);
810 macro_example_canvas->Divide(0,2);
811 TVirtualPad *pad1 = macro_example_canvas->cd(1);
812 pad1->SetGridy();
813 pad1->SetGridx();
814 distribution->Draw();
815 TVirtualPad *pad2 = macro_example_canvas->cd(2);
816 pad2->SetGridy();
817 pad2->SetGridx();
818 TH1F *shist = AliTPCCalibViewer::Integrate(distribution, mean, sigma, sigmaMax);
819 shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)");
820 shist->Draw();
821
822 return macro_example_canvas_Integrate;
823 }
824 end_macro
825 */
826
827
828 Float_t *array = histogram->GetArray();
829 Int_t nbins = histogram->GetXaxis()->GetNbins();
830 Float_t binLow = histogram->GetXaxis()->GetXmin();
831 Float_t binUp = histogram->GetXaxis()->GetXmax();
832 return Integrate(nbins, array, nbins, binLow, binUp, mean, sigma, sigmaMax, sigmaStep);
833}
834
835//_____________________________________________________________________________
836TH1F* AliBaseCalibViewer::Integrate(Int_t n, Float_t *array, Int_t nbins, Float_t binLow, Float_t binUp,
837 Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){
838 // 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"
839 // "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
840 // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
841 // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
842 // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
843 // Here the actual work is done.
844
845 Bool_t givenUnits = kTRUE;
846 if (TMath::Abs(sigma) < 1.e-10 && TMath::Abs(sigmaMax) < 1.e-10) givenUnits = kFALSE;
847 if (givenUnits) {
848 sigma = 1;
849 sigmaMax = (binUp - binLow) / 2.;
850 }
851
852 Float_t binWidth = (binUp-binLow)/(nbins - 1);
853 if (sigmaStep <= 0) sigmaStep = binWidth;
854 Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms
855 Float_t kbinLow = givenUnits ? binLow : -sigmaMax;
856 Float_t kbinUp = givenUnits ? binUp : sigmaMax;
857 TH1F *hist = 0;
858 if (givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Given x; Fraction of included data", kbins, kbinLow, kbinUp);
859 if (!givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp);
860 hist->SetDirectory(0);
861 hist->Reset();
862
863 // calculate normalization
864 // printf("calculating normalization, integrating from bin 1 to %i \n", n);
865 Double_t normalization = 0;
866 for (Int_t i = 1; i <= n; i++) {
867 normalization += array[i];
868 }
869 // printf("normalization: %f \n", normalization);
870
871 // given units: units from given histogram
872 // sigma units: in units of sigma
873 // iDelta: integrate in interval (mean +- iDelta), given units
874 // x: ofset from mean for integration, given units
875 // hist: needs
876
877 // fill histogram
878 for (Float_t iDelta = mean - sigmaMax * sigma; iDelta <= mean + sigmaMax * sigma; iDelta += sigmaStep) {
879 // integrate array
880 Double_t value = 0;
881 for (Float_t x = mean - sigmaMax * sigma; x <= iDelta; x += binWidth) {
882 value += (x <= binUp && x >= binLow) ? array[GetBin(x, nbins, binLow, binUp)] : 0;
883 }
884 if (value / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", value, normalization);
885 if (value / normalization > 100) return hist;
886 Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
887 // printf("first integration bin: %i, last integration bin: %i \n", GetBin(mean - sigmaMax * sigma, nbins, binLow, binUp), GetBin(iDelta, nbins, binLow, binUp));
888 // printf("value: %f, normalization: %f, normalized value: %f, iDelta: %f, Bin: %i \n", value, normalization, value/normalization, iDelta, bin);
889 value = (value / normalization);
890 hist->SetBinContent(bin, value);
891 }
892 return hist;
893}
894
895//_____________________________________________________________________________
896void AliBaseCalibViewer::DrawLines(TH1F *histogram, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const {
897 //
898 // Private function for SigmaCut(...) and Integrate(...)
899 // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend
900 //
901
902 // start to draw the lines, loop over requested sigmas
903 Char_t c[500];
904 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
905 if (!pm) {
906 Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]);
907 TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin));
908 //fListOfObjectsToBeDeleted->Add(lineUp);
909 lineUp->SetLineColor(color);
910 lineUp->SetLineStyle(2 + i);
911 lineUp->Draw();
912 TLine* lineLeft = new TLine(nsigma[i], histogram->GetBinContent(bin), 0, histogram->GetBinContent(bin));
913 //fListOfObjectsToBeDeleted->Add(lineLeft);
914 lineLeft->SetLineColor(color);
915 lineLeft->SetLineStyle(2 + i);
916 lineLeft->Draw();
917 sprintf(c, "Fraction(%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin));
918 legend->AddEntry(lineLeft, c, "l");
919 }
920 else { // if (pm)
921 Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]);
922 TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin));
923 //fListOfObjectsToBeDeleted->Add(lineUp1);
924 lineUp1->SetLineColor(color);
925 lineUp1->SetLineStyle(2 + i);
926 lineUp1->Draw();
927 TLine* lineLeft1 = new TLine(nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin));
928 //fListOfObjectsToBeDeleted->Add(lineLeft1);
929 lineLeft1->SetLineColor(color);
930 lineLeft1->SetLineStyle(2 + i);
931 lineLeft1->Draw();
932 sprintf(c, "Fraction(+%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin));
933 legend->AddEntry(lineLeft1, c, "l");
934 bin = histogram->GetXaxis()->FindBin(-nsigma[i]);
935 TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], histogram->GetBinContent(bin));
936 //fListOfObjectsToBeDeleted->Add(lineUp2);
937 lineUp2->SetLineColor(color);
938 lineUp2->SetLineStyle(2 + i);
939 lineUp2->Draw();
940 TLine* lineLeft2 = new TLine(-nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin));
941 //fListOfObjectsToBeDeleted->Add(lineLeft2);
942 lineLeft2->SetLineColor(color);
943 lineLeft2->SetLineStyle(2 + i);
944 lineLeft2->Draw();
945 sprintf(c, "Fraction(-%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin));
946 legend->AddEntry(lineLeft2, c, "l");
947 }
948 } // for (Int_t i = 0; i < nsigma.GetNoElements(); i++)
949}
950
951//_____________________________________________________________________________
952void AliBaseCalibViewer::DrawLines(TGraph *graph, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const {
953 //
954 // Private function for SigmaCut(...) and Integrate(...)
955 // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend
956 //
957
958 // start to draw the lines, loop over requested sigmas
959 Char_t c[500];
960 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
961 if (!pm) {
962 TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i]));
963 //fListOfObjectsToBeDeleted->Add(lineUp);
964 lineUp->SetLineColor(color);
965 lineUp->SetLineStyle(2 + i);
966 lineUp->Draw();
967 TLine* lineLeft = new TLine(nsigma[i], graph->Eval(nsigma[i]), 0, graph->Eval(nsigma[i]));
968 //fListOfObjectsToBeDeleted->Add(lineLeft);
969 lineLeft->SetLineColor(color);
970 lineLeft->SetLineStyle(2 + i);
971 lineLeft->Draw();
972 sprintf(c, "Fraction(%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i]));
973 legend->AddEntry(lineLeft, c, "l");
974 }
975 else { // if (pm)
976 TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i]));
977 //fListOfObjectsToBeDeleted->Add(lineUp1);
978 lineUp1->SetLineColor(color);
979 lineUp1->SetLineStyle(2 + i);
980 lineUp1->Draw();
981 TLine* lineLeft1 = new TLine(nsigma[i], graph->Eval(nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(nsigma[i]));
982 //fListOfObjectsToBeDeleted->Add(lineLeft1);
983 lineLeft1->SetLineColor(color);
984 lineLeft1->SetLineStyle(2 + i);
985 lineLeft1->Draw();
986 sprintf(c, "Fraction(+%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i]));
987 legend->AddEntry(lineLeft1, c, "l");
988 TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], graph->Eval(-nsigma[i]));
989 //fListOfObjectsToBeDeleted->Add(lineUp2);
990 lineUp2->SetLineColor(color);
991 lineUp2->SetLineStyle(2 + i);
992 lineUp2->Draw();
993 TLine* lineLeft2 = new TLine(-nsigma[i], graph->Eval(-nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(-nsigma[i]));
994 //fListOfObjectsToBeDeleted->Add(lineLeft2);
995 lineLeft2->SetLineColor(color);
996 lineLeft2->SetLineStyle(2 + i);
997 lineLeft2->Draw();
998 sprintf(c, "Fraction(-%f #sigma) = %f",nsigma[i], graph->Eval(-nsigma[i]));
999 legend->AddEntry(lineLeft2, c, "l");
1000 }
1001 } // for (Int_t i = 0; i < nsigma.GetNoElements(); i++)
1002}