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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>
11a2ac51 28#include <TFile.h>
29#include <TKey.h>
30#include <TGraph.h>
31#include <TDirectory.h>
32#include <TFriendElement.h>
33
7330f0e5 34#include "TTreeStream.h"
11a2ac51 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 //
e99fb5c9 124 if(&param == this) return *this;
125 TObject::operator=(param);
126
127
11a2ac51 128 fTree = param.fTree;
129 fTreeMustBeDeleted = param.fTreeMustBeDeleted;
130 fListOfObjectsToBeDeleted = param.fListOfObjectsToBeDeleted;
131 fAbbreviation = param.fAbbreviation;
132 fAppendString = param.fAppendString;
133 return (*this);
134}
135
136//_____________________________________________________________________________
137AliBaseCalibViewer::~AliBaseCalibViewer()
138{
139 //
140 // AliBaseCalibViewer destructor
141 // all objects will be deleted, the file will be closed, the pictures will disappear
142 //
143 if (fTree && fTreeMustBeDeleted) {
144 fTree->SetCacheSize(0);
145 fTree->Delete();
146 }
147 if (fFile) {
148 fFile->Close();
149 fFile = 0;
150 }
151
152 for (Int_t i = fListOfObjectsToBeDeleted->GetEntriesFast()-1; i >= 0; i--) {
153 delete fListOfObjectsToBeDeleted->At(i);
154 }
155 delete fListOfObjectsToBeDeleted;
156}
157
158//_____________________________________________________________________________
9cc39f20 159void AliBaseCalibViewer::Delete(Option_t* /*option*/) {
11a2ac51 160 //
161 // Should be called from AliBaseCalibViewerGUI class only.
162 // If you use Delete() do not call the destructor.
163 // All objects (except those contained in fListOfObjectsToBeDeleted) will be deleted, the file will be closed.
164 //
165
11a2ac51 166 if (fTree && fTreeMustBeDeleted) {
167 fTree->SetCacheSize(0);
168 fTree->Delete();
169 }
9cc39f20 170 delete fFile;
11a2ac51 171 delete fListOfObjectsToBeDeleted;
172}
173
174//_____________________________________________________________________________
175void AliBaseCalibViewer::FormatHistoLabels(TH1 *histo) const {
176 //
177 // formats title and axis labels of histo
178 // removes '.fElements'
179 //
180 if (!histo) return;
181 TString replaceString(fAppendString.Data());
182 TString *str = new TString(histo->GetTitle());
183 str->ReplaceAll(replaceString, "");
184 histo->SetTitle(str->Data());
185 delete str;
186 if (histo->GetXaxis()) {
187 str = new TString(histo->GetXaxis()->GetTitle());
188 str->ReplaceAll(replaceString, "");
189 histo->GetXaxis()->SetTitle(str->Data());
190 delete str;
191 }
192 if (histo->GetYaxis()) {
193 str = new TString(histo->GetYaxis()->GetTitle());
194 str->ReplaceAll(replaceString, "");
195 histo->GetYaxis()->SetTitle(str->Data());
196 delete str;
197 }
198 if (histo->GetZaxis()) {
199 str = new TString(histo->GetZaxis()->GetTitle());
200 str->ReplaceAll(replaceString, "");
201 histo->GetZaxis()->SetTitle(str->Data());
202 delete str;
203 }
204}
205
206//_____________________________________________________________________________
207TFriendElement* AliBaseCalibViewer::AddReferenceTree(const Char_t* filename, const Char_t* treename, const Char_t* refname){
208 //
209 // add a reference tree to the current tree
210 // by default the treename is 'tree' and the reference treename is 'R'
211 //
212 TFile *file = new TFile(filename);
213 fListOfObjectsToBeDeleted->Add(file);
214 TTree * tree = (TTree*)file->Get(treename);
215 return AddFriend(tree, refname);
216}
217
218//_____________________________________________________________________________
219TString* AliBaseCalibViewer::Fit(const Char_t* drawCommand, const Char_t* formula, const Char_t* cuts,
220 Double_t & chi2, TVectorD &fitParam, TMatrixD &covMatrix){
221 //
222 // fit an arbitrary function, specified by formula into the data, specified by drawCommand and cuts
223 // returns chi2, fitParam and covMatrix
224 // returns TString with fitted formula
225 //
226
227 TString formulaStr(formula);
228 TString drawStr(drawCommand);
229 TString cutStr(cuts);
230
231 // abbreviations:
232 drawStr.ReplaceAll(fAbbreviation, fAppendString);
233 cutStr.ReplaceAll(fAbbreviation, fAppendString);
234 formulaStr.ReplaceAll(fAbbreviation, fAppendString);
235
236 formulaStr.ReplaceAll("++", fAbbreviation);
237 TObjArray* formulaTokens = formulaStr.Tokenize(fAbbreviation.Data());
238 Int_t dim = formulaTokens->GetEntriesFast();
239
240 fitParam.ResizeTo(dim);
241 covMatrix.ResizeTo(dim,dim);
242
243 TLinearFitter* fitter = new TLinearFitter(dim+1, Form("hyp%d",dim));
244 fitter->StoreData(kTRUE);
245 fitter->ClearPoints();
246
247 Int_t entries = Draw(drawStr.Data(), cutStr.Data(), "goff");
97def841 248 if (entries == -1) {
249 delete fitter;
250 return new TString("An ERROR has occured during fitting!");
251 }
11a2ac51 252 Double_t **values = new Double_t*[dim+1] ;
253
254 for (Int_t i = 0; i < dim + 1; i++){
255 Int_t centries = 0;
256 if (i < dim) centries = fTree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff");
257 else centries = fTree->Draw(drawStr.Data(), cutStr.Data(), "goff");
258
97def841 259 if (entries != centries) {
260 delete fitter;
261 delete [] values;
262 return new TString("An ERROR has occured during fitting!");
263 }
11a2ac51 264 values[i] = new Double_t[entries];
265 memcpy(values[i], fTree->GetV1(), entries*sizeof(Double_t));
266 }
267
268 // add points to the fitter
269 for (Int_t i = 0; i < entries; i++){
270 Double_t x[1000];
271 for (Int_t j=0; j<dim;j++) x[j]=values[j][i];
272 fitter->AddPoint(x, values[dim][i], 1);
273 }
274
275 fitter->Eval();
276 fitter->GetParameters(fitParam);
277 fitter->GetCovarianceMatrix(covMatrix);
278 chi2 = fitter->GetChisquare();
11a2ac51 279
280 TString *preturnFormula = new TString(Form("( %e+",fitParam[0])), &returnFormula = *preturnFormula;
281
282 for (Int_t iparam = 0; iparam < dim; iparam++) {
283 returnFormula.Append(Form("%s*(%e)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1]));
284 if (iparam < dim-1) returnFormula.Append("+");
285 }
286 returnFormula.Append(" )");
287 delete formulaTokens;
288 delete fitter;
97def841 289 for (Int_t i = 0; i < dim + 1; i++) delete [] values[i];
11a2ac51 290 delete[] values;
291 return preturnFormula;
292}
293
294//_____________________________________________________________________________
295Double_t AliBaseCalibViewer::GetLTM(Int_t n, Double_t *array, Double_t *sigma, Double_t fraction){
296 //
297 // returns the LTM and sigma
298 //
299 Double_t *ddata = new Double_t[n];
300 Double_t mean = 0, lsigma = 0;
301 UInt_t nPoints = 0;
302 for (UInt_t i = 0; i < (UInt_t)n; i++) {
303 ddata[nPoints]= array[nPoints];
304 nPoints++;
305 }
306 Int_t hh = TMath::Min(TMath::Nint(fraction * nPoints), Int_t(n));
307 AliMathBase::EvaluateUni(nPoints, ddata, mean, lsigma, hh);
308 if (sigma) *sigma = lsigma;
309 delete [] ddata;
310 return mean;
311}
312
313//_____________________________________________________________________________
314Int_t AliBaseCalibViewer::GetBin(Float_t value, Int_t nbins, Double_t binLow, Double_t binUp){
315 // Returns the 'bin' for 'value'
316 // The interval between 'binLow' and 'binUp' is divided into 'nbins' equidistant bins
317 // avoid index out of bounds error: 'if (bin < binLow) bin = binLow' and vice versa
318 /* Begin_Latex
319 GetBin(value) = #frac{nbins - 1}{binUp - binLow} #upoint (value - binLow) +1
320 End_Latex
321 */
322
323 Int_t bin = TMath::Nint( (Float_t)(value - binLow) / (Float_t)(binUp - binLow) * (nbins-1) ) + 1;
324 // avoid index out of bounds:
325 if (value < binLow) bin = 0;
326 if (value > binUp) bin = nbins + 1;
327 return bin;
328
329}
330
331//_____________________________________________________________________________
332TH1F* AliBaseCalibViewer::SigmaCut(TH1F *histogram, Float_t mean, Float_t sigma, Float_t sigmaMax,
333 Float_t sigmaStep, Bool_t pm) {
334 //
335 // 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
336 // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'histogram'
337 // '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
338 // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
339 // sigmaStep: the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
340 // pm: Decide weather Begin_Latex t > 0 End_Latex (first case) or Begin_Latex t End_Latex arbitrary (secound case)
341 // The actual work is done on the array.
342 /* Begin_Latex
6a1caa6b 343 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
11a2ac51 344 or
6a1caa6b 345 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
11a2ac51 346 End_Latex
6a1caa6b 347 Begin_Macro(source)
11a2ac51 348 {
349 Float_t mean = 0;
350 Float_t sigma = 1.5;
351 Float_t sigmaMax = 4;
352 gROOT->SetStyle("Plain");
353 TH1F *distribution = new TH1F("Distribution1", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
354 TRandom rand(23);
355 for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
356 Float_t *ar = distribution->GetArray();
357
6a1caa6b 358 TCanvas* macro_example_canvas = new TCanvas("cAliBaseCalibViewer", "", 350, 350);
11a2ac51 359 macro_example_canvas->Divide(0,3);
360 TVirtualPad *pad1 = macro_example_canvas->cd(1);
361 pad1->SetGridy();
362 pad1->SetGridx();
363 distribution->Draw();
364 TVirtualPad *pad2 = macro_example_canvas->cd(2);
365 pad2->SetGridy();
366 pad2->SetGridx();
367
368 TH1F *shist = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax);
369 shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)");
370 shist->Draw();
371 TVirtualPad *pad3 = macro_example_canvas->cd(3);
372 pad3->SetGridy();
373 pad3->SetGridx();
374 TH1F *shistPM = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax, -1, kTRUE);
375 shistPM->Draw();
376 return macro_example_canvas;
377 }
6a1caa6b 378 End_Macro
11a2ac51 379 */
380
381 Float_t *array = histogram->GetArray();
382 Int_t nbins = histogram->GetXaxis()->GetNbins();
383 Float_t binLow = histogram->GetXaxis()->GetXmin();
384 Float_t binUp = histogram->GetXaxis()->GetXmax();
385 return AliBaseCalibViewer::SigmaCut(nbins, array, mean, sigma, nbins, binLow, binUp, sigmaMax, sigmaStep, pm);
386}
387
388//_____________________________________________________________________________
389TH1F* 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){
390 //
391 // 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
392 // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'array', 'n' specifies the length of the array
393 // '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
394 // 'nbins': number of bins, 'binLow': first bin, 'binUp': last bin
395 // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex)
396 // sigmaStep: the binsize of the generated histogram
397 // Here the actual work is done.
398
399 if (TMath::Abs(sigma) < 1.e-10) return 0;
400 Float_t binWidth = (binUp-binLow)/(nbins - 1);
401 if (sigmaStep <= 0) sigmaStep = binWidth;
402 Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms
403 if (pm) kbins = 2 * (Int_t)(sigmaMax * sigma / sigmaStep) + 1;
404 Float_t kbinLow = !pm ? 0 : -sigmaMax;
405 Float_t kbinUp = sigmaMax;
406 TH1F *hist = new TH1F("sigmaCutHisto","Cumulative; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp);
407 hist->SetDirectory(0);
408 hist->Reset();
409
410 // calculate normalization
411 Double_t normalization = 0;
412 for (Int_t i = 0; i <= n; i++) {
413 normalization += array[i];
414 }
415
416 // given units: units from given histogram
417 // sigma units: in units of sigma
418 // iDelta: integrate in interval (mean +- iDelta), given units
419 // x: ofset from mean for integration, given units
420 // hist: needs
421
422 // fill histogram
423 for (Float_t iDelta = 0; iDelta <= sigmaMax * sigma; iDelta += sigmaStep) {
424 // integrate array
425 Double_t valueP = array[GetBin(mean, nbins, binLow, binUp)];
426 Double_t valueM = array[GetBin(mean-binWidth, nbins, binLow, binUp)];
427 // add bin of mean value only once to the histogram
428 for (Float_t x = binWidth; x <= iDelta; x += binWidth) {
429 valueP += (mean + x <= binUp) ? array[GetBin(mean + x, nbins, binLow, binUp)] : 0;
430 valueM += (mean-binWidth - x >= binLow) ? array[GetBin(mean-binWidth - x, nbins, binLow, binUp)] : 0;
431 }
432
433 if (valueP / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueP, normalization);
434 if (valueP / normalization > 100) return hist;
435 if (valueM / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueM, normalization);
436 if (valueM / normalization > 100) return hist;
437 valueP = (valueP / normalization);
438 valueM = (valueM / normalization);
439 if (pm) {
440 Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
441 hist->SetBinContent(bin, valueP);
442 bin = GetBin(-iDelta/sigma, kbins, kbinLow, kbinUp);
443 hist->SetBinContent(bin, valueM);
444 }
445 else { // if (!pm)
446 Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
447 hist->SetBinContent(bin, valueP + valueM);
448 }
449 }
450 if (!pm) hist->SetMaximum(1.2);
451 return hist;
452}
453
454//_____________________________________________________________________________
9cc39f20 455TH1F* AliBaseCalibViewer::SigmaCut(Int_t /*n*/, Double_t */*array*/, Double_t /*mean*/, Double_t /*sigma*/,
456 Int_t /*nbins*/, Double_t */*xbins*/, Double_t /*sigmaMax*/){
11a2ac51 457 //
458 // SigmaCut for variable binsize
459 // NOT YET IMPLEMENTED !!!
460 //
461 printf("SigmaCut with variable binsize, Not yet implemented\n");
9cc39f20 462
11a2ac51 463 return 0;
464}
465
466//_____________________________________________________________________________
467Int_t AliBaseCalibViewer::DrawHisto1D(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
9a9e9b94 468 const Char_t *sigmas, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM) const
469{
470 //
11a2ac51 471 // Easy drawing of data, in principle the same as EasyDraw1D
9a9e9b94 472 // Difference: A line for the mean / median / LTM is drawn
11a2ac51 473 // in 'sigmas' you can specify in which distance to the mean/median/LTM you want to see a line in sigma-units, separated by ';'
474 // 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.
475 // "plotMean", "plotMedian" and "plotLTM": what kind of lines do you want to see?
9a9e9b94 476 //
477 Int_t oldOptStat = gStyle->GetOptStat();
478 gStyle->SetOptStat(0000000);
479 Double_t ltmFraction = 0.8;
480
481 TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
482 TVectorF nsigma(sigmasTokens->GetEntriesFast());
483 for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
484 TString str(((TObjString*)sigmasTokens->At(i))->GetString());
485 Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
486 nsigma[i] = sig;
487 }
488
489 TString drawStr(drawCommand);
490 Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
491 if (dangerousToDraw) {
492 Warning("DrawHisto1D", "The draw string must not contain ':' or '>>'.");
493 return -1;
494 }
495 drawStr += " >> tempHist";
496 Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts);
497 TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
11a2ac51 498 // FIXME is this histogram deleted automatically?
9a9e9b94 499 Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
500
501 Double_t mean = TMath::Mean(entries, values);
502 Double_t median = TMath::Median(entries, values);
503 Double_t sigma = TMath::RMS(entries, values);
504 Double_t maxY = htemp->GetMaximum();
505
506 TLegend * legend = new TLegend(.7,.7, .99, .99, "Statistical information");
507 //fListOfObjectsToBeDeleted->Add(legend);
508
509 if (plotMean) {
11a2ac51 510 // draw Mean
9a9e9b94 511 TLine* line = new TLine(mean, 0, mean, maxY);
512 //fListOfObjectsToBeDeleted->Add(line);
513 line->SetLineColor(kRed);
514 line->SetLineWidth(2);
515 line->SetLineStyle(1);
516 line->Draw();
517 legend->AddEntry(line, Form("Mean: %f", mean), "l");
11a2ac51 518 // draw sigma lines
9a9e9b94 519 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
520 TLine* linePlusSigma = new TLine(mean + nsigma[i] * sigma, 0, mean + nsigma[i] * sigma, maxY);
521 //fListOfObjectsToBeDeleted->Add(linePlusSigma);
522 linePlusSigma->SetLineColor(kRed);
523 linePlusSigma->SetLineStyle(2 + i);
524 linePlusSigma->Draw();
525 TLine* lineMinusSigma = new TLine(mean - nsigma[i] * sigma, 0, mean - nsigma[i] * sigma, maxY);
526 //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
527 lineMinusSigma->SetLineColor(kRed);
528 lineMinusSigma->SetLineStyle(2 + i);
529 lineMinusSigma->Draw();
530 legend->AddEntry(lineMinusSigma, Form("%i #sigma = %f",(Int_t)(nsigma[i]), (Float_t)(nsigma[i] * sigma)), "l");
531 }
532 }
533 if (plotMedian) {
11a2ac51 534 // draw median
9a9e9b94 535 TLine* line = new TLine(median, 0, median, maxY);
536 //fListOfObjectsToBeDeleted->Add(line);
537 line->SetLineColor(kBlue);
538 line->SetLineWidth(2);
539 line->SetLineStyle(1);
540 line->Draw();
541 legend->AddEntry(line, Form("Median: %f", median), "l");
11a2ac51 542 // draw sigma lines
9a9e9b94 543 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
544 TLine* linePlusSigma = new TLine(median + nsigma[i] * sigma, 0, median + nsigma[i]*sigma, maxY);
545 //fListOfObjectsToBeDeleted->Add(linePlusSigma);
546 linePlusSigma->SetLineColor(kBlue);
547 linePlusSigma->SetLineStyle(2 + i);
548 linePlusSigma->Draw();
549 TLine* lineMinusSigma = new TLine(median - nsigma[i] * sigma, 0, median - nsigma[i]*sigma, maxY);
550 //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
551 lineMinusSigma->SetLineColor(kBlue);
552 lineMinusSigma->SetLineStyle(2 + i);
553 lineMinusSigma->Draw();
554 legend->AddEntry(lineMinusSigma, Form("%i #sigma = %f",(Int_t)(nsigma[i]), (Float_t)(nsigma[i] * sigma)), "l");
555 }
556 }
557 if (plotLTM) {
11a2ac51 558 // draw LTM
9a9e9b94 559 Double_t ltmRms = 0;
560 Double_t ltm = GetLTM(entries, values, &ltmRms, ltmFraction);
561 TLine* line = new TLine(ltm, 0, ltm, maxY);
11a2ac51 562 //fListOfObjectsToBeDeleted->Add(line);
9a9e9b94 563 line->SetLineColor(kGreen+2);
564 line->SetLineWidth(2);
565 line->SetLineStyle(1);
566 line->Draw();
567 legend->AddEntry(line, Form("LTM: %f", ltm), "l");
11a2ac51 568 // draw sigma lines
9a9e9b94 569 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
570 TLine* linePlusSigma = new TLine(ltm + nsigma[i] * ltmRms, 0, ltm + nsigma[i] * ltmRms, maxY);
11a2ac51 571 //fListOfObjectsToBeDeleted->Add(linePlusSigma);
9a9e9b94 572 linePlusSigma->SetLineColor(kGreen+2);
573 linePlusSigma->SetLineStyle(2+i);
574 linePlusSigma->Draw();
575
576 TLine* lineMinusSigma = new TLine(ltm - nsigma[i] * ltmRms, 0, ltm - nsigma[i] * ltmRms, maxY);
11a2ac51 577 //fListOfObjectsToBeDeleted->Add(lineMinusSigma);
9a9e9b94 578 lineMinusSigma->SetLineColor(kGreen+2);
579 lineMinusSigma->SetLineStyle(2+i);
580 lineMinusSigma->Draw();
581 legend->AddEntry(lineMinusSigma, Form("%i #sigma = %f", (Int_t)(nsigma[i]), (Float_t)(nsigma[i] * ltmRms)), "l");
582 }
583 }
584 if (!plotMean && !plotMedian && !plotLTM) return -1;
585 legend->Draw();
586 gStyle->SetOptStat(oldOptStat);
587 return 1;
11a2ac51 588}
589
590//_____________________________________________________________________________
591Int_t AliBaseCalibViewer::SigmaCut(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
592 Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, Bool_t pm,
593 const Char_t *sigmas, Float_t sigmaStep) const {
594 //
595 // Creates a histogram, where you can see, how much of the data are inside sigma-intervals
596 // around the mean/median/LTM
597 // with drawCommand, sector and cuts you specify your input data, see EasyDraw
598 // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma)
599 // sigmaStep: the binsize of the generated histogram
600 // plotMean/plotMedian/plotLTM: specifies where to put the center
601 //
602
603 Double_t ltmFraction = 0.8;
604
605 TString drawStr(drawCommand);
606 Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>");
607 if (dangerousToDraw) {
608 Warning("SigmaCut", "The draw string must not contain ':' or '>>'.");
609 return -1;
610 }
611 drawStr += " >> tempHist";
612
613 Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff");
614 TH1F *htemp = (TH1F*)gDirectory->Get("tempHist");
615 // FIXME is this histogram deleted automatically?
616 Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers
617
618 Double_t mean = TMath::Mean(entries, values);
619 Double_t median = TMath::Median(entries, values);
620 Double_t sigma = TMath::RMS(entries, values);
621
622 TLegend * legend = new TLegend(.7,.7, .99, .99, "Cumulative");
623 //fListOfObjectsToBeDeleted->Add(legend);
624 TH1F *cutHistoMean = 0;
625 TH1F *cutHistoMedian = 0;
626 TH1F *cutHistoLTM = 0;
627
628 TObjArray *sigmasTokens = TString(sigmas).Tokenize(";");
629 TVectorF nsigma(sigmasTokens->GetEntriesFast());
630 for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) {
631 TString str(((TObjString*)sigmasTokens->At(i))->GetString());
632 Double_t sig = (str.IsFloat()) ? str.Atof() : 0;
633 nsigma[i] = sig;
634 }
635
636 if (plotMean) {
637 cutHistoMean = SigmaCut(htemp, mean, sigma, sigmaMax, sigmaStep, pm);
638 if (cutHistoMean) {
639 //fListOfObjectsToBeDeleted->Add(cutHistoMean);
640 cutHistoMean->SetLineColor(kRed);
641 legend->AddEntry(cutHistoMean, "Mean", "l");
642 cutHistoMean->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
643 cutHistoMean->Draw();
644 DrawLines(cutHistoMean, nsigma, legend, kRed, pm);
645 } // if (cutHistoMean)
646
647 }
648 if (plotMedian) {
649 cutHistoMedian = SigmaCut(htemp, median, sigma, sigmaMax, sigmaStep, pm);
650 if (cutHistoMedian) {
651 //fListOfObjectsToBeDeleted->Add(cutHistoMedian);
652 cutHistoMedian->SetLineColor(kBlue);
653 legend->AddEntry(cutHistoMedian, "Median", "l");
654 cutHistoMedian->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
655 if (plotMean && cutHistoMean) cutHistoMedian->Draw("same");
656 else cutHistoMedian->Draw();
657 DrawLines(cutHistoMedian, nsigma, legend, kBlue, pm);
658 } // if (cutHistoMedian)
659 }
660 if (plotLTM) {
661 Double_t ltmRms = 0;
662 Double_t ltm = GetLTM(entries, values, &ltmRms, ltmFraction);
663 cutHistoLTM = SigmaCut(htemp, ltm, ltmRms, sigmaMax, sigmaStep, pm);
664 if (cutHistoLTM) {
665 //fListOfObjectsToBeDeleted->Add(cutHistoLTM);
666 cutHistoLTM->SetLineColor(kGreen+2);
667 legend->AddEntry(cutHistoLTM, "LTM", "l");
668 cutHistoLTM->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle()));
669 if ((plotMean && cutHistoMean) || (plotMedian && cutHistoMedian)) cutHistoLTM->Draw("same");
670 else cutHistoLTM->Draw();
671 DrawLines(cutHistoLTM, nsigma, legend, kGreen+2, pm);
672 }
673 }
674 if (!plotMean && !plotMedian && !plotLTM) return -1;
675 legend->Draw();
676 return 1;
677}
678
679//_____________________________________________________________________________
680Int_t AliBaseCalibViewer::Integrate(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts,
9cc39f20 681 Float_t /*sigmaMax*/, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM,
682 const Char_t *sigmas, Float_t /*sigmaStep*/) const {
11a2ac51 683 //
684 // 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"
685 // "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
686 // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
687 // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
688 // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
689 // The actual work is done on the array.
690 /* Begin_Latex
6a1caa6b 691 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
11a2ac51 692 End_Latex
693 */
694
695 Double_t ltmFraction = 0.8;
11a2ac51 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 }
0dd616b4 781 delete [] index;
782 delete [] xarray;
783 delete [] yarray;
11a2ac51 784 if (!plotMean && !plotMedian && !plotLTM) return -1;
785 legend->Draw();
786 return entries;
787}
788
789//_____________________________________________________________________________
790TH1F* AliBaseCalibViewer::Integrate(TH1F *histogram, Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){
791 //
792 // 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"
793 // "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
794 // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
795 // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
796 // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
797 // The actual work is done on the array.
798 /* Begin_Latex
6a1caa6b 799 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
11a2ac51 800 End_Latex
6a1caa6b 801 Begin_Macro(source)
11a2ac51 802 {
803 Float_t mean = 0;
804 Float_t sigma = 1.5;
805 Float_t sigmaMax = 4;
806 gROOT->SetStyle("Plain");
807 TH1F *distribution = new TH1F("Distribution2", "Distribution f(x, #mu, #sigma)", 1000,-5,5);
808 TRandom rand(23);
809 for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma));
810 Float_t *ar = distribution->GetArray();
811
812 TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_Integrate", "", 350, 350);
813 macro_example_canvas->Divide(0,2);
814 TVirtualPad *pad1 = macro_example_canvas->cd(1);
815 pad1->SetGridy();
816 pad1->SetGridx();
817 distribution->Draw();
818 TVirtualPad *pad2 = macro_example_canvas->cd(2);
819 pad2->SetGridy();
820 pad2->SetGridx();
821 TH1F *shist = AliTPCCalibViewer::Integrate(distribution, mean, sigma, sigmaMax);
822 shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)");
823 shist->Draw();
824
825 return macro_example_canvas_Integrate;
826 }
6a1caa6b 827 End_Macro
11a2ac51 828 */
829
830
831 Float_t *array = histogram->GetArray();
832 Int_t nbins = histogram->GetXaxis()->GetNbins();
833 Float_t binLow = histogram->GetXaxis()->GetXmin();
834 Float_t binUp = histogram->GetXaxis()->GetXmax();
835 return Integrate(nbins, array, nbins, binLow, binUp, mean, sigma, sigmaMax, sigmaStep);
836}
837
838//_____________________________________________________________________________
839TH1F* AliBaseCalibViewer::Integrate(Int_t n, Float_t *array, Int_t nbins, Float_t binLow, Float_t binUp,
840 Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){
841 // 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"
842 // "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
843 // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate
844 // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated
845 // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used
846 // Here the actual work is done.
847
848 Bool_t givenUnits = kTRUE;
849 if (TMath::Abs(sigma) < 1.e-10 && TMath::Abs(sigmaMax) < 1.e-10) givenUnits = kFALSE;
850 if (givenUnits) {
851 sigma = 1;
852 sigmaMax = (binUp - binLow) / 2.;
853 }
854
855 Float_t binWidth = (binUp-binLow)/(nbins - 1);
856 if (sigmaStep <= 0) sigmaStep = binWidth;
857 Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms
858 Float_t kbinLow = givenUnits ? binLow : -sigmaMax;
859 Float_t kbinUp = givenUnits ? binUp : sigmaMax;
860 TH1F *hist = 0;
861 if (givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Given x; Fraction of included data", kbins, kbinLow, kbinUp);
862 if (!givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp);
863 hist->SetDirectory(0);
864 hist->Reset();
865
866 // calculate normalization
867 // printf("calculating normalization, integrating from bin 1 to %i \n", n);
868 Double_t normalization = 0;
869 for (Int_t i = 1; i <= n; i++) {
870 normalization += array[i];
871 }
872 // printf("normalization: %f \n", normalization);
873
874 // given units: units from given histogram
875 // sigma units: in units of sigma
876 // iDelta: integrate in interval (mean +- iDelta), given units
877 // x: ofset from mean for integration, given units
878 // hist: needs
879
880 // fill histogram
881 for (Float_t iDelta = mean - sigmaMax * sigma; iDelta <= mean + sigmaMax * sigma; iDelta += sigmaStep) {
882 // integrate array
883 Double_t value = 0;
884 for (Float_t x = mean - sigmaMax * sigma; x <= iDelta; x += binWidth) {
885 value += (x <= binUp && x >= binLow) ? array[GetBin(x, nbins, binLow, binUp)] : 0;
886 }
887 if (value / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", value, normalization);
888 if (value / normalization > 100) return hist;
889 Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp);
890 // printf("first integration bin: %i, last integration bin: %i \n", GetBin(mean - sigmaMax * sigma, nbins, binLow, binUp), GetBin(iDelta, nbins, binLow, binUp));
891 // printf("value: %f, normalization: %f, normalized value: %f, iDelta: %f, Bin: %i \n", value, normalization, value/normalization, iDelta, bin);
892 value = (value / normalization);
893 hist->SetBinContent(bin, value);
894 }
895 return hist;
896}
897
898//_____________________________________________________________________________
899void AliBaseCalibViewer::DrawLines(TH1F *histogram, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const {
9a9e9b94 900 //
11a2ac51 901 // Private function for SigmaCut(...) and Integrate(...)
902 // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend
9a9e9b94 903 //
904
11a2ac51 905 // start to draw the lines, loop over requested sigmas
9a9e9b94 906 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
907 if (!pm) {
908 Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]);
909 TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin));
11a2ac51 910 //fListOfObjectsToBeDeleted->Add(lineUp);
9a9e9b94 911 lineUp->SetLineColor(color);
912 lineUp->SetLineStyle(2 + i);
913 lineUp->Draw();
914 TLine* lineLeft = new TLine(nsigma[i], histogram->GetBinContent(bin), 0, histogram->GetBinContent(bin));
11a2ac51 915 //fListOfObjectsToBeDeleted->Add(lineLeft);
9a9e9b94 916 lineLeft->SetLineColor(color);
917 lineLeft->SetLineStyle(2 + i);
918 lineLeft->Draw();
919 legend->AddEntry(lineLeft, Form("Fraction(%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)), "l");
920 }
921 else { // if (pm)
922 Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]);
923 TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin));
11a2ac51 924 //fListOfObjectsToBeDeleted->Add(lineUp1);
9a9e9b94 925 lineUp1->SetLineColor(color);
926 lineUp1->SetLineStyle(2 + i);
927 lineUp1->Draw();
928 TLine* lineLeft1 = new TLine(nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin));
11a2ac51 929 //fListOfObjectsToBeDeleted->Add(lineLeft1);
9a9e9b94 930 lineLeft1->SetLineColor(color);
931 lineLeft1->SetLineStyle(2 + i);
932 lineLeft1->Draw();
933 legend->AddEntry(lineLeft1, Form("Fraction(+%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)), "l");
934 bin = histogram->GetXaxis()->FindBin(-nsigma[i]);
935 TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], histogram->GetBinContent(bin));
11a2ac51 936 //fListOfObjectsToBeDeleted->Add(lineUp2);
9a9e9b94 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));
11a2ac51 941 //fListOfObjectsToBeDeleted->Add(lineLeft2);
9a9e9b94 942 lineLeft2->SetLineColor(color);
943 lineLeft2->SetLineStyle(2 + i);
944 lineLeft2->Draw();
945 legend->AddEntry(lineLeft2, Form("Fraction(-%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)), "l");
946 }
947 } // for (Int_t i = 0; i < nsigma.GetNoElements(); i++)
11a2ac51 948}
949
950//_____________________________________________________________________________
951void AliBaseCalibViewer::DrawLines(TGraph *graph, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const {
9a9e9b94 952 //
11a2ac51 953 // Private function for SigmaCut(...) and Integrate(...)
954 // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend
9a9e9b94 955 //
956
11a2ac51 957 // start to draw the lines, loop over requested sigmas
9a9e9b94 958 for (Int_t i = 0; i < nsigma.GetNoElements(); i++) {
959 if (!pm) {
960 TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i]));
11a2ac51 961 //fListOfObjectsToBeDeleted->Add(lineUp);
9a9e9b94 962 lineUp->SetLineColor(color);
963 lineUp->SetLineStyle(2 + i);
964 lineUp->Draw();
965 TLine* lineLeft = new TLine(nsigma[i], graph->Eval(nsigma[i]), 0, graph->Eval(nsigma[i]));
11a2ac51 966 //fListOfObjectsToBeDeleted->Add(lineLeft);
9a9e9b94 967 lineLeft->SetLineColor(color);
968 lineLeft->SetLineStyle(2 + i);
969 lineLeft->Draw();
970 legend->AddEntry(lineLeft, Form("Fraction(%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i])), "l");
971 }
972 else { // if (pm)
973 TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i]));
11a2ac51 974 //fListOfObjectsToBeDeleted->Add(lineUp1);
9a9e9b94 975 lineUp1->SetLineColor(color);
976 lineUp1->SetLineStyle(2 + i);
977 lineUp1->Draw();
978 TLine* lineLeft1 = new TLine(nsigma[i], graph->Eval(nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(nsigma[i]));
11a2ac51 979 //fListOfObjectsToBeDeleted->Add(lineLeft1);
9a9e9b94 980 lineLeft1->SetLineColor(color);
981 lineLeft1->SetLineStyle(2 + i);
982 lineLeft1->Draw();
983 legend->AddEntry(lineLeft1, Form("Fraction(+%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i])), "l");
984 TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], graph->Eval(-nsigma[i]));
11a2ac51 985 //fListOfObjectsToBeDeleted->Add(lineUp2);
9a9e9b94 986 lineUp2->SetLineColor(color);
987 lineUp2->SetLineStyle(2 + i);
988 lineUp2->Draw();
989 TLine* lineLeft2 = new TLine(-nsigma[i], graph->Eval(-nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(-nsigma[i]));
11a2ac51 990 //fListOfObjectsToBeDeleted->Add(lineLeft2);
9a9e9b94 991 lineLeft2->SetLineColor(color);
992 lineLeft2->SetLineStyle(2 + i);
993 lineLeft2->Draw();
994 legend->AddEntry(lineLeft2, Form("Fraction(-%f #sigma) = %f",nsigma[i], graph->Eval(-nsigma[i])), "l");
995 }
996 } // for (Int_t i = 0; i < nsigma.GetNoElements(); i++)
11a2ac51 997}