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