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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> | |
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 | ||
37 | ClassImp(AliBaseCalibViewer) | |
38 | ||
39 | AliBaseCalibViewer::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 | //_____________________________________________________________________________ | |
54 | AliBaseCalibViewer::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 | //_____________________________________________________________________________ | |
75 | AliBaseCalibViewer::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 | //_____________________________________________________________________________ | |
95 | AliBaseCalibViewer::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 | //____________________________________________________________________________ | |
118 | AliBaseCalibViewer & 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 | //_____________________________________________________________________________ | |
133 | AliBaseCalibViewer::~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 | //_____________________________________________________________________________ | |
155 | void 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 | //_____________________________________________________________________________ | |
173 | void 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 | //_____________________________________________________________________________ | |
205 | TFriendElement* 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 | //_____________________________________________________________________________ | |
217 | TString* 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 | //_____________________________________________________________________________ | |
286 | Double_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 | //_____________________________________________________________________________ | |
305 | Int_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 | //_____________________________________________________________________________ | |
323 | TH1F* 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 | //_____________________________________________________________________________ | |
380 | 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){ | |
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 | //_____________________________________________________________________________ | |
446 | TH1F* 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 | //_____________________________________________________________________________ | |
465 | Int_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, <mRms, 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 | //_____________________________________________________________________________ | |
588 | Int_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, <mRms, 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 | //_____________________________________________________________________________ | |
677 | Int_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, <mRms, 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 | //_____________________________________________________________________________ | |
790 | TH1F* 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 | |
799 | 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 } | |
800 | End_Latex | |
801 | begin_macro(source) | |
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 | } | |
827 | end_macro | |
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 | //_____________________________________________________________________________ | |
839 | TH1F* 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 | //_____________________________________________________________________________ | |
899 | void AliBaseCalibViewer::DrawLines(TH1F *histogram, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const { | |
900 | // | |
901 | // Private function for SigmaCut(...) and Integrate(...) | |
902 | // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend | |
903 | // | |
904 | ||
905 | // start to draw the lines, loop over requested sigmas | |
906 | Char_t c[500]; | |
907 | for (Int_t i = 0; i < nsigma.GetNoElements(); i++) { | |
908 | if (!pm) { | |
909 | Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]); | |
910 | TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin)); | |
911 | //fListOfObjectsToBeDeleted->Add(lineUp); | |
912 | lineUp->SetLineColor(color); | |
913 | lineUp->SetLineStyle(2 + i); | |
914 | lineUp->Draw(); | |
915 | TLine* lineLeft = new TLine(nsigma[i], histogram->GetBinContent(bin), 0, histogram->GetBinContent(bin)); | |
916 | //fListOfObjectsToBeDeleted->Add(lineLeft); | |
917 | lineLeft->SetLineColor(color); | |
918 | lineLeft->SetLineStyle(2 + i); | |
919 | lineLeft->Draw(); | |
920 | sprintf(c, "Fraction(%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)); | |
921 | legend->AddEntry(lineLeft, c, "l"); | |
922 | } | |
923 | else { // if (pm) | |
924 | Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]); | |
925 | TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin)); | |
926 | //fListOfObjectsToBeDeleted->Add(lineUp1); | |
927 | lineUp1->SetLineColor(color); | |
928 | lineUp1->SetLineStyle(2 + i); | |
929 | lineUp1->Draw(); | |
930 | TLine* lineLeft1 = new TLine(nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin)); | |
931 | //fListOfObjectsToBeDeleted->Add(lineLeft1); | |
932 | lineLeft1->SetLineColor(color); | |
933 | lineLeft1->SetLineStyle(2 + i); | |
934 | lineLeft1->Draw(); | |
935 | sprintf(c, "Fraction(+%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)); | |
936 | legend->AddEntry(lineLeft1, c, "l"); | |
937 | bin = histogram->GetXaxis()->FindBin(-nsigma[i]); | |
938 | TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], histogram->GetBinContent(bin)); | |
939 | //fListOfObjectsToBeDeleted->Add(lineUp2); | |
940 | lineUp2->SetLineColor(color); | |
941 | lineUp2->SetLineStyle(2 + i); | |
942 | lineUp2->Draw(); | |
943 | TLine* lineLeft2 = new TLine(-nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin)); | |
944 | //fListOfObjectsToBeDeleted->Add(lineLeft2); | |
945 | lineLeft2->SetLineColor(color); | |
946 | lineLeft2->SetLineStyle(2 + i); | |
947 | lineLeft2->Draw(); | |
948 | sprintf(c, "Fraction(-%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)); | |
949 | legend->AddEntry(lineLeft2, c, "l"); | |
950 | } | |
951 | } // for (Int_t i = 0; i < nsigma.GetNoElements(); i++) | |
952 | } | |
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 | Char_t c[500]; | |
963 | for (Int_t i = 0; i < nsigma.GetNoElements(); i++) { | |
964 | if (!pm) { | |
965 | TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i])); | |
966 | //fListOfObjectsToBeDeleted->Add(lineUp); | |
967 | lineUp->SetLineColor(color); | |
968 | lineUp->SetLineStyle(2 + i); | |
969 | lineUp->Draw(); | |
970 | TLine* lineLeft = new TLine(nsigma[i], graph->Eval(nsigma[i]), 0, graph->Eval(nsigma[i])); | |
971 | //fListOfObjectsToBeDeleted->Add(lineLeft); | |
972 | lineLeft->SetLineColor(color); | |
973 | lineLeft->SetLineStyle(2 + i); | |
974 | lineLeft->Draw(); | |
975 | sprintf(c, "Fraction(%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i])); | |
976 | legend->AddEntry(lineLeft, c, "l"); | |
977 | } | |
978 | else { // if (pm) | |
979 | TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i])); | |
980 | //fListOfObjectsToBeDeleted->Add(lineUp1); | |
981 | lineUp1->SetLineColor(color); | |
982 | lineUp1->SetLineStyle(2 + i); | |
983 | lineUp1->Draw(); | |
984 | TLine* lineLeft1 = new TLine(nsigma[i], graph->Eval(nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(nsigma[i])); | |
985 | //fListOfObjectsToBeDeleted->Add(lineLeft1); | |
986 | lineLeft1->SetLineColor(color); | |
987 | lineLeft1->SetLineStyle(2 + i); | |
988 | lineLeft1->Draw(); | |
989 | sprintf(c, "Fraction(+%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i])); | |
990 | legend->AddEntry(lineLeft1, c, "l"); | |
991 | TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], graph->Eval(-nsigma[i])); | |
992 | //fListOfObjectsToBeDeleted->Add(lineUp2); | |
993 | lineUp2->SetLineColor(color); | |
994 | lineUp2->SetLineStyle(2 + i); | |
995 | lineUp2->Draw(); | |
996 | TLine* lineLeft2 = new TLine(-nsigma[i], graph->Eval(-nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(-nsigma[i])); | |
997 | //fListOfObjectsToBeDeleted->Add(lineLeft2); | |
998 | lineLeft2->SetLineColor(color); | |
999 | lineLeft2->SetLineStyle(2 + i); | |
1000 | lineLeft2->Draw(); | |
1001 | sprintf(c, "Fraction(-%f #sigma) = %f",nsigma[i], graph->Eval(-nsigma[i])); | |
1002 | legend->AddEntry(lineLeft2, c, "l"); | |
1003 | } | |
1004 | } // for (Int_t i = 0; i < nsigma.GetNoElements(); i++) | |
1005 | } |