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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"); | |
97def841 | 246 | if (entries == -1) { |
247 | delete fitter; | |
248 | return new TString("An ERROR has occured during fitting!"); | |
249 | } | |
11a2ac51 | 250 | Double_t **values = new Double_t*[dim+1] ; |
251 | ||
252 | for (Int_t i = 0; i < dim + 1; i++){ | |
253 | Int_t centries = 0; | |
254 | if (i < dim) centries = fTree->Draw(((TObjString*)formulaTokens->At(i))->GetName(), cutStr.Data(), "goff"); | |
255 | else centries = fTree->Draw(drawStr.Data(), cutStr.Data(), "goff"); | |
256 | ||
97def841 | 257 | if (entries != centries) { |
258 | delete fitter; | |
259 | delete [] values; | |
260 | return new TString("An ERROR has occured during fitting!"); | |
261 | } | |
11a2ac51 | 262 | values[i] = new Double_t[entries]; |
263 | memcpy(values[i], fTree->GetV1(), entries*sizeof(Double_t)); | |
264 | } | |
265 | ||
266 | // add points to the fitter | |
267 | for (Int_t i = 0; i < entries; i++){ | |
268 | Double_t x[1000]; | |
269 | for (Int_t j=0; j<dim;j++) x[j]=values[j][i]; | |
270 | fitter->AddPoint(x, values[dim][i], 1); | |
271 | } | |
272 | ||
273 | fitter->Eval(); | |
274 | fitter->GetParameters(fitParam); | |
275 | fitter->GetCovarianceMatrix(covMatrix); | |
276 | chi2 = fitter->GetChisquare(); | |
11a2ac51 | 277 | |
278 | TString *preturnFormula = new TString(Form("( %e+",fitParam[0])), &returnFormula = *preturnFormula; | |
279 | ||
280 | for (Int_t iparam = 0; iparam < dim; iparam++) { | |
281 | returnFormula.Append(Form("%s*(%e)",((TObjString*)formulaTokens->At(iparam))->GetName(),fitParam[iparam+1])); | |
282 | if (iparam < dim-1) returnFormula.Append("+"); | |
283 | } | |
284 | returnFormula.Append(" )"); | |
285 | delete formulaTokens; | |
286 | delete fitter; | |
97def841 | 287 | for (Int_t i = 0; i < dim + 1; i++) delete [] values[i]; |
11a2ac51 | 288 | delete[] values; |
289 | return preturnFormula; | |
290 | } | |
291 | ||
292 | //_____________________________________________________________________________ | |
293 | Double_t AliBaseCalibViewer::GetLTM(Int_t n, Double_t *array, Double_t *sigma, Double_t fraction){ | |
294 | // | |
295 | // returns the LTM and sigma | |
296 | // | |
297 | Double_t *ddata = new Double_t[n]; | |
298 | Double_t mean = 0, lsigma = 0; | |
299 | UInt_t nPoints = 0; | |
300 | for (UInt_t i = 0; i < (UInt_t)n; i++) { | |
301 | ddata[nPoints]= array[nPoints]; | |
302 | nPoints++; | |
303 | } | |
304 | Int_t hh = TMath::Min(TMath::Nint(fraction * nPoints), Int_t(n)); | |
305 | AliMathBase::EvaluateUni(nPoints, ddata, mean, lsigma, hh); | |
306 | if (sigma) *sigma = lsigma; | |
307 | delete [] ddata; | |
308 | return mean; | |
309 | } | |
310 | ||
311 | //_____________________________________________________________________________ | |
312 | Int_t AliBaseCalibViewer::GetBin(Float_t value, Int_t nbins, Double_t binLow, Double_t binUp){ | |
313 | // Returns the 'bin' for 'value' | |
314 | // The interval between 'binLow' and 'binUp' is divided into 'nbins' equidistant bins | |
315 | // avoid index out of bounds error: 'if (bin < binLow) bin = binLow' and vice versa | |
316 | /* Begin_Latex | |
317 | GetBin(value) = #frac{nbins - 1}{binUp - binLow} #upoint (value - binLow) +1 | |
318 | End_Latex | |
319 | */ | |
320 | ||
321 | Int_t bin = TMath::Nint( (Float_t)(value - binLow) / (Float_t)(binUp - binLow) * (nbins-1) ) + 1; | |
322 | // avoid index out of bounds: | |
323 | if (value < binLow) bin = 0; | |
324 | if (value > binUp) bin = nbins + 1; | |
325 | return bin; | |
326 | ||
327 | } | |
328 | ||
329 | //_____________________________________________________________________________ | |
330 | TH1F* AliBaseCalibViewer::SigmaCut(TH1F *histogram, Float_t mean, Float_t sigma, Float_t sigmaMax, | |
331 | Float_t sigmaStep, Bool_t pm) { | |
332 | // | |
333 | // 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 | |
334 | // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'histogram' | |
335 | // '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 | |
336 | // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex) | |
337 | // sigmaStep: the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used | |
338 | // pm: Decide weather Begin_Latex t > 0 End_Latex (first case) or Begin_Latex t End_Latex arbitrary (secound case) | |
339 | // The actual work is done on the array. | |
340 | /* Begin_Latex | |
341 | 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 | |
342 | or | |
343 | 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 } | |
344 | End_Latex | |
345 | begin_macro(source) | |
346 | { | |
347 | Float_t mean = 0; | |
348 | Float_t sigma = 1.5; | |
349 | Float_t sigmaMax = 4; | |
350 | gROOT->SetStyle("Plain"); | |
351 | TH1F *distribution = new TH1F("Distribution1", "Distribution f(x, #mu, #sigma)", 1000,-5,5); | |
352 | TRandom rand(23); | |
353 | for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma)); | |
354 | Float_t *ar = distribution->GetArray(); | |
355 | ||
356 | TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_SigmaCut", "", 350, 350); | |
357 | macro_example_canvas->Divide(0,3); | |
358 | TVirtualPad *pad1 = macro_example_canvas->cd(1); | |
359 | pad1->SetGridy(); | |
360 | pad1->SetGridx(); | |
361 | distribution->Draw(); | |
362 | TVirtualPad *pad2 = macro_example_canvas->cd(2); | |
363 | pad2->SetGridy(); | |
364 | pad2->SetGridx(); | |
365 | ||
366 | TH1F *shist = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax); | |
367 | shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)"); | |
368 | shist->Draw(); | |
369 | TVirtualPad *pad3 = macro_example_canvas->cd(3); | |
370 | pad3->SetGridy(); | |
371 | pad3->SetGridx(); | |
372 | TH1F *shistPM = AliTPCCalibViewer::SigmaCut(distribution, mean, sigma, sigmaMax, -1, kTRUE); | |
373 | shistPM->Draw(); | |
374 | return macro_example_canvas; | |
375 | } | |
376 | end_macro | |
377 | */ | |
378 | ||
379 | Float_t *array = histogram->GetArray(); | |
380 | Int_t nbins = histogram->GetXaxis()->GetNbins(); | |
381 | Float_t binLow = histogram->GetXaxis()->GetXmin(); | |
382 | Float_t binUp = histogram->GetXaxis()->GetXmax(); | |
383 | return AliBaseCalibViewer::SigmaCut(nbins, array, mean, sigma, nbins, binLow, binUp, sigmaMax, sigmaStep, pm); | |
384 | } | |
385 | ||
386 | //_____________________________________________________________________________ | |
387 | 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){ | |
388 | // | |
389 | // 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 | |
390 | // The data of the distribution Begin_Latex f(x, #mu, #sigma) End_Latex are given in 'array', 'n' specifies the length of the array | |
391 | // '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 | |
392 | // 'nbins': number of bins, 'binLow': first bin, 'binUp': last bin | |
393 | // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma, Begin_Latex t #sigma End_Latex) | |
394 | // sigmaStep: the binsize of the generated histogram | |
395 | // Here the actual work is done. | |
396 | ||
397 | if (TMath::Abs(sigma) < 1.e-10) return 0; | |
398 | Float_t binWidth = (binUp-binLow)/(nbins - 1); | |
399 | if (sigmaStep <= 0) sigmaStep = binWidth; | |
400 | Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms | |
401 | if (pm) kbins = 2 * (Int_t)(sigmaMax * sigma / sigmaStep) + 1; | |
402 | Float_t kbinLow = !pm ? 0 : -sigmaMax; | |
403 | Float_t kbinUp = sigmaMax; | |
404 | TH1F *hist = new TH1F("sigmaCutHisto","Cumulative; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp); | |
405 | hist->SetDirectory(0); | |
406 | hist->Reset(); | |
407 | ||
408 | // calculate normalization | |
409 | Double_t normalization = 0; | |
410 | for (Int_t i = 0; i <= n; i++) { | |
411 | normalization += array[i]; | |
412 | } | |
413 | ||
414 | // given units: units from given histogram | |
415 | // sigma units: in units of sigma | |
416 | // iDelta: integrate in interval (mean +- iDelta), given units | |
417 | // x: ofset from mean for integration, given units | |
418 | // hist: needs | |
419 | ||
420 | // fill histogram | |
421 | for (Float_t iDelta = 0; iDelta <= sigmaMax * sigma; iDelta += sigmaStep) { | |
422 | // integrate array | |
423 | Double_t valueP = array[GetBin(mean, nbins, binLow, binUp)]; | |
424 | Double_t valueM = array[GetBin(mean-binWidth, nbins, binLow, binUp)]; | |
425 | // add bin of mean value only once to the histogram | |
426 | for (Float_t x = binWidth; x <= iDelta; x += binWidth) { | |
427 | valueP += (mean + x <= binUp) ? array[GetBin(mean + x, nbins, binLow, binUp)] : 0; | |
428 | valueM += (mean-binWidth - x >= binLow) ? array[GetBin(mean-binWidth - x, nbins, binLow, binUp)] : 0; | |
429 | } | |
430 | ||
431 | if (valueP / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueP, normalization); | |
432 | if (valueP / normalization > 100) return hist; | |
433 | if (valueM / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", valueM, normalization); | |
434 | if (valueM / normalization > 100) return hist; | |
435 | valueP = (valueP / normalization); | |
436 | valueM = (valueM / normalization); | |
437 | if (pm) { | |
438 | Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp); | |
439 | hist->SetBinContent(bin, valueP); | |
440 | bin = GetBin(-iDelta/sigma, kbins, kbinLow, kbinUp); | |
441 | hist->SetBinContent(bin, valueM); | |
442 | } | |
443 | else { // if (!pm) | |
444 | Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp); | |
445 | hist->SetBinContent(bin, valueP + valueM); | |
446 | } | |
447 | } | |
448 | if (!pm) hist->SetMaximum(1.2); | |
449 | return hist; | |
450 | } | |
451 | ||
452 | //_____________________________________________________________________________ | |
453 | TH1F* AliBaseCalibViewer::SigmaCut(Int_t n, Double_t *array, Double_t mean, Double_t sigma, Int_t nbins, Double_t *xbins, Double_t sigmaMax){ | |
454 | // | |
455 | // SigmaCut for variable binsize | |
456 | // NOT YET IMPLEMENTED !!! | |
457 | // | |
458 | printf("SigmaCut with variable binsize, Not yet implemented\n"); | |
459 | // avoid compiler warnings: | |
460 | n=n; | |
461 | mean=mean; | |
462 | sigma=sigma; | |
463 | nbins=nbins; | |
464 | sigmaMax=sigmaMax; | |
465 | array=array; | |
466 | xbins=xbins; | |
467 | ||
468 | return 0; | |
469 | } | |
470 | ||
471 | //_____________________________________________________________________________ | |
472 | Int_t AliBaseCalibViewer::DrawHisto1D(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts, | |
9a9e9b94 | 473 | const Char_t *sigmas, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM) const |
474 | { | |
475 | // | |
11a2ac51 | 476 | // Easy drawing of data, in principle the same as EasyDraw1D |
9a9e9b94 | 477 | // Difference: A line for the mean / median / LTM is drawn |
11a2ac51 | 478 | // in 'sigmas' you can specify in which distance to the mean/median/LTM you want to see a line in sigma-units, separated by ';' |
479 | // 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. | |
480 | // "plotMean", "plotMedian" and "plotLTM": what kind of lines do you want to see? | |
9a9e9b94 | 481 | // |
482 | Int_t oldOptStat = gStyle->GetOptStat(); | |
483 | gStyle->SetOptStat(0000000); | |
484 | Double_t ltmFraction = 0.8; | |
485 | ||
486 | TObjArray *sigmasTokens = TString(sigmas).Tokenize(";"); | |
487 | TVectorF nsigma(sigmasTokens->GetEntriesFast()); | |
488 | for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) { | |
489 | TString str(((TObjString*)sigmasTokens->At(i))->GetString()); | |
490 | Double_t sig = (str.IsFloat()) ? str.Atof() : 0; | |
491 | nsigma[i] = sig; | |
492 | } | |
493 | ||
494 | TString drawStr(drawCommand); | |
495 | Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>"); | |
496 | if (dangerousToDraw) { | |
497 | Warning("DrawHisto1D", "The draw string must not contain ':' or '>>'."); | |
498 | return -1; | |
499 | } | |
500 | drawStr += " >> tempHist"; | |
501 | Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts); | |
502 | TH1F *htemp = (TH1F*)gDirectory->Get("tempHist"); | |
11a2ac51 | 503 | // FIXME is this histogram deleted automatically? |
9a9e9b94 | 504 | Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers |
505 | ||
506 | Double_t mean = TMath::Mean(entries, values); | |
507 | Double_t median = TMath::Median(entries, values); | |
508 | Double_t sigma = TMath::RMS(entries, values); | |
509 | Double_t maxY = htemp->GetMaximum(); | |
510 | ||
511 | TLegend * legend = new TLegend(.7,.7, .99, .99, "Statistical information"); | |
512 | //fListOfObjectsToBeDeleted->Add(legend); | |
513 | ||
514 | if (plotMean) { | |
11a2ac51 | 515 | // draw Mean |
9a9e9b94 | 516 | TLine* line = new TLine(mean, 0, mean, maxY); |
517 | //fListOfObjectsToBeDeleted->Add(line); | |
518 | line->SetLineColor(kRed); | |
519 | line->SetLineWidth(2); | |
520 | line->SetLineStyle(1); | |
521 | line->Draw(); | |
522 | legend->AddEntry(line, Form("Mean: %f", mean), "l"); | |
11a2ac51 | 523 | // draw sigma lines |
9a9e9b94 | 524 | for (Int_t i = 0; i < nsigma.GetNoElements(); i++) { |
525 | TLine* linePlusSigma = new TLine(mean + nsigma[i] * sigma, 0, mean + nsigma[i] * sigma, maxY); | |
526 | //fListOfObjectsToBeDeleted->Add(linePlusSigma); | |
527 | linePlusSigma->SetLineColor(kRed); | |
528 | linePlusSigma->SetLineStyle(2 + i); | |
529 | linePlusSigma->Draw(); | |
530 | TLine* lineMinusSigma = new TLine(mean - nsigma[i] * sigma, 0, mean - nsigma[i] * sigma, maxY); | |
531 | //fListOfObjectsToBeDeleted->Add(lineMinusSigma); | |
532 | lineMinusSigma->SetLineColor(kRed); | |
533 | lineMinusSigma->SetLineStyle(2 + i); | |
534 | lineMinusSigma->Draw(); | |
535 | legend->AddEntry(lineMinusSigma, Form("%i #sigma = %f",(Int_t)(nsigma[i]), (Float_t)(nsigma[i] * sigma)), "l"); | |
536 | } | |
537 | } | |
538 | if (plotMedian) { | |
11a2ac51 | 539 | // draw median |
9a9e9b94 | 540 | TLine* line = new TLine(median, 0, median, maxY); |
541 | //fListOfObjectsToBeDeleted->Add(line); | |
542 | line->SetLineColor(kBlue); | |
543 | line->SetLineWidth(2); | |
544 | line->SetLineStyle(1); | |
545 | line->Draw(); | |
546 | legend->AddEntry(line, Form("Median: %f", median), "l"); | |
11a2ac51 | 547 | // draw sigma lines |
9a9e9b94 | 548 | for (Int_t i = 0; i < nsigma.GetNoElements(); i++) { |
549 | TLine* linePlusSigma = new TLine(median + nsigma[i] * sigma, 0, median + nsigma[i]*sigma, maxY); | |
550 | //fListOfObjectsToBeDeleted->Add(linePlusSigma); | |
551 | linePlusSigma->SetLineColor(kBlue); | |
552 | linePlusSigma->SetLineStyle(2 + i); | |
553 | linePlusSigma->Draw(); | |
554 | TLine* lineMinusSigma = new TLine(median - nsigma[i] * sigma, 0, median - nsigma[i]*sigma, maxY); | |
555 | //fListOfObjectsToBeDeleted->Add(lineMinusSigma); | |
556 | lineMinusSigma->SetLineColor(kBlue); | |
557 | lineMinusSigma->SetLineStyle(2 + i); | |
558 | lineMinusSigma->Draw(); | |
559 | legend->AddEntry(lineMinusSigma, Form("%i #sigma = %f",(Int_t)(nsigma[i]), (Float_t)(nsigma[i] * sigma)), "l"); | |
560 | } | |
561 | } | |
562 | if (plotLTM) { | |
11a2ac51 | 563 | // draw LTM |
9a9e9b94 | 564 | Double_t ltmRms = 0; |
565 | Double_t ltm = GetLTM(entries, values, <mRms, ltmFraction); | |
566 | TLine* line = new TLine(ltm, 0, ltm, maxY); | |
11a2ac51 | 567 | //fListOfObjectsToBeDeleted->Add(line); |
9a9e9b94 | 568 | line->SetLineColor(kGreen+2); |
569 | line->SetLineWidth(2); | |
570 | line->SetLineStyle(1); | |
571 | line->Draw(); | |
572 | legend->AddEntry(line, Form("LTM: %f", ltm), "l"); | |
11a2ac51 | 573 | // draw sigma lines |
9a9e9b94 | 574 | for (Int_t i = 0; i < nsigma.GetNoElements(); i++) { |
575 | TLine* linePlusSigma = new TLine(ltm + nsigma[i] * ltmRms, 0, ltm + nsigma[i] * ltmRms, maxY); | |
11a2ac51 | 576 | //fListOfObjectsToBeDeleted->Add(linePlusSigma); |
9a9e9b94 | 577 | linePlusSigma->SetLineColor(kGreen+2); |
578 | linePlusSigma->SetLineStyle(2+i); | |
579 | linePlusSigma->Draw(); | |
580 | ||
581 | TLine* lineMinusSigma = new TLine(ltm - nsigma[i] * ltmRms, 0, ltm - nsigma[i] * ltmRms, maxY); | |
11a2ac51 | 582 | //fListOfObjectsToBeDeleted->Add(lineMinusSigma); |
9a9e9b94 | 583 | lineMinusSigma->SetLineColor(kGreen+2); |
584 | lineMinusSigma->SetLineStyle(2+i); | |
585 | lineMinusSigma->Draw(); | |
586 | legend->AddEntry(lineMinusSigma, Form("%i #sigma = %f", (Int_t)(nsigma[i]), (Float_t)(nsigma[i] * ltmRms)), "l"); | |
587 | } | |
588 | } | |
589 | if (!plotMean && !plotMedian && !plotLTM) return -1; | |
590 | legend->Draw(); | |
591 | gStyle->SetOptStat(oldOptStat); | |
592 | return 1; | |
11a2ac51 | 593 | } |
594 | ||
595 | //_____________________________________________________________________________ | |
596 | Int_t AliBaseCalibViewer::SigmaCut(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts, | |
597 | Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, Bool_t pm, | |
598 | const Char_t *sigmas, Float_t sigmaStep) const { | |
599 | // | |
600 | // Creates a histogram, where you can see, how much of the data are inside sigma-intervals | |
601 | // around the mean/median/LTM | |
602 | // with drawCommand, sector and cuts you specify your input data, see EasyDraw | |
603 | // sigmaMax: up to which sigma around the mean/median/LTM the histogram is generated (in units of sigma) | |
604 | // sigmaStep: the binsize of the generated histogram | |
605 | // plotMean/plotMedian/plotLTM: specifies where to put the center | |
606 | // | |
607 | ||
608 | Double_t ltmFraction = 0.8; | |
609 | ||
610 | TString drawStr(drawCommand); | |
611 | Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>"); | |
612 | if (dangerousToDraw) { | |
613 | Warning("SigmaCut", "The draw string must not contain ':' or '>>'."); | |
614 | return -1; | |
615 | } | |
616 | drawStr += " >> tempHist"; | |
617 | ||
618 | Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff"); | |
619 | TH1F *htemp = (TH1F*)gDirectory->Get("tempHist"); | |
620 | // FIXME is this histogram deleted automatically? | |
621 | Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers | |
622 | ||
623 | Double_t mean = TMath::Mean(entries, values); | |
624 | Double_t median = TMath::Median(entries, values); | |
625 | Double_t sigma = TMath::RMS(entries, values); | |
626 | ||
627 | TLegend * legend = new TLegend(.7,.7, .99, .99, "Cumulative"); | |
628 | //fListOfObjectsToBeDeleted->Add(legend); | |
629 | TH1F *cutHistoMean = 0; | |
630 | TH1F *cutHistoMedian = 0; | |
631 | TH1F *cutHistoLTM = 0; | |
632 | ||
633 | TObjArray *sigmasTokens = TString(sigmas).Tokenize(";"); | |
634 | TVectorF nsigma(sigmasTokens->GetEntriesFast()); | |
635 | for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) { | |
636 | TString str(((TObjString*)sigmasTokens->At(i))->GetString()); | |
637 | Double_t sig = (str.IsFloat()) ? str.Atof() : 0; | |
638 | nsigma[i] = sig; | |
639 | } | |
640 | ||
641 | if (plotMean) { | |
642 | cutHistoMean = SigmaCut(htemp, mean, sigma, sigmaMax, sigmaStep, pm); | |
643 | if (cutHistoMean) { | |
644 | //fListOfObjectsToBeDeleted->Add(cutHistoMean); | |
645 | cutHistoMean->SetLineColor(kRed); | |
646 | legend->AddEntry(cutHistoMean, "Mean", "l"); | |
647 | cutHistoMean->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle())); | |
648 | cutHistoMean->Draw(); | |
649 | DrawLines(cutHistoMean, nsigma, legend, kRed, pm); | |
650 | } // if (cutHistoMean) | |
651 | ||
652 | } | |
653 | if (plotMedian) { | |
654 | cutHistoMedian = SigmaCut(htemp, median, sigma, sigmaMax, sigmaStep, pm); | |
655 | if (cutHistoMedian) { | |
656 | //fListOfObjectsToBeDeleted->Add(cutHistoMedian); | |
657 | cutHistoMedian->SetLineColor(kBlue); | |
658 | legend->AddEntry(cutHistoMedian, "Median", "l"); | |
659 | cutHistoMedian->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle())); | |
660 | if (plotMean && cutHistoMean) cutHistoMedian->Draw("same"); | |
661 | else cutHistoMedian->Draw(); | |
662 | DrawLines(cutHistoMedian, nsigma, legend, kBlue, pm); | |
663 | } // if (cutHistoMedian) | |
664 | } | |
665 | if (plotLTM) { | |
666 | Double_t ltmRms = 0; | |
667 | Double_t ltm = GetLTM(entries, values, <mRms, ltmFraction); | |
668 | cutHistoLTM = SigmaCut(htemp, ltm, ltmRms, sigmaMax, sigmaStep, pm); | |
669 | if (cutHistoLTM) { | |
670 | //fListOfObjectsToBeDeleted->Add(cutHistoLTM); | |
671 | cutHistoLTM->SetLineColor(kGreen+2); | |
672 | legend->AddEntry(cutHistoLTM, "LTM", "l"); | |
673 | cutHistoLTM->SetTitle(Form("%s, cumulative; Multiples of #sigma; Fraction of included data", htemp->GetTitle())); | |
674 | if ((plotMean && cutHistoMean) || (plotMedian && cutHistoMedian)) cutHistoLTM->Draw("same"); | |
675 | else cutHistoLTM->Draw(); | |
676 | DrawLines(cutHistoLTM, nsigma, legend, kGreen+2, pm); | |
677 | } | |
678 | } | |
679 | if (!plotMean && !plotMedian && !plotLTM) return -1; | |
680 | legend->Draw(); | |
681 | return 1; | |
682 | } | |
683 | ||
684 | //_____________________________________________________________________________ | |
685 | Int_t AliBaseCalibViewer::Integrate(const Char_t* drawCommand, const Char_t* sector, const Char_t* cuts, | |
686 | Float_t sigmaMax, Bool_t plotMean, Bool_t plotMedian, Bool_t plotLTM, | |
687 | const Char_t *sigmas, Float_t sigmaStep) const { | |
688 | // | |
689 | // 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" | |
690 | // "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 | |
691 | // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate | |
692 | // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated | |
693 | // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used | |
694 | // The actual work is done on the array. | |
695 | /* Begin_Latex | |
696 | 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 } | |
697 | End_Latex | |
698 | */ | |
699 | ||
700 | Double_t ltmFraction = 0.8; | |
701 | // avoid compiler warnings: | |
702 | sigmaMax = sigmaMax; | |
703 | sigmaStep = sigmaStep; | |
704 | ||
705 | TString drawStr(drawCommand); | |
706 | Bool_t dangerousToDraw = drawStr.Contains(":") || drawStr.Contains(">>"); | |
707 | if (dangerousToDraw) { | |
708 | Warning("Integrate", "The draw string must not contain ':' or '>>'."); | |
709 | return -1; | |
710 | } | |
711 | drawStr += " >> tempHist"; | |
712 | ||
713 | Int_t entries = EasyDraw1D(drawStr.Data(), sector, cuts, "goff"); | |
714 | TH1F *htemp = (TH1F*)gDirectory->Get("tempHist"); | |
715 | TGraph *integralGraphMean = 0; | |
716 | TGraph *integralGraphMedian = 0; | |
717 | TGraph *integralGraphLTM = 0; | |
718 | Double_t *values = fTree->GetV1(); // value is the array containing 'entries' numbers | |
719 | Int_t *index = new Int_t[entries]; | |
720 | Float_t *xarray = new Float_t[entries]; | |
721 | Float_t *yarray = new Float_t[entries]; | |
722 | TMath::Sort(entries, values, index, kFALSE); | |
723 | ||
724 | Double_t mean = TMath::Mean(entries, values); | |
725 | Double_t median = TMath::Median(entries, values); | |
726 | Double_t sigma = TMath::RMS(entries, values); | |
727 | ||
728 | // parse sigmas string | |
729 | TObjArray *sigmasTokens = TString(sigmas).Tokenize(";"); | |
730 | TVectorF nsigma(sigmasTokens->GetEntriesFast()); | |
731 | for (Int_t i = 0; i < sigmasTokens->GetEntriesFast(); i++) { | |
732 | TString str(((TObjString*)sigmasTokens->At(i))->GetString()); | |
733 | Double_t sig = (str.IsFloat()) ? str.Atof() : 0; | |
734 | nsigma[i] = sig; | |
735 | } | |
736 | ||
737 | TLegend * legend = new TLegend(.7,.7, .99, .99, "Integrated histogram"); | |
738 | //fListOfObjectsToBeDeleted->Add(legend); | |
739 | ||
740 | if (plotMean) { | |
741 | for (Int_t i = 0; i < entries; i++) { | |
742 | xarray[i] = (values[index[i]] - mean) / sigma; | |
743 | yarray[i] = float(i) / float(entries); | |
744 | } | |
745 | integralGraphMean = new TGraph(entries, xarray, yarray); | |
746 | if (integralGraphMean) { | |
747 | //fListOfObjectsToBeDeleted->Add(integralGraphMean); | |
748 | integralGraphMean->SetLineColor(kRed); | |
749 | legend->AddEntry(integralGraphMean, "Mean", "l"); | |
750 | integralGraphMean->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle())); | |
751 | integralGraphMean->Draw("alu"); | |
752 | DrawLines(integralGraphMean, nsigma, legend, kRed, kTRUE); | |
753 | } | |
754 | } | |
755 | if (plotMedian) { | |
756 | for (Int_t i = 0; i < entries; i++) { | |
757 | xarray[i] = (values[index[i]] - median) / sigma; | |
758 | yarray[i] = float(i) / float(entries); | |
759 | } | |
760 | integralGraphMedian = new TGraph(entries, xarray, yarray); | |
761 | if (integralGraphMedian) { | |
762 | //fListOfObjectsToBeDeleted->Add(integralGraphMedian); | |
763 | integralGraphMedian->SetLineColor(kBlue); | |
764 | legend->AddEntry(integralGraphMedian, "Median", "l"); | |
765 | integralGraphMedian->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle())); | |
766 | if (plotMean && integralGraphMean) integralGraphMedian->Draw("samelu"); | |
767 | else integralGraphMedian->Draw("alu"); | |
768 | DrawLines(integralGraphMedian, nsigma, legend, kBlue, kTRUE); | |
769 | } | |
770 | } | |
771 | if (plotLTM) { | |
772 | Double_t ltmRms = 0; | |
773 | Double_t ltm = GetLTM(entries, values, <mRms, ltmFraction); | |
774 | for (Int_t i = 0; i < entries; i++) { | |
775 | xarray[i] = (values[index[i]] - ltm) / ltmRms; | |
776 | yarray[i] = float(i) / float(entries); | |
777 | } | |
778 | integralGraphLTM = new TGraph(entries, xarray, yarray); | |
779 | if (integralGraphLTM) { | |
780 | //fListOfObjectsToBeDeleted->Add(integralGraphLTM); | |
781 | integralGraphLTM->SetLineColor(kGreen+2); | |
782 | legend->AddEntry(integralGraphLTM, "LTM", "l"); | |
783 | integralGraphLTM->SetTitle(Form("%s, integrated; Multiples of #sigma; Fraction of included data", htemp->GetTitle())); | |
784 | if ((plotMean && integralGraphMean) || (plotMedian && integralGraphMedian)) integralGraphLTM->Draw("samelu"); | |
785 | else integralGraphLTM->Draw("alu"); | |
786 | DrawLines(integralGraphLTM, nsigma, legend, kGreen+2, kTRUE); | |
787 | } | |
788 | } | |
0dd616b4 | 789 | delete [] index; |
790 | delete [] xarray; | |
791 | delete [] yarray; | |
11a2ac51 | 792 | if (!plotMean && !plotMedian && !plotLTM) return -1; |
793 | legend->Draw(); | |
794 | return entries; | |
795 | } | |
796 | ||
797 | //_____________________________________________________________________________ | |
798 | TH1F* AliBaseCalibViewer::Integrate(TH1F *histogram, Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){ | |
799 | // | |
800 | // 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" | |
801 | // "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 | |
802 | // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate | |
803 | // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated | |
804 | // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used | |
805 | // The actual work is done on the array. | |
806 | /* Begin_Latex | |
807 | 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 } | |
808 | End_Latex | |
809 | begin_macro(source) | |
810 | { | |
811 | Float_t mean = 0; | |
812 | Float_t sigma = 1.5; | |
813 | Float_t sigmaMax = 4; | |
814 | gROOT->SetStyle("Plain"); | |
815 | TH1F *distribution = new TH1F("Distribution2", "Distribution f(x, #mu, #sigma)", 1000,-5,5); | |
816 | TRandom rand(23); | |
817 | for (Int_t i = 0; i <50000;i++) distribution->Fill(rand.Gaus(mean, sigma)); | |
818 | Float_t *ar = distribution->GetArray(); | |
819 | ||
820 | TCanvas* macro_example_canvas = new TCanvas("macro_example_canvas_Integrate", "", 350, 350); | |
821 | macro_example_canvas->Divide(0,2); | |
822 | TVirtualPad *pad1 = macro_example_canvas->cd(1); | |
823 | pad1->SetGridy(); | |
824 | pad1->SetGridx(); | |
825 | distribution->Draw(); | |
826 | TVirtualPad *pad2 = macro_example_canvas->cd(2); | |
827 | pad2->SetGridy(); | |
828 | pad2->SetGridx(); | |
829 | TH1F *shist = AliTPCCalibViewer::Integrate(distribution, mean, sigma, sigmaMax); | |
830 | shist->SetNameTitle("Cumulative","Cumulative S(t, #mu, #sigma)"); | |
831 | shist->Draw(); | |
832 | ||
833 | return macro_example_canvas_Integrate; | |
834 | } | |
835 | end_macro | |
836 | */ | |
837 | ||
838 | ||
839 | Float_t *array = histogram->GetArray(); | |
840 | Int_t nbins = histogram->GetXaxis()->GetNbins(); | |
841 | Float_t binLow = histogram->GetXaxis()->GetXmin(); | |
842 | Float_t binUp = histogram->GetXaxis()->GetXmax(); | |
843 | return Integrate(nbins, array, nbins, binLow, binUp, mean, sigma, sigmaMax, sigmaStep); | |
844 | } | |
845 | ||
846 | //_____________________________________________________________________________ | |
847 | TH1F* AliBaseCalibViewer::Integrate(Int_t n, Float_t *array, Int_t nbins, Float_t binLow, Float_t binUp, | |
848 | Float_t mean, Float_t sigma, Float_t sigmaMax, Float_t sigmaStep){ | |
849 | // 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" | |
850 | // "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 | |
851 | // sigmaMax: up to which sigma around the mean/median/LTM you want to integrate | |
852 | // if "igma == 0" and "sigmaMax == 0" the whole histogram is integrated | |
853 | // "sigmaStep": the binsize of the generated histogram, -1 means, that the maximal reasonable stepsize is used | |
854 | // Here the actual work is done. | |
855 | ||
856 | Bool_t givenUnits = kTRUE; | |
857 | if (TMath::Abs(sigma) < 1.e-10 && TMath::Abs(sigmaMax) < 1.e-10) givenUnits = kFALSE; | |
858 | if (givenUnits) { | |
859 | sigma = 1; | |
860 | sigmaMax = (binUp - binLow) / 2.; | |
861 | } | |
862 | ||
863 | Float_t binWidth = (binUp-binLow)/(nbins - 1); | |
864 | if (sigmaStep <= 0) sigmaStep = binWidth; | |
865 | Int_t kbins = (Int_t)(sigmaMax * sigma / sigmaStep) + 1; // + 1 due to overflow bin in histograms | |
866 | Float_t kbinLow = givenUnits ? binLow : -sigmaMax; | |
867 | Float_t kbinUp = givenUnits ? binUp : sigmaMax; | |
868 | TH1F *hist = 0; | |
869 | if (givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Given x; Fraction of included data", kbins, kbinLow, kbinUp); | |
870 | if (!givenUnits) hist = new TH1F("integratedHisto","Integrated Histogram; Multiples of #sigma; Fraction of included data", kbins, kbinLow, kbinUp); | |
871 | hist->SetDirectory(0); | |
872 | hist->Reset(); | |
873 | ||
874 | // calculate normalization | |
875 | // printf("calculating normalization, integrating from bin 1 to %i \n", n); | |
876 | Double_t normalization = 0; | |
877 | for (Int_t i = 1; i <= n; i++) { | |
878 | normalization += array[i]; | |
879 | } | |
880 | // printf("normalization: %f \n", normalization); | |
881 | ||
882 | // given units: units from given histogram | |
883 | // sigma units: in units of sigma | |
884 | // iDelta: integrate in interval (mean +- iDelta), given units | |
885 | // x: ofset from mean for integration, given units | |
886 | // hist: needs | |
887 | ||
888 | // fill histogram | |
889 | for (Float_t iDelta = mean - sigmaMax * sigma; iDelta <= mean + sigmaMax * sigma; iDelta += sigmaStep) { | |
890 | // integrate array | |
891 | Double_t value = 0; | |
892 | for (Float_t x = mean - sigmaMax * sigma; x <= iDelta; x += binWidth) { | |
893 | value += (x <= binUp && x >= binLow) ? array[GetBin(x, nbins, binLow, binUp)] : 0; | |
894 | } | |
895 | if (value / normalization > 100) printf("+++ Error, value to big: %f, normalization with %f will fail +++ \n", value, normalization); | |
896 | if (value / normalization > 100) return hist; | |
897 | Int_t bin = GetBin(iDelta/sigma, kbins, kbinLow, kbinUp); | |
898 | // printf("first integration bin: %i, last integration bin: %i \n", GetBin(mean - sigmaMax * sigma, nbins, binLow, binUp), GetBin(iDelta, nbins, binLow, binUp)); | |
899 | // printf("value: %f, normalization: %f, normalized value: %f, iDelta: %f, Bin: %i \n", value, normalization, value/normalization, iDelta, bin); | |
900 | value = (value / normalization); | |
901 | hist->SetBinContent(bin, value); | |
902 | } | |
903 | return hist; | |
904 | } | |
905 | ||
906 | //_____________________________________________________________________________ | |
907 | void AliBaseCalibViewer::DrawLines(TH1F *histogram, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const { | |
9a9e9b94 | 908 | // |
11a2ac51 | 909 | // Private function for SigmaCut(...) and Integrate(...) |
910 | // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend | |
9a9e9b94 | 911 | // |
912 | ||
11a2ac51 | 913 | // start to draw the lines, loop over requested sigmas |
9a9e9b94 | 914 | for (Int_t i = 0; i < nsigma.GetNoElements(); i++) { |
915 | if (!pm) { | |
916 | Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]); | |
917 | TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin)); | |
11a2ac51 | 918 | //fListOfObjectsToBeDeleted->Add(lineUp); |
9a9e9b94 | 919 | lineUp->SetLineColor(color); |
920 | lineUp->SetLineStyle(2 + i); | |
921 | lineUp->Draw(); | |
922 | TLine* lineLeft = new TLine(nsigma[i], histogram->GetBinContent(bin), 0, histogram->GetBinContent(bin)); | |
11a2ac51 | 923 | //fListOfObjectsToBeDeleted->Add(lineLeft); |
9a9e9b94 | 924 | lineLeft->SetLineColor(color); |
925 | lineLeft->SetLineStyle(2 + i); | |
926 | lineLeft->Draw(); | |
927 | legend->AddEntry(lineLeft, Form("Fraction(%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)), "l"); | |
928 | } | |
929 | else { // if (pm) | |
930 | Int_t bin = histogram->GetXaxis()->FindBin(nsigma[i]); | |
931 | TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], histogram->GetBinContent(bin)); | |
11a2ac51 | 932 | //fListOfObjectsToBeDeleted->Add(lineUp1); |
9a9e9b94 | 933 | lineUp1->SetLineColor(color); |
934 | lineUp1->SetLineStyle(2 + i); | |
935 | lineUp1->Draw(); | |
936 | TLine* lineLeft1 = new TLine(nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin)); | |
11a2ac51 | 937 | //fListOfObjectsToBeDeleted->Add(lineLeft1); |
9a9e9b94 | 938 | lineLeft1->SetLineColor(color); |
939 | lineLeft1->SetLineStyle(2 + i); | |
940 | lineLeft1->Draw(); | |
941 | legend->AddEntry(lineLeft1, Form("Fraction(+%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)), "l"); | |
942 | bin = histogram->GetXaxis()->FindBin(-nsigma[i]); | |
943 | TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], histogram->GetBinContent(bin)); | |
11a2ac51 | 944 | //fListOfObjectsToBeDeleted->Add(lineUp2); |
9a9e9b94 | 945 | lineUp2->SetLineColor(color); |
946 | lineUp2->SetLineStyle(2 + i); | |
947 | lineUp2->Draw(); | |
948 | TLine* lineLeft2 = new TLine(-nsigma[i], histogram->GetBinContent(bin), histogram->GetBinLowEdge(0)+histogram->GetBinWidth(0), histogram->GetBinContent(bin)); | |
11a2ac51 | 949 | //fListOfObjectsToBeDeleted->Add(lineLeft2); |
9a9e9b94 | 950 | lineLeft2->SetLineColor(color); |
951 | lineLeft2->SetLineStyle(2 + i); | |
952 | lineLeft2->Draw(); | |
953 | legend->AddEntry(lineLeft2, Form("Fraction(-%f #sigma) = %f",nsigma[i], histogram->GetBinContent(bin)), "l"); | |
954 | } | |
955 | } // for (Int_t i = 0; i < nsigma.GetNoElements(); i++) | |
11a2ac51 | 956 | } |
957 | ||
958 | //_____________________________________________________________________________ | |
959 | void AliBaseCalibViewer::DrawLines(TGraph *graph, TVectorF nsigma, TLegend *legend, Int_t color, Bool_t pm) const { | |
9a9e9b94 | 960 | // |
11a2ac51 | 961 | // Private function for SigmaCut(...) and Integrate(...) |
962 | // Draws lines into the given histogram, specified by "nsigma", the lines are addeed to the legend | |
9a9e9b94 | 963 | // |
964 | ||
11a2ac51 | 965 | // start to draw the lines, loop over requested sigmas |
9a9e9b94 | 966 | for (Int_t i = 0; i < nsigma.GetNoElements(); i++) { |
967 | if (!pm) { | |
968 | TLine* lineUp = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i])); | |
11a2ac51 | 969 | //fListOfObjectsToBeDeleted->Add(lineUp); |
9a9e9b94 | 970 | lineUp->SetLineColor(color); |
971 | lineUp->SetLineStyle(2 + i); | |
972 | lineUp->Draw(); | |
973 | TLine* lineLeft = new TLine(nsigma[i], graph->Eval(nsigma[i]), 0, graph->Eval(nsigma[i])); | |
11a2ac51 | 974 | //fListOfObjectsToBeDeleted->Add(lineLeft); |
9a9e9b94 | 975 | lineLeft->SetLineColor(color); |
976 | lineLeft->SetLineStyle(2 + i); | |
977 | lineLeft->Draw(); | |
978 | legend->AddEntry(lineLeft, Form("Fraction(%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i])), "l"); | |
979 | } | |
980 | else { // if (pm) | |
981 | TLine* lineUp1 = new TLine(nsigma[i], 0, nsigma[i], graph->Eval(nsigma[i])); | |
11a2ac51 | 982 | //fListOfObjectsToBeDeleted->Add(lineUp1); |
9a9e9b94 | 983 | lineUp1->SetLineColor(color); |
984 | lineUp1->SetLineStyle(2 + i); | |
985 | lineUp1->Draw(); | |
986 | TLine* lineLeft1 = new TLine(nsigma[i], graph->Eval(nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(nsigma[i])); | |
11a2ac51 | 987 | //fListOfObjectsToBeDeleted->Add(lineLeft1); |
9a9e9b94 | 988 | lineLeft1->SetLineColor(color); |
989 | lineLeft1->SetLineStyle(2 + i); | |
990 | lineLeft1->Draw(); | |
991 | legend->AddEntry(lineLeft1, Form("Fraction(+%f #sigma) = %f",nsigma[i], graph->Eval(nsigma[i])), "l"); | |
992 | TLine* lineUp2 = new TLine(-nsigma[i], 0, -nsigma[i], graph->Eval(-nsigma[i])); | |
11a2ac51 | 993 | //fListOfObjectsToBeDeleted->Add(lineUp2); |
9a9e9b94 | 994 | lineUp2->SetLineColor(color); |
995 | lineUp2->SetLineStyle(2 + i); | |
996 | lineUp2->Draw(); | |
997 | TLine* lineLeft2 = new TLine(-nsigma[i], graph->Eval(-nsigma[i]), graph->GetHistogram()->GetXaxis()->GetBinLowEdge(0), graph->Eval(-nsigma[i])); | |
11a2ac51 | 998 | //fListOfObjectsToBeDeleted->Add(lineLeft2); |
9a9e9b94 | 999 | lineLeft2->SetLineColor(color); |
1000 | lineLeft2->SetLineStyle(2 + i); | |
1001 | lineLeft2->Draw(); | |
1002 | legend->AddEntry(lineLeft2, Form("Fraction(-%f #sigma) = %f",nsigma[i], graph->Eval(-nsigma[i])), "l"); | |
1003 | } | |
1004 | } // for (Int_t i = 0; i < nsigma.GetNoElements(); i++) | |
11a2ac51 | 1005 | } |