canvas->SaveAs("Plot_Correlation.pdf");
}
-void fitOther(const char* fileNameMC = "multiplicityMC_3M.root", const char* folder = "Multiplicity", const char* fileNameESD = "multiplicityMC_3M.root", Bool_t chi2 = kTRUE, Int_t histID = 3, Bool_t fullPhaseSpace = kFALSE)
+void loadlibs()
{
+ gSystem->Load("libTree");
+ gSystem->Load("libVMC");
+
+ gSystem->Load("libSTEERBase");
+ gSystem->Load("libANALYSIS");
gSystem->Load("libPWG0base");
+}
+void fitOther(const char* fileNameMC = "multiplicityMC_3M.root", const char* folder = "Multiplicity", const char* fileNameESD = "multiplicityMC_3M.root", Bool_t chi2 = kTRUE, Int_t histID = 3, Bool_t fullPhaseSpace = kFALSE, Float_t beta = 1e4)
+{
+ loadlibs();
+
AliMultiplicityCorrection* mult = new AliMultiplicityCorrection(folder, folder);
TFile::Open(fileNameMC);
if (chi2)
{
- mult->SetRegularizationParameters(AliMultiplicityCorrection::kPol1, 1e4);
- mult->SetRegularizationParameters(AliMultiplicityCorrection::kNone, 0, 125); mult->SetCreateBigBin(kFALSE);
- //mult->SetRegularizationParameters(AliMultiplicityCorrection::kEntropy, 1e3);
+ mult->SetRegularizationParameters(AliMultiplicityCorrection::kPol1, beta);
+ //mult->SetCreateBigBin(kFALSE);
+ //mult->SetRegularizationParameters(AliMultiplicityCorrection::kNone, 0); //mult->SetCreateBigBin(kFALSE);
+ //mult->SetRegularizationParameters(AliMultiplicityCorrection::kNone, 0, 125); mult->SetCreateBigBin(kFALSE);
+ //mult->SetRegularizationParameters(AliMultiplicityCorrection::kEntropy, 1e5);
//mult->SetRegularizationParameters(AliMultiplicityCorrection::kLog, 1e5);
//mult->ApplyMinuitFit(histID, fullPhaseSpace, AliMultiplicityCorrection::kTrVtx, kTRUE, hist2->ProjectionY("mymchist"));
- mult->ApplyMinuitFit(histID, fullPhaseSpace, AliMultiplicityCorrection::kTrVtx, kFALSE, hist2->ProjectionY("mymchist"));
+ mult->ApplyMinuitFit(histID, fullPhaseSpace, AliMultiplicityCorrection::kTrVtx, kFALSE); //hist2->ProjectionY("mymchist"));
mult->DrawComparison("MinuitChi2", histID, fullPhaseSpace, kTRUE, hist2->ProjectionY("mymchist"));
}
else
return 0;
}
-/*void EvaluateBayesianMethod(const char* fileNameMC = "multiplicityMC.root", const char* fileNameESD = "multiplicityMC.root", const char* targetDir, Int_t histID = 3)
-{
- gSystem->mkdir(targetDir);
-
- gSystem->Load("libPWG0base");
-
- AliMultiplicityCorrection* mult = new AliMultiplicityCorrection("Multiplicity", "Multiplicity");
- TFile::Open(fileNameMC);
- mult->LoadHistograms("Multiplicity");
-
- AliMultiplicityCorrection* multESD = new AliMultiplicityCorrection("MultiplicityESD", "MultiplicityESD");
- TFile::Open(fileNameESD);
- multESD->LoadHistograms("Multiplicity");
- mult->SetMultiplicityESD(histID, multESD->GetMultiplicityESD(histID));
-
- TCanvas* canvas = new TCanvas("EvaluateBayesianMethod", "EvaluateBayesianMethod", 800, 600);
- TLegend* legend = new TLegend(0.2, 0.7, 0.58, 0.98);
- legend->SetFillColor(0);
-
- Float_t min = 1e10;
- Float_t max = 0;
-
- TGraph* first = 0;
- Int_t count = 0; // just to order the saved images...
-
- for (AliMultiplicityCorrection::EventType type = AliMultiplicityCorrection::kTrVtx; type <= AliMultiplicityCorrection::kINEL; ++type)
- {
- TGraph* fitResultsMC = new TGraph;
- fitResultsMC->SetTitle(";Weight Parameter");
- TGraph* fitResultsRes = new TGraph;
- fitResultsRes->SetTitle(";Weight Parameter");
-
- fitResultsMC->SetFillColor(0);
- fitResultsRes->SetFillColor(0);
- fitResultsMC->SetMarkerStyle(20+type);
- fitResultsRes->SetMarkerStyle(24+type);
- fitResultsRes->SetMarkerColor(kRed);
- fitResultsRes->SetLineColor(kRed);
-
- legend->AddEntry(fitResultsMC, Form("%s MC chi2", GetEventTypeName(type)));
- legend->AddEntry(fitResultsRes, Form("%s residual chi2", GetEventTypeName(type)));
-
- for (Float_t weight = 0; weight < 1.01; weight += 0.1)
- {
- Float_t chi2MC = 0;
- Float_t residuals = 0;
-
- mult->ApplyBayesianMethod(histID, kFALSE, type, weight, 100, 0, kFALSE);
- mult->DrawComparison(Form("%s/Bayesian_%02d_%d_%f", targetDir, count++, type, weight), histID, kFALSE, kTRUE, multESD->GetMultiplicityMC(histID, type)->ProjectionY());
- mult->GetComparisonResults(&chi2MC, 0, &residuals);
-
- fitResultsMC->SetPoint(fitResultsMC->GetN(), weight, chi2MC);
- fitResultsRes->SetPoint(fitResultsRes->GetN(), weight, residuals);
-
- min = TMath::Min(min, TMath::Min(chi2MC, residuals));
- max = TMath::Max(max, TMath::Max(chi2MC, residuals));
- }
-
- fitResultsMC->Print();
- fitResultsRes->Print();
-
- canvas->cd();
- fitResultsMC->Draw(Form("%s CP", (first == 0) ? "A" : "SAME"));
- fitResultsRes->Draw("SAME CP");
-
- if (first == 0)
- first = fitResultsMC;
- }
-
- gPad->SetLogy();
- printf("min = %f, max = %f\n", min, max);
- if (min <= 0)
- min = 1e-5;
- first->GetYaxis()->SetRangeUser(min * 0.5, max * 1.5);
-
- legend->Draw();
-
- canvas->SaveAs(Form("%s/%s.gif", targetDir, canvas->GetName()));
- canvas->SaveAs(Form("%s/%s.C", targetDir, canvas->GetName()));
-}
-
-void EvaluateBayesianMethodIterations(const char* fileNameMC = "multiplicityMC.root", const char* fileNameESD = "multiplicityMC.root", const char* targetDir, Int_t histID = 2)
-{
- gSystem->mkdir(targetDir);
-
- gSystem->Load("libPWG0base");
-
- AliMultiplicityCorrection* mult = new AliMultiplicityCorrection("Multiplicity", "Multiplicity");
- TFile::Open(fileNameMC);
- mult->LoadHistograms("Multiplicity");
-
- AliMultiplicityCorrection* multESD = new AliMultiplicityCorrection("MultiplicityESD", "MultiplicityESD");
- TFile::Open(fileNameESD);
- multESD->LoadHistograms("Multiplicity");
- mult->SetMultiplicityESD(histID, multESD->GetMultiplicityESD(histID));
-
- TCanvas* canvas = new TCanvas("EvaluateBayesianMethodIterations", "EvaluateBayesianMethodIterations", 800, 600);
- TLegend* legend = new TLegend(0.2, 0.7, 0.58, 0.98);
- legend->SetFillColor(0);
-
- Float_t min = 1e10;
- Float_t max = 0;
-
- TGraph* first = 0;
- Int_t count = 0; // just to order the saved images...
-
- for (AliMultiplicityCorrection::EventType type = AliMultiplicityCorrection::kTrVtx; type <= AliMultiplicityCorrection::kINEL; ++type)
- {
- TGraph* fitResultsMC = new TGraph;
- fitResultsMC->SetTitle(";Iterations");
- TGraph* fitResultsRes = new TGraph;
- fitResultsRes->SetTitle(";Iterations");
-
- fitResultsMC->SetFillColor(0);
- fitResultsRes->SetFillColor(0);
- fitResultsMC->SetMarkerStyle(20+type);
- fitResultsRes->SetMarkerStyle(24+type);
- fitResultsRes->SetMarkerColor(kRed);
- fitResultsRes->SetLineColor(kRed);
-
- legend->AddEntry(fitResultsMC, Form("%s MC chi2", GetEventTypeName(type)));
- legend->AddEntry(fitResultsRes, Form("%s residual chi2", GetEventTypeName(type)));
-
- for (Int_t iter = 5; iter <= 50; iter += 5)
- {
- Float_t chi2MC = 0;
- Float_t residuals = 0;
-
- mult->ApplyBayesianMethod(histID, kFALSE, type, 0.1, iter);
- mult->DrawComparison(Form("%s/BayesianIter_%02d_%d_%d", targetDir, count++, type, iter), histID, kFALSE, kTRUE, multESD->GetMultiplicityMC(histID, type)->ProjectionY());
- mult->GetComparisonResults(&chi2MC, 0, &residuals);
-
- fitResultsMC->SetPoint(fitResultsMC->GetN(), iter, chi2MC);
- fitResultsRes->SetPoint(fitResultsRes->GetN(), iter, residuals);
-
- min = TMath::Min(min, TMath::Min(chi2MC, residuals));
- max = TMath::Max(max, TMath::Max(chi2MC, residuals));
- }
-
- fitResultsMC->Print();
- fitResultsRes->Print();
-
- canvas->cd();
- fitResultsMC->Draw(Form("%s CP", (first == 0) ? "A" : "SAME"));
- fitResultsRes->Draw("SAME CP");
-
- if (first == 0)
- first = fitResultsMC;
- }
-
- gPad->SetLogy();
- printf("min = %f, max = %f\n", min, max);
- if (min <= 0)
- min = 1e-5;
- first->GetYaxis()->SetRangeUser(min * 0.5, max * 1.5);
-
- legend->Draw();
-
- canvas->SaveAs(Form("%s/%s.gif", targetDir, canvas->GetName()));
- canvas->SaveAs(Form("%s/%s.C", targetDir, canvas->GetName()));
-}*/
-
void EvaluateBayesianMethodIterationsSmoothing(const char* fileNameMC = "multiplicityMC.root", const char* fileNameESD = "multiplicityMC.root", const char* targetDir, Int_t histID = 3)
{
gSystem->mkdir(targetDir);
TFile* graphFile = TFile::Open(Form("%s/EvaluateBayesianMethodIterationsSmoothing.root", targetDir), "RECREATE");
Int_t colors[3] = {1, 2, 4};
- Int_t markers[12] = {20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 3};
+ Int_t markers[20] = {20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 3, 4, 5, 6};
for (AliMultiplicityCorrection::EventType type = AliMultiplicityCorrection::kTrVtx; type <= AliMultiplicityCorrection::kTrVtx; ++type)
//for (AliMultiplicityCorrection::EventType type = AliMultiplicityCorrection::kTrVtx; type <= AliMultiplicityCorrection::kINEL; ++type)
TCanvas* canvas = new TCanvas(tmp, tmp, 800, 600);
- for (Int_t i = 1; i <= 4; i++)
+ for (Int_t i = 1; i <= 5; i++)
{
- Int_t iterArray[4] = {5, 20, 50, 100};
+ Int_t iterArray[5] = {5, 20, 50, 100, -1};
//Int_t iter = i * 40 - 20;
Int_t iter = iterArray[i-1];
fitResultsMC[region]->GetYaxis()->SetTitle(Form("P_{1} in region %d", region));
fitResultsMC[region]->SetName(Form("%s_MC_%d", tmp.Data(), i * AliMultiplicityCorrection::kQualityRegions + region - 2));
fitResultsMC[region]->SetFillColor(0);
- fitResultsMC[region]->SetMarkerStyle(markers[(i-1) * AliMultiplicityCorrection::kQualityRegions + region]);
+ //fitResultsMC[region]->SetMarkerStyle(markers[(i-1) * AliMultiplicityCorrection::kQualityRegions + region]);
+ fitResultsMC[region]->SetMarkerStyle(markers[(i-1)]);
fitResultsMC[region]->SetLineColor(colors[region]);
}
canvas->SaveAs(Form("%s/%s.eps", targetDir, canvas->GetName()));
}
+void EvaluateDrawResultRegions(const char* targetDir, Int_t type = 0)
+{
+ gSystem->Load("libPWG0base");
+
+ TString name;
+ TFile* graphFile = 0;
+ if (type == -1)
+ {
+ name = "EvaluateChi2Method";
+ graphFile = TFile::Open(Form("%s/EvaluateChi2Method.root", targetDir));
+ }
+ else
+ {
+ name.Form("EvaluateBayesianMethodIterationsSmoothing_%s", GetEventTypeName(type));
+ graphFile = TFile::Open(Form("%s/EvaluateBayesianMethodIterationsSmoothing.root", targetDir));
+ }
+
+ TCanvas* canvas = new TCanvas(name, name, 800, 1200);
+ //canvas->Divide(1, AliMultiplicityCorrection::kQualityRegions, 0, 0);
+ canvas->Range(0, 0, 1, 1);
+
+ TPad* pad[3];
+ pad[0] = new TPad(Form("%s_pad1", name.Data()), "", 0.02, 0.05, 0.98, 0.35);
+ pad[1] = new TPad(Form("%s_pad2", name.Data()), "", 0.02, 0.35, 0.98, 0.65);
+ pad[2] = new TPad(Form("%s_pad3", name.Data()), "", 0.02, 0.65, 0.98, 0.95);
+
+ Float_t yMinRegion[3];
+ for (Int_t i=0; i<AliMultiplicityCorrection::kQualityRegions; ++i)
+ yMinRegion[i] = 1e20;
+
+ for (Int_t region = 1; region <= AliMultiplicityCorrection::kQualityRegions; region++)
+ {
+ canvas->cd();
+ pad[region-1]->Draw();
+ pad[region-1]->cd();
+ pad[region-1]->SetRightMargin(0.05);
+
+ if (region != 1)
+ pad[region-1]->SetBottomMargin(0);
+ if (region != AliMultiplicityCorrection::kQualityRegions)
+ pad[region-1]->SetTopMargin(0);
+
+ if (type == -1)
+ {
+ pad[region-1]->SetLogx();
+ pad[region-1]->SetLogy();
+ }
+ pad[region-1]->SetTopMargin(0.05);
+ pad[region-1]->SetGridx();
+ pad[region-1]->SetGridy();
+
+ TLegend* legend = new TLegend(0.8, 0.15, 0.98, 0.98);
+ legend->SetFillColor(0);
+
+ Int_t count = 0;
+
+ Float_t xMin = 1e20;
+ Float_t xMax = 0;
+
+ Float_t yMin = 1e20;
+ Float_t yMax = 0;
+
+ TString xaxis, yaxis;
+
+ while (1)
+ {
+ count++;
+
+ TGraph* mc = (TGraph*) graphFile->Get(Form("%s_MC_%d", name.Data(), count));
+ if (!mc)
+ break;
+
+ if (TString(mc->GetTitle()).Contains(Form("reg. %d", region)) == kFALSE)
+ continue;
+
+ xaxis = mc->GetXaxis()->GetTitle();
+ yaxis = mc->GetYaxis()->GetTitle();
+
+ mc->Print();
+
+ xMin = TMath::Min(xMin, mc->GetXaxis()->GetXmin());
+ yMin = TMath::Min(yMin, mc->GetYaxis()->GetXmin());
+
+ xMax = TMath::Max(xMax, mc->GetXaxis()->GetXmax());
+ yMax = TMath::Max(yMax, mc->GetYaxis()->GetXmax());
+
+ for (Int_t i=0; i<mc->GetN(); ++i)
+ yMinRegion[(count-1) % 3] = TMath::Min(yMinRegion[(count-1) % 3], mc->GetY()[i]);
+ }
+
+ if (type >= 0)
+ {
+ xaxis = "smoothing parameter";
+ }
+ else if (type == -1)
+ {
+ xaxis = "weight parameter";
+ xMax *= 5;
+ }
+ yaxis = "P_{1}"; // (2 <= t <= 150)";
+
+ printf("%f %f %f %f\n", xMin, xMax, yMin, yMax);
+
+ TGraph* dummy = new TGraph;
+ dummy->SetPoint(0, xMin, yMin);
+ dummy->SetPoint(1, xMax, yMax);
+ dummy->SetTitle(Form(";%s;%s", xaxis.Data(), yaxis.Data()));
+
+ dummy->SetMarkerColor(0);
+ dummy->Draw("AP");
+ dummy->GetYaxis()->SetMoreLogLabels(1);
+
+ count = 0;
+
+ while (1)
+ {
+ count++;
+
+ TGraph* mc = (TGraph*) graphFile->Get(Form("%s_MC_%d", name.Data(), count));
+ if (!mc)
+ break;
+ if (TString(mc->GetTitle()).Contains(Form("reg. %d", region)) == kFALSE)
+ continue;
+
+ printf("Loaded %d sucessful.\n", count);
+
+ if (type == -1)
+ {
+ legend->AddEntry(mc, Form("Eq. (%d) - reg. %d", 10 + (count-1) / 3, 1+ (count-1) % 3));
+ }
+ else
+ legend->AddEntry(mc);
+
+ mc->Draw("SAME PC");
+
+ }
+
+ legend->Draw();
+ }
+
+ for (Int_t i=0; i<AliMultiplicityCorrection::kQualityRegions; ++i)
+ Printf("Minimum for region %d is %f", i, yMinRegion[i]);
+
+ canvas->Modified();
+ canvas->SaveAs(Form("%s/%s.gif", targetDir, canvas->GetName()));
+ canvas->SaveAs(Form("%s/%s.eps", targetDir, canvas->GetName()));
+}
+
void EvaluateChi2Method(const char* fileNameMC = "multiplicityMC_2M.root", const char* fileNameESD = "multiplicityMC_1M_3.root", const char* targetDir, Int_t histID = 3)
{
gSystem->mkdir(targetDir);
TFile* graphFile = TFile::Open(Form("%s/EvaluateChi2Method.root", targetDir), "RECREATE");
- for (AliMultiplicityCorrection::RegularizationType type = AliMultiplicityCorrection::kPol0; type <= AliMultiplicityCorrection::kLog; ++type)
+ for (AliMultiplicityCorrection::RegularizationType type = AliMultiplicityCorrection::kPol0; type <= AliMultiplicityCorrection::kEntropy; ++type)
// for (AliMultiplicityCorrection::RegularizationType type = AliMultiplicityCorrection::kEntropy; type <= AliMultiplicityCorrection::kEntropy; ++type)
-// for (AliMultiplicityCorrection::RegularizationType type = AliMultiplicityCorrection::kPol0; type <= AliMultiplicityCorrection::kPol0; ++type)
+ //for (AliMultiplicityCorrection::RegularizationType type = AliMultiplicityCorrection::kPol1; type <= AliMultiplicityCorrection::kPol1; ++type)
{
TGraph* fitResultsMC[3];
for (Int_t region=0; region<AliMultiplicityCorrection::kQualityRegions; ++region)
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff