/************************************************************************** * Copyright(c) 1998-1999, ALICE Experiment at CERN, All rights reserved. * * * * Author: The ALICE Off-line Project. * * Contributors are mentioned in the code where appropriate. * * * * Permission to use, copy, modify and distribute this software and its * * documentation strictly for non-commercialf purposes is hereby granted * * without fee, provided that the above copyright notice appears in all * * copies and that both the copyright notice and this permission notice * * appear in the supporting documentation. The authors make no claims * * about the suitability of this software for any purpose. It is * * provided "as is" without express or implied warranty. * **************************************************************************/ /* $Id: AliTRDclusterResolution.cxx */ /////////////////////////////////////////////////////////////////////////////// // // // TRD cluster error parameterization // // // // This class is designed to produce the reference plots for a detailed study// // and parameterization of TRD cluster errors. The following effects are taken// // into account : // // - dependence with the total charge of the cluster // // - dependence with the distance from the center pad. This is monitored // for each layer individually since the pad size varies with layer // - dependence with the drift length - here the influence of anisochronity // and diffusion are searched // - dependence with the distance to the anode wire - anisochronity effects // - dependence with track angle (for y resolution) // The correlation between effects is taken into account. // // Since magnetic field plays a very important role in the TRD measurement // the ExB correction is forced by the setter function SetExB(Int_t). The // argument is the detector index, if none is specified all will be // considered. // // Two cases are of big importance. // - comparison with MC // - comparison with Kalman fit. In this case the covariance matrix of the // Kalman fit are needed. // // The functionalities implemented in this class are based on the storage // class AliTRDclusterInfo. // // The Method // ---------- // // The method to disentangle s_y and s_x is based on the relation (see also fig.) // BEGIN_LATEX // #sigma^{2} = #sigma^{2}_{y} + tg^{2}(#alpha_{L})*#sigma^{2}_{x_{d}} + tg^{2}(#phi-#alpha_{L})*(#sigma^{2}_{x_{d}}+#sigma^{2}_{x_{c}}) // END_LATEX // with // BEGIN_LATEX // #sigma^{2}_{x_{c}} #approx 0 // END_LATEX // we suppose the chamber is well calibrated for t_{0} and aligned in // radial direction. // // Clusters can be radially shifted due to three causes: // - globally shifted - due to residual misalignment/miscalibration(t0) // - locally shifted - due to different local drift velocity from the mean // - randomly shifted - due to neighboring (radial direction) clusters // charge induced by asymmetry of the TRF. // // We estimate this effects by the relations: // BEGIN_LATEX // #mu_{y} = tg(#alpha_{L})*#Delta x_{d}(...) + tg(#phi-#alpha_{L})*(#Delta x_{c}(...) + #Delta x_{d}(...)) // END_LATEX // where // BEGIN_LATEX // #Delta x_{d}(...) = ( + #delta v_{d}(x_{d}, d)) * (t + t^{*}(Q)) // END_LATEX // and we specified explicitely the variation of drift velocity parallel // with the track (x_{d}) and perpendicular to it due to anisochronity (d). // // For estimating the contribution from asymmetry of TRF the following // parameterization is being used // BEGIN_LATEX // t^{*}(Q) = #delta_{0} * #frac{Q_{t+1} - Q_{t-1}}{Q_{t-1} + Q_{t} + Q_{t+1}} // END_LATEX // // // Clusters can also be r-phi shifted due to: // - wrong PRF or wrong cuts at digits level //The following correction is applied : // BEGIN_LATEX // <#Delta y> = a + b * sin(c*y_{pw}) // END_LATEX // The Models // // Parameterization against total charge // // Obtained for B=0T at phi=0. All other effects integrated out. // BEGIN_LATEX // #sigma^{2}_{y}(Q) = #sigma^{2}_{y}(...) + b(#frac{1}{Q} - #frac{1}{Q_{0}}) // END_LATEX // For B diff 0T the error of the average ExB correction error has to be subtracted !! // // Parameterization Sx // // The parameterization of the error in the x direction can be written as // BEGIN_LATEX // #sigma_{x} = #sigma_{x}^{||} + #sigma_{x}^{#perp} // END_LATEX // // where the parallel component is given mainly by the TRF width while // the perpendicular component by the anisochronity. The model employed for // the parallel is gaus(0)+expo(3) with the following parameters // 1 C 5.49018e-01 1.23854e+00 3.84540e-04 -8.21084e-06 // 2 M 7.82999e-01 6.22531e-01 2.71272e-04 -6.88485e-05 // 3 S 2.74451e-01 1.13815e+00 2.90667e-04 1.13493e-05 // 4 E1 2.53596e-01 1.08646e+00 9.95591e-05 -2.11625e-05 // 5 E2 -2.40078e-02 4.26520e-01 4.67153e-05 -2.35392e-04 // // and perpendicular to the track is pol2 with the parameters // // Par_0 = 0.190676 +/- 0.41785 // Par_1 = -3.9269 +/- 7.49862 // Par_2 = 14.7851 +/- 27.8012 // // Parameterization Sy // // The parameterization of the error in the y direction along track uses // BEGIN_LATEX // #sigma_{y}^{||} = #sigma_{y}^{0} -a*exp(1/(x-b)) // END_LATEX // // with following values for the parameters: // 1 sy0 2.60967e-01 2.99652e-03 7.82902e-06 -1.89636e-04 // 2 a -7.68941e+00 1.87883e+00 3.84539e-04 9.38268e-07 // 3 b -3.41160e-01 7.72850e-02 1.63231e-05 2.51602e-05 // //========================================================================== // Example how to retrive reference plots from the task // void steerClErrParam(Int_t fig=0) // { // gSystem->Load("libANALYSIS.so"); // gSystem->Load("libTRDqaRec.so"); // // // initialize DB manager // AliCDBManager *cdb = AliCDBManager::Instance(); // cdb->SetDefaultStorage("local://$ALICE_ROOT/OCDB"); // cdb->SetRun(0); // // initialize magnetic field. // AliMagFCheb *field=new AliMagFCheb("Maps","Maps", 2, 1., 10., AliMagFCheb::k5kG); // AliTracker::SetFieldMap(field, kTRUE); // // AliTRDclusterResolution *res = new AliTRDclusterResolution(); // res->SetMCdata(); // res->Load("TRD.TaskClErrParam.root"); // res->SetExB(); // res->SetVisual(); // //res->SetSaveAs(); // res->SetProcessCharge(kFALSE); // res->SetProcessCenterPad(kFALSE); // //res->SetProcessMean(kFALSE); // res->SetProcessSigma(kFALSE); // if(!res->PostProcess()) return; // new TCanvas; // res->GetRefFigure(fig); // } // // Authors: // // Alexandru Bercuci // //////////////////////////////////////////////////////////////////////////// #include "AliTRDclusterResolution.h" #include "info/AliTRDclusterInfo.h" #include "AliTRDgeometry.h" #include "AliTRDcluster.h" #include "AliTRDcalibDB.h" #include "AliTRDCommonParam.h" #include "Cal/AliTRDCalROC.h" #include "Cal/AliTRDCalDet.h" #include "AliLog.h" #include "AliTracker.h" #include "AliCDBManager.h" #include "TROOT.h" #include "TObjArray.h" #include "TAxis.h" #include "TF1.h" #include "TLegend.h" #include "TGraphErrors.h" #include "TLine.h" #include "TH2I.h" #include "TH3S.h" #include "TTree.h" #include "TMath.h" #include "TLinearFitter.h" #include "TCanvas.h" #include "TSystem.h" ClassImp(AliTRDclusterResolution) const Float_t AliTRDclusterResolution::fgkTimeBinLength = 1./ AliTRDCommonParam::Instance()->GetSamplingFrequency(); //_______________________________________________________ AliTRDclusterResolution::AliTRDclusterResolution(const char *name, const char *title) : AliTRDrecoTask(name, title) ,fCanvas(0x0) ,fInfo(0x0) ,fResults(0x0) ,fAt(0x0) ,fStatus(0) ,fDet(-1) ,fExB(0.) ,fVdrift(0.) ,fLy(0) ,fX(0.) ,fY(0.) ,fZ(0.) { // Constructor memset(fR, 0, 4*sizeof(Float_t)); memset(fP, 0, 4*sizeof(Float_t)); // time drift axis fAt = new TAxis(kNTB, 0., kNTB*fgkTimeBinLength); // By default register all analysis // The user can switch them off in his steering macro SetProcess(kQRes); SetProcess(kCenter); SetProcess(kMean); SetProcess(kSigm); } //_______________________________________________________ AliTRDclusterResolution::~AliTRDclusterResolution() { // Destructor if(fCanvas) delete fCanvas; if(fAt) delete fAt; if(fResults){ fResults->Delete(); delete fResults; } } //_______________________________________________________ void AliTRDclusterResolution::ConnectInputData(Option_t *) { fInfo = dynamic_cast(GetInputData(0)); } //_______________________________________________________ void AliTRDclusterResolution::CreateOutputObjects() { OpenFile(0, "RECREATE"); fContainer = Histos(); } //_______________________________________________________ Bool_t AliTRDclusterResolution::GetRefFigure(Int_t ifig) { // Steering function to retrieve performance plots if(!fResults) return kFALSE; TLegend *leg = 0x0; TList *l = 0x0; TObjArray *arr = 0x0; TTree *t = 0x0; TH2 *h2 = 0x0;TH1 *h1 = 0x0; TGraphErrors *gm(0x0), *gs(0x0), *gp(0x0); switch(ifig){ case kQRes: if(!(arr = (TObjArray*)fResults->At(kQRes))) break; if(!(gm = (TGraphErrors*)arr->At(0))) break; if(!(gs = (TGraphErrors*)arr->At(1))) break; if(!(gp = (TGraphErrors*)arr->At(2))) break; gs->Draw("apl"); gs->GetHistogram()->GetYaxis()->SetRangeUser(-50., 700.); gs->GetHistogram()->SetXTitle("Q [a.u.]"); gs->GetHistogram()->SetYTitle("#sigma_{y} / #mu_{y} [#mum] / freq"); gm->Draw("pl"); gp->Draw("pl"); return kTRUE; case kCenter: if(!(arr = (TObjArray*)fResults->At(kCenter))) break; gPad->Divide(2, 1); l = gPad->GetListOfPrimitives(); ((TVirtualPad*)l->At(0))->cd(); ((TTree*)arr->At(0))->Draw("y:x>>h(23, 0.1, 2.4, 51, -.51, .51)", "m[0]*(ly==0&&abs(m[0])<1.e-1)", "colz"); ((TVirtualPad*)l->At(1))->cd(); leg= new TLegend(.7, .7, .9, .95); leg->SetBorderSize(0); leg->SetFillColor(0); leg->SetFillStyle(0); leg->SetHeader("TRD Plane"); for(Int_t il = 1; il<=AliTRDgeometry::kNlayer; il++){ if(!(gm = (TGraphErrors*)arr->At(il))) return kFALSE; gm->Draw(il>1?"pc":"apc"); leg->AddEntry(gm, Form("%d", il-1), "pl"); if(il>1) continue; gm->GetHistogram()->SetXTitle("t_{drift} [#mus]"); gm->GetHistogram()->SetYTitle("#sigma_{y}(x|cen=0) [#mum]"); gm->GetHistogram()->GetYaxis()->SetRangeUser(150., 500.); } leg->Draw(); return kTRUE; case kSigm: if(!(t = (TTree*)fResults->At(kSigm))) break; t->Draw("z:t>>h2x(23, 0.1, 2.4, 25, 0., 2.5)","sx*(1)", "lego2fb"); h2 = (TH2F*)gROOT->FindObject("h2x"); printf(" const Double_t sx[24][25]={\n"); for(Int_t ix=1; ix<=h2->GetNbinsX(); ix++){ printf(" {"); for(Int_t iy=1; iyGetNbinsY(); iy++){ printf("%6.4f ", h2->GetBinContent(ix, iy)); } printf("%6.4f},\n", h2->GetBinContent(ix, h2->GetNbinsY())); } printf(" };\n"); gPad->Divide(2, 1, 1.e-5, 1.e-5); l = gPad->GetListOfPrimitives(); ((TVirtualPad*)l->At(0))->cd(); h1 = h2->ProjectionX("hsx_pxx"); h1->Scale(1.e4/kND); h1->SetMarkerStyle(24); h1->SetYTitle("<#sigma_{x}> [#mum]"); h1->SetXTitle("t_{drift} [#mus]"); h1->GetXaxis()->SetRange(2, kNTB-1); h1->Draw("pc"); t->Draw("z:t>>h2y(23, 0.1, 2.4, 25, 0., 2.5)","sy*(1)", "lego2fb"); h2 = (TH2F*)gROOT->FindObject("h2y"); printf(" const Double_t sy[24][25]={\n"); for(Int_t ix=1; ix<=h2->GetNbinsX(); ix++){ printf(" {"); for(Int_t iy=1; iyGetNbinsY(); iy++){ printf("%6.4f ", h2->GetBinContent(ix, iy)); } printf("%6.4f},\n", h2->GetBinContent(ix, h2->GetNbinsY())); } printf(" };\n"); ((TVirtualPad*)l->At(1))->cd(); h1 = h2->ProjectionX("hsy_pxx"); h1->Scale(1.e4/kND); h1->SetMarkerStyle(24); h1->SetYTitle("<#sigma_{y}> [#mum]"); h1->SetXTitle("t_{drift} [#mus]"); h1->GetXaxis()->SetRange(2, kNTB-1); h1->Draw("pc"); return kTRUE; case kMean: if(!(t = (TTree*)fResults->At(kMean))) break; t->Draw("z:t>>h2x(23, 0.1, 2.4, 25, 0., 2.5)","dx*(1)", "goff"); h2 = (TH2F*)gROOT->FindObject("h2x"); printf(" const Double_t dx[24][25]={\n"); for(Int_t ix=1; ix<=h2->GetNbinsX(); ix++){ printf(" {"); for(Int_t iy=1; iyGetNbinsY(); iy++){ printf("%6.4f ", h2->GetBinContent(ix, iy)); } printf("%6.4f},\n", h2->GetBinContent(ix, h2->GetNbinsY())); } printf(" };\n"); gPad->Divide(2, 1, 1.e-5, 1.e-5); l = gPad->GetListOfPrimitives(); ((TVirtualPad*)l->At(0))->cd(); h1 = h2->ProjectionX("hdx_pxx"); h1->Scale(1.e4/kND); h1->SetMarkerStyle(24); h1->SetYTitle(" [#mum]"); h1->SetXTitle("t_{drift} [#mus]"); h1->GetXaxis()->SetRange(2, kNTB-1); h1->Draw("pc"); t->Draw("z:t>>h2y(23, 0.1, 2.4, 25, 0., 2.5)","dy*(1)", "goff"); h2 = (TH2F*)gROOT->FindObject("h2y"); printf(" const Double_t dy[24][25]={\n"); for(Int_t ix=1; ix<=h2->GetNbinsX(); ix++){ printf(" {"); for(Int_t iy=1; iyGetNbinsY(); iy++){ printf("%6.4f ", h2->GetBinContent(ix, iy)); } printf("%6.4f},\n", h2->GetBinContent(ix, h2->GetNbinsY())); } printf(" };\n"); ((TVirtualPad*)l->At(1))->cd(); h1 = h2->ProjectionX("hdy_pxx"); h1->Scale(1.e4/kND); h1->SetMarkerStyle(24); h1->SetYTitle(" [#mum]"); h1->SetXTitle("t_{drift} [#mus]"); h1->GetXaxis()->SetRange(2, kNTB-1); h1->Draw("pc"); return kTRUE; default: break; } AliWarning("No container/data found."); return kFALSE; } //_______________________________________________________ TObjArray* AliTRDclusterResolution::Histos() { // Retrieve histograms array if already build or build it if(fContainer) return fContainer; fContainer = new TObjArray(kNtasks); //fContainer->SetOwner(kTRUE); TH3S *h3 = 0x0; TObjArray *arr = 0x0; fContainer->AddAt(arr = new TObjArray(2*AliTRDgeometry::kNlayer), kCenter); arr->SetName("Center"); for(Int_t il=0; ilFindObject(Form("hCenResLy%d", il)))){ h3 = new TH3S( Form("hCenResLy%d", il), Form(" ly [%d]", il), kNTB, fAt->GetBinLowEdge(1), fAt->GetBinUpEdge(kNTB), // x 51, -.51, .51, // y 60, -.3, .3); // dy h3->SetXTitle("x [#mus]"); h3->SetYTitle("y [pw]"); h3->SetZTitle("#Delta y[cm]"); } h3->Reset(); arr->AddAt(h3, il); // add Pull plot for each layer if(!(h3=(TH3S*)gROOT->FindObject(Form("hCenPullLy%d", il)))){ h3 = new TH3S( Form("hCenPullLy%d", il), Form(" ly [%d]", il), kNTB, fAt->GetBinLowEdge(1), fAt->GetBinUpEdge(kNTB), // x 51, -.51, .51, // y 60, -4., 4.); // dy h3->SetXTitle("x [#mus]"); h3->SetYTitle("y [pw]"); h3->SetZTitle("#Delta y/#sigma_{y}"); } h3->Reset(); arr->AddAt(h3, AliTRDgeometry::kNlayer+il); } if(!(h3 = (TH3S*)gROOT->FindObject("Charge"))){ h3 = new TH3S("Charge", "dy=f(q)", 50, 2.2, 7.5, 60, -.3, .3, 60, -4., 4.); h3->SetXTitle("log(q) [a.u.]"); h3->SetYTitle("#Delta y[cm]"); h3->SetZTitle("#Delta y/#sigma_{y}"); } fContainer->AddAt(h3, kQRes); fContainer->AddAt(arr = new TObjArray(kNTB), kSigm); arr->SetName("Resolution"); for(Int_t ix=0; ixFindObject(Form("hr_x%02d", ix)))){ h3 = new TH3S( Form("hr_x%02d", ix), Form(" t_{drift}(%3.1f-%3.1f)[#mus]", fAt->GetBinLowEdge(ix+1), fAt->GetBinUpEdge(ix+1)), kND, 0., 2.5, // z 35, -.35, .35, // tgp 60, -.3, .3); // dy h3->SetXTitle("z [mm]"); h3->SetYTitle("tg#phi"); h3->SetZTitle("#Delta y[cm]"); } arr->AddAt(h3, ix); } fContainer->AddAt(arr = new TObjArray(kNTB), kMean); arr->SetName("Systematics"); for(Int_t ix=0; ixFindObject(Form("hs_x%02d", ix)))){ h3 = new TH3S( Form("hs_x%02d", ix), Form(" t_{drift}(%3.1f-%3.1f)[#mus]", fAt->GetBinLowEdge(ix+1), fAt->GetBinUpEdge(ix+1)), kND, 0., 2.5, // z 35, -.35, .35, // tgp-h tgt 60, -.3, .3); // dy h3->SetXTitle("z [mm]"); h3->SetYTitle("tg(#phi) - h*tg(#theta)"); h3->SetZTitle("#Delta y[cm]"); } arr->AddAt(h3, ix); } return fContainer; } //_______________________________________________________ void AliTRDclusterResolution::Exec(Option_t *) { // Fill container histograms if(!HasExB()) AliWarning("ExB was not set. Call SetExB() before running the task."); Int_t det, t; Float_t x, y, z, q, dy, dydx, dzdx, cov[3], covcl[3]; TH3S *h3 = 0x0; // define limits around ExB for which x contribution is negligible const Float_t kDtgPhi = 3.5e-2; //(+- 2 deg) TObjArray *arr0 = (TObjArray*)fContainer->At(kCenter); TObjArray *arr1 = (TObjArray*)fContainer->At(kSigm); TObjArray *arr2 = (TObjArray*)fContainer->At(kMean); const AliTRDclusterInfo *cli = 0x0; TIterator *iter=fInfo->MakeIterator(); while((cli=dynamic_cast((*iter)()))){ cli->GetCluster(det, x, y, z, q, t, covcl); if(fDet>=0 && fDet!=det) continue; dy = cli->GetResolution(); cli->GetGlobalPosition(y, z, dydx, dzdx, &cov[0]); // resolution as a function of cluster charge // only for phi equal exB if(TMath::Abs(dydx-fExB) < kDtgPhi){ h3 = (TH3S*)fContainer->At(kQRes); h3->Fill(TMath::Log(q), dy, dy/TMath::Sqrt(covcl[0])); printf("q=%f Log(q)=%f dy=%f pull=%f\n",q, TMath::Log(q), dy, dy/TMath::Sqrt(covcl[0])); } // do not use problematic clusters in resolution analysis // TODO define limits as calibration aware (gain) !! if(q<20. || q>250.) continue; x = (t+.5)*fgkTimeBinLength; // conservative approach !! // resolution as a function of y displacement from pad center // only for phi equal exB if(TMath::Abs(dydx-fExB) < kDtgPhi/* && TMath::Abs(x-0.675)<0.225*/){ Int_t ly(AliTRDgeometry::GetLayer(det)); h3 = (TH3S*)arr0->At(ly); h3->Fill(x, cli->GetYDisplacement(), dy); h3 = (TH3S*)arr0->At(AliTRDgeometry::kNlayer+ly); h3->Fill(x, cli->GetYDisplacement(), dy/TMath::Sqrt(covcl[0])); } Int_t ix = fAt->FindBin(x); if(ix==0 || ix == fAt->GetNbins()+1){ AliWarning(Form("Drift time %3.1f outside allowed range", x)); continue; } // fill histo for resolution (sigma) ((TH3S*)arr1->At(ix-1))->Fill(10.*cli->GetAnisochronity(), dydx, dy); // fill histo for systematic (mean) ((TH3S*)arr2->At(ix-1))->Fill(10.*cli->GetAnisochronity(), dydx-cli->GetTilt()*dzdx, dy); } PostData(0, fContainer); } //_______________________________________________________ Bool_t AliTRDclusterResolution::PostProcess() { if(!fContainer) return kFALSE; if(!HasExB()) AliWarning("ExB was not set. Call SetExB() before running the post processing."); TObjArray *arr = 0x0; TTree *t=0x0; if(!fResults){ TGraphErrors *g = 0x0; fResults = new TObjArray(kNtasks); fResults->SetOwner(); fResults->AddAt(arr = new TObjArray(3), kQRes); arr->SetOwner(); arr->AddAt(g = new TGraphErrors(), 0); g->SetLineColor(kBlue); g->SetMarkerColor(kBlue); g->SetMarkerStyle(7); arr->AddAt(g = new TGraphErrors(), 1); g->SetLineColor(kRed); g->SetMarkerColor(kRed); g->SetMarkerStyle(23); arr->AddAt(g = new TGraphErrors(), 2); g->SetLineColor(kGreen); g->SetMarkerColor(kGreen); g->SetMarkerStyle(7); // pad center dependence fResults->AddAt(arr = new TObjArray(AliTRDgeometry::kNlayer+1), kCenter); arr->SetOwner(); arr->AddAt( t = new TTree("cent", "dy=f(y,x,ly)"), 0); t->Branch("ly", &fLy, "ly/B"); t->Branch("x", &fX, "x/F"); t->Branch("y", &fY, "y/F"); t->Branch("m", &fR[0], "m[2]/F"); t->Branch("s", &fR[2], "s[2]/F"); t->Branch("pm", &fP[0], "pm[2]/F"); t->Branch("ps", &fP[2], "ps[2]/F"); for(Int_t il=1; il<=AliTRDgeometry::kNlayer; il++){ arr->AddAt(g = new TGraphErrors(), il); g->SetLineColor(il); g->SetLineStyle(il); g->SetMarkerColor(il);g->SetMarkerStyle(4); } fResults->AddAt(t = new TTree("sigm", "dy=f(dw,x,dydx)"), kSigm); t->Branch("t", &fX, "t/F"); t->Branch("z", &fZ, "z/F"); t->Branch("sx", &fR[0], "sx[2]/F"); t->Branch("sy", &fR[2], "sy[2]/F"); fResults->AddAt(t = new TTree("mean", "dy=f(dw,x,dydx - h dzdx)"), kMean); t->Branch("t", &fX, "t/F"); t->Branch("z", &fZ, "z/F"); t->Branch("dx", &fR[0], "dx[2]/F"); t->Branch("dy", &fR[2], "dy[2]/F"); } else { TObject *o = 0x0; TIterator *iter=fResults->MakeIterator(); while((o=((*iter)()))) o->Clear(); // maybe it is wrong but we should never reach this point } // precalculated value of tg^2(alpha_L) Double_t exb2 = fExB*fExB; // square of the mean value of sigma drift length. // has to come from previous calibration //Double_t sxd2 = 1.;// [mm^2] printf("ExB[%e] ExB2[%e]\n", fExB, exb2); // process resolution dependency on charge if(HasProcess(kQRes)) ProcessCharge(); // process resolution dependency on y displacement if(HasProcess(kCenter)) ProcessCenterPad(); // process resolution dependency on drift legth and drift cell width if(HasProcess(kSigm)) ProcessSigma(); // process systematic shift on drift legth and drift cell width if(HasProcess(kMean)) ProcessMean(); return kTRUE; } //_______________________________________________________ Bool_t AliTRDclusterResolution::SetExB(Int_t det, Int_t col, Int_t row) { // check OCDB AliCDBManager *cdb = AliCDBManager::Instance(); if(cdb->GetRun() < 0){ AliError("OCDB manager not properly initialized"); return kFALSE; } // check magnetic field if(TMath::Abs(AliTracker::GetBz()) < 1.e-10){ AliWarning("B=0. Magnetic field may not be initialized. Continue if you know what you are doing !"); } // set reference detector if any if(det>=0 && detGetVdriftROC(det); const AliTRDCalDet *fCalVdriftDet = fCalibration->GetVdriftDet(); fVdrift = fCalVdriftDet->GetValue(det) * fCalVdriftROC->GetValue(col, row); fExB = AliTRDCommonParam::Instance()->GetOmegaTau(fVdrift); SetBit(kExB); return kTRUE; } //_______________________________________________________ void AliTRDclusterResolution::SetVisual() { if(fCanvas) return; fCanvas = new TCanvas("clResCanvas", "Cluster Resolution Visualization", 10, 10, 600, 600); } //_______________________________________________________ void AliTRDclusterResolution::ProcessCharge() { // Resolution as a function of cluster charge. // // As described in the function ProcessCenterPad() the error parameterization for clusters for phi = a_L can be // written as: // BEGIN_LATEX // #sigma_{y}^{2} = #sigma_{y}^{2}|_{B=0} + tg^{2}(#alpha_{L})*#sigma_{x}^{2} // END_LATEX // with the contribution in case of B=0 given by: // BEGIN_LATEX // #sigma_{y}|_{B=0} = #sigma_{diff}*Gauss(0, s_{ly}) + #delta_{#sigma}(q) // END_LATEX // which further can be simplified to: // BEGIN_LATEX // <#sigma_{y}|_{B=0}>(q) = <#sigma_{y}> + #delta_{#sigma}(q) // <#sigma_{y}> = #int{f(q)#sigma_{y}dq} // END_LATEX // The results for s_y and f(q) are displayed below: //Begin_Html // //End_Html // The function has to extended to accomodate gain calibration scalling and errors. // // Author // Alexandru Bercuci TH2I *h2 = 0x0; if(!(h2 = (TH2I*)fContainer->At(kQRes))) { AliWarning("Missing dy=f(Q) histo"); return; } TF1 f("f", "gaus", -.5, .5); TAxis *ax = 0x0; TH1D *h1 = 0x0; // compute mean error on x Double_t s2x = 0.; for(Int_t ix=5; ixAt(kQRes); TGraphErrors *gqm = (TGraphErrors*)arr->At(0); TGraphErrors *gqs = (TGraphErrors*)arr->At(1); TGraphErrors *gqp = (TGraphErrors*)arr->At(2); Double_t q, n = 0., entries; ax = h2->GetXaxis(); for(Int_t ix=1; ix<=ax->GetNbins(); ix++){ q = TMath::Exp(ax->GetBinCenter(ix)); if(q<20. || q>250.) continue; // ?! h1 = h2->ProjectionY("py", ix, ix); entries = h1->GetEntries(); if(entries < 50) continue; Adjust(&f, h1); h1->Fit(&f, "Q"); // Fill sy^2 = f(q) Int_t ip = gqm->GetN(); gqm->SetPoint(ip, q, 1.e4*f.GetParameter(1)); gqm->SetPointError(ip, 0., 1.e4*f.GetParError(1)); // correct sigma for ExB effect gqs->SetPoint(ip, q, 1.e4*(f.GetParameter(2)*f.GetParameter(2)-exb2*s2x)); gqs->SetPointError(ip, 0., 1.e4*f.GetParError(2)*f.GetParameter(2)); // save probability n += entries; gqp->SetPoint(ip, q, entries); gqp->SetPointError(ip, 0., 0./*TMath::Sqrt(entries)*/); } // normalize probability and get mean sy Double_t sm = 0., sy; for(Int_t ip=gqp->GetN(); ip--;){ gqp->GetPoint(ip, q, entries); entries/=n; gqp->SetPoint(ip, q, 1.e3*entries); gqs->GetPoint(ip, q, sy); sm += entries*sy; } // error parametrization s(q) = + b(1/q-1/q0) TF1 fq("fq", "[0] + [1]/x", 20., 250.); gqs->Fit(&fq/*, "W"*/); printf("sm=%f [0]=%f [1]=%f\n", 1.e-4*sm, fq.GetParameter(0), fq.GetParameter(1)); printf(" const Float_t sq0inv = %f; // [1/q0]\n", (sm-fq.GetParameter(0))/fq.GetParameter(1)); printf(" const Float_t sqb = %f; // [cm]\n", 1.e-4*fq.GetParameter(1)); } //_______________________________________________________ void AliTRDclusterResolution::ProcessCenterPad() { // Resolution as a function of y displacement from pad center and drift length. // // Since the error parameterization of cluster r-phi position can be written as (see AliTRDcluster::SetSigmaY2()): // BEGIN_LATEX // #sigma_{y}^{2} = (#sigma_{diff}*Gauss(0, s_{ly}) + #delta_{#sigma}(q))^{2} + tg^{2}(#alpha_{L})*#sigma_{x}^{2} + tg^{2}(#phi-#alpha_{L})*#sigma_{x}^{2}+[tg(#phi-#alpha_{L})*tg(#alpha_{L})*x]^{2}/12 // END_LATEX // one can see that for phi = a_L one gets the following expression: // BEGIN_LATEX // #sigma_{y}^{2} = #sigma_{y}^{2}|_{B=0} + tg^{2}(#alpha_{L})*#sigma_{x}^{2} // END_LATEX // where we have explicitely marked the remaining term in case of absence of magnetic field. Thus one can use the // previous equation to estimate s_y for B=0 and than by comparing in magnetic field conditions one can get the s_x. // This is a simplified method to determine the error parameterization for s_x and s_y as compared to the one // implemented in ProcessSigma(). For more details on cluster error parameterization please see also // AliTRDcluster::SetSigmaY2() // // The representation of dy=f(y_cen, x_drift| layer) can be also used to estimate the systematic shift in the r-phi // coordinate resulting from imperfection in the cluster shape parameterization. From the expresion of the shift derived // in ProcessMean() with phi=exb one gets: // BEGIN_LATEX // <#Delta y>= <#delta x> * (tg(#alpha_{L})-h*dz/dx) + <#delta y - #delta x * tg(#alpha_{L})> // <#Delta y>(y_{cen})= -h*<#delta x>(x_{drift}, q_{cl}) * dz/dx + #delta y(y_{cen}, ...) // END_LATEX // where all dependences are made explicit. This last expression can be used in two ways: // - by average on the dz/dx we can determine directly dy (the method implemented here) // - by plotting as a function of dzdx one can determine both dx and dy components in an independent method. //Begin_Html // //End_Html // Author // Alexandru Bercuci TObjArray *arr = (TObjArray*)fContainer->At(kCenter); if(!arr) { AliWarning("Missing dy=f(y | x, ly) container"); return; } Double_t exb2 = fExB*fExB; Float_t s[AliTRDgeometry::kNlayer]; TF1 f("f", "gaus", -.5, .5); TF1 fp("fp", "gaus", -3.5, 3.5); TH1D *h1 = 0x0; TH2F *h2 = 0x0; TH3S *h3r=0x0, *h3p=0x0; TObjArray *arrRes = (TObjArray*)fResults->At(kCenter); TTree *t = (TTree*)arrRes->At(0); TGraphErrors *gs = 0x0; TAxis *ax = 0x0; printf(" const Float_t lSy[6][24] = {\n {"); const Int_t nl = AliTRDgeometry::kNlayer; for(Int_t il=0; ilAt(il))) continue; if(!(h3p = (TH3S*)arr->At(nl+il))) continue; gs = (TGraphErrors*)arrRes->At(il+1); fLy = il; // printf("Ly[%d]\n", il); for(Int_t ix=1; ix<=h3r->GetXaxis()->GetNbins(); ix++){ ax = h3r->GetXaxis(); ax->SetRange(ix, ix); ax = h3p->GetXaxis(); ax->SetRange(ix, ix); fX = ax->GetBinCenter(ix); // printf(" x[%2d]=%4.2f\n", ix, fX); for(Int_t iy=1; iy<=h3r->GetYaxis()->GetNbins(); iy++){ ax = h3r->GetYaxis(); ax->SetRange(iy, iy); ax = h3p->GetYaxis(); ax->SetRange(iy, iy); fY = ax->GetBinCenter(iy); // printf(" y[%2d]=%5.2f\n", iy, fY); // finish navigation in the HnSparse h1 = (TH1D*)h3r->Project3D("z"); Int_t entries = (Int_t)h1->Integral(); if(entries < 50) continue; //Adjust(&f, h1); h1->Fit(&f, "QN"); // Fill sy,my=f(y_w,x,ly) fR[0] = f.GetParameter(1); fR[1] = f.GetParError(1); fR[2] = f.GetParameter(2); fR[3] = f.GetParError(2); h1 = (TH1D*)h3p->Project3D("z"); h1->Fit(&fp, "QN"); fP[0] = fp.GetParameter(1); fP[1] = fp.GetParError(1); fP[2] = fp.GetParameter(2); fP[3] = fp.GetParError(2); //printf("ly[%d] x[%3.1f] y[%+5.2f] m[%5.3f] s[%5.3f] \n", fLy, fX, fY, fR[0], fR[2]); t->Fill(); } } t->Draw("y:x>>h(24, 0., 2.4, 51, -.51, .51)", Form("s[0]*(ly==%d&&abs(m[0])<1.e-1)", fLy), "goff"); h2=(TH2F*)gROOT->FindObject("h"); f.FixParameter(1, 0.); Int_t n = h2->GetXaxis()->GetNbins(), nn(0); s[il]=0.; printf(" {"); for(Int_t ix=1; ix<=n; ix++){ ax = h2->GetXaxis(); fX = ax->GetBinCenter(ix); h1 = h2->ProjectionY("hCenPy", ix, ix); //if((Int_t)h1->Integral() < 1.e-10) continue; // Apply lorentz angle correction // retrieve error on the drift length Double_t s2x = AliTRDcluster::GetSX(ix-1); s2x *= s2x; Int_t nnn = 0; for(Int_t iy=1; iy<=h1->GetNbinsX(); iy++){ Double_t s2 = h1->GetBinContent(iy); s2*= s2; // sigma square corrected for Lorentz angle // s2 = s2_y(y_w,x)+exb2*s2_x Double_t sy = TMath::Sqrt(TMath::Max(s2 - exb2*s2x, Double_t(0.))); if(sy<1.e-20) continue; h1->SetBinContent(iy, sy); nnn++; printf("s[%6.2f] sx[%6.2f] sy[%6.2f]\n", 1.e4*TMath::Sqrt(s2), 1.e4*TMath::Abs(fExB*AliTRDcluster::GetSX(ix-1)), 1.e4*h1->GetBinContent(iy)); } // do fit only if enough data Double_t sPRF = 0.; if(nnn>5){ h1->Fit(&f, "QN"); sPRF = f.GetParameter(2); nn++; } s[il]+=sPRF; printf("%6.4f,%s", sPRF, ix%6?" ":"\n "); Int_t jx = gs->GetN(); gs->SetPoint(jx, fX, 1.e4*sPRF); gs->SetPointError(jx, 0., 0./*f.GetParError(0)*/); } printf("\b},\n"); s[il]/=nn; f.ReleaseParameter(2); if(!fCanvas) continue; h2->Draw("lego2fb"); fCanvas->Modified(); fCanvas->Update(); if(IsSaveAs()) fCanvas->SaveAs(Form("Figures/ProcessCenter_ly[%d].gif", fLy)); else gSystem->Sleep(100); } printf(" };\n"); printf(" const Float_t lPRF[] = {" "%5.3f, %5.3f, %5.3f, %5.3f, %5.3f, %5.3f};\n", s[0], s[1], s[2], s[3], s[4], s[5]); } //_______________________________________________________ void AliTRDclusterResolution::ProcessSigma() { // As the r-phi coordinate is the only one which is measured by the TRD detector we have to rely on it to // estimate both the radial (x) and r-phi (y) errors. This method is based on the following assumptions. // The measured error in the y direction is the sum of the intrinsic contribution of the r-phi measurement // with the contribution of the radial measurement - because x is not a parameter of Alice track model (Kalman). // BEGIN_LATEX // #sigma^{2}|_{y} = #sigma^{2}_{y*} + #sigma^{2}_{x*} // END_LATEX // In the general case // BEGIN_LATEX // #sigma^{2}_{y*} = #sigma^{2}_{y} + tg^{2}(#alpha_{L})#sigma^{2}_{x_{drift}} // #sigma^{2}_{x*} = tg^{2}(#phi - #alpha_{L})*(#sigma^{2}_{x_{drift}} + #sigma^{2}_{x_{0}} + tg^{2}(#alpha_{L})*x^{2}/12) // END_LATEX // where we have explicitely show the lorentz angle correction on y and the projection of radial component on the y // direction through the track angle in the bending plane (phi). Also we have shown that the radial component in the // last equation has twp terms, the drift and the misalignment (x_0). For ideal geometry or known misalignment one // can solve the equation // BEGIN_LATEX // #sigma^{2}|_{y} = tg^{2}(#phi - #alpha_{L})*(#sigma^{2}_{x} + tg^{2}(#alpha_{L})*x^{2}/12)+ [#sigma^{2}_{y} + tg^{2}(#alpha_{L})#sigma^{2}_{x}] // END_LATEX // by fitting a straight line: // BEGIN_LATEX // #sigma^{2}|_{y} = a(x_{cl}, z_{cl}) * tg^{2}(#phi - #alpha_{L}) + b(x_{cl}, z_{cl}) // END_LATEX // the error parameterization will be given by: // BEGIN_LATEX // #sigma_{x} (x_{cl}, z_{cl}) = #sqrt{a(x_{cl}, z_{cl}) - tg^{2}(#alpha_{L})*x^{2}/12} // #sigma_{y} (x_{cl}, z_{cl}) = #sqrt{b(x_{cl}, z_{cl}) - #sigma^{2}_{x} (x_{cl}, z_{cl}) * tg^{2}(#alpha_{L})} // END_LATEX // Below there is an example of such dependency. //Begin_Html // //End_Html // // The error parameterization obtained by this method are implemented in the functions AliTRDcluster::GetSX() and // AliTRDcluster::GetSYdrift(). For an independent method to determine s_y as a function of drift length check the // function ProcessCenterPad(). One has to keep in mind that while this method return the mean s_y over the distance // to pad center distribution the other method returns the *STANDARD* value at center=0 (maximum). To recover the // standard value one has to solve the obvious equation: // BEGIN_LATEX // #sigma_{y}^{STANDARD} = #frac{<#sigma_{y}>}{#int{s exp(s^{2}/#sigma) ds}} // END_LATEX // with "" being the value calculated here and "sigma" the width of the s_y distribution calculated in // ProcessCenterPad(). // // Author // Alexandru Bercuci TObjArray *arr = (TObjArray*)fContainer->At(kSigm); if(!arr){ AliWarning("Missing dy=f(x_d, d_w) container"); return; } // init visualization TGraphErrors *ggs = 0x0; TGraph *line = 0x0; if(fCanvas){ ggs = new TGraphErrors(); line = new TGraph(); line->SetLineColor(kRed);line->SetLineWidth(2); } // init logistic support TF1 f("f", "gaus", -.5, .5); TLinearFitter gs(1,"pol1"); TH1 *hFrame=0x0; TH1D *h1 = 0x0; TH3S *h3=0x0; TAxis *ax = 0x0; Double_t exb2 = fExB*fExB, x; AliTRDcluster c; TTree *t = (TTree*)fResults->At(kSigm); for(Int_t ix=0; ixAt(ix))) continue; c.SetPadTime(ix); x = c.GetXloc(0., 1.5); fX= fAt->GetBinCenter(ix+1); for(Int_t iz=1; iz<=h3->GetXaxis()->GetNbins(); iz++){ ax = h3->GetXaxis(); ax->SetRange(iz, iz); fZ = ax->GetBinCenter(iz); // reset visualization if(fCanvas){ new(ggs) TGraphErrors(); ggs->SetMarkerStyle(7); } gs.ClearPoints(); for(Int_t ip=1; ip<=h3->GetYaxis()->GetNbins(); ip++){ ax = h3->GetYaxis(); ax->SetRange(ip, ip); Double_t tgl = ax->GetBinCenter(ip); // finish navigation in the HnSparse //if(TMath::Abs(dydx)>0.18) continue; Double_t tgg = (tgl-fExB)/(1.+tgl*fExB); Double_t tgg2 = tgg*tgg; h1 = (TH1D*)h3->Project3D("z"); Int_t entries = (Int_t)h1->Integral(); if(entries < 50) continue; //Adjust(&f, h1); h1->Fit(&f, "QN"); Double_t s2 = f.GetParameter(2)*f.GetParameter(2); Double_t s2e = 2.*f.GetParameter(2)*f.GetParError(2); // Fill sy^2 = f(tg^2(phi-a_L)) gs.AddPoint(&tgg2, s2, s2e); if(!ggs) continue; Int_t jp = ggs->GetN(); ggs->SetPoint(jp, tgg2, s2); ggs->SetPointError(jp, 0., s2e); } // TODO here a more robust fit method has to be provided // for which lower boundaries on the parameters have to // be imposed. Unfortunately the Minuit fit does not work // for the TGraph in the case of B not 0. if(gs.Eval()) continue; fR[0] = gs.GetParameter(1) - x*x*exb2/12.; printf("s2x+x2=%f ang=%f s2x=%f\n", gs.GetParameter(1), x*x*exb2/12., fR[0]); fR[0] = TMath::Max(fR[0], Float_t(4.e-4)); // s^2_y = s0^2_y + tg^2(a_L) * s^2_x // s0^2_y = f(D_L)*x + s_PRF^2 fR[2]= gs.GetParameter(0)-exb2*fR[0]; printf("s2y+s2x=%f s2y=%f\n", fR[0], fR[2]); fR[2] = TMath::Max(fR[2], Float_t(2.5e-5)); fR[0] = TMath::Sqrt(fR[0]); fR[1] = .5*gs.GetParError(1)/fR[0]; fR[2] = TMath::Sqrt(fR[2]); fR[3] = gs.GetParError(0)+exb2*exb2*gs.GetParError(1); t->Fill(); printf(" xd=%4.2f[cm] sx=%6.1f[um] sy=%5.1f[um]\n", x, 1.e4*fR[0], 1.e4*fR[2]); if(!fCanvas) continue; fCanvas->cd(); fCanvas->SetLogx(); //fCanvas->SetLogy(); if(!hFrame){ fCanvas->SetMargin(0.15, 0.01, 0.1, 0.01); hFrame=new TH1I("hFrame", "", 100, 0., .3); hFrame->SetMinimum(0.);hFrame->SetMaximum(.005); hFrame->SetXTitle("tg^{2}(#phi-#alpha_{L})"); hFrame->SetYTitle("#sigma^{2}y[cm^{2}]"); hFrame->GetYaxis()->SetTitleOffset(2.); hFrame->SetLineColor(1);hFrame->SetLineWidth(1); hFrame->Draw(); } else hFrame->Reset(); Double_t xx = 0., dxx=.2/50; for(Int_t ip=0;ip<50;ip++){ line->SetPoint(ip, xx, gs.GetParameter(0)+xx*gs.GetParameter(1)); xx+=dxx; } ggs->Draw("pl"); line->Draw("l"); fCanvas->Modified(); fCanvas->Update(); if(IsSaveAs()) fCanvas->SaveAs(Form("Figures/ProcessSigma_z[%5.3f]_x[%5.3f].gif", fZ, fX)); else gSystem->Sleep(100); } } return; } //_______________________________________________________ void AliTRDclusterResolution::ProcessMean() { // By this method the cluster shift in r-phi and radial directions can be estimated by comparing with the MC. // The resolution of the cluster corrected for pad tilt with respect to MC in the r-phi (measuring) plane can be // expressed by: // BEGIN_LATEX // #Delta y=w - y_{MC}(x_{cl}) // w = y_{cl}^{'} + h*(z_{MC}(x_{cl})-z_{cl}) // y_{MC}(x_{cl}) = y_{0} - dy/dx*x_{cl} // z_{MC}(x_{cl}) = z_{0} - dz/dx*x_{cl} // y_{cl}^{'} = y_{cl}-x_{cl}*tg(#alpha_{L}) // END_LATEX // where x_cl is the drift length attached to a cluster, y_cl is the r-phi coordinate of the cluster measured by // charge sharing on adjacent pads and y_0 and z_0 are MC reference points (as example the track references at // entrance/exit of a chamber). If we suppose that both r-phi (y) and radial (x) coordinate of the clusters are // affected by errors we can write // BEGIN_LATEX // x_{cl} = x_{cl}^{*} + #delta x // y_{cl} = y_{cl}^{*} + #delta y // END_LATEX // where the starred components are the corrected values. Thus by definition the following quantity // BEGIN_LATEX // #Delta y^{*}= w^{*} - y_{MC}(x_{cl}^{*}) // END_LATEX // has 0 average over all dependency. Using this decomposition we can write: // BEGIN_LATEX // <#Delta y>=<#Delta y^{*}> + <#delta x * (dy/dx-h*dz/dx) + #delta y - #delta x * tg(#alpha_{L})> // END_LATEX // which can be transformed to the following linear dependence: // BEGIN_LATEX // <#Delta y>= <#delta x> * (dy/dx-h*dz/dx) + <#delta y - #delta x * tg(#alpha_{L})> // END_LATEX // if expressed as function of dy/dx-h*dz/dx. Furtheremore this expression can be plotted for various clusters // i.e. we can explicitely introduce the diffusion (x_cl) and drift cell - anisochronity (z_cl) dependences. From // plotting this dependence and linear fitting it with: // BEGIN_LATEX // <#Delta y>= a(x_{cl}, z_{cl}) * (dy/dx-h*dz/dx) + b(x_{cl}, z_{cl}) // END_LATEX // the systematic shifts will be given by: // BEGIN_LATEX // #delta x (x_{cl}, z_{cl}) = a(x_{cl}, z_{cl}) // #delta y (x_{cl}, z_{cl}) = b(x_{cl}, z_{cl}) + a(x_{cl}, z_{cl}) * tg(#alpha_{L}) // END_LATEX // Below there is an example of such dependency. //Begin_Html // //End_Html // // The occurance of the radial shift is due to the following conditions // - the approximation of a constant drift velocity over the drift length (larger drift velocities close to // cathode wire plane) // - the superposition of charge tails in the amplification region (first clusters appear to be located at the // anode wire) // - the superposition of charge tails in the drift region (shift towards anode wire) // - diffusion effects which convolute with the TRF thus enlarging it // - approximate knowledge of the TRF (approximate measuring in test beam conditions) // // The occurance of the r-phi shift is due to the following conditions // - approximate model for cluster shape (LUT) // - rounding-up problems // // The numerical results for ideal simulations for the radial and r-phi shifts are displayed below and used // for the cluster reconstruction (see the functions AliTRDcluster::GetXcorr() and AliTRDcluster::GetYcorr()). //Begin_Html // // //End_Html // More details can be found in the presentation given during the TRD // software meeting at the end of 2008 and beginning of year 2009, published on indico.cern.ch. // // Author // Alexandru Bercuci TObjArray *arr = (TObjArray*)fContainer->At(kMean); if(!arr){ AliWarning("Missing dy=f(x_d, d_w) container"); return; } // init logistic support TF1 f("f", "gaus", -.5, .5); TF1 line("l", "[0]+[1]*x", -.15, .15); TGraphErrors *gm = new TGraphErrors(); TH1 *hFrame=0x0; TH1D *h1 = 0x0; TH3S *h3 =0x0; TAxis *ax = 0x0; Double_t x; AliTRDcluster c; TTree *t = (TTree*)fResults->At(kMean); for(Int_t ix=0; ixAt(ix))) continue; c.SetPadTime(ix); x = c.GetXloc(0., 1.5); fX= fAt->GetBinCenter(ix+1); for(Int_t iz=1; iz<=h3->GetXaxis()->GetNbins(); iz++){ ax = h3->GetXaxis(); ax->SetRange(iz, iz); fZ = ax->GetBinCenter(iz); // reset fitter new(gm) TGraphErrors(); gm->SetMarkerStyle(7); for(Int_t ip=1; ip<=h3->GetYaxis()->GetNbins(); ip++){ ax = h3->GetYaxis(); ax->SetRange(ip, ip); Double_t tgl = ax->GetBinCenter(ip); // finish navigation in the HnSparse h1 = (TH1D*)h3->Project3D("z"); Int_t entries = (Int_t)h1->Integral(); if(entries < 50) continue; //Adjust(&f, h1); h1->Fit(&f, "QN"); // Fill = f(dydx - h*dzdx) Int_t jp = gm->GetN(); gm->SetPoint(jp, tgl, f.GetParameter(1)); gm->SetPointError(jp, 0., f.GetParError(1)); } if(gm->GetN()<4) continue; gm->Fit(&line, "QN"); fR[0] = line.GetParameter(1); // dx fR[1] = line.GetParError(1); fR[2] = line.GetParameter(0) + fExB*fR[0]; // xs = dy - tg(a_L)*dx t->Fill(); printf(" xd=%4.2f[cm] dx=%6.2f[um] dy=%6.2f[um]\n", x, 1.e4*fR[0], 1.e4*fR[2]); if(!fCanvas) continue; fCanvas->cd(); if(!hFrame){ fCanvas->SetMargin(0.1, 0.02, 0.1, 0.01); hFrame=new TH1I("hFrame", "", 100, -.3, .3); hFrame->SetMinimum(-.1);hFrame->SetMaximum(.1); hFrame->SetXTitle("tg#phi-htg#theta"); hFrame->SetYTitle("#Delta y[cm]"); hFrame->GetYaxis()->SetTitleOffset(1.5); hFrame->SetLineColor(1);hFrame->SetLineWidth(1); hFrame->Draw(); } else hFrame->Reset(); gm->Draw("pl"); line.Draw("same"); fCanvas->Modified(); fCanvas->Update(); if(IsSaveAs()) fCanvas->SaveAs(Form("Figures/ProcessMean_Z[%5.3f]_X[%5.3f].gif", fZ, fX)); else gSystem->Sleep(100); } } }