/** **************************************************************************** * \fn static uint16_t MDIO_Read(uint8_t phy_addr,uint8_t regAddr) * Read 16 Bit from MDIO * This is not the standard Read command as used for PHY Registers. * Use this command after setting the Address with a separate address * command with no other commands between. **************************************************************************** */ uint16_t MDIO_Read(uint8_t phy_addr,uint8_t regAddr) { uint8_t i; uint16_t outval,inval; SetMDO(1); SetDirection(DIR_OUT); mdio_delay(); /* Preamble */ for(i = 0; i < 32; i++) { SetMDC(1); mdio_delay(); SetMDC(0); mdio_delay(); } //outval = 0x1800; outval = 0x0c00; outval |= (phy_addr << 5); outval |= (regAddr & 0x1f); for(i = 0; i < 14; i++, outval <<= 1) { uint8_t mdo = (outval & 0x2000) ? 1 : 0; SetMDO(mdo); SetMDC(1); mdio_delay(); SetMDC(0); mdio_delay(); } /* Bus release with one clock cycle */ SetDirection(DIR_IN); SetMDC(1); mdio_delay(); SetMDC(0); mdio_delay(); inval = 0; for(i = 0; i < 16; i++) { SetMDC(1); mdio_delay(); SetMDC(0); mdio_delay(); /* ****************************************************** * delay of data is up to 300ns from rising edge so * better sample behind falling edge ****************************************************** */ inval <<= 1; if(GetMDI()) { inval |= 1; } } /* Let the device release the bus ? */ SetMDC(1); mdio_delay(); SetMDC(0); mdio_delay(); return inval; }
TGraphErrors *MD68Point(TH1 *hp, double y) { double ymin = hp->GetMinimum(); double ymax = hp->GetMaximum(); double yDraw = y>0? y : ymin + 0.25*(ymax-ymin); TGraphErrors *g = new TGraphErrors(); MaxDensityInterval mdi = GetMDI(hp, 0.68); g->SetPoint(0, mdi.u, yDraw); g->SetPointError(0, mdi.du, 0); SetGraphProps(g, kAzure, kAzure, kAzure, kOpenSquare, 1.5); return g; }
TGraphAsymmErrors *ReducedSamplingVolume(TH1D **hmp, TGraphAsymmErrors *old) { TGraphAsymmErrors *g = (TGraphAsymmErrors *)old->Clone(); for (int t=0; t<g->GetN(); t++) { if (!hmp[t]) Error("","!hmp[%d]",t); MaxDensityInterval mdi = GetMDI(hmp[t], 0.99); g->SetPoint(t, g->GetX()[t], mdi.u); g->SetPointEYlow(t, mdi.du); g->SetPointEYhigh(t, mdi.du); } return g; }
void BayesUnfoldingExample641() { #ifdef __CINT__ // Avoid CINT badness Printf("Please compile this script (root BayesUnfoldingExample641.C+) " "or use ROOT 6."); gSystem->Exit(0); #endif if (!gROOT->IsBatch()) { Printf("Several canvases coming...adding -b flag."); gROOT->SetBatch(); } gStyle->SetOptStat(0); gStyle->SetPaintTextFormat(".2f"); if (gSystem->Getenv("TMPDIR")) gSystem->SetBuildDir(gSystem->Getenv("TMPDIR")); TRandom3 ran; TStopwatch watch; // Watch starts here. A call to Start() would reset it. TObjArray *cList = new TObjArray(); // List of drawn canvases --> PDF file // Set up the problem double bins[Nt+1] = {0}; for (int j=0; j<=Nt; j++) bins[j] = 500*TMath::Exp(0.15*j); TestProblem testprob = AtlasDiJetMass(Nt, Nr, bins, bins, apar, bpar, nevts, evtWeight); TH2D *hM = testprob.Response; TH1D *hT = testprob.xTruth; TH1D *hTmc = testprob.xTruthEst; TH1D *hMt = testprob.xIni; TH1D *hD = testprob.bNoisy; TH1D *heff = testprob.eff; SetHistProps(hT,kRed+2,kNone,kRed+2); SetHistProps(hTmc,kRed,kNone,kRed); SetHistProps(hD,kBlack,kNone,kBlack,kFullCircle,1.5); TMatrixD M = MatrixUtils::Hist2Matrix(hM); TVectorD T = MatrixUtils::Hist2Vec(hT); // \hat{T} TVectorD Tmc = MatrixUtils::Hist2Vec(hTmc); // \tilde{T} TVectorD D = MatrixUtils::Hist2Vec(hD); TVectorD eff = MatrixUtils::Hist2Vec(heff); TVectorD Pt = MatrixUtils::ElemDiv(MatrixUtils::Hist2Vec(hMt), eff); // P(t) TMatrixD Prt = MatrixUtils::DivRowsByVector(M, Pt); // P(r|t) // Compute initial sampling volume and do MCMC sampling TGraphAsymmErrors *box = HyperBox(hTmc); SetGraphProps(box, kGreen+2, kNone, kSpring, kFullSquare, 1.0); // Likelihood functor LogPoissonLikeFn llfunc(Prt, D); // Curvature regularization. // Note that this is not directly penalizing curvature of the solution. // Instead it smooths the solution divided by the trial spectrum. std::vector<double> regpars; regpars.push_back(alpha); // Regularization strength for (int i=0; i<box->GetN(); i++) regpars.push_back(box->GetY()[i]); CurvatureRegFn regfunc(regpars); TTree *tmcmc = SampleMH(nMcmcSamples, 1e4, 0.01, box, llfunc, regfunc); // Create marginal prob. distributions from MCMC std::cout << Form("Marginalizing parameters from Markov chain...") << std::flush; TH1D *hMCMC[Nt]; for (int t=0; t<Nt; t++) { double tlo = box->GetY()[t] - box->GetEYlow()[t]; double thi = box->GetY()[t] + box->GetEYhigh()[t]; hMCMC[t] = new TH1D(Form("hMCMC%d",t),"",nMcmcBins, tlo, thi); hMCMC[t]->SetTitle(Form("MCMC - point %d;" "entries;" "Marginal posterior probability",t)); // Marginalize with unit weight when using MCMC, weight by // likelihood if sampling was uniform. tmcmc->Draw(Form("T%d >> hMCMC%d",t,t), "", "goff"); hMCMC[t]->Scale(1./hMCMC[t]->Integral(1, nMcmcBins)); SetHistProps(hMCMC[t], kBlack, kYellow, kBlack, kFullCircle, 1.0); hMCMC[t]->GetYaxis()->SetTitleOffset(1.5); } Printf("Done marginalizing MCMC."); // Now compute reduced sampling volume, and do uniform sampling TGraphAsymmErrors *rbox = ReducedSamplingVolume(hMCMC, box); SetGraphProps(rbox, kBlack, kNone, kNone, kFullSquare, 1.0); TH1D *hFlat[Nt]; if (doUniformSampling) { TTree *tflat = SampleUniform(nFlatSamples, D, Prt, rbox); std::cout << Form("Marginalizing parameters from uniform volume...") << std::flush; for (int t=0; t<Nt; t++) { double tlo = rbox->GetY()[t] - rbox->GetEYlow()[t]; double thi = rbox->GetY()[t] + rbox->GetEYhigh()[t]; hFlat[t] = new TH1D(Form("hFlat%d",t),"",nFlatBins, tlo, thi); hFlat[t]->SetTitle(Form("Uniform sampling - point %d;" "dijet mass (GeV/c^{2});" "Marginal posterior probability",t)); tflat->Draw(Form("T%d >> hFlat%d",t,t), "L", "goff"); hFlat[t]->Scale(1./hFlat[t]->Integral(1,nFlatBins)); SetHistProps(hFlat[t], kBlack, kOrange, kBlack, kFullCircle, 1.0); } Printf("Done marginalizing uniform volume."); } // Unfolded spectrum from MCMC TGraphErrors *unf1 = new TGraphErrors(); SetGraphProps(unf1, kBlue, kNone, kBlue, kOpenSquare, 1.5); unf1->SetLineWidth(2); for (int t=0; t<Nt; t++) { MaxDensityInterval mdi = GetMDI(hMCMC[t], 0.68); unf1->SetPoint(t, hD->GetBinCenter(t+1), mdi.u); unf1->SetPointError(t, 0.48*hD->GetBinWidth(t+1), mdi.du); } // Unfolded spectrum from uniform sampling after volume reduction TGraphErrors *unf2 = 0; if (doUniformSampling) { unf2 = new TGraphErrors(); SetGraphProps(unf2, kRed, kNone, kRed, kOpenSquare, 1.5); unf2->SetLineWidth(2); for (int t=0; t<Nt; t++) { MaxDensityInterval mdi = GetMDI(hFlat[t], 0.68); unf2->SetPoint(t, hD->GetBinCenter(t+1), mdi.u); unf2->SetPointError(t, 0.47*hD->GetBinWidth(t+1), mdi.du); } } Printf("Drawing results..."); DrawObject(hM, "colz", "matrix", cList, 550, 500); gPad->SetLogx(); gPad->SetLogy(); gPad->SetLogz(); gPad->SetRightMargin(0.15); DrawObject(heff, "", "efficiency", cList); // Draw marginal dists. from MCMC for (int t=0; t<Nt; t++) { DrawObject(hMCMC[t], "", Form("post_%d", t), cList); gPad->SetLeftMargin(0.15); if (doUniformSampling) { hFlat[t]->Scale(1./hFlat[t]->Integral(1, nFlatBins,"width")); hFlat[t]->Draw("same"); } double ymin = hMCMC[t]->GetMinimum(); double ymax = hMCMC[t]->GetMaximum(); double yDraw = 0.25*(ymax-ymin); DataPoint(hD, hMCMC[t], t, 0.75*yDraw)->Draw("ep same"); TruePoint(hT, hMCMC[t], t, yDraw)->Draw("p same"); MD68Point(hMCMC[t], yDraw)->Draw("ep same"); } // Result! hT->GetYaxis()->SetRangeUser(0.002, 101*nevts*evtWeight); DrawObject(hT, "ep", "result", cList); gPad->SetLogy(); box->Draw("e5 same"); if (doUniformSampling || drawReducedVolume) rbox->Draw("e5 same"); hT->Draw("same"); hD->Draw("ep same"); unf1->Draw("ep same"); if (unf2) unf2->Draw("ep same"); if (printPDFs) { PrintPDFs(cList, "pdfs"); // Print individuals into ./pdfs dir PrintPDF(cList, "pdfs/mcmc_unfold_example"); // Multipage PDF } Printf("All done."); watch.Stop(); watch.Print(); return; }