def plotMCEffs(input_list, plot_name='fakerate.pdf'): '''input_list expects tuples with structure: (hist tight, hist loose, legend line, colour) ''' graphs = [] for (hist_tight, hist_loose, legend, colour, style) in input_list: g = TGraphAsymmErrors(hist_tight) g.Divide(hist_tight, hist_loose) g.GetYaxis().SetTitle('Fake rate') g.GetXaxis().SetTitle(hist_tight.GetXaxis().GetTitle()) g.GetYaxis().SetTitleOffset(1.2) g.GetYaxis().SetTitleOffset(1.3) g.SetLineColor(colour) g.SetLineStyle(style) g.SetMarkerColor(colour) g.SetMarkerStyle(19+style) g.legend = legend graphs.append(g) ratio_graphs = [] base_graph = graphs[0] for graph in graphs[1:]: g_vals = base_graph.GetY() g_data_vals = graph.GetY() g_ratio = graph.Clone('ratio') for i in xrange(graph.GetN()): ratio = g_data_vals[i]/g_vals[i] if g_vals[i] else 0. g_ratio.SetPoint(i, base_graph.GetX()[i], ratio) rel_y_low = math.sqrt((graph.GetErrorYlow(i)/g_data_vals[i])**2 + (base_graph.GetErrorYlow(i)/g_vals[i])**2) if g_data_vals[i] > 0.0000001 and g_vals[i] > 0.0000001 else 0. g_ratio.SetPointEYlow(i, rel_y_low * ratio) rel_y_high = math.sqrt((graph.GetErrorYhigh(i)/g_data_vals[i])**2 + (base_graph.GetErrorYhigh(i)/g_vals[i])**2) if g_data_vals[i] > 0.0000001 and g_vals[i] > 0.0000001 else 0. g_ratio.SetPointEYhigh(i, rel_y_high * ratio) ratio_graphs.append(g_ratio) # Gymnastics to get same label sizes etc in ratio and main plot ytp_ratio = 2. xtp_ratio = 2. # hr.GetYaxis().SetNdivisions(4) for g_ratio in ratio_graphs: g_ratio.GetYaxis().SetTitleSize(g.GetYaxis().GetTitleSize() * xtp_ratio) g_ratio.GetXaxis().SetTitleSize(g.GetXaxis().GetTitleSize() * ytp_ratio) g_ratio.GetYaxis().SetTitleOffset(g.GetYaxis().GetTitleOffset() / xtp_ratio) g_ratio.GetXaxis().SetTitleOffset(g.GetXaxis().GetTitleOffset()) # / ytp_ratio) g_ratio.GetYaxis().SetLabelSize(g.GetYaxis().GetLabelSize() * xtp_ratio) g_ratio.GetXaxis().SetLabelSize(g.GetXaxis().GetLabelSize() * ytp_ratio) g_ratio.GetXaxis().SetTitle(base_graph.GetXaxis().GetTitle()) for graph in graphs: graph.GetXaxis().SetLabelColor(0) graph.GetXaxis().SetLabelSize(0) maxy = 1.3 * max([gr.GetMaximum() for gr in graphs] + [1./1.25]) for g in graphs: g.GetYaxis().SetRangeUser(0.0011, maxy) cv, pad, padr = HistDrawer.buildCanvas() pad.cd() base_graph.Draw('AP') for graph in graphs[1:]: graph.Draw('P') legend = TLegend(0.63, 0.63, 0.93, 0.91) legend.SetFillColor(0) legend.SetFillStyle(0) legend.SetLineColor(0) legend.SetLineWidth(0) legend2 = TLegend(0.78, 0.63, 0.93, 0.91) legend2.SetFillColor(0) legend2.SetFillStyle(0) legend2.SetLineColor(0) legend2.SetLineWidth(0) for graph in graphs: if 'opp' in graph.legend: legend2.AddEntry(graph.GetName(), graph.legend, 'lep') else: legend.AddEntry(graph.GetName(), graph.legend, 'lep') legend.Draw() legend2.Draw() padr.cd() for g_ratio in ratio_graphs: g_ratio.GetYaxis().SetRangeUser(0.01, 1.99) g_ratio.GetYaxis().SetTitle('Ratio to '+base_graph.legend) g_ratio.Draw('AP' if g_ratio == ratio_graphs[0] else 'P') drawRatioLines(g_ratio) cv.Print(plot_name) g.GetYaxis().SetRangeUser(0.0001, 1) pad.SetLogy(True) cv.Print(plot_name.replace('.', '_log.'))
x.SetLabelSize(15) ''' #create ratio plot using TGraphAsymmErrors gPad.Modified() ratio = TGraphAsymmErrors() ratio.Divide(h6,h,"cl=0.683 b(1,1) mode") ratio.SetMarkerColor(1) ratio.SetMarkerStyle(21) ratio.SetLineColor(1) ratio.GetXaxis().SetTitleSize(0.1) ratio.GetXaxis().SetLabelSize(0.1) ratio.GetXaxis().SetRangeUser(200,1000) ratio.GetXaxis().SetTitle("MET (GeV)") ratio.GetYaxis().SetTitleSize(0.1) ratio.GetYaxis().SetLabelSize(0.1) ratio.GetYaxis().SetTitleOffset(0.4) ratio.GetYaxis().SetTitle("ratio") ratio.SetTitle("") #upper histogram plot is pad1 pad1 = TPad("pad1","pad1", 0, 0.3, 1, 1.0) pad1.SetBottomMargin(0) #joins upper and lower plot pad1.Draw() #lower ratio plot is pad2 c1.cd() #returns to main canvas before defining pad2 pad2 = TPad("pad2","pad2", 0, 0.05, 1, 0.3) pad2.SetTopMargin(0) #joins upper and lower plot pad2.SetBottomMargin(0.2) pad2.Draw()
def limit2HDM(): global signals signals = range(800, 2000 + 1, 50) multF = HTOBB THEORY = ['T1', 'T2'] mass, val = fillValues("./combine/AZh/AZh_M%d.txt") Obs0s = TGraph() Exp0s = TGraph() Exp1s = TGraphAsymmErrors() Exp2s = TGraphAsymmErrors() massB, valB = fillValues("./combine/BBAZh/BBAZh_M%d.txt") Obs0sB = TGraph() Exp0sB = TGraph() Exp1sB = TGraphAsymmErrors() Exp2sB = TGraphAsymmErrors() for i, m in enumerate(mass): if not m in val: print "Key Error:", m, "not in value map" continue n = Exp0s.GetN() Obs0s.SetPoint(n, m, val[m][0] * multF) Exp0s.SetPoint(n, m, val[m][3] * multF) Exp1s.SetPoint(n, m, val[m][3] * multF) Exp1s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][2] * multF, val[m][4] * multF - val[m][3] * multF) Exp2s.SetPoint(n, m, val[m][3] * multF) Exp2s.SetPointError(n, 0., 0., val[m][3] * multF - val[m][1] * multF, val[m][5] * multF - val[m][3] * multF) Obs0sB.SetPoint(n, m, valB[m][0] * multF) Exp0sB.SetPoint(n, m, valB[m][3] * multF) Exp1sB.SetPoint(n, m, valB[m][3] * multF) Exp1sB.SetPointError(n, 0., 0., valB[m][3] * multF - valB[m][2] * multF, valB[m][4] * multF - valB[m][3] * multF) Exp2sB.SetPoint(n, m, valB[m][3] * multF) Exp2sB.SetPointError(n, 0., 0., valB[m][3] * multF - valB[m][1] * multF, valB[m][5] * multF - valB[m][3] * multF) col = 629 Exp2s.SetLineWidth(2) Exp2s.SetLineStyle(1) Obs0s.SetLineWidth(3) Obs0s.SetMarkerStyle(0) Obs0s.SetLineColor(1) Exp0s.SetLineStyle(2) Exp0s.SetLineWidth(3) Exp0s.SetLineColor(1) # Exp1s.SetFillColorAlpha(col, 0.4) #kGreen+1 # Exp1s.SetLineColorAlpha(col, 0.4) # Exp2s.SetFillColorAlpha(col, 0.2) #kOrange # Exp2s.SetLineColorAlpha(col, 0.2) Exp1s.SetFillColor(417) Exp1s.SetLineColor(417) Exp2s.SetFillColor(800) Exp2s.SetLineColor(800) colB = 922 Exp2sB.SetLineWidth(2) Obs0sB.SetLineStyle(9) Obs0sB.SetLineWidth(3) Obs0sB.SetMarkerStyle(0) Obs0sB.SetLineColor(colB) Exp0sB.SetLineStyle(8) Exp0sB.SetLineWidth(3) Exp0sB.SetLineColor(colB) Exp1sB.SetFillColorAlpha(colB, 0.4) #kGreen+1 Exp1sB.SetLineColorAlpha(colB, 0.4) Exp2sB.SetFillColorAlpha(colB, 0.2) #kOrange Exp2sB.SetLineColorAlpha(colB, 0.2) Exp2s.GetXaxis().SetTitle("m_{A} (GeV)") Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25) Exp2s.GetXaxis().SetNoExponent(True) Exp2s.GetXaxis().SetMoreLogLabels(True) Exp2s.GetYaxis().SetTitle( "#sigma(A) #bf{#it{#Beta}}(A #rightarrow Zh) #bf{#it{#Beta}}(h #rightarrow bb) (fb)" ) Exp2s.GetYaxis().SetTitleOffset(1.5) Exp2s.GetYaxis().SetNoExponent(True) Exp2s.GetYaxis().SetMoreLogLabels() Theory = {} #for t in THEORY: # Theory[t] = TGraphAsymmErrors() # for m in sorted(THDM[t]['ggA'].keys()): # if m < mass[0] or m > mass[-1]: continue # Xs, Xs_Up, Xs_Down = 0., 0., 0. # Xs = THDM[t]['ggA'][m] # Xs_Up = Xs*(1.+math.sqrt((THDM['PDF']['ggA'][m][0]-1.)**2 + (THDM['QCD']['ggA'][m][0]-1.)**2)) # Xs_Down = Xs*(1.-math.sqrt((1.-THDM['PDF']['ggA'][m][1])**2 + (1.-THDM['QCD']['ggA'][m][1])**2)) # n = Theory[t].GetN() # Theory[t].SetPoint(n, m, Xs) # Theory[t].SetPointError(n, 0., 0., (Xs-Xs_Down), (Xs_Up-Xs)) # Theory[t].SetLineColor(theoryLineColor[t]) # Theory[t].SetFillColor(theoryFillColor[t]) # Theory[t].SetFillStyle(theoryFillStyle[t]) # Theory[t].SetLineWidth(2) # #Theory[t].SetLineStyle(7) c1 = TCanvas("c1", "Exclusion Limits", 800, 600) c1.cd() #SetPad(c1.GetPad(0)) c1.GetPad(0).SetTopMargin(0.06) c1.GetPad(0).SetRightMargin(0.05) c1.GetPad(0).SetLeftMargin(0.12) c1.GetPad(0).SetTicks(1, 1) c1.GetPad(0).SetLogy() Exp2s.Draw("A3") Exp1s.Draw("SAME, 3") Exp0s.Draw("SAME, L") # Exp2sB.Draw("SAME, 3") # Exp1sB.Draw("SAME, 3") Exp0sB.Draw("SAME, L") if not options.blind: Obs0s.Draw("SAME, L") Obs0sB.Draw("SAME, L") for t in THEORY: Theory[t].Draw("SAME, L3") Theory[t].Draw("SAME, L3X0Y0") #setHistStyle(Exp2s) # Exp2s.GetXaxis().SetTitleSize(0.045) # Exp2s.GetYaxis().SetTitleSize(0.04) # Exp2s.GetXaxis().SetLabelSize(0.04) # Exp2s.GetYaxis().SetLabelSize(0.04) # Exp2s.GetXaxis().SetTitleOffset(1) # Exp2s.GetYaxis().SetTitleOffset(1.25) Exp2s.GetXaxis().SetTitleSize(0.050) Exp2s.GetYaxis().SetTitleSize(0.050) Exp2s.GetXaxis().SetLabelSize(0.045) Exp2s.GetYaxis().SetLabelSize(0.045) Exp2s.GetXaxis().SetTitleOffset(0.90) Exp2s.GetYaxis().SetTitleOffset(1.25) Exp2s.GetYaxis().SetMoreLogLabels(True) Exp2s.GetYaxis().SetNoExponent(True) Exp2s.GetYaxis().SetRangeUser(0.5, 1.e3) Exp2s.GetXaxis().SetRangeUser(mass[0], mass[-1]) drawAnalysis('AZh') drawRegion('AZHsl', True) #drawCMS(LUMI, "") #Preliminary drawCMS(LUMI, "Work in Progress", suppressCMS=True) # legend leg = TLegend(0.6, 0.90, 0.99, 0.90) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) leg.SetHeader("95% CL upper limits") leg.AddEntry(None, "gg #rightarrow A #rightarrow Zh", "") #"95% CL upper limits" leg.AddEntry(Obs0s, "Observed", "l") leg.AddEntry(Exp0s, "Expected", "l") leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f") leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f") leg.AddEntry(None, "", "") leg.AddEntry(None, "bbA #rightarrow Zh", "") leg.AddEntry(Obs0sB, "Observed", "l") leg.AddEntry(Exp0sB, "Expected", "l") leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.045) leg.Draw() # latex = TLatex() # latex.SetNDC() # latex.SetTextSize(0.040) # latex.SetTextFont(42) # latex.DrawLatex(0.65, leg.GetY1()-0.045, "cos(#beta-#alpha)=0.25, tan(#beta)=1") # legB = TLegend(0.12, 0.4-4*0.3/5., 0.65, 0.4) legB = TLegend(0.15, 0.27, 0.68, 0.27) legB.SetBorderSize(0) legB.SetFillStyle(0) #1001 legB.SetFillColor(0) for t in THEORY: legB.AddEntry(Theory[t], theoryLabel[t], "fl") legB.AddEntry(None, "cos(#beta-#alpha)=0.25, tan(#beta)=1", "") legB.SetY1(legB.GetY2() - legB.GetNRows() * 0.045) legB.Draw() c1.GetPad(0).RedrawAxis() leg.Draw() c1.Update() if not gROOT.IsBatch(): raw_input("Press Enter to continue...") c1.Print("plotsLimit/Exclusion/THDM.png") c1.Print("plotsLimit/Exclusion/THDM.pdf")
def main(): from optparse import OptionParser parser = OptionParser() parser.add_option("-i", "--inputfile", dest="inputfile") parser.add_option("-o", "--ouputfile", dest="outputfile") parser.add_option("-b", "--batch", action="store_true",\ dest="isBatch", default=False) parser.add_option("--normalization", nargs=2,\ type="float", dest="norm_range") parser.add_option("--fit", nargs=2,\ type="float", dest="fit_range") (options, args) = parser.parse_args() isSaveOutput = options.outputfile is not None if not (options.inputfile): parser.error("Please specify inputfiles.") import configurations as config if options.fit_range: fit_range = options.fit_range norm_range = (fit_range[1] - 200., fit_range[1]) else: fit_range = config.fit_range norm_range = config.norm_range # Override normalization range from input if options.norm_range: norm_range = options.norm_range from Styles import formatST, formatTemplate, formatUncertainty from ROOT import TFile, TF1, TH1D, TMath, TCanvas, TLegend,\ TGraphAsymmErrors, TVectorD #input file name infile = TFile(options.inputfile, "READ") from HistoStore import HistoStore store = HistoStore() canvas = HistoStore() print "Fit range: %d - %d GeV" % fit_range print "Normalization range: %d - %d GeV" % norm_range # Fit for N in config.exclusive_multiplicities: hST = infile.Get("plots%dJets/ST" % N) if not options.isBatch: c = TCanvas("TemplateN%d" % N, "TemplateN%d" % N, 500, 500) canvas.book(c) formatST(hST) hST.Draw("e") hST.GetXaxis().SetRangeUser(fit_range[0], config.maxST) hST.GetYaxis().SetRangeUser(1e-2, 2e4) c.SetLogy(1) for i,formula in enumerate(config.templates): if N == 2: f = TF1("templateN%d_%d" % (N, i), formula, 0, 10000) elif N == 3: f = store.get("templateN2_%d" % i).Clone("templateN%d_%d" % (N, i)) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) hST.Fit(f, "QN0", "", fit_range[0], fit_range[1]) if i == 0: hST.Fit(f, "Q0", "", fit_range[0], fit_range[1]) formatTemplate(f, N, i) store.book(f) if not options.isBatch: f.Draw("same") hTemplate = hST.Clone("histoTemplateN%d_%d" % (N,i)) hTemplate.Reset() hTemplate.Eval(f) formatTemplate(hTemplate, N, i) store.book(hTemplate) if i == 0: hRef = hTemplate.Clone("ReferenceTemplateN%d_0" % N) store.book(hRef) # Print Chi-squre/Ndof print "N = %d, Chi^2/Ndof = %0.2f/%d" %\ (N, f.GetChisquare(), f.GetNDF()) if not options.isBatch: c.Update() # Calculate scale/error from OptimizationTools import OptimizeScale for histoN, templateN in [[2,3]]: hST = store.get("ReferenceTemplateN%d_0" % histoN) hTemplate = store.get("ReferenceTemplateN%d_0" % templateN) hlnL, scale, err = OptimizeScale(hST, hTemplate, norm_range) hlnL.SetName("LogLikelihood_%dto%d" % (templateN, histoN)) store.book(hlnL) for i in range(len(config.templates)): hTemplate = store.get("histoTemplateN%d_%d" % (templateN, i)) hTemplate_ = hTemplate.Clone("histoTemplateN%d_%d__RescaledToN%d" % (templateN, i, histoN)) hTemplate_.Scale(scale) store.book(hTemplate_) # Shape Uncertainty hBkgTemplate = store.get("histoTemplateN2_0") hBkgTemplate.Rebin(config.rebin) nbins = hBkgTemplate.GetNbinsX() vST = TVectorD(nbins) vBkg = TVectorD(nbins) vexl = TVectorD(nbins) vexh = TVectorD(nbins) shape_el = TVectorD(nbins) shape_eh = TVectorD(nbins) rel_shape_el = TVectorD(nbins) rel_shape_eh = TVectorD(nbins) for i in range(nbins): vST[i] = hBkgTemplate.GetBinCenter(i+1) if (vST[i] < config.com): vBkg[i] = hBkgTemplate.GetBinContent(i+1) else: vBkg[i] = 0.0 vexl[i] = 0.0 vexh[i] = 0.0 shape_el[i] = 0.0 shape_eh[i] = 0.0 rel_shape_el[i] = 0.0 rel_shape_eh[i] = 0.0 for i in range(len(config.templates)): for label in ["histoTemplateN2_%d", "histoTemplateN3_%d__RescaledToN2"]: if label % i == "histoTemplateN2_0": continue h = store.get(label % i) h.Rebin(config.rebin) for ibin in range(nbins): diff = h.GetBinContent(ibin+1) - vBkg[ibin] if diff > 0 and diff > shape_eh[ibin]: shape_eh[ibin] = diff elif diff < 0 and abs(diff) > shape_el[ibin]: shape_el[ibin] = abs(diff) # Relative Shape Uncertaincy for i in range(nbins): if vBkg[i] > 0: #rel_shape_el[i] = rel_shape_el[i] / vBkg[i] #hape_eh[i] = rel_shape_eh[i] / vBkg[i] max_err = max(shape_el[i], shape_eh[i]) shape_el[i] = max_err shape_eh[i] = max_err rel_shape_el[i] = max_err /vBkg[i] rel_shape_eh[i] = max_err /vBkg[i] else: rel_shape_el[i] = 0.0 rel_shape_eh[i] = 0.0 #print vST[i], vBkg[i], rel_shape_el[i], rel_shape_eh[i] gShapeUncertainty = TGraphAsymmErrors(vST, vBkg, vexl, vexh, shape_el, shape_eh) gShapeUncertainty.SetName("Shape_Uncertainty") formatUncertainty(gShapeUncertainty) store.book(gShapeUncertainty) gRelShapeUncertainty = TGraphAsymmErrors(vST, vexl, vexl, vexh, rel_shape_el, rel_shape_eh) gRelShapeUncertainty.SetName("Relative_Shape_Uncertainty") formatUncertainty(gRelShapeUncertainty) store.book(gRelShapeUncertainty) # Generate Backgrouds for N in config.label_for_data: hST = infile.Get("plotsN%s/ST" % N) rel_scale_err2 = 0.0 scale_factor = 1.0 for Nref in config.label_for_ref: if N == Nref: continue template = store.get("ReferenceTemplateN%s_0" % Nref) hlnL, scale, err = OptimizeScale(hST, template, norm_range) hlnL.SetName("LogLikelihood_%sto%s" % (Nref, N)) store.book(hlnL) if Nref == "2": scale_factor = scale rel_scale_err2 += err/scale * err/scale print "%s/%s %.3f +/- %.3f" % (N, Nref, scale, err) vy = TVectorD(nbins) veyh = TVectorD(nbins) veyl = TVectorD(nbins) for i in range(nbins): vy[i] = vBkg[i] * scale_factor veyh[i] = vy[i] * TMath.Sqrt(rel_scale_err2 + rel_shape_eh[i]*rel_shape_eh[i]) veyl[i] = vy[i] * TMath.Sqrt(rel_scale_err2 + rel_shape_el[i]*rel_shape_el[i]) print "Scaling uncertainty (%s): %.2f" %\ (N, TMath.sqrt(rel_scale_err2) * 100.0) gBkg = TGraphAsymmErrors(vST, vy, vexl, vexh, veyl, veyh) gBkg.SetName("BackgroundGraph_N%s" % N) formatUncertainty(gBkg) store.book(gBkg) hST.Rebin(config.rebin) hST.SetName("Data_N%s" % N) formatST(hST) store.book(hST) hBkg = hST.Clone("Background_N%s" % N) hBkg.Reset() store.book(hBkg) for i in range(nbins): ibin = hBkg.FindBin(vST[i]) hBkg.SetBinContent(ibin, vy[i]) hBkg.SetBinError(ibin, max(veyh[i], vexl[i])) from OptimizationTools import Integral hIntBkg = hBkg.Clone("IntegralBackground_N%s" % N) Integral(hIntBkg) store.book(hIntBkg) hIntData = hST.Clone("IntegralData_N%s" % N) Integral(hIntData) store.book(hIntData) # Plot Shape Uncertainty if not options.isBatch: legend_shape = TLegend(0.5544355,0.5741525,0.9495968,0.9152542) legend_shape.SetTextFont(42) legend_shape.SetFillColor(0) c = TCanvas("ShapeUncertaintyN2", "ShapeUncertaintyN2", 500, 500) canvas.book(c) gShapeUncertainty.Draw("AC3") gShapeUncertainty.GetXaxis().SetRangeUser(fit_range[0], config.maxST) gShapeUncertainty.GetYaxis().SetRangeUser(5e-2, 1.2e6) legend_shape.AddEntry(store.get("Data_N2"), "Data (N = 2)", "p") legend_shape.AddEntry(gShapeUncertainty, "Shape Uncertainty", "f") for i in range(len(config.templates)): for label in ["histoTemplateN2_%d", "histoTemplateN3_%d__RescaledToN2"]: h = store.get(label % i) h.GetXaxis().SetRangeUser(fit_range[0], config.maxST) h.Draw("histcsame") if label == "histoTemplateN2_%d": N = 2 else: N = 3 legend_shape.AddEntry(h, "Parametrization %d (N = %d)" % (i, N), "l") store.get("Data_N2").Draw("esame") c.SetLogy(1) legend_shape.Draw("plain") c.Update() if isSaveOutput: store.saveAs(options.outputfile) if not options.isBatch: raw_input("Press Enter to continue...")
def limit(): method = '' channel = "bb" if INCLUDEACC: particleP = "X" else: particleP = "Z'" particle = 'b#bar{b}' multF = ZPTOBB THEORY = ['bstar'] if INCLUDEACC: THEORY.append('SSM') suffix = "_" + BTAGGING if ISMC: suffix += "_MC" if SY: suffix += "_comb" #if method=="cls": suffix="_CLs" if INCLUDEACC: suffix += "_acc" if SY: filename = "./combine/limits/bstar/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt" else: filename = "./combine/limits/bstar/" + BTAGGING + "/" + YEAR + "_M%d.txt" if CATEGORY != "": if SY: filename = filename.replace( BTAGGING + "/combined_run2/", BTAGGING + "/single_category/combined_run2/" + CATEGORY + "_") else: filename = filename.replace( BTAGGING + "/", BTAGGING + "/single_category/" + CATEGORY + "_") suffix += "_" + CATEGORY if ISMC: filename = filename.replace(".txt", "_MC.txt") mass, val = fillValues(filename) #print "mass =",mass #print "val =", val Obs0s = TGraph() Exp0s = TGraph() Exp1s = TGraphAsymmErrors() Exp2s = TGraphAsymmErrors() Sign = TGraph() pVal = TGraph() Best = TGraphAsymmErrors() Theory = {} for i, m in enumerate(mass): if not m in val: print "Key Error:", m, "not in value map" continue if INCLUDEACC: acc_factor = ACCEPTANCE[m] else: acc_factor = 1. n = Exp0s.GetN() Obs0s.SetPoint(n, m, val[m][0] * multF * acc_factor) Exp0s.SetPoint(n, m, val[m][3] * multF * acc_factor) Exp1s.SetPoint(n, m, val[m][3] * multF * acc_factor) Exp1s.SetPointError(n, 0., 0., (val[m][3] - val[m][2]) * multF * acc_factor, (val[m][4] - val[m][3]) * multF * acc_factor) Exp2s.SetPoint(n, m, val[m][3] * multF * acc_factor) Exp2s.SetPointError(n, 0., 0., (val[m][3] - val[m][1]) * multF * acc_factor, (val[m][5] - val[m][3]) * multF * acc_factor) if len(val[m]) > 6: Sign.SetPoint(n, m, val[m][6]) if len(val[m]) > 7: pVal.SetPoint(n, m, val[m][7]) if len(val[m]) > 8: Best.SetPoint(n, m, val[m][8]) if len(val[m]) > 10: Best.SetPointError(n, 0., 0., abs(val[m][9]), val[m][10]) for t in THEORY: Theory[t] = TGraphAsymmErrors() if 'bstar' == t: x = [] y = [] with open('bstar_deta1p1_lhc13TeV2.txt') as fin: for line in fin.readlines(): x.append(float(line.split()[0])) y.append(float(line.split()[1]) * 1000) for ind in range(len(x)): Theory[t].SetPoint(ind + 1, x[ind], y[ind]) Theory[t].SetLineColor(theoryLineColor[t]) Theory[t].SetFillColor(theoryFillColor[t]) Theory[t].SetFillStyle(theoryFillStyle[t]) Theory[t].SetLineWidth(2) continue Xs_dict = HVT[t]['Z']['XS'] if t != 'SSM' else SSM['Z'] for m in sorted(Xs_dict.keys()): if INCLUDEACC and t != 'SSM': acc_factor = ACCEPTANCE[m] else: acc_factor = 1. if m < SIGNALS[0] or m > SIGNALS[-1]: continue #if m < mass[0] or m > mass[-1]: continue #if t!= 'SSM' and m>4500: continue ## I don't have the higher mass xs if m > 4500: continue XsZ, XsZ_Up, XsZ_Down = 0., 0., 0. if t != 'SSM': XsZ = 1000. * HVT[t]['Z']['XS'][m] * SSM["BrZ"][ m] #assuming the same BR as the SSM Z' one XsZ_Up = XsZ * (1. + math.hypot(HVT[t]['Z']['QCD'][m][0] - 1., HVT[t]['Z']['PDF'][m][0] - 1.)) XsZ_Down = XsZ * (1. - math.hypot(1. - HVT[t]['Z']['QCD'][m][0], 1. - HVT[t]['Z']['PDF'][m][0])) else: XsZ = 1000. * SSM['Z'][m] * SSM["BrZ"][m] XsZ_Up = XsZ * (1. + math.hypot(HVT['A1']['Z']['QCD'][m][0] - 1., HVT['A1']['Z']['PDF'][m][0] - 1.)) XsZ_Down = XsZ * (1. - math.hypot(1. - HVT['A1']['Z']['QCD'][m][0], 1. - HVT['A1']['Z']['PDF'][m][0])) n = Theory[t].GetN() Theory[t].SetPoint(n, m, XsZ * acc_factor) Theory[t].SetPointError(n, 0., 0., (XsZ - XsZ_Down) * acc_factor, (XsZ_Up - XsZ) * acc_factor) Theory[t].SetLineColor(theoryLineColor[t]) Theory[t].SetFillColor(theoryFillColor[t]) Theory[t].SetFillStyle(theoryFillStyle[t]) Theory[t].SetLineWidth(2) #Theory[t].SetLineStyle(7) Exp2s.SetLineWidth(2) Exp2s.SetLineStyle(1) Obs0s.SetLineWidth(3) Obs0s.SetMarkerStyle(0) Obs0s.SetLineColor(1) Exp0s.SetLineStyle(2) Exp0s.SetLineWidth(3) Exp1s.SetFillColor(417) #kGreen+1 Exp1s.SetLineColor(417) #kGreen+1 Exp2s.SetFillColor(800) #kOrange Exp2s.SetLineColor(800) #kOrange Exp2s.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)") Exp2s.GetXaxis().SetTitleSize(Exp2s.GetXaxis().GetTitleSize() * 1.25) Exp2s.GetXaxis().SetNoExponent(True) Exp2s.GetXaxis().SetMoreLogLabels(True) Exp2s.GetYaxis().SetTitle( "#sigma(" + particleP + ") #bf{#it{#Beta}}(" + particleP + " #rightarrow " + particle + "){} (fb)".format(" #times #Alpha" if INCLUDEACC else "")) Exp2s.GetYaxis().SetTitleOffset(1.5) Exp2s.GetYaxis().SetNoExponent(True) Exp2s.GetYaxis().SetMoreLogLabels() Sign.SetLineWidth(2) Sign.SetLineColor(629) Sign.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)") Sign.GetXaxis().SetTitleSize(Sign.GetXaxis().GetTitleSize() * 1.1) Sign.GetYaxis().SetTitle("Significance") pVal.SetLineWidth(2) pVal.SetLineColor(629) pVal.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)") pVal.GetXaxis().SetTitleSize(pVal.GetXaxis().GetTitleSize() * 1.1) pVal.GetYaxis().SetTitle("local p-Value") Best.SetLineWidth(2) Best.SetLineColor(629) Best.SetFillColor(629) Best.SetFillStyle(3003) Best.GetXaxis().SetTitle("m_{" + particleP + "} (GeV)") Best.GetXaxis().SetTitleSize(Best.GetXaxis().GetTitleSize() * 1.1) Best.GetYaxis().SetTitle("Best Fit (pb)") c1 = TCanvas("c1", "Exclusion Limits", 800, 600) c1.cd() #SetPad(c1.GetPad(0)) c1.GetPad(0).SetTopMargin(0.06) c1.GetPad(0).SetRightMargin(0.05) c1.GetPad(0).SetLeftMargin(0.12) c1.GetPad(0).SetTicks(1, 1) #c1.GetPad(0).SetGridx() #c1.GetPad(0).SetGridy() c1.GetPad(0).SetLogy() Exp2s.Draw("A3") Exp1s.Draw("SAME, 3") for t in THEORY: Theory[t].Draw("SAME, L3") Theory[t].Draw("SAME, L3X0Y0") Exp0s.Draw("SAME, L") if not options.blind: Obs0s.Draw("SAME, L") #setHistStyle(Exp2s) Exp2s.GetXaxis().SetTitleSize(0.050) Exp2s.GetYaxis().SetTitleSize(0.050) Exp2s.GetXaxis().SetLabelSize(0.045) Exp2s.GetYaxis().SetLabelSize(0.045) Exp2s.GetXaxis().SetTitleOffset(0.90) Exp2s.GetYaxis().SetTitleOffset(1.25) Exp2s.GetYaxis().SetMoreLogLabels(True) Exp2s.GetYaxis().SetNoExponent(True) if INCLUDEACC: Exp2s.GetYaxis().SetRangeUser(0.05, 5.e3) else: Exp2s.GetYaxis().SetRangeUser(0.1, 5.e3) #else: Exp2s.GetYaxis().SetRangeUser(0.1, 1.e2) #Exp2s.GetXaxis().SetRangeUser(mass[0], min(mass[-1], MAXIMUM[channel] if channel in MAXIMUM else 1.e6)) Exp2s.GetXaxis().SetRangeUser(SIGNALS[0], SIGNALS[-1]) #drawAnalysis(channel) drawAnalysis("") #drawRegion(channel, True) drawRegion("", True) #drawCMS(LUMI, "Simulation Preliminary") #Preliminary if CATEGORY == "": #drawCMS(LUMI, "Work in Progress", suppressCMS=True) drawCMS(LUMI, "", suppressCMS=True) else: #drawCMS(LUMI, "Work in Progress, "+CAT_LABELS[CATEGORY], suppressCMS=True) drawCMS(LUMI, CAT_LABELS[CATEGORY], suppressCMS=True) # legend top = 0.9 nitems = 4 + len(THEORY) leg = TLegend(0.55, top - nitems * 0.3 / 5., 0.98, top) #leg = TLegend(0.45, top-nitems*0.3/5., 0.98, top) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) leg.SetHeader("95% CL upper limits") leg.AddEntry(Obs0s, "Observed", "l") leg.AddEntry(Exp0s, "Expected", "l") leg.AddEntry(Exp1s, "#pm 1 std. deviation", "f") leg.AddEntry(Exp2s, "#pm 2 std. deviation", "f") for t in THEORY: leg.AddEntry(Theory[t], theoryLabel[t], "fl") leg.Draw() latex = TLatex() latex.SetNDC() latex.SetTextSize(0.045) latex.SetTextFont(42) #latex.DrawLatex(0.66, leg.GetY1()-0.045, particleP+" #rightarrow "+particle+"h") leg2 = TLegend(0.12, 0.225 - 2 * 0.25 / 5., 0.65, 0.225) leg2.SetBorderSize(0) leg2.SetFillStyle(0) #1001 leg2.SetFillColor(0) c1.GetPad(0).RedrawAxis() leg2.Draw() if not options.blind: Obs0s.Draw("SAME, L") c1.GetPad(0).Update() if not gROOT.IsBatch(): raw_input("Press Enter to continue...") c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".png") c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".pdf") if 'ah' in channel or 'sl' in channel: c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".C") c1.Print("combine/plotsLimit/ExclusionLimits/" + YEAR + suffix + ".root") for t in THEORY: print "Model", t, ":", for m in range(mass[0], mass[-1], 1): if not (Theory[t].Eval(m) > Obs0s.Eval(m)) == ( Theory[t].Eval(m + 1) > Obs0s.Eval(m + 1)): print m, print "" return ##FIXME # ---------- Significance ---------- c2 = TCanvas("c2", "Significance", 800, 600) c2.cd() c2.GetPad(0).SetTopMargin(0.06) c2.GetPad(0).SetRightMargin(0.05) c2.GetPad(0).SetTicks(1, 1) c2.GetPad(0).SetGridx() c2.GetPad(0).SetGridy() Sign.GetYaxis().SetRangeUser(0., 5.) Sign.Draw("AL3") #drawCMS(LUMI, "Preliminary") drawCMS(LUMI, "Work in Progress", suppressCMS=True) drawAnalysis(channel[1:3]) c2.Print("combine/plotsLimit/Significance/" + YEAR + suffix + ".png") c2.Print("combine/plotsLimit/Significance/" + YEAR + suffix + ".pdf") # c2.Print("plotsLimit/Significance/"+YEAR+suffix+".root") # c2.Print("plotsLimit/Significance/"+YEAR+suffix+".C") # ---------- p-Value ---------- c3 = TCanvas("c3", "p-Value", 800, 600) c3.cd() c3.GetPad(0).SetTopMargin(0.06) c3.GetPad(0).SetRightMargin(0.05) c3.GetPad(0).SetTicks(1, 1) c3.GetPad(0).SetGridx() c3.GetPad(0).SetGridy() c3.GetPad(0).SetLogy() pVal.Draw("AL3") pVal.GetYaxis().SetRangeUser(2.e-7, 0.5) ci = [ 1., 0.317310508, 0.045500264, 0.002699796, 0.00006334, 0.000000573303, 0.000000001973 ] line = TLine() line.SetLineColor(922) line.SetLineStyle(7) text = TLatex() text.SetTextColor(922) text.SetTextSize(0.025) text.SetTextAlign(12) for i in range(1, len(ci) - 1): line.DrawLine(pVal.GetXaxis().GetXmin(), ci[i] / 2, pVal.GetXaxis().GetXmax(), ci[i] / 2) text.DrawLatex(pVal.GetXaxis().GetXmax() * 1.01, ci[i] / 2, "%d #sigma" % i) #drawCMS(LUMI, "Preliminary") drawCMS(LUMI, "Work in Progress", suppressCMS=True) drawAnalysis(channel[1:3]) c3.Print("combine/plotsLimit/pValue/" + YEAR + suffix + ".png") c3.Print("combine/plotsLimit/pValue/" + YEAR + suffix + ".pdf") # c3.Print("plotsLimit/pValue/"+YEAR+suffix+".root") # c3.Print("plotsLimit/pValue/"+YEAR+suffix+".C") # --------- Best Fit ---------- c4 = TCanvas("c4", "Best Fit", 800, 600) c4.cd() c4.GetPad(0).SetTopMargin(0.06) c4.GetPad(0).SetRightMargin(0.05) c4.GetPad(0).SetTicks(1, 1) c4.GetPad(0).SetGridx() c4.GetPad(0).SetGridy() Best.Draw("AL3") #drawCMS(LUMI, "Preliminary") drawCMS(LUMI, "Work in Progress", suppressCMS=True) drawAnalysis(channel[1:3]) c4.Print("combine/plotsLimit/BestFit/" + YEAR + suffix + ".png") c4.Print("combine/plotsLimit/BestFit/" + YEAR + suffix + ".pdf") # c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".root") # c4.Print("plotsLimit/BestFit/"+YEAR+suffix+".C") if not gROOT.IsBatch(): raw_input("Press Enter to continue...") if 'ah' in channel: outFile = TFile("bands.root", "RECREATE") outFile.cd() pVal.Write("graph") Best.Write("best") outFile.Close()