def getGraph(self): from array import array from ROOT import TMultiGraph, TLegend, TGraphAsymmErrors n = len(self.__x) if n != len(self.__y) or n != len(self.__yErrLow) or n != len( self.__yErrHigh): raise StandardError, "The length of the x(%s), y(%s) and y error(%s,%s) lists does not match" % ( len(self.__x), len(self.__y), len( self.__yErrLow), len(self.__yErrHigh)) result = TMultiGraph() legendPosition = [ float(i) for i in self.__getStyleOption("legendPosition").split() ] legend = TLegend(*legendPosition) legend.SetFillColor(0) result.SetTitle("%s;%s;%s" % (self.__title, self.__xTitle, self.__yTitle)) #(refArrays, refLabel) = self.__getRefernceGraphArrays() #refGraph = TGraphAsymmErrors(*refArrays) #refGraph.SetLineWidth(2) #refGraph.SetLineColor(int(self.__config.get("reference","lineColor"))) #refGraph.SetFillColor(int(self.__config.get("reference","fillColor"))) #result.Add(refGraph,"L3") #legend.AddEntry(refGraph,self.__config.get("reference","name")) xErr = array("d", [0 for i in range(n)]) print "__x = ", self.__x print "__y = ", self.__y graph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr, self.__yErrLow, self.__yErrHigh) graph.SetLineWidth(2) graph.SetFillColor(0) graph.SetLineColor(int(self.__getStyleOption("lineColor"))) graph.SetMarkerColor(int(self.__getStyleOption("markerColor"))) graph.SetMarkerStyle(int(self.__getStyleOption("markerStyle"))) graph.SetMarkerSize(float(self.__getStyleOption("markerSize"))) sysGraph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr, self.__ySysErrLow, self.__ySysErrHigh) sysGraph.SetLineWidth(1) sysGraph.SetFillColor(0) sysGraph.SetLineColor(int(self.__getStyleOption("lineColor"))) sysGraph.SetMarkerColor(int(self.__getStyleOption("markerColor"))) sysGraph.SetMarkerStyle(int(self.__getStyleOption("markerStyle"))) sysGraph.SetMarkerSize(float(self.__getStyleOption("markerSize"))) result.Add(sysGraph, "[]") result.Add(graph, "P") # result.SetName("MultiPlots") # result.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle)) result.SetName("MG_%s" % (self.__title)) legend.AddEntry(graph, self.__getStyleOption("name")) #for (x,y,yErr) in zip(self.__x, self.__y, zip(self.__yErrLow,self.__yErrHigh)): # self.__addAnnotaion("hallo",x,y,yErr) return (result, legend)
def bestfit(): xmin = -10 xmax = 6 c, h = draw_canvas_histo( xmin, xmax, "Best fit #mu = #sigma/#sigma_{SM} at m_{H} = 125 GeV") # line at SM expectation of mu = 1 l = TLine() l.SetLineStyle(2) l.DrawLine(1.0, -1.0, 1.0, -1.0 + nchannels) mu = np.array([-0.4, -4.734, -2.014]) upper = np.array([2.12, 3.688, 1.80]) lower = np.array([2.08, 3.754, 1.8224]) channel = np.array([1.5, 0.5, -0.5]) zero = np.zeros(nchannels) gmu = TGraphAsymmErrors(nchannels, mu, channel, lower, upper, zero, zero) gmu.SetMarkerStyle(21) gmu.SetMarkerSize(1.5) gmu.SetLineColor(2) gmu.SetLineWidth(2) gmu.Draw("pz") #draw_disclaimer() c.RedrawAxis() c.Modified() c.Update() c.SaveAs("bestfit.pdf")
def getGraphSimple(self): from array import array from ROOT import TMultiGraph, TLegend, TGraphAsymmErrors n = len(self.__x) if n != len(self.__y) or n != len(self.__yErrLow) or n != len(self.__yErrHigh): raise StandardError, "The length of the x(%s), y(%s) and y error(%s,%s) lists does not match"%(len(self.__x), len(self.__y), len(self.__yErrLow), len(self.__yErrHigh)) legendPosition = [float(i) for i in self.__getStyleOption("legendPosition").split()] legend = TLegend(*legendPosition) legend.SetFillColor(0) xErr = array("d",[0 for i in range(n)]) print "__x = ", self.__x print "__y = ", self.__y graph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr, self.__yErrLow,self.__yErrHigh) graph.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle)) graph.SetLineWidth(2) graph.SetFillColor(0) graph.SetLineColor(int(self.__getStyleOption("lineColor"))) graph.SetMarkerColor(int(self.__getStyleOption("markerColor"))) graph.SetMarkerStyle(int(self.__getStyleOption("markerStyle"))) graph.SetMarkerSize(float(self.__getStyleOption("markerSize"))) graph.SetDrawOption("AP") return (graph, legend)
def getEff(name, den_input, num_input, rebin=0, xtitle='', ytitle=''): c = TCanvas(name + '_Canvas') legend = TLegend(0.8, 0.1, 0.999, 0.6) legend.SetFillColor(kWhite) den = den_input.Clone() num = num_input.Clone() if rebin != 0: den.Rebin(rebin) num.Rebin(rebin) error_bars = TGraphAsymmErrors() error_bars.Divide(num, den, "cl=0.683 b(1,1) mode") error_bars.SetLineWidth(3) if xtitle == '': error_bars.GetXaxis().SetTitle(num.GetXaxis().GetTitle()) else: error_bars.GetXaxis().SetTitle(xtitle) if ytitle == '': error_bars.GetYaxis().SetTitle(num.GetYaxis().GetTitle()) else: error_bars.GetYaxis().SetTitle(ytitle) error_bars.GetXaxis().SetRangeUser(400, 2000) error_bars.SetLineColor(kBlack) error_bars.SetMaximum(1.01) error_bars.SetMinimum(0.) if ytitle == '': error_bars.GetYaxis().SetTitle("Trigger rate") else: error_bars.GetYaxis().SetTitle(ytitle) error_bars.SetTitle('') error_bars.Draw('AP') c.SaveAs('pdf/' + name + '.pdf') c.Write(name + '_Canvas') error_bars.Write(name)
def makeResidHist(data, bkg): pulls = TGraphAsymmErrors(data.GetN()) pulls.SetName("Pulls") pulls.SetLineWidth(data.GetLineWidth()) pulls.SetLineStyle(data.GetLineStyle()) pulls.SetLineColor(data.GetLineColor()) pulls.SetMarkerSize(data.GetMarkerSize()) pulls.SetMarkerStyle(data.GetMarkerStyle()) pulls.SetMarkerColor(data.GetMarkerColor()) pulls.SetFillStyle(data.GetFillStyle()) pulls.SetFillColor(data.GetFillColor()) # Add histograms, calculate Poisson confidence interval on sum value for i in range(data.GetN()): x = data.GetX()[i] dyl = data.GetErrorYlow(i) dyh = data.GetErrorYhigh(i) yy = data.GetY()[i] - bkg.Interpolate(x) #bkg.GetBinContent(i+1) norm = dyl if yy > 0. else dyh if norm == 0.: yy, dyh, dyl = 0., 0., 0. else: yy /= norm dyh /= norm dyl /= norm pulls.SetPoint(i, x, yy) pulls.SetPointEYhigh(i, dyh) pulls.SetPointEYlow(i, dyl) return pulls
def convertHistToGraph(hist, useGarwood=False): alpha = 1 - 0.6827 graph = TGraphAsymmErrors(hist.GetNbinsX()) if useGarwood: lastEvent = False for i in reversed(range(hist.GetNbinsX())): N = hist.GetBinContent(i + 1) if not lastEvent and N > 0: lastEvent = True if lastEvent and N <= 0.: N = 1.e-6 L = 0 if N == 0 else ROOT.Math.gamma_quantile(alpha / 2, N, 1.) U = ROOT.Math.gamma_quantile_c(alpha / 2, N + 1, 1) graph.SetPoint(i, hist.GetXaxis().GetBinCenter(i + 1), N if not N == 0 else -1.e99) graph.SetPointError(i, 0., 0., N - L, U - N) else: for i in range(hist.GetNbinsX()): graph.SetPoint(i, hist.GetXaxis().GetBinCenter(i + 1), hist.GetBinContent(i + 1)) graph.SetPointError(i, hist.GetXaxis().GetBinWidth(i + 1) / 2., hist.GetXaxis().GetBinWidth(i + 1) / 2., hist.GetBinError(i + 1), hist.GetBinError(i + 1)) graph.SetLineWidth(hist.GetLineWidth()) graph.SetLineStyle(hist.GetLineStyle()) graph.SetLineColor(hist.GetLineColor()) graph.SetMarkerSize(hist.GetMarkerSize()) graph.SetMarkerStyle(hist.GetMarkerStyle()) graph.SetMarkerColor(hist.GetMarkerColor()) graph.SetFillStyle(hist.GetFillStyle()) graph.SetFillColor(hist.GetFillColor()) return graph
def ratioplot(): # create required parts leg = getLegend() latex = getLatex() c = TCanvas() c.SetLogy() #Draw input histograms hists = ['h_frac_recoil_', 'h_full_recoil_'] label = ['recoil with MET triggers', 'recoil without MET triggers'] combineHist(hists, label, leg) #leg.Draw() #c.SaveAs("Combinehists_D.pdf") ratio = [] h1 = f.Get('h_frac_recoil_') #h1=setHistStyle(h1,bins) h2 = f.Get('h_full_recoil_') #h2=setHistStyle(h2,bins) h3 = createRatio(h1, h2) gr = TGraphAsymmErrors(h1, h2) gr.GetXaxis().SetRangeUser(0, 1500) gr.GetYaxis().SetRangeUser(0, 1.2) gr.SetMarkerStyle(20) gr.SetMarkerSize(0.5) gr.SetLineColor(1) gr.GetYaxis().SetTitle("Trigger Efficiency") gr.GetXaxis().SetTitle("Recoil [GeV]") gr.SetTitle("") # print ("ratio",ratio ) # c, pad1, pad2 = createCanvasPads() # # # draw everything # pad1.cd() # h1.Draw() # h2.Draw("same") # to avoid clipping the bottom zero, redraw a small axis # h1.GetYaxis().SetLabelSize(0.0) # axis = TGaxis(-5, 20, -5, 220, 20, 220, 510, "") # axis.SetLabelFont(43) # axis.SetLabelSize(15) # axis.Draw() # pad2.cd() gr.Draw() latex.DrawLatex(0.41, 0.93, "Trigger Efficincy in MET Run2017E") xmin = 0.0 line = TLine(max(xmin, gr.GetXaxis().GetXmin()), 1, 1500, 1) line.SetLineColor(1) line.SetLineWidth(1) line.SetLineStyle(7) line.Draw() #h3.Draw('pl') c.SaveAs("test.pdf")
def getDataPoissonErrors(hist, kPoisson=False, drawZeroBins=False, drawXbars=False, centerBin=True): '''Make data poisson errors for a histogram with two different methods: - TH1.kPoisson - chi-squared quantile ''' # https://github.com/DESY-CMS-SUS/cmgtools-lite/blob/8_0_25/TTHAnalysis/python/plotter/mcPlots.py#L70-L102 # https://github.com/DESY-CMS-SUS/cmgtools-lite/blob/8_0_25/TTHAnalysis/python/plotter/susy-1lep/RcsDevel/plotDataPredictWithSyst.py#L12-L21 if kPoisson: hist.SetBinErrorOption(TH1D.kPoisson) Nbins = hist.GetNbinsX() xaxis = hist.GetXaxis() alpha = (1 - 0.6827) / 2. graph = TGraphAsymmErrors(Nbins) graph.SetName(hist.GetName() + "_graph") graph.SetTitle(hist.GetTitle()) for i in xrange(1, Nbins + 1): N = hist.GetBinContent(i) if N <= 0 and not drawZeroBins: continue dN = hist.GetBinError(i) yscale = 1 if centerBin: x = xaxis.GetBinCenter(i) else: x = xaxis.GetBinLowEdge(i) if N > 0 and dN > 0 and abs(dN**2 / N - 1) > 1e-4: # check is error is Poisson yscale = (dN**2 / N) N = (N / dN)**2 if kPoisson: EYlow = hist.GetBinErrorLow(i) EYup = hist.GetBinErrorUp(i) else: EYlow = (N - Math.chisquared_quantile_c(1 - alpha, 2 * N) / 2.) if N > 0 else 0 EYup = Math.chisquared_quantile_c(alpha, 2 * (N + 1)) / 2. - N y = yscale * N EXup = xaxis.GetBinUpEdge(i) - x if drawXbars else 0 EXlow = x - xaxis.GetBinLowEdge(i) if drawXbars else 0 graph.SetPoint(i - 1, x, y) graph.SetPointError(i - 1, EXlow, EXup, EYlow, EYup) #print ">>> getDataPoissonErrors - bin %2d: (x,y) = ( %3.1f - %4.2f + %4.2f, %4.2f - %4.2f + %4.2f )"%(i,x,EXlow,EXup,y,EYlow,EYup) graph.SetLineWidth(hist.GetLineWidth()) graph.SetLineColor(hist.GetLineColor()) graph.SetLineStyle(hist.GetLineStyle()) graph.SetMarkerSize(hist.GetMarkerSize()) graph.SetMarkerColor(hist.GetMarkerColor()) graph.SetMarkerStyle(hist.GetMarkerStyle()) return graph
def makeEffPlotsVars(tree, varx, vary, sel, nbinx, xmin, xmax, nbiny, ymin, ymax, xtitle, ytitle, leglabel=None, header='', addon='', option='pt', marker=20, col=1): binning = [20, 200] if args.onebin else [ 20, 30, 40, 50, 60, 70, 80, 100, 150, 200 ] if option == 'pt': _hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, len(binning) - 1, array('d', binning)) _ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, len(binning) - 1, array('d', binning)) elif option == 'eta': _hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax) _ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin, xmax) tree.Draw(varx + ' >> ' + _hist_.GetName(), sel) tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary) g_efficiency = TGraphAsymmErrors() g_efficiency.Divide(_ahist_, _hist_, "cl=0.683 b(1,1) mode") g_efficiency.GetXaxis().SetTitle(xtitle) g_efficiency.GetYaxis().SetTitle('efficiency') g_efficiency.GetYaxis().SetNdivisions(507) g_efficiency.SetLineWidth(3) g_efficiency.SetName(header) g_efficiency.SetMinimum(0.) g_efficiency.GetYaxis().SetTitleOffset(1.3) g_efficiency.SetMarkerStyle(marker) g_efficiency.SetMarkerSize(1) g_efficiency.SetMarkerColor(col) g_efficiency.SetLineColor(col) g_efficiency.Draw('ap') # save(c, 'plots/' + addon) return g_efficiency
def convertHistToGraph(hist): graph = TGraphAsymmErrors(hist.GetNbinsX()) for i in range(hist.GetNbinsX()): graph.SetPoint(i, hist.GetXaxis().GetBinCenter(i), hist.GetBinContent(i)) graph.SetPointError(i, hist.GetXaxis().GetBinWidth(i) / 2., hist.GetXaxis().GetBinWidth(i) / 2., hist.GetBinError(i), hist.GetBinError(i)) graph.SetLineWidth(hist.GetLineWidth()) graph.SetLineStyle(hist.GetLineStyle()) graph.SetLineColor(hist.GetLineColor()) graph.SetMarkerSize(hist.GetMarkerSize()) graph.SetMarkerStyle(hist.GetMarkerStyle()) graph.SetMarkerColor(hist.GetMarkerColor()) graph.SetFillStyle(hist.GetFillStyle()) graph.SetFillColor(hist.GetFillColor()) return graph
def histToGraph(hist, name='', keepErrors=True, poissonErrors=True): ## Helper method to convert a histogram to a corresponding graph # @hist TH1 object # @name name of the graph (default is name of histogram) # @keepErrors decide if the y-errors should be propagated to the graph # @poissonErrors decide if the y-errors should be calculated as Poisson errors # @return graph if not name: name = 'g%s' % (hist.GetName()) from ROOT import TGraphAsymmErrors nBins = hist.GetNbinsX() graph = TGraphAsymmErrors(nBins) graph.SetNameTitle(name, hist.GetTitle()) xAxis = hist.GetXaxis() for i in xrange(nBins): xVal = xAxis.GetBinCenter(i + 1) yVal = hist.GetBinContent(i + 1) graph.SetPoint(i, xVal, yVal) graph.SetPointEXlow(i, abs(xVal - xAxis.GetBinLowEdge(i + 1))) graph.SetPointEXhigh(i, abs(xVal - xAxis.GetBinUpEdge(i + 1))) if keepErrors: if poissonErrors: lo, hi = calculatePoissonErrors(yVal) graph.SetPointEYlow(i, lo) graph.SetPointEYhigh(i, hi) else: graph.SetPointEYlow(i, hist.GetBinErrorLow(i + 1)) graph.SetPointEYhigh(i, hist.GetBinErrorUp(i + 1)) # copy the style graph.SetMarkerStyle(hist.GetMarkerStyle()) graph.SetMarkerColor(hist.GetMarkerColor()) graph.SetMarkerSize(hist.GetMarkerSize()) graph.SetLineStyle(hist.GetLineStyle()) graph.SetLineColor(hist.GetLineColor()) graph.SetLineWidth(hist.GetLineWidth()) graph.SetFillColor(hist.GetFillColor()) graph.SetFillStyle(hist.GetFillStyle()) return graph
def makeEffPlotsVars(tree, varx, numeratorAddSelection, baseSelection, binning, xtitle='', header='', addon='', marker=20, col=1): _denomHist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, len(binning) - 1, binning) _nominatorHist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, len(binning) - 1, binning) tree.Draw(varx + ' >> ' + _denomHist_.GetName(), baseSelection) tree.Draw(varx + ' >> ' + _nominatorHist_.GetName(), baseSelection + ' && ' + numeratorAddSelection) g_eff = TGraphAsymmErrors() g_eff.Divide(_nominatorHist_, _denomHist_, "cl=0.683 b(1,1) mode") g_eff.GetXaxis().SetTitle(xtitle) g_eff.GetYaxis().SetTitle('efficiency') g_eff.GetYaxis().SetNdivisions(507) g_eff.SetLineWidth(3) g_eff.SetName(header) g_eff.SetMinimum(0.) g_eff.GetYaxis().SetTitleOffset(1.3) g_eff.SetMarkerStyle(marker) g_eff.SetMarkerSize(1) g_eff.SetMarkerColor(col) g_eff.SetLineColor(col) g_eff.Draw('ap') return g_eff
def fixPlots_withErrors(middleGraph): thisBandsGraph = TGraphAsymmErrors() thisBandsGraph.SetName(middleGraph.GetName()) thisBandsGraph.SetMarkerStyle(middleGraph.GetMarkerStyle()) thisBandsGraph.SetMarkerSize(middleGraph.GetMarkerSize()) thisBandsGraph.SetMarkerColor(middleGraph.GetMarkerColor()) thisBandsGraph.SetLineColor(middleGraph.GetLineColor()) for iPoint in xrange(0, middleGraph.GetN()): # Retrieve middle graph for (x,y) coordinates dataPointX = Double(0) dataPointY = Double(0) dataErrorX = Double(0) middleGraph.GetPoint(iPoint, dataPointX, dataPointY) dataErrorX = middleGraph.GetErrorX(iPoint) dataErrorY = middleGraph.GetErrorY(iPoint) dataPointY = fabs(dataPointY - 1) if (iPoint < middleGraph.GetN()): #if(dataPointY != 0 and iPoint < middleGraph.GetN()): print "fixPlots:", dataPointX thisBandsGraph.SetPoint(iPoint, dataPointX, dataPointY) thisBandsGraph.SetPointError(iPoint, dataErrorX, dataErrorX, dataErrorY, dataErrorY) #if (dataPointY == 0) : #thisBandsGraph.SetPoint(iPoint, dataPointX, dataPointY) #thisBandsGraph.SetPointError(iPoint, dataErrorX, dataErrorX, dataErrorY, dataErrorY) return thisBandsGraph
def doit(): frame = h.ProfileX(hname + "_frame", 1, -1, "s") gr1 = TGraphAsymmErrors(h.GetNbinsX()) gr2 = TGraphAsymmErrors(h.GetNbinsX()) gr1_aspt = TGraphAsymmErrors(h.GetNbinsX()) gr2_aspt = TGraphAsymmErrors(h.GetNbinsX()) # Apply gaussian fits for i in xrange(h.GetNbinsX()): h_py = h.ProjectionY("_py", i + 1, i + 1) if 50 <= i <= 60: # high pT, not enough entries (300 bins -> 150) h_py.Rebin(2) elif i >= 78: # low pT, resolution affected by finite bin width h_py = h.ProjectionY("_py", i + 1, i + 2) # merge i & (i+1) entries if i == 82: # even lower pT, resolution affected by finite bin width h_py = h.ProjectionY("_py", i + 1, i + 4) # merge i & (i+4) entries elif i >= 82: continue if h_py.Integral() < 20: continue r = h_py.Fit("gaus", "SNQ", "", -1, 1.2) #r = h_py.Fit("gaus", "SNQ", "", h_py.GetMean() - 0.04*5, h_py.GetMean() + 0.04*5) mean, sigma, meanErr, sigmaErr = r.Parameter(1), r.Parameter( 2), r.ParError(1), r.ParError(2) gr1.SetPoint(i, h.GetXaxis().GetBinCenter(i + 1), mean) gr1.SetPointError(i, 0, 0, sigma, sigma) gr2.SetPoint(i, h.GetXaxis().GetBinCenter(i + 1), sigma) gr2.SetPointError(i, 0, 0, sigmaErr, sigmaErr) gr1_aspt.SetPoint(i, 1.0 / h.GetXaxis().GetBinCenter(i + 1), mean) gr1_aspt.SetPointError(i, 0, 0, sigma, sigma) gr2_aspt.SetPoint(i, 1.0 / h.GetXaxis().GetBinCenter(i + 1), sigma) gr2_aspt.SetPointError(i, 0, 0, sigmaErr, sigmaErr) # Draw h.Draw("COLZ") gPad.SetLogx(0) #draw_cms_lumi() #gPad.Print("figures_perf/" + hname + "_omtf" + ".png") #gPad.Print("figures_perf/" + hname + "_omtf" + ".pdf") # frame.Reset() frame.SetBins(50, 0, 50) frame.GetXaxis().SetTitle("gen p_{T} [GeV]") frame.GetYaxis().SetTitle("#Delta(p_{T})/p_{T} bias") frame.SetMaximum(0.5) frame.SetMinimum(-0.5) frame.SetStats(0) frame.Draw() gr1_aspt.SetLineColor(col) gr1_aspt.SetMarkerColor(col) gr1_aspt.Draw("p") gPad.SetLogx() draw_cms_lumi() gPad.Print("figures_perf/" + hname + "_bias" + "_omtf" + ".png") gPad.Print("figures_perf/" + hname + "_bias" + "_omtf" + ".pdf") # frame.GetXaxis().SetTitle("gen p_{T} [GeV]") frame.GetYaxis().SetTitle("#Delta(p_{T})/p_{T} resolution") frame.SetMaximum(0.6) frame.SetMinimum(0.0) frame.SetStats(0) frame.Draw() gr2_aspt.SetLineColor(col) gr2_aspt.SetMarkerColor(col) gr2_aspt.Draw("p") #gr2_aspt.Fit("pol1", "", "", 10, 40) gPad.SetLogx() draw_cms_lumi() gPad.Print("figures_perf/" + hname + "_res" + "_omtf" + ".png") gPad.Print("figures_perf/" + hname + "_res" + "_omtf" + ".pdf") # h.cache = [frame, gr1, gr2, gr1_aspt, gr2_aspt]
def significanceSB(cutlist, labellist): basecut = labellist[0] dim = len(cutlist) significance = [0] * (dim + 1) file = {} tree = {} effs = {} hist = {} GrAsym = {} yErrorUp = {} yErrorDown = {} totEve = 0 GrAsym = TGraphAsymmErrors() cuts = "" for j, c in enumerate(cutlist): s = 0. b = 0. cuts += cutlist[0] if j == 0 else " && " + cutlist[j] print "cuts = ", cuts for num1, v in enumerate(signals): #print "Signal = ", v for num2, filename in enumerate(samples[v]['files']): #print "Signal rootfile read = ", filename file[filename] = TFile(NTUPLESIG + filename + ".root", "READ") # Read TFile tree[filename] = file[filename].Get("Events") # Read TTree nevents = float(sample[filename]['nevents']) xs = float(sample[filename]['xsec']) * float( sample[filename]['kfactor']) LumiMC = nevents / xs Weight = float(LUMI) / float(LumiMC) sig_entries = tree[filename].GetEntries(cuts) #print "s = ", float(sig_entries) * float(Weight) s += float(sig_entries) * float(Weight) print "TOT SIG = ", s for num1, k in enumerate(back): #print "backgrounds = ", k for num2, filename in enumerate(samples[k]['files']): #print "backgrounds rootfile read = ", filename file[filename] = TFile(NTUPLEDIR + filename + ".root", "READ") # Read TFile tree[filename] = file[filename].Get("Events") # Read TTree nevents = float(sample[filename]['nevents']) xs = float(sample[filename]['xsec']) * float( sample[filename]['kfactor']) LumiMC = nevents / xs Weight = float(LUMI) / float(LumiMC) bkg_entries = tree[filename].GetEntries(cuts) #print "b = ", float(bkg_entries) * float(Weight) b += float(bkg_entries) * float(Weight) print "TOT BKG = ", b ##End of cutlist #COMPUTE #print "s = ", s #print "b = ", b #print "sqrt(b) = ", math.sqrt(b) #print "significance = ", float(s/math.sqrt(b)) significance[j] = float(s / math.sqrt(b)) yErrorUp[j] = float( TEfficiency.ClopperPearson(math.sqrt(b), s, 0.68, True) - significance[j]) yErrorDown[j] = float( significance[j] - TEfficiency.ClopperPearson(math.sqrt(b), s, 0.68, False)) GrAsym.SetPoint(j, j + 0.5, significance[j]) GrAsym.SetPointError(j, 0, 0, yErrorUp[j], yErrorDown[j]) for k, cs in enumerate(labellist): GrAsym.GetHistogram().GetXaxis().Set(dim, 0, dim) GrAsym.GetHistogram().GetXaxis().SetBinLabel(k + 1, "%s" % labellist[k]) GrAsym.SetLineColor(2) GrAsym.SetLineWidth(3) GrAsym.SetMarkerStyle(8) GrAsym.SetMarkerColor(2) c1 = TCanvas("c1", "Signals Acceptance", 800, 600) c1.cd() c1.GetPad(0).SetTopMargin(0.06) c1.GetPad(0).SetRightMargin(0.05) c1.GetPad(0).SetTicks(1, 1) gStyle.SetOptStat(0) #GrAsym.SetMaximum(1.3) #GrAsym.SetMinimum(0.) GrAsym.GetHistogram().GetXaxis().SetTitle("") GrAsym.GetHistogram().GetYaxis().SetTitle("Significance (S/#sqrt{B})") GrAsym.Draw("pa") drawCMS(LUMI, "Work In Progress") drawRegion(basecut) if not os.path.exists('plots/Signal/Significance/'): os.system('mkdir -p plots/Signal/Significance/') c1.Print("plots/Signal/Significance/Sigf_SB_" + basecut + ".png") c1.Print("plots/Signal/Significance/Sigf_SB_" + basecut + ".pdf") #if not options.runBash: raw_input("Press Enter to continue...") pass
def makeBand(middleGraph, lowerGraph, upperGraph, noorder): bandsGraph = TGraphAsymmErrors() bandsGraph.SetName(middleGraph.GetName()) bandsGraph.SetMarkerStyle(middleGraph.GetMarkerStyle()) bandsGraph.SetMarkerColor(middleGraph.GetMarkerColor()) bandsGraph.SetLineColor(middleGraph.GetLineColor()) x1 = Double(0) x2 = Double(0) x3 = Double(0) y = [] y.append(Double(0)) y.append(Double(0)) y.append(Double(0)) tmp = Double(0) if lowerGraph.GetN() != upperGraph.GetN() or lowerGraph.GetN( ) != middleGraph.GetN(): print "Plots don't have the same number of points!" print "Lower: ", lowerGraph.GetN() print "Upper: ", upperGraph.GetN() print "Middle: ", middleGraph.GetN() return 0 #again a hack to forget about the 1st point for iPoint in xrange(1, lowerGraph.GetN()): middleGraph.GetPoint(iPoint, x1, y[0]) lowerGraph.GetPoint(iPoint, x1, y[1]) upperGraph.GetPoint(iPoint, x1, y[2]) if (iPoint == lowerGraph.GetN() - 1): x2 = x1 else: upperGraph.GetPoint(iPoint + 1, x2, buf) if (iPoint == 0): x3 = x1 else: upperGraph.GetPoint(iPoint - 1, x3, buf) tmp = Double(0) yup = Double(0) yce = Double(0) ydn = Double(0) if noorder == 0: yce = y[0] ydn = y[1] yup = y[2] elif noorder == 1: tmp = y[2] if y[1] < y[2]: y[2] = y[1] y[1] = tmp yce = y[0] ydn = y[1] yup = y[2] else: for p in [0, 1, 2]: for q in [0, 1, 2]: if y[q] > y[q + 1]: tmp = y[q + 1] y[q + 1] = y[q] y[q] = tmp ydn = y[0] yce = y[1] yup = y[2] bandsGraph.SetPoint(iPoint, x1, yce) ex0 = Double(lowerGraph.GetErrorX(iPoint)) binwl = Double(0) binwh = Double(0) if (ex0 == 0): binwl = (x1 - x3) / 2. binwh = (x2 - x1) / 2. if binwl == 0.: binwl = binwh if binwh == 0.: binwh = binwl else: binwl = ex0 binwh = ex0 dxl = Double(yce - ydn) dxh = Double(yup - yce) if noorder == 0: if dxl < 0: tmp = -dxl dxl = dxh dxh = tmp bandsGraph.SetPointError(iPoint, binwl, binwh, dxl, dxh) return bandsGraph
def makePlot(finname,foutname,plottitle='',masstitle='',scale=False): xsecs = resonantXsecs if 'resonant' in finname else fcncXsecs points = {} if BLIND: cls = [2.5, 16, 50, 84, 97.5] else: cls = [2.5, 16, 50, 84, 97.5,'Observed'] xaxis = [] for cl in cls: points[cl] = [] xsec=1 for l in open(finname): try: if l.strip()[0]=='#': continue if 'MASS' in l: if scale: xsec = xsecs[int(l.split()[1])] if VERBOSE: print '' stdout.write('$%6s$ & $%7.3g$'%(l.split()[1],xsec/(0.667))) xaxis.append(float(l.split()[1])) else: cl,val = parseLine(l) points[cl].append(val/xsec) if VERBOSE and (cl==50 or cl=='Observed'): stdout.write(' & $%10.4g$'%(val/xsec)) except: pass if VERBOSE: print '' N = len(xaxis) up1Sigma=[]; up2Sigma=[] down1Sigma=[]; down2Sigma=[] for iM in xrange(N): up1Sigma.append(points[84][iM]-points[50][iM]) up2Sigma.append(points[97.5][iM]-points[50][iM]) down1Sigma.append(-points[16][iM]+points[50][iM]) down2Sigma.append(-points[2.5][iM]+points[50][iM]) up1Sigma = array('f',up1Sigma) up2Sigma = array('f',up2Sigma) down1Sigma = array('f',down1Sigma) down2Sigma = array('f',down2Sigma) cent = array('f',points[50]) if not BLIND: obs = array('f',points['Observed']) xarray = array('f',xaxis) xsecarray = array('f',[xsecs[xx] for xx in xaxis]) xsecarrayLow = array('f',[0.0625*xsecs[xx] for xx in xaxis]) onearray = array('f',[1 for xx in xaxis]) graphXsec = TGraph(N,xarray,xsecarray) graphXsecLow = TGraph(N,xarray,xsecarrayLow) graphOne = TGraph(N,xarray,onearray) zeros = array('f',[0 for i in xrange(N)]) graphCent = TGraph(N,xarray,cent) if not BLIND: graphObs = TGraph(N,xarray,obs) graph1Sigma = TGraphAsymmErrors(N,xarray,cent,zeros,zeros,down1Sigma,up1Sigma) graph2Sigma = TGraphAsymmErrors(N,xarray,cent,zeros,zeros,down2Sigma,up2Sigma) c = TCanvas('c','c',700,600) c.SetLogy() c.SetLeftMargin(.15) graph2Sigma.GetXaxis().SetTitle(masstitle+' [GeV]') if scale: graph2Sigma.GetYaxis().SetTitle('Upper limit [#sigma/#sigma_{theory}]') else: graph2Sigma.GetYaxis().SetTitle("Upper limit [#sigma] [pb]") graph2Sigma.SetLineColor(5) graph1Sigma.SetLineColor(3) graph2Sigma.SetFillColor(5) graph1Sigma.SetFillColor(3) graph2Sigma.SetMinimum(0.5*min(points[2.5])) if scale: graph2Sigma.SetMaximum(10*max(max(points[97.5]),max(xsecarray),4)) else: graph2Sigma.SetMaximum(10*max(max(points[97.5]),max(xsecarray))) graphCent.SetLineWidth(2) graphCent.SetLineStyle(2) if not BLIND: graphObs.SetLineColor(1) graphObs.SetLineWidth(3) graph1Sigma.SetLineStyle(0) graph2Sigma.SetLineStyle(0) leg = TLegend(0.55,0.7,0.9,0.9) leg.AddEntry(graphCent,'Expected','L') if not BLIND: leg.AddEntry(graphObs,'Observed','L') leg.AddEntry(graph1Sigma,'1 #sigma','F') leg.AddEntry(graph2Sigma,'2 #sigma','F') leg.SetFillStyle(0) leg.SetBorderSize(0) graph2Sigma.Draw('A3') graph1Sigma.Draw('3 same') graphCent.Draw('same L') if not BLIND: graphObs.Draw('same L') if scale: graphOne.SetLineColor(2) graphOne.SetLineWidth(2) graphOne.SetLineStyle(2) graphOne.Draw('same L') else: graphXsec.SetLineColor(2) graphXsecLow.SetLineColor(4) subscript = 'SR' if 'Resonant' in plottitle else 'FC' if 'Resonant' in plottitle: leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=0.1}{m_{#chi}=100 GeV}'%(subscript,subscript),'l') else: leg.AddEntry(graphXsec,'Theory a_{%s}=b_{%s}=0.1'%(subscript,subscript),'l') # leg.AddEntry(graphXsecLow,'Theory a_{%s}=b_{%s}=0.025'%(subscript,subscript),'l') for g in [graphXsec]: g.SetLineWidth(2) g.SetLineStyle(2) g.Draw('same L') leg.Draw() label = TLatex() label.SetNDC() label.SetTextFont(62) label.SetTextAlign(11) label.DrawLatex(0.19,0.85,"CMS") label.SetTextFont(52) label.DrawLatex(0.28,0.85,"Preliminary") label.SetTextFont(42) label.SetTextSize(0.6*c.GetTopMargin()) label.DrawLatex(0.19,0.77,plottitle) if scale: if 'Resonant' in plottitle: label.DrawLatex(0.19,0.7,"a_{SR} = b_{SR} = 0.1") label.DrawLatex(0.19,0.64,"m_{#chi}=100 GeV") else: label.DrawLatex(0.19,0.7,"a_{FC} = b_{FC} = 0.1") label.SetTextSize(0.5*c.GetTopMargin()) label.SetTextFont(42) label.SetTextAlign(31) # align right label.DrawLatex(0.9, 0.94,"%.1f fb^{-1} (13 TeV)"%(plotConfig.lumi)) c.SaveAs(foutname+'.pdf') c.SaveAs(foutname+'.png')
def limit(): method = '' channel = "bb" particleP = "Z'" particle = channel multF = ZPTOBB THEORY = ['A1', 'B3'] suffix = "_" + BTAGGING if ISMC: suffix += "_MC" if SY: suffix += "_comb" #if method=="cls": suffix="_CLs" if SY: filename = "./combine/limits/MANtag_study/" + BTAGGING + "/combined_run2/" + YEAR + "_M%d.txt" else: filename = "./combine/limits/MANtag_study/" + BTAGGING + "/" + YEAR + "_M%d.txt" if CATEGORY != "": 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 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) 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() addXZH = True for m in sorted(HVT[t]['W']['XS'].keys()): if m < mass[0] or m > mass[-1]: continue if m > 4500: continue ## for now because I don't have the higher mass xs FIXME XsZ, XsZ_Up, XsZ_Down = 0., 0., 0. if addXZH: XsZ = 1000. * HVT[t]['Z']['XS'][ m] * 0.12 #temporary BR value set to 0.12 FIXME 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])) n = Theory[t].GetN() Theory[t].SetPoint(n, m, XsZ) Theory[t].SetPointError(n, 0., 0., (XsZ - XsZ_Down), (XsZ_Up - XsZ)) 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)") 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) 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 drawCMS(LUMI, "Work in Progress", 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/MANtag_study/" + YEAR + suffix + ".png") c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR + suffix + ".pdf") if 'ah' in channel or 'sl' in channel: c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + YEAR + suffix + ".C") c1.Print("combine/plotsLimit/ExclusionLimits/MANtag_study/" + 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
def makeplot_single( h1_sig=None, h1_bkg=None, h1_data=None, sig_legends_=None, bkg_legends_=None, sig_colors_=None, bkg_colors_=None, hist_name_=None, sig_scale_=1.0, dir_name_="plots", output_name_=None, extraoptions=None ): if h1_sig == None or h1_bkg == None: print("nothing to plot...") return os.system("mkdir -p "+dir_name_) os.system("cp index.php "+dir_name_) s_color = [632, 617, 839, 800, 1] b_color = [920, 2007, 2005, 2003, 2001, 2011] if sig_colors_: s_color = sig_colors_ if bkg_colors_: b_color = bkg_colors_ for idx in range(len(h1_sig)): h1_sig[idx].SetLineWidth(3) h1_sig[idx].SetLineColor(s_color[idx]) for idx in range(len(h1_bkg)): h1_bkg[idx].SetLineWidth(2) h1_bkg[idx].SetLineColor(b_color[idx]) h1_bkg[idx].SetFillColorAlpha(b_color[idx], 1) if h1_data: h1_data.SetBinErrorOption(1) h1_data.SetLineColor(1) h1_data.SetLineWidth(2) h1_data.SetMarkerColor(1) h1_data.SetMarkerStyle(20) myC = r.TCanvas("myC","myC", 600, 600) myC.SetTicky(1) pad1 = r.TPad("pad1","pad1", 0.05, 0.33,0.95, 0.97) pad1.SetBottomMargin(0.027) pad1.SetRightMargin( rightMargin ) pad1.SetLeftMargin( leftMargin ) pad2 = r.TPad("pad2","pad2", 0.05, 0.04, 0.95, 0.31) pad2.SetBottomMargin(0.4) pad2.SetTopMargin(0.05) pad2.SetRightMargin( rightMargin ) pad2.SetLeftMargin( leftMargin ) pad2.Draw() pad1.Draw() pad1.cd() for idx in range(len(h1_sig)): print("before signal scaling",h1_sig[idx].Integral()) h1_sig[idx].Scale(sig_scale_) print("after signal scaling",h1_sig[idx].Integral()) stack = r.THStack("stack", "stack") nS = np.zeros(h1_bkg[0].GetNbinsX()) eS = np.zeros(h1_bkg[0].GetNbinsX()) #hist_all is used to make the data/mc ratio. remove signal for the moment due to signal is scaled right now hist_all = h1_sig[0].Clone("hist_all") hist_all.Scale(0.0) hist_s = h1_sig[0].Clone("hist_s") hist_b = h1_bkg[0].Clone("hist_b") for idx in range(len(h1_bkg)): stack.Add(h1_bkg[idx]) for ib in range(h1_bkg[0].GetNbinsX()): nS[ib] += h1_bkg[idx].GetBinContent(ib+1) eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_bkg[idx].GetBinError(ib+1)*h1_bkg[idx].GetBinError(ib+1)) hist_all.Add(h1_bkg[idx]) if idx > 0: hist_b.Add(h1_bkg[idx]) for idx in range(len(h1_sig)): print("ggH signal yield: ", hist_s.Integral()) if idx > 0: hist_temp = h1_sig[idx].Clone(h1_sig[idx].GetName()+"_temp") #hist_all.Add(hist_temp) hist_s.Add(h1_sig[idx]) print("all signal yield: ", hist_s.Integral()) stack.SetTitle("") maxY = 0.0 if "stack_signal" in extraoptions and extraoptions["stack_signal"]: for idx in range(len(h1_sig)): h1_sig[idx].SetFillColorAlpha(s_color[idx], 1) stack.Add(h1_sig[idx]) for ib in range(h1_bkg[0].GetNbinsX()): nS[ib] += h1_sig[idx].GetBinContent(ib+1) eS[ib] = math.sqrt(eS[ib]*eS[ib] + h1_sig[idx].GetBinError(ib+1)*h1_sig[idx].GetBinError(ib+1)) if stack.GetMaximum() > maxY: maxY = stack.GetMaximum() #if "SR" in h.GetTitle(): stack.Draw("hist") else: stack.Draw("hist") if stack.GetMaximum() > maxY: maxY = stack.GetMaximum() for idx in range(len(h1_sig)): if h1_sig[idx].GetMaximum() > maxY: maxY = h1_sig[idx].GetMaximum() if "SR" in h1_bkg[0].GetTitle(): #h1_sig[idx].Draw("samehist") hist_s.Draw("samehist") ##draw stack total unc on top of total histogram box = r.TBox(0,0,1,1,) box.SetFillStyle(3002) box.SetLineWidth(0) box.SetFillColor(r.kBlack) for idx in range(h1_bkg[0].GetNbinsX()): box.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]-eS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), nS[idx]+eS[idx]) if h1_data: if h1_data.GetMaximum() > maxY: maxY = h1_data.GetMaximum()+np.sqrt(h1_data.GetMaximum()) #if not "SR" in h1_data.GetTitle() or "fail" in h1_data.GetTitle(): if True: #print("debug h1_data.GetName()",h1_data.GetName(), h1_data.GetTitle()) TGraph_data = TGraphAsymmErrors(h1_data) for i in range(TGraph_data.GetN()): #data point var_x, var_y = Double(0.), Double(0.) TGraph_data.GetPoint(i,var_x,var_y) if np.fabs(var_y) < 1e-5: TGraph_data.SetPoint(i,var_x,-1.0) TGraph_data.SetPointEYlow(i,-1) TGraph_data.SetPointEYhigh(i,-1) #print("zero bins in the data TGraph: bin",i+1) else: TGraph_data.SetPoint(i,var_x,var_y) err_low = var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)) TGraph_data.SetPointEYlow(i, var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y))) TGraph_data.SetPointEYhigh(i, (0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y) TGraph_data.SetMarkerColor(1) TGraph_data.SetMarkerSize(1) TGraph_data.SetMarkerStyle(20) TGraph_data.Draw("same P") stack.GetYaxis().SetTitle("Events") stack.GetYaxis().SetTitleOffset(1.05) stack.GetYaxis().SetTitleSize(0.08) stack.GetYaxis().SetLabelSize(0.06) #stack.GetYaxis().CenterTitle() stack.GetXaxis().SetLabelSize(0.) #stack.GetXaxis().SetLabelOffset(0.013) #if "xaxis_range" in extraoptions: # stack.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1])) leg = r.TLegend(0.2, 0.60, 0.9, 0.88) leg.SetNColumns(3) leg.SetFillStyle(0) leg.SetBorderSize(0) leg.SetTextFont(42) leg.SetTextSize(0.05) for idx in range(len(h1_bkg)): leg.AddEntry(h1_bkg[idx], bkg_legends_[idx], "F") if "SR" in hist_s.GetTitle(): leg.AddEntry(hist_s, 'HH #times {:1.2}'.format(sig_scale_), "L") leg.AddEntry(box, "Total unc", "F") if h1_data: leg.AddEntry(h1_data, "Data", "ep") leg.Draw() pad2.cd() pad2.SetGridy(1) ratio = None ratio_Low = 0.0 ratio_High = 4 if h1_data: ratio = TGraphAsymmErrors(h1_data) for i in range(ratio.GetN()): #bkg prediction imc = Double(hist_all.GetBinContent(i+1)) #data point var_x, var_y = Double(0.), Double(0.) if not ("SR" in h1_data.GetTitle() and (i>5 and i<9)): ratio.GetPoint(i,var_x,var_y) if var_y == 0.: ratio.SetPoint(i,var_x,-1.0) ratio.SetPointEYlow(i,-1) ratio.SetPointEYhigh(i,-1) continue ratio.SetPoint(i,var_x,var_y/imc) err_low = (var_y - (0.5*TMath.ChisquareQuantile(0.1586555,2.*var_y)))/imc err_high = ((0.5*TMath.ChisquareQuantile(1.-0.1586555,2.*(var_y+1))) - var_y)/imc ratio.SetPointEYlow(i, err_low) ratio.SetPointEYhigh(i, err_high) ratio.SetMarkerColor(1) ratio.SetMarkerSize(1) ratio.SetMarkerStyle(20) ratio.GetXaxis().SetTitle("j_{2} regressed mass [GeV]") #myC.Update() if "ratio_range" in extraoptions: ratio_Low = extraoptions["ratio_range"][0] ratio_High = extraoptions["ratio_range"][1] ratio.GetYaxis().SetTitle("data/mc") ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High) ratio.GetXaxis().SetRangeUser(50, 220) ratio.SetTitle("") ratio.Draw("same AP") pad2.Update() print(ratio.GetTitle(),ratio.GetName(),"debug") else: ratio = h1_sig[0].Clone("ratio") ratio_High = 0.0 for ibin in range(1,ratio.GetNbinsX()+1): s = hist_s.GetBinContent(ibin) b = hist_b.GetBinContent(ibin) L = 0.0 if b > 0.0: L = s/math.sqrt(b) if L > ratio_High: ratio_High = L ratio.SetBinContent(ibin, L) if ratio_High > 1.0: ratio_High = 1.0 ratio.GetYaxis().SetRangeUser(ratio_Low, ratio_High*1.2) ratio.GetYaxis().SetTitle("S/#sqrt{B}") ratio.Draw("samehist") ratio.SetLineColor(1) ratio.SetLineWidth(2) ratio.SetMarkerStyle(20) ratio.SetMarkerColor(1) ratio.SetFillColorAlpha(1, 0) ratio.GetXaxis().SetTitleOffset(0.94) ratio.GetXaxis().SetTitleSize(0.18) ratio.GetXaxis().SetLabelSize(0.12) ratio.GetXaxis().SetLabelOffset(0.013) ratio.GetYaxis().SetTitleOffset(0.40) ratio.GetYaxis().SetTitleSize(0.17) ratio.GetYaxis().SetLabelSize(0.13) ratio.GetYaxis().SetTickLength(0.01) ratio.GetYaxis().SetNdivisions(505) #if "xaxis_range" in extraoptions: # ratio.GetXaxis().SetRangeUser(float(extraoptions["xaxis_range"][0]),float(extraoptions["xaxis_range"][1])) #draw stack total unc on the ratio plot to present the background uncertainty box_ratio = r.TBox(0,0,1,1,) box_ratio.SetFillStyle(3002) box_ratio.SetLineWidth(0) box_ratio.SetFillColor(r.kBlack) for idx in range(h1_bkg[0].GetNbinsX()): if np.fabs(nS[idx])> 1e-06: box_ratio.DrawBox(h1_bkg[0].GetBinCenter(idx+1)-0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]-eS[idx])/nS[idx], h1_bkg[0].GetBinCenter(idx+1)+0.5*h1_bkg[0].GetBinWidth(idx+1), (nS[idx]+eS[idx])/nS[idx]) else: print("blinded Higgs peak region") if "xaxis_label" in extraoptions and extraoptions["xaxis_label"] != None: x_title = extraoptions["xaxis_label"] ratio.GetXaxis().SetTitle(x_title) ratio.GetYaxis().CenterTitle() ##########draw CMS preliminary pad1.cd() tex1 = r.TLatex(leftMargin, 0.91, "CMS") tex1.SetNDC() tex1.SetTextFont(61) tex1.SetTextSize(0.070) tex1.SetLineWidth(2) tex1.Draw() tex2 = r.TLatex(leftMargin+0.12,0.912,"Internal") tex2.SetNDC() tex2.SetTextFont(52) tex2.SetTextSize(0.055) tex2.SetLineWidth(2) tex2.Draw() lumi_value = 137 if "lumi_value" in extraoptions: lumi_value = extraoptions["lumi_value"] tex3 = r.TLatex(0.72,0.912,"%d"%lumi_value+" fb^{-1} (13 TeV)") tex3.SetNDC() tex3.SetTextFont(42) tex3.SetTextSize(0.055) tex3.SetLineWidth(2) tex3.Draw() outFile = dir_name_ if output_name_: outFile = outFile + "/" +output_name_ else: outFile = outFile + "/" + hist_name_ #print("maxY = "+str(maxY)) stack.SetMaximum(maxY*1.7) #print everything into txt file text_file = open(outFile+"_linY.txt", "w") text_file.write("bin | x ") for idx in range(len(h1_bkg)): text_file.write(" | %21s"%bkg_legends_[idx]) text_file.write(" | %21s"%("total B")) for idx in range(len(sig_legends_)): text_file.write(" | %25s"%sig_legends_[idx]) if h1_data: text_file.write(" | data | data/mc") text_file.write("\n-------------") for idx in range(24*(len(h1_bkg) + 1)+ 29*len(sig_legends_)): text_file.write("-") if h1_data: text_file.write("-------") text_file.write("\n") for ibin in range(0,h1_sig[0].GetNbinsX()+1): text_file.write("%3d"%ibin+" ") text_file.write(" | %6.3f"%h1_data.GetBinCenter(ibin)+" ") for idx in range(len(h1_bkg)): text_file.write(" | %7.3f "%h1_bkg[idx].GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%h1_bkg[idx].GetBinError(ibin)) text_file.write(" | %7.3f "%hist_b.GetBinContent(ibin)+"$\\pm$"+ " %7.3f"%hist_b.GetBinError(ibin)) for idx in range(len(sig_legends_)): text_file.write(" | %9.3f "%h1_sig[idx].GetBinContent(ibin)+"$\\pm$"+ " %9.3f"%h1_sig[idx].GetBinError(ibin)) if h1_data: text_file.write(" | %d"%h1_data.GetBinContent(ibin) + " | %7.3f "%h1_data.GetBinContent(ibin) +"$\\pm$"+ " %7.3f"%h1_data.GetBinError(ibin)) text_file.write("\n\n") #print yield table for AN text_file.write("print yield table for AN\n") bkg_all = 0 bkg_all_errsq = 0 for idx in range(len(h1_bkg)): bkg_tmp = h1_bkg[idx].GetBinContent(7)+h1_bkg[idx].GetBinContent(8)+h1_bkg[idx].GetBinContent(9) bkg_errsq_tmp = h1_bkg[idx].GetBinError(7)*h1_bkg[idx].GetBinError(7)+h1_bkg[idx].GetBinError(8)*h1_bkg[idx].GetBinError(8)+h1_bkg[idx].GetBinError(9)*h1_bkg[idx].GetBinError(9) bkg_all += bkg_tmp bkg_all_errsq += bkg_errsq_tmp text_file.write("%s"%(bkg_legends_[idx])+"& %7.2f"%(bkg_tmp)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_errsq_tmp)+"\n") text_file.write("total background & %7.2f"%(bkg_all)+"$\\pm$"+ "%7.2f"%np.sqrt(bkg_all_errsq)+"\n") text_file.write("\ggHH SM ($\kapl=1$) & %7.2f"%((h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9)))+"\n") text_file.write("\VBFHH SM ($\kapl=1$) & %7.2f"%((h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.1f"%(sig_scale_*np.sqrt(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9)))+"\n") text_file.write("HH bin 8 value %s"%h1_sig[0].GetBinContent(8)+"\n") text_file.write("HH bin 9 value %s"%h1_sig[0].GetBinContent(9)+"\n") text_file.write("HH bin 7 value %s"%h1_sig[0].GetBinContent(7)+"\n") text_file.write("HH bin 8 error %s"%h1_sig[0].GetBinError(8)+"\n") text_file.write("HH bin 9 error %s"%h1_sig[0].GetBinError(9)+"\n") text_file.write("HH bin 7 error %s"%h1_sig[0].GetBinError(7)+"\n") text_file.write("total & %7.2f"%(bkg_all+(h1_sig[0].GetBinContent(7)+h1_sig[0].GetBinContent(8)+h1_sig[0].GetBinContent(9)+h1_sig[1].GetBinContent(7)+h1_sig[1].GetBinContent(8)+h1_sig[1].GetBinContent(9))/sig_scale_)+"$\\pm$"+ "%7.2f"%(np.sqrt((h1_sig[0].GetBinError(7)*h1_sig[0].GetBinError(7)+h1_sig[0].GetBinError(8)*h1_sig[0].GetBinError(8)+h1_sig[0].GetBinError(9)*h1_sig[0].GetBinError(9))/(sig_scale_*sig_scale_)+(h1_sig[1].GetBinError(7)*h1_sig[1].GetBinError(7)+h1_sig[1].GetBinError(8)*h1_sig[1].GetBinError(8)+h1_sig[1].GetBinError(9)*h1_sig[1].GetBinError(9))/(sig_scale_*sig_scale_)+bkg_all_errsq))+"\n") text_file.close() os.system("cp "+outFile+"_linY.txt "+outFile+"_logY.txt") pad1.RedrawAxis() myC.SaveAs(outFile+"_linY.png") myC.SaveAs(outFile+"_linY.pdf") myC.SaveAs(outFile+"_linY.C") pad1.cd() stack.SetMaximum(maxY*100.0) stack.SetMinimum(0.5) pad1.SetLogy() pad1.RedrawAxis() myC.SaveAs(outFile+"_logY.png") myC.SaveAs(outFile+"_logY.pdf") myC.SaveAs(outFile+"_logY.C") #save histogram and ratio to root file outFile_root = r.TFile(outFile+".root", "recreate") outFile_root.cd() for idx in range(len(h1_bkg)): h1_bkg[idx].Write() for idx in range(len(sig_legends_)): h1_sig[idx].Write() if h1_data: h1_data.Write() ratio.Write() #outFile_root.Write() outFile_root.Close()
def getGraph(self,dset): from array import array from ROOT import TMultiGraph, TLegend, TGraphAsymmErrors n = len(self.__x) if n != len(self.__y) or n != len(self.__yErrLow) or n != len(self.__yErrHigh): raise StandardError, "The length of the x(%s), y(%s) and y error(%s,%s) lists does not match"%(len(self.__x), len(self.__y), len(self.__yErrLow), len(self.__yErrHigh)) result = TMultiGraph() legendPosition = [float(i) for i in self.__getStyleOption("legendPosition").split()] legend = TLegend(*legendPosition) legend.SetFillColor(0) result.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle)) #(refArrays, refLabel) = self.__getRefernceGraphArrays() #refGraph = TGraphAsymmErrors(*refArrays) #refGraph.SetLineWidth(2) #refGraph.SetLineColor(int(self.__config.get("reference","lineColor"))) #refGraph.SetFillColor(int(self.__config.get("reference","fillColor"))) #result.Add(refGraph,"L3") #legend.AddEntry(refGraph,self.__config.get("reference","name")) xErr = array("d",[0 for i in range(n)]) print "__x = ", self.__x print "__y = ", self.__y lst = [] for inc in range (0,n): d={} d['run']=self.__runs[inc] d['x']=self.__x[inc] d['y']=self.__y[inc] d['yErr']=self.__yErrLow[inc] d['yTitle']=self.__yTitle if self.__config.has_option(self.__section,"yMin") and self.__config.has_option(self.__section,"yMax") : d['ymin']=float(self.__config.get(self.__section,"yMin")) d['ymax']=float(self.__config.get(self.__section,"yMax")) else: d['ymin']=0 d['ymax']=0 lst.append(d) obj ={} obj[self.__title]=lst #finalObj[self.__title]=lst #finalList.append(finalObj) # save_path = './JSON_A/' #completeName = os.path.join(save_path, self.__title+".json") if not os.path.exists("JSON_RECO"): os.makedirs("JSON_RECO") if not os.path.exists("JSON_RECO/"+dset): os.makedirs("JSON_RECO/"+dset) with open("./JSON_RECO/"+dset+"/"+self.__title+".json", 'w') as outfile: json.dump(obj, outfile,indent=4) print json.dumps(obj,indent=2) graph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr, self.__yErrLow,self.__yErrHigh) graph.SetLineWidth(2) graph.SetFillColor(0) graph.SetLineColor(int(self.__getStyleOption("lineColor"))) graph.SetMarkerColor(int(self.__getStyleOption("markerColor"))) graph.SetMarkerStyle(int(self.__getStyleOption("markerStyle"))) graph.SetMarkerSize(float(self.__getStyleOption("markerSize"))) sysGraph = TGraphAsymmErrors(n, self.__x, self.__y, xErr, xErr, self.__ySysErrLow,self.__ySysErrHigh) sysGraph.SetLineWidth(1) sysGraph.SetFillColor(0) sysGraph.SetLineColor(int(self.__getStyleOption("lineColor"))) sysGraph.SetMarkerColor(int(self.__getStyleOption("markerColor"))) sysGraph.SetMarkerStyle(int(self.__getStyleOption("markerStyle"))) sysGraph.SetMarkerSize(float(self.__getStyleOption("markerSize"))) #TOMAS removed sys error from the plot #result.Add(sysGraph,"[]") result.Add(graph,"P") # result.SetName("MultiPlots") # result.SetTitle("%s;%s;%s"%(self.__title,self.__xTitle,self.__yTitle)) result.SetName("MG_%s"%(self.__title)) legend.AddEntry(graph, self.__getStyleOption("name")) #for (x,y,yErr) in zip(self.__x, self.__y, zip(self.__yErrLow,self.__yErrHigh)): # self.__addAnnotaion("hallo",x,y,yErr) return (result, legend)
gr_denom = TGraphAsymmErrors(len(rates)) gr_numer = TGraphAsymmErrors(len(rates)) for i, x in enumerate(rates): pu, emtf_rate, emtf_rate_err, emtf2026_rate, emtf2026_rate_err = x gr_denom.SetPoint(i, pu, emtf_rate) gr_denom.SetPointError(i, 0, 0, emtf_rate_err, emtf_rate_err) gr_numer.SetPoint(i, pu, emtf2026_rate) gr_numer.SetPointError(i, 0, 0, emtf2026_rate_err, emtf2026_rate_err) gr_denom.SetMarkerStyle(20) gr_denom.SetMarkerSize(1.4) gr_denom.SetMarkerColor(632) # kRed gr_denom.SetLineWidth(2) gr_denom.SetLineColor(632) # kRed gr_numer.SetMarkerStyle(20) gr_numer.SetMarkerSize(1.4) gr_numer.SetMarkerColor(600) # kBlue gr_numer.SetLineWidth(2) gr_numer.SetLineColor(600) # kBlue frame = TH1F("frame", "; PU; Trigger rate [kHz]", 100, 0, 350) frame.SetMinimum(0) frame.SetMaximum(25) frame.Draw() #gr_denom.Draw("P") gr_numer.Draw("P")
def makePlot1D(filepath, foutname, plottitle='', masstitle=''): br = 1 if 'Resonant' in plottitle else 0.68 limits = parseLimitFiles2D(filepath, br) xaxis = [] xseclist = [] xsecerr = [] cent = [] obs = [] up1 = [] up2 = [] down1 = [] down2 = [] maxval = 0 minval = 999 for m in sorted(limits): l = limits[m] xaxis.append(m) xseclist.append(l.xsec) xsecerr.append(l.xsec * .2) cent.append(l.cent) up1.append(l.up1 - l.cent) up2.append(l.up2 - l.cent) down1.append(l.cent - l.down1) down2.append(l.cent - l.down2) obs.append(l.obs) maxval = max(maxval, l.up2) minval = min(minval, l.down2) N = len(xaxis) up1Sigma = array('f', up1) up2Sigma = array('f', up2) down1Sigma = array('f', down1) down2Sigma = array('f', down2) cent = array('f', cent) obs = array('f', obs) xarray = array('f', xaxis) xsecarray = array('f', xseclist) xsecerrarray = array('f', xsecerr) zeros = array('f', [0 for i in xrange(N)]) graphXsec = TGraphErrors(N, xarray, xsecarray, zeros, xsecerrarray) graphCent = TGraph(N, xarray, cent) graphObs = TGraph(N, xarray, obs) graph1Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down1Sigma, up1Sigma) graph2Sigma = TGraphAsymmErrors(N, xarray, cent, zeros, zeros, down2Sigma, up2Sigma) c = TCanvas('c', 'c', 700, 600) c.SetLogy() c.SetLeftMargin(.15) graph2Sigma.GetXaxis().SetTitle(masstitle + ' [GeV]') graph2Sigma.GetYaxis().SetTitle( '95% C.L. upper limit [#sigma/#sigma_{theory}]') c2 = root.kOrange c1 = root.kGreen + 1 graph2Sigma.SetLineColor(c2) graph1Sigma.SetLineColor(c1) graph2Sigma.SetFillColor(c2) graph1Sigma.SetFillColor(c1) graph2Sigma.SetMinimum(0.5 * minval) graph2Sigma.SetMaximum(10 * maxval) graphCent.SetLineWidth(2) graphCent.SetLineStyle(2) graphObs.SetLineColor(1) graphObs.SetLineWidth(3) graphObs.SetMarkerStyle(20) graphObs.SetMarkerSize(1) graphObs.SetMarkerColor(1) graph1Sigma.SetLineStyle(0) graph2Sigma.SetLineStyle(0) leg = TLegend(0.55, 0.7, 0.9, 0.9) leg.AddEntry(graphCent, 'Expected', 'L') if not BLIND: leg.AddEntry(graphObs, 'Observed', 'Lp') leg.AddEntry(graph1Sigma, '1 std. dev.', 'F') leg.AddEntry(graph2Sigma, '2 std. dev.', 'F') leg.SetFillStyle(0) leg.SetBorderSize(0) graph2Sigma.Draw('A3') graph1Sigma.Draw('3 same') graphCent.Draw('same L') if not BLIND: graphObs.Draw('same Lp') subscript = 'SR' if 'Resonant' in plottitle else 'FC' coupling = '0.1' if 'Resonant' in plottitle else '0.25' graphXsec.SetLineColor(2) graphXsec.SetLineWidth(2) graphXsec.SetLineStyle(2) graphXsec.SetFillColor(2) graphXsec.SetFillStyle(3005) graphXsec.Draw('same L3') ''' if not scale: if 'Resonant' in plottitle: leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=100 GeV}'%(subscript,subscript,coupling),'l') else: leg.AddEntry(graphXsec,'Theory #splitline{a_{%s}=b_{%s}=%s}{m_{#chi}=10 GeV}'%(subscript,subscript,coupling),'l') ''' if not BLIND: findIntersect1D(graphObs, graphXsec, xaxis[0], xaxis[-1]) findIntersect1D(graphCent, graphXsec, xaxis[0], xaxis[-1]) leg.Draw() label = TLatex() label.SetNDC() label.SetTextSize(0.8 * c.GetTopMargin()) label.SetTextFont(62) label.SetTextAlign(11) label.DrawLatex(0.15, 0.94, "CMS") label.SetTextFont(52) label.SetTextSize(0.6 * c.GetTopMargin()) # label.DrawLatex(0.25,0.94,"Preliminary") label.SetTextFont(42) label.SetTextSize(0.7 * c.GetTopMargin()) label.DrawLatex(0.19, 0.83, plottitle) if 'Resonant' in plottitle: label.DrawLatex(0.19, 0.75, "a_{SR} = b_{SR} = %s" % coupling) label.DrawLatex(0.19, 0.68, "m_{#chi}=100 GeV") else: label.DrawLatex(0.19, 0.75, "g_{DM}^{V}=1,g_{q}^{V}=0.25") label.DrawLatex(0.19, 0.68, "m_{#chi}=1 GeV") label.SetTextSize(0.6 * c.GetTopMargin()) label.SetTextFont(42) label.SetTextAlign(31) # align right label.DrawLatex(0.95, 0.94, "%.1f fb^{-1} (13 TeV)" % (plotConfig.lumi)) c.SaveAs(foutname + '.pdf') c.SaveAs(foutname + '.png')
def pullsVertical_noBonly(fileName): content = filterPullFile(fileName) nbins, off = len(content), 0.10 # Graphs h_pulls = TH2F("pulls", "", 6, -3., 3., nbins, 0, nbins) S_pulls = TGraphAsymmErrors(nbins) boxes = [] canvas = TCanvas("canvas", "Pulls", 720, 300 + nbins * 18) #nbins*20) canvas.cd() canvas.SetGrid(0, 1) canvas.SetTopMargin(0.01) canvas.SetRightMargin(0.01) canvas.SetBottomMargin(0.10) canvas.SetLeftMargin(0.40) canvas.SetTicks(1, 1) for i, s in enumerate(content): l = s.split() h_pulls.GetYaxis().SetBinLabel(i + 1, l[0]) S_pulls.SetPoint(i, float(l[3]), float(i + 1) - 0.5) S_pulls.SetPointError(i, float(l[4]), float(l[4]), 0., 0.) h_pulls.GetXaxis().SetTitle("(#hat{#theta} - #theta_{0}) / #Delta#theta") h_pulls.GetXaxis().SetLabelOffset(0.0) h_pulls.GetXaxis().SetTitleOffset(0.8) h_pulls.GetXaxis().SetLabelSize(0.045) h_pulls.GetXaxis().SetTitleSize(0.050) h_pulls.GetYaxis().SetLabelSize(0.046) h_pulls.GetYaxis().SetNdivisions(nbins, 0, 0) S_pulls.SetFillColor(kBlack) S_pulls.SetLineColor(kBlack) S_pulls.SetMarkerColor(kBlack) S_pulls.SetLineWidth(2) S_pulls.SetMarkerStyle(20) S_pulls.SetMarkerSize(1) box1 = TBox(-1., 0., 1., nbins) #box1.SetFillStyle(3001) # 3001 checkered #box1.SetFillStyle(0) box1.SetFillColor(kGreen + 1) # 417 box1.SetLineWidth(2) box1.SetLineStyle(2) box1.SetLineColor(kGreen + 1) # 417 box2 = TBox(-2., 0., 2., nbins) #box2.SetFillStyle(3001) # 3001 checkered #box2.SetFillStyle(0) box2.SetFillColor(kOrange) # 800 box2.SetLineWidth(2) box2.SetLineStyle(2) box2.SetLineColor(kOrange) # 800 leg = TLegend(0.01, 0.01, 0.3, 0.15) leg.SetTextSize(0.05) leg.SetBorderSize(0) leg.SetFillStyle(0) leg.SetFillColor(0) #leg.SetNColumns(2) leg.AddEntry(S_pulls, "S+B fit", "lp") if text: leg.AddEntry(0, text, "") h_pulls.Draw("") box2.Draw() box1.Draw() S_pulls.Draw("P6SAME") leg.Draw() canvas.RedrawAxis() canvas.Print(outName + ".png") canvas.Print(outName + ".pdf") if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
else: ey_hi_ratio = y_ratio * math.hypot(ey_hi_mu / y_mu, ey_hi_ele / y_ele) grRatio.SetPoint(i, x, y_ratio) grRatio.SetPointEXlow(i, ex_lo) grRatio.SetPointEXhigh(i, ex_hi) grRatio.SetPointEYlow(i, ey_lo_ratio) grRatio.SetPointEYhigh(i, ey_hi_ratio) if True: grRatio.SetMarkerStyle(20) grRatio.SetMarkerSize(1.5) grRatio.SetMarkerColor(1) grRatio.SetLineStyle(1) grRatio.SetLineColor(1) grRatio.SetLineWidth(1) grRatio.GetXaxis().SetLabelSize(0.04) grRatio.GetXaxis().SetTitleSize(0.04) grRatio.GetXaxis().SetTitleOffset(1.25) grRatio.GetYaxis().SetLabelSize(0.04) grRatio.GetYaxis().SetTitleSize(0.04) grRatio.GetYaxis().SetTitleOffset(1.5) # grRatio.GetYaxis().SetRangeUser(ylo, yhi) grRatio.Draw("ALP") fOutput = TFile('durp.root', 'recreate') grRatio.Write('durp')
vey1l = array('d',ey1l) vey1h = array('d',ey1h) vey2l = array('d',ey2l) vey2h = array('d',ey2h) g1 = TGraphAsymmErrors(len(vx),vx,vy1,vexl,vexh,vey1l,vey1h) g2 = TGraphAsymmErrors(len(vx),vx,vy2,vexl,vexh,vey2l,vey2h) gObs = TGraph(len(vx),vx,vyObs) gExp = TGraph(len(vx),vx,vyExp) gxs = TGraph(len(vx),vx,vxs) gExpDinko = TGraph(len(massesTeV_v),massesTeV_v,xs_exp_limits) for i in range(0,len(massesTeV_v)): print "exp limit Dinko: "+str(xs_exp_limits[i]) g2.SetFillColor(ROOT.kYellow) g2.SetLineColor(ROOT.kYellow) g1.SetFillColor(ROOT.kGreen) g1.SetLineColor(ROOT.kGreen) gExp.SetLineWidth(2) gExp.SetLineStyle(9) gExpDinko.SetLineWidth(2) gExpDinko.SetLineStyle(2) gObs.SetLineWidth(2) gObs.SetLineStyle(1) gObs.SetLineColor(ROOT.kBlue+1) gObs.SetMarkerColor(ROOT.kBlue+1) gObs.SetMarkerStyle(21) gObs.SetMarkerSize(1.5) gxs.SetLineStyle(5) gxs.SetLineWidth(3) gxs.SetLineColor(ROOT.kRed)
def plotDataOverMCEff(hist_mc_tight, hist_mc_loose, hist_data_tight, hist_data_loose, plot_name='fakerate.pdf'): g = TGraphAsymmErrors(hist_mc_tight) g.Divide(hist_mc_tight, hist_mc_loose) g.GetYaxis().SetTitle('Fake rate') g.GetXaxis().SetTitle(hist_mc_tight.GetXaxis().GetTitle()) g.GetYaxis().SetTitleOffset(1.2) g.GetYaxis().SetTitleOffset(1.3) g.SetLineColor(2) g.SetMarkerColor(2) g_data = TGraphAsymmErrors(hist_data_tight) g_data.Divide(hist_data_tight, hist_data_loose) g_data.GetYaxis().SetTitle('Fake rate') g_data.GetXaxis().SetTitle(hist_data_tight.GetXaxis().GetTitle()) g_data.GetYaxis().SetTitleOffset(1.2) g_data.GetYaxis().SetTitleOffset(1.3) g_data.SetMarkerColor(1) g_vals = g.GetY() g_data_vals = g_data.GetY() g_ratio = g_data.Clone('ratio') for i in xrange(g_data.GetN()): ratio = g_data_vals[i] / g_vals[i] if g_vals[i] else 0. g_ratio.SetPoint(i, g.GetX()[i], ratio) rel_y_low = math.sqrt((g_data.GetErrorYlow(i) / g_data_vals[i])**2 + ( g.GetErrorYlow(i) / g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0. g_ratio.SetPointEYlow(i, rel_y_low * ratio) rel_y_high = math.sqrt( (g_data.GetErrorYhigh(i) / g_data_vals[i])**2 + (g.GetErrorYhigh(i) / g_vals[i])**2) if g_data_vals[i] > 0. and g_vals[i] > 0. else 0. g_ratio.SetPointEYhigh(i, rel_y_high * ratio) # Gymnastics to get same label sizes etc in ratio and main plot ytp_ratio = 2. xtp_ratio = 2. # hr.GetYaxis().SetNdivisions(4) 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_data.GetXaxis().SetLabelColor(0) g_data.GetXaxis().SetLabelSize(0) g.GetXaxis().SetLabelColor(0) g.GetXaxis().SetLabelSize(0) g_ratio.GetXaxis().SetTitle(g.GetXaxis().GetTitle()) # maxy = 1.1 * min(g.GetMaximum(), g_data.GetMaximum(), 0.2) g.GetYaxis().SetRangeUser(0.001, 0.2) cv, pad, padr = HistDrawer.buildCanvas() pad.cd() g.Draw('AP') g_data.Draw('P') legend = TLegend(0.23, 0.73, 0.43, 0.91) legend.SetFillColor(0) legend.SetFillStyle(0) legend.SetLineColor(0) legend.SetLineWidth(0) legend.AddEntry(g.GetName(), 'MC', 'lep') legend.AddEntry(g_data.GetName(), 'Observed', 'lep') legend.Draw() padr.cd() g_ratio.GetYaxis().SetRangeUser(0.51, 1.49) g_ratio.GetYaxis().SetTitle('Obs/MC') g_ratio.Draw('AP') drawRatioLines(g_ratio) cv.Print(plot_name)
def pullsVertical(fileName): content = filterPullFile(fileName) nbins, off = len(content), 0.10 b_pulls = TH1F("b_pulls", ";;Pulls", nbins, 0. - off, nbins - off) s_pulls = TH1F("s_pulls", ";;Pulls", nbins, 0. + off, nbins + off) # for i, s in enumerate(content): l = s.split() b_pulls.GetXaxis().SetBinLabel(i + 1, l[0]) s_pulls.GetXaxis().SetBinLabel(i + 1, l[0]) b_pulls.SetBinContent(i + 1, float(l[1])) b_pulls.SetBinError(i + 1, float(l[2])) s_pulls.SetBinContent(i + 1, float(l[3])) s_pulls.SetBinError(i + 1, float(l[4])) b_pulls.SetFillStyle(3005) b_pulls.SetFillColor(923) b_pulls.SetLineColor(923) b_pulls.SetLineWidth(1) b_pulls.SetMarkerStyle(20) b_pulls.SetMarkerSize(1.25) s_pulls.SetLineColor(602) s_pulls.SetMarkerColor(602) s_pulls.SetMarkerStyle(24) #24 s_pulls.SetLineWidth(1) b_pulls.GetYaxis().SetRangeUser(-2.5, 2.5) # Graphs h_pulls = TH2F("pulls", "", 6, -3., 3., nbins, 0, nbins) B_pulls = TGraphAsymmErrors(nbins) S_pulls = TGraphAsymmErrors(nbins) boxes = [] canvas = TCanvas("canvas", "Pulls", 600, 150 + nbins * 10) #nbins*20) canvas.cd() canvas.SetGrid(0, 1) canvas.GetPad(0).SetTopMargin(0.01) canvas.GetPad(0).SetRightMargin(0.01) canvas.GetPad(0).SetBottomMargin(0.05) canvas.GetPad(0).SetLeftMargin(0.25) #(0.25)#(0.065) canvas.GetPad(0).SetTicks(1, 1) for i, s in enumerate(content): l = s.split() if "1034h" in l[0]: l[0] = "CMS_PDF_13TeV" h_pulls.GetYaxis().SetBinLabel(i + 1, l[0].replace('CMS2016_', '')) #C #y1 = gStyle.GetPadBottomMargin() #y2 = 1. - gStyle.GetPadTopMargin() #h = (y2 - y1) / float(nbins) #y1 = y1 + float(i) * h #y2 = y1 + h #box = TPaveText(0, y1, 1, y2, 'NDC') #box.SetFillColor(0) #box.SetTextSize(0.02) #box.SetBorderSize(0) #box.SetTextAlign(12) #box.SetMargin(0.005) #if i % 2 == 0: # box.SetFillColor(18) #box.Draw() #boxes.append(box) B_pulls.SetPoint(i + 1, float(l[1]), float(i + 1) - 0.3) #C B_pulls.SetPointError(i + 1, float(l[2]), float(l[2]), 0., 0.) #C for i, s in enumerate(content): l = s.split() S_pulls.SetPoint(i + 1, float(l[3]), float(i + 1) - 0.7) #C S_pulls.SetPointError(i + 1, float(l[4]), float(l[4]), 0., 0.) #C h_pulls.GetXaxis().SetTitle("(#hat{#theta} - #theta_{0}) / #Delta#theta") h_pulls.GetXaxis().SetLabelOffset(-0.01) h_pulls.GetXaxis().SetTitleOffset(.6) h_pulls.GetYaxis().SetNdivisions(nbins, 0, 0) B_pulls.SetFillColor(1) B_pulls.SetLineColor(1) B_pulls.SetLineStyle(1) B_pulls.SetLineWidth(2) B_pulls.SetMarkerColor(1) B_pulls.SetMarkerStyle(20) B_pulls.SetMarkerSize(1) #(0.75) S_pulls.SetFillColor(629) S_pulls.SetLineColor(629) S_pulls.SetMarkerColor(629) S_pulls.SetLineWidth(2) S_pulls.SetMarkerStyle(20) S_pulls.SetMarkerSize(1) box1 = TBox(-1., 0., 1., nbins) box1.SetFillStyle(3001) #box1.SetFillStyle(0) box1.SetFillColor(417) box1.SetLineWidth(2) box1.SetLineStyle(2) box1.SetLineColor(417) box2 = TBox(-2., 0., 2., nbins) box2.SetFillStyle(3001) #box2.SetFillStyle(0) box2.SetFillColor(800) box2.SetLineWidth(2) box2.SetLineStyle(2) box2.SetLineColor(800) leg = TLegend(0.1, -0.05, 0.7, 0.08) leg.SetBorderSize(0) leg.SetFillStyle(0) leg.SetFillColor(0) leg.SetNColumns(2) leg.AddEntry(B_pulls, "B-only fit", "lp") leg.AddEntry(S_pulls, "S+B fit", "lp") if text: leg.AddEntry(0, text, "") h_pulls.Draw("") box2.Draw() box1.Draw() B_pulls.Draw("P6SAME") S_pulls.Draw("P6SAME") leg.Draw() # drawCMS(35867, "Preliminary") # drawAnalysis("VH") # drawRegion(outName) canvas.Print(outName + ".png") canvas.Print(outName + ".pdf") if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
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 ratioplot(): # create required parts leg = getLegend() latex = getLatex() c = SetCanvas() #c.SetLogy() #c = TCanvas() #c.SetLogy() h1 = f.Get('h_num_calo_') #'calo',pf h1 = setHistStyle(h1, bins) h2 = f.Get('h_den_calo_') h2 = setHistStyle(h2, bins) h11 = f2.Get('h_num_calo_') h11 = setHistStyle(h11, bins) h21 = f2.Get('h_den_calo_') h21 = setHistStyle(h21, bins) gr = TGraphAsymmErrors(30) #gr.Divide(h1,h2) gr = TGraphAsymmErrors(h1, h2) gr2 = TGraphAsymmErrors(h11, h21) gr2.SetMarkerStyle(20) gr2.GetXaxis().SetRangeUser(0, 1000) gr2.SetMarkerSize(1.5) gr2.SetLineColor(2) gr2.SetLineWidth(1) gr2.SetMarkerColor(2) gr.GetXaxis().SetRangeUser(0, 1000) # gr.GetYaxis().SetRangeUser(0.0001,1.2) gr.SetMarkerStyle(20) gr.SetMarkerSize(1.5) gr.SetLineColor(1) gr.SetLineWidth(1) gr.SetMarkerColor(1) gr.GetYaxis().SetTitle("Trigger Efficiency") gr.GetXaxis().SetTitle("MET [GeV]") gr.SetTitle("") #base histogram histogram_base = TH1F("histogram_base", "", 1000, 0, 1000.) histogram_base.SetTitle("") histogram_base.SetStats(0) histogram_base.SetMarkerSize(2) #histogram_base.SetMinimum(0.0) histogram_base.SetMaximum(1.2) histogram_base.GetXaxis().SetTitle("Online E_{T}^{miss} (GeV)") histogram_base.GetYaxis().SetTitle("Efficiency") histogram_base = setHistStyle(histogram_base, bins) histogram_base.Draw("HIST") # c.SaveAs() gr.Draw('P same') gr2.Draw('P same') latex.DrawLatex(0.49, 0.93, " EGamma Run2018C, 13 TeV") xmin = 0.0 line = TLine(max(xmin, gr.GetXaxis().GetXmin()), 1, 1000, 1) line.SetLineColor(1) line.SetLineWidth(1) line.SetLineStyle(7) line.Draw() leg.AddEntry(gr, 'With HBHENoise filter', 'P') leg.AddEntry(gr2, 'Without HBHENoise filter', 'P') leg.Draw() txt = 'Path: HLT_PFMETTypeOne200_HBHE_BeamHaloCleaned' texcms = AddText(txt) texcms.Draw("same") c.SaveAs('testTurnOn_EGamma.png')
]) theory_v = TVectorD(7, theory_a) mass_a = array("d", [1500, 1750, 2000, 2250, 2500, 3000, 3250, 3500]) mass_v = TVectorD(7, mass_a) err_a = array("d", [0, 0, 0, 0, 0, 0, 0, 0]) err_v = TVectorD(7, err_a) ## theory_a = array("d",[0.0006434760289,0.0005751010031,0.0005231099785,0.0005344069796,0.0005340549978]) ## theory_v = TVectorD(5,theory_a) ## mass_a = array("d",[1750,2000,2500,2750,3000]) ## mass_v = TVectorD(5,mass_a) ## err_a = array("d",[0,0,0,0,0,0]) ## err_v = TVectorD(5,err_a) theory = TGraphAsymmErrors(mass_v, theory_v, err_v, err_v, err_v, err_v) theory.SetLineColor(4) theory.SetLineStyle(9) theory.SetLineWidth(3) observed_p = TGraphAsymmErrors(massv, obsv, masserrv, masserrv, obserrv, obserrv) observed_p.SetLineColor(ROOT.kBlack) observed_p.SetLineWidth(2) observed_p.SetMarkerStyle(20) expected_p = TGraphAsymmErrors(massv, expv, masserrv, masserrv, experrv, experrv) expected_p.SetLineColor(ROOT.kBlack) expected_p.SetLineWidth(2) expected_p.SetLineStyle(2) expected68 = TGraphAsymmErrors(massv, expv, masserrv, masserrv, exp68Lv,