def legend4Plot(plot, left = False): if left: theLeg = TLegend(0.2, 0.62, 0.55, 0.92, "", "NDC") else: theLeg = TLegend(0.60, 0.62, 0.92, 0.92, "", "NDC") theLeg.SetName('theLegend') theLeg.SetBorderSize(0) theLeg.SetLineColor(0) theLeg.SetFillColor(0) theLeg.SetFillStyle(0) theLeg.SetLineWidth(0) theLeg.SetLineStyle(0) theLeg.SetTextFont(42) theLeg.SetTextSize(.045) entryCnt = 0 for obj in range(0, int(plot.numItems())): objName = plot.nameOf(obj) if (not plot.getInvisible(objName)): theObj = plot.getObject(obj) objTitle = theObj.GetTitle() if len(objTitle) < 1: objTitle = objName dopts = plot.getDrawOptions(objName).Data() # print 'obj:',theObj,'title:',objTitle,'opts:',dopts,'type:',type(dopts) if theObj.IsA().InheritsFrom('TNamed'): theLeg.AddEntry(theObj, objTitle, dopts) entryCnt += 1 theLeg.SetY1NDC(0.9 - 0.05*entryCnt - 0.005) theLeg.SetY1(theLeg.GetY1NDC()) return theLeg
def addSys(var, cut, sys): binLow = "" binHigh = "" binName = "" if "binned" in cut: binLow = cut[cut.find("LowVal") + 6:cut.find("HighVal") - 1] binHigh = cut[cut.find("HighVal") + 7:] binName = "bin_" + binLow + "_" + binHigh cut = cut[:cut.find("binned")] channel = cut weight = "eventWeightLumi" #+ ("*stitchWeight" if any([x for x in back if x.endswith('b')]) else "") cut = selection[cut] if not binLow == "": cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh weightUp = weightDown = weight varUp = varDown = var cutUp = cutDown = cut # Systematics if sys == 'CMS_scale_j': if var != "MET_sign": varUp = var.replace('pt', 'ptScaleUp') else: varUp = var.replace('sign', 'signScaleUp') if var != "MET_sign": varDown = var.replace('pt', 'ptScaleDown') else: varDown = var.replace('sign', 'signScaleDown') cutUp = cut.replace('MET_pt', 'MET_ptScaleUp') cutUp = cutUp.replace('Jets', 'JetsScaleUp') cutUp = cutUp.replace('12', '12ScaleUp') cutUp = cutUp.replace('mT>', 'mTScaleUp>') cutUp = cutUp.replace('mT2', 'mT2ScaleUp') cutDown = cut.replace('MET_pt', 'MET_ptScaleDown') cutDown = cutDown.replace('Jets', 'JetsScaleDown') cutDown = cutDown.replace('12', '12ScaleDown') cutDown = cutDown.replace('mT>', 'mTScaleDown>') cutDown = cutDown.replace('mT2', 'mT2ScaleDown') elif sys == 'CMS_res_j': if var != "MET_sign": varUp = var.replace('pt', 'ptResUp') else: varUp = var.replace('sign', 'signResUp') if var != "MET_sign": varDown = var.replace('pt', 'ptResDown') else: varDown = var.replace('sign', 'signResDown') cutUp = cut.replace('MET_pt', 'MET_ptResUp') cutUp = cutUp.replace('Jets', 'JetsResUp') cutUp = cutUp.replace('12', '12ResUp') cutUp = cutUp.replace('mT>', 'mTResUp>') cutUp = cutUp.replace('mT2', 'mT2ResUp') cutDown = cut.replace('MET_pt', 'MET_ptResDown') cutDown = cutDown.replace('Jets', 'JetsResDown') cutDown = cutDown.replace('12', '12ResDown') cutDown = cutDown.replace('mT>', 'mTResDown>') cutDown = cutDown.replace('mT2', 'mT2ResDown') elif sys == 'CMS_WqcdWeightRen': weightUp += "*WqcdWeightRenUp/WqcdWeight" weightDown += "*WqcdWeightRenDown/WqcdWeight" elif sys == 'CMS_WqcdWeightFac': weightUp += "*WqcdWeightFacUp/WqcdWeight" weightDown += "*WqcdWeightFacDown/WqcdWeight" elif sys == 'CMS_ZqcdWeightRen': weightUp += "*ZqcdWeightRenUp/ZqcdWeight" weightDown += "*ZqcdWeightRenDown/ZqcdWeight" elif sys == 'CMS_ZqcdWeightFac': weightUp += "*ZqcdWeightFacUp/ZqcdWeight" weightDown += "*ZqcdWeightFacDown/ZqcdWeight" elif sys == 'CMS_WewkWeight': weightUp += "/WewkWeight" weightDown += "" elif sys == 'CMS_ZewkWeight': weightUp += "/ZewkWeight" weightDown += "" elif sys == 'CMS_pdf': weightUp += "*PDFWeightUp/eventWeight" weightDown += "*PDFWeightDown/eventWeight" elif sys == 'CMS_HF': weightUp += "*1.20" weightDown += "*0.8" elif sys == 'CMS_eff_b': weightUp += "*bTagWeightUp/bTagWeight" weightDown += "*bTagWeightDown/bTagWeight" elif sys == 'CMS_scale_pu': weightUp += "*puWeightUp/puWeight" weightDown += "*puWeightDown/puWeight" elif sys == 'CMS_scale_top': weightUp += "/TopWeight" weightDown += "" elif sys == 'CMS_eff_trigger': weightUp += "*triggerWeightUp/triggerWeight" weightDown += "*triggerWeightDown/triggerWeight" elif sys == 'CMS_eff_e' and '2e' in cut or '1e' in channel: weightUp += "*leptonWeightUp/leptonWeight" weightDown += "*leptonWeightDown/leptonW\ eight" elif sys == 'CMS_eff_m' and '2m' in cut or '1m' in channel: weightUp += "*leptonWeightUp/leptonWeight" weightDown += "*leptonWeightDown/leptonW\ eight" elif sys == 'QCDscale_ren': weightUp += "*QCDRenWeightUp" weightDown += "*QCDRenWeightDown" elif sys == 'QCDscale_fac': weightUp += "*QCDFacWeightUp" weightDown += "*QCDFacWeightDown" # elif sys=='EWKscale_Z': weightDown += "/ZewkWeight" # elif sys=='EWKscale_W': weightDown += "/WewkWeight" else: print "Systematic", sys, "not applicable or not recognized." ### Create and fill MC histograms ### file = {} tree = {} hist = {} histUp = {} histDown = {} isBlind = BLIND and 'SR' in channel for i, s in enumerate(back + sign): tree[s] = TChain("tree") for j, ss in enumerate(sample[s]['files']): tree[s].Add(NTUPLEDIR + ss + ".root") if not binLow == "": hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events;" + ('log' if variable[var]['log'] else ''), 1, float(binLow), float(binHigh)) elif binLow == "" and variable[var]['nbins'] > 0: hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events;" + ('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max']) else: hist[s] = TH1F(s, ";" + variable[var]['title'], len(variable[var]['bins']) - 1, array('f', variable[var]['bins'])) hist[s].Sumw2() histUp[s] = hist[s].Clone(s + 'Up') histDown[s] = hist[s].Clone(s + 'Down') redFactorString = "" redFactorValue = "" #if isBlind and 'data' not in s and options.limit: if isBlind and 'data' not in s: redFactorValue = " / 15" cutstring = ("*(" + cut + ")" if len(cut) > 0 else "") cutstringUp = ("*(" + cutUp + ")" if len(cut) > 0 else "") cutstringDown = ("*(" + cutDown + ")" if len(cut) > 0 else "") if '-' in s: cutstring = cutstring.replace( cut, cut + "&& nBQuarks==" + s.split('-')[1][0]) tree[s].Project(s, var, "(" + weight + redFactorValue + ")" + cutstring) if 'HF' not in sys or 'QCDscale' not in sys: tree[s].Project(s + 'Up', varUp, "(" + weightUp + redFactorValue + ")" + cutstring) tree[s].Project( s + 'Down', varDown, "(" + weightDown + redFactorValue + ")" + cutstring) if 'HF' in sys: if s.startswith('WJ') or s.startswith('ZJ') or s.startswith( 'DYJets'): tree[s].Project( s + 'Up', varUp, "(" + weightUp + redFactorValue + ")" + cutstringUp) tree[s].Project( s + 'Down', varDown, "(" + weightDown + redFactorValue + ")" + cutstringDown) else: tree[s].Project( s + 'Up', varUp, "(" + weight + redFactorValue + ")" + cutstringUp) tree[s].Project( s + 'Down', varDown, "(" + weight + redFactorValue + ")" + cutstringDown) if 'QCDscale' in sys: if s.startswith('WJ') or s.startswith('ZJ') or s.startswith( 'DYJets'): tree[s].Project( s + 'Up', varUp, "(" + weight + redFactorValue + ")" + cutstringUp) tree[s].Project( s + 'Down', varDown, "(" + weight + redFactorValue + ")" + cutstringDown) else: tree[s].Project( s + 'Up', varUp, "(" + weightUp + redFactorValue + ")" + cutstringUp) tree[s].Project( s + 'Down', varDown, "(" + weightDown + redFactorValue + ")" + cutstringDown) hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0) hist[s].SetLineWidth(2) histUp[s].SetLineWidth(2) histDown[s].SetLineWidth(2) hist[s].SetLineColor(1) histUp[s].SetLineColor(629) histDown[s].SetLineColor(602) # Rescale normalization for QCD scales FIXME if 'QCDscale' in sys: for s in back + sign: #['TTbar', 'TTbarSL', 'ST']: if s in hist and histUp[s].Integral( ) > 0. and histDown[s].Integral() > 0.: histUp[s].Scale(hist[s].Integral() / histUp[s].Integral()) histDown[s].Scale(hist[s].Integral() / histDown[s].Integral()) hist['BkgSum'] = hist[back[0]].Clone("BkgSum") hist['BkgSum'].Reset() histUp['BkgSum'] = hist['BkgSum'].Clone("BkgSumUp") histUp['BkgSum'].SetLineColor(629) histUp['BkgSum'].Reset() histDown['BkgSum'] = hist['BkgSum'].Clone("BkgSumDown") histDown['BkgSum'].SetLineColor(602) histDown['BkgSum'].Reset() for i, s in enumerate(back): hist['BkgSum'].Add(hist[s], 1) histUp['BkgSum'].Add(histUp[s], 1) histDown['BkgSum'].Add(histDown[s], 1) for i, s in enumerate(back + sign + ['BkgSum']): addOverflow(hist[s], False) addOverflow(histUp[s], False) addOverflow(histDown[s], False) c1 = TCanvas("c1", "Signals", 800, 600) c1.cd() gStyle.SetOptStat(0) gStyle.SetOptTitle(0) if RATIO: c1.Divide(1, 2) setTopPad(c1.GetPad(1), RATIO) setBotPad(c1.GetPad(2), RATIO) c1.cd(1) c1.GetPad(bool(RATIO)).SetTopMargin(0.06) c1.GetPad(bool(RATIO)).SetRightMargin(0.06) c1.GetPad(bool(RATIO)).SetTicks(1, 1) c1.GetPad(bool(RATIO)).SetLogy() histUp['BkgSum'].SetMaximum(histUp['BkgSum'].GetMaximum() * 5) histUp['BkgSum'].Draw("HIST") histDown['BkgSum'].Draw("SAME, HIST") hist['BkgSum'].Draw("SAME, HIST") drawCMS(-1, "Simulation", False) setHistStyle(histUp['BkgSum'], 1.2 if RATIO else 1.1) if RATIO: c1.cd(2) errUp = histUp['BkgSum'].Clone("BkgUp;") errUp.Add(hist['BkgSum'], -1) errUp.Divide(hist['BkgSum']) errUp.SetTitle("") errUp.GetYaxis().SetTitle("#frac{shifted-central}{central}") errUp.GetYaxis().SetNdivisions(503) setBotStyle(errUp) errUp.GetYaxis().SetRangeUser(-0.3, 0.3) errUp.Draw("HIST") errDown = histDown['BkgSum'].Clone("BkgDown;") errDown.Add(hist['BkgSum'], -1) errDown.Divide(hist['BkgSum']) errDown.Draw("SAME, HIST") f1 = TF1("myfunc", "[0]", -100000, 10000) f1.SetLineColor(1) f1.SetLineStyle(7) f1.SetLineWidth(1) f1.SetParameter(0, 0) f1.Draw("same") leg = TLegend(0.65, 0.80, 0.95, 0.80) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetHeader(sys.replace('CMS', '').replace('_', ' ')) leg.AddEntry(histUp['BkgSum'], "Up", "l") leg.AddEntry(hist['BkgSum'], "Central", "l") leg.AddEntry(histDown['BkgSum'], "Down", "l") leg.SetY1(0.75 - leg.GetNRows() * 0.045) c1.cd(1) leg.Draw() if options.saveplots: if not os.path.exists("plotsSys_" + options.name + "/" + channel + binName): os.makedirs("plotsSys_" + options.name + "/" + channel + binName) c1.Print("plotsSys_" + options.name + "/" + channel + binName + "/" + sys + ".png") c1.Print("plotsSys_" + options.name + "/" + channel + binName + "/" + sys + ".pdf") for i, s in enumerate(back + sign): c2 = TCanvas(s + "canvas", "Signals", 800, 600) c2.cd() gStyle.SetOptStat(0) gStyle.SetOptTitle(0) c2.GetPad(0).SetTopMargin(0.06) c2.GetPad(0).SetRightMargin(0.06) c2.GetPad(0).SetTicky(2) c2.GetPad(0).SetLogy() histUp[s].SetMaximum(histUp[s].GetMaximum() * 5) histUp[s].Draw("HIST") histDown[s].Draw("SAME, HIST") hist[s].Draw("SAME, HIST") drawCMS(-1, "Simulation", False) if options.saveplots: c2.Print("plotsSys_" + options.name + "/" + channel + binName + "/" + sys + "_" + s + ".png") c2.Print("plotsSys_" + options.name + "/" + channel + binName + "/" + sys + "_" + s + ".pdf") saveHist(histUp, channel + binName, sys + 'Up') saveHist(histDown, channel + binName, sys + 'Down') print "Added systematic", sys, "to channel", channel
def plot(var, cut, norm=False, nm1=False): ### Preliminary Operations ### doBinned = False if options.mode == "binned": doBinned = True fileRead = os.path.exists("combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root") treeRead = not any( x == cut for x in ['0l', '1e', '1m', '2e', '2m', '1e1m', 'Gen', 'Trigger' ]) #(var in variable.keys()) # Read from tree #signal definition if fileRead: sign = ['ttDM_MChi1_MPhi200_scalar', 'tDM_MChi1_MPhi200_scalar'] #for postfit plot if not fileRead and not options.limit: sign = ['ttDM_MChi1_MPhi100_scalar', 'tDM_MChi1_MPhi100_scalar'] #for normal plotting #bkg definition if fileRead or options.limit: back = [ "QCD", "DYJetsToNuNu_HT", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarSL" ] #for postfit or limit if (cut).find('>250') or (cut).startswith('AH'): #for hadronic selections back = [ "QCD", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarV", "TTbar2L", "TTbar1L", "DYJetsToNuNu_HT" ] if fileRead or options.limit: back = [ "QCD", "DYJetsToLL_HT", "VV", "ST", "WJetsToLNu_HT", "TTbarSL", "DYJetsToNuNu_HT" ] #for postfit or limit binLow = "" binHigh = "" binName = "" if "binned" in cut: binLow = cut[cut.find("LowVal") + 6:cut.find("HighVal") - 1] binHigh = cut[cut.find("HighVal") + 7:] binName = "bin_" + binLow + "_" + binHigh cut = cut[:cut.find("binned")] useformula = False if 'formula' in variable[var]: useformula = True channel = cut plotdir = cut plotname = var weight = "eventWeightLumi" #*(2.2/35.9) isBlind = BLIND and ('SR' in channel or 'ps' in channel) if fileRead: isBlind = False options.saveplots = True RESIDUAL = True elif isBlind: RATIO = 0 SIGNAL = 20 else: RATIO = 4 SIGNAL = 1 RESIDUAL = False showSignal = True #('SR' in channel) cutSplit = cut.split() for s in cutSplit: if s in selection.keys(): plotdir = s cut = cut.replace(s, selection[s]) if not binLow == "": cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh #if treeRead and cut in selection: cut = cut.replace(cut, selection[cut]) # Determine Primary Dataset pd = [] if any(w in cut for w in ['1l', '1m', '2m', 'isWtoMN', 'isZtoMM', 'isTtoEM']): pd += [x for x in sample['data_obs']['files'] if 'SingleMuon' in x] if any(w in cut for w in ['1l', '1e', '2e', 'isWtoEN', 'isZtoEE']): pd += [x for x in sample['data_obs']['files'] if 'SingleElectron' in x] if any(w in cut for w in ['0l', 'isZtoNN']): pd += [x for x in sample['data_obs']['files'] if 'MET' in x] if len(pd) == 0: raw_input("Warning: Primary Dataset not recognized, continue?") print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:" print " dataset:", pd print " cut :", cut print " cut :", weight ### Create and fill MC histograms ### # Create dict file = {} tree = {} hist = {} ### Create and fill MC histograms ### for i, s in enumerate(data + back + sign): if fileRead: var = 'MET_pt' if channel.startswith('SL'): var = 'MET_sign' if channel.endswith('ZR'): var = 'FakeMET_pt' plotname = var hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events;" + ('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max']) if doBinned: bins = np.array([]) if 'bins' in variable[var].keys(): bins = np.array(variable[var]['bins']) else: binsize = (variable[var]['max'] - variable[var]['min']) / variable[var]['nbins'] bins = np.arange(variable[var]['min'], variable[var]['max'] + binsize, binsize) bins = np.append(bins, 10000) for i in range(0, len(bins) - 1): rbin = str(bins[i]) + "_" + str(bins[i + 1]) fileName = "combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root" if not any( t in s for t in ['data', 'tDM'] ) else "rootfiles_" + options.name + "/" + channel + "bin_" + rbin + ".root" histName = "shapes_fit_b/" + channel + "bin_" + rbin + "/" + s if not any( t in s for t in ['data', 'tDM']) else s file[s] = TFile(fileName, "READ") tmphist = file[s].Get(histName) if 'data' not in s: hist[s].SetMarkerSize(0) if tmphist: hist[s].SetBinContent(i + 1, tmphist.GetBinContent(1)) hist[s].SetBinError(i + 1, tmphist.GetBinError(1)) else: hist[s].SetBinContent(i + 1, 0.) hist[s].SetBinError(i + 1, 0.) else: fileName = "combinedCards_" + options.name + "/fitDiagnostics_" + options.file + ".root" if not s == 'data_obs' else "rootfiles_" + options.name + "/" + channel + binName + ".root" histName = "shapes_fit_b/" + channel + "/" + s if not s == 'data_obs' else s file[s] = TFile(fileName, "READ") tmphist = file[s].Get(histName) if tmphist == None: tmphist = hist[back[0]].Clone(s) tmphist.Reset("MICES") print "Histogram", histName, "not found in file", fileName if s == 'data_obs': hist[s] = tmphist else: hist[s] = hist['data_obs'].Clone(s) hist[s].SetMarkerSize(0) for i in range(tmphist.GetNbinsX() + 1): hist[s].SetBinContent(i + 1, tmphist.GetBinContent(i + 1)) elif treeRead: # Project from tree tree[s] = TChain("tree") for j, ss in enumerate(sample[s]['files']): if not 'data' in s or ('data' in s and ss in pd): tree[s].Add(NTUPLEDIR + ss + ".root") if not binLow == "": hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events;" + ('log' if variable[var]['log'] else ''), 1, float(binLow), float(binHigh)) elif binLow == "" and variable[var]['nbins'] > 0: hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events;" + ('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max']) else: hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events;" + ('log' if variable[var]['log'] else ''), len(variable[var]['bins']) - 1, array('f', variable[var]['bins'])) hist[s].Sumw2() redFactorString = "" redFactorValue = "" #if isBlind and 'data' in s: if isBlind and 'data' in s and options.limit: redFactorString = " && Entry$ % 15 == 1" #if isBlind and 'data' not in s: if isBlind and 'data' not in s and options.limit: redFactorValue = " / 15" cutstring = "(" + weight + redFactorValue + ")" + ( "*(" + cut + redFactorString + ")" if len(cut) > 0 else "") if '-' in s: cutstring = cutstring.replace( cut, cut + "&& nBQuarks==" + s.split('-')[1][0]) if useformula == True: tree[s].Project(s, variable[var]['formula'], cutstring) else: tree[s].Project(s, var, cutstring) if not tree[s].GetTree() == None: hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast()) else: # Histogram written to file for j, ss in enumerate(sample[s]['files']): if not 'data' in s or ('data' in s and ss in pd): file[ss] = TFile(NTUPLEDIR + ss + ".root", "R") if file[ss].IsZombie(): print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist" continue tmphist = file[ss].Get(cut + "/" + var) if tmphist == None: continue if not s in hist.keys(): hist[s] = tmphist else: hist[s].Add(tmphist) if hist[s].Integral() < 0: hist[s].Scale(0) hist[s].SetFillColor(sample[s]['fillcolor']) hist[s].SetFillStyle(sample[s]['fillstyle']) hist[s].SetLineColor(sample[s]['linecolor']) hist[s].SetLineStyle(sample[s]['linestyle']) #if 'WJetsToLNu' in s and 'SL' in channel and 'WR' in channel: hist[s].Scale(1.30) #if 'TTbar' in s and 'SL' in channel and 'TR' in channel: hist[s].Scale(0.91) hist['BkgSum'] = hist[back[0]].Clone("BkgSum") hist['BkgSum'].Reset("MICES") for i, s in enumerate(back): hist['BkgSum'].Add(hist[s], 1) if fileRead: hist['PreFit'] = hist['BkgSum'].Clone("PreFit") if doBinned: for i in range(0, len(bins) - 1): rbin = str(bins[i]) + "_" + str(bins[i + 1]) tmphist = file[back[0]].Get("shapes_prefit/" + channel + "bin_" + rbin + "/" + "total_background") if tmphist: hist['PreFit'].SetBinContent(i + 1, tmphist.GetBinContent(1)) else: hist['PreFit'].SetBinContent(i + 1, 0.) else: tmphist = file[back[0]].Get("shapes_prefit/" + channel + "/" + "total_background") for i in range(tmphist.GetNbinsX() + 1): hist['PreFit'].SetBinContent(i + 1, tmphist.GetBinContent(i + 1)) addOverflow(hist['PreFit'], False) hist['PreFit'].SetLineStyle(2) hist['PreFit'].SetLineColor(617) #923 hist['PreFit'].SetLineWidth(3) hist['PreFit'].SetFillStyle(0) hist['BkgSum'].SetFillStyle(3002) hist['BkgSum'].SetFillColor(1) # Create data and Bkg sum histograms # if options.blind or 'SR' in channel: # hist['data_obs'] = hist['BkgSum'].Clone("data_obs") # hist['data_obs'].Reset("MICES") # Set histogram style hist[data[0]].SetMarkerStyle(20) hist[data[0]].SetMarkerSize(1.25) for i, s in enumerate(data + back + sign + ['BkgSum']): addOverflow(hist[s], False) # Add overflow for i, s in enumerate(sign): hist[s].SetLineWidth(3) #for i, s in enumerate(sign): sample[s]['plot'] = True#sample[s]['plot'] and s.startswith(channel[:2]) if norm: for i, s in enumerate(sign): hist[s].Scale(hist['BkgSum'].Integral() / hist[s].Integral()) # for i, s in enumerate(back): # hist[s].SetFillStyle(3005) # hist[s].SetLineWidth(2) # #for i, s in enumerate(sign): # # hist[s].SetFillStyle(0) # if not var=="Events": # sfnorm = hist[data[0]].Integral()/hist['BkgSum'].Integral() # print "Applying SF:", sfnorm # for i, s in enumerate(back+['BkgSum']): hist[s].Scale(sfnorm) if SIGNAL > 1: if not var == "Events": for i, s in enumerate(sign): hist[s].Scale(SIGNAL) # Create stack bkg = THStack("Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events") for i, s in enumerate(back): bkg.Add(hist[s]) # Legend #leg = TLegend(0.65, 0.6, 0.95, 0.9) leg = TLegend(0.45, 0.63, 0.93, 0.92) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) leg.SetNColumns(3) leg.SetTextFont(42) if len(data) > 0: leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe") for i, s in reversed(list(enumerate(back))): leg.AddEntry(hist[s], sample[s]['label'], "f") if 'PreFit' not in hist: leg.AddEntry(hist['BkgSum'], sample['BkgSum']['label'], "f") else: leg.AddEntry(hist['BkgSum'], 'MC unc.', "l") leg.AddEntry(hist['PreFit'], sample['PreFit']['label'], "l") if showSignal: for i, s in enumerate(sign): if SIGNAL > 1: if sample[s]['plot']: leg.AddEntry(hist[s], '%s (x%d)' % (sample[s]['label'], SIGNAL), "l") else: if sample[s]['plot']: leg.AddEntry(hist[s], sample[s]['label'], "l") leg.SetY1(0.9 - leg.GetNRows() * 0.05) # --- Display --- c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800 if RATIO else 600) if RATIO: if RESIDUAL: c1.Divide(1, 3) setFitTopPad(c1.GetPad(1), RATIO) setFitBotPad(c1.GetPad(2), RATIO) setFitResPad(c1.GetPad(3), RATIO) else: c1.Divide(1, 2) setTopPad(c1.GetPad(1), RATIO) setBotPad(c1.GetPad(2), RATIO) c1.cd(1) c1.GetPad(bool(RATIO)).SetTopMargin(0.06) c1.GetPad(bool(RATIO)).SetRightMargin(0.05) c1.GetPad(bool(RATIO)).SetTicks(1, 1) log = ("log" in hist['BkgSum'].GetZaxis().GetTitle()) if log: c1.GetPad(bool(RATIO)).SetLogy() # Draw bkg.Draw("HIST") # stack hist['BkgSum'].Draw("SAME, E2") # sum of bkg if not isBlind and len(data) > 0: graph = fixData(hist[data[0]], USEGARWOOD) graph.Draw("SAME, PE") #data_graph.Draw("SAME, PE") if 'PreFit' in hist: hist['PreFit'].Draw("SAME, HIST") if showSignal: for i, s in enumerate(sign): if sample[s]['plot']: hist[s].Draw( "SAME, HIST" ) # signals Normalized, hist[s].Integral()*sample[s]['weight'] bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075) bkg.SetMaximum((5. if log else 1.25) * max( bkg.GetMaximum(), hist[data[0]].GetBinContent(hist[data[0]].GetMaximumBin()) + hist[data[0]].GetBinError(hist[data[0]].GetMaximumBin()))) if len(sign) > 0 and bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum( max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()) * 1.25) bkg.SetMinimum( max( min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin( )), hist[data[0]].GetMinimum()), 5.e-1) if log else 0.) if log: #bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4) bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e3) bkg.GetYaxis().SetMoreLogLabels(True) else: bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e3) leg.Draw() if fileRead and 'SR' in channel: drawCMS(LUMI / 15., "Preliminary") else: drawCMS(LUMI, "Preliminary") drawRegion(channel, True) drawAnalysis("DM" + channel[:2]) drawOverflow() setHistStyle(bkg, 1.2 if RATIO else 1.1) setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1) if RATIO: c1.cd(2) err = hist['BkgSum'].Clone("BkgErr;") err.SetTitle("") err.GetYaxis().SetTitle("Data / Bkg") for i in range(1, err.GetNbinsX() + 1): err.SetBinContent(i, 1) if hist['BkgSum'].GetBinContent(i) > 0: err.SetBinError( i, hist['BkgSum'].GetBinError(i) / hist['BkgSum'].GetBinContent(i)) if RESIDUAL: setFitBotStyle(err) else: setBotStyle(err) errLine = err.Clone("errLine") errLine.SetLineWidth(1) errLine.SetFillStyle(0) res = hist[data[0]].Clone("Residues") for i in range(0, res.GetNbinsX() + 1): if hist['BkgSum'].GetBinContent(i) > 0: res.SetBinContent( i, res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i)) res.SetBinError( i, res.GetBinError(i) / hist['BkgSum'].GetBinContent(i)) if RESIDUAL: setFitBotStyle(res) else: setBotStyle(res) #err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5) #err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2) err.Draw("E2") if 'PreFit' in hist: respre = hist[data[0]].Clone("ResiduesPreFit") respre.Divide(hist['PreFit']) respre.SetLineStyle(2) respre.SetLineColor(617) #923 respre.SetLineWidth(3) respre.SetFillStyle(0) respre.Draw("SAME, HIST") errLine.Draw("SAME, HIST") if not isBlind and len(data) > 0: res.Draw("SAME, PE0") #res_graph.Draw("SAME, PE0") if len(err.GetXaxis().GetBinLabel( 1)) == 0: # Bin labels: not a ordinary plot drawRatio(hist['data_obs'], hist['BkgSum']) drawStat(hist['data_obs'], hist['BkgSum']) c1.Update() if RATIO and RESIDUAL: c1.cd(3) c1.SetGrid(1, 0) resFit = hist[data[0]].Clone("Residues") resFit.Reset("MICES") resFit.SetTitle("") #resFit.GetYaxis().SetTitle("Residuals") resFit.GetYaxis().SetTitle( "#frac{Data - Bkg}{#sqrt{#sigma_{Data}^{2}+#sigma_{Bkg}^{2}}}") for i in range(0, res.GetNbinsX() + 1): if hist['BkgSum'].GetBinContent(i) > 0: resFit.SetBinContent( i, (hist[data[0]].GetBinContent(i) - hist['BkgSum'].GetBinContent(i)) / (math.sqrt( math.pow(hist['BkgSum'].GetBinError(i), 2) + math.pow(hist[data[0]].GetBinError(i), 2)))) setFitResStyle(resFit) resFit.SetLineColor(15) resFit.SetFillColor(15) resFit.SetFillStyle(1001) resFit.Draw("HIST") resFitLine = resFit.Clone("resFitLine") resFitLine.SetLineWidth(1) resFitLine.SetFillStyle(0) resFitLine.Draw("SAME, HIST") c1.Update() if gROOT.IsBatch( ) and options.saveplots: # and (treeRead and channel in selection.keys()): AddString = "" if not os.path.exists("plots_" + options.name + "/" + plotdir): os.makedirs("plots_" + options.name + "/" + plotdir) if fileRead: if RESIDUAL: AddString = "_PostFit_Residual" else: AddString = "_PostFit" #c1.Print("plots_"+options.name+"/"+plotdir+"/"+plotname+binName+AddString+".png") c1.Print("plots_" + options.name + "/" + plotdir + "/" + plotname + binName + AddString + ".pdf") # Print table printTable(hist, sign) if not gROOT.IsBatch(): raw_input("Press Enter to continue...") if gROOT.IsBatch() and not fileRead and ( var == 'MET_pt' or (channel.startswith('SL') and var == 'MET_sign') or (channel.endswith('ZR') and var == 'FakeMET_pt')): saveHist(hist, channel + binName)
def plot(var, cut, nm1=False): ### Preliminary Operations ### treeRead = True if not FILE else False # Read from tree channel = cut isBlind = BLIND showSignal = False if 'SB' in cut or 'TR' in cut else True # Determine explicit cut if treeRead: for k in sorted(alias.keys(), key=len, reverse=True): if k in cut: cut = cut.replace(k, alias[k]) # Determine Primary Dataset pd = [] if "isSingleMuonPhotonTrigger" in cut: pd = [x for x in sample['data_obs']['files'] if "MuonEG" in x] elif "isJPsiTrigger" in cut: pd = [x for x in sample['data_obs']['files'] if "Charmonium" in x] else: print "Cannot determine Primary Dataset." exit() print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:" print " dataset:", pd print " cut :", cut if isBlind and "SR" in channel and var in ["H_mass"]: cut += " && ( isMC ? 1 : !(H_mass > 86 && H_mass < 96) && !(H_mass > 120 && H_mass < 130) )" ### Create and fill MC histograms ### # Create dict file = {} tree = {} hist = {} cutstring = "(eventWeightLumi)" + ("*(" + cut + ")" if len(cut) > 0 else "") ### Create and fill MC histograms ### for i, s in enumerate(data + back + sign): hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events;" + ('logx' if variable[var]['logx'] else '') + ('logy' if variable[var]['logy'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max']) hist[s].Sumw2() tree[s] = TChain("Events") for j, ss in enumerate(sample[s]['files']): if s in data and not ss in pd: continue if YEAR == 2016 and not ('Run2016' in ss or 'Summer16' in ss): continue if YEAR == 2017 and not ('Run2017' in ss or 'Fall17' in ss): continue if YEAR == 2018 and not ('Run2018' in ss or 'Autumn18' in ss): continue for f in os.listdir(NTUPLEDIR + '/' + ss): tree[s].Add(NTUPLEDIR + '/' + ss + '/' + f) tree[s].Project(s, var, cutstring) if not tree[s].GetTree() == None: hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast()) # jobs = [] # queue = multiprocessing.Queue() # for i, s in enumerate(data+back+sign): # for j, ss in enumerate(sample[s]['files']): # if s in data and not ss in pd: continue # if YEAR == 2016 and not ('Run2016' in ss or 'Summer16' in ss): continue # if YEAR == 2017 and not ('Run2017' in ss or 'Fall17' in ss): continue # if YEAR == 2018 and not ('Run2018' in ss or 'Autumn18' in ss): continue # if treeRead: # Project from tree ## hist[s] = loopProject(s, ss, variable[var], cutstring, True) # p = multiprocessing.Process(target=parallelProject, args=(queue, s, ss, variable[var], cutstring, )) # jobs.append(p) # p.start() # else: # Histogram written to file # hist[s] = readhist(FILE, s, var, cut) # # # Wait for all jobs to finish # for job in jobs: # h = queue.get() # if not h.GetOption() in hist: hist[h.GetOption()] = h # else: hist[h.GetOption()].Add(h) # for job in jobs: # job.join() # Histogram style for i, s in enumerate(data + back + sign): hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0) hist[s].SetFillColor(sample[s]['fillcolor']) hist[s].SetFillStyle(sample[s]['fillstyle'] if not options.norm else 0) hist[s].SetLineColor(sample[s]['linecolor']) hist[s].SetLineStyle(sample[s]['linestyle']) hist[s].SetLineWidth(sample[s]['linewidth']) ### Create Bkg Sum histogram ### hist['BkgSum'] = hist['data_obs'].Clone( "BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum") hist['BkgSum'].Reset("MICES") hist['BkgSum'].SetFillStyle(3003) hist['BkgSum'].SetFillColor(1) for i, s in enumerate(back): hist['BkgSum'].Add(hist[s]) if options.norm: for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(hist[data[0]].Integral() / hist['BkgSum'].Integral()) for i, s in enumerate(sign): hist[s].Scale(hist[data[0]].Integral() / hist[s].Integral()) # Create data and Bkg sum histograms # if BLIND: # or 'SR' in channel: # hist['data_obs'] = hist['BkgSum'].Clone("data_obs") # hist['data_obs'].Reset("MICES") # Set histogram style hist['data_obs'].SetMarkerStyle(20) hist['data_obs'].SetMarkerSize(1.25) # for i, s in enumerate(data+back+sign+['BkgSum']): addOverflow(hist[s], False) # Add overflow for i, s in enumerate(sign): hist[s].SetLineWidth(3) for i, s in enumerate(sign): sample[s]['plot'] = True # Create stack bkg = THStack("Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events") for i, s in enumerate(back): bkg.Add(hist[s]) # Legend leg = TLegend(0.65, 0.6, 0.95, 0.9) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) if len(data) > 0: leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe") for i, s in reversed(list(enumerate(['BkgSum'] + back))): leg.AddEntry(hist[s], sample[s]['label'], "f") if showSignal: for i, s in enumerate(sign): if sample[s]['plot']: leg.AddEntry(hist[s], sample[s]['label'], "fl") leg.SetY1(0.9 - leg.GetNRows() * 0.04) # --- Display --- c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800 if RATIO else 600) if RATIO: c1.Divide(1, 2) setTopPad(c1.GetPad(1), RATIO) setBotPad(c1.GetPad(2), RATIO) c1.cd(1) c1.GetPad(bool(RATIO)).SetTopMargin(0.06) c1.GetPad(bool(RATIO)).SetRightMargin(0.05) c1.GetPad(bool(RATIO)).SetTicks(1, 1) logX, logY = "logx" in hist['BkgSum'].GetZaxis().GetTitle( ), "logy" in hist['BkgSum'].GetZaxis().GetTitle() if logY: c1.GetPad(bool(RATIO)).SetLogy() if logX: c1.GetPad(bool(RATIO)).SetLogx() # Draw bkg.Draw("HIST") # stack hist['BkgSum'].Draw("SAME, E2") # sum of bkg if len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data #data_graph.Draw("SAME, PE") # if showSignal: # smagn = 1. #if treeRead else 1.e2 #if logY else 1.e2 for i, s in enumerate(sign): if sample[s]['plot']: hist[s].Draw("SAME, HIST") # hist[s].Scale(smagn) # hist[s].Draw("SAME, HIST") # signals Normalized, hist[s].Integral()*sample[s]['weight'] # #textS = drawText(0.80, 0.9-leg.GetNRows()*0.05 - 0.02, stype+" (x%d)" % smagn, True) bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset() * 1.075) bkg.SetMaximum((5. if logY else 1.25) * max( bkg.GetMaximum(), hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) + hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin()))) #if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25) bkg.SetMinimum( max( min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin( )), hist['data_obs'].GetMinimum()), 5.e-1) if logY else 0.) if logY: bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4) bkg.GetYaxis().SetMoreLogLabels(True) #if logY: bkg.SetMinimum(1) leg.Draw() drawCMS(LUMI[YEAR], "Preliminary") if channel in aliasNames: drawRegion(aliasNames[channel], True) #drawAnalysis(channel) #if nm1 and not cutValue is None: drawCut(cutValue, bkg.GetMinimum(), bkg.GetMaximum()) #FIXME #if len(sign) > 0: # if channel.startswith('X') and len(sign)>0: drawNorm(0.9-0.05*(leg.GetNRows()+1), "#sigma(X) = %.1f pb" % 1.) setHistStyle(bkg, 1.2 if RATIO else 1.1) setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1) if RATIO: c1.cd(2) if logX: c1.GetPad(2).SetLogx() err = hist['BkgSum'].Clone("BkgErr;") err.SetTitle("") err.GetYaxis().SetTitle("Data / Bkg") for i in range(1, err.GetNbinsX() + 1): err.SetBinContent(i, 1) if hist['BkgSum'].GetBinContent(i) > 0: err.SetBinError( i, hist['BkgSum'].GetBinError(i) / hist['BkgSum'].GetBinContent(i)) setBotStyle(err) errLine = err.Clone("errLine") errLine.SetLineWidth(1) errLine.SetFillStyle(0) res = hist['data_obs'].Clone("Residues") for i in range(0, res.GetNbinsX() + 1): if hist['BkgSum'].GetBinContent(i) > 0: res.SetBinContent( i, res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i)) res.SetBinError( i, res.GetBinError(i) / hist['BkgSum'].GetBinContent(i)) setBotStyle(res) #err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5) #err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2) err.Draw("E2") errLine.Draw("SAME, HIST") if len(data) > 0: res.Draw("SAME, PE0") #res_graph.Draw("SAME, PE0") if len(err.GetXaxis().GetBinLabel( 1)) == 0: # Bin labels: not a ordinary plot drawRatio(hist['data_obs'], hist['BkgSum']) drawStat(hist['data_obs'], hist['BkgSum']) if var in ["H_mass"]: c1.cd(bool(RATIO)) boxZ = drawBox(XZMIN, hist['data_obs'].GetMinimum(), XZMAX, hist['data_obs'].GetMaximum() / 1.30, "Z") boxH = drawBox(XHMIN, hist['data_obs'].GetMinimum(), XHMAX, hist['data_obs'].GetMaximum() / 1.30, "H") c1.Update() if True: #gROOT.IsBatch(): varname = var.replace('.', '_').replace('()', '') if not os.path.exists("plots/" + channel): os.makedirs("plots/" + channel) c1.Print("plots/" + channel + "/" + varname + ".png") c1.Print("plots/" + channel + "/" + varname + ".pdf") # Print table printTable(hist, sign) if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
normCMS.SetLineColor(kBlue + 2) normCMS.Draw('apz') normCMS.SetMaximum(0.3) leg = TLegend(0.4, 1. - gPad.GetTopMargin() - 0.03 - 0.18, 1. - gPad.GetRightMargin() - 0.02, 1. - gPad.GetTopMargin() - 0.03, '', 'NDC') leg.SetMargin(0.15) #leg.Dump() leg.SetFillStyle(0) leg.SetBorderSize(0) leg.AddEntry(normCMS, 'CMS, |y|<2, #sqrt{s}=7 TeV', 'pe') if (D0): D0.Draw('pz') leg.AddEntry(D0, 'D#oslash, |y|<1.8, #sqrt{s}=1.96 TeV', 'pe') else: leg.SetY1(1. - gPad.GetTopMargin() - 0.03 - 0.12) if (CDF): CDF.Draw('pz') leg.AddEntry(CDF, 'CDF, |y|<0.4, #sqrt{s}=1.8 TeV', 'pe') #leg.Dump() leg.Draw('same') l.DrawLatex(1.0 - gPad.GetRightMargin() - 0.04, leg.GetY1() - 0.06, '#varUpsilon(%iS)' % resonance) gPad.SetLogy() gPad.Update() normCMS.GetXaxis().SetTitle('p_{T} (GeV/c)') normCMS.GetYaxis().SetTitle('(d#sigma/dp_{T})/#sigma_{TOT} (GeV/c)^{-1}') normCMS.GetXaxis().SetLimits(0., 30.) gPad.Modified() gPad.Print('TevatronCompare%iS.eps' % resonance) gPad.Print('TevatronCompare%iS.png' % resonance)
class Plot(object): """Structural class for representing, accessing, and maintaining references to ROOT graphical elements forming a plot, potentially with a ratio subplot. """ # Plotting 'constants' for the plot class. Ideally, one would allow these # to be flexible, but unfortunately ROOT's coordinate system is extremely # inconsistent and fragile, so it is best to fix these values here. You # can change them dynamically with Plot.Whatever = value, but it is # probably best to leave them alone. # TODO: 600x600 and 800x600 are the ATLAS default for square and # rectangular plots respectively. Fix this when everything is calm. PLOT_WIDTH = 1280 # px PLOT_HEIGHT = 1024 # px #PLOT_MARGINS = (0.125, 0.05, 0.1, 0.1) # Left, Right, Bottom, Top PLOT_MARGINS = (0.125, 0.05, 0.1, 0.07) # Left, Right, Bottom, Top PLOT_MARGINS_WITH_RATIO = (0.125, 0.05, 0.025, 0.1) PLOT_RATIO_MARGINS = (0.125, 0.05, 0.325, 0.05) PLOT_TITLE_X = 0.5 PLOT_TITLE_Y = 0.95 PLOT_TITLE_TEXT_SIZE = 0.04 PLOT_TITLE_TEXT_COLOR = 1 PLOT_TITLE_TEXT_FONT = 42 PLOT_HEADER_HEIGHT = 400 # px PLOT_LEGEND_LEFT = 0.45 PLOT_LEGEND_RIGHT = 0.95 PLOT_LEGEND_BOTTOM = 0.7 PLOT_LEGEND_BOTTOM_WITH_RATIO = 0.63 PLOT_LEGEND_TOP = 0.90 PLOT_LEGEND_TOP_WITH_RATIO = 0.86 PLOT_LEGEND_TEXT_SIZE = 0.025 PLOT_LEGEND_TEXT_SIZE_WITH_RATIO = 0.03 PLOT_LEGEND_ROW_SIZE = 0.04 PLOT_LEGEND_ROW_SIZE_WITH_RATIO = 0.045 PLOT_LEGEND_N_COLUMNS = 1 PLOT_LEGEND_PIVOT_COLUMNS = True PLOT_STAT_LEFT = 0.55 PLOT_STAT_LEFT_WITH_RATIO = 0.60 PLOT_STAT_RIGHT = 0.85 PLOT_STAT_RIGHT_WITH_RATIO = 0.93 PLOT_STAT_BOTTOM = 0.15 PLOT_STAT_BOTTOM_WITH_RATIO = 0.07 PLOT_STAT_TOP = 0.4 PLOT_STAT_TOP_WITH_RATIO = 0.45 PLOT_STAT_TEXT_FONT = 42 PLOT_STAT_TEXT_SIZE = 0.03 PLOT_STAT_TEXT_SIZE_WITH_RATIO = 0.04 PLOT_RATIO_FRACTION = 0.3 # fraction of canvas height PLOT_X_AXIS_TITLE_SIZE = 0.042 PLOT_X_AXIS_TITLE_SIZE_WITH_RATIO = 0.14 PLOT_X_AXIS_TITLE_OFFSET = 0.95 PLOT_X_AXIS_TITLE_OFFSET_WITH_RATIO = 0.96 PLOT_X_AXIS_LABEL_SIZE_WITH_RATIO = 0.12 PLOT_Y_AXIS_LABEL_OFFSET = 0.01 PLOT_Y_AXIS_TITLE_SIZE = 0.042 PLOT_Y_AXIS_TITLE_SIZE_WITH_RATIO = 0.06 PLOT_Y_AXIS_TITLE_OFFSET = 1.0 PLOT_Y_AXIS_TITLE_OFFSET_WITH_RATIO = 0.95 PLOT_Y_AXIS_LABEL_SIZE_WITH_RATIO = 0.05 PLOT_RATIO_Y_AXIS_TITLE_SIZE = 0.12 PLOT_RATIO_Y_AXIS_TITLE_OFFSET = 0.40 PLOT_RATIO_Y_AXIS_LABEL_SIZE = 0.12 PLOT_RATIO_Y_AXIS_LABEL_OFFSET = PLOT_Y_AXIS_LABEL_OFFSET PLOT_RATIO_Y_AXIS_NDIVISIONS = 504 PLOT_RATIO_Y_AXIS_MINIMUM = 0.6 PLOT_RATIO_Y_AXIS_MAXIMUM = 1.4 PLOT_ERROR_BAND_FILL_STYLE = 3254 # Diagonal lines PLOT_ERROR_BAND_FILL_COLOR = 13 # Gray PLOT_ERROR_BAND_LINE_WIDTH = 0 PLOT_ERROR_BAND_LINE_COLOR = 0 PLOT_RATIO_ERROR_BAND_FILL_STYLE = 3254 # Diagonal lines PLOT_RATIO_ERROR_BAND_FILL_COLOR = 807 # Orange PLOT_RATIO_ERROR_BAND_LINE_WIDTH = 0 PLOT_RATIO_ERROR_BAND_LINE_COLOR = 0 # Stamp settings PLOT_ATLAS_STAMP_TEXT_SIZE = 0.035 PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO = 0.05 PLOT_ATLAS_STAMP_TEXT_COLOR = 1 PLOT_ATLAS_STAMP_TEXT_FONT = 42 PLOT_ATLAS_STAMP_LEFT = 0.18 PLOT_ATLAS_STAMP_TOP = 0.875 PLOT_ATLAS_STAMP_TOP_WITH_RATIO = 0.82 # Stamp specializations PLOT_ATLAS_STAMP_ATLAS_TEXT_FONT = 72 PLOT_ATLAS_STAMP_ATLAS_LABEL_LEFT = 0.28 PLOT_ATLAS_STAMP_LUMINOSITY_OFFSET = 0.036 PLOT_ATLAS_STAMP_LUMINOSITY_OFFSET_WITH_RATIO = 0.05 PLOT_ATLAS_STAMP_LUMINOSITY_SIZE = 0.062 PLOT_ATLAS_STAMP_LUMINOSITY_SIZE_WITH_RATIO = 0.085 def __init__(self, title='', x_title=None, y_title=None, plot_header=True, ratio=False, x_range=None, y_max=None, y_log_scale=False): """Initializes a new instance of the Plot class. Args: title: The title to set for the histogram plot_header: Whether or not to include whitespace at the top of the plot for the ATLAS label and legend ratio: Whether or not to include a ratio plot x_range: A tuple of (x_min, x_max) y_max: The maximum Y axis value y_log_scale: Use log scale for Y axis """ # Store the title self._title = title self._x_title, self._y_title = x_title, y_title # Store whether or not the user wants to create a plot header self._plot_header = plot_header # Calculate a unique name for the plot components name = _rand_uuid() # Create a canvas self._canvas = TCanvas(name + '_canvas', name, int(self.PLOT_WIDTH), int(self.PLOT_HEIGHT)) SetOwnership(self._canvas, False) # Create the main plot and draw it self._plot = TPad(name + '_plot', name, 0.0, (self.PLOT_RATIO_FRACTION if ratio else 0.0), 1.0, 1.0) SetOwnership(self._plot, False) self._plot.SetMargin( *(self.PLOT_MARGINS_WITH_RATIO if ratio else self.PLOT_MARGINS)) self._plot.Draw() # HACK: Draw the plot title. # https://root.cern.ch/phpBB3/viewtopic.php?t=18282. Wonderful. self._draw_title() # Store ranges self._x_range = x_range if y_max is not None: self._set_maximum_value(y_max) # Store log scale self._y_log_scale = y_log_scale # Switch back to the context of the canvas self._canvas.cd() # Create a ratio plot and draw it if requested if ratio: self._ratio_plot = TPad(name + '_ratio', name, 0.0, 0.0, 1.0, self.PLOT_RATIO_FRACTION) SetOwnership(self._ratio_plot, False) self._ratio_plot.SetMargin(*self.PLOT_RATIO_MARGINS) self._ratio_plot.SetGridy(True) self._ratio_plot.Draw() else: self._ratio_plot = None # Track whether or not we've already drawn to the main pad self._drawn = False # Track whether or not we've already drawn to the ratio pad self._ratio_drawn = False # Track that object which sets up the axes in the main plot self._axes_object = None # Create a structure to track any histograms we generate internally # which need to be added to any legends created self._legend_extras = [] # Create lists of the cloned drawables, just to be certain self._drawables = [] self._ratio_drawables = [] def save(self, path, extensions=['pdf']): """Saves this plot to file. Args: path: The path where the plot should be saved. """ # Force an update of the canvas self._canvas.Update() # Save to file for e in extensions: self._canvas.SaveAs(path + '.' + e) def _get_maximum_value(self): """Returns the currently set maximum value (possibly None). """ if hasattr(self, '_maximum_value'): return self._maximum_value return None def _set_maximum_value(self, value): """Sets the current maximum value, possibly including room for a plot header. Args: value: The value to set """ # Check if the current value is not None, and if so, throw an error # because this property should not be set twice if self._get_maximum_value() is not None: raise RuntimeError('maximum value should not be set twice') # If the value is None, ignore it if value is None: return # If the user wants a plot header, then add space for one if self._plot_header: # Grab the plot pad height (in pixels) plot_height = (self.PLOT_HEIGHT * (self._plot.GetY2() - self._plot.GetY1())) # Adjust the height value *= (plot_height + self.PLOT_HEADER_HEIGHT) / plot_height # Set the value self._maximum_value = value def draw(self, *drawables_styles_options): """Plots a collection of plottables to the main plot pad. All TH1 objects are drawn with error bars. THStack elements are only drawn with an error band if one is provided. This method may only be called once Args: drawables_styles_options: Each argument of this function must be of the form (object, style, options), where object is one of the following: - A TH1 object - A TH2 object - A THStack object - A tuple of the form (THStack, TGraph) where the latter represents error bars - A TGraph object - A TLine object style is a tuple of the form (line_color, fill_color, marker_style), and options is a string which will be used for the options argument of the object's Draw method. Plottables will be rendered in the order provided. Axes drawing options (e.g. 'a' or 'same' should not be provided and will be set automatically). A TLine may not be the first drawable element. """ # Make sure there are drawables if len(drawables_styles_options) == 0: raise ValueError('must provide at least one plottable') # Check if we've already drawn if self._drawn: raise RuntimeError('cannot draw twice to a plot') self._drawn = True # Remove None-valued drawables drawables_styles_options = tuple( ((d, s, o) for d, s, o in drawables_styles_options if valid_drawable(d))) # Extract drawables drawables, _, _ = zip(*drawables_styles_options) # Check if there is a maximum value set, and if not, set it if self._get_maximum_value() is None: self._set_maximum_value(maximum_value(drawables)) # Move to the context of the plot pad self._plot.cd() # Iterate through and draw drawables based on type first = True for drawable, style, option in drawables_styles_options: # Check if this a tuple of histogram, error_band if isinstance(drawable, tuple): drawable, error_band = drawable else: error_band = None # Make a clone of the drawable so we don't modify it o = clone(drawable) SetOwnership(o, False) # Add it to the list of drawables self._drawables.append(o) # Set the title appropriately if not is_line(o): o.SetTitle(drawable.GetTitle()) # Style the drawable before it is drawn if style is not None: if is_line(drawable) or is_function(drawable): if isinstance(style, dict): style_line(o, **style) else: style_line(o, *style) else: if isinstance(style, dict): style_histogram(o, **style) else: style_histogram(o, *style) # Set the maximum value of the drawable if supported # HACK: I wish this could go into _handle_axes, but apparently it # can't because ROOT sucks and this has to be set on EVERY # drawable, not just the one with the axes. if is_scatter(o): o.SetMinimum(1 if self._y_log_scale else 0) if is_histo(o) or is_graph(o) or is_stack(o) or is_function(o): o.SetMaximum(self._get_maximum_value()) # With TGraph, this is sometimes necessary. Perhaps with TH1 # too. I'm not sure what happens if we set log scale, but # we'll cross that bridge then. o.SetMinimum(1 if self._y_log_scale else 0) # Include axes if we need to. Store the x-axis range. if first: if is_line(o): raise ValueError('TLine may not be first drawable') if is_graph(o): option += 'a' else: option += 'same' first = False # Draw the drawable o.Draw(option) # TODO: This method of plotting the stats box is a huge hack. We # should plot fit functions separately, and move the stats box # plotting to its own function. Home grow everything, the only # way you can make ROOT work. if is_graph(o) or is_histo(o): if len(o.GetListOfFunctions()) > 0: # HACK: Need to call Update() to paint the fit stats self._plot.Update() stats = o.FindObject("stats") if stats: stats.SetTextFont(Plot.PLOT_STAT_TEXT_FONT) stats.SetTextSize( (Plot.PLOT_STAT_TEXT_SIZE_WITH_RATIO if self._ratio_plot else Plot.PLOT_STAT_TEXT_SIZE)) stats.SetX1NDC( (Plot.PLOT_STAT_LEFT_WITH_RATIO if self._ratio_plot else Plot.PLOT_STAT_LEFT)) stats.SetY1NDC( (Plot.PLOT_STAT_BOTTOM_WITH_RATIO if self._ratio_plot else Plot.PLOT_STAT_BOTTOM)) stats.SetX2NDC( (Plot.PLOT_STAT_RIGHT_WITH_RATIO if self._ratio_plot else Plot.PLOT_STAT_RIGHT)) stats.SetY2NDC( (Plot.PLOT_STAT_TOP_WITH_RATIO if self._ratio_plot else Plot.PLOT_STAT_TOP)) # Handle axes if not is_line(o): self._handle_axes(o, option) # If there is an error band, draw it if error_band is not None: self._draw_error_band(error_band) if self._y_log_scale: self._plot.SetLogy(1) # TODO: Verify this. It breaks 2D plotting. # HACK: Need to force a redraw of plot axes due to issue with ROOT: # http://root.cern.ch/phpBB3/viewtopic.php?f=3&t=14034 #self._plot.RedrawAxis() def _handle_axes(self, drawable, option): """If there is no object currently registered as the owner of the axes drawn on the main plot, then this will set it. Args: drawable: The graph, histogram or stack whose axes were ALREADY drawn option: The option with which to draw the axes """ # If we already have an axes object, ignore this one if self._axes_object is not None: return # Grab the histogram used for axes style/range manipulation if is_stack(drawable) or is_graph(drawable): axes_histogram = drawable.GetHistogram() else: axes_histogram = drawable self._axes_object = axes_histogram # Grab the histogram used for title manipulation if is_stack(drawable): title_histogram = drawable.GetHists()[0] else: title_histogram = drawable # Grab axes x_axis, y_axis = axes_histogram.GetXaxis(), axes_histogram.GetYaxis() # Grab titles from first histogram if not set explicitly if self._x_title is None: self._x_title = title_histogram.GetXaxis().GetTitle() if self._y_title is None: self._y_title = title_histogram.GetYaxis().GetTitle() if self._x_range is not None: #x_axis.SetRangeUser(*self._x_range) x_axis.SetLimits(*self._x_range) # Style x-axis, or hide it if this plot has a ratio plot if self._ratio_plot: x_axis.SetLabelOffset(999) x_axis.SetTitleOffset(999) else: x_axis.SetTitle(self._x_title) x_axis.SetTitleSize(self.PLOT_X_AXIS_TITLE_SIZE) x_axis.SetTitleOffset(self.PLOT_X_AXIS_TITLE_OFFSET) # Style y-axis if self._ratio_plot: y_axis.SetLabelSize(self.PLOT_Y_AXIS_LABEL_SIZE_WITH_RATIO) y_axis.SetLabelOffset(self.PLOT_Y_AXIS_LABEL_OFFSET) y_axis.SetTitle(self._y_title) y_axis.SetTitleSize( (self.PLOT_Y_AXIS_TITLE_SIZE_WITH_RATIO if self._ratio_plot else self.PLOT_Y_AXIS_TITLE_SIZE)) y_axis.SetTitleOffset( (self.PLOT_Y_AXIS_TITLE_OFFSET_WITH_RATIO if self._ratio_plot else self.PLOT_Y_AXIS_TITLE_OFFSET)) # Redraw the drawable with the new style drawable.Draw(option) def _draw_error_band(self, error_band): """Draws an error band on top of histogram objects. Args: error_band: The error band to draw (a TGraphAsymmErrors) """ # Style it # HACK: Setting the marker style to 0 specifies this should be filled # in the legend error_band.SetMarkerStyle(0) error_band.SetMarkerSize(0) error_band.SetFillStyle(self.PLOT_ERROR_BAND_FILL_STYLE) error_band.SetFillColor(self.PLOT_ERROR_BAND_FILL_COLOR) error_band.SetLineWidth(self.PLOT_ERROR_BAND_LINE_WIDTH) error_band.SetLineColor(self.PLOT_ERROR_BAND_LINE_COLOR) # Draw it error_band.Draw('e2same') # Add it to the list of things we need to add to the legend self._legend_extras.append(error_band) def draw_ratio_histogram(self, histogram, draw_unity=True, error_band=None): """Draws a ratio histogram to the ratio pad. Args: histogram: The ratio histogram to draw (use ratio_histogram) draw_unity: Whether or not to draw a line at 1 error_band: An error band to draw under the ratio histogram (see owls_hep.uncertainty.ratio_uncertainty_band) The histogram X axis title is set by draw_histogram if not set explicitly. draw_ratio_histogram should therefore be called after draw_histogram. """ # Check if we've already drawn if self._ratio_drawn: raise RuntimeError('cannot draw twice to a plot') self._ratio_drawn = True # Switch to the context of the ratio pad self._ratio_plot.cd() # Clone the histogram histogram = histogram.Clone(_rand_uuid()) SetOwnership(histogram, False) # Style it x_axis, y_axis = histogram.GetXaxis(), histogram.GetYaxis() x_axis.SetTitleSize(self.PLOT_X_AXIS_TITLE_SIZE_WITH_RATIO) x_axis.SetTitleOffset(self.PLOT_X_AXIS_TITLE_OFFSET_WITH_RATIO) x_axis.SetLabelSize(self.PLOT_X_AXIS_LABEL_SIZE_WITH_RATIO) x_axis.SetTitle(self._x_title) if self._x_range: x_axis.SetLimits(*self._x_range) #x_axis.SetRangeUser(*self._x_range) else: x_axis.SetLimits(self._axes_object.GetXaxis().GetXmin(), self._axes_object.GetXaxis().GetXmax()) #x_axis.SetRangeUser(self._axes_object.GetXaxis().GetXmin(), #self._axes_object.GetXaxis().GetXmax()) y_axis.SetTitleSize(self.PLOT_RATIO_Y_AXIS_TITLE_SIZE) y_axis.SetTitleOffset(self.PLOT_RATIO_Y_AXIS_TITLE_OFFSET) y_axis.SetLabelSize(self.PLOT_RATIO_Y_AXIS_LABEL_SIZE) y_axis.SetLabelOffset(self.PLOT_RATIO_Y_AXIS_LABEL_OFFSET) y_axis.SetRangeUser(self.PLOT_RATIO_Y_AXIS_MINIMUM, self.PLOT_RATIO_Y_AXIS_MAXIMUM) y_axis.SetNdivisions(self.PLOT_RATIO_Y_AXIS_NDIVISIONS, False) # Draw it # NOTE: Have to specify E0 or points out of the vertical range won't # have their error bars drawn: # https://root.cern.ch/phpBB3/viewtopic.php?f=3&t=13329 # histogram.Draw('e0p') # NOTE: Or live with it and get rid of points from zero value bins histogram.Draw('ep') # Draw a line at unity if requested if draw_unity: # Calculate the line coordinates line_min = histogram.GetBinLowEdge(1) max_bin = histogram.GetNbinsX() line_max = (histogram.GetBinLowEdge(max_bin) + histogram.GetBinWidth(max_bin)) # Create and draw the line unit_line = TLine(line_min, 1.0, line_max, 1.0) SetOwnership(unit_line, False) unit_line.SetLineColor(2) # Red unit_line.SetLineWidth(2) unit_line.Draw('same') # If an error band was provided, draw it and add it to our legend # elements if error_band: # Keep ownership of the error band SetOwnership(error_band, False) # Style it error_band.SetMarkerSize(0) error_band.SetFillStyle(self.PLOT_RATIO_ERROR_BAND_FILL_STYLE) error_band.SetFillColor(self.PLOT_RATIO_ERROR_BAND_FILL_COLOR) error_band.SetLineWidth(self.PLOT_RATIO_ERROR_BAND_LINE_WIDTH) error_band.SetLineColor(self.PLOT_RATIO_ERROR_BAND_LINE_COLOR) # Draw it error_band.Draw('e2same') # Now, if we've drawn unity or an error band, redraw our ratio # histogram so that its point lie on top of the unity line or error # band, but use 'same' so that the axes/ticks don't cover the red line if draw_unity or error_band: # histogram.Draw('e0psame') histogram.Draw('epsame') def draw_ratios(self, drawables_styles_options, draw_unity=True, y_range=None, y_title=None): """Draws a drawable to the ratio pad. Args: drawable: The drawable to draw draw_unity: Whether or not to draw a line at 1 """ # Check if we've already drawn if self._ratio_drawn: raise RuntimeError('cannot draw twice to a plot') self._ratio_drawn = True # Switch to the context of the ratio pad self._ratio_plot.cd() # Iterate through and draw drawables based on type first = True for drawable, style, option in drawables_styles_options: # Make a clone of the drawable so we don't modify it o = clone(drawable) SetOwnership(o, False) # Add it to the list of drawables self._ratio_drawables.append(o) # Set the title appropriately if not is_line(o): o.SetTitle(drawable.GetTitle()) # Style the drawable before it is drawn if style is not None: if is_line(drawable) or is_function(drawable): style_line(o, *style) else: style_histogram(o, *style) if not is_line(o): if y_range is not None: o.SetMinimum(y_range[0]) o.SetMaximum(y_range[1]) # Include axes if we need if first: if is_line(o): raise ValueError('TLine may not be first drawable') x_axis, y_axis = o.GetXaxis(), o.GetYaxis() if is_graph(o): option += 'a' else: option += 'same' first = False # Draw the drawable o.Draw(option) x_axis.SetTitleSize(self.PLOT_X_AXIS_TITLE_SIZE_WITH_RATIO) x_axis.SetTitleOffset(self.PLOT_X_AXIS_TITLE_OFFSET_WITH_RATIO) x_axis.SetLabelSize(self.PLOT_X_AXIS_LABEL_SIZE_WITH_RATIO) x_axis.SetTitle(self._x_title) if self._x_range: x_axis.SetLimits(*self._x_range) #x_axis.SetRangeUser(*self._x_range) else: x_axis.SetLimits(self._axes_object.GetXaxis().GetXmin(), self._axes_object.GetXaxis().GetXmax()) #x_axis.SetRangeUser(self._axes_object.GetXaxis().GetXmin(), #self._axes_object.GetXaxis().GetXmax()) y_axis.SetTitleSize(self.PLOT_RATIO_Y_AXIS_TITLE_SIZE) y_axis.SetTitleOffset(self.PLOT_RATIO_Y_AXIS_TITLE_OFFSET) y_axis.SetLabelSize(self.PLOT_RATIO_Y_AXIS_LABEL_SIZE) y_axis.SetLabelOffset(self.PLOT_RATIO_Y_AXIS_LABEL_OFFSET) y_axis.SetNdivisions(self.PLOT_RATIO_Y_AXIS_NDIVISIONS, False) if y_title is not None: y_axis.SetTitle(y_title) self._ratio_plot.Update() def _draw_title(self): """Draws a title on the plot. """ title = TLatex() title.SetTextColor(self.PLOT_TITLE_TEXT_COLOR) title.SetTextFont(self.PLOT_TITLE_TEXT_FONT) title.SetNDC() title.SetTextSize(self.PLOT_TITLE_TEXT_SIZE) title.SetTextAlign(22) title.DrawLatex(self.PLOT_TITLE_X, self.PLOT_TITLE_Y, self._title) def draw_atlas_label(self, luminosity=None, sqrt_s=None, custom_label=None, atlas_label=None): """Draws an ATLAS stamp on the plot, with an optional categorization label. It is recommended that you construct the Plot with plot_header = True in order to make space for the label. Args: luminosity: The integrated luminosity, in pb^-1 sqrt_s: The center of mass energy, in MeV label: The label to put after 'ATLAS', None to exclude the 'ATLAS' categorization entirely """ # Change context to the plot pad self._plot.cd() # Create the latex object # TODO: Consider using TPaveText to overwrite drawn graphs and # histograms. At least for scatter plots. # TODO: Increase readability: Create two sets of constants - # one for plots with ratio and one for plots without. Select the # correct one in draw_ratio_histogram. stamp = TLatex() # Style it stamp.SetTextColor(self.PLOT_ATLAS_STAMP_TEXT_COLOR) stamp.SetTextSize( (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self._ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE)) stamp.SetTextFont(self.PLOT_ATLAS_STAMP_TEXT_FONT) stamp.SetNDC() top = (self.PLOT_ATLAS_STAMP_TOP_WITH_RATIO if self._ratio_plot else self.PLOT_ATLAS_STAMP_TOP) # Print an ATLAS label on top if atlas_label is not None: # Draw the label stamp.SetTextFont(self.PLOT_ATLAS_STAMP_ATLAS_TEXT_FONT) stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, 'ATLAS') stamp.SetTextFont(self.PLOT_ATLAS_STAMP_TEXT_FONT) stamp.DrawLatex(self.PLOT_ATLAS_STAMP_ATLAS_LABEL_LEFT, top, atlas_label) top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self. _ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3 # Draw the luminosity and sqrt(s) if luminosity is not None or sqrt_s is not None: text = '' if sqrt_s is not None: text += '#sqrt{{s}} = {0:.0f} TeV'.format(sqrt_s / 1.0e6) if luminosity is not None: text += ', ' if luminosity is not None: if luminosity >= 1000.0: text += '{0:.1f} fb^{{-1}}'.format(luminosity / 1000.0) elif luminosity > 100.0: text += '{0:.2f} fb^{{-1}}'.format(luminosity / 1000.0) else: text += '{0:.1f} pb^{{-1}}'.format(luminosity) stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, text) top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self. _ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3 # If requested, draw the custom label or the 'ATLAS' label, # preferring the former if custom_label is not None: # Draw each line of text, decreasing top for each step for text in [t for t in custom_label if t is not None]: stamp.DrawLatex(self.PLOT_ATLAS_STAMP_LEFT, top, text) top -= (self.PLOT_ATLAS_STAMP_TEXT_SIZE_WITH_RATIO if self. _ratio_plot else self.PLOT_ATLAS_STAMP_TEXT_SIZE) * 1.3 def draw_pave(self, texts, position): """Draw a text box at the position and fill it with text. Args: texts: String or N-tuple of strings. positon: Absolute position of the form (x1, x2, y1, y2) as an N-tuple of floats, or a string with one of the values "{top,bottom}{left,right}" """ if isinstance(position, basestring): if position == 'topleft': position = (0.15, 0.35, 0.85, 0.75) elif position == 'topright': position = (0.95, 0.75, 0.95, 0.85) elif position == 'bottomleft': position = (0.05, 0.25, 0.05, 0.15) elif position == 'topright': position = (0.95, 0.75, 0.05, 0.15) # Switch to the context of the main plot self._plot.cd() # Create the pave self._pave = TPaveText(position[0], position[2], position[1], position[3], 'NDC') SetOwnership(self._pave, False) # Add the text if isinstance(texts, basestring): self._pave.AddText(texts) else: for t in texts: self._pave.AddText(t) # Draw the pave self._pave.Draw() def draw_legend(self, use_functions=False, legend_entries=None): """Draws a legend onto the plot with the specified histograms. It is recommended that you construct the Plot with plot_header = True in order to make space for the legend. Args: drawables: The elements to include in the legend (via AddEntry) use_functions: Add associated functions to the legend """ # Check if we already have a legend if hasattr(self, '_legend'): raise RuntimeError('legend already exists on this plot') # Check if the plot has been drawn if not self._drawn: raise RuntimeError('plot must be drawn before the legend') # Remove None-valued drawables drawables = tuple((d for d in self._drawables if d is not None)) # Remove TLine objects drawables = tuple((d for d in drawables if not is_line(d))) # If we shouldn't add functions to the legend, remove them if not use_functions: drawables = tuple((d for d in drawables if not isinstance(d, TF1))) # Use only certain entries if legend_entries is not None: def get_drawable_by_title(title): for d in drawables: if d.GetTitle() == title: return d return None drawables = tuple((get_drawable_by_title(e.GetTitle()) for e in legend_entries if e is not None)) drawables = tuple((d for d in drawables if d is not None)) # Switch to the context of the main plot self._plot.cd() # Create the legend self._legend = TLegend(self.PLOT_LEGEND_LEFT, (self.PLOT_LEGEND_BOTTOM_WITH_RATIO if self._ratio_plot else self.PLOT_LEGEND_BOTTOM), self.PLOT_LEGEND_RIGHT, (self.PLOT_LEGEND_TOP_WITH_RATIO if self._ratio_plot else self.PLOT_LEGEND_TOP)) SetOwnership(self._legend, False) # Style it self._legend.SetTextSize( (self.PLOT_LEGEND_TEXT_SIZE_WITH_RATIO if self._ratio_plot else self.PLOT_LEGEND_TEXT_SIZE)) self._legend.SetBorderSize(0) self._legend.SetFillStyle(0) # transparent self._legend.SetNColumns(self.PLOT_LEGEND_N_COLUMNS) # Create a chained list of all drawables. We decompose THStack # objects in reverse order, i.e. top-to-bottom. drawables = \ list(chain(*(drawable_iterable(h, True, True) for h in drawables))) # Add anything to this list that we created internally drawables.extend(self._legend_extras) # Because ROOT draws legend entries from left-to-right across rows and # not top-to-bottom along columns, we need to do a bit of a pivot on # the list so that the histograms appear in the vertical order of the # stack if self.PLOT_LEGEND_PIVOT_COLUMNS: n_entries = len(drawables) n_col = self.PLOT_LEGEND_N_COLUMNS n_row = int(ceil(float(n_entries) / n_col)) self._legend.SetY1(self._legend.GetY2() - n_row * (self.PLOT_LEGEND_ROW_SIZE_WITH_RATIO if self. _ratio_plot else self.PLOT_LEGEND_ROW_SIZE)) legend_order = [] for r in xrange(0, n_row): for c in xrange(0, n_col): if (r * n_col + c) == n_entries: # Don't need an outer break, this would only happen on the # last row if n_row * n_col != n_entries break legend_order.append(drawables[r + c * n_row]) else: legend_order = drawables # Add the drawables for drawable in legend_order: SetOwnership(drawable, False) title = drawable.GetTitle() # HACK: Convention: legend for drawables with a non-default # marker style (data) to be drawn as line with point, and with # empty fill (signal) to be drawn as line if drawable.GetMarkerStyle() != 0: self._legend.AddEntry(drawable, title, 'ep') elif drawable.GetFillColor() == 0: self._legend.AddEntry(drawable, title, 'l') else: self._legend.AddEntry(drawable, title, 'f') # Draw the legend self._legend.Draw()
def efficiencyAll(): #signals = {'XZHeebb':['eebb'],'XZHmmbb':['mmbb'],'XZHnnbb':['nnbb'],'XZHee0b':['ee0b'],'XZHmm0b':['mm0b'],'XZHnn0b':['nn0b'],'XZHVBFeebbVBF':['eebbVBF'],'XZHVBFmmbbVBF':['mmbbVBF'],'XZHVBFnnbbVBF':['nnbbVBF'],'XZHVBFee0bVBF':['ee0bVBF'],'XZHVBFmm0bVBF':['mm0bVBF'],'XZHVBFnn0bVBF':['nn0bVBF']} labels = {'XZHeebb' : "eeb#bar{b}",'XZHmmbb' : "#mu#mub#bar{b}",'XZHnnbb' : "#nu#nub#bar{b}",'XZHee0b' : "ee0b",'XZHmm0b' : "#mu#mu0b",'XZHnn0b' : "#nu#nu0b",'XZHVBFeebbVBF' : "eeb#bar{b}VBF",'XZHVBFmmbbVBF' : "#mu#mub#bar{b}VBF",'XZHVBFnnbbVBF' : "#nu#nub#bar{b}VBF",'XZHVBFee0bVBF' : "ee0bVBF",'XZHVBFmm0bVBF' : "#mu#mu0bVBF",'XZHVBFnn0bVBF' : "#nu#nu0bVBF"} colors = {'XZHeebb' : 2, 'XZHmmbb' : 4, 'XZHnnbb' : 2,'XZHee0b' : 3, 'XZHmm0b' : 6, 'XZHnn0b' : 4,'XZHVBFeebbVBF' : 2, 'XZHVBFmmbbVBF' : 4, 'XZHVBFnnbbVBF' : 2,'XZHVBFee0bVBF' : 3, 'XZHVBFmm0bVBF' : 6, 'XZHVBFnn0bVBF' : 4} styles = {'XZHeebb' : 1, 'XZHmmbb' : 1, 'XZHnnbb' : 1,'XZHee0b' : 1, 'XZHmm0b' : 1, 'XZHnn0b' : 1,'XZHVBFeebbVBF' : 1, 'XZHVBFmmbbVBF' : 1, 'XZHVBFnnbbVBF' : 1,'XZHVBFee0bVBF' : 1, 'XZHVBFmm0bVBF' : 1, 'XZHVBFnn0bVBF' : 1} marker = {'XZHeebb' : 22, 'XZHmmbb' : 20, 'XZHnnbb' : 22,'XZHee0b' : 22, 'XZHmm0b' : 20, 'XZHnn0b' : 20, 'XZHVBFeebbVBF' : 22, 'XZHVBFmmbbVBF' : 20, 'XZHVBFnnbbVBF' : 22,'XZHVBFee0bVBF' : 22, 'XZHVBFmm0bVBF' : 20, 'XZHVBFnn0bVBF' : 20} genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000] eff = {} for signal_samples in ['ZlepHinc','ZinvHinc','ZinvHincVBF','ZlepHincVBF']: if signal_samples == 'ZinvHinc': signals = {'XZHnnbb':['nnbb'],'XZHnn0b':['nn0b']} sign_list = ['XZHnnbb','XZHnn0b'] elif signal_samples == 'ZlepHinc': signals = {'XZHeebb':['eebb'],'XZHmmbb':['mmbb'],'XZHee0b':['ee0b'],'XZHmm0b':['mm0b']} sign_list = ['XZHeebb','XZHmmbb','XZHee0b', 'XZHmm0b'] elif signal_samples == 'ZinvHincVBF': signals = {'XZHVBFnnbbVBF':['nnbbVBF'],'XZHVBFnn0bVBF':['nn0bVBF']} sign_list = ['XZHVBFnnbbVBF', 'XZHVBFnn0bVBF'] elif signal_samples == 'ZlepHincVBF': signals = {'XZHVBFeebbVBF':['eebbVBF'],'XZHVBFmmbbVBF':['mmbbVBF'],'XZHVBFee0bVBF':['ee0bVBF'],'XZHVBFmm0bVBF':['mm0bVBF']} sign_list = ['XZHVBFeebbVBF', 'XZHVBFmmbbVBF','XZHVBFee0bVBF', 'XZHVBFmm0bVBF'] for sign, channels in signals.iteritems(): treeSign = {} ngenSign = {} nevtSign = {} eff[sign] = TGraphErrors() eff[sign].SetTitle(sign) eff[sign].SetMarkerColor(colors[sign]) eff[sign].SetMarkerSize(1.25) eff[sign].SetLineColor(colors[sign]) eff[sign].SetLineWidth(2) eff[sign].SetLineStyle(styles[sign]) eff[sign].SetMarkerStyle(marker[sign]) for i, m in enumerate(genPoints): neff = 0. for channel in channels: if signal_samples == 'ZinvHinc': file_list = ['Ntuples2016/XZH/ZprimeToZHToZinvHall_narrow_M%s'%m,'Ntuples2017/XZH/ZprimeToZHToZinvHall_narrow_M%s'%m,'Ntuples2018/XZH/ZprimeToZHToZinvHall_narrow_M%s'%m] elif signal_samples == 'ZlepHinc': file_list = ['Ntuples2016/XZH/ZprimeToZHToZlepHinc_narrow_M%s'%m,'Ntuples2017/XZH/ZprimeToZHToZlepHinc_narrow_M%s'%m,'Ntuples2018/XZH/ZprimeToZHToZlepHinc_narrow_M%s'%m] elif signal_samples == 'ZinvHincVBF': file_list = ['Ntuples2016/XZHVBF/Zprime_VBF_Zh_Zinvhinc_narrow_M-%s'%m,'Ntuples2017/XZHVBF/Zprime_VBF_Zh_Zinvhinc_narrow_M-%s'%m,'Ntuples2018/XZHVBF/Zprime_VBF_Zh_Zinvhinc_narrow_M-%s'%m] elif signal_samples == 'ZlepHincVBF': file_list = ['Ntuples2016/XZHVBF/Zprime_VBF_Zh_Zlephinc_narrow_M-%s'%m,'Ntuples2017/XZHVBF/Zprime_VBF_Zh_Zlephinc_narrow_M-%s'%m,'Ntuples2018/XZHVBF/Zprime_VBF_Zh_Zlephinc_narrow_M-%s'%m] #if not 'VBF' in channel: # signMass = "XZH_M%d" % m #else: # signMass = "XZHVBF_M%d" % m ngenSign[m] = 0. nevtSign[m] = 0. #for j, ss in enumerate(sample[signMass]['files']): for j, ss in enumerate(file_list): sfile = TFile(NTUPLEDIR + ss + ".root", "READ") if not sfile.Get("Events")==None: ngenSign[m] += sfile.Get("Events").GetEntries() # From trees treeSign[m] = sfile.Get("tree") nevtSign[m] += treeSign[m].GetEntries(selection[channel] + selection['SR']) else: ngenSign[m] = -1 print "Failed reading file", NTUPLEDIR + ss + ".root" sfile.Close() if nevtSign[m] == 0 or ngenSign[m] < 0: continue # Gen Br #print "m:",m #print "nevtSign:",nevtSign[m] #print "ngenSign:",ngenSign[m] neff += nevtSign[m]/ngenSign[m] if 'ln' in sign or 'll' in sign: neff *= 1.5 n = eff[sign].GetN() eff[sign].SetPoint(n, m, neff) eff[sign].SetPointError(n, 0, 0) n = 0. #max([eff[x].GetN() for x in channels]) maxEff = 0. #sign_list = ['XZHeebb','XZHmmbb','XZHnnbb', 'XZHee0b', 'XZHmm0b', 'XZHnn0b','XZHVBFeebbVBF', 'XZHVBFmmbbVBF', 'XZHVBFnnbbVBF','XZHVBFee0bVBF', 'XZHVBFmm0bVBF', 'XZHVBFnn0bVBF'] leg = TLegend(0.15, 0.15, 0.95, 0.35) #leg = TLegend(0.15, 0.7, 0.95, 0.8) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) for sign in sign_list: #if eff[sign].GetN() > 0: leg.AddEntry(eff[sign], labels[sign], "pl") n += 1 leg.SetNColumns(int(n/3)) leg.SetY1(leg.GetY2()-n*0.045/leg.GetNColumns()) n_error = max([eff[x].GetN() for x in sign_list]) # Total efficiency eff["sum"] = TGraphErrors(n_error) eff["sum"].SetMarkerStyle(24) eff["sum"].SetMarkerColor(1) eff["sum"].SetLineWidth(2) for i in range(n_error): tot, mass = 0., 0. for sign in sign_list: if eff[sign].GetN() > i: tot += eff[sign].GetY()[i] mass = eff[sign].GetX()[i] if tot > maxEff: maxEff = tot eff["sum"].SetPoint(i, mass, tot) #legS = TLegend(0.55, 0.85-0.045, 0.95, 0.85) legS = TLegend(0.55, 0.35-0.045, 0.95, 0.35) legS.SetBorderSize(0) legS.SetFillStyle(0) #1001 legS.SetFillColor(0) legS.AddEntry(eff['sum'], "%s Total efficiency"%signal_samples, "pl") c1 = TCanvas("c1", "Signal Efficiency", 1200, 800) c1.cd(1) c1.GetPad(0).SetTicks(1, 1) c1.SetLogy() first = sign_list[0] #if eff['XZHeebb'].GetN()!=0: # first = 'XZHeebb' #else: # first = 'XZHnnbb' eff[first].Draw("APL") for sign, channels in signals.iteritems(): eff[sign].Draw("APL" if i==0 else "SAME, PL") eff["sum"].Draw("SAME, PL") leg.Draw() legS.Draw() setHistStyle(eff[first], 1.1) eff[first].SetTitle(";m_{X} (GeV);Acceptance #times efficiency") eff[first].SetMinimum(0.) eff[first].SetMaximum(max(1., maxEff*1.5)) #0.65 eff[first].GetXaxis().SetTitleSize(0.045) eff[first].GetYaxis().SetTitleSize(0.045) eff[first].GetXaxis().SetLabelSize(0.045) eff[first].GetYaxis().SetLabelSize(0.045) eff[first].GetYaxis().SetTitleOffset(1.1) eff[first].GetXaxis().SetTitleOffset(1.05) eff[first].GetXaxis().SetRangeUser(750,5500) eff[first].GetYaxis().SetRangeUser(0., 0.4) drawCMS(-1,YEAR, "Simulation") """ latex = TLatex() latex.SetNDC() latex.SetTextSize(0.05) latex.SetTextColor(1) latex.SetTextFont(42) latex.SetTextAlign(13) latex.DrawLatex(0.83, 0.99, "(13 TeV)") latex.SetTextFont(62) latex.SetTextSize(0.06) latex.DrawLatex(0.15, 0.90, "CMS") latex.SetTextSize(0.05) latex.SetTextFont(52) """ #c1.Print("plotsSignal/Efficiency/Efficiency.pdf") #c1.Print("plotsSignal/Efficiency/Efficiency.png") c1.Print("plotsSignal/Efficiency/%s_Efficiency.pdf"%signal_samples) c1.Print("plotsSignal/Efficiency/%s_Efficiency.png"%signal_samples)
def DeepCSV_pt_distribution( year): ## everything below is jsut copy&past from above from root_numpy import root2array, fill_hist, array2root import numpy.lib.recfunctions as rfn from aliases import alias_deepCSV, WP_deepCSV ### Preliminary Operations ### treeRead = True var = 'jpt_1' channel = 'preselection' cut = alias_deepCSV['preselection'] unit = '' if "GeV" in variable[var]['title']: unit = ' GeV' isBlind = BLIND and 'SR' in channel isAH = False showSignal = True stype = "HVT model B" if len(sign) > 0 and 'AZh' in sign[0]: stype = "2HDM" elif len(sign) > 0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV" if treeRead: for k in sorted(alias_deepCSV.keys(), key=len, reverse=True): if k in cut: cut = cut.replace( k, alias_deepCSV[k].format(WP=WP_deepCSV[BTAGGING][year])) print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:" print " cut :", cut ### Create and fill MC histograms ### # Create dict file = {} tree = {} hist = {} N_signal_tot = 0. N_signal_tag = 0. ### Create and fill MC histograms ### for i, s in enumerate(back + sign): if variable[var]['nbins'] > 0: hist[s] = TH1F( s, ";jet p_{T};Events / ( " + str( (variable[var]['max'] - variable[var]['min']) / variable[var]['nbins']) + unit + " );" + ('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max']) else: hist[s] = TH1F( s, ";jet p_{T};Events" + ('log' if variable[var]['log'] else ''), len(variable[var]['bins']) - 1, array('f', variable[var]['bins'])) hist[s].Sumw2() for j, ss in enumerate(sample[s]['files']): if not 'data' in s: if year == "run2" or year in ss: arr = root2array(NTUPLEDIR + ss + ".root", branches=["jpt_1", "eventWeightLumi"], selection=cut + " && jdeepCSV_1>" + str(WP_deepCSV[BTAGGING][year])) if 'signal' in ss.lower(): N_signal_tag += len(arr['jpt_1'][arr['jpt_1'] > 3500]) print "imported " + NTUPLEDIR + ss + ".root" fill_hist(hist[s], arr["jpt_1"], weights=arr["eventWeightLumi"]) arr = None arr = root2array(NTUPLEDIR + ss + ".root", branches=["jpt_2", "eventWeightLumi"], selection=cut + " && jdeepCSV_2>" + str(WP_deepCSV[BTAGGING][year])) print "imported " + NTUPLEDIR + ss + ".root" if 'signal' in ss.lower(): N_signal_tag += len(arr['jpt_2'][arr['jpt_2'] > 3500]) fill_hist(hist[s], arr["jpt_2"], weights=arr["eventWeightLumi"]) arr = None if 'signal' in ss.lower(): arr = root2array(NTUPLEDIR + ss + ".root", branches=["jpt_1", "eventWeightLumi"], selection=cut) N_signal_tot += len(arr['jpt_1'][arr['jpt_1'] > 3500]) arr = None arr = root2array(NTUPLEDIR + ss + ".root", branches=["jpt_2", "eventWeightLumi"], selection=cut) N_signal_tot += len(arr['jpt_2'][arr['jpt_2'] > 3500]) arr = None hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0) hist[s].SetFillColor(sample[s]['fillcolor']) hist[s].SetFillStyle(sample[s]['fillstyle']) hist[s].SetLineColor(sample[s]['linecolor']) hist[s].SetLineStyle(sample[s]['linestyle']) if channel.endswith('TR') and channel.replace('TR', '') in topSF: hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0]) hist['ST'].Scale(topSF[channel.replace('TR', '')][0]) hist['BkgSum'] = hist['data_obs'].Clone( "BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum") hist['BkgSum'].Reset("MICES") hist['BkgSum'].SetFillStyle(3003) hist['BkgSum'].SetFillColor(1) for i, s in enumerate(back): hist['BkgSum'].Add(hist[s]) # Create data and Bkg sum histograms if options.blind or 'SR' in channel: hist['data_obs'] = hist['BkgSum'].Clone("data_obs") hist['data_obs'].Reset("MICES") # Set histogram style hist['data_obs'].SetMarkerStyle(20) hist['data_obs'].SetMarkerSize(1.25) for i, s in enumerate(back + sign + ['BkgSum']): addOverflow(hist[s], False) # Add overflow for i, s in enumerate(sign): hist[s].SetLineWidth(3) for i, s in enumerate(sign): sample[s][ 'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2]) if isAH: for i, s in enumerate(back): hist[s].SetFillStyle(3005) hist[s].SetLineWidth(2) #for i, s in enumerate(sign): # hist[s].SetFillStyle(0) if not var == "Events": sfnorm = hist[data[0]].Integral() / hist['BkgSum'].Integral() print "Applying SF:", sfnorm for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(sfnorm) if BLIND and var.endswith("Mass"): for i, s in enumerate(data + back + ['BkgSum']): first, last = hist[s].FindBin(65), hist[s].FindBin(135) for j in range(first, last): hist[s].SetBinContent(j, -1.e-4) if BLIND and var.endswith("Tau21"): for i, s in enumerate(data): first, last = hist[s].FindBin(0), hist[s].FindBin(0.6) for j in range(first, last): hist[s].SetBinContent(j, -1.e-4) # Create stack if variable[var]['nbins'] > 0: bkg = THStack( "Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events / ( " + str( (variable[var]['max'] - variable[var]['min']) / variable[var]['nbins']) + unit + " )") else: bkg = THStack("Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events; ") for i, s in enumerate(back): bkg.Add(hist[s]) # Legend leg = TLegend(0.65, 0.6, 0.95, 0.9) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) if len(data) > 0: leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe") for i, s in reversed(list(enumerate(['BkgSum'] + back))): leg.AddEntry(hist[s], sample[s]['label'], "f") if showSignal: for i, s in enumerate(sign): if sample[s]['plot']: leg.AddEntry(hist[s], sample[s]['label'], "fl") leg.SetY1(0.9 - leg.GetNRows() * 0.05) # --- Display --- c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800 if RATIO else 600) if RATIO: c1.Divide(1, 2) setTopPad(c1.GetPad(1), RATIO) setBotPad(c1.GetPad(2), RATIO) c1.cd(1) c1.GetPad(bool(RATIO)).SetTopMargin(0.06) c1.GetPad(bool(RATIO)).SetRightMargin(0.05) c1.GetPad(bool(RATIO)).SetTicks(1, 1) log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle() if log: c1.GetPad(bool(RATIO)).SetLogy() # Draw bkg.Draw("HIST") # stack hist['BkgSum'].Draw("SAME, E2") # sum of bkg if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data if 'sync' in hist: hist['sync'].Draw("SAME, PE") #data_graph.Draw("SAME, PE") if showSignal: smagn = 1. #if treeRead else 1.e2 #if log else 1.e2 for i, s in enumerate(sign): # if sample[s]['plot']: hist[s].Scale(smagn) hist[s].Draw( "SAME, HIST" ) # signals Normalized, hist[s].Integral()*sample[s]['weight'] textS = drawText(0.80, 0.9 - leg.GetNRows() * 0.05 - 0.02, stype + " (x%d)" % smagn, True) #bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075) bkg.GetYaxis().SetTitleOffset(0.9) #bkg.GetYaxis().SetTitleOffset(2.) bkg.SetMaximum((5. if log else 1.25) * max( bkg.GetMaximum(), hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) + hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin()))) #if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25) bkg.SetMinimum( max( min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin( )), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.) if log: bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4) #bkg.GetYaxis().SetMoreLogLabels(True) bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max']) #if log: bkg.SetMinimum(1) leg.Draw() #drawCMS(LUMI[year], "Preliminary") drawCMS(LUMI[year], "", suppressCMS=True) drawRegion('XVH' + channel, True) drawAnalysis(channel) setHistStyle(bkg, 1.2 if RATIO else 1.1) setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1) if RATIO: c1.cd(2) err = hist['BkgSum'].Clone("BkgErr;") err.SetTitle("") err.GetYaxis().SetTitle("Data / MC") err.GetYaxis().SetTitleOffset(0.9) err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max']) for i in range(1, err.GetNbinsX() + 1): err.SetBinContent(i, 1) if hist['BkgSum'].GetBinContent(i) > 0: err.SetBinError( i, hist['BkgSum'].GetBinError(i) / hist['BkgSum'].GetBinContent(i)) setBotStyle(err) errLine = err.Clone("errLine") errLine.SetLineWidth(1) errLine.SetFillStyle(0) res = hist['data_obs'].Clone("Residues") for i in range(0, res.GetNbinsX() + 1): if hist['BkgSum'].GetBinContent(i) > 0: res.SetBinContent( i, res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i)) res.SetBinError( i, res.GetBinError(i) / hist['BkgSum'].GetBinContent(i)) if 'sync' in hist: res.SetMarkerColor(2) res.SetMarkerStyle(31) res.Reset() for i in range(0, res.GetNbinsX() + 1): x = hist['data_obs'].GetXaxis().GetBinCenter(i) if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0: res.SetBinContent( i, hist['data_obs'].GetBinContent( hist['data_obs'].FindBin(x)) / hist['sync'].GetBinContent(hist['sync'].FindBin(x))) res.SetBinError( i, hist['data_obs'].GetBinError( hist['data_obs'].FindBin(x)) / hist['sync'].GetBinContent(hist['sync'].FindBin(x))) setBotStyle(res) #err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5) #err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2) err.Draw("E2") errLine.Draw("SAME, HIST") if not isBlind and len(data) > 0: res.Draw("SAME, PE0") #res_graph.Draw("SAME, PE0") if len(err.GetXaxis().GetBinLabel( 1)) == 0: # Bin labels: not a ordinary plot drawRatio(hist['data_obs'], hist['BkgSum']) drawStat(hist['data_obs'], hist['BkgSum']) c1.Update() if gROOT.IsBatch(): if channel == "": channel = "nocut" varname = var.replace('.', '_').replace('()', '') if not os.path.exists("plots/" + channel): os.makedirs("plots/" + channel) suffix = '' if "b" in channel or 'mu' in channel: suffix += "_" + BTAGGING c1.Print("plots/MANtag_study/deepCSV_plots/pt_" + year + suffix + ".png") c1.Print("plots/MANtag_study/deepCSV_plots/pt_" + year + suffix + ".pdf") # Print table printTable(hist, sign) print 'deepCSV efficiency:', N_signal_tag / N_signal_tot
def plot(var, cut, year, norm=False, nm1=False): ### Preliminary Operations ### treeRead = not cut in [ "nnqq", "en", "enqq", "mn", "mnqq", "ee", "eeqq", "mm", "mmqq", "em", "emqq", "qqqq" ] # Read from tree channel = cut unit = '' if "GeV" in variable[var]['title']: unit = ' GeV' isBlind = BLIND and 'SR' in channel isAH = False #'qqqq' in channel or 'hp' in channel or 'lp' in channel showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5 stype = "HVT model B" if len(sign) > 0 and 'AZh' in sign[0]: stype = "2HDM" elif len(sign) > 0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV" if treeRead: for k in sorted(alias.keys(), key=len, reverse=True): if BTAGGING == 'semimedium': if k in cut: if ADDSELECTION: cut = cut.replace( k, aliasSM[k] + SELECTIONS[options.selection]) else: cut = cut.replace(k, aliasSM[k]) else: if k in cut: if ADDSELECTION: cut = cut.replace( k, alias[k].format(WP=working_points[BTAGGING]) + SELECTIONS[options.selection]) else: cut = cut.replace( k, alias[k].format(WP=working_points[BTAGGING])) # Determine Primary Dataset pd = sample['data_obs']['files'] print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:" print " dataset:", pd print " cut :", cut if var == 'jj_deltaEta_widejet': if "jj_deltaEta_widejet<1.1 && " in cut: print print "omitting jj_deltaEta_widejet<1.1 cut to draw the deltaEta distribution" print cut = cut.replace("jj_deltaEta_widejet<1.1 && ", "") else: print print "no 'jj_deltaEta_widejet<1.1 && ' in the cut string detected, so it cannot be ommited explicitly" print ### Create and fill MC histograms ### # Create dict file = {} tree = {} hist = {} ### Create and fill MC histograms ### for i, s in enumerate(data + back + sign): if treeRead: # Project from tree tree[s] = TChain("tree") for j, ss in enumerate(sample[s]['files']): if not 'data' in s or ('data' in s and ss in pd): if year == "run2" or year in ss: tree[s].Add(NTUPLEDIR + ss + ".root") if variable[var]['nbins'] > 0: hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events / ( " + str( (variable[var]['max'] - variable[var]['min']) / variable[var]['nbins']) + unit + " );" + ('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max']) else: hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events" + ('log' if variable[var]['log'] else ''), len(variable[var]['bins']) - 1, array('f', variable[var]['bins'])) hist[s].Sumw2() cutstring = "(eventWeightLumi)" + ("*(" + cut + ")" if len(cut) > 0 else "") tree[s].Project(s, var, cutstring) if not tree[s].GetTree() == None: hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast()) else: # Histogram written to file for j, ss in enumerate(sample[s]['files']): if not 'data' in s or ('data' in s and ss in pd): file[ss] = TFile(NTUPLEDIR + ss + ".root", "R") if file[ss].IsZombie(): print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist" continue tmphist = file[ss].Get(cut + "/" + var) if tmphist == None: continue if not s in hist.keys(): hist[s] = tmphist else: hist[s].Add(tmphist) hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0) hist[s].SetFillColor(sample[s]['fillcolor']) hist[s].SetFillStyle(sample[s]['fillstyle']) hist[s].SetLineColor(sample[s]['linecolor']) hist[s].SetLineStyle(sample[s]['linestyle']) if channel.endswith('TR') and channel.replace('TR', '') in topSF: hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0]) hist['ST'].Scale(topSF[channel.replace('TR', '')][0]) hist['BkgSum'] = hist['data_obs'].Clone( "BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum") hist['BkgSum'].Reset("MICES") hist['BkgSum'].SetFillStyle(3003) hist['BkgSum'].SetFillColor(1) for i, s in enumerate(back): hist['BkgSum'].Add(hist[s]) if options.norm: for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(hist[data[0]].Integral() / hist['BkgSum'].Integral()) # Create data and Bkg sum histograms if options.blind or 'SR' in channel: hist['data_obs'] = hist['BkgSum'].Clone("data_obs") hist['data_obs'].Reset("MICES") # Set histogram style hist['data_obs'].SetMarkerStyle(20) hist['data_obs'].SetMarkerSize(1.25) for i, s in enumerate(data + back + sign + ['BkgSum']): addOverflow(hist[s], False) # Add overflow for i, s in enumerate(sign): hist[s].SetLineWidth(3) for i, s in enumerate(sign): sample[s][ 'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2]) if isAH: for i, s in enumerate(back): hist[s].SetFillStyle(3005) hist[s].SetLineWidth(2) #for i, s in enumerate(sign): # hist[s].SetFillStyle(0) if not var == "Events": sfnorm = hist[data[0]].Integral() / hist['BkgSum'].Integral() print "Applying SF:", sfnorm for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(sfnorm) if BLIND and var.endswith("Mass"): for i, s in enumerate(data + back + ['BkgSum']): first, last = hist[s].FindBin(65), hist[s].FindBin(135) for j in range(first, last): hist[s].SetBinContent(j, -1.e-4) if BLIND and var.endswith("Tau21"): for i, s in enumerate(data): first, last = hist[s].FindBin(0), hist[s].FindBin(0.6) for j in range(first, last): hist[s].SetBinContent(j, -1.e-4) if SYNC and var == "jj_mass_widejet" and year in ["2016", "2017", "2018"]: #iFile = TFile("sync/JetHT_run" + year + "_red_cert_scan.root", "READ") #hist['sync'] = iFile.Get("Mjj") if year == '2016': iFile = TFile("sync/2016/2016_07Aug2017_1246_1p1.root", "READ") hist['sync'] = iFile.Get("h_mjj_data") elif year == '2017': iFile = TFile( "sync/2017/histos_Run2017BCDEF_17Nov2017_JEC2017_mjj1530_cemf_lt_0p8_deltaETA_lt_1p1.root", "READ") hist['sync'] = iFile.Get("h_mjj_data") elif year == '2018': iFile = TFile( "sync/2018/Double_sideband_inputs_18v10_preliminary_v2.root", "READ") hist['sync'] = iFile.Get("h_mjj") # hist['sync'] = tmp.Rebin(len(dijet_bins)-1, "sync", array('d', dijet_bins)) # hist['sync'] = tmp.Rebin(100, "sync") hist['sync'].SetMarkerStyle(31) hist['sync'].SetMarkerSize(1.25) hist['sync'].SetMarkerColor(2) print "Imported and drawing sync file" # Create stack if variable[var]['nbins'] > 0: bkg = THStack( "Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events / ( " + str( (variable[var]['max'] - variable[var]['min']) / variable[var]['nbins']) + unit + " )") else: bkg = THStack("Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events; ") for i, s in enumerate(back): bkg.Add(hist[s]) # Legend leg = TLegend(0.65, 0.6, 0.95, 0.9) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) if len(data) > 0: leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe") for i, s in reversed(list(enumerate(['BkgSum'] + back))): leg.AddEntry(hist[s], sample[s]['label'], "f") if showSignal: for i, s in enumerate(sign): if sample[s]['plot']: leg.AddEntry(hist[s], sample[s]['label'], "fl") leg.SetY1(0.9 - leg.GetNRows() * 0.05) # --- Display --- c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800 if RATIO else 600) if RATIO: c1.Divide(1, 2) setTopPad(c1.GetPad(1), RATIO) setBotPad(c1.GetPad(2), RATIO) c1.cd(1) c1.GetPad(bool(RATIO)).SetTopMargin(0.06) c1.GetPad(bool(RATIO)).SetRightMargin(0.05) c1.GetPad(bool(RATIO)).SetTicks(1, 1) log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle() if log: c1.GetPad(bool(RATIO)).SetLogy() # Draw bkg.Draw("HIST") # stack hist['BkgSum'].Draw("SAME, E2") # sum of bkg if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data if 'sync' in hist: hist['sync'].Draw("SAME, PE") #data_graph.Draw("SAME, PE") if showSignal: smagn = 1. #if treeRead else 1.e2 #if log else 1.e2 for i, s in enumerate(sign): # if sample[s]['plot']: hist[s].Scale(smagn) hist[s].Draw( "SAME, HIST" ) # signals Normalized, hist[s].Integral()*sample[s]['weight'] textS = drawText(0.80, 0.9 - leg.GetNRows() * 0.05 - 0.02, stype + " (x%d)" % smagn, True) #bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075) bkg.GetYaxis().SetTitleOffset(0.9) #bkg.GetYaxis().SetTitleOffset(2.) bkg.SetMaximum((5. if log else 1.25) * max( bkg.GetMaximum(), hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) + hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin()))) #if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25) bkg.SetMinimum( max( min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin( )), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.) if log: bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4) #bkg.GetYaxis().SetMoreLogLabels(True) bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max']) #if log: bkg.SetMinimum(1) leg.Draw() #drawCMS(LUMI[year], "Preliminary") #drawCMS(LUMI[year], "Work in Progress", suppressCMS=True) drawCMS(LUMI[year], "", suppressCMS=True) drawRegion('XVH' + channel, True) drawAnalysis(channel) setHistStyle(bkg, 1.2 if RATIO else 1.1) setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1) if RATIO: c1.cd(2) err = hist['BkgSum'].Clone("BkgErr;") err.SetTitle("") if SYNC: err.GetYaxis().SetTitle("Nano/Mini") else: err.GetYaxis().SetTitle("Data / MC") err.GetYaxis().SetTitleOffset(0.9) err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max']) for i in range(1, err.GetNbinsX() + 1): err.SetBinContent(i, 1) if hist['BkgSum'].GetBinContent(i) > 0: err.SetBinError( i, hist['BkgSum'].GetBinError(i) / hist['BkgSum'].GetBinContent(i)) setBotStyle(err) errLine = err.Clone("errLine") errLine.SetLineWidth(1) errLine.SetFillStyle(0) res = hist['data_obs'].Clone("Residues") for i in range(0, res.GetNbinsX() + 1): if hist['BkgSum'].GetBinContent(i) > 0: res.SetBinContent( i, res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i)) res.SetBinError( i, res.GetBinError(i) / hist['BkgSum'].GetBinContent(i)) if 'sync' in hist: res.SetMarkerColor(1) res.SetMarkerStyle(20) res.Reset() for i in range(0, res.GetNbinsX() + 1): x = hist['data_obs'].GetXaxis().GetBinCenter(i) if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0: res.SetBinContent( i, hist['data_obs'].GetBinContent( hist['data_obs'].FindBin(x)) / hist['sync'].GetBinContent(hist['sync'].FindBin(x))) res.SetBinError( i, hist['data_obs'].GetBinError( hist['data_obs'].FindBin(x)) / hist['sync'].GetBinContent(hist['sync'].FindBin(x))) setBotStyle(res) #err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5) #err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2) err.Draw("E2") errLine.Draw("SAME, HIST") if not isBlind and len(data) > 0: res.Draw("SAME, PE0") #res_graph.Draw("SAME, PE0") if len(err.GetXaxis().GetBinLabel( 1)) == 0: # Bin labels: not a ordinary plot drawRatio(hist['data_obs'], hist['BkgSum']) drawStat(hist['data_obs'], hist['BkgSum']) if SYNC: err.GetYaxis().SetRangeUser(0.9, 1.1) c1.Update() if gROOT.IsBatch(): if channel == "": channel = "nocut" varname = var.replace('.', '_').replace('()', '') if not os.path.exists("plots/" + channel): os.makedirs("plots/" + channel) suffix = '' if "b" in channel or 'mu' in channel: suffix += "_" + BTAGGING if ADDSELECTION: suffix += "_" + options.selection c1.Print("plots/" + channel + "/" + varname + "_" + year + suffix + ".png") c1.Print("plots/" + channel + "/" + varname + "_" + year + suffix + ".pdf") # Print table printTable(hist, sign) # if True: # sFile = TFile("sync/data_2016.root", "RECREATE") # sFile.cd() # hist['data_obs']. if not gROOT.IsBatch(): raw_input("Press Enter to continue...")
def plotPrePost(category, category2): if len(category) == 0: print "Please select a category with the -c option" exit() is2 = len(category2) > 0 if 'l' in category: category, category2, is2 = category.replace('l', 'e'), category.replace( 'l', 'm'), True isAH = len(category) >= 5 X_name = "JJ_mass" if isAH else "VH_mass" signalName = signal + category + "_M%d" % mass data = ["data_obs"] back = ["Bkg_" + category] if isAH else [ "VV_" + category, "Top_" + category, "Vjets_" + category ] sign = [signalName] if len(signal) > 0 else [] fitr = ["total", "total_signal", "total_background"] labels = { "data_obs": "Data", "VV_" + category: "VV, Vh", "Top_" + category: "t#bar{t}, t+X", "Vjets_" + category: "V+jets", "Bkg_" + category: "Bkg fit", signalName: "Signal" } if 'ee' in category or 'mm' in category: labels["Vjets_" + category] = "Z(ll)+jets" elif 'en' in category or 'mn' in category: labels["Vjets_" + category] = "W(l#nu)+jets" elif 'nn' in category: labels["Vjets_" + category] = "Z(#nu#nu),W(l#nu)+jets" lastBin = 3500. if 'ee' in category or 'mm' in category or 'nn' in category else 4500. # xs = 0. # if 'XWH' in signal or 'XVH' in signal: xs += HVT['B3']['W']['XS'][mass]*HVT['B3']['W']['BR'][mass] # if 'XZH' in signal or 'XVH' in signal: xs += HVT['B3']['Z']['XS'][mass]*HVT['B3']['Z']['BR'][mass] xs = getCrossSection(signal, category, mass) #if len(signal2)>0: xs[signal2] = getCrossSection(signal2, category, 0) histData, histPre, histPost, graphPre, graphPost = None, {}, {}, {}, {} if readData: dataFile = TFile("workspace/" + category + ".root", "READ") workspace = dataFile.Get("VH_2016") variable = workspace.var(X_name) dataset = workspace.data(data[0]) if is2: dataFile2 = TFile("workspace/" + category2 + ".root", "READ") workspace2 = dataFile2.Get("VH_2016") dataset2 = workspace2.data(data[0]) dataset.append(dataset2) data2 = dataset.createHistogram(variable, variable, variable.getBinning().numBins() / 10, 1) data1 = data2.ProjectionX() data1.SetMarkerStyle(20) data1.SetMarkerSize(1.25) data1.SetLineColor(1) histData = data1 graphData = convertHistToGraph(histData, True) width = histData.GetXaxis().GetBinWidth(1) inFile = TFile(options.fileName, "READ") if inFile == None: print "File", options.fileName, "not found" return if not inFile.GetDirectory("shapes_prefit/" + category): print "Category", category, "not recognized" return for i, h in enumerate(back + sign + fitr): histPre[h] = inFile.Get("shapes_prefit/" + category + "/" + h) if is2: histPre[h].Add( inFile.Get("shapes_prefit/" + category2 + "/" + h.replace(category, category2))) histPre[h].SetName(h + '_pre') histPre[h].SetLineColor(getColor(h, category)) histPre[h].SetFillColor(getColor(h, category)) histPre[h].SetLineWidth(3) if h in back: histPre[h].SetFillStyle(1001) elif h in fitr: histPre[h].SetFillStyle(3002) elif h in sign: histPre[h].SetTitle("m_{%s'} = %d GeV" % (signal[1], mass)) histPre[h].SetOption("HVT model B g_{V}=3") histPre[h].SetFillStyle(1) histPre[h].SetLineStyle(3) histPre[h].SetLineWidth(6) if xs > 0.: histPre[h].Scale(xs * 1000.) # histPre[h].Rebin(10) if readData: histPre[h].Scale(width) histPre[h].GetYaxis().SetTitle("Events / ( %d GeV )" % width) if 'nn' in category: histPre[h].GetXaxis().SetTitle( histPre[h].GetXaxis().GetTitle().replace( 'm_{VH}', 'm^{T}_{VH}')) for i, h in enumerate(back + sign + fitr): histPost[h] = inFile.Get("shapes_fit_b/" + category + "/" + h) if is2: histPost[h].Add( inFile.Get("shapes_fit_b/" + category2 + "/" + h.replace(category, category2))) histPost[h].SetName(h + '_post') histPost[h].SetLineColor(getColor(h, category)) histPost[h].SetFillColor(getColor(h, category)) histPost[h].SetLineWidth(3) if h in back: histPost[h].SetFillStyle(1001) elif h in fitr: histPost[h].SetFillStyle(3002) elif h in sign: histPost[h].SetTitle("m_{%s'} = %d GeV" % (signal[1], mass)) histPost[h].SetOption("HVT model B g_{V}=3") histPost[h].SetFillStyle(1) histPost[h].SetLineStyle(3) histPost[h].SetLineWidth(6) if xs > 0.: histPost[h].Scale(xs * 1000.) # histPost[h].Rebin(10) if readData: histPost[h].Scale(width) histPost[h].GetYaxis().SetTitle("Events / ( %d GeV )" % width) if 'nn' in category: histPost[h].GetXaxis().SetTitle( histPost[h].GetXaxis().GetTitle().replace( 'm_{VH}', 'm^{T}_{VH}')) # Set errors ot zero to have smooth curves for i, h in enumerate(back + sign): for i in range(histPre[h].GetNbinsX()): histPre[h].SetBinError(i + 1, 0.) for i in range(histPost[h].GetNbinsX()): histPost[h].SetBinError(i + 1, 0.) stackPre = THStack( "Pre", ";" + histPre['total'].GetXaxis().GetTitle() + ";" + histPre['total'].GetYaxis().GetTitle()) for i, s in enumerate(back): stackPre.Add(histPre[s]) stackPost = THStack( "Post", ";" + histPost['total'].GetXaxis().GetTitle() + ";" + histPost['total'].GetYaxis().GetTitle()) for i, s in enumerate(back): stackPost.Add(histPost[s]) for i, h in enumerate(back): tmpPre = histPre[back[i]].Clone(back[i] + "_stack_pre") for j in range(i): tmpPre.Add(histPre[back[j]]) graphPre[back[i]] = convertHistToGraph(tmpPre) tmpPost = histPost[back[i]].Clone(back[i] + "_stack_post") for j in range(i): tmpPost.Add(histPost[back[j]]) graphPost[back[i]] = convertHistToGraph(tmpPost) # Additional signal, if present inFiles = {} for i, f in enumerate( signals): #combine/test/mlfit_monoHnn_MZ3000_MA300.root signalName2 = f.replace('combine/test/mlfit_', '').replace('.root', '') if 'AZh' in signalName2: signalName2 = signalName2.replace('AZh', 'AZh' + category) signalSmpl2 = signalName2.replace(category, '') signal2 = signalName2.split('_M')[0].replace(category, '') try: mass2, mass2A = int(signalName2.split('_M')[1]), 0 except: mass2, mass2A = int( signalName2.split('_MZ')[1].split('_MA')[0]), int( signalName2.split('_MA')[1]) # inFiles[signalName2] = TFile(f, "READ") histSign2 = inFiles[signalName2].Get("shapes_prefit/" + category + "/" + signalName2) if is2: histSign2.Add(inFiles[signalName2].Get( "shapes_prefit/" + category2 + "/" + signalName2.replace(category, category2))) histSign2.SetName(signalName2 + '_pre') if signal2.startswith('X'): histSign2.SetTitle("m_{V'} = %d GeV" % mass2) elif signal2.startswith('A'): histSign2.SetTitle("m_{A} = %d GeV" % mass2) elif not mass2 == 0: histSign2.SetTitle("m_{Z'} = %d GeV" % mass2) if i == 0: histSign2.SetOption( "Z'-2HDM\nm_{A}=300 GeV" if 'monoH' in signalName2 else "Type-II 2HDM\ncos(#beta-#alpha) = 0.25\ntan#beta = 1") histSign2.SetLineColor(getColor(signalName2, category)) histSign2.SetFillColor(getColor(signalName2, category)) histSign2.SetFillStyle(1) histSign2.SetLineStyle(5 + i) histSign2.SetLineWidth(5) if readData: histSign2.Scale(width) xs2 = getCrossSection(signalName2, category, 0) if xs2 > 0.: histSign2.Scale(xs2 * 1000.) sign += [signalName2] histPre[signalName2] = histSign2 histPost[signalName2] = histSign2 leg = TLegend(0.6 - 0.005, 0.6, 0.925, 0.9) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) if readData: leg.AddEntry(graphData, labels[data[0]], "pe") for i, s in reversed(list(enumerate(back))): leg.AddEntry(histPre[s], labels[s], "f") #for i, s in enumerate(sign): # leg.AddEntry(histPre[s], labels[s], "fl") ### c1 = TCanvas("c1", "Pre-Post Fit", 800, 800) c1.Divide(1, 2) c1.cd(1) setTopPad(c1.GetPad(1), RATIO) setBotPad(c1.GetPad(2), RATIO) c1.GetPad(1).SetTopMargin(0.06) c1.GetPad(1).SetRightMargin(0.05) c1.GetPad(1).SetBottomMargin(0.01) c1.GetPad(1).SetTicks(1, 1) #histData.Draw("APE" if d==0 else "SAME, PE") #for i, h in enumerate(back): graphPost[h].Draw("ACL" if i==0 else "C") stackPost.Draw("C") setHistStyle(stackPost, 1.2) stackPost.SetMaximum(stackPost.GetMaximum() * 5.) stackPost.SetMinimum(max(stackPost.GetMinimum(), 0.2)) stackPost.GetXaxis().SetRangeUser(stackPost.GetXaxis().GetXmin(), lastBin) histPost['total'].SetLineWidth(1) histPost['total'].Draw("SAME, E3") histPre['total_background'].SetLineColor(921) histPre['total_background'].SetLineStyle(2) histPre['total_background'].SetLineWidth(3) histPre['total_background'].SetFillColor(1) histPre['total_background'].SetFillStyle(0) histPre['total_background'].Draw("SAME, HIST") for i, s in enumerate(sign): histPre[s].Draw("SAME, L") if readData: graphData.Draw("SAME, PE0") leg.AddEntry(histPost['total'], "Bkg. unc.", "f") leg.AddEntry(histPre['total_background'], "Pre-fit", "l") for i, s in enumerate(sign): for o in histPre[s].GetOption().split('\n'): if len(o) > 0.: leg.AddEntry(None, o, "") leg.AddEntry(histPre[s], histPre[s].GetTitle(), "l") leg.SetY1(0.9 - leg.GetNRows() * 0.060) leg.Draw() # if len(sign)>0: # latex = TLatex() # latex.SetNDC() # latex.SetTextSize(0.045) # latex.SetTextFont(42) # latex.DrawLatex(0.67, leg.GetY1()-0.045, "HVT model B g_{V}=3") drawCMS(LUMI, "") #Preliminary drawRegion('XVH' + options.category, True) drawAnalysis(category) c1.GetPad(1).SetLogy() c1.cd(2) err = histPost['total'].Clone("BkgErr;") err.SetTitle("") err.Reset("MICES") err.GetYaxis().SetTitle("(N^{data}-N^{bkg})/#sigma") setBotStyle(err, 4 + 1) err.GetXaxis().SetTitleSize(0.16) #err.GetXaxis().SetTitleOffset(1.25); err.GetYaxis().SetTitleOffset(0.33) err.GetXaxis().SetRangeUser(err.GetXaxis().GetXmin(), lastBin) err.GetYaxis().SetRangeUser(-5., 5.) err.SetLineWidth(2) err.SetLineStyle(2) err.SetFillStyle(0) err.Draw("L") #"E2" if readData: pulls = makeResidHist(graphData, histPost['total']) #setBotStyle(pulls, RATIO, False) pulls.Draw("SAME, PE0") #drawRatio(hist['data_obs'], hist['BkgSum']) #drawStat(hist['data_obs'], hist['BkgSum']) chi2, nbins, npar = 0., 0, 0 for i in range(0, pulls.GetN()): if graphData.GetY()[i] > 1.e-3: nbins = nbins + 1 chi2 += pulls.GetY()[i]**2 #drawChi2(chi2, nbins-npar, True) c1.Update() if not gROOT.IsBatch(): raw_input("Press Enter to continue...") c1.Print("plotsPrePost/BkgSR_" + options.category + ".png") c1.Print("plotsPrePost/BkgSR_" + options.category + ".pdf") ### if VERBOSE: c2 = TCanvas("c2", "Pre-Post Fit", 1200, 600) c2.Divide(2, 1) c2.cd(1) c2.GetPad(1).SetTopMargin(0.06) c2.GetPad(1).SetRightMargin(0.05) c2.GetPad(1).SetBottomMargin(0.10) c2.GetPad(1).SetTicks(1, 1) #histData.Draw("APE" if d==0 else "SAME, PE") #for i, h in enumerate(back): graphPre[h].Draw("FL" if i==0 else "SAME, FL") stackPre.Draw("HIST") histPre['total'].Draw("SAME, E2") stackPre.SetMaximum(stackPre.GetMaximum() * 5.) stackPre.SetMinimum(max(stackPre.GetMinimum(), 0.01)) histData.Draw("SAME, PE") leg.Draw() drawCMS(LUMI, "Preliminary") drawRegion('XVH' + category, True) drawAnalysis(category) c2.cd(2) c2.GetPad(2).SetTopMargin(0.06) c2.GetPad(2).SetRightMargin(0.05) c2.GetPad(2).SetBottomMargin(0.10) c2.GetPad(2).SetTicks(1, 1) #histData.Draw("APE" if d==0 else "SAME, PE") #for i, h in enumerate(back): graphPost[h].Draw("ACL" if i==0 else "C") stackPost.Draw("HIST") histPost['total'].Draw("SAME, E2") stackPost.SetMaximum(stackPost.GetMaximum() * 5.) stackPost.SetMinimum(max(stackPost.GetMinimum(), 0.01)) histData.Draw("SAME, PE") leg.Draw() drawCMS(LUMI, "Preliminary") drawRegion('XVH' + category, True) drawAnalysis(category) c2.GetPad(1).SetLogy() c2.GetPad(2).SetLogy() c2.Print("combine/test/" + signalName + "_prepost.png") c2.Print("combine/test/" + signalName + "_prepost.pdf") c2.Close()
def train_and_apply(): np.random.seed(1) ROOT.gROOT.SetBatch() #Extract data from root file tree = uproot.open("out_all.root")["outA/Tevts"] branch_mc = [ "MC_B_P", "MC_B_eta", "MC_B_phi", "MC_B_pt", "MC_D0_P", "MC_D0_eta", "MC_D0_phi", "MC_D0_pt", "MC_Dst_P", "MC_Dst_eta", "MC_Dst_phi", "MC_Dst_pt", "MC_Est_mu", "MC_M2_miss", "MC_mu_P", "MC_mu_eta", "MC_mu_phi", "MC_mu_pt", "MC_pis_P", "MC_pis_eta", "MC_pis_phi", "MC_pis_pt", "MC_q2" ] branch_rec = [ "B_P", "B_eta", "B_phi", "B_pt", "D0_P", "D0_eta", "D0_phi", "D0_pt", "Dst_P", "Dst_eta", "Dst_phi", "Dst_pt", "Est_mu", "M2_miss", "mu_P", "mu_eta", "mu_phi", "mu_pt", "pis_P", "pis_eta", "pis_phi", "pis_pt", "q2" ] nvariable = len(branch_mc) x_train = tree.array(branch_mc[0], entrystop=options.maxevents) for i in range(1, nvariable): x_train = np.vstack( (x_train, tree.array(branch_mc[i], entrystop=options.maxevents))) x_test = tree.array(branch_rec[0], entrystop=options.maxevents) for i in range(1, nvariable): x_test = np.vstack( (x_test, tree.array(branch_rec[i], entrystop=options.maxevents))) x_train = x_train.T x_test = x_test.T x_test = array2D_float(x_test) #Different type of reconstruction variables #BN normalization gamma = 0 beta = 0.2 ar = np.array(x_train) a = K.constant(ar[:, 0]) mean = K.mean(a) var = K.var(a) x_train = K.eval(K.batch_normalization(a, mean, var, gamma, beta)) for i in range(1, nvariable): a = K.constant(ar[:, i]) mean = K.mean(a) var = K.var(a) a = K.eval(K.batch_normalization(a, mean, var, gamma, beta)) x_train = np.vstack((x_train, a)) x_train = x_train.T ar = np.array(x_test) a = K.constant(ar[:, 0]) mean = K.mean(a) var = K.var(a) x_test = K.eval(K.batch_normalization(a, mean, var, gamma, beta)) for i in range(1, nvariable): a = K.constant(ar[:, i]) mean = K.mean(a) var = K.var(a) a = K.eval(K.batch_normalization(a, mean, var, gamma, beta)) x_test = np.vstack((x_test, a)) x_test = x_test.T #Add noise, remain to be improved noise = np.random.normal(loc=0.0, scale=0.01, size=x_train.shape) x_train_noisy = x_train + noise noise = np.random.normal(loc=0.0, scale=0.01, size=x_test.shape) x_test_noisy = x_test + noise x_train = np.clip(x_train, -1., 1.) x_test = np.clip(x_test, -1., 1.) x_train_noisy = np.clip(x_train_noisy, -1., 1.) x_test_noisy = np.clip(x_test_noisy, -1., 1.) # Network parameters input_shape = (x_train.shape[1], ) batch_size = 128 latent_dim = 2 # Build the Autoencoder Model # First build the Encoder Model inputs = Input(shape=input_shape, name='encoder_input') x = inputs # Shape info needed to build Decoder Model shape = K.int_shape(x) # Generate the latent vector latent = Dense(latent_dim, name='latent_vector')(x) # Instantiate Encoder Model encoder = Model(inputs, latent, name='encoder') encoder.summary() # Build the Decoder Model latent_inputs = Input(shape=(latent_dim, ), name='decoder_input') x = Dense(shape[1])(latent_inputs) x = Reshape((shape[1], ))(x) outputs = Activation('tanh', name='decoder_output')(x) # Instantiate Decoder Model decoder = Model(latent_inputs, outputs, name='decoder') decoder.summary() # Autoencoder = Encoder + Decoder # Instantiate Autoencoder Model autoencoder = Model(inputs, decoder(encoder(inputs)), name='autoencoder') autoencoder.summary() autoencoder.compile(loss='mse', optimizer='adam') # Train the autoencoder autoencoder.fit(x_train_noisy, x_train, validation_data=(x_test_noisy, x_test), epochs=options.epochs, batch_size=batch_size) # Predict the Autoencoder output from corrupted test imformation x_decoded = autoencoder.predict(x_test_noisy) # Draw Comparision Plots c = TCanvas("c", "c", 700, 700) fPads1 = TPad("pad1", "Run2", 0.0, 0.29, 1.00, 1.00) fPads2 = TPad("pad2", "", 0.00, 0.00, 1.00, 0.29) fPads1.SetBottomMargin(0.007) fPads1.SetLeftMargin(0.10) fPads1.SetRightMargin(0.03) fPads2.SetLeftMargin(0.10) fPads2.SetRightMargin(0.03) fPads2.SetBottomMargin(0.25) fPads1.Draw() fPads2.Draw() fPads1.cd() nbin = 50 min = -1. max = 1. variable = "P^{B}" lbin = (max - min) / nbin lbin = str(float((max - min) / nbin)) xtitle = branch_rec[options.branch - 1] ytitle = "Events/" + lbin + "GeV" h_rec = TH1D("h_rec", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max) h_rec.Sumw2() h_pre = TH1D("h_pre", "" + ";%s;%s" % (xtitle, ytitle), nbin, min, max) h_pre.Sumw2() for i in range(x_test_noisy.shape[0]): h_rec.Fill(x_test_noisy[i][options.branch - 1]) h_pre.Fill(x_decoded[i][options.branch - 1]) h_rec = UnderOverFlow1D(h_rec) h_pre = UnderOverFlow1D(h_pre) maxY = TMath.Max(h_rec.GetMaximum(), h_pre.GetMaximum()) h_rec.SetLineColor(2) h_rec.SetFillStyle(0) h_rec.SetLineWidth(2) h_rec.SetLineStyle(1) h_pre.SetLineColor(3) h_pre.SetFillStyle(0) h_pre.SetLineWidth(2) h_pre.SetLineStyle(1) h_rec.SetStats(0) h_pre.SetStats(0) h_rec.GetYaxis().SetRangeUser(0, maxY * 1.1) h_rec.Draw("HIST") h_pre.Draw("same HIST") h_rec.GetYaxis().SetTitleSize(0.06) h_rec.GetYaxis().SetTitleOffset(0.78) theLeg = TLegend(0.5, 0.45, 0.95, 0.82, "", "NDC") theLeg.SetName("theLegend") theLeg.SetBorderSize(0) theLeg.SetLineColor(0) theLeg.SetFillColor(0) theLeg.SetFillStyle(0) theLeg.SetLineWidth(0) theLeg.SetLineStyle(0) theLeg.SetTextFont(42) theLeg.SetTextSize(.05) theLeg.AddEntry(h_rec, "Reconstruction", "L") theLeg.AddEntry(h_pre, "Prediction", "L") theLeg.SetY1NDC(0.9 - 0.05 * 6 - 0.005) theLeg.SetY1(theLeg.GetY1NDC()) fPads1.cd() theLeg.Draw() title = TLatex( 0.91, 0.93, "AE prediction compare with reconstruction, epochs=" + str(options.epochs)) title.SetNDC() title.SetTextSize(0.05) title.SetTextFont(42) title.SetTextAlign(31) title.SetLineWidth(2) title.Draw() fPads2.cd() h_Ratio = h_pre.Clone("h_Ratio") h_Ratio.Divide(h_rec) h_Ratio.SetLineColor(1) h_Ratio.SetLineWidth(2) h_Ratio.SetMarkerStyle(8) h_Ratio.SetMarkerSize(0.7) h_Ratio.GetYaxis().SetRangeUser(0, 2) h_Ratio.GetYaxis().SetNdivisions(504, 0) h_Ratio.GetYaxis().SetTitle("Pre/Rec") h_Ratio.GetYaxis().SetTitleOffset(0.35) h_Ratio.GetYaxis().SetTitleSize(0.13) h_Ratio.GetYaxis().SetTitleSize(0.13) h_Ratio.GetYaxis().SetLabelSize(0.11) h_Ratio.GetXaxis().SetLabelSize(0.1) h_Ratio.GetXaxis().SetTitleOffset(0.8) h_Ratio.GetXaxis().SetTitleSize(0.14) h_Ratio.SetStats(0) axis1 = TGaxis(min, 1, max, 1, 0, 0, 0, "L") axis1.SetLineColor(1) axis1.SetLineWidth(1) for i in range(1, h_Ratio.GetNbinsX() + 1, 1): D = h_rec.GetBinContent(i) eD = h_rec.GetBinError(i) if D == 0: eD = 0.92 B = h_pre.GetBinContent(i) eB = h_pre.GetBinError(i) if B < 0.1 and eB >= B: eB = 0.92 Err = 0. if B != 0.: Err = TMath.Sqrt((eD * eD) / (B * B) + (D * D * eB * eB) / (B * B * B * B)) h_Ratio.SetBinContent(i, D / B) h_Ratio.SetBinError(i, Err) if B == 0.: Err = TMath.Sqrt((eD * eD) / (eB * eB) + (D * D * eB * eB) / (eB * eB * eB * eB)) h_Ratio.SetBinContent(i, D / 0.92) h_Ratio.SetBinError(i, Err) if D == 0 and B == 0: h_Ratio.SetBinContent(i, -1) h_Ratio.SetBinError(i, 0) h_Ratio.Draw("e0") axis1.Draw() c.SaveAs(branch_rec[options.branch - 1] + "_comparision.png")
nc = int((lg - 1) / 12 + 1) leg.SetNColumns(nc) extra = (2 + 0.4 * int(lg / nc + 1)) dv = (vmax - vmin) / 10. if valmin is None: grall[0].SetMinimum(vmin - dv) else: grall[0].SetMinimum(valmin) if valmax is None: grall[0].SetMaximum(vmax + dv * extra) else: grall[0].SetMaximum(valmax) if nc > 8: nc = 8 leg.SetX1(0.9 - 0.1 * nc) leg.SetY1(0.9 - 0.8 * (extra - 1) / (11 + extra)) leg.Draw() else: gr = TGraph(tree.GetSelectedRows(), tree.GetV2(), tree.GetV1()) gr.SetTitle(gtitle) gr.SetMarkerStyle(20) gr.SetMarkerSize(1.3) gr.SetMarkerColor(2) gr.SetLineColor(2) gr.GetXaxis().SetNoExponent(kTRUE) if one_run: gr.GetXaxis().SetTitle("Lumi") else: gr.GetXaxis().SetTitle("Runs") if valmin is not None: grall[0].SetMinimum(valmin)
legDistr.AddEntry(hRawYieldFDVsCut[iPt], 'Non-prompt', 'f') legDistr.AddEntry(hRawYieldsVsCutReSum[iPt], 'Prompt + non-prompt', 'l') legEff.AddEntry(hEffPromptVsCut[iPt], 'Prompt', 'lpe') legEff.AddEntry(hEffFDVsCut[iPt], 'Non-prompt', 'lpe') legFrac.AddEntry(hPromptFracVsCut[iPt], 'Prompt', 'lpe') legFrac.AddEntry(hFDFracVsCut[iPt], 'Non-prompt', 'lpe') deltaY = 0. if compareToFc: legFrac.AddEntry(gPromptFracFcVsCut[iPt], 'Prompt #it{f}_{c}', 'fp') legFrac.AddEntry(gFDFracFcVsCut[iPt], 'Non-prompt #it{f}_{c}', 'fp') deltaY += 0.1 legFrac.SetY1(0.83 - deltaY) if compareToNb: legFrac.AddEntry(gPromptFracNbVsCut[iPt], 'Prompt #it{N}_{b}', 'fp') legFrac.AddEntry(gFDFracNbVsCut[iPt], 'Non-prompt #it{N}_{b}', 'fp') deltaY += 0.1 legFrac.SetY1(0.83 - deltaY) cEff.append(TCanvas(f'cEff_{ptString}', '', 800, 800)) cEff[iPt].DrawFrame(0.5, hEffPromptVsCut[iPt].GetMinimum() / 5, nSets + 0.5, 1., f'{commonString};efficiency') cEff[iPt].SetLogy() hEffPromptVsCut[iPt].DrawCopy('same') hEffFDVsCut[iPt].DrawCopy('same') legEff.Draw()
def hvt(benchmark=['B3', 'A1']): hxs = {} hw = {} gxs = {} gw = {} mg = TMultiGraph() for m in massPoints: hxs[m] = TH2F("hxs_M%d" % m, ";;", 50, -0.04, 3.96, 100, 0., 2.) hw[m] = TH2F("hw_M%d" % m, ";;", 50, -0.04, 3.96, 50, 0., 2.) for m in massPoints: file = TFile.Open("HVT/scanHVT_M%s.root" % m, "READ") tree = file.Get("tree") for entry in range( tree.GetEntries()): # Fill mass points only if NOT excluded tree.GetEntry(entry) gH, gF = tree.gv * tree.ch, tree.g * tree.g * tree.cq / tree.gv XsBr = tree.CX0 * tree.BRbb * 1000. # in fb if XsBr < observed[m]: hxs[m].Fill(gH, gF) if tree.total_widthV0 / float(m) < width: hw[m].Fill(gH, gF) for b in range(hxs[m].GetNbinsX() * hxs[m].GetNbinsY()): hxs[m].SetBinContent(b, 1. if hxs[m].GetBinContent(b) > 0. else 0.) hw[m].SetBinContent(b, 1. if hw[m].GetBinContent(b) > 0. else 0.) #hxs[m].Smooth(20) #hw[m].Smooth(20) gxs[m] = getCurve(hxs[m]) for i, g in enumerate(gxs[m]): g.SetLineColor(massColors[m]) g.SetFillColor(massColors[m]) g.SetFillStyle(massFill[m]) #(3345 if i>1 else 3354) g.SetLineWidth(503 * (1 if i < 2 else -1)) mg.Add(g) #if m==3000: if m == massPoints[-1]: gw[m] = getCurve(hw[m]) for i, g in enumerate(gw[m]): g.SetPoint(0, 0., g.GetY()[0]) g.SetLineWidth(501 * (1 if i < 2 else -1)) g.SetLineColor(920 + 2) g.SetFillColor(920 + 1) g.SetFillStyle(3003) mg.Add(g) if options.root: outFile = TFile("plotsLimit/Model.root", "RECREATE") outFile.cd() for m in massPoints: mg[m].Write("X_M%d" % m) mgW.Write("width") outFile.Close() print "Saved histogram in file plotsLimit/Model.root, exiting..." exit() ### plot ### c1 = TCanvas("c1", "HVT Exclusion Limits", 800, 600) c1.cd() c1.GetPad(0).SetTopMargin(0.06) c1.GetPad(0).SetRightMargin(0.05) c1.GetPad(0).SetTicks(1, 1) mg.Draw("AC") #mg.GetXaxis().SetTitle("g_{V} c_{H}") mg.GetXaxis().SetTitle("Higgs and vector boson coupling g_{H}") mg.GetXaxis().SetRangeUser(-3., 3.) mg.GetXaxis().SetLabelSize(0.045) mg.GetXaxis().SetTitleSize(0.045) mg.GetXaxis().SetTitleOffset(1.) #mg.GetYaxis().SetTitle("g^{2} c_{F} / g_{V}") mg.GetYaxis().SetTitle("Fermion coupling g_{F}") mg.GetYaxis().SetLabelSize(0.045) mg.GetYaxis().SetTitleSize(0.045) mg.GetYaxis().SetTitleOffset(1.) mg.GetYaxis().SetRangeUser(-1.2, 1.2) mg.GetYaxis().SetNdivisions(505) # hxs[3500].Draw("CONTZ") drawCMS(LUMI, "Preliminary", False) # drawAnalysis("XVH"+category, False) # latex = TLatex() # latex.SetNDC() # latex.SetTextFont(62) # latex.SetTextSize(0.06) # latex.DrawLatex(0.10, 0.925, "CMS") # model B g_model = {} for i, b in enumerate(benchmark): g_model[i] = TGraph(1) g_model[i].SetTitle(models_name[b]) g_model[i].SetPoint(0, models_point[b][0], models_point[b][1]) g_model[i].SetMarkerStyle(models_style[b]) g_model[i].SetMarkerColor(models_color[b]) g_model[i].SetMarkerSize(1.5) g_model[i].Draw("PSAME") # text latex = TLatex() latex.SetTextSize(0.045) latex.SetTextFont(42) latex.SetTextColor(630) # for b in benchmark: latex.DrawLatex(models_point[b][0]+0.02, models_point[b][1]+0.02, models_name[b]) latex.SetTextColor(920 + 2) latex.DrawLatex(-2.8, -0.875, "#frac{#Gamma_{Z'}}{m_{Z'}} > %.0f%%" % (width * 100, )) #leg = TLegend(0.68, 0.60, 0.95, 0.94) leg = TLegend(0.68, 0.34, 0.95, 0.66) leg.SetBorderSize(1) leg.SetFillStyle(1001) leg.SetFillColor(0) for m in massPoints: leg.AddEntry(gxs[m][0], "m_{Z'} = %.1f TeV" % (m / 1000.), "fl") for i, b in enumerate(benchmark): leg.AddEntry(g_model[i], g_model[i].GetTitle(), "P") leg.SetY1(leg.GetY2() - leg.GetNRows() * 0.050) leg.SetMargin(0.35) leg.Draw() gxs_ = gxs[massPoints[0]][0].Clone("gxs_") gxs_.SetLineColor(1) # gxs_.SetFillColor(1) latex.SetNDC() latex.SetTextColor(1) latex.SetTextSize(0.04) latex.SetTextFont(52) latex.DrawLatex(0.15, 0.95, "q#bar{q} #rightarrow Z' #rightarrow b#bar{b}") c1.Print("plots/model/HVT.png") c1.Print("plots/model/HVT.pdf") c1.Print("plots/model/HVT.root") c1.Print("plots/model/HVT.C") #g = 0.646879, cH = 0.976246, cF = 1.02433 print "model B = [", 3 * 0.976246, ",", 0.646879 * 0.646879 * 1.02433 / 3, "]" 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 efficiency(year): import numpy as np from root_numpy import tree2array, fill_hist from aliases import AK8veto, electronVeto, muonVeto genPoints = [ 1800, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 5500, 6000, 7000, 8000 ] eff = {} vetoes = {"AK8": AK8veto, "electron": electronVeto, "muon": muonVeto} VETO = "AK8" ##could change the veto to investigate here if SEPARATE: eff_add = {} #channels = ['none', 'qq', 'bq', 'bb', 'mumu'] channels = ['qq', 'bq', 'bb', 'mumu'] for channel in channels: treeSign = {} ngenSign = {} nevtSign = {} eff[channel] = TGraphErrors() if SEPARATE: nevtSign_add = {} eff_add[channel] = TGraphErrors() for i, m in enumerate(genPoints): signName = "ZpBB_M" + str(m) ngenSign[m] = 0. nevtSign[m] = 0. if SEPARATE: nevtSign_add[m] = 0. for j, ss in enumerate(sample[signName]['files']): if year == "run2" or year in ss: sfile = TFile(NTUPLEDIR + ss + ".root", "READ") ngenSign[m] += sfile.Get("Events").GetBinContent(1) treeSign[m] = sfile.Get("tree") if BTAGGING == 'semimedium': #if SEPARATE: # temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=aliasSM[channel].replace(vetoes[VETO], "")) #else: temp_array = tree2array( treeSign[m], branches='BTagAK4Weight_deepJet', selection=aliasSM[channel]) temp_hist = TH1F('pass', 'pass', 1, 0, 1) fill_hist(temp_hist, np.zeros(len(temp_array)), weights=temp_array) nevtSign[m] += temp_hist.GetBinContent(1) temp_array = None temp_hist.Reset() if SEPARATE: temp_array = tree2array( treeSign[m], branches='BTagAK4Weight_deepJet', selection=aliasSM[channel].replace( vetoes[VETO], "")) temp_hist = TH1F('pass', 'pass', 1, 0, 1) fill_hist(temp_hist, np.zeros(len(temp_array)), weights=temp_array) nevtSign[m] += temp_hist.GetBinContent(1) temp_array = None temp_hist.Reset() else: #if SEPARATE: # temp_array = tree2array(treeSign[m], branches='BTagAK4Weight_deepJet', selection=alias[channel].format(WP=working_points[BTAGGING]).replace(vetoes[VETO], "")) #else: temp_array = tree2array( treeSign[m], branches='BTagAK4Weight_deepJet', selection=alias[channel].format( WP=working_points[BTAGGING])) temp_hist = TH1F('pass', 'pass', 1, 0, 1) fill_hist(temp_hist, np.zeros(len(temp_array)), weights=temp_array) nevtSign[m] += temp_hist.GetBinContent(1) temp_array = None temp_hist.Reset() if SEPARATE: temp_array = tree2array( treeSign[m], branches='BTagAK4Weight_deepJet', selection=alias[channel].format( WP=working_points[BTAGGING]).replace( vetoes[VETO], "")) temp_hist = TH1F('pass', 'pass', 1, 0, 1) fill_hist(temp_hist, np.zeros(len(temp_array)), weights=temp_array) nevtSign_add[m] += temp_hist.GetBinContent(1) temp_array = None temp_hist.Reset() sfile.Close() print channel, ss, ":", nevtSign[m], "/", ngenSign[ m], "=", nevtSign[m] / ngenSign[m] if nevtSign[m] == 0 or ngenSign[m] < 0: continue n = eff[channel].GetN() eff[channel].SetPoint(n, m, nevtSign[m] / ngenSign[m]) eff[channel].SetPointError(n, 0, math.sqrt(nevtSign[m]) / ngenSign[m]) if SEPARATE: eff_add[channel].SetPoint(n, m, nevtSign_add[m] / ngenSign[m]) eff_add[channel].SetPointError( n, 0, math.sqrt(nevtSign_add[m]) / ngenSign[m]) eff[channel].SetMarkerColor(color[channel]) eff[channel].SetMarkerStyle(20) eff[channel].SetLineColor(color[channel]) eff[channel].SetLineWidth(2) if SEPARATE: eff_add[channel].SetMarkerColor(color[channel] + color_shift[channel]) eff_add[channel].SetMarkerStyle(21) eff_add[channel].SetLineColor(color[channel] + color_shift[channel]) eff_add[channel].SetLineWidth(2) eff_add[channel].SetLineStyle(7) if channel == 'qq' or channel == 'none': eff[channel].SetLineStyle(3) n = max([eff[x].GetN() for x in channels]) maxEff = 0. # Total efficiency eff["sum"] = TGraphErrors(n) eff["sum"].SetMarkerStyle(24) eff["sum"].SetMarkerColor(1) eff["sum"].SetLineWidth(2) if SEPARATE: eff_add["sum"] = TGraphErrors(n) eff_add["sum"].SetMarkerStyle(25) eff_add["sum"].SetMarkerColor(1) eff_add["sum"].SetLineWidth(2) eff_add["sum"].SetLineStyle(7) for i in range(n): tot, mass = 0., 0. if SEPARATE: tot_add = 0. for channel in channels: if channel == 'qq' or channel == 'none': continue #not sure if I should include 2mu category in sum if eff[channel].GetN() > i: tot += eff[channel].GetY()[i] if SEPARATE: tot_add += eff_add[channel].GetY()[i] mass = eff[channel].GetX()[i] if tot > maxEff: maxEff = tot eff["sum"].SetPoint(i, mass, tot) if SEPARATE: eff_add["sum"].SetPoint(i, mass, tot_add) if SEPARATE: leg = TLegend(0.15, 0.50, 0.95, 0.8) else: leg = TLegend(0.15, 0.60, 0.95, 0.8) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) leg.SetNColumns(len(channels) / 4) for i, channel in enumerate(channels): if eff[channel].GetN() > 0: leg.AddEntry(eff[channel], getChannel(channel), "pl") if SEPARATE: leg.AddEntry(eff_add[channel], getChannel(channel) + " no " + VETO + "-veto", "pl") if SEPARATE: leg.SetY1(leg.GetY2() - len([x for x in channels if eff[x].GetN() > 0]) * 0.045) else: leg.SetY1(leg.GetY2() - len([x for x in channels if eff[x].GetN() > 0]) / 2. * 0.045) if SEPARATE: legS = TLegend(0.5, 0.8 - 0.045, 0.9, 0.85) else: legS = TLegend(0.5, 0.85 - 0.045, 0.9, 0.85) legS.SetBorderSize(0) legS.SetFillStyle(0) #1001 legS.SetFillColor(0) legS.AddEntry(eff['sum'], "Total b tag efficiency (1 b tag + 2 b tag + 2 #mu)", "pl") if SEPARATE: legS.AddEntry(eff_add['sum'], "Total b tag efficiency, no " + VETO + "-veto", "pl") c1 = TCanvas("c1", "Signal Efficiency", 1200, 800) c1.cd(1) eff['sum'].Draw("APL") if SEPARATE: eff_add['sum'].Draw("SAME, PL") for i, channel in enumerate(channels): eff[channel].Draw("SAME, PL") if SEPARATE: eff_add[channel].Draw("SAME, PL") leg.Draw() legS.Draw() setHistStyle(eff["sum"], 1.1) eff["sum"].SetTitle(";m_{Z'} (GeV);Acceptance #times efficiency") eff["sum"].SetMinimum(0.) eff["sum"].SetMaximum(max(1., maxEff * 1.5)) #0.65 if SEPARATE: eff_add["sum"].SetTitle(";m_{Z'} (GeV);Acceptance #times efficiency") eff_add["sum"].SetMinimum(0.) eff_add["sum"].SetMaximum(1.) eff["sum"].GetXaxis().SetTitleSize(0.045) eff["sum"].GetYaxis().SetTitleSize(0.045) eff["sum"].GetYaxis().SetTitleOffset(1.1) eff["sum"].GetXaxis().SetTitleOffset(1.05) eff["sum"].GetXaxis().SetRangeUser(1500, 8000) c1.SetTopMargin(0.05) #drawCMS(-1, "Simulation Preliminary", year=year) #Preliminary #drawCMS(-1, "Work in Progress", year=year, suppressCMS=True) drawCMS(-1, "", year=year, suppressCMS=True) drawAnalysis("") if SEPARATE: c1.Print("plots/Efficiency/" + year + "_" + BTAGGING + "_no" + VETO + "veto.pdf") c1.Print("plots/Efficiency/" + year + "_" + BTAGGING + "_no" + VETO + "veto.png") else: c1.Print("plots/Efficiency/" + year + "_" + BTAGGING + ".pdf") c1.Print("plots/Efficiency/" + year + "_" + BTAGGING + ".png") # print print "category", for m in range(0, eff["sum"].GetN()): print " & %d" % int(eff["sum"].GetX()[m]), print "\\\\", "\n\\hline" for i, channel in enumerate(channels + ["sum"]): if channel == 'sum': print "\\hline" print getChannel(channel).replace("high ", "H").replace( "low ", "L").replace("purity", "P").replace("b-tag", ""), for m in range(0, eff[channel].GetN()): print "& %.1f" % (100. * eff[channel].GetY()[m]), print "\\\\"
def Direct_Estimator(var, cut, year): from root_numpy import root2array, fill_hist, array2root import numpy.lib.recfunctions as rfn ### Preliminary Operations ### treeRead = not cut in [ "nnqq", "en", "enqq", "mn", "mnqq", "ee", "eeqq", "mm", "mmqq", "em", "emqq", "qqqq" ] # Read from tree channel = cut unit = '' if "GeV" in variable[var]['title']: unit = ' GeV' isBlind = BLIND and 'SR' in channel isAH = False #'qqqq' in channel or 'hp' in channel or 'lp' in channel showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5 stype = "HVT model B" if len(sign) > 0 and 'AZh' in sign[0]: stype = "2HDM" elif len(sign) > 0 and 'monoH' in sign[0]: stype = "Z'-2HDM m_{A}=300 GeV" if treeRead: for k in sorted(alias.keys(), key=len, reverse=True): if BTAGGING == 'semimedium': if k in cut: cut = cut.replace(k, aliasSM[k]) else: if k in cut: cut = cut.replace( k, alias[k].format(WP=working_points[BTAGGING])) print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:" print " cut :", cut if var == 'jj_deltaEta_widejet': if "jj_deltaEta_widejet<1.1 && " in cut: print print "omitting jj_deltaEta_widejet<1.1 cut to draw the deltaEta distribution" print cut = cut.replace("jj_deltaEta_widejet<1.1 && ", "") else: print print "no 'jj_deltaEta_widejet<1.1 && ' in the cut string detected, so it cannot be ommited explicitly" print ### Create and fill MC histograms ### # Create dict file = {} tree = {} hist = {} ### Create and fill MC histograms ### for i, s in enumerate(back + sign): if True: #FIXME if variable[var]['nbins'] > 0: hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events / ( " + str( (variable[var]['max'] - variable[var]['min']) / variable[var]['nbins']) + unit + " );" + ('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max']) else: hist[s] = TH1F( s, ";" + variable[var]['title'] + ";Events" + ('log' if variable[var]['log'] else ''), len(variable[var]['bins']) - 1, array('f', variable[var]['bins'])) hist[s].Sumw2() for j, ss in enumerate(sample[s]['files']): if not 'data' in s: if year == "run2" or year in ss: arr = root2array( NTUPLEDIR + ss + ".root", branches=[ var, "jpt_1", "jpt_2", "eventWeightLumi", "TMath::Abs(jflavour_1)==5 && TMath::Abs(jflavour_2)==5", "TMath::Abs(jflavour_1)==5 && TMath::Abs(jflavour_2)!=5", "TMath::Abs(jflavour_1)!=5 && TMath::Abs(jflavour_2)==5", "TMath::Abs(jflavour_1)!=5 && TMath::Abs(jflavour_2)!=5" ], selection=cut if len(cut) > 0 else "") print "imported " + NTUPLEDIR + ss + ".root" arr.dtype.names = [ var, "jpt_1", "jpt_2", "eventWeightLumi", "bb", "bq", "qb", "qq" ] MANtag_eff1 = np.array(map(MANtag_eff, arr["jpt_1"])) MANtag_eff2 = np.array(map(MANtag_eff, arr["jpt_2"])) MANtag_mis1 = np.array(map(MANtag_mis, arr["jpt_1"])) MANtag_mis2 = np.array(map(MANtag_mis, arr["jpt_2"])) MANtag_weight = np.multiply( arr["eventWeightLumi"], np.multiply(arr['bb'], np.multiply(MANtag_eff1, MANtag_eff2)) + np.multiply( arr['bq'], np.multiply(MANtag_eff1, MANtag_mis2)) + np.multiply(arr['qb'], np.multiply(MANtag_mis1, MANtag_eff2)) + np.multiply(arr['qq'], np.multiply(MANtag_mis1, MANtag_mis2))) fill_hist(hist[s], arr[var], weights=MANtag_weight) deepCSV_eff1 = np.array(map(deepCSV_eff, arr["jpt_1"])) deepCSV_eff2 = np.array(map(deepCSV_eff, arr["jpt_2"])) deepCSV_mis1 = np.array(map(deepCSV_mis, arr["jpt_1"])) deepCSV_mis2 = np.array(map(deepCSV_mis, arr["jpt_2"])) deepCSV_weight = np.multiply( arr["eventWeightLumi"], np.multiply( arr['bb'], np.multiply(deepCSV_eff1, deepCSV_eff2)) + np.multiply( arr['bq'], np.multiply(deepCSV_eff1, deepCSV_mis2)) + np.multiply( arr['qb'], np.multiply(deepCSV_mis1, deepCSV_eff2)) + np.multiply( arr['qq'], np.multiply(deepCSV_mis1, deepCSV_mis2))) if var == "jj_mass_widejet" and options.save and not "data" in ss: arr = rfn.append_fields(arr, "MANtag_weight", MANtag_weight, usemask=False) arr = rfn.append_fields(arr, "deepCSV_weight", deepCSV_weight, usemask=False) array2root(arr, NTUPLEDIR + "MANtag/" + ss + "_" + BTAGGING + ".root", treename="tree", mode='recreate') print "saved as", NTUPLEDIR + "MANtag/" + ss + "_" + BTAGGING + ".root" arr = None hist[s].Scale(sample[s]['weight'] if hist[s].Integral() >= 0 else 0) hist[s].SetFillColor(sample[s]['fillcolor']) hist[s].SetFillStyle(sample[s]['fillstyle']) hist[s].SetLineColor(sample[s]['linecolor']) hist[s].SetLineStyle(sample[s]['linestyle']) if channel.endswith('TR') and channel.replace('TR', '') in topSF: hist['TTbarSL'].Scale(topSF[channel.replace('TR', '')][0]) hist['ST'].Scale(topSF[channel.replace('TR', '')][0]) hist['BkgSum'] = hist['data_obs'].Clone( "BkgSum") if 'data_obs' in hist else hist[back[0]].Clone("BkgSum") hist['BkgSum'].Reset("MICES") hist['BkgSum'].SetFillStyle(3003) hist['BkgSum'].SetFillColor(1) for i, s in enumerate(back): hist['BkgSum'].Add(hist[s]) # Create data and Bkg sum histograms if options.blind or 'SR' in channel: hist['data_obs'] = hist['BkgSum'].Clone("data_obs") hist['data_obs'].Reset("MICES") # Set histogram style hist['data_obs'].SetMarkerStyle(20) hist['data_obs'].SetMarkerSize(1.25) for i, s in enumerate(back + sign + ['BkgSum']): addOverflow(hist[s], False) # Add overflow for i, s in enumerate(sign): hist[s].SetLineWidth(3) for i, s in enumerate(sign): sample[s][ 'plot'] = True #sample[s]['plot'] and s.startswith(channel[:2]) if isAH: for i, s in enumerate(back): hist[s].SetFillStyle(3005) hist[s].SetLineWidth(2) #for i, s in enumerate(sign): # hist[s].SetFillStyle(0) if not var == "Events": sfnorm = hist[data[0]].Integral() / hist['BkgSum'].Integral() print "Applying SF:", sfnorm for i, s in enumerate(back + ['BkgSum']): hist[s].Scale(sfnorm) if BLIND and var.endswith("Mass"): for i, s in enumerate(data + back + ['BkgSum']): first, last = hist[s].FindBin(65), hist[s].FindBin(135) for j in range(first, last): hist[s].SetBinContent(j, -1.e-4) if BLIND and var.endswith("Tau21"): for i, s in enumerate(data): first, last = hist[s].FindBin(0), hist[s].FindBin(0.6) for j in range(first, last): hist[s].SetBinContent(j, -1.e-4) # Create stack if variable[var]['nbins'] > 0: bkg = THStack( "Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events / ( " + str( (variable[var]['max'] - variable[var]['min']) / variable[var]['nbins']) + unit + " )") else: bkg = THStack("Bkg", ";" + hist['BkgSum'].GetXaxis().GetTitle() + ";Events; ") for i, s in enumerate(back): bkg.Add(hist[s]) # Legend leg = TLegend(0.65, 0.6, 0.95, 0.9) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) if len(data) > 0: leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe") for i, s in reversed(list(enumerate(['BkgSum'] + back))): leg.AddEntry(hist[s], sample[s]['label'], "f") if showSignal: for i, s in enumerate(sign): if sample[s]['plot']: leg.AddEntry(hist[s], sample[s]['label'], "fl") leg.SetY1(0.9 - leg.GetNRows() * 0.05) # --- Display --- c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800 if RATIO else 600) if RATIO: c1.Divide(1, 2) setTopPad(c1.GetPad(1), RATIO) setBotPad(c1.GetPad(2), RATIO) c1.cd(1) c1.GetPad(bool(RATIO)).SetTopMargin(0.06) c1.GetPad(bool(RATIO)).SetRightMargin(0.05) c1.GetPad(bool(RATIO)).SetTicks(1, 1) log = variable[var]['log'] #"log" in hist['BkgSum'].GetZaxis().GetTitle() if log: c1.GetPad(bool(RATIO)).SetLogy() # Draw bkg.Draw("HIST") # stack hist['BkgSum'].Draw("SAME, E2") # sum of bkg if not isBlind and len(data) > 0: hist['data_obs'].Draw("SAME, PE") # data if 'sync' in hist: hist['sync'].Draw("SAME, PE") #data_graph.Draw("SAME, PE") if showSignal: smagn = 1. #if treeRead else 1.e2 #if log else 1.e2 for i, s in enumerate(sign): # if sample[s]['plot']: hist[s].Scale(smagn) hist[s].Draw( "SAME, HIST" ) # signals Normalized, hist[s].Integral()*sample[s]['weight'] textS = drawText(0.80, 0.9 - leg.GetNRows() * 0.05 - 0.02, stype + " (x%d)" % smagn, True) #bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075) bkg.GetYaxis().SetTitleOffset(0.9) #bkg.GetYaxis().SetTitleOffset(2.) bkg.SetMaximum((5. if log else 1.25) * max( bkg.GetMaximum(), hist['data_obs'].GetBinContent(hist['data_obs'].GetMaximumBin()) + hist['data_obs'].GetBinError(hist['data_obs'].GetMaximumBin()))) #if bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25) bkg.SetMinimum( max( min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin( )), hist['data_obs'].GetMinimum()), 5.e-1) if log else 0.) if log: bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4) #bkg.GetYaxis().SetMoreLogLabels(True) bkg.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max']) #if log: bkg.SetMinimum(1) leg.Draw() #drawCMS(LUMI[year], "Preliminary") drawCMS(LUMI[year], "Work in Progress", suppressCMS=True) drawRegion('XVH' + channel, True) drawAnalysis(channel) setHistStyle(bkg, 1.2 if RATIO else 1.1) setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1) if RATIO: c1.cd(2) err = hist['BkgSum'].Clone("BkgErr;") err.SetTitle("") err.GetYaxis().SetTitle("Data / MC") err.GetYaxis().SetTitleOffset(0.9) err.GetXaxis().SetRangeUser(variable[var]['min'], variable[var]['max']) for i in range(1, err.GetNbinsX() + 1): err.SetBinContent(i, 1) if hist['BkgSum'].GetBinContent(i) > 0: err.SetBinError( i, hist['BkgSum'].GetBinError(i) / hist['BkgSum'].GetBinContent(i)) setBotStyle(err) errLine = err.Clone("errLine") errLine.SetLineWidth(1) errLine.SetFillStyle(0) res = hist['data_obs'].Clone("Residues") for i in range(0, res.GetNbinsX() + 1): if hist['BkgSum'].GetBinContent(i) > 0: res.SetBinContent( i, res.GetBinContent(i) / hist['BkgSum'].GetBinContent(i)) res.SetBinError( i, res.GetBinError(i) / hist['BkgSum'].GetBinContent(i)) if 'sync' in hist: res.SetMarkerColor(2) res.SetMarkerStyle(31) res.Reset() for i in range(0, res.GetNbinsX() + 1): x = hist['data_obs'].GetXaxis().GetBinCenter(i) if hist['sync'].GetBinContent(hist['sync'].FindBin(x)) > 0: res.SetBinContent( i, hist['data_obs'].GetBinContent( hist['data_obs'].FindBin(x)) / hist['sync'].GetBinContent(hist['sync'].FindBin(x))) res.SetBinError( i, hist['data_obs'].GetBinError( hist['data_obs'].FindBin(x)) / hist['sync'].GetBinContent(hist['sync'].FindBin(x))) setBotStyle(res) #err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5) #err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2) err.Draw("E2") errLine.Draw("SAME, HIST") if not isBlind and len(data) > 0: res.Draw("SAME, PE0") #res_graph.Draw("SAME, PE0") if len(err.GetXaxis().GetBinLabel( 1)) == 0: # Bin labels: not a ordinary plot drawRatio(hist['data_obs'], hist['BkgSum']) drawStat(hist['data_obs'], hist['BkgSum']) c1.Update() if gROOT.IsBatch(): if channel == "": channel = "nocut" varname = var.replace('.', '_').replace('()', '') if not os.path.exists("plots/" + channel): os.makedirs("plots/" + channel) suffix = '' if "b" in channel or 'mu' in channel: suffix += "_" + BTAGGING c1.Print("plots/MANtag_study/" + channel + "/" + varname + "_" + year + suffix + ".png") c1.Print("plots/MANtag_study/" + channel + "/" + varname + "_" + year + suffix + ".pdf") # Print table printTable(hist, sign)
def plot(var, cut, norm=False, nm1=False): ### Preliminary Operations ### fileRead = os.path.exists(options.file) treeRead = not any(x==cut for x in ['0l', '1e', '1m', '2e', '2m', '1e1m', 'Gen', 'Trigger'])#(var in variable.keys()) # Read from tree binLow = "" binHigh = "" binName = "" if "binned" in cut: binLow = cut[cut.find("LowVal")+6:cut.find("HighVal")-1] binHigh = cut[cut.find("HighVal")+7:] binName = "bin_"+binLow+"_"+binHigh cut = cut[:cut.find("binned")] useformula = True if 'formula' in variable[var]: print variable[var]['formula'] useformula = True channel = cut plotdir = cut plotname = var weight = "eventWeightLumi" #*(2.2/35.9) isBlind = BLIND and 'SR' in channel showSignal = True#('SR' in channel) cutSplit = cut.split() for s in cutSplit: if s in selection.keys(): plotdir = s cut = cut.replace(s, selection[s]) if not binLow == "": cut = cut + " && " + var + " > " + binLow + " && " + var + " < " + binHigh #if treeRead and cut in selection: cut = cut.replace(cut, selection[cut]) # Determine Primary Dataset pd = [] print cut if any(w in cut for w in ['1l', '1m', '2m', 'isWtoMN', 'isZtoMM', 'isTtoEM']): pd += [x for x in sample['data_obs']['files'] if 'SingleMuon' in x] if any(w in cut for w in ['1l', '1e', '2e', 'isWtoEN', 'isZtoEE']): pd += [x for x in sample['data_obs']['files'] if 'SingleElectron' in x] if any(w in cut for w in ['0l', 'isZtoNN']): pd += [x for x in sample['data_obs']['files'] if 'MET' in x] if len(pd)==0: raw_input("Warning: Primary Dataset not recognized, continue?") print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:" print " dataset:", pd print " cut :", cut print " cut :", weight ### Create and fill MC histograms ### # Create dict file = {} tree = {} hist = {} ### Create and fill MC histograms ### for i, s in enumerate(data+back+sign): if fileRead: fileName = options.file if not s=='data_obs' else "rootfiles_"+options.name+"/"+channel+binName+".root" histName = "shapes_fit_b/"+channel+"/"+s if not s=='data_obs' else s file[s] = TFile(fileName, "READ") tmphist = file[s].Get(histName) if tmphist==None: tmphist = hist[back[0]].Clone(s) tmphist.Reset("MICES") print "Histogram", histName, "not found in file", fileName if s=='data_obs': hist[s] = tmphist else: hist[s] = hist['data_obs'].Clone(s) #hist[s].Reset("MICES") hist[s].SetMarkerSize(0) for i in range(tmphist.GetNbinsX()+1): hist[s].SetBinContent(i+1, tmphist.GetBinContent(i+1)) elif treeRead: # Project from tree tree[s] = TChain("tree") for j, ss in enumerate(sample[s]['files']): if not 'data' in s or ('data' in s and ss in pd): tree[s].Add(NTUPLEDIR + ss + ".root") if not binLow == "": hist[s] = TH1F(s, ";"+variable[var]['title']+";Events;"+('log' if variable[var]['log'] else ''), 1, float(binLow), float(binHigh)) elif binLow == "" and variable[var]['nbins']>0: hist[s] = TH1F(s, ";"+variable[var]['title']+";Events;"+('log' if variable[var]['log'] else ''), variable[var]['nbins'], variable[var]['min'], variable[var]['max']) else: hist[s] = TH1F(s, ";"+variable[var]['title']+";Events;"+('log' if variable[var]['log'] else ''), len(variable[var]['bins'])-1, array('f', variable[var]['bins'])) hist[s].Sumw2() redFactorString = "" redFactorValue = "" if isBlind and 'data' in s: redFactorString = " && Entry$ % 15 == 0" if isBlind and 'data' not in s: redFactorValue = " / 15" cutstring = "("+weight+redFactorValue+")" + ("*("+cut+redFactorString+")" if len(cut)>0 else "") if '-' in s: cutstring = cutstring.replace(cut, cut + "&& nBQuarks==" + s.split('-')[1][0]) if useformula == True: tree[s].Project(s, variable[var]['formula'], cutstring) else: tree[s].Project(s, var, cutstring) if not tree[s].GetTree()==None: hist[s].SetOption("%s" % tree[s].GetTree().GetEntriesFast()) else: # Histogram written to file for j, ss in enumerate(sample[s]['files']): if not 'data' in s or ('data' in s and ss in pd): file[ss] = TFile(NTUPLEDIR + ss + ".root", "R") if file[ss].IsZombie(): print "WARNING: file", NTUPLEDIR + ss + ".root", "does not exist" continue tmphist = file[ss].Get(cut+"/"+var) if tmphist==None: continue if not s in hist.keys(): hist[s] = tmphist else: hist[s].Add(tmphist) if hist[s].Integral() < 0: hist[s].Scale(0) hist[s].SetFillColor(sample[s]['fillcolor']) hist[s].SetFillStyle(sample[s]['fillstyle']) hist[s].SetLineColor(sample[s]['linecolor']) hist[s].SetLineStyle(sample[s]['linestyle']) #if 'WJetsToLNu' in s and 'SL' in channel and 'WR' in channel: hist[s].Scale(1.30) #if 'TTbar' in s and 'SL' in channel and 'TR' in channel: hist[s].Scale(0.91) hist['BkgSum'] = hist[back[0]].Clone("BkgSum") hist['BkgSum'].Reset("MICES") for i, s in enumerate(back): hist['BkgSum'].Add(hist[s], 1) if fileRead: #hist['BkgSum'] = file[back[0]].Get("shapes_fit_b/"+channel+"/"+"total_background") tmphist = file[back[0]].Get("shapes_prefit/"+channel+"/"+"total_background") hist['PreFit'] = hist['BkgSum'].Clone("PreFit") for i in range(tmphist.GetNbinsX()+1): hist['PreFit'].SetBinContent(i+1, tmphist.GetBinContent(i+1)) hist['PreFit'].SetLineStyle(2) hist['PreFit'].SetLineColor(923) hist['PreFit'].SetLineWidth(3) hist['PreFit'].SetFillStyle(0) hist['BkgSum'].SetFillStyle(3003) hist['BkgSum'].SetFillColor(1) # Create data and Bkg sum histograms # if options.blind or 'SR' in channel: # hist['data_obs'] = hist['BkgSum'].Clone("data_obs") # hist['data_obs'].Reset("MICES") # Set histogram style hist[data[0]].SetMarkerStyle(20) hist[data[0]].SetMarkerSize(1.25) for i, s in enumerate(data+back+sign+['BkgSum']): addOverflow(hist[s], False) # Add overflow for i, s in enumerate(sign): hist[s].SetLineWidth(3) #for i, s in enumerate(sign): sample[s]['plot'] = True#sample[s]['plot'] and s.startswith(channel[:2]) if norm: for i, s in enumerate(sign): hist[s].Scale(hist['BkgSum'].Integral()/hist[s].Integral()) # for i, s in enumerate(back): # hist[s].SetFillStyle(3005) # hist[s].SetLineWidth(2) # #for i, s in enumerate(sign): # # hist[s].SetFillStyle(0) # if not var=="Events": # sfnorm = hist[data[0]].Integral()/hist['BkgSum'].Integral() # print "Applying SF:", sfnorm # for i, s in enumerate(back+['BkgSum']): hist[s].Scale(sfnorm) # Create stack bkg = THStack("Bkg", ";"+hist['BkgSum'].GetXaxis().GetTitle()+";Events") for i, s in enumerate(back): bkg.Add(hist[s]) # Legend leg = TLegend(0.65, 0.6, 0.95, 0.9) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) if len(data) > 0: leg.AddEntry(hist[data[0]], sample[data[0]]['label'], "pe") for i, s in reversed(list(enumerate(['BkgSum']+back))): leg.AddEntry(hist[s], sample[s]['label'], "f") if 'PreFit' in hist: leg.AddEntry(hist['PreFit'], sample['PreFit']['label'], "l") if showSignal: for i, s in enumerate(sign): if sample[s]['plot']: leg.AddEntry(hist[s], sample[s]['label'], "fl") leg.SetY1(0.9-leg.GetNRows()*0.05) # --- Display --- c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800 if RATIO else 600) if RATIO: c1.Divide(1, 2) setTopPad(c1.GetPad(1), RATIO) setBotPad(c1.GetPad(2), RATIO) c1.cd(1) c1.GetPad(bool(RATIO)).SetTopMargin(0.06) c1.GetPad(bool(RATIO)).SetRightMargin(0.05) c1.GetPad(bool(RATIO)).SetTicks(1, 1) log = ("log" in hist['BkgSum'].GetZaxis().GetTitle()) if log: c1.GetPad(bool(RATIO)).SetLogy() # Draw bkg.Draw("HIST") # stack hist['BkgSum'].Draw("SAME, E2") # sum of bkg if not isBlind and len(data) > 0: hist[data[0]].Draw("SAME, PE") # data #data_graph.Draw("SAME, PE") if 'PreFit' in hist: hist['PreFit'].Draw("SAME, HIST") if showSignal: for i, s in enumerate(sign): if sample[s]['plot']: hist[s].Draw("SAME, HIST") # signals Normalized, hist[s].Integral()*sample[s]['weight'] bkg.GetYaxis().SetTitleOffset(bkg.GetYaxis().GetTitleOffset()*1.075) bkg.SetMaximum((5. if log else 1.25)*max(bkg.GetMaximum(), hist[data[0]].GetBinContent(hist[data[0]].GetMaximumBin())+hist[data[0]].GetBinError(hist[data[0]].GetMaximumBin()))) if len(sign) > 0 and bkg.GetMaximum() < max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum()): bkg.SetMaximum(max(hist[sign[0]].GetMaximum(), hist[sign[-1]].GetMaximum())*1.25) bkg.SetMinimum(max(min(hist['BkgSum'].GetBinContent(hist['BkgSum'].GetMinimumBin()), hist[data[0]].GetMinimum()), 5.e-1) if log else 0.) if log: bkg.GetYaxis().SetNoExponent(bkg.GetMaximum() < 1.e4) bkg.GetYaxis().SetMoreLogLabels(True) leg.Draw() drawCMS(LUMI, "Preliminary") drawRegion(channel, True) drawAnalysis("DM"+channel[:2]) drawOverflow() setHistStyle(bkg, 1.2 if RATIO else 1.1) setHistStyle(hist['BkgSum'], 1.2 if RATIO else 1.1) if RATIO: c1.cd(2) err = hist['BkgSum'].Clone("BkgErr;") err.SetTitle("") err.GetYaxis().SetTitle("Data / Bkg") for i in range(1, err.GetNbinsX()+1): err.SetBinContent(i, 1) if hist['BkgSum'].GetBinContent(i) > 0: err.SetBinError(i, hist['BkgSum'].GetBinError(i)/hist['BkgSum'].GetBinContent(i)) setBotStyle(err) errLine = err.Clone("errLine") errLine.SetLineWidth(1) errLine.SetFillStyle(0) res = hist[data[0]].Clone("Residues") for i in range(0, res.GetNbinsX()+1): if hist['BkgSum'].GetBinContent(i) > 0: res.SetBinContent(i, res.GetBinContent(i)/hist['BkgSum'].GetBinContent(i)) res.SetBinError(i, res.GetBinError(i)/hist['BkgSum'].GetBinContent(i)) setBotStyle(res) #err.GetXaxis().SetLabelOffset(err.GetXaxis().GetLabelOffset()*5) #err.GetXaxis().SetTitleOffset(err.GetXaxis().GetTitleOffset()*2) err.Draw("E2") if 'PreFit' in hist: respre = hist['PreFit'].Clone("ResiduesPreFit") respre.Divide(hist['BkgSum']) respre.Draw("SAME, HIST") errLine.Draw("SAME, HIST") if not isBlind and len(data) > 0: res.Draw("SAME, PE0") #res_graph.Draw("SAME, PE0") if len(err.GetXaxis().GetBinLabel(1))==0: # Bin labels: not a ordinary plot drawRatio(hist['data_obs'], hist['BkgSum']) drawStat(hist['data_obs'], hist['BkgSum']) c1.Update() if gROOT.IsBatch() and options.saveplots: # and (treeRead and channel in selection.keys()): if not os.path.exists("plots_"+options.name+"/"+plotdir): os.makedirs("plots_"+options.name+"/"+plotdir) c1.Print("plots_"+options.name+"/"+plotdir+"/"+plotname+binName+".png") c1.Print("plots_"+options.name+"/"+plotdir+"/"+plotname+binName+".pdf") # Print table printTable(hist, sign) if not gROOT.IsBatch(): raw_input("Press Enter to continue...") if gROOT.IsBatch() and not fileRead and (var == 'MET_pt' or (channel.startswith('SL') and var == 'MET_sign') or (channel.endswith('ZR') and var == 'FakeMET_pt')): saveHist(hist, channel+binName)
def efficiency(stype, Zlep=True): genPoints = [800, 1000, 1200, 1400, 1600, 1800, 2000, 2500, 3000, 3500, 4000, 4500] eff = {} channels = [x for x in channelList if len(x)<5] for channel in channels: treeSign = {} ngenSign = {} nevtSign = {} eff[channel] = TGraphErrors() for i, m in enumerate(genPoints): signName = "%s_M%d" % (stype, m) #"%s_M%d" % (channel[:3], m) ngenSign[m] = 0. nevtSign[m] = 0. for j, ss in enumerate(sample[signMass]['files']): if 'nn' in channel and not 'Zinv' in ss: continue if ('en' in channel or 'mn' in channel) and not 'Wlep' in ss: continue if ('ee' in channel or 'mm' in channel) and not 'Zlep' in ss: continue if Zlep and 'Zinv' in ss: continue if not Zlep and 'Zlep' in ss: continue sfile = TFile(NTUPLEDIR + ss + ".root", "READ") if not sfile.Get("Events")==None: ngenSign[m] += sfile.Get("Events").GetEntries() # From trees treeSign[m] = sfile.Get("tree") nevtSign[m] += treeSign[m].GetEntries(selection[channel] + selection['SR']) else: ngenSign[m] = -1 print "Failed reading file", NTUPLEDIR + ss + ".root" sfile.Close() if nevtSign[m] == 0 or ngenSign[m] < 0: continue # Gen Br n = eff[channel].GetN() eff[channel].SetPoint(n, m, nevtSign[m]/ngenSign[m]) eff[channel].SetPointError(n, 0, math.sqrt(nevtSign[m])/ngenSign[m]) eff[channel].SetMarkerColor(color[channel]) eff[channel].SetMarkerStyle(20) eff[channel].SetLineColor(color[channel]) eff[channel].SetLineWidth(2) if channel.count('b')==1: eff[channel].SetLineStyle(3) n = max([eff[x].GetN() for x in channels]) maxEff = 0. # Total efficiency eff["sum"] = TGraphErrors(n) eff["sum"].SetMarkerStyle(24) eff["sum"].SetMarkerColor(1) eff["sum"].SetLineWidth(2) for i in range(n): tot, mass = 0., 0. for channel in channels: if eff[channel].GetN() > i: tot += eff[channel].GetY()[i] mass = eff[channel].GetX()[i] if tot > maxEff: maxEff = tot eff["sum"].SetPoint(i, mass, tot) leg = TLegend(0.15, 0.60, 0.95, 0.8) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) leg.SetNColumns(len(channels)/4) for i, channel in enumerate(channels): if eff[channel].GetN() > 0: leg.AddEntry(eff[channel], getChannel(channel), "pl") leg.SetY1(leg.GetY2()-len([x for x in channels if eff[x].GetN() > 0])/2.*0.045) legS = TLegend(0.55, 0.85-0.045, 0.95, 0.85) legS.SetBorderSize(0) legS.SetFillStyle(0) #1001 legS.SetFillColor(0) legS.AddEntry(eff['sum'], "Total efficiency", "pl") c1 = TCanvas("c1", "Signal Efficiency", 1200, 800) c1.cd(1) eff['sum'].Draw("APL") for i, channel in enumerate(channels): eff[channel].Draw("SAME, PL") leg.Draw() legS.Draw() setHistStyle(eff["sum"], 1.1) eff["sum"].SetTitle(";m_{"+stype[1]+"'} (GeV);Acceptance #times efficiency") eff["sum"].SetMinimum(0.) eff["sum"].SetMaximum(max(1., maxEff*1.5)) #0.65 eff["sum"].GetXaxis().SetTitleSize(0.045) eff["sum"].GetYaxis().SetTitleSize(0.045) eff["sum"].GetYaxis().SetTitleOffset(1.1) eff["sum"].GetXaxis().SetTitleOffset(1.05) eff["sum"].GetXaxis().SetRangeUser(750, 5500) if stype=='XWH' or (stype=='XZH' and Zlep): line = drawLine(750, 2./3., 4500, 2./3.) drawCMS(-1,YEAR, "Simulation") #Preliminary drawAnalysis("ZH") suffix = "" if stype=='XZH' and Zlep: suffix = "ll" elif stype=='XZH' and not Zlep: suffix = "nn" elif stype=='XWH': suffix = "ln" c1.Print("plotsSignal/Efficiency/"+stype+suffix+".pdf") c1.Print("plotsSignal/Efficiency/"+stype+suffix+".png") # print print "category", for m in range(0, eff["sum"].GetN()): print " & %d" % int(eff["sum"].GetX()[m]), print "\\\\", "\n\\hline" for i, channel in enumerate(channels+["sum"]): if channel=='sum': print "\\hline" print getChannel(channel).replace("high ", "H").replace("low ", "L").replace("purity", "P").replace("b-tag", ""), for m in range(0, eff[channel].GetN()): print "& %.1f" % (100.*eff[channel].GetY()[m]), print "\\\\"
def dijet(category): channel = 'bb' stype = channel isSB = True # relict from using Alberto's more complex script isData = not ISMC nTupleDir = NTUPLEDIR samples = data if isData else back pd = [] for sample_name in samples: if YEAR == 'run2': pd += sample[sample_name]['files'] else: pd += [x for x in sample[sample_name]['files'] if YEAR in x] print "datasets:", pd if not os.path.exists(PLOTDIR): os.makedirs(PLOTDIR) if BIAS: print "Running in BIAS mode" order = 0 RSS = {} X_mass = RooRealVar("jj_mass_widejet", "m_{jj}", X_min, X_max, "GeV") weight = RooRealVar("MANtag_weight", "", -1.e9, 1.e9) variables = RooArgSet(X_mass) variables.add(RooArgSet(weight)) if VARBINS: binsXmass = RooBinning(len(abins) - 1, abins) X_mass.setBinning(RooBinning(len(abins_narrow) - 1, abins_narrow)) plot_binning = RooBinning( int((X_mass.getMax() - X_mass.getMin()) / 100), X_mass.getMin(), X_mass.getMax()) else: X_mass.setBins(int((X_mass.getMax() - X_mass.getMin()) / 10)) binsXmass = RooBinning(int((X_mass.getMax() - X_mass.getMin()) / 100), X_mass.getMin(), X_mass.getMax()) plot_binning = binsXmass baseCut = "" print stype, "|", baseCut print " - Reading from Tree" treeBkg = TChain("tree") for ss in pd: if os.path.exists(nTupleDir + ss + "_" + BTAGGING + ".root"): treeBkg.Add(nTupleDir + ss + "_" + BTAGGING + ".root") else: print "found no file for sample:", ss setData = RooDataSet("setData", "Data (QCD+TTbar MC)", variables, RooFit.Cut(baseCut), RooFit.WeightVar(weight), RooFit.Import(treeBkg)) nevents = setData.sumEntries() dataMin, dataMax = array('d', [0.]), array('d', [0.]) setData.getRange(X_mass, dataMin, dataMax) xmin, xmax = dataMin[0], dataMax[0] lastBin = X_mass.getMax() if VARBINS: for b in narrow_bins: if b > xmax: lastBin = b break print "Imported", ( "data" if isData else "MC" ), "RooDataSet with", nevents, "events between [%.1f, %.1f]" % (xmin, xmax) #xmax = xmax+binsXmass.averageBinWidth() # start form next bin # 1 parameter print "fitting 1 parameter model" p1_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p1_1", "p1", 7.0, 0., 2000.) modelBkg1 = RooGenericPdf("Bkg1", "Bkg. fit (2 par.)", "1./pow(@0/13000, @1)", RooArgList(X_mass, p1_1)) normzBkg1 = RooRealVar( modelBkg1.GetName() + "_norm", "Number of background events", nevents, 0., 5. * nevents) #range dependent of actual number of events! modelExt1 = RooExtendPdf(modelBkg1.GetName() + "_ext", modelBkg1.GetTitle(), modelBkg1, normzBkg1) fitRes1 = modelExt1.fitTo(setData, RooFit.Extended(True), RooFit.Save(1), RooFit.SumW2Error(not isData), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1)) fitRes1.Print() RSS[1] = drawFit("Bkg1", category, X_mass, modelBkg1, setData, binsXmass, [fitRes1], normzBkg1.getVal()) # 2 parameters print "fitting 2 parameter model" p2_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_1", "p1", 0., -100., 1000.) p2_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p2_2", "p2", p1_1.getVal(), -100., 600.) modelBkg2 = RooGenericPdf("Bkg2", "Bkg. fit (3 par.)", "pow(1-@0/13000, @1) / pow(@0/13000, @2)", RooArgList(X_mass, p2_1, p2_2)) normzBkg2 = RooRealVar(modelBkg2.GetName() + "_norm", "Number of background events", nevents, 0., 5. * nevents) modelExt2 = RooExtendPdf(modelBkg2.GetName() + "_ext", modelBkg2.GetTitle(), modelBkg2, normzBkg2) fitRes2 = modelExt2.fitTo(setData, RooFit.Extended(True), RooFit.Save(1), RooFit.SumW2Error(not isData), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1)) fitRes2.Print() RSS[2] = drawFit("Bkg2", category, X_mass, modelBkg2, setData, binsXmass, [fitRes2], normzBkg2.getVal()) # 3 parameters print "fitting 3 parameter model" p3_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_1", "p1", p2_1.getVal(), -2000., 2000.) p3_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_2", "p2", p2_2.getVal(), -400., 2000.) p3_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p3_3", "p3", -2.5, -500., 500.) modelBkg3 = RooGenericPdf( "Bkg3", "Bkg. fit (4 par.)", "pow(1-@0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000))", RooArgList(X_mass, p3_1, p3_2, p3_3)) normzBkg3 = RooRealVar(modelBkg3.GetName() + "_norm", "Number of background events", nevents, 0., 5. * nevents) modelExt3 = RooExtendPdf(modelBkg3.GetName() + "_ext", modelBkg3.GetTitle(), modelBkg3, normzBkg3) fitRes3 = modelExt3.fitTo(setData, RooFit.Extended(True), RooFit.Save(1), RooFit.SumW2Error(not isData), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1)) fitRes3.Print() RSS[3] = drawFit("Bkg3", category, X_mass, modelBkg3, setData, binsXmass, [fitRes3], normzBkg3.getVal()) # 4 parameters print "fitting 4 parameter model" p4_1 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_1", "p1", p3_1.getVal(), -2000., 2000.) p4_2 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_2", "p2", p3_2.getVal(), -2000., 2000.) p4_3 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_3", "p3", p3_3.getVal(), -50., 50.) p4_4 = RooRealVar("CMS" + YEAR + "_" + category + "_p4_4", "p4", 0.1, -50., 50.) modelBkg4 = RooGenericPdf( "Bkg4", "Bkg. fit (5 par.)", "pow(1 - @0/13000, @1) / pow(@0/13000, @2+@3*log(@0/13000)+@4*pow(log(@0/13000), 2))", RooArgList(X_mass, p4_1, p4_2, p4_3, p4_4)) normzBkg4 = RooRealVar(modelBkg4.GetName() + "_norm", "Number of background events", nevents, 0., 5. * nevents) modelExt4 = RooExtendPdf(modelBkg4.GetName() + "_ext", modelBkg4.GetTitle(), modelBkg4, normzBkg4) fitRes4 = modelExt4.fitTo(setData, RooFit.Extended(True), RooFit.Save(1), RooFit.SumW2Error(not isData), RooFit.Strategy(2), RooFit.Minimizer("Minuit2"), RooFit.PrintLevel(1 if VERBOSE else -1)) fitRes4.Print() RSS[4] = drawFit("Bkg4", category, X_mass, modelBkg4, setData, binsXmass, [fitRes4], normzBkg4.getVal()) # Normalization parameters are should be set constant, but shape ones should not # if BIAS: # p1_1.setConstant(True) # p2_1.setConstant(True) # p2_2.setConstant(True) # p3_1.setConstant(True) # p3_2.setConstant(True) # p3_3.setConstant(True) # p4_1.setConstant(True) # p4_2.setConstant(True) # p4_3.setConstant(True) # p4_4.setConstant(True) normzBkg1.setConstant(True) normzBkg2.setConstant(True) normzBkg3.setConstant(True) normzBkg4.setConstant(True) #*******************************************************# # # # Fisher # # # #*******************************************************# # Fisher test with open(PLOTDIR + "/Fisher_" + category + ".tex", 'w') as fout: fout.write(r"\begin{tabular}{c|c|c|c|c}") fout.write("\n") fout.write(r"function & $\chi^2$ & RSS & ndof & F-test \\") fout.write("\n") fout.write("\hline") fout.write("\n") CL_high = False for o1 in range(1, 5): o2 = min(o1 + 1, 5) fout.write("%d par & %.2f & %.2f & %d & " % (o1 + 1, RSS[o1]["chi2"], RSS[o1]["rss"], RSS[o1]["nbins"] - RSS[o1]["npar"])) if o2 > len(RSS): fout.write(r"\\") fout.write("\n") continue #order==0 and CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1 + 1., o2 + 1., RSS[o1]["nbins"]) fout.write("CL=%.3f " % (CL)) if CL > 0.10: # The function with less parameters is enough if not CL_high: order = o1 #fout.write( "%d par are sufficient " % (o1+1)) CL_high = True else: #fout.write( "%d par are needed " % (o2+1)) if not CL_high: order = o2 fout.write(r"\\") fout.write("\n") fout.write("\hline") fout.write("\n") fout.write(r"\end{tabular}") print "saved F-test table as", PLOTDIR + "/Fisher_" + category + ".tex" #print "-"*25 #print "function & $\\chi^2$ & RSS & ndof & F-test & result \\\\" #print "\\multicolumn{6}{c}{", "Zprime_to_bb", "} \\\\" #print "\\hline" #CL_high = False #for o1 in range(1, 5): # o2 = min(o1 + 1, 5) # print "%d par & %.2f & %.2f & %d & " % (o1+1, RSS[o1]["chi2"], RSS[o1]["rss"], RSS[o1]["nbins"]-RSS[o1]["npar"]), # if o2 > len(RSS): # print "\\\\" # continue #order==0 and # CL = fisherTest(RSS[o1]['rss'], RSS[o2]['rss'], o1+1., o2+1., RSS[o1]["nbins"]) # print "%d par vs %d par CL=%f & " % (o1+1, o2+1, CL), # if CL > 0.10: # The function with less parameters is enough # if not CL_high: # order = o1 # print "%d par are sufficient" % (o1+1), # CL_high=True # else: # print "%d par are needed" % (o2+1), # if not CL_high: # order = o2 # print "\\\\" #print "\\hline" #print "-"*25 #print "@ Order is", order, "("+category+")" #order = min(3, order) #order = 2 if order == 1: modelBkg = modelBkg1 #.Clone("Bkg") modelAlt = modelBkg2 #.Clone("BkgAlt") normzBkg = normzBkg1 #.Clone("Bkg_norm") fitRes = fitRes1 elif order == 2: modelBkg = modelBkg2 #.Clone("Bkg") modelAlt = modelBkg3 #.Clone("BkgAlt") normzBkg = normzBkg2 #.Clone("Bkg_norm") fitRes = fitRes2 elif order == 3: modelBkg = modelBkg3 #.Clone("Bkg") modelAlt = modelBkg4 #.Clone("BkgAlt") normzBkg = normzBkg3 #.Clone("Bkg_norm") fitRes = fitRes3 elif order == 4: modelBkg = modelBkg4 #.Clone("Bkg") modelAlt = modelBkg3 #.Clone("BkgAlt") normzBkg = normzBkg4 #.Clone("Bkg_norm") fitRes = fitRes4 else: print "Functions with", order + 1, "or more parameters are needed to fit the background" exit() modelBkg.SetName("Bkg_" + YEAR + "_" + category) modelAlt.SetName("Alt_" + YEAR + "_" + category) normzBkg.SetName("Bkg_" + YEAR + "_" + category + "_norm") print "-" * 25 # Generate pseudo data setToys = RooDataSet() setToys.SetName("data_toys") setToys.SetTitle("Data (toys)") if not isData: print " - Generating", nevents, "events for toy data" setToys = modelBkg.generate(RooArgSet(X_mass), nevents) #setToys = modelAlt.generate(RooArgSet(X_mass), nevents) print "toy data generated" if VERBOSE: raw_input("Press Enter to continue...") #*******************************************************# # # # Plot # # # #*******************************************************# print "starting to plot" c = TCanvas("c_" + category, category, 800, 800) c.Divide(1, 2) setTopPad(c.GetPad(1), RATIO) setBotPad(c.GetPad(2), RATIO) c.cd(1) frame = X_mass.frame() setPadStyle(frame, 1.25, True) if VARBINS: frame.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin) signal = getSignal( category, stype, 2000) #replacing Alberto's getSignal by own dummy function graphData = setData.plotOn(frame, RooFit.Binning(plot_binning), RooFit.Scaling(False), RooFit.Invisible()) modelBkg.plotOn(frame, RooFit.VisualizeError(fitRes, 1, False), RooFit.LineColor(602), RooFit.FillColor(590), RooFit.FillStyle(1001), RooFit.DrawOption("FL"), RooFit.Name("1sigma")) modelBkg.plotOn(frame, RooFit.LineColor(602), RooFit.FillColor(590), RooFit.FillStyle(1001), RooFit.DrawOption("L"), RooFit.Name(modelBkg.GetName())) modelAlt.plotOn(frame, RooFit.LineStyle(7), RooFit.LineColor(613), RooFit.FillColor(609), RooFit.FillStyle(1001), RooFit.DrawOption("L"), RooFit.Name(modelAlt.GetName())) if not isSB and signal[0] is not None: # FIXME remove /(2./3.) signal[0].plotOn( frame, RooFit.Normalization(signal[1] * signal[2], RooAbsReal.NumEvent), RooFit.LineStyle(3), RooFit.LineWidth(6), RooFit.LineColor(629), RooFit.DrawOption("L"), RooFit.Name("Signal")) graphData = setData.plotOn( frame, RooFit.Binning(plot_binning), RooFit.Scaling(False), RooFit.XErrorSize(0 if not VARBINS else 1), RooFit.DataError(RooAbsData.Poisson if isData else RooAbsData.SumW2), RooFit.DrawOption("PE0"), RooFit.Name(setData.GetName())) fixData(graphData.getHist(), True, True, not isData) pulls = frame.pullHist(setData.GetName(), modelBkg.GetName(), True) chi = frame.chiSquare(setData.GetName(), modelBkg.GetName(), True) #setToys.plotOn(frame, RooFit.DataError(RooAbsData.Poisson), RooFit.DrawOption("PE0"), RooFit.MarkerColor(2)) frame.GetYaxis().SetTitle("Events / ( 100 GeV )") frame.GetYaxis().SetTitleOffset(1.05) frame.Draw() #print "frame drawn" # Get Chi2 # chi2[1] = frame.chiSquare(modelBkg1.GetName(), setData.GetName()) # chi2[2] = frame.chiSquare(modelBkg2.GetName(), setData.GetName()) # chi2[3] = frame.chiSquare(modelBkg3.GetName(), setData.GetName()) # chi2[4] = frame.chiSquare(modelBkg4.GetName(), setData.GetName()) frame.SetMaximum(frame.GetMaximum() * 10) frame.SetMinimum(max(frame.GetMinimum(), 1.e-1)) c.GetPad(1).SetLogy() drawAnalysis(category) drawRegion(category, True) #drawCMS(LUMI, "Simulation Preliminary") drawCMS(LUMI, "Work in Progress", suppressCMS=True) leg = TLegend(0.575, 0.6, 0.95, 0.9) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) leg.AddEntry(setData.GetName(), setData.GetTitle() + " (%d events)" % nevents, "PEL") leg.AddEntry(modelBkg.GetName(), modelBkg.GetTitle(), "FL") #.SetTextColor(629) leg.AddEntry(modelAlt.GetName(), modelAlt.GetTitle(), "L") if not isSB and signal[0] is not None: leg.AddEntry("Signal", signal[0].GetTitle(), "L") leg.SetY1(0.9 - leg.GetNRows() * 0.05) leg.Draw() latex = TLatex() latex.SetNDC() latex.SetTextSize(0.04) latex.SetTextFont(42) if not isSB: latex.DrawLatex(leg.GetX1() * 1.16, leg.GetY1() - 0.04, "HVT model B (g_{V}=3)") # latex.DrawLatex(0.67, leg.GetY1()-0.045, "#sigma_{X} = 1.0 pb") c.cd(2) frame_res = X_mass.frame() setPadStyle(frame_res, 1.25) frame_res.addPlotable(pulls, "P") setBotStyle(frame_res, RATIO, False) if VARBINS: frame_res.GetXaxis().SetRangeUser(X_mass.getMin(), lastBin) frame_res.GetYaxis().SetRangeUser(-5, 5) frame_res.GetYaxis().SetTitle("pulls(#sigma)") frame_res.GetYaxis().SetTitleOffset(0.3) frame_res.Draw() fixData(pulls, False, True, False) drawChi2(RSS[order]["chi2"], RSS[order]["nbins"] - (order + 1), True) line = drawLine(X_mass.getMin(), 0, lastBin, 0) if VARBINS: c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf") c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png") else: c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".pdf") c.SaveAs(PLOTDIR + "/BkgSR_" + category + ".png") #*******************************************************# # # # Generate workspace # # # #*******************************************************# if BIAS: gSystem.Load("libHiggsAnalysisCombinedLimit.so") from ROOT import RooMultiPdf cat = RooCategory("pdf_index", "Index of Pdf which is active") pdfs = RooArgList(modelBkg, modelAlt) roomultipdf = RooMultiPdf("roomultipdf", "All Pdfs", cat, pdfs) normulti = RooRealVar("roomultipdf_norm", "Number of background events", nevents, 0., 1.e6) normzBkg.setConstant( False ) ## newly put here to ensure it's freely floating in the combine fit # create workspace w = RooWorkspace("Zprime_" + YEAR, "workspace") # Dataset if isData: getattr(w, "import")(setData, RooFit.Rename("data_obs")) else: getattr(w, "import")(setToys, RooFit.Rename("data_obs")) #getattr(w, "import")(setData, RooFit.Rename("data_obs")) if BIAS: getattr(w, "import")(cat, RooFit.Rename(cat.GetName())) getattr(w, "import")(normulti, RooFit.Rename(normulti.GetName())) getattr(w, "import")(roomultipdf, RooFit.Rename(roomultipdf.GetName())) getattr(w, "import")(modelBkg, RooFit.Rename(modelBkg.GetName())) getattr(w, "import")(modelAlt, RooFit.Rename(modelAlt.GetName())) getattr(w, "import")(normzBkg, RooFit.Rename(normzBkg.GetName())) w.writeToFile(WORKDIR + "%s_%s.root" % (DATA_TYPE + "_" + YEAR, category), True) print "Workspace", WORKDIR + "%s_%s.root" % ( DATA_TYPE + "_" + YEAR, category), "saved successfully" if VERBOSE: raw_input("Press Enter to continue...")
def addPlots(plots): from ROOT import RooCurve, RooHist, TLine, TLegend, RooPlot, RooAbsData, \ SetOwnership, TGraphErrors #print plots outplot = plots[0].emptyClone(plots[0].GetName()) newMax = 0. for item in range(0, int(plots[0].numItems())): itemName = plots[0].nameOf(item) firstItem = plots[0].getObject(item) if (type(firstItem) == RooCurve): fullCurve = clipCurve(firstItem) for plot in range(1, len(plots)): nextCurve = clipCurve(plots[plot].getCurve(itemName)) fullCurve = RooCurve(fullCurve.GetName(), fullCurve.GetTitle(), fullCurve, nextCurve) fullCurve.addPoint(fullCurve.GetX()[fullCurve.GetN() - 1], 0) fullCurve.addPoint(fullCurve.GetX()[0], 0) fullCurve.SetLineColor(firstItem.GetLineColor()) fullCurve.SetLineStyle(firstItem.GetLineStyle()) fullCurve.SetFillColor(firstItem.GetFillColor()) fullCurve.SetFillStyle(firstItem.GetFillStyle()) outplot.addPlotable(fullCurve, plots[0].getDrawOptions(itemName).Data()) SetOwnership(fullCurve, False) if (type(firstItem) == RooHist): fullHist = firstItem for plot in range(1, len(plots)): nextHist = plots[plot].getHist(itemName) fullHist = addHists(fullHist, nextHist) fullHist.SetName(itemName) fullHist.SetTitle(firstItem.GetTitle()) outplot.addPlotable(fullHist, plots[0].getDrawOptions(itemName).Data()) SetOwnership(fullHist, False) if (type(firstItem) == TGraphErrors): fullErrors = firstItem for plot in range(1, len(plots)): nextErrors = plots[plot].findObject(itemName) fullErrors = addErrors(fullErrors, nextErrors) fullErrors.SetName(itemName) fullErrors.SetTitle(firstItem.GetTitle()) outplot.addObject(fullErrors, plots[0].getDrawOptions(itemName).Data()) SetOwnership(fullErrors, False) if (type(firstItem) == TLine): newLine = TLine(firstItem) newLine.SetY2(outplot.GetMaximum()) SetOwnership(newLine, False) outplot.addObject(newLine) pass if (type(firstItem) == TLegend): newLeg = TLegend(firstItem) newLeg.SetY1NDC(0.92 - \ 0.04*newLeg.GetListOfPrimitives().GetSize() - \ 0.02) newLeg.SetY1(newLeg.GetY1NDC()) SetOwnership(newLeg, False) outplot.addObject(newLeg) for plot in plots: newMax += plot.GetMaximum() outplot.SetMaximum(outplot.GetMaximum() * 1.3) outplot.GetYaxis().SetTitle("Events / GeV") ## outplot.SetMaximum(newMax) ## outplot.Print("v") return outplot
def plot(var, cut, signal): sign = signal ### Preliminary Operations ### treeRead = not 'cutflow' in var #treeRead = False channel = cut if 'inc' in cut: eventWeightLuminame = 'eventWeightLumi_nobtag' else: eventWeightLuminame = 'eventWeightLumi' if "SB" in cut or "SR" in cut: channel_name = cut[:-2] else: channel_name = cut if year in ['2016', '2017', '2018']: NTUPLEDIR = "/work/pbaertsc/heavy_resonance/Ntuples%s/" % (year) else: NTUPLEDIR = "/work/pbaertsc/heavy_resonance/" #showSignal = False if 'SB' in cut or 'TR' in cut else True #'SR' in channel or channel=='qqqq'#or len(channel)==5 if var in [ 'dijet_VBF_mass', 'deltaR_VBF', 'deltaR_HVBFjet1', 'deltaR_HVBFjet2' ]: showSignal = False else: showSignal = True if treeRead: for k in sorted(selection.keys(), key=len, reverse=True): if k in cut: cut = cut.replace(k, selection[k]) print "Plotting from", ("tree" if treeRead else "file"), var, "in", channel, "channel with:" print " cut :", cut ### Create and fill MC histograms ### # Create dict file = {} tree = {} hist = {} ### Create and fill MC histograms ### tree[sign] = TChain("tree") for j, ss in enumerate(sample[sign]['files']): tree[sign].Add(NTUPLEDIR + ss + ".root") if variable[var]['nbins'] > 0: min_value = variable[var]['min'] max_value = variable[var]['max'] title = variable[var]['title'] hist[sign] = TH1F( sign, ";" + title + ";Events;" + ('log' if variable[var]['log'] else ''), variable[var]['nbins'], min_value, max_value) else: hist[s] = TH1F(sign, ";" + variable[var]['title'], len(variable[var]['bins']) - 1, array('f', variable[var]['bins'])) hist[sign].Sumw2() cutstring = "%s" % eventWeightLuminame + ("*(" + cut + ")") tree[sign].Project(sign, var, cutstring) if not tree[sign].GetTree() == None: hist[sign].SetOption("%s" % tree[sign].GetTree().GetEntriesFast()) hist[sign].SetFillColor(sample[sign]['fillcolor']) hist[sign].SetFillStyle(sample[sign]['fillstyle']) hist[sign].SetLineColor(sample[sign]['linecolor']) hist[sign].SetLineStyle(sample[sign]['linestyle']) hist[sign].SetLineWidth(3) sample[sign]['plot'] = True # Legend leg = TLegend(0.6, 0.6, 0.9, 0.9) leg.SetBorderSize(0) leg.SetFillStyle(0) #1001 leg.SetFillColor(0) leg.AddEntry(hist[sign], sample[sign]['label'], "fl") leg.SetY1(0.9 - leg.GetNRows() * 0.05) # --- Display --- c1 = TCanvas("c1", hist.values()[0].GetXaxis().GetTitle(), 800, 800) c1.SetTopMargin(0.06) c1.SetRightMargin(0.05) c1.SetTicks(1, 1) hist[sign].Draw("HIST") leg.Draw() drawCMS(LUMI, year, "Preliminary") drawRegion('XVH' + channel, True) drawAnalysis(channel) c1.Update() if gROOT.IsBatch(): varname = var.replace('.', '_').replace('()', '') if not os.path.exists(OUTPUTDIR + channel_name): os.makedirs(OUTPUTDIR + channel_name) c1.Print("%s" % OUTPUTDIR + channel_name + "/" + varname + sign + ".png") c1.Print("%s" % OUTPUTDIR + channel_name + "/" + varname + sign + ".pdf")