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File indexing completed on 2021-08-10 22:50:04

0001 from __future__ import print_function
0002 from __future__ import absolute_import
0003 from builtins import range
0004 from .officialStyle import officialStyle
0005 from array import array
0006 from ROOT import gROOT, gStyle, TH1F, TH1D, TF1, TFile, TCanvas, TH2F, TLegend, TGraphAsymmErrors, Double, TLatex
0007 import os, copy, sys
0008 
0009 gROOT.SetBatch(True)
0010 officialStyle(gStyle)
0011 gStyle.SetOptTitle(0)
0012 #set_palette("color")
0013 #gStyle.SetPaintTextFormat("2.0f")
0014 
0015 
0016 argvs = sys.argv
0017 argc = len(argvs)
0018 
0019 if argc != 2:
0020     print('Please specify the runtype : python3 tauPOGplot.py <ZTT, ZEE, ZMM, QCD>')
0021     sys.exit(0)
0022 
0023 runtype = argvs[1]
0024 print('You selected', runtype)
0025 
0026 
0027 tlabel = 'Z #rightarrow #tau#tau'
0028 xlabel = 'gen. tau p_{T}^{vis} (GeV)'
0029 
0030 if runtype == 'QCD':
0031     tlabel = 'QCD'
0032     xlabel = 'jet p_{T} (GeV)'
0033 elif runtype == 'ZEE':
0034     tlabel = 'Z #rightarrow ee'
0035     xlabel = 'electron p_{T} (GeV)'
0036 elif runtype == 'ZMM':
0037     tlabel = 'Z #rightarrow #mu#mu'
0038     xlabel = 'muon p_{T} (GeV)'
0039 
0040 
0041 
0042 
0043 def ensureDir(directory):
0044     if not os.path.exists(directory):
0045         os.makedirs(directory)
0046 
0047 def save(canvas, name):
0048     ensureDir('compare_' + runtype)
0049     canvas.SaveAs(name.replace(' ','').replace('&&','')+'.pdf')
0050     canvas.SaveAs(name.replace(' ','').replace('&&','')+'.gif')
0051 
0052 
0053 def LegendSettings(leg, ncolumn):
0054     leg.SetNColumns(ncolumn)
0055     leg.SetBorderSize(0)
0056     leg.SetFillColor(10)
0057     leg.SetLineColor(0)
0058     leg.SetFillStyle(0)
0059     leg.SetTextSize(0.03)
0060     leg.SetTextFont(42)
0061 
0062 
0063 
0064 def makeCompareVars(tree, var, sel, leglist, nbin, xmin, xmax, xtitle, ytitle, scale, name):
0065    
0066 #    print leglist
0067 
0068     c = TCanvas()
0069 
0070     hists = []
0071     col = [1,2,4,8,6]
0072     ymax = 0
0073 
0074     for ii, isel in enumerate(sel):
0075         hist = TH1F('h_' + str(ii), 'h_' + str(ii), nbin, xmin, xmax)
0076         hist.GetXaxis().SetTitle(xtitle)
0077         hist.GetYaxis().SetTitle(ytitle)
0078         hist.GetYaxis().SetNdivisions(507)
0079         hist.SetLineColor(col[ii])
0080         hist.SetLineWidth(len(sel)+1-ii)
0081         hist.SetLineStyle(1)
0082         hist.SetMarkerSize(0)
0083         hist.SetMinimum(0)
0084         hist.Sumw2()
0085 
0086 #        print hist.GetName(), var, isel
0087         tree.Project(hist.GetName(), var, isel)
0088         hist.Scale(1./hist.GetEntries())
0089 
0090         if ymax < hist.GetMaximum():
0091             ymax = hist.GetMaximum()
0092 
0093         hists.append(hist)
0094 
0095 
0096     leg = TLegend(0.6,0.65,0.91,0.9)
0097     LegendSettings(leg,1)
0098 
0099     for ii, ihist in enumerate(hists):
0100         ihist.SetMaximum(ymax*1.2)
0101         ihist.SetMinimum(0.)
0102 
0103         if ii ==0:
0104             ihist.Draw('h')
0105         else:
0106             ihist.Draw('hsame')
0107 
0108         if leglist[ii] != 'None':
0109             leg.AddEntry(ihist, leglist[ii], "l")
0110 
0111 
0112     if leglist[0]!='None':
0113         leg.Draw()
0114 
0115 
0116 #    save(c, 'compare_' + runtype + '/compare_' + name)
0117 
0118 
0119 
0120 
0121     
0122 def overlay(hists, ytitle, header, addon):
0123 
0124     print('number of histograms = ', len(hists))
0125 
0126     canvas = TCanvas()
0127     leg = TLegend(0.2,0.7,0.5,0.9)
0128     LegendSettings(leg, 1)
0129 
0130     col = [1,2,4,6,8,9,12]
0131 
0132     ymax = -1
0133     ymin = 100
0134 
0135     for ii, hist in enumerate(hists):
0136         hist.GetYaxis().SetTitle('efficiency')
0137         hist.SetLineColor(col[ii])
0138         hist.SetMarkerColor(col[ii])
0139         hist.SetLineWidth(2)
0140         hist.SetMarkerSize(1)
0141 
0142 
0143         for ip in range(hist.GetN()):
0144             x = Double(-1)
0145             y = Double(-1)
0146             hist.GetPoint(ip, x, y)
0147 
0148             if ymin > y:
0149                 ymin = y
0150             if ymax < y:
0151                 ymax = y
0152 
0153 #
0154 #        if ymax < hist.GetMaximum():
0155 #            ymax = hist.GetMaximum()
0156 #        if ymin > hist.GetMinimum():
0157 #            ymin = hist.GetMinimum()
0158 
0159         if ii==0:
0160             hist.Draw("Azp")
0161         else:
0162             
0163             hist.Draw("pzsame")
0164  
0165 #        print hist.GetName(), hist.GetTitle()
0166         legname = hist.GetName()
0167 
0168         leg.AddEntry(hist, legname, 'lep')
0169 
0170 
0171     for hist in hists:
0172         hist.SetMaximum(ymax*2)
0173 #        hist.SetMinimum(ymin*0.5)
0174 
0175     leg.Draw()
0176 
0177     tex = TLatex( hists[-1].GetXaxis().GetXmin() + 0.01*(hists[-1].GetXaxis().GetXmax() - hists[-1].GetXaxis().GetXmin()), ymax*2.1, addon.replace('tau_',''))
0178 
0179     tex.SetTextFont(42)
0180     tex.SetTextSize(0.03)
0181     tex.Draw()
0182 
0183     tex2 = TLatex( hists[-1].GetXaxis().GetXmin() + 0.87*(hists[-1].GetXaxis().GetXmax() - hists[-1].GetXaxis().GetXmin()), ymax*2.1, tlabel)
0184 
0185     tex2.SetTextFont(42)
0186     tex2.SetTextSize(0.03)
0187     tex2.Draw()
0188 
0189 
0190 
0191     save(canvas, 'compare_' + runtype + '/' + header)
0192 
0193 
0194 
0195 def hoverlay(hists, xtitle, ytitle, name):
0196    
0197     c = TCanvas()
0198 
0199     ymax = 0
0200     for hist in hists:
0201         if ymax < hist.GetMaximum():
0202             ymax = hist.GetMaximum()
0203 
0204 
0205     leg = TLegend(0.6,0.65,0.91,0.9)
0206     LegendSettings(leg,1)
0207 
0208     for ii, ihist in enumerate(hists):
0209         ihist.SetMaximum(ymax*1.2)
0210         ihist.SetMinimum(0.)
0211         ihist.SetMarkerSize(0.)
0212         ihist.GetXaxis().SetTitle(xtitle)
0213         ihist.GetYaxis().SetTitle(ytitle)
0214 
0215         if ii ==0:
0216             ihist.Draw('h')
0217         else:
0218             ihist.Draw('hsame')
0219 
0220         leg.AddEntry(ihist, ihist.GetName(), "l")
0221 
0222 
0223     leg.Draw()
0224 
0225 
0226     save(c, 'compare_' + runtype + '/hist_' + name)
0227 
0228 
0229 
0230 
0231 def makeEffPlotsVars(tree, varx, vary, sel, nbinx, xmin, xmax, nbiny, ymin, ymax, xtitle, ytitle, leglabel = None, header='', addon='', option='pt', marker=20):
0232    
0233     binning = [20,30,40,50,60,70,80,100,150,200]
0234 
0235     c = TCanvas()
0236 
0237     if option=='pt':
0238         _hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, len(binning)-1, array('d',binning))
0239         _ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, len(binning)-1, array('d',binning))
0240     elif option=='eta':
0241         _hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, nbinx, xmin, xmax)
0242         _ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, nbinx, xmin, xmax)
0243     elif option=='nvtx':
0244         _hist_ = TH1F('h_effp_' + addon, 'h_effp' + addon, len(vbinning)-1, array('d',vbinning))
0245         _ahist_ = TH1F('ah_effp_' + addon, 'ah_effp' + addon, len(vbinning)-1, array('d',vbinning))
0246 
0247 
0248     tree.Draw(varx + ' >> ' + _hist_.GetName(), sel)
0249     tree.Draw(varx + ' >> ' + _ahist_.GetName(), sel + ' && ' + vary)
0250     
0251     g_efficiency = TGraphAsymmErrors()
0252     g_efficiency.BayesDivide(_ahist_, _hist_)
0253     g_efficiency.GetXaxis().SetTitle(xtitle)
0254     g_efficiency.GetYaxis().SetTitle('efficiency')
0255     g_efficiency.GetYaxis().SetNdivisions(507)
0256     g_efficiency.SetLineWidth(3)
0257     g_efficiency.SetName(header)    
0258     g_efficiency.SetMinimum(0.)
0259     g_efficiency.GetYaxis().SetTitleOffset(1.3)
0260     g_efficiency.SetMarkerStyle(marker)
0261     g_efficiency.SetMarkerSize(1)
0262     g_efficiency.Draw('ap')
0263 
0264 #    save(c, 'plots/' + addon)
0265 
0266     return copy.deepcopy(g_efficiency)
0267 
0268 
0269 
0270 
0271 
0272 if __name__ == '__main__':
0273 
0274     
0275     vardict = {
0276         'againstMuonLoose3':{'var':'tau_againstMuonLoose3 > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'againstMuonLoose3'},
0277         'againstMuonTight3':{'var':'tau_againstMuonTight3 > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'againstMuonTight3'},
0278 
0279         'againstElectronVLooseMVA5':{'var':'tau_againstElectronVLooseMVA5 > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'againstElectronVLooseMVA5'},
0280         'againstElectronLooseMVA5':{'var':'tau_againstElectronLooseMVA5 > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'againstElectronLooseMVA5'},
0281         'againstElectronMediumMVA5':{'var':'tau_againstElectronMediumMVA5 > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'againstElectronMediumMVA5'},
0282 
0283         'againstElectronVLooseMVA6':{'var':'tau_againstElectronVLooseMVA6 > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'againstElectronVLooseMVA6'},
0284         'againstElectronLooseMVA6':{'var':'tau_againstElectronLooseMVA6 > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'againstElectronLooseMVA6'},
0285         'againstElectronMediumMVA6':{'var':'tau_againstElectronMediumMVA6 > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'againstElectronMediumMVA6'},
0286 
0287         'byLoosePileupWeightedIsolation3Hits':{'var':'tau_byLoosePileupWeightedIsolation3Hits > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byLoosePileupWeightedIsolation3Hits'},
0288         'byMediumPileupWeightedIsolation3Hits':{'var':'tau_byMediumPileupWeightedIsolation3Hits > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byMediumPileupWeightedIsolation3Hits'},
0289         'byTightPileupWeightedIsolation3Hits':{'var':'tau_byTightPileupWeightedIsolation3Hits > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byTightPileupWeightedIsolation3Hits'},
0290 
0291         'byLooseCombinedIsolationDeltaBetaCorr3Hits':{'var':'tau_byLooseCombinedIsolationDeltaBetaCorr3Hits > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byLooseCombinedIsolationDeltaBetaCorr3Hits'},
0292         'byMediumCombinedIsolationDeltaBetaCorr3Hits':{'var':'tau_byMediumCombinedIsolationDeltaBetaCorr3Hits > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byMediumCombinedIsolationDeltaBetaCorr3Hits'},
0293         'byTightCombinedIsolationDeltaBetaCorr3Hits':{'var':'tau_byTightCombinedIsolationDeltaBetaCorr3Hits > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byTightCombinedIsolationDeltaBetaCorr3Hits'},
0294 
0295         'byLooseIsolationMVA3oldDMwLT':{'var':'tau_byLooseIsolationMVA3oldDMwLT > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byLooseIsolationMVA3oldDMwLT'},
0296         'byMediumIsolationMVA3oldDMwLT':{'var':'tau_byMediumIsolationMVA3oldDMwLT > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byMediumIsolationMVA3oldDMwLT'},
0297         'byTightIsolationMVA3oldDMwLT':{'var':'tau_byTightIsolationMVA3oldDMwLT > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byTightIsolationMVA3oldDMwLT'},
0298 
0299         'byLooseIsolationMVArun2v1DBoldDMwLT':{'var':'tau_byLooseIsolationMVArun2v1DBoldDMwLT > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byLooseIsolationMVArun2v1DBoldDMwLT'},
0300         'byMediumIsolationMVArun2v1DBoldDMwLT':{'var':'tau_byMediumIsolationMVArun2v1DBoldDMwLT > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byMediumIsolationMVArun2v1DBoldDMwLT'},
0301         'byTightIsolationMVArun2v1DBoldDMwLT':{'var':'tau_byTightIsolationMVArun2v1DBoldDMwLT > 0.5 && tau_decayModeFindingOldDMs > 0.5', 'nbin':2, 'min':-0.5, 'max':1.5, 'title':'byTightIsolationMVArun2v1DBoldDMwLT'},
0302 
0303         }
0304 
0305 
0306     reco_cut = 'tau_pt > 20 && abs(tau_eta) < 2.3'
0307     loose_id = 'tau_decayModeFindingOldDMs > 0.5 && tau_byLooseCombinedIsolationDeltaBetaCorr3Hits > 0.5'
0308 
0309     sampledict = {
0310 #        '7_6_0_pre7':{'file':'Myroot_7_6_0_pre7_' + runtype + '.root', 'col':2, 'marker':20, 'width':3},
0311 #        '7_6_0':{'file':'Myroot_7_6_0_' + runtype + '.root', 'col':1, 'marker':21, 'width':4},
0312 #        '7_6_1':{'file':'Myroot_7_6_1_' + runtype + '.root', 'col':4, 'marker':22, 'width':2},
0313         '7_6_1_v3':{'file':'Myroot_7_6_1_v3_' + runtype + '.root', 'col':3, 'marker':23, 'width':1},
0314         }
0315 
0316     for hname, hdict in sorted(vardict.items()):        
0317 
0318         hists = []
0319 
0320         for rel, rdict in sorted(sampledict.items()):
0321 
0322             if rel.find('7_6_1')==-1 and (hname.find('MVA6')!=-1 or hname.find('MVArun2')!=-1): continue
0323             
0324             tfile = TFile(rdict['file'])
0325             tree = tfile.Get('per_tau')
0326 
0327             num_sel = reco_cut
0328             den_sel = '1'
0329 
0330             if hname.find('against')!=-1: 
0331                 num_sel = '1'
0332                 den_sel = reco_cut + ' && ' + loose_id
0333 
0334             hists.append(makeEffPlotsVars(tree, 'tau_genpt', num_sel + '&&' + hdict['var'], den_sel, 30, 0, 300, hdict['nbin'], hdict['min'], hdict['max'], xlabel, hdict['title'], '', rel, rel, 'pt', rdict['marker']))
0335 
0336 
0337 #            if rel=='7_6_1' and (hname.find('MVA5')!=-1 or hname.find('IsolationMVA3')!=-1):
0338 #                xvar = hdict['var'].replace('IsolationMVA3', 'IsolationMVArun2v1DB').replace('MVA5','MVA6')
0339 #                print 'adding', xvar
0340 #
0341 #                hists.append(makeEffPlotsVars(tree, 'tau_genpt', num_sel + '&&' + xvar, den_sel, 30, 0, 300, hdict['nbin'], hdict['min'], hdict['max'], xlabel, hdict['title'], '', rel + '(' + xvar.replace('tau_','').replace('> 0.5','').replace(' && decayModeFindingOldDMs ','') + ')', rel, 'pt', rdict['marker']))
0342 
0343 
0344 
0345         overlay(hists, hname, hname, hdict['title'])
0346 
0347 
0348 
0349 
0350     hvardict = {
0351         'tau_dm':{'var':'tau_dm', 'nbin':12, 'min':0., 'max':12, 'title':'decay Mode', 'sel':'1'},
0352         'tau_mass_1prong':{'var':'tau_mass', 'nbin':30, 'min':0., 'max':2.5, 'title':'Tau mass, 1prong', 'sel':'tau_dm==0'},
0353         'tau_mass_1prongp0':{'var':'tau_mass', 'nbin':30, 'min':0., 'max':2.5, 'title':'Tau mass, 1prong+#pi^{0}', 'sel':'tau_dm==1'},
0354         'tau_mass_2prong':{'var':'tau_mass', 'nbin':30, 'min':0., 'max':2.5, 'title':'Tau mass, 2prong', 'sel':'(tau_dm==5 || tau_dm==6)'},
0355         'tau_mass_3prong':{'var':'tau_mass', 'nbin':30, 'min':0., 'max':2.5, 'title':'Tau mass, 3prong (+#pi^{0})', 'sel':'(tau_dm==10 || tau_dm==11)'},
0356         'pt_resolution_1prong':{'var':'(tau_genpt-tau_pt)/(tau_genpt)', 'nbin':30, 'min':-1., 'max':1., 'title':'pT resolution, 1prong', 'sel':'tau_dm==0'},
0357         'pt_resolution_1prongp0':{'var':'(tau_genpt-tau_pt)/(tau_genpt)', 'nbin':30, 'min':-1., 'max':1., 'title':'pT resolution, 1prong+#pi^{0}', 'sel':'tau_dm==1'},
0358         'pt_resolution_2prong':{'var':'(tau_genpt-tau_pt)/(tau_genpt)', 'nbin':30, 'min':-1., 'max':1., 'title':'pT resolution, 2prong', 'sel':'(tau_dm==5 || tau_dm==6)'},
0359         'pt_resolution_3prong':{'var':'(tau_genpt-tau_pt)/(tau_genpt)', 'nbin':30, 'min':-1., 'max':1., 'title':'pT resolution, 3prong (+#pi^{0})', 'sel':'(tau_dm==10 || tau_dm==11)'},
0360         }
0361 
0362 
0363     for hname, hdict in sorted(hvardict.items()):        
0364 
0365         hists = []
0366 
0367         if runtype != 'ZTT' and hname.find('pt_resolution')!=-1: continue
0368         
0369 
0370         for rel, rdict in sorted(sampledict.items()):
0371 
0372             tfile = TFile(rdict['file'])
0373             tree = tfile.Get('per_tau')
0374 
0375             hist = TH1F('h_' + hname + '_' + rel, 'h_' + hname + '_' + rel, hdict['nbin'], hdict['min'], hdict['max'])
0376             hist.GetYaxis().SetNdivisions(507)
0377             hist.SetLineColor(rdict['col'])
0378             hist.SetLineWidth(rdict['width'])
0379             hist.SetMinimum(0)
0380             hist.SetName(rel)
0381             hist.Sumw2()
0382 
0383             tree.Project(hist.GetName(), hdict['var'], hdict['sel'])        
0384             hist.Scale(1./hist.GetEntries())
0385 
0386             hists.append(copy.deepcopy(hist))
0387 
0388         hoverlay(hists, hdict['title'], 'a.u.', hname)