def plotSelection(histoList, name, log=False, xlabel='', normed=False):
plot = Canvas.Canvas(name, 'png', 0.15, 0.77, 0.9, 0.88, 1)
sel = []
ymax = 0.0
for histo in histoList:
_f = r.TFile(histo[0])
aux_h = copy.deepcopy(_f.Get(histo[1]))
if normed: aux_h.Scale(1 / aux_h.Integral())
if aux_h.GetMaximum() > ymax: ymax = aux_h.GetMaximum()
for h, histo in enumerate(histoList):
_f = r.TFile(histo[0])
_h = copy.deepcopy(_f.Get(histo[1]))
_f.Close()
_h.GetXaxis().SetTitle(xlabel)
if normed:
_h.Scale(1 / _h.Integral())
_h.GetYaxis().SetTitle('Event density')
else:
_h.GetYaxis().SetTitle('Events')
if log: _h.SetMaximum(10.0 * ymax)
else: _h.SetMaximum(1.3 * ymax)
option = 'HIST'
if h != 0: option = 'HIST, SAME'
plot.addHisto(_h, option, histo[3], 'l', histo[2], 1, h)
plot.save(1, 0, log, '', '', outputDir='plots')
def makeAgreementTest(lumi, histo1, histo2, ylog, label1, label2, labela, labelb, name, isData, xlabel = '', outtag = '', yshift = 0.0, LLlabel = '', rebin = 0, rmin = 0.9, rmax = 1.1, maxY = False):
## Histogram tunning
if rebin:
histo1.Rebin(rebin)
histo2.Rebin(rebin)
histo1.SetTitle('; Collinearity |#Delta#Phi|; Dimuon pairs')
histo1.SetMaximum(1.45*histo1.GetMaximum())
histo1.SetMinimum(0.0)
histo1.SetMarkerStyle(20)
histo2.SetMarkerStyle(24)
plot = Canvas.Canvas(name, 'png,pdf', 0.15, 0.66, 0.45, 0.78, 1)
plot.addHisto(histo1, 'P', label1, 'p', r.kBlack, 1, 0)
plot.addHisto(histo2, 'P,SAME', label2, 'p', r.kBlue, 1, 1)
plot.addLatex(0.16, 0.8, labela, font = 42, size = 0.037, align = 11)
plot.addLatex(0.88, 0.88, labelb, font = 42, size = 0.045, align = 31)
plot.saveRatio(1, 0, 0, '', histo1, histo2, r_ymin = rmin, r_ymax = rmax, label = 'Ratio', xlog = False, outputDir = WORKPATH + 'SymmetryResults/', maxYnumbers = maxY)
print('>>>>>>>>>> KOLMOGOROV test for ' + labela)
print('>>>>>>>>>> ' + str(histo1.KolmogorovTest(histo2)))
print('>>>>>>>>>> Chi2 test for ' + labela)
print('>>>>>>>>>> ' + str(histo1.Chi2Test(histo2, "WWP")))
return
def makePlot(lumi, lumi_str, treeDA, treeMC, var, name, nbin, xmin, xmax, theCut, cuts, labelx, logx, protection):
MC = treeMC.getTH1F(lumi, "hMC_%s"%(name), var, nbin, xmin, xmax, theCut, '', labelx)
MCS = treeMC.getStack(lumi, "hMCS_%s"%(name), var, nbin, xmin, xmax, theCut, "", labelx)
DATA = treeDA.getTH1F(lumi, "hDATA_%s"%(name), var, nbin, xmin, xmax, theCut, '', labelx)
maxValmc = MC.GetMaximum() #GetBinContent(MC.GetMaximumBin())
maxValdata = DATA.GetMaximum() #GetBinContent(DATA.GetMaximumBin())
maxVal = max(maxValmc, maxValdata)
if not logx:
MC.GetYaxis().SetRangeUser(0.1, 1.3*maxVal)
MCS.SetMaximum(1.3*maxVal)
DATA.GetYaxis().SetRangeUser(0.1, 1.3*maxVal)
else:
MC.GetYaxis().SetRangeUser(0.1, 2.0*maxVal)
MCS.SetMaximum(2.0*maxVal)
DATA.GetYaxis().SetRangeUser(0.1, 2.0*maxVal)
print "DATA integral ", DATA.Integral()
print "MC integral ", MC.Integral()
SetOwnership(MC, 0 ) # 0 = release (not keep), 1 = keep
SetOwnership(MCS, 0 ) # 0 = release (not keep), 1 = keep
SetOwnership(DATA, 0 ) # 0 = release (not keep), 1 = keep
plot = Canvas.Canvas('DataMC/%s/plot_%s'%(lumi_str,name), 'png,pdf,root', 0.65, 0.6, 0.85, 0.9)
plot.addStack(MCS, "HIST", 1, 1)
plot.addHisto(DATA, "E1,SAME", "Data", "P", r.kBlack, 1, 0)
plot.saveRatio(1, 1, logx, lumi, DATA, MC)
def makeValidation(lumi, treeMC, var, name, xlabel, logx, LLlabel=False):
hSR_MC = treeMC.getLoopTH1F('histMC_SR_' + name, 'SR_' + var, xlabel)
hCR_MC = treeMC.getLoopTH1F('histMC_CR_' + name, 'CR_' + var, xlabel)
hSR_MC.SetMarkerStyle(20)
hSR_MC.SetMarkerSize(0.8)
hCR_MC.SetMarkerStyle(20)
hCR_MC.SetMarkerSize(0.8)
luminosity = lumi
### Get maximum
maxValCR = hCR_MC.GetMaximum()
maxValSR = hSR_MC.GetMaximum()
maxVal = max(maxValCR, maxValSR)
### Set Maximum
if not logx:
hCR_MC.SetMaximum(1.3 * maxVal)
hSR_MC.SetMaximum(1.3 * maxVal)
else:
hCR_MC.SetMaximum(100.0 * maxVal)
hSR_MC.SetMaximum(100.0 * maxVal)
hCR_MC.SetMinimum(0.1)
hSR_MC.SetMaximum(0.1)
### Canvas object
plot = Canvas.Canvas('hist_' + name, 'png', 0.7, 0.79, 0.9, 0.87)
plot.addHisto(hCR_MC, 'P', 'Control region', 'l', r.kBlue, 1, 0)
plot.addHisto(hSR_MC, 'P, SAME', 'Signal region', 'l', r.kRed, 1, 0)
### Dilepton banner
if LLlabel == 'EE':
plot.addLatex(0.17, 0.84, 'e^{+}e^{-} channel')
if LLlabel == 'MM':
plot.addLatex(0.17, 0.84, '#mu^{+}#mu^{-} channel')
### Save it
#plot.save(1, 0, logx, luminosity, '')
plot.saveRatio(1,
0,
logx,
luminosity,
hSR_MC,
hCR_MC,
label="SR/CR",
outputDir='closurePlots/')
def makeDataMCPlot(lumi,
hname,
hnameDATA,
ylog,
treeMC,
treeDATA,
inputdir,
treeSI=False,
xlabel='',
outtag='',
yshift=100.0,
LLlabel='',
DATAlabel='',
extralabel='',
xlog=False):
luminosity = lumi
hSS = treeDATA.getLoopTH1F(inputdir, hnameDATA)
hOS = treeMC.getLoopStack(inputdir, hname)
# hSS tunning:
hSS.SetLineColor(r.kBlack)
hSS.SetFillColor(r.kMagenta + 1)
hSS.SetTitle('QCD, W+jets')
#print(hSS.GetEntries())
### Combine SS + OS contributions:
hBKG = r.THStack('hBKG_%s' % (hname), ';' + hOS.GetXaxis().GetTitle() +
';' + hOS.GetYaxis().GetTitle()) # Background stacked
hBKGtotal = copy.deepcopy(hSS) # Background total (for ratio)
hBKG.Add(copy.deepcopy(hSS))
for _h in hOS.GetHists():
_h.Scale(lumi / 35.87)
hBKG.Add(copy.deepcopy(_h))
hBKGtotal.Add(copy.deepcopy(_h))
nBCK = len(hOS.GetHists()) + 1
hBKGtotal.SetMarkerStyle(20) # Auxiliar to save the ratio correctly
### Signal histograms
s_histos = []
if treeSI:
hSIS = treeSI.getLoopStack(inputdir, hname)
for _i, _h in enumerate(hSIS.GetHists()):
s_histos.append(copy.deepcopy(_h))
### Data histogram
hDATA = treeDATA.getLoopTH1F(inputdir, hname)
hDATA.SetMarkerStyle(20)
hDATA.SetMarkerSize(0.8)
### Get maximum
maxValMC = hBKGtotal.GetMaximum()
maxValSI = 0 if not treeSI else max(
[s_histos[i].GetMaximum() for i in range(0, len(s_histos))])
maxValDATA = 0 if not treeDATA else hDATA.GetMaximum()
maxVal = max([maxValMC, maxValSI, maxValDATA])
### Set Maximum
if not ylog:
hBKG.SetMaximum(1.3 * maxVal)
hBKG.SetMinimum(0.0)
hBKGtotal.SetMaximum(1.3 * maxVal)
hBKGtotal.SetMinimum(0.0)
if treeSI:
for _h in s_histos:
if not yshift:
_h.SetMaximum(1.3 * maxVal)
else:
_h.SetMaximum(yshift * maxVal)
_h.SetMinimum(0.0)
if treeDATA:
if not yshift:
hDATA.SetMaximum(1.3 * maxVal)
else:
hDATA.SetMaximum(yshift * maxVal)
hDATA.SetMinimum(0.0)
else:
if not yshift:
hBKG.SetMaximum(10.0 * maxVal)
else:
hBKG.SetMaximum(yshift * maxVal)
hBKG.SetMinimum(0.1)
if not yshift:
hBKGtotal.SetMaximum(10.0 * maxVal)
else:
hBKGtotal.SetMaximum(yshift * maxVal)
hBKGtotal.SetMinimum(0.1)
if treeSI:
for _h in s_histos:
if not yshift:
_h.SetMaximum(10.0 * maxVal)
else:
_h.SetMaximum(yshift * maxVal)
_h.SetMinimum(0.1)
if treeDATA:
if not yshift:
hDATA.SetMaximum(10.0 * maxVal)
else:
hDATA.SetMaximum(yshift * maxVal)
hDATA.SetMinimum(0.1)
### -> Canvas object
if treeDATA:
plot = Canvas.Canvas(hname, 'png', 0.6, 0.65, 0.85, 0.9, 1)
else:
plot = Canvas.Canvas(hname, 'png', 0.6, 0.79, 0.9, 0.9, 1)
### Add background:
plot.addStack(hBKG, 'HIST', 1, 0) # Background
### Add signals:
if treeSI:
for i, _h in enumerate(s_histos):
_h.SetLineWidth(2) # provisional
plot.addHisto(_h, 'HIST, SAME', _h.GetTitle(), 'l',
_h.GetFillColor(), 1, i + nBCK) # Signal
### Add DATA:
if treeDATA:
plot.addHisto(hDATA, 'P, SAME', 'Data', 'p', r.kBlack, 1,
nBCK + len(s_histos))
### DATAlabel banner:
if DATAlabel:
plot.addLatex(0.17, 0.85, DATAlabel, font=62)
### Channel banner:
if LLlabel == 'EE':
plot.addLatex(0.17, 0.81, 'e^{+}e^{-} channel', font=42)
if LLlabel == 'MM':
plot.addLatex(0.17, 0.81, '#mu^{+}#mu^{-} channel', font=42)
### Extralabel:
if extralabel:
plot.addLatex(0.17, 0.76, extralabel, font=42)
### Save it
outdir = os.path.dirname(os.path.abspath(
__main__.__file__)) + '/Plots_' + outtag + '/'
if treeDATA:
plot.saveRatio(1,
0,
ylog,
luminosity,
hDATA,
hBKGtotal,
label="Data/BKG",
outputDir=outdir,
xlog=xlog)
else:
plot.save(1, 0, ylog, luminosity, '', outputDir=outdir)
print('WORKPATH: ' + WORKPATH)
r.setTDRStyle()
###########################
#### Parser object ####
###########################
parser = optparse.OptionParser(usage='usage: %prog [opts] FilenameWithSamples', version='%prog 1.0')
parser.add_option('-t', '--tag', action='store', type=str, dest='tag', default='', help='Output tag')
(opts, args) = parser.parse_args()
histo = getObject('Vertex-results/th1f_TT2018.root', 'EE_vxIvyI')
histo.GetZaxis().SetLabelSize(0.03)
histo.GetXaxis().SetTitle('Dielectron vertex x_{PV} (cm)')
histo.GetYaxis().SetTitle('Dielectron vertex y_{PV} (cm)')
plot = Canvas.Canvas('hist_EE_vxIvyI', 'png,pdf', 0.3, 0.75, 0.89, 0.8, 1)
plot.addHisto(histo, 'COLZ', '', 'f', '', 1, 0)
plot.addLatex(0.9, 0.93, 't#bar{t} (dilep.) 2018 UL', size = 0.03, align = 31)
plot.save(0, 0, 0, '', '', outputDir = WORKPATH + 'harvested/', inProgress = False, maxYnumbers = 2)
histo = getObject('Vertex-results/th1f_TT2018.root', 'EE_vx1vy1')
histo.GetZaxis().SetLabelSize(0.03)
histo.GetXaxis().SetTitle('Dielectron vertex x_{1} (cm)')
histo.GetYaxis().SetTitle('Dielectron vertex y_{1} (cm)')
plot = Canvas.Canvas('hist_EE_vx1vy1', 'png,pdf', 0.3, 0.75, 0.89, 0.8, 1)
plot.addHisto(histo, 'COLZ', '', 'f', '', 1, 0)
plot.addLatex(0.9, 0.93, 't#bar{t} (dilep.) 2018 UL', size = 0.03, align = 31)
plot.save(0, 0, 0, '', '', outputDir = WORKPATH + 'harvested/', inProgress = False, maxYnumbers = 3)
histo = getObject('Vertex-results/th1f_TT2018.root', 'EE_vx2vy2')
histo.GetZaxis().SetLabelSize(0.03)
hist_1000_350_1.SetLineWidth(2)
hist_1000_350_10 = getObject('Results/th1f_signals.root',
'hist_E_relTrkIso_log_HSS_1000_350_10_2016')
hist_1000_350_10.Scale(1. / hist_1000_350_10.Integral())
hist_1000_350_10.SetLineColor(r.kBlue - 4)
hist_1000_350_10.SetLineWidth(2)
hist_1000_350_100 = getObject(
'Results/th1f_signals.root',
'hist_E_relTrkIso_log_HSS_1000_350_100_2016')
hist_1000_350_100.Scale(1. / hist_1000_350_100.Integral())
hist_1000_350_100.SetLineColor(r.kBlue - 7)
hist_1000_350_100.SetLineWidth(2)
plot = Canvas.Canvas('hist_electron_relTrkIso',
'png,pdf',
0.17,
0.55,
0.45,
0.89,
1,
lsize=0.028)
plot.addHisto(hist_bkg, 'HIST', 'Background: Hadronic WZ, WW decays', 'f',
'', 1, 0)
plot.addHisto(hist_125_50_1, 'HIST,SAME',
'm_{H} = 125 GeV, m_{S} = 50 GeV, c#tau = 1 mm', 'l', '', 1,
1)
plot.addHisto(hist_125_50_10, 'HIST,SAME',
'm_{H} = 125 GeV, m_{S} = 50 GeV, c#tau = 10 mm', 'l', '', 1,
2)
plot.addHisto(hist_125_50_100, 'HIST,SAME',
'm_{H} = 125 GeV, m_{S} = 50 GeV, c#tau = 100 mm', 'l', '',
1, 3)
plot.addHisto(hist_400_50_1, 'HIST,SAME',
def makeSymmetryTest(lumi,
hname,
ylog,
tree,
inputdir,
label,
name,
isData,
xlabel='',
outtag='',
yshift=0.0,
LLlabel='',
rebin=0,
maxY=False):
## Get the histogram
if type(hname) != list:
hist = tree.getLoopTH1F(inputdir, hname)
else:
hist = tree.getLoopTH1F(inputdir, hname[0])
for n in range(1, len(hname)):
hist.Add(tree.getLoopTH1F(inputdir, hname[n]))
nbins = int(hist.GetNbinsX() / 2.0)
xmin = 0.0 # default
xmax = 3.14 / 2.0 # default
## Define forward and inverted backward histograms
forward = r.TH1F('Forward', ';;Event yield', nbins, xmin, xmax)
invBackward = r.TH1F('InvBackward', ';;Event yield', nbins, xmin, xmax)
for n in range(1, nbins + 1):
forward.SetBinContent(n, hist.GetBinContent(n))
invBackward.SetBinContent(n, hist.GetBinContent(2 * nbins + 1 - n))
forward.SetBinError(n, hist.GetBinError(n))
invBackward.SetBinError(n, hist.GetBinError(2 * nbins + 1 - n))
## Histogram tunning
if rebin:
forward.Rebin(rebin)
invBackward.Rebin(rebin)
forward.SetMaximum(1.45 * forward.GetMaximum())
forward.SetMinimum(0.0)
forward.SetMarkerStyle(24)
invBackward.SetMarkerStyle(24)
plot = Canvas.Canvas(name, 'png,pdf', 0.15, 0.72, 0.45, 0.82, 1)
plot.addHisto(forward, 'P',
'Forward: |#Delta#Phi|, for |#Delta#Phi| #in [0, #pi/2]',
'p', r.kGreen + 2, 1, 0)
plot.addHisto(
invBackward, 'P,SAME',
'Backward inverted: |#Delta#Phi| - #pi/2, for |#Delta#Phi| #in [#pi/2, #pi]',
'p', r.kRed + 2, 1, 1)
plot.addLatex(0.18, 0.85, label, font=62, size=0.035, align=11)
plot.saveRatio(1,
0,
0,
'',
forward,
invBackward,
r_ymin=0.8,
r_ymax=1.2,
label='Forward / Inv. Backward',
xlog=False,
outputDir=WORKPATH + 'SymmetryResults/',
maxYnumbers=maxY)
print('>>>>>>>>>> KOLMOGOROV test for ' + label)
print('>>>>>>>>>> ' + str(forward.KolmogorovTest(invBackward)))
print('>>>>>>>>>> Chi2 test for ' + label)
print('>>>>>>>>>> ' + str(forward.Chi2Test(invBackward, "WWP")))
return
def makeSignalBackgroundPlot(hname,
ylog,
treeMC,
treeDATA,
treeSI,
inputdir,
xlabel='',
outtag='',
yshift=0.0,
LLlabel='',
DATAlabel='',
extralabel=''):
SShname = hname.split('__')[0] + '_SS'
hSS = treeDATA.getLoopTH1F(inputdir, SShname)
hBKG = treeMC.getLoopTH1F(inputdir, hname)
hBKG.Add(hSS)
### Signal histograms
s_histos = []
if treeSI:
hSIS = treeSI.getLoopStack(inputdir, hname)
for _i, _h in enumerate(hSIS.GetHists()):
_h.Scale(1.0 / _h.Integral())
s_histos.append(copy.deepcopy(_h))
hBKG.Scale(1.0 / hBKG.Integral())
### Get maximum
maxBKG = hBKG.GetMaximum()
maxSI = 0 if not treeSI else max(
[s_histos[i].GetMaximum() for i in range(0, len(s_histos))])
maxVal = max([maxBKG, maxSI])
### Set Maximum
if not ylog:
hBKG.SetMaximum(1.15 * maxVal)
hBKG.SetMinimum(0.0)
if treeSI:
for _h in s_histos:
if not yshift:
_h.SetMaximum(1.15 * maxVal)
else:
_h.SetMaximum(yshift * maxVal)
_h.SetMinimum(0.0)
else:
if not yshift:
hBKG.SetMaximum(10.0 * maxVal)
else:
hBKG.SetMaximum(yshift * maxVal)
hBKG.SetMinimum(0.1)
if treeSI:
for _h in s_histos:
if not yshift:
_h.SetMaximum(10.0 * maxVal)
else:
_h.SetMaximum(yshift * maxVal)
_h.SetMinimum(0.1)
### -> Canvas object
plot = Canvas.Canvas(hname, 'png', 0.55, 0.69, 0.87, 0.87, 1)
### Add background:
hBKG.SetLineColor(r.kAzure - 7)
hBKG.SetFillColorAlpha(r.kAzure, 0.5)
hBKG.GetXaxis().SetLabelSize(0.04)
hBKG.GetXaxis().SetTitleSize(0.045)
hBKG.GetYaxis().SetLabelSize(0.04)
hBKG.GetYaxis().SetTitleSize(0.045)
#hBKG.SetFillStyle(3016)
plot.addHisto(hBKG, 'HIST', 'Background', 'f', '', 1, 0) # Background
### Add signals:
if treeSI:
for i, _h in enumerate(s_histos):
_h.SetLineWidth(2) # provisional
plot.addHisto(_h, 'HIST, SAME', _h.GetTitle(), 'l',
_h.GetFillColor(), 1, i + 1) # Signal
plot.addLatex(0.17, 0.85, 'Normalized to 1', font=62)
### Channel banner:
if LLlabel == 'EE':
plot.addLatex(0.17, 0.81, 'e^{+}e^{-} channel', font=42)
if LLlabel == 'MM':
plot.addLatex(0.17, 0.81, '#mu^{+}#mu^{-} channel', font=42)
### Extralabel:
if extralabel:
plot.addLatex(0.17, 0.76, extralabel, font=42)
### Save it
outdir = os.path.dirname(os.path.abspath(
__main__.__file__)) + '/SIvsBKG_' + outtag + '/'
plot.save(1, 0, ylog, '', '', outputDir=outdir)
#####################################
#### Construct TEfficiencies ####
#####################################
BCK_nJet = getObject('jets_DY2M/th1fs.root', 'hist_nJet')
SI_nJet = getObject('jets_signal/th1fs.root', 'hist_nJet')
BCK_nJet.Scale(1.0/BCK_nJet.Integral())
SI_nJet.Scale(1.0/SI_nJet.Integral())
BCK_nJet.SetLineWidth(2)
SI_nJet.SetLineWidth(2)
BCK_nJet.SetTitle(';Number of Jets;Normalized event yield')
SI_nJet.SetTitle('')
nJet_comp = Canvas.Canvas("nJet_comp", 'png', 0.35, 0.77, 0.67, 0.86, 1)
nJet_comp.addHisto(BCK_nJet, 'HIST', 'Monte Carlo: Z/#gamma*#rightarrowl#bar{l} (DYJetsToLL_M-50)', 'l', r.kRed+2, 1, 0)
nJet_comp.addHisto(SI_nJet, 'HIST,SAME', 'Monte Carlo: H#rightarrowXX#rightarrow4l (All masses)', 'l', r.kBlue+2, 1, 1)
nJet_comp.saveRatio(1, 0, 1, '', SI_nJet, BCK_nJet, r_ymin=0, r_ymax=10, label ="Sig/Bkg", outputDir = 'harvested_jets/')
BCK_nGenJet = getObject('jets_DY2M/th1fs.root', 'hist_nGenJet')
SI_nGenJet = getObject('jets_signal/th1fs.root', 'hist_nGenJet')
BCK_nGenJet.Scale(1.0/BCK_nGenJet.Integral())
SI_nGenJet.Scale(1.0/SI_nGenJet.Integral())
BCK_nGenJet.SetLineWidth(2)
SI_nGenJet.SetLineWidth(2)
BCK_nGenJet.SetTitle(';Number of GenJets;Normalized event yield')
SI_nGenJet.SetTitle('')
nGenJet_comp = Canvas.Canvas("nGenJet_comp", 'png', 0.35, 0.77, 0.67, 0.86, 1)
nGenJet_comp.addHisto(BCK_nGenJet, 'HIST', 'Monte Carlo: Z/#gamma*#rightarrowl#bar{l} (DYJetsToLL_M-50)', 'l', r.kRed+2, 1, 0)
'fakes_DY2M_ptmin31_sigmapt0p3_chi10/th1fs.root',
'total_DG_numberOfValidHits_clone')
SI_fake_DG_nvalid_bin1 = getObject(
'fakes_signal_ptmin31_sigmapt0p3_chi10/th1fs.root',
'total_DG_numberOfValidHits_bin1_clone')
SI_fake_DG_nvalid_bin2 = getObject(
'fakes_signal_ptmin31_sigmapt0p3_chi10/th1fs.root',
'total_DG_numberOfValidHits_bin2_clone')
SI_fake_DG_nvalid_bin3 = getObject(
'fakes_signal_ptmin31_sigmapt0p3_chi10/th1fs.root',
'total_DG_numberOfValidHits_bin3_clone')
DY_fake_DG_nvalid.SetTitle(';Numbes of valid hits N_{Hits};Fake rate')
SI_fake_DG_nvalid_bin1.SetTitle(';;')
SI_fake_DG_nvalid_bin2.SetTitle(';;')
SI_fake_DG_nvalid_bin3.SetTitle(';;')
SI_fake_DG_nvalid_bins_ = Canvas.Canvas("SI_fake_DG_nvalid_bins", 'png',
0.31, 0.73, 0.53, 0.88, 1)
SI_fake_DG_nvalid_bins_.addRate(
DY_fake_DG_nvalid,
'AP',
'Monte Carlo: Z/#gamma*#rightarrowl#bar{l} (DYJetsToLL_M-50)',
'p',
r.kRed + 2,
True,
0,
marker=24)
SI_fake_DG_nvalid_bins_.addRate(
SI_fake_DG_nvalid_bin1,
'AP,SAME',
'H#rightarrowXX#rightarrow4l (All masses): |d_{xy}| < 1 cm',
'p',
r.kBlue - 9,
parser.add_option('-t', '--tag', action='store', type=str, dest='tag', default='', help='Output tag')
(opts, args) = parser.parse_args()
#####################################
#### Construct TEfficiencies ####
#####################################
# ptmin > 31, eta cut
SI_DG_sigmapt = getObject('resolutions_signal_ptmin31_sigma0p2sep/th1fs.root', 'hist_DG_sigmapt')
SI_DG_sigmapt.SetTitle(';DG #sigma_{p_{T}}/p_{T};DG yield')
SI_DG_sigmapt.SetLineWidth(1)
SI_DG_sigmapt.SetLineColor(r.kBlack)
SI_DG_sigmapt.SetFillColorAlpha(r.kBlack, 0.25)
SI_DG_sigmapt_ = Canvas.Canvas("SI_DG_sigmapt", 'png', 0.57, 0.75, 0.87, 0.9, 1)
SI_DG_sigmapt_.addHisto(SI_DG_sigmapt, 'HIST', '', 'l', '', 1, 0)
SI_DG_sigmapt_.addLatex(0.85, 0.85, 'Monte Carlo: H#rightarrowXX#rightarrow4l (All masses)', size = 0.03, align = 31)
SI_DG_sigmapt_.addLatex(0.85, 0.8, '|#eta^{DG} < 2.4|, p_{T}^{DG} > 10 GeV', size = 0.03, align = 31)
SI_DG_sigmapt_.save(0, 0, 1, '','', ymin = 0.1, ymax = 5000000, outputDir = WORKPATH + 'harvested_resols_'+opts.tag+'/')
SI_DG_sigmapt_pass = getObject('resolutions_signal_ptmin31_sigma0p3sep/th1fs.root', 'ptres_DG_sigmapt_bin1')
SI_DG_sigmapt_nopass = getObject('resolutions_signal_ptmin31_sigma0p3sep/th1fs.root', 'ptres_DG_sigmapt_bin2')
SI_DG_sigmapt_pass.SetTitle(';(p_{T}^{reco}-p_{T}^{gen})/p^{gen}_{T};DG yield')
SI_DG_sigmapt_nopass.SetTitle(';;')
SI_DG_sigmapt_pass.SetLineWidth(2)
SI_DG_sigmapt_nopass.SetLineWidth(2)
SI_DG_sigmapt_pass.SetLineColor(r.kGreen+2)
SI_DG_sigmapt_nopass.SetLineColor(r.kRed)
SI_DG_sigmapt_pass.Scale(1.0/SI_DG_sigmapt_pass.Integral())
SI_DG_sigmapt_nopass.Scale(1.0/SI_DG_sigmapt_nopass.Integral())
#####################################
#### Construct TEfficiencies ####
#####################################
# Differences in pt
BCK_unmatched_DG_pt = getObject('fakes_DY2M_nocut_eta2/th1fs.root', 'unmatched_DG_pt')
SI_unmatched_DG_pt = getObject('fakes_signal_nocut_eta2/th1fs.root', 'unmatched_DG_pt')
BCK_unmatched_DG_pt.Scale(1.0/BCK_unmatched_DG_pt.Integral())
SI_unmatched_DG_pt.Scale(1.0/SI_unmatched_DG_pt.Integral())
BCK_unmatched_DG_pt.SetLineWidth(2)
SI_unmatched_DG_pt.SetLineWidth(2)
BCK_unmatched_DG_pt.SetTitle(';Fake DG p_{T} (GeV);Normalized fake DG yield')
SI_unmatched_DG_pt.SetTitle('')
SI_DG_pt_ = Canvas.Canvas("unmatched_comp_pt", 'png', 0.35, 0.77, 0.67, 0.86, 1)
SI_DG_pt_.addHisto(BCK_unmatched_DG_pt, 'HIST', 'Monte Carlo: Z/#gamma*#rightarrowl#bar{l} (DYJetsToLL_M-50)', 'l', r.kRed+2, 1, 0)
SI_DG_pt_.addHisto(SI_unmatched_DG_pt, 'HIST,SAME', 'Monte Carlo: H#rightarrowXX#rightarrow4l (All masses)', 'l', r.kBlue+2, 1, 1)
SI_DG_pt_.addLatex(0.9, 0.93, 'Displaced Global Muons: |#eta^{DG}| < 2', size = 0.04, align = 31)
SI_DG_pt_.saveRatio(1, 0, 1, '', SI_unmatched_DG_pt, BCK_unmatched_DG_pt, r_ymin=0, r_ymax=10, label ="Sig/Bkg", outputDir = 'harvested_diffs/')
BCK_matched_DG_pt = getObject('fakes_DY2M_nocut_eta2/th1fs.root', 'matched_DG_pt')
SI_matched_DG_pt = getObject('fakes_signal_nocut_eta2/th1fs.root', 'matched_DG_pt')
BCK_matched_DG_pt.Scale(1.0/BCK_matched_DG_pt.Integral())
SI_matched_DG_pt.Scale(1.0/SI_matched_DG_pt.Integral())
BCK_matched_DG_pt.SetLineWidth(2)
SI_matched_DG_pt.SetLineWidth(2)
BCK_matched_DG_pt.SetTitle(';True DG p_{T} (GeV);Normalized true DG yield')
SI_matched_DG_pt.SetTitle('')
SI_DG_pt_ = Canvas.Canvas("matched_comp_pt", 'png', 0.35, 0.77, 0.67, 0.86, 1)
#### -----------------
for j in range(0, ev.nEE):
iee = j
if not eval(cm.EE_BS2018): continue
if not eval(cm.EE_iso2l): continue
if not eval(cm.EE_OS): continue
hEE_dPhi_asym.Fill(abs(ev.EE_dPhi[iee]))
if ev.EE_leadingEt[iee] > 40 and ev.EE_subleadingEt[iee] > 40:
hEE_dPhi_sym.Fill(abs(ev.EE_dPhi[iee]))
hMM_dPhi_sym.SetMinimum(0.0)
hMM_dPhi_sym.SetMaximum(1.5*hMM_dPhi_asym.GetMaximum())
plot = Canvas.Canvas('MM_dPhi_ptcutsymmetry', 'png,pdf', 0.16, 0.8, 0.35, 0.89, 1, lsize = 0.035)
plot.addHisto(hMM_dPhi_sym, 'P', 'Symmetric cuts (p_{T} > 40 GeV)', 'p', r.kBlue-3, 1, 0, marker = 20)
plot.addHisto(hMM_dPhi_asym, 'P,SAME', 'Asymmetric cuts (p_{T} > 40, 25 GeV)', 'p', r.kMagenta-3, 1, 1, marker = 25)
plot.addLatex(0.9, 0.93, 'Drell-Yan (2018 UL)', font = 42, size = 0.035, align = 31)
plot.save(1, 0, 0, '', '', outputDir = WORKPATH + '/', maxYnumbers = 3)
hEE_dPhi_sym.SetMinimum(0.0)
hEE_dPhi_sym.SetMaximum(1.5*hEE_dPhi_asym.GetMaximum())
plot = Canvas.Canvas('EE_dPhi_ptcutsymmetry', 'png,pdf', 0.16, 0.8, 0.35, 0.89, 1, lsize = 0.035)
plot.addHisto(hEE_dPhi_sym, 'P', 'Symmetric cuts (p_{T} > 40 GeV)', 'p', r.kBlue-3, 1, 0, marker = 20)
plot.addHisto(hEE_dPhi_asym, 'P,SAME', 'Asymmetric cuts (p_{T} > 40, 25 GeV)', 'p', r.kMagenta-3, 1, 1, marker = 25)
plot.addLatex(0.9, 0.93, 'Drell-Yan (2018 UL)', font = 42, size = 0.035, align = 31)
plot.save(1, 0, 0, '', '', outputDir = WORKPATH + '/', maxYnumbers = 3)
for _e, ev in enumerate(_tree):
for i in range(0, ev.nDG):
if ev.DG_phi[i] > 0:
upperLegs.Fill(ev.DG_vx[i], ev.DG_vy[i])
if ev.DG_phi[i] < 0:
lowerLegs.Fill(ev.DG_vx[i], ev.DG_vy[i])
"""
PLOT = Canvas.Canvas('2Dplot', 'png', 0.6, 0.6, 0.89, 0.89, 1)
PLOT.addHisto(upperLegs, 'BOX', 'Upper legs', 'f', r.kRed, 1, 0)
PLOT.addHisto(lowerLegs, 'BOX, SAME', 'Lower legs', 'f', r.kBlue, 1, 2)
PLOT.save(1, 0, 0, '', '')
"""
lowerLegs.SetFillColor(r.kRed)
upperLegs.SetFillColor(r.kBlue)
c1 = r.TCanvas("c1", "", 600, 600)
lowerLegs.Draw("BOX")
upperLegs.Draw("BOX, SAME")
c1.SaveAs("2Dlegs.png")
allvolume = lowerLegs.Clone()
allvolume.Add(upperLegs)
allvolume.GetYaxis().SetTitle('DG muon v_{y} (cm)')
allvolume.GetXaxis().SetTitle('DG muon v_{x} (cm)')
PLOT = Canvas.Canvas('vxvyDGpop', 'png', 0.6, 0.6, 0.89, 0.89, 1)
PLOT.addHisto(allvolume, 'COLZ', '', 'f', r.kRed, 1, 0)
PLOT.save(0, 0, 0, '', '')
help='Output tag')
(opts, args) = parser.parse_args()
#####################################
#### Construct TEfficiencies ####
#####################################
# No cuts applied
SI_matched_DG_pt = getObject('fakes_signal_nocut/th1fs.root',
'matched_DG_pt')
SI_unmatched_DG_pt = getObject('fakes_signal_nocut/th1fs.root',
'unmatched_DG_pt')
SI_DG_pt = doFakeStack(SI_matched_DG_pt, SI_unmatched_DG_pt, fakedown=True)
SI_DG_pt_ = Canvas.Canvas("SI_DG_pt", 'png', 0.52, 0.81, 0.87, 0.9, 1)
SI_DG_pt_.addStack(SI_DG_pt, 'HIST', 1, 0)
SI_DG_pt_.addLatex(0.41,
0.75,
'Monte Carlo: H#rightarrowXX#rightarrow4l (All masses)',
size=0.03,
align=11)
SI_DG_pt_.addLatex(0.41, 0.71, '|#eta^{DG} < 2.4|', size=0.03, align=11)
SI_DG_pt_.save(1,
0,
0,
'',
'',
outputDir=WORKPATH + 'harvested_fakes_' + opts.tag + '/',
maxYnumbers=4)
def makeSymmetryTest2(lumi,
hname,
ylog,
tree,
inputdir,
label,
name,
isData,
xlabel='',
outtag='',
yshift=0.0,
LLlabel='',
color=r.kBlack):
## Get the histogram
if type(hname) != list:
hist = tree.getLoopTH1F(inputdir, hname)
else:
hist = tree.getLoopTH1F(inputdir, hname[0])
for n in range(1, len(hname)):
hist.Add(tree.getLoopTH1F(inputdir, hname[n]))
hist.SetMarkerStyle(20)
hist.SetMarkerSize(0.8)
## Adjust the maximum
hist.SetMaximum(2 * hist.GetMaximum())
hist.SetMinimum(0.5 * hist.GetMinimum())
## Get Mean and Median:
mean = hist.GetMean()
print('Mean: ', hist.GetMean())
y = 0.
q = np.array([0.])
prob = np.array([0.5])
y = hist.GetQuantiles(1, q, prob)
print('Median: ', q[0])
print('Reference: ', 3.14 / 2.0)
## Get populations
nbins = hist.GetNbinsX()
simbin = int(nbins / 2)
forwardErr = np.array([0.])
backwardErr = np.array([0.])
Nforward = hist.IntegralAndError(1, simbin, forwardErr)
Nbackward = hist.IntegralAndError(simbin + 1, nbins, backwardErr)
print("N in forward: ", Nforward, forwardErr[0])
print("N in backward: ", Nbackward, backwardErr[0])
fullStat = [Nforward, forwardErr[0], Nbackward, backwardErr[0]]
## Plot
plot = Canvas.Canvas(name, 'png,pdf', 0.14, 0.8, 0.3, 0.9, 1)
plot.addHisto(hist, 'P, SAME', label, 'p', color, 1, 0)
plot.addLine(mean, hist.GetMinimum(), mean, hist.GetMaximum(), r.kBlue)
plot.addLatex(0.18,
0.75,
'Mean: #bar{{|#Delta#Phi|}} = {0:.4f}'.format(mean),
font=42,
size=0.03,
align=11,
color=r.kBlue)
plot.addLine(q[0], hist.GetMinimum(), q[0], hist.GetMaximum(), r.kRed)
plot.addLatex(0.18,
0.71,
'Median: |#Delta#Phi|_{{1/2}} = {0:.4f}'.format(q[0]),
font=42,
size=0.03,
align=11,
color=r.kRed)
plot.addLatex(0.18,
0.66,
'N_{{forward}} = {0} #pm {1}'.format(int(Nforward),
int(forwardErr[0])),
font=42,
size=0.03,
align=11,
color=r.kBlack)
plot.addLatex(0.18,
0.62,
'N_{{backward}} = {0} #pm {1}'.format(
int(Nbackward), int(backwardErr[0])),
font=42,
size=0.03,
align=11,
color=r.kBlack)
plot.save(1, 0, ylog, '', '', outputDir=WORKPATH + 'SymmetryResults/')
return fullStat
def make2DPlot(h2, name, outdir):
plot = Canvas.Canvas(name, 'png', 0.17, 0.8, 0.3, 0.9, 1)
plot.addHisto(h2, 'COLZ', '', '', '', 1, 0)
plot.save(0, 0, 0, '', '', outputDir=WORKPATH + outdir)
#outputFile.cd()
#configs[point]['r_hist'].Write()
#configs[point]['dxy_hist'].Write()
print(configs)
### Harvesting
configs['400_50_1']['r_hist'].SetLineWidth(2)
configs['400_50_10']['r_hist'].SetLineWidth(2)
configs['400_50_100']['r_hist'].SetLineWidth(2)
configs['400_50_1000']['r_hist'].SetLineWidth(2)
configs['400_50_10000']['r_hist'].SetLineWidth(2)
configs['400_50_1']['r_hist'].SetMaximum(
1.4 * configs['400_50_1']['r_hist'].GetMaximum())
plot = Canvas.Canvas('hist_r_400_50', 'png,pdf', 0.14, 0.74, 0.4, 0.89, 1)
plot.addHisto(configs['400_50_1']['r_hist'].Clone(), 'HIST',
'm_{H} = 400 GeV, m_{S} = 50 GeV, c#tau = 1 mm', 'p',
r.kRed + 1, 1, 0)
plot.addHisto(configs['400_50_10']['r_hist'].Clone(), 'HIST, SAME',
'm_{H} = 400 GeV, m_{S} = 50 GeV, c#tau = 10 mm', 'p',
r.kOrange + 1, 1, 1)
plot.addHisto(configs['400_50_100']['r_hist'].Clone(), 'HIST, SAME',
'm_{H} = 400 GeV, m_{S} = 50 GeV, c#tau = 100 mm', 'p',
r.kGreen + 1, 1, 2)
plot.addHisto(configs['400_50_1000']['r_hist'].Clone(), 'HIST, SAME',
'm_{H} = 400 GeV, m_{S} = 50 GeV, c#tau = 1000 mm', 'p',
r.kBlue + 1, 1, 3)
plot.addHisto(configs['400_50_10000']['r_hist'].Clone(), 'HIST, SAME',
'm_{H} = 400 GeV, m_{S} = 50 GeV, c#tau = 10000 mm', 'p',
r.kMagenta + 1, 1, 4)
def makeCumulativeValidation(lumi,
tree,
var,
name,
xlabel,
logx,
LLlabel=False):
hSR = tree.getLoopTH1F('histMC_SR_' + name, 'SR_' + var, xlabel)
hCR = tree.getLoopTH1F('histMC_CR_' + name, 'CR_' + var, xlabel)
cumSR = r.TH1F('cumSR', '', hSR.GetNbinsX(),
hSR.GetXaxis().GetXmin(),
hSR.GetXaxis().GetXmax())
cumCR = r.TH1F('cumCR', '', hCR.GetNbinsX(),
hCR.GetXaxis().GetXmin(),
hCR.GetXaxis().GetXmax())
cumSR.GetXaxis().SetTitle('(' + xlabel + ')_{min}')
cumSR.GetYaxis().SetTitle('Number of events with ' + xlabel + ' > (' +
xlabel + ')_{min}')
cumCR.GetXaxis().SetTitle('(' + xlabel + ')_{min}')
cumCR.GetYaxis().SetTitle('Number of events with ' + xlabel + ' > (' +
xlabel + ')_{min}')
for n in range(1, hSR.GetNbinsX() + 1):
valSR = 0.0
errSR = 0.0
valCR = 0.0
errCR = 0.0
for j in range(n, hSR.GetNbinsX() + 1):
valSR = valSR + hSR.GetBinContent(j)
errSR = errSR + hSR.GetBinError(j)
valCR = valCR + hCR.GetBinContent(j)
errCR = errCR + hCR.GetBinError(j)
cumSR.SetBinContent(n, valSR)
cumSR.SetBinError(n, errSR)
cumCR.SetBinContent(n, valCR)
cumCR.SetBinError(n, errCR)
cumSR.SetMarkerStyle(20)
cumSR.SetMarkerSize(0.8)
cumCR.SetMarkerStyle(20)
cumCR.SetMarkerSize(0.8)
luminosity = lumi
### Get maximum
maxValCR = cumCR.GetMaximum()
maxValSR = cumSR.GetMaximum()
maxVal = max(maxValCR, maxValSR)
### Set Maximum
if not logx:
cumCR.SetMaximum(1.3 * maxVal)
cumSR.SetMaximum(1.3 * maxVal)
else:
cumCR.SetMaximum(100.0 * maxVal)
cumSR.SetMaximum(100.0 * maxVal)
cumCR.SetMinimum(0.1)
cumSR.SetMaximum(0.1)
### Canvas object
plot = Canvas.Canvas('hist_' + name, 'png', 0.7, 0.79, 0.9, 0.87, 1)
plot.addHisto(cumCR, 'P', 'Control region', 'l', r.kBlue, 1, 0)
plot.addHisto(cumSR, 'P, SAME', 'Signal region', 'l', r.kRed, 1, 0)
### Dilepton banner
if LLlabel == 'EE':
plot.addLatex(0.17, 0.84, 'e^{+}e^{-} channel')
if LLlabel == 'MM':
plot.addLatex(0.17, 0.84, '#mu^{+}#mu^{-} channel')
### Save it
#plot.save(1, 0, logx, luminosity, '')
plot.saveRatio(1,
0,
logx,
luminosity,
cumSR,
cumCR,
label="SR/CR",
outputDir='closureMCPlots/')
def makePromptBKGPlot(name, lumi, hname_SR, hname_CR, ylog, treeDATA, inputdir, rebin = False, limit = 0.0, xlabel = '', outtag = '', yshift = 0.0, LLlabel = '', DATAlabel = '', extralabel = '', xlog = False):
### Get histograms
luminosity = lumi
hSR_ = treeDATA.getLoopTH1F(inputdir, hname_SR)
hCR_ = treeDATA.getLoopTH1F(inputdir, hname_CR)
### rebinins:
if type(rebin) != bool:
if len(rebin) > 1:
hSR = hSR_.Rebin(len(rebin)-1, hSR_.GetName() + '_rebined', rebin)
hCR = hCR_.Rebin(len(rebin)-1, hCR_.GetName() + '_rebined', rebin)
else:
hSR = hSR_.Rebin(rebin)
hCR = hCR_.Rebin(rebin)
else:
hSR = hSR_.Clone()
hCR = hCR_.Clone()
### Blinding limits:
if limit:
for n in range(1, hSR.GetNbinsX()):
if hSR.GetBinLowEdge(n) > limit: hSR.SetBinContent(n, 0.0)
#if hCR_.GetBinLowEdge(n) > limit: hCR_.SetBinContent(n, 0.0)
hCR.SetFillColorAlpha(r.kCyan-6, 0.8)
hCR.SetLineColor(r.kCyan+3)
hSR.SetMarkerStyle(20)
hSR.SetMarkerSize(0.8)
hSR.SetMarkerColor(r.kBlack)
hCR.GetXaxis().SetTitleSize(0.045)
hSR.GetXaxis().SetTitleSize(0.045)
hCR.GetYaxis().SetTitleSize(0.045)
hSR.GetYaxis().SetTitleSize(0.045)
### Get maximum
maxValSR = hSR.GetMaximum()
maxValCR = hCR.GetMaximum()
maxVal = max([maxValSR, maxValCR])
### Set Maximum
if not ylog:
hCR.SetMaximum(1.3*maxVal)
else:
hCR.SetMaximum(100.0*maxVal)
hCR.SetMinimum(0.1)
### Canvas object
plot = Canvas.Canvas('BKGVal_'+name, 'png,pdf', 0.51, 0.69, 0.7, 0.82, 1)
plot.addHisto(hCR, 'HIST', 'Background (Data-driven)', 'f', '', 1, 0)
plot.addHisto(hSR, 'P, SAME', 'Data', 'p', '', 1, 1)
plot.addLatex(0.17, 0.8, extralabel, font = 62)
### Channel banner:
if LLlabel == 'EE':
plot.addLatex(0.17, 0.74, 'Dielectron channel', font = 42)
if LLlabel == 'MM':
plot.addLatex(0.17, 0.74, 'Dimuon channel', font = 42)
### Save it
outdir = os.path.dirname(os.path.abspath(__main__.__file__)) + '/BKGVal_' + outtag + '/'
plot.saveRatio(1, 1, ylog, luminosity, hSR, hCR, r_ymin = 0.0, r_ymax = 2.0, label="Obs./Pred.", outputDir = outdir, xlog = xlog)
hist_1d_r_cos = getObject('Results/th1f_2018ABCD.root', 'hist_1d_r_cos')
hist_1d_r_id = getObject('Results/th1f_2018ABCD.root', 'hist_1d_r_id')
hist_1d_r_chi.SetTitle(';Vertex radius r [cm]; Dimuon vertex yield')
hist_1d_r_chi.SetMaximum(100.0 * hist_1d_r_chi.GetMaximum())
hist_1d_r_chi.SetMinimum(0.1)
hist_1d_r_chi.Sumw2()
hist_1d_r_chi.SetLineColor(r.kBlack)
hist_1d_r_char.SetLineColor(r.kBlack)
hist_1d_r_cos.SetLineColor(r.kBlack)
hist_1d_r_id.SetLineColor(r.kBlack)
hist_1d_r_chi.SetFillColor(r.kMagenta - 6)
hist_1d_r_char.SetFillColor(r.kBlue - 6)
hist_1d_r_cos.SetFillColor(r.kCyan - 6)
hist_1d_r_id.SetFillColor(r.kGray)
plot = Canvas.Canvas('hist_dr', 'png,pdf', 0.14, 0.75, 0.5, 0.89, 1)
plot.addHisto(hist_1d_r_chi, 'HIST',
'Vertices passing kinematic cuts and #chi^{2}/ndf < 7.5',
'f', '', 1, 0)
plot.addHisto(hist_1d_r_char, 'HIST,SAME', 'Vertices with OS dGlobals',
'f', '', 1, 1)
plot.addHisto(hist_1d_r_cos, 'HIST,SAME',
'Vertices passing dR, mass, isolation and cos(#alpha) cuts',
'f', '', 1, 2)
plot.addHisto(hist_1d_r_id, 'HIST,SAME',
'Vertices with dGlobals passing ID', 'f', '', 1, 3)
plot.addLatex(0.9, 0.93, '2018 Data (6.0 fb)', size=0.026, align=31)
plot.save(1,
1,
1,
'',
def makeBlindedPlot(lumi,
hname_SI,
hname_bkg,
ylog,
treeDATA,
inputdir,
treeSI,
rebin=False,
lines=[],
xlabel='',
outtag='',
ymax=0.0,
LLlabel='',
DATAlabel='',
extralabel=''):
### Get histograms
luminosity = lumi
print(inputdir, hname_bkg)
hbkg_ = treeDATA.getLoopTH1F(inputdir, hname_bkg)
### rebinins:
if type(rebin) != bool:
if len(rebin) > 1:
hbkg = hbkg_.Rebin(
len(rebin) - 1,
hbkg_.GetName() + '_rebined', rebin)
else:
hbkg = hbkg_.Rebin(rebin)
else:
hbkg = hbkg_.Clone()
hbkg.SetFillColorAlpha(r.kCyan - 6, 0.8)
hbkg.SetLineColor(r.kCyan - 2)
hbkg.GetXaxis().SetTitleSize(0.045)
hbkg.GetYaxis().SetTitleSize(0.045)
### Signal histograms
s_histos = []
hSIS = treeSI.getLoopStack(inputdir, hname_SI)
for _i, _h in enumerate(hSIS.GetHists()):
#_h.Scale(lumi/35.87)
s_histos.append(copy.deepcopy(_h))
### Get maximum
maxValbkg = hbkg.GetMaximum()
maxValSI = max([s_histos[i].GetMaximum() for i in range(0, len(s_histos))])
maxVal = max([maxValSI, maxValbkg])
### Set Maximum
if not ylog:
hbkg.SetMaximum(1.3 * maxVal)
else:
hbkg.SetMaximum(100.0 * maxVal)
hbkg.SetMinimum(0.0001)
if ymax: hbkg.SetMaximum(ymax)
### Count background events
backtotal = 0.0
for n in range(1, hbkg.GetNbinsX() + 1):
if hbkg.GetBinLowEdge(n) > 20.0:
backtotal += hbkg.GetBinContent(n)
print("background total", backtotal)
### Canvas object
plot = Canvas.Canvas('Blinded_' + hname_bkg, 'png,pdf', 0.15, 0.65, 0.6,
0.89, 1)
plot.addHisto(hbkg, 'HIST', 'Background (Data-driven)', 'f', '', 1, 0)
### Add signals:
for i, _h in enumerate(s_histos):
_h.SetLineWidth(2) # provisional
masses = eval(_h.GetTitle()[3:])
legend = 'm_{H} = ' + str(masses[0]) + ' GeV, m_{X} = ' + str(
masses[1]) + ' GeV, c#tau = ' + str(masses[2]) + ' mm'
plot.addHisto(_h, 'HIST, SAME', legend, 'l', _h.GetFillColor(), 1,
i + 1) # Signal
for line in lines:
plot.addLine(line, hbkg.GetMinimum(), line, hbkg.GetMaximum(),
r.kBlack, 2)
### Extralabel
#plot.addLatex(0.17, 0.8, extralabel, font = 62)
### Save it
outdir = os.path.dirname(os.path.abspath(
__main__.__file__)) + '/SRPlots_' + outtag + '/'
plot.save(1, 1, ylog, luminosity, '', outputDir=outdir, xlog=False)
### Pt (IFCA)
BKG_IFCAeff_pt = getObject('Results/th1fDrell_Yan.root', 'eff_IFCA_pt')
SI_4mm_IFCAeff_pt = getObject('Results/th1fHXX_400_50_4mm.root',
'eff_IFCA_pt')
SI_40mm_IFCAeff_pt = getObject('Results/th1fHXX_400_50_40mm.root',
'eff_IFCA_pt')
SI_400mm_IFCAeff_pt = getObject('Results/th1fHXX_400_50_400mm.root',
'eff_IFCA_pt')
BKG_IFCAeff_pt_ = rebinAxis(BKG_IFCAeff_pt, ptbin)
SI_4mm_IFCAeff_pt_ = rebinAxis(SI_4mm_IFCAeff_pt, ptbin)
SI_40mm_IFCAeff_pt_ = rebinAxis(SI_40mm_IFCAeff_pt, ptbin)
SI_400mm_IFCAeff_pt_ = rebinAxis(SI_400mm_IFCAeff_pt, ptbin)
EFF_pt = Canvas.Canvas("EFF_IFCA_pt", 'png', 0.15, 0.77, 0.45, 0.9, 1)
EFF_pt.addRate(
SI_4mm_IFCAeff_pt_,
'AP',
'H#rightarrowXX: m_{H} = 400 GeV, m_{X} = 50 GeV, c#tau = 4 mm',
'p',
r.kBlue + 2,
True,
0,
marker=24)
EFF_pt.addRate(
SI_40mm_IFCAeff_pt_,
'AP,SAME',
'H#rightarrowXX: m_{H} = 400 GeV, m_{X} = 50 GeV, c#tau = 40 mm',
'p',
r.kBlue - 4,
def makePlot(lumi,
var,
name,
xlabel,
logx,
treeMC,
treeSI=False,
treeDATA=False,
LLlabel=False,
normed=False):
hMCS = treeMC.getLoopStack('histMC_' + name, var, xlabel)
luminosity = lumi
### MC total histogram
f = 0
for _i, _h in enumerate(hMCS.GetHists()):
if not f: hMC = copy.deepcopy(_h)
else: hMC.Add(_h, 1.)
f = 1
hMC.SetMarkerStyle(20) # Auxiliar to save the ratio correctly
### Signal histograms
if treeSI:
hSIS = treeSI.getLoopStack('histSI_' + name, var, xlabel)
s_histos = []
for _i, _h in enumerate(hSIS.GetHists()):
s_histos.append(copy.deepcopy(_h))
### Data histogram
if treeDATA:
hDATA = treeDATA.getLoopTH1F('histDATA_' + name, var, xlabel)
hDATA.SetMarkerStyle(20)
hDATA.SetMarkerSize(0.8)
hDATA.Scale(hMC.Integral() / hDATA.Integral())
# Normalization
if normed:
hMC.Scale(1.0 / hMC.Integral())
for _h in s_histos:
_h.Scale(1.0 / _h.Integral())
luminosity = 1.0
### Get maximum
maxValMC = hMC.GetMaximum()
maxValSI = 0 if not treeSI else max(
[s_histos[i].GetMaximum() for i in range(0, len(s_histos))])
maxValDATA = 0 if not treeDATA else hDATA.GetMaximum()
maxVal = max([maxValMC, maxValSI, maxValDATA])
### Set Maximum
if not logx:
hMCS.SetMaximum(1.3 * maxVal)
hMCS.SetMinimum(0.0)
hMC.SetMaximum(1.3 * maxVal)
hMC.SetMinimum(0.0)
if treeSI:
for _h in s_histos:
_h.SetMaximum(1.3 * maxVal)
_h.SetMinimum(0.0)
if treeDATA:
hDATA.SetMaximum(1.3 * maxVal)
hDATA.SetMinimum(0.0)
else:
hMCS.SetMaximum(10.0 * maxVal)
hMCS.SetMinimum(0.1)
hMC.SetMaximum(10.0 * maxVal)
hMC.SetMinimum(0.1)
if treeSI:
for _h in s_histos:
_h.SetMaximum(10.0 * maxVal)
_h.SetMinimum(0.1)
if treeDATA:
hDATA.SetMaximum(10.0 * maxVal)
hDATA.SetMinimum(0.1)
### Canvas object
plot = Canvas.Canvas('hist_' + name, 'png', 0.34, 0.68, 0.9, 0.9)
if normed: plot.addHisto(hMC, 'HIST', '', 'l', r.kBlue, 1, 0) # Background
else: plot.addStack(hMCS, 'HIST', 1, 0) # Background
if treeSI:
for _h in s_histos:
plot.addHisto(_h, 'HIST, SAME', '', 'l', _h.GetFillColor(), 1,
3) # Signal
if treeDATA:
plot.addHisto(hDATA, 'P, SAME', '', 'p', r.kBlack, 1, 4)
### Dilepton banner
if LLlabel == 'EE':
plot.addLatex(0.17, 0.86, 'e^{+}e^{-} channel')
if LLlabel == 'MM':
plot.addLatex(0.17, 0.86, '#mu^{+}#mu^{-} channel')
### Save it
#plot.save(1, 0, logx, luminosity, '', outputDir = WORKPATH + 'plots/')
plot.saveRatio(1,
0,
logx,
luminosity,
hDATA,
hMC,
label="Data/MC",
outputDir='newplots/')
def makeClosureTestMC(lumi, name, hBKG_A, hBKG_B, ylog, tree, inputdir, labelA = 'A', labelB = 'B', xlabel = '', outtag = '', yshift = 0.0, LLlabel = '', DATAlabel = '', extralabel = '', rmin = 0.0, rmax = 2.0):
print(rmin, rmax)
### Get histograms
luminosity = lumi
### Initialize cumulative histograms
cumA = hBKG_A.Clone('cumA')
cumB = hBKG_B.Clone('cumB')
cumA.Reset()
cumB.Reset()
#cumA = r.TH1F('cumA', '', hBKG_A.GetNbinsX(), hBKG_A.GetXaxis().GetXmin(), hBKG_A.GetXaxis().GetXmax())
#cumB = r.TH1F('cumB', '', hBKG_B.GetNbinsX(), hBKG_B.GetXaxis().GetXmin(), hBKG_B.GetXaxis().GetXmax())
cumA.SetTitle(';min('+xlabel+');Number of events with '+xlabel+ ' > ('+xlabel+')_{min}')
### Set cumulative values
for n in range(1, hBKG_A.GetNbinsX() + 1):
valA = 0.0
errA = 0.0
valB = 0.0
errB = 0.0
for j in range(n, hBKG_A.GetNbinsX() + 1):
valA = valA + hBKG_A.GetBinContent(j)
errA = errA + hBKG_A.GetBinError(j)
valB = valB + hBKG_B.GetBinContent(j)
errB = errB + hBKG_B.GetBinError(j)
cumA.SetBinContent(n, valA)
cumA.SetBinError(n, errA)
cumB.SetBinContent(n, valB)
cumB.SetBinError(n, errB)
### Histogram tuning
cumA.SetMarkerStyle(20)
cumA.SetMarkerSize(1)
cumA.SetMarkerColor(r.kBlue)
cumA.GetYaxis().SetTitleOffset(1.3)
cumB.SetMarkerStyle(25)
cumB.SetMarkerSize(1)
cumB.SetMarkerColor(r.kRed)
hBKG_A.SetMarkerStyle(20)
hBKG_A.SetMarkerSize(1)
hBKG_A.SetMarkerColor(r.kBlue)
hBKG_B.SetMarkerStyle(25)
hBKG_B.SetMarkerSize(1)
hBKG_B.SetMarkerColor(r.kRed)
### Get maximum
maxValA = cumA.GetMaximum()
maxValB = cumB.GetMaximum()
maxVal = max(maxValA, maxValB)
### Set Maximum
if not ylog:
cumA.SetMaximum(1.3*maxVal)
cumB.SetMaximum(1.3*maxVal)
else:
cumA.SetMaximum(100.0*maxVal)
cumB.SetMaximum(100.0*maxVal)
cumA.SetMinimum(0.1)
cumB.SetMaximum(0.1)
### Get maximum
maxValA = hBKG_A.GetMaximum()
maxValB = hBKG_B.GetMaximum()
maxVal = max(maxValA, maxValB)
### Set Maximum
if not ylog:
hBKG_A.SetMaximum(1.3*maxVal)
hBKG_B.SetMaximum(1.3*maxVal)
else:
hBKG_A.SetMaximum(100.0*maxVal)
hBKG_B.SetMaximum(100.0*maxVal)
hBKG_A.SetMinimum(0.1)
hBKG_B.SetMaximum(0.1)
### Canvas object
plot = Canvas.Canvas('closuretest_'+name, 'png,pdf', 0.5, 0.66, 0.7, 0.78, 1)
plot.addHisto(cumA, 'P', labelA, 'p', r.kBlue, 1, 0)
plot.addHisto(cumB, 'P, SAME', labelB, 'p', r.kRed, 1, 1)
### Channel banner:
if LLlabel == 'EE':
plot.addLatex(0.17, 0.75, 'Dielectron vertices', font = 42)
if LLlabel == 'MM':
plot.addLatex(0.17, 0.75, 'Dimuon vertices', font = 42)
plot.addLatex(0.85, 0.56, 'Tail cumulative of x = #int^{#infty}_{x_{min}} N(x) dx', font = 42, align = 31)
plot.addLatex(0.17, 0.81, extralabel, font = 42, align = 11, size = 0.045)
plot.addLatex(0.9, 0.88, DATAlabel, font = 42, align = 31, size = 0.045)
### Save it
outdir = os.path.dirname(os.path.abspath(__main__.__file__)) + '/ClosureTests_' + outtag + '/'
plot.saveRatio(1, 0, ylog, luminosity, cumA, cumB, r_ymin = rmin, r_ymax = rmax, label="SR/BCR", outputDir = outdir)
### Main comparison
cplot = Canvas.Canvas('plaincomparison_'+name, 'png,pdf', 0.5, 0.66, 0.7, 0.78, 1)
cplot.addHisto(hBKG_A, 'P', labelA, 'p', r.kBlue, 1, 0)
cplot.addHisto(hBKG_B, 'P, SAME', labelB, 'p', r.kRed, 1, 1)
if LLlabel == 'EE':
cplot.addLatex(0.17, 0.75, 'Dielectron vertices', font = 42)
if LLlabel == 'MM':
cplot.addLatex(0.17, 0.75, 'Dimuon vertices', font = 42)
cplot.addLatex(0.17, 0.81, extralabel, font = 42, align = 11, size = 0.045)
cplot.addLatex(0.9, 0.88, DATAlabel, font = 42, align = 31, size = 0.045)
cplot.saveRatio(1, 0, ylog, luminosity, hBKG_A, hBKG_B, label="SR/BCR", outputDir = outdir)
#### Construct TEfficiencies ####
#####################################
hSR = getObject(
'Results/th1f.root',
'hSR_HLT_Diphoton30_22_R9Id_OR_IsoCaloId_AND_HE_R9Id_Mass90')
hBCR = getObject(
'Results/th1f.root',
'hBCR_HLT_Diphoton30_22_R9Id_OR_IsoCaloId_AND_HE_R9Id_Mass90')
hSR.Sumw2()
hBCR.Sumw2()
plot = Canvas.Canvas('hist_fullPath',
'png,pdf',
0.17,
0.55,
0.45,
0.89,
1,
lsize=0.028)
plot.addHisto(hSR, 'P', 'SR (fullPath)', 'p', r.kBlue, 1, 0)
plot.addHisto(hBCR, 'P,SAME', 'BCR (fullPath)', 'p', r.kRed, 1, 1)
plot.addLatex(0.9, 0.93, '2016 UL', size=0.03, align=31)
plot.addLatex(0.75, 0.5, 'disp. Electrons:', size=0.03, align=22)
plot.addLatex(0.75, 0.44, 'E_{T} > 25 GeV', size=0.03, align=22)
plot.addLatex(0.75, 0.4, '|#eta| < 2', size=0.03, align=22)
plot.saveRatio(1,
1,
1,
'',
hSR,
hBCR,
def makeSensitivity(lumi,
treeSI,
treeMC,
var,
name,
xlabel,
logx,
LLlabel=False,
normed=False):
hMCS = treeMC.getLoopTH1F('histMC_' + name, var, xlabel)
hSIS = treeSI.getLoopStack('histSI_' + name, var, xlabel)
luminosity = lumi
### Signal histograms
s_histos = []
for _i, _h in enumerate(hSIS.GetHists()):
s_histos.append(copy.deepcopy(_h))
### Get significance values
plot = Canvas.Canvas('sensitivity_' + name, 'png', 0.5, 0.7, 0.9, 0.9, 1)
sens = []
for _i, _h in enumerate(s_histos):
gh = r.TH1F('gr', '', hMCS.GetNbinsX(),
hMCS.GetXaxis().GetXmin(),
hMCS.GetXaxis().GetXmax())
for n in range(1, hMCS.GetNbinsX() + 1):
sig_value = 0.0
back_value = 0.0
value = 0.0
for j in range(n, hMCS.GetNbinsX() + 1):
sig_value = sig_value + _h.GetBinContent(j)
back_value = back_value + hMCS.GetBinContent(j)
if sig_value + back_value == 0:
value = 0.0
else:
value = sig_value / math.sqrt(sig_value + back_value)
gh.SetBinContent(n, value)
gh.SetTitle(_h.GetTitle())
gh.SetLineWidth(2)
gh.GetYaxis().SetTitle('S/#sqrt{S + B}')
gh.GetXaxis().SetTitle(xlabel)
gh.SetLineColor(_h.GetFillColor())
gh1 = gh.Clone()
gh2 = copy.deepcopy(gh1)
sens.append(gh2)
print([sens[i].GetMaximum() for i in range(0, len(sens))])
maximum = max([sens[i].GetMaximum() for i in range(0, len(sens))])
print('maximum: ', maximum)
for _s, gh2 in enumerate(sens):
gh2.SetMaximum(1.3 * maximum)
if _s == 0:
plot.addHisto(gh2, 'l', gh2.GetTitle(), 'l', gh2.GetLineColor(), 1,
1)
else:
plot.addHisto(gh2, 'l, same', gh2.GetTitle(), 'l',
gh2.GetLineColor(), 1, 1)
### Dilepton banner
if LLlabel == 'EE':
plot.addLatex(0.17, 0.86, 'e^{+}e^{-} channel')
if LLlabel == 'MM':
plot.addLatex(0.17, 0.86, '#mu^{+}#mu^{-} channel')
### Save it
plot.save(1, 0, logx, luminosity, '', outputDir=WORKPATH + 'sensitivity/')
xmin=0,
xmax=10,
cut=allcuts,
options='',
xlabel='Dimuon #mu#mu L_{xy} (cm)')
hSI = treeSI.getTH1F(lumi=lumi,
name='hSI',
var='DMDMBase_Lxy[0]',
nbin=20,
xmin=0,
xmax=10,
cut=allcuts,
options='',
xlabel='Dimuon #mu#mu L_{xy} (cm)')
plot = Canvas.Canvas('hist_DMDM_Lxy', 'png', 0.55, 0.8, 0.9, 0.9, 1)
plot.addHisto(hBCK, 'hist', '', 'l', '', 1, 0)
plot.addHisto(hSI, 'hist, same', '', 'l', '', 1, 1)
plot.save(legend=True,
isData=False,
log=True,
lumi=lumi,
labelx='',
outputDir=outputFolder)
### --> Ejemplo de sacar la sensitivity y las cumulativas
Sensitivity, cumSig, cumBkg = computeSensitivity(hBCK, hSI, r.kGreen)
plot_sensitivity = Canvas.Canvas('hist_sensitivity', 'png', 0.55, 0.8, 0.9,
0.9, 1)
plot_sensitivity.addHisto(Sensitivity, 'hist', '', 'l', '', 1, 0)
##############################
#### Some definitions ####
##############################
#####################################
#### Construct TEfficiencies ####
#####################################
hist_dPhi = getObject('Results/th1f_all.root', 'hist_dPhi')
hist_dPhi.Rebin(4)
hist_dPhi.SetTitle(
';Dimuon collinearity |#Delta#Phi|; Dimuon vertex yield')
hist_dPhi.SetMaximum(1.2 * hist_dPhi.GetMaximum())
hist_dPhi.SetMinimum(0.0)
hist_dPhi.Sumw2()
plot = Canvas.Canvas('hist_cosmics_dPhi', 'png,pdf', 0.3, 0.75, 0.89, 0.8,
1)
plot.addHisto(hist_dPhi, 'P', '', 'p', r.kBlue, 1, 0)
plot.addLatex(0.9, 0.93, 'Cosmic data 2018', size=0.026, align=31)
plot.addLatex(0.85,
0.85,
'/NoBPTX/Run2018B-12Nov2019_UL2018-v1/AOD',
size=0.026,
align=31)
plot.addLatex(0.85,
0.8,
'HLT_L2Mu10_NoVertex_NoBPTX3BX',
size=0.026,
align=31)
plot.save(0,
1,
0,
