def produceNormalizedPlots(processes, selections, variables, colors, intLumi, pdir, lt, rt, log, hfile):
print ''
print 'Preparing normalized plots ...'
gROOT.SetBatch(True)
intLumiab = intLumi/1e+06
yl = "Normalized Event Rate"
ff = "eps"
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
hfile.cd()
nsel = 0
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v, dic in variables.iteritems() :
histos = []
i = 0
filename = '{}_{}_{}'.format(v, selstr, logstr)
leg = TLegend(0.60,0.65,0.90,0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.SetShadowColor(10)
leg.SetTextSize(0.035)
leg.SetTextFont(42)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
# rebin if needed
hh.Rebin(int(hh.GetNbinsX()/dic['bin']))
if hh.Integral(0, hh.GetNbinsX()+1) > 0:
hh.Scale(1./hh.Integral(0, hh.GetNbinsX()+1))
histos.append(hh)
cols.append(colors[p])
leg.AddEntry(hh,p,"l")
i+=1
drawNormalized(filename, yl, leg, lt, rt, ff, pdir, log, histos, cols)
print 'DONE.'
def produceStackedPlots(processes, selections, variables, colors, intLumi, pdir, delphesVersion, log, stacksig, hfile):
print ''
print 'Preparing distribution plots ...'
myStyle()
gROOT.SetBatch(True)
intLumiab = intLumi/1e+06
yl = "Events"
rt = "RECO: Delphes-{}".format(delphesVersion)
lt = "#sqrt{{s}} = 100 TeV, L = {} ab^{{-1}}".format(intLumiab)
ff = "png"
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
if stacksig:
stackstr = 'stack'
else:
stackstr = 'nostack'
hfile.cd()
nsel = 0
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v in variables.keys() :
histos = []
i = 0
filename = '{}_{}_{}_{}'.format(v, selstr, stackstr, logstr)
leg = TLegend(0.70,0.65,0.95,0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
hh.Scale(intLumi)
histos.append(hh)
cols.append(colors[p])
if i > 0:
leg.AddEntry(hh,p,"f")
else:
leg.AddEntry(hh,p,"l")
i+=1
drawStack(filename, yl, leg, lt, rt, ff, pdir, log, stacksig, histos, cols)
print 'DONE.'
def produceNormalizedPlots(processes, selections, variables, colors, intLumi,
pdir, delphesVersion, log, hfile):
print ''
print 'Preparing normalized plots ...'
myStyle()
gROOT.SetBatch(True)
intLumiab = intLumi / 1e+06
yl = "Normalized Event Rate"
rt = "RECO: Delphes-{}".format(delphesVersion)
lt = "#sqrt{{s}} = 100 TeV, L = {} ab^{{-1}}".format(intLumiab)
ff = "png"
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
hfile.cd()
nsel = 0
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v in variables.keys():
histos = []
i = 0
filename = '{}_{}_{}'.format(v, selstr, logstr)
leg = TLegend(0.60, 0.65, 0.90, 0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
if hh.Integral(0, hh.GetNbinsX() + 1) > 0:
hh.Scale(1. / hh.Integral(0, hh.GetNbinsX() + 1))
histos.append(hh)
cols.append(colors[p])
leg.AddEntry(hh, p, "l")
i += 1
drawNormalized(filename, yl, leg, lt, rt, ff, pdir, log, histos,
cols)
print 'DONE.'
graphs = {}
dymva = [
0.05, 0.2, 0.4, 0.6, 0.75, 0.825, 0.875, 0.9125, 0.9375, 0.9625, 0.9875
]
e_dymva = [
0.05, 0.1, 0.1, 0.1, 0.05, 0.025, 0.025, 0.0125, 0.0125, 0.0125, 0.0125
]
#loading DY histograms
for dirs in thelist:
histos['DY_' + dirs.GetName()] = thefile.Get(dirs.GetName() +
'/dymva_alt_dnn_0j/histo_DY')
histos['DATA_' +
dirs.GetName()] = thefile.Get(dirs.GetName() +
'/dymva_alt_dnn_0j/histo_DATA')
histos['DYDATA_' + dirs.GetName()] = TH1D.Clone(histos['DATA_' +
dirs.GetName()])
histos['AMC_' + dirs.GetName()] = TH1D.Clone(histos['DY_' +
dirs.GetName()])
histos['VV_' + dirs.GetName()] = TH1D(
'VV_' + dirs.GetName(), 'VV_' + dirs.GetName(),
histos['DY_' + dirs.GetName()].GetNbinsX(),
histos['DY_' + dirs.GetName()].GetXaxis().GetXbins().GetArray())
# CREATING k histograms
if 'in' in dirs.GetName(
) and not 'btag' in dirs.GetName() and not 'df' in dirs.GetName(
) and not 'H_' in dirs.GetName() and not 'ww_' in dirs.GetName(
) and not '1j' in dirs.GetName() and not '2j' in dirs.GetName(
) and not 'VBF' in dirs.GetName() and not 'VH' in dirs.GetName():
#print('Histos for K and R: ', dirs.GetName())
histos['k_MC_' + dirs.GetName()] = TH1D(
K_ff[iRAndKff][iRegion]['val'] = DYCalc.k_MC(RAndKff[iRAndKff]['KffFile'],RAndKff[iRAndKff]['Regions'][iRegion]['kNum'],RAndKff[iRAndKff]['Regions'][iRegion]['kDen'])
K_ff[iRAndKff][iRegion]['err'] = DYCalc.Ek_MC(RAndKff[iRAndKff]['KffFile'],RAndKff[iRAndKff]['Regions'][iRegion]['kNum'],RAndKff[iRAndKff]['Regions'][iRegion]['kDen'])
#R{out/in}
Rinout[iRAndKff][iRegion] = {}
DataR[iRAndKff][iRegion] = {}
print 'file = ', RAndKff[iRAndKff]['RFile']
Rfile[iRAndKff] = TFile(RAndKff[iRAndKff]['RFile'])
thelist[iRAndKff] = Rfile[iRAndKff].GetListOfKeys()
for dirs in thelist[iRAndKff] :
if RAndKff[iRAndKff]['Regions'][iRegion]['RNum'] in dirs.GetName():
currentdir = dirs.ReadObj()
for subdir in currentdir.GetListOfKeys():
if subdir.GetName()!='events':
continue
DataR[iRAndKff][iRegion]['NumDATA'] = Rfile[iRAndKff].Get(dirs.GetName()+'/'+subdir.GetName()+'/histo_DATA')
DataR[iRAndKff][iRegion]['RNum'] = TH1D.Clone(DataR[iRAndKff][iRegion]['NumDATA'])
currentsubdir = subdir.ReadObj()
previoussample=''
for sample in currentsubdir.GetListOfKeys():
if sample.GetName()==previoussample:
continue
previoussample = sample.GetName()
if not 'histo_DY' in sample.GetName() and not 'histo_DATA' in sample.GetName():
DataR[iRAndKff][iRegion][sample.GetName()]= Rfile[iRAndKff].Get(dirs.GetName()+'/'+subdir.GetName()+'/'+sample.GetName())
DataR[iRAndKff][iRegion]['RNum'].Add(DataR[iRAndKff][iRegion][sample.GetName()],-1)
if RAndKff[iRAndKff]['Regions'][iRegion]['RDen'] in dirs.GetName():
currentdir = dirs.ReadObj()
currentdir = dirs.ReadObj()
for subdir in currentdir.GetListOfKeys():
if subdir.GetName()!='events':
def produceStackedPlots(processes, selections, variables, colors, intLumi, pdir, lt, rt, log, stacksig, hfile, ana_tex):
print ''
print 'Preparing stacked plots ...'
gROOT.SetBatch(True)
intLumiab = intLumi/1e+06
yl = "events"
ff = "eps"
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
if stacksig:
stackstr = 'stack'
else:
stackstr = 'nostack'
hfile.cd()
nsel = 0
legsize = 0.04*float(len(processes))
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v, dic in variables.iteritems() :
histos = []
i = 0
filename = '{}_{}_{}_{}'.format(v, selstr, stackstr, logstr)
leg = TLegend(0.58,0.86 - legsize,0.86,0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.SetShadowColor(10)
leg.SetTextSize(0.035)
leg.SetTextFont(42)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
hh.Scale(intLumi)
# rebin if needed
hh.Rebin(int(hh.GetNbinsX()/dic['bin']))
histos.append(hh)
cols.append(colors[p])
# fix names if needed
leg_name = p
if p.find('m_{Z}')>=0 : leg_name = p.replace('m_{Z}', 'm_{Z\'}' )
if p.find('m_{RSG}')>=0 : leg_name = p.replace('m_{RSG}','m_{G_{RS}}')
if p.find('vv')>=0 : leg_name = p.replace('vv','VV (V=Z/W)')
if p.find('vj')>=0 : leg_name = p.replace('vj','Vj (V=Z/W)')
if p.find('tt')>=0 : leg_name = p.replace('tt','t#bar{t}')
if p.find('Drell-Yan')>=0 and ana_tex.find("e^")>=0: leg_name = p.replace('Drell-Yan','pp #rightarrow Z/#gamma* #rightarrow e^{+}e^{-}')
if p.find('Drell-Yan')>=0 and ana_tex.find("mu^")>=0: leg_name = p.replace('Drell-Yan','pp #rightarrow Z/#gamma* #rightarrow #mu^{+}#mu^{-}')
if p.find('Drell-Yan')>=0 and ana_tex.find("tau^")>=0: leg_name = p.replace('Drell-Yan','pp #rightarrow Z/#gamma* #rightarrow #tau^{+}#tau^{-}')
if i > 0:
leg.AddEntry(hh,leg_name,"f")
else:
leg.AddEntry(hh,leg_name,"l")
i+=1
drawStack(filename, yl, leg, lt, rt, ff, pdir, log, stacksig, histos, cols, ana_tex)
print 'DONE.'
def produceStackedPlots(processes, selections, variables, colors, intLumi,
pdir, lt, rt, log, stacksig, hfile):
print ''
print 'Preparing stacked plots ...'
gROOT.SetBatch(True)
intLumiab = intLumi / 1e+06
yl = "events"
ff = FORMAT
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
if stacksig:
stackstr = 'stack'
else:
stackstr = 'nostack'
hfile.cd()
nsel = 0
legsize = 0.04 * float(len(processes))
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v, dic in variables.iteritems():
histos = []
i = 0
filename = '{}_{}_{}_{}'.format(v, selstr, stackstr, logstr)
leg = TLegend(0.60, 0.86 - legsize, 0.86, 0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.SetShadowColor(10)
leg.SetTextSize(0.035)
leg.SetTextFont(42)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
hh.Scale(intLumi)
# rebin if needed
hh.Rebin(int(hh.GetNbinsX() / dic['bin']))
histos.append(hh)
cols.append(colors[p])
if i > 0:
leg.AddEntry(hh, p, "f")
else:
leg.AddEntry(hh, p, "l")
i += 1
drawStack(filename, yl, leg, lt, rt, ff, pdir, log, stacksig,
histos, cols, dic)
print 'DONE.'
def produceStackedHackedPlots(processes, selections, variables, colors,
intLumi, pdir, lt, rt, log, stacksig, hfile):
print ''
print 'Preparing stacked HACKED plots ...'
gROOT.SetBatch(True)
intLumiab = intLumi / 1e+06
yl = "events"
ff = FORMAT
logstr = ''
if log:
logstr = 'log'
else:
logstr = 'lin'
stackstr = ''
if stacksig:
stackstr = 'stack'
else:
stackstr = 'nostack'
hfile.cd()
nsel = 0
legsize = 0.04 * float(len(processes))
for s in selections:
selstr = 'sel{}'.format(int(nsel))
nsel += 1
for v, dic in variables.iteritems():
histos = []
i = 0
filename = '{}_{}_{}_{}'.format(v, selstr, stackstr, logstr)
leg = TLegend(0.60, 0.86 - legsize, 0.86, 0.88)
leg.SetFillColor(0)
leg.SetFillStyle(0)
leg.SetLineColor(0)
leg.SetShadowColor(10)
leg.SetTextSize(0.035)
leg.SetTextFont(42)
cols = []
for p in processes:
hname = '{}_{}_{}'.format(p, selstr, v)
h = hfile.Get(hname)
hh = TH1D.Clone(h)
hh.Scale(intLumi)
if p == 'HH(#kappa_{#lambda}=1.00)':
print 'here'
hh.Scale(20)
r = ROOT.TRandom3(5)
import math
'''
if '#gamma#gamma + Jets' in p and 'haa_m' in filename and 'sel0' in filename:
mean = 0
for bin in xrange(1, hh.GetNbinsX()+1):
#print '#gamma#gamma + Jets' , hh.GetBinContent(bin)
mean += hh.GetBinContent(bin)
#hh.SetBinContent(bin,r.Poisson(1750))
#hh.SetBinContent(bin,r.Gaus(1000, 0.75*math.sqrt(1000)))
mean = mean/hh.GetNbinsX()
for bin in xrange(1, hh.GetNbinsX()+1):
#hh.SetBinContent(bin,r.Gaus(mean, 0.75*math.sqrt(mean)))
hh.SetBinContent(bin,r.Gaus(hh.GetBinContent(bin), 0.99*math.sqrt(mean)))
if 'j#gamma + Jets' in p and 'haa_m' in hname and 'sel0' in filename:
mean = 0
for bin in xrange(1,hh.GetNbinsX()+1):
#print 'j#gamma + Jets', hh.GetBinContent(bin)
mean += hh.GetBinContent(bin)
#hh.SetBinContent(bin,r.Poisson(1000))
#hh.SetBinContent(bin,r.Gaus(700, 0.75*math.sqrt(700)))
mean = mean/hh.GetNbinsX()
for bin in xrange(1, hh.GetNbinsX()+1):
#hh.SetBinContent(bin,r.Gaus(mean, 0.75*math.sqrt(mean)))
hh.SetBinContent(bin,r.Gaus(hh.GetBinContent(bin), 0.99*math.sqrt(mean)))
'''
print p, hname
'''
#hh.Smooth(1)
for bin in xrange(1, hh.GetNbinsX()+1):
#print '#gamma#gamma + Jets' , hh.GetBinContent(bin)
binc = hh.GetBinContent(bin)
hh.SetBinContent(bin,r.Poisson(binc))
'''
if 'j#gamma + Jets' in p and 'sel0' in filename and 'haa_m' in hname:
href = hfile.Get('#gamma#gamma + Jets_sel0_haa_m')
href2 = TH1D.Clone(href)
href2.Scale(intLumi)
#href2.Smooth(1)
for bin in xrange(1, hh.GetNbinsX() + 1):
#print '#gamma#gamma + Jets' , hh.GetBinContent(bin)
binc = 1.2 * href2.GetBinContent(bin)
hh.SetBinContent(bin, r.Poisson(binc))
'''
if 'j#gamma + Jets' in p and 'sel0' in filename and 'hbb_m' in hname:
href = hfile.Get('#gamma#gamma + Jets_sel0_hbb_m')
href2 = TH1D.Clone(href)
href2.Scale(intLumi)
#href2.Smooth(1)
for bin in xrange(1, hh.GetNbinsX()+1):
#print '#gamma#gamma + Jets' , hh.GetBinContent(bin)
binc = 1.2*href2.GetBinContent(bin)
hh.SetBinContent(bin,r.Poisson(binc))
'''
## for bbtahtah final state
if 'top pair' in p and 'sel0' in filename and 'bdt' in hname:
for bin in xrange(1, hh.GetNbinsX() + 1):
print 'top', bin, hh.GetBinContent(bin)
if bin == 47: hh.SetBinContent(bin, 300000.)
'''
## for bbtahtal final state
if 'top pair' in p and 'sel0' in filename and 'bdt' in hname:
for bin in xrange(1, hh.GetNbinsX()+1):
print 'top', bin , hh.GetBinContent(bin)
if bin == 44: hh.SetBinContent(bin,2500000.)
if bin == 46: hh.SetBinContent(bin,1500000.)
if bin == 47: hh.SetBinContent(bin,1200000.)
if bin == 49: hh.SetBinContent(bin,600000.)
if bin == 50: hh.SetBinContent(bin,450000.)
'''
'''
## for bbbb final state
if 'QCD' in p and 'sel0' in filename and 'bdt' in hname:
for bin in xrange(1, hh.GetNbinsX()+1):
print 'qcd', bin , hh.GetBinContent(bin)
if bin == 47: hh.SetBinContent(bin,220000000.)
if bin == 47: hh.SetBinContent(bin,150000000.)
if bin == 48: hh.SetBinContent(bin,110000000.)
if bin == 49: hh.SetBinContent(bin,75000000.)
if bin == 50: hh.SetBinContent(bin,50000000.)
'''
# rebin if needed
#if bin == 47: hh.SetBinContent(bin,450000.)
hh.Rebin(int(hh.GetNbinsX() / dic['bin']))
histos.append(hh)
cols.append(colors[p])
if i > 0:
leg.AddEntry(hh, p, "f")
else:
#leg.AddEntry(hh,p+ ' x20',"l")
leg.AddEntry(hh, p, "l")
i += 1
drawStack(filename, yl, leg, lt, rt, ff, pdir, log, stacksig,
histos, cols, dic)
print 'DONE.'
print 'K file = ', RAndKff[iRAndKff]['KffFile']
Kfile[iRAndKff] = TFile(RAndKff[iRAndKff]['KffFile'])
thelist[iRAndKff] = Kfile[iRAndKff].GetListOfKeys()
# Prepare numerator and denominator for K computation
for dirs in thelist[iRAndKff] :
# K numerator
if RAndKff[iRAndKff]['Regions'][iRegion]['kNum'] in dirs.GetName():
currentdir = dirs.ReadObj()
for subdir in currentdir.GetListOfKeys():
if subdir.GetName()!='events':
continue
# Numerator: starting with total data in the actual channel (ee or mm)
K_ff_data[iRAndKff][iRegion]['NumDATA'] = Kfile[iRAndKff].Get(dirs.GetName()+'/'+subdir.GetName()+'/histo_DATA')
K_ff_data[iRAndKff][iRegion]['kNum'] = TH1D.Clone(K_ff_data[iRAndKff][iRegion]['NumDATA'])
print("Total data = {}".format(K_ff_data[iRAndKff][iRegion]['kNum'].Integral()))
currentsubdir = subdir.ReadObj()
previoussample=''
for sample in currentsubdir.GetListOfKeys():
if sample.GetName()==previoussample:
continue
previoussample = sample.GetName()
# Numerator: subtract backgrounds
if not 'histo_DY' in sample.GetName() and not 'histo_DATA' in sample.GetName():
K_ff_data[iRAndKff][iRegion][sample.GetName()] = Kfile[iRAndKff].Get(dirs.GetName()+'/'+subdir.GetName()+'/'+sample.GetName())
K_ff_data[iRAndKff][iRegion]['kNum'].Add(K_ff_data[iRAndKff][iRegion][sample.GetName()],-1)
# K denominator
if RAndKff[iRAndKff]['Regions'][iRegion]['kDen'] in dirs.GetName():
currentdir = dirs.ReadObj()
if not "histo_DY" in process.GetName(
) and not "histo_DATA" in process.GetName():
# Subtract from Data non-DY MC
print(process.GetName())
h_tmp = my_file.Get(cut + "/" + variable + "/" +
process.GetName())
h_Data.Add(h_tmp, -1)
print("Data integral after MC subtraction: {}".format(
h_Data.Integral()))
# Get DY MC histogram
h_DY = my_file.Get(cut + "/" + variable + "/histo_DY")
print("DY MC integral: {}".format(h_DY.Integral()))
# Prepare Data/MC histogram ratio
h_Ratio = TH1D.Clone(h_Data)
h_Ratio.Divide(h_DY)
print("Ratio integral: {}".format(h_Ratio.Integral()))
h_Ratio.Draw()
# Define Fit range
x_min = h_Ratio.GetXaxis().GetXmin()
x_max = h_Ratio.GetXaxis().GetXmax()
# Fit
h_Ratio.Fit(fit_func, "", "", x_min, x_max)
# Fit result
fit_result = h_Ratio.FindObject(fit_func)
# Fitting parameters
