def main() :
global samplesPhoton
baseDirPhoton = '/data/users/jkunkle/RecoPhoton/Photon_2015_11_23'
treename = 'tupel/EventTree'
filename = 'tree.root'
sampleConfPhoton = 'Modules/SinglePhoton15.py'
xsfile = 'cross_sections/photon15.py'
lumi=2093.
samplesPhoton = SampleManager(baseDirPhoton, treename, filename=filename, xsFile=xsfile, lumi=lumi, quiet=options.quiet)
samplesPhoton .ReadSamples( sampleConfPhoton )
if options.save :
ROOT.gROOT.SetBatch(True)
if options.outputDir is not None :
samplesPhoton.start_command_collection()
if options.makeAll or options.makePhotonComp :
MakePhotonCompPlots( save=options.save, detail=options.detailLevel )
samplesPhoton.run_commands(nFilesPerJob=1, treeName=treename)
def main():
global samples
if not options.baseDir.count('/eos/') and not os.path.isdir(
options.baseDir):
print 'baseDir not found!'
return
samples = SampleManager(options.baseDir,
options.treeName,
mcweight=options.mcweight,
treeNameModel=options.treeNameModel,
filename=options.fileName,
base_path_model=options.baseDirModel,
xsFile=options.xsFile,
lumi=options.lumi,
readHists=options.readHists,
quiet=options.quiet)
if options.samplesConf is not None:
samples.ReadSamples(options.samplesConf)
print 'Samples ready.\n'
print 'The draw syntax follows that of TTree.Draw. Examples : '
print 'samples.Draw(\'met_et\', \'EventWeight && passcut_ee==1\', \'(300, 0, 300)\'\n'
print 'The first argument is a branch in the tree to draw'
print 'The second argument is a set of cuts and/or weights to apply'
print 'The third argument are the bin limits to use \n'
print 'To see all available branches do ListBranches()'
def run_sp(num_path, den_path, num_conf, den_conf):
samples_num = SampleManager(num_path, 1.0, 'photons')
samples_den = SampleManager(den_path, 1.0, 'photons')
read_samples(samples_num, num_conf)
read_samples(samples_den, den_conf)
draw_command_num_incljet_0conv = 'EventWeight * ( ph_n == 1 && ph_pt > 25.0 && ph_nConvTrk == 0 && ph_pass_tight && ph_corriso_30 < 2000 )'
draw_command_num_incljet_2conv = 'EventWeight * ( ph_n == 1 && ph_pt > 25.0 && ph_nConvTrk == 2 && ph_pass_tight && ph_corriso_30 < 2000 )'
num_dist = 'ph_pt'
den_dist = 'ph_pt'
binning = [
25, 35, 45, 55, 65, 75, 85, 105, 125, 150, 175, 200, 250, 300, 400,
1000
]
MakeRatio(samples_num, samples_den, num_dist, den_dist,
draw_command_num_incljet_0conv, draw_command_num_incljet_0conv,
binning, options.outputDir + '/AllJet/Conv0/hist.root')
MakeRatio(samples_num, samples_den, num_dist, den_dist,
draw_command_num_incljet_2conv, draw_command_num_incljet_2conv,
binning, options.outputDir + '/AllJet/Conv2/hist.root')
def main() :
global samplesMuMu
global samplesElEl
baseDirMuMu = '/afs/cern.ch/work/j/jkunkle/private/CMS/Wgamgam/Output/TAndPMuMu_2014_11_27'
baseDirElEl = '/afs/cern.ch/work/j/jkunkle/private/CMS/Wgamgam/Output/TAndPElEl_2014_11_27'
treename = 'ggNtuplizer/EventTree'
filename = 'tree.root'
sampleConf = 'Modules/TAndPSampleConf.py'
samplesMuMu = SampleManager(baseDirMuMu, treename, filename=filename, xsFile=options.xsFile, lumi=options.lumi, quiet=options.quiet)
samplesElEl = SampleManager(baseDirElEl, treename, filename=filename, xsFile=options.xsFile, lumi=options.lumi, quiet=options.quiet)
samplesMuMu .ReadSamples( sampleConf )
samplesElEl .ReadSamples( sampleConf )
if options.outputDir is not None :
ROOT.gROOT.SetBatch(True)
samplesMuMu.start_command_collection()
samplesElEl.start_command_collection()
MakeMuMuPlots( )
MakeElElPlots( )
samplesMuMu.run_commands()
samplesElEl.run_commands()
def main() :
global sampManLG
global sampManLLG
global sampManFit
base_dir_lg = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepGammaNoPhID_2015_06_29/'
base_dir_llg = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepLepGammaNoPhID_2015_06_29/'
fit_dir = options.fitPath
sampManLG = SampleManager(base_dir_lg, options.treeName,filename=options.fileName, xsFile=options.xsFile, lumi=options.lumi, quiet=options.quiet)
sampManLLG = SampleManager(base_dir_llg, options.treeName,filename=options.fileName, xsFile=options.xsFile, lumi=options.lumi, quiet=options.quiet)
sampManFit = SampleManager(fit_dir, options.treeName,filename=options.fileName, xsFile=options.xsFile, lumi=options.lumi, quiet=options.quiet)
if options.samplesConf is not None :
sampManLG.ReadSamples( options.samplesConf )
sampManLLG.ReadSamples( options.samplesConf )
sampManFit.ReadSamples( options.samplesConf )
if options.outputDir is not None :
if not os.path.isdir( options.outputDir ) :
os.makedirs( options.outputDir )
#RunClosureFitting( outputDir = None )
if options.syst_file is not None :
load_syst_file( options.syst_file )
RunNomFitting( outputDir = options.outputDir, ch=options.channel, fitvar=options.fitvar, pid=options.pid)
def main() :
global samplesLLG
baseDirLLG = '/afs/cern.ch/work/j/jkunkle/private/CMS/Wgamgam/Output/LepLepGammaNoPhID_2015_07_16'
treename = 'ggNtuplizer/EventTree'
filename = 'tree.root'
sampleConfLLG = 'Modules/ZJetsTemplates.py'
samplesLLG = SampleManager(baseDirLLG , treename, filename=filename, xsFile='cross_sections/wgamgam.py', lumi=19400)
samplesLLG .ReadSamples( sampleConfLLG )
ROOT.gROOT.SetBatch(True)
binning = { 'EB' : ( 30, 0, 0.03 ), 'EE' : ( 30, 0, 0.09 ) }
pt_bins = [(15, 25), (25,40), (40,70), (70,1000000)]
all_hists = []
for eta in ['EB', 'EE'] :
for ptmin, ptmax in pt_bins :
samplesLLG.Draw('ph_sigmaIEIE[0]', 'PUWeight * ( mu_passtrig25_n>0 && mu_n==2 && ph_mediumNoSIEIE_n==1 && ph_HoverE12[0] < 0.05 && fabs( m_leplep-91.2 ) < 5 && leadPhot_sublLepDR >1 && leadPhot_leadLepDR>1 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] ) ' %( ptmin, ptmax, eta ), binning[eta] )
hist_data = samplesLLG.get_samples( name='Muon' )[0]
hist_bkg = samplesLLG.get_samples( name='RealPhotonsZg' )[0]
hist_subtracted = hist_data.hist.Clone( 'Subtracted_%s_%d-%d' %( eta, ptmin, ptmax ))
hist_subtracted.Add( hist_bkg.hist, -1 )
all_hists.append( hist_data.hist.Clone( 'Data_%s_%d-%d' %( eta, ptmin, ptmax ) ) )
all_hists.append( hist_bkg.hist.Clone( 'Zg_%s_%d-%d' %( eta, ptmin, ptmax ) ) )
all_hists.append( hist_subtracted)
out_file = ROOT.TFile.Open( options.outputFile, 'RECREATE' )
for hist in all_hists :
hist.Write()
out_file.Close()
print '^_^ Finished ^_^'
def run_caf(dil_path, gj_path, dil_conf, gj_conf):
samples_dil_ee = SampleManager(options.dil_path, 1.0, 'HWWTree_ee')
samples_dil_mm = SampleManager(options.dil_path, 1.0, 'HWWTree_mm')
samples_gj = SampleManager(options.gj_path,
1.0,
'photons',
filename='ntuple.root')
read_samples(samples_dil_ee, options.dil_conf)
read_samples(samples_dil_mm, options.dil_conf)
read_samples(samples_gj, options.gj_conf)
draw_command_dil_ee_incldphi_incljet = 'mcEventWeight * ( isLowPtCand==0 && Mll > 12000 && Mll < 60000 && Ptll > 30 && (lepID0*lepID1==-121) )'
draw_command_dil_mm_incldphi_incljet = 'mcEventWeight * ( isLowPtCand==0 && Mll > 12000 && Mll < 60000 && Ptll > 30 && (lepID0*lepID1==-169) )'
draw_command_dil_ee_incldphi_jetcut = 'mcEventWeight * ( isLowPtCand==0 && Mll > 12000 && Mll < 60000 && Ptll > 30 && (lepID0*lepID1==-121) && m_jet_n>1 )'
draw_command_dil_mm_incldphi_jetcut = 'mcEventWeight * ( isLowPtCand==0 && Mll > 12000 && Mll < 60000 && Ptll > 30 && (lepID0*lepID1==-169) && m_jet_n>1 )'
draw_command_gj_incljet = 'EventWeight * ( ph_pt > 30.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_jetcut = 'EventWeight * ( ph_pt > 30.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_n > 1 )'
dil_dist = 'Ptll'
gj_dist = 'ph_pt'
binning_incljet = range(30, 80) + [
80, 85, 90, 95, 100, 110, 120, 130, 150, 200, 300, 500, 1000
]
binning_jetcut = range(30, 80) + [
80, 85, 90, 95, 100, 110, 120, 130, 150, 200, 300, 500, 1000
]
MakeRatio(samples_dil_ee, samples_gj, dil_dist, gj_dist,
draw_command_dil_ee_incldphi_incljet, draw_command_gj_incljet,
binning_incljet, options.outputDir + '/AllJet/EE/hist.root')
MakeRatio(samples_dil_mm, samples_gj, dil_dist, gj_dist,
draw_command_dil_mm_incldphi_incljet, draw_command_gj_incljet,
binning_incljet, options.outputDir + '/AllJet/MM/hist.root')
MakeRatio(samples_dil_ee, samples_gj, dil_dist, gj_dist,
draw_command_dil_ee_incldphi_jetcut, draw_command_gj_jetcut,
binning_jetcut, options.outputDir + '/NJet2/EE/hist.root')
MakeRatio(samples_dil_mm, samples_gj, dil_dist, gj_dist,
draw_command_dil_mm_incldphi_jetcut, draw_command_gj_jetcut,
binning_jetcut, options.outputDir + '/NJet2/MM/hist.root')
def main():
global samplesWgg
global samplesWggComb
global samplesWggBkgSub
sampleConfWgg = 'Modules/WggFinal.py'
sampleConfWggBkgSub = 'Modules/WggFinalBkgSub.py'
samplesWgg = SampleManager(options.baseDir,
filename='hist.root',
xsFile='cross_sections/wgamgam.py',
lumi=19400,
readHists=True)
samplesWggComb = SampleManager(options.baseDirComb,
filename='hist.root',
xsFile='cross_sections/wgamgam.py',
lumi=19400,
readHists=True)
samplesWggBkgSub = SampleManager(options.baseDirComb,
filename='hist.root',
xsFile='cross_sections/wgamgam.py',
lumi=19400,
readHists=True)
samplesWgg.ReadSamples(sampleConfWgg)
samplesWggComb.ReadSamples(sampleConfWgg)
samplesWggBkgSub.ReadSamples(sampleConfWggBkgSub)
if options.outputDir is not None:
ROOT.gROOT.SetBatch(True)
if options.electron:
MakeElectronPlots(options.outputDir, suffix='', ylabel='Events / bin')
MakeElectronPlots(options.outputDir,
suffix='_perGeV',
ylabel='Events / 5 GeV',
logy=1)
if options.muon:
MakeMuonPlots(options.outputDir, suffix='', ylabel='Events / bin')
MakeMuonPlots(options.outputDir,
suffix='_perGeV',
ylabel='Events / 5 GeV',
logy=1)
if options.combined:
MakeCombinedPlots(options.outputDir)
print '^.^ FINSHED ^.^'
def main() :
#sampManMuG= SampleManager( options.baseDirMuG, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManElG= SampleManager( baseDirElG, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
#sampManMuG.ReadSamples( _SAMPCONF )
sampManElG.ReadSamples( _SAMPCONF )
#sampManMuG.outputs = {}
sampManElG.outputs = {}
sel_base_mu = 'mu_pt30_n==1 && mu_n==1'
#sel_base_el = 'ph_n==1 && el_n==1'
#sel_base_el = 'ph_n>=1 && el_n>=1'
sel_base_el = '1'
sel_base_el = 'ph_n>=1 && el_n==1 &&ph_pt[0]>50'
sel_base_el = 'ph_n>=1 && el_n==1 &&!( ph_eta[0]<0&&ph_phi[0]>2.3&&ph_phi[0]<2.7)&&!(ph_phi[0]>1.2&&ph_phi[0]<1.5) '
sel_base_el = 'ph_n>=1 && el_n==1'
samp = 'Z+jets'
if options.data:
samp='Data'
#eta_cuts = ['EB', 'EE']
eta_cuts = ['EB']
workspaces_to_save = {}
selections = { 'base' : {
# 'mu' : {'selection' : sel_base_mu },
'el' : { 'selection' : sel_base_el },
},
}
elefake = ROOT.RooWorkspace( 'elefake' )
#make_efake( sampManElG, 'Z+jets', sel_base_el,'EB', 'ph_eta', suffix='noOvLrm', closure=False, workspace=elefake, overlaprm=0)
f1 = ROOT.TFile("%s/output.root"%(options.outputDir),"RECREATE")
#undo scaling by cross section
# print "scale ", sampManElG.get_samples(name="DYJetsToLL_M-50")[0].scale
if not options.data: sampManElG.get_samples(name="DYJetsToLL_M-50")[0].scale=1.
#closure( sampManElG, samp, sel_base_el,'EB', plot_var = 'm_lep_ph',var = "ph_eta",varbins=np.linspace(-1.4,1.4,15),xtitle="photon #eta",mode=2)
# closure( sampManElG, samp, sel_base_el,'EB', plot_var = 'm_lep_ph',var = "ph_phi",varbins=np.linspace(-3.1416,3.1416,21),xtitle="photon #phi",mode=2)
# closure( sampManElG, samp, sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=[-2.4,-1.8,-1.4,-1,-0.6,-0.3,0,0.3,0.6,1,1.4,1.8,2.4], var="ph_eta")
#closure( sampManElG, samp, sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=range(20,100,10)+[150,160], mode=1)
#closure( sampManElG, samp, sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=[20,30,40,70,100,110], mode=1)
#sampManElG.closure( samp, sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=[20,30,40,50,70,90,110,150,160], mode=1)
sampManElG.closure( samp, sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=[0,30,40,50,60,80], mode=1)
#closure( sampManElG, samp, sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=[40,1000])
# closure( sampManElG, samp, sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=[20,1000])
# closure( sampManElG, samp, sel_base_el,'EB', plot_var = 'm_lep_ph',var="jet_n",varbins=range(0,10), mode=2)
# closure( sampManElG, 'Wgamma', sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=[0,50,250])
#elefake.writeToFile( '%s/%s.root' %( options.outputDir,elefake.GetName() ) )
#for key, can in sampManElG.outputs.iteritems() :
f1.Write()
f1.Close()
def makeMetPlots(baseDir, sampleConf, outputDir=None):
sampMan = SampleManager(baseDir, _TREENAME, filename=_FILENAME)
sampMan.ReadSamples(sampleConf)
samp_name = 'Comb'
samp = sampMan.get_samples(name=samp_name)
if not samp:
print('Could not find sample!')
return
binning_x = list(range(0, 50, 5)) + list(range(50, 300, 10)) + list(
range(300, 600, 20)) + list(range(600, 2000, 100)) + [2000, 3000]
binning_y = list(range(-1000, 1000, 10))
var = 'truemet_pt-recomet_pt:truemet_pt'
sampMan.create_hist(samp[0], var, '', (binning_x, binning_y))
thishist = samp[0].hist.Clone('met')
thishist_px = thishist.ProfileX('met_px')
can = ROOT.TCanvas('metRes', 'metRes')
thishist_px.Draw()
can.SetLogx()
if outputDir:
can.SaveAs('%s/MetResolution.pdf' % (outputDir))
else:
input('cont')
def main() :
#if options.outputDir: f1 = ROOT.TFile("%s/output.root"%(options.outputDir),"RECREATE")
sampManMuG= SampleManager( options.baseDirMuG, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI, readHists=True , weightHistName = "weighthist")
sampManMuG.ReadSamples( _SAMPCONF )
sampManElG= SampleManager( options.baseDirElG, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI ,readHists=True , weightHistName = "weighthist")
sampManElG.ReadSamples( _SAMPCONF )
print options.baseDirElG
#samples = sampManMuG
for ch, samples in zip(["mu","el"],[sampManMuG,sampManElG]):
#for ch, samples in [("el",sampManElG),]:
#hname = samples[0].walk_text()
#hname = [h[:-7] for h in hname if "_before" in h] # choose cut histograms
hname = hnames
labelname = "%i Muon Channel" %options.year if ch == "mu" else "%i Electron Channel" %options.year
#samplname = "WGToLNuG-amcatnloFXFX"
samplname = "AllTop"
for h in hname:
## prepare config
xlabel = h.split("/Filter")[-1]
hist_config = {"colors":[4,2],"drawopt":"hist","logy":True,"xunit":"","xlabel":xlabel, "ymax_scale":1.4}
label_config = {"extra_label":labelname, "extra_label_loc":(.17,.82)}
legend_config = {"legend_entries":[samplname+" before",samplname+" after"]}
## draw histogram
samples.CompareHists([h+"_before", h+"_after"],[samplname]*2,
"",hist_config=hist_config, label_config=label_config, legend_config=legend_config)
#samples[-1].hist.GetYaxis().UnZoom()
## save histogram
samples.SaveStack(xlabel+"%i%s.pdf" %(options.year, ch), options.outputDir, "base")
def ReadMuElSamples(baseDirMu=None, baseDirEl=None):
if baseDirMu is None:
baseDirMu = options.baseDirMu
if baseDirEl is None:
baseDirEl = options.baseDirEl
sampManMu = SampleManager(baseDirMu,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI)
sampManEl = SampleManager(baseDirEl,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI)
if options.samplesConf is not None:
sampManMu.ReadSamples(options.samplesConf)
sampManEl.ReadSamples(options.samplesConf)
else:
print 'Must provide a sample configuration. Exiting'
return
return sampManMu, sampManEl
def makeMuPlots(baseDirMu, sampleConf, outputDir=None, trig=False):
sampMan = SampleManager(baseDirMu, _TREENAME, filename=_FILENAME)
sampMan.ReadSamples(sampleConf)
samp_name = 'Comb'
samp = sampMan.get_samples(name=samp_name)
if not samp:
print('Could not find sample!')
return
#binning = range( 0, 50, 5) + range( 50, 500, 10 ) + range( 500, 2000, 100 )
binning = list(range(0, 50, 5)) + list(range(50, 300, 10)) + list(
range(300, 600, 20)) + list(range(600, 2000, 100)) + [2000, 3000]
var = 'truemu_pt'
name = 'MuonEff'
if trig:
labels = [
'IsoMu24', 'IsoTkMu24', 'IsoTkMu24 || IsoMu24', 'Mu17_Photon30'
]
colors = [ROOT.kBlack, ROOT.kRed, ROOT.kGreen, ROOT.kBlue]
selections = [
'', 'passTrig_IsoMu24', 'passTrig_IsoTkMu24',
'passTrig_IsoMu24 || passTrig_IsoTkMu24 ',
'passTrig_Mu17_Photon30_CaloIdL_L1ISO'
]
name += 'Trig'
else:
#labels = ['Container Muons', 'Triggered Muons', 'Tight Muons', 'Triggered Tight Muons']
labels = ['Container Muons', 'Tight Muons']
colors = [ROOT.kBlack, ROOT.kRed]
selections = [
'', 'hasTruthMatch', 'hasTruthMatch && muprobe_passTight '
]
hists = []
for idx, selection in enumerate(selections):
sampMan.create_hist(samp[0], var, selection, binning)
thissamp = sampMan.get_samples(name=samp_name)
hists.append(thissamp[0].hist.Clone('hist_%d' % idx))
print(hists)
canvas = ROOT.TCanvas(name, name)
DrawHists(canvas, hists, colors, labels, outputDir)
def main() :
global samples
sampleConf = 'Modules/StudyWgammaFinal.py'
samples = SampleManager(options.baseDir, filename='hist.root', readHists=True)
for dirname in os.listdir( options.baseDir ) :
if os.path.isdir( '%s/%s' %( options.baseDir, dirname ) ) :
samples.AddSample( dirname, path=dirname )
sample_list = samples.get_samples()
sample_pairs = []
for s in sample_list :
if s.name.count('chIsoCorr') :
samp_sieie = s.name.replace( 'chIsoCorr', 'sigmaIEIE' )
sample_pairs.append( ( s.name, samp_sieie ) )
output_dir = options.baseDir
#output_dir = None
MakeRatioComparisons( sample_pairs, ['elz_sb_fine_medium_CutMet0', 'elz_sb_fine_tight_CutMet0', 'elz_sb_two_tight_CutMet0'], ['Medium range', 'Tight range', 'Two bin fit'], output_dir, name_postfix = 'comp_elz_fitmethods' )
MakeRatioComparisons( sample_pairs, ['muz_sb_fine_medium_CutMet0', 'muz_sb_fine_tight_CutMet0', 'muz_sb_two_tight_CutMet0'], ['Medium range', 'Tight range', 'Two bin fit'], output_dir,name_postfix = 'comp_muz_fitmethods' )
MakeRatioComparisons( sample_pairs, ['mu_cr_fine_medium_CutMet0', 'mu_cr_fine_tight_CutMet0', 'mu_cr_two_medium_CutMet0'], ['Medium range', 'Tight range', 'Two bin fit'], output_dir, name_postfix='comp_mu_fitmethods' )
MakeRatioComparisons( sample_pairs, ['el_cr_fine_medium_CutMet0', 'el_cr_fine_tight_CutMet0', 'el_cr_two_medium_CutMet0'], ['Medium range', 'Tight range', 'Two bin fit'], output_dir, name_postfix='comp_el_fitmethods' )
MakeRatioComparisons( sample_pairs, ['mu_cr_fine_tight_CutMet0', 'mu_sb_fine_tight_CutMet0', ], ['Control region template', 'Sideband template'], output_dir, name_postfix='comp_mu_faketemplates' )
MakeRatioComparisons( sample_pairs, ['el_cr_fine_tight_CutMet0', 'el_sb_fine_tight_CutMet0', ], ['Control region template', 'Sideband template'], output_dir, name_postfix='comp_el_faketemplates' )
MakeRatioComparisons( sample_pairs, ['mu_cr_two_tight_CutMet0', 'mu_sb_two_tight_CutMet0', ], ['Control region template', 'Sideband template'], output_dir, name_postfix='comp_mu_twobin_faketemplates' )
MakeRatioComparisons( sample_pairs, ['el_cr_two_tight_CutMet0', 'el_sb_two_tight_CutMet0', ], ['Control region template', 'Sideband template'], output_dir, name_postfix='comp_el_twobin_faketemplates' )
MakeComparison( sample_pairs, 'muz_sb_fine_tight_CutMet0', output_dir=output_dir )
MakeComparison( sample_pairs, 'mu_sb_fine_tight_CutMet0', output_dir=output_dir )
def main():
global samplesLLG
baseDirLLG = '/afs/cern.ch/work/j/jkunkle/private/CMS/Wgamgam/Output/LepLepGammaNoPhID_2015_07_16'
treename = 'ggNtuplizer/EventTree'
filename = 'tree.root'
sampleConfLLG = 'Modules/ZJetsTemplates.py'
samplesLLG = SampleManager(baseDirLLG,
treename,
filename=filename,
xsFile='cross_sections/wgamgam.py',
lumi=19400)
samplesLLG.ReadSamples(sampleConfLLG)
ROOT.gROOT.SetBatch(True)
binning = {'EB': (30, 0, 0.03), 'EE': (30, 0, 0.09)}
pt_bins = [(15, 25), (25, 40), (40, 70), (70, 1000000)]
all_hists = []
for eta in ['EB', 'EE']:
for ptmin, ptmax in pt_bins:
samplesLLG.Draw(
'ph_sigmaIEIE[0]',
'PUWeight * ( mu_passtrig25_n>0 && mu_n==2 && ph_mediumNoSIEIE_n==1 && ph_HoverE12[0] < 0.05 && fabs( m_leplep-91.2 ) < 5 && leadPhot_sublLepDR >1 && leadPhot_leadLepDR>1 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] ) '
% (ptmin, ptmax, eta), binning[eta])
hist_data = samplesLLG.get_samples(name='Muon')[0]
hist_bkg = samplesLLG.get_samples(name='RealPhotonsZg')[0]
hist_subtracted = hist_data.hist.Clone('Subtracted_%s_%d-%d' %
(eta, ptmin, ptmax))
hist_subtracted.Add(hist_bkg.hist, -1)
all_hists.append(
hist_data.hist.Clone('Data_%s_%d-%d' % (eta, ptmin, ptmax)))
all_hists.append(
hist_bkg.hist.Clone('Zg_%s_%d-%d' % (eta, ptmin, ptmax)))
all_hists.append(hist_subtracted)
out_file = ROOT.TFile.Open(options.outputFile, 'RECREATE')
for hist in all_hists:
hist.Write()
out_file.Close()
print '^_^ Finished ^_^'
def main():
parent_dir = handle_arguments()
# Parse parent dir
samples = setup_samples(parent_dir)
# Create sample manager
sample_manager = SampleManager(samples)
# Print global stats
#sample_manager.print_global_summary()
#sample_manager.print_module_stats()
# Wait for input
read_input(sample_manager)
def main():
global samples
baseDir = '/afs/cern.ch/work/j/jkunkle/private/CMS/Wgamgam/Output/SingleLepton_2014_09_03/'
treename = 'ggNtuplizer/EventTree'
filename = 'tree.root'
sampleConf = 'Modules/Wgamgam.py'
samples = SampleManager(baseDir,
treename,
filename=filename,
xsFile=options.xsFile,
lumi=options.lumi,
quiet=options.quiet)
samples.ReadSamples(sampleConf)
#if options.save :
# ROOT.gROOT.SetBatch(True)
MakePredictions(save=False)
def main() :
width_colors = { '0.01' : ROOT.kRed, '1' : ROOT.kBlack, '5' : ROOT.kGreen, '10' : ROOT.kBlue, '20' : ROOT.kMagenta }
mass_colors = { 200 : ROOT.kBlack, 250 : ROOT.kRed, 300 : ROOT.kBlue, 350 : ROOT.kGreen, 400 : ROOT.kMagenta, 450 : ROOT.kOrange, 500 : ROOT.kCyan,
600 : ROOT.kBlack, 700 : ROOT.kRed, 800 : ROOT.kBlue, 900 : ROOT.kGreen, 1000 : ROOT.kMagenta, 1200 : ROOT.kOrange, 1400 : ROOT.kCyan,
1600 : ROOT.kBlack, 1800: ROOT.kRed, 2000 : ROOT.kBlue, 2200 : ROOT.kGreen, 2400 : ROOT.kMagenta, 2600 : ROOT.kOrange, 2800 : ROOT.kCyan, 3000 : ROOT.kBlack,
3500 : ROOT.kRed, 4000 : ROOT.kBlue,
}
sampMan = SampleManager(options.baseDir, filename='validation.root', readHists=True)
sampManMass = SampleManager(options.baseDir, filename='validation.root', readHists=True)
samples_lepnu = []
samples_qq = []
dirBaseName = 'MadGraphChargedResonance'
if options.pythia :
dirBaseName = 'PythiaChargedResonance'
for dirname in os.listdir( options.baseDir ) :
res = re.match( '%s_(WGToLNuG|WGToJJG)_M(\d{3,4})_width(0p01|\d{1,2})' %dirBaseName, dirname )
if res is not None :
mass = int( res.group(2) )
print(dirname)
print(res.group(3))
if res.group(3) == '0p01' :
width = '0.01'
else :
width = res.group(3)
if res.group(1) == 'WGToLNuG' :
continue
samples_lepnu.append( {'name' : dirname, 'mass' : mass, 'width' : width} )
elif res.group(1) == 'WGToJJG' :
samples_qq.append( {'name' : dirname, 'mass' : mass, 'width' : width} )
sampMan.AddSample( dirname, path=dirname, isActive=False, isSignal=True, sigLineStyle=1, sigLineWidth=1, plotColor=width_colors[width] )
sampManMass.AddSample( dirname, path=dirname, isActive=False, isSignal=True, sigLineStyle=1, sigLineWidth=1, plotColor=mass_colors[mass] )
if options.pythia :
samples_qq = samples_lepnu
# make the list of masses unique
draw_width_comp_hists( sampMan, 'm_lep_nu_ph', samples_lepnu, prefix='WidthCompare_ChargedResonance_WGToLNuG', xlabel='Resonance Mass [GeV]' )
draw_width_comp_hists( sampMan, 'm_q_q_ph', samples_qq, prefix='WidthCompare_ChargedResonance_WGToJJG', xlabel='Resonance Mass [GeV]' )
draw_mass_comp_hists( sampManMass, 'm_lep_nu_ph', samples_lepnu, prefix='MassCompare_ChargedResonance_WGToLNuG', xlabel='Resonance Mass [GeV]' )
draw_mass_comp_hists( sampManMass, 'm_q_q_ph', samples_qq, prefix='MassCompare_ChargedResonance_WGToJJG', xlabel='Resonance Mass [GeV]' )
def makeElPlots(baseDir, sampleConf, outputDir=None, trig=False):
sampMan = SampleManager(baseDir, _TREENAME, filename=_FILENAME)
sampMan.ReadSamples(sampleConf)
samp_name = 'Comb'
samp = sampMan.get_samples(name=samp_name)
if not samp:
print('Could not find sample!')
return
#binning = range( 0, 50, 5) + range( 50, 500, 10 ) + range( 500, 2000, 100 )
binning = list(range(0, 50, 5)) + list(range(50, 300, 10)) + list(
range(300, 600, 20)) + list(range(600, 2000, 100)) + [2000, 3000]
var = 'trueel_pt'
name = 'ElectronEff'
if trig:
labels = ['Ele24_eta2p1_WPTight_Gsf']
colors = [ROOT.kBlack]
selections = ['', 'passTrig_Ele27_eta2p1_WPTight_Gsf']
name += 'Trig'
else:
labels = [
'Container Electrons', 'Loose Electrons', 'Medium Electrons',
'Tight Electrons'
]
colors = [ROOT.kBlack, ROOT.kRed, ROOT.kGreen, ROOT.kBlue]
selections = [
'', 'hasTruthMatch', 'hasTruthMatch && elprobe_passLoose ',
'hasTruthMatch && elprobe_passMedium ',
'hasTruthMatch && elprobe_passTight '
]
hists = []
for idx, selection in enumerate(selections):
sampMan.create_hist(samp[0], var, selection, binning)
thissamp = sampMan.get_samples(name=samp_name)
hists.append(thissamp[0].hist.Clone('hist_%d' % idx))
canvas = ROOT.TCanvas(name, name)
DrawHists(canvas, hists, colors, labels, outputDir)
def main() :
sampMan = SampleManager( options.baseDir, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
if options.samplesConf is not None :
sampMan.ReadSamples( options.samplesConf )
else :
print 'Must provide a sample configuration. Exiting'
return
var = 'mt_res'
selection = 'mu_n==1 && ph_n==1'
binning = ( 100, 0, 5000 )
bkg_samp = 'MCBackground'
sampMan.create_hist( bkg_samp, var, selection, binning )
bkg_hist = sampMan.get_samples( name=bkg_samp)[0].hist
sig_normalizations = {}
for samp in sampMan.get_samples( ) :
if samp.name.count ( 'ResonanceMass' ) and samp.name.count( 'width0p01' ) :
sampMan.create_hist( samp, var, selection, binning )
samp_bins = []
for ibin in range( 0, samp.hist.GetNbinsX() ) :
samp_bins.append( (samp.hist.GetBinContent( ibin+1 ), ibin+1 ) )
samp_bins.sort()
print samp_bins
peak_sum = samp_bins[-1][0]
bkg_sum = bkg_hist.GetBinContent( samp_bins[-1][1] )
print 'Samp = %s, sig = %f, bkg = %f' %(samp.name, peak_sum, bkg_sum )
sig_normalizations[samp.name] = float( bkg_sum )/peak_sum
print sig_normalizations
invzlt = '&& abs(m_lep_ph-91)>%i'
csvmed = " && jet_CSVMedium_n>1"
nophmatch = "&& !(ph_truthMatchPh_dr[0]<ph_truthMatchEl_dr[0])"
phmatch = "&& (ph_truthMatchPh_dr[0]<ph_truthMatchEl_dr[0])"
flatphi = "ph_phi[0]+3.1416*(1+2*(ph_eta[0]>0))"
UNBLIND = "ph_hasPixSeed[0]==1 || met_pt<40"
weight = "PUWeight*NLOWeight"
pie = 3.1416
## read MuG samples
sampManMuG = SampleManager(options.baseDirMuG,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI * lumiratio,
readHists=False,
weightHistName="weighthist",
dataFrame=options.dataFrame)
## read ElG samples
sampManElG = SampleManager(options.baseDirElG,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI * lumiratio,
readHists=False,
weightHistName="weighthist",
dataFrame=options.dataFrame)
## read MuMu samples
def main():
#sampManMuG = SampleManager( options.baseDirMuG, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManMuG = SampleManager(options.baseDirMuG,
_TREENAME,
filename=_FILENAME)
#sampManElG = SampleManager( options.baseDirElG, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManElG = SampleManager(options.baseDirElG,
_TREENAME,
filename=_FILENAME)
sampManMuG.ReadSamples(_SAMPCONF)
sampManElG.ReadSamples(_SAMPCONF)
sampManMuG.outputs = OrderedDict()
sampManMuG.fitresults = OrderedDict()
sampManMuG.chi2 = OrderedDict()
sampManMuG.chi2prob = OrderedDict()
sampManElG.outputs = OrderedDict()
sampManElG.fitresults = OrderedDict()
sampManElG.chi2 = OrderedDict()
sampManElG.chi2prob = OrderedDict()
sel_base_mu = 'mu_pt30_n==1 && mu_n==1'
sel_base_el = 'el_pt30_n==1 && el_n==1'
sel_jetveto_mu = sel_base_mu + ' && jet_n == 0 '
sel_jetveto_el = sel_base_el + ' && jet_n == 0 '
#eta_cuts = ['EB', 'EE']
eta_cuts = ['EB']
workspaces_to_save = {}
xmin_m = 60
xmax_m = 4000
bin_width_m = 20
xmin_pt = xmin_m / 2
if xmin_pt < 50:
xmin_pt = 50
xmax_pt = xmax_m / 2
bin_width_pt = bin_width_m / 2.
binning_m = ((xmax_m - xmin_m) / bin_width_m, xmin_m, xmax_m)
binning_pt = ((xmax_pt - xmin_pt) / bin_width_pt, xmin_pt, xmax_pt)
xvar_m = ROOT.RooRealVar('x_m', 'x_m', xmin_m, xmax_m)
xvar_pt = ROOT.RooRealVar('x_pt', 'x_pt', xmin_pt, xmax_pt)
signal_binning_m_width1 = {
200: ((xmax_m) / 4, 0, xmax_m),
250: ((xmax_m) / 4, 0, xmax_m),
300: ((xmax_m) / 4, 0, xmax_m),
350: ((xmax_m) / 4, 0, xmax_m),
400: ((xmax_m) / 4, 0, xmax_m),
450: ((xmax_m) / 5, 0, xmax_m),
500: ((xmax_m) / 8, 0, xmax_m),
600: ((xmax_m) / 10, 0, xmax_m),
700: ((xmax_m) / 10, 0, xmax_m),
800: ((xmax_m) / 10, 0, xmax_m),
900: ((xmax_m) / 10, 0, xmax_m),
1000: ((xmax_m) / 10, 0, xmax_m),
1200: (int((xmax_m) / 15), 0, xmax_m),
1400: (int((xmax_m) / 15), 0, xmax_m),
1600: (int((xmax_m) / 15), 0, xmax_m),
1800: (int((xmax_m) / 15), 0, xmax_m),
2000: ((xmax_m) / 20, 0, xmax_m),
2200: ((xmax_m) / 20, 0, xmax_m),
2400: ((xmax_m) / 20, 0, xmax_m),
2600: ((xmax_m) / 20, 0, xmax_m),
2800: (int((xmax_m) / 25), 0, xmax_m),
3000: (int((xmax_m) / 25), 0, xmax_m),
3500: (int((xmax_m) / 40), 0, xmax_m),
4000: (int((xmax_m) / 40), 0, xmax_m),
}
signal_binning_m_width2 = {
200: ((xmax_m) / 4, 0, xmax_m),
250: ((xmax_m) / 4, 0, xmax_m),
300: ((xmax_m) / 4, 0, xmax_m),
350: ((xmax_m) / 5, 0, xmax_m),
400: ((xmax_m) / 5, 0, xmax_m),
450: ((xmax_m) / 8, 0, xmax_m),
500: ((xmax_m) / 8, 0, xmax_m),
600: ((xmax_m) / 8, 0, xmax_m),
700: ((xmax_m) / 10, 0, xmax_m),
800: ((xmax_m) / 10, 0, xmax_m),
900: (int((xmax_m) / 15), 0, xmax_m),
1000: (int((xmax_m) / 15), 0, xmax_m),
1200: ((xmax_m) / 20, 0, xmax_m),
1400: ((xmax_m) / 25, 0, xmax_m),
1600: ((xmax_m) / 25, 0, xmax_m),
1800: (int((xmax_m) / 30), 0, xmax_m),
2000: (int((xmax_m) / 30), 0, xmax_m),
2200: (int((xmax_m) / 30), 0, xmax_m),
2400: (int((xmax_m) / 30), 0, xmax_m),
2600: (int((xmax_m) / 35), 0, xmax_m),
2800: (int((xmax_m) / 35), 0, xmax_m),
3000: (int((xmax_m) / 35), 0, xmax_m),
3500: (int((xmax_m) / 40), 0, xmax_m),
4000: (int((xmax_m) / 40), 0, xmax_m),
}
signal_binning_m = [signal_binning_m_width1, signal_binning_m_width2]
signal_binning_pt = {}
for mass, binning in signal_binning_m_width1.iteritems():
pt_min = binning[1] / 2.
if pt_min < 50:
pt_min = 50
signal_binning_pt[mass] = (binning[0] / 2., pt_min, binning[2] / 2.)
kine_vars = { #'mt_incl_lepph_z' : { 'var' : 'mt_lep_met_ph' , 'xvar' : xvar_m , 'binning' : binning_m, 'signal_binning' : signal_binning_m },
#'m_incl_lepph_z' : { 'var' : 'm_lep_met_ph' , 'xvar' : xvar_m , 'binning' : binning_m, 'signal_binning' : signal_binning_m },
##'mt_rotated' : { 'var' : 'mt_rotated' , 'xvar' : xvar_m , 'binning' : binning_m, 'signal_binning' : signal_binning_m },
'mt_fulltrans': {
'var': 'mt_res',
'xvar': xvar_m,
'binning': binning_m,
'signal_binning': signal_binning_m
},
#'mt_constrwmass' : { 'var' : 'recoM_lep_nu_ph' , 'xvar' : xvar_m , 'binning' : binning_m, 'signal_binning' : signal_binning_m },
#'ph_pt' : { 'var' : 'ph_pt[0]' , 'xvar' : xvar_pt , 'binning' : binning_pt, 'signal_binning' : signal_binning_pt },
}
selections = {
'base': {
'mu': {
'selection': sel_base_mu
},
'el': {
'selection': sel_base_el
},
},
#'jetVeto' : { 'mu' : {'selection' : sel_jetveto_mu },
# 'el' : { 'selection' : sel_jetveto_el } ,
# },
}
lepg_samps = {'mu': sampManMuG, 'el': sampManElG}
#lepg_samps = {'mu' : sampManMuG }
for seltag, chdic in selections.iteritems():
print "seltag: ", seltag, "chdic: ", chdic
for ch, seldic in chdic.iteritems():
print "ch:", ch, "seldic", seldic
for name, vardata in kine_vars.iteritems():
print "name", name, "vardata", vardata #['signal_binning']
make_signal_fits(lepg_samps[ch],
seldic['selection'],
eta_cuts,
vardata['var'],
vardata['xvar'],
vardata['signal_binning'],
workspaces_to_save,
suffix='%s_%s_%s' % (ch, name, seltag),
plots_dir=options.outputDir + "/plots")
if options.outputDir is not None:
for fileid, ws in workspaces_to_save.iteritems():
#for idx, ws in enumerate(ws_list) :
#if idx == 0 :
# recreate = True
#else :
# recreate = False
ws.writeToFile('%s/%s.root' % (options.outputDir, fileid))
def main() :
global samplesWgg
global samplesZgg
global samplesWggComb
global samplesWggBkgSub
sampleConfWgg = 'Modules/WggFinal.py'
if options.zgg :
sampleConfWgg = 'Modules/ZggFinal.py'
sampleConfWggBkgSub = 'Modules/WggFinalBkgSub.py'
samplesWgg = SampleManager(options.baseDir, filename='hist.root', readHists=True)
samplesWggComb = SampleManager(options.baseDirComb, filename='hist.root', readHists=True)
#samplesWggBkgSub = SampleManager(options.baseDirComb, filename='hist.root', xsFile='cross_sections/wgamgam.py', lumi=19400, readHists=True)
samplesWgg .ReadSamples( sampleConfWgg )
samplesWggComb .ReadSamples( sampleConfWgg )
#samplesWggBkgSub .ReadSamples( sampleConfWggBkgSub )
if options.outputDir is not None :
ROOT.gROOT.SetBatch(True)
dumpStack=True
samplesWgg.activate_sample( 'AllBkg')
samplesWgg.activate_sample( 'ZggNoSyst')
samplesWgg.activate_sample( 'OtherDiPhotonNoSyst')
samplesWgg.activate_sample( 'AllBkgPlusSig')
samplesWgg.activate_sample('DiTopDiPhoton')
samplesWgg.activate_sample('SingleTopDiPhoton')
samplesWgg.activate_sample('TriBosonDiPhoton')
samplesWgg.activate_sample('TTVDiPhoton')
samplesWgg.activate_sample('WWDiPhoton')
samplesWgg.activate_sample('WZlvjjDiPhoton')
samplesWgg.activate_sample('WZlvllDiPhoton')
samplesWgg.activate_sample('WZjjllDiPhoton')
samplesWgg.activate_sample('WZlvvvDiPhoton')
samplesWgg.activate_sample('ZZDiPhoton')
if options.zgg and not options.money :
MakeZggPlots( options.outputDir, selection='muZgg', var='pt_leadph12', suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
MakeZggPlots( options.outputDir, selection='elZgg', var='pt_leadph12', suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
#MakeZggPlots( options.outputDir, selection='muZgg', var='m_ph1_ph2' , suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
#MakeZggPlots( options.outputDir, selection='muZgg', var='pt_ph1_ph2' , suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
#MakeZggPlots( options.outputDir, selection='elZgg', var='m_ph1_ph2' , suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
#MakeZggPlots( options.outputDir, selection='elZgg', var='pt_ph1_ph2' , suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
if options.electron :
MakeElectronPlots(options.outputDir, selection='elfullhighmt', var='pt_leadph12', suffix='', ylabel='Events / bin',label_style='fancy', dumpStack=dumpStack )
#MakeElectronPlots(options.outputDir, selection='elfullhighmt', var='m_ph1_ph2' , suffix='', ylabel='Events / bin',label_style='fancy', dumpStack=dumpStack )
#MakeElectronPlots(options.outputDir, selection='elfullhighmt', var='pt_ph1_ph2' , suffix='', ylabel='Events / bin',label_style='fancy', dumpStack=dumpStack )
if options.muon :
MakeMuonPlots(options.outputDir, selection='muhighmt', var='pt_leadph12', suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
#MakeMuonPlots(options.outputDir, selection='muhighmt', var='m_ph1_ph2' , suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
#MakeMuonPlots(options.outputDir, selection='muhighmt', var='pt_ph1_ph2' , suffix='', ylabel='Events / bin', label_style='fancy', dumpStack=dumpStack )
dumpStack=False
samplesWgg.deactivate_sample( 'AllBkg' )
samplesWgg.deactivate_sample( 'ZggNoSyst')
samplesWgg.deactivate_sample( 'OtherDiPhotonNoSyst')
samplesWgg.deactivate_sample( 'AllBkgPlusSig')
samplesWgg.deactivate_sample('DiTopDiPhoton')
samplesWgg.deactivate_sample('SingleTopDiPhoton')
samplesWgg.deactivate_sample('TriBosonDiPhoton')
samplesWgg.deactivate_sample('TTVDiPhoton')
samplesWgg.deactivate_sample('WWDiPhoton')
samplesWgg.deactivate_sample('WZlvjjDiPhoton')
samplesWgg.deactivate_sample('WZlvllDiPhoton')
samplesWgg.deactivate_sample('WZjjllDiPhoton')
samplesWgg.deactivate_sample('WZlvvvDiPhoton')
samplesWgg.deactivate_sample('ZZDiPhoton')
xlabel_ptg = 'p_{T}^{lead #gamma} [GeV]'
xlabel_ptgg = 'p_{T}^{#gamma #gamma} [GeV]'
xlabel_mgg = 'M_{#gamma #gamma} [GeV]'
xlabel_mee = 'M_{ee} [GeV]'
xlabel_mmm = 'M_{#mu#mu} [GeV]'
xlabel_meegg = 'M_{ee#gamma#gamma} [GeV]'
xlabel_mmmgg = 'M_{#mu#mu#gamma#gamma} [GeV]'
selections_wgg_mu = [
#------------------------------------
# Subl pT bins
#------------------------------------
#{'selection' : 'muhighmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 200, 'ymax_pergev' : 100 , 'ymin_logy' : 0.1, 'ymax_logy':200 },
#{'selection' : 'mulowmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 140, 'ymax_pergev' : 80 , 'ymin_logy' : 0.1, 'ymax_logy' :200 },
#------------------------------------
# Nominal pT bins
#------------------------------------
{'selection' : 'muhighmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 80, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'mulowmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 40, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#------------------------------------
# From OneBin
#------------------------------------
#{'selection' : 'muhighmt' , 'var' : 'm_ph1_ph2' , 'xlabel' : xlabel_mgg , 'ymax' : 60, 'ymax_pergev' : 5 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'muhighmt' , 'var' : 'pt_ph1_ph2' , 'xlabel' : xlabel_ptgg , 'ymax' : 45, 'ymax_pergev' : 7 , 'ymin_logy' : 0.5, 'ymax_logy' :30 },
#{'selection' : 'mulowmt' , 'var' : 'm_ph1_ph2' , 'xlabel' : xlabel_mgg , 'ymax' : 40, 'ymax_pergev' : 4 , 'ymin_logy' : 0.01, 'ymax_logy' :20 },
#{'selection' : 'mulowmt' , 'var' : 'pt_ph1_ph2' , 'xlabel' : xlabel_ptgg , 'ymax' : 45, 'ymax_pergev' : 7 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
]
selections_wgg_el = [
#------------------------------------
# Subl pT bins
#------------------------------------
#{'selection' : 'elfullhighmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 140, 'ymax_pergev' : 50 , 'ymin_logy' : 0.1, 'ymax_logy' :100 },
#{'selection' : 'elfulllowmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 100, 'ymax_pergev' : 30 , 'ymin_logy' : 0.1, 'ymax_logy' :100 },
#{'selection' : 'ellooselowmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 240, 'ymax_pergev' : 100 , 'ymin_logy' : 0.1, 'ymax_logy' :200 },
##{'selection' : 'elzcrhighmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 80, 'ymax_pergev' : 30 , 'ymin_logy' : 0.1, 'ymax_logy' :100 },
#------------------------------------
# Nominal pT bins
#------------------------------------
{'selection' : 'elfullhighmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 50, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'elfulllowmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 50, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'ellooselowmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 80, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
##{'selection' : 'elzcrhighmt' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 40, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#------------------------------------
# From OneBin
#------------------------------------
#{'selection' : 'elfullhighmt' , 'var' : 'm_ph1_ph2' , 'xlabel' : xlabel_mgg , 'ymax' : 30, 'ymax_pergev' : 2.5, 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'elfullhighmt' , 'var' : 'pt_ph1_ph2' , 'xlabel' : xlabel_ptgg , 'ymax' : 30, 'ymax_pergev' : 5 , 'ymin_logy' : 0.5, 'ymax_logy' :30 },
#{'selection' : 'elfulllowmt' , 'var' : 'm_ph1_ph2' , 'xlabel' : xlabel_mgg , 'ymax' : 30, 'ymax_pergev' : 3 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'elfulllowmt' , 'var' : 'pt_ph1_ph2' , 'xlabel' : xlabel_ptgg , 'ymax' : 30, 'ymax_pergev' : 5 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'ellooselowmt' , 'var' : 'm_ph1_ph2' , 'xlabel' : xlabel_mgg , 'ymax' : 50, 'ymax_pergev' : 5 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'ellooselowmt' , 'var' : 'pt_ph1_ph2' , 'xlabel' : xlabel_ptgg , 'ymax' : 50, 'ymax_pergev' : 7 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
##{'selection' : 'elzcrhighmt' , 'var' : 'm_ph1_ph2' , 'xlabel' : xlabel_mgg , 'ymax' : 30, 'ymax_pergev' : 5 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
##{'selection' : 'elzcrhighmt' , 'var' : 'pt_ph1_ph2' , 'xlabel' : xlabel_ptgg , 'ymax' : 20, 'ymax_pergev' : 7 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
]
selections_zgg = [
#{'selection' : 'elZgg' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 60, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
#{'selection' : 'muZgg' , 'var' : 'pt_leadph12' , 'xlabel' : xlabel_ptg , 'ymax' : 60, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :20 },
{'selection' : 'elZgg' , 'var' : 'm_ph1_ph2' , 'xlabel' : xlabel_mgg , 'ymax' : 40, 'ymax_pergev' : 10 , 'ymin_logy' : 0.1, 'ymax_logy' :30 },
{'selection' : 'elZgg' , 'var' : 'pt_ph1_ph2' , 'xlabel' : xlabel_ptgg , 'ymax' : 50, 'ymax_pergev' : 13, 'ymin_logy' : 0.1, 'ymax_logy' :30 },
{'selection' : 'elZgg' , 'var' : 'm_leplepphph', 'xlabel' : xlabel_meegg , 'ymax' : 50, 'ymax_pergev' : 5 , 'ymin_logy' : 0.1, 'ymax_logy' :10 },
{'selection' : 'elZgg' , 'var' : 'm_elel' , 'xlabel' : xlabel_mee , 'ymax' : 50, 'ymax_pergev' : 12 , 'ymin_logy' : 0.1, 'ymax_logy' :30 },
{'selection' : 'muZgg' , 'var' : 'm_ph1_ph2' , 'xlabel' : xlabel_mgg , 'ymax' : 50, 'ymax_pergev' : 12 , 'ymin_logy' : 0.1, 'ymax_logy' :30 },
{'selection' : 'muZgg' , 'var' : 'pt_ph1_ph2' , 'xlabel' : xlabel_ptgg , 'ymax' : 60, 'ymax_pergev' : 15 , 'ymin_logy' : 0.1, 'ymax_logy' :30 },
{'selection' : 'muZgg' , 'var' : 'm_leplepphph', 'xlabel' : xlabel_mmmgg , 'ymax' : 60, 'ymax_pergev' : 6 , 'ymin_logy' : 0.1, 'ymax_logy' :10 },
{'selection' : 'muZgg' , 'var' : 'm_mumu' , 'xlabel' : xlabel_mmm , 'ymax' : 60, 'ymax_pergev' : 12 , 'ymin_logy' : 0.1, 'ymax_logy' :30 },
]
conf_wgg_mu = make_full_configs( selections_wgg_mu, options.outputDir, dumpStack )
conf_wgg_el = make_full_configs( selections_wgg_el, options.outputDir, dumpStack )
conf_zgg = make_full_configs( selections_zgg , options.outputDir, dumpStack )
if options.aqgc :
MakeCombinedAQGCPlots( options.outputDir, dumpStack=dumpStack )
#MakeCombinedAQGCPlots( options.outputDir, var='m_ph1_ph2', dumpStack=dumpStack )
elif options.money :
if options.zgg :
MakeCombinedMoneyPlots( options.outputDir, var='pt_ph1_ph2', dumpStack=dumpStack, zgg=options.zgg, ymax=30)
MakeCombinedMoneyPlots( options.outputDir, var='m_ph1_ph2', dumpStack=dumpStack, zgg=options.zgg, ymax=20)
MakeCombinedMoneyPlots( options.outputDir, var='m_leplepphph', dumpStack=dumpStack, zgg=options.zgg, ymax=130)
#MakeCombinedMoneyPlots( options.outputDir, var='m_elel', dumpStack=dumpStack, zgg=options.zgg, ymax=130)
#MakeCombinedMoneyPlots( options.outputDir, var='m_mumu', dumpStack=dumpStack, zgg=options.zgg, ymax=130)
else :
MakeCombinedMoneyPlots( options.outputDir, var='pt_ph1_ph2', dumpStack=dumpStack, zgg=options.zgg, ymax=13)
MakeCombinedMoneyPlots( options.outputDir, var='m_ph1_ph2', dumpStack=dumpStack, zgg=options.zgg, ymax=8)
elif options.zgg :
for conf in conf_zgg :
MakeZggPlots( **conf )
else :
if options.electron :
for conf in conf_wgg_el :
MakeElectronPlots( **conf )
if options.muon:
for conf in conf_wgg_mu :
MakeMuonPlots( **conf )
print '^.^ FINSHED ^.^'
def main():
#sampManNoFilt = SampleManager( options.baseDir, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManLepFilt = SampleManager(options.baseDirLepFilt,
_TREENAME,
filename=_FILENAME)
if options.samplesConf is not None:
#sampManNoFilt.ReadSamples( options.samplesConf )
sampManLepFilt.ReadSamples(options.samplesConf)
else:
print('Must provide a sample configuration. Exiting')
return
mode_cuts = {
'electron': 'isWElDecay == 1',
'muon': 'isWMuDecay == 1',
'tau': 'isWTauDecay == 1',
'tauel': 'isWTauDecay == 1 && isWTauElDecay == 1',
'taumu': 'isWTauDecay == 1 && isWTauMuDecay == 1'
}
cuts_nofilt = OrderedDict()
#cuts_nofilt = { 'electron' : OrderedDict(), 'muon' : OrderedDict() }
cuts_lepfilt = {'electron': OrderedDict(), 'muon': OrderedDict()}
cuts_lepfilt['electron'][
'pass trigger'] = 'passTrig_HLT_Ele27_eta2p1_WPTight_Gsf'
cuts_lepfilt['muon'][
'pass trigger'] = '(passTrig_HLT_IsoTkMu24 || passTrig_HLT_IsoMu24 )'
cuts_lepfilt['electron']['At least one medium electron'] = 'el_n > 0'
cuts_lepfilt['muon']['At least one tight muon'] = 'mu_n > 0'
cuts_lepfilt['electron'][
'Electron trigger match'] = ' Sum$( el_hasTrigMatch ) > 0 && Sum$( el_pt > 30 ) > 0'
cuts_lepfilt['muon'][
'Muon trigger match'] = ' Sum$( mu_hasTrigMatch ) > 0 && Sum$( mu_pt > 25 ) > 0'
cuts_lepfilt['electron']['Second Lepton Veto'] = 'Sum$( el_pt > 10 ) == 1'
cuts_lepfilt['muon']['Second Lepton Veto'] = 'Sum$( mu_pt > 10 ) == 1'
cuts_lepfilt['electron'][
'At least one Medium Photon'] = 'Sum$( ph_passMedium ) > 0'
cuts_lepfilt['muon'][
'At least one Medium Photon'] = 'Sum$( ph_passMedium ) > 0'
cuts_lepfilt['electron'][
'At least one Photon with pt $>$ 30'] = 'Sum$( ph_pt > 30 ) > 0'
cuts_lepfilt['muon'][
'At least one Photon with pt $>$ 30'] = 'Sum$( ph_pt > 30 ) > 0'
cut_efficiencies = {}
selected_samples = []
for samp in sampManLepFilt.get_samples():
res = re.match('ResonanceMass(\d+)_width(\d+)', samp.name)
print(samp.name)
if res is not None:
width = int(res.group(2))
mass = int(res.group(1))
cut_efficiencies.setdefault(width, {})
cut_efficiencies[width].setdefault(mass, {})
selected_samples.append(samp.name)
eff_ele = get_efficiencies(sampManLepFilt, samp.name, mode_cuts,
cuts_lepfilt['electron'])
eff_mu = get_efficiencies(sampManLepFilt, samp.name, mode_cuts,
cuts_lepfilt['muon'])
cut_efficiencies[width][mass] = {
'muon': eff_mu,
'electron': eff_ele
}
if options.outputFile is not None:
dirpath = os.path.dirname(options.outputFile)
if not os.path.isdir(dirpath):
os.makedirs(dirpath)
ofile = open(options.outputFile, 'w')
pickle.dump(cut_efficiencies, ofile)
ofile.close()
def run_tnt(dil_path, gj_path, dil_conf, gj_conf):
samples_dil = SampleManager(options.dil_path, 1.0, 'events')
samples_gj = SampleManager(options.gj_path, 1.0, 'photons', 'ntuple.root')
read_samples(samples_dil, options.dil_conf)
read_samples(samples_gj, options.gj_conf)
draw_command_dil_ee_incldphi_incljet = 'EventWeight * ( ( passtrig_electron ) && IsEE && passcut_os && passcut_thirdlepveto && passcut_mll && Ptll > 25.0 )'
draw_command_dil_mm_incldphi_incljet = 'EventWeight * ( ( passtrig_muon ) && IsMM && passcut_os && passcut_thirdlepveto && passcut_mll && Ptll > 25.0 )'
draw_command_dil_ee_cutdphi_incljet = 'EventWeight * ( ( passtrig_electron ) && IsEE && passcut_os && passcut_thirdlepveto && passcut_mll && Ptll > 25.0 && passcut_dphill )'
draw_command_dil_mm_cutdphi_incljet = 'EventWeight * ( ( passtrig_muon ) && IsMM && passcut_os && passcut_thirdlepveto && passcut_mll && Ptll > 25.0 && passcut_dphill )'
draw_command_dil_ee_incldphi_jetcut = 'EventWeight * ( ( passtrig_electron ) && IsEE && passcut_os && passcut_thirdlepveto && passcut_mll && Ptll > 25.0 && jet_eta25Pt25_n == 0 )'
draw_command_dil_mm_incldphi_jetcut = 'EventWeight * ( ( passtrig_muon ) && IsMM && passcut_os && passcut_thirdlepveto && passcut_mll && Ptll > 25.0 && jet_eta25Pt25_n == 0 )'
draw_command_dil_ee_cutdphi_jetcut = 'EventWeight * ( ( passtrig_electron ) && IsEE && passcut_os && passcut_thirdlepveto && passcut_mll && Ptll > 25.0 && jet_eta25Pt25_n == 0 && passcut_dphill )'
draw_command_dil_mm_cutdphi_jetcut = 'EventWeight * ( ( passtrig_muon ) && IsMM && passcut_os && passcut_thirdlepveto && passcut_mll && Ptll > 25.0 && jet_eta25Pt25_n == 0 && passcut_dphill )'
if options.distribution == 'pt':
dil_dist = 'Ptll'
gj_dist = 'ph_pt'
draw_command_gj_ee_incldphi_incljet = 'EventWeight * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_ee_incldphi_jetcut = 'EventWeight * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_eta25Pt25_n == 0 )'
draw_command_gj_ee_cutdphi_incljet = 'EventWeight * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_ee_cutdphi_jetcut = 'EventWeight * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_eta25Pt25_n == 0 )'
draw_command_gj_mm_incldphi_incljet = 'EventWeight * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_mm_incldphi_jetcut = 'EventWeight * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_eta25Pt25_n == 0 )'
draw_command_gj_mm_cutdphi_incljet = 'EventWeight * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_mm_cutdphi_jetcut = 'EventWeight * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_eta25Pt25_n == 0 )'
binning_incljet = [
25, 30, 40, 50, 60, 70, 80, 100, 120, 150, 200, 1000
]
binning_jetcut = [25, 30, 40, 50, 60, 70, 80, 100, 150, 1000]
binning_dphill_incljet = [
25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 100, 120, 150, 200, 1000
]
binning_dphill_jetcut = [
25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 100, 150, 1000
]
elif options.distribution == 'll_dphi_met':
dil_dist = 'LL_DPhi_Met'
gj_dist = 'ph_dphi_met'
draw_command_gj_ee_incldphi_incljet = 'EventWeight * PtWeightEE * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_ee_incldphi_jetcut = 'EventWeight * PtWeightEE0J * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_eta25Pt25_n == 0 )'
draw_command_gj_ee_cutdphi_incljet = 'EventWeight * PtWeightDPhillEE * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_ee_cutdphi_jetcut = 'EventWeight * PtWeightDPhillEE0J * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_eta25Pt25_n == 0 )'
draw_command_gj_mm_incldphi_incljet = 'EventWeight * PtWeightMM * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_mm_incldphi_jetcut = 'EventWeight * PtWeightMM0J * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_eta25Pt25_n == 0 )'
draw_command_gj_mm_cutdphi_incljet = 'EventWeight * PtWeightDPhillMM * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 )'
draw_command_gj_mm_cutdphi_jetcut = 'EventWeight * PtWeightDPhillMM0J * ( ph_pt > 25.0 && lep_n == 0 && ph_n==1 && ph_nConvTrk==0 && jet_eta25Pt25_n == 0 )'
binning_incljet = [x * 0.01 for x in range(-315, 325, 10)]
binning_jetcut = [x * 0.01 for x in range(-315, 330, 15)]
binning_dphill_incljet = [x * 0.01 for x in range(-315, 345, 30)]
binning_dphill_jetcut = [x * 0.01 for x in range(-315, 345, 30)]
MakeRatio(samples_dil, samples_gj, dil_dist, gj_dist,
draw_command_dil_ee_incldphi_incljet,
draw_command_gj_ee_incldphi_incljet, binning_incljet,
options.outputDir + '/AllJet/EE/hist.root')
MakeRatio(samples_dil, samples_gj, dil_dist, gj_dist,
draw_command_dil_mm_incldphi_incljet,
draw_command_gj_mm_incldphi_incljet, binning_incljet,
options.outputDir + '/AllJet/MM/hist.root')
MakeRatio(samples_dil, samples_gj, dil_dist, gj_dist,
draw_command_dil_ee_cutdphi_incljet,
draw_command_gj_ee_cutdphi_incljet, binning_dphill_incljet,
options.outputDir + '/DPhill_AllJet/EE/hist.root')
MakeRatio(samples_dil, samples_gj, dil_dist, gj_dist,
draw_command_dil_mm_cutdphi_incljet,
draw_command_gj_mm_cutdphi_incljet, binning_dphill_incljet,
options.outputDir + '/DPhill_AllJet/MM/hist.root')
MakeRatio(samples_dil, samples_gj, dil_dist, gj_dist,
draw_command_dil_ee_incldphi_jetcut,
draw_command_gj_ee_incldphi_jetcut, binning_jetcut,
options.outputDir + '/NJet0/EE/hist.root')
MakeRatio(samples_dil, samples_gj, dil_dist, gj_dist,
draw_command_dil_mm_incldphi_jetcut,
draw_command_gj_mm_incldphi_jetcut, binning_jetcut,
options.outputDir + '/NJet0/MM/hist.root')
MakeRatio(samples_dil, samples_gj, dil_dist, gj_dist,
draw_command_dil_ee_cutdphi_jetcut,
draw_command_gj_ee_cutdphi_jetcut, binning_dphill_jetcut,
options.outputDir + '/DPhill_NJet0/EE/hist.root')
MakeRatio(samples_dil, samples_gj, dil_dist, gj_dist,
draw_command_dil_mm_cutdphi_jetcut,
draw_command_gj_mm_cutdphi_jetcut, binning_dphill_jetcut,
options.outputDir + '/DPhill_NJet0/MM/hist.root')
def main():
global samplesLLG
global samplesLG
baseDirLLG = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepLepGammaNoPhID_2015_06_29'
baseDirLG = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepGammaNoPhID_2015_06_29'
#baseDirLG = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepGammaJJNoPhID_2015_05_05'
treename = 'ggNtuplizer/EventTree'
filename = 'tree.root'
sampleConfLLG = 'Modules/Wgamgam.py'
samplesLLG = SampleManager(baseDirLLG,
treename,
filename=filename,
xsFile='cross_sections/wgamgam.py',
lumi=19400)
samplesLG = SampleManager(baseDirLG,
treename,
filename=filename,
xsFile='cross_sections/wgamgam.py',
lumi=19400)
samplesLLG.ReadSamples(sampleConfLLG)
samplesLG.ReadSamples(sampleConfLLG)
#file_bin_map = {'ElectronFakeFitsRatio' : [(15,20), (20,25), (25,30), (30,40), (40,50), (50,70), (70, 'max')] }
#file_bin_map_syst = {'ElectronFakeFitsRatioExpBkg' : [(15,20), (20,25), (25,30), (30,40), (40,50), (50,70), (70, 'max') ] }
#file_bin_map = {'ElectronFakeFitsRatioMCTemplateNDKeys' : [(15,20), (20,25), (25,30), (30, 35), (35, 40), (40, 45), (45, 50), (50,60), (60,70), (70,100), (100,'max') ] }
#file_bin_map = {'ElectronFakeFitsRatioExpBkg' : [(15,20), (20,25), (25,30), (30, 35), (35, 40), (40, 45), (45, 50), (50,60), (60,70), (70,100), (100,'max') ] }
#file_bin_map_syst = {'ElectronFakeFitsRatioExpBkg' : [(15,20), (20,25), (25,30), (30, 35), (35, 40), (40, 45), (45, 50), (50,60), (60,70), (70,100), (100,'max')] }
#file_bin_map = {'ElectronFakeFitsRatioMCTemplateNDKeys' : [(15,20), (20,25), (25,30), (30, 35), (35, 40), (40, 45), (45, 50), (50,60), (60,70), (70,100), (100,'max')] }
#file_bin_map_syst = {'ElectronFakeFitsRatioMCTemplateNDKeys' : [(15,20), (20,25), (25,30), (30, 35), (35, 40), (40, 45), (45, 50), (50,60), (60,70), (70,100), (100,'max')] }
#file_bin_map = {'ElectronFakeFitsRatio' : [(_ptbins[0], _ptbins[1]), (_ptbins[1], _ptbins[2]), (_ptbins[2], _ptbins[3]), (_ptbins[3], 'max')] }
#file_bin_map_syst = {'ElectronFakeFitsRatioExpBkg' : [(_ptbins[0], _ptbins[1]), (_ptbins[1], _ptbins[2]), (_ptbins[2], _ptbins[3]),(_ptbins[3], 'max') ] }
#file_bin_map = {'ElectronFakeFitsRatioMCTemplateNDKeys' : [(_ptbins[0], _ptbins[1]), (_ptbins[1], _ptbins[2]), (_ptbins[2], _ptbins[3]), (_ptbins[3], 'max')] }
#file_bin_map = {'ElectronFakeFitsRatioMCTemplateNDKeysSubtractZgamma' : [(_ptbins[0], _ptbins[1]), (_ptbins[1], _ptbins[2]), (_ptbins[2], _ptbins[3]), (_ptbins[3], 'max')] }
#file_bin_map_syst = {'ElectronFakeFitsRatioExpBkg' : [(_ptbins[0], _ptbins[1]), (_ptbins[1], _ptbins[2]), (_ptbins[2], _ptbins[3]),(_ptbins[3], 'max') ] }
if options.wcr:
suffix = ''
path_nom = 'ElectronFakeFitsRatio%s' % suffix
path_mctemp = 'ElectronFakeFitsRatio%sMCTemplateNDKeys' % suffix
file_bin_map = {}
file_bin_map_syst = {}
# go up to the second-to-last bin
for bidx in range(0, len(_ptbins) - 2):
file_bin_map.setdefault(path_mctemp, []).append(
(_ptbins[bidx], _ptbins[bidx + 1]))
file_bin_map_syst.setdefault(path_mctemp, []).append(
(_ptbins[bidx], _ptbins[bidx + 1]))
# do the last bin
file_bin_map[path_nom] = [(_ptbins[-2], 'max')]
file_bin_map_syst[path_nom] = [(_ptbins[-2], 'max')]
for typedir in os.listdir('%s/SinglePhotonResults' % options.baseDir):
base_dir_ele_single = options.baseDir
base_dir_jet_single = '%s/SinglePhotonResults/%s' % (
options.baseDir, typedir)
outputDirSingle = '%s/SinglePhotonWCRBackgroundEstimates/%s' % (
options.baseDir, typedir)
#MakeSingleJetBkgEstimate( base_dir_jet_single, channels=['elwzcr', 'elwzcrinvpixlead'],outputDir=outputDirSingle )
#MakeSingleEleBkgEstimate( base_dir_ele_single, base_dir_jet_single, file_bin_map, file_bin_map_syst, el_selection='elw', outputDir=outputDirSingle )
MakeSingleEleBkgEstimate(base_dir_ele_single,
base_dir_jet_single,
file_bin_map,
file_bin_map_syst,
el_selection='elwzcr',
outputDir=outputDirSingle,
namePostfix='__zcr')
#MakeSingleJetBkgEstimate( base_dir_jet_single, channels=['muw'],outputDir=outputDirSingle )
MakeSingleBkgEstimatePlots(outputDirSingle,
options.plotDir,
channels=['elwzcr'])
#MakeSingleBkgEstimatePlots( outputDirSingle, options.plotDir, channels=['muw'] )
if options.zcr:
for typedir in os.listdir('%s/SinglePhotonResults' % options.baseDir):
base_dir_jet_single = '%s/SinglePhotonResults/%s' % (
options.baseDir, typedir)
outputDirSingle = '%s/SinglePhotonZCRBackgroundEstimates/%s' % (
options.baseDir, typedir)
#MakeSingleJetBkgEstimate( base_dir_jet_single, channels=['mu', 'el', 'murealcr', 'elrealcr'], outputDir=outputDirSingle )
#MakeSingleBkgEstimatePlots( outputDirSingle, options.plotDir, channels=['mu', 'el', 'murealcr', 'elrealcr'], makeEleFake=False )
MakeSingleJetBkgEstimate(base_dir_jet_single,
channels=['mu'],
outputDir=outputDirSingle)
MakeSingleBkgEstimatePlots(outputDirSingle,
options.plotDir,
channels=['mu'],
makeEleFake=False)
print '^_^ FINSISHED ^_^'
print 'It is safe to kill the program if it is hanging'
import ROOT
from SampleManager import SampleManager
ROOT.gROOT.SetBatch(False)
baseDir = 'root://eoscms//eos/cms/store/user/jkunkle/Wgamgam/RecoOutput_2015_04_05'
treename = 'ggNtuplizer/EventTree'
filename = 'tree.root'
sampleConf = '/afs/cern.ch/user/j/jkunkle/usercode/Plotting/Modules/LepLep.py'
samples = SampleManager(
baseDir,
treename,
filename=filename,
xsFile=
'/afs/cern.ch/user/j/jkunkle/usercode/Plotting/cross_sections/wgamgam.py',
lumi=19400.)
samples.ReadSamples(sampleConf)
samples.start_command_collection()
samples.Draw('el_pfiso40[0]/el_pt[0]',
'el_n==1 && el_triggerMatch[0] && el_pt[0] > 30', (100, 0, 5))
samples.SaveStack('el_relpfiso',
'/afs/cern.ch/work/j/jkunkle/private/CMS/Plots/TEST')
samples.Draw('el_pfiso40[0]/el_pt[0]',
'el_n==1 && el_triggerMatch[0] && el_pt[0] > 30', [0, 1.5, 5])
samples.SaveStack('el_relpfiso__varBins',
def main() :
global samplesLLG
global samplesLG
baseDirLGG = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepGammaGammaNoPhID_2015_11_09'
baseDirLLG = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepLepGammaNoPhID_2015_11_09'
baseDirLG = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepGammaNoPhID_2015_11_09'
treename = 'ggNtuplizer/EventTree'
filename = 'tree.root'
sampleConfLLG = 'Modules/ZJetsTemplates.py'
samplesLLG = SampleManager(baseDirLLG , treename, filename=filename, xsFile='cross_sections/wgamgam.py', lumi=19400)
samplesLG = SampleManager(baseDirLG , treename, filename=filename, xsFile='cross_sections/wgamgam.py', lumi=19400)
samplesLGG = SampleManager(baseDirLGG , treename, filename=filename, xsFile='cross_sections/wgamgam.py', lumi=19400)
samplesLLG .ReadSamples( sampleConfLLG )
samplesLG .ReadSamples( sampleConfLLG )
samplesLGG .ReadSamples( sampleConfLLG )
ROOT.gROOT.SetBatch(True)
binning = { 'sigmaIEIE' : { 'EB' : (30, 0, 0.03), 'EE' : (30, 0, 0.09) },
'chIsoCorr' : { 'EB' : (30, 0, 45), 'EE' : (35, 0, 42) },
'neuIsoCorr' : { 'EB' : (40, -2, 38), 'EE' : (30, -2, 43) },
'phoIsoCorr' : { 'EB' : (53, -2.1, 35), 'EE' : (42, -2, 40) }
}
pt_bins = [(15, 25), (25,40), (40,70), (70,1000000)]
_sieie_cuts = { 'EB' : (0.011,0.029), 'EE' : (0.033, 0.087) }
_chIso_cuts = { 'EB' : (1.5, 19.5) , 'EE' : (1.2,20.4) }
_neuIso_cuts = { 'EB' : (1.0,20) , 'EE' : (1.5,20.5) }
_phoIso_cuts = { 'EB' : (0.7,20.3) , 'EE' : (1.0,20) }
vars = ['sigmaIEIE', 'chIsoCorr', 'phoIsoCorr']
all_hists = []
for var in vars :
for eta in ['EB', 'EE'] :
for ptmin, ptmax in pt_bins :
draw_str_fake=None
if var == 'sigmaIEIE' :
draw_str_fake = 'mu_passtrig25_n>0 && mu_n==2 && ph_mediumNoSIEIENoEleVeto_n==1 && ph_HoverE12[0] < 0.05 && fabs( m_leplep-91.2 ) < 5 && leadPhot_sublLepDR >1 && leadPhot_leadLepDR>1 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %( ptmin, ptmax, eta, var, _sieie_cuts[eta][1] )
elif var == 'chIsoCorr' :
draw_str_fake = 'mu_passtrig25_n>0 && mu_n==2 && ph_mediumNoChIsoNoEleVeto_n==1 && ph_HoverE12[0] < 0.05 && fabs( m_leplep-91.2 ) < 5 && leadPhot_sublLepDR >1 && leadPhot_leadLepDR>1 && ph_passSIEIEMedium[0] && ph_passNeuIsoCorrMedium[0] && ph_passPhoIsoCorrMedium[0] && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %( ptmin, ptmax, eta, var, _chIso_cuts[eta][1] )
elif var == 'phoIsoCorr' :
draw_str_fake = 'mu_passtrig25_n>0 && mu_n==2 && ph_mediumNoPhoIsoNoEleVeto_n==1 && ph_HoverE12[0] < 0.05 && fabs( m_leplep-91.2 ) < 5 && leadPhot_sublLepDR >1 && leadPhot_leadLepDR>1 && ph_passSIEIEMedium[0] && ph_passNeuIsoCorrMedium[0] && ph_passChIsoCorrMedium[0] && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %( ptmin, ptmax, eta, var, _phoIso_cuts[eta][1] )
samplesLLG.Draw('ph_%s[0]'%var, draw_str_fake, binning[var][eta] )
hist_data = samplesLLG.get_samples( name='Muon' )[0]
hist_bkg = samplesLLG.get_samples( name='Zg' )[0]
hist_subtracted = hist_data.hist.Clone( '%s_Subtracted_%s_%d-%d' %( var, eta, ptmin, ptmax ))
hist_subtracted.Add( hist_bkg.hist, -1 )
all_hists.append( hist_data.hist.Clone( '%s_Data_%s_%d-%d' %( var, eta, ptmin, ptmax ) ) )
all_hists.append( hist_bkg.hist.Clone( '%s_Zg_%s_%d-%d' %( var, eta, ptmin, ptmax ) ) )
all_hists.append( hist_subtracted)
draw_str_real = None
if var=='sigmaIEIE' :
#draw_str_real = 'mu_passtrig25_n>0 && mu_n==2 && ph_mediumNoSIEIEPassCSEV_n==1 && m_elel > 40 && leadPhot_leadLepDR>0.4 && ph_truthMatch_ph[0] && abs(ph_truthMatchMotherPID_ph[0]) < 25 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %(ptmin, ptmax,eta, var, _sieie_cuts[eta][1])
draw_str_real = 'mu_passtrig25_n>0 && mu_n==1 && ph_mediumNoSIEIENoEleVeto_n==1 && leadPhot_leadLepDR>0.4 && ph_truthMatch_ph[0] && (fabs(ph_pt[0]-ph_truthMatchPt_ph[0])/ph_truthMatchPt_ph[0]) < 2 && (ph_truthMatchParentage_ph[0] & 4 ) == 0 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %(ptmin, ptmax,eta, var, _sieie_cuts[eta][1])
elif var == 'chIsoCorr' :
#draw_str_real = 'mu_passtrig25_n>0 && mu_n==2 && ph_mediumNoChIsoPassCSEV_n==1 && m_elel > 40 && leadPhot_leadLepDR>0.4 && ph_truthMatch_ph[0] && abs(ph_truthMatchMotherPID_ph[0]) < 25 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %(ptmin, ptmax,eta, var, _chIso_cuts[eta][1])
draw_str_real = 'mu_passtrig25_n>0 && mu_n==1 && ph_mediumNoChIsoNoEleVeto_n==1 && leadPhot_leadLepDR>0.4 && ph_truthMatch_ph[0] && (fabs(ph_pt[0]-ph_truthMatchPt_ph[0])/ph_truthMatchPt_ph[0]) < 2 && (ph_truthMatchParentage_ph[0] & 4 ) == 0 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %(ptmin, ptmax,eta, var, _chIso_cuts[eta][1])
elif var == 'phoIsoCorr' :
#draw_str_real = 'mu_passtrig25_n>0 && mu_n==2 && ph_mediumNoPhoIsoPassCSEV_n==1 && m_elel > 40 && leadPhot_leadLepDR>0.4 && ph_truthMatch_ph[0] && abs(ph_truthMatchMotherPID_ph[0]) < 25 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %(ptmin, ptmax,eta, var, _phoIso_cuts[eta][1])
draw_str_real = 'mu_passtrig25_n>0 && mu_n==1 && ph_mediumNoPhoIsoNoEleVeto_n==1 && leadPhot_leadLepDR>0.4 && ph_truthMatch_ph[0] && (fabs(ph_pt[0]-ph_truthMatchPt_ph[0])/ph_truthMatchPt_ph[0]) < 2 && (ph_truthMatchParentage_ph[0] & 4 ) == 0 && ph_pt[0] > %d && ph_pt[0] < %d && ph_Is%s[0] && ph_%s[0] < %f ' %(ptmin, ptmax,eta, var, _phoIso_cuts[eta][1])
samplesLG.create_hist( 'Wg', 'ph_%s[0]' %var, draw_str_real, binning[var][eta],)
newhist = samplesLG.get_samples( name='Wg' )[0].hist.Clone( '%s_Wg_%s_%d-%d' %( var, eta, ptmin, ptmax ) )
all_hists.append(newhist)
#for var in vars :
# for etalead in ['EB', 'EE'] :
# for etasubl in ['EB', 'EE'] :
# if etalead=='EE' and etasubl=='EE' :
# continue
# for ptmin, ptmax in pt_bins :
# draw_str_2d = None
# draw_var = None
# if var == 'sigmaIEIE' :
# draw_str_2d = 'mu_passtrig25_n>0 && ph_HoverE12[0] < 0.05 && ph_HoverE12[1] < 0.05 && dr_ph1_ph2 > 0.4 && m_ph1_ph2 > 0.0 && ph_noSIEIEiso1299_n == 2 && chIsoCorr_leadph12 > %f && chIsoCorr_sublph12 > %f && chIsoCorr_leadph12 < 12 && chIsoCorr_sublph12 < 12 && ph_passNeuIsoCorrMedium[0] && ph_passNeuIsoCorrMedium[1] && phoIsoCorr_leadph12 > %f && phoIsoCorr_leadph12 < 9 && phoIsoCorr_sublph12 > %f && phoIsoCorr_sublph12 < 9 && is%s_leadph12 && is%s_sublph12 && pt_leadph12 > %d && pt_leadph12 < %d && sieie_leadph12 < %f && sieie_sublph12 < %f ' %( _chIso_cuts[etalead][0], _chIso_cuts[etasubl][0], _phoIso_cuts[etalead][0], _phoIso_cuts[etasubl][0], etalead, etasubl, ptmin, ptmax, _sieie_cuts[etalead][1], _sieie_cuts[etasubl][1] )
# draw_var = 'sieie_sublph12:sieie_leadph12' # y:x
# elif var == 'chIsoCorr' :
# draw_str_2d = 'mu_passtrig25_n>0 && ph_HoverE12[0] < 0.05 && ph_HoverE12[1] < 0.05 && dr_ph1_ph2 > 0.4 && m_ph1_ph2 > 0.0 && ph_n==2 && sieie_leadph12 > %f && sieie_sublph12 > %f && ph_passNeuIsoCorrMedium[0] && ph_passNeuIsoCorrMedium[1] && phoIsoCorr_leadph12 > %f && phoIsoCorr_sublph12 > %f && phoIsoCorr_leadph12 < 5 && phoIsoCorr_sublph12 < 5 && is%s_leadph12 && is%s_sublph12 && pt_leadph12 > %d && pt_leadph12 < %d && chIsoCorr_leadph12 < %f && chIsoCorr_sublph12 < %f ' %( _sieie_cuts[etalead][0], _sieie_cuts[etasubl][0], _phoIso_cuts[etalead][0], _phoIso_cuts[etasubl][0], etalead, etasubl, ptmin, ptmax, _chIso_cuts[etalead][1], _chIso_cuts[etasubl][1] )
# draw_var = 'chIsoCorr_sublph12:chIsoCorr_leadph12' # y:x
# elif var == 'phoIsoCorr' :
# draw_str_2d = 'mu_passtrig25_n>0 && ph_HoverE12[0] < 0.05 && ph_HoverE12[1] < 0.05 && dr_ph1_ph2 > 0.4 && m_ph1_ph2 > 0.0 && ph_n==2 && sieie_leadph12 > %f && sieie_sublph12 > %f && ph_passNeuIsoCorrMedium[0] && ph_passNeuIsoCorrMedium[1] && chIsoCorr_leadph12 > %f && chIsoCorr_sublph12 > %f && chIsoCorr_leadph12 < 10 && chIsoCorr_sublph12 < 10 && is%s_leadph12 && is%s_sublph12 && pt_leadph12 > %d && pt_leadph12 < %d && phoIsoCorr_leadph12 < %f && phoIsoCorr_sublph12 < %f ' %( _sieie_cuts[etalead][0], _sieie_cuts[etasubl][0], _chIso_cuts[etalead][0], _chIso_cuts[etasubl][0], etalead, etasubl, ptmin, ptmax, _phoIso_cuts[etalead][1], _phoIso_cuts[etasubl][1] )
# draw_var = 'phoIsoCorr_sublph12:phoIsoCorr_leadph12' # y:x
# binning_2d = ( binning[var][etalead][0], binning[var][etalead][1], binning[var][etalead][2], binning[var][etasubl][0], binning[var][etasubl][1], binning[var][etasubl][2] )
# samplesLGG.create_hist( 'Muon', draw_var, draw_str_2d, binning_2d )
# newhist = samplesLGG.get_samples( name='Muon' )[0].hist.Clone( '%s_2DFakeFake_%s-%s_%d-%d' %( var, etalead, etasubl, ptmin, ptmax ) )
# all_hists.append(newhist)
out_file = ROOT.TFile.Open( options.outputFile, 'RECREATE' )
for hist in all_hists :
hist.Write()
out_file.Close()
print '^_^ Finished ^_^'
import ROOT from SampleManager import SampleManager ROOT.gROOT.SetBatch(False) baseDir = 'root://eoscms//eos/cms/store/user/jkunkle/Wgamgam/RecoOutput_2015_04_05' treename = 'ggNtuplizer/EventTree' filename = 'tree.root' sampleConf= '/afs/cern.ch/user/j/jkunkle/usercode/Plotting/Modules/LepLep.py' samples = SampleManager( baseDir, treename, filename=filename, xsFile='/afs/cern.ch/user/j/jkunkle/usercode/Plotting/cross_sections/wgamgam.py', lumi=19400. ) samples.ReadSamples( sampleConf ) samples.start_command_collection() samples.Draw( 'el_pfiso40[0]/el_pt[0]', 'el_n==1 && el_triggerMatch[0] && el_pt[0] > 30', (100, 0, 5) ) samples.SaveStack( 'el_relpfiso', '/afs/cern.ch/work/j/jkunkle/private/CMS/Plots/TEST' ) samples.Draw( 'el_pfiso40[0]/el_pt[0]', 'el_n==1 && el_triggerMatch[0] && el_pt[0] > 30', [0, 1.5, 5] ) samples.SaveStack( 'el_relpfiso__varBins', '/afs/cern.ch/work/j/jkunkle/private/CMS/Plots/TEST' ) samples.DumpStack( '/afs/cern.ch/work/j/jkunkle/private/CMS/Plots/TEST' ,'el_relpfiso__varBins' ) samples.Draw( 'mu_corrIso[0]/mu_pt[0]', 'mu_n==1 && mu_triggerMatch[0] && mu_passTightNoIso[0] && mu_pt[0] > 25', (100, 0, 5) ) samples.SaveStack( 'mu_relpfiso', '/afs/cern.ch/work/j/jkunkle/private/CMS/Plots/TEST' ) samples.Draw( 'mu_corrIso[0]/mu_pt[0]', 'mu_n==1 && mu_triggerMatch[0] && mu_passTightNoIso[0] && mu_pt[0] > 25', [0, 2, 5] ) samples.SaveStack( 'mu_relpfiso__varBins', '/afs/cern.ch/work/j/jkunkle/private/CMS/Plots/TEST' ) samples.DumpStack( '/afs/cern.ch/work/j/jkunkle/private/CMS/Plots/TEST' ,'mu_relpfiso__varBins' )
def main() :
#sampManNoFilt = SampleManager( options.baseDir, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManLepFilt = SampleManager( options.baseDirLepFilt, _TREENAME, filename=_FILENAME )
if options.samplesConf is not None :
#sampManNoFilt.ReadSamples( options.samplesConf )
sampManLepFilt.ReadSamples( options.samplesConf )
else :
print 'Must provide a sample configuration. Exiting'
return
mode_cuts = { 'electron' : 'isWElDecay == 1', 'muon' : 'isWMuDecay == 1', 'tau' : 'isWTauDecay == 1' , 'tauel' : 'isWTauDecay == 1 && isWTauElDecay == 1' , 'taumu' : 'isWTauDecay == 1 && isWTauMuDecay == 1' }
cuts_nofilt = OrderedDict()
#cuts_nofilt = { 'electron' : OrderedDict(), 'muon' : OrderedDict() }
cuts_lepfilt = { 'electron' : OrderedDict(), 'muon' : OrderedDict() }
cuts_lepfilt['electron']['pass trigger'] = 'passTrig_HLT_Ele27_eta2p1_WPTight_Gsf'
cuts_lepfilt['muon'] ['pass trigger'] = '(passTrig_HLT_IsoTkMu24 || passTrig_HLT_IsoMu24 )'
cuts_lepfilt['electron']['At least one medium electron'] = 'el_n > 0'
cuts_lepfilt['muon'] ['At least one tight muon'] = 'mu_n > 0'
cuts_lepfilt['electron']['Electron trigger match'] = ' Sum$( el_hasTrigMatch ) > 0 && Sum$( el_pt > 30 ) > 0'
cuts_lepfilt['muon'] ['Muon trigger match'] = ' Sum$( mu_hasTrigMatch ) > 0 && Sum$( mu_pt > 25 ) > 0'
cuts_lepfilt['electron']['Second Lepton Veto'] = 'Sum$( el_pt > 10 ) == 1'
cuts_lepfilt['muon'] ['Second Lepton Veto'] = 'Sum$( mu_pt > 10 ) == 1'
cuts_lepfilt['electron']['At least one Medium Photon'] = 'Sum$( ph_passMedium ) > 0'
cuts_lepfilt['muon'] ['At least one Medium Photon'] = 'Sum$( ph_passMedium ) > 0'
cuts_lepfilt['electron']['At least one Photon with pt $>$ 30'] = 'Sum$( ph_pt > 30 ) > 0'
cuts_lepfilt['muon'] ['At least one Photon with pt $>$ 30'] = 'Sum$( ph_pt > 30 ) > 0'
cut_efficiencies = {}
selected_samples = []
for samp in sampManLepFilt.get_samples() :
res = re.match( 'ResonanceMass(\d+)_width(\d+)', samp.name )
print samp.name
if res is not None :
width = int(res.group(2))
mass = int(res.group(1))
cut_efficiencies.setdefault( width, {})
cut_efficiencies[width].setdefault( mass, {} )
selected_samples.append( samp.name )
eff_ele = get_efficiencies( sampManLepFilt, samp.name, mode_cuts, cuts_lepfilt['electron'] )
eff_mu = get_efficiencies( sampManLepFilt, samp.name, mode_cuts, cuts_lepfilt['muon'] )
cut_efficiencies[width][mass] = {'muon' : eff_mu, 'electron' : eff_ele}
if options.outputFile is not None :
dirpath = os.path.dirname( options.outputFile )
if not os.path.isdir( dirpath ) :
os.makedirs( dirpath )
ofile = open( options.outputFile, 'w' )
pickle.dump( cut_efficiencies, ofile )
ofile.close()
def main():
# Parse command-line options
from argparse import ArgumentParser
p = ArgumentParser()
p.add_argument('--outputDir',
default=None,
type=str,
dest='outputDir',
help='output directory for histograms')
options = p.parse_args()
if options.outputDir is not None:
ROOT.gROOT.SetBatch(True)
else:
ROOT.gROOT.SetBatch(False)
global sampManMMG
global sampManMG
base_dir_mmg = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepLepGammaNoPhID_2015_04_11'
base_dir_mg = '/afs/cern.ch/work/j/jkunkle/public/CMS/Wgamgam/Output/LepGammaNoPhID_2015_04_11'
treename = 'ggNtuplizer/EventTree'
filename = 'tree.root'
xsFile = 'cross_sections/wgamgam.py'
lumi = 19400
sampManMMG = SampleManager(base_dir_mmg,
treeName=treename,
filename=filename,
xsFile=xsFile,
lumi=lumi)
sampManMG = SampleManager(base_dir_mg,
treeName=treename,
filename=filename,
xsFile=xsFile,
lumi=lumi)
samplesConf = 'Modules/TAndPSampleConf.py'
sampManMMG.ReadSamples(samplesConf)
sampManMG.ReadSamples(samplesConf)
if options.outputDir is not None:
if not os.path.isdir(options.outputDir):
os.makedirs(options.outputDir)
subdir = 'PhotonEfficiencies'
outputDir = options.outputDir + '/' + subdir
pt_bins = [15, 20, 25, 30, 40, 50, 70]
#eff_medium_data = GetPhotonEfficiencies( data_sample='Muon' , numerator = 'mmg_medium', denominator='mmg_loose' , pt_bins=pt_bins, bkg_sample='DYJetsToLLPhOlap', outputDir=outputDir+'/DataLLCut' )
eff_eveto_data = GetPhotonEfficiencies(data_sample='Muon',
numerator='mmg_eveto',
denominator='mmg_medium',
pt_bins=pt_bins,
bkg_sample='DYJetsToLLPhOlap',
outputDir=outputDir +
'/DataLLCut')
#eff_medium_mc = GetPhotonEfficiencies( data_sample='Zgamma', numerator = 'mmg_medium', denominator='mmg_loose' , pt_bins=pt_bins, bkg_sample=None, outputDir=outputDir+'/MCLLCut' )
eff_eveto_mc = GetPhotonEfficiencies(data_sample='Zgamma',
numerator='mmg_eveto',
denominator='mmg_medium',
pt_bins=pt_bins,
bkg_sample=None,
outputDir=outputDir + '/MCLLCut')
pt_bins_high = [70]
#eff_medium_data = GetPhotonEfficienciesHighPt( data_sample='Muon' , numerator='mmg_medium', denominator='mmg_loose' , pt_min=pt_min_high, outputDir=outputDir+'/DataLLCut' )
eff_eveto_data_highpt = GetPhotonEfficienciesHighPt(
data_sample='Muon',
numerator='mg_eveto_highpt',
denominator='mg_medium_highpt',
ptbins=pt_bins_high,
useBkgEstimate=True,
outputDir=outputDir + '/DataLLCut')
#eff_medium_mc = GetPhotonEfficienciesHighPt( data_sample='Zgamma', numerator='mmg_medium', denominator='mmg_loose' , pt_min=pt_min_high, outputDir=outputDir+'/MCLLCut' )
eff_eveto_mc_highpt = GetPhotonEfficienciesHighPt(
data_sample='Wgamma',
numerator='mg_eveto_highpt',
denominator='mg_medium_highpt',
ptbins=pt_bins_high,
useBkgEstimate=False,
outputDir=outputDir + '/MCLLCut')
for bin, eff in eff_eveto_data_highpt.iteritems():
eff_eveto_data[bin] = eff
for bin, eff in eff_eveto_mc_highpt.iteritems():
eff_eveto_mc[bin] = eff
print 'Eveto data '
for bin, eff in eff_eveto_data.iteritems():
print 'Bin = %s, eff = %s' % (bin, eff)
print 'Eveto MC '
for bin, eff in eff_eveto_mc.iteritems():
print 'Bin = %s, eff = %s' % (bin, eff)
#MakeScaleFactor( eff_medium_data, eff_medium_mc, tag='sf_medium_nom' , binning=_photon_hist_eta_pt_bins, outputDir=outputDir )
MakeScaleFactor(eff_eveto_data,
eff_eveto_mc,
tag='sf_eveto_nom',
binning=_photon_hist_eta_pt_bins,
outputDir=outputDir)
def makePhPlots(baseDir,
sampleConf,
outputDir=None,
elevetoid=None,
trig=False):
sampMan = SampleManager(baseDir, _TREENAME, filename=_FILENAME)
sampMan.ReadSamples(sampleConf)
samp_name = 'Comb'
samp = sampMan.get_samples(name=samp_name)
if not samp:
print('Could not find sample!')
return
#binning = range( 0, 50, 5) + range( 50, 500, 10 ) + range( 500, 2000, 100 )
binning = list(range(0, 50, 5)) + list(range(50, 300, 10)) + list(
range(300, 600, 20)) + list(range(600, 2000, 100)) + [2000, 3000]
var = 'truephot_pt'
name = 'PhotonEff'
if trig:
labels = ['Mu17_Photon30']
colors = [ROOT.kBlack]
selections = ['', 'passTrig_Mu17_Photon30_CaloIdL_L1ISO']
name += 'Trig'
elif elevetoid is None:
labels = [
'Container Photons', 'Loose Photons', 'Medium Photons',
'Tight Photons'
]
colors = [ROOT.kBlack, ROOT.kRed, ROOT.kGreen, ROOT.kBlue]
selections = [
'', 'hasTruthMatch', 'hasTruthMatch && phprobe_passLoose ',
'hasTruthMatch && phprobe_passMedium ',
'hasTruthMatch && phprobe_passTight '
]
name += 'EleVeto%s' % elevetoid
else:
labels = [
'%s Photons' % elevetoid,
'%s Photons + CSEV' % elevetoid,
'%s Photons + PSV' % elevetoid
]
colors = [ROOT.kBlack, ROOT.kRed, ROOT.kGreen]
selections = [
'', 'hasTruthMatch',
'hasTruthMatch && phprobe_pass%s && phprobe_eleVeto ' % elevetoid,
'hasTruthMatch && phprobe_pass%s && !phprobe_hasPixSeed ' %
elevetoid
]
hists = []
for idx, selection in enumerate(selections):
sampMan.create_hist(samp[0], var, selection, binning)
thissamp = sampMan.get_samples(name=samp_name)
hists.append(thissamp[0].hist.Clone('hist_%d' % idx))
canvas = ROOT.TCanvas(name, name)
DrawHists(canvas, hists, colors, labels, outputDir)
def main() :
global sampManFake
global sampManReal
global sampManSignal
base_dir_fake = '/data/users/jkunkle/RecoPhoton/FakePhotonProbeWithTrig_2015_11_27'
base_dir_real = '/data/users/jkunkle/RecoPhoton/LepLepNoPhID_2015_11_26'
base_dir_sig = '/data/users/jkunkle/RecoPhoton/LepLepNoPhID_2015_11_26'
treeName = 'tupel/EventTree'
xsfile = 'cross_sections/photon15.py'
lumi = 2093.
sampManFake = SampleManager( base_dir_fake, treeName, filename='tree.root', xsFile=xsfile, lumi=lumi )
sampManReal = SampleManager( base_dir_real, treeName, filename='tree.root', xsFile=xsfile, lumi=lumi )
sampManSignal = SampleManager( base_dir_sig, treeName, filename='tree.root', xsFile=xsfile, lumi=lumi )
conf_fake = 'Modules/SinglePhoton15.py'
conf_real = 'Modules/LepLep15.py'
conf_signal = 'Modules/LepLep15.py'
sampManFake.ReadSamples( conf_fake )
sampManReal.ReadSamples( conf_real )
sampManSignal.ReadSamples( conf_signal )
channels = ['Muon',]
#channels = ['Muon', 'Electron']
fitvars = ['ph_chIsoCorr']
#fitvars = ['ph_sigmaIEIE']
# make fake photon templ
for fv in fitvars :
for reg in regions :
for ptmin, ptmax in ptbins :
fake_str_data = get_fake_draw_str( 'data', fv, reg, ptmin, ptmax )
fake_str_mc = get_fake_draw_str( 'mc', fv, reg, ptmin, ptmax )
draw_var_fake = get_fake_draw_var( fv )
draw_var_real = get_real_draw_var( fv )
real_str = get_real_draw_str( 'template', fv, reg, ptmin, ptmax )
data_samp_fake = sampManFake.get_samples(name='SinglePhoton')[0]
mc_samp_fake = sampManFake.get_samples(name='DiPhoton')[0]
mc_samp_real = sampManReal.get_samples(name='Zgamma')[0]
data_template_fake = clone_sample_and_draw( data_samp_fake, draw_var_fake, fake_str_data, binnings[reg][fv],sampManFake )
mc_template_fake = clone_sample_and_draw( mc_samp_fake, draw_var_fake, fake_str_mc, binnings[reg][fv],sampManFake )
mc_template_real = clone_sample_and_draw( mc_samp_real, draw_var_real, real_str, binnings[reg][fv], sampManReal )
data_template_fake.Add( mc_template_fake, -1 )
#data_template_fake.Draw()
#raw_input('cont')
#mc_template_real.Draw()
#raw_input('cont')
for ch in channels :
signal_str = get_real_draw_str( 'signal', fv, reg, ptmin, ptmax, channel=ch )
data_samp_sig = sampManReal.get_samples(name=ch)[0]
sig_data = clone_sample_and_draw( data_samp_sig, draw_var_real, signal_str, binnings[reg][fv], sampManSignal )
xvar = ROOT.RooRealVar( 'xvar', 'xvar', 0, binnings[reg][fv][2] )
fakefrac = ROOT.RooRealVar( 'fakefrac', 'fakefrac', 0.5, 0., 1. )
sig_templatehist = ROOT.RooDataHist( 'sig_hist', 'sig_hist', ROOT.RooArgList(xvar), mc_template_real )
bkg_templatehist = ROOT.RooDataHist( 'bkg_hist', 'bkg_hist', ROOT.RooArgList(xvar), data_template_fake)
sig_template = ROOT.RooHistPdf( 'sig_template', 'sig_template', ROOT.RooArgSet( xvar), sig_templatehist )
bkg_template = ROOT.RooHistPdf( 'bkg_template', 'bkg_template', ROOT.RooArgSet( xvar), bkg_templatehist)
model = ROOT.RooAddPdf( 'model', 'model', ROOT.RooArgList( sig_template, bkg_template), ROOT.RooArgList( fakefrac ) )
target_template = ROOT.RooDataHist( 'target_template', 'target_template', ROOT.RooArgList( xvar ), sig_data )
print 'RUN FIT'
result = model.fitTo( target_template, ROOT.RooFit.Range(0, loose_cuts[reg][fv]),ROOT.RooFit.Save() )
if result is not None :
result.Print()
print 'Integral bins = 1 - %d' %(mc_template_real.FindBin(cut_values[reg][fv]))
print 'Predictd bkg = ', mc_template_real.Integral( 1, mc_template_real.FindBin(cut_values[reg][fv])-1 ) * fakefrac.getValV()
frame = xvar.frame()
target_template.plotOn(frame)
model.plotOn( frame )
model.plotOn( frame , ROOT.RooFit.Components('sig_template'), ROOT.RooFit.LineStyle(ROOT.kDashed))
model.plotOn( frame , ROOT.RooFit.Components('bkg_template'), ROOT.RooFit.LineStyle(ROOT.kDashed), ROOT.RooFit.LineColor( ROOT.kRed ) )
frame.Draw()
raw_input('cont')
print result
def main():
sampManMu = SampleManager(options.baseDirMu,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI)
sampManEl = SampleManager(options.baseDirEl,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI)
sampManMu.ReadSamples(_SAMPCONF)
sampManEl.ReadSamples(_SAMPCONF)
sel_base_mu = 'mu_pt30_n==1 && mu_n==1 && ph_n==1'
sel_base_el = 'el_pt30_n==1 && el_n==1 && ph_n==1'
plot_var = 'mt_res'
configs = [
{
'tag': 'mug',
'sampMan': sampManMu,
'selection': sel_base_mu
},
{
'tag': 'elg',
'sampMan': sampManEl,
'selection': sel_base_el
},
]
sig_samples = []
for samp in sampManMu.get_samples():
print samp.name
if samp.name.count('ResonanceMass') > 0:
res = re.match(
'(MadGraph|Pythia)ResonanceMass(\d+)_width(\w+|\d+)',
samp.name)
sig_samples.append(
(samp.name, res.group(1), int(res.group(2)), res.group(3)))
for conf in configs:
for samp, samptype, mass, width in sig_samples:
suffix = '_m%d_width%s' % (mass, width)
fit_xmin = int(mass * 0.6)
xmax = mass * 1.5
nbins = 100
var = ROOT.RooRealVar('m_res%s' % suffix, 'Transverse mass [GeV]',
0.0, xmax)
var.setBins(10000, 'cache')
var.setMin('cache', -100)
var.setMax('cache', 1500)
if width == '0p01':
width_factor = 0.01
else:
width_factor = float(width)
#------------------------------
# create the model
#------------------------------
#if width == '0p01' :
# #------------------------------
# # crystal ball, has four parameters
# #------------------------------
# cb_cut = ROOT.RooRealVar('cb_cut' , 'Cut' , 0.2 , 0, 2 , '')
# cb_sigma = ROOT.RooRealVar('cb_sigma' , 'Width', 2., 0 , 60, 'GeV')
# cb_power = ROOT.RooRealVar('cb_power' , 'Power', 7.1, 0 , 100 , '')
# cb_m0 = ROOT.RooRealVar('cb_mass' , 'mass' , mass , mass*0.8, mass*1.2,'GeV')
# model = ROOT.RooCBShape('sig_model%s'%suffix, 'A Crystal Ball Lineshape', var, cb_m0, cb_sigma,cb_cut,cb_power)
#else :
##------------------------------
## Breit-Wigner, has two parameters, the resonance mass and the width
#------------------------------
bw_width_val = mass * 0.01 * width_factor
#if bw_width_val < 2. :
bw_width_val = 2.
bw_m = ROOT.RooRealVar('bw_mass', 'Resonance Mass', mass,
mass * 0.8, mass * 1.2, 'GeV')
bw_w = ROOT.RooRealVar('bw_width', 'Breit-Wigner width',
bw_width_val, -100, 100, 'GeV')
bw_m.setConstant()
bw_w.setConstant()
bw = ROOT.RooBreitWigner('bw%s' % suffix,
'A Breit-Wigner Distribution', var, bw_m,
bw_w)
##------------------------------
## Gaussian, has two parameters, the resonance mass and the width
##------------------------------
#gaus_width_val = mass*0.01*width_factor
#if gaus_width_val < 2. :
# gaus_width_val = 2.0
#gaus_m = ROOT.RooRealVar('bw_mass' , 'Resonance Mass', mass, mass*0.8, mass*1.2,'GeV')
#gaus_w = ROOT.RooRealVar('bw_width', 'Breit-Wigner width',gaus_width_val , -100, 100,'GeV')
#gaus_m.setConstant()
##gaus_w.setConstant()
#gaus = ROOT.RooGaussian('gaus%s' %suffix,'A Gaussian Distribution', var, gaus_m,gaus_w)
#------------------------------
# crystal ball, has four parameters
#------------------------------
cb_cut = ROOT.RooRealVar('cb_cut', 'Cut', 0.2, 0, 2, '')
cb_sigma = ROOT.RooRealVar('cb_sigma', 'Width', 2., 0, 60, 'GeV')
cb_power = ROOT.RooRealVar('cb_power', 'Power', 7.1, 0, 100, '')
cb_m0 = ROOT.RooRealVar('cb_mass', 'mass', 0, -200, 200, 'GeV')
cb = ROOT.RooCBShape('cb%s' % suffix, 'A Crystal Ball Lineshape',
var, cb_m0, cb_sigma, cb_cut, cb_power)
#------------------------------
# FFT convolve them to make the model
#------------------------------
model = ROOT.RooFFTConvPdf('sig_model%s' % suffix, 'Convolution',
var, bw, cb)
#------------------------------
# create the data histogram
#------------------------------
conf['sampMan'].create_hist(samp, plot_var, conf['selection'],
(nbins, 0, xmax))
hist = conf['sampMan'].get_samples(name=samp)[0].hist.Clone(
'data_muon%s' % suffix)
data_hist = ROOT.RooDataHist('target_data', 'target_data',
ROOT.RooArgList(var), hist)
model.fitTo(data_hist, ROOT.RooFit.Range(fit_xmin, mass * 1.3),
ROOT.RooFit.SumW2Error(True))
can = ROOT.TCanvas(str(uuid.uuid4()), '')
can.SetTitle('')
frame = var.frame()
frame.SetTitle('')
data_hist.plotOn(frame)
model.plotOn(frame)
model.paramOn(
frame, ROOT.RooFit.ShowConstants(True),
ROOT.RooFit.Layout(0.1, 0.5, 0.9),
ROOT.RooFit.Format("NEU", ROOT.RooFit.AutoPrecision(3)))
frame.Draw()
frame.Print()
#if width != '0p01' :
print 'bw_m = %f, err up = %f, err dn = %f ' % (
bw_m.getValV(), bw_m.getErrorHi(), bw_m.getErrorLo())
print 'bw_w = %f, err up = %f, err dn = %f ' % (
bw_w.getValV(), bw_w.getErrorHi(), bw_w.getErrorLo())
print 'cb_cut = %f, err up = %f, err dn = %f ' % (
cb_cut.getValV(), cb_cut.getErrorHi(), cb_cut.getErrorLo())
print 'cb_sigma = %f, err up = %f, err dn = %f ' % (
cb_sigma.getValV(), cb_sigma.getErrorHi(),
cb_sigma.getErrorLo())
print 'cb_power = %f, err up = %f, err dn = %f ' % (
cb_power.getValV(), cb_power.getErrorHi(),
cb_power.getErrorLo())
print 'cb_m0 = %f, err up = %f, err dn = %f ' % (
cb_m0.getValV(), cb_m0.getErrorHi(), cb_m0.getErrorLo())
#chi2 = frame.chiSquare( 'sig_model%s' %suffix, 'target_data', 4 )
chi2 = frame.chiSquare(4)
print 'Chi^2 = ', chi2
chi2_text = ROOT.TLatex()
chi2_text.SetNDC()
chi2_text.SetText(0.12, 0.45, 'Chi^2/ndf = %.01f' % chi2)
chi2_text.Draw()
result_dic = {}
result_dic['bw_m'] = (bw_m.getValV(), bw_m.getErrorHi(),
bw_m.getErrorLo())
result_dic['bw_w'] = (bw_w.getValV(), bw_w.getErrorHi(),
bw_w.getErrorLo())
result_dic['cb_cut'] = (cb_cut.getValV(), cb_cut.getErrorHi(),
cb_cut.getErrorLo())
result_dic['cb_sigma'] = (cb_sigma.getValV(),
cb_sigma.getErrorHi(),
cb_sigma.getErrorLo())
result_dic['cb_power'] = (cb_power.getValV(),
cb_power.getErrorHi(),
cb_power.getErrorLo())
result_dic['cb_m0'] = (cb_m0.getValV(), cb_m0.getErrorHi(),
cb_m0.getErrorLo())
result_dic['chi2'] = chi2
if options.outputDir is not None:
name = 'SignalFit_%s_%s_M%d_width%s' % (samptype, conf['tag'],
mass, width)
can.SaveAs('%s/%s.pdf' % (options.outputDir, name))
opfile = open('%s/%s.pickle' % (options.outputDir, name), 'w')
pickle.dump(result_dic, opfile)
opfile.close()
else:
raw_input("cont")
def main():
sampManMuNoG = SampleManager(options.baseDirMuNoG,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI)
sampManElNoG = SampleManager(options.baseDirElNoG,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI)
sampManMuNoG.ReadSamples(_SAMPCONF)
sampManElNoG.ReadSamples(_SAMPCONF)
sampManMuNoG.outputs = {}
sampManElNoG.outputs = {}
sel_base_mu = 'mu_pt30_n==1 && mu_n==1'
sel_base_el = 'el_pt35_n==1 && el_n==1'
#eta_cuts = ['EB', 'EE']
eta_cuts = ['EB']
workspaces_to_save = {}
xmin_m = 160
xmax_m = 2000
bin_width_m = 20
xmin_pt = xmin_m / 2
if xmin_pt < 50:
xmin_pt = 50
xmax_pt = xmax_m / 2
bin_width_pt = bin_width_m / 2.
binning_m = ((xmax_m - xmin_m) / bin_width_m, xmin_m, xmax_m)
binning_pt = ((xmax_pt - xmin_pt) / bin_width_pt, xmin_pt, xmax_pt)
#binning_leadjet_pt = (128, 0., 640.)
xvar_m = ROOT.RooRealVar('x_m', 'x_m', xmin_m, xmax_m)
xvar_pt = ROOT.RooRealVar('x_pt', 'x_pt', xmin_pt, xmax_pt)
kine_vars = { #'mt_incl_lepph_z' : { 'var' : 'mt_lep_met_ph' , 'xvar' : xvar_m , 'binning' : binning_m},
#'m_incl_lepph_z' : { 'var' : 'm_lep_met_ph' , 'xvar' : xvar_m , 'binning' : binning_m},
'mt_fulltrans': {
'var': 'mt_res',
'xvar': xvar_m,
'binning': binning_m
},
#'mt_constrwmass' : { 'var' : 'recoM_lep_nu_ph' , 'xvar' : xvar_m , 'binning' : binning_m},
#'ph_pt' : { 'var' : 'ph_pt[0]' , 'xvar' : xvar_pt , 'binning' : binning_pt},
}
selections = {
'base': {
'mu': {
'selection': sel_base_mu
},
# 'el' : { 'selection' : sel_base_el },
},
#'jetVeto' : { 'mu' : {'selection' : sel_jetveto_mu },
# 'el' : { 'selection' : sel_jetveto_el } ,
# },
}
workspace_signal = ROOT.RooWorkspace('workspace_signal')
workspace_wgamma = ROOT.RooWorkspace('workspace_wgamma')
workspace_wgammalo = ROOT.RooWorkspace('workspace_wgammalo')
workspace_top = ROOT.RooWorkspace('workspace_top')
workspace_zgamma = ROOT.RooWorkspace('workspace_zgamma')
wjets = ROOT.RooWorkspace('workspace_wjets')
elefake = ROOT.RooWorkspace('elefake')
for seltag, chdic in selections.items():
for ch, seldic in chdic.items():
for name, vardata in kine_vars.items():
print('MC background k-factor calculation')
make_kfactor_calc(sampManMuNoG,
'Wjets',
seldic['selection'],
False,
suffix='wjets_%s' % (ch),
workspace=wjets)
make_kfactor_calc(sampManMuNoG,
'Wgamma',
seldic['selection'],
False,
suffix='wjets_%s' % (ch),
workspace=wjets)
make_kfactor_calc(sampManMuNoG,
'Zgamma',
seldic['selection'],
False,
suffix='wjets_%s' % (ch),
workspace=wjets)
make_kfactor_calc(sampManMuNoG,
'AllTop',
seldic['selection'],
False,
suffix='wjets_%s' % (ch),
workspace=wjets)
make_kfactor_calc(sampManMuNoG,
'Z+jets',
seldic['selection'],
False,
suffix='wjets_%s' % (ch),
workspace=wjets)
make_kfactor_calc(sampManMuNoG,
'Data',
seldic['selection'],
True,
suffix='wjets_%s' % (ch),
workspace=wjets)
if options.outputDir is not None:
wjets.writeToFile('%s/outfile_kfactor.root' % (options.outputDir))
for fileid, ws_list in workspaces_to_save.items():
for idx, ws in enumerate(ws_list):
if idx == 0:
recreate = True
else:
recreate = False
ws.writeToFile(
'%s/workspace_%s.root' % (options.outputDir, fileid),
recreate)
outputFile = ROOT.TFile(
'%s/outfile_kfactor.root' % (options.outputDir), 'recreate')
for key, can in sampManMuNoG.outputs.items():
can.Write('%s' % (key))
for key, can in sampManElNoG.outputs.items():
can.Write('%s' % (key))
def main():
global samplesFF
global samplesData
if not options.baseDir.count('/eos/') and not os.path.isdir(
options.baseDir):
print 'baseDir not found!'
return
samplesFF = SampleManager(options.baseDir,
options.treeName,
mcweight=options.mcweight,
treeNameModel=options.treeNameModel,
filename=options.fileName,
base_path_model=options.baseDirModel,
xsFile=options.xsFile,
lumi=options.lumi,
readHists=options.readHists,
quiet=options.quiet)
base_dir_data = '/afs/cern.ch/work/j/jkunkle/private/CMS/Wgamgam/Output/LepGammaGammaNoPhID_2014_10_29'
samplesData = SampleManager(base_dir_data,
options.treeName,
filename=options.fileName,
xsFile=options.xsFile,
lumi=options.lumi,
quiet=options.quiet)
if options.samplesConf is not None:
samplesFF.ReadSamples(options.samplesConf)
samplesData.ReadSamples(options.samplesConf)
#cuts_den = '!ph_passChIsoCorrMedium[0] && !ph_passNeuIsoCorrMedium[0] && !ph_passPhoIsoCorrMedium[0] && ph_sigmaIEIE[0]>%f && ph_sigmaIEIE[0] < %f '
#cuts_num = 'ph_passChIsoCorrMedium[0] && ph_passNeuIsoCorrMedium[0] && ph_passPhoIsoCorrMedium[0] && %s && ph_passSIEIEMedium[0] ' %(ec)
loose_cuts = (12, 9, 9)
#loose_cuts = (20, 15, 15)
#loose_cuts = (1000000,1000000,1000000)
cuts_den = 'ph_hasPixSeed[0]==0 && !ph_passNeuIsoCorrMedium[0] && !ph_passPhoIsoCorrMedium[0] && !ph_passSIEIEMedium[0] && !ph_passChIsoCorrMedium[0] && ph_chIsoCorr[0] < %d && ph_neuIsoCorr[0] < %d && ph_phoIsoCorr[0] < %d ' % (
loose_cuts[0], loose_cuts[1], loose_cuts[2])
cuts_num = 'ph_hasPixSeed[0]==0 && ph_passNeuIsoCorrMedium[0] && ph_passPhoIsoCorrMedium[0] && ph_passSIEIEMedium[0] && ph_passChIsoCorrMedium[0] && ph_HoverE12[0] < 0.05'
regions = ['EB', 'EE']
ptbins = [15, 25, 40, 70, 1000000]
fake_factors = GetFakeFactors(cuts_den, cuts_num, regions, ptbins)
#for key, val in fake_factors.iteritems() :
# print 'Fake factors for %s' %key
# val.Draw()
# raw_input('continue')
#ApplySinglePhotonFF( cuts_den, sieie_cuts, eta_cuts, ptbins, fake_factors )
gg_regions = [('EB', 'EB')]
ApplyDiPhotonFF(loose_cuts, gg_regions, ptbins, fake_factors)
def main() :
if options.batch:
ptbins = [0,30,40,50,60,80,1000]
#ptbins = np.linspace(0,3.1416*4,37)
regions = 'aABCD'
regions = 'AB'
if options.data: regions = 'ABD'
with open('condor_template.jdl','r') as file1:
filedata = file1.read()
i=0
for r in regions:
for binlow,binhigh in zip(ptbins[0:-1],ptbins[1:]):
pyoption = '%i %g %g %s' %(options.step,binlow,binhigh,r)
if options.data: pyoption+=" --data"
filedatawrite= filedata.replace('[REPLACE]',pyoption)
writefilename = 'log/condor%i.jdl' %i
with open(writefilename,'w') as file:
file.write(filedatawrite)
condor_command = 'condor_submit %s' %writefilename
os.system(condor_command)
i+=1
return
if options.baseDirElG ==None:
options.baseDirElG = "/data/users/kakw/Resonances/LepGamma_elg_newblind_2018_09_23_beta/"
options.baseDirElG = "/data2/users/kakw/Resonances2016/LepGamma_elg_2019_10_28"
sampManElG= SampleManager( options.baseDirElG, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManElG.ReadSamples( _SAMPCONF )
#sel_base_el = 'ph_n>=1 && el_n==1 &&!( ph_eta[0]<0&&ph_phi[0]>2.3&&ph_phi[0]<2.7)&&!(ph_phi[0]>1.2&&ph_phi[0]<1.5) '
#sampManElG.deactivate_all_samples()
#sampManElG.activate_sample(["Z+jets"])
#sampManElG.get_samples(name="DYJetsToLL_M-50")[0].scale=1.
#f1 = ROOT.TFile("%s/output.root"%(options.outputDir),"RECREATE")
# elefake = ROOT.RooWorkspace( 'elefake' )
if options.step == 1:
sampManElG.deactivate_all_samples()
#sampManElG.activate_sample(["Z+jets","Zgamma"])
sampManElG.activate_sample(["Z+jets"])
sampManElG.get_samples(name="DYJetsToLL_M-50")[0].scale=1.
#ptbins = np.linspace(0,3.1416*4)
ptbins = [0,30,40,50,60,80,2000]
#ptbins = [0,80,2000]
if options.binlow:
ptbins = [float(options.binlow), float(options.binhigh)]
#sampManElG.closure( samp, sel_base_el,'EB', plot_var = 'm_lep_ph',varbins=[20,30,40,50,70,90,110,150,160], mode=1)
#fitting(sampManElG,(50,80))
#makevariableplots(sampManElG,[0,0.7,1.0,1.479,3],fitrange,basesel=base+"&&ph_pt[0]>80", tag="etaptgt80",var="abs(ph_sc_eta[0])")
#makevariableplots(sampManElG,[0,0.7,1.0,1.479,3],fitrange,basesel=base+"&&ph_pt[0]<=80",tag="etaptlt80",var="abs(ph_sc_eta[0])")
#makevariableplots(sampManElG,ptbins,fitrange,basesel=base+phmedium,tag="phi",var="(ph_phi[0]+3.1416+(ph_eta[0]>0)*3.1416*2)")
makevariableplots(sampManElG,ptbins,fitrange,basesel=baseeta+phmedium,tag="all")
#makevariableplots(sampManElG,[0,30,40,50,60,80,1000],fitrange,basesel=baseeta+passpix+ltmet,tag="regA")
#makevariableplots(sampManElG,[0,30,40,50,60,80,1000],fitrange,basesel=baseeta+failpix+ltmet,tag="regB")
#makevariableplots(sampManElG,[0,30,40,50,60,80,1000],fitrange,basesel=baseeta+passpix+gtmet,tag="regS")
#makevariableplots(sampManElG,[0,30,40,50,60,80,1000],fitrange,basesel=baseeta+failpix+gtmet,tag="regD")
elif options.step == 11:
sampManElG.deactivate_all_samples()
sampManElG.activate_sample(["Zgamma"])
sampManElG.get_samples(name="ZGTo2LG")[0].scale=1.
makevariableplots_simultaneous_zg(sampManElG,[0,30,40,50,60,80,1000],fitrange5,
basesel=baseeta+phmedium,tag="zgamma",ic = dict(bkgd="gauszg",ext='simulzg'),dobkgd=False,donorm=False)
elif options.step == 2:
print "STEP 2 SIMULTANEOUS FIT"
bkgd=["expo","gaus"]
if options.data: doData, tbase = True, "_data"
else: doData,tbase=False,""
ic = dict(bkgd=["expo","gaus"],sig="dcbp2",bkgd2="gauszg2",ext="simul2")
binvar = "ph_pt[0]"
ptbins = [0,30,40,50,60,80,2000]
#ptbins = [80,1000]
## flattened phi for both forward and backward detector. 10 degree in each bin
#ptbins = np.linspace(0,3.1416*4,72)
#binvar = "(ph_phi[0]+3.1416+(ph_eta[0]>0)*3.1416*2)"
## batch options
if options.binlow:
ptbins = [float(options.binlow), float(options.binhigh)]
tbase+=options.binlow.replace('.',"p")
if options.region:
doregions = options.region
else:
doregions = 'ABCDa'
print 'DOregions: %s bins %f %f' %(doregions,ptbins[0],ptbins[1])
#makevariableplots_simultaneous(sampManElG,[0,30,40,50,60,80,1000],fitrange1,basesel=baseeta,tag="all")
#makevariableplots_simultaneous(sampManElG,[0,30,40,50,60,80,1000],fitrange2,
# basesel=baseeta,tag="all",ic = dict(bkgd="gaus"),maxtimes=20)
if 'A' in doregions: makevariableplots_simultaneous(sampManElG,ptbins,fitrange4,
basesel=baseeta+passpix+ltmet+phmedium,tag="regA"+tbase,ic = ic,maxtimes=50,doData=doData, var=binvar)
if 'B' in doregions: makevariableplots_simultaneous(sampManElG,ptbins,fitrange4,
basesel=baseeta+failpix+ltmet+phmedium,tag="regB"+tbase,ic = ic,maxtimes=50,doData=doData, var=binvar)
if 'D' in doregions: makevariableplots_simultaneous(sampManElG,ptbins,fitrange4,
basesel=baseeta+failpix+gtmet+phmedium,tag="regD"+tbase,ic = ic ,maxtimes=50,doData=doData, var=binvar)
if not doData:
if 'a' in doregions: makevariableplots_simultaneous(sampManElG,ptbins,fitrange4,
basesel=baseeta ,tag="all",ic = ic,maxtimes=50, var= binvar)
if 'C' in doregions or 'S' in doregions:
makevariableplots_simultaneous(sampManElG,ptbins,fitrange4,
basesel=baseeta+passpix+gtmet+phmedium,tag="regS",ic = ic,maxtimes=50, var=binvar)
return
def main():
print 'DEBUG:', options.dataFrame
sampManMu = SampleManager(options.baseDirMu,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI,
weightHistName="weighthist",
dataFrame=options.dataFrame)
sampManEl = SampleManager(options.baseDirEl,
_TREENAME,
filename=_FILENAME,
xsFile=_XSFILE,
lumi=_LUMI,
weightHistName="weighthist",
dataFrame=options.dataFrame)
sampManMu.ReadSamples(_SAMPCONF)
sampManEl.ReadSamples(_SAMPCONF)
sampManMu.outputs = {}
sampManEl.outputs = {}
#Analysis signal region
#val_sel_mu = 'mu_pt30_n==1 && mu_n==1 && el_n==0 && ph_n==1 && ph_IsEB[0] && ph_pt[0] > 80 && ph_passHOverEMedium[0] && ph_passNeuIsoCorrMedium[0] && ph_passPhoIsoCorrMedium[0] && !ph_hasPixSeed[0] && ph_passEleVeto[0] && met_pt > 25 && (mt_res > 200 && mt_res < 2000 )'
#val_sel_el = 'el_pt30_n==1 && el_n==1 && mu_n==0 && ph_n==1 && ph_IsEB[0] && ph_pt[0] > 80 && ph_passHOverEMedium[0] && ph_passNeuIsoCorrMedium[0] && ph_passPhoIsoCorrMedium[0] && !ph_hasPixSeed[0] && ph_passEleVeto[0] && met_pt > 25 && (mt_res > 200 && mt_res < 2000 )'
#Wgamma enriched
val_sel_mu = 'mu_pt30_n==1 && mu_n==1 && el_n==0 && ph_n==1 && ph_IsEB[0] && ph_pt[0] > 80 && ph_passHOverETight[0] && ph_passNeuIsoCorrTight[0] && ph_passPhoIsoCorrTight[0] && !ph_hasPixSeed[0] && ph_passEleVeto[0] && met_pt > 40 && (m_lep_ph < 75 || m_lep_ph > 105)'
val_sel_el = 'el_pt30_n==1 && el_n==1 && mu_n==0 && ph_n==1 && ph_IsEB[0] && ph_pt[0] > 80 && ph_passHOverETight[0] && ph_passNeuIsoCorrTight[0] && ph_passPhoIsoCorrTight[0] && !ph_hasPixSeed[0] && ph_passEleVeto[0] && met_pt > 40 && (m_lep_ph < 75 || m_lep_ph > 105)'
#Zgamma enriched
#val_sel_mu = 'mu_n==2 && m_ll < 130. && m_ll > 50. && mu_pt_rc[0] > 52. && mu_pt_rc[1] > 30. && mu_hasTrigMatch[0] && mu_passTight[0] && mu_hasTrigMatch[1] && mu_passTight[1] && ph_n==1 && ph_IsEB[0] && ph_pt[0] > 40 && ph_passHOverETight[0] && ph_passNeuIsoCorrTight[0] && ph_passPhoIsoCorrTight[0] && !ph_hasPixSeed[0] && ph_passEleVeto[0] && (ph_pt[0] > 40.*m_llph/150.)'
#val_sel_el = ''
#W+Jets enriched
#val_sel_mu = 'mu_pt30_n==1 && mu_n==1 && mu_eta[0] > -2.4 && mu_eta[0] < 2.4 && mu_hasTrigMatch[0] && mu_passTight[0] && mt_lep_met > 50. && leadjet_pt > 30. && jet_n >= 1 && jet_eta[0] > -2.4 && jet_eta[0] < 2.4 && jet_CSVMedium_n == 0 && ph_n == 0'
#val_sel_mu = 'mu_pt30_n==1 && mu_n==1 && el_n==0 && mu_eta[0] < 2.4 && mu_passTight[0] && mt_lep_met > 50. && ph_n==1 && ph_IsEB[0] && ph_pt[0] > 25 && ph_passHOverEMedium[0] && ph_passNeuIsoCorrMedium[0] && ph_passPhoIsoCorrMedium[0] && !ph_hasPixSeed[0] && ph_passEleVeto[0]'
#val_sel_el = ''
#eta_cuts = ['EB', 'EE']
eta_cuts = ['EB']
workspaces_to_save = {}
selections = {
'base': {
'mu': {
'selection': val_sel_mu
},
'el': {
'selection': val_sel_el
},
},
}
#workspace_signal = ROOT.RooWorkspace( 'workspace_signal' )
#workspace_wgamma = ROOT.RooWorkspace( 'workspace_wgamma' )
#workspace_wgammalo = ROOT.RooWorkspace( 'workspace_wgammalo' )
#workspace_top = ROOT.RooWorkspace( 'workspace_top' )
#workspace_zgamma = ROOT.RooWorkspace( 'workspace_zgamma' )
wjets = ROOT.RooWorkspace('workspace_wjets')
#elefake = ROOT.RooWorkspace( 'elefake' )
for seltag, chdic in selections.iteritems():
for ch, seldic in chdic.iteritems():
for et in eta_cuts:
if ch == 'mu':
print 'Jet fake rate: MC - %s' % (ch)
make_wjets_matrix(sampManMu,
'WJets',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManMu,
'WGamma',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManMu,
'TTbar_DiLep',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManMu,
'TTbar_SingleLep',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManMu,
'Zgamma',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManMu,
'ZJets',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManMu,
'GJets',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManMu,
'TTGJets',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManMu,
'GammaGamma',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
print 'Jet fake rate: data - %s' % (ch)
#make_wjets_matrix( sampManMu, 'Data', seldic['selection'], et, True, suffix='data_%s_%s' %(et,ch))
if ch == 'el':
print 'Jet fake rate: MC - %s' % (ch)
make_wjets_matrix(sampManEl,
'WJets',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManEl,
'WGamma',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManEl,
'TTbar_DiLep',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManEl,
'TTbar_SingleLep',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManEl,
'Zgamma',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManEl,
'ZJets',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManEl,
'GJets',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManEl,
'TTGJets',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
make_wjets_matrix(sampManEl,
'GammaGamma',
seldic['selection'],
et,
False,
suffix='mc_%s_%s' % (et, ch))
print 'Jet fake rate: data - %s' % (ch)
#make_wjets_matrix( sampManEl, 'Data', seldic['selection'], et, True, suffix='data_%s_%s' %(et,ch))
if options.outputDir is not None:
wjets.writeToFile('%s/%s.root' % (options.outputDir, wjets.GetName()))
#for fileid, ws_list in workspaces_to_save.iteritems() :
# for idx, ws in enumerate(ws_list) :
# if idx == 0 :
# recreate = True
# else :
# recreate = False
# ws.writeToFile( '%s/workspace_%s.root' %( options.outputDir, fileid ), recreate )
outputFile = ROOT.TFile(
'%s/outfile_matrixFR_wgamma18_%s.root' %
(options.outputDir, wjets.GetName()), 'recreate')
for key, can in sampManMu.outputs.iteritems():
can.Write('%s' % (key))
for key, can in sampManEl.outputs.iteritems():
can.Write('%s' % (key))
def main() :
sampManMu = SampleManager( options.baseDirMu, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManEl = SampleManager( options.baseDirEl, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManMu.ReadSamples( _SAMPCONF )
sampManEl.ReadSamples( _SAMPCONF )
sel_base_mu = 'mu_pt30_n==1 && mu_n==1 && ph_n==1'
sel_base_el = 'el_pt30_n==1 && el_n==1 && ph_n==1'
plot_var = 'mt_res'
configs = [
{'tag' : 'mug', 'sampMan' : sampManMu, 'selection' : sel_base_mu },
{'tag' : 'elg', 'sampMan' : sampManEl, 'selection' : sel_base_el },
]
sig_samples = []
for samp in sampManMu.get_samples() :
print samp.name
if samp.name.count('ResonanceMass') > 0 :
res = re.match('(MadGraph|Pythia)ResonanceMass(\d+)_width(\w+|\d+)', samp.name )
sig_samples.append( ( samp.name, res.group(1), int(res.group(2)), res.group(3) ) )
for conf in configs :
for samp, samptype, mass, width in sig_samples :
suffix = '_m%d_width%s' %( mass, width )
fit_xmin = int(mass*0.6)
xmax = mass * 1.5
nbins = 100
var = ROOT.RooRealVar( 'm_res%s' %suffix, 'Transverse mass [GeV]', 0.0, xmax)
var.setBins(10000,'cache')
var.setMin('cache',-100)
var.setMax('cache',1500)
if width == '0p01' :
width_factor = 0.01
else :
width_factor = float( width )
#------------------------------
# create the model
#------------------------------
#if width == '0p01' :
# #------------------------------
# # crystal ball, has four parameters
# #------------------------------
# cb_cut = ROOT.RooRealVar('cb_cut' , 'Cut' , 0.2 , 0, 2 , '')
# cb_sigma = ROOT.RooRealVar('cb_sigma' , 'Width', 2., 0 , 60, 'GeV')
# cb_power = ROOT.RooRealVar('cb_power' , 'Power', 7.1, 0 , 100 , '')
# cb_m0 = ROOT.RooRealVar('cb_mass' , 'mass' , mass , mass*0.8, mass*1.2,'GeV')
# model = ROOT.RooCBShape('sig_model%s'%suffix, 'A Crystal Ball Lineshape', var, cb_m0, cb_sigma,cb_cut,cb_power)
#else :
##------------------------------
## Breit-Wigner, has two parameters, the resonance mass and the width
#------------------------------
bw_width_val = mass*0.01*width_factor
#if bw_width_val < 2. :
bw_width_val = 2.
bw_m = ROOT.RooRealVar('bw_mass' , 'Resonance Mass', mass, mass*0.8, mass*1.2,'GeV')
bw_w = ROOT.RooRealVar('bw_width', 'Breit-Wigner width',bw_width_val , -100, 100,'GeV')
bw_m.setConstant()
bw_w.setConstant()
bw = ROOT.RooBreitWigner('bw%s' %suffix,'A Breit-Wigner Distribution', var, bw_m,bw_w)
##------------------------------
## Gaussian, has two parameters, the resonance mass and the width
##------------------------------
#gaus_width_val = mass*0.01*width_factor
#if gaus_width_val < 2. :
# gaus_width_val = 2.0
#gaus_m = ROOT.RooRealVar('bw_mass' , 'Resonance Mass', mass, mass*0.8, mass*1.2,'GeV')
#gaus_w = ROOT.RooRealVar('bw_width', 'Breit-Wigner width',gaus_width_val , -100, 100,'GeV')
#gaus_m.setConstant()
##gaus_w.setConstant()
#gaus = ROOT.RooGaussian('gaus%s' %suffix,'A Gaussian Distribution', var, gaus_m,gaus_w)
#------------------------------
# crystal ball, has four parameters
#------------------------------
cb_cut = ROOT.RooRealVar('cb_cut' , 'Cut' , 0.2 , 0, 2 , '')
cb_sigma = ROOT.RooRealVar('cb_sigma' , 'Width', 2., 0 , 60, 'GeV')
cb_power = ROOT.RooRealVar('cb_power' , 'Power', 7.1, 0 , 100 , '')
cb_m0 = ROOT.RooRealVar('cb_mass' , 'mass' , 0 , -200 , 200,'GeV')
cb = ROOT.RooCBShape('cb%s'%suffix, 'A Crystal Ball Lineshape', var, cb_m0, cb_sigma,cb_cut,cb_power)
#------------------------------
# FFT convolve them to make the model
#------------------------------
model = ROOT.RooFFTConvPdf('sig_model%s'%suffix,'Convolution', var, bw, cb)
#------------------------------
# create the data histogram
#------------------------------
conf['sampMan'].create_hist(samp, plot_var, conf['selection'], ( nbins, 0, xmax ) )
hist = conf['sampMan'].get_samples(name=samp)[0].hist.Clone( 'data_muon%s' %suffix )
data_hist = ROOT.RooDataHist( 'target_data', 'target_data', ROOT.RooArgList( var ), hist )
model.fitTo( data_hist,ROOT.RooFit.Range(fit_xmin, mass*1.3),ROOT.RooFit.SumW2Error(True) )
can = ROOT.TCanvas( str(uuid.uuid4()), '' )
can.SetTitle('')
frame = var.frame()
frame.SetTitle('')
data_hist.plotOn( frame )
model.plotOn( frame )
model.paramOn(frame, ROOT.RooFit.ShowConstants(True), ROOT.RooFit.Layout(0.1,0.5,0.9), ROOT.RooFit.Format("NEU",ROOT.RooFit.AutoPrecision(3)));
frame.Draw()
frame.Print()
#if width != '0p01' :
print 'bw_m = %f, err up = %f, err dn = %f ' %(bw_m.getValV(), bw_m.getErrorHi(), bw_m.getErrorLo() )
print 'bw_w = %f, err up = %f, err dn = %f ' %(bw_w.getValV(), bw_w.getErrorHi(), bw_w.getErrorLo() )
print 'cb_cut = %f, err up = %f, err dn = %f ' %(cb_cut.getValV(), cb_cut.getErrorHi(), cb_cut.getErrorLo() )
print 'cb_sigma = %f, err up = %f, err dn = %f ' %(cb_sigma.getValV(), cb_sigma.getErrorHi(), cb_sigma.getErrorLo() )
print 'cb_power = %f, err up = %f, err dn = %f ' %(cb_power.getValV(), cb_power.getErrorHi(), cb_power.getErrorLo() )
print 'cb_m0 = %f, err up = %f, err dn = %f ' %(cb_m0.getValV(), cb_m0.getErrorHi(), cb_m0.getErrorLo() )
#chi2 = frame.chiSquare( 'sig_model%s' %suffix, 'target_data', 4 )
chi2 = frame.chiSquare( 4 )
print 'Chi^2 = ',chi2
chi2_text = ROOT.TLatex()
chi2_text.SetNDC()
chi2_text.SetText(0.12, 0.45, 'Chi^2/ndf = %.01f' %chi2 )
chi2_text.Draw()
result_dic = {}
result_dic['bw_m'] = ( bw_m.getValV(), bw_m.getErrorHi(), bw_m.getErrorLo() )
result_dic['bw_w'] = ( bw_w.getValV(), bw_w.getErrorHi(), bw_w.getErrorLo() )
result_dic['cb_cut'] = ( cb_cut.getValV(), cb_cut.getErrorHi(), cb_cut.getErrorLo() )
result_dic['cb_sigma'] = ( cb_sigma.getValV(), cb_sigma.getErrorHi(), cb_sigma.getErrorLo() )
result_dic['cb_power'] = ( cb_power.getValV(), cb_power.getErrorHi(), cb_power.getErrorLo() )
result_dic['cb_m0'] = ( cb_m0.getValV(), cb_m0.getErrorHi(), cb_m0.getErrorLo() )
result_dic['chi2'] = chi2
if options.outputDir is not None :
name = 'SignalFit_%s_%s_M%d_width%s' %( samptype, conf['tag'], mass, width )
can.SaveAs( '%s/%s.pdf' %( options.outputDir, name ) )
opfile = open( '%s/%s.pickle' %(options.outputDir, name ) ,'w')
pickle.dump( result_dic, opfile )
opfile.close()
else :
raw_input("cont")
def main() :
sampManMu = SampleManager( options.baseDirMu, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManEl = SampleManager( options.baseDirEl, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManSigMu = SampleManager( options.baseDirSigMu, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManSigEl = SampleManager( options.baseDirSigEl, _TREENAME, filename=_FILENAME, xsFile=_XSFILE, lumi=_LUMI )
sampManMu.ReadSamples( _SAMPCONF )
sampManEl.ReadSamples( _SAMPCONF )
sampManSigMu.ReadSamples( _SAMPCONF )
sampManSigEl.ReadSamples( _SAMPCONF )
sampManMu.outputs = {}
sampManEl.outputs = {}
sampManSigMu.outputs = {}
sampManSigEl.outputs = {}
sel_base_mu = 'mu_pt30_n==1 && mu_n==1'
sel_base_el = 'el_pt30_n==1 && el_n==1'
eta_cuts = ['EB', 'EE']
workspaces_to_save = {}
xmin = 160
xmax = 1000
binning = ((xmax-xmin)/20, xmin, xmax)
xvar = ROOT.RooRealVar( 'x', 'x',xmin , xmax)
if options.doSignal :
workspace_signal = ROOT.RooWorkspace( 'workspace_signal' )
make_signal_fits( sampManSigMu, sel_base_mu, eta_cuts, 'mt_lep_met_ph', workspace_signal, suffix='mu' )
make_signal_fits( sampManSigEl, sel_base_el, eta_cuts, 'mt_lep_met_ph', workspace_signal, suffix='el' )
workspaces_to_save['signal'] = []
workspaces_to_save['signal'].append(workspace_signal )
if options.doWGamma :
workspace_wgamma = ROOT.RooWorkspace( 'workspace_wgamma' )
wgamma_res_mu = get_wgamma_fit( sampManMu, sel_base_mu, eta_cuts, xvar, 'mt_lep_met_ph', binning, workspace_wgamma, suffix='mu' )
wgamma_res_el = get_wgamma_fit( sampManEl, sel_base_el, eta_cuts, xvar, 'mt_lep_met_ph', binning, workspace_wgamma, suffix='el' )
workspaces_to_save['wgamma'] = []
workspaces_to_save['wgamma'].append(workspace_wgamma)
if options.doWJets :
wjets = ROOT.RooWorkspace( 'wjets' )
#wjets_mu_EB = ROOT.RooWorkspace( 'wjets_mu_EB' )
#wjets_el_EB = ROOT.RooWorkspace( 'wjets_el_EB' )
#wjets_mu_EE = ROOT.RooWorkspace( 'wjets_mu_EE' )
#wjets_el_EE = ROOT.RooWorkspace( 'wjets_el_EE' )
wjets_res_mu = make_wjets_fit( sampManMu, 'Data', sel_base_mu, 'EB', 'mt_lep_met_ph', 'chIso', 'sigmaIEIE', binning, xvar, suffix='mu_EB', closure=False, workspace=wjets)
wjets_res_el = make_wjets_fit( sampManEl, 'Data', sel_base_el, 'EB', 'mt_lep_met_ph', 'chIso', 'sigmaIEIE', binning, xvar, suffix='el_EB', closure=False, workspace=wjets)
#wjets_res_mu = make_wjets_fit( sampManMu, 'Data', sel_base_mu, 'EE', 'mt_lep_met_ph', 'chIso', 'sigmaIEIE', binning, xvar, suffix='mu', closure=False, workspace=wjets_mu_EE )
#wjets_res_el = make_wjets_fit( sampManEl, 'Data', sel_base_el, 'EE', 'mt_lep_met_ph', 'chIso', 'sigmaIEIE', binning, xvar, suffix='el', closure=False, workspace=wjets_el_EE )
workspaces_to_save['wjets'] = []
workspaces_to_save['wjets'].append( wjets )
if options.doClosure :
closure_res_mu = make_wjets_fit( sampManMu, 'Wjets', sel_base_mu, 'EB', 'mt_lep_met_ph', 'chIso', 'sigmaIEIE', binning, xvar, suffix='mu_EB', closure=True )
closure_res_el = make_wjets_fit( sampManEl, 'Wjets', sel_base_el, 'EB', 'mt_lep_met_ph', 'chIso', 'sigmaIEIE', binning, xvar, suffix='el_EB', closure=True )
if options.outputDir is not None :
for fileid, ws_list in workspaces_to_save.iteritems() :
for idx, ws in enumerate(ws_list) :
if idx == 0 :
recreate = True
else :
recreate = False
ws.writeToFile( '%s/workspace_%s.root' %( options.outputDir, fileid ), recreate )
for key, can in sampManMu.outputs.iteritems() :
print key
can.SaveAs('%s/%s.pdf' %( options.outputDir, key ) )
for key, can in sampManEl.outputs.iteritems() :
can.SaveAs('%s/%s.pdf' %( options.outputDir, key ) )
for key, can in sampManSigMu.outputs.iteritems() :
can.SaveAs('%s/%s.pdf' %( options.outputDir, key ) )
for key, can in sampManSigEl.outputs.iteritems() :
can.SaveAs('%s/%s.pdf' %( options.outputDir, key ) )
def main():
print "Program start..."
#ROOT.gROOT.ProcessLine('TFile* f_zpt = TFile::Open("results/zpt_weight.root")')
#ROOT.gROOT.ProcessLine('TH1D* h_zpt_ratio = (TH1D*)f_zpt->Get("h_zpt_ratio")')
if doMuon:
input_antiiso_data = "inputs/awmunu/input_data.txt"
input_antiiso_wl0 = "inputs/awmunu/input_wm0.txt"
input_antiiso_wl1 = "inputs/awmunu/input_wm1.txt"
input_antiiso_wl2 = "inputs/awmunu/input_wm2.txt"
input_antiiso_ttbar = "inputs/awmunu/input_ttbar_dilepton.txt"
input_antiiso_ttbar_1lep = "inputs/awmunu/input_ttbar_singlelepton.txt"
input_antiiso_ttbar_0lep = "inputs/awmunu/input_ttbar_hadronic.txt"
input_antiiso_ww = "inputs/awmunu/input_ww.txt"
input_antiiso_wz = "inputs/awmunu/input_wz.txt"
input_antiiso_zz = "inputs/awmunu/input_zz.txt"
input_antiiso_zxx = "inputs/awmunu/input_zxx.txt"
input_antiiso_wx0 = "inputs/awmunu/input_wx0.txt"
input_antiiso_wx1 = "inputs/awmunu/input_wx1.txt"
input_antiiso_wx2 = "inputs/awmunu/input_wx2.txt"
else:
input_antiiso_data = "inputs/awenu/input_data.txt"
input_antiiso_wl0 = "inputs/awenu/input_we0.txt"
input_antiiso_wl1 = "inputs/awenu/input_we1.txt"
input_antiiso_wl2 = "inputs/awenu/input_we2.txt"
input_antiiso_ttbar = "inputs/awenu/input_ttbar_dilepton.txt"
input_antiiso_ttbar_1lep = "inputs/awenu/input_ttbar_singlelepton.txt"
input_antiiso_ttbar_0lep = "inputs/awenu/input_ttbar_hadronic.txt"
input_antiiso_ww = "inputs/awenu/input_ww.txt"
input_antiiso_wz = "inputs/awenu/input_wz.txt"
input_antiiso_zz = "inputs/awenu/input_zz.txt"
input_antiiso_zxx = "inputs/awenu/input_zxx.txt"
input_antiiso_wx0 = "inputs/awenu/input_wx0.txt"
input_antiiso_wx1 = "inputs/awenu/input_wx1.txt"
input_antiiso_wx2 = "inputs/awenu/input_wx2.txt"
## for the QCD background estimation (data-driven)
qcdnorm = -1.0
DataAisoSamp = Sample(input_antiiso_data,
isMC=False,
name="Data_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm,
legend='QCD',
color='226')
# W -> lnu
Wl0AisoSamp = Sample(input_antiiso_wl0,
isMC=True,
name="wl0_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
Wl1AisoSamp = Sample(input_antiiso_wl1,
isMC=True,
name="wl1_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
Wl2AisoSamp = Sample(input_antiiso_wl2,
isMC=True,
name="wl2_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
# ttbar
TTbarAisoSamp = Sample(input_antiiso_ttbar,
isMC=True,
name="ttbar_dilepton_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
TT1LepAisoSamp = Sample(input_antiiso_ttbar_1lep,
isMC=True,
name="ttbar_1lepton_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
TT0LepAisoSamp = Sample(input_antiiso_ttbar_0lep,
isMC=True,
name="ttbar_0lepton_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
## dibosons
WWAisoSamp = Sample(input_antiiso_ww,
isMC=True,
name="WW_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
WZAisoSamp = Sample(input_antiiso_wz,
isMC=True,
name="WZ_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
ZZAisoSamp = Sample(input_antiiso_zz,
isMC=True,
name="ZZ_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
# tau
ZXXAisoSamp = Sample(input_antiiso_zxx,
isMC=True,
name="ZXX_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
Wx0AisoSamp = Sample(input_antiiso_wx0,
isMC=True,
name="wx0_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
Wx1AisoSamp = Sample(input_antiiso_wx1,
isMC=True,
name="wx1_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
Wx2AisoSamp = Sample(input_antiiso_wx2,
isMC=True,
name="wx2_aiso",
isWSR=True,
additionalnorm=-1.0 * qcdnorm)
sampMan = SampleManager(DataAisoSamp, [
Wl0AisoSamp, Wl1AisoSamp, Wl2AisoSamp, TTbarAisoSamp, TT1LepAisoSamp,
TT0LepAisoSamp, WWAisoSamp, WZAisoSamp, ZZAisoSamp, ZXXAisoSamp,
Wx0AisoSamp, Wx1AisoSamp, Wx2AisoSamp
])
sampMan.groupMCs([
"WW_aiso", "WZ_aiso", "ZZ_aiso", "ZXX_aiso", "wx0_aiso", "wx1_aiso",
"wx2_aiso"
], "EWK", 216, "EWK")
sampMan.groupMCs(
["ttbar_dilepton_aiso", "ttbar_1lepton_aiso", "ttbar_0lepton_aiso"],
"ttbar", 96, "t#bar{t}")
label = "W#rightarrow#mu#nu" if doMuon else "W#rightarrow e#nu"
sampMan.groupMCs(['wl0_aiso', 'wl1_aiso', 'wl2_aiso'], "wlnu", 92, label)
sampMan.DefineAll("Lep_pt", "lep.Pt()")
sampMan.ApplyCutAll("Lep_pt > 25.0")
sampMan.DefineAll("w_iso1", "(relIso > 0.15 && relIso < 0.30)")
sampMan.DefineAll("w_iso2", "(relIso > 0.30 && relIso < 0.45)")
sampMan.DefineAll("w_iso3", "(relIso > 0.45 && relIso < 0.55)")
sampMan.DefineAll("w_iso4", "(relIso > 0.55 && relIso < 0.65)")
# muon and electron isolation distributions are different
# more coarse binning for electrons to make sure enough statistics
if doMuon:
sampMan.DefineAll("w_iso5", "(relIso > 0.20 && relIso < 0.25)")
sampMan.DefineAll("w_iso6", "(relIso > 0.25 && relIso < 0.30)")
sampMan.DefineAll("w_iso7", "(relIso > 0.30 && relIso < 0.35)")
sampMan.DefineAll("w_iso8", "(relIso > 0.35 && relIso < 0.40)")
sampMan.DefineAll("w_iso9", "(relIso > 0.40 && relIso < 0.45)")
sampMan.DefineAll("w_iso10", "(relIso > 0.45 && relIso < 0.50)")
sampMan.DefineAll("w_iso11", "(relIso > 0.50 && relIso < 0.55)")
sampMan.DefineAll("w_iso12", "(relIso > 0.55 && relIso < 0.60)")
sampMan.DefineAll("w_iso13", "(relIso > 0.60 && relIso < 0.65)")
isobins = [
"iso1", "iso2", "iso3", "iso4", "iso5", "iso6", "iso7", "iso8",
"iso9", "iso10", "iso11", "iso12", "iso13"
]
else:
sampMan.DefineAll("w_iso5", "(relIso > 0.20 && relIso < 0.25)")
sampMan.DefineAll("w_iso6", "(relIso > 0.25 && relIso < 0.30)")
sampMan.DefineAll("w_iso7", "(relIso > 0.30 && relIso < 0.35)")
sampMan.DefineAll("w_iso8", "(relIso > 0.35 && relIso < 0.40)")
sampMan.DefineAll("w_iso9", "(relIso > 0.40 && relIso < 0.50)")
sampMan.DefineAll("w_iso10", "(relIso > 0.50 && relIso < 0.70)")
isobins = [
"iso1", "iso2", "iso3", "iso4", "iso5", "iso6", "iso7", "iso8",
"iso9", "iso10"
]
sampMan.DefineAll("Lep_eta", "lep.Eta()")
sampMan.DefineMC("met_pt", "metVars[1]", excludes=['Data_aiso'])
sampMan.DefineMC("met_phi",
"TVector2::Phi_mpi_pi(metVarsPhi[1])",
excludes=['Data_aiso'])
# Data does not run any recoil corrections
DataAisoSamp.Define("met_pt", "metVars[0]")
DataAisoSamp.Define("met_phi", "TVector2::Phi_mpi_pi(metVarsPhi[0])")
sampMan.DefineAll("met_raw_pt", "metVars[0]")
sampMan.DefineAll("met_raw_phi", "TVector2::Phi_mpi_pi(metVarsPhi[0])")
#sampMan.DefineAll("weight_wplus", "weight_WoVpt * (q>0)", excludeGroups=['QCD'])
#sampMan.DefineAll("weight_wminus", "weight_WoVpt * (q<0)", excludeGroups=['QCD'])
#sampMan.DefineSpecificMCs("weight_wplus", "weight_WoVpt * (q>0)", sampgroupnames=['QCD'])
#sampMan.DefineSpecificMCs("weight_wminus", "weight_WoVpt * (q<0)", sampgroupnames=['QCD'])
sampMan.DefineAll("weight_wplus", "weight_WoVpt * (q>0)")
sampMan.DefineAll("weight_wminus", "weight_WoVpt * (q<0)")
#sampMan.DefineAll("weight_eta0to0p8", "fabs(Lep_eta)<0.8")
#sampMan.DefineAll("weight_eta0p8to1p4", "fabs(Lep_eta)>0.8 && fabs(Lep_eta)<1.4")
#sampMan.DefineAll("weight_eta1p4to2", "fabs(Lep_eta)<1.4 && fabs(Lep_eta)<2.0")
#sampMan.DefineAll("weight_eta2to2p4", "fabs(Lep_eta)>2.0 && fabs(Lep_eta)<2.4")
# recoil variables
sampMan.DefineAll("V2W", "UVec(lep.Pt(), lep.Phi(), met_pt, met_phi)")
sampMan.DefineAll("WpT", "V2W.Mod()")
sampMan.DefineAll("Wphi", "TVector2::Phi_mpi_pi(V2W.Phi())")
# WpT bins
sampMan.DefineAll("WpT_bin0", "WpT>0.0")
sampMan.DefineAll("WpT_bin1", "WpT<8.0")
sampMan.DefineAll("WpT_bin2", "WpT>8.0 && WpT<16.0")
sampMan.DefineAll("WpT_bin3", "WpT>16.0 && WpT<24.0")
sampMan.DefineAll("WpT_bin4", "WpT>24.0 && WpT<32.0")
sampMan.DefineAll("WpT_bin5", "WpT>32.0 && WpT<40.0")
sampMan.DefineAll("WpT_bin6", "WpT>40.0 && WpT<50.0")
sampMan.DefineAll("WpT_bin7", "WpT>50.0 && WpT<70.0")
sampMan.DefineAll("WpT_bin8", "WpT>70.0 && WpT<100.0")
wptbins = [
"WpT_bin0", "WpT_bin1", "WpT_bin2", "WpT_bin3", "WpT_bin4", "WpT_bin5",
"WpT_bin6", "WpT_bin7", "WpT_bin8"
]
# eta bins for electrons: barral and endcap
sampMan.DefineAll("lepEta_bin0", "1.0")
sampMan.DefineAll("lepEta_bin1", "abs(lep.Eta()) <= 1.4442")
sampMan.DefineAll("lepEta_bin2", "abs(lep.Eta()) > 1.4442")
if doMuon:
etabins = ["lepEta_bin0"]
else:
etabins = ["lepEta_bin0", "lepEta_bin1", "lepEta_bin2"]
# pT_ell / mT
sampMan.DefineAll("ptOvermT", "Lep_pt / mtCorr")
#for iso in ['iso1', 'iso2', 'iso3', 'iso4', 'iso5', 'iso6', 'iso7', 'iso8', 'iso9', 'iso10', 'iso11', 'iso12', 'iso13']:
for iso in isobins:
for chg in ['WoVpt', 'wplus', 'wminus']:
#for eta in ['0to0p8', '0p8to1p4', '1p4to2', '2to2p4']:
#for wpt in ["WpT_bin0", "WpT_bin1", "WpT_bin2", "WpT_bin3", "WpT_bin4", "WpT_bin5", "WpT_bin6", "WpT_bin7", "WpT_bin8"]:
for wpt in wptbins:
for lepeta in etabins:
sampMan.DefineAll(
"weight_{}_{}_{}_{}".format(chg, iso, wpt, lepeta),
"w_{} * weight_{} * {} * {}".format(
iso, chg, wpt, lepeta))
met_pt_bins = np.array([
0., 2.0, 4., 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 33, 36,
39, 42, 45, 48, 51, 55, 60, 65, 70, 75, 80, 90, 100, 110, 120, 135,
150, 165, 180, 200
])
u1_bins = np.array([
-40., -36., -32., -28., -25., -22.0, -20.0, -18, -16, -14, -12, -10,
-8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28,
30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 53, 56, 59, 64, 68, 72, 76,
80, 85, 90, 100
])
u2_bins = np.array([
-80., -70., -65., -60., -56., -52, -48, -44, -40, -37, -34, -31, -28,
-25., -22., -20, -18, -16, -14, -12, -10, -8, -6, -4, -2, 0, 2, 4, 6,
8, 10, 12, 16, 18, 20, 22, 25, 28, 31, 34, 37, 40, 44, 48, 52, 56, 60,
65, 70, 80
])
#u_bins = np.array([0., 2., 4., 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 43, 46, 49, 52, 56, 60, 64, 68, 72, 76, 80, 85, 90, 95, 100, 105, 110, 115, 120, 130, 140, 150])
u_bins = np.concatenate((np.linspace(0, 20, 11), np.linspace(24, 60, 10)))
phimin = -ROOT.TMath.Pi()
phimax = ROOT.TMath.Pi()
eta_bins = np.concatenate(
(np.linspace(-2.4, -2.0,
3), np.linspace(-1.8, 1.8, 37), np.linspace(2.0, 2.4, 3)))
pt_bins = np.concatenate(
(np.linspace(0, 15, 6), np.linspace(17, 23, 4), np.linspace(25, 35, 6),
np.linspace(36, 55, 20), np.linspace(57, 63,
4), np.linspace(66, 70, 2),
np.linspace(75, 100, 6)))
mass_bins = np.concatenate(
(np.linspace(0, 52, 14), np.linspace(55, 67,
5), np.linspace(70, 76, 3),
np.linspace(78, 86, 5), np.linspace(87, 96, 10),
np.linspace(98, 104, 5), np.linspace(106, 110, 2)))
u_bins = np.concatenate((np.linspace(0, 20, 11), np.linspace(24, 80, 15),
np.linspace(85, 109, 4), np.linspace(120, 150,
3)))
mt_bins = np.concatenate(
(np.linspace(0, 20, 6), np.linspace(22, 38,
9), np.linspace(40, 60, 21),
np.linspace(62, 80, 10), np.linspace(84, 120, 10)))
met_pt_bins = np.array([
0., 2.0, 4., 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 33, 36,
39, 42, 45, 48, 51, 55, 60, 65, 70, 75, 80
])
#for iso in ['iso1', 'iso2', 'iso3', 'iso4', 'iso5', 'iso6', 'iso7', 'iso8', 'iso9']:
#for iso in ['iso1', 'iso2', 'iso3', 'iso4', 'iso5', 'iso6', 'iso7', 'iso8', 'iso9', 'iso10', 'iso11', 'iso12', 'iso13']:
for iso in isobins:
for chg in ['WoVpt', 'wplus', 'wminus']:
#for wpt in ["WpT_bin0", "WpT_bin1", "WpT_bin2", "WpT_bin3", "WpT_bin4", "WpT_bin5", "WpT_bin6", "WpT_bin7", "WpT_bin8"]:
for wpt in wptbins:
for lepeta in etabins:
if chg == 'WoVpt':
continue
strname = "weight_{}_{}_{}_{}".format(
chg, iso, wpt, lepeta)
#outputname = "histo_wjetsAntiIso_mtcorr_" + strname
##sampMan.cacheDraw("mtCorr", outputname, mt_bins, DrawConfig(xmin=0, xmax=120, xlabel="m_{T} [GeV]", dology=False, ymax=1.0e4), weightname = strname)
#sampMan.cacheDraw("mtCorr", outputname, 25, 40, 140, DrawConfig(xmin=40, xmax=140, xlabel="m_{T} [GeV]", dology=False, ymax=1e4, donormalizebin=False, addOverflow=False, addUnderflow=False, showratio=True), weightname = strname)
outputname = "histo_wjetsAntiIso_fullrange_mtcorr_" + strname
sampMan.cacheDraw("mtCorr",
outputname,
70,
0,
140,
DrawConfig(xmin=0,
xmax=140,
xlabel="m_{T} [GeV]",
dology=False,
ymax=2e4,
donormalizebin=False,
addOverflow=False,
addUnderflow=False,
showratio=True),
weightname=strname)
#outputname = "histo_wjetsAntiIso_WpTcorr_" + strname
#sampMan.cacheDraw("WpT", outputname, u_bins, DrawConfig(xmin=0, xmax=60, xlabel="p^{W}_{T} [GeV]", dology=False, ymax=1.0e4), weightname = strname)
#outputname = "histo_wjetsAntiIso_leppt_" + strname
#sampMan.cacheDraw("Lep_pt", outputname, pt_bins, DrawConfig(xmin=25, xmax=100, xlabel="p^{#mu}_{T} [GeV]", dology=False, ymax=1.0e4), weightname = strname)
#outputname = "histo_wjetsAntiIso_metpt_" + strname
#sampMan.cacheDraw("met_pt", outputname, met_pt_bins, DrawConfig(xmin=0, xmax=80, xlabel="PF MET [GeV]", dology=False, ymax=1e4), weightname = strname)
sampMan.launchDraw()
#hmts_comp = OrderedDict()
##for iso in ['iso1', 'iso2', 'iso3', 'iso4', 'iso5', 'iso6', 'iso7', 'iso8', 'iso9']:
#for iso in ['iso1', 'iso2', 'iso3', 'iso4', 'iso5', 'iso6', 'iso7', 'iso8', 'iso9', 'iso10', 'iso11', 'iso12', 'iso13']:
# for chg in ['WoVpt', 'wplus', 'wminus']:
# for wpt in ["WpT_bin0", "WpT_bin1", "WpT_bin2", "WpT_bin3", "WpT_bin4", "WpT_bin5", "WpT_bin6", "WpT_bin7", "WpT_bin8"]:
# for lepeta in etabins:
# if chg == "WoVpt":
# continue
# strname = "weight_{}_{}_{}_{}".format(chg, iso, wpt, lepeta)
# outputname = "histo_wjetsAntiIso_mtcorr_" + strname
# hstacked = THStack2TH1(sampMan.hsmcs[outputname])
# for ibin in xrange(hstacked.GetNbinsX()+1):
# # hstacked should always be above 0
# hstacked.SetBinContent(ibin, max(hstacked.GetBinContent(ibin), 0))
# sampMan.hdatas[outputname].Add( hstacked, -1.0 )
# hmts_comp[strname] = sampMan.hdatas[outputname]
# hmts_comp[strname].SetName(outputname)
#outfile = ROOT.TFile("output_qcdshape.root", "recreate")
#for chg in ["wplus", "wminus"]:
# for i in xrange(1,13):
# for wpt in ["WpT_bin0", "WpT_bin1", "WpT_bin2", "WpT_bin3", "WpT_bin4", "WpT_bin5", "WpT_bin6", "WpT_bin7", "WpT_bin8"]:
# iso = "iso" + str(i)
# iso_next = "iso" + str(i+1)
# strname = "weight_{}_{}_{}".format(chg, iso, wpt)
# strname_next = "weight_{}_{}_{}".format(chg, iso_next, wpt)
# outputname = "histo_wjetsAntiIso_mtcorr_" + strname
# hcenter = hmts_comp[strname]
# hup = hmts_comp[strname_next].Clone(outputname+"_shapeUp")
# hdown = hmts_comp[strname_next].Clone(outputname+"_shapeDown")
# hup.Scale(hcenter.Integral() / hup.Integral())
# for ibin in xrange(1, hcenter.GetNbinsX()+1):
# center = hcenter.GetBinContent(ibin)
# up = hup.GetBinContent(ibin)
# hdown.SetBinContent(ibin, max(2*center - up, 0))
# hcenter.SetBinContent(ibin, max(center, 0))
# hup.SetBinContent(ibin, max(up, 0))
# hcenter.SetDirectory(outfile)
# hcenter.Write()
# hup.SetDirectory(outfile)
# hup.Write()
# hdown.SetDirectory(outfile)
# hdown.Write()
#outfile.Close()
# repeat the same for the full mT one
hmts_comp = OrderedDict()
#for iso in ['iso1', 'iso2', 'iso3', 'iso4', 'iso5', 'iso6', 'iso7', 'iso8', 'iso9']:
#for iso in ['iso1', 'iso2', 'iso3', 'iso4', 'iso5', 'iso6', 'iso7', 'iso8', 'iso9', 'iso10', 'iso11', 'iso12', 'iso13']:
for iso in isobins:
for chg in ['WoVpt', 'wplus', 'wminus']:
#for wpt in ["WpT_bin0", "WpT_bin1", "WpT_bin2", "WpT_bin3", "WpT_bin4", "WpT_bin5", "WpT_bin6", "WpT_bin7", "WpT_bin8"]:
for wpt in wptbins:
for lepeta in etabins:
if chg == "WoVpt":
continue
strname = "weight_{}_{}_{}_{}".format(
chg, iso, wpt, lepeta)
outputname = "histo_wjetsAntiIso_fullrange_mtcorr_" + strname
hstacked = THStack2TH1(sampMan.hsmcs[outputname])
for ibin in xrange(hstacked.GetNbinsX() + 1):
# hstacked should always be above 0
hstacked.SetBinContent(
ibin, max(hstacked.GetBinContent(ibin), 0))
sampMan.hdatas[outputname].Add(hstacked, -1.0)
hmts_comp[strname] = sampMan.hdatas[outputname]
hmts_comp[strname].SetName(outputname)
lepname = "mu" if doMuon else "e"
outfile = ROOT.TFile("output_qcdshape_fullrange_" + lepname + "nu.root",
"recreate")
for chg in ["wplus", "wminus"]:
#for i in xrange(1,13):
#for i in xrange(1,10):
for i in xrange(1, len(isobins)):
#for wpt in ["WpT_bin0", "WpT_bin1", "WpT_bin2", "WpT_bin3", "WpT_bin4", "WpT_bin5", "WpT_bin6", "WpT_bin7", "WpT_bin8"]:
for wpt in wptbins:
for lepeta in etabins:
# skip iso10 as it is used for the uncertainty cal for iso9
iso = "iso" + str(i)
iso_next = "iso" + str(i + 1)
strname = "weight_{}_{}_{}_{}".format(
chg, iso, wpt, lepeta)
strname_next = "weight_{}_{}_{}_{}".format(
chg, iso_next, wpt, lepeta)
outputname = "histo_wjetsAntiIso_fullrange_mtcorr_" + strname
hcenter = hmts_comp[strname]
hup = hmts_comp[strname_next].Clone(outputname +
"_shapeUp")
hdown = hmts_comp[strname_next].Clone(outputname +
"_shapeDown")
hup.Scale(hcenter.Integral() / hup.Integral())
for ibin in xrange(1, hcenter.GetNbinsX() + 1):
center = hcenter.GetBinContent(ibin)
up = hup.GetBinContent(ibin)
hdown.SetBinContent(ibin, max(2 * center - up, 0))
hcenter.SetBinContent(ibin, max(center, 0))
hup.SetBinContent(ibin, max(up, 0))
hcenter.SetDirectory(outfile)
hcenter.Write()
hup.SetDirectory(outfile)
hup.Write()
hdown.SetDirectory(outfile)
hdown.Write()
outfile.Close()
# calculate the shape uncertainty and save them to root file
#hmts_comp = OrderedDict()
##hwpts_comp = OrderedDict()
##hmupts_comp = OrderedDict()
##hmetpts_comp = OrderedDict()
#for iso in ['iso1', 'iso2', 'iso3', 'iso4', 'iso5', 'iso6', 'iso7', 'iso8', 'iso9']:
# for chg in ['WoVpt', 'wplus', 'wminus']:
# if chg == "WoVpt":
# continue
# strname = "weight_{}_{}".format(chg, iso)
# outputname = "histo_wjetsAntiIso_mtcorr_" + strname
# sampMan.hdatas[outputname].Add( THStack2TH1(sampMan.hsmcs[outputname]), -1 )
# hmts_comp[strname] = sampMan.hdatas[outputname]
#outputname = "histo_wjetsAntiIso_WpTcorr_" + strname
#sampMan.hdatas[outputname].Add( THStack2TH1(sampMan.hsmcs[outputname]), -1 )
#hwpts_comp[strname] = sampMan.hdatas[outputname]
#outputname = "histo_wjetsAntiIso_leppt_" + strname
#sampMan.hdatas[outputname].Add( THStack2TH1(sampMan.hsmcs[outputname]), -1 )
#hmupts_comp[strname] = sampMan.hdatas[outputname]
#outputname = "histo_wjetsAntiIso_metpt_" + strname
#sampMan.hdatas[outputname].Add( THStack2TH1(sampMan.hsmcs[outputname]), -1 )
#hmetpts_comp[strname] = sampMan.hdatas[outputname]
#labels = ["|#eta|<0.8", "0.8<|#eta|<1.4", "1.4<|#eta|<2.0", "2.0<|#eta|<2.4"]
#DrawHistos(hmts_comp.values(), labels, 0, 120, 'm_{T} [GeV]', 2e-4, 5e-1, 'A.U.', 'histo_wjetsAntiIso_comp_mT', dology=True, showratio=True, donormalize=True, mycolors=[1,2,3, 4], ratiobase=1, drawoptions=["e", "e", "e", "e"], legendoptions=['LPE', 'LPE', 'LPE', 'LPE'], yrmin=0.71, yrmax=1.29)
#DrawHistos(hwpts_comp.values(), labels, 0, 60, 'p^{W}_{T} [GeV]', 2e-3, 5e-1, 'A.U.', 'histo_wjetsAntiIso_comp_WpT', dology=True, showratio=True, donormalize=True, mycolors=[1,2,3, 4], ratiobase=1, drawoptions=["e", "e", "e", "e"], legendoptions=['LPE', 'LPE', 'LPE', 'LPE'], yrmin=0.71, yrmax=1.29)
#DrawHistos(hmupts_comp.values(), labels, 25, 100, 'p^{#mu}_{T} [GeV]', 2e-3, 5e-1, 'A.U.', 'histo_wjetsAntiIso_comp_mupt', dology=True, showratio=True, donormalize=True, mycolors=[1,2,3, 4], ratiobase=1, drawoptions=["e", "e", "e", "e"], legendoptions=['LPE', 'LPE', 'LPE', 'LPE'], yrmin=0.71, yrmax=1.29)
#DrawHistos(hmetpts_comp.values(), labels, 0, 80, 'p^{miss}_{T} [GeV]', 2e-3, 5e-1, 'A.U.', 'histo_wjetsAntiIso_comp_metpt', dology=True, showratio=True, donormalize=True, mycolors=[1,2,3, 4], ratiobase=1, drawoptions=["e", "e", "e", "e"], legendoptions=['LPE', 'LPE', 'LPE', 'LPE'], yrmin=0.71, yrmax=1.29)
#DrawHistos(hmts_comp.values(), ['0.15<I<0.30', '0.30<I<0.45', '0.45<I<0.55'], 40, 120, 'm_{T} [GeV]', 2e-4, 5e-1, 'A.U.', 'histo_wjetsAntiIso_comp_mT', dology=True, showratio=True, donormalize=True, mycolors=[1,2,3], ratiobase=1, drawoptions=["e", "e", "e"], legendoptions=['LPE', 'LPE', 'LPE'], yrmin=0.71, yrmax=1.29)
#DrawHistos(hwpts_comp.values(), ['0.15<I<0.30', '0.30<I<0.45', '0.45<I<0.55'], 0, 60, 'p^{W}_{T} [GeV]', 2e-3, 5e-1, 'A.U.', 'histo_wjetsAntiIso_comp_WpT', dology=True, showratio=True, donormalize=True, mycolors=[1,2,3], ratiobase=1, drawoptions=["e", "e", "e"], legendoptions=['LPE', 'LPE', 'LPE'], yrmin=0.71, yrmax=1.29)
#DrawHistos(hmupts_comp.values(), ['0.15<I<0.30', '0.30<I<0.45', '0.45<I<0.55'], 25, 70, 'p^{#mu}_{T} [GeV]', 2e-3, 5e-1, 'A.U.', 'histo_wjetsAntiIso_comp_mupt', dology=True, showratio=True, donormalize=True, mycolors=[1,2,3], ratiobase=1, drawoptions=["e", "e", "e"], legendoptions=['LPE', 'LPE', 'LPE'], yrmin=0.71, yrmax=1.29)
#DrawHistos(hmetpts_comp.values(), ['0.15<I<0.30', '0.30<I<0.45', '0.45<I<0.55'], 0, 80, 'p^{W}_{T} [GeV]', 2e-3, 5e-1, 'A.U.', 'histo_wjetsAntiIso_comp_metpt', dology=True, showratio=True, donormalize=True, mycolors=[1,2,3], ratiobase=1, drawoptions=["e", "e", "e"], legendoptions=['LPE', 'LPE', 'LPE'], yrmin=0.71, yrmax=1.29)
#sampMan.launchDraw()
sampMan.dumpCounts()
print "Program end..."
raw_input()
return
def main():
print "Program start..."
#ROOT.gROOT.ProcessLine('TFile* f_zpt = TFile::Open("results/zpt_weight.root")')
#ROOT.gROOT.ProcessLine('TH1D* h_zpt_ratio = (TH1D*)f_zpt->Get("h_zpt_ratio")')
if dotest:
input_data = "inputs/zmumu/input_data.txt"
input_dy = "inputs/zmumu/input_zjets.txt"
input_ttbar = "inputs/zmumu/input_ttbar_dilepton.txt"
else:
input_data = "inputs/zmumu/input_data.txt"
input_dy = "inputs/zmumu/input_zjets.txt"
input_ttbar = "inputs/zmumu/input_ttbar_dilepton.txt"
input_ttbar_1lep = "inputs/zmumu/input_ttbar_singlelepton.txt"
input_ttbar_0lep = "inputs/zmumu/input_ttbar_hadronic.txt"
input_ww = "inputs/zmumu/input_ww.txt"
input_wz = "inputs/zmumu/input_wz.txt"
input_zz = "inputs/zmumu/input_zz.txt"
input_zxx = "inputs/zmumu/input_zxx.txt"
DataSamp = Sample(input_data,
isMC=False,
legend="Data",
name="Data",
bjetVeto=VetoB)
DYSamp = Sample(input_dy,
xsec=6077.22 * 1e3,
color=92,
reweightzpt=True,
legend="Z#rightarrow#mu#mu",
name="DY",
bjetVeto=VetoB,
nmcevt=311461009200.830078 / 128007. * 126620.8)
TTbarSamp = Sample(input_ttbar,
xsec=831.76 * 0.105 * 1e3,
color=46,
reweightzpt=False,
legend="t#bar{t}",
name="ttbar2lep",
bjetVeto=VetoB,
nmcevt=360411094.548295 / 335.0 * 325.0)
if not dotest:
WWSamp = Sample(input_ww,
xsec=12.178 * 1e3,
color=38,
reweightzpt=False,
legend="WW2L",
name="WW",
bjetVeto=VetoB,
nmcevt=1000000.0)
WZSamp = Sample(input_wz,
xsec=5.26 * 1e3,
color=39,
reweightzpt=False,
legend="WZ3L",
name="WZ",
bjetVeto=VetoB,
nmcevt=885000.00)
ZZSamp = Sample(input_zz,
xsec=0.564 * 1e3,
color=37,
reweightzpt=False,
legend="ZZ2L",
name="ZZ",
bjetVeto=VetoB,
nmcevt=1000000.0)
ZXXSamp = Sample(input_zxx,
xsec=1.0,
color=40,
reweightzpt=False,
legend="ZXX",
name="ZXX",
bjetVeto=VetoB,
nmcevt=3.1146101e+11)
TT1LepSamp = Sample(input_ttbar_1lep,
xsec=831.76 * 0.219 * 1e3,
color=47,
reweightzpt=False,
legend="t#bar{t}",
name="ttbar1lep",
bjetVeto=VetoB,
nmcevt=1.2561491e+09 / 335.0 * 325.0)
TT0LepSamp = Sample(input_ttbar_0lep,
xsec=831.76 * 0.219 * 1e3,
color=48,
reweightzpt=False,
legend="t#bar{t}",
name="ttbar0lep",
bjetVeto=VetoB,
nmcevt=6.2414786e+09 / 335.0 * 325)
if not dotest:
sampMan = SampleManager(DataSamp, [
DYSamp, TTbarSamp, WWSamp, WZSamp, ZZSamp, ZXXSamp, TT1LepSamp,
TT0LepSamp
])
else:
sampMan = SampleManager(DataSamp, [DYSamp, TTbarSamp])
sampMan.groupMCs(["WW", "WZ", "ZZ", "ZXX"], "EWK", 216, "EWK")
sampMan.groupMCs(["ttbar2lep", "ttbar1lep", "ttbar0lep"], "ttbar", 96,
"t#bar{t}")
sampMan.DefineAll("zpt", "Z.Pt()")
sampMan.DefineAll("zmass", "ZMass")
sampMan.DefineAll("zy", "Z.Rapidity()")
sampMan.DefineAll("zeta", "Z.Rapidity()")
sampMan.DefineAll("zphi", "Z.Phi()")
sampMan.DefineAll("urawvec", "UVec(zpt, zphi, met_raw_pt, met_raw_phi)")
sampMan.DefineAll("uraw_pt", "urawvec.Mod()")
sampMan.DefineAll("uraw_phi", "urawvec.Phi()")
sampMan.DefineAll("uraw1",
"uraw_pt * TMath::Cos(uraw_phi + TMath::Pi() - zphi)")
sampMan.DefineAll("uraw2",
"uraw_pt * TMath::Sin(uraw_phi + TMath::Pi() - zphi)")
sampMan.DefineAll("uvec",
"UVec(zpt, zphi, met_corrected_pt, met_corrected_phi)")
sampMan.DefineAll("u_pt", "uvec.Mod()")
sampMan.DefineAll("u_phi", "uvec.Phi()")
sampMan.DefineAll("ucor1", "u_pt * TMath::Cos(u_phi + TMath::Pi() - zphi)")
sampMan.DefineAll("ucor2", "u_pt * TMath::Sin(u_phi + TMath::Pi() - zphi)")
sampMan.DefineAll("nPV", "npv")
sampMan.DefineAll("m1pt", "lep1_corr.Pt()")
sampMan.DefineAll("m1eta", "lep1_corr.Eta()")
sampMan.DefineAll("m2pt", "lep2_corr.Pt()")
sampMan.DefineAll("m2eta", "lep2_corr.Eta()")
sampMan.DefineAll("LeadMuon_pt", "(m1pt > m2pt ? m1pt : m2pt)")
sampMan.DefineAll("LeadMuon_eta", "(m1pt > m2pt ? m1eta : m2eta)")
sampMan.DefineAll("SubleadMuon_pt", "(m1pt > m2pt ? m2pt : m1pt)")
sampMan.DefineAll("SubleadMuon_eta", "(m1pt > m2pt ? m2eta : m1eta)")
#DYSamp.Define("u_pt_corr_central", "TMath::Sqrt(u1_corr_central*u1_corr_central + u2_corr_central*u2_corr_central)")
#sampMan.DefineAll("u1_corr_central", "u1" , excludes=['DY'])
met_pt_bins = np.array([
0., 2.0, 4., 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 33, 36,
39, 42, 45, 48, 51, 55, 60, 65, 70, 75, 80, 90, 100, 110, 120, 135,
150, 165, 180, 200
])
u1_bins = np.array([
-20.0, -16, -12, -10, -8, -6, -4, -2, 0, 2, 4, 6, 8, 10, 12, 14, 16,
18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 42, 44, 46, 48, 50, 53,
56, 59, 64, 68, 72, 76, 80, 85, 90, 100
])
u2_bins = np.array([
-40, -35, -30, -25., -22., -20, -18, -16, -14, -12, -10, -8, -6, -4,
-2, 0, 2, 4, 6, 8, 10, 12, 16, 18, 20, 22, 25, 30, 35, 40
])
#u_bins = np.array([0., 2., 4., 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30, 32, 34, 36, 38, 40, 43, 46, 49, 52, 56, 60, 64, 68, 72, 76, 80, 85, 90, 95, 100, 105, 110, 115, 120, 130, 140, 150])
phimin = -ROOT.TMath.Pi()
phimax = ROOT.TMath.Pi()
eta_bins = np.concatenate(
(np.linspace(-2.4, -2.0,
3), np.linspace(-1.8, 1.8, 37), np.linspace(2.0, 2.4, 3)))
pt_bins = np.concatenate((np.linspace(25, 35, 6), np.linspace(36, 55, 20),
np.linspace(57, 63, 4), np.linspace(66, 70, 2)))
mass_bins = np.concatenate(
(np.linspace(70, 76, 3), np.linspace(78, 86,
5), np.linspace(87, 96, 10),
np.linspace(98, 104, 5), np.linspace(106, 110, 2)))
u_bins = np.concatenate((np.linspace(0, 20, 11), np.linspace(24, 80, 15),
np.linspace(85, 109, 4), np.linspace(120, 150,
3)))
# z pt befor and after pt reweighting
sampMan.cacheDraw("zpt",
"histo_zjets_zpt_WoZptWeight_mumu",
u_bins,
DrawConfig(xmin=0,
xmax=150,
xlabel='p_{T}^{#mu#mu} [GeV]'),
weightname="weight_WoVpt")
sampMan.cacheDraw("zmass", "histo_zjets_zmass_mumu", mass_bins,
DrawConfig(xmin=70, xmax=110, xlabel='m_{#mu#mu} [GeV]'))
sampMan.cacheDraw(
"zeta", "histo_zjets_zeta_mumu", eta_bins,
DrawConfig(xmin=-2.5,
xmax=2.5,
xlabel='#eta_{#mu#mu} [GeV]',
ymax=1e7,
ylabel='Events / 1'))
sampMan.cacheDraw(
"zy", "histo_zjets_zrapidity_mumu", eta_bins,
DrawConfig(xmin=-2.5,
xmax=2.5,
xlabel='y_{#mu#mu} [GeV]',
ymax=1e7,
ylabel='Events / 1'))
sampMan.cacheDraw(
"nPV", "histo_nPV_mumu", 10, 0, 10,
DrawConfig(xmin=0, xmax=10, xlabel='# PV', ylabel='Events / 1'))
sampMan.cacheDraw(
"LeadMuon_pt", "histo_leadMuon_pt_mumu", pt_bins,
DrawConfig(xmin=20, xmax=70, xlabel='p_{T}(Leading #mu) [GeV]'))
sampMan.cacheDraw(
"LeadMuon_eta", "histo_leadMuon_eta_mumu", eta_bins,
DrawConfig(xmin=-2.6,
xmax=2.6,
xlabel='#eta (Leading #mu) [GeV]',
ymax=1e7,
ylabel='Events / 1'))
sampMan.cacheDraw(
"SubleadMuon_pt", "histo_subleadMuon_pt_mumu", pt_bins,
DrawConfig(xmin=20, xmax=70, xlabel='p_{T}(Subleading #mu) [GeV]'))
sampMan.cacheDraw(
"SubleadMuon_eta", "histo_subleadMuon_eta_mumu", eta_bins,
DrawConfig(xmin=-2.6,
xmax=2.6,
xlabel='#eta (Subleading #mu) [GeV]',
ymax=1e7,
ylabel='Events / 1'))
sampMan.cacheDraw("met", "histo_zjets_mumu_pfmet_pt", met_pt_bins,
DrawConfig(xmin=0, xmax=100, xlabel='PF MET [GeV]'))
sampMan.cacheDraw(
"metPhi", "histo_zjets_mumu_pfmet_phi", 30, phimin, phimax,
DrawConfig(xmin=phimin, xmax=phimax, xlabel='PF MET #phi'))
sampMan.cacheDraw("met_raw_pt", "histo_zjets_mumu_pfmet_raw_pt",
met_pt_bins,
DrawConfig(xmin=0, xmax=100, xlabel='Raw PF MET [GeV]'))
sampMan.cacheDraw(
"met_raw_phi", "histo_zjets_mumu_pfmet_raw_phi", 30, phimin, phimax,
DrawConfig(xmin=phimin, xmax=phimax, xlabel='Raw PF MET #phi'))
sampMan.cacheDraw("met_wlepcorr_pt", "histo_zjets_mumu_pfmet_wlepcorr_pt",
met_pt_bins,
DrawConfig(xmin=0, xmax=100, xlabel='PF MET [GeV]'))
sampMan.cacheDraw(
"met_wlepcorr_phi", "histo_zjets_mumu_pfmet_wlepcorr_phi", 30, phimin,
phimax, DrawConfig(xmin=phimin, xmax=phimax, xlabel='PF MET #phi'))
sampMan.cacheDraw("met_corrected_pt",
"histo_zjets_mumu_pfmet_corrected_pt", met_pt_bins,
DrawConfig(xmin=0, xmax=100, xlabel='PF MET [GeV]'))
sampMan.cacheDraw(
"met_corrected_phi", "histo_zjets_mumu_pfmet_corrected_phi", 30,
phimin, phimax,
DrawConfig(xmin=phimin, xmax=phimax, xlabel='PF MET #phi'))
sampMan.cacheDraw(
"uraw1", "histo_zjets_mumu_pfmet_uraw1_pt", u1_bins,
DrawConfig(xmin=-20.0,
xmax=100.0,
xlabel="Raw u_{#parallel} [GeV]",
extraText='#mu#mu channel'))
sampMan.cacheDraw(
"uraw2", "histo_zjets_mumu_pfmet_uraw2_pt", u2_bins,
DrawConfig(xmin=-30.0,
xmax=30.0,
xlabel="Raw u_{#perp } [GeV]",
extraText='#mu#mu channel'))
sampMan.cacheDraw(
"ucor1", "histo_zjets_mumu_pfmet_ucor1_pt", u1_bins,
DrawConfig(xmin=-20.0,
xmax=100.0,
xlabel="u_{#parallel} [GeV]",
extraText='#mu#mu channel'))
sampMan.cacheDraw(
"ucor2", "histo_zjets_mumu_pfmet_ucor2_pt", u2_bins,
DrawConfig(xmin=-30.0,
xmax=30.0,
xlabel="u_{#perp } [GeV]",
extraText='#mu#mu channel'))
sampMan.launchDraw()
sampMan.dumpCounts()
print "Program end..."
raw_input()
return