lumiF_noHIMP = 0.57
lumiG = 7.54
lumiH = 8.61
lumi = lumiB + lumiC + lumiD + lumiE + lumiF + lumiG + lumiH # luminosity
lumi_preVFP = 5.79 + 2.57 + 4.25 + 4.01 + 2.53
lumi_postVFP = 0.57 + 7.54 + 8.61
lumi_2017 = 41.48
lumi_2018 = 59.83
filename = args.dat
############# Tree creation
if not args.condor:
treeSI_2016 = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'dat/signals_2016UL.dat', Signals_2016, 'SI'),
name='SI',
isdata=0)
treeSI_2017 = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'dat/signals_2017UL.dat', Signals_2017, 'SI'),
name='SI',
isdata=0)
treeSI_2018 = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'dat/signals_2018UL.dat', Signals_2018, 'SI'),
name='SI',
isdata=0)
if doDATA:
treeDATA2016 = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleMuon2016 + DoubleEG2016, 'DATA'),
name='DATA',
isdata=1)
treeDATA2017 = Sample.Tree(fileName=helper.selectSamples(
Signals2016.append('HSS_125_50_100_2016')
Signals2016.append('HSS_125_50_1000_2016')
#Signals2016.append('HSS_125_50_10000_2016')
Signals2016.append('HSS_400_50_1_2016')
Signals2016.append('HSS_400_50_10_2016')
Signals2016.append('HSS_400_50_100_2016')
Signals2016.append('HSS_400_50_1000_2016')
#Signals2016.append('HSS_400_50_10000_2016')
Signals2016.append('HSS_1000_350_1_2016')
Signals2016.append('HSS_1000_350_10_2016')
Signals2016.append('HSS_1000_350_100_2016')
Signals2016.append('HSS_1000_350_1000_2016')
#Signals2016.append('HSS_1000_350_10000_2016')
treeSI = Sample.Tree(fileName=helper.selectSamples(
GALAPAGOPATH + 'signals_2016.dat', Signals2016, 'MC'),
name='2016',
isdata=0)
###################################
#### Loop over tree events ####
###################################
cm = CutManager.CutManager()
for b in treeSI.blocks:
for s in b.samples:
histograms['hist_E_relTrkIso_' + s.name] = r.TH1F(
"hist_E_relTrkIso_" + s.name, "", 50, 0, 1)
histograms['hist_E_relTrkIso_log_' + s.name] = r.TH1F(
"hist_E_relTrkIso_log_" + s.name, "",
len(bin_iso_log) - 1, bin_iso_log)
histograms['hist_E_relPFIso_' + s.name] = r.TH1F(
############# Signal definition
Signals = []
Signals.append('HXX_1000_350_350mm')
Signals.append('HXX_1000_350_35mm')
Signals.append('HXX_1000_150_100mm')
Signals.append('HXX_1000_150_10mm')
Signals.append('HXX_400_150_400mm')
Signals.append('HXX_400_50_400mm')
Signals.append('HXX_400_50_40mm')
Signals.append('HXX_400_50_4mm')
############# Luminosity definition
filename = 'dat/Samples_cern_fillingv2.dat'
treeSI = Sample.Tree(fileName=helper.selectSamples(WORKPATH + filename,
Signals, 'MC'),
name='SI',
isdata=0)
makeCutStability('SI_MMdPhiStability_2016',
'hMM_cutEfficiency',
12,
False,
treeSI,
WORKPATH + opts.input,
outtag='LLefficiencies',
LLlabel='MM')
makeCutStability('SI_EEdPhiStability_2016',
'hEE_cutEfficiency',
11,
False,
treeSI,
############# Background definition
Backgrounds = []
Backgrounds.append('WJetsToLNu')
Backgrounds.append('WW')
Backgrounds.append('WZ')
Backgrounds.append('ZZ')
Backgrounds.append('DYJetsToLL_M-50')
Backgrounds.append('DYJetsToLL_M-10to50')
Backgrounds.append('QCD_Pt-30to40')
Backgrounds.append('QCD_Pt-40toInf')
lumi = 36.773
############# Tree creation
treeMC = Sample.Tree(
helper.selectSamples(WORKPATH + 'dat/MC.dat', Backgrounds, 'MC'), 'MC',
0, WORKPATH + opts.inputFile)
#makeBackgroundValidation(lumi, treeMC, 'EEsel_leadingPt', 'EEsel_leadingPt', 'Leading p_{T} (GeV/c)', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'EEsel_subleadingPt', 'EEsel_subleadingPt', 'Subleading p_{T} (GeV/c)', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'EEsel_invMass', 'EEsel_invMass', 'Mass (GeV/c^{2})', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'EEsel_Chi2', 'EEsel_Chi2', 'Vertex #Chi^{2}', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'EEsel_dPhi', 'EEsel_dPhi', 'Collinearity |#Delta#Phi|', 1, 'EE')
makeCumulativeValidation(lumi, treeMC, 'EEsel_minIxy', 'EEsel_minIxy',
'|d_{0}|/#sigma_{d}', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'MMsel_leadingPt', 'MMsel_leadingPt', 'Leading p_{T} (GeV/c)', 1, 'MM')
#makeBackgroundValidation(lumi, treeMC, 'MMsel_subleadingPt', 'MMsel_subleadingPt', 'Subleading p_{T} (GeV/c)', 1, 'MM')
#makeBackgroundValidation(lumi, treeMC, 'MMsel_invMass', 'MMsel_invMass', 'Mass (GeV/c^{2})', 1, 'MM')
#makeBackgroundValidation(lumi, treeMC, 'MMsel_Chi2', 'MMsel_Chi2', 'Vertex #Chi^{2}', 1, 'MM')
#makeBackgroundValidation(lumi, treeMC, 'MMsel_dPhi', 'MMsel_dPhi', 'Collinearity |#Delta#Phi|', 1, 'MM')
DoubleMuon2016 = []
DoubleMuon2016.append('DoubleMuon_Run2016B_HIPM')
DoubleMuon2016.append('DoubleMuon_Run2016C_HIPM')
DoubleMuon2016.append('DoubleMuon_Run2016D_HIPM')
DoubleMuon2016.append('DoubleMuon_Run2016E_HIPM')
DoubleMuon2016.append('DoubleMuon_Run2016F_HIPM')
DoubleMuon2016.append('DoubleMuon_Run2016F_noHIPM')
DoubleMuon2016.append('DoubleMuon_Run2016G_noHIPM')
DoubleMuon2016.append('DoubleMuon_Run2016H_noHIPM')
########### .dat definition
filename = opts.dat
########### Data Tree's declaration
treeMuonDATA = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleMuon2016, 'DATA'),
name='DATA',
isdata=1)
###########################################
########## Read recipe
#####
recipe = DatacardManager.Recipe(opts.recipe)
datacards = recipe.dcs
###########################################
########## Loop over datacards
#####
for datacard_name in datacards.keys():
#### ---- Histograms
#### -----------------
bin_iso_log = np.logspace(-3, 2)
hist_E_relTrkIso = r.TH1F("hist_E_relTrkIso", "", 50, 0, 1)
hist_E_relTrkIso_log = r.TH1F("hist_E_relTrkIso_log", "", len(bin_iso_log) -1, bin_iso_log)
hist_E_relPFIso = r.TH1F("hist_E_relPFIso", "", 50, 0, 1)
hist_E_relPFIso_log = r.TH1F("hist_E_relPFIso_log", "", len(bin_iso_log) -1, bin_iso_log)
hist_M_relPFIso = r.TH1F("hist_M_relPFIso", "", 50, 0, 1)
hist_M_relPFIso_log = r.TH1F("hist_M_relPFIso_log", "", len(bin_iso_log) -1, bin_iso_log)
#########################
#### Load sample ####
#########################
treeBKG = Sample.Tree( fileName = helper.selectSamples(GALAPAGOPATH + 'dat/Samples_cern_UltraLegacy.dat', ['WW_postVFP', 'WZ_postVFP'], 'MC'), name = '2016', isdata = 0 )
###################################
#### Loop over tree events ####
###################################
cm = CutManager.CutManager()
for b in treeBKG.blocks:
for s in b.samples:
for t in s.ttrees:
print('New tree with:', t.GetEntries())
for e,ev in enumerate(t):
Signals2017.append('HSS_400_50_10000_2017')
Signals2018 = []
Signals2018.append('HSS_400_50_1_2018')
Signals2018.append('HSS_400_50_10_2018')
Signals2018.append('HSS_400_50_100_2018')
Signals2018.append('HSS_400_50_1000_2018')
Signals2018.append('HSS_400_50_10000_2018')
############# Luminosity definition
lumi2016 = 35.9 # fb-1
lumi2017 = 41.5 # fb-1
lumi2018 = 59.7 # fb-1
############# Galapago Tree definitions
treeSI_2016 = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'signals_2016.dat', Signals2016, 'SI'),
name='SI',
isdata=0)
treeSI_2017 = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'signals_2017.dat', Signals2017, 'SI'),
name='SI',
isdata=0)
treeSI_2018 = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'signals_2018.dat', Signals2018, 'SI'),
name='SI',
isdata=0)
treeDATA_EG2016 = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleEG2016, 'DATA'),
name='DATA',
isdata=1)
treeDATA_EG2017 = Sample.Tree(fileName=helper.selectSamples(
dest='LUMI',
help='Luminosity value')
parser.add_option('-p',
'--option',
action='store',
type=str,
dest='OPTION',
help='Option')
(opts, args) = parser.parse_args()
############ datFile definition
datFile = opts.DATFILE
############ Initialize the output root file
outputPath = opts.OUTPATH if opts.OUTPATH[-1] == '/' else opts.OUTPATH + '/'
if not os.path.exists(outputPath): os.makedirs(outputPath) # ensure the path
outputName = outputPath + 'fill_' + opts.SAMPLE + '.root'
output = r.TFile(outputName, "RECREATE")
output.Close()
sampleList = []
sampleList.append(opts.SAMPLE)
############ Tree creation
tree = Sample.Tree(helper.selectSamples(opts.DATFILE, sampleList, opts.OPTION),
opts.OPTION, 0, outputName)
############ Tree loop
tree.Loop(opts.LUMI, False, False)
DoubleMuon_list = []
DoubleMuon_list.append(DoubleMuonB)
DoubleMuon_list.append(DoubleMuonC)
DoubleMuon_list.append(DoubleMuonD)
DoubleMuon_list.append(DoubleMuonE)
DoubleMuon_list.append(DoubleMuonF)
DoubleMuon_list.append(DoubleMuonG)
DoubleMuon_list.append(DoubleMuonH)
########### .dat definition
filename = 'dat/Samples_cern_fillingv2.dat'
########### Data Tree's declaration
treeEGDATA = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleEG_list, 'DATA'),
name='DATA',
isdata=1)
treeMuonDATA = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleMuon_list, 'DATA'),
name='DATA',
isdata=1)
###########################################
########## Muon channel datacards
#####
#MMchannel_nMMe1 = DatacardManager.Channel('MMe1', 'trackIxy', 6.0, -99)
MMchannel_nMMe1_I = DatacardManager.Channel('MMe1_I', 'trackIxy', 6.0,
10.0)
MMchannel_nMMe1_II = DatacardManager.Channel('MMe1_II', 'trackIxy', 11.0,
15.0)
lumiF = 3.10
lumiG = 7.54
lumiH = 8.61
lumi_total = lumiB + lumiC + lumiD + lumiE + lumiF + lumiG + lumiH # luminosity
lumi_list = [lumiB, lumiC, lumiD, lumiE, lumiF, lumiG, lumiH]
filename = 'dat/Samples_cern_fillingv2.dat'
letter = ['B', 'C', 'D', 'E', 'F', 'G', 'H']
#
# -- QCD Correlation
#
treeEG = Sample.Tree(fileName=helper.selectSamples(WORKPATH + filename,
DoubleEG_list, 'DATA'),
name='DATA',
isdata=1)
treeMuon = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleMuon_list, 'DATA'),
name='DATA',
isdata=1)
makeSymmetryTest(lumi_total, 'hEESS0_dPhi', 0, treeEG,
WORKPATH + opts.input,
'DoubleEG_Run2016[B-H] (SS0 Region)',
'DATAEG_hEESS0_dPhi_corr', 1)
makeSymmetryTest2(lumi_total, 'hEESS0_dPhi', 0, treeEG,
WORKPATH + opts.input,
'DoubleEG_Run2016[B-H] (SS0 Region)',
'DATAEG_hEESS0_dPhi_test', 1)
makeSymmetryTest2(lumi_total,
'hEEOS0disp_dPhi',
Signals_400_50_2018.append('HSS_400_50_1_2018')
Signals_400_50_2018.append('HSS_400_50_10_2018')
Signals_400_50_2018.append('HSS_400_50_100_2018')
Signals_400_50_2018.append('HSS_400_50_1000_2018')
Signals_400_50_2018.append('HSS_400_50_10000_2018')
Signals_1000_350_2018 = []
Signals_1000_350_2018.append('HSS_1000_350_1_2018')
Signals_1000_350_2018.append('HSS_1000_350_10_2018')
Signals_1000_350_2018.append('HSS_1000_350_100_2018')
Signals_1000_350_2018.append('HSS_1000_350_1000_2018')
Signals_1000_350_2018.append('HSS_1000_350_10000_2018')
filename = 'dat/Samples_cern_UltraLegacy.dat'
#treeMC = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, Backgrounds, 'MC'), name = 'MC', isdata = 0 )
treeSI_400_50_2016 = Sample.Tree( fileName = helper.selectSamples(WORKPATH + 'signals_2016.dat', Signals_400_50_2016, 'MC'), name = 'SI', isdata = 0 )
treeSI_400_50_2017 = Sample.Tree( fileName = helper.selectSamples(WORKPATH + 'signals_2017.dat', Signals_400_50_2017, 'MC'), name = 'SI', isdata = 0 )
treeSI_400_50_2018 = Sample.Tree( fileName = helper.selectSamples(WORKPATH + 'signals_2018.dat', Signals_400_50_2018, 'MC'), name = 'SI', isdata = 0 )
treeSI_1000_350_2018 = Sample.Tree( fileName = helper.selectSamples(WORKPATH + 'signals_2018.dat', Signals_1000_350_2018, 'MC'), name = 'SI', isdata = 0 )
MMlabels = []
MMlabels.append('Muon trigger')
MMlabels.append('Good PV')
MMlabels.append('Have MM passing ID')
MMlabels.append('p_{T} > 25 GeV')
MMlabels.append('|#eta| < 2')
MMlabels.append('#Delta R > 0.2')
MMlabels.append('m_{#mu#mu} > 15 GeV')
MMlabels.append('Rel. PFIso < 0.2')
MMlabels.append('Vertex #chi^{2}/ndof < 10')
MMlabels.append('Cosmic muon rejection')
hBKG_B_rebin = hBKG_B.Rebin(len(newbin)-1, 'hBKG_B_rebin', newbin)
makeClosureTestMC(lumi = '', name = 'VV_EE_2018', hBKG_A = hBKG_A_rebin, hBKG_B = hBKG_B_rebin, ylog = True, tree = treeVV_2018, inputdir = opts.input, labelB = 'Control Region: |#Delta#Phi|_{ee} > #pi/2', labelA = 'Signal Region: |#Delta#Phi|_{ee} < #pi/2', xlabel = '|d_{xy}|/#sigma_{d}', outtag = 'EEChannelV2', yshift = 0.0, LLlabel = 'EE', DATAlabel = '2018 UL', extralabel = 'WW, WZ, ZZ (Baseline selection)')
hBKG_A = treeVV_2018.getLoopTH1F(opts.input, 'hMMSR_trackIxy')
hBKG_B = treeVV_2018.getLoopTH1F(opts.input, 'hMMBCR_trackIxy')
newbin = np.linspace(0., 25., 26)
hBKG_A_rebin = hBKG_A.Rebin(len(newbin)-1, 'hBKG_A_rebin', newbin)
hBKG_B_rebin = hBKG_B.Rebin(len(newbin)-1, 'hBKG_B_rebin', newbin)
makeClosureTestMC(lumi = '', name = 'VV_MM_2018', hBKG_A = hBKG_A_rebin, hBKG_B = hBKG_B_rebin, ylog = True, tree = treeVV_2018, inputdir = opts.input, labelB = 'Control Region: |#Delta#Phi|_{#mu#mu} > #pi/2', labelA = 'Signal Region: |#Delta#Phi|_{ee} < #pi/2', xlabel = '|d_{xy}|/#sigma_{d}', outtag = 'MMChannelV2', yshift = 0.0, LLlabel = 'MM', DATAlabel = '2018 UL', extralabel = 'WW, WZ, ZZ (Baseline selection)', rmax = 3.0)
"""
#
# -- QCD and Wjets Correlation
#
tree_EE2016_HIPM = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, DoubleEG2016_HIPM, 'MC'), name = 'DATA', isdata = 1 )
tree_MM2016_HIPM = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, DoubleMuon2016_HIPM, 'MC'), name = 'DATA', isdata = 1 )
hBKG_A = tree_EE2016_HIPM.getLoopTH1F(opts.input, 'hEEQCDSR_trackIxy')
hBKG_B = tree_EE2016_HIPM.getLoopTH1F(opts.input, 'hEEQCDBCR_trackIxy')
newbin = np.linspace(0., 25., 26)
hBKG_A_rebin = hBKG_A.Rebin(len(newbin)-1, 'hBKG_A_rebin', newbin)
hBKG_B_rebin = hBKG_B.Rebin(len(newbin)-1, 'hBKG_B_rebin', newbin)
makeClosureTestMC(lumi = '', name = 'QCD_EE_2016HIPM', hBKG_A = hBKG_A_rebin, hBKG_B = hBKG_B_rebin, ylog = True, tree = tree_EE2016_HIPM, inputdir = opts.input, labelB = 'Control Region: |#Delta#Phi|_{ee} > #pi/2', labelA = 'Signal Region: |#Delta#Phi|_{ee} < #pi/2', xlabel = '|d_{xy}|/#sigma_{d}', outtag = 'EEChannelV2', yshift = 0.0, LLlabel = 'EE', DATAlabel = 'HIP affected runs', extralabel = 'QCD Control Region', rmax = 3.0)
hBKG_A = tree_MM2016_HIPM.getLoopTH1F(opts.input, 'hMMQCDSR_trackIxy')
hBKG_B = tree_MM2016_HIPM.getLoopTH1F(opts.input, 'hMMQCDBCR_trackIxy')
newbin = np.linspace(0., 25., 26)
hBKG_A_rebin = hBKG_A.Rebin(len(newbin)-1, 'hBKG_A_rebin', newbin)
hBKG_B_rebin = hBKG_B.Rebin(len(newbin)-1, 'hBKG_B_rebin', newbin)
makeClosureTestMC(lumi = '', name = 'QCD_MM_2016HIPM', hBKG_A = hBKG_A_rebin, hBKG_B = hBKG_B_rebin, ylog = True, tree = tree_MM2016_HIPM, inputdir = opts.input, labelB = 'Control Region: |#Delta#Phi|_{#mu#mu} > #pi/2', labelA = 'Signal Region: |#Delta#Phi|_{ee} < #pi/2', xlabel = '|d_{xy}|/#sigma_{d}', outtag = 'MMChannelV2', yshift = 0.0, LLlabel = 'MM', DATAlabel = 'HIP affected runs', extralabel = 'QCD Control Region', rmax = 3.0)
lumiMuon['DoubleMuon_Run2016D'] = lumiD
lumiMuon['DoubleMuon_Run2016E'] = lumiE
lumiMuon['DoubleMuon_Run2016F'] = lumiF
lumiMuon['DoubleMuon_Run2016G'] = lumiG
lumiMuon['DoubleMuon_Run2016H'] = lumiH
lumi_Muon = 0.0
for dataset in DoubleMuon_list:
lumi_Muon += lumiMuon[dataset]
#filename = 'dat/Samples_cern_Legacy.dat'
filename = 'dat/Samples_cern_fillingv2.dat'
### Tree SI Tree
treeSI_400_50_Legacy = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'dat/Samples_cern_Legacy.dat', Signals_400_50, 'SI'),
name='SI',
isdata=0)
################################
######## DoubleEG Plots ########
################################
#treeDATA = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, DoubleEG_list, 'DATA'), name = 'DATA', isdata = 1 )
##################################
######## DoubleMuon Plots ########
##################################
treeDATA = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleMuon_list, 'DATA'),
name='DATA',
lumiH = 8.61
lumi_total = lumiB + lumiC + lumiD + lumiE + lumiF + lumiG + lumiH # luminosity
lumi_list = [lumiB, lumiC, lumiD, lumiE, lumiF, lumiG, lumiH]
filename = 'dat/Samples_cern_filling.dat'
letter = ['B', 'C', 'D', 'E', 'F', 'G', 'H']
### -> Muon plots:
MuonTrees = []
for i in range(0, len(DoubleMuon_list)):
dataset = DoubleMuon_list[i]
lumi = lumi_list[i]
treeEra = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, [dataset], 'DATA'),
name='DATA',
isdata=1)
MuonTrees.append(treeEra)
makeQCDEstimationPlot(lumi_total,
'hMM_trackIxy',
True,
MuonTrees,
DoubleMuon_list,
WORKPATH + opts.Muoninput,
outtag='QCDEstimation',
LLlabel='MM')
makeQCDEstimationPlot(lumi_total,
'hMM_trackDxy',
True,
MuonTrees,
Signals2016.append('HSS_400_50_10_'+year)
Signals2016.append('HSS_400_50_100_'+year)
Signals2016.append('HSS_400_150_1_'+year)
Signals2016.append('HSS_400_150_10_'+year)
Signals2016.append('HSS_400_150_100_'+year)
Signals2016.append('HSS_600_150_1_'+year)
Signals2016.append('HSS_600_150_10_'+year)
Signals2016.append('HSS_600_150_100_'+year)
Signals2016.append('HSS_1000_150_1_'+year)
Signals2016.append('HSS_1000_150_10_'+year)
Signals2016.append('HSS_1000_150_100_'+year)
Signals2016.append('HSS_1000_350_1_'+year)
Signals2016.append('HSS_1000_350_10_'+year)
Signals2016.append('HSS_1000_350_100_'+year)
treeSI = Sample.Tree( fileName = helper.selectSamples(GALAPAGOPATH + 'signals_'+year+'.dat', Signals2016, 'MC'), name = year, isdata = 0 )
###################################
#### Loop over tree events ####
###################################
cm = CutManager.CutManager()
rates = {}
for b in treeSI.blocks:
for s in b.samples:
rates[s.name] = {}
rates[s.name]['total'] = 0.0
rates[s.name]['passed'] = 0.0
for t in s.ttrees:
print(s.name, 'New tree with:', t.GetEntries())
# Signal.append('ScalarBosons_400_150_40')
Signal.append('DisplacedSUSY_1500_494_160')
Signal.append('DisplacedSUSY_1000_148_60')
Signal.append('DisplacedSUSY_350_148_173')
Signal.append('DisplacedSUSY_120_48_165')
############## Data definition
Data = []
Data.append('DoubleMuon-Run2016B')
############# Parameter definition
lumi = 36.773 # luminosity
############# Tree creation
treeMC = Sample.Tree(
helper.selectSamples(WORKPATH + 'dat/MC.dat', Backgrounds, 'MC'), 'MC',
0, WORKPATH + opts.outputFile)
treeSI = Sample.Tree(
helper.selectSamples(WORKPATH + 'dat/SI.dat', Signal, 'SI'), 'SI', 0,
WORKPATH + opts.outputFile)
treeDATA = Sample.Tree(
helper.selectSamples(WORKPATH + 'dat/DATA.dat', Data, 'DATA'), 'DATA',
1, WORKPATH + opts.outputFile)
############# Tree loop
if not opts.avoidLoop:
treeMC.Loop(lumi, False, False)
treeSI.Loop(lumi, False, False)
treeDATA.Loop(lumi, False, False)
makeSensitivity(lumi, treeSI, treeMC, 'SR_EEsel_minIxy', 'SR_EEsel_minIxy',
makeDataMCPlot(lumiEG[dataset], 'hEE_trackDxy', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hEE_Lxy', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hEE_Ixy', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hEE_leadingPt', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hEE_subleadingPt', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hEE_normalizedChi2', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hE_eta', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hE_dxy', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hE_dxyError', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
makeDataMCPlot(lumiEG[dataset], 'hE_charge', True, treeMC, treeDATA, WORKPATH + opts.EGinput, outtag = dataset, LLlabel = 'EE', DATAlabel = dataset, extralabel = '|Control region #Delta#Phi| > #pi/2')
"""
##### Joint luminosity
treeMC = Sample.Tree(fileName=helper.selectSamples(WORKPATH + filename,
Backgrounds, 'MC'),
name='MC',
isdata=0)
treeDATA = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleEG_list, 'DATA'),
name='DATA',
isdata=1)
makeDataMCPlot(lumi_EG,
'hEEOS0_dPhi',
'hEESS0_dPhi',
True,
treeMC,
treeDATA,
WORKPATH + opts.EGinput,
outtag='DoubleEG_Full2016',
LLlabel='EE',
#### -----------------
#### ---- Histograms
#### -----------------
hist_2d_BPregion = r.TH2F("hist_2d_BPregion", "", 150, -5, 5, 150, -5, 5)
hist_2d_20region = r.TH2F("hist_2d_20region", "", 200, -20, 20, 200, -20,
20)
hist_1d_r = r.TH1F("hist_1d_r_all", "", 100, 0, 10)
#########################
#### Load sample ####
#########################
treeA = Sample.Tree(fileName=helper.selectSamples(
GALAPAGOPATH + 'dat/Samples_cern_UltraLegacy.dat',
['DoubleMuon_Run2018' + opts.era], 'DATA'),
name='2018' + opts.era,
isdata=0)
###################################
#### Loop over tree events ####
###################################
cm = CutManager.CutManager()
for b in treeA.blocks:
for s in b.samples:
#for f in s.ftpaths:
# print(f)
for t in s.ttrees:
print('New tree with:', t.GetEntries())
cutoff = t.GetEntries() / 10
mass_rebin = np.concatenate((np.arange(0, 15, 5, float),
np.arange(15, 65, 10, float),
np.arange(65, 100, 5, float),
np.arange(100, 220, 15, float),
np.arange(220, 400, 20, float)))
# np.arange(15, 65, 10, float),
# np.arange(100, 400, 15, float)))
Ixy_reb = [0.0, 1.0, 2.0, 3.0, 4.0, 6.0, 8.0, 10.0, 14.0, 20.0, 30.0, 40.0]
Ixy_rebin = np.array(Ixy_reb)
################################
######## DoubleEG Plots ########
################################
treeDATA_HIPM = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, DoubleEG_HIPM, 'DATA'), name = 'DATA', isdata = 1 )
treeDATA_noHIPM = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, DoubleEG_noHIPM, 'DATA'), name = 'DATA', isdata = 1 )
treeDATA_2016 = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, DoubleEG_HIPM + DoubleEG_noHIPM, 'DATA'), name = 'DATA', isdata = 1 )
treeDATA_2017 = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, DoubleEG2017, 'DATA'), name = 'DATA', isdata = 1 )
treeDATA_2018 = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, EGamma2018, 'DATA'), name = 'DATA', isdata = 1 )
treeDATA_full = Sample.Tree( fileName = helper.selectSamples(WORKPATH + filename, DoubleEG_HIPM + DoubleEG_noHIPM + DoubleEG2017 + EGamma2018, 'DATA'), name = 'DATA', isdata = 1 )
#### Prompt validation
makePromptBKGPlot(name = 'EEprompt_mass_HIPM', lumi = 19.7, hname_SR = 'hEEpromptSR_mass', hname_CR = 'hEEpromptCR_mass', ylog = True, treeDATA = treeDATA_HIPM, inputdir = opts.input, xlabel = '', outtag = '', yshift = 0.0, LLlabel = 'EE', DATAlabel = '', extralabel = 'Prompt control region', xlog = False)
makePromptBKGPlot(name = 'EEprompt_mass_noHIPM', lumi = 16.15, hname_SR = 'hEEpromptSR_mass', hname_CR = 'hEEpromptCR_mass', ylog = True, treeDATA = treeDATA_noHIPM, inputdir = opts.input, xlabel = '', outtag = '', yshift = 0.0, LLlabel = 'EE', DATAlabel = '', extralabel = 'Prompt control region', xlog = False)
makePromptBKGPlot(name = 'EEprompt_mass_2016', lumi = 35.9, hname_SR = 'hEEpromptSR_mass', hname_CR = 'hEEpromptCR_mass', ylog = True, treeDATA = treeDATA_2016, inputdir = opts.input, xlabel = '', outtag = '', yshift = 0.0, LLlabel = 'EE', DATAlabel = '', extralabel = 'Prompt control region', xlog = False)
makePromptBKGPlot(name = 'EEprompt_mass_2017', lumi = 41.5, hname_SR = 'hEEpromptSR_mass', hname_CR = 'hEEpromptCR_mass', ylog = True, treeDATA = treeDATA_2017, inputdir = opts.input, xlabel = '', outtag = '', yshift = 0.0, LLlabel = 'EE', DATAlabel = '', extralabel = 'Prompt control region', xlog = False)
makePromptBKGPlot(name = 'EEprompt_mass_2018', lumi = 59.8, hname_SR = 'hEEpromptSR_mass', hname_CR = 'hEEpromptCR_mass', ylog = True, treeDATA = treeDATA_2018, inputdir = opts.input, xlabel = '', outtag = '', yshift = 0.0, LLlabel = 'EE', DATAlabel = '', extralabel = 'Prompt control region', xlog = False)
#### On-Z validation
lumiMuon['DoubleMuon_Run2016G'] = lumiG
lumiMuon['DoubleMuon_Run2016H'] = lumiH
lumi_Muon = 0.0
for dataset in DoubleMuon_list:
lumi_Muon += lumiMuon[dataset]
filename = 'dat/Samples_cern_filling.dat'
################################
######## DoubleEG Plots ########
################################
## Full luminosity
treeSI = Sample.Tree(fileName=helper.selectSamples(WORKPATH + filename,
Signals, 'MC'),
name='SI',
isdata=0)
treeMC = Sample.Tree(fileName=helper.selectSamples(WORKPATH + filename,
Backgrounds, 'MC'),
name='MC',
isdata=0)
treeMuon = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, DoubleMuon_list, 'DATA'),
name='DATA',
isdata=1)
treeEG = Sample.Tree(fileName=helper.selectSamples(WORKPATH + filename,
DoubleEG_list, 'DATA'),
name='DATA',
isdata=1)
makeSignalBackgroundPlot(hname='hMM_dPhi_full',
############# Set the TDR plot style
gROOT.ProcessLine('.L include/tdrstyle.C')
gROOT.SetBatch(1)
r.setTDRStyle()
############# Datasets definition
MuonData = []
MuonData.append('DoubleMuon-Run2016B')
ElectronData = []
ElectronData.append('DoubleEG-Run2016B')
lumi = 4.8
############# Tree creation
treeMuonDATA = Sample.Tree(
helper.selectSamples(WORKPATH + 'dat/DATA.dat', MuonData, 'DATA'),
'DATA', 1, WORKPATH + opts.inputFile)
treeElectronDATA = Sample.Tree(
helper.selectSamples(WORKPATH + 'dat/DATA.dat', ElectronData, 'DATA'),
'DATA', 1, WORKPATH + opts.inputFile)
#makeBackgroundValidation(lumi, treeMC, 'EEsel_leadingPt', 'EEsel_leadingPt', 'Leading p_{T} (GeV/c)', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'EEsel_subleadingPt', 'EEsel_subleadingPt', 'Subleading p_{T} (GeV/c)', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'EEsel_invMass', 'EEsel_invMass', 'Mass (GeV/c^{2})', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'EEsel_Chi2', 'EEsel_Chi2', 'Vertex #Chi^{2}', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'EEsel_dPhi', 'EEsel_dPhi', 'Collinearity |#Delta#Phi|', 1, 'EE')
makeCumulativeValidation(lumi, treeElectronDATA, 'SiF_EEsel_minIxy',
'SiF_EEsel_minIxy', '|d_{0}|/#sigma_{d}', 1, 'EE')
#makeBackgroundValidation(lumi, treeMC, 'MMsel_leadingPt', 'MMsel_leadingPt', 'Leading p_{T} (GeV/c)', 1, 'MM')
#makeBackgroundValidation(lumi, treeMC, 'MMsel_subleadingPt', 'MMsel_subleadingPt', 'Subleading p_{T} (GeV/c)', 1, 'MM')
def createDatacards(datacards, Backgrounds, Signals, flavor, year):
treeBKG = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + filename, Backgrounds, 'DATA'),
name='DATA',
isdata=1)
datacard_list = []
for datacard_key in datacards.keys():
datacard = datacards[datacard_key]
channels = {}
### Initialize channels with background information
for channel_name in datacard.keys():
xmin = datacard[channel_name]['bkg']['limits'][0] # minimum value
xmax = datacard[channel_name]['bkg']['limits'][1] # minimum value
directory = datacard[channel_name]['bkg']['dir']
histogram = datacard[channel_name]['bkg']['histogram']
print('>>>>> Info channel', channel_name, xmin, xmax, directory,
histogram)
channels[channel_name] = DatacardManager.Channel(
channel_name, xmin, xmax)
## Get histogram
_histo = treeBKG.getLoopTH1F(directory, histogram)
print(directory, histogram)
c1 = r.TCanvas("", "", 500, 500)
c1.SetLogy(1)
_histo.Draw('HIST')
c1.Print('Pruebafondochannel.png')
channels[channel_name].addBackground('bkg', _histo)
print(channels[channel_name].backgrounds[0].rate)
for sample in Signals:
treeSI = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'signals_' + year + '.dat', [sample], 'SI'),
name='SI',
isdata=0)
sample_split = sample.split('_')
mH = sample_split[1]
mS = sample_split[2]
ctau = sample_split[3]
sample_label = 'mH' + mH + '__' + 'mS' + mS + '__' + 'ctau' + ctau + 'mm'
## Create datacard:
datacard_name = 'Datacard__' + flavor + '__' + sample_label + '__' + year + '.txt'
output_datacard = DatacardManager.Datacard(datacard_name)
for channel_name in datacard.keys():
histogram = datacard[channel_name]['sig']['histogram']
directory = datacard[channel_name]['sig']['dir']
_histo = treeSI.getLoopTH1F(directory, histogram)
channels[channel_name].setSignal('sig', _histo)
output_datacard.addChannel(channels[channel_name])
output_datacard.saveDatacard(outputDir=_outdir)
datacard_list.append(datacard_name)
return datacard_list
### Lista de senyales que queremos incluir (tercera columna del dat):
Signals = []
Signals.append('HXX_400_50_4mm')
############# DEFINICION DE PARAMETROS
###
### Este es el valor de la luminosidad al que se normalizan los plots e.g. 35.9 fb correspondiente a 2016
lumi = 35.9 # luminosity
############# CREACION DE OBJETOS TREE DE GALAPAGO
###
### Toman como uno de los inputs la .dat file que queremos meter (en este caso dat/Samples_cern.dat)
### y una lista con los nombres de las muestras que queremos incluir de ese dat.
### Es importante indicar (cuando proceda) que isdata = 0 para que se aplique la normalizacion
treeMC = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'dat/Samples_cern.dat', Backgrounds, 'MC'),
name='MC',
isdata=0)
treeSI = Sample.Tree(fileName=helper.selectSamples(
WORKPATH + 'dat/Samples_cern.dat', Signals, 'SI'),
name='SI',
isdata=0)
############# DEFINICION DE CORTES
### Como ejemplo pongo algunos de los cortes basicos de los displaced global muon pais (DMDM)
### Hay algunos definidos en include/CutManager, pero desde aqui podeis definir los que querais
### La sintaxis es la que se utilizaria con el metodo .Draw de un TTree normal
cuts = CutManager.CutManager() # Clase CutManager
haveDMDM = cuts.brackets('nDMDMBase > 0') # Corte 1
lowPhi = cuts.brackets(
'fabs(DMDMBase_dPhi[DMDMBase_maxIxy])< 3.14/2.0') # Corte 2
if __name__ == "__main__":
print bcolors.HEADER
print '#######################################################################'
print ' Starting IFCA analysis... '
print '#######################################################################' + bcolors.ENDC
parser = optparse.OptionParser(usage='usage: %prog [opts] FilenameWithSamples', version='%prog 1.0')
parser.add_option('-s', '--samples', action='store', type=str, dest='sampleFile', default='samples.dat', help='the samples file. default \'samples.dat\'')
(opts, args) = parser.parse_args()
daDatasets = ['Data']
mcDatasets = ['DYJets', 'TTJets', 'singleT', 'TTW', 'TTZ', 'WW', 'VVV', 'VZ']
treeMC = Sample.Tree(helper.selectSamples(opts.sampleFile, mcDatasets, 'MC'), 'MC' , 0, isScan = 0)
treeDA = Sample.Tree(helper.selectSamples(opts.sampleFile, daDatasets, 'DA'), 'DATA', 1, isScan = 0)
lumi = 9.9 ; maxrun = 99276811; lumi_str = '9.9invfb'
gROOT.ProcessLine('.L include/tdrstyle.C')
gROOT.SetBatch(1)
r.setTDRStyle()
cuts = CutManager.CutManager()
labelmll = 'm_{ll} [GeV]'
labelmet = 'E_{T}^{miss} [GeV]'
labelmt2 = 'MT2 [GeV]'
labelpt2 = 'p_{T} (2nd) [GeV]'
labelnjet = "N. Jets"