try:
if sys.argv[2] == "CR":
doCR = True
elif sys.argv[2] == "SR":
doSR = True
except IndexError:
pass
keyIter = inputFile.GetListOfKeys().MakeIterator()
key = keyIter.Next()
while key:
if key.GetName().find("low") == -1 and key.GetName().find("high") == -1:
try:
nomHist = TH1D(inputFile.Get(key.GetName()))
highHist = TH1D(inputFile.Get(key.GetName() + "_high"))
lowHist = TH1D(inputFile.Get(key.GetName() + "_low"))
try:
highRatio = highHist.Integral() / nomHist.Integral()
lowRatio = lowHist.Integral() / nomHist.Integral()
#print key.GetName(),"high = ",highRatio,"low = ",lowRatio
if key.GetName().startswith("hQCD_"):
tmp = key.GetName()[5:]
if doCR:
if not key.GetName().find("ChanCR") == -1:
print "for", key.GetName()
print ' <OverallSys Name="NormQCD_%s" High="%f" Low="%f" />' % (
tmp, highRatio, lowRatio)
pass
elif doSR:
"title": "m_{ll}^{(2)} [GeV]",
"bin": 36,
"xmin": 0,
"xmax": 100
},
"higgs_m": {
"name": "higgs_m",
"title": "m_{4l} [GeV]",
"bin": 36,
"xmin": 70,
"xmax": 170
},
}
dict_histoS = {
var: TH1D(var + "S", var + "S;" + Vars[var]["title"] + ";",
Vars[var]["bin"], Vars[var]["xmin"], Vars[var]["xmax"])
for var in Vars
}
dict_histoB = {
var: TH1D(var + "B", var + "B;" + Vars[var]["title"] + ";",
Vars[var]["bin"], Vars[var]["xmin"], Vars[var]["xmax"])
for var in Vars
}
rootFileS = TFile(FileS, "read")
treeS = rootFileS.Get(treeName)
rootFileB = TFile(FileB, "read")
treeB = rootFileB.Get(treeName)
for entry in xrange(treeS.GetEntries()):
treeS.GetEntry(entry)
#Tianai
from ROOT import TH1D, gRandom
h1 = TH1D("hist","histogram",100,-5,5)
for i in xrange(10000):
hist = gRandom.Gaus()
h1.Fill(hist)
h1.Draw()
def create_yields_list(channel, varName, toppt, j1j2pt, lepjetdR, bin, low,
high, ylabel, xlabel, save, wprime, btags, List_to_use):
if (channel == 'electron'): ch = '_el'
if (channel == 'muon'): ch = '_mu'
pyfile = open(
"yields_For2DLimits_06Jun_finalbins_ScaleGenTopPt_QCD/" + channel +
"_" + save + "_Wprime" + wprime + "_Histos-" + btags +
"_dr03_lep50.py", "w")
doTTbarWeight = 'True'
if (channel == 'electron'):
cut = 'jet_0_pt_WprimeCalc >= 120 && jet_1_pt_WprimeCalc >= 40 && elec_1_pt_WprimeCalc > 50 && abs(elec_1_eta_WprimeCalc) < 2.5 && elec_1_RelIso_WprimeCalc < 0.1 && corr_met_WprimeCalc > 20 && Muon_DeltaR_LjetsTopoCalcNew > ' + lepjetdR
if (channel == 'muon'):
cut = 'jet_0_pt_WprimeCalc >= 120 && jet_1_pt_WprimeCalc >= 40 && muon_1_pt_WprimeCalc > 50 && abs(muon_1_eta_WprimeCalc) < 2.1 && muon_1_RelIso_WprimeCalc < 0.12 && corr_met_WprimeCalc > 20 && Muon_DeltaR_LjetsTopoCalcNew > ' + lepjetdR
print varName
if btags == 'zerobtags':
cutbtag = ' && ((jet_0_tag_WprimeCalc+jet_1_tag_WprimeCalc)==0) '
if btags == 'onebtags':
cutbtag = ' && ((jet_0_tag_WprimeCalc+jet_1_tag_WprimeCalc)==1) '
if btags == 'ge1btags':
cutbtag = ' && ((jet_0_tag_WprimeCalc+jet_1_tag_WprimeCalc)>=1) '
if btags == 'twobtags':
cutbtag = ' && ((jet_0_tag_WprimeCalc+jet_1_tag_WprimeCalc)==2) '
if btags == 'ge2btags':
cutbtag = ' && ( (jet_0_tag_WprimeCalc==1 && (jet_1_tag_WprimeCalc + jet_2_tag_WprimeCalc + jet_3_tag_WprimeCalc + jet_4_tag_WprimeCalc + jet_5_tag_WprimeCalc + jet_6_tag_WprimeCalc + jet_7_tag_WprimeCalc + jet_8_tag_WprimeCalc + jet_9_tag_WprimeCalc) >= 1 ) || (jet_1_tag_WprimeCalc==1 && (jet_0_tag_WprimeCalc + jet_2_tag_WprimeCalc + jet_3_tag_WprimeCalc + jet_4_tag_WprimeCalc + jet_5_tag_WprimeCalc + jet_6_tag_WprimeCalc + jet_7_tag_WprimeCalc + jet_8_tag_WprimeCalc + jet_9_tag_WprimeCalc) >= 1 ) ) '
if btags == 'final':
cutbtag = ' && ( (jet_0_tag_WprimeCalc+jet_1_tag_WprimeCalc ) >= 1 )'
if btags == 'final':
cut = cut + ' && BestTop_LjetsTopoCalcNew > 130 && BestTop_LjetsTopoCalcNew < 210 && BestTop_Pt_LjetsTopoCalcNew > ' + toppt + ' && Jet1Jet2_Pt_LjetsTopoCalcNew > ' + j1j2pt
cutwbb = ' && n_Bjets_WprimeCalc > 0' # Wb(b)
cutwcc = ' && n_Bjets_WprimeCalc==0 && n_Cjets_WprimeCalc>0' # Wc(c)
cutwjj = ' && n_Bjets_WprimeCalc==0 && n_Cjets_WprimeCalc==0' # W+light
if (doTTbarWeight == 'True'):
SFWjmu = 0.82 ## myHF120lep50, scale ttbar
SFWcmu = 1.0 * 1.66 ## myHF120lep50, scale ttbar
SFWbmu = 1.0 * 1.21 ## myHF120lep50, scale ttbar
SFWjmuPlus = 0.82 * 0.87 ## myHF120lep50, scale ttbar
SFWcmuPlus = 1.0 * 1.66 * 1.15 ## myHF120lep50, scale ttbar
SFWbmuPlus = 1.0 * 1.21 * 1.15 ## myHF120lep50, scale ttbar
SFWjmuMinus = 0.82 * 1.13 ## myHF120lep50, scale ttbar
SFWcmuMinus = 1.0 * 1.66 * 0.85 ## myHF120lep50, scale ttbar
SFWbmuMinus = 1.0 * 1.21 * 0.85 ## myHF120lep50, scale ttbar
#weight_ttbarminus = '(1.188-0.001124*(BestTop_Pt_LjetsTopoCalcNew))'
#weight_ttbarplus = '1.0'
#weight_ttbar = '(5.809*TMath::Landau(BestTop_Pt_LjetsTopoCalcNew,149.1,154))'
weight_ttbarminus = 'weight_TopPt_WprimeCalc'
weight_ttbarplus = '1.0'
weight_ttbar = 'weight_TopPt_WprimeCalc'
if (doTTbarWeight == 'False'):
SFWjmu = 0.82 ## myHF120lep50
SFWcmu = 0.93 * 1.66 ## myHF120lep50
SFWbmu = 0.93 * 1.21 ## myHF120lep50
SFWjmuPlus = 0.82 * 0.87 ## myHF120lep50
SFWcmuPlus = 0.93 * 1.66 * 1.15 ## myHF120lep50
SFWbmuPlus = 0.93 * 1.21 * 1.15 ## myHF120lep50
SFWjmuMinus = 0.82 * 1.13 ## myHF120lep50
SFWcmuMinus = 0.93 * 1.66 * 0.85 ## myHF120lep50
SFWbmuMinus = 0.93 * 1.21 * 0.85 ## myHF120lep50
weight_ttbarminus = '1.0'
weight_ttbarplus = '1.0'
weight_ttbar = '1.0'
cutzerobtags = ' && ( (jet_0_tag_WprimeCalc+jet_1_tag_WprimeCalc+jet_2_tag_WprimeCalc+jet_3_tag_WprimeCalc+jet_4_tag_WprimeCalc+jet_5_tag_WprimeCalc+jet_6_tag_WprimeCalc+jet_7_tag_WprimeCalc+jet_8_tag_WprimeCalc+jet_9_tag_WprimeCalc) == 0 )'
print wprime
#print cut + cutbtag
List = List_to_use
Variables = {}
VariablesPUup = {}
Variables0tag = {}
VariablesHFup = {}
VariablesHFdown = {}
VariablesTTbarShapeUp = {}
VariablesTTbarShapeDown = {}
background = 0
j = 0
nominalwprime = 'False'
print List
for Type in List:
if (channel == 'electron'):
prefix = 'elec_invmass_' + btags + '__'
if (channel == 'muon'):
prefix = 'mu_invmass_' + btags + '__'
suffix = ''
if (Type == 'Data_el' or Type == 'Data_mu'): suffix = 'DATA' + Type
if (Type == 'WJets' or re.match('W[1-4]Jets', Type)):
suffix = 'wjets' + Type
if (Type == 'WW'): suffix = 'scaledntb' + Type
if (Type == 'ZJets_M50' or Type == 'WW' or Type == 'T_t'
or Type == 'Tbar_t' or Type == 'T_tW' or Type == 'Tbar_tW'):
suffix = 'scaledntb' + Type
if (Type == 'TTbar_Madgraph'): suffix = 'ttbar' + Type
if (Type == 'T_s' or Type == 'Tbar_s'): suffix = 'tb' + Type
if (Type == 'WJets_JESUP'): suffix = 'wjets_jesUp' + Type
if (Type == 'TTbar_Madgraph_JESUP' or Type == 'WW_JESUP'
or Type == 'ZJets_M50_JESUP' or Type == 'T_t_JESUP'
or Type == 'Tbar_t_JESUP' or Type == 'T_tW_JESUP'
or Type == 'Tbar_tW_JESUP'):
suffix = 'scaledntb_jesUp' + Type
if (Type == 'TTbar_Madgraph_JESUP'): suffix = 'scaledall_jesUp' + Type
if (Type == 'T_s_JESUP' or Type == 'Tbar_s_JESUP'):
suffix = 'tb_jesUp' + Type
if (Type == 'WJets_JESDOWN'): suffix = 'wjets_jesDown' + Type
if (Type == 'TTbar_Madgraph_JESDOWN' or Type == 'WW_JESDOWN'
or Type == 'ZJets_M50_JESDOWN' or Type == 'T_t_JESDOWN'
or Type == 'Tbar_t_JESDOWN' or Type == 'T_tW_JESDOWN'
or Type == 'Tbar_tW_JESDOWN'):
suffix = 'scaledntb_jesDown' + Type
if (Type == 'TTbar_Madgraph_JESDOWN'):
suffix = 'scaledall_jesDown' + Type
if (Type == 'T_s_JESDOWN' or Type == 'Tbar_s_JESDOWN'):
suffix = 'tb_jesDown' + Type
if (Type == 'WJets_JERUP'): suffix = 'wjets_jerUp' + Type
if (Type == 'TTbar_Madgraph_JERUP' or Type == 'WW_JERUP'
or Type == 'ZJets_M50_JERUP' or Type == 'T_t_JERUP'
or Type == 'Tbar_t_JERUP' or Type == 'T_tW_JERUP'
or Type == 'Tbar_tW_JERUP'):
suffix = 'scaledntb_jerUp' + Type
if (Type == 'TTbar_Madgraph_JERUP'): suffix = 'scaledall_jerUp' + Type
if (Type == 'T_s_JERUP' or Type == 'Tbar_s_JERUP'):
suffix = 'tb_jerUp' + Type
if (Type == 'WJets_JERDOWN'): suffix = 'wjets_jerDown' + Type
if (Type == 'TTbar_Madgraph_JERDOWN' or Type == 'WW_JERDOWN'
or Type == 'ZJets_M50_JERDOWN' or Type == 'T_t_JERDOWN'
or Type == 'Tbar_t_JERDOWN' or Type == 'T_tW_JERDOWN'
or Type == 'Tbar_tW_JERDOWN'):
suffix = 'scaledntb_jerDown' + Type
if (Type == 'TTbar_Madgraph_JERDOWN'):
suffix = 'scaledall_jerDown' + Type
if (Type == 'T_s_JERDOWN' or Type == 'Tbar_s_JERDOWN'):
suffix = 'tb_jerDown' + Type
if (Type == 'WJets_BTAGUP'): suffix = 'wjets_btagUp' + Type
if (Type == 'TTbar_Madgraph_BTAGUP' or Type == 'WW_BTAGUP'
or Type == 'ZJets_M50_BTAGUP' or Type == 'T_t_BTAGUP'
or Type == 'Tbar_t_BTAGUP' or Type == 'T_tW_BTAGUP'
or Type == 'Tbar_tW_BTAGUP'):
suffix = 'scaledntb_btagUp' + Type
if (Type == 'TTbar_Madgraph_BTAGUP'):
suffix = 'scaledall_btagUp' + Type
if (Type == 'T_s_BTAGUP' or Type == 'Tbar_s_BTAGUP'):
suffix = 'tb_btagUp' + Type
if (Type == 'WJets_BTAGDOWN'): suffix = 'wjets_btagDown' + Type
if (Type == 'TTbar_Madgraph_BTAGDOWN' or Type == 'WW_BTAGDOWN'
or Type == 'ZJets_M50_BTAGDOWN' or Type == 'T_t_BTAGDOWN'
or Type == 'Tbar_t_BTAGDOWN' or Type == 'T_tW_BTAGDOWN'
or Type == 'Tbar_tW_BTAGDOWN'):
suffix = 'scaledntb_btagDown' + Type
if (Type == 'TTbar_Madgraph_BTAGDOWN'):
suffix = 'scaledall_btagDown' + Type
if (Type == 'T_s_BTAGDOWN' or Type == 'Tbar_s_BTAGDOWN'):
suffix = 'tb_btagDown' + Type
if (Type == 'WJets_SCALEUP'): suffix = 'wjets_q2scaleUp'
if (Type == 'WJets_SCALEDOWN'): suffix = 'wjets_q2scaleDown'
if (Type == 'WJets_MATCHINGUP'): suffix = 'wjets_matchingUp'
if (Type == 'WJets_MATCHINGDOWN'): suffix = 'wjets_matchingDown'
if (Type == 'TTbar_Madgraph_SCALEUP'): suffix = 'ttbar__q2scale__plus'
if (Type == 'TTbar_Madgraph_SCALEDOWN'):
suffix = 'ttbar__q2scale__mins'
if (Type == 'TTbar_Madgraph_MATCHINGUP'):
suffix = 'ttbar__matching__plus'
if (Type == 'TTbar_Madgraph_MATCHINGDOWN'):
suffix = 'ttbar__matching__minus'
if (Type.startswith('QCD')): suffix = 'qcd' + Type
if (Type.startswith('QCD') and Type.endswith('JESUP')):
suffix = 'qcd_jesUp' + Type
if (Type.startswith('QCD') and Type.endswith('JESDOWN')):
suffix = 'qcd_jesDown' + Type
if (Type.startswith('QCD') and Type.endswith('JERUP')):
suffix = 'qcd_jerUp' + Type
if (Type.startswith('QCD') and Type.endswith('JERDOWN')):
suffix = 'qcd_jerDown' + Type
if (Type.startswith('QCD') and Type.endswith('BTAGUP')):
suffix = 'qcd_btagUp' + Type
if (Type.startswith('QCD') and Type.endswith('BTAGDOWN')):
suffix = 'qcd_btagDown' + Type
w_suffix = 'wp'
if (wprime != 'ModRight'):
if (Type.startswith('Wprime800' + wprime)):
suffix = w_suffix + '800'
if (Type.startswith('Wprime900' + wprime)):
suffix = w_suffix + '900'
if (Type.startswith('Wprime1000' + wprime)):
suffix = w_suffix + '1000'
if (Type.startswith('Wprime1100' + wprime)):
suffix = w_suffix + '1100'
if (Type.startswith('Wprime1200' + wprime)):
suffix = w_suffix + '1200'
if (Type.startswith('Wprime1300' + wprime)):
suffix = w_suffix + '1300'
if (Type.startswith('Wprime1400' + wprime)):
suffix = w_suffix + '1400'
if (Type.startswith('Wprime1500' + wprime)):
suffix = w_suffix + '1500'
if (Type.startswith('Wprime1600' + wprime)):
suffix = w_suffix + '1600'
if (Type.startswith('Wprime1700' + wprime)):
suffix = w_suffix + '1700'
if (Type.startswith('Wprime1800' + wprime)):
suffix = w_suffix + '1800'
if (Type.startswith('Wprime1900' + wprime)):
suffix = w_suffix + '1900'
if (Type.startswith('Wprime2000' + wprime)):
suffix = w_suffix + '2000'
if (Type.startswith('Wprime2100' + wprime)):
suffix = w_suffix + '2100'
if (Type.startswith('Wprime2200' + wprime)):
suffix = w_suffix + '2200'
if (Type.startswith('Wprime2300' + wprime)):
suffix = w_suffix + '2300'
if (Type.startswith('Wprime2400' + wprime)):
suffix = w_suffix + '2400'
if (Type.startswith('Wprime2500' + wprime)):
suffix = w_suffix + '2500'
if (Type.startswith('Wprime2600' + wprime)):
suffix = w_suffix + '2600'
if (Type.startswith('Wprime2700' + wprime)):
suffix = w_suffix + '2700'
if (Type.startswith('Wprime2800' + wprime)):
suffix = w_suffix + '2800'
if (Type.startswith('Wprime2900' + wprime)):
suffix = w_suffix + '2900'
if (Type.startswith('Wprime3000' + wprime)):
suffix = w_suffix + '3000'
if (Type.endswith('_JESUP')): suffix += '__jes__plus'
if (Type.endswith('_JESDOWN')): suffix += '__jes__minus'
if (Type.endswith('_JERUP')): suffix += '__jer__plus'
if (Type.endswith('_JERDOWN')): suffix += '__jer__minus'
if (Type.endswith('_BTAGUP')): suffix += '__btag__plus'
if (Type.endswith('_BTAGDOWN')): suffix += '__btag__minus'
else:
if (Type.startswith('Wprime800Right')): suffix = w_suffix + '800'
if (Type.startswith('Wprime900Right')): suffix = w_suffix + '900'
if (Type.startswith('Wprime1000Right')): suffix = w_suffix + '1000'
if (Type.startswith('Wprime1100Right')): suffix = w_suffix + '1100'
if (Type.startswith('Wprime1200Right')): suffix = w_suffix + '1200'
if (Type.startswith('Wprime1300Right')): suffix = w_suffix + '1300'
if (Type.startswith('Wprime1400Right')): suffix = w_suffix + '1400'
if (Type.startswith('Wprime1500Right')): suffix = w_suffix + '1500'
if (Type.startswith('Wprime1600Right')): suffix = w_suffix + '1600'
if (Type.startswith('Wprime1700Right')): suffix = w_suffix + '1700'
if (Type.startswith('Wprime1800Right')): suffix = w_suffix + '1800'
if (Type.startswith('Wprime1900Right')): suffix = w_suffix + '1900'
if (Type.startswith('Wprime2000Right')): suffix = w_suffix + '2000'
if (Type.startswith('Wprime2100Right')): suffix = w_suffix + '2100'
if (Type.startswith('Wprime2200Right')): suffix = w_suffix + '2200'
if (Type.startswith('Wprime2300Right')): suffix = w_suffix + '2300'
if (Type.startswith('Wprime2400Right')): suffix = w_suffix + '2400'
if (Type.startswith('Wprime2500Right')): suffix = w_suffix + '2500'
if (Type.startswith('Wprime2600Right')): suffix = w_suffix + '2600'
if (Type.startswith('Wprime2700Right')): suffix = w_suffix + '2700'
if (Type.startswith('Wprime2800Right')): suffix = w_suffix + '2800'
if (Type.startswith('Wprime2900Right')): suffix = w_suffix + '2900'
if (Type.startswith('Wprime3000Right')): suffix = w_suffix + '3000'
if (Type.endswith('_JESUP')): suffix += '__jes__plus'
if (Type.endswith('_JESDOWN')): suffix += '__jes__minus'
if (Type.endswith('_JERUP')): suffix += '__jer__plus'
if (Type.endswith('_JERDOWN')): suffix += '__jer__minus'
if (Type.endswith('_BTAGUP')): suffix += '__btag__plus'
if (Type.endswith('_BTAGDOWN')): suffix += '__btag__minus'
histName = prefix + suffix + 'varbin'
histNamePUup = prefix + suffix + 'varbin' + '__PU__plus'
histNamePUdown = prefix + suffix + 'varbin' + '__PU__minus'
histName0tag = prefix + suffix + 'varbin' + '__0tag__plus'
histNameHFup = prefix + suffix + 'varbin' + '__hf__plus'
histNameHFdown = prefix + suffix + 'varbin' + '__hf__minus'
histNameTTbarShapeUp = prefix + suffix + 'varbin' + '__ttbarshape__plus'
histNameTTbarShapeDown = prefix + suffix + 'varbin' + '__ttbarshape__minus'
histNamePre = prefix + suffix + 'varbin' + 'Pre'
Variables[Type] = TH1D(histName, histName, bin, array('d', xlow))
Variables[Type].Sumw2()
if (channel == 'electron'):
weight = '( ((0.973*weight_PU_ABCD_PileUpCalc*weight_ElectronEff_53x_WprimeCalc)*(abs(elec_1_eta_WprimeCalc)<1.5)) + ((1.02*weight_PU_ABCD_PileUpCalc*weight_ElectronEff_53x_WprimeCalc)*(abs(elec_1_eta_WprimeCalc)>1.5 && abs(elec_1_eta_WprimeCalc)<2.5)) )'
weightPUup = '( ((0.973*weight_PU_ABCD735_PileUpCalc*weight_ElectronEff_53x_WprimeCalc)*(abs(elec_1_eta_WprimeCalc)<1.5)) + ((1.02*weight_PU_ABCD735_PileUpCalc*weight_ElectronEff_53x_WprimeCalc)*(abs(elec_1_eta_WprimeCalc)>1.5 && abs(elec_1_eta_WprimeCalc)<2.5)) )'
SF = 1.0
if (channel == 'muon'):
weight = 'weight_PU_ABCD_PileUpCalc*weight_MuonEff_WprimeCalc'
weightPUup = 'weight_PU_ABCD735_PileUpCalc*weight_MuonEff_WprimeCalc'
SF = 1.0
if (Type == 'WJets' or re.match('W[1-4]Jets', Type)):
WccHist = TH1D('WccHist', 'WccHist', bin, array('d', xlow))
WbbHist = TH1D('WbbHist', 'WbbHist', bin, array('d', xlow))
WccHist.Sumw2()
WbbHist.Sumw2()
WccHistPUup = TH1D('WccHistPUup', 'WccHistPUup', bin,
array('d', xlow))
WbbHistPUup = TH1D('WbbHistPUup', 'WbbHistPUup', bin,
array('d', xlow))
WccHistPUup.Sumw2()
WbbHistPUup.Sumw2()
WccHistHFup = TH1D('WccHistHFup', 'WccHistHFup', bin,
array('d', xlow))
WbbHistHFup = TH1D('WbbHistHFup', 'WbbHistHFup', bin,
array('d', xlow))
WccHistHFup.Sumw2()
WbbHistHFup.Sumw2()
WccHistHFdown = TH1D('WccHistHFdown', 'WccHistHFdown', bin,
array('d', xlow))
WbbHistHFdown = TH1D('WbbHistHFdown', 'WbbHistHFdown', bin,
array('d', xlow))
WccHistHFdown.Sumw2()
WbbHistHFdown.Sumw2()
WccHistPre = TH1D('WccHistPre', 'WccHistPre', bin,
array('d', xlow))
WbbHistPre = TH1D('WbbHistPre', 'WbbHistPre', bin,
array('d', xlow))
WccHistPre.Sumw2()
WbbHistPre.Sumw2()
if ((not Type.endswith('UP')) and (not Type.endswith('DOWN'))):
VariablesPUup[Type] = TH1D(histNamePUup, histNamePUup, bin,
array('d', xlow))
#VariablesPUdown[Type] = TH1D(histNamePUdown, histNamePUdown, bin, array('d',xlow))
Variables0tag[Type] = TH1D(histName0tag, histName0tag, bin,
array('d', xlow))
VariablesHFup[Type] = TH1D(histNameHFup, histNameHFup, bin,
array('d', xlow))
VariablesHFdown[Type] = TH1D(histNameHFdown, histNameHFdown, bin,
array('d', xlow))
VariablesTTbarShapeUp[Type] = TH1D(histNameTTbarShapeUp,
histNameTTbarShapeUp, bin,
array('d', xlow))
VariablesTTbarShapeDown[Type] = TH1D(histNameTTbarShapeDown,
histNameTTbarShapeDown, bin,
array('d', xlow))
VariablesPUup[Type].Sumw2()
#VariablesPUdown[Type].Sumw2()
Variables0tag[Type].Sumw2()
VariablesHFup[Type].Sumw2()
VariablesHFdown[Type].Sumw2()
VariablesTTbarShapeUp[Type].Sumw2()
VariablesTTbarShapeDown[Type].Sumw2()
print Type, histName
if (Type.startswith('Data')):
Trees[Type].Draw(var + " >> " + histName,
"(" + cut + cutbtag + ")", 'goff')
# 0 tag for data-driven shape
Trees[Type].Draw(var + " >> " + histName0tag,
"(" + cut + cutzerobtags + ")", 'goff')
elif (Type == 'WJets' or re.match('W[1-4]Jets', Type)):
Trees[Type].Draw(
var + " >> " + histName, "(" + weight + ")*(" + str(SFWjmu) +
")*(" + cut + cutwjj + cutbtag + ")", 'goff')
Trees[Type].Draw(
var + " >> " + "WbbHist", "(" + weight + ")*(" + str(SFWbmu) +
")*(" + cut + cutwbb + cutbtag + ")", 'goff')
Trees[Type].Draw(
var + " >> " + "WccHist", "(" + weight + ")*(" + str(SFWcmu) +
")*(" + cut + cutwcc + cutbtag + ")", 'goff')
Variables[Type].Add(WbbHist)
Variables[Type].Add(WccHist)
if ((not Type.endswith('UP')) and (not Type.endswith('DOWN'))):
# Pile Up
Trees[Type].Draw(
var + " >> " + histNamePUup, "(" + weightPUup + ")*(" +
str(SFWjmu) + ")*(" + cut + cutwjj + cutbtag + ")", 'goff')
Trees[Type].Draw(
var + " >> " + "WbbHistPUup", "(" + weightPUup + ")*(" +
str(SFWbmu) + ")*(" + cut + cutwbb + cutbtag + ")", 'goff')
Trees[Type].Draw(
var + " >> " + "WccHistPUup", "(" + weightPUup + ")*(" +
str(SFWcmu) + ")*(" + cut + cutwcc + cutbtag + ")", 'goff')
VariablesPUup[Type].Add(WbbHistPUup)
VariablesPUup[Type].Add(WccHistPUup)
# 0 tag shape (one sided, so only one histogram)
Trees[Type].Draw(
var + " >> " + histName0tag,
"(" + weight + ")*(" + cut + cutzerobtags + ")", 'goff')
# H.F. k-factor
Trees[Type].Draw(
var + " >> " + histNameHFup, "(" + weight + ")*(" +
str(SFWjmuPlus) + ")*(" + cut + cutwjj + cutbtag + ")",
'goff')
Trees[Type].Draw(
var + " >> " + "WbbHistHFup", "(" + weight + ")*(" +
str(SFWbmuPlus) + ")*(" + cut + cutwbb + cutbtag + ")",
'goff')
Trees[Type].Draw(
var + " >> " + "WccHistHFup", "(" + weight + ")*(" +
str(SFWcmuPlus) + ")*(" + cut + cutwcc + cutbtag + ")",
'goff')
VariablesHFup[Type].Add(WbbHistHFup)
VariablesHFup[Type].Add(WccHistHFup)
Trees[Type].Draw(
var + " >> " + histNameHFdown, "(" + weight + ")*(" +
str(SFWjmuMinus) + ")*(" + cut + cutwjj + cutbtag + ")",
'goff')
Trees[Type].Draw(
var + " >> " + "WbbHistHFdown", "(" + weight + ")*(" +
str(SFWbmuMinus) + ")*(" + cut + cutwbb + cutbtag + ")",
'goff')
Trees[Type].Draw(
var + " >> " + "WccHistHFdown", "(" + weight + ")*(" +
str(SFWcmuMinus) + ")*(" + cut + cutwcc + cutbtag + ")",
'goff')
VariablesHFdown[Type].Add(WbbHistHFdown)
VariablesHFdown[Type].Add(WccHistHFdown)
elif ((not Type.startswith('T')) and (not Type.startswith('QCD'))):
Trees[Type].Draw(var + " >> " + histNamePre,
"(" + weight + ")*(" + cut + ")", 'goff')
Trees[Type].Draw(var + " >> " + histName,
"(" + weight + ")*(" + cut + cutbtag + ")",
'goff')
if ((not Type.endswith('UP')) and (not Type.endswith('DOWN'))):
Trees[Type].Draw(
var + " >> " + histNamePUup,
"(" + weightPUup + ")*(" + cut + cutbtag + ")", 'goff')
#Trees[Type].Draw(var + " >> " + histNamePUdown, "("+weightPUdown+")*("+cut+cutbtag+")", 'goff')
Trees[Type].Draw(
var + " >> " + histName0tag,
"(" + weight + ")*(" + cut + cutzerobtags + ")", 'goff')
Trees[Type].Draw(var + " >> " + histNameHFup,
"(" + weight + ")*(" + cut + cutbtag + ")",
'goff')
Trees[Type].Draw(var + " >> " + histNameHFdown,
"(" + weight + ")*(" + cut + cutbtag + ")",
'goff')
elif (Type.startswith('TTbar')):
Trees[Type].Draw(
var + " >> " + histName, "(" + weight + ")*(" + weight_ttbar +
")*(" + cut + cutbtag + ")", 'goff')
if ((not Type.endswith('UP')) and (not Type.endswith('DOWN'))):
Trees[Type].Draw(
var + " >> " + histNamePUup, "(" + weightPUup + ")*(" +
weight_ttbar + ")*(" + cut + cutbtag + ")", 'goff')
#Trees[Type].Draw(var + " >> " + histNamePUdown, "("+weightPUdown+")*("+cut+cutbtag+")", 'goff')
Trees[Type].Draw(
var + " >> " + histName0tag, "(" + weight + ")*(" +
weight_ttbar + ")*(" + cut + cutzerobtags + ")", 'goff')
Trees[Type].Draw(
var + " >> " + histNameHFup, "(" + weight + ")*(" +
weight_ttbar + ")*(" + cut + cutbtag + ")", 'goff')
Trees[Type].Draw(
var + " >> " + histNameHFdown, "(" + weight + ")*(" +
weight_ttbar + ")*(" + cut + cutbtag + ")", 'goff')
Trees[Type].Draw(
var + " >> " + histNameTTbarShapeUp, "(" + weight + ")*(" +
weight_ttbarplus + ")*(" + cut + cutbtag + ")", 'goff')
Trees[Type].Draw(
var + " >> " + histNameTTbarShapeDown, "(" + weight +
")*(" + weight_ttbarminus + ")*(" + cut + cutbtag + ")",
'goff')
else:
#print 'cut = ',cut+cutbtag
Trees[Type].Draw(var + " >> " + histName,
"(" + weight + ")*(" + cut + cutbtag + ")",
'goff')
if ((not Type.endswith('UP')) and (not Type.endswith('DOWN'))):
Trees[Type].Draw(
var + " >> " + histNamePUup,
"(" + weightPUup + ")*(" + cut + cutbtag + ")", 'goff')
#Trees[Type].Draw(var + " >> " + histNamePUdown, "("+weightPUdown+")*("+cut+cutbtag+")", 'goff')
Trees[Type].Draw(
var + " >> " + histName0tag,
"(" + weight + ")*(" + cut + cutzerobtags + ")", 'goff')
Trees[Type].Draw(var + " >> " + histNameHFup,
"(" + weight + ")*(" + cut + cutbtag + ")",
'goff')
Trees[Type].Draw(var + " >> " + histNameHFdown,
"(" + weight + ")*(" + cut + cutbtag + ")",
'goff')
if (not Type.startswith('Data')):
#print 'EVENTS Before Scaling FOR ',Type,' = ',Variables[Type].Integral()
#print str(SF),' ',str(lumi),' ',str(xsec[Type]),' ',str(Nevents[Type])
if (channel == 'electron'): lumi = lumi_el
if (channel == 'muon'): lumi = lumi_mu
if Variables[Type].Integral() != 0:
Variables[Type].Scale(
(SF * lumi * xsec_norm[Type] / Nevents[Type]))
pyfile.write(" yields[mass+coup+'_" + Type + ch + "_" +
btags + "'] = " +
str(Variables[Type].Integral()) + "\n")
if ((not Type.endswith('UP')) and (not Type.endswith('DOWN'))):
VariablesPUup[Type].Scale(
(SF * lumi * xsec_norm[Type] / Nevents[Type]))
#VariablesPUdown[Type].Scale ( (SF*lumi*xsec_norm[Type]/Nevents[Type]) )
Variables0tag[Type].Scale(
(SF * lumi * xsec_norm[Type] / Nevents[Type]))
VariablesHFup[Type].Scale(
(SF * lumi * xsec_norm[Type] / Nevents[Type]))
VariablesHFdown[Type].Scale(
(SF * lumi * xsec_norm[Type] / Nevents[Type]))
VariablesTTbarShapeUp[Type].Scale(
(SF * lumi * xsec_norm[Type] / Nevents[Type]))
VariablesTTbarShapeDown[Type].Scale(
(SF * lumi * xsec_norm[Type] / Nevents[Type]))
pyfile.write(" yieldsPUup[mass+coup+'_" + Type + ch +
"_" + btags + "'] = " +
str(VariablesPUup[Type].Integral()) + "\n")
pyfile.write(" yields0tag[mass+coup+'_" + Type + ch +
"_" + btags + "'] = " +
str(Variables0tag[Type].Integral()) + "\n")
pyfile.write(" yieldsHFup[mass+coup+'_" + Type + ch +
"_" + btags + "'] = " +
str(VariablesHFup[Type].Integral()) + "\n")
pyfile.write(" yieldsHFdown[mass+coup+'_" + Type + ch +
"_" + btags + "'] = " +
str(VariablesHFdown[Type].Integral()) + "\n")
pyfile.write(" yieldsTTbarShapeUp[mass+coup+'_" + Type +
ch + "_" + btags + "'] = " +
str(VariablesTTbarShapeUp[Type].Integral()) +
"\n")
pyfile.write(
" yieldsTTbarShapeDown[mass+coup+'_" + Type + ch +
"_" + btags + "'] = " +
str(VariablesTTbarShapeDown[Type].Integral()) + "\n")
pyfile.close()
for line in fileinput.input(pyfile.name, inplace=1):
if '_el_el' in line:
line = line.replace('_el_el', '_el')
if '_mu_mu' in line:
line = line.replace('_mu_mu', '_mu')
if 'Data' in line:
line = line.replace('Data', 'data')
if 'WJets' in line:
line = line.replace('WJets', 'wjets')
if 'W1Jets' in line:
line = line.replace('W1Jets', 'w1jets')
if 'W2Jets' in line:
line = line.replace('W2Jets', 'w2jets')
if 'W3Jets' in line:
line = line.replace('W3Jets', 'w3jets')
if 'W4Jets' in line:
line = line.replace('W4Jets', 'w4jets')
if 'ZJets_M50' in line:
line = line.replace('ZJets_M50', 'zjets')
if 'WW' in line:
line = line.replace('WW', 'ww')
if 'T_t' in line:
line = line.replace('T_t', 't')
if 'Tbar_t' in line:
line = line.replace('Tbar_t', 'bt')
if 'tW' in line:
line = line.replace('tW', 'tw')
if 'T_s' in line:
line = line.replace('T_s', 's')
if 'Tbar_s' in line:
line = line.replace('Tbar_s', 'bs')
if 'TTbar_Madgraph' in line:
line = line.replace('TTbar_Madgraph', 'ttbar')
if 'QCD_Pt_80_170_EM' in line:
line = line.replace('QCD_Pt_80_170_EM', 'qcd80to170')
if 'QCD_Pt_170_250_EM' in line:
line = line.replace('QCD_Pt_170_250_EM', 'qcd170to250')
if 'QCD_Pt_250_350_EM' in line:
line = line.replace('QCD_Pt_250_350_EM', 'qcd250to350')
if 'QCD_Pt_350_EM' in line:
line = line.replace('QCD_Pt_350_EM', 'qcd350')
if 'Wprime' in line:
line = line.replace('Wprime', 'wp')
if 'Right' in line:
line = line.replace('Right', 'R')
if 'Left' in line:
line = line.replace('Left', 'L')
if 'Mix' in line:
line = line.replace('Mix', 'RL')
print line
HiggsHist.Add(thehist)
thehist = fitUtils.File2Hist(fitterPars.MCDirectory + \
'el_VBFHWWMH%i_CMSSW428.root' % (opts.mH),
'VBFHWW%i_el' % (opts.mH), True, 1, False)
VBFHiggsHist.Add(thehist)
thehist = fitUtils.File2Hist(fitterPars.MCDirectory + \
'el_HWWTauNuMH%i_CMSSW428.root' % (opts.mH),
'HWWTauNu%i_el' % (opts.mH), True, 1, False)
TauNuHiggsHist.Add(thehist)
extraFactor = 10.
(Ngen,otherdata) = NgenHiggs(opts.mH, pars4.includeElectrons,
pars4.includeMuons)
HiggsHist.Scale(1./float(Ngen), 'width')
SigVisual = TH1D(HiggsHist)
SigVisual.SetName('SigVisual')
SigVisual.SetLineColor(kBlue)
SigVisual.SetLineWidth(3)
SigVisual.Scale(pars4.intLumi*extraFactor*otherdata[1]*otherdata[2])
SigVisual.Print()
SigVisualLog = TH1D(SigVisual)
SigVisualLog.SetName("SigVisualLog")
NgenVBF = NgenVBFHiggs(opts.mH, pars4.includeElectrons, pars4.includeMuons)
VBFHiggsHist.Scale(1./float(NgenVBF),'width')
NgenTauNu = NgenHWWTauNu(opts.mH, pars4.includeElectrons, pars4.includeMuons)
TauNuHiggsHist.Scale(1./float(NgenTauNu), 'width')
print "Ngen Higgs:",Ngen,"VBF Higgs:",NgenVBF,"HWW -> tau nu jj:",NgenTauNu
bins = 100
print "Calculate histogram ranges"
data = []
for i in xrange(len(data1_in)):
inputs1 = data1_in[i]
output1 = data1_out[i]
#if (abs(inputs1[0])>1000): print i, inputs1[0]
if (inputs1[0] != 0):
data.append(output1[0] / inputs1[0])
xmin = min(data)
xmax = max(data)
print "Min value =", xmin, " Max value=", xmax
xmin = -0.5
xmax = 0.5
h1 = TH1D("valid1", "valid1", bins, xmin, xmax)
h1.Sumw2()
h1.SetLineWidth(2)
h1.SetLineStyle(1)
h1.SetLineColor(1)
h1.SetStats(0)
h1.SetTitle("")
h2 = TH1D("neuralnet1", "neuralnet1", bins, xmin, xmax)
h2.Sumw2()
h2.SetMarkerColor(2)
h2.SetMarkerSize(0.7)
h2.SetMarkerStyle(21)
h2.SetLineWidth(2)
h2.SetLineStyle(1)
h2.SetLineColor(1)
# Canvas
c1 = TCanvas( 'c1', 'Histogram Drawing Options', 200, 10, 700, 900 )
pad1 = TPad( 'pad1', 'The pad with the function', 0.03, 0.62, 0.50, 0.92, 21 )
pad2 = TPad( 'pad2', 'The pad with the histogram', 0.51, 0.62, 0.98, 0.92, 21 )
pad3 = TPad( 'pad3', 'The pad with the histogram', 0.03, 0.02, 0.97, 0.57, 21 )
pad1.Draw()
pad2.Draw()
pad3.Draw()
npts = len(x)
h1 = TH1D( 'h1', 'Full pulse profile', npts, 0, npts)
h2 = TH1D( 'h2', 'Diff with template', hibin-lowbin, lowbin, hibin)
h3 = TH1D( 'h3', 'Fitted pulse profile', hibin-lowbin, lowbin, hibin)
for i in xrange(npts):
h1.SetBinContent( i+1, x[i] )
#h2.SetBinContent( i+1, x[i] )
for i,xx in enumerate(x[lowbin:hibin]):
h2.SetBinContent( i+1, xx )
h3.SetBinContent( i+1, xx )
par = array( 'd', 3*[0.] )
total = TF1( 'total', 'gaus', lowbin, hibin )
# Set initial conditions
par[0] = h2.GetMaximum()
def getHists(contained_only=False):
# Define the desired energy ranges
ERanges = [(0.3, 0.5), (0.5, 0.7), (0.7, 0.9), (0.9, 1.1), (1.1, 1.3),
(1.3, 1.5), (1.5, 1.7), (1.7, 1.9), (1.9, 2.1)]
# Creating empty histograms
hist_dictionary = {}
for myE in ERanges:
myName = "h_min%s_max%s" % (myE[0], myE[1])
if contained_only: myName = "cont_h_min%s_max%s" % (myE[0], myE[1])
myTitle = "Energies from %0.1f to %0.1f;(True p - Reco p) / True p;Entries" % (
myE[0], myE[1])
if contained_only:
myTitle = "Contained Tracks: Energies from %0.1f to %0.1f;(True p - Reco p) / True p;Entries" % (
myE[0], myE[1])
hist_dictionary[myE] = TH1D(myName, myTitle, 100, -4, 4)
# Loop over energy ranges to create graphs of type (2)
for myE in ERanges:
myPlot = "(true_mom - mcs_reco_mom) / true_mom >> h_min%0.1f_max%0.1f" % (
myE[0], myE[1])
if contained_only:
myPlot = "(true_mom - mcs_reco_mom) / true_mom >> cont_h_min%0.1f_max%0.1f" % (
myE[0], myE[1])
myCut = "mcs_reco_mom > 0 && true_mom > %f && true_mom < %f" % (myE[0],
myE[1])
if contained_only: myCut += " && mu_contained == 1"
f.ana_tree.Draw(myPlot, myCut)
# Create lists for graphs (3), (4) that will be filled in the next loop
xpoints = []
filMeanVals, filSqrtHistEntries = [], []
filStdVals, filStdErrs = [], []
for myE in ERanges:
# Filter out energy ranges with 0 entries
if hist_dictionary[myE].GetEntries() != 0:
xpoints.append(myE[0] + (myE[1] - myE[0]) / 2.)
filMeanVals.append(hist_dictionary[myE].GetMean())
filSqrtHistEntries.append(
TMath.sqrt(hist_dictionary[myE].GetEntries()))
filStdVals.append(hist_dictionary[myE].GetRMS())
filStdErrs.append(hist_dictionary[myE].GetRMSError())
# Calculate error bars for graph (3): (stdDev / sqrt(entries))
yerrs = map(truediv, filStdVals, filSqrtHistEntries)
# Define a constant width (x-error bar) for each marker
xerrs = [(myE[1] - myE[0]) / 2. for myE in ERanges]
# Graph (3) and (4) setup
meanGraph = TGraphErrors()
stdGraph = TGraphErrors()
meanGraphTitle = "Mean vs. Energy;True Muon Energy (GeV);#left[p_{MC}-p_{MCS} / p_{MC}#right]"
if contained_only:
meanGraphTitle = "Contained Tracks: Mean vs. Energy;True Muon Energy (GeV);#left[p_{MC}-p_{MCS} / p_{MC}#right]"
stdGraphTitle = "Standard Deviation vs. Energy;True Muon Energy (GeV);Fractional Momentum Resolution"
if contained_only:
stdGraphTitle = "Contained Tracks: Standard Deviation vs. Energy;True Muon Energy (GeV);Fractional Momentum Resolution"
meanGraph.SetTitle(meanGraphTitle)
stdGraph.SetTitle(stdGraphTitle)
# Fill in data for graph (3)
for i in xrange(len(xpoints)):
meanGraph.SetPoint(i, xpoints[i], filMeanVals[i])
meanGraph.SetPointError(i, xerrs[i], yerrs[i])
meanGraph.Draw("ALP")
# Fill in data for graph (4)
for i in xrange(len(xpoints)):
stdGraph.SetPoint(i, xpoints[i], filStdVals[i])
stdGraph.SetPointError(i, xerrs[i], filStdErrs[i])
stdGraph.Draw("ALP")
return hist_dictionary, meanGraph, stdGraph
def __init__(self, histname, ncuts, **kwargs):
self.hist = TH1D('cutflow', 'cutflow', ncuts, 0, ncuts)
self.hist.GetXaxis().SetLabelSize(0.041)
self.nextidx = 0
self.cuts = {}
h_fit = []
chMaxYRange = 0
h_1D_sX = []
h_2D_sX = []
sXMaxYRange = 0
h_1D_sY = []
h_2D_sY = []
sYMaxYRange = 0
# sort into different cluster property
for h in sorted(histo1D):
n = histo1D[h].GetName()
if sliceL: hist1D = histo1D[h].Clone()
else:
hist1F = histo1D[h].Clone()
hist1D = TH1D()
hist1F.Copy(hist1D)
if 'ch' in n:
setHisto(hist1D, "Norm. on-trk. clu. charge (ke)",
"On-track Clusters / 1 ke", 0)
h_fit.append(fitting(hist1D))
h_1D_ch.append(hist1D)
if hist1D.GetMaximum() > chMaxYRange: chMaxYRange = hist1D.GetMaximum()
if 'X' in n:
setHisto(hist1D, "On-track cluster size x (pixel)",
"On-track Clusters / 1 pixel", 0)
h_1D_sX.append(hist1D)
if hist1D.GetMaximum() > sXMaxYRange: sXMaxYRange = hist1D.GetMaximum()
if 'Y' in n:
setHisto(hist1D, "On-Track cluster size y (pixel)",
"On-track Clusters / 1 pixel", 0)
def main(optunf="Bayes"):
optunfs = ["Bayes", "SVD", "TUnfold", "Invert", "Reverse"]
if not optunf in optunfs:
txt = "Unfolding option " + optunf + " not recognised"
raise ValueError(txt)
global hReco, hMeas, hTrue
print "==================================== TRAIN ===================================="
# Create response matrix object for 40 measured and 20
# unfolded bins:
response = RooUnfoldResponse(40, -10.0, 10.0, 20, -10.0, 10.0)
# Train with a Breit-Wigner, mean 0.3 and width 2.5.
for i in xrange(100000):
# xt= gRandom.BreitWigner( 0.3, 2.5 )
xt = gRandom.Gaus(0.0, 5.0)
x = smear(xt)
if x != None:
response.Fill(x, xt)
else:
response.Miss(xt)
print "==================================== TEST ====================================="
hTrue = TH1D("true", "Test Truth", 20, -10.0, 10.0)
hMeas = TH1D("meas", "Test Measured", 40, -10.0, 10.0)
# Test with a Gaussian, mean 0 and width 2.
for i in xrange(10000):
# xt= gRandom.Gaus( 0.0, 2.0 )
xt = gRandom.BreitWigner(0.3, 2.5)
x = smear(xt)
hTrue.Fill(xt)
if x != None:
hMeas.Fill(x)
print "==================================== UNFOLD ==================================="
print "Unfolding method:", optunf
if "Bayes" in optunf:
# Bayes unfoldung with 4 iterations
# unfold= RooUnfoldBayes( response, hMeas, 4 )
unfold = RooUnfoldBayes(response, hMeas, 10, False, True)
elif "SVD" in optunf:
# SVD unfoding with free regularisation
# unfold= RooUnfoldSvd( response, hMeas, 20 )
unfold = RooUnfoldSvd(response, hMeas)
elif "TUnfold" in optunf:
# TUnfold with fixed regularisation tau=0.002
# unfold= RooUnfoldTUnfold( response, hMeas )
unfold = RooUnfoldTUnfold(response, hMeas, 0.002)
elif "Invert" in optunf:
unfold = RooUnfoldInvert(response, hMeas)
elif "Reverse" in optunf:
unfold = RooUnfoldBayes(response, hMeas, 1)
hReco = unfold.Hreco()
# unfold.PrintTable( cout, hTrue )
unfold.PrintTable(cout, hTrue, 2)
hReco.Draw()
hMeas.Draw("SAME")
hTrue.SetLineColor(8)
hTrue.Draw("SAME")
return
def D1H_txt_to_roothist(filename, outputpath=''):
from ROOT import TFile, TCanvas, TPad, TH1D, TLatex, TStyle, gStyle, TText, gPad, TPaveText
from inspect import currentframe, getframeinfo
import os
#gStyle.SetOptStat(0)
can = TCanvas("can", "can", 200, 10, 500, 500)
if (filename[0] == "/"):
filename = filename
else:
filename = os.getcwd(
) + "/" + filename # get the path included filename
loca = len(filename)
for i in range(1, len(filename) + 1): # find the "/" location
if (filename[-i] == "/"):
loca = i - 1
break
FILENAME = filename.replace(filename[:-loca],
"") # this is the shorten filename
# print(FILENAME, "******")
fileroot = filename.replace(".txt", "_F.root")
fileroot = fileroot.replace("//", "/")
f = open(filename, "r")
lineList = f.readlines()
Nbin = (len(lineList)) # get number of bins
Line_string = str(lineList[0])
_, bin_init, _, _ = Line_string.split()
bin_init = float(bin_init) # get initial bin
Line_string = str(lineList[len(lineList) - 1])
_, _, bin_final, _ = Line_string.split()
bin_final = float(bin_final) # get final bin
f.seek(0) # reset python read line
hist = TH1D("hist", "hist", Nbin, bin_init, bin_final)
total_e = 0
for i in range(1, Nbin + 1):
Line_string = str(f.readline())
_, _, _, bin_c = Line_string.split()
bin_c = float(bin_c)
hist.SetBinContent(i, bin_c)
total_e = total_e + bin_c
total_e = int(total_e)
hist.Draw()
text = TText(hist.GetXaxis().GetBinCenter(2),
hist.GetYaxis().GetBinCenter(1),
"Recycled. Total Entry : %i" % total_e)
text.SetTextFont(10)
text.Draw()
gPad.Update()
can.Update()
if (outputpath == ''):
wf = TFile(fileroot, "RECREATE")
print(fileroot, " root file is generated !!!")
else:
if (outputpath[0] == "/"):
fileroot = outputpath + "/" + FILENAME.replace(".txt", "_F.root")
fileroot = fileroot.replace("//", "/")
elif (outputpath[0] == "~"):
fileroot = outputpath.replace(
"~", os.environ['HOME']) + "/" + FILENAME.replace(
".txt", "_F.root")
fileroot = fileroot.replace("//", "/")
else:
fileroot = os.getcwd() + "/" + outputpath + "/" + FILENAME.replace(
".txt", "_F.root")
fileroot = fileroot.replace("//", "/")
wf = TFile(fileroot, "RECREATE")
print(fileroot, " root file is generated !!!")
hist.Write()
wf.Close()
fileroot = fileroot.replace("//", "/")
# print(fileroot)
return fileroot
def main(argv):
LOADTREE_ANADIR = "/net/user/aguilar/work/IceCube/scripts/IC79/"
tr = TChain("L3CutsTree")
tr.Add(LOADTREE_ANADIR + "final.v19.nugen_numu_6308_9981f_RunID_new.root")
tr.GetEntries()
MCzenith = ctypes.c_double(0)
tr.SetBranchAddress("MC_Zr", MCzenith)
MCazimuth = ctypes.c_double(0)
tr.SetBranchAddress("MC_Ar", MCazimuth)
eneMC = ctypes.c_double(0)
tr.SetBranchAddress("MC_En", eneMC)
SMPEzenith = ctypes.c_double(0)
tr.SetBranchAddress("MPE_FINAL_MUON_Zd", SMPEzenith)
SMPEazimuth = ctypes.c_double(0)
tr.SetBranchAddress("MPE_FINAL_MUON_Ad", SMPEazimuth)
ene = ctypes.c_double(0)
tr.SetBranchAddress("MuE_FINAL_MUON_En", ene)
sigma = ctypes.c_double(0)
tr.SetBranchAddress("MPE_Pb_FINAL_MUON_SigmaDeg", sigma)
oneW = ctypes.c_double(0)
tr.SetBranchAddress("OW", oneW)
nEv = ctypes.c_double(0)
tr.SetBranchAddress("NEvents", nEv)
h = TH2D("pull", "pull", 50, 1, 9, 100, 0.01, 20)
h.SetStats(0)
h.GetXaxis().SetTitle("log_{10}(MuEx_energy [GeV])")
h.GetYaxis().SetTitle("Pull: #Delta#Psi/#sigma_{parab.}")
g = TGraphAsymmErrors(50)
medians = [[] for i in range(0, 50)]
mediansW = [[] for i in range(0, 50)]
go = TGraph(50)
go.SetMarkerStyle(22)
go.SetMarkerColor(4)
hEneW = TH1D(
"hEneW",
"MCPrimary energy; log_{10}(E [GeV]); #frac{d#Phi}{dE} [GeV^{-1} cm^{-2} s^{-1} sr^{-1} ( #frac{E}{GeV} )^{-2} ",
100, 1, 9.5)
hREneW = TH1D(
"hREneW",
"Reco energy SplineMPEMuEXDiff; log_{10}(E [GeV]); #frac{d#Phi}{dE} [GeV^{-1} cm^{-2} s^{-1} sr^{-1} ( #frac{E}{GeV} )^{-2}",
100, 1, 9)
for i in range(0, tr.GetEntries()):
tr.GetEntry(i)
#if not hem(np.radians(SMPEzenith.value)):
# continue
#if np.radians(sigma.value)>np.radians(5.):
# continue
w = weight(oneW.value, eneMC.value, nEv.value)
hEneW.Fill(log10(eneMC.value), w)
hREneW.Fill(log10(ene.value), w)
dAngle = SpaceAngleRad(MCzenith.value, MCazimuth.value,
np.radians(SMPEzenith.value),
np.radians(SMPEazimuth.value))
h.Fill(log10(ene.value), dAngle / np.radians(sigma.value), w)
if h.GetXaxis().FindBin(log10(ene.value)) > 0 and h.GetXaxis().FindBin(
log10(ene.value)) < 51:
medians[h.GetXaxis().FindBin(log10(ene.value)) - 1].append(
dAngle / np.radians(sigma.value))
mediansW[h.GetXaxis().FindBin(log10(ene.value)) - 1].append(w)
#if i>200:
#break
npoints = 0
for i in range(1, h.GetNbinsX() + 1):
if len(medians[i - 1]) > 3:
g.SetPoint(
npoints,
h.GetXaxis().GetBinCenter(i),
weighted_percentile(0.5, np.asarray(medians[i - 1]),
np.asarray(mediansW[i - 1])))
g.SetPointError(
npoints, 0., 0.,
weighted_percentile(0.5 - 0.341, np.asarray(medians[i - 1]),
np.asarray(mediansW[i - 1])),
weighted_percentile(0.5 + 0.341, np.asarray(medians[i - 1]),
np.asarray(mediansW[i - 1])))
npoints += 1
c0 = TCanvas()
hEneW.Draw()
g.Set(npoints)
g.SetMarkerStyle(20)
gStyle.SetPalette(1)
gStyle.SetOptLogz(1)
spline = TSpline5("spl", TGraph(g))
filename = "SplineforCorr_IC79.root"
#if hem(0.):
# filename=filename+"down.root"
#else:
# filename=filename+"up.root"
fout = TFile(filename, "RECREATE")
spline.Write()
fout.Close()
#print "loading mpfSigmaDegRescaledIC79V2.C"
#ROOT.gROOT.ProcessLine(".L mpfSigmaDegRescaledIC79V2.C+")
print "loading RescaledSigma_IC79.C"
ROOT.gROOT.ProcessLine(".L RescaledSigma_IC79.C+")
try:
ROOT.loadSplines()
except:
pass
npoints = 0
for i in range(1, h.GetNbinsX() + 1):
if len(medians[i - 1]) > 3:
#if hem(0.):
#plotZen=0.
#else:
#plotZen=100.
#go.SetPoint(npoints,h.GetXaxis().GetBinCenter(i),ROOT.mpfSigmaDegRescaledIC79V2(1.,pow(10,h.GetXaxis().GetBinCenter(i))))
go.SetPoint(
npoints,
h.GetXaxis().GetBinCenter(i),
ROOT.RescaledSigma_IC79(1.,
pow(10,
h.GetXaxis().GetBinCenter(i))))
npoints += 1
go.Set(npoints)
del medians[:]
del medians
del mediansW[:]
del mediansW
c = TCanvas()
c.SetLogy()
c.SetGridx()
c.SetGridy()
h.SetMinimum(1)
h.Draw("colz")
g.Draw("pl same")
go.Draw("pl same")
spline.Draw("lp same")
h2 = TH2D("pull - corrected", "pull - corrected", 50, 1, 9, 100, 0.01, 20)
h2.GetXaxis().SetTitle("log_{10}(E [GeV])")
h2.GetYaxis().SetTitle("Pull_{corr}: #Delta#Psi/f_{corr}(#sigma_{parab.})")
g2 = TGraphAsymmErrors(50)
medians2 = [[] for i in range(0, 50)]
medians2W = [[] for i in range(0, 50)]
g2.SetMarkerStyle(20)
for i in range(0, tr.GetEntries()):
tr.GetEntry(i)
#if not hem(np.radians(SMPEzenith.value)): continue
#if np.radians(sigma.value)>np.radians(5.):
# continue
w = weight(oneW.value, eneMC.value, nEv.value)
dAngle = SpaceAngleRad(MCzenith.value, MCazimuth.value,
np.radians(SMPEzenith.value),
np.radians(SMPEazimuth.value))
h2.Fill(
log10(ene.value),
dAngle / (np.radians(sigma.value) * spline.Eval(log10(ene.value))),
w)
if h.GetXaxis().FindBin(log10(ene.value)) > 0 and h.GetXaxis().FindBin(
log10(ene.value)) < 51:
medians2[h.GetXaxis().FindBin(log10(ene.value)) - 1].append(
dAngle /
(np.radians(sigma.value) * spline.Eval(log10(ene.value))))
medians2W[h.GetXaxis().FindBin(log10(ene.value)) - 1].append(w)
#if i>200:
#break
npoints = 0
for i in range(1, h2.GetNbinsX() + 1):
if len(medians2[i - 1]) > 3:
g2.SetPoint(
npoints,
h.GetXaxis().GetBinCenter(i),
weighted_percentile(0.5, np.asarray(medians2[i - 1]),
np.asarray(medians2W[i - 1])))
g2.SetPointError(
npoints, 0., 0.,
weighted_percentile(0.5 - 0.341, np.asarray(medians2[i - 1]),
np.asarray(medians2W[i - 1])),
weighted_percentile(0.5 + 0.341, np.asarray(medians2[i - 1]),
np.asarray(medians2W[i - 1])))
npoints += 1
del medians2[:]
del medians2
del medians2W[:]
del medians2W
g2.Set(npoints)
c2 = TCanvas()
c2.SetLogy()
c2.SetGridx()
c2.SetGridy()
h2.SetStats(0)
h2.SetMinimum(1)
h2.Draw("colz")
g2.Draw("pl same")
h3 = TH2D("pull - corrected xcheck", "pull - corrected xcheck", 50, 1, 9,
100, 0.01, 20)
h3.GetXaxis().SetTitle("log_{10}(E [GeV])")
h3.GetYaxis().SetTitle("Pull_{corr}: #Delta#Psi/f_{corr}(#sigma_{parab.})")
g3 = TGraphAsymmErrors(50)
medians3 = [[] for i in range(0, 50)]
medians3W = [[] for i in range(0, 50)]
g3.SetMarkerStyle(20)
SigmaBins = [(0.5, 0.6, 1), (0.6, 0.7, 2), (0.7, 0.9, 4), (0.9, 1.3, 1),
(1.3, 1.7, 2)]
h_pdf_corr = []
for b in range(len(SigmaBins)):
h_pdf_corr.append(
TH1D("h_pdf_corr" + str(b),
"h_pdf_corr" + str(b) + ";#Delta#Psi [deg]", 50, 0, 3))
h_pdf_corr[-1].SetLineColor(SigmaBins[b][2])
h_pdf_corr[-1].SetLineWidth(2)
for i in range(0, tr.GetEntries()):
tr.GetEntry(i)
#if not hem(np.radians(SMPEzenith.value)): continue
#if np.radians(sigma.value)>np.radians(5.):
# continue
w = weight(oneW.value, eneMC.value, nEv.value)
dAngle = SpaceAngleRad(MCzenith.value, MCazimuth.value,
np.radians(SMPEzenith.value),
np.radians(SMPEazimuth.value))
#pull=dAngle/ROOT.mpfSigmaDegRescaledIC79V2(np.radians(sigma.value),ene.value)
#s_cor=ROOT.mpfSigmaDegRescaledIC79V2(np.radians(sigma.value),ene.value)
pull = dAngle / ROOT.RescaledSigma_IC79(np.radians(sigma.value),
ene.value)
s_cor = ROOT.RescaledSigma_IC79(np.radians(sigma.value), ene.value)
h3.Fill(log10(ene.value), pull, w)
for b in range(len(SigmaBins)):
if SigmaBins[b][0] <= np.degrees(
s_cor) and SigmaBins[b][1] > np.degrees(s_cor):
h_pdf_corr[b].Fill(np.degrees(dAngle), w)
if h.GetXaxis().FindBin(log10(ene.value)) > 0 and h.GetXaxis().FindBin(
log10(ene.value)) < 51:
medians3[h.GetXaxis().FindBin(log10(ene.value)) - 1].append(pull)
medians3W[h.GetXaxis().FindBin(log10(ene.value)) - 1].append(w)
#if i>200:
#break
npoints = 0
for i in range(1, h2.GetNbinsX() + 1):
if len(medians3[i - 1]) > 3:
g3.SetPoint(
npoints,
h.GetXaxis().GetBinCenter(i),
weighted_percentile(0.5, np.asarray(medians3[i - 1]),
np.asarray(medians3W[i - 1])))
g3.SetPointError(
npoints, 0., 0.,
weighted_percentile(0.5 - 0.341, np.asarray(medians3[i - 1]),
np.asarray(medians3W[i - 1])),
weighted_percentile(0.5 + 0.341, np.asarray(medians3[i - 1]),
np.asarray(medians3W[i - 1])))
npoints += 1
c30 = TCanvas()
leg = ROOT.TLegend(0.7, 0.7, 1., 1.)
ffunc = []
for b in range(3):
h_pdf_corr[b].Sumw2()
if b == 0:
h_pdf_corr[b].Draw("HIST")
else:
h_pdf_corr[b].Draw("same HIST")
ffunc.append(TF1("ffunc" + str(b), CircGaus, 0.001, 3., 2))
ffunc[-1].SetLineColor(SigmaBins[b][2])
ffunc[-1].SetParameter(0, 1.)
ffunc[-1].SetParameter(1, 1.)
h_pdf_corr[b].Fit(ffunc[-1], "R")
ffunc[-1].Draw("same")
leg.AddEntry(
h_pdf_corr[b],
"range: " + str(SigmaBins[b][0]) + " to " + str(SigmaBins[b][1]) +
" #sigma_{fit}=" + '{:.2f}'.format(ffunc[-1].GetParameter(0)), "l")
leg.Draw()
c31 = TCanvas()
leg1 = ROOT.TLegend(0.7, 0.7, 1., 1.)
ffunc = []
for b in range(3, 5):
h_pdf_corr[b].Sumw2()
if b == 0:
h_pdf_corr[b].Draw("HIST")
else:
h_pdf_corr[b].Draw("same HIST")
ffunc.append(TF1("ffunc" + str(b), CircGaus, 0.001, 3., 2))
ffunc[-1].SetLineColor(SigmaBins[b][2])
ffunc[-1].SetParameter(0, 1.)
ffunc[-1].SetParameter(1, 1.)
h_pdf_corr[b].Fit(ffunc[-1], "R")
ffunc[-1].Draw("same")
leg1.AddEntry(
h_pdf_corr[b],
"range: " + str(SigmaBins[b][0]) + " to " + str(SigmaBins[b][1]) +
" #sigma_{fit}=" + '{:.2f}'.format(ffunc[-1].GetParameter(0)), "l")
leg1.Draw()
g3.Set(npoints)
c3 = TCanvas()
c3.SetLogy()
c3.SetGridx()
c3.SetGridy()
h3.SetStats(0)
h3.SetMinimum(1)
h3.Draw("colz")
g3.Draw("pl same")
c3.SaveAs("mpfSigmaDegRescaledIC79V2_corrected.png")
raw_input("Press Enter to continue...")
return legend
thefile = TFile('rootFile/plots_DYMVA_SYS_2017_v6.root')
thelist = thefile.GetListOfKeys()
histos = {}
values = {}
errors = {}
graphs = {}
dymva = [0.05,0.2,0.4,0.6,0.75,0.825,0.875,0.9125,0.9375,0.9625,0.9875]
e_dymva = [0.05,0.1,0.1,0.1,0.05,0.025,0.025,0.0125,0.0125,0.0125,0.0125]
#loading DY histograms
for dirs in thelist :
histos['DY_'+dirs.GetName()] = thefile.Get(dirs.GetName()+'/dymva_alt_dnn_0j/histo_DY')
histos['DATA_'+dirs.GetName()] = thefile.Get(dirs.GetName()+'/dymva_alt_dnn_0j/histo_DATA')
histos['DYDATA_'+dirs.GetName()] = TH1D.Clone(histos['DATA_'+dirs.GetName()])
histos['AMC_'+dirs.GetName()] = TH1D.Clone(histos['DY_'+dirs.GetName()])
histos['VV_'+dirs.GetName()] = TH1D('VV_'+dirs.GetName(), 'VV_'+dirs.GetName(), histos['DY_'+dirs.GetName()].GetNbinsX(), histos['DY_'+dirs.GetName()].GetXaxis().GetXbins().GetArray())
# CREATING k histograms
if 'in' in dirs.GetName() and not 'btag' in dirs.GetName() and not 'df' in dirs.GetName() and not 'H_' in dirs.GetName() and not 'ww_' in dirs.GetName() and not '1j' in dirs.GetName() and not '2j' in dirs.GetName() and not 'VBF' in dirs.GetName() and not 'VH' in dirs.GetName():
#print('Histos for K and R: ', dirs.GetName())
histos['k_MC_'+dirs.GetName()] = TH1D('k_MC_'+dirs.GetName(), 'k_MC_'+dirs.GetName(), histos['DY_'+dirs.GetName()].GetNbinsX(), histos['DY_'+dirs.GetName()].GetXaxis().GetXbins().GetArray())
histos['k_DATA_'+dirs.GetName()] = TH1D('k_DATA_'+dirs.GetName(), 'k_DATA_'+dirs.GetName(), histos['DATA_'+dirs.GetName()].GetNbinsX(), histos['DATA_'+dirs.GetName()].GetXaxis().GetXbins().GetArray())
# CREATING R histograms
if 'out' in dirs.GetName() and not 'df' in dirs.GetName() and not 'H_' in dirs.GetName() and not 'ww_' in dirs.GetName():
#print('R dirs', dirs.GetName())
histos['R_MC_'+dirs.GetName()] = TH1D('R_MC_'+dirs.GetName(), 'R_MC_'+dirs.GetName(), histos['DY_'+dirs.GetName()].GetNbinsX(), histos['DY_'+dirs.GetName()].GetXaxis().GetXbins().GetArray())
histos['R_DATA_'+dirs.GetName()] = TH1D('R_DATA_'+dirs.GetName(), 'R_DATA_'+dirs.GetName(), histos['DATA_'+dirs.GetName()].GetNbinsX(), histos['DATA_'+dirs.GetName()].GetXaxis().GetXbins().GetArray())
histos['DATA_fromR_'+dirs.GetName()] = TH1D('DATA_fromR_'+dirs.GetName(), 'DATA_fromR_'+dirs.GetName(), histos['DATA_'+dirs.GetName()].GetNbinsX(), histos['DATA_'+dirs.GetName()].GetXaxis().GetXbins().GetArray())
histos['R_fromDATAR_'+dirs.GetName()] = TH1D('R_fromDATAR_'+dirs.GetName(), 'R_fromDATAR_'+dirs.GetName(), histos['DATA_'+dirs.GetName()].GetNbinsX(), histos['DATA_'+dirs.GetName()].GetXaxis().GetXbins().GetArray())
bin_end = 10000
if mass >= 4000:
binsize = 1
bin_end = 2000
if mass >= 5000:
binsize = 0.2
bin_end = 1000
if mass >= 6000:
binsize = 0.1
bin_end = 500
if mass >= 6500:
binsize = 0.05
bin_end = 250
bins = int(bin_end / binsize)
hist = TH1D("dist{0}".format(mass), "dist", bins, 0, bin_end)
r = TRandom1()
expected_limits_file = op.join(args.expected_limit_dir, mass_folder,
"combined-{}.txt".format(mass))
with open(expected_limits_file, "r") as result_file:
for i, line in enumerate(result_file.readlines()):
if len(line) == 0:
continue
limit = float(line)
lum = r.Gaus(fb, 0.032 * fb)
limit2 = fb * limit / lum
#if i % 1000 == 0:
# print(limit, lum, limit2)
hist.Fill(limit2)
#hist.Fill(fb*limit/r.Gaus(fb, 0.032*fb))
def histo(*args):
histos[args[0]] = TH1D(*args)
#!/usr/bin/python
from ROOT import TH1D
import sys
data = []
fl = open(sys.argv[1])
h = TH1D("hist", "Histogram", 1000, -1, 100)
for line in fl:
h.Fill(float(line.split()[int(sys.argv[2])]))
h.Draw()
raw_input()
valphys = {}
valsyst = {}
runSyst = False
c1 = TCanvas("c1", "c1", 1)
## Import all measurements
## Meas 1
### Import the file
myfile["meas1"] = TFile("%s" % options.input)
## and then get the TTree
mytree["meas1"] = myfile["meas1"].Get("tree")
## FastFrame
### First create an empty TH1D histogram
myhist["meas1"] = TH1D("myhist_meas1", "meas1", 256, 0, 8)
### Then get the saved histogram into the new one and normalize to ns from s by *1.e9
mytree["meas1"].Draw("area*1.e9>>myhist_meas1", "", "")
today = datetime.date.today()
fTag = today.strftime("%Y%m%d")
fName = options.input
i = fName.find("/")
while i > 0:
fName = fName[i + 1:]
i = fName.find("/")
fName = fName[:-5]
myhist["meas1"].SetTitle("%s" % fName)
myhist["meas1"].GetXaxis().SetTitle("area [V#times ns]")
myhist["meas1"].GetYaxis().SetTitle("counts")
def __init__(self, name, dataType, **kwargs):
print 'TreeProducerCommon is called for', name
self.name = name
self._isData = dataType=='data'
self._isEmb = dataType=='emb'
self.doJECSys = kwargs.get('doJECSys', True ) and not self._isData
###self.isVectorLQ = kwargs.get('isVectorLQ', 'VectorLQ' in self.name )
# TREE
self.outputfile = TFile(name, 'RECREATE')
self.tree = TTree('tree','tree')
# HISTOGRAM
self.cutflow = TH1D('cutflow', 'cutflow', 25, 0, 25)
self.pileup = TH1D('pileup', 'pileup', 100, 0, 100)
#### CHECK genPartFlav
###self.flags_LTF_DM1 = TH1D('flags_LTF_DM1', "flags for l #rightarrow #tau_{h}, DM1", 18, 0, 18)
###self.flags_LTF_DM0 = TH1D('flags_LTF_DM0', "flags for l #rightarrow #tau_{h}, DM0", 18, 0, 18)
###self.flags_LTF_mis = TH1D('flags_LTF_mis', "flags for l #rightarrow #tau_{h}, DM1, wrong genPartFlav", 18, 0, 18)
###self.flags_LTF_DM1_sn1 = TH1D('flags_LTF_DM1_sn1', "flags for l #rightarrow #tau_{h}, DM1 (status!=1)", 18, 0, 18)
###self.flags_LTF_DM0_sn1 = TH1D('flags_LTF_DM0_sn1', "flags for l #rightarrow #tau_{h}, DM0 (status!=1)", 18, 0, 18)
###self.flags_LTF_mis_sn1 = TH1D('flags_LTF_mis_sn1', "flags for l #rightarrow #tau_{h}, DM1, wrong genPartFlav (status!=1)", 18, 0, 18)
###for hist in [self.flags_LTF_DM1, self.flags_LTF_DM0, self.flags_LTF_mis, self.flags_LTF_DM0_sn1, self.flags_LTF_DM1_sn1, self.flags_LTF_mis_sn1]:
### hist.GetXaxis().SetBinLabel( 1, "isPrompt" )
### hist.GetXaxis().SetBinLabel( 2, "isDirectPromptTauDecayProduct" )
### hist.GetXaxis().SetBinLabel( 3, "isHardProcess" )
### hist.GetXaxis().SetBinLabel( 4, "fromHardProcess" )
### hist.GetXaxis().SetBinLabel( 5, "isDirectHardProcessTauDecayProduct" )
### hist.GetXaxis().SetBinLabel( 6, "fromHardProcessBeforeFSR" )
### hist.GetXaxis().SetBinLabel( 7, "isFirstCopy" )
### hist.GetXaxis().SetBinLabel( 8, "isLastCopy" )
### hist.GetXaxis().SetBinLabel( 9, "isLastCopyBeforeFSR" )
### hist.GetXaxis().SetBinLabel(10, "status==1" )
### hist.GetXaxis().SetBinLabel(11, "status==23" )
### hist.GetXaxis().SetBinLabel(12, "status==44" )
### hist.GetXaxis().SetBinLabel(13, "status==51" )
### hist.GetXaxis().SetBinLabel(14, "status==52" )
### hist.GetXaxis().SetBinLabel(15, "other status" )
### hist.GetXaxis().SetLabelSize(0.041)
###self.genmatch_corr = TH2D("genmatch_corr","correlation between Tau_genPartFlav and genmatch",6,0,6,6,0,6)
###self.genmatch_corr_DM0 = TH2D("genmatch_corr_DM0","correlation between Tau_genPartFlav and genmatch for DM0",6,0,6,6,0,6)
###self.genmatch_corr_DM1 = TH2D("genmatch_corr_DM1","correlation between Tau_genPartFlav and genmatch for DM1",6,0,6,6,0,6)
#############
# EVENT #
#############
self.addBranch('run', 'i')
self.addBranch('lumi', 'i')
self.addBranch('event', 'i')
self.addBranch('isData', '?', self._isData)
self.addBranch('isEmb', '?', self._isEmb)
self.addBranch('npvs', 'i')
self.addBranch('npvsGood', 'i')
self.addBranch('metfilter', '?')
if not any(x for x in [self._isData, self._isEmb]):
self.addBranch('nPU', 'i', -1)
self.addBranch('nTrueInt', 'i', -1)
self.addBranch('LHE_Njets', 'i', -1)
##############
# WEIGHT #
##############
if not self._isData:
self.addBranch('weight', 'f', 1.)
self.addBranch('genweight', 'f', 1.)
self.addBranch('trigweight', 'f', 1.)
self.addBranch('puweight', 'f', 1.)
self.addBranch('idisoweight_1', 'f', 1.)
self.addBranch('idisoweight_2', 'f', 1.)
self.addBranch('zptweight', 'f', 1.)
self.addBranch('ttptweight', 'f', 1.)
self.addBranch('btagweight', 'f', 1.)
self.addBranch('btagweight_loose', 'f', 1.)
self.addBranch('btagweight50', 'f', 1.)
self.addBranch('btagweight50_loose', 'f', 1.)
if self._isEmb:
self.addBranch('embselweight', 'f', 1.)
############
# JETS #
############
self.addBranch('njets', 'i')
self.addBranch('njets50', 'i')
self.addBranch('ncjets', 'i')
self.addBranch('nfjets', 'i')
self.addBranch('nbtag', 'i')
self.addBranch('nbtag50', 'i')
self.addBranch('nbtag_loose', 'i')
self.addBranch('nbtag50_loose', 'i')
self.addBranch('jpt_1', 'f')
self.addBranch('jeta_1', 'f')
self.addBranch('jphi_1', 'f')
self.addBranch('jdeepb_1', 'f')
self.addBranch('jpt_2', 'f')
self.addBranch('jeta_2', 'f')
self.addBranch('jphi_2', 'f')
self.addBranch('jdeepb_2', 'f')
self.addBranch('bpt_1', 'f')
self.addBranch('beta_1', 'f')
self.addBranch('bpt_2', 'f')
self.addBranch('beta_2', 'f')
if self.doJECSys:
for uncertainty in ['jer','jes']:
for variation in ['Down','Up']:
label = '_'+uncertainty+variation
self.addBranch('njets'+label, 'i')
self.addBranch('njets50'+label, 'i')
self.addBranch('nbtag50'+label, 'i')
self.addBranch('nbtag50_loose'+label, 'i')
self.addBranch('jpt_1'+label, 'f')
self.addBranch('jpt_2'+label, 'f')
self.addBranch('met', 'f')
self.addBranch('metphi', 'f')
###self.addBranch('puppimet', 'f')
###self.addBranch('puppimetphi', 'f')
###self.addBranch('metsignificance', 'f')
###self.addBranch('metcovXX', 'f')
###self.addBranch('metcovXY', 'f')
###self.addBranch('metcovYY', 'f')
###self.addBranch('fixedGridRhoFastjetAll', 'f')
if not any(x for x in [self._isData, self._isEmb]):
self.addBranch('genmet', 'f', -1)
self.addBranch('genmetphi', 'f', -9)
#############
# OTHER #
#############
self.addBranch('pfmt_1', 'f')
self.addBranch('pfmt_2', 'f')
self.addBranch('m_vis', 'f')
self.addBranch('pt_ll', 'f')
self.addBranch('dR_ll', 'f')
self.addBranch('dphi_ll', 'f')
self.addBranch('deta_ll', 'f')
self.addBranch('chi', 'f')
self.addBranch('pzetamiss', 'f')
self.addBranch('pzetavis', 'f')
self.addBranch('dzeta', 'f')
if self.doJECSys:
for uncertainty in ['jer','jes','unclEn']:
for variation in ['Down','Up']:
label = '_'+uncertainty+variation
self.addBranch('met'+label, 'f')
self.addBranch('pfmt_1'+label, 'f')
self.addBranch('dzeta'+label, 'f')
self.addBranch('dilepton_veto', '?')
self.addBranch('extraelec_veto', '?')
self.addBranch('extramuon_veto', '?')
self.addBranch('lepton_vetos', '?')
self.addBranch('lepton_vetos_noTau', '?')
if not self._isData:
###self.addBranch('ngentauhads', 'i', -1)
###self.addBranch('ngentaus', 'i', -1)
self.addBranch('m_genboson', 'f', -1)
self.addBranch('pt_genboson', 'f', -1)
### output of createHistos.py
# open root file with found peak histograms
if len(sys.argv) > 1:
filename = sys.argv[1]
print "input filename is ", filename
f = TFile.Open(filename, "read")
dirlist = f.GetListOfKeys()
iter = dirlist.MakeIterator()
key = iter.Next()
histos = {}
bigpeaks = {}
fout = TFile.Open("summed.root", "RECREATE")
hout = TH1D("hout", "peaks", 80, 0, 80)
histo = None
while key:
if key.GetClassName() == 'TH1D':
histo = key.ReadObj()
hName = histo.GetName()
print "found histogram", hName,
histos[hName] = histo
val = fit(histo)
if val > 50:
bigpeaks[hName] = val
print val
hout.Fill(val)
key = iter.Next()
# ==============================================================================
print "==================================== TRAIN ===================================="
response= RooUnfoldResponse (40, -10.0, 10.0);
# Train with a Breit-Wigner, mean 0.3 and width 2.5.
for i in xrange(100000):
xt= gRandom.BreitWigner (0.3, 2.5);
x= smear (xt);
if x!=None:
response.Fill (x, xt);
else:
response.Miss (xt);
print "==================================== TEST ====================================="
hTrue= TH1D ("true", "Test Truth", 40, -10.0, 10.0);
hMeas= TH1D ("meas", "Test Measured", 40, -10.0, 10.0);
# Test with a Gaussian, mean 0 and width 2.
for i in xrange(10000):
xt= gRandom.Gaus (0.0, 2.0)
x= smear (xt);
hTrue.Fill(xt);
if x!=None: hMeas.Fill(x);
print "==================================== UNFOLD ==================================="
unfold= RooUnfoldBayes (response, hMeas, 4); # OR
# unfold= RooUnfoldSvd (response, hMeas, 20); # OR
# unfold= RooUnfoldTUnfold (response, hMeas); # OR
# unfold= RooUnfoldIds (response, hMeas, 3); # OR
hReco= unfold.Hreco();
def test_unroll(fname, xvars, yvars, cut="", verb=0):
"""Test unrolling of 2D histogram and create 4D response matrix."""
if verb >= 1:
print ">>> test_unroll(%s)" % (fname)
start = time()
file = TFile(fname, 'READ')
assert file and not file.IsZombie(), "Could not open %s" % (fname)
tree = file.Get('tree')
assert tree and tree.GetEntries() > 10, "Could find TTree 'tree' in %s" % (
fname)
hists = []
xvar1, xvar2 = xvars[:2]
yvar1, yvar2 = yvars[:2]
#xbins = xvars[2:]
#ybins = yvars[2:]
nxbins, xmin, xmax = xvars[2:]
nybins, ymin, ymax = yvars[2:]
#nxbins, nybins = xbins[0], ybins[0]
nbins = nxbins * nybins
hname2d = "testUnroll_%s-%s" % (vardict.get(
yvar1, yvar1), vardict.get(xvar1, xvar1))
hname2dcpp = "testUnroll_%s-%s_rolledup_cpp" % (vardict.get(
yvar1, yvar1), vardict.get(xvar1, xvar1))
hname4d = "testUnroll_response"
hnamepy = "%s_unrolled_py" % (hname2d)
hnamecpp = "%s_unrolled_cpp" % (hname2d)
xtitle = titledict.get(xvar1, xvar1)
ytitle = titledict.get(yvar1, yvar1)
xtitle1d = "bin(%s,%s)" % (xtitle, ytitle)
xtitle4d = "bin(%s,%s)" % (titledict.get(
xvar2, xvar2), titledict.get(yvar2, yvar2))
ytitle4d = xtitle1d
# CREATE HISTOGRAMS
#hist1 = TH1D(hname1,hname1,*xbins)
histpy = TH1D(hnamepy, hnamepy, nbins, 1, 1 + nbins) # unrolled
hist2d = TH2D(hname2d, hname2d, nxbins, xmin, xmax, nybins, ymin,
ymax) #*(xbins+ybins)
hist4d = TH2D(hname4d, hname4d, nbins, 1, 1 + nbins, nbins, 1,
1 + nbins) # response / migration matrix
tree.Draw("%s:%s >> %s" % (yvar1, xvar1, hname2d), cut, 'gOFF')
Unroll.SetBins(hist2d, verb) # set axis for unrolling in Unroll::GetBin
histcpp = Unroll.Unroll(hist2d, hnamecpp, False, verb) # unrolled
tree.Draw("Unroll::GetBin(%s,%s) >> %s" % (xvar1, yvar1, hnamepy), cut,
'gOFF') # unrolled
tree.Draw("Unroll::GetBin(%s,%s):Unroll::GetBin(%s,%s) >> %s" %
(xvar2, yvar2, xvar1, yvar1, hname4d), cut,
'gOFF') # x: reco, y: gen
hist2dcpp = Unroll.RollUp(histcpp, hname2dcpp, hist2d, verb) # rolled up
#hist = TH2F(hname,hname,nbins,xmin,xmax)
#tree.Draw("%s:%s >> %s"%(xvar,yvar,hname),"(%s)*%s"%(cut,weight),'gOFF')
# DRAW
for vars in [xvars, yvars]:
nabins, amin, amax = vars[2:]
for avar in vars[:2]:
hname = "testUnroll_%s" % (vardict.get(avar, avar))
hist1d = TH1D(hname, hname, nabins, amin, amax)
tree.Draw("%s >> %s" % (avar, hname), cut, 'gOFF')
atitle = titledict.get(avar, avar)
draw(hist1d, atitle)
draw(histpy, xtitle1d, nbins=nybins) # rolled up
draw(histcpp, xtitle1d, nbins=nybins) # rolled up
draw2d(hist2d, xtitle, ytitle, number=True) # original
draw2d(hist2dcpp, xtitle, ytitle, number=True) # rolled back up
draw2d(hist4d, xtitle4d, ytitle4d, nxbins=nybins,
nybins=nybins) # response matrix
# TEST Unroll::GetBin
nx, ny = 2 * nxbins, 2 * nybins # number of points to scan
xbinw = float(xmax - xmin) / nx
ybinw = float(ymax - ymin) / ny
print ">>> nxbins=%s, xmin=%s, xmax=%s" % (nxbins, xmin, xmax)
print ">>> nybins=%s, ymin=%s, ymax=%s" % (nybins, ymin, ymax)
print ">>> nx=%s, xbinw=%s, ny=%s, ybinw=%s" % (nx, xbinw, ny, ybinw)
xvals = [xmin + xbinw * i for i in range(-2, nx + 3)]
yvals = [ymin + ybinw * i for i in range(-2, ny + 3)]
#xvals = [-1,0,1,10,50,100,200]
#yvals = [-1,0,1,10,50,100,200]
print ">>>"
print ">>> %7s |" % (r'y\x') + ' '.join("%6s" % x for x in xvals)
print ">>> %8s+" % ('-' * 7) + '-' * 7 * len(xvals)
for y in yvals:
#print ">>> %7s |"%x+' '.join("%6s"%(Unroll.GetBin(x,y)) for x in xvals)
row = ">>> %7s |" % y
for x in xvals:
bin = Unroll.GetBin(x, y)
str = "%6s " % (bin)
if bin < 1 or bin >= nxbins * nybins + 1:
str = red(str)
row += str
print row
print ">>>"
print ">>> Took %.2fs" % (time() - start)
# # # # ##
#printing#
# #
plt.figure(1)
plt.title("Costfunction of (modified) Training-data")
plt.xlabel("epochs")
plt.ylabel("cost function")
x_range = [x + 1 for x in range(0, N_epochs)]
plt.plot(x_range, cost_training_data)
plt.savefig("cost_on_training_data.png")
plt.figure(2)
plt.title("f data")
plt.xlabel("epochs")
plt.ylabel("total error on validation data")
x_range = [x + 1 for x in range(0, N_epochs)]
plt.plot(x_range, error_validation_data)
plt.savefig("error_on_val_data.png")
error_on_validation_data_after_training = diff_validation[-1].reshape(
(1, validation_set.get_N()))
hist = TH1D('hist', "Errors on val data after last training epoch", 200,
-10000, 10000)
fill_hist(hist, error_on_validation_data_after_training[0])
canvas = TCanvas()
hist.GetXaxis().SetTitle("desired Chi^2- outputted Chi^2")
hist.Draw()
canvas.SaveAs('error_on_val_data_hist.png')
def compareDatasets(trigger, leg, datasets, lineColors, legendLabels, axisTitle, canvas, dataLumi, metHLTFilters, outputSuffix = None):
inputFiles = [TFile.Open("triggerEfficiency_" + x + ".root") for x in datasets]
efficiencies = [x.Get(trigger + "_" + leg) for x in inputFiles]
for iEff in range(len(efficiencies)):
SetStyle(efficiencies[iEff])
efficiencies[iEff].SetLineColor(lineColors[iEff])
efficiencies[iEff].SetMarkerColor(lineColors[iEff])
oneLine = TLine(xlo, 1.0, xhi, 1.0)
oneLine.SetLineWidth(3)
oneLine.SetLineStyle(2)
backgroundHist = TH1D("backgroundHist", "backgroundHist", 1, xlo, xhi)
backgroundHist.GetYaxis().SetTitle("Trigger Efficiency")
backgroundHist.GetYaxis().SetRangeUser(ylo, yhi)
backgroundHist.GetXaxis().SetTitle(axisTitle)
SetStyle(backgroundHist)
canvas.cd()
backgroundHist.Draw()
for eff in efficiencies:
eff.Draw("CP same")
#oneLine.Draw("same")
pt_cmsPrelim = TPaveText(0.132832, 0.859453, 0.486216, 0.906716, "brNDC")
pt_cmsPrelim.SetBorderSize(0)
pt_cmsPrelim.SetFillStyle(0)
pt_cmsPrelim.SetTextFont(62)
pt_cmsPrelim.SetTextSize(0.0374065)
if dataLumi > 0:
pt_cmsPrelim.AddText("CMS Preliminary")
else:
pt_cmsPrelim.AddText("CMS Simulation")
pt_cmsPrelim.Draw("same")
if dataLumi > 0:
pt_lumi = TPaveText(0.744361, 0.92928, 0.860902, 0.977667, "brNDC")
pt_lumi.SetBorderSize(0)
pt_lumi.SetFillStyle(0)
pt_lumi.SetTextFont(42)
pt_lumi.SetTextSize(0.0374065)
pt_lumi.AddText("{:.2f}".format(dataLumi / 1000.0) + " fb^{-1}, 13 TeV")
pt_lumi.Draw("same")
pt_leg = TPaveText(0.160401, 0.768657, 0.342105, 0.863184, "brNDC")
pt_leg.SetBorderSize(0)
pt_leg.SetFillStyle(0)
pt_leg.SetTextFont(42)
pt_leg.SetTextSize(0.025)
pt_leg.SetTextAlign(12)
if leg == "METLeg":
legLabel = ""
for filt in metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + metHLTFilters[-1]
pt_leg.AddText(legLabel)
if leg == "TrackLeg":
pt_leg.AddText(trigger + "*")
legLabel = ""
for filt in metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + metHLTFilters[-1] + " applied"
pt_leg.AddText(legLabel)
if leg == "METPath":
if trigger == "GrandOr":
pt_leg.AddText("OR of Signal Paths")
else:
if len(trigger) > 25 and len(trigger.split("_")) > 2:
firstLine = trigger.split("_")[0] + "_" + trigger.split("_")[1] + "_"
pt_leg.AddText(firstLine)
secondLine = ""
for line in trigger.split("_")[2:-1]:
secondLine += line + "_"
secondLine += trigger.split("_")[-1] + "*"
pt_leg.AddText(secondLine)
else:
pt_leg.AddText(trigger + "*")
pt_leg.Draw("same")
legendLabel = trigger
legend = TLegend(0.65, 0.75, 0.93, 0.88)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
if leg == 'METLeg':
legend.SetHeader('MET Leg')
elif leg == 'TrackLeg':
legend.SetHeader('Track Leg')
for iEff in range(len(efficiencies)):
legend.AddEntry(efficiencies[iEff], legendLabels[iEff], 'P')
legend.Draw("same")
if outputSuffix is not None:
canvas.SaveAs('compareDatasets_' + trigger + '_' + leg + '_' + outputSuffix + '.pdf')
else:
canvas.SaveAs('compareDatasets_' + trigger + '_' + leg + '_.pdf')
for inputFile in inputFiles:
inputFile.Close()
return
if not len(sys.argv) == 5:
print "usage is scaleInputHistos.py inputFileName outputFileName oldLumi newLumi"
sys.exit(1)
inputFile = TFile(sys.argv[1],"READ")
outputFile = TFile(sys.argv[2],"RECREATE")
oldLumi = float(sys.argv[3])
newLumi = float(sys.argv[4])
keyIter = inputFile.GetListOfKeys().MakeIterator()
key = keyIter.Next()
newHists = []
while key:
hist = TH1D(inputFile.Get(key.GetName()))
hist.Scale(newLumi/oldLumi)
newHists.append(hist)
key = keyIter.Next()
outputFile.cd()
for hist in newHists:
hist.Write()
outputFile.Close()
inputFile.Close()
def compare(trigger, leg, data, mc, axisTitle, canvas, dataLumi, metHLTFilters):
dataFile = TFile.Open("triggerEfficiency_" + data + ".root", "read")
mcFile = TFile.Open("triggerEfficiency_" + mc + ".root", "read")
dataEff = dataFile.Get(trigger + "_" + leg)
mcEff = mcFile.Get(trigger + "_" + leg)
SetStyle(dataEff)
SetStyle(mcEff)
mcEff.SetLineColor(600)
mcEff.SetMarkerColor(600)
oneLine = TLine(xlo, 1.0, xhi, 1.0)
oneLine.SetLineWidth(3)
oneLine.SetLineStyle(2)
backgroundHist = TH1D("backgroundHist", "backgroundHist", 1, xlo, xhi)
backgroundHist.GetYaxis().SetTitle("Trigger Efficiency")
backgroundHist.GetYaxis().SetRangeUser(ylo, yhi)
backgroundHist.GetXaxis().SetTitle(axisTitle)
SetStyle(backgroundHist)
canvas.cd()
backgroundHist.Draw()
dataEff.Draw("CP same")
mcEff.Draw("CP same")
#oneLine.Draw("same")
pt_cmsPrelim = TPaveText(0.132832, 0.859453, 0.486216, 0.906716, "brNDC")
pt_cmsPrelim.SetBorderSize(0)
pt_cmsPrelim.SetFillStyle(0)
pt_cmsPrelim.SetTextFont(62)
pt_cmsPrelim.SetTextSize(0.0374065)
pt_cmsPrelim.AddText("CMS Preliminary")
pt_cmsPrelim.Draw("same")
pt_lumi = TPaveText(0.744361, 0.92928, 0.860902, 0.977667, "brNDC")
pt_lumi.SetBorderSize(0)
pt_lumi.SetFillStyle(0)
pt_lumi.SetTextFont(42)
pt_lumi.SetTextSize(0.0374065)
pt_lumi.AddText("{:.2f}".format(dataLumi / 1000.0) + " fb^{-1}, 13 TeV")
pt_lumi.Draw("same")
pt_leg = TPaveText(0.160401, 0.768657, 0.342105, 0.863184, "brNDC")
pt_leg.SetBorderSize(0)
pt_leg.SetFillStyle(0)
pt_leg.SetTextFont(42)
pt_leg.SetTextSize(0.025)
pt_leg.SetTextAlign(12)
if leg == "METLeg":
legLabel = ""
for filt in metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + metHLTFilters[-1]
pt_leg.AddText(legLabel)
if leg == "TrackLeg":
pt_leg.AddText(trigger + "*")
legLabel = ""
for filt in metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + metHLTFilters[-1] + " applied"
pt_leg.AddText(legLabel)
if leg == "METPath":
if trigger == "GrandOr":
pt_leg.AddText("OR of Signal Paths")
else:
if len(trigger) > 25 and len(trigger.split("_")) > 2:
firstLine = trigger.split("_")[0] + "_" + trigger.split("_")[1] + "_"
pt_leg.AddText(firstLine)
secondLine = ""
for line in trigger.split("_")[2:-1]:
secondLine += line + "_"
secondLine += trigger.split("_")[-1] + "*"
pt_leg.AddText(secondLine)
else:
pt_leg.AddText(trigger + "*")
pt_leg.Draw("same")
dataLabel = '2015 data'
if '2016BC' in data:
dataLabel = '2016 B+C data'
if '2016DEFGH' in data:
dataLabel = '2016 D-H data'
if '2017' in data:
dataLabel = '2017 data'
if '2018' in data:
dataLabel = '2018 data'
legendLabel = trigger
legend = TLegend(0.65, 0.75, 0.93, 0.88)
legend.SetBorderSize(0)
legend.SetFillColor(0)
legend.SetFillStyle(0)
legend.SetTextFont(42)
if leg == 'METLeg':
legend.SetHeader('MET Leg')
elif leg == 'TrackLeg':
legend.SetHeader('Track Leg')
legend.AddEntry(dataEff, dataLabel, 'P')
legend.AddEntry(mcEff, 'W #rightarrow l#nu MC', 'P')
legend.Draw("same")
outputDirectory = 'plots_compare'
if 'BC' in data:
outputDirectory = 'plots_compareBC'
if 'DEFGH' in data:
outputDirectory = 'plots_compareDEFGH'
if not os.path.exists(outputDirectory):
os.mkdir(outputDirectory)
canvas.SaveAs(outputDirectory + '/' + trigger + '_' + leg + '.pdf')
mcFile.Close()
dataFile.Close()
return
def generatePDFs(ma=0):
""" Generate a set of TH1D's to be turned into RooDataHist objects.
Takes axion mass (in keV) as a parameter.
Full suite of diganostic plots is in ./sandbox/specPlots.py
Make the binning 0.02 keV intervals - hopefully that's fine enough.
TODO: BDM/ALP pdf Get from Kris's analysis in GAT
TODO: WIMP pdf? Get PyWIMP from Graham
"""
f = TFile("./data/inputHists.root", "RECREATE")
redondoScale = 1e19 * 0.511e-10**-2 # convert table to [cts / (keV cm^2 d)]
axData, phoData, tritData = [], [], []
with open("./data/redondoFlux.txt") as f1: # 23577 entries
lines = f1.readlines()[11:]
for line in lines:
data = line.split()
axData.append([float(data[0]), float(data[1])])
with open("./data/ge76peXS.txt") as f2: # 2499 entries, 0.01 kev intervals
lines = f2.readlines()
for line in lines:
data = line.split()
phoData.append([float(data[0]), float(data[1])])
with open("./data/TritiumSpectrum.txt") as f3: # 20000 entries
lines = f3.readlines()[1:]
for line in lines:
data = line.split()
conv = float(data[2]) # raw spectrum convolved w/ ge cross section
if conv < 0: conv = 0.
tritData.append([float(data[1]), conv])
axData, phoData, tritData = np.array(axData), np.array(phoData), np.array(
tritData)
def sig_ae(E, m):
beta = (1 - m**2. / E**2.)**(1. / 2)
return (1 -
(1. / 3.) * beta**(2. / 3.)) * (3. * E**2.) / (16. * np.pi *
(1. / 137.) *
511.**2. * beta)
kpb = 0.02
eLo, eHi = 0.1, 30.
nBins = int((eHi - eLo) / kpb)
h1 = TH1D("h1", "photoelectric", nBins, eLo, eHi) # [cm^2 / kg]
h2 = TH1D("h2", "axioelectric", nBins, eLo, eHi) # [cm^2 / kg]
h3 = TH1D("h3", "axion flux", nBins, eLo, eHi) # [cts / (keV cm^2 d)]
h4 = TH1D("h4", "convolved flux", nBins, eLo, eHi) # [cts / (keV d kg)]
h5 = TH1D("h5", "tritium", nBins, eLo, eHi) # [cts] (normalized to 1)
for i in range(nBins):
ene = i * kpb + eLo
eneLo, eneHi = ene - kpb / 2., ene + kpb / 2.
with warnings.catch_warnings():
warnings.simplefilter("ignore", category=RuntimeWarning)
# if ma>0, we ignore entries with E <= m.
# photoelectric x-section [cm^2 / kg]
idx = np.where((phoData[:, 0] >= eneLo) & (phoData[:, 0] <= eneHi))
pho = np.mean(phoData[idx][:, 1]) * 1000
if np.isnan(pho) or len(phoData[idx][:, 1]) == 0: pho = 0.
if phoData[idx][:, 1] <= ma: pho = 0.
h1.SetBinContent(i, pho)
# axioelectric x-section [cm^2 / kg]
if ene > ma: axio = pho * sig_ae(ene, ma)
else: axio = 0.
h2.SetBinContent(i, axio)
# axion flux [cts / (cm^2 d keV)]
idx = np.where((axData[:, 0] >= eneLo) & (axData[:, 0] <= eneHi))
flux = np.mean(axData[idx][:, 1]) * redondoScale
if np.isnan(flux): flux = 0.
h3.SetBinContent(i, flux)
# axion flux convolved [cts / (keV d kg)]
axConv = axio * flux
h4.SetBinContent(i, axConv)
# tritium
idx = np.where((tritData[:, 0] >= eneLo)
& (tritData[:, 0] <= eneHi))
trit = np.mean(tritData[idx][:, 1])
if np.isnan(trit): trit = 0.
h5.SetBinContent(i, trit)
h1.Write()
h2.Write()
h3.Write()
h4.Write()
h5.Write()
f.Close()
def plotEfficiency(self, doFit = False):
if hasattr(self, "Denominator") and hasattr(self, "Numerator"):
if self._rebinFactor != 1:
self.Denominator["hist"].Rebin(self._rebinFactor)
self.Numerator["hist"].Rebin(self._rebinFactor)
for ibin in range(-1, self.Numerator["hist"].GetNbinsX()+1):
if self.Numerator["hist"].GetBinContent(ibin+1) > self.Denominator["hist"].GetBinContent(ibin+1):
print('Fixing bad bin:', (ibin+1))
self.Numerator["hist"].SetBinContent(ibin+1, self.Denominator["hist"].GetBinContent(ibin+1))
self.Numerator["hist"].SetBinError(ibin+1, self.Denominator["hist"].GetBinError(ibin+1))
efficiencyGraph = TGraphAsymmErrors(self.Numerator["hist"], self.Denominator["hist"], "cp")
pt_cmsPrelim = TPaveText(0.132832, 0.859453, 0.486216, 0.906716, "brNDC")
pt_cmsPrelim.SetBorderSize(0)
pt_cmsPrelim.SetFillStyle(0)
pt_cmsPrelim.SetTextFont(62)
pt_cmsPrelim.SetTextSize(0.0374065)
pt_cmsPrelim.AddText("CMS Preliminary")
pt_lumi = TPaveText(0.744361, 0.92928, 0.860902, 0.977667, "brNDC")
pt_lumi.SetBorderSize(0)
pt_lumi.SetFillStyle(0)
pt_lumi.SetTextFont(42)
pt_lumi.SetTextSize(0.0374065)
pt_lumi.AddText(self._luminosityLabel)
pt_leg = TPaveText(0.160401, 0.768657, 0.342105, 0.863184, "brNDC")
pt_leg.SetBorderSize(0)
pt_leg.SetFillStyle(0)
pt_leg.SetTextFont(42)
pt_leg.SetTextSize(0.0349127)
pt_leg.SetTextAlign(12)
if self._leg == "METLeg":
legLabel = ""
for filt in self._metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + self._metHLTFilters[-1]
pt_leg.AddText(legLabel)
pt_leg.AddText(self._datasetLabel)
if self._leg == "TrackLeg":
pt_leg.AddText(self._path + "*")
pt_leg.AddText(self._datasetLabel)
legLabel = ""
for filt in self._metHLTFilters[:-1]:
legLabel = legLabel + filt + ", "
legLabel = legLabel + self._metHLTFilters[-1] + " applied"
pt_leg.AddText(legLabel)
if self._leg == "METPath":
if self._path == "GrandOr":
pt_leg.AddText("OR of Signal Paths")
else:
pt_leg.AddText(self._path + "*")
pt_leg.AddText(self._datasetLabel)
lumiLabel = TPaveText(0.66416, 0.937339, 0.962406, 0.992894, "brNDC")
lumiLabel.SetBorderSize(0)
lumiLabel.SetFillStyle(0)
lumiLabel.SetTextFont(42)
lumiLabel.SetTextSize(0.0387597)
lumiLabel.AddText(str (self._luminosityLabel))
oneLine = TLine(xlo, 1.0, xhi, 1.0)
oneLine.SetLineWidth(3)
oneLine.SetLineStyle(2)
backgroundHist = TH1D("backgroundHist", "backgroundHist", 1, xlo, xhi)
backgroundHist.GetYaxis().SetTitle("Trigger Efficiency")
backgroundHist.GetYaxis().SetRangeUser(ylo, yhi)
if self._leg == "METLeg" or self._leg == "METPath":
backgroundHist.GetXaxis().SetTitle(self._metLegAxisTitle)
elif self._leg == "TrackLeg":
backgroundHist.GetXaxis().SetTitle(self._trackLegAxisTitle)
SetStyle(backgroundHist)
SetStyle(efficiencyGraph)
self._canvas.cd()
backgroundHist.Draw()
efficiencyGraph.Draw("P")
pt_cmsPrelim.Draw("same")
pt_lumi.Draw("same")
pt_leg.Draw("same")
oneLine.Draw("same")
if doFit:
(fitFunc, fitText) = self.PlotFit(efficiencyGraph)
fitFunc.Draw("same")
fitText.Draw("same")
if not os.path.exists('plots_' + self.Denominator["sample"]):
os.mkdir('plots_' + self.Denominator["sample"])
if self._leg == "METPath":
self._canvas.SaveAs('plots_' + self.Denominator["sample"] + '/' + self._path + "_Efficiency.pdf")
else:
self._canvas.SaveAs('plots_' + self.Denominator["sample"] + '/' + self._path + "_" + self._leg + ".pdf")
self._fout.cd()
if self._tgraphSuffix is not None:
efficiencyGraph.Write(self._path + "_" + self._leg + "_" + self._tgraphSuffix)
if doFit:
fitFunc.Write(self._path + "_" + self._leg + "_" + self._tgraphSuffix + "_fitResult")
else:
efficiencyGraph.Write(self._path + "_" + self._leg)
if doFit:
fitFunc.Write(self._path + "_" + self._leg + "_fitResult")
else:
print("Denominator and Numerator must be defined for path ", self._path, ", leg ", self._leg)
return 0.0
print "Number of Events: ", num_events4
# this is the full off-beam file
file6_path = sys.argv[6]
file6 = TFile(file6_path, 'Read')
if(file6.IsOpen()):
print 'File ', file6_path, ' is open'
if(file6.IsOpen() == False):
quit()
dir6 = file6.AnalyzeTPCO
opt_tree6 = dir6.Get("optical_tree")
num_events6 = opt_tree6.GetEntries()
print "Total Off-Beam Events: ", num_events6
h_timing_1 = TH1D("h_timing_1", "h_timing_1", 60, 0, 20)
h_timing_2 = TH1D("h_timing_2", "h_timing_2", 60, 0, 20)
h_timing_3 = TH1D("h_timing_3", "h_timing_3", 60, 0, 20)
h_timing_4 = TH1D("h_timing_4", "h_timing_4", 60, 0, 20)
h_timing_1_2 = TH1D("h_timing_1_2", "h_timing_1_2", 60, 0, 20)
h_timing_2_2 = TH1D("h_timing_2_2", "h_timing_2_2", 60, 0, 20)
h_timing_3_2 = TH1D("h_timing_3_2", "h_timing_3_2", 60, 0, 20)
h_timing_4_2 = TH1D("h_timing_4_2", "h_timing_4_2", 60, 0, 20)
h_pe_1 = TH1D("h_pe_1", "h_pe_1", 80, 0, 2000)
h_pe_2 = TH1D("h_pe_2", "h_pe_2", 80, 0, 2000)
h_pe_3 = TH1D("h_pe_3", "h_pe_3", 80, 0, 2000)
h_pe_4 = TH1D("h_pe_4", "h_pe_4", 80, 0, 2000)
h_pe_1_2 = TH1D("h_pe_1_2", "h_pe_1_2", 80, 0, 2000)
'kkMass': [220, 0.8, 3.0],
'bdMass': [320, 4.8, 8.0],
'kpiMass': [190, 0.6, 2.5],
'bdbarMass': [320, 4.8, 8.0],
'kpibarMass': [190, 0.6, 2.5],
'tk1Pt': [100, 0., 80.],
'tk2Pt': [100, 0., 80.],
}
ntuple = TNtupleD(treeName, 'flat ntuple for roofit',
':'.join(fillNames))
print ' joint names ' + ':'.join(
fillNames) + 'with size = {0}'.format(len(fillNames))
hists = {
fillName: TH1D(treeName + '_' + fillName, fillName, num[0],
num[1], num[2])
for fillName, num in fillPars.iteritems()
}
for event in mytree:
nCand = event.candSize
for i in range(nCand):
tk1p4 = TLorentzVector(event.tk1P1[i], event.tk1P2[i],
event.tk1P3[i], event.tk1P0[i])
tk2p4 = TLorentzVector(event.tk2P1[i], event.tk2P2[i],
event.tk2P3[i], event.tk2P0[i])
pmup4 = TLorentzVector(event.pmuP1[i], event.pmuP2[i],
event.pmuP3[i], event.pmuP0[i])
nmup4 = TLorentzVector(event.nmuP1[i], event.nmuP2[i],
event.nmuP3[i], event.nmuP0[i])
mumup4 = pmup4 + nmup4