def GetBDTfile(dtype,polarity):
bdtfname = filedir+'Lb_'+dtype+'_'+polarity+'_MVA.root'
if os.path.isfile(bdtfname):
print('BDT file already created')
else:
print()
print('>>> Creating file with BDT variable')
print()
AddBDTinfo(ifname, 'DecayTree', bdtfname, 'MC',
pickled_model_path = '../PIDGen_PIDCalib_MVA/xgb_reg.pkl')
return bdtfname
def GetBDTfile(ifname):
bdtfname = ifname[0:-5] + '_MVA.root'
if os.path.isfile(bdtfname):
print('BDT file already created')
else:
print()
print('>>> Creating file with BDT variable')
print()
AddBDTinfo(ifname,
'tupleout/DecayTree',
bdtfname,
'MC',
pickled_model_path='../PIDGen_PIDCalib_MVA/xgb_reg.pkl')
return bdtfname
def LoadBDTdf(dtype,polarity):
dflist_bdt=[]
ifname = filedir+'Data/Lb_'+dtype+'_'+polarity+'.root'
bdtfname = ifname[0:-5]+'_MVA.root'
if os.path.isfile(bdtfname):
print('BDT file already created')
else:
print()
print('>>> Creating file with BDT variable')
print()
AddBDTinfo(ifname, 'tupleout/DecayTree', bdtfname, 'Data',
pickled_model_path = '../PIDGen_PIDCalib_MVA/xgb_reg.pkl')
for df in uproot.pandas.iterate(bdtfname,'DecayTree',
'bdt', entrysteps=500000):
dflist_bdt.append(df)
return dflist_bdt
def CreateSelectionTreeData(ifname, tree, dtype, polarity, nevts):
print(ifname)
bdtfname = ifname[0:-5] + '_MVA.root'
if os.path.isfile(bdtfname):
print('BDT file already created')
else:
print()
print('>>> Creating file with BDT variable')
print()
AddBDTinfo(ifname,
'tupleout/DecayTree',
bdtfname,
'Data',
pickled_model_path='../PIDGen_PIDCalib_MVA/xgb_reg.pkl')
df_bdt = read_root(bdtfname, 'DecayTree')
print('HERE')
ofname = ifname[0:-5] + '_Selections_' + datetime.now().strftime(
"%d-%m-%Y_%I-%M-%S_%p") + '.root'
of = r.TFile(ofname, 'RECREATE')
ot = r.TTree('DecayTree', 'DecayTree')
L0 = array('i', [0])
HLT1 = array('i', [0])
HLT2 = array('i', [0])
TFinal = array('i', [0])
LcMass = array('i', [0])
PIDcut = array('i', [0])
PIDCalibCut = array('i', [0])
TOTpreselection = array('i', [0])
PassBDT = array('i', [0])
RemoveDDst = array('i', [0])
FinalSelection = array('i', [0])
isIsolated = array('i', [0])
isKenriched = array('i', [0])
ot.Branch('L0', L0, 'L0/I')
ot.Branch('HLT1', HLT1, 'HLT1/I')
ot.Branch('HLT2', HLT2, 'HLT2/I')
ot.Branch('Trigger_Final', TFinal, 'Trigger_Final/I')
ot.Branch('LcMass', LcMass, 'LcMass/I')
ot.Branch('PIDcut', PIDcut, 'PIDcut/I')
ot.Branch('PIDCalibCut', PIDCalibCut, 'PIDCalibCut/I')
ot.Branch('TOTpreselection', TOTpreselection, 'TOTpreselection/I')
ot.Branch('PassBDT', PassBDT, 'PassBDT/I')
ot.Branch('RemoveDDst', RemoveDDst, 'RemoveDDst/I')
ot.Branch('FinalSelection', FinalSelection, 'FinalSelection/I')
ot.Branch('isIsolated', isIsolated, 'isIsolated/I')
ot.Branch('isKenriched', isKenriched, 'isKenriched/I')
t.SetBranchStatus('*', 0)
varsON = [
'Lb_L0Global_TIS', 'Lb_L0HadronDecision_TOS',
'Lc_Hlt1TrackMVADecision_TOS', 'Lc_Hlt1TwoTrackMVADecision_TOS',
'Lb_Hlt2XcMuXForTauB2XcMuDecision_TOS',
'Lb_Hlt2XcMuXForTauB2XcFakeMuDecision_TOS', 'Lc_M', 'p_ProbNNp',
'p_ProbNNk', 'mu_PID*', '*_P', '*_PT', 'nTracks', 'runNumber',
'eventNumber', 'Lb_ISOLATION_*', 'mu_PX', 'mu_PY', 'mu_PZ', 'mu_ID',
'Lc_PX', 'Lc_PY', 'Lc_PZ'
]
for var in varsON:
t.SetBranchStatus(var, 1)
for i in range(nevts):
if i % 500000 == 0:
print(i)
L0[0] = L0TriggerData(tree, i)
HLT1[0] = HLT1TriggerData(tree, i)
HLT2[0] = HLT2TriggerData(tree, i, dtype)
TFinal[0] = GetTriggerData(L0[0], HLT1[0], HLT2[0])
LcMass[0] = LcMussCut(tree, i)
PIDcut[0] = GetPIDCutData(tree, i, dtype)
PIDCalibCut[0] = GetPIDCalibCuts(tree, i)
TOTpreselection[0] = GetFinalPreselectionData(TFinal[0], LcMass[0],
PIDcut[0],
PIDCalibCut[0])
runN, evtN = GetRunEventNumber(tree, i)
#bdt = df_bdt.loc[(df_bdt['eventNumber']==evtN)&(df_bdt['runNumber']==runN),'bdt']
runN_2, evtN_2 = df_bdt.loc[i, 'runNumber'], df_bdt.loc[i,
'eventNumber']
if runN != runN_2 or evtN != evtN_2:
print('ERROR!!!!')
#print(runN, runN_2, evtN, evtN_2)
bdt = df_bdt.loc[i, 'bdt']
#print(bdt)
PassBDT[0] = CheckSurvivalBDT(bdt, BDTcut)
RemoveDDst[0] = CheckPProbNNcut(tree, i)
FinalSelection[0] = GetFinalSelectionData(TOTpreselection[0],
PassBDT[0], RemoveDDst[0])
isKenriched[0] = CheckIfIsKenriched(tree, i)
isIsolated[0] = CheckIfIsIsolated(tree, i)
ot.Fill()
of.Write()
of.Close()