| 1 |
nmohr |
1.1 |
#!/usr/bin/env python
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from samplesclass import sample
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from printcolor import printc
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import pickle
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import sys
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import os
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import ROOT
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import math
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import shutil
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from ROOT import TFile
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import ROOT
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from array import array
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import warnings
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warnings.filterwarnings( action='ignore', category=RuntimeWarning, message='creating converter.*' )
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#usage: ./write_regression_systematic.py path
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path=sys.argv[1]
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#load info
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infofile = open(path+'/samples.info','r')
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info = pickle.load(infofile)
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infofile.close()
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#os.mkdir(path+'/sys')
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def deltaPhi(phi1, phi2):
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result = phi1 - phi2
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while (result > math.pi): result -= 2*math.pi
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while (result <= -math.pi): result += 2*math.pi
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return result
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def corrCSV(btag, csv, flav):
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if(csv < 0.): return csv
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if(csv > 1.): return csv;
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if(flav == 0): return csv;
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if(math.fabs(flav) == 5): return btag.ib.Eval(csv)
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if(math.fabs(flav) == 4): return btag.ic.Eval(csv)
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if(math.fabs(flav) != 4 and math.fabs(flav) != 5): return btag.il.Eval(csv)
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return -10000
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for job in info:
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#print job.name
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#if job.name != 'ZH125': continue
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ROOT.gROOT.ProcessLine(
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"struct H {\
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int HiggsFlag;\
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float mass;\
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float pt;\
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float eta;\
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float phi;\
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float dR;\
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float dPhi;\
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float dEta;\
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} ;"
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)
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ROOT.gROOT.LoadMacro('../interface/btagshape.h+')
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from ROOT import BTagShape
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btagNom = BTagShape("../data/csvdiscr.root")
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btagNom.computeFunctions()
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btagUp = BTagShape("../data/csvdiscr.root")
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btagUp.computeFunctions(+1.,0.)
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btagDown = BTagShape("../data/csvdiscr.root")
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btagDown.computeFunctions(-1.,0.)
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btagFUp = BTagShape("../data/csvdiscr.root")
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btagFUp.computeFunctions(0.,+1.)
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btagFDown = BTagShape("../data/csvdiscr.root")
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btagFDown.computeFunctions(0.,-1.)
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print '\t - %s' %(job.name)
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input = TFile.Open(job.getpath(),'read')
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output = TFile.Open(job.path+'/sys/'+job.prefix+job.identifier+'.root','recreate')
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input.cd()
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obj = ROOT.TObject
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for key in ROOT.gDirectory.GetListOfKeys():
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input.cd()
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obj = key.ReadObj()
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#print obj.GetName()
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if obj.GetName() == job.tree:
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continue
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output.cd()
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#print key.GetName()
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obj.Write(key.GetName())
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nmohr |
1.3 |
input.cd()
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nmohr |
1.1 |
tree = input.Get(job.tree)
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nEntries = tree.GetEntries()
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job.addpath('/sys')
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if job.type != 'DATA':
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job.SYS = ['Nominal','JER_up','JER_down','JES_up','JES_down','beff_up','beff_down','bmis_up','bmis_down']
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H = ROOT.H()
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HNoReg = ROOT.H()
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tree.SetBranchStatus('H',0)
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output.cd()
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newtree = tree.CloneTree(0)
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hJ0 = ROOT.TLorentzVector()
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hJ1 = ROOT.TLorentzVector()
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regWeight = "../data/MVA_BDT_REG_May23.weights.xml"
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regDict = {"Jet_pt": "hJet_pt", "Jet_eta": "hJet_eta", "Jet_e": "hJet_e", "Jet_JECUnc": "hJet_JECUnc", "Jet_chf": "hJet_chf","Jet_nconstituents": "hJet_nconstituents", "Jet_vtxPt": "hJet_vtxPt", "Jet_vtx3dL": "hJet_vtx3dL", "Jet_vtx3deL": "hJet_vtx3deL"}
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regVars = ["Jet_pt","Jet_eta","Jet_e","Jet_JECUnc", "Jet_chf","Jet_nconstituents", "Jet_vtxPt", "Jet_vtx3dL", "Jet_vtx3deL"]
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#Regression branches
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applyRegression = True
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hJet_pt = array('f',[0]*2)
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hJet_e = array('f',[0]*2)
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newtree.Branch( 'H', H , 'HiggsFlag/I:mass/F:pt/F:eta:phi/F:dR/F:dPhi/F:dEta/F' )
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newtree.Branch( 'HNoReg', HNoReg , 'HiggsFlag/I:mass/F:pt/F:eta:phi/F:dR/F:dPhi/F:dEta/F' )
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Event = array('f',[0])
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METet = array('f',[0])
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rho25 = array('f',[0])
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METphi = array('f',[0])
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fRho25 = ROOT.TTreeFormula("rho25",'rho25',tree)
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fEvent = ROOT.TTreeFormula("Event",'EVENT.event',tree)
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fMETet = ROOT.TTreeFormula("METet",'METnoPU.et',tree)
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fMETphi = ROOT.TTreeFormula("METphi",'METnoPU.phi',tree)
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hJet_MET_dPhi = array('f',[0]*2)
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hJet_regWeight = array('f',[0]*2)
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hJet_MET_dPhiArray = [array('f',[0]),array('f',[0])]
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newtree.Branch('hJet_MET_dPhi',hJet_MET_dPhi,'hJet_MET_dPhi[2]/F')
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newtree.Branch('hJet_regWeight',hJet_regWeight,'hJet_regWeight[2]/F')
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readerJet0 = ROOT.TMVA.Reader("!Color:!Silent" )
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readerJet1 = ROOT.TMVA.Reader("!Color:!Silent" )
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theForms = {}
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theVars0 = {}
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for var in regVars:
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theVars0[var] = array( 'f', [ 0 ] )
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readerJet0.AddVariable(var,theVars0[var])
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theForms['form_reg_%s_0'%(regDict[var])] = ROOT.TTreeFormula("form_reg_%s_0"%(regDict[var]),'%s[0]' %(regDict[var]),tree)
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readerJet0.AddVariable( "Jet_MET_dPhi", hJet_MET_dPhiArray[0] )
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readerJet0.AddVariable( "METet", METet )
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readerJet0.AddVariable( "rho25", rho25 )
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theVars1 = {}
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for var in regVars:
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theVars1[var] = array( 'f', [ 0 ] )
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readerJet1.AddVariable(var,theVars1[var])
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theForms['form_reg_%s_1'%(regDict[var])] = ROOT.TTreeFormula("form_reg_%s_1"%(regDict[var]),'%s[1]' %(regDict[var]),tree)
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readerJet1.AddVariable( "Jet_MET_dPhi", hJet_MET_dPhiArray[1] )
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readerJet1.AddVariable( "METet", METet )
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readerJet1.AddVariable( "rho25", rho25 )
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readerJet0.BookMVA( "jet0Regression", regWeight );
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readerJet1.BookMVA( "jet1Regression", regWeight );
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bortigno |
1.4 |
#Add training Flag
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EventForTraining = array('f',[0])
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newtree.Branch('EventForTraining',EventForTraining,'EventForTraining/F')
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EventForTraining[0]=0
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bortigno |
1.6 |
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lheWeight = array('f',[0])
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newtree.Branch('lheWeight',lheWeight,'lheWeight/F')
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bortigno |
1.7 |
lheWeight[0] = 1.
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bortigno |
1.6 |
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bortigno |
1.4 |
#EventForTraining=0
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bortigno |
1.5 |
TFlag=ROOT.TTreeFormula("EventForTraining","EVENT.event%2",tree)
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nmohr |
1.1 |
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if job.type != 'DATA':
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#CSV branches
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hJet_flavour = array('f',[0]*2)
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hJet_csv = array('f',[0]*2)
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hJet_csvOld = array('f',[0]*2)
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hJet_csvUp = array('f',[0]*2)
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hJet_csvDown = array('f',[0]*2)
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hJet_csvFUp = array('f',[0]*2)
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hJet_csvFDown = array('f',[0]*2)
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newtree.Branch('hJet_csvOld',hJet_csvOld,'hJet_csvOld[2]/F')
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newtree.Branch('hJet_csvUp',hJet_csvUp,'hJet_csvUp[2]/F')
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newtree.Branch('hJet_csvDown',hJet_csvDown,'hJet_csvDown[2]/F')
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newtree.Branch('hJet_csvFUp',hJet_csvFUp,'hJet_csvFUp[2]/F')
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newtree.Branch('hJet_csvFDown',hJet_csvFDown,'hJet_csvFDown[2]/F')
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#JER branches
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hJet_pt_JER_up = array('f',[0]*2)
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newtree.Branch('hJet_pt_JER_up',hJet_pt_JER_up,'hJet_pt_JER_up[2]/F')
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hJet_pt_JER_down = array('f',[0]*2)
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newtree.Branch('hJet_pt_JER_down',hJet_pt_JER_down,'hJet_pt_JER_down[2]/F')
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hJet_e_JER_up = array('f',[0]*2)
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newtree.Branch('hJet_e_JER_up',hJet_e_JER_up,'hJet_e_JER_up[2]/F')
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hJet_e_JER_down = array('f',[0]*2)
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newtree.Branch('hJet_e_JER_down',hJet_e_JER_down,'hJet_e_JER_down[2]/F')
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H_JER = array('f',[0]*4)
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newtree.Branch('H_JER',H_JER,'mass_up:mass_down:pt_up:pt_down/F')
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#JES branches
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hJet_pt_JES_up = array('f',[0]*2)
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newtree.Branch('hJet_pt_JES_up',hJet_pt_JES_up,'hJet_pt_JES_up[2]/F')
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hJet_pt_JES_down = array('f',[0]*2)
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newtree.Branch('hJet_pt_JES_down',hJet_pt_JES_down,'hJet_pt_JES_down[2]/F')
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hJet_e_JES_up = array('f',[0]*2)
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newtree.Branch('hJet_e_JES_up',hJet_e_JES_up,'hJet_e_JES_up[2]/F')
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hJet_e_JES_down = array('f',[0]*2)
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newtree.Branch('hJet_e_JES_down',hJet_e_JES_down,'hJet_e_JES_down[2]/F')
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H_JES = array('f',[0]*4)
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newtree.Branch('H_JES',H_JES,'mass_up:mass_down:pt_up:pt_down/F')
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#iter=0
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for entry in range(0,nEntries):
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tree.GetEntry(entry)
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#fill training flag
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#iter+=1
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#if (iter%2==0):
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# EventForTraining[0]=1
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#else:
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# EventForTraining[0]=0
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#iter+=1
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bortigno |
1.4 |
# if job.type != 'DATA':
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# EventForTraining=int(not TFlag.EvalInstance())
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EventForTraining[0]=int(not TFlag.EvalInstance())
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nmohr |
1.1 |
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#get
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hJet_pt = tree.hJet_pt
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hJet_e = tree.hJet_e
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hJet_pt0 = tree.hJet_pt[0]
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hJet_pt1 = tree.hJet_pt[1]
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hJet_eta0 = tree.hJet_eta[0]
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hJet_eta1 = tree.hJet_eta[1]
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hJet_genPt0 = tree.hJet_genPt[0]
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hJet_genPt1 = tree.hJet_genPt[1]
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hJet_e0 = tree.hJet_e[0]
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hJet_e1 = tree.hJet_e[1]
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hJet_phi0 = tree.hJet_phi[0]
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hJet_phi1 = tree.hJet_phi[1]
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hJet_JECUnc0 = tree.hJet_JECUnc[0]
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hJet_JECUnc1 = tree.hJet_JECUnc[1]
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| 237 |
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| 238 |
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Event[0]=fEvent.EvalInstance()
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| 239 |
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METet[0]=fMETet.EvalInstance()
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| 240 |
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rho25[0]=fRho25.EvalInstance()
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| 241 |
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METphi[0]=fMETphi.EvalInstance()
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| 242 |
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for key, value in regDict.items():
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| 243 |
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theVars0[key][0] = theForms["form_reg_%s_0" %(value)].EvalInstance()
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| 244 |
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theVars1[key][0] = theForms["form_reg_%s_1" %(value)].EvalInstance()
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| 245 |
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for i in range(2):
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| 246 |
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hJet_MET_dPhi[i] = deltaPhi(METphi[0],tree.hJet_phi[i])
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| 247 |
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hJet_MET_dPhiArray[i][0] = deltaPhi(METphi[0],tree.hJet_phi[i])
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| 248 |
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| 249 |
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if applyRegression:
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| 250 |
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hJ0.SetPtEtaPhiE(hJet_pt0,hJet_eta0,hJet_phi0,hJet_e0)
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| 251 |
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hJ1.SetPtEtaPhiE(hJet_pt1,hJet_eta1,hJet_phi1,hJet_e1)
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| 252 |
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HNoReg.HiggsFlag = 1
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| 253 |
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HNoReg.mass = (hJ0+hJ1).M()
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| 254 |
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HNoReg.pt = (hJ0+hJ1).Pt()
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| 255 |
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HNoReg.eta = (hJ0+hJ1).Eta()
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| 256 |
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HNoReg.phi = (hJ0+hJ1).Phi()
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| 257 |
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HNoReg.dR = hJ0.DeltaR(hJ1)
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| 258 |
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HNoReg.dPhi = hJ0.DeltaPhi(hJ1)
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| 259 |
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HNoReg.dEta = abs(hJ0.Eta()-hJ1.Eta())
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| 260 |
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rPt0 = readerJet0.EvaluateRegression( "jet0Regression" )[0]
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| 261 |
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rPt1 = readerJet1.EvaluateRegression( "jet1Regression" )[0]
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| 262 |
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hJet_regWeight[0] = rPt0/hJet_pt0
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| 263 |
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hJet_regWeight[1] = rPt1/hJet_pt1
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| 264 |
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rE0 = hJet_e0*hJet_regWeight[0]
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| 265 |
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rE1 = hJet_e1*hJet_regWeight[1]
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| 266 |
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hJ0.SetPtEtaPhiE(rPt0,hJet_eta0,hJet_phi0,rE0)
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| 267 |
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hJ1.SetPtEtaPhiE(rPt1,hJet_eta1,hJet_phi1,rE1)
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| 268 |
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tree.hJet_pt[0] = rPt0
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| 269 |
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tree.hJet_pt[1] = rPt1
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| 270 |
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tree.hJet_e[0] = rE0
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| 271 |
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tree.hJet_e[1] = rE1
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| 272 |
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H.HiggsFlag = 1
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| 273 |
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H.mass = (hJ0+hJ1).M()
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| 274 |
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H.pt = (hJ0+hJ1).Pt()
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| 275 |
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|
H.eta = (hJ0+hJ1).Eta()
|
| 276 |
|
|
H.phi = (hJ0+hJ1).Phi()
|
| 277 |
|
|
H.dR = hJ0.DeltaR(hJ1)
|
| 278 |
|
|
H.dPhi = hJ0.DeltaPhi(hJ1)
|
| 279 |
|
|
H.dEta = abs(hJ0.Eta()-hJ1.Eta())
|
| 280 |
|
|
if hJet_regWeight[0] > 5. or hJet_regWeight[1] > 5.:
|
| 281 |
|
|
print 'MET %.2f' %(METet[0])
|
| 282 |
|
|
print 'rho25 %.2f' %(rho25[0])
|
| 283 |
|
|
for key, value in regDict.items():
|
| 284 |
|
|
print '%s 0: %.2f'%(key, theVars0[key][0])
|
| 285 |
|
|
print '%s 0: %.2f'%(key, theVars1[key][0])
|
| 286 |
|
|
for i in range(2):
|
| 287 |
|
|
print 'dPhi %.0f %.2f' %(i,hJet_MET_dPhiArray[i][0])
|
| 288 |
|
|
print 'corr 0 %.2f' %(hJet_regWeight[0])
|
| 289 |
|
|
print 'corr 1 %.2f' %(hJet_regWeight[1])
|
| 290 |
|
|
print 'Event %.0f' %(Event[0])
|
| 291 |
|
|
print 'rPt0 %.2f' %(rPt0)
|
| 292 |
|
|
print 'rPt1 %.2f' %(rPt1)
|
| 293 |
|
|
print 'rE0 %.2f' %(rE0)
|
| 294 |
|
|
print 'rE1 %.2f' %(rE1)
|
| 295 |
|
|
print 'Mass %.2f' %(H.mass)
|
| 296 |
|
|
|
| 297 |
|
|
if job.type == 'DATA':
|
| 298 |
|
|
newtree.Fill()
|
| 299 |
|
|
continue
|
| 300 |
|
|
|
| 301 |
|
|
for i in range(2):
|
| 302 |
|
|
flavour = tree.hJet_flavour[i]
|
| 303 |
|
|
csv = tree.hJet_csv[i]
|
| 304 |
|
|
hJet_csvOld[i] = csv
|
| 305 |
|
|
tree.hJet_csv[i] = corrCSV(btagNom,csv,flavour)
|
| 306 |
|
|
hJet_csvDown[i] = corrCSV(btagDown,csv,flavour)
|
| 307 |
|
|
hJet_csvUp[i] = corrCSV(btagUp,csv,flavour)
|
| 308 |
|
|
hJet_csvFDown[i] = corrCSV(btagFDown,csv,flavour)
|
| 309 |
|
|
hJet_csvFUp[i] = corrCSV(btagFUp,csv,flavour)
|
| 310 |
|
|
|
| 311 |
|
|
for updown in ['up','down']:
|
| 312 |
|
|
#JER
|
| 313 |
|
|
if updown == 'up':
|
| 314 |
|
|
inner = 0.06
|
| 315 |
|
|
outer = 0.1
|
| 316 |
|
|
if updown == 'down':
|
| 317 |
|
|
inner = -0.06
|
| 318 |
|
|
outer = -0.1
|
| 319 |
|
|
#Calculate
|
| 320 |
|
|
if abs(hJet_eta0)<1.1: res0 = inner
|
| 321 |
|
|
else: res0 = outer
|
| 322 |
|
|
if abs(hJet_eta1)<1.1: res1 = inner
|
| 323 |
|
|
else: res1 = outer
|
| 324 |
|
|
rPt0 = hJet_pt0 + (hJet_pt0-hJet_genPt0)*res0
|
| 325 |
|
|
rPt1 = hJet_pt1 + (hJet_pt1-hJet_genPt1)*res1
|
| 326 |
|
|
rE0 = hJet_e0*rPt0/hJet_pt0
|
| 327 |
|
|
rE1 = hJet_e1*rPt1/hJet_pt1
|
| 328 |
|
|
if applyRegression:
|
| 329 |
|
|
theVars0['Jet_pt'][0] = rPt0
|
| 330 |
|
|
theVars1['Jet_pt'][0] = rPt1
|
| 331 |
|
|
theVars0['Jet_e'][0] = rE0
|
| 332 |
|
|
theVars1['Jet_e'][0] = rE1
|
| 333 |
|
|
rPt0 = readerJet0.EvaluateRegression( "jet0Regression" )[0]
|
| 334 |
|
|
rPt1 = readerJet1.EvaluateRegression( "jet1Regression" )[0]
|
| 335 |
|
|
rE0 = hJet_e0*rPt0/hJet_pt0
|
| 336 |
|
|
rE1 = hJet_e1*rPt1/hJet_pt1
|
| 337 |
|
|
hJ0.SetPtEtaPhiE(rPt0,hJet_eta0,hJet_phi0,rE0)
|
| 338 |
|
|
hJ1.SetPtEtaPhiE(rPt1,hJet_eta1,hJet_phi1,rE1)
|
| 339 |
|
|
#Set
|
| 340 |
|
|
if updown == 'up':
|
| 341 |
|
|
hJet_pt_JER_up[0]=rPt0
|
| 342 |
|
|
hJet_pt_JER_up[1]=rPt1
|
| 343 |
|
|
hJet_e_JER_up[0]=rE0
|
| 344 |
|
|
hJet_e_JER_up[1]=rE1
|
| 345 |
|
|
H_JER[0]=(hJ0+hJ1).M()
|
| 346 |
|
|
H_JER[2]=(hJ0+hJ1).Pt()
|
| 347 |
|
|
if updown == 'down':
|
| 348 |
|
|
hJet_pt_JER_down[0]=rPt0
|
| 349 |
|
|
hJet_pt_JER_down[1]=rPt1
|
| 350 |
|
|
hJet_e_JER_down[0]=rE0
|
| 351 |
|
|
hJet_e_JER_down[1]=rE1
|
| 352 |
|
|
H_JER[1]=(hJ0+hJ1).M()
|
| 353 |
|
|
H_JER[3]=(hJ0+hJ1).Pt()
|
| 354 |
|
|
|
| 355 |
|
|
#JES
|
| 356 |
|
|
if updown == 'up':
|
| 357 |
|
|
variation=1
|
| 358 |
|
|
if updown == 'down':
|
| 359 |
|
|
variation=-1
|
| 360 |
|
|
#calculate
|
| 361 |
|
|
rPt0 = hJet_pt0*(1+variation*hJet_JECUnc0)
|
| 362 |
|
|
rPt1 = hJet_pt1*(1+variation*hJet_JECUnc1)
|
| 363 |
|
|
rE0 = hJet_e0*(1+variation*hJet_JECUnc0)
|
| 364 |
|
|
rE1 = hJet_e1*(1+variation*hJet_JECUnc1)
|
| 365 |
|
|
if applyRegression:
|
| 366 |
|
|
theVars0['Jet_pt'][0] = rPt0
|
| 367 |
|
|
theVars1['Jet_pt'][0] = rPt1
|
| 368 |
|
|
theVars0['Jet_e'][0] = rE0
|
| 369 |
|
|
theVars1['Jet_e'][0] = rE1
|
| 370 |
|
|
rPt0 = readerJet0.EvaluateRegression( "jet0Regression" )[0]
|
| 371 |
|
|
rPt1 = readerJet1.EvaluateRegression( "jet1Regression" )[0]
|
| 372 |
|
|
rE0 = hJet_e0*rPt0/hJet_pt0
|
| 373 |
|
|
rE1 = hJet_e1*rPt1/hJet_pt1
|
| 374 |
|
|
hJ0.SetPtEtaPhiE(rPt0,hJet_eta0,hJet_phi0,rE0)
|
| 375 |
|
|
hJ1.SetPtEtaPhiE(rPt1,hJet_eta1,hJet_phi1,rE1)
|
| 376 |
|
|
#Fill
|
| 377 |
|
|
if updown == 'up':
|
| 378 |
|
|
hJet_pt_JES_up[0]=rPt0
|
| 379 |
|
|
hJet_pt_JES_up[1]=rPt1
|
| 380 |
|
|
hJet_e_JES_up[0]=rE0
|
| 381 |
|
|
hJet_e_JES_up[1]=rE1
|
| 382 |
|
|
H_JES[0]=(hJ0+hJ1).M()
|
| 383 |
|
|
H_JES[2]=(hJ0+hJ1).Pt()
|
| 384 |
|
|
if updown == 'down':
|
| 385 |
|
|
hJet_pt_JES_down[0]=rPt0
|
| 386 |
|
|
hJet_pt_JES_down[1]=rPt1
|
| 387 |
|
|
hJet_e_JES_down[0]=rE0
|
| 388 |
|
|
hJet_e_JES_down[1]=rE1
|
| 389 |
|
|
H_JES[1]=(hJ0+hJ1).M()
|
| 390 |
|
|
H_JES[3]=(hJ0+hJ1).Pt()
|
| 391 |
|
|
|
| 392 |
|
|
newtree.Fill()
|
| 393 |
|
|
|
| 394 |
|
|
newtree.AutoSave()
|
| 395 |
|
|
output.Close()
|
| 396 |
|
|
|
| 397 |
|
|
#dump info
|
| 398 |
|
|
infofile = open(path+'/sys'+'/samples.info','w')
|
| 399 |
|
|
pickle.dump(info,infofile)
|
| 400 |
|
|
infofile.close()
|
