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#!/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 ROOT
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from ROOT import TFile, TTree
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import ROOT
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from array import array
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from BetterConfigParser import BetterConfigParser
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import sys
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from mvainfos import mvainfo
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from gethistofromtree import getScale
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#warnings.filterwarnings( action='ignore', category=RuntimeWarning, message='creating converter.*' )
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#usage: ./train run gui
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#CONFIGURE
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#load config
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config = BetterConfigParser()
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#config.read('./config')
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config.read('./config7TeV')
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#GLOABAL rescale from Train/Test Spliiting:
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global_rescale=2.
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#get locations:
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Wdir=config.get('Directories','Wdir')
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#systematics
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systematics=config.get('systematics','systematics')
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systematics=systematics.split(' ')
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weightF=config.get('Weights','weightF')
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def getTree(job,cut,subsample=-1):
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newinput = TFile.Open(job.getpath(),'read')
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output.cd()
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Tree = newinput.Get(job.tree)
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#Tree.SetDirectory(0)
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if subsample>-1:
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CuttedTree=Tree.CopyTree('(%s) & (%s)'%(cut,job.subcuts[subsample]))
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#print '\t--> read in %s'%job.group[subsample]
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else:
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CuttedTree=Tree.CopyTree(cut)
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#print '\t--> read in %s'%job.name
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#CuttedTree.SetDirectory(0)
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return CuttedTree
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#def getScale(job,subsample=-1):
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# input = TFile.Open(job.getpath())
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# CountWithPU = input.Get("CountWithPU")
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# CountWithPU2011B = input.Get("CountWithPU2011B")
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# #print lumi*xsecs[i]/hist.GetBinContent(1)
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# return float(job.lumi)*float(job.xsec)*float(job.sf)/(0.46502*CountWithPU.GetBinContent(1)+0.53498*CountWithPU2011B.GetBinContent(1))*2/float(job.split)
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run=sys.argv[1]
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gui=sys.argv[2]
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#CONFIG
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#factory
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factoryname=config.get('factory','factoryname')
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factorysettings=config.get('factory','factorysettings')
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#MVA
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MVAtype=config.get(run,'MVAtype')
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MVAname=run
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MVAsettings=config.get(run,'MVAsettings')
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fnameOutput = Wdir +'/weights/'+factoryname+'_'+MVAname+'.root'
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#locations
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path=config.get(run,'path')
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TCutname=config.get(run, 'treeCut')
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TCut=config.get('Cuts',TCutname)
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#print TCut
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#signals
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signals=config.get(run,'signals')
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signals=signals.split(' ')
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#backgrounds
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backgrounds=config.get(run,'backgrounds')
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backgrounds=backgrounds.split(' ')
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treeVarSet=config.get(run,'treeVarSet')
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#variables
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#TreeVar Array
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MVA_Vars={}
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MVA_Vars['Nominal']=config.get(treeVarSet,'Nominal')
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MVA_Vars['Nominal']=MVA_Vars['Nominal'].split(' ')
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#Spectators:
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#spectators=config.get(treeVarSet,'spectators')
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#spectators=spectators.split(' ')
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#TRAINING samples
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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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#Workdir
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workdir=ROOT.gDirectory.GetPath()
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TrainCut='%s && EventForTraining==1'%TCut
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EvalCut='%s && EventForTraining==0'%TCut
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#load TRAIN trees
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Tbackgrounds = []
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TbScales = []
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Tsignals = []
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TsScales = []
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output = ROOT.TFile.Open(fnameOutput, "RECREATE")
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print '\n*** TRAINING EVENTS ***\n'
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for job in info:
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if eval(job.active):
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if job.name in signals:
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print '\tREADING IN %s AS SIG'%job.name
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Tsignal = getTree(job,TrainCut)
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ROOT.gDirectory.Cd(workdir)
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TsScale = getScale(job,global_rescale)
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Tsignals.append(Tsignal)
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TsScales.append(TsScale)
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if job.name in backgrounds:
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if job.subsamples:
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print '\tREADING IN SUBSAMPLES of %s AS BKG'%job.name
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for subsample in range(0,len(job.group)):
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print '\t- %s'%job.group[subsample]
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Tbackground = getTree(job,TrainCut,subsample)
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ROOT.gDirectory.Cd(workdir)
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TbScale = getScale(job,global_rescale,subsample)
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Tbackgrounds.append(Tbackground)
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TbScales.append(TbScale)
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else:
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print '\tREADING IN %s AS BKG'%job.name
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Tbackground = getTree(job,TrainCut)
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ROOT.gDirectory.Cd(workdir)
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TbScale = getScale(job,global_rescale)
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Tbackgrounds.append(Tbackground)
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TbScales.append(TbScale)
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#load EVALUATE trees
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Ebackgrounds = []
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EbScales = []
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Esignals = []
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EsScales = []
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print '\n*** TESTING EVENTS ***\n'
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for job in info:
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if eval(job.active):
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if job.name in signals:
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print '\tREADING IN %s AS SIG'%job.name
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Esignal = getTree(job,EvalCut)
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ROOT.gDirectory.Cd(workdir)
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EsScale = getScale(job,global_rescale)
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Esignals.append(Esignal)
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EsScales.append(EsScale)
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if job.name in backgrounds:
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if job.subsamples:
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print '\tREADING IN SUBSAMPLES of %s AS BKG'%job.name
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for subsample in range(0,len(job.group)):
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print '\t- %s'%job.group[subsample]
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Ebackground = getTree(job,EvalCut,subsample)
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ROOT.gDirectory.Cd(workdir)
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EbScale = getScale(job,global_rescale,subsample)
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Ebackgrounds.append(Ebackground)
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EbScales.append(EbScale)
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else:
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print '\tREADING IN %s AS BKG'%job.name
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Ebackground = getTree(job,EvalCut)
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ROOT.gDirectory.Cd(workdir)
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EbScale = getScale(job,global_rescale)
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Ebackgrounds.append(Ebackground)
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EbScales.append(EbScale)
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#output = ROOT.TFile.Open(fnameOutput, "RECREATE")
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factory = ROOT.TMVA.Factory(factoryname, output, factorysettings)
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#set input trees
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for i in range(len(Tsignals)):
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factory.AddSignalTree(Tsignals[i], TsScales[i], ROOT.TMVA.Types.kTraining)
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factory.AddSignalTree(Esignals[i], EsScales[i], ROOT.TMVA.Types.kTesting)
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for i in range(len(Tbackgrounds)):
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if (Tbackgrounds[i].GetEntries()>0):
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factory.AddBackgroundTree(Tbackgrounds[i], TbScales[i], ROOT.TMVA.Types.kTraining)
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if (Ebackgrounds[i].GetEntries()>0):
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factory.AddBackgroundTree(Ebackgrounds[i], EbScales[i], ROOT.TMVA.Types.kTesting)
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for var in MVA_Vars['Nominal']:
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factory.AddVariable(var,'D') # add the variables
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#for var in spectators:
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# factory.AddSpectator(var,'D') #add specators
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#Execute TMVA
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factory.SetSignalWeightExpression(weightF)
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factory.Verbose()
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factory.BookMethod(MVAtype,MVAname,MVAsettings)
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factory.TrainAllMethods()
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factory.TestAllMethods()
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factory.EvaluateAllMethods()
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output.Write()
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#WRITE INFOFILE
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infofile = open(Wdir+'/weights/'+factoryname+'_'+MVAname+'.info','w')
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info=mvainfo(MVAname)
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info.factoryname=factoryname
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info.factorysettings=factorysettings
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info.MVAtype=MVAtype
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info.MVAsettings=MVAsettings
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info.weightfilepath=Wdir+'/weights'
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info.path=path
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info.varset=treeVarSet
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info.vars=MVA_Vars['Nominal']
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#info.spectators=spectators
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pickle.dump(info,infofile)
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infofile.close()
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# open the TMVA Gui
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if gui == 'gui':
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ROOT.gROOT.ProcessLine( ".L TMVAGui.C")
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ROOT.gROOT.ProcessLine( "TMVAGui(\"%s\")" % fnameOutput )
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ROOT.gApplication.Run()
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