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#!/usr/bin/env python
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import sys
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import os
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import ROOT
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from ROOT import TFile
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from array import array
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from math import sqrt
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from copy import copy
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#suppres the EvalInstace conversion warning bug
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import warnings
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warnings.filterwarnings( action='ignore', category=RuntimeWarning, message='creating converter.*' )
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from ConfigParser import SafeConfigParser
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from samplesinfo import sample
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from mvainfos import mvainfo
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import pickle
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from progbar import progbar
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from printcolor import printc
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#CONFIGURE
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#load config
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config = SafeConfigParser()
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config.read('./config')
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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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#TreeVar Array
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MVA_Vars={}
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for systematic in systematics:
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MVA_Vars[systematic]=config.get('treeVars',systematic)
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MVA_Vars[systematic]=MVA_Vars[systematic].split(' ')
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######################
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#Evaluate multi: Must Have same treeVars!!!
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Apath=sys.argv[1]
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arglist=sys.argv[2] #RTight_blavla,bsbsb
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#for axample
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#0 5 0
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#and
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#5 -1 1
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start=int(sys.argv[3])
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stop=int(sys.argv[4])
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doinfo=bool(int(sys.argv[5]))
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MVAlist=arglist.split(',')
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#CONFIG
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#factory
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factoryname=config.get('factory','factoryname')
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#MVA
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#MVAnames=[]
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#for MVA in MVAlist:
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# print MVA
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# MVAnames.append(config.get(MVA,'MVAname'))
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#print Wdir+'/weights/'+factoryname+'_'+MVAname+'.info'
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#MVAinfofiles=[]
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MVAinfos=[]
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for MVAname in MVAlist:
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MVAinfofile = open(Wdir+'/weights/'+factoryname+'_'+MVAname+'.info','r')
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MVAinfos.append(pickle.load(MVAinfofile))
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MVAinfofile.close()
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treeVarSet=MVAinfos[0].varset
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#variables
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#TreeVar Array
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MVA_Vars={}
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for systematic in systematics:
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MVA_Vars[systematic]=config.get(treeVarSet,systematic)
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MVA_Vars[systematic]=MVA_Vars[systematic].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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#progbar quatsch
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longe=40
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#Workdir
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workdir=ROOT.gDirectory.GetPath()
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#os.mkdir(Apath+'/MVAout')
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#Book TMVA readers: MVAlist=["MMCC_bla","CC5050_bla"]
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readers=[]
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for MVA in MVAlist:
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readers.append(ROOT.TMVA.Reader("!Color:!Silent"))
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#define variables and specatators
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MVA_var_buffer = []
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for i in range(len( MVA_Vars['Nominal'])):
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MVA_var_buffer.append(array( 'f', [ 0 ] ))
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for reader in readers:
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reader.AddVariable( MVA_Vars['Nominal'][i],MVA_var_buffer[i])
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#MVA_spectator_buffer = []
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#for i in range(len(spectators)):
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# MVA_spectator_buffer.append(array( 'f', [ 0 ] ))
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# for reader in readers:
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# reader.AddSpectator(spectators[i],MVA_spectator_buffer[i])
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#Load raeder
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for i in range(0,len(readers)):
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readers[i].BookMVA(MVAinfos[i].MVAname,MVAinfos[i].getweightfile())
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#--> Now the MVA is booked
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#Apply samples
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infofile = open(Apath+'/samples.info','r')
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Ainfo = pickle.load(infofile)
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infofile.close()
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#eval
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for job in Ainfo[start:stop]:
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#get trees:
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input = TFile.Open(job.getpath(),'read')
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outfile = TFile.Open(job.path+'/MVAout/'+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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outfile.cd()
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#print key.GetName()
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obj.Write(key.GetName())
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tree = input.Get(job.tree)
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nEntries = tree.GetEntries()
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outfile.cd()
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newtree = tree.CloneTree(0)
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#MCs:
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if job.type != 'DATA':
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MVA_formulas={}
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for systematic in systematics:
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#print '\t\t - ' + systematic
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MVA_formulas[systematic]=[]
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#create TTreeFormulas
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for j in range(len( MVA_Vars['Nominal'])):
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MVA_formulas[systematic].append(ROOT.TTreeFormula("MVA_formula%s_%s"%(j,systematic),MVA_Vars[systematic][j],tree))
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outfile.cd()
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#Setup Branches
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MVAbranches=[]
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for i in range(0,len(readers)):
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MVAbranches.append(array('f',[0]*9))
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newtree.Branch(MVAinfos[i].MVAname,MVAbranches[i],'nominal:JER_up:JER_down:JES_up:JES_down:beff_up:beff_down:bmis_up:bmis_down/F')
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print '\n--> ' + job.name +':'
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#progbar setup
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if nEntries >= longe:
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step=int(nEntries/longe)
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long=longe
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else:
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long=nEntries
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step = 1
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bar=progbar(long)
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#Fill event by event:
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for entry in range(0,nEntries):
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if entry % step == 0:
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bar.move()
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#load entry
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tree.GetEntry(entry)
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for systematic in systematics:
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for j in range(len( MVA_Vars['Nominal'])):
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MVA_var_buffer[j][0] = MVA_formulas[systematic][j].EvalInstance()
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for j in range(0,len(readers)):
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MVAbranches[j][systematics.index(systematic)] = readers[j].EvaluateMVA(MVAinfos[j].MVAname)
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#Fill:
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newtree.Fill()
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newtree.AutoSave()
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outfile.Close()
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#DATA:
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if job.type == 'DATA':
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#MVA Formulas
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MVA_formulas_Nominal = []
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#create TTreeFormulas
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for j in range(len( MVA_Vars['Nominal'])):
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MVA_formulas_Nominal.append(ROOT.TTreeFormula("MVA_formula%s_Nominal"%j, MVA_Vars['Nominal'][j],tree))
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outfile.cd()
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MVAbranches=[]
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for i in range(0,len(readers)):
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MVAbranches.append(array('f',[0]))
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newtree.Branch(MVAinfos[i].MVAname,MVAbranches[i],'nominal/F')
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#progbar
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print '\n--> ' + job.name +':'
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if nEntries >= longe:
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step=int(nEntries/longe)
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long=longe
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else:
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long=nEntries
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step = 1
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bar=progbar(long)
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#Fill event by event:
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for entry in range(0,nEntries):
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if entry % step == 0:
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bar.move()
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#load entry
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tree.GetEntry(entry)
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#nominal:
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for j in range(len( MVA_Vars['Nominal'])):
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MVA_var_buffer[j][0] = MVA_formulas_Nominal[j].EvalInstance()
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for j in range(0,len(readers)):
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MVAbranches[j][0]= readers[j].EvaluateMVA(MVAinfos[j].MVAname)
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newtree.Fill()
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newtree.AutoSave()
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outfile.Close()
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#Update Info:
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if doinfo:
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for job in Ainfo:
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for MVAinfo in MVAinfos:
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job.addcomment('Added MVA %s'%MVAinfo.MVAname)
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job.addpath('/MVAout')
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infofile = open(Apath+'/MVAout/samples.info','w')
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pickle.dump(Ainfo,infofile)
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infofile.close()
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