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runAnalysis.py
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#!/usr/bin/env python
import os
import glob
import math
import array
import subprocess
from subprocess import Popen
from ROOT import gROOT, gStyle, gSystem, TLatex
from sampleWrapperClass import *
from BoostedWSamples import *
from trainingClass import *
############################################
# Job steering #
############################################
parser = OptionParser()
parser.add_option('-b', action='store_true', dest='noX', default=False,help='no X11 windows')
parser.add_option('--createTrees', action='store_true', dest='createTrainingTrees', default=False,help='creates Training Trees')
parser.add_option('--makeControlPlots', action='store_true', dest='makeControlPlots', default=False, help='makeControlPlots')
parser.add_option('--makeTMVAPlots', action='store_true', dest='makeTMVAPlots', default=False, help='makeTMVAPlots')
parser.add_option('--makeSignalRegionControlPlots', action='store_true', dest='makeSignalRegionControlPlots', default=False,help='makeSignalRegionControlPlots')
parser.add_option('--makeTTBarControlPlots', action='store_true', dest='makeTTBarControlPlots', default=False, help='makeTTBarControlPlots')
parser.add_option('--makeFinalTree', action='store_true', dest='makeFinalTree', default=False, help='make Final Tree')
parser.add_option('--doTraining', action='store_true', dest='doTraining', default=False,help='does training')
parser.add_option('-m', '--trainingMethod',action="store",type="string",dest="trainingMethod",default="Likelihood")
parser.add_option('--channel',action="store",type="string",dest="channel",default="mu")
parser.add_option('--sampleToProcess',action="store",type="string",dest="sampleToProcess",default=None)
parser.add_option('-i', '--graphindex',action="store",type="int",dest="graphindex",default=0)
parser.add_option('--sourcefiledirectory',action="store",type="string",dest="sourcefiledirectory",default="/gwteray/users/gerosa/RD_Trees_Production/RD_Trees_ExoLeptonID/")
parser.add_option('--outputfiledirectory',action="store",type="string",dest="outputfiledirectory",default="/gwteray/users/gerosa/RD_Trees_Production/RD_Trees_ExoLeptonID/")
(options, args) = parser.parse_args()
############################################################
if __name__ == '__main__':
print "Welcome to the boosted analysis..."
sourcefiledirectory = options.sourcefiledirectory;
outputfiledirectory = options.outputfiledirectory;
# ---------------------------------------------------
# check if directories exists
if not os.path.isdir(outputfiledirectory+"trainingtrees_mu"): os.system("mkdir "+outputfiledirectory+"trainingtrees_mu");
if not os.path.isdir(outputfiledirectory+"trainingtrees_el"): os.system("mkdir "+outputfiledirectory+"trainingtrees_el");
if not os.path.isdir("classifier"): os.system("mkdir classifier");
# ---------------------------------------------------
# define samples, this creates some trees in the "trainingtrees" directory
isData = True;
notData = False;
CHANNEL = options.channel
if CHANNEL == 'el':
LUMI = 19.2;
elif CHANNEL == 'mu':
LUMI = 19.3;
# --------------------------------------------------
treename = "WJet"
if options.makeTMVAPlots:
sourcefiledirectory = "/uscms_data/d3/weizou/VBFHiggsAnalysis/BoostedWAnalysis2012/boostedWWAnalysis/trainingtrees_%s/"%(CHANNEL)
treename = "otree"
lumifile = "MCScaleFactors.txt"
# --------------------------------------------------- take the root RD files from what is passed to the main
boostedWSamples = Samples(CHANNEL)
boostedWSamples.SetFilePath(sourcefiledirectory)
boostedWSamples.SetTreeName(treename)
boostedWSamples.SetFileNames()
boostedWSamples.SetLumi(LUMI)
# ----------------------------------------------------
datasample = sampleWrapperClass("data",boostedWSamples.GetFileNames()["data"],CHANNEL,LUMI,LUMI,boostedWSamples.GetTreeName(),isData,outputfiledirectory)
ggH600Sample = sampleWrapperClass("ggH600",boostedWSamples.GetFileNames()["ggH600"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"ggH600")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
ggH700Sample = sampleWrapperClass("ggH700",boostedWSamples.GetFileNames()["ggH700"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"ggH700")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
ggH800Sample = sampleWrapperClass("ggH800",boostedWSamples.GetFileNames()["ggH800"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"ggH800")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
ggH900Sample = sampleWrapperClass("ggH900",boostedWSamples.GetFileNames()["ggH900"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"ggH900")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
ggH1000Sample = sampleWrapperClass("ggH1000",boostedWSamples.GetFileNames()["ggH1000"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"ggH1000")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
vbfH600Sample = sampleWrapperClass("vbfH600",boostedWSamples.GetFileNames()["vbfH600"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"vbfH600")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
vbfH700Sample = sampleWrapperClass("vbfH700",boostedWSamples.GetFileNames()["vbfH700"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"vbfH700")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
vbfH800Sample = sampleWrapperClass("vbfH800",boostedWSamples.GetFileNames()["vbfH800"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"vbfH800")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
vbfH900Sample = sampleWrapperClass("vbfH900",boostedWSamples.GetFileNames()["vbfH900"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"vbfH900")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
vbfH1000Sample = sampleWrapperClass("vbfH1000",boostedWSamples.GetFileNames()["vbfH1000"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"vbfH1000")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
rsg1000Sample_kMpl01_py = sampleWrapperClass("rsg1000_kMpl01_py",boostedWSamples.GetFileNames()["rsg1000_kMpl01_py"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"rsg1000_kMpl01_py")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
rsg1000Sample_kMpl01_hw = sampleWrapperClass("rsg1000_kMpl01_hw",boostedWSamples.GetFileNames()["rsg1000_kMpl01_hw"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"rsg1000_kMpl01_hw")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
rsg1500Sample_kMpl01_py = sampleWrapperClass("rsg1500_kMpl01_py",boostedWSamples.GetFileNames()["rsg1500_kMpl01_py"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"rsg1500_kMpl01_py")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
rsg1500Sample_kMpl01_hw = sampleWrapperClass("rsg1500_kMpl01_hw",boostedWSamples.GetFileNames()["rsg1500_kMpl01_hw"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"rsg1500_kMpl01_hw")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
rsg2000Sample_kMpl01_py = sampleWrapperClass("rsg2000_kMpl01_py",boostedWSamples.GetFileNames()["rsg2000_kMpl01_py"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"rsg2000_kMpl01_py")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M600 = sampleWrapperClass("BulkG_c0p2_M600",boostedWSamples.GetFileNames()["BulkG_c0p2_M600"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M600")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M700 = sampleWrapperClass("BulkG_c0p2_M700",boostedWSamples.GetFileNames()["BulkG_c0p2_M700"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M700")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M800 = sampleWrapperClass("BulkG_c0p2_M800",boostedWSamples.GetFileNames()["BulkG_c0p2_M800"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M800")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M900 = sampleWrapperClass("BulkG_c0p2_M900",boostedWSamples.GetFileNames()["BulkG_c0p2_M900"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M900")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1000 = sampleWrapperClass("BulkG_c0p2_M1000",boostedWSamples.GetFileNames()["BulkG_c0p2_M1000"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1000")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1100 = sampleWrapperClass("BulkG_c0p2_M1100",boostedWSamples.GetFileNames()["BulkG_c0p2_M1100"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1100")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1200 = sampleWrapperClass("BulkG_c0p2_M1200",boostedWSamples.GetFileNames()["BulkG_c0p2_M1200"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1200")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1300 = sampleWrapperClass("BulkG_c0p2_M1300",boostedWSamples.GetFileNames()["BulkG_c0p2_M1300"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1300")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1400 = sampleWrapperClass("BulkG_c0p2_M1400",boostedWSamples.GetFileNames()["BulkG_c0p2_M1400"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1400")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1500 = sampleWrapperClass("BulkG_c0p2_M1500",boostedWSamples.GetFileNames()["BulkG_c0p2_M1500"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1500")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1600 = sampleWrapperClass("BulkG_c0p2_M1600",boostedWSamples.GetFileNames()["BulkG_c0p2_M1600"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1600")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1700 = sampleWrapperClass("BulkG_c0p2_M1700",boostedWSamples.GetFileNames()["BulkG_c0p2_M1700"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1700")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1800 = sampleWrapperClass("BulkG_c0p2_M1800",boostedWSamples.GetFileNames()["BulkG_c0p2_M1800"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1800")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M1900 = sampleWrapperClass("BulkG_c0p2_M1900",boostedWSamples.GetFileNames()["BulkG_c0p2_M1900"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M1900")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M2000 = sampleWrapperClass("BulkG_c0p2_M2000",boostedWSamples.GetFileNames()["BulkG_c0p2_M2000"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M2000")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M2100 = sampleWrapperClass("BulkG_c0p2_M2100",boostedWSamples.GetFileNames()["BulkG_c0p2_M2100"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M2100")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M2200 = sampleWrapperClass("BulkG_c0p2_M2200",boostedWSamples.GetFileNames()["BulkG_c0p2_M2200"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M2200")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M2300 = sampleWrapperClass("BulkG_c0p2_M2300",boostedWSamples.GetFileNames()["BulkG_c0p2_M2300"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M2300")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M2400 = sampleWrapperClass("BulkG_c0p2_M2400",boostedWSamples.GetFileNames()["BulkG_c0p2_M2400"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M2400")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
BulkG_c0p2_M2500 = sampleWrapperClass("BulkG_c0p2_M2500",boostedWSamples.GetFileNames()["BulkG_c0p2_M2500"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"BulkG_c0p2_M2500")), LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
WJets_PythiaSample = sampleWrapperClass("WJets_Pythia",boostedWSamples.GetFileNames()["WJets_Pythia"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WJets_Pythia")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
WJets_Pythia_matchDwSample = sampleWrapperClass("WJets_Pythia_matchDw",boostedWSamples.GetFileNames()["WJets_Pythia_matchDw"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WJets_Pythia_matchDw")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
WJets_Pythia_matchUpSample = sampleWrapperClass("WJets_Pythia_matchUp",boostedWSamples.GetFileNames()["WJets_Pythia_matchUp"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WJets_Pythia_matchUp")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
WJets_Pythia_scaleDwSample = sampleWrapperClass("WJets_Pythia_scaleDw",boostedWSamples.GetFileNames()["WJets_Pythia_scaleDw"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WJets_Pythia_scaleDw")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
WJets_Pythia_scaleUpSample = sampleWrapperClass("WJets_Pythia_scaleUp",boostedWSamples.GetFileNames()["WJets_Pythia_scaleUp"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WJets_Pythia_scaleUp")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
W1Jets_PythiaSample = sampleWrapperClass("W1Jets_Pythia",boostedWSamples.GetFileNames()["W1Jets_Pythia"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"W1Jets_Pythia")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
W2Jets_PythiaSample = sampleWrapperClass("W2Jets_Pythia",boostedWSamples.GetFileNames()["W2Jets_Pythia"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"W2Jets_Pythia")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
W3Jets_PythiaSample = sampleWrapperClass("W3Jets_Pythia",boostedWSamples.GetFileNames()["W3Jets_Pythia"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"W3Jets_Pythia")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
W4Jets_PythiaSample = sampleWrapperClass("W4Jets_Pythia",boostedWSamples.GetFileNames()["W4Jets_Pythia"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"W4Jets_Pythia")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
WJets_Pythia100Sample = sampleWrapperClass("WJets_Pythia100",boostedWSamples.GetFileNames()["WJets_Pythia100"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WJets_Pythia100")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
WJets_Pythia180Sample = sampleWrapperClass("WJets_Pythia180",boostedWSamples.GetFileNames()["WJets_Pythia180"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WJets_Pythia180")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
WJets_HerwigSample = sampleWrapperClass("WJets_Herwig",boostedWSamples.GetFileNames()["WJets_Herwig"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WJets_Herwig")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
ZJetsSample = sampleWrapperClass("ZJets",boostedWSamples.GetFileNames()["ZJets"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"ZJets")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory)
TTbarSample = sampleWrapperClass("TTbar",boostedWSamples.GetFileNames()["TTbar"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"TTbar_mad")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
TTbarSample_matchDn = sampleWrapperClass("TTbar_matchDn",boostedWSamples.GetFileNames()["TTbar_matchDn"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"TTbar_matchDn")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
TTbarSample_matchUp = sampleWrapperClass("TTbar_matchUp",boostedWSamples.GetFileNames()["TTbar_matchUp"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"TTbar_matchUp")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
TTbarSample_Powheg = sampleWrapperClass("TTbar_Powheg",boostedWSamples.GetFileNames()["TTbar_Powheg"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"TTbar_Powheg")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
TTbarSample_scaleDn = sampleWrapperClass("TTbar_scaleDn",boostedWSamples.GetFileNames()["TTbar_scaleDn"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"TTbar_scaleDn")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
TTbarSample_scaleUp = sampleWrapperClass("TTbar_scaleUp",boostedWSamples.GetFileNames()["TTbar_scaleUp"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"TTbar_scaleUp")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
TTbarSample_mcanlo = sampleWrapperClass("TTbar_mcanlo",boostedWSamples.GetFileNames()["TTbar_mcanlo"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"TTbar_mcanlo")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
WWSample = sampleWrapperClass("WW",boostedWSamples.GetFileNames()["WW"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WW")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
WWSample_mcanlo = sampleWrapperClass("WW_mcanlo",boostedWSamples.GetFileNames()["WW_mcanlo"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WW_mcanlo")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
WWSample_madgraph = sampleWrapperClass("WW2jet_madgraph",boostedWSamples.GetFileNames()["WW2jet_madgraph"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WW2jet_madgraph")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
WWSample_phantom = sampleWrapperClass("WW2jet_phantom",boostedWSamples.GetFileNames()["WW2jet_phantom"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WW2jet_phantom")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
WZSample = sampleWrapperClass("WZ",boostedWSamples.GetFileNames()["WZ"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"WZ")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
ZZSample = sampleWrapperClass("ZZ",boostedWSamples.GetFileNames()["ZZ"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"ZZ")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
tchSample = sampleWrapperClass("tch",boostedWSamples.GetFileNames()["tch"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"tch")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
tWchSample = sampleWrapperClass("tWch",boostedWSamples.GetFileNames()["tWch"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"tWch")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
schSample = sampleWrapperClass("sch",boostedWSamples.GetFileNames()["sch"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"sch")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
tch_barSample = sampleWrapperClass("tch_bar",boostedWSamples.GetFileNames()["tch_bar"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"tch_bar")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
tWch_barSample = sampleWrapperClass("tWch_bar",boostedWSamples.GetFileNames()["tWch_bar"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"tWch_bar")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
sch_barSample = sampleWrapperClass("sch_bar",boostedWSamples.GetFileNames()["sch_bar"],CHANNEL,1.0/(boostedWSamples.GetLumiScaleFactor(lumifile,"sch_bar")),LUMI,boostedWSamples.GetTreeName(),notData,outputfiledirectory);
allSamples = [datasample,
ggH600Sample,ggH700Sample,ggH800Sample,ggH900Sample,ggH1000Sample,
vbfH600Sample,vbfH700Sample,vbfH800Sample,vbfH900Sample,vbfH1000Sample,
# rsg1000Sample_kMpl01_py,rsg1000Sample_kMpl01_hw,rsg1500Sample_kMpl01_py,rsg1500Sample_kMpl01_hw,rsg2000Sample_kMpl01_py,
BulkG_c0p2_M600,BulkG_c0p2_M700,BulkG_c0p2_M800,BulkG_c0p2_M900,BulkG_c0p2_M1000,BulkG_c0p2_M1100,BulkG_c0p2_M1200,
BulkG_c0p2_M1300,BulkG_c0p2_M1400,BulkG_c0p2_M1500,BulkG_c0p2_M1600,BulkG_c0p2_M1700,BulkG_c0p2_M1800,BulkG_c0p2_M1900,BulkG_c0p2_M2000,
BulkG_c0p2_M2100,BulkG_c0p2_M2200,BulkG_c0p2_M2300,BulkG_c0p2_M2400,BulkG_c0p2_M2500,
WJets_PythiaSample,WJets_Pythia_matchUpSample,WJets_Pythia_matchDwSample,WJets_Pythia_scaleUpSample,WJets_Pythia_scaleDwSample,
W1Jets_PythiaSample, W2Jets_PythiaSample,W3Jets_PythiaSample,W4Jets_PythiaSample,WJets_Pythia100Sample,
WJets_Pythia180Sample,WJets_HerwigSample,ZJetsSample,
TTbarSample,TTbarSample_matchDn,TTbarSample_matchUp,TTbarSample_scaleDn,TTbarSample_scaleUp,TTbarSample_Powheg,TTbarSample_mcanlo,
WWSample,WZSample,ZZSample,WWSample_mcanlo,WWSample_madgraph,WWSample_phantom,
tchSample,tWchSample,schSample,tch_barSample,tWch_barSample,sch_barSample];
if options.createTrainingTrees:
# ---------------------------------------------------
# create training tree
if options.sampleToProcess == None:
datasample.createTrainingTree();
WJets_Pythia180Sample.createTrainingTree();
WJets_Pythia100Sample.createTrainingTree();
ZJetsSample.createTrainingTree();
TTbarSample.createTrainingTree();
TTbarSample_matchDn.createTrainingTree();
TTbarSample_matchUp.createTrainingTree();
TTbarSample_Powheg.createTrainingTree();
TTbarSample_scaleDn.createTrainingTree();
TTbarSample_scaleUp.createTrainingTree();
WWSample.createTrainingTree();
WZSample.createTrainingTree();
ZZSample.createTrainingTree();
tchSample.createTrainingTree();
tWchSample.createTrainingTree();
schSample.createTrainingTree();
tch_barSample.createTrainingTree();
tWch_barSample.createTrainingTree();
sch_barSample.createTrainingTree();
else:
for i in range(len(allSamples)):
# print options.sampleToProcess," ",allSamples[i].getLabel();
if options.sampleToProcess == allSamples[i].getLabel():
allSamples[i].createTrainingTree();
if options.makeControlPlots:
# ---------------------------------------------------
# make control plots based on the same cuts as the training trees
if not os.path.isdir("controlPlots"): os.system("mkdir controlPlots");
#myPlotter.makeControlPlots("controlPlots","nocuts");
print "Please Check the Cuts used on the BoostedWControlPlots.py is reasonable"
Cuts = "W_pt > 200 && GroomedJet_CA8_pt[0] > 200 && ggdboostedWevt == 1 && event_met_pfmet > 50 && GroomedJet_CA8_deltaphi_METca8jet > 2.0 && GroomedJet_CA8_deltaR_lca8jet > 1.57";
if CHANNEL == "el": Cuts = Cuts + " && event_met_pfmet > 70 && W_electron_pt > 35";
if CHANNEL == "mu": Cuts = Cuts + " && W_muon_pt > 30";
Cuts = Cuts + " && GroomedJet_numberbjets_csvm > 0";
#Cuts = "W_pt > 180 && GroomedJet_CA8_pt_pr[0] > 180 && ggdboostedWevt == 1 && event_metMVA_met > 50"
#Cuts = "W_pt > 180 && GroomedJet_CA8_pt_pr[0] > 180 && ggdboostedWevt == 1 && event_metMVA_met > 50 && GroomedJet_numberjets <= 1"
#Cuts = "W_pt > 180 && GroomedJet_CA8_pt_pr[0] > 180 && ggdboostedWevt == 1 && event_metMVA_met > 50 && GroomedJet_numberbjets == 0"
print "Cuts we apply: " + Cuts
if options.noX:
p = subprocess.Popen(["python","BoostedWControlPlots.py","-b","-f","%s"%sourcefiledirectory,"-t","%s"%treename,"-l","%f"%LUMI,"-s","%s"%lumifile,"-c","%s"%Cuts,"-n","%s"%CHANNEL])
if(p.wait() != None): raw_input( 'Press ENTER to continue\n ' )
else:
p = subprocess.Popen(["python","BoostedWControlPlots.py","-f","%s"%sourcefiledirectory,"-t","%s"%treename,"-l","%f"%LUMI,"-s","%s"%lumifile,"-c","%s"%Cuts,"-n","%s"%CHANNEL])
if(p.wait() != None): raw_input( 'Press ENTER to continue\n ' )
if options.makeTTBarControlPlots:
if not os.path.isdir("controlPlots_ttbar"): os.system("mkdir controlPlots_ttbar");
print "Please Check the Cuts used on the BoostedWControlPlots.py is reasonable"
#Cuts = "W_pt > 180 && GroomedJet_CA8_pt[0] > 200 && ggdboostedWevt == 1 && event_metMVA_met > 50 && GroomedJet_CA8_tau2tau1[0]< 0.524 "
Cuts = "W_pt > 180 && GroomedJet_CA8_pt[0] > 200 && ggdboostedWevt == 1 && event_metMVA_met > 50 && GroomedJet_CA8_tau2tau1[0]< 0.524 && GroomedJet_numberbjets >= 1 "
addition = "ttbar";
print "Cuts we apply: " + Cuts
print "We don't put the cuts on the signal and try to compare the W jet performance with TTbar and Data!!!!"
print "Make Sure the numberbjets cut is the last cut in the cut sequence"
if options.noX:
p = subprocess.Popen(["python","BoostedWControlPlots.py","-b","-f","%s"%sourcefiledirectory,"-t","%s"%treename,"-l","%f"%LUMI,"-s","%s"%lumifile,"-c","%s"%Cuts,"-n","%s"%CHANNEL,"-a",addition])
if(p.wait() != None): raw_input( 'Press ENTER to continue\n ' )
else:
p = subprocess.Popen(["python","BoostedWControlPlots.py","-f","%s"%sourcefiledirectory,"-t","%s"%treename,"-l","%f"%LUMI,"-s","%s"%lumifile,"-c","%s"%Cuts,"-n","%s"%CHANNEL,"-a",addition])
if(p.wait() != None): raw_input( 'Press ENTER to continue\n ' )
if options.makeSignalRegionControlPlots:
if not os.path.isdir("controlPlots_signalregion"): os.system("mkdir controlPlots_signalregion");
print "Please Check the Cuts used on the BoostedWControlPlots.py is reasonable"
Cuts = "W_pt > 180 && GroomedJet_CA8_pt[0] > 200 && ggdboostedWevt == 1 && event_metMVA_met > 50 && GroomedJet_CA8_mass_pr[0]>=70 && GroomedJet_CA8_mass_pr[0]<=100 && boostedW_lvj_m>400 && boostedW_lvj_m<1400 && GroomedJet_CA8_tau2tau1[0]< 0.525 && GroomedJet_numberjets <1"
addition = "signalregion";
#Cuts = "W_pt > 180 && GroomedJet_CA8_pt[0] > 200 && ggdboostedWevt == 1 && event_metMVA_met > 50 && GroomedJet_CA8_mass_pr[0]>=70 && GroomedJet_CA8_mass_pr[0]<=100"
#Wtagger = "GroomedJet_CA8_tau2tau1[0]< 0.524"
#Cuts = Cuts + " && " + Wtagger;
#addition = "signalregion_withoutjetnumber";
print "Cuts we apply: " + Cuts
if options.noX:
p = subprocess.Popen(["python","BoostedWControlPlots.py","-b","-f","%s"%sourcefiledirectory,"-t","%s"%treename,"-l","%f"%LUMI,"-s","%s"%lumifile,"-c","%s"%Cuts,"-n","%s"%CHANNEL,"-a",addition])
if(p.wait() != None): raw_input( 'Press ENTER to continue\n ' )
else:
p = subprocess.Popen(["python","BoostedWControlPlots.py","-f","%s"%sourcefiledirectory,"-t","%s"%treename,"-l","%f"%LUMI,"-s","%s"%lumifile,"-c","%s"%Cuts,"-n","%s"%CHANNEL,"-a",addition])
if(p.wait() != None): raw_input( 'Press ENTER to continue\n ' )
if options.makeTMVAPlots:
# ---------------------------------------------------
# make control plots based on the same cuts as the training trees
if not os.path.isdir("controlPlots"): os.system("mkdir controlPlots");
#myPlotter.makeControlPlots("controlPlots","nocuts");
print "Please Check the Cuts used on the BoostedWTMVAPlots.py is reasonable"
#Cuts = "W_pt > 180 && GroomedJet_CA8_pt_pr[0] > 180 && ggdboostedWevt == 1 && event_metMVA_met > 50"
#Cuts = "W_pt > 180 && GroomedJet_CA8_pt_pr[0] > 180 && ggdboostedWevt == 1 && event_metMVA_met > 50 && GroomedJet_numberjets <= 1"
#Cuts = "W_pt > 180 && GroomedJet_CA8_pt_pr[0] > 180 && ggdboostedWevt == 1 && event_metMVA_met > 50 && GroomedJet_numberbjets == 0"
Cuts = "jet_mass_pr > 60 && jet_mass_pr < 100"
#Cuts = "1 > 0"
print "Cuts we apply: " + Cuts
if options.noX:
p = subprocess.Popen(["python","BoostedWTMVAPlots.py","-b","-f","%s"%sourcefiledirectory,"-t","%s"%treename,"-l","%f"%LUMI,"-s","%s"%lumifile,"-c","%s"%Cuts])
if(p.wait() != None): raw_input( 'Press ENTER to continue\n ' )
else:
p = subprocess.Popen(["python","BoostedWTMVAPlots.py","-f","%s"%sourcefiledirectory,"-t","%s"%treename,"-l","%f"%LUMI,"-s","%s"%lumifile,"-c","%s"%Cuts])
if(p.wait() != None): raw_input( 'Press ENTER to continue\n ' )
# ---------------------------------------------------
# do the training
# get the training tree names
if options.doTraining:
signalTrainingTreeName = ggH600Sample.getTrainingTreeName();
backgroundTrainingTreeNames = WJets_PythiaSample.getTrainingTreeName();
trainingMethod = options.trainingMethod
# Trainings
# --------- #1 -----------
listOfTrainingVariables1 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1"];
WWTraining1 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables1, "simple" );
# --------- #2 -----------
listOfTrainingVariables2 = ["jet_grsens_tr","jet_grsens_ft","jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore7","jet_planarflow04","jet_planarflow07"];
WWTraining2 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables2, "no subjets" );
listOfTrainingVariables2 = ["jet_grsens_tr","jet_grsens_ft","jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_sjdr","jet_rcore4","jet_rcore7","jet_planarflow04","jet_planarflow07"];
WWTraining2 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables2, "optimal" );
listOfTrainingVariables3 = ["jet_massdrop_pr"]
WWTraining3 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables3, trainingMethod, "massdrop")
listOfTrainingVariables4 = ["jet_massdrop_pr", "jet_tau2tau1"]
WWTraining4 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables4, "massdroptau2tau1")
listOfTrainingVariables5 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4"]
WWTraining5 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables5, "simplercore4")
listOfTrainingVariables6 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5"]
WWTraining6 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables6, "simplercore45")
listOfTrainingVariables7 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6"]
WWTraining7 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables7, "simplercore456")
listOfTrainingVariables8 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7"]
WWTraining8 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables8, "simplercore4567")
listOfTrainingVariables9 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04"]
WWTraining9 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables9, "simplercore4567planarflow4")
listOfTrainingVariables10 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04","jet_planarflow05"]
WWTraining10 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables10, "simplercore4567planarflow45")
listOfTrainingVariables11 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04","jet_planarflow05","jet_planarflow06"]
WWTraining11 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables11, "simplercore4567planarflow456")
listOfTrainingVariables12 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04","jet_planarflow05","jet_planarflow06","jet_planarflow07"]
WWTraining12 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables12, "simplercore4567planarflow4567")
listOfTrainingVariables13 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04","jet_planarflow05","jet_planarflow06","jet_planarflow07","jet_pt1frac"]
WWTraining13 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables13, "simplercore4567planarflow4567subjet1")
listOfTrainingVariables14 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04","jet_planarflow05","jet_planarflow06","jet_planarflow07","jet_pt2frac"]
WWTraining14 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables14, "simplercore4567planarflow4567subjet2")
listOfTrainingVariables15 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04","jet_planarflow05","jet_planarflow06","jet_planarflow07","jet_pt1frac","jet_sjdr"]
WWTraining15 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables15, "simplercore4567planarflow4567subjet1sjdr")
listOfTrainingVariables16 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04","jet_planarflow05","jet_planarflow06","jet_planarflow07","jet_pt1frac","jet_sjdr","jet_grsens_ft","jet_grsens_tr"]
WWTraining16 = trainingClass(signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables16, "simplercore4567planarflow4567subjet1sjdrgrsens_ftgrsens_tr")
listOfTrainingVariables17 = ["jet_grsens_tr","jet_grsens_ft","jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_rcore7","jet_planarflow07"];
WWTraining17 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables3, "optimal2" );
listOfTrainingVariables18 = ["jet_grsens_tr","jet_grsens_ft","jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_pt1frac","jet_sjdr","jet_jetconstituents","jet_rcore4","jet_rcore5","jet_rcore6","jet_rcore7","jet_planarflow04","jet_planarflow05","jet_planarflow06","jet_planarflow07"];
WWTraining18 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables4, "all" );
listOfTrainingVariables19 = ["jet_massdrop_pr","jet_qjetvol"];
WWTraining19 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables17, "massdropqjet" );
listOfTrainingVariables20 = ["jet_qjetvol","jet_tau2tau1"];
WWTraining20 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables18,trainingMethod,"tau2tau1qjet" );
listOfTrainingVariables21 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_pt1frac"];
WWTraining21 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables19, "simplesubjet1pt" );
listOfTrainingVariables22 = ["jet_massdrop_pr","jet_qjetvol","jet_tau2tau1","jet_pt1frac","jet_sjdr"];
WWTraining22 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables20, "simplesubjet1ptsjdr" );
listOfTrainingVariables23 = ["jet_massdrop_pr","jet_qjetvol","jet_pt1frac","jet_sjdr"];
WWTraining213= trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables21, "massdropqjetsubjet1ptsjdr" );
listOfTrainingVariables23 = ["jet_qjetvol"];
WWTraining23 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables22,trainingMethod,"qjet" );
listOfTrainingVariables24 = ["jet_tau2tau1"];
WWTraining24 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables23,trainingMethod, "tau2tau1" );
listOfTrainingVariables25 = ["jet_qjetvol","jet_tau2tau1","jet_pt1frac","jet_sjdr"];
WWTraining25 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables24, trainingMethod, "tau2tau1qjetsubjet1ptsjdr" );
listOfTrainingVariables26 = ["jet_massdrop_pr","jet_qjetvol"];
WWTraining26 = trainingClass( signalTrainingTreeName, backgroundTrainingTreeNames, listOfTrainingVariables25,trainingMethod,"massdropqjet" );
PrimaryVertex1 = 0
PrimaryVertex2 = 15
PrimaryVertex3 = 30
PrimaryVertex4 = 40
WWTraining18.doTraining( 200, 275, PrimaryVertex1, PrimaryVertex2);
WWTraining18.doTraining( 200, 275, PrimaryVertex2, PrimaryVertex3);
WWTraining18.doTraining( 200, 275, PrimaryVertex3, PrimaryVertex4);
WWTraining18.doTraining( 275, 500, PrimaryVertex1, PrimaryVertex2);
WWTraining18.doTraining( 275, 500, PrimaryVertex2, PrimaryVertex3);
WWTraining18.doTraining( 275, 500, PrimaryVertex3, PrimaryVertex4);
WWTraining18.plotTrainingResults( 200, 275, PrimaryVertex1, PrimaryVertex2);
WWTraining18.plotTrainingResults( 200, 275, PrimaryVertex2, PrimaryVertex3);
WWTraining18.plotTrainingResults( 200, 275, PrimaryVertex3, PrimaryVertex4);
WWTraining18.plotTrainingResults( 275, 500, PrimaryVertex1, PrimaryVertex2);
WWTraining18.plotTrainingResults( 275, 500, PrimaryVertex2, PrimaryVertex3);
WWTraining18.plotTrainingResults( 275, 500, PrimaryVertex3, PrimaryVertex4);
WWTraining18.doTraining( 200, 275, 0, 100);
WWTraining18.doTraining( 275, 500, 0, 100);
WWTraining18.plotTrainingResults( 200, 275, 0, 100);
WWTraining18.plotTrainingResults( 275, 500, 0, 100);
WWTraining1.doTraining( 200, 275 );
WWTraining1.doTraining( 275, 500 );
WWTraining1.doTraining( 200, 275, 0, 10);
WWTraining1.doTraining( 200, 275, 10, 20);
WWTraining1.doTraining( 200, 275, 20, 30);
WWTraining1.doTraining( 275, 500, 0, 10);
WWTraining1.doTraining( 275, 500, 10, 20);
WWTraining1.doTraining( 275, 500, 20, 30);
WWTraining22.doTraining( 200, 275, PrimaryVertex1, PrimaryVertex2);
WWTraining22.doTraining( 200, 275, PrimaryVertex2, PrimaryVertex3);
WWWTraining22.doTraining( 200, 275, PrimaryVertex3, PrimaryVertex4);
WWTraining22.doTraining( 275, 500, PrimaryVertex1, PrimaryVertex2);
WWTraining22.doTraining( 275, 500, PrimaryVertex2, PrimaryVertex3);
WWTraining22.doTraining( 275, 500, PrimaryVertex3, PrimaryVertex4);
WWTraining23.doTraining( 200, 275, PrimaryVertex1, PrimaryVertex2);
WWTraining23.doTraining( 200, 275, PrimaryVertex2, PrimaryVertex3);
WWTraining23.doTraining( 200, 275, PrimaryVertex3, PrimaryVertex4);
WWTraining23.doTraining( 275, 500, PrimaryVertex1, PrimaryVertex2);
WWTraining23.doTraining( 275, 500, PrimaryVertex2, PrimaryVertex3);
WWTraining23.doTraining( 275, 500, PrimaryVertex3, PrimaryVertex4);
WWTraining24.doTraining( 200, 275, PrimaryVertex1, PrimaryVertex2);
WWTraining24.doTraining( 200, 275, PrimaryVertex2, PrimaryVertex3);
WWTraining24.doTraining( 200, 275, PrimaryVertex3, PrimaryVertex4);
WWTraining24.doTraining( 275, 500, PrimaryVertex1, PrimaryVertex2);
WWTraining24.doTraining( 275, 500, PrimaryVertex2, PrimaryVertex3);
WWTraining24.doTraining( 275, 500, PrimaryVertex3, PrimaryVertex4);
WWTraining2.doTraining( 200, 275 );
WWTraining2.doTraining( 275, 500 );
WWTraining3.doTraining( 200, 275, 0, 100);
WWTraining3.doTraining( 275, 500, 0, 100);
WWTraining4.doTraining( 200, 275 );
WWTraining4.doTraining( 275, 500 );
WWTraining5.doTraining( 200, 275 );
WWTraining5.doTraining( 275, 500 );
WWTraining6.doTraining( 200, 275 );
WWTraining6.doTraining( 275, 500 );
WWTraining7.doTraining( 200, 275 );
WWTraining7.doTraining( 275, 500 );
WWTraining8.doTraining( 200, 275 );
WWTraining8.doTraining( 275, 500 );
WWTraining9.doTraining( 200, 275 );
WWTraining9.doTraining( 275, 500 );
WWTraining10.doTraining( 200, 275 );
WWTraining10.doTraining( 275, 500 );
WWTraining11.doTraining( 200, 275 );
WWTraining11.doTraining( 275, 500 );
WWTraining12.doTraining( 200, 275 );
WWTraining12.doTraining( 275, 500 );
WWTraining13.doTraining( 200, 275 );
WWTraining13.doTraining( 275, 500 );
WWTraining14.doTraining( 200, 275 );
WWTraining14.doTraining( 275, 500 );
WWTraining15.doTraining( 200, 275 );
WWTraining15.doTraining( 275, 500 );
WWTraining16.doTraining( 200, 275 );
WWTraining16.doTraining( 275, 500 );
if options.makeFinalTree:
# ---------------------------------------------------
# make the final trees
print "making final trees"
if not os.path.isdir("finalPlot"): os.system("mkdir finalPlot");
if options.noX: gROOT.SetBatch()
# get name from training class (if name is MassDrop, special case)
# give it the same set of variables
# give it samples
Signalefficiency = 0.6
graphindex = options.graphindex
#
rocs18ptv1 = WWTraining18.makeFinalPlotsInternal( 200, 275, PrimaryVertex1, PrimaryVertex2);
rocs18ptv2 = WWTraining18.makeFinalPlotsInternal( 200, 275, PrimaryVertex2, PrimaryVertex3);
#rocs18ptv3 = WWTraining18.makeFinalPlotsInternal( 200, 275, PrimaryVertex3, PrimaryVertex4);
PrintTable(rocs18ptv1[graphindex], rocs18ptv2[graphindex], WWTraining18, 200, 275, PrimaryVertex1, PrimaryVertex2, PrimaryVertex3, Signalefficiency)
rocs18ptv4 = WWTraining18.makeFinalPlotsInternal( 275, 500, PrimaryVertex1, PrimaryVertex2);
rocs18ptv5 = WWTraining18.makeFinalPlotsInternal( 275, 500, PrimaryVertex2, PrimaryVertex3);
#rocs18ptv6 = WWTraining18.makeFinalPlotsInternal( 275, 500, PrimaryVertex3, PrimaryVertex4);
PrintTable(rocs18ptv4[graphindex], rocs18ptv5[graphindex], WWTraining18, 275, 500, PrimaryVertex1, PrimaryVertex2, PrimaryVertex3, Signalefficiency)
#
rocs22ptv1 = WWTraining22.makeFinalPlotsInternal( 200, 275, PrimaryVertex1, PrimaryVertex2);
rocs22ptv2 = WWTraining22.makeFinalPlotsInternal( 200, 275, PrimaryVertex2, PrimaryVertex3);
#rocs22ptv3 = WWTraining22.makeFinalPlotsInternal( 200, 275, PrimaryVertex3, PrimaryVertex4);
PrintTable(rocs22ptv1[graphindex], rocs22ptv2[graphindex], WWTraining22, 200, 275, PrimaryVertex1, PrimaryVertex2, PrimaryVertex3, Signalefficiency)
rocs22ptv4 = WWTraining22.makeFinalPlotsInternal( 275, 500, PrimaryVertex1, PrimaryVertex2);
rocs22ptv5 = WWTraining22.makeFinalPlotsInternal( 275, 500, PrimaryVertex2, PrimaryVertex3);
#rocs22ptv6 = WWTraining22.makeFinalPlotsInternal( 275, 500, PrimaryVertex3, PrimaryVertex4);
PrintTable(rocs22ptv4[graphindex], rocs22ptv5[graphindex], WWTraining22, 275, 500, PrimaryVertex1, PrimaryVertex2, PrimaryVertex3, Signalefficiency)
rocs23ptv1 = WWTraining23.makeFinalPlotsInternal( 200, 275, PrimaryVertex1, PrimaryVertex2);
rocs23ptv2 = WWTraining23.makeFinalPlotsInternal( 200, 275, PrimaryVertex2, PrimaryVertex3);
#rocs23ptv3 = WWTraining23.makeFinalPlotsInternal( 200, 275, PrimaryVertex3, PrimaryVertex4);
PrintTable(rocs23ptv1[graphindex], rocs23ptv2[graphindex], WWTraining23, 200, 275, PrimaryVertex1, PrimaryVertex2, PrimaryVertex3, Signalefficiency)
rocs23ptv4 = WWTraining23.makeFinalPlotsInternal( 275, 500, PrimaryVertex1, PrimaryVertex2);
rocs23ptv5 = WWTraining23.makeFinalPlotsInternal( 275, 500, PrimaryVertex2, PrimaryVertex3);
#rocs23ptv6 = WWTraining23.makeFinalPlotsInternal( 275, 500, PrimaryVertex3, PrimaryVertex4);
PrintTable(rocs23ptv4[graphindex], rocs23ptv5[graphindex], WWTraining23, 275, 500, PrimaryVertex1, PrimaryVertex2, PrimaryVertex3, Signalefficiency)
rocs1 = WWTraining1.makeFinalPlotsInternal( 200, 275 );
rocs2 = WWTraining2.makeFinalPlotsInternal( 200, 275 );
rocs3 = WWTraining3.makeFinalPlotsInternal( 200, 275 );
rocs4 = WWTraining4.makeFinalPlotsInternal( 200, 275 );
rocs1ex = WWTraining1.makeFinalPlots( 200, 275, 0. );
rocs2ex = WWTraining2.makeFinalPlots( 200, 275, 0. );
rocs3ex = WWTraining3.makeFinalPlots( 200, 275, 0. );
rocs4ex = WWTraining4.makeFinalPlots( 200, 275, 0. );
canBdtRoc = ROOT.TCanvas("canBdtRoc","canBdtRoc",800,800);
canBdtRoc.cd();
hrl = canBdtRoc.DrawFrame(0,0,1.0,1.0);
hrl.GetXaxis().SetTitle("#epsilon_{sig}");
hrl.GetYaxis().SetTitle("1 - #epsilon_{bkg}");
canBdtRoc.SetGrid();
rocs1[0].Draw();
rocs1[1].SetLineColor(ROOT.kRed);
rocs1[1].Draw();
rocs2[0].SetLineColor(ROOT.kBlue);
rocs2[0].Draw();
rocs3[0].SetLineColor(ROOT.kMagenta);
rocs3[0].Draw();
rocs4[0].SetLineColor(ROOT.kGreen+2);
rocs4[0].Draw();
rocs1[2].SetLineWidth(2);
rocs1[2].SetLineColor(ROOT.kBlack);
rocs1[2].Draw();
rocs2[2].SetLineWidth(2);
rocs2[2].SetLineColor(ROOT.kBlue);
rocs2[2].Draw();
rocs3[2].SetLineWidth(2);
rocs3[2].SetLineColor(ROOT.kMagenta);
rocs3[2].Draw();
rocs4[2].SetLineWidth(2);
rocs4[2].SetLineColor(ROOT.kGreen+2);
rocs4[2].Draw();
rocs4[2].SetLineColor(ROOT.kCyan+2);
rocs4[2].SetLineStyle(2);
rocs4[2].Draw();
leg = ROOT.TLegend(0.25,0.2,0.55,0.5)
leg.SetFillColor(0)
leg.SetBorderSize(0)
leg.AddEntry( rocs1[0], "simple", 'l' );
leg.AddEntry( rocs1[1], "mass drop only", 'l' );
leg.AddEntry( rocs2[0], "optimal", 'l' );
leg.AddEntry( rocs3[0], "optimal2", 'l' );
leg.AddEntry( rocs4[0], "all", 'l' );
leg.AddEntry( rocs4ex[0], "all ex", 'l' );
leg.Draw();
canBdtRoc.SaveAs("finalPlot/testROC_compall.eps");
canBdtRoc.SaveAs("finalPlot/testROC_compall.png");
hs = rocs2ex[2];
hs.SetLineColor(4);
hs.Scale(1./hs.Integral());
hb = rocs2ex[3];
hb.SetLineColor(2);
hb.Scale(1./hb.Integral());
canMassPrBdtCut = ROOT.TCanvas("canMassPrBdtCut","canMassPrBdtCut",800,800);
hs.Draw("hist");
hb.Draw("histsames");
canMassPrBdtCut.SaveAs("finalPlot/testmasspr.eps");
canMassPrBdtCut.SaveAs("finalPlot/testmasspr.png");
raw_input( 'Press ENTER to continue\n ' )