pfs · nshafiei · Dec 17, 2018 · Dec 17, 2018 · Dec 17, 2018 · Dec 17, 2018
diff --git a/TopAnalysis/README.md b/TopAnalysis/README.md
@@ -0,0 +1,231 @@
+# TopLJets2015
+
+## Installation instructions
+
+These installation instructions correspond to the 2017 data/MC production.
+To install execute the following in your work area.
+Notice: if you are not creating the ntuples, you can skip the part of the instructions 
+marked with the `##OPTIONAL/##END OPTIONAL` markers.
+If compilation fails for some reason repeat the scram b...
+
+```
+cmsrel CMSSW_9_4_11
+cd CMSSW_9_4_11/src
+cmsenv
+
+##OPTIONAL (TAKES TOO LONG/MUCH SPACE, USE ONLY IF CREATING NTUPLES FROM SCRATCH)
+
+git cms-init
+
+#proton reconstruction
+#see https://twiki.cern.ch/twiki/bin/viewauth/CMS/CTPPSStandardProtonReconstruction
+git remote add ctpps git@github.com:CTPPS/cmssw.git
+git fetch ctpps
+git checkout -b test ctpps/ctpps_initial_proton_reconstruction_CMSSW_9_4_11
+git cms-addpkg CondFormats/CTPPSOpticsObjects DataFormats/ProtonReco IOMC/EventVertexGenerators IOMC/ParticleGuns RecoCTPPS/ProtonReconstruction RecoCTPPS/TotemRPLocal SimCTPPS/OpticsParameterisation Validation/CTPPS CondFormats/RunInfo CondFormats/DataRecord
+scram b -j 8
+
+#MVA v2 ids
+#photon/electron id+scale and smearing fixes for MINIAOD 2017v2 (doesn't harm 2016v3)
+git cms-merge-topic cms-egamma:EgammaID_949 
+#just adds in an extra file to have a setup function to make things easier
+git cms-merge-topic cms-egamma:EgammaPostRecoTools_940 
+
+#re-do MET to mitigate EE noise
+git cms-merge-topic cms-met:METFixEE2017_949_v2
+scram b -j 8
+
+##END OPTIONAL
+
+#higgs combination tool
+git clone https://github.com/cms-analysis/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
+cd HiggsAnalysis/CombinedLimit
+git fetch origin
+git checkout v7.0.10
+scram b -j 8
+cd -
+
+#additional tools
+mkdir TopQuarkAnalysis 
+cd TopQuarkAnalysis
+git clone -b 94x https://gitlab.cern.ch/psilva/BFragmentationAnalyzer.git
+scram b -j 8
+cd -
+
+#this package
+cd $CMSSW_BASE/src
+git clone https://github.com/pfs/TopLJets2015.git
+cd TopLJets2015
+git submodule init
+git submodule update
+scram b -j 8
+```
+
+## Running ntuple creation and checking the selection
+
+The ntuplizer is steered with test/runMiniAnalyzer_cfg.py.
+It takes several options from command line (see cfg for details).
+To run locally the ntuplizer, for testing purposes do something like:
+
+```
+cmsRun test/runMiniAnalyzer_cfg.py runOnData=False era=era2017 outFilename=MC13TeV_TTJets.root
+cmsRun test/runMiniAnalyzer_cfg.py runOnData=True  era=era2017 outFilename=Data13TeV_SinglePhoton.root
+cmsRun test/runL1PrefireAna_cfg.py runOnData=True  era=era2017 outFilename=Data13TeV_SinglePhoton_l1prefire.root
+```
+
+To submit the ntuplizer to the grid start by setting the environment for crab3.
+More details can be found in [CRAB3CheatSheet](https://twiki.cern.ch/twiki/bin/view/CMSPublic/CRAB3CheatSheet#Environment_setup)
+
+```
+source /cvmfs/cms.cern.ch/crab3/crab.sh
+```
+The following script helps submitting a list of files described in a json file.
+Partial submission can be made adding "-o csv_list" as an option.
+Adding "-s" will trigger the submission to the grid (otherwise the script only writes down the crab cfg files)
+
+```
+python scripts/submitToGrid.py -j data/era2017/samples.json -c ${CMSSW_BASE}/src/TopLJets2015/TopAnalysis/test/runMiniAnalyzer_cfg.py 
+python scripts/submitToGrid.py -j data/era2017/samples.json -c ${CMSSW_BASE}/src/TopLJets2015/TopAnalysis/test/runL1PrefireAna_cfg.py --addParents --only JetHT,SinglePhoton,SingleElectron,MuonEG,DoubleEG --lfn /store/group/cmst3/group/top/psilva/l1prefire -w grid_prefire -s
+python scripts/submitToGrid.py -j data/era2016/samples.json -c ${CMSSW_BASE}/src/TopLJets2015/TopAnalysis/test/runMiniAnalyzer_cfg.py -w grid_2016 --lumi /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions16/13TeV/Final/Cert_271036-284044_13TeV_PromptReco_Collisions16_JSON.txt --era era2016
+```
+
+As soon as ntuple production starts to finish, to move from crab output directories to a simpler directory structure which can be easily parsed by the local analysis runThe merging can be run locally if needed by using the checkProductionIntegrity.py script
+
+```
+python scripts/mergeGridOutputs.py -i /store/cmst3/group/top/psilva/ab05162/ -o /store/cmst3/group/top/RunIIReReco/ab05162/
+python scripts/mergeGridOutputs.py -i /store/cmst3/group/top/grid_2016/113427a -o /store/cmst3/group/top/RunIIReReco/113427a_2016
+```
+
+## Luminosity
+
+After ntuples are processed, you can create the list of runs/lumi sections processed using crab as:
+```
+a=(`find grid/ -maxdepth 1 | grep crab_Data `)
+for i in ${a[@]}; do
+    crab kill ${i};
+    crab status ${i};
+    crab report ${i}; 
+done
+``` 
+In case of failed jobs the missing lumis can be processed with the following script to wrap the tedious process of 
+updating the cfg with a finer grain luminosity per job and the missing lumis json
+```
+for i in ${a[@]}; do
+    python scripts/runMissingLumiSecs.py ${i}
+done
+```
+You can then run the brilcalc tool to get the integrated luminosity in total and per run 
+(see http://cms-service-lumi.web.cern.ch/cms-service-lumi/brilwsdoc.html for more details).
+The following script runs brilcalc inclusively and per trigger path, and stores the results in a ROOT file with the total integrated lumi per run.
+It takes a bit to run, depending on the number of triggers configured to use in the analysis
+```
+export PATH=$HOME/.local/bin:/cvmfs/cms-bril.cern.ch/brilconda/bin:$PATH
+python scripts/convertLumiTable.py --normtag /cvmfs/cms-bril.cern.ch/cms-lumi-pog/Normtags/normtag_PHYSICS.json 
+python scripts/convertLumiTable.py -o data/era2016/ -y 2016 --lumi /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions16/13TeV/Final/Cert_271036-284044_13TeV_PromptReco_Collisions16_JSON.txt
+```
+
+## Preparing the analysis 
+
+Correction and uncertainty files are stored under data by era directories (e.g data/era2017) in order no to mix different periods.
+
+* Pileup weighting. To update the pileup distributions run the script below. It will store the data pileup distributions for different min.bias cross section in data/pileupWgts.root
+```
+python scripts/runPileupEstimation.py --out data/era2017/pileupWgts.root
+python scripts/runPileupEstimation.py --out data/era2016/pileupWgts.root \
+       --json /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions16/13TeV/Final/Cert_271036-284044_13TeV_PromptReco_Collisions16_JSON.txt \
+       --puJson /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions16/13TeV/PileUp/pileup_latest.txt
+```
+* B-tagging. To apply corrections to the simulation one needs the expected efficiencies stored somwewhere. The script below will project the jet pT spectrum from the TTbar sample before and after applying b-tagging, to compute the expecte efficiencies. The result will be stored in data/expTageff.root
+```
+python scripts/saveExpectedBtagEff.py -i /store/cmst3/group/top/RunIIReReco/ab05162/MC13TeV_2017_TTJets      -o data/era2017/expectedBtagEff.root;
+python scripts/saveExpectedBtagEff.py -i /store/cmst3/group/top/RunIIReReco/2016/0c522df/MC13TeV_2016_TTJets -o data/era2016/expectedBtagEff.root;
+```
+* MC normalization. This will loop over all the samples available in EOS and produce a normalization cache (weights to normalize MC). The file will be available in data/genweights.pck
+```
+python scripts/produceNormalizationCache.py -i /store/cmst3/group/top/RunIIReReco/ab05162      -o data/era2017/genweights_ab05162.root
+python scripts/produceNormalizationCache.py -i /store/cmst3/group/top/RunIIReReco/2016/0c522df -o data/era2016/genweights_0c522df.root
+```
+The lepton/photon trigger/id/iso efficiencies should also be placed under data/era2017. 
+The src/EfficiencyScaleFactorsWrapper.cc  should then be updated to handle the reading of the ROOT files and the application of the scale factors
+event by event.
+
+## Updating the code
+
+Commit your changes regularly with
+```
+git commit -a -m'comment on the changes made'
+```
+Push to your forked repository
+```
+git push git@github.com:MYGITHUBLOGIN/TopLJets2015.git
+```
+From the github area of the repository cleak on the green button "Compare,review and create a pull request" to create the PR to merge with your colleagues.
+
+# Local analyses
+
+The ROOT trees created by MiniAnalyzer.cc can be analyzed with a simple executable.
+See some examples under `src`.
+The new executable should be included in `bin/analysisWrapper.cc` so that it can be used with the runLocalAnalysis.py script
+which allows to run over single files or full directories.
+See examples under test/ in ```steer*Analysis.sh```.
+To plot the output of the local analysis you can run the following:
+```
+python scripts/plotter.py -i analysis/   -j data/era2017/samples.json  -l 12870
+```
+# BDT Training
+This part currently works only for the VBF analysis. The signal and background trees must have been produced in the previous session by enabling "--mvatree" with "SEL" option.The training is done per category with the following command:
+just remember that before doing the below command do hadd for signal.root files and also background.
+```
+
+
+
+
+
+python scripts/trainVbfBDT --vbf nt=50:mns=5:md=3:abb=0.6:nc=1 --ext LowVPtHighMJJ --sig signal.root --bkg backgrounds.root --cat A:VBF --card test/analysis/VBF_Cards/LowVPtHighMJJCard
+```
+The example above is for "LowVPtHighMJJ" category. Once happy with the training, 
+
+
+cp vbf/weights/LowVPtHighMJJ_BDT_VBF0LowVPtHighMJJ* test/analysis/VBF_weights
+
+
+Update src/VBFVectorBoson.cc accordingly and compile.
+
+## Transformed BDT
+To make the background BDT distribution flat, following steps must be followed:
+
+Produce plots with the compiled version of the code including new BDT weights (see Local analysis) and
+cd test/analysis/VBF_weights
+python getInverseCDFFromPlotter.py PATH_TO_plotter.root
+Reproduce the plots to have the proper flat BDT distribution of background
+# Preparation of the data cards and workspaces
+
+This part currently works only for the VBF analysis. It assumes that there are root files in the working directory which includes the plots created in the previous section. For the example below, the root file is called plotter_LowVPtHighMJJ2016.root. Running the script below will make two data cards and a single corresponding workspace for signal region (here gamma+jets) and control region (here Z+jets) of the "LowVPtHighMJJ" the category:
+```
+python scripts/makeWorkspace.py --Chan LowVPtHighMJJ --Hist vbfmva --nBin 20 --year 2016 --shapeOnly --doSignalPH
+```
+
+The input histogram will be rebinned to have five bins. If you remove "--doSignalPH", the signal process in the signal and control region will NOT be connected via the transfer function.
+
+
+# Photon fake rate estimation and distributions
+The method is explained in AN-18-046. The FR is measured in bins of mjj separately for photons in the barrel and endcap. It is measured in a control region (CR) which has the same selection as the signal region except the 2nd jet that is required to fail the loose pileup identification.
+
+## Input data for FR measurement
+Photon data (HighVPt categories): add --CR to the SEL option in steerVBFVectorBoson.sh
+
+Jet data (LowVPt category): run steerVBFVectorBoson.sh with SELJETHT in which --CR is a default input
+## Input templates for FR measurement
+Tight template: the output of running default SEL option in steerVBFVectorBoson.sh on GJet samples
+
+Fake template: the output of running SELJETHT option with -q QCDTemp argument in steerVBFVectorBoson.sh
+## Fake rate estimation
+for preparing the PHOTON-DATA and other files, you have to hadd the files.
+Run the command below on the output of the aforementioned steps:
+```
+python scripts/createFakeRatio.py --fGdata PHOTON-DATA --fJdata JETHT-DATA --fJQCD JETHT-DATA-QCDTEMP --fGMC GJET-MC --cats HighVPtHighMJJA:HighVPtLowMJJA:LowVPtHighMJJA
+```
+Copy the output fakeRatios.root to data/eraYEAR
+## Fake rate application
+Here the distributions of not-tight photons are scaled by the estimated FR. The distributions can be used then in plotting. Need to activate --SRfake for the SEL option in steerVBFVectorBoson.sh and run on SinglePhoton data
+
diff --git a/TopAnalysis/bin/runFRcalculation.cc b/TopAnalysis/bin/runFRcalculation.cc
@@ -15,7 +15,9 @@ using namespace std;
 using namespace TMVA;
 int main( int argc, char** argv )
 {
+
   TString fGammaData("Data13TeV_SinglePhoton_2017.root"), fJetData("Data13TeV_JetHT_2017.root"), fJetQCD("Data13TeV_JetHTQCD_2017.root"), fGammaMC("MC13TeV_GJets.root"),binvar("Mjj");
+
   std::string categories;
   std::string oers;
 

diff --git a/TopAnalysis/data/era2016/fakeRatios.root b/TopAnalysis/data/era2016/fakeRatios.root
diff --git a/TopAnalysis/data/era2016/genweights_0c522df.root b/TopAnalysis/data/era2016/genweights_0c522df.root
diff --git a/TopAnalysis/data/era2016/genweights_ab05162.root b/TopAnalysis/data/era2016/genweights_ab05162.root
diff --git a/TopAnalysis/data/era2016/samples.json b/TopAnalysis/data/era2016/samples.json
@@ -1,8 +1,10 @@
 {
+
 "MC13TeV_2016_TTJets"                  : [832, 0,     "/TT_TuneCUETP8M2T4_13TeV-powheg-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "t#bar{t}", 920, true, false, 1],
 "MC13TeV_2016_TTJets_psweights"        : [832, 0,     "/TT_TuneCUETP8M2T4_PSweights_13TeV-powheg-pythia8/RunIISummer16MiniAODv3-94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "t#bar{t}", 920, true, false, 1],
 "MC13TeV_2016_TTJetsTo2L2Nu_psweights" : [87.5, 0,     "/TTTo2L2Nu_TuneCP5_PSweights_13TeV-powheg-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "t#bar{t}", 920, true, false, 1],
 "MC13TeV_2016_TTJetsTo2L2Nu_fxfx"   : [87.5, 0,     "/TTJets_Dilept_TuneCUETP8M2T4_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "t#bar{t}", 920, true, false, 1],
+
 "MC13TeV_2016_TTJets_fsrdn_ext"     : [832, 0,     "/TT_TuneCUETP8M2T4_13TeV-powheg-fsrdown-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext1-v1/MINIAODSIM", "t#bar{t} fsrdn", 920, true, false, 1],
 "MC13TeV_2016_TTJets_fsrup"         : [832, 0,     "/TT_TuneCUETP8M2T4_13TeV-powheg-fsrup-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "t#bar{t} fsrup", 920, true, false, 1],
 "MC13TeV_2016_TTJets_fsrup_ext"     : [832, 0,     "/TT_TuneCUETP8M2T4_13TeV-powheg-fsrup-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext1-v1/MINIAODSIM", "t#bar{t} fsrup", 920, true, false, 1],

diff --git a/TopAnalysis/data/era2016/vbf_samples.json b/TopAnalysis/data/era2016/vbf_samples.json
@@ -4,6 +4,7 @@
 "MC13TeV_2016_GJets_HT200to400"      : [2305,    0,  "/GJets_HT-200To400_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "#gamma+jets", "#7fc97f", false, false, 1],
 "MC13TeV_2016_GJets_HT400to600"      : [274.4,   0,  "/GJets_HT-400To600_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "#gamma+jets", "#7fc97f", false, false, 1],
 "MC13TeV_2016_GJets_HT600toInf"      : [93.46,   0,  "/GJets_HT-600ToInf_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "#gamma+jets", "#7fc97f", false, false, 1],
+
 "MC13TeV_2016_QCDEM_15to20"          : [2302200, 0,  "", "QCD", 41, false, false, 1],
 "MC13TeV_2016_QCDEM_20to30"          : [5352960, 0,  "/QCD_Pt-20to30_EMEnriched_TuneCUETP8M1_13TeV_pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "QCD", "#DCDCDC", false, false, 1],
 "MC13TeV_2016_QCDEM_30to50"          : [9928000, 0,  "/QCD_Pt-30to50_EMEnriched_TuneCUETP8M1_13TeV_pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "QCD", "#DCDCDC", false, false, 1],
@@ -21,10 +22,15 @@
 "MC13TeV_2016_WG"                    : [378.2,    0,  "/WGToLNuG_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext2-v1/MINIAODSIM", "W,Top,VV", "#386cb0", false, false, 1],
 "MC13TeV_2016_ZG"                    : [1,        0,  "/ZGTo2LG_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext1-v1/MINIAODSIM", "W,Top,VV", "#386cb0", false, false, 1],
 "MC13TeV_2016_WJets_mlm"             : [61526.7, 0,   "/WJetsToLNu_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext2-v2/MINIAODSIM", "W,Top,VV", "#386cb0",false,false,1],
-"MC13TeV_2016_DY10to50"              : [18610, 0,  "/DYJetsToLL_M-10to50_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "DY", "#fdc086", false, false, 1],
-"MC13TeV_2016_DY50toInf_0J_fxfx"     : [4757.0, 0,  "/DYToLL_0J_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_backup_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "DY", 41, false, false, 1],
+
+
+ "MC13TeV_2016_DY50toInf_0J_fxfx"     : [4757.0, 0,  "/DYToLL_0J_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_backup_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "DY", 41, false, false, 1],
+
 "MC13TeV_2016_DY50toInf_1J_fxfx"     : [859.6, 0,  "/DYToLL_1J_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext1-v1/MINIAODSIM", "DY", 41, false, false, 1],
-"MC13TeV_2016_DY50toInf_2J_fxfx"     : [340.5, 0,  "/DYToLL_2J_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "DY", 41, false, false, 1],
+
+
+ "MC13TeV_2016_DY50toInf_2J_fxfx"     : [340.5, 0,  "/DYToLL_2J_13TeV-amcatnloFXFX-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "DY", 41, false, false, 1],
+
 "MC13TeV_2016_EWKZJJ"                : [1.629,    0,  "/EWK_LLJJ_MLL-50_MJJ-120_13TeV-madgraph-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "EWK Zjj", "#f0027f", false, false, 1],
 "MC13TeV_2016_IntZJJ"                : [0.1276,       0,  "/LLJJ_INT_SM_5f_LO_13TeV_madgraph-pythia8_TuneCUETP8M1/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext1-v1/MINIAODSIM", "EWK Zjj", "#f0027f", false, false, 1],
 "MC13TeV_2016_EWKAJJ"                : [4.74,    0,  "/GJets_Mjj-500_SM_5f_TuneCUETP8M1_EWK_13TeV-madgraph-pythia8/RunIISummer16MiniAODv3-94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "EWK #gammajj", 633, false, false, 1],

diff --git a/TopAnalysis/data/era2016/vbf_syst_samples.json b/TopAnalysis/data/era2016/vbf_syst_samples.json
@@ -1,7 +1,8 @@
 {
+
 "MC13TeV_2016_DY50toInf_mlm_ext"     : [5765.4, 0,  "/DYJetsToLL_M-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext2-v2/MINIAODSIM", "DY", 41, false, false, 1],
 "MC13TeV_2016_DY50toInf_mlm" : [5765.4, 0,  "/DYJetsToLL_M-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3_ext1-v2/MINIAODSIM", "DYlo", 41, false, false, 1],
-"MC13TeV_2016_GJets_unlops"  : [14676.0,  0,  "", "#gamma+jets (unlops)", "#7fc97f", false, false, 1],
+"MC13TeV_2016_GJets_unlops"  : [14676.0,  0,   "#gamma+jets (unlops)", "#7fc97f", false, false, 1],
 "MC13TeV_2016_DY50toInf_1J_mlm"  : [5765.4, 0, "/DY1JetsToLL_M-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v1/MINIAODSIM", "DYlojet", 41, false, false, 1], 
 "MC13TeV_2016_DY50toInf_2J_mlm"  : [5765.4, 0, "/DY2JetsToLL_M-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "DYlojet", 41, false, false, 1], 
 "MC13TeV_2016_DY50toInf_3J_mlm"  : [5765.4, 0, "/DY3JetsToLL_M-50_TuneCUETP8M1_13TeV-madgraphMLM-pythia8/RunIISummer16MiniAODv3-PUMoriond17_94X_mcRun2_asymptotic_v3-v2/MINIAODSIM", "DYlojet", 41, false, false, 1], 

diff --git a/TopAnalysis/data/era2017/genweights_ab05162.root b/TopAnalysis/data/era2017/genweights_ab05162.root
diff --git a/TopAnalysis/data/era2017/vbf_samples.json b/TopAnalysis/data/era2017/vbf_samples.json
@@ -28,6 +28,8 @@
 "MC13TeV_2017_IntZJJ"                : [0.1276,       0,  "", "EWK Zjj", "#f0027f", false, false, 1],
 "MC13TeV_2017_EWKAJJ"                : [4.74,    0,  "/GJets_Mjj-500_SM_5f_TuneCP5_EWK_13TeV-madgraph-pythia8/RunIIFall17MiniAODv2-PU2017_12Apr2018_94X_mc2017_realistic_v14-v1/MINIAODSIM", "EWK #gammajj", 633, false, false, 1],
 "MC13TeV_2017_IntAJJ"                : [0.7,     0,  "/GJets_Mjj-500_SM_5f_TuneCP5_INTERFERENCE_13TeV-madgraph-pythia8/RunIIFall17MiniAODv2-PU2017_12Apr2018_94X_mc2017_realistic_v14-v1/MINIAODSIM", "EWK #gammajj", 633, false, false, 1],
+
+
 "Data13TeV_2017B_SinglePhoton"       : [1,       1, "/SinglePhoton/Run2017B-31Mar2018-v1/MINIAOD", "Data", 1, false, false, 0],
 "Data13TeV_2017C_SinglePhoton"       : [1,       1, "/SinglePhoton/Run2017C-31Mar2018-v1/MINIAOD", "Data", 1, false, false, 0],
 "Data13TeV_2017D_SinglePhoton"       : [1,       1, "/SinglePhoton/Run2017D-31Mar2018-v1/MINIAOD", "Data", 1, false, false, 0],