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CMSSWGeneration

Up to date(31/01/2022):

generation scripts

In the generation folder there are two main scripts to generate with full sim given the WLLJJ_WToLNu_EWK flow. You are encouraged to use them because of pathces and checks automatically inserted.

Old guide

Instructions on how to generate a private sample using crab in CMSSW

Assumptions:

  • LHE are available
  • premixed library is available

Prepare to get premix library:

voms-proxy-init -voms cms -rfc

LHE:

example: /afs/cern.ch/user/g/govoni/myeos/samples/2019_EFT/SSeu/SM_limit/SSeu_SMlimit_results/1441466/SSeu_SMlimit_3/unweighted_events.lhe

Generate2018

This folder allows to generate nanoAOD samples for 2018 year starting from a gridpack.

Open cmsconnect and go to /local-scratch/<username>

  1. git clone https://github.com/UniMiBAnalyses/CMSSWGeneration.git

  2. A log folder is necessary to save .log .err and .out files

    mkdir log

  3. Open wrapper.sh L8 and change - if needed - with the suffix of your gridpack.

gridpack_name="$1_<suffix of your gridpack>_tarball.tar.xz"

  1. Open submit.jdl L9 and change the first input, that is the absolute path to your gridpack. The name of the process must be replaced by $(proc), for example:

/local-scratch/<username>/gridpacks/$(proc)_slc7_amd64_gcc700_CMSSW_10_6_0_tarball.tar.xz

  1. To make scale and pdf weights available (this will be useful for nuisances at reco level), it is necessary to use CMSSW_10_6_20 sandbox. This means that you can create a CMSSW_10_6_20.tgz and then pass it as an input:
cd Generate2018/input
cmsrel CMSSW_10_6_20
cd CMSSW_10_6_20/src
cmsenv
scram b
cd ../..
tar -zcvf CMSSW_10_6_20.tgz CMSSW_10_6_20

You also need CMSSW_10_2_6.tgz, so let's repeat:

cd Generate2018/input
cmsrel CMSSW_10_2_6
cd CMSSW_10_2_6/src
cmsenv
scram b
cd ../..
tar -zcvf CMSSW_10_2_6.tgz CMSSW_10_2_6
  1. The list of input files inside input folder should be now:
  • SMP-RunIIAutumn18DRPremix-00050_1_cfg.py
  • SMP-RunIIAutumn18DRPremix-00050_2_cfg.py
  • SMP-RunIIAutumn18MiniAOD-00050_1_cfg.py
  • SMP-RunIIAutumn18NanoAODv7-00058_1_cfg.py
  • SMP-RunIIFall18wmLHEGS-00062_SM_1_cfg.py or SMP-RunIIFall18wmLHEGS-00062_EFT_1_cfg.py
  • CMSSW_10_6_20.tgz
  • CMSSW_10_2_6.tgz

Open submit.jdl L9 and change the absolute path of the other 7 input files, for example

/local-scratch/<username>/CMSSWGeneration/Generate2018/input/SMP-RunIIAutumn18DRPremix-00050_1_cfg.py

One should choose between LHEGS for SM or EFT. The SM version is without dipoleRecoil and it should be used to validate the framework and in order to compare with old DAS samples which do not had this option. For newer sample it should be on, so please use the EFT version, regardless of what you are generating.

  1. Create a folder to save all the nanoAOD files, for example output

    mkdir output

Open submit.jdl L10 and change the absolute path of your ouput folder (do not change $(proc)/$(proc)_$(Cluster)_$(Step).root), for example

transfer_output_remaps = "SMP-RunIIAutumn18NanoAODv7-00058.root = /scratch/<username>/CMSSWGeneration/Generate2018/output/$(proc)/$(proc)_$(Cluster)_$(Step).root"

  1. Open submit.jdl L3 to change the number of events you want for each .root file.

arguments = $(proc) <number of events>

  1. Open submit.sh L2 to change the number of .root files you want.

sed -i "12s/.*/Queue <number of .root files> proc in ($1)/g" submit.jdl

If your output folder has a different name, change also L3

mkdir -p <your output folder>/$1

  1. Now everything is ready, you just need to run

    ./submit.sh <name of your process>

where the name of the process is the one that appears in the gridpack.

The generation can last a few days, you can see its evolution using

`condor_q`

Generate2017

Example of outputs for 2017 production

https://github.com/latinos/LatinoAnalysis/blob/master/NanoGardener/python/framework/samples/fall17_102X_nAODv5.py

From the name of the sample:
/GluGluHToWWTo2L2Nu_M125_13TeV_powheg2_JHUGenV714_pythia8/RunIIFall17NanoAODv5-PU2017_12Apr2018_Nano1June2019_102X_mc2017_realistic_v7-v1/NANOAODSIM
Get the different steps (backward) in the generation:

NanoAOD
https://cms-pdmv.cern.ch/mcm/requests?prepid=HIG-RunIIFall17NanoAODv5-00331&page=0&shown=127

MiniAOD 
https://cms-pdmv.cern.ch/mcm/requests?prepid=HIG-RunIIFall17MiniAODv2-02464&page=0&shown=127

PreMix 
https://cms-pdmv.cern.ch/mcm/requests?prepid=HIG-RunIIFall17DRPremix-02533&page=0&shown=127

wmLHE
https://cms-pdmv.cern.ch/mcm/requests?prepid=HIG-RunIIFall17wmLHEGS-01920&page=0&shown=127

Instructions:

NanoAOD
https://twiki.cern.ch/twiki/bin/view/CMSPublic/WorkBookNanoAOD#Running_on_various_datasets_from

Produce (from RunTheMatrix):

# in: /afs/cern.ch/work/a/amassiro/ECAL/SIM/ToRebase/CMSSW_11_0_X_2019-10-06-2300/src dryRun for 'cd 250202.172_TTbar_13UP17+TTbar_13UP17+DIGIPRMXUP17_PU25_RD+RECOPRMXUP17_PU25+HARVESTUP17_PU25
cmsDriver.py TTbar_13TeV_TuneCUETP8M1_cfi  --conditions auto:phase1_2017_realistic -n 10 --era Run2_2017 --eventcontent FEVTDEBUG --relval 9000,50 -s GEN,SIM --datatier GEN-SIM --beamspot Realistic25ns13TeVEarly2017Collision --io TTbar_13UP17.io --python TTbar_13UP17.py --fileout file:step1.root  --nThreads 8 > step1_TTbar_13UP17+TTbar_13UP17+DIGIPRMXUP17_PU25_RD+RECOPRMXUP17_PU25+HARVESTUP17_PU25.log  2>&1


#    in: /afs/cern.ch/work/a/amassiro/ECAL/SIM/ToRebase/CMSSW_11_0_X_2019-10-06-2300/src dryRun for 'cd 250202.172_TTbar_13UP17+TTbar_13UP17+DIGIPRMXUP17_PU25_RD+RECOPRMXUP17_PU25+HARVESTUP17_PU25
cmsDriver.py step2  --datamix PreMix --conditions auto:phase1_2017_realistic --pileup_input das:/RelValPREMIXUP17_PU25/CMSSW_10_6_0-PU25ns_106X_mc2017_realistic_v3-v1/PREMIX --era Run2_2017 --procModifiers premix_stage2 -s DIGI:pdigi_valid,DATAMIX,L1,DIGI2RAW,HLT:@relval2017 --datatier GEN-SIM-DIGI-RAW-HLTDEBUG --eventcontent FEVTDEBUGHLT --io DIGIPRMXUP17_PU25_RD.io --python DIGIPRMXUP17_PU25_RD.py -n 100  --filein  file:step1.root  --fileout file:step2.root  --nThreads 8 > step2_TTbar_13UP17+TTbar_13UP17+DIGIPRMXUP17_PU25_RD+RECOPRMXUP17_PU25+HARVESTUP17_PU25.log  2>&1


#    in: /afs/cern.ch/work/a/amassiro/ECAL/SIM/ToRebase/CMSSW_11_0_X_2019-10-06-2300/src dryRun for 'cd 250202.172_TTbar_13UP17+TTbar_13UP17+DIGIPRMXUP17_PU25_RD+RECOPRMXUP17_PU25+HARVESTUP17_PU25
cmsDriver.py step3  --conditions auto:phase1_2017_realistic -n 10 --era Run2_2017 --eventcontent RECOSIM,MINIAODSIM,DQM --runUnscheduled  --procModifiers premix_stage2 -s RAW2DIGI,L1Reco,RECO,RECOSIM,EI,PAT,VALIDATION:@standardValidationNoHLT+@miniAODValidation,DQM:@standardDQMFakeHLT+@miniAODDQM --datatier GEN-SIM-RECO,MINIAODSIM,DQMIO --io RECOPRMXUP17_PU25.io --python RECOPRMXUP17_PU25.py --filein  file:step2.root  --fileout file:step3.root  --nThreads 8 > step3_TTbar_13UP17+TTbar_13UP17+DIGIPRMXUP17_PU25_RD+RECOPRMXUP17_PU25+HARVESTUP17_PU25.log  2>&1


#    in: /afs/cern.ch/work/a/amassiro/ECAL/SIM/ToRebase/CMSSW_11_0_X_2019-10-06-2300/src dryRun for 'cd 250202.172_TTbar_13UP17+TTbar_13UP17+DIGIPRMXUP17_PU25_RD+RECOPRMXUP17_PU25+HARVESTUP17_PU25
cmsDriver.py step4  --conditions auto:phase1_2017_realistic -s HARVESTING:@standardValidationNoHLT+@standardDQMFakeHLT+@miniAODValidation+@miniAODDQM --filetype DQM --geometry DB:Extended --era Run2_2017 --mc  --io HARVESTUP17_PU25.io --python HARVESTUP17_PU25.py -n 100  --filein file:step3_inDQM.root --fileout file:step4.root  > step4_TTbar_13UP17+TTbar_13UP17+DIGIPRMXUP17_PU25_RD+RECOPRMXUP17_PU25+HARVESTUP17_PU25.log  2>&1

Where:

/home/amassiro/Cern/Code/UniMiB/CMSSWGeneration

/afs/cern.ch/user/a/amassiro/work/Latinos/Framework/Generation

UL2020 SMEFTsim gridpack generation

In order to generate gradpacks for the LHE production stage one can take inspiration from https://github.com/GiacomoBoldrini/cmsgen . The repo contains cards to produce gridpacks for inclusive WW with EFT contributions via SMEFTsim madgraph model. The ReadMe describes the steps to produce gridpacks for the UL2020 campaign. For different campaigns or production one should carefully choose which branch of the genproduction to clone (UL2019 branch has mg 261 while master branch, as of 28/01/2020, has mg 265).