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Setup the framework

  1. This tool is based on the CMS nanoAOD tool: https://github.com/cms-nanoAOD/nanoAOD-tools, release a CMSSW (take CMSSW_10_6_29 as exmaple),
cd $CMSSW_BASE/src
git clone https://github.com/cms-nanoAOD/nanoAOD-tools.git PhysicsTools/NanoAODTools
cd PhysicsTools/NanoAODTools
cmsenv
scram b
  1. Then clone this framework using command below
git clone https://github.com/botaoguo/TAU-Trigger-NANO.git

TAU-Trigger-NANO

  1. postprocessing the nanoAOD file
cd TAU-Trigger-NANO

Then you can using two type of input parameter to run the processing, notice that file.txt should contains one root file each line, like /your/path/your_input.root in each line of the txt file

python postproc.py --input nanoAOD.root --isMC 1 --era 2022 --output ./
python postproc.py --inputFileList file.txt --isMC 1 --era 2022 --output ./

The ntuple you get would be "nanoAOD_Skim.root"

  1. loop the tuple to match Trigger Object filterbit and some other cut In this case, you should activate a env that contains ROOT package and make sure that RDF , numpy, matplotlib, sklearn and other packages imported in the file fitTurnOn.py could be used. run the command below to do trigger object match and get the "numerator and denominator" in efficiency measurements
python skimTuple_2022preEE.py --input nanoAOD_Skim.root --output sk_mc --selection DeepTau --type mc --pudata pileupfile_data.root --pumc pileupfile_mc.root
python skimTuple_2022preEE.py --input nanoAOD_data_Skim.root --output sk_data --selection DeepTau --type data

Then you will get two files, one for mc, another for data.

  1. plot the TurnOn curves run the command below
python createTurnOn_multi.py --input-data sk_data.root --input-mc sk_mc.root --output TurnOn
  1. fit the TurnOn curves run the command below
python fitTurnOn_multi.py --intput TurnOn.root --output fitTurnOn