Begin by cloning the repository,
git clone [email protected]:UMN-CMS/WrCoffea.git
cd WrCoffea
Create and source a virtual python environment,
python3 -m venv wr-env
source wr-env/bin/activate
Install the appropriate packages,
python3 -m pip install -r requirements.txt
To set up a grid UI
voms-proxy-init --rfc --voms cms -valid 192:00
Source LGC release
source /cvmfs/sft.cern.ch/lcg/views/LCG_106/x86_64-el9-gcc13-opt/setup.sh
To run a basic analysis,
python3 bin/run_analysis.py Run3Summer22 DYJets --hists --skimmed
More information can be found in the README.md file in other folders.
To preprocess the background datasets (needs to be done if a file in configs/ is updated),
python3 scripts/make_skimmed_json.py Run2Summer20UL18 bkg --umn
where Run2Summer20UL18 can be replaced with Run3Summer22 to preprocess Run3 datasets.
Signal samples can be preprocessed with,
python3 scripts/make_skimmed_json.py Run2Autumn18 sig --umn
To run a basic analysis at UMN,
python3 bin/run_analysis.py Run2Summer20UL18 DYJets --skimmed --umn --hists
where the run is given by Run2Summer20UL18 or Run3Summer22, and the process DYJets or tt.
To run over signal samples,
python3 bin/run_analysis.py Run2Autumn18 Signal --mass WR3200_N800 --umn --hists
where the possible signal points are given by Run2Autumn18_mass_points.csv
To make histograms for all signal samples, use the script
./bin/analyze_signals.sh
which executes run_analysis.py in a loop with all of the signal points.
Plotting is handled in the WR_Plotter submodule,
cd WR_Plotter
Source the appropriate LGC release. For example,
source /cvmfs/sft.cern.ch/lcg/views/LCG_104/x86_64-centos8-gcc11-opt/setup.sh
To plot a comparison of the Run2 vs. Run3 backgrounds,
python3 scripts/241120_Run2VSRun3/plot_CR.py --umn
To plot a comparison of the signal masses,
python3 scripts/241215_N3000_vs_N800/plot_SR.py --umn
The files bin/run_analysis.py and src/analyzer.py make a standard selection and standard set of histograms. Independent studies where the variables, selections, histograms etc may differ are developed in the tests folder. It is usually easiest to copy the files and start from there. Once finished, new studies can integrated into the main pipeline via the bin or python or src folders.