Batch-processing facility for EXO Leptons+Jets Ntuples to create light-weight trees, and analyzer facility for histograms, signal-background separation optimization, event-counting and limit-setting.
Darin Baumgartel (darinb@cern.ch) Feb 2014 cloned to David Morse (dmorse@cern.ch) May 2015
=============== *** Running Instructions: This requires running on the CERN LXPlus5 computing system. ****
[STEP 1]
Checking out the package
git clone git@github.com:DavidMorse/LQ2Analysis.git LQ2Analysis13TeV
cd LQ2Analysis13TeV
[STEP 2]
Running the code to get the pileup-reweighting histograms and integrated-lumi info.
./GetPULumiInfo.tcsh
[STEP 3]
Organize your NTuples by creating a CSV file which contains the NTuple Information.
CSV files should have columns like : SignalType,Xsections,N_orig,Group,CustomJson,EOSDirectory
A little info:
- SignalType: A unique identifier at the beginning of the names of the root files.
- Xsections: The cross-section to normalize the sample to (NLO is better!)
- N_orig: The original number of events in the sample... more on this in [STEP 4]
- Group: The group for the files. For instance, you might have three SignalTypes like WW, WZ, and ZZ, and want to put them in a Group called "DiBoson"
- CustomJson: The name of a Json file specifying the good runs/lumis to use. This can be the same for every data type, or different, or 0 for MC
- EOSDirectory: The eos path where the files are kept for this signaltype. Should be like a typical EOS path e.g. /store/group/..../
Please see a convenient example: NTupleInfo2016Full.csv
[STEP 4]
Get the original number of events for MC (to fill out the N_orig in the csv file).
Use the counting histograms in the ntuples to determine this. There is a way of batching this and gathering the results, as such:
python AnalyzerMakerFastLocal.py -i NTupleInfo2015Full_MiniAODv2.csv -py NTupleEvCounter.py -t PreFullLumiCountUpdate -j Cert_246908-260627_13TeV_PromptReco_Collisions15_25ns_JSON_Silver_v2.txt -p 0 -q 8nh -s 100 --FileRefresh
Some notes on the arguments:
- -i CSV File: The CSV file you wish to run on
- -t Tag name: Results will output in a directory specified by this tag-name
- -j JSON file: Not important here, needed in [STEP 5]
- -p 0: Not important here, needed in [STEP 5]
- -q 8nh: The batch queue to use.
- -s 100: Number of files to analyzer per batch job (the split number)
- --FileRefresh: The code will automatically make a list of good ntuple files and store it locally for future use, so it doesn't have to re-read directories all the time. This demands to re-read directories.
[STEP 5]
Make the analysis trees.