Scripts to make ntuples for quick trigger studies
There are two files: one for release 21 and one for release 22 (master).
The selection and trigger lists saved by these scripts are only toy exampes - you should tailor the selection for your own purposes.
To run locally, first setup AnalysisBase (in the release corresponding to your AODs). If your code gets errors about RootCore/Packages.h, after setting up the release, run this:
export ROOT_INCLUDE_PATH=$ROOT_INCLUDE_PATH:$ROOTCOREBIN/RootCore/include/
which adds the problematic file to the include path.
Then, you can simply execute the file locally. For example:
./skimTrig_r21.py --inputFiles /eos/atlas/atlasdatadisk/rucio/data18_13TeV/68/68/AOD.19448522._000080.pool.root.1 --isData True --l1rois
aside: the input file listed above is an AOD from the latest 21.3 enhanced bias processing. These are alwyas stored on CERN-PROD_DATADISK so are available locally on lxplus/testbed. To access the file paths, you can run:
rucio list-file-replicas data18_13TeV.00360026.physics_EnhancedBias.merge.AOD.r11637_r11638_p3989_tid19448522_00 --rse CERN-PROD_DATADISK
There are three command-line options, you can use ./skim_r12.py --help to see how they work:
--inputFiles : a comma-separated list of files you want to run on (a single file is ok too!)
--isData : set this to True or False depending on whether you're running on data or MC
--l1rois : if you include this flag, L1 RoIs will be saved to your output ntuple (muon, egamma, and tau).
To run on the grid, you can use the following command (after running lsetup panda and voms-proxy-init -voms atlas, and changing the outDS to someting with your username):
prun --useAthenaPackages --exec "python skimTrig_r21.py --inputFiles %IN --isData True --l1rois | tee log.txt" \
--inDS data18_13TeV.00360026.physics_EnhancedBias.merge.AOD.r11637_r11638_p3989_tid19448522_00 \
--outDS user.hrussell.testEB_360026.r11637_v01 \
--outputs tmpTrig.root --excludeFile=\*.o,\*.so,\*.a --mergeOutput
Note that if you change the name of the output file tmpTrig.root in the script: fOut = ROOT.TFile("tmpTrig.root","RECREATE"), you also need to change the name of the file in outputs (likely some regex also works, but I have not tried).
In order to analyse EnhancedBias data, you also need to add the EB weights. There are two steps here:
(1) create a tree with EB weights saved for each event number (2) add this tree as a friend in your analysis script.
For (1), first hadd together your full output from the EB dataset. Then, run the script makeFriend.py:
./makeFriend.py my_input_dataset.root
Make sure that the enhanced bias weight file inside makeFriend.py corresponds to the same run you ran on!
You'll get out a file my_input_dataset.EBweights.root. This is just a tree trig with two branches: eventNumber and EBweight.
In your analysis code, then setup like this:
data_file = 'enhancedBias.root'
weights_file = 'enhancedBias.EBweights.root'
f = ROOT.TFile.Open(data_file, 'read')
t = f.Get('trig')
if not doMC:
t.AddFriend('trig',weights_file)
print ('added a friend')
And then you can access the variable EBweight for each event, like you would any other variable.