Setup on SLC6 in a clean directory (no CMSSW) on a shared file system
$ mkdir -p ~/tth/sw
$ cd ~/tth/sw
$ wget --no-check-certificate https://raw.githubusercontent.com/jpata/tthbb13/meanalysis-80x/setup.sh
$ source setup.sh
This will download CMSSW, the tthbb code and all the dependencies.
In order to compile the code, run
$ cd ~/tth/sw/CMSSW/src
$ cmsenv
$ scram b -j 8
We use rootpy in the code, which is installed on the T3 locally in ~jpata/rootpy. In order to properly configure the environment, run the following source setenv_psi.sh before starting your work.
This will start with MiniAOD and produce a VHBB ntuple.
In order to run a quick test of the code, use the following testfile
$ python tests.py
# need to adjust which test this actual runs. MEAnalysis/python/test_MEAnalysis_heppy.py is good
Submission will proceed via crab3, explained in Step1+2.
Using the VHBB ntuple, we will run the ttH(bb) and matrix element code
This is to test the code
$ make test_MEAnalysis
#this will call TTH/MEAnalysis/python/MEAnalysis_heppy.py
$ make test_MEAnalysis_withme
#this will call TTH/MEAnalysis/python/MEAnalysis_heppy.py
To submit a few test workflows with crab do:
$ cd TTH/MEAnalysis/crab_vhbb
$ python multicrab.py --workflow testing_withme --tag my_test1
To produce all the SL/DL samples, do
$ cd TTH/MEAnalysis/crab_vhbb
$ python multicrab.py --workflow leptonic --tag May13
To prepare the dataset files in TTH/MEAnalysis/gc/datasets/{TAG}/{DATASET}, use the DAS script
$ python TTH/MEAnalysis/python/MakeDatasetFiles.py --version {TAG}
When some of the samples are done, you can produce smallis (<10GB) skims of the files using local batch jobs.
$ cd TTH/MEAnalysis/gc
$ source makeEnv.sh #make an uncommited script to properly set the environment on the batch system
v./grid-control/go.py confs/projectSkim.conf
... #wait
$ ./hadd.py /path/to/output/GC1234/
This will produce some skimmed ntuples in
/mnt/t3nfs01/data01/shome/jpata/tth/gc/projectSkim/GCe0f041d65b98:
Jul15_leptonic_v1__ttHTobb_M125_13TeV_powheg_pythia8 <= unmerged
Jul15_leptonic_v1__ttHTobb_M125_13TeV_powheg_pythia8.root <= merged file
Jul15_leptonic_v1__TTJets_SingleLeptFromTbar_TuneCUETP8M1_13TeV-madgraphMLM-pythia8
Jul15_leptonic_v1__TTJets_SingleLeptFromTbar_TuneCUETP8M1_13TeV-madgraphMLM-pythia8.root
Jul15_leptonic_v1__TTJets_SingleLeptFromT_TuneCUETP8M1_13TeV-madgraphMLM-pythia8
Jul15_leptonic_v1__TTJets_SingleLeptFromT_TuneCUETP8M1_13TeV-madgraphMLM-pythia8.root
Jul15_leptonic_v1__TTTo2L2Nu_13TeV-powheg
Jul15_leptonic_v1__TTTo2L2Nu_13TeV-powheg.root
Jul15_leptonic_v1__TT_TuneCUETP8M1_13TeV-powheg-pythia8
Jul15_leptonic_v1__TT_TuneCUETP8M1_13TeV-powheg-pythia8.root
The total processed yields can be extracted with
$ cd TTH/MEAnalysis/gc
$ ./grid-control/go.py confs/count.conf
...
$ ./hadd.py /path/to/output/GC1234/
$ python $CMSSW_BASE/src/TTH/MEAnalysis/python/getCounts.py /path/to/output/GC1234/
The counts need to be introduced to TTH/MEAnalysis/python/samples_base.py as the ngen dictionary.
In order to industrially produce all variated histograms, we create an intermediate file containing ROOT THnSparse histograms of the samples with appropriate systematics.
$ cd TTH/MEAnalysis/gc
$ ./grid-control/go.py confs/sparse.conf
...
$ hadd -f sparse.root /path/to/output/GC1234/
The output file will contain
$
TTTo2L2Nu_13TeV-powheg <- sample
-dl <- base category ({sl,dl,fh})
--sparse (THnSparseT<TArrayF>) <==== nominal distribution
--sparse_CMS_ttH_CSVHFDown (THnSparseT<TArrayF>) <==== systematically variated distributions
--sparse_CMS_ttH_CSVHFStats1Down (THnSparseT<TArrayF>)
--sparse_CMS_ttH_CSVHFStats1Up (THnSparseT<TArrayF>)
--sparse_CMS_ttH_CSVHFStats2Down (THnSparseT<TArrayF>)
--sparse_CMS_ttH_CSVHFStats2Up (THnSparseT<TArrayF>)
--sparse_CMS_ttH_CSVHFUp (THnSparseT<TArrayF>)
-sl
...
ttHTobb_M125_13TeV_powheg_pythia8
-dl
...
-sl
...
...
Configure the input file in TTH/Plotting/python/Datacards/AnalysisSpecificationSL.py, then call
cd TTH/MEAnalysis/gc
#generate the parameter csv files: analysis_groups.csv, analysis_specs.csv
python $CMSSW_BASE/src/TTH/Plotting/python/Datacards/AnalysisSpecification.py
./grid-control/go.py confs/makecategories.conf
This will create all the combine datacards ({ANALYSIS}/{CATEGORY}.root files and shapes_*.txt files) for all analyses and all the categories.
[jpata@t3ui17 gc]$ ls -1 ~/tth/gc/makecategory/GC41c32de9adb2/SL_7cat/
shapes_sl_j4_t3_blrH_mem_SL_0w2h2t_p.txt
shapes_sl_j4_t3_blrL_btag_LR_4b_2b_btagCSV_logit.txt
shapes_sl_j4_t3_mem_SL_0w2h2t_p.txt
shapes_sl_j4_tge4_mem_SL_0w2h2t_p.txt
shapes_sl_j5_t3_blrH_mem_SL_1w2h2t_p.txt
shapes_sl_j5_t3_blrL_btag_LR_4b_2b_btagCSV_logit.txt
shapes_sl_j5_t3_mem_SL_1w2h2t_p.txt
shapes_sl_j5_tge4_mem_SL_1w2h2t_p.txt
shapes_sl_jge6_t2_btag_LR_4b_2b_btagCSV_logit.txt
shapes_sl_jge6_t3_blrH_mem_SL_2w2h2t_p.txt
shapes_sl_jge6_t3_blrL_mem_SL_2w2h2t_p.txt
shapes_sl_jge6_t3_mem_SL_2w2h2t_p.txt
shapes_sl_jge6_tge4_mem_SL_2w2h2t_p.txt
sl_j4_t3_blrH.root
sl_j4_t3_blrL.root
sl_j4_t3.root
sl_j4_tge4.root
sl_j5_t3_blrH.root
sl_j5_t3_blrL.root
sl_j5_t3.root
sl_j5_tge4.root
sl_jge6_t2.root
sl_jge6_t3_blrH.root
sl_jge6_t3_blrL.root
sl_jge6_t3.root
sl_jge6_tge4.root
$ python ../test/listroot.py ~/tth/gc/makecategory/GC41c32de9adb2/SL_7cat/sl_jge6_tge4.root
ttH_hbb
-sl_jge6_tge4
--btag_LR_4b_2b_btagCSV_logit (Hist)
--jetsByPt_0_pt (Hist)
--mem_SL_2w2h2t_p (Hist)
ttbarPlusB
-sl_jge6_tge4
--btag_LR_4b_2b_btagCSV_logit (Hist)
--jetsByPt_0_pt (Hist)
--mem_SL_2w2h2t_p (Hist)
...
Configure the path to the category output in confs/makelimits.conf by setting datacardbase to the output of step 4.
cd TTH/MEAnalysis/gc
./grid-control/go.py confs/makelimits.conf
From the output of makecategory, you can make data/MC plots using code in plotlib.py and controlPlot.py. See TTH/MEAnalysis/python/joosep/controlPlot.py for an example. For this to work, you need to use the rootpy environment.
On the T3 using 10 cores, you can make about 100 pdf plots per minute.