Class for analyzing the 2015 razor ntuples
cmsrel CMSSW_10_6_20
cd CMSSW_10_6_20/src
git clone git@github.com:cms-lpc-llp/llp_analyzer.git
cd llp_analyzer
make
Instructions for setting up coffea env on LPC cluster:
cmsenv
source envSetup.sh #only do this for the first time
source coffeaenv/bin/activate #Load the coffea env everytime
To start jupyter on LPC node and forward to your local terminal, you need to connect to lpc with correct port forwarding:
ssh -Y -L localhost:8888:localhost:8888 username@cmslpc-sl7.fnal.gov
Then you can start jupyter notebook after activating the coffeaenv
voms-proxy-init --rfc --voms cms # To access file on XROOTD
jupyter notebook --no-browser #Start the jupyter server
-
Copy analyzers/DummyAnalyzer.cc and replace each instance of "DummyAnalyzer" with the name of your desired analyzer. Modify the body of the Analyze function to define your analyzer's behavior. DO NOT need to write a header file for the analyzer class; the Makefile will generate one for you automatically.
-
Do
make
. This will create an executablebin/Run<name of your analyzer>
. You can execute your analysis using this program directly or by calling it via theRazorRun
script.
After compiling,
./RazorRun_T2 <list of input files> <name of your analyzer> <options>
Example: to execute a dummy analysis that does nothing,
./RazorRun_T2 lists/TTJets_List_Test.txt DummyAnalyzer
The "options" are the following:
-d --isData
-f= --outputFile=<output filename> (optional)
-n= --optionNumber=<option number> (optional)
-l= --optionLabel=<option Label> (optional)
-h --help
./RazorRun_T2 <list of input files> llp_vH -d=${isData} -n=${option} -f=${outputfile} -l=${tag}
isData
isyes
orno
option
is 1 if run on condor, elseoption
can be any other number, if run interactively. (It just sets the directory of JEC parameters differently)tag
can beRazor2017_17Nov2017Rereco_EOY_RERECO
,Razor2016_07Aug2017Rereco
,Razor2018_17SeptEarlyReReco
(different tag for each year, defined insrc/RazorHelper.cc
)- list of input files are stored in
lists/llpntuple/V1p0/MC_Summer16/v1/
, same format as the llpntuple storage space in/mnt/hadoop/
Run scripts/make_input_list_muonsystem_sig.sh
- Check the
root_dir
, which is where the ntuples are - Check
list_dir
where you want the input list to be
First run lists/Json_from_crab_prod/run_and_lumis.py
to copy all the processed json files from lxplus to t2
Then run scripts/make_input_list_muonsystem_data.py
- The script checks if there are broken files in the ntuple directory and outputs a good file list, bad file list, and a lumi file that contains: good file lumi AND good lumi.
rootDir
dictionary stores the location of the ntuples for each run periodsampleName
stores the sample name of the ntuples for each run periodgoldenLumi
stores the good lumi list for each year
Before submitting jobs, make sure proxy and CMSSW environment is setup.
- run the
llp_MuonSystem
analyzer for signal, bkg or data:scripts_condor/submit_llp_wH_muonsystem_bkg.sh
scripts_condor/submit_llp_wH_muonsystem_sig.sh
scripts_condor/submit_llp_wH_muonsystem_data*.sh
- Check
inputfilelist
where the list you created in step 1 is stored, andoutput
where you want the output do be stored
- Normalize the ntuples
scripts_condor/submit_normalize_muonsystem_*.sh
- Check
outputDir
andinputDir
The submitJob_LPC_llp.py
script submit the analyzer Runllp_hnl_analyzer
to LPC condor queue
Example command:
python submitJob_LPC_llp.py -l Razor2018_17SeptEarlyReReco --isData no --optionNumber 01 --njobs 5 -i lists/test.txt -o HNL/
For local testing:
./Runllp_hnl_analyzer ./lists/test.txt -f=test.root --isData=no --optionLabel=Razor2018_17SeptEarlyReReco --optionNumber=01
The NormalizeNtuple macro opens a specified set of files and adds a 'weight' branch to each TTree in each file. The value of 'weight' is the same for all events in a tree and is equal to lumi * CrossSection/NEvents, where NEvents is the total number of events processed for the given dataset, and lumi is the luminosity normalized to. The cross sections can be found in the file data/xSections.dat
. To run NormalizeNtuple:
./NormalizeNtuple <input file list> [lumi]
See lists/filestonormalize/testTTJets.txt for an example input file to be used with NormalizeNtuple.
- Make sure the dataset being processed have xSections in
data/xSections.dat
- Create input file list using
scripts/create_normalize_txt.py
The script hadd_llp_bkg.sh
automatically hadd and normalize the llp_analyzer ROOT files for the background samples.
- hadd signals (hadd WminusH and WplusH)
scripts/hadd_displaced.sh
- hadd different bins of QCD/ZJetstoNunu:
hadd_qcd.sh
orhadd_ZJetToNuNu.sh
- hadd different run periods in a particular year for Data:
hadd_data*.sh
https://github.com/RazorCMS/RazorCommon
The main controller of the fit and limit setting comes from the configuration file, which defines the binning used in the binned fit, the initlial values of the shape parameters, etc.
config/run2_sideband.config
Setup combine from lxplus:
mkdir ~/work/RAZORRUN2/
cd ~/work/RAZORRUN2/
cmsrel CMSSW_7_1_5
cd CMSSW_7_1_5/src/
git clone https://github.com/RazorCMS/HiggsAnalysis-CombinedLimit.git HiggsAnalysis/CombinedLimit
cd HiggsAnalysis/CombinedLimit
git pull origin razor1dpdf_71X
scramv1 b clean; scramv1 b
cd ../..
Proceed with the usual setup of RazorAnalyzer:
git clone https://github.com/RazorCMS/RazorAnalyzer.git
cd RazorAnalyzer
make
Make output directories to make workflow easier
mkdir Datasets
mkdir FitResults; mkdir FitProjections; mkdir cards
Now convert the SM MC ntuples into a SM Cocktail RooDataSet (ignoring the QCD contirbution).
python python/DustinTuple2RooDataSet.py -c config/run2_sideband.config -b MultiJet -d Datasets/ -w -l 4000 \
root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p19_ForFullStatus20151030/MC/RazorInclusive_DYJetsToLL_M-50_HTBinned_1pb_weighted.root \
root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p19_ForFullStatus20151030/MC/RazorInclusive_DYJetsToLL_M-5to50_HTBinned_1pb_weighted.root \
root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p19_ForFullStatus20151030/MC/RazorInclusive_SingleTop_1pb_weighted.root \
root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p19_ForFullStatus20151030/MC/RazorInclusive_TTJets_TuneCUETP8M1_13TeV-amcatnloFXFX-pythia8_1pb_weighted.root \
root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p19_ForFullStatus20151030/MC/RazorInclusive_TTV_1pb_weighted.root \
root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p19_ForFullStatus20151030/MC/RazorInclusive_VV_1pb_weighted.root \
root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p19_ForFullStatus20151030/MC/RazorInclusive_WJetsToLNu_HTBinned_1pb_weighted.root \
root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p19_ForFullStatus20151030/MC/RazorInclusive_ZJetsToNuNu_HTBinned_1pb_weighted.root
Similarly you can run over the data,
python python/DustinTuple2RooDataSet.py -b MultiJet -c config/run2_sideband.config -d Datasets/ --data -l 1264 root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/RazorInclusive/V1p20_ForFullStatus20151030/Data/RazorInclusive_HTMHT_Run2015D_Oct05ReMiniAOD_PRv4_GoodLumiGolden.root
To perform the fit on the SM Cocktail,
python python/BinnedFit.py -b MultiJet -c config/run2_sideband.config -d FitResults -l 4000 Datasets/RazorInclusive_SMCocktail_weighted_lumi-4.000_0-3btag_MultiJet.root
To produce the signal templates,
python python/SMSTemplates.py -c config/run2_sideband.config -b MultiJet -d Datasets/ -l 4000 root://eoscms.cern.ch//eos/cms/store/group/phys_susy/razor/Run2Analysis/FullRazorInclusive/V1p22_ForPreappFreezing20151106/jobs/combined/SMS-T1bbbb_1500_100.root
To run Bayesian toys, marginalizing the shape parameters to estimate the systematic uncertainty in each bin, and produce a large number of other 1D fit projections, and the 2D projections in MR and Rsq,
python python/RunToys.py -b MultiJet -c config/run2_sideband.config -i FitResults/BinnedFitResults_MultiJet.root -d FitResults -t 10000
python python/PlotFit.py -b MultiJet -c config/run2_sideband.config -d FitResults -i FitResults/BinnedFitResults_MultiJet.root -t FitResults/toys_Bayes_MultiJet.root
Next, to produce the datacards and run combine, execute the following:
python python/DustinTuple2RooDataSet.py -b MultiJet -c config/run2_sideband.config -w -l 4000 -d Datasets Signals/RazorInclusive_SMS-T1bbbb_2J_mGl-1500_mLSP-100_1pb_weighted.root
python python/RunCombine.py -b MultiJet -c config/run2_sideband.config -d cards --lumi-array 4 -m T1bbbb --mGluino 1500 --mLSP 100
To submit jobs to run the limits on the data:
mkdir ~/work/RAZORRUN2/Limits/
python python/RunCombineJobs.py -i FitResults/BinnedFitResults.root -c config/run2_sideband.config --data -l 1.264 -m T1bbbb -b MultiJet -d cards/ -q 8nh
After the jobs finish, the output files will be in Limits directory we just created
cp ~/work/RAZORRUN2/Limits/cards/higgs*.root cards/
python python/GetCombine.py -m T1bbbb -b MultiJet -c config/run2_sideband.config -d cards/ -l 1.264
python python/Get2DContour.py -m T1bbbb -b MultiJet -d cards/
To make the final (expected) limit plot, we need to check out a different repository.
git clone git@github.com:RazorCMS/PlotsSMS
cd PlotsSMS
Note you have to change the smoothed cross section limit file location in config/SUS15004/T1bbbb_Exp_SUS15004.cfg. Then you can run it,
python python/makeSMSplots.py config/SUS15004/T1bbbb_Exp_SUS15004.cfg T1bbbbAsymptotic
To make an "unweighted" dataset (not needed for the preceding commands), execute
python python/RooDataSet2UnweightedDataSet.py -b MultiJet -c config/run2_sideband.config -d Datasets Datasets/RazorAnalysis_SMCocktail_weighted_lumi-4.000_0-3btag_MultiJet.root
To make a .csv file of the yields in the cards directory, run the following command
python python/WriteDataCard.py -b MultiJet -c config/run2_sideband.config -d cards Datasets/RazorInclusive_SMS-T1bbbb_2J_mGl-1500_mLSP-100_weighted_lumi-4.000_0-3btag_MultiJet.root Datasets/RazorInclusive_SMCocktail_weighted_lumi-3.0_1-3btag_MultiJet.root --print-yields