Muon TnP using egm_tnp_analysis

Quick Start

source setup.sh

python tnpEGM_fitter.py etc/config/muonTnP_AFB.py --createBins

python tnpEGM_fitter.py etc/config/muonTnP_AFB.py --createHists --condor -n 20

python tnpEGM_fitter.py etc/config/muonTnP_AFB.py --doFit --condor

python tnpEGM_fitter.py etc/config/muonTnP_AFB.py --doPlot --condor

python tnpEGM_fitter.py etc/config/muonTnP_AFB.py --sumUp --condor

Or

python tnpEGM_fitter.py etc/config/muonTnP_AFB.py --createBins --createHists --doFit --doPlot --sumUp --condor -n 20

Edit Configuration files

• edit etc/config/@@@@@@@.py (examples) muonTnP_AFB.py
• You can change muonTnP_AFB's settings. (Period, Measure, Charge)
• Or, you can make your own configuration file

Refit

For example, you want to refit 2,3,5,10 bins in flag data_altbkd.

python tnpEGM_fitter.py etc/config/muonTnP_AFB.py --doFit --doPlot --sumUp --condor --flag data_altbkd --iBin 2 3 5 10

• You can change initial values of fit parameters in etc/config/muonTnP_AFB.py
• Also, you can change the height of peak by changing values in libCpp/histFitter.C (103~106 lines)

To do

• Optimization
• Find hidden bugs

If you wanna use abseta binnings instead of eta binnings.

substitute 'eta' -> 'abseta' in these files.

etc/config/muonTnP_AFB.py

tnpEGM_fitter.py

libPython/rootUtils.py (especially, GetEffiHist function)

Plotting codes

You may need to edit these if you want.

• canvas_margin.h
• plotter.C
• check_chargeAsym.C

There are old configuration files.

They are used in old version when we use a bunch of fitting functions and select by eye

• old_codes/Muon2017idiso_original_HS.py
• old_codes/MuonTnP_AFB_old.py
• old_codes/MuonTnP_ISR.py
• old_codes/tnpEGM_fitter.py
• old_codes/binbybin_selection

egm_tnp_analysis (Old message)

General note on installation

This code can in principle run on your laptop but you need ROOT 6.10 or higher and RooFit installed to use some advanced features when creating histograms. The easiest thing to do is to set the environement in a 10_1_X release:

cmsrel CMSSW_10_1_1

cd CMSSW_10_1_1/src

cmsenv

Install stable branch

git clone -b egm_tnp_Prompt2018_v2_15pOfStat git@github.com:lsoffi/egm_tnp_analysis.git

cd egm_tnp_analysis/

source etc/scripts/setup_ROOT6.10.sh

Compile with:

make

Quick description

Package to handle analysis of tnp trees. The main tool is the python fitter

===> tnpEGM_fitter.py

The interface between the user and the fitter is solely done via the settings file.

IMPORTANT In the following we refer to this file as "settings.py", anyway that is just a template file. you should run "settings_ele.py", or "settings_pho.py", or edit them according to your needs.

===> etc/config/settings.py - set the flags (i.e. Working points) that can be tested - set the different samples and location - set the fitting bins - set the different cuts to be used - set the output directory

Help message:

python tnpEGM_fitter.py --help

The settings have always to be passed to the fitter

python tnpEGM_fitter.py etc/config/settings.py

Therefore several "settings.py" files can be setup (for different run period for instance)

The different fitting steps

Everything will be done for a specific flag (so the settings can be the same for different flags). Hence, the flag to be used must be specified each time (named myWP in following).

1. Create the bining. To each bin is associated a cut that can be tuned bin by bin in the settings.py

• After setting up the settings.py check bins

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --checkBins

• if you need additinal cuts for some bins (cleaning cuts), tune cuts in the settings.py, then recheck. Once satisfied, create the bining

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --createBins

• CAUTION: when recreacting bins, the output directory is overwritten! So be sure to not redo that once you are at step2

2. Create the histograms with the different cuts... this is the longest step. Histograms will not be re-done later

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --createHists

3. Do your first round of fits.

• nominal fit

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit

• MC fit to constrain alternate signal parameters [note this is the only MC fit that makes sense]

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --mcSig --altSig

• Alternate signal fit (using constraints from previous fits)

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --altSig

• Alternate background fit (using constraints from previous fits)

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --altBkg

4. Check fits and redo failed ones. (there is a web index.php in the plot directory to vizualize from the web)

• can redo a given bin using its bin number ib. The bin number can be found from --checkBins, directly in the ouput dir (or web interface)

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --iBin ib

• the initial parameters can be tuned for this particular bin in the settings.py file. Once the fit is good enough, do not redo all fits, just fix next failed fit. One can redo any kind of fit bin by bin. For instance the MC with altSig fit (if the constraint parameters were bad in the altSig for instance)

python tnpEGM_fitter.py etc/config/settings.py --flag myWP --doFit --mcSig --altSig --iBin ib

5. egm txt ouput file. Once all fits are fine, put everything in the egm format txt file

python tnpEGM_fitter.py etc/config/setting.py --flag myWP --sumUp

The settings file

The settings file includes all the necessary information for a given setup of fit

- General settings.

* flag: this is the Working point in the tnpTree  (pass: flagCut ; fail !flagCut). The name of the flag myWP is the one to be passed

to the fitter. One can handle complex flags with a cut string (root cut string):

flag = { 'myWP' : myWPCutString }

* baseOutDir: the output directory (will be created by the fitter)

- Sample definition.

* tnpTreeDir: the directory in the tnpTree (different for phoID, eleID, reco, hlt)

* samplesDef: these are the main info
  - data: data ntuple
  - mcNom: nominal MC sample
  - mcAlt: MC for generator syst
  - tagSel: usually same as nominal MC + different base cuts: check the tag selection syst

 The sample themselves are defined in etc/inputs/tnpSampleDef.py  (the attribute nEvts, lumi are not necessary for the fit per-se and can be omitted). 
 A list of samples for ICHEP2016 from official egm production is already setup properly in the package. 
 Then in the settings.py the sample can be specified further:
 - sample.set_mctruth() : force mc truth on a MC sample
 - sample.rename('xxx') : if a sample is used 2 times (like with 2 different sets of cuts), it has to be renamed for the second use
 - sample.set_cut(cut)  : add a cut to define the sample (like a run range for data or an additional tag selection for alt tag selection syst)
 - sample.set_weight('totWeight') : name of the weight to be used for MC reweighting (totWeight in this example). Note: the tool can handle a pu Tree to reweight a MC with different PU scenario (ask for further explanations and/or settings_rwPU.py example)

- Cuts.

* cutBase: Define here the main cut
* additionalCuts: can be used for cleaning cuts (or put additionalCuts = None)

- Fitting parameters.

Define in this section the init parameters for the different fit, can be tuned to improve convergence.

====================

Update PU weights

1.Pileup files have to be computed with: python etc/scripts/pureweight.py

Here one has to update the name of the directory whre the files will be located and the corresponding names. If needed you have to recompute the data PU distributions and copying them to eos before doing this step, see point 3.

2.This pyhton uses the following: libPython/puReweighter.py.

Here one nees to add the PU MC mix numbers that are available here: http://cmslxr.fnal.gov/source/SimGeneral/MixingModule/python/?v=CMSSW_9_4_0

3.The data PU distrubtions can be computed using the following instructions (similar to what is done in step1):

pileupCalc.py -i /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions17/13TeV/PromptReco/Cert_294927-306462_13TeV_PromptReco_Collisions17_JSON.txt --inputLumiJSON /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions17/13TeV/PileUp/pileup_latest.txt --calcMode true --minBiasXsec 69200 --maxPileupBin 100 --numPileupBins 100 pileup_2017_41fb.root

The nvtx and rho histos are not needed because we will use the pu method (type = 0) for the reweight.

NB: Before using these py in order to load the needed libraires one has to run: export PYTHONPATH=$PYTHONPATH:/afs/cern.ch/user/s/soffi/scratch0/TEST/CMSSW-10-0-0-pre3/src/egm_tnp_analysis

adding remote (Fabrice version)

git remote add origin git@github.com:fcouderc/egm_tnp_analysis.git