pyroot-plotscripts

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Refer to READMEs in the corresponding directories for specific information!

For readability there is a README for each step, if deeper adjustments have to be done:

• configs
• plottinscripts
• util WIP
• data: not used right now, change structure to have files local
• standaloneTools: files not correlated to the pyroot-plotscript but Karim insists on keeping them

Package-requirements

• export SCRAM_ARCH=slc7_amd64_gcc700
• CMSSW_11_1_0_pre4

Workflow

Configs

• create/adjust a plotconfig file named pltcfg_STR.py, that includes all samples that shall be used in the pyroot-plotscript and the nominal weight expression. Two lists of Samples
  • samplesDataControlPlots (data samples)
  • samples (simulated samples) add Sample using, with SAMPLENAMEINCSVCONFIG being the process name in the used systematics csv file STR_systematics.csv

   plotClasses.Sample(SAMPLENAMEONPLOTS,COLOR,
       PATH/TO/SAMPLE,
       SELECTION,
       SAMPLENAMEINCSVCONFIG,
       samDict=sampleDict, readTrees=doReadTrees)

  • create/adjust the nominal weight expression

   nominalweight="STR"

• create/adjust a systematics csv file STR_systematics.csv, add entries regarding the columns:
  • Uncertainty name of the uncertainty, add # to skip
  • Type type of the uncertainty
  • Construction construction type of the uncertainty, rate for lnN, variation and weight for shape
  • Up add the weight expression or SAMPLENAME for the up shape
  • Down add the weight expression or SAMPLENAME for the down shape
  • Plot use 1 to plot uncertainty
  • PROCESSES contains at least all processes used in pltcfg_STR.py, add 1 to activate shape for specific process

plottingscripts

create/adjust a plottingscript plotLimits_STR.py, define variables and analysis options in the first paragraph of the main function

Usage

Navigate to the plottinscripts directory and run the plottingscript plotLimits_STR.py

python `plotLimits_STR.py`

Cross Evaluation Workflow

• first train separate DNNs with the DRACO_MLfoy Framework on the Odd/Even subsamples by appending the options --even or --odd to the execution of the train script (this of course requires a previous preprocessing of Odd and Even MC events)
• generate a new set of DNNs with the following structure:

NAME_OF_DNN_SET
----/DNNS_OF_FIRST_JT_REGION
--------/ODD
------------checkpointfiles
--------/EVEN
------------checkpointfiles
----/DNNS_OF_SECOND_JT_REGION
...

• generate a new plt_config with the utils/tools/dNNInterfaces/MLfoyInterface.py and specify the option -x to activate the cross evaluation setup. Activating this option creates input-feature-plots and output-discriminator-plots for each jet-tag-region by combining the subfolders (EVEN/ODD). If the option is not specified when creating the new plt_config, separate plots are created for the subfolder checkpoint files.
• add the created config to the plt_config variable in your top-level-script.
• check your config file and remove the (Evt_Odd==0)*2.0 selection if it still exists and replace it by 1.0 to select all events and remove the adjusted weight.
• add the variable Evt_Odd to your variable_cfg if it does not exist yet.
• add the option crossEvaluation to your analysisOptions dictionary in the top-level-script if it does not exist yet and set it to True. If the option is set to False the c++ code that is written will evaluate all DNNs separately, so you also need a plt_config that was not created with the -x option.
• set the path to your new DNNSet in the dnnInterface dictionary in the top-level-script.
• execute the top-level-script as usual.

b tagging sf corrections

setup for deriving b tagging scale factor patches

• module util/scaleFactorCreator.py can be called in a top level script to create SF histograms
• SF are caluclated as ratios between nominal template and dummy systematic templates for each variable and process
• example configs are added in configs/sfPatch/
• example scripts are added in plottingscripts/sfPatch/derive_X.py

new SF histograms

• sf histograms are added to the data/btagSFCorrection/ directory
• three different binnings are available for each year (which one to use is still to be decided)
• sf histograms are available for all ttbar, ttbb, ttH and ttZ processes

setup for applying b tagging scale factor patches

• C++ module SFCorrectionHelper has been included
• module is loaded per default into the created C script
• add the following excerpt to the top level script

    sfCorrection = {}
    sfCorrection["sfFile"] =  pyrootdir+"/data/btagSFCorrection/sf_2018_deepJet_fineBinning.root"
    # variables for the correction
    sfCorrection["corrections"] = {}
    sfCorrection["corrections"]["HT_vs_NJet"] = ["Evt_HT_jets", "N_Jets"]
    # in root file sf histograms exist with some naming scheme
    sfCorrection["nameTemplate"] = "$BINNING__$PROCESS__$NAME"
    # SF_ is always preprended by default, that should not be changed
    # $BINNING = "_vs_".join(corrections[X])
    # DANGER: order of variables is important
    # name of corrections to be applied (should match whats defined in syst.csv or samples.py)
    sfCorrection["names"] = ["btag_NOMINAL"]

• pass information to plotParallel via pP.setSFCorrection(sfCorrection)
• new SFs can for example be applied by using variables with naming scheme

sf__$BINNING__$NAME

e.g.

sf__HT_vs_NJet__btag_NOMINAL

friend trees

friend trees can be used to get additional variables (i.e. branches) from other root ntuple files. Creation of such friend tree files is currently implemented in the karim framework for reconstruction information. The friend tree files need to have the same order of events as the original ntuple files. In the current implementation it is also required to have the same folder structure for the original ntuple files and the friend tree files:

BASEPATH/
----/SAMPLEA/
----/----/FILE1.root
----/----/FILE2.root
----/SAMPLEB/
----/----/FILE3.root
...

The current implementation of friend trees replaces the BASEPATH of the original ntuple files with a new path where the friend trees are supposed to be stored. To activate the use add a dictionary to your sample config, e.g.

friendTrees = {
	"friendTreeName": "/new/base/path/to/friend/trees/",
	...
	}

and add pP.SetUseFriendTrees(True) to your top level script.

If the name of a branch you want to access is unique between the friend trees and the original tree, you can just use the variable name. If the same variable exists in a friend tree and your original tree, the content of the original tree will be accessed when you call the plain variable name. To access any variable in a friend tree, you can use the variable name friendTreeName.variableName.