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Code to analyze ROOT trees produced by the DijetRootTreeMaker
This project forked from cmsdijet/dijetroottreeanalyzer
Code to analyze ROOT trees produced by the DijetRootTreeMaker
Introduction: ------------- This package provides a small facility to analyze one or a chain of root ntuples. A script (./scripts/make_rootNtupleClass.sh) is used to generate automatically (using the root command RootNtupleMaker->MakeClass) a class (include/rootNtupleClass.h and src/rootNtupleClass.C) with the variable definitions of a given root ntuple (to be provided by the user). The class baseClass (include/baseClass.h and src/baseClass.C) inherits from the automatically generated rootNtupleClass. baseClass provides the methods that are common to all analysis, such as the method to read a list of root files and form a chain. It will, asap, also provide a method to read a list of selection cuts. The class analysisClass (include/analysisClass.h and src/analysisClass.C) inherits from baseClass. The user's code should be placed in the method Loop() of analysisClass, which reimplements the method Loop() of rootNtupleClass. The main program (src/main.C) receives the configuration parameters (such as the input chain of root files and a file to provide a cut list) and executes the analysisClass code. Instructions: ------------- 1) Environment setup: cd CMSSW_8_0_24/src/ cmsenv (or the most recent release) 2) Download the code: git clone https://github.com/CMSDIJET/DijetRootTreeAnalyzer.git CMSDIJET/DijetRootTreeAnalyzer 3) Go inside: cd CMSDIJET/DijetRootTreeAnalyzer ======= 4) to generate the rootNtupleClass (you do not need to unless the ntuple format changes): cd CMSDIJET/DijetRootTreeAnalyzer ./scripts/make_rootNtupleClass.sh (you will be asked for input arguments) i.e. ./scripts/make_rootNtupleClass.sh -f dijetTree_signal.root -t dijets/events where the rootfile has been produced by the DijetRootTreeMaker Note that there could be minor glitches and then you can fix by hand. ======= 5) Make a symbolic link analysisClass.C by: ln -sf analysisClass_myCodeWide.C src/analysisClass.C or ln -sf analysisClass_myCodeAK8.C src/analysisClass.C or ln -sf analysisClass_myCodeCA8.C src/analysisClass.C 6) Compile to test that all is OK so far: make clean make 7) Run: ./main (you will be asked for input arguments) i.e. ./main config/inputListJets.txt config/cutFileExampleJet.txt dijets/events output/rootFile output/cutEfficiencyFile 8) All the produced files are in the directory output Notes: 1) one can have several analyses in a directory, such as src/analysisClass_myCode1.C src/analysisClass_myCode2.C src/analysisClass_myCode3.C and move the symbolic link to the one to be used: ln -sf analysisClass_myCode2.C src/analysisClass.C and compile/run as above. More details: ------------- - Example code: The src/analysisClass_template.C comes with simple example code. The example code in enclosed by #ifdef USE_EXAMPLE ... code ... #endif //end of USE_EXAMPLE The code is NOT compiled by default. In order to compile it, uncomment the line #FLAGS += -DUSE_EXAMPLE in the Makefile. - Providing cuts via file: A list of cut variable names and cut limits can be provided through a file (see config/cutFileExample.txt). The variable names in such a file have to be filled with a value calculated by the user analysisClass code, a function "fillVariableWithValue" is provided - see example code. Once all the cut variables have been filled, the cuts can be evaluated by calling "evaluateCuts" - see example code. Do not forget to reset the cuts by calling "resetCuts" at each event before filling the variables - see example code. The function "evaluateCuts" determines whether the cuts are satisfied or not, stores the pass/failed result of each cut, calculates cut efficiencies and fills histograms for each cut variable (binning provided by the cut file, see config/cutFileExample.txt). The user has access to the cut results via a set of functions (see include/baseClass.h) bool baseClass::passedCut(const string& s); bool baseClass::passedAllPreviousCuts(const string& s); bool baseClass::passedAllOtherCuts(const string& s); where the string to be passed is the cut variable name. The cuts are evaluated following the order of their apperance in the cut file (config/cutFileExample.txt). One can simply change the sequnce of line in the cut file to have the cuts applied in a different order and do cut efficiency studies. Also, the user can assign to each cut a level (0,1,2,3,4 ... n) and use a function bool baseClass::passedAllOtherSameLevelCuts(const string& s); to have the pass/failed info on all other cuts with the same level. There is actually also cuts with level=-1. These cuts are not actually evaluated, the corresponding lines in the cut file (config/cutFileExample.txt) are used to pass values to the user code (such as fiducial region limits). The user can access these values (and also those of the cuts with level >= 0) by double baseClass::getCutMinValue1(const string& s); double baseClass::getCutMaxValue1(const string& s); double baseClass::getCutMinValue2(const string& s); double baseClass::getCutMaxValue2(const string& s); - Automatic histograms for cuts The following histograms are generated for each cut variable with level >= 0: no cuts applied passedAllPreviousCuts passedAllOtherSameLevelCuts passedAllOtherCuts passedAllCut and by default only the following subset no cuts applied passedAllPreviousCuts passedAllOtherCuts is saved to the output root file. All histograms can be saved to the output root file by uncommenting the following line in the Makefile #FLAGS += -DSAVE_ALL_HISTOGRAMS - Automatic cut efficiency: the absolute and relative efficiency is calculated for each cut and stored in an output file (named output/cutEfficiencyFile.dat if the code is executed following the examples) The user has the option to implement a good run list using a JSON file. This requires two edits to the cut file and one edit to the analysisClass.C file. A line must be inserted at the beginning of the cut file with the word "JSON" first, and then the full AFS path of the desiredJSON file. For example: JSON /afs/cern.ch/cms/CAF/CMSCOMM/COMM_DQM/certification/Collisions11/7TeV/Prompt/Cert_160404-163369_7TeV_PromptReco_Collisions11_JSON.txt In addition, the user must define the JSON file selection in the cut file. This is done in the usual way: #VariableName minValue1(<) maxValue1(>=) minValue2(<) maxValue2(>=) level histoNbinsMinMax #------------ ------------ ------------- ------------ ------------- ----- ---------------- PassJSON 0 1 - - 0 2 -0.5 1.5 In the analysisClass.C file, the user must add the following line within the analysis loop: fillVariableWithValue ( "PassJSON", passJSON (run, ls, isData)); Note that the use of a JSON file (good run list) is optional. If the user does not list a JSON file in the cut file, no selection will be made. This tool was validated on May 18, 2011. The results of the validation study are here: https://twiki.cern.ch/twiki/pub/CMS/ExoticaLeptoquarkAnalyzerRootNtupleMakerV2/json_validation.pdf Additional scripts for running on several datasets: --------------------------------------------------- See ./doc/howToMakeAnalysisWithRootTuples.txt Using the Optimizer (Jeff Temple): ---------------------------------- The input cut file can also specify variables to be used in optimization studies. To do so, add a line in the file for each variable to optimize. The first field of a line must be the name of the variable, second field must be "OPT", third field either ">" or "<". (The ">" sign will pass values greater than the applied threshold, and "<" will pass those less than the threshold.) 4th and 5th fields should be the minimum and maximum thresholds you wish to apply when scanning for optimal cuts. An example of the optimization syntax is: #VariableName must be OPT > or < RangeMin RangeMax unused #------------ ----------- ------ ------------ ------------- ------ muonPt OPT > 10 55 5 This optimizer will scan 10 different values, evenly distributed over the inclusive range [RangeMin, RangeMax]. At the moment, the 6th value is not used and does not need to be specified. The optimization cuts are always run after all the other cuts in the file, and are only run when all other cuts are passed. The above line will make 10 different cuts on muonPt, at [10, 15, 20, 25, ..., 55]. ('5' in the 6th field is meaningless here.) The output of the optimization will be a 10-bin histogram, showing the number of events passing each of the 10 thresholds. Multiple optimization cuts may be applied in the same file. In the case where N optimization cuts are applied, a histogram of 10^N bins will be produced, with each bin corresponding to a unique cut combination. No more than 6 variables may be optimized at one time (limitation in the number of bins for a TH1F ~ 10^6). Since such file can become quite large, the default is to not create A file (optimizationCuts.txt in the working directory) that lists the cut values applied for each bin can be produced by uncommenting the line #FLAGS += -DCREATE_OPT_CUT_FILE in the Makefile. Since this file can be quite large (10^N lines), by default it is not created. Producing an ntuple skim (Dinko Ferencek): ------------------------------------------ The class baseClass provides the ability to produce a skimmed version of the input ntuples. In order to produce a skim, the following preliminary cut line has to be added to the cut file #VariableName value1 value2 value3 value4 level #------------ ------------ ------------- ------------ ------------- ----- produceSkim 1 - - - -1 and call the fillSkimTree() method for those events that meet the skimming criteria. One possible example is if( passedCut("all") ) fillSkimTree(); If the above preliminary cut line is not present in the cut file, is commented out or its value1 is set to 0, the skim creation will be turned off and calling the fillSkimTree() method will have no effect. JSON parser (Edmund Berry): --------------------------- See https://hypernews.cern.ch/HyperNews/CMS/get/exotica-lq/266.html PU reweight (Edmund Berry): --------------------------- See https://twiki.cern.ch/twiki/pub/CMS/Exo2011LQ1AndLQ2Analyses/PileupReweightingCode.pdf Producing a new ntuple with a subset of cutFile variables and a subset of events (Paolo, Francesco, Edmund): ------------------------------------------------------------------------------------------------------------ The class baseClass provides the ability to produce a new ntuple with a subset of the variables defined in the cutFile, and with a subset of events. In order to do so, the following preliminary cut line has to be added to the cut file #VariableName value1 value2 value3 value4 level #------------ ------------ ------------- ------------ ------------- ----- produceReducedSkim 1 - - - -1 then each variable that needs to be included in the new tree has to be flagged with SAVE in the cutFile at the end of the line where the variabole is defined, as for pT1stEle and pT2ndEle below: #VariableName minValue1(<) maxValue1(>=) minValue2(<) maxValue2(>=) level histoNbinsMinMax OptionalFlag #------------ ------------ ------------- ------------ ------------- ----- ---------------- ------------ nEleFinal 1 +inf - - 0 11 -0.5 10.5 pT1stEle 85 +inf - - 1 100 0 1000 SAVE pT2ndEle 30 +inf - - 1 100 0 1000 SAVE invMass_ee 0 80 100 +inf 1 120 0 1200 (do not put anything for those variables that do not need to be saved, such as for nEleFinaland invMass_ee) finally, call fillReducedSkimTree() in the analysisClass for the subset of events that need to be saved, e.g.: if( passedCut("nEleFinal") ) fillReducedSkimTree(); If the above preliminary cut line is not present in the cut file, is commented out or its value1 is set to 0, the skim creation will be turned off and calling the fillReducedSkimTree() method will have no effect. The new ntuple will be created in a file named as the std output root file with _reduced_skim appended before the .root and the tree name will be as in the input root file.
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