stripmaskinganalysis's Introduction

Strip Masking Analysis

This repository was developed in order to fine tune and design the single-strip masking analysis implemented in the SiStrip commissioning workflow as PedsFullNoise task.

CMSSW Setup:

cmsrel CMSSW_12_1_0 ;
cd CMSSW_12_1_0/src ;
cmsenv;		      
git-cms-init; 
git-cms-addpkg DataFormats/SiStripCommon/;
git-cms-addpkg DPGAnalysis/SiStripTools/;
git-cms-addpkg CommonTools/TrackerMap;
git cms-addpkg DQM/SiStripCommissioningAnalysis;
git cms-addpkg DQM/SiStripCommissioningClients;
git cms-addpkg DQM/SiStripCommissioningDbClients;
git cms-addpkg DQM/SiStripCommissioningSources;	 
git cms-addpkg DQM/SiStripCommissioningSummary;
git clone [email protected]:trackerpro/StripMaskingAnalysis.git TrackerDAQAnalysis/StripMaskingAnalysis
scramv1 b -j 4;

Producing the source DQM files for the Pedestal runs

cd TrackerDAQAnalysis/StripMaskingAnalysis/test;
cmsRun pedestalDQMfromDat_cfg.py partition=<partition> inputPath=<input path for the dat files> doFEDErr=<make map of bad channels> runNumber=<Run number to pick files in the /opt/cmssw directory>
cmsRun pedestalSourcefromDQM_cfg.py inputFiles=<input files> inputPath=<input path for DQM files>

Run the analysis

Using the codes located in macros directory. In particular:

fullPedestalAnalysis.C: runnning the classsification analysis for bad strips on the DQM file with per-APV histogram storing in tree format the result of test-statistics for every strip
plotPedestalAnalysis.C: plot result for good and bad strips as example i.e. noise distributions, values of the various test-statistics etc based on defined criterion
plotTestStatistics.C: plot the value/distribution of the main test statistics

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