Download the code

Download the code from github:

git clone https://github.com/GonzalezFJR/ttCMSDAS.git

or using ssh:

git clone git@github.com:GonzalezFJR/ttCMSDAS.git

The analysis

By default, the output of the analysis are histograms, filled at different levels of selection. The MC histograms are normalized to 1 pb-1 for a given input cross section. The weight is computed as:

self.weight = (cross\_section * genWeight)/(sum\_of\_gen\_weights)

Anyway, this is fully customizable.

Run the test

cd test/

This is an easy analysis that selects events with two leptons and save two histograms for the lepton pT and invariant mass. You can run the analysis by executing the file executeTest.sh:

source executeTest.sh

Or just executing the command:

python -c 'from testAnalysis import testAnalysis; testAnalysis("/gpfs/ddn/cms/user/cmsdas/2019/ttbar/dilepton_skim/", "DYJetsToLL_MLL50", eventRange = [0, 100000], xsec = 10, run = True, nSlots = 4)'

If you are not running on lxplus or you wish to run on another sample, just modify the path and sample name. By default, divides the sample in 4 pieces. The output is saved in a folder called temp. You can merge the output rootfiles:

python ../framework/merger.py -frv temp/

Secuential or multicore processing

Example of how to run the analysis on a sample (WWTo2L2Nu):

python -c 'from myAnalysis import myAnalysis; myAnalysis("/gpfs/ddn/cms/user/cmsdas/2019/ttbar/dilepton_skim/", "WWTo2L2Nu", xsec = 10, run = True)'

Example of how to run the analysis on a sample (WWTo2L2Nu) using :

python -c 'from myAnalysis import myAnalysis; myAnalysis("/gpfs/ddn/cms/user/cmsdas/2019/ttbar/dilepton_skim/", "WWTo2L2Nu", xsec = 10, nSlots = 5, run = True)'

Merge the rootfiles

You can automatically merge any number of rootfies with a name SAMPLE_[number].root into a SAMPLE.root file. Use the command:

python framework/merge.py [folder]

Explore the trees

You can explore the input samples and trees by executing the following python scritp:

python framework/fileReader.py