wmass

run the python wrapper:

python runWmassAnalyzer.py /store/group/phys_smp/Wmass/perrozzi/ntuples/ntuples_2014_05_23_53X/WMinusPOWHEG/InclWeights/ -b -q 1nd -o minusOutput python runWmassAnalyzer.py /store/group/phys_smp/Wmass/perrozzi/ntuples/ntuples_2014_05_23_53X/WPlusPOWHEG/InclWeights/ -b -q 1nd -o plusOutput

compile the code in root: root .L wmassAnalyzer.cc+ makes a wmassAnalyzer_cc.so

combine -M MaxLikelihoodFit --saveNLL datacardShapes.txt -t -1 --expectSignal=1 -S 1 do NLL save it use asimov dataset normalization with/without systematics (bounds should change)

or do all masses at once

for i in {0..201}; do combine -M MaxLikelihoodFit --saveNLL -m $i datacardMass${i}.txt -t -1 --expectSignal=1 -S 1; done

make asimov toy dataset from nominal mass (ID95)

combine datacardMass95.txt -M GenerateOnly -m 888 -t -1 --expectSignal=1 --saveToys -S 0

combining

combine datacard2.txt -M MaxLikelihoodFit --toysFile -t -1 --saveNLL

combine datacardMass${i}.txt -M MaxLikelihoodFit --toysFile higgsCombineTest.GenerateOnly.mH999.123456.root -t -1 --saveNLL -m 100${i}

for i in {0..201}; do combine -M MaxLikelihoodFit --toysFile higgsCombineTest.GenerateOnly.mH999.123456.root --saveNLL -m $i datacardMass${i}.txt -t -1 -S 1; done