$> git clone https://github.com/jwsmithers/ttgammaCutOptimisations.git ; cd ttgammaCutOptimisations
$> ./cutOptimisation.py
If cutflow is required after all cuts have been optimized:
$> ./offline_cutflow.py
Open cutOptimizations.py and scroll right to the bottom. You will see commented out functions that call main(). For the most basic test,
an allCut jobs has been uncommented. This means the optimisation will run once (for ejets and mujets) and assumes your cuts have been chosen
(look in lines ~255 -> ~266). There are two way to run this:
_ = Parallel(n_jobs=-1, verbose=5, backend="multiprocessing") \ ( delayed(main)(cutName="full_cuts",region=i,cut_range=[1],allCuts = True) for i in regs)
will run ejets and mujets in parallel. But the output is a little difficult to read. You can also just uncomment
main(cutName="full_cuts",region="ejets",cut_range=[1], allCuts = True)
to do it sequentially.
Ok, but what if you want to optimise a single variable? To do this, comment out all the main() functions at the bottom of cutOptimisations.py
and uncomment out a variable and range of cuts you would like to run over. I.e.
main(cutName="ph_HFT_MVA",region="ejets",cut_range=[0,0.05,0.10,0.15,0.20,0.25,0.3,0.35,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95])
This will cut on each value specified in [] for the ph_HFT_MVA variable in the ejets region. But before you do ./cutOptimisations.py,
you have to set the cutValue in the loop.
Go to line 252 and change ph_HFT_MVA_cutValue = 0, to ph_HFT_MVA_cutValue = cutValue. Now run the code. Printouts are useful, but also look in
outputs_ directories to gets some root files and already plotted pngs.
Systematics are quite central to this optimization. These are defined in lines ~640 -> ~685. You can always add more, but remember to account for
this in optimizationFunctions.py, _sigmaSys function.