Implement a script (e.g. make_tables.py) that takes a dataframe object (output from the looper) as input and prints out various yield tables.
Prints out yields & uncertainties for each process, total background (i.e. sum of all background MC), and ratio of each process to the total background yield.
Ideally the following options would be configurable:
- Input dataframe
- List of samples to consider as background
- List of samples to consider as signal (just ggTauTau for now, but will be nice to have this easily configurable in the future)
- Option to scale non-resonant background yields to m_gg mass window, ~[122,128], for more fair comparison with signal and resonant backgrounds
- Option to make tables separately by year
- Options to apply cuts based on columns saved in the dataframe (e.g. print yields after cutting on some value of m_tautau). The easiest way to do this would probably be to supply a
json file with a list of cuts as an input, then the script makes yield tables for each cut listed in the config file.
Should go in a directory tables under the Preselection dir.
Implement a script (e.g.
make_tables.py) that takes a dataframe object (output from the looper) as input and prints out various yield tables.Prints out yields & uncertainties for each process, total background (i.e. sum of all background MC), and ratio of each process to the total background yield.
Ideally the following options would be configurable:
jsonfile with a list of cuts as an input, then the script makes yield tables for each cut listed in the config file.Should go in a directory
tablesunder thePreselectiondir.