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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.
The text was updated successfully, but these errors were encountered:
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:
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 thePreselection
dir.The text was updated successfully, but these errors were encountered: