helee/tnp_tamsa

tnp tool for tamsa

Quick start

source setup.sh
# python tnp_tamsa.py CONFIG_FILE CONFIG_KEY
python tnp_tamsa.py config/AFBMuon_v15.py 2018_MediumID_LooseTrkIso

Configuration

In the config file, you should define a dictionary called 'Configs' as below. See more examples in config directory.

from tnpConfig import tnpConfig
Configs["2018_muonID"]=tnpConfig(
  data="/path/to/data.root",
  sim="/path/to/sim.root",
  ...
)

tnpConfig parameters

The parameters for tnpConfig class are listed below. The parameters can be prefixed by 'data_' or 'sim_' to specify it is only for certain type of sample.

• data: Path to data ROOT file(s)
• sim: Path to simulation ROOT file(s)
• tree: TTree name
• mass: Branch name or expression for the mass variable to be used as x-axis of histograms
• expr: Expression for the event selection
• test: Expression for the pass condition
• weight: Expression for the event weight
• maxweight: maximum value of absolute weight.
• hist_nbins: Number of mass bins
• hist_range: Minimum and maximum mass as a tuple. ex) (60.0, 120.0)
• bins: TnP binning
• genmatching: Expression for the gen-matching
• genmass: Expression for the generator level mass.
• method: TnP method. 'fit' or 'count'.
• fit_parameter (only for fit method): List of strings for RooWorkspace factory. You should define PDFs with name of 'sigPass', 'sigFail', 'bkgPass' and 'bkgFail'.
• fit_range (only for fit method): Mass range for fitting
• count_range: Mass range for the efficiency evaluation. It could be different from fit_range.
• option: extra options separated by space; 'saveprefit', 'fix_below20'
• systematic: Definition of systematic variations.