"""Analysis configuration module.
tdub is a Python library for physics analysis. Naturally some
properties of the analysis need to be easily modifiable for various
studies. This module houses a handful of variables that can be
modified simply by importing the module.
For example, we can call :py:func:`tdub.data.features_for` and expect
different results without changing the API usage, just changing the
configuration module ``FEATURESET_foo`` constants::
>>> from tdub.data import features_for
>>> features_for("2j2b")
['mass_lep1jet1', 'mass_lep2jet1', 'pT_jet2', ...]
>>> import tdub.config
>>> tdub.config.FEATURESET_2j2b = ["pT_jet1", "met"]
>>> features_for("2j2b")
['pT_jet1', 'met']
Similarly, we can modify the selection via this module::
>>> from tdub.data import selection_for
>>> selection_for("2j2b")
'(reg2j2b == True) & (OS == True)'
>>> import tdub.config
>>> tdub.config.SELECTION_2j2b = "(reg2j2b == True) & (OS == True) & (mass_lep1jet1 < 155)"
>>> selection_for("2j2b")
'(reg2j2b == True) & (OS == True) & (mass_lep1jet1 < 155)'
This module also contains some convenience functions for helping to
automate the process of providing some sensible defaults for some
configuration options, but not at import time (i.e. if the default
requires importing a module or parsing some data from the web).
"""
from __future__ import annotations
RANDOM_STATE = 414
"""
int: Seed for various random tasks requiring reproducibility.
"""
SELECTION_1j1b = "(reg1j1b == True) & (OS == True)"
"""
str: The numexpr selection string for the 1j1b region.
"""
SELECTION_2j1b = "(reg2j1b == True) & (OS == True)"
"""
str: The numexpr selection string for the 2j1b region.
"""
SELECTION_2j2b = "(reg2j2b == True) & (OS == True)"
"""
str: The numexpr selection string for the 2j2b region.
"""
FEATURESET_1j1b_TMVA = sorted(
[
"pTsys_lep1lep2jet1met",
"mass_lep2jet1",
"mass_lep1jet1",
"pTsys_lep1lep2",
"deltaR_lep2_jet1",
"nsoftjets",
"deltaR_lep1_lep2",
"deltapT_lep1_jet1",
"mT_lep2met",
"nsoftbjets",
"cent_lep1lep2",
"pTsys_lep1lep2jet1",
],
key=str.lower,
)
"""
list(str): List of features we use for classifiers in the 1j1b region.
"""
FEATURESET_2j1b_TMVA = sorted(
[
"mass_lep1jet2",
"psuedoContTagBin_jet1",
"mass_lep1jet1",
"mass_lep2jet1",
"mass_lep2jet2",
"pTsys_lep1lep2jet1jet2met",
"psuedoContTagBin_jet2",
"pT_jet2",
],
key=str.lower,
)
"""
list(str): List of features we use for classifiers in the 2j1b region.
"""
FEATURESET_2j2b_TMVA = sorted(
[
"mass_lep1jet2",
"mass_lep1jet1",
"deltaR_lep1_jet1",
"mass_lep2jet1",
"pTsys_lep1lep2met",
"pT_jet2",
"mass_lep2jet2",
],
key=str.lower,
)
"""
list(str): List of features we use for classifiers in the 2j2b region.
"""
FEATURESET_1j1b = sorted(
[
"cent_lep1lep2",
"deltapT_lep1_lep2",
"mass_lep1jet1",
"mass_lep2jet1",
"mass_lep2jet1met",
"mT_jet1met",
# "nsoftbjets",
# "nsoftjets",
"pT_jetS1",
"pTsys_jet1met",
"pTsys_lep1lep2",
"pTsys_lep1lep2jet1met",
],
key=str.lower,
)
"""
list(str): List of features we use for classifiers in the 1j1b region.
"""
FEATURESET_2j1b = sorted(
[
# "deltaR_lep1lep2_jet1jet2met",
# "HT_jet1jet2",
# "mass_lep2jet1",
# "pT_jet2",
"pTsys_lep1lep2",
"mass_lep1jet1",
"mass_lep1jet2",
# "mass_lep2jet2",
# "deltaR_lep1_jet1",
"deltaR_lep2_jet1",
# "psuedoContTagBin_jet1",
# "psuedoContTagBin_jet2",
"pTsys_lep1lep2jet1met",
"pTsys_lep1lep2jet1jet2met",
"HTratio_lep1lep2_lep1lep2jet1met",
"HTratio_lep1lep2_lep1lep2jet1jet2met"
# "pTHTratio_lep1lep2jet1met",
# "pTHTratio_lep1lep2jet1jet2met",
],
key=str.lower,
)
"""
list(str): List of features we use for classifiers in the 2j1b region.
"""
FEATURESET_2j2b = sorted(
[
# "deltaR_jet1_jet2",
"mass_lep1jet1",
"mass_lep1jet2",
"mass_lep2jet1",
"mass_lep2jet2",
"pT_jet2",
"pTsys_jet1jet2",
"pTsys_lep1lep2met",
],
key=str.lower,
)
"""
list(str): List of features we use for classifiers in the 2j2b region.
"""
AVOID_IN_CLF = sorted(
[
"tmva_bdt_response",
"phi_lep1",
"phi_lep2",
"phi_jet1",
"phi_jet2",
"eta_met",
"eta_jetL1",
"eta_jetS1",
"sumet",
"mass_jet1",
"mass_jet2",
"mass_jetF",
"mass_jetL1",
"mass_jetS1",
"E_jetL1",
"E_jetS1",
"E_jet1",
"E_jet2",
"pT_lep3",
"pT_jetL1",
"nbjets",
"njets",
],
key=str.lower,
)
"""
list(str): List of features to avoid in classifiers.
"""
AVOID_IN_CLF_1j1b = sorted(["_nothing"])
"""
list(str): List of features to avoid specifically in 1j1b classifiers.
"""
AVOID_IN_CLF_2j1b = sorted(
["HT_jet1jet2", "deltaR_lep1lep2_jet1jet2met", "mass_lep2jet1", "pT_jet2"]
)
"""
list(str): List of features to avoid specifically in 2j1b classifiers.
"""
AVOID_IN_CLF_2j2b = sorted(["deltaR_jet1_jet2"])
"""
list(str): List of features to avoid specifically in 2j2b classifiers.
"""
DEFAULT_SCAN_PARAMETERS = {
"max_depth": [3, 4, 5, 6, 7],
"num_leaves": [8, 16, 32, 64, 128],
"learning_rate": [0.05, 0.07, 0.1, 0.2],
"min_child_samples": [100, 250, 500, 1000, 2000],
"reg_lambda": [0],
}
"""
dict(str, list): The default grid to perform a parameter scan.
"""
META_TABLE_URL = "https://cern.ch/ddavis/tdub_data/meta.yml"
PLOTTING_META_TABLE = None
"""
dict, optional: Plotting metadata table.
"""
PLOTTING_LOGY = []
"""
list(str): Plots (defined as TRExFitter Regions) to use log scale.
"""