def determine_number_of_triggers(hist: Hist,
jet_pt: analysis_objects.JetPtBin) -> int:
""" Determine the number of triggers for the specific analysis parameters.
When retrieving the number of triggers, carefully note the information below:
.. code-block:: python
>>> hist = ROOT.TH1D("test", "test", 10, 0, 10)
>>> x = 2, y = 5
>>> hist.FindBin(x)
2
>>> hist.FindBin(x+epsilon)
2
>>> hist.FindBin(y)
6
>>> hist.FindBin(y-epsilon)
5
Note:
The bin + epsilon on the lower bin is not strictly necessary, but it is used for consistency.
Note:
This bin + epsilon technique is frequently used when determining projector bin ends.
Args:
hist: Histogram containing the number of triggers.
jet_pt: Jet pt for which we want to know the number of triggers.
Returns:
Number of triggers for the selected analysis parameters.
"""
#logger.debug(
# f"Find bin({jet_pt.range.min} + epsilon): "
# f"{hist.FindBin(jet_pt.range.min + epsilon)} to "
# f"Find bin({jet_pt.range.max} - epsilon): "
# f"{hist.FindBin(jet_pt.range.max - epsilon)}"
#)
number_of_triggers: int = hist.Integral(
hist.FindBin(jet_pt.range.min + epsilon),
hist.FindBin(jet_pt.range.max - epsilon))
logger.info(
f"n_trig for [{jet_pt.range.min}, {jet_pt.range.max}): {number_of_triggers}"
)
return number_of_triggers
def calculate_systematic_2D(
nominal: Hist, variation: Hist,
signal_dominated: analysis_objects.AnalysisBin,
background_dominated: analysis_objects.AnalysisBin) -> float:
""" Calculate a systematic in 2D.
Args:
nominal: Nominal hist.
variation: Systematically varied hist.
signal_dominated: Signal dominated region.
Background_dominated: Background dominated region.
Returns:
The systematic calculated over the specified ranges.
"""
# Calculate the nominal integral in the signal region
nominal_signal = nominal.Integral(
1,
nominal.GetXaxis().GetNbins(),
nominal.GetYaxis().FindBin(-1.0 * signal_dominated.max + epsilon),
nominal.GetYaxis().FindBin(signal_dominated.max - epsilon),
)
# And in the background dominated region
nominal_background = nominal.Integral(
1,
nominal.GetXaxis().GetNbins(),
nominal.GetYaxis().FindBin(-1.0 * background_dominated.max + epsilon),
nominal.GetYaxis().FindBin(-1.0 * background_dominated.min - epsilon),
) + nominal.Integral(
1,
nominal.GetXaxis().GetNbins(),
nominal.GetYaxis().FindBin(background_dominated.min + epsilon),
nominal.GetYaxis().FindBin(background_dominated.max - epsilon),
)
# Calculate the variation integral in the signal region
variation_signal = variation.Integral(
1,
variation.GetXaxis().GetNbins(),
variation.GetYaxis().FindBin(-1.0 * signal_dominated.max + epsilon),
variation.GetYaxis().FindBin(signal_dominated.max - epsilon),
)
# And in the background dominated region
variation_background = variation.Integral(
1,
variation.GetXaxis().GetNbins(),
variation.GetYaxis().FindBin(-1.0 * background_dominated.max +
epsilon),
variation.GetYaxis().FindBin(-1.0 * background_dominated.min -
epsilon),
) + variation.Integral(
1,
variation.GetXaxis().GetNbins(),
variation.GetYaxis().FindBin(background_dominated.min + epsilon),
variation.GetYaxis().FindBin(background_dominated.max - epsilon),
)
# Basically, signal / variation
# NOTE: There is no need to apply the correlation scale factor because it will cancel
# in the factor.
factor = (nominal_signal / nominal_background) / (variation_signal /
variation_background)
# Help out mypy because it doesn't understand ROOT
return cast(float, factor)
