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davidwalter2
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@@ -171,7 +174,7 @@ def build_graph(df, dataset):
axis_ewMll = hist.axis.Variable(massBins, name = "ewMll", underflow=False)
axis_ewPtll = hist.axis.Variable(common.ptV_binning, underflow=False, name = "ewPTll")
axis_ewAbsYll = hist.axis.Regular(50, 0, 5, name = "ewAbsYll")
df = theory_tools.define_dressed_vars(df, mode="wmass" if isW else "dilepton")
df = theory_tools.define_dressed_vars(df, mode="wmass" if isW else "wlike")
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Most of the time when people run the gen histmaker I would expect them to want dilepton observables.

axis_eta = hist.axis.Regular(25, 0, 2.5, name = "postfsrLep_absEta", overflow=True, underflow=False)
axis_pt = hist.axis.Regular(50, 20, 70, name = "postfsrLep_pt", overflow=True, underflow=True)
results.append(df_fiducial.HistoBoost("nominal_postfsr", [axis_eta, axis_pt], ["postfsrLep_absEta", "postfsrLep_pt", "nominal_weight"], storage=hist.storage.Weight()))
if args.applySelection:
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I think it's probably easier to just set the lepton type in the gen script and then make one or the other.

kdlong pushed a commit that referenced this pull request Jul 4, 2025
add flag for physics model of the fitresult used as input
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2 participants