tmp

Author: mafrahm

hbw/inference/base.py

@@ -152,8 +152,10 @@ def add_inference_categories(self: InferenceModel):
                 ]
 
             # add the category to the inference model
-            self.add_category(cat_name, **cat_kwargs)
-
+            try:
+                self.add_category(cat_name, **cat_kwargs)
+            except Exception as e:
+                from hbw.util import debugger; debugger()
             # do some customization of the inference category
             self.customize_category(self.get_category(cat_name), cat_inst)
 

hbw/production/normalized_btag.py

@@ -75,7 +75,10 @@ def normalized_btag_weights_init(self: Producer) -> None:
 @normalized_btag_weights.requires
 def normalized_btag_weights_requires(self: Producer, reqs: dict) -> None:
     from hbw.tasks.corrections import GetBtagNormalizationSF
-    reqs["btag_renormalization_sf"] = GetBtagNormalizationSF.req(self.task)
+    processes = ["tt"]
+    if "dy" in self.dataset_inst.name:
+        processes = ["dy_m50toinf", "dy_m10to50"]
+    reqs["btag_renormalization_sf"] = GetBtagNormalizationSF.req(self.task, processes=processes)
 
 
 @normalized_btag_weights.setup

hbw/production/weights.py

@@ -262,7 +262,7 @@ def combined_normalization_weights_init(self: Producer) -> None:
         normalized_pu_weights,
     },
     mc_only=True,
-    version=1,
+    version=3,
 )
 def event_weights(self: Producer, events: ak.Array, **kwargs) -> ak.Array:
     """

hbw/tasks/corrections.py

@@ -180,9 +180,19 @@ def safe_div(num, den):
                 h = self.reduce_hist(h, mode)
                 denominator = h.values()
 
+                # get axes for the output histogram
+                out_axes = []
+                for ax in h.axes:
+                    if isinstance(ax, hist.axis.Variable):
+                        out_axes.append(ax)
+                    elif isinstance(ax, hist.axis.Integer):
+                        out_axes.append(hist.axis.Variable(ax.edges, name=ax.name, label=ax.label))
+                    else:
+                        raise ValueError(f"Unsupported axis type {type(ax)}")
+
                 # calculate the scale factor and store it as a correctionlib evaluator
                 sf = safe_div(numerator, denominator)
-                sfhist = hist.Hist(*h.axes, data=sf)
+                sfhist = hist.Hist(*out_axes, data=sf)
                 sfhist.name = f"{mode_str}_{weight_name}"
                 sfhist.label = "out"
 
@@ -193,7 +203,7 @@ def safe_div(num, den):
                 # set overflow bins behavior (default is to raise an error when out of bounds)
                 # NOTE: claming seems to not work for int axes. Hopefully the number of jets considered to
                 # create these SFs is always large enough to not hit the overflow bin.
-                if any(isinstance(ax, hist.axis.Variable) for ax in h.axes):
+                if any(isinstance(ax, hist.axis.Variable) for ax in out_axes):
                     btag_renormalization.data.flow = "clamp"
 
                 # store the evaluator