Fix multi limit plots with mixed unblinding flags.

dhi/plots/limits.py

@@ -347,6 +347,7 @@ def plot_limit_scans(
     def check_values(values):
         _values = []
         for v in values:
+            if v is not None:
                 if isinstance(v, np.ndarray):
                     v = {key: v[key] for key in v.dtype.names}
                 assert "limit" in v
@@ -363,7 +364,7 @@ def plot_limit_scans(
     if observed_values:
         assert len(observed_values) == n_graphs
         observed_values = check_values(observed_values)
-        has_obs = True
+        has_obs = any(v is not None for v in observed_values)
     scan_values = expected_values[0][scan_parameter]
     has_thy = theory_values is not None
     has_thy_err = False
@@ -441,8 +442,8 @@ def plot_limit_scans(
                 scan_values.min(), scan_values.max())
 
         # observed graph
-        if has_obs:
         ov = observed_values[i]
+        if ov is not None:
             obs_mask = ~np.isnan(ov["limit"])
             obs_limit_values = ov["limit"][obs_mask]
             obs_scan_values = ov[scan_parameter][obs_mask]
@@ -645,6 +646,7 @@ def plot_limit_points(
         x_max_value = max(x_max_value, max(d["expected"]))
         if "observed" in d:
             assert isinstance(d["observed"], (float, int))
+            if not np.isnan(d["observed"]) and d["observed"] is not None:
                 has_obs = True
                 x_min_value = min(x_min_value, d["observed"])
                 x_max_value = max(x_max_value, d["observed"])
@@ -819,7 +821,7 @@ def plot_limit_points(
             fmt = lambda v: "{{:.{}f}} {{}}".format(get_digits(v)).format(v, xsec_unit)
         else:
             fmt = lambda v: "{{:.{}f}}".format(get_digits(v)).format(v)
-        if obs is None:
+        if obs is None or np.isnan(obs[0]):
             return y_label_tmpl % (label, fmt(exp))
         else:
             return y_label_tmpl_obs % (label, fmt(exp), fmt(obs[0]))

dhi/tasks/gof.py

@@ -294,7 +294,9 @@ class PlotMultipleGoodnessOfFits(PlotGoodnessOfFit, POIMultiTask, MultiDatacardT
     def requires(self):
         return [
             MergeGoodnessOfFit.req(self, datacards=datacards, toys=t, toys_per_branch=tpb, **kwargs)
-            for datacards, t, tpb, kwargs in zip(self.multi_datacards, self.toys, self.toys_per_branch, self.get_multi_task_kwargs())
+            for datacards, t, tpb, kwargs in zip(
+                self.multi_datacards, self.toys, self.toys_per_branch, self.get_multi_task_kwargs(),
+            )
         ]
 
     def output(self):

dhi/tasks/limits.py

@@ -662,14 +662,12 @@ class PlotMultipleUpperLimits(PlotUpperLimits, POIMultiTask, MultiDatacardTask):
             names = [names[i] for i in self.datacard_order]
 
         # prepare observed values
-        obs_values = None
-        if self.unblinded:
         obs_values = [
             {
                 self.scan_parameter: _limit_values[self.scan_parameter],
                 "limit": _limit_values["observed"],
-                }
-                for _limit_values in limit_values
+            } if mkwargs["unblinded"] else None
+            for _limit_values, mkwargs in zip(limit_values, self.get_multi_task_kwargs())
         ]
 
         # call the plot function
@@ -807,14 +805,12 @@ class PlotMultipleUpperLimitsByModel(PlotUpperLimits, POIMultiTask, MultiHHModel
             names = [names[i] for i in self.hh_model_order]
 
         # prepare observed values
-        obs_values = None
-        if self.unblinded:
         obs_values = [
             {
                 self.scan_parameter: _limit_values[self.scan_parameter],
                 "limit": _limit_values["observed"],
-                }
-                for _limit_values in limit_values
+            } if mkwargs["unblinded"] else None
+            for _limit_values, mkwargs in zip(limit_values, self.get_multi_task_kwargs())
         ]
 
         # call the plot function
@@ -992,11 +988,11 @@ class PlotUpperLimitsAtPoint(UpperLimitsBase, POIPlotTask, POIMultiTask, MultiDa
 
         # load limit values
         names = ["limit", "limit_p1", "limit_m1", "limit_p2", "limit_m2"]
-        if self.unblinded:
+        if any(self.unblinded):
             names.append("observed")
         limit_values = np.array(
             [
-                self.load_limits(coll["collection"][0], unblinded=self.unblinded)
+                self.load_limits(coll["collection"][0], unblinded=any(self.unblinded))
                 for coll in self.input()
             ],
             dtype=[(name, np.float32) for name in names],
@@ -1051,7 +1047,7 @@ class PlotUpperLimitsAtPoint(UpperLimitsBase, POIPlotTask, POIMultiTask, MultiDa
                 "expected": record.tolist()[:5],
                 "theory": thy_value and thy_value[0].tolist()[1:],
             }
-            if self.unblinded:
+            if any(self.unblinded):
                 entry["observed"] = float(record[5])
             data.append(entry)