Add changes from !69.

For some reasons, the source branch for !69 (closed) disappeared.

This PR recovers the changes.

dhi/plots/exclusion.py

@@ -218,9 +218,10 @@ def plot_exclusion_and_bestfit_1d(
 
     # perform scans to extract best fit values
     scans = []
+    n_intervals = 1
     for d in data:
         if not d or d.get("nll_values") is None:
-            scans.append(None)
+            scans.append([])
             continue
 
         # preprocess values
@@ -234,23 +235,68 @@ def plot_exclusion_and_bestfit_1d(
             origin="entry '{}'".format(d["name"]),
         )
 
+        # check for disjoint intervals
+        dnll2_below_1sigma = np.where(dnll2_values < 1)[0]
+        dnll2_below_1sigma_intervals = np.split(
+            dnll2_below_1sigma,
+            np.where(np.diff(dnll2_below_1sigma) != 1)[0] + 1,
+        )
+
         # evaluate the scan
-        scans.append(evaluate_likelihood_scan_1d(
-            poi_values,
-            dnll2_values,
-            poi_min=poi_min,
-            origin="entry '{}'".format(d["name"]),
-        ))
+        if len(dnll2_below_1sigma_intervals) <= 1:
+            scans.append([evaluate_likelihood_scan_1d(
+                poi_values,
+                dnll2_values,
+                poi_min=poi_min,
+                origin="entry '{}'".format(d["name"]),
+            )])
+        else:
+            warn("{} disjoint intervals of dnll2 values below 1 sigma".format(
+                len(dnll2_below_1sigma_intervals),
+            ))
+            # perform scans
+            scan_intervals = []
+            for i in range(len(dnll2_below_1sigma_intervals)):
+                dnll2_values_tmp = dnll2_values.copy()
+
+                # exclude points of other intervals
+                for j in range(len(dnll2_below_1sigma_intervals)):
+                    if i != j:
+                        dnll2_values_tmp[dnll2_below_1sigma_intervals[j]] = 1.1
+                scan_intervals.append(evaluate_likelihood_scan_1d(
+                    poi_values,
+                    dnll2_values_tmp,
+                    poi_min=None,
+                    origin="entry '{}' (interval {})".format(d["name"], i),
+                ))
+
+            # order them by minimum of dnll2
+            order_dnll2 = np.argsort([
+                dnll2_values[idx].min()
+                for idx in dnll2_below_1sigma_intervals
+            ])
+            scans.append([scan_intervals[i] for i in order_dnll2])
+            n_intervals = max(n_intervals, len(scan_intervals))
 
     # draw best fit values
     if any(scans):
         f = int(show_best_fit_error)
         g_bestfit = create_tgraph(
             n,
-            [(scan.num_min() if scan else -1e5) for scan in scans],
+            [scan[0].num_min() if scan else -1e5 for scan in scans],
             [n - i - 0.5 for i in range(n)],
-            [(f * scan.num_min.u(direction="down", default=0.0) if scan else 0) for scan in scans],
-            [(f * scan.num_min.u(direction="up", default=0.0) if scan else 0) for scan in scans],
+            [
+                f * scan[0].num_min.u(direction="down", default=0.0)
+                if scan
+                else 0.0
+                for scan in scans
+            ],
+            [
+                f * scan[0].num_min.u(direction="up", default=0.0)
+                if scan
+                else 0.0
+                for scan in scans
+            ],
             0,
             0,
         )
@@ -259,6 +305,30 @@ def plot_exclusion_and_bestfit_1d(
         draw_objs.append((g_bestfit, "PZ" + opt("E")))
         legend_entries.append((g_bestfit, "Best fit value", "P" + opt("L")))
 
+        # add potential additional intervals
+        if n_intervals > 1:
+            for idx in range(1, n_intervals):
+                g_interval = create_tgraph(
+                    n,
+                    [(scan[idx].num_min() if len(scan) > idx else -1e5) for scan in scans],
+                    [n - i - 0.5 for i in range(n)],
+                    [
+                        f * scan[idx].num_min.u(direction="down", default=0.0)
+                        if len(scan) > idx
+                        else 0.0
+                        for scan in scans
+                    ],
+                    [
+                        f * scan[idx].num_min.u(direction="up", default=0.0)
+                        if len(scan) > idx else 0.0
+                        for scan in scans
+                    ],
+                    0,
+                    0,
+                )
+                r.setup_graph(g_interval, props={"MarkerSize": 0., "LineWidth": 1})
+                draw_objs.append((g_interval, "PZ" + opt("E")))
+
     # theory prediction
     if x_min < 1:
         sm_value = poi_data[scan_parameter].sm_value
@@ -287,7 +357,10 @@ def plot_exclusion_and_bestfit_1d(
             args = (
                 label,
                 scan_label,
-                scan.num_min.str("%.2f", style="root", force_asymmetric=True, styles={"space": ""}),
+                " / ".join(
+                    s.num_min.str("%.2f", style="root", force_asymmetric=True, styles={"space": ""})
+                    for s in scan
+                ),
             )
             if style.matches("summary") and d["name"] in hh_references:
                 tmpl = label_tmpl_summary