Add rollout-quality distance, confusion, containment and router plots (gitea #76)

Picks 4 of the 7 catalog additions the issue proposed (the smaller-lift
ones; 2D joint plots, PIT calibration, and the throughput/accuracy scatter
are left for follow-up issues):

- marginal_distance_summary: a var x grouping-axis KS-statistic heatmap,
  reusing the existing marginal hist1d compute and just adding a finalize —
  a single at-a-glance regression scorecard instead of N overlay plots.
- n_sec_confusion: predicted (rollout) vs true (reference) secondary count
  per event, paired by event_id since a rollout is seeded from the same
  events as its reference file. Needed a new zero-filling primitive
  (reduce.sec_count_by_event) since a plain group_by over secondary rows
  silently drops zero-secondary events.
- shower_containment_depth_{90,95}: per-event depth containing 90%/95% of
  deposited energy, derived from the same per-event depth-bin matrix the
  longitudinal profile already computes.
- router_specialization: max gate weight vs energy per side, summarizing
  router_gating's full stacked area into the one trend line the roadmap's
  MoE writeup describes (the ~60-65% ceiling), to make a future
  lambda_balance>0 retrain's effect on specialization checkable at a glance.

Both new heatmap-shaped plots (distance summary, confusion matrix) share one
new "heatmap" Reduced kind/renderer rather than two near-identical ones.

Co-Authored-By: Claude Sonnet 5 <noreply@anthropic.com>

tests/test_analysis_reduce.py

@@ -159,6 +159,20 @@ def test_secondaries_rollout_vs_reference_align():
     assert t["pdg"].to_list() == [22, 22]
 
 
+def test_sec_count_by_event_zero_fills_events_with_no_secondaries():
+    r_phys = physical_steps(_rollout_frame(), Side.rollout)
+    r_sec = secondaries(_rollout_frame(), Side.rollout)
+    ev, n = R.sec_count_by_event(r_phys, r_sec)
+    # event 1 has one secondary track; event 2 has none and must still appear (as 0),
+    # not silently drop out of a plain group_by on the secondaries frame alone.
+    assert dict(zip(ev.tolist(), n.tolist())) == {1: 1, 2: 0}
+
+    t_all = _reference_frame()
+    t_sec = secondaries(t_all, Side.reference)
+    ev, n = R.sec_count_by_event(t_all, t_sec)
+    assert dict(zip(ev.tolist(), n.tolist())) == {1: 1, 2: 1}
+
+
 def test_leakage_fraction():
     frac = R.leakage_fraction(_rollout_frame())
     # event 1: escaped pre_E=30, deposited=90 -> 30/120 = 0.25; event 2: 0