Merge branch 'master' into fix/issue-75

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

tests/test_catalog.py

@@ -6,7 +6,13 @@ import numpy as np
 import pytest
 
 from giant.analysis import build_catalog, catalog_ids, get_spec
-from giant.analysis.catalog import Bundle, PlotSpec
+from giant.analysis.catalog import (
+    Bundle,
+    PlotSpec,
+    _containment_depths,
+    _integer_confusion,
+    _ks_statistic,
+)
 from giant.analysis.context import Context, build_context
 from tests.test_analysis_reduce import _reference_frame, _rollout_frame
 
@@ -53,6 +59,8 @@ def test_every_spec_computes_valid_reduced(bundle: Bundle):
             "single_hist",
             "router_gating",
             "router_share",
+            "router_specialization",
+            "heatmap",
             "unavailable",
         }
         assert r.title and r.xlabel
@@ -88,6 +96,14 @@ def _validate_payload(r) -> None:
             for side in ("rollout", "reference"):
                 if side in p:
                     assert cat in p[side]
+    elif r.kind == "router_specialization":
+        for side in ("rollout", "reference"):
+            if side in p:
+                assert len(p[side]["centers"]) == len(p[side]["score"])
+    elif r.kind == "heatmap":
+        assert len(p["matrix"]) == len(p["row_labels"])
+        for row in p["matrix"]:
+            assert len(row) == len(p["col_labels"])
 
 
 # ---------------------------------------------------------------------------
@@ -98,7 +114,10 @@ def _validate_payload(r) -> None:
 # sec_count_per_species via pdg-keyed sums), concat-then-finalize with
 # data-dependent edges (event_total_edep), concat-then-mean/std (shower_
 # longitudinal), concat-then-max-edge (leakage_fraction), pdg-keyed sum with a
-# ratio (species_edep_share), and a chunkable=False passthrough (router_gating).
+# ratio (species_edep_share), a chunkable=False passthrough (router_gating),
+# nested sum-merge into a scorecard (marginal_distance_summary), concat-then-
+# event-id-join (n_sec_confusion), and concat-then-per-event-derived-quantity
+# (shower_containment_depth_90, reusing the profile matrix's own merge shape).
 _CHUNK_EQUIVALENCE_IDS = [
     "marginal_edep",
     "species_edep_share",
@@ -107,6 +126,9 @@ _CHUNK_EQUIVALENCE_IDS = [
     "leakage_fraction",
     "sec_count_per_species",
     "router_gating",
+    "marginal_distance_summary",
+    "n_sec_confusion",
+    "shower_containment_depth_90",
 ]
 
 
@@ -146,3 +168,45 @@ def test_chunked_matches_unchunked(ctx: Context, spec_id: str):
     assert chunked.id == unchunked.id
     assert chunked.kind == unchunked.kind
     _assert_payload_close(unchunked.payload, chunked.payload)
+
+
+# ---------------------------------------------------------------------------
+# new (gitea #76) reductions: KS distance, confusion matrix, containment depth
+# ---------------------------------------------------------------------------
+
+
+def test_ks_statistic():
+    assert _ks_statistic([10, 10], [10, 10]) == 0.0  # identical shape -> 0
+    assert _ks_statistic([10, 0], [0, 10]) == 1.0  # fully disjoint -> 1
+    assert _ks_statistic([0, 0], [0, 0]) != _ks_statistic([0, 0], [0, 0])  # nan (no data either side)
+    assert _ks_statistic([10, 0], [0, 0]) == 1.0  # one side empty, other isn't -> maximal mismatch
+
+
+def test_integer_confusion_matches_event_pairing():
+    # true (reference) n_sec = [1, 1]; predicted (rollout) n_sec = [1, 0]
+    labels, mat = _integer_confusion(np.array([1, 1]), np.array([1, 0]))
+    assert labels == ["0", "1+"]
+    assert mat.tolist() == [[0, 0], [1, 1]]  # row=true, col=pred
+
+
+def test_integer_confusion_caps_pathological_outliers():
+    labels, mat = _integer_confusion(np.array([0, 500]), np.array([0, 0]), max_bins=5)
+    assert labels[-1] == "4+"
+    assert mat.shape == (5, 5)
+    assert mat.sum() == 2
+
+
+def test_containment_depths_simple_ramp():
+    # one event, edep concentrated in the first bin -> 90%/95% containment
+    # depth is the first bin's right edge; a zero-energy event is dropped.
+    mat = np.array([[9.0, 1.0, 0.0], [0.0, 0.0, 0.0]])
+    edges = np.array([0.0, 1.0, 2.0, 3.0])
+    depths = _containment_depths(mat, edges, 0.90)
+    assert depths.tolist() == [1.0]
+
+
+def test_n_sec_confusion_spec(bundle):
+    spec = get_spec("n_sec_confusion")
+    r = spec.finalize([spec.compute_partial(bundle)], bundle.ctx)
+    assert r.payload["row_labels"] == r.payload["col_labels"] == ["0", "1+"]
+    assert r.payload["matrix"] == [[0, 0], [1, 1]]