Add data-integrity guards against silent NaN/Inf propagation and races

- log_transform / _validate_unit_pre_dir now raise on non-finite input
  instead of letting a NaN row silently poison the persisted normalizer
  cache (norm < 1e-6 was always False for NaN, so the existing guard
  never caught it).
- encode_secondaries warns when a row's secondary energies cumulatively
  exceed e_sec, instead of silently saturating the overflowing slot's
  stick-breaking logit via the _EPS floor.
- EVENT_ID_FILE_STRIDE overflow now raises instead of silently colliding
  two files' event ids together (reintroducing train/val leakage).
- make_event_split(val_fraction=0.0) now actually holds out nothing,
  instead of always forcing at least 1 validation event.
- setup_cache.save() is now serialized with a flock, since two
  concurrent writers (a real scenario on this repo's shared
  portal/condor machines) could otherwise race and silently drop one
  writer's freshly-computed cache section.
- Documented (no behavior change) the pre_dir ≈ -ẑ antipodal rotation
  singularity in _rodrigues_axis, which is real but inherent to any
  single-valued local-frame convention.

Each fix has a regression test.

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

tests/test_dataset.py

@@ -30,6 +30,16 @@ def test_make_event_split_no_empty_sets():
     assert len(val_set) > 0
 
 
+def test_make_event_split_val_fraction_zero_holds_out_nothing():
+    """val_fraction=0.0 is an explicit "train on everything" request and
+    must not be silently overridden into holding out 1 event."""
+    rng = np.random.default_rng(3)
+    event_ids = rng.integers(0, 50, size=1000)
+    train_set, val_set = make_event_split(event_ids, val_fraction=0.0)
+    assert val_set == set()
+    assert train_set == set(np.unique(event_ids).tolist())
+
+
 def test_make_event_split_reproducible():
     event_ids = np.arange(100)
     a_tr, a_val = make_event_split(event_ids, val_fraction=0.1, seed=42)