Clamp analysis histogram bins before the i32 cast, not after

_bin_expr clipped the bin index to [0, nbins-1] only after casting it to
Int32, so the clip never got the chance to do its job: a rollout
step_length of 1.0725e10 mm against fixed edges [2.9e-5, 94.04] with 50
bins gives a raw index of ~5.7e9, which overflows i32 and fails the
strict cast, killing the whole compute-one job. Same for +/-inf.

Clamp in f64 first and cast after. NaN has no edge to clamp to, so map
it to null and drop it in the two callers (hist1d, profile_partial) —
what np.histogram does with it, and what profile_partial needs anyway
since a null bin index would break its np.add.at.

Partials computed before this change stay valid: the old code crashed on
these values rather than binning them wrong, so any chunk that produced
a partial contained none of them and its counts are unchanged here.

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

giant/analysis/reduce.py

@@ -29,8 +29,17 @@ from giant.constants import TERM_ESCAPED
 
 
 def _bin_expr(value: pl.Expr, lo: float, hi: float, nbins: int) -> pl.Expr:
-    """Uniform bin index of ``value`` over ``[lo, hi]`` into ``nbins`` bins."""
-    return ((value - lo) / (hi - lo) * nbins).floor().cast(pl.Int32).clip(0, nbins - 1)
+    """Uniform bin index of ``value`` over ``[lo, hi]`` into ``nbins`` bins.
+
+    Out-of-range values clamp into the edge bins, and the clamp deliberately
+    happens in f64 *before* the integer cast: a rollout is free to emit a wildly
+    out-of-range outlier (a step_length of 1e10 mm, say) or an inf, whose
+    unclamped bin index overflows i32 and makes the cast fail outright. NaN has
+    no edge to clamp to, so it becomes null and is dropped by the callers below
+    — the same thing ``np.histogram`` does with it.
+    """
+    idx = ((value - lo) / (hi - lo) * nbins).floor().clip(0, nbins - 1)
+    return pl.when(idx.is_nan()).then(None).otherwise(idx).cast(pl.Int32)
 
 
 def hist1d(
@@ -50,6 +59,7 @@ def hist1d(
     group = pl.lit(0, dtype=pl.Int64) if group is None else group
     res = (
         lf.select(group.alias("_g"), _bin_expr(value, lo, hi, nbins).alias("_b"))
+        .drop_nulls("_b")
         .group_by("_g", "_b")
         .agg(pl.len().alias("_n"))
         .collect(engine="streaming")
@@ -190,6 +200,7 @@ def profile_partial(
             _bin_expr(coord, lo, hi, nbins).alias("_b"),
             weight.alias("_w"),
         )
+        .drop_nulls("_b")
         .group_by("event_id", "_b")
         .agg(pl.col("_w").sum().alias("_ws"))
         .collect(engine="streaming")

giant/data/transforms.py

@@ -10,6 +10,14 @@ _EPS = 1e-8
 # the conservation it slightly softens is physically negligible (~0.001%).
 _SIMPLEX_FLOOR = 1e-5
 
+# Upper clip for a raw predicted log_mass before inv_log_transform: exp(y)
+# must stay well inside float32 range (~3.4e38, i.e. y < ~88.7) or it
+# overflows to inf, which — like the negative-mass case below — blows up the
+# next log_transform call once that mass is fed back in as conditioning.
+# 80.0 leaves comfortable headroom while still being far beyond any physical
+# particle mass a converged model would ever predict.
+_LOG_MASS_MAX = 80.0
+
 
 def log_transform(x: np.ndarray, eps: float = _EPS) -> np.ndarray:
     x = np.asarray(x, dtype=np.float32)
@@ -629,9 +637,18 @@ def decode_secondaries(
                 pre_dir[valid], dir_local[valid, i]
             )
 
-    # mass is non-negative by construction (inv_log_transform of a real
-    # number is always > 0); clip to 0 for padded/invalid slots rather than
-    # leaving a spurious small positive floor from the log inverse.
+    # log_mass is a raw model prediction, not itself the output of
+    # log_transform, so it can land far outside the range that round-trips
+    # cleanly through inv_log_transform: too negative and exp(log_mass)
+    # undershoots _EPS, making inv_log_transform go slightly negative; too
+    # positive and exp(log_mass) overflows float32 to inf. Either one then
+    # blows up the next log_transform call on this track's mass once it's
+    # fed back in as conditioning for a further rollout step
+    # (giant/rollout.py -> build_cond_features -> _physical_cond_columns).
+    # Clip log_mass to a range whose inverse is guaranteed finite and >= 0
+    # before that can happen; clip to 0 separately for padded/invalid slots
+    # rather than leaving a spurious small positive floor.
+    log_mass = np.clip(log_mass, np.log(_EPS), _LOG_MASS_MAX)
     sec_mass = np.where(sec_valid, inv_log_transform(log_mass), 0.0).astype(np.float32)
     sec_charge = np.where(sec_valid, charge, 0.0).astype(np.float32)
 

tests/test_analysis_reduce.py

@@ -102,6 +102,33 @@ def test_hist1d_overall_and_grouped():
     assert hg[11].sum() == 4
 
 
+def test_hist1d_clamps_extreme_values_and_drops_nan():
+    # A rollout can emit a wildly out-of-range step_length (or an inf/NaN); the
+    # fixed-edge binning must clamp rather than overflow the i32 bin cast.
+    lf = pl.DataFrame(
+        {"x": [5.0, 1.0725e10, float("inf"), -float("inf"), float("nan"), None]}
+    ).lazy()
+    edges = np.linspace(0.0, 50.0, 6)  # width 10
+    h = R.hist1d(lf, pl.col("x"), edges)
+    # 5 -> bin 0; 1e10 and +inf -> top bin; -inf -> bin 0; NaN/null dropped
+    assert h[0].tolist() == [2, 0, 0, 0, 2]
+
+
+def test_profile_partial_clamps_extreme_values_and_drops_nan():
+    lf = pl.DataFrame(
+        {
+            "event_id": [1, 1, 1, 1],
+            "z": [5.0, 1.0725e10, float("nan"), 45.0],
+            "w": [1.0, 2.0, 4.0, 8.0],
+        }
+    ).lazy()
+    edges = np.linspace(0.0, 50.0, 6)
+    ev, mat = R.profile_partial(lf, pl.col("z"), edges, pl.col("w"))
+    assert ev.tolist() == [1]
+    # 1e10 clamps into the top bin alongside 45; the NaN row's weight is dropped
+    assert mat[0].tolist() == [1.0, 0.0, 0.0, 0.0, 10.0]
+
+
 def test_physical_steps_drops_synthetic_rollout_rows_only():
     lf = _rollout_frame()
     phys = physical_steps(lf, Side.rollout).collect()

tests/test_phase2.py

@@ -540,3 +540,53 @@ def test_decode_secondaries_mass_charge_round_trip_with_normalizer():
     )
     assert sec_mass[0, 0] == pytest.approx(938.27208943, abs=1e-2)
     assert sec_charge[0, 0] == pytest.approx(1.0, abs=1e-4)
+
+
+def test_decode_secondaries_extreme_negative_log_mass_stays_nonnegative():
+    from giant.data.transforms import decode_secondaries, log_transform
+
+    N = 1
+    e_sec = np.array([5.0], dtype=np.float32)
+    n_sec = np.array([1])
+    pre_dir = np.array([[0.0, 0.0, 1.0]], dtype=np.float32)
+
+    sec_cont = np.zeros((N, K_MAX, 6), dtype=np.float32)
+    sec_cont[0, 0, 0] = 10.0  # stick logit -> ~all of e_sec
+    sec_cont[0, 0, 1:4] = [0, 0, 1]
+    sec_cont[0, 0, 4] = -50.0  # raw model prediction: extremely negative log_mass
+    sec_cont[0, 0, 5] = 1.0
+
+    _, _, sec_mass, _, _ = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir)
+
+    # A raw model prediction isn't itself the output of log_transform, so
+    # naively applying inv_log_transform can undershoot zero (see
+    # decode_secondaries) — which then crashes the next log_transform call
+    # once this mass is fed back in as conditioning during rollout. The
+    # float32 residual from clipping can land a hair below zero, but must
+    # stay well above -eps so log_transform(mass) stays finite.
+    assert sec_mass[0, 0] > -1e-8
+    log_transform(sec_mass[0, 0])
+
+
+def test_decode_secondaries_extreme_positive_log_mass_stays_finite():
+    from giant.data.transforms import decode_secondaries, log_transform
+
+    N = 1
+    e_sec = np.array([5.0], dtype=np.float32)
+    n_sec = np.array([1])
+    pre_dir = np.array([[0.0, 0.0, 1.0]], dtype=np.float32)
+
+    sec_cont = np.zeros((N, K_MAX, 6), dtype=np.float32)
+    sec_cont[0, 0, 0] = 10.0  # stick logit -> ~all of e_sec
+    sec_cont[0, 0, 1:4] = [0, 0, 1]
+    sec_cont[0, 0, 4] = 200.0  # raw model prediction: extremely positive log_mass
+    sec_cont[0, 0, 5] = 1.0
+
+    _, _, sec_mass, _, _ = decode_secondaries(sec_cont, n_sec, e_sec, pre_dir)
+
+    # Mirror image of the extreme-negative case above: exp(log_mass)
+    # overflows float32 to inf for an unclipped raw prediction this large,
+    # which then crashes the next log_transform call the same way a
+    # negative mass would.
+    assert np.isfinite(sec_mass[0, 0])
+    log_transform(sec_mass[0, 0])