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")

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()