Bump ruff line-length to 120 and reformat

Rejoins lines that only wrapped because they exceeded the old 88-char
limit; ruff check and the full test suite (725 passed) are unaffected.

giant/data/setup_cache.py

@@ -108,10 +108,7 @@ def normalizer_key(
     # affect which cond_cont columns are computed for real vs. zero-filled
     # (giant.data.transforms._physical_cond_columns), so both must be part of
     # the key or two mixed-axis runs could collide on the same cache entry.
-    return (
-        f"valfrac={val_fraction:.6g}_seed={seed}_pcond={particle_conditioning}"
-        f"_mcond={material_conditioning}"
-    )
+    return f"valfrac={val_fraction:.6g}_seed={seed}_pcond={particle_conditioning}_mcond={material_conditioning}"
 
 
 # Top-N-map axes: "pdg" keys match pdg_map's int
@@ -126,10 +123,7 @@ def topn_key(axis: str, n_classes: int) -> str:
     sidecar stays reusable across runs with different emb_dim (see the
     dict[int, dict] precedent `proc_maps` sets, keyed by n_experts)."""
     if axis not in _TOPN_AXIS_CASTS:
-        raise ValueError(
-            f"unknown top-N map axis {axis!r}, expected one of "
-            f"{sorted(_TOPN_AXIS_CASTS)}"
-        )
+        raise ValueError(f"unknown top-N map axis {axis!r}, expected one of {sorted(_TOPN_AXIS_CASTS)}")
     return f"{axis}:{n_classes}"
 
 
@@ -165,9 +159,7 @@ class NormalizerEntry:
             "tgt_norm": self.tgt_norm.to_dict(),
             "sec_phys_norm": self.sec_phys_norm.to_dict(),
             "n_train_steps": self.n_train_steps,
-            "energy_quantiles": np.asarray(
-                self.energy_quantiles, dtype=np.float32
-            ).tolist(),
+            "energy_quantiles": np.asarray(self.energy_quantiles, dtype=np.float32).tolist(),
         }
 
     @classmethod
@@ -234,13 +226,8 @@ class SetupCache:
                 np.array(d["event_index"]["counts"], dtype=np.int64),
             )
         proc_maps = {int(k): v for k, v in d.get("proc_maps", {}).items()}
-        normalizers = {
-            k: NormalizerEntry.from_json(v) for k, v in d.get("normalizers", {}).items()
-        }
-        topn_maps = {
-            k: topnmap_from_json(v, axis=k.split(":", 1)[0])
-            for k, v in d.get("topn_maps", {}).items()
-        }
+        normalizers = {k: NormalizerEntry.from_json(v) for k, v in d.get("normalizers", {}).items()}
+        topn_maps = {k: topnmap_from_json(v, axis=k.split(":", 1)[0]) for k, v in d.get("topn_maps", {}).items()}
         return cls(
             fingerprint=d["fingerprint"],
             git_hash=d.get("git_hash", "unknown"),
@@ -263,18 +250,14 @@ class SetupCache:
             fingerprint=other.fingerprint,
             git_hash=other.git_hash,
             vocab=other.vocab if other.vocab is not None else self.vocab,
-            event_index=(
-                other.event_index if other.event_index is not None else self.event_index
-            ),
+            event_index=(other.event_index if other.event_index is not None else self.event_index),
             proc_maps={**self.proc_maps, **other.proc_maps},
             normalizers={**self.normalizers, **other.normalizers},
             topn_maps={**self.topn_maps, **other.topn_maps},
         )
 
 
-def load(
-    data: str | Path, files: list[Path], echo=lambda *a, **k: None
-) -> SetupCache | None:
+def load(data: str | Path, files: list[Path], echo=lambda *a, **k: None) -> SetupCache | None:
     """Load and validate the sidecar for `data`; `None` on any miss (never raises).
 
     A missing file, corrupt JSON, format-version mismatch, dimension-constant
@@ -298,9 +281,7 @@ def load(
             echo("setup cache: format version changed — ignoring stale cache")
             return None
         if raw.get("dims") != _DIMS:
-            echo(
-                "setup cache: model dimension constants changed — ignoring stale cache"
-            )
+            echo("setup cache: model dimension constants changed — ignoring stale cache")
             return None
         fp = fingerprint_files(files)
         if raw.get("fingerprint") != fp:
@@ -343,9 +324,7 @@ def save(
         with open(lock_path, "a") as lock_file:
             fcntl.flock(lock_file, fcntl.LOCK_EX)
             try:
-                base = load(data, files, echo=lambda *a, **k: None) or SetupCache.empty(
-                    files
-                )
+                base = load(data, files, echo=lambda *a, **k: None) or SetupCache.empty(files)
                 merged = base.merge(sections)
                 payload = json.dumps(merged.to_json(), separators=(",", ":"))
                 tmp.write_text(payload)
@@ -353,9 +332,7 @@ def save(
             finally:
                 fcntl.flock(lock_file, fcntl.LOCK_UN)
     except OSError as exc:
-        echo(
-            f"setup cache: could not write {path} ({exc}) — continuing without caching"
-        )
+        echo(f"setup cache: could not write {path} ({exc}) — continuing without caching")
         try:
             tmp.unlink(missing_ok=True)
         except OSError:
@@ -366,16 +343,12 @@ def compute_event_index_from_files(files: list[Path]) -> tuple[np.ndarray, np.nd
     """Unique event ids + per-event row (step) counts, across all `files`."""
     if not files:
         return np.empty(0, dtype=np.int64), np.empty(0, dtype=np.int64)
-    all_ids = np.concatenate(
-        [load_event_ids(f, offset=event_id_offset(i)) for i, f in enumerate(files)]
-    )
+    all_ids = np.concatenate([load_event_ids(f, offset=event_id_offset(i)) for i, f in enumerate(files)])
     unique_ids, counts = np.unique(all_ids, return_counts=True)
     return unique_ids, counts
 
 
-def n_train_steps_for_split(
-    unique_ids: np.ndarray, counts: np.ndarray, train_events_arr: np.ndarray
-) -> int:
+def n_train_steps_for_split(unique_ids: np.ndarray, counts: np.ndarray, train_events_arr: np.ndarray) -> int:
     """Row (step) count summed over whichever `unique_ids` fall in `train_events_arr`.
 
     `train_events_arr` must be ascending and duplicate-free (as produced by