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/loader.py

@@ -115,9 +115,7 @@ def _pad_dir_col(dx: pd.Series, dy: pd.Series, dz: pd.Series, K: int) -> np.ndar
     return out
 
 
-def _df_to_dict(
-    df: pd.DataFrame, offset: int = 0, k_max: int = K_MAX
-) -> dict[str, np.ndarray]:
+def _df_to_dict(df: pd.DataFrame, offset: int = 0, k_max: int = K_MAX) -> dict[str, np.ndarray]:
     has_sec_lists = "sec_E_list" in df.columns
 
     d: dict[str, np.ndarray] = {
@@ -136,9 +134,7 @@ def _df_to_dict(
         # / ProcessRouter). Guarded like has_sec_lists: older parquet
         # conversions predating this column still load fine.
         "process": (
-            df["process"].to_numpy(dtype=object)
-            if "process" in df.columns
-            else np.full(len(df), "", dtype=object)
+            df["process"].to_numpy(dtype=object) if "process" in df.columns else np.full(len(df), "", dtype=object)
         ),
         "step_length": df["step_length"].to_numpy(dtype=np.float32),
         "post_E": df["post_E"].to_numpy(dtype=np.float32),
@@ -151,16 +147,12 @@ def _df_to_dict(
     if has_sec_lists:
         d["sec_E_list"] = _pad_list_col(df["sec_E_list"], k_max)
         d["sec_pdg_list"] = _pad_list_col_int(df["sec_pdg_list"], k_max)
-        d["sec_dir_list"] = _pad_dir_col(
-            df["sec_dx_list"], df["sec_dy_list"], df["sec_dz_list"], k_max
-        )
+        d["sec_dir_list"] = _pad_dir_col(df["sec_dx_list"], df["sec_dy_list"], df["sec_dz_list"], k_max)
 
     return d
 
 
-def load_steps(
-    path: str | Path, offset: int = 0, k_max: int = K_MAX
-) -> dict[str, np.ndarray]:
+def load_steps(path: str | Path, offset: int = 0, k_max: int = K_MAX) -> dict[str, np.ndarray]:
     return _df_to_dict(pd.read_parquet(path), offset=offset, k_max=k_max)
 
 
@@ -170,9 +162,7 @@ def load_event_ids(path: str | Path, offset: int = 0) -> np.ndarray:
     return _offset_event_id(ids, offset)
 
 
-def iter_file_chunks(
-    path: str | Path, offset: int = 0, k_max: int = K_MAX
-) -> Iterator[dict[str, np.ndarray]]:
+def iter_file_chunks(path: str | Path, offset: int = 0, k_max: int = K_MAX) -> Iterator[dict[str, np.ndarray]]:
     """Yield one parquet row-group at a time so a large file never fully loads.
 
     `k_max` sets the padded width of the sec_*_list columns (should match
@@ -214,15 +204,11 @@ def _cond_df_to_dict(df: pd.DataFrame, offset: int = 0) -> dict[str, np.ndarray]
     }
 
 
-def iter_cond_chunks(
-    path: str | Path, offset: int = 0
-) -> Iterator[dict[str, np.ndarray]]:
+def iter_cond_chunks(path: str | Path, offset: int = 0) -> Iterator[dict[str, np.ndarray]]:
     """Yield conditioning-only row-groups (no post-step columns read from disk)."""
     pf = pq.ParquetFile(path)
     for i in range(pf.num_row_groups):
-        yield _cond_df_to_dict(
-            pf.read_row_group(i, columns=_COND_COLS).to_pandas(), offset=offset
-        )
+        yield _cond_df_to_dict(pf.read_row_group(i, columns=_COND_COLS).to_pandas(), offset=offset)
 
 
 def build_index_maps(
@@ -315,9 +301,7 @@ class TopNMap:
     other_members: dict
 
 
-def build_topn_map_from_files(
-    files: list[Path], column: str, n_classes: int, cast=str
-) -> TopNMap:
+def build_topn_map_from_files(files: list[Path], column: str, n_classes: int, cast=str) -> TopNMap:
     """Scan `column` and build a frequency-capped value->index map, structurally
     identical to `build_process_map_from_files` (shares its ranking core via
     `_topn_plus_other_map`), generalized over the source column and key type.