[pre-commit.ci] pre-commit autoupdate (#48)

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.8.6 → v0.9.4](astral-sh/ruff-pre-commit@v0.8.6...v0.9.4)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

Author: pre-commit-ci[bot]

.pre-commit-config.yaml

@@ -3,7 +3,7 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.6
+    rev: v0.9.4
     hooks:
       - id: ruff
         args: [ --fix ]

scripts/bench.py

@@ -307,7 +307,7 @@ def _plot(all_res: Table, sort=True, fname='bench_results.png', paper=False):
                 lines.append(f'{const_desc} $\\approx 12{var}$')
             else:
                 lines.append(
-                    f'{const_desc}: {eval(const+"_const")}',
+                    f'{const_desc}: {eval(const + "_const")}',
                 )
             lines = '\n'.join(lines)
             ax.annotate(

scripts/compare.py

@@ -98,12 +98,12 @@ def _analyze(all_res, fname, plot=True):
         nz = denom != 0
         frac = np.abs(p1 - p2) / denom
         frac = frac[nz]
-        print(f'\tmax err {np.nanmax(frac)*100:.4g}%')
-        print(f'\t99% err {np.sort(frac)[int(len(frac)*.99)]*100:.4g}%')
-        print(f'\t90% err {np.sort(frac)[int(len(frac)*.9)]*100:.4g}%')
-        print(f'\t75% err {np.sort(frac)[int(len(frac)*.75)]*100:.4g}%')
-        print(f'\t50% err {np.sort(frac)[int(len(frac)*.50)]*100:.4g}%')
-        print(f'\trms err / mean {rms_err/rms_mean * 100:.4g}%')
+        print(f'\tmax err {np.nanmax(frac) * 100:.4g}%')
+        print(f'\t99% err {np.sort(frac)[int(len(frac) * 0.99)] * 100:.4g}%')
+        print(f'\t90% err {np.sort(frac)[int(len(frac) * 0.9)] * 100:.4g}%')
+        print(f'\t75% err {np.sort(frac)[int(len(frac) * 0.75)] * 100:.4g}%')
+        print(f'\t50% err {np.sort(frac)[int(len(frac) * 0.50)] * 100:.4g}%')
+        print(f'\trms err / mean {rms_err / rms_mean * 100:.4g}%')
 
     if plot:
         freq = f0 + df * np.arange(Nf)

scripts/sweep.py

@@ -140,9 +140,9 @@ def _analyze(all_tables, fname, plot=True, paper=False):
 
             print(f'{method} vs {ref}, N={table.meta["N"]}, Nf={table.meta["Nf"]}')
             # pretty-print stats with :.4g
-            print(f'\t99% err {bxp_stats["whishi"]*100:.4g}%')
-            print(f'\t90% err {bxp_stats["q3"]*100:.4g}%')
-            print(f'\tmed err {bxp_stats["med"]*100:.4g}%')
+            print(f'\t99% err {bxp_stats["whishi"] * 100:.4g}%')
+            print(f'\t90% err {bxp_stats["q3"] * 100:.4g}%')
+            print(f'\tmed err {bxp_stats["med"] * 100:.4g}%')
 
     if plot:
         # _plot_box(all_info, fname)