linting and formatting errors

Author: vmehtacode

app.py

@@ -9,6 +9,7 @@
 
 Goal: Show full BDH improves by >10% vs baseline, measure component interactions.
 """
+
 import json
 import sys
 from pathlib import Path

examples/ablation_study.py

@@ -6,6 +6,7 @@
 
 Goal: Show >20% improvement over Naive baseline.
 """
+
 import json
 import sys
 from pathlib import Path

examples/baseline_comparison.py

@@ -7,6 +7,7 @@
 
 Goal: Show >5% weight change for at least one constraint type.
 """
+
 import json
 import sys
 from pathlib import Path

examples/hebbian_stress_test.py

@@ -12,6 +12,7 @@
     the Transformer and Models of the Brain. arXiv:2509.26507
     https://arxiv.org/abs/2509.26507
 """
+
 import sys
 from pathlib import Path
 
@@ -139,7 +140,7 @@ def main() -> None:
         scenario_counts = Counter(all_scenarios)
         for scenario, count in scenario_counts.most_common():
             logger.info(
-                f"      {scenario:15s}: {count:3d} ({count/len(all_scenarios):.1%})"
+                f"      {scenario:15s}: {count:3d} ({count / len(all_scenarios):.1%})"
             )
 
     # 6. Standard metrics
@@ -294,7 +295,7 @@ def plot_bdh_metrics(metrics_history: list, trainer: any) -> None:
         ax5.text(
             bar.get_x() + bar.get_width() / 2.0,
             height,
-            f"{count}\n({count/total:.1%})",
+            f"{count}\n({count / total:.1%})",
             ha="center",
             va="bottom",
         )

examples/selfplay_bdh_demo.py

@@ -7,6 +7,7 @@
 References:
     See docs/selfplay_implementation.md for architecture details.
 """
+
 import sys
 from pathlib import Path
 

examples/selfplay_quick_demo.py

@@ -29,14 +29,14 @@
    ],
    "source": [
     "# Setup and imports\n",
-    "import pandas as pd\n",
-    "import polars as pl\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "from pathlib import Path\n",
     "import json\n",
     "import warnings\n",
+    "from pathlib import Path\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "import polars as pl\n",
+    "import seaborn as sns\n",
+    "\n",
     "warnings.filterwarnings(\"ignore\")\n",
     "\n",
     "plt.style.use(\"seaborn-v0_8-whitegrid\")\n",
@@ -328,12 +328,18 @@
     "print(df_pd[\"energy_kwh\"].describe())\n",
     "\n",
     "print(\"\\n=== Top 15 Appliances by Total Consumption ===\")\n",
-    "appliance_totals = df_pd.groupby(\"channel\")[\"energy_kwh\"].agg([\"sum\", \"mean\", \"median\", \"std\", \"count\"]).sort_values(\"sum\", ascending=False)\n",
+    "appliance_totals = (\n",
+    "    df_pd.groupby(\"channel\")[\"energy_kwh\"]\n",
+    "    .agg([\"sum\", \"mean\", \"median\", \"std\", \"count\"])\n",
+    "    .sort_values(\"sum\", ascending=False)\n",
+    ")\n",
     "display(appliance_totals.head(15))\n",
     "\n",
     "total_energy = df_pd[\"energy_kwh\"].sum()\n",
     "appliance_totals[\"pct_contribution\"] = (appliance_totals[\"sum\"] / total_energy) * 100\n",
-    "print(f\"\\nTop 5 appliances: {appliance_totals.head(5)['pct_contribution'].sum():.1f}% of consumption\")"
+    "print(\n",
+    "    f\"\\nTop 5 appliances: {appliance_totals.head(5)['pct_contribution'].sum():.1f}% of consumption\"\n",
+    ")"
    ]
   },
   {