diff --git a/scratches/pseco/synthetic_dataset/reward_algorithms/n03 HeatMap.ipynb b/scratches/pseco/synthetic_dataset/reward_algorithms/n03 HeatMap.ipynb
index 807f3a9..198bbbc 100644
--- a/scratches/pseco/synthetic_dataset/reward_algorithms/n03 HeatMap.ipynb	
+++ b/scratches/pseco/synthetic_dataset/reward_algorithms/n03 HeatMap.ipynb	
@@ -110,11 +110,7 @@
     "        \"Node Types\": len(stats.get(\"node_type_frequency\", {}))\n",
     "    })\n",
     "\n",
-    "fill_rate_df = pd.DataFrame(fill_rate_data)\n",
-    "\n",
-    "print(\"Fill Rate Summary for E and F:\")\n",
-    "print(fill_rate_df.to_string(index=False))\n",
-    "print(f\"\\nBoth candidates have identical dimensions: {total_cells} total cells\")\n"
+    "fill_rate_df = pd.DataFrame(fill_rate_data)\n"
    ]
   },
   {
@@ -22219,21 +22215,7 @@
     "    yaxis=dict(autorange=\"reversed\")\n",
     ")\n",
     "\n",
-    "fig.show()\n",
-    "\n",
-    "print(\"\\n\" + \"=\"*80)\n",
-    "print(\"DETAILED FILL RATE STATISTICS: E vs F\")\n",
-    "print(\"=\"*80)\n",
-    "for candidate in [\"E\", \"F\"]:\n",
-    "    stats = candidates[candidate]\n",
-    "    print(f\"\\nCandidate {candidate}:\")\n",
-    "    print(f\"  Total Cells:      {stats.get('total_cells', 'N/A'):,}\")\n",
-    "    print(f\"  Filled Cells:     {stats.get('filled_cells', 'N/A'):,}\")\n",
-    "    print(f\"  Empty Cells:      {stats.get('empty_cells', 'N/A'):,}\")\n",
-    "    print(f\"  Low Quality:      {stats.get('low_quality_cells', 'N/A'):,}\")\n",
-    "    print(f\"  Fill Rate (%):    {stats.get('fill_rate', 0) * 100:.4f}%\")\n",
-    "    print(f\"  Node Types:       {len(stats.get('node_type_frequency', {}))}\")\n",
-    "print(\"=\"*80)"
+    "fig.show()"
    ]
   },
   {