diff --git "a/scripts/plot_data.ipynb" "b/scripts/plot_data.ipynb"
new file mode 100644--- /dev/null
+++ "b/scripts/plot_data.ipynb"
@@ -0,0 +1,649 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7943b934",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/home/ubuntu/Qwen-Image-Edit-Angles\n"
+     ]
+    }
+   ],
+   "source": [
+    "%cd /home/ubuntu/Qwen-Image-Edit-Angles"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "5de64216",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "from matplotlib import pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "07aff18c",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Loaded results for qwen_base: 17 rows\n",
+      "Loaded results for qwen_fa3: 17 rows\n",
+      "Loaded results for qwen_aot: 18 rows\n",
+      "Loaded results for qwen_fa3_aot: 18 rows\n",
+      "Loaded results for qwen_fa3_aot_int8: 18 rows\n"
+     ]
+    }
+   ],
+   "source": [
+    "from qwenimage.experiment import ExperimentConfig\n",
+    "from qwenimage.experiments.experiments_qwen import ExperimentRegistry\n",
+    "\n",
+    "\n",
+    "experiment_names = ExperimentRegistry.keys()\n",
+    "report_dir = ExperimentConfig().report_dir\n",
+    "\n",
+    "# Load all CSV results\n",
+    "all_results = []\n",
+    "for name in experiment_names:\n",
+    "    csv_path = report_dir / f\"{name}.csv\"\n",
+    "    \n",
+    "    df = pd.read_csv(csv_path, index_col=0)\n",
+    "    df['experiment'] = name\n",
+    "    all_results.append(df)\n",
+    "    print(f\"Loaded results for {name}: {len(df)} rows\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "53cb7629",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>len</th>\n",
+       "      <th>experiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>QwenBaseExperiment.load</td>\n",
+       "      <td>21.085774</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QwenBaseExperiment.optimize</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Preprocessing</td>\n",
+       "      <td>0.040042</td>\n",
+       "      <td>0.002902</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>QwenImageEditPlusPipeline.encode_prompt</td>\n",
+       "      <td>0.141252</td>\n",
+       "      <td>0.082341</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Encode Prompt</td>\n",
+       "      <td>0.141295</td>\n",
+       "      <td>0.082349</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>QwenImageEditPlusPipeline._encode_vae_image</td>\n",
+       "      <td>0.102881</td>\n",
+       "      <td>0.045868</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>Prep gen</td>\n",
+       "      <td>0.105089</td>\n",
+       "      <td>0.046743</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>loop 0</td>\n",
+       "      <td>0.405985</td>\n",
+       "      <td>0.037210</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>loop 1</td>\n",
+       "      <td>0.435711</td>\n",
+       "      <td>0.038597</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>loop 2</td>\n",
+       "      <td>0.463708</td>\n",
+       "      <td>0.011147</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>loop 3</td>\n",
+       "      <td>0.450008</td>\n",
+       "      <td>0.010889</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>11</th>\n",
+       "      <td>loop</td>\n",
+       "      <td>1.755919</td>\n",
+       "      <td>0.034667</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>12</th>\n",
+       "      <td>pre decode</td>\n",
+       "      <td>0.077481</td>\n",
+       "      <td>0.002126</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>13</th>\n",
+       "      <td>vae.decode</td>\n",
+       "      <td>0.127213</td>\n",
+       "      <td>0.046457</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>post process</td>\n",
+       "      <td>0.052132</td>\n",
+       "      <td>0.043604</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>offload</td>\n",
+       "      <td>0.005029</td>\n",
+       "      <td>0.000151</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>QwenBaseExperiment.run_once</td>\n",
+       "      <td>2.304565</td>\n",
+       "      <td>0.123000</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                           name       mean       std  len  \\\n",
+       "0                       QwenBaseExperiment.load  21.085774  0.000000    1   \n",
+       "1                   QwenBaseExperiment.optimize   0.000001  0.000000    1   \n",
+       "2                                 Preprocessing   0.040042  0.002902    5   \n",
+       "3       QwenImageEditPlusPipeline.encode_prompt   0.141252  0.082341    5   \n",
+       "4                                 Encode Prompt   0.141295  0.082349    5   \n",
+       "5   QwenImageEditPlusPipeline._encode_vae_image   0.102881  0.045868    5   \n",
+       "6                                      Prep gen   0.105089  0.046743    5   \n",
+       "7                                        loop 0   0.405985  0.037210    5   \n",
+       "8                                        loop 1   0.435711  0.038597    5   \n",
+       "9                                        loop 2   0.463708  0.011147    5   \n",
+       "10                                       loop 3   0.450008  0.010889    5   \n",
+       "11                                         loop   1.755919  0.034667    5   \n",
+       "12                                   pre decode   0.077481  0.002126    5   \n",
+       "13                                   vae.decode   0.127213  0.046457    5   \n",
+       "14                                 post process   0.052132  0.043604    5   \n",
+       "15                                      offload   0.005029  0.000151    5   \n",
+       "16                  QwenBaseExperiment.run_once   2.304565  0.123000    5   \n",
+       "\n",
+       "   experiment  \n",
+       "0   qwen_base  \n",
+       "1   qwen_base  \n",
+       "2   qwen_base  \n",
+       "3   qwen_base  \n",
+       "4   qwen_base  \n",
+       "5   qwen_base  \n",
+       "6   qwen_base  \n",
+       "7   qwen_base  \n",
+       "8   qwen_base  \n",
+       "9   qwen_base  \n",
+       "10  qwen_base  \n",
+       "11  qwen_base  \n",
+       "12  qwen_base  \n",
+       "13  qwen_base  \n",
+       "14  qwen_base  \n",
+       "15  qwen_base  \n",
+       "16  qwen_base  "
+      ]
+     },
+     "execution_count": 40,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "all_results[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "id": "5bb86726",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "combined_df.shape=(88, 5)\n",
+      "combined_df.columns.tolist()=['name', 'mean', 'std', 'len', 'experiment']\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>name</th>\n",
+       "      <th>mean</th>\n",
+       "      <th>std</th>\n",
+       "      <th>len</th>\n",
+       "      <th>experiment</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>QwenBaseExperiment.load</td>\n",
+       "      <td>21.085774</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>QwenBaseExperiment.optimize</td>\n",
+       "      <td>0.000001</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Preprocessing</td>\n",
+       "      <td>0.040042</td>\n",
+       "      <td>0.002902</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>QwenImageEditPlusPipeline.encode_prompt</td>\n",
+       "      <td>0.141252</td>\n",
+       "      <td>0.082341</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Encode Prompt</td>\n",
+       "      <td>0.141295</td>\n",
+       "      <td>0.082349</td>\n",
+       "      <td>5</td>\n",
+       "      <td>qwen_base</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                                      name       mean       std  len  \\\n",
+       "0                  QwenBaseExperiment.load  21.085774  0.000000    1   \n",
+       "1              QwenBaseExperiment.optimize   0.000001  0.000000    1   \n",
+       "2                            Preprocessing   0.040042  0.002902    5   \n",
+       "3  QwenImageEditPlusPipeline.encode_prompt   0.141252  0.082341    5   \n",
+       "4                            Encode Prompt   0.141295  0.082349    5   \n",
+       "\n",
+       "  experiment  \n",
+       "0  qwen_base  \n",
+       "1  qwen_base  \n",
+       "2  qwen_base  \n",
+       "3  qwen_base  \n",
+       "4  qwen_base  "
+      ]
+     },
+     "execution_count": 42,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "combined_df = pd.concat(all_results, ignore_index=True)\n",
+    "print(f\"{combined_df.shape=}\")\n",
+    "print(f\"{combined_df.columns.tolist()=}\")\n",
+    "combined_df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5fcd8c1a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "fac9587f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 864x432 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "profile_targets = [\"loop\", \"QwenBaseExperiment.run_once\"]\n",
+    "\n",
+    "for target in profile_targets:\n",
+    "    plot_data = combined_df[combined_df['name'] == target].copy()\n",
+    "    plot_data = plot_data.sort_values('mean', ascending=False)\n",
+    "\n",
+    "    fig, ax = plt.subplots(figsize=(12, 6))\n",
+    "\n",
+    "    x_pos = range(len(plot_data))\n",
+    "    \n",
+    "    # Get the maximum (slowest) time for percentage calculation\n",
+    "    max_time = plot_data['mean'].max()\n",
+    "\n",
+    "    # Plot bars with error bars\n",
+    "    bars = ax.bar(x_pos, plot_data['mean'], yerr=plot_data['std'], \n",
+    "                    capsize=12, alpha=0.7, edgecolor='black')\n",
+    "\n",
+    "    # Customize plot\n",
+    "    ax.set_xlabel('Optimization Type', fontsize=12, fontweight='bold')\n",
+    "    ax.set_ylabel('Time (s)', fontsize=12, fontweight='bold')\n",
+    "    ax.set_title(f'Optimizations Comparison Over: {target}', \n",
+    "                    fontsize=14, fontweight='bold')\n",
+    "    ax.set_xticks(x_pos)\n",
+    "    ax.set_xticklabels([row['experiment'] for _, row in plot_data.iterrows()], \n",
+    "                        rotation=45, ha='right', fontsize=12)\n",
+    "    ax.grid(axis='y', alpha=0.3)\n",
+    "\n",
+    "    # Add value labels on top of bars with percentage decrease\n",
+    "    for i, (idx, row) in enumerate(plot_data.iterrows()):        \n",
+    "        ax.text(i - 0.2, row['mean'] + 0.01, f\"{row['mean']:.3f}s\", \n",
+    "                ha='center', va='bottom', fontsize=12)\n",
+    "        \n",
+    "        pct_decrease = ((max_time - row['mean']) / max_time) * 100\n",
+    "        ax.text(i + 0.2, row['mean'] + 0.01, f\"(-{pct_decrease:.1f}%)\", \n",
+    "                ha='center', va='bottom', fontsize=12, color='green')\n",
+    "    \n",
+    "    \n",
+    "\n",
+    "    plt.tight_layout()\n",
+    "\n",
+    "    # Save plot\n",
+    "    plot_path = report_dir / f'{target}_performance_comparison.png'\n",
+    "    plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
+    "\n",
+    "    plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "eec1ec7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1008x504 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "needs_background": "light"
+     },
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "=== Time Breakdown Summary ===\n",
+      "name               Preprocessing  Encode Prompt  Prep gen   loop  pre decode  \\\n",
+      "experiment                                                                     \n",
+      "qwen_base                   0.04          0.141     0.105  1.756       0.077   \n",
+      "qwen_fa3                    0.04          0.144     0.106  1.494       0.064   \n",
+      "qwen_aot                    0.04          0.139     0.107  1.395       0.153   \n",
+      "qwen_fa3_aot                0.04          0.140     0.107  1.150       0.125   \n",
+      "qwen_fa3_aot_int8           0.04          0.131     0.103  1.131       0.103   \n",
+      "\n",
+      "name               vae.decode  post process  offload  \n",
+      "experiment                                            \n",
+      "qwen_base               0.127         0.052    0.005  \n",
+      "qwen_fa3                0.128         0.052    0.005  \n",
+      "qwen_aot                0.127         0.053    0.005  \n",
+      "qwen_fa3_aot            0.127         0.053    0.005  \n",
+      "qwen_fa3_aot_int8       0.128         0.052    0.005  \n",
+      "\n",
+      "Total times:\n",
+      "experiment\n",
+      "qwen_base            2.304\n",
+      "qwen_fa3             2.033\n",
+      "qwen_aot             2.019\n",
+      "qwen_fa3_aot         1.747\n",
+      "qwen_fa3_aot_int8    1.693\n",
+      "dtype: float64\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Create stacked bar plot\n",
+    "stack_targets = [\"Preprocessing\", \"Encode Prompt\", \"Prep gen\", \"loop\", \"pre decode\", \"vae.decode\", \"post process\", \"offload\"]\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Filter data for the target stages\n",
+    "stack_data = combined_df[combined_df['name'].isin(stack_targets)].copy()\n",
+    "\n",
+    "# Pivot the data so experiments are rows and stages are columns\n",
+    "pivot_data = stack_data.pivot(index='experiment', columns='name', values='mean')\n",
+    "\n",
+    "# Reorder columns to match the desired order\n",
+    "pivot_data = pivot_data[stack_targets]\n",
+    "\n",
+    "# Sort by total time (highest to lowest)\n",
+    "pivot_data['total'] = pivot_data.sum(axis=1)\n",
+    "pivot_data = pivot_data.sort_values('total', ascending=False)\n",
+    "pivot_data = pivot_data.drop('total', axis=1)\n",
+    "\n",
+    "# Create the stacked bar plot\n",
+    "fig, ax = plt.subplots(figsize=(14, 7))\n",
+    "\n",
+    "# Plot stacked bars\n",
+    "pivot_data.plot(kind='bar', stacked=True, ax=ax, \n",
+    "                colormap='viridis', edgecolor='black', capsize=12, alpha=0.7, width=0.8)\n",
+    "\n",
+    "# Customize plot\n",
+    "ax.set_xlabel('Optimization Type', fontsize=12, fontweight='bold')\n",
+    "ax.set_ylabel('Time (s)', fontsize=12, fontweight='bold')\n",
+    "ax.set_title('Pipeline Time Breakdown by Optimization', \n",
+    "             fontsize=14, fontweight='bold')\n",
+    "ax.set_xticklabels(pivot_data.index, rotation=45, ha='right', fontsize=12)\n",
+    "ax.legend(title='Pipeline Stage', bbox_to_anchor=(1.05, 1), loc='upper left', fontsize=10)\n",
+    "ax.grid(axis='y', alpha=0.3)\n",
+    "\n",
+    "# Add total time labels on top of each bar with percentage improvement\n",
+    "max_time = pivot_data.sum(axis=1).max()\n",
+    "for i, (idx, row) in enumerate(pivot_data.iterrows()):\n",
+    "    total = row.sum()\n",
+    "    \n",
+    "    # Show time value\n",
+    "    ax.text(i - 0.2, total + 0.01, f'{total:.3f}s', \n",
+    "            ha='center', va='bottom', fontsize=12, fontweight='bold')\n",
+    "    \n",
+    "\n",
+    "    pct_decrease = ((max_time - total) / max_time) * 100\n",
+    "    ax.text(i + 0.2, total + 0.01, f'(-{pct_decrease:.1f}%)', \n",
+    "            ha='center', va='bottom', fontsize=12, color='green', fontweight='bold')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "\n",
+    "# Save plot\n",
+    "plot_path = report_dir / 'stacked_time_breakdown.png'\n",
+    "plt.savefig(plot_path, dpi=300, bbox_inches='tight')\n",
+    "\n",
+    "plt.show()\n",
+    "\n",
+    "# Print summary table\n",
+    "print(\"\\n=== Time Breakdown Summary ===\")\n",
+    "print(pivot_data.round(3))\n",
+    "print(f\"\\nTotal times:\")\n",
+    "print(pivot_data.sum(axis=1).round(3))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "db8bf253",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "fd23a11b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.12"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}