Graph_Generation_Test.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "518d2aa3",
   "metadata": {},
   "source": [
    "# Test code for generating graphs\n",
    "\n",
    "by Wenxuan Han"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "44966e18",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "\n",
    "# y=\"episode_reward/episode_reward\"\n",
    "# y=\"success_rate/success_rate\"\n",
    "\n",
    "plot_data = []\n",
    "AC_df = pd.read_csv(\"AC_progress\")\n",
    "TD3_df = pd.read_csv(\"td3_progress\")\n",
    "PPO_df = pd.read_csv(\"ppo_progress\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "14e99d14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>log_dir</th>\n",
       "      <th>frame_per_second</th>\n",
       "      <th>episode_reward</th>\n",
       "      <th>total_steps</th>\n",
       "      <th>total_episodes</th>\n",
       "      <th>total_time</th>\n",
       "      <th>target_q</th>\n",
       "      <th>actor_loss</th>\n",
       "      <th>critic_loss</th>\n",
       "      <th>success_rate</th>\n",
       "      <th>current_q1</th>\n",
       "      <th>reward</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>MetaDrive-Tut-Hard-v0</td>\n",
       "      <td>704</td>\n",
       "      <td>5.162734</td>\n",
       "      <td>1428</td>\n",
       "      <td>2</td>\n",
       "      <td>2.028312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>MetaDrive-Tut-Hard-v0</td>\n",
       "      <td>761</td>\n",
       "      <td>2.789586</td>\n",
       "      <td>2404</td>\n",
       "      <td>4</td>\n",
       "      <td>3.157796</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>MetaDrive-Tut-Hard-v0</td>\n",
       "      <td>777</td>\n",
       "      <td>4.075364</td>\n",
       "      <td>3405</td>\n",
       "      <td>5</td>\n",
       "      <td>4.378556</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>MetaDrive-Tut-Hard-v0</td>\n",
       "      <td>717</td>\n",
       "      <td>5.123336</td>\n",
       "      <td>4406</td>\n",
       "      <td>6</td>\n",
       "      <td>6.144996</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>MetaDrive-Tut-Hard-v0</td>\n",
       "      <td>737</td>\n",
       "      <td>5.736588</td>\n",
       "      <td>5407</td>\n",
       "      <td>7</td>\n",
       "      <td>7.330042</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0                log_dir  frame_per_second  episode_reward  \\\n",
       "0           0  MetaDrive-Tut-Hard-v0               704        5.162734   \n",
       "1           1  MetaDrive-Tut-Hard-v0               761        2.789586   \n",
       "2           2  MetaDrive-Tut-Hard-v0               777        4.075364   \n",
       "3           3  MetaDrive-Tut-Hard-v0               717        5.123336   \n",
       "4           4  MetaDrive-Tut-Hard-v0               737        5.736588   \n",
       "\n",
       "   total_steps  total_episodes  total_time  target_q  actor_loss  critic_loss  \\\n",
       "0         1428               2    2.028312       NaN         NaN          NaN   \n",
       "1         2404               4    3.157796       NaN         NaN          NaN   \n",
       "2         3405               5    4.378556       NaN         NaN          NaN   \n",
       "3         4406               6    6.144996       NaN         NaN          NaN   \n",
       "4         5407               7    7.330042       NaN         NaN          NaN   \n",
       "\n",
       "   success_rate  current_q1  reward  \n",
       "0           0.0         NaN     NaN  \n",
       "1           0.0         NaN     NaN  \n",
       "2           0.0         NaN     NaN  \n",
       "3           0.0         NaN     NaN  \n",
       "4           0.0         NaN     NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "AC_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "09ad1bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# AC_df\n",
    "for index, row in AC_df.iterrows():\n",
    "    plot_data.append({\n",
    "        'value': row['episode_reward'],\n",
    "        'total_episodes': row['total_episodes'],\n",
    "        'label': 'AC'\n",
    "    })\n",
    "\n",
    "'''\n",
    "# PPO_df\n",
    "for index, row in PPO_df.iterrows():\n",
    "    plot_data.append({\n",
    "        'value': row['episode_reward'],\n",
    "        'total_episodes': row['total_episodes'],\n",
    "        'label': 'PPO'\n",
    "    }) \n",
    "'''\n",
    "\n",
    "# TD3_df\n",
    "for index, row in TD3_df.iterrows():\n",
    "    plot_data.append({\n",
    "        'value': row['episode_reward'],\n",
    "        'total_episodes': row['total_episodes'],\n",
    "        'label': 'TD3'\n",
    "    })\n",
    "\n",
    "plot_data = pd.DataFrame(plot_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b80458e4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>value</th>\n",
       "      <th>total_episodes</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.162734</td>\n",
       "      <td>2</td>\n",
       "      <td>AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.789586</td>\n",
       "      <td>4</td>\n",
       "      <td>AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.075364</td>\n",
       "      <td>5</td>\n",
       "      <td>AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.123336</td>\n",
       "      <td>6</td>\n",
       "      <td>AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.736588</td>\n",
       "      <td>7</td>\n",
       "      <td>AC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>193</th>\n",
       "      <td>227.088248</td>\n",
       "      <td>644</td>\n",
       "      <td>TD3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>194</th>\n",
       "      <td>229.320093</td>\n",
       "      <td>648</td>\n",
       "      <td>TD3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <td>233.582745</td>\n",
       "      <td>651</td>\n",
       "      <td>TD3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>196</th>\n",
       "      <td>235.135659</td>\n",
       "      <td>653</td>\n",
       "      <td>TD3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>197</th>\n",
       "      <td>234.172182</td>\n",
       "      <td>658</td>\n",
       "      <td>TD3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>198 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          value  total_episodes label\n",
       "0      5.162734               2    AC\n",
       "1      2.789586               4    AC\n",
       "2      4.075364               5    AC\n",
       "3      5.123336               6    AC\n",
       "4      5.736588               7    AC\n",
       "..          ...             ...   ...\n",
       "193  227.088248             644   TD3\n",
       "194  229.320093             648   TD3\n",
       "195  233.582745             651   TD3\n",
       "196  235.135659             653   TD3\n",
       "197  234.172182             658   TD3\n",
       "\n",
       "[198 rows x 3 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plot_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b9f55c95",
   "metadata": {},
   "outputs": [
    {
     "data": {
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p2bJl+uc//6mEhAQNHz5cF154oYYNG+az3xkAAADAEnwAAAAAmrWq4UJYWFiD+oaFhfnM0DjRPZgcDof+/ve/67zzztM//vEPffPNNzpw4ECN4VNSUpLGjRvXoNDhVBYREeH3taGhoT5/LkdnMTXU+eefry+//FK/+93v1KpVqxqvKSsr09dff60HH3xQI0aM0Ntvv13tmqrvManh77Pw8HDjuOoydce+robct6agrKqioiK/71Wfmuri7rvv1owZMzR69Ohax52bm6tZs2bp1ltv1cUXX6zvv/8+YGMCAADAqY+vJwEAAABo1qp+uN/QAKmkpMQnHGposFCV0+nUDTfcUG3vHbvdrq5duyotLU1paWnq0qWLevXqpfbt20uq3BOotn2JWpKGhEjH/rlU/TNuqKSkJD344IO6//77tXbtWi1ZskTLly/XunXrqu25VVhYqL/97W/KycnRn/70p1qf/3jeZ0dVfY8dOyOstLTU73s6nc46Hw8NDTWOe/bsqU8++cTve/urd+/emjp1qsrLy7V8+XItXbpUK1asqHHJxN27d+vWW2/VSy+9pHPOOSfgYwEAAMCphwAKAAAAQLMWGxtrHBcWFsrhcPi91NuhQ4d82scu/dYQ//73v33Cpy5duujee+/VsGHD6hxPRUXFcT/nqaQhyxvm5OT4tFu3bn3Cz282m9WvXz/169dPd955p0pKSrR69WotXbpU8+bN81kOb9q0abryyiuVlpYmyfc9JlUundelSxe/n7vq+6zqeyw5OdnnuoYsNVjf72fVMWdmZsrr9TbafkyhoaEaMWKERowYIany92f58uX65ptvtHjxYiOwc7lceuyxx3TWWWexHB8AAABYgg8AAABA89a1a1fj2OVyaePGjX73Xb9+vU+7Q4cOxzUGh8Oh9957z2gnJCTo3Xff1YgRI+oMn5xOp0/o0JL3iSooKNDBgwf9unbDhg0+7fT09ICPJyIiQsOHD9fkyZO1ePFijRkzxnjM4/FowYIFRrvqe0yS1q1b5/fzZGVl+YRbqampxnFycrLP3krHvu66bNmypc7HO3XqZBwXFhbWe31V+fn5JxSMJiYm6rLLLtOzzz6rRYsWqVevXsZj+/btq1Z3AAAACE4EUAAAAACatQEDBvi0v/zyS7/7fvHFFz7tM844o9o1/swa2bt3r8+MlAsvvFBxcXH19vv55599loHzeDz19jmVffPNN35dN3/+fOM4JCSkxj+XuixZskT33HOPxowZo8GDB9e7XJ3dbtcDDzzgc27fvn3Gcffu3RUVFWW0G/Iemzt3rk/72NcyePBg43jFihUqLCys9575+flau3ZtndcMHDjQp/3pp5/We9+jbrjhBvXp00fDhg3ThAkT5Ha7jcfeeustTZw4URdddJFuvvnmeu+VkJCgiRMn+pzbv3+/32MBAABAy0UABQAAAKBZ69Wrl7FUmiR99NFHyszMrLffzz//rG+//dZo9+vXr9qSaFL1AKqmWUrFxcU+bYvFUu/zu1wuPfPMM9XOtWRvv/12vXtBbd26VYsWLTLaF1xwQYP35ioqKtLnn3+uDRs26PDhw1q8eHG9fY4N/yIjI41js9msiy66yGhv3LjRJySrTUFBgd58802jHRYWpuHDh/tcc/nllxvH5eXleu211+q971tvvVXvDKWzzz5b0dHRRvuDDz7Q3r176733woULjVmEOTk5io+P93k/79ixQ4sWLdLOnTu1YsWKastY1qRqgCVVzj4DAAAACKAAAAAANGsmk0m33HKL0S4pKdHtt99e5346e/bs0d133+0TOtx66601Xnts+HFs2CRJbdu29Wl/9dVXNV53VGlpqe6//379/PPPPudb+n5QO3bs0COPPFLrTK/s7Gzdddddxowli8Wi3//+9w1+nuHDh/uEHE899VS9+yu9+uqrPu2qM5Mk6cYbb/TZt+jhhx/WL7/8Uuv9SktLdffddysrK8s495vf/MZnyT1JGjFihHr06GG0p02bpq+++qrW+3777bf673//W+drkSoDtN/+9rc+45k4caLPeI61e/duPfzww0bbZDJVq4vRo0cbx06nU4888ki1gKkqp9Opt956y2iHhISoX79+9Y4fAAAALR8BFAAAAIBmb8yYMRoxYoTR3rRpk6644gp99NFHKioqMs7n5eXpzTff1Lhx43yWAbv88st17rnn1njv+Ph4n/b06dOrXZOUlOTzofrBgwd17bXX6vvvv/eZ1ZSXl6f33ntPV155pebMmVPtPiUlJX682lPbrFmzdP311/uEb+Xl5frss8901VVXaceOHcb5W265pdr+S/6IjIzU7373O6OdmZmpcePG6dNPP60WDO7YsUMPP/ywpk2bZpw77bTTdNZZZ/lcl5aWpj/96U9Gu6CgQBMmTNBzzz2nAwcO+LyWefPmacyYMfr++++N8507d9Ydd9xRbaxms1mPP/64bDabpMrZQnfddZf+/ve/+7xHs7Ky9Oyzz+r//u///J4pN3HiRJ/9l7Zt26YxY8bo/fffV0FBgXG+qKhI7733nn79618rLy/POD9hwgSf/lJlMFd12cvFixfruuuu0/Lly32WOnS5XFq9erWuv/56/fjjj8b58ePH+8zMAgAAQPAyeVvyLrgAAAAAmqX7779fn3zyidFetGiRUlJS6uxTVFSkiRMnauXKlT7nLRaL4uPj5fF4lJeXV20JvZEjR+rZZ5+V3W6v8b7btm3TJZdc4nMuISFBdrtdI0eO1EMPPSRJWrt2ra699tpqS8xZLBa1atVKRUVFKi0t9XnMZrOpc+fO2rx5s6TK2SE//fSTz0wbSUpPTzeOb7/99hqDjIZ44YUX9OKLLxrtpKQkhYaGntA9W7durXfeecfn3KxZs3z2VurWrZu2bt1qtCMiIhQdHa2cnJxq+zRdeumlevLJJ2tczjAzM1PnnXee0Z4yZYrGjBnjc43D4dANN9yg1atX+5w3mUxq1aqV7Ha78vPzq4V+bdu21fvvv682bdrU+Dr//ve/+8zoOSo2NlahoaHKzc2t9lo6deqk1157rdpMuaq+/PJL3XvvvdXCpYSEBJnNZuXk5Bjv3YSEBJ111lnGvk4DBw6s9nt/1MGDB3XLLbf4/L5LlcHX0XtnZ2dXm5V2/vnn67nnnjOCsaoyMjJ0zTXXKDs72+e8zWZTq1atJEm5ubnVauGss87Sv//971prDQAAAMGFGVAAAAAATglRUVF6/fXXdeutt/qEKW63W9nZ2crNzfUJn2JiYvTQQw/p+eefr/MD8a5du2rcuHE+53Jzc3XgwAGtW7fOOHf66afrxRdfVGxsrM+1brdbWVlZ1cKnHj166P3339fNN99snKuoqNDatWsb9LoD4dChQ9q7d+8J/dq3b1+9zzN16lSfJdxKSkp04MABn8AmIiJCDzzwgJ5++mm/9tKqjd1u17Rp0zRmzBiffby8Xq+ys7O1b9++auHTeeedp/fee6/W8EmSHnzwQT311FNKSkryOZ+fn6+DBw/6vBar1arx48fro48+qjN8kqSLLrpIb731llJTU33O5+bmKjs723jvtm/fXtOmTatzjFW1bt1aH3zwgX7961/7BJsej0fZ2dnKysryCZ9CQkJ055136vnnn68xfDo6hvfff1/9+/f3Oe90OnXgwAEdOHDAJ3yy2Wy66aab9NJLLxE+AQAAwGCt/xIAAAAAaB5sNpvuueceXXfddfrss8/0/fffa+fOnTp8+LA8Ho9atWql7t27a8SIEbr44ot99gmqy+OPP67TTjtNn3zyiXbv3q2ioiJFRkZWC0iGDx+uefPmaebMmVq6dKm2b9+uwsJCmc1mRUVFqX379urevbvOPfdc/epXv5LJZFKHDh0UEhJi7P80Y8YMnXHGGQH/vWkOQkNDNXXqVI0dO1YzZszQ2rVrlZOTo/DwcHXp0kXnnHOOxo4dW23Zw+MVHh6uKVOm6Oabb9Znn32mNWvWaOfOnSoqKpLb7VZcXJxat26twYMHa+TIkerTp49f97388st14YUXau7cuVqyZIk2bNig3NxcVVRUKDo6Wl26dNHQoUN1xRVX+B0USdKAAQP0xRdfaO7cufryyy+1YcMG5efnKyoqSh06dNCFF16osWPHKjIyss59oo4VERGhxx57TLfccovmzp2rZcuWac+ePcrPz5fH41FMTIzS09M1ePBgXXHFFUpMTKz3nqmpqXr//ff1ww8/aN68edqwYYMyMjJUXFwsq9Wq+Ph4paam6le/+pVGjRpV7wxGAAAABB+W4AMAAAAAHJdjl+DzZylFAAAAAMGBJfgAAAAAAAAAAAAQUARQAAAAAAAAAAAACCgCKAAAAAAAAAAAAAQUARQAAAAAAAAAAAACigAKAAAAAAAAAAAAAUUABQAAAAAAAAAAgIAigAIAAAAAAAAAAEBAmbxer7epBwEAAAAAAAAAAICWgxlQAAAAAAAAAAAACCgCKAAAAAAAAAAAAAQUARQAAAAAAAAAAAACigAKAAAAAAAAAAAAAUUABQAAAAAAAAAAgIAigAIAAAAAAAAAAEBAEUABAAAAAAAAAAAgoAigAAAAAAAAAAAAEFAEUAAAAAAAAAAAAAgoAigAAAAAAAAAAAAEFAEUAAAAAAAAAAAAAooACgAAAAAAAAAAAAFFAAUAAAAAAAAAAICAIoACAAAAAAAAAABAQBFAAQAAAAAAAAAAIKAIoAAAAAAAAAAAABBQBFAAAAAAAAAAAAAIKAIoAAAAAAAAAAAABBQBFAAAAAAAAAAAAAKKAAoAAAAAAAAAAAABRQAFAAAAAAAAAACAgCKAAgAAAAAAAAAAQEARQAEAAAAAAAAAACCgCKAAAAAAAAAAAAAQUARQAAAAAAAAAAAACCgCKAAAAAAAAAAAAAQUARQAAAAAAAAAAAACigAKAAAAAAAAAAAAAUUABQAAAAAAAAAAgID6fxx5F0y60i+sAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1920x1440 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(dpi=300)\n",
    "sns.set(\"paper\", style=\"darkgrid\")\n",
    "\n",
    "# Plot\n",
    "ax = sns.lineplot(\n",
    "    data=plot_data,\n",
    "    x='total_episodes',\n",
    "    y='value',\n",
    "    hue='label'\n",
    ")\n",
    "\n",
    "# Set the plot title and labels\n",
    "ax.set_title('AC vs TD3 on MetaDrive-Tut-Hard-V0')\n",
    "ax.set_xlabel('Total Episodes')\n",
    "ax.set_ylabel('Episodic Reward')\n",
    "\n",
    "# Set the legend\n",
    "ax.legend(title='Algorithm', fontsize=8, loc='upper right')\n",
    "\n",
    "# Save the plot\n",
    "plt.savefig('generalization.png', format='png', dpi=300, bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "faa30d30",
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6c3c1261",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86732fcf",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}