agentbasedmodel.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "e91fdb77-bfa1-402a-8559-80a6f8313509",
   "metadata": {},
   "outputs": [],
   "source": [
    "from mesa import Agent, Model\n",
    "from mesa.time import RandomActivation\n",
    "from mesa.space import MultiGrid\n",
    "from mesa.datacollection import DataCollector\n",
    "import mesa.batchrunner as batch_run\n",
    "\n",
    "from mesa.visualization.modules import CanvasGrid\n",
    "from mesa.visualization.ModularVisualization import ModularServer\n",
    "from mesa.visualization.modules import ChartModule\n",
    "\n",
    "def compute_gini(model):\n",
    "    agent_wealths = [agent.wealth for agent in model.schedule.agents]\n",
    "    x = sorted(agent_wealths)\n",
    "    N = model.num_agents\n",
    "    B = sum(xi * (N - i) for i, xi in enumerate(x)) / (N * sum(x))\n",
    "    return 1 + (1 / N) - 2 * B\n",
    "\n",
    "\n",
    "class MoneyAgent(Agent):\n",
    "    \"\"\"An agent with fixed initial wealth.\"\"\"\n",
    "\n",
    "    def __init__(self, unique_id, model):\n",
    "        super().__init__(unique_id, model)\n",
    "        self.wealth = 1\n",
    "\n",
    "    def step(self):\n",
    "        self.move()\n",
    "        if self.wealth > 0:\n",
    "            self.give_money()\n",
    "        \n",
    "    def move(self):\n",
    "        possible_steps = self.model.grid.get_neighborhood(\n",
    "            self.pos,\n",
    "            moore=True,\n",
    "            include_center=False)\n",
    "        new_position = self.random.choice(possible_steps)\n",
    "        self.model.grid.move_agent(self, new_position)\n",
    "        \n",
    "    def give_money(self):\n",
    "        cellmates = self.model.grid.get_cell_list_contents([self.pos])\n",
    "        if len(cellmates) > 1:\n",
    "            other = self.random.choice(cellmates)\n",
    "            other.wealth += 1\n",
    "            self.wealth -= 1\n",
    "    \n",
    "\n",
    "\n",
    "class MoneyModel(Model):\n",
    "    \"\"\"A model with some number of agents.\"\"\"\n",
    "\n",
    "    def __init__(self, N, width, height):\n",
    "        self.num_agents = N\n",
    "        self.grid = MultiGrid(width, height, True)\n",
    "        self.schedule = RandomActivation(self)\n",
    "        self.running = True\n",
    "\n",
    "        # Create agents\n",
    "        for i in range(self.num_agents):\n",
    "            a = MoneyAgent(i, self)\n",
    "            self.schedule.add(a)\n",
    "\n",
    "            # Add the agent to a random grid cell\n",
    "            x = self.random.randrange(self.grid.width)\n",
    "            y = self.random.randrange(self.grid.height)\n",
    "            self.grid.place_agent(a, (x, y))\n",
    "            \n",
    "        self.datacollector = DataCollector(\n",
    "            model_reporters={\"Gini\": compute_gini}, agent_reporters={\"Wealth\": \"wealth\"}\n",
    "        )\n",
    "        \n",
    "    def step(self):\n",
    "        self.datacollector.collect(self)\n",
    "        self.schedule.step()\n",
    "            \n",
    "            \n",
    "def agent_portrayal(agent):\n",
    "    portrayal = {\"Shape\": \"circle\",\n",
    "                 \"Filled\": \"true\",\n",
    "                 \"r\": 0.5}\n",
    "\n",
    "    if agent.wealth > 0:\n",
    "        portrayal[\"Color\"] = \"red\"\n",
    "        portrayal[\"Layer\"] = 0\n",
    "    else:\n",
    "        portrayal[\"Color\"] = \"grey\"\n",
    "        portrayal[\"Layer\"] = 1\n",
    "        portrayal[\"r\"] = 0.2\n",
    "    return portrayal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "41370a16-5baa-413a-aaea-1b62bc6ad6e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = MoneyModel(50, 10, 10)\n",
    "for i in range(100):\n",
    "    model.step()\n",
    "    \n",
    "gini = model.datacollector.get_model_vars_dataframe()\n",
    "gini.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ea1f00ff-9021-4428-97c6-a521e2bd32c9",
   "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></th>\n",
       "      <th>Wealth</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>AgentID</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">0</th>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              Wealth\n",
       "Step AgentID        \n",
       "0    0             1\n",
       "     1             1\n",
       "     2             1\n",
       "     3             1\n",
       "     4             1"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_wealth = model.datacollector.get_agent_vars_dataframe()\n",
    "agent_wealth.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4836e511-c8bb-465a-a363-b27ec7357170",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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6ZD/JfcAccEmSI8CHquru8aYCVkeUNwIHu/lmgA92q2bHaRdwb3eHwAuAT1XVxNyyN4FmgM+u/plnJ/DJqvrieCM9733A/m5A9X3gj8acB4AkF7B6t9q7x53ljKr6epJPA48Bp4FvMsQVmRN/G6EkaXMtTKFIkjZhgUtSoyxwSWqUBS5JjbLAJalRFrgkNcoCl6RGWeCS1Kj/Bd96UAT8JFksAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the wealth at the end of the simulation\n",
    "end_wealth = agent_wealth.xs(99, level=\"Step\")[\"Wealth\"]\n",
    "end_wealth.hist(bins=range(agent_wealth.Wealth.max() + 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "f37f63bc-79e6-4a23-bee5-dd8624038008",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Step'>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the wealth of a specific agent\n",
    "one_agent_wealth = agent_wealth.xs(14, level=\"AgentID\")\n",
    "one_agent_wealth.Wealth.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "882cfb82-b2a1-4038-b10b-89b6f1b4c2d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "245it [00:12, 19.64it/s]\n"
     ]
    }
   ],
   "source": [
    "# Make a batch run\n",
    "# ----------------\n",
    "\n",
    "from mesa.batchrunner import batch_run\n",
    "\n",
    "# Parameters for the model\n",
    "params = {\"width\": 10, \"height\": 10, \"N\": range(10, 500, 10)}\n",
    "\n",
    "results = batch_run(\n",
    "    MoneyModel,\n",
    "    parameters=params,\n",
    "    iterations=5,\n",
    "    max_steps=100,\n",
    "    number_processes=None,\n",
    "    data_collection_period=1,\n",
    "    display_progress=True,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "3f3ea838-5d0a-4b5a-a193-92b400ee6068",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['RunId', 'iteration', 'Step', 'width', 'height', 'N', 'Gini', 'AgentID',\n",
      "       'Wealth'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "results_df = pd.DataFrame(results)\n",
    "print(results_df.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "079bca0c-d383-4a0d-8a6f-9af87fb9c8a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f2840d89eb0>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "results_filtered = results_df[(results_df.AgentID == 0) & (results_df.Step == 100)]\n",
    "N_values = results_filtered.N.values\n",
    "gini_values = results_filtered.Gini.values\n",
    "plt.scatter(N_values, gini_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7e9867f4-1f2b-4a4c-a5d9-3f0888f5371c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize the grid\n",
    "grid = CanvasGrid(agent_portrayal, 10, 10, 500, 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "57f9df44-a381-44a5-8028-a650c6f0adf6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Interface starting at http://127.0.0.1:8522\n"
     ]
    },
    {
     "ename": "RuntimeError",
     "evalue": "This event loop is already running",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "\u001b[0;32m/tmp/ipykernel_109810/2425018795.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mserver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mport\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m8522\u001b[0m \u001b[0;31m# The default\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 11\u001b[0;31m \u001b[0mserver\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlaunch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/mamba/envs/spatial-analytics/lib/python3.9/site-packages/mesa/visualization/ModularVisualization.py\u001b[0m in \u001b[0;36mlaunch\u001b[0;34m(self, port, open_browser)\u001b[0m\n\u001b[1;32m    335\u001b[0m             \u001b[0mwebbrowser\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mopen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0murl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    336\u001b[0m         \u001b[0mtornado\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mautoreload\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 337\u001b[0;31m         \u001b[0mtornado\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mioloop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mIOLoop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/.conda/envs/mamba/envs/spatial-analytics/lib/python3.9/site-packages/tornado/platform/asyncio.py\u001b[0m in \u001b[0;36mstart\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    197\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_setup_logging\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m             \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_event_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masyncio_loop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 199\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masyncio_loop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_forever\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    200\u001b[0m         \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    201\u001b[0m             \u001b[0masyncio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_event_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mold_loop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/mamba/envs/spatial-analytics/lib/python3.9/asyncio/base_events.py\u001b[0m in \u001b[0;36mrun_forever\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    584\u001b[0m         \u001b[0;34m\"\"\"Run until stop() is called.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    585\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_closed\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 586\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    587\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_coroutine_origin_tracking\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_debug\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_thread_id\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mthreading\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_ident\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.conda/envs/mamba/envs/spatial-analytics/lib/python3.9/asyncio/base_events.py\u001b[0m in \u001b[0;36m_check_running\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    576\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_check_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    577\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mis_running\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 578\u001b[0;31m             \u001b[0;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'This event loop is already running'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    579\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mevents\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_running_loop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    580\u001b[0m             raise RuntimeError(\n",
      "\u001b[0;31mRuntimeError\u001b[0m: This event loop is already running"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Socket opened!\n",
      "{\"type\":\"reset\"}\n",
      "{\"type\":\"get_step\",\"step\":1}\n",
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      "{\"type\":\"get_step\",\"step\":217}\n",
      "{\"type\":\"get_step\",\"step\":218}\n",
      "{\"type\":\"get_step\",\"step\":219}\n",
      "{\"type\":\"get_step\",\"step\":220}\n",
      "{\"type\":\"get_step\",\"step\":221}\n",
      "{\"type\":\"get_step\",\"step\":222}\n",
      "{\"type\":\"get_step\",\"step\":223}\n",
      "{\"type\":\"get_step\",\"step\":224}\n",
      "{\"type\":\"get_step\",\"step\":225}\n",
      "{\"type\":\"get_step\",\"step\":226}\n",
      "{\"type\":\"get_step\",\"step\":227}\n",
      "{\"type\":\"get_step\",\"step\":228}\n",
      "{\"type\":\"get_step\",\"step\":229}\n",
      "{\"type\":\"get_step\",\"step\":230}\n",
      "{\"type\":\"get_step\",\"step\":231}\n",
      "{\"type\":\"get_step\",\"step\":232}\n",
      "{\"type\":\"get_step\",\"step\":233}\n",
      "{\"type\":\"get_step\",\"step\":234}\n"
     ]
    }
   ],
   "source": [
    "chart = ChartModule([{\"Label\": \"Gini\",\n",
    "                      \"Color\": \"Black\"}],\n",
    "                    data_collector_name='datacollector')\n",
    "\n",
    "server = ModularServer(MoneyModel,\n",
    "                       [grid, chart],\n",
    "                       \"Money Model\",\n",
    "                       {\"N\":100, \"width\":10, \"height\":10})\n",
    "\n",
    "server.port = 8522 # The default\n",
    "server.launch();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e8d82107-c333-4a6b-b149-4e4e96243508",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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