abm.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import\n",
    "\n",
    "#Modelling\n",
    "import mesa\n",
    "\n",
    "from mesa import Agent, Model\n",
    "\n",
    "import solara\n",
    "\n",
    "#Analysis\n",
    "from mesa.datacollection import DataCollector\n",
    "from mesa.space import MultiGrid\n",
    "from mesa.time import RandomActivation\n",
    "\n",
    "from mesa.experimental import JupyterViz\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.figure import Figure\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Callable, Optional, Type\n",
    "\n",
    "class RandomActivationByTypeFiltered(mesa.time.RandomActivationByType):\n",
    "    \"\"\"\n",
    "    A scheduler that overrides the get_type_count method to allow for filtering\n",
    "    of agents by a function before counting.\n",
    "\n",
    "    Example:\n",
    "    >>> scheduler = RandomActivationByTypeFiltered(model)\n",
    "    >>> scheduler.get_type_count(AgentA, lambda agent: agent.some_attribute > 10)\n",
    "    \"\"\"\n",
    "\n",
    "    def get_type_count(\n",
    "        self,\n",
    "        type_class: Type[mesa.Agent],\n",
    "        filter_func: Optional[Callable[[mesa.Agent], bool]] = None,\n",
    "    ) -> int:\n",
    "        \"\"\"\n",
    "        Returns the current number of agents of certain type in the queue\n",
    "        that satisfy the filter function.\n",
    "        \"\"\"\n",
    "        count = 0\n",
    "        for agent in self.agents_by_type[type_class].values():\n",
    "            if filter_func is None or filter_func(agent):\n",
    "                count += 1\n",
    "        return count"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Model\n",
    "\n",
    "class BrainModel(Model):\n",
    "    def __init__(self, Nn, Na, width, height):\n",
    "        super().__init__()\n",
    "        self.num_neurons = Nn\n",
    "        self.num_antibody = Na\n",
    "        self.grid = MultiGrid(width, height, True)\n",
    "        self.schedule = RandomActivationByTypeFiltered(self)\n",
    "        self.running = True\n",
    "        self.step_count = 0\n",
    "        self.datacollector = DataCollector(\n",
    "            model_reporters={\n",
    "            \"Total surface NMDARs\": compute_tot_nmdar,\n",
    "            \"Number of Antibodies\": lambda m: m.schedule.get_type_count(AntibAgent)   \n",
    "        },\n",
    "        agent_reporters={\n",
    "            \"Surface NMDAR Conc.\": \"NMDAR\",\n",
    "            \"Agent type\": \"type\"\n",
    "        }, \n",
    "    )\n",
    "        \n",
    "    # Create Neurons\n",
    "        for unique_id in range(self.num_neurons):\n",
    "            x = self.random.randrange(self.grid.width)\n",
    "            y = self.random.randrange(self.grid.height)\n",
    "            \n",
    "            neuron = NeuronAgent(unique_id, self)\n",
    "            self.grid.place_agent(neuron, (x, y))\n",
    "            self.schedule.add(neuron)\n",
    "\n",
    "    #Create Antibodies\n",
    "        for i in range(self.num_antibody):\n",
    "            x = self.random.randrange(self.grid.width)\n",
    "            y = self.random.randrange(self.grid.height)\n",
    "            \n",
    "            antib = AntibAgent((i,x,y), self)\n",
    "            self.grid.place_agent(antib, (x, y))\n",
    "            self.schedule.add(antib) \n",
    "\n",
    "    def step(self):\n",
    "        self.datacollector.collect(self)\n",
    "        self.schedule.step()\n",
    "        self.step_count += 1\n",
    "\n",
    "        for agent in self.schedule.agents[:]:\n",
    "            if hasattr(agent, \"remove\"):\n",
    "                agent.remove()\n",
    "\n",
    "\n",
    "#Agents\n",
    "\n",
    "class NeuronAgent(Agent):\n",
    "    def __init__(self, unique_id, model):\n",
    "        # run the __init__ method of the parent class\n",
    "        super().__init__(unique_id, model)\n",
    "        # additional initialization for the derived class\n",
    "        self.NMDAR = 1.2e12\n",
    "        self.type = \"Neuron\" \n",
    "\n",
    "    def step(self):\n",
    "        if self.model.step_count % random.randint(50,144) == 0 and self.NMDAR < 1.2e12:\n",
    "            self.NMDAR += random.randint(0,1)\n",
    "\n",
    "class AntibAgent(Agent):\n",
    "    def __init__(self, unique_id, model):\n",
    "        # run the __init__ method of the parent class\n",
    "        super().__init__(unique_id, model)\n",
    "        # additional initialization for the derived class\n",
    "        self.NMDAR = 0\n",
    "        self.type = \"Antibody\"\n",
    "        self.energy = 1200     \n",
    "        self.marked_for_removal = False    \n",
    "\n",
    "    def move(self):\n",
    "        possible_steps = self.model.grid.get_neighborhood(\n",
    "            self.pos,  # current position\n",
    "            moore=True,  # including step on the diagonal\n",
    "            include_center=False,  # to remain at current position is not allowed\n",
    "        )\n",
    "        \n",
    "        if possible_steps:\n",
    "            new_position = self.random.choice(possible_steps)\n",
    "            self.model.grid.move_agent(self, new_position)\n",
    "            self.pos = new_position\n",
    "\n",
    "    def step(self):\n",
    "        self.move()\n",
    "        self.energy -= random.randint(0,2)\n",
    "\n",
    "        #if there is a neuron nearby, internalise NMDAR\n",
    "        x, y = self.pos\n",
    "        this_cell = self.model.grid.get_cell_list_contents([self.pos])\n",
    "        neuron = [obj for obj in this_cell if isinstance(obj, NeuronAgent)]\n",
    "        \n",
    "        random_number = random.randint(1, 100) #flytta? in i loopen?\n",
    "\n",
    "        if neuron:\n",
    "             affected_neuron = self.random.choice(neuron)\n",
    "             \n",
    "             if random_number >= 50:\n",
    "                affected_neuron.NMDAR -= 1\n",
    "\n",
    "             # Internalise NMDAR, along with itself\n",
    "             \n",
    "\n",
    "             self.marked_for_removal = True #rätt placering?\n",
    "        \n",
    "        #Death from time\n",
    "        if self.energy <= 0:\n",
    "            self.marked_for_removal = True\n",
    "\n",
    "    def remove(self):\n",
    "        if self.marked_for_removal:\n",
    "            self.model.grid.remove_agent(self)\n",
    "            self.model.schedule.remove(self)\n",
    "\n",
    "\n",
    "def compute_tot_nmdar(model):\n",
    "    \n",
    "    agent_nmdars =  [agent.NMDAR for agent in model.schedule.agents]\n",
    "\n",
    "    tot = sum(agent_nmdars)\n",
    "\n",
    "    return tot    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = BrainModel(1000, 40000, 1000, 1000)\n",
    "\n",
    "for i in range(1000):\n",
    "    model.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
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       "        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>Surface NMDAR Conc.</th>\n",
       "      <th>Agent type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>AgentID</th>\n",
       "      <th></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>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">999</th>\n",
       "      <th>(39990, 983, 159)</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Antibody</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(39991, 434, 916)</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Antibody</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(39995, 698, 361)</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Antibody</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(39996, 82, 798)</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Antibody</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>(39997, 967, 687)</th>\n",
       "      <td>0.000000e+00</td>\n",
       "      <td>Antibody</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>32515260 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        Surface NMDAR Conc. Agent type\n",
       "Step AgentID                                          \n",
       "0    0                         1.200000e+12     Neuron\n",
       "     1                         1.200000e+12     Neuron\n",
       "     2                         1.200000e+12     Neuron\n",
       "     3                         1.200000e+12     Neuron\n",
       "     4                         1.200000e+12     Neuron\n",
       "...                                     ...        ...\n",
       "999  (39990, 983, 159)         0.000000e+00   Antibody\n",
       "     (39991, 434, 916)         0.000000e+00   Antibody\n",
       "     (39995, 698, 361)         0.000000e+00   Antibody\n",
       "     (39996, 82, 798)          0.000000e+00   Antibody\n",
       "     (39997, 967, 687)         0.000000e+00   Antibody\n",
       "\n",
       "[32515260 rows x 2 columns]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agent_df = model.datacollector.get_agent_vars_dataframe()\n",
    "model_df = model.datacollector.get_model_vars_dataframe()\n",
    "\n",
    "model_df\n",
    "agent_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1199999999996631.0\n",
      "24913\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "print(model_df['Total surface NMDARs'].min())\n",
    "print(model_df['Number of Antibodies'].min())\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "\n",
    "ax.plot(model_df['Number of Antibodies'], label = 'Number of Antibodies', color='red')\n",
    "ax.tick_params(axis='y', labelcolor='red')\n",
    "\n",
    "ax2 = ax.twinx()\n",
    "\n",
    "ax2.plot(model_df['Total surface NMDARs'], label = 'Total surface NMDARs', color='blue')\n",
    "ax2.tick_params(axis='y', labelcolor='blue')\n",
    "\n",
    "ax.legend(loc = 'upper left')\n",
    "ax2.legend(loc = 'upper right')\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Surface NMDAR Conc.</th>\n",
       "      <th>Agent type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Step</th>\n",
       "      <th>AgentID</th>\n",
       "      <th></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>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">999</th>\n",
       "      <th>995</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>996</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>997</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>998</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>999</th>\n",
       "      <td>1.200000e+12</td>\n",
       "      <td>Neuron</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1000000 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              Surface NMDAR Conc. Agent type\n",
       "Step AgentID                                \n",
       "0    0               1.200000e+12     Neuron\n",
       "     1               1.200000e+12     Neuron\n",
       "     2               1.200000e+12     Neuron\n",
       "     3               1.200000e+12     Neuron\n",
       "     4               1.200000e+12     Neuron\n",
       "...                           ...        ...\n",
       "999  995             1.200000e+12     Neuron\n",
       "     996             1.200000e+12     Neuron\n",
       "     997             1.200000e+12     Neuron\n",
       "     998             1.200000e+12     Neuron\n",
       "     999             1.200000e+12     Neuron\n",
       "\n",
       "[1000000 rows x 2 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "neuron_df = agent_df.loc[agent_df['Agent type'] == 'Neuron']\n",
    "\n",
    "neuron_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\ndef agent_portrayal(agent):\\n    portrayal = {\"Shape\": \"circle\",\\n                 \"Filled\": \"true\",\\n                 \"Layer\": 0,\\n                 \"Color\": \"blue\",\\n                 \"r\": 0.5}\\n    \\n    if isinstance(agent, AntibAgent):\\n        portrayal[\"Color\"] = \"red\"\\n        portrayal[\"Layer\"] = 1\\n        portrayal[\"r\"] = 0.2\\n\\n    return portrayal\\n\\ngrid = mesa.visualization.CanvasGrid(agent_portrayal, 100, 100, 500, 500)\\n\\nnmdar = mesa.visualization.ChartModule([{\"Label\": \"Total surface NMDARs\", \"Color\": \"Red\"}],\\n                                       data_collector_name=\\'datacollector\\')\\n\\nab = mesa.visualization.ChartModule([{\"Label\": \"Number of Antibodies\", \"Color\": \"Blue\"}],\\n                                       data_collector_name=\\'datacollector\\')\\n\\n\\nserver = mesa.visualization.ModularServer(\\n    BrainModel, \\n    [grid, nmdar, ab], \\n    \"Brain Model\", \\n    {\"Nn\":1000, \"Na\":500, \"width\":1000, \"height\":1000,}\\n)\\n\\nserver.port = 8521\\nserver.launch()\\n'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\n",
    "def agent_portrayal(agent):\n",
    "    portrayal = {\"Shape\": \"circle\",\n",
    "                 \"Filled\": \"true\",\n",
    "                 \"Layer\": 0,\n",
    "                 \"Color\": \"blue\",\n",
    "                 \"r\": 0.5}\n",
    "    \n",
    "    if isinstance(agent, AntibAgent):\n",
    "        portrayal[\"Color\"] = \"red\"\n",
    "        portrayal[\"Layer\"] = 1\n",
    "        portrayal[\"r\"] = 0.2\n",
    "\n",
    "    return portrayal\n",
    "\n",
    "grid = mesa.visualization.CanvasGrid(agent_portrayal, 100, 100, 500, 500)\n",
    "\n",
    "nmdar = mesa.visualization.ChartModule([{\"Label\": \"Total surface NMDARs\", \"Color\": \"Red\"}],\n",
    "                                       data_collector_name='datacollector')\n",
    "\n",
    "ab = mesa.visualization.ChartModule([{\"Label\": \"Number of Antibodies\", \"Color\": \"Blue\"}],\n",
    "                                       data_collector_name='datacollector')\n",
    "\n",
    "\n",
    "server = mesa.visualization.ModularServer(\n",
    "    BrainModel, \n",
    "    [grid, nmdar, ab], \n",
    "    \"Brain Model\", \n",
    "    {\"Nn\":1000, \"Na\":500, \"width\":1000, \"height\":1000,}\n",
    ")\n",
    "\n",
    "server.port = 8521\n",
    "server.launch()\n",
    "\"\"\""
   ]
  }
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