{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running simulations using Pop2net\n",
    "\n",
    "In the previous part of this introduction, we have learned how to use Pop2net to build, evaluate and export networks.\n",
    "In the following part of this introduction, we will learn how to use Pop2net to directly run a simulation based on the defined network.\n",
    "In this simple simulation example, we do not use any additional package to run the simulation, but only Pop2net.\n",
    "However, it is possible (and recommended) to use Pop2net in combination with a dedicated simulation framework, like Mesa or AgentPy.\n",
    "See chapter *Integration into ABM frameworks* for examples of how to integrate Pop2net into Mesa and ActorPy.\n",
    "\n",
    "In the following, we programme a simple infection simulation as an example of how to use pop2net to run simulations."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "nbsphinx": "hidden"
   },
   "outputs": [],
   "source": [
    "# this is a hidden cell which sets seeds for consistent outputs\n",
    "\n",
    "import random\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "random.seed(0)\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DataFaker\n",
    "\n",
    "In the previous example, we used example data to create the population of actors.\n",
    "In this example, we also use *fake* data, but this time we use a data generator provided by Pop2net to generate the data.\n",
    "The DataFaker creates a data set of any size, which is similar to a classic survey data set.\n",
    "`pop2net.data_fakers.soep_faker` creates data similiar to the [German Socio-Economic Panel](https://www.diw.de/en/diw_01.c.678568.en/research_data_center_soep.html).\n",
    "\n",
    "We start by creating a dataset of 1000 rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>work_hours_day</th>\n",
       "      <th>nace2_division</th>\n",
       "      <th>hid</th>\n",
       "      <th>pid</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>74.0</td>\n",
       "      <td>female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2</td>\n",
       "      <td>1434</td>\n",
       "      <td>2344</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>31.0</td>\n",
       "      <td>female</td>\n",
       "      <td>7.473384</td>\n",
       "      <td>87</td>\n",
       "      <td>1434</td>\n",
       "      <td>695</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>24.0</td>\n",
       "      <td>male</td>\n",
       "      <td>0.396244</td>\n",
       "      <td>1</td>\n",
       "      <td>1434</td>\n",
       "      <td>2077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>77.0</td>\n",
       "      <td>female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2</td>\n",
       "      <td>5711</td>\n",
       "      <td>346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>26.0</td>\n",
       "      <td>female</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>-2</td>\n",
       "      <td>5711</td>\n",
       "      <td>5288</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    age  gender  work_hours_day  nace2_division   hid   pid\n",
       "0  74.0  female        0.000000              -2  1434  2344\n",
       "1  31.0  female        7.473384              87  1434   695\n",
       "2  24.0    male        0.396244               1  1434  2077\n",
       "3  77.0  female        0.000000              -2  5711   346\n",
       "4  26.0  female        0.000000              -2  5711  5288"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pop2net as p2n\n",
    "from pop2net.data_fakers.soep import soep_faker\n",
    "\n",
    "df_soep = soep_faker.soep(size=1000, seed=1000000)\n",
    "df_soep.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating a simulation\n",
    "\n",
    "Let's start with designing the network by defining 4 location types:\n",
    "A home `Home`, to which all actors of a household are assigned, a workplace `Work`, which groups the actors according to their occupational sectors, and a school `School`, which contains age-specific classes.\n",
    "To ensure that each actor has contact with at least two other actors, we also define the location `Ring`, which connects the entire population along a ring."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import networkx as nx\n",
    "\n",
    "\n",
    "class Home(p2n.LocationDesigner):\n",
    "    def split(self, actor):\n",
    "        return actor.hid\n",
    "\n",
    "    def weight(self, actor):\n",
    "        return 12\n",
    "\n",
    "\n",
    "class Work(p2n.LocationDesigner):\n",
    "    n_actors = 10\n",
    "\n",
    "    def filter(self, actor):\n",
    "        return True if actor.work_hours_day > 0 and actor.nace2_division > 0 else False\n",
    "\n",
    "    def split(self, actor):\n",
    "        return actor.nace2_division\n",
    "\n",
    "    def weight(self, actor):\n",
    "        return actor.work_hours_day\n",
    "\n",
    "\n",
    "class School(p2n.LocationDesigner):\n",
    "    n_actors = 25\n",
    "\n",
    "    def filter(self, actor):\n",
    "        return True if 6 <= actor.age <= 18 else False\n",
    "\n",
    "    def split(self, actor):\n",
    "        return actor.age\n",
    "\n",
    "    def weight(self, actor):\n",
    "        return 6\n",
    "\n",
    "\n",
    "class Ring(p2n.LocationDesigner):\n",
    "    def setup(self):\n",
    "        self.nxgraph = nx.cycle_graph(n=len(self.model.env.actors))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we create our actor class with the attributes `infection_status` and `days_since_infection` as well as the methods `infect` and `update_infection_status`.\n",
    "\n",
    "What is relevant here is how the actor accesses other actors with which it is in contact.\n",
    "This is done using the `actor.neighbors()` method.\n",
    "On the other hand, it is relevant how the actor determines *how much* contact it has or has had with the other actors.\n",
    "This is done using the method `actor.get_actor_weight()`.\n",
    "Both methods include all locations that an actor visits."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import random\n",
    "\n",
    "\n",
    "class Actor(p2n.Actor):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.infection_status = \"susceptible\"\n",
    "        self.days_since_infection = 0\n",
    "\n",
    "    def infect(self):\n",
    "        if self.infection_status in [\"infectious\", \"symptomatic\"]:\n",
    "            for actor_v in self.neighbors():\n",
    "                if actor_v.infection_status == \"susceptible\":\n",
    "                    infection_probability = 0.01 * self.get_actor_weight(actor_v)\n",
    "\n",
    "                    if random.random() < infection_probability:\n",
    "                        actor_v.infection_status = \"exposed\"\n",
    "\n",
    "    def update_infection_status(self):\n",
    "        if self.infection_status in [\"exposed\", \"infectious\", \"symptomatic\"]:\n",
    "            self.days_since_infection += 1\n",
    "\n",
    "            if 2 <= self.days_since_infection <= 5:\n",
    "                self.infection_status = \"infectious\"\n",
    "\n",
    "            elif 6 <= self.days_since_infection <= 10:\n",
    "                self.infection_status = \"symptomatic\"\n",
    "\n",
    "            elif self.days_since_infection >= 11:\n",
    "                self.infection_status = \"recovered\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the simulation model.\n",
    "In the `__init__()` method we initialize environment and creator objects and some lists to save the data.\n",
    "Subsequently, we create the network and infect 10 randomly chosen agents.\n",
    "\n",
    "In `step()` we define what should happen in each individual time step of the simulation.\n",
    "First, the method `update_infection_status()` is to be executed for each actor and then the method `infect()` for each actor.\n",
    "Afterwards, we collect the number of actors ever infected per time step in order to visualize it after the simulation.\n",
    "\n",
    "The method `run` just executes the method `step` for a specific amount of steps."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InfectionModel:\n",
    "    def __init__(self):\n",
    "        self.env = p2n.Environment(model=self)\n",
    "        self.creator = p2n.Creator(env=self.env)\n",
    "        self.n_infected_actors = []\n",
    "        self.n_cumulative_infections = []\n",
    "\n",
    "        # Create actors and locations and add them to the environment\n",
    "        self.creator.create(\n",
    "            df=df_soep,\n",
    "            actor_class=Actor,\n",
    "            location_designers=[\n",
    "                Home,\n",
    "                Work,\n",
    "                School,\n",
    "                Ring,\n",
    "            ],\n",
    "        )\n",
    "\n",
    "        # infect 10 random actors\n",
    "        for actor in random.choices(self.env.actors, k=10):\n",
    "            actor.infection_status = \"exposed\"\n",
    "\n",
    "        self.inspector = p2n.NetworkInspector(env=self.env)\n",
    "\n",
    "    def step(self):\n",
    "        for actor in self.env.actors:\n",
    "            actor.update_infection_status()\n",
    "\n",
    "        for actor in self.env.actors:\n",
    "            actor.infect()\n",
    "\n",
    "        n_currently_infected_actors = sum(\n",
    "            [\n",
    "                1\n",
    "                for actor in self.env.actors\n",
    "                if actor.infection_status in [\"infectious\", \"symptomatic\"]\n",
    "            ]\n",
    "        )\n",
    "        self.n_infected_actors.append(n_currently_infected_actors)\n",
    "        self.n_cumulative_infections.append(\n",
    "            sum([1 for agent in self.env.actors if agent.infection_status != \"susceptible\"])\n",
    "        )\n",
    "\n",
    "    def run(self, steps=1):\n",
    "        for s in range(steps):\n",
    "            self.step()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's execute the model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = InfectionModel()\n",
    "results = model.run(steps=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Below, you can see the number of infections per time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Infections')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data1 = model.n_cumulative_infections\n",
    "plt.plot(data1)\n",
    "plt.grid()\n",
    "plt.xlabel(\"Tick\")\n",
    "plt.ylabel(\"Infections\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just because we can, let's have look at the network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(null);\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? 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       <tr style='display:@{start_display};'>\\n        \\n        <td style='text-align: right;color: #4682b4;'>start</td>\\n        <td>:</td>\\n        <td style='text-align: left;'>@start</td>\\n        \\n        </tr>\\n        \\n        <tr style='display:@{end_display};'>\\n        \\n        <td style='text-align: right;color: #4682b4;'>end</td>\\n        <td>:</td>\\n        <td style='text-align: left;'>@end</td>\\n        \\n        </tr>\\n        \\n        <tr style='display:@{weight_display};'>\\n        \\n        <td style='text-align: right;color: #4682b4;'>weight</td>\\n        <td>:</td>\\n        <td style='text-align: left;'>@weight</td>\\n        \\n        </tr>\\n        \\n        <tr style='display:@{index_display};'>\\n        \\n        <td style='text-align: right;color: #4682b4;'>index</td>\\n        <td>:</td>\\n        <td style='text-align: left;'>@index</td>\\n        \\n        </tr>\\n        \\n        <tr style='display:@{type_display};'>\\n        \\n        <td style='text-align: right;color: #4682b4;'>type</td>\\n        <td>:</td>\\n        <td style='text-align: left;'>@type</td>\\n        \\n        </tr>\\n        \\n        <tr style='display:@{label_display};'>\\n        \\n        <td style='text-align: right;color: #4682b4;'>label</td>\\n        <td>:</td>\\n        <td style='text-align: left;'>@label</td>\\n        \\n        </tr>\\n        </table></div>\",\"line_policy\":\"interp\"}}],\"logo\":null}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1017\",\"attributes\":{\"visible\":false,\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1018\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1019\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1020\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p1012\",\"attributes\":{\"visible\":false,\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p1013\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p1014\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p1015\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1016\",\"attributes\":{\"axis\":{\"id\":\"p1012\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1021\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1017\"},\"grid_line_color\":null}}]}}]}};\n  const render_items = [{\"docid\":\"ce27d470-9a41-4e6c-8fae-4d1bbec5b409\",\"roots\":{\"p1001\":\"eb060ff4-e54c-4fb3-bec6-455cfa8d969f\"},\"root_ids\":[\"p1001\"],\"notebook_comms_target\":\"p1054\"}];\n  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p1001"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.inspector.plot_bipartite_network()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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If loading BokehJS from CDN, this \\n\"+\n     \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n     \"</p>\\n\"+\n     \"<ul>\\n\"+\n     \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n     \"<li>use INLINE resources instead, as so:</li>\\n\"+\n     \"</ul>\\n\"+\n     \"<code>\\n\"+\n     \"from bokeh.resources import INLINE\\n\"+\n     \"output_notebook(resources=INLINE)\\n\"+\n     \"</code>\\n\"+\n     \"</div>\"}};\n\n  function display_loaded(error = null) {\n    const el = document.getElementById(null);\n    if (el != null) {\n      const html = (() => {\n        if (typeof root.Bokeh === \"undefined\") {\n          if (error == null) {\n            return \"BokehJS is loading ...\";\n          } else {\n            return \"BokehJS failed to load.\";\n          }\n        } else {\n          const prefix = `BokehJS ${root.Bokeh.version}`;\n          if (error == null) {\n            return `${prefix} successfully loaded.`;\n          } else {\n            return `${prefix} <b>encountered errors</b> while loading and may not function as expected.`;\n          }\n        }\n      })();\n      el.innerHTML = html;\n\n      if (error != null) {\n        const wrapper = document.createElement(\"div\");\n        wrapper.style.overflow = \"auto\";\n        wrapper.style.height = \"5em\";\n        wrapper.style.resize = \"vertical\";\n        const content = document.createElement(\"div\");\n        content.style.fontFamily = \"monospace\";\n        content.style.whiteSpace = \"pre-wrap\";\n        content.style.backgroundColor = \"rgb(255, 221, 221)\";\n        content.textContent = error.stack ?? 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 const render_items = [{\"docid\":\"9ebbad9b-cd1d-4c8f-a2d3-225e7776e685\",\"roots\":{\"p3442\":\"aabb76d4-6a83-45bb-82ed-e18d47c88dad\"},\"root_ids\":[\"p3442\"],\"notebook_comms_target\":\"p3495\"}];\n  void root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n  }\n  if (root.Bokeh !== undefined) {\n    embed_document(root);\n  } else {\n    let attempts = 0;\n    const timer = setInterval(function(root) {\n      if (root.Bokeh !== undefined) {\n        clearInterval(timer);\n        embed_document(root);\n      } else {\n        attempts++;\n        if (attempts > 100) {\n          clearInterval(timer);\n          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n        }\n      }\n    }, 10, root)\n  }\n})(window);",
      "application/vnd.bokehjs_exec.v0+json": ""
     },
     "metadata": {
      "application/vnd.bokehjs_exec.v0+json": {
       "id": "p3442"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.inspector.plot_actor_network()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Simulations on dynamic networks\n",
    "\n",
    "Up to now, we have had purely static networks that have not changed once they have been created.\n",
    "In pop2net, there are two ways to change the networks during the simulation.\n",
    "Firstly, actors can leave and join locations using the location methods `remove_actor()` and `add_actor()`, for instance.\n",
    "If a complete *move* of the actor from one location to another is not necessary, the connection weights between the actor and the location can also simply be varied.\n",
    "In the following, we will have a look at this method to create dynamic networks.\n",
    "\n",
    "We now want to add another property to the infection simulation: Actors only visit the school or workplace if they are not currently symptomatically ill.\n",
    "In order to exclude actors who are symptomatically ill from schools and workplaces, we adapt the `weight()` method in each case.\n",
    "As the weights can now change in every time step, we have to make sure that the weights are updated in every time step.\n",
    "Therefore we add the line `self.env.update_weights(location_labels=[\"Home\", \"School\", \"Work\"])` to `InfectionModel.step()` which updates the edge weights between all actors and locations of the types `Home`, `School` and `Work` in every time step."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Home(p2n.LocationDesigner):\n",
    "    def split(self, actor):\n",
    "        return actor.hid\n",
    "\n",
    "    def weight(self, actor):\n",
    "        if actor.infection_status == \"symptomatic\":\n",
    "            return 3\n",
    "        else:\n",
    "            return 12\n",
    "\n",
    "\n",
    "class Work(p2n.LocationDesigner):\n",
    "    n_actors = 10\n",
    "\n",
    "    def filter(self, actor):\n",
    "        return True if actor.work_hours_day > 0 and actor.nace2_division > 0 else False\n",
    "\n",
    "    def split(self, actor):\n",
    "        return actor.nace2_division\n",
    "\n",
    "    def weight(self, actor):\n",
    "        if actor.infection_status == \"symptomatic\":\n",
    "            return 0\n",
    "        else:\n",
    "            return actor.work_hours_day\n",
    "\n",
    "\n",
    "class School(p2n.LocationDesigner):\n",
    "    n_actors = 25\n",
    "\n",
    "    def filter(self, actor):\n",
    "        return True if 6 <= actor.age <= 18 else False\n",
    "\n",
    "    def split(self, actor):\n",
    "        return actor.age\n",
    "\n",
    "    def weight(self, actor):\n",
    "        if actor.infection_status == \"symptomatic\":\n",
    "            return 0\n",
    "        else:\n",
    "            return 6\n",
    "\n",
    "\n",
    "class Ring(p2n.LocationDesigner):\n",
    "    def setup(self):\n",
    "        self.nxgraph = nx.cycle_graph(n=len(self.model.env.actors))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "class InfectionModel:\n",
    "    def __init__(self):\n",
    "        self.env = p2n.Environment(model=self)\n",
    "        self.creator = p2n.Creator(env=self.env)\n",
    "        self.n_infected_actors = []\n",
    "        self.n_cumulative_infections = []\n",
    "\n",
    "        # Create actors and locations and add them to the environment\n",
    "        self.creator.create(\n",
    "            df=df_soep,\n",
    "            actor_class=Actor,\n",
    "            location_designers=[\n",
    "                Home,\n",
    "                Work,\n",
    "                School,\n",
    "                Ring,\n",
    "            ],\n",
    "        )\n",
    "\n",
    "        # infect 10 random actors\n",
    "        for actor in random.choices(self.env.actors, k=10):\n",
    "            actor.infection_status = \"exposed\"\n",
    "\n",
    "        self.inspector = p2n.NetworkInspector(env=self.env)\n",
    "\n",
    "    def step(self):\n",
    "        self.env.update_weights(location_labels=[\"Home\", \"School\", \"Work\"])\n",
    "\n",
    "        for actor in self.env.actors:\n",
    "            actor.update_infection_status()\n",
    "\n",
    "        for actor in self.env.actors:\n",
    "            actor.infect()\n",
    "\n",
    "        n_currently_infected_actors = sum(\n",
    "            [\n",
    "                1\n",
    "                for actor in self.env.actors\n",
    "                if actor.infection_status in [\"infectious\", \"symptomatic\"]\n",
    "            ]\n",
    "        )\n",
    "        self.n_infected_actors.append(n_currently_infected_actors)\n",
    "\n",
    "        self.n_cumulative_infections.append(\n",
    "            sum([1 for agent in self.env.actors if agent.infection_status != \"susceptible\"])\n",
    "        )\n",
    "\n",
    "    def run(self, steps=1):\n",
    "        for s in range(steps):\n",
    "            self.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Actor(p2n.Actor):\n",
    "    def __init__(self, *args, **kwargs):\n",
    "        super().__init__(*args, **kwargs)\n",
    "        self.infection_status = \"susceptible\"\n",
    "        self.days_since_infection = 0\n",
    "\n",
    "    def infect(self):\n",
    "        if self.infection_status in [\"infectious\", \"symptomatic\"]:\n",
    "            for actor_v in self.neighbors():\n",
    "                if actor_v.infection_status == \"susceptible\":\n",
    "                    contact_weight = self.get_actor_weight(actor_v)\n",
    "                    infection_probability = 0.01 * contact_weight\n",
    "\n",
    "                    if random.random() < infection_probability:\n",
    "                        actor_v.infection_status = \"exposed\"\n",
    "\n",
    "    def update_infection_status(self):\n",
    "        if self.infection_status in [\"exposed\", \"infectious\", \"symptomatic\"]:\n",
    "            self.days_since_infection += 1\n",
    "\n",
    "            if 1 <= self.days_since_infection <= 2:\n",
    "                self.infection_status = \"infectious\"\n",
    "\n",
    "            elif 3 <= self.days_since_infection <= 10:\n",
    "                self.infection_status = \"symptomatic\"\n",
    "\n",
    "            elif self.days_since_infection >= 11:\n",
    "                self.infection_status = \"recovered\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = InfectionModel()\n",
    "results = model.run(steps=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data2 = model.n_cumulative_infections\n",
    "\n",
    "plt.plot(data1, label=\"Simulation 1\")\n",
    "plt.plot(data2, label=\"Simulation 2\")\n",
    "plt.xlabel(\"Tick\")\n",
    "plt.ylabel(\"Infections\")\n",
    "plt.legend()\n",
    "plt.grid()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pop2net-py3.12",
   "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": "3.12.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}