{ "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", "To perform simulations, Pop2net relies on [AgentPy](https://agentpy.readthedocs.io/en/latest/#) - a general ABM framework.\n", "We recommend you to familiarize yourself with AgentPy first, as only some aspects of AgentPy are covered in this introduction.\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 agents.\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": [ "
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" ], "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": [ "## Designing the simulation\n", "\n", "The object class `Model` is the key component of implementing simulation models using pop2net (or AgentPy).\n", "The class `Model` integrates all elements of the simulation and determines the simulation sequences.\n", "\n", "`Model` has four core methods that structure the processes of the simulation model:\n", "\n", "1. `Model.setup()`\n", "2. `Model.step()`\n", "3. `Model.update()`\n", "4. `Model.end()`\n", "\n", "Please refer to the [AgentPy documentation](https://agentpy.readthedocs.io/en/latest/reference_model.html) for more detailed information on those methods." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In addition to an *empty* `Agent` class and an *empty* `LocationDesigner` class, we define our first own `Model` class below.\n", "First we define the method `setup()`, which is executed according to the AgentPy logic at the beginning of the simulation.\n", "Within the `setup()` method, we do nothing more than create a `Creator` as usual and then create agents and locations." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "class Agent(p2n.Agent):\n", " pass\n", "\n", "\n", "class Home(p2n.LocationDesigner):\n", " pass\n", "\n", "\n", "class InfectionModel(p2n.Model):\n", " def setup(self):\n", " self.creator = p2n.Creator(model=self)\n", " self.creator.create(\n", " df=df_soep,\n", " agent_class=Agent,\n", " location_designers=[Home],\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we execute the simulation model.\n", "Again, please refer to the [AgentPy documentation](https://agentpy.readthedocs.io/en/latest/overview.html#running-a-simulation) if needed." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Completed: 50 steps\n", "Run time: 0:00:00.694835\n", "Simulation finished\n" ] } ], "source": [ "model = InfectionModel()\n", "results = model.run(steps=50)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simulation has now run, but practically nothing has happened except that the agents and locations have been created." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "AgentList (1000 objects)" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.agents" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "LocationList (1 object)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "model.locations" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we add some code to make the model a working infection simulation.\n", "We add the attributes `infection_status` and `days_since_infection` as well as the methods `infect` and `update_infection_status` to the agent.\n", "\n", "What is relevant here is how the agent accesses other agents with which it is in contact.\n", "This is done using the `agent.neighbors()` method.\n", "On the other hand, it is relevant how the agent determines *how much* contact it has or has had with the other agents.\n", "This is done using the method `agent.get_agent_weight()`.\n", "Both methods include all locations that an agent visits." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "import random\n", "\n", "\n", "class Agent(p2n.Agent):\n", " def __init__(self, model, *args, **kwargs):\n", " super().__init__(model, *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 agent_v in self.neighbors():\n", " if agent_v.infection_status == \"susceptible\":\n", " infection_probability = 0.01 * self.get_agent_weight(agent_v)\n", "\n", " if random.random() < infection_probability:\n", " agent_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 4 locations.\n", "A home `Home`, to which all agents of a household are assigned, a workplace `Work`, which groups the agents according to their occupational sectors, and a school `School`, which contains age-specific classes.\n", "To ensure that each agent has contact with at least two other agents, we also define the location `Ring`, which connects the entire population along a ring using the predefined pop2net class `RingLocation`." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "import networkx as nx\n", "\n", "\n", "class Home(p2n.LocationDesigner):\n", " def split(self, agent):\n", " return agent.hid\n", "\n", " def weight(self, agent):\n", " return 12\n", "\n", "\n", "class Work(p2n.LocationDesigner):\n", " n_agents = 10\n", "\n", " def filter(self, agent):\n", " return True if agent.work_hours_day > 0 and agent.nace2_division > 0 else False\n", "\n", " def split(self, agent):\n", " return agent.nace2_division\n", "\n", " def weight(self, agent):\n", " return agent.work_hours_day\n", "\n", "\n", "class School(p2n.LocationDesigner):\n", " n_agents = 25\n", "\n", " def filter(self, agent):\n", " return True if 6 <= agent.age <= 18 else False\n", "\n", " def split(self, agent):\n", " return agent.age\n", "\n", " def weight(self, agent):\n", " return 6\n", "\n", "\n", "class Ring(p2n.LocationDesigner):\n", " def setup(self):\n", " self.nxgraph = nx.cycle_graph(n=len(self.model.agents))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We extend the `InfectionModel` so that 10 random agents are infected at the start of the simulation (`setup()`).\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 agent and then the method `infect()` for each agent.\n", "If you are wondering about the strange syntax, have a look [here](https://agentpy.readthedocs.io/en/latest/overview.html#agent-sequences).\n", "The method `update()` is also executed in each time step, but always after the method `step()`.\n", "In `update()` we collect the number of agents ever infected per time step in order to visualize it after the simulation." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "class InfectionModel(p2n.Model):\n", " def setup(self):\n", " self.creator = p2n.Creator(model=self)\n", " self.creator.create(\n", " df=df_soep,\n", " agent_class=Agent,\n", " location_designers=[\n", " Home,\n", " Work,\n", " School,\n", " Ring,\n", " ],\n", " )\n", "\n", " for agent in random.choices(self.agents, k=10):\n", " agent.infection_status = \"exposed\"\n", "\n", " self.inspector = p2n.NetworkInspector(model=self)\n", "\n", " def step(self):\n", " self.agents.update_infection_status()\n", " self.agents.infect()\n", "\n", " def update(self):\n", " self.record(\n", " \"cumulative_infections\",\n", " sum([1 for agent in self.agents if agent.infection_status != \"susceptible\"]),\n", " )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's execute the model:" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Completed: 50 steps\n", "Run time: 0:00:08.946991\n", "Simulation finished\n" ] } ], "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": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0, 0.5, 'Infections')" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "data1 = results.variables.InfectionModel.cumulative_infections\n", "\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": 12, "metadata": {}, "outputs": [ { "data": { "application/javascript": "'use strict';\n(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\nconst JS_MIME_TYPE = 'application/javascript';\n const HTML_MIME_TYPE = 'text/html';\n const EXEC_MIME_TYPE = 'application/vnd.bokehjs_exec.v0+json';\n const CLASS_NAME = 'output_bokeh rendered_html';\n\n /**\n * Render data to the DOM node\n */\n function render(props, node) {\n const script = document.createElement(\"script\");\n node.appendChild(script);\n }\n\n /**\n * Handle when an output is cleared or removed\n */\n function handleClearOutput(event, handle) {\n function drop(id) {\n const view = Bokeh.index.get_by_id(id)\n if (view != null) {\n view.model.document.clear()\n Bokeh.index.delete(view)\n }\n }\n\n const cell = handle.cell;\n\n const id = cell.output_area._bokeh_element_id;\n const server_id = cell.output_area._bokeh_server_id;\n\n // Clean up Bokeh references\n if (id != null) {\n drop(id)\n }\n\n if (server_id !== undefined) {\n // Clean up Bokeh references\n const cmd_clean = \"from bokeh.io.state import curstate; print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; i < script_attrs.length; i++) {\n toinsert[toinsert.length - 1].firstChild.setAttribute(script_attrs[i].name, script_attrs[i].value);\n toinsert[toinsert.length - 1].firstChild.textContent = bk_div.children[0].textContent\n }\n // store reference to server id on output_area\n output_area._bokeh_server_id = output.metadata[EXEC_MIME_TYPE][\"server_id\"];\n }\n }\n\n function register_renderer(events, OutputArea) {\n\n function append_mime(data, metadata, element) {\n // create a DOM node to render to\n const toinsert = this.create_output_subarea(\n metadata,\n CLASS_NAME,\n EXEC_MIME_TYPE\n );\n this.keyboard_manager.register_events(toinsert);\n // Render to node\n const props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n render(props, toinsert[toinsert.length - 1]);\n element.append(toinsert);\n return toinsert\n }\n\n /* Handle when an output is cleared or removed */\n events.on('clear_output.CodeCell', handleClearOutput);\n events.on('delete.Cell', handleClearOutput);\n\n /* Handle when a new output is added */\n events.on('output_added.OutputArea', handleAddOutput);\n\n /**\n * Register the mime type and append_mime function with output_area\n */\n OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n /* Is output safe? */\n safe: true,\n /* Index of renderer in `output_area.display_order` */\n index: 0\n });\n }\n\n // register the mime type if in Jupyter Notebook environment and previously unregistered\n if (root.Jupyter !== undefined) {\n const events = require('base/js/events');\n const OutputArea = require('notebook/js/outputarea').OutputArea;\n\n if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n register_renderer(events, OutputArea);\n }\n }\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
start:@start
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index:@index
type:@type
label:@label
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print(curstate().uuid_to_server['\" + server_id + \"'].get_sessions()[0].document.roots[0]._id)\";\n cell.notebook.kernel.execute(cmd_clean, {\n iopub: {\n output: function(msg) {\n const id = msg.content.text.trim()\n drop(id)\n }\n }\n });\n // Destroy server and session\n const cmd_destroy = \"import bokeh.io.notebook as ion; ion.destroy_server('\" + server_id + \"')\";\n cell.notebook.kernel.execute(cmd_destroy);\n }\n }\n\n /**\n * Handle when a new output is added\n */\n function handleAddOutput(event, handle) {\n const output_area = handle.output_area;\n const output = handle.output;\n\n // limit handleAddOutput to display_data with EXEC_MIME_TYPE content only\n if ((output.output_type != \"display_data\") || (!Object.prototype.hasOwnProperty.call(output.data, EXEC_MIME_TYPE))) {\n return\n }\n\n const toinsert = output_area.element.find(\".\" + CLASS_NAME.split(' ')[0]);\n\n if (output.metadata[EXEC_MIME_TYPE][\"id\"] !== undefined) {\n toinsert[toinsert.length - 1].firstChild.textContent = output.data[JS_MIME_TYPE];\n // store reference to embed id on output_area\n output_area._bokeh_element_id = output.metadata[EXEC_MIME_TYPE][\"id\"];\n }\n if (output.metadata[EXEC_MIME_TYPE][\"server_id\"] !== undefined) {\n const bk_div = document.createElement(\"div\");\n bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n const script_attrs = bk_div.children[0].attributes;\n for (let i = 0; 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\\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n \\n
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\",\"line_policy\":\"interp\"}}],\"logo\":null}},\"left\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p3459\",\"attributes\":{\"visible\":false,\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p3460\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p3461\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p3462\"}}}],\"below\":[{\"type\":\"object\",\"name\":\"LinearAxis\",\"id\":\"p3454\",\"attributes\":{\"visible\":false,\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p3455\",\"attributes\":{\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"BasicTickFormatter\",\"id\":\"p3456\"},\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p3457\"}}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p3458\",\"attributes\":{\"axis\":{\"id\":\"p3454\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p3463\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p3459\"},\"grid_line_color\":null}}]}}]}};\n const render_items = [{\"docid\":\"fa2f178f-ac7e-4204-825d-dea6159c5fd9\",\"roots\":{\"p3443\":\"cf7e6269-3749-4da4-b83f-8eddf7d44287\"},\"root_ids\":[\"p3443\"],\"notebook_comms_target\":\"p3496\"}];\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": "p3443" } }, "output_type": "display_data" } ], "source": [ "model.inspector.plot_agent_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, agents can leave and join locations using the location methods `remove_agent()` and `add_agent()`, for instance.\n", "If a complete *move* of the agent from one location to another is not necessary, the connection weights between the agent 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: Agents only visit the school or workplace if they are not currently symptomatically ill.\n", "In order to exclude agents 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.update_weights(location_labels=[\"Home\", \"School\", \"Work\"])` to `model.step()` which updates the edge weights between all agents and locations of the types `Home`, `School` and `Work` in every time step." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "class Home(p2n.LocationDesigner):\n", " def split(self, agent):\n", " return agent.hid\n", "\n", " def weight(self, agent):\n", " if agent.infection_status == \"symptomatic\":\n", " return 3\n", " else:\n", " return 12\n", "\n", "\n", "class Work(p2n.LocationDesigner):\n", " n_agents = 10\n", "\n", " def filter(self, agent):\n", " return True if agent.work_hours_day > 0 and agent.nace2_division > 0 else False\n", "\n", " def split(self, agent):\n", " return agent.nace2_division\n", "\n", " def weight(self, agent):\n", " if agent.infection_status == \"symptomatic\":\n", " return 0\n", " else:\n", " return agent.work_hours_day\n", "\n", "\n", "class School(p2n.LocationDesigner):\n", " n_agents = 25\n", "\n", " def filter(self, agent):\n", " return True if 6 <= agent.age <= 18 else False\n", "\n", " def split(self, agent):\n", " return agent.age\n", "\n", " def weight(self, agent):\n", " if agent.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.agents))" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "import datetime as dt\n", "\n", "\n", "class InfectionModel(p2n.Model):\n", " def setup(self):\n", " self.creator = p2n.Creator(model=self)\n", " self.creator.create(\n", " df=df_soep,\n", " agent_class=Agent,\n", " location_designers=[\n", " Home,\n", " Work,\n", " School,\n", " Ring,\n", " ],\n", " )\n", " self.date = dt.date(2022, 1, 1)\n", "\n", " for agent in random.choices(self.agents, k=10):\n", " agent.infection_status = \"exposed\"\n", "\n", " def step(self):\n", " self.update_weights(location_labels=[\"Home\", \"School\", \"Work\"])\n", " self.agents.update_infection_status()\n", " self.agents.infect()\n", "\n", " def update(self):\n", " self.record(\n", " \"cumulative_infections\",\n", " sum([1 for agent in self.agents if agent.infection_status != \"susceptible\"]),\n", " )" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "class Agent(p2n.Agent):\n", " def __init__(self, model, *args, **kwargs):\n", " super().__init__(model, *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 agent_v in self.neighbors():\n", " if agent_v.infection_status == \"susceptible\":\n", " contact_weight = self.get_agent_weight(agent_v)\n", " infection_probability = 0.01 * contact_weight\n", "\n", " if random.random() < infection_probability:\n", " agent_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": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Completed: 50 steps\n", "Run time: 0:00:08.600417\n", "Simulation finished\n" ] } ], "source": [ "model = InfectionModel()\n", "results = model.run(steps=50)" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "data2 = results.variables.InfectionModel.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": ".venv", "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": "3.13.2" } }, "nbformat": 4, "nbformat_minor": 2 }