Panel_Mesa_Example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Panel Mesa Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import mesa\n",
    "import plotly.express as px"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib.figure import Figure\n",
    "\n",
    "def mpl_plot(avg):\n",
    "    fig = Figure()\n",
    "    ax = fig.add_subplot()\n",
    "    avg.plot(ax=ax)\n",
    "    return fig\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "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(mesa.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 move(self):\n",
    "        possible_steps = self.model.grid.get_neighborhood(\n",
    "            self.pos, moore=True, include_center=False\n",
    "        )\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",
    "    def step(self):\n",
    "        self.move()\n",
    "        if self.wealth > 0:\n",
    "            self.give_money()\n",
    "\n",
    "\n",
    "class MoneyModel(mesa.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 = mesa.space.MultiGrid(width, height, True)\n",
    "        self.schedule = mesa.time.RandomActivation(self)\n",
    "        self.instant_gini=None\n",
    "        # Create agents\n",
    "        for i in range(self.num_agents):\n",
    "            a = MoneyAgent(i, self)\n",
    "            self.schedule.add(a)\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 = mesa.DataCollector(\n",
    "            model_reporters={\"Gini\": compute_gini}, agent_reporters={\"Wealth\": \"wealth\"}\n",
    "        )\n",
    "\n",
    "    def step(self):\n",
    "        self.datacollector.collect(self)\n",
    "        self.instant_gini = compute_gini(self)\n",
    "        self.schedule.step()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run(n_agents=10,steps=100,width=10,height=20,view=mpl_plot):\n",
    "    model = MoneyModel(n_agents, width, height)\n",
    "    for i in range(steps):\n",
    "        model.step()\n",
    "    \n",
    "    gini = model.datacollector.get_model_vars_dataframe()\n",
    "    \n",
    "    return mpl_plot(gini)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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i < inline_js.length; i++) {\n        inline_js[i].call(root, root.Bokeh);\n      }} else if (Date.now() < root._bokeh_timeout) {\n      setTimeout(run_inline_js, 100);\n    } else if (!root._bokeh_failed_load) {\n      console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n      root._bokeh_failed_load = true;\n    }\n  }\n\n  if (root._bokeh_is_loading === 0) {\n    console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n    run_inline_js();\n  } else {\n    load_libs(css_urls, js_urls, js_modules, function() {\n      console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n      run_inline_js();\n    });\n  }\n}(window));"
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document.createElement(\"div\");\n    bk_div.innerHTML = output.data[HTML_MIME_TYPE];\n    var script_attrs = bk_div.children[0].attributes;\n    for (var i = 0; i < script_attrs.length; i++) {\n      toinsert[toinsert.length - 1].childNodes[1].setAttribute(script_attrs[i].name, script_attrs[i].value);\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/**\n * Handle when an output is cleared or removed\n */\nfunction handle_clear_output(event, handle) {\n  var id = handle.cell.output_area._hv_plot_id;\n  var server_id = handle.cell.output_area._bokeh_server_id;\n  if (((id === undefined) || !(id in PyViz.plot_index)) && (server_id !== undefined)) { return; }\n  var comm = window.PyViz.comm_manager.get_client_comm(\"hv-extension-comm\", \"hv-extension-comm\", function () {});\n  if (server_id !== null) {\n    comm.send({event_type: 'server_delete', 'id': server_id});\n    return;\n  } else if (comm !== null) {\n    comm.send({event_type: 'delete', 'id': id});\n  }\n  delete PyViz.plot_index[id];\n  if ((window.Bokeh !== undefined) & (id in window.Bokeh.index)) {\n    var doc = window.Bokeh.index[id].model.document\n    doc.clear();\n    const i = window.Bokeh.documents.indexOf(doc);\n    if (i > -1) {\n      window.Bokeh.documents.splice(i, 1);\n    }\n  }\n}\n\n/**\n * Handle kernel restart event\n */\nfunction handle_kernel_cleanup(event, handle) {\n  delete PyViz.comms[\"hv-extension-comm\"];\n  window.PyViz.plot_index = {}\n}\n\n/**\n * Handle update_display_data messages\n */\nfunction handle_update_output(event, handle) {\n  handle_clear_output(event, {cell: {output_area: handle.output_area}})\n  handle_add_output(event, handle)\n}\n\nfunction register_renderer(events, OutputArea) {\n  function append_mime(data, metadata, element) {\n    // create a DOM node to render to\n    var 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    var props = {data: data, metadata: metadata[EXEC_MIME_TYPE]};\n    render(props, toinsert[0]);\n    element.append(toinsert);\n    return toinsert\n  }\n\n  events.on('output_added.OutputArea', handle_add_output);\n  events.on('output_updated.OutputArea', handle_update_output);\n  events.on('clear_output.CodeCell', handle_clear_output);\n  events.on('delete.Cell', handle_clear_output);\n  events.on('kernel_ready.Kernel', handle_kernel_cleanup);\n\n  OutputArea.prototype.register_mime_type(EXEC_MIME_TYPE, append_mime, {\n    safe: true,\n    index: 0\n  });\n}\n\nif (window.Jupyter !== undefined) {\n  try {\n    var events = require('base/js/events');\n    var OutputArea = require('notebook/js/outputarea').OutputArea;\n    if (OutputArea.prototype.mime_types().indexOf(EXEC_MIME_TYPE) == -1) {\n      register_renderer(events, OutputArea);\n    }\n  } catch(err) {\n  }\n}\n"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>.bk-root, .bk-root .bk:before, .bk-root .bk:after {\n",
       "  font-family: var(--jp-ui-font-size1);\n",
       "  font-size: var(--jp-ui-font-size1);\n",
       "  color: var(--jp-ui-font-color1);\n",
       "}\n",
       "</style>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import panel as pn\n",
    "pn.extension()\n",
    "\n",
    "n_steps = pn.widgets.IntSlider(name='Number of Steps', value=50, start=1, end=100)\n",
    "n_agents = pn.widgets.IntSlider(name='Number of Agents', value=50, start=1, end=100)\n",
    "width = pn.widgets.IntSlider(name='Width', value=10, start=10, end=100)\n",
    "height = pn.widgets.IntSlider(name='Height', value=10, start=10, end=100)\n",
    "interactive = pn.bind(run, steps=n_steps, width=width,height=height,view=mpl_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.holoviews_exec.v0+json": "",
      "text/html": [
       "<div id='1002'>\n",
       "  <div class=\"bk-root\" id=\"87587083-b01f-44b8-9527-e10b85db7723\" data-root-id=\"1002\"></div>\n",
       "</div>\n",
       "<script type=\"application/javascript\">(function(root) {\n",
       "  function embed_document(root) {\n",
       "    var docs_json = 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&quot; width=&quot;460px&quot; height=&quot;345px&quot; alt=&quot;&quot;&gt;&lt;/img&gt;\",\"width\":460},\"id\":\"1008\",\"type\":\"panel.models.markup.HTML\"}],\"root_ids\":[\"1002\",\"1009\",\"1010\"]},\"title\":\"Bokeh Application\",\"version\":\"2.4.3\"}};\n",
       "    var render_items = [{\"docid\":\"b23bdf99-5d77-437f-bca6-3e0ca5c34828\",\"root_ids\":[\"1002\"],\"roots\":{\"1002\":\"87587083-b01f-44b8-9527-e10b85db7723\"}}];\n",
       "    root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n",
       "    for (const render_item of render_items) {\n",
       "      for (const root_id of render_item.root_ids) {\n",
       "\tconst id_el = document.getElementById(root_id)\n",
       "\tif (id_el.children.length && (id_el.children[0].className === 'bk-root')) {\n",
       "\t  const root_el = id_el.children[0]\n",
       "\t  root_el.id = root_el.id + '-rendered'\n",
       "\t}\n",
       "      }\n",
       "    }\n",
       "  }\n",
       "  if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "    embed_document(root);\n",
       "  } else {\n",
       "    var attempts = 0;\n",
       "    var timer = setInterval(function(root) {\n",
       "      if (root.Bokeh !== undefined && root.Bokeh.Panel !== undefined) {\n",
       "        clearInterval(timer);\n",
       "        embed_document(root);\n",
       "      } else if (document.readyState == \"complete\") {\n",
       "        attempts++;\n",
       "        if (attempts > 200) {\n",
       "          clearInterval(timer);\n",
       "          console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n",
       "        }\n",
       "      }\n",
       "    }, 25, root)\n",
       "  }\n",
       "})(window);</script>"
      ],
      "text/plain": [
       "Column\n",
       "    [0] IntSlider(end=100, name='Number of Steps', start=1, value=50)\n",
       "    [1] IntSlider(end=100, name='Number of Agents', start=1, value=50)\n",
       "    [2] IntSlider(end=100, name='Width', start=10, value=10)\n",
       "    [3] IntSlider(end=100, name='Height', start=10, value=10)\n",
       "    [4] ParamFunction(function, _pane=Matplotlib)"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "application/vnd.holoviews_exec.v0+json": {
       "id": "1002"
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "first_app = pn.Column(n_steps, n_agents, width,height,interactive)\n",
    "\n",
    "first_app"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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