{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Network analysis\n",
    "\n",
    "The NetworkInspector provides tools for quickly analyzing the created network.\n",
    "\n",
    "First, let us create an example network:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LocationList (6 objects)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pop2net as p2n\n",
    "\n",
    "model = p2n.Model()\n",
    "creator = p2n.Creator(model=model)\n",
    "\n",
    "for _ in range(20):\n",
    "    agent = p2n.Agent(model=model)\n",
    "    agent.status = \"pupil\"\n",
    "\n",
    "for _ in range(4):\n",
    "    agent = p2n.Agent(model=model)\n",
    "    agent.status = \"teacher\"\n",
    "\n",
    "\n",
    "class School(p2n.LocationDesigner):\n",
    "    n_locations = 2\n",
    "    overcrowding = False\n",
    "\n",
    "    def weight(self, agent):\n",
    "        return 0.1\n",
    "\n",
    "    def stick_together(self, agent):\n",
    "        return agent.Classroom\n",
    "\n",
    "\n",
    "class PupilsInClassroom(p2n.MeltLocationDesigner):\n",
    "    n_agents = 5\n",
    "\n",
    "    def filter(self, agent):\n",
    "        return agent.status == \"pupil\"\n",
    "\n",
    "\n",
    "class TeacherInClassroom(p2n.MeltLocationDesigner):\n",
    "    n_agents = 1\n",
    "\n",
    "    def filter(self, agent):\n",
    "        return agent.status == \"teacher\"\n",
    "\n",
    "\n",
    "class Classroom(p2n.LocationDesigner):\n",
    "    def melt(self):\n",
    "        return PupilsInClassroom, TeacherInClassroom\n",
    "\n",
    "\n",
    "creator.create_locations(\n",
    "    location_designers=[\n",
    "        Classroom,\n",
    "        School,\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the following, we initialize an instance of the NetworkInspector and bind it to our model:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "inspector = p2n.NetworkInspector(model=model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network visualization\n",
    "\n",
    "The method `plot_bipartite_network()` plots the biparte network.\n",
    "Agents are displayed as circles and locations are displayed as squares.\n",
    "Using the arguments `agent_color` and `location_color`, agent and location attributes can be displayed as colors in the plot.\n",
    "With the arguments `agent_attrs` and `location_attrs` you can add attributes that should be displayed in the tooltips for each node when you move the mouse pointer over it.\n",
    "\n",
    "In the following example I display the agent attribute `status` as color and add `id` to the list of shown attribute values per agent node."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
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 const render_items = [{\"docid\":\"8d32d71d-c92f-4802-9829-c3bad6b32266\",\"roots\":{\"p2201\":\"c4ddbb3b-2f7b-412d-bb56-8561b1dbfedc\"},\"root_ids\":[\"p2201\"],\"notebook_comms_target\":\"p2291\"}];\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": "p2201"
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "inspector.plot_bipartite_network(agent_color=\"status\", agent_attrs=[\"id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may have noticed that the weight between nodes is automatically reflected in the alpha (transparency) of the edge connecting them."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `plot_agent_network()`, the agent-level network is shown:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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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   "source": [
    "inspector.plot_agent_network(agent_color=\"status\", agent_attrs=[\"id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using `plot_networks()`, both network types are plotted:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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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    "inspector.plot_networks(agent_color=\"status\", agent_attrs=[\"id\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate affiliations\n",
    "\n",
    "Using `eval_affiliations()`, the number of locations per label, the number of agents per location and the number of locations per agent can be displayed:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "______________________________________\n",
      "Number of locations\n",
      "______________________________________\n",
      "\n",
      "                count\n",
      "location_label       \n",
      "Classroom           4\n",
      "School              2\n",
      "\n",
      "\n",
      "______________________________________\n",
      "Number of agents per location\n",
      "______________________________________\n",
      "\n",
      "                mean  std   min   25%   50%   75%   max\n",
      "location_label                                         \n",
      "Classroom        6.0  0.0   6.0   6.0   6.0   6.0   6.0\n",
      "School          12.0  0.0  12.0  12.0  12.0  12.0  12.0\n",
      "\n",
      "\n",
      "______________________________________\n",
      "Number of affiliated locations per agent\n",
      "______________________________________\n",
      "\n",
      "      n_affiliated_locations\n",
      "mean                     2.0\n",
      "std                      0.0\n",
      "min                      2.0\n",
      "25%                      2.0\n",
      "50%                      2.0\n",
      "75%                      2.0\n",
      "max                      2.0\n"
     ]
    }
   ],
   "source": [
    "inspector.eval_affiliations()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Network measures\n",
    "\n",
    "Using `inspector.network_measures()`, common network measures are calculated for the agent-level network.\n",
    "If there are unconnected components within the network, the network measures are calculated for each component."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'n_nodes': 12,\n",
       "  'diameter': 0.2,\n",
       "  'density': 1.0,\n",
       "  'transitivity': 1.0,\n",
       "  'avg_clustering': 0.3472381885547426,\n",
       "  'avg_path_length': 0.14545454545454548},\n",
       " {'n_nodes': 12,\n",
       "  'diameter': 0.2,\n",
       "  'density': 1.0,\n",
       "  'transitivity': 1.0,\n",
       "  'avg_clustering': 0.3472381885547426,\n",
       "  'avg_path_length': 0.14545454545454548}]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspector.network_measures()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Coming soon ...\n",
    "\n",
    "The inspector offers several additional methods for analyzing the created network.\n",
    "Detailed documentation will be added soon."
   ]
  }
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