demo_serialized_population_2.ipynb

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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Serialized Population Demo"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Motivation\n",
    "- Saves dtk state to file\n",
    "- Loads file and continues, e.g. burn-in"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Configuration"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "{\n",
    "    ...\n",
    "    \"Serialization_Time_Steps\" : [\n",
    "          1,\n",
    "         10,\n",
    "         50\n",
    "    ],\n",
    "    \"Serialization_Type\" : \"TIMESTEP\"\n",
    "    ...\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Created Files\n",
    "- state-00001.dtk\n",
    "- state-00010.dtk\n",
    "- state-00050.dtk\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and Continue"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "...\n",
    "\"Serialized_Population_Filenames\": [\n",
    "            \"state-00050.dtk\"\n",
    "],\n",
    "\"Serialization_Type\" : \"TIMESTEP\"\n",
    "..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Manipulating a dtk File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- file access, writing, reading, compression\n",
    "- https://github.com/InstituteforDiseaseModeling/DtkTrunk/tree/master/Scripts/serialization \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pycharm Demo\n",
    "- Run eradication.exe and generate .dtk file at time step 1\n",
    "- Open dtk file in Pycharm\n",
    "- Show node content in debugger"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Demo\n",
    "- Load dtk file at timestep 1\n",
    "- Infect some indiviuals\n",
    "- Save dtk file\n",
    "- Start simulation with created dtk file\n",
    "- https://github.com/tfischle-idmod/SerializedPopulation-Tools"
   ]
  },
  {
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   "execution_count": 1,
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   "outputs": [
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   "source": [
    "import sys\n",
    "sys.path.append(\"C:\\\\Users\\\\tfischle\\\\Github\\\\SerializedPopulation-Tools\")\n",
    "import change_serialized_population as csp\n",
    "import pathlib\n",
    "import plotly\n",
    "import plotly.graph_objs as go\n",
    "\n",
    "plotly.offline.init_notebook_mode(connected=True)"
   ]
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  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "serialized_file = \"state-00001.dtk\"\n",
    "path = pathlib.PureWindowsPath(r\"C:\\Users\\tfischle\\Desktop\\Eradication_2.21\\output\", serialized_file)\n",
    "\n",
    "ser_pop = csp.SerializedPopulation(path)\n",
    "node_0 = ser_pop.nodes[0]"
   ]
  },
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   "source": [
    "# plot m_age \n",
    "ind_age = csp.getAttributeValues(node_0.individualHumans, \"m_age\")   # m_age C++ name\n",
    "\n",
    "layout = go.Layout( title=\"m_age\", \n",
    "                           bargap=0.2, bargroupgap=0.1, \n",
    "                           xaxis=dict(title=\"age in days\"),\n",
    "                           yaxis=dict(title=\"#individuals\"))\n",
    "\n",
    "data = [go.Histogram(x=ind_age)]\n",
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    ")"
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   ],
   "source": [
    "# plot number of infections\n",
    "fct = lambda ind: len(ind[\"infections\"]) > 0\n",
    "ind_values = csp.getAttributeValues(node_0.individualHumans, \"m_age\", filter_fct=fct)\n",
    "\n",
    "data = [go.Histogram(x=ind_values)]\n",
    "plotly.offline.iplot ({ 'data': data, 'layout': layout})"
   ]
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  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"__class__\": \"Infection\",\n",
      "    \"suid\": {\n",
      "        \"id\": 31\n",
      "    },\n",
      "    \"duration\": 7,\n",
      "    \"total_duration\": 9.7328,\n",
      "    \"incubation_timer\": 0,\n",
      "    \"infectious_timer\": 9.7328,\n",
      "    \"infectiousness\": 3.5,\n",
      "    \"infectiousnessByRoute\": [],\n",
      "    \"StateChange\": 0,\n",
      "    \"infection_strain\": {\n",
      "        \"cladeID\": 0,\n",
      "        \"geneticID\": 0\n",
      "    },\n",
      "    \"m_is_symptomatic\": 1,\n",
      "    \"m_is_newly_symptomatic\": 0\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "#print infection\n",
    "import json\n",
    "path_infection = pathlib.PureWindowsPath(r\"C:\\Users\\tfischle\\Desktop\\Eradication_2.21\\infection.json\")\n",
    "with open(path_infection, \"r\") as file:\n",
    "    infection = json.load(file)\n",
    "    print(json.dumps(infection, indent=4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load infection and append it to several individuals\n",
    "ser_pop.addInfection(node_0,path_infection, lambda ind: ind[\"m_age\"] > 30000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
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       "    Plotly.newPlot(\"b7d469a1-f3a2-4a1f-9a88-c063b9d7ad18\", [{\"x\": [37988.8, 31759.9, 37398, 35053.3, 59804.4, 38529.3, 51307.5], \"type\": \"histogram\", \"uid\": \"13ebc86e-ac46-4c00-8f01-d9bab2c653e8\"}], {\"bargap\": 0.2, \"bargroupgap\": 0.1, \"title\": {\"text\": \"m_age\"}, \"xaxis\": {\"title\": {\"text\": \"age in days\"}}, \"yaxis\": {\"title\": {\"text\": \"#individuals\"}}}, {\"showLink\": false, \"linkText\": \"Export to plot.ly\", \"plotlyServerURL\": \"https://plot.ly\"}); \n",
       "}\n",
       "});</script><script type=\"text/javascript\">window.addEventListener(\"resize\", function(){if (document.getElementById(\"b7d469a1-f3a2-4a1f-9a88-c063b9d7ad18\")) {window._Plotly.Plots.resize(document.getElementById(\"b7d469a1-f3a2-4a1f-9a88-c063b9d7ad18\"));};})</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all individuals with an infection\n",
    "fct = lambda ind: len(ind[\"infections\"]) > 0\n",
    "ind_values = csp.getAttributeValues(node_0.individualHumans, \"m_age\", filter_fct=fct)\n",
    "\n",
    "data = [go.Histogram(x=ind_values)]\n",
    "plotly.offline.iplot ({ 'data': data, 'layout': layout})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to file\n",
    "serialized_output_file = \"dtk-added_infections.dtk\"\n",
    "output_path = pathlib.PureWindowsPath(r\"C:\\Users\\tfischle\\Desktop\\Eradication_2.21\", serialized_output_file)\n",
    "ser_pop.close()\n",
    "ser_pop.write(output_path)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Result\n",
    "- Simulation starts with 7 infected individuals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Outlook / Future Plans / Ideas\n",
    "- Distribute created population to several nodes\n",
    "- Modularization i.e save/load parts of simulation, load parts from configuration ( e.g. laraval habitats)\n",
    "- Create synthetic population\n",
    "- Creation of individuals/infections? \n",
    "- Backwards compatibility, e.g. new/renamed variables?\n",
    "- Testing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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