SIR_model.ipynb

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    "# Creating MDF SIR Model \n",
    "An SIR model is an epidemiological model that computes the theoretical number of people infected with a contagious illness in a closed population over time. The name of this class of models derives from the fact that they involve coupled equations relating the number of susceptible people S(t), number of people infected I(t), and number of people who have recovered R(t).\n",
    "\n",
    "Susceptible-Infected-Recovered Model into making:\n",
    "1. **[Create a SIR model graph](#h1)**\n",
    "2. **[Create nodes for the SIR dynamics](#h2)**\n",
    "3. **[SIR model in working:](#h3)**\n",
    "4. **[Plots](#h4)**\n",
    "  \n",
    "\n"
   ]
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   "cell_type": "markdown",
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   "source": [
    "## 1. Create a SIR model graph <a class=\"anchor\" id=\"h1\"></a>"
   ]
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   "source": [
    "from modeci_mdf.mdf import*\n",
    "\n",
    "# Initialize the Model\n",
    "sir_model = Model(id=\"SIR_Model\")\n",
    "\n",
    "# Create a Graph within the Model\n",
    "sir_graph = Graph(id=\"SIR_Graph\")\n",
    "sir_model.graphs.append(sir_graph)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03027c9f-9311-4038-9ea6-764c9200688d",
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   "source": [
    "## 2. Create nodes for the SIR dynamics <a class=\"anchor\" id=\"h2\"></a>"
   ]
  },
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    "### 1. SIR equation Node\n",
    "\n",
    "\\begin{align*}\n",
    "\\begin{aligned}\n",
    "S &= \\text{Susceptible Population}, \\\\\n",
    "I &= \\text{Infected Population}, \\\\\n",
    "R &= \\text{Recovered Population}.\n",
    "\\end{aligned} \\\\\n",
    "\\\\\n",
    "\\frac{\\mathrm{d}S}{\\mathrm{d}t} &= -\\frac{\\beta S I}{N},\\\\\n",
    "\\frac{\\mathrm{d}I}{\\mathrm{d}t} &= \\frac{\\beta S I}{N} - \\gamma I,\\\\\n",
    "\\frac{\\mathrm{d}R}{\\mathrm{d}t} &= \\gamma I, \\quad \\beta = 0.3, \\quad \\gamma = 0.1. \\tag*{(1)}\n",
    "\\end{align*}\n",
    "\n",
    "\n",
    "\n"
   ]
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     "text": [
      "sir_node:\n",
      "    parameters:\n",
      "        gamma:\n",
      "            value: 0.1\n",
      "        beta:\n",
      "            value: 0.3\n",
      "        total_population:\n",
      "            value: 1000\n",
      "        susceptible_population:\n",
      "            default_initial_value: 999\n",
      "            time_derivative: -beta*susceptible_population*infected_population/total_population\n",
      "        infected_population:\n",
      "            default_initial_value: 1\n",
      "            time_derivative: beta*susceptible_population*infected_population/total_population\n",
      "                - gamma*infected_population\n",
      "        recovered_population:\n",
      "            default_initial_value: 0\n",
      "            time_derivative: gamma*infected_population\n",
      "    output_ports:\n",
      "        out_port1:\n",
      "            value: susceptible_population\n",
      "        out_port2:\n",
      "            value: infected_population\n",
      "        out_port3:\n",
      "            value: recovered_population\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Parameters for the model\n",
    "total_population = 1000\n",
    "initial_infected = 1\n",
    "initial_recovered = 0\n",
    "initial_susceptible = total_population - initial_infected - initial_recovered\n",
    "beta = 0.3  # Infection rate\n",
    "gamma = 0.1  # Recovery rate\n",
    "\n",
    "\n",
    "\n",
    "# SIR equation Node\n",
    "sir_node = Node(id=\"sir_node\")\n",
    "\n",
    "#parameters\n",
    "total_population = Parameter(id=\"total_population\", value=total_population)\n",
    "gamma = Parameter(id=\"gamma\", value=gamma)\n",
    "beta = Parameter(id=\"beta\", value=beta)\n",
    "\n",
    "#dynamic relationship\n",
    "susceptible_population = Parameter(id=\"susceptible_population\",\n",
    "                                    default_initial_value=initial_susceptible,\n",
    "                                    time_derivative=\"-beta*susceptible_population*infected_population/total_population\"\n",
    "                                   )\n",
    "infected_population = Parameter(id=\"infected_population\",\n",
    "                                    default_initial_value=initial_infected,\n",
    "                                    time_derivative=\"beta*susceptible_population*infected_population/total_population - gamma*infected_population\"\n",
    "                                   )\n",
    "recovered_population = Parameter(id=\"recovered_population\",\n",
    "                                  default_initial_value=initial_recovered,\n",
    "                                  time_derivative=\"gamma*infected_population\"\n",
    "                                 )\n",
    "\n",
    "#output ports\n",
    "infected_output1 = OutputPort(id=\"out_port1\",value=susceptible_population.id)\n",
    "infected_output2 = OutputPort(id=\"out_port2\",value=infected_population.id)\n",
    "infected_output3 = OutputPort(id=\"out_port3\",value=recovered_population.id)\n",
    "\n",
    "#Adding components to node\n",
    "sir_node.parameters.append(gamma)\n",
    "sir_node.parameters.append(beta)\n",
    "sir_node.parameters.append(total_population)\n",
    "sir_node.parameters.append(susceptible_population)\n",
    "sir_node.parameters.append(infected_population)\n",
    "sir_node.parameters.append(recovered_population)\n",
    "sir_node.output_ports.append(infected_output1)\n",
    "sir_node.output_ports.append(infected_output2)\n",
    "sir_node.output_ports.append(infected_output3)\n",
    "\n",
    "print(sir_node.to_yaml())"
   ]
  },
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   "cell_type": "markdown",
   "id": "4a963ec2-ec86-4bc6-973d-de3b00fc4bf3",
   "metadata": {},
   "source": [
    "### 2. Recovered Node, Infected Node, Susceptible Node"
   ]
  },
  {
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     "text": [
      "Recovered:\n",
      "    input_ports:\n",
      "        input_port: {}\n",
      "    output_ports:\n",
      "        out_port:\n",
      "            value: input_port\n",
      "\n",
      "Infected:\n",
      "    input_ports:\n",
      "        input_port: {}\n",
      "    output_ports:\n",
      "        out_port:\n",
      "            value: input_port\n",
      "\n",
      "Susceptible:\n",
      "    input_ports:\n",
      "        input_port: {}\n",
      "    output_ports:\n",
      "        out_port:\n",
      "            value: input_port\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# Recovered Node\n",
    "recovered_node = Node(id=\"Recovered\")\n",
    "recovered_input = InputPort(id=\"input_port\")\n",
    "recovered_output = OutputPort(id=\"out_port\",value=recovered_input.id)\n",
    "recovered_node.input_ports.append(recovered_input)\n",
    "recovered_node.output_ports.append(recovered_output)\n",
    "\n",
    "#Infected Node\n",
    "infected_node = Node(id=\"Infected\")\n",
    "infected_input = InputPort(id=\"input_port\")\n",
    "infected_output = OutputPort(id=\"out_port\",value=infected_input.id)\n",
    "infected_node.input_ports.append(infected_input)\n",
    "infected_node.output_ports.append(infected_output)\n",
    "\n",
    "#Susceptible Node\n",
    "susceptible_node = Node(id=\"Susceptible\")\n",
    "susceptible_input = InputPort(id=\"input_port\")\n",
    "susceptible_output = OutputPort(id=\"out_port\",value=susceptible_input.id)\n",
    "susceptible_node.input_ports.append(susceptible_input)\n",
    "susceptible_node.output_ports.append(susceptible_output)\n",
    "\n",
    "# Add nodes to the graph\n",
    "sir_graph.nodes.append(sir_node)\n",
    "sir_graph.nodes.append(recovered_node)\n",
    "sir_graph.nodes.append(infected_node)\n",
    "sir_graph.nodes.append(susceptible_node)\n",
    "\n",
    "print(recovered_node.to_yaml())\n",
    "print(infected_node.to_yaml())\n",
    "print(susceptible_node.to_yaml())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6df13868-7f06-45f3-87b7-06f5eec79133",
   "metadata": {},
   "source": [
    "### 3. Edges from SIR_node to recovered_node, infected_node, recovered_node"
   ]
  },
  {
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   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "sir_to_rec:\n",
      "    sender: sir_node\n",
      "    receiver: Recovered\n",
      "    sender_port: out_port3\n",
      "    receiver_port: input_port\n",
      "\n",
      "sir_to_inf:\n",
      "    sender: sir_node\n",
      "    receiver: Infected\n",
      "    sender_port: out_port2\n",
      "    receiver_port: input_port\n",
      "\n",
      "sir_to_sus:\n",
      "    sender: sir_node\n",
      "    receiver: Susceptible\n",
      "    sender_port: out_port1\n",
      "    receiver_port: input_port\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#sir node to recovered node\n",
    "sir_to_rec_edge = Edge(\n",
    "    id=\"sir_to_rec\",\n",
    "    sender=sir_node.id,\n",
    "    sender_port=\"out_port3\",\n",
    "    receiver=recovered_node.id,\n",
    "    receiver_port=\"input_port\",\n",
    "   \n",
    ")\n",
    "\n",
    "#sir node to infected node\n",
    "sir_to_inf_edge = Edge(\n",
    "    id=\"sir_to_inf\",\n",
    "    sender=sir_node.id,\n",
    "    sender_port=\"out_port2\",\n",
    "    receiver=infected_node.id,\n",
    "    receiver_port=\"input_port\",\n",
    ")\n",
    "\n",
    "#sir node to susceptible node\n",
    "sir_to_sus_edge = Edge(\n",
    "    id=\"sir_to_sus\",\n",
    "    sender=sir_node.id,\n",
    "    sender_port=\"out_port1\",\n",
    "    receiver=susceptible_node.id,\n",
    "    receiver_port=\"input_port\",\n",
    ")\n",
    "\n",
    "\n",
    "# Add edges to the graph\n",
    "sir_graph.edges.append(sir_to_rec_edge)\n",
    "sir_graph.edges.append(sir_to_inf_edge)\n",
    "sir_graph.edges.append(sir_to_sus_edge)\n",
    "\n",
    "print(sir_to_rec_edge.to_yaml())\n",
    "print(sir_to_inf_edge.to_yaml())\n",
    "print(sir_to_sus_edge.to_yaml())"
   ]
  },
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "### 4. Save the model to file\n",
    "\n",
    "The model can be saved to a file (serialized) in either JSON or YAML format."
   ]
  },
  {
   "cell_type": "code",
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   "id": "62953113-30be-449d-8216-2275701c5bea",
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    {
     "name": "stdout",
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     "text": [
      "Saved to SIR_Model.json\n",
      "Saved to SIR_Model.yaml\n",
      "SIR_Model:\n",
      "    format: ModECI MDF v0.4\n",
      "    generating_application: Python modeci-mdf v0.4.5\n",
      "    graphs:\n",
      "        SIR_Graph:\n",
      "            nodes:\n",
      "                sir_node:\n",
      "                    parameters:\n",
      "                        gamma:\n",
      "                            value: 0.1\n",
      "                        beta:\n",
      "                            value: 0.3\n",
      "                        total_population:\n",
      "                            value: 1000\n",
      "                        susceptible_population:\n",
      "                            default_initial_value: 999\n",
      "                            time_derivative: -beta*susceptible_population*infected_population/total_population\n",
      "                        infected_population:\n",
      "                            default_initial_value: 1\n",
      "                            time_derivative: beta*susceptible_population*infected_population/total_population\n",
      "                                - gamma*infected_population\n",
      "                        recovered_population:\n",
      "                            default_initial_value: 0\n",
      "                            time_derivative: gamma*infected_population\n",
      "                    output_ports:\n",
      "                        out_port1:\n",
      "                            value: susceptible_population\n",
      "                        out_port2:\n",
      "                            value: infected_population\n",
      "                        out_port3:\n",
      "                            value: recovered_population\n",
      "                Recovered:\n",
      "                    input_ports:\n",
      "                        input_port: {}\n",
      "                    output_ports:\n",
      "                        out_port:\n",
      "                            value: input_port\n",
      "                Infected:\n",
      "                    input_ports:\n",
      "                        input_port: {}\n",
      "                    output_ports:\n",
      "                        out_port:\n",
      "                            value: input_port\n",
      "                Susceptible:\n",
      "                    input_ports:\n",
      "                        input_port: {}\n",
      "                    output_ports:\n",
      "                        out_port:\n",
      "                            value: input_port\n",
      "            edges:\n",
      "                sir_to_rec:\n",
      "                    sender: sir_node\n",
      "                    receiver: Recovered\n",
      "                    sender_port: out_port3\n",
      "                    receiver_port: input_port\n",
      "                sir_to_inf:\n",
      "                    sender: sir_node\n",
      "                    receiver: Infected\n",
      "                    sender_port: out_port2\n",
      "                    receiver_port: input_port\n",
      "                sir_to_sus:\n",
      "                    sender: sir_node\n",
      "                    receiver: Susceptible\n",
      "                    sender_port: out_port1\n",
      "                    receiver_port: input_port\n",
      "\n"
     ]
    }
   ],
   "source": [
    "j_file = \"%s.json\" % sir_model.id\n",
    "new_file = sir_model.to_json_file(j_file)\n",
    "print(\"Saved to %s\" % j_file)\n",
    "y_file = \"%s.yaml\" % sir_model.id\n",
    "new_file = sir_model.to_yaml_file(y_file)\n",
    "print(\"Saved to %s\" % y_file)\n",
    "\n",
    "print(sir_model.to_yaml())"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e2b1ce4-4ff3-458c-862f-dfd9551e53bf",
   "metadata": {},
   "source": [
    "### 5. SIR Model graph image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "112d2278-7359-43ce-8025-0257ca87bf90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Converting MDF graph: SIR_Graph to graphviz (level: 3, format: png)\n",
      "    Node: sir_node\n",
      "    Node: Recovered\n",
      "    Node: Infected\n",
      "    Node: Susceptible\n",
      "    Edge: sir_to_rec connects sir_node to Recovered\n",
      "    Edge: sir_to_inf connects sir_node to Infected\n",
      "    Edge: sir_to_sus connects sir_node to Susceptible\n",
      "Written graph image to: sir_model.png\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sir_model.to_graph_image(\n",
    "        engine=\"dot\",\n",
    "        output_format=\"png\",\n",
    "        view_on_render=False,\n",
    "        level=3,\n",
    "        filename_root=\"sir_model\",\n",
    "        is_horizontal=True\n",
    "    )\n",
    "\n",
    "from IPython.display import Image\n",
    "Image(filename=\"sir_model.png\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3b430bd-2d1b-48b5-bc58-46c818de1e9c",
   "metadata": {},
   "source": [
    "## 3. SIR model in working: <a class=\"anchor\" id=\"h3\"></a>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "aa0c2b80-376d-4be8-b5f8-eeac763da26c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Init graph: SIR_Graph\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n"
     ]
    }
   ],
   "source": [
    "from modeci_mdf.execution_engine import EvaluableGraph\n",
    "eg = EvaluableGraph(sir_graph, verbose=False)\n",
    "eg.evaluate()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "afbc914b-fe55-4452-bbc6-73ed8ca1b44e",
   "metadata": {},
   "source": [
    "### Running for 100 days of epidemic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "051c50a4-f6a0-4e50-a478-c4e17f65c180",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "======   Evaluating at t = 0  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 999\n",
      "Infected polution: 1\n",
      "Recovered polution: 0\n",
      "======   Evaluating at t = 1  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 998.7003\n",
      "Infected polution: 1.19961009\n",
      "Recovered polution: 0.11996100900000001\n",
      "======   Evaluating at t = 2  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 998.3408847129701\n",
      "Infected polution: 1.4389350205683615\n",
      "Recovered polution: 0.26385451105683616\n",
      "======   Evaluating at t = 3  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 997.9099204145265\n",
      "Infected polution: 1.72581977806864\n",
      "Recovered polution: 0.4364364888637002\n",
      "======   Evaluating at t = 4  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 997.3932566113118\n",
      "Infected polution: 2.0696341028934038\n",
      "Recovered polution: 0.6433998991530405\n",
      "======   Evaluating at t = 5  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 996.7739848819482\n",
      "Infected polution: 2.4815579222006536\n",
      "Recovered polution: 0.8915556913731059\n",
      "======   Evaluating at t = 6  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 996.0319191683\n",
      "Infected polution: 2.974915399913633\n",
      "Recovered polution: 1.189047231364469\n",
      "======   Evaluating at t = 7  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 995.1429859597582\n",
      "Infected polution: 3.5655637181365862\n",
      "Recovered polution: 1.5456036031781277\n",
      "======   Evaluating at t = 8  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 994.0785122422294\n",
      "Infected polution: 4.272342429191955\n",
      "Recovered polution: 1.9728378460973233\n",
      "======   Evaluating at t = 9  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 992.8043991004893\n",
      "Infected polution: 5.117588293722393\n",
      "Recovered polution: 2.4845966754695628\n",
      "======   Evaluating at t = 10  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 991.2801698492515\n",
      "Infected polution: 6.1277186022560555\n",
      "Recovered polution: 3.0973685356951686\n",
      "======   Evaluating at t = 11  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 989.4578840682017\n",
      "Infected polution: 7.33388258673654\n",
      "Recovered polution: 3.8307567943688228\n",
      "======   Evaluating at t = 12  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 987.2809136843185\n",
      "Infected polution: 8.772675018388915\n",
      "Recovered polution: 4.708024296207714\n",
      "======   Evaluating at t = 13  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 984.6825853020354\n",
      "Infected polution: 10.486897611686556\n",
      "Recovered polution: 5.75671405737637\n",
      "======   Evaluating at t = 14  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 981.5847056664134\n",
      "Infected polution: 12.526341342174248\n",
      "Recovered polution: 7.009348191593795\n",
      "======   Evaluating at t = 15  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 977.8960061425828\n",
      "Infected polution: 14.948544958984098\n",
      "Recovered polution: 8.504202687492205\n",
      "======   Evaluating at t = 16  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 973.5105694186727\n",
      "Infected polution: 17.819460417586058\n",
      "Recovered polution: 10.286148729250812\n",
      "======   Evaluating at t = 17  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 968.3063395013154\n",
      "Infected polution: 21.21392332247985\n",
      "Recovered polution: 12.407541061498797\n",
      "======   Evaluating at t = 18  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 962.1438665696597\n",
      "Infected polution: 25.215784853412778\n",
      "Recovered polution: 14.929119546840075\n",
      "======   Evaluating at t = 19  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 954.8655027484243\n",
      "Infected polution: 29.917511292446527\n",
      "Recovered polution: 17.92087067608473\n",
      "======   Evaluating at t = 20  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 946.2953429100512\n",
      "Infected polution: 35.41900064545218\n",
      "Recovered polution: 21.462770740629946\n",
      "======   Evaluating at t = 21  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 936.2402923016554\n",
      "Infected polution: 41.82530923610616\n",
      "Recovered polution: 25.645301664240563\n",
      "======   Evaluating at t = 22  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 924.4927303782096\n",
      "Infected polution: 49.24293661287576\n",
      "Recovered polution: 30.56959532552814\n",
      "======   Evaluating at t = 23  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 910.835309301886\n",
      "Infected polution: 57.77430457180474\n",
      "Recovered polution: 36.34702578270861\n",
      "======   Evaluating at t = 24  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 895.0484463295777\n",
      "Infected polution: 67.51011457805396\n",
      "Recovered polution: 43.09803724051401\n",
      "======   Evaluating at t = 25  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 876.920999380192\n",
      "Infected polution: 78.51941426346607\n",
      "Recovered polution: 50.949978666860616\n",
      "======   Evaluating at t = 26  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 856.2644024121922\n",
      "Infected polution: 90.8374866367381\n",
      "Recovered polution: 60.033727330534425\n",
      "======   Evaluating at t = 27  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 832.9301305487027\n",
      "Infected polution: 104.45212185398059\n",
      "Recovered polution: 70.47893951593248\n",
      "======   Evaluating at t = 28  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 806.8297347011252\n",
      "Infected polution: 119.28943299790755\n",
      "Recovered polution: 82.40788281572324\n",
      "======   Evaluating at t = 29  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 777.9558562276204\n",
      "Infected polution: 135.20106359415516\n",
      "Recovered polution: 95.92798917513876\n",
      "======   Evaluating at t = 30  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 746.4017184702376\n",
      "Infected polution: 151.95524909644402\n",
      "Recovered polution: 111.12351408478317\n",
      "======   Evaluating at t = 31  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 712.3758207533899\n",
      "Infected polution: 169.23449777465916\n",
      "Recovered polution: 128.04696386224907\n",
      "======   Evaluating at t = 32  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 676.2082514777867\n",
      "Infected polution: 186.64237714617033\n",
      "Recovered polution: 146.7112015768661\n",
      "======   Evaluating at t = 33  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 638.3455168272859\n",
      "Infected polution: 203.7208368419269\n",
      "Recovered polution: 167.0832852610588\n",
      "======   Evaluating at t = 34  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 599.3322319625818\n",
      "Infected polution: 219.97769231026132\n",
      "Recovered polution: 189.08105449208492\n",
      "======   Evaluating at t = 35  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 559.7803155682957\n",
      "Infected polution: 234.92167768506224\n",
      "Recovered polution: 212.57322226059114\n",
      "======   Evaluating at t = 36  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 520.3289563077824\n",
      "Infected polution: 248.10047532573853\n",
      "Recovered polution: 237.383269793165\n",
      "======   Evaluating at t = 37  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 481.60079789207055\n",
      "Infected polution: 259.13604385544795\n",
      "Recovered polution: 263.2968741787098\n",
      "======   Evaluating at t = 38  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 444.16076024705706\n",
      "Infected polution: 267.7518581437783\n",
      "Recovered polution: 290.07205999308763\n",
      "======   Evaluating at t = 39  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 408.4832995858462\n",
      "Infected polution: 273.7883210748441\n",
      "Recovered polution: 317.45089210057205\n",
      "======   Evaluating at t = 40  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 374.9319125516298\n",
      "Infected polution: 277.20508262382697\n",
      "Recovered polution: 345.1714003629547\n",
      "======   Evaluating at t = 41  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 343.75200301247463\n",
      "Infected polution: 278.071515080598\n",
      "Recovered polution: 372.97855187101453\n",
      "======   Evaluating at t = 42  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 315.0757109255739\n",
      "Infected polution: 276.5484376631894\n",
      "Recovered polution: 400.6333956373335\n",
      "======   Evaluating at t = 43  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 288.93560224494803\n",
      "Infected polution: 272.8650007127044\n",
      "Recovered polution: 427.9198957086039\n",
      "======   Evaluating at t = 44  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 265.2834782512\n",
      "Infected polution: 267.29447358605864\n",
      "Recovered polution: 454.64934306720977\n",
      "======   Evaluating at t = 45  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 244.01083595013006\n",
      "Infected polution: 260.131850610828\n",
      "Recovered polution: 480.6625281282926\n",
      "======   Evaluating at t = 46  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 224.9683388526893\n",
      "Infected polution: 251.67509464412336\n",
      "Recovered polution: 505.8300375927049\n",
      "======   Evaluating at t = 47  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 207.98266046088477\n",
      "Infected polution: 242.21080190645995\n",
      "Recovered polution: 530.051117783351\n",
      "======   Evaluating at t = 48  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 192.8699663690238\n",
      "Infected polution: 232.00427848118792\n",
      "Recovered polution: 553.2515456314698\n",
      "======   Evaluating at t = 49  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 179.4459691525829\n",
      "Infected polution: 221.29352041294987\n",
      "Recovered polution: 575.3808976727647\n",
      "======   Evaluating at t = 50  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 167.5329000812763\n",
      "Infected polution: 210.28635194484787\n",
      "Recovered polution: 596.4095328672495\n",
      "======   Evaluating at t = 51  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 156.9639353646266\n",
      "Infected polution: 199.15992875678333\n",
      "Recovered polution: 616.3255257429278\n",
      "======   Evaluating at t = 52  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 147.58565750924558\n",
      "Infected polution: 188.06188059162432\n",
      "Recovered polution: 635.1317138020902\n",
      "======   Evaluating at t = 53  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 139.25908661938354\n",
      "Infected polution: 177.11249024819583\n",
      "Recovered polution: 652.8429628269098\n",
      "======   Evaluating at t = 54  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 131.85972953312907\n",
      "Infected polution: 166.40744274169606\n",
      "Recovered polution: 669.4837071010794\n",
      "======   Evaluating at t = 55  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 125.27699741546316\n",
      "Infected polution: 156.02080589980605\n",
      "Recovered polution: 685.08578769106\n",
      "======   Evaluating at t = 56  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 119.41325198622262\n",
      "Infected polution: 146.00801085282757\n",
      "Recovered polution: 699.6865887763428\n",
      "======   Evaluating at t = 57  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 114.18266456862987\n",
      "Infected polution: 136.4086848858072\n",
      "Recovered polution: 713.3274572649235\n",
      "======   Evaluating at t = 58  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 109.51001243546065\n",
      "Infected polution: 127.24925143067134\n",
      "Recovered polution: 726.0523824079906\n",
      "======   Evaluating at t = 59  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 105.32949230348788\n",
      "Infected polution: 118.54525600236165\n",
      "Recovered polution: 737.9069080082268\n",
      "======   Evaluating at t = 60  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 101.58359881457315\n",
      "Infected polution: 110.30340652025993\n",
      "Recovered polution: 748.9372486602527\n",
      "======   Evaluating at t = 61  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 98.2220937158227\n",
      "Infected polution: 102.52333532795618\n",
      "Recovered polution: 759.1895821930484\n",
      "======   Evaluating at t = 62  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 95.20107672063033\n",
      "Infected polution: 95.19910136882406\n",
      "Recovered polution: 768.7094923299309\n",
      "======   Evaluating at t = 63  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 92.48215963468579\n",
      "Infected polution: 88.32045677890272\n",
      "Recovered polution: 777.5415380078211\n",
      "======   Evaluating at t = 64  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 90.03173965983534\n",
      "Infected polution: 81.87390441241922\n",
      "Recovered polution: 785.7289284490631\n",
      "======   Evaluating at t = 65  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 87.82036464573738\n",
      "Infected polution: 75.84357281331798\n",
      "Recovered polution: 793.3132857303949\n",
      "======   Evaluating at t = 66  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 85.82218157958705\n",
      "Infected polution: 70.21193379527494\n",
      "Recovered polution: 800.3344791099224\n",
      "======   Evaluating at t = 67  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 84.01445918021744\n",
      "Infected polution: 64.96038570948963\n",
      "Recovered polution: 806.8305176808714\n",
      "======   Evaluating at t = 68  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 82.37717567816111\n",
      "Infected polution: 60.069723070254184\n",
      "Recovered polution: 812.8374899878968\n",
      "======   Evaluating at t = 69  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 80.89266343907207\n",
      "Infected polution: 55.52051073058887\n",
      "Recovered polution: 818.3895410609557\n",
      "======   Evaluating at t = 70  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 79.5453028425236\n",
      "Infected polution: 51.293378409540864\n",
      "Recovered polution: 823.5188789019098\n",
      "======   Evaluating at t = 71  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 78.32125864670267\n",
      "Infected polution: 47.36924915576983\n",
      "Recovered polution: 828.2558038174868\n",
      "======   Evaluating at t = 72  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 77.20825288219393\n",
      "Infected polution: 43.72951333049034\n",
      "Recovered polution: 832.6287551505359\n",
      "======   Evaluating at t = 73  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 76.1953690851032\n",
      "Infected polution: 40.356157919879905\n",
      "Recovered polution: 836.6643709425239\n",
      "======   Evaluating at t = 74  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 75.27288338083461\n",
      "Infected polution: 37.23185943853241\n",
      "Recovered polution: 840.3875568863771\n",
      "======   Evaluating at t = 75  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 74.43211855676414\n",
      "Infected polution: 34.34004734742446\n",
      "Recovered polution: 843.8215616211195\n",
      "======   Evaluating at t = 76  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 73.66531781414162\n",
      "Infected polution: 31.66494376316222\n",
      "Recovered polution: 846.9880559974357\n",
      "======   Evaluating at t = 77  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 72.96553537037754\n",
      "Infected polution: 29.191584259091606\n",
      "Recovered polution: 849.9072144233448\n",
      "======   Evaluating at t = 78  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 72.3265414982453\n",
      "Infected polution: 26.905823732276858\n",
      "Recovered polution: 852.5977967965725\n",
      "======   Evaluating at t = 79  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 71.74273994523021\n",
      "Infected polution: 24.794330613560255\n",
      "Recovered polution: 855.0772298579286\n",
      "======   Evaluating at t = 80  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 71.20909598123279\n",
      "Infected polution: 22.84457211273966\n",
      "Recovered polution: 857.3616870692026\n",
      "======   Evaluating at t = 81  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 70.72107358276492\n",
      "Infected polution: 21.044792701071245\n",
      "Recovered polution: 859.4661663393098\n",
      "======   Evaluating at t = 82  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 70.27458048282097\n",
      "Infected polution: 19.383987624488835\n",
      "Recovered polution: 861.4045651017586\n",
      "======   Evaluating at t = 83  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 69.86592000330242\n",
      "Infected polution: 17.851872900655213\n",
      "Recovered polution: 863.1897523918242\n",
      "======   Evaluating at t = 84  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 69.49174874610654\n",
      "Infected polution: 16.438852970467618\n",
      "Recovered polution: 864.8336376888709\n",
      "======   Evaluating at t = 85  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 69.14903935401716\n",
      "Infected polution: 15.135986940717785\n",
      "Recovered polution: 866.3472363829427\n",
      "======   Evaluating at t = 86  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 68.83504766702949\n",
      "Infected polution: 13.93495416141156\n",
      "Recovered polution: 867.7407317990838\n",
      "======   Evaluating at t = 87  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 68.5472836968479\n",
      "Infected polution: 12.82801972213186\n",
      "Recovered polution: 869.023533771297\n",
      "======   Evaluating at t = 88  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 68.28348592469938\n",
      "Infected polution: 11.80800032116006\n",
      "Recovered polution: 870.2043338034131\n",
      "======   Evaluating at t = 89  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 68.04159849758075\n",
      "Infected polution: 10.868230854117558\n",
      "Recovered polution: 871.2911568888248\n",
      "======   Evaluating at t = 90  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 67.81975095753428\n",
      "Infected polution: 10.002531981668374\n",
      "Recovered polution: 872.2914100869917\n",
      "======   Evaluating at t = 91  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 67.61624018915182\n",
      "Infected polution: 9.205178864993185\n",
      "Recovered polution: 873.211927973491\n",
      "======   Evaluating at t = 92  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 67.42951431361598\n",
      "Infected polution: 8.470871200504803\n",
      "Recovered polution: 874.0590150935415\n",
      "======   Evaluating at t = 93  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 67.25815829435702\n",
      "Infected polution: 7.794704639282721\n",
      "Recovered polution: 874.8384855574698\n",
      "======   Evaluating at t = 94  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 67.10088105081103\n",
      "Infected polution: 7.172143640002464\n",
      "Recovered polution: 875.55569992147\n",
      "======   Evaluating at t = 95  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 66.95650390363089\n",
      "Infected polution: 6.598995775090986\n",
      "Recovered polution: 876.2155994989791\n",
      "======   Evaluating at t = 96  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 66.82395019771842\n",
      "Infected polution: 6.071387487090777\n",
      "Recovered polution: 876.8227382476882\n",
      "======   Evaluating at t = 97  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 66.7022359691979\n",
      "Infected polution: 5.585741274629009\n",
      "Recovered polution: 877.3813123751511\n",
      "======   Evaluating at t = 98  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 66.59046153942894\n",
      "Infected polution: 5.1387542740213235\n",
      "Recovered polution: 877.8951878025532\n",
      "======   Evaluating at t = 99  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 66.48780393377551\n",
      "Infected polution: 4.727378192609685\n",
      "Recovered polution: 878.3679256218142\n",
      "======   Evaluating at t = 100  ======\n",
      "Evaluating graph: SIR_Graph, root nodes: ['sir_node'], with array format numpy\n",
      "Susceptible polution: 66.39351003545819\n",
      "Infected polution: 4.348800542790448\n",
      "Recovered polution: 878.8028056760932\n"
     ]
    }
   ],
   "source": [
    "dt = 1\n",
    "duration = 100\n",
    "t = 0\n",
    "times = []\n",
    "s = []\n",
    "i = []\n",
    "r = []\n",
    "while t <= duration:\n",
    "    times.append(t)\n",
    "    print(\"======   Evaluating at t = %s  ======\" % (t))\n",
    "    if t == 0:\n",
    "        eg.evaluate()  \n",
    "    else:\n",
    "        eg.evaluate(time_increment=dt)\n",
    "\n",
    "    s.append(eg.enodes[\"Susceptible\"].evaluable_outputs[\"out_port\"].curr_value)\n",
    "    i.append(eg.enodes[\"Infected\"].evaluable_outputs[\"out_port\"].curr_value)\n",
    "    r.append(eg.enodes[\"Recovered\"].evaluable_outputs[\"out_port\"].curr_value)\n",
    "    \n",
    "    t += dt\n",
    "    \n",
    "    \n",
    "    print('Susceptible polution: %s'%eg.enodes[\"Susceptible\"].evaluable_outputs[\"out_port\"].curr_value)\n",
    "    print('Infected polution: %s'%eg.enodes[\"Infected\"].evaluable_outputs[\"out_port\"].curr_value)\n",
    "    print('Recovered polution: %s'%eg.enodes[\"Recovered\"].evaluable_outputs[\"out_port\"].curr_value)\n",
    "    \n",
    "\n",
    "     "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9ea4712f-bb6d-4e5c-b046-90c8fc59d881",
   "metadata": {},
   "source": [
    "## 4. Plots <a class=\"anchor\" id=\"h4\"></a>\n",
    "### Plots of above SIR data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "77b96ed8-e847-4296-9d91-6497290c866e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Create subplots\n",
    "fig, axs = plt.subplots(1, 3, figsize=(15, 5),sharey=True)\n",
    "\n",
    "# Plotting Susceptible population\n",
    "axs[0].plot(times, s, label='Susceptible', color='blue')\n",
    "axs[0].set_xlabel('Time')\n",
    "axs[0].set_ylabel('Susceptible Population')\n",
    "axs[0].set_title('Susceptible Population over Time')\n",
    "axs[0].legend()\n",
    "axs[0].grid(True)\n",
    "\n",
    "# Plotting Infected population\n",
    "axs[1].plot(times, i, label='Infected', color='orange')\n",
    "axs[1].set_xlabel('Time')\n",
    "axs[1].set_ylabel('Infected Population')\n",
    "axs[1].set_title('Infected Population over Time')\n",
    "axs[1].legend()\n",
    "axs[1].grid(True)\n",
    "\n",
    "# Plotting Recovered population\n",
    "axs[2].plot(times, r, label='Recovered', color='green')\n",
    "axs[2].set_xlabel('Time')\n",
    "axs[2].set_ylabel('Recovered Population')\n",
    "axs[2].set_title('Recovered Population over Time')\n",
    "axs[2].legend()\n",
    "axs[2].grid(True)\n",
    "\n",
    "plt.tight_layout()  # Adjust layout to prevent overlap\n",
    "plt.show()\n",
    "\n",
    "\n",
    "# Plotting\n",
    "plt.figure(figsize=(10, 5))\n",
    "plt.plot(times, s, label='Susceptible')\n",
    "plt.plot(times, i, label='Infected')\n",
    "plt.plot(times, r, label='Recovered')\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Population')\n",
    "plt.title('Population over time')\n",
    "plt.legend()\n",
    "plt.grid(True)\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41b7bae1-e47b-49cd-9164-2438294e8a90",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf2e045d-5320-43d6-b433-0eefdca964ae",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  },
  "toc-showcode": true,
  "toc-showmarkdowntxt": true
 },
 "nbformat": 4,
 "nbformat_minor": 5
}