examples/31_nested_graphs.ipynb

{
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      },
      "source": [
        "# Nested Graphs <a target=\"_blank\" href=\"https://colab.research.google.com/github/yWorks/yfiles-jupyter-graphs/blob/main/examples/31_nested_graphs.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "\n",
        "Before using the graph widget, install all necessary packages and initialize your widget."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "id": "86ccab84-0881-4e14-b33d-51313ec2b85e",
      "metadata": {
        "id": "86ccab84-0881-4e14-b33d-51313ec2b85e",
        "outputId": "dcd553d5-178a-42c1-e094-4d191b3d537a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m15.6/15.6 MB\u001b[0m \u001b[31m27.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m139.8/139.8 kB\u001b[0m \u001b[31m4.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m2.3/2.3 MB\u001b[0m \u001b[31m24.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h"
          ]
        }
      ],
      "source": [
        "%pip install yfiles_jupyter_graphs --quiet\n",
        "from yfiles_jupyter_graphs import GraphWidget"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "99c91625-6682-4dcc-86ed-ff80a85d435d",
      "metadata": {
        "id": "99c91625-6682-4dcc-86ed-ff80a85d435d"
      },
      "source": [
        "You can also open this notebook in Google Colab when Google Colab's custom widget manager is enabled:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "id": "837b3dc2-a656-4bd1-856d-b2e4ed12b7a0",
      "metadata": {
        "id": "837b3dc2-a656-4bd1-856d-b2e4ed12b7a0"
      },
      "outputs": [],
      "source": [
        "try:\n",
        "  import google.colab\n",
        "  from google.colab import output\n",
        "  output.enable_custom_widget_manager()\n",
        "except:\n",
        "  pass"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e8af92d1-a099-4f27-b42b-7ffebff1954d",
      "metadata": {
        "id": "e8af92d1-a099-4f27-b42b-7ffebff1954d"
      },
      "source": [
        "<a target=\"_blank\" href=\"https://colab.research.google.com/github/yWorks/yfiles-jupyter-graphs/blob/main/examples/31_nested_graphs.ipynb\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8cea83a9-2373-4dce-a9a2-353022fb1676",
      "metadata": {
        "id": "8cea83a9-2373-4dce-a9a2-353022fb1676"
      },
      "source": [
        "## Sample Data\n",
        "\n",
        "The dataset for this sample is the following airport and flight routes data:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "id": "0eb47bdd-d943-4820-bbc3-29d8396204ac",
      "metadata": {
        "id": "0eb47bdd-d943-4820-bbc3-29d8396204ac"
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      "outputs": [],
      "source": [
        "airports = [\n",
        "    {\"name\": \"Los Angeles\", \"iata\": \"LAX\", \"lat\": 33.942536, \"lng\": -118.408075, \"passengers\": 65000000, \"country\": \"USA\", \"id\": \"LAX\"}, {\"name\": \"Rio de Janeiro\", \"iata\": \"GIG\", \"lat\": -22.808903, \"lng\": -43.243647, \"passengers\": 5000000, \"country\": \"Brazil\", \"id\": \"GIG\"}, {\"name\": \"Lima\", \"iata\": \"LIM\", \"lat\": -12.021889, \"lng\": -77.114319, \"passengers\": 18000000, \"country\": \"Peru\", \"id\": \"LIM\"}, {\"name\": \"London\", \"iata\": \"LHR\", \"lat\": 51.4775, \"lng\": -0.461389, \"passengers\": 61000000, \"country\": \"UK\", \"id\": \"LHR\"},\n",
        "    {\"name\": \"Frankfurt\", \"iata\": \"FRA\", \"lat\": 50.033333, \"lng\": 8.570556, \"passengers\": 48000000, \"country\": \"Germany\", \"id\": \"FRA\"}, {\"name\": \"Moscow\", \"iata\": \"SVO\", \"lat\": 55.972642, \"lng\": 37.414589, \"passengers\": 49000000, \"country\": \"Russia\", \"id\": \"SVO\"}, {\"name\": \"New Delhi\", \"iata\": \"DEL\", \"lat\": 28.5665, \"lng\": 77.103089, \"passengers\": 39000000, \"country\": \"India\", \"id\": \"DEL\"}, {\"name\": \"Shanghai\", \"iata\": \"PVG\", \"lat\": 31.143378, \"lng\": 121.805214, \"passengers\": 32000000, \"country\": \"China\", \"id\": \"PVG\"},\n",
        "    {\"name\": \"Hongkong\", \"iata\": \"HKG\", \"lat\": 22.308919, \"lng\": 113.914603, \"passengers\": 1000000, \"country\": \"China\", \"id\": \"HKG\"}, {\"name\": \"Tokio\", \"iata\": \"NRT\", \"lat\": 35.764722, \"lng\": 140.386389, \"passengers\": 15000000, \"country\": \"Japan\", \"id\": \"NRT\"}, {\"name\": \"Dubai\", \"iata\": \"DXB\", \"lat\": 25.252778, \"lng\": 55.364444, \"passengers\": 29000000, \"country\": \"UAE\", \"id\": \"DXB\"}, {\"name\": \"Dakar\", \"iata\": \"DKR\", \"lat\": 14.670833, \"lng\": -17.072778, \"passengers\": 2000000, \"country\": \"Senegal\", \"id\": \"DKR\"},\n",
        "    {\"name\": \"Johannesburg\", \"iata\": \"JNB\", \"lat\": -26.133694, \"lng\": 28.242317, \"passengers\": 9000000, \"country\": \"South Africa\", \"id\": \"JNB\"}, {\"name\": \"Sydney\", \"iata\": \"SYD\", \"lat\": -33.946111, \"lng\": 151.177222, \"passengers\": 44000000, \"country\": \"Australia\", \"id\": \"SYD\"}, {\"name\": \"Nairobi\", \"iata\": \"NBO\", \"lat\": -1.319167, \"lng\": 36.927778, \"passengers\": 900000, \"country\": \"Kenya\", \"id\": \"NBO\"}, {\"name\": \"Atlanta\", \"iata\": \"ATL\", \"lat\": 33.639167, \"lng\": -84.427778, \"passengers\": 93000000, \"country\": \"USA\", \"id\": \"ATL\"},\n",
        "    {\"name\": \"New York City\", \"iata\": \"JFK\", \"lat\": 40.63975, \"lng\": -73.778925, \"passengers\": 55000000, \"country\": \"USA\", \"id\": \"JFK\"}, {\"name\": \"Cairo\", \"iata\": \"CAI\", \"lat\": 30.121944, \"lng\": 31.405556, \"passengers\": 14000000, \"country\": \"Egypt\", \"id\": \"CAI\"}, {\"name\": \"Casablanca\", \"iata\": \"CMN\", \"lat\": 33.367467, \"lng\": -7.589967, \"passengers\": 7000000, \"country\": \"Morocco\", \"id\": \"CMN\"}, {\"name\": \"Lagos\", \"iata\": \"LOS\", \"lat\": 6.577222, \"lng\": 3.321111, \"passengers\": 5000000, \"country\": \"Nigeria\", \"id\": \"LOS\"},\n",
        "    {\"name\": \"Cape Town\", \"iata\": \"CPT\", \"lat\": -33.969444, \"lng\": 18.597222, \"passengers\": 5000000, \"country\": \"South Africa\", \"id\": \"CPT\"}, {\"name\": \"Chengdu\", \"iata\": \"CTU\", \"lat\": 30.578333, \"lng\": 103.946944, \"passengers\": 40000000, \"country\": \"China\", \"id\": \"CTU\"}, {\"name\": \"Jakarta\", \"iata\": \"CGK\", \"lat\": -6.125567, \"lng\": 106.655897, \"passengers\": 54000000, \"country\": \"Indonesia\", \"id\": \"CGK\"}, {\"name\": \"Teheran\", \"iata\": \"IKA\", \"lat\": 35.416111, \"lng\": 51.152222, \"passengers\": 8000000, \"country\": \"Iran\", \"id\": \"IKA\"},\n",
        "    {\"name\": \"Tel Aviv\", \"iata\": \"TLV\", \"lat\": 32.011389, \"lng\": 34.886667, \"passengers\": 20000000, \"country\": \"Israel\", \"id\": \"TLV\"}, {\"name\": \"Kuala Lumpur\", \"iata\": \"KUL\", \"lat\": 2.745578, \"lng\": 101.709917, \"passengers\": 25000000, \"country\": \"Malaysia\", \"id\": \"KUL\"}, {\"name\": \"Manila\", \"iata\": \"MNL\", \"lat\": 14.508647, \"lng\": 121.019581, \"passengers\": 8000000, \"country\": \"Philippines\", \"id\": \"MNL\"}, {\"name\": \"Singapur\", \"iata\": \"SIN\", \"lat\": 1.350189, \"lng\": 103.994433, \"passengers\": 32000000, \"country\": \"Singapore\", \"id\": \"SIN\"},\n",
        "    {\"name\": \"Taipeh\", \"iata\": \"TPE\", \"lat\": 25.077732, \"lng\": 121.232822, \"passengers\": 800000, \"country\": \"Taiwan\", \"id\": \"TPE\"}, {\"name\": \"Bangkok\", \"iata\": \"BKK\", \"lat\": 13.681108, \"lng\": 100.747283, \"passengers\": 65000000, \"country\": \"Thailand\", \"id\": \"BKK\"}, {\"name\": \"Istanbul\", \"iata\": \"IST\", \"lat\": 40.976922, \"lng\": 28.814606, \"passengers\": 64000000, \"country\": \"Turkey\", \"id\": \"IST\"}, {\"name\": \"Ulaanbaatar\", \"iata\": \"ULN\", \"lat\": 47.843056, \"lng\": 106.766639, \"passengers\": 1000000, \"country\": \"Mongolia\", \"id\": \"ULN\"},\n",
        "    {\"name\": \"Melbourne\", \"iata\": \"MEL\", \"lat\": -37.673333, \"lng\": 144.843333, \"passengers\": 12000000, \"country\": \"Australia\", \"id\": \"MEL\"}, {\"name\": \"Brisbane\", \"iata\": \"BNE\", \"lat\": -27.383333, \"lng\": 153.118056, \"passengers\": 23000000, \"country\": \"Australia\", \"id\": \"BNE\"}, {\"name\": \"Nadi\", \"iata\": \"NAN\", \"lat\": -17.755392, \"lng\": 177.443378, \"passengers\": 2000000, \"country\": \"Fiji\", \"id\": \"NAN\"}, {\"name\": \"Auckland\", \"iata\": \"AKL\", \"lat\": -37.008056, \"lng\": 174.791667, \"passengers\": 21000000, \"country\": \"New Zealand\", \"id\": \"AKL\"},\n",
        "    {\"name\": \"Paris\", \"iata\": \"CDG\", \"lat\": 49.009722, \"lng\": 2.547778, \"passengers\": 57000000, \"country\": \"France\", \"id\": \"CDG\"}, {\"name\": \"Madrid\", \"iata\": \"MAD\", \"lat\": 40.4675, \"lng\": -3.551944, \"passengers\": 50000000, \"country\": \"Spain\", \"id\": \"MAD\"}, {\"name\": \"Barcelona\", \"iata\": \"BCN\", \"lat\": 41.297078, \"lng\": 2.078464, \"passengers\": 41000000, \"country\": \"Spain\", \"id\": \"BCN\"}, {\"name\": \"Rome\", \"iata\": \"FCO\", \"lat\": 41.804444, \"lng\": 12.250833, \"passengers\": 29000000, \"country\": \"Italy\", \"id\": \"FCO\"},\n",
        "    {\"name\": \"Copenhagen\", \"iata\": \"CPH\", \"lat\": 55.617917, \"lng\": 12.655972, \"passengers\": 30000000, \"country\": \"Denmark\", \"id\": \"CPH\"}, {\"name\": \"Helsinki\", \"iata\": \"HEL\", \"lat\": 60.317222, \"lng\": 24.963333, \"passengers\": 5000000, \"country\": \"Finland\", \"id\": \"HEL\"}, {\"name\": \"Athens\", \"iata\": \"ATH\", \"lat\": 37.936358, \"lng\": 23.944467, \"passengers\": 22000000, \"country\": \"Greece\", \"id\": \"ATH\"}, {\"name\": \"Dublin\", \"iata\": \"DUB\", \"lat\": 53.421333, \"lng\": -6.270075, \"passengers\": 32000000, \"country\": \"Ireland\", \"id\": \"DUB\"},\n",
        "    {\"name\": \"Reykjavik\", \"iata\": \"RKV\", \"lat\": 64.13, \"lng\": -21.940556, \"passengers\": 400000, \"country\": \"Iceland\", \"id\": \"RKV\"}, {\"name\": \"Oslo\", \"iata\": \"OSL\", \"lat\": 60.193917, \"lng\": 11.100361, \"passengers\": 9000000, \"country\": \"Norway\", \"id\": \"OSL\"}, {\"name\": \"Vienna\", \"iata\": \"VIE\", \"lat\": 48.110833, \"lng\": 16.570833, \"passengers\": 10000000, \"country\": \"Austria\", \"id\": \"VIE\"}, {\"name\": \"Lisbon\", \"iata\": \"LIS\", \"lat\": 38.774167, \"lng\": -9.134167, \"passengers\": 28000000, \"country\": \"Portugal\", \"id\": \"LIS\"},\n",
        "    {\"name\": \"Stockholm\", \"iata\": \"ARN\", \"lat\": 59.651944, \"lng\": 17.918611, \"passengers\": 7000000, \"country\": \"Sweden\", \"id\": \"ARN\"}, {\"name\": \"Edinburgh\", \"iata\": \"EDI\", \"lat\": 55.95, \"lng\": -3.3725, \"passengers\": 14000000, \"country\": \"UK\", \"id\": \"EDI\"}, {\"name\": \"Chicago\", \"iata\": \"ORD\", \"lat\": 41.978603, \"lng\": -87.904842, \"passengers\": 54000000, \"country\": \"USA\", \"id\": \"ORD\"}, {\"name\": \"Dallas\", \"iata\": \"DFW\", \"lat\": 32.896828, \"lng\": -97.037997, \"passengers\": 73000000, \"country\": \"USA\", \"id\": \"DFW\"},\n",
        "    {\"name\": \"San Francisco\", \"iata\": \"SFO\", \"lat\": 37.618972, \"lng\": -122.374889, \"passengers\": 42000000, \"country\": \"USA\", \"id\": \"SFO\"}, {\"name\": \"Las Vegas\", \"iata\": \"LAS\", \"lat\": 36.080056, \"lng\": -115.15225, \"passengers\": 52000000, \"country\": \"USA\", \"id\": \"LAS\"}, {\"name\": \"Miami\", \"iata\": \"MIA\", \"lat\": 25.79325, \"lng\": -80.290556, \"passengers\": 50000000, \"country\": \"USA\", \"id\": \"MIA\"}, {\"name\": \"Toronto\", \"iata\": \"YYZ\", \"lat\": 43.677222, \"lng\": -79.630556, \"passengers\": 12000000, \"country\": \"Canada\", \"id\": \"YYZ\"},\n",
        "    {\"name\": \"Vancouver\", \"iata\": \"YVR\", \"lat\": 49.193889, \"lng\": -123.184444, \"passengers\": 19000000, \"country\": \"Canada\", \"id\": \"YVR\"}, {\"name\": \"Montreal\", \"iata\": \"YUL\", \"lat\": 45.47175, \"lng\": -73.736569, \"passengers\": 15000000, \"country\": \"Canada\", \"id\": \"YUL\"}, {\"name\": \"Mexico-City\", \"iata\": \"MEX\", \"lat\": 19.436303, \"lng\": -99.072097, \"passengers\": 46000000, \"country\": \"Mexico\", \"id\": \"MEX\"}, {\"name\": \"Guatemala-City\", \"iata\": \"GUA\", \"lat\": 14.583272, \"lng\": -90.527475, \"passengers\": 2000000, \"country\": \"Guatemala\", \"id\": \"GUA\"},\n",
        "    {\"name\": \"Buenos Aires\", \"iata\": \"EZE\", \"lat\": -34.822222, \"lng\": -58.535833, \"passengers\": 5000000, \"country\": \"Argentina\", \"id\": \"EZE\"}, {\"name\": \"Sao Paulo\", \"iata\": \"GRU\", \"lat\": -23.432075, \"lng\": -46.469511, \"passengers\": 34000000, \"country\": \"Brazil\", \"id\": \"GRU\"}, {\"name\": \"Santiago de Chile\", \"iata\": \"SCL\", \"lat\": -33.392975, \"lng\": -70.785803, \"passengers\": 20000000, \"country\": \"Chile\", \"id\": \"SCL\"}, {\"name\": \"Brasilia\", \"iata\": \"BSB\", \"lat\": -15.871111, \"lng\": -47.918611, \"passengers\": 13000000, \"country\": \"Brazil\", \"id\": \"BSB\"},\n",
        "    {\"name\": \"Bogota\", \"iata\": \"BOG\", \"lat\": 4.701594, \"lng\": -74.146947, \"passengers\": 36000000, \"country\": \"Colombia\", \"id\": \"BOG\"}, {\"name\": \"Caracas\", \"iata\": \"CCS\", \"lat\": 10.601194, \"lng\": -66.991222, \"passengers\": 8000000, \"country\": \"Venezuela\", \"id\": \"CCS\"}\n",
        "  ]\n",
        "connections = [\n",
        "    {'from': 'LAX','to': 'JFK' },   {'from': 'JFK','to': 'GIG' },    {'from': 'JFK','to': 'LIM' },    {'from': 'JFK','to': 'LHR' },    {'from': 'GIG','to': 'FRA' },    {'from': 'LIM','to': 'GIG' },    {'from': 'FRA','to': 'JFK' },    {'from': 'LHR','to': 'FRA' },    {'from': 'FRA','to': 'SVO' },    {'from': 'FRA','to': 'DXB' },    {'from': 'SVO','to': 'DEL' },\n",
        "    {'from': 'SVO','to': 'PVG' },    {'from': 'DEL','to': 'HKG' },    {'from': 'PVG','to': 'HKG' },    {'from': 'PVG','to': 'NRT' },    {'from': 'HKG','to': 'SYD' },    {'from': 'NRT','to': 'SYD' },    {'from': 'DXB','to': 'SVO' },    {'from': 'DXB','to': 'DEL' },    {'from': 'DXB','to': 'DKR' },    {'from': 'DXB','to': 'JNB' },    {'from': 'JNB','to': 'LHR' },\n",
        "    {'from': 'JNB','to': 'DKR' },    {'from': 'SYD','to': 'DXB' },    {'from': 'NBO','to': 'JNB' },    {'from': 'NBO','to': 'DXB' },    {'from': 'ATL','to': 'JFK' },    {'from': 'LAX','to': 'ATL' },    {'from': 'ATL','to': 'LHR' },    {'from': 'ATL','to': 'LIM' },    {'from': 'SCL','to': 'LIM' },    {'from': 'EZE','to': 'SCL' },    {'from': 'SCL','to': 'GRU' },\n",
        "    {'from': 'GIG','to': 'EZE' },    {'from': 'GIG','to': 'GRU' },    {'from': 'BSB','to': 'GIG' },    {'from': 'SCL','to': 'BSB' },    {'from': 'LIM','to': 'BSB' },    {'from': 'BOG','to': 'BSB' },    {'from': 'CCS','to': 'BSB' },    {'from': 'BOG','to': 'GUA' },    {'from': 'CCS','to': 'MIA' },    {'from': 'GUA','to': 'MIA' },    {'from': 'GUA','to': 'MEX' },\n",
        "    {'from': 'MEX','to': 'LAX' },    {'from': 'MEX','to': 'LAX' },    {'from': 'LAX','to': 'SFO' },    {'from': 'SFO','to': 'YVR' },    {'from': 'LAX','to': 'LAS' },    {'from': 'LAX','to': 'DFW' },    {'from': 'LAX','to': 'ORD' },    {'from': 'SFO','to': 'LAS' },    {'from': 'DFW','to': 'ATL' },    {'from': 'ATL','to': 'YYZ' },    {'from': 'ORD','to': 'YYZ' },\n",
        "    {'from': 'YYZ','to': 'YUL' },    {'from': 'YYZ','to': 'JFK' },    {'from': 'YUL','to': 'JFK' },    {'from': 'JNB','to': 'CPT' },    {'from': 'LOS','to': 'DKR' },    {'from': 'NBO','to': 'LOS' },    {'from': 'DKR','to': 'CMN' },    {'from': 'DKR','to': 'CAI' },    {'from': 'NBO','to': 'CAI' },    {'from': 'DXB','to': 'CAI' },    {'from': 'IKA','to': 'DXB' },\n",
        "    {'from': 'IST','to': 'IKA' },    {'from': 'TLV','to': 'ATH' },    {'from': 'CAI','to': 'TLV' },    {'from': 'ATH','to': 'IST' },    {'from': 'FCO','to': 'ATH' },    {'from': 'LIS','to': 'LHR' },    {'from': 'LIS','to': 'MAD' },    {'from': 'MAD','to': 'BCN' },    {'from': 'CDG','to': 'LHR' },    {'from': 'DUB','to': 'LHR' },    {'from': 'EDI','to': 'LHR' },\n",
        "    {'from': 'CDG','to': 'BCN' },    {'from': 'BCN','to': 'FCO' },    {'from': 'VIE','to': 'IST' },    {'from': 'VIE','to': 'SVO' },    {'from': 'CMN','to': 'LIS' },    {'from': 'MAD','to': 'CMN' },    {'from': 'FRA','to': 'CPH' },    {'from': 'LHR','to': 'CPH' },    {'from': 'CPH','to': 'OSL' },    {'from': 'CPH','to': 'ARN' },    {'from': 'ARN','to': 'HEL' },\n",
        "    {'from': 'HEL','to': 'SVO' },    {'from': 'ULN','to': 'PVG' },    {'from': 'CTU','to': 'PVG' },    {'from': 'PVG','to': 'TPE' },    {'from': 'CTU','to': 'HKG' },    {'from': 'TPE','to': 'HKG' },    {'from': 'HKG','to': 'MNL' },    {'from': 'BKK','to': 'HKG' },    {'from': 'SIN','to': 'KUL' },    {'from': 'SIN','to': 'BKK' },    {'from': 'CGK','to': 'SIN' },\n",
        "    {'from': 'MNL','to': 'SIN' },    {'from': 'SIN','to': 'SYD' },    {'from': 'BNE','to': 'SYD' },    {'from': 'SYD','to': 'MEL' },    {'from': 'NAN','to': 'SYD' },    {'from': 'AKL','to': 'SYD' },    {'from': 'NAN','to': 'AKL' },    {'from': 'RKV','to': 'LHR' },    {'from': 'BCN','to': 'CDG' },    {'from': 'BCN','to': 'FRA' },    {'from': 'FCO','to': 'FRA' },\n",
        "    {'from': 'BOG','to': 'MEX' },    {'from': 'BOG','to': 'GRU' },    {'from': 'ATL','to': 'MIA' },    {'from': 'FRA','to': 'IST' },    {'from': 'IST','to': 'DEL' },    {'from': 'PVG','to': 'BKK' },    {'from': 'DEL','to': 'BKK' },\n",
        "  ]"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1970927b-c195-4135-8b10-c0ad7902abab",
      "metadata": {
        "id": "1970927b-c195-4135-8b10-c0ad7902abab"
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        "Provided as data to yFiles Jupyter Graphs results in this graph:"
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        "outputId": "6c088327-c62a-4ddb-dd97-61d9b766c09a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
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            "3d2f57cdcd26430c8233bd0f301510bb"
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      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GraphWidget(layout=Layout(height='800px', width='100%'))"
            ],
            "application/vnd.jupyter.widget-view+json": {
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      ],
      "source": [
        "w = GraphWidget()\n",
        "w.edges = edges = [\n",
        "    {\"start\": connection['from'], \"end\": connection['to'], \"label\": \"\", \"properties\": {}, \"directed\": False}\n",
        "    for connection in connections\n",
        "]\n",
        "\n",
        "w.nodes = nodes = [\n",
        "    {\"id\": airport['iata'], \"properties\": {\"label\": airport['name'], 'passengers': airport['passengers'], 'country': airport['country']}, \"coordinates\": [airport['lat'], airport['lng']]}\n",
        "    for airport in airports\n",
        "]\n",
        "w.node_coordinate_mapping = 'coordinates' # also map the geo-coordinate data because they are available in the dataset\n",
        "display(w)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "66223c0a-fcfa-468e-a04b-6f29ff15055a",
      "metadata": {
        "id": "66223c0a-fcfa-468e-a04b-6f29ff15055a"
      },
      "source": [
        "Among other properties, each airport entry contains a `country` property which we can use as grouping key, such that the airports are grouped by country."
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c0b30cf2-74c3-462f-9e62-37c6fd209887",
      "metadata": {
        "id": "c0b30cf2-74c3-462f-9e62-37c6fd209887"
      },
      "source": [
        "## Different ways to specify node grouping\n",
        "\n",
        "A different parent mapping must be used, depending on whether the group nodes are already part of the data set or not.\n",
        "\n",
        "<dl>\n",
        "    <dt><code>node_parent_mapping</code></dt>\n",
        "    <dd>\n",
        "        This mapping does not create new group node and just resolves the mapped id against the given dataset.<br>\n",
        "        Should be used when the group nodes are already <b>part of</b> the given dataset.<br>\n",
        "        See <a href=#Grouping-existing-nodes---node_parent_mapping>Grouping existing nodes</a> below.\n",
        "    </dd>\n",
        "    <dt><code>node_parent_group_mapping</code></dt>\n",
        "    <dd>\n",
        "        This mapping always creates new dicts based on the given mapping.<br>\n",
        "        Should be used when the group nodes are <b>not part of</b> the given dataset.<br>\n",
        "        See <a href=#Create-new-group-nodes---node_parent_group_mapping>Create new group nodes</a> below.\n",
        "    </dd>\n",
        "</dl>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21d70393-a928-47d5-adb4-cdfc25c1bedd",
      "metadata": {
        "id": "21d70393-a928-47d5-adb4-cdfc25c1bedd"
      },
      "source": [
        "## Grouping existing nodes - `node_parent_mapping`\n",
        "\n",
        "This function should return an id for each given node object which is then used as group node in the widget for the respective child. If the parent node does not existing in the dataset, no grouping is created.\n",
        "\n",
        "### Adding parent nodes to the dataset\n",
        "\n",
        "This example dataset does not contain suitable group nodes, therefore we first add some to showcase this mapping. Therefore, we create artifical \"country\" nodes in the dataset to be able to group the nodes by their `country` property:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "id": "6c3615c0-6f70-4525-94e2-0d674f1e24d9",
      "metadata": {
        "id": "6c3615c0-6f70-4525-94e2-0d674f1e24d9"
      },
      "outputs": [],
      "source": [
        "airport_nodes = [\n",
        "    {\"id\": airport['iata'], \"properties\": {\"label\": airport['name'], 'passengers': airport['passengers'], 'country': airport['country']}, \"coordinates\": [airport['lat'], airport['lng']]}\n",
        "    for airport in airports\n",
        "]\n",
        "\n",
        "countries = []\n",
        "for airport_node in airport_nodes:\n",
        "    # collect unique countries among airports\n",
        "    airport_country = airport_node[\"properties\"][\"country\"]\n",
        "    if not any(country[\"id\"] == airport_country for country in countries):\n",
        "        countries.append({\"id\": airport_country, \"properties\": {\"label\": airport_country}})\n",
        "\n",
        "# create a nodes source with both, the unique countries (reprsenting our group nodes) and airports\n",
        "nodes_with_groups = airport_nodes + countries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "id": "45a57df5-955e-40b4-9862-f8fb24b3f526",
      "metadata": {
        "id": "45a57df5-955e-40b4-9862-f8fb24b3f526",
        "outputId": "4749864c-2eba-4dab-ed2b-f02d05831bc6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Setter for the node parent mapping property.\n",
            "\n",
            "        Parameters\n",
            "        ----------\n",
            "        node_parent_mapping: callable | str\n",
            "            A function that produces node parent IDs or the name of the property to use for the parent binding.\n",
            "            The function should have the same signature as `default_node_parent_mapping`\n",
            "            e.g. take in a node dictionary and return a str or `None` value.\n",
            "            It is expected that the returned value corresponds to the ID of another node (see `nodes`). This parent node\n",
            "            is then created as a group node that groups the corresponding child nodes.\n",
            "\n",
            "        Example\n",
            "        -------\n",
            "        .. code::\n",
            "\n",
            "           from yfiles_jupyter_graphs import GraphWidget\n",
            "           w = GraphWidget()\n",
            "           def custom_node_parent_mapping(node: dict):\n",
            "           ...\n",
            "           w.set_node_parent_mapping(custom_node_parent_mapping)\n",
            "\n",
            "        Returns\n",
            "        -------\n",
            "\n",
            "        \n"
          ]
        }
      ],
      "source": [
        "print(w.set_node_parent_mapping.__doc__)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f6ebe245-24b2-4e2f-bc48-e07eb215cbb3",
      "metadata": {
        "id": "f6ebe245-24b2-4e2f-bc48-e07eb215cbb3"
      },
      "source": [
        "For this we define a new mapping function that provides the `country` property as parent node id.\n",
        "\n",
        "Because we already enriched our datasets with respective node objects, we can use them to group the nodes by those country objects."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "id": "359f5269-2f57-4458-96b6-802c93e3fa87",
      "metadata": {
        "id": "359f5269-2f57-4458-96b6-802c93e3fa87",
        "outputId": "5acffa37-7c62-4d6e-fdb4-28eaf09cd582",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "b6e59d1cd2034df88411177d08e1cf64",
            "ac3aadd4b19c411394df2bdefaa8ecbe"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GraphWidget(layout=Layout(height='800px', width='100%'))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "b6e59d1cd2034df88411177d08e1cf64"
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                }
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          }
        }
      ],
      "source": [
        "w2 = GraphWidget()\n",
        "\n",
        "# use the country property as parent-id for each node\n",
        "w2.node_parent_mapping = \"country\"\n",
        "\n",
        "# assign the data\n",
        "w2.edges = edges\n",
        "w2.nodes = nodes_with_groups # dataset with distinct country node objects\n",
        "\n",
        "# also map the geo-coordinate data because they are available in the dataset\n",
        "w2.node_coordinate_mapping = \"coordinates\"\n",
        "\n",
        "display(w2)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "aca906e5-739d-4264-bd37-24fe7395970f",
      "metadata": {
        "id": "aca906e5-739d-4264-bd37-24fe7395970f"
      },
      "source": [
        "## Create new group nodes - `node_parent_group_mapping`\n",
        "\n",
        "This function creates new group nodes depending on the returned value. The returned value must either be a `str` which is used as label and id for the new group node (i.e. nodes with the same mapped `str` are grouped together), or it must be a `dict` with a mandatory `label` property and optional more key-value pairs that are added as properties to the group. These additional properties are also considered when resolving other node mappings (e.g. for the styling of group nodes)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "id": "172d8c2a-82fa-4907-8f86-863dc3b36269",
      "metadata": {
        "id": "172d8c2a-82fa-4907-8f86-863dc3b36269",
        "outputId": "762b3a47-6ee5-4d02-c868-c02190645937",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "2d2d3d113274414f98a6c3ee367086d4",
            "eb68a6945db54daa9d0b5c7191a884d1"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GraphWidget(layout=Layout(height='800px', width='100%'))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
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          },
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      ],
      "source": [
        "w3 = GraphWidget()\n",
        "\n",
        "# use the country property as parent-id for each node, note how the 'nodes' data does not contain distinct country node objects\n",
        "w3.node_parent_group_mapping = \"country\"\n",
        "\n",
        "# assign the data\n",
        "w3.edges = edges\n",
        "w3.nodes = nodes # does not contain distinct country objects\n",
        "\n",
        "# also map the geo-coordinate data because they are available in the dataset\n",
        "w3.node_coordinate_mapping = \"coordinates\"\n",
        "\n",
        "display(w3)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2006b777-0167-44eb-b496-5b66a55a9c04",
      "metadata": {
        "id": "2006b777-0167-44eb-b496-5b66a55a9c04"
      },
      "source": [
        "## Group node styling\n",
        "\n",
        "Similar to leaf nodes, group nodes may also be styled through the node style mappings, see also [Color Mapping](./03_color_mapping.ipynb) or [Styles Mapping](./08_styles_mapping.ipynb).\n",
        "\n",
        "For example, make the group nodes gray-ish:"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "id": "c2d3a3d7-40dc-425c-944e-a33382c67a0e",
      "metadata": {
        "id": "c2d3a3d7-40dc-425c-944e-a33382c67a0e",
        "outputId": "bfa514d4-f238-477a-f94b-78af587eb650",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "3e5504df0ca54dafa952a8bf274777b8",
            "ac76137b9cfb4ba393e45e3e433acf5a"
          ]
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GraphWidget(layout=Layout(height='800px', width='100%'))"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "3e5504df0ca54dafa952a8bf274777b8"
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          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
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          }
        }
      ],
      "source": [
        "w4 = GraphWidget()\n",
        "w4.node_parent_group_mapping = \"country\"\n",
        "w4.edges = edges\n",
        "w4.nodes = nodes\n",
        "w4.node_coordinate_mapping = 'coordinates'\n",
        "\n",
        "# assign a color mapping to style country nodes differently, otherwise use the widget's default color\n",
        "w4.node_color_mapping = lambda node : None if \"passengers\" in node[\"properties\"] else \"gray\"\n",
        "\n",
        "display(w4)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "cd6096f3-8ffb-4506-9f68-e83082efacec",
      "metadata": {
        "id": "cd6096f3-8ffb-4506-9f68-e83082efacec"
      },
      "source": [
        "## Using optional group node properties\n",
        "\n",
        "The `node_parent_group_mapping` allows to create group nodes with additional properties. These properties are also suspect to node mappings, just like properties of other node objects"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "id": "97e2f72a-858a-48f6-9a81-5a8572d4bbaf",
      "metadata": {
        "id": "97e2f72a-858a-48f6-9a81-5a8572d4bbaf",
        "outputId": "cb5655c5-d03b-4b45-d63b-8385bae39c0b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 817,
          "referenced_widgets": [
            "a42ef7eeb3064c74bf2e041753e92c5a",
            "bf1ae932a1834ad5a4a2731f7517bb7d"
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      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "GraphWidget(layout=Layout(height='800px', width='100%'))"
            ],
            "application/vnd.jupyter.widget-view+json": {
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                }
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          }
        }
      ],
      "source": [
        "w5 = GraphWidget()\n",
        "\n",
        "# assign additional properties to group nodes\n",
        "w5.node_parent_group_mapping = lambda node: {'label': node['properties']['country'], 'color': '#9F4499', 'char_count': len(node['properties']['country'])}\n",
        "\n",
        "w5.edges = edges\n",
        "w5.nodes = nodes\n",
        "w5.node_coordinate_mapping = 'coordinates'\n",
        "\n",
        "# use the additional property as color\n",
        "w5.node_color_mapping = 'color'\n",
        "\n",
        "display(w5)"
      ]
    }
  ],
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