KG_and_Time_Series_Python.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Wimmics/kgstat/blob/main/KG_and_Time_Series_Python.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hdsk9awT5amn"
      },
      "source": [
        "# Applying time series analysis to meteorological data knowledge graph WeKG-MF\n",
        "\n",
        "**maintained by the [WIMMICS team](https://team.inria.fr/wimmics/)**\n",
        "\n",
        "This notebook demonstrates how to access and query WeKG-MF RDF dataset and visualize the geographical and time series data extracted from the knowledge graph.\n",
        "\n",
        "The WeKG-MF dataset offers access to a large set of meteorological variables described through spatial and temporal dimensions.\n",
        "\n",
        "The full description of the dataset can be found here: https://github.com/Wimmics/weather-kg/tree/main/meteo.\n",
        "\n",
        "SPARQL query endpoint: http://weakg.i3s.unice.fr/sparql.\n",
        "\n",
        "\n",
        "**NOTE:** Although this notebook closely resembles the notebook in R it does not produce exactly the same results. This is due to the data updates and the difference in the default parameters of the models used by R and Python packages.\n",
        "\n",
        "**REFERENCES:**\n",
        "\n",
        "Nadia Yacoubi Ayadi, Catherine Faron, Franck Michel, Fabien Gandon, Olivier Corby. WeKG-MF: a Knowledge Graph of Observational Weather Data. ESWC 2022 - 19th Extended Semantic Web Conference, May 2022, Hersonissos, Greece. https://hal.inria.fr/hal-03657694/file/WeKG-MF-Poster-ESWC-2022.pdf\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GoPDIyiK8kI5",
        "outputId": "7ba1b351-b88e-4bfe-880a-8aec39f173e5"
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sys.version_info(major=3, minor=11, micro=11, releaselevel='final', serial=0)\n",
            "Google Colab Version: 0.0.1a2\n"
          ]
        }
      ],
      "source": [
        "import sys\n",
        "print(sys.version_info)\n",
        "\n",
        "colab = 'google.colab' in sys.modules\n",
        "if colab:\n",
        "  import google.colab\n",
        "  print(f\"Google Colab Version: {google.colab.__version__}\")\n",
        "\n",
        "import os\n",
        "conda = os.environ.get('CONDA_DEFAULT_ENV')\n",
        "\n",
        "if conda:\n",
        "  print(f'The runtime {conda} environment is a Conda distribution')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zHJCPLwMzCvO"
      },
      "source": [
        "## Setup\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Hg2IrHbmpUJ"
      },
      "source": [
        "### Enabling querying of the Knowledge Graph  \n",
        "\n",
        "In this notebook we demonstrate the use of [pycorese](https://corese-stack.github.io/corese-python/) which is a Python wrapper of [corese-core](https://corese-stack.github.io/corese-core/) Java library.\n",
        "\n",
        "**CORESE (COnceptual REsource Search Engine)** is a software platform implementing and extending the standards of the Semantic Web. It allows for creating, manipulating, parsing, serializing, querying, reasoning, and validating RDF data. A detailed description of the CORESE software can be found [here](https://corese-stack.github.io/corese-core/)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a79Od5QPzvzw",
        "outputId": "a49d60b1-b406-44a2-c731-8d4a76a10a0b"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "openjdk 11.0.25 2024-10-15\n",
            "OpenJDK Runtime Environment (build 11.0.25+9-post-Ubuntu-1ubuntu122.04)\n",
            "OpenJDK 64-Bit Server VM (build 11.0.25+9-post-Ubuntu-1ubuntu122.04, mixed mode, sharing)\n"
          ]
        }
      ],
      "source": [
        "!java --version"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQVMzYul0G45"
      },
      "source": [
        "#### Install Python interface to CORESE library\n",
        "\n",
        "``pycorese`` is the wrapper of corese-core library that allows to use CORESE in Python. Its documentaion can be found [here](https://corese-stack.github.io/corese-python/).\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "**NOTE:** This notebook uses a fixed version (`0.2.0`) of the ``pycorese`` library to ensure stability and reproducibility. However, the package is actively developed, and newer versions may be available.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "NtkY1LLC0fMu"
      },
      "outputs": [],
      "source": [
        "%pip install pycorese==0.2.0"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "f8_oGhZQ0oJp"
      },
      "source": [
        "#### Launch CORESE engine"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "gztVRi1K0rm6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        },
        "outputId": "5f884de8-6e5b-45a1-e73c-8638e11660c1"
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'4.6.1'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "from  pycorese.api import CoreseAPI\n",
        "\n",
        "corese = CoreseAPI()\n",
        "corese.loadCorese()\n",
        "corese.coreseVersion()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CM1-2YRU5amu"
      },
      "source": [
        "#### Define the dataset endpoint for the SPARQL data access\n",
        "\n",
        "NOTE: depending on the runtime environment an *https* endpoint URL may lead to an error. In this case, try to change it to *http*."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "MrW8hNlg0z7P"
      },
      "outputs": [],
      "source": [
        "endpoint = 'https://weakg.i3s.unice.fr/sparql'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dMoEFPVq5amv"
      },
      "source": [
        "#### Define prefixes to abbreviate namespaces\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "9V9rs13T5amw"
      },
      "outputs": [],
      "source": [
        "sparql_prefixes = '''\n",
        "PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>\n",
        "PREFIX dct: <http://purl.org/dc/terms/>\n",
        "PREFIX rdfs: <http://www.w3.org/2000/01/rdf-schema#>\n",
        "\n",
        "PREFIX qudt: <http://qudt.org/schema/qudt/>\n",
        "PREFIX wdt:  <http://www.wikidata.org/prop/direct/>\n",
        "PREFIX wd:   <http://www.wikidata.org/entity/>\n",
        "PREFIX geosparql:  <http://www.opengis.net/ont/geosparql#>\n",
        "PREFIX qb: <http://purl.org/linked-data/cube#>\n",
        "PREFIX sosa: <http://www.w3.org/ns/sosa/>\n",
        "\n",
        "PREFIX wekg:  <http://ns.inria.fr/meteo/>\n",
        "PREFIX weo: <http://ns.inria.fr/meteo/ontology/>\n",
        "PREFIX wep: <http://ns.inria.fr/meteo/ontology/property/>\n",
        "PREFIX wevp: <http://ns.inria.fr/meteo/vocab/weatherproperty/>\n",
        "PREFIX wes-dimension: <http://ns.inria.fr/meteo/observationslice/dimension#>\n",
        "PREFIX wes-measure: <http://ns.inria.fr/meteo/observationslice/measure#>\n",
        "PREFIX wes-attribute: <http://ns.inria.fr/meteo/observationslice/attribute#>\n",
        "PREFIX wevp-agro: <http://ns.inria.fr/meteo/vocab/agroclimatic/growingDegreeDay>\n",
        "\n",
        "PREFIX geo: <http://www.w3.org/2003/01/geo/wgs84_pos#>\n",
        "\n",
        "# returned data prefixes\n",
        "PREFIX qudtu: <http://qudt.org/vocab/unit/>\n",
        "PREFIX wd:    <http://www.wikidata.org/entity/>\n",
        "PREFIX wdt:   <http://www.wikidata.org/prop/direct/>\n",
        "PREFIX wes:   <http://ns.inria.fr/meteo/weatherstation/>\n",
        "\n",
        "'''"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VrETbDzg30FB"
      },
      "source": [
        "### Enabling visualization tools\n",
        "\n",
        "For charting the data we will be using packages:\n",
        "- `matplotlib` - generates plots, histograms, bar charts, errorcharts, scatterplots, etc., with just a few lines of code\n",
        "- `seaborn` - data visualization library based on matplotlib that provides a high-level interface for drawing attractive and informative statistical graphics\n",
        "- `folium` - makes it easy to visualize data on an  interactive `leaflet` map\n",
        "\n",
        "NOTE: `matplotlib` and `seaborn` usually come with Python distribution (at least it's true for Google Colab runtimes). If this is not the case install the me using either by running  `pip install matplotlib seaborn` or `conda install conda-forge::matplotlib conda-forge::seaborn`."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "LbaGFEPEakLd"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "7v9lbGaDByG2"
      },
      "outputs": [],
      "source": [
        "%pip install folium"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "DKuimlq9G3wa"
      },
      "outputs": [],
      "source": [
        "%pip install basemap"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "20umVR_HmwC3"
      },
      "source": [
        "### Enabling time series analysis tools\n",
        "To accomplish the time series analysis of temperature data the following packages will be used:\n",
        "\n",
        "- `pandas` - contains extensive capabilities and features for working with time series data\n",
        "- `statsmodels.tsa` - is a part of the `statsmodel` package containing model classes and functions that are useful for time series analysis.\n",
        "- `pmdarima` - a statistical library that implements `auto_arima` functionality and other time series analysis capabilities\n",
        "- `scikit-learn` - simple and efficient tools for predictive analysis\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "IZMclAjx3bxS"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pQ5_oKDQQrbQ"
      },
      "outputs": [],
      "source": [
        "%pip install pmdarima"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GoIMwsgZtA7W"
      },
      "source": [
        "Commonly used metrics for evaluating time series forecasting models include:\n",
        "\n",
        "- *Mean Absolute Error (MAE)*: measures the average absolute difference between the predicted values and the actual values. It provides a measure of the average magnitude of errors without considering their direction.\n",
        "\n",
        "- *Mean Squared Error (MSE)*: calculates the average of the squared differences between the predicted values and the actual values. It penalizes larger errors more than MAE, making it sensitive to outliers.\n",
        "\n",
        "- *Root Mean Squared Error (RMSE)*: is the square root of the MSE. It provides a measure of the average magnitude of errors in the same units as the original data.\n",
        "\n",
        "- *Mean Absolute Percentage Error (MAPE)*: calculates the average percentage difference between the predicted values and the actual values. It measures the relative accuracy of the forecast and is particularly useful when comparing forecasts across different time series with varying scales.\n",
        "\n",
        "We define the helper functions to calculates the metrics and t the prediction confidence interval which will be used in the visualization."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "ozKrHNADCR-X"
      },
      "outputs": [],
      "source": [
        "def forecast_metrics(actual, forecast, to_print=False):\n",
        "  from sklearn.metrics import mean_absolute_error, mean_squared_error, mean_absolute_percentage_error\n",
        "\n",
        "  # Calculating accuracy metrics\n",
        "  mae = mean_absolute_error(actual, forecast)\n",
        "  mse = mean_squared_error(actual, forecast)\n",
        "  rmse = np.sqrt(mse)\n",
        "  mape = mean_absolute_percentage_error(actual, forecast)\n",
        "\n",
        "  if to_print:\n",
        "    print(f'Mean Absolute Error (MSA): {mae:.4f}')\n",
        "    print(f'Mean Squared Error (MSE):  {mse:.4f}')\n",
        "    print(f'Root Mean Squared Error (RMSE): {rmse:.4f}')\n",
        "    print(f'Mean Absolute Percentage Error (MAPE): {mape:.4f}')\n",
        "\n",
        "  return   mae, mse, rmse, mape\n",
        "\n",
        "# Calculate confidence intervals\n",
        "def forecast_conf_interval(actual, forecast, ci_level=0.95):\n",
        "  from scipy.stats import norm\n",
        "  import numpy as np\n",
        "\n",
        "  residuals = actual - forecast\n",
        "\n",
        "  # Assuming normal distribution of residuals\n",
        "  z_score = norm.ppf(1 - (1 - ci_level) / 2)# 1.96 for ci_level = 0.95\n",
        "  std_residuals = np.std(residuals)\n",
        "  n = len(actual)\n",
        "\n",
        "  margin_error = z_score * (std_residuals / np.sqrt(n))\n",
        "  lower_ci = forecast - margin_error\n",
        "  upper_ci = forecast + margin_error\n",
        "\n",
        "  return lower_ci, upper_ci"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "FB4A2bEoAv4U"
      },
      "outputs": [],
      "source": [
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmjIzmSimwC5"
      },
      "source": [
        "The setup is done."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lqmLjfXO5amx"
      },
      "source": [
        "## Visualizing weather stations' locations\n",
        "The WeKG dataset has information about the location of the weather stations.\n",
        "\n",
        "Let's query the knowledge graph for the list of weather stations, their geographical coordinates, and administrative regions."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XALyE23w5amx",
        "outputId": "ccc46758-c98c-4c04-fc89-5dbb8574aaa8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(61, 7)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "query_stations = f'''\n",
        "# wdt:P131 = located in the administrative territorial entity\n",
        "# wdt:P2585 = INSEE region code\n",
        "\n",
        "SELECT distinct ?regionCode ?region\n",
        "                ?regionLabel\n",
        "                ?station ?stationName\n",
        "                ?lat ?long\n",
        "\n",
        "WHERE {{\n",
        "  SERVICE <{endpoint}>\n",
        "  {{\n",
        "        ?station  a weo:WeatherStation;\n",
        "                  rdfs:label ?stationName;\n",
        "                  geo:lat ?lat; geo:long ?long.\n",
        "\n",
        "         OPTIONAL {{?station dct:spatial/wdt:P131 ?region.\n",
        "                   ?region rdfs:label ?regionLabel;\n",
        "                           wdt:P2585 ?regionCode.}}\n",
        "  }}\n",
        "}}\n",
        "ORDER BY ?regionCode ?stationName\n",
        "'''\n",
        "graph = corese.Graph()\n",
        "\n",
        "stations_df = corese.sparqlSelect(graph=graph,\n",
        "                                  query='\\n'.join([sparql_prefixes, query_stations]),\n",
        "                                  return_dataframe=True)\n",
        "stations_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "teF9D54mAc7e"
      },
      "source": [
        "Visualize the French Meteo weather station locations on the world map.\n",
        "\n",
        "NOTE: the interactive map may not render properly in GitHub. Use [nbviewer.org](https://nbviewer.org/) to see this notebook not in a runtime environment but with this map."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "rXvy8mcd-Rnc",
        "outputId": "e132e77b-1ae7-41ce-df59-1d07b0dda4e6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Click on a marker to reveal the weather station name.\n",
            "Copy/paste as a value of the station_name variable in the next cell.\n"
          ]
        },
        {
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              "  &quot;preferCanvas&quot;: false,\n",
              "  &quot;scrollWheelZoom&quot;: false,\n",
              "  &quot;tile&quot;: &quot;CartoDB Positron&quot;,\n",
              "}\n",
              "\n",
              "                }\n",
              "            );\n",
              "\n",
              "            \n",
              "\n",
              "        \n",
              "    \n",
              "            var tile_layer_4e25bde5789d878ce99a5a6ce2dde5e6 = L.tileLayer(\n",
              "                &quot;https://tile.openstreetmap.org/{z}/{x}/{y}.png&quot;,\n",
              "                {\n",
              "  &quot;minZoom&quot;: 2,\n",
              "  &quot;maxZoom&quot;: 19,\n",
              "  &quot;maxNativeZoom&quot;: 19,\n",
              "  &quot;noWrap&quot;: false,\n",
              "  &quot;attribution&quot;: &quot;\\u0026copy; \\u003ca href=\\&quot;https://www.openstreetmap.org/copyright\\&quot;\\u003eOpenStreetMap\\u003c/a\\u003e contributors&quot;,\n",
              "  &quot;subdomains&quot;: &quot;abc&quot;,\n",
              "  &quot;detectRetina&quot;: false,\n",
              "  &quot;tms&quot;: false,\n",
              "  &quot;opacity&quot;: 1,\n",
              "}\n",
              "\n",
              "            );\n",
              "        \n",
              "    \n",
              "            tile_layer_4e25bde5789d878ce99a5a6ce2dde5e6.addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var marker_ea06c68701917de941dd8d78a19d92b0 = L.marker(\n",
              "                [-66.663167, 140.001],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_14546cee9c7652ae3e227d1209c12f72 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_ea06c68701917de941dd8d78a19d92b0.setIcon(icon_14546cee9c7652ae3e227d1209c12f72);\n",
              "        \n",
              "    \n",
              "        var popup_5e48dbaf86b61787352f22824d487213 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_8f6d3710ce134c5bbbd5810f8dd9d9aa = $(`&lt;div id=&quot;html_8f6d3710ce134c5bbbd5810f8dd9d9aa&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;DUMONT D&#x27;URVILLE&lt;/div&gt;`)[0];\n",
              "                popup_5e48dbaf86b61787352f22824d487213.setContent(html_8f6d3710ce134c5bbbd5810f8dd9d9aa);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_ea06c68701917de941dd8d78a19d92b0.bindPopup(popup_5e48dbaf86b61787352f22824d487213)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_b8f317b7f2371435db3a68b0de3db7db = L.marker(\n",
              "                [-22.344167, 40.340667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_5649f261e0a7ed1a82e96439537568dc = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_b8f317b7f2371435db3a68b0de3db7db.setIcon(icon_5649f261e0a7ed1a82e96439537568dc);\n",
              "        \n",
              "    \n",
              "        var popup_b1d75ada4ea3186e9489ae7515e396db = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_2b4b807c2bdebe03f7566e10b3cf8868 = $(`&lt;div id=&quot;html_2b4b807c2bdebe03f7566e10b3cf8868&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;EUROPA&lt;/div&gt;`)[0];\n",
              "                popup_b1d75ada4ea3186e9489ae7515e396db.setContent(html_2b4b807c2bdebe03f7566e10b3cf8868);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_b8f317b7f2371435db3a68b0de3db7db.bindPopup(popup_b1d75ada4ea3186e9489ae7515e396db)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_30f656504c68f111ca755852707bee28 = L.marker(\n",
              "                [-11.582667, 47.289667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_07f54afb8360ca38a02323c00e373526 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_30f656504c68f111ca755852707bee28.setIcon(icon_07f54afb8360ca38a02323c00e373526);\n",
              "        \n",
              "    \n",
              "        var popup_d69e2c56d450fca46ebcfc60bdee565a = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_fcc883727f3ee0002619fa48c35b8c71 = $(`&lt;div id=&quot;html_fcc883727f3ee0002619fa48c35b8c71&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;GLORIEUSES&lt;/div&gt;`)[0];\n",
              "                popup_d69e2c56d450fca46ebcfc60bdee565a.setContent(html_fcc883727f3ee0002619fa48c35b8c71);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_30f656504c68f111ca755852707bee28.bindPopup(popup_d69e2c56d450fca46ebcfc60bdee565a)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_9d7c7001a1b2b0920ffbd7d892a68151 = L.marker(\n",
              "                [-17.054667, 42.712],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_9d5168b511f867420ea1746babcdaa63 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_9d7c7001a1b2b0920ffbd7d892a68151.setIcon(icon_9d5168b511f867420ea1746babcdaa63);\n",
              "        \n",
              "    \n",
              "        var popup_9747e33964250996039225a14b32ab91 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_faa955adcd0276133de9de72df2629c1 = $(`&lt;div id=&quot;html_faa955adcd0276133de9de72df2629c1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;JUAN DE NOVA&lt;/div&gt;`)[0];\n",
              "                popup_9747e33964250996039225a14b32ab91.setContent(html_faa955adcd0276133de9de72df2629c1);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_9d7c7001a1b2b0920ffbd7d892a68151.bindPopup(popup_9747e33964250996039225a14b32ab91)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_518bd585c1144a8d320887fdc9a7a196 = L.marker(\n",
              "                [-49.352333, 70.243333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_62f655d69927231a8058ace29856f42d = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_518bd585c1144a8d320887fdc9a7a196.setIcon(icon_62f655d69927231a8058ace29856f42d);\n",
              "        \n",
              "    \n",
              "        var popup_792357ce1045e47f3c183e4a8fae6a34 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_b4fb5a08e9e5c2ea35bad6f242a6f807 = $(`&lt;div id=&quot;html_b4fb5a08e9e5c2ea35bad6f242a6f807&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;KERGUELEN&lt;/div&gt;`)[0];\n",
              "                popup_792357ce1045e47f3c183e4a8fae6a34.setContent(html_b4fb5a08e9e5c2ea35bad6f242a6f807);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_518bd585c1144a8d320887fdc9a7a196.bindPopup(popup_792357ce1045e47f3c183e4a8fae6a34)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_82af4fd0fb5692c52cb369d985e873e9 = L.marker(\n",
              "                [-37.795167, 77.569167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_48d777d31dbb75b3a0d37e641ce45c99 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_82af4fd0fb5692c52cb369d985e873e9.setIcon(icon_48d777d31dbb75b3a0d37e641ce45c99);\n",
              "        \n",
              "    \n",
              "        var popup_1c8ff5812b114100708514ed2dedd1e8 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_39c1271ae080eb6ea122ee37237acfc7 = $(`&lt;div id=&quot;html_39c1271ae080eb6ea122ee37237acfc7&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;NOUVELLE AMSTERDAM&lt;/div&gt;`)[0];\n",
              "                popup_1c8ff5812b114100708514ed2dedd1e8.setContent(html_39c1271ae080eb6ea122ee37237acfc7);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_82af4fd0fb5692c52cb369d985e873e9.bindPopup(popup_1c8ff5812b114100708514ed2dedd1e8)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_f7b0a60edeefe67797382161664f7606 = L.marker(\n",
              "                [17.9015, -62.852167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_a923fec149590eda3e807f6e841c3fea = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_f7b0a60edeefe67797382161664f7606.setIcon(icon_a923fec149590eda3e807f6e841c3fea);\n",
              "        \n",
              "    \n",
              "        var popup_f639d2c4b395dad582bcfe9888c636ae = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_4fbd215d20d2a8ee6baeab02ae93c0db = $(`&lt;div id=&quot;html_4fbd215d20d2a8ee6baeab02ae93c0db&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ST-BARTHELEMY METEO&lt;/div&gt;`)[0];\n",
              "                popup_f639d2c4b395dad582bcfe9888c636ae.setContent(html_4fbd215d20d2a8ee6baeab02ae93c0db);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_f7b0a60edeefe67797382161664f7606.bindPopup(popup_f639d2c4b395dad582bcfe9888c636ae)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_7ca719a211d4bea588b489525b388512 = L.marker(\n",
              "                [46.766333, -56.179167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_2e063db3453f2ea59d98957b6c8681ff = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_7ca719a211d4bea588b489525b388512.setIcon(icon_2e063db3453f2ea59d98957b6c8681ff);\n",
              "        \n",
              "    \n",
              "        var popup_0e8509164fa81288f6af489ba388e57d = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_566e05edfa7aa70509303320252cdf87 = $(`&lt;div id=&quot;html_566e05edfa7aa70509303320252cdf87&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ST-PIERRE&lt;/div&gt;`)[0];\n",
              "                popup_0e8509164fa81288f6af489ba388e57d.setContent(html_566e05edfa7aa70509303320252cdf87);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_7ca719a211d4bea588b489525b388512.bindPopup(popup_0e8509164fa81288f6af489ba388e57d)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_622520df05697020c83d34813964fbfe = L.marker(\n",
              "                [-15.887667, 54.520667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_56e11d08bef2e70250aeaffbded5ac04 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_622520df05697020c83d34813964fbfe.setIcon(icon_56e11d08bef2e70250aeaffbded5ac04);\n",
              "        \n",
              "    \n",
              "        var popup_8c29e009f1ecbf0b6cc6698ba2a792bd = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_2fb6471417ed4de98d8c23e90bc0f5bb = $(`&lt;div id=&quot;html_2fb6471417ed4de98d8c23e90bc0f5bb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;TROMELIN&lt;/div&gt;`)[0];\n",
              "                popup_8c29e009f1ecbf0b6cc6698ba2a792bd.setContent(html_2fb6471417ed4de98d8c23e90bc0f5bb);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_622520df05697020c83d34813964fbfe.bindPopup(popup_8c29e009f1ecbf0b6cc6698ba2a792bd)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_a1bc73e8f137e01381508a947a71ca41 = L.marker(\n",
              "                [16.335, -61.004],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_0cca2151b60d81fcc4025ed32f328625 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_a1bc73e8f137e01381508a947a71ca41.setIcon(icon_0cca2151b60d81fcc4025ed32f328625);\n",
              "        \n",
              "    \n",
              "        var popup_5a7434b6832bab582728eefa3f35cf1e = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_c81dbfb8afcc21b435c05dd638a1413f = $(`&lt;div id=&quot;html_c81dbfb8afcc21b435c05dd638a1413f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;LA DESIRADE METEO&lt;/div&gt;`)[0];\n",
              "                popup_5a7434b6832bab582728eefa3f35cf1e.setContent(html_c81dbfb8afcc21b435c05dd638a1413f);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_a1bc73e8f137e01381508a947a71ca41.bindPopup(popup_5a7434b6832bab582728eefa3f35cf1e)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_6c51e734a83c9e7db9bff31106abb8fe = L.marker(\n",
              "                [16.264, -61.516333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_f716c88f89c0a4ad509be4d9cf53dab0 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_6c51e734a83c9e7db9bff31106abb8fe.setIcon(icon_f716c88f89c0a4ad509be4d9cf53dab0);\n",
              "        \n",
              "    \n",
              "        var popup_42c24fff9b846e5506d6f0fddb1d5048 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_dcd840c7426e6439829453c499b93113 = $(`&lt;div id=&quot;html_dcd840c7426e6439829453c499b93113&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;LE RAIZET AERO&lt;/div&gt;`)[0];\n",
              "                popup_42c24fff9b846e5506d6f0fddb1d5048.setContent(html_dcd840c7426e6439829453c499b93113);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_6c51e734a83c9e7db9bff31106abb8fe.bindPopup(popup_42c24fff9b846e5506d6f0fddb1d5048)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_cdebb9c79702349dda35471e25ed12c4 = L.marker(\n",
              "                [14.595333, -60.995667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_8beca3100e6bdd6640d68602e9bc9d3a = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_cdebb9c79702349dda35471e25ed12c4.setIcon(icon_8beca3100e6bdd6640d68602e9bc9d3a);\n",
              "        \n",
              "    \n",
              "        var popup_3bcb29bb93ece4688921f85b1d19d73a = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_a257af53399f90a3584b6644adff2f56 = $(`&lt;div id=&quot;html_a257af53399f90a3584b6644adff2f56&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;LAMENTIN-AERO&lt;/div&gt;`)[0];\n",
              "                popup_3bcb29bb93ece4688921f85b1d19d73a.setContent(html_a257af53399f90a3584b6644adff2f56);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_cdebb9c79702349dda35471e25ed12c4.bindPopup(popup_3bcb29bb93ece4688921f85b1d19d73a)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_2fcc54e5eee8cc5dcc723b0f6710dc0d = L.marker(\n",
              "                [14.7745, -60.875333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_2cb35461abe6519da57ae037b913699b = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_2fcc54e5eee8cc5dcc723b0f6710dc0d.setIcon(icon_2cb35461abe6519da57ae037b913699b);\n",
              "        \n",
              "    \n",
              "        var popup_2334ecc0e372abde3b43236b14cb8b32 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_667c1f563557c9e9fdb69c0f802a41b0 = $(`&lt;div id=&quot;html_667c1f563557c9e9fdb69c0f802a41b0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;TRINITE-CARAVEL&lt;/div&gt;`)[0];\n",
              "                popup_2334ecc0e372abde3b43236b14cb8b32.setContent(html_667c1f563557c9e9fdb69c0f802a41b0);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_2fcc54e5eee8cc5dcc723b0f6710dc0d.bindPopup(popup_2334ecc0e372abde3b43236b14cb8b32)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_bea995777b482559870774279a4d2773 = L.marker(\n",
              "                [4.822333, -52.365333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_12c351b7eb89d90141aa568a2519d8f6 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_bea995777b482559870774279a4d2773.setIcon(icon_12c351b7eb89d90141aa568a2519d8f6);\n",
              "        \n",
              "    \n",
              "        var popup_2935f02e3e3118ffc807be21518618c4 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_d5ba38a0c12b87fc4f38945e47dea6e3 = $(`&lt;div id=&quot;html_d5ba38a0c12b87fc4f38945e47dea6e3&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;CAYENNE-MATOURY&lt;/div&gt;`)[0];\n",
              "                popup_2935f02e3e3118ffc807be21518618c4.setContent(html_d5ba38a0c12b87fc4f38945e47dea6e3);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_bea995777b482559870774279a4d2773.bindPopup(popup_2935f02e3e3118ffc807be21518618c4)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_ac0269e5b23cb41f3f956e11cf2bb2f2 = L.marker(\n",
              "                [3.640167, -54.028333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_22a1f21c231d9b6aa0d9450eb2623ef5 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_ac0269e5b23cb41f3f956e11cf2bb2f2.setIcon(icon_22a1f21c231d9b6aa0d9450eb2623ef5);\n",
              "        \n",
              "    \n",
              "        var popup_7e788c7e98877c197f0808b36a0ce89e = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_9ab2cbcf0ff94cecd632ed872c9ed11b = $(`&lt;div id=&quot;html_9ab2cbcf0ff94cecd632ed872c9ed11b&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MARIPASOULA&lt;/div&gt;`)[0];\n",
              "                popup_7e788c7e98877c197f0808b36a0ce89e.setContent(html_9ab2cbcf0ff94cecd632ed872c9ed11b);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_ac0269e5b23cb41f3f956e11cf2bb2f2.bindPopup(popup_7e788c7e98877c197f0808b36a0ce89e)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_3f90ef37972b953b4c18f0640dc7c8ae = L.marker(\n",
              "                [3.890667, -51.804667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_6b89fb6fc97ca02b4595a3223956e6aa = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_3f90ef37972b953b4c18f0640dc7c8ae.setIcon(icon_6b89fb6fc97ca02b4595a3223956e6aa);\n",
              "        \n",
              "    \n",
              "        var popup_89060dbcb93346ba2587a2d639ba4cf1 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_34153304558a21c400fcf460b6c8d7d1 = $(`&lt;div id=&quot;html_34153304558a21c400fcf460b6c8d7d1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;SAINT GEORGES&lt;/div&gt;`)[0];\n",
              "                popup_89060dbcb93346ba2587a2d639ba4cf1.setContent(html_34153304558a21c400fcf460b6c8d7d1);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_3f90ef37972b953b4c18f0640dc7c8ae.bindPopup(popup_89060dbcb93346ba2587a2d639ba4cf1)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_d4eae6208a78bf2796f68311e53a5543 = L.marker(\n",
              "                [5.4855, -54.031667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_4f0929d6f66f9300f7f12de3e71616d7 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_d4eae6208a78bf2796f68311e53a5543.setIcon(icon_4f0929d6f66f9300f7f12de3e71616d7);\n",
              "        \n",
              "    \n",
              "        var popup_6de49e95834c5deaaa402d28ae637cb0 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_8995e4dafa8627a375eb65c0f8557048 = $(`&lt;div id=&quot;html_8995e4dafa8627a375eb65c0f8557048&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;SAINT LAURENT&lt;/div&gt;`)[0];\n",
              "                popup_6de49e95834c5deaaa402d28ae637cb0.setContent(html_8995e4dafa8627a375eb65c0f8557048);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_d4eae6208a78bf2796f68311e53a5543.bindPopup(popup_6de49e95834c5deaaa402d28ae637cb0)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_778601c6f86544a1cce572a8eb72bd0b = L.marker(\n",
              "                [-20.8925, 55.528667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_e627d05296069ff7908a95e200874849 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_778601c6f86544a1cce572a8eb72bd0b.setIcon(icon_e627d05296069ff7908a95e200874849);\n",
              "        \n",
              "    \n",
              "        var popup_51ce9f510e48b13e92437abb392036e6 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_43e5a55af9e8eaa0cae469ef13c34666 = $(`&lt;div id=&quot;html_43e5a55af9e8eaa0cae469ef13c34666&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;GILLOT-AEROPORT&lt;/div&gt;`)[0];\n",
              "                popup_51ce9f510e48b13e92437abb392036e6.setContent(html_43e5a55af9e8eaa0cae469ef13c34666);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_778601c6f86544a1cce572a8eb72bd0b.bindPopup(popup_51ce9f510e48b13e92437abb392036e6)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_f9387e1dec5718dadb8f8b039ec36e3b = L.marker(\n",
              "                [-12.8055, 45.282833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_343b01b2e8cf963d7d9f2552c62dd886 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_f9387e1dec5718dadb8f8b039ec36e3b.setIcon(icon_343b01b2e8cf963d7d9f2552c62dd886);\n",
              "        \n",
              "    \n",
              "        var popup_b32e783105df06e860e2a6696a376aca = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_d5a650678d78cc9bb3f400cf6ef2df75 = $(`&lt;div id=&quot;html_d5a650678d78cc9bb3f400cf6ef2df75&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;PAMANDZI&lt;/div&gt;`)[0];\n",
              "                popup_b32e783105df06e860e2a6696a376aca.setContent(html_d5a650678d78cc9bb3f400cf6ef2df75);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_f9387e1dec5718dadb8f8b039ec36e3b.bindPopup(popup_b32e783105df06e860e2a6696a376aca)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_89ca5aaf323f0f5008537dd8723c8ebd = L.marker(\n",
              "                [48.716833, 2.384333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_43128664b0672dbfc9cd8f179d340f0a = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_89ca5aaf323f0f5008537dd8723c8ebd.setIcon(icon_43128664b0672dbfc9cd8f179d340f0a);\n",
              "        \n",
              "    \n",
              "        var popup_90c9a4775a11468ebc0e900a131415ca = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_00af1bf75beadb93784c561678064955 = $(`&lt;div id=&quot;html_00af1bf75beadb93784c561678064955&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ORLY&lt;/div&gt;`)[0];\n",
              "                popup_90c9a4775a11468ebc0e900a131415ca.setContent(html_00af1bf75beadb93784c561678064955);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_89ca5aaf323f0f5008537dd8723c8ebd.bindPopup(popup_90c9a4775a11468ebc0e900a131415ca)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_bfd49043780b9b1a20c2e464f19ee3c3 = L.marker(\n",
              "                [47.059167, 2.359833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_904736046c064f307214a02d8e93c5c4 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_bfd49043780b9b1a20c2e464f19ee3c3.setIcon(icon_904736046c064f307214a02d8e93c5c4);\n",
              "        \n",
              "    \n",
              "        var popup_112c3301771d88b458e72327fb3d60b2 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_aced5d2676a8cff4852da90cf0303e47 = $(`&lt;div id=&quot;html_aced5d2676a8cff4852da90cf0303e47&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BOURGES&lt;/div&gt;`)[0];\n",
              "                popup_112c3301771d88b458e72327fb3d60b2.setContent(html_aced5d2676a8cff4852da90cf0303e47);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_bfd49043780b9b1a20c2e464f19ee3c3.bindPopup(popup_112c3301771d88b458e72327fb3d60b2)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_9d13318b88b3957a2984502b1ec3e6e0 = L.marker(\n",
              "                [47.4445, 0.727333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_05fb7e66a7a6534419f761dbcec8b431 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_9d13318b88b3957a2984502b1ec3e6e0.setIcon(icon_05fb7e66a7a6534419f761dbcec8b431);\n",
              "        \n",
              "    \n",
              "        var popup_92300c6bc4b900168327326f043064c5 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_d7c31a0ffb184901efb459a9990ce369 = $(`&lt;div id=&quot;html_d7c31a0ffb184901efb459a9990ce369&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;TOURS&lt;/div&gt;`)[0];\n",
              "                popup_92300c6bc4b900168327326f043064c5.setContent(html_d7c31a0ffb184901efb459a9990ce369);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_9d13318b88b3957a2984502b1ec3e6e0.bindPopup(popup_92300c6bc4b900168327326f043064c5)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_ca117f57877ceb7a8bd645adac9c38a0 = L.marker(\n",
              "                [47.267833, 5.088333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_f48a5a13c7c1caf73740036a47b7a3ef = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_ca117f57877ceb7a8bd645adac9c38a0.setIcon(icon_f48a5a13c7c1caf73740036a47b7a3ef);\n",
              "        \n",
              "    \n",
              "        var popup_cce459caeab788f13536933a88a0a0fe = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_bb7166c1ceeea3b1add4d51956505a6c = $(`&lt;div id=&quot;html_bb7166c1ceeea3b1add4d51956505a6c&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;DIJON-LONGVIC&lt;/div&gt;`)[0];\n",
              "                popup_cce459caeab788f13536933a88a0a0fe.setContent(html_bb7166c1ceeea3b1add4d51956505a6c);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_ca117f57877ceb7a8bd645adac9c38a0.bindPopup(popup_cce459caeab788f13536933a88a0a0fe)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_74e0674068980fc19e83d3e031773d49 = L.marker(\n",
              "                [48.4455, 0.110167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_a7da38ef4fd443b19f5ac130d8238697 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_74e0674068980fc19e83d3e031773d49.setIcon(icon_a7da38ef4fd443b19f5ac130d8238697);\n",
              "        \n",
              "    \n",
              "        var popup_04ebbaecafc52fa4d75bad9e6e8f8e02 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_1c3e71c06b02acafc4830d377d411267 = $(`&lt;div id=&quot;html_1c3e71c06b02acafc4830d377d411267&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ALENCON&lt;/div&gt;`)[0];\n",
              "                popup_04ebbaecafc52fa4d75bad9e6e8f8e02.setContent(html_1c3e71c06b02acafc4830d377d411267);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_74e0674068980fc19e83d3e031773d49.bindPopup(popup_04ebbaecafc52fa4d75bad9e6e8f8e02)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_110d35f565c8b3771bea7ffacfb37a6f = L.marker(\n",
              "                [49.18, -0.456167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_cd85f6e054898261693505ff7c8c417a = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_110d35f565c8b3771bea7ffacfb37a6f.setIcon(icon_cd85f6e054898261693505ff7c8c417a);\n",
              "        \n",
              "    \n",
              "        var popup_9d62a74125a14c6dec5d0ad6bfb71a57 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_4354513c101b13007f371a5df7cf29d5 = $(`&lt;div id=&quot;html_4354513c101b13007f371a5df7cf29d5&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;CAEN-CARPIQUET&lt;/div&gt;`)[0];\n",
              "                popup_9d62a74125a14c6dec5d0ad6bfb71a57.setContent(html_4354513c101b13007f371a5df7cf29d5);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_110d35f565c8b3771bea7ffacfb37a6f.bindPopup(popup_9d62a74125a14c6dec5d0ad6bfb71a57)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_6219cb70919a4541d71012e5fe3791a0 = L.marker(\n",
              "                [49.725167, -1.939833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_972012e7516c9b824df1005897efba15 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_6219cb70919a4541d71012e5fe3791a0.setIcon(icon_972012e7516c9b824df1005897efba15);\n",
              "        \n",
              "    \n",
              "        var popup_b26a0cbba60348d14e3ad2e628585a16 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_29830861a6e577f8343dda3705a7b140 = $(`&lt;div id=&quot;html_29830861a6e577f8343dda3705a7b140&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;PTE DE LA HAGUE&lt;/div&gt;`)[0];\n",
              "                popup_b26a0cbba60348d14e3ad2e628585a16.setContent(html_29830861a6e577f8343dda3705a7b140);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_6219cb70919a4541d71012e5fe3791a0.bindPopup(popup_b26a0cbba60348d14e3ad2e628585a16)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_c9f023ed383506fca69628c59895665e = L.marker(\n",
              "                [49.383, 1.181667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_3aeebedd9db797b51d02b61449f284bb = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_c9f023ed383506fca69628c59895665e.setIcon(icon_3aeebedd9db797b51d02b61449f284bb);\n",
              "        \n",
              "    \n",
              "        var popup_b14f8280b3cd33951550ccd79e57914a = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_17abac9654b4ea4c997abbf19b83c30c = $(`&lt;div id=&quot;html_17abac9654b4ea4c997abbf19b83c30c&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ROUEN-BOOS&lt;/div&gt;`)[0];\n",
              "                popup_b14f8280b3cd33951550ccd79e57914a.setContent(html_17abac9654b4ea4c997abbf19b83c30c);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_c9f023ed383506fca69628c59895665e.bindPopup(popup_b14f8280b3cd33951550ccd79e57914a)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_6465af2e53bc7113fc9e278435bcd154 = L.marker(\n",
              "                [50.136, 1.834],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_eb77785001c2a9a0065dada4f54c6d2e = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_6465af2e53bc7113fc9e278435bcd154.setIcon(icon_eb77785001c2a9a0065dada4f54c6d2e);\n",
              "        \n",
              "    \n",
              "        var popup_23d94b9f2a3fb7767e5494c960d03101 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_a90495611746ecd4a4dbc74bbf6e736e = $(`&lt;div id=&quot;html_a90495611746ecd4a4dbc74bbf6e736e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ABBEVILLE&lt;/div&gt;`)[0];\n",
              "                popup_23d94b9f2a3fb7767e5494c960d03101.setContent(html_a90495611746ecd4a4dbc74bbf6e736e);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_6465af2e53bc7113fc9e278435bcd154.bindPopup(popup_23d94b9f2a3fb7767e5494c960d03101)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_22184bacfb569bfcd3ef0e931d635c77 = L.marker(\n",
              "                [50.57, 3.0975],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_e9c054f1eae20dd1d0ed01fba690c0b5 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_22184bacfb569bfcd3ef0e931d635c77.setIcon(icon_e9c054f1eae20dd1d0ed01fba690c0b5);\n",
              "        \n",
              "    \n",
              "        var popup_d5579762961962e3f07a8b62d6f51fd1 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_8d0ddba519e4f407772c0c44adb0a432 = $(`&lt;div id=&quot;html_8d0ddba519e4f407772c0c44adb0a432&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;LILLE-LESQUIN&lt;/div&gt;`)[0];\n",
              "                popup_d5579762961962e3f07a8b62d6f51fd1.setContent(html_8d0ddba519e4f407772c0c44adb0a432);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_22184bacfb569bfcd3ef0e931d635c77.bindPopup(popup_d5579762961962e3f07a8b62d6f51fd1)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_29da6d80473bfbcaf4fae6cb55a3279c = L.marker(\n",
              "                [47.614333, 7.51],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_cd4e54c1b8b387fd03aacd1b7b0c0fce = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_29da6d80473bfbcaf4fae6cb55a3279c.setIcon(icon_cd4e54c1b8b387fd03aacd1b7b0c0fce);\n",
              "        \n",
              "    \n",
              "        var popup_68e4aba01f69f31205db819ea7419355 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_730abc11a904d7e2c1ae4c3ccc04b9fa = $(`&lt;div id=&quot;html_730abc11a904d7e2c1ae4c3ccc04b9fa&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BALE-MULHOUSE&lt;/div&gt;`)[0];\n",
              "                popup_68e4aba01f69f31205db819ea7419355.setContent(html_730abc11a904d7e2c1ae4c3ccc04b9fa);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_29da6d80473bfbcaf4fae6cb55a3279c.bindPopup(popup_68e4aba01f69f31205db819ea7419355)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_44b00130e501f3fc2aafa7c8f8fd73b3 = L.marker(\n",
              "                [48.581, 5.959833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_2a066713826b064fe94672491f615c7d = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_44b00130e501f3fc2aafa7c8f8fd73b3.setIcon(icon_2a066713826b064fe94672491f615c7d);\n",
              "        \n",
              "    \n",
              "        var popup_6445fe95f794df2530f08ef5beba907c = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_e1041f16337f3a8d032b2f257ec54716 = $(`&lt;div id=&quot;html_e1041f16337f3a8d032b2f257ec54716&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;NANCY-OCHEY&lt;/div&gt;`)[0];\n",
              "                popup_6445fe95f794df2530f08ef5beba907c.setContent(html_e1041f16337f3a8d032b2f257ec54716);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_44b00130e501f3fc2aafa7c8f8fd73b3.bindPopup(popup_6445fe95f794df2530f08ef5beba907c)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_4a7a215842c47e39e1665d7a66e0cef0 = L.marker(\n",
              "                [49.209667, 4.155333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_49cd49ce824a1bfed7ac560a96085249 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_4a7a215842c47e39e1665d7a66e0cef0.setIcon(icon_49cd49ce824a1bfed7ac560a96085249);\n",
              "        \n",
              "    \n",
              "        var popup_0162eb3107aa4f8546e889780a3532a4 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_8fe4d4282c1e3b5e50b8ec2554c25beb = $(`&lt;div id=&quot;html_8fe4d4282c1e3b5e50b8ec2554c25beb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;REIMS-PRUNAY&lt;/div&gt;`)[0];\n",
              "                popup_0162eb3107aa4f8546e889780a3532a4.setContent(html_8fe4d4282c1e3b5e50b8ec2554c25beb);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_4a7a215842c47e39e1665d7a66e0cef0.bindPopup(popup_0162eb3107aa4f8546e889780a3532a4)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_f91e60e13f3a0cc9b2d62de642c172d3 = L.marker(\n",
              "                [48.5495, 7.640333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_232090b7f0915ae61de74ff5e0ad643d = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_f91e60e13f3a0cc9b2d62de642c172d3.setIcon(icon_232090b7f0915ae61de74ff5e0ad643d);\n",
              "        \n",
              "    \n",
              "        var popup_59fc0bbd7263bee5fb4f05c88176b11b = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_453e02b6f7cbca16eadcf22437b4c83f = $(`&lt;div id=&quot;html_453e02b6f7cbca16eadcf22437b4c83f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;STRASBOURG-ENTZHEIM&lt;/div&gt;`)[0];\n",
              "                popup_59fc0bbd7263bee5fb4f05c88176b11b.setContent(html_453e02b6f7cbca16eadcf22437b4c83f);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_f91e60e13f3a0cc9b2d62de642c172d3.bindPopup(popup_59fc0bbd7263bee5fb4f05c88176b11b)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_7544210cc107c22d80315773c20ad2bc = L.marker(\n",
              "                [48.324667, 4.02],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_d3af4a809332b9f2bf8518af0a2933ed = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_7544210cc107c22d80315773c20ad2bc.setIcon(icon_d3af4a809332b9f2bf8518af0a2933ed);\n",
              "        \n",
              "    \n",
              "        var popup_0de8c7eadfe544f81a54949ba9f30a14 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_50f33f9bbea19d3057cfb4d37d4616a6 = $(`&lt;div id=&quot;html_50f33f9bbea19d3057cfb4d37d4616a6&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;TROYES-BARBEREY&lt;/div&gt;`)[0];\n",
              "                popup_0de8c7eadfe544f81a54949ba9f30a14.setContent(html_50f33f9bbea19d3057cfb4d37d4616a6);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_7544210cc107c22d80315773c20ad2bc.bindPopup(popup_0de8c7eadfe544f81a54949ba9f30a14)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_ccdc99484e14e04d65c0a1fc4df54e51 = L.marker(\n",
              "                [47.15, -1.608833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_6f13705daec0692ea335cc92114033a7 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_ccdc99484e14e04d65c0a1fc4df54e51.setIcon(icon_6f13705daec0692ea335cc92114033a7);\n",
              "        \n",
              "    \n",
              "        var popup_4b5854ff642fc8be5d568e2197ba3be8 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_00e6a18f79191d7d2918bb81ade81961 = $(`&lt;div id=&quot;html_00e6a18f79191d7d2918bb81ade81961&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;NANTES-BOUGUENAIS&lt;/div&gt;`)[0];\n",
              "                popup_4b5854ff642fc8be5d568e2197ba3be8.setContent(html_00e6a18f79191d7d2918bb81ade81961);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_ccdc99484e14e04d65c0a1fc4df54e51.bindPopup(popup_4b5854ff642fc8be5d568e2197ba3be8)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_bffd4e909ca529637f36a6bdf01cf695 = L.marker(\n",
              "                [47.294333, -3.218333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_c5cdba9e4cfc2e5a03c4adb07c792513 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_bffd4e909ca529637f36a6bdf01cf695.setIcon(icon_c5cdba9e4cfc2e5a03c4adb07c792513);\n",
              "        \n",
              "    \n",
              "        var popup_e73d8d41a574bcf4df936e2a17c738da = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_d59dfb89fa8d8952b4bd121ff0215b39 = $(`&lt;div id=&quot;html_d59dfb89fa8d8952b4bd121ff0215b39&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BELLE ILE-LE TALUT&lt;/div&gt;`)[0];\n",
              "                popup_e73d8d41a574bcf4df936e2a17c738da.setContent(html_d59dfb89fa8d8952b4bd121ff0215b39);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_bffd4e909ca529637f36a6bdf01cf695.bindPopup(popup_e73d8d41a574bcf4df936e2a17c738da)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_a1d4a9ffcafbbeef23e7a1ba9e5a0e1d = L.marker(\n",
              "                [48.444167, -4.412],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_0890b420daf249560b860a26231bdfae = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_a1d4a9ffcafbbeef23e7a1ba9e5a0e1d.setIcon(icon_0890b420daf249560b860a26231bdfae);\n",
              "        \n",
              "    \n",
              "        var popup_e062073f59ffe3083ac44e699780de7f = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_c6c2a7a9a465d227e6c86d65b42917fb = $(`&lt;div id=&quot;html_c6c2a7a9a465d227e6c86d65b42917fb&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BREST-GUIPAVAS&lt;/div&gt;`)[0];\n",
              "                popup_e062073f59ffe3083ac44e699780de7f.setContent(html_c6c2a7a9a465d227e6c86d65b42917fb);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_a1d4a9ffcafbbeef23e7a1ba9e5a0e1d.bindPopup(popup_e062073f59ffe3083ac44e699780de7f)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_3e9a8de0b779e46a5e379d4d2a1ac901 = L.marker(\n",
              "                [48.825833, -3.473167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_093bfa999d8a39c6a0adc379462a6733 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_3e9a8de0b779e46a5e379d4d2a1ac901.setIcon(icon_093bfa999d8a39c6a0adc379462a6733);\n",
              "        \n",
              "    \n",
              "        var popup_9c666efb07c1d2cb9fa9336c920603f6 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_d580e7bd8fff1d2af0826fe120871ebd = $(`&lt;div id=&quot;html_d580e7bd8fff1d2af0826fe120871ebd&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;PLOUMANAC&#x27;H&lt;/div&gt;`)[0];\n",
              "                popup_9c666efb07c1d2cb9fa9336c920603f6.setContent(html_d580e7bd8fff1d2af0826fe120871ebd);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_3e9a8de0b779e46a5e379d4d2a1ac901.bindPopup(popup_9c666efb07c1d2cb9fa9336c920603f6)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_e42db83848da7d94f45beb2ad6f1f123 = L.marker(\n",
              "                [48.068833, -1.734],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_c3706070fc609deaf31b7b8b53b102bd = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_e42db83848da7d94f45beb2ad6f1f123.setIcon(icon_c3706070fc609deaf31b7b8b53b102bd);\n",
              "        \n",
              "    \n",
              "        var popup_da93826f28fe1174338af8c06d93a808 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_dd1b0af273bc84fa04927c08f123ccf2 = $(`&lt;div id=&quot;html_dd1b0af273bc84fa04927c08f123ccf2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;RENNES-ST JACQUES&lt;/div&gt;`)[0];\n",
              "                popup_da93826f28fe1174338af8c06d93a808.setContent(html_dd1b0af273bc84fa04927c08f123ccf2);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_e42db83848da7d94f45beb2ad6f1f123.bindPopup(popup_da93826f28fe1174338af8c06d93a808)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_69f92803fa78aeeb251c96ab578c476a = L.marker(\n",
              "                [44.830667, -0.691333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_1342ea806eb2389b0f2297a446b55592 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_69f92803fa78aeeb251c96ab578c476a.setIcon(icon_1342ea806eb2389b0f2297a446b55592);\n",
              "        \n",
              "    \n",
              "        var popup_c972766029fde5529048022fef4dacda = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_fb323237f3fda56c841c82c40744a9c9 = $(`&lt;div id=&quot;html_fb323237f3fda56c841c82c40744a9c9&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BORDEAUX-MERIGNAC&lt;/div&gt;`)[0];\n",
              "                popup_c972766029fde5529048022fef4dacda.setContent(html_fb323237f3fda56c841c82c40744a9c9);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_69f92803fa78aeeb251c96ab578c476a.bindPopup(popup_c972766029fde5529048022fef4dacda)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_8dfb400213be349268fd81523a70f600 = L.marker(\n",
              "                [45.861167, 1.175],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_05cc38894d66ee45457eedff5f411814 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_8dfb400213be349268fd81523a70f600.setIcon(icon_05cc38894d66ee45457eedff5f411814);\n",
              "        \n",
              "    \n",
              "        var popup_6b5df82205c43b2c418dea3b9affb647 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_f3bd380358775c0cfd418c0024b3afe5 = $(`&lt;div id=&quot;html_f3bd380358775c0cfd418c0024b3afe5&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;LIMOGES-BELLEGARDE&lt;/div&gt;`)[0];\n",
              "                popup_6b5df82205c43b2c418dea3b9affb647.setContent(html_f3bd380358775c0cfd418c0024b3afe5);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_8dfb400213be349268fd81523a70f600.bindPopup(popup_6b5df82205c43b2c418dea3b9affb647)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_bf9e955e9848fb0837ab09b9a929c2a8 = L.marker(\n",
              "                [43.909833, -0.500167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_d04f6e1c9f4d73b127931b94cf9d085c = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_bf9e955e9848fb0837ab09b9a929c2a8.setIcon(icon_d04f6e1c9f4d73b127931b94cf9d085c);\n",
              "        \n",
              "    \n",
              "        var popup_29fa503f57d437773f12f14236033cef = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_a808d1e2a8ec9fc8f62b4178008c0164 = $(`&lt;div id=&quot;html_a808d1e2a8ec9fc8f62b4178008c0164&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MONT-DE-MARSAN&lt;/div&gt;`)[0];\n",
              "                popup_29fa503f57d437773f12f14236033cef.setContent(html_a808d1e2a8ec9fc8f62b4178008c0164);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_bf9e955e9848fb0837ab09b9a929c2a8.bindPopup(popup_29fa503f57d437773f12f14236033cef)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_187a863a17ea1b3048a1e5a8124363c9 = L.marker(\n",
              "                [46.593833, 0.314333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_577d8dc25b4ffab3711a42c7bfd24ab3 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_187a863a17ea1b3048a1e5a8124363c9.setIcon(icon_577d8dc25b4ffab3711a42c7bfd24ab3);\n",
              "        \n",
              "    \n",
              "        var popup_6ebecfb95aa9c21e832b64f0b4de4097 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_b7f8854ca5443c7e6cf18ca95eec7bd2 = $(`&lt;div id=&quot;html_b7f8854ca5443c7e6cf18ca95eec7bd2&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;POITIERS-BIARD&lt;/div&gt;`)[0];\n",
              "                popup_6ebecfb95aa9c21e832b64f0b4de4097.setContent(html_b7f8854ca5443c7e6cf18ca95eec7bd2);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_187a863a17ea1b3048a1e5a8124363c9.bindPopup(popup_6ebecfb95aa9c21e832b64f0b4de4097)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_f03ba4ad838ebbeb9b36ce551e9e247b = L.marker(\n",
              "                [46.046833, -1.4115],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_3e65d901ea0c81c98fb17972bd59b05f = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_f03ba4ad838ebbeb9b36ce551e9e247b.setIcon(icon_3e65d901ea0c81c98fb17972bd59b05f);\n",
              "        \n",
              "    \n",
              "        var popup_c331e67a073b66a87b6c9922a894e088 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_dd427a3e6783f40f5ff1acc1c750077d = $(`&lt;div id=&quot;html_dd427a3e6783f40f5ff1acc1c750077d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;PTE DE CHASSIRON&lt;/div&gt;`)[0];\n",
              "                popup_c331e67a073b66a87b6c9922a894e088.setContent(html_dd427a3e6783f40f5ff1acc1c750077d);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_f03ba4ad838ebbeb9b36ce551e9e247b.bindPopup(popup_c331e67a073b66a87b6c9922a894e088)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_2af7045a7fd254fc3271b5c28d13e16c = L.marker(\n",
              "                [44.745, 1.396667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_d162aff0de7ca10ca29817c3c9eff685 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_2af7045a7fd254fc3271b5c28d13e16c.setIcon(icon_d162aff0de7ca10ca29817c3c9eff685);\n",
              "        \n",
              "    \n",
              "        var popup_0e2ebecac05fb124789e0df3e99f2a80 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_175a380b137cafa12e319712e77ec9f8 = $(`&lt;div id=&quot;html_175a380b137cafa12e319712e77ec9f8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;GOURDON&lt;/div&gt;`)[0];\n",
              "                popup_0e2ebecac05fb124789e0df3e99f2a80.setContent(html_175a380b137cafa12e319712e77ec9f8);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_2af7045a7fd254fc3271b5c28d13e16c.bindPopup(popup_0e2ebecac05fb124789e0df3e99f2a80)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_5a6f8e68263bd3d5573210fa761b7d9f = L.marker(\n",
              "                [44.1185, 3.0195],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_2597be9abf8797f72df2bc6e1b8d2a92 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_5a6f8e68263bd3d5573210fa761b7d9f.setIcon(icon_2597be9abf8797f72df2bc6e1b8d2a92);\n",
              "        \n",
              "    \n",
              "        var popup_5a5f208918a9aa0ba0478476b9878c68 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_66acf8f6003dd69cb426b68454ae374d = $(`&lt;div id=&quot;html_66acf8f6003dd69cb426b68454ae374d&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MILLAU&lt;/div&gt;`)[0];\n",
              "                popup_5a5f208918a9aa0ba0478476b9878c68.setContent(html_66acf8f6003dd69cb426b68454ae374d);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_5a6f8e68263bd3d5573210fa761b7d9f.bindPopup(popup_5a5f208918a9aa0ba0478476b9878c68)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_b43781ad844b25ba1d29ccba6617a5b9 = L.marker(\n",
              "                [43.577, 3.963167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_48e322a3fb3d1b5b4aed7e7bffe025de = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_b43781ad844b25ba1d29ccba6617a5b9.setIcon(icon_48e322a3fb3d1b5b4aed7e7bffe025de);\n",
              "        \n",
              "    \n",
              "        var popup_9ed5f727b4181bff4eb3a9ccd9641403 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_ef4c181a05dc10048283868da45b35f8 = $(`&lt;div id=&quot;html_ef4c181a05dc10048283868da45b35f8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MONTPELLIER&lt;/div&gt;`)[0];\n",
              "                popup_9ed5f727b4181bff4eb3a9ccd9641403.setContent(html_ef4c181a05dc10048283868da45b35f8);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_b43781ad844b25ba1d29ccba6617a5b9.bindPopup(popup_9ed5f727b4181bff4eb3a9ccd9641403)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_e4d83e84107cd469678e1be6516c3315 = L.marker(\n",
              "                [42.737167, 2.872833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_6ffc3819bd9c32bb1cf0a24d4606163d = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_e4d83e84107cd469678e1be6516c3315.setIcon(icon_6ffc3819bd9c32bb1cf0a24d4606163d);\n",
              "        \n",
              "    \n",
              "        var popup_9201d50e57491fc287afa66591cc5e4d = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_0432a75c9743493bf6659558ddd44b81 = $(`&lt;div id=&quot;html_0432a75c9743493bf6659558ddd44b81&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;PERPIGNAN&lt;/div&gt;`)[0];\n",
              "                popup_9201d50e57491fc287afa66591cc5e4d.setContent(html_0432a75c9743493bf6659558ddd44b81);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_e4d83e84107cd469678e1be6516c3315.bindPopup(popup_9201d50e57491fc287afa66591cc5e4d)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_04025fd2f462790f6f06db0b41167690 = L.marker(\n",
              "                [43.005333, 1.106833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_c5eb932bffe88a42e6aafa4ff45bca24 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_04025fd2f462790f6f06db0b41167690.setIcon(icon_c5eb932bffe88a42e6aafa4ff45bca24);\n",
              "        \n",
              "    \n",
              "        var popup_3944e5982d0e1d801f60ef9affde6237 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_c39c47c6b15d329945c2ae09a0558708 = $(`&lt;div id=&quot;html_c39c47c6b15d329945c2ae09a0558708&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;ST GIRONS&lt;/div&gt;`)[0];\n",
              "                popup_3944e5982d0e1d801f60ef9affde6237.setContent(html_c39c47c6b15d329945c2ae09a0558708);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_04025fd2f462790f6f06db0b41167690.bindPopup(popup_3944e5982d0e1d801f60ef9affde6237)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_46d018bf698b6cfb886629511cc894bc = L.marker(\n",
              "                [43.188, 0.0],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_24dd6f387c0c19d2604e7941271aee92 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_46d018bf698b6cfb886629511cc894bc.setIcon(icon_24dd6f387c0c19d2604e7941271aee92);\n",
              "        \n",
              "    \n",
              "        var popup_e78a80b7e7da9604c14882963a098e5e = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_a6e137653bb162806357c2782818bc5e = $(`&lt;div id=&quot;html_a6e137653bb162806357c2782818bc5e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;TARBES-OSSUN&lt;/div&gt;`)[0];\n",
              "                popup_e78a80b7e7da9604c14882963a098e5e.setContent(html_a6e137653bb162806357c2782818bc5e);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_46d018bf698b6cfb886629511cc894bc.bindPopup(popup_e78a80b7e7da9604c14882963a098e5e)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_2ec4703fd6bf4e0e7f7ec63616446991 = L.marker(\n",
              "                [43.621, 1.378833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_024bbd4c430ce06aedbbbcdd8a81a9c1 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_2ec4703fd6bf4e0e7f7ec63616446991.setIcon(icon_024bbd4c430ce06aedbbbcdd8a81a9c1);\n",
              "        \n",
              "    \n",
              "        var popup_bbd2553aa57ed3d75bc1af0f5c0d9f5c = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_c9a3a0ea3a72bf13cae8987288c6949a = $(`&lt;div id=&quot;html_c9a3a0ea3a72bf13cae8987288c6949a&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;TOULOUSE-BLAGNAC&lt;/div&gt;`)[0];\n",
              "                popup_bbd2553aa57ed3d75bc1af0f5c0d9f5c.setContent(html_c9a3a0ea3a72bf13cae8987288c6949a);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_2ec4703fd6bf4e0e7f7ec63616446991.bindPopup(popup_bbd2553aa57ed3d75bc1af0f5c0d9f5c)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_4b4f12f963d3207dce38cc8093e9eb39 = L.marker(\n",
              "                [45.786833, 3.149333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_c09f93dfff2237a890b0f15f06da5aa0 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_4b4f12f963d3207dce38cc8093e9eb39.setIcon(icon_c09f93dfff2237a890b0f15f06da5aa0);\n",
              "        \n",
              "    \n",
              "        var popup_2874dbf566e337467e4e15fc78454979 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_d7f7d40aef64a54f244315e843b224a0 = $(`&lt;div id=&quot;html_d7f7d40aef64a54f244315e843b224a0&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;CLERMONT-FD&lt;/div&gt;`)[0];\n",
              "                popup_2874dbf566e337467e4e15fc78454979.setContent(html_d7f7d40aef64a54f244315e843b224a0);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_4b4f12f963d3207dce38cc8093e9eb39.bindPopup(popup_2874dbf566e337467e4e15fc78454979)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_e69ac91e2ada3ddfb59a33124daea0b6 = L.marker(\n",
              "                [45.0745, 3.764],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_2f83b625c4f2402f3b46e95b8c722008 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_e69ac91e2ada3ddfb59a33124daea0b6.setIcon(icon_2f83b625c4f2402f3b46e95b8c722008);\n",
              "        \n",
              "    \n",
              "        var popup_eb88ee9b8810afebfbb8b1568e0a9770 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_3659bc77315c97245e4c6656db74a1ea = $(`&lt;div id=&quot;html_3659bc77315c97245e4c6656db74a1ea&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;LE PUY-LOUDES&lt;/div&gt;`)[0];\n",
              "                popup_eb88ee9b8810afebfbb8b1568e0a9770.setContent(html_3659bc77315c97245e4c6656db74a1ea);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_e69ac91e2ada3ddfb59a33124daea0b6.bindPopup(popup_eb88ee9b8810afebfbb8b1568e0a9770)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_6a7b197c2366febd61caacc7d6955afe = L.marker(\n",
              "                [45.7265, 5.077833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_853e5b7efa628f3c4b3a68ba6b1d14f2 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_6a7b197c2366febd61caacc7d6955afe.setIcon(icon_853e5b7efa628f3c4b3a68ba6b1d14f2);\n",
              "        \n",
              "    \n",
              "        var popup_a7a0a599d09374ca5ffc90154792822e = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_eb8710e498c625d3a7448052c4c4f37e = $(`&lt;div id=&quot;html_eb8710e498c625d3a7448052c4c4f37e&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;LYON-ST EXUPERY&lt;/div&gt;`)[0];\n",
              "                popup_a7a0a599d09374ca5ffc90154792822e.setContent(html_eb8710e498c625d3a7448052c4c4f37e);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_6a7b197c2366febd61caacc7d6955afe.bindPopup(popup_a7a0a599d09374ca5ffc90154792822e)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_b17b7d55ce75f2efeed9ffe2844e96be = L.marker(\n",
              "                [44.581167, 4.733],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_0781e5797cbd34639641021c17cf686b = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_b17b7d55ce75f2efeed9ffe2844e96be.setIcon(icon_0781e5797cbd34639641021c17cf686b);\n",
              "        \n",
              "    \n",
              "        var popup_a1c5a3a62b27dc4a9c924934a9912e4d = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_42acfc670a7ffda1d0a8936befc208ec = $(`&lt;div id=&quot;html_42acfc670a7ffda1d0a8936befc208ec&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MONTELIMAR&lt;/div&gt;`)[0];\n",
              "                popup_a1c5a3a62b27dc4a9c924934a9912e4d.setContent(html_42acfc670a7ffda1d0a8936befc208ec);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_b17b7d55ce75f2efeed9ffe2844e96be.bindPopup(popup_a1c5a3a62b27dc4a9c924934a9912e4d)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_a3b57f277198b7d6e12233862d19ee22 = L.marker(\n",
              "                [43.079333, 5.940833],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_d8b3dfa079f83d0c1c9c0cc29f11c4e6 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_a3b57f277198b7d6e12233862d19ee22.setIcon(icon_d8b3dfa079f83d0c1c9c0cc29f11c4e6);\n",
              "        \n",
              "    \n",
              "        var popup_da0436da9829b34f0600e570a6985571 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_2634621074fa9a9078fe021a6cb8938f = $(`&lt;div id=&quot;html_2634621074fa9a9078fe021a6cb8938f&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;CAP CEPET&lt;/div&gt;`)[0];\n",
              "                popup_da0436da9829b34f0600e570a6985571.setContent(html_2634621074fa9a9078fe021a6cb8938f);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_a3b57f277198b7d6e12233862d19ee22.bindPopup(popup_da0436da9829b34f0600e570a6985571)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_dad14aa60abc5d5b7e059ce4f70737f9 = L.marker(\n",
              "                [44.565667, 6.502333],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_536e2cc04df6a305e3cad57ef1cfbf32 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_dad14aa60abc5d5b7e059ce4f70737f9.setIcon(icon_536e2cc04df6a305e3cad57ef1cfbf32);\n",
              "        \n",
              "    \n",
              "        var popup_cfa9ec477168e92ef71af5e171edbf5f = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_62ea657c77eca5517140302ee6a367c1 = $(`&lt;div id=&quot;html_62ea657c77eca5517140302ee6a367c1&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;EMBRUN&lt;/div&gt;`)[0];\n",
              "                popup_cfa9ec477168e92ef71af5e171edbf5f.setContent(html_62ea657c77eca5517140302ee6a367c1);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_dad14aa60abc5d5b7e059ce4f70737f9.bindPopup(popup_cfa9ec477168e92ef71af5e171edbf5f)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_f5fa47cf7efa775a16b8639f290cf4f0 = L.marker(\n",
              "                [43.437667, 5.216],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_3fedd4bb484cbc0e63ba3e0d06f5a1ea = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_f5fa47cf7efa775a16b8639f290cf4f0.setIcon(icon_3fedd4bb484cbc0e63ba3e0d06f5a1ea);\n",
              "        \n",
              "    \n",
              "        var popup_f97d20af4c969c946ceb793921418a2a = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_6260318c8eb2f4b6c95938f13b2978ad = $(`&lt;div id=&quot;html_6260318c8eb2f4b6c95938f13b2978ad&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;MARIGNANE&lt;/div&gt;`)[0];\n",
              "                popup_f97d20af4c969c946ceb793921418a2a.setContent(html_6260318c8eb2f4b6c95938f13b2978ad);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_f5fa47cf7efa775a16b8639f290cf4f0.bindPopup(popup_f97d20af4c969c946ceb793921418a2a)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_1b5203336289214a44d515e714f6013a = L.marker(\n",
              "                [43.648833, 7.209],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_476765686fc2960676f5c618e0187b1d = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_1b5203336289214a44d515e714f6013a.setIcon(icon_476765686fc2960676f5c618e0187b1d);\n",
              "        \n",
              "    \n",
              "        var popup_aa45ebfd2eac5498d2f21c3f6dafaf3d = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_8495cf5b0938f6f2f06441653263d4c8 = $(`&lt;div id=&quot;html_8495cf5b0938f6f2f06441653263d4c8&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;NICE&lt;/div&gt;`)[0];\n",
              "                popup_aa45ebfd2eac5498d2f21c3f6dafaf3d.setContent(html_8495cf5b0938f6f2f06441653263d4c8);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_1b5203336289214a44d515e714f6013a.bindPopup(popup_aa45ebfd2eac5498d2f21c3f6dafaf3d)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_97cbf14ff2d883f9bba68f15802393fa = L.marker(\n",
              "                [41.918, 8.792667],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_c1c8095d46990cd67b291ecf2ecabbc6 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_97cbf14ff2d883f9bba68f15802393fa.setIcon(icon_c1c8095d46990cd67b291ecf2ecabbc6);\n",
              "        \n",
              "    \n",
              "        var popup_e637e692d3e021a2731a0ef548cdbc43 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_70ca3cc97de81171696e0d8102e5c370 = $(`&lt;div id=&quot;html_70ca3cc97de81171696e0d8102e5c370&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;AJACCIO&lt;/div&gt;`)[0];\n",
              "                popup_e637e692d3e021a2731a0ef548cdbc43.setContent(html_70ca3cc97de81171696e0d8102e5c370);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_97cbf14ff2d883f9bba68f15802393fa.bindPopup(popup_e637e692d3e021a2731a0ef548cdbc43)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "    \n",
              "            var marker_fdc7707b6ad749ed4aa1510231b59b15 = L.marker(\n",
              "                [42.540667, 9.485167],\n",
              "                {\n",
              "}\n",
              "            ).addTo(map_bd016499ad446963067fddd53ff91d09);\n",
              "        \n",
              "    \n",
              "            var icon_d76eef5052a4d02769f9c2c8949884e4 = L.AwesomeMarkers.icon(\n",
              "                {\n",
              "  &quot;markerColor&quot;: &quot;blue&quot;,\n",
              "  &quot;iconColor&quot;: &quot;white&quot;,\n",
              "  &quot;icon&quot;: &quot;info-sign&quot;,\n",
              "  &quot;prefix&quot;: &quot;glyphicon&quot;,\n",
              "  &quot;extraClasses&quot;: &quot;fa-rotate-0&quot;,\n",
              "}\n",
              "            );\n",
              "            marker_fdc7707b6ad749ed4aa1510231b59b15.setIcon(icon_d76eef5052a4d02769f9c2c8949884e4);\n",
              "        \n",
              "    \n",
              "        var popup_d0ec338c678ccec69e4a4fd11bd78947 = L.popup({\n",
              "  &quot;maxWidth&quot;: &quot;100%&quot;,\n",
              "});\n",
              "\n",
              "        \n",
              "            \n",
              "                var html_7f4eebb760cc485546bc4e307a2b4ed4 = $(`&lt;div id=&quot;html_7f4eebb760cc485546bc4e307a2b4ed4&quot; style=&quot;width: 100.0%; height: 100.0%;&quot;&gt;BASTIA&lt;/div&gt;`)[0];\n",
              "                popup_d0ec338c678ccec69e4a4fd11bd78947.setContent(html_7f4eebb760cc485546bc4e307a2b4ed4);\n",
              "            \n",
              "        \n",
              "\n",
              "        marker_fdc7707b6ad749ed4aa1510231b59b15.bindPopup(popup_d0ec338c678ccec69e4a4fd11bd78947)\n",
              "        ;\n",
              "\n",
              "        \n",
              "    \n",
              "&lt;/script&gt;\n",
              "&lt;/html&gt;\" width=\"800\" height=\"500\"style=\"border:none !important;\" \"allowfullscreen\" \"webkitallowfullscreen\" \"mozallowfullscreen\"></iframe>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ],
      "source": [
        "import folium\n",
        "fig = folium.Figure(width=800, height=500)\n",
        "\n",
        "map = folium.Map(min_zoom=2, #max_zoom=2,\n",
        "                        zoom_control=True, scrollWheelZoom=False,\n",
        "                        #width='95%', height='95%',\n",
        "                        tile='CartoDB Positron',\n",
        "                        max_bounds=True  ).add_to(fig)\n",
        "for index, row in stations_df.iterrows():\n",
        "    folium.Marker([row.lat, row.long],\n",
        "                  popup=row.stationName, #popup_content\n",
        "                  icon=folium.Icon(color='blue')).add_to(map)\n",
        "\n",
        "print('''Click on a marker to reveal the weather station name.\n",
        "Copy/paste as a value of the station_name variable in the next cell.''')\n",
        "\n",
        "fig"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PUazv_7xmwC8"
      },
      "source": [
        "## Plotting Average Monthly Temperatures\n",
        "\n",
        "The WeKG-MF dataset contains calculated minimum, maximum, and average daily temperatures.\n",
        "\n",
        "We can take advantage of the already aggregated daily temperatures and aggregate them further in a SPARQL query to obtain monthly temperatures and visualize the average monthly temperatures for a weather station."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "aMsRRBBxPE8v"
      },
      "outputs": [],
      "source": [
        "station_name = 'NICE'\n",
        "station_name = station_name.upper()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hn9qUq4vc551",
        "outputId": "4b480248-4bd2-45bb-dda4-99892e5cfef5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(12, 5)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "query_monthly_temps = f'''\n",
        "SELECT ?stationName\n",
        "       ?month\n",
        "       (AVG(?temp_avg) as ?avg)\n",
        "       (AVG(?temp_min) as ?low)\n",
        "       (AVG(?temp_max) as ?high)\n",
        "\n",
        "WHERE\n",
        "{{\n",
        "  SERVICE <{endpoint}>\n",
        "  {{\n",
        "    VALUES (?stationName) {{ (\"{station_name}\") }}\n",
        "    ?stationIRI rdfs:label ?stationName.\n",
        "\n",
        "    ?s a qb:Slice ;\n",
        "      wes-dimension:station ?stationIRI ;\n",
        "      wes-dimension:year ?year;\n",
        "      qb:observation [a qb:Observation ;\n",
        "                      wes-attribute:observationDate ?date ;\n",
        "                      wes-measure:avgDailyTemperature ?temp_avg;\n",
        "                      wes-measure:minDailyTemperature ?temp_min;\n",
        "                      wes-measure:maxDailyTemperature ?temp_max] .\n",
        "\n",
        "    BIND(MONTH(?date) as ?month)\n",
        "  }}\n",
        "}}\n",
        "GROUP BY ?month ?stationIRI ?stationName\n",
        "ORDER BY ?month\n",
        "'''\n",
        "monthly_temps_df = corese.sparqlSelect(graph=corese.Graph(),\n",
        "                                      query='\\n'.join([sparql_prefixes, query_monthly_temps]),\n",
        "                                      return_dataframe=True)\n",
        "\n",
        "monthly_temps_df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 395
        },
        "id": "oGlhjAXe1mxS",
        "outputId": "748a06e0-2bdc-4724-d2e6-a37582ecedd4"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "sns.set_theme(style='whitegrid')\n",
        "\n",
        "# Plot the temparaturechart\n",
        "sns.lineplot(data=monthly_temps_df.loc[:, ['month', 'high', 'low']].melt('month'),\n",
        "              x='month',\n",
        "              y= 'value',\n",
        "              hue='variable',\n",
        "              palette={'high': 'darkred', 'low': 'navy'},\n",
        "              marker='o')\n",
        "\n",
        "\n",
        "# Add labels\n",
        "for i, row in monthly_temps_df.iterrows():\n",
        "    plt.text(row.month, row.high + 0.3, f'{row.high:.1f}',\n",
        "             va='bottom', ha='center', size=10)\n",
        "    plt.text(row.month, row.low + 0.3, f'{row.low:.1f}',\n",
        "             va='bottom', ha='center', size=10)\n",
        "\n",
        "plt.title(f'Average monthly temperatures in {station_name}')\n",
        "plt.xlabel('')\n",
        "plt.ylabel('Temperature (C°)')\n",
        "\n",
        "# Set x-axis ticks to month abbreviations\n",
        "import calendar\n",
        "plt.xticks(ticks=monthly_temps_df.month,\n",
        "           labels = [calendar.month_name[m][:3] for m in monthly_temps_df.month])\n",
        "\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-Kv_tvrFmwC9"
      },
      "source": [
        "## Are the temperatures really rising?\n",
        "\n",
        "\n",
        "To answer this question we need the time series analysis tools like Time Series Decomposition and daily temperature data from the WeKG dataset for a weather station."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "I3VbbIeIWhU4"
      },
      "outputs": [],
      "source": [
        "station_name= 'NICE'\n",
        "station_name = station_name.upper()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nciDraotmwC-",
        "outputId": "f9e654c5-4918-49bd-efd2-3aeadf0adaa5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4018, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "query_daily_temps = f'''\n",
        "SELECT ?station ?stationName\n",
        "       ?date\n",
        "       ?temp_avg\n",
        "       ?temp_min\n",
        "       ?temp_max\n",
        "\n",
        "WHERE\n",
        "{{\n",
        "  SERVICE <{endpoint}>\n",
        "  {{\n",
        "    VALUES (?stationName) {{ (\"{station_name}\") }}\n",
        "    ?station rdfs:label ?stationName.\n",
        "\n",
        "    ?s a qb:Slice ;\n",
        "            wes-dimension:station ?station ;\n",
        "            qb:observation [ a qb:Observation ;\n",
        "            wes-attribute:observationDate ?date ;\n",
        "            wes-measure:avgDailyTemperature ?temp_avg;\n",
        "            wes-measure:minDailyTemperature ?temp_min;\n",
        "            wes-measure:maxDailyTemperature ?temp_max] .\n",
        "\n",
        "  }}\n",
        "}}\n",
        "\n",
        "GROUP BY ?station ?stationName ?date\n",
        "ORDER BY ?date\n",
        "'''\n",
        "daily_temps_df = corese.sparqlSelect(graph=corese.Graph(),\n",
        "                                    query='\\n'.join([sparql_prefixes, query_daily_temps]),\n",
        "                                    return_dataframe=True)\n",
        "\n",
        "daily_temps_df.shape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VwkHyNV4mwC-"
      },
      "source": [
        "### Convert Data Frame to a Time Series object\n",
        "A time series is \"a set of data collected sequentially usually at fixed intervals of time\" (Merriam-Webster 2021). Time series data has characteristics that make it very useful for analyzing phenomena that change over time.\n",
        "\n",
        "For a `pandas.DataFrame`, all it takes to create a time series is to create an index that contains the date."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "ZpnCGl_SWUdP",
        "outputId": "3460b5f6-8eab-4d24-a577-b6a40eacdd8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           count   mean   std  min   25%    50%    75%   max\n",
              "temp_avg  4018.0  16.78  5.93  2.2  11.6  16.05  21.95  30.6\n",
              "temp_min  4018.0  14.02  6.28 -1.2   8.6  13.50  19.30  28.7\n",
              "temp_max  4018.0  19.53  5.73  3.1  14.7  18.90  24.70  36.5"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-cdbb8d10-2442-4f0e-9053-533fb32b29d1\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>temp_avg</th>\n",
              "      <td>4018.0</td>\n",
              "      <td>16.78</td>\n",
              "      <td>5.93</td>\n",
              "      <td>2.2</td>\n",
              "      <td>11.6</td>\n",
              "      <td>16.05</td>\n",
              "      <td>21.95</td>\n",
              "      <td>30.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>temp_min</th>\n",
              "      <td>4018.0</td>\n",
              "      <td>14.02</td>\n",
              "      <td>6.28</td>\n",
              "      <td>-1.2</td>\n",
              "      <td>8.6</td>\n",
              "      <td>13.50</td>\n",
              "      <td>19.30</td>\n",
              "      <td>28.7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>temp_max</th>\n",
              "      <td>4018.0</td>\n",
              "      <td>19.53</td>\n",
              "      <td>5.73</td>\n",
              "      <td>3.1</td>\n",
              "      <td>14.7</td>\n",
              "      <td>18.90</td>\n",
              "      <td>24.70</td>\n",
              "      <td>36.5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "          + ' to learn more about interactive tables.';\n",
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              "  }\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "  @keyframes spin {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"daily_temps_df\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 4018.0,\n        \"max\": 4018.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          4018.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.758150821304131,\n        \"min\": 14.018367346938774,\n        \"max\": 19.534668989547036,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          16.77651816824291\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2769432171492675,\n        \"min\": 5.728066383026962,\n        \"max\": 6.2750142593971585,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          5.925852462373549\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.26789182575654,\n        \"min\": -1.2,\n        \"max\": 3.1,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.050136608962512,\n        \"min\": 8.6,\n        \"max\": 14.7,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          11.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.701388531848019,\n        \"min\": 13.5,\n        \"max\": 18.9,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          16.05\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.7001543165777266,\n        \"min\": 19.3,\n        \"max\": 24.7,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          21.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.067349669420289,\n        \"min\": 28.7,\n        \"max\": 36.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          30.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "daily_temps_df.date = pd.to_datetime(daily_temps_df.date)\n",
        "\n",
        "daily_temps_df.set_index('date', inplace=True)\n",
        "daily_temps_df.index.freq = 'D'\n",
        "\n",
        "# Print the values statistics\n",
        "pd.set_option('display.precision', 2)\n",
        "daily_temps_df.describe().transpose()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zGUyi5_kmwC_"
      },
      "source": [
        "### Time Series Decomposition\n",
        "Time series decomposition separates a time series into three fundamental components that can be added together to create the original data:\n",
        "\n",
        "- a seasonal component\n",
        "- a long-term trend component\n",
        "- a random component\n",
        "\n",
        "When using historical data to anticipate what might happen in the future, we often wish to separate the seasonal and random components to see the long-term historical trends.\n",
        "\n",
        "We use Seasonal-Trend decomposition using LOESS (STL) to decompose the high and low daily temperatures time series. STL uses locally estimated scatterplot smoothing (LOESS) to extract smooth estimates of the three components."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 990
        },
        "id": "fs1_iolCi5OP",
        "outputId": "3414ff4b-4c61-41c4-f3af-c65388cceea9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 0 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from statsmodels.tsa.seasonal import STL\n",
        "\n",
        "plt.figure(figsize=(8, 8))\n",
        "\n",
        "colors = {'high': 'darkred', 'low': 'navy'}\n",
        "\n",
        "sns.set_theme(style='whitegrid', font_scale=0.8,\n",
        "              palette=colors.values(),\n",
        "              rc= {'lines.linewidth': 0.2,\n",
        "                   'lines.markersize' : 1})\n",
        "\n",
        "trend_period = (daily_temps_df.index.max() - daily_temps_df.index.min() ).days\n",
        "\n",
        "stl_max = STL(daily_temps_df.temp_max, period=365, trend=trend_period)\n",
        "\n",
        "decomposition_max = stl_max.fit()\n",
        "decomposition_max.plot().suptitle( 'Temperature decomposition', y=1.0)\n",
        "\n",
        "sns.set_palette([colors['low']])\n",
        "\n",
        "stl_min = STL(daily_temps_df.temp_min, period=365, trend=trend_period)\n",
        "decomposition_min = stl_min.fit()\n",
        "decomposition_min.plot()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3JYPu3RxDpAq"
      },
      "source": [
        "OBSERVATIONS AND CONCLUSIONS\n",
        "\n",
        "\n",
        "---\n",
        "\n",
        "Yes, the temperatures are rising in Nice, France.\n",
        "\n",
        "- we can see the rising trend for both high and low daily temperatures\n",
        "- we can see the seasonal change in temperatures as well\n",
        "\n",
        "NOTE: you can try another station data where the trend and seasonal components may be very different."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ryd1c38ZPTz1"
      },
      "source": [
        "## Forecasting\n",
        "\n",
        "Forecasting is the process of making predictions or estimates about future outcomes, or trends based on historical data, patterns, and relationships.\n",
        "\n",
        "To demonstrate forecasting let's take the daily temperatures from 2016 through 2022 for better visualization. We  will use the years 2016 through 2021 to train a forecast model to predict the temperatures of 2022, and we will compare the predicted and actual data to evaluate how good is the forecasting model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "yVvfbHaQg6IE"
      },
      "outputs": [],
      "source": [
        "# Create monthly data from daily data\n",
        "ts_data = daily_temps_df.temp_avg.resample('M', label='left').max()\n",
        "ts_data = ts_data['2016-01-01' : '2022-12-31' ]\n",
        "\n",
        "# Split into training and test data\n",
        "training_data_monthly = ts_data.head(ts_data.shape[0] - 12)\n",
        "test_data_monthly = ts_data.tail(12)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "crYc9dRKGbta"
      },
      "source": [
        "### Forecasting monthly temperatures with Holt-Winters forecast\n",
        "To forecast future temperatures based on historical observations, we can use [Holt-Winters](https://otexts.com/fpp2/holt-winters.html) model that considers past seasonal cycles, trends, and random variation.\n",
        "\n",
        "It is a quick method but it only uses seasonal and trend components."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "qHiBIwzSm39p"
      },
      "outputs": [],
      "source": [
        "from statsmodels.tsa.holtwinters import ExponentialSmoothing\n",
        "\n",
        "model_tmax = ExponentialSmoothing(training_data_monthly,\n",
        "                                  seasonal='additive',\n",
        "                                  seasonal_periods=12).fit()\n",
        "\n",
        "forecast_monthly = model_tmax.forecast(test_data_monthly.shape[0])\n",
        "\n",
        "# Calculate error scores and a confidence interval\n",
        "ci = forecast_conf_interval(test_data_monthly, forecast_monthly)\n",
        "mae, mse, rmse, mape = forecast_metrics(test_data_monthly, forecast_monthly)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "79m7GWKOAv4f"
      },
      "source": [
        "Visulaize the actual data and forecast data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "3vM6bOZUqKb1",
        "outputId": "9012cffb-8049-4f82-f26b-5995e007c5a2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "sns.set_theme(style='whitegrid', font_scale=0.8)\n",
        "\n",
        "sns.lineplot(training_data_monthly, color='black', legend=True, label='train')\n",
        "sns.lineplot(test_data_monthly, legend=True, label='test')\n",
        "sns.lineplot(forecast_monthly , color='purple', legend=True, label='forecast')\n",
        "plt.fill_between(test_data_monthly.index, ci[0], ci[1], color='lavender')\n",
        "\n",
        "plt.title(f'Monthly Average Temperature Forecast for {station_name}\\nHolt-Winters Forecast Model')\n",
        "\n",
        "plt.xlabel(f'\\n MAE ={mae:.2f}     MSE = {mse:.2f}' +\n",
        "           f'\\nRMSA ={rmse:.2f}   MAPE = {mape:.2f}',   ha='center')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9m9qOwJ0LjYh"
      },
      "source": [
        "OBSERVATIONS AND CONCLUSIONS:\n",
        "\n",
        "---\n",
        "\n",
        "- by looking at the Monthly Temperature chart we can observe that the forecast is pretty close to reality, especially for the spring and fall months\n",
        "- on average the forecast is 1 degree off (mean absolute error- MAE)\n",
        "- the Holt-Winters model forecast's MAPE is ~5%. Not too bad\n",
        "\n",
        "\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9kNiyzFVQbau"
      },
      "source": [
        "### Forecasting monthly temperatures with Seasonal ARIMA (auto-arima)\n",
        "\n",
        "Auto Regressive Integrated Moving Average ([ARIMA]( https://otexts.com/fpp2/arima.html) ) modeling involves a detailed analysis of the training data using lags and lagged forecast errors.\n",
        "\n",
        "- *AutoRegression (AR)*: a specific type of regression model where the dependent variable depends on past values of itself (lags)\n",
        "\n",
        "- *Integrated (I)*: this component involves differencing the time series data to make it stationary (mean and variance remain constant over time). Differencing means subtracting the previous value from the current value to remove trends or seasonality and predicting the difference.\n",
        "\n",
        "- *Moving Average (MA)*: this model works by analyzing how wrong you were in predicting values for the previous time periods to make a better estimate for the current time period.\n",
        "\n",
        "ARIMA model is described by 3 parameters (p, d, q):\n",
        "  -  p - number of time lags (AR)\n",
        "  -  d - order of differencing (I)\n",
        "  -  q - size of moving average window (MA)\n",
        "\n",
        "*Seasonal ARIMA* is an extension of the ARIMA model that includes seasonal components to handle time series data with seasonal patterns. It is particularly useful for modeling and forecasting time series with monthly, quarterly, or yearly patterns.\n",
        "\n",
        "Seasonal ARIMA is represented with the additional 4 parameters:\n",
        " - P - seasonal lag order (SAR)\n",
        " - D - order of differencing for the seasonal part (SI)\n",
        " - Q - number of seasonal moving average terms (SMA)\n",
        " - s - seasonal period, indicating the length of the seasonal cycle (e.g., 12 for monthly data, 4 for quarterly)\n",
        "\n",
        "For example: SARIMA(3, 1, 1)(2, 0, 1, 12) is the model that takes into consideration 3 previous data points, has a differencing component, and considers the prediction error of the previous month's estimation. In addition, it includes the seasonal pattern over 12 months, considering data from 2 previous years and the prediction error of the previous month's estimation.  \n",
        "\n",
        "To find out the optimal combination of these parameters we can call an `auto_arima` function that would do the iteration for us based on the best `AIC` or `BIC` metrics.\n",
        "\n",
        "- AIC - Akaike Information Criterion - estimates the quality of the model based on errors and simplicity (less is better)\n",
        "- BIC - Bayesian Information Criterion - like AIC but with a heavier penalty for complexity (less is better)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "MElXzgkjCMLU",
        "outputId": "6f83fa0c-f668-443e-ddac-640515ff4912"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                     SARIMAX Results                                      \n",
              "==========================================================================================\n",
              "Dep. Variable:                                  y   No. Observations:                   71\n",
              "Model:             SARIMAX(1, 1, 0)x(1, 0, 0, 12)   Log Likelihood                -173.919\n",
              "Date:                            Fri, 17 Jan 2025   AIC                            353.837\n",
              "Time:                                    18:50:50   BIC                            360.583\n",
              "Sample:                                01-31-2016   HQIC                           356.517\n",
              "                                     - 11-30-2021                                         \n",
              "Covariance Type:                              opg                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1         -0.2513      0.147     -1.705      0.088      -0.540       0.038\n",
              "ar.S.L12       0.7348      0.107      6.870      0.000       0.525       0.944\n",
              "sigma2         7.3683      1.242      5.934      0.000       4.935       9.802\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.27   Jarque-Bera (JB):                 0.79\n",
              "Prob(Q):                              0.61   Prob(JB):                         0.67\n",
              "Heteroskedasticity (H):               1.19   Skew:                             0.22\n",
              "Prob(H) (two-sided):                  0.67   Kurtosis:                         3.29\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>                  <td>y</td>               <th>  No. Observations:  </th>    <td>71</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>SARIMAX(1, 1, 0)x(1, 0, 0, 12)</td> <th>  Log Likelihood     </th> <td>-173.919</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Fri, 17 Jan 2025</td>        <th>  AIC                </th>  <td>353.837</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>18:50:50</td>            <th>  BIC                </th>  <td>360.583</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                    <td>01-31-2016</td>           <th>  HQIC               </th>  <td>356.517</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                          <td>- 11-30-2021</td>          <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>               <td>opg</td>              <th>                     </th>     <td> </td>   \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "      <td></td>        <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>    <td>   -0.2513</td> <td>    0.147</td> <td>   -1.705</td> <td> 0.088</td> <td>   -0.540</td> <td>    0.038</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.S.L12</th> <td>    0.7348</td> <td>    0.107</td> <td>    6.870</td> <td> 0.000</td> <td>    0.525</td> <td>    0.944</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th>   <td>    7.3683</td> <td>    1.242</td> <td>    5.934</td> <td> 0.000</td> <td>    4.935</td> <td>    9.802</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.27</td> <th>  Jarque-Bera (JB):  </th> <td>0.79</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.61</td> <th>  Prob(JB):          </th> <td>0.67</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>1.19</td> <th>  Skew:              </th> <td>0.22</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.67</td> <th>  Kurtosis:          </th> <td>3.29</td>\n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using the outer product of gradients (complex-step)."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}          &               y                & \\textbf{  No. Observations:  } &     71      \\\\\n\\textbf{Model:}                  & SARIMAX(1, 1, 0)x(1, 0, 0, 12) & \\textbf{  Log Likelihood     } &  -173.919   \\\\\n\\textbf{Date:}                   &        Fri, 17 Jan 2025        & \\textbf{  AIC                } &  353.837    \\\\\n\\textbf{Time:}                   &            18:50:50            & \\textbf{  BIC                } &  360.583    \\\\\n\\textbf{Sample:}                 &           01-31-2016           & \\textbf{  HQIC               } &  356.517    \\\\\n\\textbf{}                        &          - 11-30-2021          & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}        &              opg               & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                  & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{ar.L1}    &      -0.2513  &        0.147     &    -1.705  &         0.088        &       -0.540    &        0.038     \\\\\n\\textbf{ar.S.L12} &       0.7348  &        0.107     &     6.870  &         0.000        &        0.525    &        0.944     \\\\\n\\textbf{sigma2}   &       7.3683  &        1.242     &     5.934  &         0.000        &        4.935    &        9.802     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Ljung-Box (L1) (Q):}     & 0.27 & \\textbf{  Jarque-Bera (JB):  } & 0.79  \\\\\n\\textbf{Prob(Q):}                & 0.61 & \\textbf{  Prob(JB):          } & 0.67  \\\\\n\\textbf{Heteroskedasticity (H):} & 1.19 & \\textbf{  Skew:              } & 0.22  \\\\\n\\textbf{Prob(H) (two-sided):}    & 0.67 & \\textbf{  Kurtosis:          } & 3.29  \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{SARIMAX Results}\n\\end{center}\n\nWarnings: \\newline\n [1] Covariance matrix calculated using the outer product of gradients (complex-step)."
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "from pmdarima import auto_arima\n",
        "\n",
        "arima_monthly = auto_arima( y=training_data_monthly,\n",
        "                            start_p=1,  max_p=3,\n",
        "                            d=1, max_d=2,\n",
        "                            start_q=1,  max_q=3,\n",
        "                            seasonal=True, m=12,\n",
        "                            start_P=0, max_P=2,\n",
        "                            D=0, max_D=2,\n",
        "                            start_Q=0, max_Q=2,\n",
        "                            information_criterion = 'aic', # (‘aic’, ‘bic’, ‘hqic’, ‘oob’)\n",
        "                            trace=False,\n",
        "                            #error_action='ignore',  # don't want to know if an order does not work\n",
        "                            #suppress_warnings=True,  # don't want convergence warnings\n",
        "                            stepwise=True,\n",
        "                            random_state=42)\n",
        "\n",
        "arima_monthly.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "nRyIjJV4Isu9"
      },
      "outputs": [],
      "source": [
        "# Make a forecast\n",
        "forecast_monthly = arima_monthly.predict(n_periods=12)\n",
        "\n",
        "# Calculate metrics and a confidence interval\n",
        "ci = forecast_conf_interval(test_data_monthly, forecast_monthly)\n",
        "mae, mse, rmse, mape = forecast_metrics(test_data_monthly, forecast_monthly)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "7kVm6ULJKMy4",
        "outputId": "a777a7de-8411-41fc-e421-992178be548d"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "sns.set_theme(style='whitegrid', font_scale=0.8)\n",
        "\n",
        "sns.lineplot(training_data_monthly, color='black', legend=True, label='train')\n",
        "sns.lineplot(test_data_monthly, legend=True, label='test')\n",
        "sns.lineplot(forecast_monthly , color='purple', legend=True, label='forecast')\n",
        "plt.fill_between(test_data_monthly.index, ci[0], ci[1], color='lavender',\n",
        "                 label='95% conf. int.')\n",
        "\n",
        "arima_monthly_order = arima_monthly.get_params()['order']\n",
        "arima_monthly_seasonal_order = arima_monthly.get_params()['seasonal_order']\n",
        "plt.title(f'Monthly Average Temperature Forecast for {station_name}' +\n",
        "          f'\\n Seasonal ARIMA {arima_monthly_order} {arima_monthly_seasonal_order} model')\n",
        "\n",
        "plt.xlabel(f'\\n MAE ={mae:.2f}     MSE = {mse:.2f}' +\n",
        "           f'\\nRMSA ={rmse:.2f}   MAPE = {mape:.2f}',   ha='center')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfXOcoh_lsEd"
      },
      "source": [
        "OBSERVATIONS AND CONCLUSIONS:\n",
        "\n",
        "- best seasonal ARIMA model for average monthly temperatures does not take into account the previous month data rather it take a previous year data for the same month (**0**, 1, 1) (**1**, 0, 1, 12)\n",
        "- however, it takes into account the difference in the temperatures from a previous month but not a previous year for the same month (0, **1**, 1) (1, **0**, 1, 12)\n",
        "- and it takes into account the error of estimation in a previous month's forecast and a previous year's forecast (0, 1, **1**) (1, 0, **1**, 12)\n",
        "- the metrics of ARIMA model are worse than Holts-Winters forecast.\n",
        "\n",
        "More complex models are not always the best."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jn4vrL1vlqnq"
      },
      "source": [
        "### Predicting daily temperatures with ARIMA forecast\n",
        "\n",
        "`Auto_arima` takes a long time and a lot of memory when applied to the daily data. In this case, we can just take the ARIMA parameters that we obtained in R notebook and fit the model with those parameters using `ARIMA` model from `statsmodels.tsa.arima.model`.\n",
        "\n",
        "NOTE: to compare the model results with R notebook we restrict the time period from 2016 to 2021 as it was done there."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "XI8VciQxluX8"
      },
      "outputs": [],
      "source": [
        "ts_data = daily_temps_df.temp_max['2016-01-01' : '2021-12-31' ]\n",
        "ts_data.index = pd.DatetimeIndex(ts_data.index, freq='D')\n",
        "\n",
        "# Split into training and test data\n",
        "training_data_daily = ts_data.head(ts_data.shape[0] - 365)\n",
        "test_data_daily = ts_data.tail(365)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "VrS0lDAjnLT4",
        "outputId": "0cbdb710-80ea-46b8-8749-8ad1232d6435"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<class 'statsmodels.iolib.summary.Summary'>\n",
              "\"\"\"\n",
              "                                     SARIMAX Results                                      \n",
              "==========================================================================================\n",
              "Dep. Variable:                           temp_max   No. Observations:                 1827\n",
              "Model:             ARIMA(1, 0, 2)x(0, 1, [], 365)   Log Likelihood               -3306.278\n",
              "Date:                            Fri, 17 Jan 2025   AIC                           6620.556\n",
              "Time:                                    18:50:58   BIC                           6641.706\n",
              "Sample:                                01-01-2016   HQIC                          6628.446\n",
              "                                     - 12-31-2020                                         \n",
              "Covariance Type:                     Not computed                                         \n",
              "==============================================================================\n",
              "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
              "------------------------------------------------------------------------------\n",
              "ar.L1          0.8658        nan        nan        nan         nan         nan\n",
              "ma.L1         -0.3814        nan        nan        nan         nan         nan\n",
              "ma.L2         -0.0920        nan        nan        nan         nan         nan\n",
              "sigma2         5.3907        nan        nan        nan         nan         nan\n",
              "===================================================================================\n",
              "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):                76.45\n",
              "Prob(Q):                              0.99   Prob(JB):                         0.00\n",
              "Heteroskedasticity (H):               0.94   Skew:                             0.14\n",
              "Prob(H) (two-sided):                  0.53   Kurtosis:                         4.09\n",
              "===================================================================================\n",
              "\n",
              "Warnings:\n",
              "[1] Covariance matrix not calculated.\n",
              "\"\"\""
            ],
            "text/html": [
              "<table class=\"simpletable\">\n",
              "<caption>SARIMAX Results</caption>\n",
              "<tr>\n",
              "  <th>Dep. Variable:</th>              <td>temp_max</td>            <th>  No. Observations:  </th>   <td>1827</td>   \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Model:</th>           <td>ARIMA(1, 0, 2)x(0, 1, [], 365)</td> <th>  Log Likelihood     </th> <td>-3306.278</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Date:</th>                   <td>Fri, 17 Jan 2025</td>        <th>  AIC                </th> <td>6620.556</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Time:</th>                       <td>18:50:58</td>            <th>  BIC                </th> <td>6641.706</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Sample:</th>                    <td>01-01-2016</td>           <th>  HQIC               </th> <td>6628.446</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th></th>                          <td>- 12-31-2020</td>          <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Covariance Type:</th>          <td>Not computed</td>          <th>                     </th>     <td> </td>    \n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "     <td></td>       <th>coef</th>     <th>std err</th>      <th>z</th>      <th>P>|z|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ar.L1</th>  <td>    0.8658</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L1</th>  <td>   -0.3814</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>ma.L2</th>  <td>   -0.0920</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>sigma2</th> <td>    5.3907</td> <td>      nan</td> <td>      nan</td> <td>   nan</td> <td>      nan</td> <td>      nan</td>\n",
              "</tr>\n",
              "</table>\n",
              "<table class=\"simpletable\">\n",
              "<tr>\n",
              "  <th>Ljung-Box (L1) (Q):</th>     <td>0.00</td> <th>  Jarque-Bera (JB):  </th> <td>76.45</td>\n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(Q):</th>                <td>0.99</td> <th>  Prob(JB):          </th> <td>0.00</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Heteroskedasticity (H):</th> <td>0.94</td> <th>  Skew:              </th> <td>0.14</td> \n",
              "</tr>\n",
              "<tr>\n",
              "  <th>Prob(H) (two-sided):</th>    <td>0.53</td> <th>  Kurtosis:          </th> <td>4.09</td> \n",
              "</tr>\n",
              "</table><br/><br/>Warnings:<br/>[1] Covariance matrix not calculated."
            ],
            "text/latex": "\\begin{center}\n\\begin{tabular}{lclc}\n\\toprule\n\\textbf{Dep. Variable:}          &           temp\\_max            & \\textbf{  No. Observations:  } &    1827     \\\\\n\\textbf{Model:}                  & ARIMA(1, 0, 2)x(0, 1, [], 365) & \\textbf{  Log Likelihood     } & -3306.278   \\\\\n\\textbf{Date:}                   &        Fri, 17 Jan 2025        & \\textbf{  AIC                } &  6620.556   \\\\\n\\textbf{Time:}                   &            18:50:58            & \\textbf{  BIC                } &  6641.706   \\\\\n\\textbf{Sample:}                 &           01-01-2016           & \\textbf{  HQIC               } &  6628.446   \\\\\n\\textbf{}                        &          - 12-31-2020          & \\textbf{                     } &             \\\\\n\\textbf{Covariance Type:}        &          Not computed          & \\textbf{                     } &             \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lcccccc}\n                & \\textbf{coef} & \\textbf{std err} & \\textbf{z} & \\textbf{P$> |$z$|$} & \\textbf{[0.025} & \\textbf{0.975]}  \\\\\n\\midrule\n\\textbf{ar.L1}  &       0.8658  &          nan     &       nan  &           nan        &          nan    &          nan     \\\\\n\\textbf{ma.L1}  &      -0.3814  &          nan     &       nan  &           nan        &          nan    &          nan     \\\\\n\\textbf{ma.L2}  &      -0.0920  &          nan     &       nan  &           nan        &          nan    &          nan     \\\\\n\\textbf{sigma2} &       5.3907  &          nan     &       nan  &           nan        &          nan    &          nan     \\\\\n\\bottomrule\n\\end{tabular}\n\\begin{tabular}{lclc}\n\\textbf{Ljung-Box (L1) (Q):}     & 0.00 & \\textbf{  Jarque-Bera (JB):  } & 76.45  \\\\\n\\textbf{Prob(Q):}                & 0.99 & \\textbf{  Prob(JB):          } &  0.00  \\\\\n\\textbf{Heteroskedasticity (H):} & 0.94 & \\textbf{  Skew:              } &  0.14  \\\\\n\\textbf{Prob(H) (two-sided):}    & 0.53 & \\textbf{  Kurtosis:          } &  4.09  \\\\\n\\bottomrule\n\\end{tabular}\n%\\caption{SARIMAX Results}\n\\end{center}\n\nWarnings: \\newline\n [1] Covariance matrix not calculated."
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "from statsmodels.tsa.arima.model import ARIMA\n",
        "\n",
        "arima_daily = ARIMA(training_data_daily,\n",
        "                    order=(1, 0, 2), seasonal_order=(0, 1, 0, 365))\\\n",
        "                    .fit(method='innovations_mle', low_memory=True, cov_type='none')\n",
        "\n",
        "arima_daily.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "yfRZMmTxAZoo"
      },
      "outputs": [],
      "source": [
        "# Make a forecast\n",
        "forecast_daily = arima_daily.forecast(steps=365)\n",
        "\n",
        "# Calculate metrics\n",
        "ci = forecast_conf_interval(test_data_daily, forecast_daily)\n",
        "mae, mse, rmse, mape = forecast_metrics(test_data_daily, forecast_daily)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 438
        },
        "id": "FNJV6ONgALER",
        "outputId": "6b9a8565-62d7-47e8-93cc-d151a1d18b04"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "sns.set_theme(style='whitegrid', font_scale=0.8, rc= {'lines.linewidth': 0.5})\n",
        "\n",
        "#sns.lineplot(training_data_daily, color='black', legend=True, label='train')\n",
        "sns.lineplot(test_data_daily, legend=True, label='test')\n",
        "sns.lineplot(forecast_daily , color='purple', legend=True, label='forecast')\n",
        "plt.fill_between(test_data_daily.index, ci[0], ci[1], color='lavender',\n",
        "                 label='95% conf. int.')\n",
        "\n",
        "plt.title(f'Daily Maximum Temperature Forecast for {station_name}')\n",
        "plt.xlabel(f'\\n MAE ={mae:.2f}     MSE = {mse:.2f}' +\n",
        "           f'\\nRMSA ={rmse:.2f}   MAPE = {mape:.2f}',   ha='center')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "id": "Va2DHnsXPFt4",
        "outputId": "6745cd3b-c7bc-43d9-e5e0-432cf5afa74d"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            temp_max  predicted_mean  ci low  ci high\n",
              "date                                                 \n",
              "2021-02-07      13.4           13.59   13.31    13.87\n",
              "2021-02-21      15.2           14.80   14.52    15.08\n",
              "2021-05-12      20.8           19.00   18.72    19.28\n",
              "2021-06-20      26.1           23.40   23.12    23.68\n",
              "2021-09-17      25.1           27.20   26.92    27.48"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5a8dbcb1-647e-436a-af37-a640433eebdf\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temp_max</th>\n",
              "      <th>predicted_mean</th>\n",
              "      <th>ci low</th>\n",
              "      <th>ci high</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2021-02-07</th>\n",
              "      <td>13.4</td>\n",
              "      <td>13.59</td>\n",
              "      <td>13.31</td>\n",
              "      <td>13.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-02-21</th>\n",
              "      <td>15.2</td>\n",
              "      <td>14.80</td>\n",
              "      <td>14.52</td>\n",
              "      <td>15.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-05-12</th>\n",
              "      <td>20.8</td>\n",
              "      <td>19.00</td>\n",
              "      <td>18.72</td>\n",
              "      <td>19.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-06-20</th>\n",
              "      <td>26.1</td>\n",
              "      <td>23.40</td>\n",
              "      <td>23.12</td>\n",
              "      <td>23.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021-09-17</th>\n",
              "      <td>25.1</td>\n",
              "      <td>27.20</td>\n",
              "      <td>26.92</td>\n",
              "      <td>27.48</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"            axis=1)\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2021-02-07 00:00:00\",\n        \"max\": \"2021-09-17 00:00:00\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"2021-02-21 00:00:00\",\n          \"2021-09-17 00:00:00\",\n          \"2021-05-12 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"temp_max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.709378249862239,\n        \"min\": 13.4,\n        \"max\": 26.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          15.2,\n          25.1,\n          20.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"predicted_mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.738768531922648,\n        \"min\": 13.58992455058025,\n        \"max\": 27.2,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          14.798658916909575,\n          27.2,\n          18.999999986731115\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ci low\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.7387685319226485,\n        \"min\": 13.307236810962292,\n        \"max\": 26.917312260382044,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          14.515971177291618,\n          26.917312260382044,\n          18.71731224711316\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"ci high\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5.738768531922647,\n        \"min\": 13.872612290198207,\n        \"max\": 27.482687739617955,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          15.081346656527533,\n          27.482687739617955,\n          19.28268772634907\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 33
        }
      ],
      "source": [
        "random_dates = test_data_daily.sample(5).sort_index()\n",
        "\n",
        "pd.concat ( [ random_dates,\n",
        "              forecast_daily[random_dates.index],\n",
        "              ci[0][random_dates.index].rename('ci low'),\n",
        "              ci[1][random_dates.index].rename('ci high') ],\n",
        "            axis=1)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqMbVsrcgPLM"
      },
      "source": [
        "OBSERVATIONS AND CONCLUSIONS:\n",
        "\n",
        "---\n",
        "\n",
        "- the forecast is off by 2 degrees on average, definitely not the best forecast\n",
        "- MAPE is pretty high too but at least consistent with ~13% in the R notebook\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fu5_-P1z6818"
      },
      "source": [
        "### Regression Tree(s) for Time Series Forecasting\n",
        "\n",
        "While scikit-learn models (e.g., Random Forest, Histogram-based Gradient Boosting Regression Tree) are not specifically designed for time series forecasting, they can still be applied to time series data, especially when combined with lagged features or other engineered features.\n",
        "\n",
        "For  demonstration purposes, we choose the following features:\n",
        "- a previous day's temperature\n",
        "- an average of the previous week's temperature\n",
        "- a temperature one year ago.\n",
        "\n",
        "NOTE: the `Random Forest Regressor` cannot deal with missing values we should consider an extra year."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "Tozb7qeA5xgK"
      },
      "outputs": [],
      "source": [
        "ts_data = daily_temps_df.temp_max['2016-01-01' : '2022-12-31' ]\n",
        "ts_data.index = pd.DatetimeIndex(ts_data.index, freq='D')\n",
        "\n",
        "ts_data_lagged = pd.concat(\n",
        "              [\n",
        "                  ts_data,\n",
        "                  # add a previous day as predictors\n",
        "                  ts_data.shift(1).rename(\"lagged_temp_1d\"),\n",
        "\n",
        "                  # add an average temperature for previous week\n",
        "                  ts_data.shift(1).rolling(7).mean().rename(\"lagged_mean_1wk\"),\n",
        "\n",
        "                  # add a previous year temperature on that day\n",
        "                  ts_data.shift(365).rename('lagged_temp_1y')\n",
        "              ],\n",
        "              axis=1)\n",
        "\n",
        "# Dropping N/As is required for the Random Forest model\n",
        "# but not for HistGradientBoostingRegressor\n",
        "ts_data_lagged.dropna(inplace=True)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fQ1PAyA27n_W"
      },
      "source": [
        "We demontsrate the use of `scikit-learn` `TimeSeriesSplit` to split the time series into train and test sets.\n",
        "\n",
        "NOTE: `TimeSeriesSplit` also helps with cross-validation splits that are not discussed here for brievity."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "J2qT8yNzkQsa"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "\n",
        "ts_cv = TimeSeriesSplit( n_splits=2,  # its a minimum we can have but we really need just one\n",
        "                        test_size=365 )\n",
        "\n",
        "train_idx, test_idx = list(ts_cv.split(ts_data_lagged))[1]\n",
        "\n",
        "training_data_daily_X = ts_data_lagged.iloc[train_idx].drop('temp_max', axis=1)\n",
        "test_data_daily_X     = ts_data_lagged.iloc[test_idx].drop('temp_max', axis=1)\n",
        "\n",
        "training_data_daily_y = ts_data_lagged.iloc[train_idx].temp_max\n",
        "test_data_daily_y     = ts_data_lagged.iloc[test_idx].temp_max"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BNAULKYn8TbA"
      },
      "source": [
        "Choose a model between `HistGradientBoostingRegressor` and `RandomForestRegressor` and fit it with the training data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "4p791WrGVn-b"
      },
      "outputs": [],
      "source": [
        "from sklearn.ensemble import HistGradientBoostingRegressor, RandomForestRegressor\n",
        "\n",
        "regressor_daily = RandomForestRegressor().fit(training_data_daily_X, training_data_daily_y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wmpVj1Wv8cgr"
      },
      "source": [
        "Make a forecast of temperatures for the last year. Compare the forecast values with actual values to evaluate the qualiy of the model."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "yMr1R5MAY-uJ"
      },
      "outputs": [],
      "source": [
        "# Make a forecast\n",
        "forecast_daily_y = regressor_daily.predict(test_data_daily_X)\n",
        "forecast_daily_y = pd.Series(forecast_daily_y,\n",
        "                             index = test_data_daily_y.index)\n",
        "\n",
        "# Calculate metrics\n",
        "mae, mse, rmse, mape = forecast_metrics(test_data_daily_y, forecast_daily_y)\n",
        "ci = forecast_conf_interval(test_data_daily_y, forecast_daily_y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9rnirBcHTyaK"
      },
      "source": [
        "Visualize last year's actual max daily temperatures and the predicted one to see how close they are."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 453
        },
        "id": "LmVOn-_oZjQc",
        "outputId": "937e0095-5e1c-4824-836a-903c566ba279"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.figure(figsize=(8, 4))\n",
        "sns.set_theme(style='whitegrid', font_scale=0.8, rc= {'lines.linewidth': 0.5})\n",
        "\n",
        "sns.lineplot(test_data_daily_y, legend=True, label='actual')\n",
        "sns.lineplot(forecast_daily_y , color='purple', legend=True, label='forecast')\n",
        "plt.fill_between(test_data_daily_y.index, ci[0], ci[1], color='lavender',\n",
        "                 label='95% conf. int.')\n",
        "\n",
        "model =  'Random Forest Regressor Model' if isinstance(regressor_daily, RandomForestRegressor)\\\n",
        "         else 'Histogram-based Gradient Boosting Regression Tree Model'\n",
        "\n",
        "plt.title(f'Daily Maximum Temperature Forecast for {station_name}\\n{model}')\n",
        "plt.xlabel(f'\\n MAE ={mae:.2f}     MSE = {mse:.2f}' +\n",
        "           f'\\nRMSA ={rmse:.2f}   MAPE = {mape:.2f}',   ha='center')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3zUmieWykN6"
      },
      "source": [
        "OBSERVATIONS AND CONCLUSIONS:\n",
        "\n",
        "---\n",
        "\n",
        "- the results produced by regression models are better than ARIMAs  with 8% MAPE compared to 13%\n",
        "- the fitting of the model does not take long time and a lot of computing power like ARIMA\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-_B8yW-MSZZh"
      },
      "source": [
        "---\n",
        "\n",
        "---\n",
        "\n",
        "This notebook demonstrates the basic integration of knowledge graphs data and time series analysis. It covers only a few TS models, however, it can serve as a starting point for a project that\n",
        "\n",
        "\n",
        "\n",
        "- works with time-stamped data represented by the knowledge graph\n",
        "- looks for the trends and seasonality of data over time\n",
        "- trains forecast models  \n",
        "- visualizes prediction quality metrics\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "lymU9F5gSZZh"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.10.16"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}