{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LmZg-f8THUka"
      },
      "source": [
        "# **Interactive Viewer for Global Gridded Temperature & Salinity (1960–Present)** #"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "For an interactive version of this page please visit the Google Colab:  \n",
        "[<img src=\"https://colab.research.google.com/img/colab_favicon_256px.png\" height=\"35px\" align=CENTER> Open in Google Colab ](https://colab.research.google.com/drive/122NdAxBs2LriP21uAWRtpBBB7lcgreoZ)<br>\n",
        "<sub>(To open link in new tab press Ctrl + click)</sub>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Alternatively this notebook can be opened with Binder by following the link:\n",
        "[Interactive Viewer for Global Gridded Temperature & Salinity (1960–Present)](https://mybinder.org/v2/gh/s4oceanice/literacy.s4oceanice/main?urlpath=%2Fdoc%2Ftree%2Fnotebooks_binder%2Foceanice_cora_point.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0XLmEmCEGpfu"
      },
      "source": [
        "**Purpose**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sRhTLAQyH_ji"
      },
      "source": [
        "This notebook builds an **interactive map interface (via WMS)** to browse **monthly gridded fields of ocean temperature and salinity** derived from in-situ observations.\n",
        "Users can:\n",
        "\n",
        "* Select **time, depth and variable** (temperature or salinity).\n",
        "\n",
        "* Render global maps interactively using the **ncWMS service**.\n",
        "\n",
        "* Extract **time series** and **vertical profiles** at chosen coordinates.\n",
        "\n",
        "It is designed for:\n",
        "\n",
        "* Fast visual inspection of long records (1960 → today).\n",
        "\n",
        "* Quick comparisons across depths.\n",
        "\n",
        "* Teaching/demonstration of WMS and ERDDAP capabilities."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jXBJnPJxGv9K"
      },
      "source": [
        "**Data sources**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t8CPbHmrJFSr"
      },
      "source": [
        "The layers come from the **Copernicus Marine Service product** [INSITU_GLO_PHY_TS_OA_MY_013_052](https://https://www.seanoe.org/data/00351/46219/):\n",
        "* Global objective analysis of **temperature (TEMP)** and **salinity (PSAL)**.\n",
        "\n",
        "* Monthly coverage from **January 1960 to present**.\n",
        "\n",
        "* Based on the **CORA reprocessed in-situ profile database**.\n",
        "\n",
        "* Provides **gap-filled, quality-controlled gridded T/S fields** for **climate research** and **model validation**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "oXHDKogZAAdf"
      },
      "outputs": [],
      "source": [
        "# @title\n",
        "import pandas as pd\n",
        "import requests\n",
        "import xml.etree.ElementTree as ET\n",
        "from IPython.display import display\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "from ipyleaflet import (\n",
        "                        Map,\n",
        "                        WMSLayer\n",
        "                       )\n",
        "from ipywidgets import (\n",
        "                        interact,\n",
        "                        Output,\n",
        "                        Dropdown,\n",
        "                        SelectionSlider,\n",
        "                        FloatSlider,\n",
        "                        SelectionRangeSlider,\n",
        "                        VBox,\n",
        "                        Label\n",
        "                       )\n",
        "\n",
        "\n",
        "SLA_URL = 'https://prod-erddap.emodnet-physics.eu/ncWMS/wms?SERVICE=WMS&REQUEST=GetCapabilities&VERSION=1.3.0&DATASET=INSITU_GLO_PHY_TS_OA_MY_013_052'\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p50ofr9_PHk2"
      },
      "source": [
        "**Instructions to use this Notebook**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zku0I7_JPRb4"
      },
      "source": [
        "Run each code cell by clicking the **Play button** (▶️) button on the left of every grey code block.\n",
        "Execute cells in order to activate all interactive features."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2TAphcEjPHIf"
      },
      "source": [
        "**Explaining the code**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kBLPIgDCPF78"
      },
      "source": [
        "\n",
        "**1. Importing Required Libraries**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wz0UwgyZNzvw"
      },
      "source": [
        "The notebook uses several Python packages, grouped by their role:\n",
        "\n",
        "**Data handling & requests**\n",
        "*  pandas as pd – to handle tabular data (e.g., when parsing WMS metadata or time/elevation values into DataFrames).\n",
        "\n",
        "* requests – to make HTTP requests, e.g. calling the WMS GetCapabilities endpoint.\n",
        "\n",
        "* numpy as np – to handle arrays, numeric operations.\n",
        "\n",
        "* xml.etree.ElementTree as ET – to parse XML responses from the WMS server (the GetCapabilities document is XML).\n",
        "\n",
        "**Visualization**\n",
        "\n",
        "* matplotlib.pyplot as plt – to make static plots or visualizations (e.g., time series or maps).\n",
        "\n",
        "* ipyleaflet – interactive map with WMS overlays.\n",
        "\n",
        "**Interactivity**\n",
        "\n",
        "* IPython.display.display – to display objects (tables, widgets, figures) in a Jupyter/Colab notebook.\n",
        "\n",
        "* ipywidgets – sliders, dropdowns, and range selectors."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sItkhNYrVT_g"
      },
      "source": [
        "**2. Fetching and Parsing WMS GetCapabilities Metadata**\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MVqVauBRVVCZ"
      },
      "source": [
        "This block connects to the WMS server and parse the **capabilities XML document** and extracts:\n",
        "\n",
        "* Available layers (TEMP, PSAL).\n",
        "\n",
        "* Depth levels.\n",
        "\n",
        "* Time coverage (monthly from 1960 to today).\n",
        "\n",
        "* Spatial bounds."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "a43942a8"
      },
      "outputs": [],
      "source": [
        "# @title\n",
        "# Fetch the XML data from the URL\n",
        "try:\n",
        "    response = requests.get(SLA_URL)\n",
        "    response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)\n",
        "    xml_data = response.content\n",
        "except requests.exceptions.RequestException as e:\n",
        "    print(f\"Error fetching data from {SLA_URL}: {e}\")\n",
        "    xml_data = None\n",
        "\n",
        "if xml_data:\n",
        "    # Parse the XML data\n",
        "    root = ET.fromstring(xml_data)\n",
        "\n",
        "    # Define namespaces\n",
        "    namespaces = {\n",
        "        'ns0': 'http://www.opengis.net/wms',\n",
        "        'ns2': 'http://www.w3.org/1999/xlink',\n",
        "        'ns3': 'http://reading-escience-centre.github.io/edal-java/wms'\n",
        "    }\n",
        "\n",
        "    # Extract layers, times, and elevations\n",
        "    layers = {}\n",
        "    all_times = set()\n",
        "    all_elevations = set()\n",
        "\n",
        "    for layer in root.findall('.//ns0:Layer', namespaces):\n",
        "        layer_name_element = layer.find('ns0:Name', namespaces)\n",
        "        layer_title_element = layer.find('ns0:Title', namespaces)\n",
        "\n",
        "        if layer_name_element is not None and layer_title_element is not None:\n",
        "            layer_name = layer_name_element.text\n",
        "            # Filter layers to include only those ending with 'PSAL' or 'TEMP'\n",
        "            if layer_name.endswith('PSAL') or layer_name.endswith('TEMP'):\n",
        "                layer_title = layer_title_element.text\n",
        "                layers[layer_name] = {'Title': layer_title}\n",
        "\n",
        "                # Extract BoundingBox information if available\n",
        "                bbox_element = layer.find('ns0:BoundingBox', namespaces)\n",
        "                if bbox_element is not None:\n",
        "                    bbox = {\n",
        "                        'CRS': bbox_element.get('CRS'),\n",
        "                        'minx': bbox_element.get('minx'),\n",
        "                        'miny': bbox_element.get('miny'),\n",
        "                        'maxx': bbox_element.get('maxx'),\n",
        "                        'maxy': bbox_element.get('maxy')\n",
        "                    }\n",
        "                    layers[layer_name]['BoundingBox'] = bbox\n",
        "\n",
        "\n",
        "                # Extract time values\n",
        "                times_element = layer.find('ns0:Dimension[@name=\"time\"]', namespaces)\n",
        "                if times_element is not None:\n",
        "                    times = times_element.text.split(',')\n",
        "                    layers[layer_name]['times'] = times\n",
        "                    all_times.update(times)\n",
        "\n",
        "                # Extract elevation values\n",
        "                elevations_element = layer.find('ns0:Dimension[@name=\"elevation\"]', namespaces)\n",
        "                if elevations_element is not None:\n",
        "                    elevations = elevations_element.text.split(',')\n",
        "                    layers[layer_name]['elevations'] = [float(e) for e in elevations] # Convert to float\n",
        "                    all_elevations.update([float(e) for e in elevations]) # Convert to float\n",
        "\n",
        "\n",
        "    # Convert sets to sorted lists\n",
        "    unique_times = sorted(list(all_times))\n",
        "    unique_elevations = sorted(list(all_elevations))\n",
        "\n",
        "else:\n",
        "    print(\"Could not fetch or parse XML data.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "x2xsrwBpU4uu"
      },
      "source": [
        "**3. Creating User Controls (Layer, Depth, Time)**\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SeCdTyguWN9I"
      },
      "source": [
        "This block builds interactive widgets for:\n",
        "\n",
        "* Variable (TEMP/PSAL).\n",
        "\n",
        "+ Depth level (0–5500 m).\n",
        "\n",
        "* Time step (monthly)."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "id": "f5189da5"
      },
      "outputs": [],
      "source": [
        "# @title\n",
        "# Create a dropdown for layers\n",
        "layer_dropdown = Dropdown(\n",
        "    options=layers.keys(),\n",
        "    description='Layer:',\n",
        "    disabled=False,\n",
        ")\n",
        "\n",
        "# Create a slider for unique_elevations\n",
        "elevation_slider = FloatSlider(\n",
        "    min=min(unique_elevations) if unique_elevations else 0,\n",
        "    max=max(unique_elevations) if unique_elevations else 100,\n",
        "    step=(unique_elevations[1] - unique_elevations[0]) if len(unique_elevations) > 1 else 1,\n",
        "    description='Elevation:',\n",
        "    disabled=False,\n",
        "    continuous_update=False,\n",
        "    orientation='horizontal',\n",
        "    readout=True,\n",
        ")\n",
        "\n",
        "# Create a slider for unique_times\n",
        "time_slider = SelectionSlider(\n",
        "    options=unique_times,\n",
        "    description='Time:',\n",
        "    disabled=False,\n",
        "    continuous_update=False,\n",
        "    orientation='horizontal',\n",
        "    readout=True,\n",
        ")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ExOgHLlVZcPg"
      },
      "source": [
        "**4. Interactive WMS Map**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "numlYlSMZKA1"
      },
      "source": [
        "An interactive global map:\n",
        "\n",
        "* Displays the chosen variable, depth, and time as a **WMS overlay**.\n",
        "\n",
        "* Allows clicking on the map to drop a marker and record coordinates."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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        },
        "id": "e287f46c",
        "outputId": "eeffee95-1c50-481e-9aea-96756e894085"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "dcfb3aae5f3440aa91e464db927ce058",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "interactive(children=(Dropdown(description='Layer:', options=('INSITU_GLO_PHY_TS_OA_MY_013_052/PSAL', 'INSITU_…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "749f30f336de4692b3a14efc85f7144e",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "Output()"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title\n",
        "# Create an output widget to display the map\n",
        "map_output = Output()\n",
        "\n",
        "# Function to update the map based on selections\n",
        "def update_map(layer_name, elevation, time):\n",
        "    with map_output:\n",
        "        map_output.clear_output()\n",
        "\n",
        "        # Construct the WMS URL with selected parameters\n",
        "        wms_url = (\n",
        "            f\"{SLA_URL.split('?')[0]}?\"\n",
        "            f\"SERVICE=WMS&VERSION=1.3.0&REQUEST=GetMap&FORMAT=image/png&TRANSPARENT=true&\"\n",
        "            f\"LAYERS={layer_name}&\"\n",
        "            f\"ELEVATION={elevation}&\"\n",
        "            f\"TIME={time}&\"\n",
        "            f\"CRS=EPSG:4326&STYLES=&WIDTH=256&HEIGHT=256&BBOX=-180,-90,180,90\"\n",
        "        )\n",
        "\n",
        "        # Create a WMS layer\n",
        "        wms_layer = WMSLayer(\n",
        "            url=wms_url,\n",
        "            layers=layer_name,\n",
        "            format='image/png',\n",
        "            transparent=True,\n",
        "            attribution='EMODnet Physics'\n",
        "        )\n",
        "\n",
        "        # Create a map\n",
        "        m = Map(center=(0, 0), zoom=0)\n",
        "\n",
        "        # Add the WMS layer to the map\n",
        "        m.add_layer(wms_layer)\n",
        "\n",
        "        # Variable to store the current marker\n",
        "        current_marker = None\n",
        "\n",
        "        # Add the click handler to the map\n",
        "        from ipyleaflet import Marker\n",
        "\n",
        "        def handle_map_click(**kwargs):\n",
        "            nonlocal current_marker  # Access the current_marker from the outer scope\n",
        "            if kwargs.get('type') == 'click':\n",
        "                clicked_latitude = kwargs.get('coordinates')[0]\n",
        "                clicked_longitude = kwargs.get('coordinates')[1]\n",
        "                #print(f\"Clicked coordinates: Latitude = {clicked_latitude}, Longitude = {clicked_longitude}\")\n",
        "                # You can store these coordinates in a variable or perform other actions here\n",
        "                global last_clicked_coordinates\n",
        "                last_clicked_coordinates = {'lat': clicked_latitude, 'lng': clicked_longitude}\n",
        "\n",
        "                # Remove the previous marker if it exists\n",
        "                if current_marker is not None:\n",
        "                    m.remove_layer(current_marker)\n",
        "\n",
        "                # Add a new marker at the clicked location\n",
        "                marker = Marker(location=(clicked_latitude, clicked_longitude))\n",
        "                m.add_layer(marker)\n",
        "                current_marker = marker # Update the current_marker\n",
        "\n",
        "\n",
        "        m.on_interaction(handle_map_click)\n",
        "\n",
        "\n",
        "        # Display the map\n",
        "        display(m)\n",
        "\n",
        "# Use interact to link the widgets to the update_map function\n",
        "interact(update_map, layer_name=layer_dropdown, elevation=elevation_slider, time=time_slider);\n",
        "\n",
        "# Display the output widget\n",
        "display(map_output)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OGPwv7rwZx_9"
      },
      "source": [
        "**5. Interactive Time Range Selector**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9fKoNdELZrNk"
      },
      "source": [
        "This block adds a time range selection tool using a `SelectionRangeSlider`. It lets users pick a start and end date from all available timestamps. The chosen range is displayed in real time below the slider. By default, the slider spans the full available time range (1960–2024).\n",
        "\n",
        "This provides a simple way to filter data visualization to a specific temporal window."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 62,
          "referenced_widgets": [
            "6920231f111843c2b697e9475b2330a3",
            "b509a1ebceaf4d81a209daeef125b19e",
            "40c9f7bb5f1d4969869d6cd6b78e4ef0",
            "129e9af1d06e47e7826e9c81b48e5ce2",
            "cf7ceba0bfc44837a75ef52717168574",
            "6058faf68d714878b2a8772890013781",
            "877b4101b50f4bbfb7120853b5ce8f0f"
          ]
        },
        "id": "d1wphdDL7WxW",
        "outputId": "d6129d48-b1e8-4d8e-e648-eb88fc60515b"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.jupyter.widget-view+json": {
              "model_id": "6920231f111843c2b697e9475b2330a3",
              "version_major": 2,
              "version_minor": 0
            },
            "text/plain": [
              "VBox(children=(SelectionRangeSlider(continuous_update=False, index=(0, 773), options=('1960-01-01T00:00:00.000…"
            ]
          },
          "metadata": {
            "application/vnd.jupyter.widget-view+json": {
              "colab": {
                "custom_widget_manager": {
                  "url": "https://ssl.gstatic.com/colaboratory-static/widgets/colab-cdn-widget-manager/2b70e893a8ba7c0f/manager.min.js"
                }
              }
            }
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title\n",
        "# Create a RangeSlider for unique_times\n",
        "time_range_slider = SelectionRangeSlider(\n",
        "    options=unique_times,\n",
        "    index=(0, len(unique_times)-1), # Default to selecting the full range\n",
        "    disabled=False,\n",
        "    continuous_update=False,\n",
        "    orientation='horizontal',\n",
        "    readout=False, # Set readout to False to hide the value next to the slider\n",
        ")\n",
        "\n",
        "# Create an output widget to display the selected range\n",
        "range_output = Output()\n",
        "\n",
        "# Function to display the selected range\n",
        "def display_selected_range(change):\n",
        "    with range_output:\n",
        "        range_output.clear_output()\n",
        "        selected_range = change['new']\n",
        "        # Check if the selected range is the default full range\n",
        "        if selected_range == (unique_times[0], unique_times[-1]):\n",
        "            print(f\"Range slider set to the full range: {unique_times[0]} - {unique_times[-1]}\")\n",
        "        else:\n",
        "            print(f\"Time range selected: {selected_range}\")\n",
        "\n",
        "# Observe changes in the time_range_slider and call the display function\n",
        "time_range_slider.observe(display_selected_range, names='value')\n",
        "\n",
        "# Initial display before any interaction\n",
        "with range_output:\n",
        "    print(\"Please select a time range from the slider (maximum range: 1960-01-01T00:00:00.000Z - 2024-06-01T00:00:00Z)\")\n",
        "\n",
        "\n",
        "# Arrange the slider and output in a VBox\n",
        "vbox = VBox([time_range_slider, range_output])\n",
        "\n",
        "# Display the VBox\n",
        "display(vbox)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gTUVAPMOacMW"
      },
      "source": [
        "**6. Time-Series Extraction and Plotting at Selected Location**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XKWrdeysae8i"
      },
      "source": [
        "This block takes the user’s widget selections (layer, depth, time range, and clicked coordinates) and builds a query to the ERDDAP server. It then:\n",
        "\n",
        "* Fetches the corresponding CSV dataset (temperature or salinity).\n",
        "\n",
        "* Cleans and structures the data into a pandas DataFrame.\n",
        "\n",
        "* Converts timestamps and values into usable formats.\n",
        "\n",
        "* Generates a **time-series** plot showing how the selected variable changes over the chosen period at the specified depth and coordinates.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYpyrnQUOsGY"
      },
      "source": [
        "**Note:** Run this code once you selected layer, elevation, time range and coordinates by clicking on the map. Also do it everytime you change a selection"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "cellView": "form",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601
        },
        "id": "2QnHUilk0MsH",
        "outputId": "969e941e-5cc6-424e-db36-5b30bf168ef8"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fetching data from: https://er1.s4oceanice.eu/erddap/griddap/INSITU_GLO_PHY_TS_OA_MY_013_052.csv?TEMP%5B(2013-09-01):1:(2024-06-01)%5D%5B(1.0):1:(1.0)%5D%5B(44.1428):1:(44.1428)%5D%5B(8.9428):1:(8.9428)%5D\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title\n",
        "# Define variables based on widget selections and map click\n",
        "layer = layer_dropdown.value.split('/')[-1]  # Get the last part of the layer name\n",
        "first_date = time_range_slider.value[0].split('T')[0] # Remove time component\n",
        "second_date = time_range_slider.value[1].split('T')[0] # Remove time component\n",
        "selected_elevation = elevation_slider.value\n",
        "lat = round(last_clicked_coordinates['lat'], 5) # Approximate latitude to 5 decimal places\n",
        "lon = round(last_clicked_coordinates['lng'], 5) # Approximate longitude to 5 decimal places\n",
        "\n",
        "# Construct the temporal_url based on the provided structure\n",
        "temporal_url = (\n",
        "    f'https://er1.s4oceanice.eu/erddap/griddap/INSITU_GLO_PHY_TS_OA_MY_013_052.csv?'\n",
        "    f'{layer}%5B({first_date}):1:({second_date})'\n",
        "    f'%5D%5B({selected_elevation}):1:({selected_elevation})'\n",
        "    f'%5D%5B({lat}):1:({lat})'\n",
        "    f'%5D%5B({lon}):1:({lon})%5D'\n",
        ")\n",
        "\n",
        "\n",
        "print(f\"Fetching data from: {temporal_url}\")\n",
        "\n",
        "# Fetch the data from the temporal_url\n",
        "try:\n",
        "    temporal_response = requests.get(temporal_url)\n",
        "    temporal_response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)\n",
        "    temporal_data = temporal_response.text\n",
        "\n",
        "except requests.exceptions.RequestException as e:\n",
        "    print(f\"Error fetching data from {temporal_url}: {e}\")\n",
        "    temporal_data = None\n",
        "\n",
        "if temporal_data:\n",
        "\n",
        "    from io import StringIO\n",
        "\n",
        "    try:\n",
        "        df_temporal = pd.read_csv(StringIO(temporal_data), skiprows=[0, 1], delimiter=',') # Skip both header rows and specify delimiter\n",
        "        # Manually assign column names based on the second header row in the raw data\n",
        "        # Assuming the order is always time, depth, latitude, longitude, and then the variable (PSAL or TEMP)\n",
        "        if layer == 'PSAL':\n",
        "            df_temporal.columns = ['time', 'depth', 'latitude', 'longitude', 'PSAL']\n",
        "            value_column = 'PSAL'\n",
        "        elif layer == 'TEMP':\n",
        "             df_temporal.columns = ['time', 'depth', 'latitude', 'longitude', 'TEMP']\n",
        "             value_column = 'TEMP'\n",
        "        else:\n",
        "             print(f\"Unknown layer: {layer}. Cannot determine value column.\")\n",
        "             df_temporal = None\n",
        "\n",
        "    except pd.errors.ParserError as e:\n",
        "        print(f\"Error parsing CSV data: {e}\")\n",
        "        df_temporal = None\n",
        "\n",
        "\n",
        "    if df_temporal is not None and not df_temporal.empty:\n",
        "        time_column = df_temporal.columns[0]\n",
        "\n",
        "        if df_temporal is not None and value_column in df_temporal.columns:\n",
        "\n",
        "            df_temporal[time_column] = pd.to_datetime(df_temporal[time_column], errors='coerce')\n",
        "\n",
        "            # Check if the value column is numeric and convert if necessary\n",
        "            if not pd.api.types.is_numeric_dtype(df_temporal[value_column]):\n",
        "                 try:\n",
        "                     df_temporal[value_column] = pd.to_numeric(df_temporal[value_column], errors='coerce')\n",
        "                     # print(f\"Converted '{value_column}' to numeric.\") # Commented out to hide output\n",
        "                 except ValueError:\n",
        "                     print(f\"Error converting '{value_column}' to numeric.\")\n",
        "                     df_temporal = None # Set to None if conversion fails\n",
        "\n",
        "\n",
        "            if df_temporal is not None:\n",
        "                # Drop rows with NaT in either time or value column\n",
        "                df_temporal.dropna(subset=[time_column, value_column], inplace=True)\n",
        "\n",
        "                # Plot the data\n",
        "                if not df_temporal.empty:\n",
        "                    plt.figure(figsize=(12, 6))\n",
        "                    # Use line plot for time-series data\n",
        "                    plt.plot(df_temporal[time_column], df_temporal[value_column])\n",
        "                    plt.xlabel('Time')\n",
        "                    plt.ylabel(f'{layer} at {selected_elevation}m')\n",
        "                    plt.title(f'Temporal variation of {layer} at Latitude: {lat:.2f}, Longitude: {lon:.2f}, Elevation: {selected_elevation}m')\n",
        "                    plt.grid(True)\n",
        "                    plt.show()\n",
        "                else:\n",
        "                    print(\"No data available to plot after dropping invalid entries.\")\n",
        "\n",
        "        else:\n",
        "             print(f\"Value column '{value_column}' not found in the DataFrame.\")\n",
        "\n",
        "    else:\n",
        "        print(\"No data available to process from the fetched data.\")\n",
        "\n",
        "else:\n",
        "    print(\"Could not fetch or process temporal data.\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "c0mH1UOsbBBu"
      },
      "source": [
        "**7. Depth-Profile Extraction and Plotting at Selected Location (Range of Depths)**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jnzK4c4VbEBe"
      },
      "source": [
        "This cell builds a griddap query to ERDDAP for the chosen layer (TEMP or PSAL) at your clicked latitude/longitude, over the selected time range, and across depths 1–5500 m. It then:\n",
        "\n",
        "* Parses the returned CSV, keeps the value column, and coerces it to numeric.\n",
        "\n",
        "* Drops invalid rows and plots a depth profile (value vs. depth) with the y-axis inverted.\n",
        "\n",
        "* Colors the points by the variable value and adds a colorbar for quick interpretation."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_ohIhZTOu90t"
      },
      "source": [
        "**Note:** Run this code once you selected layer, elevation, time range and coordinates by clicking on the map. Also do it everytime you change a selection.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 909
        },
        "id": "e1c3d49d-475d-417c-803a-79b94b9a9562",
        "outputId": "025cb496-be6f-447e-fcb5-118cb5feae37"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Fetching data from: https://er1.s4oceanice.eu/erddap/griddap/INSITU_GLO_PHY_TS_OA_MY_013_052.csv?TEMP%5B(2013-09-01):1:(2024-06-01)%5D%5B(1):1:(5500)%5D%5B(44.1428):1:(44.1428)%5D%5B(8.9428):1:(8.9428)%5D\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 800x1000 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# @title\n",
        "# Define variables based on widget selections and map click\n",
        "layer = layer_dropdown.value.split('/')[-1]  # Get the last part of the layer name\n",
        "first_date = time_range_slider.value[0].split('T')[0] # Remove time component\n",
        "second_date = time_range_slider.value[1].split('T')[0] # Remove time component\n",
        "selected_elevation = elevation_slider.value\n",
        "lat = round(last_clicked_coordinates['lat'], 5) # Approximate latitude to 5 decimal places\n",
        "lon = round(last_clicked_coordinates['lng'], 5) # Approximate longitude to 5 decimal places\n",
        "\n",
        "depth_url = (\n",
        "    f'https://er1.s4oceanice.eu/erddap/griddap/INSITU_GLO_PHY_TS_OA_MY_013_052.csv?'\n",
        "    f'{layer}%5B({first_date}):1:({second_date})'\n",
        "    f'%5D%5B(1):1:(5500)'\n",
        "    f'%5D%5B({lat}):1:({lat})'\n",
        "    f'%5D%5B({lon}):1:({lon})%5D'\n",
        ")\n",
        "print(f'Fetching data from: {depth_url}')\n",
        "# Fetch the data from the depth_url\n",
        "try:\n",
        "    depth_response = requests.get(depth_url)\n",
        "    depth_response.raise_for_status() # Raise an HTTPError for bad responses (4xx or 5xx)\n",
        "    depth_data = depth_response.text\n",
        "\n",
        "except requests.exceptions.RequestException as e:\n",
        "    print(f\"Error fetching data from {depth_url}: {e}\")\n",
        "    depth_data = None\n",
        "\n",
        "if depth_data:\n",
        "\n",
        "    from io import StringIO\n",
        "\n",
        "    try:\n",
        "        df_depth = pd.read_csv(StringIO(depth_data), skiprows=[0, 1], delimiter=',') # Skip both header rows and specify delimiter\n",
        "        # Manually assign column names based on the second header row in the raw data\n",
        "        # Assuming the order is always time, depth, latitude, longitude, and then the variable (PSAL or TEMP)\n",
        "        if layer == 'PSAL':\n",
        "            df_depth.columns = ['time', 'depth', 'latitude', 'longitude', 'PSAL']\n",
        "            value_column = 'PSAL'\n",
        "        elif layer == 'TEMP':\n",
        "             df_depth.columns = ['time', 'depth', 'latitude', 'longitude', 'TEMP']\n",
        "             value_column = 'TEMP'\n",
        "        else:\n",
        "             print(f\"Unknown layer: {layer}. Cannot determine value column.\")\n",
        "             df_depth = None\n",
        "\n",
        "\n",
        "    except pd.errors.ParserError as e:\n",
        "        print(f\"Error parsing CSV data: {e}\")\n",
        "        df_depth = None\n",
        "\n",
        "\n",
        "    if df_depth is not None and not df_depth.empty:\n",
        "\n",
        "        # Check if the value column is numeric and convert if necessary\n",
        "        if value_column in df_depth.columns and not pd.api.types.is_numeric_dtype(df_depth[value_column]):\n",
        "             try:\n",
        "                 df_depth[value_column] = pd.to_numeric(df_depth[value_column], errors='coerce')\n",
        "                 # print(f\"Converted '{value_column}' to numeric.\") # Commented out to hide output\n",
        "             except ValueError:\n",
        "                 print(f\"Error converting '{value_column}' to numeric.\")\n",
        "                 df_depth = None # Set to None if conversion fails\n",
        "\n",
        "\n",
        "        if df_depth is not None:\n",
        "            # Drop rows with NaT in the value column\n",
        "            df_depth.dropna(subset=[value_column], inplace=True)\n",
        "\n",
        "            # Plot the vertical profile\n",
        "            if not df_depth.empty:\n",
        "                plt.figure(figsize=(8, 10))\n",
        "                # Use the value column for coloring the points\n",
        "                scatter = plt.scatter(df_depth[value_column], df_depth['depth'], c=df_depth[value_column], cmap='viridis') # Changed to scatter plot and added coloring\n",
        "                plt.xlabel(f'{layer}')\n",
        "                plt.ylabel('Depth (m)')\n",
        "                plt.title(f'Vertical profile of {layer} at Latitude: {lat:.2f}, Longitude: {lon:.2f} for the selected time range')\n",
        "                plt.gca().invert_yaxis()  # Invert the y-axis to show depth increasing downwards\n",
        "                plt.grid(True)\n",
        "                plt.colorbar(scatter, label=f'{layer}') # Add a colorbar\n",
        "                plt.show()\n",
        "            else:\n",
        "                 print(\"No data available to plot after dropping invalid entries.\")\n",
        "\n",
        "    else:\n",
        "        print(\"No data available to process from the fetched data.\")\n",
        "\n",
        "else:\n",
        "    print(\"Could not fetch or process depth data.\")"
      ]
    }
  ],
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              "1960-05-01T00:00:00.000Z",
              "1960-06-01T00:00:00.000Z",
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              "1971-01-01T00:00:00.000Z",
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              "1977-12-01T00:00:00.000Z",
              "1978-01-01T00:00:00.000Z",
              "1978-02-01T00:00:00.000Z",
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              "1978-11-01T00:00:00.000Z",
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              "1979-01-01T00:00:00.000Z",
              "1979-02-01T00:00:00.000Z",
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              "1979-08-01T00:00:00.000Z",
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              "1979-10-01T00:00:00.000Z",
              "1979-11-01T00:00:00.000Z",
              "1979-12-01T00:00:00.000Z",
              "1980-01-01T00:00:00.000Z",
              "1980-02-01T00:00:00.000Z",
              "1980-03-01T00:00:00.000Z",
              "1980-04-01T00:00:00.000Z",
              "1980-05-01T00:00:00.000Z",
              "1980-06-01T00:00:00.000Z",
              "1980-07-01T00:00:00.000Z",
              "1980-08-01T00:00:00.000Z",
              "1980-09-01T00:00:00.000Z",
              "1980-10-01T00:00:00.000Z",
              "1980-11-01T00:00:00.000Z",
              "1980-12-01T00:00:00.000Z",
              "1981-01-01T00:00:00.000Z",
              "1981-02-01T00:00:00.000Z",
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              "1981-06-01T00:00:00.000Z",
              "1981-07-01T00:00:00.000Z",
              "1981-08-01T00:00:00.000Z",
              "1981-09-01T00:00:00.000Z",
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              "1981-11-01T00:00:00.000Z",
              "1981-12-01T00:00:00.000Z",
              "1982-01-01T00:00:00.000Z",
              "1982-02-01T00:00:00.000Z",
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              "1982-11-01T00:00:00.000Z",
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              "1984-06-01T00:00:00.000Z",
              "1984-07-01T00:00:00.000Z",
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              "1984-10-01T00:00:00.000Z",
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              "1985-01-01T00:00:00.000Z",
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              "1985-10-01T00:00:00.000Z",
              "1985-11-01T00:00:00.000Z",
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              "1986-01-01T00:00:00.000Z",
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              "1986-08-01T00:00:00.000Z",
              "1986-09-01T00:00:00.000Z",
              "1986-10-01T00:00:00.000Z",
              "1986-11-01T00:00:00.000Z",
              "1986-12-01T00:00:00.000Z",
              "1987-01-01T00:00:00.000Z",
              "1987-02-01T00:00:00.000Z",
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              "1987-06-01T00:00:00.000Z",
              "1987-07-01T00:00:00.000Z",
              "1987-08-01T00:00:00.000Z",
              "1987-09-01T00:00:00.000Z",
              "1987-10-01T00:00:00.000Z",
              "1987-11-01T00:00:00.000Z",
              "1987-12-01T00:00:00.000Z",
              "1988-01-01T00:00:00.000Z",
              "1988-02-01T00:00:00.000Z",
              "1988-03-01T00:00:00.000Z",
              "1988-04-01T00:00:00.000Z",
              "1988-05-01T00:00:00.000Z",
              "1988-06-01T00:00:00.000Z",
              "1988-07-01T00:00:00.000Z",
              "1988-08-01T00:00:00.000Z",
              "1988-09-01T00:00:00.000Z",
              "1988-10-01T00:00:00.000Z",
              "1988-11-01T00:00:00.000Z",
              "1988-12-01T00:00:00.000Z",
              "1989-01-01T00:00:00.000Z",
              "1989-02-01T00:00:00.000Z",
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              "1989-06-01T00:00:00.000Z",
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              "1989-11-01T00:00:00.000Z",
              "1989-12-01T00:00:00.000Z",
              "1990-01-01T00:00:00.000Z",
              "1990-02-01T00:00:00.000Z",
              "1990-03-01T00:00:00.000Z",
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              "1990-06-01T00:00:00.000Z",
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              "1990-08-01T00:00:00.000Z",
              "1990-09-01T00:00:00.000Z",
              "1990-10-01T00:00:00.000Z",
              "1990-11-01T00:00:00.000Z",
              "1990-12-01T00:00:00.000Z",
              "1991-01-01T00:00:00.000Z",
              "1991-02-01T00:00:00.000Z",
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              "1991-06-01T00:00:00.000Z",
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              "1991-11-01T00:00:00.000Z",
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              "1992-01-01T00:00:00.000Z",
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              "1992-08-01T00:00:00.000Z",
              "1992-09-01T00:00:00.000Z",
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              "1992-11-01T00:00:00.000Z",
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