rfvl.ipynb

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        "<a href=\"https://colab.research.google.com/github/MarcoAlbaETT/EMODnet-Physics-Documentation/blob/master/rfvl.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
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      "metadata": {
        "id": "7ydeifHy_KZ0"
      },
      "source": [
        "# **EMODnet Physics - River flow rate (RVFL) querying**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "W2Lk2xzd_KZ2"
      },
      "source": [
        "This notebook will illustrate how to build queries and make requests to [ercompwebapps.emodnet-physics.eu/erddap/tabledap/TS_RVFL.html](https://ercompwebapps.emodnet-physics.eu/erddap/tabledap/TS_RVFL.html) using Python."
      ]
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      "source": [
        "## **Setup**\n",
        "\n",
        "To begin we need to import the necessary libraries.\n",
        "\n",
        "We will also declare a variable BASE_URL which will be used in the following steps as base for building the URLs."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
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      "source": [
        "# !pip install requests pandas matplotlib\n",
        "\n",
        "import requests\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import io\n",
        "\n",
        "BASE_URL = 'https://ercompwebapps.emodnet-physics.eu/erddap/tabledap/TS_RVFL.csvp'"
      ]
    },
    {
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      "metadata": {
        "id": "6iTf1Wdm_KZ2"
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      "source": [
        "The data format specified in the BASE_URL is ```csvp```, which will return a csv with the column names (and their unit of measurment) as first row and data starting from the second.\n",
        "Other possibilities are ```csv``` (in which the first row contains the column names and the second the units of measurment) and ```csv0``` which will return only the data rows.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2GI1pHT8_KZ3"
      },
      "source": [
        "## **Get a list of platform codes**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iHEskNpp_KZ3"
      },
      "source": [
        "After the libraries have been succesfully installed and imported it is possible to make the first request, which will return a list of all the platform codes.\n",
        "These can be than used in the following requests."
      ]
    },
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              "             PLATFORMCODE  latitude (degrees_north)  longitude (degrees_east)\n",
              "0                10100000                -69.944700                 -4.234700\n",
              "1                10100000                 -4.234700                -69.944700\n",
              "2                12240000                -67.491700                 -3.337200\n",
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              "...                   ...                       ...                       ...\n",
              "3734                Westy                 53.390598                 -2.560370\n",
              "3735         Wey-Broadwey                 50.653706                 -2.473201\n",
              "3736      Witham-Claypole                 53.022354                 -0.746079\n",
              "3737        Worfe-Burcote                 52.555340                 -2.375983\n",
              "3738  Wye-BourneEndHedsor                 51.571865                 -0.708580\n",
              "\n",
              "[3739 rows x 3 columns]"
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "platforms_df",
              "summary": "{\n  \"name\": \"platforms_df\",\n  \"rows\": 3739,\n  \"fields\": [\n    {\n      \"column\": \"PLATFORMCODE\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 2435,\n        \"samples\": [\n          \"RS_GB_Tay_Ballathie\",\n          \"64560000\",\n          \"IF000126\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude (degrees_north)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 42.36511717279019,\n        \"min\": -72.7847,\n        \"max\": 70.13985,\n        \"num_unique_values\": 3640,\n        \"samples\": [\n          -49.4844,\n          46.36889,\n          -22.6628\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude (degrees_east)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.630358123942537e+35,\n        \"min\": -137.9775,\n        \"max\": 9.96921e+36,\n        \"num_unique_values\": 3652,\n        \"samples\": [\n          -48.1903,\n          -21.7211,\n          -57.8744\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ],
      "source": [
        "platforms_query = '?PLATFORMCODE%2Clatitude%2Clongitude&distinct()'\n",
        "# this will return us three columns: PLATFORMCODE, latitude and longitude (more on this in the next section)\n",
        "# the additional parameter &distinct() will ensure we will get only unique rows\n",
        "\n",
        "platform_resp = requests.get(BASE_URL + platforms_query)\n",
        "platforms_df = pd.read_csv(io.StringIO(platform_resp.text), sep=',')\n",
        "platforms_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g4Qqknls_KZ3"
      },
      "source": [
        "## **Data gathering**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IzKOfHrT_KZ3"
      },
      "source": [
        "As can be seen from [ercompwebapps.emodnet-physics.eu/erddap/tabledap/TS_RVFL.html](https://ercompwebapps.emodnet-physics.eu/erddap/tabledap/TS_RVFL.html) there is a list of possible variables that can be selected.\n",
        "\n",
        "These are:  \n",
        "PLATFORMCODE, SOURCE, SENSOR, time, TIME_QC, depth, DEPTH_QC, latitude, longitude, POSITION_QC, RVFL, RVFL_QC, RVFL_DM, url_metadata and qc_entity.\n",
        "\n",
        "Following are three examples of data queries:\n",
        "\n",
        "###  With PLATFORMCODE and time range\n",
        "\n",
        "  When building the URL to get the data a platform code can be inserted in the query to get the data relative to the platform.\n",
        "  In the following example the platform code 'RS_IT_Po_Pontelagoscuro' has been chosen and the variables are:\n",
        "\n",
        "  - time\n",
        "  - depth\n",
        "  - latitude\n",
        "  - longitude\n",
        "  - RVFL\n",
        "\n",
        "  The query will look like:\n",
        "\n",
        "  ```?time%2Cdepth%2Clatitude%2Clongitude%2CRVFL&PLATFORMCODE=%22RS_IT_Po_Pontelagoscuro%22&time%3E=2024-03-29T09%3A45%3A00Z&time%3C=2024-04-29T09%3A45%3A00Z```\n",
        "\n",
        "  It can be divided into two main parts:\n",
        "\n",
        "1. ```?time%2Cdepth%2Clatitude%2Clongitude%2CRVFL```\n",
        "\n",
        "    Where ```?``` indicates the start of query parametes and the rest is a list of variables we want as columns in the csv, separated by ```%2C```, an encoded comma(,).\n",
        "\n",
        "2. ```&PLATFORMCODE=%22RS_IT_Po_Pontelagoscuro%22&time%3E=2024-03-29T09%3A45%3A00Z&time%3C=2024-04-29T09%3A45%3A00Z```\n",
        "\n",
        "    After the list of variables we can add filters, separated by ```&```.\n",
        "\n",
        "    The platform code chosen is RS_IT_Po_Pontelagoscuro and it has to be inserted between encoded double quotes(\"), represented by ```%22```.\n",
        "\n",
        "    The syntax for the timerange is:\n",
        "\n",
        "    ```time%3E=2024-03-29T09%3A45%3A00Z&time%3C=2024-04-29T09%3A45%3A00Z```\n",
        "\n",
        "    Here the other encoded characters are ```%3E``` (>), ```%3C``` (<) and ```%3A``` (:).\n",
        "    \n",
        "    The time has to be passed as an ISO string, with the format YYYY-MM-DDThh:mm:ssZ."
      ]
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      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                  PLATFORMCODE            time (UTC)  depth (m)  \\\n",
              "0      RS_IT_Po_Pontelagoscuro  2023-05-08T07:30:00Z        0.0   \n",
              "1      RS_IT_Po_Pontelagoscuro  2023-05-08T07:45:00Z        0.0   \n",
              "2      RS_IT_Po_Pontelagoscuro  2023-05-08T08:00:00Z        0.0   \n",
              "3      RS_IT_Po_Pontelagoscuro  2023-05-08T08:15:00Z        0.0   \n",
              "4      RS_IT_Po_Pontelagoscuro  2023-05-08T08:30:00Z        0.0   \n",
              "...                        ...                   ...        ...   \n",
              "23315  RS_IT_Po_Pontelagoscuro  2024-04-28T22:00:00Z        0.0   \n",
              "23316  RS_IT_Po_Pontelagoscuro  2024-04-28T22:30:00Z        0.0   \n",
              "23317  RS_IT_Po_Pontelagoscuro  2024-04-28T23:00:00Z        0.0   \n",
              "23318  RS_IT_Po_Pontelagoscuro  2024-04-28T23:30:00Z        0.0   \n",
              "23319  RS_IT_Po_Pontelagoscuro  2024-04-29T00:00:00Z        0.0   \n",
              "\n",
              "       latitude (degrees_north)  longitude (degrees_east)  RVFL (m3/s)  \n",
              "0                       44.8883                  11.60807       675.77  \n",
              "1                       44.8883                  11.60807       667.87  \n",
              "2                       44.8883                  11.60807       667.87  \n",
              "3                       44.8883                  11.60807       667.87  \n",
              "4                       44.8883                  11.60807       660.00  \n",
              "...                         ...                       ...          ...  \n",
              "23315                   44.8883                  11.60807      1747.17  \n",
              "23316                   44.8883                  11.60807      1747.17  \n",
              "23317                   44.8883                  11.60807      1747.17  \n",
              "23318                   44.8883                  11.60807      1747.17  \n",
              "23319                   44.8883                  11.60807      1741.62  \n",
              "\n",
              "[23320 rows x 6 columns]"
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PLATFORMCODE</th>\n",
              "      <th>time (UTC)</th>\n",
              "      <th>depth (m)</th>\n",
              "      <th>latitude (degrees_north)</th>\n",
              "      <th>longitude (degrees_east)</th>\n",
              "      <th>RVFL (m3/s)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2023-05-08T07:30:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>675.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2023-05-08T07:45:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>667.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2023-05-08T08:00:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>667.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2023-05-08T08:15:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>667.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2023-05-08T08:30:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>660.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23315</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2024-04-28T22:00:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>1747.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23316</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2024-04-28T22:30:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>1747.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23317</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2024-04-28T23:00:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>1747.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23318</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2024-04-28T23:30:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>1747.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23319</th>\n",
              "      <td>RS_IT_Po_Pontelagoscuro</td>\n",
              "      <td>2024-04-29T00:00:00Z</td>\n",
              "      <td>0.0</td>\n",
              "      <td>44.8883</td>\n",
              "      <td>11.60807</td>\n",
              "      <td>1741.62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23320 rows × 6 columns</p>\n",
              "</div>\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "  .colab-df-spinner {\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @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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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data_df",
              "summary": "{\n  \"name\": \"data_df\",\n  \"rows\": 23320,\n  \"fields\": [\n    {\n      \"column\": \"PLATFORMCODE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RS_IT_Po_Pontelagoscuro\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 23318,\n        \"samples\": [\n          \"2023-05-10T12:00:00Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"depth (m)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude (degrees_north)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 44.8883,\n        \"max\": 44.8883,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          44.8883\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude (degrees_east)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 11.60807,\n        \"max\": 11.60807,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          11.60807\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RVFL (m3/s)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1014.4285813312011,\n        \"min\": 276.38,\n        \"max\": 4973.83,\n        \"num_unique_values\": 1073,\n        \"samples\": [\n          1567.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ],
      "source": [
        "platform_code = 'RS_IT_Po_Pontelagoscuro'\n",
        "\n",
        "variables = '?PLATFORMCODE%2Ctime%2Cdepth%2Clatitude%2Clongitude%2CRVFL'\n",
        "filters = f'&PLATFORMCODE=%22{platform_code}%22&time%3E=2023-04-29T00%3A00%3A00Z&time%3C=2024-04-29T00%3A00%3A00Z'\n",
        "\n",
        "data_resp = requests.get(BASE_URL + variables + filters)\n",
        "data_df = pd.read_csv(io.StringIO(data_resp.text), sep=',')\n",
        "\n",
        "data_df=data_df.sort_values(by=[\"time (UTC)\"])\n",
        "data_df.reset_index(drop=True, inplace=True)\n",
        "data_df"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  With multiple platform codes"
      ],
      "metadata": {
        "id": "MnlSGZrmRzyz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "It is possible to select multiple platform codes when querying the data. This can be done by using a regex.\n",
        "\n",
        "In this example the three platform codes used will be 'RS_CA_Dartmouth_PasdeDame', 'Roden-Rodington' and 'RS_ES_Guadalfeo_Orgiva'.\n",
        "\n",
        "To build these part of the query the regex will have this syntax:\n",
        "\n",
        "```PLATFORMCODE=~%22(platform_code_1%7Cplatform_code_2%7Cplatform_code_3)```\n",
        "\n",
        "Where ```%7C``` represents the symbol ```|``` (meaning OR).\n",
        "\n"
      ],
      "metadata": {
        "id": "yufHeR6NR8-z"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "regex_platform_code = '(RS_CA_Dartmouth_PasdeDame%7CRoden-Rodington%7CRS_ES_Guadalfeo_Orgiva)'\n",
        "\n",
        "variables = '?PLATFORMCODE%2Ctime%2Clatitude%2Clongitude%2CRVFL'\n",
        "regex_filters = f'&PLATFORMCODE=~%22{regex_platform_code}%22&time%3E=2024-02-20T00%3A00%3A00Z&time%3C=2024-04-29T00%3A00%3A00Z'\n",
        "\n",
        "regex_data_resp = requests.get(BASE_URL + variables + regex_filters)\n",
        "regex_data_df = pd.read_csv(io.StringIO(regex_data_resp.text), sep=',')\n",
        "\n",
        "unique_platform_codes = regex_data_df['PLATFORMCODE'].unique()\n",
        "print('\\nThis DataFrame contains the platform codes:', unique_platform_codes, '\\n')\n",
        "\n",
        "regex_data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "oeew627AVIqu",
        "outputId": "816b6e26-b049-40a6-f9f1-5ef9e7bec6f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
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            "\n",
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            "\n"
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            "text/plain": [
              "                   PLATFORMCODE            time (UTC)  \\\n",
              "0     RS_CA_Dartmouth_PasdeDame  2024-02-20T00:00:00Z   \n",
              "1     RS_CA_Dartmouth_PasdeDame  2024-02-20T00:15:00Z   \n",
              "2     RS_CA_Dartmouth_PasdeDame  2024-02-20T00:30:00Z   \n",
              "3     RS_CA_Dartmouth_PasdeDame  2024-02-20T00:45:00Z   \n",
              "4     RS_CA_Dartmouth_PasdeDame  2024-02-20T01:00:00Z   \n",
              "...                         ...                   ...   \n",
              "7800            Roden-Rodington  2024-04-23T09:00:00Z   \n",
              "7801            Roden-Rodington  2024-04-24T09:00:00Z   \n",
              "7802            Roden-Rodington  2024-04-25T09:00:00Z   \n",
              "7803            Roden-Rodington  2024-04-26T09:00:00Z   \n",
              "7804            Roden-Rodington  2024-04-27T09:00:00Z   \n",
              "\n",
              "      latitude (degrees_north)  longitude (degrees_east)  RVFL (m3/s)  \n",
              "0                    48.977670                -64.699690        2.046  \n",
              "1                    48.977670                -64.699690        2.046  \n",
              "2                    48.977670                -64.699690        2.046  \n",
              "3                    48.977670                -64.699690        2.033  \n",
              "4                    48.977670                -64.699690        2.033  \n",
              "...                        ...                       ...          ...  \n",
              "7800                 52.723644                 -2.609739        2.530  \n",
              "7801                 52.723644                 -2.609739        2.490  \n",
              "7802                 52.723644                 -2.609739        2.450  \n",
              "7803                 52.723644                 -2.609739        2.270  \n",
              "7804                 52.723644                 -2.609739        2.160  \n",
              "\n",
              "[7805 rows x 5 columns]"
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              "      <td>-64.699690</td>\n",
              "      <td>2.046</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RS_CA_Dartmouth_PasdeDame</td>\n",
              "      <td>2024-02-20T00:15:00Z</td>\n",
              "      <td>48.977670</td>\n",
              "      <td>-64.699690</td>\n",
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              "      <th>2</th>\n",
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              "      <td>48.977670</td>\n",
              "      <td>-64.699690</td>\n",
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              "      <th>3</th>\n",
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              "      <td>48.977670</td>\n",
              "      <td>-64.699690</td>\n",
              "      <td>2.033</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>2024-02-20T01:00:00Z</td>\n",
              "      <td>48.977670</td>\n",
              "      <td>-64.699690</td>\n",
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              "      <td>2024-04-23T09:00:00Z</td>\n",
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              "      <td>52.723644</td>\n",
              "      <td>-2.609739</td>\n",
              "      <td>2.270</td>\n",
              "    </tr>\n",
              "    <tr>\n",
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              "      <td>Roden-Rodington</td>\n",
              "      <td>2024-04-27T09:00:00Z</td>\n",
              "      <td>52.723644</td>\n",
              "      <td>-2.609739</td>\n",
              "      <td>2.160</td>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "regex_data_df",
              "summary": "{\n  \"name\": \"regex_data_df\",\n  \"rows\": 7805,\n  \"fields\": [\n    {\n      \"column\": \"PLATFORMCODE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"RS_CA_Dartmouth_PasdeDame\",\n          \"RS_ES_Guadalfeo_Orgiva\",\n          \"Roden-Rodington\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 6319,\n        \"samples\": [\n          \"2024-04-05T01:30:00Z\",\n          \"2024-04-28T06:45:00Z\",\n          \"2024-02-27T15:15:00Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude (degrees_north)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.711029468105897,\n        \"min\": 36.88466,\n        \"max\": 52.7236442565918,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          48.97767,\n          36.88466,\n          52.7236442565918\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude (degrees_east)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24.136811257603668,\n        \"min\": -64.69969,\n        \"max\": -2.609739065170288,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          -64.69969,\n          -3.41554,\n          -2.609739065170288\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RVFL (m3/s)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.823144955472152,\n        \"min\": 1.515,\n        \"max\": 175.6,\n        \"num_unique_values\": 1762,\n        \"samples\": [\n          96.02,\n          10.11,\n          58.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 97
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###  With coordinates range"
      ],
      "metadata": {
        "id": "qaWqgS56bnS3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Another possibility when querying the data is to specify a range of coordinates.\n",
        "This can be done by inserting in the query filters the following:\n",
        "\n",
        "```latitude%3E=45&latitude%3C=49&longitude%3E=10&longitude%3C=14```\n",
        "\n",
        "Effectively selecting platforms inside a square delimited by:\n",
        "\n",
        "- latitude equal or greater than 45 and equal or less than 49\n",
        "\n",
        "and\n",
        "\n",
        "- longitude equal or greater than 10 and equal or less than 14."
      ],
      "metadata": {
        "id": "R3Ua9HhUbtTE"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "coords_variables = '?PLATFORMCODE%2Clatitude%2Clongitude'\n",
        "coords_filter = '&latitude%3E=45&latitude%3C=49&longitude%3E=10&longitude%3C=14&distinct()'\n",
        "\n",
        "coords_data_resp = requests.get(BASE_URL + coords_variables + coords_filter)\n",
        "coords_data_df = pd.read_csv(io.StringIO(coords_data_resp.text), sep=',')\n",
        "coords_data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 332
        },
        "id": "03XyfGE8fQ4e",
        "outputId": "44dd9271-3964-4c42-938a-2f8edd7dbdef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                        PLATFORMCODE  latitude (degrees_north)  \\\n",
              "0                        ADN-CURRISO                 45.761665   \n",
              "1            RS_IT_Adige_BoaraPisani                 45.105480   \n",
              "2      RS_IT_Bacchiglione_Montegalda                 45.442730   \n",
              "3              RS_IT_Brenta_Barzizza                 45.777416   \n",
              "4  RS_IT_CanaleLedra_ScolmatoreCorno                 46.144040   \n",
              "5         RS_IT_Fontanelle_Monticano                 45.836945   \n",
              "6      RS_IT_Livenza_MedunaDiLivenza                 45.805107   \n",
              "7            RS_IT_Natisone_Cividale                 46.091180   \n",
              "8               RS_IT_Segusino_Piave                 45.922490   \n",
              "\n",
              "   longitude (degrees_east)  \n",
              "0                 13.496333  \n",
              "1                 11.784244  \n",
              "2                 11.672323  \n",
              "3                 11.730543  \n",
              "4                 13.024471  \n",
              "5                 12.439918  \n",
              "6                 12.610580  \n",
              "7                 13.430171  \n",
              "8                 11.948837  "
            ],
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>PLATFORMCODE</th>\n",
              "      <th>latitude (degrees_north)</th>\n",
              "      <th>longitude (degrees_east)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ADN-CURRISO</td>\n",
              "      <td>45.761665</td>\n",
              "      <td>13.496333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RS_IT_Adige_BoaraPisani</td>\n",
              "      <td>45.105480</td>\n",
              "      <td>11.784244</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>RS_IT_Bacchiglione_Montegalda</td>\n",
              "      <td>45.442730</td>\n",
              "      <td>11.672323</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>RS_IT_Brenta_Barzizza</td>\n",
              "      <td>45.777416</td>\n",
              "      <td>11.730543</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RS_IT_CanaleLedra_ScolmatoreCorno</td>\n",
              "      <td>46.144040</td>\n",
              "      <td>13.024471</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>RS_IT_Fontanelle_Monticano</td>\n",
              "      <td>45.836945</td>\n",
              "      <td>12.439918</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>RS_IT_Livenza_MedunaDiLivenza</td>\n",
              "      <td>45.805107</td>\n",
              "      <td>12.610580</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>RS_IT_Natisone_Cividale</td>\n",
              "      <td>46.091180</td>\n",
              "      <td>13.430171</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>RS_IT_Segusino_Piave</td>\n",
              "      <td>45.922490</td>\n",
              "      <td>11.948837</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "      const quickchartButtonEl =\n",
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              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "coords_data_df",
              "summary": "{\n  \"name\": \"coords_data_df\",\n  \"rows\": 9,\n  \"fields\": [\n    {\n      \"column\": \"PLATFORMCODE\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"RS_IT_Natisone_Cividale\",\n          \"RS_IT_Adige_BoaraPisani\",\n          \"RS_IT_Fontanelle_Monticano\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude (degrees_north)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3202814895505743,\n        \"min\": 45.10548,\n        \"max\": 46.14404,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          46.09118,\n          45.10548,\n          45.836945\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude (degrees_east)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7269283585206467,\n        \"min\": 11.672323,\n        \"max\": 13.496333122253418,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          13.430171,\n          11.784244,\n          12.439918\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can then select different variables and add a timerange to the filters:"
      ],
      "metadata": {
        "id": "E3CbGYPPm4UB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "coords_time_variables = '?PLATFORMCODE%2Clatitude%2Clongitude%2Cdepth%2CRVFL%2CRVFL_QC'\n",
        "coords_filter += '&latitude%3E=45&latitude%3C=49&longitude%3E=10&longitude%3C=14&time%3E=2024-04-20T00%3A00%3A00Z&time%3C=2024-04-27T00%3A00%3A00Z'\n",
        "\n",
        "coords_time_data_resp = requests.get(BASE_URL + coords_time_variables + coords_filter)\n",
        "coords_time_data_df = pd.read_csv(io.StringIO(coords_time_data_resp.text), sep=',')\n",
        "coords_time_data_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "2yf9Zl2knFx-",
        "outputId": "79e2adc6-62ce-4c86-a21a-5ee08e7c5435"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                PLATFORMCODE  latitude (degrees_north)  \\\n",
              "0    RS_IT_Adige_BoaraPisani                  45.10548   \n",
              "1    RS_IT_Adige_BoaraPisani                  45.10548   \n",
              "2    RS_IT_Adige_BoaraPisani                  45.10548   \n",
              "3    RS_IT_Adige_BoaraPisani                  45.10548   \n",
              "4    RS_IT_Adige_BoaraPisani                  45.10548   \n",
              "..                       ...                       ...   \n",
              "192     RS_IT_Segusino_Piave                  45.92249   \n",
              "193     RS_IT_Segusino_Piave                  45.92249   \n",
              "194     RS_IT_Segusino_Piave                  45.92249   \n",
              "195     RS_IT_Segusino_Piave                  45.92249   \n",
              "196     RS_IT_Segusino_Piave                  45.92249   \n",
              "\n",
              "     longitude (degrees_east)  depth (m)  RVFL (m3/s)  RVFL_QC (1)  \n",
              "0                   11.784244        0.0       286.06            0  \n",
              "1                   11.784244        0.0       287.51            0  \n",
              "2                   11.784244        0.0       290.41            0  \n",
              "3                   11.784244        0.0       291.86            0  \n",
              "4                   11.784244        0.0       293.32            0  \n",
              "..                        ...        ...          ...          ...  \n",
              "192                 11.948837        0.0        31.41            0  \n",
              "193                 11.948837        0.0        32.28            0  \n",
              "194                 11.948837        0.0        33.16            0  \n",
              "195                 11.948837        0.0        34.06            0  \n",
              "196                 11.948837        0.0        34.98            0  \n",
              "\n",
              "[197 rows x 6 columns]"
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "coords_time_data_df",
              "summary": "{\n  \"name\": \"coords_time_data_df\",\n  \"rows\": 197,\n  \"fields\": [\n    {\n      \"column\": \"PLATFORMCODE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 8,\n        \"samples\": [\n          \"RS_IT_Bacchiglione_Montegalda\",\n          \"RS_IT_Livenza_MedunaDiLivenza\",\n          \"RS_IT_Adige_BoaraPisani\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"latitude (degrees_north)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3416532911541059,\n        \"min\": 45.10548,\n        \"max\": 46.14404,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          45.44273,\n          45.805107,\n          45.10548\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"longitude (degrees_east)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5275495303544712,\n        \"min\": 11.672323,\n        \"max\": 13.430171,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          11.672323,\n          12.6105795,\n          11.784244\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"depth (m)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.0,\n        \"max\": 0.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RVFL (m3/s)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 123.96322162334596,\n        \"min\": 0.0,\n        \"max\": 360.94,\n        \"num_unique_values\": 196,\n        \"samples\": [\n          134.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RVFL_QC (1)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kI8k33kc_KZ4"
      },
      "source": [
        "## **Server-side functions**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z9O3M9Nb_KZ4"
      },
      "source": [
        "When getting the data it is also possible to use Server-side functions.\n",
        "\n",
        "These can be:\n",
        "- orderBy\n",
        "- orderByDescending\n",
        "- orderByClosest\n",
        "- orderByCount\n",
        "- orderByLimit\n",
        "- orderByMax\n",
        "- orderByMin\n",
        "- orderByMinMax\n",
        "- orderByMean\n",
        "- orderBySum\n",
        "\n",
        "In this example we will use the OrderByMean, which sorts the rows into groups and returns just one row for each group.  \n",
        "This row will have the mean of each of the other variables.\n",
        "\n",
        "The syntax is the following:\n",
        "\n",
        "```&orderByMean(%22PLATFORMCODE%2Ctime/1month%22)```\n",
        "\n",
        "inside the orderByMean() server side function we will pass PLATFORMCODE as a key\n",
        "and specify the time duration to group the data and get its mean."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 488
        },
        "id": "V_Gwet3l_KZ4",
        "outputId": "07fbaba3-561c-456f-fa9c-2678a9803360"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               PLATFORMCODE            time (UTC)  RVFL (m3/s)\n",
              "0   RS_IT_Po_Pontelagoscuro  2023-03-01T00:00:00Z   476.446042\n",
              "1   RS_IT_Po_Pontelagoscuro  2023-04-01T00:00:00Z   427.257708\n",
              "2   RS_IT_Po_Pontelagoscuro  2023-05-01T00:00:00Z  1112.957788\n",
              "3   RS_IT_Po_Pontelagoscuro  2023-06-01T00:00:00Z   976.922049\n",
              "4   RS_IT_Po_Pontelagoscuro  2023-07-01T00:00:00Z   500.729891\n",
              "5   RS_IT_Po_Pontelagoscuro  2023-08-01T00:00:00Z   448.454212\n",
              "6   RS_IT_Po_Pontelagoscuro  2023-09-01T00:00:00Z  1133.536400\n",
              "7   RS_IT_Po_Pontelagoscuro  2023-10-01T00:00:00Z  1311.416379\n",
              "8   RS_IT_Po_Pontelagoscuro  2023-11-01T00:00:00Z  2270.372392\n",
              "9   RS_IT_Po_Pontelagoscuro  2023-12-01T00:00:00Z   962.411794\n",
              "10  RS_IT_Po_Pontelagoscuro  2024-01-01T00:00:00Z  1111.184499\n",
              "11  RS_IT_Po_Pontelagoscuro  2024-02-01T00:00:00Z  1323.183123\n",
              "12  RS_IT_Po_Pontelagoscuro  2024-03-01T00:00:00Z  3138.369564\n",
              "13  RS_IT_Po_Pontelagoscuro  2024-04-01T00:00:00Z  2954.304220"
            ],
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              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-fb52b064-ae5c-4578-82e8-9dbfd4b6b579 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-fb52b064-ae5c-4578-82e8-9dbfd4b6b579');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-e2bff55d-08ed-43b7-aa21-c11d9cf4689f\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-e2bff55d-08ed-43b7-aa21-c11d9cf4689f')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-e2bff55d-08ed-43b7-aa21-c11d9cf4689f button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "ordered_data_df",
              "summary": "{\n  \"name\": \"ordered_data_df\",\n  \"rows\": 14,\n  \"fields\": [\n    {\n      \"column\": \"PLATFORMCODE\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"RS_IT_Po_Pontelagoscuro\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"time (UTC)\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"2023-12-01T00:00:00Z\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RVFL (m3/s)\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 884.8725890673709,\n        \"min\": 427.257708333333,\n        \"max\": 3138.3695639320003,\n        \"num_unique_values\": 14,\n        \"samples\": [\n          962.4117938311688\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ],
      "source": [
        "platform_code = 'RS_IT_Po_Pontelagoscuro'\n",
        "\n",
        "variables = '?PLATFORMCODE%2Ctime%2CRVFL'\n",
        "filters = f'&PLATFORMCODE=%22{platform_code}%22&time%3E=2023-03-29T09%3A45%3A00Z&time%3C=2024-04-29T09%3A45%3A00Z'\n",
        "\n",
        "server_side_func = '&orderByMean(%22PLATFORMCODE%2Ctime/1month%22)'\n",
        "\n",
        "ordered_data_resp = requests.get(BASE_URL + variables + filters + server_side_func)\n",
        "ordered_data_df = pd.read_csv(io.StringIO(ordered_data_resp.text), sep=',')\n",
        "ordered_data_df"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rQLaA0Cn_KZ4"
      },
      "source": [
        "## **Data visualization**"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ve3fVOha_KZ4"
      },
      "source": [
        "The data obtained and inserted into a DataFrame can be easily visualized by using the plot() method of the pandas DataFrame.  \n",
        "Here we will plot the values from the time ('time (UTC)') column on the x axis and the river flow rate ('RVFL') on the y axis from the ```data_df```, obtained in the first part (```With PLATFORMCODE and time range```) of the ```Data gathering``` section."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        },
        "id": "738k7xLp_KZ4",
        "outputId": "3f5d2968-d134-454b-fb4d-7e911616a0c2"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = data_df.plot(x='time (UTC)', y='RVFL (m3/s)', figsize=(15, 6), rot=0)\n",
        "\n",
        "plt.xlabel('Time (UTC)')\n",
        "plt.ylabel('RVFL (m3/s)')\n",
        "plt.title(f'{platform_code}: RVFL vs Time')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yfGUkWjs_KZ5"
      },
      "source": [
        "In this second example we will use a bar plot to visualize the mean of river flow rate (RVFL) grouped by month, obtained in the ```Server-side functions``` section."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 710
        },
        "id": "niDU5vSf_KZ5",
        "outputId": "afc8b6a3-c2c3-443a-8784-95c50860b494"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "ax = ordered_data_df.plot.bar(x='time (UTC)', y='RVFL (m3/s)', figsize=(15, 6), rot=270)\n",
        "\n",
        "plt.xlabel('Time')\n",
        "plt.ylabel('RVFL')\n",
        "plt.title(f'{platform_code}: Mean RVFL vs Time - Barplot')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "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": "3.9.0"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}