Quick start

Once the plugin is installed, it can be used directly into a Jupyter notebook.

import climetlab as cml
from dask.distributed import Client

# Instantiate a default Dask distributed client to handle data
client = Client()

cmlds = cml.load_dataset(
    "wekeo-clms-cgls-hourly-lst-global-v2",
    start="2021-01-18",
    end="2021-01-19",
)

ds = cmlds.to_xarray()
ds.LST.plot(x="lon", y="lat")

client.shutdown()

To ease the construct of the arguments for the load_dataset function, especially for those datasets with a lot of options, you can use a utility function that transforms an HDA API request into usable parameters.

Open the WEkEO Data web page, choose one of the supported dataset and select the parameters in the form, then click on the Show API request(s) button:

On the new modal, click the Copy button:

Once the request is copied into the clipboard, you can use it following the example below:

import climetlab as cml
from climetlab_wekeo_datasets import hda2cml

query = {
    "datasetId": "EO:ECMWF:DAT:CAMS_EUROPE_AIR_QUALITY_FORECASTS",
    "boundingBoxValues": [
        {
        "name": "area",
        "bbox": [
            3.694591996044645,
            35.907220595373886,
            14.939002418789217,
            42.61449357770201
        ]}
    ],
    "dateRangeSelectValues": [
        {
            "name": "date",
            "start": "2023-08-25T00:00:00.000Z",
            "end": "2023-10-31T23:59:59.999Z"
        }
    ],
    "multiStringSelectValues": [
        {
        "name": "variable",
        "value": [
            "alder_pollen",
            "ammonia"
        ]},
        ...
    ],
    "stringChoiceValues": [
        {
            "name": "format",
            "value": "netcdf"
        }
    ]
}

entry_point, arguments = hda2cml(query)
dataset = cml.load_dataset(entry_point, **arguments)