101 generalize copernicus downloader

bluemath_tk/downloaders/ecmwf/OpenData/OpenData_config.json

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+{
+    "datasets": {
+        "forecast_data": {
+            "description": "ECMWF OpenData Forecast Data",
+            "url": "https://confluence.ecmwf.int/display/DAC/ECMWF+open+data%3A+real-time+forecasts+from+IFS+and+AIFS",
+            "models": [
+                "ifs",
+                "aifs"
+            ],
+            "resolutions": [
+                "0p25"
+            ]
+        }
+    }
+}

bluemath_tk/downloaders/ecmwf/ecmwf_downloader.py

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+import json
+import os
+from typing import List, Union
+
+import xarray as xr
+from ecmwf.opendata import Client
+
+from .._base_downloaders import BaseDownloader
+
+
+class ECMWFDownloader(BaseDownloader):
+    """
+    This is the main class to download data from the ECMWF.
+
+    Attributes
+    ----------
+    product : str
+        The product to download data from. Currently only OpenData is supported.
+    product_config : dict
+        The configuration for the product to download data from.
+    client : ecmwf.opendata.Client
+        The client to interact with the ECMWF API.
+
+    Examples
+    --------
+    .. jupyter-execute::
+
+        from bluemath_tk.downloaders.ecmwf.ecmwf_downloader import ECMWFDownloader
+
+        ecmwf_downloader = ECMWFDownloader(
+            product="OpenData",
+            base_path_to_download="/path/to/ECMWF/",  # Will be created if not available
+            check=True,
+        )
+        dataset = ecmwf_downloader.download_data(
+            load_data=True,
+            param=["msl"],
+            step=[0, 240],
+            type="fc",
+        )
+        print(dataset)
+    """
+
+    products_configs = {
+        "OpenData": json.load(
+            open(
+                os.path.join(
+                    os.path.dirname(__file__), "OpenData", "OpenData_config.json"
+                )
+            )
+        )
+    }
+
+    def __init__(
+        self,
+        product: str,
+        base_path_to_download: str,
+        model: str = "ifs",
+        resolution: str = "0p25",
+        debug: bool = True,
+        check: bool = True,
+    ) -> None:
+        """
+        This is the constructor for the ECMWFDownloader class.
+
+        Parameters
+        ----------
+        product : str
+            The product to download data from. Currently only OpenData is supported.
+        base_path_to_download : str
+            The base path to download the data to.
+        model : str, optional
+            The model to download data from. Default is "ifs".
+        resolution : str, optional
+            The resolution to download data from. Default is "0p25".
+        debug : bool, optional
+            Whether to run in debug mode. Default is True.
+        check : bool, optional
+            Whether to just check the data. Default is True.
+
+        Raises
+        ------
+        ValueError
+            If the product configuration is not found.
+        """
+
+        super().__init__(
+            base_path_to_download=base_path_to_download, debug=debug, check=check
+        )
+        self._product = product
+        self._product_config = self.products_configs.get(product)
+        if self._product_config is None:
+            raise ValueError(f"{product} configuration not found")
+        self.set_logger_name(
+            f"ECMWFDownloader-{product}", level="DEBUG" if debug else "INFO"
+        )
+        if not self.check:
+            if model not in self.product_config["datasets"]["forecast_data"]["models"]:
+                raise ValueError(f"Model {model} not supported for {self.product}")
+            if (
+                resolution
+                not in self.product_config["datasets"]["forecast_data"]["resolutions"]
+            ):
+                raise ValueError(
+                    f"Resolution {resolution} not supported for {self.product}"
+                )
+            self._client = Client(
+                source="ecmwf",
+                model=model,
+                resol=resolution,
+                preserve_request_order=False,
+                infer_stream_keyword=True,
+            )
+            self.logger.info("---- DOWNLOADING DATA ----")
+        else:
+            self.logger.info("---- CHECKING DATA ----")
+
+        # Set the model and resolution parameters
+        self.model = model
+        self.resolution = resolution
+
+    @property
+    def product(self) -> str:
+        return self._product
+
+    @property
+    def product_config(self) -> dict:
+        return self._product_config
+
+    @property
+    def client(self) -> Client:
+        return self._client
+
+    def list_datasets(self) -> List[str]:
+        """
+        Lists the datasets available for the product.
+
+        Returns
+        -------
+        List[str]
+            The list of datasets available for the product.
+        """
+
+        return list(self.product_config["datasets"].keys())
+
+    def download_data(
+        self, load_data: bool = False, *args, **kwargs
+    ) -> Union[str, xr.Dataset]:
+        """
+        Downloads the data for the product.
+
+        Parameters
+        ----------
+        load_data : bool, optional
+            Whether to load the data into an xarray.Dataset. Default is False.
+        *args
+            The arguments to pass to the download function.
+        **kwargs
+            The keyword arguments to pass to the download function.
+
+        Returns
+        -------
+        Union[str, xr.Dataset]
+            The path to the downloaded file if load_data is False, otherwise the xarray.Dataset.
+
+        Raises
+        ------
+        ValueError
+            If the product is not supported.
+        """
+
+        if self.product == "OpenData":
+            downloaded_file_path = self.download_data_open_data(*args, **kwargs)
+            if load_data:
+                return xr.open_dataset(downloaded_file_path, engine="cfgrib")
+            else:
+                return downloaded_file_path
+        else:
+            raise ValueError(f"Download for product {self.product} not supported")
+
+    def download_data_open_data(
+        self,
+        force: bool = False,
+        **kwargs,
+    ) -> str:
+        """
+        Downloads the data for the OpenData product.
+
+        Parameters
+        ----------
+        force : bool, optional
+            Whether to force the download. Default is False.
+        **kwargs
+            The keyword arguments to pass to the download function.
+
+        Returns
+        -------
+        str
+            The path to the downloaded file.
+        """
+
+        if "param" in kwargs:
+            variables = kwargs["param"]
+        else:
+            variables = []
+        if "step" in kwargs:
+            steps = kwargs["step"]
+            if not isinstance(steps, list):
+                steps = [steps]
+        else:
+            steps = []
+        if "type" in kwargs:
+            type = kwargs["type"]
+        else:
+            type = "fc"
+
+        output_grib_file = os.path.join(
+            self.base_path_to_download,
+            self.product,
+            self.model,
+            self.resolution,
+            f"{'_'.join(variables)}_{'_'.join(str(step) for step in steps)}_{type}.grib2",
+        )
+        if not self.check:
+            os.makedirs(os.path.dirname(output_grib_file), exist_ok=True)
+
+        if self.check or not force:
+            if os.path.exists(output_grib_file):
+                self.logger.debug(f"{output_grib_file} already downloaded")
+            else:
+                if self.check:
+                    self.logger.debug(f"{output_grib_file} not downloaded")
+                else:
+                    self.logger.debug(f"Downloading: {output_grib_file}")
+                    self.client.retrieve(
+                        target=output_grib_file,
+                        **kwargs,
+                    )
+        else:
+            self.logger.debug(f"Downloading: {output_grib_file}")
+            self.client.retrieve(
+                target=output_grib_file,
+                **kwargs,
+            )
+
+        return output_grib_file

tests/downloaders/test_ecmwf_downloader.py

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+import tempfile
+import unittest
+
+import xarray as xr
+
+from bluemath_tk.downloaders.ecmwf.ecmwf_downloader import ECMWFDownloader
+
+
+class TestECMWFDownloader(unittest.TestCase):
+    def setUp(self):
+        self.temp_dir = tempfile.mkdtemp()
+        self.downloader = ECMWFDownloader(
+            product="OpenData",
+            base_path_to_download="OpenDataJavixu",  # self.temp_dir,
+            check=False,  # Just check paths to download, do not actually download
+        )
+
+    def test_list_datasets(self):
+        datasets = self.downloader.list_datasets()
+        self.assertIsInstance(datasets, list)
+        self.assertTrue(len(datasets) > 0)
+        print(f"Available datasets: {datasets}")
+
+    def test_download_data(self):
+        dataset = self.downloader.download_data(
+            load_data=True,
+            param=["msl"],
+            step=[0, 240],
+            type="fc",
+            force=False,
+        )
+        self.assertIsInstance(dataset, xr.Dataset)
+        print(dataset)
+
+
+if __name__ == "__main__":
+    unittest.main()