fetch_data#
- earth2studio.data.fetch_data(
- source,
- time,
- variable,
- lead_time=array([0], dtype='timedelta64[h]'),
- device='cpu',
- interp_to=None,
- interp_method='nearest',
- legacy=True,
Utility function to fetch data arrays from particular sources and load data on the target device. If desired, xarray interpolation/regridding in the spatial domain can be used by passing a target coordinate system via the optional interp_to argument.
- Parameters:
source (DataSource | ForecastSource) – The data source to fetch from
time (TimeArray) – Timestamps to return data for (UTC).
variable (VariableArray) – Strings or list of strings that refer to variables to return
lead_time (LeadTimeArray, optional) – Lead times to fetch for each provided time, by default np.array(np.timedelta64(0, “h”))
device (torch.device, optional) – Torch device to load data tensor to, by default “cpu”
interp_to (CoordSystem, optional) – If provided, the fetched data will be interpolated to the coordinates specified by lat/lon arrays in this CoordSystem
interp_method (str) – Interpolation method to use with xarray (by default ‘nearest’)
legacy (bool, optional) – If True (default), returns tuple of (torch.Tensor, CoordSystem). If False, returns xr.DataArray with numpy arrays for CPU or cupy arrays for CUDA.
- Returns:
If legacy=True: Tuple containing output tensor and coordinate OrderedDict. If legacy=False: xr.DataArray with numpy arrays (CPU) or cupy arrays (CUDA).
- Return type:
tuple[torch.Tensor, CoordSystem] | xr.DataArray
Examples using earth2studio.data.fetch_data#
Regional Downscaling to COSMO-REA (6 km and 2.2 km)
