Using UK Met Office PP files

This example uses a small PP file containing global temperature data.

We can read this data as a file source using the Iris library under the hood.

Please note, PP file support requires the scitools-iris and ncdata packages to be installed.

import earthkit.data as ekd

# we ensure the example file is available.
ekd.download_example_file("air_temp.pp")

ds = ekd.from_source("file", "air_temp.pp")

/Users/cgr/venv/dev/lib/python3.13/site-packages/array_api_compat/torch/_info.py:361: UserWarning: 'mkldnn' is no longer used as device type. So torch.device('mkldnn') will be deprecated and removed in the future. Please use other valid device types instead. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/c10/core/Device.cpp:42.)
  a = torch.empty((0,), device=torch.device(device_name, index=i))
/Users/cgr/venv/dev/lib/python3.13/site-packages/iris/fileformats/netcdf/saver.py:2857: IrisDeprecation: Saving to netcdf with legacy-style attribute handling for backwards compatibility.
This mode is deprecated since Iris 3.8, and will eventually be removed.
Please consider enabling the new split-attributes handling mode, by setting 'iris.FUTURE.save_split_attrs = True'.
  warn_deprecated(message)
/Users/cgr/venv/dev/lib/python3.13/site-packages/ncdata/xarray.py:165: FutureWarning: In a future version, xarray will not decode the variable 'forecast_period' into a timedelta64 dtype based on the presence of a timedelta-like 'units' attribute by default. Instead it will rely on the presence of a timedelta64 'dtype' attribute, which is now xarray's default way of encoding timedelta64 values.
To continue decoding into a timedelta64 dtype, either set `decode_timedelta=True` when opening this dataset, or add the attribute `dtype='timedelta64[ns]'` to this variable on disk.
To opt-in to future behavior, set `decode_timedelta=False`.
  ds = store_entrypoint.open_dataset(self, **xr_load_kwargs)

The resulting object is an Xarray fieldList.

ds.ls()

	variable	level	valid_datetime	units
0	air_temperature	None	1998-12-01T00:00:00	K

ds[0].shape

(73, 96)

ds[0].values[:5]

array([254.644, 254.644, 254.644, 254.644, 254.644], dtype=float32)

ds[0].to_latlon()

{'lat': array([[ 89.999985,  89.999985,  89.999985, ...,  89.999985,  89.999985,
          89.999985],
        [ 87.499985,  87.499985,  87.499985, ...,  87.499985,  87.499985,
          87.499985],
        [ 84.999985,  84.999985,  84.999985, ...,  84.999985,  84.999985,
          84.999985],
        ...,
        [-84.999954, -84.999954, -84.999954, ..., -84.999954, -84.999954,
         -84.999954],
        [-87.499954, -87.499954, -87.499954, ..., -87.499954, -87.499954,
         -87.499954],
        [-89.999954, -89.999954, -89.999954, ..., -89.999954, -89.999954,
         -89.999954]], shape=(73, 96), dtype=float32),
 'lon': array([[  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,
         356.2499  ],
        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,
         356.2499  ],
        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,
         356.2499  ],
        ...,
        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,
         356.2499  ],
        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,
         356.2499  ],
        [  0.      ,   3.749999,   7.499998, ..., 348.7499  , 352.4999  ,
         356.2499  ]], shape=(73, 96), dtype=float32)}