Add IFS uncompressed data

.github/workflows/ci.yml

@@ -47,7 +47,7 @@ jobs:
         run: uv python install
 
       - name: Install the package
-        run: uv sync --all-extras --all-groups && uv pip install .
+        run: uv sync --refresh --all-extras --all-groups && uv pip install .
 
       - name: Run tests
         run: uv run pytest

README.md

@@ -18,7 +18,7 @@ To download all the data used for the benchmark run the following commands:
 ```bash
 uv run python -m climatebenchpress.data_loader.datasets.esa_biomass_cci
 uv run python -m climatebenchpress.data_loader.datasets.cams
-uv run python -m climatebenchpress.data_loader.datasets.era5
+uv run python -m climatebenchpress.data_loader.datasets.ifs_uncompressed
 uv run python -m climatebenchpress.data_loader.datasets.nextgems
 uv run python -m climatebenchpress.data_loader.datasets.cmip6.access_ta
 uv run python -m climatebenchpress.data_loader.datasets.cmip6.access_tos

pyproject.toml

@@ -8,7 +8,9 @@ dependencies = [
     "cf-xarray~=0.10.0",
     "cftime~=1.6.0",
     "dask>=2024.12.0,<2025.4",
+    "earthkit-regrid~=0.5.0",
     "fsspec>=2024.10.0,<2025.4",
+    "gribscan~=0.0.14",
     "healpy~=1.18.0",
     # These versions need to be pinned to be compatible with the NextGEMS
     # catalog at https://data.nextgems-h2020.eu/online.yaml.
@@ -52,5 +54,5 @@ where = ["src"]
 addopts = ["--import-mode=importlib"]
 
 [[tool.mypy.overrides]]
-module = ["fsspec.*", "intake.*", "healpy.*"]
+module = ["fsspec.*", "intake.*", "healpy.*", "earthkit.*"]
 follow_untyped_imports = true

src/climatebenchpress/data_loader/datasets/all.py

@@ -4,4 +4,5 @@
 from .cmip6.all import *
 from .era5 import *
 from .esa_biomass_cci import *
+from .ifs_uncompressed import *
 from .nextgems import *

src/climatebenchpress/data_loader/datasets/ifs_uncompressed.py

@@ -0,0 +1,172 @@
+__all__ = ["IFSUncompressedDataset"]
+
+import argparse
+from pathlib import Path
+
+import earthkit.regrid
+import numpy as np
+import requests
+import xarray as xr
+
+from .. import (
+    monitor,
+    open_downloaded_canonicalized_dataset,
+    open_downloaded_tiny_canonicalized_dataset,
+)
+from .abc import Dataset
+
+BASE_URL = "https://object-store.os-api.cci1.ecmwf.int/esiwacebucket"
+
+
+class IFSUncompressedDataset(Dataset):
+    """Dataset for IFS uncompressed data.
+
+    Contains data from the [hplp](https://apps.ecmwf.int/ifs-experiments/rd/hplp/)
+    experiment from the Integrated Forecasting System (IFS) model. Crucially,
+    this dataset contains uncompressed 64-bit floating point data.
+    """
+
+    name = "ifs-uncompressed"
+
+    @staticmethod
+    def download(download_path: Path, progress: bool = True):
+        ds = load_hplp_data(leveltype="sfc", gridtype="reduced_gg")
+        ds = ds[["msl", "10u", "10v"]]
+        ds_regridded = regrid_to_regular(
+            ds,
+            in_grid={"grid": "O400"},
+            out_grid={"grid": [0.25, 0.25]},
+        )
+        downloadfile = download_path / "ifs_uncompressed.zarr"
+        with monitor.progress_bar(progress):
+            ds_regridded.to_zarr(
+                downloadfile, mode="w", encoding=dict(), compute=False
+            ).compute()
+
+    @staticmethod
+    def open(download_path: Path) -> xr.Dataset:
+        ds = xr.open_dataset(download_path / "ifs_uncompressed.zarr")
+
+        # Needed to make the dataset CF-compliant.
+        ds.longitude.attrs["axis"] = "X"
+        ds.latitude.attrs["axis"] = "Y"
+        ds.time.attrs["standard_name"] = "time"
+        return ds
+
+
+def load_hplp_data(leveltype=None, gridtype=None, step=None, remap=False):
+    """Function taken from: https://github.com/climet-eu/compression-lab-notebooks/blob/main/04-example-datasets/01-hplp.ipynb."""
+    if leveltype not in {"pl", "ml", "sfc", "wave"}:
+        raise ValueError(
+            f"Invalid leveltype: '{leveltype}'. Available leveltypes: pl, ml, sfc, wave"
+        )
+
+    if leveltype in {"ml", "pl"} and not gridtype:
+        raise ValueError(
+            f"Gridtype is required for leveltype '{leveltype}'. Available: reduced_gg, sh"
+        )
+
+    if remap and gridtype != "sh":
+        raise ValueError("Only 'sh' fields can be remapped.")
+
+    if leveltype == "wave" and gridtype != "reduced_ll":
+        print("Warning: Wave model data are stored on a reduced_ll grid.")
+
+    if leveltype == "sfc" and gridtype != "reduced_gg":
+        print("Warning: Surface level data are stored on a reduced_gg grid.")
+
+    if step and not (leveltype == "ml" and gridtype == "reduced_gg"):
+        print(
+            "Warning: Specifying 'step' is unnecessary for this configuration and will be ignored."
+        )
+
+    if leveltype in {"sfc", "wave"}:
+        url = f"{BASE_URL}/hplp/hplp_{leveltype}.grib"
+    elif leveltype == "ml" and gridtype == "reduced_gg":
+        if step is None:
+            raise ValueError(
+                "The ml reduced_gg data are split into two parts:\n"
+                "  - Steps: 0, 12, 24, 36, 48, 60, 72, 84, 96, 108, 120 (2020-07-21T00:00:00 to 2020-07-26T00:00:00)\n"
+                "  - Steps: 132, 144, 156, 168, 180, 192, 204, 216, 228, 240 (2020-07-26T12:00:00 to 2020-07-31T00:00:00)\n"
+                "Specify a step smaller than 120 for accessing the first part, \n"
+                "and a step greater or equal to 132 for accessing the second part."
+            )
+        if step <= 120:
+            url = f"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_0_120.grib"
+        else:
+            url = f"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}_levels_132_240.grib"
+    else:
+        url = f"{BASE_URL}/hplp/hplp_{leveltype}_{gridtype}" + (
+            "_O400.grib" if remap else ".grib"
+        )
+    ref = requests.get(f"{url}.ref").json()
+
+    print(f"Loading dataset {url}")
+
+    return xr.open_dataset(
+        "reference://",
+        engine="zarr",
+        backend_kwargs=dict(storage_options=dict(fo=ref, asynchronous=False)),
+        consolidated=False,
+    )
+
+
+def regrid_to_regular(ds, in_grid, out_grid):
+    """Regrid dataset to a regular lat-lon grid.
+
+    Parameters
+    ----------
+    ds : xr.Dataset
+        The input dataset to regrid
+    in_grid : dict
+        The input grid specification for earthkit.regrid.interpolate
+    out_grid : dict
+        The output grid specification for earthkit.regrid.interpolate. Is assumed to be
+        a regular lat-lon grid with equal spacing in latitude and longitude, e.g. {"grid": [0.25, 0.25]}.
+    """
+    out_data = {var: [] for var in ds.data_vars}
+    for var in ds.data_vars:
+        for time in ds.time:
+            r = earthkit.regrid.interpolate(
+                ds[var].sel(time=time).values,
+                in_grid=in_grid,
+                out_grid=out_grid,
+                method="linear",
+            )
+            out_data[var].append(r)
+
+    dx = out_grid["grid"][0]
+    assert (
+        out_grid["grid"][0] == out_grid["grid"][1]
+    ), "Only grids with equal latitude and longitude spacing are supported."
+    lats = np.linspace(90, -90, int(180 / dx) + 1)
+    lons = np.linspace(0, 360 - dx, int(360 / dx))
+    coords = {
+        "time": ds.time,
+        "latitude": lats,
+        "longitude": lons,
+    }
+    out_ds = xr.Dataset(
+        {
+            var: (("time", "latitude", "longitude"), out_data[var])
+            for var in ds.data_vars
+        },
+        coords=coords,
+    )
+    return out_ds
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("--basepath", type=Path, default=Path())
+    args = parser.parse_args()
+
+    ds = open_downloaded_canonicalized_dataset(
+        IFSUncompressedDataset, basepath=args.basepath
+    )
+    open_downloaded_tiny_canonicalized_dataset(
+        IFSUncompressedDataset, basepath=args.basepath
+    )
+
+    for v, da in ds.items():
+        print(f"- {v}: {da.dims}")