File size: 2,734 Bytes
9191802 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 | """Create an ERA5-style HDF5 dataset consumable by OneScience ERA5Dataset."""
from __future__ import annotations
import argparse
from pathlib import Path
import h5py
import numpy as np
from common import load_config, resolve_path
from variables import c85_from_config
def create_year(
path: Path,
year: int,
steps: int,
height: int,
width: int,
channels: list[str],
time_step_hours: int,
hdf5_config: dict,
) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with h5py.File(path, "w") as handle:
fields = handle.create_dataset(
"fields",
shape=(steps, len(channels), height, width),
dtype="float32",
chunks=tuple(min(size, limit) for size, limit in zip((steps, len(channels), height, width), hdf5_config["chunks"])),
fillvalue=0.0,
compression=hdf5_config["compression"],
compression_opts=hdf5_config["compression_level"],
)
fields.attrs["variables"] = np.asarray(channels, dtype=h5py.string_dtype("utf-8"))
fields.attrs["time_step"] = time_step_hours
fields.attrs["year"] = year
lat = np.linspace(90.0, -90.0, height, dtype=np.float32)[:, None]
lon = np.arange(width, dtype=np.float32)[None, :] * (360.0 / width)
t2m_index = channels.index("t2m")
for step in range(steps):
fields[step, t2m_index] = (
np.cos(np.deg2rad(lat)) * np.cos(np.deg2rad(lon + step * 15.0))
)
handle.create_dataset("global_means", data=np.zeros((1, len(channels), 1, 1), dtype=np.float32))
handle.create_dataset("global_stds", data=np.ones((1, len(channels), 1, 1), dtype=np.float32))
def main() -> None:
parser = argparse.ArgumentParser()
parser.add_argument("--config", default="conf/config.yaml")
args = parser.parse_args()
cfg = load_config(args.config)
data_cfg = cfg["data"]
channels, _ = c85_from_config(cfg)
root = resolve_path(cfg["paths"]["data_root"], cfg)
generated_years = set()
for split, split_cfg in data_cfg["splits"].items():
for year in split_cfg["years"]:
if year in generated_years:
raise ValueError(f"Year {year} is assigned to more than one data split")
generated_years.add(year)
create_year(
root / "data" / f"{year}.h5",
year,
split_cfg["time_steps"],
*data_cfg["grid_size"],
channels,
data_cfg["time_step_hours"],
data_cfg["hdf5"],
)
print(f"Created ERA5-compatible yearly datasets at {root / 'data'}")
if __name__ == "__main__":
main()
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