| import os |
| import sys |
| from pathlib import Path |
| root_path = Path(__file__).parent.parent |
| sys.path.append(str(root_path)) |
| import h5py |
| import numpy as np |
| import xarray as xr |
| from onescience.utils.YParams import YParams |
|
|
|
|
| |
| DATASET_DIMS = {"T": 10, "H": 721, "W": 1440, "time_step": 6} |
|
|
|
|
| def generate_fake_h5(data_dir, var_names, years, dims): |
| """ |
| 为每个年份生成一个空 h5 文件。 |
| 利用 HDF5 chunked 数据集未写入 chunk 即返回 fill_value=0 的特性， |
| 文件实际只含元数据，极小，但 shape 与真实数据完全一致。 |
| 均值/标准差也作为数据集内嵌进每年的 h5，与 era5.py 新版读取方式对应。 |
| """ |
| os.makedirs(os.path.join(data_dir, "data"), exist_ok=True) |
| T, C = dims["T"], len(var_names) |
| H, W = dims["H"], dims["W"] |
|
|
| means = np.zeros((1, C, 1, 1), dtype=np.float32) |
| stds = np.ones((1, C, 1, 1), dtype=np.float32) |
|
|
| for year in years: |
| path = os.path.join(data_dir, "data", f"{year}.h5") |
| with h5py.File(path, "w") as f: |
| ds = f.create_dataset( |
| "fields", |
| shape=(T, C, H, W), |
| dtype="float32", |
| chunks=(1, C, H, W), |
| fillvalue=0.0, |
| ) |
| ds.attrs["variables"] = var_names |
| ds.attrs["time_step"] = dims["time_step"] |
| f.create_dataset("global_means", data=means) |
| f.create_dataset("global_stds", data=stds) |
|
|
| size_kb = os.path.getsize(path) / 1024 |
| print(f" {year}.h5 shape=({T},{C},{H},{W}) " |
| f"logical={T*C*H*W*4/1024**3:.1f}GB actual={size_kb:.1f}KB") |
|
|
|
|
| def get_static(data_dir, var, name): |
| os.makedirs(data_dir, exist_ok=True) |
| ds = xr.Dataset( |
| data_vars={ |
| f"{var}": (("valid_time", "latitude", "longitude"), |
| np.random.rand(1, 721, 1440).astype(np.float32)) |
| }, |
| coords={ |
| "valid_time": ["2015-12-31"], |
| "latitude": np.linspace(90, -90, 721, dtype=np.float64), |
| "longitude": np.linspace(0, 359.75, 1440, dtype=np.float64), |
| "number": 0, |
| "expver": "", |
| }, |
| attrs={ |
| "GRIB_centre": "ecmf", |
| "GRIB_centreDescription": "European Centre for Medium-Range Weather Forecasts", |
| "GRIB_subCentre": "0", |
| "Conventions": "CF-1.7", |
| "institution": "European Centre for Medium-Range Weather Forecasts", |
| "history": "Generated manually", |
| } |
| ) |
|
|
| ds.to_netcdf(f"{data_dir}/{name}.nc") |
| arr = np.random.randn(721, 1440).astype(np.float32) |
| np.save(f'{data_dir}/land_mask.npy', arr) |
| np.save(f'{data_dir}/soil_type.npy', arr) |
| np.save(f'{data_dir}/topography.npy', arr) |
| print(f"✅ Static data: {arr.shape}, dtype: {arr.dtype}, save to {data_dir}") |
|
|
|
|
| if __name__ == "__main__": |
| cfg_datapipe = YParams("conf/config.yaml", "datapipe") |
|
|
| if cfg_datapipe.dataset.data_dir.startswith("/public/") or cfg_datapipe.dataset.data_dir.startswith("/work2/"): |
| print("请检查 config，确保各 *_dir 指向本地测试路径而非生产路径。") |
| exit() |
|
|
| years = cfg_datapipe.dataset.train_time + cfg_datapipe.dataset.val_time + cfg_datapipe.dataset.test_time |
| atm_vars = cfg_datapipe.dataset.channels |
|
|
| generate_fake_h5(cfg_datapipe.dataset.data_dir, atm_vars, years, DATASET_DIMS) |
|
|
| static_dir = os.path.join(cfg_datapipe.dataset.data_dir, "static") |
| get_static(static_dir, 'z', 'geopotential') |
| get_static(static_dir, 'lsm', 'land_sea_mask') |
|
|
|
|
| print("\n✅ Fake datasets generated.") |
|
|
