scripts/fake_data.py

import os
import h5py
import numpy as np
import xarray as xr
from onescience.utils.YParams import YParams


# Prithvi WxC 输入两个时刻、预测一个时刻，并额外接收 4 通道静态场。
def get_dims(cfg_model, cfg_data):
    H, W = int(cfg_model.n_lats_px), int(cfg_model.n_lons_px)
    if tuple(map(int, cfg_data.dataset.img_size)) != (H, W):
        raise ValueError("model grid and datapipe.dataset.img_size must match")
    input_steps, output_steps = int(cfg_model.input_size_time), 1
    samples = max(int(cfg_data.dataloader.batch_size), 2)
    T = input_steps + output_steps + samples - 1
    return {
        "T": T, "H": H, "W": W, "time_step": 6,
        "input_steps": input_steps, "output_steps": output_steps,
        "static_channels": int(cfg_model.in_channels_static),
    }


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, H, W, channels):
    os.makedirs(data_dir, exist_ok=True)
    lat = np.linspace(90, -90, H, dtype=np.float32)
    lon = np.linspace(0, 360 - 360 / W, W, dtype=np.float32)
    lat_grid = np.broadcast_to(lat[:, None], (H, W)) / 90.0
    lon_grid = np.broadcast_to(lon[None, :], (H, W)) / 180.0 - 1.0
    land_mask = (np.sin(np.deg2rad(lat_grid * 90)) > 0).astype(np.float32)
    topography = np.cos(np.deg2rad(lat_grid * 90)).astype(np.float32)
    base = [lat_grid, lon_grid, land_mask, topography]
    static = np.stack((base * ((channels + 3) // 4))[:channels]).astype(np.float32)

    ds = xr.Dataset(
        data_vars={
            "z": (("valid_time", "latitude", "longitude"), static[-1:]),
            "lsm": (("valid_time", "latitude", "longitude"), static[min(2, channels - 1):min(2, channels - 1) + 1]),
        },
        coords={
            "valid_time": ["2015-12-31"],
            "latitude": lat.astype(np.float64),
            "longitude": lon.astype(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[["z"]].to_netcdf(f"{data_dir}/geopotential.nc")
    ds[["lsm"]].to_netcdf(f"{data_dir}/land_sea_mask.nc")
    np.save(f'{data_dir}/static.npy', static)
    np.save(f'{data_dir}/land_mask.npy', land_mask)
    np.save(f'{data_dir}/soil_type.npy', np.zeros((H, W), dtype=np.float32))
    np.save(f'{data_dir}/topography.npy', topography)
    print(f"✅ Static data: {static.shape}, dtype: {static.dtype}, save to {data_dir}")


if __name__ == "__main__":
    cfg_model = YParams("conf/config.yaml", "model")
    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
    if len(atm_vars) != int(cfg_model.in_channels):
        raise ValueError("channel count must match model.in_channels")

    dims = get_dims(cfg_model, cfg_datapipe)
    generate_fake_h5(cfg_datapipe.dataset.data_dir, atm_vars, years, dims)

    static_dir = os.path.join(cfg_datapipe.dataset.data_dir, "static")
    get_static(static_dir, dims["H"], dims["W"], dims["static_channels"])

    print("\n✅ Fake datasets generated.")