Datasets:
File size: 2,786 Bytes
c84ff43 | 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 79 80 81 82 83 84 85 86 87 88 89 90 91 92 | import numpy as np
import xarray as xr
__all__ = ["chunk", "print_xarray", "save_with_progress"]
def chunk(ds, time=1, **kwargs):
if isinstance(ds, xr.DataArray):
dims = {k: v for k, v in zip(ds.dims, ds.shape)}
else:
dims = {k: v for k, v in ds.sizes.items()}
if "time" in dims:
dims["time"] = time
for k, v in kwargs.items():
if k in dims:
dims[k] = v
ds = ds.chunk(dims)
return ds
def print_xarray(ds, desp="", names=[]):
if isinstance(ds, xr.Dataset):
for v in ds:
print_xarray(ds[v], desp=desp, names=names)
return
v = ds
msg = f"{v.name.upper()} {desp}: \nshape: \033[94m{v.shape}\033[0m"
if "time" in ds.dims:
start_date = ds.time[0].dt.date.item()
end_date = ds.time[-1].dt.date.item()
msg += f", time: \033[94m{len(ds.time)} = ({start_date} ~ {end_date})\033[0m"
if "lat" in ds.dims and "lon" in ds.dims:
lat = ds.lat.values
lon = ds.lon.values
msg += f", latlon: \033[94m({lat[0]:.3f} ~ {lat[-1]:.3f}) x ({lon[0]:.3f} ~ {lon[-1]:.3f})\033[0m"
if "level" in v.dims:
if len(names) > 0:
v = v.sel(level=np.intersect1d(names, v.level))
for lvl in v.level.data:
x = v.sel(level=lvl).values
msg += f"\nlevel: {lvl:04d}, value: \033[91m{x.min():.3f} ~ {x.max():.3f}\033[0m"
elif "depth" in v.dims:
for lvl in v.depth.data:
x = v.sel(depth=lvl).values
msg += f"\ndepth: {lvl:.2f}, value: \033[91m{x.min():.3f} ~ {x.max():.3f}\033[0m"
elif "channel" in v.dims:
if len(names) > 0:
v = v.sel(channel=np.intersect1d(names, v.channel))
for ch in v.channel.data:
x = v.sel(channel=ch).values
msg += f"\nchannel: {ch}, value: \033[91m{x.min():.3f} ~ {x.max():.3f}\033[0m"
else:
x = v.values
msg += f", value: \033[91m{x.min():.3f} ~ {x.max():.3f}\033[0m"
print(msg)
def save_with_progress(ds, save_name, dtype=np.float32):
from dask.diagnostics import ProgressBar
if 'time' in ds.dims:
ds = ds.assign_coords(time=ds.time.astype(np.datetime64))
ds = ds.astype(dtype)
if 'channel' in ds.coords and ds.coords['channel'].dtype == 'object':
ds = ds.assign_coords(
channel=ds.coords['channel'].astype('U50') # 'U50' 表示最大50字符的Unicode字符串
)
if save_name.endswith(".nc"):
obj = ds.to_netcdf(save_name, compute=False)
elif save_name.endswith(".zarr"):
obj = ds.to_zarr(save_name, compute=False)
else:
raise ValueError("save_type must be 'nc' or 'zarr'!")
with ProgressBar():
obj.compute()
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