What is your issue?

After I processed the source nc file, I got an intermediate result and saved it. When I want to use the intermediate result again, I find that it does not match the saved result.

``` import math import numpy as np import pandas as pd import time import os import dateutil.parser import xarray as xr from tqdm import tqdm import time

def hour2day(Dir, file): """ 将小时分辨率的nc文件转为日分辨率的nc文件 估算O3新增的三个变量：eDownward UV radiation at the surface(uvb), surface net solar radiation(ssr), surface net thermal radiation(str), total column ozon(tco3) :param file: 待处理的小时分辨率的nc文件 """ print('start', time.strftime('%H:%M:%S',time.localtime())) # time.strftime('%H:%M:%S',time.localtime()) xs = [xr.open_dataset(os.path.join(Dir, f)) for f in file] # nc = [nc.Dataset(os.path.join(Dir, f)) for f in file] start = xr.concat([xs[0], xs[1].sel(time = xs[1]['time'][:16])], dim = 'time').coarsen(time = 24).mean() end = xs[1].sel(time = xs[1]['time'][16:-8]).coarsen(time = 24).mean() year_xs = xr.concat([start, end], dim = 'time') print('end', time.strftime('%H:%M:%S', time.localtime())) return year_xs

Dir = '/data/lcx/3_Atmos/ERA5/single/2019/' file = os.listdir(Dir) file = [f for f in file if f.endswith('.nc')] # 这里根据类型对nc文件进行选择 file.sort(key=lambda x: int(x[:4])) # 2014_end.nc, 2015.nc print(file) day_ds = hour2day(Dir, file)

day_ds = xr.open_dataset('E:/3_Atmos/O3_Mapping/temp_file/day_single_e11_2019.nc')

day_ds.to_netcdf('/code/lcx/3_Atmos/data/test.nc') # 这里的形状是 365147256，包含11个变量, 应该无缺失值 ds = xr.open_dataset('/code/lcx/3_Atmos/data/test.nc')

for var in ds.keys(): print(var, np.isnan(ds[var]).sum(),np.isnan(day_ds[var]).sum()) ```

the result is : u10 <xarray.DataArray 'u10' ()> array(1) <xarray.DataArray 'u10' ()> array(0) v10 <xarray.DataArray 'v10' ()> array(22) <xarray.DataArray 'v10' ()> array(0) t2m <xarray.DataArray 't2m' ()> array(444) <xarray.DataArray 't2m' ()> array(0) I tried to find the differences between them in: new_ds['u10'][np.where(np.isnan(new_ds['u10']))], day_ds['u10'][np.where(np.isnan(new_ds['u10']))] out: (<xarray.DataArray 'u10' (time: 1, latitude: 1, longitude: 1)> array([[[nan]]], dtype=float32) Coordinates: * longitude (longitude) float32 135.8 * latitude (latitude) float32 31.25 * time (time) datetime64[ns] 2019-08-14T03:30:00, <xarray.DataArray 'u10' (time: 1, latitude: 1, longitude: 1)> array([[[-14.674651]]], dtype=float32) Coordinates: * longitude (longitude) float32 135.8 * latitude (latitude) float32 31.25 * time (time) datetime64[ns] 2019-08-14T03:30:00)