id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type
1387341095,I_kwDOAMm_X85SsSUn,7092,"Save an nc file and open it again, the content of the data inside has changed",48684315,closed,0,,,2,2022-09-27T08:27:25Z,2023-09-16T10:35:43Z,2023-09-16T10:35:43Z,NONE,,,,"### 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 365*147*256，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)
```
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