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issues: 701062999

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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
701062999 MDU6SXNzdWU3MDEwNjI5OTk= 4422 Problem decoding times in data from OpenDAP server 6514690 closed 0     11 2020-09-14T12:45:44Z 2022-01-12T14:48:49Z 2020-10-26T09:50:18Z NONE      

What happened: I download data from an OpenDAP server using xarray directly. Then, I save the xarray.DataArray object to file. The times I get in the index are different (see output below). Note, for instance, the second element of the index in the original dataset is 2020-09-13T00:59:59.999997000, while after saving and loading again is 2020-09-13T00:59:59.999997056

What you expected to happen: I'm not sure if this is a bug or just something that we cannot rely upon, but I would expect the index time to have the same values in both objects.

Minimal Complete Verifiable Example:

```python import datetime as dt import xarray as xr import cftime

print(xr.version) print(cftime.version)

yesterday = (dt.date.today() - dt.timedelta(days=1)).strftime('%Y%m%d') src = xr.open_dataset(f'https://nomads.ncep.noaa.gov/dods/gfs_0p25_1hr/gfs{yesterday}/gfs_0p25_1hr_00z')

print('-'*60)

var = src['ugrd10m'].isel(time=slice(0, 8))

print('Original: ', var.time) var.to_netcdf('test.nc')

print('-'*60)

dst = xr.open_dataset('test.nc') print('Recovered:', dst.time)

```

Anything else we need to know?:

Output of the previous script</tt> ``` 0.16.0 1.2.1 ------------------------------------------------------------ Original: <xarray.DataArray 'time' (time: 8)> array(['2020-09-13T00:00:00.000000000', '2020-09-13T00:59:59.999997000', '2020-09-13T02:00:00.000003000', '2020-09-13T03:00:00.000000000', '2020-09-13T03:59:59.999997000', '2020-09-13T05:00:00.000003000', '2020-09-13T06:00:00.000000000', '2020-09-13T06:59:59.999997000'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2020-09-13 ... 2020-09-13T06:59:59.999997 Attributes: grads_dim: t grads_mapping: linear grads_size: 121 grads_min: 00z13sep2020 grads_step: 1hr long_name: time minimum: 00z13sep2020 maximum: 00z18sep2020 resolution: 0.041666668 ------------------------------------------------------------ Recovered: <xarray.DataArray 'time' (time: 8)> array(['2020-09-13T00:00:00.000000000', '2020-09-13T00:59:59.999997056', '2020-09-13T02:00:00.000002944', '2020-09-13T03:00:00.000000000', '2020-09-13T03:59:59.999997056', '2020-09-13T05:00:00.000002944', '2020-09-13T06:00:00.000000000', '2020-09-13T06:59:59.999997056'], dtype='datetime64[ns]') Coordinates: * time (time) datetime64[ns] 2020-09-13 ... 2020-09-13T06:59:59.999997056 Attributes: grads_dim: t grads_mapping: linear grads_size: 121 grads_min: 00z13sep2020 grads_step: 1hr long_name: time minimum: 00z13sep2020 maximum: 00z18sep2020 resolution: 0.041666668 ```

Environment:

I create a clean enviroment with:

conda create -n xarray_test python=3.6 xarray cfgrib cftime netCDF4

Output of <tt>xr.show_versions()</tt> ``` >>> xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.6.12 |Anaconda, Inc.| (default, Sep 8 2020, 23:10:56) [GCC 7.3.0] python-bits: 64 OS: Linux OS-release: 4.4.0-112-generic machine: x86_64 processor: x86_64 byteorder: little LC_ALL: en_US.utf-8 LANG: en_US.utf-8 LOCALE: en_US.UTF-8 libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.16.0 pandas: 1.1.1 numpy: 1.19.1 scipy: None netCDF4: 1.5.4 pydap: None h5netcdf: None h5py: None Nio: None zarr: None cftime: 1.2.1 nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: 0.9.8.4 iris: None bottleneck: None dask: None distributed: None matplotlib: None cartopy: None seaborn: None numbagg: None pint: None setuptools: 49.6.0.post20200814 pip: 20.2.2 conda: None pytest: None IPython: None sphinx: None ```
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