I have millisecond-resolution time data as a coordinate on a DataArray. That data loses precision when round-tripping through disk.

MCVE Code Sample

bug_data.p.zip

Unzip the data. It will result in a pickle file.

```python bug_data_path = '/path/to/unzipped/bug_data.p' tmp_path = '~/Desktop/test.nc'

with open(bug_data_path, 'rb') as f: data = pickle.load(f)

selector = dict(animal=0, timepoint=0, wavelength='410', pair=0)

before_disk_ts = data.time.sel(**selector).values[()]

data.time.encoding = {'units': 'microseconds since 1900-01-01', 'calendar': 'proleptic_gregorian'}

data.to_netcdf(tmp_path) after_disk_ts = xr.load_dataarray(tmp_path).time.sel(**selector).values[()]

print(f'before roundtrip: {before_disk_ts}') print(f' after roundtrip: {after_disk_ts}') output: before roundtrip: 2017-02-22T16:24:10.586000000 after roundtrip: 2017-02-22T16:24:10.585999872 ```

Expected Output

Before: 2017-02-22T16:24:10.586000000 After: 2017-02-22T16:24:10.586000000

Problem Description

As you can see, I lose millisecond precision in this data. (The same happens when I use millisecond in the encoding).

Versions

When I save my DataArray to disk using to_netcdf, then try to reload it as a DataArray, it fails the roundtrip. When I load it as a DataSet, I see that the coordinates have been transformed into variables.

Also, the attributes have disappeared.

MCVE Code Sample

bug_data.p.zip

Unzip the file. It should be a pickle file.

```python import pickle import xarray as xr

data = pickle.load(open('/path/to/bug_data.p', 'rb'))

print(type(data) == xr.DataArray)

>>> True

path = '/var/tmp/bug_data.nc'

data.to_netcdf(path, format='NETCDF4', mode='w')

xr.open_dataarray(path) # fails due to multiple variables

xr.open_dataset(path) # succeeds ```

Expected Output

I expect xr.open_dataarray(path) to succeed, and for the result to be equal to data.

Problem Description

The DataArray -> Disk -> DataArray roundtrip should be seamless.

Versions

Writing a DataArray to disk breaks if the filename is test.nc. I'm unsure if other names pose a similar problem.

MCVE Code Sample

```python

load data into imgs

type(imgs) == xr.core.dataarray.DataArray True

filename = '/Users/sean/Desktop/test.nc' imgs.to_netcdf(filename) xr.open_dataarray(filename)

ValueError Traceback (most recent call last) <ipython-input-132-9eaec208384d> in <module> 2 filename = '/Users/sean/Desktop/test.nc' 3 imgs.to_netcdf(filename) ----> 4 xr.open_dataarray(filename)

~/opt/anaconda3/envs/pharedox/lib/python3.7/site-packages/xarray/backends/api.py in open_dataarray(filename_or_obj, group, decode_cf, mask_and_scale, decode_times, autoclose, concat_characters, decode_coords, engine, chunks, lock, cache, drop_variables, backend_kwargs, use_cftime) 676 if len(dataset.data_vars) != 1: 677 raise ValueError( --> 678 "Given file dataset contains more than one data " 679 "variable. Please read with xarray.open_dataset and " 680 "then select the variable you want."

ValueError: Given file dataset contains more than one data variable. Please read with xarray.open_dataset and then select the variable you want.

filename = '/Users/sean/Desktop/testing.nc' imgs.to_netcdf(filename) xr.open_dataarray(filename) ```

Expected Output

Problem Description

Saving the DataArray to a file called test.nc seems like it generates a DataSet with multiple variables? Or otherwise destroys the expected formatting for xr.load_datarray.

Versions