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  • xarray · 2 ✖
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
656982083 MDU6SXNzdWU2NTY5ODIwODM= 4224 wrong time encoding after padding xzenggit 8161792 open 0     3 2020-07-15T00:46:53Z 2022-04-29T17:39:17Z   NONE      

What happened:

If I open a netcdf with default settings (contain a daily time dimension) and then pad with hourly values, even the padded dataset shows hourly time values, the hourly values cannot be saved. I think this is due to the encoding, but I'm not sure how to fix it.

What you expected to happen: I expected the final line of code give me ```python

array(['2000-01-01T00:00:00.000000000', '2000-01-01T01:00:00.000000000',

'2000-01-01T02:00:00.000000000', '2000-01-01T03:00:00.000000000',

'2000-01-01T04:00:00.000000000'], dtype='datetime64[ns]')

Instead, it outputspython

array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',

'2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',

'2000-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

```

Minimal Complete Verifiable Example:

```python import xarray as xr

time = pd.date_range("2000-01-01", freq="1D", periods=365 ) ds = xr.Dataset({"foo": ("time", np.arange(365)), "time": time}) ds.to_netcdf('test5.nc')

ds = xr.open_dataset('test5.nc') ds.time.encoding

padding

ds_hourly = ds.resample(time='1h').pad() ds_hourly.time.values[0:5]

array(['2000-01-01T00:00:00.000000000', '2000-01-01T01:00:00.000000000',

'2000-01-01T02:00:00.000000000', '2000-01-01T03:00:00.000000000',

'2000-01-01T04:00:00.000000000'], dtype='datetime64[ns]')

ds_hourly.to_netcdf('test6.nc')

load the padded data file

ds_hourly_load = xr.open_dataset('test6.nc') ds_hourly_load.time.values[0:5]

array(['2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',

'2000-01-01T00:00:00.000000000', '2000-01-01T00:00:00.000000000',

'2000-01-01T00:00:00.000000000'], dtype='datetime64[ns]')

```

Anything else we need to know?:

Environment: xarray version: '0.15.1'

Output of <tt>xr.show_versions()</tt> 
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     xarray 13221727 issue
668905666 MDU6SXNzdWU2Njg5MDU2NjY= 4291 resample function gives 0s instead of NaNs xzenggit 8161792 closed 0     3 2020-07-30T15:59:32Z 2020-08-05T16:55:58Z 2020-08-05T16:55:58Z NONE      

What happened: When I use resample(time='1d').sum(dim='time') to resample a time series with NaNs, the resampled result gives me 0s instead of NaNs, while NaNs should be the correct answer.

What you expected to happen:

NaNs should be the correct answer.

Minimal Complete Verifiable Example:

```python import xarray as xr

dates = pd.date_range('20200101', '20200601', freq='h') data = np.linspace(0, 10, num=len(dates)) data[0:30*24] = np.nan

da = xr.DataArray(data, coords=[dates], dims='time') da.plot()

Instead of NaNs, the resampled time series in January 20202 give us 0s, which not right.

da.resample(time='1d', skipna=True).sum(dim='time', skipna=True).plot() ```

Anything else we need to know?:

Did I misunderstand something here? Thanks!

Environment: xarray - '0.15.1'

Output of <tt>xr.show_versions()</tt> xarray - '0.15.1' 
{
    "url": "https://api.github.com/repos/pydata/xarray/issues/4291/reactions",
    "total_count": 0,
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}
   completed xarray 13221727 issue

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