Not really minimal but shows a real use:

```python from pathlib import Path import matplotlib.pyplot as plt import xarray as xr

create data path

daily_data_path = Path("/obs_data/") / "ASCAT" / "v1.x.x" / "data/Daily/Netcdf"

get data files and open mf dataset

data_files_2016 = daily_data_path.glob("2016/??/??/*.nc") raw_data_set = xr.open_mfdataset(data_files_2016).isel(latitude=300, longitude=0)

Resample with filling gaps by NaN's and shift to match original time axis

infilled_data_set = raw_data_set.resample(time="1D").asfreq() infilled_data_set.time.data += (raw_data_set.time.data[0] - infilled_data_set.time.data[0])

extract wind speeds

rws = raw_data_set.wind_speed iws = infilled_data_set.wind_speed.rename("resampled_wind_speed")

and plot (with a slight offset to be able to distinguish the lines)

fig, ax = plt.subplots(2, 1)

(iws+1).plot(ax=ax[0]); rws.plot(ax=ax[0]); ax[0].legend(["resampled", "original"], loc=0, ncol=2);

(iws+1).sel(time=slice("2016-08-01", "2016-10-01")).plot(ax=ax[1]); rws.sel(time=slice("2016-08-01", "2016-10-01")).plot(ax=ax[1]); ```

Note the gaps that are more clearly visible in the lower panel.