html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2007#issuecomment-880141480,https://api.github.com/repos/pydata/xarray/issues/2007,880141480,MDEyOklzc3VlQ29tbWVudDg4MDE0MTQ4MA==,223250,2021-07-14T19:10:46Z,2021-07-14T19:10:46Z,CONTRIBUTOR,"I added support for `.rolling(..., pad=False)` in #5603.  The basic implementation was simple, but getting it working for bottleneck/dask took a little more work.

That fixes, e.g., #4743, but I don't think it's a complete fix for this issue.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,307783090
https://github.com/pydata/xarray/issues/2007#issuecomment-876103462,https://api.github.com/repos/pydata/xarray/issues/2007,876103462,MDEyOklzc3VlQ29tbWVudDg3NjEwMzQ2Mg==,223250,2021-07-08T03:55:33Z,2021-07-08T03:55:33Z,CONTRIBUTOR,"> `.pad(...).rolling(..., pad=False)`

For this API, it seems that the only thing that would need to be implemented would be adding a `pad` keyword argument to rolling, defaulting to `True`.  Is that correct?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,307783090
https://github.com/pydata/xarray/issues/2007#issuecomment-392070454,https://api.github.com/repos/pydata/xarray/issues/2007,392070454,MDEyOklzc3VlQ29tbWVudDM5MjA3MDQ1NA==,17162724,2018-05-25T14:11:20Z,2018-05-25T14:11:20Z,CONTRIBUTOR,"I was going to suggest this feature so glad others are interested.

In my use case I would like to smooth a daily climatology. My colleague uses matlab and uses https://www.mathworks.com/matlabcentral/fileexchange/52688-nan-tolerant-fast-smooth

Using the `slice` solution as @mathause showed above, it would look something like (using code from http://xarray.pydata.org/en/stable/examples/weather-data.html#toy-weather-data)

```
import numpy as np
import pandas as pd
import xarray as xr

times = pd.date_range('2000-01-01', '2010-12-31', name='time')
annual_cycle = np.sin(2 * np.pi * (times.dayofyear.values / 366 - 0.28))
noise = 15 * np.random.rand(annual_cycle.size)
data = 10 + (15 * annual_cycle) + noise
da = xr.DataArray(data, coords=[times], dims='time')
#da.plot()
#Check variability at one day
#da.groupby('time.dayofyear').std('time')[0]
da_clim = da.groupby('time.dayofyear').mean('time')
_da_clim = xr.concat([da_clim[-15:], da_clim, da_clim[:15]], 'dayofyear')
da_clim_smooth = _da_clim.rolling(dayofyear=31, center=True).mean().dropna('dayofyear')
#da_clim_smooth.plot()
```
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,307783090