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/4922#issuecomment-791465015,https://api.github.com/repos/pydata/xarray/issues/4922,791465015,MDEyOklzc3VlQ29tbWVudDc5MTQ2NTAxNQ==,8881170,2021-03-05T14:47:46Z,2021-03-05T14:47:46Z,CONTRIBUTOR,"> I feel like this should not work i.e. rolling window length (6) < size along axis (3). So the bottleneck error seems right.

This is normally the case, but with `min_periods=1` it should just return the given value so long as there's at least one observation (as in case #2, where the boundaries return as normal and the middle number is smoothed).

Thanks for the pointer on #4977!","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,811321550
https://github.com/pydata/xarray/issues/4922#issuecomment-790986252,https://api.github.com/repos/pydata/xarray/issues/4922,790986252,MDEyOklzc3VlQ29tbWVudDc5MDk4NjI1Mg==,8881170,2021-03-04T22:21:37Z,2021-03-04T22:32:01Z,CONTRIBUTOR,"@dcherian, to add to the complexity here,  it's even weirder than originally reported. See my test cases below. This might alter how this bug is approached.

```python
import xarray as xr

def _rolling(ds):
    return ds.rolling(time=6, center=False, min_periods=1).mean()

# Length 3 array to test that min_periods is called in, despite asking
# for 6 time-steps of smoothing
ds = xr.DataArray([1, 2, 3], dims='time')
ds['time'] = xr.cftime_range(start='2021-01-01', freq='D', periods=3)
```

### 1. With `bottleneck` installed, `min_periods` is ignored as a kwarg with in-memory arrays.

(`bottleneck` installed)
```python
# Just apply rolling to the base array.
ds.rolling(time=6, center=False, min_periods=1).mean()

>>> ValueError: Moving window (=6) must between 1 and 3, inclusive

# Group into single day climatology groups and apply
ds.groupby('time.dayofyear').map(_rolling)

>>> ValueError: Moving window (=6) must between 1 and 1, inclusive
```

### 2. With `bottleneck` uninstalled, `min_periods` works with in-memory arrays.

(`bottleneck` uninstalled)
```python
# Just apply rolling to the base array.
ds.rolling(time=6, center=False, min_periods=1).mean()

>>> <xarray.DataArray (time: 3)>
>>> array([1. , 1.5, 2. ])
>>> Coordinates:
>>> * time     (time) object 2021-01-01 00:00:00 ... 2021-01-03 00:00:00

# Group into single day climatology groups and apply
ds.groupby('time.dayofyear').map(_rolling)

>>> <xarray.DataArray (time: 3)>
>>> array([1., 2., 3.])
>>> Coordinates:
>>>  * time     (time) object 2021-01-01 00:00:00 ... 2021-01-03 00:00:00
```

### 3. Regardless of `bottleneck`, `dask` objects ignore `min_period` when a `groupby` object.

This specifically seems like an issue with `.map()`

(independent of `bottleneck` installation)
```python
# Just apply rolling to the base array.
ds.chunk().rolling(time=6, center=False, min_periods=1).mean().compute()

>>> <xarray.DataArray (time: 3)>
>>> array([1. , 1.5, 2. ])
>>> Coordinates:
>>>   * time     (time) object 2021-01-01 00:00:00 ... 2021-01-03 00:00:00

# Group into single day climatology groups and apply
ds.chunk().groupby('time.dayofyear').map(_rolling)

>>> ValueError: For window size 6, every chunk should be larger than 3, but the smallest chunk size is 1.
>>> Rechunk your array
>>> with a larger chunk size or a chunk size that
>>> more evenly divides the shape of your array.
```

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