issue_comments: 433454137

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https://github.com/pydata/xarray/issues/2514#issuecomment-433454137 https://api.github.com/repos/pydata/xarray/issues/2514 433454137 MDEyOklzc3VlQ29tbWVudDQzMzQ1NDEzNw== 102827 2018-10-26T15:49:20Z 2018-10-31T21:14:48Z CONTRIBUTOR

EDIT: The issue of this post is now separated #2531

I think I have a fix, but wanted to write some failing tests before committing the changes. Doing this I discovered that also DataArray.rolling() does not preserve the chunksizes, apparently depending on the applied method.

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

t = pd.date_range(start='2018-01-01', end='2018-02-01', freq='H') bar = np.sin(np.arange(len(t))) baz = np.cos(np.arange(len(t)))

da_test = xr.DataArray(data=np.stack([bar, baz]), coords={'time': t, 'sensor': ['one', 'two']}, dims=('sensor', 'time'))

print(da_test.chunk({'time': 100}).rolling(time=60).mean().chunks)

print(da_test.chunk({'time': 100}).rolling(time=60).count().chunks) Output for mean: ((2,), (745,)) Output for count: ((2,), (100, 100, 100, 100, 100, 100, 100, 45)) Desired Output: ((2,), (100, 100, 100, 100, 100, 100, 100, 45)) ```

My fix solves my initial problem, but maybe if done correctly it should also solve this bug, too.

Any idea why this depends on whether .mean() or .count() is used?

I have already pushed some WIP changes. Should I already open a PR if though most new test still fail?

 
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