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/1046#issuecomment-731523316,https://api.github.com/repos/pydata/xarray/issues/1046,731523316,MDEyOklzc3VlQ29tbWVudDczMTUyMzMxNg==,26384082,2020-11-21T07:36:23Z,2020-11-21T07:36:23Z,NONE,"In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity
If this issue remains relevant, please comment here or remove the `stale` label; otherwise it will be marked as closed automatically
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,182667672
https://github.com/pydata/xarray/issues/1046#issuecomment-255246621,https://api.github.com/repos/pydata/xarray/issues/1046,255246621,MDEyOklzc3VlQ29tbWVudDI1NTI0NjYyMQ==,5572303,2016-10-20T22:32:23Z,2016-10-20T22:32:23Z,CONTRIBUTOR,"Let me exhaust a few other ideas first. I'll definitely share my thoughts here first before making any commit. Thanks.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,182667672
https://github.com/pydata/xarray/issues/1046#issuecomment-254347566,https://api.github.com/repos/pydata/xarray/issues/1046,254347566,MDEyOklzc3VlQ29tbWVudDI1NDM0NzU2Ng==,2443309,2016-10-17T22:00:32Z,2016-10-17T22:00:32Z,MEMBER,"I'm fine with this approach for now. It would be great if we could convince bottleneck to help us out with a keyword argument of some kind.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,182667672
https://github.com/pydata/xarray/issues/1046#issuecomment-253929482,https://api.github.com/repos/pydata/xarray/issues/1046,253929482,MDEyOklzc3VlQ29tbWVudDI1MzkyOTQ4Mg==,1217238,2016-10-14T21:56:42Z,2016-10-14T21:56:51Z,MEMBER,"@chunweiyuan I agree, this seems worth doing, and I think you have a pretty sensible approach here. For large arrays (especially with ndim > 1), this should add only minimal performance overhead. If you can fit this into the [existing framework](https://github.com/pydata/xarray/blob/master/xarray/core/rolling.py) for `rolling` that would be awesome!
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,182667672
https://github.com/pydata/xarray/issues/1046#issuecomment-253681067,https://api.github.com/repos/pydata/xarray/issues/1046,253681067,MDEyOklzc3VlQ29tbWVudDI1MzY4MTA2Nw==,5572303,2016-10-14T00:53:54Z,2016-10-14T00:53:54Z,CONTRIBUTOR,"My opinion is that the nan has got to go. If we want to (1) maintain pandas-consistency and (2) use bottleneck without mucking it up, then I think we need to add some logic in either rolling.reduce() or rolling._center_result().
So here's my failed attempt:
```
def reverse_and_roll_1d(data, window_size, min_periods=1):
""""""
Implements a concept to fix the end-of-array problem with
xarray.core.rolling._center_shift(),
by
1.) take slice of the back-end of the array
2.) flip it
3.) compute centered-window arithmetic
4.) flip it again
5.) replace back-end of default result with (4)
:param DataArray data: 1-D data array, with dim name 'x'.
:param int window_size: size of window.
""""""
# first the default way to computing centered window
r = data.rolling(x=window_size, center=True, min_periods=min_periods)
avg = r.mean()
# now we need to fix the back-end of the array
rev_start = len(data.x) # an index
rev_end = len(data.x) - window_size - 1 \
if len(data.data) > window_size \
else None # another index
tail_slice = slice(rev_start, rev_end, -1) # back end of array, flipped
r2 = data[dict(x=tail_slice)].\
rolling(x=window_size, center=True, min_periods=min_periods)
avg[dict(x=slice(-window_size+1, None))] = \
r2.mean()[dict(x=slice(window_size-2, None, -1))] # replacement
return avg
```
This algorithm is consistently 8 times slower than pd.DataFrame.rolling(), for various 1d array sizes.
I'm open to ideas as well :)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,182667672
https://github.com/pydata/xarray/issues/1046#issuecomment-253408063,https://api.github.com/repos/pydata/xarray/issues/1046,253408063,MDEyOklzc3VlQ29tbWVudDI1MzQwODA2Mw==,2443309,2016-10-13T03:58:32Z,2016-10-13T03:58:32Z,MEMBER,"We do try to stay consistent with pandas except for the last position. [Here's](https://github.com/pydata/xarray/blob/9cf107b522188e206bd15f4620bc3b55ca30e616/xarray/test/test_dataarray.py#L1549-L1567) the unit test where we verify that behavior.
Using `x=0` from your example in Pandas:
``` Python
In [1]: import pandas as pd
s
In [2]: data = pd.Series([0, 3, 6])
In [3]: data.rolling(3, center=True, min_periods=1).mean()
Out[3]:
0 1.5
1 3.0
2 4.5
```
If I remember correctly, and my brain is a bit like mush right now so I could be wrong, `bottleneck` and `pandas` handle this case differently so we had to make a decision. We choose to use `bottleneck` (for speed) but to do our best to stay consistent with `pandas`. Back to your example, this time just with `bottleneck`:
``` Python
In [4]: bn.move_mean(data, 3, min_count=1)
Out[4]: array([ 0. , 1.5, 3. ])
```
So, as you can see, bottleneck does something totally different that wouldn't otherwise work with `center=True` unless we did our little `shift` trick. I'm not really sure the best way to correct for this difference in the last position except to either a) try to push a `center=True` option into `bottleneck` (may not be possible), or b) write a bunch of logic on our end bridge the gap between these two (may be laborious). Of course, I'm open to ideas.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,182667672
https://github.com/pydata/xarray/issues/1046#issuecomment-253405068,https://api.github.com/repos/pydata/xarray/issues/1046,253405068,MDEyOklzc3VlQ29tbWVudDI1MzQwNTA2OA==,1217238,2016-10-13T03:37:55Z,2016-10-13T03:37:55Z,MEMBER,"I think we mostly tried to make this consistent with pandas. To be honest I don't entirely understand the logic myself.
Cc @jhamman
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