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/3216#issuecomment-1499591643,https://api.github.com/repos/pydata/xarray/issues/3216,1499591643,IC_kwDOAMm_X85ZYfPb,8453445,2023-04-06T20:34:19Z,2023-04-06T20:34:47Z,CONTRIBUTOR,"Hello!
Just adding a 👍 to this thread - and, since it is an old issue, wondering if this is on `xarray` roadmap somewhere.
Something like `.rolling(time='5M')` would be really valuable for many applications.
thanks so much for all your work!
Chiara
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,480753417
https://github.com/pydata/xarray/issues/5877#issuecomment-947906426,https://api.github.com/repos/pydata/xarray/issues/5877,947906426,IC_kwDOAMm_X844f-d6,8453445,2021-10-20T17:59:13Z,2021-10-20T17:59:13Z,CONTRIBUTOR,"Yup - just followed your suggestion and:
1) conda removed `bottleneck` and it removed xarray and pandas as well
2) conda installed `xarray` which installed `xarray`, `pandas`, and `pytz`
and now the `xr.rolling(time=3).sum()` yields:
array([ nan, nan, 0. , 0. , 0. ,
0. , 0. , 0.31 , 1.23 , 9.530001 ,
10.64 , 9.75 , 2.67 , 1.35 , 1.46 ,
0.36999997, 0.26999998, 0.25 , 0.14999999, 2.68 ,
2.56 , 2.73 , 0.39999998, 0.39999998, 0.19999999,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
dtype=float32)
could you elaborate more on the issue? is this because of some bouncing between precisions across packages?
But why do I have zeros at the beginning of the rolling sum and non zeros after having calculated a sum?
it is not consistent in the behaviour.
Thanks tho!
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1030768250
https://github.com/pydata/xarray/issues/5877#issuecomment-947195221,https://api.github.com/repos/pydata/xarray/issues/5877,947195221,IC_kwDOAMm_X844dQ1V,8453445,2021-10-20T00:02:58Z,2021-10-20T00:02:58Z,CONTRIBUTOR,"Adding a few extra observations:
```python
ds_ex.rolling(time=3).mean().pr.values
df_ex.rolling(window=3).mean().values.T
```
have a similar behaviour, in that once again `xr.rolling()` doesn't have zero where it should, but `pd.rolling` does.
But when I switch to other operations, like `var` or `std` the behaviour is the opposite, i.e.:
```
ds_ex.rolling(time=3).std().pr.values
```
array([ nan, nan, 0. , 0. , 0. ,
0. , 0. , 0.1461354 , 0.38218665, 3.631293 ,
3.367307 , 3.6156974 , 0.61356837, 0.54522127, 0.5188016 ,
0.01698606, 0.06376763, 0.05906381, 0.05098677, 1.157881 ,
1.1856455 , 1.148419 , 0.09427918, 0.09427918, 0.09427926,
0. , 0. , 0. , 0. , 0. ,
0. , 0. , 0. , 0. , 0. ],
dtype=float32)
whereas
```
df_ex.rolling(window=3).std().values.T
```
gives
array([[ nan, nan, 0.00000000e+00, 0.00000000e+00,
0.00000000e+00, 0.00000000e+00, 0.00000000e+00, 1.78978585e-01,
4.68081166e-01, 4.44740760e+00, 4.12409195e+00, 4.42830679e+00,
7.51465227e-01, 6.67757461e-01, 6.35400157e-01, 2.08166670e-02,
7.81024957e-02, 7.23417792e-02, 6.24499786e-02, 1.41810905e+00,
1.45211339e+00, 1.40652052e+00, 1.15470047e-01, 1.15470047e-01,
1.15470047e-01, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08,
9.60572442e-08, 9.60572442e-08, 9.60572442e-08, 9.60572442e-08,
9.60572442e-08, 9.60572442e-08, 9.60572442e-08]])","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,1030768250
https://github.com/pydata/xarray/issues/4793#issuecomment-758204109,https://api.github.com/repos/pydata/xarray/issues/4793,758204109,MDEyOklzc3VlQ29tbWVudDc1ODIwNDEwOQ==,8453445,2021-01-11T20:29:15Z,2021-01-11T20:29:15Z,CONTRIBUTOR,Great - I will plan on modifying it using the `air_temperature` dataset. I will work on it next week on. ,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,783630055
https://github.com/pydata/xarray/issues/2380#issuecomment-446373483,https://api.github.com/repos/pydata/xarray/issues/2380,446373483,MDEyOklzc3VlQ29tbWVudDQ0NjM3MzQ4Mw==,8453445,2018-12-11T21:43:53Z,2018-12-11T21:43:53Z,CONTRIBUTOR,"I have found a workaround, I think, in the last item of this issue.
You have to set it before running it in xarray.
https://github.com/NCAR/pynio/issues/19
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,353566871
https://github.com/pydata/xarray/issues/1844#issuecomment-418420696,https://api.github.com/repos/pydata/xarray/issues/1844,418420696,MDEyOklzc3VlQ29tbWVudDQxODQyMDY5Ng==,8453445,2018-09-04T15:53:10Z,2018-09-04T15:53:10Z,CONTRIBUTOR,"Thanks - i will give this a try!
And thanks for the clarifications.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,290023410
https://github.com/pydata/xarray/issues/1844#issuecomment-418175182,https://api.github.com/repos/pydata/xarray/issues/1844,418175182,MDEyOklzc3VlQ29tbWVudDQxODE3NTE4Mg==,8453445,2018-09-03T18:38:47Z,2018-09-03T18:38:47Z,CONTRIBUTOR,"Yes, @spencerkclark that was my initial intent. I - for some reasons, and I understand I was wrong about it, - thought that dayoftheyear would align the days always on the same grid. To be honest I have never used it until now, so I wasn't sure how it worked. I was just surprised by that behavior, which I understand is intended. It is just not explained well IMHO. If we calculate the daily climatology, the 366th day is the 31st of december of every 4 years, right? it just wasn't exactly what I expected, so I thought to put a note in this issue, which popped up when I was looking for some more details about this attribute.
Said so - is there a more suitable attribute for what I want to do? This is maybe not the best place to discuss about that, I can send an email to the mailing list.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,290023410
https://github.com/pydata/xarray/issues/1844#issuecomment-417437968,https://api.github.com/repos/pydata/xarray/issues/1844,417437968,MDEyOklzc3VlQ29tbWVudDQxNzQzNzk2OA==,8453445,2018-08-30T19:24:46Z,2018-08-30T19:24:46Z,CONTRIBUTOR,"I am commenting on this issue, because my findings seem relevant to this example.
I have just encountered an unexpected (to me) behavior of dayofyear.
I have a dataset, ds:
```
<xarray.Dataset>
Dimensions: (L: 45, S: 1168)
Coordinates:
* S (S) datetime64[ns] 1999-01-01T12:00:00 1999-01-06T12:00:00 ...
* L (L) float64 0.0 24.0 48.0 72.0 96.0 120.0 144.0 168.0 192.0 ...
Data variables:
pr (S, L) float32 2.0625568e-05 3.5336856e-05 5.2443047e-05 ...
truth (S, L) float32 2.0625568e-05 3.5336856e-05 5.2443047e-05 ...
```
S is my time coordinate. It is daily, but not continuous
```
<xarray.DataArray 'S' (S: 1168)>
array(['1999-01-01T12:00:00.000000000', '1999-01-06T12:00:00.000000000',
'1999-01-11T12:00:00.000000000', ..., '2014-12-17T12:00:00.000000000',
'2014-12-22T12:00:00.000000000', '2014-12-27T12:00:00.000000000'],
dtype='datetime64[ns]')
Coordinates:
* S (S) datetime64[ns] 1999-01-01T12:00:00 1999-01-06T12:00:00 ...
```
For example for 1999 first three months:
```
ds.S.sel(S=slice('1999-01-01','1999-03-05'))
<xarray.DataArray 'S' (S: 13)>
array(['1999-01-01T12:00:00.000000000', '1999-01-06T12:00:00.000000000',
'1999-01-11T12:00:00.000000000', '1999-01-16T12:00:00.000000000',
'1999-01-21T12:00:00.000000000', '1999-01-26T12:00:00.000000000',
'1999-01-31T12:00:00.000000000', '1999-02-05T12:00:00.000000000',
'1999-02-10T12:00:00.000000000', '1999-02-15T12:00:00.000000000',
'1999-02-20T12:00:00.000000000', '1999-02-25T12:00:00.000000000',
'1999-03-02T12:00:00.000000000'], dtype='datetime64[ns]')
Coordinates:
* S (S) datetime64[ns] 1999-01-01T12:00:00 1999-01-06T12:00:00 ...
```
and for 2008:
```
broadcasted_data.S.sel(S=slice('2008-01-01','2008-03-05'))
<xarray.DataArray 'S' (S: 13)>
array(['2008-01-01T12:00:00.000000000', '2008-01-06T12:00:00.000000000',
'2008-01-11T12:00:00.000000000', '2008-01-16T12:00:00.000000000',
'2008-01-21T12:00:00.000000000', '2008-01-26T12:00:00.000000000',
'2008-01-31T12:00:00.000000000', '2008-02-05T12:00:00.000000000',
'2008-02-10T12:00:00.000000000', '2008-02-15T12:00:00.000000000',
'2008-02-20T12:00:00.000000000', '2008-02-25T12:00:00.000000000',
'2008-03-02T12:00:00.000000000'], dtype='datetime64[ns]')
Coordinates:
* S (S) datetime64[ns] 2008-01-01T12:00:00 2008-01-06T12:00:00 ...
```
Please note, within the non leap (1999) or leap (2008) years, the days are the same. There are 73 S values per year.
However when I groupby('S.dayofyear') things are not aligned anymore starting from March.
For example, if I groupby() and print the value of dayofyear and the grouped values:
```
for k, gg in ds.groupby('S.dayofyear'):
print(k)
print(gg)
.....
51 ## 51st day of the year
<xarray.Dataset>
Dimensions: (L: 45, S: 16)
Coordinates:
* S (S) datetime64[ns] 1999-02-20T12:00:00 2000-02-20T12:00:00 ...
* L (L) float64 0.0 24.0 48.0 72.0 96.0 120.0 144.0 168.0 192.0 ...
Data variables:
pr (S, L) float32 2.8822698e-05 3.1478736e-05 3.707411e-05 ...
truth (S, L) float32 2.8387214e-05 2.8993465e-05 2.8109233e-05 ...
56 ## 56st day of the year
<xarray.Dataset>
Dimensions: (L: 45, S: 16)
Coordinates:
* S (S) datetime64[ns] 1999-02-25T12:00:00 2000-02-25T12:00:00 ...
* L (L) float64 0.0 24.0 48.0 72.0 96.0 120.0 144.0 168.0 192.0 ...
Data variables:
pr (S, L) float32 3.5827405e-05 2.27847e-05 2.8826753e-05 ...
truth (S, L) float32 2.9589286e-05 2.6589936e-05 2.7626802e-05 ...
```
up to here everything looks good, I have 16 values (one for each year of data) for each day of the year, but starting with March 2nd, they start getting split in two groups:
```
61 ## 61st day of the year
<xarray.Dataset>
Dimensions: (L: 45, S: 12)
Coordinates:
* S (S) datetime64[ns] 1999-03-02T12:00:00 2001-03-02T12:00:00 ...
* L (L) float64 0.0 24.0 48.0 72.0 96.0 120.0 144.0 168.0 192.0 ...
Data variables:
pr (S, L) float32 2.2245076e-05 2.9928206e-05 3.2708682e-05 ...
truth (S, L) float32 2.5899697e-05 2.5815236e-05 2.6628013e-05 ...
62## 62nd day of the year
<xarray.Dataset>
Dimensions: (L: 45, S: 4)
Coordinates:
* S (S) datetime64[ns] 2000-03-02T12:00:00 2004-03-02T12:00:00 ...
* L (L) float64 0.0 24.0 48.0 72.0 96.0 120.0 144.0 168.0 192.0 ...
Data variables:
pr (S, L) float32 2.3905726e-05 2.1646814e-05 1.5209519e-05 ...
truth (S, L) float32 2.4452387e-05 2.5048954e-05 2.5876538e-05 ...
66## 66th day of the year
<xarray.Dataset>
Dimensions: (L: 45, S: 12)
Coordinates:
* S (S) datetime64[ns] 1999-03-07T12:00:00 2001-03-07T12:00:00 ...
* L (L) float64 0.0 24.0 48.0 72.0 96.0 120.0 144.0 168.0 192.0 ...
Data variables:
pr (S, L) float32 2.60827e-05 4.9364742e-05 3.838778e-05 ...
truth (S, L) float32 2.6537613e-05 2.7840171e-05 2.7700215e-05 ...
67## 67th day of the year
<xarray.Dataset>
Dimensions: (L: 45, S: 4)
Coordinates:
* S (S) datetime64[ns] 2000-03-07T12:00:00 2004-03-07T12:00:00 ...
* L (L) float64 0.0 24.0 48.0 72.0 96.0 120.0 144.0 168.0 192.0 ...
Data variables:
pr (S, L) float32 1.59269e-05 2.7056101e-05 1.8332774e-05 ...
truth (S, L) float32 2.1952277e-05 2.7667278e-05 2.5342364e-05 ...
```
and so on.
This was unexpected to me. And not well document. It means that, especially when we calculate anomalies, we might not be aligning things correctly? or am I wrong?
Is there a way to group the data by the day of the year so that everything is grouped on 366 days?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,290023410
https://github.com/pydata/xarray/issues/2232#issuecomment-397016711,https://api.github.com/repos/pydata/xarray/issues/2232,397016711,MDEyOklzc3VlQ29tbWVudDM5NzAxNjcxMQ==,8453445,2018-06-13T17:16:45Z,2018-06-13T17:16:45Z,CONTRIBUTOR,"I think that if the correct way should be to use 'QS-DEC' and not 'Q-NOV' - and this was definitely true. However I run also 0.10.2 - not sure if it was fixed in the latest versions.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,332077435
https://github.com/pydata/xarray/issues/2159#issuecomment-390670582,https://api.github.com/repos/pydata/xarray/issues/2159,390670582,MDEyOklzc3VlQ29tbWVudDM5MDY3MDU4Mg==,8453445,2018-05-21T14:28:08Z,2018-05-21T14:28:08Z,CONTRIBUTOR,"Thanks for opening up this issue. This would be very helpful for the forecasting community as well, where we usually concatenate along Start time and Lead time dimensions.
Here, however, [was mentioned](https://stackoverflow.com/a/47557546/2789703) that it is quite difficult to generalize it, and he suggested a workaround.
I know that some people did it for specific datasets, so maybe it would be helpful to add an example to the documentation that shows how this can be implemented on a case by case basis?","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,324350248
https://github.com/pydata/xarray/issues/2055#issuecomment-385016657,https://api.github.com/repos/pydata/xarray/issues/2055,385016657,MDEyOklzc3VlQ29tbWVudDM4NTAxNjY1Nw==,8453445,2018-04-27T16:04:12Z,2018-04-27T16:04:12Z,CONTRIBUTOR,"Great, will do.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,314239017
https://github.com/pydata/xarray/issues/2055#issuecomment-385016272,https://api.github.com/repos/pydata/xarray/issues/2055,385016272,MDEyOklzc3VlQ29tbWVudDM4NTAxNjI3Mg==,8453445,2018-04-27T16:02:44Z,2018-04-27T16:02:44Z,CONTRIBUTOR,"> The where example might be better added to the section on where: http://xarray.pydata.org/en/v0.10.3/indexing.html#masking-with-where
I think this is not correct. the where you linked (or at least the way it is used) is for [masking](http://xarray.pydata.org/en/v0.10.3/generated/xarray.DataArray.where.html#xarray.DataArray.where).
In my example uses [xarray.where()](http://xarray.pydata.org/en/v0.10.3/generated/xarray.where.html) to assign values.
but again, I might be off, i have a limited understanding of this.","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,314239017
https://github.com/pydata/xarray/issues/2055#issuecomment-385000519,https://api.github.com/repos/pydata/xarray/issues/2055,385000519,MDEyOklzc3VlQ29tbWVudDM4NTAwMDUxOQ==,8453445,2018-04-27T15:12:39Z,2018-04-27T15:12:39Z,CONTRIBUTOR,"For example, using the tutorial data:
```
ds = xr.tutorial.load_dataset('air_temperature')
#add an empty 2D dataarray
ds['empty']= xr.full_like(ds.air.mean('time'),fill_value=0)
#modify one grid point, using where() or loc()
ds['empty'] = xr.where((ds.coords['lat']==20)&(ds.coords['lon']==260), 100, ds['empty'])
ds['empty'].loc[dict(lon=260, lat=30)] = 100
#modify an area with where() and a mask
mask = (ds.coords['lat']>20)&(ds.coords['lat']<60)&(ds.coords['lon']>220)&(ds.coords['lon']<260)
ds['empty'] = xr.where(mask, 100, ds['empty'])
#modify an area with loc()
lc = ds.coords['lon']
la = ds.coords['lat']
ds['empty'].loc[dict(lon=lc[(lc>220)&(lc<260)], lat=la[(la>20)&(la<60)])] = 100
```
these are examples that I am pretty sure are not on the website, they are I think common in climate scientists workflow, and that it took me quite a while to figure out. I was using a boolean dataarray as well as in the SO example, slowing down my work of quite a bit.
Do they make sense? I can try and add them to the documentation at [Assigning Values with indexing](http://xarray.pydata.org/en/stable/indexing.html#assigning-values-with-indexing) , or is there another place that is more relevant? ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,314239017
https://github.com/pydata/xarray/issues/2055#issuecomment-384989608,https://api.github.com/repos/pydata/xarray/issues/2055,384989608,MDEyOklzc3VlQ29tbWVudDM4NDk4OTYwOA==,8453445,2018-04-27T14:36:45Z,2018-04-27T14:36:45Z,CONTRIBUTOR,"I finally had the time to try out this SO suggestion on assigning on multiple dimensions as well (imaging being in need to modify the forcing of a model for a selected area) and it works. These are quite peculiar ways (at least for people not deep into xarray...) to assign values; I am compiling a list of them which IMHO should be added somewhere in the help. I will post them here for discussion, and to make sure they are indeed the most correct way to do it!
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,314239017
https://github.com/pydata/xarray/issues/2055#issuecomment-381254511,https://api.github.com/repos/pydata/xarray/issues/2055,381254511,MDEyOklzc3VlQ29tbWVudDM4MTI1NDUxMQ==,8453445,2018-04-13T20:38:09Z,2018-04-13T20:38:09Z,CONTRIBUTOR,"Regarding B)
I think that the current text can lead to confusion:
> Select or assign values by integer location (like numpy): x[:10] or by label (like pandas): x.loc['2014-01-01'] or x.sel(time='2014-01-01').
because selecting and assigning are discussed together. I think that should be fixed too.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,314239017
https://github.com/pydata/xarray/issues/2023#issuecomment-377249450,https://api.github.com/repos/pydata/xarray/issues/2023,377249450,MDEyOklzc3VlQ29tbWVudDM3NzI0OTQ1MA==,8453445,2018-03-29T14:16:39Z,2018-03-29T14:16:39Z,CONTRIBUTOR,"(short introduction: I created this issue, but I didn't realize I was logged in into another account)
I am not sure I have a constructive comment on how to name it.
If the future version will deprecate the attribute style access, I can see how it won't be a problem eventually, but I though to open this issue to bring it up.
how about just ""q"", since that is the name of the parameter? too short? ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,309378665
https://github.com/pydata/xarray/issues/1051#issuecomment-254904584,https://api.github.com/repos/pydata/xarray/issues/1051,254904584,MDEyOklzc3VlQ29tbWVudDI1NDkwNDU4NA==,8453445,2016-10-19T18:44:41Z,2016-10-19T18:44:41Z,CONTRIBUTOR,"I will be happy to add these info to the documentation.
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,183715595