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/1279#issuecomment-328724595,https://api.github.com/repos/pydata/xarray/issues/1279,328724595,MDEyOklzc3VlQ29tbWVudDMyODcyNDU5NQ==,4992424,2017-09-12T03:29:29Z,2017-09-12T03:29:29Z,NONE,"@shoyer - This output is usually provided as a sequence of daily netCDF files, each on a ~2 degree global grid with 24 timesteps per file (so shape 24 x 96 x 144). For convenience, I usually concatenate these files into yearly datasets, so they'll have a shape (8736 x 96 x 144). I haven't played too much with how to chunk the data, but it's not uncommon for me to load 20-50 of these files simultaneously (each holding a years worth of data) and treat each year as an ""ensemble member dimension, so my data has shape (50 x 8736 x 96 x 144). Yes, keeping everything in dask array land is preferable, I suppose.

@jhamman - Wow, that worked pretty much perfectly! There's a handful of typos (you switch from ""a"" to ""x"" halfway through), and there's a lot of room for optimization by chunksize. But it just *works*, which is absolutely ridiculous. I just pushed a ~200 GB dataset on my cluster with ~50 cores and it screamed through the calculation.

Is there anyway this could be pushed before 0.10.0? It's a killer enhancement. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,208903781
https://github.com/pydata/xarray/issues/1279#issuecomment-328314676,https://api.github.com/repos/pydata/xarray/issues/1279,328314676,MDEyOklzc3VlQ29tbWVudDMyODMxNDY3Ng==,4992424,2017-09-10T02:04:33Z,2017-09-10T02:04:33Z,NONE,"In light of #1489 is there a way to move forward here with `rolling` on `dask`-backed data structures? 

In soliciting the atmospheric chemistry community for a few illustrative examples for [gcpy](http://danielrothenberg.com/gcpy/), it's become apparent that indices computed from re-sampled timeseries would be killer, attention-grabbing functionality. For instance, the EPA air quality standard we use for ozone involves taking hourly data, computing 8-hour rolling means for each day of your dataset, and then picking the maximum of those means for each day (""MDA8 ozone""). Similar metrics exist for other pollutants.

With traditional xarray data-structures, it's *trivial* to compute this quantity (assuming we have hourly data and using the new resample API from #1272):

``` python
ds = xr.open_dataset(""hourly_ozone_data.nc"")
mda8_o3 = (
    ds['O3']
    .rolling(time=8, min_periods=6)
    .mean('time')
    .resample(time='D').max()
)
```
There's one quirk relating to timestamp the rolling data (by default `rolling` uses the *last* timestamp in a dataset, where in my application I want to label data with the *first* one) which makes that chained method a bit impractical, but it only adds like one line of code and it is totally dask-friendly. ","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,208903781
https://github.com/pydata/xarray/issues/1279#issuecomment-301489242,https://api.github.com/repos/pydata/xarray/issues/1279,301489242,MDEyOklzc3VlQ29tbWVudDMwMTQ4OTI0Mg==,4992424,2017-05-15T14:18:55Z,2017-05-15T14:18:55Z,NONE,Dask dataframes have recently been updated so that rolling operations work (dask/dask#2198). Does this open a pathway to enable rolling on dask arrays within xarray?,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,208903781