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/pull/818#issuecomment-218675077,https://api.github.com/repos/pydata/xarray/issues/818,218675077,MDEyOklzc3VlQ29tbWVudDIxODY3NTA3Nw==,167164,2016-05-12T06:54:53Z,2016-05-12T06:54:53Z,NONE,"`forcing_data.isel(lat=lat, lon=lon).values()` returns a `ValuesView`, which scikit-learn doesn't like. However, `forcing_data.isel(lat=lat, lon=lon).to_array().T` seems to work..
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146182176
https://github.com/pydata/xarray/pull/818#issuecomment-218667702,https://api.github.com/repos/pydata/xarray/issues/818,218667702,MDEyOklzc3VlQ29tbWVudDIxODY2NzcwMg==,167164,2016-05-12T06:02:55Z,2016-05-12T06:02:55Z,NONE,"@shoyer: Where does `times` come from in that code?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146182176
https://github.com/pydata/xarray/pull/818#issuecomment-218654978,https://api.github.com/repos/pydata/xarray/issues/818,218654978,MDEyOklzc3VlQ29tbWVudDIxODY1NDk3OA==,167164,2016-05-12T04:02:43Z,2016-05-12T04:03:01Z,NONE,"Example forcing data:

```
<xarray.Dataset>
Dimensions:  (lat: 360, lon: 720, time: 2928)
Coordinates:
  * lon      (lon) float64 0.25 0.75 1.25 1.75 2.25 2.75 3.25 3.75 4.25 4.75 ...
  * lat      (lat) float64 -89.75 -89.25 -88.75 -88.25 -87.75 -87.25 -86.75 ...
  * time     (time) datetime64[ns] 2012-01-01 2012-01-01T03:00:00 ...
Data variables:
    SWdown   (time, lat, lon) float64 446.5 444.9 445.3 447.8 452.4 456.3 ...
```

Where there might be an arbitrary number of data variables, and the scikit-learn input would be time (rows) by data variables (columns). I'm currently doing this:

``` python
def predict_gridded(model, forcing_data, flux_vars):
    """"""predict model results for gridded data

    :model: TODO
    :data: TODO
    :returns: TODO

    """"""
    # set prediction metadata
    prediction = forcing_data[list(forcing_data.coords)]

    # Arrays like (var, lon, lat, time)
    result = np.full([len(flux_vars),
                      forcing_data.dims['lon'],
                      forcing_data.dims['lat'],
                      forcing_data.dims['time']],
                     np.nan)
    print(""predicting for lon: "")
    for lon in range(len(forcing_data['lon'])):
        print(lon, end=', ')
        for lat in range(len(forcing_data['lat'])):
            result[:, lon, lat, :] = model.predict(
                forcing_data.isel(lat=lat, lon=lon)
                            .to_dataframe()
                            .drop(['lat', 'lon'], axis=1)
            ).T
    print("""")
    for i, fv in enumerate(flux_vars):
        prediction.update(
            {fv: xr.DataArray(result[i, :, :, :], 
                              dims=['lon', 'lat', 'time'],
                              coords=forcing_data.coords)
            }
        )

    return prediction
```

and I think it's working (still debugging, and it's pretty slow running)
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146182176
https://github.com/pydata/xarray/pull/818#issuecomment-218372591,https://api.github.com/repos/pydata/xarray/issues/818,218372591,MDEyOklzc3VlQ29tbWVudDIxODM3MjU5MQ==,167164,2016-05-11T06:24:11Z,2016-05-11T06:24:11Z,NONE,"I want to be able to run a scikit-learn model over a bunch of variables in a 3D (lat/lon/time) dataset, and return values for each coordinate point. Is something like this multi-dimensional groupby required (I'm thinking groupby(lat, lon) => 2D matrices that can be fed straight into scikit-learn), or is there already some other mechanism that could achieve something like this? Or is the best way at the moment just to create a null dataset, and loop over lat/lon and fill in the blanks as you go?
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,146182176