issue_comments: 218654978

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)