issue_comments: 393300042

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https://github.com/pydata/xarray/issues/2199#issuecomment-393300042 https://api.github.com/repos/pydata/xarray/issues/2199 393300042 MDEyOklzc3VlQ29tbWVudDM5MzMwMDA0Mg== 1550771 2018-05-30T20:06:29Z 2018-05-30T20:16:45Z NONE

I agree the accessor is the best option for now, but I have no strong opinions about the name of the accessor.

Okay thanks, given xarray's preference for accessor names to match projects I'm now leaning toward da.holoplot().

Automatic generation of DynamicMaps. Say I have a DataArray with dimensions ('time', 'lat', 'lon'); I should be able to say da.hv.plot(kdims=['lat', 'lon'] and have time become a dynamic selector.

HoloPlot explicitly does not deal with kdims and vdims instead more closely following the API of pd.DataFrame.plot and xr.DataArray. That said coordinates that are not assigned to the x/y axes will automatically result in a DynamicMap, so this will give you an image plot + a widget to select the time:

python da.holoplot(x='lon', y='lat', kind='image')

To go along with the above, lazy loading of dask-backed arrays

That should happen automatically.

Intelligent faceting which automatically links the facet kdims

You can facet in a number of ways:

python da.isel(time=slice(0, 3)).holoplot(x='lon', y='lat', kind='image', by='time')

will produce three subplots which are linked on the x- and y-axis, i.e. zooming on one will zoom on all unless you set shared_axes=False. You can also generate a grid with:

python da.isel(time=slice(0, 3)).holoplot(x='lon', y='lat', kind='image', row='time', col='some_other_coord')

Plotting not just of DataArrays but Datasets.

This is also already supported, the API here is:

python ds.holoplot(x='lon', y='lat', z=['air', 'surface'])

Will provide a widget to select between the 'air' and 'surface' data variable.

Options for projections, coastlines, etc. associated with geoviews

Currently working on that, it's basically just waiting on new HoloViews/GeoViews releases. The API here is as follows:

python air_ds.air.holoplot.quadmesh( 'lon', 'lat', ['air', 'some_other_variable'], crs=ccrs.PlateCarree(), projection=ccrs.Orthographic(-80, 30), global_extent=True, width=600, height=500, cmap='viridis' ) * gv.feature.coastline

 
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