issue_comments

Thanks for everyone's feedback, due to trademark concerns we decided to rename both the library and the API to .hvplot. There should be a release and an announcement in the coming week.

Thanks again for the feedback, I've decided to go with .holoplot in the end. I'll work on finishing some of geo related features today and get a 0.1 release and announcement out this week.

something like DataArray.hv.plot.contourf() seems too deeply nested.

Actually I suppose that's not what it would be, it could be da.hv.plot and da.hv.contourf with .plot figuring out the kind for you. I quite like that too.

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

Thanks for the feedback! I'll try to drive the pandas conversation along, but since I doubt that will be resolved in the near term so I think until then we should definitely pursue the accessor approach (which is much better than the property monkey patching we're doing now).

Personally I'd prefer DataArray.hvplot() since I think even the two extra characters make a difference and something like DataArray.hv.plot.contourf() seems too deeply nested. That said if "our preference for accessor names to match projects" is a solidly established convention I'll defer to that and go with DataArray.holoplot().

@rabernat Since you have used HoloViews with xarray in the past I'd very appreciate your input as well.