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.

I'm not strongly opposed to something like DataArray.hvplot for the accessor, it's just slightly less obvious than DataArray.holoplot.

hv would probably be too short for a good name (but of course this is totally up to you), especially because I can imagine people using hv for a variables name, which can also be accessed via attributes.

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

Oh and another big 👍 to the datashader integration. This is crucial for my datasets.

I am a big fan of holoviews and have been using it extensively for my own work in recent months. So obviously I am a big 👍 on this integration.

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

Some features I would like to see are things that go beyond the plotting capabilities associated with the matplotlib engine. For example:

• 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.
• To go along with the above, lazy loading of dask-backed arrays
• Intelligent faceting which automatically links the facet kdims
• Plotting not just of DataArrays but Datasets. The variable itself could become a dynamic selector in a dropdown menu. Basically, I just want to say ds.hv.plot() and have holoviews provide all the options I need to explore the dataset interactively. Kind of like how ncview works. At that point, we won't need ncview anymore.
• Options for projections, coastlines, etc. associated with geoviews

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.

Very cool! I also think this would be a good use case for a new accessor, perhaps DataArray.holoplot() mirroring our preference for accessor names to match projects.

An engine keyword/option could also be viable, but would require more coordination (e.g., figuring out the plotting interface, which seems to have stalled that plotting issue). That said, if pandas figured out a way to do this I'm sure we would be happy to copy it.

It looks like a good use case for accessors. The syntax could then be: DataArray.hv.plot() and would give you full flexibility.