HoloViz Roadmap, as of 3/2020

HoloViz helps coordinate between numerous independent open-source projects, each with their own developers, priorities, and agendas. In large part, the future of HoloViz is up to a distributed team of developers that focus on the areas of greatest current interest and need, including areas that specifically get current funding.

Immediate, already funded priorities for 2020 include:

  1. Ongoing maintenance, improved documentation and examples: As always, there are various bugs and usability issues reported on the issue tracker, and we will address these as time permits.

  2. Improved imaging, simulation, machine learning, and Earth science workflows: Support for working with image and other multidimensional data for visualization and machine-learning applications, including on HPC systems.

  3. Simpler deployment of large-scale visualizations: Automatic generation of slippy-map tiles for exploration of large datasets using standard web servers. Already provided in Datashader, but needs additional polishing.

  4. Better Datashader integration with external plotting libraries (HoloViews, Plotly, Matplotlib): Datashader needs to provide functions for supporting hover information, legends, colorbars, and interactivity, which each plotting library can then use.

  5. Support for maintaining Python-based projects: As maintainers of a wide range of Python projects, we are working to minimize the cost of maintaining each one, by sharing code for tracking versions, making releases, providing example scripts, managing datasets, documenting examples, testing examples (including notebooks), and building websites. Projects addressing each of these areas are being added to the PyViz Github organization and will be documented as they become mature.

  6. Integrating 3D surface and volume rendering into HoloViz: HoloViews can plot limited quantities of 3D surface or point data using Matplotlib or Plotly, but other tools are needed for larger datasets or for volumetric rendering with transparent/semitransparent voxels. Improving 3D support could be as simple as providing examples for using existing tools like IPyVolume and Vaex, or by wrapping JavaScript libraries like ThreeJS and CesiumJS. There’s a first prototype for a CesiumJS backend now available, but lots more work to do to make it practical for real use.

  7. More extensive documentation about deployment: HoloViz includes a tutorial on deployment using Bokeh server, but there are many other ways to set up live Python-backed plots and Panel dashboards or apps, including creating a Flask server or embedding into Django, along with deploying via Heroku or Google Cloud and AWS or via the Anaconda Enterprise platform or on MyBinder. Documenting and testing these possibilities takes time and effort, and any contributions or examples that will help people get started and decide between the alternatives would be very helpful.

Other things we’d like to see in HoloViz or in packages designed to work well with HoloViz include:

  1. Bokeh WebGL support: Bokeh provides good support for working interactively with small datasets using an HTML Canvas (client-side interactivity), and when combined with Datashader it can handle enormous datasets by pre-rendering them to images in Python (server-side). However, in between these two extremes there is a range of data sizes that could be addressed well by the client-side WebGL technology. Bokeh includes some WebGL support, but it is patchy and not fully implemented, so it is not the default for either direct Bokeh usage or for Bokeh with the HoloViews API. Volunteers to maintain and expand the WebGL support could make a much broader range of data sizes practical in easily redistributable standalone HTML files, and could enable new classes of high-performance interactive features.

  2. Additional plot types: HoloViews includes an extensive range of plot types (Elements) covering the typical visualizations used across many domains. However, there are always more that can be included, and some domains are not as well covered as others. Some examples that we’d like to include are:

    (And if someone wants to write and maintain a pie chart even though they are nearly always the wrong thing to do, we can probably be convinced to allow it. :-)

  3. Better native GUI support: Right now, HoloViz focuses exclusively on tools that work well in web browsers, because it aims to support the entire workflow from initial exploration to delivery of fully functional interactive applications to other stakeholders. One consequence is that it doesn’t currently support the types of interactivity provided by Matplotlib when used with its native GUI backends like Qt. It should be feasible to make the interactive functionality in HoloViews (e.g. streams and DynamicMaps) support native GUI apps as well, allowing the same user code to be applied to a wider range of use cases.

  4. Altair/Vega/Vega-lite integration: HoloViews and Bokeh provide declarative syntaxes that can be expressed in purely static form, and it should be feasible to write a translator between them and other declarative libraries like Altair, Vega, and Vega-Lite. Those libraries are currently focused primarily on statistical visualizations, not covering areas like multidimensional (image-like) plotting where HoloViews and Bokeh are also used, but they offer a common interchange format that could be helpful for interoperating with other tools. Writing an import and export facility that covers the bulk of the shared functionality should not be a major undertaking and could open up interesting new applications. For now, Altair and Vega-lite visualizations can be specified and then used directly with Panel.

  5. hvPlot/HoloViews serialization: HoloViews uses a declarative design that can be represented in a purely textual form, without any Python code. An initial implementation allows any Param-based objects (including HoloViews objects) to be represented in JSON or YAML, but it needs some polishing before it can be put into wide use for saving and restoring configurations and layouts.

  6. Better integration with ____: There are a lot of tools in the Python and other scientific software ecosystems that could be included in HoloViz or made easily usable from it. NetworkX (already usable but not fully exploited yet) is just one example of many; suggestions welcome!

  7. GUI-based plot creation: (As in business intelligence and dashboarding applications.) The powerful components available in HoloViz are ready for Python users to put together into visualizations and apps, but they would also make a very strong base for building a graphical approach for working with data, with drag and drop layouts, GUI-configurable mapping of data sources, and GUI configuration of the plot objects. HoloViews components are already declarative, which means that they can be mapped directly into GUI elements for changing their parameters dynamically. Paired with the new Intake library for declaring data sources, it would be possible to build a fully graphical interface for working with data that would have the advantage of being backed by a fully configurable, open-source set of plotting library elements, ensuring that when people outgrow the GUI framework they can easily extend and expand anything developed in it, unlike current business intelligence and dashboarding applications.

If any of the functionality above is interesting to you (or you have ideas of your own!) and can offer help with implementation, please open an issue on this repository or on the specific subproject repository involved. And if you are lucky enough to be in a position to fund our developers to work on it, please contact sales@anaconda.com.