HoloViz Roadmap, as of 11/2023#

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.

Currently ongoing or scheduled priorities 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. Better Datashader integration with external plotting libraries (HoloViews, Plotly, Matplotlib): Datashader provides its own “shading” support, turning an array into colors for display, but if shading is done by an external plotting program, the plotting program can support user-friendly features like hover information, legends, and colorbars. There is already extensive support for rendering Datashader output in Bokeh, along with some support in Matplotlib and Plotly. The core team is gradually further improving Bokeh’s support, with nearly all of Datashader’s shading functionality now or soon available interactively in Bokeh, and similar support can be added to other libraries by their users or contributors.

  3. Support for managing annotations: Users of a plot or dashboard often notice important features of the data on display, but don’t have a convenient way to capture or communicate that insight. The core team is developing annotations as a first-class component the HoloViz tools, easily added onto plots and dashboards so that users can denote regions of interest in some space (e.g. a time range on a timeseries plot), storing, restoring, and displaying that information in editable form. See holoviz/holonote for the work as it develops.

Other goals for HoloViz or packages designed to work well with HoloViz include:

  1. New and improved workflow examples in biomedical and Earth sciences, especially utilizing machine learning: Demonstrations for handling and visualization of various large, multidimensional datasets from data-intensive research domains, including on HPC systems. Update: Work is ongoing, but several such examples are now at https://projectpythia.org, holoviz-topics/neuro and https://panel.holoviz.org/gallery/index.html#app-gallery .

  2. Better deployment and exploration of large-scale visualizations: For instance, with improvements to automatic generation of tiles for exploration of large datasets using standard web servers. Incomplete work on tiling can be found in this notebook.

  3. 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.

  4. 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:

  5. Interactive Matplotlib plotting: Right now, HoloViews supports Matplotlib primarily as static PNG or SVG output. Matplotlib also supports interactive web-based plotting via ipympl, and supporting such interactivity could help make Matplotlib be a viable alternative for more use cases.

  6. 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.

  7. 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, but not with HoloViews.

  8. 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.

  9. Independent selection groups: HoloViews currently supports a single selection group. There is no current support for multiple, fully independent selection groups within the same display - each group with its own identity and selection-based actions. Support for multiple independent groups would allow for more flexible use of, and comparison between, selections.

  10. Easy to configure drilldown support: In a drilldown action, the user interacts in some way with one main plot (usually some low-dimensional reduction of the full data) selecting one (or sometimes multiple) item(s) that determine what is shown in another separate plot (usually a less aggregated view of the selected subset of the data). Supporting intuitive drilldown will either require a new API or adaptation of link_selections to safely handle the state prior to any selection.

  11. 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!

If any of the functionality above is interesting to you (or you have ideas of your own!) and you would like to help with implementation, please open an issue on this repository or on the specific subproject repository involved.