Data Visualization in Blender

Overview:

At times, you may want to create a data visualization in Blender, either to extract the 3D model of the data or to use Blender as the visualization software itself. However, Blender is often an unwieldly piece of software for new users,  especially for those unacquainted with 3D modelling paradigms. Additionally, "just modelling" the data from a dataset may be difficult or infeasible to achieve accurately or efficiently.  Programmatic tools are necessary for doing this type of visualization. There are several methods for doing this, which are listed here.

Options:

• blender-plots

  • Python-forward option for creating complex, detailed 3D plots.

  • Great for easily creating surfaces and scatterplots, especially using predefined models.

  • Best option for those familiar with python scripting

• BlenderDataVis

  • No-code, all-inclusive option for creating 2D or 3D charts.  

  • Uses locally loaded CSV files, although performs poorly on files with more than 200 rows.

  • Fewer chart options, although charts may have more detail and customization.

  • Charts may be generated with axes, titles, and labels included

  • Best option for those with a less complex dataset and less familiar with Blender

• plot-in-blender

  • Minimal-code approach to simple models and 2D and 3D visualization options.

  • Results are low-polygon, but easy to add predefined colors and texturing to.

  • Good option for those looking for highly performant models.

• Sverchok

  • Complex, minimal code option targeting architects constructing 3D models from data.

  • Has a wide variety of features, can work with tools like SciPy to import data and create graphs/surfaces

  • Great for those deeply familiar with Blender or targeting architecture applications specifically.

• Bioxel Nodes

  • Extension for volumetric data rendering, particularly for biological data

  • Focused on very high-quality results, intensive rendering processes

  • Good for those with biological applications and some prior familiarity with Blender rendering pipelines

• Use Python directly

  • It's possible to use Python and various libraries to generate charts in Blender, given the extensive scripting support it has.

  • Several varying approaches to this, but this tutorial gives an approach for a bar chart, this one for a network, and this one for astronomy (which also features using Blender for VR visualization!).

• MolecularNodes

  • Low-code tool targeting biology and chemistry datasets to create models and animations of molecules and proteins. 

  • Documentation is extensive, produces highly stylized and cinematic results.

  • Tutorials assume no knowledge of Blender, making this strong for introductory users!

• MicrosopyNodes

  • Plugin specifically targeting .tif files from microscopy outputs.

  • Great for fine-grained control and visualization of these files,  stylization and highlighting of important details. 

  • Preprint available here.

• MCell

  • Plugin, low-code tool for biology and chemistry applications focused on creating realistic simulations of molecules.

  • Prioritizes simulation and animation, highly extensive tool.

  • Great for experimentation and prerendering biological animations.

What should I use?

It depends on your application, data, and background. Different tools will provide varying levels of functionality and control over your visualization, which may require different amounts of Python or Blender knowledge to engage with. Most domain-specific tools focus mainly on biology and astronomy data and require the most knowledge to use, but also typically provide tutorials to understand the basics of Blender beforehand. 

The most critical determining factor is understanding what you want your outcome to be. Let your results choose the tool, not the other way around! In any case, working through a basic tutorial with Blender to understand the basics of the UI and modelling is often useful to examine before attempting to add any plugins or extensions to build on this. There are many, many tutorials out there, including on this wiki -- use them!