Visualising

The Visualising category defines how computed and generated outputs are interpreted, represented, and deployed beyond the internal node graph.

It operates on structured data, geometry, and analytical results, converting them into plots, dashboards, interactive maps, web interfaces, and deployable visual systems.

Visualising is divided into three main groups:

Visualising Design Tools

This group focuses on spatial and geometry-based representation.

It supports:

  • Interactive map visualisation
  • Mesh-based dashboard rendering
  • DEM plotting
  • Web-based spatial display
  • VR-ready exports

These tools externalise geometric and spatial outputs, allowing exploration of results in interactive or immersive environments.

Visualising Data Tools

This group focuses on statistical and analytical representation.

It supports:

  • Plotly-based dashboards
  • Bokeh-based dashboards
  • Seaborn statistical plots
  • DataFrame visualisation
  • Chart-based analytical summaries

These tools transform structured datasets and computed metrics into visual narratives, charts, and interactive analytical interfaces.

Visualising Supporting Tools

This group enables deployment and runtime execution of visual outputs.

It supports:

  • Dashboard assembly
  • Dashboard server execution
  • Python HTTP server utilities
  • Code snippet generation for external runtimes

These tools separate computation from deployment, allowing visual outputs to run in environments such as web servers or interactive dashboard platforms.

Structural Role

The Visualising category ensures:

  • Clear separation between computation and representation
  • Structured transformation of results into interpretable formats
  • Interactive exploration of analytical and generative outputs
  • Deployment-ready visual systems beyond the modelling interface

It completes the computational workflow by making results inspectable, communicable, and externally executable.