Visualization of grid world properties
- Dominant language
- Python
- Stars
- 252
- Forks
- 279
- Avg merge
- 8d 9h
- Merged PRs (30d)
- 2
Description
Dear MESA devs,
we are creating a custom environment inspired to the Wolf-Sheep Predation example (https://mesa.readthedocs.io/stable/examples/advanced/wolf_sheep.html)
In brief, mobile robots need to carry out collective tasks and need to communicate via pheromone traces deposited in the environment.
We modeled the pheromone value associated to each patch in the grid world using property layers.
Now we would like to visualize the pheromone value (both for debugging and for rendering example videos), however we cannot find a "proper" way to do so.
The only way we found so far is to abuse the agent abstraction by creating a "pheromone agent" in each patch of the grid world, and then rendering it (in a different way than "proper" agents---the robots).
We will probably encounter the same issue when adding obstacles and walls to our grid world: how can we rendered visually those without abusing the agent abstraction?
Is there some Solara components/methods we need to check out?
We feel we are missing something because this seems quite a basic and widely used functionality to have...(for instance, in Netlogo patches' properties can be visualized via coloring of the patches, without the need to resort to "fake" turtles)
Thanks, looking forward a kind reply.
Contributor guide
Research direction
Start with the advanced Wolf-Sheep example and inspect how its Solara visualization renders the grid and agents. Determine whether property layers, obstacles, or walls can be represented without creating fake agents. Done means the supported approach and any required example or documentation changes are clear, with pheromone values visibly rendered for debugging or videos.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 32/100