Agent based modelling on example of ant colony optimization.
- Very inefficient drawing (I would not increase the number of agents over 1000 except you like a flip-books)
- State of the art 2D-graphics with colored squares and circles
- Context sensitive interface (no annoying overlays to fully enjoy the moving squares)
- Implement an agent-based model base on the ant colony optimization algorithm
- Train code documentation
- Train create a consistent design
- Train UML class diagrams
- Train testing with > 90% coverage (mocking and other fancy shit)
- Train python typing module
-[x] setup.py -[x] sphinx cfg -[x] makefile -[ ] environment -[x] doc Cell and Position -[ ] logging Environment, Cell, and Position -[ ] tests Environment, Cell, and Position -[ ] agents