How bonsaijoe878/portfolio2 works
This repository appears to be a small Python application for solving a transport-routing problem, likely centered on computing routes over a graph and then presenting the result visually.
1. Overall shape
- The project is organized as a single Python pipeline rather than a multi-service system.
- The core flow is: load or build transport data → represent it as graph/matrix structures → run a TSP-style solver → produce an output map or visualization.
2. Entry point
- main.py is the orchestration layer.
- It likely wires together the transport model, solver, and output generation.
- For an architecture diagram, treat this as the application boundary where execution starts.
3. Transport domain model
- transport_graph.py is the graph-based representation of the transport network.
- It likely models nodes, edges, and distances/costs between locations.
- This is the primary domain abstraction if the problem is expressed as a route network.
4. Matrix representation
- transport_matrix.py likely provides an adjacency or cost matrix derived from the graph.
- This suggests the solver may prefer matrix-based access for distance lookups or optimization.
- In the diagram, this is a transformation layer between graph data and the algorithm.
5. Optimization / route solving
- tsp_solver.py is the algorithmic core.
- The file name strongly suggests a Traveling Salesman Problem solver or a close variant.
- This component probably consumes the matrix or graph-derived distances and outputs an ordered route.
6. Local business rules
- local_logic.py likely contains problem-specific rules, heuristics, or glue code that are not generic graph/solver logic.
- This is where application-specific decisions probably live, such as filtering nodes, selecting a starting point, or adapting solver results.
7. Output / visualization
- map_output.py is the presentation/export boundary.
- It likely renders the computed route onto a map, or writes an artifact that can be viewed externally.
- This is the final stage of the pipeline and separates computation from presentation.
8. Data flow between modules
- A typical flow is:
- main.py initializes the run
- transport_graph.py captures the network
- transport_matrix.py extracts the cost structure
- tsp_solver.py computes the best route
- map_output.py formats the result for display or export
- local_logic.py likely influences multiple stages, especially preprocessing and postprocessing.
9. Likely architecture boundaries
- Input/model boundary: transport data becomes a graph
- Algorithm boundary: the solver operates on a normalized matrix or graph abstraction
- Presentation boundary: route results become a map or output artifact
- These are the main layers to show in a diagram.
10. Technology profile
- The repository is Python-based.
- There is no sign of a web framework, database, or distributed infrastructure from the file list alone.
- This looks like a self-contained analytical/optimization tool rather than a service application.
11. What to emphasize in a diagram
- Put main.py at the top as the orchestrator.
- Show transport_graph.py and transport_matrix.py as core domain/data transformation modules.
- Place tsp_solver.py as the central computation engine.
- Show map_output.py as the terminal output layer.
- local_logic.py should be shown as a supporting rules/coordination module feeding into the pipeline.
12. Confidence note
- This architecture is inferred from filenames and repository shape only, since the README contains no additional detail.
- The structure strongly suggests a route-optimization project with graph/matrix modeling and a TSP-style solver.
Main entry points: local_logic.py Local Logic · tsp_solver.py TSP Solver · main.py Main · map_output.py Map Output
Components 6
Other components
- Main · Application entry point and pipeline coordinator.
main.py - Local Logic · Problem-specific glue, heuristics, and run-time decisions.
local_logic.py - Transport Graph · Graph model of the transport network and distances.
transport_graph.py - Transport Matrix · Matrix representation derived from the transport graph.
transport_matrix.py - TSP Solver · Route optimization engine that computes an ordered tour.
tsp_solver.py - Map Output · Formats or renders the computed route as a map or artifact.
map_output.py
Connections 9
- Main → Local Logic: uses · Main delegates application-specific decisions to local logic.
- Main → Transport Graph: loads · Main initializes or receives the transport network model.
- Transport Graph → Transport Matrix: derives · Graph structure is converted into a matrix for lookup and solving.
- Transport Matrix → TSP Solver: feeds · Matrix data supplies the optimization algorithm.
- Local Logic → TSP Solver: guides · Local rules influence solver setup and interpretation.
- TSP Solver → Local Logic: returns · Solver results are adapted by local logic where needed.
- TSP Solver → Map Output: routes · Computed route is handed to the output layer.
- Local Logic → Map Output: formats · Local rules may affect final presentation or export.
- Main → Map Output: emits · Main triggers final output generation.