Personal project testing out implementation of network algorithms on the London Tube Map.

Testing candidate solutions in roughandready.py before commiting these to main.py, which will have appropriate class/function structure.

• Imported tube map into a matrix
• Set-up dictionary for name references
• Function returning shortest journey in a graph
• TODO NEXT: Function returning shortest journey given fixed start station

The data used for this project is freely available here: https://commons.wikimedia.org/wiki/London_Underground_geographic_maps/CSV

Using scipy sparse matrices (time/memory saver compared to multiple adjacency lists) to store a sparse adjacency matrix which represents the tubemap as an edge-weighted, non-directional, connected network. Stations are represented by numerical indices, and so a dictionary is used to hold the key/value pairs of station numerical index to name.

• Exploring implementation of Primms and Kruskal's algorithms to produce a minimum spanning tree (MST).

• This will involve implementing Dijkstra's algorithm for shortest path to minimise the journey between any two stations.

• Visiting all stations in opimum length of time. -- Implement candidate solution using MST, random descent, steepest descent, simulated annealing, tabu.