🧭 TSP Visualizer & Optimizer TSP.py

A scratch-built, axis-separated Traveling Salesman Problem (TSP) solver with real-time 2D/3D animation using Matplotlib. This implementation avoids bundled heuristics and prioritizes semantic clarity, reproducibility, and extensibility.

🚀 Features

• Nearest-neighbor path construction
• 2-opt optimization for path refinement
• Memoized Euclidean distance calculation
• Real-time animated rendering (2D or 3D auto-detect)
• Interactive zoom and manual step-through
• Validator-ready output with timing and distance metrics

📦 Requirements only to Draw results

pip install matplotlib

🧠 Algorithm Overview

Phase	Description
nearest()	Builds initial path using greedy nearest-neighbor logic
optimize()	Applies 2-opt swaps to reduce total path distance
dist()	Memoized Euclidean distance between cities
render()	Dynamically draws path in 2D or 3D space with zoom and pan support
update()	Animation frame handler for progressive path rendering
zoom_handler()	Mouse scroll zooms in/out and pans viewport
click_handler()	Mouse click manually advances animation frame

🧮 Usage

Replace check_array = cities with your own list of city dictionaries:

cities = [
    {'name': 'A', 'x': 0, 'y': 0},
    {'name': 'B', 'x': 1, 'y': 1},
    {'name': 'C', 'x': 2, 'y': 0},
    # Optional 3D: add 'z' key
]

Then run the script:

python tsp_visualizer.py

📊 Output

Initial Distance: 123.456
Initial Solution Time (ms): 4.321
Optimized Distance: 98.765
Optimization Time (ms): 2.109
Sorted Path Length: 11
Start: {'name': 'A', 'x': 0, 'y': 0}
Penultimate: {'name': 'K', 'x': 5, 'y': 2}
End: {'name': 'A', 'x': 0, 'y': 0}

🎥 Demo

• Auto-detects 2D or 3D based on presence of 'z' key
• Colors each segment using Tableau palette
• Click to manually advance frames
• Scroll to zoom and pan

🧩 Extensibility

• Swap in alternate heuristics or optimization strategies
• Extend dist() for geodesic or weighted metrics
• Integrate with external validators or lexicons