Pathfinding Visualizer in Python

A simple pathfinding visualizer built using Python and Pygame. This project demonstrates the A* algorithm in action by visually showing how the shortest path is found on a grid. Users can interactively set the start point, end point, and obstacles (barriers) while watching the algorithm work in real time.

Youtube Video: https://www.youtube.com/watch?v=O0MvdhQdj6I&t=6s

Features

• Interactive Grid:
  • Set start and end nodes.
  • Place barriers by clicking.
  • Reset nodes with a right-click.
• A* Algorithm Visualization:
  • Visual feedback for nodes being considered (open/closed).
  • Path reconstruction once the goal is reached.
• Modular Codebase:
  • Organized into separate files for better readability and maintenance.

Project Structure

Pathfinding_Visualiser/
├── main.py         # Main file to run the visualizer and handle UI events
├── spot.py         # Contains the Spot class representing each cell in the grid
└── algorithm.py    # Implements the A* pathfinding algorithm

Prerequisites

• Python 3.x

• Pygame
  Install Pygame using pip:

  pip install pygame

Getting Started

1. Clone the Repository:

   git clone https://github.com/EgemenErin/Pathfinding_Visualizer.git
   cd Pathfinding-Visualizer

2. Run the Visualizer:

   python main.py

How to Use

• Left-click to set the start node (orange), end node (turquoise), and barriers (black).
• Right-click to reset a cell.
• Press Spacebar to run the A* algorithm.
• Press C to clear the grid and start over.

Code Overview

spot.py

Defines the Spot class which represents each cell in the grid. It handles:

• State Management: Methods to mark a spot as start, end, barrier, open, closed, or part of the final path.
• Drawing: Rendering the spot on the Pygame window.
• Neighbor Updates: Determining adjacent spots that are not barriers for the algorithm.

algorithm.py

Implements the A* pathfinding algorithm:

• Heuristic Function (h): Calculates the Manhattan distance between two points.
• Path Reconstruction (reconstruct_path): Traces back from the end node to the start node to visualize the shortest path.
• A* Algorithm (a_star_algorithm): Uses a PriorityQueue to explore nodes, updating scores and paths until the goal is reached.

main.py

Serves as the entry point and ties the modules together:

• Setup: Initializes the Pygame window and grid.
• Event Handling: Processes mouse and keyboard inputs to set nodes and run the algorithm.
• Rendering: Continuously draws the grid, updates the display, and handles user interactions.

Contributing

Contributions are welcome! If you have ideas for improvements or bug fixes, please submit a pull request or open an issue.

License

This project is open source and available under the MIT License.

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