Pathfinding with A* Algorithm Within a Large Matrix

The Pathstar library implements a pathfinding algorithm using the A* search technique to find the optimal path between a start position and a destination within a large matrix. It avoids looping through the entire matrix by using a scan function that extracts a smaller submatrix based on the start and destination positions.

Features

• Matrix Generation: Generates a random matrix with values between 0 and 9.
• A Pathfinding*: Implements the A* search algorithm to find the shortest path avoiding obstacles.
• Visualization: Prints the resulting path on the matrix using ASCII characters for easy visualization.

Usage

1. Clone the repository:

   git clone https://github.com/yourusername/pathstar.git
   cd pathstar

2. Install dependencies (e.g., NumPy):

   pip install numpy

3. Import and call the pathstar function from your code:

   from pathstar import pathstar, print_pathstar, viz_pathstar, generate_matrix
   
   # Example usage:
   M = generate_matrix(10, 10)
   obstacles = [4, 7, 2, 9]
   start_pos = (9, 7)
   dest_pos = (2, 3)
   
   path = pathstar(M, start_pos, dest_pos, obstacles)
   
   # Optionally print the path or visualize it
   print_pathstar(path)
   viz_pathstar(M, path)

   • pathstar(M, Spos, Dpos, obstacles) computes the optimal path.
   • print_pathstar(path) prints the path step by step.
   • viz_pathstar(M, path) visualizes the path on the matrix using ASCII characters.