A* path finding

Table of Contents

1. A* path finding
2. Introduction
3. Usage
4. Installation
   • Prerequisites
   • Setup
5. Tests
6. License
7. Acknowledgment

Introduction

This project is a pathfinding algorithm that uses the A* algorithm to find the shortest path between two points. The algorithm is implemented in Python and uses the Pillow library to visualize the results. The algorithm is tested using pytest and the coverage is measured using the codecov.io service. The project is set up to use GitHub actions to run the tests and measure the coverage. The results of the tests and coverage are presented in the badges above.

Usage

When the proper setup is done, the program can be run by executing the following command in the root directory of the project:

python main.py

Results

To see the results of the different tasks look into the docs folder. Here the results are presented in a png files.

Task 1

Task 2

Task 3

Task 4

Task 5

Installation

To install the A star pathfinding, one needs to have all the prerequisites installed and set up, and follow the setup guild. The following sections will guide you through the process.

Prerequisites

• Ensure that git is installed on your machine. Download Git
• Ensure Python 3.9 or newer is installed on your machine. Download Python

Setup

1. Clone the repository

git https://github.com/SverreNystad/a-star-pathfinding.git
cd a-star-pathfinding

2. Create a virtual environment (optional, but recommended)

   On Windows:

   python3 -m venv venv
   source venv/Scripts/activate

   On macOS and Linux:

   python3 -m venv venv
   source venv/bin/activate

3. Install the required packages

pip install -r requirements.txt

Tests

To run all the tests, run the following command in the root directory of the project:

pytest --cov
coverage html # To generate a coverage report

License

Licensed under the MIT License. Because sharing is caring :)

Acknowledgment

The dataset of Samfundet (https://www.samfundet.no/) is not made by me, and most of the code in the class Map_Obj was created by Håkon Måløy then later extended and documented by Xavier Sánchez-Díaz.

Thanks to the following libraries:

• Pillow for image manipulation
• Pytest for testing
• NumPy for numerical computing
• Panadas for data analysis