BigONavigator 0.2.2

A Python package to track and document the computational complexity of functions.

BigONavigator - Navigating Computational Complexity

BigONavigator is not just a Python package; it's a journey into the heart of algorithm efficiency. Originally developed as a university project, this tool has grown into a robust resource for developers, researchers, and students alike. It elegantly navigates the complexities of computational performance, providing insights into the Big O notation of algorithms with precision and clarity.

🎓 University Project Background

This project was conceived and developed as part of a university course in Computer Science, aiming to bridge theoretical concepts with practical application. It offers an educational insight into algorithm complexity, making it a perfect tool for academic projects and research.

🌟 Key Features

• Dynamic Complexity Estimation: Intuitively estimate the computational complexity of Python functions. (COMING SOON)
• Decorator-Driven Analysis: Utilize decorators to effortlessly mark and track function complexities.
• Comprehensive Complexity Table: View a summarised table of all tracked functions and their complexities, fostering a deeper understanding of algorithm performance.

🛠 Installation

pip install BigONavigator

📈 Usage

Kickstart your complexity analysis with BigONavigator:

from bigonavigator import O
from bigonavigator import show_complexity_table

@O['n']
def example_linear_function(data):
    # Implement linear time complexity operations
    pass

# Review the complexity summary
show_complexity_table()

📚 Documentation

Delve deeper into BigONavigator with our comprehensive documentation, which covers everything from setup to advanced features. Perfect for academic purposes and hands-on learning. Check it out here.

🤝 Contributing

Your contributions can help make BigONavigator an even more valuable tool for the academic and developer community:

1. Fork the Project
2. Create your Feature Branch (git checkout -b feature/YourAmazingFeature)
3. Commit your Changes (git commit -m 'Add some AmazingFeature')
4. Push to the Branch (git push origin feature/YourAmazingFeature)
5. Open a Pull Request

📞 Support & Feedback

We welcome feedback and queries! Please file any issues or suggestions on our Issues page or engage with us via Discussions.

📃 License

This project is open-source and available under the MIT License. See LICENSE for more details.