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Contributing to KAPESIT

Thank you for your interest in contributing to KAPESIT! Whether you're fixing a bug, adding a feature, or improving documentation, your contributions are highly valued. This guide will help you get started with the contribution process.

How Can You Contribute?

There are several ways you can contribute to KAPESIT:

  • Reporting Bugs: If you encounter any issues while using the project, let me know by opening a bug report.
  • Suggesting Enhancements: Have an idea for a new feature or improvement? Open an enhancement issue to discuss your thoughts.
  • Improving Documentation: Help improve the documentation by fixing typos, clarifying explanations, or adding examples.
  • Writing Code: If you're a developer, contribute by fixing issues, adding new features, or improving the existing code.
  • Optimizing Simulations: If you have experience with robotic simulations or machine learning models, help optimize the simulations for better performance or accuracy.

Contribution Process

  1. Fork the Repository: Start by forking the repository to your GitHub account.

  2. Clone Your Fork:

    git clone https://github.com/kiruthikpurpose/KAPESIT.git

  3. Create a Branch: Create a new branch for your contribution to ensure your work is isolated:

    git checkout -b feature-or-bug-description

  4. Make Changes:

    • If you're working on code, write clean, maintainable, and well-commented code.
    • Make sure your changes follow the project's coding standards and guidelines.
    • If you're adding a new feature, ensure it's properly documented.
    • If you're fixing a bug, add a test case to verify the fix.

  5. Run Tests: Before submitting your contribution, run all tests to ensure that nothing is broken.

    • For neural network-related contributions, use the test scripts provided in the repository.
    • If you're contributing to simulations, make sure to run tests in the simulation environment (Gazebo/Webots).

  6. Commit Your Changes: Write clear, descriptive commit messages:

    git commit -m "Add detailed description of your changes"

  7. Push to Your Fork:

    git push origin feature-or-bug-description

  8. Submit a Pull Request:

    • Go to the original repository on GitHub and submit a Pull Request (PR) from your fork.
    • Include a detailed description of the changes you made and the purpose behind them.
    • If applicable, include screenshots or logs of your tests.
    • Link the pull request to the relevant issue (if applicable) to provide context.

Code Guidelines

To maintain the quality and readability of the project, please adhere to the following guidelines when contributing code:

1. Style Guidelines:

  • Use consistent indentation (4 spaces for Python code).
  • Follow PEP 8 standards for Python.
  • Ensure proper naming conventions for functions, variables, and files.
  • Use descriptive variable and function names that clearly communicate their purpose.

2. Commenting:

  • Comment on your code where necessary, especially in complex sections.
  • Write docstrings for all functions and classes. Example: 
    def predict_dip(input_data):
    """
    Predicts dips in the robot's movement trajectory using trained neural networks.
    
    Args:
        input_data (numpy array): Input data representing the robot's current movement.
    
    Returns:
        bool: True if a dip is predicted, False otherwise.
    """
    pass

3. Testing:

  • Make sure to write unit tests for any new functionality.
  • Test code should be located in the tests/ folder, and should be executable using a command like: 
    python -m unittest discover -s tests
  • If you modify the neural network, ensure the model is validated through test scripts provided in the project.

4. Documentation:

  • Any changes to the code that affect the functionality should be reflected in the relevant documentation.
  • Ensure that the documentation is clear, concise, and updated as per your contributions.

Issues and Bug Reports

If you encounter a bug or would like to request a new feature, follow these steps:

  1. Search Existing Issues: First, check if there is already an open issue related to your problem or idea.
  2. Create a New Issue: If no existing issue is found, open a new one and provide detailed information, including:
    • A clear description of the bug or feature request.
    • Steps to reproduce the issue, if applicable.
    • Screenshots, logs, or relevant error messages to illustrate the problem.
  3. Label Your Issue: Use relevant labels such as bug, enhancement, documentation, etc., to categorize the issue.

Code of Conduct

This project adheres to an open, collaborative, and respectful environment. By participating, you agree to uphold the following values:

  • Be respectful and professional in your interactions.
  • Provide constructive feedback and suggestions.
  • Avoid any form of harassment, personal attacks, or inappropriate behavior.

For further details, refer to the Code of Conduct file.

Additional Resources

Here are a few additional resources to help you get started:

  • GitHub Help: If you're new to GitHub, check out GitHub's documentation on how to fork a repo, submit pull requests, etc.
  • Gazebo/Webots Documentation: Review the official documentation for these simulators to understand how to simulate environments for KAPESIT.
  • Machine Learning Resources: Check out TensorFlow and PyTorch documentation for more on building neural networks.

Thank You!

Thank you again for your interest in contributing to KAPESIT! Every contribution, no matter how big or small, helps improve the project. I look forward to your ideas, improvements, and collaboration.

If you have any questions or need help, feel free to reach out by opening an issue or contacting me directly.