Metadata-Version: 2.3
Name: k_math_kit
Version: 0.0.1
Summary: A package with some usual math functions and objects, generally concerning numerical analysis
Project-URL: Homepage, https://pypi.org/project/k-math-kit/
Project-URL: Issues, https://github.com/KpihX/k_math_kit/issues
Project-URL: Repository, https://github.com/KpihX/k_math_kit.git
Author-email: KpihX <kapoivha@gmail.com>
Maintainer-email: KpihX <kapoivha@gmail.com>
License: MIT License
License-File: LICENSE
Keywords: analysis,integration,math,numerical,polynomial
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Requires-Python: >=3.8
Description-Content-Type: text/markdown

# 📐 k_math_kit 📚

Welcome to **k_math_kit**! This toolkit is designed to make advanced mathematical computations and polynomial manipulations easier for you. Whether you are a student, educator, or professional, this library will save you time and effort in performing complex mathematical operations. Created by KpihX.

## Table of Contents

1. [Features](#features)
2. [Installation](#installation)
3. [Usage](#usage)
4. [Examples](#examples)
5. [Contributing](#contributing)
6. [License](#license)

## Features 🎉

- **Polynomial Operations**: Perform operations like addition, subtraction, and multiplication of polynomials.
- **Interpolation**: Implement Newton and Lagrange interpolation methods.
- **Integration**: Perform numerical integration using different techniques.
- **Spline Interpolation**: Generate and work with spline interpolations.
- **Taylor Series**: Compute and manipulate Taylor polynomials.

## Installation 🛠️

To get started with `k_math_kit`, you need to have Python installed on your system. You can then install the package via pip:

```sh
pip install k_math_kit
```

## Usage 🚀

Here is a quick example to get you started:

### Examples 🌟

For detailed examples, check out the `tests` directory, which contains Jupyter notebooks demonstrating various functionalities:

- `lagrange_interpolations.ipynb`
- `spline_interpolations.ipynb`

## Contributing 🤝

We welcome contributions to enhance the functionality of `k_math_kit`. If you have any ideas or improvements, please feel free to fork the repository and submit a pull request. For major changes, please open an issue to discuss what you would like to change.

### Steps to Contribute

1. Fork the repository.
2. Create your feature branch: `git checkout -b feature/your-feature-name`
3. Commit your changes: `git commit -m 'Add some feature'`
4. Push to the branch: `git push origin feature/your-feature-name`
5. Open a pull request.

## License 📜

This project is licensed under the MIT License. See the [LICENSE](./LICENSE) file for details.

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Feel free to reach out if you have any questions or feedback. Happy computing! 😊

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## Author

**KpihX**

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**Enjoy using k_math_kit and happy computing!** 🧮✨
