Welcome to the Numerical Analysis App! This Python desktop application helps you solve mathematical equations using various numerical methods. With an interactive GUI, you can easily input equations and visualize results through engaging plots. Whether you're a student or a professional, this tool provides the features you need for effective numerical analysis.
- Interactive GUI: A user-friendly interface for inputting equations.
- Visualization: View results through interactive plots.
- Solution History: Keep track of your previous solutions for easy reference.
- Customizable Settings: Adjust settings to fit your needs.
- PDF Export: Save your results in PDF format for easy sharing.
To get started with the Numerical Analysis App, follow these steps:
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Clone the repository:
git clone https://github.com/WayibKahil/Numerical-Analysis-App.git
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Navigate to the project directory:
cd Numerical-Analysis-App -
Install the required packages: Make sure you have Python installed. You can then use pip to install the necessary libraries:
pip install -r requirements.txt
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Run the application: Execute the following command to start the app:
python main.py
After installation, open the app. You will see a clean interface where you can input your equations. Here’s how to use the app:
- Input an Equation: Type your mathematical equation in the designated input field.
- Select a Method: Choose from various numerical methods available in the dropdown menu.
- Visualize Results: Click on the "Solve" button to see the results plotted on the screen.
- Export Results: Use the export feature to save your results as a PDF.
The Numerical Analysis App supports several numerical methods, including:
- Bisection Method: A root-finding method that repeatedly bisects an interval and selects a subinterval in which a root exists.
- Cramer’s Rule: A method for solving systems of linear equations using determinants.
- False Position Method: A root-finding algorithm that combines the bisection method and linear interpolation.
- Fixed Point Iteration: A method for finding roots of equations by iterating on a function.
- Gauss Elimination: A method for solving linear systems by transforming the system into an upper triangular form.
- LU Decomposition: A method that factors a matrix into the product of a lower triangular matrix and an upper triangular matrix.
- Newton-Raphson Method: An iterative method for finding successively better approximations to the roots of a real-valued function.
- Secant Method: A root-finding algorithm that uses a succession of roots of secant lines.
We welcome contributions! If you have suggestions or improvements, feel free to fork the repository and submit a pull request. Please ensure that your code adheres to our coding standards and includes appropriate tests.
- Fork the repository.
- Create a new branch:
git checkout -b feature/YourFeature
- Make your changes and commit them:
git commit -m "Add your feature" - Push to the branch:
git push origin feature/YourFeature
- Create a pull request.
This project is licensed under the MIT License. See the LICENSE file for details.
For any inquiries or issues, please reach out to the project maintainer:
- Name: Wayib Kahil
- Email: wayib.kahil@example.com
To download the latest version of the Numerical Analysis App, visit the Releases section. Download the appropriate file and execute it to start using the app.
You can also find previous versions and updates in the same section.
We would like to thank the open-source community for their contributions and support. Special thanks to the developers of the libraries used in this project.
Feel free to explore and enhance your numerical analysis skills with the Numerical Analysis App!