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Author: delemmaao

README.md

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+# 🌌 BSc Astrophysics Projects
+
+![Astrophysics](https://img.shields.io/badge/Astrophysics-Projects-blue?style=flat&logo=github)
+
+Welcome to the **BSc Astrophysics Projects** repository! Here, you will find all the astrophysics projects I completed during my final year. My main focus was on optimizing orbital transfers using gradient descent and evaluating uncertainties with the Markov Chain Monte Carlo method.
+
+## 🚀 Table of Contents
+
+- [Introduction](#introduction)
+- [Project Overview](#project-overview)
+- [Technologies Used](#technologies-used)
+- [Installation](#installation)
+- [Usage](#usage)
+- [Contributing](#contributing)
+- [License](#license)
+- [Contact](#contact)
+- [Releases](#releases)
+
+## 📚 Introduction
+
+Astrophysics combines physics and astronomy to understand celestial phenomena. In my final year, I delved into various projects that applied mathematical modeling and statistical methods to real-world problems in space science. This repository serves as a comprehensive collection of my work, showcasing the intersection of theory and practical application.
+
+## 🌠 Project Overview
+
+The projects in this repository include:
+
+1. **Orbital Transfer Optimization**: This project employs gradient descent techniques to find the most efficient paths for spacecraft maneuvers. By minimizing fuel consumption and time, we can enhance mission success rates.
+
+2. **Uncertainty Evaluation**: Using the Markov Chain Monte Carlo method, this project assesses uncertainties in orbital parameters. This approach allows for better risk management in space missions.
+
+3. **Mathematical Modeling**: Various mathematical models are developed to simulate astrophysical phenomena. These models help in predicting outcomes and understanding complex systems.
+
+4. **Data Visualization**: Visual representations of data are crucial in astrophysics. This project focuses on creating animations and plots that illustrate findings effectively.
+
+5. **Unit Testing**: Ensuring the reliability of code is vital. This section covers unit testing practices used throughout the projects.
+
+## 🛠️ Technologies Used
+
+The following technologies and methodologies are employed in this repository:
+
+- **Animation**: For visualizing data and processes.
+- **Astronomy**: The foundational science behind the projects.
+- **Astrophysics**: The application of physics to astronomical phenomena.
+- **Bayesian Inference**: A statistical method for uncertainty evaluation.
+- **Calculus**: Essential for modeling and optimization.
+- **Gradient Descent**: A key optimization technique used in various projects.
+- **LaTeX**: For documentation and presentation of mathematical equations.
+- **Markov Chain Monte Carlo**: A method for sampling from probability distributions.
+- **Mathematical Modeling**: Creating models to simulate real-world scenarios.
+- **Mathematics**: The backbone of all computations and analyses.
+- **Package Development**: Building reusable components for the projects.
+- **Physics**: The fundamental science that underpins astrophysics.
+- **Spacecraft Dynamics and Control**: Understanding the motion of spacecraft.
+- **Statistics**: For analyzing data and drawing conclusions.
+- **Unit Testing**: Ensuring code quality and reliability.
+- **Visualization**: Creating visual representations of data and results.
+- **VSCode**: The development environment used for coding.
+
+## 📥 Installation
+
+To get started with the projects, follow these steps:
+
+1. **Clone the Repository**:
+   ```bash
+   git clone https://github.com/delemmaao/BSc_Astrophysics_Projects.git
+   ```
+
+2. **Navigate to the Project Directory**:
+   ```bash
+   cd BSc_Astrophysics_Projects
+   ```
+
+3. **Install Dependencies**:
+   Depending on the specific projects you wish to run, you may need to install certain libraries. Check the individual project folders for a `requirements.txt` or `setup.py` file.
+
+## ⚙️ Usage
+
+Each project contains its own README file with detailed instructions on how to run and use the code. You can find these instructions in their respective directories.
+
+To run the main project focused on orbital transfer optimization, use the following command:
+
+```bash
+python orbital_transfer.py
+```
+
+For uncertainty evaluation using the Markov Chain Monte Carlo method, run:
+
+```bash
+python mcmc_evaluation.py
+```
+
+Feel free to explore the code and modify it as needed for your own experiments.
+
+## 🤝 Contributing
+
+Contributions are welcome! If you have suggestions or improvements, please fork the repository and submit a pull request. You can also open issues for any bugs or enhancements you find.
+
+### Steps to Contribute:
+
+1. Fork the repository.
+2. Create a new branch:
+   ```bash
+   git checkout -b feature/YourFeature
+   ```
+3. Make your changes and commit them:
+   ```bash
+   git commit -m "Add some feature"
+   ```
+4. Push to the branch:
+   ```bash
+   git push origin feature/YourFeature
+   ```
+5. Open a pull request.
+
+## 📄 License
+
+This project is licensed under the MIT License. See the [LICENSE](LICENSE) file for details.
+
+## 📞 Contact
+
+For any questions or inquiries, feel free to reach out:
+
+- **Email**: your.email@example.com
+- **GitHub**: [delemmaao](https://github.com/delemmaao)
+
+## 📦 Releases
+
+You can find the latest releases and download files from the [Releases section](https://github.com/delemmaao/BSc_Astrophysics_Projects/releases). Each release contains important updates and files that you may need to execute.
+
+Feel free to explore the various projects, and I hope you find them insightful. Happy coding!