index.md

RegressionLab Documentation

Welcome to RegressionLab, a powerful and user-friendly curve fitting application designed for scientists, engineers, students, and data analysts.

📊 What is RegressionLab?

RegressionLab is a comprehensive data analysis tool that performs curve fitting operations using multiple mathematical models. Whether you're analyzing experimental data, exploring mathematical relationships, or performing scientific research, RegressionLab provides an intuitive interface to fit your data to various equations and visualize the results.

🚀 Quick Links

• Introduction - Learn about RegressionLab's objectives and benefits.
• Installation Guide - Get started with RegressionLab.
• User Guide - Learn how to use the application.
• Configuration - Customize RegressionLab to your needs.
• API Documentation - Technical reference for developers.

📖 Table of Contents

Getting Started

1. Introduction

   • Project overview and objectives.
   • Key benefits and features.
   • Web and desktop versions.

2. Installation Guide

   • Quick installation (recommended).
   • Installation with Git.
   • Manual installation.
   • System requirements.

3. User Guide

   • Using the web version (Streamlit).
   • Using the desktop version (Tkinter).
   • Understanding operation modes.

Configuration & Customization

4. Configuration Guide
   • Changing the language.
   • Customizing plot styles.
   • Setting input/output directories.
   • UI theme customization.
   • Logging configuration.

Interface Guides

5. Streamlit Guide

   • Web interface overview.
   • Operation modes in Streamlit.
   • Tips and tricks.

6. Tkinter Guide

   • Desktop interface overview.
   • Operation modes in Tkinter.
   • Keyboard shortcuts and navigation.

For Developers

7. Extending RegressionLab

   • Adding new fitting functions.
   • Modifying equation types.
   • Code structure overview.

8. Customizing the Fitting Core

   • Replacing SciPy with other libraries.
   • Implementing custom fitting algorithms.
   • Performance considerations.

9. API Documentation

   • Core modules.
   • Fitting utilities.
   • Data loaders.
   • Plotting functions.
   • Configuration management.

Additional Information

10. Troubleshooting

    • Known issues.
    • Common problems and solutions.
    • Future updates and roadmap.

11. Contributing

    • How to contribute.
    • Development setup.
    • Code standards.
    • Pull request guidelines.

12. License

    • MIT License information.
    • Copyright details.

🔗 External Links

• Web Application: https://regressionlab.streamlit.app/
• GitHub Repository: GitHub
• Report Issues: Issue Tracker

💡 Need Help?

If you need assistance:

1. Check the User Guide for basic usage.
2. Review the Troubleshooting section.
3. Consult the API Documentation for technical details.
4. Open an issue on GitHub.

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Version: 1.1.2
Author: Alejandro Mata Ali
License: MIT