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A Tkinter GUI that allows users to test the collatz conjecture for the desired numbers and visualize them, and giving the option of exportation into a CSV file for further analysis

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Collatz Conjecture Visualizer & Data Exporter

A professional-grade Python tool that explores the $3n + 1$ problem. This application doesn't just calculate sequences; it provides a full suite for data analysis and visualization.


Note: entered numbers lose precision as they become more unspeakably high

To test unspeakably high numbers accurately or see the Research behind this project: Click Here


Features

  • Algorithmic Calculation: Fast processing of the Collatz sequence for any integer

  • Interactive Visualization: Generates dynamic line graphs using Matplotlib to show the "hailstone" peaks and valleys.

  • Data Export: Utilizes Pandas to serialize sequence data into a .csv file for external analysis in Excel or SQL.

  • Adaptive Logarithmic Scaling: Automatically switches to $Log_{10}$ visualization for unspeakably high numbers to prevent hardware overflow and maintain graph readability.


Technologies Used:

  • Programming Language: Python

  • Libraries Used: Tkinter, Pandas and Matplolib

  • Concepts: Data Engineering pipelines, State Management and Mathematical Modeling.


Collatz Sequence Visualization for the number 7,384,723 Visualization of the Collatz sequence for 7,384,723 which peaks at over 33 million.

How to Run:

Follow these steps to run the project locally:

1. Clone the repository and enter:

git clone https://github.com/saifkayyali3/CollatzStandard.git
cd CollatzStandard

2. Make a virtual environment

python -m venv venv

source venv/bin/activate # Linux/macOS
venv\Scripts\activate # Windows

3. Install the needed requirements

pip install -r requirements.txt

4. Run

python Collatz_Conjecture.py

License

This project is licensed under the MIT License – see the LICENSE file for details.

Author

Saif Kayyali

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A Tkinter GUI that allows users to test the collatz conjecture for the desired numbers and visualize them, and giving the option of exportation into a CSV file for further analysis

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