Data Science Challenges for Fusion Analysis

A repository containing Jupyter notebooks that introduce students to real-world examples using data science tools to make predictions for key Fusion parameters using real Fusion data from the Mega Amp Spherical Tokamak (MAST).

Description

This project contains a number of Jupyter notebooks designed to introduce students to data science techniques applied to fusion energy research. Using data from the Fair-MAST project, students will learn how to process, analyze, and build predictive models using real experimental fusion data.

The notebooks focus on:

• Data exploration and visualization of fusion parameters
• Predictive modeling of key fusion performance indicators
• Machine learning applications in fusion research

Data Source

The data used in this project comes from the FAIR-MAST project, which aims to make fusion research data more Findable, Accessible, Interoperable, and Reusable (FAIR). The MAST (Mega Amp Spherical Tokamak) is a fusion energy experiment based at Culham Centre for Fusion Energy in the UK.

Getting Started

Prerequisites

• Python 3.11 or higher
• uv - A faster and more reliable Python package installer and resolver

Installation

Local Installation

1. Clone the repository

   git clone https://github.com/yourusername/data-science-challenges.git

2. Navigate to the project directory

   cd data-science-challenges

3. Install uv if you don't have it already

   pip install uv

4. Create a virtual environment and install dependencies using uv

   uv venv
   uv pip install -e .

5. Activate the virtual environment

   # On Windows
   .venv\Scripts\activate
   
   # On Unix or MacOS
   source .venv/bin/activate

Google Colab Setup (for the Plasma Current notebook)

If you're using Google Colab to run the notebooks, you can install this package directly:

1. Using UV (Recommended)

   !pip install uv
   !uv pip install git+https://github.com/Simon-McIntosh/data-science-challenges.git

2. Using Pip

   !pip install git+https://github.com/Simon-McIntosh/data-science-challenges.git

3. After installation, restart your runtime for all changes to take effect by clicking on the "Runtime" menu and selecting "Restart runtime".

Usage

Running Jupyter Notebooks

To run the Jupyter notebooks, make sure you've activated your virtual environment, then:

jupyter lab --notebook-dir notebooks/

This will open a browser window with the Jupyter interface where you can select and run any of the notebooks.

Available Notebooks

1. MAST Plasma Current - Infer the value plasma current produced by CCFE's Mega Ampere Spherical Tokamak from discrete magnetic diagnostic data.
2. MAST Plasma Volume - Infer plasma volume from wide angle camera data.
3. MAST Plasma Equilibrium - Infer plasma equilibria from a diverse set of diagnostic data.

Accessing Fair-MAST Data

Find sample data in the fair_mast_data directory. For more data:

1. Visit the Fair-MAST Data Catalog
2. Use the provided API to access the complete FAIR-MAST archive

License

This project is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC BY-SA 4.0).

This means you are free to:

• Share — copy and redistribute the material in any medium or format
• Adapt — remix, transform, and build upon the material for any purpose, even commercially

Under the following terms:

• Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made.
• ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original.

See the LICENSE file for more details.