nxs-computational-tutorial-2026

Agenda

Set up

1. Log into analysis.sns.gov
2. Run script start_jupyter.sh provided by Jean B. in /SNS/EXAMPLES/NS2026/

Introductory Lectures (powerpoint) 30-45 mins.

• Intro: What is scientific software (MG)
• Version control and collaboration: Git and GitHub (MG)
• Environment management (micromamba, pixi and pip) (JB):
• Running python options: scripts, python interpreter, IDE, jupyter (JB)
• Intro to file systems at ORNL. Where are my neutron data stored? Oncat (AS)
• LLM in the scientific data pipeline (MD)

Tutorials

Malcolm tutorial

• create a git repo

Jean tutorial

• Open notebook, Explanation of notebook (shift enter, shift enter...)
• Cell: imports:
• Demo 1: Import data from ascii to numpy array. Do this multiple ways. Mention pandas.
• Demo 2: Plot with matplotlib. Make it interactive. Show errors?
• Demo 3: Extend script to for loop over multiple files
• Demo 4: Create widget to do Exercise 3.

BREAK (AS)

Zach Tutorial 2

• Exercise 4 (SciPy): Set up fit to a peak: initial conditions, define to fit, define residual, define fit range, interpret errors (variance-covariance matrix)
• Exercise 5: Use LMFIT for same process.
• Advanced Exercise 1: Event data: Inspect nxs file with HDFView,
• Load neutron data and log metadata from nxs file with h5py.
• Advanced Ex 2: histogram events (with log binning)
• Super Advanced Ex 3: Re-use fitting script, fit peaks, plot position versus experimental log.

Vibe coding a science app

• We will split into teams and compete to build the best app!

References

Environment management

• micromamba
• pixi
• pip

Python editors

• jupyter
• visual code
• pycharm

Python librairies

• matplotlib
• numpy
• widgets
• plotly __