Scientific Computing Notebooks

Reproducible Python examples for numerical methods, modelling, and simulation-style analysis.

This repo is a public collection of small, readable scientific-computing examples. The goal is to show clear explanation, clean notebooks, reproducible code, and practical numerical workflows.

What this repo demonstrates

• Clean Python structure for scientific-computing examples
• Numerical methods explained through short scripts and notebooks
• Reproducible runs with deterministic inputs and outputs
• Tutoring-friendly code that exposes assumptions and intermediate values
• Simulation-style post-processing with clear summaries

Example topics

• numerical integration
• optimization
• simulation post-processing
• uncertainty-aware analysis
• plotting and interpretation
• tensor/numerical-method utilities

Repository structure

• notebooks/: notebook versions of selected examples
• src/scicomp_demos/: reusable Python utilities
• examples/: terminal-friendly runnable scripts
• tests/: checks for the numerical routines and synthetic workflows

How to run

Run the numerical integration example:

PYTHONPATH=src python3 examples/run_numerical_integration.py

Run the simulation-style post-processing example:

PYTHONPATH=src python3 examples/run_simulation_postprocessing.py

Run tests:

PYTHONPATH=src python3 -m unittest discover -s tests

Intended audience

This repo is for students, researchers, and collaborators who want examples that are easier to read, explain, and adapt than a large research codebase.

Notes on reproducibility

The examples use synthetic or analytic inputs only. No private research data, private product data, or confidential project internals are included.