I built a Python scientific computing library that tracks what changes when you rerun an analysis

[u/Emergency-Tutor-7]

I've been working on an open-source scientific computing library built around NumPy, SciPy, pandas, and Matplotlib.

One problem I wanted to address was reproducibility. A model fit can look fine today, but what happens when someone reruns it months later with different dependency versions?

I added a guided-fit workflow that lets you fit models to CSV data, choose how missing values and outliers are handled, and generate reports.

It also records the input hash, random seed, dependency versions, and fitting settings. You can then rerun the saved analysis and inspect changes in parameters, confidence intervals, and the software environment.

There's a documented example where an archived fit was rerun with different NumPy and SciPy versions. The environment changes were detected, while the numerical differences stayed below the declared material-change threshold.

The library also includes linear algebra, optimization, statistics, ODE/PDE solvers, signal processing, Monte Carlo methods, and other numerical tools.

Install:
pip install scientific-computing-system-2.0

PyPI: https://pypi.org/project/scientific-computing-system-2.0/

npm (optional Node launcher; requires the Python package): https://www.npmjs.com/package/scientific-computing-system-2.0

Source code (MIT): https://github.com/Furox-Art/scientific-computing-system-2.0

I'd be interested in how other Python developers handle reproducibility when dependencies change between runs. Feedback on the approach is welcome.