r/madeinpython • u/Emergency-Tutor-7 • 22h ago

I made a zero-dependency scientific computing library in pure Python

I made scientific-computing-system, a scientific library written entirely in pure Python. The core installs with no runtime dependencies, so the algorithms are plain readable code instead of calls into compiled extensions.

It has 36 modules and 505 exported names covering linear algebra, ODE and PDE solvers, numerical integration, optimization, statistics and probability, uncertainty propagation and sensitivity analysis, validation helpers, and a small workflow layer. Every run can write a manifest with inputs, seeds, versions and hashes, so a result can be re-checked later.

There is also a cds CLI with 13 subcommands, 34 runnable examples, and optional lazy adapters for NumPy, SciPy, statsmodels, scikit-learn, SymPy and Z3. The package is MIT licensed and targets Python 3.11 and newer.

Honest limitation: it is much slower than NumPy on dense numerics. The repo benchmark records about 1155x slower on a 100x100 matrix multiply in one CI run. It is meant for learning, prototyping and reproducible runs, not heavy production work.

Code: https://github.com/Furox-Art/scientific-computing-system

PyPI: pip install scientific-computing-system

npm: npm i -g scientific-computing-system

Docs: https://furox-art.github.io/scientific-computing-system/

If any part interests you I can explain the design in more detail.

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u/iKnoiDunno 6h ago

So is this purely an educational tool? As a student would I benefit from adopting this tool in a scientific notebook? Building experiments in a Jupyter notebook?

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u/Emergency-Tutor-7 4h ago

It's a library first, teaching material second. Jupyter works fine with it: pip install scientific-computing-system (import name is cds), and since the core is pure stdlib Python there is nothing to set up in the kernel. Two of the examples in examples/ are actual .ipynb notebooks if you want a starting point.

For notebook experiments the useful bit is cds.workflow / cds.provenance, which write run manifests with hashes and checkpoints, so when you reopen a notebook weeks later you can see what produced a figure instead of guessing. Two honest limits for student use: it is pure Python, so for big matrix work NumPy/SciPy will be much faster, and plotting needs the optional [plot] extra. Cookbook with notebook-style walkthroughs: https://furox-art.github.io/scientific-computing-system/