scientific-computing-system
A pure-Python computational science platform for numerical methods, modeling, validation, uncertainty, scientific workflows, dimensional analysis, and reproducible research.
I wrote this because NumPy and SciPy are incredible, but they're also 20 years old and carry two decades of design decisions that don't always make sense anymore.
This is a from-scratch rethinking of what scientific computing in Python could look like if we started today. No C extensions, no Fortran legacy, no dependency hell. Just Python, type hints, and algorithms that are actually readable.
What's inside
- Linear algebra: SVD, QR, Cholesky, eigenvalues-all implemented in pure Python with proper error handling
- Optimization: gradient descent, constrained optimization, metaheuristics
- Statistics: hypothesis testing, Bayesian inference, time series
- Machine learning: PCA, clustering, simple neural nets (educational, not production)
- Quantum computing: circuit simulation, state vectors, basic gates
- Signal processing: filters, wavelets, STFT
- ODE/PDE solvers: stiff and non-stiff, symplectic integrators
The catch
It's slower than NumPy. Sometimes 10x slower, sometimes 100x. That's the price of pure Python. But it's also completely transparent-you can read every algorithm, understand every step, and modify anything without compiling C.
I use it for prototyping, for teaching, and for cases where I need to know exactly what the computer is doing. For production number crunching, I still reach for NumPy.
License
MIT.
1 maintainer
Metadata
1 maintainer
Release files for scientific-computing-system 2.0.1
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Source distribution (sdist)
| File | Size | Uploaded | |
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| scientific_computing_system-2.0.1.tar.gz | 594.6 kB | Details |
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| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| scientific_computing_system-2.0.1-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 915.3 kB
Release files / scientific_computing_system-2.0.1.tar.gz
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