I built scientific-computing-system-2.0, the NumPy build of my scientific computing project. It puts NumPy, SciPy, pandas and Matplotlib behind one typed API: dense and sparse linear algebra, statistics, optimization, integration, interpolation, signals, spectral graph theory, Monte Carlo, graphs, classical ML, time series, visualization and I/O. The current release is 5.4.5 on PyPI and npm; "2.0" is the generation name, not the version. What makes it more than a wrapper around NumPy is guided-fit. It turns a CSV into an inspectable model fit and saves a manifest with the random seed, input hash and the exact Python/NumPy/SciPy/pandas/Matplotlib versions. Later, guided-fit-rerun compares the saved analysis with the new run and reports dependency drift, confidence-interval drift and parameter drift instead of assuming the rerun is equivalent. The repo has a real archived replay from 2026-10-06: a fit saved on NumPy 1.26.4 / SciPy 1.11.4 was replayed on NumPy 2.3.5 / SciPy 1.18.1, both version warnings fired, relative CI drift was 0.003364917%, and the reliability label stayed reliable -> reliable. Current state, measured not asserted: 53 importable names, 535 public exports, 1,927 tests, a 100% blended branch-coverage gate, ruff and mypy gates, CI on Linux/Windows/macOS x Python 3.10-3.13, plus a separate determinism matrix. Every PyPI file from 5.1.0 to 5.4.5 carries a PEP 740 provenance attestation (330 files checked on 2026-10-22); npm 5.4.5 is the first npm release with a Sigstore/SLSA attestation. Quick start: pip install scientific-computing-system-2.0 import cds2 print(cds2.linalg.solve([[3.0, 1.0], [1.0, 2.0]], [9.0, 8.0])) # [2. 3.] Honest parts: the npm package is a thin launcher and still needs the Python install. The committed benchmarks are stale (measured on 3.0.0 in August) and the README says so; I have not rerun them for 5.4.5. And this is the NumPy build - if you need zero dependencies, the sibling scientific-computing-system is pure Python and much slower on dense numerics. Code: https://github.com/Furox-Art/scientific-computing-system-2.0 PyPI: pip install scientific-computing-system-2.0 npm: npm i -g scientific-computing-system-2.0 Docs: https://furox-art.github.io/scientific-computing-system-2.0/ Happy to answer questions about any module or the reproducibility design.   submitted by   /u/Emergency-Tutor-7 [link]   [comments]