scientific-computing-system

A pure-Python computational science platform for numerical methods, modeling, validation, uncertainty, scientific workflows, dimensional analysis, and reproducible research.

Docs · Getting Started · API Reference · Cookbook · Benchmarks · Project · Changelog · Contributing · Security · Code of Conduct · Citation · Paper (JOSS) · Discussions · Issues

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.

Download counts: PyPI stats

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.

The import name is cds (the distribution is scientific-computing-system). The zero-dependency core needs nothing but the standard library. The sibling NumPy build of this work is scientific-computing-system-2.0; related projects are listed in docs/related.md.

Install

Channel	Command	What you get
PyPI (the library)	pip install scientific-computing-system	The actual Python package. Zero runtime dependencies. This is what you want.
npm (a launcher shim)	npm i -g scientific-computing-system	A thin Node wrapper that runs python -m cds. It ships no Python and needs the Python distribution installed so that python -m cds resolves.

At the time of writing (2026-10-30), both registries' latest version is 2.2.21, but their 2.2.x histories differ and the two channels are not equally verifiable:

• PyPI — carries a PEP 740 provenance attestation (Sigstore-signed, binding the file digest to the publishing workflow). Verified on 2.2.3, 2.2.4, 2.2.5, 2.2.6, 2.2.8, 2.2.11, 2.2.12, 2.2.13, 2.2.14, 2.2.15 and 2.2.17 through 2.2.21. The 2.2.7 wheel and sdist return 404 from the per-file provenance endpoint, so "every release since attestations were enabled" would be false; 2.2.16 was never published.
• npm — carries a Sigstore attestation as of 2.2.3, minted by npm's OIDC trusted publisher (the registry serves both an npm publish attestation and a SLSA provenance v1 bundle for that tarball). 1.0.0 through 2.2.2 predate the publisher registration and have none, so pin 2.2.3 or newer. npm has no 2.2.7, 2.2.8, 2.2.11 or 2.2.16 at all: its 2.2.x line is 2.2.0 through 2.2.6, 2.2.12 through 2.2.15, and 2.2.17 through 2.2.21, so the latest versions match while the histories do not. npm also exposes dist.integrity (sha512), which is a content pin, not proof of origin, and dist.signatures, which is npm's registry transport signature, not build provenance.

Prefer PyPI if you need an attestation for a version older than 2.2.3. SECURITY.md has the per-channel table, the published digests to pin, the commands to verify them, and what to register to close the npm gap.

What's inside

src/cds/ holds 34 subpackages plus causal.py and sensitivity.py. That is 35 public feature modules — the cds CLI entry point is the 36th subpackage — exporting 505 names (342 functions, 163 classes), all pure standard library. tests/test_readme_surface.py derives these numbers from the package, so they cannot drift silently.

Area	Modules	What you get
Core models	cds.core	Shared Domain, Hypothesis, HypothesisStatus types
Linear algebra & calculus	cds.math_utils, cds.interpolate	SVD, QR, Cholesky, LU, power iteration, derivatives, integrals
ODE / PDE	cds.diffeq, cds.pde	Euler, RK4, adaptive RK45, implicit stiff methods, symplectic integrators; 1-D heat & wave equations
Quadrature	cds.numerical_integration	Trapezoid, Simpson 1/3 & 3/8, Romberg, Gauss-Legendre, adaptive Simpson, 2-D tensor rules
Optimization	cds.optimization	Gradient descent, Newton, Adam, Nelder-Mead, annealing, constrained search
Statistics	cds.stats, cds.probability, cds.bayes	Descriptive stats, regression, t/chi-square/ANOVA, effect sizes, nonparametric ranks, time series, multiple testing, Bayesian conjugate updates
Monte Carlo	cds.montecarlo	π estimation, Monte-Carlo integration, random walks
Machine learning	cds.ml	MLP, k-NN, k-means, CART, random forest, boosting, PCA (educational, not production)
Signal processing	cds.signals, cds.wavelets	DFT/FFT/IFFT, convolution, Butterworth design, STFT, Haar DWT
Quantum	cds.quantum	Single & multi-qubit state-vector circuits, Bell/GHZ states, entanglement
Graphs	cds.graph	BFS, DFS, Dijkstra shortest paths, Kruskal MST, topological sort, cycle detection
Scientific domains	cds.scientific, cds.genetics, cds.fractals, cds.infotheory	Physical constants & formulas, DNA analysis & alignment, fractal sets, entropy/divergence/mutual information
Symbolic modeling	cds.modeling	Expression trees, symbolic differentiation, LaTeX export, MathModel systems, root finding, fitting
Uncertainty & sensitivity	cds.uncertainty, cds.sensitivity	Analytic + correlated Monte-Carlo propagation, local & variance-based global sensitivity, identifiability
Validation & causality	cds.validation, cds.causal	Cross-method checks, drift/OOD reports, assumption-gated causal estimators
Units	cds.units	SI quantities, conversions, dimensional compatibility checks
Workflow & provenance	cds.workflow, cds.provenance	Approval-gated orchestration, run manifests, hashes, checkpoints
Data	cds.data_analysis, cds.data_io	CSV/tabular analysis, streaming I/O, optional HDF5/NetCDF
Knowledge & hypothesis	cds.knowledge, cds.hypothesis	Concept graphs, research notes, structured hypothesis generation & evaluation
NLP (educational)	cds.nlp	BPE, embeddings, attention, autograd, MiniGPT
Plotting	cds.plot	Optional matplotlib helpers ([plot] extra)
Backend adapters	cds.tools	Lazy discovery for NumPy/SciPy/statsmodels/scikit-learn/SymPy/Z3

The authoritative per-module list is the API reference.

!!! note "cds modules is a curated subset" cds modules prints 26 entries. It is not the full catalog: it omits bayes, causal, core, fractals, genetics, infotheory, interpolate, pde, and wavelets. Use the API reference or src/cds/ for the complete set.

Runnable examples and the dashboard

• examples/ — 34 runnable scripts and notebooks (32 .py, 2 .ipynb), each self-contained and dependency-free unless noted. Every tutorial page in the docs has a matching example.
• dashboard/app.py — a Streamlit dashboard: cds dashboard, or pip install "scientific-computing-system[dashboard]".

Quick Start

pip install scientific-computing-system

from cds.math_utils import svd
from cds.diffeq import solve_system
from cds.stats import linear_regression

# 1. SVD — result is an SVDResult, not a tuple
result = svd([[1.0, 2.0], [3.0, 4.0]])
print("singular_values =", [round(v, 6) for v in result.singular_values])


# 2. Damped harmonic oscillator: y'' = -y - 0.1 y'
def rhs(t, y):
    return [y[1], -y[0] - 0.1 * y[1]]


t, y = solve_system(rhs, 0.0, [1.0, 0.0], 50.0, dt=0.01)
print("final t =", round(t[-1], 2), "| final y =", [round(v, 8) for v in y[-1]])

# 3. Least-squares line fit
fit = linear_regression([1.0, 2.0, 3.0, 4.0, 5.0], [2.1, 3.9, 6.2, 7.8, 10.1])
print(f"slope = {fit.slope:.4f} | intercept = {fit.intercept:.4f} | r^2 = {fit.r_squared:.4f}")

Real output (executed verbatim; pasted from an actual run):

singular_values = [5.464986, 0.365966]
final t = 50.0 | final y = [0.07638443, 0.0264785]
slope = 1.9900 | intercept = 0.0500 | r^2 = 0.9973

CLI

The package installs a cds command with 13 subcommands:

Command	Purpose
cds --help	Full command list
cds modules	Curated module catalog (see the caveat above)
cds info	Version, module status, health summary
cds version	Show the installed version
cds constants	Table of physical constants
cds calc <formula>	Quick physics calculation (ke, gravity, wave, gas)
cds stats <numbers>	Descriptive statistics for a comma-separated list
cds sample <dist>	Draw samples from a probability distribution
cds integrate <fn>	Integrate a built-in function over [a, b]
cds hypothesis <q>	Generate scientific hypotheses for a question
cds prompt	Prompt text for a custom hypothesis generator
cds benchmark	Run the built-in benchmarks
cds plot <numbers>	ASCII plot, or PNG with --file when [plot] is installed
cds dashboard	Launch the Streamlit dashboard (needs [dashboard])

$ cds --version
System version 2.2.22
$ cds constants        # physical constants table

Optional extras

The core is zero-dependency. Everything below is opt-in:

pip install "scientific-computing-system[scientific]"  # NumPy, SciPy, statsmodels, scikit-learn, SymPy, Z3
pip install "scientific-computing-system[io]"          # HDF5 + NetCDF
pip install "scientific-computing-system[plot]"        # matplotlib
pip install "scientific-computing-system[pandas]"      # DataFrame bridge: cds.data_analysis.pandas_io
pip install "scientific-computing-system[dashboard]"   # Streamlit dashboard

The catch

Pure Python is slower than NumPy on dense numerics: the committed benchmark artifact records 100×100 matrix multiplication at 0.0696 s against NumPy's 0.000060 s, about 1155× slower. Those numbers are from one CI run whose platform, CPU, and library versions were not recorded, so treat them as order-of-magnitude only — docs/benchmarks.md states exactly what that artifact does and does not support, and docs/why-pure-python.md covers when to reach for NumPy or the 2.0 build instead.

Contributing

pip install -e ".[dev,test,docs]"   # lint/type, test, and docs tooling
pytest            # CI also enforces 100% blended coverage (statement + branch)
mkdocs serve      # docs at http://127.0.0.1:8000/

.[dev,test,docs] is the smallest environment for the default test suite and the docs. Add .[property] for the Hypothesis invariants, or .[all] for the dashboard, HDF5/NetCDF, plotting and scientific backends together.

Issues and PRs welcome; see CONTRIBUTING.md and CODE_OF_CONDUCT.md.

Related projects

Part of the Furox scientific-computing portfolio: independently versioned projects that pair agent-facing interfaces (MCP servers and Agent Skills) with deterministic, fail-closed verification.

• scientific-computing-system-2.0 — NumPy/SciPy/pandas build with replayable fit records.
• scientific-toolkit — MCP gateway over nine research tools.
• axiomize — unit-checked modeling engine with SBML, CellML and Modelica export.
• axiomize-quantum-skills-2.0 — modeling engine plus quantum-inspired reasoning bundle.
• quantum-reasoning-skill — agent skill that keeps competing hypotheses alive.
• plan-auditor — evidence-based completion gate for coding agents.
• nomosguard — deterministic security decision layer with a hash-chained ledger.
• eq-layer — interaction-policy layer that decides how an agent speaks.
• literature-evidence-mcp — literature MCP server with a hash-chained evidence ledger.
• repocraft — byte-deterministic project scaffolding.

License

MIT — see LICENSE.

Cite via CITATION.cff. codemeta.json carries the same metadata in codemeta form, and paper.md / paper.bib are the JOSS manuscript.