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. |

Both channels are published at the same version. They are not equally verifiable:

• PyPI — carries a PEP 740 provenance attestation (Sigstore-signed, binding the file digest to the publishing workflow). Verified on 2.2.1.
• npm — carries no attestation; the npmjs.com trusted publisher is not registered yet, so its publish path cannot mint one. npm does expose 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 for anything security-sensitive. 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/ — 35 runnable scripts and notebooks (33 .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 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 2.2.3
$ 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]"
pytest            # CI also enforces 100% blended coverage (statement + branch)
mkdocs serve      # docs at http://127.0.0.1:8000/

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

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.