scientific-computing-system-2.0
This is the NumPy build of scientific-computing-system, not a separate product: pick this one for NumPy/SciPy/pandas/matplotlib, the other for readable pure Python with zero runtime dependencies.
52 importable names, 535 public exports, 1,805 tests, 100% branch coverage, MIT. Every count here is generated from the tree by scripts/make_promo.py and CI, not typed by hand.
Installation
Python 3.10+. PyPI is the install path:
pip install scientific-computing-system-2.0
| Extra | Adds | Use when |
|---|---|---|
[dev] |
pytest, ruff, mypy, hypothesis, scikit-learn, networkx, pandas-stubs | contributing |
[test] |
pytest, pytest-cov, hypothesis, scikit-learn | running the suite only |
[docs] |
mkdocs, mkdocs-material, mkdocstrings | building the site |
[gpu] |
cupy-cuda12x | using the optional cds2.gpu backend |
[all] |
all of the above | everything |
From a clone: pip install -e ".[dev]".
Optional C kernels. _fast_kmeans and _fast_pagerank are compiled when a
toolchain is available and fall back silently to pure NumPy otherwise, so
CDS_PURE=1 pip install . is fully functional but slower on those two
operations.
npm (optional). A same-named npm package is a thin Node launcher shim —
five files, ~8 KB, no Python — that calls python -m cds2.cli. You still need
the PyPI install, so most people can skip it. Provenance caveats and the stale
2.0.0 on npm: docs/npm.md.
Quick start
Executed against scientific-computing-system-2.0==5.2.5:
import cds2
print(cds2.linalg.solve([[3.0, 1.0], [1.0, 2.0]], [9.0, 8.0])) # [2. 3.]
print(cds2.montecarlo.pi_estimate(n=100_000, seed=42)) # 3.13776
print(cds2.infotheory.entropy([0.25, 0.25, 0.25, 0.25])) # 2.0
print(cds2.graph.pagerank(cds2.graph.from_edges(4, [(0, 1), (0, 2), (1, 3), (2, 3)])))
# [0.1375043 0.19594362 0.19594362 0.47060846]
Guided fitting: the differentiating feature
cds2.guided_fit turns a CSV into a reproducible, inspectable model fit. Unlike
a bare curve_fit, the model is recommended and then confirmed by you, and
the run is written to a replayable manifest.
cds2 guided-fit data.csv --x time --y response \
--model exponential --missing drop --outliers exclude --report pdf
cds2 guided-fit-rerun guided-fit-results/guided_fit_manifest.json
Five model families (linear, quadratic, exponential, power, logistic),
explicit missing-data (ask/drop/interpolate) and outlier (ask/keep/exclude)
policies, PDF/HTML/Markdown reports, and a manifest carrying the input hash so a
rerun proves it used the same data. See case studies.
Command line
All ten subcommands, from cds2 --help:
cds2 info show version information
cds2 stats 1,2,3,4,5 descriptive statistics
cds2 integrate sin --a 0 --b 3.14159
cds2 linsolve --a "3,1;1,2" --b "9,8"
cds2 entropy "0.25,0.25,0.25,0.25"
cds2 units 5 --from-unit km --to-unit mile
cds2 solve --coeffs "1,-5,6"
cds2 plot 1,3,2,5,4 --file out.png
cds2 guided-fit data.csv --x time --y response
cds2 guided-fit-rerun guided-fit-results/guided_fit_manifest.json
python -m cds2 does not work — there is no cds2.__main__. Use the cds2
console script, or python -m cds2.cli.
Modules
46 flat modules plus 6 subpackages; 47 generated API pages. * marks deprecated modules.
| Module | What it does | Entry points |
|---|---|---|
cds2.linalg |
Dense linear algebra, typed results | solve, det, svd, eigh, lstsq, expm |
cds2.stats |
Tests, summaries, effect sizes | independent_t_test, permutation_test, describe, bootstrap_ci |
cds2.optimize |
Optimizers and root finding | minimize, curve_fit, find_root_scalar, linprog |
cds2.integrate |
Quadrature and ODEs | quad, integrate_2d, solve_ivp, solve_bvp, simpson |
cds2.interpolate |
Interpolation | linear_interp, cubic_spline, pchip_interpolator, rbf_interp |
cds2.signals |
FFT, spectra, filters | power_spectrum, welch_spectrum, butter_lowpass, find_peaks |
cds2.sparse |
Sparse solvers, CDS preconditioners | solve_cg, solve_gmres, truncated_svd, ilu_preconditioner |
cds2.spectral |
Spectral graph theory | laplacian, fiedler_vector, algebraic_connectivity, spectral_cluster |
cds2.calculus |
Derivatives, Jacobians, Hessians | derivative, jacobian, hessian, propagate_error |
cds2.ml |
Classical ML and metrics | LinearRegression, KMeans, PCA, KNeighborsClassifier, r2_score |
cds2.timeseries |
pandas-backed series analysis | seasonal_decompose, exponential_smoothing, acf, ljung_box |
cds2.montecarlo |
Seeded estimation | pi_estimate, mc_integrate, metropolis_hastings, parallel_mc_integrate |
cds2.graph |
Graphs and PageRank | from_edges, pagerank, connected_components, modularity |
cds2.io |
pandas I/O and summaries | read_csv, write_csv, summarize, iter_csv |
cds2.viz |
Matplotlib helpers | plot_series, plot_heatmap, plot_regression, plot_confusion_matrix |
cds2.data_analysis |
DataFrame bridge | DataSet, to_dataframe, from_dataframe |
cds2.bayes |
Conjugate updates, naive Bayes | beta_binomial_update, NaiveBayes, posterior_interval |
cds2.bayesopt |
GP-surrogate optimization | bayes_opt, GaussianProcess, expected_improvement |
cds2.infotheory |
Entropy and dependence | entropy, mutual_information, kl_divergence, permutation_entropy |
cds2.chaos |
Nonlinear dynamics | delay_embed, largest_lyapunov_exponent, hurst_exponent, bifurcation_scan |
cds2.metaheuristics |
Population and annealing | genetic_minimize, pso_minimize, simulated_annealing |
cds2.geometry |
Hulls, distances, polygons | convex_hull, closest_pair, point_in_polygon, segments_intersect |
cds2.rl |
Bandits and tabular RL | ucb1, epsilon_greedy, q_learn, GridWorld |
cds2.quality |
Statistical process control | xbar_chart, ewma_chart, cusum_chart, process_capability |
cds2.design |
Design of experiments | full_factorial, latin_hypercube, central_composite |
cds2.reliability |
Survival analysis | kaplan_meier, weibull_fit, mtbf, availability |
cds2.modeling |
Symbolic maths, pure Python | Expression, diff, integrate, solve_polynomial, to_latex |
cds2.hypothesis |
Heuristic hypothesis generation | HypothesisEngine, Hypothesis, Domain |
cds2.knowledge |
Concept graphs and retrieval | no public names yet |
cds2.scientific |
CODATA constants and formulas, CDS-native | CONSTANTS, speed_of_light, photon_energy, convert_units |
cds2.quantum |
Dense statevector circuits | QuantumCircuit, GATES |
cds2.genetics |
DNA composition and alignment | gc_content, kmer_counts, global_align, find_orfs |
cds2.epidemiology |
SIR/SEIR trajectories | simulate_sir, simulate_seir, herd_immunity_threshold |
cds2.finance |
Returns, drawdown, risk | log_returns, max_drawdown, sharpe_ratio, black_scholes |
cds2.game_theory |
Nash, dominance, zero-sum | pure_nash_equilibria, iterated_elimination, zero_sum_mixed |
cds2.combinatorial |
TSP, knapsack, assignment | assign_min_cost, knapsack_01, nearest_neighbor_tsp, two_opt |
cds2.spatial |
Contiguity and point patterns | build_weight_matrix, morans_i, gearys_c |
cds2.text |
Tokenization and similarity | tokenize, tfidf_matrix, cosine_similarity, summarize_terms |
cds2.image |
Grayscale filters, morphology | convolve2d, gaussian_blur, sobel_edges, binarize |
cds2.wavelets |
Haar decomposition | haar_dwt, haar_idwt, dwt_levels, wavelet_denoise |
cds2.pde |
Vectorised FTCS and leapfrog | heat_equation_1d, wave_equation_1d, solve_heat |
cds2.sde |
Stochastic differential equations | sde_euler_maruyama, sde_milstein, ensemble_stats |
cds2.guided_fit |
Guided reproducible fitting | run_guided_fit, recommend_model, save_manifest, rerun_manifest |
cds2.cli |
argparse front end | main, the cds2 console script |
cds2.special* |
scipy.special wrapper | 41 aliases; use scipy directly |
cds2.distributions* |
scipy.stats wrapper | 56 pdf/cdf/ppf aliases; use scipy directly |
cds2.array_api |
Array API 2023.12 namespace | matmul, cholesky, std, 21 names; no API page |
cds2.nlp |
BPE tokenizer, autograd, attention, mini-GPT | BPETokenizer, multi_head_attention, TinyGPT |
cds2.estimator |
scikit-learn compatible estimators | LinearRegressionGD, KMeansSKL, PCASKL, RidgeSGD |
cds2.bench |
Benchmark history and regression | run_regression_check, RegressionReport |
cds2.prof |
Profiling, history, regression gates | profile, timed, BenchHistory, RegressionGate |
cds2.gpu |
Optional CuPy backend | is_available, synchronize, cupy |
Examples
20 runnable scripts in examples/, all executed by the examples-smoke CI job on every push. Try bayesian_inference.py (Metropolis-Hastings vs an analytic conjugate result), experiment_fitting.py (Michaelis-Menten fit, permutation test on residuals) and signal_denoising.py (Butterworth filter, Welch spectra). Catalogue: Examples · Recipes.
Performance, measured honestly
benchmarks/results.json and docs/benchmarks.md are a real run, but a stale one: cds2 3.0.0, commit 6ea5a02, 2026-08-22, while the release is 5.2.5. Read them as "3.0.0 versus the baseline libraries on one machine", not as a claim about today.
Of the 13 measured cases, CDS v2 was slower in 4: dataframe summary 1.82x, solve 800x800 1.22x, solve 8x8 x300 1.17x and welch 1.03x. The pandas row returns strictly more information (per-column nulls and uniques), so part of that premium is extra work. The clearest win is PageRank at 0.15x versus NetworkX. There is no blanket speedup claim, and benchmark-methodology.md sets out the limits.
Documentation map
| Page | What is in it |
|---|---|
| Getting started | install plus a ten-minute tour, one section per domain |
| Modules overview | every module with its backing library |
| Examples · Recipes | runnable scripts; copy-paste snippets |
| Case studies | reproducible end-to-end workflows |
| Validation · research-readiness | what is verified, and how |
| Industrial computing | I/O and industrial use cases |
| Benchmarks · methodology | measurements and their limits |
| API reference | 47 generated module pages |
| Deprecated modules | cds2.special, cds2.distributions and how to migrate |
| npm shim | what the npm package is, and is not |
| Release process | how a release is cut |
Contributing
pip install -e ".[dev]"
pytest --cov=cds2 --cov-fail-under=100 # the gate is fail_under=100
mkdocs serve # docs at http://127.0.0.1:8000
Coverage below 100% fails CI, so new code needs tests on success and error paths. Full rules in CONTRIBUTING.md.
Deprecated modules
cds2.special and cds2.distributions are deprecated since 4.3.0. Because
__init__.py imports both eagerly, a bare import cds2 emits both
DeprecationWarnings even if you never touch them:
DeprecationWarning: cds2.special is deprecated since 4.3.0 ...
DeprecationWarning: cds2.distributions is deprecated since 4.3.0 ...
They still work; prefer scipy.special and scipy.stats directly. Migration
notes: Deprecated modules.
Project files
CHANGELOG.md · CONTRIBUTING.md · SECURITY.md · CODE_OF_CONDUCT.md · CITATION.cff · codemeta.json · LICENSE (MIT)
License
MIT.
1 maintainer
Metadata
1 maintainer
Release files for scientific-computing-system-2.0 5.2.6
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