scientific-computing-system-2.0

CDS v2 is a scientific computing platform built on the scientific Python stack — NumPy, SciPy, pandas and matplotlib. The algorithms proven in the pure-Python cognitive-discovery-system (v1.x) form its foundation; v2 rebuilds them for speed and adds new domain modules on top.

Installation

pip install scientific-computing-system-2.0

From source:

git clone https://github.com/Furox-Art/scientific-computing-system-2.0.git
cd scientific-computing-system-2.0
pip install -e .[dev]

Quick start

import numpy as np
import cds2

# Linear algebra
A = [[3.0, 1.0], [1.0, 2.0]]
b = [9.0, 8.0]
x = cds2.linalg.solve(A, b)

# Statistics
r = cds2.stats.independent_t_test([1, 2, 3, 4, 5], [3, 4, 5, 6, 7])

# Optimization
res = cds2.optimize.minimize(lambda v: (v[0] - 2) ** 2 + (v[1] + 1) ** 2, x0=[0.0, 0.0])
print(res.x)  # ~ [2.0, -1.0]

# Signals
freqs, psd = cds2.signals.power_spectrum(np.sin(np.linspace(0, 100, 1024)), fs=256.0)

# Graphs with PageRank
adj = cds2.graph.from_edges(4, [(0, 1), (0, 2), (1, 3), (2, 3)], directed=True)
scores = cds2.graph.pagerank(adj)

# Information theory
h = cds2.infotheory.entropy([0.25, 0.25, 0.25, 0.25])
mi = cds2.infotheory.mutual_information([[0.5, 0.0], [0.0, 0.5]])

# Chaos / nonlinear dynamics
series = cds2.chaos.logistic_map(3.99, length=400, seed=1)
lyap = cds2.chaos.largest_lyapunov_exponent(series)

# Bayesian conjugate updates
post = cds2.bayes.beta_binomial_update(successes=7, failures=3)
print(post.mean)  # 0.7

# Metaheuristics
res = cds2.metaheuristics.pso_minimize(lambda v: (v[0] - 3) ** 2, [(-10, 10)], seed=1)

# Geometry
area = cds2.geometry.hull_area([(0, 0), (1, 0), (0, 1)])

# Reinforcement learning
q_values, returns = cds2.rl.q_learn(cds2.rl.GridWorld(4, 4), episodes=300, seed=1)

Modules

Module	Built on	Highlights
`cds2.linalg`	NumPy	solve, det, inv, pinv, eig/eigh, SVD, least squares, cholesky, cond
`cds2.stats`	scipy.stats	t-tests, ANOVA, non-parametrics, correlations, chi-square, effect sizes, normal dist helpers
`cds2.optimize`	scipy.optimize	minimize, roots (brentq/newton/system), linprog, least squares, curve fit
`cds2.integrate`	scipy.integrate	quad, 2-D/3-D integration, ODE solvers, trapezoid/simpson
`cds2.interpolate`	scipy.interpolate	linear/cubic/pchip, lagrange, griddata, regular grids
`cds2.signals`	scipy.signal	FFT, PSD/welch/spectrogram, Butterworth filters, peaks, envelope
`cds2.montecarlo`	NumPy Generator	pi estimate, MC integration/expectation, hit-or-miss (all seedable)
`cds2.graph`	scipy.sparse.csgraph	components, Dijkstra/Bellman-Ford/Floyd-Warshall, MST, topological order, PageRank
`cds2.ml`	NumPy/SciPy	LinearRegression, LogisticRegression, KMeans++, PCA, KNN, metrics, data generators
`cds2.timeseries`	pandas	moving average, EWM, differencing, seasonal decomposition, ACF/PACF, Ljung-Box
`cds2.viz`	matplotlib	series/histogram/scatter/heatmap/spectrum/regression/confusion plots
`cds2.io`	pandas	CSV/JSON read-write, optional Excel/Parquet bridges, DataFrame summaries
`cds2.calculus`	NumPy	derivative, complex-step gradient, jacobian, hessian
`cds2.special`	scipy.special	gamma, erf family, beta, Bessels, zeta
`cds2.sparse`	scipy.sparse.linalg	CG/GMRES/BiCGSTAB solvers, Lanczos eigenpairs, truncated SVD
`cds2.distributions`	scipy.stats	t, chi2, F, exponential, uniform, lognormal, poisson, binomial (pdf/cdf/ppf)
`cds2.spectral`	scipy.sparse	Laplacians, Fiedler vector, algebraic connectivity, spectral clustering
`cds2.infotheory`	NumPy	Shannon/joint/conditional entropy, KL & Jensen-Shannon divergence, mutual information, permutation entropy
`cds2.chaos`	NumPy	delay embedding, false nearest neighbours, Lyapunov exponent, correlation dimension, sample entropy, Hurst exponent, bifurcation scans
`cds2.bayes`	scipy.stats	Beta-Binomial / Normal-Normal / Gamma-Poisson conjugate updates, credible intervals, naive Bayes, Metropolis posteriors
`cds2.metaheuristics`	NumPy	real-coded genetic algorithm, particle swarm optimization, simulated annealing
`cds2.geometry`	scipy.spatial	convex hull, closest pair, point-in-polygon, polygon area/perimeter, line-segment intersection, rotations
`cds2.rl`	NumPy	Bernoulli bandits (epsilon-greedy, UCB1), tabular Q-learning, grid-world environment
`cds2.quality`	NumPy + scipy.stats	Shewhart/EWMA/CUSUM/p control charts, Cp/Cpk capability indices, defective PPM
`cds2.design`	NumPy	full & fractional factorial DOE, Latin hypercube sampling, central composite designs

CLI

cds2 info
cds2 stats 1,2,3,4,5
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

Relationship to CDS v1.x

The original zero-dependency pure-Python line lives at Furox88/cognitive-discovery-system and remains available. v2 is an independent project that trades that constraint for the speed and breadth of the scientific Python ecosystem.

Runnable case studies live in examples/ - see the docs page for details.

Benchmarks

cds2 races the scientific stack head-to-head — and ships its own compiled C kernels where they help. Current scoreboard (full methodology in docs/benchmarks.md):

Race	Baseline	cds2/baseline
PageRank 400n (C kernel)	NetworkX	0.18x
K-Means 4k×2 k=8 (C kernel)	scikit-learn	0.72x
Linear regression 20k×10	scikit-learn	0.74x
Monte Carlo pi 2M	hand-vectorized NumPy	0.77x
solve / eigh / rfft / welch / minimize	NumPy & SciPy	~1.00x
describe 500k (adds quartiles)	SciPy	1.10x

Wrapper APIs hold parity with raw NumPy/SciPy; the KMeans Lloyd loop and PageRank power iteration are from-scratch C extensions (cds2._fast_kmeans, cds2._fast_pagerank) that beat the specialist libraries. A pure-Python fallback wheel keeps compiler-less installs working.

python benchmarks/run_benchmarks.py            # full run
python benchmarks/run_benchmarks.py --quick    # smoke run

Development

pip install -e .[dev]
pytest            # run the test suite
ruff check .      # lint

License

MIT — see LICENSE.

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scientific-computing-system-2.0 4.2.0

scientific-computing-system-2.0

Installation

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CLI

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