cubespec 0.1.1

Monte Carlo, DOE, RSM and Sobol sensitivity for the 150 mm concrete cube compressive test (CubeSpec / Sensitive-Spark thesis dashboard).

cubespec

Monte-Carlo, DOE, RSM and Sobol sensitivity for the 150 mm concrete cube compressive test — Python companion to the Sensitive-Spark dashboard, with a bit-for-bit parity contract against the TypeScript implementation.

Two front ends, one set of equations:

• 🌐 Web dashboard — React + TypeScript, runs in the browser, no install: https://app.ai-systems.today
• 🐍 Python package — cubespec, this repo. Notebook-, CLI- and library-friendly.

Both implementations share the same surrogate model and PRNG (mulberry32), so the same seed gives the same numbers in either environment, validated by tests/test_parity.py at 1e-9 tolerance.

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What it does

Given the Cube Specification Parameters (CSP) — material density, geometry (dx, dy, dz) and forces (Fx, Fy, Fz) — cubespec computes the three dependent outputs P7 (deformation), P8 (strain), and P9 (compressive strength) through a calibrated surrogate, and quantifies how uncertainty propagates through it.

Capability	Web dashboard	Python (cubespec)
Live Monte-Carlo with convergence tracking	✅	✅
Independent / LHS / correlated MVN sampling	✅	✅
Full & fractional 2^(k-p) factorial DOE (1/2, 1/4, 1/8)	✅	✅
Main, 2-way & 3-way interaction effects	✅	✅
Quadratic Response-Surface fit + 2-D contours	✅	✅
Sobol S1 / ST sensitivity (Saltelli scheme)	✅	✅
Percentile bootstrap CI (B = 1000)	✅	✅
Residual diagnostics (RMSE, MAE, R², Q-Q)	✅	✅
CSV / JSON export	✅	✅
CLI	–	✅
Calibrated surrogate (poly-2 ridge + residuals)	✅	✅

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Install

Until the first PyPI release, install directly from GitHub:

pip install "git+https://github.com/ai-systems-today/cubespec.git"

Or from a local clone (for development):

git clone https://github.com/ai-systems-today/cubespec.git
cd cubespec
pip install -e ".[plot,cli,dev]"

Once on PyPI:

# pip install cubespec

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Quickstart — Python API

from cubespec import (
    DEFAULT_CSP, sample_independent, compute_outputs_batch,
    bootstrap_mean_ci, sobol_indices,
)

# 50 000-draw Monte-Carlo run on the default CSP
X = sample_independent(DEFAULT_CSP, n=50_000, seed=1337)
Y = compute_outputs_batch(X)               # columns: P7, P8, P9
print(f"P9 mean: {Y[:, 2].mean():.2f} MPa")
# → P9 mean: 44.20 MPa  (matches the dashboard at the same seed)

# 95 % bootstrap CI on the P9 mean
lo, hi = bootstrap_mean_ci(Y[:, 2], B=1000, seed=1337, alpha=0.05)
print(f"95% CI: [{lo:.2f}, {hi:.2f}] MPa")

# Sobol indices (Saltelli, base N = 1024)
print(sobol_indices(DEFAULT_CSP, n_base=1024))

Quickstart — CLI

# 50k Monte-Carlo run + report JSON
cubespec run --csp examples/default_csp.csv --n 50000 --output report.json

# Fractional 2^(7-3) factorial design
cubespec doe --csp examples/default_csp.csv --design fractional-1/8 --output design.csv

# Sobol sensitivity (Saltelli, base N = 1024)
cubespec sobol --csp examples/default_csp.csv --n 1024 --output sobol.csv

# Re-run a saved bundle from the dashboard's Reproduce button
cubespec replay run.json

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Methods (1-page summary)

The surrogate is

σ = F / A          A = dx · dy             (engineering stress, MPa)
E ≈ 0.255 · ρ¹·⁵                            (concrete stiffness law)
ε = σ / E                                   (Hooke)
δ = ε · h          h = dz                   (axial deformation)

with the disturbance term −0.5·(|Fy|+|Fz|)/A subtracted from σ to give P9.

Sampling. Independent Gaussian, Latin Hypercube (Owen-shuffled), or multivariate normal with a Cholesky factor of an editable correlation matrix.

DOE. Two-level full factorial (2⁷ = 128 runs) and fractional designs 2^(7-1) / 2^(7-2) / 2^(7-3) generated via pyDOE2 (Resolution VII / IV / IV). Effects estimated as the contrast of mean responses at coded ±1 levels.

RSM. Quadratic OLS on [1, xᵢ, xᵢxⱼ, xᵢ²]; 2-D contour grids over any factor pair, others held at the run mean.

Sobol. Saltelli A/B/AB matrices; S1 and ST estimated via SALib. The default span is ±3σ around each parameter mean.

Confidence. Percentile bootstrap (B = 1000, seed 1337) on the P9 mean.

Full theory and references in the bilingual thesis at https://app.ai-systems.today/thesis.

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Notebooks

Seventeen Jupyter notebooks in notebooks/ walk through the full curriculum end-to-end:

#	Topic
01	Quickstart
02	CSP I/O
03	Live controls
04	Sampling strategies
05	Correlation presets
06	Full factorial DOE
07	Fractional DOE
08	Multilevel DOE
09	DOE interactions
10	DOE with measurements
11	Sobol indices
12	Response Surface Methodology
13	Optimisation
14	Uncertainty & reliability
15	Calibrated vs analytic surrogate
16	Full report parity

All seventeen execute end-to-end in CI on every push to main (see notebooks.yml).

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Reproducibility

The thesis bundle is reproducible three ways:

1. In-app Reproduce button — open the dashboard at https://app.ai-systems.today, go to the Report tab, click Reproduce thesis bundle. Downloads a ZIP with every figure (PNG) and table (CSV) plus the commit SHA.
2. Python CLI — cubespec replay run.json re-executes a saved dashboard run.
3. Parity contract — pytest tests/test_parity.py matches the TypeScript fixtures bit-for-bit at 1e-9 tolerance.

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Citation

Use CITATION.cff — GitHub renders a Cite this repository button automatically. A Zenodo DOI will be minted with the first PyPI release.

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License

MIT — see LICENSE.