Fit and test outcomes using the canonical Angell equation as a measurable model. """ Angell Framework V2: Real-model outcome testing scaffold (single-file) Author: Nicholas Reid Angell (framework) Implementation: ChatGPT (single-file scaffold) Goal Fit and test outcomes using the canonical Angell equation as a measurable model. Run baselines and ablations to produce scientific traction, not just visuals. Generate figure suite + provenance hashes so figures are evidence, not decoration. Assumptions (default) Validation target: Noma geometry-first early detection Outcome y is binary: 1 = "positive event" (e. g. , early-stage identified, severe outcome, etc. ) You can swap in any domain by remapping columns and interpretation. Run python angellᵥ2ᵥalidation. py python angellᵥ2ᵥalidation. py --csv yourdata. csv --outdir outputs CSV schema (minimal) caseᵢd, t, x, y Optional (recommended) rho, delayₒnsetₜofirstcontact, delaycontactₜoᵣeferral, delayᵣeferralₜoₜreatment Notes No fixed colors are set for plots (matplotlib defaults). If some libs are missing, the script will degrade gracefully where possible. Full Changelog: https: //github. com/NicholAI91/Angell-framework-real-model-outcome-testing. md/commits/Real-Models
Nicholas Reid Angell (Thu,) studied this question.
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