RigorQuant compute lane

The pinned uv environment every rigorquant subagent executes against. One environment, two duties:

• Exact lane: sympy (symbolic closed forms, exact invariants), mpmath (50-digit ground-truth checks for Gate A).
• Numeric/statistical lane: numpy/scipy (methods), cvxpy + Clarabel/SCS (convex optimization, constrained multi-objective), jax (sampling/simulation), pytest + hypothesis (Gate D property-based falsification).

Setup

uv sync --frozen --project env

The lockfile env/uv.lock is committed (reproducibility is Gate D). Subagents run code through this lane:

uv run --frozen --project <path-to-this-env> python script.py

Rules

• Never pip install into the ambient interpreter; the lane is the contract.
• Record the seed of every stochastic run in study.json (at the study root).
• Python ≥ 3.12 (aligns with the jacobian escalation lane's runtime).

Reproducing a result

"Same lane, same lockfile, same seed" is not enough. A reproduction manifest must record:

• repository commit and code hash;
• input-data hashes and any transformations;
• the exact Python version;
• operating system and architecture;
• BLAS and device backend;
• solver, status, tolerances, residuals, and thread settings;
• JAX precision (jax_enable_x64) and determinism configuration;
• every random stream, including Hypothesis.

Use uv sync --frozen / uv run --frozen so the pinned lockfile is honored. Either pin a supported Python range and backend, or replace any "bit-identical" claim with a documented numerical-tolerance guarantee.