Widen supported Python versions to 3.9-3.12

[adowling2]

Summary

Widens supported Python versions from >=3.9,<3.10 to >=3.9,<3.13, verified with
real runs on every claimed version plus a full from-scratch ~28-minute stochastic
reproduction rerun on 3.12. No dependency version changed and no tolerance or
reference/baseline file was touched — see the guardrails section below.

Stage 2: verification table (version × check)

Each version installed into a fresh conda env (conda create -n ... python=X.Y pip,
then pip install -e ".[dev]"), never reusing another version's env.

Check	3.9 (existing dev env)	3.10	3.11	3.12
pytest -q	204 passed, 2 deselected	204 passed, 2 deselected	204 passed, 2 deselected	204 passed, 2 deselected
test_matches_synthetic_baseline (atol=1e-10)	passed	passed	passed	passed
pytest -q tests/regression	22 passed, 2 deselected	22 passed, 2 deselected	22 passed, 2 deselected	22 passed, 2 deselected
Import contract (bits_for_gaps Julia-free/TF-lazy)	OK, version 0.1.2	OK, version 0.1.2	OK, version 0.1.2	OK, version 0.1.2
Lazy GPflow import (AnisotropicSE())	OK, ndim=2	OK, ndim=2	OK, ndim=2	OK, ndim=2
setuptools.__version__ (the <81 bound)	80.9.0	80.10.2	80.10.2	80.10.2

Every version resolved the exact same pinned stack (numpy 1.26.4, scipy 1.13.1,
tensorflow/tensorflow-macos 2.16.2, gpflow 2.9.2, tensorflow-probability 0.24.0,
tf-keras 2.16.0) from pip install -e ".[dev]" alone — no version-specific dependency
resolution surprises. All four temporary conda envs (bfg-py310, bfg-py311,
bfg-py312) were removed after use; only the pre-existing bits_for_gaps (3.9) dev
env remains.

Stage 3: reproduction on Python 3.12

In the bfg-py312 env with pip install -e ".[dev,vle]" and
PYTHON_JULIACALL_HANDLE_SIGNALS=yes:

• pytest -m vle: 2 passed — Julia/Clapeyron work fine on 3.12.

• python paper/reproduce.py: all 11 figures regenerated from paper/data/ in
  ~66 seconds wall-clock. Spot-checked two rendered PNGs (the Wilson McCabe-Thiele
  diagram and the HMC trace plot) — both render correctly, R-hat ≈ 1.005–1.009 on the
  trace plot.

• python paper/full_reproduction.py (the 15-iteration adaptive HMC + acquisition
  loop, from scratch, no archived data as input): completed in 0.47 h (28 min),
  inside the documented ~25–30 min range. Compared against the existing Python 3.9
  fresh-run numbers already recorded in paper/REPRODUCTION.md (full write-up added
  in this PR, "Cross-Python-version check" section):

  	Python 3.9 fresh run	Python 3.12 fresh run
  Max entropy, iter 1	1.4577	1.4576908
  Max entropy, iter 2	1.2470	1.2470073
  Max entropy, iter 15	-0.0084	-0.2244
  R-hat, iter 15	1.00523, 1.00730, 1.00879	1.00527, 1.00736, 1.00879
  ESS, iter 15	1468.29, 2428.09, 653.15	1468.06, 2424.94, 653.15
  Hyperparameter posterior mean	1.35645, 0.86239, 3.19502	1.35606, 0.86216, 3.19502
  Hyperparameter posterior median	1.28619, 0.81949, 3.04407	1.28501, 0.81881, 3.04407
  Test RMSE, iter 1 → 15	4.337 → 0.887 (~4.9×)	4.385 → 0.856 (~5.1×)
  Column tracking (liquid / vapor mole fraction)	within 0.03 / 0.014	within 0.027 / 0.014
  Both columns converged?	yes	yes

  Iterations 1–2's max entropy matched to 6+ significant figures — striking,
  given no bitwise-reproducibility guarantee exists across interpreters. From
  iteration 3 on, the two runs' design trajectories diverge, as expected: each
  iteration's posterior-predictive diagnostic draws from TensorFlow's ambient,
  unseeded RNG (already documented as non-reproducible in mixture.py's module
  docstring — this is the same non-reproducibility that already separates two
  independent runs within one Python version, not something new to 3.12). Despite
  that divergence, every statistical conclusion holds: R-hat/ESS stay comfortably
  converged, the hyperparameter-posterior ordering (lengthscale_2 >
  lengthscale_1) is preserved, entropy still decays and crosses zero in the same
  iteration range, test RMSE still drops by a comparable factor, and both McCabe-
  Thiele columns still converge and track each other within the same tolerance band
  already used elsewhere in this repo (0.03/0.014).

  Conclusion: Python 3.12 gives statistically equivalent science to Python 3.9.
  Run artifacts stayed in the gitignored results_remaked/; nothing was committed.

environment.yml decision

Moved to Python 3.12 (from 3.9), since Stage 3's rerun came out clean on every
axis above. The dev/reproduction environment now uses the top of the newly-supported
range, so day-to-day development and the paper/reproduce.py /
paper/full_reproduction.py scripts exercise the newest supported interpreter by
default. The file's header comment explains the decision and gives the one-line
change (python=3.12 → python=3.9) for anyone who specifically wants the
interpreter the archived published results (paper/data/, paper/reference/) were
originally produced on — every other pin in the file is unaffected either way.

No dependency version changed; no tolerance or reference file touched

• numpy, scipy, tensorflow/tensorflow-macos, gpflow, tensorflow-probability,
  tf-keras, and the setuptools<81 bound are byte-for-byte identical to what was
  on main. Confirmed in the built wheel's Requires-Dist metadata (see Stage 5
  below) and by diffing pyproject.toml's dependencies list.
• tests/integration/data/synthetic_baseline.json and paper/reference/*.json were
  not modified (git diff main -- tests/integration/data paper/reference is empty) —
  every version's test_matches_synthetic_baseline and tests/regression run passed
  against the existing files, at the existing tolerances.
• paper/data/ and paper/reference/ contents untouched.

Why Python 3.13+ is not supported (upstream constraint)

bits_for_gaps depends on GPflow, and GPflow requires numpy<2 in every release
it has ever published, including the latest (2.11.1). No NumPy 1.x release
publishes a Python 3.13 wheel — the first NumPy version that does is 2.1.0. So GPflow
(and therefore this package) cannot run on Python 3.13 at all, no matter what this
package's own pins say. TensorFlow's newest release (2.21.0) also publishes no Python
3.14 wheels. Python 3.12 is the hard ceiling until GPflow adds NumPy 2 support
upstream — no timeline is promised or implied. Documented in
docs/installation.md (a dedicated "Why not Python 3.13+?" admonition), README.md,
and CHANGELOG.md.

Stage 5: final gates

• pytest -q: 204 passed, 2 deselected on both 3.9 and 3.12 (re-verified after
  every commit on this branch).
• pytest -m vle: 2 passed on 3.9.
• ruff check .: clean.
• sphinx-build -W docs docs/_build/html (clean rebuild): clean, no warnings/errors.
• python -m build + twine check dist/*: both sdist and wheel PASSED. Wheel
  metadata confirms Requires-Python: <3.13,>=3.9 and every Requires-Dist pin
  unchanged (numpy<2,>=1.26, scipy<1.14,>=1.13, gpflow==2.9.2,
  tensorflow-probability==0.24.0, tf-keras==2.16.0, tensorflow(-macos)==2.16.2,
  setuptools<81). dist//build/ removed after inspection.

Files changed

• pyproject.toml: requires-python widened; classifiers for 3.10/3.11/3.12 added.
• .github/workflows/ci.yml: split into a lint job (ruff, once) and a test job
  matrixed across Python 3.9–3.12.
• .readthedocs.yaml: build Python 3.9 → 3.12.
• environment.yml: python=3.9 → python=3.12 (see decision above).
• docs/installation.md, README.md, CHANGELOG.md: document the widened support
  and the 3.13+ upstream-constraint explanation.
• paper/REPRODUCTION.md: new "Cross-Python-version check" section with the Stage 3
  numbers above.

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