feat: add BlackJAXNUTS integration test script#23
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Mirrors `Nautilus_jax.py` — runs `af.BlackJAXNUTS` (shipped in PyAutoFit #1256) on the standard 1D Gaussian dataset using the JAX-jitted likelihood path. Validates that the gradient-based MCMC search executes end-to-end with `use_jax=True`, recovers truth within tolerance, and produces healthy NUTS diagnostics (positive ESS, zero divergences). Hard assertions guard against silent regressions: parameter recovery must land within ±5 of (50, 25, 10), ESS > 50, divergences == 0. Not added to smoke_tests.txt — that list is intentionally a small curated subset; this script is exercised by run_all_scripts.sh. Refs PyAutoLabs/PyAutoFit#1255 Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
This was referenced May 6, 2026
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Summary
scripts/searches/BlackJAXNUTS.py— integration test foraf.BlackJAXNUTSon the standard 1D Gaussian dataset, mirroring the structure ofNautilus_jax.py.use_jax=True, callsenable_pytrees()+register_model(model)soinstance_from_vectorJIT-traces, then runsaf.BlackJAXNUTS(num_warmup=200, num_samples=500)and asserts on parameter recovery + diagnostic health.|recovered - truth| < 5per param,ess_min > 50,n_divergent == 0.smoke_tests.txt— that list is intentionally a small curated subset; this script is picked up byrun_all_scripts.shpost-merge instead.Library counterpart: PyAutoLabs/PyAutoFit#1256 (merged).
Workspace counterpart: PyAutoLabs/autofit_workspace#52.
Issue: PyAutoLabs/PyAutoFit#1255.
Test plan
python scripts/searches/BlackJAXNUTS.py— recovers (centre, normalization, sigma) close to (50, 25, 10), ESS > 100, 0 divergences. All asserts pass.🤖 Generated with Claude Code