v2026.5.1.4

PyAutoLens v2026.5.1.4

Highlights

Python 3.9–3.13 supported, 3.12 / 3.13 recommended

This release expands supported Python versions to 3.9 through 3.13 (#486). 3.12 and 3.13 are first-class recommended; 3.9, 3.10, 3.11 are supported but emit a loud (bypassable) banner on import. Silence the banner via version.python_version_check: False in your workspace's config/general.yaml. Python 3.14 is not yet supported.

Key impacts for users:

• requires-python = ">=3.9" in pyproject.toml (lower floor than before)
• Classifiers now cover 3.9, 3.10, 3.11, 3.12, 3.13
• JAX is now an optional extra: pip install autolens[jax], gated on python_version >= '3.11'. Plain pip install autolens no longer pulls JAX as a transitive dep.

HowToLens moved to its own repo

The HowToLens lecture series now lives in its own repository at PyAutoLabs/HowToLens (#468). Existing URLs/prose in the library and workspace pointing at the previous location have been updated. Clone the new repo to follow the tutorial chapters.

Performance

• Short-circuit set_snr_of_snr_light_profiles when no SNR profiles are present (#471) — eliminates a redundant traversal in models that don't use SNR-tuned profiles

Internal / Cleanup

• Remove unused pyprojroot import (#485)
• Add mask padding likelihood sanity check to test suite (#436)
• Clean up jax false positives in test_autolens/ (#488)
• Move sparse-operator parity check to autolens_workspace_test (#489) — keeps the library's unit suite numpy-only

Upstream Changes

PyAutoConf

• Support Python 3.9–3.13, first-class 3.12/3.13 (PyAutoConf#102)
• Soften Python version check with general.yaml bypass (PyAutoConf#96)

PyAutoArray

• Support Python 3.9–3.13 (PyAutoArray#293)
• Handle off-centre masks in convolver and blurring grid padding (PyAutoArray#274)
• Fix NaN gradients from jaxnnls backward pass via Jacobi preconditioning (PyAutoArray#279)
• Lower nnls_target_kappa default to 1e-11 for stable NNLS gradients (PyAutoArray#283, #284)

PyAutoFit

• Support Python 3.9–3.13 (PyAutoFit#1244)
• JAX-jitted likelihoods in Dynesty via use_jax_jit (PyAutoFit#1243)
• Skip FitException samples in compute_latent_samples (PyAutoFit#1233)

PyAutoGalaxy

• Support Python 3.9–3.13 (PyAutoGalaxy#382)
• Mark pytree_token as ephemeral in light/linear profiles (PyAutoGalaxy#374)
• xp-gate jax.scipy.special.factorial in shapelets/exponential.py (PyAutoGalaxy#385)

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Full changelog: 2026.4.13.6...2026.5.1.4