feat: rectangular mesh split — Bilinear default everywhere, RTU documented as advanced - #221
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…ed/interferometer Workspace follow-up to PyAutoArray#462 (issue PyAutoArray#461), mirroring the autolens_workspace sweep: - Imaging/multi_dataset examples: RectangularAdaptDensity/AdaptImage -> RectangularBilinearAdaptDensity/AdaptImage (fast CPU default). - Interferometer examples: -> RectangularRTUAdaptDensity/AdaptImage (pure rename of the previous kernel-CDF behaviour). - Mesh-variants docs incl. the Enzi et al. 2026 (arXiv:2606.30620) RTU citation and gradient guidance added to the pixelization likelihood_function.py and README. - Prior configs: rectangular_adapt_*.yaml -> rectangular_rtu_adapt_*.yaml plus new rectangular_bilinear_adapt_*.yaml (same priors). Scripts and configs only — notebooks regenerate at release. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WtMqU3JfmyJh8GvB7jT4Et
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llms-full.txt / workspace_index.json regenerated with autohands/regenerate_navigator.py autogalaxy after the Bilinear/RTU class-name sweep (navigator staleness CI check). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WtMqU3JfmyJh8GvB7jT4Et
No normal-workspace example uses RTU (user decision): interferometer examples switch to the RectangularBilinear* default, matching imaging; RTU stays documentation-only (gradient/GPU advanced option). Navigator catalogue regenerated. Co-Authored-By: Claude Fable 5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01WtMqU3JfmyJh8GvB7jT4Et
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Summary
Workspace follow-up to the PyAutoArray rectangular mesh split (PyAutoLabs/PyAutoArray#462, merged; task PyAutoLabs/PyAutoArray#461), mirroring PyAutoLabs/autolens_workspace#495. Every example — imaging, multi_dataset and interferometer — switches to the fast-CPU default
RectangularBilinearAdaptDensity/RectangularBilinearAdaptImage; no normal-workspace example uses RTU (user decision, 2026-08-21). RTU is documentation-only: the advanced option for GPUs and gradient-based (JAX) samplers, with the Enzi et al. 2026 (arXiv:2606.30620) citation.Scripts Changed
imaging/,interferometer/,multi_dataset/: class-name sweep toRectangularBilinear*(drop-in — no script passedbandwidth/n_knots).imaging/features/pixelization/likelihood_function.py,README.md: "Rectangular Mesh Variants" docs — Bilinear = rank-CDF fast CPU default; RTU = kernel-CDF advanced option (GPU / gradient samplers), Enzi citation, gradient caveat (over_sample_size_pixelization >= 4or RTU).config/priors/mesh/:rectangular_adapt_*.yaml→rectangular_rtu_adapt_*.yamlplus newrectangular_bilinear_adapt_*.yaml(same priors).llms-full.txt/workspace_index.json: navigator catalogue regenerated (CI staleness check).Notebooks regenerate at release.
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Test Plan
py_compile).