feat: real-data A2744 weak lensing example (weak series step 7b)#253
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scripts/weak/real_data/a2744.py — the first PyAutoLens weak-lensing analysis of real sky data: downloads the public pyRRG A2744 shape catalogue (pinned commit, provenance documented; not redistributed), projects RA/Dec to the tangent plane, applies standard quality cuts (6585 -> 382 usable shapes), builds a reduced-shear WeakDataset via the new from_arrays loader, shows the model-independent Kaiser-Squires map, and fits a spherical NFW with Nautilus. Framed honestly: training-data-grade depth gives a marginal (~1.5 sigma) tangential signal with the correct radial concentration and a null B-mode — the example demonstrates the real-data workflow, which transfers unchanged to deep survey catalogues via WeakDataset.from_fits/from_csv. Step 7b of the weak-lensing series (PyAutoLabs/PyAutoLens#588). Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
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Summary
scripts/weak/real_data/a2744.py— the first PyAutoLens weak-lensing analysis of real sky data, completing the weak-lensing series. Downloads the public pyRRG A2744 shape catalogue (commit-pinned URL, provenance documented, file not redistributed), projects RA/Dec to the tangent plane, applies the standard quality cuts (6585 → 382 usable shapes), builds a reduced-shearWeakDatasetvia the step-7a loaders (PyAutoLabs/PyAutoLens#589, merged), shows the model-independent Kaiser-Squires map, and fits a spherical NFW with Nautilus. Honest framing throughout: training-data-grade depth yields a marginal (~1.5σ) tangential signal with the correct radial concentration and a null B-mode — the example demonstrates the real-data workflow, which transfers unchanged to deep survey catalogues. Tracking issue: PyAutoLabs/PyAutoLens#588.Scripts Changed
scripts/weak/real_data/a2744.py(new) — validated end-to-end twice (initial run + rerun after the radial window/framing revision; full Nautilus fit each time).llms-full.txt/workspace_index.json— catalogue regenerated in-branch.Validation checklist (--auto run)
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