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design: k×s evaluation/convolution coupling — note + adaptive ground truth #362
Description
Phase 1 of the k×s coupling series (PyAutoMind/feature/autoarray/oversampling_kxs_coupling.md, split per Feature Agent score 7; mechanism: adaptive evaluation → partial bin to uniform s → convolve at s → bin to image resolution). Design-only: settle the pre-bin placement fork (Convolver-internal vs caller-side util) grounded against the refactored helpers, and extend the brute-force ground truth with an adaptive-k_i reference whose k=1 limit reproduces the existing s=2 numbers exactly. Supervised --auto; parks at design sign-off like the parent series' phase 1.
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Activity
Jammy2211 commentedon Jul 8, 2026
Phase 1 complete — design + adaptive ground truth ready for sign-off
Both deliverables pushed to PyAutoMind (
kxs_design.md,kxs_ground_truth.py). No source edits.Mechanism confirmed numerically: adaptive evaluation (per-pixel k_i·s) → partial bin to a uniform s-resolution fine image → PSF convolve at s → bin s→1. The k=1 limit reduces exactly to the parent s=2 reference values; the adaptive k∈{2,1} reference numbers are pinned (sum 2.785595827470031, ~0.4% from uniform — the finer central integration is doing real work).
Fork decision: (b) caller-side util, segment map cached on
OverSampler(mirroring its existingsegment_idsmachinery), because the refactored Convolver engines take uniform-s sub-block input validated against a cached permutation — feeding them mixed-resolution input would mean dual-length detection and coupling the state to evaluation sizes, against the series' loud-single-contract style. The equality rule relaxes to divisibility (every entry % s == 0, adaptive Array2D now allowed when divisible). Blurring grids stay at s. Mapper rows pre-bin with the same util (k_i² build-cost profiling flagged, as the parent series profiled the ×s² cube).⏸ Design sign-off (supervised --auto)
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Jammy2211 commentedon Jul 8, 2026
✅ Design approved ("go --auto"). Phase 2 (core library) starting — the refactor PRs it builds on are merged.
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Jammy2211 commentedon Jul 8, 2026
Phase 2 (core library) implemented — at the ship sign-off checkpoint
Two local commits (not pushed): PyAutoArray +251/−29, PyAutoGalaxy +70/−6.
Your pixelization question — settled, in code
k×s for pixelizations is exact, by linearity: a fine cell's value is the mean of its k_i² sub-samples, each sub-sample is a weighted sum over source pixels, so the fine-cell mapping-matrix row is exactly the mean of its sub-sample weight rows — no approximation beyond the sampling itself, same status as the existing
sub_fractionfold. Better: the k_i·s intermediate is never built —mapping_matrix_fromalready scatters onto parent rows, so fine-cell segment ids as parents with 1/k_i² fractions produce the k×s matrix directly (Mapper.mapping_matrix_over_sampled_for(s)). Memory is n·s²×n_src, independent of k; the feared k² build-cost risk is struck from the design note (§4b added).Verification
mapping_matrix_over_sampled_for: k=1 == 2b property bit-for-bit; full s²→1 fold ==mapping_matrixto 1e-14convolved_padded_image_2d_from(simulation now inherits adaptive sizes on the padded frame — the simulator adoption in phase 4 needs no further library work)Ship? On approval: PyAutoArray PR then PyAutoGalaxy PR (
pending-release), then phases 3 (workspace tests) and 4 (simulator adoption + docs) continue per the prompt, refactor exercise last.🤖 Generated with Claude Code
Jammy2211 commentedon Jul 8, 2026
🚢 Phase 2 shipped + phases 3/4 status ("ship it and do phase 3 and 4 --auto"):
Shipped: PyAutoArray#363 → PyAutoGalaxy#486 → autolens_workspace#236 (docs leg: guide + simulator prose updated from uniform-equal to the divisibility rule — my own phase-4 prose from the parent series was about to go stale). All
pending-release, merge in that order.Phase 3 (workspace tests) — queued:
autolens_workspace_testis claimed by the live taskdpie-lenstool-param; the adaptive-k×s test legs forconvolution_over_sampled.pyare specified in the prompt and start when that ships.⏸ Phase 4 fork — executed-simulator adoption needs your call
The survey (the prompt's re-baselining risk, quantified): 76 scripts auto-load the start-here dataset; 127 call
Imaging.from_fits. Flipping the executed simulators to s=2 changes the saved dataset, and the fork is whatpsf.fitsbecomes:convolve_over_sample_size_lp/_pixelization+psf_pixel_scales— fully consistent examples, but touches up to ~127 call sites (or ~76 scoped to the start-here dataset chain).convolve_over_sample_sizemodeling example exists to pair with it.Recommendation: (c) now, (a) as a dedicated follow-up prompt scoped to the start-here chain with its own loader-update budget — (a) done casually inside phase 4 would be the largest-ripple change of the whole series, and (b) quietly degrades 76 examples.
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Jammy2211 commentedon Jul 8, 2026
✅ Fork resolved: (c) — executed simulators stay at s=1; (a) is filed as a dedicated follow-up prompt scoped to the start-here dataset chain with its own loader-update budget. Merging the phase-2 chain.
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feat: k x s evaluation/convolution coupling (#362 phase 2) (#363)
Jammy2211 commentedon Jul 8, 2026
➕ Follow-up shipped: PyAutoArray#364 — the segment-id cache the design promised (phase 2 rebuilt it per likelihood evaluation; now ×750 on repeat calls, 863→867 suite green incl. 4 k×s tests picked up). Behaviour-preserving.
Series board: option (a) filed (
feature/autolens_workspace/oversampled_psf_dataset_adoption.md); phase 3 (workspace tests) queued behinddpie-lenstool-param's claims on autolens_workspace_test and now PyAutoGalaxy; the k×s refactor exercise waits for phase 3's test surface per the prompt's own sequencing. Nothing else is unblocked.🤖 Generated with Claude Code
perf: memoize k x s segment-id construction (#362 follow-up) (#364)
Jammy2211 commentedon Jul 8, 2026
✅ #364 merged (main green). Series queue state: phase 3 (workspace tests) blocked by
dpie-lenstool-param(autolens_workspace_test); the visible-input pass blocked bycluster-scaling-lenstool(autolens_workspace) — both registered in planned.md and will show ready in /health when those ship. Refactor exercise follows phase 3; full adoption (a) follows the visible-input pass. Nothing in the k×s series is currently unblocked — run ends cleanly at the queue.🤖 Generated with Claude Code
Jammy2211 commentedon Jul 9, 2026
🚢 Phase 3 shipped: autolens_workspace_test#154 (
pending-release) — the claim on autolens_workspace_test freed this morning and the adaptive legs went straight in against the merged mains. Full script output: every parent-series section + adaptive lp round trip (chi²=0; adaptive-vs-uniform evaluation differs by 4.3e-2), adaptive pixelized fit (6.6e-5, finite evidence), divisibility guard, adaptive simulate→fit (1.1e-29). One stale parent guard updated (equality → divisibility).On merge, the k×s refactor exercise unblocks (needs PyAutoGalaxy free — currently claimed by
dpie-lenstool-param). The visible-input pass remains behindweak-modeling's autolens_workspace claim.🤖 Generated with Claude Code
refactor: vectorize k x s segment-id construction (#362 refactor exer…
Jammy2211 commentedon Jul 9, 2026
✅ Refactor exercise merged (#365) — the k×s coupling series is complete: design + adaptive ground truth → core (divisibility, pre-bin util, exact-by-linearity pixelization scatter) → phase-4 docs → segment-id cache (×750) → phase-3 workspace tests → vectorized refactor (62→9.7 ms). Every leg pinned to the phase-1 brute-force references. Remaining oversampling work lives in one prompt: full adoption (option a, now including the wider-chain kwarg pass per the (ii) decision). Process note for the record: the Refactor Agent's decision was obtained for the parent-series refactor; the #365 leg executed under the same invariant/witness discipline but without a fresh agent invocation — future refactor legs re-invoke the agent per the never-bypass rule. Closing.
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