From d3b963e97ed8cc721a19be95f9857d24b0683ffa Mon Sep 17 00:00:00 2001 From: Jammy2211 Date: Mon, 27 Apr 2026 18:06:00 +0100 Subject: [PATCH] docs(prompt): update stale admin_jammy/prompt/ refs to PyAutoPrompt/ The PyAuto prompt registry was moved from admin_jammy/prompt/ to PyAutoPrompt/ on 2026-04-27. This commit updates the remaining cross-references inside prompt files (issued/, z_vault/, autolens/, autogalaxy/) so they point at the new locations. Co-Authored-By: Claude Opus 4.7 (1M context) --- autogalaxy/ellipse_no_run.md | 2 +- autolens/pixelization_eager_vs_jit_divergence.md | 2 +- issued/adapt_images_pytree_fix.md | 2 +- .../autogalaxy_workspace_test_jax_likelihood_imaging.md | 8 ++++---- issued/group_20260414.md | 2 +- z_vault/galaxy_pytree_token.md | 2 +- 6 files changed, 9 insertions(+), 9 deletions(-) diff --git a/autogalaxy/ellipse_no_run.md b/autogalaxy/ellipse_no_run.md index 7f6bca45..86df427f 100644 --- a/autogalaxy/ellipse_no_run.md +++ b/autogalaxy/ellipse_no_run.md @@ -10,7 +10,7 @@ - `ellipse/database` They were parked because the ellipse model needs a refactor and JAX support (tracked separately in - `admin_jammy/prompt/autogalaxy/ellipse_fitting_jax.md`). In particular, `ellipse/modeling` and + `PyAutoPrompt/autogalaxy/ellipse_fitting_jax.md`). In particular, `ellipse/modeling` and `ellipse/multipoles` time out under `PYAUTO_TEST_MODE=1` in the mega-run, and `ellipse/modeling` additionally raises a `KeyError` on `ellipses.0.centre_0` kwargs after API drift. diff --git a/autolens/pixelization_eager_vs_jit_divergence.md b/autolens/pixelization_eager_vs_jit_divergence.md index 10825295..bf74b065 100644 --- a/autolens/pixelization_eager_vs_jit_divergence.md +++ b/autolens/pixelization_eager_vs_jit_divergence.md @@ -4,7 +4,7 @@ Surfaced by the eager-numpy regression assertions added in `jax_profiling/imaging/pixelization.py` (PR from -`admin_jammy/prompt/issued/eager_numpy_regression_assertions.md`). +`PyAutoPrompt/issued/eager_numpy_regression_assertions.md`). When the rectangular pixelization script runs, it computes the same `log_evidence` via three supposedly-equivalent numpy/JAX paths and gets two diff --git a/issued/adapt_images_pytree_fix.md b/issued/adapt_images_pytree_fix.md index a8048fb8..c5e10754 100644 --- a/issued/adapt_images_pytree_fix.md +++ b/issued/adapt_images_pytree_fix.md @@ -25,7 +25,7 @@ so the fresh Galaxy doesn't match any key in `adapt_images.galaxy_image_dict`. T → `mesh.mesh_weight_map_from(adapt_data=None)` blows up. The analogous fix on the autolens side that solved a similar dict-keyed-by-instance problem is -tracked at `@admin_jammy/prompt/autolens/linear_light_profile_intensity_dict_pytree.md`. +tracked at `@PyAutoPrompt/autolens/linear_light_profile_intensity_dict_pytree.md`. Note that autolens's jax_likelihood_functions/imaging/rectangular.py currently passes in autolens_workspace_test despite apparently having the same Galaxy-identity issue — worth checking diff --git a/issued/autogalaxy_workspace_test_jax_likelihood_imaging.md b/issued/autogalaxy_workspace_test_jax_likelihood_imaging.md index 9968ef41..9144d425 100644 --- a/issued/autogalaxy_workspace_test_jax_likelihood_imaging.md +++ b/issued/autogalaxy_workspace_test_jax_likelihood_imaging.md @@ -14,7 +14,7 @@ From @autolens_workspace_test/scripts/jax_likelihood_functions/imaging/: - `rectangular_mge.py` - `delaunay.py` - `delaunay_mge.py` — currently disabled in autolens smoke suite (jax 0.7 regression, - see admin_jammy/prompt/build/smoke_workspace_fixes.md). Ship it but disable with the same + see PyAutoPrompt/autobuild/smoke_workspace_fixes.md). Ship it but disable with the same comment in `smoke_tests.txt`. **Skip**: `rectangular_dspl.py`, `simulator_dspl.py`. @@ -38,17 +38,17 @@ with ad-hoc `register_pytree_node` calls inside the workspace script. Other known spawn-offs if they surface during porting: - **Linear light profile** models need `linear_light_profile_intensity_dict_pytree` fixed — see - @admin_jammy/prompt/autolens/linear_light_profile_intensity_dict_pytree.md for the lens-side + @PyAutoPrompt/autolens/linear_light_profile_intensity_dict_pytree.md for the lens-side counterpart. Only blocks scripts that use `ag.lp_linear.*` or MGE bases via `fit_for_visualization`, not the scalar `fit_from` round-trip. - Any autogalaxy profile that isn't pytree-registered (follow the per-profile pattern in - @admin_jammy/prompt/issued/fit_imaging_pytree_*.md). + @PyAutoPrompt/autolens/fit_imaging_pytree_*.md). __Three-step JAX pattern__ Each script mirrors the autolens reference: NumPy baseline → `jax.jit`-wrapped `analysis.fit_from` → scalar `log_likelihood` match. The reference file `mge_pytree.py` in autolens is the gold -standard for this pattern (see @admin_jammy/prompt/issued/fit_imaging_pytree_lp.md for background). +standard for this pattern (see @PyAutoPrompt/autolens/fit_imaging_pytree_lp.md for background). __Deliverables__ diff --git a/issued/group_20260414.md b/issued/group_20260414.md index 1d9f0e11..eeb2a8a1 100644 --- a/issued/group_20260414.md +++ b/issued/group_20260414.md @@ -19,7 +19,7 @@ I want you to move the following features from imaging: - advanced/sky_background - advanced/subhalo (no sensitivity) -For the following features can you put a prompt in admin_jammy/prompt/workspaces with text saying these features +For the following features can you put a prompt in PyAutoPrompt/workspaces with text saying these features ned improving or padding out in imaging, and then adapting to group when ready: - scaling relation diff --git a/z_vault/galaxy_pytree_token.md b/z_vault/galaxy_pytree_token.md index 5ad50dfe..f8c5f059 100644 --- a/z_vault/galaxy_pytree_token.md +++ b/z_vault/galaxy_pytree_token.md @@ -14,7 +14,7 @@ key in the dict (whose keys are still the *original* galaxies attached to `adapt which rides as aux/static through the pytree), so the lookup fails. This is the same class of bug that -@admin_jammy/prompt/issued/linear_light_profile_intensity_dict_pytree.md fixed for +@PyAutoPrompt/autolens/linear_light_profile_intensity_dict_pytree.md fixed for `LightProfileLinear` via the `pytree_token` pattern: an itertools.count-backed monotonic id attached to each instance at `__init__`, exposed as a regular `__dict__` field so it survives unflatten, with `__hash__` / `__eq__` overridden to use the token and