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prompt: record use_adjoint_scaling resolved (PyAutoArray#478) #267

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Merged
Jammy2211 merged 1 commit into from
Aug 22, 2026
Merged

prompt: record use_adjoint_scaling resolved (PyAutoArray#478) #267

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26 changes: 22 additions & 4 deletions draft/maintenance/libraries/remove_pynufft_legacy_transformer.md
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Expand Up @@ -126,10 +126,28 @@ hidden there.

Two things found along the way that are NOT closed by this task:

1. `use_adjoint_scaling` is now a **no-op on both remaining transformers** — it
was load-bearing only for the deleted class's pynufft-internal IFFT
normalisation. Kept, with docstrings rewritten, rather than widening this
removal. Worth a separate decision.
1. ~~`use_adjoint_scaling` is now a no-op on both remaining transformers.~~
**RESOLVED 2026-08-22 — @PyAutoArray#478.** Parameter and the
`adjoint_scaling` attribute both removed from `TransformerDFT` and
`TransformerNUFFT`, and from the sole caller
`Interferometer.apply_sparse_operator`.

The history, since it matters for anyone holding old results: `TransformerDFT`
**never** applied the factor; `TransformerNUFFTPyNUFFT` did
(`image *= self.adjoint_scaling`, a Kaiser-Bessel compensation — the only real
user, deleted in #475); `TransformerNUFFT` applied it until **bd18a769
(2026-05-22)**, which removed the multiplication because "the nufftax adjoint
is already the mathematical adjoint and needs no extra scaling".

Verified numerically before removing: `True` vs `False` was bit-identical
(`0.000e+00`) on both classes, and nufftax matches the exact DFT at 1.562e-13
relative — the same figure before and after. Applying the factor would have
been a 4096x error on a 32x32 grid, not a correction.

**Caveat worth raising with users:** anyone who used `TransformerNUFFT` with
`use_adjoint_scaling=True` *before* 2026-05-22 has results that differ by
`4 * N_y * N_x` from anything regenerated today. That discrepancy dates from
bd18a769, not from #478.
2. `autolens_workspace_test/scripts/interferometer/jax_likelihood/rectangular_sparse.py`
recorded the `apply_sparse_operator` / `TransformerNUFFT` incompatibility as
being caused by pynufft's kernel-deconvolved adjoint scale. That backend is
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