regenerate-goldens

Testing & Quality

Use when a deliberate look/pipeline change has shifted NegPy's golden / characterization test values and they need updating — e.g. after changing the working-space TRC or primaries, the print curve, a lab/toning default (saturation, vibrance), or any default that alters rendered output, and a snapshot test now fails. Keywords: golden, goldens, characterization test, snapshot, regenerate, update expected values, test_scene_linear_relocation, test_characteristic_curve, _GOLDEN, look drift.

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Regenerate goldens

Overview

A few tests pin rendered output as hardcoded "golden" values — they exist to catch unintended drift in the look. When you change the pipeline on purpose (a default, the TRC, primaries, a curve), those snapshots go stale and must be re-baselined.

Core principle — this is the one thing to get right: only regenerate a golden when the change that moved it was intentional. A failing golden after an unrelated edit is a real regression — fix the code, do not bless the new numbers. Regenerating to silence a surprise defeats the entire point of the test.

Which tests carry rendered goldens

tests/test_scene_linear_relocation.py — _GOLDEN: full-engine output (6 pixels × default / expo_dark / expo_cmy). Depends on the whole creative chain, so it shifts on almost any look change: working TRC/primaries, print curve, lab/toning/finish defaults (saturation, vibrance, etc.).
tests/test_characteristic_curve.py::test_default_curve_shape — golden: the default print curve sampled at x = 0, .25, .5, .75, 1 (exposure kernel only). Shifts on TRC or print-curve changes, not on lab/saturation.

Not rendered snapshots (do not use this skill for them):

test_working_oetf.py — values are defined by the TRC formula; hand-edit if you change the curve.
test_lab_colorspace.py / test_lab_logic.py — assert structural properties / explicit inputs, not default-look pixels. A failure there usually means a real math change.
Parity tests (test_pipeline_parity.py) — relative CPU↔GPU; default-value changes apply to both sides, so they don't need regeneration.

Workflow

Confirm the change was intentional. If you can't name why the look moved, stop and investigate — that's a regression, not a golden update.
See what actually fails so you only touch real snapshots:
```
make test
```
Expect test_scene_linear_relocation and/or test_characteristic_curve if the change is look-affecting. Anything else failing is a separate signal — read it, don't auto-bless.
Print the new values:
```
uv run python .claude/skills/regenerate-goldens/regenerate.py
```
It imports each test's own image / sample-point / curve helpers, so those stay in sync; only the relocation config list is mirrored in the script — if you add or remove a config or sample point in the test, update the script to match.
Paste the printed blocks over _GOLDEN (relocation) and golden (characteristic curve). Keep the surrounding comment/docstring accurate (which TRC, which default).
Re-run to confirm green and eyeball the magnitude of the shift — a tiny tweak that moved a pixel by 0.4 is a red flag the change did more than intended:
```
make test
```
If a non-golden test (lab_logic L* tolerance, etc.) also moved, decide per-test whether the new behaviour is correct before loosening a bound — don't widen tolerances to pass.