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Persist PAE/PDE/pLDDT matrices in Python-mode scores.npz - #432
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The web server emits per-model confidence matrices, but run_inference() in Python mode only wrote scalar metrics into scores.model_idx_*.npz. Persist pae/pde/plddt alongside the scalars so downstream analysis has the same artifacts. Closes chaidiscovery#348.
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Checks are green and the PR is mergeable. Anything blocking review? Happy to adjust. |
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Description
run_folding_on_context()computes per-token PAE/PDE and per-atompLDDT, yet the per-sample
scores.model_idx_*.npzstored only thescalar ranking metrics. The confidence tensors never reached disk.
A
write_scores_npz()helper now persistspae,pdeandplddtnext to the scalar keys in the same file.
Motivation
Python-mode runs could not obtain the PAE output the web server emits
(#348). Persisting the matrices closes that gap without touching
inference behavior.
Test plan
tests/test_scores_npz.pybuilds a syntheticSampleRanking, writesthe npz, and asserts array presence and shapes.
tests/test_ranking.pypasses. The unrelated local
test_ions_parsingfailure is a macOSmultiprocessing-spawn limitation in
utils/timeout.py, reproducibleon a clean checkout.