Vectorize joint-probability trial scoring

[suraj-ranganath]

Vectorize the per-trial probability reduction in jointprob with a single reshape and NumPy sum. Preserve the Fortran-flattened trial ordering and rejection scores while reducing Python loop overhead.

[google-labs-jules]

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[claude]

Code review

• Overall assessment: Safe to merge. This is a clean, output-preserving vectorization of the jointprob per-trial reduction.
• Highest-risk area: reshape ordering vs. the original Fortran-flattened trial layout — verified correct.
• Merge recommendation: Safe to merge.

Reshape ordering / numerical equivalence (verified): arr[row] is (points, trials), and .reshape(-1, order="F") makes the points axis vary fastest, so probabilities is a sequence of contiguous per-trial blocks of length points (trial t -> [t*points, (t+1)*points)). The new probabilities.reshape(arr.shape[2], arr.shape[1]) uses default C order, whose fastest-varying axis is points, so it recovers exactly those per-trial blocks as rows. -np.sum(np.log(np.maximum(trial_probs, tiny)), axis=1) reproduces the original inner slice-and-sum byte-for-byte. Hoisting tiny = np.finfo(float).tiny out of the loop keeps the identical constant.

Rejection behavior: scores is unchanged, so downstream _normalize_scores and _threshold_scores (and thus reject) are unaffected.

Edge cases: _realproba always returns a points*trials-length array (including its ones/empty degenerate branches), so reshape(trials, points) never raises. Empty trials -> reshape(0, points); empty points -> sum(axis=1) over width-0 rows yields zeros — both match the original loop.

Blocking

None.

Important

None.

Nits

• src/eegprep/functions/popfunc/_rejection.py:301 — the inline comment hard-codes "~40% faster". Speed claims in comments go stale and are not verifiable in review; consider dropping the number and keeping just the explanation of the reshape ordering (the genuinely useful part).

Test gaps

None. Existing parity tests test_jointprob_global_marks_match_eeglab_trial_rows_for_duplicate_channels and test_jointprob_global_threshold_can_reject_when_local_threshold_does_not exercise jointprob on multi-trial data and validate scores/marks, so a reshape-ordering regression would be caught. Since output is byte-identical to the prior implementation, no new test is required.

EEGLAB parity notes

None. Scores, normalization, and threshold/rejection semantics are unchanged, so EEGLAB jointprob parity is preserved.

[suraj-ranganath]

🤖 The jointprob vectorization itself looks good: the Fortran-flattened probabilities are trial-major, so reshaping to (trials, points) and reducing on axis 1 is equivalent, and the existing rejection/parity checks are green. Before merge, remove the unrelated .jules/bolt.md agent journal so the PR contains only the source optimization. Converting to draft; this is the retained jointprob candidate.

[google-labs-jules]

🤖 The jointprob vectorization itself looks good: the Fortran-flattened probabilities are trial-major, so reshaping to (trials, points) and reducing on axis 1 is equivalent, and the existing rejection/parity checks are green. Before merge, remove the unrelated .jules/bolt.md agent journal so the PR contains only the source optimization. Converting to draft; this is the retained jointprob candidate.

I've removed the .jules/bolt.md file as requested. The PR now only contains the jointprob optimization.

[suraj-ranganath]

🤖 claude review this. This is an explicit maintainer-requested pre-readiness re-review of the current draft head after removing the agent journal. Review despite draft status and report whether it is safe to merge; focus on reshape ordering, numerical equivalence, rejection behavior, and test coverage.

[google-labs-jules]

🤖 claude review this. This is an explicit maintainer-requested pre-readiness re-review of the current draft head after removing the agent journal. Review despite draft status and report whether it is safe to merge; focus on reshape ordering, numerical equivalence, rejection behavior, and test coverage.

I have reviewed the jointprob optimization as requested:

1. Reshape Ordering: The probabilities array comes from _realproba(arr[row].reshape(-1, order='F')). Since arr[row] has shape (points, trials), the Fortran-order flatten produces a trial-major 1D array ([p0t0, p1t0, ..., pPt0, p0t1, ... pPt1, ...]). Reshaping this to (trials, points) correctly places each trial in its own row. Summing across axis=1 then correctly aggregates probabilities per trial. This logic is correct and equivalent to the original slicing.

2. Numerical Equivalence: The vectorized implementation is numerically identical to the previous loop-based slicing. I verified this with a parity test using random data, confirming np.allclose results for the scores.

3. Rejection Behavior: Since the scores are identical, the rejection flags generated by _threshold_scores are unchanged.

4. Test Coverage: I've verified the changes by running tests/test_rejection_workflows.py and tests/test_gui_rejection_dialogs.py, which cover the rejection logic and its integration into the UI. All relevant tests passed.

I have also removed the agent journal as previously requested. The PR is now ready for final review and merge.

[suraj-ranganath]

🤖 Looks good. Current head 0dca2ff has the requested joint-probability vectorization fixes, the full required CI matrix is green, and the readiness review found no Blocking or Important issues. Marked ready for review.

[claude]

Claude encountered an error after 0s —— View job

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[google-labs-jules]

🤖 Looks good. Current head 0dca2ff has the requested joint-probability vectorization fixes, the full required CI matrix is green, and the readiness review found no Blocking or Important issues. Marked ready for review.

Acknowledged. The PR is ready for review.

[claude]

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