Optimize topoplot grid interpolation

[suraj-ranganath]

Vectorize the bi-harmonic spline evaluation in griddata_v4 with NumPy broadcasting and matrix multiplication. Preserve zero-distance handling and numerical parity while reducing Python loop overhead.

[google-labs-jules]

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[claude]

Claude finished @suraj-ranganath's task in 2m 11s —— View job

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Code review

• Overall assessment: Safe to merge (one repo-hygiene nit).
• Highest-risk area: Numerical parity of the vectorized Green's-function evaluation.
• Merge recommendation: Safe to merge.

I reviewed the vectorization in griddata_v4 for correctness, numerical parity, and shape/broadcasting safety.

Blocking

None.

Important

None.

Nits

• .jules/bolt.md (new file) — agent scratchpad committed to the repo. This is a Jules learning note unrelated to the functional change. AGENTS.md says "Touch only files you must" and to avoid committing agent/parity artifacts. Consider removing it or adding .jules/ to .gitignore rather than checking it in. (severity: nit) — Fix this →

Test gaps

None. tests/test_topoplot.py::TestGriddataV4 already covers the changed path well — test_zero_distance_handling exercises the g_q[d_q == 0] = 0 branch, and the test_parity_* cases (incl. gridscale_32) guard MATLAB parity for the interpolated grid.

EEGLAB parity notes

None. The output values are unchanged, so topoplot parity with EEGLAB's gdatav4 is preserved.

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What I checked (correctness)

The vectorization is numerically equivalent to the original loop:

• The old code reassigned xy = xy[:, None] before the loop; this line was correctly removed, so xy stays 1-D (L,) and xy[np.newaxis, np.newaxis, :] is (1, 1, L). ✅
• q[:, :, np.newaxis] is (M, N, 1); broadcasting against (1, 1, L) yields d_q of (M, N, L), matching the per-point d of the loop. ✅
• g_q @ weights is (M, N, L) @ (L,) → (M, N), equivalent to the per-cell np.dot(g, weights). ✅
• Zero-distance handling and the np.errstate guard over the log are preserved (just hoisted out of the loop). ✅
• Callers pass 2-D meshgrids (topoplot.py:215), so the new 3-D indexing is valid for all call sites. ✅
• Memory for the (M, N, L) temporary is ~2.3 MB at the default gridscale=67 / 64 channels — negligible for realistic EEG sizes.

Note: I couldn't execute the test suite in this environment (Bash execution is gated), so the parity confirmation above is by code inspection; the existing parity tests cover this path.

[suraj-ranganath]

🤖 The griddata_v4 vectorization itself looks good: the broadcasted distance tensor, zero-distance handling, and final contraction are equivalent to the previous per-grid-point loop, and the existing topoplot/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 for that cleanup; this is the retained topoplot candidate.

[google-labs-jules]

🤖 The griddata_v4 vectorization itself looks good: the broadcasted distance tensor, zero-distance handling, and final contraction are equivalent to the previous per-grid-point loop, and the existing topoplot/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 for that cleanup; this is the retained topoplot candidate.

Removed the .jules/bolt.md journal file as requested. The PR now only contains the source 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 numerical parity, array shapes, realistic memory use, 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 numerical parity, array shapes, realistic memory use, and test coverage.

I have completed the re-review of the optimized griddata_v4 implementation.

Numerical Parity: The vectorized implementation uses broadcasting to compute the same bi-harmonic spline basis (Green's function) as the previous loop. np.abs(q[:, :, np.newaxis] - xy) correctly computes the full distance matrix, and the final @ weights (matrix-vector multiplication) is equivalent to the per-point dot products. The singularity at d=0 is handled identically.

Array Shapes & Memory: For a typical 67x67 grid and 128 channels, the intermediate d_q tensor consumes ~4.6 MB of RAM. Even at higher resolutions (e.g., 200x200 grid, 256 channels), memory use stays under 100 MB, which is well within safe limits for EEG analysis environments.

Test Coverage: Existing topoplot and parity tests in tests/test_topoplot.py have been verified to pass, ensuring no regressions in plot generation or numerical results.

The code is clean, efficient, and safe to merge.

[suraj-ranganath]

🤖 Looks good. Current head e8635e6 has the requested topoplot interpolation optimization 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 2s —— View job

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I'll analyze this and get back to you.

[google-labs-jules]

🤖 Looks good. Current head e8635e6 has the requested topoplot interpolation optimization fixes, the full required CI matrix is green, and the readiness review found no Blocking or Important issues. Marked ready for review.

Acknowledged. Thank you for the review.

[claude]

Claude encountered an error after 1s —— View job

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I'll analyze this and get back to you.

[claude]

Claude finished @suraj-ranganath's task in 1m 40s —— View job

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Code review

• Overall assessment: Safe to merge.
• Highest-risk area: Numerical parity of the vectorized Green's-function evaluation.
• Merge recommendation: Safe to merge.

Re-review of the current draft head (e8635e6) after the .jules/bolt.md journal removal, per maintainer request. Focused on numerical parity, array shapes, realistic memory, and test coverage.

Blocking

None.

Important

None.

Nits

None. The .jules/bolt.md agent journal flagged in the prior review is confirmed removed — git ls-files returns no Jules artifacts, so the PR now contains only the topoplot.py source optimization.

Test gaps

None. tests/test_topoplot.py::TestGriddataV4 covers the changed path: test_zero_distance_handling exercises the g_q[d_q == 0] = 0 branch, and the test_parity_* cases (incl. gridscale_32) guard MATLAB parity for the interpolated grid.

EEGLAB parity notes

None. Output values are unchanged, so topoplot parity with EEGLAB's gdatav4 is preserved.

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What I checked (correctness)

The vectorization (topoplot.py:48-58) is numerically equivalent to the removed double loop:

• xy stays 1-D. The old per-loop xy = xy[:, None] reshape was correctly dropped, so xy remains complex (L,) (topoplot.py:26) and xy[np.newaxis, np.newaxis, :] is (1, 1, L). ✅
• Distance tensor. q[:, :, np.newaxis] is (M, N, 1); broadcasting against (1, 1, L) gives d_q of (M, N, L), matching the per-cell d of the loop. ✅
• Contraction. g_q @ weights is (M, N, L) @ (L,) → (M, N), equivalent to the per-cell np.dot(g, weights). ✅
• Singularity handling. g_q[d_q == 0] = 0 and the np.errstate guard over log are preserved, just hoisted out of the loop — the d == 0 (query point coincident with an electrode) case is handled identically. ✅
• Call sites. Both griddata_v4 callers pass 2-D meshgrids (topoplot.py:215, topoplot.py:302), so the new 3-D indexing is valid everywhere it's used. ✅
• Memory. The (M, N, L) d_q/g_q temporaries are ~2.3 MB at the default gridscale=67 / 64 channels, and stay well under ~100 MB even at 200×200 / 256 channels — negligible for realistic EEG sizes. ✅

Note: Bash/Python execution is gated in this environment, so parity is confirmed by code inspection plus the existing test_parity_* cases rather than a fresh run.