⚡ Bolt: vectorize griddata_v4 in topoplot

[suraj-ranganath]

💡 What: Vectorized the query point evaluation in griddata_v4 using NumPy broadcasting and the @ operator.
🎯 Why: The original double-nested loop was a significant performance bottleneck for topographic interpolation, taking ~63ms for a standard 67x67 grid.
📊 Impact: Expected ~11x speedup in biharmonic spline interpolation (down to ~5.5ms).
🔬 Measurement: Verified with a benchmark script measuring 10 calls on a 67x67 grid.

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PR created automatically by Jules for task 3522339627691010615 started by @suraj-ranganath

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[claude]

Claude finished @suraj-ranganath's task in 2m 23s —— View job

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Review: vectorize griddata_v4 in topoplot

• Read the diff and surrounding code
• Verify numerical equivalence vs. the original loop
• Check edge cases (dtype, memory, xy reassignment)
• Post review

Verdict: LGTM. The change is a correct, behavior-preserving vectorization. Nice cleanup — it also reads better than the nested loop.

Correctness ✅

The new block is mathematically equivalent to the old double loop:

• xy.ravel() still resolves correctly. The old code reassigned xy = xy[:, None] before the loop and then called xy.ravel(). The new code drops that reassignment and calls xy.ravel() on the original 1-D xy = x + 1j*y (line 26). Both produce the same 1-D array of channel coordinates, so the removal is safe. 👍
• Broadcasting is right. (xq + 1j*yq)[..., np.newaxis] is (m, n, 1), minus xy.ravel() (N,) → d of shape (m, n, N), exactly the per-point distances the loop computed one cell at a time.
• Green's function + zero handling preserved. g = (d**2)*(log(d)-1) with g[d == 0] = 0 matches the old per-point logic, including the d == 0 case (query point coincident with a channel, where d**2*(log(0)-1) is nan and gets zeroed). The np.errstate guard still suppresses the log(0) warning.
• g @ weights is (m, n, N) @ (N,) → (m, n), equal to the old np.dot(g, weights) per cell. Output dtype stays real float, matching the old np.zeros_like(xq).

Minor notes (non-blocking)

1. Memory tradeoff. The vectorized d/g now materialize a full (m, n, N) array instead of one (N,) row at a time. For a 67×67 grid with ~64 channels that's ~2.3 MB — negligible. Worth keeping in mind only if grid resolution or channel count ever grows by orders of magnitude; topoplot grids are always small, so this is fine.
2. .jules/bolt.md is a bot bookkeeping artifact. Its header dates the work 2025-05-15, which looks stale/incorrect. Not part of the functional change — just confirm this file is intended to be checked in per the repo's Jules convention.
3. Test coverage. There's an existing tests/test_topoplot.py. Since this is a pure refactor of a numerical kernel, it'd be good to confirm those pass (and ideally that they exercise griddata_v4, including a query point exactly on a channel to cover the d == 0 path). I couldn't run them here — the CI sandbox blocked uv run pytest. Please make sure uv run pytest tests/test_topoplot.py is green before merge.

Nothing blocking. The vectorization is correct and the ~11x speedup claim is plausible given it removes a m*n Python-level loop.

Note: I was unable to run uv run pytest / a numerical old-vs-new comparison in this environment due to sandbox permission restrictions, so the equivalence above is from code inspection rather than execution.

[suraj-ranganath]

🤖 Closing as a duplicate of the retained topoplot candidate #263. It implements the same np.repeat/np.tile replacement and equivalent numerical coverage.

[google-labs-jules]

🤖 Closing as a duplicate of the retained topoplot candidate #263. It implements the same np.repeat/np.tile replacement and equivalent numerical coverage.

Understood. Acknowledging that this work is a duplicate of #263 and stopping work on this task.