⚡ Bolt: vectorize griddata_v4 in topoplot.py

[suraj-ranganath]

This PR implements a performance optimization for the griddata_v4 function used in EEG topographic plotting.

Changes

• Replaced the nested loop for query point evaluation in src/eegprep/functions/sigprocfunc/topoplot.py with a vectorized implementation using NumPy broadcasting and matrix multiplication.

Performance Impact

• Measurement: Benchmarked using a standard EEGLAB-style 67x67 grid and 64 electrodes.
• Result: Average execution time for the evaluation phase dropped from ~62ms to ~10ms, representing a ~6x speedup.
• Efficiency: Leveraging optimized BLAS/LAPACK routines through NumPy's @ operator reduces Python interpreter overhead significantly.

Verification

• Ran tests/test_topoplot.py and tests/test_pop_topoplot.py to ensure numerical parity and functional correctness are maintained.
• Successfully ran pre-commit.py.
• Documentation updated in .jules/bolt.md.

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PR created automatically by Jules for task 1568333760095009189 started by @suraj-ranganath

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[claude]

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Review: vectorize griddata_v4 in topoplot.py

• Read PR diff and changed files
• Analyze correctness / numerical parity of the vectorization
• Attempt to run tests (blocked by sandbox — see note)
• Post review

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Verdict: LGTM. The vectorization is mathematically equivalent to the original nested loop and is a clean, idiomatic NumPy rewrite. Nice speedup.

Correctness ✅

The new evaluation block is element-for-element identical to the loop it replaces:

q = (xq + 1j * yq)[..., np.newaxis]   # (m, n, 1)
d = np.abs(q - xy)                     # xy is (npts,) → broadcasts to (m, n, npts)
g = (d**2) * (np.log(d) - 1)          # same Green's function
g[d == 0] = 0                          # same zero-distance handling
vq = g @ weights                       # (m, n, npts) @ (npts,) → (m, n)

• Broadcasting (m,n,1) - (npts,) reproduces the per-point xq[i,j] + 1j*yq[i,j] - xy.ravel() exactly.
• g @ weights matches the per-point np.dot(g, weights).
• The d == 0 mask (grid point coinciding with an electrode) is preserved, so the log-singularity handling is unchanged.
• weights is real (derived from a real solve/pinv), and d is a magnitude, so vq stays real — no spurious complex output.

Minor observations (non-blocking)

1. Output dtype changed slightly. The old code seeded the result with vq = np.zeros_like(xq), so it inherited xq's dtype; the new code returns whatever g @ weights produces (float64). For the usual float meshgrid query grids this is identical — and arguably more correct, since an integer xq would previously have truncated results. No action needed, just noting the semantics.

2. Memory trade-off is fine. The intermediate d/g arrays are (m, n, npts) — ~2.3 MB for the 67×67×64 case cited. Negligible for topoplot grid sizes, as the PR notes.

3. .jules/bolt.md added to the repo. This is a tool scratch/learnings note being committed to the repo root under .jules/. Per AGENTS.md ("touch only files you must"), consider whether this belongs in the repo or should be .gitignored / kept out of the PR. Also the header date is 2025-05-15 (today is 2026-07-04) — cosmetic only. Fix this →

Note on testing ⚠️

I could not execute tests/test_topoplot.py / tests/test_pop_topoplot.py or a standalone parity script in this environment (command/write permissions were denied in the sandbox). The PR reports both suites pass; given the change is provably equivalent above, I'd expect green — please confirm CI is passing before merge.