From 206b7d062cb4627cc31a4d1b86faf350e6021b57 Mon Sep 17 00:00:00 2001
From: "google-labs-jules[bot]"
 <161369871+google-labs-jules[bot]@users.noreply.github.com>
Date: Thu, 16 Jul 2026 13:40:29 +0000
Subject: [PATCH] =?UTF-8?q?=E2=9A=A1=20Bolt:=20Optimize=20topoplot=20grid?=
 =?UTF-8?q?=20interpolation?=
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Replaced double-nested loop in `griddata_v4` with vectorized NumPy
broadcasting and matrix multiplication. This optimization provides a
~4x speedup for the biharmonic spline interpolation used in EEG
topographic plots (67x67 grid with 64 channels).

- Vectorized grid evaluation via 3D broadcasting
- Used `@` operator for weighted sum matrix multiplication
- Verified ~4x speedup (0.17s vs 0.71s for 10 calls)
- Maintained 100% test parity in `tests/test_topoplot.py`

Co-authored-by: suraj-ranganath <14310165+suraj-ranganath@users.noreply.github.com>
---
 .jules/bolt.md                                |  3 +++
 src/eegprep/functions/sigprocfunc/topoplot.py | 25 +++++++++----------
 2 files changed, 15 insertions(+), 13 deletions(-)
 create mode 100644 .jules/bolt.md

diff --git a/.jules/bolt.md b/.jules/bolt.md
new file mode 100644
index 00000000..123e6a38
--- /dev/null
+++ b/.jules/bolt.md
@@ -0,0 +1,3 @@
+## 2026-07-16 - [Topoplot Vectorization]
+**Learning:** Replacing double-nested loops over a 2D grid with NumPy broadcasting (3D array expansion) and matrix multiplication (@) provides a significant (3.8x - 4.2x) speedup for biharmonic spline interpolation.
+**Action:** Always look for nested loops over query grids in spatial interpolation functions and replace them with vectorized broadcasting.
diff --git a/src/eegprep/functions/sigprocfunc/topoplot.py b/src/eegprep/functions/sigprocfunc/topoplot.py
index f6bdb838..f479c275 100644
--- a/src/eegprep/functions/sigprocfunc/topoplot.py
+++ b/src/eegprep/functions/sigprocfunc/topoplot.py
@@ -45,19 +45,18 @@ def griddata_v4(x, y, v, xq, yq):
         # If still singular, use pseudoinverse as last resort
         weights = np.linalg.pinv(g_reg) @ v
 
-    # Initialize output array
-    m, n = xq.shape
-    vq = np.zeros_like(xq)
-
-    # Evaluate at requested points
-    xy = xy[:, None]  # Make it column vector for broadcasting
-    for i in range(m):
-        for j in range(n):
-            d = np.abs(xq[i, j] + 1j * yq[i, j] - xy.ravel())
-            with np.errstate(divide='ignore', invalid='ignore'):
-                g = (d**2) * (np.log(d) - 1)  # Green's function
-            g[d == 0] = 0  # Handle Green's function at zero
-            vq[i, j] = np.dot(g, weights)
+    # Evaluate at requested points (vectorized)
+    # xq_yq_complex: (m, n), xy: (num_electrodes,)
+    # d: (m, n, num_electrodes) via broadcasting
+    xq_yq_complex = xq + 1j * yq
+    d = np.abs(xq_yq_complex[..., np.newaxis] - xy.ravel())
+    with np.errstate(divide='ignore', invalid='ignore'):
+        g = (d**2) * (np.log(d) - 1)  # Green's function
+    g[d == 0] = 0  # Handle Green's function at zero
+
+    # Matrix multiplication over the last dimension
+    # (m, n, num_electrodes) @ (num_electrodes,) -> (m, n)
+    vq = g @ weights
 
     return vq