feat: implement cluster averaging pipeline, UI controls

src/pygui/backend/cluster_average.py

@@ -0,0 +1,60 @@
+"""Cluster Average utility for wafer sensor values."""
+
+
+from __future__ import annotations
+import math
+from pygui.backend.wafer.zwafer_models import Sensor
+
+
+def average_clusters(values: list[float], clusters: list[list[int]]) -> list[float]:
+    """Return a new list of values where members of each cluster are replaced by their mean.
+
+    NaN values are filtered out before computing the mean.
+    If all values in a cluster are NaN, they remain NaN.
+    """
+    result = list(values)
+    for cluster in clusters:
+        valid_vals = []
+        for idx in cluster:
+            if idx < len(values):
+                val = values[idx]
+                if not math.isnan(val):
+                    valid_vals.append(val)
+        
+        if valid_vals:
+            mean_val = sum(valid_vals) / len(valid_vals)
+            for idx in cluster:
+                if idx < len(result):
+                    result[idx] = mean_val
+    return result
+
+
+def group_sensors_by_radius(sensors: list[Sensor], tolerance: float = 2.0) -> list[list[int]]:
+    """Group sensor indices into clusters if their radial distances are within tolerance."""
+    # Compute radii for all sensors
+    radii = [(i, math.hypot(s.x, s.y)) for i, s in enumerate(sensors)]
+    # Filter out center sensors (r < 1.0)
+    non_center = [item for item in radii if item[1] >= 1.0]
+    if not non_center:
+        return []
+
+    # Sort by radius
+    non_center.sort(key=lambda x: x[1])
+
+    clusters: list[list[int]] = []
+    current_cluster: list[int] = [non_center[0][0]]
+    last_r = non_center[0][1]
+
+    for idx, r in non_center[1:]:
+        if r - last_r <= tolerance:
+            current_cluster.append(idx)
+        else:
+            if len(current_cluster) > 1:
+                clusters.append(current_cluster)
+            current_cluster = [idx]
+            last_r = r
+
+    if len(current_cluster) > 1:
+        clusters.append(current_cluster)
+
+    return clusters