numba small fix

Author: codegithubka

models/numba_optimized.py

@@ -431,6 +431,66 @@ def _detect_clusters_numba(
 
     return labels, sizes[:n_clusters]
 
+
+@njit(cache=True)
+def _check_percolation(
+    labels: np.ndarray,
+    sizes: np.ndarray,
+    direction: int,
+) -> Tuple[bool, int, int]:
+    """
+    Check for percolating clusters.
+    
+    Args:
+        direction: 0=horizontal, 1=vertical, 2=both
+    
+    Returns:
+        percolates, perc_label, perc_size
+    """
+    rows, cols = labels.shape
+    max_label = len(sizes)
+    
+    touches_left = np.zeros(max_label + 1, dtype=np.bool_)
+    touches_right = np.zeros(max_label + 1, dtype=np.bool_)
+    touches_top = np.zeros(max_label + 1, dtype=np.bool_)
+    touches_bottom = np.zeros(max_label + 1, dtype=np.bool_)
+    
+    for i in range(rows):
+        if labels[i, 0] > 0:
+            touches_left[labels[i, 0]] = True
+        if labels[i, cols - 1] > 0:
+            touches_right[labels[i, cols - 1]] = True
+    
+    for j in range(cols):
+        if labels[0, j] > 0:
+            touches_top[labels[0, j]] = True
+        if labels[rows - 1, j] > 0:
+            touches_bottom[labels[rows - 1, j]] = True
+    
+    best_label = 0
+    best_size = 0
+    
+    for label in range(1, max_label + 1):
+        percolates_h = touches_left[label] and touches_right[label]
+        percolates_v = touches_top[label] and touches_bottom[label]
+        
+        is_percolating = False
+        if direction == 0:
+            is_percolating = percolates_h
+        elif direction == 1:
+            is_percolating = percolates_v
+        else:
+            is_percolating = percolates_h or percolates_v
+        
+        if is_percolating:
+            cluster_size = sizes[label - 1]
+            if cluster_size > best_size:
+                best_size = cluster_size
+                best_label = label
+    
+    return best_label > 0, best_label, best_size
+
+
 # ============================================================================
 # PUBLIC API - CLUSTER DETECTION
 # ============================================================================