重构管道中心选择逻辑，优化数据处理方式

app/algorithms/burst_location/network_partitioner.py

@@ -21,25 +21,19 @@ def _to_metis_edge_weight(edge_weight):
 
 
 def pick_center_pipe(node_x, node_y, candidate_pipe, pipe_start_node, pipe_end_node):
-    data_set_t = pd.DataFrame(dtype=object)
-    data_set_t["x"] = (
-        node_x[pipe_start_node[candidate_pipe]].values
-        + node_x[pipe_start_node[candidate_pipe]].values
-    ) / 2
-    data_set_t["y"] = (
-        node_y[pipe_end_node[candidate_pipe]].values
-        + node_y[pipe_end_node[candidate_pipe]].values
-    ) / 2
-    data_set_t.index = list(candidate_pipe)
-    mean_x = data_set_t["x"].mean()
-    mean_y = data_set_t["y"].mean()
-    data_set_t["d"] = abs(data_set_t["x"] - mean_x) + abs(data_set_t["y"] - mean_y)
-    distance_t = data_set_t["d"].sort_values(ascending=True, inplace=False)
-    """if distance_t.index==[]:
-        print(candidate_pipe)
-    else:"""
-    center_t = distance_t.index[0]
-    return center_t
+    candidate_pipe_list = list(candidate_pipe)
+    start_nodes = pipe_start_node[candidate_pipe_list]
+    end_nodes = pipe_end_node[candidate_pipe_list]
+
+    x_vals = (
+        node_x[start_nodes].to_numpy() + node_x[start_nodes].to_numpy()
+    ) / 2.0
+    y_vals = (node_y[end_nodes].to_numpy() + node_y[end_nodes].to_numpy()) / 2.0
+    mean_x = float(np.mean(x_vals))
+    mean_y = float(np.mean(y_vals))
+    distance = np.abs(x_vals - mean_x) + np.abs(y_vals - mean_y)
+    center_idx = int(np.argmin(distance))
+    return candidate_pipe_list[center_idx]
 
 
 def find_new_center_pipe(
@@ -56,11 +50,8 @@ def find_new_center_pipe(
 
 def cal_area_node_linked_pipe(nodeset, node_pipe_dic):
     pipeset = []
-    nodeset = list(nodeset)
-    for i in range(len(nodeset)):
-        temp_node = nodeset[i]
-        pipe = node_pipe_dic[temp_node]
-        pipeset = pipeset + pipe
+    for temp_node in nodeset:
+        pipeset.extend(node_pipe_dic[temp_node])
     return pipeset