如何优化Abaqus Python创建近距节点集的代码以提升效率？

需要读取坐标文本文件，在Abaqus中创建最近节点集，但当前循环处理单个点耗时约1秒，效率极低。现有代码如下：

file_hole_path = 'I:/Worktem/hole_shape_xyz_nocracks_scale.txt'
def read_xyz_file(file_hole_path):  # Read hole edge shape XYZ points
    coordinates = []
    with open(file_hole_path, 'r') as file:
        for line in file:
            parts = line.strip().split()
            coordinates.append(
                (float(parts[0]), float(parts[1]), float(parts[2])))
    return tuple(coordinates)

rootAsse = mdb.models['Model1'].rootAssembly
Instance = rootAsse.instances['plate-1']
allNodes = Instance.nodes
Path_xyz = read_xyz_file(file_hole_path)
cor_nodes_label = []
previous_value = None  # Initialize a variable to store the previous value
# general cal
start_time = time.time()
for i_k in range(0, len(Path_xyz)):
    cor_nodes = allNodes.getClosest(Path_xyz[i_k]).label
    if cor_nodes != previous_value:  # Check if the current value is different from the previous value
        cor_nodes_label.append(cor_nodes)  # Append the value to the tuple y
        previous_value = cor_nodes  # Update the previous value
    # print
    total_time = time.time() - start_time
    print("Step {}/{}; Total time elapsed: {:.2f} seconds".format(i_k +
          1, len(Path_xyz), total_time))

cor_nodes_label = tuple(cor_nodes_label)
rootAsse.Set(name='Set-hole',
             nodes=allNodes.sequenceFromLabels((cor_nodes_label),))

1. 使用批量查找函数getClosestMultiple #

Abaqus的NodeArray提供了getClosestMultiple方法，支持一次性传入多个坐标点批量查找最近节点，大幅减少Python与Abaqus内核的交互次数（这是原代码效率低下的核心原因）。

2. 优化去重逻辑 #

批量获取节点后，直接用集合或有序字典去重，比循环逐个判断效率更高；用dict.fromkeys可以保留节点标签首次出现的顺序。

3. 减少冗余IO操作 #

原代码每步循环都打印耗时，频繁IO会拖慢速度，改为循环结束后打印总耗时，或每N步打印一次进度。

import time
file_hole_path = 'I:/Worktem/hole_shape_xyz_nocracks_scale.txt'

def read_xyz_file(file_hole_path):  # Read hole edge shape XYZ points
    coordinates = []
    with open(file_hole_path, 'r') as file:
        for line in file:
            parts = line.strip().split()
            coordinates.append(
                (float(parts[0]), float(parts[1]), float(parts[2])))
    return coordinates  # 返回列表即可，无需转元组

rootAsse = mdb.models['Model1'].rootAssembly
Instance = rootAsse.instances['plate-1']
allNodes = Instance.nodes
Path_xyz = read_xyz_file(file_hole_path)

start_time = time.time()

# 批量查找最近节点
closest_nodes = allNodes.getClosestMultiple(coordinates=Path_xyz)
# 提取节点标签并有序去重
cor_nodes_label = list(dict.fromkeys(node.label for node in closest_nodes))

total_time = time.time() - start_time
print(f"Total time elapsed: {total_time:.2f} seconds")

# 创建节点集
rootAsse.Set(name='Set-hole', nodes=allNodes.sequenceFromLabels(cor_nodes_label))

额外优化提示 #

• 如果坐标文件体积极大，可以分块读取并分批次批量处理，避免一次性加载过多数据占用内存。
• 确保Instance.nodes的引用已提前缓存，避免重复获取节点集合。

内容的提问来源于stack exchange，提问作者Elliot