基于python代码，实现500个节点进行随机多项式，计算运行时间和所占内存，然后将节点分区之后，再次计算得出对比结果，并绘制折线图

以下是基于 Python 代码实现500个节点进行随机多项式、计算运行时间和所占内存、节点分区、对比结果和绘制折线图的示例代码：

import random
import time
import psutil
import matplotlib.pyplot as plt

# 生成随机多项式
def generate_random_polynomial(n):
    return [random.randint(1, 10) for i in range(n)]

# 计算运行时间和所占内存
def calculate_running_time_and_memory(n):
    polynomial = generate_random_polynomial(n)
    start_time = time.time()
    process = psutil.Process()
    memory_before = process.memory_info().rss
    result = sum(polynomial)
    memory_after = process.memory_info().rss
    end_time = time.time()
    return end_time - start_time, memory_after - memory_before

# 节点分区
def partition_nodes(n, num_partitions):
    return [i % num_partitions for i in range(n)]

# 计算节点分区后的运行时间和所占内存
def calculate_running_time_and_memory_with_partition(n, num_partitions):
    partitions = partition_nodes(n, num_partitions)
    start_time = time.time()
    process = psutil.Process()
    memory_before = process.memory_info().rss
    result = 0
    for partition in range(num_partitions):
        polynomial = [p for i, p in enumerate(partitions) if i % num_partitions == partition]
        result += sum(polynomial)
    memory_after = process.memory_info().rss
    end_time = time.time()
    return end_time - start_time, memory_after - memory_before

# 计算运行时间和所占内存的列表
n_values = list(range(1, 501))
time_values = []
memory_values = []
partition_time_values = []
partition_memory_values = []
num_partitions = 10

for n in n_values:
    time_value, memory_value = calculate_running_time_and_memory(n)
    time_values.append(time_value)
    memory_values.append(memory_value)

    partition_time_value, partition_memory_value = calculate_running_time_and_memory_with_partition(n, num_partitions)
    partition_time_values.append(partition_time_value)
    partition_memory_values.append(partition_memory_value)

# 绘制折线图
plt.plot(n_values, time_values, label='Time')
plt.plot(n_values, memory_values, label='Memory')
plt.plot(n_values, partition_time_values, label='Partition Time')
plt.plot(n_values, partition_memory_values, label='Partition Memory')
plt.xlabel('Number of Nodes')
plt.ylabel('Time/Memory (Bytes)')
plt.title('Performance Comparison')
plt.legend()
plt.show()

此代码将生成500个节点的随机多项式，并比较计算该多项式的运行时间和所占内存，以及将节点分区后的运行时间和所占内存。最后，该代码使用 Matplotlib 库绘制了四条线（时间、内存、分区时间和分区内存）的折线图，以可视化比较结果。