from Topology_zoo import get_source_nodes
import subprocess, sys
from concurrent.futures import ThreadPoolExecutor, as_completed

class Multi_scenario_simulation():
    
    def __init__(self, 先验拥塞概率, n_samples, 进程数):
        self.先验拥塞概率 = 先验拥塞概率
        self.n_samples = n_samples
        self.进程数 = 进程数
        self.拓扑名称 = ['Japan']
        self.label = ['DR', 'FPR', 'F1', 'F2']
        self.prop = 0.01
    

    def 获取各拓扑的源节点列表(self):
        
        # 列举每个拓扑对应的源节点列表
        源节点列表 = {}
        for name in self.拓扑名称:
            print(f"\r 正在获取{name} 的源节点列表    ", end="")
            源节点列表[name] = get_source_nodes(name)
            print(f"\r {name} 的源节点 共有 {len(源节点列表[name])} 个")
        
        print(f"\r {源节点列表}")

        return 源节点列表

def execute_command(prob, bias, key, value, seed_n):
    for cnt in range(len(value)):
        # 构建命令行参数
        for seed_ in range(int(seed_n), int(seed_n)+30):
            command = ["python3", 'mulitprocess_heu.py', str(prob), str(bias), str(seed_), key] + [str(i) for i in value[cnt]]
            subprocess.run(command)

if __name__ == '__main__':
    prob = sys.argv[1]
    bias = sys.argv[2]
    seed_n = sys.argv[3]
    print("bias", bias, 'seed_n', seed_n)
    Mss = Multi_scenario_simulation(0.1, 10, 1)
    源节点列表 = Mss.获取各拓扑的源节点列表()

    max_workers = 10  # 设置并发执行的最大任务数量
    with ThreadPoolExecutor(max_workers=max_workers) as executor:
        futures = {executor.submit(execute_command, prob, bias, key, value, seed_n): (key, value) for key, value in 源节点列表.items()}
        for future in as_completed(futures):
            key, value = futures[future]
            try:
                future.result()
            except Exception as e:
                print(f'An exception occurred: {e}')