import liberprocess as lp

# 创建进程管理器
manager = lp.ProcessManager()

# 定义目标函数
def worker_function(name, count):
    for i in range(count):
        print(f"Worker {name}: {i}")
        time.sleep(1)

# 创建并启动进程
manager.create_process(
    target=worker_function,
    args=("worker1", 5),
    name="MyWorker"
)
manager.start()

# 等待进程完成
manager.join()

import liberprocess as lp

# 创建进程池
pool = lp.ProcessPool(processes=4)

# 提交任务
results = []
for i in range(10):
    result = pool.apply_async(worker_function, args=(f"task_{i}", 3))
    results.append(result)

# 获取结果
for result in results:
    result.get()

# 关闭进程池
pool.close()
pool.join()

import liberprocess as lp

# 队列通信
queue_comm = lp.create_queue_comm()
queue_comm.put("Hello from main process")
message = queue_comm.get()

# 管道通信
pipe_comm = lp.create_pipe_comm()
pipe_comm.send("Hello through pipe")
data = pipe_comm.recv()

# 管理器通信
manager_comm = lp.create_manager_comm()
shared_dict = manager_comm.create_dict()
shared_list = manager_comm.create_list()

import liberprocess as lp

# 创建进程监控器
monitor = lp.create_monitor(monitoring_interval=1.0)

# 添加进程到监控
monitor.add_process(process_id)

# 设置监控回调
def on_process_event(event_type, process_id, data):
    print(f"Process {process_id}: {event_type} - {data}")

monitor.set_callback(on_process_event)

# 开始监控
monitor.start_monitoring()

import liberprocess as lp

# 创建性能收集器
collector = lp.PerformanceCollector()

# 收集进程性能数据
performance_data = collector.collect_process_performance(process_id)
print(f"CPU使用率: {performance_data['cpu_percent']}%")
print(f"内存使用: {performance_data['memory_info']['rss']} bytes")

# 获取历史数据
history = collector.get_historical_data(process_id, hours=1)
stats = collector.get_statistics(process_id)

import liberprocess as lp

# 创建资源管理器
resource_manager = lp.ResourceManager()

# 设置资源限制
resource_manager.set_cpu_limit(80.0)  # CPU使用率不超过80%
resource_manager.set_memory_limit(1024 * 1024 * 1024)  # 内存不超过1GB

# 设置告警回调
def on_resource_alert(alert_level, resource_type, current_value, limit_value):
    print(f"资源告警: {resource_type} {alert_level} - 当前: {current_value}, 限制: {limit_value}")

resource_manager.set_alert_callback(on_resource_alert)

# 开始监控
resource_manager.start_monitoring()

import liberprocess as lp

# 快速创建进程
process_manager = lp.create_process(
    target=my_function,
    args=(arg1, arg2),
    name="MyProcess"
)

# 快速创建进程池
pool = lp.create_process_pool(processes=4)

# 快速创建通信对象
queue_comm = lp.create_queue_comm(maxsize=100)
pipe_comm = lp.create_pipe_comm(duplex=True)
manager_comm = lp.create_manager_comm()

# 快速创建监控器
monitor = lp.create_monitor(monitoring_interval=2.0)

# 快速创建日志记录器
logger = lp.create_logger(name="MyApp", level="DEBUG")

# 获取系统信息
system_info = lp.get_system_info()
process_info = lp.get_process_info(pid=1234)

import liberprocess as lp

# 设置全局配置
lp.Config.set_debug_mode(True)
lp.Config.set_default_log_level("DEBUG")

# 获取当前配置
config = lp.Config.get_config()
print(config)

import liberprocess as lp

# 创建日志记录器
logger = lp.Logger(
    name="MyApp",
    level="INFO",
    console_output=True,
    file_output=True,
    log_file="app.log"
)

# 记录日志
logger.info("应用程序启动")
logger.warning("这是一个警告")
logger.error("发生错误")

# 使用便捷方法
logger.log_with_context("INFO", "处理任务", {"task_id": 123, "user": "admin"})

import liberprocess as lp

# 创建数据验证器
validator = lp.DataValidator()

# 定义验证规则
rules = {
    "name": {"required": True, "type": str, "min_length": 2},
    "age": {"required": True, "type": int, "min_value": 0, "max_value": 150},
    "email": {"required": False, "type": str, "pattern": "email"}
}

# 验证数据
data = {"name": "张三", "age": 25, "email": "zhangsan@example.com"}
is_valid, errors = validator.validate(data, rules)

if is_valid:
    print("数据验证通过")
else:
    print(f"验证失败: {errors}")

import liberprocess as lp

# 创建序列化器
serializer = lp.DataSerializer()

# 序列化数据
data = {"name": "测试", "values": [1, 2, 3], "timestamp": datetime.now()}
serialized = serializer.serialize(data, format="json")

# 反序列化数据
deserialized = serializer.deserialize(serialized, format="json")

# 压缩序列化
compressed = serializer.serialize_compressed(data, format="json", compression="gzip")
decompressed = serializer.deserialize_compressed(compressed, format="json", compression="gzip")