Python历史记录管理之保存最后N个元素的完整指南

from collections import deque

# 创建最大长度为5的历史记录
history = deque(maxlen=5)

# 添加元素
for i in range(10):
    history.append(i)
    print(f"添加 {i}: {list(history)}")

# 输出结果：
# 添加 0: [0]
# 添加 1: [0, 1]
# ...
# 添加 4: [0, 1, 2, 3, 4]
# 添加 5: [1, 2, 3, 4, 5]  # 自动移除最旧元素

from collections import deque
from datetime import datetime, timedelta

class timestampedhistory:
    """带时间戳的历史记录系统"""
    
    def __init__(self, maxlen=1000):
        self.history = deque(maxlen=maxlen)
        self.timestamps = deque(maxlen=maxlen)
    
    def add(self, item):
        """添加带时间戳的记录"""
        now = datetime.now()
        self.history.append(item)
        self.timestamps.append(now)
    
    def get_recent(self, seconds=60):
        """获取最近n秒的记录"""
        cutoff = datetime.now() - timedelta(seconds=seconds)
        recent_items = []
        
        # 反向遍历提高效率
        for i in range(len(self.history)-1, -1, -1):
            if self.timestamps[i] < cutoff:
                break
            recent_items.append(self.history[i])
        
        return list(reversed(recent_items))
    
    def __str__(self):
        return f"历史记录: {len(self.history)}/{self.history.maxlen}"

# 使用示例
sensor_history = timestampedhistory(maxlen=100)
sensor_history.add(23.5)
sensor_history.add(24.1)
print(sensor_history.get_recent(30))  # 获取最近30秒的记录

class weightedhistory:
    """带权重的历史记录系统"""
    
    def __init__(self, maxlen=100, decay=0.9):
        self.history = deque(maxlen=maxlen)
        self.weights = deque(maxlen=maxlen)
        self.decay = decay  # 衰减因子
    
    def add(self, item, weight=1.0):
        """添加带权重的记录"""
        self.history.append(item)
        self.weights.append(weight)
        
        # 应用衰减因子
        for i in range(len(self.weights)):
            self.weights[i] *= self.decay
    
    def weighted_average(self):
        """计算加权平均值"""
        total = 0.0
        weight_sum = 0.0
        for item, weight in zip(self.history, self.weights):
            total += item * weight
            weight_sum += weight
        return total / weight_sum if weight_sum > 0 else 0

# 使用示例
stock_history = weightedhistory(maxlen=50, decay=0.95)
stock_history.add(150.5)  # 最新数据权重最高
stock_history.add(149.8)
print(f"加权平均股价: {stock_history.weighted_average():.2f}")

class multidimensionhistory:
    """多维度历史记录系统"""
    
    def __init__(self, maxlen=100, dimensions=3):
        self.maxlen = maxlen
        self.dimensions = dimensions
        self.history = [deque(maxlen=maxlen) for _ in range(dimensions)]
    
    def add(self, *values):
        """添加多维数据"""
        if len(values) != self.dimensions:
            raise valueerror(f"需要 {self.dimensions} 个维度数据")
        
        for i, value in enumerate(values):
            self.history[i].append(value)
    
    def get_dimension(self, index):
        """获取特定维度历史"""
        return list(self.history[index])
    
    def correlation(self, dim1, dim2):
        """计算两个维度的相关性"""
        from statistics import mean, stdev
        if len(self.history[dim1]) < 2:
            return 0
        
        x = list(self.history[dim1])
        y = list(self.history[dim2])
        
        mean_x = mean(x)
        mean_y = mean(y)
        cov = sum((a - mean_x) * (b - mean_y) for a, b in zip(x, y))
        std_x = stdev(x) if len(x) > 1 else 1
        std_y = stdev(y) if len(y) > 1 else 1
        
        return cov / (std_x * std_y * len(x))

# 使用示例
sensor_data = multidimensionhistory(maxlen=100, dimensions=3)
sensor_data.add(23.5, 45, 1013)  # 温度, 湿度, 气压
sensor_data.add(24.1, 43, 1012)
print(f"温度-湿度相关性: {sensor_data.correlation(0, 1):.2f}")

class memoryoptimizedhistory:
    """内存优化的历史记录"""
    
    def __init__(self, maxlen=1000, dtype='f4'):
        """
        :param maxlen: 最大记录数
        :param dtype: 数据类型 ('f4'=float32, 'i4'=int32等)
        """
        import numpy as np
        self.buffer = np.zeros(maxlen, dtype=dtype)
        self.index = 0
        self.count = 0
        self.maxlen = maxlen
    
    def add(self, value):
        """添加新值"""
        self.buffer[self.index] = value
        self.index = (self.index + 1) % self.maxlen
        self.count = min(self.count + 1, self.maxlen)
    
    def get_history(self):
        """获取历史记录（按时间顺序）"""
        if self.count < self.maxlen:
            return self.buffer[:self.count]
        return np.concatenate((self.buffer[self.index:], self.buffer[:self.index]))
    
    def __len__(self):
        return self.count

# 使用示例
mem_history = memoryoptimizedhistory(maxlen=10000, dtype='f4')
for i in range(15000):
    mem_history.add(i * 0.1)
print(f"内存占用: {mem_history.buffer.nbytes / 1024:.2f}kb")

from collections import deque
import threading

class threadsafehistory:
    """线程安全的历史记录"""
    
    def __init__(self, maxlen=1000):
        self.history = deque(maxlen=maxlen)
        self.lock = threading.rlock()
    
    def add(self, item):
        """添加记录（线程安全）"""
        with self.lock:
            self.history.append(item)
    
    def get_last(self, n=1):
        """获取最后n条记录"""
        with self.lock:
            if n >= len(self.history):
                return list(self.history)
            return list(self.history)[-n:]
    
    def clear(self):
        """清空历史记录"""
        with self.lock:
            self.history.clear()

# 多线程测试
def worker(history, id):
    for i in range(1000):
        history.add(f"thread-{id}:{i}")

safe_history = threadsafehistory(maxlen=5000)
threads = []
for i in range(10):
    t = threading.thread(target=worker, args=(safe_history, i))
    threads.append(t)
    t.start()

for t in threads:
    t.join()

print(f"总记录数: {len(safe_history.history)}")

import sqlite3
from collections import deque
import pickle

class persistenthistory:
    """持久化历史记录系统"""
    
    def __init__(self, maxlen=1000, db_file='history.db'):
        self.maxlen = maxlen
        self.memory_cache = deque(maxlen=maxlen)
        self.db_file = db_file
        self._init_db()
    
    def _init_db(self):
        """初始化数据库"""
        with sqlite3.connect(self.db_file) as conn:
            conn.execute("""
                create table if not exists history (
                    id integer primary key,
                    timestamp datetime default current_timestamp,
                    data blob
                )
            """)
    
    def add(self, item):
        """添加记录（内存+持久化）"""
        self.memory_cache.append(item)
        
        # 异步持久化
        threading.thread(target=self._persist_item, args=(item,)).start()
    
    def _persist_item(self, item):
        """持久化单个项目"""
        try:
            with sqlite3.connect(self.db_file) as conn:
                data_blob = pickle.dumps(item)
                conn.execute("insert into history (data) values (?)", (data_blob,))
                # 保持数据库记录不超过最大长度
                conn.execute("""
                    delete from history 
                    where id <= (
                        select id from history 
                        order by id desc 
                        limit 1 offset ?
                    )
                """, (self.maxlen,))
        except exception as e:
            print(f"持久化失败: {str(e)}")
    
    def get_full_history(self):
        """获取完整历史（内存+数据库）"""
        # 从数据库加载旧记录
        full_history = []
        try:
            with sqlite3.connect(self.db_file) as conn:
                cursor = conn.execute("select data from history order by id")
                for row in cursor:
                    full_history.append(pickle.loads(row[0]))
        except exception as e:
            print(f"数据库加载失败: {str(e)}")
        
        # 添加内存缓存
        full_history.extend(self.memory_cache)
        return full_history[-self.maxlen:]  # 确保不超过最大长度

# 使用示例
db_history = persistenthistory(maxlen=100, db_file='app_history.db')
for i in range(200):
    db_history.add(f"event-{i}")
print(f"完整历史记录: {len(db_history.get_full_history())}条")

class systemmonitor:
    """系统性能监控器"""
    
    def __init__(self, maxlen=300):  # 保留5分钟数据（每秒1个点）
        self.cpu_history = deque(maxlen=maxlen)
        self.mem_history = deque(maxlen=maxlen)
        self.net_history = deque(maxlen=maxlen)
        self.alert_history = deque(maxlen=100)  # 告警历史
    
    def collect_metrics(self):
        """收集系统指标"""
        import psutil
        # 获取cpu使用率
        cpu_percent = psutil.cpu_percent(interval=1)
        self.cpu_history.append(cpu_percent)
        
        # 获取内存使用
        mem = psutil.virtual_memory()
        self.mem_history.append(mem.percent)
        
        # 获取网络流量
        net = psutil.net_io_counters()
        self.net_history.append((net.bytes_sent, net.bytes_recv))
        
        # 检查异常
        self._check_anomalies()
    
    def _check_anomalies(self):
        """检查异常情况"""
        # cpu持续高负载检测
        if len(self.cpu_history) > 10:
            last_10 = list(self.cpu_history)[-10:]
            if min(last_10) > 80:  # 持续10秒高于80%
                self.alert_history.append({
                    "time": datetime.now(),
                    "type": "cpu",
                    "value": sum(last_10)/10
                })
        
        # 内存泄漏检测
        if len(self.mem_history) > 60:
            last_minute = list(self.mem_history)[-60:]
            if all(a < b for a, b in zip(last_minute, last_minute[1:])):
                self.alert_history.append({
                    "time": datetime.now(),
                    "type": "mem",
                    "value": last_minute[-1]
                })
    
    def generate_report(self, hours=1):
        """生成性能报告"""
        # 计算指标（每小时3600个点，但只保留300个点）
        points = min(3600 * hours, len(self.cpu_history))
        return {
            "cpu_avg": sum(list(self.cpu_history)[-points:]) / points,
            "mem_avg": sum(list(self.mem_history)[-points:]) / points,
            "alerts": list(self.alert_history)
        }

# 使用示例
monitor = systemmonitor()
# 模拟运行
for _ in range(300):
    monitor.collect_metrics()
print(monitor.generate_report())

class useractionhistory:
    """用户操作历史记录"""
    
    def __init__(self, maxlen=50):
        self.history = deque(maxlen=maxlen)
        self.undo_stack = deque(maxlen=maxlen)
        self.redo_stack = deque(maxlen=maxlen)
    
    def execute(self, action):
        """执行操作"""
        action.execute()
        self.history.append(action)
        self.undo_stack.append(action)
        self.redo_stack.clear()  # 清除重做栈
    
    def undo(self):
        """撤销操作"""
        if not self.undo_stack:
            return false
        
        action = self.undo_stack.pop()
        action.undo()
        self.redo_stack.append(action)
        return true
    
    def redo(self):
        """重做操作"""
        if not self.redo_stack:
            return false
        
        action = self.redo_stack.pop()
        action.execute()
        self.undo_stack.append(action)
        return true
    
    def get_recent_actions(self, count=10):
        """获取最近操作"""
        return list(self.history)[-count:]

# 操作基类
class action:
    def execute(self):
        pass
    
    def undo(self):
        pass

# 使用示例
class textinsertaction(action):
    def __init__(self, document, text, position):
        self.document = document
        self.text = text
        self.position = position
    
    def execute(self):
        self.document.insert(self.position, self.text)
    
    def undo(self):
        self.document.delete(self.position, len(self.text))

# 模拟文档
class document:
    def __init__(self):
        self.content = ""
    
    def insert(self, position, text):
        self.content = self.content[:position] + text + self.content[position:]
    
    def delete(self, position, length):
        self.content = self.content[:position] + self.content[position+length:]

# 测试
doc = document()
history = useractionhistory()

history.execute(textinsertaction(doc, "hello", 0))
history.execute(textinsertaction(doc, " world", 5))
print(doc.content)  # "hello world"

history.undo()
print(doc.content)  # "hello"

history.redo()
print(doc.content)  # "hello world"

class algorithmstatetracker:
    """算法状态跟踪器"""
    
    def __init__(self, maxlen=100):
        self.state_history = deque(maxlen=maxlen)
        self.parameter_history = deque(maxlen=maxlen)
        self.performance_history = deque(maxlen=maxlen)
    
    def record_state(self, state, params, performance):
        """记录算法状态"""
        self.state_history.append(state)
        self.parameter_history.append(params)
        self.performance_history.append(performance)
    
    def get_best_state(self):
        """获取最佳性能状态"""
        if not self.performance_history:
            return none
        
        # 找到最佳性能索引
        best_index = max(range(len(self.performance_history)), 
                        key=lambda i: self.performance_history[i])
        
        return {
            "state": self.state_history[best_index],
            "params": self.parameter_history[best_index],
            "performance": self.performance_history[best_index]
        }
    
    def plot_convergence(self):
        """绘制收敛曲线"""
        import matplotlib.pyplot as plt
        plt.figure(figsize=(10, 6))
        plt.plot(self.performance_history, 'o-')
        plt.title("algorithm convergence")
        plt.xlabel("iteration")
        plt.ylabel("performance")
        plt.grid(true)
        plt.show()

# 使用示例
def optimization_algorithm(tracker):
    """模拟优化算法"""
    import numpy as np
    current_state = np.random.rand(10)
    best_performance = -float('inf')
    
    for i in range(1000):
        # 生成新参数
        params = np.random.rand(3)
        
        # 评估性能（模拟）
        performance = -np.sum((current_state - params)**2)
        
        # 记录状态
        tracker.record_state(current_state.copy(), params, performance)
        
        # 更新状态
        if performance > best_performance:
            current_state = params
            best_performance = performance

# 运行算法
tracker = algorithmstatetracker()
optimization_algorithm(tracker)

# 分析结果
print(f"最佳性能: {tracker.get_best_state()['performance']:.4f}")
tracker.plot_convergence()

class robusthistory:
    """健壮的历史记录系统"""
    
    def __init__(self, maxlen=1000):
        self.history = deque(maxlen=maxlen)
        self.error_log = deque(maxlen=100)  # 错误日志
    
    def safe_add(self, item):
        """安全添加记录"""
        try:
            # 验证数据类型
            if not isinstance(item, (int, float, str)):
                raise typeerror("不支持的数据类型")
            
            self.history.append(item)
            return true
        except exception as e:
            self.error_log.append({
                "time": datetime.now(),
                "error": str(e),
                "item": str(item)
            })
            return false
    
    def get_errors(self):
        """获取错误日志"""
        return list(self.error_log)

# 使用示例
robust_hist = robusthistory()
robust_hist.safe_add(42)  # 成功
robust_hist.safe_add({"invalid": "data"})  # 失败，记录错误
print(robust_hist.get_errors())