一文带你深入了解下Python中的断言机制

def calculate_discount(price, rate):
    assert 0 <= rate <= 1, "折扣率必须在0到1之间"
    return price * rate

if not <condition>:
    raise AssertionError(<message>)

def bubble_sort(arr):
    assert isinstance(arr, list), "输入必须是列表"
    assert all(isinstance(x, (int, float)) for x in arr), "列表元素必须是数字"
    
    n = len(arr)
    for i in range(n):
        for j in range(0, n-i-1):
            if arr[j] > arr[j+1]:
                arr[j], arr[j+1] = arr[j+1], arr[j]
    return arr

def train_model(X, y):
    # 假设X应该是标准化后的数据
    assert np.allclose(X.mean(axis=0), 0), "数据未标准化"
    assert np.allclose(X.std(axis=0), 1), "数据未标准化"
    
    # 训练逻辑...

def withdraw(account, amount):
    """从账户取款
    
    Args:
        account: 账户对象，必须有balance属性
        amount: 取款金额，必须为正数且不超过余额
    """
    assert hasattr(account, 'balance'), "账户对象缺少balance属性"
    assert amount > 0, "取款金额必须为正数"
    assert amount <= account.balance, "余额不足"
    
    account.balance -= amount
    return amount

def test_stack_operations():
    s = Stack()
    assert len(s) == 0, "新栈应为空"
    
    s.push(1)
    assert len(s) == 1, "压栈后长度应为1"
    assert s.peek() == 1, "栈顶元素应为1"
    
    s.pop()
    assert len(s) == 0, "弹栈后应为空"

# 错误示范
def register_user(username):
    assert isinstance(username, str), "用户名必须是字符串"
    assert len(username) >= 3, "用户名太短"
    # ...

# 差
assert matrix.shape[0] == matrix.shape[1]

# 好
assert matrix.shape[0] == matrix.shape[1], f"矩阵不是方阵: {matrix.shape}"

# 性能敏感场景
def process_large_array(arr):
    for _ in range(1000000):
        assert arr is not None  # 每次循环都检查
        # ...

# 错误示范
assert (x := calculate_value()) > 0, "值必须为正"

# 正确做法
x = calculate_value()
assert x > 0, f"值{x}必须为正"

def assert_is_sorted(iterable, key=None):
    if key is None:
        key = lambda x: x
    for i in range(len(iterable)-1):
        assert key(iterable[i]) <= key(iterable[i+1]), \
            f"序列在索引{i}处无序: {iterable[i]} > {iterable[i+1]}"

# 使用
data = [1, 2, 4, 3, 5]
assert_is_sorted(data)  # 会触发断言

from typing import List

def process_items(items: List[int]):
    assert isinstance(items, list), "参数必须是列表"
    assert all(isinstance(x, int) for x in items), "列表元素必须是整数"
    
    # 处理逻辑...

import asyncio

async def fetch_data(url):
    assert isinstance(url, str), "URL必须是字符串"
    assert url.startswith(('http://', 'https://')), "URL格式不正确"
    
    # 异步获取逻辑...

import logging

logging.basicConfig(level=logging.DEBUG)

def critical_operation(data):
    try:
        assert data is not None, "数据不能为空"
        assert len(data) > 0, "数据不能为空列表"
    except AssertionError as e:
        logging.critical(f"断言失败: {str(e)}", exc_info=True)
        raise  # 重新抛出或处理

import os

DEBUG = os.getenv('DEBUG', '1') == '1'

def critical_function(param):
    if DEBUG:
        assert param > 0, "参数必须为正"
    # 或者更灵活的方式
    assert param > 0 or not DEBUG, "参数必须为正" if DEBUG else "参数无效"

def execute_trade(account, shares, price):
    cost = shares * price
    # 缺少断言检查cost是否为正
    account.balance -= cost  # 当shares或price为负时出错

def execute_trade(account, shares, price):
    assert shares > 0, "交易股数必须为正"
    assert price > 0, "股票价格必须为正"
    cost = shares * price
    assert cost > 0, "交易成本计算异常"
    account.balance -= cost

def normalize_data(data):
    mean = np.mean(data)
    std = np.std(data)
    # 缺少断言检查std是否为0
    normalized = (data - mean) / std  # 当std=0时产生NaN

def normalize_data(data):
    mean = np.mean(data)
    std = np.std(data)
    assert std != 0, "标准差不能为零"
    normalized = (data - mean) / std
    assert not np.any(np.isnan(normalized)), "标准化结果包含NaN"
    return normalized