高级类型检查
复杂类型验证策略
1. 自定义类型验证装饰器
def validate_types(**type_requirements):
def decorator(func):
def wrapper(*args, **kwargs):
for key, expected_type in type_requirements.items():
if key not in kwargs:
continue
if not isinstance(kwargs[key], expected_type):
raise TypeError(f"{key} 必须是 {expected_type}")
return func(*args, **kwargs)
return wrapper
return decorator
class DataProcessor:
@validate_types(user_data=dict, min_score=int)
def process_user_data(self, user_data, min_score):
filtered_data = {
k: v for k, v in user_data.items()
if isinstance(v, (int, float)) and v >= min_score
}
return filtered_data
类型检查工作流程
graph TD
A[输入数据] --> B{类型验证}
B --> |通过| C[处理数据]
B --> |失败| D[引发 TypeError]
C --> E[返回处理后的数据]
D --> F[错误处理]
2. 动态类型检查
from typing import Any, Dict, Type
def deep_type_inspection(data: Dict[str, Any],
type_map: Dict[str, Type]):
results = {}
for key, expected_type in type_map.items():
if key not in data:
results[key] = "缺失"
continue
value = data[key]
## 处理嵌套复杂类型
if isinstance(expected_type, tuple):
is_valid = any(isinstance(value, t) for t in expected_type)
else:
is_valid = isinstance(value, expected_type)
results[key] = "有效" if is_valid else "无效"
return results
## 示例用法
user_profile = {
"name": "Alice",
"age": 30,
"skills": ["Python", "数据分析"],
"metadata": {"level": "专家"}
}
type_requirements = {
"name": str,
"age": int,
"skills": list,
"metadata": (dict, type(None))
}
validation_result = deep_type_inspection(
user_profile, type_requirements
)
print(validation_result)
高级类型检查技术
| 技术 |
描述 |
复杂度 |
| 装饰器验证 |
通过装饰器进行类型检查 |
中等 |
| 动态类型映射 |
灵活的类型验证 |
高 |
| 嵌套类型检查 |
复杂类型层次结构 |
高级 |
3. 类型提示验证
from typing import Union, List, Dict
def validate_complex_structure(
data: Dict[str, Union[str, int, List[str]]]
) -> bool:
try:
for key, value in data.items():
if isinstance(value, str):
assert len(value) > 0
elif isinstance(value, int):
assert value > 0
elif isinstance(value, list):
assert all(isinstance(item, str) for item in value)
return True
except AssertionError:
return False
## LabEx Pro 提示：使用类型提示进行文档记录
错误处理与类型安全
class TypeSafeDict:
def __init__(self, initial_dict=None, type_constraints=None):
self._data = initial_dict or {}
self._constraints = type_constraints or {}
def __setitem__(self, key, value):
if key in self._constraints:
expected_type = self._constraints[key]
if not isinstance(value, expected_type):
raise TypeError(f"{key} 的类型无效")
self._data[key] = value
def __getitem__(self, key):
return self._data[key]
## 示例用法
safe_dict = TypeSafeDict(
type_constraints={
"name": str,
"age": int
}
)
关键要点
- 高级类型检查不仅仅局限于简单的
type() 和 isinstance()
- 使用装饰器、类型提示和自定义验证策略
- 实现强大的错误处理
- 考虑性能和复杂度的权衡
LabEx 建议
在你的 LabEx 项目中集成这些高级类型检查技术，以提高代码的可靠性和可维护性。
