Python-生成器
理解装饰器
装饰器是Python里面经常使用到的一个功能，通过装饰器我们能在不修改函数的情况下为函数增加一些功能，装饰器通过语法糖调用
装饰器
普通装饰器
def logger(func):
def decorator(*args, **kwargs):
print("func start")
func(*args, **kwargs)
return decorator
@logger
def func(name):
print("my name is %s" % name)
func("leon")
这是个装饰器的简单例子，整个调用其实就相当于logger(func(name)), 如果是有多个装饰的情况下，那么调用顺序是怎样的？
@A
@B
@C
def func():
pass
上面执行的的过程就是A(B(C(func())))
wraps
我们先看一个例子
def logger(func):
def decorator(*args, **kwargs):
print("func start")
func(*args, **kwargs)
return decorator
@logger
def func(name):
print("my name is %s" % name)
func("leon")
print(func.__name__) # decorator
当我们调用func.name 原本上应该输出func，但是因为加入了装饰器，导致__name__、__doc__等一些构造函数被改变了，那么如果我们如何解决这个问题呢？ 这里就需要用到functools.wraps，我们看下加入wraps试下
from functools import wraps
def logger(func):
@wraps(func)
def decorator(*args, **kwargs):
print("func start")
func(*args, **kwargs)
return decorator
@logger
def func(name):
print("my name is %s" % name)
func("leon")
print(func.__name__) # func
这里__name__是我们预期的，wraps到底做了些什么事情，这里我们看下源码
WRAPPER_ASSIGNMENTS = ('__module__', '__name__', '__qualname__', '__doc__',
'__annotations__')
WRAPPER_UPDATES = ('__dict__',)
def update_wrapper(wrapper,
wrapped,
assigned = WRAPPER_ASSIGNMENTS,
updated = WRAPPER_UPDATES):
"""Update a wrapper function to look like the wrapped function
wrapper is the function to be updated
wrapped is the original function
assigned is a tuple naming the attributes assigned directly
from the wrapped function to the wrapper function (defaults to
functools.WRAPPER_ASSIGNMENTS)
updated is a tuple naming the attributes of the wrapper that
are updated with the corresponding attribute from the wrapped
function (defaults to functools.WRAPPER_UPDATES)
"""
for attr in assigned:
try:
value = getattr(wrapped, attr)
except AttributeError:
pass
else:
setattr(wrapper, attr, value)
for attr in updated:
getattr(wrapper, attr).update(getattr(wrapped, attr, {}))
# Issue #17482: set __wrapped__ last so we don't inadvertently copy it
# from the wrapped function when updating __dict__
wrapper.__wrapped__ = wrapped
# Return the wrapper so this can be used as a decorator via partial()
return wrapper
def wraps(wrapped,
assigned = WRAPPER_ASSIGNMENTS,
updated = WRAPPER_UPDATES):
"""Decorator factory to apply update_wrapper() to a wrapper function
Returns a decorator that invokes update_wrapper() with the decorated
function as the wrapper argument and the arguments to wraps() as the
remaining arguments. Default arguments are as for update_wrapper().
This is a convenience function to simplify applying partial() to
update_wrapper().
"""
return partial(update_wrapper, wrapped=wrapped,
assigned=assigned, updated=updated)
从源码上看wraps最终是调用了update_wrapper， 这里会将___name__,__doc__等一些构造函数从原函数赋值到wrapped的的函数中
带参数的装饰器
如果我们想给装饰器加一些参数呢，这个要怎么做？
from functools import wraps
def need_logger(need=True):
def logger(func):
@wraps(func)
def decorator(*args, **kwargs):
if need:
print("func start")
func(*args, **kwargs)
return decorator
return logger
@need_logger(need=True)
def func(name):
print("my name is %s" % name)
func("leon")
如果装饰器要带参数的话，我们需要再把装饰器加一层，这个是函数的，我们也可以用类实现带参数的装饰器
class CustomLogger():
def __init__(self, need=True):
self.need = need
def __call__(self, func):
@wraps(func)
def decorator(*args, **kwargs):
if self.need:
print("func start 2")
func(*args, **kwargs)
return decorator
@CustomLogger(need=True)
def func(name):
print("my name is %s" % name)
第二种方法是不是有点不一样的感觉，通过类，我们可以将装饰器封装成一个完成包，去集成我们自己的功能
内置的三个装饰器
这里我们讲一下Python里面内置的装饰器
property
通过@property我们可以将一个函数变成一个属性进行调用，很大程度的方便了我们使用
class Demo():
def __init__(self):
self.value = 0
@property
def get_value(self):
return self.value
@get_value.setter
def set_value(self, value):
self.value = value
d = Demo()
print(d.get_value) # 0
d.set_value = 1
print(d.get_value) # 1
classmethod
classmethod和类绑定，不是和实例绑定, classmethod接收一个类参数cls, 可以修改类的状态，并将其作用到所有的类实例上
class Demo():
value = 1
@classmethod
def get_value2(cls):
cls.value += 1
return cls.value
print(Demo.get_value2()) # 2
print(Demo.value) # 2
可以看到，我们通过类方法修改的值已经作用域所有的类实例了
staticmethod
静态方法跟普通函数没什么区别，通过类和实例都可以直接该函数
class Demo():
@staticmethod
def get_value():
return "ok"
print(Demo.get_value()) # ok