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Introduction to decorators, generators and iterators in python
Introduction to decorators, generators and iterators in python
装饰器（）
１、装饰器：本质是函数；
装饰器（装饰其他函数），就是为其他函数添加附加功能；
原则：１．不能修改被装饰函数的源代码；
２．不能修改被装饰的函数的调用方式；
装饰器对被装饰的函数完全透明的，没有修改被装饰函数的代码和调用方式。
实现装饰器知识储备：
１．函数即“变量”；
２．高阶函数；
３．嵌套函数
高阶函数＋嵌套函数＝》装饰器
匿名函数（lambda表达式）
>>> calc = lambda x:x*3
>>> calc(2)
6
高阶函数：
a.把一个函数名当做实参传递给另外一个函数；
>>> def bar(): print("in the bar.....")
>>> def foo(func):
print(func)>>> foo(bar)
<function bar at 0x7f8b3653cbf8> b.返回值中包含函数名；
>>> import time
>>> def foo(): time.sleep(3) print("in the foo.....")>>> def main(func): print(func) return func>>> t = main(foo)<function foo at 0x7fb7dc9e3378>>>> t()in the foo.....在不修改源代码的情况下，统计程序运行的时间：
<span style="font-size: 16px">import time</span><br/><br/><span style="font-size: 16px">def timmer(func):</span><br/><span style="font-size: 16px"> def warpper(*args,**kwargs): ＃warpper(*args,**kwargs)万能参数，可以指定参数，也可以不指定参数</span><br/><span style="font-size: 16px"> start_time = time.time() #计算时间</span><br/><span style="font-size: 16px"> func()</span><br/><span style="font-size: 16px"> stop_time = time.time()</span><br/><span style="font-size: 16px"> print("the func run time is %s" %(stop_time-start_time)) #计算函数的运行时间</span><br/><span style="font-size: 16px"> return warpper</span><br/><br/><span style="font-size: 16px">@timmer ＃等价于test1 = timmer(test1)，因此函数的执行调用是在装饰器里面执行调用的</span><br/><span style="font-size: 16px">def test1():</span><br/><span style="font-size: 16px"> time.sleep(3)</span><br/><span style="font-size: 16px"> print("in the test1")</span><br/><br/><span style="font-size: 16px">test1()<br/>运行结果如下：<br/></span>in the test1
the func run time is 3.001983404159546
装饰器带参数的情况：
<span style="font-size: 16px; font-family: 宋体">import time</span><br/><br/><span style="font-size: 16px; font-family: 宋体">def timmer(func):</span><br/><span style="font-size: 16px; font-family: 宋体"> def warpper(*args,**kwargs):</span><br/><span style="font-size: 16px; font-family: 宋体"> start_time = time.time() #计算时间</span><br/><span style="font-size: 16px; font-family: 宋体"> func(*args,**kwargs) ＃执行函数，装饰器参数情况</span><br/><span style="font-size: 16px; font-family: 宋体"> stop_time = time.time()</span><br/><span style="font-size: 16px; font-family: 宋体"> print("the func run time is %s" %(stop_time-start_time)) #计算函数的运行时间</span><br/><span style="font-size: 16px; font-family: 宋体"> return warpper ＃返回内层函数名</span><br/><br/><span style="font-size: 16px; font-family: 宋体">@timmer</span><br/><span style="font-size: 16px; font-family: 宋体">def test1():</span><br/><span style="font-size: 16px; font-family: 宋体"> time.sleep(3)</span><br/><span style="font-size: 16px; font-family: 宋体"> print("in the test1")</span><br/><br/><span style="font-size: 16px; font-family: 宋体">@timmer ＃test2 = timmer(test2)</span><br/><span style="font-size: 16px; font-family: 宋体">def test2(name):</span><br/><span style="font-size: 16px; font-family: 宋体"> print("in the test2 %s" %name)</span><br/><br/><span style="font-size: 16px; font-family: 宋体">test1()</span><br/><span style="font-size: 16px; font-family: 宋体">test2("alex")<br/>运行结果如下：<br/></span>in the test1
the func run time is 3.0032410621643066
in the test2 alex
the func run time is 2.3603439331054688e-05
装饰器返回值情况：
import time
user,passwd = "alex","abc123"def auth(func):
def wrapper(*args,**kwargs):
username = input("Username:").strip()
password = input("Password:").strip()if user == username and passwd == password:
print("\033[32;1mUser has passed authentication.\033[0m")return func(*args,**kwargs) #实际上执行调用的函数
# res = func(*args,**kwargs)
# return res #函数返回值的情况，因为装饰器调用的时候是在装饰器调用的函数，因此返回值也在这个函数中else:
exit("\033[31;1mInvalid username or password.\033[0m")return wrapper
def index():
print("welcome to index page...")
@auth
def home():
#用户自己页面
print("welcome to home page...")return "form home..."@auth
def bbs():
print("welcome to bbs page")
index()
print(home())
bbs()
装饰器带参数的情况：
实现：１、本地验证；２、远程验证
import time
user,passwd = "alex","abc123"def auth(auth_type):'''函数的多层嵌套，先执行外层函数'''print("auth_type",auth_type)
def out_wrapper(func):
def wrapper(*args,**kwargs):
print("wrapper func args:",*args,**kwargs)if auth_type == "local":
username = input("Username:").strip()
password = input("Password:").strip()if user == username and passwd == password:
print("\033[32;1mUser has passed authentication.\033[0m")
func(*args,**kwargs) #实际上执行调用的函数
# res = func(*args,**kwargs)
# return res #函数返回值的情况，因为装饰器调用的时候是在装饰器调用的函数，因此返回值也在这个函数中else:
exit("\033[31;1mInvalid username or password.\033[0m")
elif auth_type == "ldap":
print("搞毛线lbap,傻逼．．．．")return wrapperreturn out_wrapper
def index():
print("welcome to index page...")
@auth(auth_type="local")
def home():
#用户自己页面
print("welcome to home page...")return "form home..."@auth(auth_type="ldap")
def bbs():
print("welcome to bbs page")
index()
home()
bbs() #函数没有，因为没有调用函数，函数的调用在装饰器里面，是装饰器调用了函数迭代器和生成器
生成器
通过列表生成式，我们可以直接创建一个列表。但是，受到内存限制，列表容量肯定是有限的。而且，创建一个包含100万个元素的列表，不仅占用很大的存储空间，如果我们仅仅需要访问前面几个元素，那后面绝大多数元素占用的空间都白白浪费了。
所以，如果列表元素可以按照某种算法推算出来，那我们是否可以在循环的过程中不断推算出后续的元素呢？这样就不必创建完整的list，从而节省大量的空间。在Python中，这种一边循环一边计算的机制，称为生成器：generator。
>>> l1 = (i for i in range(10))
>>> l1
>>> l1.__next__()
0
>>> l1.__next__()
1
生成器：只有在调用时才会生成相应的数据；
只有通过__next__()方法进行执行，这种能够记录程序运行的状态，yield用来生成迭代器函数。（只能往后调用，不能向前或者往后推移，只记住当前状态，因此银行的系统用来记录的时候可以使用yield函数)。
%= %= consumer(= consumer( i range(,) 运行如下： A准备吃包子了...... B准备吃包子了...... 包子1被A吃了...... 包子1被B吃了...... 包子2被A吃了...... 包子2被B吃了...... 包子3被A吃了...... 包子3被B吃了...... 包子4被A吃了...... 包子4被B吃了...... 包子5被A吃了...... 包子5被B吃了...... 包子6被A吃了...... 包子6被B吃了...... 包子7被A吃了...... 包子7被B吃了...... 包子8被A吃了...... 包子8被B吃了...... 包子9被A吃了...... 包子9被B吃了.....
迭代器
我们已经知道，可以直接作用于for循环的数据类型有以下几种：
一类是集合数据类型，如list、tuple、dict、set、str等；
一类是generator，包括生成器和带yield的generator function。
这些可以直接作用于for循环的对象统称为可迭代对象：Iterable。
可以使用isinstance()判断一个对象是否是Iterable对象
>>> from collections import Iterable
>>> isinstance([], Iterable)
True
>>> isinstance({}, Iterable)
True
>>> isinstance('abc', Iterable)
True
>>> isinstance((x for x in range(10)), Iterable)
True
>>> isinstance(100, Iterable)
FalseThe above is the detailed content of Introduction to decorators, generators and iterators in python. For more information, please follow other related articles on the PHP Chinese website!
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