Decorator concept
Passing a function as an argument to another function returns an alternative version of the function
The way to dynamically add functionality during code execution, called decorators, is essentially a function that returns a function with the purpose of adding functionality to the function without changing the original function.
Decorator 1
1. A function is also an object, and a function object can be assigned to a variable, so it can also be called through a variable.
Definition of function say ()
def fun():
print('hello python')
Assuming you want to enhance the capabilities of the say() function, such as making positive and negative judgments on arguments when calling a function, but you don't want to modify the definition of say(), you can add functionality while the code is running by using an adorner.
It can be defined as follows:
def outer(f):
def inner():
print('@@@@@@@')
f()
print('!!!!')
return inner
Look at the outer() function above, because it is a decorator, so it takes a function as a parameter and returns a function.Place decorator at function definition with python's syntax sugar
@outer
def fun():
print('hello python')
Putting the syntax sugar @outer at the definition of the fun() function is equivalent to executing a statement:
Since outer() is only a decorator and returns a function, the original fun() function still exists, but now the fun() variable with the same name points to a new function, so calling your fun() will execute the new function, that is, the inner() function returned in the outer() function.
fun = outer(fun)
Calling the fun() function not only runs fun() itself, but also prints out new content:
Example:
def outer(f):
def inner(age):
if age <=0:
age = 0
f(age)
return inner
@outer #Grammatical Sugar
def say(age):
print('%d years old' %(age))
Executing say (-12) results in
Decorator 2
Decorator implements a function timer
1. What if the decorated function has a return value?
2. How do I keep the function name and help documentation information of the decorated function?Decorator implements a function timer
1. What if the decorated function has a return value?
2. How do I keep the function name and help documentation information of the decorated function?
import random
import string
import time
li = [random.choice(string.ascii_letters) for i in range(100)]
def timeit(fun):
#@functools.wraps(fun)
def wrapper(*args,**kwargs):# Receive variable and keyword parameters
"""This is an ornament timeit"""
# Before function execution
start_time = time.time()
# Execute Function
res = fun(*args,**kwargs)
# After function execution
stop_time = time.time()
print('The run event is:%.6f' %(stop_time-start_time))
return res
return wrapper
# @timeit
# def con_add():
# s = ''
# for i in li:
# s += (i + '')
# print(s)
# @timeit
# def join_add():
# print(','.join(li))
#
# con_add()
# join_add()
@timeit
def fun_list(n):
"""This is fun_list function"""
return [2 * i for i in range(n)]
@timeit
def fun_map(n):
"""This is fun_map Function Decoration timeis decorate"""
return list(map(lambda x:x*2,range(n)))
# fun_list(10000)
# fun_map(10000)
print(fun_list.__name__) print(fun_list.__doc__) print(fun_map.__name__) print(fun_map.__doc__)
Decoration 3
To create an ornament, the following requirements are required:
- Create an add_log decorator, and the decorated function prints the log information;
- The log format is: [string time] function name: xxx, run time: xxx,
Run Return Value Result: xxx
# import time
# print(time.ctime())
import time
import functools
def add_log(func):
@functools.wraps(func)
def wrapper(*args,**kwargs):
start_time = time.time()
res = func(*args,**kwargs)
end_time = time.time()
print('[%s] Function name:%s,Runtime:%.6f,Result of running the return value'
':%d' %(time.ctime(),func.__name__,
end_time-start_time,res))
return res
return wrapper
@add_log
def add(x,y):
time.sleep(1)
return x+y
add(1,10)
Decorator 4
- Basic (parameterless decorator)
Write the decorator required_ints under the following conditions:
1). Ensure that every parameter received by the function is an integer;
2). If the parameter is not an integer, print TypeError: the parameter must be an integer
import functools
def required_ints(func):
@functools.wraps(func)
def wrapper(*args, **kwargs): # args=(1,2,..)
for i in args:
# if isinstance(i,int):
# pass
# else:
# print('Not all arguments to the function are int')
# break
if not isinstance(i, int):
print('Not all parameters of a function are int type')
break
else:
res = func(*args, **kwargs)
return res
return wrapper
@required_ints
def add(a, b):
return a + b
@required_ints
def mymax(a,b,c,d):
return max(a,b,c,d)
print(add(1,2.0))
To create an ornament, the following requirements are required:
- Create an add_log decorator, and the decorated function prints the log information;
- The log format is: [string time] function name: xxx, run time: xxx, run return value result: XXX
import time
import functools
def log(kind):
def add_log(func):
@functools.wraps(func)
def wrapper(*args,**kwargs):
start_time = time.time()
res = func(*args,**kwargs)
end_time = time.time()
print('<%s> [%s] Function name:%s,Runtime:%.6f,Result of running the return value'
':%d' %(kind,time.ctime(),func.__name__,
end_time-start_time,res))
return res
return wrapper
return add_log
@log('local2')
def add(x,y):
time.sleep(1)
return x+y
add(1,10)
Decorator 5
['root','admin','redhat']
id id+vip
Scenarios for multiple decorators:
Multiple decorators will be used to verify successful login before verifying that permissions are sufficient
import functools
import inspect
def is_login(fun):
@functools.wraps(fun)
def warapper(*args, **kwargs): # ('admin',)
if args[0] in login_session:
temp = fun(*args, **kwargs)
return temp
else:
print('Error:%s No landing success' % (args[0]))
return warapper
def is_admin(fun):
@functools.wraps(fun)
# nspect.getcallargs returns a dictionary
# key:formal parameter value:corresponding argument
def wrapper(*args, **kwargs):
inspect_res = inspect.getcallargs(fun, *args, **kwargs)
print('inspect The return value of:%s' % (inspect_res))
if inspect_res.get('name') == 'root':
temp = fun(*args, **kwargs)
return temp
else:
print('not root user,no permisson add user')
return wrapper
login_session = ['root', 'admin', 'redhat']
@is_login
@is_admin
def add_user(name):
print('add_user~~')
add_user('root')
Decorator 6
- Basic (parameterless decorator)
Write the decorator required_ints under the following conditions:
1). Ensure that every parameter received by the function is an integer;
2). If the parameter is not an integer, print TypeError: the parameter must be an integer
import functools
def required_ints(func):
@functools.wraps(func)
def wrapper(*args, **kwargs): # args=(1,2,..)
for i in args:
# if isinstance(i,int):
# pass
# else:
# print('Not all arguments to the function are int')
# break
if not isinstance(i, int):
print('Not all parameters of a function are int type')
break
else:
res = func(*args, **kwargs)
return res
return wrapper
@required_ints
def add(a, b):
return a + b
@required_ints
def mymax(a,b,c,d):
return max(a,b,c,d)
print(add(1,2.0))
Decorator 7
To create an ornament, the following requirements are required:
- Create an add_log decorator, and the decorated function prints the log information;
- The log format is: [string time] function name: xxx, run time: xxx, run return value result: XXX
import time
import functools
def log(kind):
def add_log(func):
@functools.wraps(func)
def wrapper(*args,**kwargs):
start_time = time.time()
res = func(*args,**kwargs)
end_time = time.time()
print('<%s> [%s] Function name:%s,Runtime:%.6f,Result of running the return value'
':%d' %(kind,time.ctime(),func.__name__,
end_time-start_time,res))
return res
return wrapper
return add_log
@log('local2')
def add(x,y):
time.sleep(1)
return x+y
add(1,10)
Decorator 8
- Upgraded version (decorator with parameters)
Write the decorator required_types under the following conditions:
1). When the decorator is @required_types(int,float), make sure that every parameter the function receives is
int or float type;
2). When the decorator is @required_types(list), make sure that every parameter received by the function is a list type;
3. When the decorator is @required_types(str,int), make sure that each parameter received by the function is of type STR or int;
4). If the parameter does not meet the criteria, print TypeError: the parameter must be of type XXX
import functools
def required_type(*kind):
def required(fun):
@functools.wraps(fun)
def wrapper(*args,**kwargs):
for i in args:
if not isinstance(i,kind):
print('Not all parameters of a function',kind)
break
else:
res = fun(*args,**kwargs)
return res
return wrapper
return required
@required_type(int)
def add(a, b):
return a + b
print(add(1,1.2))