import urllib2 from multiprocessing.dummy import Pool as ThreadPool urls = [ 'http://www.python.org', 'http://www.python.org/about/', 'http://www.onlamp.com/pub/a/python/2003 ] pool = ThreadPool(4) results = pool.map(urllib2.urlopen, urls) pool.close() pool.join()
对,你没有看错,只要一行代码就可以把普通的任务变成并行任务。不用手动管理线程,一切都由map自动完成。
这里演示的是多线程,如果要多进程的话只需把 from multiprocessing.dummy 改成 from multiprocessing 。
装饰器
装饰器(Decorators)是Python里一个很重要的概念,它能够使得Python代码更加简洁,用一句话概括:装饰器是修改其他函数功能的函数。PySnooper的调用主要依靠装饰器的方式,所以,了解装饰器的基本概念和使用方法更有助于理解PySnooper的使用。在这里,我先简单介绍一下装饰器的使用,如果精力有限,了解装饰器的调用方式即可。
对于Python,一切都是对象,一个函数可以作为一个对象在不同模块之间进行传递,举个例子,
def one(func):
print("now you are in function one.")
func()
def two():
print("now you are in function two")
one(two)
# 输出
>>> now you are in function one.
>>> now you are in function two.
其实这就是装饰器的核心所在,它们封装一个函数,可以用这样或那样的方式来修改它。换一种方式表达上述调用,可以用@+函数名来装饰一个函数。
def one(func):
print("now you are in function one.")
def warp():
func()
return warp
@one
def two():
print("now you are in function two.")
two()
# 输出
>>> now you are in function one.
>>> now you are in function two.
此外,在调用装饰器时还可以给函数传入参数:
def one(func):
print("now you are in function one.")
def warp(*args):
func(*args)
return warp
@one
def two(x, y):
print("now you are in function two.")
print("x value is %d, y value is %d" % (x, y))
two(5, 6)
# 输出
>>> now you are in function one.
>>> now you are in function two.
>>> x value is 5, y value is 6
另外,装饰器本身也可以接收参数,
def three(text):
def one(func):
print("now you are in function one.")
def warp(*args):
func(*args)
return warp
print("input params is {}".format(text))
return one
@three(text=5)
def two(x, y):
print("now you are in function two.")
print("x value is %d, y value is %d" % (x, y))
two(5, 6)
# 输出
>>> input params is 5
>>> now you are in function one.
>>> now you are in function two.
>>> x value is 5, y value is 6
上面讲述的就是Python装饰器的一些常用方法。
Pysnooper

作者:Jackpop
链接:https://www.zhihu.com/question/27376156/answer/699936650
来源:知乎
著作权归作者所有。商业转载请联系作者获得授权,非商业转载请注明出处。
import numpy as np
import pysnooper
@pysnooper.snoop()
def one(number):
mat = []
while number:
mat.append(np.random.normal(0, 1))
number -= 1
return mat
one(3)
# 输出
Starting var:.. number = 3
22:17:10.634566 call 6 def one(number):
22:17:10.634566 line 7 mat = []
New var:....... mat = []
22:17:10.634566 line 8 while number:
22:17:10.634566 line 9 mat.append(np.random.normal(0, 1))
Modified var:.. mat = [-0.4142847169210746]
22:17:10.634566 line 10 number -= 1
Modified var:.. number = 2
22:17:10.634566 line 8 while number:
22:17:10.634566 line 9 mat.append(np.random.normal(0, 1))
Modified var:.. mat = [-0.4142847169210746, -0.479901983375219]
22:17:10.634566 line 10 number -= 1
Modified var:.. number = 1
22:17:10.634566 line 8 while number:
22:17:10.634566 line 9 mat.append(np.random.normal(0, 1))
Modified var:.. mat = [-0.4142847169210746, -0.479901983375219, 1.0491540468063252]
22:17:10.634566 line 10 number -= 1
Modified var:.. number = 0
22:17:10.634566 line 8 while number:
22:17:10.634566 line 11 return mat
22:17:10.634566 return 11 return mat
Return value:.. [-0.4142847169210746, -0.479901983375219, 1.0491540468063252]

作者:Jackpop
链接:https://www.zhihu.com/question/27376156/answer/699936650
来源:知乎
著作权归作者所有。商业转载请联系作者获得授权,非商业转载请注明出处。
@pysnooper.snoop(prefix="funcTwo ")
def two(x, y):
z = x + y
return z
@pysnooper.snoop(prefix="funcOne ")
def one(number):
k = 0
while number:
k = two(k, number)
number -= 1
return number
one(3)
# 输出
funcOne Starting var:.. number = 3
funcOne 22:28:14.259212 call 12 def one(number):
funcOne 22:28:14.260211 line 13 k = 0
funcOne New var:....... k = 0
funcOne 22:28:14.260211 line 14 while number:
funcOne 22:28:14.260211 line 15 k = two(k, number)
funcTwo Starting var:.. x = 0
funcTwo Starting var:.. y = 3
funcTwo 22:28:14.260211 call 6 def two(x, y):
funcTwo 22:28:14.260211 line 7 z = x + y
funcTwo New var:....... z = 3
funcTwo 22:28:14.260211 line 8 return z
funcTwo 22:28:14.260211 return 8 return z
funcTwo Return value:.. 3
funcOne Modified var:.. k = 3
funcOne 22:28:14.260211 line 16 number -= 1
funcOne Modified var:.. number = 2
funcOne 22:28:14.260211 line 14 while number:
funcOne 22:28:14.260211 line 15 k = two(k, number)
funcTwo Starting var:.. x = 3
funcTwo Starting var:.. y = 2
funcTwo 22:28:14.260211 call 6 def two(x, y):
funcTwo 22:28:14.260211 line 7 z = x + y
funcTwo New var:....... z = 5
funcTwo 22:28:14.260211 line 8 return z
funcTwo 22:28:14.260211 return 8 return z
funcTwo Return value:.. 5
funcOne Modified var:.. k = 5
funcOne 22:28:14.260211 line 16 number -= 1
funcOne Modified var:.. number = 1
funcOne 22:28:14.260211 line 14 while number:
funcOne 22:28:14.260211 line 15 k = two(k, number)
funcTwo Starting var:.. x = 5
funcTwo Starting var:.. y = 1
funcTwo 22:28:14.260211 call 6 def two(x, y):
funcTwo 22:28:14.260211 line 7 z = x + y
funcTwo New var:....... z = 6
funcTwo 22:28:14.260211 line 8 return z
funcTwo 22:28:14.260211 return 8 return z
funcTwo Return value:.. 6
funcOne Modified var:.. k = 6
funcOne 22:28:14.260211 line 16 number -= 1
funcOne Modified var:.. number = 0
funcOne 22:28:14.260211 line 14 while number:
funcOne 22:28:14.260211 line 17 return number
funcOne 22:28:14.260211 return 17 return number
funcOne Return value:.. 0

class Test():
t = 10
test = Test()
@pysnooper.snoop(watch=("test.t", "x"))
# 输出
Starting var:.. number = 3
Starting var:.. test.t = 10
Starting var:.. x = 10

#### watch_explode ####
d = {
"one": 1,
"two": 1
}
@pysnooper.snoop(watch_explode="d")
# 输出
Starting var:.. number = 3
Starting var:.. d = {'one': 1, 'two': 1}
Starting var:.. d['one'] = 1
Starting var:.. d['two'] = 1
#### watch ####
d = {
"one": 1,
"two": 1
}
@pysnooper.snoop(watch="d")
# 输出
Starting var:.. d = {'one': 1, 'two': 1}
# 以看出watch_explode能够展开字典的属性值。
# 另外还有参数depth显示函数中调用函数的snoop行,默认值为1,参数值需要大于或等于1。
PySnooper输出信息主要包括以下几个部分:
- 局部变量值
- 代码片段
- 局部变量所在行号
- 返回结果
上述结果输出到控制台,还可以把日志输出到文件,
@pysnooper.snoop("debug.log")各种时间形式转换

for i in range(10):
if i == 10:
break
print(i)
else:
print('10不在里面!')
相当于:
flag = False
for i in range(10):
if i == 10:
flag = True
break
print(i)
if not flag:
print('10不在里面!')
cached_property
它的作用是将一个方法的计算结果缓存到对象的 __dict__ 当中,熟悉 Flask 的人对这个应该不陌生,Django 应该也有类似的实现。这是 werkzeug 中的源码实现:
class cached_property(property):
def __init__(self, func, name=None, doc=None):
self.__name__ = name or func.__name__
self.__module__ = func.__module__
self.__doc__ = doc or func.__doc__
self.func = func
def __set__(self, obj, value):
obj.__dict__[self.__name__] = value
def __get__(self, obj, type=None):
if obj is None:
return self
value = obj.__dict__.get(self.__name__, _missing)
if value is _missing:
value = self.func(obj)
obj.__dict__[self.__name__] = value
return value
Flask 自己实现了一个线程安全的版本,locked_cached_property:
class locked_cached_property(object):
def __init__(self, func, name=None, doc=None):
self.__name__ = name or func.__name__
self.__module__ = func.__module__
self.__doc__ = doc or func.__doc__
self.func = func
self.lock = RLock()
def __get__(self, obj, type=None):
if obj is None:
return self
with self.lock:
value = obj.__dict__.get(self.__name__, _missing)
if value is _missing:
value = self.func(obj)
obj.__dict__[self.__name__] = value
return value
更多可以前往https://www.zhihu.com/question/27376156?sort=created
来源:https://www.cnblogs.com/xingnie/p/12394509.html
