《编写高质量代码 改善Python程序的91个建议》读后程序学习小结

# coding=utf-8
# Language Reference
'''
参考书:《编写高质量代码 改善Python程序的91个建议》张颖,赖勇浩 著 2014.6
'''

from __future__ import with_statement

# assert
x, y = 1, 1
assert x == y, "not equals"

# time计时的两种方式
import timeit
t = timeit.Timer('x,y=y,x','x=1;y=2')
print(t.timeit()) # 0.110494892863
import time
t = time.time()
sum = 0
while True:
    sum += 1
    if sum > 100000:
        break
time.sleep(1)
print(time.time()-t) # 1.02999997139

# itertools 结合 yield
from itertools import islice
def fib():
    a, b = 0, 1
    while True:
        yield a
        a, b = b, a+b
print(list(islice(fib(),10))) # [0, 1, 1, 2, 3, 5, 8, 13, 21, 34]

# class 定义变量 和 nametuple 定义变量
class Seasons:
    Sprint, Summer, Autumn, Winter = range(4)
print(Seasons.Winter) # 3
from collections import namedtuple
Seasons0 = namedtuple('Seasons0','Spring Summer Autumn Winter')._make(range(4))
print(Seasons0.Winter) # 3

# isintance 可以设置多种类型的判断
print(isinstance((2,3),(str, list, tuple))) # True

# eval的漏洞,能够处理的范围太大,导致系统文件被读取,谨慎使用
str0 = '__import__("os").system("dir")'
eval(str0) # 2017/09/28  09:31     。。。

# 生成器 yield 与 迭代器 iteritems
def myenumerate(seq):
    n = -1
    for elem in reversed(seq):
        yield len(seq) + n, elem
        n = n -1
e = myenumerate([1,2,3,4,5])
print(e.next()) # (4, 5)
dict0 = {'1':1, '2':2}
d = dict0.iteritems()
print(d.next()) # ('1', 1)

# 字符串驻留机制:对于较小的字符串,为了提高系统性能保留其值的一个副本,当创建新的字符串时直接指向该副本。
a = 'hello'
b = 'hello' # b  a 的引用
print(id(a), id(b), a is b, a == b) # (53383488, 53383488, True, True)

# 可变对象list 与 不可变对象str
list1 = [1,2,3]
list2 = list1
list3 = list1[:] # 浅拷贝
list1.append(4)
print(list1, list2, list3) # ([1, 2, 3, 4], [1, 2, 3, 4], [1, 2, 3]) - list2可变
str1 = '123'
str2 = str1
str3 = str1[:]
str1 += '4'
print(str1, str2, str3) # ('1234', '123', '123') - str2不可变

# 浅拷贝, 深拷贝
# 浅拷贝和深拷贝的不同仅仅是对组合对象来说,
# 所谓的组合对象就是包含了其它对象的对象,如列表,类实例。
# 而对于数字、字符串以及其它原子类型,没有拷贝一说,产生的都是原对象的引用。
# 浅拷贝: 创建一个新的对象,其内容是原对象中元素的引用。(拷贝组合对象,不拷贝子对象)
# 浅拷贝有:切片操作、工厂函数、对象的copy()方法、copy模块中的copy函数。
# 深拷贝: 创建一个新的对象,然后递归的拷贝原对象所包含的子对象。深拷贝出来的对象与原对象没有任何关联。
# 深拷贝: 虽然实际上会共享不可变的子对象,但不影响它们的相互独立性。
# 深拷贝只有一种方式:copy模块中的deepcopy函数。
a = [[1,2]] # 组合对象
import copy
b = copy.copy(a)
print(id(a), id(b), a is b, a == b) # (58740048, 59050864, False, True)
for i, j in zip(a, b):
    print(id(i), id(j)) # (61090752, 61090752)子对象相同
b = copy.deepcopy(a)
print(id(a), id(b), a is b, a == b) # (58740048, 58737848, False, True)
for i, j in zip(a, b):
    print(id(i), id(j)) # (61090752, 61071568)子对象不同

# 再举一例
class TestCopy():
    def get_list(self, list0):
        self.list0 = list0
    def change_list(self, str):
        self.list0 += str
    def print_list(self):
        print(self.list0)
list0 = [1, 2, 3]
a = TestCopy()
a.get_list(list0)
a.print_list()  # [1, 2, 3]
b = copy.copy(a) # 浅拷贝,子对象共享
b.change_list('4')
b.print_list()  # [1, 2, 3, '4']
a.print_list()  # [1, 2, 3, '4']
c = copy.deepcopy(a) # 深拷贝,子对象独立
c.change_list('5')
c.print_list()  # [1, 2, 3, '4', '5']
b.print_list()  # [1, 2, 3, '4']
a.print_list()  # [1, 2, 3, '4']
# 赋值操作
a = [1, 2, 3]
b = copy.copy(a) # 其实是赋值操作,不属于浅拷贝,赋值对象相互独立
b.append(4)
print(a, b)  # ([1, 2, 3], [1, 2, 3, 4])

# encode, decode, gbk, utf-8
with open("test.txt", 'w') as f:
    f.write('python' + '中文测试')
with open("test.txt", 'r') as f:
        # print(f.read()) # python中文测试
        print((f.read().decode('utf-8')).encode('gbk')) # python���IJ���

# --1 = 1 = ++1 = (1)
print(+++1, ---1) # (1, -1)
print (1), (1,) # 1 (1,)
print(''.split(), ''.split(' ')) # ([], [''])

# with 调用 class, 初始化调用__enter__,退出调用__exit__
class MyContextManager():
    def __enter__(self):
        print('entering...')
    def __exit__(self, exc_type, exc_val, exc_tb):
        print('leaving...')
        if exc_type is None:
            print('no exceptions')
            return False
        elif exc_type is ValueError:
            print('value error')
            return True
        else:
            print('other error')
            return True
with MyContextManager():
    print('Testing...')
    raise(ValueError)  # entering... Testing... leaving... value error

# else 结合 for  try
for i in range(5):
    print(i),
else:
    print('for_else') # 0 1 2 3 4 for_else
try:
    print('try'),
except:
    pass
else:
    print('try_else') # try try_else

# None
a = None
b = None
print(id(a), id(b), a is b, a==b) # (505354444, 505354444, True, True)
a = 'a'
b = u'b'
print(isinstance(a,str), isinstance(b,unicode), isinstance(a, basestring), isinstance(b, basestring))
# (True, True, True, True)

#  class 操作,先调用 __init__()
class A:
    def __nonzero__(self):
        print('A.__nonzero__()')
        return True
    def __len__(self):
        print('A.__len__()')
        return  False
    def __init__(self):
        print('A.__init__()')
        self.name = 'I am A'
    def __str__(self):
        print('A.__str__()')
        return 'A.__str__{self.name}'.format(self=self)
if A():
    print('not empty') # A.__init__() A.__nonzero__() not empty
else:
    print('empty')
print(str(A())) # A.__init__() A.__str__() A.__str__I am A

# 尽量采用''.join()(效率更高),而不是 str + str
s1, s2 ,s3 = 'a', 'b', 'c'
print(s1+s2+s3, ''.join([s1, s2, s3])) # ('abc', 'abc')

# map  list 结合
list0 = [('a','b'), ('c','d')]
formatter = "choose {0[0]} and {0[1]}".format
for item in map(formatter, list0):
    print(item)  # choose a and b  choose c and d
# map 结合 type
product_info = '1-2-3'
a, b, c = map(int, product_info.split('-'))
print(a, b, c)  # (1, 2, 3)

# 格式化输出,尽量采用 format,采用 %s 输出元组时需要加逗号
itemname = list0[0] + list0[1]
print(itemname) # ('a', 'b', 'c', 'd')
print('itemname is %s' % (itemname,))   # 必须有个逗号 itemname is ('a', 'b', 'c', 'd')
print('itemname is {}'.format(itemname)) # itemname is ('a', 'b', 'c', 'd')
# 格式化输出 %2.5f,小数点后5位优先级高
print('data: %6.3f' % 123.456789123)  # data: 123.457
print('data: %2.5f' % 123.456789123)  # data: 123.45679

# class传参:__init__ 中传入可变对象 - 会在子类中继承该可变对象的值
class ChangeA():
    def __init__(self, list0 = []): # mutable 可变
        self.list0 = list0
    def addChange(self, content):
        self.list0.append(content)
a = ChangeA()
a.addChange('add change')
b = ChangeA()
print(a.list0, b.list0) # (['add change'], ['add change'])
# 函数传参:传对象或对象的引用。若可变对象 - 共享, 若不可变对象 - 生成新对象后赋值
def inc(n, list0):
    n = n + 1
    list0.append('a')
n = 3
list0 = [1]
inc(n, list0)
print(n, list0) # (3, [1, 'a'])
# 子类继承父类,传参举例
class Father():
    def print_fa(self):
        print(self.total)
    def set(self, total):
        self.total = total
class SonA(Father):
    pass
class SonB(Father):
    pass
a = SonA()
a.set([1])
a.print_fa() # [1]
b = SonB()
b.set([2])
b.print_fa() # [2]

# 在需要生成列表的时候使用列表解析
# 对于大数据处理不建议用列表解析,过多的内存消耗会导致MemoryError
print([(a,b) for a in [1,2,3] for b in [2,3,4] if a != b])
# [(1, 2), (1, 3), (1, 4), (2, 3), (2, 4), (3, 2), (3, 4)]

# class 中的 装饰符 :类方法,静态方法,实例方法
class CS():
    def instance_method(self,x):
        print(x)
    @classmethod
    def class_method(cls,x):
        print('class',x)
    @staticmethod
    def static_method(x):
        print('static',x)
CS().instance_method(1) # 1
CS().class_method(2) # ('class', 2)
CS().static_method(3) # ('static', 3)

# itemgetter 字典排序,输出为元组
dict0 = {'a':1, 'c':3, 'b':2}
from operator import itemgetter
print(sorted(dict0.viewitems(), key = itemgetter(1))) # [('a', 1), ('b', 2), ('c', 3)]

# Counter 计数
from collections import Counter
print(Counter('success')) # Counter({'s': 3, 'c': 2, 'e': 1, 'u': 1})

# 配置文件:优点:不需修改代码,改变程序行为,继承[DEFAULT]属性
with open('format.conf', 'w') as f:
    f.write('[DEFAULT]' + '\n')
    f.write('conn_str = %(dbn)s://%(user)s:%(pw)s@%(host)s:%(port)s/%(db)s' + '\n')
    f.write('dbn = mysql' + '\n')
    f.write('user = root' + '\n')
    f.write('host = localhost' + '\n')
    f.write('port = 3306' + '\n')
    f.write('[db1]' + '\n')
    f.write('user = aaa' + '\n')
    f.write('pw = ppp' + '\n')
    f.write('db = example1' + '\n')
    f.write('[db2]' + '\n')
    f.write('host = 192.168.0.110' + '\n')
    f.write('pw = www' + '\n')
    f.write('db = example2' + '\n')
from ConfigParser import  ConfigParser
conf = ConfigParser()
conf.read('format.conf')
print(conf.get('db1', 'conn_str')) # mysql://aaa:ppp@localhost:3306/example1
print(conf.get('db2', 'conn_str')) # mysql://root:[email protected]:3306/example2

# pandas - 大文件(1G)读取操作 - 需要安装 pandas
f = open('large.csv', 'wb')
f.seek(1073741824 - 1)
f.write('\0')
f.close()
import os
print(os.stat('large.csv').st_size) # 1073741824
import csv
with open('large.csv', 'rb') as csvfile:
    mycsv = csv.reader(csvfile, delimiter = ';')
    # for row in mycsv: # MemoryError
        # print(row)
# import pandas as pd
# reader = pd.read_table('large.csv', chunksize = 10, iterator = True)
# iter(reader).next()

# 序列化:把内存中的数据结构在不丢失其身份和类型信息的情况下,转成对象的文本或二进制表示。
# pickle, json, marshal, shelve
import cPickle  as pickle
my_data = {'a':1, 'b':2, 'c':3}
fp = open('picklefile.dat', 'wb')
pickle.dump(my_data, fp) # class - __getstate__(self)
fp.close()
fp = open('picklefile.dat', 'rb')
out = pickle.load(fp) # class - __setstate__(self, state)
fp.close()
print(out) # {'a': 1, 'c': 3, 'b': 2}
pickle.loads("cos\nsystem\n(S'dir'\ntR.") # 列出当前目录下所有文件,不安全 - 解决:继承类并定制化内容

# 编码器 json.JSONEncoder
try:
    import simplejson as json
except ImportError:
    import json
import datetime
d = datetime.datetime.now()
d1 = d.strftime('%Y-%m-%d %H:%M:%S')
print(d1, json.dumps(d1, cls = json.JSONEncoder)) # 也可以继承修改指定编码器json.JSONEncoder
# ('2017-09-28 11:00:46', '"2017-09-28 11:00:46"')

# traceback:出错时查看 调用栈
import sys
print(sys.getrecursionlimit()) # 最大递归深度:1000
import traceback
try:
    a = [1]
    print(a[1])
except IndexError as ex:
    print(ex) # list index out of range
    # traceback.print_exc() # 会导致程序中断
tb_type, tb_val, exc_tb = sys.exc_info()
for filename, linenum, funcname, source in traceback.extract_tb(exc_tb):
    print("%-33s:%s '%s' in %s()" % (filename, linenum, source, funcname))
    # H:/python/suggest0928.py         :353 'print(a[1])' in ()

# LOG的五个等级:DEBUG, INFO, WARNING(默认), ERROR, CRITICAL
# Logger, Handler, Formatter, Filter
import logging
logging.basicConfig(
    level = logging.DEBUG,
    filename = 'log.txt',
    filemode = 'w',
    format = '%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s',
)
logger = logging.getLogger()
logger.info('[INFO]:I am a tester')
logger.debug('test logging module')
logger.error('this is error')
logger.critical('this is critical')

# thread: 多线程底层支持,以低级原始的方式处理和控制线程,较复杂
# threading: 基于thread,操作对象化,提供丰富特性
import threading
import time
def myfunc(a, delay):
    print('calculate %s after %s' % (a, delay))
    time.sleep(delay)
    print('begin')
    res = a*a
    print('result:', res)
    return res
t1 = threading.Thread(target = myfunc, args = (2, 5))
t2 = threading.Thread(target = myfunc, args = (6, 8))
print(t1.isDaemon()) # False 守护线程,默认False
print(t2.isDaemon())
# t2.setDaemon(True) # True 表示 线程全部执行完成后,主程序才会退出
t1.start()
t2.start()

# lock, mutex, condition, event, with lock, put,get
# 生产者消费者模型
import Queue
import threading
import random
write_lock = threading.Lock()
class Producer(threading.Thread):
    def __init__(self, q, con, name):
        super(Producer, self).__init__()
        self.q = q
        self.name = name
        self.con = con
        print('Producer ', self.name, ' started')
    def run(self):
        while(1):
            global write_lock
            # self.con.acquire()
            if self.q.full():
                with write_lock:
                    print('Queue is full, producer wait')
                # self.con.wait()
            else:
                value = random.randint(0,10)
                with write_lock:
                    print(self.name, 'put value:', self.name+':'+str(value), 'into queue')
                self.q.put(self.name+':'+str(value))
                # self.con.notify()
        # self.con.release()
class Consumer(threading.Thread):
    def __init__(self, q, con, name):
        super(Consumer, self).__init__()
        self.q = q
        self.name = name
        self.con = con
        print('Consumer ', self.name, ' started')
    def run(self):
        while(1):
            global write_lock
            # self.con.acquire()
            if self.q.empty():
                with write_lock:
                    print('Queue is empty, consumer wait')
                # self.con.wait()
            else:
                value = self.q.get()
                with write_lock:
                    print(self.name, 'get value:', value, 'from queue')
                # self.con.notify()
        # self.con.release()
q = Queue.Queue(10) # 先进先出,循环队列大小10
con = threading.Condition()
p1 = Producer(q, con, 'P1')
p2 = Producer(q, con, 'P2')
c1 = Consumer(q, con, 'C1')
p1.setDaemon(False)
p2.setDaemon(False)
c1.setDaemon(False)
# p1.setDaemon(True)
# p2.setDaemon(True)
# c1.setDaemon(True)
# p1.start()
# p2.start()
# c1.start()

'''
设计模式,静态语言风格
单例模式,保证系统中一个类只有一个实例而且该实例易于被外界访
模板方法:在一个方法中定义一个算法的骨架,并将一些事先步骤延迟到子类中。
    子类在不改变算法结构的情况下,重新定义算法中的某些步骤。
    混入mixins模式:基类在运行中可以动态改变(动态性)。
'''

# 发布 publish 订阅 subscribe 松散耦合 - 中间代理人 Broker
# blinker - python-message
# 库函数:关注日志产生,不关注日志输出;
# 应用:关注日志统一放置,不关注谁产生日志。
from collections import defaultdict
route_table = defaultdict(list)
def sub(topic, callback):
    if callback in route_table[topic]:
        return
    route_table[topic].append(callback)
def pub(topic, *a, **kw):
    for func in route_table[topic]:
        func(*a, **kw)
def greeting(name):
    print('hello, %s' % name)
sub('greet', greeting) # 订阅的时候将待调用的greeting放入dictpub('greet', 'tester') # hello, tester 发布的时候调用greeting函数

# 类的状态转移,例,当telnet\注册成功后,就不再需要登录\注册了。
def workday():
    print('work hard')
def weekend():
    print('play harder')
class People(): pass
people = People()
while True:
    for i in range(1,8,1):
        if i == 6:
            people.day = weekend
        if i == 1:
            people.day = workday
        people.day()
    break

# 工厂模式
# __init__(): 在类对象创建好后,进行变量的初始化
# __new__(): 创建实例,类的构造方法,需要返回object.__new__()
class TestMode(object):
    def __init__(self):
        print('i am father')
    def test(self):
        print('test is father')
class A(TestMode):
    def __init__(self):
        print('i am A')
    def test(self):
        print('test is A')
class B(TestMode):
    def __init__(self):
        print('i am B')
    def test(self):
        print('test is B')
class FactoryTest(object):
    content = {'a':A, 'b':B}
    def __new__(cls, name):
        if name in FactoryTest.content.keys():
            print('create old %s' % name)
            return FactoryTest.content[name]()
        else:
            print('create new %s' % name)
            return TestMode()
FactoryTest('a').test() # create old a - i am A - test is A
FactoryTest('A').test() # create new A - i am father - test is father

# 局部作用域 > 嵌套作用域 > 全局作用域 > 内置作用域
a = 1
def foo(x):
    global a
    a = a * x
    def bar():
        global a
        b = a * 2
        a = b + 1
        print(a)
    return bar()
foo(1) # 3

# self 隐式传递 -- 显式 优于 隐式
# 当子类覆盖了父类的方法,但仍然想调用父类的方法
class SelfTest():
    def test(self):
        print('self test')
SelfTest.test(SelfTest()) # self test
assert id(SelfTest.__dict__['test']) == id(SelfTest.test.__func__)

# 古典类 classic class
class A: pass
# 新式类 new style class
class B(object): pass
class D(dict): pass
# 元类 metaclass
class C(type): pass
a = A
b = B()
c = C(str)
d = D()
print(type(a)) # 
print(b.__class__, type(b)) # (, )
print(c.__class__, type(c)) # (, )
print(d.__class__, type(d)) # (, )

# 菱形继承 - 应避免出现
try:
    class A(object): pass
    class B(object): pass
    class C(A, B): pass
    class D(B, A): pass
    class E(C, D): pass
except:
    print('菱形继承 - '+'order (MRO) for bases B, A')

# __dict__[] 描述符,实例调用方法为bound,类调用方法为unbound
class MyClass(object):
    def my_method(self):
        print('my method')
print(MyClass.__dict__['my_method'], MyClass.my_method)
# (, )
print(MyClass.__dict__['my_method'](MyClass()), MyClass.my_method(MyClass()))
a = MyClass()
print(a.my_method, MyClass.my_method)
# (>, )
print(a.my_method.im_self, MyClass.my_method.im_self)
# (<__main__.MyClass object at 0x0391D650>, None)

# __getattribute__()总会被调用,而__getattr__()只有在__getattribute__()中引发异常的情况下才会被调用
class AA(object):
    def __init__(self, name):
        self.name  = name
        self.x = 20
    def __getattr__(self, name):
        print('call __getattr__:', name)
        if name == 'z':
            return self.x ** 2
        elif name == 'y':
            return self.x ** 3
    def __getattribute__(self, attr):
        print('call __getattribute__:', attr)
        try:
            return super(AA, self).__getattribute__(attr)
        except KeyError:
            return 'default'
a = AA("attribute")
print(a.name) # attribute
print(a.z) # 400
if hasattr(a, 'test'): # 动态添加了 test 属性,但不会在 __dict__ 中显示
    c = a.test
    print(c) # None
else:
    print('instance a has no attribute t')
print(a.__dict__) # {'x': 20, 'name': 'attribute'} 没有‘test’

# 数据描述符:一个对象同时定义了__get__()__set__()方法,高级 - property装饰符
# 普通描述符:一种较为低级的控制属性访问机制
class Some_Class(object):
    _x = None
    def __init__(self):
        self._x = None
    @property
    def x(self):
        return self._x
    @x.setter
    def x(self, value):
        self._x = value
    @x.getter
    def x(self):
        return self._x
    @x.deleter
    def x(self):
        del self._x
obj = Some_Class()
obj.x = 10
print(obj.x + 2) # 12
print(obj.__dict__) # {'_x': 10}
del obj.x
print(obj.x) # None
print(obj.__dict__) # {}

# metaclass元类是类的模板,元类的实例为类
# 当你面临一个问题还在纠结要不要使用元类时,往往会有其他更为简单的解决方案
# 元方法可以从元类或者类中调用,不能从类的实例中调用。
# 类方法可以从类中调用,也可以从类的实例中调用
class TypeSetter(object):
    def __init__(self, fieldtype):
        print('TYpeSetter __init__', fieldtype)
        self.fieldtype = fieldtype
    def is_valid(self, value):
        return isinstance(value, self.fieldtype)
class TypeCheckMeta(type): # type为父类,是对type的重写,作为一个元类
    def __new__(cls, name, bases, dict):
        print('TypeCheckMeta __new__', name, bases, dict)
        return super(TypeCheckMeta, cls).__new__(cls, name, bases, dict)
    def __init__(self, name, bases, dict):
        self._fields = {}
        for key,value in dict.items():
            if isinstance(value, TypeSetter):
                self._fields[key] = value
    def sayHi(cls):
        print('HI')
class TypeCheck(object):
    __metaclass__ = TypeCheckMeta
    # 所有继承该类的子类都将使用元类来指导类的生成
    # 若未设置__metaclass__,使用默认的type元类来生成类
    def __setattr__(self, key, value):
        print('TypeCheck __setattr__')
        if key in self._fields:
            if not self._fields[key].is_valid(value):
                raise TypeError('Invalid type for field')
        super(TypeCheck, self).__setattr__(key, value)
class MetaTest(TypeCheck): # 由元类 TypeCheckMeta 指导生成
    name = TypeSetter(str)
    num = TypeSetter(int)
mt = MetaTest()
mt.name = 'apple'
mt.num = 100
MetaTest.sayHi() # 元方法可以从元类或者类中调用,不能从类的实例中调用。
# ('TypeCheckMeta __new__', 'TypeCheck', (,), {'__module__': '__main__', '__metaclass__': , '__setattr__': })
# ('TYpeSetter __init__', )
# ('TYpeSetter __init__', )
# ('TypeCheckMeta __new__', 'MetaTest', (,), {'__module__': '__main__', 'num': <__main__.TypeSetter object at 0x03951D50>, 'name': <__main__.TypeSetter object at 0x03951CF0>})
# TypeCheck __setattr__
# TypeCheck __setattr__
# HI

# 协议:一种松散的约定,没有相应的接口定义。
# 迭代器:统一的访问容器或集合 + 惰性求值 + 多多使用,itertools
from itertools import *
# print(''.join(i) for i in product('AB', repeat = 2))
for i in product('ABCD', repeat = 2):
    print(''.join(i)), # AA AB AC AD BA BB BC BD CA CB CC CD DA DB DC DD
print
for i in combinations('ABCD', 2): # AB AC AD BC BD CD
    print(''.join(i)),
print

# 生成器:按一定的算法生成一个序列。
# 生成器函数:使用了 yield,返回一个迭代器,以生成器的对象放回。
def fib(n):
    a, b = 1, 1
    while a < n:
        test = (yield a)
        print('test:', test)
        a, b = b, a+b
for i, f in enumerate(fib(10)):
    print(f), # 1 1 2 3 5 8
# 调用生成器函数时,函数体并不执行,当第一次调用next()方法时才开始执行,并执行到yield表达式后中止。
generator = fib(10)
print(generator, generator.next(), generator.next()) # (, 1, 1)
print(generator.send(3)) # ('test:', 3)

# yield 与 上下文管理器 结合
from contextlib import contextmanager
@contextmanager
def tag(name):
    print('<%s>' % name)
    yield
    print('<%s>' % name)
with tag('hi'):
    print('hello')
# 
# hello
# 

# GIL : Global Interpreter Lock 全局解释器锁
# sys.setcheckinterval 自动线程间切换,默认每隔100个时钟
# 单核上的多线程本质上是顺序执行的
# 多核的效率比较低,考虑 multiprocessing

'''
无论使用何种语言开发,无论开发的是何种类型,何种规模的程序,都存在这样一点相同之处。
即:一定比例的内存块的生存周期都比较短,通常是几百万条机器指令的时间,
而剩下的内存块,起生存周期比较长,甚至会从程序开始一直持续到程序结束。
'''
# 引用计数算法 - 无法解决循环引用问题 - 设置threshold阈值 gc 模块
import gc
print(gc.isenabled()) # True
print(gc.get_threshold()) # (700, 10, 10)
print(gc.garbage) # []
# 循环引用可以使一组对象的引用计数不为0,然而这些对象实际上并没有被任何外部对象所引用,
# 它们之间只是相互引用。这意味着不会再有人使用这组对象,应该回收这组对象所占用的内存空间,
# 然后由于相互引用的存在,每一个对象的引用计数都不为0,因此这些对象所占用的内存永远不会被释放。
a = []
b = []
a.append(b)
b.append(a)
print(a, b) # ([[[...]]], [[[...]]])
# python解决方案:当某些内存块M经过了3次垃圾收集的清洗之后还存活时,我们就将内存块M划到一个集合A中去,
# 而新分配的内存都划分到集合B中去。当垃圾收集开始工作时,大多数情况都只对集合B进行垃圾回收,
# 而对集合A进行垃圾回收要隔相当长一段时间后才进行,这就使得垃圾收集机制需要处理的内存少了,效率自然就提高了。
# 在这个过程中,集合B中的某些内存块由于存活时间长而会被转移到集合A中,
# 当然,集合A中实际上也存在一些垃圾,这些垃圾的回收会因为这种分代的机制而被延迟。
# Python中,总共有3“,也就是Python实际上维护了3条链表

# PyPI : Python Package Index - Python包索引
# https://pypi.python.org/pypi/{package}
# python setup.py install
# PyUnit unittest模块 - 测试代码先于被测试的代码,更有利于明确需求。
# import unittest
# unittest.main()
# 使用 Pylint 检查代码风格
# 代码审查工具:review board
# 将包发布到PyPI,供下载使用 - 这个流程需要走一遍
# 代码优化:
# 优先保证代码是可工作的
# 权衡优化的代价
# 定义性能指标,集中力量解决首要问题
# 不要忽略可读性
# 定位性能瓶颈问题 - CProfile
import cProfile
def foo():
    sum = 0
    for i in range(100):
        sum += i
    return sum
cProfile.run('foo()') # 针对 foo() 函数的运行时间分布统计

# 算法的评价 = 时间复杂度(重点) + 空间复杂度(硬件),一般采用以空间换时间的方法
# O(1)
# 循环优化:减少循环过程中的计算量,将内层计算提到上一层
# 使用不同的数据结构优化性能
# 列表 list
# 栈和队列 deque
# heapify()将序列容器转化为堆 heapq
import heapq
import random
list0 = [random.randint(0,100) for i in range(10)]
print(list0) #[55, 62, 17, 56, 82, 45, 87, 48, 65, 32]
heapq.heapify(list0)
print(list0) # [17, 32, 45, 48, 62, 55, 87, 56, 65, 82]

import array
a = array.array('c', 'string')
print(a.tostring()) # string
import sys
print(sys.getsizeof(a)) # 28
print(sys.getsizeof(list('string'))) # 72
import timeit
t = timeit.Timer("''.join(list('string'))")
print(t.timeit()) # 0.688437359057
t = timeit.Timer("a.tostring()", "import array; a = array.array('c', 'string')")
print(t.timeit()) # 0.163860804606

# set 集合的使用
list0 = [i for i in range(10)]
list1 = [i for i in range(20)]
print(set(list0)&set(list1)) # set([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

# 进程同步:multiprocessing - Pipe, Queue - 解决多核下的GIL效率问题
# 线程同步:threading - Lock, Event, Condition, Semaphore
# 线程的生命周期:创建,就绪,运行,阻塞,终止
# 避免多次创建线程 - 线程池 threadpool







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