python三大器之迭代器、生成器、装饰器

from collections.abc import Iterable
a: int = 1
print(isinstance(a, Iterable))  # False
b: str = "lalalalala" 
print(isinstance(b, Iterable))  # True
c: set = set([1, 2])
print(isinstance(c, Iterable))  # True

class MyIterable:
	def __iter__(self):
		pass
print(isinstance(MyIterable(), Iterable)) # True

from collections.abc import Iterator, Iterable
class MyIterator:
	def __init__(self, array_list):
		self.array_list = array_list
		self.index = 0
	def __iter__(self):
		return self
	def __next__(self):
		if self.index < len(self.array_list):
			val = self.array_list[self.index]
			self.index += 1
			return val
		else:
			raise StopIteration
# 父类如果是迭代器，子类也将是迭代器
class MySubIterator(MyIterator):
	def __init__(self):
		pass
myIterator = MyIterator([1, 2, 3, 4])
# 判断是否为可迭代对象
print(isinstance(myIterator, Iterable))  # True
# 判断是否为迭代器
print(isinstance(myIterator, Iterator))  # True
# 子类实例化
mySubIterator = MySubIterator()
print(isinstance(mySubIterator, Iterator))  # True
# 进行迭代
print(next(myIterator))  # 1
print(myIterator.__next__())  # 2
print(next(myIterator))  # 3
print(next(myIterator))  # 4
print(next(myIterator))  # raise StopIteration

# 列表生成式
_list = [i for i in range(10)]
print(type(_list))  # <class 'list'>
print(_list)  # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
# 生成器
_generator = (i for i in range(10))
print(type(_generator))  # <class 'generator'>
print(_generator)  # <generator object <genexpr> at 0x7fbcd92c9ba0>
# 生成器对象取值
print(_generator.__next__())  # 0
print(next(_generator)) # 1
# 注意从第三个元素开始了！
for x in _generator:
	print(x)  # 2，3，4，5，6，7，8，9

def gen_generator():
	yield 1
def func():
	return 1
print(gen_generator(), type(gen_generator()))  
# <generator object gen_generator at 0x7fe68b2c8b30> <class 'generator'>
print(func(), type(func()))  
# 1 <class 'int'>

def gen_generator2():
	yield [1, 2, 3]


s = gen_generator2()
print(next(s))  # [1, 2, 3]

def consumer():
	r = ''
	while True:
		n = yield r
		if not n:
			return
		print(f'[CONSUMER] Consuming get params.. ({n})')
		if n == 3:
			r = '500 Error'
		else:
			r = '200 OK'
def produce(c):
	c.send(None)  # 启动生成器
	n = 0
	while n < 5:
		n = n + 1
		print(f'[PRODUCER] Producing with params.. ({n})')
		r = c.send(n)  # 一旦n有值，则切换到consumer执行
		print(f'[PRODUCER] Consumer return : [{r}]')
		if not r.startswith('200'):
			print("消费者返回服务异常，则结束生产，并关闭消费者")
			c.close()  # 关闭生成器
			break
consume = consumer()
produce(consume)
# [PRODUCER] Producing with params.. (1)
# [CONSUMER] Consuming get params.. (1)
# [PRODUCER] Consumer return : [200 OK]
# [PRODUCER] Producing with params.. (2)
# [CONSUMER] Consuming get params.. (2)
# [PRODUCER] Consumer return : [200 OK]
# [PRODUCER] Producing with params.. (3)
# [CONSUMER] Consuming get params.. (3)
# [PRODUCER] Consumer return : [500 Error]
# 消费者返回服务异常，则结束生产，并关闭消费者

def my_generator(n, end_case):
	for i in range(n):
		if i == end_case:
			return f'当 i==`{i}`时，中断程序。'
		else:
			yield i
g = my_generator(5, 2)  # 调用
for _i in g:  # for循环不会显式触发异常，故而无法获取到return的值
	print(_i)
# 输出：
# 0
# 1

def my_generator2(n, end_case):
	for i in range(n):
		if i == end_case:
			return f'当 i==`{i}`时，中断程序。'
		else:
			yield i
g = my_generator2(5, 2)  # 调用
try:
	print(next(g))  # 0
	print(next(g))  # 1
	print(next(g))  # 此处要触发end_case了
except StopIteration as exc:
	print(exc.value)  # 当 i==`2`时，中断程序。

def my_generator3(n, end_case):
	for i in range(n):
		if i == end_case:
			return f'当 i==`{i}`时，中断程序。'
		else:
			yield i
def wrap_my_generator(generator):  # 将my_generator的返回值包装成一个生成器
	result = yield from generator
	yield result
g = my_generator3(5, 2)  # 调用
for _ in wrap_my_generator(g):
	print(_)
# 输出：
# 0
# 1
# 当 i==`2`时，中断程序。

# 子生成器
def average_gen():
	total = 0
	count = 0
	average = 0
	while True:
		new_num = yield average
		if new_num is None:
			break
		count += 1
		total += new_num
		average = total / count
	# 每一次return，都意味着当前协程结束。
	return total, count, average
# 委托生成器
def proxy_gen():
	while True:
		# 只有子生成器要结束（return）了，yield from左边的变量才会被赋值，后面的代码才会执行。
		total, count, average = yield from average_gen()
		print("总共传入 {} 个数值， 总和：{}，平均数：{}".fORMat(count, total, average))
# 调用方
def main():
	calc_average = proxy_gen()
	next(calc_average)  # 激活协程
	calc_average.send(10)  # 传入：10
	calc_average.send(None)  # 结束协程 send(None)等于next(calc_acerage)，也就是会走到average_gen里面的return语句
	print("================== 重开协程 ===================")
	calc_average.send(20)  # 传入：20
	calc_average.send(30)  # 传入：30
	calc_average.send(None)  # 结束协程
if __name__ == '__main__':
	main()
# 输出：
# 总共传入 1 个数值， 总和：10，平均数：10.0
# ================== 重开协程 ===================
# 总共传入 2 个数值， 总和：50，平均数：25.0

def func():   # 函数名仅仅只是个绑定内存地址的变量       
	print("i`m running") 
                     
# 这是调用                       
func()  # i`m running
# 这是对象引用，引用的是内存地址        
func2 = func  
print(func2 is func)  # True
# 通过引用进行调用  
func2()  # i`m running

def out_func():
	out_a = 10

	def inner_func(inner_x):
		return out_a + inner_x

	return inner_func


out = out_func()
print(out)  # <function out_func.<locals>.inner_func at 0x7ff378af5c10> out_func返回的是inner_func的内存地址
print(out(inner_x=2))  # 12

def decorator_get_function_name(func):
	"""
	获取正在运行函数名
	:return:
	"""

	def wrapper(*arg):
		"""
		wrapper
		:param arg:
		:return:
		"""
		print(f"当前运行方法名：{func.__name__}  with  params: {arg}")
		return func(*arg)

	return wrapper

# @func_name是python的语法糖
@decorator_get_function_name 
def test_func_add(x, y):
	print(x + y)


def test_func_sub(x, y):
	print(x - y)


test_func_add(1, 2)
# 输出：
# 当前运行方法名：test_func_add  with  params: (1, 2)
# 3
# 不使用语法糖的话也可以用以下方法，效果是一样的
decorator_get_function_name(test_func_sub)(3, 5)
# 还记得前文讲的引用吗？我们还可以换种写法达到跟?一样的效果
dec_obj = decorator_get_function_name(test_func_sub)  # 这里等同于wrapper对象
dec_obj(3,5)  # 这里等同于wrapper(3,5)
# 输出：
# 当前运行方法名：test_func_sub  with  params: (3, 5)
# -2

def login_check(func):
    def wrapper(request, *args, **kwargs):
        if not request.session.get('login_status'):
            return HttpResponseRedirect('/api/login/')
        return func(request, *args, **kwargs)

    return wrapper

@login_check
def edit_config():
	pass

"""
>  1. def login_check(func):  ==>将login_check函数加载到内存
>  ....
>  @login_check  ==>此处已经在内存中将login_check这个函数执行了！；并不需要等edit_config()实例化调用
>  2. 上例@login_check内部会执行以下操作：
>	  2.1 执行login_check函数，并将 @login_check 下面的 函数(edit_config) 作为login_check函数的参数，即：@login_check 等价于 login_check(edit_config)
>     2.2 内部就会去执行：
      def wrapper(*args):
          # 校验session...
          return func(request, *args, **kwargs)   # func是参数，此时 func 等于 edit_config，此处相当于edit_config(request, *args, **kwargs)
      return wrapper     # 返回的 wrapper，wrapper代表的是函数对象，非函数实例化对象
      2.3 其实就是将原来的 edit_config 函数塞进另外一个函数中,另一个函数当中可以做一些操作；再执行edit_config
      2.4 将执行完的 login_check 函数返回值(也就是 wrapper对象)将此返回值再重新赋值给新 edit_config，即：
      2.5 新edit_config = def wrapper:
             # 校验session...
            return 原来edit_config(request, *args, **kwargs) 
>  3. 也就是新edit_config()=login_check(edit_config):wrapper(request, *args, **kwargs):return edit_config(request, *args, **kwargs) 有点绕，大家看步骤细细理解。
"""

from functools import wraps

def w1(func):
	@wraps(func)
	def wrapper(*args, **kwargs):
		print("这里是第一个校验")
		return func(*args, **kwargs)

	return wrapper


def w2(func):
	@wraps(func)
	def wrapper(*args, **kwargs):
		print("这里是第二个校验")
		return func(*args, **kwargs)

	return wrapper


def w3(func):
	def wrapper(*args, **kwargs):
		print("这里是第三个校验")
		return func(*args, **kwargs)

	return wrapper


@w2  # 这里其实是w2(w1(f1))
@w1  # 这里是w1(f1)
def f1():
	print(f"i`m f1, at {f1}")


@w3
def f2():
	print(f"i`m f2, at {f2}")

# ====================== 实例化阶段 =====================
f1()
# 这里是第二个校验
# 这里是第一个校验
# i`m f1, at <function f1 at 0x7febc52f5e50>
f2()
# 这里是第三个校验
# i`m f2, at <function w3.<locals>.inner at 0x7febc52f5f70>

def decorator_get_function_duration(enable):
	"""
	:param enable:  是否需要统计函数执行耗时
	:return: 
	"""
	print("this is decorator_get_function_duration")

	def inner(func):
		print('this is inner in decorator_get_function_duration')

		@wraps(func)
		def wrapper(*args, **kwargs):
			print('this is a wrapper in decorator_get_function_duration.inner')
			if enable:
				start = time.time()
				print(f"函数执行前：{start}")
				result = func(*args, **kwargs)
				print('[%s]`s enable was %s it`s duration : %.3f s ' % (func.__name__, enable, time.time() - start))
			else:
				result = func(*args, **kwargs)
			return result

		return wrapper

	return inner


def decorator_1(func):
	print('this is decorator_1')

	@wraps(func)
	def wrapper(*args, **kwargs):
		print('this is a wrapper in decorator_1')
		return func(*args, **kwargs)

	return wrapper


def decorator_2(func):
	print('this is decorator_2')

	@wraps(func)
	def wrapper(*args, **kwargs):
		print('this is a wrapper in decorator_2')
		return func(*args, **kwargs)

	return wrapper


@decorator_1 # 此处相当:decorator_1(decorator_2(decorator_get_function_duration(enable=True)(fun)))
@decorator_2 # = decorator_2(decorator_get_function_duration(enable=True)(fun))
@decorator_get_function_duration(enable=True)  # = decorator_get_function_duration(enable=True)(fun)
def fun():
	time.sleep(2)
	print("fun 执行完了～")


fun()
# ======== enable=False ============
"""
this is decorator_get_function_duration
this is inner in decorator_get_function_duration
this is decorator_2
this is decorator_1
this is a wrapper in decorator_1
this is a wrapper in decorator_2
this is a wrapper in decorator_get_function_duration.inner
fun 执行完了～
"""
# ======== enable=True ============
"""
this is decorator_get_function_duration
this is inner in decorator_get_function_duration
this is decorator_2
this is decorator_1
this is a wrapper in decorator_1
this is a wrapper in decorator_2
this is a wrapper in decorator_get_function_duration.inner
函数执行前：1634635708.648994
fun 执行完了～
[fun]`s enable was True it`s duration : 2.002 s 
"""