>>> mygenerator = (x*x for x in range(3))
>>> for i in mygenerator :
... print(i)
0
1
4
看起来除了把 [] 换成 () 外没什么不同。但是，你不可以再次使用 for i in mygenerator , 因为生成器只能被迭代一次：先计算出0，然后继续计算1，然后计算4，一个跟一个的…
yield 关键字
yield 是一个类似 return 的关键字，只是这个函数返回的是个生成器。
>>> def createGenerator() :
... mylist = range(3)
... for i in mylist :
... yield i*i
...
>>> mygenerator = createGenerator() # create a generator
>>> print(mygenerator) # mygenerator is an object!
<generator object createGenerator at 0xb7555c34>
>>> for i in mygenerator:
... print(i)
0
1
4
# Here you create the method of the node object that will return the generator
def node._get_child_candidates(self, distance, min_dist, max_dist):
# Here is the code that will be called each time you use the generator object :
# If there is still a child of the node object on its left
# AND if distance is ok, return the next child
if self._leftchild and distance - max_dist < self._median:
yield self._leftchild
# If there is still a child of the node object on its right
# AND if distance is ok, return the next child
if self._rightchild and distance + max_dist >= self._median:
yield self._rightchild
# If the function arrives here, the generator will be considered empty
# there is no more than two values : the left and the right children
调用者:
# Create an empty list and a list with the current object reference
result, candidates = list(), [self]
# Loop on candidates (they contain only one element at the beginning)
while candidates:
# Get the last candidate and remove it from the list
node = candidates.pop()
# Get the distance between obj and the candidate
distance = node._get_dist(obj)
# If distance is ok, then you can fill the result
if distance <= max_dist and distance >= min_dist:
result.extend(node._values)
# Add the children of the candidate in the candidates list
# so the loop will keep running until it will have looked
# at all the children of the children of the children, etc. of the candidate
candidates.extend(node._get_child_candidates(distance, min_dist, max_dist))
return result
这个代码包含了几个小部分：
我们对一个列表进行迭代，但是迭代中列表还在不断的扩展。它是一个迭代这些嵌套的数据的简洁方式，即使这样有点危险，因为可能导致无限迭代。 candidates.extend(node._get_child_candidates(distance, min_dist, max_dist)) 穷尽了生成器的所有值，但 while 不断地在产生新的生成器，它们会产生和上一次不一样的值，既然没有作用到同一个节点上.
extend() 是一个迭代器方法，作用于迭代器，并把参数追加到迭代器的后面。
通常我们传给它一个列表参数:
>>> a = [1, 2]
>>> b = [3, 4]
>>> a.extend(b)
>>> print(a)
[1, 2, 3, 4]
>>> class Bank(): # let's create a bank, building ATMs
... crisis = False
... def create_atm(self) :
... while not self.crisis :
... yield "$100"
>>> hsbc = Bank() # when everything's ok the ATM gives you as much as you want
>>> corner_street_atm = hsbc.create_atm()
>>> print(corner_street_atm.next())
$100
>>> print(corner_street_atm.next())
$100
>>> print([corner_street_atm.next() for cash in range(5)])
['$100', '$100', '$100', '$100', '$100']
>>> hsbc.crisis = True # crisis is coming, no more money!
>>> print(corner_street_atm.next())
<type 'exceptions.StopIteration'>
>>> wall_street_atm = hsbc.create_atm() # it's even true for new ATMs
>>> print(wall_street_atm.next())
<type 'exceptions.StopIteration'>
>>> hsbc.crisis = False # trouble is, even post-crisis the ATM remains empty
>>> print(corner_street_atm.next())
<type 'exceptions.StopIteration'>
>>> brand_new_atm = hsbc.create_atm() # build a new one to get back in business
>>> for cash in brand_new_atm :
... print cash
$100
$100
$100
$100
$100
$100
$100
$100
$100
...