Welcome to the world of exotic flow control. With Python 2.2 and later versions, programmers will get some new options for making programs tick that were not available – or at least not as convenient – in earlier Python versions.
While what Python 2.2 gives us is not quite as mind-melting as the full continuations and microthreads that are possible in Stackless Python, generators and iterators do something a bit different from traditional functions and classes.
Let’s consider iterators first, since they are simpler to understand. Basically, an iterator is an object that has a
.next() method. Well, that’s not quite true; but it’s close. Actually, most iterator contexts want an object that will generate an iterator when the new
iter() built-in function is applied to it. To have a user-defined class (that has the requisite
.next() method) return an iterator, you need to have an
__iter__() method return
self. The examples will make this all clear. An iterator’s
.next() method might decide to raise a
StopIteration exception if the iteration has a logical termination.
A generator is a little more complicated and general. But the most typical use of generators will be for defining iterators; so some of the subtlety is not always worth worrying about. A generator is a function that remembers the point in the function body where it last returned. Calling a generator function a second (or nth) time jumps into the middle of the function, with all local variables intact from the last invocation.
In some ways, a generator is like the closures which were discussed in previous installments of this column discussing functional programming (see resources on the right side for more information). Like a closure, a generator “remembers” the state of its data. But a generator goes a bit further than a closure: a generator also “remembers” its position within flow-control constructs (which, in imperative programming, is something more than just data values). Continuations are still more general since they let you jump arbitrarily between execution frames, rather than returning always to the immediate caller’s context (as a generator does).
Fortunately, using a generator is much less work than understanding all the conceptual issues of program flow and state. In fact, after very little practice, generators seem as obvious as ordinary functions.
Taking a random walk
Let’s consider a fairly simple problem that we can solve in several ways — both new and old. Suppose we want a stream of positive random numbers less than one that obey a backward-looking constraint. Specifically, we want each successive number to be at least 0.4 more or less than the last one. Moreover, the stream itself is not infinite, but rather ends after a random number of steps. For the examples, we will simply end the stream when a number less than 0.1 is produced. The constraints described are a bit like one might find in a “random walk” algorithm, with the end condition resembling a “statisficing” or “local minimum” result — but certainly the requirements are simpler than most real-world ones.
In Python 2.1 or earlier, we have a few approaches to solving our problem. One approach is to simply produce and return a list of numbers in the stream. This might look like:
import random def randomwalk_list(): last, rand = 1, random.random() # init candidate elements nums =  # empty list while rand > 0.1: # threshhold terminator if abs(last-rand) >= 0.4: # accept the number last = rand nums.append(rand) # add latest candidate to nums else: print '*', # display the rejection rand = random.random() # new candidate nums.append(rand) # add the final small element return nums
Utilizing this function is as simple as:
for num in randomwalk_list(): print num,
There are a few notable limitations to the above approach. The specific example is exceedingly unlikely to produce huge lists; but just by making the threshhold terminator more stringent, we could create arbitrarily large streams (of random exact size, but of anticipatable order-of-magnitude). At a certain point, memory and performance issues can make this approach undesirable and unnecessary. This same concern got
xreadlines() added to Python in earlier versions. More significantly, many streams depend on external events, and yet should be processed as each element is available. For example, a stream might listen to a port, or wait for user inputs. Trying to create a complete list out of the stream is simply not an option in these cases.
One trick available in Python 2.1 and earlier is to use a “static” function-local variable to remember things about the last invocation of a function. Obviously, global variables could do the same job, but they cause the familiar problems with pollution of the global namespace, and allow mistakes due to non-locality. You might be surprised here if you are unfamiliar with the trick–Python does not have an “official” static scoping declaration. However, if named parameters are given mutable default values, the parameters can act as persistent memories of previous invocations. Lists, specifically, are handy mutable objects that can conveniently even hold multiple values.
Using a “static” approach, we can write a function like:
import random def randomwalk_static(last=): # init the "static" var(s) rand = random.random() # init a candidate value if last < 0.1: # threshhold terminator return None # end-of-stream flag while abs(last-rand) < 0.4: # look for usable candidate print '*', # display the rejection rand = random.random() # new candidate last = rand # update the "static" var return rand
This function is quite memory friendly. All it needs to remember is one previous value, and all it returns is a single number (not a big list of them). And a function similar to this could return successive values that depend (partly or wholly) on external events. On the down side, utilizing this function is somewhat less concise, and considerably less elegant:
num = randomwalk_static() while num is not None: print num, num = randomwalk_static()
New ways of walking
“Under the hood”, Python 2.2 sequences are all iterators. The familiar Python idiom
for elem in lst: now actually asks
lst to produce an iterator. The
for loop then repeatedly calls the
.next() method of this iterator until it encounters a
StopIteration exception. Luckily, Python programmers do not need to know what is happening here, since all the familiar built-in types produce their iterators automatically. In fact, now dictionaries have the methods
.itervalues() to produce iterators; the first is what gets used in the new idiom
for key in dct:. Likewise, the new idiom
for line in file: is supported via an iterator that calls
But given what is actually happening within the Python interpreter, it becomes obvious to use custom classes that produce their own iterators rather than exclusively use the iterators of built-in types. A custom class that enables both the direct usage of
randomwalk_list() and the element-at-a-time parsimony of
randomwalk_static is straightforward:
import random class randomwalkiter: def init(self): self.last = 1 # init the prior value self.rand = random.random() # init a candidate value def _iter(self): return self # simplest iterator creation def next(self): if self.rand < 0.1: # threshhold terminator raise StopIteration # end of iteration else: # look for usable candidate while abs(self.last-self.rand) < 0.4: print '*', # display the rejection self.rand = random.random() # new candidate self.last = self.rand # update prior value return self.rand
Use of this custom iterator looks exactly the same as for a true list generated by a function:
for num in randomwalk_iter(): print num,
In fact, even the idiom
if elem in iterator is supported, which lazily only tries as many elements of the iterator as are needed to determine the truth value (if it winds up false, it needs to try all the elements, of course).
Leaving a trail of crumbs
The above approaches are fine for the problem at hand. But none of them scale very well to the case where a routine creates a large number of local variables along the way, and winds its way into a nest of loops and conditionals. If an iterator class or a function with static (or global) variables depends on multiple data states, two problems come up. One is the mundane matter of creating multiple instance attributes or static list elements to hold each of the data values. The far more important problem is figuring out how to get back to exactly the relevant part of the flow logic that corresponds to the data states. It is awfully easy to forget about the interaction and codependence of different data.
Generators simply bypass the whole problem. A generator “returns” with the new keyword
yield, but “remembers” the exact point of execution where it returned. Next time the generator is called, it picks up where it left before — both in terms of function flow and in terms of variable values.
One does not directly write a generator in Python 2.2+. Instead, one writes a function that, when called, returns a generator. This might seem odd, but “function factories” are a familiar feature of Python, and “generator factories” are an obvious conceptual extension of this. What makes a function a generator factory in Python 2.2+ is the presence of one or more
yield statements somewhere in its body. If
return must only occur without any accompanying return value. A better choice, however, is to arrange the function bodies so that execution just “falls off the end” after all the
yields are accomplished. But if a
return is encountered, it causes the produced generator to raise a
StopIteration exception rather than yield further values.
In my opinion, the choice of syntax for generator factories was somewhat poor. A
yield statement can occur well into the body of a function, and you might be unable to determine that a function is destined to act as a generator factory anywhere within the first N lines of a function. The same thing could, of course, be true of a function factory — but being a function factory doesn’t change the actual syntax of a function body (and a function body is allowed to sometimes return a plain value; albeit probably not out of good design). To my mind, a new keyword — such as
generator in place of
def — would have been a better choice.
Quibbles over syntax aside, generators have the good manners to automatically act as iterators when called on to do so. Nothing like the
.__iter__() method of classes is needed here. Every
yield encountered becomes a return value for generator’s
.next() method. Let’s look at the simplest generator to make things clear:
>>> from future import generators >>> def gen(): yield 1 >>> g = gen() >>> g.next() 1 >>> g.next() Traceback (most recent call last): File "<pyshell#15>", line 1, in ? g.next() StopIteration
Let’s put a generator to work in our sample problem:
from future import generators # only needed for Python 2.2 import random def randomwalk_generator(): last, rand = 1, random.random() # initialize candidate elements while rand > 0.1: # threshhold terminator print '*', # display the rejection if abs(last-rand) >= 0.4: # accept the number last = rand # update prior value yield rand # return AT THIS POINT rand = random.random() # new candidate yield rand # return the final small element
The simplicity of this definition is appealing. You can utilize the generator either manually or as an iterator. In the manual case, the generator can be passed around a program, and called wherever and whenever needed (which is quite flexible). A simple example of the manual case is:
gen = randomwalk_generator() try: while 1: print gen.next(), except StopIteration: pass
Most frequently, however, you are likely to use a generator as an iterator, which is even more concise (and again looks just like an old-fashioned sequence):
for num in randomwalk_generator(): print_short(num)
It will take a little while for Python programmers to become familiar with the ins-and-outs of generators. The added power of such a simple construct is surprising at first; and even quite accomplished programmers (like the Python developers themselves) will continue to discover subtle new techniques using generators for some time, I predict.
To close, let me present one more generator example that comes from the
test_generators.py module distributed with Python 2.2. Suppose you have a tree object, and want to search its leaves in left-to-right order. Using state-monitoring variables, getting a class or function just right is difficult. Using generators makes it almost laughably easy:
>>> # A recursive generator that generates Tree leaves in in-order. >>> def inorder(t): ... if t: ... for x in inorder(t.left): ... yield x ... yield t.label ... for x in inorder(t.right): ... yield x