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SICP: Lisp Inspired Python

I just finished the lecture on streams in SICP. Python has generators, which are quite lovely. However, it's fun to imagine reimplementing generators in various ways from scratch using some of the approaches in SICP.

First of all, there are promises. A promise says that you'll do something later. In Scheme, you create a promise with "delay" and you invoke that promise with "force":
> (delay (/ 1 0))
> (force (delay (/ 1 0)))
division by zero
Here's one way to translate that to Python:
class Promise(object):

"""This is a promise to execute something on demand."""

def __init__(self, f):
"""Wrap your expression in a lambda and pass it as f."""
self.f = f

def __call__(self):
"""Call the instance to get the value of the promise.

This is memoized so that it doesn't have to compute the value
more than once since promises shouldn't do that.

if not hasattr(self, 'result'):
self.result = self.f()
return self.result

promise = Promise(lambda: 1 / 0)
print repr(promise)
print "No error yet."
print "Now, we'll force the promise, causing the error."
Easy :)

Now, let's take a look at a generator. Here's a simple counter:
def generator(start=0):
while True:
yield start
start += 1

for i in generator():
print i
Clearly, you can do this with an iterator class. However, let's instead use a closure and continuation passing style. The syntax isn't the most beautiful in the world, but it definitely does the trick ;)
def generator(start=0):
scope = locals()

def top():
while True:
return scope['start'], bottom

def bottom():
scope['start'] += 1
return top()

return top

next = generator()
while True:
(i, next) = next()
print i


Unknown said…
Nice post!

Not sure whether you've seen this before, but here's a similar post from Tim Peters on the Python mailing list from 2000:

It obviously predates generators in Python, as well as nested lexical scopes (as you can see in the i=i, inc=inc argument passing). But it's interesting how the basic semantics are very similar to your example.
Anonymous said…
Are you really implying that since you can build everything in Lisp, everything that has something is automatically inspired by Lisp? ;-)

(Afaik, Python's generators were borrowed from Icon, but I don't have time to go dig up references today.)
Anonymous said…
In the promise example, the caching can give an unexpected result if the returned value is an object, because Python's reference counting is not copy-on-write.

a = promise() = "bar"
b = promise()
# b == a, not the original value of the
# expression

On the other hand, caching might be well motivated because the expression might have a side effect, which we only expect to trigger once when we set up the promise.

So Lisp inspired Python is a bit less "functional" than the original Lisp?
jjinux said…
> Not sure whether you've seen this before, but here's a similar post from Tim Peters on the Python mailing list from 2000:

That's awesome! We probably got it from the same book. For some reason, he's not memoizing inside the promise. Oh well.

I really like his Fibonacci example:

fibs = Stream(1,
lambda: Stream(1,
lambda: sadd(fibs,

He mentions Haskell, so he was probably referring to the classic Haskell example:

fib = 1 : 1 : [ a+b | (a,b) <- zip fib (tail fib) ]
jjinux said…
> Are you really implying that since you can build everything in Lisp, everything that has something is automatically inspired by Lisp? ;-)

Of course not! I'm implying that since you can build everything in assembly, everything that has something is automatically inspired by assembly!

It just amazes me to see this stuff in a video from 1986 ;)
jjinux said…
> b == a, not the original value of the

That's actually the desired result.

I agree with you, however, side-effects make everything more complex.

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