[Raymond Hettinger]
> The approach I'm using relies on being able to exactly multiply the 0 or
> 1 bit error term mantissas by an integer (a count of how many times the
> term occurs).  With a Python dictionary keeping the counts, many steps
> are saved and the tool becomes much more memory friendly than with the
> previous queue approach.

Cool!  That's a nice approach.

For contrast, here's one that doesn't use frexp(), and is probably
faster because of that; internally, len(sums) probably won't exceed 5
in real life (but could get as large as 2K for pathological inputs,
spanning fp's full dynamic range):

def summer4(iterable):
    sums = [0.0]
    for x in iterable:
        sums.append(x)
        for i in xrange(len(sums)-2, -1, -1):
            y = sums[i]
            if abs(x) < abs(y):
                x, y = y, x
            hi = x+y
            lo = y - (hi - x)
            if lo:
                sums[i+1] = lo
            else:
                del sums[i+1]
            x = hi
        sums[0] = x
    return sum(reversed(sums), 0.0)

In effect, that keeps an arbitrarily long list of "error terms" so
that no info is lost before the final sum().  I think it's surprising
at first how short that list usually is.

> ...
> Aside from being a nice recipe, this was a good exercise in seeing what
> pure Python can do with IEEE-754 guarantees.

Now you know I how feel everytime sometime proclaims that fp
arithmetic is inherently unreliable <0.3 wink>.  Learning how to use
it correctly is indeed the blackest of obscure arts, though.

> The modest memory consumption and the typical O(n) runtime are a nice
> plus (no pun intended).

Yup, they're all emminently practical if you don't care too much about
speed.  The one above is the fastest I tried, and it still takes some
40x longer than plain sum() over a million-element random.random()
list.
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