Sometimes different languages suggests me ways to cross-pollinate
them.
(Note: probably someone has already suggested (and even implemented)
the following ideas, but I like to know why they aren't fit for
Python).
Python generators now allow me to package some code patterns common in
my code, that other (older) languages didn't allow me to extract; for
example given a sequence (array, list, etc) of items now and then I
need to scan all the upper triangle of their cross matrix:
def xpairs(seq):
len_seq = len(seq)
for i, e1 in enumerate(seq):
for j in xrange(i+1, len_seq):
yield e1, seq[j]
Or adjacent ones (there are ways to generalize the following function,
but this the most common situation for me):
def xpairwise(iterable):
return izip(iterable, islice(iterable, 1, None))
That xpairs() generator is nice, but it's not the best possible code
(but you may disagree with me, and you may think that code better than
the successive D code that uses two slices). Inside it I can't use a
list slicing because it copies subparts of the list, probably becoming
too much slow (for example if len(seq) == 1000).
Compared to Python, the D language is at lower level, so you may
expect it to have a worse syntax (but the ShedSkin compiler has shown
me once for all that you can have a language that is both high-level,
with a short & sexy syntax, and very fast), but you can define a
xpairs() iterable class in it too, and the core of that iterable class
there's a method that may look like this:
if (this.seq.length > 1)
foreach (i, e1; this.seq[0 .. $-1])
foreach (e2; this.seq[i+1 .. $]) {
result = dg(e1, e2); if (result) break; // yield
}
Where the strange last line is the yield, and the $ inside [] is the
length of the current array (a very clever idea that avoids the need
for negative indexes).
That D code is as fast or faster than the code you can back-translate
from Python, this is possible because in D arrays slices are very
light, they are a struct of <length, pointer> (in the future they may
change to a couple of pointers, to speed up the array slice scanning).
So if you slice an array you don't copy its contents, just the start-
end points of the slice. If you read/scan the slice that's all you
have, while if you write on it, D uses a Copy-On-Write strategy, that
is a just-in-time copy of the slice contents.
In practice this speeds up lot of code that manages strings and arrays
(like a XML parser, making it among the faster ones).
Being the slice a struct, it's stack allocated, so the inner foreach
doesn't even create a slice object in the heap each time the outer
foreach loops.
One problem with this strategy is that if you have a huge string, and
you keep only a little slice of it, the D garbage collector will keep
it all in memory. To avoid that problem you have to duplicate the
slice manually:
somestring[inf...sup].dup;
I think Psyco in some situations is able to manage string slices
avoiding the copy.
I think Python 3.0 too may enjoy a similar strategy of light slices +
COW for strings, lists and arrays (tuples and strings don't need the
COW).
Bye,
bearophile
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