[Numpy-discussion] Suggestion: special-case np.array(range(...)) to be faster

[Antony Lee]

Indeed:

In [1]: class C:
    def __getitem__(self, i):
        if i < 10: return i
        else: raise IndexError
    def __len__(self):
        return 10
   ...:

In [2]: np.array(C())
Out[2]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])


(omitting __len__ results in the creation of an object array, consistently
with the fact that the sequence protocol requires __len__).
Meanwhile, I found a new way to segfault numpy :-)

In [3]: class C:
    def __getitem__(self, i):
        if i < 10: return i
        else: raise IndexError
    def __len__(self):
        return 42
   ...:

In [4]: np.array(C())
Fatal Python error: Segmentation fault


2016-02-15 0:10 GMT-08:00 Nathaniel Smith <njs at pobox.com>:

> On Sun, Feb 14, 2016 at 11:41 PM, Antony Lee <antony.lee at berkeley.edu>
> wrote:
> > I wonder whether numpy is using the "old" iteration protocol (repeatedly
> > calling x[i] for increasing i until StopIteration is reached?)  A quick
> > timing shows that it is indeed slower.
>
> Yeah, I'm pretty sure that np.array doesn't know anything about
> "iterable", just about "sequence" (calling x[i] for 0 <= i <
> i.__len__()).
>
> (See Sequence vs Iterable:
> https://docs.python.org/3/library/collections.abc.html)
>
> Personally I'd like it if we could eventually make it so np.array
> specifically looks for lists and only lists, because the way it has so
> many different fallbacks right now creates all confusion between which
> objects are elements. Compare:
>
> In [5]: np.array([(1, 2), (3, 4)]).shape
> Out[5]: (2, 2)
>
> In [6]: np.array([(1, 2), (3, 4)], dtype="i4,i4").shape
> Out[6]: (2,)
>
> -n
>
> --
> Nathaniel J. Smith -- https://vorpus.org
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