On Mo, 2016-01-25 at 16:11 +0100, Sturla Molden wrote: > On 23/01/16 22:25, Sebastian Berg wrote: > > > Do you agree with this, or would it be a major inconvenience? > > I think any user of as_strided should be considered a power user. > This > is an inherently dangerous function, that can easily segfault the > process. Anyone who uses as_strided should be assumed to have taken > all > precautions. >
I am ready to accept this notion (and I guess just put repeated
warnings in the doc string).
However, two things about it, first my impression is that for a lot of
"not really power users" this function sometimes seems like a big
hammer to solve all their problems.
Second, I think even power users have sometimes wrong ideas about
numpy's ufuncs and memory overlap. This starts with operations such as
`arr[1:] += arr[:-1]` (see [1]) (for which there is at least a start on
fixing it), and only gets worse with self-overlapping arrays.
That said, I guess I could agree with you in the regard that there are
so many *other* awful ways to use as_strided, that maybe it really is
just so bad, that improving one thing doesn't actually help anyway ;).
I was actually considering adding a UserWarning when it is likely that
invalid memory is being addressed.
I still dislike that it returns something writable *as default* though.
Unless you write a single element, writing to such an array will be in
many cases unpredictable, no matter how power user you are.
- Sebastian
[1] WARNING: This is a dangerous example, we ideally want it to be
identical to arr[1:] += arr[:-1].copy() always:
In [7]: arr = np.arange(10).reshape(5, 2)
In [8]: arr[1:] += arr[:-1]
In [9]: arr
Out[9]:
array([[ 0, 1],
[ 2, 4],
[ 6, 9],
[12, 16],
[20, 25]])
In [10]: arr = np.arange(10)[::-1].copy()[::-1].reshape(5, 2)
In [11]: arr[1:] += arr[:-1] # happens to be "correct"
In [12]: arr
Out[12]:
array([[ 0, 1],
[ 2, 4],
[ 6, 8],
[10, 12],
[14, 16]])
> -1
>
> Sturla
>
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