[Numpy-discussion] numpy array casting ruled not safe
jtaylor.debian at googlemail.com
Sun Mar 8 08:35:21 EDT 2015
On 08.03.2015 11:49, Sebastian Berg wrote:
> On Sa, 2015-03-07 at 18:21 -0800, Jaime Fernández del Río wrote:
>> I note that on SO Jaime made the suggestion that take use
>> unsafe casting and throw an error on out of bounds indexes.
>> That sounds reasonable, although for sufficiently large
>> integer types an index could wrap around to a good value.
>> Maybe make it work only for npy_uintp.
>> It is mostly about consistency, and having take match what indexing
>> already does, which is to unsafely cast all integers:
>> In : np.arange(10)[np.uint64(2**64-1)]
>> Out: 9
>> I think no one has ever complained about that obviously wrong
>> behavior, but people do get annoyed if they cannot use their perfectly
>> valid uint64 array because we want to protect them from themselves.
>> Sebastian has probably given this more thought than anyone else, it
>> would be interesting to hear his thoughts on this.
> Not really, there was no change in behaviour for arrays here. Apparently
> though (which I did not realize), there was a change for numpy
> scalars/0-d arrays. Of course I think ideally "same_type" casting would
> raise an error or at least warn on out of bounds integers, but we do not
> have a mechanism for that.
> We could fix this, I think Jaime you had thought about that at some
> point? But it would require loop specializations for every integer type.
> So, I am not sure what to prefer, but for the user indexing with
> unsigned integers has to keep working without explicit cast. Of course
> the fact that it is dangerous, is bothering me a bit, even if a
> dangerous wrap-around seems unlikely in practice.
I was working on supporting arbitrary integer types as index without
This would have a few advantages, you can save memory without
sacrificing indexing performance by using smaller integers and you can
skip the negative index wraparound step for unsigned types.
But it does add quite a bit of code bloat that is essentially a
To make it really useful one also needs to adapt other functions like
where, arange, meshgrid, indices etc. to have an option to return the
smallest integer type that is sufficient for the index array.
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