On Thu, May 5, 2011 at 9:54 AM, Robert Kern wrote:
> On Thu, May 5, 2011 at 02:29, Christoph Groth wrote:
>> Has something like __index_iadd__ ever been considered seriously? Not
>> to my (limited) knowledge.
>
> Only on this list, I think. :-)
>
> I don't think it will ever happen. Only numpy r
On Thu, May 5, 2011 at 02:29, Christoph Groth wrote:
>> On Wed, May 4, 2011 at 6:19 AM, Christoph Groth wrote:
>>>
>>> Dear numpy experts,
>>>
>>> I have noticed that with Numpy 1.5.1 the operation
>>>
>>> m[::2] += 1.0
>>>
>>> takes twice as long as
>>>
>>> t = m[::2]
>>>
On Wed, May 04, 2011 at 08:57:42AM -0700, Mark Wiebe wrote:
>You'd better time this in 1.6 too. ;)
>
> [2]https://github.com/numpy/numpy/commit/f60797ba64ccf33597225d23b893b6eb11149860
Awesome work! That's all I have to say.
I find very enjoyable the generalized use of the time machine i
> On Wed, May 4, 2011 at 6:19 AM, Christoph Groth wrote:
>>
>> Dear numpy experts,
>>
>> I have noticed that with Numpy 1.5.1 the operation
>>
>> m[::2] += 1.0
>>
>> takes twice as long as
>>
>> t = m[::2]
>> t += 1.0
Mark Wiebe writes:
> You'd better tim
On Wed, May 4, 2011 at 6:19 AM, Christoph Groth wrote:
> Dear numpy experts,
>
> I have noticed that with Numpy 1.5.1 the operation
>
> m[::2] += 1.0
>
> takes twice as long as
>
> t = m[::2]
> t += 1.0
>
> where "m" is some large matrix. This is of course because the first
> snippet is equivale
On Wed, May 4, 2011 at 08:19, Christoph Groth wrote:
> Dear numpy experts,
>
> I have noticed that with Numpy 1.5.1 the operation
>
> m[::2] += 1.0
>
> takes twice as long as
>
> t = m[::2]
> t += 1.0
>
> where "m" is some large matrix. This is of course because the first
> snippet is equivalent
Dear numpy experts,
I have noticed that with Numpy 1.5.1 the operation
m[::2] += 1.0
takes twice as long as
t = m[::2]
t += 1.0
where "m" is some large matrix. This is of course because the first
snippet is equivalent to
t = m[::2]
t += 1.0
m[::2] = t
I wonder whether it would not be a good
