Thanks,
Colin W.
On 22-Jan-15 5:42 PM, Carl Kleffner
wrote:
Yes,
I build win32 as well as amd64 binaries.
Carlkl
2015-01-22 23:06 GMT+01:00 cjw :
Thanks Carl,
This is good to hear. I presume that the AMD64 is covered.
Colin
On Thu, Jan 22, 2015 at 9:29 PM, Carl Kleffner wrote:
> I took time to create mingw-w64 based wheels of numpy-1.9.1 and scipy-0.15.1
> source distributions and put them on
> https://bitbucket.org/carlkl/mingw-w64-for-python/downloads as well as on
> binstar.org. The test matrix is python-2.7 and 3
Were there any failures with the 64 bit build, or did all tests pass?
Sturla
On 22/01/15 22:29, Carl Kleffner wrote:
> I took time to create mingw-w64 based wheels of numpy-1.9.1 and
> scipy-0.15.1 source distributions and put them on
> https://bitbucket.org/carlkl/mingw-w64-for-python/downloads
Yes,
I build win32 as well as amd64 binaries.
Carlkl
2015-01-22 23:06 GMT+01:00 cjw :
> Thanks Carl,
>
> This is good to hear. I presume that the AMD64 is covered.
>
> Colin W.
>
> On 22-Jan-15 4:29 PM, Carl Kleffner wrote:
>
> I took time to create mingw-w64 based wheels of numpy-1.9.1 and
>
Thanks Carl,
This is good to hear. I presume that the AMD64 is covered.
Colin W.
On 22-Jan-15 4:29 PM, Carl Kleffner
wrote:
I took time to create mingw-w64 based wheels of numpy-1.9.1 and
scipy-0.15.1 source distributions and put them on
https
I took time to create mingw-w64 based wheels of numpy-1.9.1 and
scipy-0.15.1 source distributions and put them on
https://bitbucket.org/carlkl/mingw-w64-for-python/downloads as well as on
binstar.org. The test matrix is python-2.7 and 3.4 for both 32bit and
64bit.
Feedback is welcome.
The wheels
On Thu, Jan 22, 2015 at 3:18 PM, Charles R Harris
wrote:
>
>
> On Thu, Jan 22, 2015 at 8:08 AM, Charles R Harris
> wrote:
>>
>>
>>
>> On Thu, Jan 22, 2015 at 7:54 AM, Nathaniel Smith wrote:
>>>
>>> On Thu, Jan 22, 2015 at 2:51 PM, Charles R Harris
>>> wrote:
>>> > Hi All,
>>> >
>>> > I'm playin
Antoine Pitrou wrote:
> By always using an aligned allocator there is some overhead:
> - all arrays occupy a bit more memory by a small average amount
> (probably 16 bytes average on a 64-bit machine, for a 16 byte
> guaranteed alignment)
NumPy arrays are Python objects. They have an overhea
On Thu, Jan 22, 2015 at 8:08 AM, Charles R Harris wrote:
>
>
> On Thu, Jan 22, 2015 at 7:54 AM, Nathaniel Smith wrote:
>
>> On Thu, Jan 22, 2015 at 2:51 PM, Charles R Harris
>> wrote:
>> > Hi All,
>> >
>> > I'm playing with the idea of building a simplified datetime class on
>> top of
>> > the
On Thu, Jan 22, 2015 at 7:54 AM, Nathaniel Smith wrote:
> On Thu, Jan 22, 2015 at 2:51 PM, Charles R Harris
> wrote:
> > Hi All,
> >
> > I'm playing with the idea of building a simplified datetime class on top
> of
> > the current numpy implementation. I believe Pandas does something like
> this
On Thu, Jan 22, 2015 at 2:51 PM, Charles R Harris
wrote:
> Hi All,
>
> I'm playing with the idea of building a simplified datetime class on top of
> the current numpy implementation. I believe Pandas does something like this,
> and blaze will (does?) have a simplified version. The reason for the n
Hi All,
I'm playing with the idea of building a simplified datetime class on top of
the current numpy implementation. I believe Pandas does something like
this, and blaze will (does?) have a simplified version. The reason for the
new class would be to have an easier, and hopefully more portable, A
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