On Mon, Aug 20, 2012 at 3:51 PM, Travis Oliphant wrote:
> I'm actually not sure, why. I think the issue is making sure that the
> release manager can actually "build" NumPy without having to buy a particular
> compiler.
The MS Express editions, while not open source, are free-to-use, and work
I'm actually not sure, why. I think the issue is making sure that the release
manager can actually "build" NumPy without having to buy a particular compiler.
But, I would rather have official builds of NumPy for all platforms with a
compiler paid for by a NumPy-sponsor than not have them.
On Sun, Aug 19, 2012 at 11:39 AM, Ralf Gommers > The problem is that,
unlike 32-bit builds, they can't be made with open
> source compilers on Windows. So unless we're okay with that,
Why does it have to be built with open-source compilers? we're
building against the python.org python, yes? Which
On Thu, Aug 16, 2012 at 12:46 PM, Fabrice Silva wrote:
>
>
> Le mercredi 15 août 2012 à 20:54 +0200, Ralf Gommers a écrit :
> > I was mixing it up a bit, but yes: the _odepack extension and the C
> > source for it. Not necessary to do that at once I guess, but wrapping
> > the same function twice
On Wed, Jul 25, 2012 at 10:29 AM, Jay Bourque wrote:
> I'm actively looking at this issue since it was my pull request that broke
> this (https://github.com/numpy/numpy/pull/350). We definitely don't want to
> break this functionality for 1.7. The problem is that even though indexing
> with a subs
On Sat, Aug 18, 2012 at 9:28 AM, David Cournapeau wrote:
> Hi,
>
> I have started toying with implementing a quad precision dtype for
> numpy on supported platforms, using the __float128 + quadmath lib from
> gcc. I have noticed invalid (and unexpected) downcast to long double
> in some cases, esp
Hello,
On Mo, 2012-08-20 at 20:55 +1000, Andrew Nelson wrote:
> Dear list,
> I observe a difference when I try to load a 2D numpy array from a file
> object compared to if I supply a filename viz:
>
>
> >>> np.version.version
> '1.5.1'
> >>> f=open('fit_theoretical.txt')
> >>> a=np.loadtxt(f)
>
Dear list,
I observe a difference when I try to load a 2D numpy array from a file
object compared to if I supply a filename viz:
>>> np.version.version
'1.5.1'
>>> f=open('fit_theoretical.txt')
>>> a=np.loadtxt(f)
>>> a.shape
(1000,)
>>> a=np.loadtxt('fit_theoretical.txt')
>>> a.shape
(500, 2)
Th
