On Fri, Jun 19, 2015 at 3:05 PM, Sturla Molden
wrote:
> Charles R Harris wrote:
>
> > I'm looking to change some numpy deprecations into errors as well as
> remove
> > some deprecated functions. The problem I see is that
> > SciPy claims to support Numpy >= 1.5 and Numpy 1.5 is really, really,
>
On Fri, Jun 19, 2015 at 4:08 PM, Charles R Harris wrote:
> Hi All,
>
> I'm looking to change some numpy deprecations into errors as well as
> remove some deprecated functions. The problem I see is that
> SciPy claims to support Numpy >= 1.5 and Numpy 1.5 is really, really, old.
> So the question
Elliot Hallmark wrote:
> And I can't help but wonder if there is further configuration I need
> to make numpy faster, or if this is just a difference between out
> machines
Try to build NumPy with Intel MKL or OpenBLAS instead.
ATLAS is only efficient on the host computer on which it is built,
Debian Sid, 64-bit. I was trying to fix the problem of np.dot running very
slow.
I ended up uninstalling numpy, installing libatlas3-base through apt-get
and re-installing numpy. The performance of dot is greatly improved! But
I can't tell from any other method whether numpy is set up correctly
Charles R Harris wrote:
> I'm looking to change some numpy deprecations into errors as well as remove
> some deprecated functions. The problem I see is that
> SciPy claims to support Numpy >= 1.5 and Numpy 1.5 is really, really, old.
> So the question is, does "support" mean compiles with earlier
On Wed, Jun 17, 2015 at 11:13 PM, Nathaniel Smith wrote:
> there's some
> argument that in Python, doing explicit type checks like this is
> usually a sign that one is doing something awkward,
I tend to agree with that.
On the other hand, numpy itself is kind-of sort-of statically typed. But
Hi All,
I'm looking to change some numpy deprecations into errors as well as remove
some deprecated functions. The problem I see is that
SciPy claims to support Numpy >= 1.5 and Numpy 1.5 is really, really, old.
So the question is, does "support" mean compiles with earlier versions
of Numpy ? If t
On Fri, Jun 19, 2015 at 10:39 AM, Sebastian Berg wrote:
> No, what tile does cannot be represented that way. If it was possible
> you can achieve the same using `np.broadcast_to` basically, which was
> just added though. There are some other things you can do, like rolling
> window (adding dimens
On Fr, 2015-06-19 at 10:06 +0200, Freddy Rietdijk wrote:
> Hi,
>
>
> Having read that it is possible to basically 'copy' elements along an
> axis without actually copying the values by making use of the strides,
> I wonder whether it is possible to add this as an option to np.tile.
>
No, what t
Hi,
Having read that it is possible to basically 'copy' elements along an axis
without actually copying the values by making use of the strides, I wonder
whether it is possible to add this as an option to np.tile.
It would be easier than having to use as_strided or broadcast_arrays to
repeat data
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