On Sat, 2013-06-01 at 20:09 -0400, Warren Weckesser wrote:
> I'm using Ubuntu 12.04, so I suspect I won't be the only one who sees
> these.
>
gcc on 12.04 (precise) should be 4.6.3
See
http://packages.ubuntu.com/search?keywords=gcc&searchon=names&suite=precise§ion=all
===
Announcing PyTables 3.0.0
===
We are happy to announce PyTables 3.0.0.
PyTables 3.0.0 comes after about 5 years from the last major release
(2.0) and 7 months since the last stable release (2.4.0).
This is new major release and an important m
Thanks to the two of you!!
Chao
On Mon, Jun 3, 2013 at 11:53 AM, Sebastian Berg
wrote:
> On Mon, 2013-06-03 at 10:44 +0200, Chao YUE wrote:
> > Dear all,
> >
> > I have an array with 4 dim:
> >
> > In [24]: dd.shape
> > Out[24]: (12, 13, 120, 170)
> >
> > I would like to collapse the last two di
On 3 June 2013 08:33, David Cournapeau wrote:
> (around 50 on my own machine, but that's platform
> specific).
In my machine, it is around 10. You also have to be aware of the data
container: it is not the same to iterate over lists than over arrays.
In [6]: a = np.arange(50)
In [7]: %timeit np
On Mon, 2013-06-03 at 10:44 +0200, Chao YUE wrote:
> Dear all,
>
> I have an array with 4 dim:
>
> In [24]: dd.shape
> Out[24]: (12, 13, 120, 170)
>
> I would like to collapse the last two dim for applying np.sum(axis=-1)
>
If you use Numpy >= 1.7. you can also just use dd.sum(axis=(-1,-2))
-
On Mon, Jun 3, 2013 at 9:44 AM, Chao YUE wrote:
> Dear all,
>
> I have an array with 4 dim:
>
> In [24]: dd.shape
> Out[24]: (12, 13, 120, 170)
>
> I would like to collapse the last two dim for applying np.sum(axis=-1)
>
> In [25]: dd.reshape(12,13,-1).shape
> Out[25]: (12, 13, 20400)
>
> is there
Dear all,
I have an array with 4 dim:
In [24]: dd.shape
Out[24]: (12, 13, 120, 170)
I would like to collapse the last two dim for applying np.sum(axis=-1)
In [25]: dd.reshape(12,13,-1).shape
Out[25]: (12, 13, 20400)
is there a more general way to do this? something like
In [21]: dd.reshape(*dd
On Mon, 3 Jun 2013 07:33:23 +0100
David Cournapeau wrote:
> > While not surprising, I did not expect numpy to be so much slower (25x)...
> It is a known limitation of numpy scalars. As soon as you use array
> that are not tiny, the speed difference disappears and then favors
> numpy arrays (aroun
