On Mon, May 6, 2013 at 6:54 PM, Bago wrote:
> I submitted a patch a little while ago,
> https://github.com/numpy/numpy/pull/3107, which gave the searchsorted
> function the ability to search arrays sorted in descending order. At the
> time my approach was to detect the sortorder of the array by c
I submitted a patch a little while ago,
https://github.com/numpy/numpy/pull/3107, which gave the searchsorted
function the ability to search arrays sorted in descending order. At the
time my approach was to detect the sortorder of the array by comparing the
first and last elements. This works pret
On Mon, May 6, 2013 at 7:57 AM, Nathaniel Smith wrote:
> On Mon, May 6, 2013 at 8:52 AM, Funky Dev wrote:
> > Hi,
> >
> > I've got a project in which it turns out we need much higher precision
> > than even __float128 and playing around with a few alternatives mpfr
> > seems to be the highest pe
On Mon, 06 May 2013, Sebastian Berg wrote:
> > if you care to tune it up/extend and then I could fire it up again on
> > that box (which doesn't do anything else ATM AFAIK). Since majority of
> > time is spent actually building it (did it with ccache though) it would
> > be neat if you come up
gt; exact timings).
>
> FWIW -- just as a cruel first attempt look at
>
> http://www.onerussian.com/tmp/numpy-vbench-20130506/vb_vb_reduce.html
>
> why float16 case is so special?
Float16 is special, it is cpu-bound -- not memory bound as most
reductions -- because it is not a na
mings).
>
> FWIW -- just as a cruel first attempt look at
>
> http://www.onerussian.com/tmp/numpy-vbench-20130506/vb_vb_reduce.html
>
> why float16 case is so special?
>
> I have pushed this really coarse setup (based on some elderly copy of
> pandas' vbench) to
er numpy
> versions you will probably see something like half the speed for the
> slow axis (only got ancient or 1.7 numpy right now, so reluctant to give
> exact timings).
FWIW -- just as a cruel first attempt look at
http://www.onerussian.com/tmp/numpy-vbench-20130506/vb_vb_reduce.html
why floa
On Mon, May 6, 2013 at 8:52 AM, Funky Dev wrote:
> Hi,
>
> I've got a project in which it turns out we need much higher precision
> than even __float128 and playing around with a few alternatives mpfr
> seems to be the highest performing possibility. So I've starting
> writing a cythonized class
Hi,
I've got a project in which it turns out we need much higher precision
than even __float128 and playing around with a few alternatives mpfr
seems to be the highest performing possibility. So I've starting
writing a cythonized class mpfr_array which provides array-like
functionality but with m
Dear Python users,
I am having difficulty with numerically scaling to match line coordinates vs
grid cell size coordinates. I want to calculate the following function: F =
distance_of_crossed_line x intersected_cell_value
The problem here is that when I calculate crossed_line_length in line
c
On Mon, May 6, 2013 at 10:52 AM, Daniele Nicolodi wrote:
> On 06/05/2013 11:39, Daniele Nicolodi wrote:
>> On 06/05/2013 11:01, Robert Kern wrote:
>>> np.roll() copies all of the data every time. It does not return a
>>> view.
>>
>> Are you sure about that? Either I'm missing something, or it ret
On Mon, May 6, 2013 at 10:39 AM, Daniele Nicolodi wrote:
> On 06/05/2013 11:01, Robert Kern wrote:
>> np.roll() copies all of the data every time. It does not return a
>> view.
>
> Are you sure about that? Either I'm missing something, or it returns a
> view in my testing (with a fairly old numpy
On 06/05/2013 11:39, Daniele Nicolodi wrote:
> On 06/05/2013 11:01, Robert Kern wrote:
>> np.roll() copies all of the data every time. It does not return a
>> view.
>
> Are you sure about that? Either I'm missing something, or it returns a
> view in my testing (with a fairly old numpy, though):
On Mon, 2013-05-06 at 11:39 +0200, Daniele Nicolodi wrote:
> On 06/05/2013 11:01, Robert Kern wrote:
> > np.roll() copies all of the data every time. It does not return a
> > view.
>
> Are you sure about that? Either I'm missing something, or it returns a
> view in my testing (with a fairly old n
On 06/05/2013 11:01, Robert Kern wrote:
> np.roll() copies all of the data every time. It does not return a
> view.
Are you sure about that? Either I'm missing something, or it returns a
view in my testing (with a fairly old numpy, though):
In [209]: np.__version__
Out[209]: '1.6.2'
In [210]: v
On Thu, Feb 7, 2013 at 6:21 AM, Ondřej Čertík wrote:
> On Wed, Feb 6, 2013 at 9:20 PM, Dag Sverre Seljebotn
> wrote:
>> On 02/07/2013 12:16 AM, Matthew Brett wrote:
> [...]
>>> Can you clarify the people you think will get stuck? I think I'm
>>> right in saying that anyone with a C extension sho
On Mon, May 6, 2013 at 9:51 AM, Daniele Nicolodi wrote:
> Hello,
>
> numpy arrays are great for interfacing python with libraries that expect
> continuous memory buffers for data passing. However, libraries
> interfacing to data acquisition hardware often use those buffers as ring
> buffers where
Hello,
numpy arrays are great for interfacing python with libraries that expect
continuous memory buffers for data passing. However, libraries
interfacing to data acquisition hardware often use those buffers as ring
buffers where, once the buffer has been filled with data, new data will
be writte
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