On Sun, 2009-06-14 at 15:50 -0500, Robert Kern wrote:
> On Sun, Jun 14, 2009 at 14:31, Bryan Cole wrote:
> > I'm starting work on an application involving cpu-intensive data
> > processing using a quad-core PC. I've not worked with multi-core systems
> > previously and I'm wondering what is the bes
Robert Cimrman ntc.zcu.cz> writes:
>
> Hi,
>
> I am starting a new thread, so that it reaches the interested people.
> Let us discuss improvements to arraysetops (array set operations) at [1]
> (allowing non-unique arrays as function arguments, better naming
> conventions and documentation).
On Sun, Jun 14, 2009 at 14:31, Bryan Cole wrote:
> I'm starting work on an application involving cpu-intensive data
> processing using a quad-core PC. I've not worked with multi-core systems
> previously and I'm wondering what is the best way to utilise the
> hardware when working with numpy arrays
> In fact, I should have specified previously: I need to
> deploy on MS-Win. On first glance, I can't see that mpi4py is
> installable on Windows.
My mistake. I see it's included in Enthon, which I'm using.
Bryan
>
>
> Bryan
___
Numpy-discussion m
>
> You may want to look at MPI, e.g. mpi4py is convenient for this kind of
> work. For numerical work across processes it is close to a de facto
> standard.
>
> It requires an MPI implementation set up on your machine though (but for
> single-machine use this isn't hard to set up, typically
Bryan Cole wrote:
> I'm starting work on an application involving cpu-intensive data
> processing using a quad-core PC. I've not worked with multi-core systems
> previously and I'm wondering what is the best way to utilise the
> hardware when working with numpy arrays. I think I'm going to use the
I'm starting work on an application involving cpu-intensive data
processing using a quad-core PC. I've not worked with multi-core systems
previously and I'm wondering what is the best way to utilise the
hardware when working with numpy arrays. I think I'm going to use the
multiprocessing package, b
On Sun, Jun 14, 2009 at 10:20 AM, Tom K. wrote:
>
>
>
> jseabold wrote:
>>
>> On Mon, Jun 8, 2009 at 3:33 PM, Robert Kern wrote:
>>> On Mon, Jun 8, 2009 at 14:10, Alan G Isaac wrote:
>> Going back to Alan Isaac's example:
>> 1) beta = (X.T*X).I * X.T * Y
>> 2) beta = np.dot(np.dot(la.
On Sun, Jun 14, 2009 at 2:07 AM, David Cournapeau wrote:
>
> On Sun, Jun 14, 2009 at 4:59 PM, David Cournapeau wrote:
> > On Sun, Jun 14, 2009 at 3:51 AM, Charles R
> > Harris wrote:
> >>
> >>
> >> On Sat, Jun 13, 2009 at 12:35 PM, David Cournapeau
> >> wrote:
> >>>
> >>> On Sun, Jun 14, 2009 at
jseabold wrote:
>
> On Mon, Jun 8, 2009 at 3:33 PM, Robert Kern wrote:
>> On Mon, Jun 8, 2009 at 14:10, Alan G Isaac wrote:
> Going back to Alan Isaac's example:
> 1) beta = (X.T*X).I * X.T * Y
> 2) beta = np.dot(np.dot(la.inv(np.dot(X.T,X)),X.T),Y)
>>>
>>>
>>> Robert Kern wrote:
a = np.empty(3*n.size, np.int)
a[::3]=n
a[1::3]=m
a[2::3]=o
or
np.array(zip(n,m,o)).ravel()
but the first solution is faster, even if you have to write more :D
Emmanuelle
On Sun, Jun 14, 2009 at 04:11:29PM +0200, Robert wrote:
> whats the right way to efficiently weave arrays like this ? :
whats the right way to efficiently weave arrays like this ? :
>>> n
array([1, 2, 3, 4])
>>> m
array([11, 22, 33, 44])
>>> o
array([111, 222, 333, 444])
=>
[ 1, 11, 111, 2, 22, 222, 3, 33, 333, 4, 44, 444]
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On Sun, Jun 14, 2009 at 4:59 PM, David Cournapeau wrote:
> On Sun, Jun 14, 2009 at 3:51 AM, Charles R
> Harris wrote:
>>
>>
>> On Sat, Jun 13, 2009 at 12:35 PM, David Cournapeau
>> wrote:
>>>
>>> On Sun, Jun 14, 2009 at 3:22 AM, Charles R
>>> Harris wrote:
>>>
>>> > 1) Since reference counting is
On Sun, Jun 14, 2009 at 3:51 AM, Charles R
Harris wrote:
>
>
> On Sat, Jun 13, 2009 at 12:35 PM, David Cournapeau
> wrote:
>>
>> On Sun, Jun 14, 2009 at 3:22 AM, Charles R
>> Harris wrote:
>>
>> > 1) Since reference counting is such a pain, you should document that the
>> > constructor returns a n
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