On Sun, Jun 14, 2009 at 3:51 AM, Charles R
Harrischarlesr.har...@gmail.com wrote:
On Sat, Jun 13, 2009 at 12:35 PM, David Cournapeau courn...@gmail.com
wrote:
On Sun, Jun 14, 2009 at 3:22 AM, Charles R
Harrischarlesr.har...@gmail.com wrote:
1) Since reference counting is such a pain, you
On Sun, Jun 14, 2009 at 4:59 PM, David Cournapeaucourn...@gmail.com wrote:
On Sun, Jun 14, 2009 at 3:51 AM, Charles R
Harrischarlesr.har...@gmail.com wrote:
On Sat, Jun 13, 2009 at 12:35 PM, David Cournapeau courn...@gmail.com
wrote:
On Sun, Jun 14, 2009 at 3:22 AM, Charles R
EuroSciPy 2009 - Early Bird Deadline June 15, 2009
==
The early bird deadline for EuroSciPy 2009 is June 15, 2009.
Please register ( http://www.euroscipy.org/registration.html )
by this date to take advantage of the reduced early registration
rate.
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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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 ? :
jseabold wrote:
On Mon, Jun 8, 2009 at 3:33 PM, Robert Kernrobert.k...@gmail.com wrote:
On Mon, Jun 8, 2009 at 14:10, Alan G Isaacais...@american.edu 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
On Sun, Jun 14, 2009 at 2:07 AM, David Cournapeau courn...@gmail.com wrote:
On Sun, Jun 14, 2009 at 4:59 PM, David Cournapeaucourn...@gmail.com wrote:
On Sun, Jun 14, 2009 at 3:51 AM, Charles R
Harrischarlesr.har...@gmail.com wrote:
On Sat, Jun 13, 2009 at 12:35 PM, David Cournapeau
On Sun, Jun 14, 2009 at 10:20 AM, Tom K.t...@kraussfamily.org wrote:
jseabold wrote:
On Mon, Jun 8, 2009 at 3:33 PM, Robert Kernrobert.k...@gmail.com wrote:
On Mon, Jun 8, 2009 at 14:10, Alan G Isaacais...@american.edu wrote:
Going back to Alan Isaac's example:
1) beta = (X.T*X).I * X.T
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,
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
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 just
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
On Sun, Jun 14, 2009 at 14:31, Bryan Colebr...@cole.uklinux.net 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
Robert Cimrman cimrman3 at 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
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