On Sun, May 18, 2008 at 12:56 PM, James Snyder <[EMAIL PROTECTED]> wrote:
> I've been running out of trunk recently, and I've noted that an rc release
> has appeared and the 1.1.x branch has been regenerated.
>
> Which would be most helpful to provide feedback from?
Hmmm. I deleted the 1.1.x bran
On Sun, May 18, 2008 at 9:14 PM, Anne Archibald
<[EMAIL PROTECTED]> wrote:
> 2008/5/18 Matt Crane <[EMAIL PROTECTED]>:
>> On Sun, May 18, 2008 at 8:52 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
>>> Are there repeats?
>> No, no repeats in the first column.
>>
>> I'm going to go get a cup of coffee b
On Sun, 2008-05-18 at 12:14 -0700, rex wrote:
>
> The last relevant MKL library name change I'm aware of occurred
> when MKL 9.X was released in 2006:
>
No, they heavily changed how to link against mkl in 10. There is a whole
chapter about it in the releases notes.
> ldd shows MKL was linked:
>
David, how do these environment variables compare with yours? Are you sure
MKL is being used?
Adjusted for your local path, what does the ldd command below show?
ldd /usr/local/lib/python2.5/site-packages/numpy/linalg/lapack_lite.so
linux-gate.so.1 => (0xe000)
libmkl_lapack.so => /opt
I need it for a numerical back transformation from Laplace space.
I found mpmath, which I think will do the trick
Mark
On May 18, 6:06 pm, "Charles R Harris" <[EMAIL PROTECTED]>
wrote:
> Hi Mark,
>
> On Sun, May 18, 2008 at 9:37 AM, mark <[EMAIL PROTECTED]> wrote:
> > Hello list -
>
> > I could no
Hi -
I've been running out of trunk recently, and I've noted that an rc release
has appeared and the 1.1.x branch has been regenerated.
Which would be most helpful to provide feedback from?
>From the branch (1.1.1x) - test results on Mac OS X 10.5.2, built for
universal, using apple Python, look
David wrote:
> I have not tried with icc, but the following works for me with the
> last mkl (I have only tried numpy).
>
> [mkl]
> library_dirs = /home/david/intel/mkl/10.0.1.014/lib/32
> lapack_libs = mkl_lapack
> mkl_libs = mkl, guide
>
> (of course, adapt the library_dirs accordingly). All test
Hi Mark,
On Sun, May 18, 2008 at 9:37 AM, mark <[EMAIL PROTECTED]> wrote:
> Hello list -
>
> I could not find an option for arbitrary precision arrays in numpy.
> Did anybody implement this?
>
> I would like to use something like 80 digits precision.
>
No, we don't have this. What do you need it
Jarrod Millman wrote:
> Please test the release candidate:
> svn co http://svn.scipy.org/svn/numpy/tags/1.1.0rc1 1.1.0rc1
>
Thanks, Jarrod.
I have packaged SVN trunk from r5189 and made a Debian source package
(based on a slightly old version the Debian Python Modules Team's numpy
package with
Hello list -
I could not find an option for arbitrary precision arrays in numpy.
Did anybody implement this?
I would like to use something like 80 digits precision.
Thanks,
Mark
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On Sat, 17 May 2008 14:58:20 -0400, Anne Archibald wrote:
> numpy arrays are efficient, among other reasons, because they have
> homogeneous types. So all the elements in an array are the same type.
> (Yes, this means if you have an array of numbers only one of which
> happens to be complex, you h
Hi,
su, 2008-05-18 kello 07:16 -0600, Steven H. Rogers kirjoitti:
> Joe Harrington wrote:
> >NUMPY/SCIPY DOCUMENTATION MARATHON 2008
> > ...
> > 5. Write a new help function that optionally produces ASCII or points
> > the user's PDF or HTML reader to the right page (either local or
> > gl
On Sun, May 18, 2008 at 7:13 PM, rex <[EMAIL PROTECTED]> wrote:
>
>> I am trying to build numpy with intel icc and mkl. I don't understand
>> a lot of what I am doing.
>
> Me, too. I have built it with icc & MKL several times in the past,
> but cannot build the numpy svn with MKL now. I can build
Joe Harrington wrote:
> NUMPY/SCIPY DOCUMENTATION MARATHON 2008
> ...
> 5. Write a new help function that optionally produces ASCII or points
> the user's PDF or HTML reader to the right page (either local or
> global).
>
I can work on this. Fernando suggested this at the IPython sprin
Thanks everyone for all the comments! It helped to understand better the
advantages/disadvantages of the various options to interact with C.
Jose.
--- On Sat 05/17, Bill Spotz < [EMAIL PROTECTED] > wrote:
Just to make sure the original question gets answered, yes, numpy.i
avoids copies as
On May 17, 2008, at 9:34 AM, David Cournapeau wrote:
> Nripun Sredar wrote:
>> I have a sparse matrix 416x52. I tried to factorize this matrix using
>> svd from numpy. But it didn't produce a result and looked like it is
>> in an infinite loop.
>> I tried a similar operation using random numbers i
Jarrod Millman <[EMAIL PROTECTED]> wrote:
>Please test the release candidate:
>svn co http://svn.scipy.org/svn/numpy/tags/1.1.0rc1 1.1.0rc1
With icc & MKL it fails to find the MKL libraries.
site.cfg:
--
[DEFAULT]
library_dirs = /op
On Sun, May 18, 2008 1:14 pm, David Cournapeau wrote:
> Hi,
>
> I would like to be able to build a f2py extension in a subdir with
> distutils, that is:
>
> config.add_extension('foo/bar', source = ['foo/bar.pyf'])
A safe approach would be to create a foo/setup.py that contains
config.add_ex
Robert Kern wrote:
>
> config.add_extension('foo.bar', source=['foo/bar.pyf'])
>
Duh, should have thought about that.
thanks,
David
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On Sun, May 18, 2008 at 5:14 AM, David Cournapeau
<[EMAIL PROTECTED]> wrote:
> Hi,
>
>I would like to be able to build a f2py extension in a subdir with
> distutils, that is:
>
> config.add_extension('foo/bar', source = ['foo/bar.pyf'])
>
> But it does not work right now because of the way nump
Hi,
I would like to be able to build a f2py extension in a subdir with
distutils, that is:
config.add_extension('foo/bar', source = ['foo/bar.pyf'])
But it does not work right now because of the way numpy.distutils finds
the name of the extension. Replacing:
ext_name = extension.name.spli
> I am trying to build numpy with intel icc and mkl. I don't understand
> a lot of what I am doing.
Me, too. I have built it with icc & MKL several times in the past,
but cannot build the numpy svn with MKL now. I can build it with
icc and no MKL, and it passes all the tests with no errors.
I
2008/5/18 Matt Crane <[EMAIL PROTECTED]>:
> On Sun, May 18, 2008 at 8:52 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
>> Are there repeats?
> No, no repeats in the first column.
>
> I'm going to go get a cup of coffee before I forget to leave out any
> potentially vital information again. It's going
On Sun, May 18, 2008 at 4:02 AM, Matt Crane <[EMAIL PROTECTED]> wrote:
> On Sun, May 18, 2008 at 8:52 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
>> It depends on the sizes.
> The sizes could range from 3 to 24 with an average of around 5500.
A 24x24 boolean matrix will probably be too
On Sun, May 18, 2008 at 8:52 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> It depends on the sizes.
The sizes could range from 3 to 24 with an average of around 5500.
> Are there repeats?
No, no repeats in the first column.
I'm going to go get a cup of coffee before I forget to leave out any
p
On Sun, May 18, 2008 at 3:29 AM, Matt Crane <[EMAIL PROTECTED]> wrote:
> On Sun, May 18, 2008 at 8:08 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
>> Okay, then it's just a little bit more complicated.
>
> Thanks, and that's going to be faster - the method that I posted is
> linear in terms of the le
On Sun, May 18, 2008 at 8:08 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> Okay, then it's just a little bit more complicated.
Thanks, and that's going to be faster - the method that I posted is
linear in terms of the length of the two lists? Given that the values
in the first column are monotonica
On Sun, May 18, 2008 at 2:59 AM, Matt Crane <[EMAIL PROTECTED]> wrote:
> Sorry, I should have mentioned that no, the matching rows won't always
> be in the same position.
Okay, then it's just a little bit more complicated.
In [18]: from numpy import *
In [19]: a = array([[1, 10], [2, 20], [3, 30
On Sun, May 18, 2008 at 7:19 PM, Robert Kern <[EMAIL PROTECTED]> wrote:
> Are the matching rows always going to be the same row in each? I.e.
> you want rows i such that a[i,0]==b[i,0] rather than trying to find
> all i,j such that a[i,0]==b[j,0]?
>
> If so, then I would do the following:
>
>
> In
On Sun, May 18, 2008 at 2:04 AM, Matt Crane <[EMAIL PROTECTED]> wrote:
> Hey,
>
> I'm new to numpy but not new to python or programming in general. I
> was wondering if there's a way of using numpy to do the following or
> whether I've got what I've got and that's as good as it's going to
> get.
>
Hey,
I'm new to numpy but not new to python or programming in general. I
was wondering if there's a way of using numpy to do the following or
whether I've got what I've got and that's as good as it's going to
get.
I have two 2d arrays and I want to create another 2d array that
contains the values
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