On Sep 11, 2009, at 5:07 PM, Neal Becker wrote:
> I'd love to participate in these webinars. Problem is, AFAICT,
> gotomeeting
> only supports windows.
I'm not certain that is correct. I've participated in some of these,
and Im' running OS X (10.5). I think those were gotomeeting, althou
I'd love to participate in these webinars. Problem is, AFAICT, gotomeeting
only supports windows.
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
On Thu, Sep 10, 2009 at 14:39, David Warde-Farley wrote:
> On 10-Sep-09, at 1:09 PM, Robert Kern wrote:
>
>> One thing you can do to verify this is to change the order of
>> iteration. You will also want to profile your code. Then you can see
>> what is taking up so much time.
>>
>> http://docs.py
On 10-Sep-09, at 1:09 PM, Robert Kern wrote:
> One thing you can do to verify this is to change the order of
> iteration. You will also want to profile your code. Then you can see
> what is taking up so much time.
>
> http://docs.python.org/library/profile
Because apparently Robert is too modest
On Fri, Sep 11, 2009 at 15:46, Andrew Jaffe wrote:
> On 11/09/2009 08:33, Robert Kern wrote:
>> On Fri, Sep 11, 2009 at 10:24, Andrew Jaffe wrote:
>>> Dear all,
>>>
>>> I've got two (integer) arrays, and I want to find the indices in the
>>> first one that have entries in the second. I.E. I want a
On 11/09/2009 08:33, Robert Kern wrote:
> On Fri, Sep 11, 2009 at 10:24, Andrew Jaffe wrote:
>> Dear all,
>>
>> I've got two (integer) arrays, and I want to find the indices in the
>> first one that have entries in the second. I.E. I want all idx s.t.
>> there exists a j with a[idx]=b[j]. Here is
Hello,
The folks at stsci (Jim T.) are not able to reproduce this error with
1.4.0.dev7362 so I guess there is something wrong with my numpy
installation.
I also tried '1.4.0.dev7362' and numpy1.3 (stable) but alas, the same error!
My system:
[r...@siate numpy]# uname -a
Linux siate.iate.oac.unco
Hi all,
Ticket http://projects.scipy.org/numpy/ticket/1216 can be
closed.
Cheers,
Nils
___
NumPy-Discussion mailing list
NumPy-Discussion@scipy.org
http://mail.scipy.org/mailman/listinfo/numpy-discussion
(HTML version of email)
Greetings!
September is well upon us and it looks like it's already time for
another Scientific Computing with Python webinar. Next week, Travis
Oliphant will be hosting a presentation on regression analysis in
NumPy and SciPy. As you are probably aware, Travis wa
On Thu, Sep 10, 2009 at 10:03 AM, John [H2O] wrote:
> I have a routine that is iterating through a series of directories, loading
> files, plotting, then moving on...
>
> It runs very well for the first few iterations, but then slows tremendously
You mention plotting. I'd suggest checking that y
On Fri, Sep 11, 2009 at 10:24, Andrew Jaffe wrote:
> Dear all,
>
> I've got two (integer) arrays, and I want to find the indices in the
> first one that have entries in the second. I.E. I want all idx s.t.
> there exists a j with a[idx]=b[j]. Here is my current implementation
> (with a = pixnums, b
Dear all,
I've got two (integer) arrays, and I want to find the indices in the
first one that have entries in the second. I.E. I want all idx s.t.
there exists a j with a[idx]=b[j]. Here is my current implementation
(with a = pixnums, b=surveypix)
import numpy as np
def matchPix(pixnums, surve
On Thursday 10 September 2009 19:03:20 John [H2O] wrote:
> I have a routine that is iterating through a series of directories, loading
> files, plotting, then moving on...
>
> It runs very well for the first few iterations, but then slows tremendously
Maybe you "collect" some data into growing dat
Hi,
I'm looking for some help getting the svn trunk numpy working on Max OS
X 10.6. I've installed my own version of Python 2.6 from python.org.
I've got the following flags set:
setenv MACOSX_DEPLOYMENT_TARGET 10.6
setenv CFLAGS "-arch i386 -arch x86_64"
setenv FFLAGS "-arch i386 -arch x86_6
Hi,
In our project we define a class derived from numpy.float64 (and we add units)
and I noticed that instance creation was very slow. I found out that creating a
float64 object is fast, but creating an object from the derived class is almost
10 times slower, even if that class doesn't do anything
On Fri, Sep 11, 2009 at 5:25 AM, David Cournapeau
wrote:
> V. Armando Solé wrote:
>> David Cournapeau wrote:
>>
>>> V. Armando Solé wrote:
>>>
>>>
Hello,
It seems to point towards a packaging problem.
In python 2.5, I can do:
import numpy.core._dotblas as dotblas
V. Armando Solé wrote:
> David Cournapeau wrote:
>
>> V. Armando Solé wrote:
>>
>>
>>> Hello,
>>>
>>> It seems to point towards a packaging problem.
>>>
>>> In python 2.5, I can do:
>>>
>>> import numpy.core._dotblas as dotblas
>>> dotblas.__file__
>>>
>>> and I get:
>>>
>>> C:\\Python25
David Cournapeau wrote:
> V. Armando Solé wrote:
>
>> Hello,
>>
>> It seems to point towards a packaging problem.
>>
>> In python 2.5, I can do:
>>
>> import numpy.core._dotblas as dotblas
>> dotblas.__file__
>>
>> and I get:
>>
>> C:\\Python25\\lib\\site-packages\\numpy\\core\\_dotblas.pyd
>>
Sturla Molden wrote:
> V. Armando Solé skrev:
>
>> In python 2.6:
>>
>> >>>import numpy.core._dotblas as dotblas
>> ...
>> ImportError: No module named _dotblas
>>
>>
>
> >>> import numpy.core._dotblas as dotblas
> >>> dotblas.__file__
> 'C:\\Python26\\lib\\site-packages\\numpy\\core\
V. Armando Solé skrev:
> In python 2.6:
>
> >>>import numpy.core._dotblas as dotblas
> ...
> ImportError: No module named _dotblas
>
>>> import numpy.core._dotblas as dotblas
>>> dotblas.__file__
'C:\\Python26\\lib\\site-packages\\numpy\\core\\_dotblas.pyd'
___
V. Armando Solé wrote:
> Hello,
>
> It seems to point towards a packaging problem.
>
> In python 2.5, I can do:
>
> import numpy.core._dotblas as dotblas
> dotblas.__file__
>
> and I get:
>
> C:\\Python25\\lib\\site-packages\\numpy\\core\\_dotblas.pyd
>
That's where the error lies: if you insta
V. Armando Solé skrev:
> import numpy
> import time
> a=numpy.arange(100.)
> a.shape=1000,1000
> t0=time.time()
> b=numpy.dot(a.T,a)
> print "Elapsed time = ",time.time()-t0
>
> reports an "Elapsed time" of 1.4 seconds under python 2.5 and 15 seconds
> under python 2.6
>
My computer reports
Hello,
It seems to point towards a packaging problem.
In python 2.5, I can do:
import numpy.core._dotblas as dotblas
dotblas.__file__
and I get:
C:\\Python25\\lib\\site-packages\\numpy\\core\\_dotblas.pyd
In python 2.6:
>>>import numpy.core._dotblas as dotblas
...
ImportError: No module nam
David Cournapeau wrote:
> V. Armando Solé wrote:
>
>> Hello,
>>
>> I have found performance problems under windows when using python 2.6
>> In my case, they seem to be related to the dot product.
>>
>> The following simple script:
>>
>> import numpy
>> import time
>> a=numpy.arange(100.)
>>
V. Armando Solé wrote:
> Hello,
>
> I have found performance problems under windows when using python 2.6
> In my case, they seem to be related to the dot product.
>
> The following simple script:
>
> import numpy
> import time
> a=numpy.arange(100.)
> a.shape=1000,1000
> t0=time.time()
> b=num
Hello,
I have found performance problems under windows when using python 2.6
In my case, they seem to be related to the dot product.
The following simple script:
import numpy
import time
a=numpy.arange(100.)
a.shape=1000,1000
t0=time.time()
b=numpy.dot(a.T,a)
print "Elapsed time = ",time.tim
On Tue, Sep 8, 2009 at 6:41 PM, Charles R Harris
wrote:
>
> More precisely, 2GB for windows and 3GB for (non-PAE enabled) linux.
And just to further clarify, even with PAE enabled on linux, any
individual process has about a 3 GB address limit (there are hacks to
raise that to 3.5 or 4GB, but wi
27 matches
Mail list logo
