On Thu, Oct 15, 2009 at 8:04 AM, Kashyap Ashwin
wrote:
> Hello,
> I compiled numpy-1.3.0 from sources on Ubuntu-hardy, x86-64 (Intel) with
> MKL.
> This is my site.cfg:
> [mkl]
> # library_dirs = /opt/intel/mkl/10.0.1.014/lib/32/
> library_dirs = /opt/intel/mkl/10.2.2.025/lib/em64t
> include_dirs
Hello,
I compiled numpy-1.3.0 from sources on Ubuntu-hardy, x86-64 (Intel) with
MKL.
This is my site.cfg:
[mkl]
# library_dirs = /opt/intel/mkl/10.0.1.014/lib/32/
library_dirs = /opt/intel/mkl/10.2.2.025/lib/em64t
include_dirs = /opt/intel/mkl/10.2.2.025/include
lapack_libs = mkl_lapack
#mkl_libs =
The fix is in SVN r7530.
Mike
Michael Droettboom wrote:
> That's my bad. I will commit a fix to SVN shortly.
>
> Mike
>
> Nils Wagner wrote:
>
>> >>> numpy.__version__
>> '1.4.0.dev7528'
>>
>> ==
>> ERROR: test_from_unicode
That's my bad. I will commit a fix to SVN shortly.
Mike
Nils Wagner wrote:
> >>> numpy.__version__
> '1.4.0.dev7528'
>
> ==
> ERROR: test_from_unicode (test_defchararray.TestBasic)
> ---
>>> numpy.__version__
'1.4.0.dev7528'
==
ERROR: test_from_unicode (test_defchararray.TestBasic)
--
Traceback (most recent call last):
File
"/home/nwagner/l
I tend to use np.array to stack arrays rather than np.vstack, as I find
it does what I want with higher dimensional arrays. However, I was quite
surprised to see a large speed difference:
In [1]: import numpy as np
In [2]: N = 1e6
In [3]: M = 10
In [4]: l = [np.random.random(N) for _ in range(M
Hi,
I'm interested in constructing a recarray with fields that have two or
more dimensions. This can be done from scratch like this:
r = np.recarray((10,),dtype=[('c1',float,(3,))])
However, I am interested in appending a field to an existing recarray.
Rather than repeating existing code I w
