On Thu, Sep 15, 2011 at 5:32 AM, Travis Oliphant wrote:
> Hi all,
>
> Has there been a discussion of a 1.7.x release of NumPy? There are a
> few new features that should go into the 1.x release of NumPy, that don't
> require the ABI changes of 2.0.I thought I had heard Mark talk in
> suppo
Hi all,
Has there been a discussion of a 1.7.x release of NumPy? There are a few
new features that should go into the 1.x release of NumPy, that don't require
the ABI changes of 2.0.I thought I had heard Mark talk in support of such a
thing.
What are the plans for the release of Num
Hi all,
There were some failures in the polynomial tests earlier today, and
while investigating I saw that numpy.ma implements its own root
finder. It uses inversion of a Van der Monde matrix, which I believe
may suffer from some numerical instability problems. Given that
Charles has gone to som
On Wed, Sep 14, 2011 at 5:30 PM, Christopher Barker
wrote:
> On 9/14/11 2:41 PM, Benjamin Root wrote:
>> Are you sure the f2 code works? a.resize() takes only a shape tuple. As
>> coded, you should get an exception.
>
> wow, what an idiot!
>
> I think I just timed how long it takes to raise that
On 9/14/11 2:41 PM, Benjamin Root wrote:
> Are you sure the f2 code works? a.resize() takes only a shape tuple. As
> coded, you should get an exception.
wow, what an idiot!
I think I just timed how long it takes to raise that exception...
And when I fix that, I get a memory error.
When I fix
On Wed, Sep 14, 2011 at 4:25 PM, Christopher Barker
wrote:
> On 9/14/11 1:01 PM, Christopher Barker wrote:
> > numpy.ndarray.resize is a different method, and I'm pretty sure it
> > should be as fast or faster that np.empty + np.append.
>
> My profile:
>
> In [25]: %timeit f1 # numpy.resize()
> 10
On 9/14/11 1:01 PM, Christopher Barker wrote:
> numpy.ndarray.resize is a different method, and I'm pretty sure it
> should be as fast or faster that np.empty + np.append.
My profile:
In [25]: %timeit f1 # numpy.resize()
1000 loops, best of 3: 163 ns per loop
In [26]: %timeit f2 #numpy.ndarr
On Wed, Sep 14, 2011 at 2:45 PM, Samuel John wrote:
> Hi Nils,
>
> which version of numpy, which os?
> I can infer that you use python 2.6 in 64bit, right?
>
> Right after the beginning of the numpy.test() are some crucial information.
>
> bests
> Samuel
>
> On 14.09.2011, at 22:09, Nils Wagner
On Wed, Sep 14, 2011 at 10:45 PM, Samuel John wrote:
> Hi Nils,
>
> which version of numpy, which os?
>
Latest master. Due to https://github.com/numpy/numpy/commit/af22fc43
Travis, did you run the test suite? arange is used but not imported.
Ralf
> I can infer that you use python 2.6 in 64b
Hi Nils,
which version of numpy, which os?
I can infer that you use python 2.6 in 64bit, right?
Right after the beginning of the numpy.test() are some crucial information.
bests
Samuel
On 14.09.2011, at 22:09, Nils Wagner wrote:
> ERROR: test_polyfit (test_polynomial.TestDocs)
> -
ERROR: test_polyfit (test_polynomial.TestDocs)
--
Traceback (most recent call last):
File
"/home/nwagner/local/lib64/python2.6/site-packages/numpy/lib/tests/test_polynomial.py",
line 106, in test_polyfit
weights = arange
On 9/13/11 1:01 PM, Christopher Jordan-Squire wrote:
> Sorry, I cheated by reading the docs. :-)
me too...
> """
> numpy.resize(a, new_shape)
>
> Return a new array with the specified shape.
>
> If the new array is larger than the original array, then the new array
> is filled with repeated copie
On Wed, 14 Sep 2011, Davide wrote:
Dear list,
I'm encountering a problem with np.loadtxt.
Suppose i have a file containing three columns of data (and 10 rows), like:
0.001 0.003 0.005
0.001 0.003 0.006
0.002 0.004 0.002
0.004 0.002 0.007
0.001 0.003 0.006
0.002 0.004 0.002
0.004 0.002 0.007
0
Dear list,
I'm encountering a problem with np.loadtxt.
Suppose i have a file containing three columns of data (and 10 rows), like:
0.001 0.003 0.005
0.001 0.003 0.006
0.002 0.004 0.002
0.004 0.002 0.007
0.001 0.003 0.006
0.002 0.004 0.002
0.004 0.002 0.007
0.001 0.003 0.006
0.002 0.004 0.002
0.0
My bad, iomp5md is in compiler/lib dir, I copied it to the mkl dir and it
worked.
From: Igor Ying
To: "numpy-discussion@scipy.org"
Sent: Wednesday, September 14, 2011 1:07 PM
Subject: Re: Numpy - MKL - build error
Yes, they all are present in that directory.
It seems you are missing libiomp5.so, which is sound if you re using the
whole Composer package: the needed libs are split in two different
locations, and unfortunately, Numpy cannot cope with this last time I
checked (I think it was one of the reasons David Cournapeau created numscons
and bento).
Yes, they all are present in that directory. Also, I tried with root as login.
-r-xr-xr-x 1 root root 26342559 Aug 9 22:19 libmkl_avx.so
-r--r--r-- 1 root root 1190224 Aug 9 22:26 libmkl_blacs_ilp64.a
-r--r--r-- 1 root root 1191496 Aug 9 22:25 libmkl_blacs_intelmpi_ilp64.a
-r-xr-xr-x 1 r
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