01.12.2011 03:31, josef.p...@gmail.com kirjoitti:
[clip]
I thought np.dot is Lapack based and favors fortran order, but if the
second array is fortran ordered, then dot takes twice as long.
It uses C-LAPACK, and will make copies if the arrays are not in C-order.
--
Pauli Virtanen
Le 01/12/2011 02:44, Karl Kappler a écrit :
Also note that I have had a similar problem with much smaller arrays,
say 24 x 3076
Hi Karl,
Could you post a self-contained code with such a small array (or even
smaller. the smaller, the better...) so that we can run it and play with
it ?
--
Is this expected behavior?
np.array([-345,4,2,'ABC'])
array(['-34', '4', '2', 'ABC'], dtype='|S3')
np.version.full_version
'1.6.1'
np.version.git_revision
'68538b74483009c2c2d1644ef00397014f95a696'
Ray Jones
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Le 01/12/2011 14:52, Thouis (Ray) Jones a écrit :
Is this expected behavior?
np.array([-345,4,2,'ABC'])
array(['-34', '4', '2', 'ABC'], dtype='|S3')
With my numpy 1.5.1, I got indeed a different result:
In [1]: np.array([-345,4,2,'ABC'])
Out[1]:
array(['-345', '4', '2', 'ABC'],
On Thu, Dec 1, 2011 at 15:47, Pierre Haessig pierre.haes...@crans.org wrote:
Le 01/12/2011 14:52, Thouis (Ray) Jones a écrit :
Is this expected behavior?
np.array([-345,4,2,'ABC'])
array(['-34', '4', '2', 'ABC'], dtype='|S3')
With my numpy 1.5.1, I got indeed a different result:
In [1]:
On Thursday, December 1, 2011, Thouis Jones thouis.jo...@curie.fr wrote:
On Thu, Dec 1, 2011 at 15:47, Pierre Haessig pierre.haes...@crans.org
wrote:
Le 01/12/2011 14:52, Thouis (Ray) Jones a écrit :
Is this expected behavior?
np.array([-345,4,2,'ABC'])
array(['-34', '4', '2', 'ABC'],
On Thu, Dec 1, 2011 at 16:29, Benjamin Root ben.r...@ou.edu wrote:
Does the same problem occur if -345 comes after ABC?
Yes.
np.array(list(reversed([-345,4,2,'ABC'])))
array(['ABC', '2', '4', '-34'],
dtype='|S3')
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On Thu, Dec 1, 2011 at 6:52 AM, Thouis (Ray) Jones tho...@gmail.com wrote:
Is this expected behavior?
np.array([-345,4,2,'ABC'])
array(['-34', '4', '2', 'ABC'], dtype='|S3')
Given that strings should be the result, this looks like a bug. It's a bit
of a corner case that probably slipped
Hi,
is there any possibility to define a numpy matrix, via a smaller given
matrix, i.e. in matlab
i can do this like
a=[1 2 ; 3 4 ]
A=[a a ; a a ]
so that i finally get
A=[ [1,2,1,2]
[3,4,3,4]
[1,2,1,2]
[3,4,3,4]]
i tried different things on numpy which didn't work
On 1 Dec 2011, at 17:39, Charles R Harris wrote:
On Thu, Dec 1, 2011 at 6:52 AM, Thouis (Ray) Jones tho...@gmail.com wrote:
Is this expected behavior?
np.array([-345,4,2,'ABC'])
array(['-34', '4', '2', 'ABC'], dtype='|S3')
Given that strings should be the result, this looks like a
On Thu, Dec 1, 2011 at 10:52 AM, jonasr jonas.rueb...@web.de wrote:
Hi,
is there any possibility to define a numpy matrix, via a smaller given
matrix, i.e. in matlab
i can do this like
a=[1 2 ; 3 4 ]
A=[a a ; a a ]
so that i finally get
A=[ [1,2,1,2]
[3,4,3,4]
[1,2,1,2]
Hi,
On Thu, Dec 1, 2011 at 6:52 PM, jonasr jonas.rueb...@web.de wrote:
Hi,
is there any possibility to define a numpy matrix, via a smaller given
matrix, i.e. in matlab
i can do this like
a=[1 2 ; 3 4 ]
A=[a a ; a a ]
so that i finally get
A=[ [1,2,1,2]
[3,4,3,4]
On Thu, Dec 1, 2011 at 12:26 PM, Benjamin Root ben.r...@ou.edu wrote:
On Thu, Dec 1, 2011 at 10:52 AM, jonasr jonas.rueb...@web.de wrote:
Hi,
is there any possibility to define a numpy matrix, via a smaller given
matrix, i.e. in matlab
i can do this like
a=[1 2 ; 3 4 ]
A=[a a ; a a ]
Oops, slightly incorrect answer, but anyway my intention was more along the
lines:
In []: a= np.array([[1, 2], [3, 4]])
In []: np.c_[[a, a], [a, a]].reshape(4, 4)
Out[]:
array([[1, 2, 1, 2],
[3, 4, 3, 4],
[1, 2, 1, 2],
[3, 4, 3, 4]])
On Thu, Dec 1, 2011 at 8:16 PM,
Hi Oliver, indeed that was a typo, I should have used cut and paste. I was
using .transpose()
Olivier Delalleau-2 wrote:
I guess it's just a typo on your part, but just to make sure, you are
using
.transpose(), not .transpose, correct?
-=- Olivier
2011/11/30 Karl Kappler
On 12/1/2011 9:15 AM, Derek Homeier wrote:
np.array((2, 12,0.001+2j), dtype='|S8')
array(['2', '12', '(0.001+2'], dtype='|S8')
- notice the last value is only truncated because it had first been converted
into
a standard complex representation, so maybe the problem is already in the
way
Hi Pierre,
I was thinking about uploading some examples but strangely, when I store the
array using for example: np.save('Y',Y)
and then reload it in a new workspace, I find that the problem does not
reproduce. It would seem somehow to be
associated with the 'overhead' of the workspace I am
On 1 Dec 2011, at 21:35, Chris Barker wrote:
On 12/1/2011 9:15 AM, Derek Homeier wrote:
np.array((2, 12,0.001+2j), dtype='|S8')
array(['2', '12', '(0.001+2'], dtype='|S8')
- notice the last value is only truncated because it had first been
converted into
a standard complex
On Thu, Dec 1, 2011 at 2:47 PM, kneil magnetotellur...@gmail.com wrote:
Hi Pierre,
I was thinking about uploading some examples but strangely, when I store
the
array using for example: np.save('Y',Y)
and then reload it in a new workspace, I find that the problem does not
reproduce. It
On Mon, Nov 28, 2011 at 1:19 PM, Matthew Brett matthew.br...@gmail.comwrote:
Maybe the content could be put in
http://github.com/scipy/scipy.github.com so we can make pull requests
there?
The source is here:
https://github.com/scipy/scipy.org-new
Yo,
On Thu, Dec 1, 2011 at 8:01 PM, Jarrod Millman mill...@berkeley.edu wrote:
On Mon, Nov 28, 2011 at 1:19 PM, Matthew Brett matthew.br...@gmail.com
wrote:
Maybe the content could be put in
http://github.com/scipy/scipy.github.com so we can make pull requests
there?
The source is here:
Hi Pierre,
I confirmed with the guy who put together the machine that it is non-ECC
RAM. You know, now that i think about it, this machine seems to crash a
fair amount more often than its identical twin which sits on a desk near me.
I researched memtest a bit... downloaded and compiled it, but
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