Hi,
I am a bit unaware with that you put magic = 10 . Why?
Пятница, 19 апреля 2013, 19:05 -05:00 от Val Kalatsky :
>Here's a seed for your function:
>
>s = ' Thesampletextthatcouldbereaded thesameinbothordersArozaupalan alapuazorA
>'
>f = np.array(list(s)).view('int8').astype(float)
>f -= f.mea
Here's a seed for your function:
s = 'ThesampletextthatcouldbereadedthesameinbothordersArozaupalanalapuazorA'
f = np.array(list(s)).view('int8').astype(float)
f -= f.mean()
maybe_here = np.argmax(np.convolve(f,f))/2
magic = 10
print s[maybe_here - magic:maybe_here + magic + 1]
Let us now how to
One major advantage you can have using mkl is installing "numexpr"
compiling it with MLK.
That's a strong suggestion to easily use mkl and go faster on common
operations.
Xavier
On 20/04/2013 1:16 AM, "Matthieu Brucher"
wrote:
> The graph is a comparison of the dot calls, of course they are bett
Hello everybody,
I just have one long string
type:ThesampletextthatcouldbereadedthesameinbothordersArozaupalanalapuazorA
The result I want to take is ArozaupalanalapuazorA - which means reading
directly each letter should be the same as reading reversely ...
Is there any function which can d
On Sat, Apr 20, 2013 at 12:36 AM, Chris Barker - NOAA Federal
wrote:
> On Fri, Apr 19, 2013 at 11:31 AM, Nathaniel Smith wrote:
>> On 19 Apr 2013 19:22, "Chris Barker - NOAA Federal"
>> wrote:
>>> Anyway -- going to HDF, or netcdf, or role-your-own really seems like
>>> overkill for this. I just
19.04.2013 22:06, Chris Barker - NOAA Federal kirjoitti:
> On Fri, Apr 19, 2013 at 11:31 AM, Nathaniel Smith wrote:
>> On 19 Apr 2013 19:22, "Chris Barker - NOAA Federal"
>> wrote:
>>> Anyway -- going to HDF, or netcdf, or role-your-own really seems like
>>> overkill for this. I just need somethi
On Fri, Apr 19, 2013 at 11:31 AM, Nathaniel Smith wrote:
> On 19 Apr 2013 19:22, "Chris Barker - NOAA Federal"
> wrote:
>> Anyway -- going to HDF, or netcdf, or role-your-own really seems like
>> overkill for this. I just need something fast and simple and it
>> doesn't need to interchange with a
On 19 Apr 2013 19:22, "Chris Barker - NOAA Federal"
wrote:
> Anyway -- going to HDF, or netcdf, or role-your-own really seems like
> overkill for this. I just need something fast and simple and it
> doesn't need to interchange with anything else.
Just use pickle...?
-n
__
On Fri, Apr 19, 2013 at 10:21 AM, Robert Kern wrote:
> On Fri, Apr 19, 2013 at 8:45 PM, Chris Barker - NOAA Federal
> wrote:
>> Given that numpy scalars do exist, and have their uses -- I found this
>> wiki page to remind me:
>>
>> http://projects.scipy.org/numpy/wiki/ZeroRankArray
>>
>> It woul
On Fri, Apr 19, 2013 at 8:46 AM, Nathaniel Smith wrote:
>> Nice work -- but darn! I was hoping a change/fix to teh datetime64
>> timezone handlien could get into the next release -- oh well.
>
> That's probably too big a behavioural chance to go into a point
> release in any case...
well, dateti
On Fri, 2013-04-19 at 23:02 +0530, Robert Kern wrote:
> On Fri, Apr 19, 2013 at 9:40 PM, Sebastian Berg
> wrote:
>
> > Fun fact, array[()] will convert a 0-d array to a scalar, but do nothing
> > (or currently create a view) for other arrays. Which is actually a good
> > question. Should array[()
On Fri, Apr 19, 2013 at 9:40 PM, Sebastian Berg
wrote:
> Fun fact, array[()] will convert a 0-d array to a scalar, but do nothing
> (or currently create a view) for other arrays. Which is actually a good
> question. Should array[()] force a view or not?
Another fun fact: scalar[()] gives you a r
On Fri, Apr 19, 2013 at 8:45 PM, Chris Barker - NOAA Federal
wrote:
> Robert,
>
> As I think you wrote the code, you may have a quick answer:
>
> Given that numpy scalars do exist, and have their uses -- I found this
> wiki page to remind me:
>
> http://projects.scipy.org/numpy/wiki/ZeroRankArray
On Fri, 2013-04-19 at 08:03 -0700, Chris Barker - NOAA Federal wrote:
> On Apr 18, 2013, at 11:33 PM, Nathaniel Smith wrote:
>
>
>
> > On 18 Apr 2013 01:29, "Chris Barker - NOAA Federal"
> > wrote:
> > > This has been annoying, particular as rank-zero scalars are kind
> > of a pain.
> >
> > B
Hi folks,
In [264]: np.__version__
Out[264]: '1.7.0'
I just noticed that deep copying a rank-zero array yields a scalar --
probably not what we want.
In [242]: a1 = np.array(3)
In [243]: type(a1), a1
Out[243]: (numpy.ndarray, array(3))
In [244]: a2 = copy.deepcopy(a1)
In [245]: type(a2), a2
O
On Fri, Apr 19, 2013 at 4:17 PM, Chris Barker - NOAA Federal
wrote:
> On Fri, Apr 19, 2013 at 8:12 AM, Ondřej Čertík
> wrote:
>
>>> I'm pleased to announce the availability of the final NumPy 1.7.1 release.
>
> Nice work -- but darn! I was hoping a change/fix to teh datetime64
> timezone handlie
On Fri, Apr 19, 2013 at 8:12 AM, Ondřej Čertík wrote:
>> I'm pleased to announce the availability of the final NumPy 1.7.1 release.
Nice work -- but darn! I was hoping a change/fix to teh datetime64
timezone handlien could get into the next release -- oh well.
When do we expect the next one may
Robert,
As I think you wrote the code, you may have a quick answer:
Given that numpy scalars do exist, and have their uses -- I found this
wiki page to remind me:
http://projects.scipy.org/numpy/wiki/ZeroRankArray
It would be nice if the .npy format could support them. Would that be
a major cha
The graph is a comparison of the dot calls, of course they are better with
MKL than the default BLAS version ;)
For the rest, Numpy doesn't benefit from MKL, scipy may if they call LAPACK
functions wrapped by Numpy or Scipy (I don't remember which does the
wrapping).
Matthieu
2013/4/19 KACVINSKY
On Thu, Apr 18, 2013 at 10:04 PM, K.-Michael Aye wrote:
> On 2013-04-19 01:02:59 +, Benjamin Root said:
>> So why is there an error in the 2nd case, but no error in the first
>> case? Is there a logic to it?
>>
>> When you change a dtype like that in the first one, you aren't really
>> upcasti
On Sun, Apr 7, 2013 at 2:09 AM, Ondřej Čertík wrote:
> Hi,
>
> I'm pleased to announce the availability of the final NumPy 1.7.1 release.
>
> Sources and binary installers can be found at
> https://sourceforge.net/projects/numpy/files/NumPy/1.7.1/
>
> Only three simple bugs were fixed since 1.7.1r
Looks like the *lapack_lite files have internal calls to dgemm. I alos found
this:
http://software.intel.com/en-us/articles/numpyscipy-with-intel-mkl
So it looks like numpy/scipy performs better with MKL, regardless of how the
MKL routines are called (directly, or via a numpy/scipy interface).
On Apr 18, 2013, at 11:33 PM, Nathaniel Smith wrote:
On 18 Apr 2013 01:29, "Chris Barker - NOAA Federal"
wrote:
> This has been annoying, particular as rank-zero scalars are kind of a
pain.
BTW, while we're on the topic, can you elaborate on this? I tend to think
scalars (as opposed to 0d ndarr
For the matrix multiplication or array dot, you use BLAS3 functions as they
are more or less the same. For the rest, nothing inside Numpy uses BLAS or
LAPACK explicitelly IIRC. You have to do the calls yourself.
2013/4/19 Neal Becker
> KACVINSKY Tom wrote:
>
> > You also get highly optimized BL
KACVINSKY Tom wrote:
> You also get highly optimized BLAS routines, like dgemm and degemv.
And does numpy/scipy just then automatically use them? When I do a matrix
multiply, for example?
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h
You also get highly optimized BLAS routines, like dgemm and degemv.
From: numpy-discussion-boun...@scipy.org
[mailto:numpy-discussion-boun...@scipy.org] On Behalf Of Matthieu Brucher
Sent: Friday, April 19, 2013 9:39 AM
To: Discussion of Numerical Python
Subject: Re: [Numpy-discussion] what do I
Hi,
I think you have at least linear algebra (lapack) and dot. Basic
arithmetics will not benefit, for expm, logm... I don't know.
Matthieu
2013/4/19 Neal Becker
> What sorts of functions take advantage of MKL?
>
> Linear Algebra (equation solving)?
>
> Something like dot product?
>
> exp, lo
What sorts of functions take advantage of MKL?
Linear Algebra (equation solving)?
Something like dot product?
exp, log, trig of matrix?
basic numpy arithmetic? (add matrixes)
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