pia a Conejo en tu firma y ayúdale en sus
>> > planes de dominación mundial.
>> > ___
>> > NumPy-Discussion mailing list
>> > NumPy-Discussion@scipy.org
>> > https://mail.scipy.org/mailman/lis
2017-03-13 18:11 GMT+01:00 Julian Taylor :
> On 13.03.2017 16:21, Anne Archibald wrote:
> >
> >
> > On Mon, Mar 13, 2017 at 12:21 PM Julian Taylor
> > mailto:jtaylor.deb...@googlemail.com>>
> > wrote:
> >
> > Should it be agreed that caching is worthwhile I would propose a very
> > simple
n implemented recently (
https://github.com/numpy/numpy/pull/7997) and that is to be released in
1.13. It is a really cool (and somewhat scary) patch ;)
>
> And if you are planning on attending, please give me a shout.
>
It would be nice to attend and see you again, but unfortunatel
ire seldom copying.
>> It would be nice to see an example to understand how deep I need to go
>> inside numpy.
>>
>
Well, if copying is not a problem for you, then you can just create a new
numpy container and do the copy by yourself.
Francesc
>
>> Cheers,
>>
ion@scipy.org
> https://mail.scipy.org/mailman/listinfo/numpy-discussion
>
>
>
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eration space there are undoubtedly a number of bugs to squash.
>
> Sincerely,
>
> Robert
>
> --
> Robert McLeod, Ph.D.
> Center for Cellular Imaging and Nano Analytics (C-CINA)
> Biozentrum der Universität Basel
> Mattenstrasse 26, 4058 Basel
> Work: +41.061.387.3225 <061%20387%2032%2025>
> robert.mcl...@unibas.ch
> robert.mcl...@bsse.ethz.ch
> robbmcl...@gmail.com
>
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2016-11-04 14:36 GMT+01:00 Neal Becker :
> Francesc Alted wrote:
>
> > 2016-11-04 13:06 GMT+01:00 Neal Becker :
> >
> >> I find I often write:
> >> np.array ([some list comprehension])
> >>
> >> mainly because list comprehensions are just so
y-Discussion mailing list
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project is hosted at GitHub in:
https://github.com/pydata/numexpr
You can get the packages from PyPI as well (but not for RC releases):
http://pypi.python.org/pypi/numexpr
Share your experience
=
Let us know of any bugs, suggestions, gripes, kudos, etc. you may
have.
E
pr
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wasted 64 bytes. Pretty cheap indeed.
Francesc
>
> Thanks,
> -Øystein
>
> On Thu, May 5, 2016 at 1:55 PM, Francesc Alted wrote:
>
>> 2016-05-05 11:38 GMT+02:00 Øystein Schønning-Johansen > >:
>>
>>> Hi!
>>>
>>> I've written a lit
tion works correctly about 20% of the time I run it, and
> else it segfaults on the simd instruction in the the C function)
>
> Thanks,
> -Øystein
>
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GitHub in:
https://github.com/pydata/numexpr
You can get the packages from PyPI as well (but not for RC releases):
http://pypi.python.org/pypi/numexpr
Share your experience
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Let us know of any bugs, suggestions, gripes, kudos, etc. you may
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E
ease notes can be found in the Git repository:
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ehow.
That will probably require run-time detection of available C math libraries
(think that a numexpr binary will be able to run on different machines with
different libraries and computing capabilities), but in exchange, it will
allow for the fastest execution paths independently of the machine that
runs the code.
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ried about the time it
> takes to query the DB for a sequential ID, and then translate byte arrays.
> >
> > Any ideas? I greatly appreciate any guidance you can provide.
> >
> > Thanks,
> > Ryan
> > ___
> > NumPy-Discus
2015-12-17 12:00 GMT+01:00 Daπid :
> On 16 December 2015 at 18:59, Francesc Alted wrote:
>
>> Probably MATLAB is shipping with Intel MKL enabled, which probably is the
>> fastest LAPACK implementation out there. NumPy supports linking with MKL,
>> and actually Anaconda
need to buy a MKL license separately (which
makes sense for a commercial product).
Sorry for the confusion.
Francesc
2015-12-16 18:59 GMT+01:00 Francesc Alted :
> Hi,
>
> Probably MATLAB is shipping with Intel MKL enabled, which probably is the
> fastest LAPACK implementation out t
00)
>
> %time testx = np.linalg.solve(testA, testb)
>
> %MATLAB version
>
> testA = randn(15000);
>
> testb = randn(15000, 1);
> tic(); testx = testA \ testb; toc();
>
> ___
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s.com
http://groups.google.com/group/bcolz
License is the new BSD:
https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt
Release notes can be found in the Git repository:
https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst
**Enjoy data!**
--
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MKL is optional), so it works well as an
easy-to-deploy, easy-to-use, computational engine for projects that
don't want to adopt other solutions requiring more heavy dependencies.
What's new
==
This is a quick maintenance version that offers better handling of
MSVC symbols (#168,
dencies.
What's new
==
This is a maintenance release where an important bug in multithreading
code has been fixed (#185 Benedikt Reinartz, Francesc Alted). Also,
many harmless warnings (overflow/underflow, divide by zero and others)
in the test suite have been silenced (#183, Francesc Alted).
t
Release notes can be found in the Git repository:
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here at any point in time,
> any of us should be judged on the merit of our actions, not the
> hypotheticals of our intentions or our affiliations (commercial,
> government, academic, etc).
>
>
> Sorry for the long wall of text, I rarely post on t
groups.com
http://groups.google.es/group/blosc
Licenses
Both Blosc and its Python wrapper are distributed using the MIT license.
See:
https://github.com/Blosc/python-blosc/blob/master/LICENSES
for more details.
**Enjoy data!**
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I as well (but not for RC releases):
http://pypi.python.org/pypi/numexpr
Share your experience
=
Let us know of any bugs, suggestions, gripes, kudos, etc. you may
have.
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bcolz/blob/master/LICENSES/BCOLZ.txt
Release notes can be found in the Git repository:
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z was extremely emphatic about the size of
> the opportunity NumPy was letting slip by not formalizing *any* governance
> model. And it is a necessary first step so that e.g. we have the money to,
> say a year from now, get the right people together for a couple of days
64[s]')
Googling for a way to print UTC out of the box, the best thing I could find
is:
In [40]: [str(i.item()) for i in np.array([t], dtype="datetime64[s]")]
Out[40]: ['2015-08-26 11:52:10']
Now, is there a better way to specify that I want the datet
, thanks to all those braves that are allowing others to build on top
of NumPy's shoulders.
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2015-07-06 18:04 GMT+02:00 Jaime Fernández del Río :
> On Mon, Jul 6, 2015 at 10:18 AM, Francesc Alted wrote:
>
>> Hi,
>>
>> I have stumbled into this:
>>
>> In [62]: sa = np.fromiter(((i,i) for i in range(1000*1000)),
>> dtype=[('f0', np.int6
Oops, forgot to mention my NumPy version:
In [72]: np.__version__
Out[72]: '1.9.2'
Francesc
2015-07-06 17:18 GMT+02:00 Francesc Alted :
> Hi,
>
> I have stumbled into this:
>
> In [62]: sa = np.fromiter(((i,i) for i in range(1000*1000)), dtype=[('f0',
> np
r (i5-3380M) that should perform
quite well on unaligned data:
http://lemire.me/blog/archives/2012/05/31/data-alignment-for-speed-myth-or-reality/
So, if 4 years-old Intel architectures do not have a penalty for unaligned
access, why I am seeing that in NumPy? That
===
Announcing PyTables 3.2.0
===
We are happy to announce PyTables 3.2.0.
***
IMPORTANT NOTICE:
If you are a user of PyTables, it needs your help to keep going. Please
read the next thread as it contains important inf
...@googlegroups.com
http://groups.google.es/group/blosc
Licenses
Both Blosc and its Python wrapper are distributed using the MIT license.
See:
https://github.com/Blosc/python-blosc/blob/master/LICENSES
for more details.
**Enjoy data!**
--
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===
Announcing PyTables 3.2.0rc2
===
We are happy to announce PyTables 3.2.0rc2.
***
IMPORTANT NOTICE:
If you are a user of PyTables, it needs your help to keep going. Please
read the next thread as it contains importa
st(ast_expr) function. Pull
requests are welcome.
At any rate, which is your use case? I am curious.
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===
Announcing PyTables 3.2.0rc1
===
We are happy to announce PyTables 3.2.0rc1.
***
IMPORTANT NOTICE:
If you are a user of PyTables, it needs your help to keep going. Please
read the next thread as it contains importa
pr
Share your experience
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gt; NumPy-Discussion@scipy.org
> http://mail.scipy.org/mailman/listinfo/numpy-discussion
>
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2015-02-13 13:25 GMT+01:00 Julian Taylor :
> On 02/13/2015 01:03 PM, Francesc Alted wrote:
> > 2015-02-13 12:51 GMT+01:00 Julian Taylor > <mailto:jtaylor.deb...@googlemail.com>>:
> >
> > On 02/13/2015 11:51 AM, Francesc Alted wrote:
> > > Hi,
>
2015-02-13 12:51 GMT+01:00 Julian Taylor :
> On 02/13/2015 11:51 AM, Francesc Alted wrote:
> > Hi,
> >
> > I would like to vectorize the next computation:
> >
> > nx, ny, nz = 720, 180, 3
> > outheight = np.arange(nz) * 3
> > oro = np.arange(nx
, :, iz] = outheight[iz] + oro[ix, :]
return result
I think this should be possible by using an advanced use of broadcasting in
numpy. Anyone willing to post a solution?
Thanks,
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is the new BSD:
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Indeed it was 1.2.4 the version just released and not 1.2.7. Sorry for
the typo!
Francesc
On 7/7/14, 8:20 PM, Francesc Alted wrote:
> =
> Announcing python-blosc 1.2.4
> =
>
> What is new?
>
>
> This
://groups.google.es/group/blosc
Licenses
Both Blosc and its Python wrapper are distributed using the MIT license.
See:
https://github.com/Blosc/python-blosc/blob/master/LICENSES
for more details.
**Enjoy data!**
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, normally the
bottleneck is memory throughput.
Having said this, there are several packages that work on top of NumPy
that can use multiple cores when performing numpy operations, like
numexpr (https://github.com/pydata/numexpr), or Theano
(http://deeplearning.net/software/theano/tutorial/mu
El 18/04/14 13:39, Francesc Alted ha escrit:
> So, sqrt in numpy has barely the same speed than the one in MKL.
> Again, I wonder why :)
So by peeking into the code I have seen that you implemented sqrt using
SSE2 intrinsics. Cool!
--
Francesc
e codecs can make the storage go faster than a pure np.save(),
and most specially blosclz, lz4 and snappy. However, lz4hc and zlib
achieve the best compression ratios:
In [62]: ls -lht x*.*
-rw-r--r-- 1 faltet users 7,0M 18 abr 13:49 x-zlib.blp
-rw-r--r-- 1 faltet use
El 17/04/14 21:19, Julian Taylor ha escrit:
> On 17.04.2014 20:30, Francesc Alted wrote:
>> El 17/04/14 19:28, Julian Taylor ha escrit:
>>> On 17.04.2014 18:06, Francesc Alted wrote:
>>>
>>>> In [4]: x_unaligned = np.zeros(shape,
>>>> dtype=[(
El 17/04/14 19:28, Julian Taylor ha escrit:
> On 17.04.2014 18:06, Francesc Alted wrote:
>
>> In [4]: x_unaligned = np.zeros(shape,
>> dtype=[('y1',np.int8),('x',np.float64),('y2',np.int8,(7,))])['x']
> on arrays of this size you won&#
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;-).
>
>
> no -- it's your high tolerance for _reading_ emails...
>
> Far too many of us have a high tolerance for writing them!
Ha ha, very true!
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===
Announcing Numexpr 2.4 RC2
===
Numexpr is a fast numerical expression evaluator for NumPy. With it,
expressions that operate on arrays (like "3*a+4*b") are accelerated
and use less memory than doing the same calculation in Python.
It wears mu
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al amount of arguments it got.
> So I'm more worried about running out of stack space, though the limit
> is usually 8mb so taking 128kb for a short while should be ok.
>
> On 28.02.2014 13:32, Francesc Alted wrote:
> > Well, what numexpr is using is basically
tions we could change it if thats
> enough.
> It would bump some temporary arrays of nditer from 32kb to 128kb, I
> think that would still be fine, but getting to the point where we should
> move them onto the heap.
>
> On 28.02.2014 12:41, Francesc Alted wrote:
>> Hi Julia
its already included in these PRs.
> I'm probably still going to add gh-4284 after some though tomorrow.
>
> Cheers,
> Julian
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ges from PyPI as well (but not for RC releases):
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x27;t check. I have
>> tried to grep it tring all possible combinations of "def ndarray",
>> "self.sort", etc. Where is it?
>>
>>
>> /David.
>>
>>
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ces?
>
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ompressor:
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User's mail list:
blaze-...@continuum.io
Enjoy!
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tly as fast as weave (so I guess there were
> some performance enhancements in numexpr as well).
Err no, there have not been performance improvements in numexpr since
2.0 (that I am aware of). Maybe you are running in a multi-core machine
now and you are seeing better speedup because of
mexpr?
=
The project is hosted at Google code in:
http://code.google.com/p/numexpr/
You can get the packages from PyPI as well:
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Share your experience
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_nuevo)/numero_experimentos)
>
> desviacion_standard = np.append (desviacion_standard,
> sum(std_dev_size_medio_intuitivo)/numero_experimentos)
>
> desviacion_standard_nuevo=np.append (desviacion_standard_nuevo,
> sum(std_dev_size_medio_nuevo)/numero_experimentos)
>
> tiempos=np.append(tiempos, time.clock()-empieza)
>
> componente_y=np.append(componente_y, sum(comp_y)/numero_experimentos)
> componente_x=np.append(componente_x, sum(comp_x)/numero_experimentos)
>
> anisotropia_macroscopica_porcentual=100*(1-(componente_y/componente_x))
>
> I tryed with gc and gc.collect() and 'del'command for deleting arrays
> after his use and nothing work!
>
> What am I doing wrong? Why the memory becomes full while running (starts
> with 10% of RAM used and in 1-2hour is totally full used)?
>
> Please help me, I'm totally stuck!
> Thanks a lot!
>
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Licenses
Both Blosc and its Python wrapper are distributed using the MIT license.
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Licenses
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ur computations. I
have used this feature extensively for optimizing parts of the Blosc
compressor, and I cannot be more happier (to the point that, if it were
not for Valgrind, I could not figure out many interesting memory access
optimizati
=
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On 4/4/13 8:56 PM, Chris Barker - NOAA Federal wrote:
> On Thu, Apr 4, 2013 at 10:54 AM, Francesc Alted wrote:
>
>> That makes a difference. This can be specially important for creating
>> user-defined time origins:
>>
>> In []: np.array(int(1.5e9), dtype='dat
On 4/4/13 7:01 PM, Chris Barker - NOAA Federal wrote:
> Francesc Alted wrote:
>> When Ivan and me were discussing that, I remember us deciding that such
>> a small units would be useful mainly for the timedelta datatype, which
>> is a relative, not absolute time. We did not w
s discussion:
https://github.com/numpy/numpy/blob/master/doc/neps/datetime-proposal.rst#why-the-origin-metadata-disappeared
This is just an historical note, not that we can't change that again.
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why we decided to go with
attoseconds.
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On 3/13/13 3:53 PM, Francesc Alted wrote:
> On 3/13/13 2:45 PM, Andrea Cimatoribus wrote:
>> Hi everybody, I hope this has not been discussed before, I couldn't
>> find a solution elsewhere.
>> I need to read some binary data, and I am using numpy.fromfile to do
>>
memory, I need to read
> data skipping some records (I am reading data recorded at high frequency, so
> basically I want to read subsampling).
[clip]
You can do a fid.seek(offset) prior to np.fromfile() and the it will
read from offset. See the docstrings for `file.seek()` on how to use
recently the
> overhead, but we can do more to lower it.
Yeah. I was mainly curious about how different packages handle
unaligned arrays.
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On 3/7/13 6:47 PM, Francesc Alted wrote:
> On 3/6/13 7:42 PM, Kurt Smith wrote:
>> And regarding performance, doing simple timings shows a 30%-ish
>> slowdown for unaligned operations:
>>
>> In [36]: %timeit packed_arr['b']**2
>> 100 loops, best of
uate('sum(baligned)')
100 loops, best of 3: 2.16 ms per loop
In [17]: %timeit numexpr.evaluate('sum(bpacked)')
100 loops, best of 3: 2.08 ms per loop
Again, the unaligned case is (sligthly better). In this case numexpr is
a bit slower that NumPy because sum() is not para
=['a', 'b'], formats=['u1', 'u8']),
>> align=True)
>>
>> In [3]: dt.itemsize
>> Out[3]: 16
> Thanks! That's what I get for not checking before posting.
>
> Consider this my vote to make `aligned=True` the default
On 2/12/13 3:18 PM, Daπid wrote:
> On 12 February 2013 14:58, Francesc Alted wrote:
>> Yes, I think that's expected. Just to make sure, can you send some
>> excerpts of the errors that you are getting?
> Actually the errors are at the beginning of the process, so they are
&
).
Well, pip needs to compile the libraries prior to install them, so
compile messages are meaningful. Another question would be to reduce the
amount of compile messages by default in NumPy, but I don't think this
is realistic (and even not desirable).
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Francesc Alted
_
Exciting stuff. Thanks a lot to you and everybody implied in the release
for an amazing job.
Francesc
El 10/02/2013 2:25, "Ondřej Čertík" va escriure:
> Hi,
>
> I'm pleased to announce the availability of the final release of
> NumPy 1.7.0.
>
> Sources and binary installers can be found at
> htt
On 12/21/12 1:35 PM, Dag Sverre Seljebotn wrote:
> On 12/20/2012 03:23 PM, Francesc Alted wrote:
>> On 12/20/12 9:53 AM, Henry Gomersall wrote:
>>> On Wed, 2012-12-19 at 19:03 +0100, Francesc Alted wrote:
>>>> The only scenario that I see that this would create una
On 12/21/12 11:58 AM, Henry Gomersall wrote:
> On Fri, 2012-12-21 at 11:34 +0100, Francesc Alted wrote:
>>> Also this convolution code:
>>> https://github.com/hgomersall/SSE-convolution/blob/master/convolve.c
>>>
>>> Shows a small but repeatable speed-
On 12/20/12 7:35 PM, Henry Gomersall wrote:
> On Thu, 2012-12-20 at 15:23 +0100, Francesc Alted wrote:
>> On 12/20/12 9:53 AM, Henry Gomersall wrote:
>>> On Wed, 2012-12-19 at 19:03 +0100, Francesc Alted wrote:
>>>> The only scenario that I see that this would crea
On 12/20/12 9:53 AM, Henry Gomersall wrote:
> On Wed, 2012-12-19 at 19:03 +0100, Francesc Alted wrote:
>> The only scenario that I see that this would create unaligned arrays
>> is
>> for machines having AVX. But provided that the Intel architecture is
>> makin
d be even noticeable.
Can you tell us which difference in performance are you seeing for an
AVX-aligned array and other that is not AVX-aligned? Just curious.
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