Re: [Numpy-discussion] PyData Barcelona this May

nfo/numpy-discussion >> ___ >> NumPy-Discussion mailing list >> NumPy-Discussion@scipy.org >> https://mail.scipy.org/mailman/listinfo/numpy-discussion >> >> > > > -- > (\__/) > ( O.o) > ( > <) Este es Conejo. Copia 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/listinfo/numpy-discussion > > -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] caching large allocations on gnu/linux

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 > > > > > wrote: > > > > Should it be agreed

Re: [Numpy-discussion] PyData Barcelona this May

, but unfortunately I am quite swamped. Will see.​ ​Have fun in Barcelona!​ -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Fortran order in recarray.

gt;> 'batches', so this should require 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.​ Franc

Re: [Numpy-discussion] Fortran order in recarray.

Py-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ANN: NumExpr3 Alpha

__ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] ANN: NumExpr3 Alpha

ly, > > 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 <robert.mcl...@ethz.ch> > robbmcl...@gmail.com > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: numexpr 2.6.2 released!

Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] array comprehension

2016-11-04 14:36 GMT+01:00 Neal Becker <ndbeck...@gmail.com>: > Francesc Alted wrote: > > > 2016-11-04 13:06 GMT+01:00 Neal Becker <ndbeck...@gmail.com>: > > > >> I find I often write: > >> np.array ([some list comprehension]) > >>

Re: [Numpy-discussion] array comprehension

> NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: numexpr 2.6.1 released

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. Enjoy data! -- Fran

[Numpy-discussion] ANN: numexpr 2.6.0 released

experience = Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Calling C code that assumes SIMD aligned data.

you only wasted 64 bytes. Pretty cheap indeed. Francesc > > Thanks, > -Øystein > > On Thu, May 5, 2016 at 1:55 PM, Francesc Alted <fal...@gmail.com> wrote: > >> 2016-05-05 11:38 GMT+02:00 Øystein Schønning-Johansen <oyste...@gmail.com >> >: >> >

Re: [Numpy-discussion] Calling C code that assumes SIMD aligned data.

(BTW: the function 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 > > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.

[Numpy-discussion] ANN: bcolz 1.0.0 (final) released

/Blosc/bcolz/blob/master/RELEASE_NOTES.rst **Enjoy data!** -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: python-blosc 1.3.1

*Enjoy data!** -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: numexpr 2.5.2 released

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. Enjoy data! -- Fran

[Numpy-discussion] ANN: bcolz 1.0.0 RC2 is out!

can be found in the Git repository: https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst **Enjoy data!** -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy

[Numpy-discussion] ANN: numexpr 2.5.1 released

Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] [ANN] bcolz 1.0.0 RC1 released

can be found in the Git repository: https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst **Enjoy data!** -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy

Re: [Numpy-discussion] Fwd: Numexpr-3.0 proposal

n-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. -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: numexpr 2.5

es): http://pypi.python.org/pypi/numexpr Share your experience = Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy

Re: [Numpy-discussion] Fast Access to Container of Numpy Arrays on Disk?

list > > NumPy-Discussion@scipy.org > > https://mail.scipy.org/mailman/listinfo/numpy-discussion > > > > -- > Nathaniel J. Smith -- http://vorpus.org > ___ > NumPy-Discussion mailing list > NumPy-Discussion@scipy.org > https://mail.scipy.org/mailman/listinfo/numpy-discussion > -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] performance solving system of equations in numpy and MATLAB

2015-12-17 12:00 GMT+01:00 Daπid <davidmen...@gmail.com>: > On 16 December 2015 at 18:59, Francesc Alted <fal...@gmail.com> wrote: > >> Probably MATLAB is shipping with Intel MKL enabled, which probably is the >> fastest LAPACK implementation out there. N

Re: [Numpy-discussion] performance solving system of equations in numpy and MATLAB

will 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 <fal...@gmail.com>: > Hi, > > Probably MATLAB is shipping with Intel MKL enabled, which probably is the > fastest LAPACK

Re: [Numpy-discussion] performance solving system of equations in numpy and MATLAB

testb = np.random.randn(15000) > > %time testx = np.linalg.solve(testA, testb) > > %MATLAB version > > testA = randn(15000); > > testb = randn(15000, 1); > tic(); testx = testA \ testb; toc(); > > ___ > NumPy-Discussion

[Numpy-discussion] ANN: bcolz 0.12.0 released

://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!** -- Francesc Alted

[Numpy-discussion] ANN: numexpr 2.4.6 released

s 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, Francesc Alted

[Numpy-discussion] ANN: numexpr 2.4.5 released

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). In case you wa

[Numpy-discussion] ANN: bcolz 0.11.3 released!

notes can be found in the Git repository: https://github.com/Blosc/bcolz/blob/master/RELEASE_NOTES.rst **Enjoy data!** -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org https://mail.scipy.org/mailman/listinfo/numpy

Re: [Numpy-discussion] Governance model request

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 this list anymore. But > I was saddened to see the turn of this thread, and I hope I can contribute >

[Numpy-discussion] ANN: Numexpr 2.4.4 is out

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. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-

[Numpy-discussion] ANN: python-blosc 1.2.8 released

://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!** -- Francesc Alted

[Numpy-discussion] ANN: bcolz 0.11.0 released

/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!** -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http

Re: [Numpy-discussion] Comments on governance proposal (was: Notes from the numpy dev meeting at scipy 2015)

en tu firma y ayúdale en sus planes de dominación mundial. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Francesc Alted

Re: [Numpy-discussion] Notes from the numpy dev meeting at scipy 2015

. -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] UTC-based datetime64

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 datetimes printed always in UTC? Thanks, -- Francesc Alted

Re: [Numpy-discussion] Question about unaligned access

2015-07-06 18:04 GMT+02:00 Jaime Fernández del Río jaime.f...@gmail.com: On Mon, Jul 6, 2015 at 10:18 AM, Francesc Alted fal...@gmail.com wrote: Hi, I have stumbled into this: In [62]: sa = np.fromiter(((i,i) for i in range(1000*1000)), dtype=[('f0', np.int64), ('f1', np.int32)]) In [63

Re: [Numpy-discussion] Question about unaligned access

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 fal...@gmail.com: Hi, I have stumbled into this: In [62]: sa = np.fromiter(((i,i) for i in range(1000*1000)), dtype=[('f0', np.int64), ('f1', np.int32

[Numpy-discussion] Question about unaligned access

/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 strikes like a quite strange thing to me. Thanks, Francesc -- Francesc Alted

[Numpy-discussion] ANN: python-blosc 1.2.7 released

...@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!** -- Francesc Alted

[Numpy-discussion] ANN: PyTables 3.2.0 (final) released!

=== 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

[Numpy-discussion] ANN: PyTables 3.2.0 RC2 is out

=== 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

Re: [Numpy-discussion] ANN: numexpr 2.4.3 released

requests are welcome. At any rate, which is your use case? I am curious. -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: numexpr 2.4.3 released

, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: PyTables 3.2.0 release candidate 1 is out

=== 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

[Numpy-discussion] ANN: numexpr 2.4.1 released

= Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Introductory mail and GSoc Project Vector math library integration

-discussion -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] Vectorizing computation

, :, 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, -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion

Re: [Numpy-discussion] Vectorizing computation

2015-02-13 12:51 GMT+01:00 Julian Taylor jtaylor.deb...@googlemail.com: 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 * ny).reshape((nx, ny)) def

Re: [Numpy-discussion] Vectorizing computation

2015-02-13 13:25 GMT+01:00 Julian Taylor jtaylor.deb...@googlemail.com: On 02/13/2015 01:03 PM, Francesc Alted wrote: 2015-02-13 12:51 GMT+01:00 Julian Taylor jtaylor.deb...@googlemail.com mailto:jtaylor.deb...@googlemail.com: On 02/13/2015 11:51 AM, Francesc Alted wrote: Hi

[Numpy-discussion] ANN: bcolz 0.7.1 released

BSD: https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt **Enjoy data!** -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: bcolz 0.7.0 released

://groups.google.com/group/bcolz License is the new BSD: https://github.com/Blosc/bcolz/blob/master/LICENSES/BCOLZ.txt **Enjoy data!** -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman

[Numpy-discussion] ANN: python-blosc 1.2.7 released

://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!** -- Francesc Alted

[Numpy-discussion] [CORRECTION] python-blosc 1.2.4 released (Was: ANN: python-blosc 1.2.7 released)

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 is a maintenance release, where

Re: [Numpy-discussion] IDL vs Python parallel computing

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/multi_cores.html) -- Francesc Alted

Re: [Numpy-discussion] High-quality memory profiling for numpy in python 3.5 / volunteers needed

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 Alted

Re: [Numpy-discussion] High-quality memory profiling for numpy in python 3.5 / volunteers needed

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=[('y1',np.int8),('x',np.float64),('y2',np.int8,(7,))])['x'] on arrays

Re: [Numpy-discussion] About the npz format

-r--r-- 1 faltet users 48M 18 abr 13:47 x-lz4.blp -rw-r--r-- 1 faltet users 49M 18 abr 13:47 x-blosclz.blp -rw-r--r-- 1 faltet users 382M 18 abr 13:42 x.npy But again, we are talking about a specially nice compression case. -- Francesc Alted

Re: [Numpy-discussion] High-quality memory profiling for numpy in python 3.5 / volunteers needed

NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] High-quality memory profiling for numpy in python 3.5 / volunteers needed

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't see alignment issues you are dominated by memory bandwidth

[Numpy-discussion] ANN: numexpr 2.4 is out

know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] PEP 465 has been accepted / volunteers needed

;-). 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! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy

[Numpy-discussion] ANN: numexpr 2.4 RC2

=== 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

[Numpy-discussion] ANN: numexpr 2.4 RC1

, kudos, etc. you may have. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] last call for fixes for numpy 1.8.1rc1

to add gh-4284 after some though tomorrow. Cheers, Julian ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Francesc Alted

Re: [Numpy-discussion] last call for fixes for numpy 1.8.1rc1

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 Julian, Any chance that NPY_MAXARGS could be increased

Re: [Numpy-discussion] last call for fixes for numpy 1.8.1rc1

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 NpyIter_AdvancedNew: https://github.com/pydata

[Numpy-discussion] ANN: numexpr 2.3.1 released

): http://pypi.python.org/pypi/numexpr Share your experience = Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http

Re: [Numpy-discussion] argsort speed

___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman

[Numpy-discussion] ANN: numexpr 2.3 (final) released

= Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

[Numpy-discussion] ANN: python-blosc 1.2.0 released

://github.com/ContinuumIO/python-blosc/blob/master/LICENSES for more details. -- Francesc Alted Continuum Analytics, Inc. -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy

[Numpy-discussion] ANN: BLZ 0.6.1 has been released

! Francesc Alted Continuum Analytics, Inc. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] Catching out-of-memory error before it happens

___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy

Re: [Numpy-discussion] -ffast-math

of). Maybe you are running in a multi-core machine now and you are seeing better speedup because of this? Also, your expressions are made of transcendental functions, so linking numexpr with MKL could accelerate computations a good deal too. -- Francesc Alted

[Numpy-discussion] [ANN] numexpr 2.2 released

is hosted at Google code in: http://code.google.com/p/numexpr/ You can get the packages from PyPI as well: http://pypi.python.org/pypi/numexpr Share your experience = Let us know of any bugs, suggestions, gripes, kudos, etc. you may have. Enjoy data! -- Francesc Alted

Re: [Numpy-discussion] RAM problem during code execution - Numpya arrays

% of RAM used and in 1-2hour is totally full used)? Please help me, I'm totally stuck! Thanks a lot! ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion -- Francesc Alted

[Numpy-discussion] ANN: python-blosc 1.1 (final) released

list for Blosc at: bl...@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/FrancescAlted/python-blosc/blob/master/LICENSES for more details. Enjoy! -- Francesc Alted

[Numpy-discussion] ANN: python-blosc 1.1 RC1 available for testing

://groups.google.es/group/blosc Licenses Both Blosc and its Python wrapper are distributed using the MIT license. See: https://github.com/FrancescAlted/python-blosc/blob/master/LICENSES for more details. -- Francesc Alted ___ NumPy-Discussion mailing

Re: [Numpy-discussion] Profiling (was GSoC : Performance parity between numpy arrays and Python scalars)

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 optimizations). -- Francesc Alted ___ NumPy

[Numpy-discussion] ANN: numexpr 2.1 RC1

, suggestions, gripes, kudos, etc. you may have. Enjoy! -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] timezones and datetime64

. -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

Re: [Numpy-discussion] timezones and datetime64

that this was not needed because timestamps+timedelta would be enough. The NEP still reflects this 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. -- Francesc Alted

Re: [Numpy-discussion] timezones and datetime64

On 4/4/13 8:56 PM, Chris Barker - NOAA Federal wrote: On Thu, Apr 4, 2013 at 10:54 AM, Francesc Alted franc...@continuum.io wrote: That makes a difference. This can be specially important for creating user-defined time origins: In []: np.array(int(1.5e9), dtype='datetime64[s]') + np.array(1

Re: [Numpy-discussion] fast numpy.fromfile skipping data chunks

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 it. -- Francesc Alted

Re: [Numpy-discussion] fast numpy.fromfile skipping data chunks

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 this. Since the files are huge

Re: [Numpy-discussion] aligned / unaligned structured dtype behavior (was: GSOC 2013)

is a bit slower that NumPy because sum() is not parallelized internally. Hmm, provided that, I'm wondering if some internal copies to L1 in NumPy could help improving unaligned performance. Worth a try? -- Francesc Alted ___ NumPy-Discussion mailing

Re: [Numpy-discussion] aligned / unaligned structured dtype behavior

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 3: 2.48 ms per loop In [37]: %timeit aligned_arr['b']**2

Re: [Numpy-discussion] GSOC 2013

takes 9 bytes to host the structure, while a `aligned=True` will take 16 bytes. I'd rather let the default as it is, and in case performance is critical, you can always copy the unaligned field to a new (homogeneous) array. -- Francesc Alted ___ NumPy

Re: [Numpy-discussion] pip install numpy throwing a lot of output.

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). -- Francesc Alted ___ NumPy-Discussion mailing

Re: [Numpy-discussion] pip install numpy throwing a lot of output.

On 2/12/13 3:18 PM, Daπid wrote: On 12 February 2013 14:58, Francesc Alted franc...@continuum.io 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 out

Re: [Numpy-discussion] Byte aligned arrays

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 create unaligned arrays is for machines

Re: [Numpy-discussion] Byte aligned arrays

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-up (a few %) when using some aligned loads (as many as I

Re: [Numpy-discussion] Byte aligned arrays

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 unaligned arrays is for machines having AVX

Re: [Numpy-discussion] Byte aligned arrays

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 making great strides in fetching unaligned data

Re: [Numpy-discussion] Byte aligned arrays

data, I'd be surprised that the difference in performance would 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. -- Francesc Alted ___ NumPy

Re: [Numpy-discussion] the difference between + and np.add?

On 11/23/12 8:00 PM, Chris Barker - NOAA Federal wrote: On Thu, Nov 22, 2012 at 6:20 AM, Francesc Alted franc...@continuum.io wrote: As Nathaniel said, there is not a difference in terms of *what* is computed. However, the methods that you suggested actually differ on *how* they are computed

Re: [Numpy-discussion] Conditional update of recarray field

, so this is why it works. Would it be possible to emit a warning message in the case of faulty assignments? The only solution that I can see for this is that the fancy indexing would return a view, and not a different object, but NumPy containers are not prepared for this. -- Francesc Alted

Re: [Numpy-discussion] Conditional update of recarray field

a copy, not a view. And yes, fancy indexing returning a copy is standard for all ndarrays. Hope it is clearer now (although admittedly it is a bit strange at first sight), -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org

Re: [Numpy-discussion] the difference between + and np.add?

+01, ..., 4.9850e+07, 4.9900e+07, 4.9950e+07]) Again, the computations are the same, but how you manage memory is critical. -- Francesc Alted ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org

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