Re: [Numpy-discussion] Numpy 1.7.1
On Thu, 2013-03-21 at 18:02 -0600, Charles R Harris wrote: The Numpy 1.7.1 release process seems to have stalled. What do we need to finish up to get it going again? I think it would be nice to shoot for a release maybe the weekend after next. Talking about 1.7.1 i have a couple of bug fixes for 1.7.0 at git://github.com/akesandgren/numpy.git in the v1.7.0-hpc2n branch They are quite small. -- Ake Sandgren, HPC2N, Umea University, S-90187 Umea, Sweden Internet: a...@hpc2n.umu.se Phone: +46 90 7866134 Fax: +46 90 7866126 Mobile: +46 70 7716134 WWW: http://www.hpc2n.umu.se ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Numpy 1.7.1
On Fri, 2013-03-22 at 11:42 +, Nathaniel Smith wrote: On Fri, Mar 22, 2013 at 7:47 AM, Ake Sandgren ake.sandg...@hpc2n.umu.se wrote: On Thu, 2013-03-21 at 18:02 -0600, Charles R Harris wrote: The Numpy 1.7.1 release process seems to have stalled. What do we need to finish up to get it going again? I think it would be nice to shoot for a release maybe the weekend after next. Talking about 1.7.1 i have a couple of bug fixes for 1.7.0 at git://github.com/akesandgren/numpy.git in the v1.7.0-hpc2n branch They are quite small. Please send as PRs against master, so we can review and merge them? Done. ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
[Numpy-discussion] howto apply-along-axis?
I frequently find I have my 1d function that performs some reduction that I'd like to apply-along some axis of an n-d array. As a trivial example, def sum(u): return np.sum (u) In this case the function is probably C/C++ code, but that is irrelevant (I think). Is there a reasonably efficient way to do this within numpy? ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] howto apply-along-axis?
On 22 Mar 2013 14:09, Neal Becker ndbeck...@gmail.com wrote: I frequently find I have my 1d function that performs some reduction that I'd like to apply-along some axis of an n-d array. As a trivial example, def sum(u): return np.sum (u) In this case the function is probably C/C++ code, but that is irrelevant (I think). Is there a reasonably efficient way to do this within numpy? The core infrastructure for this sort of thing is there - search on generalized ufuncs. There's no python-level api as far as I know, though, yet. You could write a reasonable facsimile of np.vectorize for such functions using nditer. -n ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Execution time difference between 2.7 and 3.2 using numpy
On 20/03/2013 11:12 AM, Frédéric Bastien wrote: On Wed, Mar 20, 2013 at 11:01 AM, Colin J. Williams cjwilliam...@gmail.com wrote: On 20/03/2013 10:30 AM, Frédéric Bastien wrote: Hi, win32 do not mean it is a 32 bits windows. sys.platform always return win32 on 32bits and 64 bits windows even for python 64 bits. But that is a good question, is your python 32 or 64 bits? 32 bits. That explain why you have memory problem but not other people with 64 bits version. So if you want to work with bigger input, change to a python 64 bits. Fred Thanks to the people who responded to my report that numpy, with Python 3.2 was significantly slower than with Python 2.7. I have updated to numpy 1.7.0 for each of the Pythons 2.7.3, 3.2.3 and 3.3.0. The Pythons came from python.org and the Numpys from PyPi. The SciPy site still points to Source Forge, I gathered from the responses that Source Forge is no longer recommended for downloads. The tests, which are available here(http://web.ncf.ca/cjw/FP%20Summary%20over%20273-323-330.txt), show that 3.2 is slower, but not to the same degree reported before. Colin W. PS There seems also to be a Python problem with the treatment of sys.argv in Python 3.3 ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Re: [Numpy-discussion] Execution time difference between 2.7 and 3.2 using numpy
On Fri, Mar 22, 2013 at 2:39 PM, Colin J. Williams cjwilliam...@gmail.com wrote: I have updated to numpy 1.7.0 for each of the Pythons 2.7.3, 3.2.3 and 3.3.0. ... The tests, which are available here(http://web.ncf.ca/cjw/FP%20Summary%20over%20273-323-330.txt), show that 3.2 is slower, but not to the same degree reported before. Have posted your test code anywhere? Anyway, depending on how you did your timings, that looks to me like 3.* is a bit faster with small data, and pretty much within measurement error for the large datasets. And if the large ones are doing things with really big arrays (I'm assuming pretty big, as you're getting close to 32 bit memory limits...), then it's really hard to imagine how python version could make a noticeable difference -- the real work would be in the numpy code, and that's exactly the same on all python versions. If you are using BLAS or LAPACK stuff, then there might be some differences with the different builds, though I wouldn't expect so if you ar getting them from the same source. -Chris -- Christopher Barker, Ph.D. Oceanographer Emergency Response Division NOAA/NOS/ORR(206) 526-6959 voice 7600 Sand Point Way NE (206) 526-6329 fax Seattle, WA 98115 (206) 526-6317 main reception chris.bar...@noaa.gov ___ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
