Mailman 3 February 2012 - NumPy-Discussion

Moving to gcc 4.* for win32 installers ?
by David Cournapeau 28 Feb '12

28 Feb '12

Hi, I was wondering if we could finally move to a more recent version of compilers for official win32 installers. This would of course concern the next release cycle, not the ones where beta/rc are already in progress. Basically, the pros: - we will have to move at some point - gcc 4.* seem less buggy, especially C++ and fortran. - no need to maintain msvcr90 vodoo The cons: - it will most likely break the ABI - we need to recompile atlas (but I can take care of it) - the biggest: it is difficult to combine gfortran with visual studio (more exactly you cannot link gfortran runtime to a visual studio executable). The only solution I could think of would be to recompile the gfortran runtime with Visual Studio, which for some reason does not sound very appealing :) Thoughts ? cheers, David

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Re: [Numpy-discussion] [NumPy-Tickets] [NumPy] #2067: when x is an N-D array, list(x) produces surprising results
by Robert Kern 28 Feb '12

28 Feb '12

Off-Trac comments should probably go to numpy-discussion rather than back to numpy-tickets. I'm not sure why it's not read-only, but it should be. On Tue, Feb 28, 2012 at 01:15, Phillip Feldman <phillip.m.feldman(a)gmail.com> wrote: > I'd appreciate a pointer to something in the NumPy reference material > that states that a N-dim ndarray is treated as a collection of > (N-1)-dim ndarrays for purposes of iteration. I'm not sure anything states that about iteration specifically, but it's implicit from the documented behavior when given a single integer index: http://docs.scipy.org/doc/numpy/user/basics.indexing.html#single-element-in… > Phillip > > On Mon, Feb 27, 2012 at 11:45 AM, <numpy-tickets(a)scipy.org> wrote: >> #2067: when x is an N-D array, list(x) produces surprising results >> ------------------------+--------------------------------------------------- >> Reporter: pfeldman | Owner: somebody >> Type: defect | Status: new >> Priority: normal | Milestone: Unscheduled >> Component: numpy.core | Version: 1.6.1 >> Keywords: | >> ------------------------+--------------------------------------------------- >> >> Comment(by pv): >> >> The behavior does make sense, and it is what would naturally be expected. >> From the point of view of iteration, a N-dim ndarray *is* a collection of >> (N-1)-dim sub-arrays. The list constructor merely retains the consistency >> between `list(a)[j]` and `list(a[j])`. I don't see any reason to change >> this behavior, as raveling is a separate operation. >> >> -- >> Ticket URL: <http://projects.scipy.org/numpy/ticket/2067#comment:2> >> NumPy <http://projects.scipy.org/numpy> >> My example project > _______________________________________________ > NumPy-Tickets mailing list > NumPy-Tickets(a)scipy.org > http://mail.scipy.org/mailman/listinfo/numpy-tickets -- Robert Kern

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Numpy on App Engine
by Tom K. 28 Feb '12

28 Feb '12

Congrats, numpy is now available on the Google App Engine: http://googleappengine.blogspot.in/2012/02/announcing-general-availability-…

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ANN: SciPy 0.10.1 released
by Ralf Gommers 28 Feb '12

28 Feb '12

Hi all, I am pleased to announce the availability of SciPy 0.10.1. This is a maintenance release, with no new features compared to 0.10.0. Sources and binaries can be found at http://sourceforge.net/projects/scipy/files/scipy/0.10.1/, release notes are copied below. Enjoy, The SciPy developers ========================== SciPy 0.10.1 Release Notes ========================== .. contents:: SciPy 0.10.1 is a bug-fix release with no new features compared to 0.10.0. Main changes ------------ The most important changes are:: 1. The single precision routines of ``eigs`` and ``eigsh`` in ``scipy.sparse.linalg`` have been disabled (they internally use double precision now). 2. A compatibility issue related to changes in NumPy macros has been fixed, in order to make scipy 0.10.1 compile with the upcoming numpy 1.7.0 release. Other issues fixed ------------------ - #835: stats: nan propagation in stats.distributions - #1202: io: netcdf segfault - #1531: optimize: make curve_fit work with method as callable. - #1560: linalg: fixed mistake in eig_banded documentation. - #1565: ndimage: bug in ndimage.variance - #1457: ndimage: standard_deviation does not work with sequence of indexes - #1562: cluster: segfault in linkage function - #1568: stats: One-sided fisher_exact() returns `p` < 1 for 0 successful attempts - #1575: stats: zscore and zmap handle the axis keyword incorrectly

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speed of array creation from tuples
by Skipper Seabold 27 Feb '12

27 Feb '12

I am surprised by this (though maybe I shouldn't be?) It's always faster to use list comprehension to unpack lists of tuples than np.array/asarray? [~/] [1]: X = [tuple(np.random.randint(10,size=2)) for _ in range(100)] [~/] [2]: timeit np.array([x1 for _,x1 in X]) 10000 loops, best of 3: 26.4 us per loop [~/] [3]: timeit np.array(X) 1000 loops, best of 3: 378 us per loop [~/] [4]: timeit x1, x2 = np.array([x for _,x in X]), np.array([y for y,_ in X]) 10000 loops, best of 3: 53.4 us per loop [~/] [5]: timeit x1, x2 = np.array(X).T 1000 loops, best of 3: 384 us per loop Skipper

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Nature Editorial: Science Software should be Open Source
by Jason Grout 27 Feb '12

27 Feb '12

Jan Groenewald posted this link to the Sage development list, and I thought people here would be interested (and I figured people on the matplotlib, scipy, and ipython lists would see it here too): http://www.nature.com/nature/journal/v482/n7386/full/nature10836.html Thanks, Jason

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np.longlong casts to int
by Matthew Brett 27 Feb '12

27 Feb '12

Hi, I was gaily using np.longlong for casting to the highest available float type when I noticed this: In [4]: np.array([2.1], dtype=np.longlong) Out[4]: array([2], dtype=int64) whereas: In [5]: np.array([2.1], dtype=np.float128) Out[5]: array([ 2.1], dtype=float128) This on OSX snow leopard numpies 1.2 .1 -> current devel and OSX tiger PPC recent devel. I had the impression that np.float128 and np.longlong would be identical in behavior - but I guess not? Best, Matthew

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numpy ndenumerate
by Nathan Faggian 26 Feb '12

26 Feb '12

Hi, Is there a good reason for ndenumerate in numpy being slower than standard indexing? For example: --- import numpy as np def fast_itt(a): for index, value in np.ndenumerate(a): a[index] += 1 def slow_itt(a): for r in range(0, a.shape[0]): for c in range(0, a.shape[1]): a[r,c] += 1 a = np.zeros((100,100)) %timeit fast_itt(a) 10 loops, best of 3: 25.7 ms per loop %timeit slow_itt(a) 100 loops, best of 3: 13 ms per loop --- I appreciate that there are better ways of operating on arrays but there are many good reasons for permuting through indices and ndenumerate is a nice way of this... I am left wondering why it performs badly in this case. Cheers, Nathan.

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Re: [Numpy-discussion] How to modify an array
by tetsuro_kikuchi＠jesc.or.jp 26 Feb '12

26 Feb '12

Dear Olivier, Thank you very much for your help. It worked fine! Tetsuro Kikuchi Olivier Delalleau <shish(a)keba.be> 宛先: Discussion of Numerical Python <numpy-discussion(a)scipy.org> 送信者: cc: numpy-discussion-bounce 件名: Re: [Numpy-discussion] How to modify an array s(a)scipy.org 2012/02/27 10:01 Discussion of Numerical Python へ返信してください This should do what you want: array_copy = my_array.copy() array_copy[array_copy == 2] = 0 -=- Olivier Le 26 février 2012 19:53, <tetsuro_kikuchi(a)jesc.or.jp> a écrit : Dear sirs, Please allow me to ask you a beginner's question. I have an nparray whose shape is (144, 91, 1). The elements of this array are integer "0", "1" or "2", but I don't know which of the three integers is assigned to each element. I would like to make a copy of this array, and then replace only the elements whose value is "2" into "0". Could you teach how to make such a modification? Sincerely yours, Tetsuro Kikuchi _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion(a)scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion(a)scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion

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How to modify an array
by tetsuro_kikuchi＠jesc.or.jp 26 Feb '12

26 Feb '12

Dear sirs, Please allow me to ask you a beginner's question. I have an nparray whose shape is (144, 91, 1). The elements of this array are integer "0", "1" or "2", but I don't know which of the three integers is assigned to each element. I would like to make a copy of this array, and then replace only the elements whose value is "2" into "0". Could you teach how to make such a modification? Sincerely yours, Tetsuro Kikuchi

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