Mailman 3 December 2010 - NumPy-Discussion

Re: [Numpy-discussion] [PATCH] gfortran under macports
by Fabian Pedregosa 04 Dec '10

04 Dec '10

> > Correct me if I am wrong here: If you run "(sudo) gcc_select gfortran-mp-XY", where XY are the version numbers (e.g. 45 for gfortran 4.5), you should get symbolic links for the selected gcc/gfortran version. I believe that macports should probably make this clearer, and perhaps automatically when you do a "port install gccXY", but I am not sure if this needs any patching? Again, I might be wrong on this. Thanks! I didn't know about gcc_select. The correct command is "sudo gcc_select mp-gcc45" which effectively does all the symbolic links for you and works like a charm, so please ignore my previous patch. Cheers, fabian

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[PATCH] gfortran under macports
by Fabian Pedregosa 04 Dec '10

04 Dec '10

Hi all. Macports installs gfortran as part of the gcc package, but names it gfortran-mp-$version, without providing a symbolic link to a default gcfortran executable, and thus numpy.distutils is unable to find the right executable. The attached patch very simple, it just extends possible_executables with those names, but makes the build of scipy work without having to restore to obscure fc_config flags. Fabian.

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Float16 and PEP 3118
by Charles R Harris 03 Dec '10

03 Dec '10

Hi Folks, Now that the float16 type is in I was wondering if we should do anything to support it in the PEP 3118 buffer interface. This would probably affect the Cython folks as well as the people working on fixing up the structure module <http://bugs.python.org/issue3132>for Python 3.x. There is a fairly long thread about the latter and it also looks like what the Python folks are doing with structure alignment isn't going to be compatible with Numpy structured arrays. Thoughts? Chuck

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itertools.combinations to numpy
by Mario Moura 03 Dec '10

03 Dec '10

Hi Folks I have this situation >>> from timeit import Timer >>> reps = 5 >>> >>> t = Timer('itertools.combinations(range(1,10),3)', 'import itertools') >>> print sum(t.repeat(repeat=reps, number=1)) / reps 1.59740447998e-05 >>> t = Timer('itertools.combinations(range(1,100),3)', 'import itertools') >>> print sum(t.repeat(repeat=reps, number=1)) / reps 1.74999237061e-05 >>> >>> t = Timer('list(itertools.combinations(range(1,10),3))', 'import itertools') >>> print sum(t.repeat(repeat=reps, number=1)) / reps 5.31673431396e-05 >>> t = Timer('list(itertools.combinations(range(1,100),3))', 'import itertools') >>> print sum(t.repeat(repeat=reps, number=1)) / reps 0.0556231498718 >>> You can see list(itertools.combinations(range(1,100),3)) is terrible!! If you change to range(1,100000) your computer will lock. So I would like to know a good way to convert <itertools.combinations object> to ndarray? fast! without use list Is it possible? >>> x = itertools.combinations(range(1,10),3) >>> x <itertools.combinations object at 0x25f1520> >>> I tried this from http://docs.python.org/library/itertools.html?highlight=itertools#itertools… >>> numpy.fromiter(itertools.combinations(range(1,10),3), int, count=-1) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: setting an array element with a sequence. >>> and this from http://docs.python.org/library/itertools.html?highlight=itertools#itertools… import numpy from itertools import * from numpy import * def combinations(iterable, r): pool = tuple(iterable) n = len(pool) for indices in permutations(range(n), r): if sorted(indices) == list(indices): yield tuple(pool[i] for i in indices) numpy.fromiter(combinations(range(1,10),3), int, count=-1) >>> numpy.fromiter(combinations(range(1,10),3), int, count=-1) Traceback (most recent call last): File "<stdin>", line 1, in <module> ValueError: setting an array element with a sequence. >>> I like itertools.combinations performance but I need convert it to numpy. Best Regards mario

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numpy.test() Program received signal SIGABRT, Aborted.
by Nils Wagner 03 Dec '10

03 Dec '10

Hi all, I have installed the latest version of numpy. >>> numpy.__version__ '2.0.0.dev-6aacc2d' numpy.test(verbose=2) received signal SIGABRT. test_cdouble_2 (test_linalg.TestEig) ... ok test_csingle (test_linalg.TestEig) ... FAIL *** glibc detected *** /data/home/nwagner/local/bin/python: free(): invalid next size (fast): 0x000000001c2887b0 *** ======= Backtrace: ========= /lib64/libc.so.6[0x383cc71684] /lib64/libc.so.6(cfree+0x8c)[0x383cc74ccc] /data/home/nwagner/local/lib/python2.5/site-packages/numpy/core/multiarray.so[0x2b33e06f710e] (gdb) bt #0 0x000000383cc30155 in raise () from /lib64/libc.so.6 #1 0x000000383cc31bf0 in abort () from /lib64/libc.so.6 #2 0x000000383cc6a3db in __libc_message () from /lib64/libc.so.6 #3 0x000000383cc71684 in _int_free () from /lib64/libc.so.6 #4 0x000000383cc74ccc in free () from /lib64/libc.so.6 #5 0x00002b33e06f710e in array_dealloc (self=0x1c65fa00) at numpy/core/src/multiarray/arrayobject.c:209 #6 0x00000000004d6dbb in frame_dealloc (f=0x1c65eec0) at Objects/frameobject.c:416 Nils

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numpy.r_[True, False] is not a boolean array
by oc-spam66 03 Dec '10

03 Dec '10

Hello, I observe the following behavior: numpy.r_[True, False] -> array([1, 0], dtype=int8) numpy.r_[True] -> array([ True], dtype=bool) I would expect the first line to give a boolean array: array([ True, False], dtype=bool) Is it normal? Is it a bug? -- O.C. numpy.__version__ = '1.4.1'

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Pushing changes to numpy git repo problem
by Pearu Peterson 03 Dec '10

03 Dec '10

Hi, I have followed Development workflow instructions in http://docs.scipy.org/doc/numpy/dev/gitwash/ but I am having a problem with the last step: $ git push upstream ticket1679:master fatal: remote error: You can't push to git://github.com/numpy/numpy.git Use git@github.com:numpy/numpy.git What I am doing wrong? Here's some additional info: $ git remote -v show origin git@github.com:pearu/numpy.git (fetch) origin git@github.com:pearu/numpy.git (push) upstream git://github.com/numpy/numpy.git (fetch) upstream git://github.com/numpy/numpy.git (push) $ git branch -a master * ticket1679 remotes/origin/HEAD -> origin/master remotes/origin/maintenance/1.0.3.x remotes/origin/maintenance/1.1.x remotes/origin/maintenance/1.2.x remotes/origin/maintenance/1.3.x remotes/origin/maintenance/1.4.x remotes/origin/maintenance/1.5.x remotes/origin/master remotes/origin/ticket1679 remotes/upstream/maintenance/1.0.3.x remotes/upstream/maintenance/1.1.x remotes/upstream/maintenance/1.2.x remotes/upstream/maintenance/1.3.x remotes/upstream/maintenance/1.4.x remotes/upstream/maintenance/1.5.x remotes/upstream/master Thanks, Pearu

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Re: [Numpy-discussion] printoption to allow hexified floats?
by Ken Basye 02 Dec '10

02 Dec '10

Thanks for the replies. Robert is right; many numerical operations, particularly complex ones, generate different values across platforms, and we deal with these by storing the values from some platform as a reference and using allclose(), which requires extra work. But many basic operations generate the same underlying values on IEEE 754-compliant platforms but don't always format floats consistently (see http://bugs.python.org/issue1580 for a lengthy discussion on this). My impression is that Python 2.7 does a better job here, but at this point a lot of differences also crop up between 2.6 (or less) and 2.7 due to the changed formatting built into 2.7, and these are the result of formatting differences; the numbers themselves are identical (in our experience so far, at any rate). This is a current pain-point which an exact representation would alleviate. In response to David, we haven't implemented a separate print; we rely on the Numpy repr/str for ndarrays and the printoptions that allow some control over float formatting. I'm basically proposing to add a bit more control there. And thanks for the info on supported versions of Python. Ken On 12/2/10 8:14 AM, Robert Kern wrote: > On Wed, Dec 1, 2010 at 13:18, Ken Basye<kbasye1(a)jhu.edu> wrote: >> Hi Numpy folks, >> ? ? When working with floats, I prefer to have exact string >> representations in doctests and other reference-based testing; I find it >> helps a lot to avoid chasing cross-platform differences that are really >> about the string conversion rather than about numerical differences. > Unfortunately, there are still cross-platform numerical differences > that are real (but are irrelevant to the validity of the code under > test). Hex-printing for floats only helps a little to make doctests > useful for numerical code.

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Threshold
by totonixsame＠gmail.com 02 Dec '10

02 Dec '10

Hi all, I' m developing a medical software named InVesalius [1], it is a free software. It uses numpy arrays to store the medical images (CT and MRI) and the mask, the mask is used to mark the region of interest and to create 3D surfaces. Those array generally have 512x512 elements. The mask is created based in threshold, with lower and upper bound, this way: mask = numpy.zeros(medical_image.shape, dtype="uint16") mask[ numpy.logical_and( medical_image >= lower, medical_image <= upper)] = 255 Where lower and upper are the threshold bounds. Here I' m marking the array positions where medical_image is between the threshold bounds with 255, where isn' t with 0. The question is: Is there a better way to do that? Thank! [1] - svn.softwarepublico.gov.br/trac/invesalius

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broadcasting with numpy.interp
by greg whittier 02 Dec '10

02 Dec '10

Hi all, I'd like to be able to speed up the following code. def replace_dead(cube, dead): # cube.shape == (320, 640, 1200) # dead.shape == (320, 640) # cube[i,j,:] are bad points to be replaced via interpolation if dead[i,j] == True bands = np.arange(0, cube.shape[0]) for line in range(cube.shape[1]): dead_bands = bands[dead[:, line] == True] good_bands = bands[dead[:, line] == False] for sample in range(cube.shape[2]): # interp returns fp[0] for x < xp[0] and fp[-1] for x > xp[-1] cube[dead_bands, line, sample] = \ np.interp(dead_bands, good_bands, cube[good_bands, line, sample]) Removing that last loop via some sort of broadcasting seems like it should be possible, but that doesn't seem to work with interp. While interp allows the x-coordinates of interpolation points to be a multi-dimensional array, it expects the x- and y-coordinates of the data points to be 1-d arrays. Any suggestions for speeding this up? Thanks, Greg

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