Mailman 3 January 2012 - NumPy-Discussion

preferred way of testing empty arrays
by Bartosz Telenczuk 31 Jan '12

31 Jan '12

I have been using numpy for several years and I am very impressed with its flexibility. However, there is one problem that has always bothered me. Quite often I need to test consistently whether a variable is any of the following: an empty list, an empty array or None. Since both arrays and lists are ordered sequences I usually allow for both, and convert if necessary. However, when the (optional) argument is an empty list/array or None, I skip its processing and do nothing. Now, how should I test for 'emptiness'? PEP8 recommends: For sequences, (strings, lists, tuples), use the fact that empty sequences are false. >> seq = [] >> if not seq: ... print 'Hello' It works for empty numpy arrays: >> a = np.array(seq) >> if not a: ... print 'Hello" Hello but if 'a' is non-empty it raises an exception: >> a = np.array([1,2]) >> if not a: ... print 'Hello" ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() One solution is to test lengths: >> if len(seq) > 0: .... ... >> if len(a) > 0: ... ... but for None it fails again: >> opt = None >> if len(opt): ... TypeError: object of type 'NoneType' has no len() even worse we can not test for None, because it will fail if someone accidentally wraps None in an array: >> a = np.array(opt) >> if opt is not None: ... print 'hello' hello Although this behaviour is expected, it may be very confusing and it easily leads to errors. Even worse it adds unnecessary complexity in the code, because arrays, lists and None have to be handled differently. I hoped the I managed to explain the problem well. Is there a recommended way to test for empty arrays? Cheers, Bartosz

4 6

Unrealistic expectations of class Polynomial or a bug?
by eat 30 Jan '12

30 Jan '12

Hi, Short demonstration of the issue: In []: sys.version Out[]: '2.7.2 (default, Jun 12 2011, 15:08:59) [MSC v.1500 32 bit (Intel)]' In []: np.version.version Out[]: '1.6.0' In []: from numpy.polynomial import Polynomial as Poly In []: def p_tst(c): ..: p= Poly(c) ..: r= p.roots() ..: return sort(abs(p(r))) ..: Now I would expect a result more like: In []: p_tst(randn(123))[-3:] Out[]: array([ 3.41987203e-07, 2.82123675e-03, 2.82123675e-03]) be the case, but actually most result seems to be more like: In []: p_tst(randn(123))[-3:] Out[]: array([ 9.09325898e+13, 9.09325898e+13, 1.29387029e+72]) In []: p_tst(randn(123))[-3:] Out[]: array([ 8.60862087e-11, 8.60862087e-11, 6.58784520e+32]) In []: p_tst(randn(123))[-3:] Out[]: array([ 2.00545673e-09, 3.25537709e+32, 3.25537709e+32]) In []: p_tst(randn(123))[-3:] Out[]: array([ 3.22753481e-04, 1.87056454e+00, 1.87056454e+00]) In []: p_tst(randn(123))[-3:] Out[]: array([ 2.98556327e+08, 2.98556327e+08, 8.23588003e+12]) So, does this phenomena imply that - I'm testing with too high order polynomials (if so, does there exists a definite upper limit of polynomial order I'll not face this issue) or - it's just the 'nature' of computations with float values (if so, probably I should be able to tackle this regardless of the polynomial order) or - it's a nasty bug in class Polynomial Regards, eat

2 5

condense array along one dimension
by Ruby Stevenson 30 Jan '12

30 Jan '12

hi, all Say I have a three dimension array, X, Y, Z, how can I condense into two dimensions: for example, compute 2-D array with (X, Z) and summarize along Y dimensions ... is it possible? thanks Ruby

2 2

Addressing arrays
by Ted To 30 Jan '12

30 Jan '12

Hi, Is there some straightforward way to access an array by values across a subset of its dimensions? For example, if I have a three dimensional array a=(x,y,z), can I look at the values of z given particular values for x and y? Thanks, Ted

5 11

NetCDF4/numpy question
by Howard 28 Jan '12

28 Jan '12

Hi all I am a fairly recent convert to python and I have got a question that's got me stumped. I hope this is the right mailing list: here goes :) I am reading some time series data out of a netcdf file a single timestep at a time. If the data is NaN, I want to reset it to the minimum of the dataset over all timesteps (which I already know). The data is in a variable of type numpy.ma.core.MaskedArray called modelData. If I do this: for i in range(len(modelData)): if math.isnan(modelData[i]): modelData[i] = dataMin I get the effect I want, If I do this: modelData[np.isnan(modelData)] = dataMin it doesn't seem to be working. Of course I could just do the first one, but len(modelData) is about 3.5 million, and it's taking about 20 seconds to run. This is happening inside of a rendering loop, so I'd like it to be as fast as possible, and I thought the second one might be faster, and maybe it is, but it doesn't seem to be working! :) Any ideas would be much appreciated. Thanks Howard -- Howard Lander <mailto:howard@renci.org> Senior Research Software Developer Renaissance Computing Institute (RENCI) <http://www.renci.org> The University of North Carolina at Chapel Hill Duke University North Carolina State University 100 Europa Drive Suite 540 Chapel Hill, NC 27517 919-445-9651

4 7

The NumPy Mandelbrot code 16x slower than Fortran
by Ondřej Čertík 28 Jan '12

28 Jan '12

Hi, I read the Mandelbrot code using NumPy at this page: http://mentat.za.net/numpy/intro/intro.html but when I run it, it gives me integer overflows. As such, I have fixed the code, so that it doesn't overflow here: https://gist.github.com/1655320 and I have also written an equivalent Fortran program. You can compare both source codes to see that that it is pretty much one-to-one translation. The main idea in the above gist is to take an algorithm written in NumPy, and translate it directly to Fortran, without any special optimizations. So the above is my first try in Fortran. You can plot the result using this simple script (you can also just click on this gist to see the image there): https://gist.github.com/1655377 Here are my timings: Python Fortran Speedup Calculation 12.749 00.784 16.3x Saving 01.904 01.456 1.3x Total 14.653 02.240 6.5x I save the matrices to disk in an ascii format, so it's quite slow in both cases. The pure computation is however 16x faster in Fortran (in gfortran, I didn't even try Intel Fortran, that will probably be even faster). As such, I wonder how the NumPy version could be sped up? I have compiled NumPy with Lapack+Blas from source. Would anyone be willing to run the NumPy version? Just copy+paste should do it. If you want to run the Fortran version, the above gist uses some of my other modules that I use in my other programs, my goal was to see how much more complicated the Fortran code gets, compared to NumPy. As such, I put here https://gist.github.com/1655350 a single file with all the dependencies, just compile it like this: gfortran -fPIC -O3 -march=native -ffast-math -funroll-loops mandelbrot.f90 and run: $ ./a.out Iteration 1 Iteration 2 ... Iteration 100 Saving... Times: Calculation: 0.74804599999999999 Saving: 1.3640850000000002 Total: 2.1121310000000002 Let me know if you figure out something. I think the "mask" thing is quite slow, but the problem is that it needs to be there, to catch overflows (and it is there in Fortran as well, see the "where" statement, which does the same thing). Maybe there is some other way to write the same thing in NumPy? Ondrej

11 16

bug in array instanciation?
by Emmanuel Mayssat 27 Jan '12

27 Jan '12

In [20]: dt_knobs = [('pvName',(str,40)),('start','float'),('stop','float'),('mode',(str,10))] In [21]: r_knobs = np.recarray([],dtype=dt_knobs) In [22]: r_knobs Out[22]: rec.array(('\xa0\x8c\xc9\x02\x00\x00\x00\x00(\xc8v\x02\x00\x00\x00\x00\x00\xd3\x86\x02\x00\x00\x00\x00\x10\xdeJ\x02\x00\x00\x00\x00\x906\xb9\x02', 1.63e-322, 1.351330465085e-312, '\x90\xc6\xa3\x02\x00\x00\x00\x00P'), dtype=[('pvName', '|S40'), ('start', '<f8'), ('stop', '<f8'), ('mode', '|S10')]) why is the array not empty? -- E

2 1

need advice on installing NumPy onto a Windows 7 with Python2.7 (32-bit)
by William McLendon 27 Jan '12

27 Jan '12

Hi, I am trying to install NumPy (using numpy-1.6.1-win32-superpack-python2.7) on a Windows 7 machine that has 32-bit Python 2.7 installed on it using the latest installer (python-2.7.2.msi). Python is installed into the default location, C:\Python27, and as far as I can tell the registry knows about it -- or at least the windows uninstaller in the control panel does... The installation fails because the NumPy installer cannot find the Python installation. I am then prompted with a screen that should allow me to type in the location of my python installation, but the text-boxes where I should type this do not allow input so I'm kind of stuck. I did look into trying to build from source, but I don't have a C compiler on this system so setup.py died a horrible death. I'd prefer to avoid having to install Visual C++ Express on this system. Does anyone have any suggestions that might be helpful? Thanks! -William

3 4

how to cite 1Xn array as nX1 array?
by Chao YUE 27 Jan '12

27 Jan '12

Dear all, suppose I have a ndarray a: In [66]: a Out[66]: array([0, 1, 2, 3, 4]) how can use it as 5X1 array without doing a=a.reshape(5,1)? thanks Chao -- *********************************************************************************** Chao YUE Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL) UMR 1572 CEA-CNRS-UVSQ Batiment 712 - Pe 119 91191 GIF Sur YVETTE Cedex Tel: (33) 01 69 08 29 02; Fax:01.69.08.77.16 ************************************************************************************

4 4

Re: [Numpy-discussion] Problem installing NumPy with Python 3.2.2/MacOS X 10.7.2 (Samuel John)
by Hans-Martin v. Gaudecker 26 Jan '12

26 Jan '12

Hi Samuel, I realised that a couple of days ago as well… Same on Python 2.7.2 (full output from both below FWIW). I usually only need a minimal subset of SciPy, so still hoping it's only in places I don't need it. Else I shall be happy to come back to your formulas, thanks for making them! Best, Hans-Martin python Python 2.7.2 (default, Jan 11 2012, 16:23:50) [GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import scipy sci>>> scipy.test(verbose=10) Running unit tests for scipy NumPy version 2.0.0.dev-55472ca NumPy is installed in /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/numpy SciPy version 0.11.0.dev-600e81f SciPy is installed in /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy Python version 2.7.2 (default, Jan 11 2012, 16:23:50) [GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)] nose version 1.1.2 nose.config: INFO: Ignoring files matching ['^\\.', '^_', '^setup\\.py$'] nose.config: INFO: Excluding tests matching ['f2py_ext', 'f2py_f90_ext', 'gen_ext', 'pyrex_ext', 'swig_ext'] nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/fftpack/convolve.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/integrate/vode.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/interpolate/dfitpack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/interpolate/interpnd.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/io/matlab/mio5_utils.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/io/matlab/mio_utils.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/io/matlab/streams.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/lib/blas/cblas.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/lib/blas/fblas.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/lib/lapack/atlas_version.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/lib/lapack/calc_lwork.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/lib/lapack/clapack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/lib/lapack/flapack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/linalg/atlas_version.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/linalg/calc_lwork.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/linalg/cblas.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/linalg/clapack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/linalg/fblas.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/linalg/flapack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/optimize/minpack2.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/optimize/moduleTNC.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/signal/sigtools.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/signal/spectral.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/signal/spline.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/spatial/ckdtree.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/spatial/qhull.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/special/lambertw.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/special/orthogonal_eval.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/special/specfun.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/stats/futil.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/stats/mvn.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/stats/statlib.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/stats/vonmises_cython.so is executable; skipped Tests cophenet(Z) on tdist data set. ... ok Tests cophenet(Z, Y) on tdist data set. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. Correspondance should be false. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. Correspondance should be false. ... ok Tests correspond(Z, y) with empty linkage and condensed distance matrix. ... ok Tests num_obs_linkage with observation matrices of multiple sizes. ... ok Tests fcluster(Z, criterion='maxclust', t=2) on a random 3-cluster data set. ... ok Tests fcluster(Z, criterion='maxclust', t=3) on a random 3-cluster data set. ... ok Tests fcluster(Z, criterion='maxclust', t=4) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=2) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=3) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=4) on a random 3-cluster data set. ... ok Tests from_mlab_linkage on empty linkage array. ... ok Tests from_mlab_linkage on linkage array with multiple rows. ... ok Tests from_mlab_linkage on linkage array with single row. ... ok Tests inconsistency matrix calculation (depth=1) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=2) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=3) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=4) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=1, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=2, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=3, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=4, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=1) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=2) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=3) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=4) on a single linkage. ... ok Tests is_isomorphic on test case #1 (one flat cluster, different labellings) ... ok Tests is_isomorphic on test case #2 (two flat clusters, different labelings) ... ok Tests is_isomorphic on test case #3 (no flat clusters) ... ok Tests is_isomorphic on test case #4A (3 flat clusters, different labelings, isomorphic) ... ok Tests is_isomorphic on test case #4B (3 flat clusters, different labelings, nonisomorphic) ... ok Tests is_isomorphic on test case #4C (3 flat clusters, different labelings, isomorphic) ... ok Tests is_isomorphic on test case #5A (1000 observations, 2 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5B (1000 observations, 3 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5C (1000 observations, 5 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5A (1000 observations, 2 random clusters, random permutation of the labeling, slightly nonisomorphic.) Run 3 times. ... ok Tests is_isomorphic on test case #5B (1000 observations, 3 random clusters, random permutation of the labeling, slightly nonisomorphic.) Run 3 times. ... ok Tests is_isomorphic on test case #5C (1000 observations, 5 random clusters, random permutation of the labeling, slightly non-isomorphic.) Run 3 times. ... ok Tests is_monotonic(Z) on 1x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on 2x4 linkage. Expecting False. ... ok Tests is_monotonic(Z) on 2x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 1). Expecting False. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 2). Expecting False. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 3). Expecting False ... ok Tests is_monotonic(Z) on 3x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on an empty linkage. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on Iris data set. Expecting True. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Expecting True. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Perturbing. Expecting False. ... ok Tests is_valid_im(R) on im over 2 observations. ... ok Tests is_valid_im(R) on im over 3 observations. ... ok Tests is_valid_im(R) with 3 columns. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3). ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link counts. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link height means. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link height standard deviations. ... ok Tests is_valid_im(R) with 5 columns. ... ok Tests is_valid_im(R) with empty inconsistency matrix. ... ok Tests is_valid_im(R) with integer type. ... ok Tests is_valid_linkage(Z) on linkage over 2 observations. ... ok Tests is_valid_linkage(Z) on linkage over 3 observations. ... ok Tests is_valid_linkage(Z) with 3 columns. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3). ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative counts. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative distances. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative indices (left). ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative indices (right). ... ok Tests is_valid_linkage(Z) with 5 columns. ... ok Tests is_valid_linkage(Z) with empty linkage. ... ok Tests is_valid_linkage(Z) with integer type. ... ok Tests leaders using a flat clustering generated by single linkage. ... ok Tests leaves_list(Z) on a 1x4 linkage. ... ok Tests leaves_list(Z) on a 2x4 linkage. ... ok Tests leaves_list(Z) on the Iris data set using average linkage. ... ok Tests leaves_list(Z) on the Iris data set using centroid linkage. ... ok Tests leaves_list(Z) on the Iris data set using complete linkage. ... ok Tests leaves_list(Z) on the Iris data set using median linkage. ... ok Tests leaves_list(Z) on the Iris data set using single linkage. ... ok Tests leaves_list(Z) on the Iris data set using ward linkage. ... ok Tests linkage(Y, 'average') on the tdist data set. ... ok Tests linkage(Y, 'centroid') on the Q data set. ... ok Tests linkage(Y, 'complete') on the Q data set. ... ok Tests linkage(Y, 'complete') on the tdist data set. ... ok Tests linkage(Y) where Y is a 0x4 linkage matrix. Exception expected. ... ok Tests linkage(Y, 'single') on the Q data set. ... ok Tests linkage(Y, 'single') on the tdist data set. ... ok Tests linkage(Y, 'weighted') on the Q data set. ... ok Tests linkage(Y, 'weighted') on the tdist data set. ... ok Tests maxdists(Z) on the Q data set using centroid linkage. ... ok Tests maxdists(Z) on the Q data set using complete linkage. ... ok Tests maxdists(Z) on the Q data set using median linkage. ... ok Tests maxdists(Z) on the Q data set using single linkage. ... ok Tests maxdists(Z) on the Q data set using Ward linkage. ... ok Tests maxdists(Z) on empty linkage. Expecting exception. ... ok Tests maxdists(Z) on linkage with one cluster. ... ok Tests maxinconsts(Z, R) on the Q data set using centroid linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using complete linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using median linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using single linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using Ward linkage. ... ok Tests maxinconsts(Z, R) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxinconsts(Z, R) on empty linkage. Expecting exception. ... ok Tests maxinconsts(Z, R) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 0) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 0) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 0) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 0) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 1) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 1) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 1) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 1) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 2) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 2) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 2) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 2) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 3) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 3) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 3) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 3) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 3.3). Expecting exception. ... ok Tests maxRstat(Z, R, -1). Expecting exception. ... ok Tests maxRstat(Z, R, 4). Expecting exception. ... ok Tests num_obs_linkage(Z) on linkage over 2 observations. ... ok Tests num_obs_linkage(Z) on linkage over 3 observations. ... ok Tests num_obs_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3). ... ok Tests num_obs_linkage(Z) with empty linkage. ... ok Tests to_mlab_linkage on linkage array with multiple rows. ... ok Tests to_mlab_linkage on empty linkage array. ... ok Tests to_mlab_linkage on linkage array with single row. ... ok test_hierarchy.load_testing_files ... ok Ticket #505. ... ok Testing that kmeans2 init methods work. ... ok Testing simple call to kmeans2 with rank 1 data. ... ok Testing simple call to kmeans2 with rank 1 data. ... ok Testing simple call to kmeans2 and its results. ... ok Regression test for #546: fail when k arg is 0. ... ok This will cause kmean to have a cluster with no points. ... ok test_kmeans_simple (test_vq.TestKMean) ... ok test_large_features (test_vq.TestKMean) ... ok test_py_vq (test_vq.TestVq) ... ok test_py_vq2 (test_vq.TestVq) ... ok test_vq (test_vq.TestVq) ... ok Test special rank 1 vq algo, python implementation. ... ok nose.selector: INFO: /usr/local/Cellar/python/2.7.2/lib/python2.7/site-packages/scipy/cluster/tests/vq_test.py is executable; skipped test_codata.test_find ... ok test_codata.test_basic_table_parse ... ok test_codata.test_basic_lookup ... ok test_codata.test_find_all ... ok test_codata.test_find_single ... ok test_codata.test_2002_vs_2006 ... ok Check that updating stored values with exact ones worked. ... ok test_constants.test_fahrenheit_to_celcius ... ok test_constants.test_celcius_to_kelvin ... ok test_constants.test_kelvin_to_celcius ... ok test_constants.test_fahrenheit_to_kelvin ... ok test_constants.test_kelvin_to_fahrenheit ... ok test_constants.test_celcius_to_fahrenheit ... ok test_constants.test_lambda_to_nu ... ok test_constants.test_nu_to_lambda ... ok test_definition (test_basic.TestDoubleFFT) ... ok test_djbfft (test_basic.TestDoubleFFT) ... ok test_n_argument_real (test_basic.TestDoubleFFT) ... ok test_definition (test_basic.TestDoubleIFFT) ... FAIL test_definition_real (test_basic.TestDoubleIFFT) ... ok test_djbfft (test_basic.TestDoubleIFFT) ... FAIL test_random_complex (test_basic.TestDoubleIFFT) ... python(30168) malloc: *** error for object 0x104cdce88: incorrect checksum for freed object - object was probably modified after being freed. *** set a breakpoint in malloc_error_break to debug Abort trap: 6 Python 3.2.2 (default, Jan 11 2012, 16:48:20) [GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)] on darwin Type "help", "copyright", "credits" or "license" for more information. >>> import scipy >>> scipy.test(verbose=10) Running unit tests for scipy NumPy version 2.0.0.dev-55472ca NumPy is installed in /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/numpy SciPy version 0.11.0.dev-600e81f SciPy is installed in /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy Python version 3.2.2 (default, Jan 11 2012, 16:48:20) [GCC 4.2.1 (Based on Apple Inc. build 5658) (LLVM build 2335.15.00)] nose version 1.1.2 nose.config: INFO: Ignoring files matching ['^\\.', '^_', '^setup\\.py$'] nose.config: INFO: Excluding tests matching ['f2py_ext', 'f2py_f90_ext', 'gen_ext', 'pyrex_ext', 'swig_ext'] nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/fftpack/convolve.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/integrate/vode.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/interpolate/dfitpack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/interpolate/interpnd.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/io/matlab/mio5_utils.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/io/matlab/mio_utils.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/io/matlab/streams.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/lib/blas/cblas.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/lib/blas/fblas.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/lib/lapack/atlas_version.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/lib/lapack/calc_lwork.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/lib/lapack/clapack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/lib/lapack/flapack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/linalg/atlas_version.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/linalg/calc_lwork.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/linalg/cblas.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/linalg/clapack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/linalg/fblas.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/linalg/flapack.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/optimize/minpack2.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/optimize/moduleTNC.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/signal/sigtools.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/signal/spectral.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/signal/spline.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/spatial/ckdtree.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/spatial/qhull.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/special/lambertw.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/special/orthogonal_eval.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/special/specfun.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/stats/futil.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/stats/mvn.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/stats/statlib.so is executable; skipped nose.selector: INFO: /usr/local/Cellar/python3/3.2.2/lib/python3.2/site-packages/scipy/stats/vonmises_cython.so is executable; skipped Tests cophenet(Z) on tdist data set. ... ok Tests cophenet(Z, Y) on tdist data set. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. Correspondance should be false. ... ok Tests correspond(Z, y) on linkage and CDMs over observation sets of different sizes. Correspondance should be false. ... ok Tests correspond(Z, y) with empty linkage and condensed distance matrix. ... ok Tests num_obs_linkage with observation matrices of multiple sizes. ... ok Tests fcluster(Z, criterion='maxclust', t=2) on a random 3-cluster data set. ... ok Tests fcluster(Z, criterion='maxclust', t=3) on a random 3-cluster data set. ... ok Tests fcluster(Z, criterion='maxclust', t=4) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=2) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=3) on a random 3-cluster data set. ... ok Tests fclusterdata(X, criterion='maxclust', t=4) on a random 3-cluster data set. ... ok Tests from_mlab_linkage on empty linkage array. ... ok Tests from_mlab_linkage on linkage array with multiple rows. ... ok Tests from_mlab_linkage on linkage array with single row. ... ok Tests inconsistency matrix calculation (depth=1) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=2) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=3) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=4) on a complete linkage. ... ok Tests inconsistency matrix calculation (depth=1, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=2, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=3, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=4, dataset=Q) with single linkage. ... ok Tests inconsistency matrix calculation (depth=1) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=2) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=3) on a single linkage. ... ok Tests inconsistency matrix calculation (depth=4) on a single linkage. ... ok Tests is_isomorphic on test case #1 (one flat cluster, different labellings) ... ok Tests is_isomorphic on test case #2 (two flat clusters, different labelings) ... ok Tests is_isomorphic on test case #3 (no flat clusters) ... ok Tests is_isomorphic on test case #4A (3 flat clusters, different labelings, isomorphic) ... ok Tests is_isomorphic on test case #4B (3 flat clusters, different labelings, nonisomorphic) ... ok Tests is_isomorphic on test case #4C (3 flat clusters, different labelings, isomorphic) ... ok Tests is_isomorphic on test case #5A (1000 observations, 2 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5B (1000 observations, 3 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5C (1000 observations, 5 random clusters, random permutation of the labeling). Run 3 times. ... ok Tests is_isomorphic on test case #5A (1000 observations, 2 random clusters, random permutation of the labeling, slightly nonisomorphic.) Run 3 times. ... ok Tests is_isomorphic on test case #5B (1000 observations, 3 random clusters, random permutation of the labeling, slightly nonisomorphic.) Run 3 times. ... ok Tests is_isomorphic on test case #5C (1000 observations, 5 random clusters, random permutation of the labeling, slightly non-isomorphic.) Run 3 times. ... ok Tests is_monotonic(Z) on 1x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on 2x4 linkage. Expecting False. ... ok Tests is_monotonic(Z) on 2x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 1). Expecting False. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 2). Expecting False. ... ok Tests is_monotonic(Z) on 3x4 linkage (case 3). Expecting False ... ok Tests is_monotonic(Z) on 3x4 linkage. Expecting True. ... ok Tests is_monotonic(Z) on an empty linkage. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on Iris data set. Expecting True. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Expecting True. ... ok Tests is_monotonic(Z) on clustering generated by single linkage on tdist data set. Perturbing. Expecting False. ... ok Tests is_valid_im(R) on im over 2 observations. ... ok Tests is_valid_im(R) on im over 3 observations. ... ok Tests is_valid_im(R) with 3 columns. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3). ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link counts. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link height means. ... ok Tests is_valid_im(R) on im on observation sets between sizes 4 and 15 (step size 3) with negative link height standard deviations. ... ok Tests is_valid_im(R) with 5 columns. ... ok Tests is_valid_im(R) with empty inconsistency matrix. ... ok Tests is_valid_im(R) with integer type. ... ok Tests is_valid_linkage(Z) on linkage over 2 observations. ... ok Tests is_valid_linkage(Z) on linkage over 3 observations. ... ok Tests is_valid_linkage(Z) with 3 columns. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3). ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative counts. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative distances. ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative indices (left). ... ok Tests is_valid_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3) with negative indices (right). ... ok Tests is_valid_linkage(Z) with 5 columns. ... ok Tests is_valid_linkage(Z) with empty linkage. ... ok Tests is_valid_linkage(Z) with integer type. ... ok Tests leaders using a flat clustering generated by single linkage. ... ok Tests leaves_list(Z) on a 1x4 linkage. ... ok Tests leaves_list(Z) on a 2x4 linkage. ... ok Tests leaves_list(Z) on the Iris data set using average linkage. ... ok Tests leaves_list(Z) on the Iris data set using centroid linkage. ... ok Tests leaves_list(Z) on the Iris data set using complete linkage. ... ok Tests leaves_list(Z) on the Iris data set using median linkage. ... ok Tests leaves_list(Z) on the Iris data set using single linkage. ... ok Tests leaves_list(Z) on the Iris data set using ward linkage. ... ok Tests linkage(Y, 'average') on the tdist data set. ... ok Tests linkage(Y, 'centroid') on the Q data set. ... ok Tests linkage(Y, 'complete') on the Q data set. ... ok Tests linkage(Y, 'complete') on the tdist data set. ... ok Tests linkage(Y) where Y is a 0x4 linkage matrix. Exception expected. ... ok Tests linkage(Y, 'single') on the Q data set. ... ok Tests linkage(Y, 'single') on the tdist data set. ... ok Tests linkage(Y, 'weighted') on the Q data set. ... ok Tests linkage(Y, 'weighted') on the tdist data set. ... ok Tests maxdists(Z) on the Q data set using centroid linkage. ... ok Tests maxdists(Z) on the Q data set using complete linkage. ... ok Tests maxdists(Z) on the Q data set using median linkage. ... ok Tests maxdists(Z) on the Q data set using single linkage. ... ok Tests maxdists(Z) on the Q data set using Ward linkage. ... ok Tests maxdists(Z) on empty linkage. Expecting exception. ... ok Tests maxdists(Z) on linkage with one cluster. ... ok Tests maxinconsts(Z, R) on the Q data set using centroid linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using complete linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using median linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using single linkage. ... ok Tests maxinconsts(Z, R) on the Q data set using Ward linkage. ... ok Tests maxinconsts(Z, R) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxinconsts(Z, R) on empty linkage. Expecting exception. ... ok Tests maxinconsts(Z, R) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 0) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 0) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 0) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 0) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 0) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 1) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 1) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 1) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 1) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 1) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 2) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 2) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 2) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 2) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 2) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 3) on the Q data set using centroid linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using complete linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using median linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using single linkage. ... ok Tests maxRstat(Z, R, 3) on the Q data set using Ward linkage. ... ok Tests maxRstat(Z, R, 3) on linkage and inconsistency matrices with different numbers of clusters. Expecting exception. ... ok Tests maxRstat(Z, R, 3) on empty linkage. Expecting exception. ... ok Tests maxRstat(Z, R, 3) on linkage with one cluster. ... ok Tests maxRstat(Z, R, 3.3). Expecting exception. ... ok Tests maxRstat(Z, R, -1). Expecting exception. ... ok Tests maxRstat(Z, R, 4). Expecting exception. ... ok Tests num_obs_linkage(Z) on linkage over 2 observations. ... ok Tests num_obs_linkage(Z) on linkage over 3 observations. ... ok Tests num_obs_linkage(Z) on linkage on observation sets between sizes 4 and 15 (step size 3). ... ok Tests num_obs_linkage(Z) with empty linkage. ... ok Tests to_mlab_linkage on linkage array with multiple rows. ... ok Tests to_mlab_linkage on empty linkage array. ... ok Tests to_mlab_linkage on linkage array with single row. ... ok test_hierarchy.load_testing_files ... ok Ticket #505. ... ok Testing that kmeans2 init methods work. ... ok Testing simple call to kmeans2 with rank 1 data. ... ok Testing simple call to kmeans2 with rank 1 data. ... ok Testing simple call to kmeans2 and its results. ... ok Regression test for #546: fail when k arg is 0. ... ok This will cause kmean to have a cluster with no points. ... ok test_kmeans_simple (test_vq.TestKMean) ... ok test_large_features (test_vq.TestKMean) ... ok test_py_vq (test_vq.TestVq) ... ok test_py_vq2 (test_vq.TestVq) ... ok test_vq (test_vq.TestVq) ... ok Test special rank 1 vq algo, python implementation. ... ok test_codata.test_find ... ok test_codata.test_basic_table_parse ... ok test_codata.test_basic_lookup ... ok test_codata.test_find_all ... ok test_codata.test_find_single ... ok test_codata.test_2002_vs_2006 ... ok Check that updating stored values with exact ones worked. ... ok test_constants.test_fahrenheit_to_celcius ... ok test_constants.test_celcius_to_kelvin ... ok test_constants.test_kelvin_to_celcius ... ok test_constants.test_fahrenheit_to_kelvin ... ok test_constants.test_kelvin_to_fahrenheit ... ok test_constants.test_celcius_to_fahrenheit ... ok test_constants.test_lambda_to_nu ... ok test_constants.test_nu_to_lambda ... ok test_definition (test_basic.TestDoubleFFT) ... ok test_djbfft (test_basic.TestDoubleFFT) ... ok test_n_argument_real (test_basic.TestDoubleFFT) ... ok test_definition (test_basic.TestDoubleIFFT) ... python3(30179) malloc: *** error for object 0x1050ae058: incorrect checksum for freed object - object was probably modified after being freed. *** set a breakpoint in malloc_error_break to debug Abort trap: 6 > Date: Thu, 26 Jan 2012 19:01:22 +0100 > From: Samuel John <scipy(a)samueljohn.de> > Subject: Re: [Numpy-discussion] Problem installing NumPy with Python > 3.2.2/MacOS X 10.7.2 > To: Discussion of Numerical Python <numpy-discussion(a)scipy.org> > Message-ID: <AD984E1A-D359-4BA0-BDD4-81826454EFC2(a)samueljohn.de> > Content-Type: text/plain; charset=us-ascii > > Hi Hans-Martin! > > You could try my instructions recently posted to this list http://thread.gmane.org/gmane.comp.python.scientific.devel/15956/ > Basically, using llvm-gcc scipy segfaults when scipy.test() (on my system at least). > > Therefore, I created the homebrew install formula. > They work for whatever "which python" you have. But I have tested this for 2.7.2 on MacOS X 10.7.2. > > Samuel > > > On 11.01.2012, at 16:12, Hans-Martin v. Gaudecker wrote: > >> I recently upgraded to Lion and just faced the same problem with both Python 2.7.2 and Python 3.2.2 installed via the python.org installers. My hunch is that the errors are related to the fact that Apple dropped gcc-4.2 from XCode 4.2. I got gcc-4.2 via [1] then, still the same error -- who knows what else got lost in that upgrade... Previous successful builds with gcc-4.2 might have been with XCode 4.1 (or 4.2 installed on top of it). >> >> In the end I decided to re-install both Python versions via homebrew, nicely described here [2] and everything seems to work fine using LLVM. Test outputs for NumPy master under 2.7.2 and 3.2.2 are below in case they are of interest. >> >> Best, >> Hans-Martin >> >> [1] https://github.com/kennethreitz/osx-gcc-installer >> [2] http://www.thisisthegreenroom.com/2011/installing-python-numpy-scipy-matplo… > > The instructions at [2] lead to a segfault in scipy.test() for me, because it used llvm-gcc (which is the default on Lion).

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