Re: [Numpy-discussion] Problem installing NumPy with Python 3.2.2/MacOS X 10.7.2 (Samuel John)
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@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@scipy.org> Message-ID: <AD984E1A-D359-4BA0-BDD4-81826454EFC2@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-matplot...
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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Hans-Martin v. Gaudecker