Mailman 3 August 2006 - SciPy-User

scipy.odeint question
by Jelle Feringa / EZCT Architecture & Design Research Aug. 18, 2006

Aug. 18, 2006

I have a question on how/if I could use scipy.odeint for the following; I have a function that approximates a given distance on a nurbs curve. The algorithm is rather crude, so I'd like to generalize and make it more efficient. Scipy.odeint could be a suitable function to do so, but I'm having a hard time implementing it. The function I'd like to integrate with scip.odeint is the following: while 1: print 'approxLengthPLUS',approxLength if abs(distance-approxLength) < tolerance: break if approxLength < distance: tangent = vec3(RS.PointCoordinates(pointIdPLUS)) + vec3(crv.tangent(pointIdPLUS)[2]).normalize() * (1.5*vectorScale) closestPointIdPLUS = crv.closestPoint(tangent) closestCoord = crv.evaluateCrv(closestPointIdPLUS) RS.PointCoordinates(pointIdPLUS, closestCoord) else : tangent = vec3(RS.PointCoordinates(pointIdPLUS)) + vec3(crv.tangent(pointIdMIN)[2]).normalize() * (-0.75*vectorScale) closestPointIdPLUS = crv.closestPoint(tangent) closestCoord = crv.evaluateCrv(closestPointIdPLUS) RS.PointCoordinates(pointIdPLUS, closestCoord) n+=1 approxLength = crv.length(subDomain=[crvParameter, closestPointIdPLUS]) vectorScale = approxLength*(tolerance/n) Which basically scans a nurbs curve back & forth until a point is located within the tolerance. The method crv.closestPoint(tangent) returns the closest parameter of the current point The method crv.length(subDomain=[crvParameter, closestPointIdPLUS]) returns the distance from the starting point, to the point currently evaluated. Any pointers in how I could integrate such a function with scipy are greatly appreciated! Cheers, -jelle

1 0

numpy netcdf reader
by Rob Hetland Aug. 18, 2006

Aug. 18, 2006

Konrad & scipy-users, I have made some changes to the scipy.sandbox.netcdf package so that it installs correctly. (I am a total hack at distutils, but it seems to do the right thing..). This could also be installed as a stand alone package, since it does not actually depend on scipy for anything. I have tested this module on my Mac, a Redhat distribution, and a Win box (using cygwin netcdf libraries), and all seem to work fine. One issue I have is that I can't seem to write a single character. In one of the programs I use, a variable called 'sherical' is expected to have a value of 'T' or 'F'. I am unable to write a value to this variable no matter what I try. Similar zero-dimensional assignments work for floating point numbers, just not for characters. Any advice? -Rob ---- Rob Hetland, Assistant Professor Dept. of Oceanography, Texas A&M University http://pong.tamue.edu/~rob phone: 979-458-0096, fax: 979-845-6331

3 3

Re: [SciPy-user] hough transform again (stephen emslie)
by Josh Marshall Aug. 18, 2006

Aug. 18, 2006

I have some code for working with Hough transforms and their inverses. I haven't been following this thread very closely, so hopefully this is useful to someone. It hasn't been tested in isolation, but it does work fine embedded in my application. Try: import * from Hough ht = HoughTransformFromFilename("my_test_image.jpg") and then: showPiccy(ht) Note that this is *not* the standard Hough transform that operates on binary thresholded images. Rather, this an alternate "Hough" transform as described in the Shapiro & Stockman book. It takes the grayscale image and calculates the gradient at every pixel, discards those with a small magnitude and adds the rest to the Hough transform array. However, the inverse functions should be easily modifiable to be used with the real standard Hough transform. I like this form better because I am working with noisy images, which give useless results with the standard version. Cheers, Josh

2 1

Matrix Operation with index dependencies
by Maik Trömel Aug. 18, 2006

Aug. 18, 2006

Hello list! I've got a question concerning matrix operations. I'd like to fill a numpy-array (A) with values. for j in range(maxj): for i in range(maxi): y = i / 4 x = j / 4 index = (j - j / 4 * 4) * 4 + (i - i / 4 * 4) if index == xyz: C = C1 elif index == xyz +1: C = C2 elif ....... ....... A[j,i] = sum( sum( B[ x-r:x+2 , y-s:y+2 ] * C ) ) C, s, r are depending on index; x and y are depending on i and j. The problem is that it takes to long this way. Is it possible to do this without iterarting with numpy? Thanks for your help! Greetings Maik

1 0

hough transform again
by Brent Pedersen Aug. 18, 2006

Aug. 18, 2006

hi, i found this in recent archives and the script is useful. http://projects.scipy.org/pipermail/scipy-user/2006-August/008841.html has anyone written the code to go from the hough transform back to the image with the lines/edges enhanced or with non-lines removed? it's bending my mind a bit so if someone's already done it, i'd be glad of it--or any pointers. thanks. -brent [please include my email in the reply, i've subscribed to scipy-users, but not sure if it went through yet]

3 3

ANN: Python 2.4.3 Enthought Edition 1.1.0 alpha2
by Rick Ratzel Aug. 16, 2006

Aug. 16, 2006

In keeping with the "release early, release often" mantra of the open source community, Enthought is pleased to announce Python Enthought Edition Version 1.1.0 alpha2. What's New In Python 2.4.3 Enthought Edition 1.1.0 alpha2: ---------------------------------------------------------- This release focuses mainly on improving access to the Python Cheese Shop, as well as some bug fixes and enhancements to the GUI. To access the Python Cheese Shop, simply add 'http://python.org/pypi' to the 'find_links' preference under 'Preferences'. Then, click 'Install' to browse the list of packages available for installation. Changes include: * PyPI access speed greatly improved. Meta-data is also fetched "on-demand". * Bug preventing PyPI packages from downloading/installing fixed. * Packages can be "de-activated" and "re-activated" allowing users to easily select which versions, if any, are available for import. * Enstaller highlites packages which are already installed and prevents users from inadvertently changing an existing install. For a detailed list of changes made since the last release, please refer to the wiki page listed below. Links: ------ Python 2.4.3 Enthought Edition 1.1.0 download page: http://code.enthought.com/enthon/enstaller.shtml Enstaller wiki: http://enthought.com/enthought/wiki/Enstaller About Python Enthought Edition 1.1.0 alpha2: -------------------------------------------- This alpha release of 1.1.0 Python Enthought Edition is the first Enthought distribution (and the first Python distribution that we know of) to use individual egg packages (http://peak.telecommunity.com/DevCenter/PythonEggs) rather than a monolithic .exe installer. Python Enthought Edition 1.1.0 includes the Enstaller application, which manages selecting, downloading, and installing Python eggs. The initial installer program installs the standard Python software and runs a small script, which downloads, installs, and runs Enstaller. From within Enstaller, you can select individual egg packages from Enthought's egg repository, or select the "enthon-1.1.0" package, which installs everything listed in the 1.1.0 distribution. Python 2.4.3, Enthought Edition 1.1.0 is available only for Windows for the alpha2 release. It includes eggs for the following packages: Numpy SciPy IPython wxPython PIL mingw MayaVi Scientific Python VTK and many more... After installing Python Enthought Edition 1.1.0 alpha2, you can launch Enstaller again later, to upgrade, uninstall, inspect, or add new packages to your Python installation. More information is available about this and all other open source software written or released by Enthought, Inc., at http://code.enthought.com. A Note About the License ------------------------ The Enstaller application, released under the same BSD2 Open Source License as all other Enthought Open Source, is and will remain free. Access to Enthought's egg repository is free to all during the alpha and beta test phases, and will remain free for individual and academic use. After the official release of Enstaller, commercial and government users will be asked to pay a nominal, per-seat, annual fee if they'd like continued access to the egg repository (to help defray the costs of maintaining the repository). The Enstaller application may be used to access other egg repositories (e.g. the Python Cheese Shop), not just Enthought's repository. 1.1.0 alpha2 Release Notes -------------------------- This release is early alpha and has several known bugs, as well as a long list of features that have not yet been implemented. Some of the more obvious ones are listed below: * An egg of the standard documents that are typically included in Python Enthought Edition releases is not yet available. * An egg of the complete Enthought Tool Suite (ETS), a package typically available in Python Enthought Edition releases, is not yet available. However a subset of the ETS that includes Traits and TraitsUI is installed with Enstaller. * The uninstall feature does not properly uninstall a package if it is in use by any other process, including Enstaller. * The metadata about an installed package might not display in the Enstaller dialog box, in most cases because the installed package does not include metadata. This issue will improve as Enthought enhances the eggs available in the Enthought egg repository, * Packages that modify the system registry (typically, adding directories to the search PATH) require the user to log out and then log back in before the settings take effect. * Some packages that launch post-install scripts can display error messages if they fail for any reason, which can require manual intervention. These errors are usually caused by inadequate permissions for creating or moving files. * IPython may have problems when starting for the first time if a previous version of IPython was run on the system. If you see "WARNING: could not import user config", either follow the directions that follow the warning, or delete ipy_user_conf.py from the IPython directory. * Some users report that older matplotlibrc files are not compatible with the version of matplotlib installed with this release. Please refer to the matplotlib mailing list <http://sourceforge.net/mail/?group_id=80706> for further help. We are grateful to everyone who has helped test this release. If you'd like to contribute suggestions or report a bug, you can do so at https://svn.enthought.com/enthought. -- Rick Ratzel - Enthought, Inc. 515 Congress Avenue, Suite 2100 - Austin, Texas 78701 512-536-1057 x229 - Fax: 512-536-1059 http://www.enthought.com

1 0

Autocorrelation
by David Huard Aug. 16, 2006

Aug. 16, 2006

Hi, I haven't seen in Scipy a function to compute the autocorrelation of a time series. Is there one I missed ? Thanks, David

4 6

numpy test fails
by Steve Schmerler Aug. 16, 2006

Aug. 16, 2006

I've already posted this on the numpy list (for numpy 1.0b2.dev3007) but the same problem is there in 1.0b3.dev3027 (I'm running Python 2.3.5 on Debian testing): #-----------------------------------------------------------------------# numpy.test(5,5) [.....] check_2D_array (numpy.lib.tests.test_shape_base.test_vsplit) ... ok check_0D_array (numpy.lib.tests.test_shape_base.test_vstack) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_vstack) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_vstack) ... ok check_2D_array2 (numpy.lib.tests.test_shape_base.test_vstack) ... ok ====================================================================== ERROR: check_ascii (numpy.core.tests.test_multiarray.test_fromstring) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.3/site-packages/numpy/core/tests/test_multiarray.py", line 120, in check_ascii a = fromstring('1 , 2 , 3 , 4',sep=',') ValueError: don't know how to read character strings for given array type ---------------------------------------------------------------------- Ran 481 tests in 1.703s FAILED (errors=1) #-----------------------------------------------------------------------# Full logfile is attached. The scipy test runs fine. BTW, I shouldn't worry about the "Warning: No test file found in ..." messages, right? -- cheers, steve Random number generation is the art of producing pure gibberish as quickly as possible. Found 5 tests for numpy.distutils.misc_util Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core.info' from '...e-packages/numpy/core/info.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core.defchararray' from '...es/numpy/core/defchararray.pyc'> Found 4 tests for numpy.lib.getlimits Found 31 tests for numpy.core.numerictypes Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/random/tests for module <module 'numpy.random.mtrand' from '...ackages/numpy/random/mtrand.so'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/random/tests for module <module 'numpy.random.info' from '...packages/numpy/random/info.pyc'> Found 32 tests for numpy.linalg Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/testing/tests for module <module 'numpy.testing' from '...ges/numpy/testing/__init__.pyc'> Found 13 tests for numpy.core.umath Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/distutils/tests for module <module 'numpy.distutils.ccompiler' from '.../numpy/distutils/ccompiler.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/distutils/tests for module <module 'numpy.distutils.exec_command' from '...mpy/distutils/exec_command.pyc'> Found 4 tests for numpy.core.scalarmath Found 8 tests for numpy.lib.arraysetops Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/tests for module <module 'numpy.version' from '...ite-packages/numpy/version.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/distutils/tests for module <module 'numpy.distutils.info' from '...kages/numpy/distutils/info.pyc'> Found 42 tests for numpy.lib.type_check Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/fft/tests for module <module 'numpy.fft.fftpack_lite' from '...ages/numpy/fft/fftpack_lite.so'> Found 154 tests for numpy.core.multiarray Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/distutils/tests for module <module 'numpy.distutils' from '...s/numpy/distutils/__init__.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/tests for module <module 'numpy.add_newdocs' from '...packages/numpy/add_newdocs.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/distutils/tests for module <module 'numpy.distutils.__version__' from '...umpy/distutils/__version__.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/lib/tests for module <module 'numpy.lib._compiled_base' from '...es/numpy/lib/_compiled_base.so'> Found 3 tests for numpy.fft.helper Found 36 tests for numpy.core.ma Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/lib/tests for module <module 'numpy.lib.info' from '...te-packages/numpy/lib/info.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core._sort' from '...e-packages/numpy/core/_sort.so'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core.memmap' from '...packages/numpy/core/memmap.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core._internal' from '...kages/numpy/core/_internal.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/fft/tests for module <module 'numpy.fft.fftpack' from '...packages/numpy/fft/fftpack.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/linalg/tests for module <module 'numpy.linalg.lapack_lite' from '...es/numpy/linalg/lapack_lite.so'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/random/tests for module <module 'numpy.random' from '...ages/numpy/random/__init__.pyc'> Found 10 tests for numpy.lib.twodim_base Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/distutils/tests for module <module 'numpy.distutils.unixccompiler' from '...py/distutils/unixccompiler.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core.arrayprint' from '...ages/numpy/core/arrayprint.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/tests for module <module 'numpy' from '...te-packages/numpy/__init__.pyc'> Found 10 tests for numpy.core.defmatrix Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/tests for module <module 'numpy.__config__' from '...-packages/numpy/__config__.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/testing/tests for module <module 'numpy.testing.utils' from '...ckages/numpy/testing/utils.pyc'> Found 1 tests for numpy.lib.ufunclike Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core.fromnumeric' from '...ges/numpy/core/fromnumeric.pyc'> Found 4 tests for numpy.ctypeslib Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/lib/tests for module <module 'numpy.lib.scimath' from '...packages/numpy/lib/scimath.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/fft/tests for module <module 'numpy.fft' from '...ackages/numpy/fft/__init__.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/lib/tests for module <module 'numpy.lib' from '...ackages/numpy/lib/__init__.pyc'> Found 39 tests for numpy.lib.function_base Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/distutils/tests for module <module 'numpy.distutils.__config__' from '...numpy/distutils/__config__.pyc'> Found 1 tests for numpy.lib.polynomial Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/tests for module <module 'numpy._import_tools' from '...ckages/numpy/_import_tools.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/fft/tests for module <module 'numpy.fft.info' from '...te-packages/numpy/fft/info.pyc'> Found 8 tests for numpy.core.records Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core._dotblas' from '...ackages/numpy/core/_dotblas.so'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/testing/tests for module <module 'numpy.testing.numpytest' from '...es/numpy/testing/numpytest.pyc'> Found 26 tests for numpy.core.numeric Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/linalg/tests for module <module 'numpy.linalg.info' from '...packages/numpy/linalg/info.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core.__svn_version__' from '...numpy/core/__svn_version__.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/core/tests for module <module 'numpy.core' from '...ckages/numpy/core/__init__.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/testing/tests for module <module 'numpy.testing.info' from '...ackages/numpy/testing/info.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/distutils/tests for module <module 'numpy.distutils.log' from '...ckages/numpy/distutils/log.pyc'> Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/lib/tests for module <module 'numpy.lib.utils' from '...e-packages/numpy/lib/utils.pyc'> Found 4 tests for numpy.lib.index_tricks Found 46 tests for numpy.lib.shape_base Warning: No test file found in /usr/local/lib/python2.3/site-packages/numpy/lib/tests for module <module 'numpy.lib.machar' from '...-packages/numpy/lib/machar.pyc'> Found 0 tests for __main__ check_1 (numpy.distutils.tests.test_misc_util.test_appendpath) ... ok check_2 (numpy.distutils.tests.test_misc_util.test_appendpath) ... ok check_3 (numpy.distutils.tests.test_misc_util.test_appendpath) ... ok check_gpaths (numpy.distutils.tests.test_misc_util.test_gpaths) ... ok check_1 (numpy.distutils.tests.test_misc_util.test_minrelpath) ... ok check_singleton (numpy.lib.tests.test_getlimits.test_double) ... ok check_singleton (numpy.lib.tests.test_getlimits.test_longdouble) ... ok check_singleton (numpy.lib.tests.test_getlimits.test_python_float) ... ok check_singleton (numpy.lib.tests.test_getlimits.test_single) ... ok Check creation from list of list of tuples ... ok Check creation from list of tuples ... ok Check creation from tuples ... ok Check creation from list of list of tuples ... ok Check creation from list of tuples ... ok Check creation from tuples ... ok Check creation from list of list of tuples ... ok Check creation from list of tuples ... ok Check creation from tuples ... ok Check creation from list of list of tuples ... ok Check creation from list of tuples ... ok Check creation from tuples ... ok Check creation of 0-dimensional objects ... ok Check creation of multi-dimensional objects ... ok Check creation of single-dimensional objects ... ok Check creation of 0-dimensional objects ... ok Check creation of multi-dimensional objects ... ok Check creation of single-dimensional objects ... ok check_assign (numpy.core.tests.test_numerictypes.test_empty_field) ... ok Check reading the top fields of a nested array ... ok Check reading the nested fields of a nested array (1st level) ... ok Check access nested descriptors of a nested array (1st level) ... ok Check reading the nested fields of a nested array (2nd level) ... ok Check access nested descriptors of a nested array (2nd level) ... ok Check reading the top fields of a nested array ... ok Check reading the nested fields of a nested array (1st level) ... ok Check access nested descriptors of a nested array (1st level) ... ok Check reading the nested fields of a nested array (2nd level) ... ok Check access nested descriptors of a nested array (2nd level) ... ok check_access_fields (numpy.core.tests.test_numerictypes.test_read_values_plain_multiple) ... ok check_access_fields (numpy.core.tests.test_numerictypes.test_read_values_plain_single) ... ok check_cdouble (numpy.tests.test_linalg.test_det) ... ok check_csingle (numpy.tests.test_linalg.test_det) ... ok check_double (numpy.tests.test_linalg.test_det) ... ok check_single (numpy.tests.test_linalg.test_det) ... ok check_cdouble (numpy.tests.test_linalg.test_eig) ... ok check_csingle (numpy.tests.test_linalg.test_eig) ... ok check_double (numpy.tests.test_linalg.test_eig) ... ok check_single (numpy.tests.test_linalg.test_eig) ... ok check_cdouble (numpy.tests.test_linalg.test_eigvals) ... ok check_csingle (numpy.tests.test_linalg.test_eigvals) ... ok check_double (numpy.tests.test_linalg.test_eigvals) ... ok check_single (numpy.tests.test_linalg.test_eigvals) ... ok check_cdouble (numpy.tests.test_linalg.test_inv) ... ok check_csingle (numpy.tests.test_linalg.test_inv) ... ok check_double (numpy.tests.test_linalg.test_inv) ... ok check_single (numpy.tests.test_linalg.test_inv) ... ok check_cdouble (numpy.tests.test_linalg.test_lstsq) ... ok check_csingle (numpy.tests.test_linalg.test_lstsq) ... ok check_double (numpy.tests.test_linalg.test_lstsq) ... ok check_single (numpy.tests.test_linalg.test_lstsq) ... ok check_cdouble (numpy.tests.test_linalg.test_pinv) ... ok check_csingle (numpy.tests.test_linalg.test_pinv) ... ok check_double (numpy.tests.test_linalg.test_pinv) ... ok check_single (numpy.tests.test_linalg.test_pinv) ... ok check_cdouble (numpy.tests.test_linalg.test_solve) ... ok check_csingle (numpy.tests.test_linalg.test_solve) ... ok check_double (numpy.tests.test_linalg.test_solve) ... ok check_single (numpy.tests.test_linalg.test_solve) ... ok check_cdouble (numpy.tests.test_linalg.test_svd) ... ok check_csingle (numpy.tests.test_linalg.test_svd) ... ok check_double (numpy.tests.test_linalg.test_svd) ... ok check_single (numpy.tests.test_linalg.test_svd) ... ok test_attributes (numpy.core.tests.test_umath.test_choose) ... ok check_expm1 (numpy.core.tests.test_umath.test_expm1) ... ok check_floating_point (numpy.core.tests.test_umath.test_floating_point) ... ok check_log1p (numpy.core.tests.test_umath.test_log1p) ... ok check_reduce_complex (numpy.core.tests.test_umath.test_maximum) ... ok check_reduce_complex (numpy.core.tests.test_umath.test_minimum) ... ok check_power_complex (numpy.core.tests.test_umath.test_power) ... ok check_power_float (numpy.core.tests.test_umath.test_power) ... ok test_array_with_context (numpy.core.tests.test_umath.test_special_methods) ... ok test_failing_wrap (numpy.core.tests.test_umath.test_special_methods) ... ok test_old_wrap (numpy.core.tests.test_umath.test_special_methods) ... ok test_priority (numpy.core.tests.test_umath.test_special_methods) ... ok test_wrap (numpy.core.tests.test_umath.test_special_methods) ... ok check_large_types (numpy.core.tests.test_scalarmath.test_power) ... ok check_small_types (numpy.core.tests.test_scalarmath.test_power) ... ok check_type_add (numpy.core.tests.test_scalarmath.test_types) ... ok check_types (numpy.core.tests.test_scalarmath.test_types) ... ok check_intersect1d (numpy.lib.tests.test_arraysetops.test_aso) ... ok check_intersect1d_nu (numpy.lib.tests.test_arraysetops.test_aso) ... ok check_manyways (numpy.lib.tests.test_arraysetops.test_aso) ... ok check_setdiff1d (numpy.lib.tests.test_arraysetops.test_aso) ... ok check_setmember1d (numpy.lib.tests.test_arraysetops.test_aso) ... ok check_setxor1d (numpy.lib.tests.test_arraysetops.test_aso) ... ok check_union1d (numpy.lib.tests.test_arraysetops.test_aso) ... ok check_unique1d (numpy.lib.tests.test_arraysetops.test_aso) ... ok check_cmplx (numpy.lib.tests.test_type_check.test_imag) ... ok check_real (numpy.lib.tests.test_type_check.test_imag) ... ok check_fail (numpy.lib.tests.test_type_check.test_iscomplex) ... ok check_pass (numpy.lib.tests.test_type_check.test_iscomplex) ... ok check_basic (numpy.lib.tests.test_type_check.test_iscomplexobj) ... ok check_complex (numpy.lib.tests.test_type_check.test_isfinite) ... ok check_complex1 (numpy.lib.tests.test_type_check.test_isfinite) ... ok check_goodvalues (numpy.lib.tests.test_type_check.test_isfinite) ... ok check_ind (numpy.lib.tests.test_type_check.test_isfinite) ... ok check_integer (numpy.lib.tests.test_type_check.test_isfinite) ... ok check_neginf (numpy.lib.tests.test_type_check.test_isfinite) ... ok check_posinf (numpy.lib.tests.test_type_check.test_isfinite) ... ok check_goodvalues (numpy.lib.tests.test_type_check.test_isinf) ... ok check_ind (numpy.lib.tests.test_type_check.test_isinf) ... ok check_neginf (numpy.lib.tests.test_type_check.test_isinf) ... ok check_neginf_scalar (numpy.lib.tests.test_type_check.test_isinf) ... ok check_posinf (numpy.lib.tests.test_type_check.test_isinf) ... ok check_posinf_scalar (numpy.lib.tests.test_type_check.test_isinf) ... ok check_complex (numpy.lib.tests.test_type_check.test_isnan) ... ok check_complex1 (numpy.lib.tests.test_type_check.test_isnan) ... ok check_goodvalues (numpy.lib.tests.test_type_check.test_isnan) ... ok check_ind (numpy.lib.tests.test_type_check.test_isnan) ... ok check_integer (numpy.lib.tests.test_type_check.test_isnan) ... ok check_neginf (numpy.lib.tests.test_type_check.test_isnan) ... ok check_posinf (numpy.lib.tests.test_type_check.test_isnan) ... ok check_generic (numpy.lib.tests.test_type_check.test_isneginf) ... ok check_generic (numpy.lib.tests.test_type_check.test_isposinf) ... ok check_fail (numpy.lib.tests.test_type_check.test_isreal) ... ok check_pass (numpy.lib.tests.test_type_check.test_isreal) ... ok check_basic (numpy.lib.tests.test_type_check.test_isrealobj) ... ok check_basic (numpy.lib.tests.test_type_check.test_isscalar) ... ok check_default_1 (numpy.lib.tests.test_type_check.test_mintypecode) ... ok check_default_2 (numpy.lib.tests.test_type_check.test_mintypecode) ... ok check_default_3 (numpy.lib.tests.test_type_check.test_mintypecode) ... ok check_complex_bad (numpy.lib.tests.test_type_check.test_nan_to_num) ... ok check_complex_bad2 (numpy.lib.tests.test_type_check.test_nan_to_num) ... ok check_complex_good (numpy.lib.tests.test_type_check.test_nan_to_num) ... ok check_generic (numpy.lib.tests.test_type_check.test_nan_to_num) ... ok check_integer (numpy.lib.tests.test_type_check.test_nan_to_num) ... ok check_cmplx (numpy.lib.tests.test_type_check.test_real) ... ok check_real (numpy.lib.tests.test_type_check.test_real) ... ok check_basic (numpy.lib.tests.test_type_check.test_real_if_close) ... ok check_all (numpy.core.tests.test_multiarray.test_argmax) ... ok Check assignment of 0-dimensional objects with values ... ok Check assignment of multi-dimensional objects with values ... ok Check assignment of single-dimensional objects with values ... ok Check assignment of 0-dimensional objects with values ... ok Check assignment of multi-dimensional objects with values ... ok Check assignment of single-dimensional objects with values ... ok Check assignment of 0-dimensional objects with values ... ok Check assignment of multi-dimensional objects with values ... ok Check assignment of single-dimensional objects with values ... ok Check assignment of 0-dimensional objects with values ... ok Check assignment of multi-dimensional objects with values ... ok Check assignment of single-dimensional objects with values ... ok Check assignment of 0-dimensional objects with values ... ok Check assignment of multi-dimensional objects with values ... ok Check assignment of single-dimensional objects with values ... ok Check assignment of 0-dimensional objects with values ... ok Check assignment of multi-dimensional objects with values ... ok Check assignment of single-dimensional objects with values ... ok check_attributes (numpy.core.tests.test_multiarray.test_attributes) ... ok check_dtypeattr (numpy.core.tests.test_multiarray.test_attributes) ... ok check_fill (numpy.core.tests.test_multiarray.test_attributes) ... ok check_set_stridesattr (numpy.core.tests.test_multiarray.test_attributes) ... ok check_stridesattr (numpy.core.tests.test_multiarray.test_attributes) ... ok check_test_interning (numpy.core.tests.test_multiarray.test_bool) ... ok Check byteorder of 0-dimensional objects ... ok Check byteorder of multi-dimensional objects ... ok Check byteorder of single-dimensional objects ... ok Check byteorder of 0-dimensional objects ... ok Check byteorder of multi-dimensional objects ... ok Check byteorder of single-dimensional objects ... ok Check byteorder of 0-dimensional objects ... ok Check byteorder of multi-dimensional objects ... ok Check byteorder of single-dimensional objects ... ok Check byteorder of 0-dimensional objects ... ok Check byteorder of multi-dimensional objects ... ok Check byteorder of single-dimensional objects ... ok Check byteorder of 0-dimensional objects ... ok Check byteorder of multi-dimensional objects ... ok Check byteorder of single-dimensional objects ... ok Check byteorder of 0-dimensional objects ... ok Check byteorder of multi-dimensional objects ... ok Check byteorder of single-dimensional objects ... ok Check creation of 0-dimensional objects with values ... ok Check creation of multi-dimensional objects with values ... ok Check creation of single-dimensional objects with values ... ok Check creation of 0-dimensional objects with values ... ok Check creation of multi-dimensional objects with values ... ok Check creation of single-dimensional objects with values ... ok Check creation of 0-dimensional objects with values ... ok Check creation of multi-dimensional objects with values ... ok Check creation of single-dimensional objects with values ... ok Check creation of 0-dimensional objects with values ... ok Check creation of multi-dimensional objects with values ... ok Check creation of single-dimensional objects with values ... ok Check creation of 0-dimensional objects with values ... ok Check creation of multi-dimensional objects with values ... ok Check creation of single-dimensional objects with values ... ok Check creation of 0-dimensional objects with values ... ok Check creation of multi-dimensional objects with values ... ok Check creation of single-dimensional objects with values ... ok Check creation of 0-dimensional objects ... ok Check creation of multi-dimensional objects ... ok Check creation of single-dimensional objects ... ok Check creation of 0-dimensional objects ... ok Check creation of multi-dimensional objects ... ok Check creation of single-dimensional objects ... ok Check creation of 0-dimensional objects ... ok Check creation of multi-dimensional objects ... ok Check creation of single-dimensional objects ... ok check_from_attribute (numpy.core.tests.test_multiarray.test_creation) ... ok check_construction (numpy.core.tests.test_multiarray.test_dtypedescr) ... ok check_list (numpy.core.tests.test_multiarray.test_fancy_indexing) ... ok check_tuple (numpy.core.tests.test_multiarray.test_fancy_indexing) ... ok check_otherflags (numpy.core.tests.test_multiarray.test_flags) ... ok check_writeable (numpy.core.tests.test_multiarray.test_flags) ... ok check_ascii (numpy.core.tests.test_multiarray.test_fromstring) ... ERROR check_binary (numpy.core.tests.test_multiarray.test_fromstring) ... ok check_test_round (numpy.core.tests.test_multiarray.test_methods) ... ok check_transpose (numpy.core.tests.test_multiarray.test_methods) ... ok check_basic (numpy.core.tests.test_multiarray.test_newaxis) ... ok check_both (numpy.core.tests.test_multiarray.test_pickling) ... ok check_version0_float32 (numpy.core.tests.test_multiarray.test_pickling) ... ok check_version0_int8 (numpy.core.tests.test_multiarray.test_pickling) ... ok check_version0_object (numpy.core.tests.test_multiarray.test_pickling) ... ok check_version1_float32 (numpy.core.tests.test_multiarray.test_pickling) ... ok check_version1_int8 (numpy.core.tests.test_multiarray.test_pickling) ... ok check_version1_object (numpy.core.tests.test_multiarray.test_pickling) ... ok Ticket #143 ... ok Ticket #111 ... ok Ticket #119 ... ok Ticket #151 ... ok Ticket #60 ... ok Ticket #194 ... ok Ticket #50 ... ok Ticket #222 ... ok Ticket #91 ... ok Ticket #35 ... ok Ticket #105 ... ok Correct behaviour of ticket #194 ... ok Ticket #128 ... ok Ticket #64 ... ok Ticket #65 ... ok Ticket #99 ... ok Ticket #3 ... ok Ticket #71 ... ok Lexsort memory error ... ok Ticket #112 ... ok Ticket #61 ... ok Ticket #17 ... ok Ticket #125 ... ok Ticket #83 ... ok Ticket #196 ... ok Ticket #95 ... ok Ticket #126 ... ok Ticket #106 ... ok Ticket #93 ... ok Ticket #7 ... ok Ticket #62 ... ok Ticket #49 ... ok Ticket #58. ... ok Ticket #86 ... ok Ticket #239 ... ok Ticket #16 ... ok Ticket #28 ... ok Ticket #160 ... ok Ticket #202 ... ok Ticket #40 ... ok Ticket #67 ... ok Ticket #72 ... ok Ticket #47 ... ok Ticket #133 ... ok Fix in r2836 ... ok Ticket #31 ... ok Ticket #190 ... ok Ticket #79 ... ok Ticket #205 ... ok Implemented in r2840 ... ok Ticket #205 ... ok Ticket #43 ... ok check_mixed (numpy.core.tests.test_multiarray.test_string_compare) ... ok check_string (numpy.core.tests.test_multiarray.test_string_compare) ... ok check_unicode (numpy.core.tests.test_multiarray.test_string_compare) ... ok check_test_zero_rank (numpy.core.tests.test_multiarray.test_subscripting) ... ok check_constructor (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_ellipsis_subscript (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_ellipsis_subscript_assignment (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_empty_subscript (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_empty_subscript_assignment (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_invalid_newaxis (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_invalid_subscript (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_invalid_subscript_assignment (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_newaxis (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_output (numpy.core.tests.test_multiarray.test_zero_rank) ... ok check_definition (numpy.fft.tests.test_helper.test_fftfreq) ... ok check_definition (numpy.fft.tests.test_helper.test_fftshift) ... ok check_inverse (numpy.fft.tests.test_helper.test_fftshift) ... ok test_clip (numpy.core.tests.test_ma.test_array_methods) ... ok test_cumprod (numpy.core.tests.test_ma.test_array_methods) ... ok test_cumsum (numpy.core.tests.test_ma.test_array_methods) ... ok test_ptp (numpy.core.tests.test_ma.test_array_methods) ... ok test_swapaxes (numpy.core.tests.test_ma.test_array_methods) ... ok test_trace (numpy.core.tests.test_ma.test_array_methods) ... ok test_varstd (numpy.core.tests.test_ma.test_array_methods) ... ok check_testAPI (numpy.core.tests.test_ma.test_ma) ... ok Test add, sum, product. ... ok Test of basic arithmetic. ... ok check_testArrayAttributes (numpy.core.tests.test_ma.test_ma) ... ok check_testArrayMethods (numpy.core.tests.test_ma.test_ma) ... ok Test of average. ... ok More tests of average. ... ok Test of basic array creation and properties in 1 dimension. ... ok Test of basic array creation and properties in 2 dimensions. ... ok Test of conversions and indexing ... ok Tests of some subtle points of copying and sizing. ... ok Test of inplace operations and rich comparisons ... ok check_testMaPut (numpy.core.tests.test_ma.test_ma) ... ok Test of masked element ... ok Test of minumum, maximum. ... ok check_testMixedArithmetic (numpy.core.tests.test_ma.test_ma) ... ok Test of other odd features ... ok Test of pickling ... ok Test of put ... ok check_testScalarArithmetic (numpy.core.tests.test_ma.test_ma) ... ok check_testSingleElementSubscript (numpy.core.tests.test_ma.test_ma) ... ok Test of take, transpose, inner, outer products ... ok check_testToPython (numpy.core.tests.test_ma.test_ma) ... ok Test various functions such as sin, cos. ... ok Test count ... ok check_testUfuncRegression (numpy.core.tests.test_ma.test_ufuncs) ... ok test_minmax (numpy.core.tests.test_ma.test_ufuncs) ... ok test_nonzero (numpy.core.tests.test_ma.test_ufuncs) ... ok test_reduce (numpy.core.tests.test_ma.test_ufuncs) ... ok check_matrix (numpy.lib.tests.test_twodim_base.test_diag) ... ok check_vector (numpy.lib.tests.test_twodim_base.test_diag) ... ok check_2d (numpy.lib.tests.test_twodim_base.test_eye) ... ok check_basic (numpy.lib.tests.test_twodim_base.test_eye) ... ok check_diag (numpy.lib.tests.test_twodim_base.test_eye) ... ok check_diag2d (numpy.lib.tests.test_twodim_base.test_eye) ... ok check_basic (numpy.lib.tests.test_twodim_base.test_fliplr) ... ok check_basic (numpy.lib.tests.test_twodim_base.test_flipud) ... ok check_simple (numpy.lib.tests.test_twodim_base.test_histogram2d) ... ok check_basic (numpy.lib.tests.test_twodim_base.test_rot90) ... ok check_basic (numpy.core.tests.test_defmatrix.test_algebra) ... ok check_basic (numpy.core.tests.test_defmatrix.test_casting) ... ok check_basic (numpy.core.tests.test_defmatrix.test_ctor) ... ok check_basic (numpy.core.tests.test_defmatrix.test_indexing) ... ok check_instance_methods (numpy.core.tests.test_defmatrix.test_matrix_return) ... ok check_asmatrix (numpy.core.tests.test_defmatrix.test_properties) ... ok check_basic (numpy.core.tests.test_defmatrix.test_properties) ... ok check_comparisons (numpy.core.tests.test_defmatrix.test_properties) ... ok check_noaxis (numpy.core.tests.test_defmatrix.test_properties) ... ok Test whether matrix.sum(axis=1) preserves orientation. ... ok check_doctests (numpy.lib.tests.test_ufunclike.test_docs) ... ok check_dtype (numpy.tests.test_ctypeslib.test_ndpointer) ... ok check_flags (numpy.tests.test_ctypeslib.test_ndpointer) ... ok check_ndim (numpy.tests.test_ctypeslib.test_ndpointer) ... ok check_shape (numpy.tests.test_ctypeslib.test_ndpointer) ... ok check_basic (numpy.lib.tests.test_function_base.test_all) ... ok check_nd (numpy.lib.tests.test_function_base.test_all) ... ok check_basic (numpy.lib.tests.test_function_base.test_amax) ... ok check_basic (numpy.lib.tests.test_function_base.test_amin) ... ok check_basic (numpy.lib.tests.test_function_base.test_angle) ... ok check_basic (numpy.lib.tests.test_function_base.test_any) ... ok check_nd (numpy.lib.tests.test_function_base.test_any) ... ok check_basic (numpy.lib.tests.test_function_base.test_average) ... ok check_basic (numpy.lib.tests.test_function_base.test_cumprod) ... ok check_basic (numpy.lib.tests.test_function_base.test_cumsum) ... ok check_basic (numpy.lib.tests.test_function_base.test_diff) ... ok check_nd (numpy.lib.tests.test_function_base.test_diff) ... ok check_forward (numpy.lib.tests.test_function_base.test_digitize) ... ok check_random (numpy.lib.tests.test_function_base.test_digitize) ... ok check_reverse (numpy.lib.tests.test_function_base.test_digitize) ... ok check_basic (numpy.lib.tests.test_function_base.test_extins) ... ok check_both (numpy.lib.tests.test_function_base.test_extins) ... ok check_insert (numpy.lib.tests.test_function_base.test_extins) ... ok check_bartlett (numpy.lib.tests.test_function_base.test_filterwindows) ... ok check_blackman (numpy.lib.tests.test_function_base.test_filterwindows) ... ok check_hamming (numpy.lib.tests.test_function_base.test_filterwindows) ... ok check_hanning (numpy.lib.tests.test_function_base.test_filterwindows) ... ok check_simple (numpy.lib.tests.test_function_base.test_histogram) ... ok check_basic (numpy.lib.tests.test_function_base.test_linspace) ... ok check_corner (numpy.lib.tests.test_function_base.test_linspace) ... ok check_type (numpy.lib.tests.test_function_base.test_linspace) ... ok check_basic (numpy.lib.tests.test_function_base.test_logspace) ... ok check_basic (numpy.lib.tests.test_function_base.test_prod) ... ok check_basic (numpy.lib.tests.test_function_base.test_ptp) ... ok check_simple (numpy.lib.tests.test_function_base.test_sinc) ... ok check_simple (numpy.lib.tests.test_function_base.test_trapz) ... ok check_basic (numpy.lib.tests.test_function_base.test_trim_zeros) ... ok check_leading_skip (numpy.lib.tests.test_function_base.test_trim_zeros) ... ok check_trailing_skip (numpy.lib.tests.test_function_base.test_trim_zeros) ... ok check_simple (numpy.lib.tests.test_function_base.test_unique) ... ok check_simple (numpy.lib.tests.test_function_base.test_unwrap) ... ok check_large (numpy.lib.tests.test_function_base.test_vectorize) ... ok check_scalar (numpy.lib.tests.test_function_base.test_vectorize) ... ok check_simple (numpy.lib.tests.test_function_base.test_vectorize) ... ok check_doctests (numpy.lib.tests.test_polynomial.test_docs) ... ok check_fromrecords (numpy.core.tests.test_records.test_fromrecords) ... ok check_method_array (numpy.core.tests.test_records.test_fromrecords) ... ok check_method_array2 (numpy.core.tests.test_records.test_fromrecords) ... ok check_recarray_from_names (numpy.core.tests.test_records.test_fromrecords) ... ok check_recarray_from_obj (numpy.core.tests.test_records.test_fromrecords) ... ok check_recarray_fromarrays (numpy.core.tests.test_records.test_fromrecords) ... ok check_recarray_fromfile (numpy.core.tests.test_records.test_fromrecords) ... ok check_recarray_slices (numpy.core.tests.test_records.test_fromrecords) ... ok test_large (numpy.core.tests.test_numeric.test_binary_repr) ... ok test_zero (numpy.core.tests.test_numeric.test_binary_repr) ... ok test_bitwise_and (numpy.core.tests.test_numeric.test_bool_scalar) ... ok test_bitwise_or (numpy.core.tests.test_numeric.test_bool_scalar) ... ok test_bitwise_xor (numpy.core.tests.test_numeric.test_bool_scalar) ... ok test_logical (numpy.core.tests.test_numeric.test_bool_scalar) ... ok check_all (numpy.core.tests.test_numeric.test_dot) ... ok check_columnvect (numpy.core.tests.test_numeric.test_dot) ... ok check_matmat (numpy.core.tests.test_numeric.test_dot) ... ok check_matscalar (numpy.core.tests.test_numeric.test_dot) ... ok check_matvec (numpy.core.tests.test_numeric.test_dot) ... ok check_matvec2 (numpy.core.tests.test_numeric.test_dot) ... ok check_vecmat (numpy.core.tests.test_numeric.test_dot) ... ok check_vecmat2 (numpy.core.tests.test_numeric.test_dot) ... ok check_vecmat3 (numpy.core.tests.test_numeric.test_dot) ... ok check_vecobject (numpy.core.tests.test_numeric.test_dot) ... ok check_vecscalar (numpy.core.tests.test_numeric.test_dot) ... ok check_vecscalar2 (numpy.core.tests.test_numeric.test_dot) ... ok check_vecvecinner (numpy.core.tests.test_numeric.test_dot) ... ok check_vecvecouter (numpy.core.tests.test_numeric.test_dot) ... ok test_lengths (numpy.core.tests.test_numeric.test_fromiter) ... ok test_types (numpy.core.tests.test_numeric.test_fromiter) ... ok test_values (numpy.core.tests.test_numeric.test_fromiter) ... ok test_boolean (numpy.core.tests.test_numeric.test_index) ... ok test_divideerr (numpy.core.tests.test_numeric.test_seterr) ... ok test_set (numpy.core.tests.test_numeric.test_seterr) ... ok check_1d (numpy.lib.tests.test_index_tricks.test_concatenator) ... ok check_2d (numpy.lib.tests.test_index_tricks.test_concatenator) ... ok check_basic (numpy.lib.tests.test_index_tricks.test_grid) ... ok check_nd (numpy.lib.tests.test_index_tricks.test_grid) ... ok check_simple (numpy.lib.tests.test_shape_base.test_apply_along_axis) ... ok check_index_split_high_bound (numpy.lib.tests.test_shape_base.test_array_split) ... ok check_index_split_low_bound (numpy.lib.tests.test_shape_base.test_array_split) ... ok check_index_split_simple (numpy.lib.tests.test_shape_base.test_array_split) ... ok check_integer_0_split (numpy.lib.tests.test_shape_base.test_array_split) ... ok check_integer_split (numpy.lib.tests.test_shape_base.test_array_split) ... ok check_integer_split_2D_cols (numpy.lib.tests.test_shape_base.test_array_split) ... ok This will fail if we change default axis ... ok check_integer_split_2D_rows (numpy.lib.tests.test_shape_base.test_array_split) ... ok check_0D_array (numpy.lib.tests.test_shape_base.test_atleast_1d) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_atleast_1d) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_atleast_1d) ... ok check_3D_array (numpy.lib.tests.test_shape_base.test_atleast_1d) ... ok Test to make sure equivalent Travis O's r1array function ... ok check_0D_array (numpy.lib.tests.test_shape_base.test_atleast_2d) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_atleast_2d) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_atleast_2d) ... ok check_3D_array (numpy.lib.tests.test_shape_base.test_atleast_2d) ... ok Test to make sure equivalent Travis O's r2array function ... ok check_0D_array (numpy.lib.tests.test_shape_base.test_atleast_3d) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_atleast_3d) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_atleast_3d) ... ok check_3D_array (numpy.lib.tests.test_shape_base.test_atleast_3d) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_dsplit) ... ok check_3D_array (numpy.lib.tests.test_shape_base.test_dsplit) ... ok check_0D_array (numpy.lib.tests.test_shape_base.test_dstack) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_dstack) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_dstack) ... ok check_2D_array2 (numpy.lib.tests.test_shape_base.test_dstack) ... ok check_0D_array (numpy.lib.tests.test_shape_base.test_hsplit) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_hsplit) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_hsplit) ... ok check_0D_array (numpy.lib.tests.test_shape_base.test_hstack) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_hstack) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_hstack) ... ok check_rank_checking (numpy.lib.tests.test_shape_base.test_kron) ... ok check_return_type (numpy.lib.tests.test_shape_base.test_kron) ... ok check_equal_split (numpy.lib.tests.test_shape_base.test_split) ... ok check_unequal_split (numpy.lib.tests.test_shape_base.test_split) ... ok check_basic (numpy.lib.tests.test_shape_base.test_squeeze) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_vsplit) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_vsplit) ... ok check_0D_array (numpy.lib.tests.test_shape_base.test_vstack) ... ok check_1D_array (numpy.lib.tests.test_shape_base.test_vstack) ... ok check_2D_array (numpy.lib.tests.test_shape_base.test_vstack) ... ok check_2D_array2 (numpy.lib.tests.test_shape_base.test_vstack) ... ok ====================================================================== ERROR: check_ascii (numpy.core.tests.test_multiarray.test_fromstring) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/local/lib/python2.3/site-packages/numpy/core/tests/test_multiarray.py", line 120, in check_ascii a = fromstring('1 , 2 , 3 , 4',sep=',') ValueError: don't know how to read character strings for given array type ---------------------------------------------------------------------- Ran 481 tests in 1.703s FAILED (errors=1)

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Still SciPy installation problems on SUSE linux
by Sebastian Busch Aug. 15, 2006

Aug. 15, 2006

Dear list, I posted about this already some days ago -- thank you David and Eric for your advice. I tried to install SciPy the way Eric proposed. I didn't install pyfits, IPython, and F2Py. After fighting a long time with LAPACK and atlas, I decided to try lapack & blas provided by SUSE (which doesn't seem to change much, as far as I can see). Before installing again, I simply deleted the corresponding directories, e.g. /usr/lib/python2.4/site-packages/numpy/ numpy.test works fine (matplotlib is also working), however scipy.test gives an error about integers in arrays -- I attached the output. It comes from import scipy scipy.test() I hope you have again some ideas, thanks, Sebastian. Overwriting info=<function info at 0x40728924> from scipy.misc (was <function info at 0x40702064> from numpy.lib.utils) import signal -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import lib.lapack -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import cluster -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import linsolve.umfpack -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename Overwriting fft=<function fft at 0x40c79e9c> from scipy.fftpack.basic (was <module 'numpy.fft' from '/usr/lib/python2.4/site-packages/numpy/fft/__init__.pyc'> from /usr/lib/python2.4/site-packages/numpy/fft/__init__.pyc) import linsolve -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import integrate -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import optimize -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import special -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import lib.blas -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import linalg -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import maxentropy -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename import stats -> failed: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename Found 4 tests for scipy.io.array_import Found 397 tests for scipy.ndimage Warning: FAILURE importing tests for <module 'scipy.sparse.sparse' from '...ckages/scipy/sparse/sparse.pyc'> /usr/lib/python2.4/site-packages/scipy/linsolve/_superlu.py:1: ImportError: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename (in ?) Found 20 tests for scipy.fftpack.pseudo_diffs Warning: FAILURE importing tests for <module 'scipy.optimize.optimize' from '...es/scipy/optimize/optimize.pyc'> /usr/lib/python2.4/site-packages/scipy/optimize/lbfgsb.py:30: ImportError: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename (in ?) Found 5 tests for scipy.interpolate.fitpack Found 1 tests for scipy.interpolate Found 12 tests for scipy.io.mmio Found 18 tests for scipy.fftpack.basic Warning: FAILURE importing tests for <module 'scipy.linsolve.umfpack.umfpack' from '...y/linsolve/umfpack/umfpack.pyc'> /usr/lib/python2.4/site-packages/scipy/linsolve/_superlu.py:1: ImportError: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename (in ?) Warning: FAILURE importing tests for <module 'scipy.linsolve.umfpack' from '.../linsolve/umfpack/__init__.pyc'> /usr/lib/python2.4/site-packages/scipy/linsolve/_superlu.py:1: ImportError: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename (in ?) Warning: FAILURE importing tests for <module 'scipy.optimize.zeros' from '...kages/scipy/optimize/zeros.pyc'> /usr/lib/python2.4/site-packages/scipy/optimize/lbfgsb.py:30: ImportError: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename (in ?) Found 16 tests for scipy.io.mio Warning: FAILURE importing tests for <module 'scipy.special.basic' from '...ckages/scipy/special/basic.pyc'> /usr/lib/python2.4/site-packages/scipy/linalg/lapack.py:17: ImportError: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename (in ?) Warning: FAILURE importing tests for <module 'scipy.sparse' from '...ages/scipy/sparse/__init__.pyc'> /usr/lib/python2.4/site-packages/scipy/linsolve/_superlu.py:1: ImportError: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename (in ?) Found 4 tests for scipy.fftpack.helper Found 0 tests for __main__ Warning: 1000000 bytes requested, 20 bytes read. ...E................................................................................................................................................................................................................................................................................................................................................................................................................................./usr/lib/python2.4/site-packages/scipy/interpolate/fitpack2.py:410: UserWarning: The coefficients of the spline returned have been computed as the minimal norm least-squares solution of a (numerically) rank deficient system (deficiency=7). If deficiency is large, the results may be inaccurate. Deficiency may strongly depend on the value of eps. warnings.warn(message) ........................................................ ====================================================================== ERROR: check_integer (scipy.io.tests.test_array_import.test_read_array) ---------------------------------------------------------------------- Traceback (most recent call last): File "/usr/lib/python2.4/site-packages/scipy/io/tests/test_array_import.py", line 55, in check_integer from scipy import stats File "/usr/lib/python2.4/site-packages/scipy/stats/__init__.py", line 7, in ? from stats import * File "/usr/lib/python2.4/site-packages/scipy/stats/stats.py", line 191, in ? import scipy.special as special File "/usr/lib/python2.4/site-packages/scipy/special/__init__.py", line 10, in ? import orthogonal File "/usr/lib/python2.4/site-packages/scipy/special/orthogonal.py", line 66, in ? from scipy.linalg import eig File "/usr/lib/python2.4/site-packages/scipy/linalg/__init__.py", line 8, in ? from basic import * File "/usr/lib/python2.4/site-packages/scipy/linalg/basic.py", line 17, in ? from lapack import get_lapack_funcs File "/usr/lib/python2.4/site-packages/scipy/linalg/lapack.py", line 17, in ? from scipy.linalg import flapack ImportError: /usr/lib/libblas.so.3: undefined symbol: _gfortran_filename ---------------------------------------------------------------------- Ran 477 tests in 8.052s FAILED (errors=1) Don't worry about a warning regarding the number of bytes read.

2 2

SciPy 2006 PPC ISO update
by Bryce Hendrix Aug. 14, 2006

Aug. 14, 2006

I was unable to master a new PPC ISO this weekend due to a number of problems. Its not impossible, but very difficult and time consuming with the hardware available (important note: Ubuntu LiveCD does not work on G5s). Instead of the Live CD, I've made available a tar of all the files which are not provided by Ubuntu packages. The download information can be found at http://www.scipy.org/SciPy2006/LiveCD. Bryce

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